{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting with Chronos-2\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/autogluon/autogluon/blob/master/docs/tutorials/timeseries/forecasting-chronos.ipynb)\n",
    "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/autogluon/autogluon/blob/master/docs/tutorials/timeseries/forecasting-chronos.ipynb)\n",
    "\n",
    "AutoGluon-TimeSeries (AG-TS) includes the [Chronos](https://github.com/amazon-science/chronos-forecasting) family of forecasting models. Chronos models are pretrained on a large collection of real and synthetic time series data, enabling accurate out-of-the-box forecasts on new data.\n",
    "\n",
    "AG-TS provides a robust and user-friendly way to work with Chronos through the familiar `TimeSeriesPredictor` API. It allows users to backtest models, compare them with other forecasting approaches, and ensemble Chronos with other models to build robust forecasting pipelines. This tutorial demonstrates how to:\n",
    "\n",
    "- Use Chronos-2 in **zero-shot** mode to generate forecasts without dataset-specific training\n",
    "- **Fine-tune** Chronos-2 on custom data to improve accuracy\n",
    "\n",
    ":::{note}\n",
    "\n",
    "**New in v1.5:** AutoGluon now features [Chronos-2](https://arxiv.org/abs/2510.15821) — the latest version of Chronos models with _zero-shot_ support for covariates and a [90%+ win-rate](https://huggingface.co/spaces/autogluon/fev-bench) over Chronos-Bolt. The older version of this tutorial with the Chronos-Bolt model is available [here](https://auto.gluon.ai/1.4.0/tutorials/timeseries/forecasting-chronos.html).\n",
    "\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "remove-cell",
     "skip-execution"
    ]
   },
   "outputs": [],
   "source": [
    "# We use uv for faster installation\n",
    "!pip install uv\n",
    "!uv pip install -q autogluon.timeseries --system\n",
    "!uv pip uninstall -q torchaudio torchvision torchtext --system # fix incompatible package versions on Colab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting started with Chronos-2\n",
    "\n",
    "Being a pretrained model for zero-shot forecasting, Chronos is different from other models available in AG-TS. \n",
    "Specifically, by default, Chronos models do not really `fit` time series data. However, when `predict` is called, they perform _zero-shot inference_ by using the provided contextual information. In this aspect, they behave like local statistical models such as ETS or ARIMA, where all computation happens during inference. \n",
    "\n",
    "AutoGluon supports the original Chronos models (e.g., [`chronos-t5-large`](https://huggingface.co/autogluon/chronos-t5-large)), the Chronos-Bolt models (e.g., [`chronos-bolt-base`](https://huggingface.co/autogluon/chronos-bolt-base)), and the latest Chronos-2 models (e.g., [`chronos-2`](https://huggingface.co/autogluon/chronos-2)). The following table compares the capabilities of the three model families.\n",
    "\n",
    "| Capability | Chronos | Chronos-Bolt | Chronos-2 |\n",
    "|------------|---------|--------------|-----------|\n",
    "| Univariate Forecasting | ✅ | ✅ | ✅ |\n",
    "| Cross-learning across items | ❌ | ❌ | ✅ |\n",
    "| Multivariate Forecasting | ❌ | ❌ | ✅ |\n",
    "| Past-only (real/categorical) covariates | ❌ | ❌ | ✅ |\n",
    "| Known future (real/categorical) covariates | 🧩 | 🧩 | ✅ |\n",
    "| Fine-tuning support | ✅ | ✅ | ✅ |\n",
    "| Max. Context Length | 512 | 2048 | 8192 |\n",
    "| Max. Prediction Length | 64 | 64 | 1024 |\n",
    "\n",
    "\n",
    "The easiest way to get started with Chronos is through the model-specific presets. \n",
    "\n",
    "- **(recommended)** The Chronos-2 models can be accessed using the `\"chronos2_small\"` and `\"chronos2\"` presets.\n",
    "- The Chronos-Bolt️ models can be accessed using the `\"bolt_tiny\"`, `\"bolt_mini\"`, `\"bolt_small\"` and `\"bolt_base\"` presets.\n",
    "\n",
    "Alternatively, Chronos models can be combined with other time series models using presets `\"medium_quality\"`, `\"high_quality\"` and `\"best_quality\"`. More details about these presets are available in the documentation for [`TimeSeriesPredictor.fit`](https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesPredictor.fit.html).\n",
    "\n",
    "\n",
    "🧩 Chronos/Chronos-Bolt do not natively support future covariates, but they can be combined with external covariate regressors. This only models per-timestep effects, not effects across time. In contrast, Chronos-2 supports all covariate types natively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zero-shot forecasting\n",
    "\n",
    "### Univariate Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's work with a subset of the [Australian Electricity Demand dataset](https://zenodo.org/records/4659727) to see Chronos-2 in action.\n",
    "\n",
    "First, we load the dataset as a [TimeSeriesDataFrame](https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesDataFrame.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from autogluon.timeseries import TimeSeriesDataFrame, TimeSeriesPredictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>target</th>\n",
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       "                                  target\n",
       "item_id timestamp                       \n",
       "T000000 2013-03-10 00:00:00  5207.959961\n",
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = TimeSeriesDataFrame.from_path(\n",
    "    \"https://autogluon.s3.amazonaws.com/datasets/timeseries/australian_electricity_subset/test.csv\"\n",
    ")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create the [TimeSeriesPredictor](https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesPredictor.html) and select the `\"chronos2\"` presets to use the Chronos-2 (120M) model in zero-shot mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sorting the dataframe index before generating the train/test split.\n",
      "Beginning AutoGluon training...\n",
      "AutoGluon will save models to '/fsx/ansarnd/repos/autogluon/docs/tutorials/timeseries/AutogluonModels/ag-20251214_125317'\n",
      "=================== System Info ===================\n",
      "AutoGluon Version:  1.4.1b20250910\n",
      "Python Version:     3.11.11\n",
      "Operating System:   Linux\n",
      "Platform Machine:   x86_64\n",
      "Platform Version:   #38~22.04.1-Ubuntu SMP Fri Aug 22 15:44:33 UTC 2025\n",
      "CPU Count:          96\n",
      "Pytorch Version:    2.7.1+cu126\n",
      "CUDA Version:       12.6\n",
      "GPU Memory:         GPU 0: 39.38/39.38 GB\n",
      "Total GPU Memory:   Free: 39.38 GB, Allocated: 0.00 GB, Total: 39.38 GB\n",
      "GPU Count:          1\n",
      "Memory Avail:       1030.21 GB / 1121.80 GB (91.8%)\n",
      "Disk Space Avail:   1758.43 GB / 11459.15 GB (15.3%)\n",
      "===================================================\n",
      "Setting presets to: chronos2\n",
      "\n",
      "Fitting with arguments:\n",
      "{'enable_ensemble': True,\n",
      " 'eval_metric': WQL,\n",
      " 'hyperparameters': {'Chronos2': {'model_path': 'autogluon/chronos-2'}},\n",
      " 'known_covariates_names': [],\n",
      " 'num_val_windows': 1,\n",
      " 'prediction_length': 48,\n",
      " 'quantile_levels': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],\n",
      " 'random_seed': 123,\n",
      " 'refit_every_n_windows': 1,\n",
      " 'refit_full': False,\n",
      " 'skip_model_selection': True,\n",
      " 'target': 'target',\n",
      " 'verbosity': 2}\n",
      "\n",
      "Inferred time series frequency: '30min'\n",
      "Provided train_data has 172320 rows, 5 time series. Median time series length is 34464 (min=34464, max=34464). \n",
      "\n",
      "Provided data contains following columns:\n",
      "\ttarget: 'target'\n",
      "\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'WQL'\n",
      "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
      "===================================================\n",
      "\n",
      "Starting training. Start time is 2025-12-14 12:53:21\n",
      "Models that will be trained: ['Chronos2']\n",
      "Training timeseries model Chronos2. \n",
      "\t6.94    s     = Training runtime\n",
      "Training complete. Models trained: ['Chronos2']\n",
      "Total runtime: 6.97 s\n",
      "Best model: Chronos2\n"
     ]
    }
   ],
   "source": [
    "num_test_windows = 3\n",
    "prediction_length = 48\n",
    "train_data, test_data = data.train_test_split(num_test_windows * prediction_length)\n",
    "\n",
    "predictor = TimeSeriesPredictor(prediction_length=prediction_length).fit(\n",
    "    train_data,\n",
    "    presets=\"chronos2\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As promised, Chronos does not take any time to `fit`. The `fit` call merely serves as a proxy for the `TimeSeriesPredictor` to do some of its chores under the hood, such as inferring the frequency of time series and saving the predictor's state to disk. \n",
    "\n",
    "Let's use the `predict` method to generate forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Model not specified in predict, will default to the model with the best validation score: Chronos2\n"
     ]
    },
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       "                                    mean          0.1          0.2  \\\n",
       "item_id timestamp                                                    \n",
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       "        2015-02-26 02:00:00  4439.416992  4369.610352  4390.421875   \n",
       "\n",
       "                                     0.3          0.4          0.5  \\\n",
       "item_id timestamp                                                    \n",
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       "        2015-02-26 02:00:00  4412.242676  4428.110352  4439.416992   \n",
       "\n",
       "                                     0.6          0.7          0.8  \\\n",
       "item_id timestamp                                                    \n",
       "T000000 2015-02-26 00:00:00  5234.564453  5248.638672  5265.144531   \n",
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       "        2015-02-26 02:00:00  4456.724121  4474.257324  4496.027832   \n",
       "\n",
       "                                     0.9  \n",
       "item_id timestamp                         \n",
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = predictor.predict(train_data)\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a dataframe with the point forecast (`mean`) and nine quantiles which capture the uncertainty in the forecasts. Custom quantile levels can be specified as follows:\n",
    "```py\n",
    "TimeSeriesPredictor(..., quantile_levels=[0.05, 0.1, 0.5, 0.9, 0.95])\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AG-TS also makes it easy to generate predictions for multiple backtest dates and to visualize the models' predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EazmaYkTaoxiRY/F6vWzYsAGAc845h7i4uDCPqJfz1sCBFeCOBVfrkxGPUlcGUfGQPR1stu4dXxcoRqQ9ihHpiEg/HmmqwtYeJdtFeoG31psS8hdMzMTWgYOrKTnJ7C+rY+2BciXbRURERET6sX379rFo0SJeeeWV5tsyMzPxer1UVFS0WN1eWFhIZmZm8zbLly9vsa/CwsLm+5r+bbrtyG0SExPbXNUOEBUVRVTU0f1HXS5XRF5giWT6nUl7FCPSHsWItMayrObvFSMhUFUOwQaITu/4Y6JjwVcL+MAVeUlKxYi0RzEinRGpMdLRMbXd+FlEIkKLEvKTj11CvknT6ve1Byq6aVQiIiIiItIbPP300wwcOJB58+Y13zZ9+nRcLhfvvfde823btm1j//79zJo1C4BZs2axYcMGioqKmrdZuHAhiYmJjB8/vnmbI/fRtE3TPkRERET6vboyKNsFMcmde5wzBnwN4KnulmGJiEjoKNkuEuGaSshnJ0UzrZ0S8k0OJ9srW8wgExERERGR/iMYDPL0009zzTXX4HQeLmyXlJTEddddxy233ML777/PqlWr+OY3v8msWbM4+eSTAZgzZw7jx4/n61//OuvWrePdd9/l5z//OfPnz29elf7d736X3bt389Of/pStW7fy5z//mZdeeombb745LK9XREREJKL4PVCyHYI+iEro3GNtNvNVX9EtQxMRkdBRsl0kwr29obGE/KSsDpWQB5iQnYjTbqOkxkNeRX13Dk9EJCzcbjd33HEHU6dOxe12h3s4EoEUI9IexYj0B4sWLWL//v1861vfOuq+hx9+mIsuuojLL7+cM844g8zMzBal5h0OB2+++SYOh4NZs2Zx9dVX841vfIN77rmneZvc3FzeeustFi5cyJQpU3jwwQf529/+xty5c3vk9YmIiEj30LFyCFgWlO2BmiKIG9i1fbhiobYYgoHQji0EFCPSHsWI9Cfq2S4SwfyBIIu3mLKNF07K7PDjol0OxmUlsiGvktX7Kxg8ILa7higiIiIiIhFqzpw5bVa6io6O5tFHH+XRRx9t8/FDhw7l7bffPuZznHXWWaxZs+a4xikiIiLS59SWmGR7XCrYHV3bhzsOakuhoRJiU0I7PhERCRmtbBeJYFsOVVPt8ZMQ5WRqzoBOPXbWiFQA3tl4qDuGJiIiIiIiIiIiIiKfZ1lQlW++dx3HIiiHC4J+lZIXEYlwSraLRLBle0oBOHHYABz2jpWQb3LJ1EEALNpcRGWdL+RjExEJJ7/fzyuvvMKePXvw+/3hHo5EIMWItEcxIiIiIiLSOh0rH6eGSqgphJik49+XOxaqD0VcKXnFiLRHMSL9iZLtIhFs+Z4yAGbmpnb6seOzExmbmYA3EOTNDfmhHpqISFgFg0G2bt1KZWUlwWAw3MORCKQYkfYoRkREREREWqdj5eNUUwR+D7hijn9fUQngqTIJ/AiiGJH2KEakP1GyXSRCBYMWK/Y2Jdu71pPn8hMGA/DK6ryQjUtEREREREREREREWuGtg8q80Kxqh8ZS8oGIS7aLiMhhSraLRKidxTWU1/mIcTmYNKhrB2cXT83GboNV+8rZW1Ib4hGKiIiIiIiIiIiISLPaYvBWgzs+dPt0RUPVIdDqYBGRiKRku0iEWtZYQv6Eocm4nV37rzowMZrTRqUD8MoarW4XERERERERERER6RYBP1QeMH3WbbbQ7TcqATyV5ktERCKOku0iEaq5X/uwzvdrP9LlJwwC4JXVBwkGreMel4iIiIiIiIiIiIh8Tn25KfceHaIS8k0cbgj6ob4itPsVEZGQULJdJAJZlsXyPaVA1/u1N5kzPpM4t4OD5fWsO1gRgtGJiIiIiIiIiIiISAv15YAFdmfo9+2KgZpCsLSYSkQk0ijZLhKB9pfVUVjlweWwMW1I8nHtK8btaE7Yb8qvCsHoRERERDAXeSoOQN5q8HvCPRoREREREZHwCQZMMtwV1z37d8aAtw78Dd2zfxER6TIl20UiUFO/9imDk4l2OY57f6MzEgDYXlh93PsSEYkELpeLn/zkJ0yaNAmXyxXu4UgEUox0M78XirfCoXUm4V5xINwj6jTFiIiIiIhI63Ss3AUNleCpgaj47tm/Mwr89SbhHgEUI9IexYj0J0q2i0SgZbsb+7UfZwn5JqOUbBeRPsZms+F2u3E4HNhstnAPRyKQYqQbNVSZJHvJDohLhdgBUL6nsWRi76EYERERERFpnY6Vu6ChEqxA95SQB7A7THUxX2Qk2xUj0h7FiPQnSraLRJhN+ZW8sS4fgFNGpIVkn6MzzIzKHYU1IdmfiIiI9FPVhZC/BmoLITELnNEQlQABL5TuNqUTRURERERE+pNgEKoLwBXdvc9jd5jJzyIiElHCmmwPBAL84he/IDc3l5iYGEaMGMG9996LZVnN21iWxZ133klWVhYxMTHMnj2bHTt2tNhPWVkZV111FYmJiSQnJ3PddddRU9Myqbh+/XpOP/10oqOjycnJ4YEHHuiR1yjSGXVePz94cQ3eQJDZ4wZy6sjUkOx35ECTbC+t9VJao56qItL7+f1+3njjDfbt24ff7w/3cCQCKUZCLBiEsj1waC0EPJCQ3XLFRlw6VOdDVX7YhthZihERERERkdbpWLmTvNXgqQJ3N5WQb+KMhvoyc34WZooRaY9iRPqTsCbb77//fh577DH+9Kc/sWXLFu6//34eeOABHnnkkeZtHnjgAf74xz/y+OOPs2zZMuLi4pg7dy4NDQ3N21x11VVs2rSJhQsX8uabb/LRRx9xww03NN9fVVXFnDlzGDp0KKtWreK3v/0td911F0888USPvl6R9tzzxmZ2F9eSkRjFA1+aErLyKrFuJzkpMQBs1+p2EekDgsEgGzZsoLy8nGAEnGRK5FGMhJCvAYo2Q+EmcMWYxPrnj1EcLnDFQtlu8PeOiX2KERERERGR1ulYuZMaKk21L2dU9z6PKxr8DRFRSl4xIu1RjEh/0k0NRDrms88+4+KLL2bevHkADBs2jBdffJHly5cDZlX773//e37+859z8cUXA/Dss8+SkZHBa6+9xpVXXsmWLVt45513WLFiBSeeeCIAjzzyCBdeeCG/+93vyM7O5vnnn8fr9fLUU0/hdruZMGECa9eu5aGHHmqRlG/i8XjweA5fJKyqMqVZfD4fPp+vW38nfUXT70m/L8PrD/K7hTsoqfGQHOsmJdbF6Ix4puYkkx7vZn1eFa+vO8Q/VxzAZoPfXT6JBLctpL+/kelxHCirZ+uhCk4ckhiy/XaVYkTaoxiRYzkyLvT5LK1RjIRIQ4XpzV5TDPHp5uJRoI2TZHciVOZDeR4k5/ToMLtCMSIdEenHI5E6LhEREZF+w7JMu63uTrQDOKLAX2qS7VHdvIpeREQ6LKzJ9lNOOYUnnniC7du3M3r0aNatW8cnn3zCQw89BMCePXsoKChg9uzZzY9JSkripJNOYsmSJVx55ZUsWbKE5OTk5kQ7wOzZs7Hb7SxbtoxLL72UJUuWcMYZZ+B2u5u3mTt3Lvfffz/l5eUMGDCgxbjuu+8+7r777qPGu2DBAmJjY0P9a+jTFi5cGO4hRIQPDtl4da+j1fuiHBaewOHVYedlBynbupS3t4Z2DPZqO2Bn0YrNpJRuDO3Oj4NiRNqjGJHWBAKH+0IvXrwYh6P191jpvxQj3aG4Y5vt3gBs6NaRhIJiRDojUo9H6urCv6pJREREpF/z1YOnEtxx3f9cTRXGvLXd/1wiItJhYU22/+xnP6OqqoqxY8ficDgIBAL86le/4qqrrgKgoKAAgIyMjBaPy8jIaL6voKCAgQMHtrjf6XSSkpLSYpvc3Nyj9tF03+eT7bfffju33HJL889VVVXk5OQwZ84cEhPDvyK4N/D5fCxcuJDzzjsPl8sV7uGEVXWDn7se/hjw8aUTBpEW76ao2sPm/Cq2F9XgCdiIczs4a3Q6F07K4LxxA0NWPv5I3rX5vPfyRrwxqVx44YyQ77+zFCPSHsWIHIvX62XDBpPMO+ecc4iL64GTWulVFCNdEAyai0Q1RVB9CLz1EJvcuYtGlmX6tg8cDwOGdttQQ0ExIh0R6ccjTVXYRERERCRMvLWm9VZMSs88n8MF9RU981wiItIhYU22v/TSSzz//PO88MILzaXdb7rpJrKzs7nmmmvCNq6oqCiioo4u++JyuSLyAksk0+8M/v7BHsrrfAxPi+M3l0/G6bA331fj8bO3pJaRA+OJdnXvaqpx2ckA7Cyqwel0dktCvysUI9IexYi0xrKs5u8VI9IaxUgn1RRD+W6oLQMCEJ0ECWld21dMAtTkwYDBpqdghFKMSGdEaoxE4phERERE+hVfrZl03FPXWl0x4KmCgB8cYU3viIhIo7C+G99666387Gc/48orrwRg0qRJ7Nu3j/vuu49rrrmGzMxMAAoLC8nKymp+XGFhIVOnTgUgMzOToqKiFvv1+/2UlZU1Pz4zM5PCwsIW2zT93LSNSHcoqfHwt493A/CTuWNaJNoB4qOcTByU1CNjGZEej80G5XU+Smq8pCf0QB8hERERiWyWBZUHoXgrWAGISwGHu/3HHUt0ktln9SFIyW1/exERERERkd6qrgycx3kO1RnOKKgrN0l+R89cVxYRkWOzt79J96mrq8NubzkEh8NBMBgEIDc3l8zMTN57773m+6uqqli2bBmzZs0CYNasWVRUVLBq1armbRYvXkwwGOSkk05q3uajjz7C5/M1b7Nw4ULGjBlzVAl5kVD60+Kd1HoDTBmcxAUTwzuxI8btYEhKLAA7CqvDOhYRERGJAMEglO2Bwo1mRUR8RqcT7Qt21bNodwPewOFV4thspvR8VZ5ZbSEiIiIiItIXBXzQUGVWm/cUhxuCPvDW9dxziojIMYU12f6FL3yBX/3qV7z11lvs3buXV199lYceeohLL70UAJvNxk033cQvf/lLXn/9dTZs2MA3vvENsrOzueSSSwAYN24c559/Ptdffz3Lly/n008/5cYbb+TKK68kOzsbgK997Wu43W6uu+46Nm3axL/+9S/+8Ic/tOjLLhJqB8vreH7ZPgBuO39sRJRtHzUwAYDtSraLSC/ncrn40Y9+xMSJE1VCV1qlGDmGgB+qDkHeKijaDFHxEJ3c6d0s2t3ADW+V8+03y5j5ZAE/f7+C/OqAuTM6ERoqob48tGMPIcWIiIiIiEjrdKzcQd5a8Nf3bLIdABt4anr4OVtSjEh7FCPSn4S1jPwjjzzCL37xC77//e9TVFREdnY23/nOd7jzzjubt/npT39KbW0tN9xwAxUVFZx22mm88847REcf7v/4/PPPc+ONN3Luuedit9u5/PLL+eMf/9h8f1JSEgsWLGD+/PlMnz6dtLQ07rzzTm644YYefb3Svzz6/k58AYtTR6Zyysgu9jwNsdEZ8SzaUsj2ovAejImIHC+bzUZcXBxOpzMiJjNJ5FGMtKG+HIq2Ql0pOFwQP9D820n+oMV9n1YBEOWAigaL5zbUsfSgl/99LR2XwwnYoKYQ4tND/CJCQzEiIiIiItI6HSt3kLcWggGw93CaxRkFnsqefc7PUYxIexQj0p+ENdmekJDA73//e37/+9+3uY3NZuOee+7hnnvuaXOblJQUXnjhhWM+1+TJk/n444+7OlSRTjlQVse/Vx4E4ObZo8M8msPGZJqV7SojLyIi0g/VV0DBRrMCIiHjuC4I/WtTHbvK/QyItrP46wPZUOTlpncr2Fnu57kNtXxzarxZ3V5TCN5cU1ZeRERERESkL/FUmzZaPc3hPiLR7+j55xcRkRbCWkZepK96ZPEO/EGL00elceKwlHAPp9nhMvI1WJbVztYiIpHL7/fzzjvvcPDgQfx+9YSWoylGPqehEgo2mItBx5lor/EGeXiZmbj3g5nxDIixc8bQaH48yxxnPLysmrL6gEmwe2vNKvoIpBgREREREWmdjpU7wLLMuU6Pl5DHrGwPeMFX3/PP3UgxIu1RjEh/omS7SIjtK63l5dV5ANx8XuSsagcYnh6H3QaV9T7yKsJ3MCYicryCwSCrV6+mpKSEYDAY7uFIBFKMNAoGoboADq0HTxUkZB73yosnVtdQUhdkaJKDqycdXrF+5YRYxqY5qfJYPLy0sYqOKwYq88w4IoxiRERERESkdTpW7gBfnflyRre/bag53BDwgN/T88/dSDEi7VGMSH+iZLtIiP1+0Q4CQYszR6dzwpAB4R5OC9EuR/OY3t1UGObRiIiISLeqL4eCdZC3Cvz1kJB13In2otoAf11dC8BPT0nE7Ti8P4fdxv+dkQTA8xvr2Fbqg+gkU76+vvy4nldEuiYvL4+rr76a1NRUYmJimDRpEitXrmy+/9prr8Vms7X4Ov/881vso6ysjKuuuorExESSk5O57rrrqKmpabHN+vXrOf3004mOjiYnJ4cHHnigR16fiIiISNh468zq8nAk2202sDDneSIiEnZKtouE0NOf7uHVNZG5qr3JvMlZALy1Pj/MIxEREZGQsyyoLYVDG+DACqjKh9hUiEsPSS/Bh5dWU++3mJbp4sKRR19UmjU4ivNHRBO04Jm1teBwgRVQsl0kDMrLyzn11FNxuVz873//Y/PmzTz44IMMGNByQvD555/PoUOHmr9efPHFFvdfddVVbNq0iYULF/Lmm2/y0UcfccMNNzTfX1VVxZw5cxg6dCirVq3it7/9LXfddRdPPPFEj7zOnvTZrhLufXMzCzdr4rKIiEi/560x51/h6NkOYAO8SraLiESCrjdrFJEWXl+Xzz1vbgbgJ3NGMzUnObwDasOFk7K4583NrN5fQV5FPYOSw9BXSEREeoeAz/T4jk0J90ikPZYFdWVQvhdqioAgxCSDKzZkT7G91Me/NtcB8P9OS8TWxkWlL46O4Z1dDWws9pkbXNFQWwypI8J3IUqkH7r//vvJycnh6aefbr4tNzf3qO2ioqLIzMxsdR9btmzhnXfeYcWKFZx44okAPPLII1x44YX87ne/Izs7m+effx6v18tTTz2F2+1mwoQJrF27loceeqhFUr4v+GxnKU9+soeaBj/njc8I93BEREQknOrLzeTicHFEgacyfM8vIiLNlGwXCYFPdpTw45fWYllwzayhzD97ZLiH1KaMxGhmDkth2Z4y3lqfzw1njAj3kOQ4ldd6eW9rERsOVrAhrxK7zcbEQUlMGpTE7PEZJMWE8cBfRHovvweKtpoLCIOmmXLgEpk81VC2F6rywAqayRHOqJA/zW8+rSJowdwR0ZyY3fb+x6Wbz51tpT78QQunOw48NWblR1RCyMclIq17/fXXmTt3LldccQUffvghgwYN4vvf/z7XX399i+0++OADBg4cyIABAzjnnHP45S9/SWpqKgBLliwhOTm5OdEOMHv2bOx2O8uWLePSSy9lyZIlnHHGGbjd7uZt5s6dy/333095eflRK+kBPB4PHs/hHqNVVVUA+Hw+fD5fSH8PoTQ+Mx6AdQcrwj7OpucP9zgkcilGpD2KETmWI+Mi0j+fw8JbDzXl4IyBQLh6UbugvgY8DWB39PizK0a6UU0RYEF8757cqRiRjoj045GOjkvJdpHjtDGvku/8YyW+gMW8yVnc+YUJba70ihQXTclm2Z4y3lx/SMn2Xq6s1su8P37MocqGFrev3GfK9Q5JieWNG08jKVYJdxHpBF8DFG2GyoPm54oDkJGoVcmRyFcPBRuhrsSUi3d1T8Wazw54WLzXg9MOt52SeMxthyY5iHXZqPNZ7Cn3Myo1GmpLTMJdyXaRHrN7924ee+wxbrnlFu644w5WrFjBD3/4Q9xuN9dccw1gSshfdtll5ObmsmvXLu644w4uuOAClixZgsPhoKCggIEDB7bYr9PpJCUlhYKCAgAKCgqOWjGfkZHRfF9ryfb77ruPu++++6jbFyxYQGxs6CpyhFqFB8DJ9oIqXnvjbdw9f137KAsXLgz3ECTCKUakPYoRaU0gEGj+fvHixTgcEfChF7HC3DJr87theVrFiLRHMSKdEanHI3V1dR3aTsl2keOwr7SWa59eTq03wCkjUnnoy1Nw2CM/EXH+hEz+778bWX+wkv2ldQxJjdwLWr3N9sJqXl51kBi3g+zkGAYnx5CdHENWcjRRztAeUFiWxU//s45DlQ1kJ0Vz4aQsJg1OwrJg/cFK3tqQz/6yOn7877U88fUTsfeC2BSRMPN7zUr2ygOm13diliklX3kQErNVTj7SBANQstOUaE8aBDZ7tzyNZVn85lOz6vRrE2MZPuDYpxB2m40xqU7WFPjYXOJjVKrLjK2+3MSUiPSIYDDIiSeeyK9//WsApk2bxsaNG3n88cebk+1XXnll8/aTJk1i8uTJjBgxgg8++IBzzz2328Z2++23c8sttzT/XFVVRU5ODnPmzCEx8dgTesLJsiz+tP1Dimu8DJ1yCtOGJIdtLD6fj4ULF3LeeefhcmlirRxNMSLtUYzIsXi9XjZs2ADAOeecQ1xcXJhHFEEsC/LXmPOb+PQwjiMI1QUweGZYztUVI92kbK9Z/OCONbGWMQHiB7b7sEikGJGOiPTjkaYqbO1Rsl2ki4qrPXzjqeWU1HgZn5XIX74+PeTJ1O6SnhDFrBGpfLqzlDc35PP9syK37H1vUVTVwMOLtvOvFQcIWq1vMzYzgUevOoER6fEhec5/LN3Hoi1FuB12/nbNDMZnH74wecm0QVx2wiAue+wzFm0p4omPd/PdM1XFQPoOl8vF97//fd5///2IPBDrdQI+c0JXnW9KfWMzSVG703zVV0DFPohOBnv3JHRDrV/ESMV+83eJH9htiXaAD/d5WF/kI8Zp44czO7YyfVyaizUFPraW+Ll4DOZCQV2JmSAQhhKHrekXMSL9WlZWFuPHj29x27hx43j55ZfbfMzw4cNJS0tj586dnHvuuWRmZlJUVNRiG7/fT1lZWXOf98zMTAoLC1ts0/RzW73go6KiiIo6uh2Fy+WK+P+Pkwcn897WIjYX1DBzRBgvsDfqDb8zCS/FiLRHMSKtcTqdzcfKsbGxipEj1VeAtwLiksERzvNjO9htgA/C8PdRjHQDby1UH4S4JNPKr7YESrdBVEyvXPygGJHOiNTjkY6OqXdcLRWJMA2+ANc/u5J9pXXkpMTwzLdmkBAdeW8Ex3LR5GwAXl2dh8cfaGdrOZbthdWc++CHvLjcJNpnj8vgyhk5nD4qjeHpcUS7zFvt1oJq5j+/mgbf8f++N+dX8cu3tgBw+4VjWyTam0wclMRdX5gAwG/f3cbHO4qP+3lFIoXNZiM5OZmoqKiIb90R8fweKNwMxVto7gnWlGhvEpsCVYdMsrSX6PMxUlMEJdshOrFb+rM3sSyLP62oAeCqSbGkxXYsUT4uzRwXbSlp7G3ligNvnekvHyH6fIxIv3fqqaeybdu2Frdt376doUOHtvmYgwcPUlpaSlaWqUIxa9YsKioqWLVqVfM2ixcvJhgMctJJJzVv89FHH7XoZbdw4ULGjBnTagn53m7S4CQA1udVhnkk/UcwaPHW+kPsL+1YCUcRETl+OlY+hroyCHi79Tysw2yY/vHheGrFSOhV5oG3GqIar/PGpYGvziTdeyHFiPQnSraLdJJlWdz+ygbWHqggKcbFs986iYEJ0eEeVqedPyGTOLeDHUU1fO+51Uq4H4eHF26n2uNnXFYi//nuLP52zYn85vLJ/OO6k1j847PYcs/5fHTr2aTFu9laUM09b27u9HNYlkWDL8CqfWX84MU1fPFPn+D1Bzl7TDrXnjKszcd9dWYOl00bRCBoce3TK3h44XZ8geBxvFoR6fUsCwJ+UzLeUw0FG8zq6IRMiE7Cstk5UOXn9e313PdJJS9tqsNnc5t+7WW7KSqr5LU1edR4/OF+Jf1XfYWZIGGzdXsP9GV5XlYe8uK2w/XTOl6ZZXz655LtDpepoBBByXaRvu7mm29m6dKl/PrXv2bnzp288MILPPHEE8yfPx+Ampoabr31VpYuXcrevXt57733uPjiixk5ciRz584FzEr4888/n+uvv57ly5fz6aefcuONN3LllVeSnW0m737ta1/D7XZz3XXXsWnTJv71r3/xhz/8oUWZ+L5kclOy/aCS7T3l4UXbmf/Cam74x0osq40yYiIiIj0h4IeqPHBHSDlsRxR4dEzSJ9SXQ+V+iB1gzvWbuOOgptDEnohELJWRF+mkxz/czatr8nDYbTx21QnkpkXIwVUnDYhz89dvnMi3/r6CxVuL+P5zq/nz1Sf0mlL4kWJ7YTX/21gAwB+vnMqojKOTHjabjSGpsTz8lal846nlvLBsP6eMSG2uLtDEsiw8/iAef5DqBh9Ld5fxwbYilu8po7zOiy/Q8sLSqSNT+d0VU445M9Bms/GrSycRsCz+uzafP7y3gw+2F/PXr09nYGLvmyQi0iQQCPDee++Rl5dHIBCIyDJDEScYML29Kw+CpwawTI83b13zSvZqT5Br/lvK6gJfi4f+aUU135sex/qDxby8qxRvEL5yYg73f2lyeF5LB/TZGPHWQdEW8NVCYnb72x+nRxtXtX95QiwZ8R0/RhiTak4zCmuDlNUHSIlxgNMFdaWQnNMtY+2sPhsjkSYYBH+DaSUgPWrGjBm8+uqr3H777dxzzz3k5uby+9//nquuugoAh8PB+vXr+fvf/05FRQXZ2dnMmTOHe++9t0WJ9+eff54bb7yRc889F7vdzuWXX84f//jH5vuTkpJYsGAB8+fPZ/r06aSlpXHnnXdyww039Phr7gmTBiUDsKu4hhqPn/goXVbpTos2F/LI4p2AqRS2en8F04f2vYoJIiKRRsfKbagvB09V5PTQdrhN6fEwtOtSjISI3wNV+aa1n98Dsakt74+Kh5oSE3e9rJS8YkT6E50VinTCq2sO8sC7WwG464sTOGVkWphHdHxOGZnGk9fM4FvPrOC9rUX833838ZvLIzdxEon+/L658HP+hMxWE+1HOn1UOt8/awSPvr+LW/+9nl1FtXzrtGHYbDaeXbKXpz7ZQ0mN95j7iHbZ+cLkbK49dRgTspM6NMYYt4M/XDmNc8YO5OevbWTdgQoeWbyTey+Z2LEXKRKBAoEAy5Yta/5e2lFTBKW7TRl4hxNcsY09vm0Qkww2u6ncsriC1QU+nHYYn+ZiXJqL9/Y0sL8qwO3vV3NkUaQ31+dz1xcnEOOOzElafTJGAj4o3mr+jj2QaF9T4OXjAx4cNvjO9GOsam+ogs3/hZ2LYNRcmPY14t12hiY52FcZYEuJn1NzHCbu6itMVQWnu9vH354+GSORpqESynaDrwEGzzDvP9KjLrroIi666KJW74uJieHdd99tdx8pKSm88MILx9xm8uTJfPzxx10aY2+TnhBFdlI0+ZUNbMqr5KThqe0/SLpkb0ktN7+0FoDEaCdVDX5eXL5fyXYRkR6gY+U21BSZf+0RclzrjDLVw3z1JinbgxQjIVBfAYUbzSSOqITWz/PtTrACZttemGxXjEh/ESGfCiKRzbIsHvtwFw+8Y3oefv3koXz95LZ7HfYmp45M409fO4Hrn13JW+sP8etLJ2G3q4dKR+wtqeX1dfkA3HjOyA495ubZo1l/sJKPd5Tw8KLtPPPZHgDK61quIrXZYEJ2ImeNHsgZo9MZPCCGxBgXcW5Hl3vcXDx1EG6Hne89v5oVe8u6tA8R6YUq86Bos1nFnpDR5kWB5zbU8eaOBpx2+NflaUzPMsnQWm+QZ9bV8vKWOnKTndww1c1PFlVxoCbAwi2FfHFK9yd9pVH1IVOZIDGrcbJEaHn8Fp8e8LB4bwOrD3nZWmrK1F0yNoacxCPiJuAzCdTSnSa2dr5nVi4DrHgCUkfAkJMYm+YyyfZiH6fmRJlke02huRjkVHKqTwsGoGK/iRNvjfnb+xvA0bMXAEW6y6TBSeRXNrBByfZus6eklu/+YxXVDX6mDx3ArXPHcOUTS3lzfT6/uGg8STFaGSUiIj0s4DcTnyOlhDw0tuvymBXRPZxsl+MUDEDpLpNET8w+9jm+K9qcSw8YBnZ1hhaJREq2i7QjGLT4v9c38Y+l+wC4/vRcbr9gXJhHFVpnjUnH7bRT7fFzoLyOoakRdNAYwR77YBdBC84ek87EQR1bZe502Pn7N2fy1oZDPLxwO7tLagHITYvjB+eMZPb4DKKdDlwOW5eT6sdyQuMqkO2F1dR6/MSp7KVI32VZJtlVtNnMdo9puxrLhiIv935k+rz97JTE5kQ7QJzbzvwZCcyfcbh6xyWj6nlkjZ/XVu1Xsr2nBHzm7+mODfkqioKaAA98VsXC3Q1Ue1u2LBmb5uTmk46o3FJ9CN76sSlzd6T4gRCTYlbef/Br+MpzjEtz8e6uBraUNk4osztMXNaVQZySU31WMAAl282Fo6h4SBoM1QUQOHb1HpHeZPLgZN7dVMg69W0POV8gyBMf7eYP7+3A6w+SFh/Fo187gYzEKEZnxLO9sIbX1+bx9VnDwj1UERHpb7zV4Ks7usx3ONnsYAH++nCPRDqrphBqCsy5dHuT6d3xpmqYtxqiO3YNWkR6lrIsIu14ftk+/rF0HzYb3HnReL55am64hxRyLoedsZkJrD9Yyca8KiXbO6CoqoGXVx8E4MZzRnXqsXa7jS9MyeaCiZn8b2MBdpuNuRMycDq6f2ZiRmJ0c9nL9QcrmTUigk4QROT4NfVlb6gyvbHry8xJWXRimw+xLIsfL6jAG4Tzhkdz3bT2PwMuHj+AR9YU8+GOUkprPKTGR7X7GDlOtSVmxntCZkh3u7fCz1WvlpJXbUq6ZcTZmTsimlmDo5iW6SbzyD7ttcXw5o+hOh8cUWb2fdJgyJoGI2ebnuz/+aZJrC7+JeNG/R8AW4qPqN4SFWcenzLMrMKQviXgh+JtZkV7fBo4o83twaBZbSPSR0webC5ybjhYEd6B9DGlNR6ufXoFG/LMJIbTR6Xx60snkZlk3ku+OnMId7+xmeeX7efqk4d2y+RkERGRNnlqzPFupJ3H2GxmbNJ7+D3mnMnh7lg8OaPMYxoqlWwXiVCqOSFyDFUNPh5etAOAn8/rm4n2Jk39vzfla3VGR7y7qQB/0GJqTnKXewY6HXa+MCWbeZOzeiTR3mTaEDPeNQfKe+w5RaQHBINQsgPyVkHpDvDVmhn3x0i0A2ws9rG9zE+008ZvZyd36ML1yFQXk9PtBCx4c83+UL0CaUswCJUHzEm43dH+9h20pcTHl/5TQl51gNxkB//+UipLvpXBPWclc8HImJaJ9royePMWkyiPToa5v4TLn4Q598KkyyAmEVwxcO5dZlb+gWVMr/kAgJ1lfnyBxhXz7gRTRr5en0F9jt9jKhuU7YL49MOJdjAXAJtaDYj0AZMaq1rtLa2j8nPtoKRriqob+Opfl7Ihr5LkWBcPf2UKz35rJjkpsc3bXDptEG6nna0F1aoqICIiPa++HBwRuHbRFWMm21tW+9tKZKjMM+fYnenB7oyCmuLuG5OIHBcl20WO4fEPdlFW62VEehzfmNU3erS3ZeIgk4zZmF8V5pH0Dgs2FwJwwcTQrjDsCdOGJAOwZn9FWMchIiFkWVC+1/TQjhlgVhzHppqTsXa8ud2Umzt3WBTJ0R0/NLxkrOkH9+pqJdu7XV2pWdke07XJXZ+3u9zPfZ9W8eX/lFBSF2RcmpOXvpTGjOwo7K1NtijbA2/8yCT8oxLhjJ/C4Bmt94obOBamfBWA1O0vkeC24Q3CrnLT/x27wyTjdZGgb6mvgENrG1e0Dzz6vcfuAG9dOEYm0i2SY90MaUwCN63Clq4rqGzgyieWsr2whozEKF7+3ilcOm3wURMAk2PdzJuUBcC/VhwIx1BFRKS/CvhMst0V2/62Pc0ZbcrI+3S83St4asz1m5jk9svHHykqHhrKzeR1EYk4SraLtCGvop4nP9kDwM8uGIerB1ceh8PEppXteZVYmgl5TJV1PpbsKgVgzoTenWzX31qkDwj4TRK0ZBvEJJlZ7R1kWRZv7jCrTS8a3fHHAXxhTAwOG6w91MCeQyWdeqx0gmU19ke3HXe5wrL6AF9/rZRz/lHEX1bVUO21mJ7l4p9fTCC9brdJlFYfMqXpAo0rNbcvgNe+15hoT4DTfwzDTjn2E024BLBhq9jLKQMqALOKvllUPNQUgU99BXs9yzKrMvJWQ22pmejT2iQfh8v0FxTpQ5pKya/Zr0odx2v+C6vZXVzLoOQYXvrOLEakx7e57eUnDAYaK40Fgj01RBER6e881WbyqDtCk+2+Bk1u7S1qi8FbY86vO8MZY86hi7fpXFokAkVg3RORyPDgu9vw+IOclJvC7HEDwz2cbjcmMwGH3UZprZeCqgaykjqXdOlP3t9WhD9oMWpgPLlpva+//YTsJFwOGyU1HvIq6hk8IAJPFETa4XK5uP766/noo49wuSKsX1p3C/jBU2W+6srNv94ac9LvbvvidGvWFPjIqw4Q57Jx9rDo9h9whPRYB6flRPHhfg/3vLGJJ799BnZ75PRO7TMxUl8ONQUQm3xcuymuC3DVK6XsKvMw3p7HFWn7mB2/l8Gendhe3A1W4OgH2Z0QbFyRPiAXTr8FMie1/2Rx6ZAxHgo3can9E95lHmsLfFw6tvF+d7xJ0NaXd2pySKj1mRgJl2DArMgo2W4S7IlZbW/rcJsy8wF/ZJbeFOmCmbkpvLn+EMv2lPGDcA+mFztQVseqfeXYbfDi9SczJPXY5yYnD09hQKyLslovy/aUcerItB4aqYhI/6Jj5c/x1oDlN+dIkaapEoy3BkjvsadVjHRBwAeVB80E9M6y2SAh00yQtyzImBCZkz+OoBiR/qRvL9UV6aIth6p4ZU0eAP9v3rgO9a/t7aJdDkYNNB/0G/NUSv5YFmwuAGDOhIwwj6Rrol0OxmeZtgEqJS+9lc1mIz09nZiYmH7xHk0waPp5FW2FfZ/C/qVQsAFqi8z9cWmmj3YnvbnDzIaePTyaaGfnf4+3npJIlAPe313Dwwu3dfrx3alPxEjTquGgv2X/604qrKzn9y8t4IbqP7Em+ru87f4Z36x6jJz8/2Er3WES7a4YUxLxyItHQT9gg9yz4ILfdizR3mTEuQDM9HwGwIp8z+H7bDZwuqCmsMuvKRT6RIyEi98LRVvMV1RC+y0OHG4IeNW3XfqUWcNTAVi5rwyPv5UJS9IhTe25ZuamtJtoB3A67MxtrC729oZD3To2EZH+TMfKn1NXdtyVxrqVw2UmM/cgxUgX1JWZBRNRiV17vN0JCVlQXQCFG815WQRTjEh/EoFTsUTC79H3dwIwb1IWkwcnh3cwPWhCdhJbC6rZlF/JeeN7ZyK5uzX4AnywzfSZndsLS8g3mZqTzLqDlazZX8EXpmSHezgi0hbLMgnJigNQV2KSou54iE8/7hn1QcvircZk+0Wjura6eOJAF785O4GbF1XzyPu7mDAoifMnHmN1q3ROfTlU5UFsSuce563hpaW7OLBzA9N8azmJDfzK5gFH4/0ONyRkw4BhkD4aMiZDylCTcA8GTELUUw3eWtNrO2kION2dG0PuGfDZI6TU7SGTUraUpFLlCZIY1TjXNyrB9KH3VHe+fJ6EVzBoSheW72m9P3trHC6TbA9E9sUgkc4YOTCe1Dg3pbVe1h+sZMawTr5XC2DKwQPMGd/xc6sLJ2XxzxUHeHdTAfdcPBFHBFXWERGRPsjvhfqKyOzX3sQVAw1VZuV0JE8K6O+qD5nJ53ZH+9u2xe6AxEzTms1TBU5V+RGJBEq2i3zOruIa3mqcIX/jOSPDPJqeNXFQIi+v1sr2Y/lsVwl13gCZidFMGpQU7uF02bQhA/j7kn2sOaAek9I7BQIBPvroIw4dOkQgEOib5ah8DVC6Cyr2mZOpmAEdS2p10Ip8L4W1QRLcNs4Y0vX9Xjo+gQ15VTy1xcaPX1rHxEFJEdGeotfHiGWZSRZWoP1V7ZYF+WtgyxtwcAV4a/hy032N+YcSkonKHEfCyFkw5BSTwLe3UuTK7jRxFn2cn3FxaaasXeFGropdxoN1F7LqkPdwuwJXLNSWQXVh2JLtvT5GwqWmACr3dzzRDmCzmzj1e9rfVqSXsNlsnDw8lbc2HGLprlIl27ugtMbDyr1lAJ2a7D1rRCrJsS5Karws31PGrBGp3TVEEZF+S8fKR/DWgK/OTHqPVK4YM5nZW9N+1akQUYx0UkOV6dfehaqER7E7zfmVp9qce0coxYj0JyojL/I5f35/F5YFs8dlMC6riyVdeqmJjcnjTfmVYR5J5Hp3oylzOGdCRq8ufzNtSDIAm/KrVPZSeqVAIMAnn3xCYWEhgUAfjOG6MshbCWW7ICa5c0mtVlR6ggQtq/nnel+Qv62pBWDOiGiiulBC/kh3nBrHtIF2ar0BXl6Vd1z7CpVeHyN1ZVCdD7HHSCBYQdj+Drz0DXjrFtj9fmOfPiixEjngGk7pkAuoOO1OUq5+koQv3gfjvwjxaa0n2kNtxDkAXORYApgJHi1EJ0LlAfDVd/9YWtHrYyQcvHVQstO8H3X6PcmCgJLt0recPNwk2JfsLg3zSHqn97YWEbRgQnYiOSkdn6jnctiZ05icVyl5EZHuoWPlI3iqTAWwSOzX3sTuNG3AvHU99pSKkU6qLTHnvq6uVRY8ijPK7DOCKUakP1GyXeQIB8rqeG2tSRL0t1XtAOOyErHZ4FBlAyU1uhj6eYGgxaItjcn2TpQ5jERDUmJJiXPj9QfZcqg63MMRkSPVV5h+7J4qSBp03Cdib++oZ8pfCjjr70X8aUU1b+2o57zni1m42/ROvmLc8a9Cd0Yn8LVRQfN8uuh9/CzLrBy2rLYTmgXr4bXvwQe/MQlrhxtv1gy+57ibcQ1PccvAvzLoG38l9fzbSB5/DvZjJe27S+4ZgI1c3y6yKGXl55PtUQlmJn51Qc+PTTrPsqBsNzRUQEwXVvDaneCpDfmwRMKpaUX1qn3lmsDaBQs2df3c6oJJpm3NO5sKCAStdrYWERHpIl89lO8Hd/irt7XL5jDXESTyBHymRVwoq7q5Ys35dJgmr4tIS0q2ixzh8Q93EQhanD4qjak5yeEeTo+Lj3KSmxoHmBXP0tLq/eWU1npJjHZy0vDeXSbSZrNxQuPq9n8s2RfewYjIYd5aKNxs/o3PNKWXj9NfVpuVzvurAvxuSTXz/1fOwaoA2fEO/nZRCicPDkFperuDOUPAZYdthdXsLKo5/n32Z95aM0M9Jrn1+9c8D6//0PTNdrhh5By45C/c5r6d/9WOYkB8HL8/Pw274zj6wIVCUyl54ALHctYWevH4j0iI2GzmYkPFfpUX7w1qCs3Ejvh087frLIcLvJrgJ33LiPR40uLdePxB1h1QdbDOqPP6+XhHMWCqhnXWqSPSSIx2Ulx9uBS9iIhIyJXva5xs2jOl2Y+LK9pUSLM0CS3iNFSaiRBR8aHbpyvatDfw6PqLSCRQsl2kUY3Hz79XHQTgxrP736r2JhNUSr5NCzaZlXfnjsvA5ej9b5/fPn04dhu8vPog/2mMfREJI7/HJNrryyAho2vJrM/ZXOxjXaEPlx1+eXYSM7PdJEXZuG5qHAuvTmf28HZ6gXdCUlISp2ab90atbj9Ofg8EvEevarcsWPEkrPir+Tl7Glz4OzjrZ3xSm82r2xqw2+CRCwaQEhPmRHuTxlLyFzuX4A3A+qJWSsk3VEJNURgGJx3m90LpbjMByNnF9w2HuzG2/aEdm0gY2Ww2ThpuVrcv2aVS8p3x0fZiPP4gOSkxjM3s/Covt9POnAlmRfy/Vh4I9fBERERM4rpiP8SmhOT8vNs5G5Ovvp4rJS8dVF9hzudD2YqgaXGGRxOaRSJB788WiYTIyr1leP1BBg+Iab5g0h9NzDZ96jflaWX7kSzLYsHmpjKHnV95EYlOHp7KTbNHA/CL1zayo1AHZyJhVb4Pqg9BQmhWtAP8a5M5yT5veDRXT4rjpS+lse47WfzijCTi3CE+DHTFcuEQk0RTsv04+evNifiRcWBZsOxxWPMP8/PIuTDnV5A1mYagjV98UAHANybHMT3L3fNjbktjKfkptp1kUcryvM8l2212U5KxYp8prSeRqfIg1JWYagVd5XCbSSTq2y59zKzGc8el6tveKe9sNBOZ547PxNbFBMbVJw8F4I11+RRWNYRsbCIiIgQDULbH9EF3x4V7NB3jjDbH2161booowQDUFHRPKwJXNNQWqZqBSARQsl2k0dLdpvTcrH6caAcY3biqQCWAW9peWMO+0jrcTjtnjE4P93BCZv7ZIzl9VBr1vgDfe341DT71mhQJi4ZKk2yMHQD20KxIbvBbvLrNJNuvnNAD/eVsNuYMd+O0w9aCanYV63Oky7x1R6+c2PIGrP+X+X7MRXDGzc0n639ZVcOeigDpsXZuOTmEPeBC4YhS8hc6lrHi833bAaKTzUz/2uIeHZp0UEMllO8xbQ2OZyKQw2Uu/qllgPQxJzeeP67eX65j6Q6qrPfxTmPVsAsnZ3V5P1NzkpkxbAC+gMUzn+0N0ehEREQwlbdqCo5vsmlPs9lM0rVO7VUiiqfKrD53h7CEfBNXDHhr1LddJAIo2S7SqGklwsn9PNk+Mt188O8pqSUQ1Ky4Ju82Xgw6fWQacVEhLPkTZg67jYe/MpW0+Ch2FtWweKvK+PYnH24v5jf/28r3n1/FxY9+yvXPruQPi3bw/tYi/IFguIfXf1gWlO83qz1DePL1v531VHksBiU4OG1ICPqyd0ByYhKnNl4zf3u9Vrd3WUNFyxLyVYdg6Z/N96PmUjF9Pr9e6uGejyp54LMqHl1pKpPceUYSiVEReHjfWEr+QscyVh3yHn18YXeYRGzlATPrXyJHMAhle83Fm6jjnMhhs5v3O78HfyDIws2FfLarhLyKeh1zSq82Ij2OtPgoPP4gzy3dF+7h9Ar/XZtHgy/ImIwEpuUkH9e+vn36cACeX7qPWo/aVIiISAhYFlTlHz5P6U2i4qGmUFXDIklDpWml1R2x5IwBX4NJuItIWEXg1TiRnlfj8bMhz/QoP2l4SphHE16DkmOIctrxBoIcKFOPnyYLNjeWOWzsC9iXpMVHccFE87pW7ysP82j6rkDQYsXeMn799hYu+/OnvBTm3pJvrs/nmqeW8/iHu3h7QwHrDlSwcHMhDy/azjefWcEji3eGdXztcTqdXHvttYwePRqns5dPgKktMSWaY0M7Y/7FjeY9/CsTYrH3VH85ZxTzhpqk2VthLiXfa2Mk4DMr2x2NpeCtIHz0APgbIGkInPwd7vmsgSdW1/LU2lr+vLIGbwBOz4niolFd7KXd3YafiYWN6fYdxHtL2FbaSjIkZoD5v1DXc2WYe22M9KSaQqg6GNoVPQEPv12wjeufXcnX/rqMU3+zmMl3vcsnO0pC9xwiPchms3HNLFPO/JdvbeFvH+8O84gim2VZvLBsPwBXzszpcgn5JrPHZTAsNZaqBj//WXUwFEMUERH6+bGyp8qcl0QnhXskneeON4nX+opuf6p+HSMdZVlQXWDKvXcHmw1sRGzfdsWI9CdKtotg+rUHghY5KTEMHtADpXYjmN1uY3jj6naVADbyKurZmFeF3QbnjhsY7uF0i2lDkgFT/lJCb09JLWf+9n2ueHwJT3y0m9X7K/jpf9bz/17dgNcfxBcI8tH2Yv65fD8ef/ev6iyv9fJ//90EmAuUv7hoPI9ffQK/uGh8c5uEj3ZEdjlnu91OdnY2sbGx2O29+HAm4DflmW20XMl8nDYUeVme78VugyvG9ezn2pwRsc2l5N8PY7WMXhsjvnpT5aApHjb/F/LXgN0FM29gfVU8r2w1JeK+OTWOayabr9+dl3zcCYtuE5uKLXMiYFa3ry5opZS8w2UuFFTl9Vi/uV4bIz3FVw+lO83Ej1C9P9kdbM8v48mP9wAwJCUWl8NGrTfAr9/egqVeg9JL3XjOSL531gjAJNwffT+yJy2G07qDlWwtqCbKaefSaYOOe38Ou43rTssF4MlP9qhShohIiPTrY+W6MtP6yBmhk5mPpaktXQ9MYu7XMdJRnmpoqDIVB7qLM9q0PYjAcynFiPQninARDvdrPzm3f5eQbzIiPQ5Qsr3JwsYS8icOTSE1vmdKMfe0E4YMAGBjXlWPJHv7E8uyuOOVDRwsrycx2sklU7O57rRcbDZ4ftl+LvjDR0y/dyHfeGo5P3tlA1/+y1LyKrq319I9b26mtNbL6Ix4Hr1qGtedlsv5E7O47rRcfnmxSYhtzKtU39GeUFNgVo6GcNXozjIf1/7XfK7NGR5NVkJoesB3VHJSAleNNid5N7+0loPlqpLSKf4Gs7rd7jIrvZf9xdw++nysIbP45cdVAFw6Job/OyOJu88yXxnxPft37rThZwMwz7GUdYWtJNsBYlLMrP96TfwKO8uCsj3mbxEbuuNjyxHFzxccwh+0mDMiho/mT2L5HbOJj3Ky+VAV724qDNlzifQkm83GT+eO4ebZowH47bvb2F+qz7/W/HO5WdV+4aQskmPdIdnnl6bnkBzrYn9ZHX9X73YRETkeAb+ZAOzuxYux3HEqJR8pGirNOX53TtxwxZpqBt7a7nsOEWmXku0iqF/7540caGbb7SxSsh1ovvA7Z0JGmEfSfYamxpIS58YbCLIpvyrcw+lTXlubx5LdpUS77Lz1w9P5/ZXT+MVF43nqmhkkRDvZVVxLVYOftHg3idFO1h2o4KI/fsxH27tnZfn7W4t4dU0edhvcf/lkopwtE3Q5KTGkxUfhC1hsbGyvEYkCgQBLly6lqKiIQKCXTgrwe0wyyxUD9vbLafmDFgerjt2LdE+Fn6+9WkppfZAJ6S7uPzc5NGP11sBnj8DO99qfLW13cscMG5Mz3FTU+Zj//OqwTOLptTHiqwcss8p7/b/MiXniIJjxTRbsNRULohxw6ymJ4R5p5zSWkj/BvpP8/PzWt3FGmbL5VW3cH2K9NkZ6Qm0xlO8zE4FCWDHhtf1RLC+EaAfcOa0OirYwwB3k2lOGAfCH93YQ1KpU6aVsNhs/mj2KqY09yNcc0MShz6vx+Hl9nXmP/+rMISHbb4zbwY/OHQXAr97ewmc71ZZCROR49dtj5fpykyCN7mXnW0dyx/VIKfl+GyMdZVnmvCqEVQxb5Yox1w08kXc9VzEi/YmS7dLvqV/70UY0l5HXjLi9JbUs2V2KzdY3+7U3sdlsTGu6MLi/Iqxj6Usq63z86q0tAPzgnFHkpByeGX322IG8+YPTuHXuGF76ziyW3TGbt354OpMGJVFe5+Pap5eHvO/knpJafvbKegC+dWou0xorGhzJZrNx4lBz+8p9kXuROBAIsHjxYvLz83vvAXtVvjmRjzn67/B51Z4gl/+7hNOeKeKZda1PhFq0u4ErXy6hqDbI2FQnz12SSlJ0iA71Vj8LG1+GxffCwjvb7QcWFZfIo2fZSI5xsu5gJb98c0toxtEJvTZGPDWm9F9DBWx5w9w29gt43QO47xNz8nz9CfFk93DFguMWm4ov3VTOmF69mBpvsPXtopNMCTxv968I7bUx0t38HijZCXa7uXATIpWeIL9qjOEfzExgcFaWeQ+sPsS3T88lPsrJlkNVLNis1e3Su00ebPq7bjgYuZMWw+WNdfnUeQOMSI9jxrD2j38649pThnHptEEEghbff2G1KguIiBynfnusXNPYCq0DE+Ijlt0JWN1eSr7fxkhH1RSar+ik7n8umwPqIu8anmJE+hMl26XfU7/2ozUl23cW1fT73pn/WLoPgLNGp7dIlPZFJzQmWNW3PXR+u2ArJTVeRqTHcf3pw4+6f2hqHPPPHsnM3BQcdhs5KbH8+7uzuPyEwQQt+Mm/1/GPJXtDMpaNeZV86bHPKKzyMCI9jlvmjG5z2+mNsbAqgpPtvZ6nBsr2mtnytmMfjtX7glz3RhnrCk0JuLs/rOJ/Ow+3GjhQ6efbb5Tx7TfLKKwNMirFyXOXpjIgJkSHeXVlsOm/h3/e+zG8dI3p5dwWVxw5UXU8fNFgbDbzXvrOxkOhGU9fZllmNrojCja8bGanx2fCmAt5bVs9eysDpMXa+e70buz31o3cky4G4GvOxWwsaKNdhivWlL9rUJIqbKryoa4kpO0tLMvip4sqKKkLMnyAk+tPiDfvfdEJUL6XZKefb546DIDfL9qu1e3Sq00c1Jhsj+AKQeHy8Q5TuenSaYOwhbBqBpgJo/ddNokpg5OoqPNx/bMr1RJJREQ6x1dvku1RCT3/3Mv/Bs99CV78GvznOvj0D8fXg1ul5MPL12Cumdgd3b+yHczfu65Ef2+RMFKyXfo99Ws/2vD0OGw2qKz3UVbbRl/VfqDW4+ellQcA+EZjedO+rHlluxKsIbFibxnPLzM9Ke+9ZCJuZ8c+cqNdDn53xeTmpMMv/ruJxz/cdVxj+WxnCVc+sZTSWi8TByXyr+/MItbd9izt6Y0rjVbvK+/3E266TeUB8NW0O8PZG7D47tvlLM/3kuC2ccGIaCzgR++W89yGWr73VhlnPVvEoj0NOO3w3enx/PcraaTFhnDV8/p/QcADCdlw/gMQlw71ZfDBb9p+jM0GrhjOTq/mO40TTW57eQP5FW0kWMUIeM1JedAPm14xt40+Hys6gafXmooG10+LJ94doYfw3jqzeiLQxrFD7hlU2xPJtJVTuXlx69vYbOBwmH710vO8dVC+1yTB25kI1BlPrqnl3V0NuO3w+znJuB2NSbaoJFMpozKP607LJSHKydaCaj5RCWjpxSY1Jts35Vdp4sjnNE1AaK26UihEuxz85esnkp4QxbbCav6xZF+3PI+IiPRRtcWm/Lq7hyc3++pg/T9NsrQ6H8p2waZXIX9d1/fpjjevpSHySov3C+V7zcKF2B7KN7gbJ623U4VQRLpPhF6pE+k56td+tGiXg8EDTNnQ/ty3/bW1eVQ3+BmWGsuZo9LDPZxuNyUnGbsN8isbKKhsCPdwerUaj59bXlqLZcEV0wdzyojOrQ602WzcedF4fnDOSAB+87+tPLRgW5uJ72MlxP+1Yj/feGo5NR4/s4an8uL1J5MWf+xZtROyE3E77ZTWetmrEpyh560zK0fbKR9/oMrP114p5cN9HmKcNp7+Ygp/umAAs3Oj8Qbg5+9X8r9dDQQsOC3Hzf++ms7PTk0k1hXCw7v6ctjcuKp97EUwZCZc8mdToqx0J+Svbfux0UlQV8aPT0tjyuAkKut93PSvtQSUeGibrx4CDbD9HXOiHJsGE77IsjwvW0r8RDttfGVCBFZZCQbMqglvDbjioLYMKvMa+88fweFm58C5AAw99L+29+eON8l2nz6LelxlnrlAExW6UoerDnn5zWfmIt8vzkhicob78J02G8QkQ+V+ku0NXDgpC4BPlWyXXmzUwHiinHZqPH72lqotV5PKOh8HysznwsTs7iunmpkUza1zxwDw5w92Ut2gFV4iItIBAR9U7Ad3jDlG7Ul5q82E6+hkOOsOyJhkbt/xTtf3aXealfFeJV97XG2JSbbHpoZ0AvMx2Z1gBSKyb7tIf6Fku/RrRVUNrDtYAcApI5VsP1J/79tuWRbPfmZWQnx91jDs9h4+0A6DuCgnYzITAVijUvLH5ZdvbuZAWT2DkmO48wvju7QPm83Gj+eM4bbzxwLwx8U7+eVbW/D6g2w4WMk/lu7jJ/9ex+yHPmTcne9wy0trW1SiCAQtfv32Fm57eQP+oMVFk7N4+pszSIh2tfvcUU4HkxtXZa3cW9al8csx+BvM1zF6Ib+2tY4LXyhm5SGzov2JiwZwYnYUDruNR85P5uRBbuJcNq6eFMu7V6Xz3KVpjEpt/2/baetfMmNNyDLJdjAr20eeY75f81zbj3W4wAriqivij1+dRpzbwfI9ZTz2wTHKz/d3/gbw+8wqBoCRsyEmhWfWmc/iy8bGkBz9ucP3oP9w2fWGCqivMP82VJjbvDVmv0F/aMdqWWYFRl2pmTwSlQSDpkPOSTDkJEge1urqdGvcFwhYNsb6NkPJjtb37Y4Dn0rJ9zhPNVTuN8nvEF1grPEGmf92Gf4gfGF0DFdPamWySFSCmYRUcYCThqcAsHSPPntCJS8vj6uvvprU1FRiYmKYNGkSK1eubL7fsizuvPNOsrKyiImJYfbs2ezY0fL/ZllZGVdddRWJiYkkJydz3XXXUVPTckLu+vXrOf3004mOjiYnJ4cHHnigR15fJHI67IzLMsfUKiV/2MZ887sYkhJLUmw3HLMc4bJpgxieHkd5nY+nPtnbrc8lIiJ9RG2xOZeKTu75596/zPybPgZGz4GJl5mfD644vlLyTjfUFB//+KTjLMsk2rHMavOe5Iw2bRBUoVIkLJRsl37t3U0FWBZMG5JMVlLbSY/+6HCyvX+ubF+6u4xthdXEuBx8afrgcA+nx0wbkgyob/vxWLS5kH+uOIDNBg9+eUqHktvH8r2zRnDPxRMAePKTPYy/8x2+8KdP+MVrG/nPqoPsLKqhwRfkldV5zH7oQ578ZA93vb6Jcx/8gCc+2g3Aj84dxSNfnUa0q+OlxZtLySsWQs9Xb05+2pjh/Idl1dy0oIJqr8X0LBdvfzWd04dEN98f47Lz4mWpbPhuJr88O5kx3ZFkByhYfzjpO2YexCQevm/yV8y/eaug/EDb+4hJgppChibYuPviiQD87ZM9KqvbFl89VOWZigIOF0y4hANVfhbsNiu8r50Sd3jbulKoOGgS2n6fiSebExxOM6vd5jS3BfzgqTEn3a2tNu8Kb93hfbnjIWMiDDrB9Pi22yE2BVKGmgkln3u+MUMHszh4gnkJq/7V+v5tdvOlUvI9q+KA+duGsEflq1vrKagNkpPo4L5zktru0RyXAlUHOSnLfE5tzKukxhPiCSL9UHl5Oaeeeioul4v//e9/bN68mQcffJABAw5XVnnggQf44x//yOOPP86yZcuIi4tj7ty5NDQcrixx1VVXsWnTJhYuXMibb77JRx99xA033NB8f1VVFXPmzGHo0KGsWrWK3/72t9x111088cQTPfp6I0lTKfmNSrY3a5p40PS76U5Oh52bZ48G4G8f76airv+2RhMRkQ4IBkyrN4fL9NjuSZYFB5aa7wedaP4dcpIZS10pHNrQ9X27Ys2E2lCcA0rHeGvM+Xw7LQO7hTvWrGz3qUKlSDi03bBVpB94a8MhAC6cmBXmkUSepmR7fy0j/8Jy02v70hMGkRTTvSsvIskJQwbwwrL9rNlfEe6h9Eq+QJA7XjUnQt8+LTdk7Sm+MWsYMS4Ht728Hn/QIinGxZScZKYOTmJKTjIxLgd3v7GZbYXV3Pvm5ubHxbgc/ObySVw8dVCnn3N6Yy/NVfuUbA85bx20kW96Zl0NDy8zZd5+MCOeH52UgLOVyho2m62tXRw/KwjrXoQVT5rvk3Jg3BdabpM6ErKnQf4aWPMPOOeO1vflioO6g1BXwsVTB/OL1zZSUedjZ3ENozNCl9DrMzxVUL7HfJ+QDXEDee6zaoKNrQJGN02sqC02yfRB00yy2xUDdpdZjdyUzLQs8xX0Q9BnSrJX5UP1IZPEttlMQtvuNMlxewdPC3x15uJB+lhIyjYXcFpLoLrjISbF9B08oopDnNvOB7FzOc+zCteBT8BTB1GtzPh3x5nX6feaFRnSvaoLoGKfiYUQsSyLFzaaqgzXTokj3n2Med6uWKivYJBVwOABMRwsr2fVvnLOHN332/h0p/vvv5+cnByefvrp5ttyc3Obv7csi9///vf8/Oc/5+KLLwbg2WefJSMjg9dee40rr7ySLVu28M4777BixQpOPNFcAH7kkUe48MIL+d3vfkd2djbPP/88Xq+Xp556CrfbzYQJE1i7di0PPfRQi6T8kTweDx6Pp/nnqipT8tLn8+Hz9f6y3+MyzbnU+oMV3fZ6mvbbW35f6w+YY8pxmfE9MuY5Y9MYm5nA1oJq/vz+Dm6dM7rbnzPS9LYYkZ6nGJFjOTIu+srnc5tqi6GqGOLTIRDs2ecu242rthjL7sQ/aJZ5fns0jkEzse//lMD2BQQzJnZt3/YoaCiH2gqIC30aqF/FSEfVlEFDHUSn9Hws2aKgvhRqyiEhMs6hFSPSEZF+PNLRcSnZLv1WcbWH5Y0lKs+fmBnm0USekQP798r2FY2xcfGU7DCPpGc1rWxfn1eJxx8gytnDM3p7ubUHKiiq9jAg1sWP54wJ6b6vODGHGcNSCFoWuWlxR60OfOMHp/HXj3ezYHMh4zITOGtMOqeMTCOxiyvrpw81yfbthTVU1vm6vdxnZzmdTq666iqWLl2K09nLDmc8leCIOurmV7fWcdeHJtlw00kJ3HRSGJLRnmpY/Es40FjGLmMSnHaLWaH+eVO+apLtez6A+h9ATCvjtdnM7OrKPFyJg5g2JJnPdpWyfE9Ztyfbe12MBAPQUAVlpiIFycOo8MKLm8ys9GunmM/l5kR75iRzMagtTYl3uxtwm+R1XCok55gS81YQAgFzMl5VYP5OUfEmKe+vN+NpTsi7wNVYXaG2zJQ3TB1hVrEf6/kTMsxK/c8JZJ/I7l2ZDKcA1v4DTvrO0Y93x5kEcEPlsV/nceh1MdJd6sqgcItZPRPCUofrCn1sKfHjdsDl4zqw39hUqC7gpJwBHCyvZ9nuUiXbj9Prr7/O3LlzueKKK/jwww8ZNGgQ3//+97n++usB2LNnDwUFBcyePbv5MUlJSZx00kksWbKEK6+8kiVLlpCcnNycaAeYPXs2drudZcuWcemll7JkyRLOOOMM3O7DF/Xmzp3L/fffT3l5eYuV9E3uu+8+7r777qNuX7BgAbGxPVxysxtU1AI4Wbe/jDffepvu7Ei1cOHC7tt5CC3f4QBs1B3cyttvb+mR5zw92cbWAgdPf7qHnLqdJEbGdece11tiRMJHMSKtsSyLESNGAPDBBx+0XaGoTyno8WccWbiACUBh/ASW7Q4CBwEYbJvEdD6lbv9qFq8+eHxPsmv5cY+zNf0zRjrqOP9mx2P3qvA99+coRqQzIvV4pK6uY9Ui+vFVJenvFmwuIGjBlMFJ5KT0/gs6oTYi3ZSqzauop94bIMbdf5KuRdUNFFQ1YLPBxB4ocxhJhqfFMTAhiqJqD3/7eA/zzx4Z7iH1Kh9vN72wThuV3qmS7R01LC2uzfvcTjvzzx4Zsr9ZanwUuWlx7CmpZfX+cs4eOzAk+w0Vu93O0KFD2bRpE/ZjJfwiTaCxv7azZbJ9Q5GXnyysAMwK0B/NjO/5sZXvhXf/n0mO2p0w7otw4jfbLik9eAYkDzWrYde/0HrCFEz5tOpCqClkxrAUPttVyoq9ZVx98tBueynQC2OkodKUey81vZKD6WP48YJyqjwWIwY4OXtYVOOKdEf7ifZjiUk2X00CuWa1e9luqCs3q9ATssEZY1bFW42TAHx1plx92sj2E+1NopMbS8nXmZXLjSZnRfGnbZfwkPtx2PwaTLsa3HHsKPXxiw8qmTU4ih81TTapK+22ZHuvi5Hu4KmBwk0QqIeE0FZ6enGjOSGdNzKG5OgO/H6dUWCzcVKal5eBZerbftx2797NY489xi233MIdd9zBihUr+OEPf4jb7eaaa66hoMBcUM7IyGjxuIyMjOb7CgoKGDiw5TGA0+kkJSWlxTZHrpg/cp8FBQWtJttvv/12brnlluafq6qqyMnJYc6cOSQmJh61fW/jCwT5/ebFNPiDTDz5TIaltn0M1+Xn8PlYuHAh5513Hi5XZE2K/Lyqeh8lS94H4NpLzmVAbM9kvS+wLJY/sZx1ByvZGz2Cn50f2smwka43xYiEh2JE2tMvYqS+HA6uNOdIzqMnxXc3x1tbAUgfNIILTziijaX3Qqzn/0ZCQz4XDq6GgeO69gR1ZWZSdfb01iuSHad+ESMd5a2Dg8tN7/Se7tfepKEKsEHOSabFXARQjEh7Ij1GmqqwtScy/seJhMHbjSXkL5ikEvKtSYlzkxzroqLOx+6SGiZk95+kc1NvxRHp8cRF9a+3SZvNxm3nj+XH/17HHxbtYO6EDEYOVKnnjvpwh+kvfPqotDCPJDROyk1hT0ktb6zLj7hke6/lrwe/B6JbJhL+sKyGgAVzhkdz5xmJpkR8fbnpi11TYE6YvDWm15rN3rhiPB4GDIOU4RCXfvjEORgwyU1fndnW4TYne21dOPDWwu73YcmfzWOiEuHk78KoOccuLW6zmd7tHz0A2/4HM65rfXu70zx32W5mDjEzmlcoiXa0+grzNy7fB8BzlZN5b68HtwP+MHcADk+F2S5jYmiTzw6nWe0elw4Bjyn9//mTcssysedvMJMnOtrHMKqplHxxi2T7lAw3vwieyg+s/5LrOwSrnuHdgd/ilgUV1Pos1hR4+e70eKLccaYMfTDYseS+dI7fC0VbzESPxNBW8qn2BHl9u+kN+bVJnbjQFJvKycmFgI31Byv63YTPUAsGg5x44on8+te/BmDatGls3LiRxx9/nGuuuSasY4uKiiIq6ujPJZfLFZEXWDrL5YJxWYmsO1DBlsI6RmUmd+NzRf7vbNs+c341eEAMA5NCP/HgWG4+bzTXPr2C55cf4LtnjSI9oecTKeHWG2JEwksxIu3p0zFSWgx2C6Ji2t821DzVULgRAEfuKTgcR5zzxCSaHu4HluLatQCyJnTtOaJjwVcL+Fuck4Van46RjqqvhWADRKd2y8SGDomJh5oSCNabUvYRRDEi7YnUGOnomPpXFkmkUWmNh6W7zYV+9Wtvnc1mY2R6PCv3lbOruLZfJdvXHzQXgyb3s1XtTS47YRBvrM/ng23F/PQ/6/n3d0/B0Z21L/uI8lov6w9WAHDGqL5R9varM4fwzxUHeGN9Pj+7YCwDE6PDPaRmgUCAlStXUlxcTCAQiMiDsVb5GiDgNQnwRpuLfSza04AN+NlJLuxr/gEb/mP6d3eGzW6+gv7W749OgsRBkJBpku8OtylJfnA5BBr7DyUPgTNuNSunO2LkubDsMVOWfPsiGHt+69vFpkBlHtNSsnDabeRXNnCwvI7BA7rvZL9XxUgwaEqmVx0ELDyuZO7eYE6M7zoziYmJ9eDzQOZkU5q9O7iiD5eK/7ymdgBdmZ2fkGl6xVtW8wWH0SlOXE4nD/su54/uP1Gz4S1ubZhDLSYB4wnAxmIf09Ojzcprf70pKx9ivSpGQs2yoGyXqWqQmBXyi0Gvbaun3m8xcoCTE7M6sYLV7iQn2UVWXJBDtUFW7y/n1JF9YwJbOGRlZTF+/PgWt40bN46XX34ZgMxM00qrsLCQrKzD50SFhYVMnTq1eZuioqIW+/D7/ZSVlTU/PjMzk8LCwhbbNP3ctE1/NGmQSbZvzKvki/2sNdXnbWiczDwpDOdXZ45OZ2pOMmsPVPDER7v4f/PGt/8gERHpH8fKnhqoOWTOlcMhb5Vp7xWbBgNb+XwacRYcWAr7l3b9OZwxpoKZtybkq637RYx0Rk2xWWwQzlLpdidYfnM9KTb8yXbFSDcq32cWRCQPMRUFJey0RET6pQWbCwkELSYOSmRIqkrIt2VEuiljvLOof/Vtb1rZ3t9KyDex2Wz8+tJJxEc5Wb2/gr9/tjfcQ+oVPt1VgmXB6Ix4MpMiJyl9PKbkJHPi0AH4AhbPLtkX7uG0EAgEWLBgAXl5eQQCgXAPp+P8ZqXnkSdfj66sBizuyV7C8He/CSufOpxoj0o0pdoHToBBM2DY6TD0dBh6GmRNgbiBJsEO5iT9yES7zXH4PjCrV4s2w67FsO1tU75736cm0R6TCqPmwgUPdDzRDmbF+rgvmu83/aft7Wx2iE4gtnY/E7LMZ8uKvd27ur1XxYin0vzNK8z/s098owhYcOmYGL46CnNhZOA4SBoU3nF2RUwyuGPMyvhGLoeN/3daIuvjTmVbcDDx1PEt5//45tQ4Zuea989leR5wRJnJKd7abhlar4qRUKvKN60D4tKOXcGiCyzL4sWN5m/2tUmxne7LZ4tN4aQM8/dYtrs0pGPrb0499VS2bdvW4rbt27czdKhp45Gbm0tmZibvvfde8/1VVVUsW7aMWbNmATBr1iwqKipYtepw78fFixcTDAY56aSTmrf56KOP8Pl8zdssXLiQMWPGtFpCvr9oSixvaJzI259tCOP5lc1m40ezRwHwj6X7KKnx9PgYRER6o35xrFxTZEp/d8PE3g7Z85H5N31M64nwoaeaqmI1hab1U1fYbGDDrKIPsX4RIx3lqzct0Npqw9eTnNEmti0r3CNRjHQXb605ny/cDAdWQOVBU+VSwkor26VfWrDJ9Be8QKvaj6mpP/SBsrowj6RnNa9sH9w/k+0A2ckx/OyCsfz8tY38+u0txLodXDlzSLiHFdE+auzX3ldWtTe57rRcVu4r5/ll+7jxnJHd0ou+X/HUtCiHvbPMx9s7GviJ8yW+XvZfc2N0Eoy9CMbOMzORne1M3vDUQm2RObAO+syKdXeCmdlqd5hS0b46c/Bdsc+Upfc1mMS83WF6r2dPN2XqumL8xbDuRSjdCQWbIbONFWNRSVB5kJmZKazLg+V7yrl02uDWt+1vGirNpIfi7QB85hvNgGg7vzwzDlt9IaSPNZMueiN3HMSmmpX7R1xA+vrkOL4+OY6Sjd+Cz+7hRvdbOKddyVO7Ylm0p4HleV6+f6LNXCDopmR7v1VfYWLNFdMtM+BXHvKyucRPlAMuG9uFSa12JydlOXltd4ClSrYfl5tvvplTTjmFX//613z5y19m+fLlPPHEEzzxxBOASULedNNN/PKXv2TUqFHk5ubyi1/8guzsbC655BLArIQ///zzuf7663n88cfx+XzceOONXHnllWRnm9XaX/va17j77ru57rrruO2229i4cSN/+MMfePjhh8P10iNCU2J5Y34llmV1euJJX7IxjCvbAc4anc6UnGTWHajgiY92c8eFXex7KyIifYffC5UHwpccLdxkJsIDDDut9W2iEmDwTNi/BLa+DRldLCXflHxNGR7eVdd9WX2Fue4SkxzukZjzbk+VGU+4JpJI96rMNxNoBgwxLSgPrQNsvXOBRh+ile3S71iWxer9FUDfS4qF2pAUc4F0X2n/uchdWNVAUbUHuw3GZ3cx8dRHfG3mEC6Zmo0/aPGzVzbwq7c2EwiGf1ZkJLIsi4+b+rWP7lvvK3MmZDJ4QAzldT5eWZ0X7uH0fg2VZrVuoz+vrOEGxxvc6GxMtA8/By5+DGZeb3oot5doB4iKg5RcSBtpVj+njoCEgRCdYE6yYpMhKRuGzITJV8ApP4Azb4Wzb4czfwojzu56oh0gfqBZcQ+w7oW2t7PZICaZGSlmhXN3r2zvNYJBqCowJdyLNgOwNjiS84ZHE+cpNnGQktu7L4rEZ5jJHa3MrE+bcBakjcEZ9MD7v2JmY8nxVYe85jPH4TInjxIaAT+U7AB/HcR0z4rjp9ea48ZLx8aSHN21082Zw0xCbu2BChp8mqHfVTNmzODVV1/lxRdfZOLEidx77738/ve/56qrrmre5qc//Sk/+MEPuOGGG5gxYwY1NTW88847REcf/vx5/vnnGTt2LOeeey4XXnghp512WnPCHiApKYkFCxawZ88epk+fzo9//GPuvPNObrjhhh59vZFm1MAE7DaobvBT3I9XU1c1+NhbaiZvhyvZbrPZuOlcs7r9xWX78fj1viIi0u/VFkNDFUSH4dpf0A+fPGS+z5xizsnbMuo88+/+z7q+UtnV2Lf9iGpjEkLBoKkcZrdHxnm7Mwa89Sa+pe/xVEPlfjOxw2Y3ixvsDvOeJmGlZLv0O/tK66is9+F22hmTGQGlXSJYU7J9f1n/ORhrKvM4cmA8se7+XfzDbrfx8FemclNj2cW/fryHX/x3Y5hHFZl2FtVwqLKBKKedk3LD3xMplBx2G9eeMgyApz7dgxUBZah6Lb/HrCh3mmT7zjIfMTve5HbXi+b+kXPgrJ+axHhvM+lL5t8DS6H2GEl0dzwzUkzCYWdRDWW13h4YXITzVpsy8r46qC/HbznYaOVyQY7XzEJPG20Szr1ZdHLjBZ5WjidsdjPxw+6EgnWML3mXBLeNaq/FlhK/WXndUGVW/svxq843VQbiB3bL7vOq/by7qwGAa6d0fRXF8JQosuLAG7B4cemeUA2vX7rooovYsGEDDQ0NbNmyheuvv77F/TabjXvuuYeCggIaGhpYtGgRo0ePbrFNSkoKL7zwAtXV1VRWVvLUU08RHx/fYpvJkyfz8ccf09DQwMGDB7ntttu6/bVFOrfTTnayqR6xr7R/VQo7UtOq9kHJMQyIc4dtHGeOTmdgQhTVHj+f7VLVDBGRfi0YhKqD4HS3bL3WUza9CqW7zOT6md8+9iT7oaeYawj15XBwRdeez+ECvw8C/XfyX7eqKzGl/mNTwz0Sw2Yzif86LXDokyoPmutHR1blcMWa9wi//o+Hk5Lt0u+sO1gBwPisRNxO/Rc4lqZke0mNhzqvv52t+4Zw9hOMRDabjZtmj+bhr0wB4D+rDlLv1UqQz/uocVX7zNyUPllm/SszcoiPcrKzqIYlujjZdb56CJhke9CyeObdZdzjfMrcN+w0OO2mjq1kj0QZE82K+qAfNh6rd7uNAbEORqWYyUxa3Y4pNxfwmostwGZrCC6Xm1MG+k3vvnCstAg1d6y58OBpY2b9gGEw7WoA7Msf46wMk5Rfnu8xyXZ/vUrJh4KnxsRZVHzI+7Q3eXZ9HQELThnsZmxa1yeJ2Gw2bpxhLh48tGgnpf14VbD0bsNSzaSTvSX99z1sY/P5VXg/z+x2G3MnZALw7saCsI5FRETCzFMJdeXhKfldWwwrG68DjL4QBrZTGt4ZfbiS3Lb/de057U6wAprA3B2CASjfZxLcjvBNKjyKO85MAtDfvG/wNZhKmVX5Jtn++Sp1rlhzzcRTHZ7xCaBku/RD6w6Yk/2pOcnhHUgvkBTrIjHaXIw90E9Wtzcl2ycr2d7CJVMHMSg5Bq8/qP6preir/dqbJES7uHCSuTi5YHNhmEfTi/kbzImY3cm/15fzrco/4bBZ1KVNhtN/2qKfda9js8G4L5rvd7137PJ2UYnMGGjuX7FHyXZqisxKhcYS8muCIzlniIOomHhTfr2viB/YZil5wCTbBwwDXx0/8PwNgOV5XnNhKOA3M7el6ywLyveahHs3XVSs8wV5caNJKH5ranw7W7fvyonxjE+Bak+A3y3Ydtz7EwmHoalNlcL673vYusbKYZMHJ4d3IMD5E83x7MLNhWqPJSLSnzVUmiRkTydHq/LgrR+bifiJg2H61WYFcntGzjb/Hlxuzqm6KqDKciFXWxxZq9qbuOOUfO0LggEo3Q37lpivvNVm4oz7c1Xk7I0Lvxoqe36M0kzJdul3mla2Tx6sZGpHDOlHF4gsy2pOtk9SfLRgs9k4c4xJJH+4XT1gjlTvDTRPQDijj/VrP9J54w9fnFQp+S7y1QMWRbUB6pc+yXB7AbWORGJP+/7x9UyPFCPPNeXpagrh4Oq2t3PFMCPNzK5ee6CiZ8YWqXz15uTXFYtVuAkw/dovGGqZmcr2PlQpo7mUfBvHE3YnnPUzAEZVLSWNSlbke837jd1uksTSdbXFUHEA4tO67Sle2VpPlcdiaJKDc3Kjjnt/DruNu88wSft/rjjQ3OpHpDdpSrbv7cdl5Nc3nn9PiYBk+8zcFJJjXZTWelmp6joiIv2TZZm2Su6Ynn3eQ+vg1e9BxX5T/nnmtyGmg20IB59oHuOthd0fdX0MKjEdWgE/VOwz10EirfVbUzWDtqrLSeTze6BoCxRuMtdE4lIhaVDbizJcMWYxRzDYs+OUZmFNtg8bNgybzXbU1/z58wFoaGhg/vz5pKamEh8fz+WXX05hYcsVdfv372fevHnExsYycOBAbr31Vvz+ljO8PvjgA0444QSioqIYOXIkzzzzTE+9RIkwvkCQTfnmQt0UrWzvkMN92/v+BaLCKg/F1R7sNhifpWT7553VmEj+YFtRmEcSWZbsLsHjD5KdFM3ojONfyRepThuZRrTLTl5FPVsOhX9mrNPp5Mtf/jK5ubk4nd1TDjnkPFXgcPHswuV8g7cBiJ7yJRg4NswDCxF3POSeab7f8mrb29lsjEszf7NthdXdNnmjV8SIt9aUSK8txla0maBlY41tHGdm+MNT0rA7NZeSP8b7R/pYSBuNjSCXuD6jtD7IrnK/KZ1YV3rsigld0CtiJBSCASjfA3Zbt7aqeG69WdV+zZQ47DZbSPY5Y0gil+QGsSy47eX17C7WpAvpXYY2lpHfV9o/y8iX1XqbK6RFwmRml8POuWPNBcp3NqmUvIjIsfTZY2VPFTRUmfPXnrJ/mVnR7qmChCw4924YflbHH293wohzzPc7FnRtDA4neEN7bbfPxkhH1RRCTTHEdnDSRE9zRkFteKuT9vsY6Sq/Bw6th7LdEJ9u2gu21wrOHWeut3iPOGdW4r1HhTXZvmLFCg4dOtT8tXDhQgCuuOIKAG6++WbeeOMN/v3vf/Phhx+Sn5/PZZdd1vz4QCDAvHnz8Hq9fPbZZ/z973/nmWee4c4772zeZs+ePcybN4+zzz6btWvXctNNN/Htb3+bd999t2dfrESE7YXVNPiCJEQ5yU2Na/8BwpAU83s60A+S7U2r2kcNTCDG3YdWE4bIKSPTcNpt7C2t69d9Jz/v/a1mpf/ZYwdiC1FyIRLFuB2cNtJMuFgYAaXk7XY7I0eOJCkpCXtHyq6FW8APDVXsrXHyxYI/YbdZVKROwzH1y+EeWWiNnWf+PbACPG1/bgwfmIDTBtUNfvIrG7plKL0iRrw1JoG8+XUA3guewJghWcRG2Xv24k9Paa+UPMDo8wG40vUJAMvzveCKNivifaFtadMrYiQUaoqgtqRbSxseqg6wtdSP3QaXjQ1tS4yfnRxNgtvG5kNVzHn4I+56fRPltSqBKb1Df+/Z3lRVbnhaHEkxkbHiq6mU/LsbC1StSUTkGPrssXJDpWnx5jz+SkwdUrEf3rvHnAelj4Xz74PBJ3R+P00T2ws3dW0Sst0JvtAej/TZGOkIbx2U7jLnqu0lQcPFGW2Sr/7wnTv16xg5HtUFUH0IErM7/l7ljDJJ+qYFDrWlkLfKLPKQHhHWd4L09Jbldn/zm98wYsQIzjzzTCorK3nyySd54YUXOOccM3Pr6aefZty4cSxdupSTTz6ZBQsWsHnzZhYtWkRGRgZTp07l3nvv5bbbbuOuu+7C7Xbz+OOPk5uby4MPPgjAuHHj+OSTT3j44YeZO3duq+PyeDx4PIfLqlRVmXIbPp8Pn8/XHb+KPqfp9xRpv681+0ypuEmDEgkE/AQCYR5QLzAoybyh7y2pCenfMxJjZO1+Ex8TshMialyRIsoO04cms2xPOYu3FPD1k4d06/NFYox8nmVZLN5qEs9njEqN6LGGwjljUlm0pZCFmwv4/pnDwj2cXhEjzWqLobaCDzYUcK09jwaiiDvlBnw2NwT60EzTgZNwxmdgqynEv+V1rEmtTyawuWIYnljB9kobmw6WMzCuew5JIz5GqorB78e5/R1swLOB87h4qAOfzQm4IVLH3VWOeLDHQEPN0T3Gmgw7E+eSPzEyuIfhtnyWHozmirGJ4CmH+iqwhTZZE/ExcrwCfijdA5YDLHu3vd98uM9MhJiY7iLOZapJhUpqUgL/Ob+U32xO4v2dlTzz2V425lXw4rdnhuw5jiXSYyRSxyVGU5WwqgY/FXVekmN7uDdsmK0/0NSvPfyr2pucPiqNWLeD/MoGNuRVRkQveRER6SGWBdWFJkHaE7w18O7/M0nupBw49/8gMatr+8oYb9qMeauhZBekj+zc4+0uk4gLBvpWu7JwsCwo2wMNFZA0ONyjaZsrBmpKTPw5+9cxaK8W8JlJOu64zv9fdThNVUB3LBRuhPpyU3q+resvElIRM+3G6/Xy3HPPccstt2Cz2Vi1ahU+n4/Zs2c3bzN27FiGDBnCkiVLOPnkk1myZAmTJk0iI+Nwn4K5c+fyve99j02bNjFt2jSWLFnSYh9N29x0001tjuW+++7j7rvvPur2BQsWEBsb2pUafV1TtYJI8dYuO2AntqGEt99+O9zD6RUOVdgAB1sOFHfL7yySYuSz7SY+gmUHePvt/eEeTkTKCJh4+M+nm0kt29gjzxlJMfJ5h+ogr8KJ02ZRuX0Fb+8K94i6V8ALNhxszK/ihVffJrmHJoK3xrIsysrMBJkFCxb0iqoCQQtqdy0FO+yJmcj2g3Fw8GC4hxVyoxNOZ1zNf6jY9D6f+k5pc7t4px2w8fpHK6nfFfqVZb0lRoYVL2aKr45dwSxWMIGLamt4eyuw9b1wD60b1QLlbd57UsIkMqvWcYnjE54/kMXbaxrLoO1cFtJR9JYYCZ3ue7/59w5zDJXh8vD26sPPY7MCjCx8i5roLA4lzziu57gkvZRxbhuPbbazcl9Fj38ORerxSF1d368+1ZvFuB1kJEZRWOVhb2kdU/tbsr1xZXskJbSjXQ7OGpPO2xsK+N/Ggogam4hIJAkEAqxfv57S0lICgQAuV2RUKDku3hqTIO2JKmJWEBb/GioPmH7rp/+464l2MKuUU0dB8VbIW9n5ZLvDZSqFBbxmAnQI9MkY6YjaYpMMjUuDSD6HtDvB8ptV+DEDwjKEfhsjx6O22FTgSMjs/GPdcVBXZh7vqwOHG+rKzQp56XYRk2x/7bXXqKio4NprrwWgoKAAt9tNcnJyi+0yMjIoKCho3ubIRHvT/U33HWubqqoq6uvriYk5+sPl9ttv55Zbbmn+uaqqipycHObMmUNiYuJxvc7+wufzsXDhQs4777yIehN97NElQDWXnDGNuRMy2t1eYGJZHX/e8gkVPgfnnz8Huz00BxGRGCNPHVgGVDJ71gmKjzYML6jm9UeXsKfWybnnnU2Uq/tmw0ZijHzeEx/vAXZwysg0Lv3C9HAPp0e8UrSMNQcqsQZN4sKZOWEbh9fr5Xe/+x0Al112GXFxETxL01MFB1bySbGbYawFIDd3JCNPiOAZ0Mej9gqsf75MWu02LsyphfQxrW6231fG6lIvtqQsLrxwasiHEfExUlcGB5bj3PMBAP8InMeXJiRw2ZgaU14wZVhYh9dtvHWQvwaCvjZ729mSLoIP1nGp/VMerr+CsyZlE9tQDImDzIqOUA0l0mPkeAX8kL/aXFSMS+u2p7Esi3vXFgNBvnFiKicPPpwBty/5I45Dr2HZ7PgvfgJSh3ftSTw1EAxw4aAT+aRyHevzqojNncqFU7v/okGkH480VWGTyDU0NY7CKg/7SmuZmpMc7uH0GMuyWHfQrGyfkhM5K9sBLpiYxdsbCnjykz2cOTqdk4d3X5sNEZHeKhAI8OabbzZ/3yc0lZDvxmPjZsv/Cvs/MytTZ34Hsqce/z6zJptke8EG4MrOPdbhMq8/4DUrnkOgT8ZIe/weUz7ebg/Z77Fb2eyHy4qHQb+MkeMRDJgJOg5X1ypQuGKh8qB5fEKWuR5ZV2KuDTgiJhXcZ0XMb/jJJ5/kggsuIDs7/LMsoqKiiIo6epmEy+WKyAsskSySfmf13gA7iszKqOm5qREzrkg3JC0Bh92Gxx+kwhMkIzG0pZYiKUaa+gYPTUuImDFFmomDBzSvzlmTV83po9Lbf9BxiqQY+byPdpQCcO64zIgdY6jNmZDFmgOVLN5WwrWndjFpEgJH9tiM5BgBoKIM8LJoey332kz5g+gRp4LjGP2qPNXmJNgd33O95EIlMQOGnQp7P8G1+mm48IFWNxufEQN42V5Y0y1/v4iPkWADlG6Bin3UWlG8HDiDVyfH4XLUQmwiRNp4Q8WVBANHmYS75Ws9voefDp9Ek+Mv5gTbdnaVp3NCcjT4a8DpDNnqgYiPkeNVX2zK7ydkmItB3WRbqY+S+iDRThszB0XjcjT+fTa9CptfA8BmBXF98lu49PGu/f1iEsxFg7oCTh2Vzvq8KpbsKeeKGUND90LaEakxEoljkpaGpcayfE8Ze0v6VxWCQ5UNlNR4cNhtTMiOrGT7hZOyeH1dPgs3F3L931fyz++cHHFjFJG+pbzWy97SWkYMjCcxWp/dYVNdaFZ6dretb8K6F833E66AMeeHZr+Zk2D9S1C6o/OPtTtNIi+M/bt7vaby8bXFpjR3b+CMNhP9LSuyV+GLUVdqeq13dUKQ3WFi0+Ywf293nGkl4Klqc7GDhE73XXXphH379rFo0SK+/e1vN9+WmZmJ1+uloqKixbaFhYVkZmY2b1NYWHjU/U33HWubxMTEVle1S9+1Kb+SQNAiPSGKzBAnjPsyl8NOdrL5fe0v67sXiBp8AYqrPQAMGqD3hrbYbDbOHG0S7B9sKw7zaMKrst7Hyn2mDPLZYwaGeTQ957zx5rUu2VXS/H9GjsHvgao8qmzx+Pcuw26zqI/JgvTRrW8f9ENVfuNs8zior4CKA1BTaA666yvMPsPFsswJetBvvm/LCdeYfw+uMP2iWzEm3SRZd5XU4fX3ob71HVVXCrs/AODVwGlMzUlmZFLAJJ/7ej+thCxIHgI1xa3HkTMacs8E4FLHJ2wu8YEjysR+OOO/NwkGD89ot3fv/OqP95u/ycxsN1HOxgs4B1fAZ4+Y73NONj0iS7bB1i62JLLZIHYAVO7ntCHmOO2znaUtJkyIRKqhqeY9fV9ZbZhH0rPWHagAYExGAtHdWA2rKxx2G498dRozc1Oo9vi55qkV3PX6Jn780jpueWktH2wr0vuLiITMgbI6LvjDx1z658+YfNcCTrnvPe5+Y5PeZ3paQxXUl5mS7t0pbxV8/LD5fvjZMOPa0B2PZ0wy/9YWmYRvVwSUbO+y6gIo39NYPj4i0mrtc0WDv860EJDIZllQlQc2zHl8V9mPWKBgd5rrd2GsbtCfRMS7wtNPP83AgQOZN29e823Tp0/H5XLx3nuH+1Vu27aN/fv3M2vWLABmzZrFhg0bKCoqat5m4cKFJCYmMn78+OZtjtxH0zZN+5D+Y23jyf6Uwcn9oCdnaA1JiQVgf2nfTbYfalzVHuNyMCBWs4yP5bTG1ewr95aFeSTh9fGOYgJBixHpcQxJjQ33cHrMiPR4Jg5KxBewuPlfawkEdYHgmGqLwVvNm/ucnG5bA0B01liTTDxSMGBmG1cXQHwGDJoOOSfBkJPN7PWEQRCdZA6UGypNAr6utGdPlGtLzIF/TVHj9/ltJ9zTRkHOTMCClU+2usmgBAcJLvAHYXdJTfeNOxL5vVBfiVWwEYD/Bk7lminx5gTYFWNKf/VldjukDIfoRBPHrRlxFgCn2jeyudgHTndjsl0XCTqkvtzMiI9J7van+vSASbafNqSxSsHej2HBL0yfyszJcNYdMO0qc9/yv5iy9vUVZhJRZy4yu+PB18D0hArcTjsFVQ3sKu5fyUvpnYY2Hifu68PnUq2J1BLyTaJdDv52zYmMy0qkpMbDM5/t5eXVB3lldR7XPr2CL/zpExZsKgj3MEWklyusauCqvy2joKqBKKe5DJ9f2cDTn+5le2E/OwcKt/oyU0K+O0t/l2yHhXeCFTCJ8VNvOvrc/3jEJJvWWgAHVnRtH0q2d42nGoq3mSRobygf38QZDb4G079bIlt9ubkmGBPiFeiuKHMNT7pd2JPtwWCQp59+mmuuuQan8/Asr6SkJK677jpuueUW3n//fVatWsU3v/lNZs2axcknnwzAnDlzGD9+PF//+tdZt24d7777Lj//+c+ZP39+cxn47373u+zevZuf/vSnbN26lT//+c+89NJL3HzzzWF5vRI+WwvMDJ5JgyLzZD+SNSfb+/DK9rxyc/F+0IAYTcZox9TByQBsOVTdP1ejNlq81Uz0Omds/1nVDqa6wcNfnkqMy8EnO0v48/s7wz2kyFZ1CMvu4j+bqjnDvh4AW85Jh+8P+k3CqbrAlLPLnARZUyFmgElIxiRDSi5kTYLBM2DYaSYBnzXZJJ7qyqDioElcdefKiAZzwZzsE2DoLBg80zx/Q0Xbj2la3b7/Myg/cNTdNpuNMSnmUHRbfj/rOeytgdId2DyVNFguSmJHcPawKHMSHJPSrSW/I0ZUvOlNbwVbn2WdMRELG7n2Qg4WFJqJJlbA/I6kfdUFQLDby2R6AxbL8swFu1MHu2H1sybR7m+AAcPhzJ9BTCJM+SrEZ5rydc9cBP+4BF74CrxzW+cu/MSlEl2bz4mD4wH4bJcuGkjkG9a0sr20f00OWX+wAoDJjecOkSgx2sVz183klvNG8/2zRnDb+WO59pRhxLgcbMyr4oZ/rOKt9YfCPUwR6aXKa718/cll7C+rY0hKLB/99GzW3TmHU0emArBoS2E7e5CQCQagMr+bE+074K0fg7cWknLgrJ9BTDdcg86aYv49tK7zj7U7tMK5KwJ+KN5uzuNDnQjtbjY7YJm4lMhlWWZRjRUMfStJV5y5dqf/+90u7FfyFi1axP79+/nWt7511H0PP/wwF110EZdffjlnnHEGmZmZvPLKK833OxwO3nzzTRwOB7NmzeLqq6/mG9/4Bvfcc0/zNrm5ubz11lssXLiQKVOm8OCDD/K3v/2NuXPn9sjr62kb8yr598qWF7TLa73Mf2E1/1y+P0yjigyHKs0bSk5KL5p9FiFyGpPtB/pwsv1guXltg5IVH+3JSYlhQKwLbyDI1oJ+liBr5A8Eeb852Z4R5tH0vFEZCdx7yUQAHl60nSW72liZ2t9568BTxdKSaNwlm0i01RN0xsKgE839QT9UHTKJ9UHTTRJ9wDBwHKPEnM1mVrgPGGYS3kNOhowJph9TZR4EfKF/Hb468NRA2mjT+ylmAMSlQuoIc8IW9Lf+uIwJZuKAFYRVT7W6yZg0U0lka34/q5ThrYGizQCsCo7myinJOOw2k0yO7keTAhMyTFw1VJnk7JGiEvAmmn7ciRVb8ActU05NK9vb562FmgJTOaCbrS3wUuezGB1dxfg198DKxv/rg2fCBfdBUrb52RkFZ/wE80dsYoMDy+G173e8DKYzGmwWp2YGAPhkh5LtEvmaKiCV1Hip8bTxmdnHBIMWGxpXtk8eHNmfa6nxUfzw3FH89PyxfO+sEdz1xQl8+rNz+NL0wQD89t2t+AL9d4KxiHTdbS+vZ3thDRmJUTz/7ZPISIwmKdbFBROzgMMT+KUH1JebZFN3nWuV7TaJdk+1WXl+7p3d19M7s7GUfPG2zj/W4VLStStqi6D6kDl/7Y0LtByuYy+UkPCrLzcx1h191V3RJtGuUvLdLuzJ9jlz5mBZFqNHH927NDo6mkcffZSysjJqa2t55ZVXmnuxNxk6dChvv/02dXV1FBcX87vf/a7FCnmAs846izVr1uDxeNi1axfXXnttd76ksNlZVMOXHv+Mn72ygc92mgtPlmXxs1fW89b6Q9z9xmaqG7rhInwvcajCXMTNSlIytbP6xcr2CnPxfrD6tbfLZrMxqXGFSlN5yP5m5b5yyut8JMe6mDFsQLiHExZfmj6YL00fTNCCG19Yzca8/hkLx+SpBl89j671cbbdlJC3p42E+LTDifbEbFNmOTGr8z2Z7HaT+E4dDoOnQ/Jgs0reUx2aVe5+j1nRXlsKqSNNj+0jJWZDQrbpu92W/8/emcc3VpX//32zJ02TdN+32fed2Ri2AWZkBlBBBRRUQBQFFVDxhwuKqCyKqIgCoqBfREQ22ZmBYZ997+xLp/u+pE3b7Lm/P07STpm207Rps/S+X6++kjY3NyfJaXLO83mez7Pwq+LyxPvQcPCUm6eli4zdw+Mtccfdib9RvB5bAtNZNSlJbH40htHvIRhr2IogpUTMo08kbujy5gAwj8OU232i77dT+aw5LV1NIoimTRr1h/qo0sUV6nd5SfoeUsVHonJixmfggp+Llhgnk78IrnwaPvMX+OJ/Ye1vhUNGWzk8f4PoMT8UTGmcmSbWpJvKWpR2Jgoxj8WgJS1JuEyMl+r2Ey1dONw+9BoVU7Li73stNUnHXZfOJN2so7ylm2e3n+rQo6CgoDAYJ5q7WHdAVK4/8dXFPUUsAOdPF+54OyvbaOl0R2V8446uJkCOXO/0k2k9Aa/eJhycknPh/J+LtmqjRUhsb68Cd5iJyCqNSF4OKElkQ8bvg7YK0dZsNObPWKAxCDfEgD/aI1HoD1kWxTMBX2TbToSQghKwEksZdaIutitEjokZSVw0Kwd/QOamp3dS1drNM9uqeGu/WNw5vX7+t7s2yqOMDrIs9/TkzrGOwodWgjMuxPaTbOQVTs/cYIXK3ip7dAcSJdYFP1fPn5aFRj1+v0p/8emZzMqz0NLl4arHNrOlbOwq3DUaDZ/97GcpLi4+JckuZnB1sLsJtlV1sla9Rfwtd4Go9O6oExnvWbMiY2WnSxI94TKmix5sHbWiv7rTHt55Aj4RiLBXi2pjSSWE9rRJp2Zwq9TC4l6lGTg7PnceFC0Xz/ntO0W1/0mEKtsPN0ZefIjpOeLpxN9wCICj2qkUW9XivUrKHJNq5JhCpYKMKWDNF/8XJwnuUjCQtEh1hP2hvu0eR8SCQzE9R4aL3ydEa13SqFddBLqaOXv/T7lP+1dMchckZ8M5t8Pym0Fn6v9OlhzInA7mDOHocdlfITlHVFp8+LuhPbBax+y0AMl6FQ6Xj1Il2UshDigcZ33bQ9br8wpsaON0rZyk13DzeZMA+MPbR3F6lAC1goLC0PnHxnJAtJ2bkdt3fZ9jNTIz14Isw7uHh+juEwUSZq3sdYkWS6OR1NxWAa/dJtayydmioj1jFIV2EHEEg1W4otXuCO++Kq3YL0Sob3vCzJHB6GqE7hZR6BCvaAzCSS4KrgbjYo6MFGcbOGpHp6o9hM4EXQ3i/19h1IjPXY9Cv0iSxD2XzWZ2npW2bi/XPrmNX7wiLEpnBhd2/x6nVvLtTi9Or9gcZytie9iExPZGhzthgwzVwcp2xUZ+aIR6L+4dh5Xtsiyz7kA9ABfOGH8W8idj0ml4+oalLC5JxeH28eW/b+Xdw2NjhadSqZg+fTo2mw1VLPa3DgSgq5GH96u5Qf0a+VKzqOCceJ7Y4OgtkDVD2DlFCrUG0idBwVLR3z1jusiQ7RxCL0A5AJ2N4kdngbxgb/aiM8U4B7K2N6UKS/vu1oGr6c/5f+K4riZ4754+N01NE2J7TYePjgi778TsHAkEoOUYOo8dt6zFnDsFKSQwW3KjO7ZoodZC5gwhunbUi/kIPVUbM6VyjtR1gFovAkOftJwfJjE7R0aCs1Ukbox20kbFRrzPXseiwF6csg7XxIvgkj/BlE+FV/FhyYFPBT8XaneKXohDQG20sCxbJBN8fEyxkleIfUJ928vHQWW7xxfgqc0VAHxxSeFpjo5trlpSSJ7NSKPDzT82lUd7OAoKCnFCh8vb0+LzujNL+j3m/OkilvBODPdtT5i1srNV7MF15sie114Jr94qhDJzFqy8EzKnRfYx+kOShDseQN2u8O6r1kDAGzGxPWHmyED4feJ9jueqdhAtvXwe0SJwjEn4ORIJQlXtkSjEGQh9svisso9PbXCsUGZ4gmHQqnnsywtJN+s51tiJ0+tnxaR0/nndYnRqFftrO3p6p40nQlXtqUk6DFp1lEcTf1iNWpINYlER6m2eaIQq2xUb+aERqmw/2uig2zO+suIO1jmobnOi16g4e0p6tIcTdSwGLf+8bjEXTM/E7Qvwkxf3IUfCwjze8XZxqL6Tg5UN3Kx5Sfxt+qWQUiT6nydljN5CWm8WvcTSJkLOXFAbRO+ngd6XgE9UwuvMotK04AzRX06fLKrXT4c1XyQPuAZYXxgswlIaCco/hIOv9N7VoCInSQhmh6vHSd92vwfq9gCwS57EGcWpohLClDq6mcyxjtYgEjuS0kWFO4A5i25tKlrJj6dufzBI4I6Y2J6QdLeIANxoBYN8bvj4j/DWj9B7OzgQKOI/hT/DcN4PRIuM4ZBSDMUrxPVtjw/tPjozZ2aJBJ3NY+iqoqAwXIpCle3NibmXOpk39tXR6HCTkazv6Uscr+g1am67ULQ8/PO7x2jvHr9t+RQUFIbOs9uq6PL4mZJl5sxJaf0ec/40YSX/wZEm3L7ELGqJCVwd0Fouknsj6frU3QKv/0AI+UmZoqI9a0bkzn86QlbyDfvDu59KGxTble+zIdHVFFtV7fV7Ye9/wD6M9jaSFL7zocLo4+kW7gkG6+g+jkojHqPlGHQpyeqjhSK2JyA5ViOPXL0AnUZFulnHA1+YS5pZz0WzRb/7p8dhdXtduxBSsy1KVftwkCQpoa3kff4A9R0icJ9nG8D2VKEPmRYD2RYDARn21YyvXsvrg33XzpqcgUkXx5mtEcSgVfPQVQvQqVXU2J2Uj4FFaiAQ4ODBg9jtdgKx2G/M7eCvpV7u1PwTg+QVfannXCkE74B/7ETVpDTImQNas+jp5qgXVehuhxAsfW4hbJqzheV7cvbQBPaT0ZkgtVicc6AeYNlzYP414vqWR8TjBglZyR+qjaxgFrNzxO/GV1sKiH7ty/KDArI1P/zXPtHQJQmLcY1ebDolCXemCCSldBxERhL/Q94wexMOQMzOkeHi8wTdKSJctROi9QS89E3Y/wIAf/NdxFWBX/Dpc5eJdgAjYcFXxGX1Nmg5cfrjJYmFOaIH9t7qdiXJSyHmCVW2V7QmfmX7k0Hr5KuXFKHTxH/I6TPz85iSZabD5ePRD45HezgKCgoxjj8g9zhhfHV5CdIAAu/sPCsZyXq6PH62lMVm0nHcr5WddiFOOltFQm+k8HbDm3cIBzljKqz8MWTNjNz5h0LufHHZehy8YVSpSxLIRKyyPe7nyGAE/GCvECJlLFS1Nx+F174Pm/8Cz14Dz10Ph14f+v31SWLOjnGiRULPkUjgsovPFO0Y6BH6ZECGpkMRi6ko9CX+dz4K/bKoOJUPbz+Pd247l6ygwPzFxcLC7eXdNXS6x1claqiyPdemiO3DpSRdBIgSUVhtcLjxB2S0aonMZH20hxM3zAn1ba+2R3cgY0zIQn7VzPFtIf9JjDo18wttAGw8PvpZkj6fjxdffJHy8nJ8vtj7TvM6WnFV7WaVegcBVLDwK2C0iAWtzjT6WasnY0oVQnrOXLAWikV8ICAq7J1tIhEge7YQOodLci6Y0kUgYyAWflkc4+mC/S/2/HlquhDMjtZH9vslZueIz4O/QbT5Oa6bSoGhWzgDJGVEeWAxgtEmqkOCTgnmAmGROCNwlIaugAgQRWhjGLNzZLi42kXSy0j+l/sj4IPd/4YXvwGtZcjaJO7W38bdvmu4Yo6NFGMEtpTpk6FgMSDD9r8N6S5TcqzoVKJdVFWrEixQiG1CPdsrE7xn++4qO7sq7WjVUtxbyIdQqyS+v2oqAE98XE5jh+KuoqCgMDBvH2ygqtWJzaTls/PzBjxOpZJ6qts3HBqbVmzhEtdrZacd6vaKynZLLkgRkkACfnjnl9B8ROyrV9wq9tljTdpEsYf0e0Wyarj43ac/ZgjE9Rw5HZ0NogI4FtznXO2w/qciSUJvEXvi1uPwwf3CuWEo6Mzg6RzYkXCUSOg5Egk6G0UyRySdNwYjKV0U4DQfG9j9UmHYKGJ7ApNlMWA1aXt+X1ySyoSMJLo8fl7ZUxvFkY09dXaxIVb6tQ+fMyeJLND3j8TmJmAkhCzkc6xGVKox+nJLAOYW2ADYM45aU1S3dbO/tgOV1Gv7ptDLsonCIm/T8XFu6ev3se1EA18maJdeuBRKzhbXPZ1gsAnBfSwxWIRVc/ZM0Yu9eIXox164TGThj7R3vEYHqSVisz9QprRKA7M/J64f+F/Pwn6CTVRzn0hwAaKHlmPoPW24ZQ3mnKki6cFaIKq5FQSWHNG3PeBDmyvE9gWqIxxocIJaJ7K/FU6lO/jZG0mHhPp98MLXYeujIriTOoHtC+7lb+2L0Knha/MjWEUfqm6v3Ahtp7dG1On0TEsRnyN7a+yRG4eCwiiQbxOtY+o7XPj8iVvV849gVfslc3LJSKAk5gtnZDG/0IbT6+ehDceiPRwFBYUY5qnNFQBceUYhRt3ga7JQ3/Z1++vxBxTRI6J01IK7XTi3RUrEkmXY+JBYq6rUcMYNUHJWZM6NcEUIDFX8klSQu0Bcr9oc3gNJKvAqiWOD4vdCW0Vs9GoP+GHDL4VLoTEFLrofrnoW0iaJ24+8ObTzqDSALIRWhdjA0y328PrksXtMSQXmTOio7o0fKEQMRWwfR0iSxJVnFADwemldlEcztoQq23OsSj/u4XLOFFFxt7vKjr07MnZDsUKoD32eTZkf4TAeK9tDFvKLilNJMydOADFSLJ8oknI2l7WMb0tfj4OPjrWyUDoCgGrWZb0bNJ9HLGyjjVojBH+jTVyPBOZMMGdB9yDOBtPWClHZUQcnPgSg2CYev6ItMtn1MU/lJgD2yBNZVGQDSS3eB4VejKnC/cHtgNQSXJIBi+SkvvyQENs9XeBXsuL74PeK6gt9BKraZRlqd8Ebt8PLN0NrGWiMMPNzsPZBHq3MBeCKGSYykyIo7GfNhJx5ItFi21+HdJfZmSKxuLTKHrlxKCiMAulmPTq1ioBMT/uqRKO928ure0VS/1eWF0d3MBFGkiRuXz0NgH9vraSiJfHbASgoKIRPVWs3Hx5tRpLgS0Nw9zhrcjo2k5badhfvxmh1e1wiy6J6V2uKbLXojifhwEvi+pyrYMYlIz5lU7ef329xcM2LLcx9tJ45j9Tzx60OnN4hJOblLxSXDfvCe1C1RthWKwxMqKo92r3a/R748AHhXqDSwtJvQuY0MGfA1DXimKotQz+fLmQlr+ylY4KxtJA/GY1etJNoqxCulwoRQxHbxxkhIWRPlZ3AOMqarO8IVS4rle3DJddmZEqWmYAMHx4dfYvosSRU2Z6foojt4TAnzwZARUt3wiVgDMTuYDA/lHyi0Je5BVYMWhXNnR6ONnZGezhRQ3Z10FG1D40UoEuXDpkzxA0+t8iMNliiO8DRQqUWlvQyfXqy90GfDFMuEtdL/wNAsVWI7dWOAB5XYgoQJ+Op3gPA9sBUluVIYmM1lpnM8YBaI3rYuztBpaHJLAQOVUOp2Bj6PeBL/LkSFq52kYQw0n7t9aXwv5vg1VuhaisgCQF87W/hzJup8SWxoVz8f3913ij0hj/jenFZ8RG0Vp728DnZYm1fWq1UaCjENiqVRE6wpVmtPTE/v443d+L1y+RaDT0OWInEsolpnD0lA19A5sH1R6I9HAUFhRjkmW1i7XLW5AwKUk8vnhi0aq5YJIqi/hmsiFeIAD63aDuliWAMeN8LsPMf4vq0S2DBNSOueO72Brji+RZ+v8XBh1VuOr0ynV6Z3212cN4/G3nz2GnaJOUFK9vtFeAKI/6i0oK3S7GQHgifB9rKQWeMblV7ZwOBl78Dh0Vf9rKSq2DiBb23F58pLtvKwTHEZB2dGdwdY24lrzAAY20hfzJJqdBZD11NY//YCYwito8zpmYno9eo6HD5ODGOsrFDNvJKZfvIOHeqqMZ873BifRDX2MUCNk8R28PCatJSHOw/uXecWMmXN4vPzYkZoyAwJAB6jZozikU/q43HEispJxyO17Uyw70XAF3G5F7LeE+n6K+lS2BhNSldWIAPZk02+3OABA37oeU4mUkqjBoIyFDdbB+rkUYNR5MIpHXossjRu4LuAtrB7zQeSUoHrRG83fgzZwGQ1nVUVLb73IrY/km620RF+HADQh218PZd8PK3ofGAOE/eIlj9K2FVmDUTgP/s7yYgw7J8HRNTRiH4lD1bWGLKAdj26GkPn5Ut1m6ltY5xlUisEJ/kBveitfbTBM/jlN4E5jGuzhlDbl8terf/b08t7c4B2uYoKCiMS7z+AM9urwbgqqCr6FC4emkRkgQfHGmirGn8JqxHFG+32CtodJE5X9l7sPGP4vqE82D5TRFpAfbLDzsoa/ORmaTi7nOtvH5VBg99KoV8i5r6rgDfeqONyvZBKpCTcyEpU6ybKz8e+gOrtaKyeaD2b+OdznoRzzDYojeG6m3Iz9+AqukQdjmJaz0/4LuNawmcLMqas4JW8jIcXT+086o0IslCsZKPPp5u8T5Eq/BCHWyRYC9XnA4iiCK2jzO0ahWz84T18+5Ke3QHM0bIsnySjbxS2T4SQtW87x9pSqiAZo/YrtjIh82cfBswfqzky4M9pYvTEzeIOFKWTgj2bS8bv71/3j7SxtlqIbZr8+f13uBxig2RKoGXX5IE1kJxOZAYas2HwiXi+q6nkCSJoqCVfHljgifuBPxIDtHKJyUtQwQ4TKlRHlSMok+GpAxwtWMrmA5Asb8cp08WAQLF+rAXv08EhXRhWsh7naLH36u3wTNfgrJ3xd9zF8Ca38JF90LR8p5Apdcv88x+8bp/aVYE7OoHIlTdXrkRWssHPXRKqgadGhzuABWtypxQiG1yg3uNmkQV24PPK9eWuHvuWXlW8mxGZBkO1HZEezgKCgoxxDsHG2lyuEk367lgRtaQ71eQamJlsLDlqc2nd/VRGAI+18iSUE+m5Ti8d6+4nr8Yzrw1IhXz6447eXqfWLs+uCqFa+YkMSNDyyVTjLx9dSZL8nQEZPj3vkHWt5IE+YvE9ertQ39wlQYCPuEWptAXTxe0ngC9WTj3jTVyAHb8A/n125HcHewNlPBZ76/YrFpAaZOfV49+IsZScra4rNw49MfQJYm9oyKwRpdoWcifjClVVNd3NkRvDAlGAkd7FQZiXtDSbfc46W3Y7vTi9PoByFbE9hGxqDgFk05Nc6ebA3WJE1wIVWEole3hMyNX2GEfaUj8DOy2Lk9PBUtR6iiKDHHO8olCbN9c1oo/gZJyhozfx4FjJ8iXmvGjhqKl4u8+t9jUjofe3KZUkWXfNUjCxfRLxWWdsFQvCVrJl7ck+GeJ14nFK9xh0tKyQNKM3PY7kUnOhoAfa66oJJwo1XG8rhU0WnC2RXlwMYS7Q/S314cxl058CM9eI4KHtTsBGVInwLk/gk/dC7nzTglQvlPuorErQLpRxaqJo7imzpoJ+WeIpIqtg1e3a9USM9LElra0cvwmeQ3Ez3/+cyRJ6vMzbdq0ntvPPffcU26/8cYb+5yjsrKStWvXYjKZyMzM5Ac/+AE+X9/g3HvvvceCBQvQ6/VMmjSJJ598ciyeXtwR2mskrNg+TvZUM4P7n/21CZ4gqKCgEBb/3iqE8s8vykerDi/cfs2yIgD+u6OKbo8igI0Yd1dkbJldHbDup0K8T5kAZ98OxpG3hGvs8vPDd8R3yNcXJHFmQd8qeYNG4tq5Iub03wPdePyDxFVCVvIN+4c+ALVWCO2K2N6XQACaj4n3PRpV7d0t8OYdsOMJJGSe9q3ki/6fcefF0/jmIrHP+83GDty+k+ZD8Qpx2Xxo6K0E9Gaxd1Ss5KNLZ5NI6IiGhXwIlUZUuDvqozeGBEMR28ch8wptwPgR20NV7alJOgzaKGSlJRB6jbpHSHv/SGJYycuy3BPwyrcp1crhUpIuNgAnmhO/LUWo9Ua2xYBRp3yWDMTsPCtmvYZ2p5eDo5iUo1arufjiiykoKECtjp33o6W9gzT7bgB8lkKw5IkbutvAkg3GlOgNbqyQJEgpFIt2t6P/Y3LmABI4W8Fe01vZHsHPkpicI/YKNPhxyxrS01KETbrSr31gdGbQGJAMZlpVqagkmcYT+0X2d3fbiK0PY3KODIeulqFX73Q1w7qfwPqfiusGK0y6EC76DXzmLzBl1YCWm/8qFZU1n59hQqce5aBAT3X7Zmg6Nuihs7NEgLK0cvy2LxmMmTNnUldX1/Pz0Ucf9bn9hhtu6HP7/fff33Ob3+9n7dq1eDweNm7cyD/+8Q+efPJJ7rzzzp5jTpw4wdq1aznvvPPYvXs3t9xyC1/72td46623xuw5xgt5wYrvkCidaPS6hSX2nmpmrnAK3K9UtisoKASpau3mg6MiRnZlGBbyIc6enEFxmgmHy8dLu2ojPbxhE7drZVeb2IuOhIAf3v0lOGrFevns74E5PSLDu+v9dtpcAWaka/je0v7F+/NLDGSYVDQ7A7xdNkj7rNz54tJRC51DXAtLKpHUGgGxPW7nSH84aqGjGswZYyuABnyw97/wn2ugagt+ScP3vd/gx76v8cc12ZxXbOBr85PIMKmo6vDzVOlJMZOUEjCLBHVObBja46k0Yu/YXi0SDEaZhJojkcLVAV2NYBh58s6I0ZlE8r5PSb6JBIrYPg4JVbYfrOvAFaz4TmTq2sWmP9uiVLVHgnOC9lbvJ0jf9uZOD25fAElSnA+Gw8SMXrFdlhO7irkiKLYrFvKDo1GrWFwibLE3HR+9KkO1Ws2cOXNIS0uLqQX7u4cbOUsSFvL6nOliI+NzixutBdHNWh1LjCmif5irQ1ixfRKdGVJLxPXqLZTYxHtY3uoUG/8IEItzRG4+DkC1nEFRshpMKUq/9sHQmoJ92500mSYB4Gk83NPLfcBkjiESi3MkbMKxkLdXwkvfgvKPRJCt+Gy49E+w8sdQcMagvScr7D4+rHQjAVfNGoPvwYxpULgckGHTHwc9NCS2761RqjP6Q6PRkJ2d3fOTnt43UGwymfrcbrH0Bn3WrVvHgQMHeOqpp5g3bx4XXXQRd999Nw8//DAejwjIPPLII5SUlPDAAw8wffp0br75Zj73uc/x4IMPjunzjAdCInSi92xP9Mr2WXlKZbuCgkIv/oDMT17ahyzDmZPSKEoL3wVPpZK4eqmobn9uR1Wkhzhs4nKt7PeKXsgj6akuB+CjB6Fqq9jPL7lROC9FgG21bl475kIlwW8vTEGv6T8+oFVLfGGGWDc8vW+QhHRTGqQUi+vlH4Y3mAiJ7XE3R/rD7YDmo2KfOZK5Ew4BHxxdB89/DTY/DN5uXEl5fM57N8/5z+Hbi82sLBFxapNWxa1LRZL+Q9sctLuCIrkkQclZ4vqJMN7/pHToqIGu0Y/tJ8wciSTdLeB1RddCPoTGINrLeRLcZXKMCLt5SWVlJQUFBUifCBbLskxVVRWFhYURG5zC6JBnM5Ju1tPc6WZ/bTsLixK7V2iosj2Re8eNJecG+7bvqGyjw+XFYohvkSBUgZGVbECnUfKPwqUg1YRKgk63jyaHm8wETmo50Rzs1z6MzfN4Y9mENDYcamRTWQs3nD0h2sMZU3aWNfIT1UHxS/4Z4rK7FSw5YiM8nkgtEYGKpkOAJDJmTyZ7DrSWQd1eiqavBaDc7heb/rHa4I4xjvpjWIAqOYPlZnl8OB2MBJUKTOnQehxv6hTo3EqS40Swz6BfbAjHe897V7vIRDdnDn5c81F4/QeiN5wpDZZ8EyaeO+Reln/fLYJ8ZxXqKbRGoP/lUFh6I1Rthvq9ULkFCpf0e9icTLEW3V/vJBCQUanGSVLTEDl69Ci5ubkYDAaWLVvGPffc02fP/q9//YunnnqK7OxsLrnkEn76059iMonP602bNjF79myysnp7z65evZpvfvOb7N+/n/nz57Np0yYuuOCCPo+5evVqbrnllkHH5Xa7cbvdPb93dIgqYa/Xi9c7MteKWCXTLP53au1OPB7PKTGVcAm9TrHwesmyTLVdrJWzzNqYGNNoMTVT/H8ca+yko8sV045XsTRHFGITZY6MnHvfPMz7R5owaFX84MLJw34tz5+azi9fgz3V7dg7nSTpx2i9dRribo64O8DtFEnN/mFU7coB1B8/iOrwa8hI+Gd+AXnChcM71ycIyDJ3fyDWO5+fbmRyqhrvIOf93HQDf97eyUdVHo61eHrc4D6JKncR6rZyAhUb8U//9FAHAy4nROB9jbs58kmcbdB6HJydYM2NyHs9KJ4uVIdfR7X/eaSuRgBkjRH3xDVcVnkZB3wSS/J0fGthUp/58dmpBv62q5PjbX5++WE7v14pnHakguVoSv+LXF+Kr7sT9EMQcCUtyCpoPg7a5FEvAIj7ORJJ/F5orQKNafTn2pBQgc8P3R1iLkSJWJ8jQx1X2N/cJSUl1NXVkZnZN6DT2tpKSUkJfn/iV0rHO5IkMa/AytsHG9lVaU98sd0uxHalajkyFKSaKElP4kRzFzvK2zhv2mmCuzFOdZsICiV6BcZoodeoKUg1UdHSTVlzV0KL7b2V7YrYfjrOCFa276xsQ5blEQeT+yMQCHDs2DHa29sJjIH11VAxNOzEJLlxqZMx5C4QVe2SBNbC8VPVHkKSRA9oOQDNh0Gib+Zu9mw48BI0H6EkGDio7pTxuLrQmUcutsfiHOlsLMcCtKoz0GmUfu1DwmCBQABz7jSohDxvhfhc0ehEIott+Im+sThHwqa7RbhBDCaaN+yHN24XLhPmbFHJnj17yA9R5/Dz72BFzY0Lx3DO2gph6ho49CpselhU30unJkZOStVg0ECnV+ZEfQsTcyNj8ZkILFmyhCeffJKpU6dSV1fHXXfdxVlnncW+fftITk7mi1/8IkVFReTm5rJ3715++MMfcvjwYV544QUA6uvr+wjtQM/v9fX1gx7T0dGB0+nEaOx/jX3PPfdw1113nfL3devW9Yj9iYbHD6Chy+PnuZffIClCcc3169dH5kQjoNsHXW7xOVS6+X0Ox67+PGJkGcxaNZ1eiSdefIviOOgGEwtzRCG2UebI8NjWJPHUMfGBd0Wxl4rdH1Gxe/jnS9WraXXDo8+vZ5ot+s6Bsiz3JMOtW7duVPb1o0dD+HeRA8yt+gfFLe8iI7Gz6BtUa5bD7vCs/WW5/63/tiaJvY1q9CqZWUYHr+88vUvXNJuKg3YV97zbyKVF/e9XUj1TOQvw1+3jze0nCKiGuMA4dgg4NLRjByC+50h/VI/amfVeOxMb36K45V3UfhGLdmmslGVcyPHU83msLJkDbRJmjcza7G7e2t19yjnW5sIf2zT896CTDKmTqVYZ5BTO12dhdjdw4L3/Up5xfhijaoK9o/v5n3hzJJLEUDX58T3AnmiPImbXI93dp/4/9kfYYvtAQfPOzk4MhsQVWRKNeQU23j7YOC76tocq23OsipgaKWbnWTnR3MWBuo64F9tre3oLKvNjuJSkJwmxvamLpRMSt3I31Eu6OC0xA8CRZEaOBb1Ghb3bS1lzFxMzIi/O+Hw+nn322Z7ren1sVEIXOnYC0G2djMFohY46sOSO3+pblUrYyQM0HwEjvRXuIbHPUUemyoFBI+HyyVQ3dzDBPPLXKxbniK+1AgCnLlW8Dkq/9tOjSwK1luzCybAZiqmjqaWVzGQjOO2it9gAPcZPRyzOkbDwe4WFvH6QJLDWE/Dm/xNCu7UAVt4JGZPDepi/7HDgCcDiXB3L8kfY/zJcFl0HR9dDeyUcegOCLhgno1FJzEjXsrPeS2lFkyK2n8RFF13Uc33OnDksWbKEoqIinn32Wa6//nq+/vWv99w+e/ZscnJyOP/88zl+/DgTJ04c1bHdcccd3HbbbT2/d3R0UFBQwKpVq/pY2Sca9+5/j5YuDzMXr2BGzsiep9frZf369Vx44YVotdF1GztQ1wHbNpOapOUzl6yK6ljGghdadvDB0RasJbNZszj8/sxjRSzNEYXYRJkjw2dPdTs/+Ns2IMA3zynhtgvCW1/1x/uufbywqxY5YxJrLhz5+UaKx+Pht7/9LQDf/e53SUqKg8KDtnJoPCQqlMPB50H9wT2oWt7vqWifs+gzzAlzn1HR7uNrr7Rh1Ep85wwz55fokSSJ5m4/95S2AAFuXpzMlUNMYNXaXHzrDTs7W9X84ZKc/m3n5Vzk6r+gdbZykeUo8pQhfA93twnL9ILFYT2/TxKXcyRE6wloPChcEIbSkmu4yDLS4ddQb30EySsEO9mYhn/CeainX8qE5DweXt9OaZsLnRoeuTiVJXkD70tbVR08VdrNy9U6XluRhkmrQmX4PGz+E3PaXmfGqmv6TVDuF6cdJDXkLQTt6Oh6cT1HIo0sQ0MpOBogOev0x48VrnaQNFCwRMTwokCsr0dCCSOnY8hie2gjLElSH2s5AL/fz5YtW5g3b154o1SIGvMKhG3pnmp7dAcyBtR3CDE1R6lsjxjTcpJ5eQ8crBvaB00s09Ah7CuV+TF8JqSbee9wEyeaYygjbxQobwnayCuV7adFp1ExJ9/KtvI2dla0jYrYHot0e3zk+apADcbMCcLmWg6AJW/8VbWfTI/gLkHLEUAWm1lzJpizoLMBqWYbxda5HGrxUdHUwYTiKI95lNB2iqoI2ZQKRpvSr30oaJNAa8SghSZSyZBaqT5WSubiFdDZJKzkNeM0mcXVLvoLDmQh39kAb/xAHGPJg1W/gJSSsB6izuHnmX3i+++WJcljX4lgSoW5V8HOJ2H74zDh7H6TVOZk6YTYXt3GZ8Z2hHGFzWZjypQpHDt2rN/blywRVv3Hjh1j4sSJZGdns3Xr1j7HNDSISrHs7Oyey9DfTj7GYrEMWNUOoNfr+01w0Wq1MRlgiRR5KUZaujw0OLzMLYzM84yF16yx0wdAfoop6mMZC2bl2fjgaAuHGzrj4vnGwhxRiG2UORIeDR0uvvX0bjy+ABdMz+IHq6dHpI3NsonpvLCrlq3lbTHxfshyb3V93MwRbyfodKAOQzBytcNbP4aGfSCpkGZdgeaMa8NO6G3s8nPdy21UdgjX32++YWdelhaVBLvqvchAXrKaGxYko1UPbb6smmgk1+ygttPPm2VuPje9v+IPFUw4F/a/gObEuzD9U6c/sU4Hsg9UEqiH37IgLudICI8dDCYwjmICfGcjfPAbqN4mfk/OhalrkKavQWNMJSDL/L+37bx2zIVWBY+uTWVF4eDx6f93poUNJ9xUd/j549Yufnq2FaatgR1/Q+pqQlv+AUxaObTxJaVAezV4O8A0Oq9DXM+RSONqB2cLJNnC+4wabfQmcHUAnqhayUPszpGhjmnI7+quXbvYtWsXsixTWlra8/uuXbs4dOgQc+fO5cknnxzueBXGmDkFViQJqlqdtHS6T3+HOCZkI69UtkeO6cEqjEP1p7c8inUaOsT8yEiOs2q2GKIkQ4jPZU1dUR7J6NHW5aHdKfqzFKUqYvtQWFAokrp2VtqjO5AxpLyxgwlSHQDG9ELwdgubcIM1yiOLAVQqSJsI6VNFTzR/sN9R9hxxWbeb4qCVfHmiJu4E/JjdwnZZb8kAgy2644kX1BrR297npM4oKm2d9Yd7+7a7438tMmy6moEBLORddtGjvatZ9L0/70dhC+0Af94uqtqX5OlYXhDBtZK3WwSfOhvA7xn82LlXiOfgbIM3/h8EfKccMivYt720dhzPhyHQ2dnJ8ePHycnJ6ff23bt3A/TcvmzZMkpLS2lsbOw5Zv369VgsFmbMmNFzzDvvvNPnPOvXr2fZsmWj8Azin5CbVshdK1GoCbXmGiduYbPyxNpuX038J58rKCiEh8vr5+v/t4NGh5spWWYevGJuRIR2oMcpcG91O92eU9c7Cqch4Bc92zVhFNN0NsL/bhZCu0YPS26EJdeHLbS3uwN8+X8tVHb4KbSo+cZCMwaNxO4GLzuDQvvMDC0PfSoFQ3/V6QOgUUlcPUcI7E/s7uojXPZhwrnisn43eE+ztobgXsp7+nV4ouJzC+evUarmBoTA/vzXxKVKA1Mugkv+CAuuBqNIFv/L9k6eP+hELcFDF6VwXvHpx2PWqXr6tT+xp4vmbr9wzZsadAArfXboY5QkUQDQ1RT201MYBl3N4HcLV4lYQqPv/Z9QGBFDTl169913Abj22mv5wx/+kNDWbuMBi0HLxAwzxxo72V1l5/zpMWRdEUFkWT7JRl6pXI4U07PF/39ZUycurx+DNn6b8jU6RLJJVgL3Gh9tJgYrvU80J+6XcnmwX3u2xYBRF7/zfSxZUBQU2yvaojySsaOivoULpKAgYSsUFbfWwmFbXCccKhWklgjBzNkiqtqzZ8Ox9dB4mKKcoNje0jVwo7t4pquF5IAIyttSs8ILAo13jDawV+K0TQHnNvTtJ8TfNTrRszylKKrDiwp+nxCqdf04h7ja4bXvgb1SVIGffTtkzQz7Iao6fPxnf29Ve0TwOkWQQWsUAjqI5wGiil3Tj6CvNcJF98JL34LG/fDefSJ54KTPiDlBsX1fkw+/24laH2MBjCjx/e9/n0suuYSioiJqa2v52c9+hlqt5qqrruL48eM8/fTTrFmzhrS0NPbu3cutt97K2WefzZw5IhFq1apVzJgxg2uuuYb777+f+vp6fvKTn3DTTTf1VKXfeOON/OlPf+L222/nuuuuY8OGDTz77LO89tpr0XzqMUtuSGwP7lEThZpx1pprZq7YDx+ud+D1B9DGUnWSgoLCqCHLMj96oZQ9VXZsJi1//fIikg2Rq8ArSDWRZzNSY3eyo6KNsyZnROzc4wJvtxCPh9qqy1EHr94mLvUWWP5tmHh+2DbKzd1+bni1lUPNPjJMKp76bBqFVg3Xz0viuYPdWPQqzi82kJN8mliSLIsqY5VKOMHpzKDScOVME7/f4mB/k5cddR4W5fazXs6eJRKUnW1w4j04nZW8WguukNg+DlslervB6wL9KLSfkgOw6ynY/gQggzkblnwDSs7qkyS9qdrNA5tFovAvz7PyqYlDX0OdW2xgerqGg80+Nld7uHiKEWZdDvueh6ZDooVf+pShnUxvhu5WsU+LNRE4kfC5xf93rLYSlCRwd0KMDi9eCHtH8MQTT2CxWDh27BhvvfUWTqfYVA2YWaUQs8wrsAGwJ4H7trc7vTi9wr4nWxHbI0aWRU+KSUtAhqMN8V2B2BisbFfE9uETqmyvbO3G6w9EeTSjQ0hsL04fh5uQYRKqbD/S6KDD5Y3yaMaGltrjaCU/XjRgKxCZ9eO1V/tAqNQiESHgF9Xtob7t9gommMX3dbndB77EEiEAaD0uLmQzuWk2JQkjHHRmkCQMWaJ3Zaa7QvxdaxTCsi+xXZr6xecSz/uTSRuuDnjt+9ByXATpzrwVCsPvxegLyNzyVhueACzL17EsP0JV7U67qLAvXAr5CyF3vugVackRlUX9VK0DohXFyjsBSSTo7Pl3n5snpmgwaqDbByfqWiIz1gSgurqaq666iqlTp/KFL3yBtLQ0Nm/eTEZGBjqdjrfffptVq1Yxbdo0vve973H55Zfzyiuv9NxfrVbz6quvolarWbZsGVdffTVf/vKX+cUvftFzTElJCa+99hrr169n7ty5PPDAAzz++OOsXr06Gk855gmJ7TVtCVbZHhLbU8ZHkLYw1USyQYPHH+BYY3zvhxUUFIaGLMvc9coBXthVg1ol8fAXF1CUFnnXuyUTxP5xc5myngkbr1OIx+oh7LPaq+Hl7wih3ZgC5/0YJl8YttC+r9HDpc80s6veS7JO4p+fEUI7QGaSmm8tSubq2UmnF9qht+I1ZaLon9xRDz43qUY1n5kqYlFP7hmgyEVS9Va3H99w+sdSaYQo7B8fsZpT8HQHbfSHb6HfL13N8MYPYfvfARlyF8DaB2DieX0eq7HLz3febCMgw+XTjVw5M/xY4/Lg/mxjdXAvbMmBouXi+q5/Df1E2iSRfOBqD3sMCmHQ1SRe41gV27UG6G6O9ijinrA/UVpbW/n85z/Pu+++iyRJHD16lAkTJnD99deTkpLCAw88MBrjVBgFZgStwA83JK7dYqiqPTVJF9fV17GGJElMz7Gw8XgLB+s6mJ0fnxbJsiz39GzPsig28sMl22LAqFXj9Pqpau1mQgL25z7RHOzXPgqb6UQlI1lPQaqRqlYne6rs4yIr391wFIB2TQbpKi1oZJEhr9CXpAxR1d7VJCqSdWbwdDLdfwgoorw9kJBZ1d0NxzABVXImE1MNSmV7OOiSQGMiK78EdkO+XI+7sw29ySKqot2d/VdEJzJ+j7B+VJ9UTeV1wuvfh5ajImhy5m1D79f3CR7c7GBHnQga3n++LTJjBpD9kJTeG2RQqcTvoXYb9iqw5omA4ScpWQGLb4Ctj4lKkckXiT6DgFolMTNDx/Y6D3urW5hUnB+5MccxzzzzzIC3FRQU8P7775/2HEVFRbz++uuDHnPuueeya9eusMc3HglVftcknI28eD6546SyXZIkZuRY2HKilX017T1t1hQUFBITWZb5xasHeHJjOZIE91w2mzMnjUJFLMJK/oWdNWwpax2V8ycsPo9wdYLTO6R1t8Crt4r9qCkNzv2RSAINk9eOOvneejsun8wEm5rHLk5lUuoInA68LrEHzpgK8mSo3SlaQ2ky+MrcJJ490M0bx1zUOfz9i/cl58D+F6Fut7CS1w4h6WC82sh7uvrfb4yEsvfgw9+JVgYqDUz/NJxxvbB4Pwl/MKm5qTvAlFQNd59rRRqGq9/yAj1/293FpuqTEs9nXQ4VH0P1VvD5QDME6U+SxGvR1QLJ2WGPQ2EIBPxin6s1Rn7eRQqNUSShJGAsbiwJ+9295ZZb0Gq1VFZWYjL1flhcccUVvPnmmxEdnMLoMjlLCGJHEzgTu65dbPqzlarliDMtaCV/sD5++9Q53L4e54PMZGWODBdJkihJcCv5ip7KdkVsD4eFwer2HePESl5lF9bWHmO62LQarEIkVOiLSiWq22VZVLFmzQKguFMINdWdMl534lW2t9eJyvYGKR2zyQDqcSYOjwSNHgzJZFmMNMipqCSZuqO7hVOCHBAtG8Ybfk+w3cJJ27n9LwnLQK0JVtwCk88f1qk/qnTz5+3iNb33fBsF1ghVfPhcIsmkv2x+tRbSp4pkHEfDwOeYe5Wocg94Yec/+tw0O0sEN/dW2yMzXgWFUSBhxfZxZiMPvX3b99fG735YQUFhaNzzxiGe+LgcgHsvm80XFhWM2mMtC/Zt31NtV/q2D4TfK1ycXMHP30AAmo9CRy2YM09/37d/3iu0r7xzWEL7swe6ufmNNlw+mXOL9Lx4RcbIhHYAnxMMKWK/rNaIBHWv2BfPzNCyOFeHX4Z/7Rsg7pY9Gww2seY+/OoQHlAen2K7LIuEi0gla3s64d17xLxydwjb+PN+BMtuOkVoB3iqtJuN1R5MWok/r0nBpB2e+Lo4V4daghN2P7UOEdsmZ64o+PA6heg+VPRm8T8xHh3jxoLuFnC2ivZ4sYrWID6DPN3RHklcE/Z/87p167jvvvvIz+9bLTB58mQqKioiNjCF0WdKlgh0VbR04/b5ozya0SFU2Z5rU4TUSDM9R8yfg3XxG1wIWchbDBqlD/cImRC0ki9rSkyxvTyYRFCcptjIh0NP3/ZKe8TPrVarWbVqFXl5eajVsfH/m9RdDYDGnCE2xebMxOs7HilM6SJ40N0COcJKPtl+GINGwi9LVDeNvJIj1uaIu1mskx2adCGGhmlROO4xpSH5vVTqJgLQUHFQ/F0d7Ns+DGJtjoTFJwMhPjeU/ldcn3axsMEcBm3OALeua0MGrpplYu3kCApnni7hZDFQEpLOBJnTRSb9QO+pJMH8a8T1o2+Bq3fd0dO3vd4pqgcUFGKQkM16k8OdMHtwl9dPc6cI1uePExt56O3bvr9WsV1VUEhkXtpVw2MflAHw68/O5oozCkf18fJTjOTZjHj9Mjsr7KP6WKcjZtfKbodIMK3eBq0noK0c2k6I/ffpbME3PQz1pSLxednNkDs37If/V2kXt79tRwa+NNvE3y5JxaqPwN4u4AfDSU4pBqvY6wTX/V+eI9bQLx8eIGFPpYaZnxXXt/1N9G8fDEk9YmEtZufIYPjcQozWRGDNUrcHnrte7EuQoPhsuPhBmLiy3/1+jcPH/RtFHP2OMy0jStBI1quYHdz/9FS3q9RQvEJcP/He0E+mNYmkgVGwko/LORJJZBk66gAp8m0LIomkChYyJK4D9lgQ9jdBV1dXn4r2EK2trej1SoVOPJGZrCfZoMEfkBO2GjVkEZ6pVLZHnJBV3sE6B7IsR3k0w0OZH5FjQrDiuyxBP0vKW4I28kple1iE+rbvqmwjEIjs54RarWbRokVkZGTExIK92+Mj21cLgDk1SyyiDfHZYmNMUKnAmi+qENKnAiC1V1JsFe9leX2zuG0ExNockdqrAPAa05T2AsNBJxyZvFkiIOapP8jmaveI+rbH2hwJC5+7bzLPkbdEtrze0htkGwa//ridpu4Ak1I03HlWhOfpUJKQjDZInyKe30CVNsUrIDlXZN7vfbrnz6Fg0/5mPz7nOHQ7UIgLUkxaDMEKpvr2xHBxCVW1J+nUWI0jrOqLI+YW2ADYU91Op1upPlVQSESONXbyoxdLAfjO+ZP54pLRFdpBOAcuKYmNvu0xu1b2e4QwrdII4bzpoBCpB6tUlmXY9zwceEn8PvcqYbseBgFZ5uFtDn78rhAkr52XxC/PtaJWRSDBPuAXYtfJSal6i/gJunidU6RHLUFlh5+q9gG+d+Z9UbjIebvg3V8P/phqrThuBMTsHBkMb7fYR4ykst3tgI9+D6/cItqaGWyw7Nuw8ieid3o/yLLMT95tp8srszBHy5dmj7yYZ3nBJ/q2Q6/YXrNTzPuhoAq+d92Rb18Rl3MkkrjaxRyJ5ar2EFqjSAxQEteHTdhi+1lnncU///nPnt8lSSIQCHD//fdz3nnnRXRwCqOLJEk91e1HGhIzINXkCIqpyUoiSKSZlGlGrZJod3qp74jPQFGjQ4xb6dc+ckp6KtsT77OkrctDu9MLQFGqIraHw7TsZEw6NQ6Xj2MJODdOpryxgwmqOgBM1nRhw6UIqoOjNwurKlswaOVsY4ZZzJMDja4Rb/xjDaNTJGNoktLE81YID10SaA0sXSRsHs+QDvHt1+o56tCJgIknsebLafF09vZrD/hgT7A3d/E5AwZ4TsfGKjf/PeBEAu67wIZxmJaG/SIHk2eG8rmYnAOWXOhs6v92lRrmf0lcP/hKjyhfYtOQpJVw+uB4vdLnVCE2kSSpp695qM95vBN6HnkpxmH1HI1XJqQnMSE9CY8vwIZDjdEejoKCQoRxevzc9K+ddHv8LJ+YxnfPnzxmj7082A/+/SMDrIXGO6GETINFrBmNKf23KQrRVgGv3QYbHxK/TzgP5l0VltOY3RXghlda+c0mUfX5jYVm7jzLErnvPZ9LVFprTxJgVSrRQ9sjvmeT9SrmZ4s+7B9VDZBorNYKwVdSicr/w4O0/FVpxOOOMMk97vB0CRFaNQzhV5bh2Nvw7JeDiRsy5MyDtQ/A7MtAoxvwri8fcfJuuRudCu4734YqAnNneb6IZ2+q8vQWwuUtFIkE7nYhuA8VfZIQhf3eEY9L4SRcdvC746MPusEqHDGc9miPJG4JO4Jy//3389hjj3HRRRfh8Xi4/fbbmTVrFh988AH33XffaIxRYRSZnCmqhI41JKZFREhsz1DE9ohj0KqZGBRY49VKPlTZnqX0ax8xE9LFZ0kiumSUB/u1Z1sMSruBMNGoVczJF9XdOyPctz0QCFBRUYHD4SAQA5vDqvpGcglWHpgyRd/h4WzexhNaE2iTxOtkyQVgrfkoAG+U+cA9sgSNmJojgQA2rwjEm23Zom+1QnhojaBNQpWUgWxMwyB5meY9yFdfsdPukkV1QZjE1BwJB1kWCQaqoNhe9j44asX/1JzLh3VKl0/mRxvsgLDDXJgzcKBoWHi7xf/7YIHQECoVpJSIpJSB3tfJF4rAqrsD9r0AgFolMTNDvCalVYrYrhC7JFrf9vHYrx1E4sRFs7MBeKO0LsqjUVBQiCSyLPPjF0s53OAg3azn91fOi0z18hA5Z0oGkgSlNe00RLG4JWbXyl5Xr1OSSj2wiOV1wtbH4PnroXaXEJcnXQBnfnfAquZ9jR7eOOZkfZmL9WUu/m9vF7/+qJ21/27inXI3OjX8eqWV/7c8ObIJZj6XaKn0yaRso02MOyAq2c8sOI3YDsIlau5V4vrGPw68nlZpwe8bUd/2mJ0jg+F2iGSEcKnbAy99Czb8UgiSxjRYchNcdC+kTRz0rq1OP3e9L2LnNy9OHpF9/MkszNGiU0Ftp5+K9mA1skYP+UvE9eMbhn4ybZL4n/FGtmd3XM6RSNLVItpBxAMqjUiS72yI9kjilrA/WWbNmsWRI0dYsWIFn/70p+nq6uKyyy5j165dTJw4+AeLQuwxOVjZfrQxMSsOmzqDYrtZEdtHg2nZvVby8Uho06LYyI+cUGV7o8ONw5VYWZAhsb1I6dc+LEJW8rur7BE9r8/n41//+hfHjx/H54u+dWdr9WFUkoxTMgibZINS1X5aJEn0bve6IGMaAMvUh1BLsK9V4kTdyCo5YmqOOGrR4cUnq8jIzIifzVYsIUlgSgOfG6lgMQBr9Hupcfh5q1o9rL7tMTVHwsHvFT9qrRDe9wSt1AuXQUrxsE75p20Oytv9ZCapuH35KHx+ebrBaB26q4PRBikTRFZ9fzZ2ah3MvVJcP/hKz59DVvKlNfYRDVdBYTRJOLH9pMr28cZFs4STyHuHm+j2xNH3iIKCwoDIsszdrx7khV01qCT445XzyBzjAo2MZD1z820AvBtF54yYXSuf7PDUH7IM5R/Cf78Ku58WQnX6FFh1N5z7owHtnP++u5OLn2nmm6+3ccOrrdzwais/fa+dx3Z2UePwU2xV8+IXMvjirKTIO7n43EK8/SQGq3CECyain1UYtA2v8hAYzCJ84VeFW5S3e+DqdrUGAt4Rie0xO0cGQpaFUB6O01xbObz5I3jlu6JlgVoLE8+HSx+CuZ8fUiL9Lz/soNUVYEqqhhsXmoc//k9g1KqYF3Q76GMlXxKykt8+9JOpteJ/xTf8+dAfcTdHIonPDa4OkUgTLxgs4GgQiRcKYTMsb0Cr1cqPf/xjnn32WV5//XV++ctfkpMzPLtChegSqmw/kqCV7c3ByvZ0pbJ9VOjt2x6fle2Nocp2xUZ+xFgMWtKDSS3lzZHNgow2oQBiYWocLY5iiNl5orJ9f218fk4MFU/jMQDaNRliM6xULg8NQ7DKNdi33dR+rKfv2Kv7GhPG0s7TKCr2a+U0CtIsyvwYLqGq6PxFAFyg2Q3A1kZVsG97ZAMDMYvfI8R2lQZqd0LLcVGZMvvzwzrd+xUu/rJdBPB+cY4Viz6C9vEh/B5ISg/vPtZ8SM4CR33/gvukCwEJOmrAXgHAnCwReN3b4BaJPAoKMUhIbK9NFLG9p7J9/K2VZ+ZayE8x4vT6ef+wYvesoJAI/P7to/z94xMA3P+5uT2W7mPN+dMyAXhHaVPRF1kWNuCDie37X4B1Pw320rbCouvg4t+LxNQBrOPfPObk7g9EzGJ2ppZ5WVrmZGq5oMTAdfOS+OV5Vl65MqPHRSniyLKw8f4kKjWYs3tarM3N0pGklWhzBTjQNIhwGRKEASo39X+MShsU2xOrYGZQvM5gv/Yh7Me7muGD38Bz10HlRpH8nXcGrHkAzvsxWHOH9JAfVLh44ZBo1XXv+TZ06sgmaoTiJ5uqT9oLFy4DSS3+BxqPhHdCn7KHihjuTvB1iRYR8YLODB7HsIoZFEAT7h327t3b798lScJgMFBYWIherwhX8UKoZ3t5SzceXwCdZhSCa1FClmWlsn2UmZ4j5k+8iu09le2KjXxEmJCeRHOnm2NNDmYHrcMTgVC7gWyrMk+Gw8xcMRcO1zvw+gNo1YnzPXMy6vZyALyGdJElrYipQ0NnFjZnqSXi9/ZKLllk5MNKN68ec/NtT2dCuAS0VR8mC6ghk6UWw4C2hQqnQZckAkfZswGJDG8tGbSxtT4t2Le9EzSp0R7l6OP3iMCYWgv7XxJ/y50nKnb6YcMJF/d+3EGXV1S/WPUS316czKcmGjja6uPmN9rwy3D5dCOrJ47CZ1coMWAoFvIno9FB1ixgH3TUiV70J7fnMKVC1gxo2A9H1sHiG5idKSo7DrTI+FydaMKpWlFQGCNye8T2xAhmjufKdkmSWDM7h8c+KOONffVcNFspQlFQiGee/PgEf3hHJMnedelMPrcwP2pjWTk9kwfWH+Gjo824vH4MWqVFGdCbdDrQfsrVAdv/Lq4XLIVl3wJb4aCn3FHn4btvtSEDV882cfe51shXrg9GaK2sHSBpzZQCbVpor0GrS2JpnpZ3yj18VOViVuYg4n/RMtj9FDTuB58XNJ84VpJAZkSV7XGHtzvoIpAy+HEVG+HdX4v9JQgnvnlXQ9FS8V4NkS5PgB+92w7AV+clsSCSrbrkAPhcLM/X8fst8HGlG69fRquWxL4rZ65IzD6+HjL73yeegoSSsBxJPA7xPxZPLSYlSXy+dtRBcu6ACUoK/RP2qzVv3jzmz5/P/PnzmTdvXs/v8+bNY9q0aVitVr7yla/gcin/mPFAlkVPsl6DPyAnXK/lDpcPj09UxCk920eHUGX7ieYunJ5+Ko5inEaHUtkeSeYV2gB4+0BiZV4r7QZGRkGqkWSDBo8/wLEEbVkCkNxdBYDWnCbsjRWxfWjokkRQwZIHSOBq51MZbWhVcLgNjtQkRpVYV6Oouu1QpyDpk3t7DCqEhy5J9GTU6CB9MgBnq0up7AhQ3xkYVt/2uMTvEdUvXc1Q8bH427SL+90IH2z2ctMbbRxp9VHj8FPj8HOg2ddjj3ndK604PDKLc3X8+jxb5AOLnm5RmW5MAV2YYjuI9zxrNiRng6Pu1Ar3knPFZeVmAIptasxaCbcfjtYq2fgKsUluotnIj9Oe7SE+NUv0bd9wqBGXN/72xAoKCoKNx5u5+7WDAHx/1RS+srw4quOZkWMh22LA6fWzuUxZ0/Tgc4u18ECV7bv/JSrfkzLhrO8PKrT7AzJP7+viq/9rwe2H84v1/PycMRbaQVQSawxi3dsfpjTInS/2PyoNKzJEjOrjytOI5BnTQG8Rr9lA1e2QmGK73wf2yr7CsdclLOFleeCe7QEfbHkU3vqRENqTc2DFrXDJ74U1exhCe0CWufvDDqo7/OQlq/n+0mHshfrD0y32RR214LSzINVNuklFqyvAu+UnPd+Ss8Rl1dahn1