{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "555fd956",
   "metadata": {},
   "source": [
    "# RNN: Seasonal Temperature Forecasting\n",
    "\n",
    "Predict the next 24 hours of New York temperature from the previous seven days. A vanilla RNN learns a correction to the previous day’s hourly temperature curve. The forecast is a full curve, with actual future temperatures revealed only during evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7474a9fd",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2026-10-14T13:51:28.027292Z",
     "iopub.status.idle": "2026-10-14T13:51:28.757716Z",
     "shell.execute_reply": "2026-10-14T13:51:28.757144Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JAXsaw 0.1.0 · JAX 0.5.3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CudaDevice(id=0)]\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import sys, json, inspect\n",
    "ROOT = Path.cwd() if (Path.cwd() / 'examples').exists() else Path.cwd().parent\n",
    "sys.path.insert(0, str(ROOT))\n",
    "import jax\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import jaxsaw as js\n",
    "from jaxsaw import checkpoint\n",
    "from examples.forecasting import ForecastModel, train_step, infer, decode\n",
    "from IPython.display import display, Image, Code\n",
    "RUN = ROOT / 'runs/forecast-gallery-v2/rnn'\n",
    "print(f\"JAXsaw {js.__version__} · JAX {jax.__version__}\")\n",
    "print(jax.devices())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29b5fa1a",
   "metadata": {},
   "source": [
    "## Data and forecasting target\n",
    "\n",
    "[Open-Meteo’s historical API](https://open-meteo.com/en/docs/historical-weather-api) supplies hourly ERA5 reanalysis at New York coordinates for 2017–2025. The grid location is recorded below. ERA5 combines observations and a weather model; this is a retrospective forecasting experiment, not a replay of operational forecasts available at each historical date. Attribution: ERA5 / Copernicus Climate Change Service, distributed by Open-Meteo under CC BY 4.0.\n",
    "\n",
    "Inputs are standardized temperature and sine/cosine encodings of hour and day of year. Only pre-2023 temperatures fit the mean and scale. Each daily origin uses 168 known hours to predict 24 future hours. Baselines repeat the previous day or hold the latest temperature constant."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6520e83f",
   "metadata": {},
   "source": [
    "## Chronological evaluation\n",
    "\n",
    "Training targets end before 2023; validation targets fall within 2023; test targets fall within 2024–2025. Windows whose targets cross a split boundary are excluded. A validation or test forecast may use earlier observations as history, just as at inference time. No future observation enters its input.\n",
    "\n",
    "The training loader shuffles complete windows, but the split is chronological. Validation loss selects the checkpoint and controls early stopping (15 epochs without improvement). Test data are evaluated after checkpoint selection."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c93e48f7",
   "metadata": {},
   "source": [
    "## Architecture\n",
    "\n",
    "Input `[B,168,5]` → RNN with 32 hidden units → 24 forecast corrections. Add the corrections to the previous day’s standardized temperature profile.\n",
    "\n",
    "The output projection starts at zero, so the initial model equals its baseline. Training learns how to adjust it. The same `nn.Module` composition and explicit optimizer API used in the other tutorials apply here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d750666f",
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    "execution": {
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     "iopub.status.idle": "2026-10-14T13:51:28.784572Z",
     "shell.execute_reply": "2026-10-14T13:51:28.784210Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
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       ".output_html .il { color: #666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">class</span><span class=\"w\"> </span><span class=\"nc\">ForecastModel</span><span class=\"p\">(</span><span class=\"n\">nn</span><span class=\"o\">.</span><span class=\"n\">Module</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">\"\"\"Learn a recurrent correction to an available historical baseline.\"\"\"</span>\n",
       "\n",
       "    <span class=\"k\">def</span><span class=\"w\"> </span><span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">task</span><span class=\"p\">,</span> <span class=\"n\">hidden</span><span class=\"o\">=</span><span class=\"mi\">32</span><span class=\"p\">,</span> <span class=\"o\">*</span><span class=\"p\">,</span> <span class=\"n\">rngs</span><span class=\"p\">):</span>\n",
       "        <span class=\"nb\">super</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"fm\">__init__</span><span class=\"p\">()</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">task</span> <span class=\"o\">=</span> <span class=\"n\">task</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">sequence</span> <span class=\"o\">=</span> <span class=\"n\">models</span><span class=\"o\">.</span><span class=\"n\">SequenceRegressor</span><span class=\"p\">(</span>\n",
       "            <span class=\"n\">features</span><span class=\"o\">=</span><span class=\"mi\">5</span> <span class=\"k\">if</span> <span class=\"n\">task</span> <span class=\"o\">==</span> <span class=\"s2\">\"rnn\"</span> <span class=\"k\">else</span> <span class=\"mi\">4</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">hidden</span><span class=\"o\">=</span><span class=\"n\">hidden</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">outputs</span><span class=\"o\">=</span><span class=\"mi\">24</span> <span class=\"k\">if</span> <span class=\"n\">task</span> <span class=\"o\">==</span> <span class=\"s2\">\"rnn\"</span> <span class=\"k\">else</span> <span class=\"mi\">1</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">kind</span><span class=\"o\">=</span><span class=\"n\">task</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">rngs</span><span class=\"o\">=</span><span class=\"n\">rngs</span><span class=\"p\">,</span>\n",
       "        <span class=\"p\">)</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">sequence</span><span class=\"o\">.</span><span class=\"n\">head</span><span class=\"o\">.</span><span class=\"n\">weight</span><span class=\"o\">.</span><span class=\"n\">value</span> <span class=\"o\">=</span> <span class=\"n\">jnp</span><span class=\"o\">.</span><span class=\"n\">zeros_like</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">sequence</span><span class=\"o\">.</span><span class=\"n\">head</span><span class=\"o\">.</span><span class=\"n\">weight</span><span class=\"o\">.</span><span class=\"n\">value</span><span class=\"p\">)</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">sequence</span><span class=\"o\">.</span><span class=\"n\">head</span><span class=\"o\">.</span><span class=\"n\">bias</span><span class=\"o\">.</span><span class=\"n\">value</span> <span class=\"o\">=</span> <span class=\"n\">jnp</span><span class=\"o\">.</span><span class=\"n\">zeros_like</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">sequence</span><span class=\"o\">.</span><span class=\"n\">head</span><span class=\"o\">.</span><span class=\"n\">bias</span><span class=\"o\">.</span><span class=\"n\">value</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"k\">def</span><span class=\"w\"> </span><span class=\"nf\">forward</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"p\">):</span>\n",
       "        <span class=\"c1\"># Use full float32 products consistently across GPU compilation contexts.</span>\n",
       "        <span class=\"k\">with</span> <span class=\"n\">jax</span><span class=\"o\">.</span><span class=\"n\">default_matmul_precision</span><span class=\"p\">(</span><span class=\"s2\">\"highest\"</span><span class=\"p\">):</span>\n",
       "            <span class=\"n\">correction</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">sequence</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">)[</span><span class=\"s2\">\"prediction\"</span><span class=\"p\">]</span>\n",
       "        <span class=\"n\">baseline</span> <span class=\"o\">=</span> <span class=\"n\">x</span><span class=\"p\">[:,</span> <span class=\"o\">-</span><span class=\"mi\">24</span><span class=\"p\">:,</span> <span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">task</span> <span class=\"o\">==</span> <span class=\"s2\">\"rnn\"</span> <span class=\"k\">else</span> <span class=\"n\">x</span><span class=\"p\">[:,</span> <span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">:]</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">baseline</span> <span class=\"o\">+</span> <span class=\"n\">correction</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{class}\\PY{+w}{ }\\PY{n+nc}{ForecastModel}\\PY{p}{(}\\PY{n}{nn}\\PY{o}{.}\\PY{n}{Module}\\PY{p}{)}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Learn a recurrent correction to an available historical baseline.\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{k}{def}\\PY{+w}{ }\\PY{n+nf+fm}{\\PYZus{}\\PYZus{}init\\PYZus{}\\PYZus{}}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{task}\\PY{p}{,} \\PY{n}{hidden}\\PY{o}{=}\\PY{l+m+mi}{32}\\PY{p}{,} \\PY{o}{*}\\PY{p}{,} \\PY{n}{rngs}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n+nb}{super}\\PY{p}{(}\\PY{p}{)}\\PY{o}{.}\\PY{n+nf+fm}{\\PYZus{}\\PYZus{}init\\PYZus{}\\PYZus{}}\\PY{p}{(}\\PY{p}{)}\n",
       "        \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{task} \\PY{o}{=} \\PY{n}{task}\n",
       "        \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{sequence} \\PY{o}{=} \\PY{n}{models}\\PY{o}{.}\\PY{n}{SequenceRegressor}\\PY{p}{(}\n",
       "            \\PY{n}{features}\\PY{o}{=}\\PY{l+m+mi}{5} \\PY{k}{if} \\PY{n}{task} \\PY{o}{==} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{rnn}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{else} \\PY{l+m+mi}{4}\\PY{p}{,}\n",
       "            \\PY{n}{hidden}\\PY{o}{=}\\PY{n}{hidden}\\PY{p}{,}\n",
       "            \\PY{n}{outputs}\\PY{o}{=}\\PY{l+m+mi}{24} \\PY{k}{if} \\PY{n}{task} \\PY{o}{==} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{rnn}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{else} \\PY{l+m+mi}{1}\\PY{p}{,}\n",
       "            \\PY{n}{kind}\\PY{o}{=}\\PY{n}{task}\\PY{p}{,}\n",
       "            \\PY{n}{rngs}\\PY{o}{=}\\PY{n}{rngs}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "        \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{sequence}\\PY{o}{.}\\PY{n}{head}\\PY{o}{.}\\PY{n}{weight}\\PY{o}{.}\\PY{n}{value} \\PY{o}{=} \\PY{n}{jnp}\\PY{o}{.}\\PY{n}{zeros\\PYZus{}like}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{sequence}\\PY{o}{.}\\PY{n}{head}\\PY{o}{.}\\PY{n}{weight}\\PY{o}{.}\\PY{n}{value}\\PY{p}{)}\n",
       "        \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{sequence}\\PY{o}{.}\\PY{n}{head}\\PY{o}{.}\\PY{n}{bias}\\PY{o}{.}\\PY{n}{value} \\PY{o}{=} \\PY{n}{jnp}\\PY{o}{.}\\PY{n}{zeros\\PYZus{}like}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{sequence}\\PY{o}{.}\\PY{n}{head}\\PY{o}{.}\\PY{n}{bias}\\PY{o}{.}\\PY{n}{value}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{forward}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{x}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{c+c1}{\\PYZsh{} Use full float32 products consistently across GPU compilation contexts.}\n",
       "        \\PY{k}{with} \\PY{n}{jax}\\PY{o}{.}\\PY{n}{default\\PYZus{}matmul\\PYZus{}precision}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{highest}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{:}\n",
       "            \\PY{n}{correction} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{sequence}\\PY{p}{(}\\PY{n}{x}\\PY{p}{)}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{prediction}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\n",
       "        \\PY{n}{baseline} \\PY{o}{=} \\PY{n}{x}\\PY{p}{[}\\PY{p}{:}\\PY{p}{,} \\PY{o}{\\PYZhy{}}\\PY{l+m+mi}{24}\\PY{p}{:}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{]} \\PY{k}{if} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{task} \\PY{o}{==} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{rnn}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{else} \\PY{n}{x}\\PY{p}{[}\\PY{p}{:}\\PY{p}{,} \\PY{o}{\\PYZhy{}}\\PY{l+m+mi}{1}\\PY{p}{,} \\PY{o}{\\PYZhy{}}\\PY{l+m+mi}{1}\\PY{p}{:}\\PY{p}{]}\n",
       "        \\PY{k}{return} \\PY{n}{baseline} \\PY{o}{+} \\PY{n}{correction}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "class ForecastModel(nn.Module):\n",
       "    \"\"\"Learn a recurrent correction to an available historical baseline.\"\"\"\n",
       "\n",
       "    def __init__(self, task, hidden=32, *, rngs):\n",
       "        super().__init__()\n",
       "        self.task = task\n",
       "        self.sequence = models.SequenceRegressor(\n",
       "            features=5 if task == \"rnn\" else 4,\n",
       "            hidden=hidden,\n",
       "            outputs=24 if task == \"rnn\" else 1,\n",
       "            kind=task,\n",
       "            rngs=rngs,\n",
       "        )\n",
       "        self.sequence.head.weight.value = jnp.zeros_like(self.sequence.head.weight.value)\n",
       "        self.sequence.head.bias.value = jnp.zeros_like(self.sequence.head.bias.value)\n",
       "\n",
       "    def forward(self, x):\n",
       "        # Use full float32 products consistently across GPU compilation contexts.\n",
       "        with jax.default_matmul_precision(\"highest\"):\n",
       "            correction = self.sequence(x)[\"prediction\"]\n",
       "        baseline = x[:, -24:, 0] if self.task == \"rnn\" else x[:, -1, -1:]\n",
       "        return baseline + correction"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Code(inspect.getsource(ForecastModel), language=\"python\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d53f3bea",
   "metadata": {},
   "source": [
    "## Objective and optimization\n",
    "\n",
    "AdamW uses learning rate 0.001 and weight decay 0.001. Gradient norms are clipped to 1.0. Model state and optimizer state are explicit compiled-step arguments."
   ]
  },
  {
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       ".output_html .il { color: #666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"nd\">@js</span><span class=\"o\">.</span><span class=\"n\">jit</span>\n",
       "<span class=\"k\">def</span><span class=\"w\"> </span><span class=\"nf\">train_step</span><span class=\"p\">(</span><span class=\"n\">model</span><span class=\"p\">,</span> <span class=\"n\">optimizer</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">y</span><span class=\"p\">):</span>\n",
       "    <span class=\"n\">loss</span><span class=\"p\">,</span> <span class=\"n\">grads</span> <span class=\"o\">=</span> <span class=\"n\">js</span><span class=\"o\">.</span><span class=\"n\">value_and_grad</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">candidate</span><span class=\"p\">:</span> <span class=\"n\">F</span><span class=\"o\">.</span><span class=\"n\">mse_loss</span><span class=\"p\">(</span><span class=\"n\">candidate</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">),</span> <span class=\"n\">y</span><span class=\"p\">))(</span><span class=\"n\">model</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">grads</span><span class=\"p\">,</span> <span class=\"n\">norm</span> <span class=\"o\">=</span> <span class=\"n\">optim</span><span class=\"o\">.</span><span class=\"n\">clip_grad_norm</span><span class=\"p\">(</span><span class=\"n\">grads</span><span class=\"p\">,</span> <span class=\"mf\">1.0</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">optimizer</span><span class=\"o\">.</span><span class=\"n\">step</span><span class=\"p\">(</span><span class=\"n\">grads</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">loss</span><span class=\"p\">,</span> <span class=\"n\">norm</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{n+nd}{@js}\\PY{o}{.}\\PY{n}{jit}\n",
       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{train\\PYZus{}step}\\PY{p}{(}\\PY{n}{model}\\PY{p}{,} \\PY{n}{optimizer}\\PY{p}{,} \\PY{n}{x}\\PY{p}{,} \\PY{n}{y}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{n}{loss}\\PY{p}{,} \\PY{n}{grads} \\PY{o}{=} \\PY{n}{js}\\PY{o}{.}\\PY{n}{value\\PYZus{}and\\PYZus{}grad}\\PY{p}{(}\\PY{k}{lambda} \\PY{n}{candidate}\\PY{p}{:} \\PY{n}{F}\\PY{o}{.}\\PY{n}{mse\\PYZus{}loss}\\PY{p}{(}\\PY{n}{candidate}\\PY{p}{(}\\PY{n}{x}\\PY{p}{)}\\PY{p}{,} \\PY{n}{y}\\PY{p}{)}\\PY{p}{)}\\PY{p}{(}\\PY{n}{model}\\PY{p}{)}\n",
       "    \\PY{n}{grads}\\PY{p}{,} \\PY{n}{norm} \\PY{o}{=} \\PY{n}{optim}\\PY{o}{.}\\PY{n}{clip\\PYZus{}grad\\PYZus{}norm}\\PY{p}{(}\\PY{n}{grads}\\PY{p}{,} \\PY{l+m+mf}{1.0}\\PY{p}{)}\n",
       "    \\PY{n}{optimizer}\\PY{o}{.}\\PY{n}{step}\\PY{p}{(}\\PY{n}{grads}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{loss}\\PY{p}{,} \\PY{n}{norm}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "@js.jit\n",
       "def train_step(model, optimizer, x, y):\n",
       "    loss, grads = js.value_and_grad(lambda candidate: F.mse_loss(candidate(x), y))(model)\n",
       "    grads, norm = optim.clip_grad_norm(grads, 1.0)\n",
       "    optimizer.step(grads)\n",
       "    return loss, norm"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Code(inspect.getsource(train_step), language=\"python\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16855b2f",
   "metadata": {},
   "source": [
    "## Reproduce the run\n",
    "\n",
    "Select GPU 0 before starting Python or Jupyter: `CUDA_VISIBLE_DEVICES=0`. The published notebook displays saved outputs. To reproduce locally, set `TRAIN=True` and choose a fresh `RUN` directory; the command downloads missing data explicitly. Existing runs are preserved. Full training and notebook execution are local-only; CI renders saved outputs."
   ]
  },
  {
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   "id": "6c8477e7",
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     "shell.execute_reply": "2026-10-14T13:51:28.800045Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python -m examples.run rnn --download --output runs/forecast-gallery-v2/rnn --epochs 80 --limit 100000 --batch-size 64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>task</th>\n",
       "      <th>epochs</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>limit</th>\n",
       "      <th>seed</th>\n",
       "      <th>hidden</th>\n",
       "      <th>patience</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>matmul_precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rnn</td>\n",
       "      <td>80</td>\n",
       "      <td>64</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>15</td>\n",
       "      <td>0.001</td>\n",
       "      <td>highest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  task  epochs  batch_size   limit  seed  hidden  patience  learning_rate  \\\n",
       "0  rnn      80          64  100000     0      32        15          0.001   \n",
       "\n",
       "  matmul_precision  \n",
       "0          highest  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>first_origin</th>\n",
       "      <th>last_origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train</th>\n",
       "      <td>2184</td>\n",
       "      <td>2017-01-08T00</td>\n",
       "      <td>2022-12-31T00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>validation</th>\n",
       "      <td>365</td>\n",
       "      <td>2023-01-01T00</td>\n",
       "      <td>2023-12-31T00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <td>731</td>\n",
       "      <td>2024-01-01T00</td>\n",
       "      <td>2025-12-31T00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count   first_origin    last_origin\n",
       "train       2184  2017-01-08T00  2022-12-31T00\n",
       "validation   365  2023-01-01T00  2023-12-31T00\n",
       "test         731  2024-01-01T00  2025-12-31T00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source SHA-256: b02c7e876ac60b0d9e94c626faa3ad107ad93f79c8a3650dde9173f47cc01b69\n",
      "Training devices: cuda:0 NVIDIA GeForce RTX 5090\n",
      "Target normalization: 13.048811425528678 9.7767652981368\n"
     ]
    }
   ],
   "source": [
    "from examples.train_gallery import RECIPES\n",
    "import subprocess\n",
    "TRAIN = False\n",
    "command = [sys.executable, '-m', 'examples.run', 'rnn', '--download',\n",
    "           '--output', str(RUN), *RECIPES['rnn']]\n",
    "print(' '.join(part.replace(str(ROOT) + '/', '')\n",
    "               for part in ['python', *command[1:]]))\n",
    "if TRAIN:\n",
    "    subprocess.run(command, cwd=ROOT, check=True)\n",
    "if not (RUN / 'report.json').exists():\n",
    "    raise FileNotFoundError('Set TRAIN=True with a fresh RUN directory to reproduce the experiment.')\n",
    "report = json.loads((RUN / 'report.json').read_text())\n",
    "metadata = json.loads((RUN / 'data.json').read_text())\n",
    "display(pd.DataFrame([report['config']]))\n",
    "display(pd.DataFrame({split: metadata[split] for split in ('train', 'validation', 'test')}).T)\n",
    "print('Source SHA-256:', metadata['source_sha256'])\n",
    "print('Training devices:', ', '.join(report['devices']))\n",
    "print('Target normalization:', metadata['target_mean'], metadata['target_scale'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee74e520",
   "metadata": {},
   "source": [
    "## Training history\n",
    "\n",
    "The curve shows training and validation MSE in normalized target space. The selected checkpoint is the epoch with the lowest validation loss, not necessarily the last epoch."
   ]
  },
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected epoch: 75\n",
      "Completed epochs: 80\n"
     ]
    }
   ],
   "source": [
    "display(Image(filename=str(RUN / 'learning.png')))\n",
    "print('Selected epoch:', report['selected_epoch'])\n",
    "print('Completed epochs:', len(report['history']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c5a9d85",
   "metadata": {},
   "source": [
    "## Held-out evaluation\n",
    "\n",
    "MAE and RMSE are in degrees Celsius, averaged over all forecast hours and origins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f8174d16",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T13:51:28.805273Z",
     "iopub.status.busy": "2026-10-14T13:51:28.805171Z",
     "iopub.status.idle": "2026-10-14T13:51:28.810279Z",
     "shell.execute_reply": "2026-10-14T13:51:28.809831Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>1.8077</td>\n",
       "      <td>2.3978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline_daily</th>\n",
       "      <td>2.5663</td>\n",
       "      <td>3.4088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline_persistence</th>\n",
       "      <td>2.8847</td>\n",
       "      <td>3.6364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         MAE    RMSE\n",
       "model                 1.8077  2.3978\n",
       "baseline_daily        2.5663  3.4088\n",
       "baseline_persistence  2.8847  3.6364"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = pd.DataFrame(report['evaluation']).T\n",
    "display(scores.rename(columns={'mae': 'MAE', 'rmse': 'RMSE', 'log_rmse': 'Log-RMSE'}).round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9793ea20",
   "metadata": {},
   "source": [
    "## Forecasts through time\n",
    "\n",
    "The four panels use January 15, April 15, July 15, and October 15, 2024—calendar dates chosen without ranking model error. The dashed divider separates known history from the 24-hour forecast. The red curve is the model forecast; black is the observed future; dotted blue repeats yesterday. All 731 held-out daily origins are included in the aggregate metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0a9ce391",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T13:51:28.811104Z",
     "iopub.status.busy": "2026-10-14T13:51:28.811013Z",
     "iopub.status.idle": "2026-10-14T13:51:28.815325Z",
