{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "421838ed",
   "metadata": {},
   "source": [
    "# 05. PySATL CPD API: Reset Benchmarking\n",
    "\n",
    "## Section 1. Orientation\n",
    "\n",
    "The online notebook showed how one reset detector behaves on one provider. The next problem is comparative evaluation: how do we judge several reset detector configurations systematically over a dataset rather than by eye on one trace? That is the job of reset benchmarking.\n",
    "\n",
    "Reset benchmarking is built around the idea that a detector run is a whole-timeseries event sequence. Once a change point is declared, the detector resets and begins learning again. That semantics is what makes the metric families in this notebook different from the transition-centered no-reset story that follows later.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.io as pio\n",
    "pio.renderers.default = 'sphinx_gallery'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c7752ab",
   "metadata": {},
   "source": [
    "## What in this notebook\n",
    "\n",
    "By the end of the notebook, you should know how to build a threshold sweep of reset detectors, register them as benchmark entries, evaluate multiple-run classification and delay metrics, merge those metric outputs into one benchmark-ready table, and connect one chosen operating point back to a concrete trace on one provider.\n",
    "\n",
    "The notebook assumes you already know the basic provider, detector, and plotter abstractions from the earlier chapters. The focus here is on reset-specific evaluation logic and on the shape of the current reset benchmark API.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6198f19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "from pysatl_cpd.algorithms.online import ShewhartControlChart\n",
    "from pysatl_cpd.analysis.metrics.multiple_run.classification import ClassificationReport\n",
    "from pysatl_cpd.analysis.metrics.multiple_run.online import ARLMetric, MeanDelayMetric, MedianDelayMetric\n",
    "from pysatl_cpd.analysis.visualization import DrawBackend, OnlineCpdPlotter\n",
    "from pysatl_cpd.analysis.visualization.benchmarking import (\n",
    "    BenchmarkPlotter,\n",
    "    ThresholdBasedMetricVisualizer,\n",
    ")\n",
    "from pysatl_cpd.benchmark.online.reset import OnlineResetBenchmark, OnlineResetBenchmarkEntry\n",
    "from pysatl_cpd.benchmark.registry import BenchmarkRegistry\n",
    "from pysatl_cpd.core.online import OnlineResetDetector\n",
    "from pysatl_cpd.data.generator import preset_dataset\n",
    "from pysatl_cpd.data.providers.transformers import ColumnsSelectorTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48fa6c68",
   "metadata": {},
   "source": [
    "## Section 2. Building a Reset Benchmark Campaign\n",
    "\n",
    "The first benchmarking problem is campaign design: which data, which detector family, and which parameter sweep are we actually evaluating? A tutorial campaign should be small enough to run quickly but large enough to make the metrics and plots meaningful.\n",
    "\n",
    "In this chapter, we use a preset mean-shift dataset and sweep one Shewhart reset detector across several thresholds. That is enough to expose the structure of the reset benchmark API without drowning the notebook in configuration noise.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7430c8e",
   "metadata": {},
   "source": [
    "### 2.1 Choosing the dataset and feature view\n",
    "\n",
    "Even in a simple tutorial, it helps to be explicit about what the detector is observing. A preset dataset gives us labeled providers immediately, while a feature transformer makes it clear which column the univariate detector will actually consume.\n",
    "\n",
    "The reset benchmark API does not require feature transformation, but in multivariate preset datasets it is often the clearest way to define the detector input space.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9ce92a76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size: 100\n",
      "Provider names: ['extreme_mean_shifts_series_0000', 'extreme_mean_shifts_series_0001', 'extreme_mean_shifts_series_0002']\n",
      "Dataset transitions: [\"type='extreme_high'->type='extreme_low'\", \"type='baseline'->type='extreme_high'\"]\n",
      "Selected feature transformer annotation: column[feature_0]\n"
     ]
    }
   ],
   "source": [
    "FEATURE = \"feature_0\"\n",
    "THRESHOLDS = [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0]\n",
    "ERROR_MARGIN = (0, 15)\n",
    "MAX_DELAY = ERROR_MARGIN[1]\n",
    "\n",
    "dataset = preset_dataset(\"extreme_mean_shifts\", n_series=100, seed=7, series_length=500)\n",
    "feature_transformer = ColumnsSelectorTransformer(columns=[FEATURE])\n",
    "\n",
    "print(\"Dataset size:\", len(dataset))\n",
    "print(\"Provider names:\", [provider.annotation.name for provider in dataset[:3]])\n",
    "print(\"Dataset transitions:\", [repr(transition) for transition in dataset.transitions])\n",
    "print(\"Selected feature transformer annotation:\", feature_transformer.annotation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25612967",
   "metadata": {},
   "source": [
    "### 2.2 Configuring reset detectors\n",
    "\n",
    "The second problem is defining the family of detectors we want to compare. In a reset benchmark, the most common first sweep is threshold tuning: keep the algorithm and skip-period logic fixed, vary the threshold, and examine how the event sequence changes.\n",
    "\n",
    "The API makes that explicit. Each `OnlineResetDetector` is already a fully configured detector, not just an algorithm plus a threshold value stored somewhere else.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e0b7d9c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of benchmark entries: 14\n",
      "ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 0.5, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'}))\n",
      "ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 1.0, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'}))\n",
      "ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 1.5, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'}))\n",
      "ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 2.0, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'}))\n",
      "ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 2.5, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'}))\n",
      "ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 3.0, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'}))\n",
      "ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 3.5, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'}))\n",
      "ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 4.0, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'}))\n",
      "ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 4.5, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'}))\n",
      "ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 5.0, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'}))\n",
      "ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 5.5, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'}))\n",
      "ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 6.0, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'}))\n",
      "ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 6.5, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'}))\n",
      "ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 7.0, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'}))\n"
     ]
    }
   ],
   "source": [
    "entries = []\n",
    "for threshold in THRESHOLDS:\n",
    "    detector = OnlineResetDetector(\n",
    "        ShewhartControlChart(learning_period_size=100, window_size=10),\n",
    "        threshold=threshold,\n",
    "        skip_period=5,\n",
    "        collect_states=False,\n",
    "        data_transformer=feature_transformer,\n",
    "    )\n",
    "    entries.append(OnlineResetBenchmarkEntry(detector=detector))\n",
    "\n",
    "print(\"Number of benchmark entries:\", len(entries))\n",
    "for entry in entries:\n",
    "    print(entry.description)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cae428f",
   "metadata": {},
   "source": [
    "### 2.3 Why entries wrap complete detectors\n",
    "\n",
    "A benchmark entry could, in principle, have been designed as an algorithm plus a free-floating threshold. The current reset API instead chooses to wrap a fully configured detector. That is an important design decision because it keeps the runtime contract intact all the way into the benchmark layer.\n",
    "\n",
    "In practice, that means benchmark descriptions remain faithful to real detector configurations. If a detector differs by threshold, skip period, or transformer, that difference is already part of the detector description stored in the benchmark entry.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0039bf24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One concrete detector description:\n",
      "ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 0.5, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'}))\n"
     ]
    }
   ],
   "source": [
    "print(\"One concrete detector description:\")\n",
    "print(entries[0].detector.description)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4da3234",
   "metadata": {},
   "source": [
    "**Summary of Section 2:** A reset benchmark campaign is built from a labeled dataset, a feature view, and a family of fully configured reset detectors. In the current API, those configured detectors are wrapped inside `OnlineResetBenchmarkEntry` objects before benchmarking begins.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ded36fa0",
   "metadata": {},
   "source": [
    "## Section 3. Registry and Benchmark Orchestration\n",
    "\n",
    "Once the campaign exists, the next problem is execution management. Running the same detector-provider combinations repeatedly for every metric family would be wasteful and would also blur the conceptual line between \u201cdetector execution\u201d and \u201cmetric aggregation.\u201d\n",
    "\n",
    "The reset benchmark API solves that problem with two coordinating objects: a registry that caches single runs and a benchmark object that asks for metrics over a collection of entries.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fabb94c5",
   "metadata": {},
   "source": [
    "### 3.1 BenchmarkRegistry\n",
    "\n",
    "The registry exists to remember completed detector runs keyed by detector and provider descriptions. That way, once a run exists, several metrics can reuse it without re-executing the detector unnecessarily.\n",
    "\n",
    "This is important conceptually as well as computationally. It means the benchmark layer treats \u201crun the detector\u201d and \u201cscore the result\u201d as separate steps, which keeps the architecture easier to understand.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ab18d503",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Registry size before benchmarking: 0\n"
     ]
    }
   ],
   "source": [
    "registry = BenchmarkRegistry()\n",
    "print(\"Registry size before benchmarking:\", len(registry))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "457c5258",
   "metadata": {},
   "source": [
    "### 3.2 OnlineResetBenchmark\n",
    "\n",
    "The benchmark object is the orchestrator that binds a dataset and a registry together. It knows how to take a set of entries and a multiple-run metric, execute the necessary detector runs if they are missing, and return a metric result keyed by detector description.\n",
    "\n",
    "The key public method is `get_metrics_for(...)`. The benchmark itself does not hardcode one metric family; it delegates to metric objects supplied by the caller.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a248aa8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benchmark created with dataset size: 100\n",
      "Registry object attached: True\n"
     ]
    }
   ],
   "source": [
    "benchmark = OnlineResetBenchmark(dataset=dataset, registry=registry)\n",
    "print(\"Benchmark created with dataset size:\", len(dataset))\n",
    "print(\"Registry object attached:\", isinstance(benchmark._registry, BenchmarkRegistry))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "477d1b1b",
   "metadata": {},
   "source": [
    "### 3.3 Why orchestration is separate from metrics\n",
    "\n",
    "A metric should describe how to aggregate already-available runs, not how to schedule detector execution. Separating the benchmark object from the metric objects keeps those responsibilities distinct and makes the reset benchmark API easier to extend.\n",
    "\n",
    "This matters later when we combine several metric families. The benchmark orchestration stays the same even though the interpretation of the resulting runs changes from classification counts to delays or ARL.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9fa51863",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benchmark method used throughout this chapter: get_metrics_for\n"
     ]
    }
   ],
   "source": [
    "print(\"Benchmark method used throughout this chapter:\", OnlineResetBenchmark.get_metrics_for.__name__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a11e198",
   "metadata": {},
   "source": [
    "**Summary of Section 3:** The reset benchmark stack separates execution management from scoring. `BenchmarkRegistry` caches detector runs, while `OnlineResetBenchmark` orchestrates metric evaluation over a set of entries and one shared dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae08a258",
   "metadata": {},
   "source": [
    "## Section 4. Metric Families for Reset Evaluation\n",
    "\n",
    "With orchestration in place, the next problem is meaning: what exactly do we want to measure about the reset detector runs? In reset mode, the natural emphasis is whole-timeseries event sequences rather than transition-local behavior.\n",
    "\n",
    "This section introduces the three metric families used in the notebook: classification metrics, delay metrics, and average run length. Each family answers a different operational question about the same detector runs.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "825b1c42",
   "metadata": {},
   "source": [
    "### 4.1 Classification metrics\n",
    "\n",
    "The motivation for classification metrics is to answer the basic event-detection question: how many declared detections matched true change points well enough to count as correct, and how many did not? Precision, recall, and F1 summarize that behavior more compactly than raw TP, FP, and FN counts alone.\n",
    "\n",
    "In the current reset API, `ClassificationReport` is the main metric object for that purpose. It aggregates several underlying multiple-run classification metrics and returns a dictionary of the resulting scores.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0b575297",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification metric object: <pysatl_cpd.analysis.metrics.multiple_run.classification.report.ClassificationReport object at 0x7f3b678446e0>\n"
     ]
    }
   ],
   "source": [
    "classification_metric = ClassificationReport(error_margin=ERROR_MARGIN)\n",
    "print(\"Classification metric object:\", classification_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87991cea",
   "metadata": {},
   "source": [
    "### 4.2 Delay metrics\n",
    "\n",
    "A detector can be correct but still not timely. The next problem is measuring how long the detector waited after a true change before responding. That is what the delay metrics summarize.\n",
    "\n",
    "`MeanDelayMetric` and `MedianDelayMetric` take a `max_delay` parameter that caps empty or overly long delays. This keeps the metric behavior well-defined even when detections are sparse or missing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cfc8b57b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean delay metric: <pysatl_cpd.analysis.metrics.multiple_run.online.delay.MeanDelayMetric object at 0x7f3b67845400>\n",
      "Median delay metric: <pysatl_cpd.analysis.metrics.multiple_run.online.delay.MedianDelayMetric object at 0x7f3b678456a0>\n"
     ]
    }
   ],
   "source": [
    "mean_delay_metric = MeanDelayMetric(max_delay=MAX_DELAY)\n",
    "median_delay_metric = MedianDelayMetric(max_delay=MAX_DELAY)\n",
    "print(\"Mean delay metric:\", mean_delay_metric)\n",
    "print(\"Median delay metric:\", median_delay_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51e99519",
   "metadata": {},
   "source": [
    "### 4.3 Average run length\n",
    "\n",
    "The final problem is false-alarm robustness across runs. In reset mode, one useful summary is average run length: how long the detector tends to go before producing an alarm. This complements classification and delay by focusing more on alarm frequency than on matching quality.\n",
    "\n",
    "`ARLMetric` is the current multiple-run metric for that purpose. In a threshold sweep, it often reveals a tradeoff against recall or responsiveness.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3943c568",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARL metric: <pysatl_cpd.analysis.metrics.multiple_run.online.arl.ARLMetric object at 0x7f3b678457f0>\n"
     ]
    }
   ],
   "source": [
    "arl_metric = ARLMetric()\n",
    "print(\"ARL metric:\", arl_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcfd97b9",
   "metadata": {},
   "source": [
    "**Summary of Section 4:** Reset evaluation uses several metric families because no single summary captures the whole detector story. Classification metrics describe correctness, delay metrics describe timeliness, and ARL describes alarm frequency or robustness.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a179ef70",
   "metadata": {},
   "source": [
    "## Section 5. Running Metric Scenarios and Building a Benchmark Table\n",
    "\n",
    "The benchmark object returns one metric family at a time, but practical analysis usually needs a single table that combines several families. The next problem is therefore table construction: how do we merge reset benchmark outputs into one threshold-indexed result table suitable for inspection and plotting?\n",
    "\n",
    "That table-building step is not busywork. It is the bridge between raw metric dictionaries and the visualizers used later in the notebook.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a7906b",
   "metadata": {},
   "source": [
    "### 5.1 Running the benchmark for several metric families\n",
    "\n",
    "The benchmark orchestration stays fixed while only the metric object changes. That makes the code a good demonstration of the benchmark API\u2019s separation between execution scheduling and scoring semantics.\n",
    "\n",
    "We run classification, mean delay, median delay, and ARL over the same entry family. Thanks to the shared registry, these metric calls can reuse detector executions instead of rerunning everything from scratch each time.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d34416cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture benchmark_run_output\n",
    "classification_results = benchmark.get_metrics_for(entries, classification_metric)\n",
    "mean_delay_results = benchmark.get_metrics_for(entries, mean_delay_metric)\n",
    "median_delay_results = benchmark.get_metrics_for(entries, median_delay_metric)\n",
    "arl_results = benchmark.get_metrics_for(entries, arl_metric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7fbb7302",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Registry size after metric evaluation: 1400\n",
      "Number of classified detector descriptions: 14\n"
     ]
    }
   ],
   "source": [
    "print(\"Registry size after metric evaluation:\", len(registry))\n",
    "print(\"Number of classified detector descriptions:\", len(classification_results))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdf75bbf",
   "metadata": {},
   "source": [
    "### 5.2 Merging the metric outputs into one table\n",
    "\n",
    "The benchmark API deliberately returns dictionaries keyed by detector description because that is the most faithful representation of the underlying run collection. For plotting and quick inspection, though, we usually want one row per detector configuration.\n",
    "\n",
    "The code below performs that merge explicitly. The threshold is recovered from the detector description so the result table is immediately usable for threshold-oriented plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c8c7c113",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>fp</th>\n",
       "      <th>fn</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>mean_delay</th>\n",
       "      <th>median_delay</th>\n",
       "      <th>arl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>15.000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>105.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "      <td>378</td>\n",
       "      <td>179</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.070</td>\n",