utEwKxQmToahnchSNWMVhFZbvLHu2RxB1hi+0vvvgikydP5rHHHmPPnj3s2bOHxx57jKlTp/L000/zt7/9jQ0bNvCTn/xkNMarEGEkSWJylrCSP9qYWB+mTUEhNdmgUTJAR4nMZD3ZFgMBGTafiK/FvyzLPSJqliKiRoRL5woLpfUHG+hIoL7tDaGkDCVpZ1hIksSMYGJOolrJd3t8ZPvrALCkpAtBScn+HBqSJKylJUlYRgOWlr2cHexF9+re+miOLmJ422vFpc4afnWvQi8qtehl6HVCvujbfrF+DwBbmzXQ3RrN0Y0dvmA/voMvi4oGWxEULjnlMLsrwDdebcXpkzmrQM//rkjnf1ek8+0zzGhV8PYJN9Udfoqsah5Zm4JeE8HAos8tsuE9naLiPmeu6As5HHQmyJ4lLDQd9SJAFiLUj7C1DDqbUUlST4XPnorESNZRSDzyU3rFdnmwfqtxgD8gUx/cU41XsX1evo0cq4FOt4+PjjZHezgKCgrDoNbu5Oand+EPyFy2II+bzpsU7SEhSRIrpwsr+Q2KlXwvfo8QRPsTPTsbhIU8wIzPgLn/FgCyLLO1xs1l/23mRxvacXhkFmRreeiiFDSqKCRFe53CzW0gUU6SwJwJGVMhbwErgnvlrbVuXL5B1hEqtbATB9HDfqBzh/YWiYTHAU2HoX4POO3iOTYeEAJ1ctapx8syVG2BF78Je/4t/pa/BNb+DmZ8Ouxiii5PgG+81sYz+0WbzV+dZyVJN8IYkc8t2md5uyE5D/LPAFsRGm8nl08Ta7BnD5yUyFl0pri0VwhxfiiodWL/liDt/KJKPPZrD6HRg+wDe1XfvbfCaQn7v/xXv/oVf/jDH7j++uuZPXs2s2fP5vrrr+fBBx/kgQce4Etf+hIPPfQQL7744miMV2EUmJwpgr5HGhKr4jAktitV7aPHyYv/9Qcaojya8Ohw+nArzgcRZWauhUmZZjy+AG/uSwyBDKBRScoYMSEr+f217VEeyehQ3txNiSTmvMmWJbJAFYaO3iIW8BnTxO9NB7l4itgsvnqoA9k/SC+6OEHdKeZHwJAi2gwoDB+jFfz+nr7tZ7APiQDbGtXjp2+7zy2CjIdeE79POLcnANTS7Wd/k5ctNW6+/WYblR1+CixqHroohblpAeaa2/nejA5ev0RmeZ6aIquav1+aRqoxQompnm5orwFnmwgK5p8BmdOEI8FI0BohczoYbeA4ac2ZnAOpEwAZjq4DYHGesGdcf9QhgpcKCjFGlsWAJIHHF6C5M74/s5o73fgDMmqVNG73VCqVxOqZorr9jQTaAykojBdcXj83PrWD1i4PM3Mt/Pqzs8e+snkAevq2H2yM++SsiOFz8151gG11/Xx/bv87+L34rEX8n7yarTXCVhvA65fZ1+jh91scnPfPRr7wfAt7Grwk6yTuPNvCs59Lx6SNQsK83yMq209nax5Cl8SkLBtZJnD7YWd/r8PJFAXF9tpd/d+u1gongETD5xaV7F3N4rk37If26mBbqpMSNeSAENlf+S688UNoOQpqPcz7Eqy6Sxz/CXbWeXhwcwdfeK6Z2Y/Ucd3LLT3vgy8gs73WzeXPNbO+zIVOBb+70Ma5xcOIAXi6RXVxZ6MQ2btbwVIg9le5c4XzlzkLZJnPTxNrsHfLXTR2BV12zJm9bcaOrBvaY6q1oq2BPwETMMaaeOzXfjKmdDHvOpVkr3AIu7ygtLSUoqKiU/5eVFREaWkpIKzm6+qGmDGjEHVCle3HEq2yXenXPiZcOCOLp7dU8s7BBgKfnoUqGlmgw6DBIQRUm0mrOB9ECEmS+Oz8PH7z1mFe2lXDFxYVRHtIIyYQkE9qN6AIZMNlZm5iV7bX1NUxQ7KLX2xFipgaLrokkUGdNhGOrYeWY1yw2IBeDWXtAQ5WNTCjOC/aoxwRBreosFUlpYjNu8Lw0SWJAEnGVNAYMPsczJAq2Vo/EbxdwkpekxbtUY4unk6o2SEEbV0yTFsDwLZaN198oQXvSYUIRo3EY58yY3PVigx1ow3MWUxO6eTpjAZxLkMXEIEkIbcD3F2QUiICU8aUyLZM0JshaybU7hGbfrMIQFNyjqhsr/gY5n+Riycb+ePWTj6oCdBub8OaEacBDoWERadRkZVsoL7DRa3dGdcidV17sN1Ssh51nOwDR4NVM7J4cmM5Hx9rRpblmBHqFBQUBkeWZX784j72VreTYtLyyNULYyo+tHxiOnqNihq7kyMNnUzNVhyyGlvbyP/whyBJNJ77HTKnLhFtho6u6xEUf+y5lv985AbcJGklJqVqONzi61MFbtRIXDrFyPeWJZOZNMbvuSwLe2Z3lxA3k7LAlDrku0vJWZyZ3cgLZRLvV7pZXjDIOiJ/EUhq6G6G5iO94msIlVZUSicaPrfYh1hyhWDdXi3E6ZDQ7vfC/hfhwEui2h2EE0D+Yph/NWRMP8WtsKHTz10ftPP6sb6tkzeUu9lQ7mZ2ppaKdh8dbjHP0k0qHl2bysJw+7TLAehqAiQwpoqkao1e7K1CroAhjDYwWJmkc7EgW8vOei8vHXby9QVC66HkHPG+V26ChV85/WOrdeDsEAkgI02WHu/EY7/2k9Hoxdhby8TnUzza4UeBsFO2pk2bxr333ovH05s55fV6uffee5k2TVQk1dTUkJXVjyWHQkwyOSsxK9ubgwJZehwHL+KB5RPTSNKpaehwU1oTP1WrPX24lfkRUUJW8pvKWqhvd53m6NinpcuDPyAjSZBuDnOBrNDDzDwhth+o7SAQiExGvlqtZuXKleTm5qJWR3fx2lEjevs5pGSx+VH6tYeHziw2crZgMmd7Fcl6VU/QYPOx4VlBx9IcsXjEc9CZ08WmRWH4aIN92wM+0bsQOFe1m8OtfuwuOay+YrE0R4aMLIuA2NH14veCxWDOwuWTuf1tO94A2AwSE1I0LMzR8sgFGqYb7ZBSLKzm88+AlCLInil+z54tbALbq0dmIel1CrE9c5o4tyk1skJ7CGMKZM3ofR2gtx9h8yFwdTIlTcuUVA3eALy1rzbyY1BQiAC5NrFWqI3zvu2h9f54T0qdX5iCVi1R3+GisjUBRQsFhQTlsQ/KeH5nNWqVxENXLaAgNbbsfo06NWdOElboNz+9c8yd4mJxrVxzbC+TVLVMkmrIfP+HsO4n8OLX4f37AJmd+jP4T9sUkrQSqQYVXV6ZPQ1eXD4Zi15iZbGeB1fZ2P61LO67wNa/0O73inWxzyUuI+kq4HaIdbekFm2SCpeK9Xw47nj6ZFYWCEnn5cPd+AeLsejMYr0PcPzdU29Xa4KVzMNrBRmLcwQQ1fohsdyUBraCvmLhB7+FzX8WQrtaDwVLYfV9sOpukdx7ktDe6vTz+M5OLniqkdePuVBLsHaygV+vtPLc59L4wgwTGhWUNnrpcIt5dukUI/+7Ij08oT3gD86PGtBZIG8hFJwBObMhYwqYM07dX6m1wunL08XnZ4jPr2f3d/c6YRQHW241HwXnED4/VBqQ/RF1i4vZOTLaxGu/9pMxpYnEj/bqaI8kbgi7sv3hhx/m0ksvJT8/nzlz5gCi2t3v9/Pqq68CUFZWxre+9a3IjlRh1JgSrGwvb+7C4wug0yRGn1mlsn1s0GvUnDM1g9dL63n7YANzC2zRHtKQaOxQqpVHg4JUE2cUp7CtvI2X99Tw9bMnRntIIyKUlJFu1qNRJ8ZnYzSYmGFGp1HR6fZR2dpNcXrSiM+pVqtZunQpra2tUV+wy83HALBrM0jWJSkZwOGiUolFfHKW2Dy6HWCvZFFOCu+Wu9lR0cZ1wzhtzMwRnxuLLFwdzClZitg+UjQ60dOwqwmKlkPlJtZod/Kw/zNsa9Vzoa1OCMtDyCCPmTkSDn4vuBzQJJJ8mH4xAH/Y4uCE3U9mkor1V2di1SF6puvMkD5TBGE+UZ2BLglSS0SSUGu52ETLPlHRoEsCrWlogrnfKywa0yf3Js2MJuZMkTDQcgysJlFJn5wjehEefxtmfoaLpxj53WYHrx5o4QvnBE597goKUSYvxcTOSjs1cS62h9bK2eN8T2XUqZmbb2N7RRtbylopShv5WldBQWF0eftAA/e+eQiAOy+ewYrJ/ff3jja3XTiFvdXtHG3s5DMPf8z3V03lhrMmjImrZCyulR31xwHokI0k4UJd/pG4QaNnq+VTXFP7WVQS/GVNCisK9Rxo8nHC7mN6uoYJKRpUg61t5YBoVyRJQnSUJJGUGrLU1ujFXl9jFIKk1yVuCyWPD0bAJ5yZNHpRNW3LH37cQG/hwslJpGzupK4zwPsVblaWDPI9XHQm1O2G6u2w5Bt9b1Npg8/DMyxhMBbnCCDcu9QDCN3lH8LRtwAJpl8Csz4L1qI++4WALLOuzMWz+7v5oNJNsAspc7O03LPSxoyM3tdqUa6e7yw282GlmylpWuZmadEM9v/p94K7Q7zuyCCpehM6dEnCfSCleOiOiaZUUKm5eKKauz6QONbmY1e9lwU5OrAVgiVP2IEffwdmXTa0c/oiVzwVs3NkNInnfu0no1KDIVnECsyZYn4qDErYUYfly5dz4sQJfvGLXzBnzhzmzJnDL37xC06cOMHSpUsBuOaaa/jBD34Q8cEqjA7ZFgPJeg2+gEx5S+L0aVF6to8dF0wXThbx1Lc9ZCOfmTy+A0OjwWfmC7vnF3fFfzVZo0NxQIgEWrWKaUHLu0S0ktd0VAHg1qWJzbNiEx4+RhuodL1CXe1usTkEdtY6RZZ3vOIQrZXcspbU1NSBN/0KQycpQ2xgC5YAMJ3jpNIR7NvuEL3bExW/pzezXGOE9Mnsa/Tw2E7hUPXLc61YtQForxVJLHkLwJo3uNisTw5W1gQr3U3p4vVtrxa2iwHfwPf1OsFRL6pF0iaPnahtLQCtWbzXkgQlZ4u/BwOuF08WwcuPq320trWNzZgUFMIgVNke72J7fUhstyp7qiUThAXw5hMtUR6JgoLC6dhX0853n9mFLMOXlhTy5WVjkCw4TGblWXnrlrO4cEYWXr/MPW8c4ouPb457Z5Rh4ffhaxPr4HWBM/i05252aubhK1zBo8UPcmXdFbjR8eMVFs4uMqCSJGZlarlkipFJqdrBhXa/V1QUG1OFlXjhMihcLnqe5y+GrFlgTANfUDTvDq4vDSmiYrizceA9q9cpkmCTMiFvEWRMHlmCvlqD3pLFZcHaln/vP42jSqhve+txsVc6GZUG/D6xx0gUAn7xmve373ba4cPfietFy2HZzSJxN7iHkWWZd8tdXPJMMze+1saGciG0z8zQ8qvzrLzw+fQ+QnuIfDNcNcnHQqsDjatV9FfvbAR7tZhXPT/VouJZmwSZM0QVfdoUyJwu9rZFy4VTWDitCQ02MKaQLHexZpK4338PBOdEP/uk0yKpxOunMHzcDvB1x2+/9pMx2IQlvtK7fUiEVdnu9XqZNm0ar776KjfeeONojUlhjJEkiUlZZnZV2jlc72BKVmL0AFLE9rFj5bRM1CqJQ/UOqlq7Y856qz96K9uV+RFp1s7O4ecv7+dgXQdHGxw9rSrikQbFASFizMy1sLe6nf217aydkzPi8wUCAWpra+nu7iYQCJz+DqOIoVuIqZLRJipulQrK8NEliYrljOnQVg61u5g7+WLUEtR1ydQ2NpObHV6LoliZI57WKnRAvZxCVqp1dKy1xxtGmwhQ6c2QOhFV63HOVe1mS935MNcvBNgh9D2MlTkSFn4POGrEdWMKHsnI7W+34JeFneGqEq0I5iXnCLv1oWafS5J4zUypopLC0y16O9qrxcZaloPV7kYRgJEkEUQKBCB1IqRNFDaUY4XeDKnFUF8qkgWKzoS9/4GG/eDzMSFFw4x0DQeafbxZWs0Xz00bu7EpKAyBPJsIvtW0xXcwM2Qjr4jtsKQkjYffPc6WstZoD0VBQWEQ9lbbufrxLXR5/CyfmMbPL52JFOPr8zSznseuWciz26u465UDbC5rZfXvP+Cey2Zz8ZzcUXvcmFsr+93ousQ6OCsthSONE7is83aya1TUd4nxfXGWievmhVF96XMLYczrFInfGVNOFcL1wZhWaok43tMlKj61SeKyqwlajgtLckkV/EFUysuI39MnizWzJkKJ10YbV02W+dsBiQ0nXDR2+QfuPW/NF62YnG1QvxeKz+y9TaUWVfrDtJGPuTkCoip7739EIcScK3qd5WQZPvqdeB2SMmDpTX3ej5ZuP7eus/NBpYgDJmklvjwnicuni2SNUx/HLSroPU7xOuqSQG8Jvu+yqGo2WIVbmCxDwCsSAfTJQsCMVNxIpRJ7v7o9fG56Ji8ccvLGcSd3n2cVFfbFK2DPv8U+yesB7WnmoEYnKu8jREzOkdGmq1lcxmu/9k+iTxaJIpa8yH2GJShhRUS0Wi0uV/z34FU4lVm5VnZV2tlVaeeSuaO3UBtLFLF97LCZdJxRnMLmslbWH2jguhUl0R7SaQlZHioiauSxmXQsKUnjo2PNbC1vjXOxPTRPlM+RkTIj1wpURayy3efz8eSTT/Zc1+uj9x6ZvSLDU59sA138zveoErLey5sPR96A2p2YNBIzMrSUNnrZcaIxbLE9VuaIo6GcNKCRFAptiuAXEXRmEbhw2UX2f+txVqp387/Gs3HIepId9SJYdpoARqzMkbDwe0QlOUBSOvds6uRAsw+bQeLnZ5mF0G7NF5US4VREfBKdCXRB20GnPVidUS+CkQG/CBgZrJA+SQSrohGkDlkidrcGEwvMIuBVvQ2Kl3HxFCMHmh28uq+JL5479sNTUBiMkNhe254gYruyp2JhUQpqlUSN3Ul1Wzf5KbGfgK6gMN7YXWXnmr9tweHysbAohUevWYg2TtrFSZLEFWcUsqQkjVv+s5vdVXa+8+9dTM+xMDHDPCqPGXNrZb8Hm1skumdlZvGVnCT+uquL+q4AmUkqfnqWlYsnG0TyRCBUrS0JsVutFZch3A6xxtXoxZo2Yyok554+eVSjP7UtmDlTiKedDUJQ9fvE42v0onJca4z8ellvYVKGkUXZXrbX+/nvgW5uOmOQWET6FKjaAo0H+ortIYZZ2R5zcwRE4sPuf4nrh16DFbeIZILS50SygaSCRdeBtVf/2FHn4abXW6nvCqBXw1fnJvGNhWZSjZ8QS33uXgt4jV6I66kTxRzSW8Y2+fhkTGmgt7BY4yDFoKLNFWBbrYdl+XpRNR9Ktih/HyZfOPi51DqRfBLwR0Qsjsk5Mpr43GK/rhudz+WooE8Wz6m7BSwjL55KZMJeUdx0003cd999+HyD2AkqxB0hy7MtCWR5pvRsH1vizUo+JKIq9uCjw7wCGwB7quxRHcdICVW2K+0GRs7MXAuQeDbyXn+AdL/IWjVb05V+7cNFpRa2fGlThQ2/qx1qd7MwaCW/oyJ+baCdzZUAtKlSkLTKd05EkCRIzhZBjkLRxupc9R4k2cc/D2vE/IlgNn5M4XOLXpLA0UAeT+wWLaB+c76NjEATJGeJgMpIhPaTUakhKU3YXRYuEz9FIUvNRSK4GK1qMI0OUieI6hVJBQWLxd/LPwB6reQ317hpsjsGOouCQlTIDYnt9vguZlASmHtJ0muYnWcFYOsJpbpdQSHWON7UyTWPC6F9cXEq/7huMcmG8HtUR5vi9CSeu3EZZxSnEJDhgyNN0R7SmOFyuciVRdJpenYBNy9O5uLJBr6xIIl3rs7kkinGXpeCjnqxV/B2C2G9o1Ykpbo6RLKm3yNc1QqWiB9b4ciEUo1OtFVKnSCq47NmCOenlKLRWS/rTGCwceVk0ev7mf3dBEJ9v/sjbbK4bDne/+2JZCPfeLD3emcDvHkHrL+zV2iffFGP4OwPyDy2s5Mrnm+mvivAhBQNL1+ZwR0rrKRqPH0t4O3VIkHDYIXceWIfWrBEOB6YUqMntINw/cqYiibg4/wiIZCvKwuuMSUVlJwjru9/6fTnUmvFfIhg3/ZxRXerSADXJ5DYrlKLedFRI5ztFAYkbLF927ZtvPDCCxQWFrJ69Wouu+yyPj8K8cniYiG2H6jroMM1POuYWMIfkGntEgsFpbJ9bLhwhhDbt5W30uWO/WScxqDzQaYSGBoV5vaI7fHdN7dRCSBGjOnZFlQSNHe6e17XRKDR4SZXEolqltScyAlc4xGjDdRqyD9D/H58Q2/f9pouUSEQh/jswuqwW2MDVfwF9GIWgxU0BtFjT2/BjJNFqiM8uttJe7dXVL0nIj63CBoBT9SJ/qLfWJDEhZl20FuDFe2jlPSj1oiggcEi/l9jwULOmCp6t3u6RBIAQO0uAAqtGuZkagjIsGFfdRQHqaBwKnkp4v+0tctDtyc+v99kWaZOsZHvw5KSYBGDYiWvoBBTBAIydzxfisPtY1FRCk9cewZmfRSFsRGiUatYOU3E4DYeT5yiqdNRXVNNqtQJQEp2MVa9ij9dlModK6wk60+SODxdoDNC7nzRd71wmdhjWvOFxbelQPyeMVmsaWO8jcCAmDNYW+AlWSdR1eHn7bJB4izpQbHdXnnqbSq1sEJPFFqOisu0STB1rXh/tUYoPgfW/BZWfBdUGqrafVz1Qgu//qgDX0C05Hr5inSmpmpEBa+rXSRP5MyB7NnisnAp5C8WyRkGa2y1ELTkQPpkVuWIePe64y7kUALG7M8J0b1xP1RtH/w8al1QbHeP8oATlM4G8T8lxc7c8Adk7vmonW+/2cYvPmjnkR0OKuxh7j8MVtEyI1HjLBEi7HfdZrNx+eWXs3r1anJzc7FarX1+FOKTTIuBkvQkZBm2l8f/xrCt24M/ICNJkJoUA4HAcUBRWhJ5NiO+gMzOytiuQJRlWenZPsrMLRDfB0caHXTGQfLFQDQ4FBv5SGHUqZkQtLfbVxvfSRgn09jUiEXqBkBlzRPin8Lw0CUJMTokmFVvY2GOEKf3Nwfo7ozTeeMQVocenU1sXBUig94iRF9vt6iyBi437qDDLfPYIY2wORusuiNe8XRCl2hdcciXzeJcHd9fgHCEyJrZ21NyvKA1gDkD3J0i8CVJwu6+tQKAc4vFZ/KHx5qjOUoFhVOwGLQkB4WeeK1u73D5cHr9gGIjHyIRHQMVFBKBZ7ZVsbW8FZNOze+vnEdSHAvtIZZPFO2pNpe14A8k4Jq3H5oqDwPQgg3JNEh7LmebsIQ3pYoKcL1ZuGLlzBF7zZw5QjiKd8zZGK0ZfG6S+PU7b9l5r3yANUVIbO9sOFVYV2vB2zV64xxrmo+JS1shnPMDuPI/8LknYdVdkLcANDq21rj51NNNbK31kKSFe1eo+dOZbsyuOmivEnGJnHnCMcxWKBwKUopiPzkjtYSzpudjUMvUOPwcaA7GY635MHm1uL71kcH3yZJK3K6I7eHj7oTuZhGriCH+uquTR3d28coRJ3/f3cW9Hzu47pXWwd0wPolGD7K/J/FfoX/CFtufeOKJQX8U4pdQdfuWBLA8C/VrTzXp4qb/UiIQyuSPdds8e7cXj1/YnijOB6NDZrKBPJsRWYbS6jgVyOi1kVcq2yPDwsIUAB77oKw3wzbOaa8vB6ATEySlKmL7SAj1bc+eLTZ4nfXkuivITlLhl2FvZXxaJGq7xWYkYEgVgQyFyCBJkJwj+skVCrF9jWYHAH/fH6DJ3iGE6URClsHVjtwlhONKOYs/fioFrdcB1kIRUByPJKWLKiV9MmTOFH8reweAswvFOu+jEw78fsXyTiG2CFnJ19jjs5osZCFvNWox6kbe0zMRWFScikqC8pbuntdHQUEhujR0uLjndWEr/f1VU8lPMUV5RJFhZq6FZIMGh8vH/gRKZh+MrgZhgd6qyRQien94u8We3JLb/+0afWyLpeGg0UHaJG5fAOcVanD5ZL72aiv/PdBNWZuPY61eOtzB9W9yDmiThFjWeKDveVQaYRmeKPbQbWXi0pIvLpMzRautIAFZ5qfvtdPllVmYpeKNz1u4cn4mkq0I0qcJkT1voagUj7e5olJjzJ7E2flCC1l3/KQ15qJrxXvdcgzKPzr9uRQb+fBxtoKne+DPpyhwtMXL7zaJlmpXzzbxjYVmknUSx9t8rB/MDaM/9JZgi4743LuMBYoKqdBDKAs71oXSoRAS2xUhdWxZHCe2eaFq5dQkHXqNEhgaLULV7Xuq7dEdyDDx+QO0dIbaDSifJZHg5pWTMGhVbC5r5YWdNdEeTkRwNZ0AoF2dAgZb/G3GYgm1Bkxp4jJ7NgDSsfW9fdvjtErM5BZVyJrkNEVsjzQGqwiYZc8BSU2Su5E1aQ04ffDwLo/ILE8kfG6wVyEh45CNJFusZOvdIqCYnBnt0UUPg1UED71dPS4HVG0DYG6WjmSdhN0ts7+iPoqDVFA4lZCVfG2ciu31IQt5JSm1B4tBy4xcUc2UCEUMo8FTmyt4o7Qu2sOIOA6XNyFaMsYC/91exdsHIlM5J8syd/5vHw63j7kFNr6yvDgi540FNGoVS0pEdfe4sJKXZeQ24VzkNmQMfJyzTQjLRtvYjCvaJKVjTC/i0bPcXDrFgC8AP3jbzsr/a+SCp5pY9vcGGrv8Ik4Rqm4/RWzXipZtidC33e/tscr/n72k30NePuzkcIuPZB38/fozKZxzlqh4z54J6ZNEBXs899vWJ7Nqig04qW87gDkTZlwqrm99bPDqdpVaiMYKQycQgI7a0WvpNgx8AZnvrbfjCcB5xXruPtfKHWda+PKcJAAe2dEZXiGULgl8XYrYPgjDEtufe+45vvCFL7B06VIWLFjQ50chfgkJpaXV7XHbNy6EIrZHhyUTxEJ/d5UdV9BSMBYJVStnKvNjVJmbbwNgT5U9quMYLi1dHgIyqFUSaUnKXIkEBakmvnv+FAB+9fpB2rrifzPna6sCgv24E8GKLtoYbeD3Q8k54veqLb1926sd8de3PRAg2SeC7XpLpmIjH2n0lqBFmywCJMAdqe8B8PRhic6O2G5rEzZ+t7A1BCrkLKak68FpB3P2+P780RohKQ3cjh6XA1qOgrsLrVpiWb74Dv/wsCK2K8QWuTYhUse72J6l9GvvQ0j8ev9wfDryjCYnmrv4yUv7uPnfu2hMgMr/qtZu7nn9IJf+6SPm3rWOBb9Yzy9eOUC7UxHdh0tVazc/eG4vNz61oyeuN1xcXj+3/Gc3b+1vQKOSuPey2ahViZUYfeYk8Xnz8Xhol+P3YOwWiToq8wBJpl6nEI4HqmpPRCQJUkvQJdn4/VkBvrEgCateIlknoVFBl1dmc00w7pIuYjE0H+17DrUGAt7EENu7mkXCBfCTgwXsqu/7nLx+md9t7gDgxmU5WC2xZfcdKc6fVYRKgoPNPqraT4qhzL9GtB9rr4I3ftjT8u4U1Dpwd4zNYBMFl13MvRhq6/bojk72Nnqx6CXuXWlDChYHfXVeEjo17Kr3sq02jP97SQUBWTiIKPRL2GL7H//4R6699lqysrLYtWsXixcvJi0tjbKyMi666KLRGKPCGJGfYurtuV1hj/ZwRkRzsBo1w6wIZGNJcZqJzGQ9Hn+A3TEssCrJGGPD3AIbEL9ie8j6McOsT7hNeTT52lklTM1KprXLwz1vHBz2edRqNStWrCArKwu1OnoOFWpHLQA+vU0RUiOBLkls9guWiN/bylliFZu8HQ1+5DA2fDExR7qa0OAnIEtYbRkiQ1whcqhUov+ixwmTVwFQ0PwhJVYVnkBQXA0MnPwXE3MkHHzunoBIhZzJ1FQVII2vgOJAJGWIRB1bMSRlQsAHJ94H4KyglfwHx5QqU4XYIs8mLCZr2uJUbA+ulXOUyvY+rJmdDcBrpbWK6PoJDtWJdZw/IPPs9qooj2ZkVLV2c9lfNvLoB2XsrW4nIIsKsr9/fILzfvse/9udGC5eY83hemF16wvIvLRr+K9hS6ebqx/fwv9216JRSdxz2Wym5ySeqLZ8YjoA28pb8fgibwEeU2tlv5dUr1gHm1MzoLVc9A4OBIVEVzt0tYhe7caU6I0zGuhMkDYBldfFHcuT2PONHEpvzOFLs0T16p6Q4JwebO5ur+h7f5U2KLaH/50VU3MEoOmQuJCtODDxu82OPjf/50A3lR0B0k0qrj1vVjRGOCakpGeyOEe8H28dPym5zZgCy24Somn1VvjPl2HXv049gVor9p7DmBOnnCrW5sho0V4jYg+a2NAauuqPMXfHj/g/7a/51Zkassy9r32GSc3npot9yKM7wnQDVEmK68EghC22//nPf+axxx7joYceQqfTcfvtt7N+/Xq+853v0N4+PnrEJDK9Pbfj24IoJKamK2LqmCJJUo9DQiy3IwhZg6cryRijyuw8KyoJattdcVm50NuvXZknkUSrVvHry8Sm5tnt1RysG162rFqt5uyzzyYnJyeqC3aDM5hdb0pTxPZIoEsGrUlkA6dNAmSmtb6DQSNhd8Mbe4YemI2JORJMxmjCSmZq7GQ4JxQGq0hiKFwqsvSdrVydKfr0vVPuGrRve0zMkXDwucAhbFUr5GymJruFyDzeAor9YbQFbe26oWi5+Fv5h0Bv3/addW46u7qiNEAFhVMJVbbHa8/2kNiuVLb3ZUFhClOzknF5AyMSCxORo42938n/3lpFIBCGdWkM0dLp5st/30qTw82ULDMPXjGXTXes5B/XLWZSppnWLg+3/Gc3ZU0J1s5mDDh+0mv27Paq8Oxtg+yraeczf/6Y7RVtJBs0/OO6xXx+UUEkhxkzTMkyk5akw+UdnYKXWFortzm6KEC4FGWkZQhLbINN9A+2VwmRK3sWZE4fn63dzNlgzoDu3ljs3CzRwmxPQ1AwTQvayDtqwXeSiCpJIDOsyvZYmiMANB0G4LgskpE/rHSzrVbE9pzeAH/cIuJP3z6nCJMhgeM3ai1rpotknD9td1DfeVIC+oxL4TOPCqeDgBe2/RUqt3zi/jpxm29kDiMQg3NkNHDaRVK8KQb25V4nbHkU48tfZ4WqlLPU+7i47O7exKQgNywwIwHvlLs50hJGUoXGIKr4FfolbLG9srKS5ctFAMNoNOJwiAyha665hn//+9+RHZ3CmBMSSjfHsFA6FJqUyvaoEbKS3xLDCRvNPWJ7Ai+sYoAkvYbJmUJc2lMdf8lYocr2TKVaJ+IsLEpl5TRh/fbh0fi22Ez2iH7cBqUfd2RQa8CYKnovTzgPAE3lh3xtvsjK//FbNTS2x48o4W0TQfZ6OZXM1LQojyZB0VtETz3ZD8UrAFjjfRuAd6sCBFyOwe4dX3i6kTuF2F4uZzHV4gNrvqjwH+9ojZCULqzkS84Wf6vbAwEfRTYNhRY13gBsOTK+hK+f//znSJLU52fatGk9t7tcLm666SbS0tIwm81cfvnlNDT07ZNbWVnJ2rVrMZlMZGZm8oMf/ACfr2+w5r333mPBggXo9XomTZrEk08+ORZPL+7Js4mejvEqtjcoPdv7RZIkrloshL2nt1QOSyxMVI409H4n19idfDDMfYAsy7R2eTjS4GDjsWZ2VLTR6HCNyWvd5fZx3ZPbONHcRZ7NyP9dv4TPzs8nx2rknCkZvPHdszh7SgayDH/76MSojyfROHZSQsbRxs5+BeQDtR3c8cJeXt1bi9fft5r72W1VXPaXjVS1OilMNfHit5Zz5qT00R521JAkiWUTQ33bE9tKvrK6kmTJSQAJoy1L7AHyFkLuXEidAPmLILVE7CfHI2oNpBQLQS0oms/NFjHPfU1evH4ZbAVCRPV7ofnIqedIBBv5FmGRXxbIwRScCg9ubKPR4eGa5+tp7JbJs2i58sypURzk2HDF0gnMTAO7S+a2dW34T05wy5gMn30UioP7piNv9r2zWivmQyLMibHAUSdeK60p2iOBd+6GPf9GRYAN/nl4JS1S/V746A99DiuxabhokljD/21XGAnpap2obI+A60EiEnZkJjs7m9ZWIcQWFhayefNmAE6cOKFsIhKAkNge6z23T4diEx49Qu4IOyraRsXGKhI0d4rFglLZPvrMLRA9ZOPRSj5Uja9Uto8OSyeEXDCG11NZlmWamppwOp1RW3/4/AEyAiKokZyi9OOOGOYMCAR6BbOWo3xnhoeZ6WraXDI/fG7XkN7zWJgjXY0iyNogp5JiiYGNVyKi1gjbcHcXTL4QgOyWLdh0AVpcEntONAx411iYI2Hh6cQXrGyvIYuiNKOo6FYQhKzks+cIdwxvN5z4CICzioJ9248mdiC6P2bOnEldXV3Pz0cffdRz26233sorr7zCf//7X95//31qa2u57LLLem73+/2sXbsWj8fDxo0b+cc//sGTTz7JnXfe2XPMiRMnWLt2Leeddx67d+/mlltu4Wtf+xpvvfXWmD7PeCQvRYjt9e2uvgHQOKEuKLbnKJXtp/DZBfkYtCoONzjYWTm8tW4icrRBCKkTM0QS5dNbKod0P1mWqbU7eWVPLbc9u5szfvU2C+5ez6oHP+CLj2/h8r9sZPGv3mHGnW9x23920+gYHVc1rz/ATU/vZE91OzaTln9ct5isTySbaNUqvnXuRACe21Hd46qnMDSOBSvbU5PEvurZ7dV9bt94vJkvPLqJf2+t4uand7Hivg38/OX9fPeZXVz80Ifc/vxePL4AK6dl8srNK5iUmfjOUiEr+Y3HI1/wEktr5ZYqUbHcLKUKNyNDshAEbYWiot1gjer4YoKkTFHh3iXmQolNTbJOwuWTOdLqA5VGJCYANOzve19JGlYVcyzNEQBajgOisv22pcno1BIbawOsfrqJ7Y2QrFfz2ysWotckaIX1SejNKfzxU+kYNbCx2sOjOztpdfp587iT/+zvwhsApqwWB9fuhJPfP0klfo+A2B5zcyTSuB3QURMb+3K3A6qEVnuz7xau895O7cIfitsOvQKlz/c5/Lp5Yj328hEnDvcQNRyNHvxuUUGvcAphi+0rV67k5ZdfBuDaa6/l1ltv5cILL+SKK67gs5/9bMQHqDC2lKQnkZGsx+MLUFoTf5WoIRSxPXpMzjSTGrSxitU5FKpsT1PE9lGnp297tT2q4xgOPTbyyUoAcTQ4oziUmNM6LAtJr9fLX//6Vw4fPozXG52MymaHi2xEAqI1LTtmejPFPcZUIZQZLCIYIMvoyt7iwdWp6FQy7x5t499bT28nHwtzxNUixulQW5GUZIzRw5QqAkR5C0BvQfJ2cUPaPgDeOdICfl+/d4uFOTJkAn5wdaDuFlWAKnMGWq1eWOcrCAxWEXz1u6H4LPG34+8AvX3bPyxrF8k84wiNRkN2dnbPT3q6CMq3t7fzt7/9jd/97nesXLmShQsX8sQTT7Bx48aehPp169Zx4MABnnrqKebNm8dFF13E3XffzcMPP4zHI4JvjzzyCCUlJTzwwANMnz6dm2++mc997nM8+OCDUXvO8UJmsgGNSsIXkHv2r/FEQ09iqrJW/iRWo5aL5wgL238NUVBOdHz+AGXNQkj9ydoZALxzqLFnHgG4fX6e31HNjf+3gy//fStXPbaZix/6kJk/e4vl927g2//exQs7a3qS520mLZMyzeTZjKgkcHr9vLCrhvMfeJ//21Qe0SQWWZb54fN7ee9wEwatir9/9QwmZZr7PXZJSSpz8q24fQGe2qy8/0NFlmWOByvbv71S9JZ+ZU8tTo8oBHpzXx1f/fs2Ot0+ZuRYSDfraehw8+TGcv63u5Z9NR1IEnx/1RQe//IirKbx4Tq2PFjZvquyLeJFU7G0VnY3ChG1Q5MhbIw1xqiOJyZRqUR1uySBtxuVJJ1kJR8UTTOCFd2frGxXacATfrulWJojAHKbSHY/LudwzqRUrlxcCECbW2JSRhL/u3lFjxtEwiNJTCwq5K4l4tffbHSw4K8N3PhaGz98p51ffdQhHCE0enC1Q83OU88RARv5WJsjEaejXgjPuqRojwTqdoMcoFOXwau+xeRb1BTOPx8WfFncvvnPUFfac/jCHB2TUjQ4fTKvHB2ieK7WiSQMX/y1ix0LwvZW+fGPf0xeXh5Aj+Xcxo0bufTSS/nUpz4V8QEqjC2SJDG/wMa6Aw3sqbL3iCHxRo+NvCK2jzmSJLG4OJU399ez5UQLC4tioF/JJwgFsxQb+dFnbr4NEJXtgYCMShU/vbMaHEoAcTSZmWvFoFXR1u3leFMnk7Pir+qgqaGabMlLAAm1LUexkY8UGh0k54gAwMSV0FoG5R8yZf6XuH2Rml9uDXDP6we5fGFezGek++3Csrpbm6I4H4wmegtoTCIYMHElHHiJS+T3+A1zeKfCy/c9jvjva+5zQUctKtmPS9aSbgsKy4qFfC86k0i8cNRByTlw+HWo3QUBP8vy9aglOG4PUFbbyIT87GiPdsw4evQoubm5GAwGli1bxj333ENhYSE7duzA6/VywQUX9Bw7bdo0CgsL2bRpE0uXLmXTpk3Mnj2brKysnmNWr17NN7/5Tfbv38/8+fPZtGlTn3OEjrnlllsGHZfb7cbt7g3gdXSIHpperzcxA3EDkG3RU213UdHsIM0U3nda6HWKxuvl9gVo6RKB+zSTely9Z0PlCwtzeW5HNa/ureOO1VOwRUH4i+Yc+STHm7rw+mWMWhXLS2wsKrKxvcLOHc/vZU6+lS63jxd31/YI6Z9Eo5KYmJHEiklpnDslg/kFVvTa3v8Zjy/AvtoOfvn6IUprOvjp//bz3+1V3HXJDGblWUY8/t+sO8ILO2tQqyT+cMVcZueYB31dr1texC3P7uUfm05w3fICDNrYXLPG0hxpcrjpcPlQSfC5+Tn8/aMTVLU5ueP5PdS0u9he0YYsw4XTM3nw87ORJIm3DjSwq6qdbIueolQTM3KTKUgx4ff78MevWWdY5Fq0pCZpae3yUlrVyrxgwUMkOHleRP37ua0CALchA6+sBkkDMTBvYw5tMpjzwV4Jqg5mZ+j5qAp21Xn4/HQjUspENECgrRx/nzYMGnB1hv2axtQc8blRt9ciARVyDrmpZm48K509VXZK0kzcefF0kg2amPi8GzP0KXxmdgbv1zTyWrmIx05IUVPW5ufJPV3MzdLw6bzFqCo+xH/sHQI583vvG5DA1T3i/7OYmiORxuuGtirQJIM/+gndqqrtqIGdkkhqXFWixxeQYd5XUDcdRVW1CXn9nfgu/3uPG8jnZxi552MHz+zr5vPTh5jE5JPB6QB95GIssbQe6Y+hjitssX3SpEnU1dWRmSl6rV555ZVceeWVtLS0kJmZiX+8rGYSmLkhsT0OeyyD2GTZu8U/gNKzPTosLgmK7WWtfOvcaI/mVEKBIcVGfvSZmp2MUaumw+Vj84mWHouzeCBU2Z6p2MiPCjqNivkFKWwqa2FbeVtciu0dDeUAtGElTT/yIJ7CSSSlCwu4ouWw7XEhvHe1cN38ZB7Z206zy8euSjtLJ8R2Vrqqsx4Arz5FScYYTbQGSEoT9m2TL4QDL1HQsRM9Hg626qhtaiW3MN7Fdje0C6eECjmLySlq0MXf5+aoY84Ur1PufNEzz9MJlZuxFp/JuUV63il38/hHJ/j1leNDbF+yZAlPPvkkU6dOpa6ujrvuuouzzjqLffv2UV9fj06nw2az9blPVlYW9fXis6u+vr6P0B66PXTbYMd0dHTgdDoxGvsP2txzzz3cddddp/x93bp1mEzjp+2G3q8GJF5/bxP16cOrwl2/fn1kBzUEWlwAGjSSzKb33kaKn3zaMUOWIdekprY7wA+ffIdPF0cvCBuNOfJJ9rRIgJp0nZ8333yDaVqJ7ajZcLiJDYd7e7dbdTLLMwOkGkAjgVYFmUaZdD2oVXYI2Gk9dJx3DvX/ONcVwMc6iVcrVeyt6eCyRzaxIlvmovwASadZinn8sKNZQpJgVoqMWQv13fBalYq9rSK57QslPlzHt/H68cHPFZAhVa+mtcvLL59ax/Ks2LbOjYU5crRdzJFUncyG9W8x2yxR1abmpT11PcecmRXgImst7wrk0MQAAPYhSURBVKyvBUANLJIAB/gdUFoBpf2ePbHJ0qpoRcV/1m+iNjtyc+3kGP+GDRtQq6OXNGLpFC4Rddoijh31wNGPTnMPBQBvlxdQ81FFN6/vdGDrtnAO4O1o4M2d1afeYd/rYZ0/luaI0d3EqoAHt6zBqc/g7U17APG9AC18uOH07niJyvlZMCEJso1g1vp4tVLF+hoVP3zbjqVoJufzId2Vu9lwypw4EvwZPrE0R0aX6OtoK8u2kgw82yWSJiz+Dl7fKRKaNSlf5ZzGMsyuBtpe/imbJn4fJBVmL6glNXsbvfz1w2ryhlqgX7YP2Bfx5xAL65H+6O7uHtJxYYvtA/VW6OzsxGAIv/qvpqaGH/7wh7zxxht0d3czadIknnjiCRYtWtTzeD/72c/461//it1u58wzz+Qvf/kLkydP7jlHa2sr3/72t3nllVdQqVRcfvnl/OEPf8Bs7rV02rt3LzfddBPbtm0jIyODb3/729x+++1hj3c8MCdfZLbsjUPbZ4CWLiGQaVQSVqMS2I4Gi4N923dVtsVcNXMgINMaFNsV54PRR6tWcfnCPJ7aXMkj75fFldjeqFhjjjpnFAuxfXt5K19cUhjt4YSNq0lk17erU0gzxqcTTMxisIEpBdwaSCmBthNw5E1U877Ismw7r5RLbDzWHPNiu84p+mtLpjRFbB9tTGmi4iVzBpjSkLpbuCblII+3zWXDwXquLpwY7RGODJ8bHELcrJCzmJYmgU6xzzwFg1WI7AGfSNY59rawki8+k28sNPNOuZvnSlu5Za2LzHHQJuaiiy7quT5nzhyWLFlCUVERzz777IAi+Fhxxx13cNttt/X83tHRQUFBAatWrcJiGT8JbBu6Szm+p46M4mmsObskrPt6vV7Wr1/PhRdeiFY7tt8x2yvaYNc2cmwm1q49a0wfO57QljRw07/3sKFOxRXnL2Tl1IwxffxozpFPUvbucThynEVT8lizZharAzK5G8ups7vw+AMEZFg+IZXVM7PQqkfm2nIxcKvDzT1vHObV0no+rJfY2abl2mVFXLu8CMsn4kRef4D/7qjhT+8epylYWa9WSczISWZ/bQcBGVQSfH/VZG5YMfT/0+aUCn79xmE+bEni1i8sJS0p9lyOYmmO/GtrFRw4yKyiDNasWcCSTjfV/9qFXqPmwumZXDg9k/wUZe3TH0f0xzj4XhlySgFr1syK2Hk9Hg+lpSJ9YeXKlSQlRccmWZZlTuz8MUgwI9tAxuwMKDgjKmOJK/w+FpXv5/HDDTQ4Jc6ZlUsSqXAY9D4Ha6ZowRxMmPS5wN0FBUvD2mPEyhwBkA6+DAegXM5mdpaaNResEM5XCtB0BFqOgTUPJIlV82SufbmNzTUe/tC2hJXSX0l21bKmoBMypon7uNqFg0ThUkaSVRlLcyTi1O8FRwMkZ53+2NGmqwntrjpkJD7wzSDTpOKbZ+ehOvm9m/gr5P99k0zHPi4OrCNwxtcA+KjNzhvHXdTJFm5YMIR9mLMd1BrxeRGhjNtYWo/0R8iF7XQMWWwPbYQlSeLOO+/sk23u9/vZsmUL8+bNC2uQbW1tnHnmmZx33nm88cYbZGRkcPToUVJSeitP7r//fv74xz/yj3/8g5KSEn7605+yevVqDhw40CPuf+lLX6Kuro7169fj9Xq59tpr+frXv87TTz8NiBdj1apVXHDBBTzyyCOUlpZy3XXXYbPZ+PrXvx7WmMcDc/JsAFS0dGPv9mAzxd6GYDB6LcL1MSXyjiemZiej16jocPk40dLFxIz+e5lFg7ZuT0/vttQY3OwmIl8/ayJPb6nkgyNN7KtpZ1aeNdpDOi2ek6wxFbF99DgjmJiztbw1yiMZHgG7yIx2aW3KJi7SqFRgyYXa3TDxPNh+Aso/gvlfYkWuxCvl8PHxFm473XmiTJKnGQCtOV2xkR9tDFZR4e5zCpH14Ct8WreFx5nLO8ccXH2BN74THnwu/I4G1IgA0hqbpPRr7w9dkmgZ0NUIE84RYnvNDpBlzsjVsTBLzY4GP098cIwfro1cMDpesNlsTJkyhWPHjnHhhRfi8Xiw2+19qtsbGhrIzhaV/9nZ2WzdurXPORoaGnpuC12G/nbyMRaLZVBBX6/Xo9efOoe1Wm1MBlhGi8I0EXCstruG/byj8Zo1d/kAyLEax9X7FS5r5+azraKdJzeWc/vz+3jtOyvITxn7NWMs/F8dbxYVQVOzLWI8wLfOmzJqj5eXquVPX1rIlUeb+dXrBzlY18Gf3ivj8Y/LmZqVzMRMMyadmsP1Dg7VO3C4xJzOTzFiNWrZX9tBaY0IrK6emcX3V00N24nrS0uLeWpLFZWt3dz87z3864YlMdsCKRbmSHmL6Bc7JThHslO0vHyzkswzFOYVpgJl7K91RPR9PLngLppzxO7opoBGANJT09Em2UD57jk9Wi15+YXkmOqp65Y43OpnSZ4ZjKngbEVrLwNrjjhW0oG7A1SBsF7bWJkjALSVAVAm5zAlTYfWmKy03AqRMQFczeBpB1MqWjU8dFEKn/pXE3vtetry5pLashPt8XcgW1iQo9WLhG+VLFr9DZOYmiORxN0J7jYwWWGESYIRoX43ANXqAjow8+mJhlPXHBmTYMVt8MF9qPc/h3rulWC0cNUsE28cd/G/I05+fJYVg+Y0eppeD14XSH7QRjYJLlbnyFDHNOSZsGvXLnbt2oUsy5SWlvb8vmvXLg4dOsTcuXN58sknwxrkfffdR0FBAU888QSLFy+mpKSEVatWMXGiqDyRZZnf//73/OQnP+HTn/40c+bM4Z///Ce1tbW89NJLABw8eJA333yTxx9/nCVLlrBixQoeeughnnnmGWprha3Qv/71LzweD3//+9+ZOXMmV155Jd/5znf43e9+F9Z4xwtWk5biNLEB3BuHVvIhsV2pWo4eWrWK2UFBdXelPbqD+QShHnA2k3bEGfMKQ6MwzcQlc3MBeOT90/jtxQhNneJzRKuWSIlCf8XxwvzCFFQSVLc5qWt3Rns4YaNyCIstv96mCKmjgTFVJDEULBW/Nx+GrhaWFwthYk+VnU63L4oDPA2uDgyymNdGWyaoYjO4mjDokkT7AVeHENuBqc5dgMy2ej8BtyO64xsp7k6c7SLI2CBlkGfRgEZZ6/aLOQt8XshfLF4jVztUb0eSJG5cJDL1n9paRYcrNvvBjSadnZ0cP36cnJwcFi5ciFar5Z133um5/fDhw1RWVrJs2TIAli1bRmlpKY2NjT3HrF+/HovFwowZM3qOOfkcoWNC51AYnOk5Yk4eqBtatUSs0BBygLIqSamn40drpjO3wEa708tNT+/C44t+T89ocKyxE4ApWWObiL9icjqvfXsFf/nSAqZmJePyBthT3c4LO2t4anMl28rbcLh8pJt13HXpTDZ871xe+85ZfPCD87j/8jm8cvMKHr1m0bBaXiXpNfz9q4tINmjYXtHG/3u+dEC3UAU43iTmyKQYKtaIF0IOpUcaHDg9idfetbmhGpMkYjQ6WzbolTkyZIypzM0SMa09DcG1r61AXLZV9B6n0oDsB38cr49bjwFwXM5lcoZJEdpPRmuEtIngdfa8xxkmNecUif3kdkMw5lJ9UpKtWgMBrxDcFU7F2Qae7tgpvKnZCcCr7rkArJk8gAg+9VPCPdLvgV3/B8CKQj15yWo63DL/2T8Eu3R1MBHDG39x3NFmyJXt7777LgDXXnstf/jDHyJi7fbyyy+zevVqPv/5z/P++++Tl5fHt771LW644QYATpw4QX19PRdccEHPfaxWK0uWLGHTpk1ceeWVbNq0CZvN1mM7D3DBBRegUqnYsmULn/3sZ9m0aRNnn302Ol1vIHz16tXcd999tLW19amkB3C73bjdvR8kIZsAr9eL1xvHXzphMDPXQnlLN7sqWllWYgv7/qHXKRqvV71dfCikJWnHzfsVi8zJs7C9oo2dFa1cOudUO5VozZGG9i4A0pJ0yvwYQ752ZhH/213L66V1HKtvpyjt9IuRaH6O1LSKjX5msh6fL4bFvDhHr4IZORb21Xaw+VgTF8/JGfJ9T54X0fp+NnYLS2eS0vDKKlA+UyKLSg/6VPB40KRMQGorw7//f2Qv+DIF5k6qOmHj0UbOG8CSNepzpLUSLdAum7BZLMp3zlhgzIS2GsiYjUatQ+dpZ666kj3eIg5XNTFpQt9AedTnSDh0tyN3iM8cvykDHzqQ1crnTn9okkBtAJ8XdcFyVCfexX/odQK5Czm7SMckKxxrD/DPj0/wjSHYdkdzPTIUBhvX97//fS655BKKioqora3lZz/7GWq1mquuugqr1cr111/PbbfdRmpqKhaLhW9/+9ssW7aMpUtFwG3VqlXMmDGDa665hvvvv5/6+np+8pOfcNNNN/VUpd9444386U9/4vbbb+e6665jw4YNPPvss7z22mtj8vzjnVm5QiA5VOfA6w/ETTJwfbsQ27MtStLP6dBpVPzpqvlc/NBH7Kmy89t1h/nRmunRHtaY4vMHKGsS+/DJmeGL1iNFpZK4aHYOq2dmU9bcxbFGB0cbOun2+pmSZWZatoVJmeY+/3+FaSYKh7BnPR2TMpP5y5cW8pUntvLirhqm5yTz9bPjvLXNKBFKyJiYmUAWw2NElsVARrKeJoebA3XtLCxKrBZn7fWiYrkVC6kGG2iUdgJDRq1hbmEqb55oZE+9KD7Cmg91e8DeT892v2dsxxdJWkRxT1kgh3OzbdEdSyySnAPWJmivEXbywBm5Ol445OQF5wJWAbRXQWeDSF5WaYUwH89zYrSQZdHiLVaS32UZX/UONMBHgVl8dqqRpXkDFARJEsz/Emz4JRx5HRZ/DZVGz1fnJvGrjzr4xQftFFjUrCwZJKFWpRbJOYrYfgph92x/4oknIvbgZWVl/OUvf+G2227jRz/6Edu2beM73/kOOp2Or3zlK9TXi4BSVlZfoS4rK6vntvr6ejIzM/vcrtFoSE1N7XNMSUnJKecI3fZJsf2ee+7hrrvuOmW869at62Ofn8hoOyRAzds7j1DcfWjY51m/fn3kBjVENlaLsbvsjbz++utj/vgKgkCzeB8+2F/J6+oTAx431nNkR3BcktuhzI8xZoZNxQG7ijuf/oArJg69oiManyN7WsQ80ficyjwZZdICKkDFCx/sQVW9a8j3CwQCZGQIkfXdd99FFYWs5RJ3A0hQHsih7L3NY/7444kJpmXMbivDcfgD3pcuIN+koqpT4ul3duA83v/nSbTnSEbHPpYD9XIqh2s76FQ+S8aQVhYnzSSnYxeX6newp7uIpz48zOJDfde00Z4j4aAKeFjjqgOgUVvI64cccOjtKI8q1ukmU1rAMt7FV7Wdt7aXI6s0LEmXONau5rH3jpDnOMhQu05FYz0yFLq7B64+qK6u5qqrrqKlpYWMjAxWrFjB5s2be+b9gw8+iEql4vLLL8ftdrN69Wr+/Oc/99xfrVbz6quv8s1vfpNly5aRlJTEV77yFX7xi1/0HFNSUsJrr73Grbfeyh/+8Afy8/N5/PHHWb169eg96QSiINVIskGDw+XjaEMnM3Ljo199XbCyPduqCB5DoSDVxG8+N4ev/98OHvugjLMnZ7Bicnq0hzVmVLR24/EHMGrV5NmiN2dUKolJmWYmZZr51Bh2ElkxOZ2fXTKDO/+3n8c/PMHXVkxQWh5+gk63j7pgEs+kjLFPyEgE5uRZeedQI3urIye2q9VqlixZQllZGWp19Fy6nM2iAtuuSiFVaxKtoxSGzNziLHi/kd0NQdHUki8uHXV9D5QIW1iNlTkCEGgtRwWUyblMyIyP9dSYolJD6gTobhVucAYLi3KEIPteYxJySi6SoxZq98CUVb29uEcotsfSHIkY7g5R2W6IjXnW3lCB1dmMW9YSSJvGvefbkAbrpT7hXNjyKHQ1Qel/Yf7VXD8/iQNNXl487OSbr7fyf59JY3HeYMkEkiK290PYYnskCQQCLFq0iF//+tcAzJ8/n3379vHII4/wla98JWrjuuOOO3p61IOobC8oKGDVqlURqeiPBzLK23jpb9to9BlZs+acsO/v9XpZv349F1544Zj3Wdj+6kGoqmL+9ImsuWDymD62Qi9z2pw8+bsPqXepOP/CC9Br+36hRmuONGysgKOHmVqYy5o1c8bscRUgY0YbX/zbNra3anj46+di1g/+FRTNzxH71io4cpDJ+ZmsWTN/TB97vKHa38D7z+yhCQtr1iwP677RnCOBgEzbzu8CcEZxKmlnny96WilEFq8LqrbCpIuQn3sGm7OcNXkOZEMWmxo91PmTWbPmzIHvHs05srsDjkOjbOOyFTNIyyke08cft7RVQMN+JNN58PEuVqt3cDeXodIZWPOps0+x84/mHBkybgfs/Q9qArTJZhYUmFmzsBiyZkR7ZLFLRx3U7QbzauTav6F32VljOYQ85VNc4PXyXHkLHV6JWUvPoTht8Aq6WJ8jIRe2/njmmWcGva/BYODhhx/m4YcfHvCYoqKi0yYennvuuezaNfSEOYVeJEliVq6VTWUt7KttjxuxvaGnsl0RPIbKqpnZfGlJIf/aUsltz+7mzVvOJjVpfLQhOtogWrlMzjKPW5H5yjMK+c1bh2l0uNlZ2cai4sSqPB4pZUEL+XSzHqvSxm1YzM4XYntpBNuBqtVqzj//fNxud1RFMr+9BoAujU0I7Uple1jMLslBopQaR4Dmbj/ptqDY3tXQ90BJLWyxwyBW5gg+N6ruJgC8xgxMJsUho18MViG415eCzsTEVA1WvUS7W6Y9eTI2Ry00HRJie4gR2sjHzByJJM428LlA07/L4ljz9kcfczlQKk3ioUty0J+u57pKA3OvhI0Pwf4XYe5VqFRq7r/ARoc7wDvlbq5/pZXXv5hBgWWA2L1GDy57pJ9K3BNVsT0nJ6en11uI6dOn8/zzzwOQnZ0NQENDAzk5vdayDQ0NzJs3r+eYk3vIAfh8PlpbW3vun52dTUND3y+Q0O+hY05Gr9f32OKdjFarjckAy2gwrygVlQQNDjetTj9Zw9xER+M1a+kWVopZFuO4eb9ikeIMDelmHc2dHg43OVlYlNLvcWM9R9qcwhI8w2JQ5scYs2xSBsVpJspbutlYZmftEC3Do/E5osyTsWPpRLE4PdzYydEm57CCzNGYI412B5nYAUjLykZrUHqCjQpaLVgyhZ1Z0TIo/wjtoZc4a8kP4N1mDjd20e4OkG4ePNEhGnOk3V6PHmjBypnWJFTKZ8nYYM2GjnLImwdAnrecdNopbbKild2gtfZ7t5he57v9YC8H4FCgkBlpGrTGZPH/odA/lkywJ4PshsmroPRZNMfWwfQ1aNV6ptlk9rRIHGl0MnmINpOxOkdicUwK4TErzyLE9pp2vrCoINrDGRKhCtRsq5JoGA4/WTuDLSdaOdbYye