{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f230d52-dca7-4ce4-98cc-6267fc04893d",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.003185,
     "end_time": "2025-08-20T22:03:21.075254",
     "exception": false,
     "start_time": "2025-08-20T22:03:21.072069",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Normalized Mean Square Error\n",
    "\n",
    "This notebook computes the normalized mean square error of atmospheric surface pressure.\n",
    "It is compared to ERA5 observations, as well as the CESM2 large ensemble and CMIP6 model output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2292c691-9bd9-44d2-8a3f-cb90dbe2e383",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-08-20T22:03:21.081060Z",
     "iopub.status.busy": "2025-08-20T22:03:21.080499Z",
     "iopub.status.idle": "2025-08-20T22:03:34.680515Z",
     "shell.execute_reply": "2025-08-20T22:03:34.679912Z"
    },
    "papermill": {
     "duration": 13.604225,
     "end_time": "2025-08-20T22:03:34.681898",
     "exception": false,
     "start_time": "2025-08-20T22:03:21.077673",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "from nmse_utils import nmse\n",
    "from averaging_utils import seasonal_climatology_weighted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d67416c-a2d4-403b-85f4-647aa0a816eb",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.002804,
     "end_time": "2025-08-20T22:03:34.691653",
     "exception": false,
     "start_time": "2025-08-20T22:03:34.688849",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Parameters\n",
    "\n",
    "These variables are set in `config.yml`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7486e94-e493-4369-9767-90eb15c0ac3a",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-08-20T22:03:34.697218Z",
     "iopub.status.busy": "2025-08-20T22:03:34.696737Z",
     "iopub.status.idle": "2025-08-20T22:03:34.700378Z",
     "shell.execute_reply": "2025-08-20T22:03:34.699935Z"
    },
    "papermill": {
     "duration": 0.007035,
     "end_time": "2025-08-20T22:03:34.700951",
     "exception": false,
     "start_time": "2025-08-20T22:03:34.693916",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "parameters",
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "CESM_output_dir = \"\"\n",
    "case_name = \"\"\n",
    "start_date = \"\"\n",
    "end_date = \"\"\n",
    "base_case_output_dir = None\n",
    "ts_dir = None\n",
    "base_case_name = None\n",
    "base_start_date = None\n",
    "base_end_date = None\n",
    "obs_data_dir = \"\"\n",
    "validation_path = \"\"\n",
    "regridded_output = False\n",
    "base_regridded_output = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bae00d94",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-20T22:03:34.706291Z",
     "iopub.status.busy": "2025-08-20T22:03:34.705893Z",
     "iopub.status.idle": "2025-08-20T22:03:34.711645Z",
     "shell.execute_reply": "2025-08-20T22:03:34.711225Z"
    },
    "papermill": {
     "duration": 0.009277,
     "end_time": "2025-08-20T22:03:34.712560",
     "exception": false,
     "start_time": "2025-08-20T22:03:34.703283",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "case_name = \"b.e30_beta06.B1850C_LTso.ne30_t232_wgx3.192\"\n",
    "base_case_name = \"b.e30_beta06.B1850C_LTso.ne30_t232_wgx3.188\"\n",
    "CESM_output_dir = \"/glade/derecho/scratch/hannay/archive\"\n",
    "base_case_output_dir = \"/glade/derecho/scratch/gmarques/archive\"\n",
    "start_date = \"0002-01-01\"\n",
    "end_date = \"0021-12-01\"\n",
    "base_start_date = \"0002-01-01\"\n",
    "base_end_date = \"0021-12-01\"\n",
    "obs_data_dir = (\n",
    "    \"/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CUPiD_obs_data\"\n",
    ")\n",
    "ts_dir = None\n",
    "lc_kwargs = {\"threads_per_worker\": 1}\n",
    "serial = True\n",
    "regridded_output = False\n",
    "base_regridded_output = False\n",
    "validation_path = (\n",
    "    \"atm/analysis_datasets/fv0.9x1.25/seasonal_climatology/nmse_validation/PSL/\"\n",
    ")\n",
    "subset_kwargs = {}\n",
    "product = \"/glade/work/hannay/CUPiD/examples/key_metrics/computed_notebooks//atm/Global_PSL_NMSE_compare_obs_lens.ipynb\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9dfe1566-abe3-4b23-a59c-113334a0458f",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-08-20T22:03:34.718063Z",
     "iopub.status.busy": "2025-08-20T22:03:34.717641Z",
     "iopub.status.idle": "2025-08-20T22:03:34.720658Z",
     "shell.execute_reply": "2025-08-20T22:03:34.720241Z"
    },
    "papermill": {
     "duration": 0.006496,
     "end_time": "2025-08-20T22:03:34.721555",
     "exception": false,
     "start_time": "2025-08-20T22:03:34.715059",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "# Want some base case parameter defaults to equal control case values\n",
    "if base_case_name is not None:\n",
    "    if base_case_output_dir is None:\n",
    "        base_case_output_dir = CESM_output_dir\n",
    "\n",
    "    if base_start_date is None:\n",
    "        base_start_date = start_date\n",
    "\n",
    "    if base_end_date is None:\n",
    "        base_end_date = end_date\n",
    "\n",
    "    if base_regridded_output is None:\n",
    "        base_regridded_output = regridded_output\n",
    "if ts_dir is None:\n",
    "    ts_dir = CESM_output_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74c7803f-a8c5-445d-9233-0aa2663c58bd",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.002249,
     "end_time": "2025-08-20T22:03:34.726295",
     "exception": false,
     "start_time": "2025-08-20T22:03:34.724046",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Read in the current case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7f4132b5-db1f-4ae8-92df-07dd531b650e",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-08-20T22:03:34.731696Z",
     "iopub.status.busy": "2025-08-20T22:03:34.731309Z",
     "iopub.status.idle": "2025-08-20T22:03:34.734688Z",
     "shell.execute_reply": "2025-08-20T22:03:34.734294Z"
