{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6720a84f",
   "metadata": {},
   "source": [
    "# LSTM: Market Volatility Forecasting\n",
    "\n",
    "Predict the magnitude of market fluctuations over the next five trading sessions from 60 past sessions. An LSTM learns a correction to trailing historical volatility. This model forecasts volatility, not price direction or trading returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d2c4fa7",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2026-10-14T13:51:30.488502Z",
     "iopub.status.idle": "2026-10-14T13:51:31.206109Z",
     "shell.execute_reply": "2026-10-14T13:51:31.205569Z"
    },
    "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/lstm'\n",
    "print(f\"JAXsaw {js.__version__} · JAX {jax.__version__}\")\n",
    "print(jax.devices())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "012e4160",
   "metadata": {},
   "source": [
    "## Data and forecasting target\n",
    "\n",
    "Daily S&P 500 closing index values come from [FRED series SP500](https://fred.stlouisfed.org/series/SP500), sourced from S&P Dow Jones Indices LLC. The cached snapshot covers October 2016–December 2025; FRED exposes a rolling history, so later downloads may start at a different date. The source URL, exact file hash, and split dates are recorded below. Source data terms are separate from the package license.\n",
    "\n",
    "Let $r_t=\\log(P_t/P_{t-1})$. At the close of session $t$, the target is $\\sqrt{252\\,\\mathrm{mean}(r_{t+1:t+5}^2)}$: the annualized root-mean-square return over the next five sessions. This is a realized-volatility proxy, not centered sample standard deviation.\n",
    "\n",
    "The input contains 60 known returns, absolute returns, and trailing 5- and 20-session volatility. The loss is MSE in standardized log-volatility space; mean and scale are fitted only to training targets. Trailing volatility uses past returns only. Baselines use the latest trailing 5- or 20-session value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db4389f6",
   "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.\n",
    "\n",
    "Adjacent market targets overlap over five sessions. Reported errors are descriptive; they are not independent statistical trials."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cdf20d3",
   "metadata": {},
   "source": [
    "## Architecture\n",
    "\n",
    "Input `[B,60,4]` → LSTM with 16 hidden units → one correction in standardized log-volatility space. Add the correction to the latest trailing-20-session baseline, then invert the transform.\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": "38cc88c9",
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    "execution": {
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     "shell.execute_reply": "2026-10-14T13:51:31.233671Z"
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    "tags": [
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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": "2eb01341",
   "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": "62bffd55",
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "335a0a39",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2026-10-14T13:51:31.248902Z",
     "shell.execute_reply": "2026-10-14T13:51:31.248532Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python -m examples.run lstm --download --output runs/forecast-gallery-v2/lstm --epochs 80 --limit 100000 --batch-size 64 --model-width 16\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>lstm</td>\n",
       "      <td>80</td>\n",
       "      <td>64</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>16</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  lstm      80          64  100000     0      16        15          0.001   \n",
       "\n",
       "  matmul_precision  \n",
       "0          highest  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>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>1479</td>\n",
       "      <td>2017-02-10</td>\n",
       "      <td>2022-12-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>validation</th>\n",
       "      <td>246</td>\n",
       "      <td>2023-01-03</td>\n",
       "      <td>2023-12-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <td>498</td>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>2025-12-24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count first_origin last_origin\n",
       "train       1479   2017-02-10  2022-12-23\n",
       "validation   246   2023-01-03  2023-12-22\n",
       "test         498   2024-01-02  2025-12-24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source SHA-256: 4f867010611c46759a39295ac0d45d3e5d000e127546a288d6b9cdb585ed00ee\n",
      "Training devices: cuda:0 NVIDIA GeForce RTX 5090\n",
      "Target normalization: -2.130350205968994 0.7122885429625886\n"
     ]
    }
   ],
   "source": [
    "from examples.train_gallery import RECIPES\n",
    "import subprocess\n",
    "TRAIN = False\n",
    "command = [sys.executable, '-m', 'examples.run', 'lstm', '--download',\n",
    "           '--output', str(RUN), *RECIPES['lstm']]\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": "0b116c2a",
   "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: 8\n",
      "Completed epochs: 23\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": "6878ce62",
   "metadata": {},
   "source": [
    "## Held-out evaluation\n",
    "\n",
    "MAE and RMSE below are expressed in annualized volatility percentage points. Log-RMSE uses natural-log volatility. Each origin contributes one five-session target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dfa56b50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T13:51:31.252973Z",
     "iopub.status.busy": "2026-10-14T13:51:31.252889Z",
     "iopub.status.idle": "2026-10-14T13:51:31.257395Z",
     "shell.execute_reply": "2026-10-14T13:51:31.257055Z"
    }
   },
   "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",
       "      <th>Log-RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>4.5637</td>\n",
       "      <td>7.9668</td>\n",
       "      <td>0.4426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline_5d</th>\n",
       "      <td>5.6909</td>\n",
       "      <td>9.3893</td>\n",
       "      <td>0.5499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline_20d</th>\n",
       "      <td>5.6981</td>\n",
       "      <td>9.6293</td>\n",
       "      <td>0.5217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MAE    RMSE  Log-RMSE\n",
       "model         4.5637  7.9668    0.4426\n",
       "baseline_5d   5.6909  9.3893    0.5499\n",
       "baseline_20d  5.6981  9.6293    0.5217"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = pd.DataFrame(report['evaluation']).T\n",
    "scores[['mae', 'rmse']] *= 100\n",
    "display(scores.rename(columns={'mae': 'MAE', 'rmse': 'RMSE', 'log_rmse': 'Log-RMSE'}).round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15b1b059",
   "metadata": {},
   "source": [
    "## Forecasts through time\n",
    "\n",
    "The upper panel shows the known S&P 500 closing level. The lower panel compares the forecast with subsequent realized volatility and the trailing-20-session baseline. All held-out origins are shown. The model improves average error in this run but underestimates the sharp April 2025 spike; historical inputs cannot anticipate every abrupt event. The gray future outcome is aligned to its forecast start date and becomes known only after five sessions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1b20ac23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T13:51:31.258160Z",
     "iopub.status.busy": "2026-10-14T13:51:31.258074Z",
     "iopub.status.idle": "2026-10-14T13:51:31.261773Z",
     "shell.execute_reply": "2026-10-14T13:51:31.261443Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yyy+16n377bcSAKlEiRLSr7/+Kt5jycnJ0tGjR6XatWuLa3FaWpqoFxwcLIJY48ePl548eSK2RUdHSydOnJBGjRolXbhwQZTHxcXJvuS8f/++2BYbGytduHBBmjRpkvTvv//K+phZwP3x48fiPVGtWjXp5MmTYtvx48clLy8vEQQLCwuT1dV8bTk4OEj+/v7isU9JSZEOHjwolSxZUgIgNWnSJOsnIYNq1apJAHQ+7motW7aUAO0vuS5evCiuFf7+/lJQUJAkSelfkOzcuVP0q0KFClqfQVm9Zzp27Cht2rRJfK5JUvr78J9//hGPl74vs9TtZvwixdDj53RbVl+6qM9B/R5dvXq1zn1evHghPvu3bNmit638kNvP9Nzcl2QWhM3vzx213Nwb5DTgnp36WdF8fdrb20s1a9aUzp07J0lS+v3T/v37xZeeZcqUkV6/fi2rn5qaKl4DtWrVknbt2iUeg9jYWOmPP/4QX/qNHTtW7zlkdd+b1XOd0+vK69evZed34MABce98+vRpydvbW/bFJwPuRFQcMeBORER5KmPA/fHjx5KRkZFkbW0tuyFfvXq1BPxv1FhWAffMvHnzRgRMdY1MVP/hYWxsrHM0V8a+awa/58+fLykUCkmhUEjz58/XqvP69WvxR8O+fft0tpuSkiLVqVNHAiAtWLAgy2PmhPoPMwDSkSNHtLYnJiZKpUqVkgBIs2bN0ls/Y2Dxk08+kQBIw4cPz1F/Mgu4nz17VvR52bJlOvdR/9Ht7OwsJSQk6DwGAGnnzp16j7Nw4UIRWMgYoJUkSdqyZYsEpI+effHihWEnaKC5c+dKAKSWLVtqbdMcHa/rj35JkqTx48dLAKSKFStqbWvbtq0EpI/KVwdiNO3bt0+0n5uAe2aBB/XreujQobLytLQ0qVKlSpk+t5IkSZ06dZIASGPGjNHatnTpUhFQUr+mR4wYIQGQlEqlzl8sFJWAu2Zf/fz8dNY9ffq0qHvv3j3ZNnUwbPLkyXr7ph5p3rlzZ4PPR039JdRff/1lcJ1Hjx5JxsbGkqWlpd4R3K9fv5Y8PDwkANK2bdtEuXokY3Z+QXLu3DkJSB+BnvFXDpnJ7DkZOXKkCGJqBpDVQkNDxQjiTz/9VLZN87VVpUoVne+5nTt3in1CQ0MN7rMkSdLs2bPFdUrzywq1sLAw8aXFoUOHZNvatGkjrhO6RmtfunRJBEnnzp2r97yy+54JCwuTzM3NJYVCofP9WJQD7pIkSWPHjpUASA0bNtS5fd68eRKQPko+46/a8ltuPtNze1+SWRA2vz93JCn39wZFLeDu5OSk9SsQSZKkmzdvii90fvrpJ9m2tWvXimtNdHS0zmNcuHBBUigUkpmZmVb7ht73ZvZc5+a6MmfOHPGFia4R8OHh4bLR7wy4E1FxxBzuRESUr0qXLo1WrVohLi4Of//9tyhXT5b68ccf5/oYNjY2aNasGYD0vPD6tGnTBrVr1zaoTUmSMGnSJEyYMAEmJiZYv349xo8fr7Xf1q1bER0djdq1a+ODDz7Q2ZaJiQn69OkDID2HcX7y9/eX5a9VMzc3F/0LCgoyuD2lUgkgPedxXtu0aRMAwMPDA0OHDtW5z8yZMwEAkZGROHjwoM59vL290bFjR73HGThwIKysrBAWFoY9e/ZobV+xYgUAoFu3bnB2ds7WOWSlffv2AIAzZ84gLS1N737ffvutzvLOnTsDSM8xr57AEwCio6PFa2nSpEmwtLTUqvvBBx/Az88vx31XK126NAYOHKhzW6dOnQBov6aOHz+Oe/fuwdnZWe9zCwADBgwAoPt9MXLkSHTr1g0qlQofffQRli9fjmXLlgFIf87KlCmTo/MpKP379weQ/tzrylerzsHt5+eHihUrivLg4GAcOXIEJiYmmDhxot721Y/doUOHMn1t6aJ+X4eHhxtcJyAgAGlpaWjTpg18fHx07mNra4suXboAkD+n6uO9efMGcXFx2epjcnIyXr58aXA/9ZEkSXwGjRw5Em5ublr7eHh4YOTIkQCAjRs36m1rwoQJOt9zbdu2hZmZGQDg2rVr2epfv379YGRkhLCwMBw9elRr+59//gmVSgUPDw/ZNT7jtcDKykqrbu3atcV8Ahs2bMhWvzLj7u4OHx8fSJKE06dP51m7BUX9XJ89e1bn87Vy5UoA6fcppqam2WpbnRs7u/OCZJSTz/T8ui8pqM+dvLo3KCpGjhyJEiVKaJVXrVoV3bt3B6B9vVHPjfPJJ5/A3t5eZ7t16tSBt7c3kpOTdV4zgOzd92rK7XVFfT49evRA1apVteq6ubmJ9x8RUXHFgDsREeU79eSpq1evBpAePDxx4gQcHBxEcMYQu3fvRq9evVC+fHkxcZz6nzqQknECPU3+/v4GHSclJQUDBgzAvHnzYGNjg3///Rd9+/bVue+pU6cAALdu3YKbm5vefzNmzAAAMRlXfmnQoIHebaVKlQIAnRMT6tOuXTsoFArs3LkTbdu2xYYNG7Kc5MtQFy5cAJA+oZ6Rke5bkqpVq8Ld3V22f0ZZPa9KpRK9evUC8L/gulpISIj4Y3348OGGd17Ds2fPMHXqVPj5+cHJyQkmJibidVmtWjUA6ZOr6pv81NHRURZw1aR+zgDI6l+6dElMVtqiRQu9fctsm6Hq1aund7I4fa8p9fsiJiYGpUqV0vu+GDZsGAD974uVK1eiTJkyePr0KUaMGAEAGDp0qAhSFGX+/v6oUKECAGhNbpicnCyCSurAuZr6sVOpVKhWrZrex65NmzYAgLi4uGwHpDt06AAA+OqrrzB8+HDs27cvy8nv1P06cOBAptc69Zepms9p/fr14ezsjPDwcDRo0ABLlizB7du39U74BwAVKlRAlSpVkJKSggYNGmDOnDm4cuVKtr9cUHv06JF4nbZq1Urvfq1btwYAvHz5Eo8ePdK5j77rrImJCVxcXABk7zoLpH+xpZ7YV9eEqOoydWBe7dKlS+JxNOS8goKCkJKSYnC/VCoV/vrrL3Tq1AllypSBpaWl7LP3/PnzADL/7C2qvLy8RDA742fDiRMncPv2bSgUCnGdKgw5+UzPr/uSgvrcyat7g6LCkMdK832ZlpaGs2fPAkifyDWz5/DOnTsA9D+Hht73ZpSb60pycrL4Aiu/70+IiIoyBtyJiCjfde3aFQ4ODjh16hTu3bsnAjJ9+vSBhYVFlvVVKhX69u2Ljh074u+//8ajR4+QnJwMBwcHuLq6wtXVVbST2ehJXSOMdDl9+rQIkK1Zs0b8QaGLOvicmJiIZ8+e6f2nDmZpjlLOD7a2tnq3mZiYAEC2gi2NGzfGnDlzYGZmhn379qFv375wd3dH6dKlMXjwYL2jqgzx/PlzABB/NOvj4eEh2z8jQ55X9UiqPXv24MmTJ6J85cqVUKlU8PLyEsGu7Dhz5gyqVKmCGTNm4OzZs4iKioKlpSVKlCgBV1dX2Yh5fa9NQ54zQP68aT4WmT1+6scuN3LymlK/L1JSUjJ9X6i/REhISNDZvoODA3799VexXr58eSxatChX57Np0ya9wYu8HqWrHuWeMeC+Z88eREVFwczMTHwZpKZ+7FQqVaaPXWRkpKijvq6cPn1a77mpA/xA+ojFnj17IiUlBStWrEDbtm2hVCpRo0YNTJo0SQRxdPUrLi4u036pX+ea1zqlUokNGzbAxcUFN27cwOeff46qVavCwcEBnTp1wvr167VeQ8bGxti4cSPKlSuHkJAQfPXVV6hduzbs7OzQunVrLF26NFvX05y8Z/Rdc/L6Oqum/vJl69atsnO7cuUKrl+/LttHVx8NOa/U1FSDvwyIj49Hq1at0K9fP+zatQuhoaFQqVRwdHQUn73qkd+G/nKhqFF/Nqxfvx6JiYmifPny5QDSA4rlypUrlL4Bubv+5vV9SW4/d8aMGaP3+qTrOLm9NygqMjsP9TbN92VUVBSSkpIApH/RntlzqH7u9T2Hht73ZpSb60pUVBRSU1MNrktEVFwx4E5ERPnO3Nxc/HR55cqVWLt2LYD/jXzPyqpVq7BhwwYYGxtjypQpuHfvHpKSkhAVFYWIiAhERESIEa+ZjZg0NjY26Hg1atRAzZo1AQDjx4/HgwcP9O6rHm3Zq1cvSOlzo2T6Lzg42KA+FCWTJk3Co0ePsGDBAnTp0gUlSpRAWFgYAgIC0KJFC/To0SNHwaW8YsjzWr9+ffj6+iItLU38VDstLU18+ZOTEYypqano06cPoqOjUatWLezZswevX7/Gmzdv8OzZM0RERIhRakDmr83iRv2+aNCggUHvi8weG82Rp0+ePNGZniU7EhIS9AYvkpOTc9V2RuqA+4MHD8SoU+B/o5U7dOgABwcHWR31Y+fq6mrwY1e2bFkA6SML9Z2b5pcapqam2LRpE65cuYIpU6agRYsWsLKywvXr1zFv3jx4e3tj/vz5Ovv15ZdfGtSnjGk0WrVqhUePHmHt2rUYOHAgKlWqhJiYGOzatQv9+/dH7dq1ZV+GAYCPjw9u376NrVu3Yvjw4ahevToSEhJw6NAhjBo1ClWqVMl26pai7MMPP4SVlRViY2Oxbds2Ua5+vfj6+opfzRSE77//HkePHoWlpSUWLFiAkJAQJCYm4uXLl+KzVz0C+229vnXt2hWurq549eqV+KXcq1evsGXLFgA5/+XToEGDxHshJ1/m5kZRvS+JiYnRe32i/9H8Fc/evXsNeg6nTZumsy1D73uJiCjvMeBOREQFQh1cX7hwIcLCwlC9enXUrVvXoLrqXJBDhw7F9OnTUbFiRa2fGedljnFHR0ccOXIEtWrVQmhoKJo1a4a7d+/q3Fc9Miu/U8UUtlKlSmHs2LHYtm0bnj17hqCgIJFbdcuWLVi6dGm221SPvMoqFYF6e05HaqmpRzKuXr0aKpVKjHY3NzfXm6M8M2fOnEFISAiMjY2xe/dutG3bVms0Yn7kvgfkj0XGIKWmzLblp7x6XyxZsgQ7d+6EsbExqlWrhqSkJPTu3TtXvxTRDIRl/JfXgbHy5cuLn/Srg6avXr3Cv//+C0B7tDLwv8cuMjIy26OGmzdvrvfcBg0apLW/j48Ppk+fjsOHDyM6OhqHDh1C06ZNkZaWhkmTJuHq1ata/crNc2ptbY3+/fsjICAAd+/eRVhYGObMmQMLCwsx8j0jMzMzdOvWDcuWLcO1a9fw4sUL/P7773B0dERoaKjB713N90xm1xzNbbm95mSXjY0NunbtCuB/r5e0tDT89ddfAHS/XrJ7XiYmJnB0dDSoP+rP3ilTpmDs2LEoU6aMVnqp/LrGFRRTU1MMGTIEwP++3FOPdndzcxPzVLxN8uu+JLefOwEBAQZ94ZrbewP1yH/NXyxkFBMTk2nbecmQx0rzfalOTQcU3r1lbq4rjo6OItBfFO9PiIgKCgPuRERUIOrWrYsaNWqIEaTZmSw1NDQUAPRO/BQbG4tz587lvpManJyccPjwYfj6+uLJkydo3ry5zjQL6mDaxYsXszUBoZr6i4O3bXRgjRo1sGLFCnH+GSctM+S81F+4HD16VOSFzej27dvij7J69erlqs99+/aFnZ0dQkJCsH///lxPlqp+Xbq4uOj92fShQ4dy3uFM+Pr6isc4s7Q+R44cyZfjZ0X9uoiIiMhxft1r165h0qRJANIDfnv27IFSqcStW7cwbtw4nXU0v4grKu8pdZD077//RnJyMv7++28kJSXB2dkZ7dq109pf/dilpaVh7969BdZPExMTtGzZEv/++y/Mzc0hSZLs9avu16FDhzINZGWHu7s7vvjiC0yYMAGA9nVEFycnJ4wYMQJz5swBAFy+fNmgHPblypUTAaHDhw/r3U99zk5OToWSSkRzMtyIiAjxv4mJic65RDSvBYacl4+Pj8ETgGb12RscHJzpL07UwfmCfi9m9zowfPhwGBkZ4eTJk7h165b4bBg8eHC2J0stCnJ7X6JPQX3u5PbeQP2rIfXrNyOVSpXp51Je35dl9lipt9WsWVO81kxNTVG/fn0AwK5du/KkD9mVm+uKmZmZ+JVoUbw/ISIqKAy4ExFRgZkzZw4mTJiACRMm4KOPPjK4nr29PQDIRltqmjlzJt68eZMnfdTk6OiIw4cPo169eggPD0fz5s1x69Yt2T49evSAUqlESkoKxo8fn+kfaCqVCtHR0bIyOzs7ANAqLyrUeUT1sbS0BACtXxwYcl69e/cGkD7KaeXKlTr3mTJlCgDA2dk504m7DKEeXQsAs2bNwp49ewDkPGWA+nWp7yfxYWFhWLx4cQ57mzmlUon3338fADBv3jydAdBDhw7leU5yQ7333ntiIthx48ZlmaolY07phIQE9O7dG4mJiWjcuDG++eYbeHp6irzKy5cvx9atW7XaUb/ugKLznurZsyfMzc3x6tUr7Nq1S4xc7t27t85gXqVKlcRI+2+++SbLkZjZnZwTyPx9bW5uLkYnar6vP/74Y5iYmCAyMhJTp07NtP3k5GTExsYadDxA93XE0DoZ6+mjUChEvvxly5bpHJn99OlTLFu2DABEGrSC1qpVK5QqVQppaWn4888/xeulTZs2YkJWTUqlEh988AEAYO7cuTp//XH16lXxfsnOeWX12fvVV19lWr+wPt+yex3w9PRE27ZtAaT/EuratWuFPllqbuT2vkSfgvrcye29gY+PDwBg27ZtOs/9jz/+yHTUdl6/bn///XfZnBtqd+7cEamLMs7lob4v2bNnj7hX0ScnnwFZye11RX0+mzdv1jlY5fnz5/j999/zuttEREUKA+5ERFRg2rZti3nz5mHevHk6Awf6tGnTBkD6z72XL18ugncREREYN24cfvrpJzg5OeVLn5VKJQ4ePIiGDRsiIiICzZs3F5PXqbcvXLgQQPrP79u3b49z586JUVkqlQq3bt3C/Pnz4e3tjd27d8var169OgDgxo0bhRYczUyXLl3w8ccfY+/evbI/PqOiojBr1iwx8ql9+/ayeurzyjhJqab69evjww8/BAB8/vnnWLJkifijLiIiAsOGDcPmzZsBpH+pYsgEu1lRp5U5ffo00tLScjxZKpA+oay1tTUkSULPnj1F2qG0tDTs378fzZs310q/kJdmzpwJY2Nj3L59G+3btxd/1KampuLvv/9Gz549oVQq8+34mTExMcHvv/8OExMTnDx5Ek2bNsXhw4dluf4fPnyI33//HfXq1cNvv/0mqz9u3DjcvHkTSqUSf/75pwgA9+jRQ6R/GDZsmNYIRqVSKX5tsGbNGjFxW2FSKpXo2LEjAGD27Nkil7v6yx9dfvnlF9jY2ODu3bto2LAhduzYIQtuPXnyBOvWrUPLli3x5ZdfZrtPnp6emDx5Ms6ePSsLbN+/fx/9+vVDfHw8jIyMRMAFACpUqIDvvvsOAPDTTz9hwIABsmthamoqrly5ghkzZqBixYq4cuWK2DZnzhy0bdsW69atkwW6kpKS8Pfff2Pu3LkA5NeRjRs3wt/fH8uWLcPDhw9Fufr9pQ72+vn5aeXB1+frr7+GUqlEVFQUWrVqJbvmnjp1Cq1atUJ0dDQcHR2zDCbnFyMjIzGSfdWqVdi+fTuAzF8vs2bNgqmpKe7fv48PPvhA5LVXp85q164dUlNTUaFCBYwYMcLgvqg/e2fNmoV//vlHvJ8ePXqEvn374u+//870sVd/Dvz555/5PmG4psqVK8PMzAxA+rwxhoxUVn82HD9+HEDhT5aaG7m9L8lMQXzu5PbeQB38vXXrFoYPHy5+AfP69WssWLAAI0eOzDStkvp1u2XLFjGxd26kpKSgdevW+O+//wBA/Hrogw8+QFJSEkqXLi1ef2offfQRWrVqBUmS0LVrV8yaNUtMhgukT1J89OhRfPrppyhfvnyu+6hLbq4rn3zyCTw8PJCUlIQ2bdrg8OHD4n147tw5tGrVSu+vF4iIig2JiIgoD02dOlUCIHl6emar3tGjRyUAEgDp0aNHsm2vXr2SqlSpIrYbGRlJSqVSUigUEgBpxIgR0sCBAyUA0sCBA7Xa9vT0lABIa9asMajvzZo109r2+vVrqVGjRhIAydnZWbp69aps+9KlSyUzMzPRR3Nzc8nJyUkyNTUVZQCk9evXy+qlpKRIXl5eYruDg4Pk6ekpeXp6Sps3bzbkoZMkSZKaNWsmAZCmTp2ao/PTV19drv5nZ2cn2dnZycq6d+8upaWlyerdvXtXsrCwEM+Xq6urOK/Q0FCxX3R0tOwYJiYmkoODg3huAUgTJ07M8Tnr0rhxY9H2vHnzslU3o6VLl8oeCxsbG3Hezs7O0s6dO/W+rtesWZPle+XRo0d660uSJC1btkz2WNnb20vm5uYSAKlKlSrSzz//nKP3oyRJmb6nDD2Hbdu2Sba2tqJ/pqamkpOTk+ij+t+sWbNEna1bt4pyXe+BuLg4cT1o0qSJlJqaKts+c+ZM2fuwdOnSkqenp9SrV69sPwZZvcY0r1uZ0XwdqJ+brJw8eVJyc3MTdYyNjSUnJyfJ0tJS1tbQoUOzfV6a9Y2MjCQHBwfxugUgKRQKacGCBVr1VCqV9N1338lec5aWlpKTk5NkbGwsa/fkyZOinvrao1nH0dFR1k7VqlWl8PBwUUf92sp4TTUyMhJlpUqVkm7duiXrY1bPSWBgoGRvby/2sba2lqytrcW6UqmUjh8/rlUvq/eimqGfN5kJCgqSnbtSqZQSEhIyrbNx40bZZ5CdnZ3sOS1durR08+bNbJ1XcHCw5OrqKrs+az52P/zwQ6bvkXXr1sne++7u7pKnp6fk7+9v0PFzuk2SJGnIkCFiu5WVlVSmTBnJ09NTmjBhgs7HLy0tTTx3AKQtW7bo3K+g5PYzXZJyfl+S1XU9Pz931HJzbyBJktS/f3+t95D62vH5559n+vl27NgxcRxjY2OpZMmS4v7FUJqvz40bN4rPQRsbG8nKykrWr//++09nGzExMVKHDh1k52FnZye7/1U/NhkZeh3K6rnO6XVFkiTpv//+k5RKpex9aGNjIwGQbG1tpU2bNhl0TSUieltxhDsRERV5SqUSp0+fxtixY1G2bFkYGxvDxMQEzZs3x4YNGwrkZ6m2trbYv38/mjRpgsjISLRo0QKXL18W20eOHIk7d+5g4sSJ8PHxgbm5OaKjo2FjY4O6devi888/x8GDB7V+dmtiYoLDhw9j6NChKFeuHOLi4hASEoKQkBBZSobC8ssvv2DOnDlo164dKlWqBEmSkJCQgFKlSqFTp07YunUrNm/erJXSoVKlSjh69Cg6deoEFxcXvHz5UpyX5qhje3t7HD58GKtWrULz5s1ha2uL2NhYuLm54cMPP8TRo0fF6Ne80qNHDwDI8WSpmkaOHIl///0XzZs3h42NDVJTU+Hu7o7PP/8cV69eRY0aNfKiy3oNHz4cp06dQseOHeHo6IikpCQxevn8+fMGj/zNL126dMH9+/cxdepU1K9fHzY2NoiOjoa5uTl8fHwwdOhQbNu2TeRqDw0NFZPxDhkyBN27d9dq08rKChs2bIC5uTlOnDiBWbNmybZ//fXXWLRoEerWrQtTU1OEhYUhJCSkUCd3bNu2rexXPZmNVlbz9/fH3bt3MW/ePDRt2hRKpRLR0dEwNjZG1apV8dFHH+HPP/8UI1mz48CBA5g8eTKaNGmC0qVLIyEhAQBQsWJFDB48GP/99x/Gjh2rVU+hUGDGjBkICgrCqFGjULVqVRgbGyMmJgYODg5o1KgRJk2ahNOnT4s80kD663T58uXo06cPqlevDisrK7x+/RoODg5o0qQJFi5ciEuXLonJHgGgU6dOWLt2LQYPHgwfHx/Y29sjJiYGtra2qF+/PmbOnIkbN26gSpUq2Tr3Zs2a4datW5gwYQKqVq0KlUoFSZJQtWpVTJw4Ebdu3UKTJk2y/ZjmpRo1aqBWrVpivUePHln+wqdXr164ceMGRowYgQoVKiApKQkmJiaoVasWpk+fjuvXr6Nq1arZ6oenpycuXLiAIUOGoFSpUgAACwsLdOjQAfv378fkyZMzrf/RRx9h3bp1aNy4MaysrBAeHo6QkJAsJ8PMC7/++iumTZsmrsGPHz9GSEiIztQeQPovC7p16wYAb+1kqRnl9L4kKwXxuZPbe4OAgAAsWrQItWrVgqWlJVQqFfz9/fH3339nmeqtadOm+Pfff9GqVSsolUo8e/ZM3L/kRIMGDXDhwgUMGDAA9vb24j5h2LBhuHbtmshZn5GdnR127dqFPXv2oFevXihTpgySkpIQHx8Pd3d3vP/++5g9e7bOlC15JTfXlbp16yIoKAhDhw6Fu7s7UlNTYW9vj4EDB+LSpUsiTz0RUXGlkKQiMqMUERERUQHo2LEjdu/ejT59+uCvv/4q7O4QEVERUKNGDVy/fh2TJ0/GDz/8UNjdobdYcHCwSEn06NEjlC1btnA7REREBY4j3ImIiOid8fDhQzEB2SeffFLIvSEioqIgMDAQ169fh5GRUY4n0iYiIiJSY8CdiIiI3gmvX7/GJ598ApVKhQYNGhR62ggiIip8z549EymUunfvztHIRERElGsmhd0BIiIiovw0ceJEbN68GREREUhOToaJiUmO8l4TEVHx0bt3b5w6dQoRERFITU2Fra0tfvzxx8LuFhERERUDHOFORERExVpkZCQeP34MMzMz+Pn5Yd++fWjYsGFhd4uIiApRREQEwsLCYG1tjVatWiEwMFDk3SYiIiLKDU6aSkRERERERERERESUBzjCnYiIiIiIiIiIiIgoDzDgTkRERERERERERESUBxhwJyIiIiIiIiIiIiLKA8Uy4B4YGAiFQpHlvxkzZmjVDQgIQP369WFjYwNHR0e0a9cOp0+fzvR4p06dQrt27eDo6AgbGxvUr18fa9euzbROWFgYBg8ejFKlSsHCwgKVK1fG1KlTkZiYmKtzJyIiIiIiIiIiIqLCUSwnTb19+zZ+/PFHndvS0tKwfv16AMCRI0fw3nvviW1jx47FokWLYGlpiffffx+JiYk4fPgwJEnCli1b0KVLF632tm7dil69ekGlUqFp06ZwdnbG4cOHER0djQkTJmDevHlade7fvw8/Pz9ERkaievXqqFatGi5cuICHDx/C398fhw8fhrm5ed48GERERERERERERERUIIplwD0ze/fuRbt27VC6dGmEhIRAoVAAAA4dOoTWrVvDyckJZ86cQaVKlQAAZ86cQfPmzWFlZYVHjx5BqVSKtqKiolCuXDm8fv0aW7duRbdu3QAAz549Q+PGjXH//n0cPXoUzZs3l/WhcePGOHXqFEaPHo1FixYBAFJTU9GzZ09s27YNU6dOxbRp0/L9sSAiIiIiIiIiIiKivPPOBdz79euHv/76C1999RVmz54tytu1a4e9e/diwYIFGDt2rKzOmDFjsHjxYsybNw8TJkwQ5T/99BO+/PJLdO7cGdu3b5fV2bZtG7p164YOHTpg165dovz8+fNo0KABSpQogcePH8tGsj979gylS5eGjY0Nnj9/DhMTkxyfp5ubG+Li4lCmTJkct0FERERERERERERUHDx+/BjW1taIiIjI1+MUyxzu+sTFxWHHjh0AgP79+4vyhIQEHDlyBADQvXt3rXrqMs3AOQD8+++/euu0b98eFhYWOHTokCwvu7pOx44dtdLGuLq6okmTJnj16hVOnjyZ7fPTFBcXh5SUlFy1QURERERERERERFQcpKSkIC4uLt+Pk/Mh1G+hf/75B3FxcahduzaqVasmyu/cuYOkpCS4uLjAw8NDq56vry8AICgoSFZ+9epV2XZNZmZmqF69Oi5cuIC7d++iZs2aWdZRlx85cgRBQUFaqWiyQz2y/caNGzlug4iIiIiIiIiIiKg48Pb2LpDjvFMj3NWTpWqObgfSf04AQGewHQCsra2hVCrx6tUrvHnzBgDw+vVrxMTEZFpPXR4SEmLwsXTVISIiIiIiIiIiIqKi750Z4R4eHo7Dhw/D2NgYffr0kW2LjY0FAFhZWemtb21tjejoaLx58wa2traiTmb1rK2tAUAE6Q05lq46mdH3zcyDBw9QoUIFg9ogIiIiIiIiIiIiotx7Z0a4b9iwAWlpaWjdujXc3NwKuztEREREREREREREVMy8MyPc9aWTAQAbGxsAQHx8vN766oT6tra2sjrqenZ2dlnWMeRYuupkRl+O9oLKSURERERERERERERE6d6JEe63bt3C5cuXYWNjgy5dumhtV08wGhYWprN+XFwcoqOj4eDgIALhdnZ2sLe3z7SeutzT09PgY+mqQ0RERERERERERERF3zsRcF+3bh0AoFu3bjpzp3t5ecHc3BwvXrzAkydPtLZfunQJAFCzZk1ZuY+Pj2y7ppSUFFy/fh0WFhaoXLmyQXUyOxYRERERERERERERFW3FPuAuSRL++usvALrTyQCApaUlWrRoAQDYvHmz1vYtW7YAADp27Cgrb9++vWy7pt27dyMxMRGtWrWChYWFVp1du3YhKSlJVufZs2c4ceIEHBwc4O/vb9D5EREREREREREREVHRUOwD7idOnEBISAjc3d1FUF2X8ePHAwBmzZqFe/fuifIzZ85g2bJlUCqVGDJkiKzO0KFDYWdnhx07duCff/4R5c+fP8cXX3wBAJgwYYKsTv369eHv74/nz5/jyy+/FOWpqakYNWoUUlJSMHr0aJiamub8pImIiIiIiIiIiIiowBX7gLt6stS+ffvCyEj/6bZq1QpjxozBy5cvUatWLXTp0gXt2rVD06ZNkZqaijVr1kCpVMrqODo6YvXq1TAyMkL37t3RokUL9OjRA15eXrh//z7Gjx+P5s2bax1rzZo1cHJywqJFi1CzZk307t0bXl5e+Oeff9CoUSNMnjw5Lx8CIiIiIiIiIiIiIioACkmSpMLuRH5JSkpCyZIl8erVK1y9etWgvOgBAQFYsmQJbt26BTMzMzRs2BDfffcdGjVqpLfOqVOnMGvWLJw9exbJycmoVq0aPvvsMwwcOFBvndDQUEyZMgX79u1DVFQUypQpgz59+uDrr7+WpaDJKW9vbwDAjRs3ct0WERERERERERER0dusoOKlxTrg/i5jwJ2IiIiIiIiIiIgoXUHFS4t9ShkiIiIiIiIiIiIiooLAgDsRERERERERERERUR5gwJ2IiIiIiIiIiIiIKA8w4E5ERERERERERERElAcYcCciIiIiIiIiIiJ6R0iSVNhdKNYYcCciIiIiIiIiIiJ6R2zcuBEDBgzAypUr8ejRo8LuTrFjUtgdICIiIiIiIiIiIiLdJEmCQqHIs/aOHTuGmzdv4ubNm0hKSsKnn36aZ20TR7gTERERERERERERFUmxsbH49NNP0axZMxw4cCDX7b1+/RqXL18W602bNs11myTHgDsREREREREREREVqFevXuHKlSu4efMmkpOTC7s7RdbChQtx/vx5xMXFYc6cObl+rE6fPo20tDQAgKOjI7y9vfOim6SBKWWIiIiIiIiIiIiowNy8eRMjRoxAQkICAMDJyQkbNmyAo6NjIfesaDlz5gy2b98u1mNiYnD8+HG0atUqx20eO3ZMLDdp0gRGRhyPndf4iBIREREREREREVGBSEtLw/fffy+C7QDw8uXLPEmXUpxIkoS5c+dqle/YsSPHbaakpOD06dNinelk8gcD7kRERERERERERFQgtm7dijt37miVh4aGFkJviq7w8HA8fvxYq/zs2bMIDw/PUZuXLl1CXFwcAMDc3BwNGjTIVR9JNwbciYiIiIiIiIiIKN8lJSVh6dKlOreFhYUVcG+KDpVKhZMnT2LZsmUICQkBkJ52R83d3R2lS5cGkD7yfdeuXTk6ztmzZ8Vy/fr1YWFhkYtekz4MuBMREREREREREVG+e/jwId68eQMAMDMzw9dffy22vYsj3FNTU7F792707t0bY8eOxYoVK/DZZ58hNTUVN27cEPtVq1YNnTp1Eus7d+4UE59mx/Xr18VynTp1ctd50osBdyIiIiIiIiIiIsp3z549E8vu7u7w8fER60+fPs1REPltlJCQgA0bNqBz586YNm0aHj58KLaFh4fj+PHjuHXrlijz9vZGhw4dxASnERER+O+//7J1zNTUVFmbNWrUyOVZkD4MuBMREREREREREVG+i4iIEMuurq5wd3cX66mpqbKAvFp8fDzu3LkDlUqVo2OqVCpIkpSjuvnh2rVr6N69O+bPn6/zfIH0PPeawfFq1arBxcUF/v7+oiy7k6c+ePAAiYmJAABjY2N4eXnloPdkCAbciYiIiIiIiIiIKN9pBphdXV1hYWGBEiVKiLKMaWUiIyPRt29f9OvXD7NmzcrWsW7duoWvvvoK/v7++Pjjj5GcnJy7zmfTqVOn8M033+DcuXOibN++fRg2bJjscTAxMUHHjh3xzTffiLJz586JyU0VCoUIjnfu3FnsExgYiOjoaIP7o5lOpnLlyszfno8YcCciIiIiIiIiIqJ8lzHgDgAeHh6iTDPgnpycjOHDh4vJVPfv32/wSPXAwEAMGjQIhw4dQkpKCq5du4bz58/nxSkY5Ny5cxg3bhz279+Pr776CgkJCYiOjsb333+P1NRUAOmjzPv164cdO3Zg6tSp6NKlC8qUKaPVVrly5WBtbQ0AaNy4MZycnAAAKSkp2LNnj8F90gy4V69ePTenR1lgwJ2IiIiIiIiIiIjyXcaUMgBQunRpUaYOrj99+hTjx4/H48ePxbakpCRERUVleYyXL19i5syZWvngX7x4kau+GyoiIgJff/21SIHz5s0bXLlyBZs3b0ZCQgIAwNbWFsuWLcO4cePE46BQKNCtWzet9qpVqyaWTUxM0L59e7G+Y8cOg7+EYMC94DDgTkRERERERERERPlOc4S7m5sbAPkI95CQEKxbtw49e/bE2bNnteprBuz1+fHHHxETE6NVbkiwPreSkpLwxRdfaB3/+PHj2Lhxo1jv27cvatWqpVW/V69eaNKkiaysZs2asnXNtDIPHjzAjRs3suxXbGwsgoODxToD7vmLAXciIiIiIiIiIiLKV2lpaYiMjBTruka4nzhxAosWLRKTe2aUVcA9ODgYR48eFetGRv8LfWYn33lOzZs3Dzdv3tQq37x5swjCW1paokePHjrrm5qa4ueff8asWbNQtWpVNG/eHO3atZPt4+npidq1a4t1QyZP3bNnjxgJb29vrzN1DeUdBtyJiIiIiIiIiIgoX0VGRsrSvOga4a5JqVRixowZ8Pf3F2VZBdxDQkLEsru7O4YOHSrW83uE+/bt27Ft2zax7ufnp3O/rl27QqlU6m1HoVCgTZs2WLduHebNm6dzclPNUe47duzA8ePH9baXmpqKtWvXivU2bdpAoVBkdiqUSwy4ExERERERERERUb7SDJbb29uLQLKugHvnzp2xZcsWtGvXDiVLltTZhi4vX74UyyVLloSjo6NYf/XqVY77npWbN2/ip59+Eus1atTA/PnzZccH0ke3DxgwINfHa9WqlfjCQqVS4euvv8bt27d17rt3717xuBkbG6N///65Pj5ljgF3IiIiIiIiIiIiylea+dvV6WQAwMbGBp06dQIAlC9fHsuXL8d3330nRoGrA8tA1gF3zVHsTk5OcHBwEOv5FXBPTEzEV199heTkZACAg4MDfvzxR5iZmcHHx0e27+DBg+Hs7JzrY1pYWGDBggWwtrYWfdA1UWxaWhoCAgLEevv27WWPJ+UPBtyJiIiIiIiIiIgoX+kLuAPAlClTsG/fPmzatAm+vr6ybdkJuGuOcHd0dCyQEe779+/H06dPAaTnjJ89e7Y4P80JUN3c3NC3b988O26lSpUwe/ZssX7nzh2tfO5Hjx4VaXaMjIwwcODAPDs+6ceAOxEREREREREREeUrzYC7rlHWzs7OOnOLa+4bHh6OR48e4fXr1zqPoTkpq64R7uqJQ/PS1q1bxXKXLl1Qt25dsd6mTRu89957qFixIn744Qed+dhzo1GjRmjbtq1Y//XXX/HmzRsAgCRJWL16tdjWsmVLeHp65unxSTcG3ImIiIiIiIiIiChfZTbCPTOaAffo6Gj06NEDnTt31jkJamYpZdLS0kQwOq/cvHkTN2/eFOs9evSQbTczM8PcuXOxceNG1KxZM0+PrTZ69GhYWloCAGJiYnDy5EkAwOnTp3H37l2x3+DBg/Pl+KSNAXciIiIiIiIiIiLKV6GhoWI5OwF3Z2dnGBsby8revHmDAwcOaO2rmVLGyckJtra2srq6gvS58c8//4jlmjVrolKlSnnaviFcXFzQuHFjsR4WFqY1ur1x48aoXLlygfftXcWAOxEREREREREREeWbs2fP4v79+2K9XLlyBtc1MTFBiRIltMqfPHmiVaYZUHd0dISRkZGYfBVIHyGfV2JjY7Fv3z6x/uGHH+ZZ29lVsmRJsRweHo7Lly/j6tWrouzjjz8ujG69sxhwJyIiIiIiIiIionyhUqnwyy+/iPW6devCy8srW23oyvmeMXiekJCA+Ph4se7s7AwAsrQyeTnC/d9//0ViYiIAwN7eHq1atcqztrNLM+AeEREhG91ep06dfEtnQ7qZFHYHiIiIiIiIiIiIqHg6ePAg7ty5I9Y/++wznZOjZiYtLU2rLDw8XLaumU5GoVCIke2Ojo6i/NWrV9k6rj6SJMkmS+3YsSPMzc3zpO2c0PxC4vz587JtHN1e8DjCnYiIiIiIiIiIiPJcSkoKli5dKtZbtGiB6tWrZ7sdXfnHMwu4K5VKmJiYiGW1vAq4X7lyBQ8fPhTrXbt2zZN2c0pzhLumypUro379+gXcG2LAnYiIiIiIiIiIiPLc9u3bERYWBgAwNjbGqFGjctROly5dRABd7fnz50hOThbrmulinJycxHJ+jHDXnLC1fv368PT0zJN2c0pXyh0A8PX1zfavCSj3GHAnIiIiIiIiIiJ6ByQmJmLVqlVYt26dzjQteSk+Ph4rVqwQ6506dULZsmVz1FaVKlWwfft2bNy4UQSQJUnCs2fPxD6aI9w1g+yaI9zzKof7gwcPxPJ7772XJ23mho2NDWxtbbXKK1asWAi9IeZwJyIiIiIiIiIiegf89ddfIsVLQkIChg8fnq/HUge4zc3Nc30s9SjuEiVKiED706dPUbp0aQDygLu+Ee4ZJ1rNqUePHonlcuXK5UmbuVWyZEm8efNGVsaAe+HgCHciIiIiIiIiIqJiSqVS4fHjx0hLS8OmTZtE+fLly/MsAJ3Rq1evsG7dOrHet29fuLi45EnbmvnKNfO46wu4Ozg4iOW8GOEeHR0tS02T01H7eS1jWhmFQoEKFSoUUm/ebRzhTkREREREREREVExNnjwZhw8fRuXKlWVBaQBYv349Pvvss1y1n5KSgkuXLiE2NhYlSpSAt7c3Vq9ejbi4OACAnZ0dBgwYkKtjaCpZsiSuXLkCIH2Eu5ohAffw8HCsWLECJUuWhJubG0qVKoVSpUpl6/ghISFi2cbGRnaswpRx4lQPDw9YWloWUm/ebQy4ExERERERERERFUN3797F4cOHxXJGGzduRP/+/WFvb5+j9iVJwvDhw3Ht2jVR9sknn2D79u1iffDgwTrzi+eUZoBcc4S75uh1zTQymgH3+Ph4LFu2TNZey5Yt8eOPPxo8uWhwcLBYLlu2bJGZlDRjwJ3pZAoPU8oQEREREREREREVQzt27Mh0e2JiIoKCgnLcfmhoqCzYDgArV65EQkICAMDMzAw9evTIcfu6aAbcQ0JCcPz4cXz//feyLxQ0R517eHiIPO+6HD58WDZqPSsZA+5FRcaUMgy4Fx6OcCciIiIiIiIiIipmkpKSsHfv3iz3CwkJQZMmTXJ0jCdPnmiVpaSkiOXq1avDwsIiR23roxlwv3nzJsaPH6+1j+Zob2NjYwQEBODgwYN48uQJIiIiEBERgTt37iA5ORlA+iSohgbP35aAe6VKlQqpJ8SAOxERERERERER0VsqJSUFhw4dwoEDB/DmzRukpaUhLS0NcXFxeP36tc46NWrUECPTHz16lONj6wq4a6pTp06O29YnY+oUTQqFAt26dYOnp6es3N7eHt27d5eVjRs3DidOnACAYjHCPePjUpT69q5hwJ2IiIiIiIiIiOgtFBERgU8++QShoaHZqtemTRsRcNcMIGdXWFhYpttr166d47b1KVmyJEqVKiUmTLW2tkajRo3QuHFjNGrUSJazPTNly5YVAXdDH4Pk5GTZlwxFKajt5OSEhg0b4uzZs/D19UW5cuUKu0vvLAbciYiIiIiIiIiI3kKLFi3KMthubm6OKVOmYOrUqUhNTcX7778vSzeSm4C7ZvDZ29sbN27cEOvGxsaoUaNGjtvWx9jYGMuXL8exY8dQoUIF1KpVCyYm2Q9xao6CN3SEe2hoKFQqleiHh4dHto+bnxYtWoR79+6hYsWKRWYy13cRA+5ERERERERERERvmcjISBw5ckSsv//++2jUqBFMTExgbGws/lWtWhUlSpSAq6sr7t27hzZt2sjyrMfExCA6OhpKpTLbfdAMuHfu3Bm3bt0SAWlvb29YWlrm/AQz4ebmhl69euWqDc3R6cHBwZAkKcsg9ePHj8Wyh4dHjgL9+cnY2BhVqlQp7G6884rWq4KIiIiIiIiIiOgdcvLkSaxYsQKNGzfGsGHDDK63bds2pKWlAQBcXV0xY8aMTAPAtWrVQq1atQAAkiTBzs5O5HgPDg4W2wwlSZIspUyVKlVQtWpVMco9P9LJ5CXNgPubN2/w6tUrODo6ZlonMjJSLGecpJRIzaiwO0BERERERERERPQuSk1NxfTp03Hjxg0sW7YMt2/fNrjeP//8I9a7deuWrdHWCoVCa4R3dkVHRyM+Pl6su7u7o0+fPgAACwsLdO7cOdttFiSlUgl7e3uxbshj8PLlS7Hs5OSUH92iYoABdyIiIiIiIiIiokJw6dIlvHr1SqwfO3bMoHpnzpzBixcvAAAmJibo0qVLto+tmcM8JwF3zXQyNjY2sLOzQ5s2bbB7927s27cPZcqUyXabBS27edwZcCdDMOBORERERERERERUCI4ePSpbDwoKMqjevn37xHKzZs1yFPzN7Qh3zXQyHh4eIv+5m5sbbGxsst1eYcjuY6CZUoYBd9KHAXciIiIiIiIiIqICplKptALuV65cQXJycqb1EhISZCPh27Rpk6PjGxJsjoqKwuTJkzFr1iwkJibKtmkG3N3d3XPUh8KWmxHuzs7O+dInevsx4E5ERERERERERFTAgoKCZCOmASApKQnXrl3LtN6xY8dE8NvGxgaNGjXK0fE1A+5Pnz5FUlKS1j4LFy7EwYMHsX37dlnOeECeUuZtDbhrPgaPHj3Kcn/NgHtWE6zSu4sBdyIiIiIiIiIiogKUmpqKFStW6Nx24cKFTOvu379fLL/33nswNzfPUR/c3d3FRKsqlQqhoaGy7fHx8dizZ49YX79+vVhOTEzE1atXZW29jSpUqCCWnzx5goSEBL37SpKEqKgosc6UMqQPA+5EREREREREREQFRJIk/PDDDzh37pwo0xxprVme0f3793Hy5Emx/sEHH+S4HyYmJihdurRYV6eV2bNnDz7++GOMGTNGtn9cXBwkSYIkSZgxYwYeP34sttWoUSPH/ShMpUqVkn1h8fDhQ737vnnzBikpKWKdAXfShwF3IiIiIiIiIiKiArJixQrs3LlTrLdq1QrTpk0T60FBQbhx44bOur/88gskSQKQHqSvV69ervqSMY97YmIifvzxRwQFBeHy5cuyfePi4hAaGoo//vgDBw4cEOU9e/ZE5cqVc9WPwmJkZCQb5X7//n29+2qmkzExMYGdnV2+9o3eXgy4ExERERERERERFYAdO3Zg+fLlYr1WrVqYPn06vL29Ua1aNVGuuY/ahQsXcOrUKbH+2WefwdjYOFf90Qy4h4SEIDIyEvHx8Xr3//333/Hrr7+K9Xr16mH8+PG56kNh0wy4nzlzBr/99pvsVwRqmgF3JycnGBkxrEq68ZVBRERERERERESUz86cOYMffvhBrJctWxbz58+Hubk5FAoFhg8fLradOnUK169fF+uxsbGYOXOmWPfx8UGzZs1y3SdPT0+xHBwcrDWJa0YHDhwQI+zd3d0xe/ZskQf+baUZcD906BBWr16N8ePH4+nTp7L9NB8bppOhzDDgTkRERERERERElI/u3r2LL7/8EmlpaQDSA7aLFy+Gvb292Mff3x/e3t5iffPmzQD+l/P9yZMnANLToIwbNw4KhSLX/cqYUiZjwN3Y2Bj169fXqmdlZYX58+dDqVTmug+FrWLFilplKpUKQUFBsjLNEe6Ojo753i96ezHgTkRERERERERElI9++eUXkarF0tISixYtQqlSpWT7KBQK9O3bV6yfPn0aKpUKO3fulOVMHzZsGKpXr54n/dIMuCckJODOnTtivV69ejh+/Di++OILrXozZszQGah+G2mOcNcUGxsrW8+YUoZIHwbciYiIiIiIiIiI8klsbCzOnz8v1qdPn44qVaro3NfPz0/kBn/16hX27t2Ln376SWz39fXFxx9/nGd9s7GxgbOzs1i/ePGiWHZxcYG5uTk8PT3h4uIiykeOHInmzZvnWR8Km7OzMywsLLTKNQPsABAVFSWWGXCnzDDgTkRERERERERElEdu3ryJcePGYfXq1UhNTcWZM2dEKhmlUplp7nU7OzvUrFlTrE+fPh1JSUkAAHt7e8ycOTPXE6VmpDnK/caNG2JZHVRWKBSYOnUqfH198emnn2LIkCF5evzCplAokJiYqFWeMeDOEe5kqLd7VgMiIiIiIiIiIqIiQpIkTJkyBcHBwThx4gSuX78um1TU398/y4B5o0aNcOXKFQDpucTVpk2bBldX1zzvc9myZXHhwgUAEF8MAPKgcsOGDdGwYcM8P3ZR0adPH2zYsEFWxoA75RRHuBMREREREREREeWBhw8fIjg4WKwfP34cR44cEetNmjTJso1GjRpplfXp08egujmhOcJdk2aqmeKuT58+Wml+NFPIAPKA+7v02FD2MeBORERERERERESUB44dO6Z3m4mJiUGjxL28vGQjqL28vPD555/nSf900Rdwf5dGcZcqVQrr16/HkiVLRJlmgD0hIQGvXr0S6+/SY0PZx4A7ERERERERERFRHjh+/LjebXXq1IGNjU2WbSgUCjExqqurK3744QeYmZnlWR8zYsD9fzTP+eXLl5AkCQCwe/dukd7HysoqX1L7UPHBHO5ERERERERERETZEBERgV9++QVRUVEoUaIESpQoAUdHR1y/fl3s89dffyEmJgYLFixATEwMRo0aZXD7PXv2RMuWLWFlZQUrK6v8OAWhRIkSsLCw0Jo41NHRMV+PWxRpBtyTkpIQFxcHKysr/PXXX6K8c+fO+foFCL39GHAnIiIiIiIiIiLKhqVLl2L//v16t5csWRKVKlWCQqHAX3/9BUmSoFAoDG5foVAUWJ5wIyMjeHp64s6dO6LMxMQEdnZ2BXL8osTe3h5GRkZiNHtUVBQuXryI0NBQAOmPVa9evQqzi/QWYEoZIiIiIiIiIiKibAgKCsp0e/PmzWUB9uwE2wtDxrQyTk5OMDJ698KGxsbGcHBwEOsvX77En3/+KdabN28ODw+PwugavUU4wp2IiIiIiIiIiMhAiYmJCAsLE+s9e/ZEXFwcnj9/jmfPnsHDwwNDhw4txB5mn66A+7vKyclJTJh6+vRpXLp0SWzr169fYXWL3iIMuBMRERERERERERkoODhYTKZpZmaGCRMmwNjYuJB7lTsMuP+P5rlr5m6vXr06atasWRhdorfMu/fbECIiIiIiIiIiohy6f/++WC5XrtxbH2wHGHDXlHHiVLV+/foV+dRAVDQw4E5ERERERERERGSgBw8eiOUKFSoUYk/yTunSpWXBZEdHx0LsTeHSde5ubm547733CqE39DZiwJ2IiIiIiIiIiMhAxTHgbmFhgVKlSol1jnCX6927N0xMmJmbDMOAOxERERERERERkYGKY8AdAOrXry+W3+Vc5RkD7lZWVujSpUvhdIbeSsU+4P7ixQtMnDgRXl5esLS0hKOjI3x9fTFp0iTZfgEBAVAoFHr/9e7dW+8xbty4gR49esDFxQWWlpaoUaMGFi5cCJVKpbfOq1evMGbMGHh6esLc3Byenp4YO3YsoqOj8+rUiYiIiIiIiIgoD8XGxuLZs2divWLFioXYm7z12Wef4bPPPsPcuXNRtWrVwu5OockYcO/cuTNsbGwKqTf0NirWv4W4ePEiPvjgA7x8+RLe3t7o3LkzXr9+jZs3b2LBggWYO3euVh0fHx/UqlVLq7xBgwY6j3HmzBm0bNkSCQkJqF+/PsqWLYvjx49j3LhxOH36NDZt2qQ1oUJkZCT8/Pxw//59lC9fHl26dMGNGzewaNEi7N27F2fOnHmnc2URERERERERERVFmqPbra2t4erqWoi9yVv29vYYNGhQYXej0Gk+p0ZGRpkOwiXSpdgG3F+8eIE2bdogISEBO3bsQKdOnWTbz58/r7Nely5dMG3aNIOOkZKSgn79+iEhIQE///wzxo0bByD92873338fmzdvRrt27bQuVmPHjsX9+/fRrVs3bNq0SeSAGj16NH755ReMHz8eAQEB2TpfIiIiIiIiIiLKX+fOnRPL5cuX1xpkSW+/MmXKoG3btjh06BCGDBkCd3f3wu4SvWWKbUqZqVOnIjIyEnPnztUKtgPyvFQ5tW3bNjx69Ag+Pj4i2A4ANjY2WLJkCQBg/vz5sjrh4eHYsGEDzMzM8Ntvv8kmXJg7dy5cXFywfv16PH/+PNf9IyIiIiIiIiKivBEXF4dNmzaJdT8/v0LsDeWnmTNn4tSpUxg6dGhhd4XeQsUy4J6QkID169fD2toagwcPzrfj/PvvvwCA7t27a23z9fVF+fLlcf36dQQHB4vyffv2QaVSoUmTJlo/OzI3N0fHjh2RlpaGPXv25Fu/iYiIiIiIiIgoezZv3oyYmBgA6RNp9urVq5B7RPnJyKhYhk2pABTLlDIXLlzAmzdv0LhxY1haWmLv3r04ePAgEhMTUblyZfTs2ROlSpXSWffixYuYNGkSXr9+DTc3N7Ro0QLNmjXTue/Vq1cBpAfXdfH19cXDhw8RFBSEsmXLGlxn9erVCAoKys4pExERERERERFRPomLi8P69evFes+ePWFvb1+IPSKioqpYBtxv3rwJAChRogS6dOmCHTt2yLZ//fXXWLVqFfr06aNVd/fu3di9e7dYnzFjBpo1a4ZNmzZpjUh//PgxAMDDw0NnP9TlISEhuapDRERERERERESGkyQJp0+fhoWFBerUqZPl/qmpqTh69ChsbW3RoEEDrdzs69evR3R0NADAwsIC/fr1y49uE1ExUCx/G/Hq1SsAwM6dO7Fv3z78+uuveP78OYKDgzFx4kQkJCRg4MCBuHLliqhTsmRJTJs2DZcvX0ZMTAwiIiKwc+dOVKlSBceOHUOHDh2QlpYmO05sbCyA9J8R6WJtbQ0AePPmTa7qZMbb21vnP81Zs4mIiIiIiIiI3iWbNm3CmDFjMGLECCxfvjzL/Tds2IDJkyfjs88+w/jx40VwHQAiIyNlo9v79u0LBweH/Og2ERUDxTLgrlKpAKR/OzljxgyMGjUKLi4u8PT0xNy5c9GjRw+kpKRg7ty5os4HH3yAqVOnolatWrCzs4Orqys6duyI//77D5UrV8aFCxfw999/F9YpERERERERERGRARITE7Fq1Sqxvnz5cmzevDnTOsePHxfLJ06cQO/evXH+/HkAwG+//YaEhAQAgFKpxIABA/Kh10RUXBTLgLuNjY1Y1jVpqrrs2LFjBrU1evRoAMD+/ft1Hic+Pl5n3bi4OACAra1trupk5saNGzr/VahQwaD6RERERERERETFya5du0T2A7WffvoJBw4c0Lm/JEm4f/++rCwyMhKffvopJk+ejJ07d4ryYcOGyeJOREQZFcuAu6enJ4D0tC0uLi5a29UTmD5//tyg9ipVqgQACA8Pl5WXKVMGABAWFqaznrpc3Z+c1iEiIiIiIiIioqylpqbK0r+oSZKEKVOm4OzZs1rbnj9/Lkvta2JiIuocPHhQlHt5eeHDDz/Mh14TUXFSLAPutWvXBgAkJCQgKSlJa3tUVBQAGPyNpPpbUXV+dTUfHx8AwKVLl3TWU5fXrFkzV3WIiIiIiIiIiChr27dvx5MnTwAAxsbG+OWXX2Bvbw8gPRg/adIkXL9+XVbn3r17YrlEiRIICAjQGghpbm6OmTNnimA8EZE+xTLgXqZMGfj4+ECSJJ1pY9Rl6sB8VrZu3QoA8PX1lZW3b98eALBlyxatOpcvX8bDhw9RvXp1MaIeANq0aQMjIyOcOHFCa4R9UlISdu3aBWNjY7Rr186gvhERERERERERvU1UKhXu3buHnTt3Yv/+/UhNTc1xW0lJSWJ0emRkJJYsWSK2tW3bFn5+fli0aBEsLS0BpA/OHDNmDB49eiT200wnU7FiRVSpUgXr169H165dRfm4ceNQvnz5HPeTiN4dxTLgDgBffPEFAGDixImyVDBXrlzB/PnzAQAjR44U5bNnz0ZkZKSsjZSUFEyfPh2bN2+GpaWlVj74rl27oly5crh69SoWLFggyuPi4vDpp58CACZMmCCrU7JkSfTp0wfJyckYNWqU7EPliy++wIsXL/DRRx+hRIkSuTl9IiIiIiIiIqIiJykpCUOGDEGfPn0wY8YMfPPNNxg8eLBslLmhnj59inbt2qFVq1YIDAzEokWLEBsbCyA9q8Fnn30GAKhevTrmzp0rRqfHxMTgs88+Q0REBADgwYMHos2KFSsCACwtLfHNN9/gr7/+wrp169C9e/dcnTcRvTsUkiRJhd2J/DJo0CD88ccfUCqVaNSoERISEnD69GkkJSVh2LBhWL58udhXoVDA3NwcdevWRenSpfH69WtcuXIFT58+hYWFBf78809069ZN6xinT59Gq1atkJCQgAYNGsDT0xMnTpxAeHg4unfvjr///hsKhUJWJzIyEg0bNsSDBw9QoUIF1K1bFzdu3MD169dRqVIlnD17Fo6Ojrk6d29vbwDpk6oSERERERERERUFW7ZswY8//qhVbmxsjI8//hgff/wxTE1NDWpr0aJFWLduHQCgVKlSCA8PhzrM9eWXX6JHjx6y/Q8cOIBvvvlG7FOuXDmsX78egwYNEgH/GTNmMOsAUTFVUPHSYjvCHQDWrFmD5cuXo0KFCggMDMT58+fh6+uLgIAAWbAdAKZMmYKmTZsiNDQUO3bswJEjR2BlZYURI0bgypUrOoPtANCoUSP8999/+PDDD3H//n3s3LkTjo6O+Pnnn7Fp0yatYDsAODs74/z58/j888+RnJyMbdu2ISYmBqNHj8b58+dzHWwnIiIiIiIiIipq0tLS8Oeff+rdtmLFCvTv3x/Xrl0zKM3MyZMnxfLTp09FIN3d3V1nHOf999/HpEmTxPqjR49w4MABWXoZ9Qh3IqKcKtYj3N9lHOFOREREREREREXJkSNHRApgIyMj/PPPPwgLC8OsWbNEehdNtWrVwsKFC2FjY6O1LSwsDF26dNF5nGHDhmHEiBF6+zF58mQcPHgQAFCnTh1cvHgRQPoo+xMnTsDMzCy7p0ZEbwGOcCciIiIiIiIiomJjw4YNYrlFixbw8PBAw4YNsWnTJq30L0D6PHxbt27V2Zbm6PaM2rRpk2k/6tSpI5bVwXYA8PT0ZLCdiHKNAXciIiIiIiIiIspXqampCAoKEuu9e/cWy9bW1vjyyy+xbNkyrZQuFy5c0NneqVOndJZXq1YNnp6emfalevXqOstr1qyZaT0iIkMw4E5ERERERERERPnq+fPnSEtLE+tVqlTR2qdOnTrYuHEj5s2bJ8quXLmilc89Pj5eFoi3t7cXy4ZMeFqxYkWYm5trlTdu3DjLukREWWHAnYiIiIiIiIiI8tXTp0/FsrOzMywsLPTu27BhQ5iamgIAEhIScOvWLdn28+fPIyUlBQBga2uLRYsWoVq1amjdurXOyVIzMjExgZeXl1ZZvXr1DD4fIiJ9GHAnIiIiIiIiIqJ8FRYWJpZLlSqV6b4WFhaoUaOGWM+YVkYzf7ufnx+qV6+OtWvXYvbs2QbnYFdPnqjm6+sLa2trg+oSEWWGAXciIiIiIiIiIspXT548Ecvu7u5Z7q9vYlNJkmQB95ymgcmYx93f3z9H7RARZcSAOxERERERERER5SvNlDIeHh5Z7q8ZcL9y5QoSExMBAHfu3EFkZCQAwMjICI0aNcpRfzKOcGfAnYjyCgPuRERERERERESUr7I7wr1GjRpiYtPExESsXbsWAHDixAnZPkqlMkf98fDwQKtWrQAArVu3hqenZ47aISLKiAF3IiIiIiIiIiLKV5oB96xyuAOAubk5PvzwQ7EeEBCA0NDQPEknozZ79mzs378fs2fPhkKhyFVbRERqDLgTEREREREREb2jVCoVHj16hLNnz+L+/ft53n50dDRu376NV69eiTJDRrgDwIgRI+Di4gIASE5OxtSpU3Hjxg2xvUmTJrnqm0KhgJOTU67aICLKyKSwO0BERERERERERAUvNDQUX3zxBe7duyfKfvzxR5FqJbdCQkIwbNgwREVFiTJTU1MRRM+KtbU1xo8fj8mTJwMAgoKCxDZXV1dUqFAhT/pJRJSXOMKdiIiIiIiIiOgdc/36dXz88ceyYDsALF68GCkpKXlyjPnz58uC7UB6OhkjI8PDUS1btkTJkiW1yps0acI0MERUJDHgTkRERERERET0Djlx4gRGjBghS/Oi9vTpU+zevTvXxzh58iROnz6tVW5oOhk1IyMjtG/fXqs8t/nbiYjyCwPuRERERERERETviH/++QcTJkxAUlISAMDExAQzZ85E165dxT6rVq1CTExMjo+RkpKCn3/+Wec2W1vbbLenK+Bet27dbLdDRFQQGHAnIiIiIiIiIirmEhIS8Msvv+CHH36ASqUCkJ4jffHixWjbti0+/vhjmJikT/UXERGBQYMGISQkJEfH2rRpEx4/fgwgfYR6tWrVxLZ69eplu73SpUvLJjetVq0aLCwsctQ3IqL8xoA7EREREREREVExtmvXLnTo0AF//PGHKHNxccHKlStRv359AEDJkiXRv39/sT00NBTjx49HWlpato4VFRWFFStWiPVu3bphyZIl6N69OwYOHIi2bdvm6By+++47sTxixIgctUFEVBAUkiRJhd0Jynve3t4AgBs3bhRyT4iIiIiIiIiosLx58watW7dGamqqKCtfvjwWL14MNzc32b6SJGH16tVYunSpKFu4cGG28qV///332LZtG4D09DHbtm2DUqnM3Un8v+DgYABA2bJl86Q9Inq3FFS81CRfWyciIiIiIiIiokJz+/ZtEWw3NTVFz549MXToUJ251BUKBYYMGYLbt2/j6NGjAICtW7dmGXCPj4/H/fv3oVAosH37dlE+fPjwPAu2Awy0E9HbgQF3IiIiIiIiIqJi6vbt22K5du3aGDduXJZ1PvzwQxFwP3XqFCIiIrRGw6slJSWhT58+ePLkiay8bNmy6NGjRy56TkT0dmIOdyIiIiIiIiKiYurOnTtiuUqVKgbVqV+/Ptzd3QEAKpVKNmo9o8uXL2sF2wFgwoQJYhJWIqJ3CQPuRERERERERETFlOYIdy8vL4PqGBkZoWvXrmI9MDBQtj0pKQkbN27E1q1bdeZCbty4Mfz8/HLWYSKitxy/aiQiIiIiIiIiKoYSEhIQEhIi1g0NuANAixYtsGTJEgDA/fv38fz5c5QoUQIA8M8//2D+/Pk66/Xu3RsjR47MRa+JiN5uHOFORERERERERFQM3b17F5IkAQCsrKxQpkwZg+uWLl1apJUBgDNnzojllStX6qwzf/58TJw4ETY2NjnsMRHR248BdyIiIiIiIiKiYkgzf3ulSpVgZGR4GEihUKBRo0ZiXTPgnpiYqLOOt7d3DnpJRFS8MOBORERERERERFTMPHnyBIcOHRLr2Ukno6aZh/3cuXNITU0FADg5OWntW6JECTg7O+egp0RExQsD7kRERERERERExciePXvw4Ycf4tKlS6IsJwH3unXrwtTUFADw5s0bXL9+HZIkITIyUmvfatWq5bzDRETFCCdNJSIiIiIiIiJ6y0iShHXr1uHs2bNQKBQwNTWFiYkJVCoVjh8/LtvX1dUVzZo1y/YxrKysULt2bZw/fx5AelqZsmXLIjk5WWvf8uXL5+xEiIiKGQbciYiIiIiIiIjeMmfOnMHixYsz3cfBwQFDhgxBhw4dcjyRqZ+fnyzg3qpVK5371alTJ0ftExEVN0wpQ0RERERERET0lgkMDMx0u5ubG1auXInevXvnONgOyPO437x5UzYRK5A+Cr5p06aoV69ejo9BRFSccIQ7EREREREREVEBCQ8Px6lTp9CkSRO4urrKtm3ZsgXXr19Hjx494O3trbcNSZJw5swZsd61a1dUqFABqampSE1NhVKpRMuWLWFra5vr/laoUAElSpTA8+fPAQC7d+8W22rXro1ly5bByIjjOYmI1BhwJyIiIiIiIiIqACEhIRgwYADi4uKwY8cOrF27FgqFAgBw5MgR/PjjjwCAvXv34pNPPsGAAQN0BrODg4MRHh4u1keMGAFnZ+d86bNCoYCfnx927NgBALhw4YLY5uLiwmA7EVEGvCoSEREREREREeWz5ORkfP3114iLiwMA3Lp1CxEREQCAlJQU/PLLL2LftLQ0LFmyBJ999hlevHiBtLQ07N69G+vXr0dycjJOnz4t9q1cuXK+BdvVNNPKaHJxccnX4xIRvY04wp2IiIiIiIiIKJ/98ssvWvnP79y5g5IlS2Lr1q0IDQ3VqnP+/Hn06dMHVatWFSlkXrx4gQcPHoh9GjVqlL8dB9CgQQMYGxsjLS1NVs6AOxGRNo5wJyIiIiIiIiLKR8ePH8eGDRu0yu/cuQOVSoU1a9aIsrZt26JFixZiPTo6Wpavffv27bh48aJYL4iAu62tLapXr65VzoA7EZE2BtyJiIiIiIiIiPLJs2fPMH36dLGuztkOALdv38bdu3fx8uVLAICxsTHGjBmDOXPm4JtvvoG5ublWe3FxcUhJSQEAODo6ombNmvl8Bul0pZUpUaJEgRybiOhtwoA7EREREREREVE+SEtLw3fffYeYmBgAgIWFBUaPHi2237lzRzZavWrVqnB2doZCoUDXrl2xbt06VKtWTRak19S3b1+YmBRMtmBdI+k5wp2ISBsD7kRERERERERE+WD16tW4dOmSWJ80aRLatGkj1p8/f45Dhw6J9Tp16sjqly9fHmvXrsWxY8ewatUq2TYbGxt07949n3qurUqVKlAqlbKy/J6slYjobcSAOxERERERERFRHrt06RJWrFgh1j/44AN06tQJzs7OcHR0FOXXrl0Ty3Xr1tXZlpWVFWrWrImyZcuKsl69esHGxibvO66HkZGRLK2MnZ0dLCwsCuz4RERvCwbciYiIiIiIiIjyUHR0NL777juoVCoAgLu7OyZPngyFQgGFQgEvLy+tOsbGxvDx8dHbpkKhwKRJk+Di4oK6deuif//++dZ/fTTTypQqVarAj09E9DYomERfRERERERERETviDlz5uDZs2cAABMTE8yePVs2Gr1KlSo4c+aMrI63tzesrKwybbdBgwbYu3dv3nfYQK1bt8a+fftw48YNDBkypND6QURUlDHgTkRERERERESUR16+fCnLy/7ZZ5+hWrVqsn38/PywZs0aWVnG/O1FkYmJCRYtWgRJkvRO5EpE9K5jShkiIiIiIiIiojxy7NgxSJIEAHB1dUXfvn219vH19cXChQtRtWpVAIC5uTnat29foP3MDQbbiYj0K7AR7q9evYKDg4NB+164cEHvRCFEREREREREREVNamoq4uPjceTIEVHWokULGBnpHuvYuHFj+Pv7Izg4GLa2tnB2di6orhIRUT4qsBHuNWrUkH3o6CJJEr7//nv4+/sXUK+IiIiIiIiIiHLn1atX6Ny5M1q0aIGzZ8+K8hYtWmRaT6FQoFy5cgy2ExEVIwUWcA8PD8f777+PiRMnIiUlRWt7SEgImjVrhu+++w729vYF1S0iIiIiIiIiolzZsWOHmCRVzcnJCTVr1iykHhERUWEpsIB7YGAgPDw8sGDBAtSrVw83btwQ29avX49atWrh5MmTeP/99xEUFFRQ3SIiIiIiIiIiypUTJ05olTVr1gzGxsaF0BsiIipMBRZwb9KkCYKCgtC7d28EBQWhXr16mDdvHvr06YMBAwYgKSkJixYtwr59++Dm5lZQ3SIiIiIiIiIiyrHo6Ghcu3ZNVmZlZaVzslQiIir+CmzSVACws7PDn3/+ifbt22PkyJH48ssvAaTPzr1u3ToxOzcRERERERER0dvg1KlTUKlUAAAXFxesX78elpaWsLKyKuSeERFRYSjQgDuQPmv31atXER8fD0mSAADJycniw4mIiIiIiIiI6G2hmU6mcePGcHJyKsTeEBFRYSuwlDIAcPfuXTRs2BDz5s1DqVKlsGPHDvTp0wfXr19HvXr1sHDhwoLsDhERERERERFRjoWGhuL06dNivUmTJoXYGyIiKgoKLOD++++/w9fXF5cuXUL37t0RFBSEjh074s8//8Sff/4Jc3NzTJgwAR988AHCw8MLqltERERERERERNn28uVLfPbZZ4iPjwcAWFtbo379+oXcKyIiKmwFFnAfNWoUjI2NERAQgE2bNkGpVIptffr0wbVr19C0aVMcPHgQNWvWLKhuERERERERERFlS3x8PMaNG4cnT56IssmTJ8PCwqIQe0VEREVBgQXcGzVqhCtXrmDAgAE6t3t4eODIkSP48ccf8ebNm4LqFhERERERERGRwVJTU/HVV1/h5s2bomz06NFo06ZNIfaKiIiKigILuB8/fhzlypXLdB+FQoEvvvgC586dK6BeERERERERFR+pqakIDg5GSkpKYXeFqFiSJAnff/+9LG9779690b9//0LsFRERFSUFFnA3MpIfKikpCeHh4YiKitLa18fHp6C6RUREREREVCwkJyfjk08+Qffu3fHll19CkqTC7hJRsbN8+XLs2rVLrLds2RLjxo2DQqEoxF4REVFRUmABd7Xly5ejdu3asLa2hoeHByZOnCi2/fPPP+jWrRvu379f0N0iIiIiIiJ6q61atQqXL18GkP4L41u3bhVyj4iKl/DwcKxatUqs165dGzNmzICxsXEh9oqIiIqaAgu4p6WloWvXrvjkk09w69YtVK1aVWvEhY+PD7Zv345NmzYVVLeIiIiIiIjeevfu3UNAQICsbO/evYXTGaJi6urVq1CpVACAEiVKYP78+TA3Ny/kXhERUVFTYAH3JUuWYMeOHWjbti1CQkJw7do1rX0qVKiAihUr8saQiIiIiIjIQGlpaZg5cybS0tJk5QcOHEBqamoh9Yqo+Ll9+7ZYrlOnDuzs7AqxN0REVFQVWMA9ICAArq6u2LRpE1xdXfXuV61aNYSEhBRUt4iIiIiIiLQcPnwYM2fOxLlz5wq7K0JycjJSUlKQmpoq+7Xwhg0bcPPmTbGunj/r5cuX+O+//wq8n0TFlWbAvUqVKoXYEyIiKspMCupAd+7cwfvvvw9ra+tM97O2tsaLFy8KqFdERERERERyYWFh+Prrr5GWloYdO3agdevWGD9+PFxcXAqlPykpKRgzZgzOnz8vK1coFDAyMpKNbG/bti3i4+Nx7NgxAMC+ffvg5+dXoP0lKo4kSZIF3L28vAqxN0REVJQV2Ah3U1NTJCYmZrnf48ePYWtrWwA9IiIiIiIi0hYYGCgLYh88eBDdu3fHhg0bCiVFy7Fjx7SC7UB6AFCznw4ODpgwYQLatGkjys6cOSNyThNRzj158gSxsbFinQF3IiLSp8AC7t7e3rh48SLevHmjd5/nz5/jypUrqFWrVkF1i4iIiIiISObkyZNaZXFxcZg/fz4GDBiAu3fvFmh/Hjx4kOU+xsbG+O6776BUKtGgQQORViYqKgr37t3L7y4SFXuao9s9PDw4UJCIiPQqsIB7//798fLlS4wcORLJycla29PS0vDpp58iPj4eAwcOLKhuERERERERCbGxsbh8+bJY79Onj2xixLt37+Lzzz/XmqA0Pz1+/FjWn927d2PHjh3Ytm0btm7dis2bN+PgwYNo2rQpAMDOzg7e3t6iztmzZwusr0TF1Z07d8Qy87cTEVFmCizgPnz4cDRv3hwbNmyAl5cXRo4cCQC4evUqxowZg8qVK2Pr1q1o3bo1+vXrV1DdIiIiIiIqtp4+fYqVK1fi4MGDhd2Vt8bZs2dFMF2pVGLs2LHYunUrOnbsKPZ5+fJlgc47FRISIparVKkCNzc3uLu7o3Tp0vD09ES5cuVkXwoAQMOGDcVyUZr4leht8/TpUwwZMgRr1qwRZQy4ExFRZgos4G5sbIw9e/bgk08+wdOnT7F8+XIAwOXLl/HLL7/g8ePHGDZsGLZv3w6FQlFQ3SIiIiIieqtFRUVh1KhRGD16NJ4+fQogPSA8d+5cdOvWDb///jsmT56MS5cuAQBSU1Oxf/9+nWlTSJ5Oxs/PD8bGxnBwcMDUqVNhb28vtuU24P706VOcO3cuy/zqkiTJRrh7enoa1H6DBg3E8uXLlw2aT4uItK1btw5Xr16VlTF/OxERZcakIA9mYWGBX3/9FdOmTUNgYCCCg4OhUqng4eGB9957D6VKlSrI7hARERERvfVWrFghJtQcO3Ys3nvvPfz1119ISEiQ7RcYGAhfX1/8/fff+PnnnwEA8+bNQ/PmzQu6y0WWJEk4c+aMWG/SpIlse4kSJRATEwMAePbsGWrUqJGj44SHh2PAgAGIjo5G586d8d133+ndNzIyEvHx8WLd0IB79erVYW1tjbi4OKSkpODSpUto1KhRjvpL9C7LOIdC5cqVUa9evULqDRERvQ0KNOCu5uLigh49ehTGoYmIiIiIipVjx46J5YcPH+Lhw4c697tw4QIAiGA7AEydOlVWX+38+fM4cOAAOnTogFq1auVth4uwhw8f4uXLlwAAhUKB+vXry7a7uLiICUgzjnCXJAmvX7+WjYLXZ+PGjYiOjgYA7NixA7169ULlypV17quZTsbR0dHgiRpNTExQr149BAYGAkhPlcOAO1H2af7CZPz48ejdu7eYlJiIiEgXfkoQEREREb2lIiIi8Pz5c53blEol+vfvL9bv3r0rgrxqcXFxSE5OlpW9fPkS48aNw/bt2zF27FjExsbmeb+LKvWXEkD6KFalUinb7urqKpbDwsKwbds2HDlyBElJSRg4cCBatWqF+fPnQ5IkvcdITEzEzp07ZWUrVqyQrYeFhWHDhg2YOXMmfvrpJ1Fu6Oh2Nc087pw4lSj7EhISEBkZKdZbtGjBYDsREWUp30a4z5gxI8d1FQpFpj+rJCIiIiIiiFQymqysrNCvXz/069cP1tbW2LNnjxi1vXv3bq39r1y5IhvJvWPHDiQlJQEAYmNjsW/fPnTv3j2fzqBo0Qy460oZ4eLiIpY3bdokllu3bo2bN28CADZs2ABjY2OMGTNG59xU+/btw5s3b2RlR48exb1791CpUiXcunULgwYNEhO3aspNwP3hw4d49uyZ7EsDIspcWFiYWDYzM0OJEiUKsTdERPS2yLeA+7Rp06BQKLRGd2jedKq3ZSxjwJ2IiIiIKGv//fefWO7WrRv69OkDFxcX2NjYiHJfX18cPHgQALB+/XqtNk6dOiUC7mlpadi6dats+9atW/Hhhx/qDB4XJyqVChcvXhTrdevW1dpHX7BN/fiqrV+/Hra2thgyZIisXJIkbN68WWcbK1euxJw5c7Bnzx6dwXYAKFOmTKbnkJGHhwc8PDxE0PDcuXPo1KlTttogepdpppNxd3fn6HYiIjJIvgXc16xZo1V25swZLF++HB4eHujevTvKli0LID0v4datW/H48WMMHz4cfn5++dUtIiIiIqJiQaVSYd++fWK9Xr16KFeunNZ+devWFQFhzdQIavv370dycjKMjIwQHR2NZ8+eybbfu3cP169fz/EEoW+Lu3fv4vXr1wAAY2NjnbnrszO6denSpbCzs5PNXXX9+nXcuXNHrA8bNkykkzl8+DDu37+Pq1ev6m0zuyPcgfRR7lu2bAGQnlaGAXciw4WGhorl0qVLF2JPiIjobZJvAfeBAwfK1s+fP4+RI0fiq6++wowZM2BiIj/0Tz/9hClTpmD+/PkYPHhwfnWLiIiIiOitd/ToUXz99deyX5PqGpEN6E6NoikyMlLvqGu1HTt2FPuAu2Y6mapVq8p+JaCWVcDdxMQEpUqVEqNif/rpJ9ja2qJNmzYAgL///lvs6+vri2HDhuHw4cNiotslS5bIAvItWrTAkSNHxHpuA+7nzp2DSqXiKF0q1oKDg3HmzBk0a9YMpUqVylVbDLgTEVFOFNid1pQpU1ChQgX88MMPWsF2IH0Uyffff4+KFStiypQpBdUtIiIiIqK3yr179/DVV18hJSVFlFWrVg0ODg469y9dujQqV66crWMoFAq0b99erKsDwoZITEzExo0bsX79ety6dSvTCUSLkmvXronlOnXq6Nwnq4C7r68vfvvtN5EnXZIkTJ06FSdPnkRUVBQOHTok9u3ZsyeMjIwwbNgwUXby5EmRTsba2hrTp08XvwouU6ZMjgJ+devWhbGxMQAgJiZGFtAnKi7u3r2LGzduIDY2FsOHD8f8+fMxatQopKam5qpdBtyJiCgnCizgfu7cOfj4+GS5n4+Pj87Jn3LqxYsXmDhxIry8vGBpaQlHR0f4+vpi0qRJOvfftWsXmjVrBjs7O9jZ2aF58+b4999/Mz3GjRs30KNHD7i4uMDS0hI1atTAwoULoVKp9NZ59eoVxowZA09PT5ibm8PT0xNjx45FdHR0bk6XiIiIiIqx1NRUzJw5UwRlFQoFatasicmTJ+uto1AoMGrUKK3y9evX49NPP0X//v3Rr18/9O3bF3369EGfPn2wYMECvPfee2Lf+Ph4g/v4yy+/YN68eVi4cCH69++PMWPGZHpfXFTcu3dPLFetWlXnPra2tjA3N9fbRtOmTeHm5oZff/0VSqUSQHpe/FmzZmH79u3iSxJnZ2c0b94cANCyZUuUL19eqy0fHx9YWlpi5cqV+PHHH7F69WoROM8OGxsbVK9eXayfPXs2220QFWVnzpxB3759MXDgQAwfPhxRUVEA0ic8PXr0qGzf6OhoBAUFGXxN0pw0lQF3IiIyVIEF3NPS0gwaGfPw4UO9kwRl18WLF1G1alXMnz8fpqam6Ny5Mxo2bIioqCgsWLBAa/+FCxeiU6dOOH36NPz9/dGiRQucP38eHTp0wJIlS3Qe48yZM6hXrx62bNmC8uXLo1OnToiMjMS4cePQu3dvnSN6IiMjUb9+fSxevBgmJibo0qULbG1tsWjRIjRo0EDcIBARERERaQoICMDNmzfF+uLFi7F69Wq9AWI1f39/ODo6ysqqVKmCwYMHY8yYMRg3bhzGjx+PCRMmYMKECWjcuDGsra3FvnFxcQb1Ly0tTZZXHgBOnz5d5IO8iYmJspGslSpV0rmfQqHQO8rdxMQEzZo1AwCULVsWS5YsEalbIiMjsWnTJrFv165dxa9+jYyMtCZXBSByyCuVSrRq1UoE8HOiYcOGYrmoPxdE2aU50fPdu3dl2zTTOL1+/RoffvghPv74Y53xgIwSExPx/Plzsc6AOxERGarAAu7169fH+fPnsXbtWr37rF27FufOnUP9+vVzfbwXL16gTZs2SEhIwI4dO3D9+nVs3LgRe/bsQXBwME6fPi3b/86dO5g4cSLMzc1x/Phx7N27F9u3b8eVK1fg5OSEcePG4f79+7I6KSkp6NevHxISEvDzzz/j3Llz2LRpE+7duwc/Pz9s3rwZf/zxh1bfxo4di/v376Nbt264c+cONm3ahOvXr+Pzzz/H3bt3MX78+FyfPxERERG9fRISEvQOUjly5Ah+//13sd6hQwf4+fkZ1K5CocC8efPECOmuXbtmWcfKykosGzrC/caNG4iJidEq1ww2F0X3798XA2XMzc3h4eGhd9+MAffevXuja9eu+PHHH1GyZElRXqVKFdkkti9fvhTL/v7+sjZatWolUseo6Zq0Nac0A+5Xr17N1i8WiIqy1NRUBAYG6t1++fJl8Xf86dOnxfVpz549Waa70hzdbmpqKlJFERERZaXAAu7Tp0+HsbExBg8ejBYtWmDZsmXYt28f9u3bh2XLlqFly5YYPHgwTExMMH369Fwfb+rUqYiMjMTcuXPRqVMnre0Zg/qLFi1CWloaRo4cKfvDpXLlyvjmm2+QmpqKRYsWyeps27YNjx49go+PD8aNGyfKbWxsxIj4+fPny+qEh4djw4YNMDMzw2+//SbLZz937ly4uLhg/fr1sm/SiYiIiKj4i4uLQ+/evdGzZ098++23spQHN2/exHfffSfW3d3dZfefhqhZsyZWrVqFadOmYcKECVnurznCPTY21qBc7KdOndJbrp5ItCjSHFhTsWLFTFO3ODs7y9bbtm2Lb775RqSI0VStWjWtMjMzM3h5ecnKjI2NMXToULFuamqqs25OVa1aFba2tgDSA5SXLl3Ks7aJCtOVK1ey3Ec9yl3zy8yYmBg8e/Ys03ohISFiuVSpUjlK6URERO+mAgu4+/v7459//oGTkxMCAwMxatQotG/fHu3bt8eoUaNw9OhRODo6YsuWLVojPrIrISEB69evh7W1NQYPHmxQHXWe9u7du2ttU5ft2rXL4Dq+vr4oX748rl+/juDgYFG+b98+qFQqNGnSROsbcnNzc3Ts2BFpaWnYs2ePQf0mIiIiouIhMDAQT548AZB+z7h06VIAwLNnzzB+/HgkJSUBSB/csWjRItjb22f7GNWrV0eHDh1gYWGR5b42NjZiOS0tTRw/M5oB99GjR8tGeG/evDmbvS04mmko9KWTUcs4CaOu/Otq3t7eWmVVqlSBqampVnnr1q3x3nvvwcTEBMOGDTPoOTKUiYkJ6tWrJ9aZVoaKC31f8rVs2VIs7927F7GxsVq/HspqAuFHjx6JZc1rGRERUVZMst4l73To0AEPHz7Eli1bcOLECTx9+hQAULJkSTRp0gQ9evSQ3djn1IULF/DmzRs0btwYlpaW2Lt3Lw4ePIjExERUrlwZPXv2RKlSpcT+0dHRYsRN7dq1tdorXbo0nJ2dERISgtevX8POzg5A+s8xgfTgui6+vr54+PAhgoKCxE9EDamzevVqBAUF5ezkiYiIiOitdObMGdn6mjVr4Obmhq1btyIyMhJA+kjon376SSv9SH7QTCkDpKeVySwIHBkZidu3b4v1xo0bw8rKCj/++CMAYOfOnRg5cqRs5HxRoTnCPauAu6WlZabrmnQF3GvUqKFzX2NjY8ydOxepqamyX8HmlYYNG+LIkSMA0oOUEyZMgEKhyPPjEBUkzYB7v379EBMTg6pVq6Jz587477//8Pr1ayQkJGD37t148OCBrO7du3fFvAu6aAboGXAnIqLsKNCAO5A+UmbQoEEYNGhQvh1DPZFUiRIl0KVLF+zYsUO2/euvv8aqVavQp08fABDBdgcHB71/AHh4eCAyMhIhISHiJlldT1+OR3W55k/RclKHiIiIiArX3bt3sXv3bvj4+MhGTuYVlUqlc9Tx7NmzZetfffVVnsx3ZAhLS0soFAqRSiYuLk5r4lVNmnMklSxZEuXKlYObmxuWLFmC2NhYxMXF4d9//0XPnj3zve/ZIUkS7t27J9azCrj369cPu3fvBoAsz6VixYowNTVFSkqKKNMXcFfLj2A7ADRq1Egsh4aG4sGDB6hYsWK+HIsov0VHR2PBggWyoHiPHj1kf2d36tQJ69evBwBs3LhRDPhTyzjBakaabVeoUCEvuk1ERO+IAkspU5BevXoFIH0Uzb59+/Drr7/i+fPnCA4OxsSJE5GQkICBAweKfG+xsbEAtEfxaFIH4t+8eSPKsqqXV3Uy4+3trfNfxm/viYiIiCj7EhMTMX/+fHz00Uf466+/8OWXX+LixYt5fpw7d+4gOjoaAGBkZKQzXcxHH31k0GSneUWhUMgGo8TFxWW6v+ZIU39/fygUClhZWcnmU/r7779luemLgmfPnsnuvbMKQleqVAlLly7FV199hU8//TTTfU1NTVG5cmVZWfXq1XPe2Vxwc3OTjbg/fPhwofSDKLckScL48eNFilcgPVVTxkFt3bt3F7/iCAsL07r2ZJZSJjU1VTYIjiPciYgoOwp8hHt8fDwuXLiA8PDwTPNADhgwIMfHUH+Qpqam4vvvv8eoUaPEtrlz5yIkJASbN2/G3Llz8eeff+b4OERERERUfD158gSTJk3SGgW5aNEiBAQEwMgo78auaKaTqV69Oj777DOMGjVK5Atv2rQpPv/88zw7nqGsra3FgJHMAu6pqak4d+6cWNccTd2zZ09s2LABkiQhODgY58+fR8OGDfOv09l06NAhsezm5ibSR2amXr16spzomalWrRpu3LgBAHBxcdGax6kgtWzZUvTl8OHDGDFiRKH1hSinTp8+LUvBWqlSJfzwww9a+3l4eMDPz0/26xtNT58+xZs3b8SEwprCwsLEL1OMjIwKJI0XEREVHwUacJ8yZQoWLFiA+Ph4vftIkgSFQpGrgLtmHnhdk6YOHjwYmzdvxrFjx2T7Z9Yv9R8Ymh/GNjY2ePXqld56+upkdixddTKjvmHOSFe+SCIiIiIyzOnTp/Htt9/i9evXWttu3ryJ+vXro2PHjhg9ejQcHBxyfTzNgLufnx98fX0xa9YsLF68GF5eXpg+fTqMjY1zfZzsMnSEe1BQkAjMm5qayoLRHh4eaNy4MU6cOAEA2LRpU5EJuD958gS///67WPf398/zYzRo0EBMGNugQYNCzZveokULLF68GEB6uoyHDx9mOukrUVH0xx9/iGU/Pz8sXLhQ7/WxZ8+eegPuQPq1t1GjRrC2tpa9NzUnTPXw8IC5uXke9JyIiN4VBRZw/+mnnzBr1iwYGxujffv2qFy5ssFB5ezy9PQEkJ62xcXFRWu7+tvp58+fAwDKlCkDID0VTVxcnM487mFhYbK21fVevXqFsLAw1KxZ0+A6mtsMqUNEREREBUOlUmH16tVYtmyZyF0OAH369EFoaChOnjwpynbt2gUAmDp1aq6OeevWLVy+fFms+/n5AQBatWqFVq1a5art3DI04K6ZTqZOnTpaE4n26tVLBNxPnjyJsLAwvXMaFRRJkjB79mwkJiYCAJRKJT755JM8P06zZs3w+eefIyIiAkOGDMnz9rPDw8MDVapUEZPbHj58mAF3eqsEBQXh0qVLYn3IkCGZfhnp5+cHd3d3PHnyROf2r7/+GkD6ta5Fixbo378/ypcvL0vRynQyRESUXQUWcF+xYgUsLS1x4sQJ+Pr65uuxateuDQBISEhAUlKS1rfRUVFRAP432lypVKJMmTJ4/PgxLl++jMaNG8v2Dw0NRWRkJDw9PWU/MfXx8cHVq1dx6dIltGvXTqsf6hsBzWC8j4+PbJshdYiIiIgo/yUkJOCbb77B8ePHRZmFhQW+/fZbtGnTBo8fP8a5c+dkE2CePHkSKpUqV+llfv31V7FcuXJlVKtWLcdt5TXNeYcMDbjrGiXeoEEDlC1bFsHBwZAkCX///TfGjx9vUB8kScIPP/yAM2fOYPTo0Xj//fezcQb67dmzRzZR7cSJE6FUKvOkbU0KhQIDBw7M83ZzqlWrVrKA+7Bhwwq5R0TaVCoVoqKikJSUBDc3NxFU1xzdXqtWLdSqVSvTdoyNjfHhhx+KX3YA6fM03L9/X7ZfXFwcdu3ahV27dqFx48ayXzfxSykiIsquAps0NTQ0FM2aNcv3YDuQPorcx8cHkiSJtDGa1GXqwDwAtG/fHgCwZcsWrf3VZR07dpSVZ1bn8uXLePjwIapXry7L99amTRsYGRnhxIkTYoS9WlJSEnbt2gVjY2OdAXwiIiIiyj8rV66UBds9PDwQEBCANm3aAEi/x/z1119l94SvXr3K1WT1gYGBsqDvqFGj8jQ3fG4ZMsL9xYsXsuCVroC7QqFAz549xfqOHTuQkJBgUB9OnjyJbdu2ISIiAosWLTK065mKiorCzz//LNb9/f3xwQcf5EnbRV2LFi3E8v379xEcHFx4nSHS4dmzZ+jatSvatGmDzp07o1u3boiJicHDhw9lf98b+kVWp06dxCA8IyMjTJ8+HS1atECJEiV0jo4/efKkLEc8A+5ERJRdBXY37+bmpjNVS3754osvAKSPVAkPDxflV65cwfz58wEAI0eOFOVjxoyBsbExfv/9d9kfPffu3cP3338PExMTjBkzRnaMrl27oly5crh69SoWLFggyuPi4vDpp58CACZMmCCrU7JkSfTp0wfJycmyibDUfX7x4gU++ugjlChRIrcPARERERFlg2Ygp1GjRli3bh0qVqwo28fX1xdTp06VjUI/f/58to+VmpqKX3/9FZMmTRJltWrVypcc4rmhef+ubw4izcCUm5ubSKGYUfv27UV7cXFxsnqZUafuAdIDcZnNu2So+fPnIyYmBgBgaWmJyZMnF2pu9YJUpkwZVK5cWawfOXKkEHtDb5OYmBgsXrwYAwYMwO7du/PtOBs3bpSlgHny5Al27tyJtWvXirIKFSoYfL1UKpWYPn06vL29MWbMGHh5eeGnn37Cnj17cObMGRw4cADTpk3Tut6rMeBORETZVWAB9969eyMwMDDTn6Lmpb59+2LgwIG4du0aqlWrhvbt26NFixZo2LAhoqKiMGzYMPTo0UPs7+Xlhblz5yIpKQlNmjRBu3bt0KVLF/j4+ODly5f4+eeftT6ATU1NsX79elhaWmL8+PFo2LAhevXqhUqVKuHMmTPo3r27zm/dFy5ciAoVKmDr1q2oUqUKevfujRo1amDx4sWoVKmSbLQNEREREeW/N2/eyEb6Tpw4MdP5hjQnBf3vv/+ydaywsDAMHToUa9asEXnizczMMG7cuCIX9DVkhPu1a9fEco0aNTJtq0qVKmL98ePHBvUhY+qH0NBQg+rpc/LkSezfv1+sf/rpp3Bzc8tVm28bzVHuv/32G9auXYuIiAit/ZKTk2WDl+jddebMGXTp0gVr167FzZs3MXPmTERGRubbsTLatWsX9u7dK9YHDhyYrV8DtWrVCn/88Qf69esnKzcyMoKjoyM6dOiADRs2YPHixahfv77Y7uLiwoA7ERFlW4EF3KdNm4aqVauiU6dOWjfN+WXNmjVYvnw5KlSogMDAQJw/fx6+vr4ICAjA8uXLtfYfN24cdu7cCT8/P5w4cQKHDx9G3bp1sWvXLnz++ec6j9GoUSP8999/+PDDD3H//n3s3LkTjo6O+Pnnn7Fp0yadfzQ5Ozvj/Pnz+Pzzz5GcnIxt27YhJiYGo0ePxvnz5+Ho6JjnjwURERER6Xfjxg2xbG9vj9KlS2e6v2ZA5tKlS7JfLWZm79696NevH65fvy7KypQpg9WrV8Pb2zubvc5/eRlwByAb/W5IwD0sLExrv+wG3FNSUhAYGIiwsDDExcVh9uzZsv5qDsJ5V2ScjHfx4sUYPXq0rOz169fo1q0bOnbsiHXr1hVk96iIefbsGSZPnow3b96IsrS0NGzfvj3PjxUZGakzXvDw4UOkpaUBSP/VeF7N5aBJoVCgUaNG+O2337B+/XpMmDABy5cvh6mpaZ4fi4iIircCmzS1Xbt2UKlUCAwMRNWqVeHp6QkPDw+d30orFAocPnw418dUKBQYNmxYtiYC6tixo1au9qx4e3vrzOOeGUdHRyxevFg2eQsRERERFQ7NALi3t3eWI819fHxgZmaG5ORkxMfH4/r161lO3rd582bMmTNHVtapUydMnDhRNjlpUZJVwD0lJUVMwAkANWvWzLQ9T09PsRwSEpLl8U+ePKlVlt2A+/fff4/du3fDwsICdevWxbNnzwAAJiYm+Pbbb3XmcC7uypYti8qVK+Pu3bui7OHDh4iLixPP+Z49e8So9x07dqB///6F0lcqXCqVCtOnT0dsbKzWtm3btmHQoEEwMcm7sIJmelc3NzekpKTg5cuXsn369euXp8fUpUqVKrJf5BAREWVHgQXcAwMDxXJaWhoePnyI/2PvPsOjqva3j9+TDgkk9JLQe++9ShULWBAEUUCxoCjtqIg0EUSPqKAidjhiFxugoCAgTZAmVQg91BDSSCF9nhd5sv97MpOQkJk0vp/r4jp7rd3W5MQkc8/av3Xy5EmHxxa2R2kBAABQvJkD96ZNm173eB8fH7Vo0cIoJ/PHH39kG7hbrVYtWbLEaPv5+enFF19U3759b3zQ+cD8QYCjwD04OFiJiYmS0sstmmuDO2Ke4Z6TwH3btm12fTktRZMhozxFQkKCTYA/evRo1alTJ1fXKk5mz56tDz74QBs2bDD6rly5YgTu5gWEXVU6BIXfd999Z7NOxbhx47R48WKlpqYqNDRUW7ZsUc+ePZ12P3M5mU6dOiktLU0///yz0RcQEKC77rrLafcDAMAV8q2kzKlTp3L8L6sgHgAAAHA2q9Wa68Bdsi3L8dtvv2VbVubo0aO6fPmypPTJJcuWLSv0YbuU/sFABkeBu7mcTMOGDeXl5ZXt9cyB+8WLF5WUlJTlsZGRkQ4XpM3NDPe0tDS72bGSVKtWLY0ePTrH1ymO6tatq9dff10VKlQw+jKC9ZiYGO3evdvoj42NNT5Ywc3j9OnTNk9k9+jRQyNHjtQtt9xi9JnrqudVWlqaduzYYbQ7deqk7t272xxz//33y8fHx2n3BADAFfJthrv58VEAAACgsDh//ryioqKMdk5rqfft21fz589XcnKyIiIitH37dnXt2tXhsebZws2aNbtujfjCwlxSJj4+3m5/bj+oCAoKkru7u1JTU5WWlqbz58+rVq1aDo/99ddfHX6IkZvA/erVq3Z9FotF06ZNu+6HAzeLChUqKCwsTJKM/926datRLztDRESEqlSpku/jQ8FISUnRzJkzjQ9aypQpoxdffFEWi0W9evXSunXrJOV9EWOzffv2GT+L3d3d1a5dO3l6esrf31/R0dHy8/O7KddcAAAUPfk2wx0AAAAojMwLplavXl3+/v45Oq906dI2Afuvv/6a5bGbN282tjPP2CzMsispk5CQYFP+4XoLpkrpddMDAwONdlZlZaxWq00ZiQEDBhjb4eHhWS7gmllERIRNu3z58po8ebJatGiRo/NvBuXLlze2M2a4mz8gyuDoSQEUX0uXLrX52fjiiy+qbNmykqSKFSsa/Tn9vrh8+bKOHz8uq9Wa5THmRVjbtGmjUqVKycfHRwsXLtTw4cP17rvv5vjnMwAABSnfZrgDAAAAhVHGrF4pfTHJ3Lj99tuNGth//vmnYmNjbcqwHDt2TD/88IP+/fdfo69bt255G3A+Ms9wz7xo4qpVqxQdHS1JKlGihDp16pSja1avXt2ow55VPfZDhw7ZlJkcM2aM1q9fb8y2PX78uI4cOaL9+/frrrvuUrt27Rxexxy4V6pUSb/88kuOxngzMZeUCQsLU3JysrZu3Wp3XOYPL1B8/fvvv/roo4+M9p133mlTp938IU1ERIRSUlKyXcT09OnTGjZsmJKTk/Xiiy/q7rvvtjsmJibGmDUvyeaYpk2b5rjUFwAAhYHLAnc3Nze5ubnp8OHDql+/vtzd3XN8rsViybYGJgAAAOAsMTExxnbp0qVzdW6XLl2McgeJiYlav369Bg4cKCl9RueYMWNsZmMHBgaqdu3azhl4PjB/eBAfHy+r1SqLxaK0tDR9+eWXxr6BAweqVKlSObpmThZONc9ub926tWrUqKGgoCCdOHFCkvTEE08oOTlZUvrCqqtXr3ZY19kcEpcpUyZH47vZZJ7hvnv3bodPEBC43xysVqvmzZtnlBSqXLmyJk+ebHOM+XvGarUqIiLCZtZ7ZsuXLzf+e507d64aN26sn376SQkJCfLw8JCHh4cuXbpkfKDm7++vHj16OPulAQCQb1wWuFevXl0Wi0Wenp6SpGrVqslisbjqdgAAAMANMdf5zmlonMHT01N9+/bV8uXLJaWXlckI3H/44Qe74LJ///5F6m9ic0kZq9Wqa9euqWTJktqyZYsxO91isWjYsGE5vqY5cHc0w/3atWv6/fffjXbG17NatWpG4J4R3knpH5j8/fffDkv1mEPijHIYsJV5hvuff/7p8LiMcjMo3g4fPqzDhw8b7VmzZtl88CZJPj4+8vPzM556CQsLyzZwDw0NtWk/8MAD2Y7hjjvuYI0FAECR5rLA/fTp09m2AQAAgMLAXColt4G7JN12221G4L57925dunRJ5cuXt6lHLElPPfVUroLpwsBcUkZKr+NesmRJff7550Zfz549FRQUlONr1qhRw9g+cuSILly4oKpVqxp9f/zxh/FBha+vr3r37i1JqlWrljZu3Ojwmhs3bnQYuJvrS5crVy7HY7yZmGcrh4WF6fz580Y74+kNiRnuNwvzz61WrVqpbdu2Do+rUKGC8bPzeh/GZA7ss+Ph4eGw5AwAAEUJNdwBAABwUzPPcM9tSRkpfbHQatWq6ezZs7JarVqzZo2CgoKMEMrT01O//vprkSxp4uXlJQ8PD6PcY1xcnK5cuaI9e/YYx4wYMSJX12zcuLF8fX0VFxena9eu6cUXX9RHH31k1IA2l5Pp16+fSpQoIUkaNGiQfv/9d8XGxuruu+9WuXLl9MYbb0hKX+QzNTXVrowlM9yvzzzD/ezZs8a2xWLRgAED9PXXX0sicL8ZxMfH67fffjPagwYNyvLY8uXL69SpU5KuH7hHRUU57G/VqpVatGihlJQUo4RNjx49cr2WBgAAhQ2BOwAAAG5q5hruNzLDPSOY/PDDDyWll5Uxz6bu3bt3kQzbpfTX5uvra8xyjouL01dffWXsb9q0qZo3b56ra/r6+mrKlCmaPn26JOnAgQNatGiRxo8frzNnzmjv3r3GsebALygoSD/++KPc3NwkpT+ZsHDhQqWkpCgqKkr79+9Xq1atbO5F4H595sDdrGnTpqpbt67RJnAv3pKSkvTee+8pPj5eUvp/p3369Mny+My1/7MTGRnpsH/SpElq1KjRDYwWAIDCza2gBwAAAAAUpLwG7lJ6WZkMJ0+e1M6dO4324MGDb3xwhYC5rMzJkye1du1aoz1ixIgbqkk/YMAAoza7JC1btkxbtmzRihUrjL46deqoSZMmNudlhO1SepkKc7kLR+VmCNyvLyAgwO7JACl9prE5VDWX50HxEhsbq1GjRhlPM0jp/406Wog4Q24Cd0cz3L29vQnbAQDFFoE7AAAAbmp5LSkjpc++btGihV1/nTp1HPYXJeaFU5csWWKUfqhSpYp69ux5w9d99tlnVbt2baM9c+ZMrVy50mgPGjToumG++f4bN26U1Wq12U/gfn1ubm424WmGHj162HzNCNyLr40bNyo4ONhoV61aVWPGjMn2nMy1/7PjaIb7888/n8tRAgBQdBC4AwAA4KbmjBnuku0s9wyDBw++oRnghYl5hntISIixPWzYMKPu+o0oUaKE5s2bJ29vb0lSdHS0EZB7eHg4/HpmZl4o9fz58zp+/LjRtlqtNoE7i6ZmLXPgXr16ddWsWdMmcI+NjVViYmJ+Dw35YN++fcZ2hw4d9NVXXzn8EMbMXIoouxnuycnJxiLIktS+fXs98cQTuuOOO/IwYgAACjcCdwAAABQqVqtVSUlJ+XKvxMREm3vlJXDv27evPD09jXaJEiU0YMCAPI2vMDAH7uY+c0mYG1WnTh0999xzdv09e/ZUQEDAdc+vWLGiTdkZc1mZ+Ph4m4CYGe5Zy1zHvXv37rJYLHZfs6xqcaNo279/v7Hdv39/h//NZ5bTGe6Zy8m8+eabGjNmjE15KAAAiht+ywEAACBfhIaGKjY2Nttjdu3apbvuuku33nqr1qxZ4/IxmWe3SzdeUibj3K5duxrtAQMGyM/P74avV1hUq1bNru+ee+5x2msbOHCg3QcT5sVSrydzWZkM5tntbm5u8vf3v+ExFneZZzP36NFDkuTl5WXzIRRlZYqf2NhYnTx50mjndBFk84c0kZGRRqmpzMyBu4+PT7Z14QEAKC4I3AEAAOBya9eu1e23365bb71VZ8+etdtvtVr19ddf66mnntL58+d19epVzZw5U9u2bXPpuMyBu6enp1He5EY988wzql+/vlq0aKHHH388r8MrFB577DHdcsstRtvb21tDhw512vUtFoumTJmi+vXrS0oP/Nq3b5/j882B+9GjR3Xx4kVJtuFwVguDIp05cPf397cJXc2leAjci5+DBw8aax/4+/urevXqOTrP/D2TlpaW5dMP5v4yZcrkYaQAABQdN1508QbFxcUZb2xKlSqVo8fVAAAAULR99913kqSEhAStXLlSTz75pLEvKSlJr776qlasWGFzTmpqqiZMmKCmTZvq4Ycftpk97izmBVNLlSqV53rr1apV05dffpnXYRUq/v7+ev3113Xw4EH9+eef6tKliypXruzUe/j6+mrp0qU6fPiw6tevn6twvGbNmqpevbpRX/7PP//U/fffbxP0UU4mey1btjS2b7vtNpuvf9myZXX69GlJtk8NoHgw129v2rRpjku9lChRQr6+vkZ99rCwMId1380z3HNSJgoAgOLA5TPcr169qrfeeku9evVS+fLlVbp0aQUGBiowMFClS5dW+fLl1atXLy1YsMDmDQ8AAACKh5SUFB0+fNhoZ8yojIqK0qVLl/T444/bhO0VK1ZUyZIlJaXPnNy/f7+mTJly3XI0N8JZC6beDJo2baqnnnrKJpx1Ji8vL7Vs2dL4/z6nLBaLzQz8DRs2SLKdjU3gnr02bdrolVde0aRJk/TUU0/Z7Ms8w91qtdqVYkLRdeDAAWM7p+VkMpgDdnMdeDNmuAMAbkYuDdxXrFihWrVq6T//+Y82btyoiIgI+fv7q2rVqqpatar8/f0VERGhjRs3avLkyapdu7ZWrlzpyiEBAAAgn506dUoJCQlGe+/evRo1apT69OmjO+64wy7w+eyzz/Tmm2+qSpUqRn9CQoKOHj3q9LGZg8O81G9HwTKXldm7d6/Cw8O1d+9eo4/APXsWi0X9+vXT8OHD7Wpsm0PVS5cu6emnn1avXr30wQcf5Pcw4WRpaWk2P39btGiRq/PN3xuvv/66nn32WZuf9ZLtDHfWUQAA3CxcFrj/9ddfuvfeexUXF6enn35amzZtUmxsrCIiInT27FmdPXtWERERio2N1aZNmzRu3DjFxcXp3nvv1Y4dO1w1LAAAAOSzQ4cO2bSTk5Pt+qT0hTLff/99lS9fXm3bttXPP/9sE9AcP378uve6evWq1q1bl2U9YUfHZ2CGe9HVpEkTYyZ2Wlqa+vfvr99++83Y7+wSODeTwMBAY3vFihXavn27rFarPvroI6P2N4qmq1evGiVhJKlevXq5Or9Ro0Y27Q0bNujZZ59VYmKi0WcO3JnhDgC4WbgscJ8zZ47c3Nz0559/asGCBeratavDx0NLliyprl27auHChVq/fr0k6eWXX3bVsAAAAJDPHIXrZp6ennruuec0bdo0eXl5Gf1ubm7q16+f0b5e4H7q1Cndd999mjJlisaMGWMT+mSFkjLFg5ubm3r06OFwX2BgoO699958HlHxERQUZGynpqba7DOHtSh64uPjbdp+fn65On/MmDEaOXKkqlWrZvT99ddfmjJlipKTkyXZlpShhjsA4GbhssB9+/bt6t27tzp06JDjczp16qS+ffvqr7/+ctWwAAAAkM8OHjyY5b5Zs2Zp9erVGjJkiMMFS+vWrWtsnzhxIsvrnDp1So8//rhRt/vMmTN2i7A6QuBefJjLymS47bbb9MUXX9iUJ0LumGe4Z8YiqkWbOXAvUaJErhYrltID+qefflrfffed+vbta/Rv3rxZU6dOVUpKCjPcAQA3JZcF7klJSXb1/3LCy8tLSUlJLhgRAAAA8tu1a9eyDMorV66s2267LdtZj+bA/fjx40pLS7M7JiUlRS+88IJd+Ld06dLr/l1JSZnio23btjYliEaNGqWXXnop17N2YSu7DysI3Is28xMKuV2s2MzDw0Mvv/yy3eLFM2bMsFm8mBnuAICbhcsC90aNGun333/XyZMnc3zO8ePHtXbtWrtacAAAACia9u3b5zAkl6QePXrIzS37P0fr1KljbMfHx+vvv//W7t27FRwcrIsXLyo2NlZffPGFw3IzoaGh153lzqKpxYeXl5dee+019ezZU9OnT9dTTz3l8KkJ5I6Pj48qVKjgcN+VK1fyeTRwJnPg7uvrm6dreXh46JVXXlHXrl2Nvt9//12nTp0y2sxwBwDcLDxcdeEnn3xSDz/8sDp27Kjp06fr3nvvVdWqVR0ee/HiRX333XeaM2eOrl27pnHjxrlqWAAAAMgnkZGRmjt3rtFu06aNgoODjZB70KBB172Gn5+fqlatqgsXLkjSdf9OvPvuu2WxWPTDDz9IktauXavBgwdneTwlZYqXtm3bqm3btgU9jGInMDBQYWFhdv3McC/azCVl8jLDPYOnp6dee+01TZo0STt27LDbzwx3AMDNwmWB+6hRo/TPP//o7bff1oQJEzRhwgRVqFBBQUFBxi/z+Ph4nTt3zvjjzWq1asKECXrooYdcNSwAAADkg+TkZD3//PO6ePGipPRFLceMGaPo6GgtXbpUffr0Uf369XN0rTp16hiBe3YCAgI0btw4HThwwAjcL1++nO05BO7A9QUGBuqff/6x6zeXC0HR46ySMmbe3t564403NH78eO3evdtmH4E7AOBm4bLAXZIWLFigO+64Q2+++aY2bNigy5cvO3zT4+3trV69emnSpEnq3bu3K4cEAAAAF0lNTTUW3Zs/f7727Nlj7HvmmWfUrl07SVKfPn1ydd26detq8+bNRtvT01Oenp42szPd3d01bdo0+fv7q1y5ckb/9Wbgmmu4U1IGcCyrhVOZ4V60mX+G5rWkjJmPj4/eeustjRs3Tvv375eUXvKJn7EAgJuFSwN3Kf0NVZ8+fZSQkKCjR48qJCREsbGxktIfEa5evboaNGhwQwusAgAAoOClpaXp+eef17Zt2/Too4/Kz89P33//vbH/tttu0wMPPHDD12/QoIFN+5133lHbtm2VkpKiuLg4xcTEyN/f35ihXrZsWePYuLg4JSYmytvb2+G1meEOXF9WgTsz3Is2Z9Zwz6xkyZJ6++239eKLL2r79u0aOXLkddfsAACguHB54J7Bx8dHLVq0UIsWLfLrlgAAAMgHu3fv1oYNGyRJ7777rjHLXZKaNGmiF198MU+LV/bo0UOtWrXS0aNHNXHiRKNGt4eHh/z9/eXv729zfOaF+SIjI1W5cmW766amptoETgTugGME7sWTK0rKmPn5+WnhwoVKSEhggh0A4KaSb4E7AAAAiqe9e/fatFNTUyVJ5cuX1/z587OcXZ5Tnp6e+uijj5SSkiIPj+v/+erl5aVSpUoZs9fDw8MdBu4ZT11moNwB4FhQUJDD/oIsKZOSkqJ///1XDRo0kJeXV4GNoyhzVUmZzAjbAQA3m3wJ3Pfu3auVK1dq//79OnPmjPHmp1SpUqpRo4aaN2+uO++8U61atcqP4QAAAMCJHC2m6OXlpfnz56tChQpOu09OwvYMZcqUMf7mzCoUDAsLs7m2KwMnoCgrV66cvL29lZiYaNMfHh4uq9VqPMGSlJSkhIQEl394dfnyZY0bN04nT55U48aNtXTpUsqV3ABz4O6KGe4AANysXBq4nz59Wg8//LD+/PNPSZLVarU7Zvfu3frhhx/00ksvqWfPnvrkk09Us2ZNVw4LAAAATpKSkqKDBw/a9Hl6emrGjBlq2rRpAY0qPSAMCQmRlHXgfu7cOWO7atWqBHZAFiwWi9q3b2+zeLGUHrDHxcXJz89Pp0+f1jPPPKOLFy9q2rRpGjRokEvGEhoaqscee0znz5+XJB0+fFghISG8h7wBrqzhDgDAzcxlgfuFCxfUsWNHXb58Wc2bN9fgwYPVunVrBQUFGb/M4+LidO7cOe3Zs0ffffedNmzYoE6dOmn37t2qWrWqq4YGAACATGJjY/XWW2/Jw8ND48ePz/Fsx+PHjxuzJN3d3bVy5Up5enra1VHPb+b75yRwz6pkBoB006dP15o1a9SsWTONGTPGKB0VHh6ua9eu6emnn9bFixclST///LPLAvePP/7YCNszELjfGGa4AwDgGi4L3KdPn67Lly/rzTff1IQJE7I8rnnz5rrttts0bdo0vfnmm/rPf/6jGTNm6OOPP3bV0AAAAJDJokWL9PPPP0tKn+09cuTIHJ1nLifTqFEjVaxY0RXDy7Vy5coZ21kF7ubQLqtFIQGkK1u2rIYPHy4p/QOtK1euSJLOnDmj999/3wjbJSkqKspl4zhy5IhdX8bTLMgdZrgDAOAaLntuds2aNerQoUO2YXtmkyZNUocOHbR69WpXDQsAAACZXLt2Td99953R/uabb7I81mq16vPPP9dLL72kc+fOad++fca+li1bunKYuZKTGe7mwJ0Z7kDOmT/QeumllxQcHGyzP/OCxM6UEfSbnTlzxmX3K87MgTsz3AEAcB6XzXCPiIhQ9+7dc31ejRo1HC68BQAAgLyxWq364osvdOrUKTVq1Eht27ZVjRo1tG7dOpvjMkpFOLJixQotWLBAkhQdHa2jR48a+1q0aOGScd+IsmXLGtuRkZEOj6GkDHBjzIF7dHS03f6MBYudLS0tzeEHaMxwvzHmkjLMcAcAwHlcFrhXr15dmzdvVnx8fI4/LY+Pj9fmzZtVrVo1Vw0LAADgprVy5UojLM8oH2MOzjKEh4c7/BsuPj5eixcvNto7d+7UtWvXjHbjxo1dMOobY35d4eHhdvtTU1N14cIFo03gDuSco58bXbp00datWyVJycnJSkxMlLe3t1PvGxUV5fADQQL3G0PgDgCAa7ispMzQoUN14cIF9e/fX/v377/u8fv371f//v116dIlozYgAAAA8iYhIUErVqzQkSNH9PXXX9vtDw8PdxhInzhxwq5v2bJlNuUczGF7qVKlCk39dsm2pIyjGe6hoaFKSUkx2tRwB3LO/ASJJHXu3Flz58616XPFLHdHP6skKSwszCY8xvWlpaWxaCoAAC7ishnuU6dO1dq1a7V161a1atVKderUUevWrRUUFGT8Mo+Pj9e5c+e0Z88enThxQlarVR07dtQLL7zgqmEBAADcNNLS0jRp0iT9/fffdvvq1KnjMFTPcPz4cTVr1sxoh4WFadmyZVkeX7duXVkslrwN2InMM3AzZsW6u7srOjpaH3/8sf766y9jf/ny5eXj41MQwwSKpKZNmxrbTZo00WuvvSYfHx95eHgYH2TFxsaqfPnyTr2vOXCvUqWKIiIilJiYKEk6e/asGjRo4NT7FWeZP6BghjsAAM7jssDdx8dHGzdu1Msvv6xFixbp+PHjOn78uCQZb8asVqtxvL+/v8aNG6dp06Y5/dFDAACAm9Hy5csdhu1t2rTRBx98oKioKO3Zs0e7du1ScnKyLly4oB07dkiS8Xdbhvfee08JCQlZ3qtOnTrOHXwemWe4p6WlKTo6WpGRkZo8ebJN7XaJcjJAbvXo0UPPPfecrl69qvvvv18lSpSQlP6kS8YTJa5YONX8hE2FChVUsmRJ44PDkJAQAvdcyBy4M8MdAADncVngLkne3t6aM2eOZs6cqa1bt2rfvn0KCQkx/vjy8/NT9erV1aJFC3Xp0kWenp6uHA4AAMBN4/z583rnnXcc7rvvvvskSQEBAerVq5d69eolSfr666+NwP3YsWPG8UePHtWqVauMdoUKFRQWFmZzzcIWuPv6+srb29uY/frzzz9r6dKliouLszuWwB3IHTc3Nw0ZMsSu38/PzwjcXVFSxhy4lytXTuXKlTMC9zNnzjj9fsWZOXD39PSUl5dXAY4GAIDixaWBewZPT0/17NlTPXv2zI/bAQAA3BTi4+M1fvx4hYSEqEuXLrrrrrvUrFkzWSwWzZ8/36ixXqpUKSUkJCg5OVmBgYFZ/k1Wt25dY3vPnj3at2+fmjRpooULFxpPJtaqVUtDhw7Vq6++anNuYQvcLRaLypQpo0uXLkmSFi1alOWxVatWza9hAcVaqVKljG1XzHA3l5QpX768TRkUFk7NHfOHj8xuBwDAufIlcAcAAIDzbdq0SXv37pUkrVixQitWrFCtWrXUtm1bbd682TjuueeeU+3atbV582b1799fHh6O/wQ0B+6S9Mgjj9gdM378eAUEBNj1165dOw+vxDXKli1rBO4ZSpYsqZEjR2rx4sVGX/Xq1fN7aECx5OfnZ2znxwx380LNBw8eNNZqwPWZZ7hTvx0AAOcq0MA9JSVF4eHh8vX1tfnjDAAAANd3/vx5u75Tp07p1KlTRrt169a69dZbZbFYrlvfOCAgQJUrV7YLqTO0b99eXbp0sav9W758eYchfEErW7asTTsoKEhvvPGG6tSpo+joaH355ZcqXbq0OnbsWEAjBIoXVwfumWe4t2rVShaLRVarVSEhIVq3bp369+/v9PsWR8xwBwDAddxcdeGkpCSlpqY63Ld7924NGDBApUqVUtWqVeXv76/69evr7bffdtVwAAAAih3zbM/SpUvb7Xd3d9dzzz1nLFifExMmTFCFChXs+i0WiyZMmCCLxSJfX1+bMiyFrZxMhubNmxvb7du31//+9z9jrBMnTtTnn3+uH374oVB+WAAURflZUqZcuXKqVq2a+vTpY/R99NFHWb4HhS0CdwAAXMdlgXuJEiX02GOP2fX/+uuv6tq1q3777TelpKSocuXKKlmypI4fP66JEydqxIgRrhoSAABAsWIO3B966CH99NNPGj16tCpUqCCLxaLx48fblYm5nj59+mj16tX6+++/deeddxr9gwYNUv369Y22OWQvrIH78OHD9Z///EezZs3S22+/LX9/f2OfxWJRw4YNCdsBJ3J14G7+mVe+fHlJ0qOPPmp8qHj69GmtX7/e6fctKmJiYpSUlJSjY81PKvG0OQAAzuWywN1qtRqLa2WIi4vTyJEjlZSUpKlTpyo8PFznz5/X1atXtWrVKlWuXFlfffWVfvzxR1cNCwAAoNgwh08VKlRQUFCQnnrqKf3666/666+/NHz48Bu+tpubm6ZPn66ZM2dq4sSJev755232Z4Txbm5uuu222274Pq7k4+Oj+++/X3fccUeWdesBOI8rS8okJCTYzMouV66cpPT1I/r27Wv0//nnn069b1Gxbt069enTR3fccYcuX7583eOZ4Q4AgOu4LHB35IcfflB4eLgefPBBzZkzx3j02WKx6LbbbtOXX34pq9WqTz/9ND+HBQAAUCSFhYUZ2xmzPaX0v62cETC7ubnpzjvv1AMPPCBPT0+bfb169dKPP/6oX375RQ0bNszzvQAUfebAPTcz3FNSUpSWlmZsZ14nQrL9gNFisdis0dCrVy9je9euXXYTv4o7q9Wq999/X6mpqYqIiNBPP/103XPMX2MCdwAAnCtfA/e9e/fKYrFo0qRJDvf36NFDLVu21K5du/JzWAAAAEWO1Wq1m+Ge36pVq1Yg9wVQOJlLyuR0hvuuXbvUu3dv3XPPPQoNDdXIkSPVvXt3ffLJJzbHmeu3BwQE2Hyo2LZtW2P7ypUrOnPmTJb3u3Tpkvbv31+sQvng4GCdPn3aaO/cufO655hnuPv6+rpiWAAA3LTyNXDP+BS9Xr16WR5Tv359RURE5NeQAACApOXLl+utt96yCXBRuEVHRyslJcVom2e4A0BBuJGSMs8//7zi4uJ07tw5TZ48WUePHpUkLV68WGvXrtWJEye0bt06/fDDD8Y5GeVkMgQEBNisV5HVBK7z58/rvvvu08MPP6wlS5bk+HUVdr///rtN+8CBA7p27ZpNX0pKis3vDGa4AwDgOvlazLJ69eqS0t8glihRwuExycnJLNoCAEA+2rZtm1599VVJ0tWrVzVz5swCHhFywlxOxtvb22ZmKQAUBPPPIfMM6qwkJCQoOjraaB85csRm/wsvvODwvMyBu5Q+y/348eOSpN27d2vw4MF2x8ydO9cIot977z09/PDD1x1jYWe1Wu0C95SUFO3du1edO3eWlP6h+ptvvqnU1FQFBgYqKCjI5ikAZrgDAOBcLp3hvnz5ctWuXdv4t3DhQknSwYMHszzn0KFDqly5siuHBQAATF555RVje+XKlQU4EuSG+WmE8uXLy2KxFOBoACD3JWX++eefG7pP165d7frMZWV2797tsGTM33//fUP3K8wOHDigixcv2vVnlJVJSUnRu+++q6SkJKWmpiokJETbtm3T+fPnjWOZ4Q4AgHO5dIZ7bGysw8VyvvjiC/Xp08euf9euXQoODtaIESNcOSwAAGBy6dIlm7bVaiW8vUGhoaGKjo5W/fr1XX6vrBZMBYCCYn5SOT4+XikpKdku4Lxjx47rXtPd3V3Vq1dXrVq1VLt2bbVo0UIdO3a0O65169ayWCyyWq2KiIjQyZMnVadOHWN/UlJSLl9N0fDbb78Z2+7u7kpNTZX0fx8u7Nu377oL2FatWtV1AwQA4CbkssA9Y5X53Dhx4oRGjhyp+++/3wUjAgAAmV24cMGu7+rVq/L39y+A0RRtBw4c0JNPPqlr167pueee05AhQ1x6v4JeMBUAMstc2iouLi7b3yfZLe756quvqm7dugoKCso2tM9QunRp1a9f36gBv2vXLpvA/fDhwzbHly1b9rrXLOxSUlK0bt06oz1kyBB99dVXktIXUo2OjtbWrVuN/a1atdIDDzygc+fO6ezZs7p48aIaNWqk9u3b5/vYAQAozvK1hvv1DB06VEOHDi3oYQAAcNNw9Hj9xYsXCdxzKSEhQTNnzjRqA69fv97lgTsz3AEUNiVLljRmmUvpZWWy+n0SFRVlhOOOtGnTRmXKlMnV/du2bWsTuJvfW2ZeSNVRyZmiZs+ePQoPD5ckeXh46JFHHtGaNWsUGRkpq9WqgwcPasuWLcbxvXv3Vs+ePQtotAAA3DxcWsMdAAAUnNjYWC1cuFAffvihUlJSHB7jKHDPXGLmZrR27VqNGzfOZuZgdhYtWqSQkBCjHRUV5fQxpaSk6Mcff9S0adP0yCOP6LvvvjP2EbgDKAzc3NxsFuDMrpRJVnXWJcnf318BAQG5vr+5jvuePXtsnrres2ePzbGJiYm5vn5hYy4n06lTJwUEBKhp06ZG39q1a3Xy5Emj3aVLl3wdHwAAN6tCNcMdAAA4z2effaZly5ZJSi85cvfdd9vsT0lJcfg4v6PF124m58+f17Rp05Samqq///5bdevWlY+Pj0qWLKnSpUvbHb97927jEf4MkZGRTh3TpUuX9OKLL2rfvn0O9xO4AygsSpUqZQTt2QXumQNwsxo1atzQWiKtWrWSm5ub0tLSFB0drePHj6t+/fqKi4uz+/lZ1AP35ORkrV+/3mj3799fktSsWTNt3rxZkrRq1Spjf/Xq1VWtWrX8HSQAADcpZrgDAFBM/fPPP8a2o2D9m2++cRgM3yyBe2Jionbs2GH3Nfj666+NRefS0tI0ePBgDRw4UAMGDNDx48dtjo2Pj9fs2bPtrh0VFeW0cgVnzpzRyJEjswzbJWq4Ayg8zAunxsTEZHnc3r17je3q1avb7KtVq9YN37thw4ZGO6OMzM8//2wXsKempmb59FdR8NdffxlfX29vb3Xv3l2S1Lx5c4fHd+3aNd/GBgDAzY7AHQCAYshqterEiRNGO3Od3CtXrujDDz90eO7NEri/9NJLeuqppzRkyBBjFmZMTIx+/vlnu2PT0tKUmJio5cuX2/S//fbbOn/+vCTZzMZMTU3NdmZnTl28eFFPPvmkUaNXktq1a2d3HDPcARQW5oVTs/o5ePXqVR07dsxo33PPPTb7a9asecP3N5eV2bVrl1JSUuyeQsqQkJBww/cpaOZyMt27d1fJkiUlSY0aNZKbm/3b/Ntuuy3fxgYAwM2OwB0AgGIoIiJC0dHRRjskJETx8fGS0kvJzJ49W3FxcZKk0qVLa/Lkycaxxb2Ge3JyskJCQvT7779LSi//smPHDknSTz/9ZHydHNm2bZsxc33Hjh02Afzo0aNtQo681nG/cuWKnnzySYWGhhp9U6dO1eLFizV+/HibY5nhDqCwyMkM93/++cf4WRoQEKBbbrnFZr+zAvc9e/bojz/+yPKD5KJaVubatWv6888/jXa/fv2MbV9fX9WpU8fm+AYNGtjM/AcAAK5F4A4AQDFkXiRNSp/xHhwcrLS0NM2aNUvbtm0z9j355JNq1KiR0S6ugXtSUpIefvhh9erVS8OHD7fZd/z4caWkpOjrr782+tq1a2c3S/DChQs6c+aMYmNjbUrJ1K9fX48++qhNjfe8BO7R0dEaN26czp49a/RNmTLFmAU6bNgwtW7dWpLUo0cPh7XlAaAgmBc7vXDhgsNjzOVkWrVqpSpVqsjf319S+sKr9erVu+H7t2zZUu7u7pLSZ9i/9dZbxj5zMC0V3cB98+bNxux8X19fde7c2WZ/5nD9rrvuyq+hAQAAsWgqAADFkrmcTIYjR47o999/15o1a4y+W265Rffcc48uX75s9EVGRiohIUE+Pj75Mtb88ueff2r//v0O9wUHB2vdunXGbHIPDw/Nnj1b58+fV0hIiJYsWWKE33/99ZeOHTtmc+ysWbPk6empMmXKGEH7jS6cGhcXp2eeecamXvy4ceM0ePBgo+3h4aFFixbp/PnzdrWPAaAgNWzYUCtWrJAkHThwQMePH9eBAwfUu3dv48NB84KprVu3lpubm6ZOnarPPvtM/fr1U+XKlW/4/iVLllSTJk2Mn/dXrlwx9g0dOlQbNmxQcnKypKIbuGc8oSWl/x739va22d+kSROtXLnSaN966635NjYAAJAPgXtKSor+/vtvnT17Vt7e3mrevLlq167t6tsCAHBTyzzDXZI++eQTmxC4ffv2mjt3rtzc3FShQgW5u7sbi4VeunQpT4/0F0bBwcFZ7jt69KjCwsKMdr9+/VShQgVVqFBBLVu21OnTp/XZZ59Jkt544w2bc8eMGaP69etLsp3ZeSMz3FNTUzV58mQdOnTI6Bs9erRGjRpld6ynp2ex+/8IQNFnXrTz0KFDGjFihFJSUrRp0ya99dZbio+P15EjR4xjMp7W6d27t3r37u2UMbRp08buA9by5curWbNm8vb2LtKBe3JyslEGTZL69u1rd0zfvn314YcfKiIiQqNHj7apqw8AAFzPpSVlVq1apZo1a6pbt24aPny47r33XtWrV0+33nqrzUw6AADgXKdOnbLrM4ftjRs31uuvvy4vLy9Jkru7uypWrGjsL44Lp5pnjGd26dIlHT582Gg/8MADNvszP66foVGjRjZhuDlwv5EZ7lu2bNGuXbuM9n333acnn3wy19cBgIJSt25dlShRwminpKRISi+DYrVatX//fuPDXT8/P9WtW9fpYzDXcc/QrVs3ubm52cwGL4qB+/79+3Xt2jVJkre3t8PX6u/vrx9//FFff/21xo4dm99DBADgpueywH3v3r269957deHCBfn6+qpVq1aqW7eu3NzctHbtWg0cONBVtwYA4KZmtVodznDPUKtWLb399tvy9fW16a9SpYqxnVXd3aIsc+Ce1RN37dq1U4MGDWz6WrRoYbMQoJQ+w3zWrFny8Pi/BwbzOsPdXGahffv2evbZZ2WxWHJ9HQAoKB4eHmrcuLHDfRERETY/58z11p2pRYsWNj+bpfT1LiTZlEsrioG7eXZ7q1at7MrJZPD19TXefwMAgPzlst++r7/+upKTk/Xoo4/q0qVL2rVrl44ePap9+/apXr162rlzp9auXeuq2wMAcNMKDw9XdHS00TaHGZUrV9a7775rEwxnMNcCP3bsmEvHmN9iY2NtZu2vWLFC3377rdq1a2d3bObZ7VJ6uP7iiy+qWrVqKlWqlCpUqKDp06erTp06NsflNXDft2+fsd29e3eCEgBFkrmsjNnFixftFkx1BR8fHzVr1sxolyhRwvh5n/Fkl1Q0A/ft27cb2x06dCjAkQAAgKy47F3c5s2bVbNmTb333nsqWbKk0d+4cWMtWrRIVqtVmzdvdtXtAQC46aSmpuqnn37SI488YvSVLVtWd999tySpUqVKWrRokSpVquTw/CZNmhjbBw4ccO1g85l5EVlfX19jNn/mmew1a9bMsnxM37599eOPP2rDhg1avXq1brvtNrtjypQpY2znNnBPSEiwqWvcokWLXJ0PAIVFVoH7mTNndPDgQaPdpk0bl43BHEZ36tTJmAlelEvKREdH699//zXaHTt2LMDRAACArLhs0dTQ0FDdddddDh8R7NSpk3EMAADIuzNnzmj69Ok2dcil9NDjueee07333qvq1atn+ei5JJvZgMePH9e1a9ds6vBKUlpami5cuKDAwMAiVerEXE6mTp06xtgzFjvNMHz48DzNKs/LDPd///3XqHXs4+OjevXq3fA4AKAgmX+fmK1bt85YsNTHx0cNGzZ02RiGDh2qvXv3KiYmRk8//bTRb/49mJCQ4LL7u8KGDRtktVolSeXKlXNJ/XsAAJB3LgvcU1JS7GqdZsiY8Z7xxxYAALhx586d0xNPPKGwsDCjz93dXb169dKkSZPk5uaWo/C2Vq1aKlmypOLj45Wamqp///1XrVu3NvZbrVY9/vjj2rt3r1q3bq3XX39d/v7+LnlNzmYO3M0BRfPmzWWxWGS1WlWmTBmHs9ZzwzzDPbeLpprLyTRt2tSu/jAAFBUBAQFq3bq1Tb12Sdq2bZux3bx5c5f+nCtVqpQWLVpk119UZ7ivWLFC8+bNM9rt27cvUh98AwBwM6EwKAAARdjly5f11FNP2YTt/fv3188//6x58+apQoUKOb6Wu7t7tmVljh8/btTe3bNnjx599FFFRETk8RXkj6wC96CgIE2dOlXdu3fX66+/brOY3o3Iywx3c+BOORkARd0rr7yiyZMna8CAAUZfamqqsW3+QDc/FcXA/cyZM5o7d67x9fPx8dHIkSMLeFQAACArLp06tWXLFj388MO53m+xWPTJJ5+4cmgAABR5kZGReuqpp3T+/Hmj78UXXzRqtt+Ipk2baufOnZJkU2dXkk6ePGnX/t///qeJEyfe8P1cKTExURs2bNCKFStsFunL/Aj+3XffnaevmZl5hntsbKySk5Pl6el53fOsVqv2799vtLOqfwwARUX58uU1bNgwrVu3TqtXr7bbX1CBu/mD1aISuK9fv94I28uWLau3336bcjIAABRiLg3cjx8/bjOjLKf7CdwBAMheTEyMxo0bp1OnThl9EydOzHNw3LRpU2P7wIEDslqtxiPrp0+ftjvevHhbYXHx4kUtW7ZMa9as0dWrV232eXt729VtdybzDHcpfYG78uXLX/e80NBQRUdHG23z/w8AUJRlLFJt5unpafNEVX4yz3BPSkoqkDHk1ubNm43tIUOGuLT2PQAAyDuXBe5Llixx1aUBALipXbt2TePHj9fRo0eNvscee0wPPPBAnq9tDnqvXLminTt3qn379pJkE+5nMIfEhUFiYqJGjRql8PBwu33Vq1fX008/neUaM87g4+Mjb29vY9ZkVFRUjgJ384cZFSpUKDK18QHgehwF7k2bNs12EW9XKmqLpkZFRdmUeOvatWsBjgYAAOSEywL3gq4p17NnT/35559Z7l+9erVuvfVWoz1r1iy99NJLWR7//PPP69VXX3W4b+vWrZo7d662b9+upKQkNW7cWOPGjdNDDz2U5fXOnTun6dOn67ffflNERISqV6+uYcOG6YUXXshz/VgAQPFltVo1Y8YMm/IjDzzwgB599FGnXL9cuXJq3LixDh8+LEmaOXOmvvrqKwUEBBSJwP3UqVM2YbuPj4/69u2rQYMGqUWLFvmywFyZMmV06dIlSTlfONVcrqdWrVouGRcAFIQyZcrYfBApFVw5Gano1XDfunWrrFarpPQPZBs0aFDAIwIAANfj0pIyhcG9997rcCZbYGCgw+O7dOnisB5emzZtHB7//fffa+jQoUpLS1P37t1Vvnx5/fHHHxo5cqT279+v+fPn251z/PhxderUSVeuXFHTpk3VrVs37dq1S7Nnz9Yff/yhP/74o8BmfAAACrdffvlFGzZsMNp33323JkyY4NQgeerUqRo1apRSUlIUFhaml19+Wa+99ppCQkLsjo2OjrYpO1PQQkNDje3AwEB9+eWX8vX1zdcxBAQE5DpwN3+YQeAOoDixWCyqUqWKzZM8rVq1KrDxFKXA/cyZM/rxxx+NdteuXQvN71sAAJC1AgvcT5w4oTp16jjcN3fuXL344otOuc/8+fNVs2bNHB8/ZswYjRo1KkfHRkRE6OGHH1Zqaqq+//573XPPPZLS3+x37dpVb7zxhu644w717NnT5rxRo0bpypUreuaZZ7Rw4UJJUkpKioYMGaIff/xR8+bN06xZs3I8ZgDAzeHSpUt6/fXXjXb79u01ZcoUp7/5btiwocaNG6cFCxZIkv7880+98847Sk5Otjs2OTlZ165dU8mSJa973StXrujzzz9XzZo1dddddzl1zBkuX75sbAcGBuZ72C6lL2iX4cMPP1SLFi1UqVKlbM8hcAdQnJkDd3d39wJdGLqoBO6//PKLZs+ebSyWKlFOBgCAosKtoG5cr149Va5cWZMnTzZmgUnpC77NmDGjoIaVKx9//LGuXr2qQYMGGWG7JFWqVEn//e9/JUlvvPGGzTl///23tm7dqooVKxrHSJKHh4cWL14sT09Pvf3220pJScmfFwEAKBKsVqtmz56tuLg4SZKfn59mzJghd3d3l9xv+PDh6tixo9H+4osvjO3M4XFOy8q88847+vzzzzVnzhz9888/ThlnZubA/Xoht6uYS9adPn1aY8eOzbZOsNVqJXAHUKyZ67g3bNgwRx/SukpRCNxPnz6tV155xSZsDwwMVIcOHQpwVAAAIKcKLHDv06ePvLy89NZbb6l+/fr68MMPNWrUKC1cuLDA3iDn1i+//CJJGjx4sN2+22+/XT4+Plq3bp3Nm+yMc+688067sjGVKlVSt27dFBkZqS1btrhw5ACAoub777/X33//bbT/85//qHLlyi67n5ubm2bNmmUzWztDvXr1bGaO5zRwP3jwoLFtXgDOmcyBe4UKFVxyj+sZMGCAHn/8caMdEhKiZcuWZXl8ZGSkzdewdu3aLh0fAOQ3c832W265pQBHUvgD9+TkZE2bNs0Ym7+/vyZNmqRly5ax1hcAAEVEgZWU+f333yWlLwIzdepUjR07VpLUsmVL/fzzz067zyeffKLw8HC5ubmpfv36uuuuu1S9evUsj1+/fr3++ecfJSQkKCgoSAMGDMiyfvu+ffskOV70x8vLS02bNtWuXbsUHBxsPDaZ3TkZ/evXr9f+/fvtStEAAG5OV65cMcq7SFKPHj10++23u/y+5cuX14wZMzRhwgSb/lq1aunkyZPGbPuoqKjrXstqtdqE4RcuXHDmUA3me1SsWNEl97gei8WiRx99VDExMfryyy8lSf/73/80cOBAh5MKzLPb/f39VaZMmXwbKwDkh759++ratWtKTEzUvffeW6BjMQfu2T19VFA+/PBDHTlyxGjPmDFDPXr0KMARAQCA3CqwGe4ZOnXqpOrVqxsrr/fr10/VqlVz2vXnzJmjxYsXa9GiRRo/frzq1q2rl19+Ocvjly1bpoULF+qDDz7Q9OnT1bZtWw0ePFixsbE2x129etWYjRYUFOTwWhn9Z86cMfoyFpzLzTkAgJvbhg0bjFDA399fU6dOzbdF07p27aphw4bZ9NWsWVP+/v5GOycz3GNiYnTt2jWjffHiRecN0qQwlJTJ8NhjjxlPCCQkJOj99993eJw5cGd2O4DiyN3dXXfffbfuv/9+eXp6FuhYCvMM9z179mjp0qVG++677yZsBwCgCHJZ4P7VV1+pdu3aWrx4cZbHJCUl6Z577tGXX36pdu3a6e6779Ybb7yhQ4cO5fn+3bt317Jly3TixAnFx8fr6NGjmjt3rjw8PDRjxgxjsdIMdevW1fz583Xo0CHFxsbq7Nmz+uKLLxQYGKjvv/9eDz74oM3x5gA+qxqEGY/bx8TE2J2Xm3Oy06RJE4f/Tpw4kaPzAQCF37Zt24zt22+/XeXKlcvX+z/99NOqX7++pPTZ261bt7YJ3K9evXrda5iDcMk1M9wzz6IvqJIyGfz8/Iwn+CRp3bp1DsMdc+Cem4XeAQC5Zy7LkpSUVIAjsRUbG6sZM2YYE9GqV6+uSZMmFfCoAADAjXBp4B4aGqrhw4c73B8XF6dbb71VK1asULdu3bR+/Xq98847cnd317vvvpvn+8+ePVsjRoxQ7dq1VaJECdWvX19Tp07VTz/9JEmaNWuWzUy7ESNGaPLkyWrcuLF8fX0VFBSk4cOHa+fOnSpXrpx++uknbd++Pc/jAgAUb8ePH3dqffLk5GTt2rXLaHfu3Nlp184pLy8vLV68WOPGjdM777yjatWq5XqGe2hoqE374sWLRqjgLLGxsTa/2wt6hrsk3XbbbUa4c+3aNe3evdvuGPP3CwumAoBrFdYZ7q+99pouXbokKf2JgDlz5qhEiRIFPCoAAHAjXBa479u3T926dbN5Q54hIiJCt9xyizZu3Kj+/ftrzZo18vX1VZUqVdSxY0etW7fOVcNSv3791LZtW0VFRWnHjh3XPb5KlSoaPXq0JGnNmjVGv5+fn7EdHx/v8NyM2ralSpWyOy8352Tn0KFDDv/VqVMnR+cDAJwjMTFR//3vf3X//fdr9OjR2S6SmdmJEyf03nvvad26dXYh9L59+4wQ2dvbW61atXLquHPK399fo0aNUseOHY12hhsJ3K9du5bjxVZzyjy73dPTUwEBAU69/o3w9vZWhw4djPbmzZtt9h8/flyHDx822m3bts23sQHAzagwBu7r1q3T6tWrjfZjjz2mxo0bF+CIAABAXrgscA8NDXUYtl+4cEHdunXTrl27dM8992jFihU2j/UFBgbq/PnzrhqWJKlevXqScl4/1tHxpUuXNl7fuXPnHJ6X0V+jRg2jL2PB1tycAwAo3E6ePKmRI0fq22+/NfoWLVqko0ePZntebGysZs2apfvvv1+ffvqppkyZohkzZtgs4mYuJ9OmTRuboKAgmX/H52TR1MwlZSQ5/fd95gVT86vO/fV069bN2N68ebPNhyrmheIbN25slO4BALiGl5eXsV1YFk01//3QsmVLjRo1quAGAwAA8sxlgXvFihW1detWmz9iDh06pM6dO+vff//ViBEj9M0339gtmnP27FmXL6QTGRkp6f/qpd/o8S1atJCUvrhNZsnJyTp48KB8fHxs3jxnd465v3nz5jkaGwCgYG3btk0PPvigjh8/btOfkpKi559/Xt99953xiLhZWFiYHn30Ua1atcomgF29erVGjx6tc+fOyWq1asuWLca+Tp06ue6F5FJeZ7hLzl841XyPihUrOvXaedG1a1dj+9KlS8b3SlJSkn799Vdj36BBg/J9bABwszFP9iosM9zNfyc88sgjcnd3L8DRAACAvHJZ4H733XfrwoUL6tmzp95//3298MIL6tChg0JCQtSzZ0999tlndn9IhISEaNeuXWrYsKGrhqWwsDDjce7WrVtf93ir1aoff/zR4fG33367JGn58uV2561atUoJCQnq06ePzR91GeesXLnS7g+80NBQbd68WWXKlFGXLl1y8aoAAAXBarVq3rx5Nj/Pe/fubWyfO3dOr732mu644w4NGzZM7733nk6fPq34+Hg99thjOnbsmHGseTHtY8eOacSIEXrllVd08uRJo78wBe7mci3ZBe5Wq1UxMTEOA3dnL5waFhZmbBemwL18+fJq0qSJ0d60aZOk9NnuGV87b29v9e/fv0DGBwA3k8wlZZy9nkhuWa1WhYeHG+2CXvAbAADkncsC9zlz5qh169b6+++/9dRTT+m1115TfHy8WrdurU2bNumVV15RWlqacXxMTIxGjRqlxMREDR06NE/33rZtm3766Selpqba9J8+fVp333234uLiNHDgQAUFBUlKf4O+aNEixcTE2BwfGxursWPHaseOHapcubLuuecem/1jxoxR6dKl9fPPP+uHH34w+i9fvqznnntOkjR58mSbc9q3b68uXbro8uXLev75543+lJQUPfnkk0pOTtYzzzzj8ln+AIC8u3Dhgs0s7bfeekuvvfaaHnnkEbtjjx07pk8//VQPPPCAZsyYobNnzxr7HnzwQa1du1YPPPCA0RcbG2t84CtJPXr0UM2aNV3zQm7A9Wa4p6Sk6Ntvv9Vdd92lW265RX///bfdMc6e4Z65pExhkrmsjJT+5F+G7t2726wPAwBwDXPgbrValZycXICjSV/TxPzBfZkyZQpwNAAAwBk8XHXhUqVK6a+//tLXX3+tHTt2yMPDQ3fccYf69OmjadOmadq0aVq8eLG6deumlJQUrV+/XhEREWrSpImeeuqpPN07ODhYo0ePVuXKldW6dWsFBATozJkz2r17txISEtSkSRN99NFHxvFxcXEaN26cpkyZonbt2qlKlSoKCwvTnj17FB4eroCAAC1fvtxm9qEklS1bVp9++qmGDBmiwYMHq2fPnipXrpzWrVunqKgoTZo0ST179rQb35IlS9SpUyctXLhQ69evV+PGjbVz506dPHlSnTt31gsvvJCn1w8AyB/79u0ztqtXr26EqmPHjlWnTp20YcMGbd68WSEhIcZxiYmJ2rhxo9G+7777NH78eEnSxIkT1aRJE7388svGQqlS+oLbU6ZMcfGryZ3rBe5z5szRqlWrsr2Gs2e4F9aSMlJ6oP7+++9LSg/aw8PDdebMGWN/xnoxAADXyrwWSmJiok1d98z7pk+frkuXLmnq1KkueRI7IiLC2LZYLIViwW8AAJA3LgvcJcnT01MPPvigHnzwQZv+OXPmqFKlSpo+fbq+/vpro79nz5768ssv87wgXIcOHYyZ6Tt37lRkZKR8fX3VsmVL3XfffRo7dqxKlChhHF+uXDk9//zz2r59u4KDg7Vt2za5u7urVq1aGjVqlCZOnKjAwECH97r33nu1adMmzZkzR9u3b1dSUpIaN26scePGaeTIkQ7PqVevnvbu3asZM2ZozZo1+vHHH1W9enVNnz5dU6dOLTQL4gEAsmcO3DPW6MjQsmVLtWzZUhMnTtSZM2e0YcMGLVq0yObRdXd3d7vfFf369VOdOnX07LPPGkH9xIkTC90j5ubAPSYmRikpKfLwSP+z4ujRo9cN2yXnBu5Wq9Xmg43CFrjXq1dPlSpVUmhoqFGb3xy4s1g6AOQPR4F7qVKlHB777bffav369ZKkCRMmaM2aNU4fjzlwDwgIoH47AADFgEsD9+w8/fTTGj16tLZu3aro6Gg1adLEpr5pXjRq1Ejvvfdejo8vVaqUXn311Ru+X5cuXbR69epcnVOtWjUtWbLkhu8JACh4+/fvN7YzB+5mNWrU0KhRo3T27Fn9/PPPRn/v3r1VuXJlu+Pr1Kmjzz77TCtXrlSlSpXUq1cv5w7cCcyBu5Qeumc8Br906dIcXePChQuyWq2yWCx5Hs+OHTt07tw5o21esLwwsFgs6t69u7777jtJ0saNG23GS+AOAPnDUeCelbVr1xrbV65cccl4zIE75WQAACgeXFbDPSf8/PzUv39/DRkyxGlhOwAA+SE2NlbHjx832s2bN7/uOWPHjrUpTzZ8+PAsj/Xz89OwYcMKZdguSb6+vjaz8DLKypw5c0br1q0z+kePHp3lNRITE21mpefFZ599Zmx36dJF1apVc8p1nclcx33Lli1KSUmRlB7GF8bxAkBxZLFY7BZOzUp+PHlsDtzLlSvn8vsBAADXK7AZ7hkSEhIUFRWl8uXLG4+iAwBQmJw5c0Zvv/224uPj5e/vL39/fyUkJBjlYUqXLp2jBU3Lly+vBQsW6H//+5969uyppk2bunjkrpNRZzY8PFySFBUVJUn63//+Z3xdatasqSeeeMLuia66desaH1b89ddfeZ7dfeTIEZtFWR966KE8Xc9V2rRpoxIlSujatWs2pYWqVq1KOTkAyEdeXl5G0J5d4J5VbXdnYoY7AADFj8sS7piYGP37778KCAhw+Fj3sWPHNG7cOG3YsEGpqany8vLSoEGD9NZbb6lKlSquGhYAALkSGxur8ePH25T/yKxZs2Zyc8vZQ2OtW7dW69atnTW8AuXv728E7tHR0bp06ZJ++eUXY/+oUaOMOvX/+9//JKWvfeLr62sE7tu2bdP999+fp3H8/vvvxnbjxo0L7dfX29tbHTp0sFk0V6KcDADkN29vb8XExEgq+MA9MjLS2C5btqzL7wcAAFzPZSVlPvvsM3Xq1MnhommXLl1St27dtG7dOqWkpMhqtSoxMVHfffedbrnlFl27ds1VwwIAIMesVqvmzJmTbdguSZ06dcqnERUu5jruERER+vzzz5WamipJqly5sm699VZJ0qOPPqoBAwaoc+fOeuihh9S5c2fjvN27dyshISFP47h48aKx3b17d6fUhHcVc1mZDNWrVy+AkQDAzcv8VFF2v4Pyu6QMgTsAAMWDy2a4//nnn3Jzc9OIESPs9r388su6fPmyypYtqyVLlqhXr146duyYHn/8ce3atUuLFy/WpEmTXDU0AAByZOvWrTb1yO+8805Vr15d0dHRxr86depo8ODBBTjKglO9enXt3btXkrR69WodOnTI2PfQQw8ZpeJ8fHz08ssvG/sqVaokX19fxcXFKTExUbt371aXLl1ueByXL1+2uXZh1rVrV1ksFpuSMsxwB4D8ldMa7plnuKekpDi9DCqBOwAAxY/LAvf9+/erWbNmqlixok1/WlqavvrqK1ksFr3yyiu68847JUktW7bUDz/8oNq1a+unn34icAcAFLi//vrL2G7VqpWmTZtms1Doza5Xr176+eefJUl79uwx+suWLauBAwdmeZ6Hh4fat2+vDRs2SEovK5OXwD0sLMzYrlChwg1fJz+UK1dOTZo00cGDB40+AncAyF8+Pj7Gdm4C97i4OJunu5yBkjIAABQ/Lispc/nyZTVo0MCuf//+/YqKipKHh4ddzdbAwEB16NBB//77r6uGBQBAjplnbPfr14+wPZMOHTo4DB6GDx9uE2Y4Yi4rs23bthseg9VqtQncM3/QXxhlLitD4A4A+SunM9w9PT1t2o5Knx46dEgTJ07Up59+apRVy42MtVAkAncAAIoLlwXucXFxSk5OtuvfvXu3JKl58+YqXbq03f6goCBdvXrVVcMCACBHkpOTFRwcbLQbN25cgKMpnDw8PNSrVy+bPj8/vxyV2DEH7mfPntXZs2dvaAxRUVE2f28UhcC9R48exnZAQECRGDMAFCc5DdzN5b+k9Pe4mc2dO1ebN2/We++9pxkzZiglJSXH40hJSVF0dLTRLlOmTI7PBQAAhZfLAvcKFSroyJEjdv1btmyRxWJR+/btHZ6XkJDg9Mf0AADIrRMnTigpKUlSerBcr169Ah5R4dS3b1+b9tChQ+Xn53fd8ypVqqQ6deoY7a1bt97Q/c3120uUKCFfX98buk5+qlu3rsaNG6dGjRppypQphXqRVwAojnIauGcOz+Pj423aUVFRNh/O//bbb3r77bdzPI6oqCibNjPcAQAoHlwWuGeUhlm5cqXRFxYWph9++EFS+qP5jhw+fFhVq1Z11bAAAMgRczmZ+vXr29VxRbo2bdqoWrVqkqTSpUvblYvLjjPKypgD9woVKhSZ8HrUqFFatmyZ+vTpU9BDAYCbjrnsmaMyMRkyB+6ZZ7gfOHDA7pwffvjBbmZ8VswLppYoUUIlSpTI0XkAAKBwc1ngPm7cOFmtVt1333166KGHNHnyZLVr104xMTGqVq2abr/9drtzTp48qeDgYLVo0cJVwwIAIEfMgXuTJk0KcCSFm7u7u9555x09/fTT+vDDD3P1OLx5odTdu3crISEh1/c3B+6UZgEA5IT5d5U59M7sejPc9+/fb3dOQkKC3XFZMd+b2e0AABQfLgvcb7nlFs2aNUvJycn6/PPPtWDBAoWEhKhEiRJasmSJPDw87M55//33JUn9+/d31bAAAMiRw4cPG9vUb89eUFCQRo4cqbp16+bqvBYtWqhkyZKS0h/p37NnT67vbV4wtUKFCrk+HwBw8zEH7pGRkVked73A3dEM9+td04zAHQCA4sk+9XaiGTNm6I477tAPP/ygsLAwVatWTQ888IBq1arl8Hhvb2+NHz9et956qyuHBQBAtqKjo3Xy5EmjTeDuGp6enmrXrp3+/PNPSellZcxlZnKCGe4AgNwqV66csR0eHp7lceZFuSXbkjIpKSk2T8OZRUVFKSgo6LrjMAfuLJgKAEDx4dLAXZJat26t1q1b5+jYl19+2cWjAQDg+t544w2lpaVJkkqVKqWaNWsW7ICKsc6dO9sE7rnFDHcAQG6ZZ5PfaEmZEydOGPXf3d3dVb58eYWGhkqyXww1K+aw3/whAAAAKNpcVlIGAICiaOPGjfr111+N9qhRo+Tu7l6AIyrezHXcQ0JCdO7cuVydzwx3AEBumQP3Gy0pY67f3qBBA1WuXNlo5zRwzwjoJX6HAQBQnBC4AwBuKgcOHNDXX3+t3377TUeOHLF5Mx0VFaV58+YZ7WbNmmnEiBEFMcybRuXKlVW7dm2jvXXr1lydb57hTlgBAMgJc+AeHR1tF6xnyC5w37lzp7HdrFkzBQQEGO2cBu78DgMAoHhyeUkZAAAKi+DgYD3yyCNGuRgpff2QBg0aqHHjxjpz5ozxeLe3t7dmzpzJ7PZ80LlzZ6Nm/rZt2zR06NAcnZeQkKCrV68abUrKAAByIvMCpREREQ4D76wC96SkJG3fvt3o79ixozZu3Gi0cxq485QWAADFEzPcAQA3jc2bN9uE7ZKUmJio/fv36+uvv9Zff/1l9I8dO5ba7fnEvFDqrl27lJCQcN1z9uzZo8cee8xou7m5Uf8WAJAjPj4+KlmypNHOqo575sA9Y9HUPXv2GOG7t7e32rVrl+sZ7larlcAdAIBiisAdAHDTOHPmjE3bw8Pxg14tWrTQsGHD8mNIkNSyZUt5e3tLSv8A5PTp09kev3z5cj3xxBM6fPiw0Ve2bNks//8EACCznCycmtUM902bNhl97du3l4+Pj8qUKWP05SRwj4qKUnJystGuVKlSjsYNAAAKP96ZAgBuGubAfcaMGbr11lsVHBysw4cP69ChQzpy5IjKli1LKZl85uXlpbJly+rixYuSZFMmxiwtLU3vvvuuPvvsM7t9t9xyi0vHCAAoXsqWLWss1J1V4G4OxKX0wN1qtWrLli1GX/fu3SUp1zPczQumlihRQr6+vjkdOgAAKOQI3AEANwWr1WoTuNeoUUNeXl5q2rSpmjZtWoAjgySVKlXKCNxjY2Pt9icmJmrWrFlau3at0VexYkU9/PDDqlKlijp06JBvYwUAFH03MsM9Li5OJ06c0IULF4y+rl27Ssp94J55wVSLxZKTYQMAgCKAwB0AUCgcP35cq1atUq9evdS8eXOnXz8iIsImyK1Ro4bT74Eb5+fnZ2xnDtyjoqI0efJk7du3z+irV6+eFixYwCP4AIAbkpPAPTU11aYdHx+vzZs3G+1GjRoZC3bnZYY7v8sAACheCNwBAAXuwoULGjNmjGJjY/XNN9/oiy++UO3atZ16j5CQEGPb39/f5o0xCp75UXpz4G61WvXUU0/p6NGjRl/Hjh316quv2oT0AADkhnmh7fDwcIfHOKrhbg7cu3XrZmyb/66Ijo5WampqtuXpzAumZoT2AACgeGDRVACASyUnJxs1Uh1JSUnR9OnTjZA1OTlZL730kt2b3LzKXE4GhUtWM9xDQ0NtwvZBgwZpwYIFhO0AgDwxL3IaGRnp8JjMf4vExMTowIEDRjujfrtkG7hbrVbFxMRke//MJWUAAEDxQeAOAHCZhIQEDR48WHfddZdeeuklpaWlGftiYmK0efNmjR071qZUiCQdOnRIX3/9tcNrXrlyRW+99ZYef/xxPfTQQ1q1alWOxkLgXriVKlXK2DaHFOYQxM/PT9OmTZOHBw/oAQDy5kZquKelpclqtUpKn5XeoEEDY5+vr6/NjPbrlZWhpAwAAMUX71gBAC6zfft2nT9/XpK0cuVKpaSkyN3dXQcOHLAJwDP4+fkZs5uXL1+uESNG2Ow/d+6cxo4dayyuKUlz585V165dr1si5vTp08Y2gXvhk9UMd3PgXqZMGRaVAwA4hTlwz2lJGbOuXbva/E6yWCwKCAgwrnW9wJ2SMgAAFF/McAcAuIw55Jak1atXa9WqVQ7D9q5du+qTTz4x2ufOnbN5M3r69Gk99thjNmG7lF6C5p9//rnuWJjhXrhlFbibAwvz4/8AAOSFOXCPioqyeQovQ3Jycpbnm8vJZDD/nrpe4G4uKcMMdwAAihcCdwCAyzgK1s38/f3VvXt3ffDBB1qwYIHq1KmjqlWrGvv37NkjSTpx4oQef/xxmwDebO/evdneJyQkxJhpLxG4F0bmwD0uLs7YNgcW/v7++TkkAEAxZl40NTU1VVevXrU7JqsZ7t7e3mrXrp1dv/lpu+wC99jYWJvfddRwBwCgeKGkDADAZTIH7s2bN1fDhg3VtGlTNWvWTEFBQXYlQlq1aqULFy5ISg/Sa9Wqpaeeesp442qxWPTCCy8oKSlJ8+fPN47LysaNGzV79mylpqZKkkqWLKmgoCBnvUQ4SU5KylyvbBAAADnl5+cnT09PYxZ7eHi4ze+ZtLQ0h7PeJal9+/by8fGx6zefn/H7KyoqSsHBwQoODtbJkydVqVIl9ezZ0zjOw8OD328AABQzBO4AAJewWq02JWU+/vhjtWzZ8rrntWrVSr/88oskadOmTVq7dq0x68zNzU0zZszQHXfcoWPHjhnnHDlyRLGxsXazpGfOnKmNGzfaXP/pp5+Wl5fXjb8wuERWi6ZSUgYA4AoWi0Vly5Y1Fi81f8ArZV+/vVu3bg77zcH58uXL9d133zl8Ou/s2bPGdrly5eTmxoPnAAAUJ/xmBwC4RGRkpM3j2Tkt49K6dWtjOywszLiGu7u75syZozvuuEOSVKdOHSOkTUtL0/79+22u8+qrr9qE7d7e3po3b57uu+++G3o9cK2c1HBnBiAAwJmyWzj1egumOmL+PRUaGpplKTzz2jOUSwMAoPghcAcAuIS5nIy/v3+Ow9Jq1arZ1FWV0h+3fvXVV9WvXz+jz83NzWbGvLmszIYNG7R69Wqj3aZNG33xxRfq27dvLl8F8guBOwAgv5mfnIqIiLDZl1Xg3rBhwyxrrtesWdNhv7+/v83CqJcuXbLZBwAAihdKygAAXMJcTqZGjRp2tdqzYrFY1Lp1a61du1aS5OXlpf/+978OZ5O1adNGmzdvliT9/fffeuqppxQZGalXXnnFOKZ58+Z677335O7unodXA1czB+7JyclKTEyUt7c3JWUAAC5j/oA/p4F7VuVkJKlPnz46d+6czpw5o9q1a6tevXqqX7++KlSooG+++cZYe8aMwB0AgOKHwB0AriM5Odl483T69GlFR0frtttuU7169Qp6aIVa5sA9N0aNGqUdO3bI29tbM2fOVMeOHR0e1759e2P78OHDunLlil577TWjDmvG+YTthZ+5hruUPss9c+DODHcAgDOZS8pkDtwzFlPN4Ovrq0qVKmno0KFZXs/Dw0NjxoxxuC+r32GlS5fO4WgBAEBRQeAOAJmEhoZq+fLlOn78uM6cOaPz588rNTXV5pgffvhBH3zwgRo2bFhAoyz8zCVlsnrEOisNGjTQ77//Lin9zWtW6tWrp0qVKik0NFRWq1WzZ8/Wtm3bjP1PP/10rsN+FAxvb2+5u7sb/63FxMSoTJkyio6ONo4hcAcAOFNuSsr88ccf2f5Ncj1Z/Q5jhjsAAMUPNdwB3DRiY2N14MABmwDPkeeff15LlizR5s2bFRISYhe2S1JcXJzGjx+vCxcuuGq4RV5eZrhL6UH79d7YWiwWde/e3Wibw/a2bdtqyJAhub4vCobFYrGr43716lWlpaUZfZSUAQA4U05Lyri7u+cpbJcI3AEAuJkwwx3ATSEmJkYjR45USEiILBaLGjRooHbt2qlt27Zq1aqVSpYsKUmKjIzUwYMH7c53d3dXYGCggoKCtGPHDqWmpio8PFyLFi3S3Llz8/vlFHpnz57VuXPnjHbt2rVddq/u3bvru+++s+nz9fXVjBkz5ObG58pFiZ+fn/GB2KhRo2z2ubu7y9fXtwBGBQAorrIrKWMO3PMatktZB+s8vQUAQPFD4A7gpvDxxx8rJCREkmS1WnXkyBEdOXJEy5Ytk7u7u0aMGKGnn35ahw4dMs4JCAjQtGnTVLNmTQUGBsrT01OS9M033+j111+XJG3fvl1paWkEu5msWrXK2K5du7aqVavmsnu1adNGJUuWVHx8vNE3ceJEVa1a1WX3hGuYZ7hnFhAQkOOFdwEAyInMgbvVajV+15gD94y/AfOCGe4AANw8SIgAFHtnz57VN998k+X+1NRU/e9//9P27dttAvfmzZurZ8+eqlmzps0brdtvv90I2KOjo3XixAnXDb4ISk1NtQnc77zzTpcGpV5eXurSpYvR7tKliwYNGuSy+8F1Mi+cakY5GQCAs5kD98TERJsP7509w93Hx0fe3t52/SyaCgBA8cMMdwDF3nvvvWe8aapcubLmz5+vf/75Rzt37tSuXbsUFxcnSXr99ddVsWJF47wmTZo4vJ6fn58aNmyow4cPS5J2796tevXqufhVFF5Wq1UnTpzQpk2btGXLFu3fv9/Y5+7urgEDBrh8DOPHj1d4eLj8/Pw0bdo0ZkIXUdeb4Q4AgDNlPD1ltVolpc9yzyhflpycbBznjMDdYrHI399fly9ftulnhjsAAMUPgTuAYi0lJUWbNm0y2k899ZQaNmyohg0b6v7779e///6rhx56SFarVWfOnNGZM2eMY5s2bZrlddu0aWMTuN9///2uexGF1P79+7VmzRpt3rxZFy9edHhM586dVb58eZePpXLlyvrwww9dfh+4FoE7ACA/ubu7KyAgQJGRkZKk8PBwowyes2e4S+m/yzIH7vx+AwCg+KGkDIBi7dSpU0pMTJSU/qbqlltusdnfqFEj3XPPPQ7Pbdy4cZbXbdOmjbG9YcMGDRkyREuXLs12LFeuXNEHH3ygX3/9NYejL7y2bNmihx9+WN9++22WYbvFYtGwYcPyeWQoyrIL3CkpAwBwBXNZmYzgXXJN4O5oNnt25dQAAEDRxAx3AMVaxix0SapTp458fHzsjnnyySe1YcMGRUREGH3Vq1fP9g1Qy5Yt5ebmprS0NEnSyZMntWjRIg0YMECVKlUyjtu9e7c2bdqkgIAAff311woPD5ckVaxYUW3bts3z6ysojmriN27cWN27d1fDhg115coV1a5dW82bNy+A0aGoyu6/OWYAAgBcoVy5csZ6PBl/p0mum+FuVqpUKbm7uzvl2gAAoPAgcAdQrJkD96xqsvv7++u5557TlClTjL66detme93Mddyl9FrmJ0+eNAL3c+fO6emnn1ZSUpLd+Tt27NC5c+cUHByshx56SJUrV87V6ypIV65c0Y4dO4z2U089pTvvvDNfSsegeMuom+sIgQQAwBXMT1CZJ1/kxwx36rcDAFA8UVIGQLFmDsQbNWqU5XG9e/e2KTfTo0eP61576NChdn3mGvCffPKJw7Bdkr799lvNmTNH3377rV566aXr3qsw+f33342Z/ZUqVdLIkSMJ2+EU2ZWUcfR0CgAAeZWTkjKenp5OuVfmGe4E7gAAFE/McAdQbCUlJenYsWNGO7ua7BaLRbNnz9Znn30mX19f9e/f/7rXv/3229W6dWu9++67+u233yRJISEhktJnt2dXqz0uLs7Y3rlzp86ePWss0lVYBQcHa8WKFfr666+NvgEDBsjNjc9u4RyZA/eWLVvqn3/+kbe3t2677bYCGhUAoDgrV66csZ3fJWUI3AEAKJ4I3AEUW8eOHTPeLHl7e1+3TEyJEiX0+OOP5+oeVapUUZMmTewC908++USpqamSpKCgIC1fvlxXr15Vv379HF5n5cqVevLJJ3N17/xitVr1xRdf6N1337V58ymlB+6As8THx9u033jjDQUHB6tGjRo2MxABAHAWSsoAAABnY1oigCLDarXq4MGDOnDgQI6ON5eTqV+/vtPeLGVWvXp1YzskJMRudvsjjzwiDw8PlS1bVhUrVnR4jV9++cUI6AubNWvWaMGCBXZhe+vWrVWnTp0CGhWKo/bt2xtPTFSrVk3+/v5q165dlv/dAACQV+YZ7q4O3JnhDgDAzYEZ7gCKjC1btmjixImSpAcffFDPPPOMLBZLlsfv3r3b2M6ufnte1ahRw9i+ePGiFi9ebDO73TwLvEGDBrp8+bLdNUJDQ7Vz50517NjRZeO8UatXrza2K1asqEGDBsnf3z9HZXeA3KhcubJmzpypHTt26IEHHijo4QAAbgLmJ6iY4Q4AAJyBwB1AkfH7778b28uWLVPJkiX16KOPOjw2ISFBW7duNdodOnRw2biqVKkid3d3paamymq1GuVlpP+b3Z6hYcOG2rx5s8PrrFy5slAG7uY6+C+++KK6dOlSgKNBcXf77bfr9ttvL+hhAABuEubAPTY2VomJifL29maGOwAAuGGUlAFQaFitVl25ciXL/cePH7dpf/DBBzahutn27dt17do1SZKvr69Lg2wPDw8FBQXZ9Wee3S6lz3A3a968ubG9ceNGxcTEuGaQNygqKkphYWFGu379+gU4GgAAAOcy13CXpMjISEnMcAcAADeOwB1AoWC1WvXss8/q1ltv1aRJk5SUlGSzPzU11ViQ1GzevHmKi4uz6//jjz+M7a5du8rb29v5gzYx13HPkHl2u5Q+w91s3LhxxmynxMREm1n8hYH5Qw5/f3+VL1++AEcDAADgXN7e3vLz8zPaGWVlXBG4lyxZUp6enkabwB0AgOKJwB1AoXD27Flt3LhRkrRp0ybNnTtXVqvV2H/x4kUlJibanXfp0iW9++67Nn1JSUnatGmT0e7du7drBm1iruMuOZ7dLkmVKlVSq1atJKXPdm/evLnNcStXrnTtQHPJXE6mXr162dbMBwAAKIoc1XF3ReBusVhUq1YtSZKbm5vd348AAKB4IHAHUCgEBwfbtH/55RctWbLEaJ88edLYDgwMtKnd/t133+mnn34y2n///bcx693Hx0edO3d20aj/T+YZ7o5mt0vpb7TeeecdLV68WB9++KE8PDx05513GvsPHjyoU6dOuXy8OZU5cAcAAChuHAXuycnJRp+zAndJeuGFF3TLLbfohRdeUJUqVZx2XQAAUHgQuAPIV1FRUfr888+1Z88em/7M9dkl6b333tO6desk2QbutWrV0ujRo20C4Hnz5unvv/+WJG3ZssXo79Spk3x8fJz6Ghxp3LixsZ3V7PYMPj4+ateunXx9fSWl10U313YvTLPczf+/1K1btwBHAgAA4Br5NcNdkpo1a6bXX39dd999t9OuCQAAChcCdwD5JjY2ViNHjtSCBQv02GOPaerUqYqKipJkO5PabObMmTp48KBN4F67dm15eXnprbfeUrly5SSl13h/7rnndPLkSZvAvVu3bq57QSYNGzbUpEmT1KdPH73xxhu5fmNmnuX+yy+/2LzJKyipqak6ceKE0WbBVAAAUByZA/fw8HBJrgvcAQBA8UfgDiDfvPHGGzp//rzR/v333zV16lRJtoH7M888o1KlSklKX0h08uTJ2rZtm7G/du3akqTKlSvrrbfeMhZEjY2N1eOPP65Lly4Zx+ZHOZkMw4cP16uvvqo6derk+txbb73VeDMXHh6u7du3O3t4uXb27Fmjbr6bm5tRcxQAAKA4yc8Z7gAAoPgjcAeQL9avX++wVMru3bsVGRmpCxcuGH19+vTRf//7X7m7u0tKD6AzZsJL/xe4S+mlXObMmWMs5hkZGWmzr3z58s5+KS4REBCg7t27G+0VK1YU4GjS7dy509iuVq1avpTmAQAAyG/mwD3jb0kCdwAAcKMI3AG43JUrVzR37lyjXb9+fSMgT01NNeq0S5Kvr6+qVKmidu3a6YUXXnB4vZo1a9q0b7nlFj3zzDN2x3Xt2tUJo88/5rIyO3bskNVqdcl9oqOjNX36dM2aNUsxMTEOj0lLS9NXX31ltPPzSQEAAID8REkZAADgTATuAFzKarVq9uzZio6OlpS+YOi8efNsFjz99ddfje169eoZYfxdd92lJ554wuZ61apVU8mSJe3uM2LECLvFp/KrfruztGzZ0tiOi4vTlStXXHKfV155RatXr9aqVav0/fffOzxm06ZNCgkJkZReTmbo0KEuGQsAAEBBu15JGU9Pz3wfEwAAKLoI3AG41PLly23qr0+cOFE1atSwCdwPHDhgbJv7JWnMmDFatmyZ2rVrp7Jly2rs2LEO72OxWPT888+rb9++ktJnvTds2NCZL8XlSpUqZSwCK0mnT592+j3++ecf/fHHH0bbvCiq2RdffGFs9+zZU0FBQU4fCwAAQGFg/vsrKipKqampSk5ONvqY4Q4AAHKDvxwAuMzp06e1YMECo92tWzfdc889kuyD9Qx169a162vUqJEWL1583ft5eHho3rx5mjJlivz9/W9s0AWsRo0axqPMZ86cUbt27Zx27bS0NL355ps2feYFZjMcOnRIe/fuNdojRoxw2hgAAAAKmzJlyhjbVqtVUVFRlJQBAAA3jBnuAFzCarVq1qxZSkxMlJT+RmbatGlGuZisAvdWrVrl+d5FNWyX0gP3DM6e4f7bb7/p8OHDNn2OAvfPP//c2G7evLmaN2/u1HEAAAAUJr6+vvL29jbaERERBO4AAOCG8ZcDgBw7d+6cfvzxR4WFhSkmJkaxsbGKiYlRQkKCunfvrgkTJsjNLf1zvMOHD+vgwYPGudOmTbN5XLd+/fp212/QoIFq167t+hdSiJkXhHVm4J6QkKB3333Xrv/y5ctKTU2Vu7u7JOnixYtav369sf+BBx5w2hgAAAAKI4vForJly+rixYuSCNwBAEDe8JcDgByxWq2aNGmSTp486XD/l19+qWbNmhk11FevXm3sa9WqlXr06GFzfJkyZVS2bFljYSpJuu2221ww8qLFPMP9zJkzTrvul19+qdDQUEnpbxoz3kSmpqYqLCxMlStXliR99dVXSk1NlSQFBgaqZ8+eThsDAABAYVWmTBkCdwAA4BT85QAgR+Li4rIM2zNMmzZN33zzjSIjI23C4qyC9Li4OJt2//798z7QIs48w/3ixYtKSEiQj49Pnq555coVLV261Gjff//9WrVqlaKioiSll5WpXLmyYmNj9fPPPxvHDRs2zJj5DgAAUJyZn8QMDw8ncAcAADeMvxwA5EjGQp6S5O7urgkTJqhUqVJKTU3Vyy+/LCl9tvQ///xjc56np6d69+7t8JrDhg0zguC6deuqfPnyLhl7UVKlShV5eXkpKSlJkhQSEuKw/I4jYWFh+uKLL3Ty5EklJydr+PDh6tatmz744APFx8dLSq9v/8gjj2j37t02gbsk/fjjj8aHIKVKldLAgQOd/OoAAAAKp7Jlyxrbly5dsgncPT09C2JIAACgiCJwB5Aj5tIvZcqU0bBhw4z2tm3b9Mcffzg8r2vXripdurTDfffee6/Wrl2rhIQEvfTSS84dcBHl7u6uatWq6cSJE5LS67jnNHCfN2+eNm3aZLQPHDigt99+22bW+mOPPaZSpUqpcuXK+vfffyX935vKr7/+2jju3nvvVcmSJZ3xkgAAAAq9wMBAY/v7779XcnKy0WaGOwAAyA23gh4AgKLBPMPdPANIksaMGWMslurh4aEyZcoY+wYNGpTlNatUqaIff/xRa9asUYMGDZw84qLLXFbmemV8MlitVu3evdumLyEhQRMnTlRaWpqk9Prw9957ryQZNdul9MB948aNNjXehw4dmpeXAAAAUKQMHDjQ+BvXHLZLBO4AACB3CNwB5Ig5cDfXuJSkevXq6bXXXtPAgQP10UcfadmyZXrooYc0ffp0de3aNdvrurm5yWKxuGTMRVXt2rWN7c8//1wHDhy47jlXrlyxq4kv2dbJHz9+vPGGsVKlSkb/pUuXtGvXLqN9yy23qEKFCjc0dgAAgKKofPnyeu211xyuX0PgDgAAcoPAHUCOmEvKZJ7hLqWHtDNmzFCzZs1UuXJlPfPMM9nObkfW7rjjDqOcS0JCgiZMmGDzgYcjp0+ftmln/hCjXbt26tatm9GuUqWKsX3p0iUdPnzYaLdp0+ZGhw4AAFBktWrVSh07drTrJ3AHAAC5QeAOIEeuF7jDeQIDAzV//nxjga7o6Gj98ssv2Z5z6tQpY7t169bq1KmT0bZYLJowYYJNCG8uKXP27FkFBwcb7SZNmuT5NQAAABRF5kkJGQjcAQBAbhC4A8iR7ErKwPnat2+vhx56yGhntShtBnPgXqtWLT300ENGXf3Bgwfb1cg3B+6JiYlKSUmRJHl5ealu3bp5Hj8AAEBRZP4bKQOBOwAAyI1iG7j37NlTFosly39r1qxxeN7SpUvVvn17+fn5qWzZsrrtttu0bdu2bO+1detW3XbbbSpbtqz8/PzUvn17ffbZZ9mec+7cOY0ePVpVq1aVj4+P6tevr5kzZyohIeGGXzOQWykpKTp48KDD2t+ZmWe4E7jnj759+xrbhw4d0qVLl7I89syZM8Z2rVq11LZtW3344Yd65ZVX9Oyzz9odX7ZsWXl5edn1169f35hZDwAAcLNxFLjztxEAAMiNYv9R/b333is/Pz+7/sDAQLu+CRMmaOHChSpRooT69eunhIQErV27Vr///ruWL1+uu+66y+6c77//XkOHDlVaWpq6d++u8uXL648//tDIkSO1f/9+zZ8/3+6c48ePq1OnTrpy5YqaNm2qbt26adeuXZo9e7b++OMP/fHHH/L29nbK6wdOnTql//3vf6pUqZKGDBliE5a/+OKL+uOPP+Tv768JEybojjvuyHIBU0rK5L86deqoevXqCgkJkSStX79ew4cPd3iseYZ7zZo1JUktW7bM8toWi0U1atTQsWPHbPobN26ct0EDAAAUYZSUAQAAeVVsZ7hnmD9/vpYuXWr3r1mzZjbHrVu3TgsXLlS5cuW0b98+/fTTT1qzZo02bdokd3d3jR49WlFRUTbnRERE6OGHH1ZqaqqWL1+ujRs3avny5Tpy5Ijq1q2rN954Qxs3brQb06hRo3TlyhU988wzOnDggL755hsdPXpUd999t7Zu3ap58+a58CuCm0lwcLDGjBmjVatW6ZNPPtHAgQO1fPlySdLFixeNMiXR0dF66aWX9OSTT+rs2bN217FarZSUKQAWi0W9evUy2uvWrZPVarU7LjY2VmFhYUY7I3C/nqFDh9r1EbgDAICbGYE7AADIq2IfuOfUm2++KUmaNm2a6tWrZ/R36tRJTzzxhKKiovTJJ5/YnPPxxx/r6tWrGjRokO655x6jv1KlSvrvf/8rSXrjjTdszvn777+1detWVaxY0ThGSv8jbvHixfL09NTbb79t1FMGblRwcLDGjh2r6Ohooy8xMVFvvvmmEhISHNYE37lzp+6//34tWbLE5nswLi5OiYmJRpsZ7vmnd+/exvb+/fv15Zdf2h1z+vRpY7tEiRKqVKlSjq595513qmrVqjZ9LJgKAABuZuXKlZO7u7tNH4E7AADIDQJ3SdeuXdP69eslpS8umFlG38qVK236f/nllyzPuf322+Xj46N169bZ1GXPOOfOO++0KxtTqVIldevWTZGRkdqyZUseXhFudseOHbML2zMkJSXp/Pnzxve8JJUvX97YTkxM1KJFizRixAgdPHhQkm05GTc3N/n7+7tw9DBr2LChWrV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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": "c26581a2",
   "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": 8,
   "id": "96f37164",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T13:51:31.262811Z",
     "iopub.status.busy": "2026-10-14T13:51:31.262727Z",
     "iopub.status.idle": "2026-10-14T13:51:32.050237Z",
     "shell.execute_reply": "2026-10-14T13:51:32.049734Z"
    }
   },
   "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>2023-12-29</td>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>2024-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>2024-01-03</td>\n",
       "      <td>2024-01-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-01-03</td>\n",
       "      <td>2024-01-04</td>\n",
       "      <td>2024-01-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-01-04</td>\n",
       "      <td>2024-01-05</td>\n",
       "      <td>2024-01-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-01-05</td>\n",
       "      <td>2024-01-08</td>\n",
       "      <td>2024-01-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Issued after Forecast begins Outcome available\n",
       "0   2023-12-29      2024-01-02        2024-01-08\n",
       "1   2024-01-02      2024-01-03        2024-01-09\n",
       "2   2024-01-03      2024-01-04        2024-01-10\n",
       "3   2024-01-04      2024-01-05        2024-01-11\n",
       "4   2024-01-05      2024-01-08        2024-01-12"
      ]
     },
     "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('lstm', 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": "14dc18e4",
   "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. `history` contains index levels for the price panel; `history_returns` contains the known returns. Volatility arrays store fractions; multiply by 100 for percentage labels. `future_prices` are held-out outcomes and must not be shown as known history before their timestamps.\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."
   ]
  }
 ],
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  "jaxsaw": {
   "executed_utc": "2026-10-14T13:51:32Z",
   "gpu_selection": "0",
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   "run_directory": "runs/forecast-gallery-v2/lstm",
   "source_sha256": "02684a148a5b6e1a1a862449463212820591512ae42d4f10526ab1b985e8d941",
   "task": "lstm",
   "source_rename": {
    "package_from": "neuraljaxlib",
    "package_to": "jaxsaw",
    "alias_from": "nj",
    "alias_to": "js"
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