3P7eWvX144eOVRgnCkIdiLO3P89lnWaVRcOD2L/8/em4fHddZn/59zZl80i2a07973PbEdEsjixCSGQglLKUtpgTZp2hcCLbT9UQqEQl8KZWlJKYUS3rKEQCmEJJA4+2IncRzvqxZb+y6NttHs5/fHMzOybNmWpZk5M/LzuS5flmaO5jySHp05z3N/v/f9ywOdPHakR4rt55G2kC+RAtlcSeW2H+nMnNieL6hjXQBEzF4w2UWWtGTWFFlNNBRbaBkKc7Q3zI2pzPaJQYjFwJj8eRrMomO3EBkVBRmTmpkSjwuMshjworhrIDgI472omsbmUgNPt2s0qvVcw3Mw1DJ1rHrlBRgLnkRCFHbnSVb7ZDjKksGnQYHyqkX4nbMs5Fx+B7z2AzEXTj2eXCcrfPuOYt758wGO9kf5XVOIj266yL2b0QKRCYjH5DX5HHTdFX7DG97AqVOnpj12+vRp6urqAGFJV15ezlNPPZV+fnR0lFdeeYXt27cDsH37dgKBAPv3708f8/TTT5NIJNi6dWv6mOeff35ant3u3btZvnz5BRbyEoHdbExnaR1qD+g7mCukf0xUXJUUyTdWPVEUhQ01HgAO5tEcGkjOj1m/+UgyhqIo7FwtCpweP9aj82guzeC4sEm6nIAnmT8lRRZuWOpH0+AD338lvdFyOTRNIxAIEA6H0TQty6O8kMHus6iKRlgzYSoql0J7NnGWiDyoZW8Wn7ftwWdTWeEVv/c9zYMzfpnecyQ8LDaFggYXar5keV0NWJzgLBfV2r6lKGjcZDjA8UGNyOTYtEP1niOzJhYiOtwGwEmthuU+VdpnXg6TVWQNhkanOjN6jkAwAMAqv+iqON618DakJYXHmiohkBwtEIEkntDoTdrIV+poCV6I2MwGvvkHGzAbVJ480cuxrgIVNa6QxuT9/bKyq9se/Pa1otj8d0e78/u+Qwea+2VBxnxJvZc09Y8zEY5l5DXz5V7ZMika5jSrW3TmSq6YNdVC/zjaExTZ3SgQD0OykAEAo1l068ZmbxueL3OE5FqpS/OxxGeSYvulMBihYj3UbIWSFWypEOuil0NCj2Ok/ZxjTRCZ3R7dxcibOZIpwqMQGgFzfrxftT/3Q9YrzUxgpWrdTbP/QpMNVv+++Pjgj0AT8YxWo8LtS8Tfz+G+6MW+WhTnxCPimiFJo+vO8L333svLL7/Ml770JZqamvjJT37Cd7/7Xe655x5ACDMf//jH+eIXv8jDDz/MkSNH+OAHP0hlZSVvf/vbAdEJ/+Y3v5mPfvSjvPrqq7z00kv8xV/8BX/wB39AZWUlAH/4h3+I2Wzmwx/+MMeOHeNnP/sZ3/zmN6dZxUsuJFUVeTiDFkS5YEpslxvbepOPYvvghBRR9eS2pNj+zMk+wrG4zqO5OAPjqaIMOU9ywb/94SbWVLkYnIjwvu+9TOvgxGW/JhqNcv/993PixIlpxXS5YqxX5MYNKV6wycK9rGLzioVM2WqwekT17OknuL5adHPuaeyf8cv0niOxUVFUFDK6xCJVkjuKks5VtdsAeLNxP5GEwumO6YUZes+RWRMNERoUG0gdhmp8NuQG0mxwloqsQW+9+KfF4cTDAKwqEz+/453D+o1PIkmyulKsu493jxJP5P8mZP9YmFhCw6AqlMoC9ytmdaWbNy4Tzk4vNA7oPJrckLaRv8qF1BuW+nGYDXSNhDhUYPts2SZVcC3F9rlTWmSlwm1F08hYIU++3Cs7w8LR1uTw5U03aaGxNiW290VER6qzVDwxfE4Xs8EiLMNjs89hzpc5QmBKbF9a4pDNEJfDYAKHH/xL2LJY6GcPDybjBYKD4h+AahJiamLu+7d5M0cyRWgE4lHxd6Q3PUdYcvYnADxV/F6Uqk1X9vVr3yXcQsa64MRvph4uFftXR3ovUXhjSBXnzC9mYKGh65Xnmmuu4X//93/56U9/ypo1a7jvvvv4xje+wfve9770MZ/61Kf4y7/8S/70T/+Ua665hvHxcX73u99htU4t6n784x+zYsUKbrnlFu644w6uv/56vvvd76afd7vdPPHEE5w5c4bNmzfzyU9+ks9+9rP86Z/+aU6/30JjXVIoPdQR0HUcV4oU2/OHDTXiZu5ge/5sZEoRVV821ngoLbIwFo5dtBs1H5DzJLe4bSb++0+2srysiN7RMH/8g315v9kcCYgK8FGDG2yyuj6rGC3gKBX57akO1VO/5Q11wmbyhcb+vKyQVifEplDc6hELEUnusHnB4oLydQBsV46jkuBQ+4CwfSs0ImMYR0WHQcRZLTbC5Jy6PDYvWL1iQyTljNHyNACrSkU37rHusYt9tUSSMxr8DuxmA6FoIp1bnM90BsQmfLnLiuEqzd+eL9cv8QHwUtPCF9sTCY2WflFIm3JPvFqxmgzcvLIMgN8e6b7ksYPj4XS399VAc3KOLC6RYvt8SHW3Hy6wfdzL4Y2La6XDWyLz2ufI6ioR13dkILludiet5IfPTh1kMEEiVpji2UhKbPezZJYRURLBuvoyTCo0TjqJ2fziwZ4j4n+DCRLRwpwT2UDTYLwnP1zmwmMknroPlQT/E7+eRdt//8qLTKwuWP9e8fH+/yeKCIC1pWKv4exInJHwRfZPFAVQrqg452pA9zKft7zlLRw5coRQKMSJEyf46Ec/Ou15RVH4whe+QE9PD6FQiCeffJJly5ZNO6a4uJif/OQnjI2NMTIywn/913/hdE6/QVu3bh0vvPACoVCIjo4OPv3pT2f9eyt01p+T95OPm9gzEYzEmIiIaisptuvPuho3igLtQ5MMjufHG3PaRl7OD11QVYXbVosNhify2Ep+IG0jL8WMXOF1mPnRR7bisZtoGZjgxTzffIyPiA0y0bUsrydZx1kCaLB0p/i87xhbvUHMqkbnSJiWgcu7IeQaU0jMYcXqlZ3tuUY1CCt5ZxmYbNiZZKXSxuGeCEQLLHMukYCRTmyxERKags1XLYR22dl+eVQDuCpEzuCSHWJDYPgsDJ1hhd+EgkbfeDRdqCuR6IVBVVhVITbBj84QbRCKxhmZzJ9OoO4RsalW6ZHXobly/VKxmf3q2SFC0fx1+8oEgxMRIvEEigIVcs5w+xrhvvPboz0X3WfrGQnx5m++wO3feIG2wQK7b5kDiYSWdjZbJDPb58W6qoWX2x6PxSjRhgBw+8rzo5u0AEm56HROKAyPT4I72cU80nnhwQVoCx0fFoXJ3RRTX3J1F3ZdKdaiYtYkI7b6bYvFg30nxf8GE8Siwi5cAuExmBwR0XV6s+ffUCf6aE2U8n3LH7G6co7zfu07RTzH5CAc/hkAXptKjUvMiWOXspJXFIhIsf1cdBfbJfnLinIXFqNKIBjleHdhZIkNjImLv81kwGE26DwaictqSlcm54OVvKZpaRHV55Aiql6kctt3H+/N2+7lVFGGT3a255SSIgtvWy8srH6xv0Pn0VwadVzkxkVNLtlhmgusHmEl7/BB8WLQEtibH+HacnEr++zJXn3HNwO2iHDvMDm9QvST5BZ7ssihbA0A16onONQfn3fmXM6JR2CwGYBWrZRFfofYZDQYdR5YgeDwizw6sx0qNorHTjyCw6zS4BbXjxMyt12SB0zltk9fd2uaxjvu38Mbv/JM3ohuXYGU2C67C+fK4hInZS4LkViC/a354wKXDXpHhWjjd1owGeQW5I3LS7CaVNqGgjNafYeicf7sR/vpHwsTiSf43bFLd8AvBAbGw0TjGqoiHDMkc2dtqmlqAcUUDPecxagkiGoGvMVlUmyfI26bibpiYcF/tHsCPMnO9rHzrjGKKgpVC4zIkBDb+/BR7MoDIbSQUA1sqRVFn8e1evHYUHPyOaOI4pJiuyA0IopR9C58b3sZGh8ngcK90T/nphVlKMoc3aZMNtj8IfHxoQchKu7z1yWt5A/3XcpK3gQR6RR3LvJOV3JRzEaVm1eIDJeHD3XpPJrZ0T+eXMgVmed+kZFklPXVHiA/xPaxcIxIXNifSOcD/di2yIfLamRgPMLrbfm3uRSKxhkLxwAokWJ7znnnZrHoe/xYT151cZ2PKSQswrHIPO6cYDQLq7vQGCxLdre3PMeb6sVG/3On8kxsj0Wwx8Wiw+ry6zyYqxSrG8wOKFkBwLXqSU4HIDhWYJuPsRAMNQFwUqtlmRdx3ZHMDksR2H2iC2HJDvFY60sArPSLgoXjHfkbayO5epgS26dfo5r6xjnePcrIZJS///XRvHCc6wqINbcU2+eOoii8YYm4P8h3N6f50j0i5kuFW4qoAHazkRuXiX22bzzZyGRkytlA0zQ+++ujHDpn72T38Ty7x80CXck5UuayYpQFGfMi5ZJydnBiwbhmjHQL0a8fL0abU66958GaVDFGbxhcyc72if7pBxnNEC6Mhrtz0UZF0UDE6kPJB4vvAmNLQwkAz08kizACbdMPkDbySQv5Pv0LfiIT8MLXAPhhfCeva8vYtXSe9+Qr3gLOUtGY8KqI5l5blsptv8S+rMEsviaxMN5vMoG8i5FckrdtEB2GvznYRSJPO1DPJZ3XLgWyvGFjrQeAfWeH9B0IU93KTosRq0l2GeqFyaBySzKr7ndH889KfnBCVO2ZDSoum+wczDVrqlwsLysiEkvwyOH8LfSyhoU4Y3B4ZWd7rnCWiO7U2m3Cripwlh0+sUH9ytlAfm0oJTctopqBIrdP58FcpRhMwkbe2wDANvUkCQ2OtfWKhXKhEI8QHmgF4GSilmWuhCgikMweZxnEIlB/vXCZGO+BvhOsKhUbccc6A/qOTyJB3P8AHOsanbbufqFxSoh97nQ/v82De+d0Z7sUT+fF9Umxfc8CF9t7RqeEVIngg9fVYVAVnjzRy53/voe2wSD7W4f5u/89ykOvdaAq8JU71wGwv3U4byL5skV38poiCzLmT0mRhSKrkYQmBPeFQLDvDADDqlcWnM6TtcnCvmODgLtKPBgchOg51xiDWXS2xmO5H+A8MAXF/ZFi98mYvzmweYnQf54YrRMPjPdBKHkNUZCd7SBE7skh/S3kX/kPmOhn3FTM/42+h0UeQ7qIfM4YTLDtHvHx8V9D7wnWJXPbD1/KRt5oEYUYBRg9kS2k2C65JDcuL6XIYqRrJMT+POxAPZ+02C67lvOG7YuFyPB6a2Ba1bYeyBzu/GFnMrf96ZN9Oo/kQqYs5KVDhh4oisI7N4sq63y2knfGRAGRpcgvxfZcYSkCZwUoBqjYAEB952NUOiAc09jbkkfdqcmYgQHclHg9+o7lasZeDMWLwGDCq4yxWOniUHewsHLbY2GiyQzCfks1TrOifzV9oWHziAIF1QhV14jHTj3G6hJRrX+8p8CiBSQLkiUlTixGlfFwjNahqWtUquu52is6Vj7/m2OMhfR1/ukakTbymSDV2X64c4SRYP66Oc2XnhEppJ7PdYv9/PgjW/E5zBzvHuWN//wMd/77Hn76qugk/JvbV/Dua2pYXekiocFTebhmziSpzvYKeU2ZN4qisLRUCEGNvQvj/iY6LPYExg0eESsmmTNrkrntR4ZUsLjFujoRg5FzupgN5qR4NkMOcyIB3Udg6Ex+dTqHRjDFxb2TxVUq47bmgN/toMFroptiIiYXoEHfEfGkaiq8KLZsEBqBaAhMdv3G0PYynHgYgL8M3UUIC7+33J6ZvetFb4K6N4CWgGe+yOpkz0j7aJzhycTMX2Mwi0KMfLoe6IwU2yWXxGoysHONyFf+9cFOnUdzeaTYnn8s8juocFuJxBO81qpvd/vAuMzhzhe2L/ajKHBmYIK+0fyqgJuaJ1JA1Yu3b6zCoCocaAvQ1Ddz/o+qqmzatAm/34+q5v52xpsQBWgOt08u5nKJq0J0pi6+BQDl7Au8qVo4lTx3Xm67rnNkXGyK9mtu/G65KaQbVjfYvOBfDsBW9SSHemMQFpsFel9HZkV4HOuE2GTUXDVi/uudEVdomB2i8CIyBkvEtYPWvaxKdgC0DEUIhheu0CUpDIwGlRVJ+9/DHQEAIrEELycLyb75Bxup99npHQ3ztSdO6zVMQNrIZ4oyl5WlpU40Dfa2LNzu9pSNfLkU26exbZGP3/zl9axP2jo7LUbetqGS7//RFj56wyIAbl0lCtSfXOBW8t3SLSOjLEmK7U198xfH8uJeeVTsRYfMxWCSe3nzIeWi0zamMRKcFGtrgKGzUwddSjyLBoVDVM9haH8VRjryY46MiLXSkOakxFukzxgWAJtr3YBCl1k4w9F7QvxvMIn18xzd4fJijswXTRMNFUYdYyz6jsOTnwPgocQtPBNdw7YqM3dtzuB+0xv/ShQ1jXbiPvR96t1ir+3IxXLbFVX8bGRne5oCneGSXPJ764WVyGNHeojGL1LJkif0JzuXS5zyJj1fUBSF6xaLqv2XmvTtOkyJqLKzXX/cNlM6T+zlM/pHDJzL1DyRCzm9KCmycNNykRn1i/0zF3oZjUbe/OY3U11djdGYW7E7HAnj1USOmbu4LKfnvuqxecFRCmWrxKIv2M9bvWcBeP709K4fXefIiMiM69c8+N3S8ls3jBZwlIB/GQDXqCfZ0wOJSZGJrOccmTUDpzBqUYKaBa+/XAjtelbTFyqOUohHofa65LVjgNKRY/htChpwqmPhCl2SwmHbomIAfv6a2DQ+2B4gGInjc5jZWOPhC29bA8BPXmkjEtNnXR6KxhlKRi5VuqXYPl+uhtz23mRhdbm0kb+ASo+Nn991Hb++5w3s//sdfPMPNnLLyrJ0h9qOZPTaC40D+RWXlGFSBRkV8pqSEZaWCrGxqX/+Yns+3Ctbkvbgms0r7cHnicduTjvlHOuLgDuVz906dVCqQ3Ym8Sw6KYR4V5V4vv8UxkRE9zlCQLiAdWl+Kovd+oxhAXDNolIADsbqxQNDTeJ/g1lEcs3RSj4friPzJjwKEwOimF8Phlvht38DsRB7tbX8f5E/Yn2Zie+9tRirMYOOrDYv3PAJ8fHRX/DnticxEOfIpazkQVwbJIAU2yWz4LrFPvxOM0MTkbxfBMrO9vzkDUuE98hLOs+fKRt5OT/yga0NYl68kk/Wz8h5ki/cuUlYyf/mUBdanuUrD/d3oyoaMU3FVVyq93CuLhRF5MsZrUI0AzaPPIFR0WgZDNE2mB/24JODokhkCBdOqyzw0hWHD0qE2L5dPcFgCI60Fkhuu6ZBn+goOKXVsNxnALMdjHJOXTE2ryhSUIDa7eKxxt+yKmklf6wjvwr/JFcn799ah6oI4fVkzygvNvYDcN0SP6qqcMNSP3azgUg8QfuwPu93qbx2h9mAy1agG6Z5RCq3Xe+i9GwiO9svjdmosr7Gg8VouOC51ZUuqjw2JqNxXmzM7724+TAVTSHnSCZId7YvEBt5e1g4OxicPlEwKZkXqdz2I0OqEM0h3RmeRgEiM4hn0QmxPlENQnSMR4UNvd4kbfC7NR9VXlnoPlc2N4iGl2cnasUDw8kiDIMZ4uGr2yp8YkD8DPRwmAu0kXjsryE8ysHEYj4cvpfFfis/fJsPpzkL0u6im6D+BtASvDvwnzxq/jvC7a9f/HiDCUKjmR9HgSLFdsllMRpUdq0V1jK/Odil82guTb/sXM5LUhX7R7tGCATnVgmXCWTHcn6xNdm984rsbJfMwBuXlWBUFToDk7QNXbihrGkaExMTxGKxnIvxY/1CSB3Gherw5/TcEsDuF4v7OiG2W7r2sSlZ8/DcOd3tes6RSEDcLwUNbhQ9rcYkYHFB+VpQDJQrQ1QrAzx7ZgKiQV3nyKyIhdEGRUfByUQNy9wJsHp1HlSBYraD3QfhsXQMBW0vs9qfzG3vCug3NokkSU2xndvXiHX39184wwvJQuUbkmspRVFo8ItN5Jb+CV3GeK6FfEbyIa9yti4qTkdrpRoHFhKaptEju5bnjKIo7FgpbnKfPLFwreS7A3KOZJKU2H5mYILYPN1J8+Fe2RMT74U2d6kQ/STzYk1SbD86pEKRiI1lvGf6QQaz6OQ9n9DoVISeooKWQItHdZ8j2rAQ2zs1H5Ve6QA2VxaXOPDaDByK14sHxnsgGgHVKIoq5ii258N1ZF7EIqIgxZL7eMB42z4i/3M36kQfTYlK/jjy1+xa5eXBd/jxWLMk6yoK7PgHuOajRA12VqjtfGzgc9B5cObjjWaIjEMiv92wc4UU2yWz4vc2CCv53cd7SSTy98I4IDvb85Iyl5UlyTy6l3XsYk7ND7+cH3nBtfVCbG/qG08L3PnAVGe7XMjpicNiZFOtEJVmclWJRqN885vf5OjRo0Sjuc3aDQ4JIXVEdYksYEluMRhFFX7xElBNEB7lbX5h2/70iamNAj3nSGJMbIiGTS4xRol+mGzCQrxY5J9eq5zg2XaR267nHJkV8TDhgbMANFLNYpcmrznzwVkqNopqtiYr8ANcazkDwPGehdH9JSl8PnyDyMn89cEuDrUHALh+6VRh36ISsdHXkgF74LmQ6kCtkHntGaHIamJJ8nd6uCOg72CywFg4RjAi7M+ljfzcuHWVEMOePNGX13txcyUWT9A3lhTbZWd7Rqjy2LCZhAvKTEXrV4Lu98rREF5NxD+5vOUiIkoyL9Ji+yBQlIzEGztfbLckxbNz4isSCQgFpn4HqgG0BNFIRPf1VGRIiO1dmo/y4twLogsFRVHYXOOiVSsjppjFummw8dLRArNA9+vIfAkOioJti2vWXzIRSTA8mUgXF4RjGsf6ozx7NkQwOjtRuvnFX8DvPo05PsFriWV8yvIZ7n9HA/+8w3tlQnt8Dj9z1Qgb30foXT/hifhmDGhEX/g6aDOM3WAWEQPx/NnX1xMptktmxYYaLzaTgbFwjJaB/NyM0jRN2sjnMW9YLCzD9YwiSHcsO6SImg94HWZWlIs8sVfzqLs9XZQhO9t157pkBMWePLPWTHUtT6guaeesF44SsHugdCUAO9QDALzUPMR4WH8rO3VCdNjHLR5pd6g3ipLMbV8OwFb1JAf7YXh4WOeBzYJYhPiIcNIYt1ZiNhpE8YBkblg9wkpei0PZWgDWBPcBcHIgRjwytw0kiSSTbKr1srHWQySeIKHBohIHlecI24t072wXYnuVFMUyxrpqDwCHOkb0HUgWSHW1u20mbOYLbdIll+fahmIcZgMD42FO9ozpPZyM0zsWJqGByaDgd8j1dyZQVYXFpeK9oqkvP/dvZ0tkSGRxT2pm/H6/XFdlgFUVQjA8O5Jg0poU20MBmDxnT86YFM/OFVejQdHZfL6Ntqb/2js2LGzwx03FWC3y/mQ+bK73kUCl3VgjHug/Kf5XgOhVuFbSNBjtEg0fyuVl1HhC499fG2Pjd3vY+J89LL+/m+3/1cOqf+9m10/7+dDDQ+z4734ea5y8ZJd/94v/zeLj/4aBBL9JvIHD6/6eBz+4ku3VV/g+OTEAE30w0jmnXPUil4fvOv6Mcc2KabQVjj984UEGs+j+n2MxxkJDiu2SWWFQFdZUiTfkQ+35uQgcDcWIJC2SpEiWf6Ss5PUUzdIdy7IYI2/Y2pC0ks+j3PbBCSm25wupHMs9zQN51ckRHxWV3yGj7FrWDYsTnOVQtgqA0pEDNLg0InGNZ0/1XeaLs485JK5pit0rqoIl+mJxQdkKAG4wHkdD4fnTPfmf2x4ewzop5rPJXS42t0zSGnHOmO3gLIPQCFRvBsA/fBCbEUJxONO9cPNwJYXFR65flP44ZSGfYlGJEFDODOgrtldKu+eMsb5GdBkuxM72KQt5KX7MFbNR5ZrkmnlvHq2ZM0V38ppS7raiqjKaIlOkHDMaC1xsH+lpBkQWt9dVpPNoFgZ+p5lihxkNaJqwg01cX+g9MXVQqlP1XNvw6KT43HDePtm53e86oYwLl7u41SejBubJNYtEbvuhaFJsHxJ/gxjMEFl4BV+XJTQCwQFRtH0Z2kdjvPeXg/zfPWNEkg3gkTh0jyeIa+C2KPhtKl3jcf78t8O8/1eDPN48SSg2fU8iceDHVBz/PgC/Nr+FG97zKf7kuhrMhit4j9Q0GO8FxQAVG8BbL2IgRrtE4cwVUFNWwv2xt4lP9v/gQlFdNYpi9quxGGMGpNgumTWpiusjnfkptqe62l1WI1aTrJrON7Yu8qEq0DIwkd6kyTWDMos779i6SHQu51Nu+1RRhrxJ15v1NR4cZgPDwSjHu2fIDNMJZUJYhMfMrqnMMknuKSqHsnUAKINNvLlWLFJ+d7TnUl+VE2wRsRlqdhZP2a5J9MPihIpNoKhU0kcFgzx3dhIi87PWzDpDZzAQJ6hZKPd5RVe7SQoW86KoHFDEpgPi2rGqWCyJj3UWgNuB5Kpg5+oyqr1CzL5xeem05xb5kzbyOrnNdafEU2kjnzHWVqXE9pHCzDK9BCmxvUxayM+L65IugXubF15RWFfqmuKS15RMsrRMCNPNBS62j/e0ADCoFqPYi3UezcJAURSWlYl7iVMB0lFbDDSec5AqxLrzO9vFC0x/wYTOne2JOJZkcbLBWSLdD+bJmio3ZoMyJbYHWsX/hqs0l3tyOOnocGkdoXc8zu89OMCrXREcJoWv7PBw8s8reOGPSvnlu/y8/CdlHPzTcl74UCn/51onZgO81B7hzx4d5prv9fA3u4cYbj8Be/4Vdd9/AvBv8TvZ/va78HjdVz7usV4w2qBiPbiroWw1VF8DrkqxBxJoh8nArF5qbZmJ78dvZ0D1i+KDfd+f+UDZ2Q5IsV1yBayrFn/ch/K04lpayOc3bpuJtcmCjZd0sJKfjMSZSObFySzu/OHaZJX+yZ4xhiciOo9GZMYNB1OZ7fJaojcmg5ouyNiTR5tL5sl+ABSrR3a264nNC5UbwOyEeIS3u04D8OzJPsIxHSvsw+NYNLHQsLn8lzlYkhOMFnBXgVdkIW9Xj/FcR5xEvlfnJ237zmrlLPEaxJyXzA+bV/xzlIDZAfEwtzqaADjelZ8FxZKrD6NB5YE/vpZvvGcDNy4vmfZcQ7KzfWA8wuhk7nMvO1Od7dJGPmOsrHBhVBWGJiJ0DOtTlJ4tekZlZ3sm2L5I3E++0jJELL6whI5UZ7vMa88sixdIZ3s0mcU9avCASe7PZIrlyWKM0yMG8NaJB4fOTD9IMYjO2FQRWGhkhkYDTf/O9vE+DFqMmKZi95Rc/njJJbGaDKytcHBSqxUPjIoIRQwmkf19teVyh8dnFR35hedHGA4lWO4z8th7S3j3KjtWo0KN28imCjPlTgOKomAzqXxim4vd7yvlIxsdLHVM8ueJn/CJlj/B+9u74ej/APC16Dtxb3s/pR7nlY85EQc0KF0FDrGfKqL1fFC5EWq3Qfla8fsMX34/ZF2piTBmvq79oXjg2K8gdJ7DlsEoflYSKbZLZk+qs/141yjRPLzB75ddy3nP9an85ebc25+l8trNRhWnRXai5gt+p4XFyU3DfWf1724fmoigaaAq4LXLoox8INXJ8WIe5bbbwmIsBkexrJzWE9UAnhooWwPAsonXKbNpjEfiukaWkMxrD2oWPG6PfuOQTMfug9KVANxgPMZgSOF4W/4U8VyApqVt+1q0CupdqujQl8wP1SAKL2JhqNgIwPbE6wAc75nI/2gByVXDklInb99YhXJeB5nTYqTMJda7ZwZz686hado5me2yCzVTWE0GVlQI4ePwAstt75ad7RlhVaULl9XIWDjG0a78cfvKBGm3DBlNkVGWJjuXm/vH8yqO7UrRRkUWd8jku9C+XDJnlpWL95xTAVV0vQIkf9Zp7F7RHTs5LLqZQyMXdveqBpHVrCejnQD04qWieA4dwJIL2FJfzMlEsrN9chgmh67eXO7I+GUbbJ4+E+LRphAGBf7lNi91nstrDnXWCT7jfZInjPdyt/E3lCoBJjQLT8Q38+eR/8Pzxe/iD9d75jbmREzsU5ovEj9nKYLiBvAvhckRIbpfglUlJlQFfjy5lZijHBJROPPM9IMMFgiPyLU0UmyXXAH1PjtFViPhWILTvfnXCSQ72/OfNywWFdkvNQ3k3CIvVYxR4rRcsGkl0Zd8spJPzZNihxmDzIzLC65fKq4b+84M6dutfA5FMTFXLUXFMo9bb2zFUCUEM6X3MLfViVtbXa3kx4XY3q+5KfG69BuHZDoWZ7ow4wbjcQBe1KH4b9bEI4QHRTdPs1ZBvVuRee2Zwu4XjhjlYj4smjwMwPGBGFpEnxxsieRKaPDrk9seCEYJRUXRfbnsVM4oqcaGhZbb3is72zOCQVXYloduX5mgS7plZIW6Yjsmg0IwEqd7tHDFMdO4WNPFbLLIPZOkO9sDmrCVBhjrhnMLM4wWIdqNdAoL+VgIjOf9nSoGSOgsto+0A9Cl+ajyFek7lgXClkWlDONiAI94oPeE2PdKxPQvrsglqeKCS3S2B6MJ/v5ZUSj54Y0OVpecd52KR6HvOJx8BPb8Kzz6Sfjvd8D/exvs+VeU8CjY/UQ3/QnfXfY97op9kifYxj/e4p37nnQiKn5fhss0kHlqwVMt9q8uodHYTSpLi42AQrf3WvFg28vTD7paizFmQIrtklmjKEraSj4fK66l2J7/bKrzYjGq9I2Fae7P7ebQYCqHW1rI5x3bkxsHesQLnM/UPJHXkXxheVkRfqeZyWicA22B9OOqqrJ27Vq8Xi+qmtvbGY8mcn0d7lKZx603ZgfUXic+HmnnjmpRlbv7eA8aij5zZLwXgH48+N1SbM8bzEXCNk0x4E8MUq30c6gnwtrVK3W5jlyWWIjIsOgwGTKWYbNaRWa7ZP6Y7SK7vXgxAM6xM9iVMIMh6BvKvzWORHI+i0pSue25XU+lLOT9TgsWoyGn517orM/zyL65kupalsUZ82cqtz2PCwXngOxszw5Gg5ouzGqcR7OUnmtuAHtIrKtUp/+ymcmS2bM0KbZ3jycYMZYCihDURzqnH2gvFiL8WI9whTrfXUA1oGpRXecIgZTY7qfK68j9+RcgG2s9AByLJ63k+0VcH4oyJzFV7+vInImHhVh+ic72b74yRudYnKoiAx/fmiz2iIzDoQfh0b+CH74VfvXn8PxXhUV8537hFAAi2mzV78Pb78e05YPce1MNT76/lEf+oIQ1pfPQLuLJzvbLFSipBvAtEUXok5dufltbKl7rNeN68UDv0ekCvdEsrhHRhRWHNBcKaIZL8oGpiuv824hK2YRLsT1/sZoMXFMvMrr3tOS2i3lAxgzkLdcv8aMoIre9T+eqazlP8g9FUbgu6Yqx55yCDKPRyFvf+lbq6uowGnPXXR6bHMWBmKcen8wE0x1FEd3KznLQNK5JHMBt1hgKRjnQMarLHAkHuoFkZ7vcOMwfDEZw1wjLNERu+5FBjbfuuD7nc2RWxCKYxkVGXsRWJjpJzu8mkcwdZxm4qsDuQ9HivNUpNpGOd+rvsiORXI5FSQHl7EBubeSnLOTltSjTpPZZjnaOFrTl8/n0jIg5I8X2+bM9uR7ad3aISCz/Yh3nSndyjkj3g8yzpFQUZjXNI7ddrzU3AJqGJyocw6zusst3aUpmjdtmSv/NNY6bp7rb+09MP9Bkg3gEQgFAu7DRQDFg1OL6zREgPtQKQLfmo1KK7RnB77RQXmTiRCq3fahF/K+qMAcXMF2vI/MhFhHODRe59jQORvn+QfHz+MKNbuxaCA78CH76XnjlO9D5mihOMNmgeBHUvQHWvxdu+jt41wPwnh/D9R8DZ2n6NRd5jazwz9PFIxEF0yz/FixFULJMfK+X+N2uS4r/vwuuEMUHoQD0nZw6QDWCFped7UixXXKFrKtKdbYH9B3IDKQ726VIltdct0SfiuyB5Pzwyc72vMPrMKevLc836tvdPiW2y3mST6Ss5Hef6NN5JDDSL6q9JzQLXn+ZzqORAGB1QcU6AIxdr3FLjdgEeOakPvNlckgIpMO4cVjlPUleYfdC6SoAtqvH6ZtU6BvMvwJSAIIDWGNibGZXqah8l04amcPmEdeOcnHtuNWUtJLvDOg3JolklixOdrbn2kZedqBmj6WlTqwmlfFwjJaBuQtj+UQoGmc4KByHKlxyzsyXZWVOfA4zoWiCg+0BvYeTEcKxOANJZ7lKj5wjmWZJqeiynI/YrivBIazJIndHcYW0kc8wy8pSue2ki5EZbLzwQJsXxvtnjs9TVGEtntCvACg8JGK3eimm2CXF9kyxptLFqVRu+6hwD8BgFl3bVwuxEGjMuAbXNI3PPz9CLAE7GqzcYm+Ghz4A+74H4TERW7by7XDrffAHP4Z3fA92/iNs/TNYeht46y+eqT5f4lHhADlbiirAvwyCw6I7fQbWJDvb9w8Y0MrXigfPPHfhgVEptkuxXXJFrKvxAHCqZ4xQND+yc1NIG/nC4PolQjR75eww8RwW7cuO5fzmhqWiQ/j50/26jiO12PfJeZJX3LqyDKOqcKJ7NG2Dp2kakUiEeDyOdol8oUwzNiDE9iHcGG3SIjwvsLig+hrxcc9hrqkQC4HjnQFd5kg0IMT2oNE186aERD/MznRhxvWGY0CCw63dOZ8js2JAbHb1aR4qPHaROS/JHKpBdBGULAdgXfwIAMd6JiCRX2scieR8UtbAZ4eC5LIJeipbWYpimcZoUFlTmbSSb8/TIrArJJXXbjMZcNnk/dB8URSF7YsXVm57T7KAx2JU8dqlkJppliY720/Pw0ZerzU3gDZ0BoBezUO5z5vTc18NLC9P5raPGMBTJx4MtF54oNkuOpqtM+x9qAa0eJxIKKjbeioRELFbUUsxiowayBhrqos5mepsH+uBeEKI7dFJYVN+Beh5HZkX8ZmFZ4AnWkK82B7BbID/W/0S/ObjEBwEq0d0r7/9O3DDx6HhBrAVi7+hXKFpVxa7oSii4MZbL/LbZ1gLryoxYVBgIJhgrGyreLDz9ekHGUwQWhj3sPNBiu2SK6LSbcXnMBNLaJzoHtV7ONPol2JqQbC60o3LamQsFKM9hwVxAzKLO6954zIhtr/YNKCrdWLKAUHOk/zC6zDzpuQcefiQEDKj0Shf/epXOXLkCNFoNGdjmRwW5x9R3VdWLSrJHkYzLLpRLP5CI2w0io2Zxp6ALnNEGxPZghGTW3Zg5BtmB1RsAMVAKcPUK728+sILOZ8js6JP2Di2aBXUe41glOJWxrFNOR2UhDuwEub4YHxO9ogSSS6p9towGRRC0QSBSO7O25kW26XdczaYiuwL6DqOTNFzTl67Ip1ZMkI6WmuB5LZ3BcQcqfTY5BzJAitSYmrv+Jz3WPRacwME+5oB6NT8VJT4c3ruq4GpznZFRCvBhZntKZylYJqhC1c1EI1H+eo3vqXbesqYjN1K2Evl2juDrKly06xVEsMgOrwDZ8R+SzxySRF6JvS8jsyL8ISIojuPUEzjvhdGAY2flD+Ib9/XhHV7yQrY9XXRve7U+Zp1pbEbqgH8S6GoTBRXnIfVqLDMJ34Why0bxINDzRA6R9gxWiAydtUXrkuxXXJFKIrCuuqUlXz+VKvEExqDSbG9VHa25zUGdaoi+/RI7hZU6c52OT/yko21HpwWI0MTEY516VfIMzCRKsqQNvL5xu9tEDliDx/q0rUaNpLM455QXSKrSJIfuCqhTNhZLRp6AQWNgQl9FnJqUDh0JCweOUfyDdUgNpN8SwDYqp68zBfohKahDYkNxpZEBQ1eM5ikuJVxLC5w1YDVg0KCNcoZzo7C+PjcO8DynX/6p39CURQ+/vGPpx+78cYbURRl2r+77rpr2te1tbWxa9cu7HY7paWl/PVf/zWx2PSulmeffZZNmzZhsVhYsmQJDzzwQA6+o6sTo0GlzicK/vomc7eekjby2WV9TbKzPY/2WeZDT7Kzvdwl378yxbUNxYAoyIjFCz+3Xea1Z5cGvwOzQcRTpIqlComJHnEv3Icfm10WuWea5SmxfUhDKyoXD473QuwK1tCKqq+wFpnAGg0AYHTLiL9MsqbKTQQTzYkK8UDfCVHMEI9c1Gp8wREZn1G0/s7+cTpGY3zR9lO2DD4sHlx0M7z5n8DXkONBnoemgcKVi+0g9htKVooc94kLHXRSue0vj5eDo1RktLe+MHWA0SLmxlWe2y7FdskVM1VxnT+LwOFghISWdL5wSJEs30lZyesitksRNS8xGdR0EcbzjfpZyac722VRRt6xY2UZVpNK62BQ1/efxKio8gyZXDNWuUp0wlIEtcLOytz9GnUu/W5xLSGxMFHsXjlH8hGbJ93NfL16RN+xXIx4hMiQyMZr1iqo9ZjBKDeiM47JCnYP+EXxxRstTQCc7BzScVDZY9++ffzHf/wH69atu+C5j370o3R3d6f/feUrX0k/F4/H2bVrF5FIhD179vDDH/6QBx54gM9+9rPpY86cOcOuXbu46aabOHjwIB//+Mf5yEc+wuOPP56T7+1qJGUl35dD/STVqVwhO9uzwupKYdF7undMV6evTNF9Tme7JDMs8jtwWoyEogma+gs/N1cW8GQXo0FlaZmwks83Z9LZEBk8C8CoyTc34UhySZaUOlEUGA4lGFD8okg8ERXdqrNFMeia186IsJAf1ewUe336jWMBUuay4HcYp6zkB5tFcYWmXR1iaiwivs/z3BL2tIf511fH+AvDr3i/9oh4cPXvw42fFvsMepOIir/lubo8WF2iQz8RF9nz57C2TLzm4b4Y1G4TD7a9PHWAwSxcD67y3Ha5Ayi5YqY62wP6DuQcUnntxXYzRoOsIcl3rkuK7WfGFELROCZT9jv/pI18/vPGpX52H+/l+dP93HPTEl3GkC7KcMh5km84LEZuXVXObw518euDXazcqc8cUSeERXjcLDvb8wpzEdRfDy//O4x1c13JIA+NunM/jkQCW3RYDMlZnPvzSy6P2QE118Lx/+V69Sivc53eI7qQWIh4Mn9wxFSKzWaXtojZwlEC3gboeI3tptMQgmMdQ2xZr/fAMsv4+Djve9/7+M///E+++MUvXvC83W6nvLx8xq994oknOH78OE8++SRlZWVs2LCB++67j09/+tN87nOfw2w2853vfIeGhga+9rWvAbBy5UpefPFFvv71r7Nz584ZXzccDhMOT3XGjI4KISAajRaWxaRO1BcLcaovpOTk5xVPaOkMbr/dKH9HWaDSZcZkUAhG4rQOjFHtnb8Amfo96fH76hoOAlDqNMv5kkFWVRTx6tlhDrYOsdhX2HOkc1jEtpQVyTmSLZaVOTnWNcqxzgA3LbtyMfLc30vO358DovB00uwnqikg50hGMSpQ67XTOhTk+LDCGz21KEPNxHpPovmWXfwLtWSnGwAK0Zh+c0QZaMEIdGgllHmd8jqSYVaVF3HybC1vM+whMXyWeDwBCQ1CQbBd5GetaTDQCIkYqEYwWojaStNPF8x9fmQCIhGwWkRePdA+GuOe3w7xh+oT/JXp5wDEl95BYsufgmJKH6crkQhoRkioc79mWovBsxj6T0KCdHzmKr+QkY/0RYiu3YLpxMNoPUenO+3ENGEtb3Zd+hyJ+NQ/NDDbdb0fmQ2zHZcU2yVXzNqk2N7UP85EOIbDov80Gk5aP8uu9sJgkd9BmctC72iY/W0Bblwx8+ZepojEEoxMiouiFNvzl1Ru++ttw4yHY1hyXDeTSGgMpmzki+S1JB/5vfWV/OZQF48c7uKvb12syxhMk0nnBatXil/5hKqCb5nImRo4za28wkPclvtxhAIYEdbKdrfMFsxLzE4oWwU2L47Jc6q1YxEgTywqoyHME8JFI+4oFXbnkuxgdQm7PGB5QnTyHOudhOgkmBZOp90999zDrl272LFjx4xi+49//GN+9KMfUV5ezlvf+lb+/u//HrtdZHPu3buXtWvXUlY2Zc+5c+dO7r77bo4dO8bGjRvZu3cvO3bsmPaaO3funGZXfz5f/vKX+fznP3/B40888UT63JKLM96nAAb6J2H37t1ZP99IBGIJIyoa+198GlXGK2cFv9lA96TCTx97ltXezHW352KOnM/B0yqgMtjRxGOPNeb8/AsVe1j8XB/dcwRbz6GMva4ec+RQY3KOtDfy2GOnc37+qwFtSLxXPHewkUWTp6746+PxKYvwp59+GoPBkMHRXZotI20AdCtlPPbM3pyd92rCpYm/wV81wyKtnBqaaTzbwelQx4zHX9vyDUpHj9DpvZazvpsZdiwhfo4TS67nSH3/U6wHOjU/7R1dPPbYRTLnJXPCElI5odUAMDHcy9OvJ+dF0zHg2KxfJx4/nP4413Nk/ohC03AcvnnUwLLwSf7B8v8AOFn++5xy/j4c1s+d9aKcfCpDLzSc/AexBJgUA8Mh+MHZMj6KgjI5yNMvHyZkPqfRpPkgcHDOZ9TjfmQ2BIPBWR2nv0oqKThKi6xUuK10j4Q42jnC1kX6W7UMSrG9oFAUha31xTx8uJvXcyC2DyXnh0FV8NikOJav1Pkc1PnstA4Gebl5kDctzW1X6MhkNL1Q8MnO9rzkTctKcNtM9I2Fea1VH5tfe2QQAKOzWHa25xs2D1RtgYHTrJ18FfQQ28eF80FAc+Dz6NBZL7k8qgHsJVC1GZqenXo8FgS8eo1qOsOtGLUIEc2A3eVPV5NLsoC5CMrXAQqu+DAlBHil24MWGkVZIGL7gw8+yOuvv86+fftmfP4P//APqauro7KyksOHD/PpT3+aU6dO8ctf/hKAnp6eaUI7kP68p6fnkseMjo4yOTmJzXbhz/Jv//Zv+cQnPpH+fHR0lJqaGm677TZcLllgcjkq2gL8tPlVuoIKO3bswGzO7jr4UMcI7H+FUpeVt+x6U1bPdTXz+Nghuo/24q1byR3X18/79aLRKLt37+bWW2/NiZvcufxX+yswNMLN2zZz66rSy3+BZFbEDnXz7C+OMGb2cscdW+f9enrOkX8/sxcYY8cbtnBjsvBeklnczYP86oH9jChO7rjj+iv++kgkwpEjInbp5ptvxuHI0T2pphE/+GcAVJf6uGPnbTKeKwucMjdx5LkWTGYLlZXLYPgllseOs2TTn194cHQS44EDKGjUDr1E7dBLJCo2Mnntx0hOkdzOEUB98hXogA7Nz51v2kBtZUXOzn01oB7r5b4HRYSjM9TNHatcwko+EYearWCc4d5zchjaX4Wi5LpgrJdI+RZ9riPzYbQbug+BuxJN07j3iREiwW6+bf0WRhIkytax+OYPsdhWpPdIpxMcEsXklZsy83qRCZgMwGgnTAb4Xb+F3S1hhq3lULwYhpq4xXEabe07xfETg+L8VZsv8ZpBaH8l2bykQDwCNduIKkbd7kdmQ8qF7XLIdyrJnFhX7aZ7JMThjvwQ24eDUmwvNNZVu3j4cDdHOrOfvZyyBvc5zKiyDSOvuWGpn9bBNp473Z9zsT01T9w2E2ajjKPIR8xGldvXlPPgvnaeOt6HHn/Nzpio6rQW+UQ3tSR/sLqg/gY49BN8E40UMbvK04ySFNv7NQ8l7gJYRF6t2DxQuXG62B6Z0Gs0FzIgOo/atDJqPRaZ155NVBV8i8BVCaOdbDE08tuxa2jqGmTp8rLLf32e097ezsc+9jF2796N1TrzPPrTP/3T9Mdr166loqKCW265hebmZhYvzp6LjMViwWK5sLjRZDLl5QZLvrGuthiTQWE0Cn0TcRoc2f2ZDUwIl7AKj03+frLIsnIXjx3tpWUgmNGfsx5/V6k87mqfQ86ZDLKhTqyRT/aMoaiGjMUo6jlH6vxFco5kiTXVopD07FCQmKZiM19ZR6mmTXUt53SOBIcwaZMAOP2VmKwLowAy31hZKYrDT48oGFYtB0AdakLV4mA873fd3wJoYLKLQtX2l1G7D2AKDaYPyfV1JDTUigHo0vzUlnrldSTDbKgtpg8PA5oLvzKKqf841G4Vex6JEJhm2O8IRkAFjEnJUQVNmbIZL5j7fCUGBgUMKv/+2hiPN43zU/O38DMCjlLUN34C1ZmPzRUxsBZBpn7GJg84POBwQ+d+3lIPu1vgd80hPrVkPQw1Yew7AoZ3i+MtVkiExRy4mBNoJAZEwO4TQns4CiajsOInf+fIbMckd4klc2JdtQeAwzkQSmdDqnPZK8X2gmFdlXhTOtI5Ou0GPhv0p8R2aSGf99y4THQ9PHOqL+vz4nxS88TvlNeRfOb6pcKa+9XWYVasWIHb7UbNleidiOPRAgA4PbJDJ+8wWqBiHRRVomgJdpgOcybuxeLM3RzRxvsA6Nfc+D2yMzNvsTihfB2aYmaVdpoxzYQaDek9qikGhJVqi1ZBvdck5rYke1jd4FsKwB1Fwup49/FekTlY4Ozfv5++vj42bdqE0WjEaDTy3HPP8a1vfQuj0TjNHjbF1q2iU7KpqQmA8vJyent7px2T+jyV836xY1wu14xd7ZL5YzUZWFUh3mdebwtk/XwpUazCLYt/ssnSUtEhdbpvXOeRzI9wLE7fmFhbVXnkNSCTNPgcOC1GQtEETf2FO0/Gw7F01J+8rmQPv9OC32lB0+B079jlv+A8VFXN/ZobICAs5Ps1N2VeuabKFivKk+85QzESRVXC8SkegY4Z3JAGknEgrmq4/Z+EUAaoY52sWFST+zkCxIbEPJmw+DGZ5XUk01R7bbitRo4n6sQD/SeFS5ymQfgiXb7hcVCmt8Woiag+15H5EJ4Ag5Fnzob4yp4xPmH8Bdeop8BggevuAW+93iOcmUQ8O1FoNi8UL+aWskksBjgTiNNhXy2eGzgnBsZghlgYYpfYW4lNgoZwSViALMzvSpJ11iVz2w93BPQdSJKU2F5slyJZobCyoghV0RgYj9A1kt0N7oExKaIWCtct8WE2qHQMT9IykNuu1IFxcR2RRRn5zbX1opvjRO8EO25/Kw0NDRiNuTHqSUwMCssoTcHjL/yOxwWJ3QfVWwD4A9MLPBtZzKRnUc7mSCTQDUA/HvxFcnM5bzE7weZBrVrHu3iEmthpRsdnZwuWdRIJtMEWQIjtDV7zgsoOz0tMdigVue1bFLFZ8OSZSYjq4I6RYW655RaOHDnCwYMH0/+2bNnC+973Pg4ePDhjZuLBgwcBqKgQVpzbt2/nyJEj9PX1pY/ZvXs3LpeLVatWpY956qnp2YC7d+9m+/btWfrOJACbaj0AHGgPZP1cPWmxXV6PssmyMicATb1jOS88ziSp+WI1qdJ9MMOoqsLqSiE+Hu7Ij+aXudAVEF3LLquRImv+dbAtJFZWCEH1ZM+V3+sajUbe8Y535HTNDaANnQGgQyuhslRGDGSLBr8Ds0ElGNVonzBAVdJ6um3vhQenxHZvrfi/SBRcGicHeMctW3M+RwCMYyKjXbOXXLyLVjJnFEVhTWURx7R68cCgKMTFaIaJgZm/KBSYXiiugJGELteReREe48y4kf/zu2HWK03cZXxEPL7u3VB3g75juxyGLN13uWtw+iq5sUrcnz48ukQ8Pt4LE/1T505E4VKNDJHgBQUZCwkptkvmxLoqDwCtg0FGglF9B8M5YrtcyBUMVpOBSrv4+HCWN4hSImqJFFHzHrvZyNZFQkx9vvEiN29ZIlWUIedJflPqslLns6Np8HoONpfPZXxQLOYGKaLYm9uYA8kssbph2U4ANiaOUK300RVUIBbJyelDQ2KODOPCYZPXkrxFNYDNh6lqIwC3qq9zrHcyZ/PkksQjRIfbATijVVDrtWRvwSwRmGxQthaAsshZVBIc6EvQPzik88DmT1FREWvWrJn2z+Fw4PP5WLNmDc3Nzdx3333s37+fs2fP8vDDD/PBD36QN77xjaxbtw6A2267jVWrVvGBD3yAQ4cO8fjjj/OZz3yGe+65J20Df9ddd9HS0sKnPvUpTp48yf33389DDz3Evffeq+e3v+DZWCMK4HPR2d4lO9tzQp3PgVFVmIjE024ChUhnUkit8thQFvCGql6sTboEHs0Tp8m5kJ4jXrvOI1n4pLqXT3RfurP9wVfb+IufvM5/7z3L2QF945Um+0Thaafmp6w4zzKRFxBGg8rSZJHXiREjVG4QT/QeufDgwWT3avEi8b8j6fQXHBTiWq6JhrBGhIW92V2e+/NfJayp8k51to8IJwFMDtHZHp2cfnAsDJHJ6RFoqgmieRTXNhtiESKREP/nmSiRSJhv2b6LSkKsF9e/N//jJLO1d2Awgm8xdywSBRO/OGtBc1eL59peFv8rinA+iE1e5EW4sCBjgZHns0OSr7jtJup84qb4cGdA38EgM9sLlVqHqIY6lOWK7MGUPXjRwr2YLyRuXC5u2p893Z/T8w5OSAeEQuGaZHf7a2cDOT3v2IAQUodwY3XkYz6TBLMdSldB2VpUNN5neIquIBC5ctvEuRAbEZ3tk0a3WFhK8hd7MVSsJ4HCKrWVjo72Sy8Kc0UshDYqrjWjplKsDs+CrvzOCxQFKteDwYIaj3C7txsNhWdO9Og9sqxjNpt58sknue2221ixYgWf/OQnufPOO/nNb36TPsZgMPDII49gMBjYvn0773//+/ngBz/IF77whfQxDQ0NPProo+zevZv169fzta99je9973vs3LlTj2/rqiHV2X6yZ4yJcCyr5+oZEdfHcim2ZxWzUaXeLzJQGwvYSr5zWMyXSmkhnxXWVqci+QpYbB9OFWTIa0q2WVEunBAu1dmeSGh8/jfHeeRwN3//62Pc+NVn+cD3X9HNYWOyX4jtA6oPq0XOkWySnh8jBigRjkUE2mDynOtLPAJDZwF4fHIV//LyKE8OeMRzk0Pi+Vwz0gHAhGbB55PuB9lidbVnqrN9rAeiETBZhU14+Lw9lugkxEPThVTVKDqZC4l4mK/tC3OkP8GnrP9LbaJDFBhsu0vsN+UriZhoKsimy4PNwy0ry9NW8gGvKM6m+9DUMao6/fpxLvGomA8LuJlAiu2SOZPObc8D66rBcZnZXojUOsWNe7bjCAZkFndBcdNycaO87+ww4QujRLPGwJi4jvhlZ3vec219MUbiRF77OQcPHiQSyVHX8rAQUkcV94KuxCx4HCWw5GYiGOkzLeXa0OtEJnJzr5LKbA+b3GJhKclf7MVEbKXcp9zL55VPYOk9IKrx9SY0ijkkujQSjjKRLy/JPjYPFDcA8Hb3KQB2nx6CRELHQWWHZ599lm984xsA1NTU8NxzzzE4OEgoFKKxsZGvfOUruFzT81Hr6up47LHHCAaD9Pf389WvfvUCG8gbb7yRAwcOEA6HaW5u5kMf+lCOvqOrlzKXlWKLRkKDQ1l2++mWNvI5I2Ul3ziHfOV8IdW1XO2V8yUbpDrbj3eNEosX5vtUykZeFmRknxVpG/mLx1N0BiaZjMYxGRS2NojC9hcaB+gaGuNLX/pSTtfcAInhZBa3ybegRZl8IB0zEDCAzQVFFaAloPXFqYOGz4IWZxQHf7avhG+9Os6zgx4AJsZH+dIDv835HEl1WXdoJVT7XZc5WDJXNtZ4OKuVM6FZhJg7cCqZta3NLLYn4tP3QQwmIqFJXa4jc2VPYz/fPRJntXKWPyZZgLz2Tihbre/ALkc8KoT2LF8zncXlaSv5PdHl4sH+U1MHWFww0QeRGRwNopMQDy/o/VQptkvmzLqq/MltT3W2+6TYXlCkxPYjHSMkEtmrmE1ncTsW7sV8IdHgd1BbbCca1zg9krtuvlRRhsxsz3+uadDHwj0W6AJg3OCSmWD5jNUNNdtJWKfmydhYICenNk4KR46ExSPnSL5jtIjNpCSOYMeFVnh6MHAKBY2A5sDncU234ZNkD5MdfEsB2MwJAF5ojxEKXnm+qUSSS+qT66n9rcNZO0ciodE7Km3kc8WSUiF8NPYWbmd7WkiVxRlZod7nwGkxEo4lCtYBoeucqAFJdllS6sSgKgSCUXpHZy4sbUrOo0V+Jz/7s+3UJ51MW/r1sX82JLO4Y/YSKbZnmVRn+6nBmCgyrd4inuh4beqgAWEhfzheDyi8a5WNgNEPQFSve+XAlNhe45eug9mi2mvD77RwUqsVD/QdE/8brTDeJyzDU0SDdE/Aod5zBHWDCTT944dnSyAY4d5fNaKh8Pclzwv7+JIVsO4P9B7a5UnEhLtitq+ZNi93LBbrgR8NLBaPjXbCZPJaYHYIoX1ihmjYWChZFLBwr+tSbJfMmXXVKbFd3852TdMYnhAXbtnZXliU28FqUhkLx2jJYibUgLSRLygURUl3tx8P5F5slw4I+U+9z65L8UxiTFgKh40u2bWcz1iKwO5FXXJz+qHGntGcdKimOpKxFwsLL0l+Y/elPyyLdaGF8qCLcKAREHntDV7Tgq76zitM9nS3gnfsNFVFBkJxeOnUwreSlxQ2DUVJsb0te2L7wESYaFxDVaBErqeyztLSZGd7Xx68J82RqTxuKaRmA1VVWF0pBLJCtZLvCogCHtnZnn0sRgOLS0Q8xYmLWMmnxPYlyetP6v/mfn2KOeyT4v5LcZbKe+Esk3I+ODscIRhToHKTeKL32NRBA00AHNUa8FgU/vkWD85iUbRsCOtzDUoMtQLQqfmp8Tl0GcPVgKIobKnzcCxRLx5IrlWFoDo+vXs5NMI9uyf45EMH+fCvB2gbiYl9s3jhiO0/fqWN3vEYi1xwrTH5vdZfn9/28SlSIrYhy3uVRgs3ryzHoGjsHSkmZvMLN4zOV6eOMdthpFM4HZxLPjQ3ZBkptkvmzJoqN4oiLOX6xkK6jWM8HCOStM4qtkuRrJAwKLC6QiwSs+mQIEXUwiOV235iWMlZTljKAUEWZeQ/iqKwud6T8/OqE8IiPGbxSLE9n1EUKCqHmm3ph/raGrOfxx2PYouJzQaTQx/3BckVYp3qglisdNI9MDC9Ol8Pkp0jLVoF9V6T7GzPFSYrVG0GFJSJft5WLTaXnzw1Q0W+RJJHpMT211uHs+YU1pO0kC8psmAyyC2kbLM0ZSPfN65bXvJ8kUJq9kk1vxy5wuaXRELj7MAEYyF9xY9OaSOfU1Ldyye6Ly22L06K7Kn/delsnwxgTYjz2jzlsoA5y/idFvxOMxpweswCpSsBRdhAB0Quemp9cjxRT31RHEY7KSouB8Ci6ZPHPdnXDEA3fkrdRbqM4WphU72f41qd+CQgihwwWCB6Tm57PEZwfJTPTv5fnrR8is/33MXjP/kG//tq04WCa56iaRq/PiDm/D1rNdThM+KJig36DepKSMRyVhRQVFzGWp9ojutyrhEPdh2YOsDqhtAwBIemf2F4bMFf0+VKSTJnHBYjS0rEDdiV3uBnklRXu81kwGZe2H+wC5G1VSmxPTtzKJ7QGJoQImqJtAcvGLYt8mExqgxHFJr6sr/A0zQtXZQh50lhsLnOm/NzmkNCdNGsHmkRnu84SsFdk/7U3LM/+1W0EwMoaMQ1Bbsr9/NTMgfUqaWQU5mkva0V4jrmyCUSaMlFfXOiggavWYrtucTbAO5qAN5uPcAddfCm+gLoYpBc1VQ6wGZSGQ3FstaBKPPac0uD34FBVRgLxegbm9nyOZ9JJLSpznYppGaNNclYx6Nds9tH+eXrHbzz3/ew9nOPc+NXn2XXt14knsUov0sRiyfoSUZTVEv3g5ywJrnv9mLjzEWETf3ndbaX6NjZnrQHH9BclHqkPXguSBVjnBwziX0Ov4hWovEJIZQOCmH7qFbPIo8JiiqpdiQIaPp1lMeHxTwJWXyoJrmHl00213nTne3aSCfEE6LBwWiC4bNCdI9N0to7xAZVzJVqZYCPGh5hx6F7GQ0WRmf7ie4xTvdNYFY13uztEN3aVg8UL9J7aLMjEQWzMzfnsnnZXi0aGl9JrBCP9Z2Yel41AgqM9049pmkQGlnwbiVSbJfMi3XVHgAO6Si2DyXz2oulhXxBsja5SDzYHsjK6w8HI6TWkDJmoHCwmQ1sbRBi1Ustg1k/33g4RjgmHDJ80gGhINhcO9U5nKuNIltEbE6ojmLZ2Z7vmO3gqkp/mpM87uRCYhA3xW65MVSIjPQ062ttFg8TGxbV9Ge0Cqp9rmkFAZIsY3FAqbCSXxY8yP03wZuXyk4ZSX5jUGB9ssM1W7nt3UnhVOa15waL0UBdMi/5dG/hWckPTkSIxBKoCpTLOZM1VleKv/uT3WOXXQtpmsZnf32M11qHmYiIDsO2oSCdw/rc8/SNhYknNEwGRRa654hd6ypRFNjTPEjH8PROZE3TpmzkS6bbyOvS2Z7snO3Q/JSXSLewXLCiXNzvngwYhXC6ZId44vBP4cwLEA8TxsIZrZyGYjNUrGdxbQ1dml+3MRvHOgHQHKXZt82+ylld6eaMWkNMU1Fik+mCGBwlMNEvijEiQQa7hNA+iBdt+18yjo0iZZKu9iYdRz97fn1QzKmbaxQcw0nh2FNbGBbyIGITc5WFbjCyfYlwpP3lyDLxWKAVwufcV1hdYo8snCzaioXFP9UEkwFRzLAAkbs3knmxodYDwHOn+3Ubw9CEqPb2OmSXYSGS2hw63j1KJJb5C22qW9lrN0nbwwJjU60Q2w+1Z7+YJ2UhbzcbsJvljXohsLx8SgA53ZubivuimNjEthb5RSWvJL8pKkt/WBXvIBqc2TYxY4yLmIF+zY3PnaOKYklG0YbbIKZfNBLRSZSxLgDGLGVYnR79xnI1YrJDedIGr/+kvmORSK6Ajck1+WvZEtuTHahSOM0d6dz2HN3jZpJUV3uZyyrX31mkwe/AalKZjMY5O3hpQbR/PMx4OIaqwO8+fgPLklEFTf36FHOk5ki524qqyjVVLqjy2LhusQ+A/9nfOe25gfEII5NRFAUWJbPdUzby/eO5d9fQhoTLU4dWQmWxLHrMBSuS8Z4nBuNin2P170P5epEB/cwXAWhRatBQqS9xg8HI4kVL6NRLbI9FsIWFDmF2l+szhqsIs1FlZWUxTVqymaH3iPhfUcFZAoGzMN5DbEgUyvSaq1HW3kmraTEAY/0dOoz6ykgkNB4+JK6Nb19mgZ6j4omUy0O+M9EPFte0mLxss2VJJSYV9o6XEbN6hQtGx8tTB5gdEJmA0S7R1R4Nir2Wp78A//12+P5t8NAH4Ym/h4mFE90m73wl8+LNq8sxqgqH2gM06lR1PZS0kS92yIrYQqS22IbbZiISS2Slcn9gLJnDLSumC44NNUnXgxw4Z6SKMuQ8KRxMRgMTFj/tcTeHW3sv/wXzJTqJQ0tlx5Vk/3ySeaPaXCyuKGap1kKD0kNTa1t287iTne39mgdfUYFUP1/lqKrK4sWL8RhDqGg4Jzv17Wwf7cYYC5LQFExFJQveYi3vMNmhYiOgQHAARrv1HpFEMiuuSUbr7GkayErGdyqzvVLayOeMpaVCYGrsK0CxfVhmcecCg6qkrZ+Pd126oLR1UHQyV3psrCh3sbRMzK8mneZXl4wZ0IV3bhZROb94vZ3EOW4IqXlQ47VjNYloTpfVRJnLgoZCSVUtLpcLNUduS6H+FgA6NT9lHv1syq8m0p3t/ZNoRpsQxG79PNi8IgcaOJRoAKChVFx3yr0OehU/Khp1ltGczhFGO1HQCGkmin1llz9eMm821RdzLJXbPnB66gmjVfwb78My3g5A0CGuNWOOegCU8W4WV5fkdo5cIa+cGaJ7JIzLDDc2OKDvuHiibK2+A5sNoRHQgLJVops8R9hdxWwoNQAKHc714sGO1847yCfmy/BZcV2JTk4dk4iJhpX+E2BcOA6z+TnDJQVDSZGFm1YI24if79enUmk4mcddbJed7YWIoiisSlZRnuzJvNg+OCFF1EJlXZULBY2O4UkGs1xRPZgW2xfOG/xCx2g04lt/E09GlnKyI/tRA6mu5ZBmwu0tzf75JPPGaDTynvf+AW/mGcxKjK7Wk8K2KkskRnsA0dnud8nNw0LAaDTynve8h8WlNozEKY91oIX0i0ZiQHRTd2p+qjxWmdeea4xmcJWDN7mR1HVQ1+FIJLNlS50Xs1GlayREcxYsf1OZ7bKzPXcsTXYen+qZnSvPMyf7+Lv/PcLf/M9hPvWLQzx8qCubw7skUkjNHasqk2J79+zE9lQ8gd7OCanOdlmQkVvevLoCp8VI+9Akr54dSj9+fl57isUlTuKoFK+9iUWLFmE05sYBMDooumMDBj9mi3zfyQVLSp0YVIWRyRi9FENkHGweuPU+0b0M7I8lxfYyDyD2csP2cozEuc1yIKdzJGVj3qn5qSmV8W25YHO9n8MJ0ameFqJT2IsBjZKQ+Ns1uiogHkVLZp27Q528501rcjtHrpCUhfwddRrWiU7RhW0wQ9mai3+RpkFwCAIdons7NCrcICLjYv9wrAfikewOPBoU3eMly8GZ431K1cD2evH3tzchotjoPTr9GLNdFAD0nRDNKaNdoCWIqHbib/lXuOnvYds9oit/gSDFdsm8efeWGgB++XoH0Xju8xYGk2K7zOMuXFJ20NnobO8fS4qoRVJsLzSKrCbKkuvvg+2BrJ6rf1w6IBQiK1MV2ANZvoEFEmNTXct+ryfr55NkCIubTmMtAMHeFrEYyRKRgNjY7seD1yk3DwuKInEvW0cPA3292XVAuBT9okugRaug3mPKXeaaZApbMZSuEh9LK3lJgWAzG7i2XuTavtCY+Xi37hGZ2Z5rVidF1BOzyONOJDT+z08P8JNX2nhwXzsPvdbBJx86yGQymzvXSCE1d6SaFi7X2d6WtJmvLRZdwilRNSWy5pqU+4EsyMgtNrOBt6yrAOAX5zRLNffNLLanPs9GEdclGRFjC1v98l44R1hNBhb5xfXhxJgV4nGxHipfAzf9fwz7NvFYfCuldnA4pqz9VbewFVdCQzO+btYYER3UHVoJNX5Pbs99lbKpzstTiU0AaEMtMNY37XnN6qEmLv52PRWLYKwXh0/Mj4p4pxCh85RwLM6jR4Sj2duWmqcEY3dVspBgpi8aE9cq1QgVa8ErilGYGIRYDOx+cJbBeL+weM9GPnkiLs7nWyqy5XVg+1LhLPGz4WRu+0g7TJ53T2IpEpbyIx2QjMx7LVrPB16uoLd0Oyy+KZdDzjpSbJfMmxuXl+B3mhkYj/Dsqdxnt6c6231SbC9YliVtzE5lobM9lcUt50dhUucUm0sH2gJZPc9AsijDJ8X2gmJVqY3Vylm6h0aIhbObsxwcFJWu/bjxumV2XMGgKAxbxcLDPNaeVYvw6IhYoI2pLsxmeS0pJOJWL6M4MCoJutpO65fbPtgEpMR2o+xs1wOzA0pXio+HmvUdi0RyBdywVOSmvtCY2czDREKjd0TcJ8vO9tzR4HdiMxmYjMY5M3BpoatnNMRYOIZRVfjkrcsoshqJxjWa9RJSU53tXimkZptZd7YPTe9sT4vtveNZiZ64HF2yIEM3Ulbyjx3pZiIs7MFTNvJLSvJDbLcExZoq4SgFg1xT5YpUE9TJIU10o6bWzUtu4dnVX2QCGw0eI5im7gXsJcINyhYdzulYYwNngGRnu895maMlmcDvtGDyVnM40YCCBqd/N+35ga5WLEqUMc1KZc1iKFtNeUUNCU2hmFEiIz06jfzy/NeLZxkLxahwwNa6oqm8du9iON/2PhIUnezxCJSshJprwVsvLNxrt0PdNqi/Dqo3Q8UGqNoMJgeMdIqvzSQT/aKb3VsPipLZ154lGxdXYjbAwUk/UVuJKCo4N7c9hdUFdh+hYXF9P6HVsacjwpt/NsJTbblv3M0mUmyXzBuTQeUdm8QN20Ovtef8/ENB2dle6CwvT9nkZUNsF5tDJbKzvSCpLxKL/2x3tqfnibSRLxgikQgPff9bvMHSzVbtCGd6s1tNHRwSFZjDilsKqQVCJBLhn//5n3luYhkRjJRF20mEs7dZpCWjBsImDxhktE0hkJojh48cod0oqtHHes/ok9sej6INC+u9Zq2Seq91QWWXFQxmB5SvAxSYHILRTr1HJJHMihuWlgCwt3mQcCxzHc1DwQiReAJFgTKXFNtzhUFVWFEhhI9jXZeON2npT3Ut2/nLW5ayMtntnA3XuNkw1bUs50u2WVFehKIIN7++sYsXCqZs5OuTYnuD34GqwFg4Rt9YduPaZqIrIMYqO9tzz+Y6Lw1+B8FInF8lbZNTYvvi8zvbS5wYiVPd9jiHDx8mEsm+mxyhESwxce0yu8ovFLokWWN1pbCD3nN2VGS1R6YKts4ERGHGIt/0v9niqqVEMHJ/4gMcPnQoN3MECPYLsb1P8eMpcuTknBLYVOPmt/Gt4pPWF6c9N9zZCMBZpQZzkQ98iyhZvo1Wyolg5Bu/a8zddeQKaB2c4BtPCne5v9qooZqs0HtMPFm2aupATRMW6NEg+BZD9bVQshRM5/xNGM3ib8eY3C9UVXBVQPUWKFkB4VFhL5+JLvdIUEQ8+JboumdgtVrZUmkFFFrTue37Zz7YZCPcL4rZO4w1rC4xMRzS+PDuGPvbdIzyyzDyXUuSEd6VrI58+mRf2rY7VwylM9vlhmShsjTZ2d4zGmIkmFlrmQGZxV3QpDrbD7UHSFzGQnE+pOeJLMooKKKagahiYrV6huOd2RXbIwGxGTGuuoVVlKQgiEajJBBVvktpp62rO2vnUoPC3Sdu8YBqyNp5JJklGo2SSCQYKxJiuxJoh1juN56JhYgnbTNbtXJqS2T+oC6YHcLyL5kxSNtefccjkcySFeVF+J0WJqNxXm8NZOx1u5OiWInTgskgt49yScpK/nIW4S0DQhBZVCIEh+VlqYg2fTrbu0ZSYrtdl/NfTdjNRhpS1s/dFy+uaD3PRt5iNFDnEx/n3CIc2dmuJ4qi8P5tohv5W0810j8WpmdUXOcv1tluIEEikYBEDroPA6KBa0hz4vMsnAzfQmDXWhEx8GLTAB1xz7Ss6TMBUcTXcN4cqaldREgzEVVMJHLokhEfEgXKIasPxSj38HLF9cvK+G3iGgC0wSZhYZ4k2i/E9n5zlYjlAhSHjy6TuN7ENEVcR/IITdP4//73KOFYgjdUwDvWFkNwMG11TsX61IEw1g1WtxDOy1aJTu3ZYrRAyTLR5W5OdbnP471XS4hxehvA4Z/762SI7Yt8ALwYS7rDpYoVzkfTsIyIQhmDp4ZfvsvPn6yzsKtBZVPNwrney9WSJCMsLStiQ42HeELj1wdz2wGSspEvlp3tBYvLaqIyaUt4ui+zFfiDMou7oCm3g82kMhaOZdUKUc6TwmaN0srxzktvRM6XxKiwvZo0umXXcgESxoRDCdN6tilreWHmkFhsajZvVl5fkl00/1IAnKFOfTrbw+OoE8IdIWgpxWyTloi6oCgiY690hfi88zV9xyORzBJVVc6xks9ctJvMa9ePVJfhscuJ7UmxdFFSBFlWJv7Xo7N9IhwjkCyer5Sd7Tkhldt+MQeE0VCU4eTvpNY3VQAxldueW7F9ZDLKWNK+XM4RfXj/tlqqPDZ6R8N85ldHALEP4rZPX+OWFFlwWs8pMo/l4P44IETUDq2ESp8sPM0ltT471y32oWnw8+MTIs4qGa11ZlhcQ+pLpgti9aUuujVfzsdqGk/qDo4yWeSeQ25fV8WYtYoTiRoULQGNj6efs46IjuWQvRosU24Do0WLcz7O2fK/Bzp5sWkAiwH+8ToFxWSDHnFNxFEK7uqk0N4DFheUrxWd63PFWQpVyS736KTolJ/L3tR4r1iveuvmPpYM8oYVlQD8aDCZ2z7aCcHAhQdO9GONjxHTVFxldViMCp+93sG3bjSg6GSDnw2k2C7JGG/fIP64njjem9Pzpmzkpdhe2KTygTJtJZ/qWJZZ3IWJQYG1VWKRlc3c9ikHBDlPCpHV6llOdGfXdiglgkXNblCl2F5o9JtFbnugu1lYf2WaSBBzXGxWGpy533CQzJ+iqjUAVMU6hMVbrhlsRtXiBDULjiLvtDxESY6xuqByMyy+GZbcpvdoJJJZk43c9lTHY4VbdqDmmlRn+7GukUvmarckM91THc5L053tuRfbU3ntLquRIqu8X84Fqy7jgNCWtJD3O804LVPC6VQed24dEFJd7cUOM3azdAvTA4vRwF/vXA7A48fE/u2S0gutuBVFYfG5nczz6cScLUOi67FDK6HKv3A6HQuF91xTA8AvDvYRN7sgJN5/0jbypZ5px1tNBoYMOe6sjcewh8S8NXvKcnvuqxyrycD7NvqmrOTPviT+1zRKJsXfrtFXP91avWRFbgc5S0LROF989AQAH1uXoL5CxDHRuFv8718mOtKDA2B2CqHdmoECIJNVdLlXbwFnubCVDw4KUX82TPSD0SYKw/PE1WFDXQl+u0pTxMekrQLQoO2lCw8cEgUZzVolS8unihYM6sIR2kGK7ZIMcstK8Sa3v3WY4WBuMjhi8US6clpmthc2y8ozvymgado5HctyfhQq66uTYnsWc9sHkvPEJ+dJQeJUJpkYbEeLZK/a3jgpusQ0q1d2thcgkx7RtawGWrPTtZwsxghpJopcsrO9ECldLGzi/MoII90ts1/wZooBkRV3RiunzmsEQ34snq9KLEVQuR42vE8I7hJJgXD9ErHhfbRrhMHxzMRhpMTTctnZnnOWlRVhUBWGg1G6Ry6ex92SFEsXJcX2ZUmxvWN4kolkB3GuSM2XKq+0kM8Vqc72490zi+2pvPba4um/k5RleK5t5Kcs5OU1RU9+b31luqAHpoovzudmW/PUJ+HsF2Ykhs4CQmyv9kqXp1yzc3U5LquRzkCIlwJeUAz0Dw0zEQVVgZrzxHaASes5gncubMLHulFJENEMePyV2T+fZBrv31bLbq4FINF/EiZHITiISxslrikUVy4B09T7jbN2g04jvTQH2wMMTUQosSl8dJNDREUGB6cixJYmC66jYShuAJsnswOweaFyA1RuFM08Ix0wGZgW33ABwSGR0162BuzFmR3PPFBVhR3LxB7YMfNa8WD7qxcclxhoAuCEVssKTzQ3BVw6IMV2ScaoKbazoryIeELj+dOZq6a/FIFJIbQrCnhsUvwoZFLZcpnsbO8ZDRGJJzCoCqVFcjFXqGyoSXW2D2fl9UPROOPJjSjZ2V641ETO0Decve52a0RYhCuOYvGmIykozOWrAPCG2rMjto8LsX0AN8WuCztDJPmPw1NKJ6UA9LSeStsm5oz+kwC0aBXUe4x5U6l+VWKyiQ6GRG5FKolkvpS6rKwoL0LT4KXmwct/wSw40iHurZaWSdEj11hNBpYmBbCLWcmHovG0wJ2ykS92mNNrmlxbhHcOp/La5do7V6Q6288MTBCMXPi+1Tok5kAqoz1F6m+6qS/HcyQltku3DF1RVYW/uX2q4/T8vPYUntLqqU/GurJejBoaOAtALz78Llm0k2usJgO/v7EKgJ8dG4fSVZwZEuJflcuIxXShG4Xmqpr6JCdRA20AdGl+ako82T+fZBql/lJWLmqgKVGJqsXhte8R6RNF4y1aJQ3l3mnr2KrFqxjX8u96/9rZIQCuLUtgsic71ht3izx0V5XoPI9HwWAUhdjZQDWAuwqqr4GS5UnBf1gI7yOdIid+vE/YzY90QiIOpavAWZKd8cyDHasqAHhwbKN4oG2vKMQ4h4neZGc7NdRaQmK/ZSy37ti5QIrtkoxyy0qxSfn0qczlxF2KVF6722bCaJDTuZBJVeCf6h27pE3eldDYKypv6312zEY5PwqVVGf76d6xrHRn9I+Jzh+zQcVllVZ2hcp6tZnjHUPZeXFNwxkVxR5mZ45t0iQZwb94AwCLaadvKJD5E4yLRUK/5sEnxfbCRDXQYxa5ZxMDWXJAuBiJBAw2AnA6UU2DxyTFdj1RFJHTp8h7R0nh8cZlYgPu2VN9836teELjUNJZalOtdG3Rg1WVl87jbh0MomlQZDVOc3JL5bY39uljEV7lyb+N9YVKaZEVv9OCpsHJGRoXWgdEZ3udb7pwmbIHH5yIMDGHyNi5MuV+IOeI3tywtITb15RjUBXesGTmNa6/ZkqQV7oOZCeO6xy0QDsAQbMf1SyLdvTg3Ukr+SeO9TBkLOGMVg5AQ/HMvw+z75zc6GgOindGxBzp0PzUlORPd+9Vg6ryJ9ur+XH8FvH5iYdRn/0SAI3UUuyfbu1fW1pMo3ZO0U4sN07Il2PfWbG/d02JJtZ8mganfiuerN0OZru43pnsogg7m5jtQmyvuw5qt0HlJihbDZ56KCoH31JhY1+1CVz56ebwhuVVWI3wi+A6QkX1okP/9R9OO0YZFGL7mK0ag8Uu8uurNoGzYkG5h8odBElG2ZG0kn+ucYBYDtxjhpJie7FdWj8XOktKnagKBILRtPg5X1KW9EtLs1SFJskJZS4rFW4rCe3iXR3zYXBiKmpAkR3LBYOiKNTW1lJsiaMAa9UzHO8KZOdkk8MYEYUedk9pds4hyTipOeJwOLDUbAagQhmiuaU5810ZYz0A9GtufE65eVgonDtHFEVhvGgRAOpoB8Qycy8yK+JhtOGzAJzWqqn32RbUgrMgsRRNzxuUSAqEm5aL+5RnTvYRT8zvve507xgTkThOizFdGC3JLasrRdHxxdZA51rIn7uOSf2+mnIstqe7lqXYnlMulds+1dk+XWx3WIzpoojeHNYXptwPZGd7fvBvf7iJ1z9zK0svco1fWeXGqY1Qp7UT6zqYddtf03gXAAlnCRjkPq8erK50s6bKRTSu8fv3v8QvTybdU8o8Mx7vqVhCndZOudaDEsluMQZAqL8FgE6thBqfdN3RgzUNlRzzv5lPRT9KCDPGqLjX6DdXo1inX0uMBpUeUy11WjteQxAlnsM3nIsQT2i83irE9i2VyTV3/wkItIru8uW3i8eiQbD7RXd7LlANwq7eXSWs68tWQcV6kfHurQNH/jb+2CxGbqgvAhSe8b5LPHj6sakCrVgYe7ATAJO3GswO8b16aqFizYLa+5BiuySjrK/24HdamAjHaR7NvmiVFttlXnvBYzUZqE9am53KUG57anNB2h4WPqkNo+b+zG8YDSSLO3zSQr6gMJlMvP/972dVQwUmYqxWznKyK5Ada7tk1/Kw5sTjkZ1dhUJqjixduhRTkY9eg6jKH+8+nfmu5bFuQHS2+2VsScEwbY6YTCR8ywBwTnblxgYxRTQo7OKAJq2aap87d+eWzIzFOS1vUCIpFLbUe3FZjQwHo7w+zwim1Nevr3FjUGVBqh6svoSICtAyIISvRedZQKfWTqd7cyu2tw2JTdVqmdmeU9Yk58lvj3Zf4BLYls5sv9B5aXEypqBnMnd/3x1Jsb2mWIrt+YBBVXDbLy5y1PpdRFUDH+LnJPpOQzhzsY8XEB7DHBPXOmNRuYxu05HP7FqF126idTDIq8kO4PoS14zHNqzdxge1X/Bn/IShjlNZjxqYTFpRD6o+HHb5XqMLVjeffZOX3xluYlf4HzmeqCOuKQy6V824fppwL+ZD/Jwdhr2YtBxaqVyEUz1jjIVjOE2wojT5XnQy2dVeugq8DeLjRDzzWe0LmFtXCnetfx/aDEUVYs/t4E/Fk0NnUEkwqBVR5XOD1aPfQLOMFNslGUVVFW5eIf64jg7nQGwPCrHdK8X2BcHy8szmtjemxXbZiVHoLCoRmwNnBjJfST0wLsT2c60XJYXDuKWcmMGGTYkQ7D+bpTxuIbb3aR58bnk9KVSGHEvEB8NnMp7HnRhJiu24KS6Si/5CxV69GoDyeBcEA7k78WAzSjxCWDMRt5VgtssiQd0x2bKXzyeRZBGTQeXGZHf7kyfml4N4oC0AwMYaWWioF6mO5c7AJIHghdarLclM9kX+6UKqXjbyKWH3/C5qSXZ595YazEaVl5oGefRId/rxcCxO96i4553pd5LK6e7NodjenizIqCmWc6RQCFduBcA50QbDbdkTU5MW8sOaE3+xfN/Rk22LfLz0NzfzmV0rKS2yoCqwpX5my3anx89ZQy0AXaf3Z3ydfT7xYZHZHrb6pfuBXigKaxbV8JNb4wxbatgV+Uc2h7+DtWIlGGdoPChNrrEjbRDKvFvplbIvmde+sQSMFruYs81PiScX3wyqKmzQVZMowJbMiptX16AAhwcSBFa+Vzx4/FfiZzkkimROJGpZ4TeJzvYFihTbJRknZSV/dFjJWPb2xRiWNvILiqkK/PmL7Zqm0Zi2kZdvjoVOqlujJRud7WmxXXa2FySKSjzZjeqbbGFiIvPV9omxVB63W+ZxFzBh30oguVGU4aKM6OjUHPE65eZhoVKyaAMxTcXJJNpgY9Y7M9L0HQegWauk1mMAg3w/ygucpbmzDZRIMsiOVWI9/tSJ+eW2pzrbN9V55jskyRxxWU3UJkXJmbrbWwbE2qihZPr9aarYvGc0zGQsy4NMMhqKpuO5pNieW+r9Du5+02IAvvCb44yFROdg+9AkmgYOswHfDA0qKQfAnuw7PwMwHo6l54gU2wuHmmXr6dM8GIlC52vZKW4HSEYqdWp+qvzS5Ulv7GYjH7lhES9++mZe+bsdrKm6+O+kz70BgHjvcQhnt8jLNC6sqHGWClFUog82L2vKbfzsbU78diMBilhbZp0xhstZv4mYplKsDUPfCUjkIHf4EqTE9mtLE8I2/uj/CKc5qwcWvVEcFA2KLPVs57UvIPweJ5sqxL3GrxI3gN0n3FB+9E4S+/8fACe0Olb4DAs6rk3Xq9LnPvc5FEWZ9m/FihXp50OhEPfccw8+nw+n08mdd95Jb+/06uy2tjZ27dqF3W6ntLSUv/7rvyYWm76aePbZZ9m0aRMWi4UlS5bwwAMP5OLbu2q5fqkfs1FlKKxkvZI6daNeLDtSFwTpzvYM2N31j4UZDcVQFWjwS3Gs0El1a6S6NzLJwHgys71IihuFRCQS4etf/zpHjhwhUb4egLXKGU52zc8ydSZCQyI7rh8PXmkRXjCcO0cikQjGynUAlEfbM75JlCrICJk8GEwLJ29qoXP+HKkpL+WsJuIGAp2ns96ZkaZXiO2ntGqWelQwyvejvMBTB956vUchkVwxb1pWglFVaOobp3VwbvfOgWAkfd8tO9v1JWUlf7Rr5ILnUq5fi/zTN4PdNhPlLnHP2pOjVJRUV7vfaabIKu+Fcs3dNy6m3menbyzM13c3AtCWzmt3oMxgyZ1qSugKZr9RBqa62j12Ey45RwqCSCRC755f8p/KB4hgJNH5evZy24fOANChlVDlle5C+YLZqFJyib2ySCTC02NL+GfuojjYApEs6gCJBM6QWHdbPBXZO4/k8pidYCtmqWWIx97l5IFbFa5fUjJj/ENteSn/rNzNP3MXkY4DUzneOqBpWlps31KuQnAIDvxYPLnszWBLOjhEJsHmEznqkllz+yqRK/+lvZO8vuTPRRZ7eBR1Qvzddhhr8ThmLspYKOheArR69Wq6u7vT/1588cX0c/feey+/+c1v+PnPf85zzz1HV1cX73jHO9LPx+Nxdu3aRSQSYc+ePfzwhz/kgQce4LOf/Wz6mDNnzrBr1y5uuukmDh48yMc//nE+8pGP8Pjjj+f0+7yasJuNbF8kLk5Pn+zP6rlkZ/vCItXZ3tg7RiIxv8VeqtCjzufAapJvjoVOyka+bShINJ7ZKkjZ2V64TE5OEo/H0VJiu9pCS1/mbanCwyJLeVRxYzbJeVJIpOYIgHepsECs1npIBNoyeh4lOABA1OzJ6OtKss+5c8RkNNBhFDaII71ncye2D5wGoDFRzXKfQYrt+YKiyLxQSUHitpm4Jmn3+uQcu9sPtAcAUbQsI9v0ZVOtKHb44Z5WgpGpxpKhiQiBoOhgnqm4PNW13B3MzXWsNZ0NLjuW9cBqMvCFt60B4IE9Z3jqRC9nBy5t67+60o1BVRiNKnSPZP+eJyW2yzlSWETCIWKKKI6Y7D6Ztdx2LdAKQIfmp0rOkYIiGtcIKnbqEp2Md53K3onGezAQI6apeEqqsnceyeVRFPAvBe8iSowhbiwPo9hcMx66pNRJRLEQVOwEu07pKrZ3DE/SOxrGqMKGShvs/4EYT1EFrH/31IEyr31OvH9bA7fVKkTi8M59K/nZlp/Bzi+xv/w9fC36Tjq8W0XUwExxAwsE3cV2o9FIeXl5+p/fLyogRkZG+P73v8+//Mu/cPPNN7N582Z+8IMfsGfPHl5++WUAnnjiCY4fP86PfvQjNmzYwO233859993Ht7/9bSIRIcJ+5zvfoaGhga997WusXLmSv/iLv+Cd73wnX//613X7nq8Gbknmtj91Krti+1BycSk3ABYG9T47FqNKMBLn7By7MFKkrOiXSAv5BUG5y4rdbCCW0GgbyuyNmcxsL3y0yk0ArFTaaO0ZyPjrx5N53EGjS1RmSgqSsuolDGtOTEqcwdYTEM+Qr2oigSk0CIBmld1/hc6wvR6ARKADojkQ2+NRtGHRyXNaq2aF37ygF58SiSQ33LJS5LY/Ncfc9gOtwiloY60nU0OSzJE/3FpLlcdGZ2CSr+8+nX48Fa9V6bZiM19YXJ4qZO/JkdieWr/X+6SrnF68cVkJb1lXQUKDD//wNb7znMhIrb2I2G4zG1iZdBc80H6hc0KmaZN57QWPZbwNAtnJbY8OngVEZ3tF8cyinSS/URWN1uOvQiySnRME2gHo1nxUl8ycIS/JIVYXVKyFmmuhfJ2wYZ8Bs3FKfowPncmeO8YseK1VdLWv8SnYJvvg5KPiibXvnupqj4XBaJZ57XPA6nBx/5vdvGeZgYQGn34uxJbdi/jz/t/nX+PvYKnfIubJAi5o1z2ErrGxkcrKSqxWK9u3b+fLX/4ytbW17N+/n2g0yo4dO9LHrlixgtraWvbu3cu2bdvYu3cva9eupaysLH3Mzp07ufvuuzl27BgbN25k7969014jdczHP/7xi44pHA4TDofTn4+Oii65aDRKNBrN0He+sLlhkdhsPtQ+QvfweNY6RgfHxSao26rK302Bkfp9nf97W1VRxIH2EV5vHaLGM/d5c6pH/N0u9tvl3ChQzp8j9T47x7vHaOwZoXYec+N8+sfE9d5jNci5UkCc+7uKuhsIGZxY4+OE+5qJTk6Im+MMoY2Ljeqw2Us0DmhynhQC0+ZINIrZbKZFrWezdpRA12k8obHMZHANncWkRYloBjRnmbyOFBDnz5FoNErYswTGwRLsJhqaAFuWf5+hAMYR4Z5xWqumvtgmHFwy7OIimTsXu2fNF/J1XBJ92bGyjC8+eoJXzwwxGopesWVzqrM91VUt0Q+HxcgX376GP35gH99/8Qxv21DFmio3LSkL+ZKZ72WWJ8X27hzZyKciCy4m7Epyw1fftZ7SIis/2HOGvuQ6t6744gUQG2vcHO0a5UB7gLdvqsnq2GRne2EzoLmpZBDaX4GqjWDNbK56bLgdMzBm8mGxLFyL4YXOZOcxYSVvzLwYrgVaUYBO/NSUSrE9b7C6Z309cIW7YKQDihuyPKiZ2XdWFJNeU5KAfd8DLQElK2HF7VMHRSfBJPPa54SqYiwq5Z+2DVPmKeLb+8YZCE7ta6wrTiz4IgZdxfatW7fywAMPsHz5crq7u/n85z/PDTfcwNGjR+np6cFsNuPxeKZ9TVlZGT09PQD09PRME9pTz6eeu9Qxo6OjTE5OYrNd+Ab+5S9/mc9//vMXPP7EE09gt8ubwtlS4zDQPqHwrV88zbbS7OQ/dQ0YAIUTB/YRbMrKKSRZZvfu3dM+d0VVQOXhFw9h6jww59fdd1LMjYmuJh57rHF+g5ToSmqOWCJibvz2xf2EWzJ3TekeFnPl+IFXCGTR8UqSWVK2zwBPP/MMG011NMSPoQRaeeyJJzN6rq3Jzvb+hJvHfve7jL62JHtMmyNPP43BYCCm1rA5fpS+ni6OP/l8Rs5TOnKI7UCLVslIMMpjjz2WkdeVZJ+Z5khrSCz+iqM9PLbnCCjHsjoGZ6ibWxIxgpqFkMXPM8f74LicQ/nI+fes+UIwOHvHn3/6p3/ib//2b/nYxz7GN77xDQBCoRCf/OQnefDBBwmHw+zcuZP7779/2hq6ra2Nu+++m2eeeQan08kf/dEf8eUvfxmjcWo74dlnn+UTn/gEx44do6amhs985jN86EMfytS3KblC6v0OFpc4aO6f4JmTfbxtw+ztVhMJjYNtAUB2tucLN60o5S3rKnjkcDd/88vD/PjD22jpT4ntMwupKyqE2N4xrsw7om02pGzkZWe7vlhNBj771lW8eU05n/rFIdqGgmyq81z0+A01Hv77lXYOJP/ms0n7sKj8qPHKfdVC5NXEct5u2AOdr8NkIONiu3GsE4C4rRQMuvcGSuaIZ+y0ENvtmRfDg31ncCCiBjYWL2zBbqFiJAadB6Bma0YbZGbLvjOis/2Nrh44uA8UFTZ+YMpZTtNEVEbJSpnXPlccPpRhE5/YbOLPNpXTOBTj1GCUWEJjZ/WIKGRYwOj67nX77VNVI+vWrWPr1q3U1dXx0EMPzSiC54q//du/5ROf+ET689HRUWpqarjttttwuaSVzWyIRqP8tv1J2icMDJgruOOODVk5z9+89iSQYNetN8rq2AIjGo2ye/dubr31VkymqU6L2KFunvvFEUaMXu64Y+ucXlvTNP7h4LNAlDtvfQOrK+XfbSFy/hxptDRx4NkWLP5a7rhjdUbOEYrGCe59CoB33L6DYhlJUTBEIhGOHDkCwM0334wWfQpOHKM21sqbb/gYalF5xs4VO3wPAL5iH3fccUfGXleSXc6fIw6Hg1/3HYXO31ITbeHaN6wDd/W8z6O+cBxaoEmrYuOSCu7YuX7erynJDTPNkaLjbYR/+RlsSpg7qidg3buzanOm7P8BnIAmrZKNpQp3vGEduGUGYT5xsXvWfCHlwnY59u3bx3/8x3+wbt26aY/fe++9PProo/z85z/H7XbzF3/xF7zjHe/gpZdeAkRRyq5duygvL2fPnj10d3fzwQ9+EJPJxJe+9CUAzpw5w65du7jrrrv48Y9/zFNPPcVHPvIRKioq2LlzZ2a/Ycms2bW2gm893cTP9rVfkdje1D/OWDiG3WxId0dL9Oezb13F86f7Odo5yvovPIHZIGxZF82Q1w6wssKF3WwgGIlzum+ctTXZ7QJMie0XyweX5JZrG4rZ/Yk3EQhGKSm6uCvcpmRBzfHuMULROFZT9sSFNtnZXtC8lljB2w17iPQcxzzWDZ7azN0jRyYwR0WUgdFddpmDJflMXbyV2GArRk9txl97srcZBxAw+LBYpftBoRLtOoQpmlk3ytkwPBGhsU9E8GzSkgX17hqo3jJ1UHgELEVyPT4fbF4oqoBAGw53FRvKzWwoNwt7/pAZTAs7Mi+vSsU8Hg/Lli2jqamJW2+9lUgkQiAQmNbd3tvbS3m52EAvLy/n1VdfnfYavb296edS/6ceO/cYl8t1UUHfYrFgsVx4M2oymfJygyVfWePV+F0HvNg0SBw14zftI5NRJqPCiqLc48BkyqvpLJkl5/9dba73AXCiewxNMUzLdpktA+NhApNRFAWWV3gwZXHBKMk+qTmytFwUTZwdnMzYtbixX1TXu20mSt12lAWcG7PQ0M7JiTOZTFgbNsGJH7FMaWVwNEhlcYberyMTmOJj4jyuEnkfUECcP0dMJhNq5QboBH+kUyzwMvH7HBTuKY2JKkpddjlHCoiZ5sji6nJatEpWKm0YhppQlUR2F4T9xwE4rdWw0qdgshVlZl5KMk6+rgVnM6bx8XHe97738Z//+Z988YtfTD8+MjLC97//fX7yk59w8803A/CDH/yAlStX8vLLL7Nt2zaeeOIJjh8/zpNPPklZWRkbNmzgvvvu49Of/jSf+9znMJvNfOc736GhoYGvfe1rAKxcuZIXX3yRr3/96xcV22V02/y5XMTBOzZW8K/PNLGneZDGnsCsO45faR4AYG2VCy0RJ5qIX+YrJLnAazXw1Xeu5b5HT9I+PEkkGTeyqsJ50TmwsdrFSy3D7G0eYEV59gonQtE4PaMi4q/SZZZ/w3mE5zKRi6UOAy6TxmgUDrQOsqUuO9ERmqalbeQrXCY5RwqEc39Prd7rSIz9APNYG9HeRiheCpYMNbb0ncYEjGh23J5iOT8KiHN/V12an3qlh6bDL1BXsRFMmY2UjQ+3AhC2lBBNKCDnSUFw/t/zaHcTruAYGHPrTvBKSz8Ai9wKtkFRcB8vWU1CMYoINy0BE6NQthoUk5xf88FRDsOdEA5OuQaEQ6CYgZl/tgslui2v1Mnx8XGam5v5wAc+wObNmzGZTDz11FPceeedAJw6dYq2tja2b98OwPbt2/nHf/xH+vr6KC0tBYS9n8vlYtWqVeljzrfz3L17d/o1JNmj2gFlLgu9o2H2Ng9y04rSjL5+c7+oRipzWXBY8moqS+ZBnc+Ox24iEIxysmeUddWeK36N071CGKvx2rGZpdC+UFjkFzdiLQPjGXvNpuR1ZEmpUwrtBYaiKFRUVBAIBFAUBUP1ZgBWKm0c7h6gsn55Zk40IITUAc2Fwyur7AuJ8+cIgLt2LeFXjdiVSeg9ChVr5n+iwdMANGpVrHRmdkNBkl1mmiNVHhuPadWspI2xvjO4Y5PZE9vjsfQ15nSiik0+04Kv9Jbowz333MOuXbvYsWPHNLF9//79RKNRduzYkX5sxYoV1NbWsnfvXrZt28bevXtZu3btNFv5nTt3cvfdd3Ps2DE2btzI3r17p71G6piPf/zjFx2TjG7LHJeKOFjhVjkRUPmnh17g9+oSFz3uXH5+SkQ3+WIDMholD/mrFTARhc6gggJ0H9lD95GZj/VEFcDAI6+eomzkRNbG1B0EMGIzaOx99slsGsJIskB9kcrhIYWfPvEyfVXZiRwYiUA4ZkRB49CeZzl65T0VEh1IJBLpRjXFYOZVbQXblBOcOvAizV2Zc5BMxXJ1aiUEAmPyvaeAOHeOnJpcSr3WQ0vTKY6pT2X8XFuTYvuA4uex3/42468vyQ6pOTIRCqFooI608diLB4C5R8fOhYdbxf1tpTlMouswKvC8up3R1zumH9hyDMhulNzVw8CFDx1//JJfUejRbboqlH/1V3/FW9/6Vurq6ujq6uIf/uEfMBgMvPe978XtdvPhD3+YT3ziExQXF+NyufjLv/xLtm/fzrZt2wC47bbbWLVqFR/4wAf4yle+Qk9PD5/5zGe455570p3pd911F//2b//Gpz71Kf7kT/6Ep59+moceeohHH31Uz2/9qkBR4OblJfx0XwdPnujNuNje1DclkkkWDoqisL7aw3On+znUHpiT2J6aG0vl3FhQNCTzCAfGI4xMRnHb5t9dlr6OlMi5UmiYTCb++I//mMcee0x09ZWtIYqJImWSvvZG2Lod1Azs4vSJrtMWrQKfS86TQuKCOQLUlXlp1KpZo5xF6z2OEguDcR4CuabBYDMAjVo1PpfMKS0kZpojRoNKn7UOonsID3VAJCis0LJBdAJt+CwKcFqr5r0lFjBKS0RJZnnwwQd5/fXX2bdv3wXP9fT0YDabpznJAZSVldHT05M+5lyhPfV86rlLHTM6Osrk5OSMjnIyum3+zCbiwNzQx90/OciBEQvfuu1Nl3UNi8YT/H+vPwvE+PAd17GuOrOZvJLc4mvu59EHDtAesnD77Tdmrbj4yRN9cOggi8vc7Nq1LSvnkGSHaDTKU51PcngIJh3Zi4Dc3zoM+/dR5bHx1re8MSvnkGSH1HvN4sr1/OZX29mmnmD12PMsv/WPoGpLRqzk1RdPQYvI4r5181Ju2bAkAyOX5IrUHFGGw3D2BZaGDlK9+ZNQtjJzJ9E0OPgRACorK2W8X4ERjUb5zH89gtofx5sY4o7aEKx6W05z0X/4n68CAT5cdhpjUxjN5OD6zavEWj8Rh9EeqJCRbhkjOASd+8FaBIoBJgagZCUU1894+EKJbtNVbO/o6OC9730vg4ODlJSUcP311/Pyyy9TUlICwNe//nVUVeXOO+8kHA6zc+dO7r///vTXGwwGHnnkEe6++262b9+Ow+Hgj/7oj/jCF76QPqahoYFHH32Ue++9l29+85tUV1fzve99T2bH5YibVwix/akTfXzx7VpGF3epznYpki081tcIsf1g+wgfmIMJRWNvcm6UybmxkHBajJQWWegbC3NmYIINNZ55v2ZTn3BBkEU7CwCDkX5rHZWhJmL9zRCbBHMGhM/+UwA0JyqpKZIiWKFT7bXzm0Qta9SzTPY2Yo8G5ye2j3ZBNEhMUzmrlVMsxfYFQdC9BAbAON4NkYnsnWhyGMa6AWhVqqkt8WSmSEgiSdLe3s7HPvYxdu/ejdWaX64JMrotc1zqZ3br6grKXCfoHQ3zTOMgb1lXecnXOtAxxHg4RrHDzMY6H6oqW5QLmY11PkyqxnAwytnhMMvKsmMl3xEQkRB1fof8+y1AGopEN/uB9hGMRmNWijK6RiMA1PrkHClUblpRyv/VtvJ57QGMY12Y+o9DxWqwZqAoa0g4hbVppbyhxC3nSIFStuX3iZ/5Gg3xs4x1HKSobFnmXLvG+0CLkNAUvGXVco4UIJUuKyf7almttGLqeR2W3wqW4pycOxSNc6RzBIBNiIYapXgxJqeIsmVyAFwl4K0Cg5xbGcFVChMVMNYjxHZrEdhdl43My9e14GzHpOtuzoMPPkhXVxfhcJiOjg4efPBBFi9enH7earXy7W9/m6GhISYmJvjlL3+ZzmJPUVdXx2OPPUYwGKS/v5+vfvWrGI3TawhuvPFGDhw4QDgcprm5mQ996EO5+PYkwPaGYuxmAz2jIU72jGX0tZuTHamLpUi24NhQI27WD3UE5vT1jUkBdVlp9nLpJPqwKNnd3tKfGSt56ZCxsAh6hHW8dewMREOZedGkxXOLVkGxM7+ECsmVYzUZ6LQsAiAy1DZ/ITXpfNCqlRHFSLlbFmQsBJQS0YXhivSmxfCs0HsCRYszptkocnkx2D3ZO5fkqmT//v309fWxadMmjEYjRqOR5557jm9961sYjUbKysqIRCIEAoFpX9fb25ted5eXl9Pb23vB86nnLnWMy+WasatdkjuMBpX3bKkB4Kevtl32+OdO9wFww1K/FNoXAGajSr1TCKmvtAxm7TytQ+J+qt4nIyAKkRonmAwKA+NhOoYns3KO9iHxujVeOUcKlSKriRWLG3gxsVY80PSEKBydL5pGol247+xPLKOyWO7jFSorly3jgGEdAC37n4RQIHMvHhD3MD14qS4rydzrSnJGjVPj9cRSAIKdxyE0krNzH2oPEI1rlNrANZTM3qkQc5VYCBIJ8C2WQnsmURQoXQV114l/tVvBmVnX63xEtk5IsorFZGB90gY8VUGUKaT988IlZR3f3D/OaCh6RV+raVq6sCNblfsS/ViU/Htv6Z9/p2EsnuDMgHgdKbYXHtFolG9/+9scO3aMaFRcJ9SK1QD4Qu0QnV2ezuXQkhbhLVoFfpcUDAqJmeYIwKh7BQCm8U6YDMzvJL1CbG/Uqqlxm3BYdDWNklwhF5sjnuoVBDULRpKZ6rFw5k+uadAhNhZPaTUsL1alhbwk49xyyy0cOXKEgwcPpv9t2bKF973vfemPTSYTTz01lat56tQp2tra2L5d2Ett376dI0eO0NfXlz5m9+7duFwuVq1alT7m3NdIHZN6DYm+vPuaGhQFXmoapLH30gXwz57qB+BNy+RG9kJhiUuI7S+fGcraOVoHxX13XbF0+ClETCqsrBB7J/tbMyCezkDbkJgjtbIgo6A4/15555pyHo4n39s79sNotxCp5sNIO2rgLADHDCtxO+V1pJA4d47EYjEmFt8OgG/gFRjru8xXz57EQBMAnZqfmlJfxl5Xkn1Sc6T55DE6HWLPLjrQDGO9Yk2cA15Lvrfd7AugDIk9PuquE+cf7wdPDTjkvW/GMVqE+4nFCWZ7RmJH8h0ptkuyzsoKkbl3ont22QazIRyLp2/WpUi28PA7LVR7bWgaHO24siKNs4NBAsEoZqPK8nIpti80FvmTne0D8+9sbxsKEo1rWE0qVR4pcBQamqYxMjJCNBpFS96gu+s3A9CgtRGdzID7QSKBNnwGgC61khKPzJAtJGaaIwCJUrHAc8SGYbBF5HPNlf4TADRqVSwvldeRQuNic6S+1MUpTXSC0n8yY8U704iFoOcQAK8llrPCZwCTnEOSzFJUVMSaNWum/XM4HPh8PtasWYPb7ebDH/4wn/jEJ3jmmWfYv38/f/zHf8z27dvZtk3kLt92222sWrWKD3zgAxw6dIjHH3+cz3zmM9xzzz1pG/i77rqLlpYWPvWpT3Hy5Enuv/9+HnroIe699149v31JkmqvndtWlQHwhUeOT7venUvfWIhjXWLNfsNSueG4UEiJ7a+0DF30dz9fzg6KAuY6KaQWLBuTEW2vt2VHbG9P7t/VFMs5Ukicf69866oydie2ENZMEBwQebzhee71tjwHwMlEDfYij4xUKjDOnyNrbn4vIc1EtdbD2aMvQjQzbhljXSLer0srodwrdYBC4tw5Eq7aTlxTcIc6RVF7OLMuyBdj31lRcHi7/ah4wFkuOtlDAbC4oLjhqhCCJdlHvoNJss6KZIVsJsX2swNBEhoUWYyUFM0ja1WSt6xPLvYOXqGV/MF2sThcXenCbJSXuIXGlI38/DvbU+4Yi0uc0iZzgVC89FoAKpQhOs+env8LjnaixsNENAMWdxmK0Tz/15ToTll5FWcTQnSg+yBE5lGYMSDmWWOiSkaXLCDqfQ6OJeoASAw2ZWyTaBqRiXQMwb7EMlb4LVJsl+jC17/+dd7ylrdw55138sY3vpHy8nJ++ctfpp83GAw88sgjGAwGtm/fzvvf/34++MEP8oUvfCF9TENDA48++ii7d+9m/fr1fO1rX+N73/seO3fu1ONbkszA392xErNR5YXGAX57tGfGY144PQDAmiqXXGMvIOqKhJ38wHiYloH5r6HOJxJL0Jm0Hq/3y47UQmVLnReAFxoHslKUkWqWqfHKe51CprTIyrLaSp5ObBAPND01f6vwsy8C8HJiJZVuGdtW6PjK6zhq2wJAz5Fn5u8klyTe/hoAvaZKDJnKgZfknMUNi3hNE/GPWvPTObGSjye0tGvL+tgx8WDJclANEAkK0d0s718kmUEqUZKssyrZ2X6yZyxjN+1N5+S1K7LyaEGyIWklf6g9cEVfd7BNHL8hKdZLFhaL/KKC9ezgBInE/K4nTf0yr32hodiL6VZEBtBwx3GIReb3gv2ierpVK08XekgKnzqfnQPaEvFJ37G557ZrmqjGBpq0KpaVS+eDhUKlx8YppR6A8GAHhDPglHE+Ix0w2gWIfMrlZQ6ZESfJCc8++yzf+MY30p9brVa+/e1vMzQ0xMTEBL/85S/TWewp6urqeOyxxwgGg/T39/PVr34Vo3F6bMaNN97IgQMHCIfDNDc386EPfSgH341kttT5HNz9psUA3PfIcSbCsQuOee60sJC/cdnCz1O8mjCpsL7aDYju9kzTGZgkoYHVpFIqizQKluuX+DAbVc4MTNDYl9n7nlA0Tu9YCIBa2dle8OxcU85vUlbyXa/DyDyt5NtfAeDlxCqqZDFG4aMomNa8DYBFo/uIDHfN/zXjMYr6hNje7lgr10wFzJtWlPK0Jhwpx9sOwnjmogYuxqmeMcZCMYqMCdxDwlmOqk2i6cLqhqKKrI9BcvUgxXZJ1llS6sSgKgSCUXpGQxl5zXReuxTJFizr0zZmgSsq0jiYFOc31nqzMCqJ3lR7bZgNKqFoIl0dP1fS15ESeR1ZSPTYxEZyuP8MROfZvZO0CG/RKlhSJruWFwp1PjuvJ5aKTwabIDRH552JfgiPktAUmrVKllUWZ26QEl0xqApBj6i4V8e7ITiY+ZO0vgTA6UQVRqsTf7HMHpRIJNnl7hsXU1Nso3skxL8+3TTtuXhC4/nGZF77cmkhv9C4tl6sjV9qGsj4a6ct5IsdshGigHFajNywxA/A7y7ifjFXOgOTaBo4zAaKHdIprNDZubqcpxMbGdesoqu97aW5W8mPdkMytu2VxAq5j7dAWP2mdzOCg1JlmIOvPDX34vYU7a9gigcJaA7ixcuk3XcBU2w3oy3eAYBj/AwEWuc/Py7Da62i0PBd3kaUUAAMFqi+VnS1O/xgMF76BSSSK0CK7ZKsYzUZWJzsCMyUlXyz7Ehd8KyrdmM3G+gfC3N4lrntoWic48k5tlF2ti9IjAaVlZWie/Rw5/zshppl0c6CZMItBDLLaAZu2pMW4S1aBUulRfiCoc7nSIvtWqAdxnvn1o3RJ4ox2rRSYoqZRXKOLCgcNeuIawqW+DgMt0IsnLkXj8egYx8g8to3lwJm2eklkUiyi9Vk4B/eshqA7z7fzL88cYpYPEEoGucLvzlGIBilyGqU66gFyI4Vwq3g8WM99GaoASJF26AogJZ57YXPztXC1eTxY5kV29vOyWuXBRmFT53PQX2Zj90J0Z1KyzNzt5JvfhoQee3DuHjD8srMDFKiK0ZnMWc81wEQbnoeJofn94JNuwHYk1jN2hpZoFzo/N4N13A8UYeKxsiJZ+be/DBL9p0V8+8O8+viAf9ScJYJp0KrJ6vnllx9SLFdkhNWJq3kT3SPZeT1zs1alixMrCYDNy0XmwIXyxU8n2Ndo0TjGj6HmWppP7VgSdkgHr7CiIFz0TSN5mTuuxTbFxZq+VoAfOF2mJxfQYaWtAhv1ipZKi3CFwxOi5FhxxKCmgUlHhaieXQOThk9RwBo1Kqo95qxmgwZHqlET5bXVtKiJTf8+o7NbY5cjOgEWs9RAPYllnNHvSLz2iUSSU7YsaqMD2yrI6HBt55u4p3f2cuub73AD/e2AvDnNy7BaJDbRAuNNVUuttR5iSU0/t/esxl97cY+sccj89oLnx2rylAVsa/SPk8XuXPpOEdslywMdq6p4OG4EFPpOgiBjrkVL595HoC9iVUs9hop88ri5QWBouC/9l0ArA/vZ7i7RQibcyTeIubJi4m1bF8i3XcKnTUNlRyxXwNAf8sBmMi8606KYCTGsyd7AVgdSortVVsgEQGjBSzymiPJLHIVJckJKbH9eAY62xMJjZYB2ZF6NfDmNaKy+ndHu2dlJZ+ykN9Q45EV0wuYddUeAA51BOb8Gj2jIcbDMQyqQp1PbgwVIoqi4Pf7sVqt0/7eixaJCvtKrRvGuiARn/M54gMtALRRQY3fM6/xSnLPxeYIwLJKL4e1ReKT7kNzc0HoPQaIvPblpVIoLUQuNUfWVLk5rtUBoA02QXQycyce70MbEteXw8oybqm3SLFdIpHkjPvevoZvvXcjRVYjB9sDNPdPUFJk+f/bu/P4qKrzj+OfmclM9g1C9gAhEAj7jiAIooJAEeuGSl2wFheoorUudUGtW20tWrdaS6H9uYCKVisooALKJvu+hy1AIGxJSEKSycz9/XFhNAKZDJJMMvm+X6+8au42Z6YPk3vuc85zmDy6B3cNyPB386SG/LpvOgDvfr+HE+Xnfn/8Uyt35wPQ6WQfTeqvRuEOeqabyyKdz9ntpybLNFOyvd45273ylV1SWODuQL4Rbq57vOtb30vJu92wZxEAS9xZXJiuwe310dliJLXrEA5ZGhFlKWHtkq/OfamBsiIsueY621sdbWmWqJnt9c1pMWK1kdhpMACpJZspO7Lr/Pa1f2TG2lyOl7noG76P0OJ9YLFCi37m8x9HBDj0PFjOLyXbpVb8MLP95yfb9+WfoNTpxmGzkqbZywHt4jbxOIKs7DpSwpaD3qsi/DjZLoHr1Mz29fsKqXCdw+hpftThbxyGI0h/Cusju93OmDFjaNOmDXa73bM9tXkWR4xIgnBTmnOOSVSAsiKCSswRsO7IZGz2kPPRbKlFZ4sRgLbJ0T+s2354K5SdQ+Wdw1sA2OZOIVMl5OulqmKkdWIkm04m208czoGy87iW3M7vsBouDhoxtExuQkRYKATpO0ZEas8VnZL54t5+DG6XwHXdU5k9/iJPVTEJTIPaJZIaG0p+iZOPV+09L9csKqtg8wHzGU/XZjHn5ZriX5fXQCn5lXvyAeio5zT1ztnuldPjwunYNI6Zrp7mhuy5UJTn28WPbIf8PQB8786iT8u489VsqUVn7U+FRJEbPwAAR85CKDl6bi+w81usRgV73E1IS0nF4tCku/rmTDHSp09/9tGEEIuTlcsW/fylBs5i2rIcAO6OOzmrPTYdYpqBsxQi4kET9eQ8U4ZBakVWkvkQetfh4p89inr7yfXam8eFqcRdgIsIDuKiVmaJoC/Wee/src4x/zh3bhpTk80SP2vRJIJwh40TTpfn+8BX2w6a57VSdYyAExsZwgZLJgCHd28492T7ke3mNYwoEhvHgFV/bwJJ2+SoH5Lt+buh5LBvpe0qyj1rtm8xmpKpZQYCTojdRmFUawCM4/uh9BwfEP1UeQnGyfXal7lbMyzdAmGN1dEXkVqXGhvGWzd158VrOhEb7vB3c6SG2awWbu3THIB/LdiJ233uJX1PWZOTj9uAlJhQkqI1ESIQDDqZbF+++xiHjpf97OsVl1V4Klx2bxb7s68ndcc13dP4n9ssJW8cWGv2n0/kV/8CW78AYJO7KYWWSHq3SqqBVoo/pfQZCUDXijXk7Nh0blUHt38FwAJ3e3q3aKznMgHCHtGIvMYXAODcu8r3wTrVsO3gcZbvPobNAj0qVpkbkzqZs9sBgvUMR84/fUNJrWgSEUzjcAdug2rNUK5Kdp5KyDckQzyl5KtOth8pKiPn6AksFuikEdMBzWa10MGzbvu5rcl9Kkmv75HAdDCyPQCuw9nnNmMZPInUbCOZVgnR56tpUkdkJUWxyt3S/KX4EBTs821gxt5l4CyhyAhhk9GUzGQ9PAxEwSkdAQgvPwLHD0LFz3/oTFkhRXvNJQhWG5lckmpAiL5jRESk5o3skUZEcBDZh4qZv+3Qz77eit3mYPeuSqIGjOSYUDqlRmMYMHvjz5/dvmZvPi63QXJ0CMkxGpARSIZ1TGK1tR0HjRgsFaWQ/Q0c2129AczOUtj4PwC+cXemfZMgoiP1bCbQNM66iL22FIItTrYs+wpKfX9+594xH4CF7g70bqUKPAHDZiet+xAAOlWsISc399wnypzF1JOz2n+ZdBT7EbMqIS36g7ME7GFar11qhJLtUissFst5KyV/qvxzyya6EWsILs1KIMhqYcvB4+yoYhbzqRLyGU0iiAqxn/U4CQyn1gRcfY7rtm/XoJ16z+l08o9//IPNmzfjdDor7XMndwUgtmSnOULWlxnLp+RtBGCHO4lWCboJr4+qipHmjcMpsTdit/tkh33fCijNr/7Fd34LwHJ3a4KsVpo30ajo+qiqGAFIb96CXMNcu5QD66D05y+HRNEh7Me2AWCJa0l4SLDWihMRkVoRGWLnuu5pALy7ZM/Pvt7KPWayvZsqywWUwdWc8FAdK3ZpQEZ9VtW9clSIncvaJfGJq6+5YctMKMiB4sPeL3xkG+xfAcDHrn70SY85zy2X2lJlf8oRRn7aZQDE5i3GKNjn27OZ/D1Yj27HbVjYGdKWtHit114fnS1G4toNpMASSbSlhBUrl/lWGcOLsgoXH680l8y5vfFawIDIZGjSGspLzMHuWipSaoCS7VJrTpWS/7nJ9uyTCdcMJckahOgwO70zzBuqL6tYN0zrtTcsHU8m29eeQ7Ld7TbYfPJ7qJXWWa63DMPg8OHDlJaWYvykwxbfuhflho0oo9AsZ+cs8f36h8yRrzuMJFrGK5FaH1UVIzarhTZJkaw0TpaSP7TFnLlc3c5/zhIAlrpbk9HYoWVt6qmqYgSgfVoMG93muu3Goa0/fy05lxNjz2JC3CUcN0LpmJEG9lDQ2oMiIlJLbuhpJtvnbsn7WWXC3W6DlSdntndr1ui8tE3qhqHtzXLei7KPcLS4/Gdda/nJGFEJ+frJ273y1d3S+EfFLygyQqBwH+yYD8d2gKuiqovC6vfBcLPBSCfbSOFCrddeb3mLkfSLfgVAF/cmNm5c69va7es/BmCD0Yy2qY00QLmeOmuMhDXiSFxP87/3r8YozD1vrzl7w0GOlThJDIPMkpMl5BM7QlCIuSRguL5zpGboyaDUmvMxs90wDLYcMEsCa0ZqwzHkZGdv+oq9uM6wtpzbbTBn40FAyfaGouPJMvKbc49T6vRt3acdh4spLK0gxG6ldaKS7YGoXXoaG4x0AEpzVp5TOaqKQ+bM050k0TxeJZ4DUVbSj9dt3wUnjkL52SuoeDjLYP9qAJa529A6PqzG2ij+lZUYxUbDTLaXHtsLRQfPba3BU0oLObblOwC+c3dkYLNgc712rT0oIiK1pFVCJJ3SYnC5DT5dve+cr5N9qIjC0gpC7TbaJKlPFUiax4XTLjkKl9tgVhUTHrxxu40fqh9oQEZA6tsyDntkHG9V/MLcsGE6FOyHQ5vBeeLMJ5Uc9azXPq3iIhxW6N4ioZZaLLUtvFkXdjoysVoMdi3/Eo7tArfb+4kuJ6x+D4AZrgvo3SIOLJaabazUrqBgEjsNBuAC90rW7jkEZdV4HlMN/11l3t/8ulke1n3LzY3p/c24sgaphLzUGD3ZkVpzKtm+Off4GUe7VceuIyUUllbgCLKSqbK+DcYvOiURHWon+1Axn605/YHAJ6v2sfnAcSKDgxjaIckPLZTalhobSuNwBxVuw+cBPKtOdvg7psRg12zUgBQfG8FmWyYABfu3+b5ue8lRgvJ3AVAY1gxHiJKpgahtpWR7jrmGXHVKlx1YC6X5OAlirdGCTA3+C1ihDhv5EeZ3iTM/1+z8n8Nagx6lBVj3mSUzd4R3JjxI67WLiEjtu7ZbKgAfLt97zs9mTq3X3iktWn2qAHTqucrMdec+03BbXhHHSysIc9g8lS4lsNisFkb2bMYk11COEg0lR8zZ7Ud3mMt0FR2qfEJpIWz5Ao7uwIWV/7l60zXJTmi4+lMByxaEvfvNAFxcMovNm9dD8SEvJ2Eu23Z4C+WGjQ9d/bmgZZMabqj4Q1jbyzlhCSHRcoyla9ab3yE/U0l5BQu2m8tZXGX7DjAgNh1Su0JZIYTGqA8uNUZ3xFJrMppE4AiycryswrNesq9OlYxumxSlDl0DEhViZ8xFLQCYOGcbTtcPoyBLnS7+Mtss9zx2YEsahTv80kapXRaLxTO7fc3JJQSqa9XJ47tobcGAVtCoEwBBBbugpBrrxv3Yru+w4GavEUds4wSwBZ3/BorftU2OYpPRjDxiwVUGOUvh+AHvpeR3mTOTN1taUIaDzCSVxQxklqT2AISXHjAT7WXnWKHJ7YZ9K4kp24fTsGFP7QI2OwTr4aKIiNSu4R2TcQRZ2XLwOOv3ndvftRWeEvK6DwpEwzr8/FLyy3eb5aI7p8VoyaUAdtuFzbEFh/NX51XmhvUfQmi0WV1u3wo4sN4c0FxeDAc3wNYvAfjO6MwxohjZJdF/jZdakdrvJrLtmYRZyjjy/ftmVbmqlhpwu2D5JABmuXsQHhlNitZrD0wRTSiM6wpA0MHVOI/sNsu8/wzfbTtMWYWbFuHlNNr7tbmxxcUQFGyu1x6VDFbbz225yBnpbkdqjSPISq90s3TUd9t8THyctHavOZuoU6pGIDU0oy9sTlyEgz1HS/hw+V7P9kkLdpJbUEpKTCi39mnuvwZKrfth3XbfZhmu2pMPKNke6BzNzbWfYstzoTDXt1LyuxcDsMzdWkuWBLA2iZEYFisfV/Q1N+xaAKX5VVdCqCg3jwPmO7MAaN9Uo+wDWUp6O/YacdhwwYE1Zin5c5kFWJqPseMbAJa4s+jarBE4wrReu4iI1LroMDuD25kJrg9X5JzTNVbsUbI9kP24lPzscywlrwEZDUNMmIPb+qYz1XUx+0gwl+Wa8XsIiTJ/ju2CvcvMZbiO50HOEgCmOi8iOdzCL7q39Gv7pRaERBPa7x4ALnQuZuOK7yB3DRTlnV5S3jAgbxPGdjNJ+p7rEoZkhIBd1QYDkiOMuHYDAbjIWMG3O/Kh6NyXLwH46uTfrLGNV2ApzTdLxre+3Ey0O8LMZdxEaoiS7VKr+raMA+C7bdUoGXMGp2a2n0qyScMR5ghi7MXmTfir32zjeKmTjfsLeXNeNgC/H9yaELtGpjUkndLMQTerT34vVEdRWQVbDpizN7o0Vac/kGW0ymKPuwlWDNi3HE4cq/7Je5cCsMKdSat4lTwMVGGOINIbhzPd1c/ccGiTObO9NP/sJxXuM8vIA0vdbUiMCCIxJrTmGyt+075pI+a6OgNg7F9tlr8sP4cKTcWHObHLXC9uHl3p2LgCQhtrVL2IiPjFNSdLyX+6ej+lTpdP5x4tLmfHIXMga5c09akC1alS8jPOsZS8ku0Nx6/7tSAsJJgxZffgtIbA4S0w80HzPjc6xUyUlh2HrTOg+BDHCeMbdxdu69EYu5ZsC3wWC8ldBrEizOx329a+j3H8gDkI49RyA6cGMxfsheWTsVSUku1OYr0tizv7p2u99gBmazucCoLIsOYyb2MuHDv32e0ut8E3mw8CcFn5yVntqT0hIt6sUhfWRJXlpEYp2S61ql8rc/bXkh1HKavwrUNX4XJ7SpydSrJJw3Jjr6YkR4eQW1BKhydnM/Rv31FUVkGHlGiu6JTs7+ZJLeuSFovNamHHoWJ2Hq7erOW1e/NxG5ASE0pCVEgNt1BqksViITo6GrvdjuUMHa+OaY1ZYZhrLZfmbq5eeXAA5wmMgxsAc2Z7ZmLUeW231B5vMQKQlRTFNiOVQyHpYLhhx1zI3wslR08/uLQQcpZB8SHcWFjhzqRzqgZj1GfViZF2yVF8RxcAKg5shPITZiz4oqIMDm4kJH8rAIdiuxGM2yyxKSIi4gd9W8aRFB1CwQkns3ycubzyZBI1o0k4sVrGLWD9nFLyh46XsftICRaLBrnXZ9W5V4aTSz/2a8EGI537rQ9j2BzmAOWZD8LRneZs0m2zYO00AJ52/opgh53rL8iorbciNaS6MUJ4E1Ivu5tiI5jW7u3smDsZQhuZS/7tW24uMZC/B/I24j5ZDew910Du7BxCbJye99ZnXmMkOoWyJubSbXGHF5Nz6Ng5z25ftfsoR0oq6ObYQ1T+RrBYIesKc2kCDIjUshVSs5Rsl1rVJjGSuIhgTjhdnlGu1ZV9qJgTThfhDhvpcRqF1BAFB9n4/eWtPb+HOWx0bRrDX67thNWqUY4NTWy4gwtPVsv4dPW+ap1zqoR8Z5WQr/fsdjtjx46lXbt22O320/bHhjvYEWyW+T5xeLeZPK2qPPgpOUuxVJRSaISxz5ZC6+RG57vpUku8xQiY67YDfBcywNyw53tzZvu+FXAk20ySgjlQ49huyF0NQI4tjWJC6dxUJcjqs+rESJgjCFdqL8oMO/byfMjfCcWHfCslX3IEdi3AisE6d3Mym6WALQgc4efnjYiIiPjIZrVwfY+mALw5Lxu3u/p/177enAdArxa6DwpkzePCaZtklpKf6ePs9hUn12vPjI8kOvTM91hS91XnXvmUW/u2ICbUxv+KMnnQ9hBuS5DZd/poNEz/NSz/FwCT7dfzoWsAN3UIJyJa3yH1XbVjxGoloVUPFiXdituwkJE3h4LPH4fQGAiJNgdlHFgHW7/AenQHZYadb+0XctuF6WDXRJn6zGuMOCIIbzcEgOts83l/u+2cZ7fPWbcHgN9FfmVuaNIG4rPMZ4HBURCm53tSs5Rsl1pltVro1+pUKXnf1m1fc7JUdPuUaGxKrDZYv+ySyvzfD2DZo5ey4anBfHz3hbRO1MzChurKzuYI109X78eoRuLDs157WkwNtkrqirKErgBEFO0yE6gnzjBb+ad2LwJghbsVnRIc2EI0uCuQtU0yk+3vO/uao54L95rlxYKCzdH1uxfD3uVwaIu574g5M3mRyxzI0bl5nN/aLrWnX1Yqi91tzV/2rYLjueYag9VhGFCYi5G7CoA5ru5ckGQxy2k6dP8iIiL+c2uf5kQEB7H5wHHmbDpYrXMqXG7PTPgh7TVDLNBd1TUFgP9bvLta/e1T5m0xl468oIUSGw1FRHAQL4/sQuNQKx8WZjGi9ElWB3fDwGIOYgbesf2Sp45fgcNqcOuFGSoN3tCENeaSX4zkzah7KDPsRB9ahnP6XeagjOhkXFtnw/LJALxeMYLbusUR2ijJv22WmmexQLurKQ+KIMlylP1bV1BWdMxMuPsywN0wmLMpj3iO0atknrktczAEOaCsCCKTwKbBX1KzlGyXWncq2b7Ax2T7qfXaOylJ1uA1axxOk8jgqksUSYMwqF0iIXYrOw8Xs25fQZXHGobB6hyzooZK2TUMjVt2I8fdBLtRbibRC3PB7T77CYYBOUsAWO5uTdeUCK2nHODanZzZvuJoKGXxnc2NG/8LwZEQlQxWK5w4Bke2Q94m2GaOkP6svDtWC3RIURnwhmBg2xS+cXcGoGL/GnNgxuGt4Dzh/eTSAji2EyN3LQDf0pWOUaUnO/tBNdhqERGRqkWH2bm5dzMAXv1mW7WSqd/vPMrR4nJiw+z01sz2gHdt9zRC7Ta2HDzOkh3VGLgMuN0G35ysfnBJVkJNNk/qmAFtEvh6/IXc0DaUdUYLriz4HReWvsJLzmt42Hk7jxVfS4gNHr8wnPgEJVEbHKsVa2xzbr7sAh4MfpR8Ixx7wU748hHy/3UNtpVTAJjovJrZ4Vdwbc9mqgTWUETEEdTyYgBGuL7ii9xI8xnM8eqXk8/es5cdxyoYY5+JzaiA6DRoeYnZZw8KhnBNlJCap2S71Lq+J8s+r99fwJGismqft3avmUjrmKoH2yJiiggO4rK25oyK/67aX+Wxe4+d4HBROXabxZNgk/rL6XQyefJktmzZgtPpPOMxHZrGMd3dz/xl10Jzdntp/tkvWnIUctcAsMLIpGtzzcSoz6oTI/FRIXRKjcZtwJKIS8yN2+ZA9jxzoEVwpNkpc4TBolcBgz2xF7DY3Y7MJmGEBytZWp9VJ0YAmjaJYmdkDwCs+bvA6jg5CCPb+2j7okOwbQ5Wdzkb3M2ITEjHEWSBMCUoRETE/27v14Iwh431+wo9s5GrMuNkOfHB7RIJsumRYqCLDrXzy5Oz2/+zeFe1zlm/v4C842WEO2z00sz2eq2698o/FhMdxfM39OG/1zbiN23dxMYl8JrrKmbYLmFcJysLbwjjpoGdNeg0QPgcI2GNiIxvyr0DW3KN8QL/qBhGoRFKjMsczPM3YySWjtfy/vBQgqI1ICMQVCtGgqOxth0BQH/rWr5ctxeC7HBoszl43Ru3i/8s2EYMx/mV7WtzW+YQcETAiXyISDCXLBCpYbozlloXHxVCm8RIDAMWZh+p1jllFS425RYC0DElpgZbJyL1zYhOZin5/63dj6uKtQZX7jFntbdNjibErtnK9Z1hGOTm5nLixImzzsLpmBrDZ+6LzOOPbIP8vVB8lr87hgE5S+HEMZyGjdXuDLo2j6+p5kstqE6MAAzraHbi3yq4AFK6g+GCb56GrbNPXsgN8543lyEIb8IH0bcDqpARCKobIwBtW7ch252EFTfsnAcR8ZC/GwqrGOh1/CAczYZss8M/xTWYC5KDzPXiQjR4VERE/K9RuINfXWDObv+bl9ntFS43s9abs8yGdlASpKG4pXdzAGZvPMj+fO9Vfb7aZM5q79eqCcFB6nfXZ77cK1diD6Fzxy48emkaM37hZu2vrCwfZeeB7kE0bt4OQtWPChQ+x4jFAk3a0CIjk/9c0YjoPrfxabcpfJP8G5Y1v4s7f3Uj4ztUEJuYrv5SgKhWjFitkNKN8sZtsVoM2h+bw+rCSCgvNhPuztIqX+O/Szbx73UnuMU2mxBKzeR6m2HgOpncj045z+9K5MyUbBe/uCizCQDfbfU+chpgy4HjOF0GsWF20hqF1mTTRKSeuSizCTFhdg4dL2NR9tmXp9B67Q1PRHAQaS0yWeLOwoJhJsgKdp+5FNWJY7BrAQAbjOakxoQQHR1Tq+0V/xjS3nxYvGRfGYe63gvp/X9IsL83EiYNNgdiWIOg5x0szzdL2XVuppk6DcnArAS+cXcBwL17sVmKzh4Khzadef324wfh4DrYtxyKD3HMiOAzVx8uaFIGEYmazSMiInXG7f3SCQ6ysmpPvmc99jNZuusoR4rLiQmz0ztDFVoaitaJkfRu0RiX2+CdJbu9Hv/N5oMAXJKlgcsNmj0EEjtCancioxsTbLNAfJY5YFUaNlsQxGWSnNGekS3d3NTazcAh19Hj0qtxOAuhUTo0buHvVkptC4vFkTUYgOts83ly7hFc4QlwPA8OrDXXXT+D9XsO8dCM3YRzgjtDZpkbMwdDaLT5nC+8MYTq2Y3UDiXbxS9Ords+d0sepU6X1+PXnCwh3yE1Rut0i0gljiArw07OrPh45b4zHnO4qIzpK/cC0CtdN1kNyaB2yXzkMme3s2exOYM9dw3k7/mh/LNhQH4O5G0AYJm7NV2TQ5UMayDSGoXRKS0GtwFf5obBheOh5aWAAUUHwe0Eqx3aXYWreX/WHXYD0DlNMzIakm4ZSXxvNZPtxv5VUHzMnJVjGHBgvVkuHqCiDAr2mol2gO1fAfC+ayCNIkLo2MRqdvhFRETqiPjIEG7vlw7AY//dwLHi8jMeN/NkCflBbROwq4R8g3JLn+YATF2WU+UzvAMFpazfV4jFAhe3UVK1wbNazeR6SjezgliUZpfKSRYLxDY3YyM8wRy8XLDPjJG4THNJN2lYgqMh4xJcwdHEW/Jpf3Q2/1lfClFJZnzsX2Uu/ehygtsNzlIO79vJHf9ZTpkLnor7hlBXkZlYb3MFuF1QUW6u3W7VPYvUDkWa+EWv9MakxIRyuKicSQt2ej1+TU4+AJ20XruInME13VIB+O/qfSzbdfS0/c/P3Mzx0graJUcxqF1ibTdP/OjSdknMdPWi2AiGkiNwZLs5I/XAesjbaCbZC/aaN+67vgPgW3dHujVTIrUh+cXJATszdlmgohQufhSG/hUGPg5X/A1Gvgu9xrDtwDGKKyyEO2y0jI/wc6ulNgXZ7YQ17856d3Ns7nJY+pa5IzzOHJBxYD3kroPdi2D/anNfaQHsX4ULC+9UXMrt7W3YQ1VCXkRE6p7fDmxFq/gIDheV8dT/Npy23+U2+HK9OWNZJeQbnkuz4kmJCeVocTlTFu0663Ffn5zV3jkthriI4FpqndR5Vpu5XrImT8lPhTeG5M5m0r1RBsS3gSCHv1sl/mC1QnQqtpNrtz8c9D7vLt5JbjEQlQzlx2HfCti1EHZ9R3H2Im57bwP7itz0iMrn6tLp5nUyB0FEHJQVmt87YXF+e0vS8CjZLn7hCLLywOBMAN6cl82RorKzHnvoeJlnBHWP5pqRKiKn69I0lmu7pWIY8PsP11BSXuHZt2zXUc+s9j9e2R6bVR28hiQhKoTMlDhmunqZG9Z/Yia6QqLh2G5zlnvuGlj1H3BXsMjdnu/cHejaXDfkDcmQDuYgnO/3lpJXYkDFCUjtCi0vMcsfRsaD28Wag+Z3S4fUaH2XNEAD2yTwYsVIAIztc6Dg5HrtEfGACwpyzIeJUUkQ1hjWfwzALFcPSoKbcH16mfmgQDM1RESkjgmx2/jztZ2wWuC/q/cz+0fl5IvLKrj/g9UcLiojOtTOhS11n9zQBNms3HeZ+Qzvb19vO+va7d+cXK/90qyEWmubiNRzp/pPSR3AEe7v1og/hTWCNsMw4loTYSnlMf7JA3OO8tm2UpYUNCLPGQo2G063wd1fl7P2iIVGIVYmJ3yApaLUnMXe8Qaz+lxZEUSnavCG1Col28VvRnRKoX1KFEVlFbz6zfazHvfaN9soKXfRKTXaU35eROSnHh/elqToEHYdKeHFL7cAUOFy8/h/1wNwfY80ujbVbOWGaFD7ZKa6LjZ/2fs9rHoPHGFmhy46xZzZvm8FBlYed95ClMNCRpL+3jQkqbFhdGkagwF8eTgOio+aM9x/rPgwS4+dXK9dJeQbpMvap7DR0ZHFrrZYDBd8/+YPO8Mam98pwZFgscL+VRibZwAwueJybu0YSnhwkFl6XqSWvPnmm3Ts2JGoqCiioqLo3bs3X3zxhWf/gAEDsFgslX7uvPPOStfYs2cPw4YNIywsjPj4eH7/+99TUVFR6Zh58+bRtWtXgoODadmyJVOmTKmNtyci51nntBjGXJQBwP0frOH+aav5YFkOV76+kE9X78dmtfDo0CyVkG+gruqSQvdmsZSUu3hmxsbT9heccLJg+2EABqqEvIiI+Co4CsIaY7ngLtzWIAbY1pC4/2vu+fIY1398hJ7/KWDg1GJGfl7O/BwXIUEWpvXZQ0TOPMACXX5lrtVemm9OsIlUJR6pXbpDFr+xWi38YUgWAO8s2c3Ow8WnHbPnSAnvLd0DwEOXt9F67SJyVlEhdv50dUcApizaxXVvLabPC9+w+cBxokPtPHh5Gz+3UM630NBQbDbvM0QHtUtihdGav1Zca25Y9g/Y+F/zv11OWPwaABujLyLbSKFLogOrI6SGWi21qboxAjDsZEnUqZvKcMZmmGtwlxdB2XEoOsj24lA+3VICQP/MJjXWZqldvsRIeEQU93YP+2F2+64FcPgMA0ZLC2Dus1gwmFoxgA22NtySWQ5hsRAScx5bL1K11NRUXnjhBVasWMHy5csZOHAgI0aMYMOGH0pE/+Y3vyE3N9fz8+KLL3r2uVwuhg0bRnl5OYsWLeLf//43U6ZM4YknnvAcs3PnToYNG8bFF1/M6tWrGT9+PLfffjuzZs2q1fcqIufH+Etb0Sk1mqKyCj5etY8Hp69lW14R8ZHBvP+bC7iuR5q/myh+YrVaPJXiZq47wLdbD3n2zd2cx5CXv6Wswk1ao1DaJEb6saVyPvlyrywNk2JEvKl2jFgsEJkIYY2xdhsNwB+D/8P18XtoHm3DAuzId7HygBOrBd4YHEmrTW+Y56Z0hxYXg+E2Z7XHNgO7nutJ7VKyXfyqT8s4BrRuQoXb4IlP11Phclfa/9KcLThdBv1axdFHpcpExIuLMptwQ8+mACzdeZS842U4bFb+eGV7GoWrdFAgcTgc3HfffXTo0AGHo+r/b1vGR9CicQh/q7iS7clXmBsXvALvXQ+Th0BBDoY9nFetowDo1lTrKQcCX2IE4IrOyUSGBLEx9zgvr7ZAoxbgLAXDwAiOZsJyOxVug0uz4umd0bgW3oHUNF9jBKuV67unkB/ZitmublgwYO4z5oCMUwwDvv0LFB9mnyWBpypu5ob24cTayswydlZ1v6T2DB8+nKFDh9KqVSsyMzN59tlniYiIYMmSJZ5jwsLCSExM9PxERUV59s2ePZuNGzfyzjvv0LlzZ4YMGcIf//hHXn/9dcrLywH4+9//Tnp6Oi+99BJZWVmMGzeOa665hokTJ9b6+xWRny/EbuOju/owdcwF3NG/BVlJUVzSJp4Z9/SjZ7qW9WvospKiuKV3cwB++/4qrnx9ISNeX8joKcvYX1BK00ZhvHpDV02UCRA+3ytLg6MYEW98jpHQWLMSZdYV0KQ1YUYJLxQ/wbwBu1g9JpG3f9GIO7tF8M6AEgau/T0c2wX2MOh+m1ky/sQx8xqRyTX+3kR+KsjfDRD5w9AsFmcf4btth5nw2QaeubI9FouF1Tn5fLraXAvzIc1IFZFqmjC8LW2TIgkPDiI9LpwWTSKIDrX7u1niZ4PaJfP3b3fwF9cN/L1VGWybBUUn16K0OViaNIovt4YTZIHLO6b6t7HiF/GRIbxwVUfGvreSN77dQe+MHvRt3gJsDr7YeIiFu1biCLLyxC/a+bup4kf28Gge6mrw/Pwb6WndQsyxXfDpOLjib+B2wap3YNd3uLAxpvQebPZQftPOMMvLh2ngqPiPy+Xiww8/pLi4mN69e3u2v/vuu7zzzjskJiYyfPhwHn/8ccLCwgBYvHgxHTp0ICHhh7V3Bw8ezF133cWGDRvo0qULixcv5tJLL630WoMHD2b8+PFVtqesrIyysjLP74WFhQA4nU6cTufPfbsNwqnPSZ+XnM3PiZFuaVF0S4vigUtbnnY9CRznEiPjBqQzc91+DhSWsTonHwCrBW7t3Yzxl7Qk1GFTrAQQ/a0RbxQj4o1PMWINhpA4KMiBy/+CbdYjWPPWY8x+hIi2VzMwvDED7cVYl34ArjKMoBBcHW/EiGsNTiecKIHkVmBYzN+lXqjr3yPVbZeS7eJ3mQmRvHJ9Z+56dyXvfr+H+MgQnC43//h2BwC/6JhE+xTNMhSR6gmx27jp5Gh7kVN+0clMtn+52+CzwfdyRctL4cRRiEwmz5bAHZ+4AINxFzQis6lGwDZUwzomsWB7U95fuofxH6zl5ZGdCbaX8szn5rqUd/bPoGnjMD+3UvwqtBGDM6P4x0Yr1x96jGkhzxKdvxs+HA3lx82lKYDnnTewwUjn5YujSQw6BpEtVcZO/GLdunX07t2b0tJSIiIi+OSTT2jbti0AN954I82aNSM5OZm1a9fy0EMPsWXLFj7++GMADhw4UCnRDnh+P3DgQJXHFBYWcuLECUJDQ8/Yrueff56nnnrqtO2zZ8/2JPuleubMmePvJkgdpxgRb3yNkXGZsK/YgtMNTjckhxkkGtnM/Sq7hloo/qbvEfFGMSLe+B4jx7Ak3U8X5z9JO7YI24YPK+09FNGWlc3GUOpuBCv3/rBjx2pg9c9srfhDXf0eKSkpqdZxSrZLnXB5+yQeH9aWpz/fyMSvtnq292sVx5NXaAaZiIhU5nQ6eeeddzhy5AhOpxO7verqBe1Torm7bxpvLMjhoa/zaT2yM63T7BiGwR8+P0Z+WQXtGlsZO6hDLb0DqWm+xsgpT/yiLSt2H2XrwSJ+Nel7z/bU2FDuHpBRU80VPzinGAlyYGncgie7r2Lk7KZcVfoE7zueJf7EUQBKw1N5ouRaPnD14JetQ7mypQ1OBEFEfA2/G5Eza926NatXr6agoICPPvqIW265hfnz59O2bVvGjBnjOa5Dhw4kJSVxySWXkJ2dTUZGzX7fPfLII9x///2e3wsLC0lLS2PQoEGVStnL2TmdTubMmcNll11W7b9x0rAoRsQbxYhUxel0MnXqVI4ePcpvfvMbDYaT0yhGxJtzjpHD2+HwVohuBt3+iGv9R1j2rQC3E9wu3I1bEdP5RgaGxprHu5xQfBiSOkNkQpWXlrqnrt+PnKrC5o2S7VJn3NY3nX35J5i0YCcpMaE8Mbwtg9omaK0nERE5jWEY7Nmzx/Pf1fG7oR1Yu6+ABTsLufN/h7m2fQQLc8pYmFOO3Wrw0ogM7KERNdlsqUXnEiMAoQ4bb/6qGxM+3UBuwQlcbgOb1cIfR7QnxG6rqeaKH5xrjBCRQMemjfl8RBH3ftucqw4/ySjb18x1dWZpaRvAQlqUjacHREPpYQiPM9eNE/EDh8NBy5ZmGehu3bqxbNkyXnnlFd56663Tju3VqxcA27dvJyMjg8TERJYuXVrpmIMHDwKQmJjo+d9T2358TFRU1FlntQMEBwcTHBx82na73V4nH7DUZfrMxBvFiHijGJEzMQyDnJwcAIKCghQjchrFiHhzzjES1xxO5IGz0OxLdx5p/pxk/enxJUchOhFiksCq5zb1VV29H6lum5RslzrlsWFZXNMtlfS4cD3QFhGR88pmtfC3X/Vi+Cvz2Fno5MVFxz37HrognDYZ6X5sndQlGU0ieOf2Xv5uhtRVNjs0ak7GiZV8fE08L33vYOKaG6gAwuwWUiJtvHRZDJFBFVDqhKhk0OBRqSPcbneltdJ/bPXq1QAkJSUB0Lt3b5599lny8vKIjzerM8yZM4eoqChPKfrevXszc+bMSteZM2dOpXXhRUREREREqs0eCo1awP7VEBwJ1irSmBWlgAVimyvRLn6lZLvUKRaLhawklQ0UEZGa0SjcwVu39OQPH64gMaSC3k3D6NsihlbNUs0EmohIdYTHQ3gcjtJjPNI3nocujMICP1RkcrugcL/Z4Y9QGTvxj0ceeYQhQ4bQtGlTjh8/znvvvce8efOYNWsW2dnZvPfeewwdOpTGjRuzdu1a7rvvPi666CI6duwIwKBBg2jbti033XQTL774IgcOHOCxxx5j7Nixnlnpd955J6+99hoPPvggt912G9988w0ffPABM2bM8OdbFxERERGR+iwyCaLyoHAfRDSBoJAzH1d8BGKaQVjj2m2fyE8o2S4iIiINSvuUGD4bfwkYhmabisi5sQWZI+0PrIWig1jD43/4PjEMKDpgrhUXl6nR9eI3eXl53HzzzeTm5hIdHU3Hjh2ZNWsWl112GTk5OXz11Ve8/PLLFBcXk5aWxtVXX81jjz3mOd9ms/H5559z11130bt3b8LDw7nlllt4+umnPcekp6czY8YM7rvvPl555RVSU1P55z//yeDBg/3xlkVEREREJBDYgiCxg5lkP7YTQqLMWe6nGAaU5kNQMMQ21fM98Tsl20VERKRh0o24iPwcEfGQ1AXyNpqz2EOjweWE8mIIiYH4tmA/y+h7kVowadKks+5LS0tj/vz5Xq/RrFmz08rE/9SAAQNYtWqVz+0TERERERE5qyAHxGeBIwyObIfSAjP5bguC0uNm8j2uFYRE+7ulIkq2i4iIiIiInJPwxpDSFfI2Q2mhOao+Jg6iUyuPuhcRERERERER31it0CgdwhrBiXw4ngsVZZDQHiITzUS8SB2gZLuIiIjUS3a7HZfL5e9mSB2mGBFvzkuMOMIhuQu4nWBzqGqGiIiIiAQE9afEG8WIeHPeYiQk2vyJaWpWlAty/PxripxHVn83QERERMRXDoeD3//+93Ts2BGHQzfYcjrFiHhzXmPEajVntSvRLiIiIiIBQP0p8UYxIt7USIxYLEq0S52kZLuIiIiIiIiIiIiIiIiIiIiPlGwXERERERERERERERERERHxkdZsFxERkXqnoqKCadOmcejQISoqKrDb7f5uktQxihHxRjEiIiIiInJmulcWbxQj4o1iRBoSJdtFRESk3nG73WRnZ3v+W+SnFCPijWJEREREROTMdK8s3ihGxBvFiDQkKiMvIiIiIiIiIiIiIiIiIiLiIyXbRUREREREREREREREREREfKRku4iIiIiIiIiIiIiIiIiIiI+UbBcREREREREREREREREREfGRku0iIiIiIiIiIiIiIiIiIiI+CvJ3A+oDwzAAKCws9HNL6g+n00lJSQmFhYXY7XZ/N0fqIMWIeKMYkaqUl5dTWloKmH+fXS6Xn1skdY1iRLxRjEh11PX7kVN91FN91vpKfW7f1fXYFP9TjIg3ihGpiu6VxRvFiHijGJHqqOv3I9Xtc1uM+t4rrwV79+4lLS3N380QEREREREROU1OTg6pqan+bsY5U59bRERERERE6ipvfW4l26vB7Xazf/9+IiMjsVgs/m5OvVBYWEhaWho5OTlERUX5uzlSBylGxBvFiHijGBFvFCPijWJEvKnrMWIYBsePHyc5ORmrtf6uEqc+t+/qemyK/ylGxBvFiHijGBFvFCPijWJEvKnrMVLdPrfKyFeD1Wqt17ME/CkqKqpO/gORukMxIt4oRsQbxYh4oxgRbxQj4k1djpHo6Gh/N+FnU5/73NXl2JS6QTEi3ihGxBvFiHijGBFvFCPiTV2Oker0uevv0HcRERERERERERERERERERE/UbJdRERERERERERERERERETER0q2S40IDg5mwoQJBAcH+7spUkcpRsQbxYh4oxgRbxQj4o1iRLxRjEhdpdgUbxQj4o1iRLxRjIg3ihHxRjEi3gRKjFgMwzD83QgREREREREREREREREREZH6RDPbRUREREREREREREREREREfKRku4iIiIiIiIiIiIiIiIiIiI+UbBcREREREREREREREREREfGRku0iIiIiIiIiIiIiIiIiIiI+UrJdRERERERERERERERERETER0q213PPP/88PXr0IDIykvj4eK688kq2bNlS6ZjS0lLGjh1L48aNiYiI4Oqrr+bgwYOVjrnnnnvo1q0bwcHBdO7c+bTX2bVrFxaL5bSfJUuWeG3j66+/TvPmzQkJCaFXr14sXbrUs+/o0aP89re/pXXr1oSGhtK0aVPuueceCgoKqrzmvHnzGDFiBElJSYSHh9O5c2fefffdSsd8/PHHdO/enZiYGM8x//d//+e1vYFGMXL2GPmxqVOnYrFYuPLKK722N9AoRs4eI1OmTDmtvSEhIV7bG2gUI1V/j+Tn5zN27FiSkpIIDg4mMzOTmTNnem1zIFGMnD1GBgwYcMY2Dxs2zGubA4lipOrvkZdfftlz7bS0NO677z5KS0u9tjmQ1PcYAbjjjjvIyMggNDSUJk2aMGLECDZv3uz1umvXrqVfv36EhISQlpbGiy++WGn/hg0buPrqq2nevDkWi4WXX37Z6zXl/Knvsak+d81TjKjP7Y1iRH1ubxQj6nN7oxhRn9sbxYj63N7U9xiBut3nVrK9nps/fz5jx45lyZIlzJkzB6fTyaBBgyguLvYcc9999/G///2PDz/8kPnz57N//36uuuqq06512223MXLkyCpf76uvviI3N9fz061btyqPnzZtGvfffz8TJkxg5cqVdOrUicGDB5OXlwfA/v372b9/P3/5y19Yv349U6ZM4csvv+TXv/51ldddtGgRHTt2ZPr06axdu5bRo0dz88038/nnn3uOadSoEY8++iiLFy/2HDN69GhmzZpV5bUDjWLk7DFyyq5du3jggQfo169fldcMVIqRqmMkKiqqUnt3795d5XUDkWLk7DFSXl7OZZddxq5du/joo4/YsmULb7/9NikpKVVeO9AoRs4eIx9//HGltq5fvx6bzca1115b5bUDjWLk7DHy3nvv8fDDDzNhwgQ2bdrEpEmTmDZtGn/4wx+qvHagqe8xAtCtWzcmT57Mpk2bmDVrFoZhMGjQIFwu11mvW1hYyKBBg2jWrBkrVqzgz3/+M08++ST/+Mc/PMeUlJTQokULXnjhBRITE6tsp5x/9T021eeueYoR9bm9UYyoz+2NYkR9bm8UI+pze6MYUZ/bm/oeI1DH+9yGBJS8vDwDMObPn28YhmHk5+cbdrvd+PDDDz3HbNq0yQCMxYsXn3b+hAkTjE6dOp22fefOnQZgrFq1yqf29OzZ0xg7dqznd5fLZSQnJxvPP//8Wc/54IMPDIfDYTidTp9ea+jQocbo0aOrPKZLly7GY4895tN1A41ipHKMVFRUGH369DH++c9/GrfccosxYsQIn64ZiBQjP8TI5MmTjejoaJ+u0RAoRn6IkTfffNNo0aKFUV5e7tN1Ap1i5Oz3IxMnTjQiIyONoqIin64baBQjP8TI2LFjjYEDB1Y65v777zcuvPBCn64baAIhRtasWWMAxvbt2896zBtvvGHExsYaZWVlnm0PPfSQ0bp16zMe36xZM2PixIk+tV3Or0CITfW5a5ZiRH1ubxQj6nN7oxhRn9sbxYj63N4oRtTn9iYQYqQu9bk1sz3AnCqr0ahRIwBWrFiB0+nk0ksv9RzTpk0bmjZtyuLFi32+/hVXXEF8fDx9+/bls88+q/LY8vJyVqxYUem1rVYrl156aZWvXVBQQFRUFEFBQT61raCgwPO+f8owDL7++mu2bNnCRRdd5NN1A41ipHKMPP3008THx3sdJdeQKEYqx0hRURHNmjUjLS2NESNGsGHDBp+uGYgUIz/EyGeffUbv3r0ZO3YsCQkJtG/fnueee67KEZUNgWLkzPcjAJMmTeL6668nPDzcp+sGGsXIDzHSp08fVqxY4SmPtmPHDmbOnMnQoUN9um6gqe8xUlxczOTJk0lPTyctLe2s1168eDEXXXQRDofDs23w4MFs2bKFY8eO+fiupDbU99g89R7U5645ihH1ub1RjKjP7Y1iRH1ubxQj6nN7oxhRn9ub+h4jda3PrWR7AHG73YwfP54LL7yQ9u3bA3DgwAEcDgcxMTGVjk1ISODAgQPVvnZERAQvvfQSH374ITNmzKBv375ceeWVVf4jOXz4MC6Xi4SEhGq/9uHDh/njH//ImDFjqt02gA8++IBly5YxevToStsLCgqIiIjA4XAwbNgwXn31VS677DKfrh1IFCOVY2TBggVMmjSJt99+26drBTLFSOUYad26Nf/617/49NNPeeedd3C73fTp04e9e/f6dO1AohipHCM7duzgo48+wuVyMXPmTB5//HFeeuklnnnmGZ+uHUgUI6ffj5yydOlS1q9fz+233+7TdQONYqRyjNx44408/fTT9O3bF7vdTkZGBgMGDGhwJe1+rD7HyBtvvEFERAQRERF88cUXzJkzp1Kn/qcOHDhwxuue2id1S32OzR+foz53zVGMqM/tjWJEfW5vFCPqc3ujGFGf2xvFiPrc3tTnGKmrfW4l2wPI2LFjWb9+PVOnTj3v146Li+P++++nV69e9OjRgxdeeIFf/epX/PnPfwbgu+++8wR4REQE7777rs+vUVhYyLBhw2jbti1PPvmkZ3u7du081x0yZMhp582dO5fRo0fz9ttv065du0r7IiMjWb16NcuWLePZZ5/l/vvvZ968eT63LVAoRn6IkePHj3PTTTfx9ttvExcXd25vOgApRip/j/Tu3Zubb76Zzp07079/fz7++GOaNGnCW2+95fsHECAUI5VjxO12Ex8fzz/+8Q+6devGyJEjefTRR/n73//u+wcQIBQjp9+PnDJp0iQ6dOhAz549fW5XIFGMVI6RefPm8dxzz/HGG2+wcuVKPv74Y2bMmMEf//hH3z+AAFGfY2TUqFGsWrWK+fPnk5mZyXXXXUdpaSngPUak7qvPsQnqc9cGxYj63N4oRtTn9kYxoj63N4oR9bm9UYyoz+1NfY6Rutrn9q3+gtRZ48aN4/PPP+fbb78lNTXVsz0xMZHy8nLy8/MrjUg5ePAgiYmJP+s1e/XqxZw5cwDo3r07q1ev9uxLSEggODgYm83GwYMHK513ptc+fvw4l19+OZGRkXzyySfY7XbPvpkzZ+J0OgEIDQ2tdN78+fMZPnw4EydO5Oabbz6tjVarlZYtWwLQuXNnNm3axPPPP8+AAQPO+X3XV4qRyjGSnZ3Nrl27GD58uGeb2+0GICgoiC1btpCRkfEz3n39oxg58/fIj9ntdrp06cL27dt9fq+BQDFyeowkJSVht9ux2WyebVlZWRw4cIDy8vIqR1YGIsXI2b9HiouLmTp1Kk8//fQ5v9dAoBg5PUYef/xxbrrpJs/siw4dOlBcXMyYMWN49NFHsVob1vjo+h4j0dHRREdH06pVKy644AJiY2P55JNPuOGGG84YI4mJiWe87ql9UnfU99hUn7vmKUbU5/ZGMaI+tzeKEfW5vVGMqM/tjWJEfW5v6nuM1Nk+t8+rvEud4na7jbFjxxrJycnG1q1bT9ufn59v2O1246OPPvJs27x5swEYixcvPu34CRMmGJ06darWa99+++1Gly5dqjymZ8+exrhx4zy/u1wuIyUlxXj++ec92woKCowLLrjA6N+/v1FcXFyt1zYMw5g7d64RHh5uvPbaa9U+Z/To0Ub//v2rfXwgUIycOUZOnDhhrFu3rtLPiBEjjIEDBxrr1q0zysrKqv069Z1ipPrfIxUVFUbr1q2N++67r9qvEQgUI2ePkUceecRo1qyZ4XK5PNtefvllIykpqdqvEQgUI96/RyZPnmwEBwcbhw8frva1A4li5Owx0rVrV+PBBx+stO29994zQkNDjYqKimq/Tn0XCDHyU6WlpUZoaKgxefLksx7zxhtvGLGxsUZ5ebln2yOPPGK0bt36jMc3a9bMmDhxYpVtlfMrEGJTfe6apRhRn9sbxYj63N4oRtTn9kYxoj63N4oR9bm9CYQY+am61OdWsr2eu+uuu4zo6Ghj3rx5Rm5uruenpKTEc8ydd95pNG3a1Pjmm2+M5cuXG7179zZ69+5d6Trbtm0zVq1aZdxxxx1GZmamsWrVKmPVqlWezs+UKVOM9957z9i0aZOxadMm49lnnzWsVqvxr3/9q8r2TZ061QgODjamTJlibNy40RgzZowRExNjHDhwwDAM8wu0V69eRocOHYzt27dXeg9VfdF98803RlhYmPHII49UOufIkSOeY5577jlj9uzZRnZ2trFx40bjL3/5ixEUFGS8/fbbPn/O9Zli5Owx8lO33HKLMWLECG8facBRjJw9Rp566ilj1qxZRnZ2trFixQrj+uuvN0JCQowNGzb4/DnXZ4qRs8fInj17jMjISGPcuHHGli1bjM8//9yIj483nnnmGZ8/5/pMMeL9b03fvn2NkSNHVvszDTSKkbPHyIQJE4zIyEjj/fffN3bs2GHMnj3byMjIMK677jqfP+f6rL7HSHZ2tvHcc88Zy5cvN3bv3m0sXLjQGD58uNGoUSPj4MGDZ71ufn6+kZCQYNx0003G+vXrjalTpxphYWHGW2+95TmmrKzM8z6SkpKMBx54wFi1apWxbds2nz9n8V19j031uWueYkR9bm8UI+pze6MYUZ/bG8WI+tzeKEbU5/amvsdIXe9zK9lezwFn/PnxSI4TJ04Yd999txEbG2uEhYUZv/zlL43c3NxK1+nfv/8Zr7Nz507DMMx/IFlZWUZYWJgRFRVl9OzZ0/jwww+r1cZXX33VaNq0qeFwOIyePXsaS5Ys8eybO3fuWd/Dqdc+k1tuueWM5/x4BP2jjz5qtGzZ0ggJCTFiY2ON3r17G1OnTq1WmwOJYuTsMXKmcxpix18xcvYYGT9+vOd1ExISjKFDhxorV66sVpsDiWKk6u+RRYsWGb169TKCg4ONFi1aGM8++2yDGhlrGIoRbzFyaiTw7Nmzq9XWQKQYOXuMOJ1O48knnzQyMjKMkJAQIy0tzbj77ruNY8eOVavdgaK+x8i+ffuMIUOGGPHx8YbdbjdSU1ONG2+80di8ebPX665Zs8bo27evERwcbKSkpBgvvPBCpf07d+70+b5Wzp/6Hpvqc9c8xYj63N4oRtTn9kYxoj63N4oR9bm9UYyoz+1NfY+Rut7nthiGYSAiIiIiIiIiIiIiIiIiIiLVZvV3A0REREREREREREREREREROobJdtFRERERERERERERERERER8pGS7iIiIiIiIiIiIiIiIiIiIj5RsFxERERERERERERERERER8ZGS7SIiIiIiIiIiIiIiIiIiIj5Ssl1ERERERERERERERERERMRHSraLiIiIiIiIiIiIiIiIiIj4SMl2EREREREREREREZF64NZbb+XKK6/0dzNERETkpCB/N0BEREREREREREREpKGzWCxV7p8wYQKvvPIKhmHUUotERETEGyXbRURERERERERERET8LDc31/Pf06ZN44knnmDLli2ebREREURERPijaSIiInIWKiMvIiIiIiIiIiIiIuJniYmJnp/o6GgsFkulbREREaeVkR8wYAC//e1vGT9+PLGxsSQkJPD2229TXFzM6NGjiYyMpGXLlnzxxReVXmv9+vUMGTKEiIgIEhISuOmmmzh8+HAtv2MREZH6T8l2EREREREREREREZF66t///jdxcXEsXbqU3/72t9x1111ce+219OnTh5UrVzJo0CBuuukmSkpKAMjPz2fgwIF06dKF5cuX8+WXX3Lw4EGuu+46P78TERGR+kfJdhERERERERERERGReqpTp0489thjtGrVikceeYSQkBDi4uL4zW9+Q6tWrXjiiSc4cuQIa9euBeC1116jS5cuPPfcc7Rp04YuXbrwr3/9i7lz57J161Y/vxsREZH6RWu2i4iIiIiIiIiIiIjUUx07dvT8t81mo3HjxnTo0MGzLSEhAYC8vDwA1qxZw9y5c8+4/nt2djaZmZk13GIREZHAoWS7iIiIiIiIiIiIiEg9ZbfbK/1usVgqbbNYLAC43W4AioqKGD58OH/6059Ou1ZSUlINtlRERCTwKNkuIiIiIiIiIiIiItJAdO3alenTp9O8eXOCgpQiEBER+Tm0ZruIiIiIiIiIiIiISAMxduxYjh49yg033MCyZcvIzs5m1qxZjB49GpfL5e/miYiI1CtKtouIiIiIiIiIiIiINBDJycksXLgQl8vFoEGD6NChA+PHjycmJgarVSkDERERX1gMwzD83QgREREREREREREREREREZH6RMPUREREREREREREREREREREfKRku4iIiIiIiIiIiIiIiIiIiI+UbBcREREREREREREREREREfFRkL8bICIiIiIiInWT2+2mvLzc380QER/Z7XZsNpu/myEiIiIiIhLwlGwXERERERGR05SXl7Nz507cbre/myIi5yAmJobExEQsFou/myIiIiIiIhKwlGwXERERERGRSgzDIDc3F5vNRlpaGlarViATqS8Mw6CkpIS8vDwAkpKS/NwiERERERGRwKVku4iIiIiIiFRSUVFBSUkJycnJhIWF+bs5IuKj0NBQAPLy8oiPj1dJeRERERERkRqi6QkiIiIiIiJSicvlAsDhcPi5JSJyrk4NlHE6nX5uiYiIiIiISOBSsl1ERERERETOSGs9i9Rf+vcrIiIiIiJS85RsFxERERERERERERERERER8ZGS7SIiIiIiItLgNG/enJdfftnfzThvAu39iIiIiIiIiNQHSraLiIiIiIhIQMnJyeG2224jOTkZh8NBs2bNuPfeezly5Ii/myZ+cOutt2KxWE772b59u7+bdk6mTJlCTEyMv5shIiIiIiIiKNkuIiIiIiIiAWTHjh10796dbdu28f7777N9+3b+/ve/8/XXX9O7d2+OHj3ql3a5XC7cbrdfXlvg8ssvJzc3t9JPenq6z9cpLy+vgdaJiIiIiIhIfaVku4iIiIiIiASMsWPH4nA4mD17Nv3796dp06YMGTKEr776in379vHoo496jj1+/Dg33HAD4eHhpKSk8Prrr3v2GYbBk08+SdOmTQkODiY5OZl77rnHs7+srIwHHniAlJQUwsPD6dWrF/PmzfPsPzX7+LPPPqNt27YEBwfzz3/+k5CQEPLz8yu1+d5772XgwIGe3xcsWEC/fv0IDQ0lLS2Ne+65h+LiYs/+vLw8hg8fTmhoKOnp6bz77rvn8RMMTMHBwSQmJlb6sdlszJ8/n549exIcHExSUhIPP/wwFRUVnvMGDBjAuHHjGD9+PHFxcQwePBiA9evXM2TIECIiIkhISOCmm27i8OHDnvPcbjcvvvgiLVu2JDg4mKZNm/Lss8969j/00ENkZmYSFhZGixYtePzxx3E6nZ79a9as4eKLLyYyMpKoqCi6devG8uXLmTdvHqNHj6agoMAzQ//JJ5+s+Q9QREREREREzkjJdhEREREREamSYRiUlFf45ccwjGq38+jRo8yaNYu7776b0NDQSvsSExMZNWoU06ZN81zzz3/+M506dWLVqlU8/PDD3HvvvcyZMweA6dOnM3HiRN566y22bdvGf//7Xzp06OC53rhx41i8eDFTp05l7dq1XHvttVx++eVs27bNc0xJSQl/+tOf+Oc//8mGDRsYNWoUMTExTJ8+3XOMy+Vi2rRpjBo1CoDs7Gwuv/xyrr76atauXcu0adNYsGAB48aN85xz6623kpOTw9y5c/noo4944403yMvL8+H/0fPEMKC8uPZ/fIiJquzbt4+hQ4fSo0cP1qxZw5tvvsmkSZN45plnKh3373//G4fDwcKFC/n73/9Ofn4+AwcOpEuXLixfvpwvv/ySgwcPct1113nOeeSRR3jhhRd4/PHH2bhxI++99x4JCQme/ZGRkUyZMoWNGzfyyiuv8PbbbzNx4kTP/lGjRpGamsqyZctYsWIFDz/8MHa7nT59+vDyyy8TFRXlmaH/wAMPnJfPQ0RERERERHxnMXx5ciEiIiIiIiIBr7S0lJ07d5Kenk5ISAgl5RW0fWKWX9qy8enBhDmCqnXs999/zwUXXMAnn3zClVdeedr+iRMncv/993Pw4EF69uxJVlYWX3zxhWf/9ddfT2FhITNnzuSvf/0rb731FuvXr8dut1e6zp49e2jRogV79uwhOTnZs/3SSy+lZ8+ePPfcc0yZMoXRo0ezevVqOnXq5Dlm/PjxrFu3jq+//hqA2bNnc8UVV3DgwAFiYmK4/fbbsdlsvPXWW55zFixYQP/+/SkuLmbPnj20bt2apUuX0qNHDwA2b95MVlYWEydOZPz48dX6rM6L8mJ4Ltn7cefbH/aDI7zah99666288847hISEeLYNGTKEzMxMpk+fzqZNm7BYLAC88cYbPPTQQxQUFGC1WhkwYACFhYWsXLnSc+4zzzzDd999x6xZP/yb2Lt3L2lpaWzZsoWkpCSaNGnCa6+9xu23316tNv7lL39h6tSpLF++HICoqCheffVVbrnlltOOnTJlCuPHjz+tQsJP/fTfsYiIiIiIiJx/1XtiISIiIiIiIlJPVHdMee/evU/7/eWXXwbg2muv5eWXX6ZFixZcfvnlDB06lOHDhxMUFMS6detwuVxkZmZWOr+srIzGjRt7fnc4HHTs2LHSMaNGjeKCCy5g//79JCcn8+677zJs2DBiYmIAs3z42rVrK5WGNwwDt9vNzp072bp1K0FBQXTr1s2zv02bNp7z5cwuvvhi3nzzTc/v4eHhjB07lt69e3sS7QAXXnghRUVF7N27l6ZNmwJU+qzB/P9o7ty5REREnPY62dnZ5OfnU1ZWxiWXXHLW9kybNo2//e1vZGdnU1RUREVFBVFRUZ79999/P7fffjv/93//x6WXXsq1115LRkbGOb9/ERERERERqRlKtouIiIiIiEiVQu02Nj492G+vXV0tW7bEYrGwadMmfvnLX562f9OmTcTGxtKkSROv1zo1S/mrr75izpw53H333fz5z39m/vz5FBUVYbPZWLFiBTZb5fb9OAEbGhpaKZEL0KNHDzIyMpg6dSp33XUXn3zyCVOmTPHsLyoq4o477qi0PvwpTZs2ZevWrV7bXmvsYeYsc3+8ro/Cw8Np2bLlOb1ceHjlWfRFRUUMHz6cP/3pT6cdm5SUxI4dO6q83uLFixk1ahRPPfUUgwcPJjo6mqlTp/LSSy95jnnyySe58cYbmTFjBl988QUTJkxg6tSpZ4xrERERERER8R8l20VERERERKRKFoul2qXc/alx48ZcdtllvPHGG9x3332V1m0/cOAA7777LjfffLMnAb5kyZJK5y9ZsoSsrCzP76GhoQwfPpzhw4czduxY2rRpw7p16+jSpQsul4u8vDz69evncztHjRrFu+++S2pqKlarlWHDhnn2de3alY0bN541MdymTRsqKipYsWKFp4z8li1bvJYUrxEWi0/l3OuarKwspk+fjmEYnphYuHAhkZGRpKamnvW8rl27Mn36dJo3b05Q0On/Llq1akVoaChff/31GcvIL1q0iGbNmvHoo496tu3evfu04zIzM8nMzOS+++7jhhtuYPLkyfzyl7/E4XDgcrnO5S2LiIiIiIjIeWb1dwNEREREREREzpfXXnuNsrIyBg8ezLfffktOTg5ffvkll112GSkpKTz77LOeYxcuXMiLL77I1q1bef311/nwww+59957AXNd7EmTJrF+/Xp27NjBO++8Q2hoKM2aNSMzM5NRo0Zx88038/HHH7Nz506WLl3K888/z4wZM7y2cdSoUaxcuZJnn32Wa665huDgYM++hx56iEWLFjFu3DhWr17Ntm3b+PTTTxk3bhwArVu35vLLL+eOO+7g+++/Z8WKFdx+++2VBhZI9dx9993k5OTw29/+ls2bN/Ppp58yYcIE7r//fqzWsz8uGTt2LEePHuWGG25g2bJlZGdnM2vWLEaPHo3L5SIkJISHHnqIBx98kP/85z9kZ2ezZMkSJk2aBJjJ+D179jB16lSys7P529/+xieffOK5/okTJxg3bhzz5s1j9+7dLFy4kGXLlnkGgjRv3pyioiK+/vprDh8+TElJSc1+UCIiIiIiInJWSraLiIiIiIhIwGjVqhXLly+nRYsWXHfddWRkZDBmzBguvvhiFi9eTKNGjTzH/u53v2P58uV06dKFZ555hr/+9a8MHmyWy4+JieHtt9/mwgsvpGPHjnz11Vf873//86zJPnnyZG6++WZ+97vf0bp1a6688kqWLVvmWee7Ki1btqRnz56sXbuWUaNGVdrXsWNH5s+fz9atW+nXrx9dunThiSeeIDk52XPM5MmTSU5Opn///lx11VWMGTOG+Pj48/HxNSgpKSnMnDmTpUuX0qlTJ+68805+/etf89hjj1V5XnJyMgsXLsTlcjFo0CA6dOjA+PHjiYmJ8STpH3/8cX73u9/xxBNPkJWVxciRI8nLywPgiiuu4L777mPcuHF07tyZRYsW8fjjj3uub7PZOHLkCDfffDOZmZlcd911DBkyhKeeegqAPn36cOeddzJy5EiaNGnCiy++WEOfkIiIiIiIiHhjMQzD8HcjREREREREpO4oLS1l586dpKenExIS4u/miMg50L9jERERERGRmqeZ7SIiIiIiIiIiIiIiIiIiIj5Ssl1ERERERERERERERERERMRHSraLiIiIiIiIiIiIiIiIiIj4SMl2ERERERERERERERERERERHynZLiIiIiIiImdkGIa/myAi50j/fkVERERERGqeku0iIiIiIiJSic1mA6C8vNzPLRGRc1VSUgKA3W73c0tEREREREQCV5C/GyAiIiIiIiJ1S1BQEGFhYRw6dAi73Y7VqnHaIvWFYRiUlJSQl5dHTEyMZ/CMiIiIiIiInH8WQ3XFRERERERE5CfKy8vZuXMnbrfb300RkXMQExNDYmIiFovF300REREREREJWEq2i4iIiIiIyBm53W6Vkheph+x2u2a0i4iIiIiI1AIl20VERERERERERERERERERHykhfdERERERERERERERERERER8pGS7iIiIiIiIiIiIiIiIiIiIj5RsFxERERERERERERERERER8ZGS7SIiIiIiIiIiIiIiIiIiIj5Ssl1ERERERERERERERERERMRHSraLiIiIiIiIiIiIiIiIiIj4SMl2ERERERERERERERERERERH/0/I6213juEBW8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate predictions for multiple windows\n",