    },
    "papermill": {
     "duration": 0.006631,
     "end_time": "2025-08-20T22:03:34.735187",
     "exception": false,
     "start_time": "2025-08-20T22:03:34.728556",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "def fix_time_dim(dat):\n",
    "    \"\"\"CESM2 output sets time as the end of the averaging interval (e.g. January average is midnight on February 1st);\n",
    "    This function sets the time dimension to the midpoint of the averaging interval.\n",
    "    Note that CESM3 output sets time to the midpoint already, so this function should not change CESM3 data.\"\"\"\n",
    "    if \"time\" not in dat.dims:\n",
    "        return dat\n",
    "    if \"bounds\" not in dat.time.attrs:\n",
    "        return dat\n",
    "    time_bounds_avg = dat[dat.time.attrs[\"bounds\"]].mean(\"nbnd\")\n",
    "    time_bounds_avg.attrs = dat.time.attrs\n",
    "    dat = dat.assign_coords({\"time\": time_bounds_avg})\n",
    "    return xr.decode_cf(dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "caf05d8b-a711-40fb-b88a-c10472a49d30",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-20T22:03:34.740576Z",
     "iopub.status.busy": "2025-08-20T22:03:34.740178Z",
     "iopub.status.idle": "2025-08-20T22:03:34.744290Z",
     "shell.execute_reply": "2025-08-20T22:03:34.743852Z"
    },
    "papermill": {
     "duration": 0.00733,
     "end_time": "2025-08-20T22:03:34.744869",
     "exception": false,
     "start_time": "2025-08-20T22:03:34.737539",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/glade/derecho/scratch/hannay/archive/b.e30_beta06.B1850C_LTso.ne30_t232_wgx3.192/atm/proc/tseries\n"
     ]
    }
   ],
   "source": [
    "if regridded_output:\n",
    "    file_path = f\"{ts_dir}/{case_name}/atm/proc/tseries/regrid\"\n",
    "else:\n",
    "    file_path = f\"{ts_dir}/{case_name}/atm/proc/tseries\"\n",
    "print(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "318b8c9a-344f-41d5-87be-593847e4b6f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-20T22:03:34.750320Z",
     "iopub.status.busy": "2025-08-20T22:03:34.750015Z",
     "iopub.status.idle": "2025-08-20T22:03:34.754075Z",
     "shell.execute_reply": "2025-08-20T22:03:34.753625Z"
    },
    "papermill": {
     "duration": 0.007365,
     "end_time": "2025-08-20T22:03:34.754649",
     "exception": false,
     "start_time": "2025-08-20T22:03:34.747284",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/glade/derecho/scratch/hannay/archive/b.e30_beta06.B1850C_LTso.ne30_t232_wgx3.188/atm/proc/tseries\n"
     ]
    }
   ],
   "source": [
    "if base_case_name is not None:\n",
    "    if base_regridded_output:\n",
    "        base_file_path = f\"{ts_dir}/{base_case_name}/atm/proc/tseries/regrid\"\n",
    "    else:\n",
    "        base_file_path = f\"{ts_dir}/{base_case_name}/atm/proc/tseries\"\n",
    "    print(base_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ccca8e3a-a52f-4202-9704-9d4470eda984",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-08-20T22:03:34.760224Z",
     "iopub.status.busy": "2025-08-20T22:03:34.759912Z",
     "iopub.status.idle": "2025-08-20T22:03:55.980041Z",
     "shell.execute_reply": "2025-08-20T22:03:55.979443Z"
    },
    "papermill": {
     "duration": 21.224296,
     "end_time": "2025-08-20T22:03:55.981446",
     "exception": false,
     "start_time": "2025-08-20T22:03:34.757150",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "dat = (\n",
    "    fix_time_dim(xr.open_mfdataset(f\"{file_path}/*PSL*.nc\", decode_times=False))\n",
    "    .sel(time=slice(start_date, end_date))\n",
    "    .PSL\n",
    "    / 100.0\n",
    ")\n",
    "\n",
    "# Ensure all datasets have the same coordinates as the output data\n",
    "# (Avoid round-off level differences since all data should be on the same grid)\n",
    "lon = dat.lon.data\n",
    "lat = dat.lat.data\n",
    "\n",
    "if base_case_name is not None:\n",
    "    base_dat = (\n",
    "        fix_time_dim(\n",
    "            xr.open_mfdataset(f\"{base_file_path}/*PSL*.nc\", decode_times=False)\n",
    "        )\n",
    "        .sel(time=slice(base_start_date, base_end_date))\n",
    "        .assign_coords({\"lon\": lon, \"lat\": lat})\n",
    "        .PSL\n",
    "        / 100.0\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "073a2ad0-81e6-4817-9024-4b9b718fabb4",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-08-20T22:03:55.989871Z",
     "iopub.status.busy": "2025-08-20T22:03:55.989565Z",
     "iopub.status.idle": "2025-08-20T22:03:57.906505Z",
     "shell.execute_reply": "2025-08-20T22:03:57.905896Z"
    },
    "papermill": {
     "duration": 1.920989,
     "end_time": "2025-08-20T22:03:57.907384",
     "exception": false,
     "start_time": "2025-08-20T22:03:55.986395",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "# --Compute seasonal and annual means\n",
    "dat = seasonal_climatology_weighted(dat).load()\n",
    "\n",
    "if base_case_name is not None:\n",
    "    base_dat = seasonal_climatology_weighted(base_dat).load()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0527e3e-cd26-46b5-8c1e-08882109e12e",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.002461,
     "end_time": "2025-08-20T22:03:57.912759",
     "exception": false,
     "start_time": "2025-08-20T22:03:57.910298",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Read in validation data and other CMIP models for comparison (precomputed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "126e65b3-2b8c-400c-af02-2ad0b0f82e6e",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-08-20T22:03:57.918822Z",
     "iopub.status.busy": "2025-08-20T22:03:57.918459Z",
     "iopub.status.idle": "2025-08-20T22:04:01.501082Z",
     "shell.execute_reply": "2025-08-20T22:04:01.500607Z"
    },
    "papermill": {
     "duration": 3.586958,
     "end_time": "2025-08-20T22:04:01.502286",
     "exception": false,
     "start_time": "2025-08-20T22:03:57.915328",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "# ---ERA5\n",
    "era5 = xr.open_dataset(\n",
    "    os.path.join(obs_data_dir, validation_path, \"PSL_ERA5.nc\")\n",
    ").assign_coords({\"lon\": lon, \"lat\": lat})\n",
    "era5 = era5 / 100.0  # convert to hPa\n",
    "\n",
    "# ---CESM2\n",
    "lens2 = xr.open_dataset(\n",
    "    os.path.join(obs_data_dir, validation_path, \"PSL_LENS2.nc\")\n",
    ").assign_coords({\"lon\": lon, \"lat\": lat})\n",
    "lens2 = lens2 / 100.0  # convert to hPa\n",
    "\n",
    "# ---CMIP6\n",
    "modelfiles = sorted(\n",
    "    glob.glob(f\"{os.path.join(obs_data_dir,validation_path)}/CMIP6/*.nc\")\n",
    ")\n",
    "datcmip6 = [\n",
    "    xr.open_dataset(ifile).assign_coords({\"lon\": lon, \"lat\": lat}).mean(\"M\")\n",
    "    for ifile in modelfiles\n",
    "]\n",
    "datcmip6 = xr.concat(datcmip6, dim=\"model\")\n",
    "datcmip6 = datcmip6 / 100.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22cc331d-413c-4a87-bd89-812ad118cf8c",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.002611,
     "end_time": "2025-08-20T22:04:01.512374",
     "exception": false,
     "start_time": "2025-08-20T22:04:01.509763",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Compute the NMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6857717d-7514-45b5-ba33-a774f38b7c3e",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-08-20T22:04:01.518384Z",
     "iopub.status.busy": "2025-08-20T22:04:01.517967Z",
     "iopub.status.idle": "2025-08-20T22:04:02.364674Z",
     "shell.execute_reply": "2025-08-20T22:04:02.364201Z"
    },
    "papermill": {
     "duration": 0.850916,
     "end_time": "2025-08-20T22:04:02.365773",
     "exception": false,
     "start_time": "2025-08-20T22:04:01.514857",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "nmse_dat = []\n",
    "nmse_cesm2 = []\n",
    "nmse_cmip6 = []\n",
    "if base_case_name is not None:\n",
    "    nmse_base_dat = []\n",
    "else:\n",
    "    nmse_base_dat = {key: None for key in [\"AM\", \"DJF\", \"MAM\", \"JJA\", \"SON\"]}\n",
    "for ivar in era5.data_vars:\n",
    "    nmse_dat.append(nmse(era5[ivar], dat[ivar]))\n",
    "    nmse_cesm2.append(nmse(era5[ivar], lens2[ivar]))\n",
    "    nmse_cmip6.append(nmse(era5[ivar], datcmip6[ivar]))\n",
    "    if base_case_name is not None:\n",
    "        nmse_base_dat.append(nmse(era5[ivar], base_dat[ivar]))\n",
    "nmse_dat = xr.merge(nmse_dat)\n",
    "nmse_cesm2 = xr.merge(nmse_cesm2)\n",
    "nmse_cmip6 = xr.merge(nmse_cmip6)\n",
    "if base_case_name is not None:\n",
    "    nmse_base_dat = xr.merge(nmse_base_dat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1014f119-fc3f-428b-99ca-ab9de700148d",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.002511,
     "end_time": "2025-08-20T22:04:02.371193",
     "exception": false,
     "start_time": "2025-08-20T22:04:02.368682",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Set up the plot panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53494900-0145-4ab2-85b8-5ed6ae347892",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-08-20T22:04:02.377041Z",
     "iopub.status.busy": "2025-08-20T22:04:02.376751Z",
     "iopub.status.idle": "2025-08-20T22:04:02.381916Z",
     "shell.execute_reply": "2025-08-20T22:04:02.381501Z"
    },
    "papermill": {
     "duration": 0.008702,
     "end_time": "2025-08-20T22:04:02.382435",
     "exception": false,
     "start_time": "2025-08-20T22:04:02.373733",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "def plotnmse(fig, cmip6, cesm2, cesm3, cesm_baseline, x1, x2, y1, y2, titlestr):\n",
    "    ax = fig.add_axes([x1, y1, x2 - x1, y2 - y1])\n",
    "\n",
    "    cmip6 = cmip6.sortby(cmip6, ascending=False)\n",
    "    binedges = np.arange(0, cmip6.size, 1)\n",
    "    ax.bar(\n",
    "        binedges,\n",
    "        cmip6,\n",
    "        width=1,\n",
    "        bottom=0,\n",
    "        edgecolor=\"black\",\n",
    "        color=\"gray\",\n",
    "        label=\"CMIP6\",\n",
    "    )\n",
    "\n",
    "    ax.plot(cmip6.size + 1, cesm3, \"o\", color=\"blue\", label=\"THIS RUN\")\n",
    "    if cesm_baseline is not None:\n",
    "        ax.plot(cmip6.size + 1, cesm_baseline, \"x\", color=\"red\", label=\"BASELINE\")\n",
    "\n",
    "    ax.fill_between(\n",
    "        np.arange(0, cmip6.size + 3, 1) - 0.5,\n",
    "        np.arange(0, cmip6.size + 3, 1) * 0 + np.array(cesm2.min()),\n",
    "        np.arange(0, cmip6.size + 3, 1) * 0 + np.array(cesm2.max()),\n",
    "        color=\"salmon\",\n",
    "        alpha=0.5,\n",
    "        label=\"LENS2\",\n",
    "    )\n",
    "\n",
    "    ax.set_xlim(-0.5, cmip6.size + 2 - 0.5)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_ylabel(\"NMSE\", fontsize=14)\n",
    "    ax.set_title(titlestr, fontsize=16)\n",
    "\n",
    "    ax.legend()\n",
    "\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "56b4cd99-a27e-4f28-86c2-8013e7c7bc78",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-08-20T22:04:02.388245Z",
     "iopub.status.busy": "2025-08-20T22:04:02.387889Z",
     "iopub.status.idle": "2025-08-20T22:04:03.455623Z",
     "shell.execute_reply": "2025-08-20T22:04:03.455132Z"
    },
    "papermill": {
     "duration": 1.071266,
     "end_time": "2025-08-20T22:04:03.456246",
     "exception": false,
