{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "gasoline-importance-0",
   "metadata": {},
   "source": [
    "# Gasoline: Which variables are the strongest predictors for supply and demand?\n",
    "\n",
    "This notebook ranks individual predictors for next-month U.S. supply and demand. \n",
    "\n",
    "Supply and demand are separate targets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "gasoline-importance-1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:16:45.881340Z",
     "iopub.status.busy": "2026-09-16T21:16:45.881092Z",
     "iopub.status.idle": "2026-09-16T21:16:47.000808Z",
     "shell.execute_reply": "2026-09-16T21:16:47.000082Z"
    }
   },
   "outputs": [],
   "source": [
    "FUEL = 'gasoline'\n",
    "\n",
    "# Load the scientific stack used by the analysis. FUEL is the only setting that\n",
    "# differs between the crude and gasoline versions of this notebook.\n",
    "from pathlib import Path\n",
    "import sys, hashlib, platform\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import Ridge\n",
    "import sklearn\n",
    "\n",
    "# Show all ranking rows and keep the long, descriptive predictor names readable.\n",
    "pd.set_option('display.max_rows', 60)\n",
    "pd.set_option('display.max_colwidth', 100)\n",
    "\n",
    "# Locate the shared project root whether the notebook is launched from this\n",
    "# product folder or from a parent directory, then make both data loaders importable.\n",
    "ROOT = next(p for p in [Path.cwd(), *Path.cwd().parents] if (p/'oil/us_snd_crude/crude_data.py').exists())\n",
    "for fuel in ['crude', 'gasoline']:\n",
    "    sys.path.insert(0, str(ROOT/f'oil/us_snd_{fuel}'))\n",
    "from crude_data import load_panel as load_crude, FLOWS as CRUDE_FLOWS\n",
    "from gasoline_data import load_panel as load_gasoline, FLOWS as GAS_FLOWS\n",
    "\n",
    "# Load monthly PADD-level histories for both products. The second product is\n",
    "# needed later for cross-market predictors such as crude inputs for gasoline.\n",
    "panels = {'crude': load_crude(), 'gasoline': load_gasoline()}\n",
    "national = {}\n",
    "for fuel, panel in panels.items():\n",
    "    # A national observation is valid only when all five PADD regions are present.\n",
    "    assert panel.groupby('month').padd.nunique().eq(5).all()\n",
    "    cols = (CRUDE_FLOWS + ['stock_kb', 'cdu_capacity_kbd']) if fuel == 'crude' else GAS_FLOWS + ['stock_kb']\n",
    "    # min_count=5 prevents a missing PADD from silently producing a partial U.S. total.\n",
    "    # asfreq also exposes any missing month in the national time series.\n",
    "    z = panel.groupby('month')[cols].sum(min_count=5).asfreq('MS')\n",
    "    assert z.notna().all().all()\n",
    "\n",
    "    # Crude flows already arrive as daily rates. Gasoline flows are stored as\n",
    "    # monthly thousand-barrel totals, so divide by the exact days in each month.\n",
    "    # Stocks are point-in-time levels and are converted from thousand to million barrels.\n",
    "    if fuel == 'gasoline':\n",
    "        for c in GAS_FLOWS:\n",
    "            z[c.replace('_kb', '_kbd')] = z[c] / z.index.days_in_month\n",
    "    z['stock_mb'] = z.stock_kb / 1000\n",
    "    z['change_mb'] = z.stock_mb.diff()\n",
    "\n",
    "    # These derived series summarize trade and the simplified physical balance.\n",
    "    # The core balance excludes adjustments, transfers and other secondary flows.\n",
    "    z['net_imports_kbd'] = z.imports_kbd - z.exports_kbd\n",
    "    z['core_balance_kbd'] = z.production_kbd + z.net_imports_kbd - z.demand_kbd\n",
    "    if fuel == 'crude':\n",
    "        # Refinery utilization compares actual crude inputs with operable capacity.\n",
    "        z['utilization'] = z.demand_kbd / z.cdu_capacity_kbd\n",
    "    national[fuel] = z\n",
    "\n",
    "# Record the data range and source-file hash so an executed notebook identifies\n",
    "# the exact local EIA snapshots used to produce its rankings.\n",
    "manifest = []\n",
    "for fuel in national:\n",
    "    path = ROOT/f'oil/us_snd_{fuel}/data/eia_observations.csv'\n",
    "    manifest.append({'fuel': fuel, 'start': national[fuel].index.min(), 'end': national[fuel].index.max(),\n",
    "                     'months': len(national[fuel]), 'sha256': hashlib.sha256(path.read_bytes()).hexdigest()})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gasoline-importance-2",
   "metadata": {},
   "source": [
    "## How variable importance is measured\n",
    "\n",
    "We test each variable separately while accounting for seasonal patterns (which variables are most important after controlling for season). Variables that reduce prediction errors the most are the most important.\n",
    "\n",
    "The test uses six historical periods, with each prediction based only on earlier data.\n",
    "\n",
    "We remove each variable from the model containing all inputs and refit to measure the information it adds alongside the other variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "gasoline-importance-3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:16:47.003034Z",
     "iopub.status.busy": "2026-09-16T21:16:47.002810Z",
     "iopub.status.idle": "2026-09-16T21:16:47.029114Z",
     "shell.execute_reply": "2026-09-16T21:16:47.028630Z"
    }
   },
   "outputs": [],
   "source": [
    "# Select the product being ranked and align the other product to the same calendar.\n",
    "z = national[FUEL]\n",
    "other_fuel = 'gasoline' if FUEL == 'crude' else 'crude'\n",
    "other = national[other_fuel].reindex(z.index)\n",
    "demand_name = 'Crude refinery inputs' if FUEL == 'crude' else 'Gasoline product supplied'\n",
    "production_name = 'Crude production' if FUEL == 'crude' else 'Gasoline net production'\n",
    "\n",
    "# Rank predictors separately for gross supply and demand. Gross supply includes\n",
    "# domestic production and imports; exports remain an explanatory outflow.\n",
    "targets = pd.DataFrame({\n",
    "    'Supply: production + imports': z.production_kbd + z.imports_kbd,\n",
    "    'Demand: ' + demand_name.lower(): z.demand_kbd,\n",
    "})\n",
    "\n",
    "# Twelve month indicators form the seasonality-only benchmark. Each later\n",
    "# single-variable test asks whether a predictor improves on this calendar model.\n",
    "X = pd.DataFrame(index=z.index)\n",
    "for month in range(1, 13):\n",
    "    X[f'Calendar month {month:02}'] = (X.index.month == month).astype(float)\n",
    "calendar = list(X.columns)\n",
    "\n",
    "def history(name, series):\n",
    "    \"\"\"Add one-month and smoothed three-month lags without using forecast-month data.\"\"\"\n",
    "    # Shift before rolling: for a forecast at month t, these features end at t-1.\n",
    "    X[f'{name} — previous month'] = series.shift(1)\n",
    "    X[f'{name} — previous 3-month average'] = series.shift(1).rolling(3).mean()\n",
    "\n",
    "# Add recent histories of the product's main supply, trade and demand variables.\n",
    "history(production_name, z.production_kbd)\n",
    "history(FUEL.title() + ' imports', z.imports_kbd)\n",
    "history(FUEL.title() + ' exports', z.exports_kbd)\n",
    "history(demand_name, z.demand_kbd)\n",
    "history('Supply (production + imports)', targets.iloc[:, 0])\n",
    "history('Core balance (production + imports − exports − demand)', z.core_balance_kbd)\n",
    "\n",
    "# Inventory features describe the latest level, recent changes, and the deviation\n",
    "# from the same point one year earlier. Both levels in the last feature precede t.\n",
    "X['Inventory level — previous month'] = z.stock_mb.shift(1)\n",
    "X['Inventory change — previous month'] = z.change_mb.shift(1)\n",
    "X['Inventory change — previous 3-month average'] = z.change_mb.shift(1).rolling(3).mean()\n",
    "X['Inventory level minus year-earlier level'] = z.stock_mb.shift(1) - z.stock_mb.shift(13)\n",
    "\n",
    "# Include the remaining balance components available for each product.\n",
    "for column, name in [('adjustments_kbd', 'Adjustments'), ('net_receipts_kbd', 'Net regional receipts')]:\n",
    "    history(name, z[column])\n",
    "if FUEL == 'crude':\n",
    "    history('Transfers', z.transfers_kbd)\n",
    "    history('Direct crude use', z.direct_use_kbd)\n",
    "else:\n",
    "    history('Biofuel production', z.biofuels_kbd)\n",
    "\n",
    "# Refinery operations can affect crude demand and gasoline output, so both\n",
    "# notebooks receive lagged crude utilization and physical distillation capacity.\n",
    "crude = national['crude'].reindex(z.index)\n",
    "X['Crude refinery utilization — previous month'] = crude.utilization.shift(1)\n",
    "X['Crude distillation capacity — previous month'] = crude.cdu_capacity_kbd.shift(1)\n",
    "\n",
    "# Cross-product features test whether activity in the linked petroleum market\n",
    "# contains information beyond the selected product's own recent history.\n",
    "other_demand = 'Gasoline product supplied' if other_fuel == 'gasoline' else 'Crude refinery inputs'\n",
    "other_production = 'Gasoline net production' if other_fuel == 'gasoline' else 'Crude production'\n",
    "X[f'{other_demand} — previous month'] = other.demand_kbd.shift(1)\n",
    "X[f'{other_production} — previous month'] = other.production_kbd.shift(1)\n",
    "X[f'{other_fuel.title()} inventory change — previous month'] = other.change_mb.shift(1)\n",
    "\n",
    "# Use one common sample for every comparison. This prevents a feature from looking\n",
    "# better merely because it was evaluated on a different set of months.\n",
    "keep = X.notna().all(axis=1) & targets.notna().all(axis=1)\n",
    "X, targets = X.loc[keep], targets.loc[keep]\n",
    "\n",
    "# Exclude March 2020 through March 2021 and any target whose lag window reaches\n",
    "# that disruption. Offsets 0..13 cover the target month and the longest lag used.\n",
    "eligible = pd.Series(True, index=X.index)\n",
    "for offset in range(14):\n",
    "    dates = X.index - pd.DateOffset(months=offset)\n",
    "    eligible &= ~((dates >= pd.Timestamp('2020-03-01')) & (dates <= pd.Timestamp('2021-03-01')))\n",
    "\n",
    "# Score the latest 72 eligible observations in six consecutive 12-month blocks.\n",
    "# Every block is trained on all eligible observations strictly before that block.\n",
    "scored_dates = X.index[eligible][-72:]\n",
    "assert len(scored_dates) == 72\n",
    "# A block can span a calendar gap because excluded COVID-related dates are absent.\n",
    "folds = [pd.DatetimeIndex(d) for d in np.array_split(scored_dates, 6)]\n",
    "features = [c for c in X if c not in calendar]\n",
    "\n",
    "# Fail early if alignment, missing values, or duplicate feature names could make\n",
    "# the ranking invalid or silently change the observations being compared.\n",
    "assert X.index.is_monotonic_increasing and not X.index.has_duplicates\n",
    "assert np.isfinite(X.to_numpy()).all() and np.isfinite(targets.to_numpy()).all()\n",
    "assert len(features) == len(set(features))\n",
    "periods = pd.DataFrame([{\n",
    "    'Period': i + 1, 'First scored month': dates.min().strftime('%Y-%m'),\n",
    "    'Last scored month': dates.max().strftime('%Y-%m'), 'Scored months': len(dates),\n",
    "    'Earlier training months': int(((X.index < dates.min()) & eligible).sum()),\n",
    "} for i, dates in enumerate(folds)])\n",
    "# Require a meaningful training history even for the earliest validation block.\n",
    "assert periods['Earlier training months'].min() >= 36\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "gasoline-importance-4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:16:47.030654Z",
     "iopub.status.busy": "2026-09-16T21:16:47.030537Z",
     "iopub.status.idle": "2026-09-16T21:16:53.292785Z",
     "shell.execute_reply": "2026-09-16T21:16:53.292325Z"
    }
   },
   "outputs": [],
   "source": [
    "# Keep regularization fixed so alpha is not tuned on the periods used to rank\n",
    "# predictors. Standardization puts differently scaled inputs on comparable footing.\n",
    "ALPHA = 10\n",
    "\n",
    "def errors(y, columns):\n",
    "    \"\"\"Return out-of-sample absolute errors from expanding-window validation.\"\"\"\n",
    "    blocks = []\n",
    "    for period, dates in enumerate(folds, 1):\n",
    "        # Training uses only eligible observations before the first scored month.\n",
    "        # This chronological split prevents future observations from leaking backward.\n",
    "        train = (X.index < dates.min()) & eligible\n",
    "        assert X.index[train].max() < dates.min()\n",
    "        model = make_pipeline(StandardScaler(), Ridge(alpha=ALPHA))\n",
    "        model.fit(X.loc[train, columns], y.loc[train])\n",
    "        prediction = model.predict(X.loc[dates, columns])\n",
    "        blocks.append(pd.DataFrame({\n",
    "            'period': period,\n",
    "            # Absolute error keeps the result in the target's thousand-b/d units.\n",
    "            'error': np.abs(y.loc[dates].to_numpy() - prediction),\n",
    "        }, index=dates))\n",
    "    return pd.concat(blocks)\n",
    "\n",
    "rankings, calendar_importance = {}, []\n",
    "for target_name in targets:\n",
    "    y = targets[target_name]\n",
    "\n",
    "    # These two reference models support the two definitions of importance:\n",
    "    # calendar-only for individual value, and all inputs for conditional value.\n",
    "    calendar_error = errors(y, calendar)\n",
    "    full_error = errors(y, list(X.columns))\n",
    "    rows = []\n",
    "    for feature in features:\n",
    "        # Individual test: add one feature to the calendar-only baseline.\n",
    "        added = errors(y, calendar + [feature])\n",
    "        # Conditional test: remove one feature from the full model and refit.\n",
    "        removed = errors(y, [c for c in X if c != feature])\n",
    "\n",
    "        # Positive gain means the feature reduces error beyond seasonality.\n",
    "        # Positive conditional value means the full model gets worse without it.\n",
    "        gain = calendar_error.error - added.error\n",
    "        conditional = removed.error - full_error.error\n",
    "        by_period = gain.groupby(calendar_error.period).mean()\n",
    "        rows.append({\n",
    "            'Predictor': feature,\n",
    "            'Error reduction vs calendar (thousand b/d)': gain.mean(),\n",
    "            'Error reduction vs calendar (%)': 100 * gain.mean() / calendar_error.error.mean(),\n",
    "            'Periods helped (of 6)': int((by_period > 0).sum()),\n",
    "            'Error increase when removed (thousand b/d)': conditional.mean(),\n",
    "            'Periods hurt by removal (of 6)': int((conditional.groupby(full_error.period).mean() > 0).sum()),\n",
    "            # Retain fold-level gains to show whether the average is historically stable.\n",
    "            **{f'Period {i} gain': by_period.loc[i] for i in range(1, 7)},\n",
    "        })\n",
    "\n",
    "    # The main rank is based on individual improvement over calendar seasonality.\n",
    "    table = pd.DataFrame(rows).sort_values(\n",
    "        'Error reduction vs calendar (thousand b/d)', ascending=False\n",
    "    ).reset_index(drop=True)\n",
    "    table.index = table.index + 1\n",
    "    table.index.name = 'Rank'\n",
    "    rankings[target_name] = table\n",
    "\n",
    "    # Treat all 12 calendar indicators as one conceptual predictor when measuring\n",
    "    # how much seasonality contributes alongside every noncalendar feature.\n",
    "    without_calendar = errors(y, features)\n",
    "    calendar_importance.append({\n",
    "        'Target': target_name,\n",
    "        'Predictor': 'Calendar month (all 12 month indicators)',\n",
    "        'Error increase when removed (thousand b/d)': (without_calendar.error - full_error.error).mean(),\n",
    "    })\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gasoline-importance-5",
   "metadata": {},
   "source": [
    "## Most important variables\n",
    "\n",
    "The top three individual predictors for each target appear first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "gasoline-importance-6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:16:53.294814Z",
     "iopub.status.busy": "2026-09-16T21:16:53.294646Z",
     "iopub.status.idle": "2026-09-16T21:16:53.304754Z",
     "shell.execute_reply": "2026-09-16T21:16:53.304356Z"
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   },
   "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>Target</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Predictor</th>\n",
       "      <th>Error reduction (thousand b/d)</th>\n",
       "      <th>Periods helped (of 6)</th>\n",
       "      <th>Adds information alongside other inputs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Supply: production + imports</td>\n",
       "      <td>1</td>\n",
       "      <td>Supply (production + imports) — previous 3-month average</td>\n",
       "      <td>184.63</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Supply: production + imports</td>\n",
       "      <td>2</td>\n",
       "      <td>Crude distillation capacity — previous month</td>\n",
       "      <td>178.07</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Supply: production + imports</td>\n",
       "      <td>3</td>\n",
       "      <td>Gasoline net production — previous 3-month average</td>\n",
       "      <td>161.67</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Demand: gasoline product supplied</td>\n",
       "      <td>1</td>\n",
       "      <td>Gasoline product supplied — previous 3-month average</td>\n",
       "      <td>93.92</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Demand: gasoline product supplied</td>\n",
       "      <td>2</td>\n",
       "      <td>Gasoline product supplied — previous month</td>\n",
       "      <td>69.71</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Demand: gasoline product supplied</td>\n",
       "      <td>3</td>\n",
       "      <td>Inventory level — previous month</td>\n",
       "      <td>36.00</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Target  Rank  \\\n",
       "0       Supply: production + imports     1   \n",
       "1       Supply: production + imports     2   \n",
       "2       Supply: production + imports     3   \n",
       "3  Demand: gasoline product supplied     1   \n",
       "4  Demand: gasoline product supplied     2   \n",
       "5  Demand: gasoline product supplied     3   \n",
       "\n",
       "                                                  Predictor  \\\n",
       "0  Supply (production + imports) — previous 3-month average   \n",
       "1              Crude distillation capacity — previous month   \n",
       "2        Gasoline net production — previous 3-month average   \n",
       "3      Gasoline product supplied — previous 3-month average   \n",
       "4                Gasoline product supplied — previous month   \n",
       "5                          Inventory level — previous month   \n",
       "\n",
       "   Error reduction (thousand b/d)  Periods helped (of 6)  \\\n",
       "0                          184.63                      5   \n",
       "1                          178.07                      5   \n",
       "2                          161.67                      5   \n",
       "3                           93.92                      6   \n",
       "4                           69.71                      5   \n",
       "5                           36.00                      3   \n",
       "\n",
       "   Adds information alongside other inputs  \n",
       "0                                     True  \n",
       "1                                     True  \n",
       "2                                     True  \n",
       "3                                     True  \n",
       "4                                     True  \n",
       "5                                    False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build a compact table from the full rankings. Only predictors that improve on\n",
    "# the calendar baseline are eligible, and at most three are shown for each target.\n",
    "gain_column = 'Error reduction vs calendar (thousand b/d)'\n",
    "conditional_column = 'Error increase when removed (thousand b/d)'\n",
    "summary = []\n",
    "for target_name, table in rankings.items():\n",
    "    positive = table[table[gain_column] > 0].head(3)\n",
    "    for rank, row in positive.iterrows():\n",
    "        summary.append({\n",
    "            'Target': target_name,\n",
    "            'Rank': rank,\n",
    "            'Predictor': row.Predictor,\n",
    "            'Error reduction (thousand b/d)': row[gain_column],\n",
    "            'Periods helped (of 6)': row['Periods helped (of 6)'],\n",
    "            # This flag distinguishes a strong stand-alone predictor from one that\n",
    "            # still contributes after correlated inputs enter the full model.\n",
    "            'Adds information alongside other inputs': bool(row[conditional_column] > 0),\n",
    "        })\n",
    "display(pd.DataFrame(summary).round(2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gasoline-discussion-0",
   "metadata": {},
   "source": [
    "For gasoline supply, the previous three month average of production plus imports was most important.\n",
    "\n",
    "For gasoline demand, the previous three month average of product supplied was most important."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gasoline-importance-7",
   "metadata": {},
   "source": [
    "## Individual predictor rankings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gasoline-importance-8-heading-1",
   "metadata": {},
   "source": [
    "### Supply: production + imports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "gasoline-importance-8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:16:53.307146Z",
     "iopub.status.busy": "2026-09-16T21:16:53.306607Z",
     "iopub.status.idle": "2026-09-16T21:16:53.490523Z",
     "shell.execute_reply": "2026-09-16T21:16:53.490047Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4d+8eevbsCS2t//u/WcbGxujYsSNOnDiBt2/fFjr2xo0bw8TERPxsY2MDa2trcYbY+/fvkZiYiPbt28PQ0BBZWVniT8uWLfH+/XucOHFCZf21atUCAHTp0gUbN27EP//8U+gY8zIxMUGbNm3ktnXv3h05OTk4dOiQ3PYOHTrAwMBAbl8/Pz8cOnQo31loI0aMwMOHD7Fp0yYAQE5ODpYtW4ZWrVqJM6lWr16NrKwslC1bVq24CxNLx44d5T7v27cPABSWP3bu3BlGRkbi0rj9+/cDAAICAuTKdenSBTo6n/bI8ISEBGRnZ2PIkCGftL8y+/btQ9OmTeHg4CC3PTAwEG/fvlWYwZP3fLu7uwOAwrL9vA4ePCh3berq6uLQoUP49ddfFbZ/6sP+JRIJWrZsKX7W0dGBs7MzSpUqhWrVqonbLSws5K6t3PKer/r166Ns2bLi+Tx+/DjevXuncP4dHBzQpEkTpUvo846h/NSuXRvPnz+Hv78/tm3bJjfLObfr16/j+vXratWX3/1MHU2aNIG5ubn4+c2bNzh58iQ6deoEY2Njcbu2tjZ69uyJu3fvKszIKmjcHDx4EMDH6yO3Tp06KVwvcXFxaNy4Mezs7OTugy1atJCrS92+LAq2traoXbu23DZ3d3e5MbZ//35UrlwZHh4ecuW6d+9eYP2Wlpbw8/NDTEyMuIT52bNn2LZtG3r16pXvPWX//v1o2rQpbGxsxG3a2tro2rWrWm1TZfny5ahevToMDAygo6MDXV1dJCYmKiybLYzPuTfHxcVBIpGgR48ecuPC1tYWHh4e4gzH48eP4/379yqv9fy8ffsWBw8eRJcuXT7p2ZRFcf9wdnaGubk5xo8fj+XLlyMlJaXQcRAxSUhERETfjJIlS0IqlSr9BX/dunVISkrC9u3bFb7r3r07lixZgv79+2P37t04deoUkpKSYGVlJfeL86JFizB+/HjExsaicePGsLCwQLt27cRlQLJlesqWOtvZ2SEnJ+eTlpNaWloqbNPX1xdje/LkCbKysrB48WKFpIosMZLfL8GNGjVCbGwssrKy0KtXL5QuXRpVqlRReNZiYeT+pVPG1tZWjFfZ9rzbPnz4gNevX6s8huxZXLLnpsXFxeHWrVtqLdFTpTCx5D3PT548gY6OjsIviBKJBLa2tmK7Zf+b91g6OjpKz7U6Hj16BAAoXbr0J+2vzJMnT1SOZdn3ueWNXV9fHwAKTD7VqFEDSUlJcj/Vq1dH69atFbbnfpZoYRgaGsolGABAT08PFhYWCmX19PSUPs9S1djIe15V9Vne/jI0NESJEiXUbkPPnj0RGRmJ27dvo2PHjrC2tkadOnWQkJCgdh25FXQ/U0fetj579gyCIBTpuJGVz3tPUXa9PHjwADt27FC4D1auXBnA/90Hi7ov81PQ/Rv42EZV40sdffv2xT///CPGv379emRkZCgknPL63OMqM2/ePAwePBh16tTB5s2bceLECSQlJaF58+aflIjOLyZ1780PHjyAIAiwsbFRGBsnTpwQx4Wqe7Oqbbk9e/YM2dnZn3wPLor7h6mpKQ4ePIiqVavixx9/ROXKlWFnZ4fQ0FClz5kkUoZvNyYiIqJvhra2Npo0aYI9e/YgPT1d7v9su7q6AoDCTKQXL14gLi4OoaGhmDBhgrg9IyNDfLaSjJGREaZOnYqpU6fiwYMH4iwcPz8/XL58WfxlMD09XSG2e/fuQUtLS27WTVExNzcXZ+qomklWrly5fOto27Yt2rZti4yMDJw4cQJhYWHo3r07HB0dUa9ePTHBkvu5eoDq5GPuZzzJ3L9/H4DiL82y7Xm36enpyc1GUmb48OHo3Lkzzpw5gyVLlsDFxQU+Pj757pOfwsSS94UilpaWyMrKwqNHj+QShYIg4P79++KMTVn779+/D3t7e7FcVlaWwi+CuftdljwBFPtddry7d+8qzPz7VJaWlirHMvAxKV8UTExMULNmTYVtlpaWCtuLk6qx4ezsDAAFXv95+0vZC2kK0qdPH/Tp0wdv3rzBoUOHEBoaitatW+Pq1atqz5aVKeh+po68bTA3N4eWllaRjhtZvz548KDA66VkyZJwd3fHzJkzldaVO8lclH35uSwtLVWOL3X4+vrCzs4OUVFR8PX1RVRUFOrUqSP+d68ojquvr69w/wcUk76//fYbvL29sWzZMrntn/tMvM+5N5csWRISiQSHDx+Wu4/KyLblvjcrO1Z+z/q0sLCAtrb2J78gpKjuH25ubtiwYQMEQcD58+cRHR2NadOmQSqVyv1/HCJVOJOQiIiIvikhISHIzs7GoEGD1PrLuUQigSAICr84rFq1Kt+lrjY2NggMDIS/vz+uXLmCt2/fokKFCrC3t8e6desgCIJY9s2bN9i8ebP4xuOiZmhoiMaNG+Ovv/6Cu7s7atasqfCj7gw1fX19eHl5ITw8HMDHNyUCEH85On/+vFx5ZTMzgY+/EOb9bt26ddDS0kKjRo3ktm/ZskVu5tarV6+wY8cONGzYENra2vnG2759e5QpUwZjxozB3r17ERQU9EnJl6KIRfZ2ydwvJACAzZs3482bN+L33t7eAIC1a9fKldu4caPCy2JU9fuOHTvkPn///ffQ1tZW+MU8r7wzmPLTtGlT7Nu3T+FNs7/++isMDQ1Rt25dter5VuQ9X8eOHcPt27fF81mvXj1IpVKF83/37l1x6bY61DlHRkZGaNGiBSZOnIgPHz7g0qVL6jdECWX3M1ksQMGzQXPHVadOHWzZskVun5ycHPz2228oXbo0XFxcChWb7H6R96Unf/zxh8L10rp1a1y8eBHly5dXeh9UNhNVVV8Wtu2fo3Hjxrh06RLOnTsnt33dunVq7S/7I1FsbCwOHz6M5ORkubfT53fcxMREuT/qZGdnK33BjKOjo8J9aN++fQqz+CQSicJ/T8+fP5/vS5LU8Tn35tatW0MQBPzzzz9Kx4WbmxsAoG7dujAwMFB5redHKpXCy8sLmzZtynfmvqpxVVT3DxmJRAIPDw/Mnz8fZmZmOHPmTKH2J83FmYRERET0TfH09MTPP/+MYcOGoXr16vjhhx9QuXJlcXbL5s2bAUBcplOiRAk0atQIP/30E0qWLAlHR0ccPHgQq1evhpmZmVzdderUQevWreHu7g5zc3OkpqZizZo1csm/OXPmICAgAK1bt8bAgQORkZGBn376Cc+fP8fs2bO/WLsXLlyIBg0aoGHDhhg8eDAcHR3x6tUrXL9+HTt27BCfl6fM5MmTcffuXTRt2hSlS5fG8+fPsXDhQujq6sLLywvAx+cWVqhQAWPHjkVWVhbMzc2xdetWlW9strS0xODBg5GWlgYXFxfs3LkTK1euxODBg1GmTBm5stra2vDx8cHo0aORk5OD8PBwvHz5ssC3Msv2HTJkCMaPHw8jIyOF5XX9+vVDTEwMbty4odbsoM+JxcfHB76+vhg/fjxevnwJT09PnD9/HqGhoahWrRp69uwJAKhUqRJ69OiBBQsWQFdXF82aNcPFixcxd+5cheVjLVu2hIWFBfr164dp06ZBR0cH0dHRuHPnjlw5R0dH/Pjjj5g+fTrevXsHf39/mJqaIiUlBY8fPxbjd3Nzw5YtW7Bs2TLUqFEDWlpaKmfrhYaGis94mzx5MiwsLLB27VrEx8djzpw5MDU1LbBPviXJycno378/OnfujDt37mDixImwt7dHUFAQAMDMzAyTJk3Cjz/+iF69esHf3x9PnjzB1KlTYWBggNDQULWOo+ocDRgwAFKpFJ6enihVqhTu37+PsLAwmJqairNUAYgzGwt6LqE69zNZ8iQ8PBwtWrSAtrY23N3doaenp7LesLAw+Pj4oHHjxhg7diz09PSwdOlSXLx4EevXry90Er9y5crw9/dHRESEOFv80qVLiIiIgKmpqdzzX6dNm4aEhATUr18fw4cPR4UKFfD+/XvcunULO3fuxPLly1G6dGm1+rJKlSoAgF9++QUmJiYwMDBAuXLlPvmRAPkZOXIkIiMj0apVK8yYMQM2NjZYu3at2jM6gY9LjsPDw9G9e3dIpVK1niv4v//9D9u3b0eTJk0wefJkGBoa4ueff1Z4YzbwcYn2pEmTMHnyZHh5eSElJQVLlixRuA+0bt0a06dPR2hoKLy8vHDlyhVMmzYN5cqVU0jqFsbn3Js9PT3xww8/oE+fPkhOTkajRo1gZGSE9PR0HDlyBG5ubhg8eDDMzc0xduxYzJgxQ+5anzJlilpLsOfNm4cGDRqgTp06mDBhApydnfHgwQNs374dK1asgImJSb7j6nPvH3FxcVi6dCnatWsHJycnCIKALVu24Pnz5581w540TPG9M4WIiIjoyzl79qzQp08foVy5coK+vr5gYGAgODs7C7169RISExPlyt69e1fo2LGjYG5uLpiYmAjNmzcXLl68qPDWxgkTJgg1a9YUzM3NBX19fcHJyUkYNWqU8PjxY7n6YmNjhTp16ggGBgaCkZGR0LRpU+Ho0aNyZQrzduMhQ4YotE/ZGyVv3rwp9O3bV7C3txd0dXUFKysroX79+uLbV1WJi4sTWrRoIdjb2wt6enqCtbW10LJlS+Hw4cNy5a5evSp8//33QokSJQQrKyth2LBhQnx8vNK3G1euXFk4cOCAULNmTUFfX18oVaqU8OOPP8q9aVP2dsjw8HBh6tSpQunSpQU9PT2hWrVqwu7duwvsL5lbt24JAIRBgwYpfCd7C6Sy/fL2nbqxyN6g+ejRI4V63r17J4wfP14oW7asoKurK5QqVUoYPHiw8OzZM7lyGRkZwpgxYwRra2vBwMBAqFu3rnD8+HGl5/XUqVNC/fr1BSMjI8He3l4IDQ0VVq1apbRdv/76q1CrVi3BwMBAMDY2FqpVqyb39s2nT58KnTp1EszMzASJRCL35looedPohQsXBD8/P8HU1FTQ09MTPDw85OoThP97o/CmTZuU9mne8urw8vLK942pqqh6u7GRkZHSY1SuXFlhe9myZYVWrVqJn2Vjb8+ePULPnj0FMzMz8S2k165dU9h/1apVgru7u6CnpyeYmpoKbdu2Fd+qW1BMgqD6HMXExAiNGzcWbGxsBD09PcHOzk7o0qWLcP78eYX4Vb3BOjd17mcZGRlC//79BSsrKzEW2ZhTdW8SBEE4fPiw0KRJE8HIyEiQSqVC3bp1hR07dsiVkfVrUlKS3HZl5/D9+/fC6NGjFa4XU1NThbeEP3r0SBg+fLhQrlw5QVdXV7CwsBBq1KghTJw4UXj9+nWh+nLBggVCuXLlBG1t7QLHsqq3GysbY8ru9SkpKYKPj49gYGAgWFhYCP369RO2bdtW4NuNc6tfv74AQOVbzpVd40ePHhXq1q0r6OvrC7a2tsK4ceOEX375RaEtGRkZQnBwsODg4CBIpVLBy8tLOHv2rMI9KyMjQxg7dqxgb28vGBgYCNWrVxdiY2NV/vdN3bcbf+69WRAEITIyUqhTp444LsuXLy/06tVLSE5OFsvk5OQIYWFhgoODg6Cnpye4u7sLO3bsELy8vAp8u7EgfDyPnTt3FiwtLQU9PT2hTJkyQmBgoPD+/XuxTH7j6nPuH5cvXxb8/f2F8uXLC1KpVDA1NRVq164tREdH59vHRLlJBCHXWhgiIiIios/k7e2Nx48f4+LFi/mWu3XrFsqVK4effvoJY8eO/eTjLV68GMOHD8fFixfFFxQUVlHFQt+e6Oho9OnTB0lJSV/VMxI13bFjx+Dp6Ym1a9eq9RZg+m/ivZno38XlxkRERET0n/TXX3/h5s2bmDZtGtq2bfvJCUIi+rolJCTg+PHjqFGjBqRSKc6dO4fZs2fju+++Q4cOHYo7PCKibwaThERERET0n9S+fXvcv38fDRs2xPLly4s7HCL6QkqUKIE9e/ZgwYIFePXqFUqWLIkWLVogLCxMfAM4ERF9Pi43JiIiIiIiIiIi0nBaBRchIiIiIiIiIiKibxmThERERERERERERBqOSUIiIiIiIiIiIiINxxeXEBHRNyEnJwf37t2DiYkJJBJJcYdDRERERET0VRAEAa9evYKdnR20tFTPF2SSkIiIvgn37t2Dg4NDcYdBRERERET0Vbpz5w5Kly6t8nsmCYmI6JtgYmICAEhJSYG9vX0xR0NERERERPR1ePnyJRwcHMTfmVRhkpCIiL4JsiXGJiYmKFGiRDFHQ0RERERE9HUp6LFMfHEJERERERERERGRhmOSkIiIiIiIiIiISMMxSUhERERERERERKThmCQkIiIiIiIiIiLScEwSEhERERERERERaTgmCYmIiIiIiIiIiDQck4REREREREREREQajklCIiIiIiIiIiIiDcckIRERERERERERkYZjkpCIiIiIiIiIiEjDMUlIRERERERERESk4ZgkJCIiIiIiIiIi0nBMEhIREREREREREWk4JgmJiIiIiIiIiIg0HJOEREREREREREREGo5JQiIiIiIiIiIiIg3HJCEREREREREREZGGY5KQiIiIiIiIiIhIwzFJSEREREREREREpOGYJCQiIiIiIiIiItJwTBISERERERERERFpOCYJiYiIiIiIiIiINByThERERERERERERBqOSUIiIiIiIiIiIiINxyQhERERERERERGRhtMp7gCIiIiKUoPwIOiUMCzuMIiIiIiISINcn7mhuEP4bJxJSEREREREREREpOGYJCQiIiIiIiIiItJwTBISERERERERERFpOCYJiYiIiIiIiIiINByThERERERERERERBqOSUIiIiIiIiIiIiINxyQhERERERERERGRhmOSkIiIiIiIiIiISMMxSUhERERERERERKThmCQkIiIiIiIiIiLScEwSEhERERERERERaTgmCYmIiIiIiIiIiDTcfzZJOGXKFFStWvWz69m3bx8qVqyInJyczw/qEwQGBqJdu3Zf/DiOjo5YsGDBFz/O5zhw4AAkEgmeP39e3KEUmYcPH8LKygr//PNPcYfyxd26dQsSiQRnz54t7lD+84rq/kZERERERESkrkIlCR8+fIiBAweiTJky0NfXh62tLXx9fXH8+PEvFd8XFxwcjIkTJ0JL6z+bL5UTHR0NMzMzhe1JSUn44Ycf/v2ACqF+/fpIT0+HqalpcYeikre3N0aOHKl2eWtra/Ts2ROhoaFfLqivhIODA9LT01GlSpVii+HIkSPw9PSEpaUlpFIpKlasiPnz5xdbPOqQSCSIjY0t7jCIiIiIiIhIw+kUpnDHjh2RmZmJmJgYODk54cGDB0hMTMTTp0+/VHxf1LFjx3Dt2jV07tz5s+r58OED9PT0iiiqL8PKyupfP2Z0dDSio6Nx4MABtcrr6enB1tb2ywb1iTIzM6Grq/tJ+/bp0we1a9fGTz/9BHNz8yKO7PNlZ2dDIpF8dqJcW1u72M+fkZERhg4dCnd3dxgZGeHIkSMYOHAgjIyMvvok+X/V51wbRERERERE9PVQOyvw/PlzHDlyBOHh4WjcuDHKli2L2rVrIyQkBK1atQKgfLnh8+fPIZFIxESRbElpfHw8PDw8YGBggDp16uDChQviPrLZcLGxsXBxcYGBgQF8fHxw584dpbEdOnQIurq6uH//vtz2MWPGoFGjRirbtGHDBnz//fcwMDAQt8mW+a1YsQIODg4wNDRE586d5ZbAypYIh4WFwc7ODi4uLgCACxcuoEmTJpBKpbC0tMQPP/yA169fi/tlZ2dj9OjRMDMzg6WlJYKDgyEIglxMypYFV61aFVOmTJHr0x9++AE2NjYwMDBAlSpVEBcXhwMHDqBPnz548eIFJBIJJBKJuF/eetPS0tC2bVsYGxujRIkS6NKlCx48eKDQD2vWrIGjoyNMTU3RrVs3vHr1SmV/fq68y41l4yAuLg4VKlSAoaEhOnXqhDdv3iAmJgaOjo4wNzfHsGHDkJ2dLdbj6OiI6dOno3v37jA2NoadnR0WL14sdyx12x8ZGQknJyfo6+ujd+/eOHjwIBYuXCj2761bt/Ds2TMEBATAysoKUqkU3333HaKiosS63NzcYGtri61bt36xvpMpzPUVFxcHV1dX6Ovr4/bt2/jw4QOCg4Nhb28PIyMj1KlTR7xuX7x4AalUil27dskdb8uWLTAyMsLr16+VXv8HDx5E7dq1oa+vj1KlSmHChAnIysoSv1dnvE+ZMkWcvWxnZ4fhw4erbH+1atXg7++PypUrw9HRET169ICvry8OHz6cb795e3tj2LBhGDlyJMzNzWFjY4NffvkFb968QZ8+fWBiYoLy5cvjzz//lNuvoPZ5e3tj+PDhCA4OhoWFBWxtbeXa5ujoCABo3749JBKJ+FmmMNffkydP4O/vj9KlS8PQ0BBubm5Yv369+P2KFStgb2+v8GiFNm3aoHfv3uLnHTt2oEaNGjAwMICTkxOmTp0q1yaJRILly5ejbdu2MDIywowZM5CdnY1+/fqhXLlykEqlqFChAhYuXCh3nKysLAwfPly8/40fPx69e/eWe9yCIAiYM2cOnJycIJVK4eHhgT/++ENlm4mIiIiIiKjoqJ0kNDY2hrGxMWJjY5GRkfHZBx43bhzmzp2LpKQkWFtbo02bNsjMzBS/f/v2LWbOnImYmBgcPXoUL1++RLdu3ZTW1ahRIzg5OWHNmjXitqysLPz222/o06ePyhgOHTqEmjVrKmy/fv06Nm7ciB07dmDXrl04e/YshgwZIlcmMTERqampSEhIQFxcHN6+fYvmzZvD3NwcSUlJ2LRpE/bu3YuhQ4eK+0RERCAyMhKrV6/GkSNH8PTp00InjnJyctCiRQscO3YMv/32G1JSUjB79mxoa2ujfv36WLBgAUqUKIH09HSkp6dj7NixCnUIgoB27drh6dOnOHjwIBISEnDjxg107dpVrtyNGzcQGxuLuLg4xMXF4eDBg5g9e3ah4v1cb9++xaJFi7Bhwwbs2rULBw4cQIcOHbBz507s3LkTa9aswS+//KKQSPjpp5/g7u6OM2fOICQkBKNGjUJCQgIA9dsvGwebN2/G2bNnsWjRItSrVw8DBgwQ+9fBwQGTJk1CSkoK/vzzT6SmpmLZsmUoWbKkXF21a9cuMFFVlNS5vsLCwrBq1SpcunQJ1tbW6NOnD44ePYoNGzbg/Pnz6Ny5M5o3b45r167B1NQUrVq1wtq1a+WOs27dOjHZmtc///yDli1bolatWjh37hyWLVuG1atXY8aMGWq3448//sD8+fOxYsUKXLt2DbGxsXBzc1N7/7/++gvHjh2Dl5dXgWVjYmJQsmRJnDp1CsOGDcPgwYPRuXNn1K9fH2fOnIGvry969uyJt2/fFqp9MTExMDIywsmTJzFnzhxMmzZNHItJSUkAgKioKKSnp4ufgcJff+/fv0eNGjUQFxeHixcv4ocffkDPnj1x8uRJAEDnzp3x+PFj7N+/X9zn2bNn2L17NwICAgAAu3fvRo8ePTB8+HCkpKRgxYoViI6OxsyZM+WOFRoairZt2+LChQvo27cvcnJyULp0aWzcuBEpKSmYPHkyfvzxR2zcuFHcJzw8HGvXrkVUVJR4T8+7zPp///sfoqKisGzZMly6dAmjRo1Cjx49cPDgwQLPHxEREREREX0etZcb6+joIDo6GgMGDMDy5ctRvXp1eHl5oVu3bnB3dy/0gUNDQ+Hj4wPg4y/RpUuXxtatW9GlSxcAH5ewLVmyBHXq1BHLVKpUCadOnULt2rUV6uvXrx+ioqIwbtw4AEB8fDzevn0r1qfMrVu3YGdnp7D9/fv3YkwAsHjxYrRq1QoRERHickojIyOsWrVKXGa8cuVKvHv3Dr/++iuMjIwAAEuWLIGfnx/Cw8NhY2ODBQsWICQkBB07dgQALF++HLt37y5Uv+3duxenTp1CamqqOIPRyclJ/N7U1BQSiSTfZZ979+7F+fPncfPmTTg4OAD4OGOpcuXKSEpKQq1atQB8TEhGR0fDxMQEANCzZ08kJiYqJAy+pMzMTCxbtgzly5cHAHTq1Alr1qzBgwcPYGxsDFdXVzRu3Bj79++XS/J5enpiwoQJAAAXFxccPXoU8+fPh4+Pj9rt//DhA9asWSO3VFtPTw+GhoZy/ZuWloZq1aqJCee8s8EAwN7eHn/99VfRdk4+1Lm+li5dCg8PDwAfE1Lr16/H3bt3xWti7Nix2LVrF6KiojBr1iwEBASgV69eePv2LQwNDfHy5UvEx8dj8+bNSmNYunQpHBwcsGTJEkgkElSsWBH37t3D+PHjMXnyZLWWN6elpcHW1hbNmjWDrq4uypQpo/T6z6t06dJ49OgRsrKyMGXKFPTv37/AfTw8PPC///0PABASEoLZs2ejZMmSGDBgAABg8uTJWLZsGc6fP4+6deuq3T53d3fxmZTfffcdlixZgsTERPj4+Ihjy8zMTOGaLez1Z29vL/dHgWHDhmHXrl3YtGkT6tSpAwsLCzRv3hzr1q1D06ZNAQCbNm2ChYWF+HnmzJmYMGGCOLPQyckJ06dPR3BwsNxzNbt3746+ffvKHX/q1Kniv8uVK4djx45h48aN4phbvHgxQkJC0L59ewAf7487d+4U93nz5g3mzZuHffv2oV69euLxjxw5ghUrVihN9GZkZMj90erly5dK+4aIiIiIiIgKVqiHkHXs2BH37t3D9u3b4evriwMHDqB69eqIjo4u9IFlvwQCgIWFBSpUqIDU1FRxm46Ojtwsv4oVK8LMzEyuTG6BgYG4fv06Tpw4AQCIjIxEly5dxISdMu/evZNbaixTpkwZMUEoizUnJwdXrlwRt7m5uck9hzA1NRUeHh5yx/P09BT3e/HiBdLT0+XanbeN6jh79ixKly4tJgg/RWpqKhwcHMQEGQC4uroq9K+jo6OYoACAUqVK4eHDhyrrTUtLE2ecGhsbY9CgQTh8+LDCtsIwNDQUE4QAYGNjA0dHR7mZazY2Ngpx5e5n2WdZ29Rtf9myZdV6luPgwYOxYcMGVK1aFcHBwTh27JhCGalUKs5AUyZ3H6nzM2vWrHxjKuj60tPTk0vunzlzBoIgwMXFRe44Bw8exI0bNwAArVq1go6ODrZv3w4A2Lx5M0xMTPD9998rjSE1NRX16tWDRCIRt3l6euL169e4e/duvvHLdO7cGe/evYOTkxMGDBiArVu3yi19VeXw4cNITk7G8uXLsWDBAnHZbd7xmHtmZO7+0NbWhqWlpdysRRsbGwAQx5q67cv7R5SCriOZwl5/2dnZmDlzJtzd3WFpaQljY2Ps2bMHaWlpYpmAgABs3rxZTKytXbsW3bp1g7a2NgDg9OnTmDZtmlwfyWbO5h6/yu5by5cvR82aNWFlZQVjY2OsXLlSPPaLFy/w4MEDuQSvtrY2atSoIX5OSUnB+/fv4ePjI3f8X3/9VRyDeYWFhcHU1FT8yX1NExERERERUeEU6sUlAMTnA/r4+GDy5Mno378/QkNDERgYKM6cyf2cvdxLHAuS+5dtZZ9VbQM+vkXWz88PUVFRcHJyws6dOwt8YUbJkiXx7NkztePKfey8yUdBEFTGpmq7MlpaWgrPKczdh1KpVO26VFEVa97teV9GIJFIFJ5nlpudnZ3c8+i2bNmCzZs3yyViSpQoUahYlcVQ2LhylwPUb39+CebcWrRogdu3byM+Ph579+5F06ZNMWTIEMydO1cs8/Tp03wTjrn7TR0WFhaFKg/Ij0OpVCr3OScnB9ra2jh9+rSYMJKRJWT19PTQqVMnrFu3Dt26dcO6devQtWtX6Ogov40o62fZ2JZtL2i8Ozg44MqVK0hISMDevXsRFBSEn376CQcPHsz3ZRnlypUD8DGZ/+DBA0yZMgX+/v6oWbOmXF/LEn9AwWNNFrNsrKnTPlX1qjNeC7tfREQE5s+fjwULFsDNzQ1GRkYYOXIkPnz4IJbx8/NDTk4O4uPjUatWLRw+fBjz5s0Tv8/JycHUqVPRoUMHhfpz/0El77WxceNGjBo1ChEREahXrx5MTEzw008/iUudc7cht9znXta2+Ph42Nvby5XT19dX2uaQkBCMHj1a/Pzy5UsmComIiIiIiD5RoZOEebm6uorPlZIlQdLT01GtWjUAqpMfJ06cQJkyZQB8fC7W1atXUbFiRfH7rKwsJCcnizNPrly5gufPn8uVyat///7o1q0bSpcujfLly8PT0zPf2KtVq4aUlBSF7Wlpabh375647PL48ePQ0tLKd/aeq6srYmJi8ObNG/EX6KNHj4r7mZqaolSpUjhx4oT4MpWsrCycPn0a1atXF+uxsrJCenq6+Pnly5e4efOm+Nnd3R13797F1atXlcajp6cn9xIPVbGmpaXhzp074i/UKSkpePHiBSpVqpTvvvnR0dGBs7Oz+Nna2hpSqVRu279FNqM092fZ2Pmc9qvqXysrKwQGBiIwMBANGzYUnwkoc/HiRXh7e6ust6j7qKDrK69q1aohOzsbDx8+RMOGDVWWCwgIwPfff49Lly5h//79mD59usqyrq6u2Lx5s1wy7dixYzAxMRGTQAWNd+BjQrNNmzZo06YNhgwZgooVK+LChQty101+BEEQZ84V5XhUp33q0NXVLfCaVcfhw4fRtm1b9OjRA8DHpNu1a9fkxrRUKkWHDh2wdu1aXL9+HS4uLnKz+apXr44rV64Uuo8OHz6M+vXrIygoSNyWe/afqakpbGxscOrUKXF8ZWdn46+//kLVqlUBQHyJTlpamlrPkAQ+Jg9VJRCJiIiIiIiocNROEj558gSdO3dG37594e7uDhMTEyQnJ2POnDlo27YtgI+/gNatWxezZ8+Go6MjHj9+LD7jK69p06bB0tISNjY2mDhxIkqWLCn3lktdXV0MGzYMixYtgq6uLoYOHYq6devm+zwyX19fmJqaYsaMGZg2bVqBbfL19UVMTIzCdgMDA/Tu3Rtz587Fy5cvMXz4cHTp0iXf5/wFBAQgNDQUvXv3xpQpU/Do0SMMGzYMPXv2FGcrjRgxArNnz8Z3332HSpUqYd68eXJvTQaAJk2aIDo6Gn5+fjA3N8ekSZPkZnZ5eXmhUaNG6NixI+bNmwdnZ2dcvnwZEokEzZs3h6OjI16/fo3ExER4eHjA0NAQhoaGcsdo1qwZ3N3dERAQgAULFiArKwtBQUHw8vIq9PLnr9XRo0cxZ84ctGvXDgkJCdi0aRPi4+MBfF77HR0dcfLkSdy6dQvGxsawsLDAlClTUKNGDVSuXBkZGRmIi4uTS8y8ffsWp0+fLnCJcFEq6PrKy8XFRXzmYEREBKpVq4bHjx9j3759cHNzQ8uWLQF8HH82NjYICAiAo6Mj6tatq7LOoKAgLFiwAMOGDcPQoUNx5coVhIaGYvTo0eKs44LGe3R0NLKzs1GnTh0YGhpizZo1kEqlKFu2rNJj/vzzzyhTpoyYED1y5Ajmzp2LYcOGFbYLC6RO+9Th6OiIxMREeHp6Ql9fH+bm5p8Uj7OzMzZv3oxjx47B3Nwc8+bNw/379xUS3wEBAfDz88OlS5fEhKLM5MmT0bp1azg4OKBz587Q0tLC+fPnceHChXxfOOPs7Ixff/0Vu3fvRrly5bBmzRokJSWJMzqBj89IDAsLg7OzMypWrIjFixfj2bNnYoLVxMQEY8eOxahRo5CTk4MGDRrg5cuXOHbsGIyNjeXewExERERERERFr1BvN65Tpw7mz5+PRo0aoUqVKpg0aRIGDBiAJUuWiOUiIyORmZmJmjVrYsSIESp/sZw9ezZGjBiBGjVqID09Hdu3b5d7xp+hoSHGjx+P7t27o169epBKpdiwYUP+jdHSQmBgILKzs9GrV68C29SjRw+kpKTIPWsQ+PgLb4cOHdCyZUt8//33qFKlCpYuXZpvXYaGhti9ezeePn2KWrVqoVOnTmjatKlc34wZMwa9evVCYGCguCRP9hB/mZCQEDRq1AitW7dGy5Yt0a5dO7ln8gEfnwVXq1Yt+Pv7w9XVFcHBweJMpPr162PQoEHo2rUrrKysMGfOHIVYJRIJYmNjYW5ujkaNGqFZs2ZwcnLC77//XmCf/VeMGTMGp0+fRrVq1TB9+nRERETA19cXwOe1f+zYsdDW1oarqyusrKyQlpYGPT09hISEwN3dHY0aNYK2trbcWN22bRvKlCmT7wy9olbQ9aVMVFQUevXqhTFjxqBChQpo06YNTp48Kbd8UyKRwN/fH+fOnRPfiKuKvb09du7ciVOnTsHDwwODBg1Cv3795P5wUNB4NzMzw8qVK+Hp6Ql3d3ckJiZix44dsLS0VHrMnJwchISEoGrVqqhZsyYWL16M2bNnq/VHg8JSp33qiIiIQEJCAhwcHMQZ2J9i0qRJqF69Onx9feHt7Q1bW1ulieEmTZrAwsICV65cQffu3eW+8/X1RVxcHBISElCrVi3UrVsX8+bNU5mUlRk0aBA6dOiArl27ok6dOnjy5IncrEIAGD9+PPz9/dGrVy/Uq1cPxsbG8PX1lVvGPH36dEyePBlhYWGoVKkSfH19sWPHDrlkIxEREREREX0ZEiHvA8G+sAMHDqBx48Z49uwZzMzMlJaJjo7GyJEjFWbZqWPAgAF48OCB+HKFggQHB+PFixdYsWIFAGDKlCmIjY0t9DPi6Ovh6OiIkSNHYuTIkcUdCgCgdu3aGDlypEJC5ktQ5/oi+hrk5OSgUqVK6NKlS77L1gvj5cuXMDU1RdmhftApYVjwDkREREREREXk+sz8J7YVJ9nvSi9evMj3XRGf/UzCr8WLFy+QlJSEtWvXYtu2bWrvN3HiRPz888/Izs5WeGED0ed6+PAhOnXqBH9//+IOhahY3b59G3v27IGXlxcyMjKwZMkS3Lx5819JnhMREREREVHBvpkkYdu2bXHq1CkMHDgQPj4+au9namqKH3/88QtGRprM2toawcHBxR0GUbHT0tJCdHQ0xo4dC0EQUKVKFezdu/ezXpZERERERERERedfX25MRET0JXC5MRERERERFZdvYbmx+q/gJCIiIiIiIiIiom8Sk4REREREREREREQajklCIiIiIiIiIiIiDcckIRERERERERERkYZjkpCIiIiIiIiIiEjDMUlIRERERERERESk4ZgkJCIiIiIiIiIi0nBMEhIREREREREREWk4JgmJiIiIiIiIiIg0nE5xB0BERFSUjoxfitKlSxd3GERERERERP8pnElIRERERERERESk4ZgkJCIiIiIiIiIi0nBMEhIREREREREREWk4JgmJiIiIiIiIiIg0HJOEREREREREREREGo5JQiIiIiIiIiIiIg3HJCEREREREREREZGGY5KQiIiIiIiIiIhIwzFJSEREREREREREpOF0ijsAIiKiotQgPAg6JQyLOwwiIiIiIiom12duKO4Q/pM4k5CIiIiIiIiIiEjDMUlIRERERERERESk4ZgkJCIiIiIiIiIi0nBMEhIREREREREREWk4JgmJiIiIiIiIiIg0HJOEREREREREREREGo5JQiIiIiIiIiIiIg3HJCEREREREREREZGGY5KQiIiIiIiIiIhIwzFJSEREREREREREpOGYJCQiIiIiIiIiItJwTBISERERERERERFpOCYJ6bNNmTIFVatWLfJ6vb29MXLkSPGzo6MjFixY8Mn1RUdHw8zMTPycN+7AwEC0a9fuk+sv6nq+BXnPIf2fL3Xd/Jfx2iEiIiIiIio+TBJ+g+7fv49hw4bByckJ+vr6cHBwgJ+fHxITE4s7tM+SlJSEH374Qa2yyhKKXbt2xdWrV4ssnlu3bkEikeDs2bNy2xcuXIjo6OgiO85/2ZYtWzB9+nTx8+cmer8lY8eO/c9fk59K1bVDRERERERExUenuAOgonXr1i14enrCzMwMc+bMgbu7OzIzM7F7924MGTIEly9fVrpfZmYmdHV1/+VoC8fKyuqz9pdKpZBKpUUUjWqmpqZf/Bj/FRYWFsUdQpH78OED9PT0PrseY2NjGBsbF0FERERERERERJ+PMwm/MUFBQZBIJDh16hQ6deoEFxcXVK5cGaNHj8aJEyfEchKJBMuXL0fbtm1hZGSEGTNmKCzHBYDY2FhIJBK5bbNnz4aNjQ1MTEzQr18/vH//XiGOqKgoVKpUCQYGBqhYsSKWLl2ab9xv3rxBr169YGxsjFKlSiEiIkKhTN5ZaFOmTEGZMmWgr68POzs7DB8+HMDHJa63b9/GqFGjIJFIxPiVtS8/u3btQoMGDWBmZgZLS0u0bt0aN27cEL8vV64cAKBatWqQSCTw9vYGoLhkMiMjA8OHD4e1tTUMDAzQoEEDJCUlid8fOHAAEokEiYmJqFmzJgwNDVG/fn1cuXIl3/ju3r2Lbt26wcLCAkZGRqhZsyZOnjwJALhx4wbatm0LGxsbGBsbo1atWti7d69Cf06fPh3du3eHsbEx7OzssHjxYrky8+bNg5ubG4yMjODg4ICgoCC8fv1arszRo0fh5eUFQ0NDmJubw9fXF8+ePQMgv9xY2Xl58+YNSpQogT/++EOuzh07dsDIyAivXr3Ktw8+l+xcTZ06FdbW1ihRogQGDhyIDx8+iGW8vb0xdOhQjB49GiVLloSPjw8AICUlBS1btoSxsTFsbGzQs2dPPH78GACwYsUK2NvbIycnR+54bdq0Qe/evQEoLjfOycnBtGnTULp0aejr66Nq1arYtWuX+L1snDx//lzcdvbsWUgkEty6dQsAcPv2bfj5+cHc3BxGRkaoXLkydu7cqbL9jo6OmDFjhnjtlS1bFtu2bcOjR4/Qtm1bGBsbw83NDcnJyXL7bd68GZUrV4a+vj4cHR0VrldHR0fMmjULffv2hYmJCcqUKYNffvlF/F7VtSMzd+5clCpVCpaWlhgyZAgyMzNVtoGIiIiIiIiKBpOE35CnT59i165dGDJkCIyMjBS+z5sgCw0NRdu2bXHhwgX07dtXrWNs3LgRoaGhmDlzJpKTk1GqVCmFBODKlSsxceJEzJw5E6mpqZg1axYmTZqEmJgYlfWOGzcO+/fvx9atW7Fnzx4cOHAAp0+fVln+jz/+wPz587FixQpcu3YNsbGxcHNzA/BxiWvp0qUxbdo0pKenIz09Xa225fXmzRuMHj0aSUlJSExMhJaWFtq3by8mfk6dOgUA2Lt3L9LT07Flyxal9QQHB2Pz5s2IiYnBmTNn4OzsDF9fXzx9+lSu3MSJExEREYHk5GTo6Ojke05ev34NLy8v3Lt3D9u3b8e5c+cQHBwsxvb69Wu0bNkSe/fuxV9//QVfX1/4+fkhLS1Nrp6ffvoJ7u7uOHPmDEJCQjBq1CgkJCSI32tpaWHRokW4ePEiYmJisG/fPgQHB4vfnz17Fk2bNkXlypVx/PhxHDlyBH5+fsjOzlaIWdl5MTIyQrdu3RAVFSVXNioqCp06dYKJiYnKPigqiYmJSE1Nxf79+7F+/Xps3boVU6dOlSsTExMDHR0dHD16FCtWrEB6ejq8vLxQtWpVJCcnY9euXXjw4AG6dOkCAOjcuTMeP36M/fv3i3U8e/YMu3fvRkBAgNI4Fi5ciIiICMydOxfnz5+Hr68v2rRpg2vXrqndliFDhiAjIwOHDh3ChQsXEB4eXuBsxfnz58PT0xN//fUXWrVqhZ49e6JXr17o0aOHOF579eoFQRAAAKdPn0aXLl3QrVs3XLhwAVOmTMGkSZMUlthHRESgZs2a+OuvvxAUFITBgweLM5nzu3b279+PGzduYP/+/YiJiUF0dLTK5fsZGRl4+fKl3A8RERERERF9Gi43/oZcv34dgiCgYsWKapXv3r272slBmQULFqBv377o378/AGDGjBnYu3ev3GzC6dOnIyIiAh06dADwcdZQSkoKVqxYIc6iyu3169dYvXo1fv31V3GWVkxMDEqXLq0yjrS0NNja2qJZs2bQ1dVFmTJlULt2bQAfl7hqa2vDxMQEtra2hWpfbh07dpT7vHr1alhbWyMlJQVVqlQRlz9bWlqqPM6bN2+wbNkyREdHo0WLFgA+JlETEhKwevVqjBs3Tiw7c+ZMeHl5AQAmTJiAVq1a4f379zAwMFCod926dXj06BGSkpLEJb3Ozs7i9x4eHvDw8BA/z5gxA1u3bsX27dsxdOhQcbunpycmTJgAAHBxccHRo0cxf/588TzkfulIuXLlMH36dAwePFhMDM+ZMwc1a9aUSxRXrlxZaV+oOi/9+/dH/fr1ce/ePdjZ2eHx48eIi4uTS1Z+SXp6eoiMjIShoSEqV66MadOmYdy4cZg+fTq0tD7+HcXZ2Rlz5swR95k8eTKqV6+OWbNmidsiIyPh4OCAq1evwsXFBc2bN8e6devQtGlTAMCmTZtgYWEhfs5r7ty5GD9+PLp16wYACA8Px/79+7FgwQL8/PPParUlLS0NHTt2FBPmTk5OBe7TsmVLDBw4UGzXsmXLUKtWLXTu3BkAMH78eNSrVw8PHjyAra0t5s2bh6ZNm2LSpEkAPo6blJQU/PTTTwgMDJSrNygoSKxj/vz5OHDgACpWrJjvtWNubo4lS5ZAW1sbFStWRKtWrZCYmIgBAwYoxB4WFqaQ0CUiIiIiIqJPw5mE3xDZTJ+8y4NVqVmzZqGPkZqainr16slty/350aNHuHPnDvr16yc+c83Y2BgzZsyQW6qb240bN/Dhwwe5eiwsLFChQgWVcXTu3Bnv3r2Dk5MTBgwYgK1btyIrK6vQ7cnPjRs30L17dzg5OaFEiRLiEsm8s/EKqiMzMxOenp7iNl1dXdSuXRupqalyZd3d3cV/lypVCgDw8OFDpfWePXsW1apVU/nMvzdv3iA4OBiurq4wMzODsbExLl++rBC7snOZO679+/fDx8cH9vb2MDExQa9evfDkyRO8efNGjENV0ktdtWvXRuXKlfHrr78CANasWYMyZcqgUaNGSsuvXbtWbmyp85PfOfPw8IChoaFcH7x+/Rp37twRt+W9Vk6fPo39+/fLHUOWnJeN84CAAGzevBkZGRli3N26dYO2trZCDC9fvsS9e/fkxgnwMYmbd5zkZ/jw4ZgxYwY8PT0RGhqK8+fPF7hP7nFnY2MDAGKSMfc22VhMTU1VGue1a9fkZpDmrlcikcDW1lbleM6tcuXKcn1UqlQplfuFhITgxYsX4k/uc0ZERERERESFw5mE35DvvvsOEokEqampcs/EUyXvkmQtLS0x0ShT2GeByZa7rly5EnXq1JH7TllyBIDCMdXh4OCAK1euICEhAXv37kVQUBB++uknHDx4sMhewOLn5wcHBwesXLkSdnZ2yMnJQZUqVeSeV1cQVYlbQRAUtuWOW/Zd3mfayRT0ApZx48Zh9+7dmDt3LpydnSGVStGpUye1Ypcd+/bt22jZsiUGDRqE6dOnw8LCAkeOHEG/fv3EcVFUL4Lp378/lixZggkTJiAqKgp9+vRRmexu06aNwtgqiJ2dXaFjyn38vNdKTk4O/Pz8EB4errCfLMHr5+eHnJwcxMfHo1atWjh8+DDmzZun9jEB+XEim9WY+3rJe332798fvr6+iI+Px549exAWFoaIiAgMGzZM5TGVjbv8xqKysavsGs57HUokEpXj+VP309fXh76+foF1EhERERERUcE4k/AbYmFhAV9fX/z888/iTK/ccr/wQBkrKyu8evVKbt+zZ8/KlalUqZLcC1AAyH22sbGBvb09/v77bzg7O8v9yGbi5eXs7AxdXV25ep49e4arV6/mG69UKkWbNm2waNEiHDhwAMePH8eFCxcAfFxCquy5eOp68uQJUlNT8b///Q9NmzZFpUqVxJdxyMjecJvfcZydnaGnp4cjR46I2zIzM5GcnIxKlSp9cnzu7u44e/aswnMNZQ4fPozAwEC0b98ebm5usLW1FV9ukZuycymbEZecnIysrCxERESgbt26cHFxwb179xTiSExMVDtuVeelR48eSEtLw6JFi3Dp0iWly9JlTExMFMZWQT86Oqr/HnLu3Dm8e/dOrg+MjY3zXe5evXp1XLp0CY6OjgrHkiUUpVIpOnTogLVr12L9+vVwcXFBjRo1lNZXokQJ2NnZyY0TADh27Jg4TmRLdHM/YzPv9Ql8TKAPGjQIW7ZswZgxY7By5UqV7fgUrq6uSuN0cXFR+YeAvNS5doiIiIiIiOjfxSThN2bp0qXIzs5G7dq1sXnzZly7dg2pqalYtGiRwtLSvOrUqQNDQ0P8+OOPuH79OtatW6fwwoARI0YgMjISkZGRuHr1KkJDQ3Hp0iW5MlOmTEFYWBgWLlyIq1ev4sKFC4iKilI5i8rY2Bj9+vXDuHHjkJiYiIsXLyIwMFCcOaVMdHQ0Vq9ejYsXL+Lvv//GmjVrIJVKUbZsWQAf36566NAh/PPPP+IbZwvD3NwclpaW+OWXX3D9+nXs27cPo0ePlitjbW0NqVQqvrTixYsXCvUYGRlh8ODBGDduHHbt2oWUlBQMGDAAb9++Rb9+/Qodl4y/vz9sbW3Rrl07HD16FH///Tc2b96M48ePA/iYnNyyZQvOnj2Lc+fOoXv37kpnYx09ehRz5szB1atX8fPPP2PTpk0YMWIEAKB8+fLIysrC4sWLxT5evny53P4hISFISkpCUFAQzp8/j8uXL2PZsmUq+1zVeTE3N0eHDh0wbtw4fP/99/km6Irahw8f0K9fP6SkpODPP/9EaGgohg4dmu/4GzJkCJ4+fQp/f3+cOnUKf//9N/bs2YO+ffvKJb4CAgIQHx+PyMhI9OjRI984xo0bh/DwcPz++++4cuUKJkyYgLNnz4rnw9nZGQ4ODpgyZQquXr2K+Ph4hbcKjxw5Ert378bNmzdx5swZ7Nu377OS0cqMGTMGiYmJmD59Oq5evYqYmBgsWbIEY8eOVbsOda4dIiIiIiIi+ncxSfiNKVeuHM6cOYPGjRtjzJgxqFKlCnx8fJCYmIhly5blu6+FhQV+++037Ny5E25ubli/fj2mTJkiV6Zr166YPHkyxo8fjxo1auD27dsYPHiwXJn+/ftj1apViI6OhpubG7y8vBAdHa1yJiHw8S27jRo1Qps2bdCsWTM0aNBA5awr4OObmleuXAlPT09xNtuOHTtgaWkJAJg2bRpu3bqF8uXLizOwCkNLSwsbNmzA6dOnUaVKFYwaNQo//fSTXBkdHR0sWrQIK1asgJ2dHdq2bau0rtmzZ6Njx47o2bMnqlevjuvXr2P37t0wNzcvdFwyenp62LNnD6ytrdGyZUu4ublh9uzZ4kyu+fPnw9zcHPXr14efnx98fX1RvXp1hXrGjBmD06dPo1q1auILZ3x9fQEAVatWxbx58xAeHo4qVapg7dq1CAsLk9vfxcUFe/bswblz51C7dm3Uq1cP27ZtUzlzL7/z0q9fP3z48KHQL9P5XE2bNsV3332HRo0aoUuXLvDz81MY93nZ2dnh6NGjyM7Ohq+vL6pUqYIRI0bA1NRULrnYpEkTWFhY4MqVK+jevXu+dQ4fPhxjxozBmDFj4Obmhl27dmH79u347rvvAHxchrt+/XpcvnwZHh4eCA8Px4wZM+TqyM7OxpAhQ1CpUiU0b94cFSpUUHj7+OeqXr06Nm7ciA0bNqBKlSqYPHkypk2bJvfSkoKoe+0QERERERHRv0cifMoD4YjoP8/R0REjR46Ue4NxcVq7di1GjBiBe/fuictRv7TAwEA8f/4csbGx/8rx6Mt6+fIlTE1NUXaoH3RKGBa8AxERERERfZOuz9xQ3CF8VWS/K7148QIlSpRQWY4vLiGiYvX27VvcvHkTYWFhGDhw4L+WICQiIiIiIiKi/8PlxkRUrObMmYOqVavCxsYGISEhxR0OERERERERkUbicmMiIvomcLkxEREREREBXG6cl7rLjTmTkIiIiIiIiIiISMMxSUhERERERERERKThmCQkIiIiIiIiIiLScEwSEhERERERERERaTgmCYmIiIiIiIiIiDQck4REREREREREREQajklCIiIiIiIiIiIiDcckIRERERERERERkYZjkpCIiIiIiIiIiEjD6RR3AEREREXpyPilKF26dHGHQURERERE9J/CmYREREREREREREQajklCIiIiIiIiIiIiDcckIRERERERERERkYZjkpCIiIiIiIiIiEjDMUlIRERERERERESk4ZgkJCIiIiIiIiIi0nBMEhIREREREREREWk4JgmJiIiIiIiIiIg0nE5xB0BERFSUGoQHQaeEYXGHQURERET0RV2fuaG4Q6BvDGcSEhERERERERERaTgmCYmIiIiIiIiIiDQck4REREREREREREQajklCIiIiIiIiIiIiDcckIRERERERERERkYZjkpCIiIiIiIiIiEjDMUlIRERERERERESk4ZgkJCIiIiIiIiIi0nBMEhIREREREREREWk4JgmJiIiIiIiIiIg0HJOEREREREREREREGo5JQiIiIiIiIiIiIg3HJCEREREREREREZGGY5LwX+Tt7Y2RI0eKnx0dHbFgwYJii+e/Ljo6GmZmZl/8OIGBgWjXrt0XP863gGO6aBw4cAASiQTPnz8v7lCIiIiIiIhIQ2hEkvD+/fsYMWIEnJ2dYWBgABsbGzRo0ADLly/H27dviy2upKQk/PDDD8V2fHXdunULEokEZ8+eLe5QvihV7Vy4cCGio6OLJab/muIe00+ePEHz5s1hZ2cHfX19ODg4YOjQoXj58mWxxVSQvH88ICIiIiIiIioOOsUdwJf2999/w9PTE2ZmZpg1axbc3NyQlZWFq1evIjIyEnZ2dmjTpk2xxGZlZVUsx/2aZGdnQyKRQEvr681Xm5qaFncIX1xmZiZ0dXU/u57iHtNaWlpo27YtZsyYASsrK1y/fh1DhgzB06dPsW7dumKN7Vv14cMH6OnpFXcYRERERERE9Jm+3sxMEQkKCoKOjg6Sk5PRpUsXVKpUCW5ubujYsSPi4+Ph5+cnlp03bx7c3NxgZGQEBwcHBAUF4fXr1+L3t2/fhp+fH8zNzWFkZITKlStj586d4vcHDx5E7dq1oa+vj1KlSmHChAnIyspSGVvepZkSiQSrVq1C+/btYWhoiO+++w7bt2+X2yclJQUtW7aEsbExbGxs0LNnTzx+/FjlMWRLcnfv3o1KlSrB2NgYzZs3R3p6uly5qKgoVKpUCQYGBqhYsSKWLl0qfleuXDkAQLVq1SCRSODt7a30WLIlkvHx8fDw8ICBgQHq1KmDCxcuKMQTFxcHV1dX6Ovr4/bt23j27Bl69eoFc3NzGBoaokWLFrh27ZpCW8qUKQNDQ0O0b98eT548kfte2bLgkSNHysWbk5OD8PBwODs7Q19fH2XKlMHMmTPzbWfeejMyMjB8+HBYW1vDwMAADRo0QFJSkkI/JCYmombNmjA0NET9+vVx5coVpf1W1CQSCZYtW4YWLVpAKpWiXLly2LRpk/i9bMbkxo0b4e3tDQMDA/z2228A8h8H9erVw4QJE+SO9ejRI+jq6mL//v0AFMd0Wloa2rZtC2NjY5QoUQJdunTBgwcPxO/VOWd//PEH3NzcIJVKYWlpiWbNmuHNmzdK225ubo7BgwejZs2aKFu2LJo2bYqgoCAcPnw43z6bMmUKqlatisjISJQpUwbGxsYYPHgwsrOzMWfOHNja2sLa2locK+q2T1bvmjVr4OjoCFNTU3Tr1g2vXr0S23/w4EEsXLgQEokEEokEt27dEvc/ffp0ocbQ+PHj4eLiAkNDQzg5OWHSpEnIzMwEAFy5cgUSiQSXL1+W22fevHlwdHSEIAgACr7HeHt7Y+jQoRg9ejRKliwJHx8fsZ787p8AsHLlSjg4OIjX8Lx58xQeGbBjxw7UqFEDBgYGcHJywtSpU/O9jxIREREREVHR+KaThE+ePMGePXswZMgQGBkZKS0jkUjEf2tpaWHRokW4ePEiYmJisG/fPgQHB4vfDxkyBBkZGTh06BAuXLiA8PBwGBsbAwD++ecftGzZErVq1cK5c+ewbNkyrF69GjNmzChUzFOnTkWXLl1w/vx5tGzZEgEBAXj69CkAID09HV5eXqhatSqSk5Oxa9cuPHjwAF26dMm3zrdv32Lu3LlYs2YNDh06hLS0NIwdO1b8fuXKlZg4cSJmzpyJ1NRUzJo1C5MmTUJMTAwA4NSpUwCAvXv3Ij09HVu2bMn3eOPGjcPcuXORlJQEa2trtGnTRkxUyOIJCwvDqlWrcOnSJVhbWyMwMBDJycnYvn07jh8/DkEQ0LJlS3G/kydPom/fvggKCsLZs2fRuHHjQvctAISEhCA8PByTJk1CSkoK1q1bBxsbm0K1Mzg4GJs3b0ZMTAzOnDkDZ2dn+Pr6iudJZuLEiYiIiEBycjJ0dHTQt2/fQsf7qSZNmoSOHTvi3Llz6NGjB/z9/ZGamipXZvz48Rg+fDhSU1Ph6+tb4DgICAjA+vXrxWQSAPz++++wsbGBl5eXQgyCIKBdu3Z4+vQpDh48iISEBNy4cQNdu3ZVux3p6enw9/dH3759kZqaigMHDqBDhw5yMeTn3r172LJli9L48rpx4wb+/PNP7Nq1C+vXr0dkZCRatWqFu3fv4uDBgwgPD8f//vc/nDhxolDtu3HjBmJjYxEXF4e4uDgcPHgQs2fPBvBxKXu9evUwYMAApKenIz09HQ4ODuK+hR1DJiYmiI6ORkpKChYuXIiVK1di/vz5AIAKFSqgRo0aWLt2rdw+69atQ/fu3SGRSNS+x8TExEBHRwdHjx7FihUrABR8/zx69CgGDRqEESNG4OzZs/Dx8VFIuu7evRs9evTA8OHDkZKSghUrViA6OlqhnExGRgZevnwp90NERERERESf5ptebnz9+nUIgoAKFSrIbS9ZsiTev38P4GPiLzw8HADkngtWrlw5TJ8+HYMHDxZnU6WlpaFjx45wc3MDADg5OYnlly5dCgcHByxZsgQSiQQVK1bEvXv3MH78eEyePFnt5bSBgYHw9/cHAMyaNQuLFy/GqVOn0Lx5cyxbtgzVq1fHrFmzxPKRkZFwcHDA1atX4eLiorTOzMxMLF++HOXLlwcADB06FNOmTRO/nz59OiIiItChQwex7bJf0Hv37i0uIbW0tIStrW2BbQgNDRVnF8XExKB06dLYunWrmGjIzMzE0qVL4eHhAQC4du0atm/fjqNHj6J+/foAgLVr18LBwQGxsbHo3LkzFi5cCF9fX3Emm4uLC44dO4Zdu3ap1a8A8OrVKyxcuBBLlixB7969AQDly5dHgwYNAECtdr558wbLli1DdHQ0WrRoAeBjkjUhIQGrV6/GuHHjxLIzZ84Uk1MTJkxAq1at8P79exgYGKgd86fq3Lkz+vfvD+Dj+U1ISMDixYvlZgaOHDlSPOeycvmNg65du2LUqFE4cuQIGjZsCOD/EkzKxvfevXtx/vx53Lx5U0x8rVmzBpUrV0ZSUhJq1apVYDvS09ORlZWFDh06oGzZsgAgXn/58ff3x7Zt2/Du3Tv4+flh1apVBe6Tk5ODyMhImJiYwNXVFY0bN8aVK1ewc+dOaGlpoUKFCggPD8eBAwdQt25dtduXk5OD6OhomJiYAAB69uyJxMREzJw5E6amptDT04OhoaHSMVfYMfS///1P/LejoyPGjBmD33//XUzWBQQEYMmSJZg+fToA4OrVqzh9+jR+/fVXAFD7HuPs7Iw5c+bIHbug++fixYvRokUL8Q8Usms4Li5Orr0TJkwQr08nJydMnz4dwcHBCA0NVWhvWFgYpk6dqrQviIiIiIiIqHC+6ZmEMrlnCwIfZ4ydPXsWlStXRkZGhrh9//798PHxgb29PUxMTNCrVy88efJEXNo4fPhwzJgxA56enggNDcX58+fFfVNTU1GvXj25Y3l6euL169e4e/eu2rG6u7uL/zYyMoKJiQkePnwI4OPSw/3798PY2Fj8qVixIoCPs5VUMTQ0FBOEAFCqVCmxzkePHuHOnTvo16+fXL0zZszIt8781KtXT/y3hYUFKlSoIDeLTU9PT66dqamp0NHRQZ06dcRtlpaWcvvJ+lfVcdSRmpqKjIwMNG3atFD75Xbjxg1kZmbC09NT3Karq4vatWsrzNTL3cZSpUoBgNjvebVo0UKu/wv6kSUoVVHWV3njq1mzpvhvdcaBlZUVfHx8xJloN2/exPHjxxEQEKA0htTUVDg4OMjNjHN1dYWZmZlCLKp4eHigadOmcHNzQ+fOnbFy5Uo8e/aswP3mz5+PM2fOIDY2Fjdu3MDo0aPF73K3b9CgQeJ2R0dHMZEHADY2NnB1dZVLgNrY2IjnUN325a039/VXkMKMIeDj0uwGDRrA1tYWxsbGmDRpEtLS0sTvu3Xrhtu3b4uzIdeuXYuqVavC1dUVgPr3mNxjR6ag++eVK1dQu3ZtuX3yfj59+jSmTZsmd3zZLEtlL5kKCQnBixcvxJ87d+6o7BsiIiIiIiLK3zc9k9DZ2VnpM7hkMwClUqm47fbt22jZsiUGDRqE6dOnw8LCAkeOHEG/fv3EJa/9+/eHr68v4uPjsWfPHoSFhSEiIgLDhg2DIAgKyUjZksi82/OT9+UREokEOTk5AD7OSPLz8xNnPuYmSyCoW6csNlndK1eulEvSAYC2trbacRckdx9IpVK5z6qWjubuU3WWl2ppaSmUy73MOff5/lSqzqmy85+732Xfyfo7r1WrVuHdu3dqx/EpbckbX+4l+OqOg4CAAIwYMQKLFy/GunXrULlyZXFGaF7K+iTv9oLOmba2NhISEnDs2DHs2bMHixcvxsSJE3Hy5EnxGZLK2NrawtbWFhUrVoSlpSUaNmyISZMmoVSpUnJvry5RooT4b2XXSX7XozrtU1WvqnGQV2HG0IkTJ9CtWzdMnToVvr6+MDU1xYYNGxARESGWKVWqFBo3box169ahbt26WL9+PQYOHCh+r+49Ju/jG9S5f+Z3j8x9/KlTp8rNcJVRNntSX18f+vr6SvuDiIiIiIiICuebThJaWlrCx8cHS5YswbBhw1Q+lxAAkpOTkZWVhYiICHHm0MaNGxXKOTg4YNCgQRg0aBBCQkKwcuVKDBs2DK6urti8ebPcL8LHjh2DiYkJ7O3ti6Q91atXx+bNm+Ho6AgdnaI5dTY2NrC3t8fff/+tckaY7M2l2dnZatV54sQJlClTBgDw7NkzXL16VZyNpIyrqyuysrJw8uRJcbnxkydPcPXqVVSqVEksI5v9lPs4uVlZWeHixYty286ePSsmWr777jtIpVIkJiaKS3EL205nZ2fo6enhyJEj6N69O4CPSa3k5GS55ZaFVVRjRObEiRPo1auX3Odq1aqpLK/OOACAdu3aYeDAgdi1axfWrVuHnj17qizr6uqKtLQ03LlzR5xtl5KSghcvXojntaBzBnxMjnl6esLT0xOTJ09G2bJlsXXrVrnZgfmRJaJks4adnZ3V2q8g6rRPHXp6empfW/k5evQoypYti4kTJ4rbbt++rVAuICAA48ePh7+/P27cuIFu3bqJ333qPUad+2fFihXF537m3i+36tWr48qVK0V2joiIiIiIiEh93/xy46VLlyIrKws1a9bE77//jtTUVFy5cgW//fYbLl++LM6SKl++PLKysrB48WL8/fffWLNmDZYvXy5X18iRI7F7927cvHkTZ86cwb59+8RkQFBQEO7cuYNhw4bh8uXL2LZtG0JDQzF69Gi1n0dYkCFDhuDp06fw9/fHqVOn8Pfff2PPnj3o27fvZyUZpkyZgrCwMCxcuBBXr17FhQsXEBUVhXnz5gEArK2tIZVKxZcYvHjxIt/6pk2bhsTERFy8eBGBgYEoWbKkwhtsc/vuu+/Qtm1bDBgwAEeOHBFftmFvb4+2bdsC+LjUe9euXZgzZw6uXr2KJUuWKDyPsEmTJkhOTsavv/6Ka9euITQ0VC4BZWBggPHjxyM4OBi//vorbty4gRMnTmD16tVqt9PIyAiDBw/GuHHjsGvXLqSkpGDAgAF4+/Yt+vXrp1Z//xs2bdqEyMhIXL16FaGhoTh16hSGDh2a7z4FjQPgY/vbtm2LSZMmITU1VUyUKtOsWTO4u7sjICAAZ86cwalTp9CrVy94eXmJy1ULOmcnT57ErFmzkJycjLS0NGzZsgWPHj1SmYTbuXMnoqKicPHiRdy6dQs7d+7E4MGD4enpCUdHx0L0YMHUaZ86HB0dcfLkSdy6dQuPHz9We5ZhXs7OzkhLS8OGDRtw48YNLFq0CFu3blUo16FDB7x8+RKDBw9G48aN5RLUn3qPUef+OWzYMOzcuRPz5s3DtWvXsGLFCvz5559yswsnT56MX3/9FVOmTMGlS5eQmpqK33//Xe5Zi0RERERERPRlfPNJwvLly+Ovv/5Cs2bNEBISAg8PD9SsWROLFy/G2LFjxQf4V61aFfPmzUN4eDiqVKmCtWvXIiwsTK6u7OxsDBkyBJUqVULz5s1RoUIF8aH89vb22LlzJ06dOgUPDw8MGjQI/fr1K9Jfbu3s7HD06FFkZ2fD19cXVapUwYgRI2BqavpZicj+/ftj1apViI6OhpubG7y8vBAdHS0u59TR0cGiRYuwYsUK2NnZiYk7VWbPno0RI0agRo0aSE9Px/bt28VZeqpERUWhRo0aaN26NerVqwdBELBz505xRlndunWxatUqLF68GFWrVsWePXsU+tbX1xeTJk1CcHAwatWqhVevXsnNpgM+vvV3zJgxmDx5MipVqoSuXbuKz3hTt52zZ89Gx44d0bNnT1SvXh3Xr1/H7t27YW5unm8b/01Tp07Fhg0b4O7ujpiYGKxdu1Z87pwqBY0DmYCAAJw7dw4NGzYUZ4wqI5FIEBsbC3NzczRq1AjNmjWDk5MTfv/9d7FMQeesRIkSOHToEFq2bAkXFxf873//Q0REhMpnMkqlUqxcuRINGjRApUqVMHLkSLRu3Vru5RhFRZ32qWPs2LHQ1taGq6srrKys5J4hWBht27bFqFGjMHToUFStWhXHjh3DpEmTFMqVKFECfn5+OHfunMKs0U+9x6hz//T09MTy5csxb948eHh4YNeuXRg1apTcMmJfX1/ExcUhISEBtWrVQt26dTFv3jzxpTVERERERET05UgEdR72RqSGAwcOoHHjxnj27BnMzMyKOxyNJZFIsHXr1nxnbxJ9DQYMGIDLly/j8OHDRVLfy5cvYWpqirJD/aBTwrBI6iQiIiIi+lpdn7mhuEOg/wjZ70ovXryQezZ/Xt/0MwmJiOjrMXfuXPj4+MDIyAh//vknYmJixNnYREREREREVLyYJCQion/FqVOnMGfOHLx69QpOTk5YtGiR0pcIERERERER0b+PSUIqMt7e3uDq9eLHc0BfK2VvjCciIiIiIqKvwzf/4hIiIiIiIiIiIiLKH5OEREREREREREREGo5JQiIiIiIiIiIiIg3HJCEREREREREREZGGY5KQiIiIiIiIiIhIwzFJSEREREREREREpOGYJCQiIiIiIiIiItJwTBISERERERERERFpOCYJiYiIiIiIiIiINJxOcQdARERUlI6MX4rSpUsXdxhERERERET/KZxJSEREREREREREpOGYJCQiIiIiIiIiItJwTBISERERERERERFpOCYJiYiIiIiIiIiINByThERERERERERERBqOSUIiIiIiIiIiIiINxyQhERERERERERGRhmOSkIiIiIiIiIiISMPpFHcARERERalBeBB0ShgWdxhEREREREXq+swNxR0CfeM4k5CIiIiIiIiIiEjDMUlIRERERERERESk4ZgkJCIiIiIiIiIi0nBMEhIREREREREREWk4JgmJiIiIiIiIiIg0HJOEREREREREREREGo5JQiIiIiIiIiIiIg3HJCEREREREREREZGGY5KQiIiIiIiIiIhIwzFJSEREREREREREpOGYJCQiIiIiIiIiItJwTBISERERERERERFpOCYJNcCUKVNQtWrVz65n3759qFixInJycj4/qE8QGBiIdu3affHjODo6YsGCBV/8OJ/jwIEDkEgkeP78eXGHUmQePnwIKysr/PPPP8Udyhd369YtSCQSnD17trhD+Wp8i2OaiIiIiIjov4RJwi/k4cOHGDhwIMqUKQN9fX3Y2trC19cXx48fL+7QPllwcDAmTpwILa1vY9hER0fDzMxMYXtSUhJ++OGHfz+gQqhfvz7S09Nhampa3KGo5O3tjZEjR6pd3traGj179kRoaOiXC+or4eDggPT0dFSpUqW4QykWhR0bRERERERE9OV9G9mer1DHjh1x7tw5xMTE4OrVq9i+fTu8vb3x9OnT4g7tkxw7dgzXrl1D586dP6ueDx8+FFFEX46VlRUMDQ3/1WNGR0fD29tb7fJ6enqwtbWFRCL5ckF9oszMzE/et0+fPli7di2ePXtWhBEVnezs7CKZSautrQ1bW1vo6OgUQVREREREREREn49Jwi/g+fPnOHLkCMLDw9G4cWOULVsWtWvXRkhICFq1agVA+XLD58+fQyKR4MCBAwD+b/ldfHw8PDw8YGBggDp16uDChQviPrLZcLGxsXBxcYGBgQF8fHxw584dpbEdOnQIurq6uH//vtz2MWPGoFGjRirbtGHDBnz//fcwMDAQt8mWMa9YsQIODg4wNDRE586d5ZYLypYIh4WFwc7ODi4uLgCACxcuoEmTJpBKpbC0tMQPP/yA169fi/tlZ2dj9OjRMDMzg6WlJYKDgyEIglxMypYFV61aFVOmTJHr0x9++AE2NjYwMDBAlSpVEBcXhwMHDqBPnz548eIFJBIJJBKJuF/eetPS0tC2bVsYGxujRIkS6NKlCx48eKDQD2vWrIGjoyNMTU3RrVs3vHr1SmV/fq68SzNl4yAuLg4VKlSAoaEhOnXqhDdv3iAmJgaOjo4wNzfHsGHDkJ2dLdbj6OiI6dOno3v37jA2NoadnR0WL14sdyx12x8ZGQknJyfo6+ujd+/eOHjwIBYuXCj2761bt/Ds2TMEBATAysoKUqkU3333HaKiosS63NzcYGtri61bt36xvpMpzPUVFxcHV1dX6Ovr4/bt2/jw4QOCg4Nhb28PIyMj1KlTR7xuX7x4AalUil27dskdb8uWLTAyMsLr16+VXv8HDx5E7dq1oa+vj1KlSmHChAnIysoSv1dnvE+ZMkWcvWxnZ4fhw4erbH/u81amTBkYGxtj8ODByM7Oxpw5c2Brawtra2vMnDlTbr/PvR4CAwOVjg2Z06dPo2bNmjA0NET9+vVx5coVlW0gIiIiIiKiosMk4RdgbGwMY2NjxMbGIiMj47PrGzduHObOnYukpCRYW1ujTZs2crO13r59i5kzZyImJgZHjx7Fy5cv0a1bN6V1NWrUCE5OTlizZo24LSsrC7/99hv69OmjMoZDhw6hZs2aCtuvX7+OjRs3YseOHdi1axfOnj2LIUOGyJVJTExEamoqEhISEBcXh7dv36J58+YwNzdHUlISNm3ahL1792Lo0KHiPhEREYiMjMTq1atx5MgRPH36tNCJo5ycHLRo0QLHjh3Db7/9hpSUFMyePRva2tqoX78+FixYgBIlSiA9PR3p6ekYO3asQh2CIKBdu3Z4+vQpDh48iISEBNy4cQNdu3aVK3fjxg3ExsYiLi4OcXFxOHjwIGbPnl2oeD/X27dvsWjRImzYsAG7du3CgQMH0KFDB+zcuRM7d+7EmjVr8Msvv+CPP/6Q2++nn36Cu7s7zpw5g5CQEIwaNQoJCQkA1G+/bBxs3rwZZ8+exaJFi1CvXj0MGDBA7F8HBwdMmjQJKSkp+PPPP5Gamoply5ahZMmScnXVrl0bhw8f/rKdlYs611dYWBhWrVqFS5cuwdraGn369MHRo0exYcMGnD9/Hp07d0bz5s1x7do1mJqaolWrVli7dq3ccdatWycm1/L6559/0LJlS9SqVQvnzp3DsmXLsHr1asyYMUPtdvzxxx+YP38+VqxYgWvXriE2NhZubm757nPjxg38+eef2LVrF9avX4/IyEi0atUKd+/excGDBxEeHo7//e9/OHHiBICiuR4WLlyodGzITJw4EREREUhOToaOjg769u2rdh8QERERERHRp+Naty9AR0cH0dHRGDBgAJYvX47q1avDy8sL3bp1g7u7e6HrCw0NhY+PDwAgJiYGpUuXxtatW9GlSxcAH5d3LlmyBHXq1BHLVKpUCadOnULt2rUV6uvXrx+ioqIwbtw4AEB8fDzevn0r1qfMrVu3YGdnp7D9/fv3YkwAsHjxYrRq1QoRERGwtbUFABgZGWHVqlXQ09MDAKxcuRLv3r3Dr7/+CiMjIwDAkiVL4Ofnh/DwcNjY2GDBggUICQlBx44dAQDLly/H7t27C9Vve/fuxalTp5CamirOYHRychK/NzU1hUQiEeNUVcf58+dx8+ZNMZGxZs0aVK5cGUlJSahVqxaAjwnJ6OhomJiYAAB69uyJxMREhVlYX1JmZiaWLVuG8uXLAwA6deqENWvW4MGDBzA2NoarqysaN26M/fv3yyV1PD09MWHCBACAi4sLjh49ivnz58PHx0ft9n/48AFr1qyBlZWVWK+enh4MDQ3l+jctLQ3VqlUTE86Ojo4K7bC3t8dff/1VtJ2TD3Wur6VLl8LDwwPAxwTY+vXrcffuXfGaGDt2LHbt2oWoqCjMmjULAQEB6NWrF96+fQtDQ0O8fPkS8fHx2Lx5s9IYli5dCgcHByxZsgQSiQQVK1bEvXv3MH78eEyePFmt54CmpaXB1tYWzZo1g66uLsqUKaP0+s8tJycHkZGRMDExEcfHlStXsHPnTmhpaaFChQoIDw/HgQMHULdu3SK5HkxNTZWODZmZM2fCy8sLADBhwgS0atUK79+/l5vFLJORkSH3h5iXL18W2E9ERERERESkHGcSfiEdO3bEvXv3sH37dvj6+uLAgQOoXr06oqOjC11XvXr1xH9bWFigQoUKSE1NFbfp6OjIzfKrWLEizMzM5MrkFhgYiOvXr4uzgyIjI9GlSxcxYafMu3fvlP6SXqZMGTFBKIs1JydHbomgm5ubmCAEgNTUVHh4eMgdz9PTU9zvxYsXSE9Pl2t33jaq4+zZsyhdurSYIPwUqampcHBwkJvp5OrqqtC/jo6OYkIEAEqVKoWHDx+qrDctLU2ccWpsbIxBgwbh8OHDCtsKw9DQUEwQAoCNjQ0cHR3lZq7Z2NgoxJW7n2WfZW1Tt/1ly5aVSxCqMnjwYGzYsAFVq1ZFcHAwjh07plBGKpXi7du3KuvI3Ufq/MyaNSvfmAq6vvT09OSS+2fOnIEgCHBxcZE7zsGDB3Hjxg0AQKtWraCjo4Pt27cDADZv3gwTExN8//33SmNITU1FvXr15J4x6enpidevX+Pu3bv5xi/TuXNnvHv3Dk5OThgwYAC2bt0qt1xZmbzj1sbGBq6urnJJydxj5ktdD7nl7utSpUoBgMp9w8LCYGpqKv7kjouIiIiIiIgKhzMJvyDZ8wF9fHwwefJk9O/fH6GhoQgMDBR/Cc/9nL3CvPAh7wsrlL3AQtVLLaytreHn54eoqCg4OTlh586d4vPUVClZsqRaL5OQHTP3sfMmHwVBUBlbYV7EoaWlpfCcwtx9KJVK1a5LFVWx5t2uq6sr971EIsn3BRd2dnZyz6PbsmULNm/eLLdEtUSJEoWKVVkMhY0rdzlA/fbnl2DOrUWLFrh9+zbi4+Oxd+9eNG3aFEOGDMHcuXPFMk+fPs034Zi739RhYWFRqPKA/DiUSqVyn3NycqCtrY3Tp09DW1tbbj9ZQlZPTw+dOnXCunXr0K1bN6xbtw5du3ZV+aISZf0sG9uy7QWNdwcHB1y5cgUJCQnYu3cvgoKC8NNPP+HgwYMK40CmsGPmS10PqmKS1alq35CQEIwePVr8/PLlSyYKiYiIiIiIPhGThP8iV1dXxMbGAoCYBElPT0e1atUAqE5+nDhxAmXKlAEAPHv2DFevXkXFihXF77OyspCcnCwuLbxy5QqeP38uVyav/v37o1u3bihdujTKly8PT0/PfGOvVq0aUlJSFLanpaXh3r174rLL48ePQ0tLK9/Ze66uroiJicGbN2/E5NLRo0fF/UxNTVGqVCmcOHFCfJlKVlYWTp8+jerVq4v1WFlZIT09Xfz88uVL3Lx5U/zs7u6Ou3fv4urVq0rj0dPTk3uJh6pY09LScOfOHTH5kJKSghcvXqBSpUr57psfHR0dODs7i5+tra0hlUrltv1bZDNKc3+WjZ3Pab+q/rWyskJgYCACAwPRsGFD8ZmAMhcvXsz3Tc9F3UcFXV95VatWDdnZ2Xj48CEaNmyoslxAQAC+//57XLp0Cfv378f06dNVlnV1dcXmzZvlkm3Hjh2DiYkJ7O3tARQ83oGPCc02bdqgTZs2GDJkCCpWrIgLFy7IXTefo6iuB3WuPXXo6+tDX1//s+shIiIiIiIiLjf+Ip48eYImTZrgt99+E5/ftWnTJsyZMwdt27YF8PGX+bp162L27NlISUnBoUOH8L///U9pfdOmTUNiYiIuXryIwMBAlCxZEu3atRO/19XVxbBhw3Dy5EmcOXMGffr0Qd26dfN9Hpmvry9MTU0xY8aMfF9Ykrv8kSNHFLYbGBigd+/eOHfuHA4fPozhw4ejS5cu+T7nLyAgQNzv4sWL2L9/P4YNG4aePXvCxsYGADBixAjMnj0bW7duxeXLlxEUFCT31mQAaNKkCdasWYPDhw/j4sWL6N27t9zMLi8vLzRq1AgdO3ZEQkICbt68Kb6kAfi4JPL169dITEzE48ePlS5xbdasGdzd3REQEIAzZ87g1KlT6NWrF7y8vAq9/PlrdfToUcyZMwdXr17Fzz//jE2bNmHEiBEAPq/9jo6OOHnyJG7duoXHjx8jJycHkydPxrZt23D9+nVcunQJcXFxcsmlt2/f4vTp0yqX5X4JBV1febm4uIjPHNyyZQtu3ryJpKQkhIeHY+fOnWI5Ly8v2NjYICAgAI6Ojqhbt67KOoOCgnDnzh0MGzYMly9fxrZt2xAaGorRo0eLs44LGu/R0dFYvXo1Ll68iL///htr1qyBVCpF2bJlP7+T/r+iuh6UjQ0iIiIiIiIqXkwSfgHGxsaoU6cO5s+fj0aNGqFKlSqYNGkSBgwYgCVLlojlIiMjkZmZiZo1a2LEiBEq32Q6e/ZsjBgxAjVq1EB6ejq2b98u94w/Q0NDjB8/Ht27d0e9evUglUqxYcOGfGPU0tJCYGAgsrOz0atXrwLb1KNHD6SkpMg9axD4OKurQ4cOaNmyJb7//ntUqVIFS5cuzbcuQ0ND7N69G0+fPkWtWrXQqVMnNG3aVK5vxowZg169eiEwMBD16tWDiYkJ2rdvL1dPSEgIGjVqhNatW6Nly5Zo166d3DP5gI/PgqtVqxb8/f3h6uqK4OBgcQZT/fr1MWjQIHTt2hVWVlaYM2eOQqwSiQSxsbEwNzdHo0aN0KxZMzg5OeH3338vsM/+K8aMGYPTp0+jWrVqmD59OiIiIuDr6wvg89o/duxYaGtrw9XVFVZWVkhLS4Oenh5CQkLg7u6ORo0aQVtbW26sbtu2DWXKlMl3hl5RK+j6UiYqKgq9evXCmDFjUKFCBbRp0wYnT56UW+oqkUjg7++Pc+fOISAgIN/67O3tsXPnTpw6dQoeHh4YNGgQ+vXrJ/eHg4LGu5mZGVauXAlPT0+4u7sjMTERO3bsgKWl5Sf2jKKiuh6UjQ0iIiIiIiIqXhIh70Ou6Ktx4MABNG7cGM+ePYOZmZnSMtHR0Rg5cqTCLDt1DBgwAA8ePBBfrlCQ4OBgvHjxAitWrAAATJkyBbGxsYV+Rhx9PRwdHTFy5EiMHDmyuEMBANSuXRsjR45E9+7dv/ix1Lm+6L/l5cuXMDU1RdmhftApYVjc4RARERERFanrM/OfDESkiux3pRcvXuT7/gPOJNRAL168wN69e7F27VoMGzZM7f0mTpyIsmXLFsmzxIjyevjwITp16gR/f//iDoWIiIiIiIhI4/DFJRqobdu2OHXqFAYOHAgfHx+19zM1NcWPP/74BSMjTWZtbY3g4ODiDoOIiIiIiIhII3G5MRERfRO43JiIiIiIvmVcbkyfisuNiYiIiIiIiIiISC1MEhIREREREREREWk4JgmJiIiIiIiIiIg0HJOEREREREREREREGo5JQiIiIiIiIiIiIg3HJCEREREREREREZGGY5KQiIiIiIiIiIhIwzFJSEREREREREREpOGYJCQiIiIiIiIiItJwOsUdABERUVE6Mn4pSpcuXdxhEBERERER/adwJiEREREREREREZGGY5KQiIiIiIiIiIhIwzFJSEREREREREREpOGYJCQiIiIiIiIiItJwTBISERERERERERFpOCYJiYiIiIiIiIiINByThERERERERERERBqOSUIiIiIiIiIiIiINxyQhERERERERERGRhtMp7gCIiIiKUoPwIOiUMCzuMIiIiIjoK3V95obiDoHoq8SZhERERERERERERBqOSUIiIiIiIiIiIiINxyQhERERERERERGRhmOSkIiIiIiIiIiISMMxSUhERERERERERKThmCQkIiIiIiIiIiLScEwSEhERERERERERaTgmCYmIiIiIiIiIiDQck4REREREREREREQajklCIiIiIiIiIiIiDcckIRERERERERERkYZjkpCIiIiIiIiIiEjDMUlIXyVvb2+MHDlS/Ozo6IgFCxYUWzz/ddHR0TAzM/vixwkMDES7du2++HG+BRzTiiQSCWJjY4s7DCIiIiIiIo3EJCHJuX//PkaMGAFnZ2cYGBjAxsYGDRo0wPLly/H27dtiiyspKQk//PBDsR1fXbdu3YJEIsHZs2eLO5QvSlU7Fy5ciOjo6GKJ6b/mvzKmv4QpU6agatWqxR0GERERERER5aJT3AHQ1+Pvv/+Gp6cnzMzMMGvWLLi5uSErKwtXr15FZGQk7Ozs0KZNm2KJzcrKqliO+zXJzs6GRCKBltbXm9s3NTUt7hC+uMzMTOjq6n52PRzTRERERERE9DX5erMN9K8LCgqCjo4OkpOT0aVLF1SqVAlubm7o2LEj4uPj4efnJ5adN28e3NzcYGRkBAcHBwQFBeH169fi97dv34afnx/Mzc1hZGSEypUrY+fOneL3Bw8eRO3ataGvr49SpUphwoQJyMrKUhlb3qWZEokEq1atQvv27WFoaIjvvvsO27dvl9snJSUFLVu2hLGxMWxsbNCzZ088fvxY5TFkS3J3796NSpUqwdjYGM2bN0d6erpcuaioKFSqVAkGBgaoWLEili5dKn5Xrlw5AEC1atUgkUjg7e2t9FgHDhyARCJBfHw8PDw8YGBggDp16uDChQsK8cTFxcHV1RX6+vq4ffs2nj17hl69esHc3ByGhoZo0aIFrl27ptCWMmXKwNDQEO3bt8eTJ0/kvle2LHjkyJFy8ebk5CA8PBzOzs7Q19dHmTJlMHPmzHzbmbfejIwMDB8+HNbW1jAwMECDBg2QlJSk0A+JiYmoWbMmDA0NUb9+fVy5ckVpvxU1iUSCZcuWoUWLFpBKpShXrhw2bdokfi+bMblx40Z4e3vDwMAAv/32G4D8x0G9evUwYcIEuWM9evQIurq62L9/PwDFMZ2Wloa2bdvC2NgYJUqUQJcuXfDgwQPxe3XO2R9//AE3NzdIpVJYWlqiWbNmePPmjdK2y/p+9+7dqFatGqRSKZo0aYKHDx/izz//RKVKlVCiRAn4+/vLzSL+3HMaHR2NqVOn4ty5c5BIJJBIJHKzTx8/fpzvdU1ERERERERfBpOEBAB48uQJ9uzZgyFDhsDIyEhpGYlEIv5bS0sLixYtwsWLFxETE4N9+/YhODhY/H7IkCHIyMjAoUOHcOHCBYSHh8PY2BgA8M8//6Bly5aoVasWzp07h2XLlmH16tWYMWNGoWKeOnUqunTpgvPnz6Nly5YICAjA06dPAQDp6enw8vJC1apVkZycjF27duHBgwfo0qVLvnW+ffsWc+fOxZo1a3Do0CGkpaVh7Nix4vcrV67ExIkTMXPmTKSmpmLWrFmYNGkSYmJiAACnTp0CAOzduxfp6enYsmVLvscbN24c5s6di6SkJFhbW6NNmzbIzMyUiycsLAyrVq3CpUuXYG1tjcDAQCQnJ2P79u04fvw4BEFAy5Ytxf1OnjyJvn37IigoCGfPnkXjxo0L3bcAEBISgvDwcEyaNAkpKSlYt24dbGxsCtXO4OBgbN68GTExMThz5gycnZ3h6+srnieZiRMnIiIiAsnJydDR0UHfvn0LHe+nmjRpEjp27Ihz586hR48e8Pf3R2pqqlyZ8ePHY/jw4UhNTYWvr2+B4yAgIADr16+HIAhiHb///jtsbGzg5eWlEIMgCGjXrh2ePn2KgwcPIiEhATdu3EDXrl3Vbkd6ejr8/f3Rt29fpKam4sCBA+jQoYNcDMpMmTIFS5YswbFjx3Dnzh106dIFCxYswLp16xAfH4+EhAQsXrxYLP+557Rr164YM2YMKleujPT0dKSnp8u1M7/rOq+MjAy8fPlS7oeIiIiIiIg+DZcbEwDg+vXrEAQBFSpUkNtesmRJvH//HsDHxF94eDgAyL1UpFy5cpg+fToGDx4szqZKS0tDx44d4ebmBgBwcnISyy9duhQODg5YsmQJJBIJKlasiHv37mH8+PGYPHmy2stpAwMD4e/vDwCYNWsWFi9ejFOnTqF58+ZYtmwZqlevjlmzZonlIyMj4eDggKtXr8LFxUVpnZmZmVi+fDnKly8PABg6dCimTZsmfj99+nRERESgQ4cOYttTUlKwYsUK9O7dW1xCamlpCVtb2wLbEBoaCh8fHwBATEwMSpcuja1bt4rJzMzMTCxduhQeHh4AgGvXrmH79u04evQo6tevDwBYu3YtHBwcEBsbi86dO2PhwoXw9fUVZ7K5uLjg2LFj2LVrl1r9CgCvXr3CwoULsWTJEvTu3RsAUL58eTRo0AAA1GrnmzdvsGzZMkRHR6NFixYAPiZZExISsHr1aowbN04sO3PmTDF5NmHCBLRq1Qrv37+HgYGB2jF/qs6dO6N///4APp5fWVIs98zAkSNHiudcVi6/cdC1a1eMGjUKR44cQcOGDQEA69atQ/fu3ZWO77179+L8+fO4efMmHBwcAABr1qxB5cqVkZSUhFq1ahXYjvT0dGRlZaFDhw4oW7YsAIjXX35mzJgBT09PAEC/fv0QEhKCGzduiNdsp06dsH//fowfP75IzqlUKoWxsTF0dHSUjp38ruu8wsLCMHXq1ALbSERERERERAXjTEKSk3u2IPBxxtjZs2dRuXJlZGRkiNv3798PHx8f2Nvbw8TEBL169cKTJ0/EpY3Dhw8Xkw+hoaE4f/68uG9qairq1asndyxPT0+8fv0ad+/eVTtWd3d38d9GRkYwMTHBw4cPAQCnT5/G/v37YWxsLP5UrFgRAHDjxg2VdRoaGooJQgAoVaqUWOejR49w584d9OvXT67eGTNm5FtnfurVqyf+28LCAhUqVJCbxaanpyfXztTUVOjo6KBOnTriNktLS7n9ZP2r6jjqSE1NRUZGBpo2bVqo/XK7ceMGMjMzxQQUAOjq6qJ27doKM/Vyt7FUqVIAIPZ7Xi1atJDr/4J+ZMksVZT1Vd74atasKf5bnXFgZWUFHx8frF27FgBw8+ZNHD9+HAEBAUpjSE1NhYODg5ggBABXV1eYmZkpxKKKh4cHmjZtCjc3N3Tu3BkrV67Es2fPCtwvd9/b2NjA0NBQLqlvY2MjnosvdU5V7Zf3us4rJCQEL168EH/u3Pl/7N15dE/X/v/x14c0kdkcU4ghISJmWrSGlhvVBpdrTJGaSmKq2W0RYmhU1FRDqxFVY6+higaNmZoFlRAxRdu0qlqKGjL8/vDN+eWTWRtN28/zsVbW8jmfc/Z5731OrOVl73Ou5dg+AAAAACBzzCSEJKlKlSoymUw6d+6c2fbUsMDW1tbYdvXqVbVp00YDBgxQcHCwihYtqv3796tPnz7Gkte+ffvKx8dHW7Zs0fbt2zV9+nSFhoZq8ODBSklJyRBGpi6JTL89O+lfHmEymZScnCzp8fP0fH19jZmPaaUGFrltM7W21LY//PBDs5BOkgoWLJjrunOSdgxsbW3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",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "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>Predictor</th>\n",
       "      <th>Error reduction vs calendar (thousand b/d)</th>\n",
       "      <th>Error reduction vs calendar (%)</th>\n",
       "      <th>Periods helped (of 6)</th>\n",
       "      <th>Error increase when removed (thousand b/d)</th>\n",
       "      <th>Periods hurt by removal (of 6)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Supply (production + imports) — previous 3-month average</td>\n",
       "      <td>184.63</td>\n",
       "      <td>53.35</td>\n",
       "      <td>5</td>\n",
       "      <td>0.68</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Crude distillation capacity — previous month</td>\n",
       "      <td>178.07</td>\n",
       "      <td>51.46</td>\n",
       "      <td>5</td>\n",
       "      <td>4.47</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gasoline net production — previous 3-month average</td>\n",
       "      <td>161.67</td>\n",
       "      <td>46.71</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Supply (production + imports) — previous month</td>\n",
       "      <td>160.77</td>\n",
       "      <td>46.46</td>\n",
       "      <td>4</td>\n",
       "      <td>0.44</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gasoline net production — previous month</td>\n",
       "      <td>150.09</td>\n",
       "      <td>43.37</td>\n",
       "      <td>5</td>\n",
       "      <td>0.79</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Crude refinery inputs — previous month</td>\n",
       "      <td>136.48</td>\n",
       "      <td>39.44</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gasoline exports — previous month</td>\n",
       "      <td>103.76</td>\n",
       "      <td>29.98</td>\n",
       "      <td>3</td>\n",
       "      <td>0.54</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Gasoline exports — previous 3-month average</td>\n",
       "      <td>98.01</td>\n",
       "      <td>28.32</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Inventory level — previous month</td>\n",
       "      <td>87.24</td>\n",
       "      <td>25.21</td>\n",
       "      <td>3</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Crude refinery utilization — previous month</td>\n",
       "      <td>60.80</td>\n",
       "      <td>17.57</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Gasoline product supplied — previous 3-month average</td>\n",
       "      <td>37.61</td>\n",
       "      <td>10.87</td>\n",
       "      <td>2</td>\n",
       "      <td>7.26</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gasoline imports — previous 3-month average</td>\n",
       "      <td>21.29</td>\n",
       "      <td>6.15</td>\n",
       "      <td>4</td>\n",
       "      <td>0.34</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gasoline product supplied — previous month</td>\n",
       "      <td>18.97</td>\n",
       "      <td>5.48</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Adjustments — previous 3-month average</td>\n",
       "      <td>16.13</td>\n",
       "      <td>4.66</td>\n",
       "      <td>4</td>\n",
       "      <td>0.26</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Adjustments — previous month</td>\n",
       "      <td>15.21</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4</td>\n",
       "      <td>0.96</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Gasoline imports — previous month</td>\n",
       "      <td>12.01</td>\n",
       "      <td>3.47</td>\n",
       "      <td>4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Core balance (production + imports − exports − demand) — previous month</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.31</td>\n",
       "      <td>3</td>\n",
       "      <td>0.02</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Core balance (production + imports − exports − demand) — previous 3-month average</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.19</td>\n",
       "      <td>3</td>\n",
       "      <td>0.21</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Inventory change — previous month</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.02</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Net regional receipts — previous month</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Net regional receipts — previous 3-month average</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Inventory level minus year-earlier level</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>2</td>\n",
       "      <td>0.36</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Inventory change — previous 3-month average</td>\n",
       "      <td>-2.52</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Crude inventory change — previous month</td>\n",
       "      <td>-5.61</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>2</td>\n",
       "      <td>7.60</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Crude production — previous month</td>\n",
       "      <td>-23.87</td>\n",
       "      <td>-6.90</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Biofuel production — previous 3-month average</td>\n",
       "      <td>-39.50</td>\n",
       "      <td>-11.41</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.47</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Biofuel production — previous month</td>\n",
       "      <td>-41.11</td>\n",
       "      <td>-11.88</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                              Predictor  \\\n",
       "Rank                                                                                      \n",
       "1                              Supply (production + imports) — previous 3-month average   \n",
       "2                                          Crude distillation capacity — previous month   \n",
       "3                                    Gasoline net production — previous 3-month average   \n",
       "4                                        Supply (production + imports) — previous month   \n",
       "5                                              Gasoline net production — previous month   \n",
       "6                                                Crude refinery inputs — previous month   \n",
       "7                                                     Gasoline exports — previous month   \n",
       "8                                           Gasoline exports — previous 3-month average   \n",
       "9                                                      Inventory level — previous month   \n",
       "10                                          Crude refinery utilization — previous month   \n",
       "11                                 Gasoline product supplied — previous 3-month average   \n",
       "12                                          Gasoline imports — previous 3-month average   \n",
       "13                                           Gasoline product supplied — previous month   \n",
       "14                                               Adjustments — previous 3-month average   \n",
       "15                                                         Adjustments — previous month   \n",
       "16                                                    Gasoline imports — previous month   \n",
       "17              Core balance (production + imports − exports − demand) — previous month   \n",
       "18    Core balance (production + imports − exports − demand) — previous 3-month average   \n",
       "19                                                    Inventory change — previous month   \n",
       "20                                               Net regional receipts — previous month   \n",
       "21                                     Net regional receipts — previous 3-month average   \n",
       "22                                             Inventory level minus year-earlier level   \n",
       "23                                          Inventory change — previous 3-month average   \n",
       "24                                              Crude inventory change — previous month   \n",
       "25                                                    Crude production — previous month   \n",
       "26                                        Biofuel production — previous 3-month average   \n",
       "27                                                  Biofuel production — previous month   \n",
       "\n",
       "      Error reduction vs calendar (thousand b/d)  \\\n",
       "Rank                                               \n",
       "1                                         184.63   \n",
       "2                                         178.07   \n",
       "3                                         161.67   \n",
       "4                                         160.77   \n",
       "5                                         150.09   \n",
       "6                                         136.48   \n",
       "7                                         103.76   \n",
       "8                                          98.01   \n",
       "9                                          87.24   \n",
       "10                                         60.80   \n",
       "11                                         37.61   \n",
       "12                                         21.29   \n",
       "13                                         18.97   \n",
       "14                                         16.13   \n",
       "15                                         15.21   \n",
       "16                                         12.01   \n",
       "17                                          1.08   \n",
       "18                                          0.67   \n",
       "19                                          0.07   \n",
       "20                                          0.00   \n",
       "21                                          0.00   \n",
       "22                                         -0.04   \n",
       "23                                         -2.52   \n",
       "24                                         -5.61   \n",
       "25                                        -23.87   \n",
       "26                                        -39.50   \n",
       "27                                        -41.11   \n",
       "\n",
       "      Error reduction vs calendar (%)  Periods helped (of 6)  \\\n",
       "Rank                                                           \n",
       "1                               53.35                      5   \n",
       "2                               51.46                      5   \n",
       "3                               46.71                      5   \n",
       "4                               46.46                      4   \n",
       "5                               43.37                      5   \n",
       "6                               39.44                      5   \n",
       "7                               29.98                      3   \n",
       "8                               28.32                      3   \n",
       "9                               25.21                      3   \n",
       "10                              17.57                      4   \n",
       "11                              10.87                      2   \n",
       "12                               6.15                      4   \n",
       "13                               5.48                      2   \n",
       "14                               4.66                      4   \n",
       "15                               4.40                      4   \n",
       "16                               3.47                      4   \n",
       "17                               0.31                      3   \n",
       "18                               0.19                      3   \n",
       "19                               0.02                      3   \n",
       "20                               0.00                      0   \n",
       "21                               0.00                      0   \n",
       "22                              -0.01                      2   \n",
       "23                              -0.73                      3   \n",
       "24                              -1.62                      2   \n",
       "25                              -6.90                      2   \n",
       "26                             -11.41                      2   \n",
       "27                             -11.88                      2   \n",
       "\n",
       "      Error increase when removed (thousand b/d)  \\\n",
       "Rank                                               \n",
       "1                                           0.68   \n",
       "2                                           4.47   \n",
       "3                                           0.45   \n",
       "4                                           0.44   \n",
       "5                                           0.79   \n",
       "6                                          -0.26   \n",
       "7                                           0.54   \n",
       "8                                          -0.03   \n",
       "9                                           0.20   \n",
       "10                                         -1.10   \n",
       "11                                          7.26   \n",
       "12                                          0.34   \n",
       "13                                          0.75   \n",
       "14                                          0.26   \n",
       "15                                          0.96   \n",
       "16                                          0.50   \n",
       "17                                          0.02   \n",
       "18                                          0.21   \n",
       "19                                         -0.00   \n",
       "20                                          0.00   \n",
       "21                                          0.00   \n",
       "22                                          0.36   \n",
       "23                                          0.43   \n",
       "24                                          7.60   \n",
       "25                                         -1.46   \n",
       "26                                         -2.47   \n",
       "27                                         -1.10   \n",
       "\n",
       "      Periods hurt by removal (of 6)  \n",
       "Rank                                  \n",
       "1                                  5  \n",
       "2                                  3  \n",
       "3                                  4  \n",
       "4                                  4  \n",
       "5                                  5  \n",
       "6                                  0  \n",
       "7                                  3  \n",
       "8                                  3  \n",
       "9                                  3  \n",
       "10                                 2  \n",
       "11                                 4  \n",
       "12                                 4  \n",
       "13                                 4  \n",
       "14                                 4  \n",
       "15                                 4  \n",
       "16                                 3  \n",
       "17                                 3  \n",
       "18                                 3  \n",
       "19                                 4  \n",
       "20                                 2  \n",
       "21                                 2  \n",
       "22                                 2  \n",
       "23                                 4  \n",
       "24                                 6  \n",
       "25                                 2  \n",
       "26                                 3  \n",
       "27                                 2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot and print the individual rankings for supply and demand separately.\n",
    "target_name = 'Supply: production + imports'\n",
    "table = rankings[target_name]\n",
    "# Reverse the top 12 so the highest-ranked predictor appears at the top of barh.\n",
    "top = table.head(12).iloc[::-1]\n",
    "fig, ax = plt.subplots(figsize=(13, 7))\n",
    "# Green bars improve on calendar; red bars increase average error.\n",
    "colors = ['#237a57' if value > 0 else '#b65a52' for value in top[gain_column]]\n",
    "ax.barh(top.Predictor, top[gain_column], color=colors)\n",
    "ax.axvline(0, color='black', linewidth=0.8)\n",
    "ax.set_title(FUEL.title() + ' ' + target_name.lower() + ': strongest individual predictors')\n",
    "ax.set_xlabel('Reduction in average absolute error vs calendar month (thousand barrels/day)')\n",
    "ax.set_ylabel('')\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Period-level columns are shown later; omit them here to keep this table readable.\n",
    "display(table.drop(columns=[f'Period {i} gain' for i in range(1, 7)]).round(2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gasoline-importance-8-heading-2",
   "metadata": {},
   "source": [
    "### Demand: gasoline product supplied\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "gasoline-importance-8-second",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:16:53.491698Z",
     "iopub.status.busy": "2026-09-16T21:16:53.491579Z",
     "iopub.status.idle": "2026-09-16T21:16:53.596165Z",
     "shell.execute_reply": "2026-09-16T21:16:53.595602Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "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>Predictor</th>\n",
       "      <th>Error reduction vs calendar (thousand b/d)</th>\n",
       "      <th>Error reduction vs calendar (%)</th>\n",
       "      <th>Periods helped (of 6)</th>\n",
       "      <th>Error increase when removed (thousand b/d)</th>\n",
       "      <th>Periods hurt by removal (of 6)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gasoline product supplied — previous 3-month average</td>\n",
       "      <td>93.92</td>\n",
       "      <td>46.40</td>\n",
       "      <td>6</td>\n",
       "      <td>9.18</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gasoline product supplied — previous month</td>\n",
       "      <td>69.71</td>\n",
       "      <td>34.44</td>\n",
       "      <td>5</td>\n",
       "      <td>0.80</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inventory level — previous month</td>\n",
       "      <td>36.00</td>\n",
       "      <td>17.78</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Supply (production + imports) — previous month</td>\n",
       "      <td>34.98</td>\n",
       "      <td>17.28</td>\n",
       "      <td>2</td>\n",
       "      <td>0.09</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Supply (production + imports) — previous 3-month average</td>\n",
       "      <td>32.65</td>\n",
       "      <td>16.13</td>\n",
       "      <td>2</td>\n",
       "      <td>0.79</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Crude distillation capacity — previous month</td>\n",
       "      <td>23.54</td>\n",
       "      <td>11.63</td>\n",
       "      <td>2</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gasoline net production — previous month</td>\n",
       "      <td>20.54</td>\n",
       "      <td>10.15</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Gasoline net production — previous 3-month average</td>\n",
       "      <td>19.48</td>\n",
       "      <td>9.63</td>\n",
       "      <td>2</td>\n",
       "      <td>0.77</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Crude refinery inputs — previous month</td>\n",
       "      <td>10.04</td>\n",
       "      <td>4.96</td>\n",
       "      <td>2</td>\n",
       "      <td>0.22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Inventory level minus year-earlier level</td>\n",
       "      <td>7.46</td>\n",
       "      <td>3.69</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Core balance (production + imports − exports − demand) — previous month</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1.33</td>\n",
       "      <td>5</td>\n",
       "      <td>0.06</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Adjustments — previous month</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1.14</td>\n",
       "      <td>5</td>\n",
       "      <td>1.85</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Core balance (production + imports − exports − demand) — previous 3-month average</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.03</td>\n",
       "      <td>6</td>\n",
       "      <td>0.03</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Inventory change — previous month</td>\n",
       "      <td>1.87</td>\n",
       "      <td>0.93</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Adjustments — previous 3-month average</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.75</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Inventory change — previous 3-month average</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.55</td>\n",
       "      <td>4</td>\n",
       "      <td>0.19</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Net regional receipts — previous month</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Net regional receipts — previous 3-month average</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Crude inventory change — previous month</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>2</td>\n",
       "      <td>6.02</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Gasoline imports — previous month</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>2</td>\n",
       "      <td>0.70</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Gasoline imports — previous 3-month average</td>\n",
       "      <td>-3.83</td>\n",
       "      <td>-1.89</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Crude refinery utilization — previous month</td>\n",
       "      <td>-5.86</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Gasoline exports — previous month</td>\n",
       "      <td>-13.91</td>\n",
       "      <td>-6.87</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Gasoline exports — previous 3-month average</td>\n",
       "      <td>-14.31</td>\n",
       "      <td>-7.07</td>\n",
       "      <td>2</td>\n",
       "      <td>1.43</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Biofuel production — previous month</td>\n",
       "      <td>-55.27</td>\n",
       "      <td>-27.31</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Crude production — previous month</td>\n",
       "      <td>-56.05</td>\n",
       "      <td>-27.69</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.41</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Biofuel production — previous 3-month average</td>\n",
       "      <td>-57.60</td>\n",
       "      <td>-28.46</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                              Predictor  \\\n",
       "Rank                                                                                      \n",
       "1                                  Gasoline product supplied — previous 3-month average   \n",
       "2                                            Gasoline product supplied — previous month   \n",
       "3                                                      Inventory level — previous month   \n",
       "4                                        Supply (production + imports) — previous month   \n",
       "5                              Supply (production + imports) — previous 3-month average   \n",
       "6                                          Crude distillation capacity — previous month   \n",
       "7                                              Gasoline net production — previous month   \n",
       "8                                    Gasoline net production — previous 3-month average   \n",
       "9                                                Crude refinery inputs — previous month   \n",
       "10                                             Inventory level minus year-earlier level   \n",
       "11              Core balance (production + imports − exports − demand) — previous month   \n",
       "12                                                         Adjustments — previous month   \n",
       "13    Core balance (production + imports − exports − demand) — previous 3-month average   \n",
       "14                                                    Inventory change — previous month   \n",