       "      <td>13.575</td>\n",
       "      <td>15.0</td>\n",
       "      <td>108.870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>59</td>\n",
       "      <td>325</td>\n",
       "      <td>141</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.202</td>\n",
       "      <td>11.185</td>\n",
       "      <td>15.0</td>\n",
       "      <td>115.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>93</td>\n",
       "      <td>213</td>\n",
       "      <td>107</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.368</td>\n",
       "      <td>9.215</td>\n",
       "      <td>15.0</td>\n",
       "      <td>130.739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.5</td>\n",
       "      <td>150</td>\n",
       "      <td>92</td>\n",
       "      <td>50</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.679</td>\n",
       "      <td>5.410</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>187</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.914</td>\n",
       "      <td>3.220</td>\n",
       "      <td>2.0</td>\n",
       "      <td>163.895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.5</td>\n",
       "      <td>199</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.990</td>\n",
       "      <td>2.790</td>\n",
       "      <td>3.0</td>\n",
       "      <td>167.243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.150</td>\n",
       "      <td>3.0</td>\n",
       "      <td>168.285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.5</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.565</td>\n",
       "      <td>3.0</td>\n",
       "      <td>168.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.0</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.010</td>\n",
       "      <td>4.0</td>\n",
       "      <td>168.655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.5</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.505</td>\n",
       "      <td>4.0</td>\n",
       "      <td>168.895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.0</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.975</td>\n",
       "      <td>5.0</td>\n",
       "      <td>169.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6.5</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>5.525</td>\n",
       "      <td>5.0</td>\n",
       "      <td>169.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7.0</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.055</td>\n",
       "      <td>6.0</td>\n",
       "      <td>169.565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    threshold   tp   fp   fn  precision  recall     f1  mean_delay  \\\n",
       "0         0.5    0  400  200      0.000   0.000  0.000      15.000   \n",
       "1         1.0   21  378  179      0.053   0.105  0.070      13.575   \n",
       "2         1.5   59  325  141      0.154   0.295  0.202      11.185   \n",
       "3         2.0   93  213  107      0.304   0.465  0.368       9.215   \n",
       "4         2.5  150   92   50      0.620   0.750  0.679       5.410   \n",
       "5         3.0  187   22   13      0.895   0.935  0.914       3.220   \n",
       "6         3.5  199    3    1      0.985   0.995  0.990       2.790   \n",
       "7         4.0  200    0    0      1.000   1.000  1.000       3.150   \n",
       "8         4.5  200    0    0      1.000   1.000  1.000       3.565   \n",
       "9         5.0  200    0    0      1.000   1.000  1.000       4.010   \n",
       "10        5.5  200    0    0      1.000   1.000  1.000       4.505   \n",
       "11        6.0  200    0    0      1.000   1.000  1.000       4.975   \n",
       "12        6.5  200    0    0      1.000   1.000  1.000       5.525   \n",
       "13        7.0  200    0    0      1.000   1.000  1.000       6.055   \n",
       "\n",
       "    median_delay      arl  \n",
       "0           15.0  105.120  \n",
       "1           15.0  108.870  \n",
       "2           15.0  115.281  \n",
       "3           15.0  130.739  \n",
       "4            2.0  151.525  \n",
       "5            2.0  163.895  \n",
       "6            3.0  167.243  \n",
       "7            3.0  168.285  \n",
       "8            3.0  168.480  \n",
       "9            4.0  168.655  \n",
       "10           4.0  168.895  \n",
       "11           5.0  169.105  \n",
       "12           5.0  169.370  \n",
       "13           6.0  169.565  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows = []\n",
    "for description, report in classification_results.items():\n",
    "    rows.append(\n",
    "        {\n",
    "            \"threshold\": float(description.parameters[\"threshold\"]),\n",
    "            **report,\n",
    "            \"mean_delay\": float(mean_delay_results[description]),\n",
    "            \"median_delay\": float(median_delay_results[description]),\n",
    "            \"arl\": float(arl_results[description]),\n",
    "        }\n",
    "    )\n",
    "\n",
    "reset_table = pd.DataFrame(rows).sort_values(\"threshold\").reset_index(drop=True)\n",
    "display(reset_table.round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd96fa05",
   "metadata": {},
   "source": [
    "### 5.3 Reading the reset table\n",
    "\n",
    "A table is only useful if we know how to read it. In a reset threshold sweep, increasing the threshold often reduces false alarms and can improve precision, but it may also lower recall or alter the delay profile.\n",
    "\n",
    "The table below is therefore not just a storage format. It is the central object that reveals how the detector\u2019s operating point changes across the threshold sweep.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "89dbd330",
   "metadata": {},
   "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>threshold</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>mean_delay</th>\n",
       "      <th>median_delay</th>\n",
       "      <th>arl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>15.000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>105.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.070</td>\n",
       "      <td>13.575</td>\n",
       "      <td>15.0</td>\n",
       "      <td>108.870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.202</td>\n",
       "      <td>11.185</td>\n",
       "      <td>15.0</td>\n",
       "      <td>115.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.368</td>\n",
       "      <td>9.215</td>\n",
       "      <td>15.0</td>\n",
       "      <td>130.739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.5</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.679</td>\n",
       "      <td>5.410</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.914</td>\n",
       "      <td>3.220</td>\n",
       "      <td>2.0</td>\n",
       "      <td>163.895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.5</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.990</td>\n",
       "      <td>2.790</td>\n",
       "      <td>3.0</td>\n",
       "      <td>167.243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.150</td>\n",
       "      <td>3.0</td>\n",
       "      <td>168.285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.5</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.565</td>\n",
       "      <td>3.0</td>\n",
       "      <td>168.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.010</td>\n",
       "      <td>4.0</td>\n",
       "      <td>168.655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.5</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.505</td>\n",
       "      <td>4.0</td>\n",
       "      <td>168.895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.975</td>\n",
       "      <td>5.0</td>\n",
       "      <td>169.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6.5</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>5.525</td>\n",
       "      <td>5.0</td>\n",
       "      <td>169.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.055</td>\n",
       "      <td>6.0</td>\n",
       "      <td>169.565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    threshold  precision  recall     f1  mean_delay  median_delay      arl\n",
       "0         0.5      0.000   0.000  0.000      15.000          15.0  105.120\n",
       "1         1.0      0.053   0.105  0.070      13.575          15.0  108.870\n",
       "2         1.5      0.154   0.295  0.202      11.185          15.0  115.281\n",
       "3         2.0      0.304   0.465  0.368       9.215          15.0  130.739\n",
       "4         2.5      0.620   0.750  0.679       5.410           2.0  151.525\n",
       "5         3.0      0.895   0.935  0.914       3.220           2.0  163.895\n",
       "6         3.5      0.985   0.995  0.990       2.790           3.0  167.243\n",
       "7         4.0      1.000   1.000  1.000       3.150           3.0  168.285\n",
       "8         4.5      1.000   1.000  1.000       3.565           3.0  168.480\n",
       "9         5.0      1.000   1.000  1.000       4.010           4.0  168.655\n",
       "10        5.5      1.000   1.000  1.000       4.505           4.0  168.895\n",
       "11        6.0      1.000   1.000  1.000       4.975           5.0  169.105\n",
       "12        6.5      1.000   1.000  1.000       5.525           5.0  169.370\n",
       "13        7.0      1.000   1.000  1.000       6.055           6.0  169.565"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(reset_table[[\"threshold\", \"precision\", \"recall\", \"f1\", \"mean_delay\", \"median_delay\", \"arl\"]].round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55315435",
   "metadata": {},
   "source": [
    "**Summary of Section 5:** Reset benchmark outputs become most useful once merged into one threshold-indexed table. That table is the handoff point between metric computation and visual or comparative interpretation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "366aa68d",
   "metadata": {},
   "source": [
    "## Section 6. Visualizing Reset Benchmark Results\n",
    "\n",
    "Once the reset table exists, the next problem is summary. Threshold sweeps are difficult to compare by reading a raw table alone, especially when several metric columns move at once. This is where the benchmark visualization layer becomes useful again.\n",
    "\n",
    "The plotting section in this notebook does not re-teach the visualization API from first principles. Instead, it applies the visualizer patterns from notebook 04 to a real reset benchmark table.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b87be7ea",
   "metadata": {},
   "source": [
    "### 6.1 Reset benchmark plots in matplotlib\n",
    "\n",
    "The matplotlib figure below is designed as a compact operating-point dashboard. It mirrors the structure used in earlier visualization examples, but now the inputs come from a real reset benchmark run.\n",
    "\n",
    "The `BenchmarkPlotter` coordinates four metric views here: F1, precision-recall threshold curves, delay curves, and ARL. That combination gives a good first read of the threshold sweep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "172e29ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eGIah1YfD9Z9Vx3T2coLSrTeuoeLt5pRlNngVP6+/Pf6rKO7pnPl5HzdnOThwSxsA4OYYo+ecLcfn3bt3V3JysrZv366YmBg9//zzdzw+T01N1ciRI/Xaa6/p7NmzmjhxosaOHXvLXzBv95rc3Nz08ssv66WXXpKLi4vatGmjyMhIHTp0SCNHjryhvQkTJqhp06aqW7euUlJS9NNPP2W+/n8aPXq0pk2bpn/9618aO3asjh07pokTJ2rcuHEUP2CepKvS9s+lrTOuzUBvMEBq+6zkV/PWl6Vm6GJMki5cTdLFsGuF8QvREX/9maSwmCSl/W2NRA8XRwX+VRBvGFRCPeuXuzaT3PfasQAft8y7PgEAtmFqIT0+Pl4nT57MfHzmzBnt3btXpUqVUsWKFfXqq6/qwoUL+vLLLyVJTz75pD799FO99NJLGjFihH777TctXbpUP//8s1kvoVArX768Nm3apJdfflldu3ZVSkqKKlWqpO7du8vBwUE+Pj7auHGjpk2bptjYWFWqVEnvv/++evToIUl67LHH5OHhof/85z968cUX5enpqfr162duHOXk5KSPP/5Yr7/+uiZMmKB27dppw4YNSkxM1LFjx5SWlrs1Xy9evKjGjRtnPn7vvff03nvvqUOHDtqwYYOtugWwS2cvJ2jSj4e04VikOtTw09C7Kv01Y/yvDw8XlfBwkYsTg2kAwK0xRs8/+TE+r1evns3G5507d1b16tXVvn17paSkaNCgQZo0adIdvSZJGj9+vJycnDRhwgRdvHhR5cqV05NPPpltey4uLnr11Vd19uxZubu7q127dvr666+zPTcwMFC//PKLXnzxRTVs2FClSpXK/I8AoMDFR0hbpks7vpAyUqUmQ6TW/5JKXtsw90p8ikKvXiuQXy+OX7iapIsx12aYRyWkZjZlsUgB3m6ZS6w0qOB7bRb59dnkJdzl4+6UpztPAAB5ZzFutW17PtuwYYPuvvvuG44PHTpU8+fP17Bhw3T27NksRdQNGzboueee0+HDh1WhQgWNHz9ew4YNy/FzxsbGytfXVzExMdmuv3jmzBlVrlyZNSz/IbulXcxWHL5eVqtVERER8vf3Z1ZNASsqfZ+UmqHPNpzUzN9Py8/bVRN611HXOgGF5t9pbhWVfrc39Ls56HfzmNX3txqHFiaM0YsOW47Rhw0bpujoaH3//fe2CVeE3e57jvdvcxTafo8OkTZ9LO35SnJwlpqPlFqNlryvbcKcnJahqSuPat6ms5mXuDs7KrDk9aK4W2aB/HqRPMDHrVBNgCm0fW/n6Hdz0O/mMLPfczNGN3VGeseOHXWrOv78+fOzvWbPnj35mAoACj/DMLTmcLgm/3hYkXEpeqJDFY3uWE3uLjnbAA0AgJthjA4AORB5TPpzmnRgqeTqI7V7QWrxmOReMvOUE+Fx+tfiPTodmaBXetRS22plVKGku3zdnYvsxBcAKM6K1BrpAADp3JUETVpxSOv/Wsblv4+1VOUynmbHAgAAAOzfxT3SHx9IR36UvMtJ97whNR16bbPQvxiGof9uC9GbPx1WxVIe+n5MG9UpX3jvRAIA5AyFdAAoIpLTMjRjwynN/P2U/LxcNevRpkV6GRcAAFB4ZHenAYC/GIZ0brP0x/vSqXVSycpS74+khgMlJ9csp0YlpOqlZfu19ki4HmlVUf/Xsw53jQKAnaCQDgBFwNrD4Zr04yFFxKbo8fZVNOZulnEBAAAA8pVhSCfWXCugh26V/OtKD3wh1ekjOd5YTtl08rKeW7JXaRlWzX60qbrWLVvwmQEA+YZCOgAUYueuJGjyj4f129EIta/hp69GsowLAAAAkK+sGdLhH64t4RJ+QKrQXBq0RKrRTcrmbtDUdKveX3NMszeeVuuqpfVB/0YK8GFzZACwNxTSs2G1Ws2OgBzg6wR7lpyWoc82nNJnfy3jMvORpupWl2VcAAAAgHyTnirtXyL9+aEUdUqqcrc09CcpuG22BXRJOh0Zr2e+3qsjYbF6pXstjWpXRQ4OjNkBwB5RSP8bFxcXOTg46OLFi/Lz85OLiwtFq78YhqH09HQ5OTmZ3ieGYSg1NVWRkZFycHCQi4uLqXkAW1t35NoyLpdikjOXcfFw4e0aAAAAyBepidLuL6XNH0uxF6RavaQH5kiBTW96iWEY+mbXeU1acUgBPm5aPrq1GlQoUXCZAQAFjsrM3zg4OKhy5coKCwvTxYsXzY5TqBiGIavVKgcHB9ML6dd5eHioYsWKcnBwMDsKYBMhVxL1+k+HtPZIhNpVL6MFw1uoip+X2bEAAAAA+5QULe34XNr6mZR0Var/kNT2Wcm/9i0vi0lM07+/P6Cf94epf7MKmti7rjxdKa8AgL3jnf4fXFxcVLFiRaWnpysjI8PsOIWG1WrVlStXVLp06UJRuHZ0dCwUs+MBW0hOy9DM309pxoZTKuPpos8GN1H3emX5/gYAAADyQ3yktHXGtSJ6eorU+BGpzdNSyeDbXrr9TJSeW7JXsclp+vThxurVoHz+5wUAFAoU0rNhsVjk7OwsZ2dns6MUGlarVc7OznJzcysUhXTAXvx9GZdR7apobCeWcQEAAMXH/Pnz9eyzzyo6OlqSNGnSJH3//ffau3evqblgp2LOS5s/kXYtkBwcpWYjpLvGSN5lb3tpeoZVH/92Up/+dkLNKpXS0oF3KbCEewGEBgAUFlRrAMAEoVGJmvzj/5ZxmT+8haqyjAsAALgDZ8+eVeXKlbVnzx41atTI7DhA4XH5pLTpQ2nf15Krt9T2OanFKMmjVI4uD41K1DNf79G+8zF6tksNjbm7mhzZUBQAih0K6QBQgJLTMjTr99OaseGkSrGMCwAAxUZqaqpcXFzMjpFjRS0vkK2w/dKfH0iHvpe8AqQuk6WmwyTXnE9g+WHvBb323UH5ejhr6RN3qWmlkvkWFwBQuLFGBwAUkN+Ohqvrhxv16foTGtG2stY930E96pejiA4AgB3q2LGjxo4dq2effVZlypRRt27dJEkHDx5Ujx495OXlpYCAAD366KO6fPly5nXLli1T/fr15e7urtKlS6tLly5KSEjI/Pznn3+u2rVry83NTbVr19bMmTMzP1e5cmVJUuPGjWWxWNSxY8cc5x02bJj69Omjt956S+XLl1fNmjUlSaGhoerfv79KlCihUqVK6f7779fZs2ezXDt37lzVrVtXrq6uKleunMaOHZv5uQ8++ED169eXp6engoKCNHr0aMXHx+c4F5AnIVulhQ9Js9pJF/dIvT6UntkntR6b4yJ6XHKaxi3Zq2e+3qtOtf31yzPtKKIDQDFHIR0A8lloVKIeW7BTI+bvVMVSHlr5bHu93L0Wa6EDAGDnFixYIBcXF23atEkzZ85UdHS0OnXqpMaNG2vnzp1auXKlwsPD1b9/f0lSWFiYBg0apBEjRujIkSPasGGD+vXrJ8MwJEkLFy7UhAkT9NZbb+nIkSN66623NGnSJC1YsECStH37dknS2rVrFRYWpuXLl0uSNmzYIIvFckMB/J/WrVunY8eOac2aNfrpp5+Ulpambt26ydvbW3/88Yc2bdokLy8vde/eXampqZKkzz77TGPGjNHjjz+uAwcOaMWKFapWrVpmmw4ODvr444916NAhLViwQL/99pteeuklm/YzIEkyDOnkWmleT2luNyk6VOo3Rxq7S2o2XHJ2y3FTe0Ku6t6P/9Tqw+H6cEBDfTSwsXzc2EMNAIo7qjgAkE+S0zI0e+NpTV9/bRmXGYObqAfLuAAAYBupidLl4wX7nGVqSC4eOT69evXqevfddzMfv/nmm2rcuLHefvvtzGNz585VUFCQjh8/rvj4eKWnp6tfv36qVKmSJKl+/fqZ506cOFHvv/+++vXrJ0kKDg7WwYMHNXv2bA0bNkx+fn6SpNKlS6ts2f9tnujh4aGaNWvK2fnWhUBPT099/vnnmUu6/Pe//5XVatXnn3+eOX6ZN2+eSpQooQ0bNqhr165688039fzzz+uZZ57JbKd58+aZf3/22Wcz/x4cHKw333xTTz75pGbMmJGzTgRux2qVjv4o/fG+FLZPCmwqDVwk1eghOeRu7mCG1dDM30/pgzXHVT/QV1+NbKFKpT3zKTgAoKihkA4A+WD9sQhNWnFIF64m6bF2VfSvTtXk6cpbLgAANnP5uDS7Q8E+5+O/S+Ub5fj0pk2bZnm8b98+rV+/Xl5eNy4tcerUKXXt2lWdO3dW/fr11a1bN3Xt2lUPPvigSpYsqYSEBJ06dUojR47UqFGjMq9LT0+Xr6/vLXO0aNFCR48evW3e+vXrZ1kXfd++fTp58qS8vb2znJecnKxTp04pIiJCFy9eVOfOnW/a5tq1azVlyhQdPXpUsbGxSk9PV3JyshITE+XhkfP/lABukJEmHVh2bQ30y8elyu2lIT9IlTtIeZi4cjE6Sc8t2avtZ6M0pmM1PdOlupwduYkfAPA/VHUAwIZCoxL1xk+HtfpwuNpUK60vhjZTNX/v218IAAByp0yNa4Xtgn7OXPD0zDqTNT4+Xr1799bUqVNvOLdcuXJydHTUmjVrtHnzZq1evVqffPKJ/u///k/btm3LLDrPmTNHLVu2lCQZhqH09HS5urrm8QXdPm/Tpk21cOHCG8718/OTw21m+549e1a9evXSU089pbfeekulSpXSn3/+qZEjRyo1NZVCOvImLUnat0ja9LEUEyLV7CndP0MKan77a2/i1wNhemX5AXm4OGrxqFZqVaW0DQMDAOwFhXQAsIHktAzN2Xhan64/qZIeLpr+cBP1rM8yLgAA5BsXj1zNDi8MmjRpom+//VbBwcFycsr+VzGLxaI2bdqoTZs2mjBhgipVqqTvvvtO48aNU/ny5XX69GkNHjxY0v8K6dfbuj6bPCMjw2Z5lyxZIn9/f/n4+GR7TnBwsNatW6e77777hs/t2rVLVqtV77//fmbRfenSpTbJhmIoPUWee+bIcvBLKfGyVO8Bqe3XUkDdPDeZmJquN346rMXbQ9WjXllN6VdfJTxcbn8hAKBY4j4lALhDG45FqPu0jfpo3QkNaxOsdc930L0NylFEBwAAWYwZM0ZRUVEaNGiQduzYoVOnTmnVqlUaPny4MjIytG3bNr399tvauXOnQkJCtHz5ckVGRqp27dqSpMmTJ2vKlCn6+OOPdfz4cR04cEALFizQBx98IEny9/eXu7t75iamMTExkq5tQlqrVi1duHAhV3kHDx6sMmXK6P7779cff/yhM2fOaMOGDXr66ad1/vx5SdKkSZP0/vvv6+OPP9aJEye0e/duffLJJ5KkatWqKS0tTZ988olOnz6tr776SjNnzrRVd6KYsax8WV47PpJq9pD+tUt64PM7KqIfvBCjXp/8qe/3XNTUB+prxuAmFNEBALdEIR0A8uj81UQ98dVODZu3Q+VLuGvls+30ao/arIUOAACyVb58eW3atEkZGRnq2rWr6tevr2effVYlSpSQg4ODfHx8tHHjRvXs2VM1atTQa6+9pvfff189evSQJD322GP6/PPPNW/ePNWvX18dO3bUl19+qcqVK0uSnJyc9PHHH2vWrFkqX7687r//fklSYmKijh07prS0tFzl9fDw0MaNG1WxYkX169dPtWvX1siRI5WcnJw5Q33o0KGaNm2aZsyYobp166pXr146ceKEJKlhw4b64IMPNHXqVNWrV08LFy7UlClTbNWdKE5OrJVl9wLFtn1NRq9pUqkqeW7KajU0Z+Np9Z2xSe7Ojvrp6bYa0Lwik2AAALdlMQzDMDtEQYqNjZWvr69iYmJuensibmS1WhURESF/f//broUI26HfzXOrvk9J/98yLr7uzhrfq47urc8MdFvge94c9Ls56HfzmNX3jENv7lZ9k5ycrDNnzqhy5cpyc3MzKWHh9PelXRiH2M7tvud4/y5gSVelGXfJ8Kut8HtmyD8gIM/9HhGbrOe/2ac/TlzW4+2r6PmuNeTq5GjjwPaH73lz0O/moN/NYWa/52aMzrRJAMiFDcciNPnHwwqNStTItpX1r87V5cUMdAAAACB//PqylJoo476PpeS8/4fRuiPhenHZfjk6WPTVyBZqV93PhiEBAMUB1R8AyIHzVxP1xk+HtepQuO6qUlqzH22q6gHeZscCAAAA7NfhFdL+JVLfWZJPoJQckesmktMyNOWXI1qw5Zw61/LXuw82UGkv13wICwCwdxTSAeAWUtIzNHfTaX3y2wn5ujvrk0GN1YuNRAEAAID8FR8p/fSsVKuX1GCAlIdVaY9ditPTi/fo7JUEvX5/XT3aqhLjeABAnlFIB4Cb2Ho2RtO+OqLzV5M0om1lPc0yLgAAAED+M4xrRXRJ6jVNslhyVUg3DENfbjmnt345osqlPbVibFvVLMvdpACAO0NFCAD+ISU9Q899vVe/HLykVpVLafaQZqrBMi4AAABAwdi/VDr6k9T/K8krd2uZX4lP0UvL9mvd0QgNax2sV3rUkpszG4oCAO4chXQA+IevtpzTykOXNKl7sB5tV0uOjgy8AQAwm9VqNTsCigm+10wWc0H65UWpfn+pzn25unTj8Ug9/80+ZVgNzR3WTJ1qBeRTSABAcUQhHQD+Ji45TdPXn9RDTSuoe63SrKEIAIDJXFxc5ODgoIsXL8rPz08uLi78fP6LYRhKT0+Xk5MTfWIDhmEoNTVVkZGRcnBwkIuLi9mRih/DkFb8S3LxkHq+m+PLUtIz9N6qY5rzxxm1q15G7z/UUP4+bvkYFABQHFFIB4C/mbPxtBJTM/R052pSSpzZcQAAKPYcHBxUuXJlhYWF6eLFi2bHKVQMw5DVapWDgwOFdBvy8PBQxYoV5eDgYHaU4mfXPOnUOmnwMsm9ZI4uORUZr6cX79Hx8Di9dm9tjWhTWQ4O/HsAANgehXQA+EtkXIo+//OMhrUOVjlfd0VEUEgHAKAwcHFxUcWKFZWenq6MjAyz4xQaVqtVV65cUenSpSn62oijoyMz/M0SdUZa9ZrUZKhU/Z7bnm4YhpbsCNXkHw+rXAk3fTe6jeoF+hZAUABAcUUhHQD+8slvJ+TkYNFTHauaHQUAAPyDxWKRs7OznJ2dzY5SaFitVjk7O8vNzY1COoo2q1X6frTkWVrq9tZtT49OTNWryw/o14OXNKhFRY3vVVseLpQ3AAD5i580ACAp5EqiFm0L0biuNVTCw4VNpgAAAICCsu0zKWSzNOxnydX7lqduPX1Fz3+zX4mpGZr5SBN1r1eugEICAIo7CukAIOn9NcdUytNFw1tXNjsKAAAAUHxEHpPWTpZajZaC2970tPQMqz7bdEFf7ryklpVL6cMBjVTO170AgwIAijsK6QCKvUMXY/TD3ot6q289ubs4mh0HAAAAKB4y0qXvnpRKVJQ6T7jlqfM2n9V/d17S8/fU0FMdq8mRDUUBAAWMQjqAYu8/q46pchlP9W8WZHYUAAAAoPj480MpbK80co3kfPPZ5anpVs3ddFY965TW6I5V5UARHQBgAnakAVCsbT19RRuOReqFrjXl7MhbIgAAAFAgwvZLv78jtR0nVWh2y1N/2HtB4bEpGty0bAGFAwDgRlSNABRbhmHonV+Pqn6gr3rUY1AOAAAAFIj0lGtLuvjVkjq8fMtTrVZDszeeVpfa/gou5VZAAQEAuBGFdADF1qpD4dobGq2Xu9fi9lAAAACgoGx4R7p8XOo7U3JyufWpxyN0IiJeo9pVLqBwAABkj0I6gGIpPcOq91YfU9tqZdS2ehmz4wAAAADFQ+gOadM0qeMrUtn6tz191u+n1bhiCTWrVDL/swEAcAsU0gEUS8t3X9DJiHi91L2m2VEAAACA4iE1Ufr+Sal8Y6nNs7c9fW9otLadidIT7avIYuEOUgCAuZzMDgAABS05LUMfrj2ue+uXU4MKJcyOAwAAABQP6yZLMeelgYslx9uXI2ZvPKXKZTx1T52ykoz8zwcAwC0wIx1AsfPVlnOKiEvR811rmB0FAAAAKB7ObJS2zZS6TJL8bj8OP3clQSsPXtJj7SrLkf2MAACFAIV0AMVKbHKapm84qQHNg1TFz8vsOAAAAID9S46Vvh8jVWortXgiR5d8/scZlfRw0QNNKuRzOAAAcoZCOoBiZdbvp5SclqFnOlc3OwoAAABQPKz+PykpSuozXXK4fRkiKiFV3+wK1dDWwXJzdiyAgAAA3B5rpAMoNiJikzX3z7Ma3qayAnzczI4DAAAA2L/jq6XdX0q9P5JKBufoki+3nJVFFj3aqlL+ZgMAIBeYkQ6g2Pj4txNycXLQkx2qmh0FAAAAsH+JUdKKf0nVukhNhubokqTUDC3YfFYDmgeppKdLPgcEACDnKKQDKBbOXk7Q19tDNbpjVfm6O5sdBwAAALB/v7wopSdJ930iWXK2YeiyXaGKSUrTyLaV8zkcAAC5w9IuAIqF91YfUxkvVw1tHWx2FAAAAMD+HfpOOrhM6ve55FM+R5dkWA19/ucZ9axfTkGlPPI5IAAAucOMdAB27+CFGP20P0zPdqnOZkUAAABAfouPkH4aJ9W+T6r/YI4vW3Xoks5dSdQT7VmKEQBQ+FBIB2D3pq48qip+nnqwaQWzowAAAAD2zTCkH5+RLA5Srw9zvKSLYRia9fspta5aWvUr+OZzSAAAco9COgC7tvnkZf1x4rJe7FpTTo685QEAAAD5at/X0rFfpN4fSZ5lcnzZtjNR2nc+Ro+3r5KP4QAAyDuqSgDslmEYmrryqBoGlVD3emXNjgMAAADYt5jz0q8vSw0GSrV75erS2RtPq1ZZb3Wo4ZdP4QAAuDMU0gHYrZUHL2nf+Ri93L2mLDm8pRQAAABAHhiG9MNYycVT6jE1V5eeCI/Tb0cjNKpdFcbtAIBCy8nsAACQH9IzrPrP6mNqX8NPravm/JZSAAAAAHmw8wvp9HrpkeWSe4lcXTp742mV9XFT74bl8ycbAAA2wIx0AHbpm13ndToyQS91q2l2FAAAAMC+RZ2WVo+Xmo2QqnXO1aWXYpL1/d4LGtm2slycKFEAAAovfkoBsDvJaRmatva4ejcsr3qBvmbHAQAAAOyXNUP6frTk6Sfd80auL5+3+YzcnBw1sEVQPoQDAMB2WNoFgN2Zv/msrsSn6vl7apgdBQAAALBvW2dIIVulYT9Lrl65ujQuOU2Ltobo4VYV5e3mnE8BAQCwDWakA7ArMYlpmrH+pAa2CFJwGU+z4wAAAAD2K+KotO4N6a4xUnCbXF/+9fZQJadnaESbyvkQDgAA26KQDsCuzNx4SmkZhp7uXN3sKAAAAID9ykiTvntCKhksdRqf68tT06364s8z6tMoUAE+brbPBwCAjbG0CwC7ER6brHmbzuixtlXk781gHAAAAMg3f3wgXTogPbZGcs792PvHfRd1KTZZj7evkg/hAACwPWakA7Ab09aekJuzox7vwGAcAAAAyDcX90ob35XaPS8FNs315YZhaM4fp9Wplr+qB3jbPh8AAPmAQjoAu3A6Ml5Ld4ZqTMdq8mGjIgAAACB/pKdI3z0p+deW2r+YpyZ+Px6po5fimI0OAChSWNoFgF14f/VxBXi76tG7KpkdBQAAALBf69+WrpyUnvhdcnLJUxOzN55Wwwq+alm5lI3DAQCQf5iRDqDI238+Wj8fCNOz99SQm7Oj2XEAAAAA+xSyTdr8sXT3v6WAunlq4sD5GG0+dUVPdKgqi8Vi44AAAOQf0wvp06dPV3BwsNzc3NSyZUtt3779ludPmzZNNWvWlLu7u4KCgvTcc88pOTm5gNICKIymrjyqav5e6tc40OwoAADYBcboAG6QmiB9/+S1NdHbPJPnZmZtPKVKpT3UrW5ZG4YDACD/mVpIX7JkicaNG6eJEydq9+7datiwobp166aIiIhsz1+0aJFeeeUVTZw4UUeOHNEXX3yhJUuW6N///ncBJwdQWPx54rI2nbyiF7vVlJOj6f83CABAkccYHUC21k6SYsOkPjMlh7zdBRoalahfDoTpsbaV5ejAbHQAQNFiatXpgw8+0KhRozR8+HDVqVNHM2fOlIeHh+bOnZvt+Zs3b1abNm308MMPKzg4WF27dtWgQYNuO0MGgH2yWg1NXXlUjSuWUNc6AWbHAQDALjBGB3CD0xuk7bOleyZLZarluZkv/jyjEh4uerBpkO2yAQBQQEwrpKempmrXrl3q0qXL/8I4OKhLly7asmVLtte0bt1au3btyhyUnz59Wr/88ot69uxZIJkBFC6/HAzTgQsxerl7LdZXBADABhijA7hBcoz0w1gpuJ3UfFSem7makKolO0L1aKtKcndhXyMAQNHjZNYTX758WRkZGQoIyDqLNCAgQEePHs32mocffliXL19W27ZtZRiG0tPT9eSTT97yttGUlBSlpKRkPo6NjZUkWa1WWa1WG7yS4sFqtcowDPqsgNHvN5eWYdV7q46pQw0/tQguafM+ou/NQb+bg343B/1uHrP6vih8rRmjFy28j5ijuPW7ZeW/paRoGUM/vXYgj6/7yy1nZcjQIy2D8tR3xa3fCxP63hz0uznod3OY2e+5eU7TCul5sWHDBr399tuaMWOGWrZsqZMnT+qZZ57RG2+8ofHjx2d7zZQpUzR58uQbjkdGRrIBUi5YrVbFxMTIMAw5OLAOdUGh32/uu/2ROnclUW90r3TTNVvvBH1vDvrdHPS7Oeh385jV93FxcQX2XAWJMbp5eB8xR3Hqd9dz61Vy738V0+FNJaW6SXkcdyenWzV/0xndW7u0MhJjFJGY+zaKU78XNvS9Oeh3c9Dv5jCz33MzRjetkF6mTBk5OjoqPDw8y/Hw8HCVLZv97t3jx4/Xo48+qscee0ySVL9+fSUkJOjxxx/X//3f/2Xb0a+++qrGjRuX+Tg2NlZBQUHy8/OTj4+PDV+RfbNarbJYLPLz8+ONpADR79lLSs3QvB0HdV+j8mpTNzhfnoO+Nwf9bg763Rz0u3nM6ns3N7cCe668YoxetPA+Yo5i0++JUbL8MVFG9a7ybj9a3newlOKibSGKSU7X2Htqy7+0Z57aKDb9XgjR9+ag381Bv5vDzH7PzRjdtEK6i4uLmjZtqnXr1qlPnz6SrnXaunXrNHbs2GyvSUxMvKEzHR2vra1mGEa217i6usrV1fWG4w4ODvyDyCWLxUK/mYB+v9H8Lad1NTFVz99TM1/7hb43B/1uDvrdHPS7eczo+6LwdWaMXvTwPmKOYtHvv74opadI930ii2Pe1zTPsBr6YtNZda9XVpX9vO8oUrHo90KKvjcH/W4O+t0cZvV7bp7P1KVdxo0bp6FDh6pZs2Zq0aKFpk2bpoSEBA0fPlySNGTIEAUGBmrKlCmSpN69e+uDDz5Q48aNM28bHT9+vHr37p05WAdg36ITUzXz91N6uEVFVSztYXYcAADsDmN0ADr4rXRoufTAF5J39nej5NSaw5d05nKCPhzQyDbZAAAwiamF9AEDBigyMlITJkzQpUuX1KhRI61cuTJzc6OQkJAs/yvw2muvyWKx6LXXXtOFCxfk5+en3r1766233jLrJQAoYJ9tOKUMq6GxnaqbHQUAALvEGB0o5uLCpZ+fl+r0keo9cEdNGYahWRtPq2XlUmoUVMIm8QAAMIvpm42OHTv2preJbtiwIctjJycnTZw4URMnTiyAZAAKm7CYJM3ffFZPtK8iP+8bbwcHAAC2wRgdKKYMQ/rxacnBWbr3A+kO1kWXpJ3nrmpPSLTmDmtmo4AAAJjH9EI6AOTUR2tPyNPVSaPaVzE7CgAAAGB/9i6Sjq+UBi6WPEvfcXOzfj+t6v5e6ljD3wbhAAAwF6vmAygSTkbEa+nOUI25u5q83ZzNjgMAAADYl+hQaeUrUqPBUq2ed9zcyYg4rT0SrlHtq8jB4c5mtgMAUBhQSAdQJLy36pjK+brrkVYVzY4CAAAA2BerVfphtOTqI3WfYpMm52w8owAfV93fqLxN2gMAwGwU0gEUentCrmrloUt67p4acnVyNDsOAAAAYF92fiGd2Sjd/6nk5nvHzUXEJuu7PRc0vE1lxu8AALtBIR1AoWYYhqauPKqaAd7q2zjQ7DgAAACAfblySlozQWr+mFT1bps0OX/zWbk4OejhltxNCgCwHxTSARRqG09c1tbTUXqxW005srYiAAAAYDvWDOn7pySvAOme123SZHxKuv679ZwGtQiSD3sbAQDsiJPZAQDgZqxWQ1N/PapmlUqqc21/s+MAAAAA9mXLp1LodmnESsnF0yZNfr09RImpGRreprJN2gMAoLBgRjqAQuunA2E6HBarl3vUksXCbHQAAADAZsIPS7+9KbX+l1SxlU2aTMuwau6fZ3Rfo/IqX8LdJm0CAFBYUEgHUCilplv1/upj6lzLX82DS5kdBwAAALAfGWnSd09IpapId/+fzZr9eX+YLsYk6/H2VWzWJgAAhQWFdACF0pIdIQqJStSL3WuaHQUAAACwLxvfk8IPSX1nSs5uNmnSMAzN2nhaHWr4qVZZH5u0CQBAYUIhHUChk5iaro/WnVTfRoEMwgEAAABburhH2vgfqf2LUvnGNmv2jxOXdSQsVk8wGx0AYKcopAModOb+eUaxSWl67p4aZkcBAAAA7EdasvTdk1LZelL7F2za9OyNp1Uv0Ed3VS1t03YBACgsKKQDKFSuJqRq1u+nNbhVRQWV8jA7DgAAAGA/Nn8sRZ2W+s6SHJ1t1uzBCzH68+RlPdG+qiwWi83aBQCgMKGQDqBQmb7+pKyGoTF3VzM7CgAAAGA/0pKlbbOkJkMl/9o2bXrOH6dVoaS7etQra9N2AQAoTCikAyg0LkQn6cut5zSqfRWV8XI1Ow4AAABgPw4slRKvSK2esmmz568m6qf9YXqsbWU5OVJiAADYL37KASg0pq05Lm9XJz3Wjg2KAAAAAJsxDGnLDKlGd6l0VZs2/cWfZ+Tt5qT+zYNs2i4AAIUNhXQAhcKJ8Dh9u/u8/tWpmrxcncyOAwAAANiP0+ulyCPSXaNt2mxMYpqW7AjVkFaV5OHCGB4AYN8opAMoFP6z6pjKl3DXoJYVzY4CAAAA2JctM6SA+lJwO5s2+99t55RhNTSkdbBN2wUAoDCikA7AdLvOXdXqw+F6vmsNuTo5mh0HAAAAsB+Rx6STa67NRrdYbNZsclqG5m06qweaVmB/IwBAsUAhHYCpDMPQ1JVHVaust+5vGGh2HAAAAMC+bP1M8vSX6j1g02a/23NBVxJSNIr9jQAAxQSFdACm2nAsUtvPROml7jXl4GC7GTIAAABAsZcYJe37WmrxuORku1njVquhOX+cVtc6AapcxtNm7QIAUJhRSAdgGqv12mz0FsGldHdNf7PjAAAAAPZl51xJhtRshE2bXXskXKcjE/REh6o2bRcAgMKMQjoA06zYd1FHL8Xp5R41ZbHheo0AAABAsZeeKm2fIzUYIHmWtmnTszeeVvPgkmpSsaRN2wUAoDCjkA7AFKnpVr2/5pi61A5Q00qlzI4DAAAA2JdD30nxl6RWo23a7K5zUdp57qoeb89sdABA8UIhHYApFm8P0YWrSXqpe02zowAAAAD2xTCkrdOlqp0l/1o2bXrW76dV1c9TnWuxNCMAoHihkA6gwCWkpOuT306oX5MKqhHgbXYcAAAAwL6c2ySF7ZPusu1s9NOR8VpzJFyj2lWRgwNLMwIAihcK6QAK3Od/nFFsUrqeu6eG2VEAAAAA+7NlhuRX69qMdBua88cZlfFyVZ/GgTZtFwCAooBCOoACdSU+RbM3ntKjd1VSYAl3s+MAAAAA9uXKKenYL1KrpySL7WaNR8al6Nvd5zWsdbDcnB1t1i4AAEUFhXQABWr6+lNysFg05u5qZkcBAAAA7M+2WZJHKanBAJs2u2DzWTk5WPRIy0o2bRcAgKKCQjqAAnP+aqL+u/WcHm9fRaU8XcyOAwAAANiXpGhpz3+lZiMkZ9vd/ZmQkq6vtp7TwOYV5evhbLN2AQAoSiikAygwH6w5Lh93Z41oW9nsKAAAAID92f2llJEqNX/Mps0u3Rmq+JR0jWgbbNN2AQAoSiikAygQxy7F6bs9F/R052rydHUyOw4AAABgXzLSpe2zpfoPSt5lbdZseoZVX/x5Rr0blFOFkh42axcAgKKGQjqAAvGfVUcVVNJDA5tXNDsKAAB26dSpU3rttdc0aNAgRURESJJ+/fVXHTp0yORkAArEkRVSTKjUarRNm/3l4CWdv5qkx9tXtWm7AAAUNRTSAeS7HWejtPZIhJ7vWkMuTrztAABga7///rvq16+vbdu2afny5YqPj5ck7du3TxMnTjQ5HYACsXWGFNxOKtfAZk0ahqFZv59Su+plVKe8j83aBQCgKKKiBSBfGYahqb8eVZ1yPurdoLzZcQAAsEuvvPKK3nzzTa1Zs0YuLv/b0LtTp07aunWrickAFIjQHdL5HTafjb751BUduhirx9tXsWm7AAAURRTSAeSr345GaOe5q3qpe005OFjMjgMAgF06cOCA+vbte8Nxf39/Xb582YREAArU1ulSqSpSje42bXbWxtOqU85HbauVsWm7AAAURRTSAeSbDKuhd1ceU6sqpdShhp/ZcQAAsFslSpRQWFjYDcf37NmjwMBAExIBKDDRodLhFVLLpyQH2/2KfyQsVhuPR+qJDlVksTAhBgAACukA8s33ey7oWHicXupei8E3AAD5aODAgXr55Zd16dIlWSwWWa1Wbdq0SS+88IKGDBlidjwA+Wn7LMnVS2r0sE2bnb3xtAJLuKtn/XI2bRcAgKKKQjqAfJGSnqEP1hxXt7oBalKxpNlxAACwa2+//bZq1aqloKAgxcfHq06dOmrfvr1at26t1157zex4APJLSry060up6bBrxXQbuRidpB/3XdSItpXl7EjZAAAASXIyOwAA+7Ri70VdiE7SghHNzY4CAIDdc3Fx0Zw5czR+/HgdPHhQ8fHxaty4sapXr252NAD5ae9CKTVeavG4TZud++cZebg4amDzIJu2CwBAUUYhHUC+WLgtRO2ql1E1f2+zowAAUGxUrFhRFStWNDsGgIJgzZC2fibV7SP5VrBZszFJaVq8PUTD2gTL05WSAQAA1/FTEYDNHboYo72h0Zr5SFOzowAAUCyMGDHilp+fO3duASUBUGCOr5SunpEe+MKmzS7aFqK0DENDWwfbtF0AAIo6CukAbG7RthAF+Liqc21/s6MAAFAsXL16NcvjtLQ0HTx4UNHR0erUqZNJqQDkqy3TpaCWUgXbTV5JSc/Q3E1n1K9JoPy93WzWLgAA9oBCOgCbik9J1/d7LmgkGxMBAFBgvvvuuxuOWa1WPfXUU6pataoJiQDkq4t7pXObpIcW2LTZH/ZcVGRcih5rV8Wm7QIAYA+ocgGwqRV7LyopLUMDWrA+KwAAZnJwcNC4ceP04Ycfmh0FgK1tnSH5VpRq9bJZk1arodl/nFaX2gGq5u9ls3YBALAXFNIB2IxhGFq47ZzurumvwBLuZscBAKDYO3XqlNLT082OAcCWYsOkg99KLZ+QHG13k/n6YxE6GRGvJzswGx0AgOywtAsAm9l/PkaHLsbq+a41zI4CAECxMm7cuCyPDcNQWFiYfv75Zw0dOtSkVADyxY45kpOb1ORRmzY76/fTalKxhJoFl7JpuwAA2AsK6QBsZuG2cwos4a4ONdhkFACAgrRnz54sjx0cHOTn56f3339fI0aMMCkVAJtLTZR2zpUaPyq5+dqs2T0hV7X9bJRmPmK7jUsBALA3FNIB2ERMUpp+3Bem0R2rytHBYnYcAACKlfXr15sdAUBB2P+1lBR9bVkXG5q98bQql/HUPXUCbNouAAD2hDXSAdjE93suKDXDqgHNg8yOAgAAANgfq1Xa+plU616pVGWbNXv2coJWHrqkx9pVZkIMAAC3wIx0AHfMMAwt2haie2oHyN/Hzew4AAAUC40bN5bFkrOi1+7du/M5DYB8d2qddPm41Psjmzb7+Z+nVdrTRQ80qWDTdgEAsDcU0gHcsV3nrupYeJxe61Xb7CgAABQbffr0MTsCgIK0ZbpUrpFU8S6bNXk5PkXf7DyvsXdXk5uzo83aBQDAHlFIB3DHFm4LUcVSHmpTtYzZUQAAKDYmTpxodgQABSX8sHR6vdRvjpTDO1Fy4sst5+RgseiRVpVs1iYAAPaKNdIB3JGrCan6+UCYHm5ZUQ6sqQgAAADY3tYZknd5qU4fmzWZlJqhr7ac1YDmQSrp6WKzdgEAsFfMSAdwR77dfV6GYejBpqypCACAWTIyMvThhx9q6dKlCgkJUWpqapbPR0VFmZQMwB2Lj5T2L5U6viI52a7g/c2uUMUmp2tkW9ttXAoAgD1jRjqAPLu+yWj3euVUxsvV7DgAABRbkydP1gcffKABAwYoJiZG48aNU79+/eTg4KBJkyaZHQ/Andg5V3JwlJoOs1mT6RlWzfnjtHrWL6egUh42axcAAHtGIR1Anm05fUWnLydocMuKZkcBAKBYW7hwoebMmaPnn39eTk5OGjRokD7//HNNmDBBW7duNTsegLxKT5F2fC41HCR5lLJZsysPXVJoVJKeaF/FZm0CAGDvKKQDyLNF20JU1c9TLSvbblAPAABy79KlS6pfv74kycvLSzExMZKkXr166eeffzYzGoA7cWCZlBAhtXrKZk0ahqHZG0+rddXSqhfoa7N2AQCwdxTSAeTJ5fgUrTp0SQ+3rCSLhU1GAQAwU4UKFRQWFiZJqlq1qlavXi1J2rFjh1xdWX4NKJIM49omo9W7SWWq26zZraejtP98jB5nNjoAALlCIR1Annyz87wsFoseaBJodhQAAIq9vn37at26dZKkf/3rXxo/fryqV6+uIUOGaMSIESanA5AnZzZK4Qelu0bbtNnZG0+pVllvdajhZ9N2AQCwd05mBwBQ9FithhZtP6deDcqphIeL2XEAACj23nnnncy/DxgwQJUqVdLmzZtVvXp19e7d28RkAPJsy3QpoJ5UuYPNmjx2KU7rj0Xqg/4NuasUAIBcopAOINf+OHlZoVFJmjaATUYBACgMkpOT5ebmlvm4VatWatWqlYmJANyRyyekE6uk+6dLNix4z998RmV93NS7YXmbtQkAQHHB0i4Acm3RtnOqVdZbTSqWNDsKAACQ5O/vr6FDh2rNmjWyWq1mxwFwp7Z+Jnn6SfUetFmT8Snp+mHvRQ1sESRnR0oBAADkFj89AeRKeGyy1h6J0MMtK3I7KAAAhcSCBQuUmJio+++/X4GBgXr22We1c+dOs2MByIvEKGnfYqn5Y5Kz2+3Pz6EVey8qOS1D/ZsF2axNAACKE9ML6dOnT1dwcLDc3NzUsmVLbd++/ZbnR0dHa8yYMSpXrpxcXV1Vo0YN/fLLLwWUFsCSHaFycXRQn8ZsMgoAQGHRt29fffPNNwoPD9fbb7+tw4cPq1WrVqpRo4Zef/31XLfHGB0w0a75kjVDajbSps0u3h6iu2v6q3wJd5u2CwBAcWFqIX3JkiUaN26cJk6cqN27d6thw4bq1q2bIiIisj0/NTVV99xzj86ePatly5bp2LFjmjNnjgIDKegBBSHDaujr7SG6v1F5+bg5mx0HAAD8g7e3t4YPH67Vq1dr//798vT01OTJk3PVBmN0wEQZadL2OVKDhyQvP5s1e+B8jA5ciNGgFuxxBABAXpm62egHH3ygUaNGafjw4ZKkmTNn6ueff9bcuXP1yiuv3HD+3LlzFRUVpc2bN8vZ+VoRLzg4uCAjA8XahmMRuhiTrIdbMgAHAKAwSk5O1ooVK7Ro0SKtXLlSAQEBevHFF3PVBmN0wESHvpfiLkqtRtu02cU7QlTWx00da9quOA8AQHFj2oz01NRU7dq1S126dPlfGAcHdenSRVu2bMn2mhUrVuiuu+7SmDFjFBAQoHr16untt99WRkZGQcUGirWF20JUP9BXDSqUMDsKAAD4m1WrVmno0KEKCAjQU089pYCAAK1evVrnzp3TO++8k+N2GKMDJjIMaet0qUpHKaCuzZpNSEnXD3suqH/zIDmxySgAAHlm2oz0y5cvKyMjQwEBAVmOBwQE6OjRo9lec/r0af32228aPHiwfvnlF508eVKjR49WWlqaJk6cmO01KSkpSklJyXwcGxsrSbJarbJarTZ6NfbParXKMAz6rIAVpn6/cDVJ649F6K0+9QpFnvxWmPq+OKHfzUG/m4N+N49ZfZ+fz9e3b1/16tVLX375pXr27Jk5Mzy3GKMXLbyPmCPf+j1kixwu7pF10BLJhm2v2HtBiWkZeqhpYJH+XuH73Tz0vTnod3PQ7+Yws99z85ymLu2SW1arVf7+/po9e7YcHR3VtGlTXbhwQf/5z39uOkifMmVKtutCRkZGKjk5Ob8j2w2r1aqYmBgZhiEHB2YxFJTC1O9zN1+Qu7ODWpVzuukaqfakMPV9cUK/m4N+Nwf9bh6z+j4uLi7f2g4PD5e3t3e+tX8rjNHNw/uIOfKr30v8Pk1OJSrrsk8DyYbj7a82n9ZdlXzknBqniIj8ex/Kb3y/m4e+Nwf9bg763Rxm9ntuxuimFdLLlCkjR0dHhYeHZzkeHh6usmXLZntNuXLl5OzsLEdHx8xjtWvX1qVLl5SamioXF5cbrnn11Vc1bty4zMexsbEKCgqSn5+ffHx8bPRq7J/VapXFYpGfnx9vJAWosPR7WoZVPx05qL6NKyi4QjnTchSkwtL3xQ39bg763Rz0u3nM6ns3N7d8a9tWRXTG6EUL7yPmyJd+v3pOlrNrZfR8T/4B2f9by4tDF2N0ODxRsx5pIn9/f5u1awa+381D35uDfjcH/W4OM/s9N2N00wrpLi4uatq0qdatW6c+ffpIutZp69at09ixY7O9pk2bNlq0aJGsVmtmpx4/flzlypXLdoAuSa6urnJ1db3huIODA/8gcslisdBvJigM/b7+cLgi41I0uGWlYvX1Lwx9XxzR7+ag381Bv5vHjL4vCl9nxuhFD+8j5rB5v++YLbn5ytJwkGTDr+WSnefl7+2qzrUD7OJ7hO9389D35qDfzUG/m8Osfs/N85n6HTFu3DjNmTNHCxYs0JEjR/TUU08pISFBw4cPlyQNGTJEr776aub5Tz31lKKiovTMM8/o+PHj+vnnn/X2229rzJgxZr0EoFhYuC1ETSqWUJ3yzBADAMDeMUYHClhyrLT7K6nZCMnFw2bNJqam6/s9FzWATUYBALAJU9dIHzBggCIjIzVhwgRdunRJjRo10sqVKzM3NwoJCcnyvwJBQUFatWqVnnvuOTVo0ECBgYF65pln9PLLL5v1EgC7d+5Kgv44cVnvPdTQ7CgAAKAAMEYHCtier6T0ZKn5KJs2+9O+MCWkpqt/syCbtgsAQHFl+majY8eOveltohs2bLjh2F133aWtW7fmcyoA1y3aHiIfNyf1alA81kYHAACM0YECY82Qts2U6vWTfGw73l60PUTtq/spqJTtZrkDAFCcmV5IB1B4paRnaNnO83qgaQW5OTve/gIAAFBg+vXrl+Nzly9fno9JAOTZ0Z+k6BCp/1c2bfbwxVjtDY3WzEea2rRdAACKMwrpAG5q1aFwXUlI1eCWFc2OAgAA/sHX19fsCADu1JYZUqU2UvlGNm326x0h8vN2Vefa/jZtFwCA4oxCOoCbWrj1nFpULqVq/t5mRwEAAP8wb948syMAuBPnd0mhW6UBC23abFJqhr7bfUFDWleSM5uMAgBgM3n6qRodHa3PP/9cr776qqKioiRJu3fv1oULF2waDoB5TkbEa9uZKGajAwAAAPlh63SpZLBUs4dNm/1p/0XFpaRrYHPG8QAA2FKuZ6Tv379fXbp0ka+vr86ePatRo0apVKlSWr58uUJCQvTll1/mR04ABWzRthCV8nRR93plzY4CAACy0bhxY1kslhydu3v37nxOAyBXYs5Lh76Xur0tOdh2L6LF20PUrnoZNhkFAMDGcl1IHzdunIYNG6Z3331X3t7/W+6hZ8+eevjhh20aDoA5ktMy9O3u8xrYPEiuTmwyCgBAYdSnTx+zIwDIq+2zJRdPqfFgmzZ79FKsdodE67PBTWzaLgAAyEMhfceOHZo1a9YNxwMDA3Xp0iWbhAJgrp/3hykmKU2DWnA7KAAAhdXEiRPNjgAgL1LipV3zpSZDJFfb7kX09fZQlfFyVZc6ATZtFwAA5GGNdFdXV8XGxt5w/Pjx4/Lz87NJKADmWrQ9RG2rlVFwGU+zowAAAAD2Zd9iKSVOavmETZtNSs3Q8t3n9VCzCmwyCgBAPsj1T9f77rtPr7/+utLS0iRJFotFISEhevnll/XAAw/YPCCAgnUkLFa7zl1lk1EAAIqQjIwMvffee2rRooXKli2rUqVKZfkAUEhYrdLWz6Ta90klbDve/uVAmGKT0zWweZBN2wUAANfkupD+/vvvKz4+Xv7+/kpKSlKHDh1UrVo1eXt766233sqPjAAK0KJtIfLz5nZQAACKksmTJ+uDDz7QgAEDFBMTo3Hjxqlfv35ycHDQpEmTzI4H4LoTq6SoU9JdY2ze9OK/7iqtVJq7SgEAyA+5XiPd19dXa9as0Z9//qn9+/crPj5eTZo0UZcuXfIjH4AClJCSru/2XNCw1sHcDgoAQBGycOFCzZkzR/fee68mTZqkQYMGqWrVqmrQoIG2bt2qp59+2uyIACRpy3QpsJkU1MKmzR4Pj9POc1c1/WE2GQUAIL/kupB+Xdu2bdW2bVtbZgFgsh/3XVRCaroGtuB2UAAAipJLly6pfv36kiQvLy/FxMRIknr16qXx48ebGQ3AdWH7pbN/SA/Os3nTi7eHqLSni+7hrlIAAPJNrgvpr7/++i0/P2HChDyHAWCuRdtD1LGGnyqU9DA7CgAAyIUKFSooLCxMFStWVNWqVbV69Wo1adJEO3bskKurq9nxAEjX1kb3Dbq2ProNJadlaPnuCxrYIkguTtxVCgBAfsl1If27777L8jgtLU1nzpyRk5OTqlatSiEdKKIOnI/R/vMx+nxIM7OjAACAXOrbt6/WrVunli1b6l//+pceeeQRffHFFwoJCdFzzz1ndjwAceHSwWVSp/GSY55vDM/WrwfDFJOUpoHNbbt5KQAAyCrXP8H37Nlzw7HY2FgNGzZMffv2tUkoAAVv0fZzKufrpo41/cyOAgAAcumdd97J/PuAAQNUqVIlbd68WdWrV1fv3r1NTAZAkrTjc8nBWWoyxOZNL94WqtZVS6tyGTYZBQAgP9nkv8J9fHw0efJk9e7dW48++qgtmgRQgOKS0/TD3ot6on1VObHJKAAARV6rVq3UqlUrs2MAkKS0JGnnF1LjRyT3EjZt+mREnLafjdIngxrbtF0AAHAjm1XMYmJiMjc1AlC0fL/3olLSrRrQnE1GAQAoiqZMmaK5c+fecHzu3LmaOnWqCYkAZNq/VEqMklo9afOmF28PVSlPF3WtyyajAADkt1zPSP/444+zPDYMQ2FhYfrqq6/Uo0cPmwUDUDAMw9DCrefUuZa/yvq6mR0HAADkwaxZs7Ro0aIbjtetW1cDBw7Uyy+/bEIqADKMa5uM1uwplapi06aT0zL07e7z6t8sSK5OjjZtGwAA3CjXhfQPP/wwy2MHBwf5+flp6NChevXVV20WDEDB2B0SraOX4vRKj1pmRwEAAHl06dIllStX7objfn5+CgsLMyERAEnSqd+kyCPSve/ZvOlVhy4pOjFNA7mrFACAApHrQvqZM2fyIwcAkyzaFqIKJd3VvjqbjAIAUFQFBQVp06ZNqly5cpbjmzZtUvny5U1KBUBbpktlG0iV2ti86UXbQtSqSilV8fOyedsAAOBGNtlsFEDRFJOYpp/2X9QzXarLwcFidhwAAJBHo0aN0rPPPqu0tDR16tRJkrRu3Tq99NJLev75501OBxRTEUelU+ukvrMki23H2qci47XtTJQ+GtjIpu0CAICby1EhvV+/fjlucPny5XkOA6Bgfbv7vDKshh5qyu2gAAAUZS+++KKuXLmi0aNHKzU1VZLk5uaml19+meUXAbNsnSF5lZXq5vz36Zz6enuISno4q1vdsjZvGwAAZC9HhXRfX9/8zgGggBmGoYXbzqlbvbLy83Y1Ow4AALgDFotFU6dO1fjx43XkyBG5u7urevXqcnXlZzxgioQr0v4lUvsXJCcXmzadkp6hZbvO64EmFeTmzCajAAAUlBwV0ufNm5ffOQAUsO1nonQqMkFv3F/P7CgAAMBGLl26pKioKLVv316urq4yDEMWGy8pASAHds699mfTETZvetWhcF1NTNPAFhVt3jYAALg5B7MDADDHwm0hqlLGU3dVLW12FAAAcIeuXLmizp07q0aNGurZs6fCwsIkSSNHjmSNdKCgpadIO+ZIDQdKnrYfay/eFqIWlUupmj+bjAIAUJDyVEhftmyZ+vfvr1atWqlJkyZZPgAUflfiU/TrwTANalGRWWoAANiB5557Ts7OzgoJCZGHh0fm8QEDBmjlypUmJgOKoYPLpfhwqdVomzd9OjJeW05f0cPMRgcAoMDlupD+8ccfa/jw4QoICNCePXvUokULlS5dWqdPn1aPHj3yIyMAG1u267wsFoseaFrB7CgAAMAGVq9eralTp6pChaw/26tXr65z586ZlAoohgxD2jpdqtZF8qtp8+aX7AiVr7uzutdjk1EAAApargvpM2bM0OzZs/XJJ5/IxcVFL730ktasWaOnn35aMTEx+ZERgA1ZrYYWbQ9Rz3plVcrTthsfAQAAcyQkJGSZiX5dVFQUG44CBensn9KlA/kyGz0lPUPfsMkoAACmyXUhPSQkRK1bt5Ykubu7Ky4uTpL06KOPavHixbZNB8DmNp+6onNXEjW4VSWzowAAABtp166dvvzyy8zHFotFVqtV7777ru6++24TkwHFzNYZkl9tqWonmze95nC4ohJSNahFkM3bBgAAt+eU2wvKli2rqKgoVapUSRUrVtTWrVvVsGFDnTlzRoZh5EdGADa0cNs51QjwUrNKJc2OAgAAbOTdd99V586dtXPnTqWmpuqll17SoUOHFBUVpU2bNpkdDygerpySjv0q3fexlA/7EC3eHqLmwSVVPcDb5m0DAIDby/WM9E6dOmnFihWSpOHDh+u5557TPffcowEDBqhv3742DwjAdiJik7XmcLgeZpNRAADsSr169XT8+HG1bdtW999/vxISEtSvXz/t2bNHVatWNTseUDxsmyl5lJbq97d502cvJ2jTySsaxCajAACYJtcz0mfPni2r1SpJGjNmjEqXLq3Nmzfrvvvu0xNPPGHzgABsZ+nOUDk5WtS3CZuMAgBgb3x9ffV///d/WY4lJyfrvffe0wsvvGBSKqCYSIqW9iyUWo+VnN1s3vzXO0Ll4+aknvXL2bxtAACQM7kupDs4OMjB4X8T2QcOHKiBAwfaNBQA28uwGlq8PVS9G5SXr7uz2XEAAICNREZGatu2bXJxcVHnzp3l6OiotLQ0zZgxQ1OmTFF6ejqFdCC/7V4gWdOk5o/ZvOnUdKuW7QpVPzYZBQDAVLle2qVatWqaNGmSjh8/nh95AOSTjccjdSE6iU1GAQCwI3/++aeqV6+u++67Tz169FDr1q11+PBh1a1bV7NmzdKkSZMUGhpqdkzAvmWkS9tmS/Ufkrz8bd782iPhuhyfyrIuAACYLNeF9DFjxujnn39W7dq11bx5c3300Ue6dOlSfmQDYEMLt51TnXI+aljB1+woAADARl577TX17NlT+/fv17hx47Rjxw717dtXb7/9tg4fPqwnn3xS7u7uZscE7NuRH6TY81Krp/Kl+cXbQ9S0UknVLMsmowAAmCnXhfTnnntOO3bs0JEjR9SzZ09Nnz5dQUFB6tq1q7788sv8yAjgDl2MTtJvRyM0uBWbjAIAYE8OHDig1157TfXq1dPrr78ui8Wid999Vw8++KDZ0YDiwTCkLdOlyu2lsvVt3nzIlUT9ceIys9EBACgEcl1Iv65GjRqaPHmyjh8/rj/++EORkZEaPny4LbMBsJGvd4TK3dlR9zcKNDsKAACwoatXr6pMmTKSJHd3d3l4eKhevXompwKKkdDt0oVdUqsx+dL81ztC5O3mpHvZZBQAANPlerPRv9u+fbsWLVqkJUuWKDY2Vg899JCtcgGwkfQMq5bsCNH9jQPl5XpH/+QBAEAhdPjw4cylFg3D0LFjx5SQkJDlnAYNGpgRDbB/W6dLpatJ1bvavOm0DKuW7jyvfo0D5e7CJqMAAJgt11W148ePa+HChVq8eLHOnDmjTp06aerUqerXr5+8vLzyIyOAO7DuaITCY1P0MLeDAgBglzp37izDMDIf9+rVS5JksVhkGIYsFosyMjLMigfYr+gQ6ciPUo93JYc83+x9U+uOhOtyfIoGtWQcDwBAYZDrQnqtWrXUvHlzjRkzRgMHDlRAQEB+5AJgI4u2hahhUAnVC2STUQAA7M2ZM2fMjgAUW5btsyVXH6nRw/nS/qLtoWpcsYRqlfXJl/YBAEDu5LqQfuzYMVWvXj0/sgCwsZAridp4IlJTH+B2bgAA7NG6det03333Za6TDqBgWFLjpT1fSs1GSi6eNm8/NCpRfzCOBwCgUMn1/WcU0YGiY/GOEHm5Oql3g/JmRwEAAPngv//9rypUqKDWrVtr6tSpOnLkiNmRgGLB/ei3UlqS1OLxfGl/yY5Qebk4qVcDNhkFAKCwsP1CbgAKhdR0q77ZGaoHmlRgcyIAAOzUb7/9prCwMI0ePVq7du1Sy5YtVb16dT3//PPauHGjrFar2REB+2PNkMeBL6U690u+gTZv/tomo6Hq0zhQHi65vokcAADkEwrpgJ1affiSLsen6mE2JwIAwK6VLFlSjzzyiJYuXarLly/rk08+UVJSkgYPHix/f38NGTJEy5YtU0JCgtlRAftw/Fc5xZ2X0XJ0vjT/29EIRcSlaFALxvEAABQmFNIBO7VoW4iaB5dUjQBvs6MAAIAC4uLiou7du2vGjBkKDQ3VypUrFRwcrDfeeEMffPCB2fGAoi8lTpZ1k5VarrkU2CRfnmLx9hA1DCqhOuXZZBQAgMKE+8QAO3Q6Ml6bT13RtAGNzI4CAABM1KxZMzVr1kyvv/660tLSzI4DFG2GIf00Toq7pJh+01U6H57i/NVE/X48Uu/0q58PrQMAgDths0J6aGioJk6cqLlz59qqSQB5tHh7iEp6OKt7vbJmRwEAAAUgIyND8+fP17p16xQREZFlbXSLxaJ169bJ2dnZxISAHdi7UDqwVEbf2cooEZwvT7F0R6g8XZzUq0H5fGkfAADknc0K6VFRUVqwYAGFdMBkyWkZ+mbXeT3UtILcnNlkFACA4uCZZ57R/Pnzde+996pevXqyWCxmRwLsS8RR6ZcXpcaPSvUfkiIibP4U6RlWLdkZqvsblZenKzePAwBQ2OT4p/OKFStu+fnTp0/fcRgAd27lwUuKTkxjcyIAAIqRr7/+WkuXLlXPnj3NjgLYn7QkadlwqURFqce7+fY0649FKjyWTUYBACisclxI79OnjywWiwzDuOk5zHwBzLdw2zm1rlpaVfy8zI4CAAAKiIuLi6pVq2Z2DMA+rXxFijojPb5ecvGQ/rZ0ki0t3h6iBhV8VS/QN1/aBwAAd8YhpyeWK1dOy5cvl9VqzfZj9+7d+ZkTQA4cD4/TjrNX9XBLZrEAAFCcPP/88/roo49uOekFQB4c/FbaNV/qMVXyr51vT3MhOkkbjkUwGx0AgEIsxzPSmzZtql27dun+++/P9vO3m60OIP8t2haiMl4u6lqHTUYBAChO/vzzT61fv16//vqr6tate8PGosuXLzcpGVCERZ2WVjwj1XtQajIkX59q6Y5QuTs7qndDNhkFAKCwynEh/cUXX1RCQsJNP1+tWjWtX7/eJqEA5F5Saoa+3X1ej7aqJBenHN9sAgAA7ECJEiXUt29fs2MA9iM9RfpmuORZRur1oZSPy5imZ1i1dGeo7msUKC82GQUAoNDK8U/pdu3a3fLznp6e6tChwx0HApA3P+6/qPiUdG4HBQCgGJo3b57ZEQD7smaiFHFYGrlGcvPJ16f6/XikwmKS9TDjeAAACrUcF9JPnz6typUrs6EoUEgt3Bai9tX9FFTKw+woAADAJJGRkTp27JgkqWbNmvLz8zM5EVAEHf1F2vaZ1H2qVL5Rvj/d4u0hqhfoo/oV2GQUAIDCLMfrP1SvXl2RkZGZjwcMGKDw8PB8CQUgdw5eiNG+0Gg2GQUAoJhKSEjQiBEjVK5cObVv317t27dX+fLlNXLkSCUmJpodDyg6okOl75+Sat4rtXwi358uLCZJvx1lk1EAAIqCHBfS/7mR6C+//HLLNdMBFJxF20MU4OOqzrX8zY4CAABMMG7cOP3+++/68ccfFR0drejoaP3www/6/fff9fzzz5sdDygaMtKkb0dKrt7S/Z/m67ro1y3dcV5uzo66j01GAQAo9NjJBCji4lPS9cOeCxrZroqcHNlkFACA4ujbb7/VsmXL1LFjx8xjPXv2lLu7u/r376/PPvvMvHBAUbH+ben8Tmn4r5JHqXx/ugyroSU7QnRfw/LydnPO9+cDAAB3JsdVN4vFcsP66KyXDpjvh70XlJSWoYHNg8yOAgAATJKYmKiAgIAbjvv7+7O0C5ATJ9dJf34odR4vVWxZIE+58XikLsYks6wLAABFRI5npBuGoWHDhsnV1VWSlJycrCeffFKenp5Zzlu+fLltEwK4KcMwtHBriDrVClD5Eu5mxwEAACa56667NHHiRH355Zdyc3OTJCUlJWny5Mm66667TE4HFHJx4dJ3T0hVO0mtnymwp120PUR1yvmoAZuMAgBQJOS4kD506NAsjx955BGbhwGQO/vOx+hwWKxe7FbT7CgAAMBEH330kbp166YKFSqoYcOGkqR9+/bJzc1Nq1atMjkdUIhZM6Tlj0kWB6nvLMmhYJZKDI9N1m9HIzTpvrrc6Q0AQBGR40L6vHnz8jMHgDxYuPWcAku4q30NP7OjAAAAE9WrV08nTpzQwoULdfToUUnSoEGDNHjwYLm7c9cacFN/fCCd+UMa8oPkVXBj6m92hsrF0UH3N2KTUQAAigo2GwWKqJikNP24/6LG3l1Njg7MYgEAoLjz8PDQqFGjzI4BFB3nNksb3pY6vCRV6VBgT2u1Glq8PVS9G5aTD5uMAgBQZFBIB4qo73afV3qGof7N2GQUAIDiaMWKFerRo4ecnZ21YsWKW5573333FVAqoIhIuCItGylVvEtq/1KBPvUfJy/rQnQSm4wCAFDEFMwCcLcxffp0BQcHy83NTS1bttT27dtzdN3XX38ti8WiPn365G9AoJAxDEOLtofonjoB8vdxMzsOAAAwQZ8+fXT16tXMv9/so2/fvrlum/E57JphSN8/JaUnSw98LjkW7PyyxdtCVKustxoFlSjQ5wUAAHfG9EL6kiVLNG7cOE2cOFG7d+9Ww4YN1a1bN0VERNzyurNnz+qFF15Qu3btCigpUHjsPHdVx8PjNbhlJbOjAAAAk1itVvn7+2f+/WYfGRkZuWqX8Tns3pbp0olV1zYX9SnYNcojYpO19ki4Hm5ZkU1GAQAoYkwvpH/wwQcaNWqUhg8frjp16mjmzJny8PDQ3Llzb3pNRkaGBg8erMmTJ6tKlSoFmBYoHBZuPadKpT3Uumpps6MAAIBCKjo6Ok/XMT6HXTu/S1o7UWr9L6lG1wJ/+m92nZeTo0X3Nwos8OcGAAB3xtRCempqqnbt2qUuXbpkHnNwcFCXLl20ZcuWm173+uuvy9/fXyNHjiyImEChEpWQql8OXtLDLSrKgU1GAQCApKlTp2rJkiWZjx966CGVKlVKgYGB2rdvX47bYXwOu5YULS0bLpVrJHWaUOBPb7Ua+npHiHo1KC9fdzYZBQCgqDF1s9HLly8rIyNDAQEBWY4HBATo6NGj2V7z559/6osvvtDevXtz9BwpKSlKSUnJfBwbGyvpf7e/ImesVqsMw6DPClh2/b5sZ6hkGOrXuDxfj3zE97w56Hdz0O/moN/NY1bf5+fzzZw5UwsXLpQkrVmzRmvXrtXKlSu1dOlSvfjii1q9enWO2imI8bnEGN1WeB/JBcOQZcXTUtJVGUNWSA5OUh77La/9/seJywqNStKH/SvwNcsDvt/NQ9+bg343B/1uDjP7PTfPaWohPbfi4uL06KOPas6cOSpTpkyOrpkyZYomT558w/HIyEglJyfbOqLdslqtiomJkWEYcnAwfUWgYuOf/W4Yhr7ackYdq5VQRmKMIhLNTmi/+J43B/1uDvrdHPS7eczq+7i4uHxr+9KlSwoKCpIk/fTTT+rfv7+6du2q4OBgtWzZMt+eNy/jc4kxuq3wPpJz7ocWy/fID7ra9WOlpLpJt1nz/1by2u8L/jylqqXdFOiaets9B3Ajvt/NQ9+bg343B/1uDjP7PTdjdFML6WXKlJGjo6PCw8OzHA8PD1fZsmVvOP/UqVM6e/asevfunXns+v8aODk56dixY6patWqWa1599VWNGzcu83FsbKyCgoLk5+cnHx8fW74cu2a1WmWxWOTn58cbSQH6Z79vPnVFodEp+s9DjeTvX8rseHaN73lz0O/moN/NQb+bx6y+d3Nzy7e2S5YsqdDQUAUFBWnlypV68803JUmGYeRqs9GCGJ9LjNFthfeRHLp0QJbNU2Q0GynfVo/ecXN56ffIuBRtPBWj/+tZ64Y7PpAzfL+bh743B/1uDvrdHGb2e27G6KYW0l1cXNS0aVOtW7dOffr0kXSt49atW6exY8fecH6tWrV04MCBLMdee+01xcXF6aOPPsqchfN3rq6ucnV1veG4g4MD/yByyWKx0G8m+Hu/L94Rqmr+XmpZpbQsFtZHz298z5uDfjcH/W4O+t08ZvR9fj5Xv3799PDDD6t69eq6cuWKevToIUnas2ePqlWrluN2CmJ8LjFGtyXeR24jJV76dqRUpobU7W1ZbNRPue335XsuytHBon5Ngvha3QG+381D35uDfjcH/W4Os/o9N89n+tIu48aN09ChQ9WsWTO1aNFC06ZNU0JCgoYPHy5JGjJkiAIDAzVlyhS5ubmpXr16Wa4vUaKEJN1wHLA3kXEpWnXwkv7dszZFdAAAkMWHH36o4OBghYaG6t1335WXl5ckKSwsTKNHj85VW4zPYVd+eUGKvSg9sVFyzr+7Qm7l+iaj9zYoJ18PNhkFAKCoMr2QPmDAAEVGRmrChAm6dOmSGjVqpJUrV2be7hYSEsL/AAGSlu4MlaODRQ80qWB2FAAAUMg4OzvrhRdeuOH4c889l+u2GJ/DbuxdJO1bLPWdLZXJ+Z0Ztrbl9BWdu5Ko9x9qaFoGAABw50wvpEvS2LFjs71VVJI2bNhwy2vnz59v+0BAIWO1Glq8PUS9GpRnFgsAAJAkrVixQj169JCzs7NWrFhxy3Pvu+++XLXN+BxFXuRx6efnpUaDpYYDTI2yaHuIqvt7qWmlkqbmAAAAd6ZQFNIB3NofJy/r/NUkfTyootlRAABAIdGnTx9dunRJ/v7+meuZZ8diseRqw1GgyEtLkr4ZJvlWkHr+x9Qol+NTtPrQJb3ag+UZAQAo6iikA0XA4u2hqlXWW42DSpgdBQAAFBJWqzXbvwPF3qp/S1GnpFG/SS6epkb5dtd5WSwW9WsSaGoOAABw51jcECjkIuJTte5ohAa3qsQsFgAAAOBWDn0n7ZwrdX9HCqhrahTDuLY84731y6mEh4upWQAAwJ2jkA4Ucj8evCxXJwf1aVTe7CgAAKCQevrpp/Xxxx/fcPzTTz/Vs88+W/CBADNEnZFWPC3V7Sc1HWZ2Gm05fUVnryRqUAuWZwQAwB5QSAcKsfQMq344eFn3NSwvbzc2GQUAANn79ttv1aZNmxuOt27dWsuWLTMhEVDA0lOlZcMlj1JS74+kQnAn5+Ltoarq56nmwWwyCgCAPaCQDhRivxy8pIj4NA1qEWR2FAAAUIhduXJFvr6+Nxz38fHR5cuXTUgEFLB1k6VLB6UH50luPman0ZX4FK06eEmDWlRkeUYAAOwEhXSgkAqNStSEHw6pQ9USqh944y/GAAAA11WrVk0rV6684fivv/6qKlWqmJAIKEDHVkpbPpXueV0KbGJ2GknS8t0XJEkPNKlgchIAAGArTmYHAHCjlPQMjVm0W77uznrtnkpmxwEAAIXcuHHjNHbsWEVGRqpTp06SpHXr1un999/XtGnTzA0H5KeYC9L3T0o1e0qtnjI7jaT/bTLao35ZlfRkk1EAAOwFhXSgEHrzpyM6Ghanb55sJW/nFLPjAACAQm7EiBFKSUnRW2+9pTfeeEOSFBwcrM8++0xDhgwxOR2QTzLSpW9HSs4e0v3TC8W66JK07UyUTl9O0Nv96psdBQAA2BCFdKCQWbHvor7aek5v9Kmn+oG+ioiIMDsSAAAoAp566ik99dRTioyMlLu7u7y8vMyOBOSv39+RQrdLw36+tsloIbF4e4iqlPFUy8qFJxMAALhzrJEOFCKnIuP16rf7dV/D8nqkZUWz4wAAgCIkPT1da9eu1fLly2UYhiTp4sWLio+PNzkZkA9OrZc2vid1+j+p0l1mp8l0NSFVvx5gk1EAAOwRM9KBQiIpNUOj/7tbZX3dNKVffVkslsxfggEAAG7l3Llz6t69u0JCQpSSkqJ77rlH3t7emjp1qlJSUjRz5kyzIwK2Ex8hLX9cqtJRavOc2Wmy+Hb3eUnSA03ZZBQAAHvDjHSgkBj/w0Gdi0rQjMFN5enK/3EBAICce+aZZ9SsWTNdvXpV7u7umcf79u2rdevWmZgMsDGrVVo+6trf+82WHArPr7TXNxntVq+sSrHJKAAAdodqHVAILN0RqmW7zuv9hxqqZllvs+MAAIAi5o8//tDmzZvl4pK1eBccHKwLFy6YlArIB39+IJ3+XRryveTlb3aaLHacvapTkQl6o089s6MAAIB8UHj++x4opo6ExWr8Dwc1sHkQt4ACAIA8sVqtysjIuOH4+fPn5e3Nf9LDTpzbIq1/W2r/wrVlXQqZxdtDFFzaQ3dVKW12FAAAkA8opAMmiktO0+iFu1XFz0uT7qtrdhwAAFBEde3aVdOmTct8bLFYFB8fr4kTJ6pnz57mBQNsJTFK+nakFNRS6vCK2WluEJ2Yqp8PhLHJKAAAdoylXQCTGIahV749oMi4FP34r7Zyc3Y0OxIAACii3nvvPXXv3l116tRRcnKyHn74YZ04cUJlypTR4sWLzY4H3BnDkL4fLaUlSQ98LjkWvl9jl+++IMMwuMMUAAA7VvhGIEAx8eWWc/r5QJhmDG6iymU8zY4DAACKsKCgIO3bt09