    "predictions_per_window = predictor.backtest_predictions(test_data, num_val_windows=num_test_windows)\n",
    "\n",
    "# Plot predictions for the first two time series\n",
    "item_ids = test_data.item_ids[:2].tolist()\n",
    "all_predictions = pd.concat(predictions_per_window)\n",
    "predictor.plot(test_data, all_predictions, max_history_length=300, item_ids=item_ids)\n",
    "\n",
    "# Optional: Plot the cutoff dates with dashed vertical lines\n",
    "for cutoff in range(-num_test_windows * prediction_length, 0, prediction_length):\n",
    "    for i, ax in enumerate(plt.gcf().axes):\n",
    "        cutoff_timestamp = test_data.loc[item_ids[i]].index[cutoff]\n",
    "        ax.axvline(cutoff_timestamp, color='gray', linestyle='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting with covariates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous example showed Chronos-2 in action on a univariate forecasting task, i.e., only the historical data of the target time series for making predictions. However, in real-world scenarios, additional exogenous information related to the target series (e.g., weather forecasts, holidays, promotions) is often available. These exogenous time series, often referred to as covariates, may either be observed only in the past (past-only) or also in the forecast horizon (known future). Leveraging this information when making predictions can improve forecast accuracy. \n",
    "\n",
    "Chronos-2 natively supports (dynamic) covariates, past-only and known-future, real-valued or categorical. Let's see how we can use Chronos-2 to forecast with covariates on a **Electrical Load Forecasting** task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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></th>\n",
       "      <th>load</th>\n",
       "      <th>airtemperature</th>\n",
       "      <th>dewtemperature</th>\n",
       "      <th>sealvlpressure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Bull_education_Magaret</th>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>9.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1028.699951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 01:00:00</th>\n",
       "      <td>2.7908</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1028.800049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 02:00:00</th>\n",
       "      <td>3.7210</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1029.599976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 03:00:00</th>\n",
       "      <td>2.7908</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1029.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 04:00:00</th>\n",
       "      <td>9.3025</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1029.599976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              load  airtemperature  \\\n",
       "item_id                timestamp                                     \n",
       "Bull_education_Magaret 2016-01-01 00:00:00  0.0000             9.4   \n",
       "                       2016-01-01 01:00:00  2.7908             8.9   \n",
       "                       2016-01-01 02:00:00  3.7210             8.9   \n",
       "                       2016-01-01 03:00:00  2.7908             8.3   \n",
       "                       2016-01-01 04:00:00  9.3025             7.8   \n",
       "\n",
       "                                            dewtemperature  sealvlpressure  \n",
       "item_id                timestamp                                            \n",
       "Bull_education_Magaret 2016-01-01 00:00:00             3.3     1028.699951  \n",
       "                       2016-01-01 01:00:00             2.2     1028.800049  \n",
       "                       2016-01-01 02:00:00             2.2     1029.599976  \n",
       "                       2016-01-01 03:00:00             1.7     1029.500000  \n",
       "                       2016-01-01 04:00:00             1.7     1029.599976  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = TimeSeriesDataFrame.from_path(\n",
    "    \"https://autogluon.s3.amazonaws.com/datasets/timeseries/bull/test.parquet\", id_column=\"id\"\n",
    ")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal is to forecast next day's (24 hours) load using historical load and known weather covariates: air temperature, dew temperature and sea level pressure. Since future weather information is not known in advance, weather forecasts are typically used as known covariates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sorting the dataframe index before generating the train/test split.\n"
     ]
    }
   ],
   "source": [
    "prediction_length = 24\n",
    "train_data, test_data = data.train_test_split(prediction_length=prediction_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code uses Chronos-2 in the TimeSeriesPredictor to forecast the `load` for the next 24 hours. We use the _univariate_ [Chronos-Bolt (Small)](https://huggingface.co/autogluon/chronos-bolt-small) model as a baseline for comparison.\n",
    "\n",
    "Note that we have specified the target column we are interested in forecasting and the names of known covariates while constructing the TimeSeriesPredictor. Any other columns, if present, will be used as past-only covariates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Beginning AutoGluon training... Time limit = 60s\n",
      "AutoGluon will save models to '/fsx/ansarnd/repos/autogluon/docs/tutorials/timeseries/AutogluonModels/ag-20251214_125334'\n",
      "=================== System Info ===================\n",
      "AutoGluon Version:  1.4.1b20250910\n",
      "Python Version:     3.11.11\n",
      "Operating System:   Linux\n",
      "Platform Machine:   x86_64\n",
      "Platform Version:   #38~22.04.1-Ubuntu SMP Fri Aug 22 15:44:33 UTC 2025\n",
      "CPU Count:          96\n",
      "Pytorch Version:    2.7.1+cu126\n",
      "CUDA Version:       12.6\n",
      "GPU Memory:         GPU 0: 39.37/39.38 GB\n",
      "Total GPU Memory:   Free: 39.37 GB, Allocated: 0.01 GB, Total: 39.38 GB\n",
      "GPU Count:          1\n",
      "Memory Avail:       1029.12 GB / 1121.80 GB (91.7%)\n",
      "Disk Space Avail:   1758.43 GB / 11459.15 GB (15.3%)\n",
      "===================================================\n",
      "\n",
      "Fitting with arguments:\n",
      "{'enable_ensemble': False,\n",
      " 'eval_metric': MASE,\n",
      " 'hyperparameters': {'Chronos': {}, 'Chronos2': {}},\n",
      " 'known_covariates_names': ['airtemperature',\n",
      "                            'dewtemperature',\n",
      "                            'sealvlpressure'],\n",
      " 'num_val_windows': 1,\n",
      " 'prediction_length': 24,\n",
      " 'quantile_levels': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],\n",
      " 'random_seed': 123,\n",
      " 'refit_every_n_windows': 1,\n",
      " 'refit_full': False,\n",
      " 'skip_model_selection': False,\n",
      " 'target': 'load',\n",
      " 'time_limit': 60,\n",
      " 'verbosity': 2}\n",
      "\n",
      "Inferred time series frequency: 'h'\n",
      "Provided train_data has 718320 rows, 41 time series. Median time series length is 17520 (min=17520, max=17520). \n",
      "\n",
      "Provided data contains following columns:\n",
      "\ttarget: 'load'\n",
      "\tknown_covariates:\n",
      "\t\tcategorical:        []\n",
      "\t\tcontinuous (float): ['airtemperature', 'dewtemperature', 'sealvlpressure']\n",
      "\n",
      "To learn how to fix incorrectly inferred types, please see documentation for TimeSeriesPredictor.fit\n",
      "\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'MASE'\n",
      "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
      "===================================================\n",
      "\n",
      "Starting training. Start time is 2025-12-14 12:53:34\n",
      "Models that will be trained: ['Chronos[autogluon__chronos-bolt-small]', 'Chronos2']\n",
      "Training timeseries model Chronos[autogluon__chronos-bolt-small]. Training for up to 29.9s of the 59.9s of remaining time.\n",
      "\t-1.0865       = Validation score (-MASE)\n",
      "\t1.23    s     = Training runtime\n",
      "\t0.58    s     = Validation (prediction) runtime\n",
      "Training timeseries model Chronos2. Training for up to 58.0s of the 58.0s of remaining time.\n",
      "\t-0.8172       = Validation score (-MASE)\n",
      "\t0.78    s     = Training runtime\n",
      "\t1.60    s     = Validation (prediction) runtime\n",
      "Training complete. Models trained: ['Chronos[autogluon__chronos-bolt-small]', 'Chronos2']\n",
      "Total runtime: 4.27 s\n",
      "Best model: Chronos2\n",
      "Best model score: -0.8172\n"
     ]
    }
   ],
   "source": [
    "predictor = TimeSeriesPredictor(\n",
    "    prediction_length=prediction_length,\n",
    "    target=\"load\",\n",
    "    known_covariates_names=[\"airtemperature\", \"dewtemperature\", \"sealvlpressure\"],\n",
    "    eval_metric=\"MASE\",\n",
    ").fit(\n",
    "    train_data,\n",
    "    hyperparameters={\"Chronos\": {}, \"Chronos2\": {}},\n",
    "    enable_ensemble=False,\n",
    "    time_limit=60,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the predictor has been fit, we can evaluate it on the test dataset and generate the leaderboard. We see that Chronos-2, which utilizes covariates, produces a significantly more accurate forecast on the test set compared to Chronos-Bolt, which does not utilize covariates.\n",
    "\n",
    "Note that all AutoGluon-TimeSeries models report scores in a \"higher is better\" format, meaning that most forecasting error metrics like MASE are multiplied by -1 when reported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Additional data provided, testing on additional data. Resulting leaderboard will be sorted according to test score (`score_test`).\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>score_test</th>\n",
       "      <th>score_val</th>\n",
       "      <th>pred_time_test</th>\n",
       "      <th>pred_time_val</th>\n",
       "      <th>fit_time_marginal</th>\n",
       "      <th>fit_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chronos2</td>\n",
       "      <td>-0.696239</td>\n",
       "      <td>-0.817203</td>\n",
       "      <td>2.261695</td>\n",
       "      <td>1.600290</td>\n",
       "      <td>0.782884</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chronos[autogluon__chronos-bolt-small]</td>\n",
       "      <td>-1.278404</td>\n",
       "      <td>-1.086471</td>\n",
       "      <td>0.618992</td>\n",
       "      <td>0.576958</td>\n",
       "      <td>1.233093</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    model  score_test  score_val  \\\n",
       "0                                Chronos2   -0.696239  -0.817203   \n",
       "1  Chronos[autogluon__chronos-bolt-small]   -1.278404  -1.086471   \n",
       "\n",
       "   pred_time_test  pred_time_val  fit_time_marginal  fit_order  \n",
       "0        2.261695       1.600290           0.782884          2  \n",
       "1        0.618992       0.576958           1.233093          1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor.leaderboard(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the predictor to compute features importances to understand which exogenous features are affecting the prediction accuracy the most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing feature importance\n",
      "Subsample_size 50 is larger than the number of items in the data and will be ignored\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "      <th>stdev</th>\n",
       "      <th>n</th>\n",
       "      <th>p99_low</th>\n",
       "      <th>p99_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>airtemperature</th>\n",
       "      <td>0.324309</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.324309</td>\n",
       "      <td>0.324309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dewtemperature</th>\n",
       "      <td>0.057110</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.057110</td>\n",
       "      <td>0.057110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sealvlpressure</th>\n",
       "      <td>0.038278</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.038278</td>\n",
       "      <td>0.038278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                importance  stdev    n   p99_low  p99_high\n",
       "airtemperature    0.324309    0.0  5.0  0.324309  0.324309\n",
       "dewtemperature    0.057110    0.0  5.0  0.057110  0.057110\n",
       "sealvlpressure    0.038278    0.0  5.0  0.038278  0.038278"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor.feature_importance(test_data, model=\"Chronos2\", relative_scores=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With `relative_scores=True`, this method returns relative (percentage) improvements in the `eval_metric` due to each feature. In this example, the `airtemperature` feature is the most important for accurate forecasting, yielding a ~32% error reduction on the test set.\n",
    "\n",
    "Note that covariates may not always be useful and using more covariates does not necessarily imply more accurate forecasts. With Chronos-2, AutoGluon makes it easy for users to quickly validate different configurations and find ones that perform best on held-out data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-tuning \n",
    "\n",
    "We have seen above how Chronos-2 models can produce forecasts in zero-shot mode, both with and without covariates. AutoGluon also makes it easy to fine-tune Chronos models on a specific dataset to maximize the predictive accuracy.\n",
    "\n",
    "The following snippet specifies two settings for the Chronos-2 model: zero-shot and fine-tuned. `TimeSeriesPredictor` will perform a lightweight fine-tuning of the pretrained model on the provided training data. We add name suffixes to easily identify the zero-shot and fine-tuned versions of the model.\n",
    "\n",
    ":::{note}\n",
    "\n",
    "If you are fine-tuning on a machine with multiple GPUs, we strongly recommend setting the `CUDA_VISIBLE_DEVICES` environment variable to ensure that only a single GPU is visible. \n",
    "\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Beginning AutoGluon training... Time limit = 300s\n",
      "AutoGluon will save models to '/fsx/ansarnd/repos/autogluon/docs/tutorials/timeseries/AutogluonModels/ag-20251214_125418'\n",
      "=================== System Info ===================\n",
      "AutoGluon Version:  1.4.1b20250910\n",
      "Python Version:     3.11.11\n",
      "Operating System:   Linux\n",
      "Platform Machine:   x86_64\n",
      "Platform Version:   #38~22.04.1-Ubuntu SMP Fri Aug 22 15:44:33 UTC 2025\n",
      "CPU Count:          96\n",
      "Pytorch Version:    2.7.1+cu126\n",
      "CUDA Version:       12.6\n",
      "GPU Memory:         GPU 0: 39.37/39.38 GB\n",
      "Total GPU Memory:   Free: 39.37 GB, Allocated: 0.01 GB, Total: 39.38 GB\n",
      "GPU Count:          1\n",
      "Memory Avail:       1028.93 GB / 1121.80 GB (91.7%)\n",
      "Disk Space Avail:   1758.40 GB / 11459.15 GB (15.3%)\n",
      "===================================================\n",
      "\n",
      "Fitting with arguments:\n",
      "{'enable_ensemble': False,\n",
      " 'eval_metric': MASE,\n",
      " 'hyperparameters': {'Chronos2': [{'ag_args': {'name_suffix': 'ZeroShot'}},\n",
      "                                  {'ag_args': {'name_suffix': 'FineTuned'},\n",
      "                                   'fine_tune': True}]},\n",
      " 'known_covariates_names': ['airtemperature',\n",
      "                            'dewtemperature',\n",
      "                            'sealvlpressure'],\n",
      " 'num_val_windows': 1,\n",
      " 'prediction_length': 24,\n",
      " 'quantile_levels': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],\n",
      " 'random_seed': 123,\n",
      " 'refit_every_n_windows': 1,\n",
      " 'refit_full': False,\n",
      " 'skip_model_selection': False,\n",
      " 'target': 'load',\n",
      " 'time_limit': 300,\n",
      " 'verbosity': 2}\n",
      "\n",
      "Inferred time series frequency: 'h'\n",
      "Provided train_data has 718320 rows, 41 time series. Median time series length is 17520 (min=17520, max=17520). \n",
      "\n",
      "Provided data contains following columns:\n",
      "\ttarget: 'load'\n",
      "\tknown_covariates:\n",
      "\t\tcategorical:        []\n",
      "\t\tcontinuous (float): ['airtemperature', 'dewtemperature', 'sealvlpressure']\n",
      "\n",
      "To learn how to fix incorrectly inferred types, please see documentation for TimeSeriesPredictor.fit\n",
      "\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'MASE'\n",
      "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
      "===================================================\n",
      "\n",
      "Starting training. Start time is 2025-12-14 12:54:19\n",
      "Models that will be trained: ['Chronos2ZeroShot', 'Chronos2FineTuned']\n",
      "Training timeseries model Chronos2ZeroShot. Training for up to 149.9s of the 299.9s of remaining time.\n",
      "\t-0.8172       = Validation score (-MASE)\n",
      "\t0.89    s     = Training runtime\n",
      "\t1.59    s     = Validation (prediction) runtime\n",
      "Training timeseries model Chronos2FineTuned. Training for up to 297.4s of the 297.4s of remaining time.\n",
      "\t-0.8029       = Validation score (-MASE)\n",
      "\t123.89  s     = Training runtime\n",
      "\t0.77    s     = Validation (prediction) runtime\n",
      "Training complete. Models trained: ['Chronos2ZeroShot', 'Chronos2FineTuned']\n",
      "Total runtime: 127.20 s\n",
      "Best model: Chronos2FineTuned\n",
      "Best model score: -0.8029\n"
     ]
    }
   ],
   "source": [
    "predictor = TimeSeriesPredictor(\n",
    "    prediction_length=prediction_length,\n",
    "    target=\"load\",\n",
    "    known_covariates_names=[\"airtemperature\", \"dewtemperature\", \"sealvlpressure\"],\n",
    "    eval_metric=\"MASE\",\n",
    ").fit(\n",
    "    train_data=train_data,\n",
    "    hyperparameters={\n",
    "        \"Chronos2\": [\n",
    "            # Zero-shot model\n",
    "            {\"ag_args\": {\"name_suffix\": \"ZeroShot\"}},\n",
    "            # Fine-tuned model\n",
    "            {\"fine_tune\": True, \"ag_args\": {\"name_suffix\": \"FineTuned\"}},\n",
    "        ]\n",
    "    },\n",
    "    time_limit=300,  # time limit in seconds\n",
    "    enable_ensemble=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we used the default fine-tuning configuration for Chronos-2 by only specifying `\"fine_tune\": True`. By default, Chronos-2 is fine-tuned with a low-rank adapter (LoRA) to reduce memory and disk footprint. AutoGluon makes it easy to change other parameters for fine-tuning such as the mode, number of steps or learning rate.\n",
    "```python\n",
    "predictor.fit(\n",
    "    ...,\n",
    "    hyperparameters={\"Chronos2\": {\"fine_tune\": True, \"fine_tune_mode\": \"full\", \"fine_tune_lr\": 1e-4, \"fine_tune_steps\": 2000, \"fine_tune_batch_size\": 32}},\n",
    ")\n",
    "```\n",
    "\n",
    "For the full list of fine-tuning options, see the Chronos-2 documentation in [Forecasting Model Zoo](forecasting-model-zoo.md#autogluon.timeseries.models.Chronos2Model).\n",
    "\n",
    "\n",
    "After fitting, we can evaluate the two model variants on the test data and generate a leaderboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Additional data provided, testing on additional data. Resulting leaderboard will be sorted according to test score (`score_test`).\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>score_test</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chronos2FineTuned</td>\n",
       "      <td>-0.677888</td>\n",
       "      <td>-0.802909</td>\n",
       "      <td>2.510460</td>\n",
       "      <td>0.773595</td>\n",
       "      <td>123.887496</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chronos2ZeroShot</td>\n",
       "      <td>-0.696239</td>\n",
       "      <td>-0.817203</td>\n",
       "      <td>2.385682</td>\n",
       "      <td>1.586984</td>\n",
       "      <td>0.885827</td>\n",
       "      <td>1</td>\n",
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       "               model  score_test  score_val  pred_time_test  pred_time_val  \\\n",
       "0  Chronos2FineTuned   -0.677888  -0.802909        2.510460       0.773595   \n",
       "1   Chronos2ZeroShot   -0.696239  -0.817203        2.385682       1.586984   \n",
       "\n",
       "   fit_time_marginal  fit_order  \n",
       "0         123.887496          2  \n",
       "1           0.885827          1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor.leaderboard(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fine-tuning resulted in a more accurate model, as shown by the better `score_test` on the test set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FAQ\n",
    "\n",
    "\n",
    "#### How accurate is Chronos-2?\n",
    "\n",
    "Chronos-2 is the best performing (last updated: Dec 2025) time series foundation model across multiple benchmarks, including [fev-bench](https://huggingface.co/spaces/autogluon/fev-bench), [GIFT-Eval](https://huggingface.co/spaces/Salesforce/GIFT-Eval) and [Chronos Bench II](https://arxiv.org/abs/2403.07815). Details empirical results can be found in the [Chronos-2 technical report](https://arxiv.org/abs/2510.15821). The accuracy of Chronos-2 often exceeds statistical baseline models and task-specific deep learning models such as `DeepAR` and `TemporalFusionTransformer`.\n",
    "\n",
    "#### Does fine-tuning always improve Chronos-2's forecasting accuracy?\n",
    "\n",
    "Fine-tuning a foundation model like Chronos-2 involves many hyperparameter choices. AG-TS provides reasonable defaults that performed well in large-scale benchmarking, but they may not be optimal for every use case. We recommend fine-tuning only when you have a reasonable number of time series and sufficient historical data (e.g., >100 time series with a median history length larger than `3 * prediction_length`), as limited data can lead to overfitting or degraded performance. If you observe degraded accuracy, we recommend increasing the size of the training data and experimenting with different fine-tuning hyperparameters.\n",
    "\n",
    "Alternatively, you can use an ensemble of zero-shot Chronos-2 and fine-tuned Chronos-2 (Small) to construct a robust predictor, available via the `chronos2_ensemble` preset:\n",
    "\n",
    "```py\n",
    "predictor = TimeSeriesPredictor(prediction_length=prediction_length).fit(\n",
    "    ...,\n",
    "    presets=\"chronos2_ensemble\",\n",
    ")\n",
    "```\n",
    "\n",
    "#### What is the recommended hardware for running Chronos models?\n",
    "\n",
    "We recommend using a machine with a GPU for best performance, especially for fine-tuning. For reference, we tested the models on AWS `g5.2xlarge` instances with NVIDIA A10G GPUs (24 GiB GPU memory) and 32 GiB of system memory. However, Chronos-2, Chronos-Bolt, and Chronos (up to small size) can also run on consumer GPUs and CPUs with reasonable inference times.\n",
    "\n",
    "#### Why do my predictions change with the `batch_size`?\n",
    "\n",
    "By default, AutoGluon enables Chronos-2’s cross_learning mode, where the model makes joint predictions across time series within a batch. This often improves accuracy but also makes results sensitive to the `batch_size`. You can disable this mode with:\n",
    "\n",
    "```python\n",
    "predictor.fit(\n",
    "    ...,\n",
    "    hyperparameters={\"Chronos2\": {\"cross_learning\": False}},\n",
    ")\n",
    "```\n",
    "\n",
    "#### Where can I ask specific questions on Chronos?\n",
    "\n",
    "Members of the AutoGluon team are among the core developers of Chronos. So you can ask Chronos-related questions on [AutoGluon's GitHub](https://github.com/autogluon/autogluon) or on [Chronos' GitHub](https://github.com/amazon-science/chronos-forecasting/discussions)."
   ]
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