       "15                                               Adjustments — previous 3-month average   \n",
       "16                                          Inventory change — previous 3-month average   \n",
       "17                                               Net regional receipts — previous month   \n",
       "18                                     Net regional receipts — previous 3-month average   \n",
       "19                                              Crude inventory change — previous month   \n",
       "20                                                    Gasoline imports — previous month   \n",
       "21                                          Gasoline imports — previous 3-month average   \n",
       "22                                          Crude refinery utilization — previous month   \n",
       "23                                                    Gasoline exports — previous month   \n",
       "24                                          Gasoline exports — previous 3-month average   \n",
       "25                                                  Biofuel production — previous month   \n",
       "26                                                    Crude production — previous month   \n",
       "27                                        Biofuel production — previous 3-month average   \n",
       "\n",
       "      Error reduction vs calendar (thousand b/d)  \\\n",
       "Rank                                               \n",
       "1                                          93.92   \n",
       "2                                          69.71   \n",
       "3                                          36.00   \n",
       "4                                          34.98   \n",
       "5                                          32.65   \n",
       "6                                          23.54   \n",
       "7                                          20.54   \n",
       "8                                          19.48   \n",
       "9                                          10.04   \n",
       "10                                          7.46   \n",
       "11                                          2.69   \n",
       "12                                          2.30   \n",
       "13                                          2.08   \n",
       "14                                          1.87   \n",
       "15                                          1.51   \n",
       "16                                          1.10   \n",
       "17                                          0.00   \n",
       "18                                          0.00   \n",
       "19                                         -0.34   \n",
       "20                                         -1.03   \n",
       "21                                         -3.83   \n",
       "22                                         -5.86   \n",
       "23                                        -13.91   \n",
       "24                                        -14.31   \n",
       "25                                        -55.27   \n",
       "26                                        -56.05   \n",
       "27                                        -57.60   \n",
       "\n",
       "      Error reduction vs calendar (%)  Periods helped (of 6)  \\\n",
       "Rank                                                           \n",
       "1                               46.40                      6   \n",
       "2                               34.44                      5   \n",
       "3                               17.78                      3   \n",
       "4                               17.28                      2   \n",
       "5                               16.13                      2   \n",
       "6                               11.63                      2   \n",
       "7                               10.15                      2   \n",
       "8                                9.63                      2   \n",
       "9                                4.96                      2   \n",
       "10                               3.69                      4   \n",
       "11                               1.33                      5   \n",
       "12                               1.14                      5   \n",
       "13                               1.03                      6   \n",
       "14                               0.93                      5   \n",
       "15                               0.75                      5   \n",
       "16                               0.55                      4   \n",
       "17                               0.00                      1   \n",
       "18                               0.00                      1   \n",
       "19                              -0.17                      2   \n",
       "20                              -0.51                      2   \n",
       "21                              -1.89                      1   \n",
       "22                              -2.90                      2   \n",
       "23                              -6.87                      2   \n",
       "24                              -7.07                      2   \n",
       "25                             -27.31                      2   \n",
       "26                             -27.69                      2   \n",
       "27                             -28.46                      2   \n",
       "\n",
       "      Error increase when removed (thousand b/d)  \\\n",
       "Rank                                               \n",
       "1                                           9.18   \n",
       "2                                           0.80   \n",
       "3                                          -1.27   \n",
       "4                                           0.09   \n",
       "5                                           0.79   \n",
       "6                                           3.73   \n",
       "7                                          -0.00   \n",
       "8                                           0.77   \n",
       "9                                           0.22   \n",
       "10                                         -0.77   \n",
       "11                                          0.06   \n",
       "12                                          1.85   \n",
       "13                                          0.03   \n",
       "14                                         -0.03   \n",
       "15                                         -0.39   \n",
       "16                                          0.19   \n",
       "17                                         -0.00   \n",
       "18                                         -0.00   \n",
       "19                                          6.02   \n",
       "20                                          0.70   \n",
       "21                                         -0.32   \n",
       "22                                         -0.90   \n",
       "23                                         -0.14   \n",
       "24                                          1.43   \n",
       "25                                         -1.12   \n",
       "26                                         -2.41   \n",
       "27                                         -1.12   \n",
       "\n",
       "      Periods hurt by removal (of 6)  \n",
       "Rank                                  \n",
       "1                                  6  \n",
       "2                                  5  \n",
       "3                                  4  \n",
       "4                                  5  \n",
       "5                                  6  \n",
       "6                                  3  \n",
       "7                                  4  \n",
       "8                                  5  \n",
       "9                                  3  \n",
       "10                                 3  \n",
       "11                                 4  \n",
       "12                                 6  \n",
       "13                                 3  \n",
       "14                                 3  \n",
       "15                                 2  \n",
       "16                                 4  \n",
       "17                                 0  \n",
       "18                                 0  \n",
       "19                                 6  \n",
       "20                                 6  \n",
       "21                                 2  \n",
       "22                                 3  \n",
       "23                                 2  \n",
       "24                                 4  \n",
       "25                                 2  \n",
       "26                                 4  \n",
       "27                                 2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_name = 'Demand: gasoline product supplied'\n",
    "table = rankings[target_name]\n",
    "# Reverse the top 12 so the highest-ranked predictor appears at the top of barh.\n",
    "top = table.head(12).iloc[::-1]\n",
    "fig, ax = plt.subplots(figsize=(13, 7))\n",
    "# Green bars improve on calendar; red bars increase average error.\n",
    "colors = ['#237a57' if value > 0 else '#b65a52' for value in top[gain_column]]\n",
    "ax.barh(top.Predictor, top[gain_column], color=colors)\n",
    "ax.axvline(0, color='black', linewidth=0.8)\n",
    "ax.set_title(FUEL.title() + ' ' + target_name.lower() + ': strongest individual predictors')\n",
    "ax.set_xlabel('Reduction in average absolute error vs calendar month (thousand barrels/day)')\n",
    "ax.set_ylabel('')\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Period-level columns are shown later; omit them here to keep this table readable.\n",
    "display(table.drop(columns=[f'Period {i} gain' for i in range(1, 7)]).round(2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gasoline-discussion-1",
   "metadata": {},
   "source": [
    "The three-month average of supply reduces error by about 185 thousand barrels/day versus calendar month alone.\n",
    "\n",
    "For demand, the three-month average of product supplied reduces error by about 94 thousand barrels/day."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gasoline-importance-9",
   "metadata": {},
   "source": [
    "## Which variables matter alongside the others?\n",
    "\n",
    "These rankings remove one input at a time from a ridge model. Positive values mean that removing the input makes predictions worse. Negative values mean predictions improve without it. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "gasoline-importance-10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:16:53.597458Z",
     "iopub.status.busy": "2026-09-16T21:16:53.597319Z",
     "iopub.status.idle": "2026-09-16T21:16:53.816708Z",
     "shell.execute_reply": "2026-09-16T21:16:53.816243Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V1cXPhSluLHfu3BEACNWqVRM+fPgglsvJyRGsrKwEFxcXIScnR1z+6tUrwczMTGjSpIm4rFGjRoKVlZXw7t07cdnLly8FExMTIe8/leT72rhxo0K8+c9Hq1atBCMjI+HRo0cFHuP27dsFAEJ8fLzCugEDBgg2Njbi53379gkAhNDQUEm5rVu3CgCEH3/8UVxmY2MjaGtrS67Xu3fvBBMTE2HYsGEFxiOXnZ0tZGVliT/r168XAEiWZWVlSc5rQfK3R3k78fLykpQbN26cAEAYM2aMZHnnzp0FExMTyTIAgo6OjvDgwQNJzI6OjkL16tXFZb169RK0tLSE9PR0yfZt27YVdHV1hefPn0tiat68uUL8BV2jxMREAYCwe/fuQo9/9uzZgrq6unDkyJFCy928eVMAIGzatEkQBEE4ceKEAECYMmWKYGdnJ5bz8PCQtF35/T1y5EhJfaGhoQIAISMjQ1zm5uYmuLm5iZ83bdokABDWrl1baGwABHNzc+Hly5fisgcPHghqamrC/PnzC912z549AgDhwIED4rLs7GzByspK6Nq1a4Hbydugu7u75NlV0P2ed52y+zNvvR8+fBBq1KghjB8/XlwubwP169cXcnNzxeVpaWmCpqamMHjwYHFZQECA5LmQnp4uaGhoCKNHj5bs69WrV4KFhYXQo0cPQRA+Pt8BCEuWLCkwvuLKzs4WXr9+Lejp6QlLly4Vl/fs2bPAewOAcOfOHUEQBCElJUUAIDkHgiAIUVFRAgDJPVuQ/M+8krTFAQMGCHp6epJyJX3GqaurC6mpqZKypfF8ySsnJ0fIysoSNm3aJKirqwtPnz4V17Vv317yjM4r/7kp6bOoXbt2knLbtm0TAAinT58uMFZlCmrvyu6Vr7/+WjAzM5P8WyM7O1uoVauWULlyZfG+KO51vnr1qgBA8Pf3l5SLiYlRaGO1atUSOnfuXOix5L/vStKGhw0bJujr60u+EwVBEBYtWiQAEK5evVrovvPKzc0VsrKyhKNHjwoAhIsXLxYYoyAoPneVkR9L/nN65swZAYAwffp0cVlB7U5+TV1cXITs7Gxx+dmzZwUAQkxMjLjM0dFRqFevnpCVlSWpo0OHDoKlpaX43S6/1v379y80/vwx5G1X8nOS/74eOXKkoK2tLXneFve7Xdl5zhuv/DknCILg7Oys9Px36NBBqFu3brGO69mzZ0KtWrUECwsLISUlpVjb/NtxuDEREdEXwtTUFMePH0dCQgIWLFiATp064fr165g2bRpcXFzw5MmTT65b3mNHrkmTJrCxsRGH5coZGBigY8eOkmV9+vRBbm4ujh07VmD9LVq0QJ06dbBixQpx2erVqyGTyTB06FAAgJubG7KzszFr1qxixVySWDp27Cj563Zqairu37+Pfv36SXow6evro2vXrvjjjz/w9u1bvHnzBgkJCejSpQu0tbUl+/by8ipWnPm9ffsWR48eRY8ePVCxYsVPqiM/eW+F/H/h7969O/T09BAXFydZXrduXVSpUkX8rK2tDXt7e6XDE/OrVq0aNDU1xR9fX18AkCzT1NREUFDQJx9Phw4dJJ+dnJwAAO3bt1dY/vTpU4Uhge7u7pKhQOrq6ujZsydu3rwpDs86fPgw3N3dYW1tLdnWx8cHb9++VRh23rVr12LHX716dRgbG8Pf3x+rV69GcnKy0nKzZs1CdnY23NzcCq2vWrVqsLW1xaFDhwAABw8ehIuLC/r27Ys7d+7g1q1byMzMxIkTJ9C6dWuF7fPfJ7Vr1waAQq/377//Dm1tbQwaNKjQ2ICPw60NDAzEz+bm5jAzMyuyPbVt2xYWFhaSnjr79+/H/fv3Ffa7evVq1K9fH9ra2tDQ0ICmpibi4uKQkpKiUG/++70g2dnZmDdvHmrWrIly5cpBQ0MD5cqVw40bN5TW26dPH8kwNhsbGzRp0kThOZnX/v37kZ2djf79+yM7O1v80dbWhpubG44cOQIAMDExQbVq1bBw4UIsXrwYFy5cQG5ubpHHAACvX7+Gv78/qlevDg0NDWhoaEBfXx9v3ryRHEd8fHyB90Ze8uPJ/73Qo0cPpa+7KIlPaYtAyZ9xtWvXhr29vdK6Puf5cuHCBXTs2BGmpqZQV1eHpqYm+vfvj5ycHFy/fr3QYyjs2EryLPrUc1jS9i735s0bnDlzBt26dZNMIqOuro5+/frhzz//VBjuXFSMR48eBfCxTeXVrVs3hTbWsGFD/P7775g6dSqOHDmCd+/eFXqcQMna8G+//YaWLVvCyspKco+2bdtWEmtBbt++jT59+sDCwkJsE/JnemHntbjkx5K/7Tds2BBOTk4Kbb8w7du3l/Q2zX9dbt68iWvXronnLe/5aNeuHTIyMhSudUm+GwuirL28f/8ejx49kiwvznd7aWjYsCEuXryIkSNHYv/+/Qq9W/MKDQ1FcnIy9u7dC0dHx1KL4UvGJCEREdEXxtXVFf7+/ti+fTvu37+P8ePHIy0t7bMmL7GwsFC6LP9wnvzvYMm7bVFDf8aMGYO4uDikpqYiKysLa9euRbdu3ZTuuzhKEoulpaXks3x9/uUAYGVlhdzcXDx79gzPnj1Dbm5ugefnUzx79gw5OTml+pL1v//+GxoaGgpJR5lMpvQ6mpqaKtShpaVVrF++fv31VyQkJIg/AQEBACBZlpCQICZ/P4WJiYnkc7ly5Qpd/v79e8nywq6X/Fz8/fffBV7/vOXklJUtSPny5XH06FHUrVsX06dPh7OzM6ysrBAQEKD0/W/F4e7uLv4yeOjQIXh4eMDFxQXm5uY4dOgQTp48iXfv3ilNEua/3vIhVYVd78ePH8PKykrhfafKfGp70tDQQL9+/bBr1y5xGFdERAQsLS3h6ekpllu8eDFGjBiBRo0aYceOHfjjjz+QkJCANm3aKN1Hca/VhAkTMHPmTHTu3Bm//vorzpw5g4SEBNSpU0dpvcV9TuYlfy1BgwYNFBLpW7duFf+4I5PJEBcXB09PT4SGhqJ+/fqoWLEixowZU+QQ3z59+iA8PByDBw/G/v37cfbsWSQkJKBixYqS4/j777+L9SyTH0/+5RoaGkqvdUl8SluUx1SSZ1xhbeBTny/p6en45ptv8Ndff2Hp0qXiH+3kf/wqzvNTmZI+iz71HJa0vcs9e/YMgiCUaozy8vm/x5W1sWXLlsHf3x+7d+9Gy5YtYWJigs6dO+PGjRsFxlySNvzw4UP8+uuvCven/D2whf0B9vXr1/jmm29w5swZBAcH48iRI0hISMDOnTslx/s5ivr3SlH/9sqrqOsif15NmjRJ4XyMHDkSgOL5KMl346fGJVec7/bSMG3aNCxatAh//PEH2rZtC1NTU7i7uyMxMVGhbHJyMqysrP61E7F9Cr6TkIiI6AumqamJgIAA/PDDD7hy5Yq4XEtLS+nLowv6R9SDBw+ULqtevbpkmbL38Mm3LeqXxz59+sDf3x8rVqzA119/jQcPHsDPz6/QbQpTkljyv8Ravj4jI0Ohjvv370NNTQ3GxsYQBAEymazA85OXvKdh/vOe/5ybmJhAXV29VP/qbWpqiuzsbDx+/FjyS7QgCHjw4AEaNGhQavtycXGRfJa3O1dX11Lbx+cq7HrJr72pqWmB1x+AwsvHSzqbsYuLC7Zs2QJBEHDp0iVEREQgKCgIOjo6mDp1aonqAj4mCdevX4+zZ8/izJkzmDFjBgCgVatWOHjwIO7evQt9ff1Smz2yYsWKOHHiBHJzc4uVKPxUAwcOxMKFC8V3Xu3Zswfjxo2T9Hb56aef0KJFC6xatUqybUHJs+Jeq59++gn9+/fHvHnzJMufPHkieRerXEHtqrBnn7wd/fzzz7CxsSk0HhsbG6xfvx4AcP36dWzbtg2BgYH48OEDVq9erXSbFy9e4LfffkNAQICkXWVmZorvcpQzNTUt1rNMfjwPHjxApUqVxOXZ2dml+ot4SZT0GfdPzD6+e/duvHnzBjt37pRcy6ImlilKSZ9Fn6qk7V3O2NgYampqpRqjvI09fPiwyDamp6eH2bNnY/bs2Xj48KHYq9DLywvXrl0rtP7itOEKFSqgdu3amDt3rtK65IlQZQ4fPoz79+/jyJEjkh7hRb1HtCTy/nsl/x8X79+/X6oTZcjrmjZtGrp06aK0TP53V/4T91pBivPdnvffYnnfU1qS0TYaGhqYMGECJkyYgOfPn+PQoUOYPn06PD09ce/ePejq6oplLS0tC+y1/F/FnoRERERfCGX/QAf+bzhL3n/I2tra4tKlS5Jyhw8fVhiWKRcVFSX5fOrUKdy9e1dhQoBXr15hz549kmXR0dFQU1MrcsILbW1tDB06FJGRkVi8eDHq1q2Lpk2bFrpNYT4nFgcHB1SqVAnR0dEQBEFc/ubNG+zYsUOc8VhPTw8NGzbEzp07Jb3VXr16pTCDrLm5ObS1tRXO+y+//CL5LJ8Vdfv27YX+o7W4vUOAjwkk4OMvgXnt2LEDb968Ederiri4OEkSOScnB1u3bkW1atXEX7Lc3d3FX/Dy2rRpE3R1dYuVbCvONZLJZKhTpw5++OEHGBkZ4fz5859ySHB3d4dMJsPMmTMlbbx169aIj4/HwYMH0bx581KbzKht27Z4//690tm6S5OTkxMaNWqEjRs3Ijo6GpmZmRg4cKCkjEwmk/yyBwCXLl1SOhN5SSirNzY2Fn/99ZfS8jExMZLnxd27d3Hq1KlCJ53x9PSEhoYGbt26BVdXV6U/ytjb22PGjBlwcXEptM3IZDIIgqBwHOvWrVOYdb5ly5YF3ht5yY8n//fCtm3bijXb6z/hS3jGyZMhec+1IAhYu3atQtni9swGSudZVBwlbe9yenp6aNSoEXbu3Ck5ptzcXPz000+oXLlyiZMk8udX/rb3888/F9rGzM3N4ePjg969eyM1NRVv375VWq4kbbhDhw64cuUKqlWrpvT+LCxJqKxNAMCaNWsK3KakWrVqBUCx7SckJCAlJUXS9kvS7pRxcHBAjRo1cPHixQKfV3lfLfG/VpzvdvkMxfn/LZb/32xA8c6XkZERunXrBj8/Pzx9+lScHVlu1apVJRry/V/AnoRERERfCE9PT1SuXBleXl5wdHREbm4ukpKSEBYWBn19fYwdO1Ys269fP8ycOROzZs2Cm5sbkpOTER4eLs5el19iYiIGDx6M7t274969e/j+++9RqVIlcXiJnKmpKUaMGIH09HTY29tj7969WLt2LUaMGCF5x11BRo4cidDQUJw7dw7r1q2TrDt69Cjc3d0xa9asYr2X8HNiUVNTQ2hoKLy9vdGhQwcMGzYMmZmZWLhwIZ4/f44FCxaIZefMmYM2bdrAw8MDEydORE5ODkJCQqCnpyfpqSOTydC3b19s2LAB1apVQ506dXD27FmlM4cuXrwYzZo1Q6NGjTB16lRUr14dDx8+xJ49e7BmzRoYGBigVq1aAIAff/wRBgYG0NbWhp2dndJeSx4eHvD09IS/vz9evnyJpk2bijN/1qtXD/369SvyfP6XVKhQAa1atcLMmTPFGRCvXbuGLVu2iGUCAgLEd1HNmjULJiYmiIqKQmxsLEJDQwu8V/Iq6BqdPn0aK1euROfOnVG1alUIgoCdO3fi+fPn8PDwELcPCgpCUFAQ4uLiinwvoZmZGWrVqoUDBw6gZcuWYk+G1q1b4+nTp3j69CkWL178KadLqd69e2Pjxo0YPnw4UlNT0bJlS+Tm5uLMmTNwcnJCr169Sm1fgwYNwrBhw3D//n00adJEoadKhw4dMGfOHAQEBMDNzQ2pqakICgqCnZ3dZyWtOnTogIiICDg6OqJ27do4d+4cFi5cWOCrAB49eoTvvvsOQ4YMwYsXLxAQEABtbW1xJk5lbG1tERQUhO+//x63b99GmzZtYGxsjIcPH+Ls2bNiL6lLly5h1KhR6N69O2rUqIFy5crh8OHDuHTpUqE9Tw0NDdG8eXMsXLgQFSpUgK2tLY4ePYr169cr9A6bMWMG9uzZg1atWmHWrFnQ1dXFihUrxFl/5ZycnNC3b18sWbIEmpqaaN26Na5cuSLOFl0WvoRnnIeHB8qVK4fevXtjypQpeP/+PVatWoVnz54plHVxccHOnTuxatUqfPXVV+KM6MqUxrOoOEra3vOaP38+PDw80LJlS0yaNAnlypXDypUrceXKFcTExJS4N5mzszN69+6NsLAwqKuro1WrVrh69SrCwsJQvnx5Se/lRo0aoUOHDqhduzaMjY2RkpKCzZs3i3/MU6YkbTgoKAgHDx5EkyZNMGbMGDg4OOD9+/dIS0vD3r17sXr16gLPUZMmTWBsbIzhw4cjICAAmpqaiIqKwsWLF0t0Pgrj4OCAoUOHYvny5VBTU0Pbtm3F2Y2tra0xfvx4sWxJ2l1B1qxZg7Zt28LT0xM+Pj6oVKkSnj59ipSUFJw/fx7bt28vtWMrqeJ8t7dr1w4mJibw9fVFUFAQNDQ0EBERgXv37inUJ+/xv3XrVlStWhXa2tpwcXGBl5cXatWqBVdXV1SsWBF3797FkiVLYGNjgxo1akjqcHd3x927d3Hz5s1//Pi/GGU1YwoRERFJbd26VejTp49Qo0YNQV9fX9DU1BSqVKki9OvXT0hOTpaUzczMFKZMmSJYW1sLOjo6gpubm5CUlFTg7MYHDhwQ+vXrJxgZGQk6OjpCu3bthBs3bkjqdHNzE5ydnYUjR44Irq6ugpaWlmBpaSlMnz5dYRY8FDC7siAIQosWLQQTExPh7du3kuXymRsL2u5TYpHPprdw4UKl9ezevVto1KiRoK2tLejp6Qnu7u7CyZMnFcrt2bNHqF27tlCuXDmhSpUqwoIFC5TOoPfixQth8ODBgrm5uaCnpyd4eXkJaWlpSo8rOTlZ6N69u2BqairW6+PjI7x//14ss2TJEsHOzk5QV1eXzAqYf3ZjQfg4Q7G/v79gY2MjaGpqCpaWlsKIESOEZ8+eScrZ2NgI7du3V3pOi5plURl5G/oUBc1uvH37dqX7SEhIkCyXX4PHjx+LywAIfn5+wsqVK4Vq1aoJmpqagqOjoxAVFaWw/8uXLwteXl5C+fLlhXLlygl16tRRmP22oJjklF2ja9euCb179xaqVasm6OjoCOXLlxcaNmwoREREKI1f2QzWyowfP14AIMydO1eyvEaNGgIA4dKlS5LlBZ03+THl3a+y6//u3Tth1qxZQo0aNYRy5coJpqamQqtWrYRTp06JZeTnO7/817YwL168EHR0dAqcTTkzM1OYNGmSUKlSJUFbW1uoX7++sHv3boX7oLD7XdnMms+ePRN8fX0FMzMzQVdXV2jWrJlw/PhxhXMhP1+bN28WxowZI1SsWFHQ0tISvvnmGyExMVGyn4Jm1ty9e7fQsmVLwdDQUNDS0hJsbGyEbt26CYcOHRIEQRAePnwo+Pj4CI6OjoKenp6gr68v1K5dW/jhhx8ks5Eq8+effwpdu3YVjI2NBQMDA6FNmzbClStXlF6DkydPCl9//bWgpaUlWFhYCJMnTxZ+/PFHhVk/MzMzhYkTJwpmZmaCtra28PXXXwunT58u9nXN/8wrSVtUNruxIHz+M640ni+//vqrUKdOHUFbW1uoVKmSMHnyZOH3339XOIanT58K3bp1E4yMjASZTCZpE8q+Dz7nWVScmbsFofjtvaD6jh8/LrRq1UrQ09MTdHR0hK+//lr49ddfi3UulV3n9+/fCxMmTFBoY+XLl5fMSjx16lTB1dVVMDY2FrS0tISqVasK48ePF548eSKWUXbflaQNP378WBgzZoxgZ2cnaGpqCiYmJsJXX30lfP/998Lr168LPa+nTp0SGjduLOjq6goVK1YUBg8eLJw/f77AmXzzKu73bk5OjhASEiLY29sLmpqaQoUKFYS+ffsK9+7dk5QrqN0V9mxU1h4vXrwo9OjRQzAzMxM0NTUFCwsLoVWrVsLq1avFMgVd64IUNrtx3nssb915n0kl+W4/e/as0KRJE0FPT0+oVKmSEBAQIKxbt06hzrS0NOHbb78VDAwMBADi90lYWJjQpEkToUKFCuK/z3x9fYW0tDSFfbm5uRU4k/l/lUwQ8vSpJyIiov+UiIgIDBw4EAkJCUX+tblFixZ48uSJ5N2HJfXo0SPY2Nhg9OjRnzXRSmnEQv9NMpkMfn5+CA8PL+tQiIioBE6dOoWmTZsiKioKffr0Ketw6AvC7/YvB4cbExER0Wf7888/cfv2bSxcuBBqamqSodFERESkWg4ePIjTp0/jq6++go6ODi5evIgFCxagRo0aBU6aQURlj0lCIiIi+mzr1q1DUFAQbG1tERUVJZltkIiIiFSLoaEhDhw4gCVLluDVq1eoUKEC2rZti/nz54sz1BLRl4fDjYmIiIiIiIiIiFScWtFFiIiIiIiIiIiI6L+MSUIiIiIiIiIiIiIVxyQhERERERERERGRiuPEJURE9J+Qm5uL+/fvw8DAADKZrKzDISIiIiIi+iIIgoBXr17BysoKamoF9xdkkpCIiP4T7t+/D2tr67IOg4iIiIiI6It07949VK5cucD1TBISEdF/goGBAQAgOTkZlSpVKuNoiIiIiIiIvgwvX76EtbW1+DtTQZgkJCKi/wT5EGMDAwMYGhqWcTRERERERERflqJey8SJS4iIiIiIiIiIiFQck4REREREREREREQqjklCIiIiIiIiIiIiFcckIRERERERERERkYpjkpCIiIiIiIiIiEjFMUlIRERERERERESk4pgkJCIiIiIiIiIiUnFMEhIREREREREREak4JgmJiIiIiIiIiIhUHJOEREREREREREREKo5JQiIiIiIiIiIiIhXHJCEREREREREREZGKY5KQiIiIiIiIiIhIxTFJSEREREREREREpOKYJCQiIiIiIiIiIlJxTBISERERERERERGpOCYJiYiIiIiIiIiIVByThERERERERERERCqOSUIiIiIiIiIiIiIVxyQhERERERERERGRimOSkIiIiIiIiIiISMUxSUhERERERERERKTimCQkIiIiIiIiIiJScUwSEhERERERERERqTgmCYmIiIiIiIiIiFScRlkHQEREVJqahYyEhqFuWYdBREREREQq5ObcLWUdwmdjT0IiIiIiIiIiIiIVxyQhERERERERERGRimOSkIiIiIiIiIiISMUxSUhERERERERERKTimCQkIiIiIiIiIiJScUwSEhERERERERERqTgmCYmIiIiIiIiIiFQck4REREREREREREQqjklCIiIiIiIiIiIiFcckIRERERERERERkYpjkpCIiIiIiIiIiEjFMUlIRERERERERESk4pgkpDITGBiIunXrflYdLVq0wLhx40olHlXi4+ODzp07l3UYKq002v9/DdslERERERFR2WGSkEQPHjzA6NGjUbVqVWhpacHa2hpeXl6Ii4sr69AKtHPnTsyZM6esw1DAZAcVZdKkSV/0vfVPSktLg0wmQ1JSUlmHQkRERERERP+fRlkHQF+GtLQ0NG3aFEZGRggNDUXt2rWRlZWF/fv3w8/PD9euXVO6XVZWFjQ1Nf/H0f4fExOTMtv3/8KHDx9Qrly5sg6D8iita6Kvrw99ff1SiIiIiIiIiIjo87EnIQEARo4cCZlMhrNnz6Jbt26wt7eHs7MzJkyYgD/++EMsJ5PJsHr1anTq1Al6enoIDg5GREQEjIyMJPXt3r0bMplMsmzBggUwNzeHgYEBfH198f79e4U4Nm7cCCcnJ2hra8PR0RErV64sNO78w41tbW0xb948DBo0CAYGBqhSpQp+/PFHcX3jxo0xdepUSR2PHz+GpqYm4uPjAXxMAk2ZMgWVKlWCnp4eGjVqhCNHjojl5ce7f/9+ODk5QV9fH23atEFGRgaAj8NIIyMj8csvv0Amk0Emk4nbX758Ga1atYKOjg5MTU0xdOhQvH79Wqxb3gNx/vz5sLKygr29PYKCguDi4qJw7F999RVmzZpV4Lm5evUq2rdvD0NDQxgYGOCbb77BrVu3JGUWLVoES0tLmJqaws/PD1lZWeK6n376Ca6urjAwMICFhQX69OmDR48eieuPHDkCmUyGuLg4uLq6QldXF02aNEFqaqpkH8HBwTAzM4OBgQEGDx6MqVOnKgyzLel1Ly3y8z179myYmZnB0NAQw4YNw4cPH8QyLVq0wKhRozBhwgRUqFABHh4eAIDk5GS0a9cO+vr6MDc3R79+/fDkyRMAwJo1a1CpUiXk5uZK9texY0cMGDAAgOJw49zcXAQFBaFy5crQ0tJC3bp1sW/fPnG9/Hw/f/5cXJaUlASZTIa0tDQAwN27d+Hl5QVjY2Po6enB2dkZe/fuLfD4bW1tERwcjP79+0NfXx82Njb45Zdf8PjxY3Tq1An6+vpwcXFBYmKiZLsdO3bA2dkZWlpasLW1RVhYmEK9hd2HdnZ2AIB69epBJpOhRYsWku0La5dERERERET0z2CSkPD06VPs27cPfn5+0NPTU1ifPwEYEBCATp064fLlyxg0aFCx9rFt2zYEBARg7ty5SExMhKWlpUIiaO3atfj+++8xd+5cpKSkYN68eZg5cyYiIyNLdDxhYWFwdXXFhQsXMHLkSIwYMULsCent7Y2YmBgIgiCW37p1K8zNzeHm5gYAGDhwIE6ePIktW7bg0qVL6N69O9q0aYMbN26I27x9+xaLFi3C5s2bcezYMaSnp2PSpEkAPg4j7dGjh5g4zMjIQJMmTfD27Vu0adMGxsbGSEhIwPbt23Ho0CGMGjVKEn9cXBxSUlJw8OBB/Pbbbxg0aBCSk5ORkJAglrl06RIuXLgAHx8fpefgr7/+QvPmzaGtrY3Dhw/j3LlzGDRoELKzs8Uy8fHxuHXrFuLj4xEZGYmIiAhERESI6z98+IA5c+bg4sWL2L17N+7cuaN0f99//z3CwsKQmJgIDQ0NSZuIiorC3LlzERISgnPnzqFKlSpYtWqVZPvSuu6fSn6+4+PjERMTg127dmH27NmSMpGRkdDQ0MDJkyexZs0aZGRkwM3NDXXr1kViYiL27duHhw8fokePHgCA7t2748mTJ2LiGQCePXuG/fv3w9vbW2kcS5cuRVhYGBYtWoRLly7B09MTHTt2lLS7ovj5+SEzMxPHjh3D5cuXERISUmRvxR9++AFNmzbFhQsX0L59e/Tr1w/9+/dH3759cf78eVSvXh39+/cX75lz586hR48e6NWrFy5fvozAwEDMnDlT0naAwu/Ds2fPAgAOHTqEjIwM7Ny5U9yuqHZJRERERERE/wwONybcvHkTgiDA0dGxWOX79OlT7OSg3JIlSzBo0CAMHjwYwMfeZYcOHZL0JpwzZw7CwsLQpUsXAB97GyUnJ2PNmjVi76viaNeuHUaOHAkA8Pf3xw8//IAjR47A0dERPXv2xPjx43HixAl88803AIDo6Gj06dMHampquHXrFmJiYvDnn3/CysoKwMek3759+7Bx40bMmzcPwMdh1qtXr0a1atUAAKNGjUJQUBCAj8NIdXR0kJmZCQsLCzGuyMhIvHv3Dps2bRKTseHh4fDy8kJISAjMzc0BAHp6eli3bp1kSKunpyc2btyIBg0aAPjY887NzQ1Vq1ZVeg5WrFiB8uXLY8uWLeJwcHt7e0kZY2NjhIeHQ11dHY6Ojmjfvj3i4uIwZMgQAJBc46pVq2LZsmVo2LAhXr9+LUk8zZ07V0ywTp06Fe3bt8f79++hra2N5cuXw9fXFwMHDgQAzJo1CwcOHJD0niyt6/6pypUrhw0bNkBXVxfOzs4ICgrC5MmTMWfOHKipffw7SvXq1REaGipuM2vWLNSvX19sDwCwYcMGWFtb4/r167C3t0ebNm0QHR0Nd3d3AMD27dthYmIifs5v0aJF8Pf3R69evQAAISEhiI+Px5IlS7BixYpiHUt6ejq6du0q9jwtqH3k1a5dOwwbNkw8rlWrVqFBgwbo3r07gI/3UOPGjfHw4UNYWFhg8eLFcHd3x8yZMwF8bFfJyclYuHChJIlc2H1YsWJFAICpqankHgGKbpd5ZWZmIjMzU/z88uXLYp0nIiIiIiIiUsSehCT2EMo/PLggrq6uJd5HSkoKGjduLFmW9/Pjx49x7949+Pr6iu9q09fXR3BwsMIQ2aLUrl1b/H+ZTAYLCwtxmGzFihXh4eGBqKgoAMCdO3dw+vRpsXfX+fPnIQgC7O3tJXEcPXpUEoeurq6YIAQAS0tLyVDcgs5BnTp1JL01mzZtitzcXMkQXRcXF4V33g0ZMgQxMTF4//49srKyEBUVVWiiNikpCd98802h74t0dnaGurp6gcdw4cIFdOrUCTY2NjAwMBCHhKanp0vqyXu+LS0tAUCsJzU1FQ0bNpSUz/v5U657VFSUpGxxfvLHnFedOnWgq6srfm7cuDFev36Ne/fuicvyt/lz584hPj5esg95kl0et7e3N3bs2CEmsaKiotCrVy/JOZd7+fIl7t+/j6ZNm0qWN23aFCkpKQXGnt+YMWMQHByMpk2bIiAgAJcuXSpym7zXT56ozju8Xb5Mfk1TUlKUxnnjxg3k5OQorTf/fViYotplXvPnz0f58uXFH2tr6yLrJyIiIiIiIuXYk5BQo0YNyGQypKSkFGtG3vxDktXU1CTDdwGU+B1i8ne3rV27Fo0aNZKsU5ZUKUz+xJhMJpO8G87b2xtjx47F8uXLER0dDWdnZ9SpU0eMQ11dHefOnVPYb97ec8r2kf8c5CcIQoGJ2LzLlQ359vLygpaWFnbt2gUtLS1kZmaia9euBe5LR0en0FiAws/Tmzdv8O233+Lbb7/FTz/9hIoVKyI9PR2enp6S9/Xlr0d+HHnPd/5jznuePuW6d+zYUaFsUeS9QkuisGuSm5sr9gDNT54o9fLyQm5uLmJjY9GgQQMcP34cixcvLvY+AWmbkfdqzHv+8t9ngwcPhqenJ2JjY3HgwAHMnz8fYWFhGD16dIH7VHb9CrumytqxsrZf1H1YnHiK2m7atGmYMGGC+Pnly5dMFBIREREREX0iJgkJJiYm8PT0xIoVKzBmzBiFhMjz588V3kuYV8WKFfHq1Su8efNG3DYpKUlSxsnJCX/88Qf69+8vLss7IYq5uTkqVaqE27dvF/jOttLSuXNnDBs2DPv27UN0dDT69esnrqtXrx5ycnLw6NEjcTjypyhXrpykVxUA1KxZE5GRkZLzdPLkSaipqSkMBc5PQ0MDAwYMwMaNG6GlpYVevXpJer/lV7t2bURGRn7y7NPXrl3DkydPsGDBAjHpkn/yiuJwcHDA2bNnJec4bz2fct0NDAxgYGBQ4lgKcvHiRbx7905MrP7xxx/Q19dH5cqVC9ymfv362LFjB2xtbaGhofwxqqOjgy5duiAqKgo3b96Evb09vvrqK6VlDQ0NYWVlhRMnTqB58+bi8lOnTok9L+VDdDMyMmBsbAxA8T4DAGtrawwfPhzDhw/HtGnTsHbt2kKThCVVs2ZNnDhxQrLs1KlTsLe3L3ZCX95TNv89UlJaWlrQ0tL6rDqIiIiIiIjoIw43JgDAypUrkZOTg4YNG2LHjh24ceMGUlJSsGzZMoVhwvk1atQIurq6mD59Om7evIno6GiFiQbGjh2LDRs2YMOGDbh+/ToCAgJw9epVSZnAwEDMnz8fS5cuxfXr13H58mVs3LixyN5XJaWnp4dOnTph5syZSElJQZ8+fcR19vb28Pb2Rv/+/bFz507cuXMHCQkJCAkJKXSW2PxsbW1x6dIlpKam4smTJ8jKyoK3tze0tbUxYMAAXLlyBfHx8Rg9ejT69esnDukszODBg3H48GH8/vvvRb4TctSoUXj58iV69eqFxMRE3LhxA5s3b1aYebggVapUQbly5bB8+XLcvn0be/bswZw5c4q1bV6jR4/G+vXrERkZiRs3biA4OBiXLl2S9ET7X133gnz48AG+vr5ITk7G77//joCAAIwaNUrsuaeMn58fnj59it69e+Ps2bO4ffs2Dhw4gEGDBkkSX97e3oiNjcWGDRvQt2/fQuOYPHkyQkJCsHXrVqSmpmLq1KlISkrC2LFjAXx8L6K1tTUCAwNx/fp1xMbGKswqPG7cOOzfvx937tzB+fPncfjwYTg5OX3G2VE0ceJExMXFYc6cObh+/ToiIyMRHh4uTtxTHGZmZtDR0REnfHnx4kWpxkhEREREREQlxyQhAfg4WcT58+fRsmVLTJw4EbVq1YKHhwfi4uIUZqPNz8TEBD/99BP27t0LFxcXxMTEIDAwUFKmZ8+emDVrFvz9/fHVV1/h7t27GDFihKTM4MGDsW7dOkRERMDFxQVubm6IiIiAnZ1daR8uvL29cfHiRXzzzTeoUqWKZN3GjRvRv39/TJw4EQ4ODujYsSPOnDlTomGMQ4YMgYODA1xdXVGxYkWcPHkSurq62L9/P54+fYoGDRqgW7ducHd3R3h4eLHqrFGjBpo0aQIHB4cih9uampri8OHDeP36Ndzc3PDVV19h7dq1xe5VWLFiRURERGD79u2oWbMmFixYgEWLFhVr27y8vb0xbdo0TJo0CfXr1xdnSNbW1hbL/C+vuzLu7u6oUaMGmjdvjh49esDLy0uh/eZnZWWFkydPIicnB56enqhVqxbGjh2L8uXLS5KLrVq1gomJCVJTUyXJaGXGjBmDiRMnYuLEiXBxccG+ffuwZ88e1KhRA8DHYbgxMTG4du0a6tSpg5CQEAQHB0vqyMnJgZ+fH5ycnNCmTRs4ODgozCL+uerXr49t27Zhy5YtqFWrFmbNmoWgoKACZ9pWRkNDA8uWLcOaNWtgZWWFTp06lWqMREREREREVHIyoagXqRHRF0E+A/WwYcMk72H7t/Hw8ICFhQU2b95c1qHAx8cHz58/x+7du8s6FCoFL1++RPny5WEzygsahgUPxyciIiIiIiptN+duKesQCiT/XenFixcwNDQssBzfSUj0L/Do0SNs3rwZf/31FwYOHFjW4RTb27dvsXr1anh6ekJdXR0xMTE4dOgQDh48WNahEREREREREVEeTBIS/QuYm5ujQoUK+PHHH8VJK/4NZDIZ9u7di+DgYGRmZsLBwQE7duxA69atyzo0IiIiIiIiIsqDSUKif4F/61sBdHR0cOjQobIOo0D5J9ghIiIiIiIiUlWcuISIiIiIiIiIiEjFMUlIRERERERERESk4pgkJCIiIiIiIiIiUnFMEhIREREREREREak4JgmJiIiIiIiIiIhUHJOEREREREREREREKo5JQiIiIiIiIiIiIhXHJCEREREREREREZGKY5KQiIiIiIiIiIhIxWmUdQBERESl6YT/SlSuXLmswyAiIiIiIvpXYU9CIiIiIiIiIiIiFcckIRERERERERERkYpjkpCIiIiIiIiIiEjFMUlIRERERERERESk4pgkJCIiIiIiIiIiUnFMEhIREREREREREak4JgmJiIiIiIiIiIhUHJOEREREREREREREKo5JQiIiIiIiIiIiIhWnUdYBEBERlaZmISOhYahb1mEQEREREdE/6ObcLWUdwn8OexISERERERERERGpOCYJiYiIiIiIiIiIVByThERERERERERERCqOSUIiIiIiIiIiIiIVxyQhERERERERERGRimOSkIiIiIiIiIiISMUxSUhERERERERERKTimCQkIiIiIiIiIiJScUwSEhERERERERERqTgmCYmIiIiIiIiIiFQck4REREREREREREQqjklCIiIiIiIiIiIiFcckYQm1aNEC48aNEz/b2tpiyZIlZRbPPyEwMBB169Yt6zDKzD9xjVXlnMpkMuzevbusw/jXi4iIgJGRUVmHQURERERERCrkX5MkfPDgAcaOHYvq1atDW1sb5ubmaNasGVavXo23b9+WWVwJCQkYOnRome3/3+DIkSOQyWR4/vx5WYfySXiNiy8jIwNt27Yts/2npqaiZcuWMDc3h7a2NqpWrYoZM2YgKyurzGIqyn/xDw1ERERERET076NR1gEUx+3bt9G0aVMYGRlh3rx5cHFxQXZ2Nq5fv44NGzbAysoKHTt2LJPYKlasWCb7LUpWVhY0NTXLOoz/hC/1GpcWQRCQk5MDDY3PfxxYWFiUQkSfTlNTE/3790f9+vVhZGSEixcvYsiQIcjNzcW8efPKNLb/qg8fPqBcuXJlHQYRERERERF9pn9FT8KRI0dCQ0MDiYmJ6NGjB5ycnODi4oKuXbsiNjYWXl5eYtnFixfDxcUFenp6sLa2xsiRI/H69Wtx/d27d+Hl5QVjY2Po6enB2dkZe/fuFdcfPXoUDRs2hJaWFiwtLTF16lRkZ2cXGFv+XkAymQzr1q3Dd999B11dXdSoUQN79uyRbJOcnIx27dpBX18f5ubm6NevH548eVLgPuRDD3fv3g17e3toa2vDw8MD9+7dE8vIh7Nu2LABVatWhZaWFgRBQHp6Ojp16gR9fX0YGhqiR48eePjwoaT+BQsWwNzcHAYGBvD19cX79+8l6/MPvwWAzp07w8fHR/ycmZmJKVOmwNraGlpaWqhRowbWr1+PtLQ0tGzZEgBgbGwMmUwm2S6vwq6NsuGXu3fvhkwmUzgHa9asgbW1NXR1ddG9e3dJD0YfHx907twZs2fPhpmZGQwNDTFs2DB8+PChwPOf/xq/ePECQ4cOFbdv1aoVLl68WKJz+k9JS0uDTCbDli1b0KRJE2hra8PZ2RlHjhwRy8h7du7fvx+urq7Q0tLC8ePHIQgCQkNDUbVqVejo6KBOnTr4+eefAQC5ubmoXLkyVq9eLdnf+fPnIZPJcPv2bQCKw40vX76MVq1aQUdHB6amphg6dKjkfixO21q5ciVq1Kgh9iDu1q1bgcdftWpVDBw4EHXq1IGNjQ06duwIb29vHD9+vNDzJm8X8+bNg7m5OYyMjDB79mxkZ2dj8uTJMDExQeXKlbFhwwbJdkUdn7zeRYsWwdLSEqampvDz8xN7NrZo0QJ3797F+PHjIZPJJO0ZAPbv3w8nJyfo6+ujTZs2yMjIKPAYcnJy4OvrCzs7O+jo6MDBwQFLly6V1KWtra3Qo3fMmDFwc3MTP586dQrNmzeHjo4OrK2tMWbMGLx580Zcb2tri+DgYPj4+KB8+fIYMmQIAMDf3x/29vbQ1dVF1apVMXPmTIUenMHBwTAzM4OBgQEGDx6MqVOnKgzD37hxI5ycnKCtrQ1HR0esXLmywGMmIiIiIiKi0vPFJwn//vtvHDhwAH5+ftDT01NaJu8v1mpqali2bBmuXLmCyMhIHD58GFOmTBHX+/n5ITMzE8eOHcPly5cREhICfX19AMBff/2Fdu3aoUGDBrh48SJWrVqF9evXIzg4uEQxz549Gz169MClS5fQrl07eHt74+nTpwA+Dsd0c3ND3bp1kZiYiH379uHhw4fo0aNHoXW+ffsWc+fORWRkJE6ePImXL1+iV69ekjI3b97Etm3bsGPHDiQlJQH4mHB5+vQpjh49ioMHD+LWrVvo2bOnuM22bdsQEBCAuXPnIjExEZaWlp/0S3n//v2xZcsWLFu2DCkpKVi9ejX09fVhbW2NHTt2APg4FDQjI0OSuMirsGtTXPJz8Ouvv2Lfvn1ISkqCn5+fpExcXBxSUlIQHx+PmJgY7Nq1C7Nnzy5W/YIgoH379njw4AH27t2Lc+fOoX79+nB3dxevcWmd088xefJkTJw4ERcuXECTJk3QsWNH/P3335IyU6ZMwfz585GSkoLatWtjxowZ2LhxI1atWoWrV69i/Pjx6Nu3L44ePQo1NTX06tULUVFRkjqio6PRuHFjVK1aVSGGt2/fok2bNjA2NkZCQgK2b9+OQ4cOYdSoUcU+jsTERIwZMwZBQUFITU3Fvn370Lx582Jvf/PmTezbt0+SBCvI4cOHcf/+fRw7dgyLFy9GYGAgOnToAGNjY5w5cwbDhw/H8OHDxeR8cY8vPj4et27dQnx8PCIjIxEREYGIiAgAwM6dO1G5cmUEBQUhIyNDkgR8+/YtFi1ahM2bN+PYsWNIT0/HpEmTCoxfnsjdtm0bkpOTMWvWLEyfPh3btm0DALRu3RpGRkbi/Qh8TCxu27YN3t7eAD4mPT09PdGlSxdcunQJW7duxYkTJxSOaeHChahVqxbOnTuHmTNnAgAMDAwQERGB5ORkLF26FGvXrsUPP/wgbhMVFYW5c+ciJCQE586dQ5UqVbBq1SpJvWvXrsX333+PuXPnIiUlBfPmzcPMmTMRGRmp9JgzMzPx8uVLyQ8RERERERF9mi9+uPHNmzchCAIcHBwkyytUqCD2zvLz80NISAgASHol2dnZYc6cORgxYoSYpElPT0fXrl3h4uICAJLkxsqVK2FtbY3w8HDIZDI4Ojri/v378Pf3x6xZs6CmVrycqo+PD3r37g0AmDdvHpYvX46zZ8+iTZs2WLVqFerXry8Z+rhhwwZYW1vj+vXrsLe3V1pnVlYWwsPD0ahRIwBAZGQknJyccPbsWTRs2BDAx2F/mzdvFofHHjx4EJcuXcKdO3dgbW0NANi8eTOcnZ2RkJCABg0aYMmSJRg0aBAGDx4M4GNPn0OHDpWo59v169exbds2HDx4EK1bt1Y4ryYmJgAAMzOzQidjKOzaFNf79+8RGRmJypUrAwCWL1+O9u3bIywsTBwKW65cOWzYsAG6urpwdnZGUFAQJk+ejDlz5hR5jePj43H58mU8evQIWlpaAIBFixZh9+7d+PnnnzF06NBSOaefa9SoUejatSsAYNWqVdi3bx/Wr18vSZgHBQXBw8MDAPDmzRssXrwYhw8fRuPGjQF8PP8nTpzAmjVr4ObmBm9vbyxevBh3796FjY0NcnNzsWXLFkyfPl1pDFFRUXj37h02bdokJvjDw8Ph5eWFkJAQmJubF3kc6enp0NPTQ4cOHWBgYAAbGxvUq1evyO2aNGmC8+fPIzMzE0OHDkVQUFCR25iYmGDZsmVQU1ODg4MDQkND8fbtW/H4pk2bhgULFuDkyZNiwrQ4x2dsbIzw8HCoq6vD0dER7du3R1xcHIYMGQITExOoq6vDwMBAYah2VlYWVq9ejWrVqgH4eE0LOw5NTU1JstvOzg6nTp3Ctm3b0KNHD6irq6Nnz56Ijo6Gr68vgI8J82fPnqF79+4APib/+vTpIz5Ha9SogWXLlsHNzQ2rVq2CtrY2AKBVq1YKCcsZM2aI/29ra4uJEydi69atYptbvnw5fH19MXDgQADArFmzcODAAUnPyzlz5iAsLAxdunQRjyE5ORlr1qzBgAEDFI55/vz5xU7wExERERERUeG++J6EcvmH4Z09exZJSUlwdnZGZmamuDw+Ph4eHh6oVKkSDAwM0L9/f/z999/icLkxY8YgODgYTZs2RUBAAC5duiRum5KSgsaNG0v21bRpU7x+/Rp//vlnsWOtXbu2+P96enowMDDAo0ePAADnzp1DfHw89PX1xR9HR0cAwK1btwqsU0NDA66uruJnR0dHGBkZISUlRVxmY2MjeX9eSkoKrK2txQQhANSsWVOynfyY88r/uShJSUlQV1cvVm+twhR2bYqrSpUqYoIQ+Hgsubm5SE1NFZfVqVMHurq6kjKvX7+WDN8uyLlz5/D69WuYmppKruGdO3fE6/cp59TZ2VlSX1E/w4cPL7S+vPuTt528bQWApD0lJyfj/fv38PDwkOxn06ZN4nHVq1cPjo6OiImJAfBxaP6jR48K7AWbkpKCOnXqSHoAN23aVOF6FMbDwwM2NjaoWrUq+vXrh6ioqGJNVLR161acP38e0dHRiI2NxaJFiwB8TDrmPb68yXpnZ2dJktjc3FxMWAOAuro6TE1NxXu5uMfn7OwMdXV18bOlpaVYR2F0dXXFBGFxt1u9ejVcXV1RsWJF6OvrY+3atUhPTxfXe3t748iRI7h//z6Aj4ncdu3awdjYGMDH9h0RESE5R56ensjNzcWdO3fEevK2Hbmff/4ZzZo1g4WFBfT19TFz5kzJvlNTU8U/aMjl/fz48WPcu3cPvr6+kv0HBwcX+GycNm0aXrx4If4U5x4mIiIiIiIi5b74noTVq1eHTCbDtWvXJMvlvcx0dHTEZXfv3kW7du0wfPhwzJkzByYmJjhx4gR8fX3Fd2MNHjwYnp6eiI2NxYEDBzB//nyEhYVh9OjREARBIRkpCAIAxSRlYfJPGCKTyZCbmwvg45BAeU+j/CwtLQutV1kMeZflH46t7HgKW14QNTU18TzI5X3XWN5r8DkKuzZFxVAQ+XEW53iLUyY3NxeWlpaSd/zJFdZLsih79+4t0Qy8hoaGJd5H/uPL217k7TM2NhaVKlWSlJP3mAQ+Jpmio6MxdepUREdHw9PTExUqVFC6v8LamXx5UdfVwMAA58+fx5EjR3DgwAHMmjULgYGBSEhIKPR8yxPjNWvWRE5ODoYOHYqJEyfCyspKHIoP/F8vV0D5fVvYvVyc4yuoXnkdhVG2Xf5zlde2bdswfvx4hIWFoXHjxjAwMMDChQtx5swZsUzDhg1RrVo1bNmyBSNGjMCuXbuwceNGcX1ubi6GDRuGMWPGKNRfpUoV8f/zP2v++OMP9OrVC7Nnz4anpyfKly+PLVu2ICwsTOEY8sp7PPJzsnbtWrHHtFzeJGteWlpakvZJREREREREn+6L70loamoKDw8PhIeHS16er0xiYiKys7MRFhaGr7/+Gvb29mKPmbysra0xfPhw7Ny5ExMnTsTatWsBfEwonDp1SvKL66lTp2BgYKCQOPlU9evXx9WrV2Fra4vq1atLfgp65yIAZGdnIzExUfycmpqK58+fi70QlalZsybS09MlvWuSk5Px4sULODk5AQCcnJzwxx9/SLbL/7lixYqSd6Xl5OTgypUr4mcXFxfk5ubi6NGjSuOQz3yak5NTYKxyBV2bihUr4tWrV5I2kDfZI5eeni655qdPn4aamppkGPfFixfx7t07yfHq6+tLeiAWpH79+njw4AE0NDQUrp88WVacc5qfjY2NQn2F/ZiZmRVaX979ZWdn49y5c0W2FS0tLaSnpyvsK29P1D59+uDy5cs4d+4cfv75Z/FddgXVmZSUJLlmJ0+elFyPotoW8LEnZOvWrREaGopLly4hLS0Nhw8fLvT48xIEAVlZWRAEQeG65U0SllRxjq84ypUrV6x7oyjHjx9HkyZNMHLkSNSrVw/Vq1dX2gOvT58+iIqKwq+//go1NTW0b99eXCd/Pilrc4XNYHzy5EnY2Njg+++/h6urK2rUqIG7d+9Kyjg4OODs2bOSZXmfaebm5qhUqRJu376tsG87O7tPPS1ERERERERUTF98khD4+K7A7OxsuLq6YuvWrUhJSUFqaip++uknXLt2TexlUq1aNWRnZ2P58uW4ffs2Nm/erDAb67hx47B//37cuXMH58+fx+HDh8WE2ciRI3Hv3j2MHj0a165dwy+//IKAgABMmDCh2O8jLIqfnx+ePn2K3r174+zZs7h9+zYOHDiAQYMGFZoo0NTUxOjRo3HmzBmcP38eAwcOxNdff60wfC+v1q1bo3bt2vD29sb58+dx9uxZ9O/fH25ubuJwwbFjx2LDhg3YsGEDrl+/joCAAFy9elVST6tWrRAbG4vY2Fhcu3YNI0eOlMyQamtriwEDBmDQoEHYvXs37ty5gyNHjogTJtjY2EAmk+G3337D48ePJe8gy6uwa9OoUSPo6upi+vTpuHnzJqKjo8XJH/LS1tbGgAEDcPHiRRw/fhxjxoxBjx49JO97+/DhA3x9fZGcnIzff/8dAQEBGDVqVLGucevWrdG4cWN07twZ+/fvR1paGk6dOoUZM2aICY/inNN/2ooVK7Br1y5cu3YNfn5+ePbsGQYNGlRgeQMDA0yaNAnjx49HZGQkbt26hQsXLmDFihWSSSPs7OzQpEkT+Pr6Ijs7G506dSqwTm9vb/F6XLlyBfHx8Rg9ejT69esnvq+vqLb122+/YdmyZUhKSsLdu3exadMm5ObmKryjVC4qKgrbtm1DSkoKbt++je3bt2PatGno2bMnNDRKt+N0cY6vOGxtbXHs2DH89ddfhc5yXpTq1asjMTER+/fvx/Xr1zFz5kwkJCQojfv8+fOYO3cuunXrJr5nEPg4Q/Hp06fh5+eHpKQk3LhxA3v27MHo0aOL3Hd6ejq2bNmCW7duYdmyZdi1a5ekzOjRo7F+/XpERkbixo0bCA4OxqVLlxRmKJ8/fz6WLl2K69ev4/Lly9i4cSMWL178yeeFiIiIiIiIiudfkSSsVq0aLly4gNatW2PatGmoU6cOXF1dsXz5ckyaNAlz5swBANStWxeLFy9GSEgIatWqhaioKMyfP19SV05ODvz8/ODk5IQ2bdrAwcFBnNSkUqVK2Lt3L86ePYs6depg+PDh8PX1lbyQ/3NZWVnh5MmTyMnJgaenJ2rVqoWxY8eifPnyhSapdHV14e/vjz59+qBx48bQ0dHBli1bCt2XTCbD7t27YWxsjObNm6N169aoWrUqtm7dKpbp2bMnZs2aBX9/f3z11Ve4e/cuRowYIaln0KBBGDBggJhgtLOzQ8uWLSVlVq1ahW7dumHkyJFwdHTEkCFDxB5WlSpVwuzZszF16lSYm5sXOLttYdfGxMQEP/30E/bu3QsXFxfExMQgMDBQoY7q1aujS5cuaNeuHb799lvUqlVLYWZhd3d31KhRA82bN0ePHj3g5eWltK6CzunevXvRvHlzDBo0CPb29ujVqxfS0tLExFBxzuk/bcGCBQgJCUGdOnVw/Phx/PLLLwUOC5abM2cOZs2ahfnz58PJyQmenp749ddfFXpxeXt74+LFi+jSpUuhQ811dXWxf/9+PH36FA0aNEC3bt3g7u6O8PBwsUxRbcvIyAg7d+5Eq1at4OTkhNWrVyMmJgbOzs5K96mhoYGQkBA0bNgQtWvXRmBgIPz8/LBu3brinLYSKc7xFUdQUBDS0tJQrVo1yTtFS2r48OHo0qULevbsiUaNGuHvv//GyJEjFcrVqFEDDRo0wKVLlxR6gtauXRtHjx7FjRs38M0336BevXqYOXNmka9C6NSpE8aPH49Ro0ahbt26OHXqlDjrsZy3tzemTZuGSZMmoX79+rhz5w58fHwkScrBgwdj3bp1iIiIgIuLC9zc3BAREcGehERERERERP8DMqGwl1zRFyEiIgLjxo2T9LAiRYGBgdi9e7fSYchyPj4+eP78OXbv3v0/i+t/KS0tDXZ2drhw4QLq1q1b1uEQFcrDwwMWFhbYvHlzqdT38uVLlC9fHjajvKBhqFv0BkRERERE9K91c27hHafo/8h/V3rx4kWhcxx88ROXEBHRv9/bt2+xevVqeHp6Ql1dHTExMTh06BAOHjxY1qERERERERERmCQkIqL/AflQ/eDgYGRmZsLBwQE7duxA69atyzo0IiIiIiIiAocbExHRfwSHGxMRERERqQ4ONy6+4g43/ldMXEJERERERERERET/HCYJiYiIiIiIiIiIVByThERERERERERERCqOSUIiIiIiIiIiIiIVxyQhERERERERERGRimOSkIiIiIiIiIiISMUxSUhERERERERERKTimCQkIiIiIiIiIiJScUwSEhERERERERERqTiNsg6AiIioNJ3wX4nKlSuXdRhERERERET/KuxJSEREREREREREpOKYJCQiIiIiIiIiIlJxTBISERERERERERGpOCYJiYiIiIiIiIiIVByThERERERERERERCqOSUIiIiIiIiIiIiIVxyQhERERERERERGRimOSkIiIiIiIiIiISMVplHUAREREpalZyEhoGOqWdRhERET/GTfnbinrEIiI6H+APQmJiIiIiIiIiIhUHJOEREREREREREREKo5JQiIiIiIiIiIiIhXHJCEREREREREREZGKY5KQiIiIiIiIiIhIxTFJSEREREREREREpOKYJCQiIiIiIiIiIlJxTBISERERERERERGpOCYJiYiIiIiIiIiIVByThERERERERERERCqOSUIiIiIiIiIiIiIVxyQhERERERERERGRimOSkIiIiIiIiIiISMUxSUifLTAwEHXr1i31elu0aIFx48aJn21tbbFkyZJPri8iIgJGRkbi5/xx+/j4oHPnzp9cf2nX81+Q/xrS//mn7pt/M947REREREREZYdJwv+gBw8eYPTo0ahatSq0tLRgbW0NLy8vxMXFlXVonyUhIQFDhw4tVlllCcWePXvi+vXrpRZPWloaZDIZkpKSJMuXLl2KiIiIUtvPv9nOnTsxZ84c8fPnJnr/SyZNmvSvvyc/VUH3DhEREREREZUdjbIOgEpXWloamjZtCiMjI4SGhqJ27drIysrC/v374efnh2vXrindLisrC5qamv/jaEumYsWKn7W9jo4OdHR0SimagpUvX/4f38e/hYmJSVmHUOo+fPiAcuXKfXY9+vr60NfXL4WIiIiIiIiIiD4fexL+x4wcORIymQxnz55Ft27dYG9vD2dnZ0yYMAF//PGHWE4mk2H16tXo1KkT9PT0EBwcrDAcFwB2794NmUwmWbZgwQKYm5vDwMAAvr6+eP/+vUIcGzduhJOTE7S1teHo6IiVK1cWGvebN2/Qv39/6Ovrw9LSEmFhYQpl8vdCCwwMRJUqVaClpQUrKyuMGTMGwMchrnfv3sX48eMhk8nE+JUdX2H27duHZs2awcjICKampujQoQNu3bolrrezswMA1KtXDzKZDC1atACgOGQyMzMTY8aMgZmZGbS1tdGsWTMkJCSI648cOQKZTIa4uDi4urpCV1cXTZo0QWpqaqHx/fnnn+jVqxdMTEygp6cHV1dXnDlzBgBw69YtdOrUCebm5tDX10eDBg1w6NAhhfM5Z84c9OnTB/r6+rCyssLy5cslZRYvXgwXFxfo6enB2toaI0eOxOvXryVlTp48CTc3N+jq6sLY2Bienp549uwZAOlwY2XX5c2bNzA0NMTPP/8sqfPXX3+Fnp4eXr16Veg5+FzyazV79myYmZnB0NAQw4YNw4cPH8QyLVq0wKhRozBhwgRUqFABHh4eAIDk5GS0a9cO+vr6MDc3R79+/fDkyRMAwJo1a1CpUiXk5uZK9texY0cMGDAAgOJw49zcXAQFBaFy5crQ0tJC3bp1sW/fPnG9vJ08f/5cXJaUlASZTIa0tDQAwN27d+Hl5QVjY2Po6enB2dkZe/fuLfD4bW1tERwcLN57NjY2+OWXX/D48WN06tQJ+vr6cHFxQWJiomS7HTt2wNnZGVpaWrC1tVW4X21tbTFv3jwMGjQIBgYGqFKlCn788UdxfUH3jtyiRYtgaWkJU1NT+Pn5ISsrq8BjICIiIiIiotLBJOF/yNOnT7Fv3z74+flBT09PYX3+BFlAQAA6deqEy5cvY9CgQcXax7Zt2xAQEIC5c+ciMTERlpaWCgnAtWvX4vvvv8fcuXORkpKCefPmYebMmYiMjCyw3smTJyM+Ph67du3CgQMHcOTIEZw7d67A8j///DN++OEHrFmzBjdu3MDu3bvh4uIC4OMQ18qVKyMoKAgZGRnIyMgo1rHl9+bNG0yYMAEJCQmIi4uDmpoavvvuOzHxc/bsWQDAoUOHkJGRgZ07dyqtZ8qUKdixYwciIyNx/vx5VK9eHZ6ennj69Kmk3Pfff4+wsDAkJiZCQ0Oj0Gvy+vVruLm54f79+9izZw8uXryIKVOmiLG9fv0a7dq1w6FDh3DhwgV4enrCy8sL6enpknoWLlyI2rVr4/z585g2bRrGjx+PgwcPiuvV1NSwbNkyXLlyBZGRkTh8+DCmTJkirk9KSoK7uzucnZ1x+vRpnDhxAl5eXsjJyVGIWdl10dPTQ69evbBx40ZJ2Y0bN6Jbt24wMDAo8ByUlri4OKSkpCA+Ph4xMTHYtWsXZs+eLSkTGRkJDQ0NnDx5EmvWrEFGRgbc3NxQt25dJCYmYt++fXj48CF69OgBAOjevTuePHmC+Ph4sY5nz55h//798Pb2VhrH0qVLERYWhkWLFuHSpUvw9PREx44dcePGjWIfi5+fHzIzM3Hs2DFcvnwZISEhRfZW/OGHH9C0aVNcuHAB7du3R79+/dC/f3/07dtXbK/9+/eHIAgAgHPnzqFHjx7o1asXLl++jMDAQMycOVNhiH1YWBhcXV1x4cIFjBw5EiNGjBB7Mhd278THx+PWrVuIj49HZGQkIiIiChy+n5mZiZcvX0p+iIiIiIiI6NNwuPF/yM2bNyEIAhwdHYtVvk+fPsVODsotWbIEgwYNwuDBgwEAwcHBOHTokKQ34Zw5cxAWFoYuXboA+NhrKDk5GWvWrBF7UeX1+vVrrF+/Hps2bRJ7aUVGRqJy5coFxpGeng4LCwu0bt0ampqaqFKlCho2bAjg4xBXdXV1GBgYwMLCokTHl1fXrl0ln9evXw8zMzMkJyejVq1a4vBnU1PTAvfz5s0brFq1ChEREWjbti2Aj0nUgwcPYv369Zg8ebJYdu7cuXBzcwMATJ06Fe3bt8f79++hra2tUG90dDQeP36MhIQEcUhv9erVxfV16tRBnTp1xM/BwcHYtWsX9uzZg1GjRonLmzZtiqlTpwIA7O3tcfLkSfzwww/idcg76YidnR3mzJmDESNGiInh0NBQuLq6ShLFzs7OSs9FQddl8ODBaNKkCe7fvw8rKys8efIEv/32myRZ+U8qV64cNmzYAF1dXTg7OyMoKAiTJ0/GnDlzoKb28e8o1atXR2hoqLjNrFmzUL9+fcybN09ctmHDBlhbW+P69euwt7dHmzZtEB0dDXd3dwDA9u3bYWJiIn7Ob9GiRfD390evXr0AACEhIYiPj8eSJUuwYsWKYh1Leno6unbtKibMq1atWuQ27dq1w7Bhw8TjWrVqFRo0aIDu3bsDAPz9/dG4cWM8fPgQFhYWWLx4Mdzd3TFz5kwAH9tNcnIyFi5cCB8fH0m9I0eOFOv44YcfcOTIETg6OhZ67xgbGyM8PBzq6upwdHRE+/btERcXhyFDhijEPn/+fIWELhEREREREX0a9iT8D5H39Mk/PLggrq6uJd5HSkoKGjduLFmW9/Pjx49x7949+Pr6iu9c09fXR3BwsGSobl63bt3Chw8fJPWYmJjAwcGhwDi6d++Od+/eoWrVqhgyZAh27dqF7OzsEh9PYW7duoU+ffqgatWqMDQ0FIdI5u+NV1QdWVlZaNq0qbhMU1MTDRs2REpKiqRs7dq1xf+3tLQEADx69EhpvUlJSahXr16B7/x78+YNpkyZgpo1a8LIyAj6+vq4du2aQuzKrmXeuOLj4+Hh4YFKlSrBwMAA/fv3x99//403b96IcRSU9Cquhg0bwtnZGZs2bQIAbN68GVWqVEHz5s2Vlo+KipK0reL8FHbN6tSpA11dXck5eP36Ne7duycuy3+vnDt3DvHx8ZJ9yJPz8nbu7e2NHTt2IDMzU4y7V69eUFdXV4jh5cuXuH//vqSdAB+TuPnbSWHGjBmD4OBgNG3aFAEBAbh06VKR2+Rtd+bm5gAgJhnzLpO3xZSUFKVx3rhxQ9KDNG+9MpkMFhYWBbbnvJydnSXnyNLSssDtpk2bhhcvXog/ea8ZERERERERlQx7Ev6H1KhRAzKZDCkpKZJ34hUk/5BkNTU1MdEoV9J3gcmHu65duxaNGjWSrFOWHAGgsM/isLa2RmpqKg4ePIhDhw5h5MiRWLhwIY4ePVpqE7B4eXnB2toaa9euhZWVFXJzc1GrVi3J++qKUlDiVhAEhWV545avy/9OO7miJmCZPHky9u/fj0WLFqF69erQ0dFBt27dihW7fN93795Fu3btMHz4cMyZMwcmJiY4ceIEfH19xXZRWhPBDB48GOHh4Zg6dSo2btyIgQMHFpjs7tixo0LbKoqVlVWJY8q7//z3Sm5uLry8vBASEqKwnTzB6+XlhdzcXMTGxqJBgwY4fvw4Fi9eXOx9AtJ2Iu/VmPd+yX9/Dh48GJ6enoiNjcWBAwcwf/58hIWFYfTo0QXuU1m7K6wtKmu7yu7h/PehTCYrsD1/6nZaWlrQ0tIqsk4iIiIiIiIqGnsS/oeYmJjA09MTK1asEHt65ZV3wgNlKlasiFevXkm2TUpKkpRxcnKSTIACQPLZ3NwclSpVwu3bt1G9enXJj7wnXn7Vq1eHpqampJ5nz57h+vXrhcaro6ODjh07YtmyZThy5AhOnz6Ny5cvA/g4hFTZe/GK6++//0ZKSgpmzJgBd3d3ODk5iZNxyMlnuC1sP9WrV0e5cuVw4sQJcVlWVhYSExPh5OT0yfHVrl0bSUlJCu81lDt+/Dh8fHzw3XffwcXFBRYWFuLkFnkpu5byHnGJiYnIzs5GWFgYvv76a9jb2+P+/fsKccTFxRU77oKuS9++fZGeno5ly5bh6tWrSoelyxkYGCi0raJ+NDQK/nvIxYsX8e7dO8k50NfXL3S4e/369XH16lXY2toq7EueUNTR0UGXLl0QFRWFmJgY2Nvb46uvvlJan6GhIaysrCTtBABOnTolthP5EN2879jMf38CHxPow4cPx86dOzFx4kSsXbu2wOP4FDVr1lQap729fYF/CMivOPcOERERERER/W8xSfgfs3LlSuTk5KBhw4bYsWMHbty4gZSUFCxbtkxhaGl+jRo1gq6uLqZPn46bN28iOjpaYcKAsWPHYsOGDdiwYQOuX7+OgIAAXL16VVImMDAQ8+fPx9KlS3H9+nVcvnwZGzduLLAXlb6+Pnx9fTF58mTExcXhypUr8PHxEXtOKRMREYH169fjypUruH37NjZv3gwdHR3Y2NgA+Di76rFjx/DXX3+JM86WhLGxMUxNTfHjjz/i5s2bOHz4MCZMmCApY2ZmBh0dHXHSihcvXijUo6enhxEjRmDy5MnYt28fkpOTMWTIELx9+xa+vr4ljkuud+/esLCwQOfOnXHy5Encvn0bO3bswOnTpwF8TE7u3LkTSUlJuHjxIvr06aO0N9bJkycRGhqK69evY8WKFdi+fTvGjh0LAKhWrRqys7OxfPly8RyvXr1asv20adOQkJCAkSNH4tKlS7h27RpWrVpV4Dkv6LoYGxujS5cumDx5Mr799ttCE3Sl7cOHD/D19UVycjJ+//13BAQEYNSoUYW2Pz8/Pzx9+hS9e/fG2bNncfv2bRw4cACDBg2SJL68vb0RGxuLDRs2oG/fvoXGMXnyZISEhGDr1q1ITU3F1KlTkZSUJF6P6tWrw9raGoGBgbh+/TpiY2MVZhUeN24c9u/fjzt37uD8+fM4fPjwZyWjlZk4cSLi4uIwZ84cXL9+HZGRkQgPD8ekSZOKXUdx7h0iIiIiIiL632KS8D/Gzs4O58+fR8uWLTFx4kTUqlULHh4eiIuLw6pVqwrd1sTEBD/99BP27t0LFxcXxMTEIDAwUFKmZ8+emDVrFvz9/fHVV1/h7t27GDFihKTM4MGDsW7dOkRERMDFxQVubm6IiIgosCch8HGW3ebNm6Njx45o3bo1mjVrVmCvK+DjTM1r165F06ZNxd5sv/76K0xNTQEAQUFBSEtLQ7Vq1cQeWCWhpqaGLVu24Ny5c6hVqxbGjx+PhQsXSspoaGhg2bJlWLNmDaysrNCpUyeldS1YsABdu3ZFv379UL9+fdy8eRP79++HsbFxieOSK1euHA4cOAAzMzO0a9cOLi4uWLBggdiT64cffoCxsTGaNGkCLy8veHp6on79+gr1TJw4EefOnUO9evXECWc8PT0BAHXr1sXixYsREhKCWrVqISoqCvPnz5dsb29vjwMHDuDixYto2LAhGjdujF9++aXAnnuFXRdfX198+PChxJPpfC53d3fUqFEDzZs3R48ePeDl5aXQ7vOzsrLCyZMnkZOTA09PT9SqVQtjx45F+fLlJcnFVq1awcTEBKmpqejTp0+hdY4ZMwYTJ07ExIkT4eLign379mHPnj2oUaMGgI/DcGNiYnDt2jXUqVMHISEhCA4OltSRk5MDPz8/ODk5oU2bNnBwcFCYffxz1a9fH9u2bcOWLVtQq1YtzJo1C0FBQZJJS4pS3HuHiIiIiIiI/ndkwqe8EI6I/vVsbW0xbtw4yQzGZSkqKgpjx47F/fv3xeGo/zQfHx88f/4cu3fv/p/sj/5ZL1++RPny5WEzygsahrpFb0BERETFcnPulrIOgYiIPoP8d6UXL17A0NCwwHKcuISIytTbt29x584dzJ8/H8OGDfufJQiJiIiIiIiI6P9wuDERlanQ0FDUrVsX5ubmmDZtWlmHQ0RERERERKSSONyYiIj+EzjcmIiI6J/B4cZERP9uxR1uzJ6EREREREREREREKo5JQiIiIiIiIiIiIhXHJCEREREREREREZGKY5KQiIiIiIiIiIhIxTFJSEREREREREREpOKYJCQiIiIiIiIiIlJxTBISERERERERERGpOCYJiYiIiIiIiIiIVByThERERERERERERCpOo6wDICIiKk0n/FeicuXKZR0GERERERHRvwp7EhIREREREREREak4JgmJiIiIiIiIiIhUHJOEREREREREREREKo5JQiIiIiIiIiIiIhXHJCEREREREREREZGKY5KQiIiIiIiIiIhIxTFJSEREREREREREpOKYJCQiIiIiIiIiIlJxGmUdABERUWlqFjISGoa6ZR0G5XFz7payDoGIiIiIiIrAnoREREREREREREQqjklCIiIiIiIiIiIiFcckIRERERERERERkYpjkpCIiIiIiIiIiEjFMUlIRERERERERESk4pgkJCIiIiIiIiIiUnFMEhIREREREREREak4JgmJiIiIiIiIiIhUHJOEREREREREREREKo5JQiIiIiIiIiIiIhXHJCEREREREREREZGKY5KQiIiIiIiIiIhIxTFJSFSGAgMDUbdu3QI/03/TkSNHIJPJ8Pz587IO5YsREREBIyOjsg6DiIiIiIhIZTFJSFSKTp06BXV1dbRp0+aTtp80aRLi4uJKLZ4WLVpg3LhxpVbf52IS9KMmTZogIyMD5cuXL+tQyoStrS2WLFlS1mEQERERERFRHkwSEpWiDRs2YPTo0Thx4gTS09NLvL2+vj5MTU3/gcioNGRlZZVKPeXKlYOFhQVkMlmp1EdERERERET0uZgkJColb968wbZt2zBixAh06NABERERCmUWLFgAc3NzGBgYwNfXF+/fv5esz9/TTllPwM6dO8PHx0f8vHLlStSoUQPa2towNzdHt27dAAA+Pj44evQoli5dCplMBplMhrS0NHGo6/79+1GvXj3o6OigVatWePToEX7//Xc4OTnB0NAQvXv3xtu3b8X9CIKA0NBQVK1aFTo6OqhTpw5+/vlncb283ri4OLi6ukJXVxdNmjRBamoqgI/DSWfPno2LFy+K8cjPUWBgIKpUqQItLS1YWVlhzJgxn3AFSkY+vHX37t2wt7eHtrY2PDw8cO/ePbGM/Hps2LABVatWhZaWFgRBwIsXLzB06FCYmZnB0NAQrVq1wsWLFwEAqampkMlkuHbtmmR/ixcvhq2tLQRBUDrceMeOHXB2doaWlhZsbW0RFhYm2V4mk2H37t2SZUZGRuI5/PDhA0aNGgVLS0toa2vD1tYW8+fPL/D4fXx80LlzZ8ybNw/m5uYwMjLC7NmzkZ2djcmTJ8PExASVK1fGhg0bJNtdvnwZrVq1go6ODkxNTTF06FC8fv1aod5FixbB0tISpqam8PPzExOsLVq0wN27dzF+/HixHeS1f/9+ODk5QV9fH23atEFGRkaBx0BERERERESlh0lColKydetWODg4wMHBAX379sXGjRshCIK4ftu2bQgICMDcuXORmJgIS0tLrFy58rP2mZiYiDFjxiAoKAipqanYt28fmjdvDgBYunQpGjdujCFDhiAjIwMZGRmwtrYWtw0MDER4eDhOnTqFe/fuoUePHliyZAmio6MRGxuLgwcPYvny5WL5GTNmYOPGjVi1ahWuXr2K8ePHo2/fvjh69Kgkpu+//x5hYWFITEyEhoYGBg0aBADo2bMnJk6cCGdnZzGenj174ueff8YPP/yANWvW4MaNG9i9ezdcXFw+67wU19u3bzF37lxERkbi5MmTePnyJXr16iUpc/PmTWzbtg07duxAUlISAKB9+/Z48OAB9u7di3PnzqF+/fpwd3fH06dP4eDggK+++gpRUVGSeqKjo9GnTx+lvQfPnTuHHj16oFevXrh8+TICAwMxc+ZMpYnmgixbtgx79uzBtm3bkJqaip9++gm2traFbnP48GHcv38fx44dw+LFixEYGIgOHTrA2NgYZ86cwfDhwzF8+HAxcfr27Vu0adMGxsbGSEhIwPbt23Ho0CGMGjVKUm98fDxu3bqF+Ph4REZGIiIiQjyWnTt3onLlyggKChLbgdzbt2+xaNEibN68GceOHUN6ejomTZpU7HNAREREREREn06jrAMg+q9Yv349+vbtCwBo06YNXr9+jbi4OLRu3RoAsGTJEgwaNAiDBw8GAAQHB+PQoUMKvQlLIj09HXp6eujQoQMMDAxgY2ODevXqAQDKly+PcuXKQVdXFxYWFgrbBgcHo2nTpgAAX19fTJs2Dbdu3ULVqlUBAN26dUN8fDz8/f3x5s0bLF68GIcPH0bjxo0BAFWrVsWJEyewZs0auLm5ifXOnTtX/Dx16lS0b98e79+/h46ODvT19aGhoSGJJz09HRYWFmjdujU0NTVRpUoVNGzY8JPPSUlkZWUhPDwcjRo1AgBERkbCyckJZ8+eFWP48OEDNm/ejIoVKwL4mFi7fPkyHj16BC0tLQDAokWLsHv3bvz8888YOnQovL29ER4ejjlz5gAArl+/jnPnzmHTpk1K41i8eDHc3d0xc+ZMAIC9vT2Sk5OxcOFCSa/RwqSnp6NGjRpo1qwZZDIZbGxsitzGxMQEy5Ytg5qaGhwcHBAaGoq3b99i+vTpAIBp06ZhwYIFOHnyJHr16oWoqCi8e/cOmzZtgp6eHgAgPDwcXl5eCAkJgbm5OQDA2NgY4eHhUFdXh6OjI9q3b4+4uDgMGTIEJiYmUFdXh4GBgUK7zMrKwurVq1GtWjUAwKhRoxAUFFRg/JmZmcjMzBQ/v3z5sljnioiIiIiIiBSxJyFRKUhNTcXZs2fFXmgaGhro2bOnZKhmSkqKmGCTy/+5pDw8PGBjY4OqVauiX79+iIqKkgwRLkzt2rXF/zc3N4eurq6YIJQve/ToEQAgOTkZ79+/h4eHB/T19cWfTZs24datWwXWa2lpCQBiPcp0794d7969Q9WqVTFkyBDs2rUL2dnZSsump6dL9l+cn/w9+vLS0NCAq6ur+NnR0RFGRkZISUkRl9nY2IgJQuBjr7/Xr1/D1NRUsp87d+6I56JXr164e/cu/vjjDwBAVFQU6tati5o1ayqNIyUlRUzYyjVt2hQ3btxATk5OgfHn5ePjg6SkJDg4OGDMmDE4cOBAkds4OztDTe3/vgbMzc0lvTjV1dVhamoqXr+UlBTUqVNHTBDK48zNzRWHlcvrVVdXFz9bWloW2gbkdHV1xQRhcbabP38+ypcvL/7k7SlLREREREREJcOehESlYP369cjOzkalSpXEZYIgQFNTE8+ePYOxsfEn1aumpiYZsgxIJ88wMDDA+fPnceTIERw4cACzZs1CYGAgEhISYGRkVGjdmpqa4v/LZDLJZ/my3NxcABD/GxsbKzlGAGJvuoLqzbu9MtbW1khNTcXBgwdx6NAhjBw5EgsXLsTRo0cVYrKyshKH/BaXvHdbQZQN/827LG9CDPh4LJaWljhy5IjCdvJzbmlpiZYtWyI6Ohpff/01YmJiMGzYsAJjEARBIY78110mkxXaFurXr487d+7g999/x6FDh9CjRw+0bt1a8t7I/JRd88LagbI485YrrN7C2kBh2+U/5rymTZuGCRMmiJ9fvnzJRCEREREREdEnYpKQ6DNlZ2dj06ZNCAsLw7fffitZ17VrV0RFRWHUqFFwcnLCH3/8gf79+4vr5T3NClKxYkXJO9tycnJw5coVtGzZUlymoaGB1q1bo3Xr1ggICICRkREOHz6MLl26oFy5csXuiVaYmjVrQktLC+np6ZKhxSVVUDw6Ojro2LEjOnbsCD8/Pzg6OuLy5cuoX7++pJyGhgaqV6/+yfvPLzs7G4mJieLQ4tTUVDx//hyOjo4FblO/fn08ePAAGhoahb7zz9vbG/7+/ujduzdu3bql8K7DvGrWrIkTJ05Ilp06dQr29vZij7z8beHGjRsKvUYNDQ3Rs2dP9OzZE926dUObNm3w9OlTmJiYFLjvkqhZsyYiIyPx5s0bMXl68uRJqKmpwd7evtj1lFa71NLSUkhSExERERER0adhkpDoM/3222949uwZfH19Ub58ecm6bt26Yf369Rg1ahTGjh2LAQMGwNXVFc2aNUNUVBSuXr0qGeKbX6tWrTBhwgTExsaiWrVq+OGHHyQz4v7222+4ffs2mjdvDmNjY+zduxe5ublwcHAAANja2uLMmTNIS0uDvr7+JyeLDAwMMGnSJIwfPx65ublo1qwZXr58iVOnTkFfXx8DBgwoVj22tra4c+cOkpKSULlyZRgYGCAmJgY5OTlo1KgRdHV1sXnzZujo6BTrnXqfS1NTE6NHj8ayZcugqamJUaNG4euvvy70nYitW7dG48aN0blzZ4SEhMDBwQH379/H3r170blzZ3H4cpcuXTBixAiMGDECLVu2VOiBmdfEiRPRoEEDzJkzBz179sTp06cRHh4umdimVatWCA8Px9dff43c3Fz4+/tLet798MMPsLS0RN26daGmpobt27fDwsKiyB6lJeHt7Y2AgAAMGDAAgYGBePz4MUaPHo1+/foV2WMzL1tbWxw7dgy9evWClpYWKlSoUGoxEhERERER0afhOwmJPtP69evRunVrhQQh8LEnYVJSEs6fP4+ePXti1qxZ8Pf3x1dffYW7d+9ixIgRhdY9aNAgDBgwAP3794ebmxvs7OwkvQiNjIywc+dOtGrVCk5OTli9ejViYmLg7OwMAJg0aRLU1dVRs2ZNVKxYEenp6Z98nHPmzMGsWbMwf/58ODk5wdPTE7/++ivs7OyKXUfXrl3Rpk0btGzZEhUrVkRMTAyMjIywdu1aNG3aFLVr10ZcXBx+/fVXmJqafnKsxaWrqwt/f3/06dMHjRs3ho6ODrZs2VLoNjKZDHv37kXz5s0xaNAg2Nvbo1evXkhLS5MkygwNDeHl5YWLFy/C29u70Drr16+Pbdu2YcuWLahVqxZmzZqFoKAgyaQlYWFhsLa2RvPmzdGnTx9MmjQJurq64np9fX2EhITA1dUVDRo0QFpaGvbu3St55+Dn0tXVxf79+/H06VM0aNAA3bp1g7u7O8LDw0tUT1BQENLS0lCtWjXJ+x6JiIiIiIio7MiEwl74RET/U9OmTcPx48cVhp5S6YuIiMC4ceMkPTPp3+3ly5coX748bEZ5QcNQt+gN6H/m5tzCk+9ERERERPTPkf+u9OLFCxgaGhZYjj0Jib4AgiDg1q1biIuLE3sBEhERERERERH9rzBJSPQFePHiBWrWrIly5cph+vTpZR0OEREREREREakYTlxC9AUwMjJCZmZmWYehUnx8fCTv/CMiIiIiIiJSZexJSEREREREREREpOKYJCQiIiIiIiIiIlJxTBISERERERERERGpOCYJiYiIiIiIiIiIVByThERERERERERERCqOSUIiIiIiIiIiIiIVxyQhERERERERERGRimOSkIiIiIiIiIiISMUxSUhERERERERERKTiNMo6ACIiotJ0wn8lKleuXNZhEBERERER/auwJyEREREREREREZGKY5KQiIiIiIiIiIhIxTFJSEREREREREREpOKYJCQiIiIiIiIiIlJxTBISERERERERERGpOCYJiYiIiIiIiIiIVByThERERERERERERCqOSUIiIiIiIiIiIiIVxyQhERERERERERGRitMo6wCIiIhKU7OQkdAw1C3rMP4Tbs7dUtYhEBERERHR/wh7EhIREREREREREak4JgmJiIiIiIiIiIhUHJOEREREREREREREKo5JQiIiIiIiIiIiIhXHJCEREREREREREZGKY5KQiIiIiIiIiIhIxTFJSEREREREREREpOKYJCQiIiIiIiIiIlJxTBISERERERERERGpOCYJiYiIiIiIiIiIVByThERERERERERERCqOSUIiIiIiIiIiIiIVxyQhfZFatGiBcePGiZ9tbW2xZMmSMovn3y4iIgJGRkb/+H58fHzQuXPnf3w//wVs04pkMhl2795d1mEQERERERGpJCYJSeLBgwcYO3YsqlevDm1tbZibm6NZs2ZYvXo13r59W2ZxJSQkYOjQoWW2/+JKS0uDTCZDUlJSWYfyjyroOJcuXYqIiIgyienf5t/Spv8JgYGBqFu3blmHQURERERERHlolHUA9OW4ffs2mjZtCiMjI8ybNw8uLi7Izs7G9evXsWHDBlhZWaFjx45lElvFihXLZL9fkpycHMhkMqipfbm5/fLly5d1CP+4rKwsaGpqfnY9bNNERERERET0Jflysw30Pzdy5EhoaGggMTERPXr0gJOTE1xcXNC1a1fExsbCy8tLLLt48WK4uLhAT08P1tbWGDlyJF6/fi2uv3v3Lry8vGBsbAw9PT04Oztj79694vqjR4+iYcOG0NLSgqWlJaZOnYrs7OwCY8s/NFMmk2HdunX47rvvoKurixo1amDPnj2SbZKTk9GuXTvo6+vD3Nwc/fr1w5MnTwrch3xI7v79++Hk5AR9fX20adMGGRkZknIbN26Ek5MTtLW14ejoiJUrV4rr7OzsAAD16tWDTCZDixYtlO7ryJEjkMlkiI2NRZ06daCtrY1GjRrh8uXLCvH89ttvqFmzJrS0tHD37l08e/YM/fv3h7GxMXR1ddG2bVvcuHFD4ViqVKkCXV1dfPfdd/j7778l65UNCx43bpwk3tzcXISEhKB69erQ0tJClSpVMHfu3EKPM3+9mZmZGDNmDMzMzKCtrY1mzZohISFB4TzExcXB1dUVurq6aNKkCVJTU5Wet9Imk8mwatUqtG3bFjo6OrCzs8P27dvF9fIek9u2bUOLFi2gra2Nn376CUDh7aBx48aYOnWqZF+PHz+GpqYm4uPjASi26fT0dHTq1An6+vowNDREjx498PDhQ3F9ca7Zzz//DBcXF+jo6MDU1BStW7fGmzdvlB67/Nzv378f9erVg46ODlq1aoVHjx7h999/h5OTEwwNDdG7d29JL+LPvaYRERGYPXs2Ll68CJlMBplMJul9+uTJk0LvayIiIiIiIvpnMElIAIC///4bBw4cgJ+fH/T09JSWkclk4v+rqalh2bJluHLlCiIjI3H48GFMmTJFXO/n54fMzEwcO3YMly9fRkhICPT19QEAf/31F9q1a4cGDRrg4sWLWLVqFdavX4/g4OASxTx79mz06NEDly5dQrt27eDt7Y2nT58CADIyMuDm5oa6desiMTER+/btw8OHD9GjR49C63z79i0WLVqEzZs349ixY0hPT8ekSZPE9WvXrsX333+PuXPnIiUlBfPmzcPMmTMRGRkJADh79iwA4NChQ8jIyMDOnTsL3d/kyZOxaNEiJCQkwMzMDB07dkRWVpYknvnz52PdunW4evUqzMzM4OPjg8TEROzZswenT5+GIAho166duN2ZM2cwaNAgjBw5EklJSWjZsmWJzy0ATJs2DSEhIZg5cyaSk5MRHR0Nc3PzEh3nlClTsGPHDkRGRuL8+fOoXr06PD09xesk9/333yMsLAyJiYnQ0NDAoEGDShzvp5o5cya6du2Kixcvom/fvujduzdSUlIkZfz9/TFmzBikpKTA09OzyHbg7e2NmJgYCIIg1rF161aYm5vDzc1NIQZBENC5c2c8ffoUR48excGDB3Hr1i307Nmz2MeRkZGB3r17Y9CgQUhJScGRI0fQpUsXSQzKBAYGIjw8HKdOncK9e/fQo0cPLFmyBNHR0YiNjcXBgwexfPlysfznXtOePXti4sSJcHZ2RkZGBjIyMiTHWdh9nV9mZiZevnwp+SEiIiIiIqJPw+HGBAC4efMmBEGAg4ODZHmFChXw/v17AB8TfyEhIQAgmVTEzs4Oc+bMwYgRI8TeVOnp6ejatStcXFwAAFWrVhXLr1y5EtbW1ggPD4dMJoOjoyPu378Pf39/zJo1q9jDaX18fNC7d28AwLx587B8+XKcPXsWbdq0wapVq1C/fn3MmzdPLL9hwwZYW1vj+vXrsLe3V1pnVlYWVq9ejWrVqgEARo0ahaCgIHH9nDlzEBYWhi5duojHnpycjDVr1mDAgAHiEFJTU1NYWFgUeQwBAQHw8PAAAERGRqJy5crYtWuXmMzMysrCypUrUadOHQDAjRs3sGfPHpw8eRJNmjQBAERFRcHa2hq7d+9G9+7dsXTpUnh6eoo92ezt7XHq1Cns27evWOcVAF69eoWlS5ciPDwcAwYMAABUq1YNzZo1A4BiHeebN2+watUqREREoG3btgA+JlkPHjyI9evXY/LkyWLZuXPnismzqVOnon379nj//j20tbWLHfOn6t69OwYPHgzg4/WVJ8Xy9gwcN26ceM3l5QprBz179sT48eNx4sQJfPPNNwCA6Oho9OnTR2n7PnToEC5duoQ7d+7A2toaALB582Y4OzsjISEBDRo0KPI4MjIykJ2djS5dusDGxgYAxPuvMMHBwWjatCkAwNfXF9OmTcOtW7fEe7Zbt26Ij4+Hv79/qVxTHR0d6OvrQ0NDQ2nbKey+zm/+/PmYPXt2kcdIRERERERERWNPQpLI21sQ+NhjLCkpCc7OzsjMzBSXx8fHw8PDA5UqVYKBgQH69++Pv//+WxzaOGbMGDH5EBAQgEuXLonbpqSkoHHjxpJ9NW3aFK9fv8aff/5Z7Fhr164t/r+enh4MDAzw6NEjAMC5c+cQHx8PfX198cfR0REAcOvWrQLr1NXVFROEAGBpaSnW+fjxY9y7dw++vr6SeoODgwutszCNGzcW/9/ExAQODg6SXmzlypWTHGdKSgo0NDTQqFEjcZmpqalkO/n5LWg/xZGSkoLMzEy4u7uXaLu8bt26haysLDEBBQCamppo2LChQk+9vMdoaWkJAOJ5z69t27aS81/UjzyZVRBl5yp/fK6uruL/F6cdVKxYER4eHoiKigIA3LlzB6dPn4a3t7fSGFJSUmBtbS0mCAGgZs2aMDIyUoilIHXq1IG7uztcXFzQvXt3rF27Fs+ePStyu7zn3tzcHLq6upKkvrm5uXgt/qlrWtB2+e/r/KZNm4YXL16IP/fu3SuyfiIiIiIiIlKOPQkJAFC9enXIZDJcu3ZNslyeLNDR0RGX3b17F+3atcPw4cMxZ84cmJiY4MSJE/D19RWHvA4ePBienp6IjY3FgQMHMH/+fISFhWH06NEQBEEhGSkfEpl/eWHyTx4hk8mQm5sL4OP79Ly8vMSej3nJExbFrVMem7zutWvX/j/27jwup/T/H/jrVtqTkCiRJBUtdmlGtiYylrFkiWRPScYSzUgRklGWGvFBShNhEFNN9kINCdlK2WM0jK3GMqju3x++nV936x2ZZtyv5+PR49E593Wu876uc5378ejddZ0jkaQDADk5OanjrkrJPlBWVpbYrmjpaMk+rWp5KfB+uXjpciWXOZe83h+qomta3vUv2e/FnxX3d2mbN2/G69evpY7jQ9pSOr6SS/ClHQeOjo7w8PBAcHAwtm/fjrZt2wozQksrr09K76/qmsnJyeHw4cNISUnBoUOHEBwcjO+//x5nzpwRniFZntJ9X9l99amuaUXHlT5/aYqKilBUVKyyTiIiIiIiIqoaZxISgPez0WxtbRESElLhiw6KpaWloaCgAIGBgejWrRuMjIzw4MGDMuX09PTg4uKCvXv3Ys6cOdi0aROA9zOkUlJSJBIeKSkpUFdXh66ubo20p0OHDrh69Sr09fVhaGgo8VPRMxeroq2tDV1dXdy6datMncVJGAUFBQDv30QsjdOnTwu/P3v2DNnZ2cKMx/KYmpqioKAAZ86cEfY9efIE2dnZMDExEcqUrLf0eYD3M91Kv5AlPT1d+L1169ZQVlbG0aNHy41DmnYaGhpCQUEBp06dEva9e/cOaWlpQqwfQldXt0z/V/ZT1Zgqr68quwbSjAMAGDJkCP7++28kJCRg+/btGDt2bIV1mpqaIicnR2ImXEZGBvLy8oS+quqaAe8TatbW1li8eDEuXLgABQUF7Nu3r9L2V0dNXVMFBQWp7xEiIiIiIiL6ZzBJSIL169ejoKAAnTp1ws6dO5GZmYmsrCz89NNPuHbtmjBLqlWrVigoKEBwcDBu3bqFyMhIbNiwQaKuWbNm4eDBg7h9+zbOnz+PY8eOCUkEV1dX3Lt3D+7u7rh27Rr2798PHx8fzJ49W+rnEVbFzc0NT58+xejRo5Gamopbt27h0KFDmDhx4kclJ3x9feHv74+1a9ciOzsbly9fxtatWxEUFAQAaNy4MZSVlYUXpeTl5VVa35IlS3D06FFcuXIFzs7OaNSoUZk32JbUunVrDB48GFOmTMGpU6eEl23o6upi8ODBAN4v9U5ISMDKlSuRnZ2NkJCQMs8j7N27N9LS0rBt2zZcv34dPj4+uHLlivC5kpIS5s+fD09PT2zbtg03b97E6dOnsWXLFqnbqaqqiunTp2PevHlISEhARkYGpkyZglevXmHSpElS9fc/Yffu3QgLC0N2djZ8fHyQmpqKGTNmVHpMVeMAeN/+wYMHw9vbG5mZmRgzZkyF9fXt2xfm5uZwdHTE+fPnkZqaCicnJ9jY2AhLnau6ZmfOnMHy5cuRlpaGnJwc7N27F3/++edHJWRLq6lrqq+vj9u3byM9PR2PHz+WeJQBERERERER1Q4mCUnQqlUrXLhwAX379oWXlxcsLCzQqVMnBAcHY+7cufDz8wMAWFpaIigoCAEBAWjXrh2ioqLg7+8vUVdhYSHc3NxgYmKCfv36oU2bNsKLIHR1dREfH4/U1FRYWFjAxcUFkyZNwsKFC2usLTo6OkhOTkZhYSHs7OzQrl07eHh4QEND46MSkZMnT8bmzZsRHh4OMzMz2NjYIDw8XJhBJi8vj3Xr1mHjxo3Q0dEREncVWbFiBTw8PNCxY0fk5ubiwIEDwiy9imzduhUdO3bE119/DSsrK4jFYsTHxwvLNLt164bNmzcjODgYlpaWOHToUJm+tbOzg7e3Nzw9PdG5c2f89ddfcHJykijj7e2NOXPmYNGiRTAxMcHIkSOFZ8NJ284VK1Zg2LBhGDduHDp06IAbN27g4MGD0NTUrLSN/6TFixcjOjoa5ubmiIiIQFRUFExNTSs9pqpxUMzR0REXL17El19+iebNm1dYn0gkQkxMDDQ1NdGjRw/07dsXBgYG2Llzp1CmqmtWr149nDhxAvb29jAyMsLChQsRGBhY5TMZq6smrumwYcPQr18/9OrVC1paWtixY0eNxkhERERERETVJxJL8wAzIqpRiYmJ6NWrF549e4b69evXdjgySyQSYd++fZXO3qT/jvz8fGhoaKDFjIGQr6dS2+F8Fm4si67tEIiIiIiI6CMV/62Ul5eHevXqVViOMwmJiIiIiIiIiIhkHJOEREREREREREREMk6+tgMgkkU9e/YEV/rXPl4DIiIiIiIiovc4k5CIiIiIiIiIiEjGMUlIREREREREREQk45gkJCIiIiIiIiIiknFMEhIREREREREREck4JgmJiIiIiIiIiIhkHJOEREREREREREREMo5JQiIiIiIiIiIiIhnHJCEREREREREREZGMY5KQiIiIiIiIiIhIxsnXdgBEREQ16dT89WjWrFlth0FERERERPSfwpmEREREREREREREMo5JQiIiIiIiIiIiIhnHJCEREREREREREZGMY5KQiIiIiIiIiIhIxjFJSEREREREREREJOOYJCQiIiIiIiIiIpJxTBISERERERERERHJOCYJiYiIiIiIiIiIZJx8bQdARERUk74IcIV8PZXaDuM/58ay6NoOgYiIiIiIahFnEhIREREREREREck4JgmJiIiIiIiIiIhkHJOEREREREREREREMo5JQiIiIiIiIiIiIhnHJCEREREREREREZGMY5KQiIiIiIiIiIhIxjFJSEREREREREREJOOYJCQiIiIiIiIiIpJxTBISERERERERERHJOCYJiYiIiIiIiIiIZByThERERERERERERDKOSUIiIiIiIiIiIiIZxyQhERERERERERGRjGOSkD6Znj17YtasWcK2vr4+1qxZU2vxfAq+vr6wtLSs7TBqzae4xrLSpyKRCDExMbUdxr/GnTt3IBKJkJ6eXtuhEBERERERyST52g6Aat4ff/wBf39/xMXF4f79+9DQ0EDr1q0xduxYODk5QUVFpVbiOnv2LFRVVWvl3P8ViYmJ6NWrF549e4b69evXdjjVxmssvdzcXGhqatZ2GLXC2dkZz58/Z5KUiIiIiIjoX4RJws/MrVu3YG1tjfr162P58uUwMzNDQUEBsrOzERYWBh0dHQwaNKhWYtPS0qqV81bl3bt3qFu3bm2H8Vn4t17jmiIWi1FYWAh5+Y//6mzSpEkNRERERERERERUM7jc+DPj6uoKeXl5pKWlwcHBASYmJjAzM8OwYcMQFxeHgQMHCmWDgoJgZmYGVVVV6OnpwdXVFS9evBA+v3v3LgYOHAhNTU2oqqqibdu2iI+PFz5PSkpCly5doKioiKZNm2LBggUoKCioMLbSS1FFIhE2b96Mb775BioqKmjdujUOHDggcUxGRgbs7e2hpqYGbW1tjBs3Do8fP67wHOHh4ahfvz5iYmJgZGQEJSUl2Nra4t69e0KZ4uWsYWFhMDAwgKKiIsRiMXJycjB48GCoqamhXr16cHBwwMOHDyXqX7FiBbS1taGuro5Jkybh77//lvi89PJbABgyZAicnZ2F7Tdv3sDT0xN6enpQVFRE69atsWXLFty5cwe9evUCAGhqakIkEkkcV1Jl16a4D0qKiYmBSCQq0wcbN26Enp4eVFRUMGLECDx//lwo4+zsjCFDhmDx4sVo3Lgx6tWrh2nTpuHt27cV9n/pa5yXl4epU6cKx/fu3RsXL16sVp9+KsXLW6Ojo9G9e3coKSmhbdu2SExMFMokJiZCJBLh4MGD6NSpExQVFXHy5EmIxWKsXLkSBgYGUFZWhoWFBX7++WcAQFFREZo1a4YNGzZInO/8+fMQiUS4desWgLLLjS9fvozevXtDWVkZDRs2xNSpUyXuR2nG1vr169G6dWsoKSlBW1sbw4cPr7D9xeMkNjYWbdq0gYqKCoYPH46XL18iIiIC+vr60NTUhLu7OwoLC4Xjnj17BicnJ2hqakJFRQX9+/fH9evXy9R78OBBmJiYQE1NDf369UNubi6A92MvIiIC+/fvh0gkgkgkkujzW7duoVevXlBRUYGFhQV+++23CttARERERERENYdJws/IkydPcOjQIbi5uVW45LNkoqhOnTpYt24drly5goiICBw7dgyenp7C525ubnjz5g1OnDiBy5cvIyAgAGpqagCA33//Hfb29ujcuTMuXryI0NBQbNmyBUuXLq1WzIsXL4aDgwMuXboEe3t7ODo64unTpwDeL8e0sbGBpaUl0tLSkJCQgIcPH8LBwaHSOl+9eoVly5YhIiICycnJyM/Px6hRoyTK3LhxA7t27cKePXuEZ6ANGTIET58+RVJSEg4fPoybN29i5MiRwjG7du2Cj48Pli1bhrS0NDRt2hTr16+vVnsBwMnJCdHR0Vi3bh0yMzOxYcMGqKmpQU9PD3v27AEAZGVlITc3F2vXri23jsqujbSK++CXX35BQkIC0tPT4ebmJlHm6NGjyMzMxPHjx7Fjxw7s27cPixcvlqp+sViMAQMG4I8//kB8fDzOnTuHDh06oE+fPsI1rqk+/Rjz5s3DnDlzcOHCBXTv3h2DBg3CkydPJMp4enrC398fmZmZMDc3x8KFC7F161aEhobi6tWr+PbbbzF27FgkJSWhTp06GDVqFKKioiTq2L59O6ysrGBgYFAmhlevXqFfv37Q1NTE2bNnsXv3bhw5cgQzZsyQuh1paWmYOXMmlixZgqysLCQkJKBHjx6VHvPq1SusW7cO0dHRSEhIQGJiIoYOHYr4+HjEx8cjMjIS//vf/4QEKPA+eZyWloYDBw7gt99+g1gshr29Pd69eydR76pVqxAZGYkTJ04gJycHc+fOBQDMnTsXDg4OQuIwNzcX3bt3F479/vvvMXfuXKSnp8PIyAijR4+u8J8Pb968QX5+vsQPERERERERfRguN/6M3LhxA2KxGG3atJHY36hRI2F2lpubGwICAgBAYlZSy5Yt4efnh+nTpwtJmpycHAwbNgxmZmYAIJHcWL9+PfT09BASEgKRSARjY2M8ePAA8+fPx6JFi1CnjnT5Z2dnZ4wePRoAsHz5cgQHByM1NRX9+vVDaGgoOnTogOXLlwvlw8LCoKenh+zsbBgZGZVb57t37xASEoKuXbsCACIiImBiYoLU1FR06dIFAPD27VtERkYKy2MPHz6MS5cu4fbt29DT0wMAREZGom3btjh79iw6d+6MNWvWYOLEiZg8eTIAYOnSpThy5Ei1Zr5lZ2dj165dOHz4MPr27VumXxs0aAAAaNy4caXPJKzs2kjr77//RkREBJo1awYACA4OxoABAxAYGCgshVVQUEBYWBhUVFTQtm1bLFmyBPPmzYOfn1+V1/j48eO4fPkyHj16BEVFRQDAqlWrEBMTg59//hlTp06tkT79WDNmzMCwYcMAAKGhoUhISMCWLVskEuZLliyBra0tAODly5cICgrCsWPHYGVlBeB9/586dQobN26EjY0NHB0dERQUhLt376JFixYoKipCdHQ0vvvuu3JjiIqKwuvXr7Ft2zYhwR8SEoKBAwciICAA2traVbYjJycHqqqq+Prrr6Guro4WLVqgffv2lR7z7t07hIaGolWrVgCA4cOHIzIyEg8fPoSamhpMTU3Rq1cvHD9+HCNHjsT169dx4MABJCcnC4m9qKgo6OnpISYmBiNGjBDq3bBhg1DvjBkzsGTJEgCAmpoalJWV8ebNm3KXXM+dOxcDBgwA8P6fCG3btsWNGzdgbGxcpqy/v7/USWsiIiIiIiKqHGcSfoZKzhYEgNTUVKSnp6Nt27Z48+aNsP/48eOwtbWFrq4u1NXV4eTkhCdPnuDly5cAgJkzZ2Lp0qWwtraGj48PLl26JBybmZkJKysriXNZW1vjxYsXuH//vtSxmpubC7+rqqpCXV0djx49AgCcO3cOx48fh5qamvBTnCi4efNmhXXKy8ujU6dOwraxsTHq16+PzMxMYV+LFi0knp+XmZkJPT09IUEIAKamphLHFbe5pNLbVUlPT4ecnBxsbGyqdVxplV0baTVv3lxIEALv21JUVISsrCxhn4WFhcSLbqysrPDixQuJ5dsVOXfuHF68eIGGDRtKXMPbt28L1+9D+rRt27YS9VX14+LiUml9Jc9XPHZKjhUAEuMpIyMDf//9N2xtbSXOs23bNqFd7du3h7GxMXbs2AHg/dL8R48eVTgLNjMzExYWFhIzgK2trctcj8rY2tqiRYsWMDAwwLhx4xAVFYVXr15VeoyKioqQyAMAbW1t6OvrS8xK1dbWFu7JzMxMyMvLCwl4AGjYsCHatGkj0Wel623atKlQR1VKfic0bdoUACo81svLC3l5ecKPNOOSiIiIiIiIyseZhJ8RQ0NDiEQiXLt2TWJ/8SwzZWVlYd/du3dhb28PFxcX+Pn5oUGDBjh16hQmTZokLBucPHky7OzsEBcXh0OHDsHf3x+BgYFwd3eHWCwuk4wUi8UAyiYpK1P6hSEikQhFRUUA3j/brXgmVWnFyYOKlBdDyX2ll2OX157K9lekTp06Qj8UK7kMs+Q1+BiVXZuqYqhIcTulaa80ZYqKitC0aVOJ580V+5g3N8fHx0vVnmL16tWr9jlKt6/keCken3FxcdDV1ZUoVzxjEgAcHR2xfft2LFiwANu3b4ednR0aNWpU7vkqG2fF+6u6rurq6jh//jwSExNx6NAhLFq0CL6+vjh79myF/V3e/VfZPVn6/BXFX14dFR1bWUzFdRafvzRFRUWJPiciIiIiIqIPx5mEn5GGDRvC1tYWISEhwmzAiqSlpaGgoACBgYHo1q0bjIyM8ODBgzLl9PT04OLigr1792LOnDnYtGkTgPez7FJSUiT+8E9JSYG6unqZxMmH6tChA65evQp9fX0YGhpK/FT0zEUAKCgoQFpamrCdlZWF58+fl7tcsZipqSlycnIkZiJlZGQgLy8PJiYmAAATExOcPn1a4rjS21paWsILGgCgsLAQV65cEbbNzMxQVFSEpKSkcuNQUFAQjqtKRddGS0sLf/31l8QYKH7uYkk5OTkS1/y3335DnTp1JJZxX7x4Ea9fv5Zor5qamsQMxIp06NABf/zxB+Tl5ctcv+JkmTR9WlqLFi3K1FfZT+PGjSutr+T5CgoKcO7cuSrHiqKiInJycsqcq+RM1DFjxuDy5cs4d+4cfv75Zzg6OlZaZ3p6usQ1S05OlrgeVY0t4P1MyL59+2LlypW4dOkS7ty5g2PHjlXa/uowNTVFQUEBzpw5I+x78uQJsrOzhftEGgoKClKNcSIiIiIiIvrnMEn4mVm/fj0KCgrQqVMn7Ny5E5mZmcjKysJPP/2Ea9euQU5ODgDQqlUrFBQUIDg4GLdu3UJkZGSZt7HOmjULBw8exO3bt3H+/HkcO3ZMSAS4urri3r17cHd3x7Vr17B//374+Phg9uzZUj+PsCpubm54+vQpRo8ejdTUVNy6dQuHDh3CxIkTK00w1K1bF+7u7jhz5gzOnz+PCRMmoFu3bsLzCMvTt29fmJubw9HREefPn0dqaiqcnJxgY2MjLDX18PBAWFgYwsLCkJ2dDR8fH1y9elWint69eyMuLg5xcXG4du0aXF1dJd4YrK+vj/Hjx2PixImIiYnB7du3kZiYiF27dgF4nwATiUSIjY3Fn3/+KfF225IquzZdu3aFiooKvvvuO9y4cQPbt29HeHh4mTqUlJQwfvx4XLx4ESdPnsTMmTPh4OAg8Zy4t2/fYtKkScjIyMCvv/4KHx8fzJgxQ6pr3LdvX1hZWWHIkCE4ePAg7ty5g5SUFCxcuFBI4krTp5/ajz/+iH379uHatWtwc3PDs2fPMHHixArLq6urY+7cufj2228RERGBmzdv4sKFC/jxxx8REREhlGvZsiW6d++OSZMmoaCgAIMHD66wTkdHR+F6XLlyBcePH4e7uzvGjRsnPI+wqrEVGxuLdevWIT09HXfv3sW2bdtQVFRU5hmlH6N169YYPHgwpkyZglOnTuHixYsYO3YsdHV1K21fafr6+rh06RKysrLw+PHjas0MJSIiIiIiok+DScLPTKtWrXDhwgX07dsXXl5esLCwQKdOnRAcHIy5c+fCz88PAGBpaYmgoCAEBASgXbt2iIqKgr+/v0RdhYWFcHNzg4mJCfr164c2bdoILzXR1dVFfHw8UlNTYWFhARcXF0yaNAkLFy6ssbbo6OggOTkZhYWFsLOzQ7t27eDh4QENDY1Kk1QqKiqYP38+xowZAysrKygrKyM6OrrSc4lEIsTExEBTUxM9evRA3759YWBggJ07dwplRo4ciUWLFmH+/Pno2LEj7t69i+nTp0vUM3HiRIwfP15IMLZs2RK9evWSKBMaGorhw4fD1dUVxsbGmDJlijCDTFdXF4sXL8aCBQugra1d4dttK7s2DRo0wE8//YT4+HiYmZlhx44d8PX1LVOHoaEhhg4dCnt7e3z11Vdo165dmTcL9+nTB61bt0aPHj3g4OCAgQMHlltXRX0aHx+PHj16YOLEiTAyMsKoUaNw584dIfElTZ9+aitWrEBAQAAsLCxw8uRJ7N+/v8JlwcX8/PywaNEi+Pv7w8TEBHZ2dvjll1/QsmVLiXKOjo64ePEihg4dWulScxUVFRw8eBBPnz5F586dMXz4cPTp0wchISFCmarGVv369bF371707t0bJiYm2LBhA3bs2IG2bdt+YM+Ub+vWrejYsSO+/vprWFlZQSwWIz4+vswS48pMmTIFbdq0QadOnaClpYXk5OQajZGIiIiIiIiqTySW9kFRRP8B4eHhmDVrlsQMKyrL19cXMTEx5S5DLubs7Iznz58jJibmH4vrn3Tnzh20bNkSFy5cgKWlZW2HQzUgPz8fGhoaaDFjIOTrqVR9AEm4sazyf6YQEREREdF/U/HfSnl5eZU+t58zCYmIiIiIiIiIiGQck4REREREREREREQyjsuNiYjos8Dlxh+Hy42JiIiIiD5PXG5MREREREREREREUmGSkIiIiIiIiIiISMYxSUhERERERERERCTjmCQkIiIiIiIiIiKScUwSEhERERERERERyTgmCYmIiIiIiIiIiGQck4REREREREREREQyjklCIiIiIiIiIiIiGcckIRERERERERERkYyTr+0AiIiIatKp+evRrFmz2g6DiIiIiIjoP4UzCYmIiIiIiIiIiGQck4REREREREREREQyjklCIiIiIiIiIiIiGcckIRERERERERERkYxjkpCIiIiIiIiIiEjGMUlIREREREREREQk45gkJCIiIiIiIiIiknFMEhIREREREREREck4+doOgIiIqCZ9EeAK+XoqtR3Gv9KNZdG1HQIREREREf1LcSYhERERERERERGRjGOSkIiIiIiIiIiISMYxSUhERERERERERCTjmCQkIiIiIiIiIiKScUwSEhERERERERERyTgmCYmIiIiIiIiIiGQck4REREREREREREQyjklCIiIiIiIiIiIiGcckIRERERERERERkYxjkpCIiIiIiIiIiEjGMUlIREREREREREQk45gkJCIiIiIiIiIiknH/2SShr68vLC0tP7qeY8eOwdjYGEVFRR8f1AdwdnbGkCFDPvl59PX1sWbNmk9+no+RmJgIkUiE58+f13YoNebRo0fQ0tLC77//XtuhfHJ37tyBSCRCenp6bYfyn1dT329ERERERERE0qpWkvDRo0eYNm0amjdvDkVFRTRp0gR2dnb47bffPlV8n5ynpye+//571Knzn82XSggPD0f9+vXL7D979iymTp36zwdUDd27d0dubi40NDRqO5QK9ezZE7NmzZK6fOPGjTFu3Dj4+Ph8uqD+JfT09JCbm4t27drVWgynTp2CtbU1GjZsCGVlZRgbG2P16tW1Fo80RCIRYmJiajsMIiIiIiIiknHy1Sk8bNgwvHv3DhERETAwMMDDhw9x9OhRPH369FPF90mlpKTg+vXrGDFixEfV8/btWygoKNRQVJ+GlpbWP37O8PBwhIeHIzExUaryCgoKaNKkyacN6gO9e/cOdevW/aBjJ0yYgC5duuCHH36ApqZmDUf28QoLCyESiT46US4nJ1fr109VVRUzZsyAubk5VFVVcerUKUybNg2qqqr/+iT5f9XH3BtERERERET07yF1VuD58+c4deoUAgIC0KtXL7Ro0QJdunSBl5cXBgwYAKD85YbPnz+HSCQSEkXFS0rj4uJgYWEBJSUldO3aFZcvXxaOKZ4NFxMTAyMjIygpKcHW1hb37t0rN7YTJ06gbt26+OOPPyT2z5kzBz169KiwTdHR0fjqq6+gpKQk7Cte5rdx40bo6elBRUUFI0aMkFgCW7xE2N/fHzo6OjAyMgIAXL58Gb1794aysjIaNmyIqVOn4sWLF8JxhYWFmD17NurXr4+GDRvC09MTYrFYIqbylgVbWlrC19dXok+nTp0KbW1tKCkpoV27doiNjUViYiImTJiAvLw8iEQiiEQi4bjS9ebk5GDw4MFQU1NDvXr14ODggIcPH5bph8jISOjr60NDQwOjRo3CX3/9VWF/fqzSy42Lx0FsbCzatGkDFRUVDB8+HC9fvkRERAT09fWhqakJd3d3FBYWCvXo6+vDz88PY8aMgZqaGnR0dBAcHCxxLmnbHxYWBgMDAygqKmL8+PFISkrC2rVrhf69c+cOnj17BkdHR2hpaUFZWRmtW7fG1q1bhbrMzMzQpEkT7Nu375P1XbHq3F+xsbEwNTWFoqIi7t69i7dv38LT0xO6urpQVVVF165dhfs2Ly8PysrKSEhIkDjf3r17oaqqihcvXpR7/yclJaFLly5QVFRE06ZNsWDBAhQUFAifSzPefX19hdnLOjo6mDlzZoXtb9++PUaPHo22bdtCX18fY8eOhZ2dHU6ePFlpv/Xs2RPu7u6YNWsWNDU1oa2tjf/97394+fIlJkyYAHV1dbRq1Qq//vqrxHFVta9nz56YOXMmPD090aBBAzRp0kSibfr6+gCAb775BiKRSNguVp3778mTJxg9ejSaNWsGFRUVmJmZYceOHcLnGzduhK6ubplHKwwaNAjjx48Xtn/55Rd07NgRSkpKMDAwwOLFiyXaJBKJsGHDBgwePBiqqqpYunQpCgsLMWnSJLRs2RLKyspo06YN1q5dK3GegoICzJw5U/j+mz9/PsaPHy/xuAWxWIyVK1fCwMAAysrKsLCwwM8//1xhm4mIiIiIiKjmSJ0kVFNTg5qaGmJiYvDmzZuPPvG8efOwatUqnD17Fo0bN8agQYPw7t074fNXr15h2bJliIiIQHJyMvLz8zFq1Khy6+rRowcMDAwQGRkp7CsoKMBPP/2ECRMmVBjDiRMn0KlTpzL7b9y4gV27duGXX35BQkIC0tPT4ebmJlHm6NGjyMzMxOHDhxEbG4tXr16hX79+0NTUxNmzZ7F7924cOXIEM2bMEI4JDAxEWFgYtmzZglOnTuHp06fVThwVFRWhf//+SElJwU8//YSMjAysWLECcnJy6N69O9asWYN69eohNzcXubm5mDt3bpk6xGIxhgwZgqdPnyIpKQmHDx/GzZs3MXLkSIlyN2/eRExMDGJjYxEbG4ukpCSsWLGiWvF+rFevXmHdunWIjo5GQkICEhMTMXToUMTHxyM+Ph6RkZH43//+VyaR8MMPP8Dc3Bznz5+Hl5cXvv32Wxw+fBiA9O0vHgd79uxBeno61q1bBysrK0yZMkXoXz09PXh7eyMjIwO//vorMjMzERoaikaNGknU1aVLlyoTVTVJmvvL398fmzdvxtWrV9G4cWNMmDABycnJiI6OxqVLlzBixAj069cP169fh4aGBgYMGICoqCiJ82zfvl1Itpb2+++/w97eHp07d8bFixcRGhqKLVu2YOnSpVK34+eff8bq1auxceNGXL9+HTExMTAzM5P6+AsXLiAlJQU2NjZVlo2IiECjRo2QmpoKd3d3TJ8+HSNGjED37t1x/vx52NnZYdy4cXj16lW12hcREQFVVVWcOXMGK1euxJIlS4SxePbsWQDA1q1bkZubK2wD1b///v77b3Ts2BGxsbG4cuUKpk6dinHjxuHMmTMAgBEjRuDx48c4fvy4cMyzZ89w8OBBODo6AgAOHjyIsWPHYubMmcjIyMDGjRsRHh6OZcuWSZzLx8cHgwcPxuXLlzFx4kQUFRWhWbNm2LVrFzIyMrBo0SJ899132LVrl3BMQEAAoqKisHXrVuE7vfQy64ULF2Lr1q0IDQ3F1atX8e2332Ls2LFISkqq8voRERERERHRx5F6ubG8vDzCw8MxZcoUbNiwAR06dICNjQ1GjRoFc3Pzap/Yx8cHtra2AN7/Ed2sWTPs27cPDg4OAN4vYQsJCUHXrl2FMiYmJkhNTUWXLl3K1Ddp0iRs3boV8+bNAwDExcXh1atXQn3luXPnDnR0dMrs//vvv4WYACA4OBgDBgxAYGCgsJxSVVUVmzdvFpYZb9q0Ca9fv8a2bdugqqoKAAgJCcHAgQMREBAAbW1trFmzBl5eXhg2bBgAYMOGDTh48GC1+u3IkSNITU1FZmamMIPRwMBA+FxDQwMikajSZZ9HjhzBpUuXcPv2bejp6QF4P2Opbdu2OHv2LDp37gzgfUIyPDwc6urqAIBx48bh6NGjZRIGn9K7d+8QGhqKVq1aAQCGDx+OyMhIPHz4EGpqajA1NUWvXr1w/PhxiSSftbU1FixYAAAwMjJCcnIyVq9eDVtbW6nb//btW0RGRkos1VZQUICKiopE/+bk5KB9+/ZCwrn0bDAA0NXVxYULF2q2cyohzf21fv16WFhYAHifkNqxYwfu378v3BNz585FQkICtm7diuXLl8PR0RFOTk549eoVVFRUkJ+fj7i4OOzZs6fcGNavXw89PT2EhIRAJBLB2NgYDx48wPz587Fo0SKpljfn5OSgSZMm6Nu3L+rWrYvmzZuXe/+X1qxZM/z5558oKCiAr68vJk+eXOUxFhYWWLhwIQDAy8sLK1asQKNGjTBlyhQAwKJFixAaGopLly6hW7duUrfP3NxceCZl69atERISgqNHj8LW1lYYW/Xr1y9zz1b3/tPV1ZX4p4C7uzsSEhKwe/dudO3aFQ0aNEC/fv2wfft29OnTBwCwe/duNGjQQNhetmwZFixYIMwsNDAwgJ+fHzw9PSWeqzlmzBhMnDhR4vyLFy8Wfm/ZsiVSUlKwa9cuYcwFBwfDy8sL33zzDYD334/x8fHCMS9fvkRQUBCOHTsGKysr4fynTp3Cxo0by030vnnzRuKfVvn5+eX2DREREREREVWtWg8hGzZsGB48eIADBw7Azs4OiYmJ6NChA8LDw6t94uI/AgGgQYMGaNOmDTIzM4V98vLyErP8jI2NUb9+fYkyJTk7O+PGjRs4ffo0ACAsLAwODg5Cwq48r1+/llhqXKx58+ZCgrA41qKiImRlZQn7zMzMJJ5DmJmZCQsLC4nzWVtbC8fl5eUhNzdXot2l2yiN9PR0NGvWTEgQfojMzEzo6ekJCTIAMDU1LdO/+vr6QoICAJo2bYpHjx5VWG9OTo4w41RNTQ0uLi44efJkmX3VoaKiIiQIAUBbWxv6+voSM9e0tbXLxFWyn4u3i9smbftbtGgh1bMcp0+fjujoaFhaWsLT0xMpKSllyigrKwsz0MpTso+k+Vm+fHmlMVV1fykoKEgk98+fPw+xWAwjIyOJ8yQlJeHmzZsAgAEDBkBeXh4HDhwAAOzZswfq6ur46quvyo0hMzMTVlZWEIlEwj5ra2u8ePEC9+/frzT+YiNGjMDr169hYGCAKVOmYN++fRJLXyty8uRJpKWlYcOGDVizZo2w7Lb0eCw5M7Jkf8jJyaFhw4YSsxa1tbUBQBhr0rav9D9RqrqPilX3/issLMSyZctgbm6Ohg0bQk1NDYcOHUJOTo5QxtHREXv27BESa1FRURg1ahTk5OQAAOfOncOSJUsk+qh45mzJ8Vve99aGDRvQqVMnaGlpQU1NDZs2bRLOnZeXh4cPH0okeOXk5NCxY0dhOyMjA3///TdsbW0lzr9t2zZhDJbm7+8PDQ0N4afkPU1ERERERETVU60XlwAQng9oa2uLRYsWYfLkyfDx8YGzs7Mwc6bkc/ZKLnGsSsk/tsvbrmgf8P4tsgMHDsTWrVthYGCA+Pj4Kl+Y0ahRIzx79kzquEqeu3TyUSwWVxhbRfvLU6dOnTLPKSzZh8rKylLXVZGKYi29v/TLCEQiUZnnmZWko6Mj8Ty6vXv3Ys+ePRKJmHr16lUr1vJiqG5cJcsB0re/sgRzSf3798fdu3cRFxeHI0eOoE+fPnBzc8OqVauEMk+fPq004Viy36TRoEGDapUHJMehsrKyxHZRURHk5ORw7tw5IWFUrDghq6CggOHDh2P79u0YNWoUtm/fjpEjR0JevvyvkfL6uXhsF++varzr6ekhKysLhw8fxpEjR+Dq6ooffvgBSUlJlb4so2XLlgDeJ/MfPnwIX19fjB49Gp06dZLo6+LEH1D1WCuOuXisSdO+iuqVZrxW97jAwECsXr0aa9asgZmZGVRVVTFr1iy8fftWKDNw4EAUFRUhLi4OnTt3xsmTJxEUFCR8XlRUhMWLF2Po0KFl6i/5D5XS98auXbvw7bffIjAwEFZWVlBXV8cPP/wgLHUu2YaSSl774rbFxcVBV1dXopyiomK5bfby8sLs2bOF7fz8fCYKiYiIiIiIPlC1k4SlmZqaCs+VKk6C5Obmon379gAqTn6cPn0azZs3B/D+uVjZ2dkwNjYWPi8oKEBaWpow8yQrKwvPnz+XKFPa5MmTMWrUKDRr1gytWrWCtbV1pbG3b98eGRkZZfbn5OTgwYMHwrLL3377DXXq1Kl09p6pqSkiIiLw8uVL4Q/o5ORk4TgNDQ00bdoUp0+fFl6mUlBQgHPnzqFDhw5CPVpaWsjNzRW28/Pzcfv2bWHb3Nwc9+/fR3Z2drnxKCgoSLzEo6JYc3JycO/ePeEP6oyMDOTl5cHExKTSYysjLy8PQ0NDYbtx48ZQVlaW2PdPKZ5RWnK7eOx8TPsr6l8tLS04OzvD2dkZX375pfBMwGJXrlxBz549K6y3pvuoqvurtPbt26OwsBCPHj3Cl19+WWE5R0dHfPXVV7h69SqOHz8OPz+/Csuamppiz549Esm0lJQUqKurC0mgqsY78D6hOWjQIAwaNAhubm4wNjbG5cuXJe6byojFYmHmXE2OR2naJ426detWec9K4+TJkxg8eDDGjh0L4H3S7fr16xJjWllZGUOHDkVUVBRu3LgBIyMjidl8HTp0QFZWVrX76OTJk+jevTtcXV2FfSVn/2loaEBbWxupqanC+CosLMSFCxdgaWkJAMJLdHJycqR6hiTwPnlYUQKRiIiIiIiIqkfqJOGTJ08wYsQITJw4Eebm5lBXV0daWhpWrlyJwYMHA3j/B2i3bt2wYsUK6Ovr4/Hjx8IzvkpbsmQJGjZsCG1tbXz//fdo1KiRxFsu69atC3d3d6xbtw5169bFjBkz0K1bt0qfR2ZnZwcNDQ0sXboUS5YsqbJNdnZ2iIiIKLNfSUkJ48ePx6pVq5Cfn4+ZM2fCwcGh0uf8OTo6wsfHB+PHj4evry/+/PNPuLu7Y9y4ccJsJQ8PD6xYsQKtW7eGiYkJgoKCJN6aDAC9e/dGeHg4Bg4cCE1NTXh7e0vM7LKxsUGPHj0wbNgwBAUFwdDQENeuXYNIJEK/fv2gr6+PFy9e4OjRo7CwsICKigpUVFQkztG3b1+Ym5vD0dERa9asQUFBAVxdXWFjY1Pt5c//VsnJyVi5ciWGDBmCw4cPY/fu3YiLiwPwce3X19fHmTNncOfOHaipqaFBgwbw9fVFx44d0bZtW7x58waxsbESiZlXr17h3LlzVS4RrklV3V+lGRkZCc8cDAwMRPv27fH48WMcO3YMZmZmsLe3B/B+/Glra8PR0RH6+vro1q1bhXW6urpizZo1cHd3x4wZM5CVlQUfHx/Mnj1bmHVc1XgPDw9HYWEhunbtChUVFURGRkJZWRktWrQo95w//vgjmjdvLiRET506hVWrVsHd3b26XVgladonDX19fRw9ehTW1tZQVFSEpqbmB8VjaGiIPXv2ICUlBZqamggKCsIff/xRJvHt6OiIgQMH4urVq0JCsdiiRYvw9ddfQ09PDyNGjECdOnVw6dIlXL58udIXzhgaGmLbtm04ePAgWrZsicjISJw9e1aY0Qm8f0aiv78/DA0NYWxsjODgYDx79kxIsKqrq2Pu3Ln49ttvUVRUhC+++AL5+flISUmBmpqaxBuYiYiIiIiIqOZV6+3GXbt2xerVq9GjRw+0a9cO3t7emDJlCkJCQoRyYWFhePfuHTp16gQPD48K/7BcsWIFPDw80LFjR+Tm5uLAgQMSz/hTUVHB/PnzMWbMGFhZWUFZWRnR0dGVN6ZOHTg7O6OwsBBOTk5Vtmns2LHIyMiQeNYg8P4P3qFDh8Le3h5fffUV2rVrh/Xr11dal4qKCg4ePIinT5+ic+fOGD58OPr06SPRN3PmzIGTkxOcnZ2FJXnFD/Ev5uXlhR49euDrr7+Gvb09hgwZIvFMPuD9s+A6d+6M0aNHw9TUFJ6ensJMpO7du8PFxQUjR46ElpYWVq5cWSZWkUiEmJgYaGpqokePHujbty8MDAywc+fOKvvsv2LOnDk4d+4c2rdvDz8/PwQGBsLOzg7Ax7V/7ty5kJOTg6mpKbS0tJCTkwMFBQV4eXnB3NwcPXr0gJycnMRY3b9/P5o3b17pDL2aVtX9VZ6tW7fCyckJc+bMQZs2bTBo0CCcOXNGYvmmSCTC6NGjcfHiReGNuBXR1dVFfHw8UlNTYWFhARcXF0yaNEniHwdVjff69etj06ZNsLa2hrm5OY4ePYpffvkFDRs2LPecRUVF8PLygqWlJTp16oTg4GCsWLFCqn8aVJc07ZNGYGAgDh8+DD09PWEG9ofw9vZGhw4dYGdnh549e6JJkyblJoZ79+6NBg0aICsrC2PGjJH4zM7ODrGxsTh8+DA6d+6Mbt26ISgoqMKkbDEXFxcMHToUI0eORNeuXfHkyROJWYUAMH/+fIwePRpOTk6wsrKCmpoa7OzsJJYx+/n5YdGiRfD394eJiQns7Ozwyy+/SCQbiYiIiIiI6NMQiUs/EOwTS0xMRK9evfDs2TPUr1+/3DLh4eGYNWtWmVl20pgyZQoePnwovFyhKp6ensjLy8PGjRsBAL6+voiJian2M+Lo30NfXx+zZs3CrFmzajsUAECXLl0wa9asMgmZT0Ga+4vo36CoqAgmJiZwcHCodNl6deTn50NDQwMtZgyEfD2Vqg+QQTeWVf7PNiIiIiIi+vwU/62Ul5dX6bsiPvqZhP8WeXl5OHv2LKKiorB//36pj/v+++/x448/orCwsMwLG4g+1qNHjzB8+HCMHj26tkMhqlV3797FoUOHYGNjgzdv3iAkJAS3b9/+R5LnREREREREVLXPJkk4ePBgpKamYtq0abC1tZX6OA0NDXz33XefMDKSZY0bN4anp2dth0FU6+rUqYPw8HDMnTsXYrEY7dq1w5EjRz7qZUlERERERERUc/7x5cZERESfApcbV43LjYmIiIiIZI+0y42lfwUnERERERERERERfZaYJCQiIiIiIiIiIpJxTBISERERERERERHJOCYJiYiIiIiIiIiIZByThERERERERERERDKOSUIiIiIiIiIiIiIZxyQhERERERERERGRjGOSkIiIiIiIiIiISMYxSUhERERERERERCTjmCQkIiIiIiIiIiKScfK1HQAREVFNOjV/PZo1a1bbYRAREREREf2ncCYhERERERERERGRjGOSkIiIiIiIiIiISMYxSUhERERERERERCTjmCQkIiIiIiIiIiKScUwSEhERERERERERyTgmCYmIiIiIiIiIiGQck4REREREREREREQyjklCIiIiIiIiIiIiGSdf2wEQERHVpC8CXCFfT6W2w/hkbiyLru0QiIiIiIjoM8SZhERERERERERERDKOSUIiIiIiIiIiIiIZxyQhERERERERERGRjGOSkIiIiIiIiIiISMYxSUhERERERERERCTjmCQkIiIiIiIiIiKScUwSEhERERERERERyTgmCYmIiIiIiIiIiGQck4REREREREREREQyjklCIiIiIiIiIiIiGcckIRERERERERERkYxjkpCIiIiIiIiIiEjGMUlI9An17NkTs2bNErb19fWxZs2aWouH/j18fX1haWlZ22H8qzg7O2PIkCG1HQYREREREZFMYpKQPkt//PEHPDw8YGhoCCUlJWhra+OLL77Ahg0b8OrVq1qL6+zZs5g6dWqtnf/foHTiVFbNnTsXR48ere0wasWdO3cgEomQnp5e26EQERERERHR/5Gv7QCIatqtW7dgbW2N+vXrY/ny5TAzM0NBQQGys7MRFhYGHR0dDBo0qFZi09LSqpXz/hu8e/cOdevWre0wPtrbt2+hoKDw0fWoqalBTU2tBiIiIiIiIiIi+nicSUifHVdXV8jLyyMtLQ0ODg4wMTGBmZkZhg0bhri4OAwcOFAoGxQUBDMzM6iqqkJPTw+urq548eKF8Pndu3cxcOBAaGpqQlVVFW3btkV8fLzweVJSErp06QJFRUU0bdoUCxYsQEFBQYWxlV5uLBKJsHnzZnzzzTdQUVFB69atceDAAYljMjIyYG9vDzU1NWhra2PcuHF4/PhxpX2QkpKCHj16QFlZGXp6epg5cyZevnwJANi2bRvU1NRw/fp1oby7uzuMjIyEMvr6+vDz88OYMWOgpqYGHR0dBAcHS5wjJycHgwcPhpqaGurVqwcHBwc8fPhQ+Lx4OW1YWBgMDAygqKiI8ePHIykpCWvXroVIJIJIJMKdO3fw7NkzODo6QktLC8rKymjdujW2bt1aaRtrQvHy1sWLF6Nx48aoV68epk2bhrdv3wplevbsiRkzZmD27Nlo1KgRbG1tAVR+XTZu3AhdXV0UFRVJnG/QoEEYP368RP8UKyoqwpIlS9CsWTMoKirC0tISCQkJwueJiYkQiUR4/vy5sC89PV3oQ6Dq8Vqavr4+li5dCicnJ6ipqaFFixbYv38//vzzT+HampmZIS0tTeK4PXv2oG3btlBUVIS+vj4CAwPL1Lt8+XJMnDgR6urqaN68Of73v/8Jn7ds2RIA0L59e4hEIvTs2VPi+FWrVqFp06Zo2LAh3Nzc8O7duwrbQERERERERDWDSUL6rDx58gSHDh2Cm5sbVFVVyy0jEomE3+vUqYN169bhypUriIiIwLFjx+Dp6Sl87ubmhjdv3uDEiRO4fPkyAgIChNlfv//+O+zt7dG5c2dcvHgRoaGh2LJlC5YuXVqtmBcvXgwHBwdcunQJ9vb2cHR0xNOnTwEAubm5sLGxgaWlJdLS0pCQkICHDx/CwcGhwvouX74MOzs7DB06FJcuXcLOnTtx6tQpzJgxAwDg5OQknKegoAAJCQnYuHEjoqKiJPrshx9+gLm5Oc6fPw8vLy98++23OHz4MABALBZjyJAhePr0KZKSknD48GHcvHkTI0eOlIjlxo0b2LVrF/bs2YP09HSsW7cOVlZWmDJlCnJzc5Gbmws9PT14e3sjIyMDv/76KzIzMxEaGopGjRpVqx8/1NGjR5GZmYnjx49jx44d2LdvHxYvXixRJiIiAvLy8khOTsbGjRurvC4jRozA48ePcfz4caGOZ8+e4eDBg3B0dCw3jrVr1yIwMBCrVq3CpUuXYGdnh0GDBkkkc6tS2XityOrVq2FtbY0LFy5gwIABGDduHJycnDB27FicP38ehoaGcHJyglgsBgCcO3cODg4OGDVqFC5fvgxfX194e3sjPDxcot7AwEB06tQJFy5cgKurK6ZPn45r164BAFJTUwEAR44cQW5uLvbu3Sscd/z4cdy8eRPHjx9HREQEwsPDy9RNRERERERENY/LjemzcuPGDYjFYrRp00Zif6NGjfD3338DeJ9ICQgIAACJZ+O1bNkSfn5+mD59OtavXw/g/Wy5YcOGwczMDABgYGAglF+/fj309PQQEhICkUgEY2NjPHjwAPPnz8eiRYtQp450OXhnZ2eMHj0aALB8+XIEBwcjNTUV/fr1Q2hoKDp06IDly5cL5cPCwqCnp4fs7GwYGRmVqe+HH37AmDFjhLa1bt0a69atg42NDUJDQ6GkpISNGzfC3NwcM2fOxN69e+Hj44POnTtL1GNtbY0FCxYAAIyMjJCcnIzVq1fD1tYWR44cwaVLl3D79m3o6ekBACIjI9G2bVucPXtWqOvt27eIjIyUWGatoKAAFRUVNGnSRNiXk5OD9u3bo1OnTgDez0T7pygoKCAsLAwqKipo27YtlixZgnnz5sHPz0+4hoaGhli5cqVwzKJFi6q8Lv369cP27dvRp08fAMDu3bvRoEEDYbu0VatWYf78+Rg1ahQAICAgAMePH8eaNWvw448/StWWysZrRezt7TFt2jShXaGhoejcuTNGjBgBAJg/fz6srKzw8OFDNGnSBEFBQejTpw+8vb0BvB8bGRkZ+OGHH+Ds7CxRr6urq1DH6tWrkZiYCGNjY2E8NGzYUGIcAICmpiZCQkIgJycHY2NjDBgwAEePHsWUKVPKxP7mzRu8efNG2M7Pz5eqn4iIiIiIiKgsziSkz1LJ2YLA+5lL6enpaNu2rURS4fjx47C1tYWuri7U1dXh5OSEJ0+eCMtuZ86ciaVLl8La2ho+Pj64dOmScGxmZiasrKwkzmVtbY0XL17g/v37Usdqbm4u/K6qqgp1dXU8evQIwPtZW8ePHxeeX6empgZjY2MAwM2bN8ut79y5cwgPD5c4xs7ODkVFRbh9+zaA94mYLVu2IDQ0FK1atRKSgSVZWVmV2c7MzBTarqenJyQIAcDU1BT169cXygBAixYtpHoO4/Tp0xEdHQ1LS0t4enoiJSWlwrJRUVESbZPmJycnp8L6LCwsoKKiItHOFy9e4N69e8K+4uRlMWmui6OjI/bs2SOMt6ioKIwaNQpycnJlYsjPz8eDBw9gbW0tsd/a2lqiP6tS2XitSMnxp62tDQBCkrHkvuIxmZmZWW6c169fR2FhYbn1ikQiNGnSRKijMm3btpXoo6ZNm1Z4nL+/PzQ0NISfkuORiIiIiIiIqoczCemzYmhoCJFIJCxrLFY8o0pZWVnYd/fuXdjb28PFxQV+fn5o0KABTp06hUmTJgnPQJs8eTLs7OwQFxeHQ4cOwd/fH4GBgXB3d4dYLC6TjCxekll6f2VKv8xDJBIJz7IrKirCwIEDhZmPJTVt2rTc+oqKijBt2jTMnDmzzGfNmzcXfj9x4gTk5OTw4MEDvHz5EvXq1asy1uJ2ldf28vZXtOS7tP79++Pu3buIi4vDkSNH0KdPH7i5uWHVqlVlyg4aNAhdu3aVqt5iOjo61SoPoNJ2SHNdBg4ciKKiIsTFxaFz5844efIkgoKCpD4nINmfxbMai8cYgDLP6qtsvFak5PgrPld5+4rHZGXjvqJ6i+sp/YzGquKp6jgvLy/Mnj1b2M7Pz2eikIiIiIiI6AMxSUiflYYNG8LW1hYhISFwd3evNEmVlpaGgoICBAYGCgmYXbt2lSmnp6cHFxcXuLi4wMvLC5s2bYK7uztMTU2xZ88eiaRJSkoK1NXVoaurWyPt6dChA/bs2QN9fX3Iy0t3u3bo0AFXr16FoaFhhWVSUlKwcuVK/PLLL1iwYAHc3d0REREhUeb06dNltotny5mamiInJwf37t0TkjIZGRnIy8uDiYlJpfEpKChIzDgrpqWlBWdnZzg7O+PLL7/EvHnzyk0SqqurQ11dvdJzVMfFixfx+vVrIYF8+vRpqKmpoVmzZhUeI811UVZWxtChQxEVFYUbN27AyMgIHTt2LLdsvXr1oKOjg1OnTqFHjx7C/pSUFHTp0gXA/38zdm5uLjQ1NQG8f3FJaRWN15piamqKU6dOSexLSUmBkZFRubMky1P8dujyxkF1KCoqQlFR8aPqICIiIiIiove43Jg+O+vXr0dBQQE6deqEnTt3IjMzE1lZWfjpp59w7do1IZHRqlUrFBQUIDg4GLdu3UJkZCQ2bNggUdesWbNw8OBB3L59G+fPn8exY8eEJJirqyvu3bsHd3d3XLt2Dfv374ePjw9mz54t9fMIq+Lm5oanT59i9OjRSE1Nxa1bt3Do0CFMnDixwgTL/Pnz8dtvv8HNzQ3p6em4fv06Dhw4ICSK/vrrL4wbNw7u7u7o378/tm/fjl27dmH37t0S9SQnJ2PlypXIzs7Gjz/+iN27d8PDwwMA0LdvX5ibm8PR0RHnz59HamoqnJycYGNjU2Zpbmn6+vo4c+YM7ty5g8ePH6OoqAiLFi3C/v37cePGDVy9ehWxsbFVJhtrytu3bzFp0iThxSk+Pj6YMWNGpddQ2uvi6OiIuLg4hIWFYezYsZXGMW/ePAQEBGDnzp3IysrCggULkJ6eLvS5oaEh9PT04Ovri+zsbMTFxZV5q3Bl47WmzJkzB0ePHoWfnx+ys7MRERGBkJAQzJ07V+o6GjduDGVlZeGFL3l5eTUaIxEREREREVUfk4T02WnVqhUuXLiAvn37wsvLCxYWFujUqROCg4Mxd+5c+Pn5AQAsLS0RFBSEgIAAtGvXDlFRUfD395eoq7CwEG5ubjAxMUG/fv3Qpk0b4aUmurq6iI+PR2pqKiwsLODi4oJJkyZh4cKFNdYWHR0dJCcno7CwEHZ2dmjXrh08PDygoaFRYRLL3NwcSUlJuH79Or788ku0b98e3t7ewjJYDw8PqKqqCi/daNu2LQICAuDi4oLff/9dqGfOnDk4d+4c2rdvDz8/PwQGBsLOzg7A+yWgMTEx0NTURI8ePdC3b18YGBhg586dVbZp7ty5kJOTg6mpKbS0tJCTkwMFBQV4eXnB3NwcPXr0gJycHKKjoz+2+6TSp08ftG7dGj169ICDgwMGDhwIX1/fSo+R9rr07t0bDRo0QFZWFsaMGVNpnTNnzsScOXMwZ84cmJmZISEhAQcOHEDr1q0BvF+Gu2PHDly7dg0WFhYICAgo8ybtysZrTenQoQN27dqF6OhotGvXDosWLcKSJUskXlpSFXl5eaxbtw4bN26Ejo4OBg8eXKMxEhERERERUfWJxOU9TIqIZJq+vj5mzZol8fbnz5GzszOeP3+OmJiY2g6FakB+fj40NDTQYsZAyNdTqfqA/6gby/6ZBDoREREREX0eiv9WysvLq/R9BJxJSEREREREREREJOOYJCQiIiIiIiIiIpJxfLsxEZVx586d2g7hHxEeHl7bIRARERERERH9K3AmIRERERERERERkYxjkpCIiIiIiIiIiEjGMUlIREREREREREQk45gkJCIiIiIiIiIiknFMEhIREREREREREck4JgmJiIiIiIiIiIhkHJOEREREREREREREMo5JQiIiIiIiIiIiIhnHJCEREREREREREZGMk6/tAIiIiGrSqfnr0axZs9oOg4iIiIiI6D+FMwmJiIiIiIiIiIhkHJOEREREREREREREMo5JQiIiIiIiIiIiIhnHJCEREREREREREZGMY5KQiIiIiIiIiIhIxjFJSEREREREREREJOOYJCQiIiIiIiIiIpJxTBISERERERERERHJOCYJiYiIiIiIiIiIZJx8bQdARERUk74IcIV8PZXaDqPG3FgWXdshEBERERGRDOBMQiIiIiIiIiIiIhnHJCEREREREREREZGMY5KQiIiIiIiIiIhIxjFJSEREREREREREJOOYJCQiIiIiIiIiIpJxTBISERERERERERHJOCYJiYiIiIiIiIiIZByThERERERERERERDKOSUIiIiIiIiIiIiIZxyQhERERERERERGRjGOSkIiIiIiIiIiISMYxSUhERERERERERCTjmCQk+kR69uyJWbNmCdv6+vpYs2bNJz1neHg46tev/0nPQTXD2dkZQ4YMqe0w/lVK3zNERERERET0z2GSkD47f/zxBzw8PGBoaAglJSVoa2vjiy++wIYNG/Dq1atai+vs2bOYOnXqJz3HyJEjkZ2d/UnP8bH+iWTpf8HatWsRHh5e22HUisTERIhEIjx//ry2QyEiIiIiIqL/I1/bARDVpFu3bsHa2hr169fH8uXLYWZmhoKCAmRnZyMsLAw6OjoYNGhQrcSmpaX1yc+hrKwMZWXlT36eD/H27VsoKCjUdhgfrabaoaGhUQPREBEREREREdUMziSkz4qrqyvk5eWRlpYGBwcHmJiYwMzMDMOGDUNcXBwGDhwolA0KCoKZmRlUVVWhp6cHV1dXvHjxQvj87t27GDhwIDQ1NaGqqoq2bdsiPj5e+DwpKQldunSBoqIimjZtigULFqCgoKDC2ErPoBOJRNi8eTO++eYbqKiooHXr1jhw4IDEMRkZGbC3t4eamhq0tbUxbtw4PH78uMJzlF5u7OvrC0tLS4SFhaF58+ZQU1PD9OnTUVhYiJUrV6JJkyZo3Lgxli1bJlGPSCRCaGgo+vfvD2VlZbRs2RK7d++WKHP58mX07t0bysrKaNiwIaZOnSrRf8XLaf39/aGjowMjIyP07NkTd+/exbfffguRSASRSCRVX38qPXv2xIwZMzBjxgzUr18fDRs2xMKFCyEWi4Uy+vr6WLp0KZydnaGhoYEpU6YAAFJSUtCjRw8oKytDT08PM2fOxMuXLwEAXl5e6NatW5nzmZubw8fHB0DZ5cZv3rzBzJkz0bhxYygpKeGLL77A2bNnhc/LW0oeExMj9CEAXLx4Eb169YK6ujrq1auHjh07Ii0trcL2i0QibNy4EV9//TVUVFRgYmKC3377DTdu3EDPnj2hqqoKKysr3Lx5U+K40NBQtGrVCgoKCmjTpg0iIyPL1FvR2L5z5w569eoFANDU1IRIJIKzs7NwbFFRETw9PdGgQQM0adIEvr6+FcZPRERERERENYdJQvpsPHnyBIcOHYKbmxtUVVXLLVMyoVKnTh2sW7cOV65cQUREBI4dOwZPT0/hczc3N7x58wYnTpzA5cuXERAQADU1NQDA77//Dnt7e3Tu3BkXL15EaGgotmzZgqVLl1Yr5sWLF8PBwQGXLl2Cvb09HB0d8fTpUwBAbm4ubGxsYGlpibS0NCQkJODhw4dwcHCo1jlu3ryJX3/9FQkJCdixYwfCwsIwYMAA3L9/H0lJSQgICMDChQtx+vRpieO8vb0xbNgwXLx4EWPHjsXo0aORmZkJAHj16hX69esHTU1NnD17Frt378aRI0cwY8YMiTqOHj2KzMxMHD58GLGxsdi7dy+aNWuGJUuWIDc3F7m5uVX29acWEREBeXl5nDlzBuvWrcPq1auxefNmiTI//PAD2rVrh3PnzsHb2xuXL1+GnZ0dhg4dikuXLmHnzp04deqU0H5HR0ecOXNGIrl29epVXL58GY6OjuXG4enpiT179iAiIgLnz5+HoaEh7OzshPEgDUdHRzRr1gxnz57FuXPnsGDBAtStW7fSY/z8/ODk5IT09HQYGxtjzJgxmDZtGry8vIQEY8nrum/fPnh4eGDOnDm4cuUKpk2bhgkTJuD48eMS9VY0tvX09LBnzx4AQFZWFnJzc7F27VrhuIiICKiqquLMmTNYuXIllixZgsOHD5cb+5s3b5Cfny/xQ0RERERERB+Gy43ps3Hjxg2IxWK0adNGYn+jRo3w999/A3ifjAoICAAAiRcktGzZEn5+fpg+fTrWr18PAMjJycGwYcNgZmYGADAwMBDKr1+/Hnp6eggJCYFIJIKxsTEePHiA+fPnY9GiRahTR7r8u7OzM0aPHg0AWL58OYKDg5Gamop+/fohNDQUHTp0wPLly4XyYWFh0NPTQ3Z2NoyMjKQ6R1FREcLCwqCurg5TU1P06tULWVlZiI+PR506ddCmTRsEBAQgMTFRYvbbiBEjMHnyZADvE0mHDx9GcHAw1q9fj6ioKLx+/Rrbtm0TErIhISEYOHAgAgICoK2tDQBQVVXF5s2bJZbnysnJQV1dHU2aNBH2VdbXn5qenh5Wr14NkUiENm3a4PLly1i9erUwYxAAevfujblz5wrbTk5OGDNmjDCGWrdujXXr1sHGxgahoaFo164dzM3NsX37dnh7ewMAoqKi0Llz53Kv28uXLxEaGorw8HD0798fALBp0yYcPnwYW7Zswbx586RqS05ODubNmwdjY2MhrqpMmDBBSDzPnz8fVlZW8Pb2hp2dHQDAw8MDEyZMEMqvWrUKzs7OcHV1BQDMnj0bp0+fxqpVq4QZgkDlY7tBgwYAgMaNG5eZHVlytmXr1q0REhKCo0ePwtbWtkzs/v7+WLx4sVR9Q0RERERERJXjTEL67JScLQgAqampSE9PR9u2bfHmzRth//Hjx2FrawtdXV2oq6vDyckJT548EZaMzpw5E0uXLoW1tTV8fHxw6dIl4djMzExYWVlJnMva2hovXrzA/fv3pY7V3Nxc+F1VVRXq6up49OgRAODcuXM4fvw41NTUhJ/i5E/p5Z+V0dfXh7q6urCtra0NU1NTiUSmtra2cN5iVlZWZbaLZxJmZmbCwsJCYsamtbU1ioqKkJWVJewzMzOT6vl9lfV1acuXL5foE2l+KtOtWzeJ62hlZYXr16+jsLBQ2NepUyeJY86dO4fw8HCJc9jZ2aGoqAi3b98G8H5WX1RUFABALBZjx44dFc4ivHnzJt69ewdra2thX926ddGlSxehz6Uxe/ZsTJ48GX379sWKFSukGiclx2Bxcrc4WVu87++//xZm6WVmZkrECby/9qXjrGxsSxsPADRt2rTC47y8vJCXlyf83Lt3r8r6iYiIiIiIqHycSUifDUNDQ4hEIly7dk1if/GstJIv9Lh79y7s7e3h4uICPz8/NGjQAKdOncKkSZPw7t07AMDkyZNhZ2eHuLg4HDp0CP7+/ggMDIS7uzvEYnGZZGTxc+xK769M6aWgIpEIRUVFAN7PACyemVda06ZNP+oclZ23MsVtK6/9pcsAqHDZd2mV9XVpLi4u1V5y/bFKt6OoqAjTpk3DzJkzy5Rt3rw5AGDMmDFYsGABzp8/j9evX+PevXsYNWpUufVXNHZK9nOdOnUknpUIQBirxXx9fTFmzBjExcXh119/hY+PD6Kjo/HNN99U2LaSY6H4XOXtKzk+KouzvHqLj5FmjFXnOEVFRSgqKlZZJxEREREREVWNMwnps9GwYUPY2toiJCREmA1YkbS0NBQUFCAwMBDdunWDkZERHjx4UKacnp4eXFxcsHfvXsyZMwebNm0CAJiamiIlJUUiaZOSkgJ1dXXo6urWSHs6dOiAq1evQl9fH4aGhhI/0ibfPkbpZxSePn1amMloamqK9PR0iX5OTk5GnTp1qlwGraCgIDFLr1hFfV1agwYNyvRHVT/VbWfr1q0hJydX4THF16a8cxXPnGzWrBl69OiBqKgoREVFoW/fvsJMvdKKjzt16pSw7927d0hLS4OJiQmA92/H/uuvvyT6PD09vUxdRkZG+Pbbb3Ho0CEMHToUW7durbT91WViYiIRJ/B+7BfHKY3iPipvHBAREREREVHtYJKQPivr169HQUEBOnXqhJ07dyIzMxNZWVn46aefcO3aNSHx06pVKxQUFCA4OBi3bt1CZGQkNmzYIFHXrFmzcPDgQdy+fRvnz5/HsWPHhESIq6sr7t27B3d3d1y7dg379++Hj48PZs+eLfXzCKvi5uaGp0+fYvTo0UhNTcWtW7dw6NAhTJw48R9JruzevRthYWHIzs6Gj48PUlNTJV7MoaSkhPHjx+PKlSs4fvw43N3dMW7cuAoTYcX09fVx4sQJ/P7778Kbmivr60/t3r17mD17NrKysrBjxw4EBwfDw8Oj0mPmz5+P3377DW5ubkhPT8f169dx4MCBMjMfHR0dER0djd27d2Ps2LEV1qeqqorp06dj3rx5SEhIQEZGBqZMmYJXr15h0qRJAICuXbtCRUUF3333HW7cuIHt27cjPDxcqOP169eYMWMGEhMTcffuXSQnJ+Ps2bM13o/z5s1DeHg4NmzYgOvXryMoKAh79+6VeGZjVVq0aAGRSITY2Fj8+eefEm/FJiIiIiIiotrBJCF9Vlq1aoULFy6gb9++8PLygoWFBTp16oTg4GDMnTsXfn5+AABLS0sEBQUhICAA7dq1Q1RUFPz9/SXqKiwshJubG0xMTNCvXz+0adNGeKmJrq4u4uPjkZqaCgsLC7i4uGDSpElYuHBhjbVFR0cHycnJKCwshJ2dHdq1awcPDw9oaGjUWCKyMosXL0Z0dDTMzc0RERGBqKgomJqaAgBUVFRw8OBBPH36FJ07d8bw4cPRp08fhISEVFnvkiVLcOfOHbRq1QpaWloAKu/rT83JyQmvX79Gly5d4ObmBnd3d0ydOrXSY8zNzZGUlITr16/jyy+/RPv27eHt7V1mGfiIESPw5MkTvHr1CkOGDKm0zhUrVmDYsGEYN24cOnTogBs3buDgwYPQ1NQE8H4G5U8//YT4+HiYmZlhx44d8PX1FY6Xk5PDkydP4OTkBCMjIzg4OKB///41/mKPIUOGYO3atfjhhx/Qtm1bbNy4EVu3bkXPnj2lrkNXVxeLFy/GggULoK2tXeat2ERERERERPTPE4lLP+SKiGSeSCTCvn37qkxs/df17NkTlpaWWLNmTW2HQjUgPz8fGhoaaDFjIOTrqdR2ODXmxrLo2g6BiIiIiIj+w4r/VsrLy0O9evUqLMeZhERERERERERERDKOSUIiIiIiIiIiIiIZJ1/bARDRv4+sPIUgMTGxtkMgIiIiIiIi+lfgTEIiIiIiIiIiIiIZxyQhERERERERERGRjGOSkIiIiIiIiIiISMYxSUhERERERERERCTjmCQkIiIiIiIiIiKScUwSEhERERERERERyTgmCYmIiIiIiIiIiGQck4REREREREREREQyjklCIiIiIiIiIiIiGSdf2wEQERHVpFPz16NZs2a1HQYREREREdF/CmcSEhERERERERERyTgmCYmIiIiIiIiIiGQck4REREREREREREQyjklCIiIiIiIiIiIiGcckIRERERERERERkYxjkpCIiIiIiIiIiEjGMUlIREREREREREQk45gkJCIiIiIiIiIiknHytR0AERFRTfoiwBXy9VRqO4xqu7EsurZDICIiIiIiGcaZhERERERERERERDKOSUIiIiIiIiIiIiIZxyQhERERERERERGRjGOSkIiIiIiIiIiISMYxSUhERERERERERCTjmCQkIiIiIiIiIiKScUwSEhERERERERERyTgmCYmIiIiIiIiIiGQck4REREREREREREQyjklCIiIiIiIiIiIiGcckIRERERERERERkYxjkpCIiIiIiIiIiEjGMUlIREREREREREQk45gk/Af17NkTs2bNErb19fWxZs2aWovnvy48PBz169f/5OdxdnbGkCFDPvl5Pgcc0zUjMTERIpEIz58/r+1QiIiIiIiISEbIRJLwjz/+gIeHBwwNDaGkpARtbW188cUX2LBhA169elVrcZ09exZTp06ttfNL686dOxCJREhPT6/tUD6pitq5du1ahIeH10pM/zW1PaafPHmCfv36QUdHB4qKitDT08OMGTOQn59fazFVpfQ/D4iIiIiIiIhqg3xtB/Cp3bp1C9bW1qhfvz6WL18OMzMzFBQUIDs7G2FhYdDR0cGgQYNqJTYtLa1aOe+/SWFhIUQiEerU+ffmqzU0NGo7hE/u3bt3qFu37kfXU9tjuk6dOhg8eDCWLl0KLS0t3LhxA25ubnj69Cm2b99eq7F9rt6+fQsFBYXaDoOIiIiIiIg+0r83M1NDXF1dIS8vj7S0NDg4OMDExARmZmYYNmwY4uLiMHDgQKFsUFAQzMzMoKqqCj09Pbi6uuLFixfC53fv3sXAgQOhqakJVVVVtG3bFvHx8cLnSUlJ6NKlCxQVFdG0aVMsWLAABQUFFcZWemmmSCTC5s2b8c0330BFRQWtW7fGgQMHJI7JyMiAvb091NTUoK2tjXHjxuHx48cVnqN4Se7BgwdhYmICNTU19OvXD7m5uRLltm7dChMTEygpKcHY2Bjr168XPmvZsiUAoH379hCJROjZs2e55ypeIhkXFwcLCwsoKSmha9euuHz5cpl4YmNjYWpqCkVFRdy9exfPnj2Dk5MTNDU1oaKigv79++P69etl2tK8eXOoqKjgm2++wZMnTyQ+L29Z8KxZsyTiLSoqQkBAAAwNDaGoqIjmzZtj2bJllbazdL1v3rzBzJkz0bhxYygpKeGLL77A2bNny/TD0aNH0alTJ6ioqKB79+7Iysoqt99qmkgkQmhoKPr37w9lZWW0bNkSu3fvFj4vnjG5a9cu9OzZE0pKSvjpp58AVD4OrKyssGDBAolz/fnnn6hbty6OHz8OoOyYzsnJweDBg6GmpoZ69erBwcEBDx8+FD6X5pr9/PPPMDMzg7KyMho2bIi+ffvi5cuX5bZdU1MT06dPR6dOndCiRQv06dMHrq6uOHnyZKV95uvrC0tLS4SFhaF58+ZQU1PD9OnTUVhYiJUrV6JJkyZo3LixMFakbV9xvZGRkdDX14eGhgZGjRqFv/76S2h/UlIS1q5dC5FIBJFIhDt37gjHnzt3rlpjaP78+TAyMoKKigoMDAzg7e2Nd+/eAQCysrIgEolw7do1iWOCgoKgr68PsVgMoOrvmJ49e2LGjBmYPXs2GjVqBFtbW6Geyr4/AWDTpk3Q09MT7uGgoKAyjwz45Zdf0LFjRygpKcHAwACLFy+u9HuUiIiIiIiIasZnnSR88uQJDh06BDc3N6iqqpZbRiQSCb/XqVMH69atw5UrVxAREYFjx47B09NT+NzNzQ1v3rzBiRMncPnyZQQEBEBNTQ0A8Pvvv8Pe3h6dO3fGxYsXERoaii1btmDp0qXVinnx4sVwcHDApUuXYG9vD0dHRzx9+hQAkJubCxsbG1haWiItLQ0JCQl4+PAhHBwcKq3z1atXWLVqFSIjI3HixAnk5ORg7ty5wuebNm3C999/j2XLliEzMxPLly+Ht7c3IiIiAACpqakAgCNHjiA3Nxd79+6t9Hzz5s3DqlWrcPbsWTRu3BiDBg0SEhXF8fj7+2Pz5s24evUqGjduDGdnZ6SlpeHAgQP47bffIBaLYW9vLxx35swZTJw4Ea6urkhPT0evXr2q3bcA4OXlhYCAAHh7eyMjIwPbt2+HtrZ2tdrp6emJPXv2ICIiAufPn4ehoSHs7OyE61Ts+++/R2BgINLS0iAvL4+JEydWO94P5e3tjWHDhuHixYsYO3YsRo8ejczMTIky8+fPx8yZM5GZmQk7O7sqx4GjoyN27NghJJMAYOfOndDW1oaNjU2ZGMRiMYYMGYKnT58iKSkJhw8fxs2bNzFy5Eip25Gbm4vRo0dj4sSJyMzMRGJiIoYOHSoRQ2UePHiAvXv3lhtfaTdv3sSvv/6KhIQE7NixA2FhYRgwYADu37+PpKQkBAQEYOHChTh9+nS12nfz5k3ExMQgNjYWsbGxSEpKwooVKwC8X8puZWWFKVOmIDc3F7m5udDT0xOOre4YUldXR3h4ODIyMrB27Vps2rQJq1evBgC0adMGHTt2RFRUlMQx27dvx5gxYyASiaT+jomIiIC8vDySk5OxceNGAFV/fyYnJ8PFxQUeHh5IT0+Hra1tmaTrwYMHMXbsWMycORMZGRnYuHEjwsPDy5Qr9ubNG+Tn50v8EBERERER0QcSf8ZOnz4tBiDeu3evxP6GDRuKVVVVxaqqqmJPT88Kj9+1a5e4YcOGwraZmZnY19e33LLfffeduE2bNuKioiJh348//ihWU1MTFxYWisVisdjGxkbs4eEhfN6iRQvx6tWrhW0A4oULFwrbL168EItEIvGvv/4qFovFYm9vb/FXX30lcd579+6JAYizsrLKjWvr1q1iAOIbN25IxKWtrS1s6+npibdv3y5xnJ+fn9jKykosFovFt2/fFgMQX7hwodxzFDt+/LgYgDg6OlrY9+TJE7GysrJ4586dEvGkp6cLZbKzs8UAxMnJycK+x48fi5WVlcW7du0Si8Vi8ejRo8X9+vWTON/IkSPFGhoawvb48ePFgwcPlijj4eEhtrGxEYvFYnF+fr5YUVFRvGnTpnLjr6idJet98eKFuG7duuKoqCjh87dv34p1dHTEK1eulOiHI0eOCGXi4uLEAMSvX78u99w1CYDYxcVFYl/Xrl3F06dPF4vF/7+da9askShT1Th49OiRWF5eXnzixAnhcysrK/G8efOE7ZJj+tChQ2I5OTlxTk6O8PnVq1fFAMSpqalisbjqa3bu3DkxAPGdO3eq1QejRo0SKysriwGIBw4cWGW/+/j4iFVUVMT5+fnCPjs7O7G+vr5w/4rFYnGbNm3E/v7+UrevvHrnzZsn7tq1q7Bd+ntBLK65MbRy5Upxx44dhe2goCCxgYGBsJ2VlSUGIL569apYLJbuO8bGxkZsaWlZ5blLf3+OHDlSPGDAAIkyjo6OEvfwl19+KV6+fLlEmcjISHHTpk3LPYePj48YQJmfFjMGilt9N/I/90NERERERPQp5OXliQGI8/LyKi33Wc8kLFZytiDwfsZYeno62rZtizdv3gj7jx8/DltbW+jq6kJdXR1OTk548uSJsLRx5syZWLp0KaytreHj44NLly4Jx2ZmZsLKykriXNbW1njx4gXu378vdazm5ubC76qqqlBXV8ejR48AvF96ePz4caipqQk/xsbGAN7PVqqIiooKWrVqJWw3bdpUqPPPP//EvXv3MGnSJIl6ly5dWmmdlbGyshJ+b9CgAdq0aSMxi01BQUGinZmZmZCXl0fXrl2FfQ0bNpQ4rrh/KzqPNDIzM/HmzRv06dOnWseVdPPmTbx79w7W1tbCvrp166JLly5lZuqVbGPTpk0BQOj30vr37y/R/1X99O/fv9I4y+ur0vF16tRJ+F2acaClpQVbW1thJtrt27fx22+/wdHRsdwYMjMzoaenJzEzztTUFPXr1y8TS0UsLCzQp08fmJmZYcSIEdi0aROePXtW5XGrV6/G+fPnERMTg5s3b2L27NnCZyXb5+LiIuzX19eHurq6sK2trQ1TU1OJ52Vqa2sL11Da9pWut+T9V5XqjCHg/dLsL774Ak2aNIGamhq8vb2Rk5MjfD5q1CjcvXtXmA0ZFRUFS0tLmJqaApD+O6bk2ClW1fdnVlYWunTpInFM6e1z585hyZIlEucvnmVZ3kumvLy8kJeXJ/zcu3evwr4hIiIiIiKiyn3WLy4xNDQs9xlcBgYGAABlZWVh3927d2Fvbw8XFxf4+fmhQYMGOHXqFCZNmiQseZ08eTLs7OwQFxeHQ4cOwd/fH4GBgXB3d4dYLC6TjBT/35LI0vsrU/rlESKRCEVFRQDeP09v4MCBCAgIKHNccQJB2jqLYyuue9OmTRJJOgCQk5OTOu6qlOwDZWVliW1xBUtHS/ZpRWVKqlOnTplyJZc5l7zeH6qia1re9S/Z78WfFfd3aZs3b8br16+ljuND2lI6vpJL8KUdB46OjvDw8EBwcDC2b9+Otm3bwsLCotzzldcnpfdXdc3k5ORw+PBhpKSk4NChQwgODsb333+PM2fOCM+QLE+TJk3QpEkTGBsbo2HDhvjyyy/h7e2Npk2bSry9ul69esLv5d0nld2P0rSvonorGgelVWcMnT59GqNGjcLixYthZ2cHDQ0NREdHIzAwUCjTtGlT9OrVC9u3b0e3bt2wY8cOTJs2Tfhc2u+Y0o9vkOb7s7LvyJLnX7x4MYYOHVrm/EpKSmX2KSoqQlFRsdz+ICIiIiIiour5rJOEDRs2hK2tLUJCQuDu7l7hcwkBIC0tDQUFBQgMDBRmDu3atatMOT09Pbi4uMDFxQVeXl7YtGkT3N3dYWpqij179kj8IZySkgJ1dXXo6urWSHs6dOiAPXv2QF9fH/LyNXPptLW1oauri1u3blU4I6z4zaWFhYVS1Xn69Gk0b94cAPDs2TNkZ2cLs5HKY2pqioKCApw5cwbdu3cH8P55ktnZ2TAxMRHKFM9+KnmekrS0tHDlyhWJfenp6UKipXXr1lBWVsbRo0cxefLkD2qnoaEhFBQUcOrUKYwZMwbA+6RWWloaZs2aVeFxVampMVLs9OnTcHJykthu3759heWlGQcAMGTIEEybNg0JCQnYvn07xo0bV2FZU1NT5OTk4N69e8Jsu4yMDOTl5QnXtaprBrxPjllbW8Pa2hqLFi1CixYtsG/fPonZgZUpTkQVzxo2NDSU6riqSNM+aSgoKEh9b1UmOTkZLVq0wPfffy/su3v3bplyjo6OmD9/PkaPHo2bN29i1KhRwmcf+h0jzfensbGx8NzPkseV1KFDB2RlZdXYNSIiIiIiIiLpffbLjdevX4+CggJ06tQJO3fuRGZmJrKysvDTTz/h2rVrwiypVq1aoaCgAMHBwbh16xYiIyOxYcMGibpmzZqFgwcP4vbt2zh//jyOHTsmJANcXV1x7949uLu749q1a9i/fz98fHwwe/ZsieWKH8PNzQ1Pnz7F6NGjkZqailu3buHQoUOYOHHiRyUZfH194e/vj7Vr1yI7OxuXL1/G1q1bERQUBABo3LgxlJWVhZcY5OXlVVrfkiVLcPToUVy5cgXOzs5o1KhRmTfYltS6dWsMHjwYU6ZMwalTp4SXbejq6mLw4MEA3i/1TkhIwMqVK5GdnY2QkBAkJCRI1NO7d2+kpaVh27ZtuH79Onx8fCQSUEpKSpg/fz48PT2xbds23Lx5E6dPn8aWLVukbqeqqiqmT5+OefPmISEhARkZGZgyZQpevXqFSZMmSdXf/4Tdu3cjLCwM2dnZ8PHxQWpqKmbMmFHpMVWNA+B9+wcPHgxvb29kZmYKidLy9O3bF+bm5nB0dMT58+eRmpoKJycn2NjYCMtVq7pmZ86cwfLly5GWloacnBzs3bsXf/75Z4VJuPj4eGzduhVXrlzBnTt3EB8fj+nTp8Pa2hr6+vrV6MGqSdM+aejr6+PMmTO4c+cOHj9+LPUsw9IMDQ2Rk5OD6Oho3Lx5E+vWrcO+ffvKlBs6dCjy8/Mxffp09OrVSyJB/aHfMdJ8f7q7uyM+Ph5BQUG4fv06Nm7ciF9//VViduGiRYuwbds2+Pr64urVq8jMzMTOnTuxcOHCD+oTIiIiIiIikt5nnyRs1aoVLly4gL59+8LLywsWFhbo1KkTgoODMXfuXPj5+QEALC0tERQUhICAALRr1w5RUVHw9/eXqKuwsBBubm4wMTFBv3790KZNG6xfvx7A+5lg8fHxSE1NhYWFBVxcXDBp0qQa/eNWR0cHycnJKCwshJ2dHdq1awcPDw9oaGh8VCJy8uTJ2Lx5M8LDw2FmZgYbGxuEh4cLyznl5eWxbt06bNy4ETo6OkLiriIrVqyAh4cHOnbsiNzcXBw4cECYpVeRrVu3omPHjvj6669hZWUFsViM+Ph4YUZZt27dsHnzZgQHB8PS0hKHDh0q07d2dnbw9vaGp6cnOnfujL/++ktiNh3w/q2/c+bMwaJFi2BiYoKRI0cKz3iTtp0rVqzAsGHDMG7cOHTo0AE3btzAwYMHoampWWkb/0mLFy9GdHQ0zM3NERERgaioKOG5cxWpahwUc3R0xMWLF/Hll18KM0bLIxKJEBMTA01NTfTo0QN9+/aFgYEBdu7cKZSp6prVq1cPJ06cgL29PYyMjLBw4UIEBgZW+ExGZWVlbNq0CV988QVMTEwwa9YsfP3114iNjZWm26pFmvZJY+7cuZCTk4OpqSm0tLQkniFYHYMHD8a3336LGTNmwNLSEikpKfD29i5Trl69ehg4cCAuXrxYZtboh37HSPP9aW1tjQ0bNiAoKAgWFhZISEjAt99+K7GM2M7ODrGxsTh8+DA6d+6Mbt26ISgoCC1atPigPiEiIiIiIiLpicTSPOyNSAqJiYno1asXnj17hvr169d2ODJLJBJh3759lc7eJPo3mDJlCq5du4aTJ0/WSH35+fnQ0NBAixkDIV9PpUbq/CfdWBZd2yEQEREREdFnqPhvpby8PIln85f2WT+TkIiI/j1WrVoFW1tbqKqq4tdff0VERIQwG5uIiIiIiIhqF5OERET0j0hNTcXKlSvx119/wcDAAOvWrSv3JUJERERERET0z2OSkGpMz549wdXrtY/XgP6tyntjPBEREREREf07fPYvLiEiIiIiIiIiIqLKMUlIREREREREREQk45gkJCIiIiIiIiIiknFMEhIREREREREREck4JgmJiIiIiIiIiIhkHJOEREREREREREREMo5JQiIiIiIiIiIiIhnHJCEREREREREREZGMY5KQiIiIiIiIiIhIxsnXdgBEREQ16dT89WjWrFlth0FERERERPSfwpmEREREREREREREMo5JQiIiIiIiIiIiIhnHJCEREREREREREZGMY5KQiIiIiIiIiIhIxjFJSEREREREREREJOOYJCQiIiIiIiIiIpJxTBISERERERERERHJOCYJiYiIiIiIiIiIZJx8bQdARERUk74IcIV8PZXaDqPabiyLru0QiIiIiIhIhnEmIRERERERERERkYxjkpCIiIiIiIiIiEjGMUlIREREREREREQk45gkJCIiIiIiIiIiknFMEhIREREREREREck4JgmJiIiIiIiIiIhkHJOEREREREREREREMo5JQiIiIiIiIiIiIhnHJCEREREREREREZGMY5KQiIiIiIiIiIhIxjFJSEREREREREREJOOYJCQiIiIiIiIiIpJxTBLKAF9fX1haWn50PceOHYOxsTGKioo+PqgP4OzsjCFDhnzy8+jr62PNmjWf/DwfIzExESKRCM+fP6/tUGrMo0ePoKWlhd9//722Q/nk7ty5A5FIhPT09NoO5V/jcxzTRERERERE/yVMEn4ijx49wrRp09C8eXMoKiqiSZMmsLOzw2+//VbboX0wT09PfP/996hT5/MYNuHh4ahfv36Z/WfPnsXUqVP/+YCqoXv37sjNzYWGhkZth1Khnj17YtasWVKXb9y4McaNGwcfH59PF9S/hJ6eHnJzc9GuXbvaDqVWVHdsEBERERER0af3eWR7/oWGDRuGixcvIiIiAtnZ2Thw4AB69uyJp0+f1nZoHyQlJQXXr1/HiBEjPqqet2/f1lBEn46WlhZUVFT+0XOGh4ejZ8+eUpdXUFBAkyZNIBKJPl1QH+jdu3cffOyECRMQFRWFZ8+e1WBENaewsLBGZtLKycmhSZMmkJeXr4GoiIiIiIiIiD4ek4SfwPPnz3Hq1CkEBASgV69eaNGiBbp06QIvLy8MGDAAQPnLDZ8/fw6RSITExEQA/3/5XVxcHCwsLKCkpISuXbvi8uXLwjHFs+FiYmJgZGQEJSUl2Nra4t69e+XGduLECdStWxd//PGHxP45c+agR48eFbYpOjoaX331FZSUlIR9xcuYN27cCD09PaioqGDEiBESywWLlwj7+/tDR0cHRkZGAIDLly+jd+/eUFZWRsOGDTF16lS8ePFCOK6wsBCzZ89G/fr10bBhQ3h6ekIsFkvEVN6yYEtLS/j6+kr06dSpU6GtrQ0lJSW0a9cOsbGxSExMxIQJE5CXlweRSASRSCQcV7renJwcDB48GGpqaqhXrx4cHBzw8OHDMv0QGRkJfX19aGhoYNSoUfjrr78q7M+PVXppZvE4iI2NRZs2baCiooLhw4fj5cuXiIiIgL6+PjQ1NeHu7o7CwkKhHn19ffj5+WHMmDFQU1ODjo4OgoODJc4lbfvDwsJgYGAARUVFjB8/HklJSVi7dq3Qv3fu3MGzZ8/g6OgILS0tKCsro3Xr1ti6datQl5mZGZo0aYJ9+/Z9sr4rVp37KzY2FqamplBUVMTdu3fx9u1beHp6QldXF6qqqujatatw3+bl5UFZWRkJCQkS59u7dy9UVVXx4sWLcu//pKQkdOnSBYqKimjatCkWLFiAgoIC4XNpxruvr68we1lHRwczZ86ssP0lr1vz5s2hpqaG6dOno7CwECtXrkSTJk3QuHFjLFu2TOK4j70fnJ2dyx0bxc6dO4dOnTpBRUUF3bt3R1ZWVoVtICIiIiIioprDJOEnoKamBjU1NcTExODNmzcfXd+8efOwatUqnD17Fo0bN8agQYMkZmu9evUKy5YtQ0REBJKTk5Gfn49Ro0aVW1ePHj1gYGCAyMhIYV9BQQF++uknTJgwocIYTpw4gU6dOpXZf+PGDezatQu//PILEhISkJ6eDjc3N4kyR48eRWZmJg4fPozY2Fi8evUK/fr1g6amJs6ePYvdu3fjyJEjmDFjhnBMYGAgwsLCsGXLFpw6dQpPnz6tduKoqKgI/fv3R0pKCn766SdkZGRgxYoVkJOTQ/fu3bFmzRrUq1cPubm5yM3Nxdy5c8vUIRaLMWTIEDx9+hRJSUk4fPgwbt68iZEjR0qUu3nzJmJiYhAbG4vY2FgkJSVhxYoV1Yr3Y7169Qrr1q1DdHQ0EhISkJiYiKFDhyI+Ph7x8fGIjIzE//73P/z8888Sx/3www8wNzfH+fPn4eXlhW+//RaHDx8GIH37i8fBnj17kJ6ejnXr1sHKygpTpkwR+ldPTw/e3t7IyMjAr7/+iszMTISGhqJRo0YSdXXp0gUnT578tJ1VgjT3l7+/PzZv3oyrV6+icePGmDBhApKTkxEdHY1Lly5hxIgR6NevH65fvw4NDQ0MGDAAUVFREufZvn27kFwr7ffff4e9vT06d+6MixcvIjQ0FFu2bMHSpUulbsfPP/+M1atXY+PGjbh+/TpiYmJgZmZW6TE3b97Er7/+ioSEBOzYsQNhYWEYMGAA7t+/j6SkJAQEBGDhwoU4ffo0gJq5H9auXVvu2Cj2/fffIzAwEGlpaZCXl8fEiROl7gMiIiIiIiL6cFzr9gnIy8sjPDwcU6ZMwYYNG9ChQwfY2Nhg1KhRMDc3r3Z9Pj4+sLW1BQBERESgWbNm2LdvHxwcHAC8X94ZEhKCrl27CmVMTEyQmpqKLl26lKlv0qRJ2Lp1K+bNmwcAiIuLw6tXr4T6ynPnzh3o6OiU2f/3338LMQFAcHAwBgwYgMDAQDRp0gQAoKqqis2bN0NBQQEAsGnTJrx+/Rrbtm2DqqoqACAkJAQDBw5EQEAAtLW1sWbNGnh5eWHYsGEAgA0bNuDgwYPV6rcjR44gNTUVmZmZwgxGAwMD4XMNDQ2IRCIhzorquHTpEm7fvi0kMiIjI9G2bVucPXsWnTt3BvA+IRkeHg51dXUAwLhx43D06NEys7A+pXfv3iE0NBStWrUCAAwfPhyRkZF4+PAh1NTUYGpqil69euH48eMSSR1ra2ssWLAAAGBkZITk5GSsXr0atra2Urf/7du3iIyMhJaWllCvgoICVFRUJPo3JycH7du3FxLO+vr6Zdqhq6uLCxcu1GznVEKa+2v9+vWwsLAA8D4BtmPHDty/f1+4J+bOnYuEhARs3boVy5cvh6OjI5ycnPDq1SuoqKggPz8fcXFx2LNnT7kxrF+/Hnp6eggJCYFIJIKxsTEePHiA+fPnY9GiRVI9BzQnJwdNmjRB3759UbduXTRv3rzc+7+koqIihIWFQV1dXRgfWVlZiI+PR506ddCmTRsEBAQgMTER3bp1q5H7QUNDo9yxUWzZsmWwsbEBACxYsAADBgzA33//LTGLudibN28k/hGTn59fZT8RERERERFR+TiT8BMZNmwYHjx4gAMHDsDOzg6JiYno0KEDwsPDq12XlZWV8HuDBg3Qpk0bZGZmCvvk5eUlZvkZGxujfv36EmVKcnZ2xo0bN4TZQWFhYXBwcBASduV5/fp1uX+kN2/eXEgQFsdaVFQksUTQzMxMSBACQGZmJiwsLCTOZ21tLRyXl5eH3NxciXaXbqM00tPT0axZMyFB+CEyMzOhp6cnMdPJ1NS0TP/q6+sLCREAaNq0KR49elRhvTk5OcKMUzU1Nbi4uODkyZNl9lWHioqKkCAEAG1tbejr60vMXNPW1i4TV8l+Lt4ubpu07W/RooVEgrAi06dPR3R0NCwtLeHp6YmUlJQyZZSVlfHq1asK6yjZR9L8LF++vNKYqrq/FBQUJJL758+fh1gshpGRkcR5kpKScPPmTQDAgAEDIC8vjwMHDgAA9uzZA3V1dXz11VflxpCZmQkrKyuJZ0xaW1vjxYsXuH//fqXxFxsxYgRev34NAwMDTJkyBfv27ZNYrlye0uNWW1sbpqamEknJkmPmU90PJZXs66ZNmwJAhcf6+/tDQ0ND+CkZFxEREREREVUPZxJ+QsXPB7S1tcWiRYswefJk+Pj4wNnZWfgjvORz9qrzwofSL6wo7wUWFb3UonHjxhg4cCC2bt0KAwMDxMfHC89Tq0ijRo2keplE8TlLnrt08lEsFlcYW3VexFGnTp0yzyks2YfKyspS11WRimItvb9u3boSn4tEokpfcKGjoyPxPLq9e/diz549EktU69WrV61Yy4uhunGVLAdI3/7KEswl9e/fH3fv3kVcXByOHDmCPn36wM3NDatWrRLKPH36tNKEY8l+k0aDBg2qVR6QHIfKysoS20VFRZCTk8O5c+cgJycncVxxQlZBQQHDhw/H9u3bMWrUKGzfvh0jR46s8EUl5fVz8dgu3l/VeNfT00NWVhYOHz6MI0eOwNXVFT/88AOSkpLKjINi1R0zn+p+qCim4jorOtbLywuzZ88WtvPz85koJCIiIiIi+kBMEv6DTE1NERMTAwBCEiQ3Nxft27cHUHHy4/Tp02jevDkA4NmzZ8jOzoaxsbHweUFBAdLS0oSlhVlZWXj+/LlEmdImT56MUaNGoVmzZmjVqhWsra0rjb19+/bIyMgosz8nJwcPHjwQll3+9ttvqFOnTqWz90xNTREREYGXL18KyaXk5GThOA0NDTRt2hSnT58WXqZSUFCAc+fOoUOHDkI9WlpayM3NFbbz8/Nx+/ZtYdvc3Bz3799HdnZ2ufEoKChIvMSjolhzcnJw7949IfmQkZGBvLw8mJiYVHpsZeTl5WFoaChsN27cGMrKyhL7/inFM0pLbhePnY9pf0X9q6WlBWdnZzg7O+PLL78UnglY7MqVK5W+6bmm+6iq+6u09u3bo7CwEI8ePcKXX35ZYTlHR0d89dVXuHr1Ko4fPw4/P78Ky5qammLPnj0SybaUlBSoq6tDV1cXQNXjHXif0Bw0aBAGDRoENzc3GBsb4/LlyxL3zceoqftBmntPGoqKilBUVPzoeoiIiIiIiIjLjT+JJ0+eoHfv3vjpp5+E53ft3r0bK1euxODBgwG8/2O+W7duWLFiBTIyMnDixAksXLiw3PqWLFmCo0eP4sqVK3B2dkajRo0wZMgQ4fO6devC3d0dZ86cwfnz5zFhwgR069at0ueR2dnZQUNDA0uXLq30hSUly586darMfiUlJYwfPx4XL17EyZMnMXPmTDg4OFT6nD9HR0fhuCtXruD48eNwd3fHuHHjoK2tDQDw8PDAihUrsG/fPly7dg2urq4Sb00GgN69eyMyMhInT57ElStXMH78eImZXTY2NujRoweGDRuGw4cP4/bt28JLGoD3SyJfvHiBo0eP4vHjx+Uuce3bty/Mzc3h6OiI8+fPIzU1FU5OTrCxsan28ud/q+TkZKxcuRLZ2dn48ccfsXv3bnh4eAD4uPbr6+vjzJkzuHPnDh4/foyioiIsWrQI+/fvx40bN3D16lXExsZKJJdevXqFc+fOVbgs91Oo6v4qzcjISHjm4N69e3H79m2cPXsWAQEBiI+PF8rZ2NhAW1sbjo6O0NfXR7du3Sqs09XVFffu3YO7uzuuXbuG/fv3w8fHB7NnzxZmHVc13sPDw7FlyxZcuXIFt27dQmRkJJSVldGiRYuP76T/U1P3Q3ljg4iIiIiIiGoXk4SfgJqaGrp27YrVq1ejR48eaNeuHby9vTFlyhSEhIQI5cLCwvDu3Tt06tQJHh4eFb7JdMWKFfDw8EDHjh2Rm5uLAwcOSDzjT0VFBfPnz8eYMWNgZWUFZWVlREdHVxpjnTp14OzsjMLCQjg5OVXZprFjxyIjI0PiWYPA+1ldQ4cOhb29Pb766iu0a9cO69evr7QuFRUVHDx4EE+fPkXnzp0xfPhw9OnTR6Jv5syZAycnJzg7O8PKygrq6ur45ptvJOrx8vJCjx498PXXX8Pe3h5DhgyReCYf8P5ZcJ07d8bo0aNhamoKT09PYQZT9+7d4eLigpEjR0JLSwsrV64sE6tIJEJMTAw0NTXRo0cP9O3bFwYGBti5c2eVffZfMWfOHJw7dw7t27eHn58fAgMDYWdnB+Dj2j937lzIycnB1NQUWlpayMnJgYKCAry8vGBubo4ePXpATk5OYqzu378fzZs3r3SGXk2r6v4qz9atW+Hk5IQ5c+agTZs2GDRoEM6cOSOx1FUkEmH06NG4ePEiHB0dK61PV1cX8fHxSE1NhYWFBVxcXDBp0iSJfxxUNd7r16+PTZs2wdraGubm5jh69Ch++eUXNGzY8AN7pqyauh/KGxtERERERERUu0Ti0g+5on+NxMRE9OrVC8+ePUP9+vXLLRMeHo5Zs2aVmWUnjSlTpuDhw4fCyxWq4unpiby8PGzcuBEA4Ovri5iYmGo/I47+PfT19TFr1izMmjWrtkMBAHTp0gWzZs3CmDFjPvm5pLm/6L8lPz8fGhoaaDFjIOTrqdR2ONV2Y1nl/9whIiIiIiL6EMV/K+Xl5VX6/gPOJJRBeXl5OHLkCKKiouDu7i71cd9//z1atGhRI88SIyrt0aNHGD58OEaPHl3boRARERERERHJHL64RAYNHjwYqampmDZtGmxtbaU+TkNDA999990njIxkWePGjeHp6VnbYRARERERERHJJC43JiKizwKXGxMREREREZXF5cZEREREREREREQkFSYJiYiIiIiIiIiIZByThERERERERERERDKOSUIiIiIiIiIiIiIZxyQhERERERERERGRjGOSkIiIiIiIiIiISMYxSUhERERERERERCTjmCQkIiIiIiIiIiKScUwSEhERERERERERyTj52g6AiIioJp2avx7NmjWr7TCIiIiIiIj+UziTkIiIiIiIiIiISMYxSUhERERERERERCTjmCQkIiIiIiIiIiKScUwSEhERERERERERyTgmCYmIiIiIiIiIiGQck4RERERERERERMRJKt4AAD17SURBVEQyjklCIiIiIiIiIiIiGcckIRERERERERERkYxjkpCIiIiIiIiIiEjGydd2AERERDXpiwBXyNdTqe0wquXGsujaDoGIiIiIiGQcZxISERERERERERHJuP/X3n1HVXGtbQB/DiC9KIgI0mwgSFHEKKCABYkYgsZYUTGWiA17L1iimBhbvNGoiVhivfZGsYtdUWwgKqKgkhAbiFFU2N8fXubjwKFINEfk+a111nJm9ux5955yOK97ZpgkJCIiIiIiIiIiquCYJCQiIiIiIiIiIqrgmCQkIiIiIiIiIiKq4JgkJCIiIiIiIiIiquCYJCQiIiIiIiIiIqrgmCQkIiIiIiIiIiKq4JgkJCIiIiIiIiIiquCYJCQiIiIiIiIiIqrgmCQkIiIiIiIiIiKq4JgkJCIiIiIiIiIiquCYJCQiIiIiIiIiIqrgmCSkT8K0adPQoEEDZYdRoa1atQqVK1dWdhifBG9vbwwfPlzZYRAREREREVEFwiThR6Z3795o3769ssMohEk4KkmXLl1w48YNpcawdOlSODk5QV9fH/r6+nBzc0NERIRSYyrOkSNHIJPJ8PTpU2WHQkRERERERBUck4T0r3r9+rWyQ6ACXr169V7q0dLSQrVq1d5LXWVlbm6OOXPm4Pz58zh//jxatmyJgIAAXLt2TalxfaqEEHjz5o2ywyAiIiIiIqL3gEnCj5y3tzdCQkIwduxYGBoaonr16pg2bZq0vFu3bujatavcOq9fv0bVqlURHh4O4O0P+R9++AG1atWClpYWnJ2dsWXLFql83mimgwcPwtXVFdra2nB3d0diYiKAt7eRTp8+HZcuXYJMJoNMJsOqVasAACkpKQgICICuri709fXRuXNn/Pnnn1LdeSMQV65ciVq1akFDQwOrV6+GkZERsrOz5eLu2LEjevXqVWRf3Lt3D127doWhoSF0dHTg6uqKM2fOyJVZu3YtrK2tYWBggK5du+LZs2fSssjISDRr1gyVK1eGkZERvvjiCyQlJUnL79y5A5lMhm3btqFFixbQ1taGs7MzTp06JbeNFStWwMLCAtra2ujQoQPmz59f6Dbb3bt3o1GjRtDU1EStWrUwffr0fyWZktffy5Ytk2Ls1KmT3Ei1vNGqYWFhMDMzg42NDQDg/v376NKlC6pUqQIjIyMEBATgzp07AICoqChoamoWGvEWEhICLy8vAIpvN166dClq164NdXV12NraYu3atdKyvP6Oi4uT5j19+hQymQxHjhwBADx58gSBgYEwNjaGlpYW6tatKx3Xivj7+8PPzw82NjawsbHBrFmzoKuri9OnTxfbbzKZDMuWLcMXX3wBbW1t2NnZ4dSpU7h16xa8vb2ho6MDNzc3ueOlpPbl1fvrr7+iQ4cO0NbWRt26dbFr1y6p/S1atAAAVKlSBTKZDL1795bWzc3NLfK8V+TcuXPw8fFB1apVYWBgAC8vL1y4cEFa/j6vFVFRUXB1dYWGhgZiYmKQlJSEgIAAmJiYQFdXF40bN8aBAwfktpWWloZ27dpBS0sLNWvWxPr162FtbY2FCxdKZTIyMvDtt9+iWrVq0NfXR8uWLXHp0qVi201ERERERETvB5OE5cDq1auho6ODM2fO4IcffsCMGTOwf/9+AEBgYCB27dqFrKwsqXxUVBSeP3+Ojh07AgAmT56M8PBwLF26FNeuXcOIESPQo0cPHD16VG47kyZNwrx583D+/HmoqamhT58+AN7eRjpq1CjUr18faWlpSEtLQ5cuXSCEQPv27fH48WMcPXoU+/fvR1JSErp06SJX761bt7B582Zs3boVcXFx6Ny5M3JycqRkCQA8fPgQe/bswTfffKOwD7KysuDl5YUHDx5g165duHTpEsaOHYvc3FypTFJSEnbs2IE9e/Zgz549OHr0KObMmSMtf/78OUaOHIlz587h4MGDUFFRQYcOHeTqyOuH0aNHIy4uDjY2NujWrZuU4Dtx4gSCg4MxbNgwxMXFwcfHB7NmzZJbPyoqCj169EBISAji4+OxbNkyrFq1qlC5DyWvv3fv3o3IyEjExcVh8ODBcmUOHjyIhIQE7N+/H3v27MHff/+NFi1aQFdXF8eOHcPx48ehq6uLzz//HK9evULr1q1RuXJlbN26VaojJycHmzdvRmBgoMI4tm/fjmHDhmHUqFG4evUqBgwYgG+++QaHDx8udVumTJmC+Ph4REREICEhAUuXLkXVqlVLtW5OTg42btyI58+fw83NrcTyM2fORK9evRAXF4d69eqhe/fuGDBgACZMmIDz588DAIYMGfLO7Zs+fTo6d+6My5cvw8/PD4GBgXj8+DEsLCyk/kxMTERaWhoWLVokrVfcea/Is2fPEBQUhJiYGJw+fRp169aFn5+flCh/n9eKsWPHIiwsDAkJCXByckJWVhb8/Pxw4MABXLx4Eb6+vvD390dKSoq0Tq9evfDgwQMcOXIEW7duxfLly5Geni4tF0KgXbt2+OOPP7Bv3z7ExsbCxcUFrVq1wuPHjxW2OTs7G5mZmXIfIiIiIiIiKiNBH5WgoCAREBAgTXt5eYlmzZrJlWncuLEYN26cEEKIV69eiapVq4o1a9ZIy7t16yY6deokhBAiKytLaGpqipMnT8rV0bdvX9GtWzchhBCHDx8WAMSBAwek5Xv37hUAxIsXL4QQQoSGhgpnZ2e5OqKjo4WqqqpISUmR5l27dk0AEGfPnpXWq1SpkkhPT5dbd+DAgaJt27bS9MKFC0WtWrVEbm6uwn5ZtmyZ0NPTE48ePVK4PDQ0VGhra4vMzExp3pgxY0STJk0UlhdCiPT0dAFAXLlyRQghRHJysgAgfv3110LtSUhIEEII0aVLF9GuXTu5egIDA4WBgYE03bx5czF79my5MmvXrhWmpqZFxvK+hIaGClVVVZGamirNi4iIECoqKiItLU0I8fYYMzExEdnZ2VKZ3377Tdja2sr1f3Z2ttDS0hJRUVFCCCFCQkJEy5YtpeVRUVFCXV1dPH78WAghRHh4uFw/uLu7i/79+8vF16lTJ+Hn5yeE+P/+vnjxorT8yZMnAoA4fPiwEEIIf39/8c0337xTH1y+fFno6OgIVVVVYWBgIPbu3VviOgDE5MmTpelTp04JAOK3336T5m3YsEFoamqWun2K6s3KyhIymUxEREQIIf7/3Hvy5IlcPSWd96Xx5s0boaenJ3bv3i2EeL/Xih07dpS4fXt7e7F48WIhhBAJCQkCgDh37py0/ObNmwKAWLBggRBCiIMHDwp9fX3x8uVLuXpq164tli1bpnAboaGhAkChj9UQf1F7Ypdy9SEiIiIiIvpQMjIyBACRkZFRbDmOJCwHnJyc5KZNTU2lETiVKlVCp06dsG7dOgBvR8vt3LlTGt0VHx+Ply9fwsfHB7q6utJnzZo1hW6dzL8dU1NTAJAb6VNQQkICLCwsYGFhIc2zt7dH5cqVkZCQIM2zsrKCsbGx3Lr9+/dHdHQ07t+/DwAIDw9H7969IZPJFG4rLi4ODRs2hKGhYZHxWFtbQ09PT64N+eNPSkpC9+7dUatWLejr66NmzZoAIDfaCSi+HxITE/HZZ5/JlS84HRsbixkzZsj1d//+/ZGWloa///67UNwxMTFyZUvziYmJKbIfLC0tYW5uLk27ubkhNzdXun0cABwdHaGuri4X861bt6Cnpydtw9DQEC9fvpSOk8DAQBw5cgQPHjwAAKxbtw5+fn6oUqWKwjgSEhLg4eEhN8/Dw0Pu2CjJwIEDsXHjRjRo0ABjx47FyZMnS1zH1tYWcXFxOH36NAYOHIigoCDEx8cDAGbPni3Xj/n3ff79bmJiAuBtP+Wf9/LlS2m0Wmnbl79eHR0d6OnpFXteKVoPKHw8F5Seno7g4GDY2NjAwMAABgYGyMrKktr4Pq8Vrq6uctPPnz/H2LFjpfNfV1cX169fl7admJgINTU1uLi4SOvUqVNH7tiJjY1FVlYWjIyM5LafnJxcaPt5JkyYgIyMDOmTmppabJ8SERERERFR0dSUHQCVrFKlSnLTMplM7hbZwMBAeHl5IT09Hfv374empibatm0LAFK5vXv3okaNGnL1aGhoFLmdvGRdwVtx8xNCKEzqFZyvo6NTqEzDhg3h7OyMNWvWwNfXF1euXMHu3buL3JaWllaRyxTFn9eG/PH7+/vDwsICK1asgJmZGXJzc+Hg4FDoxR3F9YOiNgsh5KZzc3Mxffp0fPXVV4Vi1NTULDTP1dVV7rl8pVFwXxYnL97i9klubi4aNWokJZDyy0vwfvbZZ6hduzY2btyIgQMHYvv27cU+H7DgNgH5/lNRUZHm5Sn4Ypu2bdvi7t272Lt3Lw4cOIBWrVph8ODB+PHHH4vcprq6OurUqQPgbd+eO3cOixYtwrJlyxAcHIzOnTtLZc3MzKR/K9rvJZ0TxbVPUb156xR3XpV1vd69e+Ovv/7CwoULYWVlBQ0NDbi5uckd3+/rWlHw+BkzZgyioqLw448/ok6dOtDS0sLXX38tbbvgOZIn//zc3FyYmppKz6PMr+CzLvPHVTA2IiIiIiIiKhsmCT8B7u7usLCwwKZNmxAREYFOnTpJo8Ts7e2hoaGBlJQU6QUTZaGuro6cnBy5efb29khJSUFqaqo0mjA+Ph4ZGRmws7Mrsc5+/fphwYIFuH//Plq3bi03IrEgJycn/Prrr3j8+HGxowmL8ujRIyQkJGDZsmVo3rw5AOD48ePvXE+9evVw9uxZuXl5z6vL4+LigsTERClRVRItLa1Sly2NlJQUPHjwQEqAnTp1CioqKtILShRxcXHBpk2bpBdGFKV79+5Yt24dzM3NoaKignbt2hVZ1s7ODsePH5d7Gc3JkyelYyMv+ZiWloaGDRsCgMJkqbGxMXr37o3evXujefPmGDNmTLFJwoKEENJLcgwNDct0/ChSUvtKI+88LXhulUVMTAyWLFkCPz8/AEBqaioePnwoV+ZDXStiYmLQu3dvdOjQAcDbZ4jmvfQGeHvevHnzBhcvXkSjRo0AvH12Zv4X4bi4uOCPP/6AmpoarK2t37H1RERERERE9E8xSfgJkMlk6N69O3755RfcuHFD7sUJenp6GD16NEaMGIHc3Fw0a9YMmZmZOHnyJHR1dREUFFSqbVhbWyM5ORlxcXEwNzeHnp4eWrduDScnJwQGBmLhwoV48+YNBg0aBC8vr0K3IyoSGBiI0aNHY8WKFVizZk2xZbt164bZs2dLb+U1NTXFxYsXYWZmVqqXUuS9sXf58uUwNTVFSkoKxo8fX6q25zd06FB4enpi/vz58Pf3x6FDhxARESE3emzq1Kn44osvYGFhgU6dOkFFRQWXL1/GlStX8N13373zNt+VpqYmgoKC8OOPPyIzMxMhISHo3LkzqlevXuQ6gYGBmDt3LgICAjBjxgyYm5sjJSUF27Ztw5gxY6TblwMDAzF9+nTMmjULX3/9tcKRkXnGjBmDzp07Sy+f2L17N7Zt2ya99VZLSwtNmzbFnDlzYG1tjYcPH2Ly5MlydUydOhWNGjVC/fr1kZ2djT179hSbhJs4cSLatm0LCwsLPHv2DBs3bsSRI0cQGRn5Ll1YKiW1rzSsrKwgk8mwZ88e+Pn5QUtLC7q6umWKp06dOli7di1cXV2RmZmJMWPGFBqB+6GuFXXq1MG2bdvg7+8PmUyGKVOmyI16rFevHlq3bo1vv/0WS5cuRaVKlTBq1ChoaWlJ507r1q3h5uaG9u3b4/vvv4etrS0ePHiAffv2oX379qW6phAREREREVHZ8ZmEn4jAwEDEx8ejRo0ahZ6TNnPmTEydOhVhYWGws7ODr68vdu/eLT2TrzQ6duyIzz//HC1atICxsTE2bNgAmUyGHTt2oEqVKvD09ETr1q1Rq1YtbNq0qVR16uvro2PHjtDV1UX79u2LLauuro7o6GhUq1YNfn5+cHR0xJw5c6CqqlqqbamoqGDjxo2IjY2Fg4MDRowYgblz55Zq3fw8PDzwyy+/YP78+XB2dkZkZCRGjBghlyzz9fXFnj17sH//fjRu3BhNmzbF/PnzYWVl9c7bK4s6dergq6++gp+fH9q0aQMHBwcsWbKk2HW0tbVx7NgxWFpa4quvvoKdnR369OmDFy9eyI0srFu3Lho3bozLly8X+VbjPO3bt8eiRYswd+5c1K9fH8uWLUN4eDi8vb2lMitXrsTr16/h6uqKYcOGFUqiqqurY8KECXBycoKnpydUVVWxcePGIrf5559/omfPnrC1tUWrVq1w5swZREZGwsfHp9hYy6I07StJjRo1MH36dIwfPx4mJiZyb09+VytXrsSTJ0/QsGFD9OzZEyEhIahWrVqhch/iWrFgwQJUqVIF7u7u8Pf3h6+vr9zzBwFgzZo1MDExgaenJzp06ID+/ftDT09POndkMhn27dsHT09P9OnTBzY2NujatSvu3LkjPSOSiIiIiIiIPhyZKOphUUT/Ah8fH9jZ2eGnn35Sdihl1r9/f1y/fr3Yl4n8W6ZNm4YdO3a88zMOif5t9+7dg4WFhfSsyfchMzMTBgYGsBriDzV97fdS57/l1qyik99ERERERET/RN5vpYyMjGIfMcbbjUkpHj9+jOjoaBw6dAj/+c9/lB3OO/nxxx/h4+MDHR0dREREYPXq1SWO1COq6A4dOoSsrCw4OjoiLS0NY8eOhbW1NTw9PZUdGhEREREREYFJQlISFxcXPHnyRHr2WHly9uxZ/PDDD3j27Blq1aqFn376Cf369VN2WEQftdevX2PixIm4ffs29PT04O7ujnXr1hV6izMREREREREpB283JiKiTwJvNyYiIiIiIiqstLcb88UlREREREREREREFRyThERERERERERERBUck4REREREREREREQVHJOEREREREREREREFRyThERERERERERERBUck4REREREREREREQVHJOEREREREREREREFRyThERERERERERERBUck4REREREREREREQVnJqyAyAiInqfjo9bAnNzc2WHQUREREREVK5wJCEREREREREREVEFxyQhERERERERERFRBcckIRERERERERERUQXHJCEREREREREREVEFxyQhERERERERERFRBcckIRERERERERERUQXHJCEREREREREREVEFxyQhERERERERERFRBcckIRERERERERERUQXHJCEREREREREREVEFxyQhERERERERERFRBcckIRERERERERERUQXHJCEREREREREREVEFxyQhERERERERERFRBcckIRERERERERERUQXHJCEREREREREREVEFxyQhERERERERERFRBcckIRERERERERERUQXHJCEREREREREREVEFxyQhERERERERERFRBcckIRERERERERERUQXHJCEREREREREREVEFxyQhERERERERERFRBcckIRERERERERERUQXHJCEREREREREREVEFxyQhERERERERERFRBaem7ACIiIjeByEEAODZs2fIzMxUcjREREREREQfh7zfR3m/mYrCJCEREX0SHj16BACwt7dXciREREREREQfn2fPnsHAwKDI5UwSEhHRJ8HQ0BAAkJKSUuwXHylXZmYmLCwskJqaCn19fWWHQ0Xgfvr4cR+VD9xP5QP308eP+6h84H76eAkh8OzZM5iZmRVbjklCIiL6JKiovH3MroGBAf8oKQf09fW5n8oB7qePH/dR+cD9VD5wP338uI/KB+6nj1NpBlLwxSVEREREREREREQVHJOEREREREREREREFRyThERE9EnQ0NBAaGgoNDQ0lB0KFYP7qXzgfvr4cR+VD9xP5QP308eP+6h84H4q/2SipPcfExERERERERER0SeNIwmJiIiIiIiIiIgqOCYJiYiIiIiIiIiIKjgmCYmIiIiIiIiIiCo4JgmJiOiTsGTJEtSsWROamppo1KgRYmJilB0S5XPs2DH4+/vDzMwMMpkMO3bsUHZIVEBYWBgaN24MPT09VKtWDe3bt0diYqKyw6ICli5dCicnJ+jr60NfXx9ubm6IiIhQdlhUjLCwMMhkMgwfPlzZoVA+06ZNg0wmk/tUr15d2WGRAvfv30ePHj1gZGQEbW1tNGjQALGxscoOi/KxtrYudD7JZDIMHjxY2aHRO2KSkIiIyr1NmzZh+PDhmDRpEi5evIjmzZujbdu2SElJUXZo9D/Pnz+Hs7Mz/vOf/yg7FCrC0aNHMXjwYJw+fRr79+/Hmzdv0KZNGzx//lzZoVE+5ubmmDNnDs6fP4/z58+jZcuWCAgIwLVr15QdGilw7tw5LF++HE5OTsoOhRSoX78+0tLSpM+VK1eUHRIV8OTJE3h4eKBSpUqIiIhAfHw85s2bh8qVKys7NMrn3LlzcufS/v37AQCdOnVScmT0rvh2YyIiKveaNGkCFxcXLF26VJpnZ2eH9u3bIywsTImRkSIymQzbt29H+/btlR0KFeOvv/5CtWrVcPToUXh6eio7HCqGoaEh5s6di759+yo7FMonKysLLi4uWLJkCb777js0aNAACxcuVHZY9D/Tpk3Djh07EBcXp+xQqBjjx4/HiRMneIdIOTN8+HDs2bMHN2/ehEwmU3Y49A44kpCIiMq1V69eITY2Fm3atJGb36ZNG5w8eVJJURGVfxkZGQDeJqDo45STk4ONGzfi+fPncHNzU3Y4VMDgwYPRrl07tG7dWtmhUBFu3rwJMzMz1KxZE127dsXt27eVHRIVsGvXLri6uqJTp06oVq0aGjZsiBUrVig7LCrGq1ev8Pvvv6NPnz5MEJZDTBISEVG59vDhQ+Tk5MDExERuvomJCf744w8lRUVUvgkhMHLkSDRr1gwODg7KDocKuHLlCnR1daGhoYHg4GBs374d9vb2yg6L8tm4cSMuXLjA0ewfsSZNmmDNmjWIiorCihUr8Mcff8Dd3R2PHj1SdmiUz+3bt7F06VLUrVsXUVFRCA4ORkhICNasWaPs0KgIO3bswNOnT9G7d29lh0JloKbsAIiIiN6Hgv9TKYTg/14SldGQIUNw+fJlHD9+XNmhkAK2traIi4vD06dPsXXrVgQFBeHo0aNMFH4kUlNTMWzYMERHR0NTU1PZ4VAR2rZtK/3b0dERbm5uqF27NlavXo2RI0cqMTLKLzc3F66urpg9ezYAoGHDhrh27RqWLl2KXr16KTk6UuS3335D27ZtYWZmpuxQqAw4kpCIiMq1qlWrQlVVtdCowfT09EKjC4moZEOHDsWuXbtw+PBhmJubKzscUkBdXR116tSBq6srwsLC4OzsjEWLFik7LPqf2NhYpKeno1GjRlBTU4OamhqOHj2Kn376CWpqasjJyVF2iKSAjo4OHB0dcfPmTWWHQvmYmpoW+g8QOzs7vpzuI3X37l0cOHAA/fr1U3YoVEZMEhIRUbmmrq6ORo0aSW9Ry7N//364u7srKSqi8kcIgSFDhmDbtm04dOgQatasqeyQqJSEEMjOzlZ2GPQ/rVq1wpUrVxAXFyd9XF1dERgYiLi4OKiqqio7RFIgOzsbCQkJMDU1VXYolI+HhwcSExPl5t24cQNWVlZKioiKEx4ejmrVqqFdu3bKDoXKiLcbExFRuTdy5Ej07NkTrq6ucHNzw/Lly5GSkoLg4GBlh0b/k5WVhVu3bknTycnJiIuLg6GhISwtLZUYGeUZPHgw1q9fj507d0JPT08anWtgYAAtLS0lR0d5Jk6ciLZt28LCwgLPnj3Dxo0bceTIEURGRio7NPofPT29Qs/y1NHRgZGREZ/x+REZPXo0/P39YWlpifT0dHz33XfIzMxEUFCQskOjfEaMGAF3d3fMnj0bnTt3xtmzZ7F8+XIsX75c2aFRAbm5uQgPD0dQUBDU1JhqKq+454iIqNzr0qULHj16hBkzZiAtLQ0ODg7Yt28f/5f5I3L+/Hm0aNFCms573lNQUBBWrVqlpKgov6VLlwIAvL295eaHh4fz4eMfkT///BM9e/ZEWloaDAwM4OTkhMjISPj4+Cg7NKJy5d69e+jWrRsePnwIY2NjNG3aFKdPn+bfDh+Zxo0bY/v27ZgwYQJmzJiBmjVrYuHChQgMDFR2aFTAgQMHkJKSgj59+ig7FPoHZEIIoewgiIiIiIiIiIiISHn4TEIiIiIiIiIiIqIKjklCIiIiIiIiIiKiCo5JQiIiIiIiIiIiogqOSUIiIiIiIiIiIqIKjklCIiIiIiIiIiKiCo5JQiIiIiIiIiIiogqOSUIiIiIiIiIiIqIKjklCIiIiIiIiIiKiCo5JQiIiIqJywtvbG8OHD1d2GJ+k3r17o3379h+s/jt37kAmkyEuLu6DbeNTtWPHDtSpUweqqqo8/hU4cuQIZDIZnj59quxQykwmk2HHjh3vvV4hBL799lsYGhpK598/uY5aW1tj4cKF7zXGohw6dAj16tVDbm5useU+VN99DKZNm4YGDRoUufxjPPY/REwf4vupcePG2LZt23utkz4NTBISERFRufKhkzkfs23btmHmzJnKDoP+JSX9QK4oBgwYgK+//hqpqamYOXNmhb4GKOLu7o60tDQYGBgoO5SPTmRkJFatWoU9e/YgLS0NDg4Oha6jihJ/q1atQuXKlQvVd+7cOXz77bcfOOq3xo4di0mTJkFF5e1Pdl4PPl13796FhoYGMjMz/7VtTpkyBePHjy8xCU0VD5OERERERPnk5OR8tH80GxoaQk9PT9lhvLOPuU9JsVevXimc//r16zLVV9b1srKykJ6eDl9fX5iZmb3X47+oNhZU1tj/Lerq6qhevTpkMpmyQ/nXlHbfJSUlwdTUFO7u7qhevTrU1NT+0XXU2NgY2traZVr3XZw8eRI3b95Ep06dPvi2qDBFx5cQAm/evPkg29u5cye8vb2hr6//QepXpF27dsjIyEBUVNS/tk0qH5gkJCIionLN29sbISEhGDt2LAwNDVG9enVMmzZNrszTp0/x7bffwsTEBJqamnBwcMCePXsA/P+IkT179sDe3h4aGhq4e/cuXr16hbFjx6JGjRrQ0dFBkyZNcOTIEanOR48eoVu3bjA3N4e2tjYcHR2xYcMGue1u2bIFjo6O0NLSgpGREVq3bo3nz59Ly8PDw2FnZwdNTU3Uq1cPS5YsKbGt+W+Ts7a2xuzZs9GnTx/o6enB0tISy5cvL7aOyMhINGvWDJUrV4aRkRG++OILJCUlScvd3Nwwfvx4uXX++usvVKpUCYcPHwaAEvumqD49d+4cfHx8ULVqVRgYGMDLywsXLlyQ29b169fRrFkzaGpqwt7eHgcOHCh0O939+/fRpUsXVKlSBUZGRggICMCdO3eKbHNOTg769u2LmjVrQktLC7a2tli0aJHCstOnT0e1atWgr6+PAQMGyP1YLG5/5ubmYsaMGTA3N4eGhgYaNGiAyMjIImNSNFJpx44dUqJn1apVmD59Oi5dugSZTAaZTIZVq1YBADIyMvDtt99KcbZs2RKXLl0qclul6bO80XlhYWEwMzODjY2NdIv05s2b4e3tDU1NTfz+++8ltrWo9RSZP38+HB0doaOjAwsLCwwaNAhZWVkA3t62l5fMadmyJWQyGby9vbF69Wrs3LlT6pe8Y68sbVQkb8TWypUrUatWLWhoaEAIUWK/51/P0tISurq6GDhwIHJycvDDDz+gevXqqFatGmbNmiW3vZSUFAQEBEBXVxf6+vro3Lkz/vzzTwBAYmIiZDIZrl+/XqjfrK2tIYQodHtj3rEVFRUFOzs76Orq4vPPP0daWpq0/ps3bxASEiJdB8aNG4egoKBiR2iW5ppXmuvxzZs34enpKZ3j+/fvL3Kb+esdMmQIRo4ciapVq8LHxwcAEB8fDz8/P+jq6sLExAQ9e/bEw4cPAbzd30OHDkVKSgpkMhmsra2luvKuo97e3rh79y5GjBghdzx98803yMjIkObltaHgqEOZTIZff/0VHTp0gLa2NurWrYtdu3bJxb5r1y7UrVsXWlpaaNGiBVavXl3i7agbN25EmzZtoKmpCaD46wEAPHz4sNgYjh49is8++wwaGhowNTXF+PHj5RJeikZTNmjQQG7fTZs2DZaWltDQ0ICZmRlCQkKkZb///jtcXV2hp6eH6tWro3v37khPT5eW5x2jBw8ehKurK7S1teHu7o7ExES5bc6ZMwcmJibQ09ND37598fLlyyL7KL8TJ07A2dkZmpqaaNKkCa5cuSItK+1xW/D4yos5KioKrq6u0NDQQExMDIQQ+OGHH1CrVi1oaWnB2dkZW7ZsKTK2u3fvwt/fH1WqVIGOjg7q16+Pffv2yZXZuXMnvvzySwBvv69GjhwpnZtjx46FEEKufEnf4y1btsSQIUPk1nn06BE0NDRw6NAhAICqqir8/PwK9QURBBEREVE5EhQUJAICAqRpLy8voa+vL6ZNmyZu3LghVq9eLWQymYiOjhZCCJGTkyOaNm0q6tevL6Kjo0VSUpLYvXu32LdvnxBCiPDwcFGpUiXh7u4uTpw4Ia5fvy6ysrJE9+7dhbu7uzh27Ji4deuWmDt3rtDQ0BA3btwQQghx7949MXfuXHHx4kWRlJQkfvrpJ6GqqipOnz4thBDiwYMHQk1NTcyfP18kJyeLy5cvi59//lk8e/ZMCCHE8uXLhampqdi6dau4ffu22Lp1qzA0NBSrVq0qsu1eXl5i2LBh0rSVlZUwNDQUP//8s7h586YICwsTKioqIiEhocg6tmzZIrZu3Spu3LghLl68KPz9/YWjo6PIyckRQgixePFiYWlpKXJzc6V1Fi9eLGrUqCGVKalviurTgwcPirVr14r4+HgRHx8v+vbtK0xMTERmZqa0r2xtbYWPj4+Ii4sTMTEx4rPPPhMAxPbt24UQQjx//lzUrVtX9OnTR1y+fFnEx8eL7t27C1tbW5Gdna2wza9evRJTp04VZ8+eFbdv3xa///670NbWFps2bZI7rnR1dUWXLl3E1atXxZ49e4SxsbGYOHFiqfbn/Pnzhb6+vtiwYYO4fv26GDt2rKhUqZLUJ8nJyQKAuHjxotRHBgYGcnFu375d5P15/vfff4tRo0aJ+vXri7S0NJGWlib+/vtvkZubKzw8PIS/v784d+6cuHHjhhg1apQwMjISjx49Utj+0vRZXvt79uwprl69Kq5cuSLFbG1tLR2n9+/fL3VbC66nyIIFC8ShQ4fE7du3xcGDB4Wtra0YOHCgEEKI7OxskZiYKACIrVu3irS0NJGRkSE6d+4sPv/8c6lfsrOzy9xGRUJDQ4WOjo7w9fUVFy5cEJcuXSpVv4eGhgpdXV3x9ddfi2vXroldu3YJdXV14evrK4YOHSquX78uVq5cKQCIU6dOCSGEyM3NFQ0bNhTNmjUT58+fF6dPnxYuLi7Cy8tLiqdRo0Zi8uTJcjE2atRITJgwQQghxOHDhwUA8eTJE+nYqlSpkmjdurU4d+6ciI2NFXZ2dqJ79+7S+t99950wNDQU27ZtEwkJCSI4OFjo6+vLXVsLKumaJ0TprscODg7C29tbXLx4URw9elQ0bNhQ7hxXxMvLS+jq6ooxY8aI69evi4SEBPHgwQNRtWpVMWHCBJGQkCAuXLggfHx8RIsWLYQQQjx9+lTMmDFDmJubi7S0NJGeni7VlXcdffTokTA3NxczZsyQO54WLlwo9PX1pXl557mVlZVYsGCBFBcAYW5uLtavXy9u3rwpQkJChK6urnRMJCcni0qVKonRo0eL69eviw0bNogaNWrI7S9FnJ2dxZw5c6Tpoq4HpYnh3r17QltbWwwaNEgkJCSI7du3i6pVq4rQ0FCp/oLtyoshr8x///tfoa+vL/bt2yfu3r0rzpw5I5YvXy6V/e2338S+fftEUlKSOHXqlGjatKlo27attDzvGG3SpIk4cuSIuHbtmmjevLlwd3eXymzatEmoq6uLFStWiOvXr4tJkyYJPT094ezsXGQ/5dVrZ2cnoqOjxeXLl8UXX3whrK2txatXr6T2l+a4LXh85dXt5OQkoqOjxa1bt8TDhw/FxIkTRb169URkZKRISkoS4eHhQkNDQxw5ckQuprz9265dO+Hj4yMuX74s/f1x9OhRadtPnjwRlSpVEikpKUIIIb7//nthYGAgtmzZIn1P6unpyZ2bJX2Pr1u3TlSpUkW8fPlSWmfRokXC2tpa7rt9yZIlwtrausj+pYqJSUIiIiIqVxQlCZs1ayZXpnHjxmLcuHFCCCGioqKEioqKSExMVFhfeHi4ACDi4uKkebdu3RIymaxQYqNVq1bSD3NF/Pz8xKhRo4QQQsTGxgoA4s6dOwrLWlhYiPXr18vNmzlzpnBzcyuyfkVJwh49ekjTubm5olq1amLp0qVF1lFQenq6ACAlTNLT04Wampo4duyYVMbNzU2MGTNGCFG6vlHUp4q8efNG6Onpid27dwshhIiIiBBqamoiLS1NKrN//365BMJvv/0mbG1t5X7oZGdnCy0tLREVFVXqdg8aNEh07NhRmg4KChKGhobi+fPn0rylS5cKXV1dkZOTU+L+NDMzE7NmzZKb17hxYzFo0CAhxLsnCYV4m3Qq+AP54MGDQl9fX+7HnxBC1K5dWyxbtkxhbKXps6CgIGFiYiKXaM2LeeHChWVqa8H1SmPz5s3CyMhImn7y5IkAIA4fPizNK3gN+CdtVCQ0NFRUqlRJSioJUbp+Dw0NFdra2lLSWwghfH19hbW1tfTjXQghbG1tRVhYmBBCiOjoaKGqqiolCIQQ4tq1awKAOHv2rBDibQK6Vq1a0vK8xOm1a9eEEIqThADErVu3pHV+/vlnYWJiIk2bmJiIuXPnStNv3rwRlpaWxSYJFcl/zROidNdjVVVVkZqaKi2PiIgoVZKwQYMGcvOmTJki2rRpIzcvNTVVAJCu9wsWLBBWVlaF6ip4HS2YIFN0fioqC0AugZuVlSVkMpmIiIgQQggxbtw44eDgIFfHpEmTSkwSGhgYiDVr1sjNU3Q9KE0MEydOLHRu/Pzzz9K1rag+yJ8knDdvnrCxsZESbyU5e/asACAlV/OO0QMHDkhl9u7dKwCIFy9eCCHefs8EBwfL1dOkSZNSJQk3btwozXv06JHQ0tKS+0+gghQdtwWPr7y6d+zYIc3LysoSmpqa4uTJk3Jl+/btK7p16ya3Xt7+dXR0FNOmTSsylnXr1gkXFxdp2tTUVC5B/Pr1a2Fubl7suVnwe/zly5fC0NBQrg8aNGhQKI6dO3cKFRUVuesTEW83JiIionLPyclJbtrU1FS61SkuLg7m5uZF3loIvH2mV/46Lly4ACEEbGxsoKurK32OHj0q3dKTk5ODWbNmwcnJCUZGRtDV1UV0dDRSUlIAAM7OzmjVqhUcHR3RqVMnrFixAk+ePAHw9vbd1NRU9O3bV67+7777Tu6WoXdtu0wmQ/Xq1eVu8yooKSkJ3bt3R61ataCvr4+aNWsCgBS3sbExfHx8sG7dOgBAcnIyTp06hcDAwFL3jaI+BYD09HQEBwfDxsYGBgYGMDAwQFZWlrTtxMREWFhYoHr16tI6n332mVwdsbGxuHXrFvT09KRtGxoa4uXLl8X23S+//AJXV1cYGxtDV1cXK1askLabx9nZWe55Y25ubsjKykJqamqx+zMzMxMPHjyAh4eHXH0eHh5ISEgoMqayiI2NRVZWlnTM5X2Sk5OLbH9p+8zR0RHq6uqF1nd1dZX+/S5tzb9eUQ4fPgwfHx/UqFEDenp66NWrFx49eiR3W35p/NM2FmRlZQVjY2O5+kvT79bW1nLPuzMxMYG9vb308om8eXnnaEJCAiwsLGBhYSEtt7e3R+XKlaX+7Nq1K+7evYvTp08DANatW4cGDRrA3t6+yPi1tbVRu3ZtaTr/NTEjIwN//vmn3LmlqqqKRo0aFdsnJV3z8hR3PU5ISIClpSXMzc2l5W5ubsVuN0/B4yk2NhaHDx+W2x/16tUDgHe+jv4T+duro6MDPT09qb2JiYlo3LixXPmC1zRFXrx4Id1q/E9jSEhIgJubm9wzKz08PJCVlYV79+6Vqv5OnTrhxYsXqFWrFvr374/t27fL3a588eJFBAQEwMrKCnp6evD29gaAYo8NU1NTACgUZ36lPTbylzM0NIStra10/pT2uC3qepV/fnx8PF6+fAkfHx+5427NmjVFHnMhISH47rvv4OHhgdDQUFy+fFluef5bjTMyMpCWlibXHjU1tUKxlfQ9rqGhgR49emDlypUA3v4ddOnSJfTu3VuuHi0tLeTm5iI7O1th7FQxqSk7ACIiIqJ/qlKlSnLTMplMelGGlpZWietraWnJ/YDKzc2FqqoqYmNjoaqqKldWV1cXADBv3jwsWLAACxculJ6pNnz4cOkZdqqqqti/fz9OnjyJ6OhoLF68GJMmTcKZM2ekRNSKFSvQpEkTufoLbu+ftF0Rf39/WFhYYMWKFTAzM0Nubi4cHBzknr0XGBiIYcOGYfHixVi/fj3q168PZ2fnUvcNULhPgbfPCPvrr7+wcOFCWFlZQUNDA25ubtK2hRAlvnwhNzcXjRo1kpKY+eVP6uS3efNmjBgxAvPmzYObmxv09PQwd+5cnDlzptht5ZHJZMXuTyMjI6lcfsW1R0VFpdBzpkrzgozc3FyYmprKPQMyj6K3seatU5o+09HRUbi+ovmlaWtR9eW5e/cu/Pz8EBwcjJkzZ8LQ0BDHjx9H37593/llIf+0jQUVLFfafld0PhZ3jhZ1jOSfb2pqihYtWmD9+vVo2rQpNmzYgAEDBhQbv6JtFjzeFO3D4pR0zStu2/nbW1BpX7iiaJ/4+/vj+++/L1Q2LwH1b3jX/VtSPwNA1apVpf+E+JAx5M0v6XpkYWGBxMRE7N+/HwcOHMCgQYMwd+5cHD16FK9evUKbNm3Qpk0b/P777zA2NkZKSgp8fX2LPTbytv2hXmqVV39pj9vSXP/yYt27dy9q1KghV05DQ0Ph+v369YOvry/27t2L6OhohIWFYd68eRg6dChev36NyMhITJgw4Z3aVprv8X79+qFBgwa4d+8eVq5ciVatWsHKykqunsePH0NbW7tUfydRxcEkIREREX3SnJyccO/ePdy4caPY0YT5NWzYEDk5OUhPT0fz5s0VlomJiUFAQAB69OgB4O2Ph5s3b8LOzk4qI5PJ4OHhAQ8PD0ydOhVWVlbYvn07Ro4ciRo1auD27dvSCL1/w6NHj5CQkIBly5ZJ7Tp+/Hihcu3bt8eAAQMQGRmJ9evXo2fPntKy0vRNUWJiYrBkyRL4+fkBAFJTU6WXDABAvXr1kJKSgj///BMmJiYAgHPnzsnV4eLigk2bNkkvjyjtdt3d3TFo0CBpnqJRH5cuXcKLFy+kH0ynT5+Grq6uNOqpuP1pZmaG48ePw9PTU6rv5MmTRY4aMjY2xrNnz/D8+XPpR2hcXJxcGXV1deTk5BRq/x9//AE1NTXpRQwlKUufFUVfX/+d21qU8+fP482bN5g3b5400m7z5s0lrldUv7yvNipSln4vDXt7e6SkpCA1NVUaTRgfH4+MjAy5a0lgYCDGjRuHbt26ISkpCV27di3zNg0MDGBiYoKzZ89K53BOTg4uXryIBg0aFLleaa55Jclr74MHD2BmZgYAOHXqVJna4eLigq1bt8La2hpqamX/WavoeFI0ryzq1atX6CUV58+fL3G9hg0bIj4+/r3EZG9vj61bt8olC0+ePAk9PT0p0WVsbCz3YpvMzEwkJyfL1aOlpYUvv/wSX375JQYPHox69erhypUrEELg4cOHmDNnjnQMl6aNBdnZ2eH06dPo1auXNC9v9GxJTp8+DUtLSwDAkydPcOPGDWlU6fs4bvPkvYgrJSUFXl5epV7PwsICwcHBCA4OxoQJE7BixQoMHToUhw8fRuXKlaXzzsDAAKampjh9+rR0fX3z5g1iY2Ph4uICoPTf446OjnB1dcWKFSuwfv16LF68uFCZq1evSvUS5eHtxkRERPRJ8/LygqenJzp27Ij9+/cjOTkZERERxb551sbGBoGBgejVqxe2bduG5ORknDt3Dt9//730g69OnTrSyLKEhAQMGDAAf/zxh1THmTNnMHv2bJw/fx4pKSnYtm0b/vrrL+mHybRp0xAWFoZFixbhxo0buHLlCsLDwzF//vwP1hd5b31dvnw5bt26hUOHDmHkyJGFyuno6CAgIABTpkxBQkICunfv/k59U5Q6depg7dq1SEhIwJkzZxAYGCg3gsHHxwe1a9dGUFAQLl++jBMnTmDSpEkA/n9USGBgIKpWrYqAgADExMQgOTkZR48exbBhw4q8da5OnTo4f/48oqKicOPGDUyZMqVQ8hF4+9bmvn37Ij4+HhEREQgNDcWQIUOgoqJS4v4cM2YMvv/+e2zatAmJiYkYP3484uLiMGzYMIUxNWnSBNra2pg4cSJu3bqF9evXy72tFHh762pycjLi4uLw8OFDZGdno3Xr1nBzc0P79u0RFRWFO3fu4OTJk5g8eXKRP8zL0mfFede2FqV27dp48+YNFi9ejNu3b2Pt2rX45ZdfSlzP2toaly9fRmJiIh4+fIjXr1+/9zYWVJZ+L229Tk5OCAwMxIULF3D27Fn06tULXl5ecrcYfvXVV8jMzMTAgQPRokWLQqOY3tXQoUMRFhaGnTt3IjExEcOGDcOTJ0+KHdVX0jWvNFq3bg1bW1v06tULly5dQkxMjHSOv6vBgwfj8ePH6NatG86ePYvbt28jOjoaffr0eadkmrW1NY4dO4b79+9L/2lhbW2NrKwsHDx4EA8fPsTff/9dphgHDBiA69evY9y4cbhx4wY2b94snefF9bWvr2+hxI+i60FpDBo0CKmpqRg6dCiuX7+OnTt3IjQ0FCNHjpSS8y1btsTatWsRExODq1evIigoSG6k+KpVq/Dbb7/h6tWr0rmqpaUFKysrWFpaQl1dXTqPd+3ahZkzZ75jTwHDhg3DypUrsXLlSty4cQOhoaG4du1aqdadMWMGDh48iKtXr6J3796oWrWq9Kbu93Hc5tHT08Po0aMxYsQIrF69GklJSbh48SJ+/vlnrF69WuE6w4cPR1RUFJKTk3HhwgUcOnRI+t7YtWuXdKtx/n6YM2cOtm/fjuvXr2PQoEFyb8Iu7fc48HY04Zw5c5CTk4MOHToUWh4TE4M2bdqUqS/o08UkIREREX3ytm7disaNG6Nbt26wt7fH2LFjS/wRGR4ejl69emHUqFGwtbXFl19+iTNnzkgjJaZMmQIXFxf4+vrC29sb1atXl36UAG9HXB07dgx+fn6wsbHB5MmTMW/ePLRt2xbA2z/ef/31V6xatQqOjo7w8vLCqlWrpGcLfQgqKirYuHEjYmNj4eDggBEjRmDu3LkKywYGBuLSpUto3ry5NEIjT0l9U5SVK1fiyZMnaNiwIXr27ImQkBBUq1ZNWq6qqoodO3YgKysLjRs3Rr9+/TB58mQAkJ7Ppa2tjWPHjsHS0hJfffUV7Ozs0KdPH7x48aLIEWTBwcH46quv0KVLFzRp0gSPHj2SG1WYp1WrVqhbty48PT3RuXNn+Pv7Y9q0aQBK3p8hISEYNWoURo0aBUdHR0RGRmLXrl2oW7euwpgMDQ3x+++/Y9++fXB0dMSGDRukbeXp2LEjPv/8c7Ro0QLGxsbYsGEDZDIZ9u3bB09PT/Tp0wc2Njbo2rUr7ty5I42+LKgsfVacd21rURo0aID58+fj+++/h4ODA9atW4ewsLAS1+vfvz9sbW2lZ0yeOHHivbexoLL0e2nr3bFjB6pUqQJPT0+0bt0atWrVwqZNm+TK6evrw9/fH5cuXXovo4/zRiX26tULbm5u0NXVha+vb7HPwSvpmlcaKioq2L59O7Kzs/HZZ5+hX79+mDVrVpnaYGZmhhMnTiAnJwe+vr5wcHDAsGHDYGBgIPcMyJLMmDEDd+7cQe3ataVb093d3REcHIwuXbrA2NgYP/zwQ5lirFmzJrZs2YJt27bByckJS5culZKiRd2eCgA9evRAfHw8EhMTpXmKrgelUaNGDezbtw9nz56Fs7MzgoOD0bdvX+naCgATJkyAp6cnvvjiC/j5+aF9+/Zyz7SsXLkyVqxYAQ8PDzg5OeHgwYPYvXs3jIyMYGxsjFWrVuG///0v7O3tMWfOHPz444/v2lXo0qULpk6dinHjxqFRo0a4e/cuBg4cWKp158yZg2HDhqFRo0ZIS0vDrl27pGePvo/jNr+ZM2di6tSpCAsLg52dHXx9fbF79+4iv7tzcnIwePBg2NnZ4fPPP4etrS2WLFkC4G2SMCAgQK78qFGj0KtXL/Tu3Vt6PEb+BN+7fI9369YNampq6N69e6Fz+/79+zh58iS++eabMvcFfZpkojQPRSAiIiIiUoITJ06gWbNmuHXrltyPViJ6f3Jzc2FnZ4fOnTuXaRQYld6sWbPwyy+/IDU1tdhyY8eORUZGBpYtW/YvRUb/pgsXLqBly5b466+/Cj1T8n1JTU2FtbU1zp07V+i24jFjxiAjIwPLly//INum8ovPJCQiIiKij8b27duhq6uLunXr4tatWxg2bBg8PDyYICR6j+7evYvo6Gh4eXkhOzsb//nPf5CcnCz3aAF6P5YsWYLGjRvDyMgIJ06cwNy5czFkyJAS15s0aRJ+/vln5OTkvPMLrejjl/eohQ+RIHz9+jXS0tIwfvx4NG3aVOFzB6tVq4bRo0e/921T+ceRhERERET00VizZg1mzpyJ1NRUVK1aFa1bt8a8efOkNwgT0T+XmpqKrl274urVqxBCwMHBAXPmzJF7GQ29HyNGjMCmTZvw+PFjWFpaomfPnpgwYcI/etkKUXGOHDmCFi1awMbGBlu2bIGjo6OyQ6JyhElCIiIiIiIiIiKiCo4vLiEiIiIiIiIiIqrgmCQkIiIiIiIiIiKq4JgkJCIiIiIiIiIiquCYJCQiIiIiIiIiIqrgmCQkIiIiIiIiIiKq4JgkJCIiIiIiIiIiquCYJCQiIiIiIiIiIqrgmCQkIiIiIiIiIiKq4JgkJCIiIiIiIiIiquD+D3tSRvD4XyICAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "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>Target</th>\n",
       "      <th>Predictor</th>\n",
       "      <th>Error increase when removed (thousand b/d)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Supply: production + imports</td>\n",
       "      <td>Calendar month (all 12 month indicators)</td>\n",
       "      <td>47.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Demand: gasoline product supplied</td>\n",
       "      <td>Calendar month (all 12 month indicators)</td>\n",
       "      <td>39.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Target  \\\n",
       "0       Supply: production + imports   \n",
       "1  Demand: gasoline product supplied   \n",
       "\n",
       "                                  Predictor  \\\n",
       "0  Calendar month (all 12 month indicators)   \n",
       "1  Calendar month (all 12 month indicators)   \n",
       "\n",
       "   Error increase when removed (thousand b/d)  \n",
       "0                                       47.96  \n",
       "1                                       39.84  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reorder the same ranking tables by conditional importance: the change in\n",
    "# full-model error when one predictor is removed and the model is refitted.\n",
    "for target_name, table in rankings.items():\n",
    "    ordered = table.sort_values(conditional_column, ascending=False).head(12).iloc[::-1]\n",
    "    fig, ax = plt.subplots(figsize=(13, 7))\n",
    "    # Green means removal hurts predictions; red means removal improves them.\n",
    "    ax.barh(ordered.Predictor, ordered[conditional_column],\n",
    "            color=['#237a57' if v > 0 else '#b65a52' for v in ordered[conditional_column]])\n",