LlizRvn37FB8fr5EjR2rw4MFZNh8FiqStn0nHf5UGLZF8A81Oc4Prm4x2rVtWZbxczY4DAADyCYV0wAR7Q6P15s+HNax1sHrWL2d2HAAAUISlpaWpVq1a+umnnzR48GANHjzY7EiA7VzYLa2ZIN01VqrZ3ew02dp17qpORMSzVCMAAHaONdKBAhadmKoxC3erbnlf/btnbbPjAACAIs7Z2VnJyclmxwBsLzlGWjZcKltf6jzR7DQ3tWh7iCqxySgAAHaPQjpQgKxWQ88v3aeE1HRNH9xELk78EwQAAHduzJgxmjp1qtLT082OAtiGYUg/PnNtk9EH50pOLmYnylZMYpp+3h+mgc0rysGBTUYBALBnLO0CFKBZG09r3dEIzR3WTIElWK8UAADYxo4dO7Ru3TqtXr1a9evXl6dn1v1Xli9fblIyII92zZcOfSc9NF8qVdnsNDf13Z7zyrAaepBNRgEAsHsU0oECsu30Fb23+phGd6yqTrUCzI4DAADsSIkSJfTAAw+YHQOwjfBD0spXpGYjpLp9zU5zU9c2GQ1V17oB8vNmk1EAAOwdhXSgAETGpehfi/eoWaWSGndPDbPjAAAAOzNv3jyzIwC2kZogfTNMKl1N6va22WluaU9otI6Fx+m1Xux7BABAccACzUA+y7AaeubrPbIa0ieDGsvJkX92AADANqxWq6ZOnao2bdqoefPmeuWVV5SUlGR2LCDvfnlRijkvPThPci7cSyEu3h6qoFLualO1jNlRAABAAaCiB+Szj9Ye19bTV/TxoEby93EzOw4AALAjb731lv7973/Ly8tLgYGB+uijjzRmzBizYwF5s+9rae9C6d4PJL/CfRdnXHK6fj7AJqMAABQnFNKBfPT78Uh9sv6kxt1TQ62ZqQIAAGzsyy+/1IwZM7Rq1Sp9//33+vHHH7Vw4UJZrVazowG5c/mE9NM4qeEgqdEgs9Pc1qpjUUrPMPRQMzYZBQCguKCQDuSTsJgkPbdkr9pX99PojtXMjgMAAOxQSEiIevbsmfm4S5cuslgsunjxoompgFxKS5a+GS75lJd6vmd2mtsyDEPfHbiszrX95e/NHacAABQXFNKBfJCWYdXYRXvk6uSgDwc04nZPAACQL9LT0+XmlrWQ5+zsrLS0NJMSAXmw+v+ky8elh+ZLrl5mp7mtfedjdOpykgY1DzI7CgAAKEBOZgcA7NG7K49qX2i0ljxxl0p5upgdBwAA2CnDMDRs2DC5urpmHktOTtaTTz4pT0/PzGPLly83Ix5we4d/kHZ8fm1d9LL1zE5zW1aroY9/O6my3i5qW42lGwEAKE4opAM2turQJc3544xeu7e2mlYqaXYcAABgx4YOHXrDsUceecSEJEAeXD0r/fAvqc79UrMRZqfJkfdWH9PvxyP1bu+q3HUKAEAxQyEdsKGQK4l64Zt96l63rEa2rWx2HAAAYOfmzZtndgQgb9JTpWUjJPcSUu+PJUvhL0p/u+u8Zmw4pVd71FS7KoV/CRoAAGBbrJEO2EhyWoZGL9qlkh4uevehBrIUgV8GAAAAAFP89oYUtl96aN61Ynoht/NslF5dfkAPNa2gx5gwAwBAsUQhHbCRN346rOPh8ZoxuIl83JzNjgMAAAAUTiHbpM2fSJ3HS4FNzU5zW6FRiXriq11qVLGE3upbnwkzAAAUUxTSARv4Ye8FLdwWokm966peoK/ZcQAAAIDCKS1J+mG0VKGZdNdYs9PcVlxymh5bsFOerk6a+UhTuTjxKzQAAMUVa6QDd+hkRJxeXX5AfRsHalCLILPjAAAAAIXX+rek6FBp4CLJwdHsNLeUYTX0zNd7dTE6SctHt9b/s3ffYU1dbxzAvxnsDTJEhjiqqLhQce+K1qq490Rtq9Za66i1dbTuan+OWq1a0SrWUUetsy7cC9x7AW7Bwd7J/f1BSY0MQYGTwPfzPDyQm3PvffMSLidvTs6xNTMUHRIREREJxLfTid5DQkoaPlt7Ds7WJpjmV4Uf8yQiIiIiys6Ds8DJxUCzbwD7CqKjeauZu64j6GYEFvWqgfKOFqLDISIiIsFYSCd6R5Ik4dutV/DwVSKW9K4JMyN+wIOIiIiIKEupSelTupSsrhdTuqw/cx8rjoVi0seV0LSCg+hwiIiISAew8kf0jjacfYAt5x/hf92rcYQKEREREVFODs8CXoUBnxwBFLr9MvTk3Rf4dtsV9PZxQ//6pUWHQ0RERDqCI9KJ3sHVx9GYtP0qetZxQ8caLqLDISIiIiLSXY9CgOMLgCbjAQdP0dHkKOx5PD4LDIFPGVtMaV+ZUzcSERGRBgvpRHkUk5SK4YHnUN7BHJPbVRIdDhERERGR7kpLBrYNA5yqAg1GiY4mR9GJqfBffRa2pob4pZc3DBR8uUxERET/0e3P1BHpGEmSMP7PS3gRl4LVg+rA2EAhOiQiIiIiIt11eA7w4i7wyWGdntIlTaXGiHXn8DwuBVuH1YeVqYHokIiIiEjH8C12ojwIOB6G3Vee4seuVeFuZyY6HCIiIiIi3fX4PHDsf0CTcYBjZdHR5OiHHddw8u4LLOldE2XszUWHQ0RERDpId4cEEOmYc/dfYcau6/Bv6IHWVUqKDoeIiIiISHelpQDbhgOOlYCGX4qOJkdrToZh9clwTO9YBfXLlRAdDhEREekoFtKJcuFVfApGBJ5DVRcrfN2mouhwiIiIiIh029G5wPObwNAgQKG706QcvR2JKX9fw8AGpdHbx110OERERKTDOLUL0Vuo1RK+3HgBiakq/NyrJhcdIiIiIiLKyZNLwNF5QKMxgJOX6GiydSciDsMCz6FhuRKY+JGn6HCIiIhIx3FEOtFbLDl8F4dvRSJgQG04W5uIDoeIiIiISHepUoFtwwD7ikCjr0RHk61X8SnwX30WTpbGWNSrBpQcLENERERvwUI6UQ5O3n2Bef/cxIhm5dC0goPocIiIiIiIdNvRn4CIa8CQg4DSUHQ0WUpJU+OzwBDEJKbir+ENYWmsu1PPEBERke5gIZ0oGxGxSfj8j/Pw8bDDqJYfiA6HiIiIiEi3Pb0CHPkRaDQacK4uOposSZKEyduvICT8FQIH14WbnanokIiIiEhPsJBOlAWVWsLIP85DJgMW9KwOhVwmOiQiIiIiIt2lSgX+GgbYlQMajxUdTbZWHg/DH2ce4McuVVHHw1Z0OERERKRHWEgnysL/9t3CmdCXWDekLhwsjEWHQ0RERESk247PTx+RPng/oDQSHU2WDt2IwPSd1/BJ4zLoWstVdDhERESkZ7iiCtEbDt2MwM+H7uCrVhVQt4yd6HCIiIiIiHTbs2tA0GygwRdAqZqio8nSzaex+PyP82he0RHjWlcUHQ4RERHpIRbSiV7zOCoRX264gGYV7PFZk7KiwyEiIiIi0m2qtPQpXWzLAE3Gi44mS8/jkuG/+ixcbEwwvwenbSQiIqJ3w6ldiP6VkqbG8HXnYGaoxE/dqkPODjYRERERUc5OLgKeXAT89wEGujclYnKaCp+uCUFSqgrrh9aFuRFfAhMREdG7YS+C6F+zdt/AlUfR2PhJPdiYGYoOh4iIiIhIt0XcAA7NAOqNAFxqiY4mE0mSMGHLZVx6FI0/htSFi42p6JCIiIhIj3FqFyIAe648wcrjofjmI0/UcLMRHQ4RERERkW5Tq4C/hgPW7kCzb0RHk6Wlh+9hy7lHmNO5Krzd2ccnIiKi98MR6VTshT2Px9hNl/CRlxMG1C8tOhwiIiIiIt13cjHwKATw/wcwMBEdTSZ7rz7FnL038HnzcvCrUUp0OERERFQEcEQ6FWtJqSoMCzwHO3NDzOpcFTIZ50UnIiIiIsrR89vAwWlAveGAax3R0WRy9XE0Rq2/gDZVnPBlyw9Eh0NERERFBEekU7E29e9ruBsZh63DGsDS2EB0OEREREREui1jShcrF6DZRNHRZBIRm4TBq4NRzsEc87pWh1zOgTJERESUP1hIp2Jr6/mH+OPMfczu7IVKzpaiwyEiIiIi0n2nlwIPzgADdwOGurV4Z1KqCkN/D4FKLWF5v1owMVSIDomIiIiKEBbSqVi69SwW32y5gk41S6FbLVfR4RARERER6b4Xd4ED3wM+nwLu9URHo0WSJIz78xJuPI3Bxk/qwcnKWHRIREREVMSwkE7FTnxyGoYFnoOrrQmm+VXhvOhERERERG+jVqdP6WJREmjxnehoMll08A62X3yMxb1qoqqLtehwiIiIqAhiIZ2KFUmSMHHrZTyOSsT2EQ1hasg/ASIiIiKitzqzDLh/EhiwEzA0Ex2Nlh2XHuOnfbcw+sMP0LZqSdHhEBERURHFKiIVK3+ceYBtFx5jQY/qKOdgLjocIiIiIiLd9/IecGAqUGcoULqh6Gi0XHwQha82XkT7as74vHk50eEQERFRESYXHQAALF68GKVLl4axsTF8fHxw5syZbNsuX74cjRo1go2NDWxsbNCyZcsc2xNluPIoGlP+voo+dd3QoXop0eEQERER6Sz2z0lDrQb++hwwswdaTBYdjZYn0YkY8nswPEtaYk6XqpyykYiIiAqU8EL6hg0bMHr0aEyePBnnzp1DtWrV4Ovri4iIiCzbBwUFoWfPnjh06BBOnjwJV1dXtGrVCo8ePSrkyEmfRCemYljgOVRwtMB3H1cSHQ4RERGRzmL/nLQE/waEHwPaLwKMdOcTnQkpaRjyezCUchmW9fOGsYFCdEhERERUxAkvpP/0008YMmQIBg4ciEqVKmHp0qUwNTXFypUrs2wfGBiIYcOGoXr16qhYsSJWrFgBtVqNAwcOFHLkpC8kScK4Py/iVUIKFveqCSMlO9lERERE2WH/nDRehQH7JgO1BgFlmoiORkOtlvDVxou4FxmPFf1rw8HCWHRIREREVAwILaSnpKQgJCQELVu21GyTy+Vo2bIlTp48matjJCQkIDU1Fba2tgUVJum5X4LuYu/VZ5jXtRrc7ExFh0NERESks9g/Jw1JArZ/DpjaAh9+LzoaLT/tu4U9V59ifvfqqORsKTocIiIiKiaELjb6/PlzqFQqODo6am13dHTEjRs3cnWM8ePHw9nZWauz/7rk5GQkJydrbsfExAAA1Go11Gr1O0Ze/KjVakiSpHc5+/1kOH7cexNftCiHlp4Oehe/vua9KGDuxWDexWDexWDexRGVe334XRdG/xxgHz2/FOhzOSQA8tAjUPfZChiYpc+VrgO2XXiEnw/dwfjWFYT173n9FoN5F4e5F4N5F4N5F0Nk3vNyTqGF9Pc1a9YsrF+/HkFBQTA2zvrjfDNnzsTUqVMzbY+MjERSUlJBh1hkqNVqREdHQ5IkyOXCZwTKlR1Xn2PavnD0rOmAHlUss53XU5fpY96LCuZeDOZdDOZdDOZdHFG5j42NLbRziZKb/jnAPnp+Kajnsjz2EUr88y0SPLshxrwSoCP96MuP4/D15lv4yNMOfhXMhPXvef0Wg3kXh7kXg3kXg3kXQ2Te89JHF1pIL1GiBBQKBZ49e6a1/dmzZ3Bycspx37lz52LWrFnYv38/qlatmm27CRMmYPTo0ZrbMTExcHV1hb29PSwt+THA3FKr1ZDJZLC3t9eLC8mOS08wY384etVxxQ8dKkMmk4kO6Z3oW96LEuZeDOZdDOZdDOZdHFG5z6mwrCsKo38OsI+eXwrkuSxJkP3zKWBiA+P2P8LYSDd+H49eJeLrnZdR1cUa83p6C133iNdvMZh3cZh7MZh3MZh3MUTmPS99dKGFdENDQ3h7e+PAgQPw8/MDAM3CRCNGjMh2vzlz5mD69OnYu3cvatWqleM5jIyMYGRklGm7XC7nH0QeyWQyvcjb/mvPMHrjRfhVL4Vpfl6Qy/WziJ5BX/JeFDH3YjDvYjDvYjDv4ojIvT78ngujfw6wj56f8v25HLIauHcI6LMZMhPr/Dnme4pLTsOQNSEwNVLg177eMDE0EB0Sr9+CMO/iMPdiMO9iMO9iiMp7Xs4nfGqX0aNHo3///qhVqxbq1KmD+fPnIz4+HgMHDgQA9OvXD6VKlcLMmTMBALNnz8akSZOwbt06lC5dGk+fPgUAmJubw9zcXNjjIN1w7PZzDFt3Di09HTGnS1W9L6ITERERFTb2z4ux6IfA3olAjT5AueznuC9MKrWEL/44j4evErFlWH3YmWd+A4aIiIioMAgvpHfv3h2RkZGYNGkSnj59iurVq2PPnj2aBY7u37+v9c7AkiVLkJKSgi5dumgdZ/LkyZgyZUphhk465mzYSwz5PRj1y9phYc8aUCr4ziERERFRXrF/XkxJEvD3F4CRBdBquuhoNObsuYFDNyPw24Da+MDRQnQ4REREVIwJL6QDwIgRI7L9qGhQUJDW7bCwsIIPiPTOpYdRGBRwFtVcrbC0jzcMlSyiExEREb0r9s+LoQuBwJ39QK9NgI5M6bIx+AF+PXIPkz6uhGYVHESHQ0RERMUcq42k924+jUW/lWdQztEcK/rXhrGBuIWHiIiIiIj0TsxjYM83QLVewAetREcDADh97wUmbr2MnnXcMLBBadHhEBEREbGQTvot9Hk8eq84DWcrE6waUAfmRjrxIQsiIiIiIv0gScDfowADE6D1DNHRAADCX8Tj07UhqOVui+87VIZMxnWPiIiISDxWHUlvPXyVgN7LT8Ha1ABr/OvAytRAdEhERERERPrl4nrg9l6g53rAxEZ0NIhJSoX/6mBYmRhgSZ+aMOC6R0RERKQjWEgnvfQsJgm9V5yGUiHHWn8f2JkbiQ6JiIiIiEi/xD4F9owHqnYHKrQRHQ3SVGqMWHceETFJ2Dq8AaxNDUWHRERERKTBQjrpnRdxyeiz4jRS0tTY+Ek9OFkZiw6JiIiIiEi/ZEzpojACWs8SHQ0AYNrO6zh+5zlWD6yDsvbmosMhIiIi0sJCOumV6MRU9Ft5Bq8SUrHxk7pwtTUVHRIRERERkf65vAm4tRvoHgiY2oqOBmtPhWPViTD84FcFDcuXEB0OERERUSaccI70RnxyGgYGnMHDV4lYO7gOynCUChERERFR3sU+A3aPA6p0Bjw/Fh0Njt95jsnbr2JA/dLoW9dddDhEREREWWIhnfRCUqoKg1cH49azOPw+qA4qOlmKDomIiIiISP9IErBzNCBTAG1+FB0N7kXG4bO1Iahf1g7ftvUUHQ4RERFRtji1C+m8lDQ1PlsbgvMPXmGNvw+quVqLDomIiIiISD9d3QLc2AF0+x0wsxMaSlRCCvxXB8Pewgg/96oJpYLjvIiIiEh3sZBOOi1NpcaoDedx/M4L/DagFmqXFj9/IxERERGRXoqLBHaOASr5AZU6CA0lVaXGsMBzeJWQgr+GN4CViYHQeIiIiIjehoV00llqtYRxmy9h79VnWNrHG43K24sOiYiIiIhIf+36CpDJgI/mCg0jJU2Nr7dcwtmwl1jj7wN3OzOh8RARERHlBgvppJMkScKk7Vew9fwjLOhRAx9WchQdEhERERGR/rq6Fbj2F9BlJWAuboBKRGwShgeew4UHUZjbtRrqlhE7vQwRERFRbrGQTjpHkiTM3H0Da0/dx5zOVdG+mrPokIiIiIiI9Ff88/QpXTzbAZU7CQvjwoMofLomBCpJwvqhdeHtzmkbiYiISH+wkE46Z8GB21h25B6mtKuEbrVdRYdDRERERKTfdo8DJBXQ9qf0qV0E2Bj8AN9uu4JKJS3xa19vOFoaC4mDiIiI6F2xkE46ZdmRu5i//zbGta6AAQ08RIdDRERERKTfrv8NXNkMdFoBmDsU+ulTVWpM23ENq0+Go3stV3zvVxlGSkWhx0FERET0vlhIJ52x5lQ4Zuy6gRHNymFY03KiwyEiIiIi0m8JL4Edo4EKbQGvLoV++udxyRgWeA7nwl/hB78q6OPjBpmgEfFERERE74uFdNIJm0Me4rttVzCwQWl81eoD0eEQEREREem/3eMBVQrwceFP6XL5YTQ+WROMFJUa64bURR0PzodORERE+o2FdBJu1+UnGPvnRfSo7YpJH1fiKBUiIiIiovd1YxdweSPQ8VfAwqlQT7055CEmbL0MTycLLO3rjZJWJoV6fiIiIqKCwEI6CXXwxjOM/OM82lVzxvSOXiyiExERERG9r4SXwI5RwAetgardC+20qSo1Zuy6joDjYeji7YJpflVgbMD50ImIiKhoYCGdhDlx5zk+XXsOzSs6YG7XalDIWUQnIiIiInpve78BUpOAj/9XaFO6vIhLxoh153Em7CWmtq+MfvXcOUiGiIiIihQW0kmIkPBXGPx7MHw8bLGoVw0YKOSiQyIiIiIi0n+39gIX/wA6/AJYOhfKKa88isYna0KQlKpC4GAf1C1jVyjnJSIiIipMLKRTobvyKBoDAs6gSikrLOtbC0ZKftyTiIiIiOi9JUYBf38BlPsQqN6rUE7514VHGL/5Eso7pM+HXsqa86ETERFR0cRCOhWqW89i0fe30yhjb46VA2rDxJBFdCIiIiKifLF3IpASD7SbX+BTuqSp1Ji95waWHw1FpxqlMKOTF+dDJyIioiKNhXQqNGHP49FnxWk4Whpj9cDaMDfi04+IiIiIKF/c3g9cWAu0WwhYuRToqV7Fp2DEH+dw6t5LTPq4EgY2KM350ImIiKjIYyWTCsWjqET0XnEa5sZKrPH3gbWpoeiQiIiIiIiKhqRo4O+RQJlmQM1+BXqqa49jMHRNMOKT07DGvw7qly1RoOcjIiIi0hUspFOBi4hNQp8VpyGXA4GDfWBvYSQ6JCIiIiKiIkO2fzKQFAO0X1SgU7r8ffExxv55EWVKmOOPIXXhamtaYOciIiIi0jUspFOBehWfgr4rziAxRYVNn9ZDSSsuPkRERERElF8MHxyD7Nxq4OP/AdauBXIOlVrCnL038Ovhe+hQ3RmzOlXlWkdERERU7LCQTgUmJikV/VaewfO4ZGz4pB5HrBARERER5afkWFgd/g5S6caQeQ8skFNEJaTg8z/O4/id55j4kScGN/LgfOhERERULLGQTgUiISUNgwLOIvxFPNYPrYdyDuaiQyIiIiIiKjokCbJ/JgJJUZDaLyqQ4vaNpzEY+nsIYpJS8fsgHzQsz/nQiYiIqPhiIZ3yXVKqCkN/D8H1JzFYO9gHlZwtRYdERERERFR0pCYC2z+H7PImxDSZBgtrt3w/xa7LTzBm00W42ZoicLAPP11KRERExR4L6ZSvUlVqjFh3DsHhL7FqYB3UcLMRHRIRERERUdER+wxY3wt4dhXqLgFILFEfFvl4eJVawrx/buKXoLv4uGpJzOlSFaaGfNlIRERExB4R5RuVWsKXGy7g8K1IrOhfG3XL2IkOiYiIiIio6HhyEfijJyCpgYG7gJLVgYiIfDt8dEIqvthwHkduReLrNhXxSeMynA+diIiI6F8spFO+UKsljN98CbuvPMXiXjXR5AN70SERERERERUd17YDWz8B7CsAPdYBls6AWp1vh7/1LBZDfw/Gy/gUBAysw/48ERER0RtYSKf3JkkSpv59FZvPPcT/ulVH6ypOokMiIiIiIioaJAk4Og84+ANQuSPQ4RfAMH/nK99z5Qm+2ngRLjam+PvzhnC3M8vX4xMREREVBSyk03uRJAmz99zE6pPhmNnJC341SokOiYiIiIioaEhNArZ/DlzeCDT5Gmj6NZCPU62o1RL+t/8WFh28g4+8nPBjl2owM+JLRCIiIqKssJdE7+Xng3ew9PBdfPdxJfSs4yY6HCIiIiKioiH2GbChN/D0MtBlJVClc74ePiYpFV+uv4CDNyMw1rcChjUty/nQiYiIiHLAQjq9sxVH72HevlsY0+oD+Df0EB0OEREREVHR8ORS+qKi6rT0RUVLeefr4e9ExGLo7yGIjEvGygG10ayCQ74en4iIiKgoYiGd3sm60/cxbed1fNa0LIY3Kyc6HCIiIiKiouH6DmDLEKBEeaDn+vRFRfPRvmvP8OWGCyhpZYztIxrCowTnQyciIiLKDRbSKc+2nX+Eidsuo389d4zzrcCPgBIRERERvS9JAo79BBz4HqjUAfBbmq+LiqrVEhYcuI0FB27Dt7Ij5nWrDnPOh05ERESUa+w5UZ7svfoUX226iC41XTC5XWUW0YmIiIiI3ldqEvD3SODSBqDJ+PSFReXyfDt8bFIqvtxwEQduPMNXH36A4c3KQS5nP56IiIgoL1hIp1w7GRaNcX/fRZsqTpjVuSo730RERERE7ysuAljfK31R0c6/AV5d8vXwdyPjMPT3YETEJGNFv1po4emYr8cnIiIiKi5YSKdcCboZia//vovG5e3xv+7VoWARnYiIiIjo/Ty9DKzrkb6o6IBdgEv+Lip64PozjFp/AQ6WRtg2ogHK2pvn6/GJiIiIihMW0ilHdyLiMGv3Dey//gx13S3xc8/qMFDk38dMiYiIiIiKpRs7gc1DALuy6YuKWpXKt0Or1RIWH7qDn/bfQouKjvhf92qwMDbIt+MTERERFUcspFOWXsQlY8GB2wg8fR8lrYyxsEd11HaUw8hAITo0IiIiIiL9JUnA8fnA/qmAZzug41LA0CzfDh+XnIYxGy9iz9WnGNWyPEY2L88pGYmIiIjyAQvppCUpVYWA42H45dAdQAaM862A/vVLw1AhQ0REhOjwiIiIiIj0V1oy8PcXwMU/gMZjgabf5OuiomHP4zHk92A8iU7Csr7eaFXZKd+OTURERFTcsZBOANI//vn3pceYs+cmnsUkoU9dd4xsUR62Zob/3q8WHCERERERkR6LiwQ29AYeXwA6rQCqds3XwwfdjMDIP86jhLkRtg2vj3IOFvl6fCIiIqLijoV0wpnQl5i+8xouPoxGq0qOWONfB2W4EBERERERUf54egX4owegSgEG7gJcauXboSVJwpLDdzH3n1toVsEB83tUhyXnQyciIiLKdyykF2Ohz+Mxa/d17L36DF6lrLB+aF3ULWMnOiwiIiIioqLjxi5g82DArsy/i4q65Nuh45PT8O2uUBy4/QqfNy+HL1t+wPnQiYiIiAoIC+nF0Kv4FCw4cBtrT4XDwcII87tXR/tqzux0ExERERHlF0kCji8A9k8BPD8GOv6ab4uKxialYsu5R1h5PBSRMUn4pVcNfFTVOV+OTURERERZYyG9GElOU+H3E+FYdPA21BIwutUHGNTAA8YGCtGhEREREREVHa8vKtpoDNBsYr4sKnrrWSx+PxmGreceISlNjVaVHNHvo9Lw8eSiokREREQFjYX0YkCSJOy8/ASz99zA46gk9Krjhi9alkcJcyPRoRERERERFS1xkcCGPsDj8/myqGiqSo1/rj7D7yfDcDr0JewtjODfqAx61XGDg4UhIiIi8ilwIiIiIsoJC+lFXEj4S0zbeR3n70ehpacDAgbURjkHC9FhEREREREVPc+uAut6AGlJwICdgGvtdz5UREwS1p25jz/O3MezmGTUKW2LRT1rwLeyEwyV6aPb1Wp1fkVORERERG/BQnoRFf4iHnP23MTOy09Q2dkS6wb7oH65EqLDIiIiIiIqmm7uTl9U1MYD6LkLsHbN8yEkScKZ0Jf4/VQ49l55CgOFHB1rlkLfuu7wLGlZAEETERERUW6xkF7ERCekYtHB21h9Mgx2ZkaY17UaOtYoxYVEiYiIiIgKgiQBJxYC+yYDFdumLypqZJ6nQ8Qnp2Hr+UdYeyocN57GokwJM0xs64nO3i6wNDYooMCJiIiIKC9YSC8iUtLUWHMqHAsP3EaqSo2RzctjcKMyMDHkQqJERERERAUiLRnY8SVwIRBo9BXQ7Ns8LSp6JyIOa0+FY3PIQ8SnpKGFpyMmtvVEg7IlOBCGiIiISMewkK7nJEnCnitPMWvPDTx4mYDutd3w5Yfl4WBhLDo0IiIiIqKiK/55+qKij84BHZcB1brnarc0lRoHbkTg95NhOH7nBezMDNG3njt613VHKWuTAg6aiIiIiN4VC+l67Pz9V5i+8zqCw1+haQV7LO9XCx84ciFRIiIiIqIC9ewa8Ed3IDURGLADcK3z1l2exyVjw9kHCDwVjsfRSajhZo3/da+Gj7xKwkjJT5ESERER6ToW0vXQg5cJmLP3Jv6++BgVnSywxr8OGpW3Fx0WEREREVHRd3MPsNk/fVHRATkvKipJEs7dj8Kak2HYdfkpZDKgQ3Vn9KtXGlVKWRVi0ERERET0vlhI1yPRian45dAdBBwPg7WpAeZ0rorO3i5QcP5EIiIiIqKCJUnAiUXAvklAhY+ATsuyXVQ0MUWF7Rcf4feT4bj6OAZutqYY61sBXWu5wNrUsJADJyIiIqL8wEK6HkhVqRF4KhwLDtxGUqoaw5qVxZBGZWBmxF8fEREREVGBS0sGdowGLqwFGn4JNJ+U5aKiYc/jsfZUODaFPERMUiqaVXDAGN8KaFLenouHEhEREek5VmJ1mCRJ+OfaM8zafQNhL+LRzdsVo1t9AEdLLiRKRERERFQo4p8DG/oCj4KBjr8C1Xpo3a1SSwi6GYHfT4bj8K1IWJsaoEdtV/T2cYebnamgoImIiIgov7GQrqMuPYzC9J3XcTr0JRqVL4FfeteEZ0lL0WERERERERUfry8q2n8H4OajuetVfAo2BD9A4OlwPHiZiKouVvixS1W0q+YMYwMuHkpERERU1LCQrmMeRSXixz03sO3CY3zgaI5VA2ujyQf2kMn4UVAiIiIiokJzay/wpz9g4w4M2AlYuwEALj6Iwu8nw/H3pceABHxcrSQW9SyN6q7WYuMlIiIiogLFQrqOiE1KxS9Bd/HbsVBYGhtgZicvdPV2gVKRee5FIiIiIiIqIJIEnFwM/PMtUKEN0Gk5kuQm2BHyEGtOhuHiw2iUsjbBqJbl0b2WK+zMjURHTERERESFQCeqtIsXL0bp0qVhbGwMHx8fnDlzJsf2mzZtQsWKFWFsbAwvLy/s2rWrkCLNf2kqNdacCkfTH4MQcDwUnzQug6CxTdGzjhuL6EREREQkRLHtn6elANtHAP9MBBp8gQetlmPWwYeoN/MAxmy6CEsTAyzvVwtHxjXDsKblWEQnIiIiKkaEV2o3bNiA0aNHY/LkyTh37hyqVasGX19fREREZNn+xIkT6NmzJ/z9/XH+/Hn4+fnBz88PV65cKeTI348kSThw/Rl85x/BpL+uoGkFBxwa0xRftaoAcyN+UICIiIiIxCiu/XPEvwDW+EG6tBE36v6IwY8/RuO5hxF4Ohwda7jg4FdNsMbfBx9WcoRCzmkXiYiIiIobmSRJksgAfHx8ULt2bfz8888AALVaDVdXV3z++ef4+uuvM7Xv3r074uPjsWPHDs22unXronr16li6dOlbzxcTEwMrKytER0fD0lLM4p1XHkVjxq7rOHH3BeqXtcM3H3miSikrIbHkllqtRkREBBwcHCCXC3//pdhg3sVh7sVg3sVg3sVg3sURlXtd6IfmRmH3zwEdyE3EdagDuyM5IQZjFeOwI8odniUt0a+eOzpUd4apoW4OdOF1RAzmXQzmXRzmXgzmXQzmXQyRec9LP1ToMyIlJQUhISFo2bKlZptcLkfLli1x8uTJLPc5efKkVnsA8PX1zba9LnkSnYivNl5Eu5+P4VlMEn7rXwuBg310vohORERERMVDceufA0D4qa1IWtoct6MktEmYCrl7Pfz5aT3sGtkQPeu46WwRnYiIiIgKl9Be4fPnz6FSqeDo6Ki13dHRETdu3Mhyn6dPn2bZ/unTp1m2T05ORnJysuZ2TEwMgPR3OtRq9fuEnyeSJGFAwFk8j03G1HaV0KO2K5QKOSRJguAPBeSKWq2GJEmFmjNi3kVi7sVg3sVg3sVg3sURlXt9+F0XRv8c0J0++ssnYXDaPRhn5NVxpd5cbKhXEfYW6fOe60M/ndcRMZh3MZh3cZh7MZh3MZh3MUTmPS/nLPLDK2bOnImpU6dm2h4ZGYmkpKRCjeXbFi5wtjSCmZECL188L9Rzvy+1Wo3o6GhIksSPthQi5l0c5l4M5l0M5l0M5l0cUbmPjY0ttHPpOp3poytMcbHRcnhUrIPySiWkxGhEJBbe6d8XryNiMO9iMO/iMPdiMO9iMO9iiMx7XvroQgvpJUqUgEKhwLNnz7S2P3v2DE5OTlnu4+TklKf2EyZMwOjRozW3Y2Ji4OrqCnt7+0Kff9HBoVBPl6/UajVkMhns7e15ISlEzLs4zL0YzLsYzLsYzLs4onJvbGxcaOd6V4XRPwd0rI/e3K9Qz5efeB0Rg3kXg3kXh7kXg3kXg3kXQ2Te89JHF1pINzQ0hLe3Nw4cOAA/Pz8A6Yk7cOAARowYkeU+9erVw4EDBzBq1CjNtn379qFevXpZtjcyMoKRkVGm7XK5nH8QeSSTyZg3AZh3cZh7MZh3MZh3MZh3cUTkXh9+z4XRPwfYR89PvI6IwbyLwbyLw9yLwbyLwbyLISrveTmf8KldRo8ejf79+6NWrVqoU6cO5s+fj/j4eAwcOBAA0K9fP5QqVQozZ84EAHzxxRdo0qQJ5s2bh7Zt22L9+vUIDg7GsmXLRD4MIiIiIqIigf1zIiIiIqLMhBfSu3fvjsjISEyaNAlPnz5F9erVsWfPHs2CRffv39d6Z6B+/fpYt24dvv32W3zzzTcoX748tm3bhipVqoh6CERERERERQb750REREREmQkvpAPAiBEjsv2oaFBQUKZtXbt2RdeuXQs4KiIiIiKi4on9cyIiIiIibZzsh4iIiIiIiIiIiIgoByykExERERERERERERHlgIV0IiIiIiIiIiIiIqIcsJBORERERERERERERJQDFtKJiIiIiIiIiIiIiHLAQjoRERERERERERERUQ5YSCciIiIiIiIiIiIiygEL6UREREREREREREREOWAhnYiIiIiIiIiIiIgoByykExERERERERERERHlgIV0IiIiIiIiIiIiIqIcKEUHUNgkSQIAxMTECI5Ev6jVasTGxsLY2BhyOd9/KSzMuzjMvRjMuxjMuxjMuziicp/R/8zoj9J/2Ed/N7yOiMG8i8G8i8Pci8G8i8G8iyEy73npoxe7QnpsbCwAwNXVVXAkRERERFQcxcbGwsrKSnQYOoV9dCIiIiISKTd9dJlUzIbEqNVqPH78GBYWFpDJZKLD0RsxMTFwdXXFgwcPYGlpKTqcYoN5F4e5F4N5F4N5F4N5F0dU7iVJQmxsLJydnTnC6Q3so78bXkfEYN7FYN7FYe7FYN7FYN7FEJn3vPTRi92IdLlcDhcXF9Fh6C1LS0teSARg3sVh7sVg3sVg3sVg3sURkXuORM8a++jvh9cRMZh3MZh3cZh7MZh3MZh3MUTlPbd9dA6FISIiIiIiIiIiIiLKAQvpREREREREREREREQ5YCGdcsXIyAiTJ0+GkZGR6FCKFeZdHOZeDOZdDOZdDOZdHOaeigo+l8Vg3sVg3sVh7sVg3sVg3sXQl7wXu8VGiYiIiIiIiIiIiIjygiPSiYiIiIiIiIiIiIhywEI6EREREREREREREVEOWEgnIiIiIiIiIiIiIsoBC+lERERERERERERERDlgIZ1ydOTIEbRr1w7Ozs6QyWTYtm2b6JCKhZkzZ6J27dqwsLCAg4MD/Pz8cPPmTdFhFXlLlixB1apVYWlpCUtLS9SrVw+7d+8WHVaxM2vWLMhkMowaNUp0KEXelClTIJPJtL4qVqwoOqxi4dGjR+jTpw/s7OxgYmICLy8vBAcHiw6rSCtdunSm57tMJsPw4cNFh0aUZ+yji8E+uhjso+sG9tELD/vo4rCPXvj0rY/OQjrlKD4+HtWqVcPixYtFh1KsHD58GMOHD8epU6ewb98+pKamolWrVoiPjxcdWpHm4uKCWbNmISQkBMHBwWjevDk6dOiAq1evig6t2Dh79ix+/fVXVK1aVXQoxUblypXx5MkTzdexY8dEh1TkvXr1Cg0aNICBgQF2796Na9euYd68ebCxsREdWpF29uxZref6vn37AABdu3YVHBlR3rGPLgb76GKwjy4e++iFj330wsc+uhj61kdXig6AdFubNm3Qpk0b0WEUO3v27NG6vWrVKjg4OCAkJASNGzcWFFXR165dO63b06dPx5IlS3Dq1ClUrlxZUFTFR1xcHHr37o3ly5dj2rRposMpNpRKJZycnESHUazMnj0brq6uCAgI0Gzz8PAQGFHxYG9vr3V71qxZKFu2LJo0aSIoIqJ3xz66GOyji8E+uljso4vBPnrhYx9dDH3ro3NEOpEeiI6OBgDY2toKjqT4UKlUWL9+PeLj41GvXj3R4RQLw4cPR9u2bdGyZUvRoRQrt2/fhrOzM8qUKYPevXvj/v37okMq8rZv345atWqha9eucHBwQI0aNbB8+XLRYRUrKSkpWLt2LQYNGgSZTCY6HCLSU+yjFz720Qsf++hisI9e+NhHF08f+ugckU6k49RqNUaNGoUGDRqgSpUqosMp8i5fvox69eohKSkJ5ubm2Lp1KypVqiQ6rCJv/fr1OHfuHM6ePSs6lGLFx8cHq1atQoUKFfDkyRNMnToVjRo1wpUrV2BhYSE6vCLr3r17WLJkCUaPHo1vvvkGZ8+exciRI2FoaIj+/fuLDq9Y2LZtG6KiojBgwADRoRCRnmIfvXCxjy4G++hisI8uBvvo4ulDH52FdCIdN3z4cFy5coVzohWSChUq4MKFC4iOjsaff/6J/v374/Dhw+yoF6AHDx7giy++wL59+2BsbCw6nGLl9WkBqlatCh8fH7i7u2Pjxo3w9/cXGFnRplarUatWLcyYMQMAUKNGDVy5cgVLly5lJ72Q/Pbbb2jTpg2cnZ1Fh0JEeop99MLFPnrhYx9dHPbRxWAfXTx96KNzahciHTZixAjs2LEDhw4dgouLi+hwigVDQ0OUK1cO3t7emDlzJqpVq4YFCxaIDqtICwkJQUREBGrWrAmlUgmlUonDhw9j4cKFUCqVUKlUokMsNqytrfHBBx/gzp07okMp0kqWLJnphb+npyc/sltIwsPDsX//fgwePFh0KESkp9hHL3zsoxc+9tF1B/vohYN9dLH0pY/OEelEOkiSJHz++efYunUrgoKCuMCFQGq1GsnJyaLDKNJatGiBy5cva20bOHAgKlasiPHjx0OhUAiKrPiJi4vD3bt30bdvX9GhFGkNGjTAzZs3tbbdunUL7u7ugiIqXgICAuDg4IC2bduKDoWI9Az76LqDffSCxz667mAfvXCwjy6WvvTRWUinHMXFxWm96xkaGooLFy7A1tYWbm5uAiMr2oYPH45169bhr7/+goWFBZ4+fQoAsLKygomJieDoiq4JEyagTZs2cHNzQ2xsLNatW4egoCDs3btXdGhFmoWFRaa5Rc3MzGBnZ8c5RwvYmDFj0K5dO7i7u+Px48eYPHkyFAoFevbsKTq0Iu3LL79E/fr1MWPGDHTr1g1nzpzBsmXLsGzZMtGhFXlqtRoBAQHo378/lEp2g0l/sY8uBvvoYrCPLgb76OKwjy4G++ji6FMfXbejI+GCg4PRrFkzze3Ro0cDAPr3749Vq1YJiqroW7JkCQCgadOmWtsDAgJ0etEFfRcREYF+/frhyZMnsLKyQtWqVbF37158+OGHokMjKhAPHz5Ez5498eLFC9jb26Nhw4Y4deoU7O3tRYdWpNWuXRtbt27FhAkT8P3338PDwwPz589H7969RYdW5O3fvx/379/HoEGDRIdC9F7YRxeDfXQx2Een4oZ9dDHYRxdHn/roMkmSJNFBEBERERERERERERHpKi42SkRERERERERERESUAxbSiYiIiIiIiIiIiIhywEI6EREREREREREREVEOWEgnIiIiIiIiIiIiIsoBC+lERERERERERERERDlgIZ2IiIiIiIiIiIiIKAcspBMRERERERERERER5YCFdCKiIiQoKAgymQxRUVGFet5Vq1bB2tr6vY4RFhYGmUyGCxcuZNtG1OMjIiIiInpX7KMTERUNLKQTEekJmUyW49eUKVNEh0hEREREVKywj05EVHwoRQdARES58+TJE83PGzZswKRJk3Dz5k3NNnNzcwQHB+f5uCkpKTA0NMyXGImIiIiIihP20YmIig+OSCci0hNOTk6aLysrK8hkMq1t5ubmmrYhISGoVasWTE1NUb9+fa3O/JQpU1C9enWsWLECHh4eMDY2BgBERUVh8ODBsLe3h6WlJZo3b46LFy9q9rt48SKaNWsGCwsLWFpawtvbO9OLgr1798LT0xPm5uZo3bq11gsLtVqN77//Hi4uLjAyMkL16tWxZ8+eHB/zrl278MEHH8DExATNmjVDWFjY+6SQiIiIiChfsY/OPjoRFR8spBMRFUETJ07EvHnzEBwcDKVSiUGDBmndf+fOHWzevBlbtmzRzHfYtWtXREREYPfu3QgJCUHNmjXRokULvHz5EgDQu3dvuLi44OzZswgJCcHXX38NAwMDzTETEhIwd+5crFmzBkeOHMH9+/cxZswYzf0LFizAvHnzMHfuXFy6dAm+vr5o3749bt++neVjePDgATp16oR27drhwoULGDx4ML7++ut8zhQRERERUeFgH52ISM9JRESkdwICAiQrK6tM2w8dOiQBkPbv36/ZtnPnTgmAlJiYKEmSJE2ePFkyMDCQIiIiNG2OHj0qWVpaSklJSVrHK1u2rPTrr79KkiRJFhYW0qpVq7KNB4B0584dzbbFixdLjo6OmtvOzs7S9OnTtfarXbu2NGzYMEmSJCk0NFQCIJ0/f16SJEmaMGGCVKlSJa3248ePlwBIr169yjIOIiIiIiJR2Ed/lWUcRERFBUekExEVQVWrVtX8XLJkSQBARESEZpu7uzvs7e01ty9evIi4uDjY2dnB3Nxc8xUaGoq7d+8CAEaPHo3BgwejZcuWmDVrlmZ7BlNTU5QtW1brvBnnjImJwePHj9GgQQOtfRo0aIDr169n+RiuX78OHx8frW316tXLdQ6IiIiIiHQJ++hERPqNi40SERVBr3+cUyaTAUif/zCDmZmZVvu4uDiULFkSQUFBmY5lbW0NIH3exl69emHnzp3YvXs3Jk+ejPXr16Njx46ZzplxXkmS8uPhEBERERHpPfbRiYj0G0ekExERatasiadPn0KpVKJcuXJaXyVKlNC0++CDD/Dll1/in3/+QadOnRAQEJCr41taWsLZ2RnHjx/X2n78+HFUqlQpy308PT1x5swZrW2nTp3K4yMjIiIiItJP7KMTEekWFtKJiAgtW7ZEvXr14Ofnh3/++QdhYWE4ceIEJk6ciODgYCQmJmLEiBEICgpCeHg4jh8/jrNnz8LT0zPX5xg7dixmz56NDRs24ObNm/j6669x4cIFfPHFF1m2//TTT3H79m2MHTsWN2/exLp167Bq1ap8esRERERERLqNfXQiIt3CqV2IiAgymQy7du3CxIkTMXDgQERGRsLJyQmNGzeGo6MjFAoFXrx4gX79+uHZs2coUaIEOnXqhKlTp+b6HCNHjkR0dDS++uorREREoFKlSti+fTvKly+fZXs3Nzds3rwZX375JRYtWoQ6depgxowZGDRoUH49bCIiIiIincU+OhGRbpFJnByLiIiIiIiIiIiIiChbnNqFiIiIiIiIiIiIiCgHLKQTEREREREREREREeWAhXQiIiIiIiIiIiIiohywkE5ERERERERERERElAMW0omIiIiIiIiIiIiIcsBCOhERERERERERERFRDlhIJyIiIiIiIiIiIiLKAQvpREREREREREREREQ5YCGdiIiIiIiIiIiIiCgHLKQTEREREREREREREeWAhXQiIiIiIiIiIiIiohywkE5ERERERERERERElAMW0omIiIiIiIiIiIiIcsBCOhERERERERERERFRDlhIJyIiIiIiIiIiIiLKAQvpREREREREREREREQ5YCGdiIiIiIiIiIiIiCgHLKQTEZGW0qVLY8CAAaLD0JgyZQpkMtk77TtgwACULl06fwMiIiIiIirmmjZtiqZNm4oO460yXks8f/5cdChEVASwkE5ElEerVq2CTCbTfBkbG8PZ2Rm+vr5YuHAhYmNj3/nY165dw5QpUxAWFpZ/AWfhxIkTmDJlCqKiogr0PERERERE7+KXX36BTCaDj4+P6FBID8yYMQPbtm0THQYRFXEspBMRvaPvv/8ea9aswZIlS/D5558DAEaNGgUvLy9cunTpnY557do1TJ06tVAK6VOnTs2ykH7z5k0sX768QM9PRERERJSTwMBAlC5dGmfOnMGdO3dEh0M6joV0IioMLKQTEb2jNm3aoE+fPhg4cCAmTJiAvXv3Yv/+/YiIiED79u2RmJgoOsR3YmRkBAMDA9FhEBEREVExFRoaihMnTuCnn36Cvb09AgMDCz0GtVqNpKSkQj/vu9CnWImI9BkL6URE+ah58+b47rvvEB4ejrVr12rdd+PGDXTp0gW2trYwNjZGrVq1sH37ds39q1atQteuXQEAzZo100wdExQUpGmze/duNGrUCGZmZrCwsEDbtm1x9erVTHHcuHED3bp1g729PUxMTFChQgVMnDgRQPo8gWPHjgUAeHh4aM6TMQo+qznS7927h65du8LW1hampqaoW7cudu7cqdUmKCgIMpkMGzduxPTp0+Hi4gJjY2O0aNEi16OIjh07htq1a8PY2Bhly5bFr7/+mm3btWvXwtvbGyYmJrC1tUWPHj3w4MGDt55j7ty5qF+/Puzs7GBiYgJvb2/8+eefWm2aNGmCatWqZbl/hQoV4Ovrm6vHQ0RERER5FxgYCBsbG7Rt2xZdunTRKqSnpqbC1tYWAwcOzLRfTEwMjI2NMWbMGM225ORkTJ48GeXKlYORkRFcXV0xbtw4JCcna+0rk8kwYsQIBAYGonLlyjAyMsKePXsA5K7/CACJiYkYOXIkSpQoAQsLC7Rv3x6PHj2CTCbDlClTtNo+evQIgwYNgqOjI4yMjFC5cmWsXLkyV/nJLtaM/vjrrx8AICwsDDKZDKtWrdJsGzBgAMzNzfHo0SP4+fnB3Nwc9vb2GDNmDFQqVa7ieFNec71t2zZUqVJF8/gz8v26oKAg1KpVS+v1wZtrKMlkMsTHx2P16tWa1zZvvp6JiorCgAEDYG1tDSsrKwwcOBAJCQnv9DiJqPhSig6AiKio6du3L7755hv8888/GDJkCADg6tWraNCgAUqVKoWvv/4aZmZm2LhxI/z8/LB582Z07NgRjRs3xsiRI7Fw4UJ888038PT0BADN9zVr1qB///7w9fXF7NmzkZCQgCVLlqBhw4Y4f/68ZlHNS5cuoVGjRjAwMMDQoUNRunRp3L17F3///TemT5+OTp064datW/jjjz/wv//9DyVKlAAA2NvbZ/