{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**This is a fixed-text formatted version of a Jupyter notebook**\n",
    "\n",
    "- Try online[![Binder](https://static.mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.19?urlpath=lab/tree/tutorials/api/model_management.ipynb)\n",
    "- You may download all the notebooks in the documentation as a\n",
    "[tar file](../../_downloads/notebooks-0.19.tar).\n",
    "- **Source files:**\n",
    "[model_management.ipynb](../../_static/notebooks/model_management.ipynb) |\n",
    "[model_management.py](../../_static/notebooks/model_management.py)\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling\n",
    "\n",
    "## Aim\n",
    "\n",
    "The main aim of this tutorial is to illustrate model management in Gammapy, specially how to distribute multiple models across multiple datasets. We also show some convenience functions built in gammapy for handling multiple model components.\n",
    "\n",
    "**Note: Since gammapy v0.18, the responsibility of model management is left totally upon the user. All models, including background models, have to be explicitly defined.** To keep track of the used models, we define a global `Models` object (which is a collection of `SkyModel` objects) to which we append and delete models.\n",
    "\n",
    "\n",
    "## Prerequisites\n",
    "\n",
    "- Knowledge of 3D analysis, dataset reduction and fitting see [analysis notebook](../starting/analysis_2.ipynb)\n",
    "- Understanding of gammapy models [see the models tutorial](models.ipynb)\n",
    "- Analysis of the [galactic center with Fermi-LAT](../data/fermi_lat.ipynb)\n",
    "- Analysis of the [galactic center with CTA-DC1](../analysis/3D/analysis_3d.ipynb)\n",
    "\n",
    "## Proposed approach\n",
    "\n",
    "To show how datasets interact with models, we use two pre-computed datasets on the galactic center, one from Fermi-LAT and the other from simulated CTA (DC1) data. We demonstrate\n",
    "\n",
    "- Adding background models for each dataset\n",
    "- Sharing a model between multiple datasets\n",
    "\n",
    "We then load models from the Fermi 3FHL catalog to show some convenience handling for multiple `Models` together\n",
    "\n",
    "- accessing models from a catalog\n",
    "- selecting models contributing to a given region\n",
    "- adding and removing models\n",
    "- freezing and thawing multiple model parameters together\n",
    "- serialising models\n",
    "\n",
    "\n",
    "For computational purposes, we do not perform any fitting in this notebook.\n",
    "\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:50.506699Z",
     "iopub.status.busy": "2021-11-22T21:09:50.505857Z",
     "iopub.status.idle": "2021-11-22T21:09:50.673431Z",
     "shell.execute_reply": "2021-11-22T21:09:50.673616Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:50.675937Z",
     "iopub.status.busy": "2021-11-22T21:09:50.675635Z",
     "iopub.status.idle": "2021-11-22T21:09:51.202555Z",
     "shell.execute_reply": "2021-11-22T21:09:51.202731Z"
    }
   },
   "outputs": [],
   "source": [
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from gammapy.maps import Map\n",
    "from gammapy.datasets import MapDataset, Datasets\n",
    "from gammapy.modeling.models import (\n",
    "    PointSpatialModel,\n",
    "    SkyModel,\n",
    "    TemplateSpatialModel,\n",
    "    PowerLawNormSpectralModel,\n",
    "    Models,\n",
    "    create_fermi_isotropic_diffuse_model,\n",
    "    FoVBackgroundModel,\n",
    ")\n",
    "from regions import CircleSkyRegion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:51.204675Z",
     "iopub.status.busy": "2021-11-22T21:09:51.204378Z",
     "iopub.status.idle": "2021-11-22T21:09:51.205598Z",
     "shell.execute_reply": "2021-11-22T21:09:51.205754Z"
    }
   },
   "outputs": [],
   "source": [
    "from gammapy.modeling.models import GaussianSpatialModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the datasets\n",
    "\n",
    "First, we read some precomputed Fermi and CTA datasets, and create a `Datasets` object containing the two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:51.207881Z",
     "iopub.status.busy": "2021-11-22T21:09:51.207587Z",
     "iopub.status.idle": "2021-11-22T21:09:52.353122Z",
     "shell.execute_reply": "2021-11-22T21:09:52.353314Z"
    }
   },
   "outputs": [],
   "source": [
    "fermi_dataset = MapDataset.read(\n",
    "    \"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc.fits.gz\", name=\"fermi_dataset\"\n",
    ")\n",
    "cta_dataset = MapDataset.read(\n",
    "    \"$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz\", name=\"cta_dataset\"\n",
    ")\n",
    "datasets = Datasets([fermi_dataset, cta_dataset])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the counts maps to see the region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:52.356817Z",
     "iopub.status.busy": "2021-11-22T21:09:52.356510Z",
     "iopub.status.idle": "2021-11-22T21:09:52.889381Z",
     "shell.execute_reply": "2021-11-22T21:09:52.889572Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "ax1 = plt.subplot(121, projection=fermi_dataset.counts.geom.wcs)\n",
    "ax2 = plt.subplot(122, projection=cta_dataset.counts.geom.wcs)\n",
    "\n",
    "\n",
    "datasets[0].counts.sum_over_axes().smooth(0.05 * u.deg).plot(\n",
    "    ax=ax1, stretch=\"sqrt\", add_cbar=True\n",
    ")\n",
    "datasets[1].counts.sum_over_axes().smooth(0.05 * u.deg).plot(\n",
    "    ax=ax2, stretch=\"sqrt\", add_cbar=True\n",
    ")\n",
    "ax1.set_title(\"Fermi counts\")\n",
    "ax2.set_title(\"CTA counts\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:52.891553Z",
     "iopub.status.busy": "2021-11-22T21:09:52.891234Z",
     "iopub.status.idle": "2021-11-22T21:09:52.947010Z",
     "shell.execute_reply": "2021-11-22T21:09:52.947278Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=2</i>\n",
       "<table id=\"table4886470464\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>name</th><th>counts</th><th>background</th><th>excess</th><th>sqrt_ts</th><th>npred</th><th>npred_background</th><th>npred_signal</th><th>exposure_min</th><th>exposure_max</th><th>livetime</th><th>ontime</th><th>counts_rate</th><th>background_rate</th><th>excess_rate</th><th>n_bins</th><th>n_fit_bins</th><th>stat_type</th><th>stat_sum</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>m2 s</th><th>m2 s</th><th>s</th><th>s</th><th>1 / s</th><th>1 / s</th><th>1 / s</th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>str11</th><th>int64</th><th>float64</th><th>float32</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>str4</th><th>float64</th></tr></thead>\n",
       "<tr><td>stacked</td><td>27727</td><td>23934.755859375</td><td>3792.2441</td><td>23.90421023961601</td><td>23934.76235388656</td><td>23934.755859375</td><td>nan</td><td>31375018.0</td><td>33704756.0</td><td>nan</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>880000</td><td>633600</td><td>cash</td><td>nan</td></tr>\n",
       "<tr><td>cta_dataset</td><td>104317</td><td>91507.6953125</td><td>12809.305</td><td>41.41009347393684</td><td>91507.68628538586</td><td>91507.6953125</td><td>nan</td><td>62842028.0</td><td>19024205824.0</td><td>5292.0001029780005</td><td>5400.0</td><td>19.712206721480793</td><td>17.291703237308198</td><td>2.4205034841725985</td><td>768000</td><td>691680</td><td>cash</td><td>nan</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=2>\n",
       "    name    counts    background     excess       sqrt_ts            npred       ...    excess_rate     n_bins n_fit_bins stat_type stat_sum\n",
       "                                                                                 ...       1 / s                                            \n",
       "   str11    int64      float64      float32       float64           float64      ...      float64       int64    int64       str4   float64 \n",
       "----------- ------ --------------- --------- ----------------- ----------------- ... ------------------ ------ ---------- --------- --------\n",
       "    stacked  27727 23934.755859375 3792.2441 23.90421023961601 23934.76235388656 ...                nan 880000     633600      cash      nan\n",
       "cta_dataset 104317   91507.6953125 12809.305 41.41009347393684 91507.68628538586 ... 2.4205034841725985 768000     691680      cash      nan"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.info_table(cumulative=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:52.949002Z",
     "iopub.status.busy": "2021-11-22T21:09:52.948688Z",
     "iopub.status.idle": "2021-11-22T21:09:52.950286Z",
     "shell.execute_reply": "2021-11-22T21:09:52.950486Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets\n",
      "--------\n",
      "\n",
      "Dataset 0: \n",
      "\n",
      "  Type       : MapDataset\n",
      "  Name       : fermi_dataset\n",
      "  Instrument : \n",
      "  Models     : \n",
      "\n",
      "Dataset 1: \n",
      "\n",
      "  Type       : MapDataset\n",
      "  Name       : cta_dataset\n",
      "  Instrument : \n",
      "  Models     : \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that while the datasets have an associated background map, they currently do not have any associated background model. This will be added in the following section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assigning background models to datasets\n",
    "\n",
    "For any IACT dataset (in this case `cta_dataset`) , we have to create a `FoVBackgroundModel`. Note that `FoVBackgroundModel`\n",
    " must be specified to one dataset only\n",
    "\n",
    "For Fermi-LAT, the background contribution is taken from a diffuse isotropic template. To convert this into a gammapy `SkyModel`, use the helper function `create_fermi_isotropic_diffuse_model()` \n",
    "\n",
    "To attach a model on a particular dataset it is necessary to specify the `datasets_names`. Otherwise, by default, the model will be applied to all the datasets in `datasets` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we must create a global `Models` object which acts as the container for all models used in a particular analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:52.952255Z",
     "iopub.status.busy": "2021-11-22T21:09:52.951974Z",
     "iopub.status.idle": "2021-11-22T21:09:52.953279Z",
     "shell.execute_reply": "2021-11-22T21:09:52.953463Z"
    }
   },
   "outputs": [],
   "source": [
    "models = Models()  # global models object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:52.955404Z",
     "iopub.status.busy": "2021-11-22T21:09:52.955079Z",
     "iopub.status.idle": "2021-11-22T21:09:52.956218Z",
     "shell.execute_reply": "2021-11-22T21:09:52.956400Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the FoV background model for CTA data\n",
    "\n",
    "bkg_model = FoVBackgroundModel(dataset_name=cta_dataset.name)\n",
    "models.append(bkg_model)  # Add the bkg_model to models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:52.958223Z",
     "iopub.status.busy": "2021-11-22T21:09:52.957899Z",
     "iopub.status.idle": "2021-11-22T21:09:52.962771Z",
     "shell.execute_reply": "2021-11-22T21:09:52.962960Z"
    }
   },
   "outputs": [],
   "source": [
    "# Read the fermi isotropic diffuse background model\n",
    "\n",
    "diffuse_iso = create_fermi_isotropic_diffuse_model(\n",
    "    filename=\"$GAMMAPY_DATA/fermi_3fhl/iso_P8R2_SOURCE_V6_v06.txt\",\n",
    ")\n",
    "diffuse_iso.datasets_names = fermi_dataset.name  # specifying the dataset name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:52.964742Z",
     "iopub.status.busy": "2021-11-22T21:09:52.964440Z",
     "iopub.status.idle": "2021-11-22T21:09:52.965609Z",
     "shell.execute_reply": "2021-11-22T21:09:52.965788Z"
    }
   },
   "outputs": [],
   "source": [
    "models.append(diffuse_iso)  # Add the fermi_bkg_model to models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:52.967656Z",
     "iopub.status.busy": "2021-11-22T21:09:52.967346Z",
     "iopub.status.idle": "2021-11-22T21:09:52.968383Z",
     "shell.execute_reply": "2021-11-22T21:09:52.968724Z"
    }
   },
   "outputs": [],
   "source": [
    "# Now, add the models to datasets\n",
    "datasets.models = models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:52.970376Z",
     "iopub.status.busy": "2021-11-22T21:09:52.969989Z",
     "iopub.status.idle": "2021-11-22T21:09:52.971390Z",
     "shell.execute_reply": "2021-11-22T21:09:52.971734Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets\n",
      "--------\n",
      "\n",
      "Dataset 0: \n",
      "\n",
      "  Type       : MapDataset\n",
      "  Name       : fermi_dataset\n",
      "  Instrument : \n",
      "  Models     : ['fermi-diffuse-iso']\n",
      "\n",
      "Dataset 1: \n",
      "\n",
      "  Type       : MapDataset\n",
      "  Name       : cta_dataset\n",
      "  Instrument : \n",
      "  Models     : ['cta_dataset-bkg']\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# You can see that each dataset lists the correct associated models\n",
    "print(datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add a model on multiple datasets \n",
    "\n",
    "In this section, we show how to add a model to multiple datasets. For this,  we specify a list of `datasets_names` to the model. Alternatively, not specifying any `datasets_names` will add it to all the datasets.\n",
    "\n",
    "For this example, we use a template model of the galactic diffuse emission to be shared between the two datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:52.973932Z",
     "iopub.status.busy": "2021-11-22T21:09:52.973637Z",
     "iopub.status.idle": "2021-11-22T21:09:52.998291Z",
     "shell.execute_reply": "2021-11-22T21:09:52.998451Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the diffuse model\n",
    "diffuse_galactic_fermi = Map.read(\n",
    "    \"$GAMMAPY_DATA/fermi-3fhl-gc/gll_iem_v06_gc.fits.gz\"\n",
    ")\n",
    "\n",
    "template_diffuse = TemplateSpatialModel(\n",
    "    diffuse_galactic_fermi, normalize=False\n",
    ")  # the template model in this case is already a full 3D model, it should not be normalised\n",
    "\n",
    "diffuse_iem = SkyModel(\n",
    "    spectral_model=PowerLawNormSpectralModel(),\n",
    "    spatial_model=template_diffuse,\n",
    "    name=\"diffuse-iem\",\n",
    "    datasets_names=[\n",
    "        cta_dataset.name,\n",
    "        fermi_dataset.name,\n",
    "    ],  # specifying list of dataset names\n",
    ")  # A power law spectral correction is applied in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.000088Z",
     "iopub.status.busy": "2021-11-22T21:09:52.999806Z",
     "iopub.status.idle": "2021-11-22T21:09:53.000944Z",
     "shell.execute_reply": "2021-11-22T21:09:53.001132Z"
    }
   },
   "outputs": [],
   "source": [
    "# Now, add the diffuse model to the global models list\n",
    "models.append(diffuse_iem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.003156Z",
     "iopub.status.busy": "2021-11-22T21:09:53.002878Z",
     "iopub.status.idle": "2021-11-22T21:09:53.004100Z",
     "shell.execute_reply": "2021-11-22T21:09:53.004331Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets\n",
      "--------\n",
      "\n",
      "Dataset 0: \n",
      "\n",
      "  Type       : MapDataset\n",
      "  Name       : fermi_dataset\n",
      "  Instrument : \n",
      "  Models     : ['fermi-diffuse-iso', 'diffuse-iem']\n",
      "\n",
      "Dataset 1: \n",
      "\n",
      "  Type       : MapDataset\n",
      "  Name       : cta_dataset\n",
      "  Instrument : \n",
      "  Models     : ['cta_dataset-bkg', 'diffuse-iem']\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# add it to the datasets, and inspect\n",
    "datasets.models = models\n",
    "print(datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `diffuse-iem` model is correctly present on both. Now, you can proceed with the fit. For computational purposes, we skip it in this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.005991Z",
     "iopub.status.busy": "2021-11-22T21:09:53.005704Z",
     "iopub.status.idle": "2021-11-22T21:09:53.007083Z",
     "shell.execute_reply": "2021-11-22T21:09:53.006800Z"
    }
   },
   "outputs": [],
   "source": [
    "#%%time\n",
    "# fit2 = Fit(datasets)\n",
    "# result2 = fit2.run()\n",
    "# print(result2.success)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading models from a catalog\n",
    "\n",
    "We now load the Fermi 3FHL catalog and demonstrate some convenience functions. For more details on using gammapy catalog, see here[catalogs.ipynb]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.008704Z",
     "iopub.status.busy": "2021-11-22T21:09:53.008420Z",
     "iopub.status.idle": "2021-11-22T21:09:53.109233Z",
     "shell.execute_reply": "2021-11-22T21:09:53.109408Z"
    }
   },
   "outputs": [],
   "source": [
    "from gammapy.catalog import SourceCatalog3FHL\n",
    "\n",
    "catalog = SourceCatalog3FHL()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first choose some relevant models from the catalog and create a new `Models` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.111964Z",
     "iopub.status.busy": "2021-11-22T21:09:53.111679Z",
     "iopub.status.idle": "2021-11-22T21:09:53.455821Z",
     "shell.execute_reply": "2021-11-22T21:09:53.455997Z"
    }
   },
   "outputs": [],
   "source": [
    "gc_sep = catalog.positions.separation(\n",
    "    SkyCoord(0, 0, unit=\"deg\", frame=\"galactic\")\n",
    ")\n",
    "models_3fhl = [\n",
    "    _.sky_model() for k, _ in enumerate(catalog) if gc_sep[k].value < 8\n",
    "]\n",
    "models_3fhl = Models(models_3fhl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.458036Z",
     "iopub.status.busy": "2021-11-22T21:09:53.457732Z",
     "iopub.status.idle": "2021-11-22T21:09:53.459142Z",
     "shell.execute_reply": "2021-11-22T21:09:53.459325Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(models_3fhl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting models contributing to a given region\n",
    "\n",
    "We now use `Models.select_region()` to get a subset of models contributing to a particular region. You can also use `Models.select_mask()` to get models lying inside the `True` region of a mask map`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.461474Z",
     "iopub.status.busy": "2021-11-22T21:09:53.461175Z",
     "iopub.status.idle": "2021-11-22T21:09:53.462513Z",
     "shell.execute_reply": "2021-11-22T21:09:53.462698Z"
    }
   },
   "outputs": [],
   "source": [
    "region = CircleSkyRegion(\n",
    "    center=SkyCoord(0, 0, unit=\"deg\", frame=\"galactic\"), radius=3.0 * u.deg\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.520421Z",
     "iopub.status.busy": "2021-11-22T21:09:53.467073Z",
     "iopub.status.idle": "2021-11-22T21:09:53.521767Z",
     "shell.execute_reply": "2021-11-22T21:09:53.522074Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_selected = models_3fhl.select_region(region)\n",
    "len(models_selected)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now want to assign `models_3fhl` to the Fermi dataset, and `models_selected` to both the CTA and Fermi datasets. For this, we explicitlty mention the `datasets_names` to the former, and leave it `None` (default) for the latter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.524282Z",
     "iopub.status.busy": "2021-11-22T21:09:53.523987Z",
     "iopub.status.idle": "2021-11-22T21:09:53.525043Z",
     "shell.execute_reply": "2021-11-22T21:09:53.525218Z"
    }
   },
   "outputs": [],
   "source": [
    "for model in models_3fhl:\n",
    "    if model not in models_selected:\n",
    "        model.datasets_names = fermi_dataset.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.528793Z",
     "iopub.status.busy": "2021-11-22T21:09:53.528503Z",
     "iopub.status.idle": "2021-11-22T21:09:53.529695Z",
     "shell.execute_reply": "2021-11-22T21:09:53.529918Z"
    }
   },
   "outputs": [],
   "source": [
    "# assign the models to datasets\n",
    "datasets.models = models_3fhl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see the models on a particular dataset, you can simply see"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.531900Z",
     "iopub.status.busy": "2021-11-22T21:09:53.531573Z",
     "iopub.status.idle": "2021-11-22T21:09:53.532754Z",
     "shell.execute_reply": "2021-11-22T21:09:53.533073Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fermi dataset models:  ['3FHL J1731.7-3003', '3FHL J1732.6-3131', '3FHL J1741.8-2536', '3FHL J1744.5-2609', '3FHL J1745.6-2900', '3FHL J1745.8-3028e', '3FHL J1746.2-2852', '3FHL J1747.2-2959', '3FHL J1747.2-2822', '3FHL J1748.0-2446', '3FHL J1748.1-2903', '3FHL J1748.6-2816', '3FHL J1753.8-2537', '3FHL J1800.5-2343e', '3FHL J1800.7-2357', '3FHL J1801.5-2450', '3FHL J1801.6-2327', '3FHL J1802.3-3043', '3FHL J1809.8-2332', '3FHL J1811.2-2800']\n",
      "\n",
      " CTA dataset models:  ['3FHL J1744.5-2609', '3FHL J1745.6-2900', '3FHL J1745.8-3028e', '3FHL J1746.2-2852', '3FHL J1747.2-2959', '3FHL J1747.2-2822', '3FHL J1748.1-2903', '3FHL J1748.6-2816']\n"
     ]
    }
   ],
   "source": [
    "print(\"Fermi dataset models: \", datasets[0].models.names)\n",
    "print(\"\\n CTA dataset models: \", datasets[1].models.names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining two `Models`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Models` can be extended simply as as python lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.534927Z",
     "iopub.status.busy": "2021-11-22T21:09:53.534627Z",
     "iopub.status.idle": "2021-11-22T21:09:53.535890Z",
     "shell.execute_reply": "2021-11-22T21:09:53.536110Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    }
   ],
   "source": [
    "models.extend(models_selected)\n",
    "print(len(models))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting models from a list\n",
    "\n",
    "A `Model` can be selected from a list of `Models` by specifying its index or its name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.538025Z",
     "iopub.status.busy": "2021-11-22T21:09:53.537720Z",
     "iopub.status.idle": "2021-11-22T21:09:53.539018Z",
     "shell.execute_reply": "2021-11-22T21:09:53.539230Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1731.7-3003\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      2.742   +/-    0.50             \n",
      "    amplitude               :   2.59e-12   +/- 7.6e-13 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     17.603       GeV         \n",
      "    lon_0                   :    262.949   +/-    0.02 deg         \n",
      "    lat_0                   :    -30.051   +/-    0.02 deg         \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = models_3fhl[0]\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.540969Z",
     "iopub.status.busy": "2021-11-22T21:09:53.540679Z",
     "iopub.status.idle": "2021-11-22T21:09:53.542026Z",
     "shell.execute_reply": "2021-11-22T21:09:53.542202Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1731.7-3003\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      2.742   +/-    0.50             \n",
      "    amplitude               :   2.59e-12   +/- 7.6e-13 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     17.603       GeV         \n",
      "    lon_0                   :    262.949   +/-    0.02 deg         \n",
      "    lat_0                   :    -30.051   +/-    0.02 deg         \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Alternatively\n",
    "model = models_3fhl[\"3FHL J1731.7-3003\"]\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Models.select` can be used to select all models satisfying a list of conditions.\n",
    "To select all models applied on the cta_dataset with the characters `1748` in the name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.544446Z",
     "iopub.status.busy": "2021-11-22T21:09:53.544127Z",
     "iopub.status.idle": "2021-11-22T21:09:53.545258Z",
     "shell.execute_reply": "2021-11-22T21:09:53.545487Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models\n",
      "\n",
      "Component 0: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1748.1-2903\n",
      "  Datasets names            : None\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      4.687   +/-    1.02             \n",
      "    amplitude               :   1.24e-11   +/- 3.3e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     12.596       GeV         \n",
      "    lon_0                   :    267.037   +/-    0.02 deg         \n",
      "    lat_0                   :    -29.062   +/-    0.02 deg         \n",
      "\n",
      "Component 1: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1748.6-2816\n",
      "  Datasets names            : None\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      4.345   +/-    0.66             \n",
      "    amplitude               :   1.76e-11   +/- 3.6e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     13.199       GeV         \n",
      "    lon_0                   :    267.160   +/-    0.02 deg         \n",
      "    lat_0                   :    -28.278   +/-    0.01 deg         \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "models = models_3fhl.select(\n",
    "    datasets_names=cta_dataset.name, name_substring=\"1748\"\n",
    ")\n",
    "print(models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `Models.select()` combines the different conditions with an `AND` operator. If one needs to combine conditions with a `OR` operator, the `Models.selection_mask()` method can generate a boolean array that can be used for selection. For ex:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.548190Z",
     "iopub.status.busy": "2021-11-22T21:09:53.547885Z",
     "iopub.status.idle": "2021-11-22T21:09:53.549031Z",
     "shell.execute_reply": "2021-11-22T21:09:53.549202Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models\n",
      "\n",
      "Component 0: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1731.7-3003\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      2.742   +/-    0.50             \n",
      "    amplitude               :   2.59e-12   +/- 7.6e-13 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     17.603       GeV         \n",
      "    lon_0                   :    262.949   +/-    0.02 deg         \n",
      "    lat_0                   :    -30.051   +/-    0.02 deg         \n",
      "\n",
      "Component 1: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1748.0-2446\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      3.480   +/-    0.58             \n",
      "    amplitude               :   7.17e-12   +/- 1.6e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     14.796       GeV         \n",
      "    lon_0                   :    267.012   +/-    0.02 deg         \n",
      "    lat_0                   :    -24.775   +/-    0.01 deg         \n",
      "\n",
      "Component 2: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1748.1-2903\n",
      "  Datasets names            : None\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      4.687   +/-    1.02             \n",
      "    amplitude               :   1.24e-11   +/- 3.3e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     12.596       GeV         \n",
      "    lon_0                   :    267.037   +/-    0.02 deg         \n",
      "    lat_0                   :    -29.062   +/-    0.02 deg         \n",
      "\n",
      "Component 3: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1748.6-2816\n",
      "  Datasets names            : None\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      4.345   +/-    0.66             \n",
      "    amplitude               :   1.76e-11   +/- 3.6e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     13.199       GeV         \n",
      "    lon_0                   :    267.160   +/-    0.02 deg         \n",
      "    lat_0                   :    -28.278   +/-    0.01 deg         \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "selection_mask = models_3fhl.selection_mask(\n",
    "    name_substring=\"1748\"\n",
    ") | models_3fhl.selection_mask(name_substring=\"1731\")\n",
    "\n",
    "models_OR = models_3fhl[selection_mask]\n",
    "print(models_OR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing a model from a dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any addition or removal of a model must happen through the global models object, which must then be re-applied on the dataset(s). Note that operations **cannot** be directly performed on `dataset.models()`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.550797Z",
     "iopub.status.busy": "2021-11-22T21:09:53.550515Z",
     "iopub.status.idle": "2021-11-22T21:09:53.551924Z",
     "shell.execute_reply": "2021-11-22T21:09:53.551747Z"
    }
   },
   "outputs": [],
   "source": [
    "# cta_dataset.models.remove()\n",
    "# * this is forbidden *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.553935Z",