    "    ax.axvline(0, color='black', linewidth=0.8)\n",
    "    ax.set_title(target_name + ': which variables add information alongside all other inputs?')\n",
    "    ax.set_xlabel('Increase in average absolute error after removing and refitting (thousand barrels/day)')\n",
    "    fig.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Calendar month is removed as a 12-column block, so report it separately from\n",
    "# the one-column removal rankings plotted above.\n",
    "display(pd.DataFrame(calendar_importance).round(2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gasoline-discussion-2",
   "metadata": {},
   "source": [
    "The previous month's crude inventory change adds the most information in the removal test for gasoline supply, as removing it increases error by about 7.6 thousand barrels/day.\n",
    "\n",
    "For gasoline demand, the three-month average of product supplied adds the most information, as removing it increases error by about 9.2 thousand barrels/day.\n",
    "\n",
    "Crude inventory change is weak on its own, as above it did not reduce error for either target, but it adds information alongside the other inputs. Removing it also increases demand error by about 6.0 thousand barrels/day."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gasoline-importance-11",
   "metadata": {},
   "source": [
    "## How consistent is each predictor?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gasoline-importance-12-heading-1",
   "metadata": {},
   "source": [
    "### Supply: production + imports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "gasoline-importance-12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:16:53.818162Z",
     "iopub.status.busy": "2026-09-16T21:16:53.818012Z",
     "iopub.status.idle": "2026-09-16T21:16:53.825325Z",
     "shell.execute_reply": "2026-09-16T21:16:53.824845Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Period 1 gain</th>\n",
       "      <th>Period 2 gain</th>\n",
       "      <th>Period 3 gain</th>\n",
       "      <th>Period 4 gain</th>\n",
       "      <th>Period 5 gain</th>\n",
       "      <th>Period 6 gain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predictor</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Supply (production + imports) — previous 3-month average</th>\n",
       "      <td>573.80</td>\n",
       "      <td>393.90</td>\n",
       "      <td>68.35</td>\n",
       "      <td>62.81</td>\n",
       "      <td>11.89</td>\n",
       "      <td>-2.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crude distillation capacity — previous month</th>\n",
       "      <td>528.58</td>\n",
       "      <td>373.17</td>\n",
       "      <td>41.02</td>\n",
       "      <td>109.87</td>\n",
       "      <td>-6.10</td>\n",
       "      <td>21.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gasoline net production — previous 3-month average</th>\n",
       "      <td>513.70</td>\n",
       "      <td>313.83</td>\n",
       "      <td>89.43</td>\n",
       "      <td>84.20</td>\n",
       "      <td>14.64</td>\n",
       "      <td>-45.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supply (production + imports) — previous month</th>\n",
       "      <td>543.46</td>\n",
       "      <td>375.31</td>\n",
       "      <td>11.24</td>\n",
       "      <td>46.20</td>\n",
       "      <td>-3.54</td>\n",
       "      <td>-8.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gasoline net production — previous month</th>\n",
       "      <td>484.04</td>\n",
       "      <td>321.54</td>\n",
       "      <td>47.90</td>\n",
       "      <td>68.14</td>\n",
       "      <td>24.41</td>\n",
       "      <td>-45.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crude refinery inputs — previous month</th>\n",
       "      <td>522.67</td>\n",
       "      <td>324.75</td>\n",
       "      <td>25.71</td>\n",
       "      <td>72.84</td>\n",
       "      <td>8.85</td>\n",
       "      <td>-135.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gasoline exports — previous month</th>\n",
       "      <td>489.40</td>\n",
       "      <td>325.83</td>\n",
       "      <td>-150.65</td>\n",
       "      <td>55.17</td>\n",
       "      <td>-18.86</td>\n",
       "      <td>-78.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gasoline exports — previous 3-month average</th>\n",
       "      <td>537.42</td>\n",
       "      <td>330.84</td>\n",
       "      <td>-184.64</td>\n",
       "      <td>40.68</td>\n",
       "      <td>-38.04</td>\n",
       "      <td>-98.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inventory level — previous month</th>\n",
       "      <td>426.06</td>\n",
       "      <td>224.20</td>\n",
       "      <td>-53.82</td>\n",
       "      <td>32.33</td>\n",
       "      <td>-18.61</td>\n",
       "      <td>-86.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crude refinery utilization — previous month</th>\n",
       "      <td>329.52</td>\n",
       "      <td>116.82</td>\n",
       "      <td>-4.85</td>\n",
       "      <td>40.64</td>\n",
       "      <td>39.94</td>\n",
       "      <td>-157.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          Period 1 gain  \\\n",
       "Predictor                                                                 \n",
       "Supply (production + imports) — previous 3-month average         573.80   \n",
       "Crude distillation capacity — previous month                     528.58   \n",
       "Gasoline net production — previous 3-month average               513.70   \n",
       "Supply (production + imports) — previous month                   543.46   \n",
       "Gasoline net production — previous month                         484.04   \n",
       "Crude refinery inputs — previous month                           522.67   \n",
       "Gasoline exports — previous month                                489.40   \n",
       "Gasoline exports — previous 3-month average                      537.42   \n",
       "Inventory level — previous month                                 426.06   \n",
       "Crude refinery utilization — previous month                      329.52   \n",
       "\n",
       "                                                          Period 2 gain  \\\n",
       "Predictor                                                                 \n",
       "Supply (production + imports) — previous 3-month average         393.90   \n",
       "Crude distillation capacity — previous month                     373.17   \n",
       "Gasoline net production — previous 3-month average               313.83   \n",
       "Supply (production + imports) — previous month                   375.31   \n",
       "Gasoline net production — previous month                         321.54   \n",
       "Crude refinery inputs — previous month                           324.75   \n",
       "Gasoline exports — previous month                                325.83   \n",
       "Gasoline exports — previous 3-month average                      330.84   \n",
       "Inventory level — previous month                                 224.20   \n",
       "Crude refinery utilization — previous month                      116.82   \n",
       "\n",
       "                                                          Period 3 gain  \\\n",
       "Predictor                                                                 \n",
       "Supply (production + imports) — previous 3-month average          68.35   \n",
       "Crude distillation capacity — previous month                      41.02   \n",
       "Gasoline net production — previous 3-month average                89.43   \n",
       "Supply (production + imports) — previous month                    11.24   \n",
       "Gasoline net production — previous month                          47.90   \n",
       "Crude refinery inputs — previous month                            25.71   \n",
       "Gasoline exports — previous month                               -150.65   \n",
       "Gasoline exports — previous 3-month average                     -184.64   \n",
       "Inventory level — previous month                                 -53.82   \n",
       "Crude refinery utilization — previous month                       -4.85   \n",
       "\n",
       "                                                          Period 4 gain  \\\n",
       "Predictor                                                                 \n",
       "Supply (production + imports) — previous 3-month average          62.81   \n",
       "Crude distillation capacity — previous month                     109.87   \n",
       "Gasoline net production — previous 3-month average                84.20   \n",
       "Supply (production + imports) — previous month                    46.20   \n",
       "Gasoline net production — previous month                          68.14   \n",
       "Crude refinery inputs — previous month                            72.84   \n",
       "Gasoline exports — previous month                                 55.17   \n",
       "Gasoline exports — previous 3-month average                       40.68   \n",
       "Inventory level — previous month                                  32.33   \n",
       "Crude refinery utilization — previous month                       40.64   \n",
       "\n",
       "                                                          Period 5 gain  \\\n",
       "Predictor                                                                 \n",
       "Supply (production + imports) — previous 3-month average          11.89   \n",
       "Crude distillation capacity — previous month                      -6.10   \n",
       "Gasoline net production — previous 3-month average                14.64   \n",
       "Supply (production + imports) — previous month                    -3.54   \n",
       "Gasoline net production — previous month                          24.41   \n",
       "Crude refinery inputs — previous month                             8.85   \n",
       "Gasoline exports — previous month                                -18.86   \n",
       "Gasoline exports — previous 3-month average                      -38.04   \n",
       "Inventory level — previous month                                 -18.61   \n",
       "Crude refinery utilization — previous month                       39.94   \n",
       "\n",
       "                                                          Period 6 gain  \n",
       "Predictor                                                                \n",
       "Supply (production + imports) — previous 3-month average          -2.96  \n",
       "Crude distillation capacity — previous month                      21.89  \n",
       "Gasoline net production — previous 3-month average               -45.81  \n",
       "Supply (production + imports) — previous month                    -8.03  \n",
       "Gasoline net production — previous month                         -45.47  \n",
       "Crude refinery inputs — previous month                          -135.96  \n",
       "Gasoline exports — previous month                                -78.32  \n",
       "Gasoline exports — previous 3-month average                      -98.22  \n",
       "Inventory level — previous month                                 -86.71  \n",
       "Crude refinery utilization — previous month                     -157.29  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the top ten individual predictors' gains within each validation block.\n",
    "# This reveals whether a high overall average is broad or driven by one period.\n",
    "target_name = 'Supply: production + imports'\n",
    "table = rankings[target_name]\n",
    "columns = [f'Period {i} gain' for i in range(1, 7)]\n",
    "display(table.head(10).set_index('Predictor')[columns].round(2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gasoline-importance-12-heading-2",
   "metadata": {},
   "source": [
    "### Demand: gasoline product supplied\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "gasoline-importance-12-second",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:16:53.826510Z",
     "iopub.status.busy": "2026-09-16T21:16:53.826342Z",
     "iopub.status.idle": "2026-09-16T21:16:53.834279Z",
     "shell.execute_reply": "2026-09-16T21:16:53.833616Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Period 1 gain</th>\n",
       "      <th>Period 2 gain</th>\n",
       "      <th>Period 3 gain</th>\n",
       "      <th>Period 4 gain</th>\n",
       "      <th>Period 5 gain</th>\n",
       "      <th>Period 6 gain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predictor</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gasoline product supplied — previous 3-month average</th>\n",
       "      <td>226.69</td>\n",
       "      <td>150.58</td>\n",
       "      <td>85.88</td>\n",
       "      <td>25.94</td>\n",
       "      <td>27.13</td>\n",
       "      <td>47.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gasoline product supplied — previous month</th>\n",
       "      <td>190.40</td>\n",
       "      <td>104.94</td>\n",
       "      <td>77.60</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>13.83</td>\n",
       "      <td>37.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inventory level — previous month</th>\n",
       "      <td>215.47</td>\n",
       "      <td>92.19</td>\n",
       "      <td>38.08</td>\n",
       "      <td>-38.57</td>\n",
       "      <td>-28.44</td>\n",
       "      <td>-62.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supply (production + imports) — previous month</th>\n",
       "      <td>225.69</td>\n",
       "      <td>126.03</td>\n",
       "      <td>-35.80</td>\n",
       "      <td>-59.75</td>\n",
       "      <td>-20.37</td>\n",
       "      <td>-25.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supply (production + imports) — previous 3-month average</th>\n",
       "      <td>251.82</td>\n",
       "      <td>133.68</td>\n",
       "      <td>-43.69</td>\n",
       "      <td>-67.88</td>\n",
       "      <td>-39.45</td>\n",
       "      <td>-38.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crude distillation capacity — previous month</th>\n",
       "      <td>237.86</td>\n",
       "      <td>101.40</td>\n",
       "      <td>-10.18</td>\n",
       "      <td>-91.18</td>\n",
       "      <td>-81.24</td>\n",
       "      <td>-15.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gasoline net production — previous month</th>\n",
       "      <td>191.01</td>\n",
       "      <td>96.98</td>\n",
       "      <td>-43.01</td>\n",
       "      <td>-47.45</td>\n",
       "      <td>-31.98</td>\n",
       "      <td>-42.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gasoline net production — previous 3-month average</th>\n",
       "      <td>218.50</td>\n",
       "      <td>102.63</td>\n",
       "      <td>-51.28</td>\n",
       "      <td>-55.10</td>\n",
       "      <td>-45.04</td>\n",
       "      <td>-52.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crude refinery inputs — previous month</th>\n",
       "      <td>212.07</td>\n",
       "      <td>106.04</td>\n",
       "      <td>-36.26</td>\n",
       "      <td>-49.34</td>\n",
       "      <td>-69.32</td>\n",
       "      <td>-102.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inventory level minus year-earlier level</th>\n",
       "      <td>17.81</td>\n",
       "      <td>-20.13</td>\n",
       "      <td>46.06</td>\n",
       "      <td>-13.31</td>\n",
       "      <td>9.74</td>\n",
       "      <td>4.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          Period 1 gain  \\\n",
       "Predictor                                                                 \n",
       "Gasoline product supplied — previous 3-month average             226.69   \n",
       "Gasoline product supplied — previous month                       190.40   \n",
       "Inventory level — previous month                                 215.47   \n",
       "Supply (production + imports) — previous month                   225.69   \n",
       "Supply (production + imports) — previous 3-month average         251.82   \n",
       "Crude distillation capacity — previous month                     237.86   \n",
       "Gasoline net production — previous month                         191.01   \n",
       "Gasoline net production — previous 3-month average               218.50   \n",
       "Crude refinery inputs — previous month                           212.07   \n",
       "Inventory level minus year-earlier level                          17.81   \n",
       "\n",
       "                                                          Period 2 gain  \\\n",
       "Predictor                                                                 \n",
       "Gasoline product supplied — previous 3-month average             150.58   \n",
       "Gasoline product supplied — previous month                       104.94   \n",
       "Inventory level — previous month                                  92.19   \n",
       "Supply (production + imports) — previous month                   126.03   \n",
       "Supply (production + imports) — previous 3-month average         133.68   \n",
       "Crude distillation capacity — previous month                     101.40   \n",
       "Gasoline net production — previous month                          96.98   \n",
       "Gasoline net production — previous 3-month average               102.63   \n",
       "Crude refinery inputs — previous month                           106.04   \n",
       "Inventory level minus year-earlier level                         -20.13   \n",
       "\n",
       "                                                          Period 3 gain  \\\n",
       "Predictor                                                                 \n",
       "Gasoline product supplied — previous 3-month average              85.88   \n",
       "Gasoline product supplied — previous month                        77.60   \n",
       "Inventory level — previous month                                  38.08   \n",
       "Supply (production + imports) — previous month                   -35.80   \n",
       "Supply (production + imports) — previous 3-month average         -43.69   \n",
       "Crude distillation capacity — previous month                     -10.18   \n",
       "Gasoline net production — previous month                         -43.01   \n",
       "Gasoline net production — previous 3-month average               -51.28   \n",
       "Crude refinery inputs — previous month                           -36.26   \n",
       "Inventory level minus year-earlier level                          46.06   \n",
       "\n",
       "                                                          Period 4 gain  \\\n",
       "Predictor                                                                 \n",
       "Gasoline product supplied — previous 3-month average              25.94   \n",
       "Gasoline product supplied — previous month                        -6.00   \n",
       "Inventory level — previous month                                 -38.57   \n",
       "Supply (production + imports) — previous month                   -59.75   \n",
       "Supply (production + imports) — previous 3-month average         -67.88   \n",
       "Crude distillation capacity — previous month                     -91.18   \n",
       "Gasoline net production — previous month                         -47.45   \n",
       "Gasoline net production — previous 3-month average               -55.10   \n",
       "Crude refinery inputs — previous month                           -49.34   \n",
       "Inventory level minus year-earlier level                         -13.31   \n",
       "\n",
       "                                                          Period 5 gain  \\\n",
       "Predictor                                                                 \n",
       "Gasoline product supplied — previous 3-month average              27.13   \n",
       "Gasoline product supplied — previous month                        13.83   \n",
       "Inventory level — previous month                                 -28.44   \n",
       "Supply (production + imports) — previous month                   -20.37   \n",
       "Supply (production + imports) — previous 3-month average         -39.45   \n",
       "Crude distillation capacity — previous month                     -81.24   \n",
       "Gasoline net production — previous month                         -31.98   \n",
       "Gasoline net production — previous 3-month average               -45.04   \n",
       "Crude refinery inputs — previous month                           -69.32   \n",
       "Inventory level minus year-earlier level                           9.74   \n",
       "\n",
       "                                                          Period 6 gain  \n",
       "Predictor                                                                \n",
       "Gasoline product supplied — previous 3-month average              47.31  \n",
       "Gasoline product supplied — previous month                        37.52  \n",
       "Inventory level — previous month                                 -62.75  \n",
       "Supply (production + imports) — previous month                   -25.95  \n",
       "Supply (production + imports) — previous 3-month average         -38.60  \n",
       "Crude distillation capacity — previous month                     -15.43  \n",
       "Gasoline net production — previous month                         -42.32  \n",
       "Gasoline net production — previous 3-month average               -52.81  \n",
       "Crude refinery inputs — previous month                          -102.94  \n",
       "Inventory level minus year-earlier level                           4.60  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_name = 'Demand: gasoline product supplied'\n",
    "table = rankings[target_name]\n",
    "columns = [f'Period {i} gain' for i in range(1, 7)]\n",
    "display(table.head(10).set_index('Predictor')[columns].round(2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "gasoline-importance-12-periods",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T21:16:53.835600Z",
     "iopub.status.busy": "2026-09-16T21:16:53.835462Z",
     "iopub.status.idle": "2026-09-16T21:16:53.840387Z",
     "shell.execute_reply": "2026-09-16T21:16:53.839928Z"
    }
   },
   "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>Period</th>\n",
       "      <th>First scored month</th>\n",
       "      <th>Last scored month</th>\n",
       "      <th>Scored months</th>\n",
       "      <th>Earlier training months</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2018-05</td>\n",
       "      <td>2019-04</td>\n",
       "      <td>12</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-05</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>12</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2022-07</td>\n",
       "      <td>2023-06</td>\n",
       "      <td>12</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2023-07</td>\n",
       "      <td>2024-06</td>\n",
       "      <td>12</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2024-07</td>\n",
       "      <td>2025-06</td>\n",
       "      <td>12</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2025-07</td>\n",
       "      <td>2026-06</td>\n",
       "      <td>12</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Period First scored month Last scored month  Scored months  \\\n",
       "0       1            2018-05           2019-04             12   \n",
       "1       2            2019-05           2022-06             12   \n",
       "2       3            2022-07           2023-06             12   \n",
       "3       4            2023-07           2024-06             12   \n",
       "4       5            2024-07           2025-06             12   \n",
       "5       6            2025-07           2026-06             12   \n",
       "\n",
       "   Earlier training months  \n",
       "0                      123  \n",
       "1                      135  \n",
       "2                      147  \n",
       "3                      159  \n",
       "4                      171  \n",
       "5                      183  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print the exact scored dates and the amount of earlier training data available\n",
    "# for each block so the expanding-window design can be audited from the notebook.\n",
    "display(periods)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gasoline-discussion-3",
   "metadata": {},
   "source": [
    "The three-month average of supply, crude distillation capacity and the three-month average of gasoline net production help predict supply in five of six periods when tested individually.\n",
    "\n",
    "For demand, the three-month average of product supplied helps in all six periods. The previous month's product supplied helps in five periods, while inventory level helps in three.\n",
    "\n",
    "Crude inventory change helps individually in only two periods for each target, but removing it from the full model makes predictions worse in all six periods for both supply and demand."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gasoline-conclusion",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "For gasoline supply, recent supply levels are the strongest individual predictors, with crude distillation capacity and gasoline net production as other strong predictors. For gasoline demand, recent product supplied is strongest. The three-month averages of supply and product supplied are stronger than their previous-month values.\n",
    "\n",
    "Crude inventory change is weak on its own, but adds information alongside the other variables for both gasoline supply and demand. Gasoline inventory level is less consistent and does not improve the full demand model on average."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7a21071",
   "metadata": {},
   "source": [
    "## Comparing crude and gasoline\n",
    "\n",
    "Gasoline is easier to predict compared to crude based on these tests as its errors are smaller both in barrels/day and relative to the size of supply or demand.\n",
    "\n",
    "The strongest predictors are similar, but the variables that add information alongside the other inputs differ.\n",
    "\n",
    "Table below shows average absolute error by the average target level to account for crude volumes being larger than gasoline volumes.\n",
    "\n",
    "| Target | Full-model error, thousand barrels/day | Error as % of average target | Best individual predictor + calendar: error % |\n",
    "|---|---:|---:|---:|\n",
    "| Crude supply | 354 | 1.82% | 1.81% |\n",
    "| Gasoline supply | 138 | 1.40% | 1.63% |\n",
    "| Crude demand | 338 | 2.07% | 1.57% |\n",
    "| Gasoline demand | 110 | 1.22% | 1.20% |\n",
    "\n",
    "Gasoline has lower relative errors for both targets, even when using just the strongest individual predictor plus calendar effects.\n",
    "\n",
    "However, crude improves more relative to the baseline of only using the month of the year. Its full model reduces supply error by 89%, compared with 60% for gasoline. For demand, the reductions are 57% and 46%. This means crude has larger error reductions.\n",
    "\n",
    "### Do the important predictors differ?\n",
    "\n",
    "The strongest individual predictor are the same.\n",
    "\n",
    "| Target | Crude | Gasoline |\n",
    "|---|---|---|\n",
    "| Supply | Previous three-month average of production + imports | Previous three-month average of production + imports |\n",
    "| Demand | Previous three-month average of refinery inputs | Previous three-month average of product supplied |\n",
    "\n",
    "Second most important predictors were:\n",
    "\n",
    "- **Crude supply:** recent crude production \n",
    "- **Crude demand:** gasoline production\n",
    "\n",
    "- **Gasoline supply:** crude distillation capacity\n",
    "- **Gasoline demand:** recent product supplied\n",
    "\n",
    "\n",
    "| Target | Largest error increase when a variable is removed |\n",
    "|---|---|\n",
    "| Crude supply | Crude year-over-year inventory difference: **17.1 thousand b/d** |\n",
    "| Crude demand | Crude year-over-year inventory difference: **19.4 thousand b/d** |\n",
    "| Gasoline supply | Previous-month crude inventory change: **7.6 thousand b/d** |\n",
    "| Gasoline demand | Three-month average gasoline product supplied: **9.2 thousand b/d** |\n",
    "\n",
    "Previous-month crude inventory change also helps the full gasoline demand model: removing it increases error by 6.0 thousand b/d."
   ]
  }
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