l4nj17hvr16yMhIQEjR46EnZ0dVq9ejfbt2+PPP/9Ex44dtdrPmjULcrkcY8aMQXR0NObMmYPevXvj9OnTOebt8uXLaNWqFezt7TFlyhSkpaVh8uTJcHR0zNR2+vTp+O6779CtWzcMHjwYkZGRWLRoERo3bozz58/D2to62/MsWLAA7du3R+/evZGSkoL169eja9eu2LFjB9q2bav5HQ4ZMgRXrlxBlSpVNPuePXsWt27dwrfffpvjYyEiIiKidxcYGIhOnTrB0NAQPXv2xJIlS3D27FnUrl0bBgYG6NixI7Zs2YJff/0VhoaGmv22bduG5ORk9OjRA0D6SO327dvj2LFjGDp0KDw9PXH58mX873//w61btzJNBXLw4EFs3LgRI0aMQIkSJTT969z0H4H04vTGjRvRt29f1K1bF4cPH9a6P8OzZ89Qt25dTUHZ3t4eu3fvhr+/P2JiYjBq1Ki35iirWPO6/pFKpYKvry98fHwwd+5c7N+/H/PmzUPZsmXx2Wef5elYec31sWPHsGXLFgwbNgwWFhZYuHAhOnfujPv378POzg4AcP78ebRu3RolS5bE1KlToVKp8P3332d63bJmzRoMHjwYderUwdChQwEAZcuW1WrTrVs3eHh4YObMmTh37hxWrFgBBwcHzJ49O0+Pk4iKOYmIiPIkICBAAiCdPXs22zZWVlZSjRo1NLdbtGgheXl5SUlJSZptarVaql+/vlS+fHnNtk2bNkkApEOHDmkdLzY2VrK2tpaGDBmitf3p06eSlZWV1vbGjRtLFhYWUnh4uFZbtVqt+fnHH3+UAEihoaGZYnd3d5f69++vuT1q1CgJgHT06FGteDw8PKTSpUtLKpVKkiRJOnTokARA8vT0lJKTkzVtFyxYIAGQLl++nFWqNPz8/CRjY2OtuK9duyYpFArp9X9XYWFhkkKhkKZPn661/+XLlyWlUqm1vX///pK7u7tWu4SEBK3bKSkpUpUqVaTmzZtrtkVFRUnGxsbS+PHjtdqOHDlSMjMzk+Li4nJ8LERERET0boKDgyUA0r59+yRJSu/Duri4SF988YWmzd69eyUA0t9//62170cffSSVKVNGc3vNmjWSXC7X6sdKkiQtXbpUAiAdP35csw2AJJfLpatXr2aKKTf9x5CQEAmANGrUKK22AwYMkABIkydP1mzz9/eXSpYsKT1//lyrbY8ePSQrK6tM53tTdrFm9MfffC0RGhoqAZACAgI02/r37y8BkL7//nuttjVq1JC8vb1zPL8kSVKTJk2kJk2aaG7nNdeGhobSnTt3NNsuXrwoAZAWLVqk2dauXTvJ1NRUevTokWbb7du3JaVSKb1ZzjIzM9N6DZNh8uTJEgBp0KBBWts7duwo2dnZvfVxEhG9jlO7EBEVAHNzc8TGxgIAXr58iYMHD6Jbt26IjY3F8+fP8fz5c7x48QK+vr64ffs2Hj16lOPx9u3bh6ioKPTs2VOz//Pnz6FQKODj44NDhw4BACIjI3HkyBEMGjQIbm5uWsd4/eOPebFr1y7UqVMHDRs21Hp8Q4cORVhYGK5du6bVfuDAgVojgxo1agQgfXqY7KhUKuzduxd+fn5acXt6emaaRmXLli1Qq9Xo1q2bVi6cnJxQvnx5TS6yY2Jiovn51atXiI6ORqNGjXDu3DnNdisrK3To0AF//PEHJEnSxLhhwwb4+fnBzMwsx3MQERER0bsJDAyEo6MjmjVrBiC9D9u9e3esX79eM+VI8+bNUaJECWzYsEGz36tXr7Bv3z50795ds23Tpk3w9PRExYoVtfqNzZs3B4BM/cYmTZqgUqVKmWLKTf8xY1qSYcOGae37+eefa92WJAmbN29Gu3btIEmSVly+vr6Ijo7WOm52sos1rz799FOt240aNcqx356dvOa6ZcuWWqPGq1atCktLS825VSoV9u/fDz8/Pzg7O2valStXDm3atMlzfFk9zhcvXiAmJibPxyKi4otTuxARFYC4uDg4ODgAAO7cuQNJkvDdd9/hu+++y7J9REQESpUqle3xbt++DQCajuibLC0tAfxXrH59OpL3FR4eDh8fn0zbM6acCQ8P1zrfmwV8GxsbAOkvOrITGRmJxMRElC9fPtN9FSpUwK5duzS3b9++DUmSsmwL4K0Lpe7YsQPTpk3DhQsXtOZrfPONhn79+mHDhg04evQoGjdujP379+PZs2fo27dvjscnIiIionejUqmwfv16NGvWDKGhoZrtPj4+mDdvHg4cOIBWrVpBqVSic+fOWLduHZKTk2FkZIQtW7YgNTVVq5B++/ZtXL9+PdspDCMiIrRue3h4ZNkuN/3H8PBwyOXyTMcoV66c1u3IyEhERUVh2bJlWLZsWa7iykp2seaFsbFxptzY2Njk2G/PTl5z/eZrhjfPHRERgcTExEz5AzLnNDdyeo2S8VqKiOhtWEgnIspnDx8+RHR0tKaDp1arAQBjxozJdpHKt3UGM46xZs0aODk5ZbpfqdSdy7lCochye8bI7velVqshk8mwe/fuLM9lbm6e7b5Hjx5F+/bt0bhxY/zyyy8oWbIkDAwMEBAQgHXr1mm19fX1haOjI9auXYvGjRtj7dq1cHJyQsuWLfPlcRARERGRtoMHD+LJkydYv3491q9fn+n+wMBAtGrVCgDQo0cP/Prrr9i9ezf8/PywceNGVKxYUWvBeLVaDS8vL/z0009Zns/V1VXr9usjzzPkpf+YGxn9+j59+qB///5Ztqlatepbj5NVrNl9AjW7xUOz67e/i7zmuqBfM7ypsM9HREWT7lReiIiKiDVr1gCApmhepkwZAOkjpd9WhM2u85vxsUcHB4ccj5FxritXrrzTebLi7u6OmzdvZtp+48YNzf3vy97eHiYmJpqR969789xly5aFJEnw8PDABx98kKfzbN68GcbGxti7dy+MjIw02wMCAjK1VSgU6NWrF1atWoXZs2dj27ZtGDJkSL6+4CAiIiKi/wQGBsLBwQGLFy/OdN+WLVuwdetWLF26FCYmJmjcuDFKliyJDRs2oGHDhjh48CAmTpyotU/ZsmVx8eJFtGjR4p2nOcxt/9Hd3R1qtRqhoaFan5y8c+eOVjt7e3tYWFhApVLl+wCNjFHWby46Gh4enq/nyUp+5Pp1Dg4OMDY2zpQ/IHNOgXefxpKIKC84RzoRUT46ePAgfvjhB3h4eKB3794A0juBTZs2xa+//oonT55k2icyMlLzc8bc2292fn19fWFpaYkZM2YgNTU122PY29ujcePGWLlyJe7fv6/V5vXRFtmdJysfffQRzpw5g5MnT2q2xcfHY9myZShdunS+zM2oUCjg6+uLbdu2acV9/fp17N27V6ttp06doFAoMHXq1EwjSCRJwosXL3I8j0wm0xqVExYWhm3btmXZvm/fvnj16hU++eQTxMXFoU+fPu/w6IiIiIjobRITE7FlyxZ8/PHH6NKlS6avESNGIDY2Ftu3bwcAyOVydOnSBX///TfWrFmDtLQ0rWldAKBbt2549OgRli9fnuX54uPj3xpXbvuPGYNofvnlF63tixYtynS8zp07Y/PmzVkOfnn9tUFeubu7Q6FQ4MiRI1rb34ypIORHrl+nUCjQsmVLbNu2DY8fP9Zsv3PnDnbv3p2pvZmZWa5e2xARvQ+OSCcieke7d+/GjRs3kJaWhmfPnuHgwYPYt28f3N3dsX37dhgbG2vaLl68GA0bNoSXlxeGDBmCMmXK4NmzZzh58iQePnyIixcvAgCqV68OhUKB2bNnIzo6GkZGRmjevDkcHBywZMkS9O3bFzVr1kSPHj1gb2+P+/fvY+fOnWjQoAF+/vlnAMDChQvRsGFD1KxZE0OHDoWHhwfCwsKwc+dOXLhwAQDg7e0NAJg4cSJ69OgBAwMDtGvXLstFNL/++mv88ccfaNOmDUaOHAlbW1usXr0aoaGh2Lx5M+Ty/HlPdurUqdizZw8aNWqEYcOGIS0tDYsWLULlypVx6dIlTbuyZcti2rRpmDBhAsLCwuDn5wcLCwuEhoZi69atGDp0KMaMGZPlOdq2bYuffvoJrVu3Rq9evRAREYHFixejXLlyWufIUKNGDVSpUkWzeFLNmjXz5bESERERkbbt27cjNjYW7du3z/L+unXrwt7eHoGBgZqCeffu3bFo0SJMnjwZXl5emjV8MvTt2xcbN27Ep59+ikOHDqFBgwZQqVS4ceMGNm7ciL1796JWrVo5xpXb/qO3tzc6d+6M+fPn48WLF6hbty4OHz6MW7duAdAeMT1r1iwcOnQIPj4+GDJkCCpVqoSXL1/i3Llz2L9/P16+fPlOObSyskLXrl2xaNEiyGQylC1bFjt27MjVnOvvKz9y/aYpU6bgn3/+QYMGDfDZZ59BpVLh559/RpUqVTSvazJ4e3tj//79+Omnn+Ds7AwPD48s13kiInovEhER5UlAQIAEQPNlaGgoOTk5SR9++KG0YMECKSYmJsv97t69K/Xr109ycnKSDAwMpFKlSkkff/yx9Oeff2q1W758uVSmTBlJoVBIAKRDhw5p7jt06JDk6+srWVlZScbGxlLZsmWlAQMGSMHBwVrHuHLlitSxY0fJ2tpaMjY2lipUqCB99913Wm1++OEHqVSpUpJcLpcASKGhoZIkSZK7u7vUv3//TLF36dJFc7w6depIO3bs0Gpz6NAhCYC0adMmre2hoaESACkgIOAtmZWkw4cPS97e3pKhoaFUpkwZaenSpdLkyZOlrP5dbd68WWrYsKFkZmYmmZmZSRUrVpSGDx8u3bx5U9Omf//+kru7u9Z+v/32m1S+fHnJyMhIqlixohQQEJDtOSRJkubMmSMBkGbMmPHW+ImIiIjo3bRr104yNjaW4uPjs20zYMAAycDAQHr+/LkkSZKkVqslV1dXCYA0bdq0LPdJSUmRZs+eLVWuXFkyMjKSbGxsJG9vb2nq1KlSdHS0ph0Aafjw4VkeI7f9x/j4eGn48OGSra2tZG5uLvn5+Uk3b96UAEizZs3Savvs2TNp+PDhkqurq2RgYCA5OTlJLVq0kJYtW/bWXOUUa2RkpNS5c2fJ1NRUsrGxkT755BPpypUrmfrj/fv3l8zMzDLtn1O/+HVNmjSRmjRporXtfXOd1euQAwcOSDVq1JAMDQ2lsmXLSitWrJC++uorydjYWKvdjRs3pMaNG0smJiYSAM1xMh5PZGSkVvuM13QZr4GIiHJDJklcWYGIiCg7CxYswJdffomwsDC4ubmJDoeIiIiI9MiFCxdQo0YNrF27VjP1I70fPz8/XL16Ncv1lYiIChLnSCciIsqGJEn47bff0KRJExbRiYiIiChHiYmJmbbNnz8fcrkcjRs3FhCR/nszp7dv38auXbvQtGlTMQERUbHGOdKJiIjeEB8fj+3bt+PQoUO4fPky/vrrL9EhEREREZGOmzNnDkJCQtCsWTMolUrs3r0bu3fvxtChQ+Hq6io6PL1UpkwZDBgwAGXKlEF4eDiWLFkCQ0NDjBs3TnRoRFQMcWoXIiKiN4SFhcHDwwPW1tYYNmwYpk+fLjokIiIiItJx+/btw9SpU3Ht2jXExcXBzc0Nffv2xcSJE6FUchzjuxg4cCAOHTqEp0+fwsjICPXq1cOMGTNQs2ZN0aERUTHEQjoRERERERERERERUQ44RzoRERERERERERERUQ5YSCciIiIiIiIiIiIiygEL6UREREREREREREREOSh2q12o1Wo8fvwYFhYWkMlkosMhIiIiomJCkiTExsbC2dkZcjnHs7yOfXQiIiIiEiEvffRiV0h//PgxXF1dRYdBRERERMXUgwcP4OLiIjoMncI+OhERERGJlJs+erErpFtYWABIT46lpaXgaPSHWq1GZGQk7O3tOYKqEDHv4jD3YjDvYjDvYjDv4ojKfUxMDFxdXTX9UfoP++jvhtcRMZh3MZh3cZh7MZh3MZh3MUTmPS999GJXSM/4qKilpSU76XmgVquRlJQES0tLXkgKEfMuDnMvBvMuBvMuBvMujujcc+qSzNhHfzein8vFFfMuBvMuDnMvBvMuBvMuhi7kPTd9dD4jiIiIiIiIiIiIiIhywEI6EREREREREREREVEOWEgnIiIiIiIiIiIiIspBsZsjnYiIiAqXSqVCamqq6DB0jlqtRmpqKpKSkjj/YiEryNwbGhry91mAeD3Rpg/XEf5NEBERUVHBQjoREREVCEmS8PTpU0RFRYkORSdJkgS1Wo3Y2FguPlnICjL3crkcHh4eMDQ0zNfjFne8nmRNH64j/JsgIiKiooKFdCIiIioQGUUvBwcHmJqa6myRRxRJkpCWlgalUsncFLKCyr1arcbjx4/x5MkTuLm58feaj3g9yZquX0f4N0FERERFCQvpRERElO9UKpWm6GVnZyc6HJ2k6wWwoqwgc29vb4/Hjx8jLS0NBgYG+Xrs4orXk+zpw3WEfxNERERUVHCyOiIiIsp3GXMYm5qaCo6EqHBlTF+hUqkER1J08Hqi3/g3QUREREUFC+lERERUYHR1hCRRQeFzvuAwt/qJvzciIiIqKlhIJyIiIiLhgoKCIJPJ8rSYZOnSpTF//vwCi6k4OHLkCNq1awdnZ2fIZDJs27ZN636ZTJbl148//qhp8/LlS/Tu3RuWlpawtraGv78/4uLiCvmRkCjv8rdLREREpI9YSCciIiISKCwsDDKZDBcuXBAdChVD8fHxqFatGhYvXpzl/U+ePNH6WrlyJWQyGTp37qxp07t3b1y9ehX79u3Djh07cOTIEQwdOrSwHgK9htcTIiIiooLDxUaJiIiIspGSkqKZ35eoKGrTpg3atGmT7f1OTk5at//66y80a9YMZcqUAQBcv34de/bswdmzZ1GrVi0AwKJFi/DRRx9h7ty5cHZ2Lrjg9UxRvJ5kzF9PREREVBxwRDoRERHRv5o2bYoRI0Zg1KhRKFGiBHx9fQEAV65cQZs2bWBubg5HR0f07dsXz58/1+z3559/wsvLCyYmJrCzs0PLli0RHx+vuX/FihXw9PSEsbExKlasiF9++UVzX0ZBskaNGpDJZGjatGmu4x0wYAD8/PwwY8YMODo6wtraGt9//z3S0tIwduxY2NrawsXFBQEBAVr7PXjwAN26dYO1tTVsbW3RoUMHhIWFae4/e/YsPvzwQ5QoUQJWVlZo0qQJzp07p3UMmUyGFStWoGPHjjA1NUX58uWxffv2XMe+a9cufPDBBzAxMUGzZs20zp/h2LFjaNSoEUxMTODq6oqRI0dq5fVNP/30E7y8vGBmZgZXV1cMGzZMM8VIfHw8LC0t8eeff2rts23bNpiZmSE2NjbXsRdXz549w86dO+Hv76/ZdvLkSVhbW2uK6ADQsmVLyOVynD59OttjJScnIyYmRusLANRqdZZfkiTp3VfTpk0xfPhwfPHFF5rriSRJuHz5cqbrSWRkpGa/TZs2ZbqexMXFae5fvny55nri6emJJUuWAAAkSYKHhwcA7etJbuM9c+ZMpr/7kJAQrTYymQy//PIL2rdvDzMzM0ybNg2SJGnOn9NXdr9bff0qio9JH76Yd+a+uH0x78x7cfoSmffc4oh0IiIiotesXr0an332GY4fPw4AiIqKQvPmzTF48GD873//Q2JiIsaPH49u3brh4MGDePLkCXr27Ik5c+agY8eOiI2NxdGjRzXFpcDAQEyaNAk///wzatSogfPnz2PIkCEwNTVF7969cfr0afj4+GD//v2oXLmyZsRqUFAQmjVrhtDQUJQuXTrbeA8ePAgXFxccOXIEx48fh7+/P06cOIHGjRvj9OnT2LBhAz755BN8+OGHcHFxQWpqKnx9fVGvXj0cPXoUSqUS06ZNQ+vWrXHp0iUYGhoiNjYW/fv3x6JFiyBJEubNm4ePPvoIt2/fhoWFhebcU6dOxZw5c/Djjz9i0aJF6N27N8LDw2Fra5tjjh88eIBOnTph+PDhGDp0KIKDg/HVV19ptbl79y5at26NadOmYeXKlYiMjMSIESMwYsSITG8MZJDL5Vi4cCE8PDxw7949DBs2DOPGjcMvv/wCMzMz9OjRAwEBAVrTkgQEBKBLly5aj4uytnr1alhYWKBTp06abU+fPoWDg4NWO6VSCVtbWzx9+jTbY82cORNTp07NtD0yMhJJSUla21JTU6FWq5GWloa0tLT3fBSFR5Ik/P777xg6dCiCgoIAAM+fP0eLFi0wcOBAzJkzB4mJiZg4cSK6deuGf/75B0+ePEGvXr0wc+ZMdOjQAbGxsTh+/DhSU1ORlpaGdevWYfLkyZg/fz6qV6+OCxcu4LPPPoOZmRn69euHEydOoH79+tizZw8qVaoEQ0NDpKWl4fDhw/jwww9x69atbK8nUVFR6N27N3766SdIkoT58+ejbdu2uHbtWqa/++nTp+PHH3+EUqlEaGgoAGT7+0lLS4NarcaLFy9gYGCQ73kWQa1WIzo6GpIkQS7n2LTCwryLw9yLwbyLwbyLITLveRlQw0J6IXoYdgs2RoCZoUJ0KHknSVBEvwCUcYBMJjqa4kOSIEtKA+Dw1qZERLouMUWFu5GFvwBhWXtzmOThf2/58uUxZ84cze1p06ahRo0amDFjhmbbypUr4erqilu3biEuLg5paWno1KkT3N3dAQBeXl6atpMnT8a8efM0xUcPDw9cu3YNy5YtQ+/evWFvbw8AsLOz05pGw9TUFBUqVHhr4cnW1hYLFy6EXC5HhQoVMGfOHCQkJOCbb74BAEyYMAGzZs3CsWPH0KNHD2zYsAFqtRorVqyA7N//6QEBAbC2tkZQUBBatWqF5s2ba51j2bJlsLa2xuHDh/Hxxx9rtg8YMAA9e/YEAMyYMQMLFy7EmTNn0Lp16xxjXrJkCcqWLYt58+YBACpUqIDLly9j9uzZmjYzZ85E7969MWrUKADpv5eFCxeiSZMmWLJkCYyNjTMdN6MtkL4Q6bRp0/Dpp59qPgEwePBg1K9fH0+ePIG9vT0iIiKwa9cu7N+/P8d4Kd3KlSvRu3fvLHOfVxMmTMDo0aM1t2NiYuDq6gp7e3tYWlpqtU1KSkJsbCyUSiWUyv9evoi4puTleiKTyVC+fHnMnTtXsy3jejJr1izNtpUrV8LNzQ337t3TXE+6dOmiuZ7UqFFD0/aHH37A3Llz0bVrVwDpfxfXr1/Hb7/9hkGDBmmuIQ4ODnBxcdHsZ2FhgQoVKsDExEQrh6/78MMPtW4vX74cNjY2OH78uNbffc+ePbU+lXD//n0AyPT7yaBUKiGXy2FnZ5cvzx1doFarIZPJYG9vzyJLIWLexWHuxWDexWDeC54kSUhVSUhRqZGqUiM1TY3UVBVipWSULWEPpbJw66Z56Z+wkF5I1GoJsWv6wEV1U3Qo70QOwF50EMWQHIC9oTkwLhSQF605NYmo+LkbGYePFx0r9PPu+LwhqpSyynV7b29vrdsXL17EoUOHYG5unqnt3bt30apVK7Ro0QJeXl7w9fVFq1at0KVLF9jY2CA+Ph53796Fv78/hgwZotkvLS0NVlY5x1SnTh3cuHHjrfFWrlxZq5Pv6OiIKlWqaG4rFArY2dkhIiJC83ju3LmTaQR2UlIS7t69CyB9Co9vv/0WQUFBiIiIgEqlQkJCgqZglqFq1aqan83MzGBpaak5T06uX78OHx8frW316tXTun3x4kVcunQJgYGBmm0ZH/cMDQ2Fp6dnpuPu378fM2fOxI0bNxATE4O0tDQkJSUhISEBpqamqFOnDipXrozVq1djzJgxWLt2Ldzd3dG4ceO3xlzcHT16FDdv3sSGDRu0tjs5OWX6naelpeHly5eZ5ld/nZGREYyMjDJtl8vlmV60yuVyyGQyzVeGe8/j0e7n4+/ycN7Zu1xPXo/50qVLOHToUJafgLh3757melK1atVsryeDBw/WWsw143ryen7ezJWPj89bryfZ/d0/ePBA61i1a9fWup3dOV+/XyaTZfm71WdF8THpA+ZdHOZeDOZdDH3Ou0otISVN/V+RWqVGalp60Tol7b9t6fdLSH2tbfr9kqZN8mvtU1WSZv//jiNpnSf9vOnHfH0/7eOkb8vOxUnOsDIs3E+w5eX3zEJ6IZHLZTD2+x+GbQ8GAHzduiLcbE0FR5V7aklC1KtXsLaxgZwj0guN+vF5yPd9B3XMY8C2tOhwiIjeS1l7c+z4vKGQ8+aFmZmZ1u24uDi0a9dOa7R0hpIlS0KhUGDfvn04ceIE/vnnHyxatAgTJ07E6dOnYWqa/r9++fLlmQrH+dUxf3PEukwmy3Jbxtx/cXFx8Pb21ipQZ8gYHd+/f3+8ePECCxYsgLu7O4yMjFCvXj2kpKS89dx5mWMwJ3Fxcfjkk08wcuTITPe5ubll2hYWFoaPP/4Yn332GaZPnw5bW1scO3YM/v7+SElJ0fwuBg8ejMWLF2PMmDFYtWoVBg4cmGXxj7T99ttv8Pb2RrVq1bS216tXD1FRUQgJCdG8CXXw4EGo1epMz/n8JuKaomvXE0mSkJaWluWbEnmV27/7Nx8TERERaVOr0wvMSakqJKepkZyqRlKaCsmpaiSnqZD0xvfktLe31W7z7/c3tiWlqaFSZ1+kzg2ZDDBQyGGkkMNAKYeBQgYDhRyGCnn699e3KdO3GSnlMDdSwlD5XzsDhRwGStkb+8lh+O++Bv8e31Ahg6FSDoVMhvjYaBgb6PYsHiykFyIPrwaY5OaNgavOou32BCzp44mG5UuIDit31GqkmEYADg6AHr4jp7fMHIB93wFR91lIJyK9Z2KoyNNITl1Rs2ZNbN68GaVLl852SgSZTIYGDRqgQYMGmDRpEtzd3bF161aMHj0azs7OuHfvHnr37q21T0YBLGNOdJVKVeCPBUh/PBs2bICDg0OmKTQyHD9+HL/88gs++ugjAOlzmr++uOr78vT0zLQw6alTpzLFee3aNZQrVy5XxwwJCYFarca8efM0b1Js3LgxU7s+ffpg3Lhx+Pnnn3Ht2jX079//HR9F0RAXF4c7d+5oboeGhuLChQuwtbXVvGERExODTZs2aabieZ2npydat26NIUOGYOnSpUhNTcWIESPQo0cPODs7F2js+nhNye/rScZ1JONY73M9Kei/eyIiooKmGY397yjr10dha7alZbPt3ylGktNUeBkdC6VhFFJUbxarcypy//tzavqx8srYQA4jpULz3Ugph7FB+ncjAzmMlQpYGCthr1TAKKNNFm0NlemFbYM3Ct+vF7gNldqF8P8K5TIo5Fl/wqygqdVqRESoYaDQ7ZojC+mFzMnKGJs+rYfhgecwIOAMZnbyQtdarqLDIl1l9e/cllH3c25HREQFZvjw4Vi+fDl69uyJcePGwdbWFnfu3MH69euxYsUKBAcH48CBA2jVqhUcHBxw+vRpREZGaqYemTp1KkaOHAkrKyu0bt0aycnJCA4OxsuXLzFy5Eg4ODjAxMQEe/bsgYuLC4yNjWFlZYUzZ86gX79+OHDgAEqVKpVvj6d379748ccf0aFDB3z//fdwcXFBeHg4tmzZgnHjxsHFxQXly5fHmjVrUKtWLcTExGDs2LEwMTHJtxg+/fRTzJs3D2PHjsXgwYMREhKCVatWabUZP3486tatixEjRmDw4MEwMzPDtWvXsG/fPvz888+ZjlmuXDmkpqZi0aJFaNeuHY4fP46lS5dmamdjY4NOnTrh66+/RqtWrbTmkS6OgoOD0axZM83tjHnL+/fvr/mdrF+/HpIkaebDf1NgYCBGjBiBFi1aQC6Xo3Pnzli4cGGBx66P8vt6kpSUhDNnziA6OhpfffXVe11PCvrvnoiIig5JkjRF5pR/R0Ynp6rSt6VqF6yT3yha51zUlrSK2ilvtE19o+idEUPG9vcZjC2TAYb/FpeVcsDU0EBTwE4vWP9XrLY0Nsiy4G1skLltdgVvI4PXit8KOT8hqSdYSBfA3EiJFf1rYdJfVzD2z0t4+CoRo1qW5x8NZWZgApWpPeRR4aIjISIqtpydnXH8+HGMHz8erVq1QnJyMtzd3dG6dWvI5XJYWlriyJEjmD9/PmJiYuDu7o558+ahTZs2ANKnEjE1NcWPP/6IsWPHwszMDF5eXvjiiy8ApC/Et3DhQnz//feYNGkSGjVqhKCgICQkJODmzZtITU3N18djamqKI0eOYPz48ejUqRNiY2NRqlQptGjRQjNC/bfffsPQoUNRs2ZNuLq6YsaMGRgzZky+xeDm5obNmzfjyy+/xKJFi1CnTh3MmDEDgwYN0rSpWrUqDh8+jIkTJ6JRo0aQJAlly5ZF9+7dszxmtWrV8NNPP2H27NmYMGECGjdujJkzZ6Jfv36Z2g4aNAjr1q3DwIED8+0x6aumTZtCknJ+1Tl06FCtObnfZGtri3Xr1uV3aEVSQVxPqlSpollo932uJwX9d09ERO/v9UUaXy9c/1fQVv1X2E77b7R0xrbMbVRv3Jf1toz9klPVSP63aP2uMkZHa6b6+HcUdca2N6cCsTIx+G9UtVIOQ4UCBkoZjN44RsZ3I+V/I6yz3K78b5S20b/HMlTIofx3JHT6yOgIODg46OUc6VSwZNLbes5FTExMDKysrBAdHZ3tx5kLiyRJ+CXoLn7cexOda7pgZicvGCp184+UFxIx1Go10n5tBgPHipB1+lV0OMUKn/NiMO9iFETek5KSEBoaCg8Pjzytgl6cvD4lA99ML1y///47Ro8ejUePHuXL3NKvy+m5r0v9UF2TU254PcmePlxHiuLvj/0VMZh3cYpy7tVqCanq9MUP0/6dCiRNs9hi+ve0fwvXaSrthRhTVRLS1OlF5TT1m/ukj65Oe61t6uvHVqcvyJimzr5dikqNxORUqCDTzImdolLjXap4chk0I6MNFfL/vmttU2juM8r4rlRoitBa+2lNI/Jam3+/Z4zE1ppaRE9GXhfl57suE5n3vPTROSJdIJlMhuHNysHFxgRjN13Ck+hELOnjDSuTwl2dlnSbysIFBpzahYiI6L0lJCTgyZMnmD17NgYPHqyZT5qIiIjyjyRJUKn/GzmdMSVHxldKWnqx+PXpOV4vUL8+dUfGMV6/T7Mt7b/i85vnSVFJr92vfXxNYVwtvffCjK8zUMiglP9XNFbK0xdbNJCnj4RWahZZlP17O32hRVMDBQxMDGAgl2m1U8plSE1OhK2VRabC9etF69e3GWVT2M4YbU1E74eFdB3QoXopOFoaY+jvwei69AQCBtZBKWvOR0jpVBalgHsXRIdBRESUa59++inWrl2b5X19+vTJcu7ywjBnzhxMnz4djRs3xvjx44XEQEREpMtiklLx4GUCHrxMxMNXCXj4KhGPXiUgOj4RMkXof0Xt14vhbxa433HU9JsMlekjow2U/xWfNQsmKmWZpgAxNpDDwlipNT2I8t+i9uvFbMMcitrpxW95elFbmV7MzliM0UDx38+Z9i+ABRo5MppI97CQriPqlrHDlmH1MSDgLPwWH0fAgNqoUspKdFikA1QWpYCYx4AqFVDw0wpERKT7vv/++2znVhY5pcmUKVMwZcoUzXQYRERExU1SqgoPX/1XKH/wKjG9cP7vtujE/9ZSMDaQw9XGFM7WJrAwUsLczDi9QK18ff7p/4rLhoqM0dgKzajs14vQr89ZnV4Af21fpfYxFAVQmCYiel8spOuQcg4W2DqsAfxXn0W3X09ica+aaFbRQXRYJJjKohRkkhqIfgjYeogOh4iI6K0cHBzg4MA+DBERUWFLVanxJCrp3yJ5enE8/Xt60TwyNlnTVimXoZSNCVxtTOFVygptqpSEi40JXG1N4WpjihLmhpDJZBwZTUT0LxbSdYy9hRHWD62LkX9cgP/qs/i+QxX0qesuOiwSSGVRKv2HqPsspBMRERERERVjarWEiNhk7UL5ayPKn8Ykaeb9lskAJ0tjuNiYoLSdGRqWt4drRqHc1hROlsZQyDnqm4got1hI10Gmhkr82tcbP+y4hm+3XcGDVwkY71sRcv6DK5a0CulERERExZSUHxPuUqHj740obyRJQlRCapajyR++TMDDqESkpKk17e3MDOFiYwIXW1NUd7WBq236CHNXW1M4WxvDSKkQ+GiIiIoWFtJ1lEIuw+R2leBiY4Lpu67j0atEzO1aDcYG/CdY7CgMIVmUhIyFdCIiIiqGDAzS14hJSEiAiYmJ4Ggor1JSUgAACgVfxxBliE9Oy3I0ecbinnHJ/63jYW6k1Ey30rSCg1ah3MXGBGZGLOsQERUWXnF1mEwmw+BGZVDK2gSjNlxA399OY1nfWrAxMxQdGhU2K1cgKlx0FERERESFTqFQwNraGhEREQAAU1NTLkD3r4yFc5VKpU7mRK1WIzIyEqamplAq+dKTiqc0lRr7r0dg5+UnuP8iHg9eJeJlfIrmfkOlPL1QbmOKWqVt0LFGKc0c5S42JrA2NdDJv28iouKIvRk90MarJBwsjTHk92B0XnICAQNrw93OTHRYVJis3Ti1CxERERVbTk5OAKApplM6SZKgVqshl8t1ttAml8vh5uams/ERFZSImCSsP/sA607fx9OYJFQpZYlKJS3R0tNRM5rc1dYU9uZGnMaViEhPsJCuJ7zdbbDls/oYuOosOv1yAsv710JNNxvRYVFhsXYH7p8UHQURERGREDKZDCVLloSDgwNSU1NFh6Mz1Go1Xrx4ATs7O8jlctHhZMnQ0FBnYyPKb5Ik4dS9l1h7Khx7rz6FgUIOvxrO6O3jjiqlrESHR0RE74mFdD1SuoQZNn9WH0N/D0bPZaewoEcNtK7iJDosKgSStRtkMY+BtBRAyal9iIio8A0YMABRUVHYtm0bAKBp06aoXr065s+fLyymKVOmYNu2bbhw4UKu2oeFhcHDwwPnzp1DlSpVCjY4KhAKhYJzbb9GrVbDwMAAxsbGLFYTCRSTlIotIQ+x9vR93ImIQ1l7M0xs64lONV1gZWIgOjwiIsonLKTrGVszQ6wd7IOvNl7EZ4Eh+LZtJfg39BAdFhU0azcAEhDzELAtIzoaIiLKR2FhYShTpgzOnz+P6tWriw4n17Zs2aJZBJKIiKg4uvo4GmtPhWPb+cdIVanRqrIjvu9QGfXK2HE6IyKiIoiFdD1kbKDAop414LLHBD/suIYHLxPw3ceVoOC8akWXtVv691fhLKQTERWilJQUGBryk0BZsbW1FR0CERFRoUtKVWHX5SdYeyoc5+5HwdHSCJ82KYsedVzhaGksOjwiIipA/PyfnpLLZZjwkSd+8KuC30+G4dO1IUhMUYkOiwqKZSkAMi44SkRUwJo2bYoRI0Zg1KhRKFGiBHx9fQEAV65cQZs2bWBubg5HR0f07dsXz58/1+z3559/wsvLCyYmJrCzs0PLli0RHx+vuX/FihXw9PSEsbExKlasiF9++UVzX5ky6W+Q1qhRAzKZDE2bNs11vAMGDICfnx9mzJgBR0dHWFtb4/vvv0daWhrGjh0LW1tbuLi4ICAgQGu/Bw8eoFu3brC2toatrS06dOiAsLAwzf0qlQqjR4+GtbU17OzsMG7cOEiSlClXo0aN0txes2YNatWqBQsLCzg5OaFXr15aC0MGBQVBJpPhwIEDqFWrFkxNTVG/fn3cvHkz14931qxZcHR0hIWFBfz9/ZGUlJSpTU65fpNKpYK/vz88PDxgYmKCChUqYMGCBZr7jxw5AgMDAzx9+lRrv1GjRqFRo0a5jpuIiPTf/RcJmLn7OurNPIDRGy/C1FCJpX28cXx8c3zRsjyL6ERExQAL6Xqub113rOhfC8fvPEePZScRGZssOiQqCEojwNKZhXQiokKwevVqGBoa4vjx41i6dCmioqLQvHlz1KhRA8HBwdizZw+ePXuGbt26AQCePHmCnj17YtCgQbh+/TqCgoLQqVMnTeE5MDAQkyZNwvTp03H9+nXMmDED3333HVavXg0AOH36NABg//79ePLkCbZs2QLgv8Lz6wXurBw8eBCPHz/GkSNH8NNPP2Hy5Mn4+OOPYWNjg9OnT+PTTz/FJ598gocPHwIAUlNT4evrCwsLCxw9ehTHjx+Hubk5WrdujZSUFADAvHnzsGrVKqxcuRLHjh3Dy5cvsXXr1hzjSE1NxQ8//ICLFy9i27ZtCAsLw4ABAzK1mzhxIubNm4fg4GAolUoMGjQoV7+XjRs3YsqUKZgxYwaCg4NRsmTJTEXyt+X6TWq1Gi4uLti0aROuXbuGSZMm4ZtvvsHGjRsBAI0bN0aZMmWwZs0arccZGBiY67iJiEh/qdQS9l97hgEBZ9Bk7iH8cfo+OtV0wcGvmmDtYB+0ruIEpYJlFSKi4oJTuxQBzSs6YsPQehi0+iw6LTmOgAF1UM7BXHRYlN+s3VhIJyL9lpIAPL9V+Oct8QFgaJrr5uXLl8ecOXM0t6dNm4YaNWpgxowZmm0rV66Eq6srbt26hbi4OKSlpaFTp05wd3cHAHh5eWnaTp48GfPmzUOnTp0AAB4eHrh27RqWLVuG3r17w97eHgBgZ2cHJ6f/FhE3NTVFhQoV3joPua2tLRYuXAi5XI4KFSpgzpw5SEhIwDfffAMAmDBhAmbNmoVjx46hR48e2LBhA9RqNVasWKGZvzUgIADW1tYICgpCq1atMH/+fEyYMEET89KlS7F3794c43i9sFymTBksXLgQtWvXRlxcHMzN/+uXTJ8+HU2aNAEAfP3112jbti2SkpJgbJzzSL758+fD398f/v7+ANJ/L/v379calZ5drn/99Vf0798/0zENDAwwdepUzW0PDw+cPHkSGzdu1LxR4u/vj4CAAIwdOxYA8PfffyMpKUlzPxERFT3P45Kx4ewDrDt9H4+iEuFVygqzO1VFu2rOMDHkgsdERMUVC+lFhJeLFbYOq4+BAWfReckJLOvrDZ8ydqLDovxk7Qa8ChMdBRHRu3t+C1jWpPDPO/Qw4Fw91829vb21bl+8eBGHDh3SKgZnuHv3Llq1aoUWLVrAy8sLvr6+aNWqFbp06QIbGxvEx8fj7t278Pf3x5AhQzT7paWlwcrKKsc46tSpgxs3brw13sqVK0Mu/280nKOjI6pUqaK5rVAoYGdnp5lm5eLFi7hz5w4sLCy0jpOUlIS7d+8iOjoaT548gY+Pj+Y+pVKJWrVqZZre5XUhISGYMmUKLl68iFevXkGtVgMA7t+/j0qVKmnaVa1aVfNzyZIlAQARERFwc3PL8XFev34dn376qda2evXq4dChQwDwzrlevHgxVq5cifv37yMxMREpKSlai74OGDAA3377LU6dOoW6deti1apV6NatG8zMzHKMl4iI9IskSQgOf4U1J8Ox+8oTyGUytKvmjL513VHN1Vp0eEREpANYSC9CXGxM8edn9fHpmhD0/e0M5narhvbVnEWHRfnF2g0IPSI6CiKid1fig/Sitojz5sGbBdK4uDi0a9cOs2fPztS2ZMmSUCgU2LdvH06cOIF//vkHixYtwsSJE3H69GmYmqaPhF++fLlWYRqAVvH7fbw5Yl0mk2W5LaOwHRcXB29vbwQGBmY6Vsbo+LyKj4+Hr68vfH19ERgYCHt7e9y/fx++vr6a6WKyijdjRHxGbO8jLi4OQNa5ViiyHj24fv16jBkzBvPmzUO9evVgYWGBH3/8UTPdDgA4ODigXbt2CAgIgIeHB3bv3o2goKD3jpeIiHRDXHIatp5/hMBT4bjxNBal7UwxvnVFdPF2gbUpFxwnIqL/sJBexFiZGGD1oDr4evMljPzjPB6+SsBnTcpqXqiSHrN2B2KfAGnJ6XOmExHpG0PTPI0M1xU1a9bE5s2bUbp0aSiVWXedZDIZGjRogAYNGmDSpElwd3fH1q1bMXr0aDg7O+PevXvo3bu31j6SJCEtLQ2Ghukv0lWqwlk0vGbNmtiwYQMcHBxgaWmZZZuSJUvi9OnTaNy4MYD0Ud0hISGoWbNmlu1v3LiBFy9eYNasWXB1dQUABAcH52vcnp6eOH36NPr166fZdurUKc3Pjo6O2eY6O8ePH0f9+vUxbNgwzba7d+9majd48GD07NkTLi4uKFu2LBo0aPAej4SIiHTBjacxWHsqHFvPPUJiqgotPR0xsa0nGpQtAbmcr5+JiCgznVoV48iRI2jXrh2cnZ0hk8mwbdu2bNt++umnkMlkmD9/fqHFpy8MlXLM61YNI1uUx5w9N/HN1itIU73/SC8SzPrfj7xHPxQbBxFRMTN8+HC8fPkSPXv2xNmzZ3H37l3s3bsXAwcOhEqlwunTpzULYN6/fx9btmxBZGQkPD09AQBTp07FzJkzsXDhQty6dQuXL19GQEAAfvrpJwDpI55NTEw0i5hGR0cDAM6cOYOKFSvi0aNH+fp4evfujRIlSqBDhw44evQoQkNDERQUhJEjR2oWJP3iiy8wa9YsbNu2DTdu3MCwYcMQFRWV7THd3NxgaGiIRYsW4d69e9i+fTt++OGHfI37iy++wMqVKxEQEIBbt25h8uTJuHr1qlabt+X6TeXLl0dwcDD27t2LW7du4bvvvsPZs2cztfP19YWlpSWmTZuGgQMH5uvjIiKiwpOSpsZfFx6h29KTaD3/KPZefQb/hh44Nr45lvWrhUbl7VlEJyKibOlUIT0+Ph7VqlXD4sWLc2y3detWnDp1Cs7OnLYkOzKZDKM//ABzulTFpuAH8F8djLjkNNFh0fvIKKRHhYuNg4iomHF2dsbx48ehUqnQqlUreHl5YdSoUbC2toZcLoelpSWOHDmCjz76CB988AG+/fZbzJs3D23atAGQPpp5xYoVCAgIgJeXF5o0aYJVq1bBw8MDQPr84wsXLsSvv/4KZ2dndOjQAQCQkJCAmzdvIjU1NV8fj6mpKY4cOQI3Nzd06tQJnp6e8Pf3R1JSkmaE+ldffYW+ffuif//+milPOnbsmO0x7e3tsWrVKmzatAmVKlXCrFmzMHfu3HyNu3v37vjuu+8wbtw4eHt7Izw8HJ999plWm7fl+k2ffPIJOnXqhO7du8PHxwcvXrzQGp2eQS6XY8CAAVCpVFoj4omISD88fJWAH/feQP1ZB/DF+guQy4Gfe9XA8fHNMbpVBThbm4gOkYiI9IBMymnVKIFkMhm2bt0KPz8/re2PHj2Cj48P9u7di7Zt22LUqFEYNWpUro8bExMDKysrREdHZ/tx5qLm2O3n+GxtCFxtTREwsDYcLY3zfAy1Wo2IiAg4ODjk25yu9HZaeVenAdMdgbY/AbU4Gq6g8TkvBvMuRkHkPSkpCaGhofDw8ICxcd7/7xQHGVO7KJVKTsFWyN4l9/7+/oiMjMT27dtzbJfTc7849kNzi7l5N/y/KQbzLkZe865WSzhyOxJrT4Xj4I0ImBkq0almKfSp647yjhZv3Z/+w+e8GMy7GMy7GCLznpd+qF7Nka5Wq9G3b1+MHTsWlStXztU+ycnJSE5O1tyOiYnRHCs/FrbSB/XL2mLDJ3XhvzoYfouP47f+tVDRKW8dB7VaDUmSik3OdIVW3uVKyCxKAq/CIfH3UOD4nBeDeRejIPKeccyML8paRm6Yo8KX29xHR0fj8uXLWLduHf7666+3ts94zmfV1+S1jYio4L2MT8Gm4AcIPH0f918mwLOkJab5eaFDdWeYGelVCYSIiHSMXv0XmT17NpRKJUaOHJnrfWbOnImpU6dm2h4ZGYmkpKT8DE+n2cqBX7uWx1fb7qDr0hOY+XFZ1HHL/WgftVqN6OhoSJLEd+QK0Zt5tzVzhurZbURHRIgOrcjjc14M5l2Mgsh7amoq1Go10tLSkJbGqcWyIkmSZoFRjkgHqlWrhvv372d53+LFi9GrV698O1dect+hQwecPXsWQ4cORbNmzd76fE5LS4NarcaLFy9gYGCgdV9sbOz7BU5ERFmSJAnnH0Rh7clw7Lj8BJCAtlVL4n/dq6OmmzX/zxIRUb7Qm0J6SEgIFixYgHPnzuXpn+CECRMwevRoze2YmBi4urrC3t6+2H1s1MEB2DzcCZ//cR6jt93BjI5V0MXbJVf7qtVqyGQy2Nvbs7hViN7Mu8y+LAxe3oWRg4Po0Io8PufFYN7FKIi8JyUlITY2FkqlEkql3nQ3hHiz2Fpc7dq1K9v54B0dHQvkeZSb3AcFBeXpmEqlEnK5HHZ2dpmmduE0R0RE+SshJQ1/XXiMtafCcfVxDFxtTTD6ww/Q1dsFduZGosMjIqIiRm9e2R49ehQRERFwc3PTbFOpVPjqq68wf/58hIWFZbmfkZERjIwy/wOVy+XFskhjaWKIFf1rY9JfVzBu82U8jErCly3L5+rNCZlMVmzzJpJW3m3cgXuHIOPvoFDwOS8G8y5GfuddLpdDJpNpvigzSZI0uWGOgNKlSxfauQoy9xnP+az+nnhdIyLKH3ci4rD2VDg2n3uIuOQ0NK/ggDGtKqDxB/ZQyPk/lYiICobeFNL79u2Lli1bam3z9fVF3759MXAgF17MCwOFHDM6esHFxhQ/7r2Jh68SMKtTVRgq+eJO51m7AXHPgNREwIAryxOR7uPc31Tc8DlPRFQw1GoJB2+/wt/bQ3Hy3kvYmRmiT1139KrjBldbU9HhERFRMaBThfS4uDjcuXNHczs0NBQXLlyAra0t3NzcYGdnp9XewMAATk5OqFChQmGHqvdkMhmGNysHFxsTjN10CU+jk7CkjzesTPjxcp1m7Z7+PfohUKK82FiIiHKQMWVGQkICTEz4xh8VHykpKQAAhUIhOBIioqJlwYHbWHToHrzdbbCgR3W0ruIEIyWvtUREVHh0qpAeHByMZs2aaW5nzG3ev39/rFq1SlBURVuH6qXgZGmMoWtC0HXpCawcUBsuNnw3X2dZ/zu1UVQ4C+lEpNMUCgWsra0R8e/iyKamppy+5A2SJCEtLQ1KpZK5KWQFlXu1Wo3IyEiYmppybQAionx05VE0fjl8D/4+JTGxQ3VOlUVERELoVA+/adOmefo4bHbzolPe+JSxw+bP6mNAwBl0/OUEAgbURpVSVqLDoqxYlgJkCiDqvuhIiIjeysnJCQA0xXTSJkkS1Gq1Zj55KjwFmXu5XA43Nzf+TomI8kmqSo1xf15CeQdzDKjjJDocIiIqxnSqkE7ilHMwx9ZhDTB49Vl0+/Ukfu5VA80rOooOi96kUKYX01+Fi46EiOitZDIZSpYsCQcHB6SmpooOR+eo1Wq8ePECdnZ2HFlXyAoy94aGhvx9EhHlo18P38XNZ7HY8lk9GCiSRYdDRETFGAvppGFvYYQ/htbFF+svYPDqYEztUAV967qLDoveZO3GEelEpFcUCgXni86CWq2GgYEBjI2NWXgtZMw9EZF+uPUsFgsP3MHQxmXgVcqKn3IjIiKh+MqBtJgaKrG0jzf61SuN77Zdwcxd16FW5366HSoENu4spBMRERERUZGmUksY9+cluNia4IsWXB+KiIjE44h0ykQhl2FK+8pwtTXFtJ3X8DAqEXM7e4kOizJYuwG394mOgoiIiIiIqMAEHA/FxYdR+PPTejA2UECtVosOiYiIijmOSKds+Tf0wJLeNbH/2jP0WXkW0YlpokMiIL2QHh8BpCSIjoSIiIiIiCjfhT6Px497b2JgfQ94u9uKDoeIiAgAC+n0Fq2rlMQfQ+siNDIOo7bdRqqKowCEs3ZL/x79QGwcRERERERE+UytljB+8yU4WhpjjO8HosMhIiLSYCGd3qqmmw1+G1ALtyISsPjQXdHhkPW/C8BynnQiIiIiIipiAk+H40zoS8zq7AVTQ85GS0REuoOFdMqVai7WGFCnJBYH3cXFB1GiwyneLEoCciUQFS46EiIiIiIionzz4GUCZu6+gd4+bqhftoTocIiIiLSwkE65NrBOSXg6WWD0xgtISlWJDqf4UigBy1IckU5EREREREWGJEn4ZutlWJsY4Os2FUWHQ0RElAkL6ZRrSoUM87pWxYNXifhx703R4RRv1m7AK45IJyIiIiKiomFT8EMcvf0cMzp5wcLYQHQ4REREmbCQTnlS3tECY1tVwG/HQnHy7gvR4RRf1u4ckU5EREREREXC0+gk/LDzGjrXdEHTCg6iwyEiIsoSC+mUZ4MaeqCOhy3GbLqI2KRU0eEUTzYspBMRERERkf6TJAnfbrsMYwMFvvvYU3Q4RERE2WIhnfJMIZdhXtdqiEpIwbQd10WHUzxZuwEJz4GUeNGREBERERERvbPtFx9j//UITPOrAmtTQ9HhEBERZYuFdHonrram+PbjStgQ/AAHrj8THU7xY+2W/p2j0omIiIiISE89j0vGlO1X8XHVkvCt7CQ6HCIiohyxkE7vrEdtVzStYI/xmy/jZXyK6HCKFxbSiYiIiIhIz03efhUymQxT21cWHQoREdFbsZBO70wmk2FO56pIU6vx3bYrkCRJdEjFh0VJQG7AQjoREREREemlPVeeYOelJ5jSvjLszI1Eh0NERPRWLKTTe3GwNMYPHapg5+Un2H7xsehwig+5ArByAaLCRUdCREREeuzIkSNo164dnJ2dIZPJsG3btkxtrl+/jvbt28PKygpmZmaoXbs27t//7838pKQkDB8+HHZ2djA3N0fnzp3x7Bmn/iOi7EUlpODbbVfxYSVHtKtaUnQ4REREucJCOr23dtWc8XHVkpj011U8jU4SHU7xYe3GEelERET0XuLj41GtWjUsXrw4y/vv3r2Lhg0bomLFiggKCsKlS5fw3XffwdjYWNPmyy+/xN9//41Nmzbh8OHDePz4MTp16lRYD4GI9ND3O64hOU2FaX5VIJPJRIdDRESUK0rRAVDR8EOHKmg1/wjGb76EVQNrszNUGKzdgKeXRUdBREREeqxNmzZo06ZNtvdPnDgRH330EebMmaPZVrZsWc3P0dHR+O2337Bu3To0b94cABAQEABPT0+cOnUKdevWLbjgiUgvHboRgS3nHmFOl6pwtDR++w5EREQ6goV0yhc2ZoaY07kqBq46i3Vn7qO3j7vokIo+a3fgxk7RURAREVERpVarsXPnTowbNw6+vr44f/48PDw8MGHCBPj5+QEAQkJCkJqaipYtW2r2q1ixItzc3HDy5MlsC+nJyclITk7W3I6JidGcU61WF9yDKmLUajUkSWLOChnz/u5iklLxzdbLaFS+BDrXcM5TDpl3cZh7MZh3MZh3MUTmPS/nZCGd8k2zig7oWccV03deR8NyJeBuZyY6pKLNxh1IfAkkxwJGFqKjISIioiImIiICcXFxmDVrFqZNm4bZs2djz5496NSpEw4dOoQmTZrg6dOnMDQ0hLW1tda+jo6OePr0abbHnjlzJqZOnZppe2RkJJKSOFVgbqnVakRHR0OSJMjlnLWzsDDv727W/nBEJ6RgdCMnREZG5mlf5l0c5l4M5l0M5l0MkXmPjY3NdVsW0ilfTWxbCcfuPMeYTRexfmg9KOSc4qXAWLulf496ADhWEhsLERERFTkZo3M6dOiAL7/8EgBQvXp1nDhxAkuXLkWTJk3e+dgTJkzA6NGjNbdjYmLg6uoKe3t7WFpavl/gxYharYZMJoO9vT1f7Bci5v3dnLj7AtuuPMf37SuhWjnXPO/PvIvD3IvBvIvBvIshMu+vr/3zNiykU74yN1Jibpdq6LH8FFYcvYdPmpR9+070bjSF9HAW0omIiCjflShRAkqlEpUqafczPD09cezYMQCAk5MTUlJSEBUVpTUq/dmzZ3Bycsr22EZGRjAyMsq0XS6X80VrHslkMuZNAOY9bxJS0jBh62X4eNiiT93SkL/jgCvmXRzmXgzmXQzmXQxRec/L+fiMoHznU8YOgxt6YN4/t3Dzae4/HkF5ZO4EKAyBqPuiIyEiIqIiyNDQELVr18bNmze1tt+6dQvu7unr4Xh7e8PAwAAHDhzQ3H/z5k3cv38f9erVK9R4iUh3/bj3JiJjkzG7c9V3LqITERGJxhHpVCC+alUBQTcjMXrjBWwd1gCGSr5nk+/kcsDKlYV0IiIiemdxcXG4c+eO5nZoaCguXLgAW1tbuLm5YezYsejevTsaN26MZs2aYc+ePfj7778RFBQEALCysoK/vz9Gjx4NW1tbWFpa4vPPP0e9evWyXWiUiIqX4LCXWHUiDBM/8kTpElxHi4iI9Berm1QgjA0U+Klbddx8GoufD94WHU7RZe2WPrULERER0TsIDg5GjRo1UKNGDQDA6NGjUaNGDUyaNAkA0LFjRyxduhRz5syBl5cXVqxYgc2bN6Nhw4aaY/zvf//Dxx9/jM6dO6Nx48ZwcnLCli1bhDweItItSakqjPvzEqq5WGNgAw/R4RAREb0XjkinAuPlYoXPm5fHwoO30dzTEdVdrUWHVPRYuwFPLoiOgoiIiPRU06ZNIUlSjm0GDRqEQYMGZXu/sbExFi9ejMWLF+d3eESk5+bvv42HrxKxs683FJzShYiI9BxHpFOBGtasLCo7W2L0xgtISlWJDqfosXYDXnFEOhERERER6ZaLD6Kw7MhdfNGyPMo7WogOh4iI6L2xkE4FykAhx0/dquHhq0TM3nNDdDhFj7U7kBQFJEWLjoSIiIiIiAgAkJKmxvjNl+BZ0hJDG5cRHQ4REVG+YCGdClw5BwuM862AgONhOHHnuehwihYb9/TvUQ/ExkFERERERPSvX4Lu4E5EHOZ0qQoDBcsORERUNPA/GhWKQQ084ONhi7F/XkJMUqrocIoOa7f071H3xcZBREREREQE4PqTGPx88A6GNS2Lys5WosMhIiLKNyykU6GQy2WY27UaohNT8cPf10SHU3SYOQAKIyCK86QTEREREZFYaSo1xv15CWXszTC8eTnR4RAREf2fvfsOj6pM3zj+nUlPSDLpIZBJ6J1QpSsISlEUBBFFAXtDVPxZcG24KspaEOta0RXE3kBRRJTeCb0TkhAISQjpPTO/P4LZRUCSzCQn5f5cF9cw55w5554nA8k8eed9nUqNdKkxkYHePH55O77YdIQlu44bHad+MJvBEqkR6SIiIiIiYrh3V8Sx82gms8bG4OHqYnQcERERp1IjXWrUuB6RDG4byvSvt3Eip9DoOPWDJUqNdBERERERMdSBlBnnTCcAAJO2SURBVBxe+XUftwxoTpdIi9FxREREnE6NdKlRJpOJmWM6UWqz89i3O7Db7UZHqvssVk3tIiIiIiIihim12Xnoy600sXgx7ZLWRscRERGpFmqkS40L9fXkmVGd+GlHMt/FHjU6Tt1nsWpEuoiIiIiIGOaj1YfZnJDBC2M64+mmKV1ERKR+UiNdDHFZ58ZcERPBE9/t4FhmvtFx6jaLFQoyIT/D6CQiIiIiItLAxJ/IZdbPe5jUJ4oLmgUaHUdERKTaqJEuhnn6yg54ubvw0JfbNMWLIyxRZbcalS4iIiIiIjXIbrfzyFfbCfLx4KFhbY2OIyIiUq3USBfDWLzdeWFMZ1bsT+OTdWoCV1mAGukiIiIiIlLzPl2fyJpDJ3hhTGd8PFyNjiMiIlKt1EgXQw1sE8p1vaw8t2g3h9NyjY5TN/mEgKunGukiIiIiIlJjjmbk89yPuxnfM5L+rYKNjiMiIlLt1EgXw/1jRDtCfD144IutlNo0xUulmUynFhyNNzqJiIiIiIg0AHa7nUe/2Y6PhwuPXtbO6DgiIiI1Qo10MZyPhysvjYthc8JJ3ll+yOg4dZPFqhHpIiIiIiJSI77enMTve1N5bnQn/DzdjI4jIiJSI9RIl1qhZ3Qgtw1ozitL9rEnOcvoOHWPJUqNdBERERERqXYpWQXM+GEno7pEMLhdmNFxREREaowa6VJr3H9Ja6KDvbn/s60UldiMjlO3/Dki3a6pcUREREREpHrY7XYe+3YH7q5mnhzZweg4IiIiNUqNdKk1PN1ceHlcF/Yfz2bO0v1Gx6lbLFYozIKCDKOTiIiIiIhIPbVo+zF+2XWcp6/sSICPu9FxREREapQa6VKrdGziz72DW/Hm7wfYnHDS6Dh1hyWq7PakFhwVERERERHnS88t4snvdjK8YzgjOjU2Oo6IiEiNq1WN9OXLlzNy5EgiIiIwmUx8++235fuKi4t5+OGH6dSpEz4+PkRERDBx4kSOHj1qXGCpFncObEGnphb+7/Ot5BeVGh2nbrBYy241T7qIiIiIiFSDGT/spNRuZ8aVmtJFREQaplrVSM/NzSUmJoY33njjjH15eXls3ryZxx9/nM2bN/P111+zd+9errjiCgOSSnVydTHz0tUxJGXk88LiPUbHqRt8gsHNW410ERERERFxuiW7jvNd7FGeHNmeUF9Po+OIiIgYwtXoAP9r+PDhDB8+/Kz7/P39WbJkyWnbXn/9dS644AISEhKwWq01EVFqSMvQRjwyvC0zftjFJe3D6Ncy2OhItZvJ9N8FR0VERERERJwkM7+Yf3yznUFtQhjVpYnRcURERAxTq0akV1ZmZiYmkwmLxWJ0FKkGk/pE06d5EA9+sZWsgmKj49R+FitkaI50ERERERFxnmcX7SK/qJTnruqEyWQyOo6IiIhhatWI9MooKCjg4Ycf5tprr8XPz++cxxUWFlJYWFh+PysrCwCbzYbNZqv2nPWFzWbDbrfXeM1mjenE8DkrmPH9Tv41tnONXrs2qEzdTf6RkLAau17XTmHUa76hU92NobobQ3U3jlG119daROqa5ftS+XzjEWZe1YnG/l5GxxERETFUnWykFxcXM27cOOx2O2+99dbfHjtz5kxmzJhxxvbU1FQKCgqqK2K9Y7PZyMzMxG63YzbX3AcZ3ID7LmzKM0viuSDCk4taWmrs2rVBZeru7RpEo5PxpBw/XjbVizjEqNd8Q6e6G0N1N4bqbhyjap+dnV1j1xIRcVROYQnTv95Ov5ZBjO8ZaXQcERERw9W5RvqfTfT4+Hh+++23vx2NDjB9+nSmTZtWfj8rK4vIyEhCQkLO+1j5L5vNhslkIiQkpMbf7N8YEsLaI/nMWpbIoM5RBDfyqNHrG6lSdW/aHvPaPEJ93cA7sGYC1mNGvuYbMtXdGKq7MVR34xhVe09PLdAnInXHCz/tIT23iAW39daULiIiItSxRvqfTfT9+/ezbNkygoKCzvsYDw8PPDzObLyazWa9aa0kk8lkWN1mXtWZobOX8/h3O3n7+u4N6ge5Ctc9IAoAc1YiNNLirM5g5Gu+IVPdjaG6G0N1N44RtdfXWUTqirWHTvCftfE8NbI9kYHeRscRERGpFWrVT/M5OTnExsYSGxsLQFxcHLGxsSQkJFBcXMzYsWPZuHEj8+bNo7S0lOTkZJKTkykqKjI2uFS7EF8Pnh3VkZ93HuebLUlGx6mdLGWNdE5qwVEREREREama/KJSHv5qGz2iApjYJ9roOCIiIrVGrRqRvnHjRgYNGlR+/88pWSZNmsRTTz3F999/D0CXLl1Oe9yyZcsYOHBgTcUUgwzv1JjRXZvw5Pc76d08iAiLFrs5jXcguPlARoLRSUREREREpI56eclejmUW8MHknpjNDeeTwCIiIudTqxrpAwcOxG63n3P/3+2ThuGpKzqw5uAJHvpyGx/fdIF+sPtfJlPZ9C5qpIuIiIiISBVsTjjJ+yvjeGhYW1qENDI6joiISK1Sq6Z2ETkffy83Zo3tzMoDaXyyTlOYnMFiVSNdREREREQqrbCklIe+3EbHJv7c0r+Z0XFERERqHTXSpc65sHUIN/SO4rkfdxOXlmt0nNrFYoUM/YJBREREREQq57WlB4g/kcussZ1xdVGrQERE5K/03VHqpOkj2hLu58kDn8dSUmozOk7t8eeIdE2DJCIiIiIiFbQjKZO3/jjIlEGtaBvuZ3QcERGRWkmNdKmTvN1deWlcDLGJGfx7+SGj49QeligozoO8E0YnERERERGROqC41MaDX26jVWgj7hzYwug4IiIitZYa6VJndY8K5LYLWzD7133sOppldJzawWItu9X0LiIiIiIiUgFv/36Qfcez+dfYGNxd1SIQERE5F32XlDrt/kta0SKkEdM+j6WwpNToOMYrb6RrwVEREREREfl7+45nM+e3/dx+YXM6NfU3Oo6IiEitpka61Gkeri68PK4LB1NzePXX/UbHMZ5XALj7wkmNSBcRERERkXMrtdl58MttWAO9mTq4ldFxREREaj010qXOax/hx31DWvP2HwfZFJ9udBxjmUz/XXBURERERETkHD5YGce2IxnMGtsZTzcXo+OIiIjUemqkS71w+4XNiYm08MDnW8krKjE6jrECotRIFxERERGRc4pLy+XFX/ZyY99mdI8KNDqOiIhInaBGutQLri5mXro6huSsAp7/aY/RcYylEekiIiIiInIONpudh7/cRpifJ/83tLXRcUREROoMNdKl3mge0ojpw9vx8Zp4VuxPNTqOcf5spNvtRicREREREZFa5pN18aw/nM7zYzrh7e5qdBwREZE6Q410qVdu6B1Fv5ZBPPjFNjLzi42OYwyLFUryIbcB/zJBRERERETOkJiex/M/7WFCLyt9WwQbHUdERKROcfjXz3FxcaxYsYL4+Hjy8vIICQmha9eu9OnTB09PT2dkFKkws9nEv8bGMHT2cmZ8v5OXr+lidKSaZ4kqu81IgEahxmYREREREZFa4/XfDtDIw5VHhrc1OoqIiEidU+VG+rx583j11VfZuHEjYWFhRERE4OXlRXp6OgcPHsTT05MJEybw8MMPExUV5czMIn8rwuLFUyM78MAXW7m0QxjDOjY2OlLNsljLbjPioWkPY7OIiIiIiEitkFNYwg/bjnL7hS3w9XQzOo6IiEidU6VGeteuXXF3d2fy5Ml89dVXREZGnra/sLCQNWvWsGDBAnr06MGbb77J1Vdf7ZTAIhVxVbcm/LwzmUe/2UH3qEBCfD2MjlRzvCzg4a8FR0VEREREpNzCrUfJLy7l6h5NjY4iIiJSJ1VpjvTnn3+edevWcdddd53RRAfw8PBg4MCBvP322+zZs4fmzZs7HFSkMkwmE89d1QkT8Og327E3tIU3LVY4GW90ChERERERqSUWbEjkotYhRFi8jI4iIiJSJ1WpkT506NAKHxsUFET37t2rchkRhwQ38uDZ0Z1Ysus4X21OMjpOzbJYNSJdREREREQA2JOcRWxiBuN7njkQTkRERCqmSo30/7V582a2b99efv+7775j1KhRPProoxQVFTl6ehGHDOsYzphuTXniux3sPpZldJyaExClRrqIiIic1/Llyxk5ciQRERGYTCa+/fbb0/ZPnjwZk8l02p9hw4addkx6ejoTJkzAz88Pi8XCzTffTE5OTg0+CxE5n882JBLk487FbcOMjiIiIlJnOdxIv/3229m3bx8Ahw4dYvz48Xh7e/PFF1/w0EMPORxQxFH/HNWB6CAfbvloI2k5hUbHqRkWK2QmQkOb0kZEREQqJTc3l5iYGN54441zHjNs2DCOHTtW/ufTTz89bf+ECRPYuXMnS5YsYeHChSxfvpzbbrutuqOLSAUVFJfyzZYkxnRvirurwy0AERGRBsvh76L79u2jS5cuAHzxxRdceOGFzJ8/n7lz5/LVV185enoRh3m7u/LepB4Ulti485NNFJaUGh2p+lmsUFIAOceNTiIiIiK12PDhw3nmmWcYPXr0OY/x8PAgPDy8/E9AQED5vt27d7N48WLee+89evXqRf/+/XnttddYsGABR48erYmnICLn8cuu42TkFTOuh6Z1ERERcYSroyew2+3YbDYAfv31Vy6//HIAIiMjSUtLc/T0Ik4RYfHinYndGf/OWh77ZgezxnbGZDIZHav6WKxltxkJ4BtubBYRERGp037//XdCQ0MJCAjg4osv5plnniEoKAiANWvWYLFY6NGjR/nxQ4YMwWw2s27dunM26AsLCyks/O8nBbOyyqbgs9ls5e8t5PxsNttp78ekZtS1ui9Yn0CPqACaB3vXmcxnU9fqXp+o9sZQ3Y2huhvDyLpX5poON9J79OjBM888w5AhQ/jjjz946623AIiLiyMsTPOvSe3RzRrAC2M6cf9nW2kd5sutFzY3OlL1+d9GeuQFxmYRERGROmvYsGFcddVVNGvWjIMHD/Loo48yfPhw1qxZg4uLC8nJyYSGhp72GFdXVwIDA0lOTj7neWfOnMmMGTPO2J6amkpBQYHTn0d9ZbPZyMzMxG63YzZryo6aUpfqnpRZyOqDJ3j80mhSUlKMjuOQulT3+ka1N4bqbgzV3RhG1j07O7vCxzrcSH/llVe4/vrr+fbbb/nHP/5By5YtAfjyyy/p27evo6cXcarRXZuy73gOz/20m5ahjRjUNvT8D6qLPP3B0wIZ8UYnERERkTps/Pjx5X/v1KkTnTt3pkWLFvz+++8MHjy4yuedPn0606ZNK7+flZVFZGQkISEh+Pn5OZS5IbHZbJhMJkJCQvRmvwbVpbp/snUfjTxcuaZvK7zdHX77b6i6VPf6RrU3hupuDNXdGEbW3dPTs8LHOvydNCYmhu3bt5+x/V//+heurnX7G7XUTw9e2oYDKTnc8+kWvr6rL63DfI2OVD0s1rIR6SIiIiJO0rx5c4KDgzlw4ACDBw8mPDz8jFGuJSUlpKenEx5+7unlPDw88PDwOGO72WzWm9ZKMplMqpsB6kLdS0ptfLkpiSu7RNDI093oOE5RF+peX6n2xlDdjaG6G8Ooulfmeg4na968OSdOnDhje0FBAa1bt3b09CJOZzabeOWaLjQN8OLmjzaQnltkdKTqYbHCSY1IFxEREec5cuQIJ06coHHjxgD06dOHjIwMNm3aVH7Mb7/9hs1mo1evXkbFFBFg+f5UkrMKGN/TanQUERGResHhRvrhw4cpLS09Y3thYSFHjhxx9PQi1aKRhyvvTepBXmEpd36yiaKSeriIhCVKI9JFRETkb+Xk5BAbG0tsbCxQts5RbGwsCQkJ5OTk8OCDD7J27VoOHz7M0qVLufLKK2nZsiVDhw4FoF27dgwbNoxbb72V9evXs2rVKqZMmcL48eOJiIgw8JmJyIL1ibRv7EfHJpouSURExBmqPPfK999/X/73n3/+GX9///L7paWlLF26lGbNmjmWTqQaNQ3w5u0bunPdu2t58vsdPDe6EyaTyehYzhMQBZmJYLOBPo4kIiIiZ7Fx40YGDRpUfv/PecsnTZrEW2+9xbZt2/joo4/IyMggIiKCSy+9lH/+85+nTcsyb948pkyZwuDBgzGbzYwZM4Y5c+bU+HMRkf9KyS5g6Z4UnhzZvn69xxERETFQlRvpo0aNAsrmr5k0adJp+9zc3IiOjuall15yKJxIdesZHchzozvx4JfbaBXqy03969EvfyxWKC2CnOPg19joNCIiIlILDRw4ELvdfs79P//883nPERgYyPz5850ZS0Qc9NWmJFzMJq6MaWJ0FBERkXqjyo10m61sKoxmzZqxYcMGgoODnRZKpCZd3SOS/Sk5PLNoFy1CG3FR6xCjIzmH5dRciBnxaqSLiIiIiDQQdrudzzYkMKJjOP7ebkbHERERqTccnu8hLi5OTXSp8x4e1paBbUKZMn8zB1JyjI7jHOWNdM2TLiIiIiLSUKyLS+fwiTyu0SKjIiIiTlXlEen/a+nSpSxdupSUlJTykep/+uCDD5xxCZFq5WI28er4Lox5azW3fLSBb+/uh8Xb3ehYjvHwBa/AshHpIiIiIiLSIHy2IZHoIG96Nw80OoqIiEi94vCI9BkzZnDppZeydOlS0tLSOHny5Gl/ROoKX0833pvYk8z8Yu6at5niUtv5H1TbWawakS4iIiIi0kBk5hfz4/ZjjOsZqUVGRUREnMzhEelvv/02c+fO5YYbbnBGHhFDWYO8eev67lz/3jpm/LCTZ0Z1MjqSY9RIFxERERFpML6PTaLEZmdst6ZGRxEREal3HB6RXlRURN++fZ2RRaRW6N08iGdGdeSTtQn8Z81ho+M4xmKFk5raRURERESkIViwIZGL24YS6udpdBQREZF6x+FG+i233ML8+fOdkUWk1hh/gZUb+0Xz1A+7WLk/zeg4VRcQDZlHwFZqdBIREREREalGO5Iy2Xk0i/E9I42OIiIiUi85PLVLQUEB77zzDr/++iudO3fGzc3ttP0vv/yyo5cQMcQ/RrTjYGoud83bxLd396N5SCOjI1WexQq2YshOBv8mRqcREREREZFqsmBDAmF+HlzUOsToKCIiIvWSw430bdu20aVLFwB27Nhx2j4tbiJ1mauLmdev68roN1Zxy0cb+eaufvh7u53/gbWJxVp2m5GgRrqIiIiISD2VX1TKd1uOMqlvNK4uDn/wXERERM7C4Ub6smXLnJFDpFby83TjvUk9GfXGKqZ8upkPJ/esWz+Y+p/6WGdGPET1MTaLiIiIiIhUix+3HyO7sIRxPTSti4iISHWpQx1BEWM0C/bhrQndWHPwBM8s2m10nMrxaATeQWUj0kVEREREpF76bEMifVsEYQ3yNjqKiIhIvVWlEelXXXUVc+fOxc/Pj6uuuupvj/3666+rFEykNunbMpinrujAY9/uoFVYIyb0ijI6UsVZospGpIuIiIiISL1zMDWH9YfTeXV8F6OjiIiI1GtVaqT7+/uXz3/u7+/v1EAitdX1vaPYdzybJ7/bSbNgH/q2CDY6UsVYrBqRLiIiIiJST32+IRF/LzeGdgg3OoqIiEi9VqVG+ocffnjWv4vUd09c3p5DqbncNW8z397Vj+hgH6MjnZ/FCsdijU4hIiIiIiJOVlxq46vNRxjdtQmebi5GxxEREanXnDZHempqKitXrmTlypWkpqY667QitYqri5k3rutGgLc7t3y8kayCYqMjnZ/FCplHwFZqdBIREREREXGipbtTSMsp4pqeWmRURESkujncSM/NzeWmm26icePGXHjhhVx44YVERERw8803k5eX54yMIrWKv7cb703qQUpWAVM/3UKpzW50pL8XEA22Esg6anQSERERERFxos82JBATaaFdYz+jo4iIiNR7DjfSp02bxh9//MEPP/xARkYGGRkZfPfdd/zxxx888MADzsgoUuu0CGnEGxO6sWJ/Gs/9uNvoOH/PYi271TzpIiIiIiL1xtGMfP7Yl8p4jUYXERGpEQ430r/66ivef/99hg8fjp+fH35+fowYMYJ3332XL7/80hkZRWqlAa1CePyydry/Mo7PNtTiJrX/qR+s1UgXEREREak3vtx0BE83F0bGRBgdRUREpEGo0mKj/ysvL4+wsLAztoeGhmpqF6n3JvWNZl9KDo99u4PoIB96NQ8yOtKZ3L3BJwQy4o1OIiIiIiIiTmCz2flsQyKXd25MIw+H39aLiIhIBTg8Ir1Pnz48+eSTFBQUlG/Lz89nxowZ9OnTp1LnWr58OSNHjiQiIgKTycS333572n673c4TTzxB48aN8fLyYsiQIezfv9/RpyBSZSaTiRlXdKB7VAB3zttMYnot/eWRxaoR6SIiIiIi9cSqg2kkZeRzTU+r0VFEREQaDIcb6a+++iqrVq2iadOmDB48mMGDBxMZGcnq1at59dVXK3Wu3NxcYmJieOONN866f9asWcyZM4e3336bdevW4ePjw9ChQ09r4ovUNDcXM29N6I6vpyu3fLSRnMISoyOdyRKlRrqIiIiISD2xYEMiLUMb0c1qMTqKiIhIg+HwZ8A6duzI/v37mTdvHnv27AHg2muvZcKECXh5eVXqXMOHD2f48OFn3We325k9ezaPPfYYV155JQAff/wxYWFhfPvtt4wfP96xJyLigAAfd96f1IPRb6zm3k+38M7EHriYTUbH+i+LFZI2Gp1CREREREQclJ5bxC87k3l4WFtMplr0nkNERKSec8pkat7e3tx6663OONU5xcXFkZyczJAhQ8q3+fv706tXL9asWaNGuhiuZagvc67rys1zNzDr5z1MH97O6Ej/ZbFCZhKUloCL5lAUEREREamrvt58BIDRXZsYnERERKRhcUpHbe/evbz22mvs3r0bgHbt2jFlyhTatm3rjNMDkJycDHDGwqZhYWHl+86msLCQwsLC8vtZWVkA2Gw2bDab0/LVdzabDbvdrpqdx0Wtgpk+vC3P/riHliE+jOnW1KHzOa3u/pGY7aXYMo+UNdXlvPSaN4bqbgzV3Riqu3GMqr2+1iLiKLu9bJHRS9uHE9TIw+g4IiIiDYrDjfSvvvqK8ePH06NHj/LFRdeuXUunTp1YsGABY8aMcTikI2bOnMmMGTPO2J6amqq51SvBZrORmZmJ3W7HbHZ4av167fJW3mzrEMSj3+zA31xE54hGVT6Xs+ruYmtECJARt5WiJp5VPk9Dote8MVR3Y6juxlDdjWNU7bOzs2vsWiJSP21JzGB/Sg6PX97e6CgiIiINjsON9Iceeojp06fz9NNPn7b9ySef5KGHHnJaIz08PByA48eP07hx4/Ltx48fp0uXLud83PTp05k2bVr5/aysLCIjIwkJCcHPz88p2RoCm82GyWQiJCREb/Yr4F/XhJD8wXqmL4rj27v60iSgcusF/MlpdQ/wBcBiyobQ0KqfpwHRa94YqrsxVHdjqO7GMar2np76ZbaIOOaz9Yk0sXjRv2Ww0VFEREQaHIcb6ceOHWPixIlnbL/++uv517/+5ejpyzVr1ozw8HCWLl1a3jjPyspi3bp13Hnnned8nIeHBx4eZ37kzWw2601rJZlMJtWtgjzdzbx9fXeufGMVt/5nE1/d2Rcfj6r9c3NK3T18oFEY5sxE0NevwvSaN4bqbgzV3Riqu3GMqL2+ziLiiJzCEn7YdpTbL2yB2axFRkVERGqawz/NDxw4kBUrVpyxfeXKlQwYMKBS58rJySE2NpbY2FigbIHR2NhYEhISMJlM3HfffTzzzDN8//33bN++nYkTJxIREcGoUaMcfRoiThfUyIP3J/UkMT2P+z+LxWazGxvIYoWMeGMziIiIiIhIlSzcepT84lKu7uHYOkwiIiJSNQ6PSL/iiit4+OGH2bRpE7179wbK5kj/4osvmDFjBt9///1px/6djRs3MmjQoPL7f07JMmnSJObOnctDDz1Ebm4ut912GxkZGfTv35/FixfrY7JSa7UJ92XOtV255eONvLRkLw8Odd4CvJVmsUJGgnHXFxERERGRKluwIZGLWocQYanatJEiIiLiGIcb6XfddRcAb775Jm+++eZZ90HZx2dLS0v/9lwDBw7Ebj/3qF2TycTTTz99xnzsIrXZ4HZhPDKsLTN/2kOrUF9GdW1iTBBLFCSuN+baIiIiIiJSZXuSs4hNzODt67sZHUVERKTBcriRbrPZnJFDpF677cLm7D2ezUNfbSMqyJuu1oCaD2GxQlYSlBaDi1vNX19ERERERKrksw2JBPm4c3HbMKOjiIiINFha8UikBphMJmZe1YlOTfy59eNNHM3Ir/kQFivYbWXNdBERERERqRMKikv5ZksSY7o3xd1Vb+FFRESMou/CIjXEw9WFt6/vjoermVs/3kheUUnNBrBEld2e1IKjIiIiIiJ1xS+7jpORV8y4HpFGRxEREWnQ1EgXqUEhvh68O7EHcWm5PPD5Vmy2c68J4HSWUz94a8FREREREZE647MNCfSMDqBlaCOjo4iIiDRoaqSL1LD2EX68ck0XftqRzOyl+2vuwq4e4NtYjXQRERERkToi4UQeqw6c4JqeVqOjiIiINHhqpIsYYGiHcB4c2oY5S/fzw9ajNXdhi1WNdBERERGROuKLTYn4ergyolO40VFEREQaPKc00g8ePMhjjz3GtddeS0pKCgA//fQTO3fudMbpReqluwa2YFSXCP7vi61sO5JRMxe1WCFDc6SLiIiIiNR2JaU2vth4hCu6RODt7mp0HBERkQbP4Ub6H3/8QadOnVi3bh1ff/01OTk5AGzdupUnn3zS4YAi9ZXJZOL5MZ1p29iPWz/eSHJmQfVfVCPSRURERETqhOX7U0nOKmC8pnURERGpFRxupD/yyCM888wzLFmyBHd39/LtF198MWvXrnX09CL1mqebC+/e0B2zycRt/9lIflFp9V7QEgVZR6GkqHqvIyIiIiIiDlmwPpH2jf3o2MTP6CgiIiKCExrp27dvZ/To0WdsDw0NJS0tzdHTi9R7oX6evDuxB/uOZ/Pgl1ux2+3VdzGLFbBD1pHqu4aIiIiIiDgkJbuApXtSGH9BJCaTyeg4IiIighMa6RaLhWPHjp2xfcuWLTRp0sTR04s0CB2b+PPyuC4s3HaM1347UH0Xspz6WKimdxERERERqbW+2pSEq9nElTF6Ty0iIlJbONxIHz9+PA8//DDJycmYTCZsNhurVq3i//7v/5g4caIzMoo0CCM6Neb+Ia15eck+ftp+5i+nnMK/KWCCk1pwVERERESkNrLb7Xy2IYHhHcPx93YzOo6IiIic4nAj/bnnnqNt27ZERkaSk5ND+/btufDCC+nbty+PPfaYMzKKNBhTB7fk8s6Nuf/zWHYkZTr/Aq4e4BehEekiIiIiIrXUurh0Dp/I4xotMioiIlKrONxId3d359133+XgwYMsXLiQTz75hD179vCf//wHFxcXZ2QUaTBMJhMvXh1D6zBfbv14IylZBc6/iMWqRrqIiIiISC312YZEooO86d080OgoIiIi8j8cbqT/yWq1MmLECMaNG0erVq2cdVqRBsfTzYV3buhBqc3OHfM2U1hic+4F1EgXEREREamVMvOL+XH7Mcb11CKjIiIitY1rVR40bdq0Ch/78ssvV+USIg1auL8n707swbh/r2Hmr/G8OTHceSe3WCFuufPOJyIiIiIiTvF9bBIlNjtjuzU1OoqIiIj8RZUa6Vu2bKnQcfoNukjVxURaePrKDjz81Xa2J2USExngnBNbrJB9DEoKy+ZMFxERERGRWmHBhkQubhtKqJ+n0VFERETkL6o0tcuyZcsq9Oe3335zdl6RBmV0lwjCfd35aHW8805qiSq7zTzivHOKiIhInbR8+XJGjhxJREQEJpOJb7/99pzH3nHHHZhMJmbPnn3a9vT0dCZMmICfnx8Wi4Wbb76ZnJyc6g0uUg/tSMpk59EsxveMNDqKiIiInIXT5kgHSExMJDEx0ZmnFGnQXF3MjI0JYeG2o6RmFzrnpBZr2W2GE5vzIiIiUifl5uYSExPDG2+88bfHffPNN6xdu5aIiIgz9k2YMIGdO3eyZMkSFi5cyPLly7ntttuqK7JIvbVgQwJhfh5c1DrE6CgiIiJyFg430ktKSnj88cfx9/cnOjqa6Oho/P39eeyxxyguLnZGRpEGbWTHYMxmE5+ud9ICoX5NwGTWgqMiIiLC8OHDeeaZZxg9evQ5j0lKSuKee+5h3rx5uLm5nbZv9+7dLF68mPfee49evXrRv39/XnvtNRYsWMDRo0erO75IvZFfVMp3W45ydfdIXF2cOt5NREREnKRKc6T/r3vuuYevv/6aWbNm0adPHwDWrFnDU089xYkTJ3jrrbccDinSkPl7ujK6SxM+WRvPHRe1wN3VwR+sXd3BNwJOakS6iIiI/D2bzcYNN9zAgw8+SIcOHc7Yv2bNGiwWCz169CjfNmTIEMxmM+vWrTtng76wsJDCwv9+2i4rK6v8ejabzcnPov6y2WzY7XbVrIZVR90XbTtKdmEJY7s30dfzHPR6N45qbwzV3RiquzGMrHtlrulwI33+/PksWLCA4cOHl2/r3LkzkZGRXHvttWqkizjBxD5RfLohkZ92HOPKLk0cP2FAlEaki4iIyHm98MILuLq6MnXq1LPuT05OJjQ09LRtrq6uBAYGkpycfM7zzpw5kxkzZpyxPTU1lYKCAsdCNyA2m43MzEzsdjtms0Yx15TqqPsnaw7RI9IXz5IcUlK0xsDZ6PVuHNXeGKq7MVR3YxhZ9+zs7Aof63Aj3cPDg+jo6DO2N2vWDHd3d0dPLyJAm3Bf+rYIYu7qw85ppFuscOKg4+cRERGRemvTpk28+uqrbN68GZPJ5NRzT58+nWnTppXfz8rKIjIykpCQEPz8/Jx6rfrMZrNhMpkICQnRm/0a5Oy6H0rNITYph9nXxJzxiyn5L73ejaPaG0N1N4bqbgwj6+7p6VnhYx1upE+ZMoV//vOffPjhh3h4eABlH9V89tlnmTJliqOnF5FTJveN5rb/bCI2MYMukRbHTmaxwsFlTsklIiIi9dOKFStISUnBarWWbystLeWBBx5g9uzZHD58mPDwcFJSUk57XElJCenp6YSHh5/z3B4eHuXvHf6X2WzWm9ZKMplMqpsBnFn3LzYl4e/lxrCOjfV1PA+93o2j2htDdTeG6m4Mo+pemes53EjfsmULS5cupWnTpsTExACwdetWioqKGDx4MFdddVX5sV9//bWjlxNpsAa3CyMy0IuPVh+myzVdHDuZxQo5yVCcD25eTsknIiIi9csNN9zAkCFDTts2dOhQbrjhBm688UYA+vTpQ0ZGBps2baJ79+4A/Pbbb9hsNnr16lXjmUXqmuJSG19tPsLork3wdHMxOo6IiIj8DYcb6RaLhTFjxpy2LTIy0tHTishfuJhNTOoTzQuL9zB9RFtCfSv+0ZMzWE6NLMs8AsGtnBNQREREDHXo0CHuuOMOfvnllwo/JicnhwMHDpTfj4uLIzY2lsDAQKxWK0FBQacd7+bmRnh4OG3atAGgXbt2DBs2jFtvvZW3336b4uJipkyZwvjx44mIiHDOExOpx5buTiEtp4hreuo9tIiISG3ncCP9ww8/dEYOEamAq3tE8tIv+5i/LoH7hrSu+oksUWW3GfFqpIuIiNQT2dnZLF26tFKP2bhxI4MGDSq//+e85ZMmTWLu3LkVOse8efOYMmUKgwcPxmw2M2bMGObMmVOpHCIN1WcbEoiJtNCusdYGEBERqe0cbqSLSM3x93JjTPcmfLI2gbsGtsTdtYrzRvk1AZMLZCQ4N6CIiIjUKQMHDsRut1f4+MOHD5+xLTAwkPnz5zsxlUjDcDQjnz/2pfLs6E5GRxEREZEKcHj29hMnTnD33XfTvn17goODCQwMPO2PiDjXpD7RpOUUsmj70aqfxMW1rJmuRrqIiIiIiCG+3HQETzcXRsZoGiQREZG6wOER6TfccAMHDhzg5ptvJiwsDJPJ5IxcInIOrcJ8GdAqmA9XHWZUlyZV/zdnscLJeOeGExERERGR87LZ7Hy2IZHLOzemkYc+KC4iIlIXOPwde8WKFaxcuZKYmBhn5BGRCpjcN5qbP9rIlsQMulkDqnaSgChI3evcYCIiIlJtunbt+re/QM/Ly6vBNCLiiFUH00jKyOeanlajo4iIiEgFOdxIb9u2Lfn5+c7IIiIVNKhNKFFB3sxddbjqjXSLFfYvcW4wERERqTajRo0yOoKIOMmCDYm0DG1EN6vF6CgiIiJSQQ430t98800eeeQRnnjiCTp27Iibm9tp+/38tPq4iLOZzSYm9olm5o+7+cdl7Qjz86z8SSxWyE2B4nxw83J+SBEREXGqJ5980ugIIuIE6blF/LIzmYeHtdXUqCIiInWIw4uNWiwWsrKyuPjiiwkNDSUgIICAgAAsFgsBAVUcKSsi53V1j6a4u5qZt7aK85xbTn2MVAuOioiI1AsFBQW8+OKLRscQkfP4evMRAEZ3bWJwEhEREakMh0ekT5gwATc3N+bPn6/FRkVqkJ+nG2O7N2XeugTuvrglHq4ulTvB/zbSQ9o4P6CIiIg4XWpqKuvWrcPd3Z3Bgwfj4uJCcXExb775JjNnzqSkpIT/+7//MzqmiJyD3V62yOil7cMJauRhdBwRERGpBIcb6Tt27GDLli20aaNGnEhNm9Q3mo/XxLNw6zHGdG9auQf7RoDZFTKqOKJdREREatTKlSu5/PLLycrKwmQy0aNHDz788ENGjRqFq6srTz31FJMmTTI6poj8jS2JGexPyeHxy9sbHUVEREQqyeGpXXr06EFiYqIzsohIJbUIacRFrUOYu/owdru9cg92cQW/JpraRUREpI547LHHGDFiBNu2bWPatGls2LCB0aNH89xzz7Fr1y7uuOMOvLy07olIbfbZ+kSaWLzo3zLY6CgiIiJSSQ430u+55x7uvfde5s6dy6ZNm9i2bdtpf0Skek3uF832pEw2J5ys/IMtVjXSRURE6ojt27fz2GOP0bFjR55++mlMJhOzZs1i7NixRkcTkQrIKSzhh21HGdcjErNZU6KKiIjUNQ5P7XLNNdcAcNNNN5VvM5lM2O12TCYTpaWljl5CRP7GRa1CaBbsw4erDtM9KrByD7ZEQcqu6gkmIiIiTnXy5EmCg8tGsXp5eeHt7U3Hjh0NTiUiFbVw61Hyi0u5ukclp2QUERGRWsHhRnpcXJwzcohIFZnNJib1ieKfi3ZzLDOfxv6V+Eh3QBTsW1x94URERMSpdu3aRXJyMlC2aOHevXvJzc097ZjOnTsbEU1EzmPBhkQuah1ChEVTMImIiNRFDjfSo6KinJFDRBwwpntTXvxlH5+sjefBoW0r/kCLFfLSoCgX3H2qL6CIiIg4xeDBg09bF+Xyyy8/bb8+ESpSO+1JziI2MYO3r+9mdBQRERGpIocb6X/atWsXCQkJFBUVnbb9iiuucNYlROQcfD3dGNu9KfPXJXDPxa3wdHOp2AMt1rLbjEQIrUQDXkRERGpcRT4Jmp2dXQNJRKSyPtuQSHAjdy5uG2Z0FBEREakihxvphw4dYvTo0Wzfvr18bnQoGw0DaESMSA2Z1DeauasP8/3WsgWMKqS8kR6vRrqIiEgtd65PgmZnZ/Ppp5/y/vvvs3HjRv38LVLLFBSX8s2WJMb1iMTd1Wx0HBEREakih7+L33vvvTRr1oyUlBS8vb3ZuXMny5cvp0ePHvz+++9OiCgiFdEs2IdBbUKYu+rwaR/5/lu+jcHsBhkJ1RtOREREnG758uVMmjSJxo0b8+KLLzJo0CDWrl1rdCwR+Ytfdh0nI6+44oNdREREpFZyeET6mjVr+O233wgODsZsNmM2m+nfvz8zZ85k6tSpbNmyxRk5RaQCJvdrxqQP1rPh8EkuaBZ4/geYXcC/admIdBEREan1kpOTmTt3Lu+//z5ZWVmMGzeOwsJCvv32W9q3b290PBE5i882JNAzOoCWoY2MjiIiIiIOcHhEemlpKb6+vgAEBwdz9OhRoOyjp3v37nX09CJSCQNaBtM8xIe5q88/h2o5i1Uj0kVEROqAkSNH0qZNG7Zt28bs2bM5evQor732mtGxRORvJKbnserACa7paTU6ioiIiDjI4RHpHTt2ZOvWrTRr1oxevXoxa9Ys3N3deeedd2jevLkzMopIBZnNJib3jWbGD7tIysinicXr/A+yWOH4juoPJyIiIg756aefmDp1KnfeeSetWrUyOo6IVMDnGxPx9XBlRKdwo6OIiIiIgxwekf7YY49hs9kAePrpp4mLi2PAgAH8+OOPzJkzx+GAf1VaWsrjjz9Os2bN8PLyokWLFvzzn/+s+JzQIvXcmG5N8XZz4ZO1FZyuxRIFJzW1i4iISG23cuVKsrOz6d69O7169eL1118nLS3N6Fgicg4lpTa+2HiEK7pE4O3u8Bg2ERERMZjD382HDh1a/veWLVuyZ88e0tPTCQgIwGQyOXr6M7zwwgu89dZbfPTRR3To0IGNGzdy44034u/vz9SpU51+PZG6xsfDlXE9I/l0fQL3Dm6Fp5vL3z8gIAry06EwGzx8ayakiIiIVFrv3r3p3bs3s2fP5rPPPuODDz5g2rRp2Gw2lixZQmRkZPmUiyJivOX7U0nOKmC8pnURERGpFxwekf5XWVlZLF++vNrmR1+9ejVXXnkll112GdHR0YwdO5ZLL72U9evXV8v1ROqiiX2iyMwv5rvYpPMfbDn1g31GYvWGEhEREafw8fHhpptuYuXKlWzfvp0HHniA559/ntDQUK644gqj44nIKQvWJ9K+sR8dm/gZHUVEREScwOER6ePGjePCCy9kypQp5Ofn06NHDw4fPozdbmfBggWMGTPGGTnL9e3bl3feeYd9+/bRunVrtm7dysqVK3n55ZfPenxhYSGFhYXl97OysgCw2WzlU9LI+dlsNux2u2pWw6pa98gALy5uE8oHqw4ztluTv/90iF9TzIDt5GEIaetQ3vpEr3ljqO7GUN2Nobobx6jaV8f12rRpw6xZs5g5cyY//PADH3zwgdOvISKVl5JdwNI9KTw5sn21fFJbREREap7DjfTly5fzj3/8A4BvvvkGu91ORkYGH330Ec8884zTG+mPPPIIWVlZtG3bFhcXF0pLS3n22WeZMGHCWY+fOXMmM2bMOGN7amoqBQUFTs1Wn9lsNjIzM7Hb7ZjNTv8gg5yDI3W/sr0/U79OYfHmQ3SP/JuPedvNhJndyEncSZ6lm4OJ6w+95o2huhtDdTeG6m4co2qfnZ1dbed2cXFh1KhRjBo1qtquISIV99WmJFzNJq6MaWJ0FBEREXEShxvpmZmZBAYGArB48WLGjBmDt7c3l112GQ8++KDDAf/q888/Z968ecyfP58OHToQGxvLfffdR0REBJMmTTrj+OnTpzNt2rTy+1lZWURGRhISEoKfnz5iV1E2mw2TyURISIje7NcgR+p+WUgIc1Yc5bvdmQzv3uLvD7ZE4lt6kkahoQ6krV/0mjeG6m4M1d0YqrtxjKq9p6dnjV1LRIxjt9v5bEMCwzuG4+/tZnQcERERcRKHG+mRkZGsWbOGwMBAFi9ezIIFCwA4efJktbxZePDBB3nkkUcYP348AJ06dSI+Pp6ZM2eetZHu4eGBh