     "start_time": "2025-08-20T22:04:02.384980",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABfUAAAQGCAYAAACer1CDAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XdcU9f7B/BPEiDsvRwIDnBvcaLgXrXa1r1Xa5fWtn47bdEuV21ttcNRwa2tWrfiAicOVNy4QUUQ2ZtAcn5/HG9IyE1IIDj6e96vV17o3ePcc5PnnvscCWOMgRBCCCGEEEIIIYQQQgghLzzp894AQgghhBBCCCGEEEIIIYQYh4L6hBBCCCGEEEIIIYQQQshLgoL6hBBCCCGEEEIIIYQQQshLgoL6hBBCCCGEEEIIIYQQQshLgoL6hBBCCCGEEEIIIYQQQshLgoL6hBBCCCGEEEIIIYQQQshLgoL6hBBCCCGEEEIIIYQQQshLgoL6hBBCCCGEEEIIIYQQQshLgoL6hBBCCCGEEEIIIYQQQshLgoL6hBBCyAtixMxEjJiZ+Lw347matzoN3d69j+S0kue9KYQQQgghhBBCyAvJ4nlvACGEEMO6vXvfpOkP/14LyWklGPnVIwQ2ssa89z1Fp7t2rwjvL3iM3u3t8OlYN/XweavTEHEqD0v+54VGteXq4Uolw87juTh4Jg8JScUoKmZwtJPC09UCjevI0audHfx9rMrdvvBdmVi9J1trmNxSgmruFujUzAbDeznCzkb7mbOwP8395fj5Qy/R5eqbRtgfAPj9Ey808JPrzPvhz49x8VYRNs+pAVcnWbn78F+Tm69C2K5M3EhQICmtBLn5KjjayeDjZYFBwQ7o3MIGEolEZ768AhVW7c7C0dh8ZGQr4eIoQ5cWthjX30nnHL4oYm8W4qNFKRjbzxHjX3E2yzIVxQwbD2Tj4Jk8PE4vgYOtFO2b2GDiAGe95UmlYog4lYe90Xm490iBkhLAw0WGxnXkmDrUBbbW5R8/zbItkEoBJ3spGvrJMbSHA5rVs9YaX6hQYcfRXNy8r8CtBwo8TCkBY8D6b6vD203/18ILNwqx6WA27iUWIytPBTcnGRr6WWFEL0fUral93e+LzsX8Nel6l6XvOjt7rQDrI7Jx64ECjAEBtawwsrcjAhvZ6F3Wk8wSbIvKxdnrBUhKLUGRgtdL/j5WCG5li+6BdrC00C27hgjbP3mgE0b2dlIPF+oYY3m5yrDhuxomrftFlZxWgg0R2YhLKEJKhhL5hSq4OMpQu5olhnR3RKsG1qLzpWcpsXJnJk5dKUBOvgperhbo0dYOI3o5mnxeBEJ9ffj3WjrjsnKVOBZbgOjLBYh/pEBqlhI2cika+FrhjW4OomXp6IV8HI7Jw52HxcjIUULFAC9XC7RuYI1hPR3g4ax9XTxMKcaR8/k4c60Qj56UICtXCRcHGVoEyDGqjxNqeVtWaL9edBUtA2ISnxTjwOk83HpQjJv3FUjLUpZ7vSiKGTYdyMbhmDw8Si2B3FKCRrXlGN3XCU3qat/XC4pUOB5bgJOXC3DnoQIpGUpYWgB1a1hhQGd7dA+0M3n/D53Nw+bDOUhIKoaFDGhUR44Jrzihvq/udwqAl5O/dmQh9mYhCooYanpYoH+QPQZ2sYdUqlv2TbmPVEUZNOV+XtH7iCGm1BUZOUrsPZGLmw8UuHlfgeQ0JQCI1gmGCHV62e/BVcXUe5wpx+Ty7UIcv1iA2JuFeJyuREGRCt5u/Hv1yN5OsLfV/U5x4HQeLt8pws37Ctx7pEBxCfDJGFf06WBv8r6Zek3/F8swIYQ8D1RjEULIC25sP0edYav3ZMPORoI3ujo8k21Qqhg+++0JzsUVws1JhuBWtnCylyEtS4n7j4vxb2QOrK0kRgX1BV1a2sCvGv/SnpGtwulrBVgXkY3oKwX4/RNvWFlWLOBjyLJtmfhpuvhDgf/PsnKV2Budh0Z+VghqZgsHOykyc5SIvlyAWctT0b+THT4epf2Dt6BIhQ9/fozbD4vRuoE1urWxw92HCmw+nIPYm4X45WMv2MhfzMC+OalUDF8tfYKz1wrR0M8KQS0c8ehJMfZF5yEmrhC//89bJyCjKGaYtfwJTl0pRJ0alujd3h5WFsDjDCXOXC1AXoGTUUF9Qb+OdnB3lqmXnZBcgtNXCnDqSgG+neKBDk1LAwaZOSr8uTUTAA86O9hKkZ2nMrj8rZE5WPJPBuxtJOjcwhZODjI8fMx/kB+9kI8573mitUhAr1MzG9StqfvD3Eaue20fPJOHH8LT4GQvRa92dpBIgKjz+fh0yRN8Md4NPdrqBuEOnc3Dj2vTUVTMEFDLCj3a2sHeWor0bCUu3CzE/DXpOHAmDws/MM81b28j1amPcwsYtkbmwMtVht7ttbdRLIjyskp8UoLIc3loVEeORrXlsLWWIjWzBCcvFeD01RRMetUJo/o4ac2TnqXEuwuS8SRDiU7NbFDTyxJX7hQhfFcWrt0twg/veogGNyvjyPl8LNqYAXdnGVoGyOHubIEnmSU4dqEAZ64VYsprzhjWU/scHr+YjzsPi1Hf1wpuT6/V2w8V+DcqB/tP5eKXj71Qu3rpvS1sZxYiz+WjdnVLdGxmAztrCe4+KsaBM/k4eqEA86Z66DxM+y+oSBnQ5/LtIqzekw2pFPD1tkRGttLg9Ipiho9/eYyrdxWoU8MSr3ZxQG6+Csdi8zH958eY/aY7OjW31Vr+nFVpcLSTolV9a3RuYYHMHCWOxebj+7A0XL1bhGnDXI3e93X7svDXjix4usowoLM9CooYImPyMG3hY8x73xMtArTPd3xSMab+mIwiBUNIK1u4O8tw5mohFv+dgbuJCp37qan3EXOXQVPv5xW5jxhial2RkFSMFTuyIJEANTwsYG0lQaGCVXj9z4Kp9zhTj8msFanIylWhaV05erazhgRA7K1CbDyQg6OxBVg8wwsuDtrfRVbuzMTjdCWc7KVwdZThcbrh69AQU6/p/1oZJoSQ54WC+oQQ8oITa028ek827G2kZmtpXJ5DZ/NxLq4QgY2s8f07HrCQaQdi0rOUSM0y7cdAl5a26Nam9EeMopjhvQXJuPOwGIdi8tC3Ai2FDKnuYYHYm0U4c7UAbRvrb/n7/5G3uwV2/lgTsjLnNb9QhfcWPMbuE3l4vauDVmBr04Fs3H5YjOE9HfDWay7q4cKbGJsOZD+z8vk8RZzOw9lrhejaxhYzJ7ip32jYG52LBWvSsWxbJj4bpx3AWbE9E6euFOLNQc4Y0Us7wKhSmR6Y6NfJXuutGoAHC75ZkYpNB7K1gvpOdlLMn+qBgFpWcLST4dMlKTh7rVDvskuUDGE7M2FnLcGKL6vB07X0q+Px2Hx8vSwV6/ZliQf1m9sY1eIvJ1+FXzelw8leiqWfeavXMbK3E6bMScKvm9LRrokNHDSC5GeuFmBOeBrsbaX49m13tGmofU0zxnD8YgH2nMgtd/3GsrfVrXOT00qwNTIH3m4W/+ny3qyeHNsW1NQJwqdmlmDK3GSs2p2FgV0ctB5kLNuWiZR0JT4Y7oKBXfgDaMYY5q9JR8SpPEScNn89X9PLEj+844G2ja21tvV+32K8Nz8Zf+3IRPdAW7hrtL6fMcpN9CHynhO5+HFdOlbtzsKsNz3UwwMbWWNkb903VA7H5OG7lWn4eUMGwr6qZtb9ehFUpAzoXZa/NZb8zwt1a1hCbiVF72mG30j8NyoHV+8qENzKFjMnukH2dBtG93XE23OSsXBdOlrWt1Y/DHVzkuHLCW4IbmWr9X1l0kBnvDc/GduO5KJXOzvRN/fKephSjPBdWajpaYHfP/WG/dMWv6+F2OO9+Y95Gfm6mtb9c9GGdOQVMPzwrgfaN+F108RXGT5bkoLdJ/LQrY0dWtYvrTNNvY+Yuwyaej839T5SHlPrilrelvj5Q0/4+1jB1lqKcbMf4cHjFzdlX0XucaYek8HdHNGrnZ36waQw/S8bM7DjWC5W787CB8O1H2TNGO2GGh4W8HazwPqILKzYnlXhfTT1mv6vlWFCCHleKKhPCCGkXNfuFgEABgTZ6wT0AcDVSVbptDVWlhJ0D7TDnYeZuHlfgb4dKrU4HWP7OWHBmjQs356JwEbWoulkXhQ5+Sos/TcD0ZcKkFfIULu6JUb3dUSnZrblz6zh4q1CbDqQjWv3FCgoUsHTxQIhrW0xqo8jrK1KfzzK9LSWtbWWIrChNRKSivHoSYk6qM8Yw+4TebCRSzCmn3bLzJG9nfBvVC72nMzDuP5OFT7OShXD+n1Z2BOdhycZJfB04akLhnZ3EG3da8y+aqZ+Wr0nWysNlPDK9YPHxdhzIhfnbhQi5ekr7F6uFghqYYsxfRxhU6YF/e7jPGj85kBnrX3t28Eefx/IRuS5PEwbVppO50lmCf6NykHTenKdgD4As7VcbtuIB4yycrUfttlYS3UC4IZk56qQV8jQpK5cK6APAO2a2EAi4S3eKuPI+XzkFjCMf8VRax1uTjK80c0Ry7dl4sj5fLwSxAMYShXDL5syoGLA15PcRdN+SCT8rYJ2z+kBXm6BCv8czMbRC/l4nKGETMLryUa1ecoOzf0sVKiwcX82Is/lIzmtBNZWUjSqbYVRfXTTiugjpGJa/211nL5SgH+jcpCUVgIXRxn6drDHmL6OomXrxMV8bI3Kwa37ChQVM9TwsETv9nYY3N1Bq17QlyrH3dkCjWvLcfxiAR6nl8DeltcR+YUqRJ7LQzV3C7zauTTwJJFIMHmgMw6czsPu47kmB/U109Fp/ltIn9GqvnjLzlpeluja2g67juc+DQ6XHn99b4UFt7LFj+vS8eiJdrBQ34Oqbm3sEL4rCwlJxcjKVcLJ3vh7okrF8NonifB0lWH5F6WBrMwcJd74LBGMAb985ImmGi1Xv1mRiqjz+dg8twZcHfm6lEqeomb3yTykZpbAw8UC/TrYIaSNHUZ/rZ1m5MDpPMxZlYb2Tazxw7vaafqEFFSdmtvg2yn8gYapZcCQ6u4WqO5u/M/QE5cKAADj+jtplcsaHpbo09EeWw7n4MiFfHV5qlvTSidYCACujjIMCLLHih1ZuHiryKig/r7oPChVwOg+juqAPgDUrm6Fnu3ssPNYLs7fKFSnUHnwuBiXbhehRYBcHdAHAAuZBJNedcb5BY+x+0SuVlDf1PuIOctgRe7npt5HDKlIXeHqKFOX+YrSTBMXcSpPK5XdT9NL374wR/1s6j2uIsdE7PuERMLP6Y5jubh4q0hnvNjD+Ioy9Zr+L5VhQgh5nv477wUTQgipMg52/HaR+KSKW0I9baSsL8hcGTU9LdC/kz3uPCzGwTP5Zl++uZQogRm/PMbVO0Xo1c4OPdva4sHjYny9NBUHz+SVv4CndhzNwUeLUnDtngIdmtrgtRAHuDvLsG5fNv73awqKS8pvEa4oZrhwsxASCeBbrTSNysOUEqRlKdGkrlwnxY6VpQTN6smRmqmsVHn57Z8M/HM4B4ENrTEo2AFKFcOyfzOxaGNGhfe1RYC1OkVKc385xvZzVH+EYM2x2Hzsjc5DdXcL9GpnhwGdHeBgK8XG/dn43+IUlChLj5uimCEuXgEfLwvRHKytG9qguIT3XyE4dqEAShUQ3NIW+YUqHDyTh/X7srD3ZC6eZJrv+oq5zlucmZISS4yLoxRO9lLcS1TobN+ZqwVgDGhZXzywcfthMf45lI2N+3lwO79QPPgfe5Nva5uGugGGwKfDLt4q1Ji+CEmpJWhcx6rcPN5VkcarPIwxfLo4BWv2ZsPBToZXOtmjXyd71K5uieMX8/EotfQ4KooZZvySgtV7smFtJcEb3RzRqbkNLt4qwoc/P8bRC6bVVX9uzUD47iw0rC3HgKcBolW7sxC2U7cF5ortmfhqaSoePi5Bl5a2GNjFAVaWEiz9NxPf/pVq1PqycpW4Hq+AtRXvF0Vw9W4RikuANg10H6C6OclQu4Yl4uIVUBSb9mbK2H6O8HKVqf8tfDo1Kz84I3saG5IZ+evn1BUeSParbnxuZ+Ght6n3MKlUgmb+ctxNLNZ6EBd7qwjs6SGKvakdlLt4uxC+3hZawc35a9LUaUkGBTsgsJE1tkTm4Ld/dOvNnu3s0CPQFqeuFOLfqBz18MSUYiz+OwNuTjLMGFV+ihp9ZcCchFQe1dx0A3zVnta9F24Y18pWaFEvMzImXFo/6ZYxoX66pBEwFYKnYvVZAz8r2NtItAKsFbmPGGJqGXwW93NDqqquKE+9mlZ4/WkKy7o1LbXqE+E8mKt+NvUeZ85jYiHUey9Rd1EvWxkmhJDniVrqE0LIf1jikxKE78oUHZeaaXy6nM4tbLFhfzZW7sxEcnoJ2jexgb+PVaVbSmkqUqjUQeumRrZ+MtXYfk7YfyYPYbsyEdLatsIdNW4+nI3cfONbJ/fpYG90x1tpWUrU8rbAvPc91T9shnR3xJQ5yfh1Uzo6NrMpN996fBIPytStaYkfp3nC0a70PAmvWP8blYOhPcrkB89XYfPhbDDGW1+fvlqAlAwlxvZzRE3P0sBWYgr/YVTDQ3yfanjy4Q9TSrTmM8WN+wos/8JbnSZjfH8nTP3xMXYdz0WPtrbqXKum7KvQ8i7iVB6a+8tF06X0bGeHId11O6FbvScL4buyEHUuX537NvFJMVRM/3Go+fQ4JKaUoE3Dp/uVwAMzeYUqjJudhDSNtFWWFsDkgc4Y0l23xZ0he07k4sxVHoBUFDM8eFyC01cL4O9jiUkDdffRFBKJBFOHumBOeBre/D4ZQc1t4GQvQ+KTYkRfLkBQc96Ro5itkTla/7ezkWDaUFf0bKedO9hQedIsS4Ird/gxbKmnVfbzdu9RMa7HKxDU3AbfTPHQGqcoZlBqPBgS3i7pEWiLz8eXpt14o6sD3p3P04q0aWhtdB8Lt+4rsOLLauoUDGP6KTEm9BH+PZKDsf2d1OU65jrvsLFtY2vMetNd/TYLYwyLNmZg57FcHL2Qjy4ttd8OSk4rwb7oXKgYkJapxMnLBcjNV+HDEa5a2ygET4TzV1YNDwvceViMR6kl6r5VjDH+FWdcvFWEx+lKk9Id5ReqcPRCPqwsJWhaT/z+cjw2H7cfKlCkYIhPKsbZ64Wo5ibDBCPXcz2+CPFJPDd/RfpTaBlgjRMXC3DxVpH6uMfeKIS9DQ+WX7hZqG6FGp9UjIxsFbq0KD0/5+MKceBMPurXssKijzwhf3pOx/RxwltzkkTXOX2EK67eU2Dpv5lo7i+Hr7clvgtLQ6GC4du33URbyRpbBszJyV6KxCdAUpoSftW015GUxsuaZh2hj1LFcOB0HiQS41spJz4pgY1cIvo2orp+0ggWPkwpBgDRe59EIkEND0vcuK9AoUIFaytphe4j+lSkDD6L+7nB9VdRXVGeej5WGGwrxdbIHNSraSVan5irfjb1HmfOY7L3JP9e3caMrfKr0stYhgkh5HmioD4hhPyHPXpSopVipKICalnhkzFu+H1zBrYfycX2I/xVcQ9nGVo1sMZrIQ4IqGVai+CjF/JxP5n/+M3IUeHU5QI8yeQdgnVuUTWvxLo6yTC4mwPW7s3G9qM5GNzNtOCpYMvhHJM6FGsRYG10UB8AJrzirJXmqJa3Jfp0tMP2I7k4cbFAJyha1q5jOVCqgKlDXLSC3AAwvKcjNh/KweGYfN2gfoFKq7xYyIAprzljaA/tDpnznra4trMR/8Fl9/QHbl5BxdOyvB7ioJX32sZaijH9nDB7RSr2n8pTB/Uruq/6eDiLn6dBwfYI35WFc3GF6qB+XgEPztrrOQ62IschM5f/e9XuLLRpYI0F0zzh6SLDpVtFWLg+HX9syYSPl6VW2oby7Dmp+waHs70Uvdvbw9Ol8g/eurWxg5O9DD+EpWqty7eaJXp3sNMpB9XcLfDBcBe0aWgNdycZMnJUOH2lACt3ZmHu6jQ42ku10uII5UnsONrIpZBKtY9h+tNWux7OL3bTQ7mV7kNDK0sJoPH2wL5TebCQAW8O0k67UbemFfq0t8fO48Zd84LR/Zy0cio72cvQqbktIk7l4cHjYtSpwevpbU/r8I9GuGql4pJIJHhzkDN2Hc/F4Zg80aC+Zh1hI5fgkzFuOtsnnK+qrCNM8fOGdGRkqzDhFSe96RxOXCrQSsFRv5YVZk5yM6r1eW6BCvNWpUEq4XVmRTT35w8bLtwsLA3q3yxEc39r+HhZYGtULhTFDFaWEnXL3+YaHbQeePpQfHQ/R3VAH+D3vde7Oojmy7a1lmLmBDdMW/gY361MQ5uG1riRoMDwng56UxkZWwbMqV1jG1y7p8CaPVn4YkJpTv2k1BLsO8nLsjEP2sN2ZuHuo2L07WCn1UeMIXkFKjg7iJcZsXJcXtm3tZE8nY7B2qpi9xExFS2Dz+J+bnD9L1hdoclc9bOp9zhzHZPbDxRYvScLLg5SDBdJz/OieVnLMCGEPE8U1CeEkP+wwEbWmPe+p+i4a/eK8P6Cx0Yvq1c7O4S0skXM9QJcuVOEm/cVuHpXgYhTeThwmud7fbWLQ/kLeurohQIcvVCgNaxzCxvMetO9SvPdD+/piF3HcrFuXzb6drDX+yPAkA3f1aiCLeMsZECj2rrBhmZ15dh+JBe3HyrK/QF5LV4BADhzrRDn4nRTEshkEtx/XKwz3NvNAod/rwWliuFJhhKHY/Kwcmcmrt4tQuhkd52OdKuSWGtaYdjth6XbXtF91Ycxhn3Redh3Kg/xj4qRV6CCZt+1aSZ2CF2W6ulvShcHGWa9VdpCun1TG/xvtCs+++0JNh/KNimov+R/XuqOchXFDIlPirF2bzaW/JOBB4+LdTrHM9Xe6Fws2pCOV7s44LUQB7g5SvHgcQlP3/JnKt4f4qJOYwAAzf2t0dy/NCDo7SbFwGAHVPewwKdLniB8Z9Zzy3X/LPh6W6JOdUscOpuPlIzHCGpug6Z15fCvZaWVTiCvQIWk1BL4elvAw0X3K3nzADl2HjfumhcEiKRbcn/68CNXI6Bx/V4RrOUS7Dkp3pGw3FKC+8m6LZ9bBFjj8O+1UKJkSE4rwa7juZi7Og1xCUWYOrRy5ayqrNieiUNn89G2kTVG9tEf2Pp0rBs+HeuG3AIVbj9Q4K8dmXh7bjJmv+WhN8AN8GsudNkT3H9cgkmvOqnfCDJVnRqWcLKXqtPspGcpcf9xCQZ0toePlyU2HsjB1btFaFnfGrFP06K18C+tJ+8k8rqwSR3durOxyDBBw9pyjOvvhJU7s3gL2VpWmPiqs97pn0cZeKObAw6fy0fkuXzcf1yMlgHWyC1Q4diFfHi5WeBuYnG5aZV2Hc/F+ohs1POxxPtDXAxP/JIprwzui85Fcpr29RzU3Bb1KpmezVhibzea8gbj81AV9fOzlJRagi/+eAKlimHmRA+T+vgQI7zJpKlFgHWF67uyXvQyTAghL6oX905KCCHkhWNlKUHHZrbo+LTDVkUxw6aD2QjbmYXfNmcgqLmt0R3mzpzohm5t7KBUMtx/XIylWzNxLLYAYbuydNJ5CDF+lYH0ocK48p4H2FpLMaqPI37bnImNB7IxyUDw4nlwtJOKdmjp8jTVUZ6evOSacvL4NOv2VewtDZlUAm83C4zs7QSpVIJl/2Zi14lcDHz60Ka8Vk/ltZoyhljLSBcHKaQS7fVWdl/LWvx3BrYdyYWniwwdm9nA1VEGy6ffllbvydbqi8DuaYvLXD3HIV/kOAjztKov12ohDfB8u5YWPPVQRVlZSlC7uhW+GO+GuAQFdh7LxbCejhUOntx/XIyf16ejfRMbvDe4NBBWz8cK30zxwLjZj7BiRyb6drDT6US4rMBGNvB0keHmA4W6xTFQWp5yC1Q6gYeCIhVUKu1jKKT9MiWF2LMkk0mwcLonwndn4XhsAf7YkgmApxB5LcQBo/o4QiaVqMuHi55WwK4mXPMCsWtOCHaqNBaTnaeCUgWDb3IVKvSv10ImQU1PS7z9uguKFAz/RuWibWMb9cMaYTuqso4wxqrdWVgfkY2W9eWY/Za7UTma7W2kaBFgjbnveWLc7EeYuyoN67+tLtpJvKKY4eulT3DhRhFG9nbEqD5OIks0jkQiQXN/OY5eKEB6thIXnrbGbxFgjeruFpBJecv9FgFyXLxVBL9qllr1ZH4hg1TC7yFluZSTKi+ohS3CdmWBMaB/kL3ovpZVXhkwJ1trKRZ/7IXVe7Jw/GI+th3JgbODDAM626N9UxtM/ynFYNByb3Quft6QjjrVLbFgqme5dZUmOxupSeW4vLKf/7Rlvq310/qvAvcRTcaUwYhTeTodpXq7WaCej9UzuZ+Lvd0ovMH4otQVZZmzfjb1HlfZY5KcVoKPFj1GVq4Ss970MEuqurJvMgnMEdR/GcowIYS8qCioTwghpMKsLCUY09cJ564X4tLtIly5W6STrqE8MhkPQn4zxQOTvkvCun3ZCGpuq5XOR/ginp2n/8eT0LmgMV/aX+3igK2ROdhyOAevBRv/doGgKnPqZ+epoFIxncC+0FGgnRHBCCFYsOunmpXOcRzY0BrL/gUu3ixSB/WF/KT6Oh0T8pvW1JMP1hiZOUrU8tLOfZqRw1vNa55jc+5rRo4S24/mok4NSyz5n5dW0D09S6kTAK3hYQmpRP9xEHLkaubF9fGyBFAgmitWKpXA1lqqt0NZU8hkEvj7WCIptQS3HygqHNSPuVaIEqX4D3crSwka15HjcAxvPVvft/y+MBztpUjJUGoF9Wt4WuDGfQUSn5ToBDzEylKTp31unL9RiAkDKrRbVc7JXoYPhrli2lCG+8kluHCTd0YavisLFlJgZB8ndXnNyBF/OGHKNW8q4RratqBmpZfVpqE1th/NxcWbheqArpDbOFFPnvPEJyWQSoDqVdSxKsAD+qt2Z6GFvxzfv+OhlZLGGHY2UjSsLceJiwVITCnR6iwc4IGor5Y+wdlrhRje0wGTK9l/BcCvs6MXCnDxZiEu3iqCk70UdWpYQiKRoL6vFWJvFuHeo2Jk5arQrY32vdbWWgIV4/eQsteRUJbEFJcw/BCeCokEsLOW4K8dmejQ1EYrjVN5xMqAudnbSvHuYBe8O1i7lf2+aP62SX1f8Ra7e0/mYuG6dPh6W+LHDzxNbrFcw8MC1+4pkJ6l1Gm0oK6fNHJ5Czm7hdz6mhjjb1K5OcnUHXpW5D4iMLYM/vyhl/79ewb3c0NvN74IdYUYc9bPpt7jKnNMhIB+WpYSoZPd0aGpea5H4U0mc3tZyjAhhLyo6HElIYSQSrOWVz4ti5WlBG+/7gzGgOXbMrXG2dtI4ekqw8PHxergfVnX7vIWPHVrlN8JlqWFBBMGOKNQwbBqj26e4fJsOZyD1Xuyjf6UfWXYkBIlcO2ebkvtS087B61Xs/xXjRv68aDntXtF5UxZvtSn6WZkGr9Da3pawM1Jhit3ilBQpB2AVhQzXLpdBDcnmd5Oy4xx+bbutgvD6tUsPcem7qtUaLUs8tZHUmoJGOMdKJZtRS8cf01WlhI08LPCg8clouf43PUCWFqUbiNQ2rlrQpJuwCczR4msXBW8XM3zw1N4CGboDZfylDzt1FXfdZf1tI8AYzqdzitQ4UFyCextJOrWqUBpLvGY67rpk84+HaaZzqdFgBzV3C1w9a4CF27ozqNJUVyJnTcDiUQC32qWGBTsgAVTeSq0k5d52jE7GymquVsg8UkJnmTqlh+hVWJdI655UzXws0J2nko08GgqdR2h8SCyUW05LC2AmLhCMKZ9DtKylLiXWIwGflbqBzumEK5hpYGCHb4rE6t2Z6G5vxw/vOehcz0bK02k/gO0A1FDezjgrdfMk86lNK9+0dN8+nJ1OrqW9a1xPb4Ip56WH81rAgDqPu0v4cpd3brqqsgwwfJtmbj9oBhj+jrhkzFuyMpVYe6qNJ3zZohYGXhWDp3NBwB0ba3boGDvyVz8uC4dPt6WWDjdU29ufEOE4xxzvUBnnFA/NdNIg2SoPouLVyC3gKmnASp2HwHMVwafxf3ckKqsK8ojFFexusSc9bOp97iKHpPktBJ8+PNjpGYq8dUkd3Rqblojm2ftv1KGCSHkeaKgPiGEkHIdjsnD+Ru6Py4A4MqdIly8WQSZFGgokgveFJ2a28LfxxLn4gpx6bb2j59e7eygVAFL/83U2Y4nGSXYdDAHUinQPdC43KbdA21Rr6Yl9pzQzdNZng3f1cDh32sZ/TH19eSwXZnqYCoA3E8uxr6TebCzkaBT89JWVyVKhvvJxUh8oh2YGxhsD5mUp5JJSdfdt9x8FW49KH1wcPuBQvTV/+w8Jf7angkAaNuodL0SiQT9O9mhoIhhTZmHIusjspCTr0L/TnaV6htha1QOUjV+SBcUqtTr0sxfa+q+OjxtIf8kQzdILQTTr94tgkrjR/6TjBKdB02C/kH2AIDl27XL5d7oXCQkl6Bra+2OZJv7y+HrbYHzN4q0gkSMMazYwdcRUiY4lZalxP3kYr3pGcTcvK/AlTv8ujSUT7s8Qqv4XSdy8SRD+/iev1GICzcL4eIo1WrFfEXkAUiRQoWF69JRVMzQtY122QhpbQc7Gwn+jcrROodpWUpsOZwNexsJgluVHhOZVIIPhrlAKgG++SsV5/UE9k9eyses5U8qtuOVkJRagniRhzZCi0/NQEzv9nYoUQIrtmmXn3uPFNh3il/zQRrXfFYuLwv6HrIY6/UQ/tbNgjXpostKz1JqPXi6Hl8