     "iopub.status.busy": "2021-11-22T21:09:53.553643Z",
     "iopub.status.idle": "2021-11-22T21:09:53.555176Z",
     "shell.execute_reply": "2021-11-22T21:09:53.555394Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the model '3FHL J1744.5-2609'\n",
    "models_3fhl.remove(\"3FHL J1744.5-2609\")\n",
    "len(models_3fhl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.558950Z",
     "iopub.status.busy": "2021-11-22T21:09:53.558662Z",
     "iopub.status.idle": "2021-11-22T21:09:53.559971Z",
     "shell.execute_reply": "2021-11-22T21:09:53.560128Z"
    }
   },
   "outputs": [],
   "source": [
    "# After any operation on models, it must be re-applied on the datasets\n",
    "datasets.models = models_3fhl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see the models applied on a dataset, you can simply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.562649Z",
     "iopub.status.busy": "2021-11-22T21:09:53.562291Z",
     "iopub.status.idle": "2021-11-22T21:09:53.563639Z",
     "shell.execute_reply": "2021-11-22T21:09:53.563817Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['3FHL J1731.7-3003',\n",
       " '3FHL J1732.6-3131',\n",
       " '3FHL J1741.8-2536',\n",
       " '3FHL J1745.6-2900',\n",
       " '3FHL J1745.8-3028e',\n",
       " '3FHL J1746.2-2852',\n",
       " '3FHL J1747.2-2959',\n",
       " '3FHL J1747.2-2822',\n",
       " '3FHL J1748.0-2446',\n",
       " '3FHL J1748.1-2903',\n",
       " '3FHL J1748.6-2816',\n",
       " '3FHL J1753.8-2537',\n",
       " '3FHL J1800.5-2343e',\n",
       " '3FHL J1800.7-2357',\n",
       " '3FHL J1801.5-2450',\n",
       " '3FHL J1801.6-2327',\n",
       " '3FHL J1802.3-3043',\n",
       " '3FHL J1809.8-2332',\n",
       " '3FHL J1811.2-2800']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.models.names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting models on a (counts) map\n",
    "\n",
    "The spatial regions of `Models` can be plotted on a given geom using `Models.plot_regions()`. You can also use `Models.plot_positions()` to plot the centers of each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:53.573443Z",
     "iopub.status.busy": "2021-11-22T21:09:53.566578Z",
     "iopub.status.idle": "2021-11-22T21:09:54.101953Z",
     "shell.execute_reply": "2021-11-22T21:09:54.102246Z"
    },
    "nbsphinx-thumbnail": {
     "tooltip": "Multiple datasets and models interaction in Gammapy."
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RRhlF5HS71Yxy/BJj/AMAH38jaQQkg4IGphqryqopOKRR6I21ZUDSG5nK4PgeSxzZi2uf3q/Gdtcbw9EjGbbtDJg2zhWwzcDYuasCBlQnUum5ME5+7VnfZYOUgBLGtlyRstB1tkFifRXUFvmrDnoDO5SmSezjBMZONfn/pjM3XAWjLDfvJ9hQYNM05mbp3SsH/ebr1a2R15Fh9u6PZJ+Zd20SQIGTB7Vk6+auHgRWc5lYmDSlvnzdJ+ImS7dkyjyDI+pCpZbeMgCueukySlLWvetMf11nba1rlQfGtaILutb2AvyGyRG5ts951EAy3YUBADPrRwoSPdC3hNMXJ0uARTDM+hRld791QoR58z6OkxYluOEzZibX011d3clVdAKHz5ICMMMm0CbunN5/vHK6380joBxllFFGGcXJ6X1pjXI6pUGa8ffrlVr30dl7NYYP5D4aJgoWldwDDBANoKJfTJPr0QABi/maFWFDaOR6g3Pe23VcZ7bimb8lYIaG+WBokqmT8gV3nGCShrKCSQXDXK/GfMiQHsamAbYXDPPjd5cZqYPO1sHVWC4ggamDzq4ZwKOAJw8qCWgauUfXKxJYeyFrTHdgZQQ17aPWwnrXWnWBVl2wjScCqvk9aRK717p1mdSxB4u1vNs2t9WSPuN118+k3+d+qGBQyz8cl3rpusnBRTmf44RNg7Smkdd5vbMfT2TgEpD3nCjQivSL6zg5sdLLhEYxAaJ6gI1R9XBQ8W6tjXz0WSNFGr4P5J6Ov+VZMHg/WHUHZr+RdJaxkycvp/fdPALKUUYZZZRRRE73LOgop1MapCilfm3VAODkf6Cc1Wdv0zVQvEa/a71yMOTz7xUY28ierIYiAasyqIAF1xiYTblHWQx1d4Q7toyd8a6XNXBMtlZZF29M0/hnABmmScBGxm1Yk0ZQ25sbrgfrLAcZpiI/ZykPQLNP6xPV3bPJClVQxPuDMG88z2ODjgTIdV0JUhTcEBgpWKrpnWVYBuB82rynyaiE4Nd/ZrNFcKpu1ASOWh5dR7nSAgdtyXzPJf+F9JhO/lZgx2A+BJTzfANdn7X91aWb/Vldndl/6ObL/j3oTUBiTZcDEJN2HHQgIL7rry7yKsFuTQj+6BWh4FQnrvT6xh3jnAKfFZyIUn3r84f31t6OOo5OVk73u3kElKOMMsooozg5vS+tUU6naCRWZRlqoEiFuIymkrqt0fDtUZpS2jsnKIGQXqOgUJk5b/joOV1ySWZigpIh8i6ZQBkMZZnB3fQWfVJBQO/+BxYBeWEgC8ABxHh3kW41WNFwL+ouxVyvCQ8ksMgILmNoa98DYJHraoF+9PrptAQ0hXtyZwCI6SiI8+BypS3BlF9bWuimqde7yh72JfCqiQeTEGZW6z+sP5Q1tfOu7IvLyt7PbD0ykPoP+9ikWwRRnJRgBFfWZ5aD3QzrGoXF7fvE2EcBvCo6iRSakn0GXKCmvgy4pUmxrKFyDO5aAkeyjnqvnyzh8RaL42tZ8JwBwOdvurbq88pfz/roc+f45fS+m0dAOcooo4wyipPT+9Ia5XRLA1vrp4BSDSxlJ3W/OLICg9taNsjRLQbHUMOtkXQ04Iw36pR10DSYTm1NV2Gc5osHQ783A13dJov7FXQSKPXGoGoAHpafoFzZRDWyfdTRGktWM9SVyZm44xpMiYxSwao6UOkZRgXX/lpA3Fv7xetqAXtqAFHXK7YZHUTpI+oOq6CWZeVHXTLn/eKkx8TV2wNfFQ9AlzGhTWNMtZYHsHWknKxgwJ3hXqQy11yJmyatjQWM4R/0hbLvDJM1rW0Dozoj8ON46DPbOXc67WQdqY4PugbX6k7X2ZhBpXeNx5LffeV/zQ95koZlniy5h88cjT7dYlFnvLZH+ZxaJjWX3E7KePxy/O/mEMIbALwcwIUY4wvyse8E8DeRPPH/B4AvjTFuH5bOCChHGWWUUUYZZZQbEgISsnrcL3KZMd7AZv0VyBVrLsVtctItrokEFg1THwFSg3V4pk/d3ujyyrIvrPvMRjHTVUMVfX2rgGUsXugs+izBpF8b59eBUbo+7WOo4EXdFSH1UL1ofZlvzehVlkndIWv1WiZk7hSQ8j6yu8qcKtPngWqN9fXpt60xz7W1jXq/DyDk3Xx5nw/aQ/HrJ4/Shaap3wSHbY72O0fJpA7pw9xIvQ6GoEEwUFXkqWnkz0RYUg9q+b93wT7TJYbZB1vihI8Hkb59/IQHy+zryW+dNGJ5gHLMr8gFtXt1gkb1oEX1rrfezVafSx7g+3WVTI/M67LI1adUfgTAqwH8mBz7VQDfHGPsQgj/CsA3A/jGwxIZAeUoo4wyyigiJ7NOY8ks6E8D+Oh8ySaA7Rjjx1XufTeAJ5C9EmOMLzr2Ao5yQxKRprJptA3GHw35poyOSRDWw9zRgDJS7GF5ebZC1z4WEVvddbyPAXg6pEA4+7C1VA1sc3Qa4UMafZnOUJ3MnjHAjLp1ejdFbg+h9aFQF8pQDnnkuiFHrlWwruwjdeH1WFufqQCWvzWKLWVhTZ3Um8FW9Dc/XKcHJBA97+ppafCdBVBVAS4LQLECPHldrDzOlgFVoFxz2DQYtvQAcn/uF+/19VhwZwWKbUg0Yur+Pob1hQRhHEcEwIftddkgB5HC4tjghAr78UolnZreNY0+62ToI7k8GrVXy+TdYnVt79BuojNO2AxRfdl/UAbw0TXNyuR7oNhDIt/KvRqwS0UDiKn4bqNjkhMYNY/nDuZ2fLxyMu/mGONbQgh3u2O/Ij9/C8DnHZXOCChHGWWUUUZxcvwvLVRmQWOMr+T/IYTvBvD4Iff/9RjjxZMo2Cg3LnTfbGBrjTpk9zY1xrM0+Ryvr7mW+SAzNBRrLm36W4PxeJufLKe6xpIh7GEgkgwrAVFPsNgvut8yzcGAz+AyOiCwjM0Kcu8KEjig27CvJ/VMZlSBck2HZLgI+mpbNgxGubBmPlInYEBJAQR1o5FB2W6MGKvbgyjYrK1l9GBEAaOC89p1tbS6QwCsFx7z4EqDzDS9BboZ8u4XmUsVptc5QOnr7LdQ8Vu5LCu7gqoqCJJ2Vf0ddFYXZaHpNVCrjrLWtX5N5n0Ys7U2EkDOZ4IC3b7Pe5HKuFeX9KH/owz4dZjUgoBxzAGli77mxWcIr6XLrHpLqEfAyct1vZvPhRDul9+vjTG+9hru/zIAP33URSOgHGWUUUYZReTmzYJSQggBwCsAfOqxZzzKTRUafzNkBkGYDW8IK3tQYw4AM9w1aI0ahJ55VKERyGsUaDJNXUNFcNvm70lbAhVuf7ACMzzpsqrueEMkWVIsMEBAgKN5DkFUYICSeQwM0ZL61YRG8goMTAAAZlZnZS/VDbQWNEbBjGdeKQqY+DlojKWcTstzR4mC1ZrrsL9moW+58vB+Pa9S2+eSdZ+6qLr+cyBgzK+55Kfryut8lFoPhpWR9UBa6xIa6Qf9Yp+YtCkqsGfKtU8r0Kcb8DIXbu0T3o3XA/IFlUpdawz+gt5mpVupToaoe7rfPqSH9W8PtNVdvYc9mwY3WrlO759C1nQjsc37Uq4iKjFOCmBe97v54vV69YQQvhVJHT9x1LUjoBxllFFGGcXJTZ8F/WsA3hdj/JMl5yOAXwkhRAA/dI2zq6PcBGmQGD3AmL8rSIZa4f4m99A88u6pXrwrHoNv+Px5Xo/5dVF6XtOgAbmCxei0HpQwva634Do0cP2aUNAwFmaLWz3Q8G1ga9z8huvDOkGUOloAVhUdNFgMANNk18oFJqsxNnZw8RXw4YPkLHP3JGBWoOSD5dQAoj9frDmtXEdGjUwZWTa9bu6BUlOyjQqEFlhHAdcExAooWUd+H0ieWi7+P5tZ2pMjLG+2Z3USoSnTXWkTUFVmkxKaBCan03qerLOOr9AsrkXVcmk/UMDu3Vp14gYox6Df69PXCzCX2pXczrrdj24LRK8Iur/6vDgmCUx1/1V/bVFG2ITUChb3fo25gnwe6ZrJk93c4+RS9hJC+BKkZSqfFmNcNuc3yAgoRxlllFFGcXJzZ0EBfAGAnzzk/CfFGB8JIZwH8KshhD+OMb7lOvMa5QSE7mDsOWT+lL3zRiZQN5onMON/MFSxnMX02wwE9926a8nQEWyqyytBKJCMRnULBNxavmxQB5RspxquJClbYacYCIi6OAxMAgIqG1SDoMzFwKUOeJ+yQARFrQAhCsFTDTjS8A8u35pbY038ukIfqVTPMSAQhXkq+Bvqm9NS8KZl8+6uBIfebXYAsw5QKqhU8MP8DgTUArZGNEo7MViS7sEJlGCXMmkM7Pe5PWNT6s23C+8nqFSdcS0sWcqqdBbRlWVgH25l4kHb2k9m+H0ua92hl38aAE3FlZfij7cOpDZIk1WaNstcA5WAgcoJSvDHe/jMYYIMctQg6VFZ3tlsCQPr0j0ZuTmAMoTwUqQgPJ8SY9y7mntGQDnKKKOMMorIzd08OYTQAvhcAH9xaYlifCR/Xwgh/AyAFwMYAeUpEgVo7D1q6OlvH6FV90Uk0+CjTgYsRmnVe1WUxdRtSJgWAaXuc6lrKXld19dd/4a8Gwx7S/o9JHUbDl/OYiN6lAyLrgc7DKipUU9wNGz7ULlPARKvU9ZMwZM/NgBRYfaUmdKASwp0agzksjoUvyv3kYGsubcOrqAK2uVaBYUEWAAs+EslPa8Tr29AtsFoZAKEZe5LoOb16wGlHm96Y7z7HsCsrItniXmvgkqeb5p6kCXvpkoZmHTksdGX99R+exfXZd1Wx4eWz5dJy39G9iQlcOU6Zp1gOmy7ES/LwF7RZwl8G2Mmi0kISWuK8rl3cnJiAfN+EsBLkLyMHgbwbUhRXc8gTeACwG/FGL/qsHRGQDnKKKOMMoqTmwcoAfwNAH8cY3y4djKEcBZAE2N8Iv//GQC+/WYWcJSjhQCNYI5Co0+NON3SonHnWiQmbsUZzpO+dCdtJA01ZAjmmC7XJJLd0+idamTPZikPrrEa1lv2BnCBMmon85sAWMXiqNEIlSyXil/7VTCtnpljWXqr8FGAzYt3L6SBrCymB5R+mwnmNTBuBJZZN9691YNQX+5aubyQ+VMgy2uVwVQd1NxufbAhv+0D0/NrF71+NS8yqpMMvHX7Fq/HFQGUs5kxZcHpKZL1QwaWPbAyA1YcQPa6IxDUcvpy4JDzw3plBaWwvncgbC9g29UUaTItHC6+/Q5juz1TH/o6a9nINbrekudZn9rekUNfcPn7/VUHl+58jMF8Wrl1GWA9Hjn+d3OM8Qsqh19/remMgHKUUUYZZRSRmzcLGmN8PYDPh3N3DSHcBeB1McaXAXg6gJ/Js6QtgH8bY/ylYy/gKDcsNdBIad05H8SG9zKIjBrzbQt0s5JRZH5NJW2yigND2dgWDWqwegAVOqDpXPpyzWBwi2HdNMAZAX38JqaobSugZacOlsnAvkna3PBzGUBgnm0GJt7Q82DOgy6V2lYdQMlCkh2cyz0KyjybWWPZDgPGyvx5sKrBg5pmcc/MZULgV1tjWQO2ywD7pAFiC9y2asdmM2HTGlvL6EH5ssA2B3kmYmFdrvQ7Xc8IWFoK8AiADrrF9vK6ZF1UfwrYegC9265mmV6uJiDNoPe+/F3Thx+z6Z9c1r5kPCl8vvjgPXzWMAo1YIDTV6VBGaEXSLrkGmidtKptZXT8cnO9h65VRkA5yiijjDKKk5s2C4oY49+tHHsEwMvy/+8E8MJjL9Aoxy41l1TAQB9dDf3G9gRuwCKYHMBOtviWBeNR8Qyld/fjusghDQdyaET6ffuabOw3KBmqqbOkug7V7UV8eZcdpwz78uWLadgDi0GDum4RxDZA4dapRroCZQad4fYePkCNGtTeHXSoL1m/vgRlBDkE4bomUPVLgKRrOz2Q0LxXpqUrJ9M4DJj2uRwEp5oX05grSK6UoSZkGM9MjWmrpa3bp/A3ddf3i2tUKZ5J1bJolNdmSdvp3p963Lvcsgy1KK263Q5P+C07GrnWi9/3lHkvAFjXT3W9LOsLLJZRx+QB6hM6OnHFSRxd6+zL3SMD3s7GRcx51tz2h0kxLLbX8ckIKEcZZZRRRrll5PS+tEY5naJ7uCmIojuYBgbp+8QGzh1ICRWDntK2GILg6D3qEurLQgOS69LUQO36krXxbJF3i1XAQlc/z2bpd+hy8Bspn7r6Qn7rNh41loOgge63BSilYd6XRnSDHAGzN1dFBVxNY2sjV6aL7qCUuWO9ij0ZHej37rPMkwFygPLeldaALGDRUDVqaddZxE9eVwuUo+Xx/6uLrrYhj3O/Rx9spwZkfL+jbtSFs+bSq5MVeqxg1VTPKNfYsi9P3D1D2fo6s8o6MZAMy6x5sTwcA+yzHhSR1fP9mNvTJMWUru/erduXrbYfpx+nRVFZB5TurARxzFfZx4lcM2nLAFatpBElXUrIibTIZeltvTTv0+fdyQFJyul9N4+AcpRRRhllFJHT7VYzyukUnaGn2ylgDEbNEFb3u6YxI3c2K/epG/Jo6gab2IcASiaiB5I7JiNy6o3CrKmBX1t/56+prU9TlhXIrBsWo2B6sMOkaKAuE55T4De4NUrdFIh0MFdFZQQHl9HW3DGBcvsHps/vpkkMI+vo2aV5pX5DHUVfTVOuzaQ7sjJ0CioJPhXka+RZLat3HaUbp2+bok2kbAq6tS5NRiQKrrVNubZO93T0adX0UQOBKxn0aJTVBrYvqorPT4VlJZj061lroHYAxZJOgwSmOOlBwEVX0gYls91kINjI/V504sjXaXBHxmKUaH538puglYF6VA0E4y2Ewc2/IfrVKM06+TPsTdkbcOR2JQv7uUpaJyOn+908AspRRhlllFGcnN6X1iinU/xaSAU1A7irgJTen2OCvTEKNFQ9KKuxk8yTQsPPg7YGyfANFbDhA8t4o5fna2u+hjIoUJVrlNkcqtovbuNRExr7tfuXbamibE0rwJJssabF9XHL1rERzM17FMjcM17qUuwZPAJJ5qsgctKUWzrUgJcCYXXv9O3BQD5zLAIxdeukxN4mMlgWn69e73UzuB13tm6RssCmSzpB6kQhyK4BRc/+AuXen6pz1nGOkg1UUOwjmC4TArNYOUbAz75+0GHpXpYsdwPrUypsN7KhmgSBnu77qJGl1Q1cgZ66oGrdW05o5d/Mz+erDCQnbmprJmuuyccvp/fdPALKUUYZZZRRRE73LOgop1f8BuI08HrU3U5JDvmIjOx9LYBpZ/v6QdJbZqwqgKXhdwUla5DJJrsIy1kbNdqB8rgHPDXA4c9NBEAwLabtQWXsrd4KdGrlWxZlcogUiiGWD6ZSlwndHLt6HXwdWa6uXzwP5HpB2jSfo2vr1AWnUWCnW2lowBzmraDUu+YqoNN0h/ZzIFzdiLWulLkAXuq8OO8mOBRUEhQ1mQ3z25NoOpMGwLRcP+qBsboMe2Gd1aV10AlsfE36FNiqkXMNrI95ffpyAjZuCNaGLXncumfup6nl0G/AAkbp3p9d1hvHq/ZnP1Hly0QX94XIvaqLfhHwNRktqq4032KtMg7fC1cn1E5GTve7eQSUo4wyyiijODm9L61Rbg3x6ynnztAnyFF3NrIDXAM4GI+9MR49ShbLB/vQ7UHavh50Y2HPumzIKthRwEZD/6h1k7xewekyt8daGsqWoHKfj5DKeyd5fSmltm2LAsse6cdkZmmRsVxWVi2zMnw++MzgGqugqbX1khpMh/cPVXbAUs95XanU3D2XXavgadlei/wmOO7k2DL3Uj2vLrjMaxkg9Owk1+cSRBJoDsxwFgWzjDx6gEUhBKF7pgdITR54cywyxgqygHItooJt3yeHiQG5369LbGBspuqIfZSeBWQXqaIG9nxAPr4CcU1FHfQNHhMZVFb7uehnIr81bZZdXfw9mKTr7MnI6X03j4BylFFGGWUUJ6f3pTXK6RQCFm+EEiQ2SP8ocOT/lE4+gDEPDIwBlC6sNOAaWFCQATA0ybA94wz5WiTH4Vje7kEjuw5ujL3dr3sK1tjCBabIAaTaFg5qTCPXu8kMjq4b9Mwm3Q0BFK61rNuB6EmL1QBoZpb2AKAqbBxQlkHLrecVcFJWpNyMJuvBt65xrDGGHkyqO613XeU1dBmlDPtQCoDTqL018XXU8vpARdoPautLWV5/T61NCdKHiZG2LAv/P5B1mweoA8qhDMCwnY6KehLoFhwEhDoe57A9XX3f13LR+0DHKser6pqMoQL2QT8wwN/CQGyTJ5f0GcFzCmKZlNaP/6+wrq7hGUCMW4zwuVLslan9AaZPfd6drJzed/MIKEcZZZRRRhE53W41o5xO6ZFc1SgauKNBMi5pJCpb5tlCdTmjm2az5LymT1HjvkV2dczodQIzRMkw0FWOdfAbR+p6LiAbr7kAPjIqXQ9rUrA2+ZpiDVxv68MAY2dYMO4nuQzwTcRaH9YtZgOY7BTZYo7wDougyddHQZ0HlPM+AQwCQj2v4GplmhjQGphUPTAf/fagUutXEx+BVtMlA+0BWlH3/L93Z6WoyzawuNWH1lvL6YMdkREkw8xJgSC6ZyCiee67ynwuuEdjkQkESlayl/81imwBMh3Q6gDMYIAUSP0q9GU/HFyVpZ97F/aB3eTHgVFeM4WN+2HSKAM7eiroZJIv74Grk67nZh04YUO9ULR8HAtFX/QLLFHq8WTfnqf73TwCylFGGWWUUZyc3pfWKKdTIoB9lEEygBLs+c3GIdf0MOPXB9HR/2trqFTmfQlKBhAzy+vIYGyJshvqxkn3XOat9wz5NsYOHhawZah770D0zEAGI1oSLDMfBZgrXcmu1dgyAsHCpbMvt3TR7VSUjWKd/LYgDC6jro3U74pcx3N6DfWvQI5thIqOjgoMM1zfYwFw8v6ae66WX9lAv6ZSmWmgXJNZLQeyDpdRnCI+gFCDw+vq3aXnfbkvqWcHh3JpGihdMfnt1/oVAbSWpMm+O7h3Or3ovpB+nLKvtQCmzeKazbkDiNSPD9Ck4NK/ovo+TWgRUNaaTQFPLYBOcL9jL7rJF4fMkvrnlLrua2Ta45fT+24eAeUop1JCCB8N4KcAfASAb40x/usTzOv/APBTMcYfvYZ77gbwLgArMcYlr5xRRhlllA8NUUNKg3YA1zav3qCyj6Scn1Su7VEa+B5kEMiELkU65Wm67ilIm81s7zvPOBxVB2VpNKBO7IGZgFkCxRUBrvoS6Sv/z4FEFcEYNgZ/GYxaMXp9kBU12On213r2hdfIvbpdB88pC0fgo4CSZfDrJpnPsq0uam6dKv7aGjDUYx5MKtDVtHQtoNaJ1zIwzjIA6BnTWh/wovXUtH0wIaY1THLk4z6SrF8fCSxOHHBceoDJa7T95j3QdzbpwskIMqEd0vlhUsJ9U3ieYHKZ27QGcSJgXEGZroLKYpKpt+BbBzBGVdOd8FqUXg/6vGngnl99Ao8+KFitfp4dPr2w7+RkBJSjnFb5ZwB+I8b48SedUYzxM08y/RDCSwD8eIzxWX8W8hnlz7qcjFtNCOENAF4O4EKM8QX52L0A/h6A9+fLviXG+IuVe18K4PuR3vuvizF+x7EXcJRjlZora03mlXMMgqGgkkIDUPOpsZRcCwmUzFnXlGsZdR/EwUgX8OcZnzlKMKSBUYo1ZHoOFmhEy6xuwD4vBZ6FUT0D2gws6YrbowTS01xY7rlHIECWqEXJGiqgnvflmkbPWCnQGvTSlMcIJnXdoq4jPIxdHfYg7MV115VlmYusB3wEdDUw7Ndjal/w7GDfW1Rbn6+CdwWSOsExR3kN01bGUgPxqG7m8pv9Vq9V11e27dWKNq3fo7Tvc5k5iSH3+MA+tTRJInJst1jsb8xHAxEtK5/PR8cHJ7KuuN+8rkEaJ34fTaap40KBuQZtavvKRIQr48kF4qGMLq+jjHI98lwkhvKaJITQjozhKKPcqJzIS+tHALwawI+5498bY/yuZTeFECYAXgPg0wE8DOB3Qwg/H2P8rydRyFGuTxjQQtcgedbCbwTug+sABhgZGIPg0gM7DyJrrnq18z5ojAcZkyYZpgR4NSOxQw7mko3tSVuub1PXPwI+7wbnGSVvgFM3M5RBUeYAzuTfWsYheFGl3gxk0iCxkuqG6rfFIBPm97pUWcbU8biyo2R99XwNRHTuHIPN6D6NEwmmw7SUhayBkkIPAgLpNqts66RdvK7mWsr1qrWgNEBuwwqo9GUr3D4dcCSY1G1B/LYiA6MO61t+skWZM44/P+Y0yJROLgy6kaBUZDJVlrHb/p6avnQSRrcaaVCuaVU3dEqEBSPi5EptHSPr64GwelRwHK2g1GUxRnsbp8pM3lxQefzv5iWTvVsAfhrA3QDeDeAVMcYPHJbOEY/gUUa5+RJC+HUAfx3Aq0MIuyGEjw4hfFcI4aEQwvtCCD8YQrgtX/uSEMLDIYRvDCE8CuDfhBDuDSH8uxDCj4cQngghvD2E8FEhhG8OIVwIIfxpCOEzJL/fCCF8xRFlmuQyXAwhvBPAZ7nzXxpCeCDn984Qwt/Px88C+D8A3JXrshtCuCuE8OIQwm+GELZDCO8NIbw6hDDN94QQwvfmsj4eQviDEAIH+ZmaLpblc1xtMsqHmvTX8TlcYoxvAXDpOgrzYgDviDG+M8Y4Q5po+uzrSGeUUUYZZZRRbmE5/ncz0mTvS92xbwLwazHGjwTwa/n3oTICylFOncQYPxXAfwLw1THGdQD/AMBHAfg4pDWVzwTwz+WWOwFsIbGaX5mP/U0AbwTwFAD/BcAvI/X3ZwL4dgA/dI3F+ntIMzgfD+BFAD7Pnb+Qz28A+FIA3xtC+AsxxssAPhPAIzHG9fx5BGki658AOAfgLwP4NAD/MKf1GQA+Odd5E8ArATyWz/2rmi4OyWeUUa5RNDbfNb20zoUQ7pfPV1YSr8lX50mTN4QQnlI5/0wAfyq/H87HRjlFEpBcLVfyd4tFd1RlEIZ1WPLNdJjGWgusrebPNK3Bmja29k+jZIbMPNLFcsWxTX5N3gKj6M5Dylor71zYouL/JewkXVfJ5A6bwuf6qvtclPuYDoON7MOCj1Amks4KSjfP0MgaORgLtTIFVldT9FUNVMP6+DV6Xo+HfVQfsxmwvw/s7QGX82c2K3VNZm42s4+PMDtpU1lZ3olE2GVbK8vG/lATZQrVHdqvO9V6s5x6D5ne1VX7fzotg/uwbgdSpxpbp7rV69kOXp/7+8DeLPUFstg9ShZc2bK+ct7X068z1aBKq/ysLgZamshH3VrbRpjxdlGXOma4jyb7PNuDbb66mtNvKi7uKJ8tgLnLT5CeJXwm8VsZS92rU99oXpfL4JmOXaZ7MuDqut/Nh6dan+z9bACMK/KjAD7nqHRGl9dRTrWEEAISmPvzMcZL+dj/CuDfAvjmfFkP4NtijFfyeQD4TzHGX86//x2AzwXwHTHGeQjhpwC8NoSwGWPcvsqivALA98UY/zSn+S8BvIQnY4z/Ua79v0MIvwLgrwH4vVpiMca3ys93hxB+CMCnAPg+pGfq7QA+BsDvxBgfuAZdjDLKMch1udVcjDG+6Brv+QEA/wLpTfkvAHw3gC9z19Q8+U4uiN4o1yV0D1vB4joqBYzexNGtQxRMnmltqwnAjGkGSAFKd8fD1vSp8ar3z/vkxsa1aeqiSMNQXelYTjWcFBjRTbPHYiAhNXQJKocy9+Xm6dw7kvcxKApFOz/Xfa3A1oz6fTS9tOLiqOvCPKjm2sfWWYrdEqCpaen6SAJUXsN8QwVgDPt+dgYq2taAJPOslcHLvF9cu3hY0J/amsZa+qyDjxrL+xrZOsZv7+EDGvk2OJD6axk9GON6QY4voHQVp/h9IIHSTbMGmL2+amtP9fqmUr++t8kMD+y1bsM6USw+1DVIkHeXZp56D/8nYAQWoxr7wESqD78mUs/5AEde5rDnQo+TBFfX9W4+F0K4X36/Nsb42iPueXqM8b0AEGN8bwjh/FGZjIBylNMuTwOwBuCtGSgCi67t748x7rv73if/P4lk7M7lNwCsA9i+ynLchZIleVBPhhA+E8C3IbGHTS7z25clFkL4KADfg8R2riGNxbcCQIzx10MIr0ZaN/acEMLPAPgGAKs4WhejjHKDcvMW/scYh3EaQvhhAL9QuexhAM+W388CMLLvp1BozGpQCwDFRuRe1NDj/S3KyJwM1a9GN2BgoQYmFQQpUCmMe0lLjfqWSBgl86HRIVUUrHI/SaCMHql56gObRrcHlT0MeBLUQu5lOhOU0TO1LoMRXimzZ6KWrYv0QW9qQKzGuBEQ+oAytQA+ZLB0D0+yjNNpyXz5tGvl5DVedOKAYIb9rM/IzK+Bq9WXQkb8KFDG/73OawylBtth+bhmU0E3G9VPXDTut6qhl+O9u3YZ2wzUy8rjtXoP+TeOJe1L3etWIzpmCEJreRb7ueZjfs0jmUjWtQYGPQivAfGaFBMokibHaY+TNMiu+918PZO91ywjoBzltMtFJAD4/Bjje5ZcczPYiveiNGqfw39CCGcA/AcAfwfAz8UYD0IIP4vFoGIqP4DkivsFMcYnQgj/GOJGm7dJ+dd5VuhNAP4pEmA9TBcjazPKMcnNAZQhhGdwFhTA/wTgDyuX/S6AjwwhPA/AewB8PoAvvCkFHOWqhYagN2qVNVgWsIJGYBFhsTfgoGyKB4o1MKTgQhkvbgcyXJcZytgvMkYho7gOFqSnqeRRMHLCtCiY9IyFt5OHwDi5PCwjDW0fuVJd9mrRM72o/paxeQuBZ9pFt2GKsnfM8zCXxlo+jATLa+d9iqSp19Ct9Mx0ebmX1aFtS1C5wIjlvhOlHWtMZE2WAUXKSmt9ClgEt7V2WnAbdmBZ+1vbAiuz1B90f1G6UGuxr8a9Y1Lpy97l2DOpy/qS6kX7yRxWJ42Cq8DQT8AM4Bk2YTPPY3KGxf0mea8CSv9c8aKTWUO+sLHHNGturHwmMFhUbULi+OXmvJsBvI/v5xDCM5CWdR0qI6Ac5VRLjLHPzMX3hhC+OsZ4IYTwTAAvoEvrTZI3AfjaEMIvALiMcoHyFCnw3vsBdJmt/AyYcfw+AE8NIdwRY3w8H7sdwA6A3RDCxyCtE30/AIQQPgHpOfV7Oa99APOr0EUtn1FGuQ45/pdWCOEnkdzEz4UQHkaaIHlJCOHjkGycdwNgMKu7kLYHeVmMsQshfDXSOugJgDfEGP/o2As4yg1Jg/QgbFAann59kX7zPFAafF3+cwVmQKsBOzBKImqA11wb+76+4XgAsJLZKa4Laxpg2qd1agezMvIjDVCmzW9G21QDt0FmO1G6/fl9BweAIdcog0NmlIaulmHIawmD5MGLj2aq+w4yCY386t2G/Z6MXWduyR5k85h3meQ6V1/22JbXhaZ0da21qa+vB5Ue4Hj3WwW4jBSrzPcC26vpdeWWKCpkV1nf2jU1NpD5siyqn6ZJa19Zl76zJzWBVOGOvZjlVQvdaoG0H2M/szYBSjdzD4Cj19URhWmW/K9reDlZo1vw6JZDZAu5lnjoy1gEgz3E7Vzu4xjTNIf8eV7rJNdM8uTESm9A/2TkpgHKnwfwJQC+I3//3FE3jIBylFtBvhEpCM9vhRDOIbEUP4BkYN4s+WEkd9a3IQHB7wLwqQCQGcavRQKdZwD870iDEfn8H2dj+p15C4R7kFxYX4u03+Z/QQrP/Kn5lg0A3wvgw5DA5C/n/IBDdFHLZwzMM8q1y8m4vMYYv6By+PVLrn0EwMvk9y8CWNifcpTTIwHGOAEY3DjV7Y5ASN02AQNd6sp2BenGlS6ly/WUNM4L9z+ReW8ZEgwwUE4u0vC/btHBNVme6es64EDuVzDJ/S7JmuiekTRiycQpeGGgFd343oOhRurX9AbMa2CSboBePJgkuKEuVZ/Mj4DqMLbRu0MuW2+n+fp1dApA/HpFgju/PlFBrHct1W/N1x+vATi6L3Lbiq5fBLH8fwB5oq9G6s7rJxmlcEuZZS6lvv9O2sTULpSvMaA5fKM+SXM9Ums3DVbDOg9rF3M7FcGtlqStIE3Z+8NYe58uJ4M4znSsMb3BXb6SnpatqZz3E2H6FuR3bf/KBlkn7OMnivdObI/o2mTvdwB4UwjhywE8BOBvH5lOjKOX3CijjDLKKEle9KLnxfvv/7Zrvi+EL33rzVinMcrxSwjh2Uj7g96JZLG8Nsb4/Zkp/nEATwD4ohjj7rI0njUJ8WskgI4CLQ3K49c0eRat64BZb/c1yBFfp8Da2qKBTnBBNoMgrdjjz5VFP0BiNM4C2FgDblsDbltN5ZrNgN1dYGcX2IO5wHHNlbKGypawnmdQRsOk0c6IproWdNmautnMImBq2hAdqnsqUH57ZrdtU8RMRutUkOuBoS+LX/vGPOia6t1egZLtZDre9dGDUe9uW2MjtVy1utYAsd/PsXYfj6lL7kGOusoItNQlQbnvbyq+3sqQ6rpSfrMvMz9fzq6zKK/7s+T6mfFMwbgBJaPnQRDdPNum3j/394Ene+vTujbai3fzXLa/qg8m1cJ0Xds/VMHk3KXDcafpKRupzeH35mxyvf0kB/fC7GHPrFrZNd0zyJFjc9upO+5b/uJfxP3331/zOr4uOe3v5luOoQwhvDHG+KoPdjluZRl1eDwy6vHGZdThjcvx6/DmBeUZ5dRIB+DrY4y/F0K4HSnw168irQv/GiRviS8G8IPLEggoXfvmBAh9adBxCw7acrp5fN9LAB4YSJugXFOpwrVyNOi7bLQPbmsKFGBGTy1qat8vMmNnVoHVWXL584FxNGCPGrhq0HsGy8u8M7an5mZK8azuAsNSAUX8KOOn21tw2w1KLzqulZftqaDQ6+sw0Ws6B6yYn04u+EkBoL6u00fpXaYXgtXatf53DSB691kCVXVPVWCkQZIosSn1yzS8WzfBtgYqUr3r9jQElMtEibPeHVNgq7oPTXJ1VZbSj5lamk2+zkd7VmBZkyD19mVntGNlJBU01hhPimcmGY1VQfTQD6lbLAeyml8DeyYciK543/HL6X43X8Uj4NTJ536wC/BnQEYdViSE8IMhhN3KZ5kRNerxxmXU4Y3LCeiwv47PKLeqxBjfG2P8vfz/EwAeQNrrkwEhe9TjeywIDWRlsZSVHK5j3o6lqpYP9X0jAQMhhUspksvsFSS2cwArjTEjK7D/tWJzl/4AxORerYeCydraq5gZp9oei0OAkd62i9A9B1UIhtvMnOnef7r/orrsalvoejwP/nS7jCE/B06ZLsEo9wf0+xEGuZbMk9cpQa4CqrnoYDYr92Hkdiy+PJo3y6/3+Ppwu5RlbKqK9kvvjkuX09o9yoLWIs1qWRT08+O3I7kqoC7/F3u0omQLNcBTDxsnswxcr3j21tdPvvXJ711QB5awKfvotEmMnoJNCvWprOGgE5TjllsLTVGO5YDF8exlglLPfgKH4Jn1OkAZAEjBdZ/P7bsP99M8GTm97+ZbjqEcZZSTkhjjVwH4qg92OUYZ5YMrp3sWdJSTlRDC3QA+HsBvA/jvAN4I4HEcEVk3olwbqEYpP1wHpSxlA1uLSJczGryARICtsHYUbxSqO2uUfHht26f8GQlyYFAyyNvfN7aVoCA0FoW00Jf8P6ybhLjEZptunvVDkNX3yZhX1gN9qquyvCwfsOjaSlEABxgA8syfX7/HqJTKElJHqldlOjV9pq3rRHmfph9z/bWd6E466NSxjH6fSV0/SPCqzCBdRXWP0pXW2D9NZ9gj84jHnKZdrI2Ucii4XLZFBn/PXX7K6Hsm1bcZZdIAc+oCJUvXwAEyoFh/q2U8gPXXOZL6W1nXy/1UIWryeTUoVdjouWZxDe8cFs3Yj2NdR0tRV+JJn8qnGF33hvUTVssYQq8jQPo/SkbSg0vv8uvd3E9+AeHpfjffioByLYQQAeu8+sKYyDG+lKJcw5kMvtw0jTnSbEOAvSRaOceXBM+zM/GlxA7IQaabpfI4qfuZlMvXJSLN4rBx+HLkLC/LxjRY9xbljFFE2riQafBlHgCshBA5sxPkvl7KARh9rxGxotzDwV17qeqY9Xn0KF14JjA3AeqCM1KqJ+qe5WBdKfqQ1Gv50GSfYBv1kg9gD9YeZV+5ko9rP5oAOBsCo45jJtdoGSfye4Ky7OyDOsvnNwfvJV1fVsD2LmOa+pBnXldcmViHIPXVPtjBZvrYr0lVsJzUD+QYxxtdQoJ89Divow55XvsbyzPHYpuzbrohOttu7s5rf6CetN/qy4NlVXcWvddf31Q+bK9l6y507RXLekbyUDtHx5G2ke7vN2WIxWOV0/vSGuXkJISwjrQF0j+OMe4gBSD75EOufyMyQ34bgL39RQOzlQ5c2xePAVGABCb5HPXRG/2aOmDxN9NUQMl3PXozdIHFfecaJKOW69aUPasxZHTNRV+Oc66dpGulRqfUwe03nB/eSb0FxqlJzVBXsDfowQEaIIPiDPpVd8roKXvjpRasR8uk4HPItyvf0Xo/t9fwxxWE1ZjSZQxhTRQMa166R+Iyd2TPims5a4wwt6BRcM5JBKZXA+3Lyl2Lbjuw8ATF3SLgo7CP+jW3+h7370L0BvKVdWQ/BerACnJOQZsfn1o/SuwXr2Eb6XYlXZOA5ZxrT6V8y4TZ6Htdt0nhRAQnsmrv7Ea+1R4AFiGenn/Xu96FEMJlOf3mG1+ecnrfzbcioESLtCP9OQBbSICuQ1o0T0N4NX/vIu1cv5s/Z+Q+fbHxBbSb057KvQ1S2M21/Hsjp7+f79lAauLd/HtdykmQt5Ov3wLwFKRNDffyeaaxno9dRNq5+yxSZ72U095Eetmu5mtnAM7n35eQdvpm3Wc5z2nOdzun/aycF/LxaS7fXr5nPf/ezmlNkTafaXPZWbe9XKY+H1MdXMrlYn128jm6J+zn41soX6bM51zW9W7+pr5ZB5ahl+vYFnehBC8rSLuir+X8Z0h7E7Dcd+ZzBFVc4P5w/t7KnzbXi3meF91vZ92/W/TGPnYJqT0bpBCxqu9L+drNfH4fwDtz3djfeG4n13NLdMX2Zd02cnrU4zoMFO3kPKkn1pv9hP2WfWMvp8nxRd1wwflMruGxrXzvo7C+hJxnm9PezMd2c74NUptxHKiO9yXP3XwN9c4+xH7FNC/BngOrOb8t0Q31uJfT2cjHHs1lYR9B/n87p0NdXYQtwl/LdWTZ+Zjfd+XiGGB5CTSbnAbLOZX0Hpa878zfHKOQtDcBPPnn/hyOX07vS2uUk5EQwgoSmPyJGOObr+aebBy9CgDuDCHu5eN8ppNto/GoBirkWh4kwOIY0QlBz5YdtQbOG8K9XKcTQF40OA1ggVQU/CEDPhrsygCtoFybGHNZ/ZqzAmRVylGrizI7qgOygQo2+PGurl2XQOUVGMPHQDG8LjQYAqZ4WbbGc2ARs1U5aYQB7hfv5f10kQVK9tYzlDUX1YHVFgDnt7U4ymVUgfeya2v1VBfgZRFhKTXAtCyPGkjXvhjl/6ZJbPpsJv2zAsx0ypHAiWNCJ5TZL2tqaNz/A8hyfUEZa4qW2+tyqLdLX9dWF+2Yjfy59A/PHtbKz/7sXa25tyXBJMtB8kc9KbwrbW3tNO1OAHje856Hxx577GylODcgp/fdfMsBygYGenr5vQ8zFNfkHI3tVZiRx980VnsYk7ea76PhtgUzoIFy9pOGNAETDXYCig2kh/b7YEwHYLNCNEqZPw13Gq80/lskoxIwEHgbUsedyodgWFksGrKsp87aUj8E4wSjNApYPhrM+zAjgUCI6e5K+fThxM+O3Evd7cDaRx9qkLRa0cW2pE/mcF3S5/G5lIegYBtl2xJwrUp5AJsAWIeBD+0/rfymnzz1sir3sz96ffAYwRLruypps1/pRAP1T+DHCQ3eN4OBLgWAPM97OLHCOiC3AaRNqFv2C9U3wRX7JfudAihO5Ozk3+zf1HMj17fuOvY9/l7D4nibSV7abpxkgeiP/YKTA+zjmibLp7pkmhw7zHNddMl6sC31ecIJAV6zh3JMELxuwNqFYw35N9N7FGmssYwcD2zn45fT7VYzyvFLCCEgbeHyQIzxe64nDT5zvFHVA8PaJHqJAKWrGnA4yANKQzQ0dt2ytZdqILMcWjaCVBW6ZmokU45ZnYBuURrunnldKLv871mQ4L61HEP98v9dVnKNvVIApoYy630l1/cgA8qmMXdUFa4zrIG/eb8IND175rdC8W6dvowalZd10sBAwCK4AEogTECpDKpnchWo6j3eLZX3Lqytc+e4l2fN/RYo3W9r/dP32xpo9WBNXZPPTMtzBJVdX4I9H2W37VP7M5IyUHoDMPJr35f7r3rxadfKz/LpWGI+Rb1QnjtsEqARPXN8a8CgpvJNV3EV9meuY47uPgBDFNfa2K5NCNU8oo5XTve7+ZYDlARrl2AA7goM5NDwpTFGFoEvBBrHKyiBBGBAgkZig2TIkanchzEse5L/8LCHGcxTlK6pZCIIfgjglMXcz9fTSCfLsoFkDG/DjNt9pA69h/SCugx7GfMFQqOXhjtFQeUWSraHBiukPMyTRu45GLBocrkuyHXMUwE/gRTrq0CHgGlXjhM4beV8CDQIBjdRGizMlw8X6qiRcwoOCOAnMFZy6tLUB9I+rA3pnngZxhoRYDE/5sXzys4S+J1DOQlBvROMsF2YLsHmqpwng8t+yDbkvdofCSDZH8lg8jyw2A8IQNeQJjX4wNSxpGCXumM+gPXhDTnOdlyF9R012shwsu6bMFbcT7Cwr2ifB6y/kkkl4CSgVy8D5tPI8RnMK4DHgdQnOfFyAda+BKrs60yvk/85qcLJmjXJj2N0H+VYYVp8NnESi+W5DaOMcsPySUhM49tDCL+fj31L3v/zmkXXZlE6lNEP6cLPyVXAxkcPA558rtGYbttFMOkDsHBisYEZm2QxmH6D5ZElvag7/QCA+xK0Ms0eBgi5JpTna9nUghXx98BGQZah9ECYoRAykVoPbn8wlLkzdpKAjesuG9j2Fxq0RhlDlsezecuYNYquA1QGVYFGA6Bpl7vNepdaIIPlfvGaWmRV308Y+MizuApua+ymd1Pl90S+51J/gkpep/rU8qiLs+brgystgNqmri8FZ7X9POc9hnXErVw7BYZtRIAEUjmWPLCs7a/qQaUeY/9XG1WPD/eh1GkNeHN9p65h1KYiiATKgFUAyqBJks5QL0ljJZfNLwOjFNGqsTiWP9TklgOUDZJhSkNxGwZMgNKoZ8PSgCXzB5RUNQ1iggoCQwU/yh7uw1xYlbnkIKHBpyznWaTOSSO3cEGQfFlHGs6UHmbce3fDNaSX79TVnXqhUUsjngNawQDrT7C1ipLdZNpAyUR5kMyHEvVEQ1x1uQFjVTelnltyf5fPrSMNUmWWN5DAWIfkarqTy7ABYw6Zxh4MoCFfuyPnCORYxzUkwLiKEnARgJzJ6ewjAUpl6mqsH6Qt+D/BzSasrymIIPjiRAbBxDqsPShsR4J7snIEqoABcPZjAitlH1l+wCZPlA3jNdonV2FrXQEDhRwnZCrXkNrrXC7bJVjf6GGgmw9qpsf8ppIu24rrMHUc0lBch7XFDspxQ5C8B3PnZZqQ+5j2nXL/HgzMc+3pdq4HAbpvz1XYJFQnabA9INdpn+VzjmCzr6TNSYPn4iTkCAt7lD9TEmP8z7jBSXV6zPCZSQMLsL7rjVIaZHx3No0ZuoD1wgMAzaw05NXIppEMyXsFZRAgzY9peFap2E+Q5+SB69dfT+Q4k2GQG/QGjL2rLxVdM0D9797lCwBN/jEEg+mNjdT75nJvA2Mn1UifNCWY0+iXwCJg12+ve6Bso8PWCdZcTXkP98fU8wugyd3ftsDKtGSitOzc91NdZFn2wiW1x8I2LjWpbWniJyZ0naYyeQQ0/AaA6AC1X8NaY1fnohvdbqdW5Bro4+RwC4lbIeBr3gN9Z3EMAGMwFajWXNC1TYf1xlI2fw8nPlh+Buo6rA08yBvcUxvrBzo5ovX3ayb9w49AWieh1FWY+hvqi1LvJxeg5/S+m285QEnDkiweWS3ADFN1F1R3VXVJu5SP0Qjfzce3JB/ep0amzqrQ6FPAxjRpyK4irdUi09Bj0W2QoFCvWZNjBKgXYa6cNIj5ouaLnCACMHCiZeX1BHkELay/Ar4eJchR1oo64LVbKNe9UVQndPHbgnW8M7C1MudFj8yT7bcvaahRfQmlO+IEZqBz4FOXqqv9XG8/SUDGT2yKwcgn4JoB+IDLFznPLdGZMkxrsH67AXtAsb6e3dWJDGX0prB+rhMmvdy3i5IZXYcBXAJHNeJalOttfZlYN/6mocJrd1GycOzfawAekjxWs861PTv5sA4Ez9S3Tk4QuD0p9SSIZv04bqhz1oVjg3XXzx5KRpRtw/Ps1+el3gSzHBfKurJ91lCOf8AmDNZh4++ctMO+pMN2Yz3I0nLixxEVxySn261mlNMpHPv67OT44RpDGmkqitlodHL9kr+OoICyjFlckf8VWFWvbc0498wUy7PSlSBXmR2KGpMKhNXNl3XTexR80xjWADtaRZYhAECXgE+tDGqrFEHKHMjj+kXdk3JhC5au/OYWIEAJyPib3+oSOe9zf1gCQoe69sYs1SKc1kCt385EwQfZxyHPzupRc5WurdH0x2tAucY6LutTvr41QK19wLvq6rYzxdY8eYCpK3ZweRfuvSj7tN9Ogy7EBSsPYzBr25rU6siJm9hYnxxconPm2gw9Ethrsl66vpzoKSaAXF4DOKz0c/ZDtkuPMpAhsBwg8hnFSarGXafPAj5rTuYNerrfzbccoGyQDDACwlb+J2CiUdbBgA5fbHzh7SOtTSJYpNF6AYvrK2mYr8EM2LtgDMcBktvtdi7HJqyT9kgsGgFLCwOtNMQJzGi0X5R7yYLs5rzIzBGs7OT6b8LWkgIGuGh000BVw5sGPUHGNlIwEGVjt+R/SHk4iOiyt46ScSHLQuMeoh/qZoYUnIhleC7SgCUY4kpm1n0Lpv/tfM1zYIC7y+UnG93I/dT1FMnA5/0E151cf1HSYPnZJwhyWIdtJP1vAHialDkisVtsoz2Ua1N3kPrFJkzYfzl50efzO7leQNLzXUiBnSKMCVc2jwwzJysIYhs5vweb9CBY28/X3ZnzehQG8gjklan8AIwhnko+7AvsG03W0bb89kCX7cr6cdxxzLC/8T5lJbdgEy+s+0Upz5rLHzBX1odhbaMAfT/f/yhKpnEfNj5ZD50E4LjbhAFUSpt1eydsbFNvBIkKUjlxM0V6Lm3n3+wX51BOIB2vnN6X1iinUxoAZ9rS8PPbAzCCNVCybjXWr2acqEHsQZq3ZWuRLeGuUwNeA8foNyORTnp7pvp6Eyh7xsSbf8sioPN4WzN+JQ1lRIeosZLeUHaWI5eZQGF1NX3I2tAdcNKmNXmFPhoDOzTKa8BxcFutgCJlC2fCrDFqrmeZmV7blmzysv0cCWqm01Qvv3UKQRFQAksPJFnugqGW4zWmtcbg0p1Wty6pXa/HPDOsazKB7Ja5BKzWylhEXJX8NV+6jPpIw14vRR6w/umBpJ9IUOlFF+pSzET5nKD7KSDfvYFOssZDpF4sRps9THTigy7pzEefOyxa69pLvQoIsoctjeQ4bduTk9P7br7lAOUEBuxo8AM2KGhE0gBW17tVSYfAgEadAiYOrnUkgMAOQpZjHcmg28jXbsOYRWVWlF3k/Xz5MH0azvtyLdlCAkEyQzSOaTizvIAZtUyH6RNoEeRpfnD3NnK+QcmkatrnJA3WnXXifQQIPKZMMVmmqaRDV0DqrkECLJwYUMNd6837exjgUNaLQIF1YZ6cZFDWkmCHLNw6bH0ajzMtBX0EZI/ndHRN2wpSH2J78h62KY9BjhNIKNCjHgADuASUOyiB7znJn3VhvwOAJ2AM33Y+fx6l+zZQ9s/tnMZtcoxglGNmN+etTK6CW2UIgbJfeyZ3D9bfdlGOc8AmZQhyN2H94zaU61h5/BJsTG3CANk2Sldh9iUyz2TLN5EAJp89CtpZN/YtMtANrI04TnS8MI+HULKcW7CJnHWYaz/7qPaLO3AScvwvrRDCGwC8HMCFGOML8rHvBPA3kar+PwB8aYxxu3Lvu5G67hxAF2N80bEXcJQbkhDcXoRiBLf9IpBSMKksmm6PVFt7BpRupHpPtVwu35735vR0zV2NDWP+K30ZuGPYSksN/3zeu+wO2xWgbgTTWFd2iPXUa1gPGrfcJgswYx8ogQTBCkGXMnnMj8CyxjgtY9aA0hVWrx/cHJvF64dgQX3Jfuq9wKKLooJbzUsjraqrLrdG4bW1OhBkXM26Sc1Ty6afBX0Aw/6bnl0lEPZ1IZhUPRxWj9o5z/5qkKMDKSPtEXU9VwY39osTKJNKu/BbJyFUPwrm/LpPXkf2lWVSu4ig0r+W+B4FzNONon1Oj9UmJ3RcNyjbuEHZpkA5nukCDKRngEanPhkZAeWxSYQxdTS2gGTo7cDYEQIHbr1BIxEoWRYFV/tyb5PTJ+NEQLWJEgzREKXRS/DawoBFK9czLX5oiAPGGtHg5LUEkWrw02inUavsCA1zNWoJArZgIHFHyqcuepB0eB6S1iZsiwbW9VlYBOUESGRKCW7oPsq2Ilgj87MBM6KpGwIisnyQ+5RNZF31Q3aJ7UFQ3KJkfwnSCYL48qeboYL3DfmfbUBdsK9dRLl1xgYSk/246JegQcF1L2kTOChbzvblQ2sbBoqBsr+wTZku01HgvSppK8DmsT13jOlso1z7yDw0PbJxTMO/E6g/wBhv6lnZbUi+ZDIJSFsYY8uNwsm476AcqwS5nDBg+7D/EcSvSV4KyDlWtPw9ykBbm+4+9h+2I4Eh85+4dKmHmaRPUMxnT4u0Tc0ugKfjuOXE3Gp+BMCrAfyYHPtVAN8cY+xCCP8KwDcD+MYl9//1GOPFkyjYKMcgYTlz0TQZhOVzsWIc+n0nGZnRG6czN/3PZJaQWKWR6f4na6Xr1dSgpgzuhF05safRI0NnESMB2xqI4JOGp2fQaLwS1FHo2hslDWViCQSWbvOQD7YtcGYVOLtmgHJhfZsDJYcFK9IItCp+H0zP9Hm308L9sl+81kexHfafbC19BWLB9b2aHOZyq2ktY998WvxeWIcp1zRIbblsixafpwe2k8bWV877bJ82Jdvry0KZu98abZVtqG1JQKnRT7W/a/o1AMk6qZ45gcBz877sb77vEVQWesThHgkMntNgcSx4EFljqFUffE4pQww5N1yDxTHNLYSOivp8/TK6vB67rMNc0lgBdVMjsJjB1nrRLZCGmb6EaOwqE0dDHyg3QCYzcQXlWkIFQo/AjE+gXLcGd4+yk/zuYWwk68hyKrhguqyvMoKsG2CM7S4MxBG8rsr1ysqQGdpDYq9UGvn4sujDp4FtH0GQRSDSwUAgGbVHYEa0AiIPEJWpgfw+i0RhKJOoOl+F+cDfDnO9egdKILMOY8EfR7nVxkyuUV0QVBKAse7bct1zkEAtffDZPziJ0SOBEQJAniMQYp/RNAl02Q4KXgi2VlEy5Qq8OdmhDz91AdW+CMmnl3QJ9MhkbsLafwspeBH7kp+8UMZ9Q+rH/qRtyPo1sD1ACQ4jrE+zDpwg4ljchE1IcPwTvG2idB/m2NuFAUamyzqTuaSePOvM9lUwyfqtomz/czmPbZTPJD4X2B9qz7yTkeN/acUY3xJCuNsd+xX5+VsAPu/YMx7lpooyA2rs6nozjXwKlO5mnCirRbDsugziKtaaB4tMT4+p8XyAxJzWgM0A5vqS9ZtMk+sr3TJ9VNH9/HDUQB+cMPL7FS5zfdRy9FgMhuLZ2iGCbL/o4kjdncns5NqaAbKjItvWAJIHjSw79eNZMroRF2vXluh8NrM2ZnAdbf+j9nxs+nJ9LYHbvFIPraPWlXlRR0BKt8ZqEazNu+XpWwGza0VlTKjOdLuVIf+m3KvzihzXCLWeCeSxnH31id6gBGNzZADVLwaT4vjhemJfbr99iILJIe/eADbXLmv7Dn1E7tE1iXqMzwp1I1eXcZXD2sefalHfCoXCZ1V1ggjlFiYnIyOgPDaZIzEJl2Cg7zzS+rvHYBEkyRCxE5KJoQFJw3YVyehdz8fOwwzANVjk0StIboKPw4xKGs181nBQns95Pwp7oe1ImjQ2L8EC7dA4vRMliCKzR0aDIGIXtoaTZaCx38l9NJCnOe1NmFG9iRI8n0NynzuABcTZEd3QSH8o5/0cWKRdgsBJvv5M/vDeBqXBDxioYARcrYcCJLolKhu2l9N+luiQYJWM1VOR+gsD6DwBi5pJoEA9buV0t2EuuJfyh2BnNad7Bmnt3Q4siE+bj5P1JKjg91rWP8HKedG/spMEqrxX9URGnvldymX4CBjLdwmLwIbsKPseJxI28ocgVIF0j9Qf2Oc6AO8Uvd+Z9ftepH5OwE3AxDHI/wkiec25/Glge5Qq0G1gbCHHpIJApsl2eggGcldhY7+VNFZzWS/CGEllL7dgzwiWkyD5Esp2pb7vhPXVnZzmTj6+BRsvBPLsZzuwgFKr+Xqyj0xHxwOfN3sw74wGxpQfr1z3LOi5EML98vu1McbXXsP9Xwbgpw8p1K+EECKAH7rGdEe5GRLN8NaooxzP/NGjHvn0MFkAYLDAPd5Y1m1Jir0d3TVkXAhi1EDW6K+DgS7gBlgEgYObnKsUmUzPfKl4RoxMHoXG6xB9E4v59BAAV0mf7pQ05Lk2r0EJApqmXCt5mDGuYNu7zMbemDkPXmvr7OY9MJ9Zfh6001VXg/V4lq6mxxpwLtyUqbMeg5v2BIsMVaEHYbH149k2BSUaoZX9TYFr2y4GTaq5Ew/rUqXPVsEb9YDSYwYo1/ypGzajrJL5rkVB5bia5Iwa5H7XoggIxLWkfnwSVM6krRfcjKXs/GZZWA/WQV3kl7ku+4mapskTQ1Jf5tujHjBqItdRX71UjmXUMh+/jAzlsQqNrF2UzBDBA0EHQUHnrlMjv5f0yJitwwzRgGT0kVm7DGMmyX6QsQTMOOWHhvI5yWMHxhrxnq2c5yaM8bgAA6G8TgEjpPysww4M7PKeHrZObRPGwBI8PAzbWoNlamBue6pzGt7UD10s1QOJs0lkl8gwPQxzVVRGlewLfdG53xfrSWDMBwbZG3ZcAuKZpH8W5qqseuL1yuwQSBBIKVPNvkSjfw2J2SQzR5DEunjjniAQKF2S2U4UAqcNGHBR0KKupsh1DKJfTpCw/FuiWwJyjhfWXyca2B78huiTa3SYFoEd21eZN7KAPczdlOOEkx/sU3QpjpKXAme1y9Zh45N6uh1pgoBl3YWNRV5HXes41/pzrPO6DViAH7b7FMbwN7AgPQSzOnHNvsC+OkEZbZj5s8xipxbrnulBwOu0Huz/Pcr+dfxyXS+ti9e7tjGE8K1I1fuJJZd8UozxkRDCeQC/GkL44xjjW64nr1FORmIs10BpkBq/hciVyjm1KXsY++K3Z6CR6kVBlJpdHsNpWZiPsoyDMdqVgGPYA7NisC4TXS/pGTUvHiDxXeQjWaoO/KbuBD+DYX+IhedBkP/fl6molzPWl7mKNih1SPdNr4vZrGTaCLjmPTBtDJDrvplaRg8mNW26wq60QJwaUNN2VQA/sJTTMh391uNcF0pZkb7k9ajbhAAGYGvA0Yv2O0Zf9eWqgUndA5W22xAEqnL9sic/A0HxXo5fpuEZYmW4WZ0BKObjbHcNnKQTAA0WXXIJ3AjwCld5NfZ43IH0QbKhyezIeh4AaLrFCMoquq+tTmB1sDF5FY+H65QRUA4SQpgAuB/Ae2KMLw8h3AXgx5Hswy+KMe4edv8EycC7gGTkbSK5EbJxycJQGrluC+U6PqBcv0Y2gkCwRWIXPAu4C3MLVMBK9zsChKlcN5M0dlG67BHM3IHEcl1BfT0khUCjl+MsO+tHN0p2agI5PU8GkfUg8zlFCTYU/BAM00iewrY/UH3TNZGMGo3wTRhoJhAgm7WB0ugne8f6r6LcomKGBPL5kOmQGMTbkR4KbEdl+rS9IPey7jT4Wxjg2IEBB7qrKsik7rx+z4neGkmfQItspALti7A1pgRtFPajweUF1n7s0x0M7K+7/Aj8ObnC/sdjnDSg7glCqVv2J4JKAh9OAPBZTp2w755HatsA4P2SFut0SfRLoZsYATH7GkGbguVOPjpRQFCok0vUVSP38vwmjNFk/VukduTYvpjT7yUPlonCCQC6X7NunGQCrA+yb1PX60gTK0/A2mMN9ixgGnswjwP2h+OVm/fSCiF8CVKwnk+LMVZ3d4gxPpK/L4QQfgbAiwGMgPIUia5h9r2HhqEaXRw7PlQ/DdfBjbGSnjcyl4FJit/3scEiUAPqxuf+LBuwPdDNjCkkiBrK4YxhlpOGLtk6nx/vHYBkbwwuyzyk15vLcA8z6r2b3QCmBbCwfANz5D5aDy2TX5fn66D1UKmxmzXQqewiv3ndinwTTOrejqoXzyotbAGT/9c1gjxGncQldV7m/qjH1D1a862lt0w0OFLNNVjT0jYd6ojFvq8PVAV2reu/Gm2V99EdVvWtAacU8OlWH5RJZi29q7T24a6rgy8eW3F18Gs/adcFpLz8Vi+6xpa6GtonG2y0jai7K0jjvZHjfushHqudO1k5/ndzCOGfAPgKpGq8HSk43v7hdy3KB4Oh/DoAD8AIna8F8DUAPgzAFwP4wcNuJvKnQbyFZFTpXjkEWmvu//MwA28b5foqum7S8KSBz+vIehAwKVsHlGvnCEZ4fAfmYrgv97Bcm0gGJYHQNoyZY4emEb4Oc9elscyHiAedNFZp/APmgsh6EBwqUGYet8HAE9duESASENBYpqHNehPMMX0KQSQNdbrBsn69HFuHsarUGRkjttljMEDY5+u5ThJIoJnpK9BShlFB44YcU+CsAIugkuUjmN93+Uxh/VJBEEEx2Sj2QaBkr5VJVMD3mOh2E7ZVioK8KVL7EQgR7Gq709WFbUyA1sAiGA+GidxHYMNrZnKNsskbuXx3wVjdJ/P9IeuROulFh2TeWP+Lolvqhu7tvIfl2JF68kOd8nlA3bGvsQ8zDZZlB6mPbuTr2UeoE44ZtiGF/WZXyuNFJ6HIvPP3StaT6lK8nIY+5oHs8cnNc6sJIbwUKQjPp8QY95ZccxZAE2N8Iv//GQC+/aYUcJSrlh6LQKjWP2vrixRYRpRbivA5VGyH4NKrGXMc61oWAkm/TYdfqweUTBLd/1pkQ35WAiCgZGMUwPbAsC5tMIIlX97bdcCsX5ygG+qTDVwFMQvsZf6mW6IHihrtU4MP+UAr/F/X5dXWydXu03spHnxreYr6NZbPEIF2aiCrxowyX5aF1ygwbVsMwZyG+rs2V2Cr60LnWHRt1jKoHnWtJwHM4N7aJNZL2XDtewssrOhQge5BBxzMFpljdYWtPb31fdjA8hr6cU6b7HYLmRBBOQYPWyPY9zaulK1V6fKLbNabVxqFwW6Ge/rSRXeZDM+M/IIcmFNXlr7Pus7tQgNCxx3rqmPZvxV5jYJKoHzWHL8c/7s5hPBMJBx2T4zxyRDCmwB8PlIQvWuSmwooQwjPAvBZAP4XAP/ffJjP3B71ScNCnkQKokKV7uYELsOCWtCg34IZwDRcCWhodPM3OzTZlW2YgU1Qxg5C45oudmQV6L4HGKCgsajGOUHFOmy9FJBYV4KTNZgrLOtDQEPDc0fq0QP4KCwa3oABLuYzQwIhBHUsK2DMWQfgfaLPHmbIeuaL9eF11MG2lOFOGEjYzvl9HGz95bthgI7XqYHNaKtACXbfgRJwXcplmMJcNbfz9xYMjBIkzAD8Qf7eyvq4DcCfwFygz6EM8LKedb0NA0MEh2S8lGlluQnG+bDZgPW7LuvzvOiXrshsHwV5BIu6gTf7GVnOdZTBgziD06Pcx3UVBtruhLloPwwD9uzzd8ImXwjSNqV8gDHL7LMPw9hB9i32/03YGN2BrQsl4GvlWA9jC2fyUcOTbcsXIMcgX6LaBy7BxrCCUE5ubOVyXYKx441L/1GUbb+JMgryjuiG3hBM8wJsfN2Vz8+znh4W/c5g6yY55hrR/ZM4ATls4dR1SgjhJwG8BGmt5cMAvg0pqusZJDdWAPitGONXZc+V18UYX4YUyPZn8vkWwL+NMf7SsRdwlBsSff/pu1JdTOewZ5YHd0DdhYyfubumAFvyf+O++T+Naf5Wo/cwN1Y+c5VZnSNtI0JgqQxM25hR3ksaOrmIPgEMBWZ9X64rZZmXuc3CnQNMR4NePWAVIKxpEVB50LdMFkBlV+rOryv0brE1BpDfem1oUkChahRXAQi+bKyT3xKGeRLkKXPFtZkEd+wXreSh5StYZQGj6hatLrdeb8sAKgF15/oG82KwoVq+gxDkwvrCYZMoBMYEveyECiRZTA2Sw/WLta1watuDqJARVZdcwOxI7TuTvozyCpgXk27RMQDOLk0YAEkvWr62tfE5zfl4ppKMJ/Nh2n5vW5ZdJ5oP28LoWOQE3s3IZnUI4QDJtHjkehO5mfJ9AP4ZEhlHeTWANyLFu/nCoxIgODoPA39PwNY20RgESkNxCjOaCSzvhLk18qPgk8bjFkrmjeWgOxorwxnMSzAgSmOSxjKZR3bATq6lkEVj2VgvZUB72N6N/hmrL04a2QoGt1FuBcFrlf1jfnswhmkXZRCZTvIieKfxTjDF67n2kLMHUySWScuigKuHrSXUMrZSLgLnDdHVdr5uktuD+iZw8iCMDyPVwe0ww59tqICboPsKkgsngc4aSjfRffc/ARcZuAYWfIXggd9nUK455Ho8sq+cwNiBrT8lQ6fp3YH00HtEjjUog1oBZVtyf7VNqS/HB/sa+y7TY51a2FY9BJ4cJwT+ChbZV9g+7J+dpMnysf/tSzqeqSOLS5BHd1GOeR1n7Fur8tmE9S+gDOzF5wHrTEC5A1vXuyV5NJI+66ITDnx5qZeATnjpC5Z9Z0/SXRf9HKvEeCIvrRjjF1QOv37JtY8AeFn+/50AXnjsBRrlWMWvWyRg1OdtDQQqmNRvDxrjkuM+HQWwKjQQWywa/t7oVXZO12lpUJBhD8geaZ9NAQNAGeTFjyZvYOu6UC27ZzuOAs7eoNP9ErtuccsSCs/5dX90BdVya1AcL/5+Pc6AOsqQ1oL1eJbSt4vXnQdkCigVUHvmleVT5krT8EGGtEyqt8MitxK4dqLbmgtr01ib0B2U6Xo33MNccpUJBVL/Y/9Rvdbqp7rnPb3cT8+smms1UO+PXh+q50mzCBKZJu2elqBazuk+j3y+eI+FBouTB4AFpGL+ZIzpdaDAkOnUwKI+s/yes1reY5frfzcvDZgXY3xPCOG7kFb4PQngV1z09auWmwYoQwgvR9rQ+q0hhJfweIzxQQCffMS9bwTwuXpsFcbM0QAjKKGh1cIA4BSJEdjPv2nAbqBkOC6hNPQ2kADG7aiDhB5mzEP+34VFACUgIgglG0UDcgrbmJ0gj+CCA4dARdm4RvLYRcnOsk4EF35gEDQyHR6fwphZGsnn8jkCRJZ9CnvY3AkD8ASTlFUkgMQXM/XxP2AAoUUJQAkOyXwSbKhRTuZmHRaEhwBSwS31wIfAmfzNvNlGU6QH3EFOd1v0Qn2yjB8B64Neh8z7oqRLAEBAQFZuFyXgIOu2ijSyeV4/l6VMPUrXXPYn1pWswS6sX/E824j63UW5N+kGEhPKbT8IrCg8xr40gzGOB6IXTmQwza18/5Ws421Y++o44TGWlYCSALWR9AlSWQ62KfW0AwOUvI7gr0W5TkPrSeNR89iETThccNdyfLOuOhnBccWy9JJuB2PS+Ywi8Nx0afM50Oe03vWudyGEwG4BAG+OMb4KNyInMws6yoeIDFt/YBH8TdxvjrF55XoacD3K7TPIuChrwG9OinmAq2krQ6MsFlAa+vOudKf1UWOLgCAoAQfBZNehiH7ZYNEFj6IMiIp/9mqeniEd0pI6Mh9ucaFAhnVvmnI9tkZ6JVAh68djdPdU90keV2Cn7pwKrMgQXo1w/8va/pFaB/09pC1gbWD6JB3v0to0ZXn1208azLsy2I5GB1ZdKRDX/SjVFZmRT6lDpqvg8lpkAD5tycQvE+rI6xa9BdE7bI9FTXpYk9kt9sMFQOzLgco62UpeKyjtHd7Xw4Isda7OGkk3SLt0XQKwfNb459ay+hLY0garPfeOVa7v3bw0YF4I4SkAPhvA85DMkH8XQvjiGOOPX2smN5Oh/CQAfyuE8DJkrBZC+PEY4xcfdWM2jl4FACGESDAHWEOTiaBxTuNU2SzeQ/aCxl0PM/oUsCkzxs7C2Qka8TQsaSwSkO1KuTpJm2kqC+KNX4KPRu6jca356kACEoAhECWoYd1pFLOuGyjdY1U/vGY1/95CetEwrT2Ue33ymiksmIgybg2McWH9OBEAmKG+g3IrB4I3tsFGRUcE+9Qf60yDnswo66/6Yh4EpCswF9mzUl6gjJLK8tDlQtMkoJ8gTU4Q9BMQ0DBg3yPYWsv5b8P6EduDZWcUXM7QEQwRRGmfUl1wEmQdFsBqHdbGU8mjk3I/C/ZgpPsq8+IkB3XNfr2P1BdCrucWyoeuuqBytpG6ZZ/Zgk3WcEyzPoCBSp0EuRPGYjYwELaD0i2ZfYuMJevUotQhAa1OwPAZcBvSpATH1QZsDLFf+2fEHsxdlfXo5Js6bWHjRid2FIhyYmA3t9Xznvc8PPbYYxrY+MYk4uoiSIwyigiNKhpUh4lf80hDrJamvoMBe36QnViINgmgJWhBBYg1i/9XXV0JqGDgtACl8ruBARBNe8EdEwbONE+umavpgHVYtvYUKNcNFuddPuo6qYDyoAOaGdB0ZbmVtWL9Jm0GF70BLQXIQ31y2jUw491fffmGIDsdBndcf3+tngoiFfTVRIPo+DQUUOpHj7O83BuV6xo5kTDPZRjcn/O3B4c8zjWePu0hCq0DeittbjfYNf46XlvbSkNF7x0iBcv9/B2WfC/TrwaiUkA5lFfLKf/7tW8Estzuh+/KZQx+MVaE3WiaJeOIwHK2eL6X72V1JdhmuZTMOFY5mXfz3wDwrhjj+wEghPBmAH8FKVjqNclNA5Qxxm9GWi+DzFB+w9WAyZqoodjCojMq2wUYkCFoBEqgsw1zESRYI1Aj2OqQQJLS7ArolP0jQALMYGa+7GjKWPB6Gous21Tu0a7DerJjb8IMWH48e0NjlIYqXeSmko6C0x0YIKfxzRcq2RTAXClpgF9CGQRJ9cO60uDmNeui43VYu2oa1CXrQ5C5iQQW5ygZUQUGTIOAnWziJZfWKtIDQRlKSDn88FWGlPpblW8+XNg3VpAAOQEk8yaj91RYQCaeo762YGD0QPJWMEUd7KEEycriE8hxvfG6pLsv1yjgugAD0QrWyNxfQDlZMYWtzSTIYhv6cRJQzu7r5AHToo7ZN9cljW2UbtvPyGnRjZrpst8rUNuHRS/ekXr1MA8Fji/AWGXW6wwM7LJuHFcccwSpCh518oXtRcaY+tcXYy/3+T5Ove7DGPfjk5NxeR3lz7ZwKyOgbmweBowo/pwakRxLQ9AOd62yDsr+MXLqYaIAqnBLdNepy6uuSau5ZhYuipJGqFyvboq8BsigQ+73e2tS/PYqXpaVjb/nXY5s6QDWEBhHwOSKK3vNZbjtjLEDyvr6NYtaPgKo+cwYSa7P1E+NXa1taK/bUAzunO76xrWHXsegQBp91bOUZBV131CoLqVM876sg2eL+97SU5djX1/V3YGwwXq91uUw0UmGg872e1S7q8kvyNpEEcdCTei+u5Af7FlQ21rIPze8xwOfB/oc0DFCm3Uoo/Sh1rEA3lMBs3K80i7jMU5mUfyyKSUVjl9O5N38EIC/FEJYQ3KM+zSknTiuWW4aoDwuYaej8UkmskUy1DhrT1aGIGuGxLiQydyGrRHbQTKOAetnarwB5npI4ERDkMwNWRAgrTmk4TtDYt22Ua7lW4cZ/QQUF2HsFMutoLTN+U+RXDKfg3KdmrIoBEPPyfcSWJI9JJjdhLGoWr4pEhNzFhaNk+BvFwbeaZDvil7WUEZWXUNaADVBMvgfhwVoYdsoK8PBuQHbIH4Gm0RgHXVbBhrcH4kUwYMgaz+3B2Ab1G+iBC53oGTsHkMaVTswwLmV06BeL8AMfcDA4nmU0Xx3UEaA3YIBlRbA3TAw8jiMoZvBwM26pEcXj13RLUHrJtL4eDtKUNrIdSzHJflNALSe9X0XUpu/TY7TUFTZRLk2kunqhA77A/MiSOVEBNuAE0K7cpxjpIcFoOF4ekjqBqT2Woe5sG9L2tQnxwpQsodbKIN3XcrXPZKvnck9fz6XZxv2DGG9WdceZSCmLVd+fZ/peOVYUHlOTuOd+TeBNuvhrz82uVbfqlE+5IXjyR9Tt7kepasq+78akD6aq67B5LUNUGwKr6JsGIFTI6CM68LU1RCw63lNwZy5cg5GpIARD0YHt0aUk2eDbirgbUEaCXrjylDoFnVmqmny1g3NYn56DSAAzl2j7CSBlTKTNdfagy6BSu9ay3yURfQuiEAGlUvq5cuv4K7GCms9z0wX148OEWRbK5OCyTPTtP6zVl8NHFMD2b681EeQctb6XS3ojqbrQXVtLaqXw3AI89IJdcDGM9dVtr0Bu9q2Nn4s+3zVntUAUkynALAsW76B25gAZiMqmGSEaQLVYjuiPpXdT6boZIPfQoXnm74cw5oumVOyk5zs8t6DxyrH/G6OMf52COHfA/g9pKr+FwCvvZ60PiiAMsb4GwB+43ru5T6UBCsbsBl+uqiRtaShS9Cja5PUlY4AYJb/V/Clrm8EThwUysAQXO7JOYKvi/l/BaLKIG0isQwtjGViWkyPg2dfyruT8+Yg3JA6sWE5wMnOsew0pGnI0zgnsMrvsIH94D3TXB8CRgKdTRgwvQRjX5j++3PZIozhuYgSxBI4EGARXBOQaBvSYFfmcQYLXgNJg+eYF0T3+0igOcBclTkZQT1Q/+quecWl0cBAIPsS8yVgO5fvVdZJJyIuwyKK9jD30TWU63JXsg7PwtYSkankQ5l5sk8wr5V87N1STjJybH/2Lx4jA0c9ENwzbY4NToAA1n844UMXWAJZ1l8nIXo5BtE15LdObDAvgkDVOcswk+tnsHWx6o3A5wT7B18G7GuNpMV8ZpIGxyz1zrGlIJFjgWVbg01+7cImoVhulmENaWKHEy68Zg8WMOhpGGWUD74EAFOCqsr5psEQZEPfTzQAgZJV4PiftMmIJ9tEA9W7LFK84cytH2goT5CMRHSLrNYygOaBJJ+zfusPGqtDIBWULEyPMsiK5rsMDFEZdJ1UMOYN406uGQClgF5fRwXDHpgNLq5yXgENg+wouGTdyFDq2kIFUH4bkkbrROO+M+Z1mYtrDeD59qO+CBimbtZjGfNKAD0VQHmUy6iWrQY0FWCzXRYYwtliml6GegsQPSpYkpbBl/0gs/h+vTD7+CQbAxpoqqaK6jE92NuXX2dIIKljOko+LRa35mBVCCbJrgLlJM4BkFy62SaiN/327cXAPG0vE+X5fNeX7GQj/7e+3qdcYozfhhR1/YbklmMoW9g2DgRjUyTmg8bgGsyQBeylRSBJI5OAlACE7pFAGRWSxibZE5ajRbn9ATsUy8a8aQiuooxMScoeMMZjB6XhSBdErQfLoOeZjxrjaqAyf2UpCRyA0r1vBwYIyAgS5NLQ7SQttslWvv5RGDCg7h6GrXk8iwSIHoa5fbK860iMIcuirBJgDxGWj+VRV80V2Ia8PYxpBAwATWFrJi+5+8nO0oNFXUwJfh6HubUSfBG07EhZWS9l6pgPASx1yP5JMMhJj0aOsx7rsPVD+0gPPu5dqCwZ02ebrsImCZgvYMBnBwZOW9i4YZ5knzk+2AdYX7LvBFQ66dKgdH3meOC44UQL0+NYoTcCz5Od1Xy1X7J/q547SY965FhRfStY5USJT0vLqy8QTiIRPHJy5RKsrwKp/98u6W6j7NdaJ+p/Kmmy/7L+xy4nFOV1lA8N0X7MA8sYpWGDeSyCLyAZ+meyMU+Dm0xnn41bb7h1vbm9DmwPKuxFLqhn5QpgBVsrrxN2NH4VjCqYrOkELEM24JlfDah4YECDXu8BMOwdWBMFhZouRddE+q1T1K3TrwtVF1G93+dBJs6zaRS6Q9a2bmGeaqDWALHfmqIAlU4vTbu49UjfW/k9Q+zL41louqfOZqkNtI7DWlNfJqQ+QvA3nUof7YD9fXMHnjudEbCrkD2eTu16v7en1scDqOI8SnfOFSwC7WGvSsDGodxPW9O3N0FiI+dZpqNcchtFjiI6pggmi7Es1zEJdQ8+TB81BpP14ERO0y+unWyAYVuWw1zQr1tO+bv5lgOUE9haqlUYkONsPRk2ZeQIPAkKCRQ9A0KDT+9T45EGNq8lI9fAjHsCBoKMdViUVBqyBAcEGkBy36SrH0EPz2sZ1mCgbwNmCCu4Y7lZVgIsAgb2c5b1LBKAYhoKmsmEKgBgmQnqeC2BOUEFdaY6meb81Bin0cyHEw0K1n1Prt2AtZ+6D5Jl6pCAFfVJ/exI+dUlkuCI5WW/uoJyi5ddlP2LgJX95wC2lQhnwslMb8JcrakvArhLKIETJ0u283UEIwpQFPTQ4GHktT3Y1isEuNqm21KGDRjDeknKo31X2frnwPb/3BHd80Nwzb6g/YzgVhk7svSsD8ct+0kDazsdixtSth7mJky9so/S+CMY1DbmPZxgUtCqTPRdkhfddRuXF69nOypQp64a2MQJhX12FTYJwbIr28o8Oc4B2yv0xB7gp/ilNcrplAhg1pfeA3xu+201ChdIeVl5g7C2l12PCtuI0uZs+5K143OnR+myxu2lNC1lQpom7TfJdzvrU5NGH3gwV1W6B7J8VwCcyQ9hv/E7AQFBBlAa3DUjVfcspBAg6b2eRVMwWATcEUCn5aoF/umzUa0AYpmLqmcx/fpJ6sLnfTVSBZRYzOPMFOgaK2/NZdW7n/o1jjxHMPnkfmIVed3gQuvSZTkGoqO3SQhGimW7kxFVqQXnIfveNFa3SVsG/NG2YfpMh206aYDJFJi4tGsTHuxvvpkXmr0yiaDAsvYs8PVT4WPCr7P20ZyBxXXGg23p+h7z9G2unhC0s5b1Ry7Da1FOIpyYnOJ38y0HKGncb8MMRDICNND2YIYxgdA6Ukzcx2HrB7cAPAWpQz6CxKwpqFhHMii5jhA5nzUko/aOnMdjKLf9oKFNJogAjGXfQbkv3RUYq0NwtI2SEdlAaaQqk8jr7sznyKQSzGzAjP1d0VEPM1ifhK31Y35PQQJOD8E2YZ8iGbPUEQHhO7MON3I55gA+kK87l49fhq17nOTrFGjR+KfhvpXPX8k6Jpu5AQNICqxWUboRktllm52DMWwXYaDtvOiLeT6cr0Eu51Nha0mZPsHMBmxCgYwq8ydgWIcBcIIZgpQLcu3dKIPHXIAxlWwv3rOey74pOmC/2oRNPkxhAJCA6e5cN4ItAiOWk32L17Bf0zWbgHMdZQTSVZjLM89TFwTgLYzRZh+icPKHYP9SLiuBJpnyZ6F011UvA5b/DGyd7iWUkwp8ZgDmhs421ZeTejs0SM8IZSOViUc+Rm8JpsuJH+qUhuk6Ur/aBPCHcu1ZWH9km++hbCv2vXXYNjLHJhGn+qU1yumUHqkvqoHHfYfnSMBMwRpwdMBCBrUBxNBD6dbm3WX5nKV7bQcLaAaY0Ql377CfXG8GL6Ov+nJ7UWaKMrh6CgNLO6LLf2oRMAlWhvL2ZtAWZZSK+Mim6q6qZVGAUAOTGnymxggyb4JlSmxLsOj3ZSxYaSRlKNghWPN5+zyXLR9TVlPv9eWgblamxhIqSxYFyDNfuu4qqCSbeGVm0V1VvzUgr3VkOQZ313yOjGWV1czloCswj9e2+Si2szkE3KhL8bLAUvxfv5m2fgMVkJnrwjJSL36fUdWrlk1ZWXoZaGAsLUutPFcjfiJBn0m9fJo+fWIuqz7jWizq+URA5Sl/N99ygBIw9pHgqEHJhKkRBpgx/CQMLJF5uYz0kN+FAS51fSP4JNAjmOqQwCnzoVHJplbmg2CJedNwp5slgY8yFgomCRJ2YCwFy8BjZO9YXzIZPNfLcWULKWRWvFG95q5p5d5zch31uSn3fAD2zllHGVyB4Isg4/Z8/ArKtWwE3AoaCfqZJ+unAHIDBjZofLOvrLvrt2Dt1KMMeNPD2oWMVycfBYzbsP7XwNyIyRA+BRbEhX22hblF7iMBFqZHwMAJAsCAEPXGe1gvMresH8FPj3KckP3dzseoA8DA0UOw/tLAwC37CMEqQSDbguVjnsrMMn9+k4U7I+2jUXbZv8iyc29OjiU/TjmJ1CL1sR0pbwtre9aVOjoLm4zgmFqVNDkee8mjR7k+le+P3ZwPYCBxF9buLP8qbJ9Q1ndbdMw24jOCbUmATEB+/HK63WpGOZ1CQEmpRjnM4EwZNRpnC8YozMBvGnMrBMpAHkDJAPbu493h1Oghe+gZjcJlLb8c+S4Z8shlU6PYg6G+L4FoRBn0pOkMWAAGXq5IeYfARBlY0k1UGSbPdJKdVGCi7ptc/+jdWym6NnIoqxreHYY9IT0LpltYqC51f84aCKrpj1Jsx9GX62cbV+55v3jcbztyZrqoFwU0Ci4UIOt1CiZrLr1MR8s7aazP6D1sB4LdGjvp96rUvTSVSVWQ6cE0ZXA1b/LyFplUoEun7q+pfY1Ar8HhkXW9Tvo8IAm4a2NGQfag697GMce5ZsntyIASWA75yjndAsa3E/Mj66teE0UkWvegWoYbT+YVerrfzbccoKQhS5BIg3JdfvcwdzoFl4/CgAXBFQ3EHZjhL5MkBRNFkEN2EDCjngYly8d8CeRoQBKoMKAKJM/iRSP5KWBiedRVkNfvyf0ElwRX1AXryHRosLJeZKOY36aUfxPlOlBN9xEYq7qd60dwQ8aYA0/XLhLMrOfzl2HGfAd70ROo0FBHzvcSzKieSTo838IAotoHZPH4myCVxruyWOswt1ICHhr3BAjMb1/up/FPV+anIj2cyNKuyzXsj+yHZN7pDkkmjOUmkOuQ+jXzB8zVFijZabbjBMYMQ9JSIMP0CD61H2+IngiY2BYcexyfHBvU1ybKiYEe1j6UA7mPOmYYc+0PHCNq5EHqTrDqwSf75UyuJYDflms2Yft26sQRDdUW5Zjxk0p6HevK8axAmPVkX9iBsZusJ8c8YM8B9rvbcMwy7kM5ynUIt5His5r9VoEYgIEB1Of0MvGsBTR9BQx9yVDS+FS2FEjPPg2sA5TAt8npFu5+vQEBNW6BxIJyPZVn9NRYDlIIlonrKcn2+aiwB1geCZeAYY7F9YfM2wcbapoEVm5bLQFlrcwa2ZXi3QL1mKbR97ausxY4SdlEgiS/ttEDEc/YqXgmzwu30lAG04MYTWfel2CJfdADz7kDzkyboIxp8bqmSewWI7wugC0YoPIgbdImhpK6m83S/5yAYD19mZQB5u+h7Pn3yhJENLCnnU2MeFa8xmgPEWM753o7JJz7dL/Ytr4/cnwwf9+8fM5w3C4DuMAiI6r1VPaYz5so5eK7HLBAPbrNCUXXBZ+InPJ38y0HKOcwILIJM1CV8aBRSyOOgOaSpEMDUUEGjW8aojToaNzvw0ATDXyCkB1LumDTmDaNQJZzHcZMkDV5Mv/u5JyyYQQ3LBNBIVBGOmXeBAMERBFm7NOQ1fozDwIKZX6Yt4JIBWW7KNdb3oVy/d4ODLicgTEzaitsw9xFOeOkAJF1Y2hrgl6mS3DBFzCZRTJCChhZT4JA3q/sqLqBKntHg4Dtu400o3w7SsDD+/gw4uw9AeMUFi32fM77EVg/5kRDI9eTDdyEgWX2aw5mAjtOKhD0MG/dCPwcDEiy/tqXN7G4NnICMxy5ppJ9jcAKMNdtAi2unVUmk/83MCON5emQJhsIYHdR9m8y7QRt1KuOH+quRem23cHGOgEcQSWv34C5MlP3zJdluIRyEkpBo+9bOvnFPsFjnMAAShfkVdj6Z+qY+n4oX/tcHLec7lnQUU6n8HkM2OQPn918txx2rxcabjypYMevT7viXyYw5pHjhWVaQV0aYNgGxBueAwCYpXWiEQb8ViRfNUiVoeM6TGU7Bte9bIT2vT0PjgLZ1MdQ9qYEasGdI/N12yqwtrYIKHW4MxDSdGrgSLewUMDStotuqDw3sFS53hBQpsyoZ9MKfaPMs4j02yejnzpvmhJ0EwypG+4y1lXFB0DydVPA7MGkRsUFFhlE1l3X6Pp0lLHWc5OpgUgy9o2wlYABTA0qo0GPijLlaqp+AANSBIW6XrLV+koduFcnYOVqtN+gnGhllGXWz09o8P+uy/osm6Gw+f0+sF6nS1l2LF5Xm7zQCaBle0sSnPO2pl9y4Q3L6X4333KAskcyLLdga5AmKPdDJFhQdzAaqPqwJhNGY45GI8Emr+fzpYO5CK7JMU1rXa5XYKnur8rcEVyyXjPYHog0jJmuggrdjwcw8OyN7E3YA33fnSNoow4IkjalPLyeBvdZGMN4JX+4Vo36p44UxDBPwF7GNPYVTFDYxtxugiB+Jx/jnkRqeAMGCNkuLAeBDNnZdbmOIJcb2KqeCQpor3Q5/8GYkGvoPtWJrunCy3wJygjyD1AGYuFEBcEWAWQv97HtCdTVzZa6AmztJKRePWzrjM382YFFuqXu2Z838mcH9gAng9jkdrgi7bGCxRl/gsnNfI73tvl61SvzV5bYA2ymC5TuojouKWwL9kG2N/tzA4uuzL64ARs7fB5wvND7gGwxnyXbML2fh01e8Bj7G3+zfVl31kMnSNimZ5HWEOuzSD8nIqf4pTXK6RXfa/S3jvGI+gw/5Bq9n/1cjWMPDhbc/CQd/b+WrweTNUDJ725m9WFdIMCF9w5AoCkDDynu7YHC/bfGwqgcNixVFwq4ue3F6qp9FABo2v4epkkGUUGlB1tqkKsrrLJzCyyhgDN+1wIPFVuxOB0QYHnWlK6aeg/ZZNWXv68G1H26+tuLMplePMu7jJlbBorUlbmZ2TUEXvx/psAv/38mv+DZHzVQFtMh28l6kskEzL7QugRhTVm+GvNKQKbS5bHUoJwoUmZ8mHxozNW0QbkPrLLb2gdrkwUs07LJC38diYviOOrvXcHIABbd6I9NTvG7+ZYDlAHGlBF0rcv/6sZKoKSGNo3JTZRBaJR9AAzAIaf7UL7/w5GYKBrVZJho3Hc57V2U0R0BcxVtYMwQ2bZH8jUNjKHZQBnpVF1oeyTDdQvGJCrjchHmTkpD9iIMMG7key44/TY5XcDACkEJ9bGNMvDPGlLgmrtyuts53VWUgXCoewLSu2FAg7ogs7mdy63BFFi3HhbwhaDnLBID+ChKcLWfyzaDMVQEbgrSCQQVcLGtWH/qB3IPXXRbpDW1QOkSzXIQXJDt4z0djPWdZp1oVF4CV83nAMA7YK6td8IC/ACp/TgWgNQHyHAh1++dMLAIlAFrNnIabDOCqR62tod1uiDXrMo1ZKEvwcDhPoCnI/WdR2HutZdgbCB1uwljYd8h126iZCH5zTHMY5dgD7c+fz5K6jhDCrzEcXURFlCKQLGBrZMGLNorgf0Z2HNjPV+ziRSs63GUz4JLotu1nN9uPn8utwfH/CaMkWX5L8LGBcHq3Ujt9BiOWSKOffPkUT40hM9MNbiKKKrufwJLyPGa1JhJz2YMa+r6VA6/zYcX38NpmNbc4ni875Pr4aw3tpHPw2kP9DO73peN5Rryk3LU9uEc9rp05VeD1oMvXVdIw1zBZI2dHNLt7b6JYy816qXWT/XO497e9QCU+0Dyt99j1AOPGpuk4gGMgrbofjdSlmXl9HXUc8qS1sBmrUzLjinrSxCv4IzXeHYTKME51xnX1gaS5T9se5VaPXUdJovNb75PyTozDQV2K5m55npPjhWdzGEaMf/QvR6BElSS5ee9Cib91j2HtYXvH3qN6mHep/JxzbMGAWOgsQZlX+Vz4ETfnKf83XxLAkoawrtIhtZFmLuauo1dgLE9ZBaUoaTbnQLHDfkfMLDDY9xQnh2JnYhpEUiQfdjM53dhzAY7XQNjdLZgW1gQOKxV7ico7SQdunHeBXM7JPgkI0Lwx7oQbCnzw2MEgwTvu5LPPiyATCdpPhPG9CloJQtEQ7qFsVkEUqswV2ZeQyDHZwFBCVmibRhzSECh9VtDCoJDoKkgkwY5QdQaDOQpUNmABT4huGbfIeOpQA1yzRoSUGBb3Q7gvTBmigCJ6QM2AfBE/kzyfU/CgM0MJQtOUN3D2OgNlOB7X9LXdiZoIdDxLwzqapKvuSg6ZHtQH2zHHZQseCv5NEiAMsAAtjKyQOo/m5LeVNKF/KbLrbqXrsAmBgjgqB+OZ54/gI2NDakHJzZ43TYMLDNvBc4sA587PVJ7dSjB6w6MHT2br1UvBWVi9RnFMcw+tQkDzicnJ+NWE0J4A4CXA7gQY3xBPrYF4KeR8PG7AbwixviByr0vBfD9SN3xdTHG7zj2Ao5yQ8JJHTL9QNmXh03CYYaZAr4g6WiaHkxSFBgUxmHOU9Ou7ZU4rG+TPJcZn+qm2bZAO7NgcSpzAJgZaKKQFWr6EkDre/xqhUXUwCYL1+TjEwcoCSb9usHD2DTP8nn3RAUrhz02PGOpZZ27clwtE+uBTK0M/piueV0GVr1eWW7PTh4GTH16+u3ZTooPjLOMSdO214mDlcws9/mlT+ClrDvT0ci0vo5kzTl+vBAIzgD02fBiX/CglqLbawAYlt9w0pxVbTorXzHpwPMogbeWX6Pf+jXEvIZrO7UNdd9aTkSQzKhtU0LX+ZpuqJ+TkdHl9VhFIzp18mHHV+CyjcUAOWrctnKMxvk5mOumLsuYogRXCuQ6mNFJJpDgj4YmYMYnQVkn1225NMimkhnifcxrHWZs0ogHjPFjHWeSz7qkzbITuJJBYnrefbCBAaEdGHgnMCFzxbQ3kcAQ19txEgAwox2w4AP7sG08CAIeFZ3wc1uuy6V8PdMEjLlifnfkcpDdYx/YRAkIed8ujNlWl0MCEg8c2RZr8j+NqlWkBw63jllBYpKof4KuDdiDlu6zl2FrD89J+XsYUCSg28g6YXm1zASO7Ac8R52y3xOosK+znmxjujlvo5xc2Ue5tpAPWOqPs4nq+nlJ6sO+cjeMeeVMIMe4jiOdMOA368lHrLpTE6CxngShDLLDyRk+BxpJl3kr4F2R4xT2IcDAM3WtQHZN9LAJ6yvbcox9dwc2BlZhbcTnxG1IoPURpMmfZazODcnJvLR+BMCrAfyYHPsmAL8WY/yOEMI35d/fqDeFECYAXgPg05GI5d8NIfx8jPG/nkQhR7k+aWDB5gjq2P+VpezlW40zzybWWEmg7JreIFdRA1S3RmAafQ90YlirAeojlapBTkC5gnJimYHDACyASgUHE7mvR/ncU/HMJHXiy6Mf3YakzSBaXV7VjVWBj4IVMnlD3bo6OFoW3XaZ++AyF0S9Rsvl09X8dZuUw/JnNNKiHEigQoMZLWMttTy6dYgvj163THwZlfWetAYmz0wNUDESq0/Hx2UhoLxt1djTob5NuR5W3X59Pec9FvaYLPSXv7nvNcd40yX9+CBOfW/jHQCm0nboDLCRCWyQJ106a1sF8hQfxMmzqkAJ6nRyRANfAcmVlvvWck0vg3zpOFWZyDH1Ljiiix+PjIDy+ISzIjRmN2EAg4YkDWa6AhIoKKCkEawGMAHUFRi7M5X7+H5gXsr20OhchbketjBgB5QvS97HstwJW7e1CdsTEygN5F7yaWBBQVjPDdEB67gLA6E0oKkTpqUMyR5Ktpdlp0GsIA8wl0HWiyCDDxECARrz+/l7FdaeB5K+AkhOEqzCZrTVPXkNycBmWQjoyFxB8iCo2YK1Axki9gH2K4JvtvUUxmRxUoGTEKtyH8EhWVeuL2S7EUhxcoB1o0H1KMwdkgzZtuTJOhKQsB5sd4J7wNpwDeYKrPXcReprW7D+yGtWYWOL7aPsOoHtJoyR4AthHRbMhv1tLV/3CGyyhv2bY4Q6ZfsCBog5mbMNa1d1fWVfop7Pw/oIJ2eU1SODyLrUJgMA6+8EjgR21Dsnn8iG8h4+G5juOuy5RYC7AQPVPK7n2N5k/XUcsQ4XADwDxyzxZGZBY4xvCSHc7Q5/NoCX5P9/FMBvwAFKAC8G8I4Y4zsBIITwU/m+EVCeIqFHBVCCyRkMRCnI0/sa+VYWRiMzAougQw3YYp9D2P8KULkGDTBj3TNeNGAXolSKod40yfD14LCwe2fJiGdefHZ691Y+t/yIo04G/bQlMCYA0frrWjZ+NGIrdUnAfGVW7hHo11Uu235CAVHj2qgmnkWi6ymP+f0rNZ9aHWtumxR9dE0aY9s0v8F9uZJWLT3dz5Ln6cZcm9RYAMJdCYRVf4C1jQbPYd1bB/r9GOC1BJVMDzCmrgCzfQ6c48YUYPu8Hsaw6fllb4khMivLBxTBc+gGC5STwB3yREqX1x3n8ikA1DgNqmPPcrNw0bUlx6l6Lc2RgCWBdo8ygJc2pV8bOXfnG/f7WOWE3s3HJbccoAwwI5WgiQDnImx7inPuGhqBCgDOIRnTc6QZ/4sA/jvMINyARZjcRupkdCul8UfAtitlfBbMSLyQ878LyQWTsxg07Jt83cM53afC2DmmqwCJII+GJYHwPpKxfgkGWnqUa+SaXLYdmDvuXSijsN6Z8344p7OJkgVqkIx1sqwEB+zivHY7/25ga8jUcF4HcA8WB6ca0ZtIbbKT6zrJ6a4itd1mTvtJmCsFQQGQGMELsPWvNPAJ1gBjetdgwVQ4QTDN+V3KetmAsXDvhBn9TPcSrF2ZhzJ5d4g+CBA3sz4eFd0Btl/p76MEQuzDZFQfRWL4CIKmMIbr6fmebUlXAQn7DgHTuXxcARHLuQrgafm6CFuD/CiMsSag5f9TAB+GxEg+ltPZgblnb+V83oEySA4nO6ZYHIvsg8+EsaaX8r0M4ATY5Ay3/bgEc3FWJpbtvIPUT8gCUscXUb40nwNzr2e/5thX8KjMIicknoIE9i/l67eQxmOPNN66fIwTAA1Sv1uHMZgEkRyHQLnd0LHJ9b20zoUQ7pffr40xvvaIe54eY3wvAMQY3xtCOF+55pkA/lR+PwzgE6+ngKOcnLC/AzbRA5TGKY2ttvJRMDlc78AKDfuDzqKPemaKYE9FGR2CJgVnnvXs+5LJmMyAaYdhn8O+t8lD3a5Eny0d7ECP5UxkK/d5Y56B37w+aq6rClB0DWTN9Y/1LFz/KmXT+6qA6hrAJCVK+wGLkVC9m3Gxh2VTuoV6oFd7bJGl1LS7rr6PpS/zsscgJzu4hyOvIxtcW4fr0+d1/K3AmTJpgHmlnJNm8bnftomFVmDpRcFxsS1GX9ZV1wwOZUb5LuS7mGBR662MfNtY+RS803bSPk9geQUWhEf3nxzqesjryffVzl3LQDu+bh5ET+Sj2w9pk/KYkj4nFoxnyHQElMcmEWYUq1tYC3ND5EuNLzSe34YxNmT0AGNr1H2NxjSvIXO3jzQLS5BKQEmmgWCWL1QFXLpol+lq+Rm1VMu+jnJfP5aNOmA+ys62MOZLhYCO955DstQ+IOUneN1FMlz3YMwm82ZZFJiwzrui506OcQgoC7MNc6fUACcbSAY025rt4xkaAlcFMhQ+IAgOleGh7tdRGkDU29QdVyaaaROI9CjdqZX1IlgdjAuUTGoDA1UE+WTdWSemo2612l+R7+HLhe7DCpwJpPiOmblznFzYlPPsKwrE2Va8Rvsb68ixoP2Q+enDhuNAWQyyxTNY3yaTy8kUnn8CFtyJetWXzi4seNcZlGOEuluH9blOvtlfOSa0/xBgbiK9ODblWkg5OaZZB/aZM7D10n3Of0V0yA/HP9lVsp9zpPW0u7DgSe/CMUvE9b60LsYYX3TMpQHq7+iabT7KB1GaAEzzA6LJD02Oc8CMMwWRBGXLZvUVJHpGy2+9QGnzwPNMlU9XjVu/GTuDcvDZfoBkmLZ5YPrNziHl5/WNO1YzSnXMMz+/tQjvU1bG7xO5ACilfgqm/RpFv0G9gqBlsT94je5B6NPndTWh26k34A9Lp3DrbS1iadOU+y/qvdp3yMgpm1gLNHRU2Xlu2bpRXc+odWEfG/qhgGkfVIZ7TFImbTlB4tc+AtYfWAYtqwJ1vwbUA/LgUCPdPjUYDvvyUGept+9/BN1eXxxfDEpZG0/KgmpQnxZpLIbOJof4TNC6efDrRevkfw/N5NqG6bBMatv559yJyPW/m2+K3HKAkg1IA4xGKddW0kBUVk9BnD7AL8I2Mqdheg7GjBAMEoTOYEzfWSTGiS+BTq5l2ajcCyj3oFR3PwpdhfaRZmcalEFF6IZKt0qykvxoHXoYKGOdWS4CiI1cBwJaZYPUFZHgRIFQ5+5rYPso8r4ZSqaLelUw9AiSce0XautwIWijPpkmGSCmuQ3rFwT/yOUgaNuXdHV2jG3HPNZQumYRyGzD+txGPs4HoqZPHTE99o9NqeMGUtsy6A63YCGA4KQHGbMNGNgBrC/w/ybrkcByB+UkCQEO9cZ6kAkkuOQ4UaZRJwPYFldgjKKOs06OzZBYPwIngmKgnGRYz+XYkd/Mu4WNaW2rC7BxwTS9a/EObKKGAab0maH6XIGNF45pnZBaE91chLGJz4BNVnEygG2+h/K5w37LclDUoNZ+CZSTKdpmWu7jl7i4SOfk5H0hhGdkdvIZWAw8DSRG8tny+1mwwNijnBYJxtb4qKbsu3w2cM04ZTDUejHwDjHqAQGVwlICGNgfpseuTDbTb8je53LSmGfefJbynUQ7w485Go80ML3Bzfr7NZE0nOk2S5BFY5UyAFRh9RbcGPP3MtbNs3+8xruw6jG93rOG3oX2sDxYJg/gtB6eIdZ7FwDlIf3Cr3FU9pBrCwsw3SzqqiZ+zZ7Wj2l6/el9nOTw9w2T433pYq3RS30wKbrqDntvNtbH6eKsa2kBA92+DvO+LP9EyjSsMcTiZPBQD36kDzBdzd/3EWX/1eWbgPEA5bgCyqjIHA+FSzqWS+P+5xpQprts/XYtkJf2Y30v6/0+z+OTm/puvma55QAlDT8arAQ8HqjxHFAauhT+TwOSAHRLzhGUKDi9mI8p47MJMxzVhZDGLc+piyGNeBrIdM3bRhmQh4Y+XRIJDBoki4r5KFNKlk8HDQ10AkayTYxIyUFBlkhZX+qhgTFOnGFek98EXzT+dyTtLZirMfLxbdiDhPcoO7YNAzznc9oPw9asEegDJcPKsgEGRmjc97A1g2T/yMjyoQCnF7JDBFwEDgR9l2GgkaCI7cr3yKpcf0XKRaPljnz8MZT9lgy1gjHtVywjH7YE03y4ccKBule2DzB3ygbJktdrbkfqIx+AuTjr5MImzNjicU2fwIc6XJfzLNO61EWPsV16WF99IuuIfUc9CqgX7a+cAAKsb3pWkhNHnsXWyQYyjApsqetNGBuxiuSyfgCb4GBfXoVNWkxgfQooJzM0bz4D2DbK0k5h7v3HLjd3FvTnAXwJgO/I3z9XueZ3AXxkCOF5AN4D4PMBfOHNKuAoVyeeRmaf9scIJtl3aViqAcvrgEWj1Mu8L5keZeoAFAF2aIRzzz4PoIqIoVIHnbSisdvIh9fzeRRgAHLSZjDTWzqe9Wkk3SjHgfK9pKJukjU20deNLB2P+c3gPRuzbMsKBpDRNZy6R6QCNqBc28osautjp1MUa1dDk5hID1oVcNaYKYqWrxUwxz6wIvXwTDXTqzGYwTWGB90sHwO8eDa8Bjy1bENebT0g1bxPYMqXg+JdhPt+cW2slj32BryH/tChiPSq7cxIxRwbjaTFvUT95IECzXlnE+h81xVlQsnw1dYxAnksMd18bFkEVp3YYPlqbrN+bHjdD/umar+QvPy2KMcuI0N5vDJBWn91Acloo7GtwIIMwhS27mobth/fXk7jPBJAIVBak2tpCNPAY/oEnBfzNVuw/eD4MtmGgUUKAYsCUcAaYCN/06C+lOtIULEK27aB9z+ay3V3/v0ISpdVGu/Mv5W0uEcgQagyaGro7+Z0qaMOtgYMMBB6Eca6EUBtoAQ+BB6bcpysY4u03o56Gtz6zp3D7OLFYXAqYAXqAFInANj+q0juhsyXhjkfBLx/Jh8FRmo8TEUnM5TtSh2Q5eND986czkUYWL+M1MYbsOid+7nul2BrTQk0CIDPIoGrnXzukpSBffYcEiC8DIt6y3YAbG2s6nNVjjUojZ6HJX9ODqhLKEEgQR1QMoJrsJcQj7E8F2H9gu2xLfqf5DqdQQJsBInqBvwoLIDSKtJaR07IKPDkuGabIKe3k38TqO3AJijYludQssxkCTl+uqxv1nEz64B9hYbpDOX+ewTTBMAdbI9ZjsE+538+n38UaY+NCyipu+OReCJ7XYUQfhIpAM+5EMLDAL4NCUi+KYTw5UiE9t/O196FtD3Iy2KMXQjhqwH8MlJ3eEOM8Y+OvYAfwrJkS5e7APw40uPmi2KMu4ckYWsJnbFF1gGwZ7VnNMhS8LmzbKJEwSIn6tlVaQBrYBIayQQpypx4AKWukzw/FeNZR4SCTZ7js4jrHs/A1rQBJViqMSoEk3PRV5BvZQa5RYS6/HlQ7Rk9denVjwcm8w7DHokECB78UQbdC/gs6uT0rO6PCuZYhqkDpQsuh7n8unZWwS9Qgil1vdTzZLH5YToEDB4Q6bYVvjyAAVHVpZZjGYD040BdVJseaNqyz/tJkSFKbGP9X6O4Ml3WyU+26PlGyjCbJZdS1olbaswBoDe7oIWt1+3FwFDdKavH96ROIlEtDRYDdAGLE1C0HbQ99PlR07FOYGjd/SSCun7X3LkJwDX/GsNZA8rHIyfzbj4uueUApTIwgBmKyv6R4duQ68lkqvFPV0A1oik8NpPrmOYuyu0LGFWTL5MeBvpowHMdFJmK7ZwfATEZLpaTAGIDxky+W+qggJl1WYdF+9yBAQka1gRJgAHJczAWRHVMvSoIpRCsUa8KIK8gMVo7KAe4gmgCKuTzZFWVtWmRgPK3vOY1+GevfCUuIRn66jqpgJ11pMGiddA6KXBSPXm3yn2Ubo4ExwpWWRfqj2wh239T8l+HuUVyAmQPBui4UHxF0uMkRi/5d/maddh6um1XHraVX7NLnTXuuj1YIBoGjuhgoIptCZTMP5k9lnM/p0GdMn19uBI4dXId+w8ZwjW5Z4pkmF2RfJURn2Z9ENyyH54VnSrTty1le1q+V91rb8vfd2Q9PIJyH8gGZZRautbuw9qI5e9QuqcD1kepA+5luQMDu75PcWxzMoe643i/VSTG+AVLTn1a5dpHALxMfv8igF88oaKNUt/S5WsBfA3SXN8XA/jBQ1OIydj14fu5HVIDFJuRzytWVw8zVpX1qrlmApXIju5/bxA2sDR9YBgCKWXYWiRDu0cZVIcsCOsTpS58LnGrjoGhbMzwPugygHTlZ7qsAo1rglN+zqza9hJD3Vy9PWjjdQoqPWujTC4BNoDh5dM05XWqW9WdB7Z63Eeg9dt3AMbUKRjx6wBreXt3V5YBSAB80FWTfvvIt11XgjO9X397IFmLTguYfgn8ai7E/OZepfxdY8mYFj9kK1UXDcx1m8dqLst9bzpRIKvl83Wa9w4AtmU/1Hsp3IaDayX5zcu4XK3FYmRV/l5pbfJkcN+FAbsg1/to0V48qPTrSGuRi7U/si6eDS28BU4v5jtRueUAJR/oNMjpVtfKcSA18BnYvok03tdQGoK8VtdM0WBdlWM0OCeSHo3MJ1AuKlYwo4zZBoy92oEBlFWYUerBKwEHjUvvdsfybsNYIgInloH/04idoWRMAwwY8hqCFwIrgj3qbg9mxCtI5f0EyXuSjwfnvVxPt0DKGoBPvOce/PVXvAIvuPde/MEDDxTuzTS4qQc+kFblOPNX4KiTB/zQ2Ge5CNAJUpkH25/1ZT259xqvYV/RrTOY7iow7IMIyAMTZrQomOJnE9Z32N/JuG7BQMw+bAuauegJMOYNKLf7YJtwX7UnpC4EfFv52AzleFlH6WZOPXDSYEPS4m/ez/vYRjrpwd/cJqaH9Tm2L/sDYEBZdf8kDKCxz+7D+hpfbA3M5Zyg8lxOl+NEnw0cuxso11prHVu5d1fu1wmh3XxM9aFjg/fti/63JU9OApzIJsqn2K1mlOOXJVu6cLu1HlcRvDBGYarysQFIogQ4gAAgGmsuPXWhAxZBpRq9zMeLB5d+LdywEbqASX6KgCJZCzRgCSbJbh10QNubV0cLM0gnfDBJmbT8vJ95APYcnQCYNgYk19YMTB62ftGzkDWmheBDjwGmB2U0Y1YwDXAfGVfZHg8mfdv59Zf+0/dl+p0DlOpWyfKyzAoW6PLKfjZpgNgKoHRg2+vJ/197JM77Rd0ru6kgyLutrkhZmAcnW/z+msPaWKdDtoWCbP1fAbgHqRo4qAYofb302gb1dlz22uC4ob3n12fqXrQTf961VdMAfWusq+6bOdh1HuD2JVjXtvZ92QcY0kmrKHkpeGXUYcAmI07sDXqK3823HKDkTASZCBr8CpAIEGi4cdzQOOf1vIeGIGAvP89wAQZQdmGusDTulf2iMH8CMoKdBhYVlfluo4xeeycMaO2gNKD58lQXObJDFOpoFWaYEkjyecNzBLLMowbYqCe6Ou7C1qqRWboEM9bPogQz1F0nx5g3Df1nPve5+L/e/e6hDn/8H/8jAOCrvvM78Ymf9VnD8ZfffTcee/BBwJWBBjnBsRoYyv6yDcgecXKiRwnmWcYm12cf6UFH/RFs80G4LucAm41rYetjqCvq8iIS6CSIvYxFY4zgjB8y4pzZO4cEgi7n9Lgv4eX8TQCtfXdX0u3ztQS1Gr2MZdiEAfNWdOnbmOOS5Scw43h6Ckq36ADg/XI9P+yD1DMDH6ltpvmyjNQR+/MGjNHmMea1k/PgWlH2RbaTrkHm+OX40LHFcnEsE5hyooe60sks1pFjjhNH1Cn7Kydt9DnFfs4xdQbHLKd8r6tRbpq8GsAbkRwhvvCoi3sAe5Vuw2euj0xKNig2CYw1ks7w6Qw4LHO5FBwGYHnMCg3Go5u8M31vEHuXWo5XGpFangF8qj76ReOe+eg3/++yC4kyjWSACCinUwOT6gbp0/N5LpMacCDgYZqHsWXL8vDXKitaA/bKnPpy1Nhnz6ZSzwRY/H8q6QJlVFtNUwGV9h/vbup1xv5b0/2yAEksK4Gx6oigxLNkHqyojvm/Rmj1kwMKYPs+A20YmOJYoMu3uhR7RthHBK4Bce2TbQt0MxunTE7XYaotMbQFyjQ9uzrUUyaxFEwOOgCGfSaZ+XR6eB/1zxrGutC+SDCsYHKYYMAJyQm+m0MImwBeB+AFSGbpl8UYf/Na0rgqQBlCeC6Aj4wx/p8hhNsAtDHGJ4667ySEIO42lK5knPnQDktASEOuQ8ksTmEG4TqsUyv7SRAY8nXbKMErAaQymTSG2ew0JmmIk3FQY7FHyWJtwMDLo3IfhcCO/7PMCpgUSCsrx3quw5g7ZYZoqJL1287pbCANIoIwBTwKShvYgmsaypdgRj8ZVgW3ewD+5MEH8fp778WX33svAOBjMohUMPnae+/F+x58cFhbd0nKyrwIntnmZHk6+VDfQAI9F2Dsz1o+xjo1MPaZv8kEK6vWwowK9jUCM0ZyBUpWrXX37udy3YEEdNiv9uSeM7AZvTVJr4etsbwMc8eWiciBUWR/4Hh5DDbxQUD4bhijyXZlHwHMnZppUKdk/w5y/urCEmEuu5wAUcaQwpfLPozlZdtykgawSRJOxFDf7FcNDIzqeNiE9XW2Gddq8hjPr6Ps8xswIM62X4exvkxrFfa8IIjmPQrm2X783cH6Xw/r43xGsRzU3bEDSmAElLeInOS7Ocb4IIBPPiL/NwL4XCD1Q31H6RphAMNm9lVwoRfCJrbmyF1xhoKB8q55XjxI8uv2dG2fruOisUrXXbp8Kmil0eoZJ2VegOR2qGWuuU56Q9wb42SSlu1vuIxFKsolRrTPdxkTRYA8bUu91cAg01E9e8axxl7VAOzgCo16oJ8B5Li0aiBd0/bROSdSf6+H6PLzQXvYjxW8Mg3VTy1YUMEON2U55q5snp304NrXXcEt29yzl5To6qcAss19djZbBEhtU44d32/0/6L9YfYGhct76KG1IsAMsHKHpsxH/x/YQyyCSVab792YLwy91bc2QcJ0G9G7r5u61WqkYh+99l3vehdCCJcl6TfHGF+1mOM1yMm9m78fwC/FGD8vhEAz8ZrkSEAZQvh7AL4SCQN9OFLI9h9EZd3LzRAu8J/ANorfgYHCdRhYIVgi6Olgm7CfQwpwcQEGNmgskqkhMHk4X0eDjiCT/5PR3EYCf+tIQX/OIRn3XFN4BvZiugRjIFoAH5Hv4wLkbZSRTlvYJug0Qs/DBo02JK/hy531pcHf53y35XqW+XYAfwLbLJ6uq9TPB3Iad8Pc9m7L6cxRgkVlJNWFlwDvHMrN7GcAfuK++3AbgC/MoFLlx+69Fz98333DLPFq1sEeFtftkWG6gnL7k81cvgswYP1cGFBiP2L7U94NM+qZB3VJXT0964L7Z16C7QfawgysbdhWMhswIH5bboMpyoAsOrLZpgRJClqoD4IoPmQJfgk+ZzkfMqjshw3KaMAENFtSjzukHE+ILrdhL40DpL5+Fmk3+v18/wZK6bJONKoh81b3GPZx1YH2d4496oMsIidZ9pCY2/Wsnw3YViUM/tQi9WmuV6JO2T70FNhACgzEsXwOyZvgTP7swaLh+kkupql9aBuL7Dj1sgFzv/VuyawnJxqOXUZAeerlNLybs3H0KgB4WghxH2WkQ7XVmj4F+vDGaG07CcACbdAQ5INADb0aUOGaNooHsHqcMrAucm/sF0FiTWhI8nmKfM+8ByYzoJ0ZMND1goNeBIR4AMAAPIcByaVuiQ6ceqDqdeHTb2FlmrRWdqah5ajlXTPUVfz6SKCcVFTg5fMcJiFQgrqmsbVsA5g5Qk8+bRVtF6Z10JXghaDf17+WnteLsqEEeRqUSa9jv9aPn6ChLgYQygmRJaDPA/WV1qK+cuKFtgSvV/aawFrr5+s5eAR05aQ7weRtDRYY8NokhO/vrH+BlwVgKqAEzL6oiQesfkKgCuRh/YPlUcKiB/C85z0Pjz322Nkl2V6fnMC7OYSwgTR5+HcBIMZI+HNNcjUM5T8C8GIAv50z+pMQwvlrzei4RJkiwAzHVn7TACTgA4wdZIei+yJdDcnSkRkhGAKscxJwrsFcZi8iGZQ02mlMKsPCzsWZ0AbJCCdDtYlkNOq6CQIkZUMJvhQYzlAa/mQvCNz0w3rze4ZkYJMhoxDA0AgGzNglACaw50NhAnPXXMk62UYC2Lsw8LsNA/iAvBRynS8D+OH77sMz8vpJym+86U14zX33De3NfTvZHtN8v5YXoitOKJBRYps3sC07OFHAupNV2pV7CcTJGBOIkm2bwIAE66bRTbXfEowxT4JxwCY5yGYDNknCSQf2p9th7BaBj06StJI226+FtRv7B/MD0kuErNwWbO0ngfwK0v4NveS1AXM7JbtA/bE9qKcOFp22QxrXnPVTNpZtso1yOxRInXRiB6g/1AhGtR0nWXcEh9v5Gl3DQZ2Qrd/Nx6m7NSTQVwQegY15jt0tpDHOyRO+eDgZotfPkMbBZi7fJN9zO1IgIeT7Hs15+T3vblji6d7rapRBTtW7GbAxwGeF9qIGSNsReCOst+v4XKyBODVmVWpGoka65L381gAq6vpIVo6gru/tObYsHxVGaGWwDtZjDmAlAzQFSBS/353uR0hASVAJlGxS35fBc3jcb8vRCdhYYIaxyKIq0KDb7bI1mx5QLAMGNVfCvk9MmK7zq4ExPa7M8qTJHiGSpq9nrax9nyfvRC+8R11HVZQJrLme1ljS2nY27McDgJS+yfSXTZJ0nenLTxDUord2lfbyYM2Ddt0Spm2BZlZVx1Jcw/JMpDwrLbDSJbuCE06rSOuDa+6nvt20DkMALZRgkrYY7+UEPm2GFlhYjwkIMO0s/dp2LUPdXL15T9dbmU4krgFwI+/mcyGE++X3a2OMr5XfH4a0+ujfhBBeCOCtAL4uxqjs6pFyNYDySoxxFkJqnhDCidgwVysEMjr7P4WtceMMF41MNjA72+CnjeSG2KJkMrZhRuy6pM8JKBr/PQzAnIO53+7AgMMlmIskDUV+1F2VjAWN8ydhW3roLAvLTRdXAgca2udRBkXxLAbTZ57K3gLlWksFlJoHQfc+zLXvAOZ6SBZrA8a2kA3sJW112d1HubavBfC85z8fB7MZfudnfxYv/pzPwXOf/3ysIhnZBFQKzlgHpsn2J5hk3gpICBQIhNlPeD/TvYSyP3DyYTcf38j3PJF/09VTARDdNTlRwY8yqsy7z/fTBfYSbHJhG2kd4h0wFrbJ39v5o9NKZLlvc3Vg/XRN6FquC9ub/WwzX39Z7ptJXgRNbHPkNMkOa32R89yFAcpWztF9WV8CHOd7MIaRLCjPcVKB6eiETifnWG66vQK2FnUH5u1AoMg2JiBl29NDgq7FQHookklkvoBtC8PJm3eifEHTCPd9c5bryX1OudaW4HUt/7+DE5CRobwV5NjezbUtXWKMr7/WdBqUDCVgfZ3PAO6hp9EfdRudWnpDOl1y6/NbXdTAnneLU5dBNcL5/2FGIOvA5y2ZCTU6WW8N0Hc1opFPadwTNHqGUo1roGSrVNo2BaDxjJECTn5TdwqwyeqpS1+N1fSgT9f+8ZgCFjKdQALCvvweDB72f61etXP+UaY65MRDDZTFvh5p2Ive6yco/HU6KaATJH4niALICBg+6Bb1XtORnp9n48ZPqigu8exzm9vpIPe/plvUQY2F5r1cV6gsZdsAq70FtVppLULssnarsfC6bYeOyxaWb+gxRL/l+9VPjGges1kG6igDa/lJJ6AcL9Svth/H/4m9Qa/v3XwxxviiQ863AP4CgK+JMf52COH7AXwTgP/5WjK5GkD5f4cQvgXAbSGETwfwDwH879eSCSWE8GyksOR3Iun7tTHG77+W/a7I9vACGrhPwNgnHxBmB2boAaWB790Jh1kO+WwgsQNPSFoEXwRpagATTJLdIOCggUtQoQCC4HNPvmlsEwxB0mny8V2YWyTTI3sGGIgii0tAGWBuf57F3EO5TQbBM2AgnXVWw51MKdugha3TpG74DfnN+q3nz0ffcw/mBwf4Jy9+Mf7kbW/Ds1/4Qnzzj/wIXvixH4uLDzyACANsnAVnO3VSFoZz1rVzBJn5OT2AVLp1sG1uh4E89hf2k1bq1KDUkR9QLFsPc8sU75hhLR8ZwxUYiNCZce0rT8DYtQalS+YObIKjA3Dm3Dk8cvFisQ6QDCJ1RXaR+q/lOUcCcGT0OQ4IvggmNSQ4H6oE9Nswdpv9DrAJHf6mPjkpQFaVIHAjp6UsOduJbc+240QR0wWsr+skgoJhHTtMS8EhkKM8wvqBsrF8uFGHmyjdXGdyjmOEE1vrsPHM5wz1xnry2cU6HLuMQXluFTm2d/MhW7pctXBiBrBnnu9FcyQQWduKgwwfJypr0gOD6+FR4g09Mo481AqQWxrkhv/DJu4alK6rAyPTl6CU169IXjXGAxAjHKWBrWXzAJnpeOZJjwNlGSncX7C23yJQrivVdZhHRUVV99hC733JmPb9YiCTZeBX9XQYG3oY4GM5PCCubYFCEH3Yvo6+/jX2VfMZ9rHsy/v82k69p+bu6etEwMNsiy15ALRyX81dlGl43YQmAUlew/XAyt4tc1NV127A2n11FQj5Je+jtqr4yYFa2ZrevIQGO2xqbKdnoxX0F8BfAOU+LM4FPcBWctuzXjrhQLmpzjwn925+GMDDMcbfzr//PRKgvCa5GkD5TQC+HMDbAfx9pL3AXnetGWXpAHx9jPH3Qgi3A3hrCOFXAfwdXOV+VzSoyFywX1xCMsa4dotAi9fRsFSXx3P5/0tIrqtqZPawdV9NTncViW27JOncCQO4BDV0DVUgxXKv52M7MEO8A/D7MHbmXFbEFMYk7sIYLzKEF1FGqqVhS2NzN+ezBRt0dwLDBsp9TkPLqqB7A2bAAwbICKYJjtqcDvNg2ciGNUg7lm/mOhNsPz2new7JlY8ulE9cvowv/IRPQN916AE88ra34Qs/4RPw3LvuwvukTNyfELC+wLZWNlDZyz3YthpbsDWDZ/OHoGAOY594XYuSHSVbRsaVIPaOXI/3w9xD+QjwrplbSEzYWSSA+B5YRFgCYvbTbVh/vJDLuQnrV2wTrfe/eM1r8PWvfOUAlvewOBmwirQusMl5XMn/s9/t5nLS9ZLv9Dvz/3cBeCaMJWVQohbAn8//7+R7H8r12cz1exylOzTBH6SMZNtXYGtGD2B9bRu23lf7AccMyzLNOo6wCRsylrswF2eCa0669DDAfidsXbWCUYLOGWy/WLbduXzu0VwvfX71MKDfoPQQAIzVeRasjbdhwJlj89jlFG+ePMogx/luvmFRQKmApJdjLez5dhiTV8MGnNgCFtdoNlg0TIs1UCjf7UAyFKezZDR647pmsw1LFyqFo5GrQJjPcAYb8WCS/6vh3TTlnpXcNN7LMkbLHxsAR1OyqkzjKNu071OdlRnzgJdpEEwq68R7+M6rAUXdf5HHaoa7Z2d9PZeVX6+rnffs6GHCOhG4UbyLpM+/tl6UQkDoJwTUDZjf3I/RuxpzT9WFMSDto+3tJxy0fqzPPLcD96rsOgPWrOvK1CYPlk1gsB9PsRxE1vqhr2MBXl1bqZsuAfW0t3L7+qk+dO0jn0e0W/lNndRctrmuFr15KZzYZC8LftxJxvhoCOFPQwgfHWP8b0jr8P/rtaZz5BCKMfYAfjh/bkhijO8F8N78/xMhhAdgtiif+YfudxVhIA0o2bgG5na2h7QpOUHZnTAjnM0xRfkCoFFHI29dztGt9jzKrTzoDqvGIA1LdTulkQsYM7qBZGwSqPZyL5lTBryhYa/jcBsl8NVN6cmCEMCq8cx0WX5lhwhOCRT1fzJTHGR0DQaM3SIDuJOPszGZDoEZ7+emtBMY4PnjBx8cDHWWCV2H9zz0EAADzsi6vYxyL9JNlGxTL+mQIVYWim3Fsq3JtbyX4IHgVFlPMoub+ZrbkNriEdi2IVOkPqhs5i6S+yr7IF1cOfPGPHeRQN5lWD9kHQiYeC9B6hTAR91zD17yilfgr9x7L379gQeKNcHr8n8v9zOiqvZf1pvXTWFBY9h3LqMERldgbmwNbL0hAZ+u37yE8uHdwvrrLiygVCvlZDtQOA4Z8ZSAjX2U4/Nx0eEd+bc+Dzg5sAoD1oz+Ctj4Zh6cQFIXVzL8m1IO1mcP1j+VZVyVY/o8W0MZvGsi1/BZxmfEscrIUJ56Oc5383GIjl2gBJU1cAl3zr/49V3H+3SvZ3231FzTvNCLgOORzxx05bq1grFDCXqHd0lffmjjqT1BZlLZmhqg8AATEEDpjHOeJ7voDfcaYzaRtDRSq7dL/TYX2n50T+b2E7U9JP2+hDVZBnxr5fE6W7bOzYtvxxpg8tfSBVRZ02VyGKvm02ZZuc+psmZAyQhPGnOz1TWa6kasoHc2s7Qmru0pOokQ3DXKKB42IcFAPVqnWv/Uvu37+KQBJtNF/VHvDcp7ayxqrV+x/N4tW8FoaGyN8VA2yWMIMoRyb0l9HqkevaeBrrcGFr+PXU7u3fw1AH4iR3h9J4AvvdYElgLKEMLbcchSgBjjn7/WzFz6dwP4eKSAAv8d17DfFQHJAYxFOQczGunKR7aDbo00uGkE78jvVZghvg8zLplGn/Nbh63HIiC7DcbOkBlpYYwcwSJd22gc03BlxKup3E+D/jJsbdy2XAOU7oYbMHehNZihugEzqvdh6/E2YWGbCY4IWDfz/dz6g/XZlzRpDNOIWEMy0umySQOYnzthLBJf5tTvDswgJ4B5CmxdLIWs0q4cV0aOYIb9g223AQMVCsTIMj/LtQH7FgH5DozBPYs0KMgWsV9wkTkZM050EIxzAkHr38NYUZaHjBxgExFXkNxc92Ds3hMwVp2TBnc+97n4adnH84/yPp5f+Z3fif9Ntl75/9x9Nx7K+3jy+ax65oTE7bDgR6zjFKWhwf64nX+TqaNr2WWYO7GyeWRfe7mXY2INxi6yfnSR1XHHdNdQurhTrxO5Rj0TFISxThxvlH25ls+TDZhLqk6KUGfsxyzTU/O5x6WMnIyi3jQPTmio50WTr72Yj5MxBWxS4rk4ZhldXk+1nPS7+UaEE0hAPZgNYxxEuX7FXUMpjMe+nBjkRE8LYyV8UBIa82qsSnLFt66vLFgt2EQW69TlAUsAUqzNlA9dP5etfyzK05cghoarBuMZmEBXPz1PnWl0Um8I+20w6P66sOVHX5aNYIf7Jw6Ata2DzBpQPopdpHttze1RwWRkJ8DidV6vtXz8NcxzLuCLALu2FrL2v6+jgkkG0lG2zOtcy6KBowBUt3zh9YcxqzWQzvrxvmXsql6vaz9VvDuw78dMv5bWIJ25AyuYpJtt05T1X7YPpu8DBRu5BPDquFVXe3Wl9RM2OnnVoMw75o+68h+rnOC7Ocb4+wBedCNpHMZQvjx//6P8/cb8/UW4wWj1IYR1AP8BwD+OMdJe/+SrvX8Fi3te0VjUYxuwGUkep8G7g8QgKau1CgOH/Cgzx+AtZPPWYSwagRNBrRqESn/TkORxurluwJg9Xj/Pvy/CjG4CSMDA3QYMGNNwbSVtZaPUHZbgjYBQ3Vgh9/QwA5uueMqakF3hYuth42cYI+ddPa/AAOss11EBhcZZVvBC41vdpQjWn4a0rQkBJr83YW3JMhDMsy3PwB4mB1KPTRhgWsvnVpEAL4OlsHw7MMDewSKIaiApAh+272UY8FGgxnqzLXQSBTCQRd1OAew9+CB+7N578XfuvRcA8PwMIv+cgMnX33sv+swA78MYOQUrWv7VnOcTKLcM2UUaK7fDgjJ5ANbDmGqCL7YBJz62YCCfdWG7tCj76zbKiQNk/T4F5sZKN+VnoBT2R4KwBrY/JsckJzX4Ibi7CJtoUfZUJ2PYrzgZNMtlom4BM7YvwtqMfZrPJx7bgRnOzJO/OUHEyZUTkRFQnmY5sXfzSYo+rxUUqnhXVjXcdD3j4PmQwYwCryHgj7JCeSaK6zRb2HO5QZ2hbPMDYHBbzGU6yNe0vRmqFBqYXEfGcrFMs5mBLW8Iq/HPNWxaL2DRdZL1I3Cmvgjw6DKr38uitdZcFZcBOjXalflUcFtjFYGjWUAtj2eC/bpYz9zNe6CflemwLQc2zgEgv9aubU2XNVdlrydfdn57ZtIzbgqKFbAp0157DAfXPh6keRbc/2abYQkY89f69HW7EnSL65R9meeSRo0hbZoy0i0D5NDLqc0vXAXANYbcy2GTDPoBkieBuvOqePfmXsaaZ3f5bNO12scup/jdvBRQ5k2NEUL4pBjjJ8mpbwoh/D8Avv16MgwhrCCByZ+IMb75Ku8ZNk/m2gtGkCSoaVCyPAQ+2ygDlrDCDYwpIENBQ7qX+9lBOpQzGJoH06VRvgeb3aAoqJ3BQCFZt00YmKPR68ErXf0IPllOGuA7MLZ1F7Zvnm9kggm65p2BueExD7pysq5qBNDwB4CnnjuHJy9eND/zfI26ItPAbmEsJV1V6Sa5DttShEDiLGxd2wyl+xH1yeAoWkcF9nso1yNSZwTeu3IdZ8sVrLZIwImAez+X6SwMAJItuwQDAptIYOvJ/KFrL8v+GBJzxXWBZIRZToKvKyjZWwLfczC3TN3f9Cfvuw8ABlCp8lP33oufve++gYHuJD3qmxFYORkA2Jh7PP/PsQSYiyl1q+yvsoacxCEDS7D1NCQfeNZxF2UkWqbZS3oE+BwLd+Q8r8CA4JOwSLnsy5wUIcN9HsZQis05jN19+TT5PrYP++R2TiNAgD0MAJPRBOxZw0kejiG+Hlh2MrQ7MIBNzwi9lv8f++bJI0N5quWk3s03KoeuVTlE+MzX3xQach2NOB6HAcHpNAX88JFFyXIMBmdmPDSNqTN0vTHdthYlE7B3Ayc1V/py8tiDSa4xqzF2HuDp+WlrDKUGGvEf1m82K90kWX4a3wRGBJoK/BTo6L0sH1nYVgG6u1a/tY6MTKogmADDl3Pu7vUukBTdEsUHVEKX0q/JAAyaRebar6NcBiZVN8tAXA34el0xD01P85pXrqP4vSW9/v0erPzoVjS+Lnq95tugHFfs33MBlQrIa68NZaw5GVFzX2Va8770cgISqOy6siyahgYj0nb1dfF1BKzvTRxw9B4DvrzeDZg2GL2iTsTl9ZS/mw9jKClnQwh/Ncb4nwEghPBXUBJIVy0hxTd/PYAHYozfc7X36ebJbQjxD2GgYBMls9bAWDwyXgSbNCI3YAbxBgwsXIKBJRp3ND65/otjdQsG3KawLQi2YcCMYGMjp83012DBPQgoWySQcBdSh/xATu98TjsA+JN8jLOrrMclmHF6Seq9Knlq2QigyZSuIjXoZi73BRjgAsyQ3YCtD7wLycj9ute8Bv+/V74SH8jpXUYCSw/D9snrcn03YYOOL2Ua4QQwbK8ZjDWmUX8Ac8F8IqfD6L78PCOnfUXyfgxl1NpG8qLuA2z9LfMi20UgTBBLsE831+38eQ4MDNE1lu6/jwL4iFz+ABtAm7C9mYDEXm3l8p7P6Z7NZQESoGH+BzDQvYv0ANsA8Ev33YcPv+cefJLs4/k7b3oTXnvffcNWOcg6eFLKfidswuQSEoPP8cA60H38PFI/4WQKJ1cIntiOFNU9ARkfuFOY2/EHYGzwObm2yXmSEf19GDu/nvU2h+3n+gHYJMYECbg+FSkoUAtrs6fk+tAF9jLKgD2c7NjJ9d/MH07e8Bqyt+uwZwzdn/nsYd9QIZO+Jbppcj10gmcTtq0RdfwX8rH5LbJ58ijHLsf2bh5llFFGGeUWkFP8br4aQPnlAN4QQqDNtA3gy64zv09CAoZvDyH8fj72LTHGX7zaBGjEb+VPi2T8KuhTl7o1GAtEA5AgkSCDoAIwwIh8bAMGMhoYECUwBGztk7JJbeU65k+3TrI6NDQJXs4gGbicobkCm5E9C3NDJBvEGV412KmLXUmb7CXLpa6JreSxiTKYD0HoWdjef7cD+Ph77sGLXvEKfOy99+J3HnhgAGJkVQEDbyzXDBaJtoexYVdQuvJR12SQqE/ddL6Hufluwpgvtq8y2PwwT7pOzpCAFIVGO8uijDYB6k6+l+ycBjhqchm5/pN9YSOXm771vG4GY7fO5PMEGQSRbLPLOe/bYAF0yGSyjx1kXTzv+c9HN5vht372Z/GXPudzcOfzn48npS6AuY31Od33w9xCd6TsnKA4L/pQl1TAWDdOQmif58TBBoxJ1PG2J/dwAuacpP9uyfNJGMjihADbk32Fwn57BhaQg+CMLCevI2hlnQmkz8HGP9uf40X7NSRtMoo8x3Ky/1O3ZJn5zGH+u7Co0ryf42VdyvcMpAmV9+KYJeImx0If5TrlON/Nxy76rKbw2dcgPX/OoPQw6eVbJ6R8FNUGxu7xo+usam5uoUlMR5uv8fs7LnP3G7Yo6eyL5eO3she6vkvdXnVNHNfs6fYjLCfZF61faLKHTl8vKwO0kL3yLnrqUsj7lIFSllLdRAcmEEDfJraxmZl+jgqEpGXQvL1bLd2L9ZxnKGuMUc3l0d/Hc34tZE0/g50h6Q4uz325HpB1j1InzY8eTMqm1Zhg/9u7HavU2EdfHs1H3aL1Ws9S8tqiXSSNeZ/7Y5c+uiZUmcVlAUjJIE6nQJwaS+llyB+lnVK7zrOTgLGNNR1qe9RYSj8OAXNBrj1P9P95b6wkPemucmhcm5zyd/ORgDLG+FYALwwhbAAIMcbHrzezPJN6vZ4xAJIRdx7GLETY2kYasTTCuC6NgJIs0y6MAaOhRuOT43Um6TSwACsbWFzPtQdzw92GAToOinUk8LsLc/u7A8aAQtIhkwGUATxYJgYbISjjvTSwN1Gynqyfus62MIC8jhIIAMbaAmWU0qc997n4IQn68u5fTUFfPve7vxP/9DNtnd6n3X039h58cHALZlkIjBhghkY3pE7euF6Bufjy/jkMbNPwX0cCWh9A6caqLNNcjisY34OtASVzS10BBijIUBPsss+xXASJdBUm20WdMkIpv9k2NK4ILDnpwXQIPJ+EAUpOLKzmfBidFQCefs89iAcH+JcvfjEeeNvb8B9f+EJ8xY/8CD7mYz8Wlx54YGB9J3LfNhJwI0BjuTdhbr+sJ3W0KXWgjndhfVpZb4JejjGWWbcaAGzyYDWfIwBjP+Lk0ZbU/UlYoCm2J/sVmWvWF7AJHAI7yjaMMaewzHwesM8pe6/BsBidl9fpZBTHQSu/AdP5VH6voezj7KMbMHdbjoPjl9PtVjNKkuN8Nx9LeVAGolCjkNK7c1Ok9Uu1PREHQ5YGY2tGru51d1iUSqajLn/c2mI6Tf8Dttatdj8g+yp2i2vdVRRgKVjinn5M88qsdNvUtaKsj49iqvrh8enUAvNo+XXdXtMYCFi27o6iZWQ+CoZYLq8rgoqjtt5Qt8nBlfYQYFGTmvGvoNMDY4oGUdJy+3Rr60y99D2qe6H6cs9h9fM6q6VZW//I9b3LdF5Lh3XU4DZaPg869bum22HCJ08qtG1e5zhbzLMov/wTJU32W41AHFtXjk6eF64tdLIg9uayeq3iQaefQOj5AnYTQkO1chvw2cLYGycW4fWUv5uPBJQhhH/ufgMAYozffkJlOlRo3LLhgDTL+STsBUUjjiCP4f8JoGjo0bBToEA2gYCD/5MBfTqMFW0lP4I3Gp40/sgUEpyQAYlyboZkILPcgBmXeyjXnpGZbCTdLcnzLCwqJI37/C7GOmwfPgLkp8Hc9R6HMWYsJ1mzGYB3Pvgg3njvvXjVvfcCAO7+9AQiP1zA5I/eey/2H3wQz8m/FSBxEpJAmUCWDKUCLTJaNK5vz/V8JOtE9UDj/CCnQz2yjsr00Bggy9PAGCHqm8cJ6PgM4bObrs5sbxr8gK1D3ENyteXxVtI5gDGRClIAixRKELYFc53czundBWPfQ86HzD0APHr5Mr71Ez4Bs67DAYB3vO1t+LpP+AScv+uuYeKEY4d13JM6ETARBLK/6h5wyhTsy2cHNjbork2AReA/Q3KXvZjTINNHHbO+gI0H1pfjaws2MbIn6TAN6pXlamBAfAVl8KoAC4DFewiadfxswBj92+T+FjYZwTWbrA/1uiHXA8ZMbsNY3A3Y84d7nfJZxWumsHXDj4h+jl3GfShPvZy2dzP3NgZskswH3VFDi9tqeJaLz1/KAIAqx2iM1gxxZWd0jeEEBhwIFLkFh6bPdCjcL3Glt6jXDUp2yAsBGQGKGveQe7m3IJaBpa68TwHPSgvMp1ZPCn+HBgOrqGlybaavc229WI0RZLRXvweiX3Pn09bJAO7hRz15lswDIa37wDz1AKPuzp2etM5NX64zZV7sQ2wnZY15DdeB+vu97mq//dpQfx37qe+zCp503akyo14/DEZFpvrKzNLStquBbn+Nlk3zmOa+Np0C7T4wa8tJi9qa3KEeuR1ms+QtwJd206T0GH1WgbBn12tRgJumBKw1xn2Z1Po7J6AGvN7Znue1wFOAez4dnuWNySl+Nx8JKGGed0Cyd14O4IGTKc7RsgfgQdgaszWUjGEDC2qzhjI6KcEkQcYOzIjbhgWpUUBC91oac0/kY8+GvUDpdkkXNkb3JPDZhe2BSbC0Cdv0/LF83VNyHVeQ1mvRRXUjH3sf0howAlW6cwIGbGl4T2HurluwLVFWYevutnN9lLlrYAFlgAQOtvP/MwCvvu8+dAC+NINKlR+691687r77Cvclsl3UK43/O5Haj8FS9pGArW4K22Q9PJbPbeRjZII2sz4ehYGT5+ZrL4j+tmCMFF2dzyOBgpjr+IF8fhuprZ8FY7zyBFVRr2fl74s5rxkM0BPsEvzwswpbq0e28VJOp4OxUE+FAVzAQO4WDFxP8r2AsaIEWA8++OAQlGYILtR1eNdDDxVr8ng9x8RarhfHDfL/V5AmGz4s30O34E0p3w5s31eyfNxKh5MeQOkiy2MPwdq7Q1prynIRDBKkc/KGExRkdSewcUd348tI4wuw4FxcX/t4LhcBNdsLKCccOLFCYP0IrK1XYBMZB0guwwBwj9RlFWl9KutH3fdI/WYb5v7aI7UpnzNAuXb7IlJfJzgFbA3urSAhhI8G8NNy6MMA/PMY4/fJNS8B8HMA3pUPvfmDBZBuATlV72a+D2lMaeh9iv9fmSp/jTf0l7FWNFLV8FXDvOsSEOBkadOZwbjMlRJYbog2sOcGAbG6Quo3/68xXsr4efCl19JIZzkV8CxjQPV/ggwCWgoZRR8x05efoi6yZM5ouMdmcZ9Dfk+apH9lJmtGvK97DfzU2FUCPQ8k9FpNp5Yn9TCp6MLvHen3rDxKd+oWqnsjqrAfkimvTY7U8tL8FiIbowTYy4CTfut1taA8QxThaUq7bRNoJWPJPkYA3qDMnzorgKH0QfYtnvNbqFAXB91imfmtbLVKzV3c61Pv7XqbDO8ATF0/0HZjWlMsBnT6UJIjAWWM8bv1dwjhuwD8/ImV6AjhrD4ZBBq/ZN8AMw5piNNY3szn6UbHdAjOAHuZrcr1PWyt2g7KgDxkMNjpGiSDli56BJBkE2kQk5mk2yFnc8nQ8F6+oJkeYEa7GvfUQwdjN8ig0kBuYfsrrubrrqDcSmED5o6ojB512AH4sfvuw0fccw/+mgR9ecub3oTXZbC5D9uWZE3Kz3eZuhXuw1yTkcu0Agvasw0DnD3M1ZO62JD/O5jbLEFgAwNWBCR3wNaJsryXULp6KsMJmGtrAwuIROBI2YSxhMomK4ALsAikjVxHuRPGIm8D+FNYMKI78n0aAOkM0lo6BqvpYSCHYGgVFhQKsEkQAjH2JcDGA105Gfn2TP4mA07ARFaOxuM6rN8AxqwBZQRUneQ5B9M/YO6fHItrsEi2HGvs3wTeyuyzvZUd2YC5iEaU64KbfC3zJ4DTySfq5jzM62EXCYDv5fJT39Q/wfA5mJs1tyrYhQHOdViQrm3Y84ll5LOO/Zzjkfq/oTUENTmhSHIxxv8G4OMAIIQwAfAeAD9TufQ/xf8/e28eZ9lV1nt/9z67Tp2uqSvVlU7SCRkYk44YQBJUcNarDK+gYtCrONzB4V69XvW9eFHfS7WzIopDvMD1XoOCIAKiOAEO6GWQmYgQppB0CKHT6a6urq6qPnVqn73fP9b67efZq3Z1QuiGQnp9Pqfr9D57r3nt9fye3/M8q66fctYr8K8s7ba9WT7ZWrdd51C2vsf/+KMwuu5r8neCmgS5OgrhOksRXGTY0u5T1tofhvGFkZ7xmNZNwm1ax5xugJQCPX9Wo+olcz8c0+LrkoIZz7x5FjBlBNN6V5WZ06ZCs2fExlUwAd4JcKQRUJVkEuj7RPcLGKT96o/K8L93CfneDFj5qT0Tbtw86EjNEj1YTuvTBcrStuv+9GiMHGMrfX3SPmqxjElfeVDp/Q99XTyA8lFaUxbRz5c0Eq8HW61jeNhuKp32Ueqf2PI5Lsw8eKKwNeXz8sA79UvtYhs98NeZpD4174zSxdLYYe12pdaRJ3T3daNswvZy9L2EyXLnMu5LYfJpp38FUV7TNEXQLH/Wkgd3EkzFDpYE7b6ELv/x4yuzug332wLbIyuuYMKqQNmUuy8nCIM5BmRXsAPd5QcmQCbhcISxEGsEQLAePwKf0I44WWFC+DQWMKTEQKHAtRggtQ+MAVFbIIAUBTrx4GMDO79xGhO8FVTmihj05T2veQ2PftrTuPLaa1l0bTuGgbop2ofb655lVzcxdNAWltU+JdmnewZwjgAsNmMe+ggMNM7Y2LjOYYGafFleeaAyBMy9yaLMeKvYh2J+pSQQABvG8gUc1ty1KeDS2P/L8fp8vE8s2slYpoDfHgJLdRIzpRRwFwMnMAkGYGSOKkAmhm/e9bn6XfNaLKNn8fxRMsu0z5n0YA9szDU3Z2J9NAfF+s1ia0TzQQwgru1SmGwQ1pxfI+vYepIZrM73nMBMb0/GPpvHzMDFcgroaj5IAYNr1+VYJGSx3QJ9fcx8VnWQkknzpk768GLCOp5x7Tvm+lDjKCWK8tK8L6AJtnRW07nftL4GuE1HYJxPZyV9VvdmvV8jVmqZyaeylf7vgWCuF4hL24TKwq57RqZ5nu2MVC83s/Q8/i2rIAB7hlSCoMALtJkjr9BJwaTK1/My3UsFS4G7iWL7eYoeSHngrHo0QnDVbrNn4by/ntqUHq+RHhkiJiita5X2vbsuodvXAYw5SvtP3zV2AtOVA8uehUqFcd83OvpE+WVJX3X16f1NddK3qlcKrJp2uD7xdU9BnNg5mWWmZxvqOBOfNO/EKqfnZXYxYVsdYNKfwdn4AtMe3y4z1XRud51f6pN8K/O0XXSPhQdwXdvNTueNpuDZK0ZS5Y6eTduse3digptr2DtM8SZ0NqayzZMH6nz7+J7V9LkMKLMsex9mOacI/D97Lit1xvpggWQk9Emo26QtTF+OMRNggpcEsxls89NeJkBXuu+aF2IbxfJJGF7AwKmASR5/m8b8+iSki+mSiVAR7xPjCXb0gMpVmRUWuVKAUYJw4T5zrhwPImSeKWF+4D4SpiXY6noaBOhBBw+ytbXFc264gU/ecgsXXXcd33/zzVxzzTXcceutzdEUAmgCJtAG96uxHZOEcfWMcsoQTWBHeXjzR29qKpZJ46ckBUEX2BGrpH6awUC78pCgJP+7LYIZ7irGkgsUrWKgSKyhzC8nMF/IMo7fpQQAsRLrMYGxgv4cTn3EFuqVIvPfIWGe+bMuvbmoF4Y0R3C/z7r6CvReQvuleRpj08VCa57ontVY1rQrQybk6hOVPxGf17oUsNqkHfl4gFkfVNjYC5iv0B7bSYL5qTf/Vh3BgOi6q4eu+81D+Svol18jfZeHZ/2l0NKRMxAAtH+H9LDgWDMEU/fT2BirzWrvfLxH9dV8nYl1v5NzkB7YprWYZdk73f9fVNf1i3a499uAl+3w25dkWXYLwcL4/63r+v0PpDL/2tNu25vzDAYTTgDGLAKgzaT3iEKZE7AhCt0egFS0TNWKiuaMvsY3smznMdlvs30NS1gaMKyAURXAmZRGig7pAU1zhiJtrJuzXeCGNmjru3p4Zi5l4Jq2qm9SobgMAUvS8/6agDuFmWtOxBeTN53UPT6qrU+pUO1Bg/o5DXYzdiDLAywxR960Nk3qC8+UehDURLVVvu7Zrj5XwCbVVelMQLKrD3ZiulJwkoKqrlelZ8AmoqAl4L6TqWYKhLxPbFmGeZ/6Q45ptyWtT9eZk13MrAf0mpteIeLHRAqBnbYID/CgrahJ+0djLzDqr/m+8meypu3088iDubTtZ9rSdvpNcqGm1dj9v8tHXHnllSkPznr6XAaUBL8MpRK4p67rDr3VZyZNErT6C5h/3xoGngSwSoIv2sWYwH0H7aAv12LCvlitLcy/8COYD57YrKMEoXkRM8W7izZbo/rtw4RMsW8SIC8kCJ5H4vV1wgRddPflGEAW8BL4EDgVg7SO+RgKQIJthAKdM4SgLvvjM5/EhFsBpAMYkJTppwTrDaBaX+eZ11/P3vj2vu2WW3jn9ddz8YEDjV/mAdduL1gPY98IIJzGGCiBNe+Ls4iB0xUsYq/A+rGYj3wIVzFfyXnCGC1j5qhTsc230zYJnoll6Zm7Yj7zWORc9R8YA6X5tubGYxM7G7SI+e4lgFCBE7FZRzFmbX/8fsLVR6y2ANye+JvaM4j9ovE5hZmfLmJ+uWBzeA5TVqifF+J3zeMRdpajANQ8geFTQKxF2mBd82OeMPfBovlq3s/G+08l7ZbppuaK2nskliPQd4Cwvo4RgJQUMA+NdZRT2QJtE+DjhPWj+SxwLmF3LfahzNXFOn4UWxcCefOYf+1x10d5fH411lvWArpXgPVuDICqzbKY0LtMTL7K8hvXPGE+rWDn1Z7V9MDNao7Vdf3Y+7opy7I+8I3Aszt+fjdwRV3Xa1mWPQl4DfCwB1KZz4O0q/bmLDOBj5EJX+lMkhJLexWYXyDQmPjltMGTQE3KUIyqaHYfC6orEz7zHAaD8Fw/5rM5ioCLtrtBjyAIUnYHPulKKSCQIK7yPVua+uJ1RRzdCcSkLJyOM/AAy/dhmYBCb/Z6pkisqdmhN9FUgJ+mv/J2+yhhq9oOenw7ZPrbVYZP28wrq446xXzkP1om+eqetH3p9xSYKKWmpWl7PJA5U38qn4nCAs2kdexi4AQmBbakQPBrYqd2doE3X5ZXnLQsBLC6to6s8fOmbJubao14xY+UC7lf5OwM7Lyfb9qvXiGi94veB3np1ofLq9Nm3qWd2EOv4CoqA44FpujPaSvHuoqrmn/OcvpXYPL6c3VdP9NfyLLsD9Jrn6mkgdNa8pp8f2ahhEgBkC3M5G+ACf8Svgv3m0wX+xjjIhNXfVceFWYuKsFYYGMu5p+5ssAC4ZwiMCkbrnwJnjnGZonJWMGAy4ggWCoIzF20mVYxIV67IkFZ7ZU/opjKGSzAytCVs4aZ0faB1cPBSk1Rr3JgrSz58J13Nv3fd79pPNQ3I8xHU/1XEsCSTDc96BIjKWF7iLGmqr/MakvazOhUrMciBu49qBAgkonwAhblVkrWCcxvcRM7ObxyfSslheontnURA6oCmb2k//081DEtOQbGdb+Y6mnCXFMwmHVsHcifUXXzZrBaC4NYH2+2XRDArUw5FzAzTGivEZn0VrSjEM9igqKekyJDeYjhVH19tOYVwpq40PXrDOavukU7Yq9n61J/1ilsfvp3xBCLMDyP+Vofc/XTOuwTxm6SMP4r8bqUFwWmDFFfqD8XMKZWbKfatOae90fg+Dmnd8xCzOM4AcyLjVdeUlSd9XRuz7p6IvDuuq7vSX+o63rVff/LLMt+J8uyxbquj6X3nk+7a2+OQWYbgVXRULUn+RnVXKugX7SBjgTd1BdMv3nTybJq+0ZDAIWFQFsCYCSkbwJV2Wa/GgBctQXEXJVN25CAP/mSeTCZRoRMv6cmixLGdSSF8vbmkk0fxTqngrevWwp4UtDiGSAPintRcPB19YygN7FNmbedAJOOa0nH2T8nplV9qONkvG9gmrfyU/TRFLh7/8S0LTsxdmndU3APtHw703y76qd6bckUhXYf+rHxjKGSVyp4/2CNiZ+HXazl/TUt9cemeDCZgkGV3+XDms7XtC763lW/cdU9f7YpJ3Lrf0VJ9gFyusbYz7kuxlbXZZKeVRZ8T7KPZDVVsfWuYPscOOvpc/kcSgKR16Qsywrgi85Nde47jbEAMj1McFvDBEYJZWsYC6J7xFIVGLvlTS0l4IuR0XMCZpdj4EQgaeDyHWERWmWeCRZkxoMYMVzQ9hcssei1JQY8BBh1/xbGXM5gZrUKGgImeGsKlpjwS/xN7Vfevl5+0XjTxpz2WZorGICTcKw+Kd3/9XcPJmSIKZKNusyXJWDLlFTAXTLGacznUHVXnh4UpH3g+1AgRL97+UXgu471Vah4aAMJgQMpDmYxTZb6TOyhDqzX72q/gJJeWCUBgI6wyLAbMQ+Zw065a2Dmr/78Sw+8FNRnksAWSnEiULpM+4gaAfUBsHdxkXuPHWtMq1W+nlVfz8VyFVxJZqLq7zXMFFXs+pAAlk7E74vQnB97AcYyywQ9c32ivpyI/eAZxwqLDox7vqJ9pI9MnFexs2TFRs9jihviszJf1jVomwDPY9YCYpA9WJyJZWqjEvu+6p4p3EcBpfy7S++Rc5LOvRb029nB3DXLsosJTFudZdkN0BDB59P2tKv25todQpnlARRJ8df1foZuoU8AJT3/0CfPnPnAdj55oVP5jCPYLMso8FbtfUECYXpoPck9ntVR/gJBEsi7GLYzsVOp4Ovb6lmYFoDxfZH0of4KqAnk+uNCUjDq6+wBgf+96GgfsO2cS9+2LDcwOdE3dlbHmYi5kzDf7wdmWfnrvMO0b3wZYKBS9wnobEYtc/pslof6+HFOGTv/zE5HVnTd6wF6ep/v067x9/VP56BnKFWeTD3lI9m1Xjzg61I4qB5d7J1vlw/G5FlJn8dOgaJ8u3zAHg8Gz2QV4PPQ/R7Up4oY//1M6055+vuKAibivJNcVuDeSdBWMNFWYJ2T9LnKUGZZ9mzgJ4E9WZat6jJB/tnJL+acpxJj6eZihbRJCRiJgRlhbKCAYUlbWJMgL0FOpphThDMntfkJcFyGAU2Z1+W0jzQQENEklI+YBEUIAnHm/i9WTgzKfPxtDousKdZPfTCHsR2qR4Vt4D3aR3MoWIhAjqKrrsX/i12UWacHdAKrap/O25Ov3BH3m4Rd9a/MMWewBSgmSAKy9gsBugkssI+UBGq/QASuvRLoNfZiigQ2PBPn2SIJ6Tkm2AscqT0yn82wMznHtMdaZp1zGLO7gvkkCiRvxns8Cy2lg8ZF83Y53iOg6llVMOA5iYGzPgZg/DzP4n1i5lUvAZpl7NgY9YdY6mngB266id96xjMaxlyKF9UZjCnwEWH3u/+rX9S/GkONvRQzJTa/9H8B19PYGG/GPPa6cdB61bugoL2+td5leislgRh6MH9UmaTKT3rs+s2vGfUV7nnNDb0XwJRWc7QVI1IelLGcOex4EvmZCpzLpHoz5neAEAn4rKdztGllWTYFfB3w/e7aDwDUdf0C4OnAD2ZZVhKG+9vq2kOV82m37s2QCJruusBel5y1E3ug1GWi6YVHyna+hQMYKQvRy4Mvmjff88JwGkxjjAs+4+rr/cLEvAlM+mM9fH19tFgw89iUHfTP+KTjN3zKknL0PXP103XPmqbMV+pz1sUCekDq66x2Nexi0pdeeJ/oW0RZaJtWep/Qfj/6g+Y5vaoKAKLc3laV49vkGdCt0oL+eJbSl5+C7zR1gbEu1iwFJL5vwdjkbRGNkzHXXPFnOqb1Sk2lpTToUlooPx8pdqezLLeB47Ltl5gebdIFJpV2Ov5E9QGa42T8+J6pDT7vrqBWXcxuWrZ+36bccL81c9Ghxpz2vG7ANPZu8Aqsc5Y+FwFlXde/CPxilmW/WNd1l6/LZyVJeBfAk8mamBRtXIpcCm0mSiBJgiDurz+6Q2Z5YqEkZM9hrIvMY1dpR4ztYwBDwWZUPy/4KsCKZyVlRuvLLDEWSG0QOF2hbTbnzQsV3VIsh0zmVAeZR+rawOUr1lBmfGALRwL0KYzduQALPhTfQw2IE4CYd20Q8BfzJ8FegFdAQveqT2VaKeDvI3uuYAfOr8S6qN82MKbXA/8CO2dU/SxwJgZJ5svzsbzj2LEfU7FM+XSqXgpiJMBbEJi3lZjfnvgR+yqwMIWxbGDsuOb7APMFPE2YX/Pxd/Wfntc88vND86J0faT1oGi1c7FuOpLjEQcP8pgbb+SLlpb4wK23NqanAngCXVuEs1InY50mMJAmECqljvokI8wdgS/VFWzuyC9U5tB6aSm4zTy2ZsUQ6z2g9auIJar3LG2WVGNZYWw4sX0CvnuxebISr11G28QY2mtH1wtsnlSYBcEstvZmMOuHO7DxVp46Dmg1/j1AAOznBFCeo8OT67rewFxsde0F7vtvA799Tgr/V5J2694MJviV1fbzUb2MpSNGJNjuZI7XZZbm78/z4DuXnguYsoN6xucp4dcL555d2Eno9wxdr4A9g7bPpmd/UoBRu3p7sNXlW6g6eMCTOQFX9fUgsTknMGGIPPhNz8z04NAD3xREeTAp1lHPVxWMI7Am6QcP/ATgesX234r4AhXDqwxSFsw/l+dtAJ/lZkVUx7pvxjpuJeMh0K1x8yArBXJixNJowC0Az3a/xi5/Ue9jOq5sP0sBV8oe9nILwtOlXPCgK2VVu/Lzz3lmMV036ttxAvZS09SuudT0W7lzPVKwl4JH37aUxUzz8ff7775PvSKnmTvV9rFpxtXl5RUEvv99N+z0zjpr6RztzWcjnYmhvLqu6w8Cf5xl2WPS3+u6fvc5rdkOqU8wO5VQOiYAgkVoWKaKsJnpvD/5pV2NsUN7CIegrWGgFELAj4ogdK5iQUbm4/c7sGAnF8Yyj2BAV+BFrIOE7zXah7ynrBSu7mJxpjHwsEYQHvtYQJNlDNiKWZRgL2B5DDurchUzY9yHAawBJkAPYp/JJ0WCvMCwmCLi83NY1NmTWBCWfixbgFOA0QPmHDtipQQeHq+fgiZSrFcaCNgJPBW0j+coMOZmnrYpo5i2PkEQ1701YRzBTEWHrh+Xsci4EpBOx3pfQojSqnvFfm0QwAcYQz4BfHEfPj4KfrOqc0WbpZYiQmz5B7BjNqRIERO7P/5dxYBaRZibGs9VgvB2Icbwql8FpHJsnQyB/VdcwavvuAOlY2/9CwBu/F/PZf/jn9xc/+9XXsn48GGmY3s3gNvib9rUNc8FxLw5p/xjfcAZ34+aK1q/UtAInGlu9uP1izEwrb5dx6IaP6gPCzmcHFrAITALgpXYD0OMicxpHwGyHwN9uk+BvGYwk90VzB9VpvACp1rnc67/FzGnf+UxiflLChTvcf/fwIJ6ndW0y81qPt/Tbt2ba0x4k/VLTnc0xJyo/actGKbAsOuvvu/ERkg49mA1TS0wlQimafIRaFNmSuaZU1NtZqzruA2Blpx2kJyU1UyFZ18v+aalTJ6Anjd5zM9QhpI3uYRuH8Sso91dbUuBmWd6U/CqCxlVA0j98S0AdZTYR6NwxIuEfQ98trG7CbLwzGRXX6b+i3pGvpseiKTj0SrXXdeczJJ+SudhVUVlZMccTn0BvfLDA0AfcdV/5CucXk/7IFVW7BQIKR13z7QKNAps9XNj4j3b2tVOn/y8E5vqAZxXbqT96QF7qkDS99GoLW+l7L5XojRRY5UXbPOrLorQ9woKlrs8zhlLucv35h0BJfBjwPcBz+v4rQa++pzU6D6SAqZIqIK2CaSYxWmMrRFIWMDApKIkevayTxD6JMQKJApIySTQM1Iy3ZSQXrgPhIk2h/m75dhmK8BT0j5vUizq0LVL5U7H+3QOoX6L75XWwfLyLdF9BQZCCwyMC6hsEATqeff8JCGapkBu6fpFUVnB2BnVZYiZTKofwAB0SdvXcyH2k8wvlwnCu/piDQuQpPHfSwDGqpuelynkHtcX+qxiJquKJrtC2/Ea2uOg+p7CxlUgGwzErMa/MqHsx98UyGfsXzy0zSQFlDYx9hTCWKjdk658zcscA2lqozez3nTfxXgLNIH5Qt6NgfKNw4f5o6UlnrG0BMDilwQQ6cHk3y8tceLw4cZ0VfNiLv6ueav+1xiKARUbKKAG1vfxPd069kTjNYmtX8/2S8EgJYHmgvLIgOGobd6rea/AQHqnnHJ9IUXVZqy/rA1UvxMY+Je59yoGaPW+If4mVtVbTHjfL62nGfd7hR1ZU2FgVp9zknbxpnU+7c69mToG6aAd7AZsbetvz0keqWmcTOxgO/OwE3voBc3UbDUVplNBslXPRHj119Oon10RXSXg+7J93RQ8xOeXAlXVVcKsPxrCPyc/w8EggMqJRJrzZcu0NG2TB5oCUg0blrfrtw2gJ2PgQdmZ+rcLaKn8hv2JbGJZBjA5GgWQ4cdVfd9zbKbvN5l5psGdUnCUAmbl3ShHysCe5qP22KXmw4pMrDb6fk37IO2jdL4pKEwKkLzJttpUdwDAlBFVO7uCQPk50sUudtXdJ5WVsuUqe3PUbrPG1bO9Pi+ZKYcLNAKBB9ZdwLILOIMDt7Sm7LYytxIT4wZguud6WD2acgXeaY/ZOWMod/HevCOgrOv6++LXJ9Z13ZJbsiwbdDzyGUk+gImOmJih7U82gwns3rwR2gKsIqRK6BxgB6KL/ZDAqnPlBKR0j0CTZwXnMbCxEe9fiL+fxI4qkUDsgYt8rAR6KkzglJY3xwRysR1ad95XRferPqu0fS4l8BaxThkBXClYi/eR875gKltnANauHh4cCDioXTIxVNRLrbe98SMBWeUU7lmZwAqw5FhQFAFcHfsg5qjGTFjBlAsb2Jzo0QaKHuRqTixi80emuwK0U5j5lvpJwGahgFFp/nj3lgHMrLm27ieAEQGkVdpMntolUCjgKZZWwGUCY8LAmGSxa+oTAT09J8WITHgHBKbvLYcOsQf4xggqfXrt0hKvP3QIMJAupnvWlas1JJNQAS2BcTHvAqPq4xJjFsXSq80CjGDAbhVT6nggK0XKNDYvVjFzVWL/LkR2YWLVQJsApTetVqRXvxa03gQEBVgL2v65AvrzWOAkmduKude7QOOuOZJj5sc5NtYrsT0+ONBZSbtcC/r5nnbr3gxBoNK+BYlWHxO2/dmFVdXeN3La7FEqcKZCsweS0AZWWxK6PwXBWL97YbsLQHhw50FqnQic/n4vbKZCcRf4SOvs6+MBpWfX/N/0qAmfj8Bk6qOq370Jb+pf2cVceVZ4W4Yd9JwXvgV6PICRkC8w2XWWZ8N2qhJl2eqDsQMIMoFN6+2rKZbTR+Xvl1UTcMi3OwVPE27MU/CueejL7ZqDKaDz4MaPm1iwLiZcAAnaPpJd50HeVz0ExlIFkcpJwaRXrqge/X5QChRF+Nsrwrim6wPa7dFwp8eAnAlMdiVvHpzTXqvN0UMjOD10QNbVIc02VZyMK6jKeG8yn8962uV785kYSqW3AI+5H9c+o0nsoIRImbdO0Gakptw1CbUCiDJLlDA3hQWJGWLMob6DmY/6wCZjV4YAodg7ATuZl24QhNM1DDiqnAI7I1CC9zwGBgTgZMIncCiAPXB12iQIw/to+19dFp/1R4aI8YM2UyOGcj925qeEZB17oih+ekZTXfcJ9EjQF0NX0PZDFZg66dpHfE6AToBSz6k/xOaoHB/ARsFowNgdL+xoHGVeKaWEQOU8202CVYcLML+6Vfe8lAV7SwNJYMea+DmVxzwuiOUKcGo8VH+ZHwt8FPH6FjYfvZ+kTHR72PE1AmKTBAWHlCECgjItFbN786FDXHTwII+78camDe94xSv4s0OHGoUOrk2aMzkW4VhgSgoXrYEcY/1w5c4SfFS1ftXfmsMb2FmeYlqVn94FUhysYabOigKbxf+fds9OR2FBEZMFApU84yzFiECxlFLeEmIFO7tWa6igfb6lZ5s13zPMnHwOmzsV7XkEppRZ4RylXbxpnU9N2pV7s5LWf07yznVCc13Z/uP9KruCx0BbYEuZvjRpCgtUwM4CvRdM0999MJl+shB9xFQl1b9LYO5aVgJR9yWEqq3y25wctOvkmV6ZPKYAz/dVFzsk4L1TUKOdXgtFAVmRZJaitzh4HkjuxITJ784DhS5muTFz7QCt6ZiKQZYpZSeDlGoxqoosr+hTbmPWvC9p7tqix73ZbFrnrqLVVf77TiyXD2LkjxPR/VKqiDXVNYG9NIpxGgwqZfT9b7DdD9IrOVQG0Jz92iugPzJwORx2j3sD9PI2s+rL8X7AaXCgrvUr5l37reZ9XQWgORrBxgacrtryqd5bPqZDz9VBabIf8hrFd5m0zedsC93Fe/OOgDKGb7+UEEnu0Zgyfg6T9z/jaRMzLSywQ891TuBp7DiNAwQhVcLeMu2IpRLGwY4GkMkrmBAssDTCfCk9k/ZQ2szMCsYsCCiJbdCzAiXH6H65yBy2j4GZIeZbKBO7inak1FEszx9PMSQsin3AwaSOpwkg7jQ2sB+NZe/HQO4yJhSrT09jQHiSdoRT3a8J5oGcksDGMcx/TCB8EjM3zOPvp2Ify/R3Ajjs+k/MnuonYHYVFvxoRGDgxOYJuAvED7HALfKzmyYAhJPYOZACs3rZbMR2CIydiv0478oaEPwu/ZgWWNRQgaYexhBfHPPKseMoVuNnGVMkKL9J7AgX9ZGXgQQ8ZYq5TvAl9mvqCBYw6rJrr6Ucjbjtz1/DQ5/yNC6/9loWMJ9jgfI81nPFXRdrqXvmCb6cK7GvtH4XsfUyHeskhZFYVbG0Yla9skJnSkhBQqzLsfj7UYx57ce81mM9TgJHSphbs4A5WkeaZ9pc1J/z7voRjLGUguAAbR/ledom1yvYpiU/UpK26f0iiwiZTgvcap4dAD7CWU5yhjufdmXarXszRFCCzWnNosa0u4JMQE9gwj2fw7ZD1YFtIEQMjJibqqIJnrHlgFkKSppynNDZxXimLORk35jAMwn9O2CbVvKBRVRHmbX6aJo7+SIKRPrIsmqngKR81jww7BW0jlXwKTUXVLvP5A/p722AXZo8InWaAH1NAyKlfeTLS4GtAHC2Q2d7oNOAjaR9RZGY9HYNKAEs51XV+Nw2oLEIXzKgV1X0Ei1CRrUjKOyqr9I2wO0AnA/O45/titLr56Laq+NYwMyCfZnpdyXvF5kqdXx/eta+58otI5hUvTRXffKmzprDur8BlG46qR7pMSRdiqMu9ljljCpT+OvkCG/ZRrymd5ZXSjRzp7J9vhpBcT/H/VNKu3xvPhND+fXA9xBIrV9z108RQpZ/1pJABrT937SPyM9Kxzzo+kx8TmaPFab9l9AmwX463nvSlStA0o/5L2KsiIRqMDaR5DcJ1yozBVn6bYiZscmfSxNVoE5HgshELnd5Cdj1MWFXwU4q135dmyAI2Pp/4e6V6WGOMSxiGzV5VD+BmMo9oz4ZYIyytD5z8fdNjHXKMeEcl5e017LnUl00poV7VveonWOXn/rdfwTqxeKWmDJBpo+TGNsrdlZ+fXtoj736cI028PdmoDltpUQZ81EkUoFbL5zh+lnneApgqL4rmB8mLp9p2oExND5+3qp8jcmlBw9SbW3xuzfcwPpttzD9kOv4xptvZu8117ASo73Kd1G+y1OYQkdsPdi6O4UxoXpxDzFmTmx8atqusRcDLFZDfSOlUY4Fp/LMuNYcGHM7j5l6QwCsmlPQZt/BgN4wtk/zg5iHIveuYGM9AOZnwh6wEVH4AAuCtB7r3sfmFJhp/jzGdEpppHHXGj/7qd6uOj6fdlPalXtzXW9XGvqozVqLOooA2oJbTgQ9iUzvkxeQJdQpNcKn+54KxikI8qawTSCUygTkVFBOGclUyO1iI9NrHvimPld53vYhTZM/DkT5jksLXKN3jAfVng32bU/7OAVx/lxDn9J29graFaqSjIo48nmvYfx8v3hfugYEuPHbaS6MI5iY8Dcn9UzNO7dVrXB+mDvZ9cbvKQuY5y6TvAfVuBOJ5XlFL98e9ThNvjgfEXWrbAO49NUsgNlLwJb6SGaceW4MYhNlN2HTPEuelpMqdTzI67Io8Ax4URhATCPo+rWoPHTuaBp11ddrnPQTGNBO52iXxYBnx72VnZelcdfGSR/494zK9vLIucF9u3tv3hFQ1nX9YuDFWZZ9S13Xr/oM1umMSQLo/vh/CagaSDEH0DYRncPM1qawyJ06KkEmlLNYVFcdliygsYYJpjJTrDFBUC8MMVjykZLpbYYJmmBMFJgJ3Tx2lIZMCWUiJ188+Y4JRIGZ34kFkcnmDMaMSgDecs8pSmrLZyDmvY4FUlE/zGOAR8BQLIvaP4GZ0ioJTKqfvVmtAJH8yvz44doq9kpCPa4+qvMQYyjHsf6nMCXDLOZXO0PbVFR1qbAjL2R2PEf7WAn1vfoFVz6uj064PtA8kqmmxrNw/bdMO/Kv2qr+GQBXunZPYkqCEUEBIrDjfRk1PwR0NS8FTDaxyKnq+9Pr6/z89dczVZZMAnfdcgu/fP317D1woPE/ncYA2IrrS+/LWLpr/kxWKQlGbDdZ38JY+rVYb7VDDKOCKnnA5RUeA8J4y/wVbN4rGBEYMBWAncV8sKXQEcAVaJVpuJLml9bSSixzhrCxbI5sHBfihn8ytkNmuKuYX6lXxqh8ryQQsDxXe9Zu1oJ+vqfdujfLokDv0tTvygvUEsjAmaI5UzYJkQKN3kwwz9vBbbqAXBfDomd1b5fpY2qi2AW8WqwEbTDUBYb9/0sHDtKjH3y5XWydZ6pS30iBSTFA0Bai07p3HY+iCLKw3b9SArtMbrc1OI87R5WM+g6dMkHZtKVIbvV18mdHpuPd+OGWAbT5In0fnEk50Zjq+sIjQuk6ncGDyiyPBRQTUPSgih1ebrUakeVVA7gaEBPz6woe5UGSzlvcxhYmoOz++BzDdh9HpRTkpeel9uKGpPMaldKzRlW2N1PVX1kT+L7syr+obK57cNxqv6vfOOkH5X1fSqmGJS9N1geTa5uy9BxhvWrt+ekiKwPJ82di6j6ttMv35vtsd13Xr8qy7MnAtThZt67rnzmXFdsplZhvlAT84xgjogrKPE6+XDILzd3feewMvyw+Mw9cBOyLm9tgZEBhhSD0SQgXIFyJZUqYFEsjdmpMAKCb8d55V0eBqEUMUIo1EyMqVlIgGAwsg4GtfZjw7s/RFOMmoVmLp4dFrZzHzOokWAtE+6BA8jcVsBcoUXCj9ZifhHLlI8F/Jn4Xo+bXvGfL5Peoxaw2SigHA8bygRy4ZwvMPLlHG/jJnFTsjj8+Q+3ZizHfU7SZoDkM9BcYUIYAIDcxECxQqXsE6sUQz9Nm3o5j4EhAWX2wTpgzV7q26LgW9bH8ZnW0iYB1H5jLg2kH2DEf0r7JxFW+hqtAdfhww8rpvMleWXLyzjubds8A8/MRNK1aEB2tUzHfxPv3YC9ejYE3P9b4DmmbdoIpPTzrWMS2CJRX7nkxkZqvEnTF/s9hCgIpcDRu8qf0LDKYQiDD/Bgr7AzSmRwGlc3LPQRBbzn26wRBETUYQDW0NTyLsctqq+afTPNnafs4qz5nP9W7+qyr8ymk3bY315h5GLSZx/Q+/173kUbBBL5GcIv3psKrUhoMpksAvy8BUwClMdUVgKns/MKyNIDVJUh7RiwFm9DOxzNNetYDJj0LNOcf+rPy/DEMCiiyOTTg51lMsb4CxylI9iyS6uFNjsUcNUxRQ3VNQp61G1COYbTpKhHBVtr5RUGvLOlVVXM8SNpv/poAlNqTRq1Nmceuv/reMm1NUWxZMh5VO/rotuaRmMkmH6LfQ68NrPOcoqjo97uZQF9/P76enVTyZqfeJNSz6Ckrq+fUr749iiarslV+mpr5nVzr+pseUaM5nyZvUdAwe/cxjn5tlK5NYvW7wG1XUju3SpOXUxPXbc/ETwoot0qTsVV9yb5nP+3uvfk+AWWWZS8gyHxfBfwu8HTg7ee4XjumiiBETWB+iPK5kvmmBEoFjdGxBhUW1GNI8KmaxQTMY5jv4WQ0bdkXNSViTtYxQW8NM4kUAJAQKmaoIIDFqSlY2zChkpiXyhNb4k3oBBLE2Gy6ewTQvKCs99k45psTDpoXSOph/pIlJmiL+RHoWsDYI4EBATw9p5eAfpcZriJ6ig0T8PRmrDInPubaIgABZnYosFRj4FppNdZR0WEFUrTUZmI5i64/VW8xqvL/lDmolAY1Bv4EMjQ+OcY2K/ngE0P3uRhj/WSbnyfP+T4U2BeDWRLGQn6rWy5vsXOa5zIZXQCumoJjGwZ6BJJ0btKgCi/9qTLIBWUJpyoDLGDzeR5jPiUIap5J2dFbM2FMbKHMjTexOQ5tNlzftX7AfIDBznhciGXKb1PrXOMun1QpkVSWB64L8VqNizycw1RloFf+zZp/F2DvihKbFz5vlae2DAYhEMHWMNRHlgWrbtxOVDAY2rrzTOu86xNoA0qZU+eEd5+fO2c97WIt6PkU0m7bm33y71pvat8wM0SLgn7bXG4nU9UWyMq7AZuSgFurPh33pecH6v2Ysg95HqNg51bXnsvTf3wE2xSr1An7pDro/n4HAEqTgIS+q47ex055KDhQE0gk6QNvStiLbWoFaYnIJKuCb2BexetNeNk9cQDjCI+2DEx6+0LfEQUBhFaR2Su3yPKyfT+uTmVJUbaDwDRgV8xirN9OigQlYxQdEPSdEcGtAJ3KajGSPuPmk7Xb7PLTvVkBRVW1gjqlTLoPRJSeg+iVHiPXH+PKwJs3ye4C0zu90n2EYM33LqvKFFynShzNZQ9svVl5CsR0zQ+975N0Pqflt3xjXXu9D6fqlvaBnquj8ncn5VfF9vdXVbWPsFEUXO3jyuPcAErO2d6cZVkPeCfwibqun/JA8rg/zOyX1nX9hVmW/XNd14eyLHse8OoHUtjZSH0CA6NgLiVtM9cVAiMkgXYGOxpDwFETaIX2BlcRhMg+Ycw2NmyiKoiHgsUQy4cgBFbxnntdXQTQNoCVDQMKYlhzzEzuSHzmUoKEcCTmczEWWORCLHjMCPhC95vMDo/GegpkzAOXxzqXhIPnxeaJFfPmuRCARU6o7wUYAJW54RwWLGbLXZfw7M0axV6uxbZu0mZ0BITy2D5oB2KRqSaYL6jMZwX49DvYgfL92Pb9GIstQCtQIRNGgclT8VPG/hSAVV/dgx1ifyUwlYfIYI3mijbrdglwwVRko6rwsl9bs3lVVSFwzzDevwB8LOY1T2DK5V86wCLKaq6CzSdi/84Bi4tQHYWNoc3FTeDIyPpsvYxH7Ixgqg8XjawPxLwep+3HuIoFiplx91IG9nOKcJ4lWLRaMYxSjMxjgHQVW69g83IDM1dt/K4wBYiAu/c5FAs8is9fhq0lAW8B1X2EI12mp6BaDf0gS4Hc5SnAprpPENaSrBIKwvzSvBwSNsiNURvwy7dSPo/rhPWtNSAl1Cx2TInY1eOxHy/E3hlSfJSYkul8+rxMu2pvVpLCKAcKB7I8uwIBMHT5JaZyuVIe/0kxSspSedNNz2r55IXMLr9EL9wmBFbDVHaBFx1xkLZH5yGORm12URgnbU8XmPb5+fMuZUromanCsXhd/m2pEK4bMhXe/BjZtrIMStSiCMzk1DTMTAdNuai30RZsrENxOgDLFFSmFKyX7n1j85zGhLYoQrCbsmyYzKxwg5X3golpVTXAd8fUoK5JY00bWq49Vh7kFSRBh7Yxm7UxkmUZ6pNGrMlzekVFURlY86BwpyNUGtYt6SrPcKtKO81jH23Wg1XfVTIp98BVPoNdXZgOoXw4faRXgee0Tf6oDs+27zR0qdImVT6pL339Ut/q9KzbVp9hMr8ImZSl1KNS0Dda+vic7s9pA9TPsfQjwK1Y6ItPOd0fQCl8sJFl2QGCjHPVAy3w00097IgDsSXelHGFACjEKoiZHBCEwVnaR4eIddT7eRaYnwuTzR+wKlO5nDZLoHkrsDjCAKdYHJk4gpn9qVNV70HM70JgNmpKBUxyYG+WMVvXDXiTyasE3oyweU9XVlcBM5lsZpjAqnLTJHApE2L5dt2L+ZDO0z4DdA5jB8v43HLMbx4TnGddX3hWzvvb+IWsfvFCtMZO5qAaD6UZtvugqU0lBgQGGABRfrPJc2vxGfWhQIcA5unKghbNYfPx4vj/SdqbvGz9ZRKyMbT5631sxXpVWJRWAZQ4NVrnts1gJp4jAmhtNKzYmYUCZDIBVx+XI2OKffsHmK8ihLFcjb97k+qJ2M6yDIyfWGyBfbHbPQzUqa0CUwJGCoilIFm6R/NslTB/BG6lkJF5rObVHozZXMPml+q8Z2Cb356RzSHP+m/Rjizbw47k0dxUHxexjI1R6Gsf5Ar9hr23xFaK2VT/aC34temDG+l9JqXUOdm4drmfxvnUpF21N4MpUSXze7CYCsz3ZZbWlfx5fP7TJZCmpJc3IU2TZz38s54t8YL4TofFdwWBqSqawDkpoOz3221K65N+9/9P/6ZAojnSwoGNrryyIo8gqyCwh/X2DhSjV/QCMzm1B+bmwmewJ9w3PA1rk1CswnAiAMyuQUkb5/9fTCSdaBRhVrj6FBMd6H+880Ro0MgE9CdoAgVFsKwdQmCq8SVN+6w18XqWv/5GMNmAX9rIXQF6xHRpbqTMXFUlfrRp0bkFTFL2aYAnnxSFVL/7Y2UUzMf78+r51AS8a1tITbd7RVCiT/TN11nneIKtBSl50jMgu0xm0zns57yYT9UhZSZTQOkjH/uzQ2VWXlYm36ob9df7WTZKCPebZKPC3XtW0znam7Msuwx4MvDzwI890HzuD6D88yzL5oHnAu8mNOl/PdACP90kgX7Ffc8xf7+TmPJAjIAmwxzRrKQyYJRjpnUCZQqrrIVQFOEMnb2jtgZDwp/KEWiaj+Up6Ib3g5slCLsSlCVYLxAYrX0zUE9M82+e/m957JOfzJWPeQzT+/ZRTE5y8u67OfahD/G+P/1T3vrKV7J25EgDLMaEeg4iOyITYLFXSnOxbwRA5DuniSAhWoLvJG2WscIYYgnMg8VFRseOtaJbevPBXuyTPXlYsAKeGk8FllnBgpuIhdN9EupHmBmjzHoltAvYqX+lYFjA/F1PYWBK8+XC2KYtDEjrxSDfQQViEvj0JsMFQQlQlaHv5wi+dNIMb2yEeTQYWPS9rdICvsgKZujao3mteSwwrGsCnbPYURzHCUB+brkdeGhmBu5dszMWN93zAiR6RUlBMYMpbgSe9JGp6SRhnl0wBXum4MSymU4LEF2WjN8yFrV3Iva75p4A7b74f431PgID2u8HU9JT2NzQPBW7qeA188AgmvNqgxAo7NEWai/sw+zI2FW9L2SVcApLJbYm1F+eQV/B5tUQUwioLWAKDb1HBL4FSsfxe+7KktJA81ptOWc+lOcB5edC2lV7M0RT1twYiom+sZPjcrvwB9sFaaUUa3ghOxUsfRILIsHaP+eDeXSxL55NrRLTUn3fStrgo7LmuTGYKntcbW+jb2ufKNCX7fakAjXs7Lvpn9Ffz06mPoet+4sigCxvj1mWUDlwKUavPxnA5MxMAJPz8zAT1dSjUdQg9GB1FfLTwadypwM2G8AqNtL5W7YmR2JSKmDb3CPDaoHEBBB7JFH02qC1BH8OSFbk9Ps24DsqPLzvZDOgBmg9W96aiLSbl86NbcW48ms3B0QKp4qPraSri4ImGqkHk1ofRdSG9hxgE0hTGidleF9if66n6iumcs/A6unXgyISCxCXJWyWtpf1Y9mTPnAF3YA5HeI8t/romnwlvfmw11l4trPsGIPUbD/XJ2+PmTebTZ85e+kB782LWZa90/3/RXVdv8j9//nAs2hzTp9yuk9AWdf1z8avr8qy7M8JstDVn06hn27awA609+zTOjTBduQHJaZSAui4MlA06/KCILydrgLDozOnFheDVUdZhusrK7CyZoE8xgTAMtd3Jo2YqaaOOPAsRBHLFiul8qeKnMf96H/l0f/5WXzkzW/mlpe/nDf8yI+wdfQoG6MRFz3oUq543HU84hu/hW9YWuLtL34xf7m0xMlTp0J7Z6BcCY3tAXsLKMro/4GLXJrDahUEXwEoz+YqWAuYSSq0Qaf6HeApN93E7z7jGQ1jKgFb0TULzJxVoEGmjT7AzmnXb2J2leRftoCdcSlGSqB4EzOHXcUA2IE8BKNRPwsQy590CpjtB3ZpFPtjoR/2ytVVWBuaqa9P2sIGwOwMbK24CLRViOapOXGigkGkqeWD630vif+fwVg6MKAxdO2ewwCjWC6xgKsYi1thARlkqiEFivpWQExgXYAlJygA6ir6+EZNer8M9dB+U2Kbx6mo0Oj3YRh3h4Wp8P4bDg0wD2m/cAWeNedk7qw+lsnRYACzwzBm6jcx4lKUyEx7b3y+qoxBVMCinDCmc9EceW4ubHL3HjEz6y0CkBQAnonXLyTMlbKEtSqYrqpdAq0nMesBAcINzAx7CjNJ1zjl7rvWmcZ4DovEW2JHiuQY43vW03lAuevTbtub5WMt3z0xFM3vCThMBUMvnKZBetKUyOgt1tALnkpiHnRvV5Iw3Aj7SX39GXSpYC2h3QviqT9lcx6fM3XMO/Lqqpf6JGUh/V9/zEfj61lsP9BebWz8EMVOivXzkrgYNrF7g8nIUE6Hz9zeIHgURWAo9Vy5FQHW0OyD8xyqWFlFQm11Ti8G+nGTormvHeSmhcp8/VINhAd6YhW7ouwUPagCIO0xYpBXO+LgNpp3kRFcIB51Y11FlnKHbHp5mGcpMPJNAFOSQJxTRSQ0OljznZJn6nRvVbX9gaXYgG5Q2RVMyK/jFKh5NtADy1QRIoJiJxCmfvDmsQLQYv11X5dJbB3Xf44phNKp0oB7zCpLcoqa5RlQiCNfGSPpLezcjD276YHtzcfqun5s1w9Zlj0FOFrX9buyLPvKT6Nmn1p027quN4HNLMv+mGBB+hlPU8DjCnhbaaDhAuBBWATJCjODFUs4TWAgxI7sBR40BzMlTGwEoDJbwJVXhmhpYpSuvjqAytEI7rgD7r7b6lK4j+zCBd7E3u0n+MkNCKaws7QZlT0EofPixUW+/Y9fRm+ix199+9dw+B3vp6rCZrBnD+ydhj3lnRx785187K9ey2Dffh7/nF/kJ97xDl72lCexcsfHWFsLk/0CwgYyPx8E8CMr8ViIPLJWM4FxLYbBD3QFE1wHWBAWsSvjWPd5zJxV7NI1jz/II2+8kWt/fgn++dZGCFaeF2AM2mplZrxiFU8RmLUtYnRd7FgUgdujGCi9GGOwAD6JBTqaj3mtxjFYiPUdVTBZwGIZrs1MwYmNkPdEHAOAmQHMzSxy97FjDEemcJ0v4MCB8P30MLyctIeeXIWtUdTEFzCzEYIvQXj5rKwEoKrAKgK8s8CFBUyX8AnseBYBb+K9JzHQLLZvM46VQM69WHTdeUwhMABOlnB8NeQ/FftOoJzYT/0cJuI7SgGKxrHfpgvTFB7ZoDGxrbANYHkV7l0NQG8hD/2m43g+sGHMppQ8ZayL5sGF44o4YAABAABJREFU0SxOBwzPxfHZwtjke0s4uRK+Ky+9VueAAxiTqD44PKKJurwHuGgQxnFtDY4cDczxxgYsr9jRJXohTsd8FPVW4H8dYGTHuExjzLWA3l7sWJL9mGm95ujcFOQb9q46DlzlhMLJPty6EcotMH/TPYQ5cEnMczm2/TbOcqp391lX59P2tBv2ZkV5TQPEQJs1lPAGTnit3LFT8TdPAOn/3gfTC7dpeSkITQX2rvt3ZKM68ukqwwPEKgqvY3evTICbPJxQ3zy/A4ju5W1QmCaBRJGMAvMCljudn9nJ2kFk4DxSmGhTnv1+BJeTYTMUuBqNAuAs1g28VRUNVKiqwCBWVeiAPLP7+pOBKdWgl7H3FOinKwkkqj7yj4RQjsDtaETDXG7LoxdNQnIoe5D3yEabTOTJ0SENEuq1GVKpBH2fVVV7DKtqG0PmgaCavA3P075P11rng5ZtENiVh2fL/f/BGMVUQZOCMvntpnX2XePL8N3W6j73aSIRY9H2C9qKnbROapdnOxW9vl/aOnFdvy2lzG5jQYHJnkCLjUytA5THVukUQ7QD9Jz1dG725scD35hl2ZOI3ndZlr2kruvv/FQz+pQApUsPyDw4y7JvAH6DMH9+t67rX4q+Hy8hyE/fUdf12pny6PXgov2wcHc7qqeEbjANg0zz9mEBToYYk1GWATReMohBywawdw5GEe1MTUUz0oFN/KOVHaXhg/ycrswvSozSADNvmyEIk5NFmIAz8dpUH2YmZ/muv/kbbnv96/mHn/7vDPoVq7EXpmLZk31jetbWYG3tKH/yPf+eL/qBH+C7/uH/8gdf9RWs3flRAIZlUAxubIQ2ejZIGtLRyNivGdpRXUsMTOaYKcIGMH/FFfzyHXc043HPm/4CgCf/xnP5vq98cnP9R6+8kmOHDzOLsZEZdrbgVB+WR7ZgJ4nHKWBBYQZYlM0qXtuLnTNZYkybfNFyjK3S4l4F5pzwkeewdxB85yC8gKZnAsv2Zb95E7/3zc8I7Y6AcmoqfEajAB4vuHSRB+07RlnC5MIi60ePURTB5LMoYGE+gMu6guXlNvMoYDIg+i5gpotajMPYToEv3BwSQ6X5vgfzaRWzKP9G9cMmZjo7ib0M1WdlZeavA8IclaZydiaO/YYpSaby8IzqLLZ6HhNcZmYCu/vJYajzydgO77yugD1aX4OB6/NRANvr2Ho6himDJjDT5wLzUe5jx9coKmtOAIvLQ+DusP4aX8sc+pHJp2oHiRIrPo0FPzqFbXoCylNx4HplO4DVXOzLlbLt9zzeoDmORgqD03E88grWSrMmULlDLPLxvgIGZVC03IdS+oGn8wzl52r6rO3NYAJZWYZ3ZXokgWcixQZU7jnJ4IW7X8970JeCKu9vJtA13mFxdJkKan/3h6afyQQxFYw9G+kZxzzWVaxtVbVBpWdxuqLDesF7J59Un89EBJST0WIqPU/S92Ur0Ix8CVOU4Bte9AyA5lkEmRPtCvqK9ieMtZu8ADZPOJqsQMF0GnZRZrd53Hw8APSIyx9VIjA6mAymJjOz4btSWcLG6RAsaDh0FFfdbmfXwI42ydJ7ZJYr016xn6M245oVbOvHLCcEGEq61s/BdF56RtEf/aHUy2GrAwSmAXs8eKw7gF0XyPWpMVuPv3VFWU7XlQ9g5VPXnJ4cEfQLWLAun/xa9OB5OAx7p3q/rILsmypQspzW+Znb6lmZ36TeSWDvI99W/xHp04DsCCplLXdO0lnem+u6fjbwbIDIUP6/DwRMwgMHlJ9yX8WQtDcBXwfcBbwjy7I/A74L+GHgwcB3Ai84Uz7lGI4cCd/FcPQxX0WdpZdD4yc5TwArxwkCn7SGEmIHA7hgITB6p1bDeMkUDmwRDYchj3WCsKg1JxNCL4yKrVD95gkMWJ6HCJsFQdicmoKnvOxl3PaWt/DSZz2LPQRfTeW71fFCKYpYpwre9oIXUAJPf+WreN3Tv5j1ldMcXw5lVKtQ5NEss29gcmMjCKmrsb4DbEMfYOaj6kexuiPgrsOH+fOlJZ6ytATARU8IIPJSByb/dmmJ7PDhxndxlfYRKZMye8CEc7FnYEBhEQNgEuD7mLLAm9TqZbBIZIByMz8+5e6bILBUfv/Y2AgvoIu+4CAP/6Ybedhjljj1sVtb9yhS68wMPP4Xb2Lx757BcAjjb76J23/lGVRVYLCHw3BvPw9mn70imh4XoT6Nhk3aNUwRMhfrepoAYvo5zFdmTiHwpLnXo/28fB3nY59NRmGCYWSK+4FtL0s4dtT68VTsNzGTG2X4PjcVzVeHEVzGfPv9MA6rQxpfvskiAPKNDQv81+/DYNge04hP2UPIbxAFIK99XF0N/TUf7y2w9az/y6RVfbIa+0VRldUXM1hk4SFwz9Dm9XQRFEj9flSsLweAJzZxhrZfxBhjXgvVK9Z5ooDZHHojU3JsERjiY5iSRn6W6mspoU7HT4Udd7QnjruY15Ma3zIA4T1VO7jXWUvng/J8LqfP2t4Mpjjxxy5I8PICJ9BYOTSRE6EVIVEMBNjeJ+EtNa3zkVXzPKxNnTOvZ9NjJbSnehaqTxC4fV0b81EnCEugVGCRrvPvGjxBG1R6H0vV1/ugpR8fNdP35za8V9Ayec3zdnCTziQwVNVBq9Z1c+usRe87OA6gUGapDTWbGeiCkPdjl+Adz3JoYLwdzcjsFoJk3gKOQv2Z3afn+hNhY56fh4UFE9z6k1BNwuo9sHwcVk6GTXh4OiI138bcnvEBiARqfV08m1sF4Nmuq0NwKTiPf3tlySAPfVGWcR4lSm/Pxvl56lnvcce66krbwFW+/bovNw12o9SUF+taF2ZV45Nvfgpu8zwoPprTZwZxWsSN05u7+7x2SjK114tvE2BEK7Jz7ZQoPTdEkqPlxlXRdreBnZnGlL2tKjveT2TWWU+7fG/eEVBmWfZaujenjLbV4f1NNwAfrev6YzH/lwNPxeKvVNwP7eomcHtlApdYFm9GKlZilSBc5ljwE7Ei/qiJfj8IlvsW4PRGmGzT0YqjLGF9zfwncWXIZrrGAvtAmykVqF3oG8tVEd4dWyU87Ju/ndlLLuElT3kq+zENzb75UBdpeYZDAzazc0H7Ol4Jeb3nBS/g6q/5Ch7zI8/mzT/7P4LJkWvbIN/uby8/S/WD/MAEVgS+5P+lhTYA/vbQIQrgGyKo9Onvl5Z446FDDbuoKKgSwFWv4dCC/AwIZphib3F1WcOC1mhyyCRBjKSibgIszNkLSofHix2dIDCT+/fHvtl/BV/2qjuaup94W2Bbv/55z20B5Fc+4UqK7DDz83DJdQe58CtuhIW/Z8/WFjzmRh75tT/D8Q+8n/37bZ/R/jg7Y0GeIMyBcRXYzI2ozfLzdsu1d1RFEJ2b/0+Rw4FYxnAUwMZKvF+MsvpoXIaXZwVckNteu1XC3FHzmVwnzFEBN70MB4NQ1+EwvIgX+vEokgImBzA/tJfnRAmT8eUspYXmeh7rpmi2Gl8/F6T13CxDfvsLmCtgtGEgr3J1VH01F0ZYMBswRljjPh37oIwaxV5uzKg25BnCfBO7mWPBdMT6KmCUwHpVhXHQvJV20keVVXs1xkNsA5NFQ+n+P8SUL3qXTGFHIQ2B+T5cFPv/45zttLsPT/58T7t1bwbbi0uAUZvNgISxpH12m8zeCidIQ2Qd4j3++RTA6W+XuZuUsmIuU3/L0ahtDqgkYJYCOl/umYLkeFCp5AX2LqZHv8sn0pftfdJ8ft7H0l/z4Ftt9+C+qEZk+cgaJqawqzEpYCpLGG5Cvm4NElDs94OPZTEBe66Ay/4NfORFsPLRAERl6urNandK6hAwMCeT06IIJrZze2HxQjhwCcxeEh+cBr4WLvojuOcTcOQeOHYMVvPo7zlqm3jkuQFDTxe22p61NQYpCy5ArN8htle+m3EZlWOyfJN+NWqZssp0ssm+NOCopHMPZaaZdpNYaV/ttEoawsw1JW1u2gWwPUAPtO/L8/ZcVn397/1+AKBb0SJpamo7/k6/+3r5NhVFcN/SS0rFVtCMje5NIzJD6D/J7tp//YuuaxfcydpiE8tLln5nP53bvbmu6zcCb3ygz5+JofzVB/jbTulS2rLPXcDjCBHq/oAgN/7b+8okx1iCZSzwhSYUROayD/moHZVSAt08weRSgFFpXAVF1+ycsS0rK/Z3edmE01ls85wugnJPnSk2SZFWlTY2gvCs4B0V8CXPXuJtP/19POSqMXVlEbimpsJLYRi1NjKVmZqyl8GptdCOPVPwwd/5SZ74qnfw1l//dfL8BLN5ADPelGJ1NeRVFMGkcIqweY/iC2qSICivxz6ZiP+fLi0QipjCfzh0iAMHD/KFN97YtO9fXvEK/vLQoUYQF5ifd31QERbxGgYWF+I949LOghQAHWKmzUMM+Crf04SFPBXvWY99PK7Cy2OiCOaBAwLjNj9vL6q1uw7zsT/4FR78zGcBcMHjtrOtt730+Tz9TXewLT3yf9r3H/wX9gHPiP/9i6+7ktU7D7NnKoI6p92eiRTd5hBW14x9nCCYN28NzcxSYyLz0j42njMzMRT3ipm/SsIUwNoEelFrNj9jLOnpoQE0sesrGPMG9mI+vdF+6VeYfy7AvcfCsxOEI0Mk1IxGAXx64K9ANzJpHgLjkZmJUpkZ82AjzG1c3XzyyoI17PxTsaB9zMdTprwzM2F+yEE/debXO6LEmG2xjdPYESdDYG8Z2lsUYQ1tVAZGxRrOE3xKT1bGekLbJ1OKGpWt8QMLVKBnZYZexPYPhucoNDnsai3o+bQ792YfUj9asJFV5hPpU0XbV6lHDBAX17z3aWoJehVk5X0zbynIFAD1fmnNb/qU4d3gLTcn+t3mozsxO6nwngLfLlNXf78XfgUmJ/o7l5kG60kFeXLL14NxKcA2R9qbysgcjaAaRKYudkaLoVSn1RFMnrJQ+EJAvX3wuD+2ihz/p/D32h+F/Y+36y+9AVZuTzosquIETqsqli8/SwHKCHr7E7D3AOydCJri2asIp0RD8Kx/GPAlcNH7YpsiIFyNf0cjGl/NBkwmk0ZA23euzprUs6l5rv42Jr1pMKCA7HvFqGEMdZyIN50WTk+TDzyl+/K828RZ1dDHAzwfYMqbzELbTNvPTQ9iq6QeaRfomkCz/i92Mr0u+bcr+W7NcjPvDg8aobONWazaQNezk37/3cm0Y4zd31yL7yfJULKy0PvqnKZdvDfvCCjruv6Hs1xWl+xT13V9GPjyMz6YZX8AfDM4f8SZYPq1NgxM5FGMRcgxi4YC87lbxoQ2TVr5SQ6HQXiemQk+moNBCLiytgbHl4NiK8/DK0oMwvzAonjmWBTQVUxL289DNMhyZJ09TRCWH/FVj6XXg97H/4FHPDyA1uEQir2L3Pa+Y6ys2CbQ78PFF7eB4YWLgUmdnYH85O3c8Xev58FPuZHV331hs2juXDU/rZwQJGQgOo/gE7qK1b8oo58XAaSNS4tUuULIa44Afi6+9lrGoxEf+8vX8OAnPY0D113L/ik4thHM/AoseMpifAlslLbw9mCgW+yLmKacIKBPEPxfZTK7L/6+EZ977EyIuqt30PvLMA+uAC5djNq85TDOl18e+nB5OYLqKZg6cCW3/94SV33v0rZ5d9dLlvjkiw/R21rhyu/Z/ntXOvziJarlw6ythf/viSB2zwA++tEwJmIyRx+GlTgvpqLmbmIY+7cfXlpSbIwqE9TmHGjrFTBbhsijYrBXV+HOKvTRQgTTa2twdNVA/hyBrV0E9g0Ds7u6amz0FnD3cgBqk4RAQ0dj+Xcuw77lcF3s4YBQ9tZaqK+iCD84KkGOrVgAKIEj//IZYgzgkTi+B0bh/3uxeSRz1bWI9EosgNQc8KDF0N/3HAnAebOMSpGpqMwpg7wwF9t7omprJ+XjK1/NS2fMJHZjI4DDmtC2jQhKZSkxg8XclgBcVWH+qk8nCXNTx4ro3uPYBjggBJ8SuJ6ObRYwHhOUScrz9ttvJ8syH5D51XVdP5MHmmqodwpgcT591tNu3pvB9j4pVrtmkkAc0EQgbwXTKE0w22kmSjj0LIv/m7KXXihuCeSO4fDPyhxvMGizk10pNUGVkO5/26kuXc/7+ncFKNH/BR76/e1MsITgAvebAxVekPcAdyJXcIHJNsjzqSwDy1eNodh0JqAVsAF3vwwOfHu4tu+Lw18PJm/5Ddi4e3ujd0IoApODPQYq8zzU8dr/Bms3wwUPBX7IZSZB5wbga4LfxwXA+/4DVB92HV22B8mzjAKDHlB7X9OuuvokVtOD4Kqi5dDHdsCyk7IkZfBa1d5B4aH7BN5SwJYy5V1N8N97xfamjzu+p0GFBBa965ZIEh/5WHXaaWroWQFeKaHyEZ1J7wgPJrfKGCEWtkV1he2gtCQM2RhTSHszWaXCPT/m829vPhNDebbTXYRgrEqXYUe/nTHFAXgmwIVZVo8xP4SpfjA5U4TXEUFYqys7MF3BRGRaCEFA77nJuLERJtrcXAB2ApmDQdBQzs0ZK3TnWiijP4yRP6sYTTKCgGFpAKmszKetcC/twQAe8qT/h7v/9tVA0BRq4l/7327iAzc+o/VykKaoKOCiqxZZu/cYm8PQztUIfDde/ed8wTd/MyvPf2GzoYtxWcHYnJx28BId9aHgMBPxOQXUEaMkELAFLF5zkPHWFr9zww2cfP8tXPKo6/jG/3Mz+6+9hhPvuLV1lMUGQfjOKgMOmRsvAU+ZbBbx7wAaZlLnU05Ftq8/CveIOduML5OGjYya5c1RFP43YG4lnsM8CKBj8eBBLv6aG/m7bznI1BUHueirjW098rev4L2/cYjTG7By0yEuvxzyr146wyyFIy9f4qO/e4iNjVDuVlTcbg7D96NHaZjm0xFNNKyUe1muERQQGTCOwEHnlWov2NiIADAKDNKoK7BNvhEUBQJgVMauTQFzM2aCrUhpG4RnNG8a5QsW0bQf7zni2j0f/w6HsFwa4FddR6NwTT6BMhPfhObIH437LAFMDeP/J2mbBQuM5qFJKP7fPmBhEIBjXQX2t4rPFqWZQIu57hVhbZ52ZUXymCyWOxgE095eHpVLlQE6bRi6Vy/Sqb4pmVZi3gtFqNPpOL57I7soRc8YuAM7RzUnvCzlZ5oDh7GItRDy03q+6qqrOH78uLfi/7TTLlaCnk9nP52VvfmiLKvFOlaEeSv/Jq2XruTlxpTFyytzAchpW3tI4PSMDpgwqn0zZStTAVPlNAA3NwZlasoApQd1MnWT+WGXAO8F5PQQ+p0AYtOvCU7ZyfxPf1MQMeoSrvOcPK+aGAy+rvoe+qWil5eRGcxaz7ceku9lPmp3JsAnXhr+ClT69P7fgX/+zXh/r/1bg3zGBmZ9IByBSjV67sGw78sh+8eYwWGCyg5s5sxa/qPXQ38jnKXpA/80TGIsy7e5i6HsGrgurUDXPQqAVI2D61JpIL91mxuXLgVAWkTWUT0/F1NA2coj3rPVkbe/t4tVryozIx+Ngmw2HFp3pGX7tikAj5S2k30YVjuX459PWVgFQkxTegyR6itLQdhumRW7pHlnjQnygt4TqVlrQdvaTKb8n29782cSUL4DeFiWZVcRTkr4Nu6HGU2aVOGVobEWORaJcYixMH3MJLIgCL77igAmNodtbYw0KJp0iqYKtsHkeTQ5XQuTaZUgGIodatwISptoAm0DQmV1WHK/D9MPehj3vuUvqKKQOzMDMw8+yOVPvJHLv2iJY7fe2iw2bZJ7puAxh27iH37oGU2dV1eDKV/53vey/6d/uhVlSiBNEUYF3ATaxJLMYMcfyLxP/btCOzjOGFhfWef511/PSlkyAE698xZe8JjrmT1wgCF2/EcV++kezGRPwCB2Scv2XaApNb+ciZ9xaT9UBPYY7PwysZ4Qxq8szbx2YfIKvv9tdzRjfvqfg8/kI3/suVz4eDNzHY9GTD7oWm77aOjf/fvh439wiAddeJD8kQY6fVp+0yt4/02HWF4OComtkYFX+d8uV5At2zN5Ho4wKUs4FUHgFmbCqTkN7bMZV1YNaOcEECPmem0tvPi0Ja7HZyXoqZ8VTbWKQGdrZABJAuC+xeA/vDI0dkxzqcaOhiF+PzU0c07Nl2EE1lLRTWOBabROBTJVZykScgxserFjoggbz9pGeG425uvZ+xXMFGWOECynqgJjOSasYf++GGDKJylDqtgvm1UwAxfDKHaxIrxTFmdMqeGZjDKC+Lq0Daci+oxiJjpi6QeYb+hxzCFuDJzAwKbSOuY/ezZTXXeTEmcjZVl2BzZNyvRsrCzLMkK00ScRhuZ76rp+97mpzfkU01nZm2G7iarM3TWdGtDm/moP8GftjeP7fCLf7l8IbWEyjaIvpZF/JgVwut6Y9I3a13zAEA8oG+alpHUIekpSpUJfWu594Q6fZxdb01WmB+NKaSCirMgpqBqh2tfNA5deE55zYjuw1M0NSwfNCDfmncDHfx8mLw2AT+nuN8AHX7DdQa8FVmsDXk2AnCKwkXuvgK98jT23/Lbwd+abgGu3d1IrvQfqN8aXtPPFrCYcoOxtH6CdaOmWRiJvd6S/p+mrsRvUACpT4Oi7Ne3u9N6ueeSZQf1/XG0HpL6aKduYsunpx6etkT2nMyHlpuWVGh4IemWL5qBX8mS5vR8akFy2AaVMe319ZDKcpq6jPpRyXFmYnJHm4zlBAU1Z2kmW7hNIoyw/d26O53JvPhvpMwYo67ousyz7IeB1hHH7P3Vdv/9TzWciqjpXaEe+XMfo5yFBKFzAGIUJgrB2ycV2YP36RhDAZ2bi2YzRN0xM5NqaaVoENrVIpmK5q9FPs1fY4e2e7YAAavcSwOjaRmQicthz8aVU45Kvf6tN17X3BJDz5T/33CaCKsBLHnslxeZhLvqCgxz4uhuZf9gSG7fc2gTsWS9hcnmZPRdcwFwEoGuVHaA+IjJ8WKCRIWauOEcI2jIcwtZG6FdpYWSOt4D5vh25/XDj3ziI+Z4uS9buvJM6tn9hITJga+bvKhOoPOZXxWt9LMBMhZnEKiqEwOwGUJXmX7hWGkgeVy56phcQYn53HD7MW39xiS959hIAe74w9K8Hk+/81SU+8trX8NW/eTO9S66huv1Wqgre+15YnLmWPVsj1t75Gma/+OkAbLzjlex59NOYOHAtd95p82Y0ggv3hzptbAQTxYKo0a6ib+cgWg6Nom9hZSBqFNuueT2PscUrbm71CXnIh4/K/Hw19zRGYunXgSMjmFs2pcIQO45D8wJCHbOhAclerMskdqzFCDOpPkZYl7NxvE7F8d7AgKoYc0UzXXRjrvI17mK0ieWsEeb6hRhz3ihGoknNqLK5pr4cl+G5Jpz3MOQvpYn6VgzudBQq1zeCmbbWdR+LrtuAbLfRSk7K8uA7tko4h3bKjaH6exkLSHWJGyMBzTHheYHXCucrWoU+PidRXmHHI9vOUvqquq6P7fDbEwmOTw8j+PH9z/j3fDpH6WztzWm0RWgLZl3fe4T3RQnkka2RUKnz8bpM+PzfrsgZdd4OSuPxSpePVh6RbevUi/72QDz3lx1Ifd92Esq7mCbVc6ejS5RS5ietn75vlcFKo1cEtCBTwV4R+ryLXR0LVFZOgk07Pi2sQUOFfR9cDtUWHP1H2P/lMPPgpENcFFWfj8+78T/M4PQn4bb/Aw/5d+G3hfhq6HsweQ/RKclduxt4M+2jSnYY2K7JBrT8O33012LizH1D7uw/7WzMelTu6C+olOXb529aTTXBB+vxqXZD01XFlP1TnjsFgfLWzR6ojkuTj70Zbd3VJViZm6Mduo2O+7W3lxafwrczLROiAjnptzzuz7osYNl6T2CKeO8frr/63uCQfPu76lykXWzxet+AMsuyNwDfWtf1Svz/BcDL67r++k+1sLqu/xL4y0/1OZ+G4yCIbmIH2k8TBKtB/D5DGOT9Tss4GgWW6O67gz8kmDliVZktN5g54dpaOApiZSWeQVgGk749wIVzYeIcWYEPEfzYFABEbMR6rOOAaMa4YWzl3Bz0xutM91Y5/OIlrvjuJQBmHh3AjQeTAN/5zjsAWI+A8/GHnsvcF9k9z7/yStZGIyrg46MgbPYJh97LZHALe9WKyTuAsZgy+53thzMiP0EQ/OcxLU6BRc+dIPhk7p8JfbG2Fp4fYOaUx6Lp4YXYmZICFhdgR3pIQI860QbQKshKhkWNlQnzPGYGOxk3/j1rAUivElk44EAfLqoCMHjtTx6iLOHL/r8l0vS3S0v8+aFD9IF3PvZ6LjlwgOmZ0JaT/YOsntzitf/p65nNj/LlN11HBvzfn1liffgLPOF5N7M1fw2ju25thJBP3m0b/exMaOi4hOPHTJO/J4f911zDF93wKC64bD8bH/8o97z/g9z9gTuox2PKMvj5DQimklUVlBg68iLL4ePHDHgNCD6DMvc9XkZlQERlJ4dB4PtI7L8rCceJfHwUxnomXl8m5DsV58DxOF+aiKlT0C/Dc6uYUgJsHZRx/ullrYiwk9iRPxtY0B2x+fKH1DxZifNkGgOjeVyHOXYGpd84ZK69Gn+TwCgT13sIc/LKfmCHP6H7Ylm9Ei7YsPbo2JyZWHfVc3E+KAtORhZysGF+pQrMdRQDzz3sGBApvS4sYKpsm6EXGJPbj+Mk8Dk/iO3ZsLzOZvosRyZ/KvD7dV3XwD9lWTafZdkldV1/8rNWo12adtve3NQr/vVWBTsp1WXJUOIEs4rWER3yl9JZeGIrUtyh5PHBOG8Lt7AzeyBBU2Z4KSt6ppS7+npSypsXpszP/UlnAp9qv9xlPGhIGSHQHlk1z3bVH4wp7vkMynEYmLIMIKrcMiA1MQ+jey1T+QvOPjiAybd8L5y4FaYfCo85FK6f/Kijh53Z6U5J95UlfPB/hvIf8f0dN76N8Db/WsLOdy/hTT+A8dEg2I02t9N9DWjOt6PzFqiM7KmC8qjzmra4/Bq6r2f36dnRaBt4G1e2f6WKhy7s7u8TK9Z19mrXvEv9G9PgOF3sfle+/ggSr6zp6sK0PZ45HY3M9NdHYe4C0gqAMybs0756Chrcmkrxu7daKCLL4Nvrz5LVe6YryQILaKynGmbzHO+bu/zUkPvFUC5qwwKo6/pElmX7z12VzpykNdiL2TBrMwIbaFnP+85fI0RmnBuETUNCtxjIsgxRKyGcR6mJrrNwNyNo3BMn3WhkwTv6mJC8RhAEZea3Gq/NxXsmCD50w+VjZLMX877fPsR4DA/+d0vb2vvuX1uiKOAL/0v4bToCTg8m3/ILS4yPH+bKx34JK3fc0fiwyXxvCgNoYH5fs/1woOwm4VNGU4V1AoA4gZkjbmCAQIyJ8j2+FvJqTBUiQD+1ZsK1BP9R/F7F6z7irWdSJ9w1+XTK/HMQ6z8mAIStMvhOTkqbpvZgIGsivkCGwB/+j0PsecRBHusi1N76ylfwZ4cONWzVqCy55847OTAXGeDxOr95/fV8x0tfytt+9ln8ydd9QXgZTx/g7ltv5dbHXs/igw+0BZ+R+foCnCotWuv8ZMaX/tcf5tE/+CNkec6xW97O5vI9zHzt1/OlD7mGwcIi/3LzC3nn83+RuWPHG1+ek6uwfCwerzGM+xjWbjCH8y2M5ZWP5BADjerHqSmbBzJFVt/1+zAR/VUVtGaWuFYis6o1KBZPfo4VYd5rzDV2c+rjeM8RzLRV63aE2UUOaUewBZuDsqo5RWRYi7AhrWDHc8zGNo7WrK6ae2VpjLlehuvAJ4H1KgBCBeEZAZdFIH1qFEFzGRQVK5hvsOotUD2P+aWK1QRjVzcjcyqfSr03ctrHnog5Ffsxgh0Dlnxa6YGb1SxmWfZO9/8X1XX9ou258/osy2rghR2/d0UcvZQwJOdTO+2qvRlMsJISCWzNjpN7tEb8vTlmzibFnBf0lLoYlTOZhXaZ8KVJbNBEsT2IhxdSvRAPbfArH09oC+36/UzBT1JhVCZ9Z2I9VIZkFC+At0jDqh3BtiwNWKdgWM/XZUVWbkHZiy+cjkEAeOSPwtv+m/2/HEdKdAX+4TthazOAuI1/htd/K0xfbC8YafqaPJ25q88vL0MQIAXMee/zYc/lcPkT7dnVt8LWy2DhEcAGZH9CkGRmYfykEIFwZQXW1kNeo1HwAxVrCQFQktMyT23q5k1WS6urQGOeA2PLzw+At9Euy21HabRiGrnuTcFglpuZaToPu5Qs/p7UxSstr+uZNN8UrPXyYA2glEXZR3O5C+C6bmjlXVVmNrs5MsaxBfIwtxxo73/aKxGuT8oqaPe19yVWW7YpfMp2PlVSpt5d8rGkMmutc5L+FZi8VlmWXV7X9Z0AWZZdwTmSY+5Pku1ygbEnJ7FAHxJMe9jZUtIUCpxAAJMLCzGYzUb4jCtj2XSO3upqEMb7/RCxVecE3btqbNkFMc8e9n4sMUA3wlgUmcutrMDht76DS677UlZWfo/DLz7E/MMPsvAEAzkfe+0r+MD/PMSeQRCIH/oflrb1x7t/bYm3/fIh8hwe+lVfyokP3MI05kM2SxBoJajKr2wT2BiZv57Y3SHhFbxCO2iKFugybZZQwVfGIwOKFZCvWhASCRBiXWR2KNZGQVZWsSA9A3evTJlHhIAtEvLLWFcB5YkyRNQEM78qiNq3KoDPldiGi6+9lnI04qOvfQ0P+3+exsI11zZml2KJ1oHBahjT1dXDHLj6IF9w4428YWmJmY0QZv3Y3XeyUUKPkrW77qTfD6arzb5T0fjnrMZ2XHThhXzHy1/M5Pw8f/Xvns7yv7yntZkXBUwuXsK1P/jTfMeb38ebnvUf+cTfB1/bzYiqc7WrtCA4Mm0VGy0f2jqWfW/sm0uxyKMQyt1XBGCTY2vq0sLGbw8xym4R2NbhMNy3GvMQSFKfaw0OCXNMZuA6BzYaAzRp5J4ZYMqE1He3it+l3NB8GxPmeh03kyFthcJk1PRIuziK+S9Xlmfh+mRIjNJKOyKthNxB9DldXbP5J6Aq0CpgeRGmmFH56lNif09h7zC9o0aYP6fqvFWGeS7TonnOTXqAWtBjqU9kR3p8Xdd3R+DzhizLPljX9T+63zsjjj6g2vzrT7tqb84wAasR7tguYHnwqLXdw44N0Rrz4EwpPebA+0t687wzmfF1gUt//IZAZRfD4gGmzqH1+fhD033yZaRJ9/q2+mNLuiwzff7e79TnIQYIwnujLNuRdNN+6SV1K0uYGI0MLHmQp2A5sw+BA18XwN2JD7kBmIDyzsa8s+VQt3XH9k5IO8QPnP5fFFC4fGYfEgSPT/wtXPa1kF8MH/0I7Ktg68dgENWToxEMXw/VFKwdCwB1NIqgctPQeDOBosTSApra1GtaCKipc+ybyFzWZeX6tyKrRm1Q6VKWzN90bLpS6hvr14X3uU3npS/PP5+58r25ufJosL0DiClwTcFfGlgqfa5rjaiMzRI7dojtAE2yZun+n6gmmlS6e4v4JV1THgRvswpwAFV7u8rUZlVjCvKqo75nM32uM5Q/BbwpyzKFKv9y4PvOXZXuXypoB+LZwg5PF/twD0AJ+1YDGDwV72UF9pUBUCqVJawetXMCl5dtomn8iiL6hY0Ce7ccy3zYXLh3ZS2YJ46xiJbEukg4lV/iVgkfeM1f8Ngf/SmGzx4wGAzZ86BrqbZG3Pt/X8PiE57GBQ+/lj2DYB576rWHOHH1QS5wgPPeN76Cj/yvQxRFAMBXPeVbedPP/HQjyK8T/NNmI2szDk1vAibUtCNNCmjKf2+AnRGoQCxiKgUkcgxYCjhOEoB3TmCNCgKoUNAXlbMXY0vVxxXRn5NgKriBgclTwIOBi+YjoBmGPGVKO4nFc1PAlwEhqMoW5jd64cGDlFtbPP+GG1i/5RYWr7uOb7r5Zi645hpuv/VWiHWdu+IKfvaOO5r+PvyGYG78xOc+l6ufbAzxz1x5JScOH6bYiNFdS2POpPyoRuHv4tVX85/+5m/42J+8mHc/7zmcWmk75s/Nhb8n7/ok//AT/5m7/vplfPlv/TGv/o5v58hb30hZBmA2GIRyNkoDPGXsV5mCal3UBDAp8+sx4XzEURWZsdWgYKmrYC58L8b8yld1X8zfCyinMdYzJ4CiCzGgp+BPYkl1BIjAm4DlFBbwZh9hjo4wxr+n+sRyZjDTaS+8bhDWn/rDg9utUVA+SBki4VfzS0BvAjOXVVKAqiGmaGrOzsSir46wdUHMX4Go5Nu7GsucxgDyGrA/buDDkY1VRlgLM4R32Xq8vxra/Fo4BztXXXPO/DTqur47/j2aZdmfEGL6e0B5Fw8w4ujnYdp1e7MsDAramgEPKrVmxU6mYDI9oiM1j0vBpMBXSyDGTOh05qJSy8TQgUzvO5kK912mpx5cenNR/T2TGWGaUuFcYHIyqUsXWPWmhmKLZALp9xbvy+bBQp5vB+8y+ctHFT3i8SD9SZh9EHyHM0L4xBvD30f+GFz6lXb9z74a1j8RChttJR2x5SrQEeyn3EJBa4DIUG6FCK9iAy94OIy34E+eHMxpL/pC+LLnwak9sPyO8JzYTAf0mlRGJnE4pDlLsulkh55KD6LVoV3sZWX3VlULcxa+b+PgZ3nV9P04mUNNFUubC1UVlBF138bbRxqWNd2WY57Bulnrsufm4E5zOmVM049n9VSWwKTPs3TrJsXg6bpR2Q27j0Vwr7F93CdVIyXsurZEyd4QhrcL+KQ+qM36wWQEv/R8nZR/es/ZTudybz4b6T4BZV3Xf51l2WOALybsAz96hoAK5zzJ/FHn4+0jbB5HMJNIMQMSCteBQRUASp8gsPbW4M5/CecjXhCBpc7qU6COmQEsLtoREGtrwWpCPl5DQhCSO1YDUyDzvHmCv9vKMPw+jwmyYhxy4NiHP8bH3/xmrnr697P6oTcw3NjiDU+9gePvv4WJB13HE194M5c+6hqyY7cGf8DLAuA88sbXcPFXPo3+ZdeysREW82WP/zIG+/Zz95vfGIK/YELwJ0oL5nEvFkkSAgCQYH0y3lNhkV4nCaBUqQT+Jf6VYH8XFpU1j2MCBipGhGDexLLn43OX90MQpLIMgnp/GJ6fLcJGurUR8lzBQO7HgOWVIMCXBFC/RgArau8wzgcxw/ti/fZHEHV6fZ1fvf56tsoyLP5bbuF511/P6MABwExCs8OHedPPLfGEn14C4IqvCyDSg8m/Xlri3sOHGzPnCyLAW6VtXjo1gMlsgu/5wz/kH372Zzn8qheyd87OQ1xdDc8/uG8RBuf6cOf/fROv+K7v5mm/9/v85qMfTXX8OBNF2DjWyzBmAjwCVhLUMsz0eosA0hcIgGlUhf5bA+6NajcBojVC/96Lram98e9dVchUDPUMppnrx/+vEOolACQQmROA0Zgwv/Q5EO8HY6r1HYxpnHdjfEds03z8bSVeXyAEuLmY9ot9dbVtUj3AzHJV3jR2dMc+wlo4hmk+B4RzQ+X3uUZYP7MY01LG56cJ74DhMMzRWcy8u8R8Q5tNqAq+sSuE/I8Rxk+mripD62kDuBOY28XayjRlWTYN5HVdn4rf/w3wM8ltfwb8UJZlLycE4zl53n+yO+22vVlrwIPFCovg7M9603tKfvM9B+o8g5JGqPTCNLTBlz+aQyaeElJTc9KdmBuV78tKn0kF4ZYpYBRKJeCnZq/pEQbK19/jWdKdArKkaZupI+aC4oX4rYS9EiuZnnVZVzASq1lW9MshWVXBvR+FdzwXrv9v4WaBSA8mb/kNOBl3fLGBqc9hntMcxeHRy2jTkHi5ZXkKHBa9gAiGq/BHXw1VbNAn3gWveiLMXQqbx9odK/PUPGvTUgKtzQDm7c7yv1f+e91Nszm/yTyC0mauNJoL86fsMdoWlMe7zHQBrix35y5WBiAbsFcmVXIAsE/bPDVNmncyOdf8F4Hry0pTepRPVTmQ6fG5624/RF1J74uSbrMVvV+8mb3vp9wNkQChFPzeFcmzqfi1TJuZ9KA0NdUvMZZSQ7qLLVPPWdoRUGZZdnVd1x+MGxaYlvjyaGbz7nNfvY56YSYy2rymCYLlChZ1siAIhZoAEj7BJsgWQYuyNbLvJ3HRFstgmtrvhw2r34eZYdsHakgQ7O7FWIhJYDw0UCGTRJmVZlhEzr9bOsR3v/71vOE/vpOXfPn1DKMp5em7b+ElX349D330AaoTMPmgg5SbW/zNt93AJ999C3MPv44n/OrNcOE1lIcP83XPv4m3/fx/p4pvqDnMb1IgK8fMkJQkYGtz16LpY8AaDAiDnW3Zx4SIzdjGqdheRfcsMFNM5aMxuHcUjoSamQkL/OgwAvky+KmJURy5Z1fiZ8GNk8ZiKrZ7y13LMZauV4X/bx4+TIVFHl0GhmXJ8M47A/uHgYPX/X+HGJXw1UtLpOlPl5Z406FDzGIvD9XH55MDxdwiX3jjt3H63qP8y++9kJkZ017OzYXvw9Xwt1cEs1JFDl5+y+v58Ctfzo2/+7u8/Ju+iXHZDnAjhlh1EIAEUxBMYaCzwvz0xBQeIc5JjOUV2NmI+cxhL+at2MY5zG9yRFg/qosYUgEhAaNTmAn2NGH+la7+Uxho8+t7BtMAVrFO41iHdWiiBSsphLcEs2ot9Juelcmp2FEx8gLRApHasEbuN42r+mLa5beOOc/neVBmTRRxDUVTHpnAK9DQBna+p1hfAdwVLKq06pXHfjxX1N05Mqu5CPiTcDIIBfCHERT9AEBd1y8gBIZ5EiE47gbwveekJp/DabfvzdBmCfTOUPLMZEHCakRpTL5XXcFlJCB7IbbL/8kH4ulKYn5SoV3C5Whk9ygytwCeNw/t0RbqJTh7QKm2qCzl1fRRUg/vw6nfUwzjmc9UMG+AiWtnV/u7wLFnV1XvqoIBI7I8h7f8QvhBoNKn9z4/AMomeE0EhqOtdiPyHIoo1ZdbAWhVY/NrVOCcbZ0QocXJw9vpLk7D8Y8aaEuZRIFYn1KHtGay1dZwgWHvb9lFtblPVsSgRr6z+5O0GNmybICnbvXzWODPR/uVz2ITA6iidXyNz8t/L4Xhi+33qGs0r8WKezCaMujbuh7LX3JN3lGP2Owd53KTD2ZllNO24++56z7p/eMDYwFUI1Pigilw02BdQBNTSd9luSe5uHZ5pCkFn+dmCz1ne/NZSWdiKH+MYD7zvI7fauCrz0mN7iMpQIaOFwBjMKQFncPAm4TAMSEi6Vr8lDGPLWB5NZjEgkUW7REjZJYwtxHYyo2hddhJjDlTUBuwg9hVDtjEFMBQMJk9wCf/+Z9586/8Mjc85/l8+Ou+mvWNUw0IXF8uqd5xJ1NTMFes86bvvp6NUyUbG3D0Tbfw3sdcz8xll/HE//VSDr/7PbzvZa/g9DCUv48Q/fTesm2SOMQ0KUWs2xA7XmIrfhfr1cN8T7cw8DCIeZbumtIG5lspbfQc5jtGLGsZmFgzjdsQY8gEzAQ09P8T8XcpCMTgaLwFYgQuSixgCljAn3Vs3mgeya9vFWNRV4FXHjrEvoMHuc4F8XnHK17BKw8d4uLYVzJhhPaBt3pfPfGmm7jiCV/GX37Hk5idCwLIcBgY2kFksgar4e/pjRgVlnBsRVHAu577U3z3e+9i7sorWbvjjobtGsQ+3qJ9NAexnQoGI+2c/BLV3gybH33MB1OMq5g8KROUr8ZUigcBPZl04757gD8X8/KKAo2hgJLqJNA1ga0pvbClnRxiJrHzrr69IgilmxXsiRuczFoFph+CrXX9FsnaxndSfaf5fNLd33PlTRCCdWlzXyNYNkwQgN+obCsqtgjM7QwGKGV+W8U+UbAvzSfNYTHCs5gp8NlM5yqSXF3XHwOu67j+Ave9Bv7z2S/9X1XalXsztE3AJIxJMeV/b/wtEzCjIHjQFjxJ7s2cENvF4Illk1A8LoMg7oVq6BawBaQ84CwcJlFqmYjGRntQqe8pKPTRb/XbTqDPC9wpsE7r44FuE9DHPev9TVPTRl+GomyKCfbM6YTMQN/yCzD/MHjY0yyDj70W3vVr7UoLjOl7q6NzGv/MhjXcaqPZLlq4NdA1rSNHwCaQQKHPp+ucDjGHlXYgusHk8DTjUbVtXhZFxUS/ap9v6Tu0KSNLEFfYxdJjPdJx9f/3YyaWbVy1AaAHU/7Zneajv08m5MojZR61jnt5O6hTel9XSoP86Lva4tddP49tcc972caDQ9y1LguCPA++mKmSi3jtvhRPPnmg6M/aTQGmB59nM33ORnmt61q+GE+s63rof8uybNDxyGckjQnmYHtoH/oObcFYYFIgCcxE038knE1X8VxD95zA6ClgMAzfZXq2Ej8l4TkfeWrsnpXPoFKJCdLqxDf82q8zddWDeeYb38T/ecpTOPrxjzcaFR0Su/aBw4wvDt9Ho3io+/wM3/BbvwX9Pi+88UZmtyyIzQKwZwCXDRapjh1rzC/VvpXYLxcShNJl1xdiG4n1P03bZLd2da8wbZIAmYT/Ybw+g/nT9dz39Vhufzk818fYUpUrM0qNncoUM6TnJHh/AmOJxSKpHhXGmo4xJYC2kUG8tkaI5lltBL87CEF8xqMRH/rT1/CIpz6Ny669tvGJExsHbV/NKvbvQ687yMGn38jpY0dZ/sB7gzlRFJzm5mB6Bmbnwgt0c2ja7T6BIa8rKDa2+OBrX8tVT3kKb/3t324UBJkrX8yYgixdhLHJq3FMteDnMeC2jrG7OeYnK2Avc1P55uZxnBaikkV9LOWBfG0FtDzLJyC4iYHNizDgr3UpxYJ8LEvsLFONuZQUE9ixQWIo6yrkczJ+75dWD4HF+XivQN8KtumsY0oKrV/1pQCtLAA0FjKtyV1/yz9MDOoEbTZVDP5x17fzmEng2P2Vv3GJmff2CBYSZzXt8khyn+9pt+7NSl5LLzCpdZtayCjVVVDGVJUpEcUMQFT8VG1G0wvGnm3o5c5qsoxmgLFiKSjzWEZ5KUk413XPQnpQ6IPfKM/0/D5/vEJ6b6vvKtcfZft6lVzzeaodMvv1PqV6TkcqNCaB7lnP/vq+S8suRiVZvgnVBFzwCBiP4La/gIc8OQDM0Wb7oarC+xZ2Ivc0pYOTu9725z/qb0pxyydSLKc6RODVA1sxljJ5bTV4y/IbbTIeVZweWpRVPabASVkxEfw8tzkgdoDeiOw0Fnqkl7dv0zWxkh506T6NeR8byy4GsgvndqWu37pMVD2D2YX5fTlS6jQ4vWz/vo1ILkJ0ebD9V39zTI7QX1339U//duFGvTP8CQU7dY3eR96k1ZvyV8m9O8zuTy/t8r35TAyl0luAx9yPa5+RJGF0BjObk7+gWAQwwKJJJ0F5DRPoJPSLXRH7mROEtQIDKuu0zQqH7vf5+PwGRs+LuRRIaVhHbALq2ingj3/4h/myH/sxfvhd7+LVv/RLvPOFL2Rqfb1hs06uwifuCmxWXhRc/x+/i8f/9HN4+6texR8961n0yrLFvgIUa/AdN9/E7z39GYCBwGG8byPW/SRBWMfVX8xPTQDwBUGgPYmBL9mLVxiYlGYmx8DkDCasK+jPCBO0j9E+HkFKgCFtVlfllrG8SOI1EUIVuEf5iA06haUKmzseTOlwbTFV/T4UG6EtiwcPsrW1xU033MDq+2/h0sdcx9e98GYuuuYa7r711hbbNgAWr7iCF7pgPh97XQjmk+U53/9JUy/81kOvZG3tMFNTgZGc7AdfPwh/ZX61UQW267a3v53LbrihOYoiz0MkXQF9tfkYNqfFBHifJjDmWX6Ss5j5qwCX5oEY6J5r5wTRtyL2myICizHNXV7KR+BSzLcUE9NFaOdqZUdhCLTNYT6/IywA0wIGCKXAKdz3E5WZr04Ca0NjruexI1OkuNH7Qmukpq0Y6mFRaqVI0LoGey+ddt9XY19cGvNRlGFpNgWGt7DjVzTfB5jvmVeESbSSZYB8W89mqmFXO/6fT03aVXsztAGk3kFesNJ+KwVfC1+4jwAltP2ntJdDG+ikLIrYzrK0/HpA7sBSGtUSaMz0vK+jBFxvIuoBsOoC3YSav970Qweo7MJcYhVbfl6uL3zeHiT6qK3p2Xy5a2/aj81ZgIlUrWtN5NfFawKYfMlXwLH3w0XXwdf/DsxcBcsfonW2ZFVtl4KrqomEmvad+t86Zmx5NZ2Y0/KJbMqqDdSORnbYve5xUVgBsqLszl8dEsFkPSpD0JuR+RSqnyf6sQxFdfJRYlX/xu/SlYWZmAoo9g58Ib2rnkB94HomLriKPRPT1MUU2eQ0dVUx+uSHGX3ig4zu/iDrd7yP6gNvRbt6L7cx90qLFCCmYM4rVTTGnjG/LyCaDoEU4r6fdH2rbAPLtB4qryiCRXRVGdGR0zZ1LfLu+mjuNgqR5PdGuZ2sA2867OUkP3O9yw2YnOAVTOcy7fa9eUdAmWXZxQRZaE+WZY/G3utzbI/6/xlLPUIIwBUCKDpKAEaLtJkxaf8FlPq0g84osugsJsCmgFE+gprIAjAVZv45h521SPz/XHz2AoKQvx7LhbYAKV+pUbz3Vb/2a7z7L/+SZ/zsz3Lj//gffOBv/oa73/52Vg4fpp9lTO/bx9WPeQwHn/Iklm99H8//9m/nfW95C/tc+8VCAlxw8CBXf8uN7L9miQ/feit3EwTxMcawHsf81cpYjwtifU/Fuh0lBE7ZF9siRvY0xvLJdFTmxWsEYXcx3ruMmTGein23x/WNAqVIeD5MADsCK2LhljFwK3CpMfask+aCQP5K0ueD2J5jMX8F7lHgljtWLI+Prq/z/15/PbNlSQEcf/stvO7665k8cKCV51oc+/LwYf50aYmnLi0B8OCvD0F8BguLKL1haYm7bzvMfDR3rSoz01xdDYBI7JdeqKv33MPcRRexTDS9rIzR2o8JYQI69xKCGBXAlRggXI1jqj6cjddOYWbEl8ZxPokpXuZp+8D+S2UWATJ7/UQsR6yozmldoG3WOo8pAu4uQ33X4v0zhKA6JRZEy/tGyldzAgs0RLxf0VuOxusXY8GzPhzzuTjW5RbMdPpCjNGWP2OFsZCj2L4Drh5aN2Lvh7EO++PvMzE/sdyz2Ea0QRifPN7/YIyh9UBR76Bj8f9ifeeB2zj75q5A0ILupKY9nz7rabfuzRX2/vcmX55B8GDSm8EKeOqTsg6tcuJ7h9wwQBfbsSVwhAl+pXBC/Mjdwh/nkZoXekCZCteeZEt9ErvMWL3gngrUErh9/kUB465OYLsJYZdQ24sCzLii5dOW9pP3GxWwVFJdxyWNL17v9Crc/KVAFQDUPbfAH34V7Lko+kuOt6PtpGDPVKlvWma4/rnWTc5MdpvN9GZj8ipwXBSEgELxnvGoaqxii6JiooobsIIE+QkVg/GkEXuVGlAik9bmiJVkMnrfS1/nomBi76UUX/yjcPBbYWuD+vY3Mj78Fkb/dDNb6+tsbmxQrq8zrguKix7OxCVXM3nlo9n7Nd9Pb3aRE//wUu792z9g7WPv7+wSzTfPeu/0fk/PjdR4ZPl2IKjffVllGZTfm0M7tq9Ou6Js1wva0Wczt97KOE6pyXyLMU3ask3Zg7GLTh3RMptv6kYbQHatKb27PGPq33X6nDOGchfvzWdiKL8e+B5C2PbnYZvWKvCT57ZaZ04DaPz3IOyis2w/zF6NE8MmQU8mY2ICtjDQouMHBMpmMB8y5dvHzOwq2qaEAlVgLKgmqC9Pm6YYn/XYpuMf/CB/9K3fyviCC3jIN34jl3zBF/Dwxz6WrfGY9RMn+Oi73sVbfuEXuPe22/hHzO+tAC6+4gqe45ix2/46MGNPfu5z+XEXmfTHr7yS04cPNyDaA/DNWBeBhAnM5FD+d2uxv+doB/0BY7am4t9ljPGV+a9Av/pugB33IpNcMAHktCtjBTN11HmWYjz3YIAKbJELbPtzNWcIYyqApTJVzhomvB8/fDgARSzC5mZZcvLOO5sAL5ozYkVfd+gQk8A3RFDp098tLfH6Q4eAsJkLUA4G8UzUyvryExgAnn3oQ7nrIx9hGfMbFQDSC8yDIHCMK2YmXhGCv8xgL8xNzBxTyhLvp3DM9Yf66G5sbOfjvWJLBxibN0/bxFaKG89giLmXkmCAjf2x+LzMcgfxmhRBw+R7jrHP8v9djfXThjIiRJz15rzSRorV7hHAteqiunsfVSkdZJ6tAEI5ZqIrIL/lyshje/Qe07iQPDsdr625/hLzK1bzXKTdbFZzPu3OvVn+yRK40kiIShK49H4fu+tpSo8X6UpdDIxM7PxPEipHIxOUy9JYHQ8IvaDszwncRmDFshQV04POFMT5etZVELxTBqcJSpQbkEuBahfT2TBc7p48j+OQG7D0SUBSTJLvt5broQO+qlPvxO3txkKQ/k98rGmIZ8kaljBW3rOt/qiVicIYsoxq++C2BkLBd4QGAvjzAZ30c891lAeH+luUI3rFqBOkjkdVC0ClZsbN7ZWzR2wCAtXoHM6GFRUAvegL4XE/Dg/5erL3/m/4/a+E4x+BCuoRjIcwGrZZ0eHdt8F7/sqUFgcOMv2lz+RhS3/FaPmT3HXzf+fUP/99q7vUtymg8793YV3P0LcipibD4gHZVhnApDcNPhMA6gqmNREtlsRSqmztm1sY6+6fO1PyVg7KJ48voWbtYR8wi6euPLz5q95RxP/LlefzcW/eEVDWdf1i4MVZln1LXdev+gzW6YxJIKyPafz7BIZhGRO+c2ygC6yhMlPNMdZD5miKACl2xbOUAiQCQAqK4gOOSDCUcOiFyBFt1bEEf7GeOWZetwJMnjjBu1784sa0TiaDCwQGURM2xwJzFIcP83dLS01E0od8QwCR/piLVy8tcffhw41vooDUHGbeexLzzxLwBgOPFeYXmWNmfDMY6JYPpsDDTPxsYqyXQLxeErhx6BFYw3nXp2LqBKKbF0OsYx3rUrjrUjYM3UdKA7VJoHrG5XUEY5MFWNXfI8LcylyeUioISE0Dbz50iEuSYD4f+ONX8PeHDjUvKgkW49JejFJaaC5UMb9Lr76a297xDjMXi3kcwYCYrnm/zlnMrLfnflO9dTxMPz6vPlRS0JsV15ejWOYqAezo2fTdLkWF1oN8M30QqwKLLAtmzimljYC8QNgUZtoMFtBGiiGBXNx9GscCC+4zwiLHrmLgUe8FgUuB6w3CcSUD7HzIPdh8VSCdKVcfvY9UfzHwPcysXua8JSY8L7s6VK7OYOyPFCFn29ym3uVa0M/3tFv3Zlm+aO+VEta/Szyuqdy9uOteQOu5e/SsWAzv4wgGIMoyMhS0mQQwIU8RXPt96CfCsfIXIOo6oD0FgDutFx/wQ4FMPLPZFc22wUxJub5u3lRv6wx1KOILJj0jsKpo+fKPnPDvj3bwJqj6f8NSqoIKaKOjNcqSrZG1Ubgzy2kKSclLf76xb1tqqpvnFRllG+ko+E40Z03zrirIy4osrxhHFrh1WkhFcw5nnlcURbsj0zHwzLOBydhxo8wy1VmXEUw2/bFnD9kTfwMe/hR4+2/B6/4LbJ5sOisry21mpP6v/2101wdYe8mzOfx/foq5x30zV/6X32XjY+/lzhf8EFvLn2zq3nWGq28fmHm1b5efM728my33z0sZ4dlMn7p8LVMML4WCPv1RW56GqACv2vLG9rkSv2uOYe+civY6LCuT8f37QtZwXZhVMliOAU9PsJwL66HdvjefiaFU+qIsy/62rusVgCzLLgB+vK7rnz6nNdshjQnMiGcOIAzqBAZqIAiTm5hQK52WfM0EUMp4j3wz8+S6QMQ6AQz13UdlS5shxqt0zyvSnWeQwMBuhgnqMoedcPmCsTfe92o/Bvg0qf/80CFK4N9EUOnTK5eWeOmhQ42pow8KUsR+6REiqQo8bWDCsIDBjGuvFqFYzX3Y8R5KNcbArsb8h1gQmFG8pr4WUFiIdToR/19iPnVz8do6Zj67hQn7xLzmYx5a6BsY8CS2R6yWwGCfMMfUB7pPgEBA8hQG2gRy51W/PLykLrz2WsrRiH/+sz/j0d/yLVzy6C9kbgrKjQiEKiiqCI5jZr0igvnSjrSYBh58ww2868UvbsCblAHHaQchElMmn0bNLb2HPLARYPJBpcBCcE/Ev1tY4BgxkIsYiFawG7FnUsjMYxGPxeB6JcUGdm6jgK4UOPpMYWtDwWwEgMWya/zATFMFfDWf9LuAmICq/DL1rBhPKT18vsuxPr3Yr5rvmnsFYSMbVAFc6jcP9NV/UxigVPlSTImdFBiVImyA+X4WGMg/22k3+2mcT03aVXtzSTzjGTvHWO+R9Bw5DyzlU+zZSGi/r5rn4n+6AIgXDuXDmSbtk55pSa0T5SPpBc5UUPVsHbQF9NT/sOefcQ3z4E1sYZYHn7rUJ9IDu1Z02SowQdA2V/RsariwXZD3oDgFG6qL6u7NXS2fih4JMnNg0psD57mxhL5vBUJUpzynOS4jtRzVtaKq6DHaVi5Vte0cRqUqSv9l2VYQNPk6jCpw6VMKwFI2L9RhK14UOxkAtsBkVUF+ySPh214OR94Nv/UIGJ1qD3CsRJ5X246OSZm41hiOKo7+/Su5902v5cpn/iTX/tZ7uf23f5ATb3514xfpWWatnzJpl4L/+Px9H/g8fErXi7cASMH4ToyihlLlDwZW7kgKGCywoo+yKl2FNcbKKaIs1sVSKgiPPvFS652TBgdLfSrH7h6RQpI9zkXazXvz/QGUT6zrujGjqev6RJZlTwI+K5tWQRAgP0YQaGXGeiT+Fcg7imn5C+yw8lMEgVI+lQrQs5e2NmIaOyNOk6PEmDmBKYE8z9AcIwiL8/HvHRhoLAmC9SksmFCPIKzLxFSmQ2CLBsyE8OOxbpdgPn8lcGu897ZDh5g+eJDHO2bsH1/xCm6KYPPC2B+N+SbGgsqU8hi2iGYwze4MAehtEEDXKmZOKHZS4G0eC/5yO23BeQ4D7aPYJw+iHZQHDHSDsYB3Y5rwijAPFDRmIV6XX+vJ2LYTmJ/oPtpmvErLGCjcj7FMdbym8xIVfKWgfbwImFC1XsFcDObzCzfcwL233MLUq1/J5V/2Fcw+5BqOvu/Wpu8uIZrfVnZ8jdp+abzn4U99KmVd8/43v7kB3lUs62LaLNVqbN/FGGC/w9UX4JEYsFY7T2DsqsCkFAxH43MC/AKj84R561+kesHuiXX8MObrvAA8lDAHtwjr9gh2nqXWqthZsLWocu/GFCEyQb0olqUjSTQelbt+masnBNBZYT5f98b7NjHlyZCwbmURcWF8dhzvvxgDffK91Oa1J+azGuu/L+YhP0/lI2ZnDQPRMqOdw5Q/q/G+cczvitim2zi7abeHJj+fmrSr9ma9+6YIa1XKUym6fOqSKf27vqTNLraAphMCqbYDUO0fqcCpe7oAqjcd9SCmxXqWgdEU61jfjzXSdZxCnW8HdAIoOrZBwVo8gEsF8TRyZr+/HRw3oCC2Y6JD4vP3+eA/OaGzPEjw9/cKmKwq8rza9ps/bkTX86RffdtT0JECdG8i2ytgYnKBYuNYqw3+b9q+tK3p/8XOFUVQ8NbFzs/68YKgDChK6OWuo+KNPvBQ/kXfS/HEX6b+6x+Hf/6DJP8qKhGqbcGXlGUKsFVv35d5tckdNz+HE//0pzziOX/GxN4L+cSfvbB5piu4U1d/pQF4/XPps+l1jaEAcQoyvUm4PzKlUWC4vp3oh7Obt0qzIJSCVspZLyNDe703/Zf87Uo57aitsP190ZVkRSRLNllMeWX22Uq7fW++P4Cyl2XZZF3XmwBZlu2hfSzdZzyJjZjB/Okk1HqT1gMYgzHCtKcyiRV4W8WCc6zF+6cIpqWeicpd3l4DIYYud+XPYWaGMu2UsLtCEEYL2gfOi4ER66NFIu2HD+hzAgs4IjA4IgChGeBB117L1mjEu17zGr7oaU/j8muvbQWk0UTP3XeBAQnraxhbpIUlVkRC+yp2vIQCjcinsMJY5Arzj5zHIq1uxj6QRltJvpGNJsnVUeaq85imWwycJrQA1joBrMvkeIo2ozMZ+1LAaxjrclHM6zjGNKuf5KsrNlwmpXr+XtV1fZ2fuv56emVJH3jp9/57/ut73sPsFz+B1ffd2pjvTmCsJMBmaS+3GaCen+d7fvM3+b1//+/ZqusG6JUYSybFhxg9vXC9eSuY6fEsBrhXYpvFPKsPj2BzbwFTKpzCxlTAK3f9m2Njrbmg8XGyTdN398Z7pfTReIvBU9TgHsbuDTE/53lXnubrGnbEj9ZuTphzMveeoQ2ENb/1vhbIFPs4Jiie/FFAUirpOV+nHJvHFcamCzSK5YTgzynrhznMFFfrWgG+xhgjK4XXWU+73KzmfGrSrtqb5QOfY8okJa3p1A+J5J6GVagsT6WKMC99JFkwax6tBa0vL3CmZrdgbIl8KLsYGc/69QpjUM7k09j4LCa/KXnmsalTFKj7/SBE7xlsZ3PEstW5meR2CfK+3KpyPqOV+aZ5wNYFVr3vHRiIUp1klrg1apsnduWheqQgWgGAqo665FUy9snz/e+4iY0/eEbTn7DdrHdb3yVJzHAT2bZjrNI5IVa1iR5LAJMh0nBFVo0a5OdZu+xRz6T3tUtsveDx9FY+0gCDLuDYlFNuvycFlZ4N1pj0cxh+7N188Ce+nEf83Ovpze7j9hf/Qmc5afKBnhqQmigLlLqYXuUt5YZYbYHGft+VXRrzmNYhx0yLx/1whMhkaTKNNyfV+lfy7KNvq2Sminak15yk/phMc19g0pcp9ynF09C+fVbTLt+b7w+gfAnwt1mW/R7h3fzvgBef01qdIakvZe4mhlEgU2yVAJ5Yg1WCoCbApYAf0izoebEEMs8TgyazN4E9b8IqxkoAQ6ak8hMDMwHElSeNRoEFrSliPlOxDPmQaZIL9IFNWgUU6RME2YcfPEi9tcWzbriBk7fcwiXXXce/u/lmHn7NNbz71lsbtkj3y1dSgu80Bkoq7IB7tV9gskqug/mZqg9zDPTr/wLl6/G+/QQgIfNDsb8ezAvUgYG5fbT9VT37JHBzGvO5nceYWeXtAavmlsCKWCLNAeK9pzHgINZvksBwCjhsAcPDh1nEWNrbTp7kl7792/nJP/1T3vOmNzG69dYG5ORlEFg2yzYAWrjiCv7ry17Gm179at76N3/TRE0VUyrTSWnthrTByBZmgj2FJQFFAZ4F7IgbsX4CWnsJL8YVTJjTC3SCMG+nsbFWX2rO55gpuX7XhiAT2ZX4f7GRqoPqtIWtGbGn/r2q9gqgz8Y2DQlgzY/XBGY+qrqoHgLUUtIcxywaBgQW3StJJLyqDpu4o07yYNJ8jDD+fr7pfaO5oeN0cpevZ2vAjjtZiXn56NFnO+1mx//zqUm7bm+WwlL7o4CdN3v1wb5SvyafcrYDSr13xEKKUfBKnk33nAeT3jdTwKUoEpPUantUSi/AaU9XdNjUp1EsYxeYEjjzB8LreQjsYRGZyS4w2WVqqf8LGPujUPR7WYb/p+1JWagGRNIGTGNX981RaN/IAcl+Px6zVXSztykoLcsQiE6xA3z5vt7qgxRM9i45SP9RN7L22iXqo7caq8l9p9TvNgXlAlDbGF5sbnjGTWeeKmqsTIGVqgp4xFMonvQrnH7BV1Esf2TbcZc7mY/KtzUdJz17X8B86+jH+PCzn8BDnvM66rzPR//XktVph9QFHs+kPEn7yCs1UuWMGGAPin0+fv74QFhi67dK2wvPJqZqAKzGme1xCVJgKUtG/56Ry9vQfc46oGR37833CSjruv6VLMveB3wNoR9/tq7r153zmu2Q1JcSQiUoi+2Txl+CtMCI95fU9w33e475i42SzwAz0RSg1IRRREylinaEpxG2aQqsioVYpR1dVGazYjtknou7X2flyZZbYLLGjjE4tr7OT19/PaOyZB745C238N+vv57+gQMQ7xEjdAHtiLWqh1hcgcrCfQQIBVDE0IidkqCufAaYj6UApQCnWDEljZsAvYCuwKhAfu7aLjODEcbi+MUtECNmdoW2MK48pQiQYmEFM7Ndce3SNdVJ0U/BXnQy89KL5zSBgfrE297Gi378x3nOP/4jf/eLv8gbf/u3OT0aBR/Z0hjyCnj005/Od950E3/2y7/MS3791xvlgffv24uZqPq6qe+WYz33Y4BEbL1AqcZAfpSn4n3TmEAmNl/91MOYfa0/tV3jsYaxgepbsbnEvJW/lD/qZykUFHwGzHdQDKsESwFarQcFA5ECQr8ddW0VQNU80ntF56GCRTj2744TGPjU2Pv2juPvU5FhKNdogvrItHWKwOD0XFlzrh9yTBiXcqnGgDKE+XSCs7uxKu32s67Op5B2294M7X1TrhR633tQ6WWi3F0rq/a+4wU7Dya1JmW94AGpYhbo3eID+hD/7/3TvMCcmrw2dewQpFtmejtQGd5E0gvd+usBqD87Mi1rJ0CpfIrConICrbMrBUhS8CTQ4uul7x4kp+aOWxiAVVTWrbJtouvrKbCnfEajACZ94B7fH57Zy3MoFq/ggkN3NPcN3xei108/7blMPtICDq793JWMTx5u/u8VA2diK/3/06NYUkVACtxSBm+bguHKxzPx9P/N6d99MtWRD1IlDHLat/6TAsqUSfagcieQWJ44wod+6ms5+Jvv4cQtb+L42/9mW7v93/Ral7l1nptJcxpMKp3bzTMd89bXw4PKNPXc/B7Evrgvyxztzzv9BpGxrDBTbHcd93yFyRZSCHvTWI8r9Fefs512+958fxhK6rr+K+CvznFd7nfywWSGGKunADjSimrTmSEIbBlB6FvFhLY1TKs6pJ1y2oFL/ATzETPnaG92e7ANbJU2WPFAE0zA9SykTCpXCOzGHAEMyNdNbV1xdRAYvQs4efhwy+dsCxiVJWt33tkKJOS/VxhDojZqUS5iAu6QIODOYAtIgALaC1mMrYQL9aN8UD24XMVA7IgAhGTGrHzVRikDpDDwi1dmgGLaNB6aDzkGVmfiMzIhVBKDJaZV32Vq2499IoWATFw9yPUMoQAghHH80Etfym+//e089Vd/lS/7kR/hQ298I0c/+EFOfvCDVBMTXPyoR3HwKU+hmJzkpic9iTve9S5mMYXEpmu7fBVlTi2T4BnavoQzWDAh9aGeX4h1F8gaxPykYBEwFViVQkP9qD7UWAwJ4G3k6pG7Z8HWqGfmwI4PAfPFPYZFV9a6vSz25QqmQFAemwRQrLmoj94HMi8XY6g6T2HM5zC2cR5bm177uBn7SGv/OLYec2jMwioMLAqsF9ickPnyImZtoHeJDzIlRdUGxo5CmO/HOctpl5vVnE+WdtPerL3XW4tojiv5PVBJU03voDRpTXjLCD0jsJhGYpQSV+yo7m3yzLeDyi5heqe/9xdMeh83Cd3p8SRdZqo+GFDKknYJ772E0fRg8kz+cF0AOo2yCjTnd7bAQwlbeQCTEyPYLKxt6hu1LQ300nWmocoTSGv65q7D9P5siblvXAJgEEGkB5On/2qJrXsNTPq+8qlrrFr9mPzuwaTvJ5+Pj7KrOtc5FNOzTHz7H7L5R99DefidTX7+2V5Oc2aixkNmuM2ZoNV2X1wPSHdirpXKE/fy4V/5Lq559u/zrv/4aLZW7u0Ek56dh+6gUFXSH1Vl7dfvvi7qjzK5xyc/3z1A9ynPg8KidgD/gSS9E7zLThowLPXBzGlbFnnlefrO85/7Ar0PKO3yvfk+AWWWZV8M/BZwDUZOrNd1PXeO69aZxtgh33OYgHocY00kHF4Zf9OE+UR8dhUziZTQ2495SKAWgCoIwvGReM8axn4IJEpwFyPzUGxjlPlehQW8GRKAnxi7ESEwi0yFTmP+ZWofGIhewQCmAPM0gW2UMC4hXvcLEAyxYEFiwzzjojYKcA0wc6JlQkCU6+L1o/E5AbY1jLWT4C/TWQkYAsKVq4cAyEWYxlljIrCbu/aq/46wPVrmCmYWuZdgFquAPLh6iYFT2zWeqzHfK+Oz3hRSgMnXRQC1H58Rm3rC3at5Ooex6Msf+Qi/8dSn0nvkI3nYF30RD7r6aq7+7u+G8ZiPv+99/Pr3fz/vfNvbuKyq2EMAHLOxHSdifS7DItjqxTbAwG+PwEyqj2WKq6Au8keUSbAUB1K6TBCC0JzC1pfuB1N0QJgbK3Ec9YKdo81GS0nQc/cNgMtjX67FvKTk8dFQwTSHnsHUvaUbJ7VFPopap/sx8C3BdM19rorPrGBzayGWKVM3zQOB4WVofCX3YoGbbt8w312NwRYWUVZs5YCwbjXPPWCVYmWTMBfHRIWR659zoaysYVeb1ZxPIe22vdkLV3KpkMIE2gJcalLmUypPejM3TUsp7vTe92BUEWO7cJ6Ufl3goQvcpFFVPRDsOkNPgnkKRCR467zKLhNCn4dMKFMhH9qCvsxOPTsJ5n/XFWTIg5cuc0qVkfaP8hCAUPvLErYcSM7yUB/5g/r2dbFZvt9UHw9I8xyOv+oQdQ17n7q0rU6n/2qJjb88tG380np3lelT15hAewzT/qoi2OsC/BNP/CWqD7+O8Qfb+h5fj/S5rbLtF6l5JGDmzULtqJN2nbtA2cn3/B33vP7FXP3sF/O+n3hSS4GiCMNp2/zxKGpTOnf0LETQ1dFPXX2TXvP+mt70Oe2zogjKCx95leS7B4C4vwKR2vsb5S+GEbzPtZ7zeQsweUtJucfoIzB5LgDlbt+b7w9D+dvAtwF/DDwW+C4CZvqspBIDd2CDLl85AcoNglCpvpcppUxY5zH2aYEg8K1i/lKeeRHgGNCOBCpNxQomZMtMTSyINBoCsDLRHGB+ZQJ5MsfccGXIx2rL5S+mSgBRE15AVxN5DhNOBeoEfiqMGRR4k3lq6a6p7yQoqL5jLJIlmGmlBAkPtGQKK4ZKgGchfqqY10lXjgCe2CGvHZqPZer/YjL1fy1mAYrj7vlZjL2UECIlwhTGIikKrwfCPSw67N2YwkF1EXskrbgYTZk4nsKizs7G34+8733c/r73taL9iknzeXgBbBTznIv5nHL3SsiqMEA2iPXdcM/ppStQlZrTQtvPUH2quSOwU7l7BOhVd4F3/V8KEc1hjV0qXB7FLA9ywjoGW4s6R3QlXhcQ1gsdDJz5us+760XsuwkMvN2LAT+Nq+ayGPa15PoKZg6sdai6rGBrQH2k41c0dwSWK+z9JEWQZ24vxNaszF8HLt+zmna5FvR8atKu2pv9+9IDPTBTcB0lAt0CnRQrSpV73kd8xZWRsgyeyfRg1uedCq4S3lNzVn+4u57zESw7GZySTsDQBdDSVFfmw5iySCpHAMa3IS3Ps13yh0xNNVP/PA+W89yC/yg/AR75PY6x8j0YyHOoHZDU876OCkDkwYRnntQPlAYqT/35ISYfdJDBY25s7hu99xUM//pQJ0AWKN0pmqg/9mUnMOn7Mo3umgI5f6136SPpfcG3MHze1TtGAz4TwPWmto3JZz+wdEqTJWz2Tfng252mXg4f//3n8Kj/+R72Pe7rufetr2vK8aa8vj9UB8+S+wBAmlvqkzrOj7SP0vnbtbekIDQNfHV/9qMUVOr/VUUrXoQsx8DeQd51LLVoEHitkuvQPncyBZTnZAvd5Xvz/QGU1HX90SzLenVdj4Hfy7LsLee4XjsmCfUrmFAHbSZJQpaYC4GSGQxsDWifRydhrXB5gR3y7gGgBEQ/aQWAZN454fKbwwThHmESzmFspQCNfD/WMGAgP6stjAUSoKwwRlBgcujKlWC6jrEwMlsUcJvBAEaBAcQ9mF+mgOsw3q9zKgeYL+GyyxuXn/padQFbnPI7myAIGgqkpLFSX/mkZ9VmPSeAqHMkxVypf+cwECEAJmFDJllrWBRQmXRqjkxhjOomBsLEOlcE9kgCDphZ7pzrVwlGYgo9qBpjCg0/PwS4wcCtyhnE9mQYG6w5hGuj5rsPbqM5sEY7JLeEsWm2z30xlCcx5c0CbcZe61JKhdI9KzPkPPbnLGamrABMmqvK52IMsGu9b8VnFQlXgbE8+F+hrXC5HFMsEfNVKglAdg4D3eo/zwQq70n3jFhirROxmGrH3Vg0Z81JKW1w9QRbN6l5sfpN7Du0/UTPdtrNfhrnk6XdtDdDm6H0cq3eJ2LWNc8lzKXh9X3AHrEKShICvbmrV0pJ6QW2r6d5KHlmxkfVFHATK5SauHYxi0o+Dw/8UhZJ93bl4fNJBXIAiqg4rELbdY/PV6DRl5GyoOk5gSmIbkxeBfoSYFUUUXFY7dw/VRVMY1MQr2izviwPLH3/i4UtLrmWuhyx9S+vYeILnkbvomtbwWl8XxVF25xUv/kAPl0MnOqSgnPln+cWlKkrVRX0v+EXGf3tz1GeWtlmBpqap6o/NOfGedtEWIGPJvvtcsdl+P9gYHWTX6v3RVS/UpXc9Ue/xGXP+AnuefPrmnplebvtesa3R2O+VbbnTHpvXdE629K3r8tst2uupHXxqRWd2Pc5NqZjTMmLu6b3gNxJdJ/eRXVyfxo8DMxaT9YYysvLyeeSoYTdvTffH0C5kWVZH3hvlmW/QjhGbfrcVmvnNEEwjZRQJoFxEfNdFEO5goFMgSYJwasY6AATfBWVSWBRYFAb5SK2+YlJ0v0SnE9hDIL+ltjGNxuf9ULoEIv3PiJEplTAE+W9grGheq8IXEmYX3O/q29k9uoZzSEGktRGRe2U/583K1AdB7EMXRfrpDosYOyv+q3v8vCMlcCaBOtpjMH1QEgvDvVBhYGuffE31Ukms1XMX0yqALteLAIFqtuKq888BuY8OBdY0XqewUBmRQAOqpsWljRiOi9USX0s1q1HAETqwxnMvFvjr/kkrdhJzDfPzxO9IDXHephJ9HTMfxkDPpp7K7TZhROYH5/mS+WeEeDz60BAWPPSKxp0v1jrWcysuySYjOocSD2jOnrG81S8dx4LJLQRn8uwtT7EmHPN243Yp94kV37QK5h1gM9DR5/grktRI3AqcLdGUCz498Ix2oou9ZFA90qsp5QTWiP6v5QbYBuVNtAun7Pz6fMm7aq92Zu8+qQ9SPuL3ru95LvWd6rY8sJcapKm9wzYnqHfS/e8FHJlzNeDjDP5G+a5CekCUE27EqDiTQL1f59PyjjeF9DoegasjjqSQcwQtAPRCDB2+W+CsU+e4SoKYzQ9o+rPvPR9pKMqADM1dP2wVbZNZFtHjKiMBGB44NcClAcOwniLU792A1sfv4XswHXMfufNjPddw+jjt25jENPvHnSn17uYs/Qen3+Wm0mo2E7l3V+8lPyKL2H95qdTpYqDHcrwJp1pX3gWWvcrgFO/v51lTo9w8ZFr7/27P+Ly7/45Zh5xPSvvf0czLrVrj/KRSa3y2HYeaL5dSdN11Ekz5xKw6tvVK6BwY+HLH1dA/K2uzIc6Z7v/ow+Uo+mbAkv9rZP7/O+5++7Bao+2NZRkuC52Mhnqz4t0fwDlMwn9+EPAjxIi53/LuazUmZIGfz82sJqr0jaIOZKfoIT6FYxlm4+/L2NC+wGMJRq5e2doC9pixGS6uh8TOBdiXluEzVJRKldiXSYJ5mvTWOTTDYJ/50LMQ4K3GB0vUE8RQK2eEzNUxft9AJp7CBKGhGcxH8Q27ccWhDbgTQywD929AiolxuCsYKzcHGZWOB/rdAzTSHv2UWUJLAgAH8DMazfddZm+iglVPyvIjMwRdZ+YQc0PsYq4/vQgbcbdK7NQmSKKpRT7vBLvXcR842T+uYAB9pnYHm/uKAFoEwu4dHHM41R85irM7FQKD99+AX6xuydif9e0jwtRoCYIJpbzsSwBuBWMrVx0/aH1VcX+XcUAkcZFio4vjteOYYBnNfaF8lrFQKQUNBqHE7HdK+6eAQEsSumQA3fGZ7QWwSwUxA4eifWYp53EwC9i7wCVVxLGMI/1X8S0itoUPozNJdXdbxxesaN168Gk1uwAmytzmAJAkTAXCONTEMZLfTHANlBZLaxgAaNOcfZTvcvNas6nJu2qvRkMPPokJYj2SymppCyRUkxJTLzeNUDDZNaYEsin+/LL9EF6BCgbk8cOIVipi6mENphMAWTK3nSZNXqTRn9fA+TyNhDwZXaBn/SoBs+qef80mU52+Y/65/IELKRtTQGu2thL2+oA1Thvm5lC21dynLRNx0U0DPF4nVPPu56qLEO7PnoLJ59zPfncgW3RUP3fNKX9mTJz6Th2jV/t+kltFJje86h/y/A9r2K0Pmye62L81FYBujz5ZHkAaJqDetb3tcbT/+YBpQ8CFOZByeGX/xpX/Nv/xvKzb2wB1HFlgLcr4JQHk2kfiwWt87bSQSA1HYsGNMa/E4WVkbLkVQVDN2ZnIug0tbp8KMHkJw8mvaWDntXc9wC1wmQryV+S0SQTnGt2crfvzfcJKOu6Vuis08Chc1ud+04+UuIsQWD0GoI12qBrhAm5MsPpYyaI2tj0rNgWCfEFBih1vx9PTZ5+vG8PBjiLxUXWjh0DjK3LsckHJmSuuvw2MAA4iH+1UQsATWKTfEQwvZvFBGqZya1hjJfAgeo6jy0Kv2DEyqxijA2YQC0BW4tJgr5AAK4ftYmr3eqDAcZkCpwL6Km96g/lI/ZO/ScmWAtZYymFAhhYFOAR4FMZx7AAPepLCT8a+xEWxEaRfOcw5YHG0jOT3vxT5o5es9YnjJf6ZUwYU2+Tv+na4fvH9y8Y0yaFyiqmYPDhrWVCvIEx4GqbmF6tA4276oPrMwEl9bWYU7B5rLwEpuZdXQTe1cersZ/3YqyF8tackWJhKv7/CDb31Ed+vkkxULjPDDZmGqchpnXUevNO9poTGkspK3IsQrNApebxxdh6V700PoW7V+MDJkALSFbxHjGw6kd911oYc27OotzNm9b5FNJu25vB3kn+/a9Pqrn3ysUcE/K07vUOBIu83aPt24TLywt/SnIl8L5SsJ1t8WknINK0sTJQIJ/CMz3jfdo8m+XPeryvMrvKT691AZ/0frE8YgfFTJ3paIqd3gViHlvsbQLq0jp6SVvmnEQmSoxXjoEKH4hmfOIwVQTJdqRGyfjUnQ0zmyV1T4FPF5gcu2s6szMNxKR6pM93MdqDx30XJ17yg2xFwcBHu/VlpMn3mWcYFdk0DXQjsCgz2TQPf683C/74a1/MQ7/v58knB7A1bN3rz0TtUpB0KUt8H6tO3gcyDR7UZfLagMui3d9gLLie8wG3/JFfPnnrOg8se9h7xL8/vEJqnDwvmUJEjYgHyc6pqat/7lyk3bw37wgo4/lWOyoD6rr+wk+loCzLvgP4ifjfNeAH67q+Jf72bcCzgN+v6/r5Z8pHFSpo2/boXSUBT0KeFx4XMDZNQqTf1AQu5lw+EliliFrDmC0xiCtY4BWxkiPgR266iec94xmNVkT1kD+kF0ZVBwnovv4ZbZA7dPcKkFXApZhw6Zm4Piakqz0FtsmCCbIlITiJbMJ13QvCRzEgVbk8PYvpNTViQD0orjBTVDDmaCpeH2EKAF/+AmaurD5RG/xkFsjZhwE+MaIrrm56R+tFI5CgNTuBRbvFtbnENO16RkdeZNjh2qqTwLmAnkDQMQykVdgLSn0549qn+ScGVWa/OfZSFRPs+8UrTNQH2rhzzOdXZphiRaU4UN95RYfqKoFPfpAq2wNrAfa9WHAgbWMVxjZOuHz2YPNFbc0wkzW90KcIc2KErWXNa20cun8es0wAW4ez2JFCe7BIyxsu3/nYdimmPIjVOaSe1Re4l5JmgJ0huYId7aJ5tUFgJmXqN3K/6R0g31tdG3FuTF7rml0dSe7zPZ3tvflspp2EqSq5prXaT37zFiqeodQz2uNTJkLvUN2Xxfv8Xx8ECM7MTnl2xJ+5p2sCk1uj7UI2Lv/UhLXeATg2dam6BWSfGmbTsX9iHXdiglJhXr+l5/55dqwLcPs+0e91ZWCrAQF5GwSkfxW8paqCqWNdWJlnSur74bDtA9pi2ty9O9U/TWLnqiooCBSQqBcnap82mAQzA/Vpz+UHySZnWPvAm8kzG/NxZSyc74f0+07JB7xRfcclO4K91Je2FUjn5CqrH7mFhUd9OSvvev22sjwg7GKku+4VmzrZN6VJL5kDvq07mRh7MOl9lwUqewVMlNvBYpcyyZuv6qM4Jn6NpSyk/+7BpcDkWvKRnOAVZucK8+32vflMDOVTznJZtwNfUdf1iSzLngi8CHhc/O3bgOuBl2ZZNlPX9dpOmYhFECOivk2BmYRhL5BKaJWmXwxb6e5V/jIN1CYmtkLC9jxB+BxgR4AooMvVwOzBg3zljTfyxqUlPnTrrS2GU5FHSwzoaUPMMV+1TYxVKjFfR9VBDCzYYikxcLbm8hU7soCBAm9WKQYHzAR4CmMyob1w/ObtFY/S3nh2R4weGMAsaUeShfaRIyMM4KsuErA92If2YvZmBwJaAr1zWOCX0uXpAan+rzpuYqBWws2AML9OEJhwAQMJ+DqWYgIzy13GgIWYzgpTLMxgYERJ90vBUGHMllhhKQHU/lOxnWJWh+4eJd2v/l8lzJd9WNCrCYJiwStPBIYFOu/BTJqV5yq2jrQWBbr6mP8y2DxcweahrikCq3/5b2L+UeoLD/oKDCB7hYaUANPYmK8Rxs+D7qO0BVyNO9g6EJD1rK03adX8VlI+elfIbFrzS8GvPCupOSwW25sQjt09a/HvHs5+2s2O/+fTWd+bz2rSnE/ImxaY84BS61ZKk3HyDLQD9EjQS+Vw7081iSmN/e9NsLsdgFeXmWPLvJHtQvpOgFL3+utnMsP04MgD1xQYSWAf+42XwFL1EmbLl+/BogT3FJDo9zO1JzX7hTOzbmnbBRrT4C0t4JHk4/03R6MAjMRSpuV4ZivNt8tst5fUL70+UbTniwLgVAly6M0t0n/Qo1j/8D9RbtXb2u59XX2Zns3zzKfAuq+zV7DovnEyhulfP4eU37F3vpGFRz2e1fe8vrkvBXS6rj72rG6qLOnl0OtvX0Pe9DZlkVMFh0/eZ3SigHE/tGNP1I7vZF1wX0nvIA98Ksz9JwWk8uP2MS9SMJmyk+cKTDb13cV7846A0pnTnJVU1/Vb3H//iXCMnpK3Ykn9bFtJZ+F5X0dtXmJlVuNHvlga+PmkkAswYDlw92rDKjH/LQnWixhTuB7v/QLgoVdcwc/fcYdV9ON/C8APvOC5XPzldgDv9115JccOH26EVUW++6BryxwB+K1jwrwWwjwm6K4QfMD62Hl4EjjFfKldFUHw3I/ZkQtcL7OdkVTSpn8KE8wfijGxfmHNEQTgk5hPmoToo7QBZImdSyiGczn2t8CI6q0FKyBTuHGAMI5i7y7B2LSc4EO64vpVc2Tgvs9ipqBivVZcny5gAZMExOcxMLYanz/lxkvnX8qkcg7z+z2JzR0BKIFo/R+MTdX/i9hWHSUjFmsaE5bE8Kl/VzH2XO/uY1gUVfX1yfjsDGZGfBID9kXsW431MrYmpDzwLLvGVWtFgFryj8xXc8wsWXPWn10ntn9P7FP5HkvBojm5nzaTrTr5DUR9rvp8GJv/U9i5qgvY+F5FOyLvw2O5d8X67o/P1NixQPJbVh+UwGEMqB+lLUyLBdc1KQ1WMOA6h21qmsuL8f/3cvbTbjar+XxPZ3tvPp/Op/Pp00+Xfd9NjO7+EKNPfPCzXZX7TCsfeAcPvvEHP9vVOJ8eQNrNe/OZGErgnB2e/O8Bf9rrq4F3Ai+p6/qMcSY2MXCiJEFWwEtM1SQmHMvcUIxNgTn4z9H2ZVyh7ZcnoRBMsBMLMUMU4g8f5q2/uMSXPHsp/PCgrwFogck/Xlrik4cPN4ebS4gUIzbE2FQwvz+ZgYpRmcIYjgGGwAWixMjlLi/VV0AQ2uyM2MgCY3cFsAV2BFRUthg9LxyDgUHdI3bUC/eTtBkmzzgJ8M27/hCIUb8N3H2zWGAeaDOVHlQIoK5iYHuKoNkQaPVMsiZ4j/bB9mrLFhaRVGOhKISnMdA1E9s7g/nn6Z2Q0/ajE9AbEEDUCu1zSRcJQGbDlQ1hDkorr9/FpGmcdb5ihQE9HTcibZxYQLVV4y2gqb5VX6ZmHvMY+JdptZg0gXjNE82PK12faNy86XOFAXqvCFEdNc/VpwOCMmnC5Vu639MklnEGG0sBZ81tKTM0f+cwMAw2b8TAam7l2Bqaiu0QoynALaWL3lFaV2uYQmERM8tWfaSwOduAcreb1ZxPIZ2jvfmsJL13ExKtSQXtd2mGrXF/VI9/H/n3jA/Ao/d7z/0fzNw1LbfvWKE0pWyWNx1MmbmUnezKp4sx6iWV8j58XT6M/ixJcP6GVfyMnJ+ZY+Wqjnx9IJSd2pyzM4ukdsv/0qe6aveZnu06YsMzm74eO5Wn7zI/9lF51b7UfDdt35n8ZXcyC/a/jatQxlYJVTL2g8sPMv/4Gzn57r/m5Btv7vRx9Wd1bsV8NRdSVtqbFHcx4T6lbF3KwqaBkPIcNu76CIOLr2iuqe8as9KiXee0HfqeziONte9Lv1ZkCtv0e9nu8536v1fAYGBrZTRq91F6jm3X+wDacldXEilTYa4nMr1fJ8h1n012crfvzfcJKDnLhydnWfZVBED5BF2r6/rFwIvvz/Myq9OE8JuJhHUJsBltFsZPItHX3hwWaHy6VuP/H4wFv1AQEz0r0HYqlv/XP3mIffvg4d+3tK3eL19a4mWHDjVmkQIOJW2AoTpoompCy6xQ7SlpU/R6RtckvE+5fNKNXQBOAvVUkk+BmehJMNBvAn7zGAs6om2CnAJ51dkLxRpP1c2bEHpGU/kLZA/cdY3hFBYQSWWJydL4i/UrMYE/NaEsCRFawQI6rGPmJmJ4lT8Y8MoxP1bNU5lCHnP9IwC4hoE57xOcY+eACmDJbFKAsMBefnp5qn0a1/3YvNccKDCAqOc0tv7c01Vs/DQPoN3nAnICRuoHsAA7XqiUUkIv/RnML3YVA5Bi81Rvvbx1dIDaod8F4jRfFKxG81V94F94M7TfCVPxr59zR2P9BPo0f/fRBpoj2u+kGQKo1fion6RgmHd1lyWCFDdad2q372MwNj0H9u+0M36aaTdrQc+nJp3VvflsJz+F0r0nTz7eHNUrH/Wsnte7Uj7KXZFdc6JgWrX3xxwootDb7+8MKsGE33wHobrL/8v/1fedzGjTJEE5Fbh9OR5U+msQzEe96aM3qUzLlansTqAXHCh3INbXU3lXrtyuyLFbpYG9LvNa5ZknfZTl24GRgKQv3+eXBsjpSt6sNzU7TfNU8CIlPy7F4hU88n/f0fx24u1/AcDM1V/K3sd8A/ByAG774Sspjx3uNItWmfJBVV/4ozf08VFslUfXHOlSYNROoaBysq11eoOpbdGB/TmWzRromLf+mBTfnjQaclq/1K9y7MpK14mfIxNFaMdk3wHosm02rUA7ep+k1Rbg80eAQNtPssLM7kUYSI6QfChF/WcaTCqdi705y7IHAb+PxRR8UV3Xv/Gp5nN/AOUDPjw5y7L/DPzH+N8nEZTqvws8sa7r4/e3klmW/QHwzRAmwUK8roGUj5Eik64ShEOxBh4EFh3PShisMLAGBopKLODOMhaYR0yGB1Ov/fFD/PhXXgcP/6am/m97xSt4xaFDzYTTcyqzwo7d0G8S2KvYrgnMpE5mlypXi8eb/wp8SvhWWUMMjIzcR8K8AJ5nTwssuqXq6lk5sZo5Bir0dwUz//PKgDmMwRIIWaBt+gcmhHv2USyQhO+LaUeg1Th7EOBZU2hryFX+imu7AOt6/Ks5pGc8EJ+Lvy/T9p+bx8DFADtiZZYwnwTshpjJqeqsdmpOCjzqZaax1fgKaK9ggPliwoLTviiWtyIcXbPXXVN/Lca878UUCQJavu/6hPESWJ6J/XAv4SV8yuWpiMMaN81N5SnQqnH2pquaW14hofJ9vx/Dgm6N3DNiHsGOf1EeF8fr+r8UDL5uWuteeO257zJrFugXQPZ5eBZS7dN6UP6XuXv0TvH7xgr2TtA9K8Cggttvv50sy9bd7a+u6/qZPMBUw6720zifLD3QvfkzlVJQ6fcpr4CRIsu/67TOKyza8QTdyQuKBQYYerR9tyaKACZ7TvBNhfNGYIsvvBQoQRtEdQWf8d9TgdyDIg8QPTj0dSO2LQ1Yo6iouj52gENBZdLy5fcndrEr+E4q2G8L9uPAVwqut7GdVZtFTPNPy9B9dRXH07Vp7J7z4GUn9q5zTHFluLHrYqA9YBYjLIA2PHKYj//BEg965hIAF9wQLNF6U3NNOUdfscTo6OFW3fW9l7eZXM/6+U96/mfKNne1z/ex77N+39qUlRsUe7YfWevPMU370isG0si4EM/mTPreBwOC7dFzdZamr+u2o2dw+SeAtaoMBIIxlT7gpHyzvYyxE8mXHi/kgaT/eIu9zySYPId7cwn8eF3X786ybBZ4V5Zlb6jr+gOfSib3B1A+4MOT67q+CbgJIMuyywmmrc+s6/rDn0olo3D0TIC9WVYvYAIWtBkCASQIwq0HEhr4teQ+MKFvgTaTJiHYM1wCFhIIBxjA6a3B2sTDmdoacfffvYZLvuppHLj22m3nbOHKkDArwLUaf5cgXGFOwz3awXQEbDxYEtD0gqk28D5m9iphXhS+wGhFOxiO8tBkETjwjKUHJRCAiRgWMX5Dl5eYNt/XM+76kLYPpurvQfcGdvQIbowEELw57iQB6AgQ63pOOIJihfa4rhBeKIqaqj7VnJDpdObKUX+rLrOYv67MJ8SM+uATMqlQniTtUFJfC4zou/pZeXhWXhGC1a+qp/z2NAcmCf6ZAjtiFsX8gq0N39dgLLqEQpmEewWDmFP1v2eH1zFhUmX6NQKmzNAzV2JHyAyx+Qc2HnPxM4H5AKt9OQFQax34ftZ7JF1PUu7ksX8mXV7eBFtAF5e3Z/HVZ1rTGq8cC9akfvD5ae6U8f93EN5xV111FcePHz97B9rv8rOuzqcmPeC9+TOZ/FQadfyud5XWk9hKrc0uZY5nF6Bt3lrF3xVVMwU83uxTQq0/wF3PiAXpEj7Fcu0EGtP7lFJQmLKOHgik7OKY7eA2BZU+n66jGlImqOv3rtQFUDz7mt6bmgWnZr7+vMG0Lk2eFWztABT9+YsK0uN/AyuzFwWHLtAs89L03EN9igKKKgQ88u2pKjj8+4eoKrjiu5e21fETL13i2CsPtcbfj5HmVMqqCqzVSf95M1jYeU51mf2mLHNVQb25Tj451fw//ajvlLfv15QFPRMz7M9ChRDNN53XjYLA9UEDphOQ6efJuGrL9tBt5tqqD7b3pqmX3DN0Hw8mJft+psEkcM725rquP0nYP6jr+lSWZbcSDo4464DymYTx+SE+vcOT/wfBUux3siwDKOu6fuynmsmIcPSAfMqgHXxkLl47hpmlpgI47h4Je5pEB2ibfq5iR2UMYgOK+KzYhmMuz6mDBzl2cosXftMNfOCWW3jwddfxQzffzJ5rruH2W29tytkf8787lrcfE4QlwE8TBPXlWIcL4/fl2JZF2n5WAj5eaC5o+5wJWK273+ddW8W0LGNnWB7ATFsL4F8wVkmC/hwGgrTgBNoE0DyAVP/2Y/4erKl9l7k8xd7tx4IObcRnc4zpAZsDmtze5FfAVUDKmy1cjh0N8U5sbszH35cxcCsws0bbT1WgcJG2yeZpzBdUz1W0A9ysYXPSKyykBBH7JXNcr3zQfZ4hG9A+ImQLY4CPxPsnYl03CODkXnfPAgZCZzBQrHlTEoIorcT/f4LwUq4wC4ERJihqnizEPLS28tg/ekl701yNudau5rVXoOzHAjtNYQGSwMbtqOv7mZjHhstXbO+s6y+Nncryc4X4nJQe2lz07lC757HNS+MOtm6msTM6ZfYs39gBZv4t5lvjOg3cFvN8EGc/nQeUnxPpbO3N5zydCVTqfSfFChhT4IFi5u6Vsgy2ux+kQs1OwEe/+XMC8zyaaVZtwbmLhUujgzZtPcO9XeCv7ignT/JoTHjv57pMQXMXoBTb1WX629QDAz9pvdPzGuuOPusyufW/pYDFP+/97qDNBue5mUF2tQcMJHb1qZgzlZ2anXpA5/vHH1VSV3DHzYfYc8VB9n/ljU3eJ97z99z5+4caRrBT2VDFebtD33QxlS0m2YE5XfN+kEp+fKSkqCqo8wnqqmoi1spPV893zQmB4/RMTZlQV26c0n72oDM12+4Cqzrf1bPJ6TPqBnXJTiDSJ5myKtCeko8mL1PXIXZ8WBeY9ED2M5ke4N68mGXZO93/X1TX9Yu6bsyy7Erg0cDbPtVC7hNQuohyQz6Nw5Pruv4PwH94oM8r6RgGgQgJbxUWzVNARSZiEgYFZsSEiCUReyCQAzbx/EbnwZmA6ozLfwjcu77Of7r+esZlGcwob7mF773+evoHDrCMgT0JphJoVS4Y26Gw5yVBEB8QTOxKLBgQ2Aat+nvgdCHmA3ga8wMVwymh3QvYi7RDJAuMqv+9kKu+n8EWnDcHFRg55uo2564LmBaYVkjvnlVXtvpBwFJ9JsC5J47NR10fDuJ1gRVdS5npOcx8Nl2rAkDL8f8apxmMURVrpOfFGM5jQGyLoDxYw/pcbJ4HIxoH9e2Caz8Y2BaIKd1zmoP6vwdmmnOeIVvBlAR6Xs/MYVGQNaZimGdi3RVcSmvKt83PGR17o3kmUFphgqCYbCk0vNAo31EfnENrSW1bxEyM7yEASGgzymqHxuxIfH4/FjRpnbDGpFxReRoT2D6PNHf1e8/9prrrPjDlifpmhWB+W9A+j3UOG+MN2oourZ95zn7a7Y7/51NIZ2tv/kylLlCp953WuwDMJGaqlrvf9e7XWpL/k9h+iO+8+MKUqaJSc9agB2+JQOtZKn8+YerD1mUK22Veuc1k1NUpBZP3F5DB9jx2SqkJa2qqupO5ZGpeuRNQ9sxZCoSU/BmeadkCKR7gCeB4M89x3mYTgZb/ncaxK6CQPwMyZVBTdtaPtcxRvQmnQKXumb7iWqqtEcfe9Bou/Iqns+fAg1vl7MRep315pv7r5TT+gl2guyuvrqRnpi95BOt3fpjhcHs+XsnglQZ+nP1caoJDxbYqYE669lS+jk/pUsg06y72swfLfg42a8D3o/JI/up6oxihzS7mmGwB5je5RhtIejPXzxaY/DT25mP3h8DLsmwGeBXwX+u6Xv1UC9kRUGZZ9lTgsmi2SpZlbyPgE4Bn1XX9yk+1sLORBMA0SSTICSBBm/GTAFt1PC+WxQNNsMmSslpiaCTY5VjkTtXjtsOH2e/qVgCUJaM772wJ2xIoBeBGtP0QFeFSAnsKHj249eBOm62EUPlc6tlVTLAWYBHY0H0Lrj+HBCFbpps6n3Pe3S/GR2aNnsWaIQgHAiMCj3pWwKLvfhO7tYz56alNAjEegCgvMbsCNtMEs0YxQxp7PecBTeX6rIrtE3iaxkC4+s0rGip3r5QLYtPEfHVFcvVsl9e8e4Z33vWRxm/N/a4+FliUUkAmYwIfUhKoz/fHPCdomwOLFVWd5KPpnwWLeqs+9WBc/SuApbprHD2g9OBR61jzASzAkfLVPFrGxmBAoGY0tjpSaOA+M+67mExcv+kdoEiTqstlSf8KsO/HmH6Nodp5Qax3Ff9mhHko5YfGSG2uCLYlXomk+moz8woaPS/Fju+js5XO+1Du3nS29+Ysy64Gfg94DPBTdV3/qvvt24BnAb9f1/XzP926eyFM74ncfXRdwdC8CVvfffS+SYVEcBG5y+2shQ9QUjlGDLYDLg80RqP2dX9Onk9d4KTX8Xvjn8fOgHBcuX0yEbp3AqTp85TdZpA+dYFgDy5SltObcXrmzIM0b7LoTY89G6Y8+smz0DYtTYF1yib6/vFAf0d2kGDS2QUgd+pL5eOZs6qC6SsPUpVbvOsHbmDjY7eQHfpj5g5+MdNXXsPpO2/tzGcncK6yt1xdeh33+zqqn3diy5U0TgLCex50Nasf+2D7TE3aCpX76kP9Ln/QzIHvPLe5nbZZ93jWMQWLntlUgCY/BgKmzfNJvbawdafb0i2tcvfi7pF8mTKTHlB+NsCk0rnam7MsmyCAyZfWdf3qB5LHmV41zyJEkFOaBK4nyNe/B3xWAKUONgfbhCrMhGyKIIRfTNvfUQKqF8QFBHDXJGhuxWdWMQA6gubgd5WbYZFFJeRJKBaAnMHAizZJRanzrMcqJrhOYcya6iOmRwKx17Dou4RmCd7HYh0VsGgNA6UDzPzQsyZ58nfdPTOmzT4R2yew5YG7nt/CzFCh3fce3Hlma40ATiU8SABfoc0eqRz5D/p71fe4/hBI8v8XiKqw8/0ELlV/DyD9vFI9SqwvdZ8HnwL/Aj0ahxmCCavAiHwyPciu3PMemPWxoyTUd2vYnFU+q7SDXCgPrYU9mOJAc8CPidbFDBa5Vf2nOnqgVGGAzCsKxOou02Y8F2ibIUupsuXyKpPfla/au4j5wW649mutCpzreII+pjjZwACoV/qMMH9POflrM5FyxTPqGne970uMUeb/b+/fw2zL7rJe/DNmrapdvbNTKTY7naQJSTpIiB00N5r7TQQPhKMEBRLEqIAoHEBReLj6aAc853BTUQkgoBKumgMx5AdIQAFJhACdkDYJCQrpC02n09m9U6murq699qo5fn/M8a7xjrHm2reu3Xvt3uN9nlW11ryM2xxzzu873u/4DnJQJR8QIO3fZXiwrqdy3UCGyi5iq/R6Fl+kR4HIogHTsFI46nfzGeDvAy8d2ffylPZPhRBOxBj3Ro65JPjA09g+3eu6H3VP6lkvzwUfgHFy2VEuI4UdS58N2nOzgXQqEqmTEi2fIJVlMmFppM3apVT7BBGuZcTOI1WOqZE1CfV0dcwoCUqE9XBaur96OZfNf3M3U1esvHyuQDmRdBdNyGpUHcHUyYgrgmN18Tzqdhgjv75vjCCvdYPiKWXU28HLvgxzhezgYX7vK24lxBldBzu3vwHOPcK5/YfnxHlMCR9La0wprB/wNfmbl9VIW60KzoM9GdE//uEfxe6d7y7yqgdR/L+jVq19gMHL2JG2V+nWfbru8x7xWOkcskiK+770WnI7WOtXY004owze09mxGsSSHelk0gedl9wujxmu1Ls5DPMQ/x3wrhjjv7jcdM5HKDdijH9iv9+UIrM+GEK4ahP/ZVS64Twjz/GTIbnP4GLY2TG75CiXJyhdFPcsbTdEpezcl457DrmDHjAQtrsszeMMcdunad8G8Lx0/CMM7ngPMszFfFLK8wHyYurqtE8lE5+HyATrRjIB3CWPnGymc/Yo12sU8RFxE4lx90r1z5vS/7vIxvRJa/s9BjfBLTLhuCmldy+ZoOrmdpVFx2+R3TV9EOAEeT6pSOWMUvFUGbbJ7rJYGT9Ijjbakd0WdqwOO2RjXvlK+XK1UwS7S+fcS0mC1E/kUqnrqHSnDPMLRarUFk+19DU4oPbQtfA2UXnVP06R3XpPkSPuimRvW7rb6f9phvl2T2W4XmfIREfkS/21t/Z9MnkOwWmGvvcU8iCKCDCUrtR66E4o5ymLWKr+Hdmleovsfjxj6PNas1EKM9W5wh55zutGKuezKa/nFnAzpfv3SfKc2ftSG54kk81tcr/VcwLyfSfyrUivuvYqqwYNbqR0oXmAfH+eSufck47bIkfflRp6KuWvMK66XmfIczWPFM3lddVxpO/mGOMDwAMhhM8d2a1bPNr3R41lpNIJpfq2iKUXRt87++9p9GTlQXa5BuBESibJMO3Sje2kazJJg0hm8E6WGNluELvhrHNDtc2PlcsnI/seLXo1BGXU1zEXxmV1mvUsRIv1IEY6t1bMnJDFfkinnk+3sZF/y03Sz5G75TJX3bq8y4jNmIrXdaVKWbuOXohIydX24P67i2u1+47/wYd/yT9m/757Fgji+ZbUUBlr8qt9aqOaUI4R3zhSj7rdTv75j+OPf+b7RwcZ6jwgt19X9QURcq1bWfd/KZR12p7e2GCFt7kHNVJbnJstuq3qu54f9SvMvRjkwSWRxj2QnFC6MqnmuwJ87uJx5d7Nn8QwJ//tIYS3pW3fGmP8pUtJ5HyE8kP8R4zxa+znk7lKcDc+YK6SbJBJEpRLR0hBuy/tk8okMiFDT8ajlADIBu5u9X/PzpM74F7K3zulRkW07MhZBuLzIFnhEjEVgRGh3UznPpLOn1r6e2TispfKfMNI/ur8tZuqtj9Anq/l6pvac9O2KU1Xp3bI7o0aTXa3JI0sK03duK5AimyoXj4woDwF77A654A8Ki0iOKVsA5VDipwifz6BrCTtMBBmKVSqD5aPyKhIr/qMq9gqjx5UM2sTKEfMYOgPImQiwx1Zpdogk8OHqjptkhXyvvqoT0MmN3r4qoyuoqpsHdlVVyq5jj1DdiXZJM9zUluLQG6RBzR0XZ18qw2dHErF1DxZRTTWPaj7TO2qdt8kz6k8QamKPoGsYnwgbfN5q6qH+tIDlHMzdQ+7elkr5jpGzxNsv6vaqrfKdoLhuh5jCK92mhzUyV+MelmKuEK+5w4Y3GWPGldoFPSCa12FED4d+HngzrTptTHGbz/60lzTeCzfza9lGK/5yRjjQxc6+FIwRipro7CnjCUghUHPoEgZUIPqu1jwekrrkGzUykiHkgB2XV7KAMbJk7BM2Rmtb3We/2eyuJzI2BzNMYzNLfM6+Xa5Io4Z+cuUvo7cXr5wveBtKlI4tl6m0jis8p2rk9YR5mreSBvILdRJp8pVXyu5qEK5TIhUynMkclKplDWcsM6V2dkiCdq7810QOiZP/Sh23/OH8zbxOZxKb1RprPKCYd90uki6l6Huo3WfPP6Um3jSc1/Eg7/7hkIxHXOtHWsDJ5PaPkbIxyLYgimllmc9R7LrMkkVZtZPtayMk7y6q7h6qePqc6Rkum3r32Wv9lU6VxNX4t0cY3wTRzBgeD5C+TshhK+IMf6Ibwwh/D3gdx9txpcLRaUU6ZDBLpLZk41ouQaqo/Vkw1wqjoxPGYXqmGoYbYeSfLoSs5X+y/C738q0wWDsbpMN5QmD4bpFNqpFEN3dTgE7Pkh29fWyOjGRSlnP99uhdImckJVBpSfyqPOkqDkBUh5OSEWwNZojUtVTEpQtSqVJ6rKTRxkMIqsiB7rZ1dabZDXuwI5Tm52mHFjYJbsxq31FFNcs3ePV9xPkvqT8VS+pYFJ+fZ+XU+3bVe05sf9aUkTXdJtyzo36jAisCBYpvUMyydQAhdxC3U1D18rdn1Xu4wzkddvKpTKJqOpe2yUTqyem4+sHs19Ptb0r5WrPjjzI4QMPcgtXGlspTfUdKJXlnkVCuUvuj1qGYIfF+1sDOJD7f5/y7CmV3207V/1LAzEzFh+mOk793QmlBo2Ok5dM2Un1kFKr9tD9qjQ27Bg9644SMXK117p6Y4zx/7wiJXh84DF7N8cYXw28etl+XyP6clCTynpQDMrBmbPk5+YaWZ1cNmiv49zY7MkqpQz32g3P3TdlOPsSDrA4j3LM9VQqjZQ+V8q0P/9YHtHU0x9zyazJgLuQ1seMuYzWa0XWKprSk3Jb5+nlcmXRVUUPbuN5iJz5dn0O+8X6iyTO5+JNSldSV9V0XK0Oq41mKa2Dg/K6uooLA7mp3VVrl0+R5vf9xs/xlP/jSzn9r755tL3Ppz47aZ2rxEZ4nXCJcCqo0TLl0/PsOviwz/4S3vfrP0c8d8DaZHEOrF+D85XT26q+Z8aU5DHlc6y8a+ma+jl1f+37TBg131rQM0GXS3aO/ivCa61I7tnvKUdDJo98jegr924+EpyPUP5D4HUhhL8OvDVtezHDgPpLr3C5lmKNrH5skufUSQmQESa1Rx1JKsNJBpcyBbIQadmwc7bJBGOHTEA6yiAZIoT+4nMjXY17XzrnQ8nBOo6ncniwHRGZ4ylfBf0QQXAF0dWzjvxSVRAeleU0pUshlGS0S9sD+Zl+0srveclYF7kUUerJKqkM7F3bf4I8v81VXRn8Uss2KW9+XT8ntCIQEzvmgDwCLZV4m3y9VGeHyIiIsFRAXZsTdv4e2Z1R12E7/VZEMBEt9UsRXD2InISozUW2nGSpLfTg207pP8RwfT9IJnLHyBFF1cfVLq5OQ+7P76d0IVUZdF9pwOIByjm96mOepuQKKZS6x1RX3ZPqM7ru6s+bDFLLw9ZO7jara34DuY+qPDeSiaQPiogc6np731L6mynNeoBmWqWF7XOPBx+4Okl2v3b1Xnn6AIja0NOZkue3Kj89hyCTeFe4XSE/zzt/5XBUa101PPp3cwjhq4GvSD9fEmO873zHL4OvER1CuCxTx0llTSj9ntEgoOIPQF5exDMWiRQ0qFTfK3OCNltclmCMoNQumx5hdMyQHiNnXZfdT+s1GGtj3gnlMgO9/l4TvHrupB8zRhhcLapdLtUGWv+xbsciSEpFcvw8J/NOKn2NRalTY+TV59HV+5yw12s51mrtbAZnp1n983LFqs3lurnZ5WAzOn5OQLshnbt+5vv4hP/wFt75b/8f+r3dhbrWZXFVd22k3Wol2cmkXyt96nP8mgB82Ev+Jn/wPf/X0jmwunaKmFsvPXJY1bseWKiJpQ+S1EGU1iaL6uS8D2yU1zp4PoxDzwuHZDfZVPIGc++p2sXV3Vwvl0zCFVgjesWxlFCmeRWfGEL4DPI0wF+MMf7aY1KyJVBgCxVcFx6yMSjj8Rh5kq1I1AY5+MWOnduR5505aXKDc9PSnpDnsEnVWKN0XZS6sEPu6DIY5dIn9zuVT+mfsfzd0BXBcVc6KTO7qS4qj9pIx4qwKH/tw8qtdpDRfEAmUiKHTqpPpPPPWPtJgT1j5+wxkNsdsvullGYRoXpEaYus4ugBIHIucrNJJqduhEBpqHSUkb9EXES4RZal8G2lTyAvAaJrqAeOrqErZSKUum4qpyt97pLphNBJp/rllLwmKCkftbHXzQdSdO1EzGfkeaa7lATFSb2TqDPpuyvbgsr4AfJgzIx8raWi+bXtyMuUOBnyKI7eTq7+PsRAql1B1LmaY3XC6u1KoPqwBhY20jlPsPZSfxPRVbvO7JxpKv9NaZvKchOZ6CodtcmE4TmjdnXFUvfdLuWAgtpH7dFX25W3cEAeYDhKXKZbzVGtdfUJIYQ7GG73b4gxvvOySvM4xVG8m1OE2FddifJdDry7+eCfD8K5sejvt3qwcOxZpe9rLGKZe2BNWg5HyImf466YMK58Olmcl+s8x8lw71iuGNWEQ2Xv+0U3Tz9n7B6fu4NOMgHoa4IwG/Y5eRBqddLr5MqTtsGFA+B4Gq52nrNyOMZIkhMSEcHZDM5Ny4ECL7cT6b4vFWlXr7v0QtIcyIP77ub+N/0Sz/qCr+Q9P/7dC3Mna7Ll/aXO37GW8hpzh16mKNZ99UM/9jPp1jf44NvflElrX5ZJULnrdSdhXCFXP9VAi66RjpUXgcq/fp70c8KpjJ6O5X1o//35sJ4PKYjkgX203nNNJmeU7//LJZNXCpf5bn5McD6FEoD0krqqJNJxlmyEyV0SsrEKWRW5k1LZUicR+ZFxusFAbqS2nSYrXZCJ6AaDoiLVYSele386bpscJGWHbEAeZ3iR7VESg9OUoyGqiwxQGcMyzu8iK2oiQF06V3K9iJBUzi3y3LXTqdzaNmNYbuEJ5EAjaiepICIFMmJV1m2yG6LUmzNkgqt6iqxgZZaxvZ2276WyPhv4SIbR5h1ri62qjkpDxOd+MsHbIis/MsZlnGvB2nspDXiNUm3b7wfSRwTnJvK1vR94t7W/ynOGHCBHBMsJ+j1k9Ut5nEq/n8ig1EmdExmF7B6MlVWkQ0RNgx4HDBPUdM1r5fAEA7mUqy3k602q63EGBXCP3Fc0GKK+qP7cMSjtTySTKfW1e8n9xQdFSL/vT2XftmskpdgHemBQFLesPXbsmKekc3bJ7qmnyAMY6pPq03ILv59smHrf0YDOAcN1P0l+bkCO9KxrKBdb5fM0hn6m/nrS6gdD0K6ddK7a8Klpn0jpHjkw0JalpfvNB9KewtHiUUSSO4q1rt4KPDPGuBdCeAnwOobHQkOFo3o3hxCeyjBPcgvoQwhfB9xyOeuQPRqMkcqu+q/jDsnPEmHdjq1dYHX/yfCcp9UvGu+uAJ0PY8dIUVE6dTCcMTJ5vv9ji8BrTcKaSNZKoqtZEzt/mdLpytR8rcfZEA1V6SsyrlArtl72ZS6OTshqIr1s7ugowbLyOjmtFWVfYmW+eP2sVL+coDqh80ityqOe2+dtIbXzXT/63XzqD7+Bu1/z/XTsF0rssmBIHSXhVf7Ku56HOeYGWquw/n1tfY0/89X/gvf88DexsR6ZhUy0hcPqetSEr+/LNTGL9LvkJdfZkh19DibUUQ44KO21amTIy+7rxdbQZt3TEgx0r3u0ddklCjIoAumfWpn0PFYFqx6B/YKEctUg9zoZiDIARSJluGLbpHaIYOxQqmJOdM6QSYLIhAjajMHo7yzNHStPTyYUMhSlWGm9OhnNIgRTS0dk4rjtU1n0uyeTgnWyYikDVHUXkXoi2bjdIRMv7dcak/qtejixlGqndjpBJta14ifC56NFSmub0g1ZCqvq1ZEVP+3rUz0PrZ23LW1dI7XdBiVh3mVR3XHjX9v9IUKVlhNsV798+zaZ9E6traSmTsgumX59pWIpYJPaSnVXHd0tU/VQP1I+6svHycRwm3xtROJvYFD9dinvHVK5NKfvDHkO7gnycioi57q3zpJdrXetvOpPGlBQG+jB7YpsT0lIDyj7oyKyqU1OWDuds3bX9XRi3ls76b7VXCwFaPL+4MTtFPlaq29vk1Xm+639BQUyUjt43TdTfnuW7nHyWrQ7lH1M5PEk+ZpvkO/lK/JuuYJRXi+01pWTmBjjL4UQfiCEcCrGePrKlKghxng/w5KrVx31M/hgyTG6F/SeWWbIOIEcc3uFRRXnfBhzY3XUUUYhG9Jj+SrvpQpirdhU+2t1dMw9ctl8y2VkL3Tj9ZNbqy+f4mS0Ln+tNrmyVBvFTgyXtUddB5GSjY1FBRAymXQFUOUQmazdWjXX1Zc5UVrzOZu9Xc9ZJk3nkuI5ncJD73wH97/pDXz0N/5r3vWdf2ehPc5HtL3ey+Z/LlOXvW3q457xRV/H7IMP8NDvvo6NjZI4S1Vc5iI95pKq9vJt7gIbJ0ZY1R/JwXgcPo9z2dqmgoIpQbYLIHv8iVDKVtJ7WJHWfVC2JpKyb1aWs614BPZrklBKTRKRkDEJmbBBNvwOqn0y2E6k7U5MdZ7Sk+EnwuPuZserfU6+9u24LUqj/jh5nqI6sW4CKOdiidCo0yufnoEUqG4yhqd2/iYDoZQ7q9rggGzcniYb9jP77m6dblCLJMttUEFhRIS8LLouUtQ2GAx43bS6qdUmIjhOaDsyARNpUlvNUjuKgMg1FfI8G/+s2XevE5QPErWD3G31cNL2LXLfgmzkn7DfIs5+fW4ku2hupvLKuV79WPV14uUK4dT266N20jXXtmMM13+fQQ1TOZ5Ivsa6Nk6Y676va7fJMIfzA+m4LWs79W0NcEihlZKnttsm9wu1/xbl4IMe8mrfM+S+526pmlu5T17qBsprrv1qOw3kiByuM/SL3o7XfpXBB0tOpLREbtVf6kEiobfPup3vAxgaVBCBFlE8Y8dtU8691HVaYm8+alyJif8Xs9ZVUsveF2OMIYSPZWiWB4++NA2rivORSr/fNEC0xvAc1XPBbVV1441qe03wnBj1PXRmyCqYjqtfsKhc1a6ZntfYOpCHKZ9l5G2MbHhwlppU1uUbq5+np+1OZKSA1sf6fNGaKNZEUGrf4axMY1m9alLo7eFzFWFI1/Pc2Bg+6xt5TUsna8uW1fByuGtx7DNRqss6X24mGWBdlwmVSOXZaa7377zya/ncn38rN37Gy3jg1/7TUoLscwyn08U28LZadn1dVe8oVUWAJ978XJ75Jd/M2//BxxWReusBDSfm3tZKa0wV9TIwKdtTy4oc2jkdZT9SGj6Xte7ftWv1vAzkyK3nyPe43FtJ/+X9JTIpe2CMSK4smUy4VoPyrCQ0CV/GpRRBkQQRDhmqkBUYERMZvDp2kxz9UkRTBpurUa5y6bwT6Zgd8otOZEDGsYxRuYbKIIR8A5ygNFBlpMuod1Vnn9JddpscqGhKnn953NKXsiLSJnKmfEQw1SHUBsetDVSnvercXbK74Yn0eWoqx+nUNs8gK5pqR9Vhi+yiqzZ05VKulm5Aq+6nyEa8E3H3od8hq4i6Prq+u3YdpHaqH9UPFxEFuUfqmul6bVFG/L2ffB1kAIE9XNP/SBmMZYusmn2Q8kE3ZSCmnqaIxhnLW9dNgwm6nj4w4H0Y8v20Z8dCvn8OGAYPTlf7tF/EdptMxk5SzrnVvaE+oPZTH9J94tdABOy45fcwQ5ArkV0dJ1IokqrjXfHV8wHy/aD7bGJpHGfoxx25r+ocKJ8pfl97WxwnDwpBnpOrtES6z5IHvDpKEn2SRe+Gk1bno0aMyw2XR4nRta4YHg/EGH8I+ALgq0IIM4ZH8stjjCv8Cm24Ehgjlf4MrJ99UBJKvcd8ML/wwqjIy5hiBIsk0Y+TsezH1RBBG1XcjHDVSzD4uoNOFhS1VKTNsazsvn8Z+T2fIqi5cb5v7Bxvo9r1d6xdum5wdzyWSKGvb+kEvlb2nAjWLrq1y6ZHX/VONZaGVF53R9VH13ps0MDdZr3O5/Ye5re//uV82o+8gTf9we/xyH3vWWwIhmva97ZuqhGy2gW3bkPIJHDedtW+9VNP54X//L/wnh/8R5x733sWotXW10SQGq2lder1IGui13X5Hqvdc709lYfPMfbBEm/HMbVynh55bqRI5SHZ407v+IfJwS310SC33qszS3OVcQXfzUeCa45QynhVp5AqcxM54MYhgyLzR9W5JynVBpFHXZ+99F8dsWPwAzogLwZ/ipJMyRjdIZOWbbISAeXoaE+OIqr9IoI7advJdMz9ZPfZE+R5badTXk+nnGsng3M7le8Bssoh5eomcrAgGdMzS8frrpvyJiujHhg7DBEzROhdOZowGOI3pPTvs3KcIKueUqp0LU6mzw0MD4VN8lzGLp17b/ot419trTLro2A3ItgiBSI5J8jkRUa6+sY2WVXWNRK5FXmVUrmR0j3NMDdOgwunU1pPJS8aJ8KE1V/10sDESXJ/U97ez0+lbft2/oelsqjvixTt2HU5RXaB3WQIqyl1cpuB6DxMOS/3JmtnDWRI3dZ9oHqKwImg+qCNroEGLVx9hjLYlO6PPpXrQy0Nv48UAXkt/YdyEECDTNvWZven/7oX9smuo/p9lx0jcn88bdtJn5Pka/o+8mCWynBDKt9TyYMoCiwk4rid0pFBrAET3R9/QL5v1Wbqd09I5TrF0P5XAlfCreZi1rqKMX4/8P1Hn3vDtYa++q/vug9EKGUMeoCvDUrI2ISBuMjlDxaVEt921khdfUxtHK91pfHr7qNjCl3XwaTPBMaDmUynzJeyqM+X+le7QtYGt/Koy63tYwRSpMzPVQRTdwGt4eRC6zrWqMmslMljmwOp9OVZmGWVsHZNrcvvavNaN8z5rOs+RkhqRa6uS00afX+tNOt6eNlCBx/4g9/nD37gn/Lx3/8rvPmrP4v9P71zdFkSJ86uFrqFXpP3sbaocexDPpQX/stf4U9f+2944Fd/olCaXQ1UOj5gcqH+NF9PMh2zPllst7pNhdlsqN8hZfvVZLJOQ8ecY7jvD8lLgJxN30Uy3X7at/+uTPb2uVbQXF6PECKBIgknGAwzjVL6EgauokAZKGZC6c4qI1wkR0biTQwWkIzQk+RgMHIjdDVN5BYyYRNZ0TYRRJE5Gdf6rXO2yYa8lA7VRSTwwP4rX22TsuPqmOq6QSYkekmLECi9nkxkvHwblr5e7l4utc+elWHH6gn5Zpeb5UGVjgwF3fyQiY4b7378FqVio3o+1cqmOmyl7buUhEL9oSPPr1tjcYFsfTQydsBAWEQ4se9Svr1MOmeHbAApTV/CRWqqE897LQ8ZVV5nLG2lsWfHqgy61juU5EnXQwMATyTP+1N6pxiIzYw88CEC54sHa16oiLQe5Oo7G5am7i1d+548sCA1WfeuBk8m6ZhtyqBcrsBjbev3l/dBEeJd8kDFBuVyNzOySqqXmMrh94aeQRv2UT8XyRbRhGEgIJD7xDpZldR9qnapB0GW2BGPCqs+Ctpw/UDPYjf+fKDKVceJfZ/Zby0jMn+GT7IqJaNVKkxNGGv3yKJsRiwmk8WlJupj9b+eS1iro7PZQCbdfXKMHAmuDLrR7uqjn1urXyrTmHvowlw3S1P7Dq1OPjdR7VITPw/KsrGRyWRNRJxg1C7Ha+n4s9OU/ma5LIWgQDF1gJ56nU9XKWsSOfbb27xWbKW0qt5/9DM/CDHyiT/6Rn7naz+Hvfe8vWjXQxb7wXxO48h1GGsnEUT/Pdk6yZ/7zl/k/W96HX/ymn9RqN8+cOGE/XwkvCactVuzH+PX67BKayyIUq126hqpfbB8Z31eHaFeAuSg+k21XfaH84Nr6VW36u/ma45QrpGXyBC53GZ4UXyQ3Jlm5KUS6pdOTzkxtyO7MEodIh17hvxSkyrjrp8y+tIgXhGgxVUFd5fsyMa+yuVG9gElUVYZpJT0lK6a2HYZpe5WJ8IiA1vfO8pAPlKQdm3/AYMKIhdLuWW6uqv2UTp75EiqO1aOPUtTdZT6sku+ydft3NMsqlOqk7trqv57ZJdctaWum/rFAVnhuQH4U3LfkCI0SeVYT+nckPLQHEgsfdXvfrIbpFRY5Sdi0FO6cvaUy6M8gfwS+aDVq7OPiLbO6ciuHCKEuynfbcp5jAoO9UQrv67rJuWaTT35/oJS0T5IZXWCrOv3CFnJXLP09ED3NLrUxlrKR/1KyqrKI0Kp9hW5PUEm22pzkTfl6y8QVyV9MAEGDwClob69Qw4K5dddI566l5SP+sLM/mub0NtHwabcTcfXpayfM/rtngju3n1UWOV5Gg3XF9x+8vtqYvv0bNTxeobrc8zS8PldXZ/XGFxGBH1tw7H9taJVkItuMa1lRrgIi4x8ubc6MVxw+3NDe5bdYGNP4doY9OJg2L5GJlJ9P7RD7UI7ptyN7avXe/T6u7suLJLJen6ek0hXwA7tN2RlV6TflVwngPqInHtEWhEdlcHn/Lm67HWvlde6z0wmmaw63vOaH+LcQ2f4+B/4Vd76LV/IQ29/Y6HYOjn3NqrXpRwbgIBFMvkhL/gUnv/Kn+S9v/Iz/PG//dZ5PrUC6YqgY5n7s2+r5+z6kjxOFM83KDOWx4Jbdl8uGTJGIt2V1Qkk9t3tAL1/r0Ws8rv5miOUkey6B9mYlYEo0iJjWy8U3TMyGHWuk6/a2J+QVactMhmaWhoTcoRGGf77lJ1ehFZETER1j5LEyUjeJZMLvRi1T+5xE9un/6rHHnl0Rse6oTu1/afISppIm4iJIIK5wWK6qrO+q+3ut7w2yC6zInw92Q0TSjKtMpymnHuneoog+/UVmdQ12rH8tsmEV9fYR6g0qEAq5xaDka65jTLopVZOGAjZQ5TKlZRElUeum2pXkTxBLo4q08Ty2GQglPuWnqtrM7JqrjT2qo8bXBtksidytEXuT3uUbiBOXh8mB6FRX94lT3wXqVcf1cNdbSqVW/eef2aU85wmZLVOBGvP2s7vQfUNvxdF9lzpVB9zdVSDGOpLkNdznFH2kXPkwQudu5P+P53ymaLrcGDbdd+4QqkBFEHXSwNON5CfaT5opQf2Dvm5dwNHi1UPTd5wfULPDB+8hHJqApTvHSn5Z8nPmjEXV2HM6NY6g9rv5465lDqZqt1VfX7emNEuI19KWr0UR11GR29kVWUv3FcTga7LOdYWY2TSlSY/Zmzumwhj35cDwYcsupnWCqSTU3fv9bw1v7TvB7Io1bluU5FsRV91YjiZwGEqZ90mGgw4R5mezncltF53c6xd+x7+5L+8htlDH+BF/+9reO8bfpL3/Og/oX/kkaKdfSkUBRwaUw1r8g9pWZS4xkd++T/m2V/097j9H38Zp9/8y0wmy5e48fLV19zV91q9dGhfvRTK+VyVxxRe71fehr60iZZ+EWlU5NZ9SlLpnm1jRPJafb2t+rv5miOUuo9uJJOQ0+ROtUVWZHYp15a7L527yUBmttO5Ii87ZENWCqHyEVFysiaC42XpGeY/TcjzKe+FuTugux2KEG2Q53fukknscTIpctK5k8r4gKWn+gsiCx3Zve8MmZhtpbLu2H4Zr65G7pPnp6rup1IeO+mcE5QuozKgpXo9FXhWOv8MeR1Bvadl8Et5eoA8R1QkRYRI6eym7Wesjttk90gnRGpv0u8brf2gVLRkhGhJFvWj+xiu0RbZZVlESAMOH8lAvu4nk3K9q3pysBqlu5XqKCI0TWXysorwiJw4IRXhUhucStt2KInTGfJ8W7WzD8IoTc/rJnK/EjmWkbadtt1F6Q6tNt9O5+6QiZ36ug/I6J66izyHtCO7AatPSvl9BsOcykNr43vJbs1qn52Ux8mUpqL7TlIaJ9Ox6ou69jsM188HLw7IxqgrjSKYHeV9s52ug/qNVOJDO0+qq+q7nvLWdfQBGH2X2qvByQOGdU174MkcMVbcrabh+oa6Zm1T+3bdO/Iq0PvsHMwXtJ+f15ffa7dJwY3cZf/9uHpphrla1y9XMGvj211TvXwihm6M+5p9ntbMzlck1DoKaI2xNnGFry6zE8quG44fC8rjCqATx1qRHF2nsSI0ctn0/bUy6ARsQVGb5DUl6/YYUw+9bH493WW2XnKlrvuDv/ur/ObL/xwf/Q3fxyf+9Lv5Xz/4rdzzSz8D9AWBq6PXHvbQJ0Lsbqpzd+LJhKd91st5zpd+I4+8/37+f5/7Ih55//1F2cai6NYYI5Z1O9T1GmtvJ/M1UfR2rknnsrz9HtB64h65Vbapq5PqqrLpe/t9zWLF383XHKGUEbpNJooyyNWR9NtVSRlvUgw37f8JMmHZIxvNNzIYhw+lj9zlpD6cJKt7XjaRpE0GVxuX509Y3iI6rvxBJigiWK4C7ZMJb2fnaZvIkcqwSxnV6jjlOo0ySkVOJpTBi9S+WHk12qMRISwPlf2klevA0haR1Qv/uB3no0siPSIpOkdkVvk6GZJKK0VHbav6qQ7KT33kwM6fMBj/+2RXRylKIkHqN27wQzb2dcyNKV098Jw8iwDfQF4/6eFUlpNk0n+GTMxVXz1PVD/1bRE39UMdo3KLzJ5gUOPkoqLrvE0m5VIsVY4byMROJNJVam3fIV/LPfLAxsTOUf/Xvaq+KTLv5LIjD5xItfc+3Vt6qvMJK4fymFlZ1H5SJEUI5RYnVVbzZSN5nVhda9VDzxpBbabrvEsOIuVqifqkP4M6S+8M5fWtBxSk+Ppz4yixyhP/GxqgfK7rN5TPEr1D9Lw+AB7pYZJunHpemcikRwt10uColRQhdKViNqb+9f1iwB7HWgfdZDEPN9YP+6z+yeg+rI7TsUpD31Xnet6goHmRY6rh+dRLoY7Q6ueEriSmUhlrt1ZvY//t9VMU0jHiuey7k+4xxXiuTC+5Pr72oiuKIqg15oMHkxS4ae80b7/tb7D95z+R53z19/Cc/+s7uecXf4q7Xvfj7P7xH8z72vrGML90yDQFm5nlOh/2cMOHfAg3f97f4KO+9Ot56O4/5nf/2Tdwz6/9StHuTlA1yOFLjDi5W4Yx5X4ZnMS7ujx2TD2vddlx6l+aPynvqJpQamC+JpC9/b7Wscrv5muSUO6RCy7lQ8ZkZ/9lqEmBc0O1JwfbEfmBgWBBfiEdT8efY9GldpvSdXJGVuz0HNDaOD15zta2bevIRLa3MioNKTw6XmRRRr2M0BnlfDmphVi+MrD9xSvj39WwbbLi6iRb7SqSs2fHYeV3Y1o3vciXSI+7H4oAybDvKCPk6vrqGJ3jhrYeJDuU7pXarrb2wQa105RMsnWtH07tsE7pWqtjHmYYZJgyEBEnHft2fYKdq/bzdp6Qo5OJJGHnq62Vtw9E+CCAiMmm/VbfF+lSe3dkNbOzYzTYsEZWwpUn5HmRUD6kdR2OkfuaPl5vWCRIJ8hLmqivbFkeOlfpaFTyTPqt+2WHPDBwwsq2R4b6ptru0NppRl6SaMfy1f3v96qIrNrM+wdkjwJB99mEoT/oWaKBKV0fKdpOsN2W8TbdTtvOcPRY9Yn/DQ2C3oluMM4o38vuaSBDdD29tGQoe1AZWHSfHCMXvsyFY5kSJAIzdm/VSlad71gezPJC7svUufq3DH2RSqFWZet5kZ7eGPnyOrirIowEAJqV6R32g2I8FjhnGaEWmTw4GD5OKr0cTvR8u5OoUJXby+bzKgUP5uRk0fOoVTmfLyrs/M/f4s1f8Unc8MzncdPnvIJP+ZE3cPbBB9h5+28xve/dTN/7bvr3/SH99BHonsBmOE4Xn8DWyZv5kFs+hqd83Key/ZHP471v/GV+82u+gAd+//aFgRGfVxtSOTzK7MzacllgJh3vKu6Y+3Oo2m/pch/94n0Hef6oH68ySY1WBFcXkGQ3uTJZE8jHy+ts1d/N1xyhlOEOpQEN2VCXQSljf41FVW+X3Mk2GJQPGcQ7ZDXyBnIH7ljspE4GRcz0Xca+VA03bnWciMOUbPCqHm6wd2RjvWeRSHh7qI4yYv0jMq5ztihVlontd9dQEQGRMFf4lI/qtW/fXWkSuRJ527F03FhW+XVt/eFwv33XM17Ee5dMAlz1dXX3wM7xcugaqOyQyZYrypDJ1p5t78j9RuV7gLL+InUqu9SsQFZCfYQNSkK+aeeK6IkUivAoQMsG2UVXZVKeIufKZ4tMkvZTWXbIy6xIMdZDXO3l911POWqocvkARk92+1b/3mIgt7rfNNI4qc7V8Y+Q3Z1dwdNghO6hbfILxgdD1KdUX5VFxx6Qg2DpXhBp3CQPaLi6uEGe+6rRU78vTtg1UPvskAm6rpXfR8rP66lnmoi42vdKYJUn/jc0OOr3gb+n/Zl6QPY8WAP6aX42O+SyKbfSeimQZa6oOtfJXe0Sej5jsJ5bVhM0z8P3z/qc5xhpcrdHJ021IjW2zuCFyK/KLNdRWFQ2u/Sy8PwPZ+Vi93UgHhj216TdCYbI5JxQqkzp/3yNS/3uynaZpzmizK1VddR8UL+eXv8afn3WJmX7u5vnYQ8f/N/v5AN/+M28819/Kx/6wk/mQ5/3Ap70Ec/jKX/hr3H8w59LmKxzeLDP7JF9pg8/zN6f3sv9v/97/N53fhunf/9/wGy6UIYiQiolwYvJWDi0usiNto4SrE+cZMJXB0lyd+plbeHKv7dtPSAztu7kfB7sbLiHZY8r0mv96e3zeMQqv5uvOUIpyJCUgeWGvPbvMQQ20aKmTr50vAhYpDQQZcR7J1W6Ol8GushpR1ZcZLBL9entGChdAIWt6ljd2zIunSTqBenuh0pL30V0t8nus/dbfTYYSKOP9qiMShcGYiSipWP9hu0oXZCVr4xxqTAyuI8xEHWVQy/1GSUZ9LYTobnH0lTb6/gDhrl/W6lcKsMpspvhnuV5nDxXTSRDpP3GlIYbKVJAIatcaqP6eh2nnKPpBMCvoQLkqP9J+RX82kBJ8NQGTnbXWHTpnpGDQKkPOenzgQLltUsmdro+W+SBDbWt0vLyKt3jZLdwyG6w6stO2rBz1D49+fqKPKvfnbLj1P+8z51kUJA1gCCSh5V5aun15AGBA3JwJpVdauHpdL4URZVTCypr0EQDKzPgKeTrpusrQrqd6rJGqdI7CdZHAz26/tj/o0SElXaraWio0VffdV/q/a3n6AHDoBSUA66a17zGQCQnZOVmzAVzbF6j769JYO0SOqZeOsbUsrH9c9fZartcWfXdVak67VpdqveNlWmMVPp5Tk5EOMeOcxW0Xl5Cx5yjJNpykT17AI8cpHl6JALqbeSkth+Idx1xdt5OFamMNmjgIw4epbdQbPuhHDWpUl8RodQ2KbGFS2jf8+BbfpMP3vGbc/dUpT+dwv4+7O0NBFp5SfmsBw/mv63vqK2974iszWZDW0qh9AETtX29NM7YwId/r5Xfufq5pB/6YIHqPJsN103KpNupVP/rz+MRq/5uvuYI5TFyZEUnN5DngMn4PMMQtAMG4/C5lH7Wp8kGqZSNnoFMiFyctnQnDIRF5+5QGo7uviZFYUIOQPNsshpzD5mgyKDfId8sx60MqqsUmK207V7KaLFSUSErPjKuRbIg33B7VrddyjX33ODfolQBRUJFzk4CzyHPvZNRDPllLtLh7nwi+cKNKa8ZpevqE9P/w1RfXas94D2UD5Ce8oEjgqJlGfTZI5ObGZkoiAyKQKgtnkFWr3bJxFXEUHVx98fj6Vj1waeTl6dRWfWQPJvSUbrqY/upXU5Yuuo3u2RFW/1Y7a/66fzjZGWxS/U4mdL1AQDvBydTmU+SydwTyJHVlL7Ip97LIoanyP1OZRdhk7p7kNpnh+xdoPqonUS8AsPgUJ/aSUrflOHeegh4f0rPlWysXDIqz5DJpoxP3T/K/7id61BddJ/rOXScoa++z9I6AB5kMHR0rdUHlMca8AFyv9whB0XSM+gUWal8kpXjWencI8WKu9U0NIzB7VU3LPXM97lXgeGdMLGPBpYDyVieloqWz3l0xUWkYIzMyeh2V8JlCuf5CNwY4dTxTmbrtR6Xud7Whn8R9CQ97GuFdKwc2l+T6Mkkk/ExhdTdVsdUqZpUqq0hq2PTaakursPc/RfKPOdp2bU67BaXDvE6TSaJpFZtGKu6igCJcNXtuazdnDw5+e/7MpiN6rq/n+dPQrl+5bKBjAVyXpHh2axULseU8boOYy6svr12v+77FAxqSd/R+WqPmV1fTQc6V338Xq84/+MbK/5uvuYIpdahFKHYTdtFIl39cQVMxpsIoBQKdUYZuiITMmKhVAEDJbk7Thktcpe8HIYM510yKdOSEDLGVT4R3T3yC9Fd9noygYFSrfT26O18yNFd3d3Tb0CVW+XsyIRCbqFuiENWg0QITpBJYO1mKEN939I4sGuyacfcSBmsRWlE8hIdIi7KR4RQCp7IFGRyovN0nZSvu++qPicoSYK7FKrMugYine5GOyMre674ucoN+fqJoKm9IauMUh63yOtTquy75MGLk+RRdrmr7pP7rPq9lFJvM/VDpbFj7VcPBHi51faCD4BAvs/Uz3RPiqTp+4RSLVWddJ5UZB+g6aw9VZY+1V1trGvgaqRIHCmNKXlwRu2vtlE+WwztLmVji9z/ZpaHuxCT2lMk/gGrP+R+onJq0OiJ5P60Rx5oUtlUdrn87HLlHuCr/NJqaLgYjJFKfXw5CD03OoZnqK/rOulLpW0+l2yWDXp3/Zv0eR6YlCiRg9o90MmOG+lukI+5aLpiNJuVxrpUyLGonmMurAog5ErUWpfmZlak0wPYjClTTiLqpVlUFqWzzM3V22JBwaM8Zh44qYdJV9YFBvVuLZWpJiveznFjnMyPkcSxSLo6V8dP0svDFVifh+mqoL7HfshD50z6gfDqfLn2njXFvHbLrUmx8nLS6tGNa8Loc0PHFOX6vPqaHPb2frXyHE7K/uBRhiHXfz5PcjZcd8159vvWI+tfd2QyYZXfzdccoZQx91QGt0mN4st4d6IDWXXUeTLw5S66TSZEtVHrxqcTJRHBUwwh+2UAi9DJ2JXR54an1vRzsqv8dI7qJMLraghpn/afIC8+P6nS2mEwZs9Y/iIZJ+wYJ3tb1s4iI+4CKYgMiISeISt+ajtvP52vOs7IwYlkyD+J4WUuwukkYY+sOonkQF6yRQRL5d23NFQ31VXtI/KnNtykVEidqKpNRKTUnkpfJEHKm7vhOnlQm2oA4Cwl2cfax8n6I1W+Tm51LVVe9W/1SamYylv11yCC1EilqYe0lFCVTfeR9m1TRvRVn/Qy+CBIX9VV132fPJCjY0SgNm2fexeIXIokOmlUXjrHBwywtvD0BZ0/YXi+qK00X0PXVvemrqPa3gealJY/F3T99FzQ9dhkIK7HyQM2eu7oWun6kM59IO37MBoaGgTdO/57jFRqYGrC8E7WgJ2f36c/MnbHlKCaEEB2M62jncK4ke7LWogEja1BWaehstQuq3PX3EkmQlhZavdEn9+nbSJKIlMircvcHf2/gtnUxu/Y/DgpmR4YyfePEWEo3VFVfgXhgZJcnp3mstdz/7zMDs0LVVtpEGKsPDUhrfeJCKpdRaA8EmqXHvCdXSOdOw9YNFvMq14WRHl6/l7mw1lJRnWtgLmLbD0YsdDHrD97n5p15bljSu5Y+Qo31z4rkX6/6pS19Alcf2Ry1XHNEUopMFITT1DO6ZNx15NJggz7+8mqlAzMk+SlKERM9aKZWboyTE+QSYWI3JqlJ8Nyh+wWKPIlouOG56al566TkI1WlWWbkhDLwJRhLuPXzzlNvhHdsO/IyzycIRMnGfuqxz4l4ZQqtU4ZUOgM2UCWIubkWnV15U6unCqvlKBavdXSKzAMJKhdO4ZrJ1Kk/JXedOS/roPaTO3gRMAVM7W12hvLQ/sgkwx/z6gdZLRI0fR2O23pKy2lo2e20twm9z+VVWmds3YR8XHyIdKptGvyCGVf0nXToIvImeqg/r5FHoTQ4IWIZU9WV2Fw/bzP2kdto7Iqj12yW6gfo3Nc6av/q91ULyfwT2EY0FH5XDV0FdAVUt2vrnh62x1neCbpWuiYHTv2JKVLsF8bJ7Qd5XVSO2vgBzKJl4oyYmc9akRY6Yn/DQ0Xi7761O/1eZTU6rxQbRdx7NIDww3seeTWvnQbrNVFN67rtSRjT7GmYj3fcYyseD51cJM54espInmenWbyWxPK8ykfh5bGmJrqhLBok5EyiUD4nElPd5lLZd2O+u4fBeKBkiT7kiqQBwoU8VZpeV6HszIokEef9XLUZRhThn0+p9bodDfTeXteoN5rk3yMlqYRIdSSIJ7e+dpRWOuGoDt+TVWPei6l0pFaLRfaZQF5ahdWP94xD/TDcE/qoyIH8vxYqZRrXH9Y9XfzY04oQwi3Am8GXhZj/Nm07eXANwI/HmP8vvOdL4PvrWRCuUle2HyPPGdRBiYMHfV+sjF4gmwcyyCXK+wWmTiJ4OySidEW2cXvj1PeN5ED4RwwEAWRnlsYiM/DDHO8HqB0GZyQjXKdL8NThjXk+W1nyMugqKwi2FITIRPmbfISJ7vWBk4wRWYmDPPmZMzKaFZbiICLCB6mMp9O6UstfE76fyaV49kp7bMMc77kbqjy75EVF+UtZedMyu/pZON9i0XXWbWblEbVUe0lYuVEY5eB5Ih0BCvvNnkO24eeOsUHT5+ek10NHoicbJDnuUEm2HKdvjGVS/3ClfPtdM6upSPidB956Zhdcl85aXmpDGfsmFN2jNIS6fEBgE2Gvou19ykrK+Q+4ERdfVzXSgqw0JP7ghOobcZV8BmZ1Ok6dul4DYo4od8hewvspzKrLzyBvAyLyq4Bl0fI9/HTycq3ronIqcr7Psp5rA+kfdvkey2QSXcH3MUwj1PXUoMUHXl+rNpBpHmHvM5ll+p8kvxs8YEoKfRKw933jgQrPk+joeFC0LPVf/sg55giqUjbXdrX1eklhejsNBvzY/DAJ4IIaL1OodTDWr1x18plyledfkHqZpm4KU2ff1cHchnLR7/dXVTrRXqd9F3kVW0wVn5XHH1eqbvA1suGLFPe5vlaGcaWcfE85udV210RrAcEzrG4vVYFtU1BcsbcRX0OaE2ex1xulf+xDWuLWakiq86uVHd1XUeI7xjq7fV6qmqrYlAg1UXzUdcnzF1aReYfOWAeiXfWl+/y+TUgB7bze7ezT5/269xDO/e6wYq/mx9TQhlCWAO+C3hDtevlwK3AT4UQTsQY9xZOTnBDVQaWDMET5IAVenGcIRvKN6Z96pCuJsoglpHt7ndSF2T8dpa+DGGlp2ut/z6vLpJd2nYp1S0pcjKQpVC4m6TSURmhNORV1j2y4uPlUR1E/ERURDJk8H5oOkfRcbcpFRUZtTMG1ek05fwxd/eUUrWT0pcRrraSu6PaWERL6cnAF+G7lzLCrsjdSTJ5UTtJMTpu26VKibjvWz6bDMrPIZl4i2D941e9ir//spcV6rLaVmQBy1uKllRjGS4inyIfG2QFj3Tck8hBhE5XaXn7C8fJUXi3yG63J2zfe8hqodLZt7R1DZSO+pmrsyJoMrqclLqy7Cqo+lpPHpTp7dqozc+R+6sGXNQ/VU5XRNV/pcp620wYVG21uQgb1v7Kv6OM/Hza0vN+qWutdhFRV//VAIjXVwMbuv7KS/emBmpguMf2yN4Cunf0jOkYrp2TR9X/GEeLVY8k19BwKejto/dOJBuvwjLFw88FCDNYmw338dhalGMGuwzs2A8qzXoiH1LNalXJ5yuOpTlGSDwC6XyumaVfr9U4ppCN1r/PZPLctCQ/Ii91+T0gT+0eDIsKptdPddc8zrHlTmrlzQm1K79OVJX/sjatSX2tKB/OSuKoNHybCGXtPht7FuaO1mt/Qhn4SemqDBsbjC7vUbRD1Z5zkpl+12WrBy/qutcEVG2owYmDg/I6n5tkJXWty8rkWSOTUh8ltNU2s796nKDMqn1j5z7eserv5sdaofxa4OcYyKNDUbujfR+FjEMZuz3ZKJax651sSlYDRQYd7l6qNEQyZva9IyuFfi9PKAOWuEEto15piBS5UXuCcXdYyIRS+WibjGfsHFeQ3OjVMSr3cctvSkkuZODrRpdhq3rXZH6frKz46O8+Ayk4sOP6Kk211y75mp20c1QHQXV5wNLryMqfyOqeteFm9VEb7FfH6bqLqIlYzBjUoRfecguf9UVfxHNuu423vetdhbuj1G4oo9zukaO6Kj/1R+Ul5a72YKgVJ/Vr9Rfv90qnt2O87hppV1/6oLW9+usBgxJWt58GP7bS+buWvvrPLuXgjq6zSOoWWV3dt3REEHUv6FzdnyqLSP3U9m2nNKXeqh+fIA8Qibjp/twjRzt2kjghR7G9lzzQIJLoRFt1mtp1EUSYd8h9Wv1az4KOgfydIhNQPbtOkQNoQeni63VUmY6nY88AT+OIseKjoA0NF4uLNThl6HbV9/oe92fzOtDNynczY9+7bHRLCTucZGI5T79f/D+myNVEaSx4zdj9O0akXMWsVU4dP1/WYzae/qGloX2aEwqZwHgZl7mt+vHuRiliFI081m01Rrq7rgyAE/vcF7qRPLz+RVRZkj3Sw8Y075O7qRNBX6LF0/Fr5nMnl6FWtc8l5e8gGVA+71CoybcUzMOKGB5aPWNVBlemvU+p3I/sDxFnFXXW39te5rnCO8tLu0BePk7vRieXglxcdQ/2ZGLQUw4IXVevqhV/Nz9mhDKE8GHA5wOfwSKhfC1wO/CTMcaHzpfOIcsVwZ30fd+O7+1YbVcnPUlJIGWYixBAnrfYk4nDvqWhjxpShM4VCW2T8bxv+1xplGqh54MMbpE4kWKRURE4T8vJGpTuhUpXxm6tQMkIP01pcIswiAgpLScGfi1EBqTEyaA+sI/I9oySLOxTqrcHts8JH5SuuPqIEGmAYIOBdMq4d8Lvebsi9qRnPpPfv+suhHf/4i8C8C3f8z18/Od+7nz7Fz7rWdx3993FTeSDGPqtPiUioLZX2Xv7LgKq+vZkwqzfIkC6flT7XX3TvqdSBqhxoq+PHtxKc5u89IcGckRMNdCgPuf5qu7HyQRYhPOktQeU67aK3Ps9rXtL/VP325PIZNbVXu+bJ8nEUPf3JsOL6SyZtCmvTft9grysyg5ZsRa5FmrCrL6rdnEFU9AAzl5Ke9v2qQ1UFpVdbebGkNkRR45VnqfR0HCUEHmUgSrXVz1//Dmq4510QjnHCzsvAOs98yVIRJbkSnhuhExNKEmj3FNdMfN5ae5GuszY9DzkqqnjnZBItRyLvKp06rIwo5g7OkaWVOaxOXee9jyCbp/VRuW/1pVq21LSLBW0aq95tF3KZ3hNwOrgQLIT5kF5gN5evvP1LHWd+qykiazVcwkvdL0EbycRS1+SZYxIj7liOwmX0jpvr6odxq4bZEVSZHJ/Wg6uhtQ4a4lEqlhuX3hRx+ZKYscdVsdDvkdl//TVB8q0Ho9Y5XfzY6lQfh/wTTHGwxBKETLG+Grg1ctODCH8BPBX9VvGloiBDDAZyU6mtH3K4pywpzEYliIpUgu9UU6QSSdkUtOTyd0e5XwoldFVFxGqXUqXOakhIkfHyQ+w41a2HbIaJwVKJFdEQMd6vifI0VRnZGLqyqwIgbapnU6QjWG1JQwqi6fvRELXQ9dEN7fqp3ac2DaVX9dOBvnE2tqhh4fqo/OdkM/so/qpf8h1csPOd7XyfXffzQ/edhtfddttADw3kUgnkz9422380d13z4mr2tIVPl1r1UkDGFCqfDpHpEbKpVRCESVBAw26zrMqXdVHZFxEzpVfV6TV5/Rb522Tr+9Z8jUSKd8iK+NSCNVnIfcdH0zZIl8/9QXdQ3vkgRHVzwcfdH/tMxDkbTLJ0nYRUN1rPtig/ZBfgLoOfUpP988WZeRb3V9O3LVP+2tlXNdD7TtjeCGeIbu37qXzz1q67nHgg1nqL2rPjuGZcOeddxJCeJiM18YYX8FlIkZW2q2moeFSMGas1m5QTjJqV9guvWBEGnVrLJu7LGVljRw8a51kqKeEz81YWOqi6wa3xppM1nPmIBNBuRMulKGyxEU8fPkOyGk7eRyLrKolOty9tCaa9bIhfr5IZr1kRw2v42GfCajvv5B7bv1bH1cn++pYJ9veBnpun8NIos6Zwdq0vCZ9v1gvv54+AOD5jrlJj7nw1mtX+nzd882RdGI4VufzHS9lWH3t4GAgk7IlXE309tE95krjvKx9PvbcyDk6zy+njhEJrUkl1fGPR6z6u/mKEsoQwlcDX5F+Pgn4j4lMngJeEkKYxRhfd6F0knH0CoDjIcRnkd3DRKJk4J4kqyl+/2gblFFRDykJhlzgegaD9EQ6R6TQyaSMYxnj2LEiertkoqNyirjphpTxrptOxrUMSOUlQ9+VGN1AtbvcFllR0bHKRypp7aZ4Jn30cpX74EnyfLANchATEU2RVileOmaHMliNu/btkQOPbKf877W22bB8T5MDwCgPdzN6hrXPPZTzXjdtn9r1JBmbZHdPH0l77StfyQngFYlUOl51223861e+ck6u1JdURpE0lVHkWfVUO28wuMXukFXhTTtffUbXczOV/w/ICqzS1MN1N7XxRkp3J23zwQjV2wnhGSunK/lnGNyMdY7uN7k1i0iqfFKFjzOsq/gnDG2v/HcpidH9lMqb7kHdJ8rT1cPTlt8W2c14hxyMSO2ue0wuok6kH075q19ukwN6iRirvbYpg/yIrKrP6l7YIhNxPUPcXbUH3m3bttP5H0x5baf8TjG8XD+Q8nkyw9zmSHaV3U5tvHbzzTz44IM+FfdRY5XdahoaLhYX6sZOJHvO7/a6znD/uT3ngUR8m4zo3v778QDTHkKf52NqXl6twrkbpbZLbVNgFCiJokOKm9S+C81HrNeGHHMl9fUT6zJ6nmMKndKsly8RnDRqzqnv8//nc+11MrlATK1tahI4T5vSPsK+y6ZimtMQYTwfQXMy6C6yWqJEhNHbQdfX233ZdQtV+6hMIrKHfRnER2UdW2fUyznPe5ryp4yVoP4eyP27nr/mbsvL4PdXZ9/dRdbdXcdI5eMdq/xuvqKEMsb4KuBV9fYQwo8Bv3AxZLKGHvDuvgcluVJn36FUqIQ+bXt/+i0jVg8ZV7h0nvaLmLnB7+rlLtkIlZLkLm5KV3kep4xQKiP0DFkBkeEvYgmZLLg6dcLSnNg+DWC6aiJlTMazyrDH4nIrykdKUa2EOk6cOsXO6dOFUun1cALsJE7t5WS7TtuVaKUnwu/qm/qIp33cfu9a+b0s2DaAn33lK/kzt9zCJ3zRF823/fxrXsP3vvKVc4VNJNCvzxZZtXK3yi2G0TiRFZXXiaHKoProGrlKJgVQ11jKmSuOW2TXTymz3n9FXHUv6Xys/Z7A8KJQOl5mKF27O3Lf9rREtlzF3aFs821Ksqu6+T2mcmkw5F5LD7KLtfLyuvr10DY9AyArq4F8j4jI7qbfPeV1UN10D3hf3E556FrreaJ66Rr5c0f3odJQ++xYHdfJbat7UHU4SkRW+6XV0HAxcOO//sRqf+32qntO94KTwjXKOV9OQp2gCoFS3dJxvvxBB8ymJRGTayqU6pMb+OcjfjCkJ9JXB8Jxl1D910fLi8BidFonTzU5Op86Vs+rrJej8OPH0hjbXiuW9VxO1V+YVMd7oJxJVcdzlDaE3nM+l+8cDNF/9xYjvNZuwjVJ889YxNuxuZj1XNmxwYFzXZmWCGWd95rV1/uUK95rI/1Nc0r9vtF7yQdj/B7pGFkrlVL1dyKqe0M2jtq7/lxvWPV38xUllFcCisAphUAPcL0EZDzLxU2jGD7SSNp3H2XETRmsSkuqpow2kZHOfntndyK6Q1ZrZLRLdZRqJ8Oyo4xYKZKq8oiUbDO4m55N6dTqqQiM1BEZpWonkZ+Z5XHcPhM7RuVSB5EB3pED8agdOyvLP33Vq/jyl71srtKIaLgyC5lkSzFSWj4A0NuxTmZl5Mso1/UWeRcZ3qjOlUp0xsqE/YZMgrU8yYc/73mcm075tde9jr/w0pfyUc973gJhVf/YYlA7T6bfO5T9S1AeM7IyqHKKRIgQ9bZPbpwighoIENnXdw1sOMkWKYNMhHbJ/fapad8BmXyvUbpj308m8z4IorZWedTvsfbRPehESP11m3Jwxkc+1Tc0uKE+en/K66Sdq7z27Rz1B+UlRbUnq5FjLydX0Un5nrD21f0ml9XjwA12LAwEcI3hOaM+e4I88FMTcO97+5TPuA8yvExuIK8Bu88VWDIkYYXfWQ0NF40xI9Q/Pk9Lfd7f6T4o6s/welF1T0eKDbZtrTrOSa270M4SOdncHI8YKvdYyAveOyHsLJ86qihkF1qtWajz5vPr+nKZEGAeRMiJkdYUrElnTaakDh7OFonZrC8D8Dg86M3YfEv/7v890q2usfJT5FEn2ZDVS6/bjHJun7ttOhSNfcYwIDCZLpZ1mZrqLshq7/WNSyParkQ6ea5dmP166lpJEfdjpWQKtRvz2WmenlGJvtmtNf12pXFBhaYcnIVSoOhYnEPp8y113PX4nlrlOl8VQhlj/NuXe66McFdKoFQXe0oVSC+FWs3aIbvNSfly49wNYBmlytMNdj9eBMoNZhntTi5E4Fy9UB18pEfqUE289slrX+rj7nwywKVgKf+tap8bspuWvtpadVOZ9qr0pHRuAx99yy38lS/6Ip5/22188F3vmqtuMsxlPIt4iKDskEmP2vCEHb9FJoVuyKvtNJAAmXyJ4GwxuAVqaQUn8DLYpQKq/TcYXu5PvuUWpufO8aUf+7G88447eNbzn893/NiPcfOf/bP873e9a368Xw+RXL+uKrPafde+n7FyHrd2UN1VjxPkwYxT5L61Re4rOmefvPyEFDkvy4RSMd9gUCP1cpQ6p/tMypiraiq/+ifkPqJ9kNVIlVGE0YmU+lcN1V9EWQTRB0dOpraRYqmyuSGI1fMYmTDXpNBHQ/V8cCNRfUd90cn/jDzC+kHysiVKa9fS8IEgV4+VPuT+uEX5HFC7ycDZ48os8LzKL62GhgvBn+f6PbOPiNxk5NiZ/Z9UnzoPQSrLGnl9Sz1/1kaO6yiVrkPyPL0e6A8GA98Xq4ccxAcyWZiXe1Ya7vXcvZ5BoRO5qBW5moQ4fK7lnKD05cDfGMHTcRr4mrdFxQi0RAgsksyakIk4wqKy15PzckKzkfIWUVPQmD69TBSoxudNOrnRfFh/PvfVcfMm66vlLaqHqbfBbJoJV+hgfVqqnF5vtcVYJFmfn+lE39tkPk+xh/V03LEZnO1KUlsPPgiK1nquqrcP0Pj78nxT/boOJlavOuqrBmO96TyIDyx0oevmnbXK9bzmFEopKyKIMgL1ABehnJDnxk3IrogyAqW2iTyJ4Mm4E0RO1NlPkImA8hEhlcE9YzB0ZTzvp2NuJKts95CN4107X8bujtVHBroIgFSYGZks+YjRgR1zv7XF8ZSv3ANvJM9TxOoCmUh3ZAVJeWynY57zzGfyqxYN9V0pGup3fM/38AlVNNTdu++ekxERrmcwkCMYCOe7yQRf7aQ23k/HiFSeTOe9h0wuNsnXVufJeN8jK1JbZMOc6rtejncB/cMP8xduvZXZbMazgPvvuIMvufVWnnbTTfM2EYHZJStR25TEZzf9v59Sgdu0Y9WHzqQy3JS2vZs8T2+Sjj9J6Yb6DIZRw0fIRFD1OEV2+TzB8DB+yNpJBOcRFl8Kd42UUwRV7SpyrPKIHEsF3KJ0ab2PrG7rwSjyKyKI7RfxU3urXz6D7Pq5Q1YePV3d30r7fjvuBJmc69khwqu26YDnVvVUeUSYn2FpnUvteNqOVZozSoXzeMp7msp1f0rvQ8iLN2+TlV4NWui5s02+F456jn49J6yh4VpEf56PbAb180Dp8u/HOXx9Mxm/ggfjEanU8ZVAQ6R0nZXL3zk7f73PRK92+axdKoWaCEodPKv0+5KsCmNkcEwhE5nzNQW179DK48d5QBtvi4VnjOXpZMnrPVdQq5N1zTwvJ7ETFl1+RbZUjzkJI78H9R6dUJJ7V0LV5HWgp3qdRZWz7lOQ5tIasRWpHCOTczdZU6+LqL99rrsrqP5ePCT3jbnKWV3T9ekwACHM+uF95aQOygA5St/r3Kdzu6pPiVTO/Lj0vfa80YCLrq/XB0b60uMUq/5uvuYIJWRVQ0a4G8Yy4PcZCJMUDhE8GZjadoasZJ4gr4UoJW6DUvF7Ktmw0zlblFEZD+y7yKQeJF4GH/kUeRKkTEkxkkoq90MZpZsMN5ncCEXYpNzJkFU5H7DjRHpEHKUUqR17S+s0WUXZVpvcfTc/fNtt/N3bbgPgzyYS6WTyh267jXvuvntef9VR9dLLU2Ra12BiZfVzRVhULhnYIgpqK29fyIocLHZ6V8lElraAM3ffXcwznAEHsxnvu+cesLromPvJap22qWwi0Qe2X+XtKefX6sEq4qn+qvaZUarE+5T9arvK39VnKF+Suo9cOVSep8hkEbK6LgIuV9Ma/iIWkVQbQA6ypLRPU/Y3paHBH7VXrTiqfiqz2nbDtu/Zd2/fKUNApE2G0dAD4H2UUWE3yM8ANwR0v0Im6ROyQbmd6iSiPCFfOw1CqI4+ELZLeR9C7nfq8yK8ewxBek4Bf8rRY5VfWg0Nl4LzEUsY7rVjlFMT3ECu4WSyjuDfkdUVVyb9fnLlTK6zrnAWZU8nyvgv8urKICw16Zy7dpKJRcdALrtZPk/5HNq5InS+ruGyuVtz10SxdDLhctUVStXJCY6rdLWLq6ujTgCVXk3Wla6T9Vna0U/L4/z94SRG6TqZLEjebJiD2vV5mkIRzdTS9jKpvnX9dX0OGQYSmC0GsKnX5/T5kMXAQ9UWav+apE1SvqHPfVpKpvbRjw8AaDDE+9ZZlTP9V/v74I2iu3qaytfvOR/sUTueJdsMU8r3+fWEVa7vNUcoXR1xgiXDVuRQSp4I4SGDkbdLNtDk/uZKmAxguXLK4Jex+iSyUiOyIGVS0H4Z+SJIuhF2yaqRbgyRYK+jHmZSLVUGEYxj5PlWO2SXV7WJ6uQGsZQutY/qIXKj562I5QlKI5u07Qkp/x9J0U6/LpFKxw/cdhv/7pWvLOY1qg1EDh5OZVonE2SRIT10dD3VLifTd5G33tI9xeIyGFq7dD8du1Wl74MTIurb1jZqe5ELlUnKmciPBid2yNdBgw3uAiUleJtMJnVNnfCovb1dNCihsuheUH+bkNfd1LGqhx7AStsf4DMrt8jfNvl+klK/Re47uiaqm/q0rokbbSGd/0Syi6ny7Sn7uhNh5a02Ie3fYfGe0/XcsPM1YKKBlW3Lw++Vh8muUSqbrqnuDV1n/Vf9OtuvdlIdDyxP79sH5KjMKrPSPEY5L3jGoFj7PbrDotF6VFj1UdCGhkuFk4dardGzyZ8hPgDlx3l6HmBHqo+e8R7ltQ7Ig/1fq87TtmX3dV/96FLhfA6kIyTS4+61HUnhm2WS4updl16OHgXUo31Ccp21vObt2dv8TRaVXNXRCaHyrAP1LFNDYTHgS5ca25d18YijPlDr19GvoS9d4WR/LOCQyusEqau2q628v9Ukui6LjvH5sH11styDa0KppV0mdq6n7W1Tt6dUR19rE8p+qfIqHe9TsgVsTKFIpx6ccdI5ZVzxV18V4Txg8ACq341uZzze31ur/m6+5gjlGqU7KZQBO2Qs92T3Q70YZBzLKHyIrIDpplBkr87SkEG6QV6PzxUEEQLScR+W0pZ7ntKbkG+GU+n4uh5QjpTKyDywsmwyKKVa026H7JKnc/VyrAmlHhA+960eLRLhkhEMmdS6kiKl5rte+UpuueUW/pJFQ33Da17DD6ZoqJAJg4idiLgTihNkTFhc4kQGu4iVyuXtonT0MlF/ULuLwOn7pn2OW3urfjpO5ZR6qcEGlVn7ppaGXIq30u/j5P6mc3esPAdkAqpybpGJkx6gUsdE/JxsS+l1widi5HVRGd19c+wB7SSpNpTUR4RazYVSJT5kIG71/m07V2RP94T3/Q07R/ekynHK8vY6ej6bKa9tO1ftoIGfAztX+UIm0bp/dD/tUb68nVDXgwpu0Ir865rdSH5WaJBoZmmrHJCve533UWKVX1oNDZeCfuTjqpkrik7kls2xrNOu53XV906t/kA2zLH/UrnciIeK6PWL5dFSEDWZFLpucKesCW1vhfVyTxI52agIlMqx1lnb9JVBn8hMrSZKxXT7qq6jSLlvk0JXuxYXiqBIHAORc2LuGFMgVXcnoe66PD/XFN2FKKv2fT63MV2PfrZYfuXredVkyttgjCx16WWi/OogRnOiPitdcif2fWzQonbTnQ92pPalz1FY6/voHEasKa+/9/E6P3+P16q+zhehPJ9Keb28s1a5ntccoYSswuyRDc1NSmInQ3knHX8SeDaDG+UD5A4pI1MG4tl0zhlKo05G5zssv62Ux10pzy6l9WEj5XXFaD+do3RFAvbJc+REgJz4OFnpKUdqDqpzlJ/IgJaAOE02nEVCXdFUfk9lmM/lhFtp3U8OpCO3y49M0VD/x+texye99KU843nP4y5K5cpVsb30EWn1EUSRQ8hzT30UzAltTRRkqLuLqfqJFOADymikSvOAHORl18qpa/RUBuJygqF/3Jc+yl/13LT/PaV7p0jMGUpSs0lWsnV9u5SnK+JS2kQgJwz9ubPj7k/11PU6SNt0DdU3lJeraKet/XWu2gvL90S6Lk9jCEJzXzpX7a5j963NIA9+zKzOTyf3333KYDm75KBOE/vcQhnhd588p1DXRnk8lUzeda/tpnbbTuWSl4DaXURdZdJLUtdj29pR+Zs31XzgYR94JtmtVvXZIs9XVp2xckmNVF/SvaOBjwk5MFg9d+cosMovrYaGi4Ub4646OoEQ3EWyNuLPRyp1ns9/lJrjZFLpOpny727sz8lOReJqIia4W6ggghM65gFQagVI/+W+O5/z2VNEefU09X/uJjvL6ZRfBtRKrvJZp4x6Ow+QU+dJGRDH1bli7cuUt+owgQX3y8Pqv1CTVD9G8/9gMRqtt0kYaacxdRryIMbY9fRrr3ZzUiioXHVAI1/iZTiwTFdwBVdl1KnLSLn6tQeX8mu7bEDFB4hhnDxPqmN8MGJGnmMsu3NsAPx6wCrX9ZojlFo2xEcON22/P5xFKNzQc/cDKXpO+Nw4FJHQPqUng9OJmAz0feC9lo6M0tPkjr9FVtycaHSUCpNuGm2Tgb7PQBDkBidy6Q+MmZ1bKygaAVWZpaLIqFWbyqX2fZRLsMhwF1n7qBQN9e9/7MfyR3fcwcnnP59v+7Ef4zl/9s9yz7veVURercmwULthqowiTd5RdZ2cSJxicEf+IOMPJr8JZZQrv+OUSh9kEujqn8ovcrpv6TmB17EiD96fvGx6mWxYGjpWRFuEdp1M/tQHRYiVnq752ACGFEAnXcfJ5FDt6i5e6js+MCMyq3r3lO2rvg1ZRfNrum2/VXelr2NPkYm+7kF/iSjf+mWs+00DKt63TzBc0x2ye7ArvRPyPGENgmyS50GqrLrP1GekNGtgwAmp7uFHyIamlhZxsq7yqt1VZ10vDTb5y1Nzvq4EVt2tpqHhYlAbuU4onURBGejDj3WiVc+RK4hf+i9SCfmZ7csdCHN3ShbVogWC0ZU767UV5a5av+c0v09pdH2RzCikPs3SA63vKaLBenmUZh3spjiWTIJqla6nnFNZtHWf22dtMqilY0GCfNthn8mn5yMCetby9f1YWcbaQlFH9RLzOZ4ezCf2A6GTWnjYl+9H9Y2xtu+WfPe+6P3F98/bgkXCu9bluZ5j7wv1T9lb/h5aNj+1jlS8rC8pbdV9WZAiT2PC4tzkep5yP/K5XrDq7+ZrjlBCqea4kSklQiMXO2Sj+gyZkPWU5KYmEjIgpULKFXKXMkiMK4jYtvsp3Qp3yCrESfL8PO3fTed3ZDe2Ok2VR4arG/+QjXVBBrMMX09LF30rpav/IlAiccrL20TqsIzcCbD58MN8wa23cnI24zjwwB138Pm33srJFA1VZMbbXORq28ojg11EzI2BDfsvNWk3bRNB3AQeJA8UQCZh9UCC/vdkkjWx7XJDFbkQCe4tTVdIXUHWcfoukqi2cDVZEIFyld3b6Th5Lqiuvfp9Z7+lxqsOO2mfVF4n0z5QcWBpQ75e3i9EnkRWZQyobVVOv566Z0TelKeUTLWNiLfKqHaCRUV9aumJ1G/Z+TpHL2ApzboWKr8+J6tyq55QPi90nyhPtZPfi3oxq2661iKtGplW2kpL10/PIl0ff1krTe+rxxkI61FjlV9aDQ0XizEy6e+C+hj9roPJdCwqOApMUs8lO6AkR4raPFY2ba8Hxy6oCrKcxEGlrlkeNbrqeCWpBdT76aBU1vMbPWiOzyWs03Y1awy+iL3cH1WeNfJyX2P5jmHumklZ5r6HbgbTfpGw+HWA8XZS2msdC8F5alIJQ7vXAXL03A/2W4R3XhD7PesX1fKabHo/W4f53M054a3Ivuc3d9tl/HnvfXJhDU3yvaBgPGNuxB64Z2HAw76LVF7qe6e+j68HrHI9rzlCGRiMqCeT3cjOMBjrpxhcOx8mqx3uMiZFAcqlDXz0xwmGlKITlIb2tu0/oLwR3CUV29ZZuu7SJ1JzI9lQFhGc2XFTsvojg1znqgwyQP0BJMIhVwE3wDdTm51KbfanltYZssvhLqVqo3bQtkfuvnte1pCOOT6bcXDPPTw1HbvLoouCjHgnI1DOdxThkKEuY11tJ7IngidCKgW5ozQgROCdfLjSuFmV0V1XRXI1oOEkordjdP00ICGirPYTYdglP1Sd5Lpr7IQyGFBNzGUgeT86RUnQRDJdQZNq7KRXZEufDTtGAw+bli9kY0DbXdVVe2tww19CUM7H9ftG18cV9M7S1YCCD/y4t4Bfe+WvPqVtTsh1vBRhbdc+9S3loXLpmqgvqhzqTxsMz4pahXTvCpFbv4+3yNfTyf6upSPSu8z4aWi4WIQQvgT4pvRzD/iqGOMdad/LgW8EfjzG+H1Xp4SPDj6IWZNJwd33PECJz3mU8uUEzQnTmBLpA0j6H8lEqrPtHrUV26b/btQrUA0suinW0HPE34F1PdYolds1/94PLrOxW1Qrzwel666k3jbuwuvLq7j9NIMiwA2wEM1WkFuwsD7JJLS3BL3tXGlbhrkLc0WolebCciT2v45Gq36kQcE1a08nwrEvBy1qiKzNqu2dDTiobMtQzP+EBQV4Xp9+se+MdYN6TqhHrh0jk35f+JxMJ5/1/YTt66vfDVcf1xyhlD+23BBkWG2TjT+RIFdh3I1QbpKbdpyTT7kB+twpGZDbDAa73OGkPri609t5TgTU2CIdO3b+0xheYGfI8+s27Xg3sLfJ8wUhP5xc8ZJhqu0i2KrTpqXlqq5IsoxbGbYyYmv3OyeYU4ZgRFgeIu0iAKqvExoRA5EotaPK6OXu7bgNBgIlwioyKUKp67Vt+Xh7iYipP/i10rNVx9T7t62cOt6v05Rhzq4UVeUBpSolgjm1jx6OqsOutXVHJooa7HiAUjlXP1E7Q9mXROLUN3ZtW00adR01ICIldsagjM1SGU+RrwOULsuu2qr8TppdEVRfUBtCSbQ27Zgx12BXS71N96s0Dijd0PXicuV4zPh0ouxGqkg4qQ3kqgrD9VFbblCuR6ltqrPuMV1ztZMT/k0750q8SFfdrabhyHEn8Gkxxg+EED4H+GHg49K+lwO3Aj8VQjgRY9xblsgqQs8cfa9REwkdW0fF9Dl8Os8Jk5MyD04iQqlnQ8+i+58TloXy94u/tRyHlKkOmCQyOietfUkE6gHAur4iOx1lkCG1BQyk0tVKYL48iJPUQl3tly+fUkPujnoWzgcue5hNc9q18tcxELO1rnRHXRaZVddCUWrrJT9UzmV1gkxqFYW22FfVS/2AlM86w/VaWJKlywGW9L1LHyensLi+pMouAj42V3b+vc9tMTYn1K+tr81ZR+ZVkB/vQ56PE0RY7GvrduyYklmTy2Wf6wmr/m6+5ghlz7DQu9SxGxkWF78fuIfsLikVT4bmJgOB0023Tw60ckAZKXWXbNB25AiZU4YAIhP7LcPxDNkN76nkqI76/wB5bTpXlERgn8Rwk51MZbmXTBg2yMFSZBDLbVGG7JQh0I+U2C07RkRLxqncFjdSm92b8jxNJp67ZOP1GencnVT+k2Q18D4WH2qu5JxOx55J27bJrrlPJK+huZvK4QqXu9bukgkmZJVOZVH5XE10wuAja89I7bObyn+CrHSqnPrtBMMVrJNkpfyJDER6h5I47VGuNenkRuThGWRioEEGL+tJMgEVCd6hdD19Rtr2QKrTPWQSeJJSccfa11X7B6x9pRyrzTVQcxelsu8jhCKeMly20n8RzL2UR89w/W+ixHb6v0PuW2esrbawFyali64rgLrXNYik/t6l/B0a6FDbn6Kc96ly6cW9n34/vSrDTQxrQh4A7ycT9B2yp8G9lK75eractHbTIJbnrzx20u8tK4tI8jM5eqzyS6vhaBFj/C37+WaGLi54XJMrEf/pimPMwBcBrNHZR0bvWrWtPn6NxflpNeFxN0ffLzd4EQuP2BlrYpAInas+yr/vM6FSpgqYo+OhVFzn5euSsd+XRFrkDvve9yVhWWjbrgpOUxFKtVMdtXVibaD5jr424ZzsjZDJCcxH9aRKLsP6JBPBCZkMSi1UQJt5W/f5d8cQ7XW+HEjaX0ePZeR3TVpd7YRMlP33JOUrIl9Hzq3JrxRCX3KE6jiR4zEy6XM/hb4fdxNXTAD3RvIHhfqnEyDdBz7AAmW6qovn5/MwZ9X+65FUrnJ9rzlCCdmg2mBwcRXpk7EvkgbZoDzOYGhPbXtHNvjdgJVLpY5xtUQ3j6se6uDCFtk9VQqVDND6JhDROM3wYjlGVmF2Kdenk9G+zWCIPkAmhiIrKscui/Oz1DauRMp4leFad1a5d0p9U/t0lqafp+2qm9pI5EVtOwE+nLy8ihMtwdUqDSDohaRrtpfKd9LyqVVZqjL6MWp/1WtKuVyJCKLa0x90GnmU0u1um1NyQBcpYkpLba3r7PX17xoYORg5Rsqcl139+QTDQ9uXuJHR44MhWFmhHESRAq/rpvZUf4eB8OteFMnWAIfuP5E4tY0Uum1L67TV0ZVLXTt5GXi5z9h3pSPjQqOmnaWlj19H3Zd6lmyn9ERMT5AJvq7HKTJJ1DU9oAyN7nWVMqw2kleA3GSPk70NNBBzhkwglZ5UWi+/tl0JrPJLq+GK4suB/2K/XwvcDvxkjPGh8VOuDZzvXpGB7AFd3CPF3VVro/4Y+VlylkXm7c+mOj8RKZEwJ5OKehqNaMnWqBXKmiCPRRmt0ZGVPYB+mp99Y+ir/3Va3r5KM3bM10V0MjNXvhKJmkyyYjdLD7dIbs8xeDAjJ9ZjhLJWKrt+IIfzdSQ7OLYBGxu5XNP00pj1WSnt+nKtxppo1QGcvH1q+DqSdVndrVbHnZslF9g+E71lhMpJva6F6rqMTI4FV1I/Vz4dixFz3R6CoVy+HIj2zed5shgp1tOr1VUR1FqVvR7JJKx2nUOMy27X1cTm5mb86I/+6KtdjGsad955JzfffPPVLsY1j9aOjx6tDR893v72t3P27NkjU4+eHEL8q5dx3g/DW2KMH3NU5Wh4bBFC+AvADwCfHGN88CLP+QlA3eX4+Y69WujI0wc0mLXN4BX0JMolenzO4zJXyDpgj449pJyy4ANcUiKLSLHdovuoE61aufF8nFCOzclTmtPpomukzpl0A4ESoTk7hbOzxTo4mcbSgZHot+epk5a0cKIkF8/JJJdlNoODA9ifLgbUYyRvte9GVy6bscz1VerjuVleX3J9AjdswrHNrGJOp8Pn4GAI5qMBztqg998TSuLkcy3rOtREer5vSXl97UuV75E+D8aqH2x05VzX882NhJy2K9n1+pi1Iq4+ob415o47JUe29fugnmupwd6zlH17Zr81eKspOy5u1P1jlXDy5EnOnDmzb5teG2N8xeWmt+rv5mtOoTx79uz+7bff/oSrXY5rGSGEhx988MHWho8SrR0fPVobPnqEEB4+6jRX9QXdcDQIIXw18BXp50sYxPcfBT7nYskkQDKOXpHSXNnRaVfX3AiWwTyGmigpAQ+U4uRKUWDr9FzN9Miey+aw1QFe6rUMx+7NOsCPUAde8Xo5mZHLbA1XpOq8pej6aXK3PCS74Op7VxGdZZgrdGQDtY7MOqYCzvrheJHENcuvjhDbG9GF/H2tyyqlt01/cH5F0LHOQEoXSHVf9kNFifW8vIxOvOs6zK/ZtJyfOWF8Pcz6+5jb7jIVW/2qnk9cD3J4u6itpC6OXfbz3XvCfBkbMkmtVcpVxs0338xR2zerXOdrjlA2NDQ0NFw5rPrE/4ZHjxjjq4BXAYQQnsHg2vqKGOP/uqoFu4KQASqD9BzZkJbR6gQqYGsQmiulq5X6X88J9ONqjKl4kElNvYyEG+w1sfJImYWLZfoRusHl1CEls3at9LlzIhI+L66G8lybDGVX9NT5+pddmccYqVOQHydWczdPcuAXtUNdb6p9IpXa1vWZrIX6wtnXOnKsyLbv66flVBG1k7fZhIFMuhI7L4ud6Pkq+I5jrmqOlMvPq6H2vxBROzfL16yOZjwfcLA2c3fYeWTaWV4WZxkhVXq6jvWapD5Ismw0Ssc4mXRV8np6V636u7kRyoaGhoaGAqv80mo4cvwThrhSPxBCAJg93lyXazIphfKQwdXODaHajbP3RCwtj0IJ5T3jbrJQzplc5hI6zz/tWzOipbLXebj601m6Im7rieyJVDqZdQXMFTuVe2Lflb+7m4pMrnVlxFQwotSNq2+FG2c/zLU8TNsg/5cyVpNKd0eur5fgEV/n7UlJtnR9+5TndJoVypDUymIdx+nQX7wd5umnctb5zvPu8vXuyCqk2sTbpo6eCwPB88EHze2sVeOZEdRDyrJo/qpcqjUfVNd60pX908sn92Rtk+oK+T7wQQh3cV2nvMdcYTyfy2p9TJtDudp1vhYJ5WuvdgEeB2hteDRo7fjo0drw0ePI23CVX1oNR4sY498B/s7VLseVggxdN0Ldhc4jYLvbah2kBztX6matBNXqpK+vuEaOJOqqoBMWD5JzaMa6E2EPRuP5hREiAyVBcLfW2Swff25mwWfIipLUqJhISk1ovZzaJwI4RqygJLGu1GHKmpP2Gh6MaNkczjHUil7ocp2cUD5yULrnTiZDO4hoMi0VSv8+RhCFWg1WMRXNdx5ldVbOJ1T9Nowo1oMjkAMuev+YH0xWSFVmn4c7YajfwlImlMR3nqQGISj7hM8f9XYRuYRMCD06blXU4rdH+73eySSsdr2vOUL5aCa0NgxobXg0aO346NHa8NHjqNtw1d1qGhouFcvI5FnKNV09gE7tzujpSN2sXQX92HqpEG13YqE05/n0FBnX5dbuw/IwYEmUTierdkIdAKh2I9xI5fRlNEK/uL6lpzHPh+UEBAbiVixNweL3MZdItbeTyHrJkTF1TflLFawD3ExSfWb9EJhI5x3bsLmKXXbHlYuqiGvtrurLb8R++VxZL5ur3wpCowELH9io1/T0uYtjcxW9HReIpqVZBwhy0q8yHtr1G+szuh4ikNh2kf96SRFg9F3j96nc0T2K/vX6flr1d/M1RygbGhoaGq4sVvml1dBwuZChqsiS58hLJfUMhrv6vq8vuYwALnO3XJY3lOQr9pkYaNmLudtnXy55JCVqrnaSXQmlUM7z6sulILouB8fRds15dNVH5XBlUERKS20U8wBdgbOPylKTEhif+1fPDVXS/cg+kUgNBPhSJPXanZCVWEWQ9TKFLhNNGNTBRw5S+81KhXY+h7C62IcsBviZ2fEd1fEVMffmkCqpfinIUBcxrNd3VLtIISzqb9+76vj5/Nx++WesvGPqqP67q7TfEx651QdHfABGgyVYPt7/m0q52nVuhLKhoaGhYY5VHwVtaLgc1IawL1fgrqmQje2x+0BG8LH0u55rWKtCUhMPGUgKZGLX9+Uae5DdMGepbFpPWHmLNBxjIFUKAuNQ0JXCbZFMdKSEuQLokTmdZMG46+pYu/ixh6Zq1fNEBSeMY+TKt7siucFi0BjVcz6vz+YDumuvL2Oi/+76SnI5BejMLdiVTdWziMhb1VFlVlvM22SWiVWsjle9YZEoCmPPZ1fWJ+7W3Oc+OFfKTVn1vndwkF2u67m9rkj6/GPlPRZ1F3Kf9mvpy9LUKr7X0dPye/d6JZKw+u/mi3hMPPYIIfz7EMIDIYR32LaTIYRfDSH87/T/Q2zf94QQbg8hfNrVKfHqI4RwVwjh7SGEt4UQbk/bbgoh/FoI4edDCCeudhlXHSGEzw4h/GEI4Y9CCN+ctrU2NIQQNkMIvxtCuCOE8M4QwivT9ttCCH+a+t/bQggvSdufFUJ4xLb/kKX16em+/u6rVZ+rhUttx7TvW1Lf/MMQwv9h2y+5HfvL+DQ0rCq8j9bEsiYu0T4ynKXmCfO1D8nr/82Dsthx7tYnZeZwltw+E7GoSZ0TXSd9IlTHGNbMPN7B8c1h/cTN9JHbYk0wSXXQPEmfv1YbgQXBlMpWu7BW59TupYepfudmaW1LU+3mQXmw9iW7eDpZcZVS10aoXUlVN5HwaT/kr/Ukp9PU5qksIvcq/yR9nEDqM50yX7tSLrvnZudX7+bPxnTMXBmmVOrGiJL6Vr02qj9v3QUYbF5plz+Q+4/asFDIU94H6bM/G9b/9Gumvu+Rhp30TsjruOo+UFnVnw/s4+qkVHbdQxok0bqqdTdu75rVfjevqkL5Y8D3Az9u274Z+G8xxu9Mxvw3A98UQnhu2v+p6bz//hiW81rDX4gxnrbffx/4WuDZwN8Afmj0rAZCCGsMYfY/C7gX+L0QwuuBv0lrQ8dZ4DNijHshhHXgTSGE/5L2/csY4/eOnPPHMcYXjGz/KuBTgH8WQnhujPHdV6bIK4lLascQwi3Ay4HnATcB/zWE8JwY4yGX0Y7X+0u74fGJMeKmwDwbI8e58QyLroZ14J4Zpduezjm0/YfKgFIF9UAmTq7GyOQxhiAqGxuwvrF83uDcxdPq41AAIg+YIihQzJjy5v9dzfTy12qK3HRVz5rQQ6lYeURXX89Qabp7q+robpjnMPXM2ltYTxWul3Gp23DM7ZN+UUHz9l1YhqNfDGpUw91Ez6f0OJn0c4uBDLseHsxJadd94pDhPvBrJDV8bN6lyidV9Bjl8jFaSmRWfbDz5pGCq3pLUfVjGjJW+d28ktcqxvibwJlq8+cBr07fXw28NH1fIw9eBRouBWq7ntZ2F8LHAn8UY3xPjHEK/EeGPtna0BAH7KWfGny83AXPO7Jdcl217WW04+cB/zHGeDbGeCfwRwx9Fi6xHV0xWcVR0IaGhoaGhusNq/5uXklCuQRPiTG+FyD9vzF9fydwHHgT8INXr3grjwj8SgjhLSGEv5u2fT/wb4GvBH7yqpXs2sCHAX9iv+9N21obVgghrIUQ3gY8APxqjPF30q6vCSH8z+TS/iF2ys0hhN8PIfz3EMKn2PYfBX4L6GKM73psSr86uMR2XNY/4TLacZVfWg0NlwtXbKRS6nu9DEhPDo6iqJtazmHZwvGusElhkRuslDkFXjmw/Mfmzel/HYBHy3nIvVURSKW0afmQQmkjq1rR0u4s3XWy22LtulvD1dP6o3Y8Sxn8SIqV/o8pbV53tUcd2XT+SQ8dD2xUK2JyG67n/klBUzpS9DzSqSD35Gmf0xt79tVlhTKqqR/naqF/FAhK7q7u/lmveRkYn7s4X6OyX3TtVp8/Z/89EJBv93PUZt5fVK518txLBTlS3Ze59KquXm5XLM/X94Tr9Z2zyu/mVXV5vSTEGL/2apfhGsAnxRjvCyHcCPxqCOHdSQn+1KtdsGsEY8pOjDHeTWvDAsnN8gUhhG3gP4cQPpphsOc7GN4z3wH8c+DLgPcCz4gxPhhCeDHwuhDC82KMuzHGNwBvuCqVWAFcYjuO9s+UziW146pP/G9ouBy4q2bt8jplMOB9zuOUcp5aHT0TSjdV3+YugXUZandPkcXaxbCOHttVv90ds166os5zbImTOm13J8X2KUqq5kb2ltaydSJ7lj9HPN86oqzg81q9zL5/QhVdlUXXyq76X6O3L2pHX4dxluZcyoV3tC5V4j7n0Ofier0VTVhES5jPw53k3/NIveR062unh78IsubIKv2ZHeNuubXLsdJ2N23lAYsEVmkW7seURKZO52LcWH2+Zk1K4fp9P636u/laUijfF0J4GkD6/8BVLs81hRjjfen/A8B/JrvDNVwc7gU+3H4/HbjvKpXlmkCMcQf4DeCzY4zvizEexhh74EdI/S+5aD6Yvr8F+GPgOVenxKuJi2lHWv9saLgoOKGT+ig1y/dJsZE66Uqbq3B11EupklJxXGGak8GR86BUJI9RqptOGmKfg8WcSwFnzs1yMJxi6QcuPOdAiuaky1FjRWoUgGY2y0a+UBMP1V8EtSbBWF1Eno5RBp4Zi/rq607O/1vgGVhOXp0867cTVOUT+xzEZx6Qp8/XWsepfuuTPI9VQZE2NmBzAza6oU5SET2fupy1ctel+Zz6TNK1mHS5P6j8S+tCqY5iber92tXxGvX5dZ4+gqnBhnOzrODqOo8pmkVfto/fF/WSOVdDcWu4NFxLhPL1wN9K3/8W8PNXsSzXFEIITwghPFHfgb8EvOP8ZzVU+D3gI0MIN4cQNhgCoLz+Kpdp5RBCeHJS1Agh3AB8JvBuDQYlfD6p/6Xj19L3ZwMfCbznMS30CuJS25GhL748hHAshHAzQzv+7uXmv8puNQ0Nlwvvr7Xrq1z/3M1P0VbdNfXQjpXKU6+TOEaonDB5OWJ1vpMe/67yzxjcL6fTYakHRTA9Ny2J5KEpZcvu0Xk50k4Fp1mfZEIDOTpqrdQ5mfF6Fst7sEiMpcpqv5NOtemYO7CnfTFkcr6UBqUrrxRhz3dmhPJgCo/0A+lyUqVzNzfK6LoilvpMEgGsibK7y3q6yyCi727N3g5j7qF+vV2Vnth+J2id7R9T4QUn5p6nuxR7MCnIbtT+UWTkebtT3odn7b/cwvVp75rVfjevpMtrCOFngE8HToUQ7gX+KfCdwGtCCF8O3AN84dUr4TWHpzC4zMFwzX86xvjLV7dI1xZijLMQwtcwuA6uAf8+zd9tKPE04NWJJHbAa2KMvxBC+IkQwgsY3nF3AX8vHf+pwLeHEGQ7fGWM8cxjX+yVwyW1Y4zxnSGE1wB/wPBu/urkMnvJWHW3moaGR4sxUjmmGELpaqjfsPw+qV386rzGVL26bGNzC+s8Dhmil/YHA4nxeZOaF7hMnfQIqooqN+nzmo36iJiqXI4xMunfVY8x1aI+Hoa2WSOT+746pnaVjP1AKusIszJqnSQtc31VHZ3kuSsolKroOqUyOTELema+tvPtiYirfv7f20LlFQ77ckkUlVNrZo5hbHM9V3NeVkoF8XKUJe//7u7qbuIeOblWtlWOWpGWeioy6XON67mY1xtW/d0cYrzcAIwNDQ0NDY83PCmE+ImXcd4vw1tijB9z5AVquCYQQrgmjAkZ11o/7wSwlT4nGCL8+fIJIhKuBikNd2mF0nWxIAhkY9uVUMjurb6GX+3yWZObek6b5t3NFcXkgijVx8vl/wUP9rM+EpTG3T+FOnjOPG0jaWoTJxlgCmE6yV1La2XS275WKZV+fY6fd8zyqZdAGcvfr436ydyFuStVyXrNSs27VF59Uj3PzcrroLTr+azuxuvk3tNTPo6++u7kVYMGuj5136tdjZVG7ZocRo6tCaHXrV5Hsq/+uyrp98YB8FD67KTPPplYXkuE8sUvfjG33377kUWoX/V380oqlA0NDQ0NVw/Xygu7oeFS4cqXftdGqoxod8esXQvHImy6MV8b4FAGrKFKtyYFfp7KVCtNRcCXPqthSsOVSIfnJSNQrpU1iRHJ6focTKZOsyAt6Rz6Mp9AJpX1NfB22CArZ05mvM5OHtWmTpI8zd4qXF/nzrYr8quro8H+y812bJ1KJ5Qie96OCqwzrdLW+o1rVbrz8o3k55FU58fZfqy9XJ30bfUAg5O8vtrv5FrXz9tcJBzb766zOr4uq6unPpigucpjyqSX9XrFKte/EcqGhoaGhjlW3a2moeHRop5j5Aar1JUNFlUk7Jh12y6juZ4X53PY/FwnKmNlg5IwORkIth2MkPaUahb5XlY01dqV1pc2GVsyQ9/XzYVzrIxezvV0QOfMparP3BXW9jtxl6ukK68KDiSS4agVUN/uxaivtcOvv7uDzudcJsIt0jidpvOMUM7r0uX/k0k6Zlr2IQUWkgttZ2nXpLRWJCHPd53XVW6ys0Wle54f44qx2spdf9UmTiT9PvD+5fl0VRrLyK9Ip9LVXMx67mRzdc1Y9XdzI5QNDQ0NDQVW+aXV0HBUcIIhI9jXjqyJpODqihvlMpLHfH9rtXLMPbYmNSpTHdDG4e6SPp/QDXonlXUdtI5gTSbn6VeExglJTU7W0vcNr4zV3/Mfc8OVGirFTiQXhjIcHEA/XVweROnVxV8WsMZJqV/HWt1cNr/QyaOUycM+z+tUEB1BwXRqAlfsT58xYqr6y5UZMpn0YzTfte5jDqmOrhw6Kazn1wrnUxnr/uvLj/jarH6e3w/Kc0aeP+kKZSOTGavcDo1QNjQ0NDTMseqjoA0NR4FaofSAOYdc2DjSuZ0d60FoaiPY1cCe7OZXK5+1e6ITLp87OD+ny/PuatfHsTScpK4tOWehrn0O8lPPmauDs4gc1C7Dvf0vymrfQ1LsJpMc9GZsDuGsH5+754Ro7spLJsWuhImo6PqJPIpITqxdtd6l1qqcE3cjkvP26yEa2XO1cq0vVb1ZPwRDcnW5JvWq83Sa17iEVIFJed5hX51btbW3v8/THYs4OzZfV2nUAxu1qqn0/frX0Xp1jPfBtmTI+bHq7+ZGKBsaGhoaCqzyS6uh4dHCyY0HB9FHAXLGXFKlvOhcGcsiIp7HzI7xiJradpZFNdPnoNUKoKt4gs+/O5fUK48EWqtBIlA1KTg3K10oBQ/w4y6zOnRi9XSlqYgw2pXEuY7M6qRSyqTmK7pyJ8VyMs35OTF1KE2ld9hDP1sk/D2Z6HekAEkWcKfvq7mp9r0mcH7MbLbolqp+4GU4NDdXd3v1tM7N0lIxdVtVZPR88EPG5qHqGCefY+7edZquPHtfXWc8yq/uC6wMPcvJ5DIX5esVq9wWjVA2NDQ0NBRY5ZdWQ8NRoFYopwzRJF1Zg/G5Zk7E6uUsluU1j1I6SYSvL103Zcy72yWUSprfl3UAHW3zMrq7rIjk2L0tN8raDbPvF5dTUTqudmLf9V95dZRurK7q0S+WZ06uKiWwJlpehrrsKieUUVOdUNfHh/Spl01xxN6C/3RVe1dqZn186JLBbXXuyOrmmEIsYno4y8FqpPwdktuSVO61blBH19I2DXx4//X2GovOqraoAyK5olkQYts+5mZdK5q91aOnJI+uHtdzJ9s7acAqt8NFjGs0NFxZhBCeEkL46RDCe0IIbwkh/HYI4fMvcM6zQgjvON8x5zn3b4cQbrLfPxpCuOUiz/30EMIvXE6+F5n+TSGEn03fXxBCeMllpHFbCOEbjr50DdcD3HC4lE9Dw7UGJ5MHDIRyH9hLv2XQxuqj4CEiW76wu5NUV+4i2egfi9Tphr7S8PTmxnulXp3PVVUQkV2bDK6cc+XQynq2+jzSl2sBqizz9R0ncGwyKHpF8JqqPvMydFktXE8KpOooInFoS2/IzfPgIH+m0zzH0OeArjMsAXPMPsuIvq9PeT71TW0rwha6kpw6RC7rNSonk7xP133SlYMWWktz2ZIgh33ZD2uFUwR2zM1ZRG4sGrH+189vtc06g1KvdtLx6g/qG+cYn+uoa1OvBar7QcF31N+mLHd1bRiw6u/mplA2XFWEEALwOuDVMca/nrY9E/grVzDbvw28A7gPIMb4d65gXpeEGON9wBekny8APgb4patWoIbrEu0l3vB4hxvVU0oVT1Fe5fYqg7hWc+TaCYORLDc/WFyCoicb/WtduaZjEfmUcnmG0XLP8jy9yXmsuLk6OcKanKS6sqjzVCcZsZ3t8/UqZx3000y0Ibfl+eBzMkn5dJTrOM7Laqqmu5/6HD2v17wMriKq7SldPHV87f7r0NxOL08NJ8x+nH9cJVW6Oq4OxjN2zZwgenRfudfWKNZNTcR17NrUEVlFBp246j6Ruugo2pzFoFaejpaN0TItWiKkjuzqZKi9jzJWuS2aQtlwtfEZwDTG+EPaEGO8O8b4b2CuRL4xhPDW9FlY1/V8x4QQvjGE8PYQwh0hhO8MIXwBA0n7qRDC20IIN4QQfiOE8DHp+M9OadwRQvhvF1uJEMIXp3zeEUL4Ltu+F0L4v1N6bw4hPCVt/4j0+/dCCN8eQtizurwjhLABfDvwslTOl9XKYzruWen7t4UQ/jCE8F+Bj7JjPiKE8MtJ+X1jCOG5F1unhusTqz4K2tBwVHA1sVYppcppnb0xI9fX4XMDeUqpvBUqo6leXo5YnTPmYupK0SyRq+l0IECFQmUqWB1cRm6sykflVX31e0pWX8GWFyEHzpmTqG4g4Jvps0GpVrhbp39EZF2JBVsiRArlFM6meYQi4vO1QruB4G5sZKK7ntRYJ1lS8tSeUuD0mSto/aCU6gPLAwVJSdU8SCeV/qkJoqudSmc6zddyDLr+ImtO0mapvc7NcuCeZc/kvvru8x/rfX7MOftInVc+alNdd7XvWB38v8/lrOdNNoVyEav+bm4KZcPVxvOAt55n/wPAZ8UYD0IIHwn8DAMhvOAxIYTPAV4KfFyMcT+EcDLGeCaE8DXAN8QYbwcYRFIIITwZ+BHgU2OMd4YQTl5MBZL77HcBLwY+APxKCOGlMcbXAU8A3hxj/LYQwncDXwH8M+BfAf8qxvgzIYSvrNOMMU5DCP8E+JgY49ekfG5bkv+LgZcDL2S4p98KvCXt/mHgK2OM/zuE8HHADzCQ+IaGpWgv8YbrBYYvSbIAABnoSURBVOrrHpxHiokM9w0W15TsKNd5dBWvDibixLObJbWIxSAt0Y6v5565Mb5gKCbFUiTFg8FIWVNwmXqOmn+Uvuet3yJgaxVBUpCgrl/uVklFopcFsxG07mSqWhEMSO0/sbz9vDHMA99Yuep2FVw5XifVq1uss5P4biQhd2tetpYkZNVVuzfI5LTGWPvWc0HrpVLmx/eZHGoAAxbbVX3PBzXGgjF5P6nz9Siu5yjXwPTy+2BOc3e9OKxymzRC2bBSCCG8CvhkBtXyVoZn+veHEF7A8Fx7zshpy475TOA/xBj3AWKMZy6Q/ccDvxljvPMijxduBX4jxvj+VIefAj6VwZV3CmjO5VuAz0rfP4GB7AL8NPC9F5nXGD4F+M+qZwjh9en/CeATgf9PpJlheklDw3mxyi+thoajhBOfKYNRJKVSalCPKX52rpNBGdfuQirS6MuRnCUHTXHV0YmB3AXldugRRwUZ6HODvWce3VUqmf7XqqAI5FmyOuRKU00apD6uT+BYFQFVS4rUaya6+iVipuA1Ch6znsrrS6oouitk91Z3u5VKJzIp5XHWj5PL+bIeLD7X6rUxx5Zl8OBAXj8PvhM6FtbxjHVCVpZaMRaxCiwnnrreY5GHLwTV3Qc2/Pr4cR25Tznk+u3ba3fWsTyhHCARyVTfnZLnU9ZrT46le71jldujEcqGq413An9NP2KMXx1COAXcnjb9Q+B9wPMZnkkHI2ksO2bZNJRluNTj/bxlOBdj9IHnR3PPKdq8sGnfx8rdATsxxhc8ijwbrjOs+lpXDQ1XAjKAnVQ6udpkkUAKtfFdLxIPWfnaoJxHN6n+e5oik77OpObB+T3qbqkAx5IiODa/UuRWClTtbip4oJ0NMpmcuOI5yy6aToTGlnvo+kzyui65j/YwmWXi6HMea7iauzYpI6zO14E0YuuRV53g1lF7x6Byz0n7rKx3Ua6uXLrlYgIlOdn0frLGonvsWreoZEPZDxdIcFUXnwvrc3uhVBiVlhTKer9HDva86+VBlkUGxrZ7QB8RyrZcyPmx6u/mJbduQ8Njhl8DNkMIX2Xbjtv3JwHvjTH2wCsY9+hYdsyvAF8WQjgOYC6sDwFPHEnnt4FPCyHcXB1/IfxOOu9UCGEN+GLgv1/gnDeTifTLlxxTl/Mu4EWpbC8Cbk7bfxP4/DQf9InAXwaIMe4Cd4YQvjCdE0IIz7/IOjVcx+gv49PQcK3C+3Ht9qpor24cy8D3wCWwuFg81T6fX+kGvT6ugtbG2Zw0WTm8/G7En03HK2roPHIqpbujQ+qfjP81BneWTVI0141xggqlO6mrn6qrExifY6iIqJr36CrfodVX7bNOJrabm8Pn2MaiOrhQt65sr0P7KHLvaL1U/tliBNZQET9vZ82H9Gi102lKx9TiWZ/bB1Lbm0vx3MVWeVItA9PlqLF1f/B+6GTS66oBg3VLc6wZPc9jdo7P6RTxVBP5veBurT5H1++x2t3V02rIWOV3cyOUDVcVSb17KQMhuzOE8LvAq4FvSof8APC3QghvZnBlfXgkmdFjYoy/DLweuD2E8DZAAW1+DPghBeWxsrwf+LvAa0MIdwD/aUmx/2II4V59gGcB3wL8OnAH8NYY489foOpfB/yjVN+nAR8cOebXgVsUlAf4OeBkqstXAf8rlfutqaxvS8e80dL4EuDLU33eCXzeBcrV0NDQcF2iJkW1UuKBXI5h7pfVeTp+vvYk2fCu81pGaObp9Xk5jdpAlDttbbQfMgSwmc3grMhNn8s2V/qqOvlSGvMlHyaLpK1W4UTYRGBmlAFX5nWtz+nyMiK+TuW5FBBH56uck24oy8YGrBuprIPlOHyZlrH29/ZUu7gb55yM+ZzJLqcLaWmPRBzV3vqu5U7qZVhEuAuyDQsKJSySAqm0k8kisa37rhM7P19LrXhgIh/QEImv8/VIsw5dbx9MUbGkhI8RSQ+C1dTJaxshe+M1NDQ8Vkiq6SMxxhhCeDnwxTHGRvYarjpuCCH+mcs47x3wlhhjHTCr4TpBCOGaNyakzmnO4AlgK3220+8b0r6xKJYeFRYG0llHvKyDwdSGfu3C6Ea8b9O5rgJ6Hspbx/pi8jpXxru7gq7b+cc7OH48kzVfK9HXafTosT43U+6TIqvHKMmf0nSCOlf5+tIVdEImj5ubOQ1fr1IRV+ftYETrsM8ktY5QWrexz4Mllf2GbsjXSbUUyGKJE0qonc9a+ys/xzqDa5bqCHlA4OysnI866Uq1WO3l13PeBva9cK1dsq8uu8Pbys/z6MTYfpFMtbf66ZRMJHcZ1n3dY5FUXut48YtfzO233345015Hserv5jaHsqHh6uDFDIGEArADfNnVLU5DQ0YbHW64XjGmUrrq4uvrueHs94y7jm6SFR8dW+dTkxvBXRwdvnajq1vublgb9142L6NcbZ3oSJ2Ua+qa5g/OMpGCTN7qCKIqT91GM4DpcJ7POyzqJqJKqdyORVrVR0FxZrNSBfV5lGu6ILO8b5krqcqrgYEJud4ie4Irx/U6nh2L81WduNfVXyCjfVmfsb4wm5XRexVgSflHxtfr9H7sSizVcV6vQ8oyaJ/IpPqcK5zqA36s1Nna5bWpkxfGKrdNI5QNDVcBMcY3MgQRamhYOazyS6uh4UpB/X7GYBz5Ugb1+pC1ouWKjxM1nxPZ2/H670a0K2Vj89kUFKV2Y1ynNPy9TMvmxI3VW2VfAza60o10rkLOsioJWZn0ci2LBDp3YZ2VkVpdSVREVmFOSiq1cTbLCmc9v1TlEqEryOsEur5cqsPL2XXMo+VCqbKtGZlWOXxgwMurtOhLgj8vn+Vbk/6FwD9exvRjrhSzSMa8fx2r0vBBB/2uBzVqONkciwzrKrnOlyrv6Yng+rqTbe3JS8Mqt00jlA0NDQ0Nc6x6JLmGhisNVyhrJcWDmMC4K6q+u4ElQ9/JYB1x1ZcKkYIktWds/h8jx2Lpjbks1qjnwymdmkzKvVOuoyqHB90Zc6l0EixSJZfStX6I9OpraHo5PKKoE1r9htL11N1P+/THI8uKLPbpAouQqW2WBfaZB/GpWOHhbJHIz+ue5oUG5UUu15gaLYxFiXVC6fXzfjrmsloPcMzTsHPHCLEH6fFye//FtnsZFKDHVfk1cl8XdG+5qtreO+fHqr+bG6FsaGhoaCiwyi+thoYrCVcpRSYn1UcG9yalkVwTSqU3s+9umMsI1+86WMoyl0gnQQqmojL7/rHvE7Lh74TDCZUCvfgcRy0R4sFy5MJYt4HWx3L3SAWgkcunCPWCm2+XXUxrA9WXKBmby1irjpDUxq48R8GADvtSjVR6dRRetVMP80aur2UNV0VjBxspwXOME0nBy7Sgutr/ep6nCHi9jqgHYLLiFwTOiaW7P9dpLFMSfYkQRtKoUZPhmtw2LMcqt08jlA0NDQ0Nc6z6KGhDw2MBGbnLSKWUGFcr6/tmxvJ5lq5siWSJIB5nmKu3EAiHTMLAosfaHEfIytKYagUlqdT59ZzGOmDOzMikuzjWy16oTbzu2rfGuOtnn5TENWMxHlxH6mSh0ImgVSS3xixdSLnW1ms8zq+B6mn1wtvIy8tyIikcVp3BSV0dIGmZUjmfo0pJ3rC8nfjJoHfi2VXneXl0TL3GpeBk2tfvlDJdB3nyNGvyO7P/Y66uDRfGqr+bG6FsaGhoaCiwyi+thqNFCOHzgO8gc6ivizG+Ke17OfCNwI/HGL/vqhXyMYYbw1OyiqYgO3J9rQPc+Pn1PeQEZEH1SpD6qeUwILt4HkwXlcB1mC9hURMYR22IitxIlVyvlgSZ16MilE4GFMm1DgIzYVF5dDdIkV0nOh2DKudBdNZFkvtybt5hpU7Gfnkwl7kamlQ/udbWxNKJm6czd/+1/BfW1WRx7utCuiPXpg5iI2PcXXQVoMhVbocH1nGIDLt76xi8X1/oONVPaY8tR+JQX1WE23MM980ei8uENGXy4rHK7dQIZUNDQ0NDgVV+aTUcOf4b8Pq0hNGfB14DPDftezlwK/BTIYQTMca9q1XIxxpuxE/JQXo0l3KTHLzHz4HFuWa+XSRlTE0TSZxMymii0+nw35edEMmp5x56WspTipKXRfv9fF8KRKiXBPHgRCKWTpTdjVJLsNTqWa3wnUt51/Mp5XZbk2URNBEuT7c4ztpgLW1Yr9x5VcdZn11dXXF1sjcjR38VWS0CL1nZRQxns1y2aOlL5XWI4HeWhru26tq7EjwxkjwPliT11trX1VSdCyVZrl1Pa+Xd++zYGpXeFaPV8QB4hLxUSL1ESM/49WtYxCq3USOUDQ0NDQ1zrLpbTcPRoiKJT2DR9oTS4+26ggxdV+fcEK7hcwuX3Uvu/jiGenH7QyM7buj7/tjn7bY6xrxMmrMp9XBOcoyUnZtlcqW6OzmWOidi2Vu6gtL3KLPrk1Jx1HxHbyMFbfFAPb7W4lzlq0hnjTGiXi/nITip9Ou0bG4pfUkMXXmVi7Jfu1lytcUIufehs5SBi1SmyaRUNX0JE5VrjbwepdrJAw5BVnd9vq4rmsfSd5HSmJTcun/7PN9lbtSwqLw7mdRakyKVGphpy4RcPFb93dwIZUNDQ0NDgVV+aTUcPUIInw/8v8CNwOfartcCtwM/GWN86GqU7WpC5M1J5YUUFVehXO2CRffE883BW1Ye/+8QcYGsrNVRPN3FckHN60ulrlZYl0X4FFzVA1uLM7nUToxQAvTT4Ri5zJ4jRwOFgVRKmZxHdLWMaz7Zsfx61GWtFcq5Ujly/vkwdzntMhGsBwPOGdtWn/CoqNH+F/WxdJYNStTHeV3mvymvn5TjYu7lxObMTvPI0WwknboMY9sEDawcjHzqpU7aO+fisMrtFGK81FuooaGhoeHxivUQ4qnLOO9+eEuM8WOOvEANjxlCCJ8K/JMY42de5PE/AfxVgK7rjr/whS+8ksV73OPOO+/k5ptvvtrFuObR2vHRo7Xho8fv//7v0/f9vm16bYzxFZeb3qq/mxuhbGhoaGiYYz2EePIyznugEcprBiGErwa+Iv18SYzxPtt3J3BrjPH0Jab5cIzxCUdYzOsOrQ2PBq0dHz1aGz56HHUbrvq7ubm8NjQ0NDQUWGW3moZHjxjjq4BXAYQQ/kwIIaSgPC9iiKPy4FUtYENDQ0PDAlb53dwIZUNDQ0PDHKs+8b/hyPHXgL8ZQjjHED/jZbG5LjU0NDSsFFb93dwIZUNDQ0NDgVV+aTUcLWKM3wV81xEk9dojSON6R2vDo0Frx0eP1oaPHkfehqv8bm5zKBsaGhoa5lgLIZ64jPN22xzKhoaGhoaGK4JVfzcvWcmnoaGhoaGhoaGhoaGhoeH8aC6vDQ0NDQ0FVtmtpqGhoaGh4XrEKr+bm0LZ0NDQ0FCgv4xPw/WHEMJaCOH3Qwi/kH7fFEL4tRDCz4cQLsc767pBCOHDQwi/HkJ4VwjhnSGEf5C2tza8RIQQPjuE8IchhD8KIXxz2tba0RBC2Awh/G4I4Y7U316Ztt8WQvjTEMLb0uclaft6COHVIYS3pz76LZbWp4cQbg8hfPfVqs/VwqW2Y9r350MIv52Of3sIYTNtv+R2XOV3c1MoGxoaGhrmWPVIcg0rhX8AvAvYSr//PvC1wLOBvwH80FUq17WAGfD1Mca3hhCeCLwlhPCrwN+kteFFI4SwxrAEzmcB9wK/F0J4Pa0da5wFPiPGuBdCWAfeFEL4L2nfv4wxfm91/BcCx2KMfy6EcBz4gxDCz8QY7wK+CvgU4J+FEJ4bY3z3Y1WJFcAltWMIYQL8JPCKGOMdIYQPBc6l3ZfUjqv+bm4KZUNDQ0NDgVUeBW1YDYQQng58LvCjtnmN3CXC1SjXtYIY43tjjG9N3x9iIOYfRmvDS8XHAn8UY3xPjHEK/Efg82jtWCAO2Es/19PnfFE5I/CERIhuAKbAbtrXkfnNddW2l9GOfwn4nzHGO9L5D8YYD9O+S27HVX43N0LZ0NDQ0DCH3m5H/dIac0ur9ocQwr9O+/9nCOFFR1OjhiuE7wO+kfLyfz/wb4GvZBiVb7gIhBCeBbwQ+B1aG14qPgz4E/t9b9rW2rFCclF/G/AA8Ksxxt9Ju74mPXP/fQjhQ9K2nwUeBt4L3AN8b4zxTNr3o8BvAV2M8V2PXQ1WA5fYjs8BYgjhDSGEt4YQvtGSuqR2vFrv5otFI5QNDQ0NDQWO+qVlbmmfA9wCfHEI4ZbqsM8BPjJ9/i7wg0dRl4ajRwjh/wQeiDG+xbfHGO+OMX5qjPEvJ9Wt4QJI8/t+Dvi6GONua8NLxpiyE1s7LiLGeBhjfAHwdOBjQwgfzfCc/QjgBQzk8Z+nwz8WOARuAm4Gvj6E8OyUzhtijC+KMX79Y1uD1cAltuME+GTgS9L/zw8h/MWUziW341V6N18UGqFsaGhoaChwBUZBl7mlOT4P+PHkUvRmYDuE8LSjqE/DkeOTgL8SQriL4Vp+RgihqUCXiDQH6+eAn4oxvvZql+caxb3Ah9vvpwP3XaWyXBOIMe4AvwF8dozxfYkg9cCPMDyrAf468MsxxnMxxgeA/wG0dYYNF9mO9wL/PcZ4Osa4D/wScNneN1fp3XxRaEF5GhoaGhocbwBOXcZ5myGE2+33D8cYfzh9H3NL+7jq/GWua++9jLI0XEHEGL8F+BYYIhUC3xBj/BtXs0zXGkIIAfh3wLtijP/iapfnGsbvAR8ZQrgZ+FPg5QxkqMEQQngycC7GuBNCuAH4TOC7QghPizHqGfv5wDvS93vIA0XHgY9ncHO/rnEZ7fgG4BtTYKMp8GnAv7zM7K/Wu/mi0AhlQ0NDQ8McMcbPvgLJjrqlXcYxDQ2PF3wS8Arg7Wk+FsC3xhh/6eoV6dpDjHEWQvgaBmN7Dfj3McZ3XuVirSKeBrw6uTh2wGtijL8QQviJEMILGJ61dwF/Lx3/KuA/MBCjAPyHGOP/fMxLvXq4pHaMMX4ghPAvGAY+IvBLMcZfvJyMr+K7+aLQCGVDQ0NDw5XGxbilNde1axAxxt9gcPtquATEGN/EdRYh80ohkfBGxM+DRAZfOLL9FUuO32NYOqTBcKntmPb9JKsbGOrI3rttDmVDQ0NDw5XG3C0thLDB4Jb2+uqY1wN/M0V7/Xjgg+ZC1NDQ0NDQ0HC0uJh380WhKZQNDQ0NDVcUy9zSQghfmfb/EIPC8BLgj4B94EuvVnkbGhoaGhoe7zhKl/EQY5ui0tDQ0NDQ0NDQ0NDQ0HDpaC6vDQ0NDQ0NDQ0NDQ0NDZeFRigbGhoaGhoaGhoaGhoaLguNUDY0NDQ0NDQ0NDQ0NDRcFhqhbGhoaGhoaGhoKBBCeEoI4adDCO8JIbwlhPDbIYTPv8A5zwohvON8x5zn3L8dQrjJfv9oCOGWizz300MIv3A5+V5k+jeFEH42fX9BCOEll5HGbSGEbzj60jU0XH00QtnQ0NDQ0NDQ0DBHCCEArwN+M8b47BjjixmWFHj6Fcz2bwNzQhlj/Dsxxj+4gvldNGKM98UYvyD9fAFDROqGhoaERigbGhoaGhoaGhocnwFM05I+AMQY744x/huYK5FvDCG8NX0+sU7gfMeEEL4xhPD2EMIdIYTvDCF8AfAxwE+FEN4WQrghhPAbIYSPScd/dkrjjhDCf7vYSoQQvjjl844QwnfZ9r0Qwv+d0ntzCOEpaftHpN+/F0L49hDCntXlHWmtvm8HXpbK+bJaeUzHPSt9/7YQwh+GEP4r8FF2zEeEEH45Kb9vDCE892Lr1NCwimiEsqGhoaGhoaGhwfE84K3n2f8A8FkxxhcBLwP+9cUeE0L4HOClwMfFGJ8PfHeM8WeB24EviTG+IMb4iBIJITwZ+BHgr6Xjv/BiKpDcZ7+LgRy/ALg1hPDStPsJwJtTer8JfEXa/q+AfxVjvBW4r04zxjgF/gnwn1I5/9N58peq+0LgrwK32u4fBr42Kb/fAPzAxdSpoWFVMbnaBWhoaGhoaGhoaFhdhBBeBXwyg2p5K7AOfH8I4QXAIfCckdOWHfOZwH+IMe4DxBjPXCD7j2dwvb3zIo8XbgV+I8b4/lSHnwI+lcGVdwpozuVbgM9K3z+BgewC/DTwvReZ1xg+BfjPqmcI4fXp/wngE4H/b/AsBuDYo8inoeGqoxHKhoaGhoaGhoYGxzuBv6YfMcavDiGcYlARAf4h8D7g+QzebgcjaSw7JgDxEspyqcf7ectwLsaoNA95dPbwjNLjb9O+j5W7A3ZijC94FHk2NKwUmstrQ0NDQ0NDQ0OD49eAzRDCV9m24/b9ScB7Y4w98ApgbSSNZcf8CvBlIYTjACGEk2n7Q8ATR9L5beDTQgg3V8dfCL+TzjsVQlgDvhj47xc4581kIv3yJcfU5bwLeFEq24uAm9P23wQ+P80HfSLwlwFijLvAnSGEL0znhBDC8y+yTg0NK4lGKBsaGhoaGhoaGuZI6t1LGQjZnSGE3wVeDXxTOuQHgL8VQngzgyvrwyPJjB4TY/xl4PXA7SGEtzHMIQT4MeCHFJTHyvJ+4O8Crw0h3AEsm7f4F0MI9+oDPAv4FuDXgTuAt8YYf/4CVf864B+l+j4N+ODIMb8O3KKgPMDPASdTXb4K+F+p3G9NZX1bOuaNlsaXAF+e6vNO4PMuUK6GhpVGyIp/Q0NDQ0NDQ0NDw/WJpJo+EmOMIYSXA18cY2xkr6HhAmhzKBsaGhoaGhoaGhrgxQyBhAKwA3zZ1S1OQ8O1gaZQNjQ0NDQ0NDQ0NDQ0NFwW2hzKhoaGhoaGhoaGhoaGhstCI5QNDQ0NDQ0NDQ0NDQ0Nl4VGKBsaGhoaGhoaGhoaGhouC41QNjQ0NDQ0NDQ0NDQ0NFwWGqFsaGhoaGhoaGhoaGhouCz8/wEUziRwkpedAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 5))\n",
    "ax1 = plt.subplot(121, projection=fermi_dataset.counts.geom.wcs)\n",
    "ax2 = plt.subplot(122, projection=cta_dataset.counts.geom.wcs)\n",
    "\n",
    "for ax, dataset in zip([ax1, ax2], datasets):\n",
    "    dataset.counts.sum_over_axes().smooth(0.05 * u.deg).plot(\n",
    "        ax=ax, stretch=\"sqrt\", add_cbar=True, cmap=\"afmhot\"\n",
    "    )\n",
    "    dataset.models.plot_regions(ax=ax, color=\"white\")\n",
    "    ax.set_title(dataset.name);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Freezing and unfreezing model parameters\n",
    "\n",
    "For a given model, any parameter can be (un)frozen individually. Additionally, `model.freeze` and `model.unfreeze` can be used to freeze and unfreeze all parameters in one go."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:54.104428Z",
     "iopub.status.busy": "2021-11-22T21:09:54.104097Z",
     "iopub.status.idle": "2021-11-22T21:09:54.106030Z",
     "shell.execute_reply": "2021-11-22T21:09:54.106367Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1731.7-3003\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      2.742   +/-    0.50             \n",
      "    amplitude               :   2.59e-12   +/- 7.6e-13 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     17.603       GeV         \n",
      "    lon_0                   :    262.949   +/-    0.02 deg         \n",
      "    lat_0                   :    -30.051   +/-    0.02 deg         \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = models_3fhl[0]\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:54.108410Z",
     "iopub.status.busy": "2021-11-22T21:09:54.108056Z",
     "iopub.status.idle": "2021-11-22T21:09:54.109283Z",
     "shell.execute_reply": "2021-11-22T21:09:54.109518Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1731.7-3003\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index        (frozen)   :      2.742                   \n",
      "    amplitude               :   2.59e-12   +/- 7.6e-13 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     17.603       GeV         \n",
      "    lon_0                   :    262.949   +/-    0.02 deg         \n",
      "    lat_0                   :    -30.051   +/-    0.02 deg         \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# To freeze a single parameter\n",
    "model.spectral_model.index.frozen = True\n",
    "print(model)  # index is now frozen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:54.111249Z",
     "iopub.status.busy": "2021-11-22T21:09:54.110905Z",
     "iopub.status.idle": "2021-11-22T21:09:54.112078Z",
     "shell.execute_reply": "2021-11-22T21:09:54.112233Z"
    }
   },
   "outputs": [],
   "source": [
    "# To unfreeze a parameter\n",
    "model.spectral_model.index.frozen = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:54.114685Z",
     "iopub.status.busy": "2021-11-22T21:09:54.114308Z",
     "iopub.status.idle": "2021-11-22T21:09:54.115830Z",
     "shell.execute_reply": "2021-11-22T21:09:54.115663Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1731.7-3003\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index        (frozen)   :      2.742                   \n",
      "    amplitude    (frozen)   :   2.59e-12       1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     17.603       GeV         \n",
      "    lon_0        (frozen)   :    262.949       deg         \n",
      "    lat_0        (frozen)   :    -30.051       deg         \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# To freeze all parameters of a model\n",
    "model.freeze()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:54.117728Z",
     "iopub.status.busy": "2021-11-22T21:09:54.117402Z",
     "iopub.status.idle": "2021-11-22T21:09:54.118747Z",
     "shell.execute_reply": "2021-11-22T21:09:54.118939Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1731.7-3003\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      2.742   +/-    0.50             \n",
      "    amplitude               :   2.59e-12   +/- 7.6e-13 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     17.603       GeV         \n",
      "    lon_0                   :    262.949   +/-    0.02 deg         \n",
      "    lat_0                   :    -30.051   +/-    0.02 deg         \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# To unfreeze all parameters (except parameters which must remain frozen)\n",
    "model.unfreeze()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only spectral or spatial or temporal components of a model can also be frozen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:54.120895Z",
     "iopub.status.busy": "2021-11-22T21:09:54.120430Z",
     "iopub.status.idle": "2021-11-22T21:09:54.122670Z",
     "shell.execute_reply": "2021-11-22T21:09:54.122860Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1731.7-3003\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      2.742   +/-    0.50             \n",
      "    amplitude               :   2.59e-12   +/- 7.6e-13 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     17.603       GeV         \n",
      "    lon_0        (frozen)   :    262.949       deg         \n",
      "    lat_0        (frozen)   :    -30.051       deg         \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# To freeze spatial components\n",
    "model.freeze(\"spatial\")\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check if all the parameters of a model are frozen, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:54.124873Z",
     "iopub.status.busy": "2021-11-22T21:09:54.124558Z",
     "iopub.status.idle": "2021-11-22T21:09:54.126131Z",
     "shell.execute_reply": "2021-11-22T21:09:54.126322Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.frozen  # False because spectral components are not frozen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:54.128163Z",
     "iopub.status.busy": "2021-11-22T21:09:54.127863Z",
     "iopub.status.idle": "2021-11-22T21:09:54.129574Z",
     "shell.execute_reply": "2021-11-22T21:09:54.129859Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.spatial_model.frozen  # all spatial components are frozen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same operations can be performed on `Models` directly - to perform on a list of models at once, eg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:54.132350Z",
     "iopub.status.busy": "2021-11-22T21:09:54.132014Z",
     "iopub.status.idle": "2021-11-22T21:09:54.133185Z",
     "shell.execute_reply": "2021-11-22T21:09:54.133377Z"
    }
   },
   "outputs": [],
   "source": [
    "models_selected.freeze()  # freeze all parameters of all models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:54.135193Z",
     "iopub.status.busy": "2021-11-22T21:09:54.134905Z",
     "iopub.status.idle": "2021-11-22T21:09:54.136101Z",
     "shell.execute_reply": "2021-11-22T21:09:54.136287Z"
    }
   },
   "outputs": [],
   "source": [
    "models_selected.unfreeze()  # unfreeze all parameters of all models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:54.138926Z",
     "iopub.status.busy": "2021-11-22T21:09:54.137689Z",
     "iopub.status.idle": "2021-11-22T21:09:54.140309Z",
     "shell.execute_reply": "2021-11-22T21:09:54.140541Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['index',\n",
       " 'amplitude',\n",
       " 'lon_0',\n",
       " 'lat_0',\n",
       " 'index',\n",
       " 'amplitude',\n",
       " 'lon_0',\n",
       " 'lat_0',\n",
       " 'index',\n",
       " 'amplitude',\n",
       " 'lon_0',\n",
       " 'lat_0',\n",
       " 'r_0',\n",
       " 'index',\n",
       " 'amplitude',\n",
       " 'lon_0',\n",
       " 'lat_0',\n",
       " 'index',\n",
       " 'amplitude',\n",
       " 'lon_0',\n",
       " 'lat_0',\n",
       " 'index',\n",
       " 'amplitude',\n",
       " 'lon_0',\n",
       " 'lat_0',\n",
       " 'index',\n",
       " 'amplitude',\n",
       " 'lon_0',\n",
       " 'lat_0',\n",
       " 'index',\n",
       " 'amplitude',\n",
       " 'lon_0',\n",
       " 'lat_0']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the free parameters in the models\n",
    "models_selected.parameters.free_parameters.names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are more functionalities which you can explore. In general, using `help()` on any function is a quick and useful way to access the documentation. For ex, `Models.unfreeze_all` will unfreeze all parameters, even those which are fixed by default. To see its usage, you can simply type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:54.142345Z",
     "iopub.status.busy": "2021-11-22T21:09:54.142033Z",
     "iopub.status.idle": "2021-11-22T21:09:54.143426Z",
     "shell.execute_reply": "2021-11-22T21:09:54.143606Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method unfreeze in module gammapy.modeling.models.core:\n",
      "\n",
      "unfreeze(model_type=None) method of gammapy.modeling.models.core.Models instance\n",
      "    Restore parameters frozen status to default depending on model type\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    model_type : {None, \"spatial\", \"spectral\"}\n",
      "       restore frozen status to default for all parameters or only spatial or only spectral\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(models_selected.unfreeze)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Serialising models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Models` can be (independently of `Datasets`) written to/ read from a disk as yaml files. \n",
    "Datasets are always serialised along with their associated models, ie, with yaml and fits files.\n",
    "eg:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:54.147057Z",
     "iopub.status.busy": "2021-11-22T21:09:54.146264Z",
     "iopub.status.idle": "2021-11-22T21:09:54.879726Z",
     "shell.execute_reply": "2021-11-22T21:09:54.879931Z"
    }
   },
   "outputs": [],
   "source": [
    "# To save only the models\n",
    "models_3fhl.write(\"3fhl_models.yaml\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:54.882218Z",
     "iopub.status.busy": "2021-11-22T21:09:54.881911Z",
     "iopub.status.idle": "2021-11-22T21:09:55.101119Z",
     "shell.execute_reply": "2021-11-22T21:09:55.101311Z"
    }
   },
   "outputs": [],
   "source": [
    "# To save datasets and models\n",
    "datasets.write(filename=\"datasets-gc.yaml\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:55.103741Z",
     "iopub.status.busy": "2021-11-22T21:09:55.103142Z",
     "iopub.status.idle": "2021-11-22T21:09:55.266115Z",
     "shell.execute_reply": "2021-11-22T21:09:55.266355Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models\n",
      "\n",
      "Component 0: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1731.7-3003\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      2.742   +/-    0.50             \n",
      "    amplitude               :   2.59e-12   +/- 7.6e-13 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     17.603       GeV         \n",
      "    lon_0        (frozen)   :    262.949       deg         \n",
      "    lat_0        (frozen)   :    -30.051       deg         \n",
      "\n",
      "Component 1: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1732.6-3131\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      5.151   +/-    0.84             \n",
      "    amplitude               :   2.78e-11   +/- 4.5e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     12.216       GeV         \n",
      "    lon_0                   :    263.156   +/-    0.01 deg         \n",
      "    lat_0                   :    -31.518   +/-    0.01 deg         \n",
      "\n",
      "Component 2: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1741.8-2536\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      2.196   +/-    0.30             \n",
      "    amplitude               :   1.25e-12   +/- 3.2e-13 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     25.438       GeV         \n",
      "    lon_0                   :    265.457   +/-    0.02 deg         \n",
      "    lat_0                   :    -25.610   +/-    0.02 deg         \n",
      "\n",
      "Component 3: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1745.6-2900\n",
      "  Datasets names            : None\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      2.727   +/-    0.10             \n",
      "    amplitude               :   4.54e-11   +/- 2.7e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     18.370       GeV         \n",
      "    lon_0                   :    266.419   +/-    0.01 deg         \n",
      "    lat_0                   :    -29.011   +/-    0.00 deg         \n",
      "\n",
      "Component 4: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1745.8-3028e\n",
      "  Datasets names            : None\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : DiskSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      1.988   +/-    0.11             \n",
      "    amplitude               :   6.30e-12   +/- 7.0e-13 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     34.165       GeV         \n",
      "    lon_0                   :    266.453   +/-    0.00 deg         \n",
      "    lat_0                   :    -30.475   +/-    0.00 deg         \n",
      "    r_0                     :      0.528   +/-    0.00 deg         \n",
      "    e            (frozen)   :      0.000                   \n",
      "    phi          (frozen)   :      0.000       deg         \n",
      "    edge_width   (frozen)   :      0.010                   \n",
      "\n",
      "Component 5: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1746.2-2852\n",
      "  Datasets names            : None\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      3.253   +/-    0.31             \n",
      "    amplitude               :   2.43e-11   +/- 3.2e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     15.207       GeV         \n",
      "    lon_0                   :    266.564   +/-    0.01 deg         \n",
      "    lat_0                   :    -28.878   +/-    0.01 deg         \n",
      "\n",
      "Component 6: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1747.2-2959\n",
      "  Datasets names            : None\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      3.704   +/-    0.71             \n",
      "    amplitude               :   6.59e-12   +/- 1.8e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     14.624       GeV         \n",
      "    lon_0                   :    266.802   +/-    0.02 deg         \n",
      "    lat_0                   :    -29.995   +/-    0.02 deg         \n",
      "\n",
      "Component 7: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1747.2-2822\n",
      "  Datasets names            : None\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      2.680   +/-    0.38             \n",
      "    amplitude               :   4.91e-12   +/- 1.2e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     18.330       GeV         \n",
      "    lon_0                   :    266.824   +/-    0.02 deg         \n",
      "    lat_0                   :    -28.367   +/-    0.01 deg         \n",
      "\n",
      "Component 8: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1748.0-2446\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      3.480   +/-    0.58             \n",
      "    amplitude               :   7.17e-12   +/- 1.6e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     14.796       GeV         \n",
      "    lon_0                   :    267.012   +/-    0.02 deg         \n",
      "    lat_0                   :    -24.775   +/-    0.01 deg         \n",
      "\n",
      "Component 9: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1748.1-2903\n",
      "  Datasets names            : None\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      4.687   +/-    1.02             \n",
      "    amplitude               :   1.24e-11   +/- 3.3e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     12.596       GeV         \n",
      "    lon_0                   :    267.037   +/-    0.02 deg         \n",
      "    lat_0                   :    -29.062   +/-    0.02 deg         \n",
      "\n",
      "Component 10: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1748.6-2816\n",
      "  Datasets names            : None\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      4.345   +/-    0.66             \n",
      "    amplitude               :   1.76e-11   +/- 3.6e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     13.199       GeV         \n",
      "    lon_0                   :    267.160   +/-    0.02 deg         \n",
      "    lat_0                   :    -28.278   +/-    0.01 deg         \n",
      "\n",
      "Component 11: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1753.8-2537\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      3.658   +/-    0.48             \n",
      "    amplitude               :   1.50e-11   +/- 2.4e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     14.320       GeV         \n",
      "    lon_0                   :    268.450   +/-    0.01 deg         \n",
      "    lat_0                   :    -25.630   +/-    0.01 deg         \n",
      "\n",
      "Component 12: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1800.5-2343e\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : DiskSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      2.346   +/-    0.07             \n",
      "    amplitude               :   4.34e-11   +/- 2.4e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     23.149       GeV         \n",
      "    lon_0                   :    270.144   +/-    0.00 deg         \n",
      "    lat_0                   :    -23.719   +/-    0.00 deg         \n",
      "    r_0                     :      0.638   +/-    0.00 deg         \n",
      "    e            (frozen)   :      0.000                   \n",
      "    phi          (frozen)   :      0.000       deg         \n",
      "    edge_width   (frozen)   :      0.010                   \n",
      "\n",
      "Component 13: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1800.7-2357\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      3.576   +/-    0.61             \n",
      "    amplitude               :   1.08e-11   +/- 2.8e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     14.223       GeV         \n",
      "    lon_0                   :    270.184   +/-    0.02 deg         \n",
      "    lat_0                   :    -23.953   +/-    0.02 deg         \n",
      "\n",
      "Component 14: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1801.5-2450\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      4.524   +/-    1.03             \n",
      "    amplitude               :   9.48e-12   +/- 2.7e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     12.892       GeV         \n",
      "    lon_0                   :    270.381   +/-    0.03 deg         \n",
      "    lat_0                   :    -24.837   +/-    0.02 deg         \n",
      "\n",
      "Component 15: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1801.6-2327\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      5.050   +/-    1.23             \n",
      "    amplitude               :   2.42e-11   +/- 5.8e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     12.124       GeV         \n",
      "    lon_0                   :    270.403   +/-    0.02 deg         \n",
      "    lat_0                   :    -23.465   +/-    0.02 deg         \n",
      "\n",
      "Component 16: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1802.3-3043\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      6.222   +/-    1.29             \n",
      "    amplitude               :   1.48e-11   +/- 3.5e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     11.876       GeV         \n",
      "    lon_0                   :    270.599   +/-    0.02 deg         \n",
      "    lat_0                   :    -30.722   +/-    0.02 deg         \n",
      "\n",
      "Component 17: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1809.8-2332\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      4.220   +/-    0.26             \n",
      "    amplitude               :   6.26e-11   +/- 4.8e-12 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     13.446       GeV         \n",
      "    lon_0                   :    272.459   +/-    0.01 deg         \n",
      "    lat_0                   :    -23.539   +/-    0.01 deg         \n",
      "\n",
      "Component 18: SkyModel\n",
      "\n",
      "  Name                      : 3FHL J1811.2-2800\n",
      "  Datasets names            : fermi_dataset\n",
      "  Spectral model type       : PowerLawSpectralModel\n",
      "  Spatial  model type       : PointSpatialModel\n",
      "  Temporal model type       : \n",
      "  Parameters:\n",
      "    index                   :      2.557   +/-    0.49             \n",
      "    amplitude               :   1.30e-12   +/- 4.2e-13 1 / (cm2 GeV s)\n",
      "    reference    (frozen)   :     19.437       GeV         \n",
      "    lon_0                   :    272.804   +/-    0.02 deg         \n",
      "    lat_0                   :    -28.003   +/-    0.02 deg         \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# To read only models\n",
    "models = Models.read(\"3fhl_models.yaml\")\n",
    "print(models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-22T21:09:55.268200Z",
     "iopub.status.busy": "2021-11-22T21:09:55.267916Z",
     "iopub.status.idle": "2021-11-22T21:09:55.281257Z",
     "shell.execute_reply": "2021-11-22T21:09:55.281490Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets\n",
      "--------\n",
      "\n",
      "Dataset 0: \n",
      "\n",
      "  Type       : MapDataset\n",
      "  Name       : fermi_dataset\n",
      "  Instrument : \n",
      "  Models     : ['3FHL J1731.7-3003', '3FHL J1732.6-3131', '3FHL J1741.8-2536', '3FHL J1745.6-2900', '3FHL J1745.8-3028e', '3FHL J1746.2-2852', '3FHL J1747.2-2959', '3FHL J1747.2-2822', '3FHL J1748.0-2446', '3FHL J1748.1-2903', '3FHL J1748.6-2816', '3FHL J1753.8-2537', '3FHL J1800.5-2343e', '3FHL J1800.7-2357', '3FHL J1801.5-2450', '3FHL J1801.6-2327', '3FHL J1802.3-3043', '3FHL J1809.8-2332', '3FHL J1811.2-2800']\n",
      "\n",
      "Dataset 1: \n",
      "\n",
      "  Type       : MapDataset\n",
      "  Name       : cta_dataset\n",
      "  Instrument : \n",
      "  Models     : ['3FHL J1745.6-2900', '3FHL J1745.8-3028e', '3FHL J1746.2-2852', '3FHL J1747.2-2959', '3FHL J1747.2-2822', '3FHL J1748.1-2903', '3FHL J1748.6-2816']\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# To read datasets with models\n",
    "datasets_read = Datasets.read(\"datasets-gc.yaml\")\n",
    "print(datasets)"
   ]
  }
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