     "shell.execute_reply": "2026-10-14T13:51:28.814928Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dNZZr37692Lx5s3SNCSFEfHy82LZtm/Dy8hIARKdOnTSW++GHHwTw/oulrVu3SrGmpaWJyMhI8b///U8MHjw42/taCCEGDhwoAAgPDw9x5MgRkZqaKoR4f+2EhYWJJUuWiG+++Ubn8saSPuEuhBA///yzdC958eKFWtnMEu65uQeovmitUqWKxno9PT2l68DMzEx631NR7des7r+6HD16VEqirlixQqxZs0Z6rzx27JhULi+uFyG0n0uJiYnSl3dOTk7i1KlTOaprbqiOvY2NjZDL5WLQoEHSl3dxcXFi0aJF0v6ZNm2axvJPnjyRjn/nzp3FP//8I93jnz9/LqZNmya1D7Zv366xvK62UWpqqvTe5uzsLP7880+RkpIihBDi5s2bwsfHRy1hmtOE++7du6V1fPHFF+Lhw4dCiPcJ3v/973/SFwf16tWTrm2VnCbcs7N8Vk6fPi0lkOfMmaN2HUVFRYmDBw+K/v37i8jISLXlrl27Jn0JPHjwYHHr1i2pfuHh4WLEiBECeP/l/t9//62x3by+F48fP166fqdNmybevHkjhBDi9evX4ttvv5X2sbZ7req929nZWVhZWYn58+dL791RUVFqX/4dPXrUIPuXiIjyJybciYg+ABkT7kII0alTJykpoVQqpemZJdxDQ0OFubm5sLa2FlevXtW6rbdv30q9x7R9YO3Ro4cA3vfuff78uUhMTJR6ENWuXVtrjx9DJtxbt24tgPc9yOPj4zXmHzhwINOeS1mZN2+eACCaNm2qMS8oKEhKZmf8MCyEECEhIQJ438P45cuX2dquPgnat2/fSh/6Dxw4oLVMSkqKqFGjhgAgFixYoDZPdR4BELNmzdJYNiYmRq3H2fr16zXK3L9/X5p/+vRptXnpzz0XFxetPc5u3bol9cafO3eu2rz//e9/Anj/KwXVFwEZ/fPPP0ImkwkrKyuN9auSTObm5lp7+Otry5YtUtIro7xOuAMQv/76q9bl09LSRNmyZQUAsWzZMp3b6dChgwAgRo8enWnM2mSVBG7VqpUAIMqUKaP2BZbK5cuXpQRYxl+zpN+XtWvX1npNCSHE999/LyWKtN1fhBBi27ZtAoAoWLCglCgTQog7d+5IvRInTZqkZ62zFhERIeRyuZDJZBpfZKjuUbp62eqiT8Jd9aXfhg0bchK20WRMuMfHx4tixYoJAGq//BAi84R7bu4BV69elZJkUVFR0vTw8HDpeu7Vq5cAILZu3aq2zlKlSgkAYs2aNTndBdKvKOzt7aUe+Rmvuby4XoTQPJeio6OlY1SsWDFx/fr1HNczN9Ife133xnHjxkn7KKMBAwYIAKJXr146t5HZFy+62kabN2+W5qX/hZ5KXFyc9KVNbhLuquvXx8dH6/HbtWuXtI0tW7aozcsPCfc5c+YIAKJFixbZWk7VEWDKlCk6y6iuny+++CJb6zb0vTgiIkK6BnXFqzpHLS0tNX6NkP69W9e9XNVGGzRokNr0nO5fIiLKnziGOxHRB+qnn36Cubk5rl69io0bN+q1zJo1a5CWloZWrVqhSpUqWsvY29ujY8eOAKB1XOOlS5eiVKlSeP78Ofr3748JEybg33//hb29PTZu3AhLS8sc1ykr0dHRUkwTJ06EtbW1RpmWLVuibt26Od5G27ZtAQDnzp1DWlqa2rz+/fvDxsYGERER2Ldvn8ayqgfode7c2SgPx9u6dSuio6NRrVo1tGzZUmsZCwsL9OzZE4D24wcACoUCY8aM0Zju4OAg7buSJUuiV69eGmU8PT1RpkwZAMC1a9d0xjps2DAUKlRIY3r58uXx5ZdfAoD0MEGVVatWAQCGDx8OR0dHreutUaMGvL29kZycjOPHj2st06pVK1SrVk1nbFlRnQMPHjzAs2fPcrweQ3B2dsbQoUO1zjt16hTu3buHggULYtCgQTrX0a9fPwC6z4ecyng92tjYaJSpVq0aOnfuDACZ3qcmTpwIc3NzrfNU58W4ceN03l86duwIBwcHREVFqY23vHbtWiiVSri4uEhjGBtCsWLFUKVKFQghcPbsWbV5Tk5OAKA2nrehGGvdqnGHM44Xn1PW1tbS8zmWLl2qMea/Lrm5B1SuXBkuLi4QQqhNV/3dpEkTNGnSBABw7NgxaX54eDhCQ0MBQO0ZDdn1888/w8vLC7GxsYiJiUG5cuWkZ64AeXe9ZPTkyRP4+Pjg5MmTKFeuHM6ePYuKFStmp2pqtD2zIie+++47rdO/+OILAO+fh6J6aDMAJCYmYsOGDQCAb775Rud6Vfe7f//9F8+fP9crFtV7Uf369dG0aVON+TY2Npg0aZJe69Ll2rVruH37NoD3ddd2/Nq3b4/atWsDyPz4m4rq/vPy5UuN9pEuYWFhOHbsGCwsLLQ+P0FFddyOHDmi97oBw9+Lt27ditTUVCgUCkyePFlrme+++w5yuRwpKSkICQnRWqZEiRLo37+/1nkdOnQAoNmGysn+JSKi/IsJdyKiD1S5cuWkh5FOmzZN54NM0ztz5gwA4NChQyhcuLDOf6tXrwYA6SF+6Tk6OmLjxo2wsLDAgQMHsGjRIgDAkiVLULp0aUNVT6vLly9LD5pSJU60yWwe8P5hdjNmzEDdunXh4uICCwsLKYlQoUIFAO8frprxwW1OTk7o3r07gP9LrquEh4fj8OHDAIAhQ4Zkr2J6Uh2/27dvZ3r8vv/+eykmbSpUqABbW1ut89zc3AAANWvW1Jl8U5XJ7MF2+hyfa9euSedtWloazp8/D+D9w+Eyq9+dO3cyrV/9+vV1blslNjYW8+bNg6+vLwoVKgQrKyvpHEifCIuIiMhyXcZUq1YtWFlZaZ2nOh9iYmJQtGhRnftr8ODBAHTvr5y6fPmy9PDKZs2a6SzXvHlzAOrHOyNdxywyMlKKe+DAgTrrWKRIEenBeenrqUrANG/eHAqFIlv1UyqV2LBhAzp06ICSJUvC2tpa7UGDqodWZzxH2rVrBwBYtGgRevbsiR07duh8kHF2qdY9efJkDBkyBAcOHND5kE1T8/f3R7ly5ZCcnIxp06ZlWT6394D0D5RMn1BX/d2kSRMpoa5tfunSpVGyZMkc19fa2hpz5syRXv/8889qXwrnxfWS0X///Yd69erh+vXrqFu3Ls6cOZOrOhpKgQIFpC9uMypatKj0d/r3mEuXLkkPi2zRooXOc8Pb21taRt973j///AMgd+0KfbdhYWEBX19fneVUx19VPj9p2rQpFAoFrly5Ah8fH6xatUr6skoX1fuUUqlEhQoVdB63Vq1aAQDi4uLw6tUrtXXk5b1Ytd9r1aoFBwcHrWWcnZ1Rs2ZNtfIZ1apVS2cbSnWOv379Wm16TvYvERHlXxamDoCIiHIuICAAwcHBePjwIZYuXYqRI0dmWv7JkycA3n+giYuLy3L96XuXpVenTh2MHj0av/zyCwCge/fu6N27dzajVzd69Ghs3rxZ6zxVL+MXL15I04oVK6ZzXcWLF9c579y5c2jTpg2io6OlaXZ2drCxsYFMJkNaWpr0gSwuLk6jp/qwYcOwevVq7Nu3D5GRkVIcK1euhFKphJeXl5T0MTTV8UtMTJQSD5nRdfzs7e11LmNhYaF3mcy+5Mns+Kjmpaam4vXr13Bzc8Pr16+RlJQEIPNEfnq66qetZ316d+/eRdOmTdU+nNvY2MDJyQlmZu/7Iqh6RupznRhTZnVRnQ8pKSl69eRMSEgwWFxA9q/H9Mc7I131VNURgN5J6/Tnhere4e7urtey6dfRrl07tZ7SVlZWKFCggNTL/vXr10hJSdE4R3r16oWLFy/it99+w6ZNm6Tes2XKlEGLFi0wYMAA1KhRI1vxqEycOBH//vsv/vzzT6xYsQIrVqyATCaDt7c3WrVqhUGDBsHLyytH6zY0c3Nz/Pjjj+jSpQs2bNiAiRMnonLlyjrLG+Ie0LhxY2zdulUtoZ6+h3uhQoXg7u6O27dv49mzZyhcuLA0Pze921XS98rP2EM/L66XjFRfALi5ueHQoUOws7PTazlj0+f9BVB/j0l/L9C357qu94iMVMcmJ+2Ks2fPSr9KyGjhwoXSl/SqbRQsWBByuTzL7aQ/X/ILT09PrFy5EsOGDcO5c+dw7tw5AICrqysaN26MXr16oUOHDmqJZtVxUyqVOTpueX0v1udcALI+TjlpQ+Vk/xIRUf7FHu5ERB+wYsWKSUn2WbNmST08dVH9RPWbb76BeP8cj0z/nThxQut6oqOjsWXLFun15cuXs9x2VmJiYvD8+XOt/wwlNTUVPXv2RHR0NKpWrYp9+/bh7du3iI2NxfPnz/Hs2TOphyUAqTdierVr10b16tWRlpYmDX+QlpYm/SpA1ZvYGFTHr3v37nodP32HccgP0v98ev/+/XrVTzVkRUZZDbXg7++PiIgIeHh4YMuWLXj16hXi4uLw4sULPHv2DJGRkVJZbedAXsqsLqp9VqdOHb32l6nrkhld9Ux/Xty+fVuvOqYf6iKniYkff/wRx48fh7W1NRYsWIDw8HAkJibi1atXePbsGZ49e4Y6deoA0H6OBAUF4c6dO/jpp5/QunVrODk54f79+1i8eDFq1qypdUgnfVhaWmLz5s24evUqpk+fjiZNmsDGxgY3btxAYGAgvL29pS9Cs2PNmjVGOUc6d+6MOnXqQKlU6hyeQcUQ9wBVL+Q7d+7gyZMnuH//Ph4/fgxvb28pSZ1xWBlDJtzzir7DyXTt2hVWVlZ4/vw5hg8fbpBhKlT7fs2aNbleV3akjz0hIUGv88NYX36nl5ycrLPtYugvOfOD3r17Izw8HEuXLkX37t1RokQJvHz5En/++Sc6duwIX19ftV/dqI6bm5ub3u9THh4e0vL59V5sLNndv0RElH8x4U5E9IGbPHkynJ2d8eLFiywTLYULFwaQ+6ElBg8ejEePHqFYsWJwcXHBvXv38PXXX+dqnekTPrqShOl79aVPimaka965c+cQHh4Oc3Nz7NmzB61bt9bohaTPmN3Dhg0DAPzxxx9QKpVSb3e5XK5zzE5DMNTxywv6HB8LCwsUKFAAAKShfQDj1u/x48fSMCMbN27El19+KcWgYohx21V1yeyXCDExMbnahqnPh/TXY2ZD76jmpT/e+lLVEchZPXO6j1Q9IadPn44xY8agZMmSGsn7rM6TMmXKYMqUKdi3bx9evXqFc+fOSc/HWLhwIXbt2pWtmNKrUqUKZs6ciaNHjyI6OhpHjhxBw4YNkZaWJvWCzy9Uvaz379+PkydP6ixniHtA+fLlpWN+7NgxteFkVNIPK3P37l3p/MztkCFZyYvrJaM2bdpg+/btkMvlWL9+Pfr27fvBjg2d23tBZlTHJiftikaNGun15Z9qG1FRUdIvObRRHf+Mv2LIi/cUfRUoUABDhw7Fpk2b8OjRI9y/fx+TJ0+GTCbD6dOn1b4IUx23qKioHP1iLK/vxar9ntVwcrqOkyFkZ/8SEVH+xYQ7EdEHztnZWeo5+Msvv+Dly5c6y6rGfT1y5IheQ5Jos2LFCoSEhMDMzAzr1q2TenmvXbtW50O+VMN05Lb3ZPXq1aV16XpgJqA+Pm96jx8/BvD+57m6fi585MiRLOPo1asXHBwcEB4ejoMHDxrkYan67CPV8bt06ZJRHshoSJkdH9W8ypUrSz8Jt7S0lB4Wt3v3bqPFpToHAOh8sGpm54DqOAGZHytnZ2eN7WV04cIFnfP0oTofnj17ZpLxftNfj0ePHtVZTrU/q1Spku2HKnt4eEjXak7Oi3r16gEADh8+nK17nuq46TpHwsLCcP/+fb3XZ2Zmhs8//xwhISHSGNqqZz6kLwNk/z5pYWGBpk2bYu/evZDL5RBC6HUfyyu+vr5o3bo1gMwfdmmoe0D6cdzTDyejkr6Hu2q+l5cXihQpkuNt6iMvrhdt2rRpg507d0KhUGDjxo3o1asXUlNTc73evJb+eRaGfo9Qjcedk3ZFdreRmpqa6RdPquNfq1Yttem5fU8xVDtMG09PT8yePVt60Hr6e5vqfSotLQ379+/P9rrz+l6cfmx2XV9gREdHq431bmyZ7V8iIsq/mHAnIvoIjBw5EsWLF0dsbCx++OEHneUGDBgACwsLREVFYcaMGZmuMzk5WWOYmNu3b0s/v/3mm2/QuHFjfPHFFxgxYgSA9z2/tT3gSfXgqfTjpueEk5MTWrRoAQAIDAzUmkA7cuSI1IM5I9WYurqGqomIiMCvv/6aZRy2trbo27cvgPdD+ezbtw9A7h6Wqs8+6tq1K5ycnJCSkoJx48Zl+sFZqVTmen/nxtKlS7WOuX3nzh2EhIQAgDS2rYpq/+3bt0/ap7pkfNiYvtKPq6ytF3BsbCxmzZqlc/n0D1HLbP9WqVIFAPD3339rTZDcvn0b27Zt0ydknRo3biw9eHDs2LFITk7OtHxO95kuTk5OaNmyJQBg3rx5WsdL/vfff7F161YAQM+ePXO0HdUwTatWrcKVK1cyLZuxjn5+fjA3N8erV6+yvOelpzpPdPUUz2x4lMx6r5qbm0tJw/Rf3gD63QMyW7dcLpeGGsm4blObPXs2zMzMcOHChUzPe0PcA9In1E+cOAEzMzO1oUWKFSuGsmXLIjQ0VBoKLC+Gk8mr60Wbli1bYteuXbC2tsaff/6JHj166PWg9fzE1tZWSjjOmTMHjx49yrR8du53qveiv/76S+tQegkJCZg3b57+wWpRuXJl6aHss2bN0vpLg3379klJ84zHX/We8uTJE62J9RcvXmg8zD09Q7TDMrv/AJAeEpz+/lO2bFnp+ps6dWqWvfAzHre8vhd36dIFFhYWSExMVHsIcno//fQTkpKSYGlpiS5duujcRnblZP8SEVE+JoiIKN+bMWOGACDc3d11llm5cqUAoPbv+PHjGuVmzpwpze/bt6+4fv26NC8lJUVcuXJFzJw5U5QoUUKcPn1ampeYmCiqVKkiAIg6deqIlJQUaV5CQoKoWLGiACA+//xztXlCCHHv3j1pm5s3b875jhBC/P3338Lc3FwAEE2aNBH//fefFPvmzZuFs7OzcHJy0rq/oqOjha2trQAgGjZsKO7cuSOEECI1NVUcOHBAeHp6ChcXFynW0NBQnXFcv35dbV97eXnlql4rVqwQAISzs7O4ffu2znJr1qyRttm6dWtx/vx5kZaWJoQQIi0tTdy6dUsEBgaKcuXKiXXr1qktqzqPfH19da6/f//+AoDo37+/zjK+vr4CgJgxY4ba9NDQUCk2R0dHUbVqVXHx4kUhhBBKpVIcPnxYuLu7CwCiRIkSIiYmRm351NRU0axZMwFAWFlZiR9++EFERkZK89+9eyeOHTsmRowYIRwdHTXiUq179erVOmNPS0sTJUuWFACEt7e3+Oeff6R5Z8+eFdWrV1c7BzJeQ3FxccLKykoAEHPnzhVKpVLrdqKjo4WdnZ10TajO0+TkZLFjxw5RtGhRUaBAAZ3XtT7HQQghjhw5IiwsLKTr8siRIyI5OVma/+DBA7FkyRJRs2ZN8cMPP2S6Lm2y2qeXLl0SlpaWAoBo0KCBuHbtmhDi/X7eu3evKFq0qAAgPD09RWxsrNqyx48fl/ZzZmJjY0WlSpWk8+q3334TUVFR0vw3b96Iffv2ib59+4oKFSpoLD958mRpOwMHDhR3796V5sXExIhNmzaJjh07qi3Tp08fAUDY29uLrVu3Sve0hw8fip49ewqZTCacnZ21XgdVqlQRI0eOFMePHxfv3r2TpkdGRoqvv/5aiuXAgQNqy/Xu3VsAEPXq1ROvX7/Wui/c3NzE5MmTxblz50RiYqI0/d69e6Jr164CgDAzMxM3b97MdJ8amuqekNn5qtqn6f9llNt7gBDq7zcARI0aNTTKDB06VK1Mbt+XVNKf09ref/PiehFC93V79OhRYWNjIwCIjh07iqSkpBzXNSdWr16dZVsm/ftIxvfgJ0+eSPuoaNGi4n//+594+/atNP/FixciJCREdOzYUbRo0UJj3bqOTUpKiqhevboAIAoUKCBCQkJEamqqEEKIW7duCV9fX6ldoevY6mP37t3SOjp27CgePnwohHj/vrB+/Xrh4OAg3QNU21dJS0uTjquXl5f4+++/hVKpFGlpaeL48eOifPny0nuKtnPk8OHDAoAwNzcXZ86cyVH8gwYNEl27dhUhISHi+fPn0vTY2FixZMkS6b1xypQpastdv35dej8sV66c2LFjh0hISJDmR0REiP/973+iSZMmYtCgQWrLmuJePH78eAFAyGQyMX36dPHmzRshxPv3mu+++05a7ptvvtHYR/q8d+u6DnK6f4mIKH9iwp2I6AOgT8I9NTVVlCtXLsuEu1KpFNOmTRMymUwqZ21tLVxcXKREturfX3/9JS03cuRI6UPPgwcPNNZ748YNYW1tLQCIb7/9VmN+06ZNpfXa29sLd3d34e7uLhYsWJDt/bFs2TK1+B0dHYVcLpc+zM2fP1/n/lqyZIlaHe3s7IRCoRAARMGCBcWuXbv0SrgLIUSDBg2ksoGBgdmuR3qvX78Wrq6u0voKFiwo7aNz585p1EH1wQuAkMvlwsXFRUrkqP6tX79ebbm8TLhv2rRJ2NvbS/tYleQBIJycnMTff/+tdd0xMTGiXbt2avVwcHAQTk5OasfcwsJCY1l9Eu5CvE96qJLUAISNjY0Un62trThy5Eim19DAgQPVli1ZsqRwd3cX48ePVyu3cuVKtZjt7e2l4/b555+LRYsW5TrhLoQQ27dvl/Y1AGFpaSlcXFyka0L1b9asWVmuKyN99ummTZvUzkcHBwfpmgLef7ly69YtjeWyk0CMjIwUn3/+uVReJpMJJycnKUGl+lemTBmNZVNTU8VXX32lcd07OztLxydj8jYsLEy4ubmpnW+Ojo7S659++knndaDaZ+njVH3Rp/o3duxYjThPnjwpxWNubi6KFCki3QNU0q/DzMxMODs7q+1rmUyWo3tqbumTcH/48KHaeaLruOfmHqBSokQJqdzEiRM15m/atElt/emTW7mRVcJdtW1jXy+ZXbcnTpyQzsd27drladI9twl3Id4nwD/77DO166BAgQIa11izZs00ls3s2Dx48EDtvJHL5dI1b2VlJXbu3JnlsdXH/Pnz1c5jJycntfOhUqVKal8ypXfgwAG193kbGxvp3ClbtqzYuHGjznMkJSVFeHl5SfOdnZ2l+8uWLVv0il31vpT+Ppr+iwjg/RdJ6ZPbKn/99ZcoXLiwVM7c3Fy4uLhI7UbVv4wJd1Pci5OSkkS3bt007rVmZmbStJ49e6p9uZ1xH+Uk4Z6b/UtERPkPE+5ERB8AfRLuQgixbds2tYZ5Zh8Kr1+/LkaMGCHKly8v7OzshIWFhShYsKCoV6+emDhxojh79qxUNn2vrIy9ptNbvHix9OHk2LFjavPevHkjxo4dKz777DO15ELGD0j6Onv2rGjfvr0oUKCAUCgU4rPPPhNTpkwRb9++zfJD/d69e0WjRo2kZLunp6cYOXKkiIyMzPLDfnoLFy6UPpi/fPkyR/VI7/bt26JHjx6iWLFiah+qtR3H0NBQMWHCBFGlShXh4OAgzM3NhbOzs6hZs6YYOXKkOHz4sNTzXSUvE+6hoaHizp07ol+/fqJYsWLCyspKFCtWTAwePFg8fvw4y32xb98+0b17d1GyZEkhl8uFlZWVKF68uGjRooWYPXu21i999E24C/H+/Gnbtq2U7ChZsqTw9/eXeqJntu8TExNFQECAqFSpktoXCdr22b59+0STJk2Eg4ODsLa2FhUrVhQ///yzSEpKyvQ8zU7CXQghnj9/LmbMmCFq164tnJ2dhbm5uXBwcBBVqlQRgwYNEtu3b89RYk3ffXrv3j0xdOhQ4enpKeRyubCzsxNVq1YVM2fO1Pglg0p2EohCvE+cb9y4UXTo0EEULVpUWFlZCYVCITw8PET79u1FUFCQePLkic7l//rrL9G7d2/pnHJychLe3t5i4MCB4siRIxrlHz9+LAYOHCiKFi0qLCwshJubm2jXrp04ePCgEEL3dXDu3Dkxc+ZM0bRpU1G6dGlhY2MjrKyshLu7u+jevbs4evSozhj37dsnmjVrJlxcXNSSOyqHDh0SU6ZMET4+PsLd3V0oFAqhUChEmTJlhL+/v9ovNvKSPgl3IYQYNWpUlgl3lZzcA1T69esnbWP//v0a858/fy4lPb29vfWqoz70SbgLYfzrJavr9vTp09KXdK1bt1b7tYQxGSLhLsT7e/CyZctEixYtRKFChYSFhYWwsbERZcqUEV27dhXLly/X+iuRrI7Nq1evxLhx40SpUqWElZWVcHNzE19++aV0XRki4S7E+1869OnTR5QoUUJYWVkJR0dH8fnnn4sFCxZkeSzOnz8v2rVrJ5ydnYVcLhdly5YVkydPFm/fvs3yHImIiBCDBg2S6qcqq897phBC3L9/X/z666+iU6dOoly5csLJyUlYWFiIQoUKiebNm4s//vhDo2d+em/fvhWBgYGiYcOGUicPOzs7Ub58edGnTx8RHBysNZlsinuxEEKEhISI1q1bC1dXV2FhYSFcXV1F69atxbZt23Quk5uEe273LxER5S8yIYzw5BQiIqJPQPv27bFnzx707NkTGzZsMHU4JhcWFoZSpUoBAEJDQ+Hh4WHagIiIiIiIiIjyGJ+4QURElAMPHz6UHuo3fPhwE0dDRERERERERPkBE+5ERETZ9PbtWwwfPhxKpRJ16tSBj4+PqUMiIiIiIiIionzAwtQBEBERfSgmTJiALVu24NmzZ0hOToaFhQWCgoJMHRYRERERERER5RPs4U5ERKSnqKgoPHr0CFZWVqhbty4OHDiAzz//3NRhEREREREREVE+wYemEhEREREREREREREZAHu4ExEREREREREREREZABPuREREREREREREREQGwIQ7EREREREREREREZEBMOFORERERERERERERGQATLgTERERERERERERERkAE+5ERERERERERERERAbAhDsRERERERERERERkQEw4U5EREREREREREREZABMuBMRERERERERERERGQAT7kREREREREREREREBsCEOxERERERERERERGRATDhTkRERERERERERERkAEy4ExEREREREREREREZABPuREREREREREREREQGwIQ7EREREREREREREZEBMOFORERERERERERERGQATLgTERERERERERERERkAE+5ERERERERERERERAbAhDsRERERERERERERkQEw4U5EREREREREREREZABMuBMRERERERERERERGQAT7kREREREREREREREBsCEOxERERERERERERGRATDhTkRERERERERERERkAEy4ExEREREREREREREZABPuREREREREREREREQGwIQ7EREREREREREREZEBMOFORERERERERERERGQATLgTERERERERERERERkAE+5ERCb26tUrrF69Gn369EGFChVga2sLuVyO4sWLo2PHjti+fXuW64iNjUVAQAAqVaoEOzs7ODo6olatWvjll1+QnJysc7nIyEgsXrwYXbt2RZkyZWBtbQ1ra2uUKlUKPXv2xLFjx3JUp2HDhkEmk0Emk8HDwyNH68gop3WMjo7Gzp07MX36dLRr1w5FihSRYluzZo1BYsvtNvz8/KTymf1LTU01SLxEREREHwO2o/WT0zrmRYzx8fHYv38/Zs2ahc6dO8Pd3V1ad0BAQJbLBwQE6NWOvn//fq7iJCLKDgtTB0BE9KkrXLiwWiJVoVDA0tISkZGRiIyMxM6dO9G6dWuEhITAxsZGY/nw8HA0atQIYWFhAAAbGxskJSXhn3/+wT///IPg4GAcPXoUzs7Oass9fvwY7u7uEEJI02xsbCCEQFhYGMLCwrBp0yYMGDAAy5cvh7m5uV71OX78OJYvX56DPaFbTusIADt27IC/v79B4zHWNhQKBRwdHXXOl8lkud4GERER0ceC7eis5aYdnRcxXrx4EW3atMn1eiwtLVGgQAGd8y0smP4iorzDHu5ERCaWmpqK2rVrY/HixXjw4AESEhLw7t07hIaGYuDAgQCA/fv3Y+jQoVqXbd++PcLCwlCkSBEcPnwYcXFxiI+Px6ZNm2Bvb48rV66gT58+GsumpaVBCIGmTZti7dq1iIyMRFxcHN69e4ebN2/iiy++AAD88ccfevUuAd73UBk8eDAsLCxQs2bNnO8UA9VRpXDhwmjdujWmTp2Kbdu2GSQuY2yje/fuePbsmc5/+n5YIyIiIvoUsB2dOUO0o40dIwA4OzujadOmmDhxIjZu3IjChQtnex316tXLtB1tqF8LEBHpRRARkUkdO3Ys0/lDhw4VAAQA8ejRI7V5K1eulOadPXtWY9kNGzZI848cOaI2Lzo6Wly6dEnndpVKpWjVqpUAIOzs7ERCQkKWdRkzZowAIKZOnSr69+8vAAh3d/csl8tMbuoohBCpqaka01TlV69enavYDLUN1b7q37+/QeIhIiIi+hSwHZ253Laj8yJGbe1od3d3AUDMmDEjy+VnzJghAAhfX99cxUFEZEjs4U5EZGKNGzfOdL6qdw4A/PPPP2rz1q5dK62jbt26Gsv26NEDpUqVAgD873//U5vn6OiI6tWr69yuTCbDgAEDAADv3r3D7du3M43z/Pnz+PXXX/HZZ5/hu+++y7RsduSmjgDypFc4e54TERER5T22ozOX23Z0XsTIdjQRfYyYcCciyucUCoX0d1pamvR3fHw8zpw5AwBo3bq11mVlMhlatWoFADh06JDBtp1RUlISBgwYACEEli9frrZcbuRFHYmIiIjo48R2tGHqaKwYiYg+Vky4ExHlcydOnJD+rlSpkvT37du3oVQqAQAVK1bUubxq3rNnz/D69escbdvKygqfffaZznLff/89bt++jYEDB8LX1zdb28hMXtQxPzl69Cg+++wzKBQKODg4oFKlShgzZgzu3btn6tCIiIiIPjhsRxumjsaK0ZBu3ryJihUrwsbGBnZ2dvDy8sLgwYNx5coVU4dGRJ8gJtyJiPKx6OhozJ49GwDg4+MDLy8vad6TJ0+kv4sVK6ZzHennpV8mK6GhoVi6dCmA9w/zdHBw0FruypUrmDt3Ltzc3DBv3jy9168PY9cxv4mIiMDDhw9hY2OD+Ph43LhxAwsXLkTFihWxZMkSU4dHRERE9MFgO9owdTRmjIYUFRWF27dvw9raGklJSbh79y5WrlyJGjVqGHQIHCIifTDhTkSUTymVSvTt2xdPnz6FQqHAokWL1ObHxsZKf9vY2OhcT/p56ZfJTEJCArp27Yr4+HgULFgQP//8s9ZyqampGDBgAFJTU/Hrr7/CyclJr/Xry5h1zE+qV6+ORYsWISwsDElJSXj9+jXevn2LrVu3wtPTE8nJyRgxYgS2bt1q6lCJiIiI8j22ow1TR2PHaAhly5bF3LlzcefOHSQmJuLVq1eIi4vDwYMHUaNGDQgh8OOPP+KXX34xdahE9Alhwp2IKJ8aPXo09uzZAwD4/fffUbly5TzZbmpqKnr16oVLly7B0tISwcHBKFq0qNayP//8M65evYp27dqhW7dueRLfx2jUqFH46quv4O7uLj04ysbGBp07d8aFCxekh1mNHz8eQghThkpERESU77EdbRgfQoy9e/fGxIkT8dlnn8HS0hLA+2F8WrRogb/++gu1atUCAAQEBCAmJsaUoRLRJ4QJdyKifGjChAlST5wFCxZgwIABGmXs7e2lv+Pj43WuK/289Mtok5aWht69e2PHjh2wsLDAhg0b0KJFC61lb926hR9++AF2dnZYvHhxpuvVplatWihcuLDGv86dO2uN11B1zA5t8RUuXBijR4822Day4uLigm+//RYAEB4eznEoiYiIiDLBdrRmvDmpY17EaGwKhQI//fQTAODdu3c4evRonm2biD5tFqYOgIiI1E2aNEn6yWNgYCDGjBmjtVz63jKRkZE6e+5ERkZqXSajtLQ09OnTB3/++SfMzc2xfv16fPnllzrLf/XVV0hOTsbMmTPh7OyMd+/eqc1PTU0FAAghpHlyuVzqefLy5Us8f/5cY73pH9Zk6Dpml7b4AOR575i6detKfz98+BDVq1fP0+0TERERfQjYjjZcOzovYswLGdvRRER5gT3ciYjykYkTJ0oPI5o7dy7Gjx+vs2z58uVhZvb+Nn7jxg2d5VTzChcujAIFCmgto+qRs2nTJulDQvfu3TONNTQ0FAAwZcoU2Nvba/wLDg4GADx69Eia9vvvv0vLh4WFQQih8e/EiRNGqWNOaItPCIE1a9YYbBtERERElHtsRxu2HZ0XMRIRfayYcCciyicmTJiAwMBAAO8/JEycODHT8jY2Nqhfvz4A4MCBA1rLCCFw8OBBAND5k9a0tDT06tULmzdvlj4k9OjRI6fVMChD1fFDd/78eelv1XjuRERERPQe29Ga2I5+j+1oIjIFJtyJiPKBCRMmqP38NasPCSr9+/cHABw/fhwXLlzQmL9lyxbpp5P9+vXTmK/qkfPnn3/CwsICwcHBen9I0NVrRfVPFZu7u7s0TdfPeo1Zx/wuq4egvn79Whp7skSJEqhWrVpehEVERET0QWA72jh1zKsYcyOrdnRSUhKmTp0KALC1tUXTpk3zIiwiIibciYhMLf1Yk/Pnz8/0568Z9e/fH5UqVYIQAl26dJEeBKRUKrFlyxYMHjwYANC6dWuNBqZqrMnNmzdLD3bK6uevppCbOqpERUWp/VN59+6d2vTMHiiVlZxuY/369ejcuTO2bt2KFy9eSNMTEhKwY8cO1K1bV/ogNG/ePOmnwURERESfOrajM2eIdnReePPmjVp7WalUAnj/QNf00zOOI3/q1Ck0a9YM69atQ0REhDQ9JSUFR48ehY+Pj/RFw/Tp0+Hk5JRndSKiT5tMZPWVIBERGc2jR4/g7u4OADAzM4Orq2um5SdMmIAJEyaoTQsLC0Pjxo0RFhYG4P3PR5VKJRITEwEA1apVw9GjR+Hs7Ky23KlTp+Dr6wsAsLS0zHLs84ULF2brg4Sfnx/Wrl0Ld3d3KbacymkdVWQymV7bmTFjBgICAnIUY063sWbNGvj7+0uvbW1toVAoEB0djbS0NADvH0A1f/58jBgxIkexEREREX1s2I7WT27b0XkRo4eHB8LDw7Ms179/f7VnKZ04cQKNGzeWXltbW8PW1hYxMTFISUkB8P7cmDx5Mn788cdcxUhElB0Wpg6AiOhTpuq9ofr7+fPnmZbP2KsDeN9AvXbtGgIDA7Ft2zaEhobC0tIS3t7e6NmzJ0aOHAkrK6tMt52SkpLlthMSErKqjtHktI4fgsaNG+PHH3/EuXPncPv2bbx69QoxMTFwcHBAmTJl0KRJEwwdOpRjThIRERGlw3a0fj7mdnSlSpUQGBiIc+fO4fr164iKikJ0dDRsbGxQoUIF+Pj4YMiQIahUqZKpQyWiTwx7uBMRERERERERERERGQAHgiUiIiIiIiIiIiIiMgAm3ImIiIiIiIiIiIiIDIAJdyIiIiIiIiIiIiIiA2DCnYiIiIiIiIiIiIjIAJhwJyIiIiIiIiIiIiIyACbciYiIiIiIiIiIiIgMgAl3IiIiIiIiIiIiIiIDYMKdiIiIiIiIiIiIiMgAmHAnIiIiIiIiIiIiIjIAJtyJiIiIiIiIiIiIiAzAwtQBfGoKFy6MuLg4lCxZ0tShEBEREdEH7tGjR7C1tcWzZ89MHYrRsR1NRERERIZizHY0E+55LC4uDikpKaYOg4iIKFuEEHj16hUAwMXFBTKZzMQREREApKSkIC4uztRh5Am2o4mI6EPEdjRR/mTMdjQT7nlM1SPn5s2bJo6EiIhIf8nJyViwYAEAYOzYsbCysjJxREQEAN7e3qYOIc+wHU1ERB8itqOJ8idjtqM5hjsRERERERERERERkQEw4U5EREREREREREREZAAcUoaIiIiyZGZmhooVK0p/ExERERFR1tiOJvr0MOFOREREWbKwsEDbtm1NHQYRERER0QeF7WiiTw+/WiMiIiIiIiIiIiIiMgD2cCciIqIsCSGQkpICALC0tIRMJsvVuoQQhgqN6KMjk8lydY0RERFR/mHIdjQRfRiYcCciIqIspaSkYMGCBQCAsWPHwsrKKtvrSExMxJMnT5CcnMyEO1EmZDIZrKysULRoUSgUClOHQ0RERLlgiHY0EX1YOKQMERERGV1iYiIePXqEpKQkJtuJsiCEQFJSEh49eoTExERTh0NERERERNnAHu5ERERkdE+ePEFaWhoUCgWKFSsGCws2QYh0SU1NRWRkpPSrkNKlS5s6JCIiIiIi0hM/7RIREZFRCSGQnJwMAChWrBh/RkuUBSsrKxQrVgwPHjyQhmDieK9ERERERB8GDilDRERERpX+Ians2U6kH9W1wocMExERERF9WJhwJyIiIiIiIiIiIiIyACbciYiIiIiIiIiIiIgMgAl3IiIiypKZmRm8vLzg5eUFMzM2Hz51fn5+sLOz06usTCZDQECAcQMiIiIiyqfYjib69PBKJyIioixZWFigY8eO6NixI8dhz2DNmjWQyWT4559/1KbHxMSgdu3aUCgUOHDggImi+zBt2LABQUFBpg6DiIiIKNfYjib69PBKJyIiIjKwt2/fokWLFrh27Rq2b9+OVq1amTokk0lISMj2h8sNGzbgxo0bGDNmjHGCIiIiIiIiMhIm3ImIiIgMKDY2Fi1btsTVq1exbds2tG7d2tQhmZRCoTB1CACA1NRUKJVKWFlZmToUIiIiIiL6iHFIGSIiIspScnIy5syZgzlz5iA5OdnU4eRb7969Q6tWrXD58mVs3boVbdu2VZuvGvs8MjISHTt2hJ2dHVxdXTFhwgSkpaWplY2Li8P48eNRokQJyOVyeHl5ITAwEEIIqUznzp1RvXp1teXat28PmUyGXbt2SdMuXLgAmUyG/fv3A/i/YXDOnDmDcePGwdXVFba2tujUqRNevnypd331qUfGMdxjY2MxZswYeHh4QC6Xo1ChQmjevDkuX74MAGjUqBH27t2L8PBwyGQyyGQyeHh4SMu/ePECAwcOhJubGxQKBapUqYK1a9eqbTMsLAwymQyBgYEICgqCp6cn5HI5Ll68CFtbW4wePVqjLhERETA3N8fs2bP1rj8RERFRVtiOJvr0sIc7ERERkQHExcWhdevW+PvvvxESEoJ27dppLZeWloaWLVuiTp06CAwMxJEjR/DLL7/A09MTw4cPBwAIIdChQwccP34cAwcORNWqVXHw4EFMnDgRkZGRWLBgAQDAx8cHO3fuxNu3b+Hg4AAhBM6cOQMzMzOcPn0aHTp0AACcPn0aZmZmqF+/vlosI0eOhLOzM2bMmIGwsDAEBQXh66+/xubNm7Osrz710GbYsGEICQnB119/jQoVKuDVq1f466+/cPv2bVSvXh1Tp05FTEwMIiIipHqqHtCakJCARo0a4f79+/j6669RqlQpbNmyBX5+foiOjtZIpK9evRqJiYkYMmQI5HI5SpYsiU6dOmHz5s2YP38+zM3NpbIbN26EEAK9e/fOsu5ERERERES6MOFOREREZAD9+/fHkydPsGXLFinRrU1iYiK6d++OadOmAXifgK5evTpWrVolJap37dqFY8eOYdasWZg6dSoA4KuvvkLXrl2xcOFCfP311/D09ISPjw+USiXOnDmD1q1b48aNG3jz5g26du2K06dPS9s8ffo0qlSpAgcHB7VYXFxccOjQIchkMgCAUqnEr7/+ipiYGDg6OmZaX33qoc3evXsxePBg/PLLL9K0SZMmSX83b94cxYoVw5s3b9CnTx+1ZZcvX47bt29j/fr1UmJ82LBh8PX1xXfffYcBAwbA3t5eKh8REYH79+/D1dVVmtavXz8EBwfj8OHDamPrr1+/Hg0bNkTJkiUzrTcREREREVFmOKQMERERmYQQAnFxcfnuX/ohW7Lj+fPnUCgUKFGiRJZlhw0bpvbax8cHDx8+lF7v27cP5ubmGDVqlFq58ePHQwghDQ1TrVo12NnZ4dSpUwDeJ9aLFy+Ofv364fLly4iPj4cQAn/99Rd8fHw04hgyZIiUbFfFkZaWhvDwcL3qnFU9tHFycsKFCxfw5MkTvbaR3r59+1C4cGH07NlTmmZpaYlRo0bh3bt3OHnypFr5Ll26qCXbAaBZs2YoWrQogoODpWk3btzAtWvXNBL8RERERERE2cUe7kRERGQS8fHx8Pf3N3UYGlavXg1bW9tsL7ds2TKMGzcOrVq1wunTp+Hl5aW1nEKh0EgCOzs7482bN9Lr8PBwFC1aVK23NgCUL19emg8A5ubmqFu3rtSb/fTp0/Dx8UGDBg2QlpaG8+fPw83NDa9fv9aacM/Ym9vZ2RkA1GLRRZ96aDN37lz0798fJUqUQI0aNdCmTRv069cPpUuXznKb4eHhKFu2LMzM1PuMZNwvKqVKldJYh5mZGXr37o0lS5YgPj4eNjY2CA4OhkKhQNeuXbOMgYiIiIiIKDPs4U5ERERkABUqVMC+ffuQkJCA5s2b4/Hjx1rLpR833BAaNGiAv//+G4mJiVLC3cnJCRUrVsTp06elZLy2hLuuWPTp5Z/TenTr1g0PHz7Eb7/9hqJFi2LevHnw9vaWeu0bkrW1tdbp/fr1w7t377Bjxw4IIbBhwwa0a9cuy2F0iIiIiIiIssKEOxEREZGB1K5dGzt27MCLFy/QvHlzvHz5MkfrcXd3x5MnTxAbG6s2/b///pPmq/j4+CA5ORkbN25EZGSklFhv2LChlHD/7LPP4ObmlsNaGV6RIkUwYsQI7NixA6GhoXBxccGPP/4ozU8/zE167u7uuHfvHpRKpdp0bfslMxUrVkS1atUQHByM06dP49GjR+jbt28Oa0NERERERPR/OKQMERERZcnMzEwa8iPjcB45ZWNjg9WrVxtkXYZkY2OTq+WbNm2KjRs3omvXrmjVqhWOHz+u8bDSrLRp0wbLly/HokWLMGXKFGn6ggULIJPJ0Lp1a2lanTp1YGlpiTlz5qBAgQLw9vYG8D4Rv3r1ajg5Oak9HNSU0tLS8O7dO7We5IUKFULRokWRlJQkTbO1tUVMTIzG8m3atMGhQ4ewefNmaRz31NRU/Pbbb7Czs4Ovr6/esfTt2xeTJk2CXC6Hi4uL2j4lIiIiMhRjtKOJKH9jwp2IiIiyZGFhYfDxrWUyWY7GSv8QdOrUCStWrMCAAQPQoUMHHDhwAAqFQu/l27dvj8aNG2Pq1KkICwtDlSpVcOjQIezcuRNjxoyBp6enVNbGxgY1atTA+fPn0b59e6l3eMOGDaUHwWobTsYUYmNjUbx4cXz55ZeoUqUK7OzscOTIEfz999/45ZdfpHI1atTA5s2bMW7cONSqVQt2dnZo3749hgwZgmXLlsHPzw+XLl2Ch4cHQkJCcObMGQQFBWmMeZ+ZXr16YdKkSdi+fTuGDx8OS0tLY1SZiIiIPnHGaEcTUf7GhDsRERGREfj7++P169eYMGECunbtiu3bt+u9rJmZGXbt2oXp06dj8+bNWL16NTw8PDBv3jyMHz9eo7yPjw/Onz+PBg0aSNMKFy6MMmXK4P79+/km4W5jY4MRI0bg0KFD2LZtG5RKJcqUKYPFixdj+PDhUrkRI0bg6tWrWL16NRYsWAB3d3e0b98e1tbWOHHiBCZPnoy1a9fi7du38PLywurVq+Hn55etWNzc3NCiRQvs27ePw8kQEREREZHByIQ+T8Uig1H9zPvmzZsmjoSIiChvKJVK3LlzBwDg5eXFn9JSvtGpUydcv34d9+/fN3UoGvS9bj6ltuWnVFciIiIiMi5jti35iZeIiIiylJycjPnz52P+/PlITk42dThEufb06VPs3buXvduJiIjIqNiOJvr0cEgZIiIi0ktKSoqpQyDKtdDQUJw5cwYrV66EpaUlhg4dauqQiIiI6CPHdjTRp+Wj7eE+f/58dO7cGWXLloWjoyPkcjnc3d3Rr18/XL9+Xedya9asQe3atWFnZ4cCBQqgTZs2OHv2bB5GTkRERETGcvLkSfTt2xehoaFYu3YtChcubOqQiIiIiIjoI/LRJtx/+ukn7N+/HwUKFEDTpk3Rtm1bKBQKrFu3DjVq1MCePXs0lhkzZgz8/f1x48YNNGvWDLVr18bhw4fRsGFD7NixI+8rQUREREQG5efnByEEwsPD8eWXX5o6HCIiIiIi+sh8tEPK7Ny5EzVq1IBCoVCbvnjxYnz11VcYNGgQIiIiYGHxfhccOXIECxcuhIuLC86dO4eyZcsCAM6dO4dGjRrB398fjRo1gpOTU15XhYiIiIiIiIiIiIg+AB9tD/f69etrJNsBYMSIEfD09MTz589x69Ytafr8+fMBAN99952UbAeAunXrYtiwYYiOjsaqVauMHzgRERERERERERERfZA+2oR7ZiwtLQEAVlZWAICEhAQcO3YMALT+tFg1bffu3XkUIRERERERERERERF9aD7aIWV0WbduHe7cuYOyZctKPdnv3LmDpKQkuLq6onjx4hrLVK9eHQBw7dq1PI2ViIgov5DJZChRooT0NxERERERZY3taKJPz0efcJ83bx5u3ryJuLg43L59Gzdv3kTRokWxceNGmJubAwAePXoEAFqT7QBga2sLJycnvHnzBrGxsbC3t8+z+ImIiPIDS0tL9OrVy9RhEBERERF9UNiOJvr0fPQJ94MHD+Lo0aPSa3d3d/zvf/9DjRo1pGnv3r0DANjY2Ohcj62tLaKjo/VOuHt7e2ud/uDBA3h6euobPhERERHRJ4XtaCIiIiL6kH30Y7gfOXIEQgi8efMGp06dQtmyZeHr64sff/zR1KERERERERERERER0Ufko+/hruLk5AQfHx/s27cPdevWxbRp09CiRQvUqlULdnZ2AID4+Hidy8fFxQGA3sPJ3Lx5U+t0XT12iIiI8rPk5GQsXboUADBs2DDpweNERIbGdjQREX1M2I4m+vR89D3cM7K0tET37t0hhMDu3bsBACVLlgQAREREaF0mLi4O0dHRcHZ25vjtRET0yUpISEBCQoKpwyADkslkCAgIMHUYRERERB81tqOJPi2fXMIdAAoWLAgAePnyJQDAy8sLcrkcL1++RGRkpEb5y5cvAwAqV66cd0ESERHRB2fx4sWQyWSoU6dOjpZ/8uQJAgICcPXqVcMGRkRERERERHnik0y4nzx5EgCkhy5ZW1ujSZMmAIAtW7ZolA8JCQEAtG/fPo8iJCIiog9RcHAwPDw8cPHiRdy/fz/byz958gQzZ85kwp2IiIiIiOgD9VEm3M+cOYMDBw5AqVSqTU9JScFvv/2GdevWwdraGt27d5fmjRs3DgAwa9Ys3Lt3T5p+7tw5LFu2DE5OThg4cGDeVICIiIg+OKGhoTh79izmz58PV1dXBAcHmzokIiIiIiIiymMfZcL93r17aN26Ndzc3NCqVSv07t0bLVu2hLu7O0aNGgUrKyusWbMGJUqUkJZp1qwZRo8ejVevXqFq1aro2LEj2rRpg4YNGyI1NRWrV6+Gk5OT6SpFRERE+VpwcDCcnZ3Rtm1bfPnll1oT7tHR0Rg7diw8PDwgl8tRvHhx9OvXD1FRUThx4gRq1aoFAPD394dMJoNMJsOaNWsAAB4eHvDz89NYZ6NGjdCoUSPpdXJyMqZPn44aNWrA0dERtra28PHxwfHjx41RbSIiIiIiIkrHwtQBGIOvry++/fZbnDx5EteuXUNUVBSsrKzg4eGBL7/8EqNGjUKZMmU0lgsKCkLVqlWxaNEiHD58GFZWVmjWrBmmTZuGevXqmaAmRERE9KEIDg5G586dYWVlhZ49e2LJkiX4+++/pST6u3fv4OPjg9u3b2PAgAGoXr06oqKisGvXLkRERKB8+fL4/vvvMX36dAwZMgQ+Pj4AkO02yNu3b7Fy5Ur07NkTgwcPRmxsLFatWoWWLVvi4sWLqFq1qqGrTkRERERERP/fR5lwL1WqFH788cccLevn56e19xgREdGnTCaToXDhwtLfhiCEQExMjEHWZUiOjo7ZruOlS5fw33//4bfffgMANGjQAMWLF0dwcLCUcJ83bx5u3LiBbdu2oVOnTtKy3333HYQQkMlkaN26NaZPn466deuiT58+OYrf2dkZYWFhsLKykqYNHjwY5cqVw2+//YZVq1blaL1ERERElH3GaEcTUf72USbciYiIyLAsLS3Rv39/g64zJiYGzs7OBl2nIbx58ybbw8gFBwfDzc0NjRs3BvD+w1T37t2xfv16/PLLLzA3N8fWrVtRpUoVtWS7iiE/fJmbm8Pc3BwAoFQqER0dDaVSiZo1a+Ly5csG2w4RERERZc0Y7Wgiyt8+yjHciYiIiPJKWloaNm3ahMaNGyM0NBT379/H/fv3UadOHTx//hxHjx4FADx48AAVK1bMk5jWrl2LypUrQ6FQwMXFBa6urti7d2++/EUBERERERHRx4Q93ImIiIhy4dixY3j69Ck2bdqETZs2acwPDg5GixYtcr0dXb3g09LSpB7tALB+/Xr4+fmhY8eOmDhxIgoVKgRzc3PMnj0bDx48yHUcREREREREpBsT7kRERJSllJQUrFy5EgAwaNAgWFpa5nqdjo6OePPmTa7XY2iOjo7ZKh8cHIxChQrh999/15i3bds2bN++HUuXLoWnpydu3LiR6boyG1rG2dkZ0dHRGtPDw8NRunRp6XVISAhKly6Nbdu2qa1vxowZetSGiIiIiAzJGO1oIsrfmHAnIiKiLAkh8PbtW+lvQ5DJZNkeKz2/SUhIwLZt29C1a1d8+eWXGvOLFi2KjRs3YteuXejSpQu+//57bN++XWMcd9VDU21tbQFAa2Ld09MTp0+fRnJysvRA1D179uDx48dqCXdVb3fVOgHgwoULOHfuHEqWLGmQehMRERGRfozRjiai/I0JdyIiIqIc2rVrF2JjY9GhQwet8z///HO4uroiODgYGzZsQEhICLp27YoBAwagRo0aeP36NXbt2oWlS5eiSpUq8PT0hJOTE5YuXQp7e3vY2tqiTp06KFWqFAYNGoSQkBC0atUK3bp1w4MHD7B+/Xp4enqqbbNdu3bYtm0bOnXqhLZt2yI0NBRLly5FhQoV8O7du7zYLURERERERJ8sPjSViIiIKIeCg4OhUCjQvHlzrfPNzMzQtm1bHDhwAElJSTh9+jSGDx+Offv2YdSoUVi8eDG8vLxQvHhxAIClpSXWrl0Lc3NzDBs2DD179sTJkycBAC1btsQvv/yCu3fvYsyYMTh37hz27NkjLavi5+eHn376Cf/++y9GjRqFgwcPYv369ahZs6ZxdwYRERERERFBJvh7ljzl7e0NALh586aJIyEiItJfcnIyFixYAAAYO3asNKSJPpRKJe7cuQMA8PLygpkZv+8nyoq+182n1Lb8lOpKREQfj9y0o4nIeIzZtuQnXiIiIiIiIiIiIiIiA2DCnYiIiIiIiIiIiIjIAPjQVCIiIsqSTCaDi4uL9DcREREREWWN7WiiTw8T7kRERJQlS0tLDBo0yNRhEBERERF9UNiOJvr0cEgZIiIiIiIiIiIiIiIDYMKdiIiIiIiIiIiIiMgAOKQMERERZSklJQVr164FAPTv3x+WlpYmjoiIiIiIKP9jO5ro08OEOxEREWVJCIFXr15JfxMRERERUdbYjib69HBIGSIiIiIiIiIiIiIiA2DCnYiIiIiIiIiIiIjIAJhwJyIiIiIiIiIiIiIyACbciYiIiD4RBw4cQNWqVaFQKCCTyRAdHW3qkIiIiIiIiD4qTLgTERER5cKaNWsgk8mkfxYWFihWrBj8/PwQGRmpUb5Ro0aQyWRo3769xrywsDDIZDIEBgZK006cOCGt+9KlSxrL+Pn5wc7OLss4X716hW7dusHa2hq///471q1bB1tb22zW9sOyePFirFmzxtRhEBERERHRJ8TC1AEQERFR/ieTyeDg4CD9TZq+//57lCpVComJiTh//jzWrFmDv/76Czdu3IBCodAov2fPHly6dAk1atTQexsBAQHYvXt3juL7+++/ERsbix9++AHNmjXL0To+NIsXL0bBggXh5+dn6lCIiIjoE8V2NNGnhwl3IiIiypKlpSWGDx9u6jDytdatW6NmzZoAgEGDBqFgwYKYM2cOdu3ahW7duqmVLVmyJGJjYzFz5kzs2rVLr/VXrVoVe/bsweXLl1G9evVsx/fixQsAgJOTU7aX1SUuLu6j7yVPRERElBtsRxN9ejikDBEREZER+Pj4AAAePHigMc/e3h5jx47F7t27cfnyZb3WN3LkSDg7OyMgICDbsTRq1Aj9+/cHANSqVQsymUyt1/eWLVtQo0YNWFtbo2DBgujTp4/GcDiqoWsePHiANm3awN7eHr179wYAKJVKBAUFwdvbGwqFAm5ubhg6dCjevHmjEcv+/fvh6+sLe3t7ODg4oFatWtiwYYM0//Tp0+jatStKliwJuVyOEiVKYOzYsUhISFBbz7Nnz+Dv74/ixYtDLpejSJEi+OKLLxAWFgYA8PDwwM2bN3Hy5ElpSJ5GjRple98RERERERFlB3u4ExERERmBKvHr7Oysdf7o0aOxYMECBAQE6NXL3cHBAWPHjsX06dOz3ct96tSp8PLywvLly6Whbzw9PQG8H4Pe398ftWrVwuzZs/H8+XMsXLgQZ86cwZUrV9R6xKempqJly5Zo0KABAgMDYWNjAwAYOnSotJ5Ro0YhNDQUixYtwpUrV3DmzBlYWlpK2xowYAC8vb0xZcoUODk54cqVKzhw4AB69eoF4H3yPz4+HsOHD4eLiwsuXryI3377DREREdiyZYsUS5cuXXDz5k2MHDkSHh4eePHiBQ4fPoxHjx7Bw8MDQUFBGDlyJOzs7DB16lQAgJubm977jIiIiIiIKCeYcCciIqIspaSkSL2Qe/XqJSVQc0MIgaS3sblej6HJHexzNL5mTEwMoqKikJiYiAsXLmDmzJmQy+Vo166d1vIODg4YM2YMZsyYoXcCfdSoUViwYAFmzpyJnTt36h1b8+bNERkZieXLl6sNfZOSkoJvvvkGFStWxKlTp6Sx5hs0aIB27dpJ21JJSkpC165dMXv2bGnaX3/9hZUrVyI4OFhKmgNA48aN0apVK2zZsgW9evVCTEwMRo0ahdq1a+PEiRNq49oLIaS/58yZA2tra+n1kCFDUKZMGXz77bd49OgRSpYsiejoaJw9exbz5s3DhAkTpLJTpkyR/u7YsSO+++47qcc+ERERkSkYox1NRPkbE+5ERESUJSEEnj17Jv1tCElvY7GoTC2DrMuQvr7/NxSODtleLuODSD08PLB+/XoUL15c5zKjR49GUFCQ3gl0R0dHKUl/5coVVKtWLdtxpvfPP//gxYsXCAgIUEuAt23bFuXKlcPevXvVEu4ANMYg3bJlCxwdHdG8eXNERUVJ02vUqAE7OzscP34cvXr1wuHDhxEbG4vJkydrPEQ2/Rcc6ZPtcXFxSEhIQL169SCEwJUrV1CyZElYW1vDysoKJ06cwMCBA3X+ioCIiIjI1IzRjiai/I1juBMREREZwO+//47Dhw8jJCQEbdq0QVRUFORyeabLqBLou3btwpUrV/TazujRo+Hk5JSjsdwzCg8PBwB4eXlpzCtXrpw0X8XCwkLjC4R79+4hJiYGhQoVgqurq9q/d+/eSQ9rVY1lX7FixUxjevToEfz8/FCgQAHY2dnB1dUVvr6+AN7/igAA5HI55syZg/3798PNzQ0NGzbE3LlzpQ+zREREREREpsIe7kREREQGULt2bWmolo4dO6JBgwbo1asX7ty5Azs7O53LqcZynzlzJoKCgrLcjipJHxAQoHeS3lDkcjnMzNT7ayiVShQqVAjBwcFal3F1ddV7/WlpaWjevDlev36Nb775BuXKlYOtrS0iIyPh5+cHpVIplR0zZgzat2+PHTt24ODBg5g2bRpmz56NY8eO5brnPxERERERUU4x4U5EREQmIXewx9f3/zZ1GBrkDva5Xoe5uTlmz56Nxo0bY9GiRZg8ebLOsukT6P3799dr/WPGjJGGokn/UNPscnd3BwDcuXMHTZo0UZt3584daX5mPD09ceTIEdSvX19tOBht5QDgxo0bKFOmjNYy169fx927d7F27Vr069dPmn748GGd6xw/fjzGjx+Pe/fuoWrVqvjll1+wfv16AMjRWPxERERERES5wSFliIiIyCRkMhkUjg757p+hkrSNGjVC7dq1ERQUhMTExEzLjhkzBk5OTvj+++/1WrcqSb9z505cvXo1xzHWrFkThQoVwtKlS5GUlCRN379/P27fvo22bdtmuY5u3bohLS0NP/zwg8a81NRUREdHAwBatGgBe3t7zJ49W2N/qMYzNTc3V3ut+nvhwoVq5ePj4zXW4enpCXt7e7V62NraStsnIiIiIiLKC+zhTkRERGQkEydORNeuXbFmzRoMGzZMZzlHR0eMHj1a4wGlmVENRfPvv//C1tY2R/FZWlpizpw58Pf3h6+vL3r27Innz59j4cKF8PDwwNixY7Nch6+vL4YOHYrZs2fj6tWraNGiBSwtLXHv3j1s2bIFCxcuxJdffgkHBwcsWLAAgwYNQq1atdCrVy84Ozvj33//RXx8PNauXYty5crB09MTEyZMQGRkJBwcHLB161a8efNGbZt3795F06ZN0a1bN1SoUAEWFhbYvn07nj9/jh49ekjlatSogSVLlmDWrFkoU6YMChUqpNGTn4iIiIiIyJCYcCciIiK9ZDZcCGnXuXNneHp6IjAwEIMHD5Z6cGujGiZG9WDQrDg5OWHMmDHZStJr4+fnBxsbG/z888/45ptvYGtri06dOmHOnDl6D1ezdOlS1KhRA8uWLcO3334LCwsLeHh4oE+fPqhfv75UbuDAgShUqBB+/vln/PDDD7C0tES5cuWkxL6lpSV2796NUaNGYfbs2VAoFOjUqRO+/vprVKlSRVpPiRIl0LNnTxw9ehTr1q2DhYUFypUrhz///BNdunSRyk2fPh3h4eGYO3cuYmNj4evry4Q7ERER5Tm2o4k+LTKR/je7ZHTe3t4AgJs3b5o4EiIioryhVCpx584dAICXl5fGQzeJSJO+182n1Lb8lOpKRERERMZlzLYlP/ESERERERERERERERkAE+5ERERERERERERERAbAMdyJiIgoSykpKdiyZQsAoGvXrrC0tDRxRERERERE+R/b0USfHibciYiIKEtCCDx+/Fj6m4iIiIiIssZ2NNGnh0PKEBEREREREREREREZABPuREREREREREREREQGwIQ7EREREREREREREZEBMOFORERERERERERERGQATLgTERERERERERERERmAhakDICIiog+DpaWlqUMgIiIiIvrgsB1N9Glhwp2IiIiyZGVlhXHjxpk6DCIiIiKiDwrb0USfHibciYiIiIiIiIiIiD5SQghERETgwoUL0r8HDx6gYMGCKFasGIoXL671fycnJ8hkMlOH/8Fhwp2IiIiIKAsBAQGYOXMmhBCmDoWIiIiIKFNxcXG4ePGiWoL96dOnGuWePHmCa9eu6VxPtWrVsG7dOnh7exsz3I8OH5pKREREWUpNTcWWLVuwZcsWpKammjqcfGXNmjWQyWTSPwsLCxQrVgx+fn6IjIw0dXjZdvbsWQQEBCA6OtrUoei0ePFirFmzxtRhEBEREWWJ7WjKa1u3bkXx4sXRpEkTTJkyBTt27NCabNfHlStX0KhRI1y9etWwQX7k8ryHe3JyMm7fvo2XL18iOjoaTk5OcHV1Rfny5WFlZZXX4RAREZEelEolHj58KP1Nmr7//nuUKlUKiYmJOH/+PNasWYO//voLN27cgEKhMHV4ejt79ixmzpwJPz8/ODk5mTocrRYvXoyCBQvCz8/P1KF8ktieJyIi0h/b0ZSXgoOD0a9fP53nmoeHB+rUqYM6deqgUqVKiI6ORmRkJCIiIhARESH9HRkZieTkZABAVFQUGjdujIMHD6J27dp5WZ0PVp4k3F++fIk1a9Zg7969uHjxIpKSkjTKyOVy1K5dG+3atUP//v3h6uqaF6ERERERGUTr1q1Rs2ZNAMCgQYNQsGBBzJkzB7t27UK3bt1MHB1lJTU1FUqlkgljHdieJyIiIsrfVq1ahcGDB0tDIFpbW6N+/fpSgr127dpwc3PTa11CCGzatAl9+/ZFWloaoqOj0axZM+zbtw8NGjQwZjU+CkYdUub+/fvo2bMnSpQogW+++QanTp2CnZ0d6tWrh3bt2qFXr15o27Yt6tWrB1tbW5w6dQqTJk1CiRIl0KtXL9y/f9+Y4REREREZjY+PDwDgwYMHatP/++8/fPnllyhQoAAUCgVq1qyJXbt2qZVRDVNz6tQpDB06FC4uLnBwcEC/fv3w5s0bjW3t378fPj4+sLW1hb29Pdq2bYubN2+qlbl27Rr8/PxQunRpKBQKFC5cGAMGDMCrV6+kMgEBAZg4cSIAoFSpUtIwOWFhYVrrOGPGDFhaWuLly5ca84YMGQInJyckJiZmK85nz57B398fxYsXh1wuR5EiRfDFF19IMXh4eODmzZs4efKkFF+jRo2k5aOjozFmzBiUKFECcrkcZcqUwZw5c9R6+YSFhUEmkyEwMBBBQUHw9PSEXC7HrVu3AAB//fUXatWqBYVCAU9PTyxbtkxr/VevXo0mTZqgUKFCkMvlqFChApYsWaJWpn///ihYsCBSUlI0lm/RogW8vLy0rju/YHueiIiIKP/7/fffMWjQICnZ7urqivPnz+Pw4cOYNWsW2rdvr3eyHQBkMhl69uyJLVu2wNLSEgAQGxuLli1b4tixY0apw8fEaD3cv/76a6xYsQJpaWlo3LgxevXqhUaNGqFUqVI6l3n48CGOHz+ODRs24M8//8TWrVsxZMgQ/Pbbb8YKk4iIiMgoVAliZ2dnadrNmzdRv359FCtWDJMnT4atrS3+/PNPdOzYEVu3bkWnTp3U1vH111/DyckJAQEBuHPnDpYsWYLw8HCcOHECMpkMALBu3Tr0798fLVu2xJw5cxAfH48lS5agQYMGuHLlCjw8PAAAhw8fxsOHD+Hv74/ChQvj5s2bWL58OW7evInz589DJpOhc+fOuHv3LjZu3IgFCxagYMGCAKCzp3Lfvn3x/fffY/Pmzfj666+l6cnJyQgJCUGXLl2k4XT0jbNLly64efMmRo4cCQ8PD7x48QKHDx/Go0eP4OHhgaCgIIwcORJ2dnaYOnUqAEgfHuLj4+Hr64vIyEgMHToUJUuWxNmzZzFlyhQ8ffoUQUFBavGvXr0aiYmJGDJkCORyOQoUKIDr16+jRYsWcHV1RUBAAFJTUzFjxgytH1CWLFkCb29vdOjQARYWFti9ezdGjBgBpVKJr776StpH//vf/3Dw4EG0a9dOWvbZs2c4duwYZsyYof0EygfYniciIiLK/+bPn4/x48dLr4sUKYKjR4+ifPnyuV53p06dsGPHDnTu3BlJSUmIj49H27ZtsX37drRq1SrX6/9oCSOxtrYWo0ePFpGRkTlaPiIiQowaNUrY2NgYODLTqlChgqhQoYKpwyAiIsqWpKQk8fPPP4uff/5ZJCUlZWvZtLQ0cevWLXHr1i2RlpamMT85JVXEJSSJuIQkkZScojE/NS1Nmh+fqH3b8YlJUhlt20hMTpHmJ6ekasxPSU3VOl0fq1evFgDEkSNHxMuXL8Xjx49FSEiIcHV1FXK5XDx+/Fgq27RpU1GpUiWRmJgoTVMqlaJevXqibNmyGuusUaOGSE5OlqbPnTtXABA7d+4UQggRGxsrnJycxODBg9VievbsmXB0dFSbHh8frxH7xo0bBQBx6tQpadq8efMEABEaGqpX/evWrSvq1KmjNm3btm0CgDh+/Hi24nzz5o0AIObNm5fpNr29vYWvr6/G9B9++EHY2tqKu3fvqk2fPHmyMDc3F48ePRJCCBEaGioACAcHB/HixQu1sh07dhQKhUKEh4dL027duiXMzc1Fxqaztn3asmVLUbp0ael1WlqaKF68uOjevbtaufnz5wuZTCYePnyotY5ZXTcqxmxb5rf2PNvRRET0IcpNO5ooK7NmzRIApH8lSpQQ9+7dM/h2Dh8+LKytraXtWFlZiR07dhh8O3nJmG1Low0p8/DhQwQFBaFo0aI5Wr5YsWJYuHChxs+wiYiI6ONy6OItTPp9Kyb9vhWbj/6jMf/Rs9fS/O//2Kt1HdNX7JLKPImK0ZgffPCCNP/4pTsa8/+5HY5DF2/lqh7NmjWDq6srSpQogS+//BK2trbYtWsXihcvDgB4/fo1jh07hm7duiE2NhZRUVGIiorCq1ev0LJlS9y7dw+RkZFq6xwyZIj0E04AGD58OCwsLLBv3z4A73utR0dHo2fPntL6oqKiYG5ujjp16uD48ePSstbW1tLfiYmJiIqKwueffw4AuHz5co7r3a9fP1y4cEGtzRYcHIwSJUrA19c3W3FaW1vDysoKJ06c0Dp0Tla2bNkCHx8fODs7q22nWbNmSEtLw6lTp9TKd+nSRa33flpaGg4ePIiOHTuiZMmS0vTy5cujZcuWGttLv09jYmIQFRUFX19fPHz4EDEx789DMzMz9O7dG7t27UJsbKzaPqpXr16mvcVNje15IiIiovxJCIFp06bhu+++k6aVLl0ap06dQpkyZQy+vWbNmuHgwYOws7MD8P4XrV9++SW2bNli8G19DIyWcC9cuHC+Wg8RERGRMf3+++84fPgwQkJC0KZNG0RFRUEul0vz79+/LzWMXV1d1f6phhV58eKF2jrLli2r9trOzg5FihSRhqu5d+8eAKBJkyYa6zx06JDa+l6/fo3Ro0fDzc0N1tbWcHV1lZK9quRwTnTv3h1yuRzBwcHSuvbs2YPevXtLw97oG6dcLsecOXOwf/9+uLm5oWHDhpg7dy6ePXumVyz37t3DgQMHNLbRrFkzAJr7N2Oy++XLl0hISNDY7wC0jrV+5swZNGvWDLa2tnBycoKrqyu+/fZbaT+o9OvXDwkJCdi+fTsA4M6dO7h06RL69u2rV71Mhe15IiIiovxHCIGJEydi1qxZ0rTPPvsMp06dkoZpNAYfHx8cPnwYjo6OAIDU1FT06NEDmzZtMto2P1RGG8OdiIiIPh5WVlb45ptvTB1Gvla7dm3UrFkTANCxY0c0aNAAvXr1wp07d2BnZyc9tHPChAlae0sDyHZvFNU6161bpzWpaWHxf029bt264ezZs5g4cSKqVq0qxdSqVSu1B4pml7OzM9q1a4fg4GBMnz4dISEhSEpKQp8+fXIU55gxY9C+fXvs2LEDBw8exLRp0zB79mwcO3YM1apVyzQWpVKJ5s2bY9KkSVrnf/bZZ2qv0/dQz64HDx6gadOmKFeuHObPn48SJUrAysoK+/btw4IFC9T2aYUKFVCjRg2sX78e/fr1w/r162FlZYVu3brlePtERET0YWA7mgxtwoQJmD9/vvTa29sbR44cyZNODp9//jmOHTuG5s2b4/Xr11AqlRgyZAhatGiBAgUKGH37HwqjJdyFEGjRogVCQ0Oxbt061K1bN9Py586dQ9++fVG2bFns37/fWGERERFRPtOidgU0rv6+97CFueaP70oWLoC5X3UBAPz/DtMavh/cAUK8/1thpdm86d2yDno0qwUAsLQw15hfs7y7tLwhmJubY/bs2WjcuDEWLVqEyZMno3Tp0u+3b2kp9bjOyr1799C4cWPp9bt37/D06VO0adMGAODp6QkAKFSoUKbrfPPmDY4ePYqZM2di+vTpauvPSKZrJ2eiX79++OKLL/D3338jODgY1apVg7e3tzRf3zjTlx8/fjzGjx+Pe/fuoWrVqvjll1+wfv36TGP09PTEu3fv9N6/Gbm6usLa2lrrfrlzR30oot27dyMpKQm7du1SG34m/TA+6fXr1w/jxo3D06dPsWHDBrRt21btgbr5EdvzRERERPnL8ePH1ZLt1apVw6FDh1CwYME8i6F69eo4ceIEGjZsiOjoaMTGxmLhwoWYOXNmnsWQ3xltSJmdO3fi6NGjaNGiRZaNcwCoW7cuWrVqhUOHDmHvXu3jsxIREdHHx9LCHDYKK9gorGBlqZksNzczk+Zby620rsNabiWVMTPTbN7ILS2k+doS7hbm5lqn50ajRo1Qu3ZtBAUFITExEYUKFUKjRo2wbNkyPH36VKP8y5cvNaYtX74cKSkp0uslS5YgNTUVrVu3BgC0bNkSDg4O+Omnn9TKZVynufn7uokM3yoEBQVpLGNrawsAiI6O1q+iAFq3bo2CBQtizpw5OHnypFrv9uzEGR8fj8TERLV5np6esLe3R1JSklqM2uLr1q0bzp07h4MHD2rMi46ORmpqaqb1MDc3R8uWLbFjxw48evRImn779m2NdWrbpzExMVi9erXWdffs2RMymQyjR4/Gw4cPNfZRfsT2PBEREVH+kZqaitGjR0uvq1SpgqNHj+Zpsl2lUqVKGDdunPR64cKFuRqm8mNjtB7uGzduhLm5uVovqqxMmzYNy5YtQ3BwMNq2bWus0IiIiCibUlNTsWfPHgBAu3bt1IYAId0mTpyIrl27Ys2aNRg2bBh+//13NGjQAJUqVcLgwYNRunRpPH/+HOfOnUNERAT+/fdfteWTk5PRtGlTdOvWDXfu3MHixYvRoEEDdOjQAQDg4OCAJUuWoG/fvqhevTp69OgBV1dXPHr0CHv37kX9+vWxaNEiODg4SOOhp6SkoFixYjh06BBCQ0M1Yq5RowYAYOrUqejRowcsLS3Rvn17KRGvjaWlJXr06IFFixbB3NwcPXv2VJuvb5x3796V6luhQgVYWFhg+/bteP78OXr06KEW45IlSzBr1iyUKVMGhQoVQpMmTTBx4kTs2rUL7dq1g5+fH2rUqIG4uDhcv34dISEhCAsLy/IDycyZM3HgwAH4+PhgxIgRSE1NxW+//QZvb29cu3ZNKteiRQtYWVmhffv2GDp0KN69e4cVK1agUKFCWr9QcXV1RatWrbBlyxY4OTl9EG1dtueJiIhyj+1oMpTly5fj+vXraq9N+YvJkSNHIjAwEG/fvkVMTAwWLVqEqVOnmiyefEUYiYeHh6hTp062l/v8889FqVKljBBR/lChQgVRoUIFU4dBRESULUlJSeLnn38WP//8s0hKSsrWsmlpaeLWrVvi1q1bIi0tzUgRms7q1asFAPH3339rzEtLSxOenp7C09NTpKamCiGEePDggejXr58oXLiwsLS0FMWKFRPt2rUTISEhGus8efKkGDJkiHB2dhZ2dnaid+/e4tWrVxrbOX78uGjZsqVwdHQUCoVCeHp6Cj8/P/HPP/9IZSIiIkSnTp2Ek5OTcHR0FF27dhVPnjwRAMSMGTPU1vfDDz+IYsWKCTMzMwFAhIaGZrkfLl68KACIFi1a6CyTVZxRUVHiq6++EuXKlRO2trbC0dFR1KlTR/z5559q63n27Jlo27atsLe3FwCEr6+vNC82NlZMmTJFlClTRlhZWYmCBQuKevXqicDAQJGcnCyEECI0NFQAEPPmzdMa58mTJ0WNGjWElZWVKF26tFi6dKmYMWOGyNh03rVrl6hcubJQKBTCw8NDzJkzR/zxxx8699mff/4pAIghQ4ZkuT/1vW6M2bbMb+15tqOJiOhDlJt2NJHKq1evRIECBQQAAUD4+fmZOiQhhBDTpk2TYipQoIB4+/atqUPSmzHbljIhDDli6f+xtrZGx44dsXHjxmwt17NnT+zcuRPx8fHGCMvkVOOZ3rx508SREBER6S85ORkLFiwAAIwdOxZWVtqHdtFGqVRK4197eXlpHfKF1K1Zswb+/v74+++/pQex5nf//vsvqlativ/973/o27evqcPJl3bu3ImOHTvi1KlT8PHxybSsvteNMduW+a09z3Y0ERF9iHLTjiZS+frrr/H7778DAOzt7XH37t08eUhqVl6/fg13d3e8e/cOADBnzhxMmjTJxFHpx5htS6N94rW0tERycnK2l0tJSZHGxCQiIiKiD8OKFStgZ2eHzp07mzqUfGvFihUoXbo0GjRoYOpQ9ML2PBEREZHpXb9+HUuWLJFeT5s2LV8k2wGgQIEC+Prrr6XXgYGBiIuLM2FE+YPREu5FihTB7du3s73crVu3ULRoUSNERERERESGtnv3bsyZMwfLly/H4MGDMx3r/VO1adMmfPvtt9i7dy9Gjx4NmUxm6pD0wvY8ERERkWkJITB69GgolUoAQNmyZdUenJofjBs3DjY2NgCAly9fYvny5SaOyPSMlnD38fHBnTt3cOHCBb2XOX/+PP777z80bNjQWGERERERkQGNHDkSAQEBaNOmDWbOnGnqcPKlnj174rfffsPAgQMxYsQIU4ejN7bniYiIiExr27ZtOH78uPR6wYIF+W5YIldXVwwbNkx6PXfuXCQkJJgwItMzWsJ98ODBEELA398fUVFRWZaPioqCv78/ZDIZBg0aZKywiIiIiPI9Pz8/CCE+iPHbw8LCkJCQgB07dsDe3t7U4eRLQgjExsZi5cqVsLCwMHU4emN7noiIiMh0EhISMH78eOl169at0bZtWxNGpNuECROgUCgAAM+ePcOqVatMHJFpGS3hXqdOHQwYMAD//fcfqlSpghUrVuDt27ca5d6+fYvly5ejcuXKuHv3LgYMGIA6deoYKywiIiIiItID2/NEREREphMYGIjw8HAAgIWFhfTw3fyoSJEiGDx4sPR6zpw5SEpKMmFEpiUTQghjrTw1NRV9+/bF5s2bIZPJIJPJULp0abi6ugJ4P67Pw4cPIYSAEAI9evTAunXrPuqHLBnzCbhERETGIoRASkoKgPcPUszOGNRKpRJ37twBAHh5ecHMzGjf9xN9NPS9bozdtsxP7Xm2o4mI6EOUm3Y0fboeP34MLy8vaWiWcePG4ZdffjFxVJmLiIiAp6cnkpOTAQDLli3DkCFDTByVbsZsWxr1E6+FhQU2btyIzZs3o2bNmlAqlbh//z7OnTuHc+fO4f79+1AqlahVqxb+/PNPbNiw4aNOthMREX2oZDIZrKysYGVlle0PCaokHfA+eUdEWVNdK+mvH1Nge56IiCh3ctOOpk/XpEmTpGR7oUKFMH36dBNHlLXixYtjwIAB0uvZs2dLXzZ9aozawz2jV69e4erVq3j16hUAwMXFBVWqVEHBggXzKgSTY88cIiL6FD18+BBJSUlQKBQoVqzYBzWONVFeS01NRWRkJBITEyGXy1G6dGmdZfO6bWnK9jzb0URERPQpOH36tNoD6FeuXImBAweaMCL9hYeHo0yZMlLnkT/++AP+/v4mjko7Y7Yt8zThTvygQEREH6bU1FQcPHgQANCyZctsJ8wTExPx6NEjpKWlGSM8oo+Subk5SpYsKT2ASptPqW35KdWViIg+HrltR9OnJS0tDTVr1sTVq1cBADVq1MDFixc/qGE5Bw4ciD/++AMA4Onpif/++y9fnvcf7JAyRERE9HFQKpW4ceMGbty4AaVSme3lFQoFSpYsCblczp/SEmVBJpNBLpdnmWwnIiKi/C+37Wj6tCxfvlxKtgPAr7/++kEl2wHg22+/lYYYfPDgATZt2mTiiPKe0b9eOHfuHC5evIjChQujQ4cOsLa2NvYmiYiIKB9SKBQoXbq09HBFItLO1OO2Z8T2PBEREZHxhYeHY9KkSdLr3r17o169eiaMKGc8PT3Rq1cvrFu3DgAwa9YsVKtWDeXLl//gvjzIKaMm3IcOHYqVK1eiUqVKCAsLw7Rp03D48GG4u7sbc7NERESUj+W3ZCIR6cb2PBEREZHxCSEwaNAgvHv3DgDg7OyMefPmmTiqnJs6dSrWr18PIQTu3LmDihUrwt7eHrVq1UKdOnWkf4ULFzZ1qEZhtK8Vtm7dipUrVyIwMBBXr17FjRs3EBUVhdGjRxtrk0REREREZCBszxMRERHljZUrV+LIkSPS619//RVFihQxYUS54+XlhZ49e6pNi42NxbFjxzB79mx07NgRRYoUgbu7O7p164ZffvkFb968MVG0hme0hPvRo0cBAEOGDAEAFC9eHE2bNsWpU6eMtUlJfHw8duzYgYEDB8LLywsKhQK2traoUqUKvv/+e+nbIm3WrFmD2rVrw87ODgUKFECbNm1w9uxZo8dMRERERJSfmLI9T0RERPSpePToEcaPHy+9bt++PXr37m3CiAxj0aJFGD16NCpXrqxzKJlHjx5hy5YtmDBhQh5HZ1xGS7gXL14cAPD48WNp2uPHj+Hi4mKsTUo2bNiATp064Y8//oC5uTk6dOgAHx8fhIaGYsaMGahVqxZevHihsdyYMWPg7++PGzduoFmzZqhduzYOHz6Mhg0bYseOHUaPm4iIiIgovzBle56IiIjoUyCEwODBgxEbGwsAcHJywtKlSz+KITidnZ0RFBSEf//9FzExMThx4gTmzJmDzp07o1ixYmplP/vsMzg7O5soUsMz2hju/fv3R2BgIPr06YPp06fj9OnTuHjxIgIDA421SYmlpSWGDBmCMWPGoHz58tL0p0+fom3btrhy5QrGjBmDDRs2SPOOHDmChQsXwsXFBefOnUPZsmUBvH9IVKNGjeDv749GjRrBycnJ6PETEREREZmaKdvzRERERJ+CVatW4dChQ9LrX3/9FUWLFjVhRMZhZ2cHX19f+Pr6StMiIyNx4cIFXLhw4aNKtgOATAghjLXya9euYezYsfjnn3/g5uaG4cOHY8yYMSb9lubcuXOoV68e5HI53r59CysrKwBAmzZtsH//fixYsABjxoxRW2b06NH49ddfERgYqPYTj5zw9vYGANy8eTNX6yEiIspLQggkJCQAAKytrT+KHhdEHwNjty3zU3ue7WgiIjI0IQSeP3+O+/fv4969e7h//z4iIiJQpkwZjB8/HjY2NgbZBtvRpM2jR49QsWJFqXd7u3btsGvXrnx9jsS8S8DVe48R9vQVAKB/m7omjijnjNm2NGrCPT+Kj4+Hra0tAODJkycoUqQIEhIS4OzsjKSkJDx+/Fj6+azK6dOn0bBhQ/j6+uLEiRO52j4/KBARERGRoXxKbctPqa5ERGQcSUlJuH37tlqCXZXszKhKlSqYPHkyzM3N8zhK+hQIIdC6dWscPHgQwPuhZG7evJnve7c/fBKF+RsPAwAsLcwR+PWXMDc32ojlRmXMtqXRhpTJrx4+fAjg/bAzBQoUAADcuXMHSUlJcHV11Ui2A0D16tUBvO/hQ0REREREREREH5Z///0XixYtQkxMjN7lV69ejYEDB+brHsf0YVq9erWUbAeAoKCgfJNsT1Mqsf/cDZQtXghe7oXV5pUo5AxzMzOkKZVISU3Dk6holHArYKJI869PLuG+cOFCAECrVq0gl8sBvP8JBwCtyXYAsLW1hZOTE968eYPY2FjY29vnTbBERET5RGpqKo4dOwYAaNKkCSwsPrkmBBERERF9gNLS0rBlyxZs374dugZ5KFKkCMqUKYOyZcvixo0buHjxIgDg0KFDKFq0KNq0aZPj7bMdTRlFRERg7Nix0uu2bduiX79+Jozo/0S/i8eavedwP+IFztg8wJR+reBgay3Nt7QwR+0KHpBbWcCjsAtcHO1MGG3+ZbSrPCoqCgULFsw36wGAffv2YdWqVbC0tMQPP/wgTX/37h0AZDo2l62tLaKjo/VOuKt+lpDRgwcP4Onpmc3IiYiITEupVOLKlSsAgEaNGpk2GCLKE6Zqz7MdTUREhvLmzRssXLgQt27dkqZZWlqiYsWKUoK9TJkysLP7v6Rh48aNERAQgAcPHgAA1q5di8KFC0ujH2QX29GUnhACQ4YMwdu3bwEAjo6OWLZsWb75FUVsXCLCnka9/zs+Ebv/uobeLeuolcn4WiUpJRU7T11Fm7qVYGcjN3qs+ZnRBtkpVaoUpkyZgqioqBwt/+LFC0yaNAmlSpUySDz//fcf+vTpAyEE5s2bhypVqhhkvUREREREH6P81p4nIiLKjmvXrmHSpElqyfYiRYrgxx9/xJQpU9C1a1dUrVpVLdkOAHK5HN988w1cXFwAvE+QLly4UBodgSg31q5di/3790uvg4KCUKxYMRNGpK6EWwF0afz+y6Wa5dylv7MSHRuPoE1HcOrqPazYdRopqWnGDDPfM1oP965du2LevHlYsGABWrVqhZ49e8LX1xeFCxfWuczTp09x4sQJbNiwAYcOHUJaWhr69++f61giIyPRqlUrvHnzBuPGjcPo0aPV5qturvHx8TrXERcXBwB6Dyeja8B9XT12iIiIiIjyE1O159mOJiKi3FAqlQgJCcHWrVvVhpCpV68ehg4dCmtr60yWfs/JyQmTJ0/GtGnTkJiYiISEBPz888/46aef4OTkZMTo6WP26tUrtaFkWrdubZC8p6E1qFwGBR3tUM69sN4976/ei8DjF28AAA8iX+Kvf++hcY1yxgwzXzNawv2PP/7A2LFj8d1332HPnj3YvXs3AKBo0aLw8vKCs7Mz7O3tERsbi9evX+POnTt4+vQpAMDMzAzt27fHDz/8kOuG9evXr9GiRQuEh4fD398fgYGBGmVKliwJ4P0YStrExcUhOjpaipmIiIiI6GOXX9rzRERE+oqOjsbChQvVvry1tLSEv78/mjZtmq1hO9zd3TF69GjMnTsXQghERUVh3rx5mDFjBqysrIwRPn3kfvzxR0RHRwMAHBwcsHz5cpMOJXMn/BmuPYjEl42rq8Uhk8lQ3qNIttblW60sIl68xvmboWhc3Qu+1T4zdLgfFKM+qaFSpUrYuXMnIiIisGrVKuzZswdXr15FZGSkZiAWFqhZsybatm2LAQMG6HyAaXa8e/cOrVu3xq1bt9C5c2esWLFC64ns5eUFuVyOly9fIjIyUuOnHJcvXwYAVK5cOdcxERERERF9KEzdniciItLX3bt3MW/ePMTExEjTChcujHHjxsHDwyNH66xRowb69euHtWvXAgDu3buHxYsXY9SoUTAzM9oozfQRCg0NxaJFi6TX3333nUnbSgcv3MSev65BAChS0BENKpfJ1fpkMhl6NK+Fip7FULVsCcME+QHLk0cjFy9eHDNmzMCMGTMQFxeHW7du4cWLF4iJiYGjoyMKFSoEb2/vTB9aml1JSUn44osvcPHiRbRs2RIbN26Eubm51rLW1tZo0qQJ9u/fjy1btmDMmDFq80NCQgAA7du3N1h8REREREQfClO054mIiPQVGhqKH3/8EQkJCdK0unXrYujQobl+b2rTpg2ePHmCw4cPAwDOnj2LokWLolu3brlaL31apk6dipSUFADvR9oYOXKkyWKJT0zGmWsPoBpwaeepq6jhVRLW8tz9csPC3JzJ9v8vTxLu6dna2qJWrVpG3UZaWhp69uyJY8eOwcfHB9u2bcvy5z7jxo3D/v37MWvWLLRt2xZly5YFAJw7dw7Lli2Dk5MTBg4caNS4iYiIiIjyu7xozxMREenryZMnasl2CwsL+Pn5oXnz5gYZrkMmk8Hf3x/Pnz/HtWvXALzvmJmWloYvvviCXzZTlv755x9s3LhRej1r1iwoFAqTxWOjsMLYHs3we8hxpCqVGNiufq6T7Zl5+ioGz17FoGrZEiYdQicvyUT6J0h8JBYuXCj1Uu/UqRMcHBy0lgsMDETBggWl12PGjMHChQthY2OD5s2bIzk5GYcPH4YQAiEhIejYsWOuY1ONYanrYVBERET5kRACb9++BfB+vMFPpaFElN99Sm3LT6muRESkn6ioKEyfPh1RUVEA3j9DZMKECahZs6bBtxUXF4fvvvtObVg1e3t7dOnSBc2bN4elpaXW5diO/rQJIdC4cWOcPHkSAFClShVcvnw5XwxJ9C4+CUqhhINt1g8Szqm7j59j5a6/EJ+YDPfCLhja0ceo28sOY7Yt87yHe1548+aN9Pf27dt1lgsICFBLuAcFBaFq1apYtGgRDh8+DCsrKzRr1gzTpk1DvXr1jBozERFRfiaTyeDo6GjqMIiIiIiIAABv377FrFmzpGQ7AIwYMcIoyXbg/S+8Jk+ejICAALx69QoAEBsbizVr1mDv3r3o0aMH6tevr5FIZTv607Zv3z4p2Q4A8+bNyxfJdgCws5Ebdf2vYt5hybaTSElNAwCkpKbCztq428wvPsoe7vkZe+YQERERkaF8Sm3LT6muRESUufj4eMycOROhoaHStAEDBqBVq1Z5su3du3djz549SEpKUpvn7u6O3r17o0qVKuzJTkhNTUWVKlVw69YtAECLFi1w8ODBPI/jQeRLnLp6D31a1oGlhfbnWxrL01cx2H36X1x7EInhnXzhXbponm4/M+zhTkRERCaVlpaGU6dOAQAaNmyo80HkRERERETGlJycjLlz56ol27t165YnyXYAsLGxQffu3dGyZUuEhITg6NGjSEt734M3PDwcP/30E7y9vdG7d2+UKVOG7ehP2Nq1a6Vku0wmw9y5c/M8hogXb7B0+0kkJKUgNi4BQzo2hMJK+/BHxlDExRFDOjZExIs3KObqpDH/3I2HiHz5Bp18q8E8n/T8N4SPpyZERERkNGlpabh48SIuXrwofaAgIiIiIspLqampWLBggZTEBIA2bdqgS5cueR6Lk5MTBg0ahAULFmgMQ3zz5k18++232LBhA9vRn6i4uDhMnz5det2vXz9UqVIlz+M4eeUuEpJSAAAPn0ThSVRMlstEhz7C5RXrsNNvJPYOG4+bm3cgPup1ruIoXshZ41cfySmp2P3XNTx//fajSrYD7OFORERERERERET5nFKpxJIlS3Dp0iVpmu//Y++uw6O4ugAO/3Y3SgKEhOAEDRY0QIK7heJWSilQWqy0aKEUbfEipUhboDjFtVhwtwR3SdAAIUCEuO3u90fKlHzBSXYi530ennJHds6WyNkzd86tXZsuXbqo2r4lV65cDBgwgBYtWrBixQouXbqk7CtRooRqcQl1zZgxg0ePHgFgaWnJuHHjVImjY4PKxOsNnLl+j+7NqlM4T/Ykx8THxPLgxCnu7D3E7b2HCL51N9H+axu2gUZDnkrlKdKoLoUb1SF7yWIf/X137KIvoRFR9Gld66NeJzWSgrsQQgghhBBCCCGESLWMRiNLlizhyJEjyrZKlSrRu3fvVLMAZeHChRk1ahQXL15kxYoVWFlZUaFCBeLi4tQOTZjYkydP+OWXX5TxgAEDyJ8/vyqx6HRavvCoQu0KzhTM/V+xPSoomJvbdnNn7yHuHTpBXGTkm1/IaOTRqXM8OnWOIxN+JXO+PBRpWIcijetSsF7NDyq+1yjnTHa7zOTPaf/e56Z2qhXcY2JiCAoKwtLSEnv79Pc/VgghhBBCiPRM8nkhhBCmsmfPHnbu3KmMXVxcGDBgQKrsh162bFlKly5NeHi4LJyaQY0dO5bw8HAAHBwcGDZsmKrxaDUapdhu0Ou5uGwNRybMIOZ56GvPcSjhTKH6NYkNi+D27gOEP36SaH/Yg0ecX7yS84tXUqL1J3wyb/p7f72bm+koUyTv+7+hNMDktwHnz59PhQoVsLGxIV++fHz//ffKvo0bN9KmTRt8fX1NHZYQQgghhBDiHUg+L4QQwpR8fX1ZvHixMi5SpAhDhw7FwsJCxajeTKvVkiVLFrXDECq4efMm8+bNU8ajRo3Czs7OZNc/cfk2z8OjXrnv8flLrGjSgb1Df05SbDfLZE2RxnVpMPUnepzdz5dHtlHnpx9oNH0svS4e5ot9m6g+rB+5XMsmed3rm7Zzdd0/KfJ+0iqTzXDX6/W0a9eOLVu2YG5uTsmSJbly5UqiY8qVK0e7du2oWLEiI0aMMFVoQgghhBBCiLeQfF4IIYSphYaGMn36dGWx0axZszJ06FCsra1VjkyIVxs+fDjx8fFAQpuhPn36mOzal249ZMUuL7LYWNG9WXWK5ssBQHTIc45O/I3zS1aB0agcb5HZltIdW1O4YR3yVa2MmZXlK19Xo9GQs2wpcpYtRdXBfYkIeMrtfYc5v3AFARcTcsGDoyZRqH4tMjnIU49gwhnuc+bM4Z9//sHDw4N79+4lWkTihSJFilC0aFE8PT1NFZYQQgghhBDiHUg+L4QQwpQMBgOzZs0iMDAQSJg1PnDgQLJly6ZyZEK82vHjx9mwYYMynjhxosmexAiNiGKZ54l//x7N2n2n0ev1XFmzmUXVPDi/eGWiYnvJds3pftyTehNHUrBujdcW21/FJqcjZTq1pcWS2ZhnygRAVFAIh8ZMSd43lYaZbIb7kiVLyJkzJ2vWrMHGxua1x5UqVSrRitNCCCGEUJ+5uTndu3dX/i6EyHgknxdCCGFK69at4+LFi8r4888/p1SpUipG9GEkj84YDAYDAwYMUMaVK1emffv2Jrt+5kxWtKhRjvUHzqLTaWlVKi/rWnflwYlTiY6zdy5MgyljcKpR5aOvmTV/Xmr82J8DoyYBcGXNJkp1aEmBWlU/+rXTOpPNcL9x4wbu7u5vTM4BbGxsePr0qYmiEkIIIcS70Gg0ODo64ujoKIs/CZFBST4vhBDCVM6ePZtoprC7uzvNmjVTMaIP96o8+tixY3Ts2JG4uDiVoxPJZdmyZZw69V9xe9q0aWi1pls6U6PRULO8MwM+rU9N8xh2tuiUqNhulsmamqMG0/XgP8lSbH+hQo8vyFnORRnvGTyauKjoZHv9tMpk//Lm5uZER7/9f/j9+/fJnDmzCSISQgghhBBCvCvJ54UQQpjCkydPmD17tjLOkycPffr0STeTPv7++2/q1avHmjVr+PbbbzG+1OZDpE2hoaEMGzZMGX/66afUqlXL5HHo4+Lw/XUWt0aPw/BvH3mAok0b8OXR7bj364kumVvcaHU6Gv06Ho1OB0DI3fuc/PXPZL1GWmSygruLiwtnzpwhLCzstcc8efKE8+fPU758eVOFJYQQQoh3oNfrOXr0KEePHlUWrRJCZCySzwshhEhpsbGxTJ8+nYiICAAsLS0ZPHgwmf7tE50WvZxHx8fHs2PHDmJjYwGYP38+M2bMUDlC8bHGjx9PQEAAANbW1kyZYppe5nq9Qfl7VFAw69t358LSNco229w5ab1iLq2W/k7W/HlTLI6cZUtRsXdXZXxqzgKeXr2RYtdLC0xWcP/iiy8IDAykd+/eyg+Wl+n1evr27UtkZCRdu3Z9xSsIIYQQQi16vZ5jx45x7NgxKbgLkUFJPi+EECKlLVq0iDt37ijjXr16kT9/fhUj+ngv59EGg4FFixZRpcp/LT2+//57tm7dqmKE4mPcvHmT3377TRn/8MMPODk5pfh1w6NimLh0B8cu+vL0ug9/N26P3zFvZX/uiuXovHs9RRrVTfFYAKoN+Y4s/xb1DfHx7B48GqPB8Jaz0i+TFdx79uxJnTp1WLVqFcWLF6d3794AXLhwgf79+1OsWDE2bNhAw4YN+fzzz00VlhBCCCGEEOIdSD4vhBAiJe3fv5/9+/cr4yZNmlCjRg0VI0oZVlZWbN68mQIFCgBgNBr57LPPOH/+vLqBiQ8yaNAgpRe/k5MTQ4YMSfFrGgwGlu04QUBwGKv2nGLGlIWE3PVT9pds15xPNy/HNleOFI/lBQubTDSYMkYZ+58+z/klq0x2/dTGZAV3nU7Hjh076NOnD48ePWL+/PkAnDt3jtmzZ3P//n169OjB5s2b001fLiGEEEIIIdILyeeFEEKkFF9fXxYuXKiMixUrRpcuXVSMKGXlzJmTbdu2KWueRERE0Lx5c/z9/VWOTLwPT09Ptm/froynTZtmkvZHQaGR+D0JUsa6gAA0ABoNNUcOpukfUzGzskzxOP5f4Qa1KdHmv8WNj4ybTph/gMnjSA3MTHkxKysrfv/9d3766ScOHjzI3bt3MRgM5MuXj7p165InTx5ThiOEEEIIIYR4D5LPCyGESG7nzp1jxowZyizhLFmyMHDgQMzMTFqyMil9bCxF8+VnzZo1NGvWDIPBwIMHD2jRogWHDh1K0z3rM4rY2FgGDBigjGvXrk27du1Mcm07awvcbpzjqMYWXWQEWbyPY54pE5/MnUpRjwYmieF16o77kTv7DhPzPJTY8Aj2/ziOlkvmqBqTGkz208vV1ZUiRYqwbt06HB0dad++vakuLYQQQgghhPhIks8LIYRIbrt372bhwoUYjUYAtFotAwYMwMHBQeXIUo7RYGDXgBE8Pn+ZtmsW8Ntvv9GvXz8ATp8+TZcuXVi7di1arcmaUogPMHv2bG7evAkkfN3OmjUrRZ/wiw55zt2Dx7iz7wh39x8h4slTcmm1GHVmZM2fl9bL/8DRpUSKXf9d2eTITp2fhrJr4EgAfLbvwWfHXpybqnsjwNRM9t1748YNzM3NTXU5IYQQQgghRDKSfF4IIURyMRgMLFu2jAULFijFdisrK4YOHUrp0qVVji5lHR4/navrthDkc5uVHp/yad2G9O3bV9m/YcMGRo0apWKE4m0CAgIYO3asMu7Vqxdly5ZN1msYDQYeX7jMiel/sKRVN2a6NmBbj4FcWb2RiCdPAdAYDOR3LcPnu9alimL7C6U/b0e+qpWV8b5hYzNcaxmTFdydnZ0JDAw01eWEEEIIIYQQyUjyeSGEEMkhOjqaadOmsW3bNmWbvb09Y8eOxdXVVcXIUp7RaMTc2loZRwQ8ZXXzTgxu15HGjRsr2ydOnMjSpUvVCFG8gxEjRhAaGgpAtmzZGDduXLK9dvjjJ+wePJo/S9fk7wZtObBwFefKViGgbUcML0180Gi1lPvyM9pvWIKNY+p6IkSj0dBw+s/oLBLiDfcPYGmt5lzftP0tZ6YfGuOLW4kpbPbs2QwZMoTz589TokTquetiai4uLgBcuXJF5UiEEEKId2cwGAgISJiVkDNnTnnEVYhUwpS5pdr5vOTRQgiR9gUHB/PLL79w+/ZtZVuhQoX44YcfsLe3VzGylPOqPPri8rXs+X4MRoMBAK25ObWnjKbLpJ+5evUqAObm5uzdu5datWqpFrtI6syZM1SuXFl5MmPWrFl89913yfLaN7fuYs/3o4kKCgEgPktWHn71DUbLhAVQM9+7TQ19KEUa1KJA7WpY22dLluumFK9Z8zkybnqibcVbNaXBlDFYZ7NTJ6iXpGRuabKCO0Dv3r3ZtGkTw4YNo3nz5jg5OWFhYWGqy6cK8kFBCCGEEEIkF1Pnlmrm85JHCyFE2nb//n0mTZqU6GkpV1dXBgwYgJWVlYqRqePW7gNs/XoA8VHRyrYy/XvwxawpPH2a0DLEwcGBy5cvkytXLrXCFC8xGo3UqFGD48ePAwm5yfnz5z96gd+Y0DD2/Tieq2s3J9qu0WqJ/uxz/PMXQQO0rVOBOhXTziRmo9HI5RXr2T9yInERkcp2m5yONP5tAoUb1FYxunRScNfpdEDC/+y3LSKg0WiIj483RVgmJx8UhBBCCCFEcjFlbql2Pi95tBBCpF0XLlzg119/JSoqStnm4eFB165dM/STk/5nLrCxU09lRjNA7haN+HrFX0THxADw2WefsXLlSpUiFC9bsWIFnTt3VsZ79+6lfv36H/Wafse88fxuGKF+D5VtZpmsqTViECXbt8AyaxaWeZ6kYokClCmS96OupZaQe37s/O5HHpw4lWh7ua6fUvunH7CwtVElrnRRcC9YsOB7rdZ7586dFIxGPfJBQQghRFqk1+s5ffo0AJUqVVIKb0IIdZkyt1Q7n5c8Wggh0qarV68ybtw49Ho9kHBTtmvXrjRt2lTlyEzjbXl00K07bPj0a57fe6Bs07k4M+TQDvQklOx27dpFo0aNTBe0SCI6OpqiRYvy8GFCYbx169Zs3Ljxg18vPiaWY5N/49Tvi+Cl0mzuiuVo+vsUshUp+LEhpypGg4HTc5dwdMKv6GPjlO1ZC+an6ZxfyOte0eQxpWRu+XHPPLyHu3fvmupSQgghhEhmer2egwcPAlChQgUpuAuRAUk+L4QQ4n35+/szbdo0pdhuaWlJ//79qVSpksqRmc7b8mj7IoXotH01Gzv1IuBiQuFPf8WHQflKMvvhdaKNBr755hsuXbqE9UsLrgrTWrRokVJst7CwYNq0aR/8Wk+v3mB7n+95dvWmsk2j05FnyEDqdW1Ptux2HxtuqqPRaqn8TXcK1q2BZ98feHIpYa2C53f9WN2iM5W//ZpqQ7/DzDJ9tB7PuM/tCCGEEEIIIYQQQogUER4ezuTJkwkPDwcSFgEdNWpUhiq2vyubnI58+s8yCtSprmzLER3Pt3ZO5NRZcOvWLSZNmqRihBlbbGwskydPVsY9e/akcOHC7/06+thYvGbO5++GbRMV2+2LFqLKigWc0GRm+pp9+D54kixxp0aOJYvx+c41VBnUB82/7aSMBgPes+Zz79AxlaNLPlJwF0IIIYQQQgghhBDJJj4+nunTp+Pv769s69u3L8WKFVMxqtTNwtaWNivmUqp9C2VbTp0F/e2cqGKVlcmTJ3P9+nUVI8y4li5dip+fH5Bw42jo0KHv/Rr3Dp9gaZ2WHBk/PVFLlQpfdabN9jVsvvYIg9FIZHQsyz1PEv/vUyHpkc7Cgho/DuCz7avIVrggACXbNqNIo7rqBpaMTNZSZtmyZe91fJcuXVIoEiGEEEIIIcT7knxeCCHEuzAajSxYsCBRX+QOHTpQrVo1FaNKG3QWFnj8PoXMefPg9dtcAMw1Wtra5qRYTBjffd2T3UcOvdeaKuLjxMXFMXHiRGX85Zdfkj9//nc+P8w/gIOjJ3Nj845E221yOtJk1iQK1asJwKf1K/H3Li+0Gg1ftaiBWQZo4ZmnUnm6HNjMyV//pPK3X6kdTrIyWcG9W7du7/QDwWg0otFoJEEXQgghhBAiFZF8XgghxLvYunUr+/fvV8Y1atSgbdu2KkaUtmg0GmqOGEheN1c8+w0j6lkQAGUsM5P/6gMWjBlPj7GjVI4y41ixYoWyjo2ZmRk//vjjO52nj4vj7F/LOT5lNnERkf/t0Ggo160jNYcPxMouq7K5UsmC2Ge1JSIqBqec9sn5FlI180zW1Bw5SO0wkp3JCu6jR49+ZYJuMBjw8/Pj0KFD3Llzh27dulGgQAFThSWEEEIIIYR4B5LPCyGEeJtTp06xYsUKZVy8eHF69+4tM7I/QOGGtel64B88v/2Be4eOA2CnMyf4z+Xs1VhQb9RgtGYmK+tlSPHx8UyYMEEZf/HFFxQsWPCt5/kd82bvsLEEXvdJtD2Xa1kaTBlDrnKlX3le4TzZPypekXqY7Dvzp59+euP++Ph4Bg0axJo1azh16pRpghJCCCGEEEK8E8nnhRBCvMmdO3eYNWsWRqMRgBw5cjBkyBAsLCxUjiztss2Vg3ZrF7Jn4q+c+20+Oo0GLRrO/7GIp6fP03TuNLLmz6t2mOnWmjVr8PX1BUCr1TJ8+PA3Hh8fHcPuwaO4uvafRNut7e2oOfJ7ynzeVlko9GlIGI52mVMmcKG6VLNoqpmZGTNmzMDa2pphw4apHY4QQgghXmJmZsZnn33GZ599hpnMpBFCvILk80IIkXEFBQXxyy+/EBMTA6D8LsiSJYvKkanvY/NojVZLo5Hfo+vWmmf6WGX7Q++zLKvTktN/LiYuMio5QxaAXq9PNLu9U6dOFC1a9I3nHJ30W+Jiu0ZD2S6f0v3ETsp+0V4ptp+9cZ9xi7az/8x15QaVSF9STcEdQKfTUbFiRfbs2aN2KEIIIYR4iVarxcnJCScnJ7TaVJU+CCFSEcnnhRAi44mOjmbKlCkEBSX0GtdqtQwaNIh8+fKpHFnqkFx59IDJE9ibLwuno58r22JCwzg4ejILKjfkzLylxEfHJEfIAtiwYQPXrl0DEvrqjxgx4o3Hhz9+wvlF/7VTylnOhc93rqXR9LFY22dTtt/1D2T5zpMYjEY2HjzHzpNXXvVyIo1LdZ+YHz9+TEREhNphCCGEEEIIIT6A5PNCCJGxLFy4kNu3byvj7t27U65cORUjSp/MzMz4/a/5rI14wsowf6INemVfxJOnHBg5kQWVG3Bu4QriY2Lf8EribQwGA+PHj1fGHTp0oESJEm8859ScBcoND5scjnTcupLcrmWTHGdhriNzJisAMllZ4FrcKRkjF6lFqim4GwwGZs+ezYkTJyhbNukXpBBCCCHUo9frOXv2LGfPnkWv17/9BCFEhiP5vBBCZDxnzpzh0KFDytjDw4NGjRqpGFHqk5x5tLu7O7179+ZcTBi/BN/lWGwovNSmJvzxE/YNG8tCt4acX7IKfawU3j/EP//8w6VLl5Txu8xuv7B0tTJ269cDc2urVx6bJ7sd33dqSNF8jnzdvAY57aXtUnpksias9erVe+2+8PBw7ty5Q1BQEFqtljFjxpgqLCGEEEK8A71er7SIKF26NDqdTuWIhBCmlhHzeYPBQGhoKHZ2dmqHIoQQqU54eDjz589XxkWLFqVLly4qRpQ6JXcePXHiRDZt2sTjx4/ZHPqYfWFPqZvJnqrWdpihASDs0WP2DvkJr5nzqdizC8VaNCFL3twf/V4yAqPRyLhx45RxmzZtKFOmzBvP8Z7913+z23M6UrbLp288PouNNf071Eej0Xx8wCJVMlnB/eDBg2/cb2ZmRo0aNRg9ejT169c3TVBCCCGEEEKId5IR8/mLFy/yyy+/UK1aNTw8PN66WJoQQmQky5YtIzg4GABzc3O++eYbmZRhAnZ2dsycOZNPP00o6oYZ9WyJeMrBqGDqWmejilVWzDQJDS3CHjzi4OjJHBw9mSwlilKuQytKtGpK1vx51XwLqdr27ds5d+6cMh45cuQbjw9/HJBodrt7v56vnd3+Mim2p28mK7jfuXPntfssLCzInj075ubmpgpHCCGEEEII8R4yYj7v6emJXq/nyJEjHDlyBGdnZzw8PKhSpQpmZib7KCWEEKnO2bNnE92Ibd++vSySakIdOnRAp9OxefNmvLy88PHxIdQQzz//Ft7rW9tT2SorZi8VdUOv+3Jk7DSOjJ1GrENWHGu6U63b55Sq5i7F33/9/+z25s2bU6FChTee4z3rL/T/9sy3yelImS86JDnmgs8DShfOg06Xajp7ixRmsiyxQIECprqUEEIIIYQQIplltHw+KiqK+/fvJ9rm4+ODj48Py5cvp2HDhjRo0EDazQghMpyIiAjmzZunjIsWLUrz5s1VjChjatu2LW3btgUgMDAQb29vvLy88PLy4qC3N/uD71DFKitlLTPjqLNIdK5F4HOeb96N5+bdLNLo0dd2o1mv7tSuXTvd3Tx/H3v27MHb21sZjxo16o3Hh/kHcGHZGmXs3r9Xktntl28/5K8tR8jnaEfnJlXIlyNb8gYtUiWT3Vrp3r07ixYteutxS5YsoXv37iaISAghhBBCCPGuMlo+b21tzezZsxk4cCAlSpRItC84OJi1a9fyzTffMGfOHG7fvq1SlEIIYXpLly5VWsmYmZlJK5lUwMHBAQ8PD3766Sc8PT159uwZ3tev8vm834js9Am78tiyPzqYJ/FJF1HNbdSR+8BpJrX4lNy5cvHll1+ydetWoqOjVXgn6jEajYwdO1YZN2nShMqVK7/xnJdnt9vmykHZ/5vdHhkdy6o9pwB48DSEtftOYzQakzlykRqZrOC+ZMkSjh49+tbjjh07xtKlS00QkRBCCCGEEOJdZcR83szMjKpVqzJ27FgmT55M7dq1E7WSiY+P5/DhwwwbNow//viDuLg4FaMVQoiUd+7cuUStZDp06CCtZFIhjUaDs7MznTt3ZvacOey5cIaNT+/TesdKzHp2xL9wboK0/xV+dRoNHjbZaRefiY3L/qZFixY4OjrSsWNH1q5dS0REhIrvxjQOHDjAsWPHlPG7zG6/uPy/2e1u/XthZmWZ6BitVkPpwgn98s10Wj5r6CbtezKIVNd4MDY2Vu6MCiGEEEIIkUal13y+cOHC9O3bl86dO7N37152796tzPCEhEVlAwICGDJkCLa2tipGKoQQKSMyMjJRK5kiRYpIK5k0xMrKiqpVq1K1alWYkLDt8s697O43HEPwcwCKWmRikF0B1oY/5mp4OGvWrGHNmjXY2dnx1Vdf0bdvXwoVKqTiu0gZer2eQYMGKeN69epRrVq1N57jPXP+f7Pbc+ekbOf2SY6xsjDns4aVqVAsP8+eh5M7e9bkDVykWqmqW7/RaOTs2bM4OjqqHYoQQgghXmJmZka7du1o166dLBQohHitjJDPZ82albZt2/L777/Tv3//RIWHa9euMWLECB4/fqxihEIIkTKWLl1KUFAQkJAb9unTJ13eYE1uqTmPLt2kAX1P76N4q6bKNhutji+z5KWljSNmJMzGDgkJYfr06RQpUoRWrVqxf//+dNUaZcGCBVy4cEEZjx8//o3Hhz16nGh2u/srZre/rESBXNQoW/TjAxVpRop+p9erVy/ReOfOnUm2vRAfH8+tW7d4/PgxX3zxRUqGJYQQQoj3pNVqKVKkiNphCCFMTPL51zMzM6N69eq4u7szb948Dh06BIC/vz8jRoxgyJAhSXq/CyFEWnX+/HkOHDigjNu1a4eTk5OKEaUdqT2PtsySmWbzf6VgnersGz6e+MgoAGpYZ6NijrwsenaXu2EhQMKN9X/++Yd//vkHFxcX+vXrR+fOncmUKZOK7+DjBAcHM2LECGXcuXPnhKcA3sBr5nz0sQlt5Gxz56TM5+1SNEaR9miMKXhLSqv9bwK9RqN5690vc3NzmjRpwsKFC8mePXtKhaUqFxcXAK5cuaJyJEIIIYQQIq1L6dwyNeXzqTmPNhqNbNiwgbVr1yrbzM3N+eabb6hevbqKkQkhxMeLjIxk8ODBBAYGAgkttiZMmCCz29OhQJ9bbOsxiKdXrivbdFaWUNuN+V6Hue7rk+ScbNmy8fXXX9O3b18KFChgynCTxYABA5g5cyYANjY23Lx5kzx58rz2+NCH/ix0a6gU3Ov/MpoK3T9X9t/0C8BMp6NwnvRZ10xPUjK3TNEZ7nfu3AESEtDChQvTrl07pk6d+spjLSwsyJ49O+bm5ikZkhDpyqNHj9i1axfZsmWjbt26ZM0q/cCEEClDr9dz9epVAEqVKiUfsITIICSffzcajYZ27dqRM2dO/vzzT+Lj44mLi2PmzJkEBATQunVrWSRNCJFmLVu2TCm263Q6vvnmG8kF30NayqMdnIvw+c61HB47lbN/LQdAHx0Du47wXfbs2PZuxjrfy2zbu0c5Jzg4mKlTpzJ9+nRatmxJv379qF27dpr4vXf16lXmzJmjjEeMGPHGYjuA18x5SrE9c55clPn8v97tUTGxLNtxkufhkdStWIJm1ctgYZ662ggJ00jRf/WX72yNGTOGChUqpMm7XUKkNvHx8WzZsoX169cTHx8PwNq1a6levToeHh4ULlxY5QiFEOmNXq9nx44dABQvXjxVf1AQQiQfyeffT82aNcmePTtTp04lPDwcgNWrV/P48WN69uyZ6nr3CiHE29y7d4/9+/cr4/bt20srmfeU1vJoMytL6k0cSYHa1djZ70eigkIAiHwWSOT67TS0taF7vx84FBHIwjWrld93BoOBTZs2sWnTJsqUKUO/fv3o1KlTqm03YzQaGTBgAHq9Hkh4cmPgwIFvPCf0oT+X/l6vjN0H9MLM0kIZ7zt9nZDwSAAOnbtJ1dKFZaHUDMpkGd+YMWNMdSkh0rVbt24xd+5c7t27l2h7fHw8hw4d4tChQxQvXhwPDw/c3Nzkg50QQgghkoXk8++mZMmSTJgwgUmTJimLpx48eJCHDx/SsGFDKlWqhK2trcpRCiHEu9m3b5/ydycnJ1q0aKFiNMKUijSuR9dDW/GaOZ9LK9YRHxUNQGx4BLdWbqKAhTkru/TB1z4Tv6/6m1u3binnXrp0iR49evDDDz/Qo0cPvvnmm1R3o2bLli3s2fPfTP1ff/0VKyurN57j9dtcDHH/zm7Pm5vSnRL3bs+cyQr7LDYEhUbQyK2UFNszMNUqcSEhIYSFhb22D2Rq+0YUQm0xMTGsWbOG7du3J/q+yZYtG5DwGNcLN27c4MaNG9jb29OoUSMaNGhAlixZTB6zEEIIIdIvyedfL3fu3EyYMIGpU6dy/XpCH1wfHx98fHzQarW4uLjg7u6Om5sbdnZ26gYrhBCvERsby5EjR5RxkyZNZEJXBmObKwf1J42k6uBvOLtgOecXriA65DkA+tg4bqz9BzQapjdrinG0G3+uWM7u3buV84OCgvjll1+YOnUqHTt2ZNy4caniifzo6GgGDRqkjBs2bPjWm0n3j57k4rL/1mr5/9ntALUrFKNmeWdu3g8gn2O25A1apCkpumjq/3v8+DEjR45ky5YtSv+vVwal0ShtMtKb1LzYk0i9Ll26xPz58wkICEi0vUGDBnTu3BkLCwu8vLzw9PTk5s2bSc43NzenefPmtG3bNkP2VRVCfLzY2FhmzJgBwMCBA7GwsHjLGUIIUzB1bqlmPp8W8+i4uDj+/PNPjh49+sr9Go2G4sWL4+bmhru7O46OjiaOUAghXu/o0aPMmjULAEtLS+bPn4+1tbXKUaU96SmPjg2P4OLf6zj9xyLC/RPXJyyzZqHR9LEYihVkzpw5LF26lIiIiETHmJub06tXL0aNGkWOHDlMGXoikyZNYvjw4UDCugQXL16kVKlSrz0+zD+A5fVbE/k0IffJWjA/Xx7dkaTgLtKWNLto6sv8/f2pXLkyjx49Im/evDg6OvLkyROqVq3K7du3CQgIQKPRULVqVSkICvGv8PBwli9fzoEDBxJtz507N7169Ur0C6F69epUr14dX19fPD09OX78uNKLLC4ujo0bN+Ll5UWvXr0oUaKESd+HEEIIIdI+yeffn7m5Od999x3169fnxIkTeHt7ExISouw3Go1cv36d69evs2zZMrJnz07RokVxdnbG2dmZQoUKYWlpqd4bEEJkaC/3bq9ataoU2wUWtjZU6t2NCt07cXX9Vk7N/osg34QF1mOeh7L16wGU7tSWGb9MYcKECSxatIg5c+Yoi7DHxcUxZ84clixZwuDBgxk8eDCZM2c26Xt4+PAhEyZMUMbffvvtG4vt+thYtn7VXym26ywtaL7gNym2izfSmupC48eP59GjR4wdOxY/Pz88PDzQaDQcO3YMf39/Dh48SIkSJdBoNHh6epoqLCFSpYCAAJYtW8a3336bqNiu1Wpp1aoVU6dOfe0vhKJFi/Ldd9/x559/0r59e7Jm/a9n2MOHDxkzZgwLFy4kKioqxd+HEEIIIdIPyec/jEajwcXFha+//pq5c+cyduxYPvnkE7Jnz57k2GfPnnHy5EmWL1/O6NGj6dq1Kz/88AN//fUXBw8efONTBUIIkZwCAgK4fPmyMq5Xr56K0YjURmdhQZlObfny2A6azJqEuc1/C6NeXrmB5fXbEH3Xj0GDBuHj48PSpUsTtZoLDw/n559/pkiRIsyZM4fY2FiTxT5s2DBl5n327Nn56aef3nj8wTFTeHTqnDKuP2k0ucqVTskQRTpgspYyRYoUAVAWUfjyyy9ZtmyZMgMXEoqBxYsXp3///onuNqUnafFRWGEaRqORy5cv4+npyZkzZ5L0Qy1UqBC9e/emUKFC7/W60dHRrF69Gk9Pz0Sv6eDgQI8ePXB1dU2W+IUQ6Vt6ehRWiPTElLml2vl8esujjUYjd+7c4eTJk3h5eeHv7//Wc3Q6HQ0bNqRNmzbS+10IkaJWr17Nxo0bAciTJw8zZsxAo9GoHFXalBHy6JA799ne53v8z1xQtmnNzKgxfACV+36FRqslOjqaP//8kwkTJiS5gVy4cGEGDhz4xt9tZmZm1KhRg3z58n1wnCdOnKBatWrKeN68efTs2fO1x1/buI3tvQYr49Kd2tJk5sRExxiNRpZ5nqREgVxUKJYfC3NZ5yCtSMnc0mQFdysrKz755BM2bNgAwNdff83ixYuJjIxM9Jhk8+bNuX79Oj4+PqYIy+TS2wcF8fGio6M5cuQInp6ePHjwIMl+a2tr2rRpQ7NmzdDpdB98nZs3bzJ37twk16hRowbdunWTRVWFEG9kMBiUNSKKFSuGVmuyh+SEEG9gytxS7Xw+vefRQUFBysKqvr6+3Lp1i5iYmFcea2lpSfPmzWnevLm0eBBCJDuDwcA333xDUFAQAJ07d37rgpLi9TJKHq2Pi+PE1Dmc/G0evFRqdKpVFY85v5A5d04Anj9/ztSpU5kxYwaRkZHvdQ2dTkfr1q3p168fNWrUeK+bQAaDAXd3d06fPg1AhQoVOHXq1GvrLM+u+/B34/bERyZ0B8hRphSfbV+FubVVouN8HzzhtzX7ALC2NGdsjxZYS7uZNCFd9HD//2Lei7tWDx8+TLRCsZWVFQ8fPjRVWEKo4sWMpqNHj3LgwIEkC4lAQp92Dw8PateunSwfpIoVK8Yvv/zC5s2b2bhxozIb7ejRo1y4cIEePXpQpUqVj76OECJ90mq1sv6DEBmc5PMpy97eHnd3d9zd3QHQ6/U8ePBAKcBfuHBBmREYExPD+vXr2b17N23btqVhw4aYmcmMOiFE8jh//rxSbNfpdNSuXVvliNK2jJJH68zNqTF8IAVqV2fHN0MIe/QYgPuHT7C0dgs85kymSKO6ZM2alfHjx9O3b1/Gjh3LX3/9lehpuTfR6/WsX7+e9evXU758efr168dnn32GlZXVa88xGo3cvn2bZcuWKcV2gJkzZ7622B4TFs4/3b5Viu2WWbPQYtGsJMV2gBOXbyt/d8ppL8V2AZhwhnulSpXQaDScOnUKgCVLlvDVV18xc+ZMvv32WwAiIyMpWrQo1tbWyqOq6U16n5kjXu/FXW1vb2+8vLx4+vTpK4+rUKECHh4elC1bNsXufPv5+TF37txEM880Gg3jxo2jWLFiKXJNIYQQQiQ/U+aWaufzGT2PjouLY/fu3WzcuJGwsLBE+3LkyEHHjh2pVq1aup05KYQwnWnTpuHt7Q2Am5sb33//vcoRibQmKjiE3YNG47Ntl7JNo9PRaukcijROvB6Aj48PU6dO5erVq298TT8/P+7fv59ke/bs2enZsyd9+vQhX758BAcHK3UXLy8vvL29efbsWaJzOnbsyKpVq155HaPRyJbu/RPF3nrFPIo0qvPK4+89DuT4pVucuX6Pjg3dqFSiwBvfh0g90kVLmaFDhzJz5kwePHiAo6MjQUFBFCxYkLi4OPr370/evHn5+++/OX36NH369GHOnDmmCMvkMvoHhYxGr9dz9epV5Yd8SEjIK4+zsrKiTp06NGnShDx58pgkNoPBwM6dO1m1apXyuLKrqyvDhg0zyfWFEGlLRnkUVoi0xpS5pdr5vOTRCSIjI9myZQvbt29P0nKmYMGCNGjQADc3N+nxLoT4IM+fP6d3797KjONhw4bJul8fKaPm0Uajkcsr1rNvxARlpriZlSXt1i0iX5VK7/16er2eHTt2MGvWLPbu3Ztkv06no2DBgm+94Z8pUyauX79O/vz5X7n/1B+LODTmF2VcZfA31BjW/63xxcTFo9NqMPuIVsDCtNJFwf3ChQtMnjyZ3r17K48jrVq1ii+//JLY2Fg0Gg1GoxEXFxeOHj1K1qxZTRGWyckHhYzhwYMH7Ny5k+PHjxMeHv7a44oXL061atWoXbs2mTJleu1xKens2bNMnjxZGU+bNi3R6uFCCAEZY7EnIdIiU+aWaufzkkcnFhISwvr169m3b1+SR/E1Gg3FixfH3d0dNzc3HB0dVYpSCJHWbN26leXLlwMJra7++OOPDFMgTikZPY8OuHiVNa2+IDYsoTZimTULHbf8jWOp4h/8mleuXGHOnDksW7bsnfvAFytWDHd3d/r06UPVqlVfeYzf8VOsbdMV47+/VwvWqUGb1fPRShE9XUoXBffXuX//Pjt27CA4OJhixYrRokULzM3N1QwpRckHhfTLYDBw7tw5PD09uXjx4iuP0Wq1uLi44ObmRuXKlbG3tzdxlEkZjUaGDh3KvXv3AKhVq5byWLgQQryQ0T8oCJFapYbc0lT5fGp4r6mRv78/q1ev5sSJE689pnDhwkp/eFM9TSmESHuMRiODBg1S1uFo06YNHTt2VDmqtE/yaPA75s36T79CHxMLgE1ORzrtWE1Wp3wf9brBwcEsWrSIOXPmcPfuXWW7g4OD8nvP3d39neov4Y+fsKxeayKfJrSfyZwvD1/s3UAmB/XrNiJlpIuC+8WLF9FqtZQuXdoUl0u15INC+hMZGcmBAwfYuXMnAQEBSfabmZlRtmxZ3N3dqVixYpIFx1KDo0ePMmvWLCDhMazZs2eTPXt2laMSQqQm8kFBiNTJlLml2vm85NFvdufOHQ4fPoyXl1eSXrUvy58/vzLzvUCBAmg0GhNGKYRIzW7cuMGoUaOU8ezZs8mZM6eKEaUPkkcn8Nmxly1ffofRYAAgW+GCdNy2EhtHh49+bb1ez8GDBwkKCsLV1ZXChQu/9++3zV374rsjoVWNzsKcjttWkrtC2dceH6/XS/uYNC4lc0uTLWVfvnx5ateuzYEDB0x1SSFS1MOHD9m5cycHDx5M0j8ToECBAjRp0oSqVauq1i7mXVWtWpVVq1bx9OlT9Ho927Zto1u3bmqHJYQQQohURPL51K1QoUIUKlSILl26cPv2bWWxOH9//0TH+fn54efnx/r168mZM6cy+69o0aJSfBcig9u/f7/y99KlS0uxXSQr56YNaDhtLLsHjQQg+PZdNn7Wg083L8PC1vajXlun01G/fv0PPv/mtt1KsR2gzrgf31hsj4vX8/PCrRTJ60jVMkUo5pQTrfwOFS8xWcHd3t5eHl8U6cb27dtZtmwZ//+AiEajoXLlyjRt2pSSJUummQ8tOp2O5s2bs2jRIgD27dtH27ZtyZw5s8qRCSGEECK1kHw+bdBoNBQpUoQiRYrw2Wef4efnh7e3N15eXkoLwRcCAgLYsmULW7ZswcHBgcqVK1OtWjVKlCihUvRCLQaDgYcPH6LT6bC3t8fKykrtkISJRUVFcfz4cWVcr149FaMR6VXZL9oT+SyQoxMTZvwHXLjCP92+o/WKeZhZqjPzP/p5KPt+GKuM81d3o/yXnd54zqVbDwkJj+LMjftcvPWQib1bYa1S/CJ1MlnBvUqVKly6dMlUlxMixezbt4+lS5cm2mZjY0P9+vVp3Lhxml2Uqm7duqxbt46wsDBiYmLYtWsX7dq1UzssIYQQQqQSks+nPRqNBicnJ5ycnGjXrh2PHz/Gy8sLb29vfHx8Eh0bGBjIzp072blzJ82aNaNLly4qRS1M7ebNm8ydO5cHDx4o2zJlyoSDgwP29vbKHwcHB5ycnHB2dk4zE4vEuzt+/Ljy5LaNjQ1ubm4qRyTSK/cBvYh8FsjZ+csAuHfoOJ7f/sAnc6epsjjp4bHTiHjyFACdpQWNpo9768+48zf9lL9XKJZfiu0iCZMV3MeMGUONGjWYPn06gwcPNtVlhUhWJ0+eZP78+crYwcGBtm3bUrNmTSwtLVWM7ONZWlri4eHB2rVrAfD09KR58+Zp/n0JIYQQInlIPp/25cqVi5YtW9KyZUsCAwPx9vbG29ubq1evJnpyc9u2bRQrVowqVaqoGK1IaVFRUaxatYpdu3YleXI3MjKSyMhI/Pz8kpxXokQJPv/8c4oXL26qUIUJvNxOpmbNmhm2z7hIeRqNhrrjfiQqMIhrG7YBcGPzDqzts1F/8iiT3tDzO36Ki8vWKONq339LtiIF33pe16ZVqViiAMcv36JamSIpGKFIq0y2aOqyZcs4dOgQS5YsoWzZsnzyySc4OTm99lG19DqjQhZ7SrsuXrzIpEmT0Ov1AGTLlo1x48aRI0cOlSNLPmFhYXzzzTfKzIbu3bvTpEkTlaMSQqQGer2eq1evAlCqVCl0skCQEKmCKXNLtfN5yaNTzvPnzzl9+jTbtm3j4cOHQMIM12nTpuHg8PGL2YnU59y5c/z1119JFtjVarUY/l3Q8G0qVapEp06dyJcvX0qEKEzIz88v0Y3UKVOmULBgQfUCSmckj341fWwsmzr34e6Bo8q2/NXdqDlyMHkqlU/x68dHx7C0TguCb90FwNGlOJ33bEBnbp7i1xapQ0rmliYruGu1WjQaTaI756+6a2U0GtFoNEpRM72RDwpp082bNxk3bpxSiLa1teXnn38mf/78KkeW/JYsWcKOHTsAcHR0ZNasWZIQCCGEEKmUKXNLtfN5yaNTnr+/P0OHDlVyXhcXF0aNGoVWq1U5MpFcQkNDWbp0KUeOHEm0PW/evPTu3RtnZ2dCQkIICgpS/gQGBhIUFIS/vz++vr6JztNoNNSpU4f27duTPXt2U74VkYyWLl3K9u3bgYQFmH/55ReVIxIZRWx4BGvbduPx2YuJthdpUo8awwfiWLJYil376KTfOPnrnwBotFo6ea4ht+vrF0oV6U9K5pYmaykzevRo6fMm0qR79+4xadIk5YOHpaUlP/74Y7ostgM0a9aMXbt2odfrefr0KSdOnKBGjRpqhyWEEEIIlUk+n/7lzp2b7t278+efCQWIK1eusGXLFlq1aqVuYOKjGY1Gjh07xuLFiwkLC1O263Q6WrVqRZs2bTD/d1bni57tr3L58mVWrFjBrVu3lNc9cOAAR48excPDg1atWmFra5vyb0gkm/j4eA4fPqyM69evr2I0IqOxsLWhzcp57Og9hLsH/5vpfmvnfm7tOkCpds2pNrQfdgWTt/7y9NpNvGf9pYxde3whxXaRrEw2w10kkJk5acvjx48ZPXo0ISEhAJiZmfHjjz9SpkwZdQNLYXPmzFGSrgIFCjBlyhT5gC1EBmcwGLhz5w6QMPNJZjsKkTpkpNwyI71XNRmNRmbMmMHJkyeBhILsuHHjKFq0qMqRiQ9hNBq5fv06mzZt4vz584n2FS1alN69e+Pk5PTer+nl5cWqVavw9/dPtM/GxoaWLVvStGlT6QGeRpw8eZJff/0VAHNzc+bPn4+NjY3KUaUvr8ujn4WEExAcSqmCueXzNgmLpx6Z8CuPzyVeoF1rbk7Zzu2pMqgPtrk+vqWvQa9n1Sef4X/mAgBZ8uel2+GtWNi+/ev+rv8z8uewR6eTz0LpQUrmlvIVIsRrBAUFMX78eKXYrtFoGDBgQLovtgO0aNFC+fu9e/eSJOdCiIwnPj6e9evXs379euLj49UORwghRArRaDT07NlT6d2u1+uZPXs20dHRKkcm3kdsbCwHDhzghx9+YMyYMYnyeUtLS7p06cL48ePfu9gOCV8jVapUYfr06fTs2ZNs2bIp+yIiIli5ciX9+vVj37596bZVbHqh1+vZtGmTMq5SpYoU21PA6/LorUcv8OfGQ8xat5/7AUEqRpg6FKhdjc93raPF4tnYF/tvIVJDXBznF69kgVtDjkyYQUxY+Edd5/yilUqxHaDBlDHvVGx/9CyEX1fvZeT8zWw6dI7o2LiPikOkb6oU3C9cuMBff/3FpEmT2LJli7I9JiaG0NBQNUISIpHw8HAmTJjAkydPlG29e/fGzc1NxahMx8nJCVdXV2X8zz//qBiNEEIIIVIbyefTN1tbW7777jtlxqW/vz+LFy9WOSrxLgIDA1m1ahV9+vThzz//5O7du4n2ly1blunTp9OsWbOPflrNzMyMBg0aMHPmTDp27Ii1tbWyLygoiHnz5vH999/j7e2NPFifOu3atUuZeQ3QuHFjFaPJWPwCgjhz4z4APn5PeBz4XOWIUgeNRkOxZo3odngrTWZPJnO+PMq++KhovH6by0L3RpxfsgrDB0wCCn3oz5EJvyrjkm2bUbhB7beeZzAaWb3nFAaDkbDIGM7f9MPcTNa6E69n0oL7jRs3qFatGq6urvTu3ZuRI0eyefNmZf/KlSvJli0bO3fuNGVYQihezAQZOXIkfn5+yvYuXbpQt25dFSMzvZYtWyp/v3r1Kj4+PipGI4QQQojUQPL5jKNUqVK0bt1aGR84cEBpMyNSlxdtY2bMmEHfvn3ZtGlToj7tAGXKlGHo0KGMGDGCHDk+viXDy6ysrGjTpg1z5syhWbNmmJn9t1Tcw4cPmTZtGiNHjuTq1avJel3xcQIDA1m9erUyrlWrFsWKpdwClSIxMzMdxZ1yApDX0Y5KJQuqG1Aqo9XpKN2xNV+d3EW9iSPJ5Oig7It8GsjeIT+xpHYLbu0++M439IxGI3uH/kxcRCQAVtnsqDNu+LsFZDRSobgTluYJP9/a16+ITlpsijcw2VeHn58ftWrV4uTJkzRv3pwpU6Yk+abo0KEDFhYWbNiwwVRhCQEknQny6NEjZV/btm1p1qyZitGpo0SJEhQvXlwZyyx3IYQQImOTfD7jadeuHc7Ozsp43rx5PHv2TMWIxAtGoxEfHx/+/vtv+vfvz+jRozlx4gQGg0E5xsLCggYNGjBt2jRGjRpFpUqVUrRPdObMmenSpQszZ86kdu3aia7l4+PDTz/9xOTJk7l//36KxSDe3eLFi5VWUba2tnTp0kXliDKW3A5Z+a59Pfq2rUP7ehXR/t/3pt5gYI/3VSKjY1WKMHUws7TAtccXfO29h6rf98XM2krZF3TzFps+78W6dl/y5PL1175GbEQk/ucucmL679zefUDZXnfcj9i8VMh/E61WS13X4oz88hNa165A6cJ5P/xNiQzB7O2HJI+xY8fy7NkzFixYQPfu3QEYMmRIomNsbGwoX748Xl5epgpLZGBGo5EbN27g6emJl5dXouT0hebNm9OhQwcVolOfRqOhZcuWTJkyBYBTp07x8OFD8uaVXyxCCCFERiT5fMZjZmbGd999x9ChQ4mOjiYiIoLZs2czZswYWTxbBQaDgevXr+Pl5YW3tzeBgYGvPM7R0ZHGjRtTr149bG1tTRxlwvX79u1L8+bNWblyJWfPnlX2nT17lrNnz1K+fHk8PDwoV66cfC2p4MyZM3h7eyvjzp07kyVLFhUjyrhKFsz9yu0nLt/mnyMX2HnyCnUrFqdZ9bImjix1sbC1ofoP/Sjb5VOOTfqNy6s3wb83/e8fPsGyeq0o3bE1Zb/owPP7D3h2zYdn12/y7LoPz+89SPJ6BepUp1SHlkm2v022zJmoX6nER78fkf6ZrOC+c+dOypYtqyTnr1OwYEF2795toqhERmQwGDh69Cjbt29P1K/uBUtLS2rVqkWTJk3Inz+/ChGmHq6uruTLl48HDx5gNBrZtGkT3377rdphCSGEEEIFks9nTLly5eKrr77i999/B+DatWts3ryZNm3aqBxZxhAfH8/ly5fx9vbm1KlTPH/++j7PLi4ueHh4UKlSpVRRxHZycmLYsGFcu3aNFStWcPPmTWXf+fPnOX/+PLlz56ZJkybUrl2bTJkyqRhtxhEdHc2iRYuUcYkSJahTp456AYkkYuLi2XH8svL32DhZfPiFzLlz0mTWJFx7dOHgT79w//CJhB1GI5dXbeTyqo1vfQ2zTNY0nPpzij7xI4TJCu5PnjyhevXqbz0uLi6OyMhIE0QkMqL4+HhmzZr1yv6Tjo6ONGnShLp166oyEyQ10mq1tGjRgj/++AOAw4cPU79+fUqWLKlyZEIIIYQwNcnnM65atWpx7tw5jh8/DsCaNWsIDQ2lY8eOWFlZveVs8b5iY2O5cOECXl5enDlzhoiIiFcep9FoKFmyJG5ubri5uZE9e3YTR/puSpYsybhx4zh9+jRr1qxJ1FLmxYK8q1evpk6dOjRp0oTcuV8941ckj/Xr1/P06VMAdDodPXr0SBU3aMR/NEDNckXZf+Y60TFx1KrgnOSYoNAIMllZYGVhbvoAU4EcZUrSfv1i7uw9zMGffiHo5q03Hm+WyZrsxYuSvYQzZbt8il3Bt0+ujIvXc/n2Q8o755fivHhvJiu4Ozg4vFOvtps3bybLL9gzZ86wZ88evL298fb25uHDhwBvXUxhyZIl/PHHH1y9ehULCwuqVKnCyJEjqVat2kfHJNQVGxvLjBkzOHPmTKLtqW0mSGpTs2ZNtm/fzr179wBYsGABv/zyS6LFkIQQ6Z9Op6Nhw4bK34UQGY+p83mRemg0Gnr06IGPjw9Pnz7FaDSyY8cOTp06Rc+ePSlXrpzaIaZ5UVFRnD17Fi8vL86dO0dMTMwrj9PpdJQuXRp3d3cqV65M1qxZTRzph9FoNFSuXJlKlSpx5coVduzYwZkzZ5TP51FRUXh6erJz507Kly9PyZIlsbe3T/RHbu58vPv377N9+3Zl3Lx58wz/VLcpvG8ebWFuhkfV0tSrVAJfvydkz5p0QuCafae59eAprsWdaFWrPJmsLJI97tROo9FQuGFtCtatzqUV6/Ge/Rfh/gFkK1qI7CWKkb2EM9lLOpO9RDGyOuVF8571nr2nrrH9+CWKOeXk0/qVyGkvbZfEuzNZxax69eps3ryZ8+fPU758+Vcec+jQIS5fvky3bt0++nrjxo1770UeBwwYwMyZM7G2tqZRo0ZER0ezZ88edu/ezfr162nVqtVHxyXUERMTw7Rp07hw4YKyrUyZMnTt2hUnJycVI0v9Xsx6GDVqFEajET8/P7Zt2ybfD0JkMDqdDldXV7XDEEKoyNT5vEhdbGxs+OGHH/j111959OgRAE+fPmXChAnUrl2brl27ylOiLzEajdy6dQtvb2+ePHnyxmMjIiK4evUqcXFxr9xvbm5OuXLlqFKlChUrVsTGxiYlQjYJjUZD6dKlKV26NAEBAezatYv9+/crT8UYjUbOnTvHuXPnkpxrY2OjFN8dHByS/N3BwQEbGxuZifoaBoOB+fPno9cntCfJkSMHbdu2VTmqjOFD82hLczNcCudJsv1JcBhXbif8HD5+6RZt61RIcsz8zYd5HhGFnW0mGrqVpGDuxE/AGIzGJAu1plVaMzPKde1Iua4dMRqNyfIz4ElwGLu8rgBw834ARy740K5uxY9+XZFxmKzg/v3337Np0yZatmzJ3LlzadSoUaL9+/fvp1u3bpiZmTFgwICPvl7VqlUpW7YslStXpnLlyhQsWPC1swQA9u7dy8yZM3FwcODEiRM4Oyc8snPixAnq1KnDl19+SZ06dbCzs/vo2IRpRUdH88svv3DlyhVlW6VKlRg4cCDm5hnz8av3VaxYMRo0aMCePXuAhMcQq1WrRo4cOVSOTAghhBCmYup8XqQ+Tk5OTJkyhY0bN/LPP/8ohbtDhw5x/vx5vvzyS6pWrZphC57vuqjpu7CyssLV1ZUqVapQvnz5dDm7O2fOnHTp0oUOHTpw+PBhPD09lSfTXyUiIoKIiAj8/Pxee4y5uTkODg5kz56dihUrUrduXekN/6/9+/cn6qP/1VdfYWlpqWJE4kP5P3uOtaU5UTFxWFuaY/mKtjJ+T4IJDovkHkHULJ+0Jc3mQ+c4eeUOdrbW5HXMRhePKuniZ3dyvQdznZbiBXJx5fYjstpa80m1jL1orXh/GuPbeqwkoz/++IP+/ftjMBjIlCkTkZGR2NraotVqCQ0NRaPR8Mcff9CzZ89kv7aVlRUxMTGvbSnTtGlTPD09mTFjRpIPCP3792fWrFlMmzaNwYMHf1QcLi4uAImKvyLlREZGMmnSJG7cuKFsq1q1Kt999520RHlPERERDBgwQFmoqUKFCgwbNixd/FIWQrydwWDgwYMHAOTLl09acAmRSpg6t1Qzn5c8OnW5e/cu8+bN49atxH1zK1asyNdff42Dg4NKkZnWi0VNvby8OHXqFKGhoR/8WjY2NlSuXBl3d3fKlCmDhUXGahFhNBq5dOkSZ8+e5dmzZwQHBxMYGEhwcPBbW8O+jqWlpdIbPm/evMkccdoREhLCwIEDlfUAqlSpwqBBg1SOKuNIiTw6JjaO09fvERwWSbPqiYvBBqORAb+twWBI+L4Z2e0TcjkkboeyaOsxzt5MaBNXtXRhPm/s/tExpTdGo5ELvg8w02kpXTjj/vxIz1IytzRpwR3g5MmTTJ48mf379xMeHg4kFMPr1KnDiBEj3mkhpg/xpoJ7VFQU2bJlIyYmBj8/P/Lly5do/5EjR6hVqxa1a9fm4MGDHxWHfFAwnfDwcCZMmJDoQ0CtWrXo06eP9B/+QEePHmXWrFnKeNCgQVSpUkXFiIQQpvJiHQyAgQMHZrgigBCplRq5pVr5vOTRqY9er2fHjh2sWbOG2NhYZbu1tTWff/45DRo0SHc3aMPDw/H19cXHxwdfX19u3Ljx2kWCNRoNJUqUoHTp0m+c7KPVailUqBClSpWSSUGvoNfref78OYGBgQQFBSn/ffHnxfh1LXleKFeuHB4eHpQvXz7dfV2+zaxZszh69CiQ8P05Y8YM7O3tVY4q4zB1Hm0wGPB9+JSQsEieh0dRu0IxLMwT/2z5ddUebj96BsCwL5qQL0e2RPufBoehNxiTFOqFSE9SMrc0+W/zKlWqsHnzZoxGI8+ePcNgMJA9e3ZVC6A3btwgJiYGR0fHJMV2QOm1dfHiRVOHJj5QaGgo48aNUxb6BKhfv76swP6RqlevzoEDB7h06RIAixcvpmzZsvKYphBCCJGBpMZ8XqhDp9PRvHlzKleuzPz587l8+TKQMKFpwYIFHDt2jF69epEnT9IexKlBRETEG4u0RqORwMDARAV2f3//N77my4uaVqpUSVqSJgOdTqf0a38do9FIeHi4UoC/evUq+/btU2Z0A1y4cIELFy6QK1cumjRpQp06dTLE55iLFy8qxXaAjh07SrE9ndNqtRTLn/ONx3zVvAbBYRGER8UkKbYD7DhxmVPX7lKyYC48qpSmcF7HlApXiHRJtdvnGo0GR8fU8Q17/37CYzSvKrZDwqN9dnZ2BAcHExYWRubMmU0ZnnhPwcHBjBs3TnlkC6BJkyZ8+eWX0v7kI2k0Gnr06MHgwYOJi4sjODiY1atX0717d7VDE0IIIYSJpaZ8XqgrV65cjBo1igMHDrB8+XKlyHnt2jWGDBlC+/btadasWaqYvf3w4UO8vLzw8vLizp07yfKaLy9q6urqKovHqkCj0ZA5c2YyZ85MgQIFcHV1pX379hw9epQdO3Yk6vv++PFjlixZwsqVKylfvjxubm5pfjHa14mLi2PhwoXKuEiRIjRu3FjFiERqkdXWmqy21q/cFxoRxdkbCXWya3cfU7V0EVOGppqHT0OwNDcju538DBcfT5WM5/jx4xw5ckRZ3T5PnjzUqFEjxR4/fZsXj8K+6e62jY0NISEh71xwf/FYwv+7desWRYpkjB9WphYUFMTu3bvZs2cPYWFhyvYWLVrw+eefS7E9meTKlYvWrVuzdu1aAHbt2kXt2rXl61oIIYTIQFIyn5c8Om3SaDTUq1cPV1dXFi5ciJeXF5BQ8Fu5ciXHjx+nd+/eFC5c2KRxGY1G7t27x8mTJ/Hy8nrjopzvKnPmzDg7O1O0aFGcnZ0pXrx4ulzUNK2ztLSkfv361KtXj6tXr+Lp6cmpU6eUNrOxsbF4e3vj7e2NTqejTJkyuLu7U7lyZbJkSR9tNHbt2qU8lfFi8pQ88S3eJiQ8ihz2mfF/9hw7W2vKFU06OfX4xVvYZbammFNOzNLBE25Go5E1e0/h9ySYJlVcqF+pRLp4X0I9Ji24X7p0iW7dunH+/HkA5Rfdi0JouXLlWLJkCWXLyuq/4t0YjUZu3ryJp6cnXl5e6PX6RPvbtWtH+/btpdiezFq2bMnRo0d59OgRRqOR+fPnM3HiRHmUXAghhEjnJJ8Xb2NnZ8fgwYPx9vZmwYIFhISEAAmLrA4fPpxmzZrRvn17LC0tUyyGqKgobt26xblz5/D29iYgIOCDX8vc3JyCBQsmKrDnyJFDPl+kIRqNBhcXF1xcXHjy5Am7d+9O0m5Gr9dz/vx5zp8/z/z58ylVqhRubm7UrFkzzT6xEB4ezoYNG5Rxw4YNTX7DS6RNTjntGd7FAx+/J0REx6LTJb5JExevZ+Ohs0THxmNtac637epSIFfaXij7gu8Dpaf91qMXKZo3B0XyyVN84sOZrOB+48YNateuTUhICPny5aNdu3YULFgQgHv37rFhwwbOnz9PnTp1OH78OCVKlDBVaMov0NctdgMov4zftZ3M6xruv27Gjng/cXFxHD9+HE9PT27fvp1kv4WFBR07dqRZs2YqRJf+mZub06NHD37++WcA7ty5w65du2jatKnKkQkhhBAipZgqn5c8On1wc3PDxcWF5cuXs3//fiBhIb8tW7bg5eVFy5YtyZEjh9Kb+0N7aRsMBh48eMDNmzeVXusPHjxQbga9SsGCBXF3d8fNzY2cOd/c59jMzExmBKcjOXLkoHPnznTs2JFLly7h5eXFqVOnEj0hbTQauXLlCleuXGHNmjW0atUKDw+PFL1JlBI2b96s1DGsrKxo3769yhGJtESj0VDM6dU/H2/eDyA6Nh4Avd5AboespgwtRcTrDdhaWxIeFUPpwnmk2C4+mskK7sOHDyckJIRhw4YxduzYJP37pkyZwujRo5k0aRIjRoxIdCc2pTk5OQEk6vn9soiICEJCQsiWLZv0b1dZdHQ0W7ZsYc+ePTx//jzJ/uzZs9OkSRPq1auXZmcipBUuLi7Url2bQ4cOAbBmzRqqVKkiC/AIIYQQ6VRqzudF6mRjY0Pv3r2pUaMG8+bNU2aaBwQEMH/+/ETHWltbK8V3e3t7smTJ8sZZ5PHx8dy7d49bt24RHR391liKFSuGm5sb7u7uby2yi/TPzMyMChUqUKFCBXr06MG1a9fw9vbGy8uL4OBg5bjIyEhWrlyJp6cn7du3p27dumniqd5nz57h6empjFu2bEnWrGm/KCpSB2srC8oVzcfVu/6UKpQHC/OkpcWnwWE4Zks79bNKJQrgUigPe09do1LJAmqHI9IBjfFNt/6Tkb29PXnz5uXSpUtvPK5MmTI8fPiQoKCgZL2+lZUVMTExr5zpEBUVRbZs2YiJieHBgwfkzZs30f4jR45Qq1YtateuzcGDBz8qjhczc143c0e83v3795kxY8Yr+y66uLjg4eFBxYoV00QClF6EhoYyYMAAZR2E4sWL4+7urnxQcnBwIFu2bKligSwhxMfR6/WcPn0agEqVKsnPWiFSCVPmlmrn85JHp20xMTGsXbuWbdu2vXH2eXKxtrbG2dmZSpUq4ebmJpNCxDsxGAz4+vpy8uRJ9u/fn+Qp+Dx58vDZZ5/h5uaWqtsKzZkzh8OHDwOQLVs2Zs6cKesMqCi95tExcfFERseSLXPiJ5T8AoKY8vcuyhbNR/Ma5cjlkD7WRBDpT0rmliargsXFxb1TL8eyZcty9+7dlA/oJdbW1tSrVw9PT0/WrVvHgAEDEu1fv349AM2bNzdpXOI/Bw4cYOHChcTGxirbzM3NqVmzJh4eHhQoIHcg1ZAlSxY6d+7M3LlzgYRHzW/cuJHkuKxZs2Jvb0/u3Llp3LgxJUuWNHWoQoiPpNPpcHd3VzsMIYSKUnM+L1I/S0tLvvjiC6pXr46npyePHz8mKCiIoKCgJOswvS+tVouTk5PSY93Z2Zk8efJIKxjx3rRaLcWKFaNYsWK0bt2azZs3s3PnTuLi4gB49OgR06dPx9nZmc8//5xSpUqpHHFSd+/e5ciRI8q4Q4cOUmxXWXrNoy3NzbB8xez2f45cwEhCX/TA0Ah+6Nw4Vd+gEiIlmGyGe40aNYiPj+fkyZNvPK5KlSqYmZlx9OjRZL3+m2a4A+zdu5eGDRvi4ODAiRMncHZ2BuDEiRPUrVsXa2tr7ty5g52d3UfFITNz3k90dDQLFy5U2pa80KxZM1q1apVuVo9PywwGA2PHjuXq1avvfI6rqyudOnVS2jkJIYQQ4sOYMrdUO5+XPDp9MhgMhIaGEhQURGBgoPLflxe0fBWNRkOOHDlwdnamcOHCaa6/tkg7nj17xtq1azl06FCSeoKrqyt9+vRJVe1aJkyYwIULFwDIly8fU6dOTTczqkXqFxUTx6x1+/ELSHjKrU/r2rgUzqNyVK8WExePuZkOrdwMyLBSMrc0WcHd09OTZs2a8ddff9G9e/dXHrN48WK+/vprtm7d+tGLL27fvp1x48YpY29vb4xGY6K7iqNGjeKTTz5RxgMGDGDmzJlkypSJhg0bEhsby549ezAajaxfv55WrVp9VEwgHxTeh5+fH7/++muiFjKZM2fm22+/pUKFCipGJv5feHg4Bw4cwN/fX5mpFBQURGho6GvP0Wg01KpVi08//ZTs2bObMFohxIcwGAxK792cOXPKrEEhUglT5pamzuf/n+TRQgg1+fn5sWrVKqU1yAs5cuRg2LBh5MuXT6XI/nPhwgUmTJigjIcNG4arq6uKEQnIeHm0wWjk/E0/rtx5ROfG7ql2dvvK3d74PQmmda3yr10gVqRv6aKljI2NDX369KFHjx4sWbKETz/9VGkDcu/ePdauXcvRo0fp06cPtra2Sr+xF2rVqvVe13v69CleXl5Jtr+87enTp4n2/fbbb5QvX545c+awZ88eLCwsaNCgAaNGjaJatWrvdX3xcQ4ePMjChQuJiYlRthUvXpz+/ftLcTYVsrW1fWXLpbi4OIKDgwkMDOTp06fs2rULHx8fAIxGI4cOHeL48eM0btyY1q1by6LEQqRi8fHxLFu2DICBAwdiYWGhckRCCFMzdT4vhBCpSf78+Rk6dCjXr19nxYoVSivNJ0+eMGrUKAYPHkzp0qVVi89gMPD3338rYxcXF5molkpktDxaq9HgWtwJ1+JJn2g3GAxsO3aJuhWLkzmTeq2O/J8958Tl2xiNRmat20/PljUpW1T9m2Yi/TDZDHetVotGo1Eewfr/O1yv2/7Cx/b1Sy1kZs6bxcTEsHDhwiSL07Zo0YKOHTvK4ptpnNFo5NSpU6xatSrJ4rfW1ta0bNmSpk2bSo9BIVKh2NhYZsyYAWSMDwpCpBWmzC3VzucljxZCpBZGo5GtW7eyYsUK5WefTqejV69e1KlTR5WYDh06xO+//66MJ06cSNGiRVWJRSQmefR/1u0/w6FzN8me1ZberWurtqDq3lPX2Hz4PAA5smVmRNem6HTp+8kDkVS6mOHepUuXVPsYiUgdnj9/zoQJExItsmVra0vfvn2pWLGieoGJZKPRaHBzc6NixYocPHiQtWvXEhwcDEBUVBSrV69mz5499OjRQx59FEIIIVIZyeeFECKBRqOhRYsW5MyZk9mzZxMbG4ter+ePP/7g8ePHfPrppyb9eRkbG8vq1auVcbVq1aTYLlKdG/cec+jcTQCePQ9nt/cVunhUVSWWBpVLUjivI5sPnaN+pRJSbBfJzmQF9yVLlpjqUiINCgoKYty4cYlmPRcrVowBAwZIC5l0SKfTUb9+fWrUqIGnpyebN28mMjISgMDAQCZPnkz16tX58ssvZWFcIYQQIpWQfF4IIRJzd3fHwcGBX375hefPnwOwceNGAgIC6NOnj8lmMnt6ehIYGAgkfNbq1KmTSa4rxPso5pSTVrXKs/nweZxy2vNpg8qqxlM4T3YGdmygagwi/ZJbOEJ1z549Y8yYMYmK7R4eHvz0009SbE/nLC0tadWqFXPmzKF58+bodDpl37Fjxxg4cCCHDx/GRJ2vhBBCCCGEEOK9FC1alAkTJiRaNPXYsWOMGzeO0NDQFL9+WFgYmzZtUsZNmjQhR44cKX5dId6XRqOhQeWS9GxZk16tamFprn7LYI1GI0/viRQhBXehqoCAAMaMGaOs2A3QoUMHunXrJv3aMxBbW1u++OILpkyZgrOzs7I9LCyMOXPmMHny5CSLHAshhBBCCCFEapAjRw7GjRtHmTJllG03btxgxIgReHt7K200U8LGjRuVp4UzZcpEmzZtUuxaQiSHskXzkdXWOsl2/2fP8fF7QkxcvApRCZG8TFrRjImJYfXq1Rw6dAh/f39iYmJeeZxGo2Hfvn2mDE2o4NGjR4wdO5agoCBlW6dOnWjVqpV6QQlV5c+fn3HjxrFz505WrVql/Iw4d+4cgwYNolOnTjRu3BitVu4VCiGEEGqQfF4IIV7NxsaGH3/8kQULFrB//34gYYLZtGnTAHBwcMDZ2ZmiRYvi7OxM4cKFsbS0/ODrGQwGrly5ws6dO5VtrVu3JnPmzB/3RoRQQVhkNHM3HyLweQTmZjqmftsWs5eegP9Y0bFxrNt/hkxWFjRxd8HG+sO/94R4FyYruN+/f58GDRpw69att7aHkMc50r/79+8zbtw4pc8dQLdu3WjatKmKUYnUQKvV0rRpUypVqsRff/3FhQsXgIQP+IsXL1ZazTg4OKgcqRAZi06no3r16srfhRAZj+TzQgjxZmZmZvTq1YucOXOyatWqRPsCAwMJDAzk5MmTQMLnHicnJ6UA7+zsTJ48ed44uSg+Pp6rV6/i5eXFqVOnCAkJUfY5ODjg4eGRIu9LfBzJo98sNi6e+ZuPEPg8AoDcDlmTFNsNRiN//XOE3A5ZKZjbgZIFc2Nu9u7/L/d4X8Pryh0ATl6+zVfNa1CiQK7kexNC/B+TFdz79euHr68v9erVo3///hQuXBhbW1tTXV6kInfu3GH8+PGEhYUp23r06EHDhg1VjEqkNjly5GD48OEcOXKEJUuWEB4eDsDNmzf59ddfGTdunMx0F8KEdDodNWrUUDsMIYSKJJ8XQoi302g0tG7dmiJFirBnzx58fX2VBU1fZjAYuHv3Lnfv3mXv3r0AWFtbU7RoUaUIX7RoUTJlysSlS5fw8vLi9OnTyuei/9e5c2eTLdIq3o/k0W+m1Woo55wPMzMt9x4HUTB30sl1T4PDuHTrIZduPUQDTPm2XZKCu95gICIqhsjoOHI5ZEm0r7xzPnZ5XUm4nkZDXke7lHo7QgAmLLjv27cPZ2dndu7cKb25MzBfX18mTJhARETCnUuNRkOfPn2oU6eOuoGJVEmj0VCrVi3KlSvH4sWLOX78OAA+Pj7s3buXRo0aqRyhEEIIkXFIPi+EEO+ubNmylC1bFoCgoCB8fX3x8fHB19cXX1/fV7bkioqK4tKlS1y6dEnZZmZmRnz8q3taa7VaXFxcaNKkCZUrV06ZNyJECjPT6WhQuSQNKpfEYDAQE6dPcsxd//9uWuV0yIK1pXmSY0bN30JoRBSlCuXmmzZ1Eu3LlyMb+XNkI4+jHU2quJA5k1Wyvw8hXmayTNnCwgJXV1dJzjMgg8HAtWvX8PLy4uDBg0RHRwMJycF3332nPFolxOtkzZqVAQMGAChF95UrV+Lm5oadnZ16gQmRgRiNRp49ewZA9uzZpV2EEBmQ5PNCCPFh7O3tcXNzw83NDUj4jPzgwQN8fHyUIryfn98r23X9f7HdzMyMsmXLUqVKFSpWrCg929MAyaPfnVarxdoy6ZPsBXM70LJmOe4+DsTRLunXfHBYJKERUQDcuBdAVEws1pb/PfGh0WgY2rmx/L8XJmOybLlatWr4+vqa6nJCZfHx8Vy+fJmTJ09y+vRpQkNDE+3X6XQMHDhQSTiEeBfdunXj/PnzREZGEhkZyZIlS5RCvBAiZcXFxbFo0SIABg4cKI8sC5EBST4vhBDJ40X/dicnJ+rXrw9AdHQ0t27dUgrwPj4+BAcHA2BpaUmFChVwd3enQoUKZMqUSc3wxXuSPPrj5bTPQkO3Uq/d//IM+Cw2VjwLCSd/TvtEx0ixXZiSyQruP//8M7Vq1eKvv/6iR48eprqsMKHY2FjOnz+Pl5cXZ86cITIy8pXHWVtb079/f1xdXU0coUjr7Ozs6NSpEwsWLAASZrvXrVuXcuXKqRyZEEIIkf5JPi+EECnHysoKFxcXXFxclG2BgYEEBwfj5OQkRVoh3qBs0bwM7+qBRqMhl30WKa4L1Zms4O7q6sru3bvp3Lkzf//9N40aNSJv3ryvXfSwS5cupgpNfCS9Xs+hQ4dYt27dKxeDgYQZ7WXKlMHd3Z3KlSuTJUuWVx4nxNs0aNCAgwcPKjPsFixYwPTp0yUBFUIIIVKY5PNCCGFaDg4OODgkXUBSCJGYTqslT3Y7tcMQQmHSBox79uzhyZMn3L17l6NHj77yGKPRiEajkQQ9DTAajZw+fZqVK1fy8OHDJPstLCwoX7487u7uuLq6YmNjo0KUIr3RarX07NmTYcOGYTAYCAgIYOPGjXTs2FHt0IQQQoh0T/J5IYQQQggh3sxkBfepU6fy888/Y2lpSevWrSlcuDC2tramurxIZteuXWPlypXcuHEj0XYzMzPc3d1xd3enfPnyWFnJys8i+RUsWJCmTZuybds2AP755x9q1KhBvnz5VI5MCCGESL8knxdCCCGEEOLtTFZw//PPP8mSJQteXl4UL17cVJcVyez+/fusXLmSs2fPJtqu0WioVasWHTp0wNHRUaXoREbSoUMHTpw4QWBgIHq9ngULFjBmzBjp1SaEEEKkEMnnhRBCCCGEeDuTFdwfP35Mw4YNJTlPA4xGI2FhYQQFBSl/AgMDefDgAd7e3hiNxkTHu7q60qlTJ5ycnFSKWGREVlZWdO/enalTpwJw9epVDh06RJ06ddQNTAghhEinJJ8XQgghhBDi7UxWcC9SpAgGg8FUlxOvodfrCQkJITAwMElB/eVxXFzcW1+rWLFifP7555QsWdIEkQuRVOXKlalUqRKnT58GYPny5bi6usqivEKkAJ1Oh5ubm/J3IUTGI/m8EEII8f4kjxYi4zFZwb1379788MMP3L17l4IFC5rqsgJYv349Z86cISgoiJCQkCQz1N9X3rx5+eyzz6hcubK07xCq6969O5cuXSImJoawsDD+/vtvvvnmG7XDEiLd0el01K1bV+0whBAqknxeCCGEeH+SRwuR8Zis4N63b19u3bpFzZo1GTduHA0bNiRv3rymunyG9uTJE27duvXe51lYWODg4IC9vb3yp1ChQri7u8tdWZFqZM+enQ4dOrB8+XIADh48SJ06dShVqpTKkQkhhBDpi+TzQgghhBBCvJ3JCu4vCrRGo5GvvvrqjcdqNBri4+NNEVaGYG9vn2Sbra1tkmK6vb19om02NjYyg12kCU2bNuXw4cPcu3cPgHnz5jF69GgcHBxUjkyI9MNoNBIaGgpAlixZ5PeDEBmQ5PNCCCHE+5M8WoiMx2QF9/z588sPFZVUqVKFvHnzJiqmW1hYqB2WEMlGp9PRs2dPRo4cidFoxN/fn6FDh/Ldd99Rvnx5tcMTIl2Ii4tj7ty5AAwcOFB+jwiRAUk+L4QQQrw/yaOFyHhMVnC/e/euqS4l/k/BggWlz6ZI95ydnWnVqhWbNm0CICwsjIkTJ9K6dWs6dOggbZCEEEKIjyT5vBBCCCGEEG+nVTsAIYRILh07dqR3796Ym5sr2zZt2sTYsWMJCgpSMTIhhBBCCCGEEEIIkRGoVnCPiYnB399fimBCiGSj0WioV68ekyZNSrSI27Vr1xg6dCgXLlxQMTohhBAifZF8XgghhBBCiKRMXnCfP38+FSpUwMbGhnz58vH9998r+zZu3EibNm3w9fU1dVhCiHTEycmJSZMmUaNGDWVbaGgoEydOZPXq1RgMBhWjE0IIIdI2yeeFEEIIIYR4PZMV3PV6Pa1bt6ZPnz5cu3aNkiVLYjQaEx1Trlw5Nm/ezJo1a0wVlhAinbKysuK7776jV69eSosZo9HIxo0bGTduHCEhIeoGKIQQQqQxks8LIYQQQgjxdiYruM+ZM4d//vkHDw8P7t27x6VLl5IcU6RIEYoWLYqnp6epwhJCpGMajYb69eszceJEcufOrWy/cuUKY8aMITAwUMXohBBCiLRF8nkhhBBCCCHezmQF9yVLlpAzZ07WrFlDzpw5X3tcqVKluHfvnqnCEkJkAAUKFGDy5MlUr15d2ebv78+YMWN48uSJipEJkXZotVoqVKhAhQoV0GplzXUhMiLJ54UQQoj3J3m0EBmPyb7Tb9y4gbu7OzY2Nm88zsbGhqdPn5ooKiFERmFtbU2/fv3o2LGjsu3JkyeMGTOGx48fqxiZEGmDmZkZjRo1olGjRpiZmakdjhBCBZLPCyGEEO9P8mghMh6TFdzNzc2Jjo5+63H3798nc+bMJohICJHRaDQa2rRpQ5cuXZRtgYGBjBkzhgcPHqgYmRBCCJH6ST4vhBBCCCHE25ms4O7i4sKZM2cICwt77TFPnjzh/PnzlC9f3lRhCSEyoGbNmvHVV18p4+DgYH766Sd5/F2INzAajURGRhIZGZlkkUQhRMYg+bwQQgjx/iSPFiLjMVnB/YsvviAwMJDevXsTGxubZL9er6dv375ERkbStWtXU4UlhMigGjduTO/evdFoNACEhoby888/c/v2bZUjEyJ1iouLY/bs2cyePZu4uDi1wxFCqEDyeSGEEOL9SR4tRMaTYgX3woUL88MPPyjjnj17UqdOHVatWkXx4sXp3bs3ABcuXKB///4UK1aMDRs20LBhQz7//POUCksIIRT16tWjb9++StE9PDycsWPHcvPmTZUjE2mZr68vjx49UjsMIYT4aJLPCyGEEEII8f5SrOB+9+7dRIsl6XQ6duzYQZ8+fXj06BHz588H4Ny5c8yePZv79+/To0cPNm/erBS/hBAipdWqVYuBAwei0+kAiIyMZPz48Vy9elXlyERatG3bNoYPH86AAQNYuXIler1e7ZBMwmAwqB2CSMMMRiN3/Z+pHYZ4BcnnhRBCCCGEeH8mXR7ZysqK33//nZ9++omDBw9y9+5dDAYD+fLlo27duuTJk8eU4QghBABVqlTBzMyMX3/9lfj4eKKjoxk/fjxdu3alUaNGUjQQ7+Tx48esWrVKGW/evJnr168zYMAA7O3tVYwsZd1++JQVu73p1rQq+XOm3/cpUs4e76tsPXqRRu6l+KRaGXRak3U8FB9A8nkhhBBCCCHezKQF9xccHR1p3769GpcWQohXqlSpEkOHDmXq1KnExcURHx/PwoULuXLlCr179yZTpkxqhyhSMaPRyMKFC5P0ZLx+/TpDhgzhu+++S5cLCIZHxbBo23FCwiOZvmoPnRq54VaqkNphiTTEx+8J245dAmC311V0Gi2fVC+jclTiXUg+L4QQQgghxKvJFCIhhPhX+fLlGTlyJHZ2dsq2kydP8sMPP3Dnzh31AhOp3vHjx7lw4YIyLl68uPL3sLAwJk6cyKpVq9Jdi5kb9x7zPDwSSGgLkt0us8oRibTGxtqCHNkSvm4csthQt2Lxt5whhBBCCCGEEKlbis5wP3/+PGPHjv2gc0ePHp3M0QghxNuVLFmSKVOmMHv2bC5dSph1GRAQwMiRI+natSsNGzaUFjMikYiICJYuXaqMy5Urx/Dhwzlw4ECiWe+bNm3i+vXr9O/fP920mKlYogC2mSxZsv0EDSqXoHCe7GqHJFK5S7cecvzSLdrXq4h9FhvyZLdj6OeNWLv/DLXKO5PJykLtEMX/kXxeCCGEEEKI96MxGo3GlHhhrVb7QUUpo9GIRqNJd7MAX3BxcQHgypUrKkcihHgTg8HAhg0bWL9+PS//mKxWrRo9e/aUFjNCsWDBAnbv3g2Aubk506dPJ1euXADcv3+fX3/9lUePHinHZ8mShX79+lG2bFlV4v1Q8fHx7Nq1C4DGjRtjZvbfPfvwqBhsrCyS/N7X6w1otRq5SSUAiImLZ8KSHQSFRmBhpqNzkyq4Fnd64zkBQaFYWphhZys/c18nJXPL1JbPSx4thBAiLXpTHi2EUE9K5pYp+l1epEgRqlevnpKXEEKIFKHVamnfvj0lSpRg1qxZPH/+HEhoHXLnzh0GDhxIwYIF1Q1SqM7X15c9e/Yo47Zt2yrFdgAnJycmTZrEX3/9xdGjRwEIDQ1lwoQJ1K5dm6ZNm6aZryMzMzM++eSTV+6ztbZ85fY1+04TExfPZw0rY2VhnpLhiTTA++odgkIjAIg3GMjtkPWNx8fExfPXP0cIj4qhXsUS1K7gjKV8HZmc5PNCCCHEx3lTHi2ESJ9StOBeo0YNFi1alJKXEEKIFFWmTBmmTp3KzJkzlbue/v7+jBgxgl69elGrVi2VIxRq0ev1zJ8/X3kCIm/evLRo0SLJcdbW1nz33XeUKlWKxYsXExcXh9Fo5ODBgxw8eJCSJUvi4eFB5cqV0el0pn4bKcb76h2OX7oFgF9AEH3b1sEhq63KUQk11ShbFAszMzYdOke1MkXInf3NBfe1+07zOCgUgL2nr1HbtZgpwhT/R/J5IYQQQggh3o88xyKEEG9hZ2fHqFGjWL9+PRs2bMBoNBIXF8ecOXMICAigXbt20jIjA/L09OTu3bvKuEePHq99PFSj0dCgQQOKFi3KjBkz8Pf3V/Zdu3aNa9eu4eDgQOPGjalfvz6ZM6e+xUdffN1DQuucN33NG41Gzt64r4wtzHSvnQUvMg6NRoO7SyFcCufBwuzNN5eMRiOF8mTn7I37xMXrqVa6MJbmib+/DEYjRqMRnVabkmELIYQQQnyU98mjhRDpg3xCEUKId6DVaunQoQMjRoxIVAxdt24dc+bMURIokTE8e/aMNWvWKOM6depQqlSpt55XsGBBpk2bxrfffkuRIkUS7QsMDGTlypX07t2buXPncufOHVJomZUPEhcXx4wZM5gxY8Zbv941Gg09W9akSRUX7Gwz0bt1bWkFIhS21pZYmL95zodGo6FG2aL89FVzPKqUplaFpLPbr9/156cFW9njfZWwyOiUClcIIYQQ4qO8Tx4thEgfZIa7EEK8h7JlyzJx4kQmT57Mw4cPAThy5AjPnj1jyJAh2NpKy4yMYPHixcTExACQOXNmOnfu/M7nmpubU6tWLWrWrImPjw87duzAy8tLWVwwLi6O/fv3s3//fnLmzIm7uzvu7u4UKVIEbRqayavVamlWvSz1K5XE2lKK7SKpW7sPcm7BcvK4VaBiz65YZkn6ZEdWW2s+qV7mlecfPHeT4LBI/jlygaDQCD5tUDmlQxZCCCGEEEKIt0o7n9yFECKVyJkzJ+PGjVNWtIaEtiAjRozg8ePHKkYmTOH06dOcOnVKGX/xxRdkyZLlvV9Ho9FQrFgxBgwYwO+//06bNm2SvE5AQABbtmxhxIgR9O3bl0WLFnH16lUMBsNHvw9TeVWxPTwqhn+OnCf+35sMIv1as/cUB8/eSPI1G/74CVt7DODugaMc/2U2f1Wsj9fM+cRGRL7T64ZGRHHjXoAyLu+cP8kxAUGhPA0O+7g3IIQQQgghhBDvKcVmuKelYoAQQrwvW1tbRowYwbx58zh06BDw32KqQ4YMoUSJEipHKFJCdHQ0CxcuVMYlS5akdu3aH/269vb2dOzYkTZt2nD8+HH27NmDj49PomMCAwPZuXMnO3fuJEuWLFSuXJm6detSrFjaWkgyLl7PX/8c4dbDp9z1D+Tr5jWwkf7u6dKNe485csEXAK+rd+nTuhZZbKwBOD51DvGRUcqx0SHPOTJ+OmfmLcG9X0/KdfsMM6vXf11ksbHm56+bc+SCL5dvP6Ro/hxJjvE8cZnT1++R19GOuhWLU8WlcDK/w/RP8nkhhBBCCCHen8xwF0KID2RmZsY333zDp59+qmwLCwtj3LhxHD9+XMXIREpZt24dgYGBAOh0Onr06JGsix5ZWFhQp04dJkyYwB9//MGXX35JqVKlklwjNDSUffv2MXLkSIYPH86RI0eIj49PtjhS0oEzN7j18CkAPn5POH39nsoRiZSy9dhF5e8aUBbODfS5xaUV65V92pcWG458GsiBUZNY4NaQC0tXo4+Nfe3r22XORPMaZfmxi0eShVPj4vVcvv0IgIdPQyD1LIcghBBCCCGESOek4C6EEB9Bo9HQtm1b+vXrh9m/RaO4uDh+++03Fi5ciI+PT6pa+FJ8uGPHjrFt2zZl3LJlS/Lly5di18uePTseHh789NNPzJ8/n549e1KuXDl0Ol2i43x9fZk9ezbffPMN69atIyQkJMViSg71KhXHrVRBANxdClGrvLO6AYkU06tVLdxKFUSr0fBZw8rKGgRHJ8zA+G87IbtCBeh57gCVv/saM2sr5dxw/wD2fD+GRdU8uLh8Lc/vP3ivn6UhYZFky5IJSCj0VyxRIMkxAUGh0tZICCGEEEIIkew0RqkEmdSLns9XrlxRORIhRHK7fv06U6ZMITw8PNF2BwcHKleuTJUqVShRokSaWvhSJDh79ixTp05VFjbNmTMn06dPx8LCwuSxREREcPr0afbv38+1a9eS7NfpdFSrVg0PDw+KFi2abNeNjY1lxowZAAwcOPCj3rvRaMTryh0qlSyA2f/dQDh3049nIWGUd86PY7aki2iKtOdpSBiOdgn/lg+9z7Lqk8+Ufc0X/Ebxlh4ARAQ8xWvmvH9ntscleZ1Mjg7krliO3K7lyFOpHLkqlMHiLQtVBwSFEhIeSXGnXIm2GwwGxi7aTmx8PDXKFqVWhWLKDPy0JiPllhnpvQohhEg/kjOPFkIkn5TMLaXgbmLyQUGI9M3f359Jkya9dvHUF7233d3dKV26tDIrXqRe169fZ/z48cT+29oic+bM/Pzzzyk6u/1d3blzB09PT44dO0ZcXNICpaurKwMGDMDKyuoVZ7+f+Ph4ZYZ/s2bNUuxr97c1+/B98ASA1rXLU79SyRS5jjA9o9HI6madeOh9FoBcrmX5fOfapC2THvpzcvofXFq5QZkJ/0oaDdlLOJPbtSy2eXJhbZcVS7ssWNnZYWWXBatsWbHKmhUruyzo/u+D7aVbD5m3+TAAWo2Gn75ujn0Wm+R9wyaSkXLLjPRehRBCpB+myqOFEO9HCu7piHxQECL9i4qK4uDBg3h7e3P16tXXtkGwsrKiSJEiFC1aFGdnZ4oWLYq9vb2JoxVvcvfuXX766SciIyMBsLa2ZvTo0RQpUkTlyBILDQ1l79697N69m6CgoET7WrduzWefffaaM1OXsMhohs/drHzPfN+pEQVzOyQ65qZfAFqNhvw57bE0lw8rqcmxi76ULJj7tYVrX8+9bO7SVxl32LwMp+rur3294Nv3OPX7Qu4fPkHI3fsfFZtllsxkzpebzHlykyVfbvxyF+CC0Ro94JLXgZ5t6iQpyqcVGSm3zEjvVQghhBBCpCwpuKcj8kFBiIzl+fPnnD59Gi8vLy5duqS0JHkdBweHRAX44sWLJ+nZLUzj0aNHjB49mtDQUADMzc0ZPny48nM8NYqPj+fUqVNs3boVX19fIOEmwe+//47tW1pvpAZhkdEcPu/DeR8/oqLjGNuzBdr/m/3866o93H70DK1GQ+cm7riVKqRStOJlxy76smrPKexsrfm2XV1yOWRNtN8QH8+S2i0IunkLgEINatN21fx3fv3IZ0H4n72I/9kL+J+5wOOzF4kJDfuomPXWmQgrXxGre3fI9OQx5bp2pN6kkcm6ELIpZKTcMiO9VyGEEEIIkbKk4J6OyAcFITKuiIgIzpw5g7e3N+fPn1dalLxJ3rx5GThwIE5OTiaIULwQGBjIqFGjePbsGQBarZbvv/+eSpUqqRzZu4mMjKRv375EREQA0K5dOzp06KByVO8nIioGm//rqa3XG/h+znri4hNuXA3+rCGF8mRXIzzxkoCgUMYv2aE8mVAwtwODP2uYqHB9cfk6dg8amTDQaOh68B8cSxX/4GsaDQaCfO/gf+YCT69cJyo4hOjg50SHvPgTSnTIcwyvaLX0Jg2n/Uy5rh0/OC41ZKTcMiO9VyGEEEIIkbJSMreUZ7GFEMJEbGxsqFWrFrVq1SImJoZbt27h6+uLj48Pvr6+BAYGJjnn4cOH/Pjjj3z11VfUrVs3zc28TItCQ0MZP368UmwH+Oabb9JMsR0gU6ZMfPLJJ6xduxYAT09PmjVrRqZMmT74NU292NP/F9sBwqNiKJTbgXuPg4jXG8iXI1uSY67cfkS2LJnIk90uReMT/8lpn4UO9Suydu9pstha8+Un1RL9rIqLjOL4lFnK2KVDq48qtgNotFocihXBodjr2zsZjUbiIiKJDnlOxJNnhD30J/Shf8J/H/gT9sifsAf+RDx5qpxzYOREHCuWJ0/pEh8VnxBCCCHEC7JoqhAZjxTchRBCBZaWlpQqVYpSpUop24KCghIV4F/cZY2Li2Pu3LlcvXqVr7/+OlkWwBSvFhkZycSJE3n48KGy7csvv6RWrVoqRvVhPDw82Lp1K1FRUURERLBz507atGmjdlgfJautNf061MdgMBAYGoG5WeJ2S0+Cw1i8/Rh6g5EO9StRtXRhlSLNeGqWc8bGypLc2bPikDVx+6Kz85cR/jhhIVydpQXVh/UzSUwajQYLWxssbG3Iki8PuV3LvvK48McBLK3bivDwKIIrV+WXrScZYu9AvjyOJolTCCGEEEIIkb5o1Q5ACCFEAnt7e9zc3Pj8888ZM2YMw4cPJ3PmzMr+w4cP8+OPP3L//sctHiheLTY2lilTpnD79m1lW4cOHfDw8FAxqg9nY2OTKPbt27cTHR2tYkTJR6vV4miXOdE2g9HI4m3HiI6NJy5ez4YDZwmNiFIpwozJtbgTuf+vd3tkYBBes/7r1e7a4wuy5Mtj6tDeyDZXTjxmTyagQ2eeV6+F3sKSvxdtUDssIYQQQgghRBolBXchhEilypcvz9SpUylZsqSy7eHDhwwfPpwDBw6oGFn6Ex8fz4wZM7h69aqyrWnTprRt21bFqD7eJ598ojwRERYWxu7du1WOKOVoNRqa1yiLjVXCI7qfN3Yni421ylGlT0+Cw7hy+9E7Hes1Yy6xYeEAWGbNglu/nikZ2gcr3KA25ez+a2P0yGjG5S27VIxICCGEEEIIkVZJwV0IIVIxe3t7Ro8eTatWrZRtsbGx/Pnnn/z+++/pZsaymgwGA3/++SdnzpxRttWuXZsuXbqk+Z75mTNnplGjRsp469atxMTEqBhRyipVKA/DunjQvl5FKhTLr3Y46VJIWCRz1h9g3ubDnLp2983H3vPj3KKVyth9QC+ss9mlbIAfoeP3PbANfobdwb3kmzuLA4NGEvrg3W4sCCGEEEIIIcQLUnAXQohUTqfT0alTJ3788cdELWYOHTrE8OHDU2RF7YzCaDSyZMkSjhw5omyrVKkSvXv3RqtNH78imzdvrizM9Pz5c/bu3atyRCkrW+ZM1K5QLMl2g9HIHu+rhIRFqhBV+mA0Glm49RhBoREYjEaWeZ4kICj0tccfmzwTQ1wcAJnz5sb16y9MFeoHsbS2Ymi3ZjhePIs2NoaY56Fs7zUYQ3y82qEJIYQQQggh0pD0UU0QQogMoEKFCkyZMoXixYsr2x48eMDPP//MpEmTuHv3rnrBpVHr1q1j586dyrh06dIMGDAAnU73hrPSlqxZs9KwYUNlvGXLFmJjY1WMSB27va7yz5EL/LxoG54nLqsdTpqk0WjoUL8ittYJrVfa1KlATvssrzzW/+xFrq3fqoyr/9APMyvLVx6bmtgXLUSDKWOU8UPvs5yY/oeKEQkhhBBCCCHSGim4CyFEGuLg4MCYMWNo2bJlou3nzp3jhx9+YPbs2Tx58kSl6NKWHTt2sH79emVcpEgRhgwZoswGT09atGiBubk5AMHBwezfv/+9X0Or1VK4cGEKFy6c5mb/3/UPZPvxSwDExevRGwwqR5R25c9pz8CODWhTpwJ1XYu/8pio4BC29higjLOXKkapDi1feWxq5PJpK0q1b6GMT/76J37HvFWMSAghhBBpWVrOo4UQH0ZjNBqNageRkbi4uABICwghxEe7fv06K1as4MaNG4m2m5mZ0ahRI9q0aUOWLK+efZrRHTx4kD/++G/War58+fj5558TtexJbxYtWqTM5ndwcGDWrFlKET690xsMHL94ix0nLmMwGPjp6xZYW2aM925qRoOBjZ16cWffYQA0Wi0dNi4lf3U3lSN7PzFh4Syr15rHBi1xDo7keXyfrgf/wdo+m9qhJZGRcsuM9F6FEEIIIUTKSsncUm6tCSFEGlWiRAnGjh3L0KFDyZcvn7I9Pj6eHTt28N1337F+/XoePXoki6u+xNvbm7lz5ypjR0dHRo4cma6L7ZAwy/1Fq5zAwEAOHTqkckSmo9NqqVnemZ++akafNrVfWWxft/8Mp6/fIyRcery/YDAaOX39Hu8zN+PEr38oxXaAGsMHprliO0CEUUNgr28J6NSNoPqNCQmNYGf/4e/1/0IIIYQQQgiRMZmpHYAQQogPp9FoqFSpEq6urhw6dIi1a9cSGBgIQFRUFGvXrmXt2rUAZMqUCQcHB+zt7ZU/Dg4OFChQgKJFi6LRaNR8KyZx+fJlfvvtNwz/thTJmjUro0aNwt7eXuXIUl727NmpW7eusmjqpk2bqFOnDmZmGScVsLQwp2Du7Em23/QL4NC5mxw6d5MieR0Z2LFBov0xcfEcPHuDbJkzUSx/TuwyZzJVyKoxGo1sPHiWg2dvcv2uP581ckP3lkeg7+w7zPEpc5RxUY/6uPXrkdKhpojMmawIjf+3uG5mRqh7dW7t3MXeH36m3oQR6DLI0yFCCCGEEEKI95dxPmULIUQ6ptVqqVu3LtWrV2fnzp1s2rSJiIiIRMdERkYSGRmJn59fkvMrVarE119/na4Lz76+vvzyyy/Ex8cDYGNjw8iRI8mVK5fKkZlOq1atOHDgAHq9nqdPn3LkyBHq1q37TufGxsYyZ05CMfXbb79NV73utx65qPzdztY6yf6g0Ai2Hk04xkynpWfLmpQqlMdk8anh2KVbHDx7E4CTV+7gkNUWj6qlX3v88/sP2N5nCPw7A9yuUAE85vySZm/kmZvpaFC5BBsPnsPueRBWPtcBuLB4FcG37tJ8wW9YZ7NTN0ghhBBCpAnpOY8WQryatJQRQoh0xMLCghYtWjBnzqsaA68AAQAASURBVBxatWqFo6PjOy3Mc/r0aQYOHMiePXuU2d/pyfPnz5k8eTIxMTFAwv+nYcOGUaBAAZUjM60cOXJQs2ZNZbxp0yb0ev07nx8XF0dcXFxKhKaqjg0rUc45H5msLF45ez0k7L82MzbWlhTKk3SWfHpTsbgTRfI6AgkLpdZ5zQKpAPHRMWzp3o/o4BAAzKytaLl4FpZZ0nabphplizKoYwOG9/+MAg7/vZf7h0+wonEHAn1uqRidEEIIIdKS9JpHCyFeTWa4CyFEOmRjY0OnTp3o1KkTBoOBkJAQgoKClD+BgYEEBQXh5+fH3bt3gYQWNH/99RfHjh2jZ8+e5MmTfmbwLly4kNDQUAB0Oh3ff/89xYu/voCYnrVp04ZDhw5hNBp5/Pgxx44do1atWmqHpaq8jtno0SLhRoThFT26rS3NKVc0Hzf9Avi0fiWsLdP/rCRrSwv6tq3D5sPn8aha+o2LzO4fPp6AC/8tNNRo+lgcXUqYIswUZWFuRuF/bzp03LKCXf2Hc33TdgBC7txjZZNPafbXDArVq/mmlxFCCCGEEEJkMFJwF0KIdE6r1So92/+f0WjkwIEDLFu2jMjIhFm8V69eZciQIbRv355mzZql+R7fJ06c4OTJk8q4S5culC9fXr2AVJYrVy5q1KjBkSNHAFi+fDnFihX7oNY69w4dJ/jOPcp17ZhmW4f8P+0r3kfB3Nnp0bImsXHxWJgn/X649ziQmNh4ijnlNEWIJmNhbkaH+pXeeMyllRu4uHytMi735WeUat8ypUMzOXNrKz6ZN53sJZw5Ouk3AGJCw9j4WU/qjB2Ga88u6eZ7QAghhBBCCPFxpKWMEEJkYBqNhnr16jFjxgzc3NyU7XFxcaxcuZIRI0Zw+/ZtFSP8OKGhoSxcuFAZlyxZksaNG6sYUerQpk0bdDodkNBuZ/z48QQFBb3Xa0Q+C2LHN0PZO+Qn/un2LVFBwSkRaqryqmJ7vF7P8p0nmbVuPyt3exMZHatCZOp4cuka+374WRnnci1L3XHDVYwoZWk0GrK0aUXmyZPRZUro9W80GDgwciK7B41CH5tx/u2FEEIIIYQQrycFdyGEEGTLlo3vv/+ewYMHY2dnp2y/c+cOw4cPZ9SoUfz2228sW7aMbdu2cfz4ca5fv87Tp0+VRUhTo5dbyVhYWNCnT5936mmf3uXNm5eePXsq4ydPnjBhwgTCw8Pf+TUOjvmFiCdPAfDdsZeldVpy/5hXssea2u32usrjwISvseOXbuEf+DzR/qiYWC7deohfQBBhkdFqhPhOwiKjWeZ5gpDwyLcfDESHPOefL78lPjphXQRrh2y0WDQLs3TcbufsjfvM23SYSyHROEybim3e3Mq+S3+vY1277kQGvt+NKyGEEEIIIUT6k7b7BAghhEhW7u7uuLi48Pfff7N//34ADAYDN27ceON5zs7O9OrVCycnJ1OE+U5OnjzJiRMnlHGnTp0+qG1KelW3bl0iIiJYtmwZAH5+fkyaNIlRo0ZhZWX11vNrjRpMxJOn3Dt4DIBw/wDWtu5KlQG9qDrkW3Tmr+/5nZ6UKZKXi74PePA0hFrlnZWFRl949PQ58zYfBsDW2pLJ37RRI8y3Wrf/DGdv3OfSrYd0qF+JyiULvvZYo9HIzn4/8vzeAwA0Wi3N5v1KlpcK0OmN0Wjk2EVf9P8uKn3eP4QBG5Zx9LuhPDp1DoAHJ07xd6N2tP57Lo4li6kZrhBCCCGEEEJFMs1PCCFEIra2tvTu3ZvRo0eTM+e79aT28fFh3Lhx+Pv7p3B07yY0NJQFCxYo45IlS9KkSRMVI0qdmjVrRps2/xWAfXx8mDp1KnFxcUmO1Wg05M+fn/z586PRaLDNlYN2axZQ+6ehaF8U141GTs6Yy+oWnQm552eqt6Gq/DntGfJ5Y9rWdaVFzXJJ9r88Y9wucyZThvbObvoFcPbGfQCiYuKIiIp54/HXN23H13OfMq4+rD8FaldL0RjVptFo+LpFTfLntCeTlQX92tfDqYgTHTYto1SHVspxofcfEnLnvnqBCiGEECLV+f88WgiR/mmMRqNR7SAyEhcXFwCuXLmiciRCCPF2cXFx3Lhxg6dPnxIYGEhQUJDyJzAwkLCwsETHOzo6Mm7cuFcu0GpKv/32G8ePHwcSWslMnTqV3LnT7+zbj2E0Glm4cCG7d+9Wtrm5uTFw4EClz/vbPL5wme09BxN8+66yzSKzLQ2n/UzJNs2SO+Q05eyN+2w/dpHg8CiK5c9B79a11Q4pCaPRyPFLt9h06Bx5HbPR/9P6r1w8FiAyMIjF1ZsSFZjQs79A7Wq0W7sQTQZp1RQeGUNYVDS5HbIq24xGI6f/WMShn6dS48cBVBnY26QxZaTcMiO9VyGEEEIIkbJSMreUgruJyQcFIUR6Ehsby5EjR5g3b56yLW/evPz8889kyZJFlZi8vLyYPn26Mu7atSuffPKJKrGkFQaDgTlz5nD06FFlW506dejTp887z8KJDY9g34/jubJ6Y6LtLp+2pv7kkVjY2iZrzGmN0WgkXm/A3CzxTYyQsEiy2lqnitlOQaERGIxGsmd9/b/V9j7fc239VgDMMlnT7fBW7ArkN1WIqdrjC5fJWdbF5P+WGSm3zEjvVQghhBBCpKyUzC0zxnQkIYQQKcLCwoL69evTrVs3ZdvDhw+ZNGkSUVFRJo/n/1vJlChRAg8PD5PHkdZotVq++eYbXF1dlW0HDx5k+fLlvOt9eQtbGzxmT6LZ/F+xyPxfwfbKmk0sr9+Wp1euJ3vcaYlGo0lSbL/z6BkTl3my48RllaJKzD6LzRuL7bd2H1SK7QA1fxwgxXYSbqZsP3aJUHvHVHHjRAghhBBCCKEuKbj/n6ioKEaPHk2xYsWwsrIiT548dO/enYcPH6odmhBCpFpNmzalXbt2yvjWrVtMmTKF2NhYk8axePFinj9/DoC5uTl9+vRBm0FaXXwsMzMzBg0aRMmSJZVt27ZtY9OmTUDC0wyzZs1i1qxZb/x3LdH6E7oc2EyeyhWUbcG377LC41Mur96Ucm8gjbn14Cmz1+0nMjoWzxOXOXD2zQsTqy0mLJy9Q8Yo49wVy1GhxxcqRpQ6GAwG1uw7jefJy8zffJh7jwPVDkkIIYQQqcy75tFCiPRDqhAviY6Opl69eowbN47w8HBatmxJ/vz5Wbx4MRUqVOD27dtqhyiEEKlW+/btEy1MeuXKFX777Tf0er1Jru/t7c2xY8eU8WeffSZ929+ThYUFP/zwA4UKFVK2rV69WunvHhUV9U5PLtgVyE/HLX9TZVAf+HfGb3xUNDu/G8buQaOIj37zopwZQU77LNhntQHATKclm4kXVF2525sj530wvOMTDEfGTSfs0WMAtObmNP5tAtp37PGfngUEh+F15Q4AOrm5J4QQQojXeNc8WgiRPpipHUBqMn78eE6ePEnVqlXZvXs3tv/2m/31118ZPHgw3bt35+DBg+oGKYQQqZRGo6Fbt25ERERw5MgRAE6fPs3cuXM/eKZ5ZGQkvr6++Pr68uDBAwwGw2uPvXTpkvL34sWL07Rp0/d/E4JMmTIxfPhwRo8ejb+/PwDXr1+ndu33W+xTa2ZGjR8HkNe9Ijv6fE9UUAgAF5evJeDCFZovmpmh25HYZrLk27Z1+X3jQdrWcaVEgVzJ9tonr9zm2p2Ef7sc9ln4pFqZRPuv3vXn+KVbAJy7eZ+vmtfAxtryta/34MRpzi9eqYyrDOxN9hLOyRZvWpbbIStfN6/B2v2n6dO6NrleWkxVCCGEEEIIkTFJwf1fsbGxzJkzB4Dff/9dKbYDDBo0iKVLl3Lo0CHOnDlDxYoV1QpTCCFSNa1WS58+fYiMjOTMmTMAHDp0CBsbG7p27frG/sZ6vZ779+/j4+ODr68vPj4+PHr06J17iL8grWQ+XtasWRk1ahSjRo3C1dWVr776ivj4+A96rUL1avLFvk1s+ao/j89eBCDg4hWW129D0z+mUqRRnWSMPG2xy5yJH79o8kFfq1Excdx/HIhDVluy2yXuux72P/buOyyKqwsD+LvA0puAgoKACmrA3rB3DUaNgsau2KLGrolRo4klJjGWmNhi7D2W2HvvBSzYsAEKCgoqTZqwC/f7g4+JG6q4sJT39zz7uHvvzOyZy4Jnzs7ciX+HG4+eAQAq2ZbOUHAvX6aU9FyZkgoDPXmW76N8l4RjE6ZJr60+qQy3ccM+ON7izLViOXzv0BE6POOfiIiIiIjAgrvk0qVLiImJQaVKlVC7du0M/d27d8edO3dw4MABFtyJiLKho6ODCRMm4JdffpHu9n348GH4+vpCRyfz/3aEEHj16pVa5jTs3bs3ypUr99HbKemsrKzwyy+/wMzM7KNvBGlqVw699m/B2R/m4tbaLQCApJi32NN3ONzGj0CTKWNL7PQkmRXbtx73wdMXbwAAnzWuhtqV7VX6L94OwPaT1yAAdG5aA5+6uar0O5S1zNV768p10PdTt2wL/lcWLENUYFDaC5kMny6aA21d3VxtvyRhsZ2IiIiIiNKx4P5/t2/fBgDUqVMn0/709jt37hRYTERERZWuri4mTZqEH3/8EYGBaVNXpE9P8qGMjIzg5OSEihUrQl9fP9tlbW1tUb9+/Ty9D2Vkbm6utm3p6Omi7a8/wLZBbRyb+D2UCWlzWHr/vgIvb95Gw4lfoVzdWtDRz3pqk5IiIiYOLyPSbv6b8C7jl1BJCgXSr/sIepnxJp0O1hbo1KQ6ZDJZpnPD68l10LV5LbhUKAtrC9Ms4wi/cx8+S1dLr+sO90LZujU/cG+IiIiIiIhKFhbc/+/Zs7RLr+3s7DLtT28PDg4usJiIiIoyQ0NDTJ06FbNmzcLz589ztY62tjYcHR3h5OQEZ2dnODk5oWzZsh99hjXljz179uCTTz5B1apVc73OJ906o3S1T7B/0BhE+qfdjPzZ+St4dv4KtPV0Ua5eLZRv4obyTdxQtm5N6OjxbOqsGOjJoa+bMZXT05XDvWG1LNfTleugbf1Pst12qlKJY+OnQfz/psdmDnZoMmXcxwVMRERERERUArDg/n9xcXEA0gpEmTEyMgIAxMbG5mp7rq6umbYHBgaiUqVKeYiQiKjoMTU1xU8//YRHjx7lOAe4iYkJHB0docvpKgolmUwGGxsb6XlcXBwGDRqEmJgYfPrppxg7dizc3XM3H7lVFSf0O74Tx8ZPx6N9R6T2lKRkPL/kg+eXfAAsgY6+HsrWq4XyTRqgXN1asKzqDGObMsX+C5hOTWqgZZ0kAIBtafMM/TWdy6NaRVuULmUCrXwai2vL1+LV3fvS6/YLf4SuUeY5EpG6MY8mIqLi5L95NBEVfyy4ExFRvtLX10fNmpyGoqiTy+Xw8vKSXq9ZswYxMWnTnhw7dgzHjh2Dk5MTxowZg4EDB8LUNOupSgBA19gYnVYtQsX2LfFg5wGE+NyQpplJp3yXhOcXvfH8orfUpmdqAssqTrCq6qzyr5F16WJzAFOhnFW2/VZmxtn2f6yoJ8G4Mn+p9Lpab084tGicr+9JREREVFz9N48mouKPBff/MzZOO3hNSEjItD8+Ph5A2hmYuZF+o8D/yuqMHSIioqLE2toa1apVw71796S2gIAAjBs3DtOmTcPAgQMxevRoVKlSJcttyGQyuPboCtceXZGSnIywW/fSznC/7INQn5sZCvAAkPQ2Fi+u+eLFNV+VdiPr0nAbPwK1B/eBLBdn2VPWzs+eD+W7tDPsjcqURsvZUzQcEZU0zKOJiIiIqCjjEen/2dvbAwBCQkIy7U9vd3BwKLCYiIiICisPDw/cuXMHZ86cgYeHh8pUMnFxcVi6dCmqVq2KDh064MyZMzluT1tXF7YN6qDhhBH4YudajPH3Qe9Df6Pp1PFwbNUUxjZlsl0/Pvw1Tk/9ETu7D8LbkBcfvX8lVciV6/A/dEJ63fLHKdA3N9NgREREREREREULz3D/v/TpDm7evJlpf3p7jRo1CiwmIiKiwkKhUGD16tUAgKFDh0Iul0Mmk6Fly5Zo2bIlgoKCsHz5cqxatQrR0dHSekePHsXRo0cxZ84cfPfdd7me9iW9AG/boI7U9i46Bm8e+iPiUQDePAxAxCN/vHkYgITXb6Rlnl24ivXNO6P1T9Pg2suj2EwzUxCEEDg781fptU2dGqjq0VGDEREREREVfZnl0URUvPEM9/9r0qQJzMzMEBgYiFu3bmXo/+effwAAnTt3LuDIiIiINE8Igbdv3+Lt27cQQmTod3R0xLx58xASEoK//vorw9QP06dPx+DBg5GcnJznGPTNzWDXsB5qevVCm1+mo8fuDRh5/xKG3z4H547tpOWSY+NwdOxU7B0wEvGv3mSzRXrfo71HEHbzjvS65czJ/MKCiIiI6CPllEcTUfHDgvv/6erqYvTo0QCAUaNGSXO2A8Bvv/2GO3fuoEWLFqhbt66mQiQiIir0jIyMMGzYMNy9excnT56Es7Oz1Ld+/Xq4u7sjKipKre9pUs4Gn69bgg7LfoWe6b/3Wgk8ehrrm3fC4wPH1Pp+xZEyKRnn5yyQXjt3bAe7RvU0GBEREREREVHRxIL7e6ZPnw43NzdcvnwZzs7O6NmzJxo2bIivv/4apUuXxtq1azUdIhERUZEgk8nQpk0bXLlyBc2aNZPaz5w5g8aNG+Pp06dqfz/XHl0x8MJBOLRsIrUnRkRh/+CxODTia7yLjlHrexYnt9ZuwdtnoQAALR0dNPv+aw1HREREREREVDSx4P4efX19nDlzBt9//z0MDQ2xd+9eBAcHY+DAgbh58yYqVqyo6RCJiIiKFEtLS5w4cQJ9+/aV2h4+fAg3NzdcvXpV7e9nUs4G3XesQdt5M6BjaCC1P9h1EKvqtcWZ6T8jwj9Q7e9blCVGRePKwuXS65pevWBRqYIGIyIiIiIiIiq6WHD/DwMDA8yePRsBAQFISkrCy5cvsW7dOtjZ2Wk6NCIioiJJT08PmzZtwowZM6S2169fo1WrVtI9UtRJJpOh1qA+8DqzT+Wmq0kxb3Hjrw1Y1/gzbO/aHw/3HEbKR8wpX1xc/e1PJMW8BQDomhij0TejNBxRwUtKSuKcqkREREREpBY6mg6AiIiIij+ZTIaZM2eiYsWKGDp0KBQKBd69e4cvvvgCv/76KyZNmqT2G3SWquiAnvs34/qf63Bl/jIoEhKkvueXfPD8kg8MS1uiWu9uqNG/B8wdy2e5LSEEUhUKaOvqqjVGTYsOeg7fNVuk127jhsPQykKDERUMIQT8/Pywa9cu7N69G3fu3IGenh5sbW1hZ2cHW1tbled2dnaoVKkSSpcurenQiYiIiIiokGPBnYiIiHIkk8lgaWkpPc+rAQMGwN7eHh4eHoiOjgYATJ48Gfv27cO4cePg4eEBuVyujpABAFra2mgweihqevXCg3/249b6v/Hm/mOpP+F1BHwWr4TPklUoV68WtLS1oUhMhCLxHZSJ76BISEh7npAIACjtWhUNxn6JKl06QEtbW21xasqFOQuRqlAAAExsy6LOsAEajij/CCFw/fp1qcju7++v0p+UlIQnT57gyZMnWW7D0dERbm5u0qNOnTrQ19fP79CJiIioCFNXHk1ERYdM8PrZAuXq6goA8PPz03AkREREmvPw4UN07NgxQ3HT1tYWI0eOxJdffpkvZxMLIfDy+i3cWr8Nj/cfgfJdUp62U6qiI9zGD8cn3TtDW41fEBSkF9dvYWuHntLrDst+hWuPrpoLKB/ExsbCx8cHBw4cwO7du/H8+XO1bl8ul6NmzZoqRXhnZ+cCPZguSbllSdpXIiIiIspf+ZlbsuBewHigQERElOb169fo27cvTpw4kaFPT08Pffr0wZgxY1C7du18ef/EqGjc374XtzdsQ2TA0zxtw9TeFm5jh8G1lyd09IrOdDNCCGzr1AehPjcBAGWqu6D/yV2QaRXd2/ukpKTAz88P3t7e0sPPzy/budnr1auHbt26oVOnThBCICQkBKGhoRn+ff78uXRFRk7OnTuH5s2bq2mvclaScsuStK9ERERElL9YcC9GeKBARESkysfHB0uWLMH27duh+P/0Ju9r1qwZhg8fjs6dO8PU1FTt7y+EQOjVG3jzyB86+vqQG+hDx8AAckN9yA0MoGOQ9u+7t29x4891eLjnMERqqso2jMtao8Hooaje7wso371DbGgYYl+GITb0ZdrzFy/xNjQMSTExKN/EDU2mjIOeibHa9yW3Hh88jv2Dxkive+xeD/tmjTQWT24JIfDmzRuVgviTJ0/g4+OD69evIz4+Ptv1ZTIZmjZtim7dusHDwwP29va5ft/nz5+rFPNv3LiBxMREleW0tbURExMDIyOjPO/jhypJuWVJ2lciIiIiyl8suBcjPFAgIqKiSKFQYMOGDQAALy8vtc6zni4sLAwrV67En3/+ibCwsAz9urq6aNeuHTw9PfH555/DyspK7THkRlRgELz/+Av3d+5HqlKp0ifT0spQjM+MuaM9Oq1eBJua1fIrzCylJCdjXdNOiH4aDACo2K4lPLf+VeBxZCf9pqZ79+6Fn5+fVFx/8eIFkpI+bBogU1NTNG7cGF27dkXXrl1hbW2tlhgVCgXu3bunUoTX19fHzZs31bL93CpJuWVJ2lciIio+CiKPJqIPx4J7McIDBSIiKoqSk5OxaNEiAMCECROgq5t/06ckJyfjn3/+weLFi+Ht7Z3pMtra2mjRogU8PT3h4eGBcuXK5Vs8WYl5FgKfJatwb+supCRnPDM/J1pyOVrMmIQ6wwbk25zfIjUVceGv8fZZCGKeh+Lts1C89L2DwKOnAaR9QeB1/gCsqjjly/t/CCEEbty4Id3U9PHjxzmv9B/a2tqoUaOGypzqVapUgVYBTZWTnJycr78bmSlJuWVJ2lciIio+CjKPJqLcy8/cUkftWyQiIiL6CLq6uujTpw/69OkDb29vrFy5Evv27UNERIS0TEpKCk6fPo3Tp09j9OjRaNy4MYYNG4aePXtCX1+/QOI0s7dDu/mz0HDiSFxftga3N26HMvEdAEBHXw8m5crCxNbmvX9tEBMcAp+lqwEhkKpQ4Mz0n/Hs4lW4//EzDCxKfVQ8qSkpeOHji4Bjp/D63kPEPAtFbOiLbL8MqNH/C40W21NSUnD58mXs3r0bu3fvxrNnz3K1nqGhIWxtbWFnZwc7OzvUqFEDDRs2RJ06dWBoaJjPUWeNB9BERERERMSCOxERERVa6Wcp//XXXzh//jx27dqFPXv24OXLlyrLXb58GZcvX8akSZMwfPhwjBgxAra2tgUSo0lZa7Sa8x2aTBmLtyEvYVTGCvqlzLM8a92xdVMcGvEN4sNfAwACj57GxlZd0fGvhbBrWO+D3luRkIjgc5cQcOQUAo+fQWJEVK7XNbAwR+NJY3JeMB8oFAosWrQIv/32G8LDw7Ncrl69enB3d4ejo6NUYLe1tYW5edbjS0REREREpEksuBMREVGhp6Ojg9atW6N169ZYsmQJrl69it27d2PXrl0ICgqSlnv9+jXmzJmDuXPnonv37hg7diwaNmxYIMVZXWNjWFV1znE5+6YNMeDMPhwZPRlBpy8AAGJfhGF71wFo8u0YNBg3DFra2pmuK4RAYkQUnpw4i4AjJxF09pJ0Vn12DCxLwbS8LczK28LU3hZm5e1QsX1LGFmX/rCdVIObN29i6NCh8PX1zdCX15uaEhERERERFRYsuBMREVGRoqWlhcaNG6Nx48aYP38+bt68iZUrV2LTpk1ITEwEACiVSmzbtg3btm1DvXr1MGbMGPTo0aPAppvJiVFpS3T7eyWuLV+Liz8tQqpSCZGSgou//I7A42dgbGON5Ph4JMf9+1D8/9//3qj1fVo6OrBv2hCObZqhVEVHmNnbwtSuHHSNjQpw7zKXmJiImTNnYuHChUhJSZHa079M8fT0VOtNTYmIiIiIiDSBBXciIiIqsmQyGerWrYu//voLv/zyC9asWYOlS5eqzAV+/fp1eHl54csvv0StWrVUbqhZqVIljU1NItPSQoPRQ2HXsB4ODp+It89CAQAvb9z+oO3oGhuhQtsWcP6sLSq0aQ49U5P8CPejnD17Fl9++SUCAgJU2ocMGYK5c+fCyspKQ5ERERERERGpFwvuRERElCOZTAZTU1PpeWFkYWGBSZMmYcKECThw4AAWL16Ms2fPSv3Jycnw8fGBj48PlixZAgCwtLREgwYN4ObmhkaNGqFFixbQ09Mr0LjL1auFAaf34tj46fA/eCxX6xhZl4ZThzZw6tAW5Zu4QUevcN6sMzo6Gt9++y1WrVql0l6xYkWsWrUKrVu31lBkRERERAWjKOTRRKReMiGE0HQQJYmrqysAwM/PT8OREBERFX937tzBkiVLsHXrViQkJOS4vImJCTp16oRu3brB3d0dRkYFNxWLEAJPT51HmO9d6BjoQ9fYKIuHMYzKWEGmpVVgsX2oV69e4fjx45g8eTJevHghtWtpaWHixImYNWsWDA0NNRhh8VGScsuStK9ERERElL/yM7dkwb2A8UCBiIio4CUnJ+POnTvw9vaWHo8fP852HQMDA7i7u8PT0xOdOnWCubl5wQRbxLx79w43b95UGdv3b2SbrkaNGlizZg3q1atX8EEWYyUptyxJ+0pERERE+YsF92KEBwpERESFQ1RUlDTFzNWrV3H27Nksz4KXy+Vo06YNvLy80KNHD2gV4rPLC4Kvry/Wrl2LK1eu4Pbt21BmcyNXPT09/PDDD5g0aRLkcnkBRlkylKTcsiTtKxERERHlLxbcixEeKBARUVGkUCiwdetWAECfPn2KZeE0ISEBx44dw+7du3HgwAHExMRkulzNmjUxd+5cfPrppyVuHs7AwEBMnz4d27Zty3HZsmXLokWLFpg5cyaqVKlSANGVTCUptyxJ+0pERMVHScijiYqi/MwtedNUIiIiypEQAmFhYdLz4sjQ0BAeHh7w8PBAcnIyTp8+jV27dmHv3r148+aNtNzt27fRoUMHtGrVCr/++ivq16+vwagLRnh4OObMmYMVK1Zkeja7gYEB6tWrBzc3N+lhZ2dX4r6QICIiIvqvkpBHE5EqFtyJiIiI/kNXVxfu7u5wd3fHn3/+iUuXLmH58uXYsWOHtMyZM2fQoEEDdO/eHT/99BMqV66swYjzR2xsLBYsWICFCxciPj5epa969eoYOXIkGjZsiGrVqkFHh2klERERERFRyZ6AlIiIiCgHOjo6aNGiBbZv347r16+jTZs2Kv3//PMPXFxc8NVXX+Hly5cailK9kpOTsWTJElSqVAmzZ89WKbY7ODhg48aN8PX1xYgRI1CrVi0W24mIiIiIiP6PR0dEREREuVS3bl2cPHkSJ06cwOTJk+Hr6wsASElJwYoVK7B69WrY2trCzs4uy3/t7e0L9VQrJ0+exIgRIxAYGKjSbmlpienTp+Orr76Cnp6ehqIjIiIiIiIq3FhwJyIiIvpA7dq1Q5s2bbBjxw5MmzYNT548AQAolUoEBwcjODg4y3Xr1KmDTZs2wcXFpaDCzZXIyEh8/fXXWL9+vUq7oaEhJk6ciG+++QZmZmaaCY6IiIiIiKiI4JQyRERERHmgpaWFXr164cGDB1i6dCnKlCmTq/Vu3ryJ+vXrY+PGjfkcYe4IIbBz5064uLioFNu1tbXx1VdfISAgAD/++COL7URERERERLnAM9yJiIgoVwwMDDQdQqGkq6uLUaNGYdiwYfD390doaChCQ0MREhKS4d/Xr18DABISEuDl5YWzZ89i6dKlMDQ01EjsL168wMiRI7Fv3z6V9jp16mDNmjWoVauWRuIiIiIiKk6YRxOVLDIhhNB0ECWJq6srAMDPz0/DkRAREVFBO3ToEAYMGIDIyEipzdXVFTt37sQnn3zy0duPj4/HyZMn8eLFC5QtW1aaO75MmTLQ1taWlktNTcXq1asxadIkvH37VmrX19fH7NmzMWHCBN4ItYgoSbllSdpXIiIiIspf+Zlb8kiKiIiIqIB07NgRvr6+6NWrF65cuQIgLcGrX78+VqxYgX79+n3wNmNiYnDw4EHs3r0bR44cQWJiYoZldHR0UK5cOenGraGhobh8+bLKMi1btsSqVavg5OSUt50jIiIiIiIiFtyJiIiICpK9vT3OnTuH7777DgsWLACQdmZ6//79cfbsWSxZsiTHy45fv36N/fv3Y9euXTh58iQUCkW2yyuVSjx79gzPnj3L0GdmZob58+dj6NChkMlked8xIiIiIiIiYsGdiIiIcqZQKLBz504AwBdffAG5XK7hiIo2uVyO+fPno3nz5vDy8kJUVBQAYM2aNTh37hycnZ2zXDcmJgZXr15Fampqpv2mpqaoXbs2Xr16hdDQUJUpY/6ra9euWLZsGcqVK/dxO0REREREmWIeTVTysOBOREREORJC4Pnz59JzUo/OnTvD19cXPXv2hLe3NwAgICAAAQEBH7QdS0tLdO3aFZ6enmjTpg309PSkvtjY2Aw3co2Ojkbr1q3x6aef8qx2IiIionzEPJqo5GHBnYiIiEiDHBwccP78eUydOhW//fZbrtcrV64cPDw80K1bNzRr1izLm5yamJigatWqqFq1qrpCJiIiIiIioiyw4E5ERESkYbq6uli4cCEGDBiAy5cvZ3v2k5aWFmrWrAk3NzdoaWkVYJRERERERESUExbciYiIiAqJmjVrombNmpoOg4iIiIiIiPKIp0UREREREREREREREakBC+5ERERERERERERERGrAKWWIiIgoV+RyuaZDICIiIiIqcphHE5UsLLgTERFRjnR1dTFx4kRNh0FEREREVKQwjyYqeTilDBERERERERERERGRGrDgTkRERERERERERESkBpxShoiIiHKkVCqxZ88eAICHhwd0dJhCEBERERHlhHk0UcnD33IiIiLKUWpqKp48eSI9JyIiIiKinDGPJip5OKUMEREREREREREREZEasOBORERERERERERERKQGLLgTEREREREREREREakBC+5ERERERERERERERGrAgjsRERERERERERERkRrIhBBC00GUJCYmJlAoFKhUqZKmQyEiIso1IQQiIiIAAJaWlpDJZBqOiIgAIDAwEHK5HLGxsZoOJd8xjyYioqKIeTRR4ZSfebSO2rdI2TIyMkJ8fPwHrRMYGAgAPLhQI46p+nFM1Y9jql4cz48jk8lgZWWl0sYxVT+OqfoV9zGVy+UwMjLSdBgFgnl04cAxVT+OqXpxPNWPY/pxmEcXDI6p+hX3Mc3PPJpnuBcBrq6uAAA/Pz8NR1J8cEzVj2OqfhxT9eJ4qh/HVP04purHMS3Z+PNXP46p+nFM1YvjqX4cU/XjmKofx1T9OKZ5xznciYiIiIiIiIiIiIjUgAV3IiIiIiIiIiIiIiI1YMGdiIiIiIiIiIiIiEgNWHAnIiIiIiIiIiIiIlIDFtyJiIiIiIiIiIiIiNRAJoQQmg6CiIiIiIiIiIiIiKio4xnuRERERERERERERERqwII7EREREREREREREZEasOBORERERERERERERKQGLLgTEREREREREREREakBC+5ERERERERERERERGrAgjsRERERERERERERkRqw4F5InT17FjKZLMtHw4YNs1w3JCQEgwYNQrly5aCvr4/KlStjxowZePfuXQHuQdHw448/SmO6efPmLJfjmGbuzp07GD16NBo2bIhy5cpBT08PZmZmaNSoEZYsWQKFQpHluhzTzD18+BC//vorWrVqBSsrK8jlctjY2MDT0xMXLlzIdl2Oaebi4+OxadMmjBkzBm5ubtDT04NMJsPMmTNzXJdjmrnExET88MMPqFy5MvT19VGuXDkMHjwYoaGhmg6t0Lpx4wbmzp0LT09P2NnZSf/35GT9+vVo0KABjI2NYWFhgc8++wyXL18ugIgLt4SEBOzduxdDhgxBlSpVoK+vDyMjI9SsWROzZ89GXFxclutyTIs/5tEFg3n0x2EerX7Mo9WPebT6MY/+cMyj1Yt5dAERVCidOXNGABCVKlUSXl5eGR6zZ8/OdD1/f39hZWUlAIhq1aqJHj16iIoVKwoAokmTJuLdu3cFvCeF18OHD4Wenp6QyWQCgNi0aVOmy3FMs7ZkyRIBQDg4OIg2bdqIXr16iTZt2gh9fX0BQLRo0UIkJSVlWI9jmjVbW1sBQBgbG4u2bduKHj16iGrVqgkAQiaTiUWLFmW6Hsc0a76+vgJAhseMGTOyXY9jmrnExETRsGFDAUCULVtW9OjRQzRo0EAAEKVLlxaBgYGaDrFQ6tKlS6afw+yMGzdOABAGBgaiS5cu4tNPPxU6OjpCW1tb7Nmzp2ACL6RWrVoljeEnn3wivvjiC/Hpp58KExMTAUBUrVpVhIeHZ1iPY1oyMI/Of8yjPx7zaPVjHq1+zKPVi3l03jCPVi/m0QWDBfdCKv1AwcvL64PWa9KkiQAgxo4dK7UpFArh4eGRq/8YS4rU1FTRvHlzYW1tLf3xzupAgWOatcDAwEyTgrCwMCm5XbJkSYZ+jmnW2rRpIzZu3CgSExNV2lesWCEACG1tbeHn55dhPY5p1gICAsSQIUPEihUrxI0bN8Ts2bNzNSYc08xNmzZNABCNGjUSsbGxUvvChQulAgFlNHfuXPH999+L/fv3i5cvXwo9Pb1sDxROnDghAAhLS0vx+PFjqf3y5ctCV1dXmJubi6ioqAKIvHBav369GDZsmLh//75K+4sXL0Tt2rUFANG7d2+VPo5pycE8On8xj1YP5tHqxzxa/ZhHqxfz6LxhHq1ezKMLBgvuhVReDhS8vb0FAFGmTJkM3xiHhYUJuVwuSpUqJRQKhZqjLXpWrlwpAIjNmzcLLy+vLA8UOKZ5t2nTJgFAeHh4qLRzTPOuffv2AoCYOXOmSjvH9MP88ssvOSb6HNPMJSUlCTMzMwFA3Lx5M0N/jRo1BABx/fp1DURXtOR0oNChQwcBINOz8caOHSsAiAULFuRjhEXX5cuXBQChp6encnYox7TkYB6dv5hH5z/m0erHPFo9mEfnHfNo9WEenX+YR6sP53AvRg4dOgQA6Ny5M/T09FT6rK2t0axZM0RFReHixYuaCK/QCAsLw7fffos2bdqgb9++2S7LMc07uVwOANDV1VVp55jmXc2aNQEAL168UGnnmKofxzRzly5dQkxMDCpVqoTatWtn6O/evTsA4MCBAwUdWrGSmJiI06dPA/h3TN/Hcc5e+t/KpKQkREREAOCYUs74dz93mEcXDObR6sc8uuBwTDPHPLpgMOf7OMyj1YcF90LO398fU6dOxbBhw/Ddd9/h8OHDSE1NzXTZ27dvAwDq1KmTaX96+507d/In2CJi7NixSExMxJ9//pnjshzTvImKisLChQsBAB07dlTp45jm3ZMnTwAANjY2Ku0cU/XjmGaO41IwHj16hKSkJJQuXRp2dnYZ+jnO2Uv/WymXy2FhYQGAY1pSMY9WP+bR+Y95dP5gHl1wOKaZ47gUDOZ8H4d5tProaDoAyt7ly5cz3PG3evXq2LVrF5ydnVXanz17BgCZ/gK83x4cHJwPkRYNBw8exM6dOzFr1qwM45cZjmnu+Pv746effkJqairCw8Nx+fJlxMXFYcSIERnOfuKY5k1gYCAOHjwIAPj8889V+jim6scxzRzHpWDkNM5GRkYwNzdHVFQUYmNjYWJiUpDhFXp//PEHAMDd3V06s45jWjIxj1Yv5tH5g3l0/mMeXbA4ppnjuBQM5nwfh3m0+vAM90LKzMwMkyZNwtWrVxEREYGIiAicOnUKDRs2xN27d9G+fXvExMSorBMXFwcAMDQ0zHSbRkZGAIDY2Nj8Db6QiouLw8iRI1G5cmVMnjw51+sAHNOchIeHY8OGDdi0aROOHz+OuLg4jB07Fr/++iu0tFT/zHBMP5xSqcTAgQORlJSEnj17om7duir9HFP145hmjuNSMHIaZ4BjnZXDhw9jzZo1kMvl+PHHH6V2jmnJwjxa/ZhH5x/m0fmLeXTB45hmjuNSMJjz5R3zaPXiGe75xMPDAw8ePPigdTZu3IgGDRoAAGrXrp1hXq/WrVvj4sWLaNWqFS5cuIDly5dj6tSpaou5sPvYMf3uu+/w/PlznDp1KsNcciXVx45puqZNm0IIgZSUFDx79gx79uzBrFmzcOTIERw/fhyOjo5qjLpwU9eYvm/s2LG4ePEiKlasiOXLl39siEVOfowpERU/Dx8+RL9+/SCEwPz586U5KKnoYR6tfsyj1Y95tPoxj1Y/5tFElBvMo9WPBfd88vTpUzx69OiD1klISMhxGW1tbUyePBkXLlzAsWPHVA4UjI2Ns91OfHw8ABTZyzs+Zkx9fHywbNky9O/fH61bt871+hzTjLL7nGpra6NChQqYOHEiHB0d0a1bN4wZM0bl5hkc04yyG9OffvoJf/75J6ytrXHs2DFpHrX3cUwzys3f0+wU9zHNK45LwchpnAGO9X+FhobC3d0dUVFRmDhxIsaNG6fSzzEtWphHqx/zaPVjHq1+zKPVj3l04cFxKRjM+T4c8+j8wYJ7Prl161a+bTt9zsSXL1+qtNvb28PX1xchISGZrpfe7uDgkG+x5aePGdP0m2TdvXsXLVu2VOl7+PAhgLSEbPXq1XB3d8eUKVMAcEw/hoeHB4yNjXH06FEkJydDV1cXAMf0Q6xYsQLTp0+HmZkZjh49Cicnp0yX45iqX3Ef07yyt7cHAI5LPstpnOPj4xEdHY1SpUoxqQUQGRmJ9u3bIzg4GIMGDcKCBQsyLMMxLVqYR6sf82j1Yx6tfsyj1Y95dOHBPLpgMOf7MMyj8w/ncC+CoqKiAPw7R1K69Es+bt68mel66e01atTIx+gKt1u3buHcuXMqj/DwcABpBwznzp2TDhwAjunHkMlksLCwgFKplD6zAMc0t7Zt24ZRo0bB0NAQhw4dQq1atbJclmOqfhzTzHFcCkaVKlWgp6eH169fIzQ0NEM/x/lfcXFx6NChA+7fvw9PT0+sWrUKMpksw3IcU0rHPDrvmEcXHObRH4d5tGZxTDPHcSkYzPlyj3l0PhNU5EyaNEkAEEOGDFFp9/b2FgBEmTJlxLt371T6wsLChFwuF6VKlRLJyckFGW6h5+XlJQCITZs2ZejjmOZdYGCgkMlkwtTUVCiVSqmdY5qzQ4cOCblcLnR1dcWxY8dyXJ5j+mF++eUXAUDMmDEjy2U4pplLSkoSZmZmAoDw9fXN0F+jRg0BQFy/fr3ggyti9PT0RHZpWIcOHQQAsWjRogx9Y8eOFQDEggUL8jHCwu/du3eidevWAoD49NNPRVJSUrbLc0xJCObR6sY8On8wj8475tH5i3l03jGPVh/m0R+PeXT+Y8G9kFq0aJF49uyZSltqaqpYsWKF0NHRETKZLNM/xE2aNBEAxLhx46Q2hUIhPD09c/yPsaTK7kBBCI5pdhYvXixevnyZof3hw4fCzc1NABCjR4/O0M8xzdrFixeFgYGB0NHREXv27Mn1ehzT3MvNgYIQHNOsTJs2TQAQjRs3FnFxcVL7woULBQDRokULzQVXhOR0oHDixAkBQFhaWorHjx9L7ZcvXxZ6enrC3NxcREVFFUCkhZNSqRQeHh4CgGjWrJmIj4/PcR2OacnBPLrgMI/OO+bR6sc8Ov8xj/44zKPVg3n0x2EeXTBkQgjxMWfIU/5wdHRESEgI6tSpgwoVKuDdu3e4e/cunj59Ci0tLfzxxx8YPXp0hvX8/f3RqFEjREREoHr16nBxccG1a9fw5MkTNG7cGKdPn4aenp4G9qjwGjhwIDZs2IBNmzahX79+Gfo5pllzdHTE8+fPUbNmTTg5OUEIgeDgYNy4cQOpqalo3rw5Dh06JN1kIx3HNGulSpVCdHQ0KlSogObNm2e6TNOmTTF06FCVNo5p9jw8PKT5el+8eIHnz5/D1tYWdnZ2AICyZctiz549KutwTDP37t07tGzZEt7e3ihbtiyaNWuG4OBgeHt7o3Tp0rh69SoqVqyo6TALnUOHDuHHH3+UXvv4+EAIATc3N6nt+++/R8eOHaXX48ePxx9//AFDQ0O0a9cOycnJOHHiBIQQ+Oeff9C1a9eC3IVC5Y8//sD48eMBpP1+m5qaZrrcggULYGVlJb3mmJYMzKMLDvPovGMerX7Mo/MH82j1YR6dN8yj1Yt5dAHRVKWfsrd48WLRqVMnUaFCBWFkZCR0dXWFg4OD6Nevn/Dx8cl23WfPnomBAwcKGxsboaurK5ycnMT3338vEhMTCyj6oiWnM3OE4JhmZfPmzaJPnz6icuXKwtTUVMjlcmFtbS0+/fRTsX79epGSkpLluhzTzAHI8eHl5ZXpuhzTrDk4OGQ7pg4ODpmuxzHNXEJCgvj+++9FpUqVhK6urrCxsREDBw4Uz58/13Rohda6dety/N1et25dpuvVrVtXGBoaCnNzc+Hu7i4uXbpU8DtQyMyYMSNXfy+fPn2aYV2OafHHPLrgMI/OO+bR6sc8On8wj1Yv5tEfjnm0ejGPLhg8w52IiIiIiIiIiIiISA20NB0AEREREREREREREVFxwII7EREREREREREREZEasOBORERERERERERERKQGLLgTEREREREREREREakBC+5ERERERERERERERGrAgjsRERERERERERERkRqw4E5EREREREREREREpAYsuBMRERERERERERERqQEL7kREREREREREREREasCCOxERERERERERERGRGrDgTkRERERERERERESkBiy4E1GhJJPJIJPJsl1m/fr1kMlkGDhwYMEEpUGvX7/GgAEDULZsWWhra0Mmk2H9+vWaDkst4uPjMXbsWJQvXx46OjqQyWSYOXOmpsMiIiIiKpKYR6tiHk1ERAWNBXcioiJgyJAh2LRpE8qUKYPevXvDy8sLTk5Omg5LLaZOnYolS5ZAX18fPXr0gJeXF2rVqqXpsAqloKAgyGQytGzZMl/fZ+bMmRo5GJXJZHB0dCzQ9yQiIqLijXk0Acyjiahg6Wg6ACIiyl5ycjIOHz4MR0dH+Pr6QkureH1XunfvXhgYGMDX1xfGxsaaDoeIiIiIignm0UREpAnF638bIqJiKCwsDCkpKXBwcCh2BwkAEBISgjJlyvAggYiIiIjUink0ERFpQvH7H4eICIBSqcSSJUtQt25dGBsbw9jYGA0aNMCff/6JlJSUDMs7OjpmOdfl2bNnM53jcuDAgZDJZDh79iyOHTuGVq1awdzcHDKZDNHR0QCA4OBgfPXVV6hcuTIMDQ1hYWEBV1dXDB8+HI8ePcpxPxwdHeHg4AAAOHfunDQnZ/rlgu9fGvn27VtMnDgRFSpUgFwux/jx46Xt3L9/H3379kXZsmWhq6sLW1tbDBgwINMY3t/fV69eYciQIbCxsYGRkRGaNm2Ky5cvS8uuWLECNWrUgIGBAcqXL4+ZM2ciNTU1x/0CgJYtW0Imk0EIgeDgYGnf/vtzyGvsYWFhGDp0KOzs7KCjo4Pff/9dWu758+cYPXo0KlWqBH19fVhYWKBTp04q+/ZfDx48wJAhQ+Do6Ag9PT2UKVMGTZo0wYIFC6BUKqXlAgICMHPmTDRq1Ag2NjbQ1dWFnZ0dBgwYgMePH2e67dx8TmbOnIkKFSoAUP0sfMj8q4cPH0a7du1ga2sLPT09lCtXDk2bNsWsWbOkZRwdHaXXgwYNUnmfs2fPAgDevXuHNWvWoEuXLqhYsSIMDAxgbm6O5s2bY9u2bZm+d3a/L7///rv0c//vZyG/L/slIiIiVcyjmUczj86IeTQRfQhOKUNExU5KSgq6dOmCw4cPw9TUFO3atYMQAqdPn8bIkSNx4sQJ/PPPP2o7y2Xr1q1YvXo16tWrhw4dOiAwMBAymQzPnz9HnTp1EBkZCWdnZ3z22WdISUlBcHAwVq1ahUaNGqFKlSrZbrt79+4ICgrCrl27YG1tDXd3dwCAlZWVynKJiYlo0aIFgoOD0aJFC9SpUwelSpUCAJw6dQqdO3dGYmIiateujZYtW+Lhw4fYtGkT9uzZg8OHD6NZs2YZ3jsqKgqNGjVCSkoKWrZsiaCgIFy6dAnt2rWDj48PVq5ciVWrVqFVq1ZwcHDAuXPnMGvWLCgUCvz00085jpu7uzscHR2xYcMGGBkZoXv37hmWyWvsr1+/Rv369aFUKtG0aVO8e/cOhoaGAIArV66gY8eOiIqKQpUqVdCxY0e8fv0ax44dw9GjR7Flyxb07NlTZXs7d+5E//79kZSUhE8++QQeHh6IiYmBn58fJk2ahKFDh8Lc3BwAsHr1asybNw/VqlVD/fr1oaenh/v372PTpk3Yt28fLly4gBo1akjbzu3npFatWujWrVuGzwIANG3aNMfxXrZsGUaPHg1tbW00adIELVq0wJs3b/DgwQPMnDkTM2bMAJD2mTt58iRu376NJk2aqMxxamNjAyDtAHXo0KEoV64cqlSpggYNGiAsLAyXL1/GhQsX8PDhwyxv2JXZ70vt2rXh5eWV6WehatWqOe4bERERqQfzaObRzKMzYh5NRB9MEBEVQgBETn+i1q1bJwAILy8vlfYFCxYIAMLV1VWEhYVJ7S9evBBVqlQRAMSSJUtU1nFwcMjy/c6cOZPp+3h5eUlxbtu2LcN6P/zwgwAgRo8enaEvODhYBAQEZLt/6Z4+fSoAiBYtWmTZB0A0atRIREVFqfTHxcUJa2trAUAsXbpUpe+3334TAISdnZ1ITEzMsL8ARL9+/URycrLUN2PGDAFAuLi4iHLlyqnsg5+fn9DV1RWGhoYiNjY2V/smRNrP2sHBIUP7x8bu4eGh0ieEEDExMaJs2bJCW1tbbN68WaXv2rVrolSpUsLY2Fi8evVKan/8+LHQ19cXOjo6YsuWLSrrpKamimPHjol3795JbVeuXBFPnjzJsD9r164VAESrVq1U2j/kc5LdZyEn9vb2QiaTiWvXrmXYhzNnzqi0pf+c161bl+m23rx5I06cOCFSU1NV2p88eSIcHR2FlpaWePr0qUpfTr8vQmT9WSAiIqLcYx79L+bRzKPTMY8mooLEgjsRFUrpCUVuHv9N4O3t7QUAcezYsQzb3b9/vwAgnJycVNo/5kChY8eOma731VdfCQBi7969ud/xTOT2QOG/CaAQ/yanjRo1ynTbdevWFQBUkub0/TU1NRWRkZEqy0dHRwuZTCYAiNWrV2fYnoeHhwCQIfHMTlbJ4cfErqenJ0JCQjKss2jRIgFAfP3115luM/0A5LfffpPa0n+OI0aMyPU+ZaVJkyZCJpOJ6OjoDNvPzefkYw4UDAwMRKlSpXK1bE4HCtlZtWqVACAWL16s0p7T74sQPFAgIiJSB+bR/2IezTw6HfNoIipInFKGiAo1Ly+vLPsCAgJw6dIllbZnz57h2bNnKF26NNq3b59hnU6dOsHc3BwBAQEICwuTLu37GJ9//nmm7XXr1gUAfPfdd9DW1kbbtm2hr6//0e+XmbJly6JevXoZ2i9cuAAA6Nu3b6br9evXDzdu3MCFCxcyLFOvXj3pctp0ZmZmsLCwQERERKbjW7FiRQDAy5cv87Qf6oq9Tp06sLW1zbDO8ePHAQCenp6ZbjP9slofHx+p7eTJkwCA4cOH5zr2uLg4HDhwALdu3UJkZCQUCgWAtHERQiAwMBB16tQBUHCfk7p16+LixYsYMmQIJk6cCFdX14/e5sWLF3H27FmEhobi3bt3EEJIP3t/f/9M18nq94WIiIjUi3l07jCPZh6dE+bRRPShWHAnokJt/fr12fb990DhxYsXACDdIOm/ZDIZHBwcEB0djdDQULUcKNjb22faPnDgQBw/fhw7duxA586doa+vj/r168Pd3R2DBw9Wy3vnFEP6eKTfHOq/0ttDQ0Mz9GWWaAOAsbExIiIiMu03NjYGACQlJeUUco4+JvasxiMoKAgA0KRJk2zf+82bN9Lz58+fAwAqVaqU7TrpTp8+jV69euH169dZLhMbGys9L6jPybJly9C1a1esXbsWa9euhbW1NVq0aAFPT090794d2traud5WTEwMPD09cfr06SyXeX8f35fVz4aIiIjUi3n0x8XAPFoV82jm0USUeyy4E1GJk34X99xKTU3Ntj+rsyi0tbWxfft2TJkyBfv27cPp06fh7e2NCxcuYO7cuTh69CgaN278QbF8aAw5yW4scroZlrpulpVX2cWe1Xik/yy7d+8OIyOjLNfP6w2G4uLi0KNHD0RGRuKHH35Ar1694ODgAAMDA8hkMvTp0wd///03hBDSOgX1OalRowbu37+Po0eP4vDhwzh79ix27NiBHTt2oFGjRjh79ix0dXVzta3Jkyfj9OnTaNGiBWbNmoVq1arB3Nwc2traOH78OD799FOVfXxffp2dRkRERPmPefS/mEdnjnl09phHE5UMLLgTUbFSrlw5AEBwcHCWy6T3vX9mSXqCFBcXJ51dki79zIy8ql27NmrXro2ZM2fi7du3mDlzJhYtWoTx48erXHKZH3Iaj/QzVbI6C0eT8iN2Ozs7PHr0CFOmTJEuQc1J+fLl4e/vj8DAQNSqVSvbZS9cuICIiAh0794ds2bNytD/5MmTLNctiM+Jvr4+unbtiq5duwIA/Pz80KdPH1y5cgWrV6/GyJEjc7WdPXv2QFtbG/v374epqalKX3b7SERERIUX82hVzKNVMY9mHk1EuafZr1WJiNTM3t4e9vb2eP36NU6dOpWh/9ChQ4iKioKTk5PKJYZly5YFADx+/DjDOidOnFBbfKampvjll18gk8lw7949tW03K+lzKf7999+Z9m/evFllucIkP2Jv164dgLREN7fatm0LAFi5cmWOy0ZFRQFIOyD5r4CAANy8eTNX75nV5yT9gFapVOZqOzlxdXXFqFGjAOCD3icqKgqmpqYZDhIAYMeOHXmORy6Xq23fiIiI6MMwj1bFPFoV82hVzKOJKDssuBNRsTNmzBgAwMSJE1Xm/wsLC8OkSZMAAOPGjVNZp0WLFgCAX375BSkpKVL733//nWWimpNNmzZlejBw5MgRCCFQvnz5PG33Q/To0QPW1ta4ePFihkR38eLFuH79OmxtbdGtW7d8j+VD5Ufsw4cPR5kyZTBv3jysXLkyw2XOSqUSx44dU/m5jR8/Hvr6+li1ahW2b9+usrwQAidOnJDm2qxcuTIAYPfu3SqfvejoaAwZMkS66dP7PuRzYmVlBblcjsDAQJXPaU4SEhKwePFiREdHq7Snpqbi6NGjAKDyPulnRT169CjT7VWuXBlRUVEZxmPRokU4c+ZMruP6r3LlyiE8PDxDnERERFQwmEf/i3m0KubR0SrtzKOJKFuCiKgQAiBy+hO1bt06AUB4eXmptCuVStGhQwcBQJiZmQkPDw/RtWtXYWJiIgCIrl27ipSUFJV1wsLCROnSpQUAUblyZdG9e3dRs2ZNoa2tLSZMmJDp+3h5eQkA4syZM5nG16VLFwFAVKpUSXTt2lX07t1bNGzYUMhkMqGlpSV27NiRq7F4+vSpACBatGjxQX3pTp48KQwMDAQAUbduXdG7d29Ru3ZtAUAYGxuL8+fPqyx/5syZTPc3nYODQ5Y/mxkzZggAYt26dbnaNyHSftYODg4FErsQQly5ckVYWVkJAKJ8+fKiQ4cOok+fPqJ169bC3NxcABB79uxRWefvv/8WcrlcABAuLi6iV69eokOHDqJ8+fICgIiKipKWbdeunQAgzM3NRdeuXUXXrl2Fubm5cHJykj4T739mPvRz0rlzZwFAuLq6iv79+4shQ4aItWvXZjvGUVFRAoCQy+WiYcOGolevXsLT01OK39HRUbx580ZaPjQ0VOjr6wttbW3h7u4uBg8eLIYMGSIePnwohBBi8+bN0u9os2bNRO/evYWLi4vQ0tLK8++LEEKMGTNGABAVKlQQffv2FUOGDBHz5s3Ldt+IiIhIFfPofzGPZh79PubRRFRQWHAnokLpYw4UhBBCoVCIP/74Q9SuXVsYGhoKQ0NDUa9ePbFs2TKhVCoz3d6DBw9Ep06dhImJiTAyMhLNmzcXp0+fzjL5zCnxOXfunBg1apSoVauWsLS0FPr6+qJixYqiV69e4tq1a7kZBiHExx8oCCHEvXv3RO/evYW1tbWQy+WibNmyol+/flLi977CdKCg7tjTvXz5Unz77bfC1dVV+nxUqlRJdOnSRaxfv17ExsZmWOf27duiX79+wtbWVsjlclGmTBnRpEkTsXDhQqFQKKTlEhISxLRp04Szs7PQ09MT5cuXFyNGjBBv3rzJ9DPzoZ+T8PBw0b9/f2FjYyO0tbVztb8KhUIsW7ZMeHp6ikqVKglDQ0Nhbm4uatSoIWbNmiUiIiIyrHPs2DHRpEkTYWxsLP0+vh/3oUOHRMOGDYWJiYkwNzcXbdu2FWfPns3z74sQQsTFxYnRo0eL8uXLCx0dnVx9tomIiEgV8+h/MY9mHv0+5tFEVFBkQmRx+2MiIiIiIiIiIiIiIso1zuFORERERERERERERKQGLLgTEREREREREREREakBC+5ERERERERERERERGrAgjsRERERERERERERkRqw4E5EREREREREREREpAYsuBMRERERERERERERqQEL7kREREREREREREREasCCOxERERERERERERGRGrDgTkRERERERERERESkBiy4ExERERERERERERGpAQvuRERERERERERERERqwII7EREREREREREREZEasOBORERERERERERERKQGLLgTEREREREREREREakBC+5ERERERERERERERGrAgjsRERERERERERERkRqw4E5EREREREREREREpAYsuBMRERERERERERERqQEL7kREREREREREREREasCCOxERERERERERERGRGrDgTkRERERERERERESkBiy4ExERERERERERERGpAQvuRERERERERERERERqwII7EREREREREREREZEasOBORERERERERERERKQGLLgTEREREREREREREakBC+5ERERERERERERERGrAgjsRERERERERERERkRqw4E5EREREREREREREpAYsuBMRERERERERERERqQEL7kREREREREREREREasCCOxGRhkVERGDdunXo168fXFxcYGRkBD09PdjZ2aFr167Ys2dPjtuIjY3FzJkzUb16dRgbG8PMzAz169fHwoULkZycnOV6oaGhWL58Ob744gs4OTnBwMAABgYGqFChAnr37o3Tp0/naZ9GjBgBmUwGmUwGR0fHPG3jv/Kyj0FBQVIcuXkMGjQoT7FFR0dj3759+OGHH9CpUyeULVtW2ub69etzXH/gwIG5ik+pVOYpPiIiIqLiiHl09hISEnDkyBHMmTMHnp6ecHBwkLY9c+bMXG8nPDwcX3/9NapUqQIDAwNYWFigWbNmWL16NYQQHxUj82giKo50NB0AEVFJZ2Njo5IA6uvrQy6XIzQ0FKGhodi3bx86dOiAf/75B4aGhhnWDw4ORsuWLREUFAQAMDQ0RFJSEq5fv47r169jy5YtOHXqFEqVKqWy3vPnz+Hg4KCSJBsaGkIIgaCgIAQFBWHbtm0YPHgwVq5cCW1t7Vztz5kzZ7By5co8jETW8rqP2trasLa2znbb7969Q0xMDACgfv36eYpv7969eS7Wv09fXx9mZmZZ9stkso9+DyIiIqLignl09nx8fPDZZ5991DZu3LiBTz/9FBEREQAAY2NjxMbG4uLFi7h48SL++ecf7N+/H7q6unnaPvNoIiqOeIY7EZGGKZVKNGjQAMuXL0dgYCASExMRFxeHp0+fYsiQIQCAI0eOYPjw4Zmu27lzZwQFBaFs2bI4ceIE4uPjkZCQgG3btsHExAS+vr7o169fhnVTUlIghECbNm2wYcMGhIaGIj4+HnFxcfDz80OXLl0AAGvXrs31GTAJCQn48ssvoaOjg3r16uV9UNS0j+XLl0dYWFi2j/79+wMADAwM0KdPnzzHaWNjgw4dOmDatGnYvXt3nrbRs2fPbGPN7cEaERERUUnAPDpnpUqVQps2bTBp0iT8/fffsLGxyfW6MTEx6NSpEyIiIlC1alVcu3YNsbGxiI+Px9KlSyGXy3Hs2DGMHz/+o2JkHk1ExY4gIiKNOn36dLb9w4cPFwAEAPHs2TOVvtWrV0t9ly9fzrDu1q1bpf6TJ0+q9EVHR4sbN25k+b6pqanC3d1dABDGxsYiMTExx30ZP368ACCmTZsmvLy8BADh4OCQ43rZ+Zh9zEliYqIoVaqUACD69euX5xiVSmWGtvSY1q1bl+P66WPl5eWV5xiIiIiIShrm0dnLLEd1cHAQAMSMGTNyXH/69OkCgDAwMBBPnjzJ0P/zzz8LAEJbW1s8evRIbTEyjyaioo5nuBMRaVirVq2y7U8/OwcArl+/rtK3YcMGaRuNGjXKsG6vXr1QoUIFAMDGjRtV+szMzFCnTp0s31cmk2Hw4MEAgLi4ODx48CDbOK9evYrFixejcuXKmD59erbLfoiP2cec7N69G1FRUQCAoUOH5jlGnjFDREREVPCYR2fvY3PU9P1+fyzeN2bMGBgbGyMlJQVbtmzRSIxERIURC+5ERIWcvr6+9DwlJUV6npCQgEuXLgEAOnTokOm6MpkM7u7uAIDjx4+r7b3/KykpCYMHD4YQAitXrlRZ72Pk9z6uWbMGAODs7IwWLVp8RKREREREVNiU5Dz6Yz169AjPnj0DkPUYGRsbo1mzZgDyNkZERMUVC+5ERIXc2bNnpefVq1eXnj948ACpqakAgGrVqmW5fnpfWFgYIiMj8/Teurq6qFy5cpbLzZ49Gw8ePMCQIUPUWrjOz3188uQJzpw5A0D17CdNOnXqFCpXrgx9fX2YmpqievXqGD9+PPz9/TUdGhEREVGRU5Lz6I9179496Xluxuj+/fv5HlN2mEcTUWHCgjsRUSEWHR2NX375BQDQrFkzVKlSRep78eKF9NzW1jbLbbzf9/46OXn69ClWrFgBIO0mRKamppku5+vri3nz5sHa2hrz58/P9fZzIz/3ce3atRBCQEdHB15eXnkPUo1CQkLw5MkTGBoaIiEhAffu3cMff/yBatWq4c8//9R0eERERERFRknPoz/Wh47R27dvERcXl+9xZYV5NBEVJiy4ExEVUqmpqejfvz9evnwJfX19LF26VKU/NjZWem5oaJjldt7ve3+d7CQmJuKLL75AQkICrKysMHfu3EyXUyqVGDx4MJRKJRYvXgxzc/NcbT+38msfU1JSsH79egBAx44dYWNjk/cg1aBOnTpYunQpgoKCkJSUhMjISLx9+xa7du1CpUqVkJycjJEjR2LXrl0ajZOIiIioKGAe/fHyc4zUiXk0ERVGLLgTERVS48aNw8GDBwEAy5YtQ40aNQrkfZVKJfr06YMbN25ALpdjy5YtKFeuXKbLzp07F7du3UKnTp3Qo0ePAolPHY4ePYrQ0FAAH3ezVHUZO3YsRo0aBQcHB+nGUYaGhvD09IS3t7d0k6qvv/4aQghNhkpERERU6DGPLjmYRxNRYcSCOxFRIfTNN99IZ+IsWrQIgwcPzrCMiYmJ9DwhISHLbb3f9/46mUlJSUHfvn2xd+9e6OjoYOvWrWjfvn2my96/fx8//vgjjI2NsXz58my3m5n69evDxsYmw8PT0zPTeNW1jwCwevVqAGmXwGZ1EygAmcZnY2ODcePG5fge6mJpaYnvvvsOABAcHAxfX98Ce28iIiKiooZ5tHp87BgxjyaikkxH0wEQEZGqb7/9FgsXLgQALFiwAOPHj890uffPlgkNDc3yzJ30M7n/u85/paSkoF+/ftixYwe0tbWxefNmdO/ePcvlR40aheTkZMyaNQulSpXKMGejUqkEAAghpD49PT3I5XIAwOvXrxEeHp5hu+/fkErd+wgA4eHh0hlPAwcOlM6EyWrZzMTExGT7HurWqFEj6fmTJ09Qp06dAn1/IiIioqKAefSH3dg1O/8do6zmoU8fI1NTUxgbG0vtzKOJqCTjGe5ERIXIpEmTpBsmzZs3D19//XWWy37yySfQ0kr7M37v3r0sl0vvs7GxgYWFRabLpJ+Rs23bNukgoWfPntnG+vTpUwDA1KlTYWJikuGxZcsWAMCzZ8+ktmXLlknrBwUFQQiR4XH27Nl82cd0GzduhFKphEwmy/SMp/dlFp8QQpr/nYiIiIgKB+bRqnn0x6pWrZr0PDdj5OLiotLOPJqISjIW3ImIColvvvkGCxYsAJB2kDBp0qRslzc0NESTJk0ApM1JnhkhBI4dOwYAWV7SmpKSgj59+mD79u3SQUKvXr3yuhtqpa59fN+aNWsAAK1atULFihXVFGn+unr1qvQ8fR5KIiIiIkrDPFr9KleuDHt7ewBZj1F8fDwuXLgAIHd5uCYwjyYiTWDBnYioEPjmm29ULn/N6SAhnZeXFwDgzJkz8Pb2ztC/c+dOPHnyBAAwYMCADP3pZ+Ts2LEDOjo62LJlS64PErI6syb9kR6bg4OD1JbVZb35uY/vu3jxIh49egSgcNwsFUCON2+KjIzEzz//DAAoX748ateuXRBhERERERUJzKPzh0wmk/Z727ZtCAoKyrDMsmXLEBcXB21tbfTt27dA4wOYRxNR4cWCOxGRhr0/1+Rvv/2W7eWv/+Xl5YXq1atDCIFu3brh1KlTAIDU1FTs3LkTX375JQCgQ4cOaNOmjcq66XNNbt++XbqxU06Xv2rCx+zjf6XfLNXCwkKtN5UCgDdv3qg80sXFxam0//emU5s3b4anpyd27dqFV69eSe2JiYnYu3cvGjVqJB3szZ8/X7r8mYiIiKikYx6ds6ioKJVcNDU1FUDazU7fb//vPPJA2pcZNjY2SEhIQMeOHXHjxg0AQHJyMv788098//33AIBhw4ahcuXKeY6ReTQRFTcykdNXgkRElG+ePXsGBwcHAICWlhZKly6d7fLffPMNvvnmG5W2oKAgtGrVSjrrxNDQEKmpqXj37h0AoHbt2jh16hRKlSqlst758+fRokULAIBcLs9x7vM//vjjgw4kBg4ciA0bNsDBwSHTM2I+RF738X1v375F2bJlkZCQgLFjx+KPP/74qJj+SyaT5Wq5GTNmYObMmdLr9evXY9CgQdJrIyMj6OvrIzo6GikpKQDSbpL122+/YeTIkWqNmYiIiKioYh6dO46OjggODs5xOS8vr0znV79x4wY+/fRTREREAABMTEzw7t07KBQKAGlTyezfvx96enp5jpF5NBEVNzqaDoCIqCRLP8Mk/Xl4eHi2y2d25omjoyPu3LmDBQsWYPfu3Xj69CnkcjlcXV3Ru3dvjBkzBrq6utm+t0KhyPG9ExMTc9qdfJPXfXzftm3bpLNiCst0MkDaXPI//fQTrly5ggcPHiAiIgIxMTEwNTWFk5MTWrdujeHDh3POSSIiIqL3MI8uGHXr1oWfnx9+/fVXHDx4EM+fP4eRkRGqVasGLy8vDB48WGNnjjOPJqLCime4ExERERERERERERGpASewIiIiIiIiIiIiIiJSAxbciYiIiIiIiIiIiIjUgAV3IiIiIiIiIiIiIiI1YMGdiIiIiIiIiIiIiEgNWHAnIiIiIiIiIiIiIlIDFtyJiIiIiIiIiIiIiNSABXciIiIiIiIiIiIiIjVgwZ2IiIiIiIiIiIiISA1YcCciIiIiIiIiIiIiUgMW3ImIiIiIiIiIiIiI1IAFdyIiIiIiIiIiIiIiNdDRdAAljY2NDeLj42Fvb6/pUIiIiIioiHv27BmMjIwQFham6VDyHfNoIiIiIlKX/MyjWXAvYPHx8VAoFJoOg4iI6IMIIRAREQEAsLS0hEwm03BERAQACoUC8fHxmg6jQDCPJiKiooh5NFHhlJ95NAvuBSz9jBw/Pz8NR0JERJR7ycnJWLRoEQBgwoQJ0NXV1XBERAQArq6umg6hwDCPJiKiooh5NFHhlJ95NOdwJyIiIiIiIiIiIiJSA57hTkRERDnS0tJCtWrVpOdERERERJQz5tFEJQ8L7kRERJQjHR0ddOzYUdNhEBEREREVKcyjiUoefrVGRERERERERERERKQGPMOdiIiIciSEgEKhAADI5XLIZDINR0REREREVPgxjyYqeXiGOxEREeVIoVBg0aJFWLRokXTAQERERERE2WMeTVTysOBORERERERERERERKQGLLgTEREREREREREREakBC+5ERERERERERERERGrAgjsRERERERERERERkRqw4E5EREREREREREREpAYsuBMRERERERERERERqYGOpgMgIiKiwk9LSwtVqlSRnhMVF2FhYbh9+zbs7e1RpUoVfr6JiIhIrZhHE5U8LLgTERFRjnR0dNC1a1dNh0GkFkFBQdizZw927dqFy5cvQwgBADAzM0P9+vXh5uYmPcqUKaPhaImIiKgoYx5NVPKw4E5ERERExd6jR4+wa9cu7N69Gzdu3Mh0mZiYGJw8eRInT56U2hwdHVUK8LVr14aBgUFBhU1EREREREUMC+5EREREVCy9ePECq1evxo4dO+Dn55flcpaWloiMjJTOdH9fUFAQgoKCsH37dgBpZ6nVrFlTpQjv7OzMS8SJiIiIiAgAC+5ERESUC8nJyVi0aBEAYMKECdDV1dVwRESZE0LA29sbixcvxs6dO6FUKjNdrnLlyujWrRu6deuGOnXqIDY2FtevX4e3tzd8fHzg7e2Nly9fZlhPqVTixo0buHHjBpYvXw4AMDc3l4rvQ4cORfny5fN1H4mIiKjoYB5NVPKw4E5ERERERV5SUhJ27tyJxYsX49q1a5kuU6tWLXh6esLT0xMuLi6QyWRSn6mpKVq3bo3WrVsDSCvch4SEwNvbW3pcv34diYmJGbYbHR2NY8eO4dixY+jRo0f+7CARERERERUJLLgTERERUZEVFhaGFStWYMWKFQgPD8/QX758eYwYMQI9e/ZEpUqVcr1dmUyG8uXLo3z58ujevTuAtLPb7927B29vb1y9ehXe3t54+PChNBWNiYkJqlatqp4dIyIiIiKiIokFdyIiIiIqcpKTk/Hdd99h8eLFUCgUGfqbN2+OsWPHokuXLtDRUU/Kq6Ojg1q1aqFWrVoYPnw4gLQbrV67dg3e3t549+4dtLW11fJeRERERERUNLHgTkRERERFSlBQEHr06JFh6hg9PT307dsXY8aMQa1atQokFjMzM7Rt2xZt27YtkPcjIiIiIqLCjQV3IiIiIioy9u3bh4EDByI6Olpqs7W1xahRo/Dll1/CyspKc8EREREREVGJx4I7ERERERV6ycnJmDJlChYtWqTS/uWXX+KPP/6AgYGBhiIjIiIiIiL6FwvuRERElCMtLS1UrFhRek5UkIKDg9GzZ094e3tLbUZGRvjrr7/Qt29fDUZGRERElD3m0UQlDwvuRERElCMdHR188cUXmg6DSqADBw7Ay8sLUVFRUlu1atWwc+dOVK1aVYOREREREeWMeTRRycOv1oiIiIio0Hnz5g0mTpyIzz//XKXYPmTIEHh7e7PYTkREREREhRLPcCciIiKiQuPOnTtYvHgxtmzZgnfv3knthoaG+PPPPzFgwAANRkdERERERJQ9FtyJiIgoR8nJyVi6dCkAYPTo0dDV1dVwRFScKJVK7N+/H4sXL8a5c+cy9Lu4uGDnzp1wcXHRQHREREREecc8mqjkYcGdiIiIckWhUGg6BCpmIiMjsXr1aixbtgzPnj3L0F+mTBl89dVXmDRpEoyMjDQQIREREdHHYx5NVLIU2zncf/vtN3h6esLZ2RlmZmbQ09ODg4MDBgwYgLt372a53vr169GgQQMYGxvDwsICn332GS5fvlyAkRMREREVf3/++Sfs7OwwefLkDMX2unXrYuPGjXj27BlmzpzJYjsRERERERUZxfYM959//hnx8fGoUaMGqlevDgDw8/PDpk2bsG3bNuzevRudOnVSWWf8+PH4448/YGBggPbt2+Pdu3c4ceIEjh8/jn/++Qddu3bVwJ4QEVFhFhkZiYCAAPj7+yMgIADBwcGwsLDA559/jqZNm0JLq9h+t02UZ6dPn8aoUaPQqscwGBiZwPvoTkS/CkX37t0xduxYNGzYEDKZTNNhEhERERERfbBiW3Dft28f6tatC319fZX25cuXY9SoURg6dChCQkKgo5M2BCdPnsQff/wBS0tLXLlyBc7OzgCAK1euoGXLlhg0aBBatmwJc3Pzgt4VIiIqJFJTU/Ho0SP4+/tLBfaIiIgMy8XFxWHp0qXYv38/+vTpg9q1a7N4SCXS/aCX8H8ejuCXEfiscXU42ZVBREQE+vfvDyEEol+9wCcde8PRpQ6qOZbBcM/W/F0hIiIiIqIirdiedtekSZMMxXYAGDlyJCpVqoTw8HDcv39fav/tt98AANOnT5eK7QDQqFEjjBgxAtHR0VizZk3+B05ERIVSdHQ0pk6dihkzZmDz5s3w9vbOtNj+vmfPnmHu3LmYOXMmHj9+XECREhUep68/xAmfB3j8/BUCQ19DCIGhQ4fixYsXgEyGiJf/TiVTysyMxXYiIiIiIiryim3BPTtyuRwApDtDJyYm4vTp0wCA7t27Z1g+ve3AgQMFFCERERUmERERmDFjBp4+fZppv6WlJdzc3NCvXz989913aNq0qUr/gwcPMH36dCxYsAChoaEFETJRoeBY1lJ6HvQyAqtWrcLevXvTGv5ffC9nZQZduQ46NHLVTJBERERERERqVGynlMnKpk2b8OjRIzg7O0tnsj969AhJSUkoXbo07OzsMqxTp04dAMCdO3cKNFYiItK8V69eYfbs2Xj16pXUVrVqVVSpUgXOzs5wcnKChYWFyjq1atVC586dsXXrVty+fVtq9/HxwbVr19CqVSvUqFEDFhYW0iN9irPCSiaToXz58tJzotyobG+NsIi3cCxrCR1lAj7rM17qa9euHb6bMBIA8OJNDEyNDDKsv+vMTRjq66K8dSlUq2hbUGETERERqQ3zaKKSp3Af3avB/Pnz4efnh/j4eDx48AB+fn4oV64c/v77b2hrawNIu+QfQKbFdgAwMjKCubk5oqKiEBsbCxMTkwKLn4iINOfly5eYPXu2ytQxvXr1gqenZ47rVqhQAdOmTcO9e/ewZcsWBAYGAgCEEDh9+rR0ZVU6MzMzlQK8g4MD6tWrl6GYrylyuRx9+vTRdBhUiIW+joZtaXOVtsrlrVG5vDWSkpLQqFEjJCYmAgCsrKywYcMG6abCdmVKZdheyKsonL35CAJAKRNDVBuWseC+6ehVGOnrwcmuNKo62EBXXuxTWyIiIipimEcTlTzF/qjk2LFjOHXqlPTawcEBGzduRN26daW2uLg4AIChoWGW2zEyMkJ0dHSuC+6urplfFh0YGIhKlSrlNnwiItKQkJAQ/Pjjj4iKipLaBgwYgE6dOn3QdqpVq4aff/4ZV69exd9//42wsLBMl4uJiUFMTIzKtDWrV69G5cqV0aBBAzRs2BBlypTJ284Q5bOLdwKw/cQ1dGleC23rf5Khf9q0afD19ZVer1mzBmXLls12m4ev3IP4/3Nz44w5WpJCCW+/tN+X0zceYqRnC7hUKJf3naBCg3k0ERERERVlxb7gfvLkSQBpN7u7e/cuZs+ejRYtWmDOnDmYNm2ahqMjIqLCKDg4GD/++CPevn0rtQ0dOhTt27fP0/ZkMhkaNWqE+vXr4+zZs7h58yYiIiIQGRmJmJiYbNd9/PgxHj9+jM2bN6NChQpo0KAB3Nzcsrwqi6igefs9xbYT1wAAe8/fgqGeLhrX+LcoeuLECSxcuFB6PXLkSHz++ec5brdnm3ooU8oEz8MjUe4/Z84DQHRsgvRcX1eOyvbWGZYJDHkNa0tTGBvofcguERERERER5ZlMCCFyXqz4UCgUaNSoEW7evAlvb2/Ur18f+/fvR5cuXVC7dm3cvHkz0/VKlSqF6OhovH379qOmlEk/Y8fPzy/P2yAqaRITE+Hr6wsfHx9ERETA3NwcFhYWsLS0VJmCw8LCQroZMlFePXnyBHPmzJGufpLJZBg+fDhat26dL++nUCgQFRWFyMhIREZGIiIiAq9evYKvr6/KvPH/ZWtri1atWqF169YwNjbOl9jel5ycjBUrVgAARowYwd81kkTHJuD37afwJiYO5sYGGPNFa1hbmAIA3rx5gxo1auDly5cAABcXF1y/fh0GBhnna/9Qb+MTcfluIF68joGhgS56ta2v0p+SkoqpK/bgXZICle2t0a1lHZS1Mvvo9y1sSlJuWZL2lYiIig/m0USFU37mlsX+DPf/ksvl6NmzJ27cuIEDBw6gfv36sLe3B5A2fUBm4uPjER0djVKlSnH+dqICEhcXh+vXr8Pb2xt37tyBQqHI1XomJiZwdXWFu7s7PvnkE96Uhj7I48eP8dNPP0nzTGtpaWHUqFFo1qxZvr2nXC5HmTJlMkwXI4RAcHAwrl69Cm9vb4SGhqr0h4aGYvPmzdixYweaN2+ODh06SDdjyi/p40L0PnMTQ4zt0Robj1xFv0/dYGWe9gWQEAJDhgyRiu26urr4+++/1VJsBwBTIwO4N6yWZf/j5+FIeJcsPTfQl6vlfYmIiIg+FPNoopKlxBXcgbQbdQHA69evAQBVqlSBnp4eXr9+jdDQUNjaqt6UK/2s9xo1ahRsoEQlTHR0NHx8fODt7Q0/Pz+kpqZ+8DZiY2Nx9epVXL16FQ4ODujQoQOaNm3KswgoR3fu3MH8+fORlJQEANDW1sa4cePQsGFDjcQjk8ng6OgIR0dH9OrVCyEhIdLvx/vzvCcnJ+PkyZM4efIkqlevjg4dOqBOnTrSzSiJCoKFqRHG92yj0rZixQrs379fev3rr78WaC4Vn5gEE0N9xCa8Q53K9pnOA/8mJg5WZvl/hQgREREREZUcJbLgfu7cOQCQbrpkYGCA1q1b48iRI9i5cyfGjx+vsvw///wDAOjcuXOBxklUUiQkJGDLli04efIksprlysjICPXq1UOVKlUQGxsrzX8dFRWFiIgIxMTEZFg3ODgYK1aswJYtW9CmTRu0b99e+sKN6H2nT5/GqlWrkJKSAgDQ0dHBxIkTUa9ePQ1H9i87OzvY2dnB09MTr169wqlTp3Dy5EnExsZKy9y9exd3795FmTJl4O7ujlatWsHIyEiDUVNxk6xQ4vCVe3Bv6Ap93czPGE9KSsJff/2FyZMnS23u7u4YO3ZsQYUJAKj3iSPqVLFHYOgbGBtmnMM99HU0ftl4BFXsrdGidmVUr2TLq6KIiIiIiOijFcuC+6VLlxAbG4v27durnOGnUCiwYsUKbNq0CQYGBujZs6fUN3HiRBw5cgRz5sxBx44d4ezsDAC4cuUK/vrrL5ibm2PIkCEFvi9Exd3169exevVqREZGZugzMzOTbhDp4uICHZ2s/2QplUpER0fj6dOnOHbsGO7cuSP1xcbGYu/evdi/fz8aNGiAzz77DFWrVs2X/aGiJTU1Fdu2bcPevXulNj09PXzzzTeoWbOm5gLLQZkyZdC7d29069YNly5dwuHDhxEcHCz1v3r1Chs3bsSePXswefJkVK5cWYPRUnHxNj4RK/ddQNDLCLx4HY3hHs2h/V6elf77NH36dJWrMEqXLo3169dnetWFECJfi9xaWlpwLl8m075zvo8BAI+epU09U72SbabLERERERERfYhiWXD39/fHoEGDYGVlhbp168LS0hJv3rzB3bt38fLlS+jr62P9+vUqc922bdsW48aNwx9//IFatWqhXbt2SE5OxokTJyCEwLp162Bubq65nSIqZqKjo7Fu3TpcuXJFpd3S0hJubm5o2LAhKleunOtpMXR0dGBlZQUrKyvUr18fISEhOHr0KM6dOydNEZKamipNN9OgQQMMGTIEpUqVUvu+UdGQnJyMpUuX4urVq1Kbubk5Jk+eLF0BVdjp6uqiVatWaNmyJR4+fIgjR47Ax8dHmo4pNjYWP/74I6ZOnQoXFxcNR0tF3dV7TxH0MgIAcD/oJS7eDkCL2pUhhMCxY8cwdepU3Lp1S2UdY2Nj/P3337C2tkZiVDRe3bmPsFv3EH77HsJu30Pcy1coW6cGGk4YAcfWzQrsDPNUIfDyTYz0ukWdyjy7nYiIiIiI1EImspq/oQh7+vQpVq9ejXPnzuHJkyd48+YNdHV14ejoiNatW2Ps2LFwcnLKdN3169dj6dKlePDgAXR1ddGwYUN8//33aNy4sVpiy8874BIVBUIInD9/Hhs2bEBcXJzUrqOjg27duqFLly7Znsn+oeLj43HmzBkcPXoUr169UukzMjJC//790apVKxZaSpiYmBjMmzcP/v7+Ulv58uUxderUIj/t0Js3b3Ds2DEcOnQISqUSQFph/ttvv/2o+bOTk5OxaNEiAMCECRN4X4QSKFUIrD1wCbf8n6NpTSd80boubly/jilTpuDMmTMqy+ro6GBUjz7o7FIT8U+CEX77HmKCM785fTqb2tXR6JtRqNiuZYH8TRZCIDD0Na7ce4JebetDrqOd7++ZH0pSblmS9pWIiIoP5tFEhVN+5pbFsuBemPFAgUqyV69eYdWqVbh9+7ZKe5UqVTB8+HDY2dnl23unpqbi5s2b2LdvHx49eqTSV61aNQwbNgw2Njb59v5UeISEhGDu3LkqX8DUrFkTEyZMgKFhxpsqFlW3b9/GvHnzoFAoAAByuRwTJ05E3bp187Q9hUKBrVu3AgD69OkDuTzz+bupeEtWKOH7+DmsjWSYNGmSdJ+b9/Xs2RMDXevi3uLVeXqPMtVd0OibUXBybw2ZBm/+m6RQQk9e+C8GLUm5ZUnaVyIiKj6YRxMVTiy4FyM8UKCSSKlU4ujRo9i+fbs0vQsA6Ovro0+fPhnut5CfUlNTcfr0aWzatAmJiYlSu66uLnr06IGOHTtCW7tonuVIObt37x4WLlyI+Ph4qa1t27YYPHiwWq+sKCz8/Pwwd+5c6fdOW1sb48ePh5ubm4Yjo8IuVQi8iY5DmVImGfru3buHtm3bIjw8XKW9bdu2+OWXX5B06jKuLFiW6XYNLMxhXbPa/x+u0DM1wfXla/H01PkMy5Z2rYKGE0eicqf2BV54P3PjIc7fDsDY7q1QyrRw33i4JOWWJWlfiYiIiCh/seBejPBAgUqS1NRUXL58Gdu3b89QmKlduza+/PJLjU3fERkZidWrV+P69esq7RUrVsSIESPg6Oiokbgo/1y4cAHLly9HSkqK1NavXz907ty5WE8p9OjRI/z888/SF0xaWloYPXo0mjZtquHIqLBKSU3FxiNX4ffkBb7u3Q5lrcykPl9fX7Rr1w4RERFSW506dTB37ly0bdMGZ6b/jJurNkl9Jnbl4NKtM6xrpRXYTe3KZfr79tL3Dq4uXI7AY2cy9BmXtYaRdWnom5lC18QEeqbG0DMzhZ6pMfRNTaFnborSLlVh9YkztNTwhemFW/7Yfirt/wZLUyOM6dEaVmbGH73d/FKScsuStK9ERERElL9YcC9GeKBAJYEQArdv38bWrVsRFBSk0mdiYoJBgwahSZMmGi9yCiFw9epVrF27FjEx/948T1tbG926dUO3bt00HiOpR0hICCZNmiQV2+VyOcaMGYOGDRtqOLKCERgYiDlz5khn9stkMnz11Vdo2bKlWrYf9Ta+0J8FTLl3zNsPBy7eAQBYmBrhmz7tYGpkAG9vb7i7uyM6OhpA2pc3y5Ytw7Bhw4DUVByb8D38tu2WtmNZxQndd66FSVnrXL93+J37uPrbcvgfOvHBccuNDFGuXi2Uq1877VGvFvRMM56hn5MTPvex70La1GeWpkYY37NNof58l6TcsiTtKxERERHlr/zMLYvf9fNEpFEBAQHYsmVLhj9Y2traaNOmDXr06AFTU1MNRadKJpOhUaNGqF69OjZu3IizZ88CAFJSUrBjxw5ER0dj8ODBBTbdDeUPIQQ2bNggFdtNTU0xefJkODs7aziyglOpUiXMmDEDP/74I2JjYyGEwPLly5GcnIz27dvnahsKhQKrV6fNyT106FBp7slzvo+x9/wtDOncBNUq2ubbPlDBiYj5d8olKzMjyHW0cfHiRXz22WeIjY0FkPY3fcuWLejZsyeUSck4NOIb+B88Jq1nXdMV3bavhqGlxQe9t3UNF3RZvxSv/R7iym9/4vGBY0Auzw1RxCcg+NxlBJ+7nNYgk8HqE2fY1q+Ncg3qwK5RfZiVz/kz2q6BC3S0tXD6xiOM6dG6UBfbiYiIqPDLKo8mouKLZ7gXMJ6ZQ8XVixcvsG3bNly9ejVDX6NGjdCrVy+ULVtWA5Hl3p07d/DXX3/h9evXUlvLli0xYsQIFt2LsOvXr2PevHnS62+++QYNGjTQYESaExISgtmzZ0tnKANp/y85OztLD3Nz80zXTU5OxqJFiwAAEyZMgK6uLq7ce4Itx7wBANpaWviyS1MW3YuJwJDXOHL1HgZ3aoIrly7g888/R0JCAoC0K0R27NiBrl27QpGQiH0DRyPozEVpXbtG9eGxZQX0TD5+GpaY56GIfByIpLdxSHr7FklvY5EUE5v279u3SHobh7iwV3h9/xHEe9NFZcW0vC3sGtVH+cb1Ub5xA5g5ls/ySqZ3yQro66oeEL+OjsWdgBA42lihvHUp6BaCm6qWpNyyJO0rEREVH5nl0USkeTzDnYgKrdTUVOzcuRN79uxBamqqSl+1atXQt29fVKpUSUPRfZgaNWpg3rx5+Pnnn+Hv7w8AOHv2LJRKJUaNGsWbqRZBCoUCGzZskF5Xr14d9evX12BEmmVnZ4dZs2Zh9uzZ0hzcfn5+KgmGlZUVnJycpAJ8hQoVoKenl+n2ypQygb6uDt4lK2FqpI+ylmaZLkdFTyW70hjdvRWOHDkCDw8P6ca7enp62LNnDzp06ICkt7HY3Wc4Qr1vSOtVaNsCn69dDLmBvlriMCtvm6uz0pPjExB+6x5eXPNF6DVfvLjmi3dR0RmWe/s8FPefh+L+jr0A0uaHL9+4PuwaN4CTexsYlfn3viL/LbYDwMPgMOw5dwsA4GBjgUl9P83TfhERERERUfHFgjsR5VlUVBQWL16c4dvAChUqoE+fPqhRo0aRmwPdyMgI06dPx9y5c/HgwQMAwMWLF5GcnIzx48dDR4d/NouSQ4cOSTfs1dLSwqBBg4rcZ1LdypYti1mzZuHnn3/GixcvMvS/efMGb968ka5W0dLSgoeHBzw8PDIsW8m2NEZ3b40tx7wxrEszWBbiG0vSh9u3bx+++OILKBQKAIChoSH279+PNm3aIOFNJHb1HIrwO//+/a/SpQM+Wz4P2ho4a0vXyBDlmzRA+SZpV68IIRAV+BShPr544XMTIVeuI+pJUIb14l6G48Gug3iw6yBOTZ4N507tUWtQH9g1qpfp34qgl//eLNbe2jLf9oeIiIiIiIouVo6IKE/u3r2LxYsXq9xstHTp0ujduzcaN25cpKdgMTAwwHfffYd58+bh7t27AAAfHx8sWLAAEydO5CWARURkZCR27dolvf70009hZ2enwYgKjzJlymDBggV4/Pgx/P394e/vj4CAAERGRmZYNjU1FaVKlcpyW45lLTHVqwO0SvgXGUVdskKpMj3Kjh070LdvXyiVSgBpN7w+dOgQmjVrhtcPHmPfgFGIDnomLV+93xdot2AWtArJlUAymQwWThVh4VQR1ft0AwDEhYUj5Mp1PL/sg+eXryHycaDKOqlKJR7tPYxHew/Dsqozag3sDZceXVSmxrErUwoRMfF4Fh4Jx7IsuBMRERERUUYsuBPRB0lNTcU///yDXbt24f1bQDRu3BjDhg2DoaGhBqNTHz09PUyePBkLFy6Er68vAODmzZuYN28eJk2alOUUG1R4bN26VZoGw8TEBF988YWGIypcdHR04OLiAhcXF6ktMjJSpQAfGBiIpKQkODk5ZbutzIrtypQU+D15iZrO/JKjsEtMSsZP6w+jdhV7fNaoGq5cuojevXtL04SZmZnh2LFjcHNzg/+hEzg88lso/j+fOwDU/WoQWs6aXOivHjG2sUZVj46o6tERABD/OgIhV64h5PI1+B8+gbiX4dKyEQ/9cWrKbJyfvQAuPT5HrYG9Udq1KlrVqYJWdapAqVAgKT4BsS/DoYiPR3J8AhRxCVAkJKJiuxaa2kUiIiIiIioEWHAnolyLjo7G4sWLce/ePalNLpdj4MCBaNu2baEvtnwoXV1dTJo0Cb///jt8fHwApN1Y9eeff8aUKVNgYGCg4QgpK48fP8b58+el1z179oSxMac7yYmFhQXc3Nzg5uYGAEhJSUFISAhsbW0z3KMhOykpqVh38DJuB4SgS7OaaNfAJeeVSGOOXPFDdFwiztx4hFuPn2HTLxOkn7elpSWOHz+O2rVq4dK8Jbgyf+m/K8pkaDZtIhqM/bJI/v03Km2JKp+7o8rn7mg1ZyoCj5/FrXVbEXz2krSMIiEBt9dvw+3122BSzgbK5GQo4hOgTHyX5XYnvvSDFqcfIyIiIiIqsYrunA9EVKDu3buHSZMmqRTbbWxsMGfOHLRr165IFltyQ0dHB+PHj0fjxo2ltgcPHuCnn35CXFycBiOjrKSmpmLdunXSawcHB7Rt21aDERVd2tracHBwgI6ODmQyGSwtLWFpaZnj7/veC7dwOyAEALDvwm3cfPQs2+VJc5QpKbj3JFR6HRH0AMFBQQDS/v4dPXoU1SpXxr5BY1SK7bomxvDYvAJu44YVi7//Wjo6cP6sLb7YuRZDrh5D3a8GQd9c9SbAsS/CkPgmMttiO5B2A1ciIiKidB+SRxNR8cDTb4goW0II7NmzB9u3b1eZQqZRo0YYPnx4sZlCJjs6OjoYO3Ys5HI5zp07ByDtDOrJkydjwoQJOU63QQXr3LlzCAz8d27mQYMGFel7ChQWcrkcQ4cOzdWyrepUwb3AULyOjoNrxXKo4WSbz9FRXuloa2NKf3ecvvEI5274Yf78GVLftGnTUMmiNLa490TEowCp3cKpArpuWg4Lp4qaCDnflarkiFazp6Dp1PF4tPcwbq3bijDfu1kur6WjA11jI8iNDCE3MkRKsqIAoyUiIqLC7kPyaCIqHlhwJ6JsXbhwAdu2bZNe6+joYODAgcX6rPbMaGlp4auvvoJcLsfJkycBAK9fv8b333+PAQMGwN3dvUSNR2GVkJCArVu3Sq8bNWqkMkc5qZdCoYCvry8aNGig0m5haoRxPdvg2FU/eLasA51CciNNypyuXAdNXO0xoncnpCjTisU1atRA38YtsLl9dyTFvJWWrdiuJTquWAA9UxNNhVtg5Ab6qNbbE9V6e+LNowDEh7+WCuu6/y+u6xoZQps30iYiIiIiovew4E5EWYqMjFSZmsPa2hoTJkxAxYrF86zGnGhpaeHLL7+ElZWVdMZ/SkoK1q1bBz8/P3z11VcwMjLSdJgl2q5duxATEwMgbQ7+/v37azii4kupVKJv377Yu3cvdu7ciS5duqj0mxsbomfb+pmu+zIiBjYWpvySqhCZMmWKNJWMtrY2ZnXqjv39R0K8N3e/2/gRaDJlLLRK4BcoVlWcYFWFVzMREREREVHOeI09EWVKCIGVK1ciPj4eAGBgYIAZM2aU2GJ7OplMBk9PT8yYMQOlSpWS2n18fDBlyhQ8efJEg9GVbC9evMDhw4el1126dIGVlZUGIypeFAoFVq9ejdWrV0OhUGDs2LHYuXMnFAoFunfvjh07duRqO6GvozFv8zGsOXAJiUnJ+Rw1ZeX9KcLOnDmD5cuXS69ndOuDgJWbpWK7jqEBOq/+Hc2mTSiRxXYiIiKij/HfPJqIij8W3IkoUxcuXMDNmzel1/3792fx8j0uLi749ddfUb16daktPDwc06dPx9GjR1WKWVQwNmzYgJSUFACAlZUVPv/8cw1HVLwIIRAREYGIiAgIITBw4ECYm5sDSDvbvXfv3ti4cWO220hKVmDtgYtQKFNwy/85lu06y98VDXgbn4h5m4/hbmAoYmNjMWTIEKmvjms1WNz2l16blrdFn0PbUKVLB02ESkRERFTk/TePJqLijwV3IsogKipKZSqZGjVqoE2bNhqMqHAyNzfHtGnT0KNHD2lqDKVSibVr12LRokVISEjQcIQlx/Xr1+Hr6yu97t+/P/T09DQYUfHXoEEDnD59GpaWlgCA1NRUDBw4ECtXrsxyHYUyFeYm/95o+bPG1TmtjAbsOXcLz19F4a+95zH1t3V4+vQpgLSpZCa5tURiRCQAQMdAHz33bUKZalU1GS4REREREVGRwoI7EakQQmDVqlUqU8mMGDGCRbEsaGlpoXv37vj+++9hZmYmtV+9ehVjxozBtm3bEBkZqcEIi7/Xr1+rTIfh4uKChg0bajCikqN27do4efgIrK2tAaT9/Rg+fDiWLFmS6fLGhnoY1a0lOjauDveGrnBxLJthmZT35gwn9YuOTcC9J6HS63PHD0jPp305AqEHT0qvG00cCbPytgUaHxERERERUVHHgjsRqbh48SKuX78uveZUMrlTrVo1zJ8/H9WqVZPaYmNjsXv3bowaNQq///47Hj9+zEsI1Sw5ORm//fYb4uLiAKTdKHXIkCH8gqiA3FixHj5fTcbhLX+jXLlyUvvYsWMxf/78TNfR0tJCh0bV0KlJjQx9r6JiMf2vfTh46Q6i43iFSH4wNzHE5H7uKGdpiqiwZ/C7ehoAUM3VFZWD30jztpeq6Ii6Xw3SZKhERERERERFEgvuRCSJjo7G2rVrpdfVq1fnVDIfwNzcHNOnT0ePHj2gq6srtaekpODy5cuYPn06vvvuO5w/f543y1GT9evXIzAwUHo9bNgwlC9fXoMRlRx3t/yDM9//grfPQuE99nscXLcR9vb2Uv+3336LH3/88YO+ZDrv+xixCe9w9Kof/tx9Lj/CJgBW5sZ46XsCe1b8DJGaCm1tbfzSdzBeXvt3WqbWv0yHjp5uNlshIiIiIiKizLDgTkQA0qaCWLlypTSVjL6+PoYPH84zhT9Q+hQzK1asQL9+/TJcHRAYGIilS5di5MiR2Lp1K27fvi2NOX2Ys2fP4uTJf6e/aNeuHZo3b67BiEqW5Lh/P7fxr17j0qip2PvnKlSqVElq/+GHHzBt2rRcFd1TUlJx49Ez6XXTmk4ZlolLTILy/zfGpbw7d+4cli7+A4lxbwEAU8ZPwLNN/0j9zh3bo0LrZpoKj4iIiIiIqEjT0XQARFQ4XLp0SWUqmX79+qFMmTIajKhoMzY2xueff46OHTvixo0bOHLkCPz8/KT+mJgY7N27F3v37gUAlCtXDk5OTnBycoKzszMcHBygo8M/0VkJCgrCqlWrpNeVKlXCwIEDNRdQCSCTyWBqaio9rzvcC7omxjg+YTpEaireRcfg/MjJ2Pn7H+gz9Rs8fPgQAPDLL78gNjYW8+bNg4GBQZbb19bWwrSBn+HK3UBcfxiMBi4VMiyz55wvbjwMhl2ZUmhT7xPUrsyrGXLj8fNwONmWhpaWFp4+fYo+ffpIfa6urmihbYzbryMApN0otdWcqZoKlYiIiKjY+W8eTUTFn0xwQuEC5erqCgAqhTciTYuOjsbEiROlebCrVauG6dOnQ0uLF8GoU3BwMI4cOYILFy7kOKWMXC5HhQoV0Lx5c7Rt25Y/i/fEx8djypQpCA8PBwCYmJjg119/5b0GNOTR/qM4NOIbpP7/M62tp4vmC2dj0E8zcPfuXWk5W1tbzJo1C15eXjl+mSSEyPRg5Md1hxAemXZW9oAODTMtypOqB0EvsXzXWVS2t8Zn9Z3wadvW0jRM2traOLl1O26MmS7N3d506ng0nPiVJkOmD1SScsuStK9ERERElL/yM7dkBYeohBNCYPXq1VKxXU9PDyNGjGCBNx84ODhgxIgR0nQztWvXhrGxcabLKhQKPH78GKtXr8YPP/yAkJCQAo62cEpNTcXSpUulYrtMJsPYsWNZbNegKp+7w2Pzn9Ax0AcApCQl4+y4aVg9cSrq1KkjLRcaGoqhQ4eiRo0a2Lt3b7bTzGRWbE9SKBH59t9pbBxsLDMs8yg4DP+cvoGkZN4jAQCi3sZjw+ErEAAePQvHjGV/q9zzYPny5Xi5cZdUbDev4IB6o4ZoKFoiIiIiIqLigWe4FzCemUOFSXR0NA4ePIj9+/dLbUOHDkX79u01GFXJIoRAeHg4/P394e/vj4CAAAQFBUGpVKosp6OjA09PT3Tt2rVETzWzZ88e/P3339Lrnj17olu3bhqMiNKFXL2O3X2GIzk27cs7yGRoOmsydvjfxbJly5CcnKyyfKNGjfDrr7+iWbPczxWuUKYg9HU0noVHollNJ5XCfLJCiZ83HMGbmDhYmBphQIeGcLIr2dNivUtWYMsxb/g+fo7UFCV2/vE93rwIBgD8+uuv6OhYGUdGTZaW77ZtFSq04X0QipqSlFuWpH0lIiIiovyVn7llya3alCDR0dF48+YNHB0dS3Shjv4VGBiII0eO4PLlyyqFXVdXV7Rt21aDkZU8MpkMNjY2sLGxkQqPCoUCQUFBOHToEC5fvgwAUCqV2LFjB65cuYIRI0bA2dlZk2FrxN27d7Ft2zbpdZ06deDh4aHBiEoWhUKBrVu3AgD69OkDuVyu0m/XsB567t2Ef3oMRmJEFCAELv4wFz2nf41xjx9jxowZ2Lhxo3Rm+5UrV9C8eXN06tQJP//8M6pXr55jDHIdbTiWtYRj2Yxnt5+9+RhvYtKK/dGxCdCT8/87fV05+rWvj+MHduG27w2p2D5lyhSMG/EV1jR0l5Z1+qwti+1ERERE+SCnPJqIih8ejZYAPj4+WL16NeRyORwdHeHs7CzdmLFMmTK8aUcJoVQq4e3tjSNHjuDx48cZ+kuVKoWvvvqKU8kUAnK5HM7Ozhg/fjyaNWuGVatWITIyEgDw/PlzTJ8+HZ999hl69uwJfX19DUdbMCIiIvD7779LxdoyZcpg9OjR/LwWICEEwsLCpOeZsa7hgl4HtuCf7oMR+yJt2QtzFqJ+TAzWrlmDr7/+Gt999x0OHjworXPw4EEcPHgQJiYmsLW1hZ2dXab/Vq5cOcspmACgVd0qSFIoceLafbSpWxXlrS3UuPdFU0pKCvr374+dO3dKbcOGDcPPP/+MM9N/RsLrNwD+f6PUH3mjVCIiIqL8kJs8moiKFxbcS4CAgAAAad+qpk9bkc7U1BROTk5SAd7JyQlGRkaaCpXywdu3b3HixAkcP34cUVFRGfpLlSqF9u3bo127dtKd06nwqFu3Lj755BNs3boVx48fB5CWpB06dAjXrl3DsGHDUKNGDQ1Hmb9SU1Px+++/IzY2FkDaFxJff/11tsVX0hxL50rodXArdnYbhOinaWdUX1uyGi+8b8J9yVwcOHAAFy5cwOTJk3HlyhVpvdjYWDx8+BAPHz7MdLtaWlpwcXGBm5sb3Nzc0LBhQ7i4uEBbWxtA2tnvnZvWQJ0q9ihtnvGzEZ+YhGRlCkqZGObDXhc+Qgh89dVXKsX2nj17Yvny5Xjz4DF812yR2t3GDYeZvZ0mwiQiIiIiIip2WHAvAZRKJeRyORSKjDeRe/v2LW7evImbN29KbeXKlZMK8M7OzrC3t+dUNEVQamoqTp06hc2bNyMxMTFDf+XKldGhQwe4ubnx51vIGRoaYujQoWjSpAlWrFiBly9fAgBevXqFOXPmoEqVKujQoQMaNGhQLH+Wx48fx6NHj6TXQ4YMQYUKFTQYEeXErLwteh/Ygp09BuPN/bQrakJ9bmJDqy5oOfNbNB3YG5cuXcK+ffswffr0XM2Zl5qainv37uHevXtYs2YNAMDY2Bj16tWDm5sbGjdujA4dOsC2tHmm6+8+54sbD4NRt6oD2jdwgbVF8fuCUQiBfRduo0n1Slgwdw5WrVol9bm7u2Pjxo1ASgqOT/weIiUFAGDuaI/6vFEqERERERGR2hS/ygxlMHbsWCiVSgQFBSEgIEC6MWN60e6/Xrx4gRcvXuD8+fMA0s4mrVixospZ8KVLl+ZUNIXYixcv8Ndff+HBgwcq7To6OlJRqlKlShqKjvLqk08+wfz587Fr1y7s27cPqampAIBHjx7h0aNHsLCwQPv27dG2bdtic7VCZGSkyk1SmzZtitatW2swIsotI+vS6LV/C05+OwsPd6dNIaNMSMTJb2fB//BJfPr7T+jatSu6dOmC8PBwhIaGIjQ0FCEhIRn+ff78ORISEjK8R1xcHM6ePYuzZ88CAJycnPDTTz+he/fuKtMNPQh6CW+/pwAAb7+ncHOtUCwL7lf9nuLktQc46XMf3jf9AZkMEAJNmjTBrl27oKuri5OTZ+PljdvSOq1/mQ4dfT0NRk1ERERERFS8sOBeQujo6EhTx7i7p90kLS4uTqUA7+/vj7i4uAzrKhQKqaCXzszMTKUA7+TkBEPDknGZfmGmVCpx8OBB7Ny5U+WKBkNDQ3Tq1Alt27aFubm55gKkj