k+oAkOa0EGyL4GyxtG5fWc3Y2UnRtbYek1BLsOFaat58xhhXbM6FipdeOqQxdwwAP6K/ek42m9eT44V3DAX1FMdP7QHBvdC7i4hWo4WGhbn0tzDPzTx6IGtLdAW+/br787LWrW8HFQYrjsfl4mFKClhr3jpYB1ihRApsP50AigVZgGIC6A++1e7K1zlV6lhJbI3NE13f2WgG2ROagcR0rjO7riKAWtnglyB7n4grx90HteUwtA0DF6i59xFJr/HMoG+fiChHU3AYNygS995zgAf1aXhZY+IGn3jQq5enTwQ4yKbB2X7bWftx7pMCB03mo7mGh1eeCj5clmtWTI/ZmEU5dKa3jS5QMK3fye1j/Ttpl39T7iDnL4LO4nxtSlXVFeRxspZBIgFQ9dYmp9bM+pt7jKnJMNAP6Mye6o3OLFz+g/18pw4QQ8jzR40pCCCHlunZPga2ROXB3lqFZPTk8XS1QUsKQkFyMc9cLoWLAm4Oc4eFc+dvKuP5OmPlnKsJ3ZuGnD0t/KI/q7YjzcYXYF52Ha3eL0LqhNWytpXicXoKTlwpQUMTw9uvOT9OblE8ikeDNQc74dMkTnVyvz5Obkwy5+Sq8+UMy2je2Rl4hw+GYPChKGD4e5aaVYiY1U4nx3yTBy1Wm9Xp77epW+GC4KxZtTMe42Ulo14TnZM4rZEhKLcHFW4Xo094eH47knRruO5WHPSdz0cJfDi83C9hYSfA4XYlTV/hx7dLSBt0DtX8gDuvpiJOXCrDxQA5uPShGQC0r3ElU4MzVQtSraYlhPfV3SGmM+rWs8OYPyQhpbQsrCwmOxeYjOU2J/p3stFq0mbqvtbwt4eYkQ+S5fFhbSeDhLAMkPCWTm5MMXVra4OiFArw9Lxmt6lsjI1uJU1cK0bK+HEmpug8NerWzQ9S5fETG5CM5tQTNA6yRlFqCYxfy4ekiw1uDnLWml0kl+GSMGz7+JQWf//YEQS1s4ekiw+U7RYiLV8DfxxIjemkfuxXbMxFxKg+fjHFFnw66wY09J3Jx5ioPHhWXAI9SS3DyUj5KlMCEAU46aTT+2JKhbsV/N5EHbv/cmqFOBzGilyNqefPrqFFtOXq0tcXBM/mY8G0S7zfjaUe50U9bDE8d4qLVQnfawsfwrWaJAB9LuDlbIDNHifNxhUjJUKJuTUud1+sdbKWYNtQVc1alYcrcZHRtbQupBIg8n4+MbBU+H+emDuQK2ja2wefj3fDj2nTM+CUF9WtZoVEdK9jKpcjIUSL2VhEePSlB6wYVz/d75Hw+HjwWf+DXrY0tAhuJB3PuPFTg62WpqO9rhdrVLeHqKENqphInLuZDKgWGdi89v8N7OuLUlQIcOJOP+8klaNnAGlk5SkSez0eJkuGzce5a1/y/UfwtobH9HDH+FecK71vbxjYY09cRa/ZmY0zoIwQ2toGXqwWy85R4lFKCS3eKMHGAE3yr8dzG6/dl4/KdInX9L5PycnbmagGKS4DB3RzQtJ72sX5zkDNibxbi100ZOB9XiJqelrh8pwhX7hQhsJE1elewc8mW9XmKmtkrUtGusTWsLCWoU90K7ZvaYF90LlbvyYZMCjTwtcKmA7p9Bmh27qgoZnh/wWPUrm6JujUt4e5sgbwCFeLii3DrQTFs5BJ8Mka789efN6Qj5nohXB2lsJFLEL4rU2cdlekEtLm/NaLO56u3VdC4jhUsLYDMXBXq1rTUSePRuoE1egTa4uDZfEz6Lgkdm9mguIThyPl8NPCTI/pygVafM5k5SsxbnQZbuQRfTijtb+Ddwc64eKsQK3dmomV9a3UqvIqUAX11V1auEn9uLT1uSiWQlafCvNVp6mFlU30M/SIRLQKs1Sk0Lt4qws37CtSvZYX/jdGe9vyNQixcnw7GgGb1rLHjqO5DjXo1rRBkRODTx8tS3ZHw5O+TENzSFoVF/L5comT4eKSrTify00e4YuqPyQhd9gTBrWzh7iTDmWuFuJtYjH6d7HRynJt6HzF3GazI/dyU+0h5KlJXaJaV9KdvimgOe/t153JTLdlYS1Hf1wqXbhdh3uo01PS0gETCG4d4uVqYXD/rU5F7nKnH5MOfH+NxuhKNalvhbqICdxN13/gse8/YfSJX/RBemH7PydLc9Z2a2Rh1jQCmX9P/tTJMCCHPCwX1CSGElGtodwdUd7dAzPUC3EhQ4OTlAiiVDC6OMnRuaYsBne21WqpVRsdmtqhfywqxt4pw/kaherlyKyl+mu6F7UdzEHkuH/tP5aFQweBkL0XL+tZ4PcTB5M7AAhvZoGV9OS7cqHyaGnOxkAELpnli2bZM7D+dh9wCFepUt8Lofo7o1Mz4llevBNmjXk1L/HM4B5duFeHkpQLY2Ujh6SLD4G6O6N2+9AdhcEsb5BWocP1eES7fLkKhgsHRToqmdeXo2c4O3drY6rRwspFL8dOHXli9OwtHL+Tj4q1CuDrKMLibA8b2d1L/KKqo94a44Mi5fOw+mYfUzBJ4uFjgrUHOGNJDtw8EU/ZVJpXgm7fcsWxbJg6cyUN+IW99162NHextpPh0jBu8XLNwLDYf/0blwMvVAoO7O2BEL0f0mvpAZ90yqQTfTvHAxgPZOHA6j7e6s5Wid3s7THzVWbTj6Ia15fjtEy+s2p2FCzcKkV+ogqerBe8grrejycduz8nSzuukEp57ukWANV7tYi9aZo5eyNd5kHX0QmmL0t7t7bR+yH421g3N6lkj4lQujl/M59ednRQdmtlgWA9HdWt+wZDuDrh2rwgx1wuRk6+CpYUEtbx5CprXQuxFc5v3bGcHJ3sp1kdkqzvj8/exwmdjHfUGz7sH2qGZvxz/RuUi5nqBuk5wtJfCv6YVRvVxRM+2FQscA8CtB8W49UA8PU3dmpZ6t6u+rxVG9HLExVuFOH2lALkFKrg4ytC6oTWG9XTUSqNhZSnBwg88sXF/NiLP5WPL4WzILSVoVk+OUb0ddYKk5jRhgDOa+Vtja2QOLsQVIrdABUc7KbzdLDCuvxN6aLz11K+TPaysJLiRoEBMXCFKShicHWRo19gG/YPsRfOouznJ8Nsn3li5IxOnrhbg1JUCeLpYYPwrThje01G0Q3BjvNLJHo/TSnD4XD7W7cuGUsXLbPumNuq3rpQq4J9D4q3TgdJgubVcgvGvOCH2RiEu3ChCVm4+LGQSeLlZ4I1uDhjczUEnHZawjvRsld6OhoVOQCuiRYAcUefzdd6AkVtJ0dBPjku3i3RS7wg+HesGH29L7DuZi21HcuDubIE3ujqgZQNrRF8u0ApAzl+ThvRsFb4Y76a1rdZWUsyc6I73FyTj+7BU/PmZN2zk0gqVAX0KiphOp5uFZYaVDer3aGuH2JuFPOWWhKfcmPKaM14LcdBJQ5KSzlOpAcDO47kQ07u9ndEBy9F9neDtZoEth3Ow42guLCz4G1DjX3HSeUMAAPyqWeL3T7zx145MnLlaiIIiFWp4WOL9IS4YFKz7YNbU+4i5y2BF7uem3kcMqUhdIdZpq+awcf2d4GREA//Px7nh980ZOHExH3mFDIzxh9lerhZmrZ9NvceZekyEc3HtnkI0hSOgG9S/cqdI5zheefrgAAC8XGVGXyOmXtP/tTJMCCHPi4SZkjCREEIIIYQQQshLY/cJ3lnsB8Nd1B2eE0IIIYSQlxvl1CeEEEIIIYSQl1x6llK3z5nMEqzdmwWpFOjQxPjW9IQQQggh5MVG6XcIIYQQQggh5CW3fn82Tl8pQNN6cjg7yJCSXoJTVwqQX8gwrr8TPF3ppx8hhBBCyH8FfbMjhBBCSJW5/UCB4xfzy53O281CtBNYQiorOa0E+6LF82prsreVYnC3ynXwTEwXe7MQsTcLy53O2I5NXySbD2cjN19V7nSV6VhXU9tG1khIKsbpKwXIyVfxToRrWGFgF3t0D6x4/xKEEEIIIeTFQ0F9QgghhFSZ2w8VejtB09TcX05BfVIlktNKjCqDXq4yCuo/B7E3C406P6Z0bPqi2HI4R6cjRjGV6VhXU9vGNmhrQoe1hBBCCCHk5UUd5RJCCCGEEEIIIYQQQgghLwnqKJcQQgghhBBCCCGEEEIIeUlQUJ8QQgghhBBCCCGEEEIIeUlQUJ8QQgghhBBCCCGEEEIIeUlQUJ8QQgj5/yI+HpBIgPHjn/eWPF9+fvxDCCGEEEIIIYS8hCioTwghlSUESst+7OyAZs2A2bOB3FzDy1i5snS+K1cMT3viBDBkCFCjBmBlBbi4AA0aACNHAqtWaU8bFSW+bZqfQYO055FI+PLKIyz77be1h48fX7rss2fF5w0J4eOTk0uH6TuOmp8WLcrfLs3lCx+pFHB2Bjp1ApYuBVQq3XnCw/m0s2bpX66+afz8+HBvbyAvT3xeY4/rf9XFi8CECfyacHMDrK2BunWBoUOBmBj98926xafx8ABsbPj8S5aIn8MXxaxZ/HxHRZlvmcnJwOTJQLVq/NgFBADffAMoFPrnyckBQkOBJk0AW1t+DbRqxeskYwllW/MjlwO1awNvvcWv27Lu3OHH4NVXeT0lkZT/EKWkhNeDHTrwc+3gADRqBHzyiXY9IdCsZ8p+9F1nKhUvO82a8bLk4cHL1q1bhrft3Dlg0iTA35/X6zY2vOyOGQMcOGB4Xn2E7T91Snu4UMcY+/kvPaCLiuL3sYYNeVm1tQXq1wcmTgRu3NA/39mzQL9+/F5oZwe0bQusX1+5bZFI+H1EzK1bwA8/AF26ANWr8/uwjw8wdiwQFyc+z9y5QK9efDobG14HtmkD/PQTkJ+vO/3x48DHHwOtW5fWlw0aAJ9+CmRmVm7fXmQVLQNijh4FZswAunYFnJyMu14yM4Gvv+Z1hIMD4O4OBAbyeqOwUHd6xoCtW/k6qlUr3d4pU4C7d03b3qIiXqcHBPDzXa0ar/PF6j9BRAQvp46OfHtDQvgwfUy5j1RFGTTlfl6R+0h5TKkrYmOBL74Aevfm22uoTjBEqNPDwyu+3caqyD3O2GNSXAxs2cKvoYYN+bQODkC7dsDvvwNKpe48+fnAwoX8mm7QgH8Xl0jEvzcYw9Rr+r9YhgkhpAyL570BhBDyn1G3LjB6NP83Y8CTJ8DevfwLXUQEcOwYIJOJzysE9RkD/voL+Pln8enCw/mPWwsL/iXc3x8oKOA/Hvfs4V94x43Tna91a+CVV8SXWZWB5k8/BQ4fNm0ezeNYlre3acv6+GPA3p7/2EhI4D++334buHAB+PNP05ZljMeP+Q+Yr782/7JfdmfP8jLaoQMQHMx/EN69C+zcCWzeDKxerXver10DOnbkPwyHDuU/ivbuBaZOBS5dApYtez778qwlJ/Mfzg8e8IdwAQH8x2poKBAdDezezX8sa7p/H+jWjR/jHj2A/v150Oj2bf7DPDTU+PXLZMDMmaX/z8wETp8Gli/n19SFCzxYKTh2jD84kMn4j39DQSnBsGF8WfXqAcOH8wcHp04BCxYAa9cC58+LX/8ffMCDf5rc3cXX8fbbfJsbNeJl6PFjYNMmYP9+4ORJPlyTSsUDCD//zOvcbt34j3NLS35cd+/m2/bNN8BXX5W/j8Zo0UL33MTGAtu38+umbFDJ2AedL4ODB3m5bteOB9KsrIDr13ndsH49v/a7dtWeJyqqdNrhw3mgZ+tWYNQoHjj64gvzb+dXX/Fy06QJMHAgD6hevgysWcPrsogIoHNn7XmWLuXlsmdPwNOTP+iPiuL3qNWrefmztS2dfvBgIDUVCAriDwuEh4Tz5/Pr9+RJvpz/moqUAX1WruQNHWxtgVq1gOxsw9NnZvLvSnfv8uM+ZQqvM4V7zr//8od4mnXtjBn8wUy1arxudnTkD7CXLwc2bODnqUmT8rdVpeJlKSKC7/vrr/OAYFgYX+fp07r137p1/J7p7s6/90kkwN9/A3368Hpp1Cjt6U29j5i7DJp6P6/IfcQQU+uKbduAOXP49AEB/Fi86Ey9x5lyTO7c4WXCwaH0XpiVxb/DvfcesG8fv09JJKXzpKTwawQAfH35g4P09Irvn6nX9H+tDBNCiBhGCCGkcu7dYwxgrHdv3XGFhYy1bMnHHz4sPn9cHB8/ZAhjfn6MubkxVlSkO11eHmMODow5OjJ2+bLueIWCsf37tYdFRvJlT5li/P4AjNWvX/50+pY9bhwfXrcu/7t3r+68wcF8XFJS6TBDx9FUYstnjLFbtxizs2NMImHszh3tcWFhfJ7QUP3L1TeNry9jlpaM1arFz1FKiu68xh7XqiQc43Hjnv26CwrEh1+5wpi1NWOenoypVNrjunTh27t7d+kwhYKx7t0NX1Pl8fXln6oSGsq3LzLSPMsbO5Yv7/ffS4epVKXX2sqV2tOXlDAWGMiYjY34MSouNn7dvr6MyeXi4959l6//66+1h9+5w1h0NGP5+fz/crnh4336NF9O27b8/Gr64AM+bvZs7eHCvt+7Z9x+HD7Mp+/cmdfLgoMHeX3QpYvuPJ9/zudp0YKx27d1x+fnMzZ/PmOffmrcNohtf3R0+dMaUzf9F+irIw4e5Pvfpo328OJifp+Ryxk7f750eHY2Y40bM2ZhwdjNmxXbFoDfR8SEhTEWG6s7fMMGPl+jRrrj9O3bmDF8niVLtIfPncvYo0faw1Qqxt55h0//7rvl7cHLydQyYMjZs/z+UlLCr7Py7n3z5vFpPvxQe3hREa9PAcaOHCkdnpTEmFTKv7dlZWnP8/PPfPoJE4zb1pUr+fTDh2vfB4XhY8dqT5+ezpizM2Pu7ozdv186/NEjxry9+bj0dO15TL2PmLsMmno/N/U+YkhF6oorVxg7d45vY1KS4TrBEKH+Dgur2LYby9R7nKnH5OFDXnby8rSXk5vLr0uAsb//1h6Xk8N/l6Sl8f/37m3afbssU6/p/1IZJoQQPSj9DiGEVCW5vLRV2ZMn4tP89Rf/O3Ysb3WVlsZbu5R15QpPp9G1q3jLL0tL3grwRREaylu3fvYZfwPhRVCvHm/tyhhv+WtOUilvkZOTA3z7rXmXXRWuXAH69uUtsxwdgQEDeCskUzDGW0516sSXYWvLU0qsXKk7rbW1+DIaN+YtmFJStFtd3bzJ3zzp2pW/lSKwtAS+/57/e/ly07a3rIwM4M03AS8v/hp127bAjh3i0xq7ryEhpeltunYtTZOi+cp1ZCR/46Z+ff4mib09X5bYmwc5ObylXZ062qmuJBLeilAq1T0OmzfzNyOE19TLsjDTi5p9+vC/Zeu2OnWA9u35MTWGkKaiZ09+fjX178//pqRUfDuB0mP03Xe8XhZ0785bKh49ysuc4PZt3prPzY23QKxbV3eZNjbA//5nWjojc7p1i6e0ql2bX1/u7jy90scf6057/z5PISSkbatZk///wQPj1yf0RZGXB3z0EV+WXM7TD2zeLD6PQsFbMrdqVZquoXNn8etMXx3RvTtv4Xn7tvbww4d569GRI4GWLUuHOzjw1vQlJbylsymEtHIAcOSIdqojIX3G+PFA8+a68w4fzlv0Xrum26pX374NHsz/lt23Tz/lrb81SSSlb4QcOWLkDmmIjeXLmD5de/g//5SmDCybhsXbm9fPmlJTeeotT09eDwYG8lbsYmlGJk/mwxYu1N0eIQXVL7+UDjO1DBjSpg2/v+h7Q7IsoR7SvN8A/HoRvltp1kPx8byFvXBP0GRqvSXUT3Pnard0njCBH/9Nm/i9QPDPP/zNgqlTtd+SqlaNn9/MTD6NoCL3EXOWwYrcz029jxhSkbqicWNeb5W9J5li/Hh+DgH+V7M+0WSO+tnUe5ypx6RGDeCdd7TfKAJ4vfHRR/zfZcuEvT2/dlxdjd8PQ0y9pv9LZZgQQvSgoD4hhFQlhaI0SCCWJqGkhL9W7uHBA2Rjx/LhQqBfk/Cl+N69FzufuMDfnwdML17kr4m/KIQHDOYKbGoaO5Y/cFm61PR8us/S3bs8EFFSArz7Ls/1vGcPH3b9unHLYIw/hJo0iQd5Ro7kAZy8PD5MeOW6PHfu8FzJPj78AYNAyEffq5fuPG3b8pQrFQlsCRQKnpbmxAmeumDMGJ4Pe9Ag3fJqyr6OH88fHAF8uaGh/KMZSJs3j/84DAwE3n+fLzs1lad7KBuQjY7mKSB69tQNBFSrBjRtylMzaOZ73rSJ/x0yhAcF/vyTB4v++af8/j1MsX8//9uqVeWW07gx/3vwIC+Tmvbs4X+7dROfd/duvm8//wwcOiSe1xfg5cnOjpfxsnr35n81y1N4OF/WlCn8oY8hmgGUZ+XRI34drFvH7y3Tp/OgsocHsHix9rS3bvGytnIlTy/y8cf8nK1cyYMkpgRKi4v5Nbl3L08RMno0v4aHDi0tD4KiIn5shTI9aRKfPiGBpxpZssS4dUZH8wdwZR9mG6ojhGGm1hF+fqXpj3x9S6/f0FDjUh0JAUBj7y+7d/O/xqRoqcjyNTVvzr9HREZqDxeOY34+r0sE16/z9B2aDwZzc3n9tnw5fyj5wQc86DxiBE/bUdYvv/DpvvhC+0H6pk08jUafPsC0aeVvu74yYE5CPbRvn/bw4mJeN9nY8PRxAn9/HoA9cUI74A6UX29pKizkx71+fV7myurVi19Lmv1vGCr7YvVZRe4j+lSkDFb1/bwy669oXWGMQYN4XQfwv5r1icBc9bOp9zhzHpPK1EvPw8tYhgkhRI+XpOYlhJCXwO3bpR2oMsaDdBERQGIib/EZEKA7z65d/EfztGn8y6W/P//ReOAAD8ZptsCqW5d/0T9/nv9QHD+e50cNCCi/1UpMjP4OYPv04S1JqsLXX/OHFl99xYM+Vlblz6N5HMtq3760dXBF3LjBv3RbWvIv4eYmlfIOFF99lecgr2hnjZmZwKJFps1jqIPfso4d49un+UbB6tU8CP3++zw4Wp4VK/j+TZrEg8bCjyOFgrc+XbiQB3pat9aeLzaW56otLubBPaHFbtk+DoSO3fz9ddctkfC3LmJieCCqbMsxYyQl8WDUqVOlP/A++ohfY++/z8+hg4Pp+zp+PG/BeeQI/7dYx3p//MFbV2sqKeGtv375hQfKatUq/zgIwy9e5A9qhHy5QsfDx48DH37IgzkCDw+ed9mUDv9KSrTLV3Y2cOYMDxQNG1b6MLKimjblx3zJEh5Y692bB8rPnOGBpq++4gFkMe+/r/3/gACey1rzQUNeHj/fTZqI15XCsdXsTPDECf7XmKDc87BlC68nfvlFNyhatpX422/zFsNLl/IW1oJly/hDi7ff5kFLYzx6xANQkZGl9fnIkfwB2U8/aQc8vvmGB0JmzeL3AiGYmJPDj+vHH/PzWr269jqiovinqIifk127+FsIZfuaMXRtuLjwecrrBLksPz++vbNnl/7bWGfOAFev8uNTtp8HwaJF/LxlZvIyFhPDj5mx15DwZpBYYKk8EgkPyG/bxsuI0PdEZCSvD44d4/8W+gMQgv+aQf158/ibCO+9p/1QZuJE8beC7Oz49di+Pa8jz5/nbyO+/TZv6S+07i/L2DJgTpMn834RFi7k5yUwkK9/3z7+QGH9et5aWeDmxlvo/u9//F4i3DMuX+bX01tv8Zb05blzhzfUMFTHA/w4CG8MGCr7YvVZRe4j+lSkDFb1/bwy669oXWGMQYP4tb59O/+3WKeu5qifK3KPM+cxqUy99Dy8jGWYEEL0ed75fwgh5KUn5CnX93n1VcauXROf95VX+DRnz5YO++MP8RzSjPH8jB06aC/f1pbncwwL43kmNQl57w19fv5Zex5z5dQXckXPnKm7HkM59Q19Pvig/O3SXP7HH/M81DNn8tzFtrZ8+IIFuvNUNqe+Zt7xzp15DlPNPKWm5NQ35liU/ZiyXBcXngdVk0rFWJMmfLxmjl59mjXj/ROI5UC+dKn0+JclHEPh4+XFWESE7nRvvsnHHzggvv5u3fj4svlSjeHry+c9cUJ33Hvv8XFr1pQOM3VfK5pTf8sWPl94eOmw77/nw5YvF59n4kQ+/uTJ0mFyOR8mk/F87w8eMPbkCWO//sqYlRVjTk7GHzfhWIl9mjVjbM+e8pdhbB7ZhQt53xSa6+jXj7GLF3WnXbmSH68HD/h5uX6dsenT+T67uTGWmFg6bWIiX1anTuLrPXqUj3/rrdJhDRrwYXFx5W93RVQ2p/6vv/Jhy5YZnvf+/dI872X7rFCpGGvY0PhrXigLd++Kj3N1Lf2/UsnrmXr1dNfLGGM7dvBlLV6sO064foRPvXqMxcToTtezJx9/65b49tapw8t7RZiaPzszk5cZqdTwdV/2eho9muedNsaFC/we5unJr+eK+OUXvt5//uH/T04uvT+3bau9z4MH83GPH5cO8/Pj17NYvzFCvmyx3OE//sjHjR/PWFCQbl7qsowtA8YyJv82Yzxf+OjR2uuWShmbNk3/MV+3jjF7e+15Onbk9YoxTpzg84waJT5+9Wo+/ocfSof5+/Nh+vpHkckYCwgo/X9F7iNiKloGzXE/r0w+8srWFVWVU99c9XNF7nHmqj+XLuXL6dat/Gkrm1Nfk7HXdFkvaxkmhBA9qKU+IYSYS+/e2q9tp6TwFs/TpgEdO/IWp5qt9ZOSeAqDBg34K7aCYcN4KoWwMN5CVbMVW506wMmTvLXzwYM8b/bJk3w9hw7x1tZ79+qmg5gyRbcl9LPyySe8BdL33/PWfGVzz5ZV9jhWhlge30WLeEvoqjRvHj/nn36qm5LCGH5+PDRQVVq25C0oNUkkQFAQz7V/8aL2WyJl5efz1ojVq/PUJ2UVF/O/cXG648aP55/CQt7yaeFCntt/3jzjU/aYg6Wl+BsqnTsDv/3Gr7HRoyu3r/rk5AA//shbzN65w1vZaXr0yPhliRHSc73yivY2T53K3xyaN4+n+Jo507jlyeXaaRmys4ELF/hbAP3785a4w4ZVfHsZ47l6160Dfv2Vt2i0teVvAkybxt9eOnhQO/WFkKdY0KABb8Vra8vflvn5Z2DBgopv04vulVd4fyXvvcff7OrTh1+/Zd8Iu3CB/w0O1m0RLZEAXbrwNCvlXfMCZ2fdt0wAngM6Orr0/zdu8NbN1auL9zkg9MMgdt3MmsU/eXm8Vfg33/CUEitX8rcCXjSFhfyNg7g4fp8z9BZMfDz/m5zMW8J/8gl/4y4igh9Dfe7d4+dcqQQ2bixtZW8qoTV9ZCR/y0izNX5yMr8/Fhbya/7IEf7mjKcnnyY7m29/48b8jZ+yOnbk+yHmo4/4vVDItz9tmm7uek3PowykpvIUKSkpPC1Sp078WOzYwd8q2bWLt8R1cSmd57vv+LbNmsXftnBx4feOjz7ix/Tvv/W/ZfSyKa8Mir3VMn26/rdWzEnf242mvGnzPJi7fn7Wdu/mb8v5+gJr11Z+ecKbTJrGj9fuk6gyXuQyTAghFURBfUIIqSqenvx184ICnrZj7lztTjVXreJfLMeM0Z7PxYV3Wrp5M+/Iqnt33WW3aKGd3zcqigcgIyOB33/nwbYXhYMD8OWX/Ivx/Pn8R/CzkpTEO/orKOAPVYT85w0alOYYFUifdjNjqL8CYZwwrT4dOvDA5LZt/GGL2Dl8noQgTVlC7vCsLMPzZ2TwQGxiouFOQssGqzVZW/O0K+HhPMD36ac8MCnkTBby6+vbFqFT3fIeEunj5iZ+HsseA3PsqyaFggf9zp/nD1fGjOHbYmHBA2arVmmnyzH2OGj2R+DkxANUr76qO/2AATyoL6ToqQhHRx6E2LyZpwX7/PPKBfXDwviDv19+0e7EsXdvfg01asQD2Mbkq500iQf1hfQ5QMWOobc3D9ImJvJc1y+a2rV5EH32bP4gV+gUs359nlZryBD+f2Hf9PUL4O3N/5Z3zQs0j5EmCwvtujM9nf+9epV/9DF03djZlXbC2qYNT03Rs2dpQNmY86pve82lqAh47TV+r/78c5473hje3vz7Qb16PBXcxx+X9oVRVkICDxA/ecLTLomluTFWkyb8+AnB/MhIXv80a8aD+vPm8YYCHh58fZrXtVCWxAL6gOG+JyQSfk8UHnK/955x21teGTCnjz7i+37xIj8eAC8/b77Jv