4fHGdvNZrPetFaSyWRS3QzgSN0n92vGE9/tICmjgMhA73MfaImCzARM+tqeRq95Y6juxlDdjaG6G8eI2uvrLNIwrItL5/CJPGZe1dnoKCIiIuJEDv80f9999zFhwgSaNm1KREQEAwcOBMqmfOnUqZOjpz9DXl7eGW9CXFxcNL+oyFlc1a0JPh6ufLI2/u8PtFghI6FmQomIiIiI1GOfbUgkOsib3s0DjY4iIiIiTuRwI/2uu+5i7dq1fPDBB6xcubK8yd28eXOeeeYZhwP+1ciRI3n22WdZtGgRhw8f5ptvvuHll19m9OjRTr+WSF3n7e7K+J6RfLo+gbyiknMfqEa6iIiIiIjDMvOL+XH7Mcb1jNQioyIiIvWMw1O7AHTv3p3u3buftu2yyy5zxqnP8Nprr/H4449z1113kZKSQkREBLfffjtPPPFEtVxPpK6b2Cea91bG8e2Wo1zXy3r2gyxRkH8SCrLAU2sHiIiIiIhUxfexSZTY7Izt1tToKCIiIuJkTmmk1yRfX19mz57N7NmzjY4iUidEBnozpF0Yc1fHce0F5xgZExBVdpuRAOEdazagiIiIiEg9sWBDIhe3DSXUT4sLi4iI1Dda8UikAbixXzT7juew5uCJsx9gOTVSXdO7iIiIiIhUyY6kTHYezWJ8z0ijo4iIiEg1UCNdpAHo0zyINmG+fLj68NkP8AkFFw810kVEREREqmjBhgTC/Dy4qHWI0VFERESkGqiRLtIAmEwmJveL5tfdx0lMzzvzALMZLJGQEV/z4URERERE6rj8olK+23KUq7tH4uqit9kiIiL1kVPmSM/IyGD9+vWkpKRgs9lO2zdx4kRnXEJEHDSqSxOe/2kPH60+zGOXtz/zAItVI9JFRERERKrgx+3HyC4sYVwPTesiIiJSXzncSP/hhx+YMGECOTk5+Pn5nbaQoclkUiNdpJbwcndh/AWRzF+XwP2XtMbH4y///C1RkLTRmHAiIiIiInXYZxsS6dsiCGuQt9FRREREpJo4/JmzBx54gJtuuomcnBwyMjI4efJk+Z/09HRnZBQRJ7mhdxS5hSV8vSXpzJ0akS4iIiIiUmkHU3NYfzida7TIqIiISL3mcCM9KSmJqVOn4u2t37yL1HZNA7y5tH04c1fFYbfbT99psUJBJuRnGJJNRERERKQu+nxDIv5ebgztEG50FBEREalGDjfShw4dysaNmg5CpK6Y3C+ag6m5rDyQdvoOS1TZrUali4iIiIhUSHGpja82H2F01yZ4urkYHUdERESqkcNzpF922WU8+OCD7Nq1i06dOuHm5nba/iuuuMLRS4iIE/VqFkjbcF/mrjrMgFYh/90R8D+N9MadjQknIiIiIlKHLN2dQlpOkaZ1ERERaQAcbqTfeuutADz99NNn7DOZTJSWljp6CRFxIpPJxI39onnk6+0cTsslOtinbIdPCLh6akS6iIiIiEgFfbYhgZhIC+0a+xkdRURERKqZw1O72Gy2c/5RE12kdrqySxP8vdz4eE38fzeaTFpwVERERESkgo5m5PPHvlTGazS6iIhIg+BwI11E6h5PNxeuvcDKFxsTySks+e8OixUy4s/9QBERERERAeCrzUl4urkwMibC6CgiIiJSA6o0tcucOXO47bbb8PT0ZM6cOX977NSpU6sUTESq1w29o3hn+SG+3nyEiX2iyzZarJC43tBcIiIiIiK1nc1u5/ONR7i8c2MaeTg8Y6qIiIjUAVX6jv/KK68wYcIEPD09eeWVV855nMlkUiNdpJaKsHgxrEM4c1cf5vpeUZjNJrBEwfYvwW4vm+pFRERERETOsCEhm6SMfK7paTU6ioiIiNSQKjXS4+Lizvp3EalbJveL5uq317B8fyoD24SWjUgvzIKCDPAKMDqeiIiIiEit9P2ONFqG+NDNajE6ioiIiNQQzZEu0oD1iAqgQ4Qfc1cfLttgiSq7Pal50kVEREREziY9t4g/DmYwrmckJn2KU0REpMGoUiP9+eefJz8/v0LHrlu3jkWLFlXlMiJSzUwmE5P7RvP73lQOpeaUjUgHyEgwNpiIiIiISC31zZYkAEZ30SKjIiIiDUmVGum7du3CarVy11138dNPP5Gamlq+r6SkhG3btvHmm2/St29frrnmGnx9fZ0WWESca2RMBIE+7ny8Jh58gsHNW410ERGRBmL58uWMHDmSiIgITCYT33777Wn7n3rqKdq2bYuPjw8BAQEMGTKEdevWnXZMeno6EyZMwM/PD4vFws0330xOTk4NPguRmlNQXMoHqw5zcasAghp5GB1HREREalCVGukff/wxv/76K8XFxVx33XWEh4fj7u6Or68vHh4edO3alQ8++ICJEyeyZ88eLrzwQmfnFhEn8XRz4boLrHyxMZHswpKyUelqpIuIiDQIubm5xMTE8MYbb5x1f+vWrXn99dfZvn07K1euJDo6mksvvfS0gTQTJkxg586dLFmyhIULF7J8+XJuu+22mnoKIjXqk7XxpGQXckvvxkZHERERkRpWpcVGAWJiYnj33Xf597//zbZt24iPjyc/P5/g4GC6dOlCcHCwM3OKSDW6vncUb/1xkC83HeFGNdJFREQajOHDhzN8+PBz7r/uuutOu//yyy/z/vvvs23bNgYPHszu3btZvHgxGzZsoEePHgC89tprjBgxghdffJGICE19IfVHTmEJb/5+kLHdmmAN8DQ6joiIiNSwKjfS/2Q2m+nSpQtdunRxQhwRMUK4vyfDO4bz0erDTG5nxZSw2uhIIiIiUssUFRXxzjvv4O/vT0xMDABr1qzBYrGUN9EBhgwZgtlsZt26dYwePfqs5yosLKSwsLD8flZWFgA2mw2bzVaNz6J+sdls2O121ayGvLf8IDkFxdw9sDn24hzVvYbp9W4c1d4YqrsxVHdjGFn3ylzT4Ua6iNQPN/aLZsxbazhYHEjLjASw28FkMjqWiIiIGGzhwoWMHz+evLw8GjduzJIlS8o/fZqcnExoaOhpx7u6uhIYGEhycvI5zzlz5kxmzJhxxvbU1FQKCgqc+wTqMZvNRmZmJna7HbO5SrN2SgVl5pfwzopDXNU5BNeibDJU9xqn17txVHtjqO7GUN2NYWTds7OzK3ysGukiAkA3awCdmvjzU5IH9xTlQP5J8A40OpaIiIgYbNCgQcTGxpKWlsa7777LuHHjWLdu3RkN9MqYPn0606ZNK7+flZVFZGQkISEh+Pn5OSN2g2Cz2TCZTISEhOjNfjX74Kc9mDDxwPCOBHi7qe4G0OvdOKq9MVR3Y6juxjCy7p6eFZ+uTY10EQHAZDJxY79oPvxiC/d4ABnxaqSLiIgIPj4+tGzZkpYtW9K7d29atWrF+++/z/Tp0wkPDyclJeW040tKSkhPTyc8PPyc5/Tw8MDDw+OM7WazWW9aK8lkMqlu1Sw5s4CP1sRz+4XNCfHzKn+zr7rXPNXdOKq9MVR3Y6juxjCq7pW5nl4RIlLuss6NyfM+tSjYyXhjw4iIiEitZLPZyuc379OnDxkZGWzatKl8/2+//YbNZqNXr15GRRRxqtd+24+Xuwu3XNjc6CgiIiJiIIcb6R9++CF5eXnOyCIiBvNwdeGyXh3Js3uQnxpndBwRERGpZjk5OcTGxhIbGwtAXFwcsbGxJCQkkJuby6OPPsratWuJj49n06ZN3HTTTSQlJXH11VcD0K5dO4YNG8att97K+vXrWbVqFVOmTGH8+PFEREQY+MxEnCP+RC6fbUjkzota4OfpZnQcERERMZDDjfRHHnmE8PBwbr75ZlavXu2MTCJioOt7R5FoD+XQgd1GRxEREZFqtnHjRrp27UrXrl0BmDZtGl27duWJJ57AxcWFPXv2MGbMGFq3bs3IkSM5ceIEK1asoEOHDuXnmDdvHm3btmXw4MGMGDGC/v3788477xj1lESc6pUl+wj0cWdin2ijo4iIiIjBHJ4jPSkpiR9++IG5c+cycOBAmjdvzo033sikSZP+dl5EEamdQv08SfFtStbRg5Ta7LiYTUZHEhERkWoycOBA7Hb7Ofd//fXX5z1HYGAg8+fPd2YskVphT3IW3209yj+v7IiXu4vRcURERMRgDo9Id3V1ZfTo0Xz33XckJiZy6623Mm/ePKxWK1dccQXfffcdNpvNGVlFpIaEWVsTVJLMsj0p5z9YRERERKQeeumXfUQGeDOuR6TRUURERKQWcOpio2FhYfTv358+ffpgNpvZvn07kyZNokWLFvz+++/OvJSIVKOQyFZEmVOZu0rzpIuIiIhIw7M54SRLdh3n/kta4e7q1LfNIiIiUkc55SeC48eP8+KLL9KhQwcGDhxIVlYWCxcuJC4ujqSkJMaNG8ekSZOccSkRqQkWK54UsutgHPuPZxudRkRERESkRr34817ahPlyRUwTo6OIiIhILeFwI33kyJFERkYyd+5cbr31VpKSkvj0008ZMmQIAD4+PjzwwAMkJiY6HFZEaoglCoBOPpnMXX3Y2CwiIiIiIjVo1YE0Vh88wQOXttZ6QSIiIlLO4cVGQ0ND+eOPP+jTp885jwkJCSEuTlNEiNQZFisA41rZ+L/NSTw0tC3+3m4GhxIRERERqV52u51ZP++lS6SFS9qHGR1HREREahGHR6S///77f9tEBzCZTERFRTl6KRGpKV4B4O7LRSF5lNhsfL5RnygRERERkfrvl13H2ZqYwUND22AyaTS6iIiI/JfDI9IBcnNz+eOPP0hISKCoqOi0fVOnTnXGJUSkJplMYLHSKP8oIzsP4aM1h7mpfzN9tFVERERE6q1Sm52XftlLv5ZB9G0ZbHQcERERqWUcbqRv2bKFESNGkJeXR25uLoGBgaSlpeHt7U1oaKga6SJ1VUAUZCQw6aJovt6SxNLdx7m0Q7jRqUREREREqsV3sUnsO57DC2M6Gx1FREREaiGHp3a5//77GTlyJCdPnsTLy4u1a9cSHx9P9+7defHFF52RUUSMYLFCRgIxkRa6WS1adFRERERE6q2iEhuv/LqPS9uH0dUaYHQcERERqYUcbqTHxsbywAMPYDabcXFxobCwkMjISGbNmsWjjz7qjIwiYoRTjXTsdib3a8bqgyfYk5xldCoREREREaf7bGMiR07m88ClbYyOIiIiIrWUw410Nzc3zOay04SGhpKQkACAv78/iYlaoFCkzrJYoSQfclMZ3jGcMD8PPtKodBERERGpZ/KLSnlt6X5GdWlCm3Bfo+OIiIhILeVwI71r165s2LABgIsuuognnniCefPmcd9999GxY0eHA4qIQSzWstuMBNxczFzfK4pvtiRxMrfo7x8nIiIiIlKHfLTmMOm5Rdw/pLXRUURERKQWc7iR/txzz9G4cWMAnn32WQICArjzzjtJTU3lnXfecTigiBjEElV2mxEPwLW9rNhsZR97FRERERGpD7IKinnr94OMvyASa5C30XFERESkFnN19AQ9evQo/3toaCiLFy929JQiUht4WcDDv2yedCC4kQcjYyL4z5p4bunfDFcXh38PJyIiIiJiqHeXH6KwpJR7Lm5ldBQRERGp5dQJE5Fzs1jhZHz53cl9o0nKyOfX3ccNDCUiIiIi4ri0nELeXxnHpL7RhPl5Gh1HREREarkqjUjv2rUrJpOpQsdu3ry5KpcQkdrAYi0fkQ7Qqak/PaIC+HDVYYZ1bGxgMBERERERx7y57CAuJhN3XNjC6CgiIiJSB1SpkT5q1CgnxxCRWikgCvYvOW3T5H7RTJm/hV1Hs2gf4WdQMBERERGRqkvKyOeTtfHcc3FLAnzcjY4jIiIidUCVGulPPvmks3OISG1ksUJmItjtcOpTKEM7hBPu58lHqw/zwtjOBgcUEREREam8Ob/ux9fTlRv7NzM6ioiIiNQRTpkjPSMjg/fee4/p06eTnp4OlE3pkpSU5IzTnyEpKYnrr7+eoKAgvLy86NSpExs3bqyWa4k0aBYrlBRATkr5JjcXMzf0ieLb2CTSc4sMDCciIiIiUnkHU3P4cvMR7hrUkkYeVRpbJiIiIg2Qw430bdu20bp1a1544QVefPFFMjIyAPj666+ZPn26o6c/w8mTJ+nXrx9ubm789NNP7Nq1i5deeomAgACnX0ukwbNYy24z4k/bfO0FVuzAgg0JZz5GRERERKQWe3nJPsJ8PZjQy2p0FBEREalDHG6kT5s2jcmTJ7N//348Pf+70vmIESNYvny5o6c/wwsvvEBkZCQffvghF1xwAc2aNePSSy+lRQstECPidOWN9NMb5oE+7ozqEsF/1sRTXGozIJiIiEjl2e129iZnM2fpfpKz9KkqkYZoR1Imi7Yd494hrfB0czE6joiIiNQhDjfSN2zYwO23337G9iZNmpCcnOzo6c/w/fff06NHD66++mpCQ0Pp2rUr7777rtOvIyKApz94Ws4YkQ4wqW80xzIL+GXn8ZrPJSIiUkF2u53tRzKZtXgPg1/6g6Gzl/OflXs5dCLf6GgiYoCXftlL82AfxnRranQUERERqWMcnhDOw8ODrKysM7bv27ePkJAQR09/hkOHDvHWW28xbdo0Hn30UTZs2MDUqVNxd3dn0qRJZxxfWFhIYWFh+f0/s9psNmw2jaStKJvNht1uV81qWG2ou8lihZPx2P+SoV24LxdEB/DhqjiGdwwzKF31qQ21b4hUd2Oo7sZQ3auPzWZnS2IGi3cm8/PO4xw5mY/Fy40h7UJ4tcN+OuyYxUm3WdhsNfuJRn2tRYy14XA6y/am8vp1XXF1ccpyYSIiItKAONxIv+KKK3j66af5/PPPATCZTCQkJPDwww8zZswYhwP+lc1mo0ePHjz33HMAdO3alR07dvD222+ftZE+c+ZMZsyYccb21NRUCgoKnJ6vvrLZbGRmZmK32zGb9UNnTakNdbd4hWFKOcDJlJQz9o3qEMCjiw6xfMdh2oZ6G5Cu+tSG2jdEqrsxVHdjqO7OVWKzszUph2UHTvLHgQxSc4sJ8nblopYBPDyoKT0bpRKw6hE8dq0lv9lQ0k2BFKak1Gjts7Oza+xaInI6u93OrMV7aN/YjxEdGxsdR0REROoghxvpL730EmPHjiU0NJT8/HwuuugikpOT6dOnD88++6wzMp6mcePGtG/f/rRt7dq146uvvjrr8dOnT2fatGnl97OysoiMjCQkJAQ/Pz+n56uvbDYbJpOJkJAQvdmvQbWh7qbQVrD/Z0JDQ8/YNzYomNdWHuWHPVlc2DG65sNVo9pQ+4ZIdTeG6m4M1d1xRSU2Vh88weKdyfy66zjpecU09vfk8i5NGNYhjG7WAFxK8zGteAkWvwb+TbFd9wVuzS+mUWpqjdf+f9cTEpGa9ce+VDYcPsmHk3tiNpuMjiMiIiJ1kMONdH9/f5YsWcLKlSvZtm0bOTk5dOvWjSFDhjgj3xn69evH3r17T9u2b98+oqKiznq8h4cHHh4eZ2w3m81601pJJpNJdTOA4XUPjIbMREwAf8ngbjYzsU80ryzZx/QR7QhudOa/tbrM8No3UKq7MVR3Y6julVdQXMof+1JZvCOZX3cfJ7ughOggb8b1tDK8Yzidm/pjMp1qku1dDD89CNnJMOAB6H8fJjcvOPVLjJquvb7OIsaw2ez86+e99IwOYGAb508/KiIiIg2Dw430P/Xv35/+/fs763TndP/999O3b1+ee+45xo0bx/r163nnnXd45513qv3aIg2SxQqlRZBzHPzO/Bjs+J6RzP51HwvWJzDl4lYGBBQRkfoup7CEZXtSWLwjmWV7U8grKqV1WCNu7NeM4R3DaRvu+9/mOUBGIix+BPYshOaD4IZvIahm50MXkdrjpx3J7Dyaxee39zn9/woRERGRSnCokW6z2Zg7dy5ff/01hw8fxmQy0axZM8aOHcsNN9xQLT+k9OzZk2+++Ybp06fz9NNP06xZM2bPns2ECROcfi0RoayRDpCRcNZGeoCPO6O7NuE/a+O5/aIWuGnhJhERcYLMvGJ+3X2cn3Yks3x/KkUlNjo18efuQS0Z1jGcFiGNznxQaTGseQP+eAE8/GDsh9BhNKhxJtJglZTaeGnJXi5qHcIFzQKNjiMiIiJ1WJUb6Xa7nSuuuIIff/yRmJgYOnXqhN1uZ/fu3UyePJmvv/6ab7/91olR/+vyyy/n8ssvr5Zzi8hf+EeW3WbEg7XXWQ+Z3C+aBRsSWbwjmZExETUYTkRE6pO0nEJ+2XmcxTuTWX0gjRKbne5RATw0tA1DO4QTGfg3C1sfXgWLpkHaPuh1BwycDp5aD0ekoft6cxKHUnOZM76r0VFERESkjqtyI33u3LksX76cpUuXMmjQoNP2/fbbb4waNYqPP/6YiRMnOhxSRAzk6QdeAWWN9HNoG+5Hn+ZBzF19WI10ERGplOTMAhbvOMZPO5LZcDgdgF7NgnhiZHuGdggnzO88C3TmpMKSx2Hrp9D0ArjtD2jcuQaSi0htV1hSyuxf93FZp8Z0bOJvdBwRERGp46rcSP/000959NFHz2iiA1x88cU88sgjzJs3T410kfrAElU2tcvfmNwvmtv/s4ltRzLo3NRSM7lERKROSkzP46dTzfMtCRm4uZjo2yKY50Z34pL2YQRVZPFqWylsmgtLZ4DJDCPnQNcbzlgYW0QarvnrEkjOKuD+S1obHUVERETqgSo30rdt28asWbPOuX/48OHMmTOnqqcXkdrEYj1vI31IuzCaWLyYu+owL1/TpWZyiYhInXEgJZvFO5LLF/3zcDVzYesQXh4Xw+B2Yfh7uVX8ZEdjy6ZxSdoEXa+HIU+DT1C1ZReRuie3sITXfzvA2O5NaRl6ljUVRERERCqpyo309PR0wsLCzrk/LCyMkydPVvX0IlKbWKywZ9HfHuJiNjGpbxT/+nkvj4xoS6jveT6KLyIi9ZrdbmfXsazy5vmBlBx83F0Y1DaUOwe2YFCbUHw8KvmjaEEm/PYMbHgPQtrBTT+DtXf1PAERqdM+XBVHdkEJ9w7RaHQRERFxjio30ktLS3F1PffDXVxcKCkpqerpRaQ2sURB5pGyj9GbXc552DU9rLyyZD+frkvk3iGtajCgiIjUBna7ndjEjPLmeUJ6Hn6ergxpH8bDw9oyoFUwnm7n/j7yNyeG7V/Cz49CUS5c8nTZgqIulRjFLiINRkZeEf9efogJva00sXgZHUdERETqiSo30u12O5MnT8bD4+xzWBYWFlY5lIjUMgFRYCuG7GTwb3LOw/y93RjdrQmfrIvnzoEtcHfVPLUiIvVdqc3OxsPp/LQjmZ93JnMss4AgH3cu7RDGPzt2pE/zIMe+H6Tugx8fgLjl0P5KGDrzb78XiYi8/cchSm127hrY0ugoIiIiUo9UuZE+adKk8x6jhUZF6gmLtew2I+G8zYvJfaOZvy6Bn3Yc48ouanSIiNRHxaU21h46wU87kvllZzJpOUWE+XkwrEM4wzo2pmd0AK4uDv4ytSgPVrwEq14t+94z4UtodYlznoCI1FspWQXMXR3HLf2bE+JbgYWLRURERCqoyo30Dz/80Jk5RKQ2848su81IgKg+f3to6zBf+rUM4sNVh9VIFxGpR0pKbaw9lM6i7UdZvCOZk3nFNA3wYnTXJgzr2JiukRbMZpNzLrbvZ/jx/8o+CTVgGvS/H9w0PYOInN9rvx3Aw9WFWy9sbnQUERERqWeq3EgXkQbEoxF4B0FGfIUOn9y3Gbd+vJEtCSfpag2o5nAiIlJdSm121sWdYOG2YyzekUx6bhGRgV5c09PKZZ0a07GJHyaTk5rnABmJsPgR2LMQmg+CG76FoBbOO7+I1GsJJ/L4dH0CD1zaBn8vraEgIiIizqVGuohUjMVa4Ub6xW1DiQz04qPVh9VIFxGpY0ptdjYcTmfRtmP8tOMYaTlFNLF4cXX3plzWuTGdmvg7t3kOUFoMa96AP14ADz8Y+yF0GA3Ovo6I1Guzl+4jwMedyX2jjY4iIiIi9ZAa6SJSMZaosqldKsDFbGJSn2heWLyHqYNb0TykUTWHExERR9hsdjbGn2TRtqP8uCOZ1OxCIvw9Gd21CZd1jiCmaTU0z/90eBUsmgZp+6DXHTBwOnj6Vc+1RKTe2nc8m2+2JPH0FR3wcncxOo6IiIjUQ2qki0jFWKxwLLbCh197gZX56xK4e/4WvrmrL55uekMjIlKb2Gx2NiecZOG2Y/y4/Rgp2YWE+3lyRUwEl3VuTJemTpzz/GxyUmHJ47D1U2jaE277Axp3rr7riUi99tIve2liKZt6SkRERKQ6qJEuIhVjsULmEbCVgvn8TXEfD1dev64bo95cxT8X7uLZ0Z1qIKSIiPwdm83OlsQMFp1qnidnFRDm58FlnRtzeefGdI0MqN7mOZR9H9k0F5bOAEww8lXoOhHM5uq9rojUW1sTM/h553FeujoGd1f9XyIiIiLVQ410EakYSxTYSiDrKFgiK/SQ9hF+PDmyPf/4Zgd9WgRxeeeIag4pIiJ/Zbfbif2f5vnRzAJCfD24rFNjLuvcmO7WGmie/+lobNk0LkmboOv1MGQG+ATXzLVFpN761897aRXaiFFdmxgdRUREROoxNdJFpGICospuMxIq3EgHuO4CK2sOnuCRr7bTMcKf6GCfagooIiJ/stvtbDuSyaLtx1i07RhJGfkEN/JgRKdwLuvUmB7RgbjUVPMcoCATfnsGNrwHIW3hxsUQ1afmri8i9dbqA2msPJDG29d3r9n/10RERKTBUSNdRCrG/1TzPCMB6Ffhh5lMJmZe1YmRr63k7vmb+epOzZcuIlId7HY7O49msXDbMRZtP0piej5BPu4M6xjO5Z0juKBZDTfPy0LBjq/g50ehMAcuebpsQVEXt5rNISL1kt1u51+/7CWmqT9DO4QZHUdERETqOTXSRaRi3L3BJ+RUI71yfD3deP26blz15mqe+3E3T1/ZsRoCiog0PHa7nV3Hsli07RiLth8j/kQeAd5uDOtYNud5r2aBuLoYNF9w2n5Y9ADE/QHtr4ShM8Ff0y6IiPP8ujuFLQkZfHJzL0wmjUYXERGR6qVGuohUnMUKGfFVemjHJv48fnk7Hv9uJ72bBzGiU2MnhxMRaRjsdjt7krPLm+dxablYvN0Y1iGcZ0Z1pE/zIOOa5wBFebDiJVj1alnjfMKX0OoS4/KISL1ks9l58ee99GkeRL+WQUbHERERkQZAS5qLSMVZrFUakf6n63tHMaJTOA9/uY34E7lODCYiUr/Z7Xb2Jmfz8i97GfzyHwx/dQX/WRtPz+gAPrrpAjb8YwjPj+nMgFYhxjbR9/0Mb/aC1XNgwDS4a62a6LXc8uXLGTlyJBEREZhMJr799tvyfcXFxTz88MN06tQJHx8fIiIimDhxIkePHj3tHOnp6UyYMAE/Pz8sFgs333wzOTk5NfxMpKH5YdtR9h7P5sFhbTQaXURERGqERqSLSMVZoiBpU5UfbjKZeH5MZy6fs5Ip87fw5Z198HDVfOkiIuey/3j2qTnPj3EgJQdfT1eGdgjn8cvb069FMO6utWRMREYiLH4E9iyE5gPh+m8guKXRqaQCcnNziYmJ4aabbuKqq646bV9eXh6bN2/m8ccfJyYmhpMnT3LvvfdyxRVXsHHjxvLjJkyYwLFjx1iyZAnFxcXceOON3HbbbcyfP7+mn440EMWlNl5eso8h7cLoZg0wOo6IiIg0EGqki0jFWayQmQSlJeBStf8+/DzdeOO6box5azUzf9zDU1d0cHJIEZG67UBKzqlpW46y73gOvh6uXNIhjEdHtKVfy+Da9QvI0mJY8wb88QJ4+MHYD6HDaNDo0Dpj+PDhDB8+/Kz7/P39WbJkyWnbXn/9dS644AISEhKwWq3s3r2bxYsXs2HDBnr06AHAa6+9xogRI3jxxReJiIio9ucgDc/nGxNJSM/j7eu7Gx1FREREGhA10kWk4ixRYC+FrCQIiKryaTo19efREW156odd9G4exLCO4U4MKSJSR9jtmPPSIKuE+JP5/LYnhV93p3AgJQcfD1cubBXM4wOackHzwFPN81LIO2506v9K2wc/PVx22+sOGDgdPP2MTiXVLDMzE5PJhMViAWDNmjVYLJbyJjrAkCFDMJvNrFu3jtGjRxuUVOqrguJS5izdzxUxEbRrrP9zREREpOaokS4iFWexlt1mJDjUSAeY1DeatYfSefDLrXSI8CMy0NsJAUVE6gZ7ehx530wjNPE3AKKAG0/9wfPUQQdO/anNmvaE2/6Axp2NTiI1oKCggIcffphrr70WP7+yBmZycjKhoaGnHefq6kpgYCDJycnnPFdhYSGFhYXl97OysgCw2WzYbLZqSF8/2Ww27HZ7g6rZR6vjOJFTxL2DWxr2vBti3WsD1d04qr0xVHdjqO7GMLLulbmmGukiUnGWyLJbBxYc/ZPJZOKFsZ25bM4KpszfzBd39K09c/2KiFSD3MISVu89Cqtf5cLkj8m0+/Kc/Q6aRkZzQXQgnZv6416bpm05HzcvsPYFs/7vbgiKi4sZN24cdrudt956y+HzzZw5kxkzZpyxPTU1lYKCAofP31DYbDYyMzOx2+2YG8C/xdzCUt5YdoCRHYLwLs0lJcWYxesbWt1rC9XdOKq9MVR3Y6juxjCy7tnZ2RU+Vo10Eak4Ny9oFOaURjqAv1fZfOlj317NC4v38Pjl7Z1yXhGR2sBut3MwNYff96aybG8KboeX87j5A6LMKawLG4/twge53d+DyIhw/ZAutdqfTfT4+Hh+++238tHoAOHh4aSkpJx2fElJCenp6YSHn3vqtunTpzNt2rTy+1lZWURGRhISEnLa+eXv2Ww2TCYTISEhDeL/kdm/7qewxM6DIzoR6u95/gdUk4ZW99pCdTeOam8M1d0YqrsxjKy7p2fFf6ZQI11EKsdidVojHSAm0sIjw9vxz4W76NUskEs7aL50Eam78opKWHPwBMv2pvD73lSOnMynqWsmL/p9Rm/X3ymI6I3rqG/oF9oOm812RgNSpLb5s4m+f/9+li1bRlBQ0Gn7+/TpQ0ZGBps2baJ797KFH3/77TdsNhu9evU653k9PDzw8PA4Y7vZbNab1koymUwNom4ncgp5f2Uck/pGExFg/JSADaXutY3qbhzV3hiquzFUd2MYVffKXE+NdBGpHIsVMuKdesqb+kWz9tAJ/u+LrfwY4UfTWvDmSESkIux2O3FpuSzbm8rve1NYF5dOUYkNa6A3l7QJ5FrzElrtmI0JTxj1Np4x48FkMjq2SLmcnBwOHPjvZPxxcXHExsYSGBhI48aNGTt2LJs3b2bhwoWUlpaWz3seGBiIu7s77dq1Y9iwYdx66628/fbbFBcXM2XKFMaPH09ERIRRT0vqobd+P4jJZOKOi1oYHUVEREQaKDXSRaRyLFZIXO/UU5pMJl4cG8OIOSu459MtfH57H9xc9JtfEamd8otKWXvoBL/vTWHZ3lQS0vNwdzXTq1kgjwxry8A2ITQr2I1p0T2QvB163AiDnwCvAKOji5xh48aNDBo0qPz+n9OtTJo0iaeeeorvv/8egC5dupz2uGXLljFw4EAA5s2bx5QpUxg8eDBms5kxY8YwZ86cGskvDcOxzHw+XhvP3QNbEujjbnQcERERaaDUSBeRyrFEQVYSlBaDi5vTTuvv7cbr13Xl6rfX8K+f9/LoiHZOO7eIiKMOp+WWN87XHjpBYYmNpgFeDGoTysA2IfRpEYS3uyvkpcPSx2DTXGjcGW5ZCk27Gx1f5JwGDhyI3W4/5/6/2/enwMBA5s+f78xYIqeZs3Q/jTxcuXlAM6OjiIiISAOmRrqIVI7FCnZbWTM9INqpp+5qDeDhYW159sfd9GoWyOB2YU49v4hIRRUUl7IuLp1le1L4Y18qcWm5uLmY6NUsiAeHtmFgm1BahPhg+nOaFrsdYufDL49DaREMnwU9bwazi7FPRESkjotLy+XzjUeYPrwtjTz09lVERESMo59ERKRyLFFltyfjnd5IB7hlQDPWxZ3ggS+28uPUAURYvJx+DRGRs0lMzysfdb76YBoFxTYi/D0Z2DaUR0e0o2+LIHzO1sQ5vgsWPQAJq6HT1XDpM+CrhZNFRJzhlSX7CPX14PreUUZHERERkQZOjXQRqRz/pmW3GQnVcnqTycSLV8cw4tWy+dIX3NZb86WLSLUoLCllQ9xJlu1N4fe9KRxMzcXVbKJndCD3D2nNoLahtApt9N9R52ecIAf+eAHWvgkBzWDid9B8YI0+BxGR+mzX0Sy+33qUmVd1wtNNn/ARERERY6mRLiKV4+YJvo2rrZEOYPF257XrujLu32t56Zd9PDK8bbVdS0QaliMn8/h9byq/nxp1nldUSrifJwPbhPDg0Lb0axmEr+d51n+w22HPQvjpEchLg4GPQN+p4OpRM09CRKSBeOmXvUQHeTO2e1Ojo4iIiIiokS4iVWCxVmsjHaB7VCAPDm3D8z/toVezQAa1Da3W64lI/VRUYmPj4XR+35fKsj0p7E/JwcVsontUAPdc3IqBbUJoG+577lHnf5UeBz89BPt/gVZDYcSsapnmSkSkodsUn87SPSnMubarPp0oIiIitYIa6SJSeTXQSAe4bUBz1h06wbTPY/nx3gE09td86SJyfscy8/l9b1njfNWBNHKLSgn19WBgmxDuv6Q1/VoG4+91nlHnf1VSCKvmwIoXwTsYrpkHbS+DijbgRUSkwux2O7MW76VtuC+Xd2psdBwRERERQI10EakKixXiV1f7ZcxmEy+N68KIV1cw9dMtfHprb1w1IklE/qK41Mam+LK5zv/Ym8qe5GzMJugeFcBdg1oysE0I7Rv7VXzU+V8d+r1sMdGTh6HP3XDRw+Du48ynICIi/2PF/jTWxaXz/qQemM36haWIiIjUDmqki0jlWayQdRRKisDVvVovFehTNl/6+HfW8sqv+3hwqOZLFxE4mVvEsr0pLN2dwvJ9qWQXlhDcyJ2LWocy5eKWDGgZgr93JUed/1V2Mvz8D9jxJUT1g2s+gdB2znkCIiJyVna7nX/9vJfuUQFcrKn9REREpBZRI11EKs8SBdgh6wgENq/2y/WMDuSBS1sza/FeLmgWxEWtQ6r9miJS+xxMzWHp7uP8uiuFjfHp2OwQ09SfWwY05+K2oXSI8HPOyMXSEtj4Pvz2DLi4w6i3IWa8pnEREakBi3cksz0pkwW39a76J4lEREREqoEa6SJSeRZr2W1GQo000gHuuLAF6w6lM+2zsvnSw/w8a+S6ImKcklNTtvy6+zhLd6dwKC0XTzcz/VsG8+zoTgxuG0qos/8vOLIRFt4Pyduhx41w8ePgHejca4iIyFmV2uy8+MteBrQKpnfzIKPjiIiIiJxGjXQRqTz/poAJTsbX2CXNZhMvj4thxJyy+dLn3dJL86WL1ENZBcUs35fKr7uOs2xvKpn5xYT4ejCkXSiPjmhHv5bBeLm7OP/Ceemw9GnYNBfCO8Etv0LTHs6/joiInNM3W5I4mJrL7Gu6Gh1FRERE5AxqpItI5bl6gG/jshHpNSiokQdzxnfl2nfX8urS/TxwaZsavb6IVI/E9LzyUedrD52gxGanXWM/JvaJYki7MDo18a++xebsdtj6KfzyOJQWwfBZ0PNmMFdDs15ERM6psKSUV5bsY3jHcDo19Tc6joiIiMgZ1EgXkaoJiKrxRjpAr+ZBTLukNS8t2UevZkH0bxVc4xlExDE2m53YIxnl853vPZ6Nm4uJ3s2DeGJkey5uG0rTAO/qD3J8Fyx6ABJWQ6er4dJnwDe8+q8rIiJnWLA+kWOZ+Xx0U0+jo4iIiIiclRrpIlI1FmuNTu3yv+4a2JJ1cenc99kWfpw6wPlzJIuI0+UVlbBifxpLdx/ntz0ppOUUEeDtxqC2odw7pBUDWgXj6+lWM2EKc+CPF2DtmxDQDCZ+B80H1sy1RUTkDHlFJbz22wGu6taUlqG+RscREREROSs10kWkaixWiFthyKXL5kvvwog5K7h3QSyf3NILl+qa9kFEqiw5s4Cle47z667jrDp4gqISGy1CfBjTvSlD2oXRzRpQs/927XbYsxB+egTy0mDgI9B3atl0VSIiYpgPVx0mM7+Iewe3MjqKiIiIyDmpkS4iVWOxQvYxKCk0pAkV4uvBq+O7cP1765izdD/3X9K6xjOIyOnsdjs7j2aVz3e+PSkTF7OJntEBPDS0DYPbhdEs2MeYcOlx8NPDsP9naDUURsyCgGhjsoiISLnMvGL+/cdBJvSKIjKwBqb1EhEREakiNdJFpGosVsAOmUcgqIUhEfq2CObewa2ZvXQfvZoF0rel5ksXqWkFxaWsOXSCX3eVTdlyLLMAX09XLmodwi0DmnFR6xAs3u7GBSwphFVzYMWL4B0M18yDtpeBSZ9iERGpDd5ZcZDiUjt3DTLm50kRERGRilIjXUSqxhJVdpsRb1gjHWDKxS1ZF3eCqQti+fHe/oT6ar50keqWllPIb3tSWLr7OCv2p5FXVEpkoBfDOoZzSbswejYLxM3FbHRMOPR72WKiJw9Dn7vhoofB3aAR8SIicoaU7AI+WHmYG/tF62c4ERERqfXqdCP9+eefZ/r06dx7773Mnj3b6DgiDYtfEzCZISPB0BguZhOzx3dhxKsruf+zWD6+SfOlizib3W5nf0oOS3YdZ+nu42xJzACga6SFKRe3ZEi7MFqFNsJUW0Z5ZyfDz/+AHV9CVD+45hMIbWd0KhER+Ys3lx3EzcXE7RdqNLqIiIjUfnW2kb5hwwb+/e9/07lzZ6OjiDRMru7gGwEn441OQqivZ9l86e+v441lB5iqhapEHFZcamN9XDq/7j7Or7uPk5iej5ebCxe2DuaFMZ25uG0owY1q2SKdpSWw8X347RlwcYdRb0PMeE3jIiJSCx05mce8dfHcN6Q1/t5uRscREREROa862UjPyclhwoQJvPvuuzzzzDNGxxFpuCxWw0ek/6lfy2DuubgVs3/dxwXNAundPMjoSCJ1TmZ+Mcv3p7Fk13H+2JdKdkEJ4X6eDGkfyuB2YfRpHoSnm4vRMc/uyEZYeD8kb4ceN8LFj4N3oNGpRETkLApLSpnxwy78vdy5sV+00XFEREREKqRONtLvvvtuLrvsMoYMGXLeRnphYSGFhYXl97OysgCw2WzYbLZqzVmf2Gw27Ha7albDanvdTRYrpB/EXkvy3TOoBesPnWDqp1tYeE8/h0bL1vba11eqe82y2ezsPZ7Nxu27Sdq5krj0Amx2O1FB3jzTzkJMU38iA8FEAtgS4ADUxq+M6cAvsOkjCO+E/aZfoGmPsh21/HWk17txjKq9vtYikJxZwJ3zNrEzKYtXx3fB271OviUVERGRBqjO/dSyYMECNm/ezIYNGyp0/MyZM5kxY8YZ21NTUykoKHB2vHrLZrORmZmJ3W7HbK4FC8g1ELW97o3cgvBKX0pqSorRUcr9Y3ATJs7bzT2fbOSV0S0xV3FKh9pe+/pKda9+qTlFrE/IZn1CFpvj0xlVtJD7XL/Cx1QIf36yPhvYfepPHWBzb0ROv3+Q1+E6MLtALfo/6e/o9W4co2qfnZ1dY9cSqY3WHTrB3fM34+Zi5vM7+tAl0mJ0JBEREZEKq1ON9MTERO69916WLFmCp2fFVnWfPn0606ZNK7+flZVFZGQkISEh+Pn5VVfUesdms2EymQgJCdGb/RpU6+vepB3mTamEBvqBa8X+TVa30FCYPd6DyXM38tXuHO4eWLXFq2p97esp1d358otKWX84nRX701h5II19x3MAGBeSyCLPdwghjpJuN3O87XUEhTaum3V3b0QjNy8aGZ2jkvR6N45Rta/oz68i9Y3dbmfu6sM8u2g3PaIDeP26brVvnQ0RERGR86hTjfRNmzaRkpJCt27dyreVlpayfPlyXn/9dQoLC3FxOX3uVg8PDzw8zvwhzWw2601rJZlMJtXNALW67gFRAJizkiC49izweVGbMO4e2JJXluyjV7MgLmhWtXmSa3Xt6zHV3TE2m51dx7JYsT+NFftT2Xj4JEWlNhr7e9K/ZTDT+gUxMOF1PHd8Ck16wOUf4hLWCXtKCmbfUNW9hun1bhwjaq+vszRE+UWlTP96G9/GHuXWAc14eFhbXF30b0FERETqnjrVSB88eDDbt28/bduNN95I27Ztefjhh89oootINbNYy24z4mtVIx3gviGtWB+XztRPt7Boan+CNOpJ6rFjmfllI85PjTpPzy3C292F3s2DmD6iLQNaBdMi2BvTlv/AkifLHnT5bOg2CczmWj+XuIiI1E0JJ/K4/ZNNHE7LZc61XbkiJsLoSCIiIiJVVqca6b6+vnTs2PG0bT4+PgQFBZ2xXURqgF9TMLlARoLRSc7g6mJmzrVdGTFnBdM+38qHk3tiNldtvnSR2iavqIR1h9JZvj+VFfvTOJCSg8kEnZr4c+0FkQxoFUI3awDurqdG/B3bBh9MgyMboMsEuORp8Ak29kmIiEi99vveFO5dEIvF241v7u5L23BNqykiIiJ1W51qpItILePiCn5NamUjHSDc35OXx8Uw+cMN/Hv5Ie6s4nzpIkaz2ezsOJpZPl3LpviTFJfaaWLxYkCrYO4b0op+LYIJ8HE//YEFWbDsOVj/bwhpCzf+BFF9jXkSIiLSINhsdt764yAv/rKXga1DmD2+K/5ebud/oIiIiEgtV+cb6b///rvREUQaNosVTsYbneKcBrYJ5c6BLXjxl730jA6gR3TV5ksXqWlJGfms3J/K8v1prD6Qxsm8YnzcXejTIojHLmtP/1bBNA/2wWQ6yyct7HbY+TUsfhQKs2HIDOh9J7iokSEiItUnu6CYBz7fyi+7jnPv4FbcO7iVPhEoIiIi9Uadb6SLiMEsVkjbZ3SKv/XAJa3ZEJfOPZ9u4cepA84ctStSC+QUlrD24AlWHkhj+f5UDqXmYjZB56YWru8dxYBWIXS1WnA73wJtaQfgx/+DQ8ug3UgY9jz4N62ZJyEiIg3WgZRsbvvPJlKzCnlvYg+GtA8zOpKIiIiIU6mRLiKOCYiCA78aneJv/Tlf+mVzVvDAF1t5b2IPjY4Sw5Xa7GxPymTFvlRWHEhjc/xJSmx2mgZ4MaBVCA9e2oa+LYLx967gKPLifFjxMqyaDb6N4bovoPWl1focREREABbvOMYDn2+lSYAX39/Tn2bBPkZHEhEREXE6NdJFxDEWK+SmlDXx3LyMTnNOERYvXh7XhRvnbuC9lYe47ULNly41LzE9j5UHyuY5X3XgBJn5xTTycKVPiyCeHNmeAa1CiAryPvt0LX9n/5KyUehZR6HffTBgWq3+9ygiIvVDqc3Oi7/s5a3fD3JZ58bMGtMZHw+9xRQREZH6ST/liIhjLNay24xECGltbJbzGNQ2lNsvbM6sxXvpHhVI96gAoyNJPZddUMyaU9O1rNifRlxa2XQtXSItTOobzYWtgomJrMB0LeeSeQQWT4fd30Ozi2DClxDcyrlPQkRE5CxO5hYxdcEWVh1I49ERbbl1QPPK/yJYREREpA5RI11EHFPeSI+v9Y10gP8b2oYNh9OZ+ukWFk3tj8Vb86WL85SU2tiWlMmKfWmsPJDK5oQMSm12rIHeDGgVzMPD2tKnRRD+Xg4u+llaDOvehmUzwaMRjHkfOo4BNTBERKQG7EjK5Pb/bCK/uJRPbu5F35bBRkcSERERqXZqpIuIY3wjwORS1kivA9xczLx2XTdGvLqC//tiG+9O7K7RU1JlNpudfSnZrDl4grWHTrDm4AmyCkrw9XSlX4tgZlzRgQGtgokKcuJcsQlrYeE0SN0NF9wGgx4FT3/nnV9ERORvfLXpCI9+s5024b68dX13mlg0lZiIiIg0DGqki4hjXFzBvylkJBidpMKaWLx46eoYbvl4I++vjOOWAc2NjiR1hN1u52BqDmsOnmDNoROsPZROem4R7i5mulgt3NS/GQNahRDT1B/Xqk7Xci65J2DJExD7CTTpAbf9Do1jnHsNERGRcygqsfHMol18vCaeq7s35Z+jOuLp5mJ0LBEREZEao0a6iDjOYq1TjXSAIe3DuKV/M15YvIce0YF0ibQYHUlqIbvdzuETef/TOD9BanYhrmYTMZEWrrvASp8WQXSzBuDlXk3NBJsNtnwMvz4FdjtcPhu6TQKzkxv1IiIi55CSVcBd8zaz9UgGz4zqyIReVn2iT0RERBocNdJFxHGWKEjZZXSKSntoWFs2xp/k7nmb+XHqAPy9HZy3Wuo8u91OYno+aw6lsfZQOmsOniA5qwAXs4mOTfwZ060pfVoE0SMqAB+PGvgWemwbLJoGRzZAlwkwZAY0Cqn+64qIiJyyKT6dOz/ZjMkEC27ro8XaRUREpMFSI11EHGexwr7FRqeoNHdXM69d25XL5qzgwS+38u8bNF96Q5SUkV824vzUPOdJGfmYTNAxwp8rukTQp3kQPaID8PWswV+0FGTBsudg/b8huA3c+BNE9a2564uISINnt9v5ZG08Ty/cRdfIAF6f0JVQX0+jY4mIiIgYRo10EXFcQBTkpUFRLrg7cVHFGhAZ6M2/ro7h9v9sYu7qw9zYr5nRkaSaHc8qKG+crzl0goT0PADaNfZjaIdw+rQI4oJmgfh7GfAJBbsddn4Nix+FwuyyEei97wQXfVpCRERqTkFxKY99u4MvNx1hct9o/nFZO9ycvfaHiIiISB2jRrqIOM5iLbvNSITQtsZmqYKhHcK5sV80z/24m27WAGI0X3q9kppdyNpDp+Y4P3iCQ2m5ALQOa8SgNiH0aRFEr2ZBBPi4Gxs07QD8+H9waBm0GwnDni9byFdERKQGHTmZxx2fbOJASg6vXBPD6K76XiQiIiICaqSLiDOUN9IT6mQjHWD68HZsjj/JlE83s/CeAfh6VNPCkVLt0nOLyhrnp0acH0jJAaBFiA99WwbxwKVt6NU8kOBGHgYnPaU4H1a8DKtmg29juO4LaH2p0alERKQBWrk/jXs+3YyPhytf3dmXDhH+RkcSERERqTXUSBcRx/k2BrMrZMQbnaTK3F3NvH5dN0bMWcEjX23j9Wu7GB1JKigzr5i1cf+d43xPcjYA0UHe9GkRxD0Xt6RP8yBC/WrhvK77fy0bhZ55BPrfB/2ngbu30alERKSBsdvt/Hv5IWYt3kP/ViHMGd8Fi7fBn9QSERERqWXUSBcRx5ldyqagqMONdDg1X/rYztzxyWb+szaBYS28jI4kZ5FVUMyGuPTyEee7jmVht0PTAC/6NA/itgub06dFEI39a/HXLzMJFj8Cu7+HZhfBhC8guJXRqUREpAHKKSzhwS+28tOOZKYMasn9l7TGxazF10VERET+So10EXEOS1TZ1C513LCOjZnUJ4rnftzNxjaBXNyxmP6tQgj1rYWjmRuInMISNhxOZ+2pxvmOpExsdojw96R3iyAm9Y2mT/MgIgPrwEju0mJY9zYsmwkejWDM+9BxDJjUsBARkZp3MDWH2/+zieTMAt6+vjvDOoYbHUlERESk1lIjXUScw2KF4zuMTuEUj17WDk83F37deZSFu7YBZQtT9m0RTP+WwfRqHoivp5vBKeuvgmIbK/ansS4unTWHTrDtSCalNjuhvh70aRHEdRdY6dMiCGugN6a61IBOWAsLp0HqbrjgNhj0KHhq7lkRETHGLzuTeeDzrYT6efDt3f1oGdrI6EgiIiIitZoa6SLiHJYo2LPI6BRO4eHqwsPD2nBjtwBMXv6sjUtn1YE0luw6ztzVh3Exm+jc1J/+LYPp2yKYblEWPFy1OGlVJWcWsDnhJFsSTrI5/iRbj2RSYrMT3MidXs2DGNOtKX1aBNE82KduNc7/lHsCljwBsZ9Ak+5w6zKI6GJ0KhERaaBKbXZm/7qP1347wNAOYbx4dYwGCIiIiIhUgBrpIuIcFivkp0NhNnj4Gp3GaUJ8PbiySxOu7NIEu91OQnoeqw6cYNWBND5ZG89rvx3A081Mz+hA+rUMpl+LYNpH+Glu0XMoKC5l59FMtiRknGqeZ3AsswCAJhYvukT6M7C5L5fERNEm3K9uNs7/ZLPBlo/h16fAboPLX4Fuk8FsNjqZiEi55cuX869//YtNmzZx7NgxvvnmG0aNGlW+/+uvv+btt99m06ZNpKens2XLFrp06XLaOQoKCnjggQdYsGABhYWFDB06lDfffJOwsLCafTJyXpl5xUxdsIXl+1N5cGgb7hrYom5/rxURERGpQWqki4hzBESV3WYkQlh7Y7NUE5PJRFSQD1FBPlzXy4rNZmd3charDqSx6sAJXv11P8//tAd/Lzf6tgiib8tg+rUIolldHUntILvdzpGT+eUN8y2JGew6mklxqR1PNzOdm1i4oksEXSMD6Gq1EObnic1mIyUlhdBQ37pds2PbYNE0OLIBYq6DS56GRiFGpxIROUNubi4xMTHcdNNNXHXVVWfd379/f8aNG8ett9561nPcf//9LFq0iC+++AJ/f3+mTJnCVVddxapVq6o7vlTC7mNZ3P6fTWQVFPPRjRdwYWt9XxIRERGpDDXSRcQ5LNay24yEettI/yuz2USHCH86RPhz24UtKCqxEZuYwcoDaaw+kMaM73dSYrPT2N+zbLR6yyD6tQgm1K9+LlyaV1TCtiOnjzZPyykEIDrIm67WAMZ0a0I3awBtwn1xc6mHI7MLsmDZc7D+3xDcBib/CNH9jE4lInJOw4cPZ/jw4efcf8MNNwBw+PDhs+7PzMzk/fffZ/78+Vx88cUAfPjhh7Rr1461a9fSu3dvp2eWyvsuNomHv9pG8+BGzLulV91YoFtERESkllEjXUSco1E4mN3KGukNlLurmQuaBXJBs0CmXdKanMISNsSls/JAGqsOpPHlpiMAtAxtdGp+9SB6twjCz5nzkhYXlM3FnZvmvHOehd1uJyO/mGMZBRzLyudYZgFp2YXY7eDmYmKovxeTwz1p7O9JuL8n3u6nvt0UAvtP/Tn7ifHJzQUfH6hrI9JLiyF2HhRkwpCnoPdd4KI5Z0Wkftu0aRPFxcUMGTKkfFvbtm2xWq2sWbNGjXSDFZfamPnjHj5YFcdVXZvw7OhOeLlrXRcRERGRqlAjXUScw2wGSyRkxBudpNZo5OHKoLahDGobCkBaTiFrDpbNr750T9nCpWYTdGpqof+p0erdogLwdKviG9wDv8Ki/4PMRPAJdeIzARt2ikvsFJfaKCq1UVxqw2aHACDEbKK7ixn3RibcXMy4ms2YADJP/akEE+BtK8VkrqNv8iN7waXPlP1bEBFpAJKTk3F3d8disZy2PSwsjOTk5HM+rrCwkMLCwvL7WVlZANhsNmw2W7VkrY9sNht2u/2sNUvNLmTqglg2xZ/kqZHtuaG3FZPJpPo6wd/VXaqP6m4c1d4YqrsxVHdjGFn3ylxTjXQRcR6LVY30vxHcyIORMRGMjIkAIOFEHqsOlo1WX7A+kTeWHcTDtWzh0r6nGusdm/iff+HSrKOw+BHY9R1ED4DrPoeQ1lXOabPZOZiaUz49y+aEk+xPycFuBz9PV7pay+Y072oNoEukBX8v5426tttspKakEBoaikmLcoqI1FszZ85kxowZZ2xPTU2loKDAgER1k81mIzMzE7vdjvl/vm/uOJbL9EUHsdnsvD6mNV2aeJGammpg0vrlXHWX6qW6G0e1N4bqbgzV3RhG1j07O7vCx6qRLiLOY4mCY7FGp6gzrEHeWIOsXHtB2cKle49nn1q4NI3XfzvArMV78fN0pU+LIPq1DKZvi2BahPzPwqWlJbDubfh9Jrh5w1XvQaexlZ4SJSOviC2JGWULgiacJDYhg+zCEswmaB3mS/eoQG4d0Jyu1gCaB/tgPl9jX0REGozw8HCKiorIyMg4bVT68ePHCQ8PP+fjpk+fzrRp08rvZ2VlERkZSUhICH5+ftUZuV6x2WyYTCZCQkLK33R+uj6BGT/spVMTf16/rith9XRtFiOdre5S/VR346j2xlDdjaG6G8PIunt6VvxnJTXSRcR5LFbY/b3RKeoks9lEu8Z+tGvsxy0DmlNUYmPrkQxWHUhj9YET/HPhLopL7YT7edK3ZRBXBCTQb+9M3E7sgZ63wKB/gJflvNcpKbWx7/h/R5tvSTzJodRcAAJ93OlmtXDHwBZ0jbTQOdJCIw99mxARkXPr3r07bm5uLF26lDFjxgCwd+9eEhIS6NOnzzkf5+HhgYeHxxnbzWaz3rRWkslkwmw2U1Rq56nvd7JgQyLX97byxOUdcHdVLavLn3XX67Vmqe7GUe2NobobQ3U3hlF1r8z11CEREeexREH+SSjIAk+NJnOE+6kpXnpGB3LfEMgtLGH94XS27D5Ah13PMrDoF2JtzXm70b8ILe5N34MF9GlejL/36dOspOUUlk/PsiXhJNuOZJJXVIrrqcZ9/5bBTL24FV2tFqyB3v8d7S4iIg1CTk4OBw4cKL8fFxdHbGwsgYGBWK1W0tPTSUhI4OjRo0BZkxzKRqKHh4fj7+/PzTffzLRp0wgMDMTPz4977rmHPn36aKHRGnQ0I5+7529hd3I2s8Z2ZlwPrdUhIiIi4mxqpIuI81isZbcZCRDe0dgs9YyPm5lBuYsZtPdJMJeSM2QWR3xHEHAwgz/2pfLxmviyhUub+HNBs0BSsssa6AnpeQCE+nrQzRrAvYNb0dUaQKcm/ni519EFPUVExGk2btzIoEGDyu//Od3KpEmTmDt3Lt9//z033nhj+f7x48cD8OSTT/LUU08B8Morr2A2mxkzZgyFhYUMHTqUN998s+aeRAO3KTGbJxZvx9PNha/u6Eunpv5GRxIRERGpl9RIFxHnCYgqu1Uj3bmSd8CiaZC4DmKuhUueplGjUC4HLo8pG3GWmJ7H6oNprDpwgkXbjhHm78mQdmF0iypbFDTC31OjzUVE5AwDBw7Ebrefc//kyZOZPHny357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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter = (\n",
    "    BenchmarkPlotter()\n",
    "    .set_metrics(\n",
    "        {\n",
    "            \"f1_plot\": ThresholdBasedMetricVisualizer(y_metrics=[\"f1\"], title=\"F1\", ylabel=\"F1 value\"),\n",
    "            \"precision_recall_plot\": ThresholdBasedMetricVisualizer(\n",
    "                y_metrics=[\"precision\", \"recall\"],\n",
    "                title=\"Precision and Recall\",\n",
    "                ylabel=\"Precision/Recall value\",\n",
    "            ),\n",
    "            \"delay_plot\": ThresholdBasedMetricVisualizer(\n",
    "                y_metrics=[\"mean_delay\", \"median_delay\"],\n",
    "                title=\"Detection delay\",\n",
    "                ylabel=\"Delay (in sample points)\",\n",
    "            ),\n",
    "            \"arl_plot\": ThresholdBasedMetricVisualizer(y_metrics=[\"arl\"], title=\"Average run length\", ylabel=\"ARL\"),\n",
    "        }\n",
    "    )\n",
    "    .set_benchmark_tables({\"reset\": reset_table})\n",
    ")\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
    "plotter.draw(\n",
    "    figure=fig,\n",
    "    axes={\n",
    "        \"f1_plot\": axes[0, 0],\n",
    "        \"precision_recall_plot\": axes[0, 1],\n",
    "        \"delay_plot\": axes[1, 0],\n",
    "        \"arl_plot\": axes[1, 1],\n",
    "    },\n",
    ")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9a4483b",
   "metadata": {},
   "source": [
    "### 6.2 Reset benchmark plots in plotly\n",
    "\n",
    "The same operating-point dashboard is also useful in plotly when interactive inspection is desirable. This plot is the plotly twin of the previous matplotlib figure, not a different analytical message.\n",
    "\n",
    "Showing both backends makes the plotting layer feel less like a special-case utility and more like a reusable reporting interface that can adapt to the user\u2019s preferred output medium.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e2399cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "line": {
          "dash": "solid",
          "width": 1
         },
         "mode": "lines",
         "name": "reset: f1",
         "type": "scatter",
         "x": {
          "bdata": "AAAAAAAA4D8AAAAAAADwPwAAAAAAAPg/AAAAAAAAAEAAAAAAAAAEQAAAAAAAAAhAAAAAAAAADEAAAAAAAAAQQAAAAAAAABJAAAAAAAAAFEAAAAAAAAAWQAAAAAAAABhAAAAAAAAAGkAAAAAAAAAcQA==",
          "dtype": "f8"
         },
         "xaxis": "x",
         "y": {
          "bdata": "AAAAAAAAAABVvcO6LfOxP9CdO3fu3Mk/vpQxu5OG1z9XLrhVLrjlPyMIax35Qu0/wijg0Hyu7z8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPw==",
          "dtype": "f8"
         },
         "yaxis": "y"
        },
        {
         "line": {
          "dash": "solid",
          "width": 1
         },
         "mode": "lines",
         "name": "reset: precision",
         "type": "scatter",
         "x": {
          "bdata": "AAAAAAAA4D8AAAAAAADwPwAAAAAAAPg/AAAAAAAAAEAAAAAAAAAEQAAAAAAAAAhAAAAAAAAADEAAAAAAAAAQQAAAAAAAABJAAAAAAAAAFEAAAAAAAAAWQAAAAAAAABhAAAAAAAAAGkAAAAAAAAAcQA==",
          "dtype": "f8"
         },
         "xaxis": "x2",
         "y": {
          "bdata": "AAAAAAAAAAAor6G8hvKqP6uqqqqqqsM/c3Nzc3Nz0z95P3Kar9XjPxvKayivoew/5K6fLVaG7z8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPw==",
          "dtype": "f8"
         },
         "yaxis": "y2"
        },
        {
         "line": {
          "dash": "solid",
          "width": 1
         },
         "mode": "lines",
         "name": "reset: recall",
         "type": "scatter",
         "x": {
          "bdata": "AAAAAAAA4D8AAAAAAADwPwAAAAAAAPg/AAAAAAAAAEAAAAAAAAAEQAAAAAAAAAhAAAAAAAAADEAAAAAAAAAQQAAAAAAAABJAAAAAAAAAFEAAAAAAAAAWQAAAAAAAABhAAAAAAAAAGkAAAAAAAAAcQA==",
          "dtype": "f8"
         },
         "xaxis": "x2",
         "y": {
          "bdata": "AAAAAAAAAADhehSuR+G6P+F6FK5H4dI/w/UoXI/C3T8AAAAAAADoP+xRuB6F6+0/16NwPQrX7z8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPw==",
          "dtype": "f8"
         },
         "yaxis": "y2"
        },
        {
         "line": {
          "dash": "solid",
          "width": 1
         },
         "mode": "lines",
         "name": "reset: mean_delay",
         "type": "scatter",
         "x": {
          "bdata": "AAAAAAAA4D8AAAAAAADwPwAAAAAAAPg/AAAAAAAAAEAAAAAAAAAEQAAAAAAAAAhAAAAAAAAADEAAAAAAAAAQQAAAAAAAABJAAAAAAAAAFEAAAAAAAAAWQAAAAAAAABhAAAAAAAAAGkAAAAAAAAAcQA==",
          "dtype": "f8"
         },
         "xaxis": "x3",
         "y": {
          "bdata": "AAAAAAAALkBmZmZmZiYrQB+F61G4XiZArkfhehRuIkCkcD0K16MVQMP1KFyPwglAUrgehetRBkAzMzMzMzMJQIXrUbgehQxACtejcD0KEECF61G4HgUSQGZmZmZm5hNAmpmZmZkZFkC4HoXrUTgYQA==",
          "dtype": "f8"
         },
         "yaxis": "y3"
        },
        {
         "line": {
          "dash": "solid",
          "width": 1
         },
         "mode": "lines",
         "name": "reset: median_delay",
         "type": "scatter",
         "x": {
          "bdata": "AAAAAAAA4D8AAAAAAADwPwAAAAAAAPg/AAAAAAAAAEAAAAAAAAAEQAAAAAAAAAhAAAAAAAAADEAAAAAAAAAQQAAAAAAAABJAAAAAAAAAFEAAAAAAAAAWQAAAAAAAABhAAAAAAAAAGkAAAAAAAAAcQA==",
          "dtype": "f8"
         },
         "xaxis": "x3",
         "y": {
          "bdata": "AAAAAAAALkAAAAAAAAAuQAAAAAAAAC5AAAAAAAAALkAAAAAAAAAAQAAAAAAAAABAAAAAAAAACEAAAAAAAAAIQAAAAAAAAAhAAAAAAAAAEEAAAAAAAAAQQAAAAAAAABRAAAAAAAAAFEAAAAAAAAAYQA==",
          "dtype": "f8"
         },
         "yaxis": "y3"
        },
        {
         "line": {
          "dash": "solid",
          "width": 1
         },
         "mode": "lines",
         "name": "reset: arl",
         "type": "scatter",
         "x": {
          "bdata": "AAAAAAAA4D8AAAAAAADwPwAAAAAAAPg/AAAAAAAAAEAAAAAAAAAEQAAAAAAAAAhAAAAAAAAADEAAAAAAAAAQQAAAAAAAABJAAAAAAAAAFEAAAAAAAAAWQAAAAAAAABhAAAAAAAAAGkAAAAAAAAAcQA==",
          "dtype": "f8"
         },
         "xaxis": "x4",
         "y": {
          "bdata": "SOF6FK5HWkB7L+q9qDdbQAAAAAAA0lxATaL3TKJXYEDPDoEby/BiQMprKK+hfGRA188WK8PnZECF61G4HgllQI/C9ShcD2VAKVyPwvUUZUBxPQrXoxxlQI/C9ShcI2VApHA9CtcrZUCuR+F6FDJlQA==",
          "dtype": "f8"
         },
         "yaxis": "y4"
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   "source": [
    "plotter_plotly = (\n",
    "    BenchmarkPlotter()\n",
    "    .set_metrics(\n",
    "        {\n",
    "            \"f1_plot\": ThresholdBasedMetricVisualizer(\n",
    "                y_metrics=[\"f1\"], title=\"F1\", ylabel=\"F1 value\", backend=\"plotly\"\n",
    "            ),\n",
    "            \"precision_recall_plot\": ThresholdBasedMetricVisualizer(\n",
    "                backend=\"plotly\",\n",
    "                y_metrics=[\"precision\", \"recall\"],\n",
    "                title=\"Precision and Recall\",\n",
    "                ylabel=\"Precision/Recall value\",\n",
    "            ),\n",
    "            \"delay_plot\": ThresholdBasedMetricVisualizer(\n",
    "                backend=\"plotly\",\n",
    "                y_metrics=[\"mean_delay\", \"median_delay\"],\n",
    "                title=\"Detection delay\",\n",
    "                ylabel=\"Delay (in sample points)\",\n",
    "            ),\n",
    "            \"arl_plot\": ThresholdBasedMetricVisualizer(\n",
    "                backend=\"plotly\",\n",
    "                y_metrics=[\"arl\"],\n",
    "                title=\"Average run length\",\n",
    "                ylabel=\"ARL\",\n",
    "            ),\n",
    "        }\n",
    "    )\n",
    "    .set_benchmark_tables({\"reset\": reset_table})\n",
    ")\n",
    "plotter_plotly.backend = \"plotly\"\n",
    "\n",
    "fig = make_subplots(\n",
    "    rows=2,\n",
    "    cols=2,\n",
    "    subplot_titles=(\"F1\", \"Precision and Recall\", \"Detection delay\", \"Average run length\"),\n",
    ")\n",
    "fig = plotter_plotly.draw(\n",
    "    figure=fig,\n",
    "    axes={\n",
    "        \"f1_plot\": (1, 1),\n",
    "        \"precision_recall_plot\": (1, 2),\n",
    "        \"delay_plot\": (2, 1),\n",
    "        \"arl_plot\": (2, 2),\n",
    "    },\n",
    ")\n",
    "fig.update_layout(height=800, width=1200, title_text=\"Reset benchmark metrics (plotly)\")"
   ]
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  {
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   "id": "9ff1834d",
   "metadata": {},
   "source": [
    "### 6.3 Interpreting the plotted tradeoffs\n",
    "\n",
    "Plots are only useful if they sharpen interpretation. In a reset threshold sweep, a rising threshold usually trades sensitivity for robustness, but the exact tradeoff depends on the algorithm, skip-period logic, and dataset family.\n",
    "\n",
    "The code below highlights one simple summary: the threshold with the highest F1 score. That value is not always the \u201cbest\u201d operating point in practice, but it is a clear anchor for the trace-level inspection that follows.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "42b34581",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>tp</th>\n",
       "      <td>200.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fp</th>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fn</th>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1</th>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_delay</th>\n",
       "      <td>3.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_delay</th>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arl</th>\n",
       "      <td>168.285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              best_row\n",
       "threshold        4.000\n",
       "tp             200.000\n",
       "fp               0.000\n",
       "fn               0.000\n",
       "precision        1.000\n",
       "recall           1.000\n",
       "f1               1.000\n",
       "mean_delay       3.150\n",
       "median_delay     3.000\n",
       "arl            168.285"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_row = reset_table.sort_values([\"f1\", \"precision\", \"recall\"], ascending=False).iloc[0]\n",
    "display(best_row.to_frame(name=\"best_row\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35c308e9",
   "metadata": {},
   "source": [
    "**Summary of Section 6:** Reset benchmark plots compress the threshold sweep into a small number of interpretable views. The table is still the source of truth, but the figures make the operating-point tradeoffs much easier to see and discuss.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bb1eacc",
   "metadata": {},
   "source": [
    "## Section 7. Explaining a Metric With One Concrete Trace\n",
    "\n",
    "A threshold sweep tells us which operating point looks strong globally, but it does not by itself explain why. The final practical problem in this notebook is therefore local interpretation: how do we connect the winning threshold back to one real detector run?\n",
    "\n",
    "This is where the earlier online and visualization notebooks come back into the story. We pick one detector configuration from the sweep, run it on one provider with state collection enabled, and inspect the resulting trace.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d248886b",
   "metadata": {},
   "source": [
    "### 7.1 Picking the winning threshold\n",
    "\n",
    "A single best row makes the later trace discussion concrete. The goal is not to claim that F1 is always the right selection criterion, only to choose one threshold from the sweep using a transparent rule.\n",
    "\n",
    "The code below uses the highest-F1 row as the anchor point for local inspection. That makes the later explanation directly tied to the same benchmark table we just plotted.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "614c8318",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen threshold for trace inspection: 4.0\n"
     ]
    }
   ],
   "source": [
    "best_threshold = float(best_row[\"threshold\"])\n",
    "print(\"Chosen threshold for trace inspection:\", best_threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eebe344f",
   "metadata": {},
   "source": [
    "### 7.2 Retrieving one cached run from the benchmark registry\n",
    "\n",
    "The benchmark sweep already ran every detector-provider pair, so trace explanation should reuse that cached execution instead of rerunning a detector. This keeps the local inspection tied to the exact benchmark result being explained.\n",
    "\n",
    "We choose the entry whose threshold matches the best row, then look up the corresponding provider run in `benchmark.registry.executions_registry`. For plotting, we apply the same feature transformer used by the benchmark so the time-series panel shows only the column that was actually passed to the detector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1a74beb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Provider name: extreme_mean_shifts_series_0000\n",
      "Ground-truth change points: [167, 334]\n",
      "Detected change points: [170, 337]\n",
      "Registry key: SingleRunDescription(detector_description=ChangePointDetectorDescription(name='online_reset_detector', parameters=frozendict({'algorithm': OnlineAlgorithmDescription(name='ShewhartControlChart', configuration=w = 10), 'threshold': 4.0, 'skip_period': 5, 'max_runlength': None, 'collect_states': False, 'data_transformer': 'column[feature_0]'})), provider_description=TimeseriesAnnotation(name='extreme_mean_shifts_series_0000', source=None, metadata=frozendict({'preset': 'extreme_mean_shifts'})))\n"
     ]
    }
   ],
   "source": [
    "provider = dataset[0]\n",
    "inspection_entry = next(\n",
    "    entry for entry in entries if float(entry.description.parameters[\"threshold\"]) == best_threshold\n",
    ")\n",
    "registry_items = list(benchmark.registry.executions_registry.items())\n",
    "matching_items = [\n",
    "    (descr, single_run)\n",
    "    for descr, single_run in registry_items\n",
    "    if descr.detector_description == inspection_entry.description\n",
    "    and descr.provider_description.name == provider.annotation.name\n",
    "]\n",
    "\n",
    "if not matching_items:\n",
    "    available_names = sorted({descr.provider_description.name for descr, _ in registry_items})\n",
    "    raise LookupError(\n",
    "        f\"No cached run found for provider '{provider.annotation.name}'. Available names count: {len(available_names)}\"\n",
    "    )\n",
    "\n",
    "key, inspection_run = matching_items[0]\n",
    "inspection_trace = inspection_run.trace\n",
    "provider = inspection_run.provider\n",
    "transformer = inspection_entry.detector.data_transformer\n",
    "transformed_provider = transformer.transform(provider) if transformer is not None else provider\n",
    "\n",
    "print(\"Provider name:\", provider.annotation.name)\n",
    "print(\"Ground-truth change points:\", list(provider.change_points))\n",
    "print(\"Detected change points:\", inspection_trace.detected_change_points)\n",
    "print(\"Registry key:\", key)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3798d646",
   "metadata": {},
   "source": [
    "### 7.3 Visualizing the winning operating point\n",
    "\n",
    "The final problem is explanation. We want one figure that makes the benchmark result legible in terms of signal, detections, and timing on a real run. This closes the loop from threshold sweep back to one concrete event sequence.\n",
    "\n",
    "`OnlineCpdPlotter` is a good fit here because it already knows how to combine the provider, the trace, and the ground-truth change points into one compact diagnostic figure. The provider shown below is the transformed one, so the time-series panel contains only the feature column used for detection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "303d1d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter = OnlineCpdPlotter(\n",
    "    backend=DrawBackend.MATPLOTLIB,\n",
    "    data_provider=transformed_provider,\n",
    "    detection_trace=inspection_trace,\n",
    ")\n",
    "plotter.set_ground_truth(list(provider.change_points), margin=10)\n",
    "plotter.set_legend_axis(\"detection_function\")\n",
    "fig, axes = plt.subplots(3, 1, figsize=(14, 10), sharex=True)\n",
    "fig.suptitle(f\"Reset detector trace at threshold={best_threshold:.2f}\", fontsize=16)\n",
    "fig = plotter.draw(\n",
    "    figure=fig,\n",
    "    axes={\"timeseries\": axes[0], \"detection_function\": axes[1], \"processing_time\": axes[2]},\n",
    ")\n",
    "plt.tight_layout()\n",
    "fig.show()"
   ]
  },
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    "**Summary of Section 7:** A reset benchmark result becomes much easier to trust once it is connected back to one concrete trace. The table tells us which threshold looks strong globally, and the trace helps explain what that operating point actually looks like on a real series.\n"
   ]
  },
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    "## Section 8. Final Recap\n",
    "\n",
    "This chapter built a full reset benchmark workflow around the current API. We created a detector sweep, separated execution orchestration from metric semantics, combined several metric families into one threshold-indexed table, visualized the resulting tradeoffs, and tied one selected operating point back to a concrete trace.\n",
    "\n",
    "That is the reset benchmark story in PySATL-CPD. The next chapter changes the evaluation semantics entirely: no-reset benchmarking keeps the detector running continuously and shifts attention from whole-timeseries event sequences toward transition-aware classification and ARL-by-state analysis.\n"
   ]
  },
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