6arq4vevXujUaNGWLt2rcr0G5GRkdi2bRt27dqFJk2aoEOHDkX+TPD169dLV2gYGRnBy8tLwxHRh9A3M0WnvxbC+bO2OPntTCRGRgMAgs9ewobmndH65+lw6dFFunFw3bp1M91OamoqHj58CG9vb+lx9+5dpPz/LO10AQEB6NmzJ+rWrYtff/0Vbdq0AQC8S1bC1MgAb+MTUc7KDM52ZTK8h0KZArmOtnoHoAAlvEvG3nO+aS9kMpS2dQSEQM2aNXHw4EEYGhrCb/te3Fr771Qy1fp0Q8W2LTQTMBERERERUTElE7xjQ4FydXUFgFxdPl/QhBAIDw+X5nkPCAjA06dPMxQ0MiOTyVCuXDlUrFgRenrZnylnb2+P1q1bQ1dXV12hE4AnT55gxYoVCAoKUmlv0KABhgwZglKlSmkmMMpXgYGBOHLkCC5fvgylUpmhv2rVqmjYsCGcnJxQoUIFyOVyDUSZN76+vvjll1+k18OGDUPbtm01GFHJlpycjEWLFgEAJkyY8MF/w+NfvcHxr79H4NHTKu1On7VFuwWzYVTa8oO2l5CQgBs3bsDHxweXLl3C/v37M/x/1a5dO8ydOxd16tSBMiUFt/xDoCfXQfVKtirLJSYpMGvNAbhUKItqFW1Rp4r9B8VSGCQmJuKrb76HzLIi5Hr6+Hv+JNiUtsDFixdhbW2N8Dv38XfHXlC+SwIAWNeqht4HtvLs9mKgMOeW6laS9pWIiIqPj82jiSh/5GduyYJ7AStqBwoKhQJBQUEqZ8GHh4d/9HbLli2L4cOHw8XFRQ1RlmzJycnYsWMHDh48KE0xAgDm5uYYMmQI3NzcNBgdFZTo6GicPHkSx48fR3R0dKbLaGtrw9HRUboyxdnZGTY2NoVyeqikpCRMnDgRr1+/BgBUqVIFs2bNUpkmhApWcnIyVqxYAQAYMWJEng4UhBDw274Xp7+bg+TYf6+oMrAshfqjhsC1l+cHF97TPXr0CNOmTcOuXbsy9PXq1Qtz5szJciqtszcf4Z8zafcycSxriW/6tFfpTxUCCe+SYWxQOIvTb968gYeHBy5evAhdfQOUtq0AU12Bo0ePwt7eHomRUdjUrhvePgsFABhYmKPfyd0wK2+bw5apKChqueXHKEn7SkRExYc68mgiUj8W3IuR4nCg8PbtWwQEBKhMRxMfH5+nbbVr1w59+/bldDR5FBAQgMWLFyMsLEylvXXr1ujXrx+MjY01FBlpilKpxNWrV3HkyBH4+/vnuLyxsTGqVKmC+vXro169eoVm7vetW7di7969ANK+KPj1119hb1/0zjqmzL0NeYGj477Ds/NXVNq15HI4d2yHml49Ub6JW56+DLp69SqmTJmCc+fOqbTr6OigX79+6NGjB1q3bq1yNdaibScRGJr25U7L2pXRvbXq1DYvI2Lw0/rDsDI3RhV7a3i2qA093cJxtYi/vz8+++wzBAQESG1t2rTBP//8A3Nzc6SmpGB3r2EIOnsRACDT0kL3HWvg0KKxpkImNSsOuWVulaR9JSIiIqL8xYJ7MVIcDxSEEHj58iUCAgIQEhKC7D5S0dHROH/+vMoyFhYWGDp0KOrVq1cQ4RYb3t7eWLJkCZKTk6U2a2trDB8+HNWqVdNgZFRY+Pv748qVK/D398eTJ09U5vXPjJaWFlxcXNCgQQM0aNAAFhYWBRSpqmfPnmHy5MnS9CCff/45+vXrp5FYKP+I1FTcWvc3zs2eD2VCYob+UhUdUWNAD7j29ICh1Yd9FoVIO7t78uTJuHv3boZ+U1NTdO7cGZ6ennB3d4eunh5uPX6OB8FhqFPZHq4Vy6ksf+XeE2w55p0Wl4khZn/5eaG4MuTChQvo2rUrIiMjpbbBgwfjzz//lM6cuvjzIlxdtELqb/7DN2gw5ssCj5XyT3HMLbNSkvaViIiIiPIXC+7FCA8U0s7K/vPPP/H8+XOV9saNG2PQoEEwMzPTUGRFgxACBw8exObNm6UvLmQyGTp16oQePXrkOIc+lUxKpRLPnj2TrkoJCAhAaGhotutUrlwZbm5u0o0sIyMjERERofJvZGQkoqKiYGxsjJYtW6JVq1YfdWVFamoqZs6cKd0MtnTp0li4cCH09fXzvE0q3GJfhOH2xu24t+UfxIW9ytCvrSuHc8f2qOrxGcrWrQWjMla53nZKSgq2bt2K77//HsHBwZkuY2DwP/buOyyKqwsD+LuFXqSIqCCigg0FQcWOJfZeE3vX2BJbrNHYjbFEo0ZjIyafJXY09hY1VmzYEBU7iIIU6Sxbvj+IEwmgoOzswr6/5/EJc2dm52wUOHvmzrlmaNWqFTp37oy2bdtm+zto16lr+OtqxsLhjX0qoEtjn0z7U9LSsfVYIDzdnFHZtQTMTTM/Jhz8JAInr4QgLjEZSSlpGNK+Aco6OWQ6JjU1Fdu2bcPatWsRFhaGqlWrolatWqhVqxZ8fX2zLHi9efNmjP5mElwqeuPGmUNQq1WYP38+Jk+eLNwMeHDwOPb2Gymc4962Bdr7/6QXNwso/xhSbmlI75WIiIiItIsF90KEHxQyKJVKBAQEYPfu3ZkWerS0tES/fv3g5+fHgkA2VCoV/P39cezYMWHM3Nwc48ePR9WqVXUYGRVESUlJePDggbDwZGxs7Ce/pomJCfz8/NCqVSs4Ozvn+fyTJ08K/Q0BYNKkSULBn3QrPT0dO3bsAAB069Yt3xfgVSuVeHj0FG7+vg2PT/4N5JCeWJdyQnHvqihR3QslfDzh6OkBI3Oz9752Wloadu3ahV27duHQoUNISck6o/4tBwcHODs7w8nJSfivk5MzbIsVh8bYCjWqVkSp4pn7zF+++wS/Hcxoj9O2XlW0rJ35KaOrIU/x64HzAAB7awvMGNRWWI/gxYsXWL16NdasWSOsWZCdChUqCAX48PBwzJ8/H+2GTIZLBU/EvAxD46qlMKRvd+H4mNBH2NSsKxSJGS3f7MqXQ+8j22HMVmOFjiHllob0XomIqPDQdh5NRB+HBfdChB8UMgsLC8OaNWtw7969TOOWlpZwc3MTFnZ0c3ODlZWVjqLUD8nJyVi2bBmCgoKEMQcHB0yZMuWjCptE71Kr1QgNDcWlS5dw6dIlREZmnWmcV1WrVkWrVq3g4+OTq8VO4+PjMWbMGCQmZiym6evri2+++eaT46D8oVAosHTpUgDA2LFjtbrY05tnYbi1eSdubd6JpFc5F6EBQCKTwaFSeZT09UH1L/vBtmzp9x6fnJyMw4cPY9euXdi/fz/i4+PzFFvx4sUxbNgwDBs2DI6OjgCA9fvOIuhBxlNbDbzc8EXTmpnOeRgehaV/HAcAdGpYDU2qV8TFixexfPly7Ny5ExpIULvV5wgOPIXYV+9/8uStcp610LLvaGF7QJu6qF4x470rEhOxqcXniLn/EABgbGmB3sd2ws6tbJ7eKxUMhpRbGtJ7JSKiwkPMPJqIco8F90KEHxSyUqvVOHr0KLZs2YLU1NQcjytevLhQgHd3d4erqyvkcrmIkerO69evsWDBAjx79kwYc3d3x4QJE7K0GSD6VBqNBk+fPsXFixcRGBiIsLAwSKVS2Nraws7ODvb29rCzsxP+2NjY4NatWzh+/DgSEhKyvJ6joyNatGgBX19fODg45Pj0ysqVK3HmzBkAgKmpKZYuXQp7e/tsjyXx6eKDgio9HY+OnUbwjn2IuHI925Yz75KbmqDON6NQY8QAyHIxcygtLQ0nTpzA7t27ERAQgOjo6FzHZmRkhO7du+Orr76ChYMzroU8xc2H4Sjn5IAvO/plOjYxJQ1XQ55CokrH/VtXsWbVCly5ckXYX6FGAzTtPhwAoE6IQlNPZ9y6dUu4AfZuj/a3TMws0LLnl3CuVAOVXItjROdGkEgk0Gg0+HPQaNz/84hwbIeNK+Heplmu3xsVLIaUWxrSeyUiosKDBXci/cSCeyHCDwo5e/36Nfz9/TMVId5HLpejTJkymWbBOzo6FrpWNI8ePcIPP/yQqd1H7dq1MWrUKP6iJlGkpKTAxMTkg7PUFQoFzp07h4MHD+bYL7tIkSKZvmfd3Nxgbm6O27dvY/bs2cJx/fv3R+vWrfP1fdCn0YcPCgkvXiLi2k1EXLuBiKs38SroNtKTk7Mc5+BRES2WzUXxarlvtaVSqfD8+XOEh4cjLCwsx/++Xcz3XbVr18bXX3+NTp07QyKRwsTYCCkpKbh+/bpQNL906RKePHmS7bV7TVgIG8eMJ5XqVi2Hns19hX0ajQZBt4MReDUIt68FIvDSRQQHB6Nx48bYuHEj4lLVsDY3RVGbjFYxN/+3A0fHTRPOrzVmGBp8OzbX/x+o4DGk3NKQ3isRERUe+pBHE1FWLLgXIvyg8GFJSUl4+PAhHjx4ICzwmNtH/q2srLIU8z5lAUddUigUuHjxItatW4e0tDRhvEOHDujRo0euWnQQ6YJGo0FISAgOHTqES5cu4UO/ZpycnJCcnCzcVCpTpgzmz58PmUwmRriUS/r4QUGtUiH6XijCLlzBxR9XIyny3/YzEqkUPl/2Q71JX8PYwjxfrhcVFYV169Zh1apV2S46XKJECbRo0QK3bt3CjRs3Mq1Rkp2GDRviq6++gqObJ/6+EYrwqDhM6dsKTg42mY47ceUu9pwOgpFchrpVy6Fbk+zXNUiKisavdVshNe4NAMC1UX10/mMtpPxeKtQMKbc0pPdKRESFhz7m0USk3dzSMPpxUIFiYWEBT09PeHp6Asgo3kVFRWUqwD9+/Bjp6elZzk1ISMD169dx/fp1YaxEiRJCAd7d3R2lS5fW21Y0qampuHbtGgIDA3Ht2rVMLXakUikGDx6Mpk2b6jBCog+TSCSoVKkSKlWqhNevX+PIkSO4ePEiXr16le3x7xYuJRIJhgwZwmI75YpUJoND5QpwqFwBlbq0xZnZi3Hzf9sBABq1GldX/4oHB46i+eLZcG1c/5Ov5+DggKlTp2LChAkICAjA8uXLcfbsWWF/REQENm7c+N7XKFGiBNq0aYNRo0bBy8tLGK/n6YawyNgsxXYAeBKR0eomXamCkTzn743TMxYIxXZjSwu0XDGfxXYiIiIiIiKR6WfVkegdEokExYoVQ7FixVCvXj0AgFKpxNOnT4UC/IMHDxAREZHt+REREYiIiBB6QxsZGaFMmTJwcXHJ1If6bV9qc3NzUdvSJCYm4urVq7h06RJu3LiR7Y0EMzMzjB8/XrgJQVRQFC1aFL169UKvXr0QHx+P0NBQ4Xs2NDQUSUlJmY5v0aIF3NzcdBQtFWSmNkXQ/Mc5qNilLY6N+w6xj54AAOKfhWPn54NQ+fMOaDR7Mszt7T75WkZGRujWrRu6deuGa9euYfny5di6dSsUCkWm48zMzFCjRg3UqlVL+OPs7Jzt7xiJRIJSjtnHplZrIJNKoVKr4Voi+3UNnp65gOAd+4Tt+t+Og2Vxx094l0RERERERPQx2FJGZHwUVnsSExOzFPOyW8DxQ0xMTISFID/UtsXc3DzbRSTt7OxgamoKIGOGfmJiIqKjoxETEyP8iY6ORmRkJEJCQrLtCfw2lurVq6Nbt25wcnLK83sh0mcajQYREREIDQ3Fo0ePUKRIEbRr105vn0AxdAqFAitXrgQAvV9DIj0lFRd/XI3LK9dD/U5bF5mJMco2bYQKHVuhXPPGMDI3y7drRkZG4rfffsOzZ89QpUoV1KpVC1WqVMm3f8/pShXCImPhaGcNc9PM/++VqWn4rWF74SaDY7Uq6HV4O2e3GwhDyi0N6b0SEVHhUZDyaCJDwh7uhQg/KIhHo9Hg1atXmVrRPHny5IM9dfOLhYUFzM3NERcXl+2s9fed93ZGpKenJ38ZExF9pKg7ITgydhpeXr+VZZ+RuTnKtWyCih1bwbWJH+QmBfdn7bkfluPC4p8BZPSu7310Jxy9PHQcFYnFkHJLQ3qvRERERKRd7OFO9BEkEgmKFy+O4sWLo0GDBgCA9PR0PHnyBA8fPkRkZGSm2eaxsbH5WoxPSkrK0i4jJ0WKFEHNmjVRq1YteHh4cIYvEVE+cPCoiJ6HtuH6uv/hwo+rkRobJ+xLT05GyO79CNm9HybWVnBr1RTl27eAg0dFWJUsLmprsU8RE/oIgcvXCts+Q/qw2E5ERERERKRDnOEuMs7M0V9qtRoJCQlCAT4hIQEf+vZISEjI0iomNjY22/OMjY2F9jO2trZCGxpXV1dUqFDhg+1riIjo46kUCjw9fQEhAQcRevAYFIk53xCVm5vBrqwrbN3KwO6fP7ZuZWBXzhXGlpYiRv1+Go0G2zv1xfNzgQAAq5LFMeDcAb2KkbTPkHJLQ3qvRERERKRdnOFOJAKpVIoiRYqgSJEiKFOmzEe/jkqlwps3bxAdHY3k5GTY2trCzs4OFhYWBWbGJBHRfymVSuzZswcA0KlTpwL3JI7M2BhlmzVE2WYNoUydjccn/0bIngN4ePQvKJNTMh2rTE5B5O27iLx9N8vr2LmXRWm/uijdsA5K1asFE2urXF0/Ne4NXt24g5jQx7Ap4wKXBrUhMzL6pPd0Z1uAUGwHgCbzp7HYTkRERKRnCnoeTUR5J/p3uUKhwN27dxEVFYW4uDjY2NjAwcEBlSpVYq9qKhRkMpmwcCoRUWGhVqvx6NEj4euCTG5qAvfWTeHeuikUScl4dPQU7u09iBeXg5AUGfXec2MePELMg0e4vmETJFIpintXRWm/OnBpWBcla3hDbmKMtIRERN4MxsugW3gZdBuvgm4j7smzTK9jZmeD8u1aoGKntnCuUwOSPD7llBITi9MzfxC2y7VsArfWTfP0GkQfi/k8ERFR7hWmPJqIckeUgntUVBQ2btyIAwcOIDAwEGlpaVmOMTExga+vL9q2bYt+/frBwcFBjNCIiIjIgBlbmKNip9ao2Kk1ACAtPgExDx8jNvQxYh4+yfhv6GPEPnoCZUpqpnM1ajUirt5AxNUbuLj0F8jNTGFZ3DGjuP6BlmQpMXG48ds23PhtGyyLF0OFjq1RsVMbFPeumqunoU7PWoSU6FgAGQvAfvb9dD5FRVrFfJ6IiIiIKHe0WnAPDQ3F9OnTsWfPHigUCgBA0aJFUb16ddjZ2cHa2hpv3rxBbGwsQkJCcObMGZw5cwbTpk1D586dMXv2bLi5uWkzRCIiIiKBibUVSnh7ooS3Z6ZxjVqNuCfP8fzcJTw9fR7Pzl4UCt5vKVNSEff4aY6vbWprAzu3Mnh18w5UaQphPPFlJK7+shFXf9mIIq6lUL5dC5Tw8UKxqpVQxMU5SyH9+fnLuL1ll7Bdd9JXsHYu+SlvmyhHzOeJiIiIiPJGawX3UaNGYd26dVCpVGjcuDF69uyJRo0avbc39qNHj/DXX39hy5Yt2L59O3bt2oWhQ4dixYoV2gqTiIiI6IMkUilsy5aGbdnS8OzzOTRqNaLu3MPTM+fx9PR5hF28kmkGvIm1FRy9POBYrQqKe1WBY7UqQvE8LT4BDw4eR8ieA3h6+jw0KpVw3psnz3F5xfp/X6eINYpVqYhiVSqhWNXKcPCoiGMTZgj7HTwqovrQvuL8TyCDw3yeiIiIiCjvJBrNB555/kjm5uYYOnQoJk6ciJIl8z7rKjw8HAsXLsT69euRlJSkhQh1Q5sr4BIREWmLQqHA0qVLAQBjx45ln+b/UKYpEHElCCmxcXCoVB42ZVxy1Zc9OToG9/88gpA9BxF24fIHW9FkIpGg16FtKFHd6xMip4JOm7mlvuXzzKOJiKggYh5NpJ+0mVtqbYb7o0ePULx48Y8+38nJCT/99BOmTJmSj1ERERER5T+5iTFK1fPN83nm9nao1r8HqvXvgYSIV7i/9xDCLl1D5O1gvHny/L3nVuvfg8V20irm80REREREeae1gvunJOfaeB0iIiIifWZVwhHVh/VH9WH9AWQs4Bp56y4ib9/957/BiL73EGqlEvYV3VH/27G6DZgKPebzRERERER5p7WWMpQ9PgpLREREH0uZmobEiFewLFkcchM+jkyGlVsa0nslIiIiIu3SZm754eaiH0mj0aBZs2Zwc3PDhQsXPnj8hQsX4ObmhlatWmkrJCIiIqICTW5qApsyLiy2kyiYzxMRERER5Z3WCu579+7FiRMn0Lx5c9SpU+eDx9epUwctW7bE0aNHceDAAW2FRUREREREucB8noiIiIgo77RWcN+6dStkMhm+++67XJ8zffp0SKVSbN68WVthERER0UdQKpUICAhAQEAAlEqlrsMhIhEwnyciIvp0zKOJDI/WCu6BgYGoXr16nhZJcnR0RI0aNXDx4kVthUVEREQfQa1W4969e7h37x7UarWuwyEiETCfJyIi+nTMo4kMj9YK7i9fvkSZMmXyfJ6rqytevnyphYiIiIiIiCi3mM8TEREREeWd1gruRkZGUCgUeT4vPT0dMplMCxEREREREVFuMZ8nIiIiIso7rRXcS5Qogbt37+b5vODgYJQsWVILERERERERUW4xnyciIiIiyjutFdwbNGiAe/fu4dKlS7k+5+LFiwgJCYGfn5+2wiIiIiIiolxgPk9ERERElHdaK7gPGTIEGo0GAwYMwOvXrz94/OvXrzFgwABIJBIMHjxYW2EREREREVEuMJ8nIiIiIso7rRXca9WqhYEDByIkJAReXl5Yt24d4uPjsxwXHx+PtWvXwtPTE/fv38fAgQNRq1YtbYVFRERERES5wHyeiIiIiCjvJBqNRqOtF1cqlejTpw+2bdsGiUQCiUSCsmXLwsHBAQAQFRWFR48eQaPRQKPRoHv37vjf//5XqBdZ8vDwAADcuXNHx5EQERHlnkajQXp6OoCMhRQlEomOIyIiQPu5pT7l88yjiYioIGIeTaSftJlbam2GOwDI5XJs3boV27ZtQ40aNaBWqxEaGooLFy7gwoULCA0NhVqtRs2aNbF9+3Zs2bKlUBfbiYiICiqJRAJjY2MYGxvzQwKRAWE+T0RE9GmYRxMZHrkYF+nWrRu6deuG6OhoBAUFITo6GgBgb28PLy8vFC1aVIwwiIiIiIjoIzCfJyIiIiLKHVEK7m/Z29vjs88+E/OSRERElA+USiWOHDkCAGjRogXkclFTCCLSE8zniYiI8oZ5NJHh0WpLGSIiIioc1Go1bt++jdu3b0OtVus6HCIiIiKiAoF5NJHh0fpttQsXLiAwMBDFixdH+/btYWZmpu1LEhERERFRPmE+T0RERESUe1qd4f7ll1+ifv36+PXXX/Hll1/Cy8sLT58+1eYliYiIiIgonzCfJyIiIiLKG60V3Hft2oX169dj8eLFCAoKwu3bt/H69WuMHj1aW5ckIiIiIqJ8wnyeiIiIiCjvtFZwP3HiBABg6NChAABnZ2d89tlnOHPmjLYuKUhOTkZAQAAGDRqEChUqwNTUFBYWFvDy8sLs2bORmJiY47kbN26Er68vLC0tYWdnh9atW+P8+fNaj5mIiIiISJ/oMp8nIiIiIiqotFZwd3Z2BgA8f/5cGHv+/Dns7e21dUnBli1b0KlTJ/j7+0Mmk6F9+/Zo0KABHj9+jBkzZqBmzZqIjIzMct6YMWMwYMAA3L59G02bNoWvry+OHTsGPz8/BAQEaD1uIiIiIiJ9oct8noiIiIiooNJawb1fv34oUqQIevfujX379mHChAkIDAzE8OHDtXVJgZGREYYOHYrg4GAEBwdj+/btOHz4MO7duwdvb2+EhIRgzJgxmc45fvw4fvrpJ9jb2+PGjRsICAjA4cOHcebMGchkMgwYMABxcXFaj52IiIiISB/oMp8notxRKBTYtm0bRo8ejZ9//jnbiWVEREQkLolGo9Fo68Vv3ryJsWPH4sqVK3B0dMTw4cMxZswYSCQSbV3ygy5cuIC6devCxMQE8fHxMDY2BgC0bt0ahw4dwtKlS7MU40ePHo3ly5dj8eLFGD9+/Cdd38PDAwBw586dT3odIiIiMWk0GqSkpAAAzMzMdPq7nIj+pe3cUp/yeebRRJndvXsXa9aswYsXL4QxmUyG5s2bo0uXLrC2ttZhdET0FvNoIv2kzdxSqwV3fZScnAwLCwsAwIsXL1CiRAmkpKTA1tYWaWlpeP78ufD47Ft///03/Pz80LBhQ5w6deqTrs8PCkRERESUXwwptzSk90r0PsnJydiyZQuOHj2a4zGmpqZo37492rZtC1NTUxGjIyIiKhi0mVtqraWMvnr06BGAjLYzdnZ2AIB79+4hLS0NDg4OWYrtAODj4wMgY4YPERERERERkS5cvXoV48ePz1Rsl0gk8PPzQ9GiRYWx1NRUbN++HV999RUOHz4MpVKpi3CJiIgMklzXAYjtp59+AgC0bNkSJiYmAIBnz54BQLbFdgCwsLCAjY0NYmNjkZCQACsrK3GCJSIi0hNKpRInT54EADRp0gRyucGlEERERDrz5s0b/Prrrzh//nym8VKlSmHYsGFwd3eHQqHA0aNHsXv3biQmJgrn+fv748CBA+jRowfq1KnDdhZEImMeTWR4tPZd/vr160x32HX9OgBw8OBBbNiwAUZGRpgzZ44w/jYZMTc3z/FcCwsLxMXF5brg/vaxhP96+PAhypUrl8fIiYiIdEutVuP69esAgEaNGuk2GCISha7yeebRRP/SaDT4+++/8dtvvyEhIUEYl8lk6NKlCzp27CgU74yNjdG2bVs0adIEe/fuxYEDB6BQKAAAr169wrJly3D79m0MHjwYUqnBPexOpDPMo4kMj9Z+y5YpUwZTpkzB69evP+r8yMhITJw4EWXKlMmXeEJCQtC7d29oNBosWrQIXl5e+fK6RERERESFkb7l80SGJioqCvPnz8fKlSszFdvLly+PRYsWoWvXrtnOlDU3N0ePHj2wfPlyNG3aNFNx/fjx4/jll1+gVqtFeQ9ERESGSGsz3Lt164ZFixZh6dKlaNmyJXr06IGGDRuiePHiOZ4TERGBU6dOCQvAqFQq9OvX75NjCQ8PR8uWLREbG4tx48Zh9OjRmfZbWloCyFh8JidJSUkAkOt2Mjk13M9pxg4RERERkT7RVT7PPJoMnVqtxuHDh7F161akpaUJ4yYmJujZsydatGiRqxnqdnZ2GDp0KNq2bYuff/4ZDx48AACcOnUK6enpGDlyJFtbEBERaYHWfrv6+/tj7NixmDZtGvbv348///wTAFCyZElUqFABtra2sLKyQkJCAmJiYnDv3j1EREQAAKRSKdq1a4c5c+Z8cmIdExOD5s2b4+nTpxgwYAAWL16c5RgXFxcAQFhYWLavkZSUhLi4OCFmIiIiIqLCTl/yeSJDEhYWhtWrVwvF8be8vLwwdOhQODg45Pk1S5YsiWnTpmHBggW4e/cuAODcuXNIT0/HmDFjWHQnIiLKZ1r9zVq1alXs3bsXYWFh2LBhA/bv34+goCCEh4dnDUQuR40aNdCmTRsMHDgwxwVM8yIxMRGtWrVCcHAwOnfujHXr1mW7QEyFChVgYmKCqKgohIeHw8nJKdP+a9euAQA8PT0/OSYiIiIiooJC1/k8kaFQKpXYs2cPdu/eDZVKJYxbWVmhX79+aNCgwSctdmpmZoapU6di4cKFuHXrFgAgMDAQixcvxrhx42BsbPzJ74GIiIgyiHIr29nZGTNmzMCMGTOQlJSE4OBgREZG4s2bNyhSpAiKFSsGDw+P9y5amldpaWno0KEDAgMD0aJFC2zduhUymSzbY83MzNCkSRMcOnQIO3bswJgxYzLt37lzJwCgXbt2+RYfEREREVFBoYt8nshQPHjwAL/88gueP3+eabxevXro378/ihQpki/XMTExwaRJk7BkyRJhAcdr165h4cKFmDBhAkxMTPLlOkRERIZO9GfHLCwsULNmTa1eQ6VSoUePHjh58iQaNGiA3bt3f/CO/bhx43Do0CHMnTsXbdq0gbu7OwDgwoULWLNmDWxsbDBo0CCtxk1EREREpO/EyOeJCruEhARcuXIFly5dwvXr16HRaIR99vb2GDx4MKpXr57v1zU2NsaECROwbNkyBAYGAgBu3ryJ+fPnY/LkyTAzM8v3axIRERkaiebd3+yFxE8//STMUu/UqROsra2zPW7x4sUoWrSosD1mzBj89NNPMDc3R7NmzaBQKHDs2DFoNBrs3LkTHTt2/OTY3vawzGkxKCIiIn2k0WgQHx8PALC2tv6kx9qJKP8YUm5pSO+VCqfY2FgEBgYiMDAQd+7cgVqtznJM8+bN0bNnT60/LaJUKrFy5UqcP39eGHN3d8fUqVNhYWGh1WsTGRrm0UT6SZu5ZaFcHSU2Nlb4es+ePTkeN3PmzEwF92XLlqFatWpYuXIljh07BmNjYzRt2hTTp09H3bp1tRozERGRPpNIJPn2SDsREZGhiImJwfnz53Hp0iXcv38fOc13c3JywpdffomKFSuKEpdcLsfXX38NY2NjnDp1CkBGa5vZs2dj0qRJsLOzEyUOIkPAPJrI8BTKGe76jDNziIiIiCi/GFJuaUjvlQqHR48eYc6cOUhKSsp2v42NDXx9feHr6wsPD48c1xzTJrVajQ0bNuDYsWPCmL29PSZPnozSpUuLHg8REZFYOMOdiIiIdEqlUuHMmTMAAD8/P50UBYiIiAoKlUqF1atXZym2Ozg4oFatWvD19UX58uUhlUp1FGEGqVSKwYMHw8TEBPv37wcAREdHY/r06Rg3bhyqVaum0/iICgPm0USGhwV3IiIi+iCVSiUsrlavXj1+UCAiInqPQ4cO4enTp8J2+/btUbduXZQpU0bv+jdLJBL07dsXxYoVw6+//gqNRoPU1FQsWLAAAwcORPPmzXUdIlGBxjyayPDo9nY6ERERERERUSHy+vVrbNu2Tdhu3LgxevfujbJly+pdsf1dLVu2xKRJk2BiYgIgo93M+vXr8fvvv2e7wCsRERFljwV3IiIiIiIionzi7++PtLQ0AICVlRV69+6t44hyz8fHB3PmzIGtra0wtn//fvz444/CeyIiIqL301nBPS0tDREREYiJidFVCERERERE9JGYzxNldfnyZVy5ckXY7tOnD6ysrHQYUd65urpi/vz5cHV1FcYCAwMxc+ZMxMXF6SwuIiKigkL0Hu5r167F6tWrcevWLWg0GvTr1w/+/v4AgN27d2PTpk1YuHAh3NzcxA6NiIiIiIg+gPk8UfZSU1OF7wUAqFy5Mho2bKjDiD6evb09Zs2ahWXLluH69esAgIcPH2LMmDHvvYEgkUhQpEgR2NnZwd7eHnZ2dsIfe3t72NraQi7nUnJEALDzr6uIeP0GNlbmqFOlLNyci+k6JCLKJ6L9plOpVOjatSv27dsHIyMjVKpUCXfu3Ml0jJeXF7p27Yrq1avj22+/FSs0IiIiIiL6AObzRO+3fft2REdHAwBkMhmGDBmi1z3bP8TMzAwTJ07Exo0bceTIEQBAcnIykpOT33vey5cv37vfxcUFPXv2hI+PT77FSlQQPX4RjacvM35mVHBxzLL/1LV7ePA8El0a+8DO2kLs8IjoE4jWUmblypXYu3cvWrVqhadPn+LWrVtZjilXrhzc3Nxw6NAhscIiIiIiIqJcYD5PlLMnT57g4MGDwnaHDh3g5OSkw4jyh0wmw8CBA9G/f3/IZLJ8ec1nz55hwYIFWL58OeLj4/PlNYkKorjEf29e2ViaZ9n/+k0iboSGYe7Ggzh9/b6YoRHRJxJthvvGjRvh6OiIbdu2wcIi5ztzlStXxtWrV8UKi4iIiHLByMgIAwcOFL4mIsPDfJ4oe2q1GuvWrYNarQYAODo6onPnzjqOKv9IJBK0bt0a9erVw9OnT997rEqlQlxcHGJiYhAdHY2YmBjhz3+L62fPnsWNGzfQv39/1K9fv0A/DUD0Pjnl0d2b1kRsQjLiEpNRzC5zqyaNRoPYhIyCvCJdmak4T0T6T7SC+71799C8efP3JucAYGFhgaioKJGiIiIiotyQSCRwcHDQdRhEpEPM54myd/z4cTx48EDYHjJkCIyNjXUYkXYUKVIEnp6eH31+eno6nj17Bn9/f+H/V0JCAlasWIGzZ89iyJAhKFq0aH6FS4WIUqlEbGwsoqOjkZqaChsbG9jb28PS0rJA3KjJKY+uWu79T8FUcy+F0OeRMDczQavaVbQVHhVCyakKPH8VA4lEAhsrcxS1sYS0AHyvFCaiFdyNjIyQmpr6weOePXtW4FZxJyIiIiIq7JjPE2UVFxeHLVu2CNv16tX7pKJ0YWZkZIRy5cphzpw5OHz4MLZu3Yq0tDQAwPXr1zFu3Dj07NkTzZs3h1QqWvdb0hMqlQrXr1/Hs2fPhKci3j4l8ebNm2zPMTIyyrJAr729PapVq4YSJUqI/A7yl0QiQc1KrqhYujjik1JhbJS1fBcVlwAHG/6+NUSJyWm4GRqGmIQklCxqA58KLpn2P3kZjVW7TgnbS0d/Dqk8c1uwszdDYSSTwcvdGabGfII5v4lWcPfw8MDVq1eRkJCQYwIeGRmJoKAg1K5dW6ywiIiIKBdUKhUuXLgAAKhTp06+9XElooKD+TxRVhs3bhQWETU3N0e/fv10HJH+k0qlaN26NWrUqIF169bhxo0bAIDU1FT4+/vj3LlzGDZsWKHogU+5k5CQgMWLF+Pu3bt5Oi89PR2vXr3Cq1evMo1LpVIMHjwYTZs2zc8wP9qn5NFW5qawMjfNMh78+AVW7zmDht7uaF/fK9uCPBVOKWnpmPfbASQkZ9ywrFGxdJaCu42lmfC1hakxjORZ/80dunAbbxJTsP+cOUZ0boQSRYtoN3ADI9pt4z59+iA6OhrDhg2DQqHIsl+lUmHkyJFITk5mkkJERKRnVCoVzp07h3PnzkGlUuk6HCLSAebzRJnduHED58+fF7Z79uwJGxsb3QVUwBQrVgxTp07FqFGjYGlpKYzfu3cPkydPxunTp3UYHYnl5cuXmDZtWq6K7dbW1nB0dPxgyya1Wo21a9di06ZNwtoKupTfeXSaIh1bj12GRqPBqWv34b//XD5ESQWFmYkRWtT6t8VQXGJKlmNsLM1ha2UOG0sz2FlnbQWoUqsRn5Tx1GJsQjLuPXuV5Rj6NKLdAhs6dCh27NiBrVu34vz582jRogWAjCRl9OjR2L9/Px4/fozmzZujV69eYoVFRERERES5wHye6F8xMTFYvXq1sO3u7q43s2kLEolEAj8/P3h5eeHXX38VbmCkpaXh559/RnBwMAYOHAgTE5NcvZ5arcalS5dw//59NG7cGC4uLh8+iXQmJCQEixYtQkJCgjDm4eGB0qVLZ2oVY29vD1tbW2HBUY1Gg6SkJKHlzLsL9F67dg1xcXEAgH379uHVq1f46quvCtW6CnGJKcKMdplUilZ12N/d0DTyKY/XbxIQG5+Msk5Z1wcwNzXGnKEdAGR8v/xXulKFiqUdcffJSzTwckdDb3etx2xoJJrs/s9rSWpqKsaPH4/169cjPT090z6ZTIaBAwfip59+gqlp1sdlCgsPDw8AwJ07d3QcCRERUe4pFAosXboUADB27NhC9aGFqCATO7fUZT7PPJr0RWpqKmbOnIlHjx4ByGhfsWDBAri6uuo2sELg8uXLWL16NRITE4WxUqVKYdy4cR9sMXPz5k1s3rwZjx8/BgCYmppi6dKlsLe312rM9HHOnj2LVatWQalUCmPdu3dHp06dPmkh1NevX2PBggV49uyZMObu7o4JEybo7AkUbeTR6UoV9p+7CTtrCzT0Lp/ncwFk22aE9I9ao9HagqcPw6LgWtIeMgNdN0ObuaWoBfe3oqKicOrUKTx58gRqtRrOzs5o3LgxSpYsKXYoouMHBSIiKohYcCfST7rKLXWRzzOPJn2gVqvx448/IjAwUBgbOHAgWrZsqcOoCpfXr19j2bJluH//vjBmYmKCIUOGwM/PL8vxDx8+xJYtW3Dr1q0s+3x9ffHNN99oNV7KG41Ggz179uCPP/4QxuRyOUaMGIH69evnyzWSk5OxbNkyBAUFCWMODg6YMmUKnJ2d8+UaeSF2Hp2mSEdcYgoc7awzjavVagQGP8GB87fQwMsdzWtV1moc9OmSUxVYt+9v1KlSFr6Vy4h23dCwSLg42hX6tQG0mVuK9n/Ox8cH5cqVw44dO+Dg4IBu3bqJdWkiIiIiIvpEzOeJgD/++CNTsb1ly5YstuezokWLYubMmfjjjz+wb98+ABktZlauXCm0mDE2NkZERAT++OMPYTHK7AQGBuLKlSuoUaOGWOHTeyiVSqxduxanTp0SxqysrDBhwgRUrFgx365jbm6OSZMmwd/fH8eOHQOQcaN42rRpGD9+PKpWrZpv19I3Go0G205cQdCDMPRoVhM1K7kK+05cCcHevzMWKT52ORj1PMvBwiyjXVNyqgJHLt1Bi1oeMDflxBp98DouEat3n8Kr2AQ8Cn8NO2sLuDkX0/p1H4ZFYeXOv+BczBZfdvTLdtFe+jDRnhm4d++e0G+LiIiIiIgKFubzZOhOnTqFgIAAYdvb25sLBGuJXC5H7969MXHiRFhY/Lvg38mTJzF16lSsXbsW48aNy1Js9/DwwLx581CpUiVhzN/fH6mpqaLFTtlLTEzEvHnzMhXbS5QogXnz5uVrsf0tmUyGwYMHo2/fvkKLmuTkZMyfPx8nT57M9+vpiwu3HyEw+AkU6Ur8dvACrt9/LuyrW7UczEwyfo8r0lV4GB4FALh27xnm/HoAJ66E4M+zN3QSN2UvKTVjkXqVWp3p71JbYuKTsHbvGShVajyJiMYve05n2wOePky0gru7uzuio6PFuhwREREREeUj5vNkyIKDg7FmzRphu1SpUhg9ejRkMvZA1qYaNWpg4cKFcHf/d0G/Z8+e4fjx41CpVMJY6dKlMXXqVHz33Xdwd3fHkCFDhL+b169fY+fOnaLHTv969eoVpk+fnqltQ6VKlTB37lwUL15ca9eVSCRo27Ytxo8fL7RxUalU+OWXX7Blyxao1WqtXVtXrMxNhRnqZUsWhafbv2sfWJiZoLlvZdSs5IrpA9vA0y2jvU5UXAISkjNuSp29EYqnL/m7Xh8UtbHE0I5+MJLL0LpOFXRt7KP1axaxNEO18hmLTctlUnRu6PNJayoYMtF6uK9YsQITJkxAUFCQVu5eFhTsPUlERAWRWq3Gq1evAACOjo6QGujCOkT6RszcUtf5PPNo0pWXL19i6tSpwkKeRYoUwbx581CsmPYf7acMSqUSW7Zswf79+zONFytWDF988QXq1auXJTfZunUr9uzZAyBjYdsffvgBpUuXFi1mynD//n0sXLgQ8fHxwlj9+vUxfPhwUZ+aCg0NxQ8//IA3b94IY3Xr1sWIESO03lNd7Dw6Jj4J205cQfemNWFrZZ5pn0ajyVJAVapUWPC/w4h+k4RWtaugSY0KkPNmot6IjU+CrbXFhw/MJxqNBieuhKCIpVmmlkSFUaFZNHXYsGHYs2cPJk+ejHbt2sHFxcXgFl3jBwUiIiIiyi9i55a6zOd1kUer1Wrs2rULKpUK3bt3F+26pD8SExPx7bffIiIiAgBgZGSEGTNmoHz58jqOzDBdvnwZv/76KwCgXbt2aNasGeTy7JemUygUGD9+vFDodHd3x5w5czhpQEQXLlzAypUrkZ6eLox17doV3bp108ms2aioKCxYsADPn//bmqNChQqYMGECrK2t33Nm4RcWGQtTEyMULWKp61AMkkqlxpmg+/CrVh4ymf7+jFKr1YBEAmkhmfVeKArubx/nyu5uWpagJBIolUoxwhIdC+5ERERElF/EzC11nc+LnUcnJiZi+fLlCAoKAgBMnDiRCy8aGKVSifnz5+P27dvC2OjRo1GvXj0dRkW5+Rn0VlBQEObPny9sDxkyBM2aNdNWaPQPjUaDvXv3YsuWLcKYTCbD8OHD4efnp8PIMvq4//jjj7h586Yw5ujoiClTpqBkyZI6jIwMVXKqAhv+PIt7z16hnqcbujetoZdtXDQaDbafuIrk1DT0blkbRvKC/xSENnPL7G8Fa0GpUqX08h8MERERfZhKpcKVK1cAZPRTZc9aIsNjaPm8TCZDVFSUsL1y5UosWLBAq/2GSX9oNBqsX78+U7G9W7duLLbrgbz8HKpWrRrq1q2L8+fPAwC2bNmCmjVrwsbGRkvRkVKpxPr16zMtTGphYYEJEyagcuXKOowsg7m5OSZPnowNGzbgxIkTADJ6zH/77bdai5F5NL3P0Ut3cO9ZxpM4526GopxTUfhWLqPjqLI6de0e/r7xAAAQm5CMoR39YGlmouOo9JdoBfcnT56IdSkiIiLKZyqVCqdOnQIAeHt784MCkQEytHzezMwM48ePx5QpU5CWlobk5GQsWbIEc+fOhYkJP2AWdmfPns1UMKxfvz66du2qw4joY/Xr1w/Xr19HSkoKkpKS8Pvvv+Prr7/WdViF0tufk7du3RLG9HH2uFwux9ChQ1G8eHFs3rwZAJCUlIQ5c+ZoZRZ+QcyjNRoNgh6EwcRIhspl9OfvrjBqXbcqQsOj8CQiGtUrlob3P4uW6hO1RoM7jyOEbZVaDeNCMMNdm/S3MRAREREREZEOOTs7Y/jw4cL206dPsX79eoi4DBbpQGJiIn777Tdhu3z58hg2bJhBPeFRmNja2qJHjx7C9tmzZzO1E6H8ERUVhWnTpmUqtleoUAHz5s3Tq2L7WxKJBB06dMC4ceOExVtVKhVWrlyJ7du3G/TP+Zj4JPyy5ww2/HkWW49dRpoi/cMn0UczNpLjy45+6NSwGvq3rqOXrVqkEgmGd2qIOlXKws7aAl929IOxkWhzuAskFtyJiIiIiIhyULduXbRu3VrYPn36tNCGgAqnzZs3Iz4+HgBgYmKC0aNHi7Y4MGlH8+bNUa5cOWF7w4YNUCgUOoyocAkNDcXUqVMRFhYmjNWtWxfTp0/X+8VIa9eujRkzZmSKc+fOnVi9erXBFt0V6SqEPH0JIKN1yMELtz9wBuWWRqNBQnJqlnErc1N8VqOSXt/Ylcmk6NncFxN6NYe1hZmuw9F7ot2O+P333/N0fN++fbUUCRERERER5ZUh5/O9e/fGw4cPce/ePQCAv78/ypQpk6mAR4VDSEhIphsqXbt2hYODgw4jovwglUoxdOhQTJ48GRqNBhEREQgICMDnn3+u69AKvMDAQCxfvjzTDYzOnTvj888/h1RaMOZ4li9fHvPnz8f333+P8PBwAMCpU6fg5+eHKlWq6Dg68RW3t0Yz30o4fPEOKrg4op6nm65DKhRUajW2n7iK4Mcv8E3P5ihiWfCK1hKJBFbmproOo0AQreDev3//XN2pebvieGFK0ImIiIiICjpDzuflcjnGjh2LSZMm4c2bN1Aqlfjxxx+xYMECWFlZ6To8yidKpRLr1q0Ttl1cXNCmTRsdRkT5qUyZMmjdujUOHDgAAAgICICPjw/c3FhM/BgajQb79+/Hpk2bhJngMpkMQ4cORePGjXUcXd4VK1YMc+bMwZw5c/D48WMAQFBQkEEW3AGguW9lODnYoJq7YS2Yri1qjQZrA/7GnccvAABrAs5g9BefwaSAt2XRaDSIjE3A/WevUN7FEY52+v1Ei5hE+5v97rvvsv0mVavVeP78OU6fPo3Hjx+jf//+KF26tFhhERERERFRLhh6Pm9nZ4exY8di9uzZUKvViIqKwvLlyzFlypQCM4uT3u/AgQN4/vy5sD1kyBDI5QW7GEKZff7557h48SKio6OhVCoxa9YsjB49GjVq1NB1aAWKSqWCv78/jh07JoyZm5vjm2++KdAFaktLSzRs2FAouL/bj97QGBvJ9XLxzoJKKpGgkmtxoeAulUqQnq4q8AX3dfvO4mZoRiupDg280My3so4j0h+i/c3OnDnzvfuVSiXGjRuHbdu24fLly+IERUREREREucJ8HqhcuTJ69uyJTZs2AQBu3LiBnTt3si1FIRAZGYkdO3YI202bNkWFChV0GBFpg5mZGYYOHYoffvgBarUaaWlpWLRoEfr165dprQbKWXJyMpYtW4agoCBhzMHBAVOmTIGzs7PuAssn794wePLkCRITE2FpaanDiPRLaFgkTIzkKOVop+tQCpxGPhUQFZeI+KQU9GlZu1AsOupU1EYouN979ooF93fozVQMuVyOpUuXwszMDJMnT9Z1OERERPQOuVyOHj16oEePHpztR0TZMpR8vl27dvD19RW2d+3ahevXr+swIvpUGo0G/v7+Qg9qa2tr9OzZU8dRkbZ4e3tj6tSpMDPL6J+s0WiwceNG+Pv7Q6VS6Tg6/fb69WvMmDEjU7Hd3d0d8+bNKxTFdgAoVaoUihQpAiDj30ZwcPAnv2ZhyaNfxcRj7d6/sXTbCdx6GK7rcAqkLo28MaBtvUJRbAeA8i6OAAAnBxu4FOdNmHfpTcEdyOj3Vb169UyPJREREZHuSaVSuLi4wMXFha0TiChHhpDPSyQSjBgxAiVKlACQUZBZvnw5IiMjdRwZfaxLly7h2rVrwnbfvn05o7WQ8/T0xJw5c1C0aFFh7PDhw1i0aBFSU1N1GJn+evToEb799ls8ffpUGKtduzZmzJgBGxsb3QWWzyQSCTw8PITt27dvf/JrFoY8WqVSY23AGSSnKqBIV+LXA+eRkMzvlexoNBr8dTUEl+8+ybJPKpVCWoh64pctWRTfD++EKX1boX19L12Ho1f07jv95cuXSEpK0nUYRERERET0EQwhnzc3N8f48eNhYmICAEhKSsKKFSuEhQOp4EhJScGvv/4qbFetWhUNGjTQYUQkFhcXF8yfPx/lypUTxq5du4bvvvsOMTExOoxM/1y9ehUzZsxAbGysMNahQweMGTMGxsbGOoxMO95tK5MfBffCQCaTokuT6jA1zpiZ3btFLViZm+o4Kv2jVqux4+RV7Dp1HZuPXEJoWOG+GS+TSfnvIAd68wyDWq3Gzz//jAsXLmR6RJOIiIh0T6VS4caNGwAALy8vyGQyHUdERPrG0PJ5FxcXDB06FCtWrAAA3Lt3D9evX4ePj4+OI6O82LZtm1BElMvlGDRoULaLA1PhZGNjgxkzZmDFihXC2hNPnjzB1KlTMXnyZLi6uuo2QD3w4sULLFmyBEqlEkDGDN3BgwejadOmOo5Me94tuIeFhSE2Nha2trYf/XqFJY+u7FoCY7s3w6MXUfCpwAVVs3Ml5CnOBD0AAChVauz66xom9m6hd79X3jwPx5Wf/RF28TJMrKxgWcJR+GP1zteWjg5QKdIRH/YCb56FI/55OOLDwt/5+gVUaQrUmTAKNYb11/Xb0iuiFdybNGmS477ExEQ8fvwYMTExkEqlmDFjhlhhERERUS6oVCqhRUSVKlUK7AcFIvp4zOezatCgAc6ePSv0cN+2bRuqVatWYFsGGJpHjx7h0KFDwnanTp1QsmRJHUZEumBqaorx48dj06ZN2L9/PwAgJiYG06dPx+jRo1GjRg0dR6hb27dvF4rtZmZmGD9+PDw9PXUclXY5OjqiaNGieP36NQDgzp07qF+//ke/XmHKo50cbODkYJNlXK1WIyE5DUUszcQPSo/UrOQKCzMT7D97E5BIMLyTn14V22MfPsGln9YgeMc+qP/5vs4Pp2cuhHvrpijiUjjWcsgPohXcT5069d79crkc9evXx3fffYfPPvtMnKCIiIiIiChXmM9n74svvhAK7o8fP0ZgYCBq166t46joQ9RqNdatWye0ASpRogQ6duyo26BIZ6RSKfr27YvixYtjw4YN0Gg0SEtLw8KFC9GhQwd88cUXBXqxy4/15MkTnD9/XtgeMmRIoS+2Axl93KtUqSL83rt9+/YnFdwNwaELd3Am6D76tqoDj7KGe+NSIpHAo0xJVHYtgXSlSm8WR426ex+Xlq3BvYCD0KjV+f76GpUKp1f/jmK9uqOUoy2cHD7+iZDCQrS/+cePH+e4z9jYGEWLFoWRkZFY4RARERERUR4wn89e2bJl4evri8DAQAAZs9x9fX05y13PHT16FA8fPhS2hwwZYpD/fimz5s2bw8HBAUuXLhUWT927dy9CQkIwZswY2Nvb6zhCcW3fvl34ulSpUqhbt64OoxHXfwvulLO7TyJw+OJtaACs3nMaA9rURfWKpXUdlk5JJBK9KLa/vHEbl5b+ggcHsi5mb+3ihBpf9oeRhTkSIl4iMSISiRGvkBjxCgkRL5ES/e+aDVIjI1g7l0QRFydYO5eEdSknFHFxQvT9R7i07Be8btkOT4qWAY5cQrOalVhwh4gF99KlDfubjYiIiIioIGM+n7MvvvgCly9fhkajQXh4OM6ePQs/Pz9dh0U5UKlU2LVrl7DdoEGDTD2bybB5e3tj3rx5+PHHHxEeHg4gY42GiRMnYtSoUfD29tZxhOIIDQ3FlStXhO0vvvjCoG4kvvszITIyEpGRkShWrJgOI9JfyakKyOUypCtVcLC1QuUyhjvDXV9EP3iI0zMW4tGxU1n22ZZzRa0xw1CpS1vI3nOjWZmmQNKrSEiNjGDp6ABJNt//ytQ03N6yC29eRwlj95+/ypf3UNCJ9tNy4MCB8Pf3/+BxGzduxMCBA0WIiIiIiIiIcov5fM5KlSqFevXqCds7duwQeh6T/rl16xbevHkDAJDJZOjTp4+OIyJ9U6pUKXz//feZbpwlJCTg+++/x5YtW6BSqXQYnTj++OMP4ety5cqhZs2aOoxGfHZ2dpnWdOAs95xVr1gaE3u1gHMxWwxqWw9mJob1tNCbxBQcCwxGWrruf+8rU9Nw7ofl+L1RhyzF9qKVyqPt2qUYcO4gqnTv9N5iOwDITYxRxMUZViUcsy22A4Dc1ATVh/eH2dOMpyCN4mLgZGsltGszZKIV3Ddu3IizZ89+8Lhz587ht99+EyEiIiIiIiLKLebz79etWzdh9uerV68+2POedOfvv/8Wvvb29oaNjY3ugiG9ZWpqipEjR2LYsGGZ2g0FBARg9uzZiImJ0WF02hUcHIybN28K21988YVeLfwolndnubPg/n4lihbBpN4ZRff/io1PgkoLfcP1xZFLd7D37xuYuf5PXLz9SGdxPPv7An5r2B4XFv8MlSJdGHf08kCH335Gv1N7UbFTa0jzedHeav27w1KRAueff4TTL8tR9t5Ng/x58V969zyQQqEo0Cs2ExEREREZMkPN50uUKIFGjRoJ27t27YJCodBdQJSt1NRUod8+ALb+ofeSSCRo0qQJvv/+ezg5OQnjd+/exYQJExAUFKS74LREo9Fkmt1esWJFeHl56TAi3flvwZ2zdt8vuyJrSlo6lu/8Cyt2nERcYrIOotKumPgknLuZsR5IQnIqUhXiz3JPfh2DgyMnYnvn/oh99EQYtypZHO1/XYHex3bBvXXTHGepfypjS0v4DO4NeUI8AODqmo1IT07RyrUKEr0quGs0Gly7dg0ODg66DoWIiIjeIZfL0bVrV3Tt2hVyue4XACIi/WTo+fy7PyOjo6Nx/PhxHUdE/3XlyhWkpaUBAMzMzODj46PjiKggcHFxwffff4/69esLY29bzJw5c0aHkeW/GzduICQkRNju3r27wc5WrVy5svB1XFyc0NM/rww1j9ZoNNh6LBBRsQkIDYvCos1HC13R3cRIjsY+5WEkl8HWyhz1PMuJdm2NRoNbm3fCv24rBG/fK4xLpFJU/7IfBpw7gPJtm4vy/eszpA/k5mYAgJToWNzavFPr19R3Wv1Ob9KkSabtw4cPZxl7S6lU4uHDh3j58iV76BEREekZqVSKcuXESyCJSD8wn8+bokWLomnTpjh8+DAAYM+ePWjSpAlMTU11HBm99W47mdq1a8PY2FiH0VBBYmpqiq+++goeHh7w9/dHeno6NBoNVq1aBXNzc9SoUUPXIX4yjUaDbdu2Cduenp6Zis6GxtraGq6urnjy5AmAjFnuzs7OeX4dQ82jVWo1pO8Ueyu7loC1eeH6fWhhZoKODb3RuHpFvH6TCCO5OE/4xTx8jKNjpyPswuVM445eHmi+ZA4cvTy0cl2NRoPY2FiEhYUhPDwcYWFhSEtLw+effw6vPp/j6pqMloKXf94Ar35fQGbAv2O1WnB/t2+hRCLBy5cv8fLlyxyPNzIyQtu2bbF48WJthkVERERERLnAfD7vOnXqhJMnT0KhUODNmzc4fPgwOnbsqOuwCMCbN29w48YNYbtBgwY6jIYKIolEgs8++wzlypXDvHnz8ObNG6jVaixduhRTpkzJ1IKkILp8+TIePnwobHfv3l2H0eiHKlWqZCq4t2zZUrcBFSBymQz9WteBawl7xCYko6NftUL7tEQRSzMUsTQT5VoJL15ia5seSImOFcaMLMzRYOpYVBvUK1c92uPj43HgwAFcvnz5vYtAazQaxMTEZCqwp6amZjlu+fLlOL3/IK77b0G6kTFeWtnh1NZ9+Kxf1497k4WAVgvujx9nrFKr0WhQtmxZdO3aFYsWLcr2WGNjYxQtWjTTYiRERESkH1QqFYKDgwFkPF5riP2ZiQwR8/m8s7W1RcuWLbFv3z4AwN69e9G8eXOYm5vrODI6f/481P8s3GdnZ2fQM3fp07i6uuLbb7/FzJkzkZycjPT0dCxcuBAzZswosDOZ1Wp1ptntNWrUgJubmw4j0g9VqlTB/v37AQB37tyBWq0WFsjOLUPOoyUSCRr5VNB1GIWGWqXCgeETMhXb3ds0Q5P502BVsvh7z42Ojsa+ffuwa9cuHDt2LF/XmXnw4AGmfj8PXkOG4rl1RlvBM4F30Lh3p3xfpLWg0GrBvXTp0sLXM2bMgLe3d6YxIiIiKhhUKhUOHjwIAKhQoYJBfVAgMmTM5z9O+/btcezYMaSkpCApKQkHDhxAt27ddB2WwTt79qzwdf369fNcNCN6l6urKyZPnoy5c+dCoVAgNTUV8+fPx6xZsz6q7YiuXbhwAc+fPxe2P//8cx1Goz8qVqwIqVQKtVqNpKQkPHnyBGXLls3TazCPzt7ruEQUsTQTrQ1LfoqMTUAxWyvRr3tx6WqEnf934e/PfvgO3gN75Xh8REQE9uzZg927d+PUqVPvnc2eFyYmJnBycoKZmRnu3LkDANi4cSN+XFoP+OcSb2yLIvTgcZRv1yJfrlnQiLZaw4wZM8S6FBERERER5TPm87lnbW2NNm3aYOfOjEXD9u/fj5YtW8LKSvwP55QhIiICDx48ELbZTobyQ8WKFTF+/HgsXLgQKpUKCQkJmDt3LubMmVOgFo9WqVTYvn27sF2nTh24urrqLiA9Ym5ujnLlygk/P27fvp3ngjtl9exVDFbtOgX3UsUwoE3dAnUD9NmrGCzcdARVyzmhbb2qcHKwFeW6YRev4MKin4Xtip3aoNqAntkeGxgYiIkTJ+LMmTPQaDTZHmNvb4/WrVvD3t7+vde1srKCs7MznJ2d4eTkBCcnJ9jb20MikSA9PR1+fn64ePEiAGDRj/PQefR8yOLjYRL2DBcu/QV3kRZu1Tc6Wx45Li4OCQkJOf7Fu7i4iBwRERERERHlFvP592vbti0OHTqEpKQkpKSkYN++fejVK+dZaKRd7y6WWqpUKT6pQfnG29sbo0aNwvLly4V+x3PnzsWsWbNgY2Oj6/By5cyZM4iIiACQ0QKEs9szq1q1qlBwv3PnDtq3b6/jiAq28Kg4LN9+AqkKJa7ffw5z0yvo0cxX12Hl2v6zNwEAtx6GIy4xBRN7ab+gnBIbhwPDvoHmn7ZoRUo7o9niWVmum5SUhOnTp+Onn34SWqi9q0SJEujUqRO6dOkCPz8/yOWfVhY2MjLC5s2bUa1aNSQkJCDi+ROE7f8N9W4/hgRAFICnp87BtXH9T7pOQSRqwf3ly5eYNm0a9u3bh+jo6ByPk0gkUCqVIkZGREREREQfwnw+98zNzdGhQwds2bIFAHDgwAGEh4fD3d0d7u7uKFu2LPu6i0Sj0WRqJ8PZ7ZTf6tWrh+TkZKxbtw5AxhMV8+bNw8yZM2FhYaHj6N4vPT1deBoHAPz8/ODk5KTDiPRPlSpVsHv3bgBAcHAwlErlJxcqDZmjnRXKlnRA8JMISKUSuJdy1HVIuZaqSEdiSpqw3bZuVa0X2zUaDY6OnY6E8IybYlK5HG3X/AgT68xPzR0/fhxDhw4V1t95y9XVFV26dEHnzp1Ru3btfH+aoGzZsvj555/Rt29fAMDeU0dQp3ojyJ6+AABcXLaGBXdtioiIQM2aNfHixQs4OTnBwcEBkZGRqFOnDh49eoRXr15BIpGgTp06Br/QEhERERGRvmE+n3ctW7bEgQMH8ObNGyiVSly5cgVXrlwBkHFTwtnZGW5ubnB3d0fFihULZN/ngiA0NBQvX74UtuvXN7wP/qR9zZo1Q1JSknCT7enTp1iwYAG+/fZbmJqa6ji6nB06dAhRUVEAAJlMhq5du+o4Iv1Tvnx5GBkZIT09HWlpaQgNDUXFihV1HVaBJZfJMLh9fazd+zca+1SAR9mSug4p10yNjTChV3PcDA3HrYdhqFymhNaveeO3P/DgwFFhu96U0ShR3UvYjomJwfjx47Fx48ZM55UvXx6rV69G48aNtX5ToHfv3jh8+LDw82/d7csYZpVx4y7sfCDCA6/ByddHqzHoG9GaJM2dOxcvXrzA7Nmz8fz5c7Rq1QoSiQTnzp1DREQETp06hYoVK0IikeDQoUNihUVERERERLnAfD7vTE1N0a9fv2wXyNNoNHj+/Dn++usvrF27FuPGjcOqVavybUEz+te77WQqV66MokWL6jAaKsw6dOiQqd3IvXv3MGjQIEybNg0bN27EuXPnEBkZmWMrLrGdP38emzdvFrYbN24MR8eCM9tYLMbGxihfvrywffv2bR1GUzgYG8kxskujbIvtfxy/jEMXbuPukwgo0vXvaTmJRAIvd2f0bllb64XsqLv3cWr698J26Ub14DtqMICMPGLHjh2oXLlypmK7TCbD1KlTcePGDTRp0kSU/ukSiQSrVq0S1n54mJaEF7J/W9pc+mmt1mPQN6LNcD98+DDKlCmDadOmZbvfz88PR48eRYUKFTBnzhzMmzdPrNCIiIiIiOgDmM9/nPr168PT0xMPHjzAgwcPEBoaitDQUCQnJ2c59tSpU0hOTsaYMWPYriCfKJVKnD9/XthmOxnSJolEgl69eiExMREnT54EkNGy5f79+7h//75wnLW1tfB0i7e3t04W4QwKCsKKFSuE4r+trS17t79HlSpVcOfOHQAZfdz5JMCny64QnJKmwLkboXh7S2r6gDZwtLMWNzA9kZ6cgv1Dx0GZmtHCxqyoHVqv/AESqRQvXrzAyJEjERAQkOkcHx8fbNiwAdWqVRM93iJFimDTpk3w8/ODWq3G0aQ4fF6lDqSpKXh09C9c+mktfIb2hZGZ/j7xk59Em+EeHh6e6S/87SyPtLR/ex85OTmhcePGmVbHJiIiIt2Ty+Xo0KEDOnTowCIQkYFiPv/xrK2tUb16dXTv3h3Tpk2Dv78/li5dihEjRqB58+YoWfLfGX6BgYFYvHgxFAqFDiMuPG7evIn4+HgAGb/LateureOIqLCTSCQYOnQo2rRpk2POFB8fj2vXrmHbtm2YPHkygoKCRI3x3r17WLJkifBEjaWlJaZNm1ZgFnnVhapVqwpf37t3L08/o5lH597TlzFCsd3c1BjFbK3ee7wYDpy/hev3n4l+3VPffY/okAfCduuVP8DC0QHBwcGoXr16pmK7qakpFi5ciEuXLumk2P5WvXr1MH36dHj5tYbfd8sQ2bUH4mvUAgD8PXcJ/Ou0xJ3tAcLir4WZaAV3a+vMd6Te/iAPDw/PNG5qappljIiIiHRLKpWiYsWKqFixYr4vtENEBQPz+fwjlUrh5OSERo0aYfDgwVi4cCF8fP7tbXrt2jUsXLgw080M+jjvtpPx8fHR+wUsqXCQSqXo168ffvvtN8yfPx8DBgxA/fr1Ubx48WyP//3330VrJ/W2t/zbny8mJiaYOnUqSpUqJcr1C6qyZcsKvfiVSiVCQkJyfS7z6Nyzt7ZA6zpVULlMCVRyLZHtLPj4pBTR4jlx5S4OXbgN//3ncf7WQ9Gue//PI7jx2zZhu8aIgSjzmR9u3LiBhg0bZlqXpHHjxrh16xYmTJigFzd0pk2bhhJFbYR/6ykurtAg4+8xITwCh0ZOwv8+64ynp8+/72UKPNG+011cXPDs2b93hKpUqQIAOHjwoDCWnJyMc+fOoUQJ7S86QEREREREucd8XnuMjY3xzTffwNfXVxi7efMmvv/+e6SkiFdYKGxSUlJw+fJlYdvPz0+H0ZAhMjIygpubG1q1aoWvv/4ay5cvx4YNGzBlyhR07txZKCaGhYXhr7/+0no8L1++xNy5c5GUlAQgY+b1xIkT4ebmpvVrF3RyuRyVKlUSttnHXTscbK3Qum5VjOjcCAPa1M2073VcIn7dfw4zN+xHXGLWtmz5LTElDUcuBQPI6Jd+6to9KEW4MfbmeTiOjP23fZ+jlwcafDsWly9fRuPGjfH69WsAGU/TrFixAidOnNCr72G5XI5lC2ZBrVYjJSkBjx7cxsNi1pAa/XszIPL2XezoOgA7vxiMqOB7OoxWe0QruDdp0gQ3b94UVr9u3749LCwsMGHCBEyePBkrVqxA48aN8erVK7Rq1UqssIiIiCgX1Go1QkJCEBISArUBPAJIRFkxn9cuuVyOMWPGoG7dfwsMwcHBmDdvXrb93unDLl++LLR9sLCwgLe3t44jIgKsrKzg7e2N7t27o2HDhsL49u3bkZqaqrXrxsTEYM6cOXjz5g2AjGLdmDFjMrVKofd7e6MZgNDPPTeYR386lVqNn7afwNV7z6BIV+LAuVtav6almQnGfPEZrC3MULSIJUZ2aQx5Noug5ydFUjL29h2JtDcZrdCMLMzRdu1SXLx8GU2bNkVsbCyAjLZ+mzdvxqhRo0RZFDWvKri7obqjBP4zh+Hg/37CmuDLuF7dDeU7ZM4Pn5z8G7837ojDo6ciIeKVjqLVDtEK7r169ULnzp0RHJxxd8jOzg5r1qyBRqPBwoULMWbMGFy+fBmVK1fmAktERER6RqlUYu/evdi7dy+USqWuwyEiHWA+r31yuRxff/01GjVqJIzdv38fs2fPRmJiou4CK6DebSdTu3ZtGBkZ6TAaoqw+//xz4d9lXFwc9u/fr5XrJCQkYO7cucINUwAYPnx4pqdq6MPeLbjntPh1dphHfzqZVIrmtSoL22GRsaLMNi9Z1AbjejTFqK6NUcTSTKvX0qjVODRyEiJv3xXGmi2aiRvPHqNFixbCeiRGRkbYvn07evToodV4PtWgvj3Rp3dvYXvT/n3YEPUY3f7cDOc6NYVxjVqN21t2YUOt5nh29qIuQtUK0Zr7eHl5YevWrZnGevTogXr16uHgwYOIjY1F+fLl0b59eyZCRERERER6hvm8OKRSKYYNGwa5XI7jx48DAB49eoRZs2Zh2rRpKFKkiI4jLBji4uJw8+ZNYbtBgwY6jIYoe0WLFkWbNm2ExQ/37duHpk2b5uvipSkpKfj+++8RFhYmjPXr1y/TjT3KndKlS8PS0hKJiYnQaDS4e/cuqlevruuwDEbdKuVw40EYfCu7okYlV0hFmtldtIilKNc5t3AFHhw4KmxXHz4Az61N0bFVK+HpFxMTE+zatQtt2rQRJaZPtWbNGkRFReHw4cMAMn7GKRQK7Nq1Cy/OXMCZ2YsR8+ARAMDE2grFvT11GW6+Em2G+82bN7PtceXi4oJhw4ZhypQp6NKlC5NzIiIiIiI9xHxePFKpFEOGDEHr1q2FsadPn2LatGk4ceIEF1PNhXPnzkGj0QAA7O3tUbFiRR1HRJS9jh07wsrKCgCQmpqKnTt35ttrx8fH44cffkBoaKgw1qVLlwJTrNM3UqkUHh4ewva7N/VI+2QyKUZ1bQzfymW0UmwPDYtEwOnrUP/zu0NMIXsO4uKSVcK2a5MGSKheCe3btxeK7WZmZti/f3+B+v41MzNDQEAA2rdvD7mRMQDg8OHDaNu2LYrXr4X+Z/5Es8WzYO5QFPUnj4axhbmOI84/ohXcq1Wrhq+++kqsyxERERERUT5iPi8uiUSCfv36oUOHDsLYq1evsGbNGgwfPhybN28WFk6jrM6ePSt83aBBA0ilon30JcoTc3NzdOnSRdg+fvw4Xrx48cmvGxISgokTJwptwACgRYsW+Pzzzz/5tQ3Zu21ljh07hpCQEB1GQ/lBo9HgyKU7WL79JI5fCcFvBy9ApRKv1/7LoFs4/PVkYdvOvSzUbRuhS9euwjoklpaWOHz4MJo2bSpaXPnFyMgIfUZPx8BpP8HIxBQA8Ndff6Fly5ZITE6GV7/uGBx4BB7dO+k40vwlWtZhZ2eHkiVLinU5IiIiIiLKR8znxSeRSNCzZ0988cUXmRZFS0xMxN69ezFy5EgsWbIEwcHBwmxuAq5cuYKHDx8K22wnQ/quefPmcHR0BJCxwOaWLVs++rXUajUCAgIwc+ZMxMTECON+fn4YMGCAXi6wWJDUrVtXeCJBqVRi0aJFePWqcC32WNB86u8/iUSCN4kpwsz2oPvP8ThCnBvaiS9fIaDPCChTM55cM7UpAtnnrdGjfz+h37+1tTWOHj0KPz8/UWLKTyq1Guv3ncWp6w9gZFEEAyYuEH4GnTt3Ds2aNUNsbCyMLS0hlYvW9VwUohXca9eujVu3tL+KMBERERER5T/m87ohkUjQpUsXLFmyBM2bN4eJiYmwT6PR4NKlS5g5cyYmTpyIkydPCrPhDJFSqcTmzZuxcOFCYax06dIoVaqUDqMi+jC5XJ5pAcTAwMCPmjn9toXMli1boFZnzNCVyWTo378/Ro4cySc98oGVlRUmTJgA+T/FwYSEBPzwww+5XkCV8o9KrcbZG6FY+sdxpCs/bQHVTg29UcrRDnbWFhjbvSncnIvlU5Q5S09JRUDfkUh8GQkAkMhkkHdtiQHjRgvfv3Z2djh58iTq1Kmj9Xi0QSaVwsHWSth2dSuPQUOHCduBgYFo0qRJoXxiT7SftjNmzMC9e/ewZMkSsS5JRERERET5hPm8bjk7O2Pw4MH45Zdf0LdvX2E27FtPnz7FL7/8guHDh2PLli2F8sPr+0RHR2PWrFnYu3evMGZsbIz+/fvrLiiiPKhTpw7KlSsnbG/atClPM3dDQkIwadIkXL9+XRhzcHDAnDlz0Lp1a85sz0cVK1bEsGH/Fg3DwsKwdOlSqFSfVvSl3FOp1Fi46Qj+OH4Zj168xtkbDz7p9YzkMgxtXx+T+7SEawn7fIoyZxqNBkfGfIuX1/+dyCBv6YfhC+YI3/cODg7466+/CvzCvB0aeMGznBOqlHXCuO5NsWbVSowaNUrYHxQUhEaNGuHly5c6jDL/STQiPXv4+++/4/Tp09i4cSM8PT3Rpk0buLi4wNTUNNvj+/btK0ZYonu7wMadO3d0HAkREVHuqVQqoQdo5cqVIZPJdBwREQHi5pa6zueZR2emVqtx7do1HDp0KNsnD6RSKXx9fdG6dWtUqFChUBfbgoKCsGLFCiQkJAhjTk5OGDduHGe3U4ESHByMmTNnCtvjx49HrVq13nuOWq3Gn3/+ia1btwqzYgGgRo0aGDFiBCwtLbUVrsH7448/sHv3bmG7efPmGDRoUJaft8yjtWP7iSs4E5RRaLexNMOswe0hk71/XnFicho2HbmEz2pUhHsp7c9iz8nFpb/g7Pyl/w74VMaEowHCZokSJXDixAlUqlRJ/OC0QJGuhFwmFZ6y0Wg0mDhxIhYvXiwcU758eZw4cQLOzs6ixaXN3FK0grtUKoVEIsl0hza7pE+j0UAikRTaO4P8oEBERERE+UXM3FLX+Tzz6JyFhYXh4MGD+Pvvv5GWlpZlf5kyZdCqVSvUrVsXxsbGH3UNpVKJO3fu4NatW5BKpbCzs4OdnR3s7e1hb28Pa2tr0dtVqFQqbNu2DQEBAZnG/fz8MHjw4BxvBhHpsx9++AFXr14FkFF0W7JkidC+5F1qtRr379/Hnj17Ms1ql8lk6NWrF9q0aVOob7TpA7VajWXLluHixYvC2IABA9CqVSsdRmU4EpJTMdt/P7zcnNGmnidsrczfe3x8UgoWbjqKuMRkWFuYYXKfFrC2MBMp2n89OHAMe/v/O8NbXao4plw/g7e3y0qVKoWTJ0/Czc1N9NjEpNFoMGPGDMyZMwcAYGZmhqNHj6J+/fqixaDN3FK0jvTfffcdf9gTERERERVQzOf1l7OzM4YOHYqePXvir7/+wuHDhxEVFSXsf/z4MVatWoX//e9/qFevHsqXLw83Nzc4Ojq+9+9UoVDg5s2buHjxIq5evYqkpKQcj5XJZLC1tRUK8W+L8e/+19bWFkZGRvnynmNiYvDTTz/h7t27wpixsTEGDRqERo0a8d8qFVi9evXCtWvXoNFoEBERgePHj6Nly5YAMm58BQcHIzAwEIGBgYiLi8t0rr29PcaOHYvy5cvrIHLDI5VKMWrUKLx+/RqhoaEAgI0bN8LR0RE+Pj46jq7wszI3xazB7WFumrsbyZZmJlCpMyYDxCel4NzNh2hVp4o2Q8wi4cVLHBo1SdhWWVtiVtBZodhetmxZnDhxAq6urqLGpQtJqQq41WmFWXPNMH/OLOzbt0/UYru2iTbDnTJwZg4RERVEarUajx8/BpAxU5KLbhHpB0PKLQ3pvX4qtVqNq1ev4tChQ7h9+3aOx1lZWcHNzQ3u7u5wc3ODm5sbZDIZrl+/jkuXLuHatWvZzpj/FNbW1rC3t4etrW2WgvzbP+bmGbMUlUol4uLiEBMTg+jo6Ez/vXXrVpYWMmPHjoWLi0u+xkukC2vXrsXx48cBZHzPDBkyBFevXsWVK1eQmJiY7Tk+Pj4YNWoUW8joQFxcHKZMmYLo6GgAGTN158yZI/w8+m8erVEqoUhKhlqRDqVCIfxX9c7XakU6NGo1inl6wMJB+z3FC4v4pBTcCA3D7UcvMKhtPRgbZZ5nvPXYZVy4/RAdGnihSfWKot+cDeg7AqGHTgAA1EZyLIkMRaQqY7FzXbRU0ZVXMfH4Zc9pRMUlopyTA9rWKAN3t3IfPjGfFYqWMpSBHxSIiKggUigUWLo0o8/g2LFjP7olARHlL0PKLQ3pveanZ8+e4dChQ/j777+hUCg+eLxMJsuxHZCRkRE8PT1hZWWVqfidkpKSrzGbmZnB2NgY8fHxuVo0skGDBhgyZAhbyFChERsbi6+//vqDN7ykUik8PDzg5+eHBg0acEKEDj19+hTTp09HamoqgIwFL+fNmwcbG5sseXTItgAcHTc9V68rMzGG96DeqDV6KMzsbLUWf2GgUqkxefVupKSlAwCGtG8AL/fMxevwqDio1Gq4ONqJHt9/W8lsT3iJy2nxADJynOPHj6N48eKix6ULJ6+GYPepf1th9Wzmi7qehavgLlpLmXfduHEDgYGBeP36NTw8PNC+fXsAQFpaGtLS0mBtba2LsIiIiIiIKBeYzxccLi4u+PLLL9GzZ09cuHAB9+/fx4MHDxAREZHt8f8ttpuamsLHxwe+vr7w9vaGmVnWfrcpKSmZCvDv/nk79ubNm1zHnJKSkqsivpGREQYNGoTGjRuzhQwVKra2tmjXrh127tyZZZ9cLoenpydq166N6tWrw8rKSgcR0n+VLl0aY8aMwQ8//ACNRoOoqCgsWrQIM2bMyHKsNA+ttVRpClxZ5Y+b/9uOmiMHovqX/WFsaZGfoRcaMpkUlV1L4Oq9ZwCAG6HPsxTcnRxsdBAZkBafgBOTZwvboYpkodherVo1HDt2DEWLFtVJbLrQ2KcCXkTF4eKdx2hX3xN1qpbVdUj5TtSC+7179zBgwABcunRJGOvXr5+QoG/ZsgWDBw/GgQMHhB5lRERERESkH5jPF1xWVlZo3rw5mjdvDgBITExEaGgoQkND8eDBA4SGhgotWiwsLFCjRg3UqlULnp6eH3yqyczMDE5OTnBycsrxGKVSidjY2CxF+f9uZze73srKKkvrGXt7e3h5ecHOTvxZikRiaNeuHS5duoTnz5/DxMQE3t7eqFWrFry9vYW2S6RffHx80LdvX/z2228AgAcPHmD9+vUYPHhwpuPkJpl/pkrlckiNjSA3NobM2Fj4OiU2DqmxcQAARUIizi1YjusbNqP22OHw7PtFltchoFr5Unj04jWqlS+F6hX0p8XY33N/ROLLSABAukaNXYmvAAC+vr44fPgwbG0N6+kFiUSC7s1qokYlV1QsXThn9YtWcH/+/Dn8/PwQFRWF9u3bo0GDBpgwYUKmYz7//HOMGDECu3btYoJORERERKRHmM8XLpaWlqhWrRqqVasGANBoNHj16hWSk5Ph4uICuTx/PyrK5XI4ODjAwcEhx2PUajXi4+MRExODtLQ0YRFWtjEjQ2RmZoZ58+YhMjISxYsX5/dBAdG6dWu8ePECx44dg52dHVq1apXlGLfWzfDVwyuQGhlBZmwEqUyW7WulJ6fg+oZNuPTTWqS9yZgNnRwVjZNT5+LK6l9Rb9LXqNS1XY7nGyIvN2dUcy+lV089hQdeQ9DGrcL2ieQYvFano06dOjh8+LDBPhUol8kKbbEdELHgPnv2bLx+/Rrr16/HwIEDASBLgm5hYYFq1aplmjFDRERERES6x3y+cJNIJDrvHSuVSmFjYwMbGxudxkGkL0xNTbkQcAEjkUgwYMAAGBkZoX379rCzs8uyfobcxDhXs9ONzM3g+9UQePb5HJdXbsDVtb9BmZLRIz7+eTgOjZqEwJXr0WLpXJSsUU0bb6fA0bd1DFQKBY6O/w74Zz2Sl8o0nEqJQbly5bBv3z6DLbYbAtH+JR4+fBienp5Ccp4TV1dXhIeHixQVERERERHlBvN5IiKiD5PL5ejfv3+OLa80Gg369OmDtWvXIjk5+YOvZ2pTBA2mjcPgwGOoNqAnpO88gRQd8gB/tOuFy6v8c7XI9H/jCL90FQ+P/pXncyl3Lq/cgOiQB8L2zsRXsCxSBPv37zeonu2GSLSCe2RkJCpUqPDB49LT03P1A4eIiIiIiMTDfJ6IiOjTXbp0CZs2bcKXX34JZ2dnTJo0CU+fPv3geZbFi6HpwhkYeP4QKnVtB/zTNkWtVOL0jB8Q0Gc4Uv7p+f4hr0MeYEfXAdjatif29BqGk1PmfspbomzEPHyMC0tWCdvnU+IQpknHzp07UbFiRR1GRmIQreBub2+PZ8+effC4+/fvo0SJEp98vatXr2LBggXo3LkznJ2dIZFIctXDaePGjfD19YWlpSXs7OzQunVrnD9//pPjISIiKshkMhmaNWuGZs2aQcY+kUQGSex8noiIqDD4bx69fPlyYV9sbCwWLlyIsmXLokuXLjh9+vQHZ5vblHFBm9WL0X3fJliV/LcV2MMjf+H3Jp3w4vL1HM9Ni0/AX9Pm47dGHfDszAVh/PqGTXh29uInvEt6l0ajwdFx30H1TzuhNyolDiW/xqpVq9C0aVMdR0diEK3gXq9ePVy+fBlBQUE5HnP69Gncvn0bjRo1+uTrzZkzB1OmTMGePXty/UjrmDFjMGDAANy+fRtNmzaFr68vjh07Bj8/PwQEBHxyTERERAWVTCaDj48PfHx8WHAnMlBi5/NERESFwX/z6BkzZmDUqFGwtLQUjlGr1di9ezcaNWqEatWqYcOGDUhJSXnv6zrXroE+J/egTNOGwlhC2Av80b43Lv+8ARq1WhjXqNW4tWUXNtRugatrfoNGpcryekfGTkN68vuvSblze+tuhJ0PFLYDkiIxfMxoDB06VIdRkZhEK7h/88030Gg06NChAw4dOgTVf765T548iT59+kAul2PMmDGffL06depg+vTp2LdvHyIiImBiYvLe448fP46ffvoJ9vb2uHHjBgICAnD48GGcOXMGMpkMAwYMQFxc3CfHRURERERUEImdzxMRERVGFSpUwIoVKxAWFoZly5ahXLlymfbfvHkTgwcPhrOzM2bPno20tLQcX8vc3g6dN/8CvxkTIPlnUoxaqcTpmQuxp88IpMTEIuLaTWxu9QWOjJ6K5Kho4dwirqXQaM4U4bw3T57j3A/Ls70O5V5SVDSOTp4tbN9OS4Rrs4ZYtGiRDqMisUk0Iq6MsGrVKowePRpqtRrm5uZITk6GpaUlpFIp4uPjIZFIsGrVKq3c8TE1NUVaWlqOj+a0bt0ahw4dwtKlS7N8QBg9ejSWL1+OxYsXY/z48Z8Uh4eHBwDgzp07n/Q6REREYlKr1QgLCwMAODs7QyoV7Z49Eb2H2LmlLvN55tFERFQQfSiPVqvVOHToEJYvX46jR49mOb9ixYpYv3496tWr997rhAdew/6h45AQHiGMmdnZICUmLtNxcjNT1Br9JWqOHAS5qQnOzFmCwOVrAQASqRQ9D21DCR/Pj3mrBGBDx96IPXcZAJCqVuNACTMcvXAOVlZWOo6M/kubuaWon5ZHjBiBv//+G+3atYNEIoFGo0FCQgLS0tLQokULnD59WiePV6SkpODkyZMAgK5du2bZ/3bszz//FDUuIiIifaFUKrF161Zs3boVSqVS1+EQkY7oaz5PRESkrz6UR0ulUrRp0wZHjhzBnTt3MHz4cJibmwv7Q0JC0KBBA4waNQoJCQk5XsfJ1wd9T+5B2WaNhLH/FtsrdGiFgecPoc74EZCbZnSCqPPNSNiWcwWQ0XrmyJhvhd7jlDcXtuwQiu0AcE6ehu2HDrDYboBEn55Wu3ZtBAQE4M2bN3j16hUiIiKQkJCAgwcPfvBunbbcu3cPaWlpcHBwgLOzc5b9Pj4+ADIe6yEiIiIiMmT6mM8TEREVBpUrV8aqVasQHh6Or7/+GhKJBEDGIpw///wzKleujAMHDuR4vpmdLTptWo2GMycKrWIAoGil8vh8z29ot34ZrJ1LZjrHyMwULZbOE7Zf372PS//MeKe8OfDdfOHr56o0zAzYjlKlSukwItIVnT0PLpFI4ODgAEdHR50vvvbs2TMAyLbYDgAWFhawsbFBbGzse+8mEhEREREZCn3K54mIiAoTGxsb/PTTTzh37hwqV64sjIeFhaFt27bo1asXoqKisj1XIpWi5shB6HnoD3j1+wLNf5yDvif3wKV+7Ryv51ynBqoN6ClsX/zxF7wOeZB/b8gA/LV9N2zeJAvbnqOHoFadnP+fU+Em18VFz58/j7///hsvXrwAAJQsWRL169fX2YyYxMREAMj0yM5/WVhYIC4uDgkJCbl6FORtH6D/evjwYZYFMYiIiIiIChJt5vPMo4mIiDLUqVMH165dw/fff4/58+cjPT0dALBlyxYcOXIEy5YtQ69evYSZ8O8q4e2JEt6578XeYPp4PDz6FxLCI6BOT8eRMd+ix4GtkPKmeq7s/W4e3k7jjTGWYtz0KTqNh3RL1IL7rVu30L9/fwQFBQGAsIDp2x8MXl5e2LhxIzw9uTgDEREREZG+YT5PREQkLhMTE8ycORPdunXDoEGDcOnSJQBAdHQ0+vTpg/nz5+Orr75Cnz59YGlp+fHXsbJEs0WzsLtnxlosEVdv4Pq6/6H6sP758TYKtZMHDqHY63hAktFIpGr/HlkWxyXDIlrB/d69e2jYsCHi4uLg7OyMrl27wtXVFQDw9OlT7Nq1C0FBQWjUqBHOnz+PihUrihWa8AMpOTk5x2OSkpIAINcLHeS0wm1OM3aIiIiIiPSZWPk882giIqKsPDw8cO7cOaxcuRJTp04Valh3797FiBEjMGXKFAwaNAgjR45E2bJlP+oaZZs1ROVu7RG8Yx8A4O/vl6Fcy89g48o+5DnRaDT438Rp8Pin2J4mk6DrtIk6jop0TbTbLVOnTkVcXBwmT56MR48e4ccff8TXX3+Nr7/+GkuWLMHDhw8xZcoUxMXF4dtvvxUrLACAi4sLgIxeWNlJSkpCXFwcbG1tubIwERERERkkfc7niYiIDIFMJsPo0aNx584ddOjQIdO+N2/e4Mcff4Sbmxs6dOiAEydOCE+i5UWjOVNgVtQOAKBMTsHRcdM+6nUMxdEjR1AiIlbYdu3QEkZmpjqMiPSBRCPSd42dnR2cnJxw69at9x5XtWpVhIeHIyYmJl+vb2pqirS0tGx/SKSkpMDW1hZpaWkICwuDk5NTpv1///03/Pz80LBhQ5w6deqT4ng7MyenmTtERET6SKVS4cqVKwCAGjVqcIFEIj0hZm6p63yeeTQRERVE2syjHzx4gJ9//hn+/v5ISEjIst/DwwODBw9G586dhcmmuRGy5yD2Dx0rbLdYOhdVe3fLl5gLE41Gg05VfFA/MuNpAzWA4TfPwKqEo24Do1zRZm4p2gz39PT0XPVy9PT0FBaBEIuZmRmaNGkCANixY0eW/Tt37gQAtGvXTtS4iIiI9IVMJkOtWrVQq1YtFtuJDJQ+5/NERET6Spt5tLu7O5YtW4bw8HCsWLEC7u7umfbfuXMHY8eORenSpeHr64sffvgBDx48+ODrVujYCm6tPhO2T347H/f2Hc7X2AuDffv2oXjYa2G7WINaLLYTABEL7l5eXnj48OEHj3v48CG8vLxEiCizcePGAQDmzp2b6YfPhQsXsGbNGtjY2GDQoEGix0VEREREpA/0PZ8nIiIyVFZWVhg1ahRCQkJw8OBBtGzZMssxly9fxuTJk1G+fHl4enpi1qxZuHXrVradICQSCZounAET64y2yunJyfhz0GicmvkD1Eql1t9PQaBWq7Fk8rdwMzYXxppNGq3DiEifiFZw//bbb3H58mX4+/vneMyvv/6Ky5cvY+rUqZ98vQMHDqB27drCH4VCAQCZxg4cOCAc37RpU4wePRrR0dGoVq0aOnbsiNatW8PPzw9KpRK//vorbGxsPjkuIiKigkitViMiIgIRERFQq9W6DoeIdEDsfJ6IiKgwEDOPlkqlaNWqFQ4dOoSQkBBMmjQJbm5uWY67desWZs6cCU9PT1SpUgUXLlzIcoxlcUe0W78MxlaWwtiVn/2xo+sAJEW+znK8odmxYweKhUUJ21buZVDS10eHEZE+Ea2H+5kzZ7B9+3asXr0a9erVwxdffIHSpUsDAJ4+fYrt27fj7NmzGD58OD7//PMs5/v5+eXpehs3bsSAAQPee8yvv/6K/v37Zzlv5cqVuHv3LoyNjVG7dm1Mnz4ddevWzdP1c8Lek0REVBApFAosXboUADB27FgYGxvrOCIiAsTNLcXO5/+LeTQRERVEus6jNRoNbt++jd27d2PXrl3ZrsViYWGBP//8E40bN86yL/bhE+wd8BVe370vjFmWcET7DT+hZE1vrcaur5RKJXwqeaB7rBrGkoy5zC1XLECV7p10HBnlhTZzS9EK7lKpFBKJRHhURSKRZNqf0/hbKpVKuwGKhB8UiIioINL1BwUiyp6YuaWu83nm0UREVBDpWx794MEDofh++fJlYdzU1BR79uzJth2NIikZx8ZPx91d+4UxqZERGs+ZgmoDe+b4u7+w2rhxIzaOGIe2Fg4AAGObIhhx62/ITU10HBnlhTZzS3m+v2IO+vbta3DfgEREREREhQXzeSIiooLP3d0dkyZNwqRJk3D8+HG0b98eKSkpSE1NRYcOHbBjxw60b98+0znGFuZovXoxStTwxqnp30OtVEKdno4Tk2cj4moQmi2eDSNzMx29I3EpFArMnjkL3U1thDGfAT1YbKdMRCu4b9y4UaxLERERERFRPmM+T0REVLg0bdoUhw8fRps2bZCYmAiFQoEuXbpgy5Yt6NatW6ZjJRIJfAb3hqNnZewb+DWSXmX0Lw/esQ9RwffQauVCFKtSURdvQ1QbNmyAeUQU7KydAAASmRReA3rqOCrSN6ItmkpERERERERERET6w8/PD8eOHUORIkUAZPQn7969OzZt2pTt8U6+Puh7Yg+c69QUxqLu3MP/PuuE45NmIyU2ToywdSIlJQVz585FfVNbYax8u5awKuGow6hIH7HgTkREREREREREZKBq166NkydPws7ODgCgVqvRt29fbNiwIdvjLRwd0G3Xr6gxYqAwplGrEeS/Gf61W+Dm/7ZDXUjWYnzX6tWroX71Gm7G5sKYz5A+OoyI9JVoLWUAIC0tDX/88QdOnz6NiIgIpKWlZXucRCLBiRMnxAyNiIiIiIg+gPk8ERFR4eTj44NTp07hs88+Q1RUFDQaDQYPHozU1FSMHDkyy/EyIyM0mjUJro3q4cSUOYh9+AQAkBITh6PjpuPGb3/gswXfoWSNauK+ES25desWZs+ejaZmNsKYo6cHStb01l1QpLdEK7g/e/YMTZs2xcOHD6HRaN57LBdjIiIi0i8ymQz16tUTviYiw8N8noiIKO8KUh5dtWpVnD59Gp999hkiIiIAAKNGjcLjx48xaNAgVKpUKcs5ro3ro/+ZP3F1zW+4sHgV0pOTAQCvbtzBllZfwKN7Z/hNHw+LYkVFfS/5KTQ0FM2bN4ciPgE+dg7CuPeQPsx5KFsSzYey5XzSsWNH7Nu3D02aNMHo0aNRtmxZWFpa5nh86dKlxQhLdB4eHgCAO3fu6DgSIiIiIiroxMwtdZ3PM48mIiISR2hoKD777DM8e/Ys03ilSpXQuXNndOnSBdWqVctSbE6IeIUzsxbi7q79mcaNrSzRcMYEePXrrvXY81t4eDjq16+PJ0+eoKGZLdpaZBTczYra4cvrpyA3NdFxhPSxtJlbilZwt7KyQsmSJXHnzh3I5aJ2stEr/KBARERERPlFzNxS1/k882giIiLxPH36FE2aNMGjR4+y3V+mTBl07twZnTt3Ru3atSGV/rtMZNiFKzgxZTai7tzLdE7NUYPg992EAjMrPDo6Gn5+fggODoaFRIaxNqVRRJaRA9UeOwz1p47VcYT0KbSZW4q2aKqxsTF8fHwMuthORERUUGk0GkRFRQn9HInI8DCfJyIiyruCmkeXLl0aV65cwdy5c+HtnbVP+ePHj7FkyRLUq1cPLi4umD17Nl69egUAcK5TA32O78ZnC76DSRFr4ZzLKzfg8NdToVYqRXsfHysxMRGtW7dGcHAwjCDBQOuSQrFdKpfDa0BPHUdI+ky0gnvdunURGhoq1uWIiIgoH6Wnp8Pf3x/+/v5IT0/XdThEpAPM54mIiPKuIOfRtra2+Pbbb3Ht2jU8evQIixcvRt26dbMcFx4ejhkzZsDFxQX9+vXD1atXIZXL4T2oFwacPQC16hk4AAA3ZElEQVQHj4rCsXf+2I29/UchPSVVzLeSJ2lpaejYsSMCAwMhAdDLqgRcjMyE/Q2mj4dVCUfdBUh6T7SC+6xZs3D37l2sW7dOrEsSEREREVE+YT5PRERkuMqUKYPx48fj3LlzCA8Px8qVK9GkSZNMrWQUCgV+//131KhRA/Xq1cO2bdtgYm+LL/b+D851agrHPTzyF3Z2G4jUN/G6eCvvpVQq0aNHD5w4cQIA0MGiGDxM/l2zptrAXqgxfICuwqMCQrTnQX18fHD06FH07t0bmzZtQvPmzeHk5JTpG/Ndffv2FSs0IiIiIiL6AObzREREBAAlS5bEyJEjMXLkSLx69Qrr1q3DqlWrEBERIRxz/vx5nD9/HiVLlsSIESMwaNUPuPjtfIQePA4ACL90FX+074Wu2zbAsngxXb2VTNRqNYYMGYI9e/YAABqa2aKemY2wv1zLJmgy/9sC04OedEfUBozHjh1DZGQknjx5grNnz2Z7jEajgUQiYYJORERERKRnmM8TERHRuxwdHTFt2jRMnDgRu3fvxvLly3HhwgVh/4sXLzBt2jQsWbIESxYtRhVbG9zevBMA8Dr4Pra06Y5u2/1hW85VR+8gg0ajwTfffIONGzcCADyNLdHWwkHYX9zHE23X/AipTKajCKkgEa3gvmjRIsyaNQsmJibo1KkTypYtC0tLyw+fSEREREREOsd8noiIiHJibGyM7t27o3v37rh8+TJWrFiBP/74Q+hbHxsbi4GDB6FZ06b4qv8XuLdxGwAg/lk4trbriS5b18GhSkUoU9OgSkuDMk0BVWoalGkZfwCgaMXykJsY53vsGo0GU6dOxdKlSwEAZeRm6FmkJPDPGrdFSjuj06ZfYGRu9p5XIfqXaAX31atXw9raGpcuXUKFChXEuiwREREREeUD5vNERESUGzVr1sTvv/+OhQsXYs2aNVi8eDESExMBAMeOH8e58+cxu303aI5nzIRPjorG/5p2/uDrFqtaGT32b8nXwrdGo8G4ceOwbNkyAICDzAiD7EpBps7Yb2prgy5/rIOFg32+XZMKP9EWTX358iUaNmzI5JyIiIiIqABiPk9ERER5Ubx4ccyYMQN37txB69athfHk5GR888dvCCxmDkkeWrRE3grGuYXL8y0+tVqNESNGCMV2S4kMw4uWgck/xXaZiTE6/v4z7NzK5ts1yTCIVnAvV64c1Gq1WJcjIiKifCSTyeDr6wtfX1/I2LeQyCAxnyciIso75tGAi4sL9u/fj82bN6No0aLC+I7gIPwaHw61mUn2J0okkJubwcjCXBi6unojIq7f/OSYVCoVBg0ahF9++QUAYCKRYFQJd1ip/r12658Xwrl2jU++Fhke0Qruw4YNw19//YUnT56IdUkiIiLKJzKZDI0bN0bjxo0N9oMCkaFjPk9ERJR3zKMzSCQS9OzZE3fv3kWvXr2E8Tsp8Zjy/BY2mSkQWL0cnrb3g2JEDzgsnIxqW1ai1dFt6HflGGzLugIANGo1joz+FiqF4qNjSU9PR58+fYQFUm2lckwoURH2in8nFjSaNQkVOrT66GuQYROth/vIkSPx8OFDNGjQAHPmzEGzZs3g5OQk1uWJiIiIiOgTMJ8nIiKiT1W0aFFs2rQJvXr1wpdffonnz59DDeDG8ye48fxJjudVtXFAX7ktAOD13fu49NNa1J0wKs/XVygU6N69O/bs2QMAcJWbYrB9aZgoVMIx3oN6o/qw/nl+baK3JBqNRiPGhd7exdNoNJBIJO8PSiKBUqkUIyzReXh4AADu3Lmj40iIiIhyT6PRID4+HgBgbW39wd/lRCQOMXNLXefzzKOJiKggYh6ds4SEBHz77bdYuXIlclOe7GRRDHXNbAAAaglgMaI3On85GCVKlMjV9VJTU9G1a1ccOHAAAFDDxBrdrItD+s6lfb8aggbTxkEiFa0pCOmINnNL0Wa4lypVij9UiIiICqj09HShv+HYsWNhbGys44iISGzM54mIiPKOeXTOrKyssHz5cnz99de4fPkywsPDERYWhrCwMOHriIgIqFQZs88PJkehkrEFbGVGkGqAe8vWo9TsGahdty46d+6Mhg0bQi7PvtSpVqsxadIkHDt2DBIAbcyLoqG5HfBPsV1mbITmP86FxxcdxXnzVKiJVnBnr0ciIiIiooKL+TwRERFpg5ubG9zc3LLdp1Kp8OrVK9y+fRsBAQE4uWsfuqiNAAAuRqaob2qD0+fO4dy5c7m6lolEip5WxVHZ2FIYM3ewR4eNK+Hk6/Ppb4YIIi6aSkRERERERERERJRbMpkMJUuWRPPmzbFq1SqcffEUDo3rCvtbmNvDXmqUq9eykxphVJFSmYrtDh4V0OvIDhbbKV+JNsP9v9LS0hATEwMTExPY2dnpKgwiIiIiIvoIzOeJiIhIbDKZDJ+vWYpf67VBctRrGEmkGO9RE5tVcXgeFpbtOVIAFSQmaG1UBObvzD12a/UZWq9aBGNLC5GiJ0Mh+gz3tWvXwtvbGxYWFnB2dsY333wj7Nu9ezc6d+6M0NBQscMiIiIiIqJcYD5PREREumRma4OmP3wnbBtFvMamCdPw5s2bTH+e3Q7GgcmzML9UVXQ1ss1UbPf9eig6bFzJYjtphWgFd5VKhU6dOmH48OG4e/cuKlWqlGUFYi8vLwQEBGDbtm1ihUVERERERLnAfJ6IiIj0Rfl2LeDeprmwfWbWIsSHR0CtVCL00HHs6j4E66p/hos/rkbSqyjhOJmxEVr9/AP8po+HRMpO26Qdov3LWrlyJfbu3YtWrVrh6dOnuHXrVpZjypUrBzc3Nxw6dEissIiIiIiIKBeYzxMREZE+afrDdzC1KQIAUCQmYXfPoVjr3RgBfUfi8YkzwDsTA6RGRqjQsTV6Hd4Bj8876ihiMhSi9XDfuHEjHB0dsW3bNlhY5Py4RuXKlXH16lWxwiIiIqJckEql8Pb2Fr4mIsPDfJ6IiCjvmEdrj4WjAxrNmYLDX00GALwOvp/lGJsypeHZ93NU6d4Z5kW55gyJQ7SC+71799C8efP3JucAYGFhgaioqPceQ0REROKSy+Vo3rz5hw8kokKL+TwREVHeMY/WLo8vOiJk9348+eusMCY1MoJ766bw6vcFStWrxdYxJDrRCu5GRkZITU394HHPnj2DlZWVCBEREREREVFuMZ8nIiIifSORSNBqxQIc+noKUmJiUaF9S3h07wwLB3tdh0YGTLSCu4eHB65evYqEhIQcE/DIyEgEBQWhdu3aYoVFREREuaDRaJCSkgIAMDMzg0Qi0XFERCQ25vNERER5xzxa+ywcHdB123pdh0EkEO2Zij59+iA6OhrDhg2DQqHIsl+lUmHkyJFITk5Gv379xAqLiIiIciE9PR0rVqzAihUrkJ6erutwiEgHmM8TERHlHfNoIsOjtYJ72bJlMWnSJGF76NChaNSoEbZu3YoKFSpg2LBhAIAbN25g9OjRKF++PHbt2oVmzZqhV69e2gqLiIiIiIhygfk8EREREVHeaa3g/uTJk0yLJclkMhw8eBDDhw/HixcvsHbtWgDA9evXsWLFCjx79gxDhgxBQEAAH68hIiIiItIx5vNERERERHknWg93ADA1NcXPP/+MmTNn4tSpU3jy5AnUajWcnZ3RuHFjlCxZUsxwiIiIiIgoD5jPExERERG9n6gF97ccHBzQrVs3XVyaiIiIiIg+EfN5IiIiIqLsibZoKhERERERERERERFRYabVGe5BQUGYPXv2R5373Xff5XM0RERERESUF8zniYiIiIjyRqsF9xs3buDGjRt5Okej0UAikTBBJyIi0iNSqRRVqlQRviYiw8B8noiI6NMwjyYyPFotuJcrVw716tXT5iWIiIhIBHK5HG3atNF1GEQkMubzREREn4Z5NJHh0WrBvX79+vD399fmJYiIiIiISEuYzxMRERER5Y1WC+5ERERUOGg0GqSnpwMAjIyMIJFIdBwREREREZH+Yx5NZHjYPIqIiIg+KD09HUuXLsXSpUuFDwxERERERPR+zKOJDA8L7kRERERERERERERE+YAFdyIiIiIiIiIiIiKifKC1Hu5qtVpbL01ERERERFrGfJ6IiIiIKO84w52IiIiIiIiIiIiIKB+w4E5ERERERERERERElA9YcCciIiIiIiIiIiIiygda6+FOREREhYdUKkWFChWEr4mIiIiI6MOYRxMZHhbciYiI6IPkcjk6duyo6zCIiIiIiAoU5tFEhoe31oiIiIiIiIiIiIiI8gEL7kRERERERERERERE+YAtZYiIiOiDFAoFli5dCgAYO3YsjI2NdRwREREREZH+Yx5NZHg4w52IiIiIiIiIiIiIKB+w4E5ERERERERERERElA9YcCciIiIiIiIiIiIiygcsuBMRERERERERERER5QMW3ImIiIiIiIiIiIiI8gEL7kRERERERERERERE+UCu6wCIiIhI/0mlUpQtW1b4moiIiIiIPox5NJHhYcGdiIiIPkgul6Nbt266DoOIiIiIqEBhHk1keHhrjYiIiIiIiIiIiIgoH7DgTkRERERERERERESUD9hShoiIiD5IoVBg5cqVAIBRo0bB2NhYxxEREREREek/5tFEhocFdyIiIsqV9PR0XYdARERERFTgMI8mMixsKUNERERERERERERElA9YcCciIiIiIiIiIiIiygcsuBMRERERERERERER5QMW3ImIiIiIiIiIiIiI8gEL7kRERERERERERERE+UCu6wCIiIhI/0kkEpQqVUr4moiIiIiIPox5NJHhYcGdiIiIPsjIyAg9e/bUdRhERERERAUK82giw8OWMkRERERERERERERE+YAFdyIiIiIiIiIiIiKifMCC+3+kpKTgu+++Q/ny5WFqaoqSJUti4MCBCA8P13VoREREOqNQKLB8+XIsX74cCoVC1+EQERERERUIzKOJDA8L7u9ITU1FkyZNMGfOHCQmJqJDhw4oVaoUfv31V3h7e+PRo0e6DpGIiEhnUlJSkJKSouswiIiIiIgKFObRRIaFBfd3zJ0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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image(filename=str(RUN / 'forecast.png')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09bdb743",
   "metadata": {},
   "source": [
    "## Error by forecast horizon\n",
    "\n",
    "Comparing error at each lead time shows how performance changes across the next day. The model has no future weather observations or numerical-weather forecast inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d00a4b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T13:51:28.816025Z",
     "iopub.status.busy": "2026-10-14T13:51:28.815936Z",
     "iopub.status.idle": "2026-10-14T13:51:28.818459Z",
     "shell.execute_reply": "2026-10-14T13:51:28.818068Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image(filename=str(RUN / 'horizon_errors.png')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9029b6a",
   "metadata": {},
   "source": [
    "## Restore the model and reproduce predictions\n",
    "\n",
    "Load the selected checkpoint into the same architecture. The comparison below checks inference against the saved prediction records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5966a671",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T13:51:28.819239Z",
     "iopub.status.busy": "2026-10-14T13:51:28.819154Z",
     "iopub.status.idle": "2026-10-14T13:51:29.535187Z",
     "shell.execute_reply": "2026-10-14T13:51:29.534609Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved predictions reproduced from the checkpoint.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Issued after</th>\n",
       "      <th>Forecast begins</th>\n",
       "      <th>Outcome available</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-12-31T23</td>\n",
       "      <td>2024-01-01T00</td>\n",
       "      <td>2024-01-01T23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-01T23</td>\n",
       "      <td>2024-01-02T00</td>\n",
       "      <td>2024-01-02T23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-01-02T23</td>\n",
       "      <td>2024-01-03T00</td>\n",
       "      <td>2024-01-03T23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-01-03T23</td>\n",
       "      <td>2024-01-04T00</td>\n",
       "      <td>2024-01-04T23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-01-04T23</td>\n",
       "      <td>2024-01-05T00</td>\n",
       "      <td>2024-01-05T23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Issued after Forecast begins Outcome available\n",
       "0  2023-12-31T23   2024-01-01T00     2024-01-01T23\n",
       "1  2024-01-01T23   2024-01-02T00     2024-01-02T23\n",
       "2  2024-01-02T23   2024-01-03T00     2024-01-03T23\n",
       "3  2024-01-03T23   2024-01-04T00     2024-01-04T23\n",
       "4  2024-01-04T23   2024-01-05T00     2024-01-05T23"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with np.load(RUN / 'predictions.npz', allow_pickle=False) as archive:\n",
    "    records = {name: archive[name] for name in archive.files}\n",
    "model = ForecastModel('rnn', hidden=report['config']['hidden'], rngs=js.Rngs(params=0))\n",
    "checkpoint.restore(RUN / 'best.npz', model=model)\n",
    "model.eval()\n",
    "replayed = decode(infer(model, records['inputs'][:64], batch_size=64), metadata)\n",
    "np.testing.assert_allclose(replayed, records['predictions'][:64], rtol=1e-5, atol=1e-5)\n",
    "print('Saved predictions reproduced from the checkpoint.')\n",
    "display(pd.DataFrame({'Issued after': records['issued_at'][:5],\n",
    "                      'Forecast begins': records['origins'][:5],\n",
    "                      'Outcome available': records['outcome_available_at'][:5]}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8bbb6e5",
   "metadata": {},
   "source": [
    "## Visualization artifacts\n",
    "\n",
    "`predictions.npz` retains every held-out forecast, baseline, target, input window, and timestamp. `visualization.json` describes the fields. `issued_at` marks the final known observation; `outcome_available_at` marks when the full target can be revealed. Temperature histories are in °C.\n",
    "\n",
    "These records support a rolling square animation without retraining. Checkpoints, source data, and full prediction arrays remain local and gitignored. The notebook embeds the evaluated plots and metrics. This is a single-seed demonstration; improvements over these baselines do not establish performance across cities, market periods, or model configurations."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.20"
  },
  "jaxsaw": {
   "executed_utc": "2026-10-14T13:51:29Z",
   "gpu_selection": "0",
   "kind": "model",
   "requires_figure": true,
   "run_directory": "runs/forecast-gallery-v2/rnn",
   "source_sha256": "212bad722462e78fa6af3a8ce2cff2467f8a37056563357fee29f91b1dac48f8",
   "task": "rnn",
   "source_rename": {
    "package_from": "neuraljaxlib",
    "package_to": "jaxsaw",
    "alias_from": "nj",
    "alias_to": "js"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}