6u98w4POgr3gcOHecOLDz/UPu+dOvEHAHXq8HHlBfUrWscL87i5aU+fl8e3V9/0xq5DkzFlUOz+OH48D4hW9f08M1N8/UKQ9kWoK/StF6h8/VzZMqRvHkPHJCICeOMNvu2HD+t2SFsRixbxsqYpJMQ8Qf0XvQwTQkgFUVCfEEKqmpC7XbOTOKA0wP/ll/wj5q+/jAsIh4TwQM7EifzL9YsU1Af4j+FffuGtZ8vmwH4WbGz4Mdq9m/9YnziRtxLXzHspfGFPS9O/HCFXtTE//ubMAXbu5MHqs2dN296qzqmfkiI+/PFj/re8/RN+tLRuXbngsEDoqPfYsdKgvlgOWAFjvO+F6tV13zgwVloaD0KWDeyXPQbm3tft23ldMHky72xS08aNPKivydBxEIZLpTyAJKhfn5dVsdZlwrCCgopsvbY6dXhA6d49XmYr2ppN6CxU7Ed2w4Y8WHDunHHLElre5eeXDrOz48u4d48Hu8rmHBbLldupE39YeujQi5tXv1kzHpwoLubHZ+9e/qbDsGH82ujUqbT8CuW6LGG4uQMRwvLeeIM//KkMCwteNi5e5Ndg3758uOa1UbbvjowMfg107Fi5dRtSWMgD1RERvMX9Dz+YvozAQP4gX+iEsaz4eL7vjx7xBzevvFKJDUZpXv3Nm3kQPyqqtJVwUBB/gykysvTBr+Y1KZxT4S2LsvSVMYC/kfTpp7yj3owMXv9FRZX/gFygrwyY0+7dfPuEgL4moQ7QrIcM1VseHvyhdXS0dv8FYurW5cfBUB0PaNdP/v78ONy6pRvU1ze95jixdZS9jwiMLYOG3i6s6vt5eW83Pu+6Qh9z1c8VucdV5pjs28cfZrq78/pCrNxUhPAmk7m9DGWYEEIqyMhvUoQQQipMaLGo2Yrx6FH+xbBuXd6yVOzj6spbp2VkGLeeF/mLpJUVf+iQn2+4tXNVa9CAtxB89Eg3aN60Kf+rmUKiLGGc2I9+sXVNmMCDAH//bdp2Cq3OTPmY4sIF8RayQuvm5s0Nz+/gwIOt16/rvipdEUK6GQuNtgZCCgux9EVnzvD1BgdXfJ3FxbyT3LKOHeN/hTdhKrKvwg9qpVJ33J07/K9YK3ph3Zrat+epMA4c0P3BmZTEUwO1a8fffBAIAahr13SXJwwzR8u3kpLS1mmG3nApj0LB/4oFC5VKXgeWTSmmz+nT/G/Z/QsO5mVeswW/QEgZolmexo/n53HZMv1BTIHmmxXPg5BKavZsHtRnjLcUBkrL8dGjuuWHMd3ybi4NG/JAVExMaYqqyhCrI4TzJVZHCMMqWkdIpeLXr0AzoD9jBm/hXhG5ubzlp4VIO6v4eF4PJibyVvwDB1ZsHWUJdeu6dcDNm6X1hZ0db4Rw+DAP1AkPAASOjvy6un1b/Jo4eVJ8fSUlwKhR/Pr75x+eeufYMdMfgoiVAXNSKHh9JtRHmoT91ayHDNVb+uYRY23Nj/uNG7qtlAFeluVyXs8LDJV9sfqsIvcRwHxlsKrv5+WpyrqiPIa+D5izfjb1HlfRY7JvH6/7XFx4PVGvXvnb9jz9V8owIYTo87yT+hNCyEtP6Hy0d2/dcUplaWdz775bOnzsWP0dZwk+/FC7I8G7d/m/s7N1p83N5R2zAYzNnVs6XF9ntoYY26GrsR3lClQqxlq0YMzCorSzQLGOcsWOo6nEOuIVJCczZmPDO3HMytIeJ3TgJ3ZeDhzgnc/5+el2Tle2o1xBYiJfV716pnWUW1U0O+CdOVN73KpV4p2dZWbyTkjLdv4ldOg8eLBup7uM8fKq2Rna8ePinfpduMCYoyMvF2U7T+vSRbdDRYWCsR49+PDDhw3vrz5C+evWjS9PcP06P19OTtrXman7umSJboe3gvXr+bhPPtEeHhVV2kls2Y6Yhfri999Lh6lUpdfaypW62yOX847gHj4sHZ6dza9BgLGDB7XnuX2b77/m8WBMf9lmrLTTzUaNxMcLyuscbs4cvpzu3RkrLNQeN2sWH/fGG6XDkpL49pb18GFpB7cbN2qPO3yYD+/cmbGiotLhBw/yTq27dNFd3uef83latxbvHLaggHfu+9ln+vdNn8p2lHvmjHYHpoIFC/i0mh2td+3Kh61YoT3tihXi13xCAi8LeXnaw3199Z9Hoc7V9OmnfNi0abrlijHGLl/W3ocjR8Q71Y2I4NeGk5P29VdczDtzlMt5PSLIzmascWNep9y4Ib695XF353W9mIICxnr14vv20UflLys+XrxjSIWCsUmT+HImTdIed+8eP9YWFrxDaHO6epWv08OD/716tXTczJn8WDs7846wy/rySz7Pe+9pDxe+D4jdP7/4gg//9FP+/8JCxpo25ftWtvybWgYY0193aTKmU02hE8+y98bCwtJxmh07b9jAhzVuzO+TmsLDS+sOY6xcyacfPlx7/4XhY8dqT5+ezo+Fu7t2J6qPHjHm7c3PX3q69jym3kfMXQYrez+vTCejla0rKtNR7q5dfN5Zs8THm1o/62PqPa4ix2TvXj69t3fFOpF/1h3l/pfKMCGE6CFhrCp74iOEkP8H4uN5fuO6dXlee8GTJ7wVy/XrvIOr06f567HZ2fyvVMpff9fXwv7yZd4ivEUL3rI6Npbn4La25p15Nm7M08okJvJWmenp/BXao0dL08pERfFXTlu31v+6qbMzz3cvkEh4i7zXXhOfvlYt3jGcsOyynfCOH89TiERH63ZEGhHB86YLhJz3ho5jWcakmQkJ4fm3NZev6eOPgZ9+4ssKDS0dfuMG75gsJYW3uGnblreyunSJt06yteUpLoKCtJfn58fPpWZnooLPPy/trLR+fdM6VDU34Rh37szTGLRty9M/3LzJ3wpxcODnrWHD0nnCw/kbB+PGlXZyCPDwzYQJ/FxXqwb06MFfPX78mO/j6dPA+vXA8OF8+hYt+DXRqRMvQyUl/Hjv38+X9dNP2uUQ4K3KO3bkqWKGDuXL37ePnw+x9DXG8vPjrSyrVePnrH9/3lp2wwbecnbNGt6ytKL7eu0aTyNUvTrPme/kxD/vvMOX37QpPxf9+vHpbtzg1/CgQTydSmiodjlPSuKtKB8+5NdlQABvwXfiBM87v2ePbhqLxYt5i1g3Nz6PXM7TRcTH87zUS5fqHpOEBP76vmYrdz8/vl7NTnVzcnidFBnJ38LZs0c7TVhqqnanx2vW8Lpq8ODSYZplKSeH1xXXrvH19enDp4+O5m9TuLryfwudwEZF8dbFQUH8jRhXV75fu3bxlorjxvE8/WU7HnzzTWDFCp6jv39/fv42beJ16smTfLgmlYrvx88/89bw3brx82VpyY/TwYM8jdN33+lPoaaPUE/266c/P/g33/BrRbgGNcvF9Om8/5SQEN5S0tGRH789e3gLygsXSjtevXGDH6u0NP6GSKNGfNodO3j6hOPHtTvYFerPyEjtTl+FciGWIkGYR/NnRVER78PhwAFerwcH831NTOT3t4sXte8Tzs58ewID+T2zoIBf60eP8mO+fr12GQL4Nvbuzcv3iBH8OGzdys9PRc6LYNgw/obVG2/w+65MxstM06al587bm9//xGh27rhtG19O5848hYO7Oy97Bw8CDx7w+8KRI9p5tYXrsX173f5fBJXpBNTbm2+Dlxe/dwk0+/H54APdN9pycvh9Iy6O709QEK8f/v6bp1HbuZMfm7Fj+fRHjvDrpmVLfq4tLfnwq1d5jvzq1XlZFdKLVKQM6Ku7jh/n1zvA7z179vByKNy/GzTgfXUIYmP5/V/YR6Gj3IgI4O5d/j3q+PHS1uxKJb8XREXxcv3qq/zau3iRl3m5nJ/jst8XxCiVvHxFRPC6PiSEr3PLFqBGDd4SuOx3mbVr+f3F3Z3fe6RSXp89fszr3LLfo0y9j5i7DJp6Pzf1PlIeU+uKuLjS724FBbyMe3mVfod1d+cd3pcnPZ3XxXI5T/0o1PdC2TO1fjbE1HucKcckLo5/jysq4uVNrK8ZPz9e92maMaM0deWBA/yNmzfeAOztS49DgwbG7Z+p1/R/rQwTQoiY5/1UgRBCXnqarZ81P3I5b5n90UeMPXlSOv2ff4q3zBPTujWf9tw53lpsyxbG3nqLsebNeQstmYy3OA8KYuynn3gLQk2aref0fcq2Gilv+ubNtZdtbEt9QbdupcsSa6lf3scYhlrqM8Zb69va8pZuZVuzPXrEz1mDBrzVtlzOWzO99RZjt26JL89Qa+bMTMZcXV+slvrjxjF26RJjffow5uDAmL09Y/37M3bliu48Qithfa2hNm3irZRcXHhLyho1GAsJ4S2YNcv96tWMDRrEj5WNDWNWVvzfI0cydvKk/m2+cYO3kHdz48e4cWPGfv2VvwVTUUKL47Q0xiZP5i3a5XLG2rRhbPt2/fMZu6+M8ZaaTZvy5Za9zu7e5S3PPTx4OQwM5C3LhWuqbEt9xni5nDiRMS8vfuzq1eOtscu2bNe0YwdvtWdvz5i1Na9Pli3Tf0zEWtAJwzU/FhaM1azJ2KhRjF28qLssY67lsrKyeCvgxo35tlpa8pbSb73FWzprun+fn7dmzfi5sLDg5aNnT90W+pqUSl52Gjfm58XNjZet8lpznz3Lj329eqV1gp8fYyNGMLZ/v+F59RHqSUMfofWkWEv9U6d43dukCW+Va2PDmL8/bxWv2XJXEB/P2IQJjFWrxo9XtWr8/2WPLWOl9WdkpPZwU1vqM8ZYSQljS5cy1qkTfyNHLmesVi1e9/zxh3ar60WL+PCaNfl01tZ8nyZPFq+bBKdP8/mcnPhxaNOGsbVr9U9vjKQkxoYO5fdZqVS7Bbqwr4Y+mscuIYG/ede6NS9zMhnf1vbtGZs3T/ztH3PdC/UZNowvY9gw7eEFBaV11r//is+bksK/v7i7l9YrW7cy9uOP2vOlpzPm48OYnZ34NSa80TRqVOmwipQBfXWXcN3o+4i1ur55k18XtWrxOsjGhtfjs2frvrnCGK9/581jrFUrXpdbWPD7wsiR/E0UUxQW8vXUq8freC8vXu+UfUtO0969vPWwvT3/dOnC2L59+qc35T5SFWXQlPt5Re4j5TGlrijv+7MpLa537+b3eRsb8W03pX42pCL3OGOPiTG/J8SuKbHvEPrqyvKYek3/F8swIYSUQS31CSGEEEIIIYRU3OjRPE//tWvab3sRQgghhJAqQR3lEkIIIYQQQggpX1KS7rAjR4CNG3lKDgroE0IIIYQ8ExbPewMIIYQQQgghhLwE+vXjeaFbtOB9Al27xvNKy2S8Lw9CCCGEEPJMUPodQgghhLzctm3jnRyWJyREu+NPQswlKop/ytOiBe8QmTxb4eHiHfyWNWgQP0cvi8xM3c5s9alMx7qaFi3iaXbu3OGdyjo7805lP/+cd8RKCCGEEEKeCQrqE0IIIeTlNn48sGpV+dOFhpovsEWIplmzgNmzy59u3DgeYCbPVkgITxFTnrAwXp+8LOLjgdq1jZuWfvIRQgghhPynUFCfEEIIIYQQQgghhBBCCHlJUEe5hBBCCCGEEEIIIYQQQshLgoL6hBBCCCGEEEIIIYQQQshLgoL6hBBCCCGEEEIIIYQQQshLgoL6hBBCCCGEEEIIIYQQQshLgoL6hBBCCCEvmaioKEgkEsyaNet5bwohhBBCCCGEkGeMgvqEEEIIIVUsMjISw4YNg4+PD+RyOVxdXREUFISff/4ZhYWFovP4+fnBz8/v2W4oqVIpKSmYM2cOBg8ejNq1a0MikUAikRicR6VSYcmSJWjVqhVsbW3h6OiI4OBg7NixQ3R6YZmGPg8ePNCaJyMjAzNmzEC9evUgl8vh4eGBwYMH4+rVqxXaz+TkZEyePBnVqlWDtbU1AgIC8M0330ChUJjlmJTn1q1bGDp0KDw8PGBjY4NmzZphyZIlUKlUOtPeuXMHs2bNwquvvooaNWpAIpFU6Lp7Vg/aYmNj8dVXX6F9+/bw9PSEXC5HnTp18O677yIxMVHvfMYeE8YY9u7di3feeQfNmjWDk5MTbG1t0bx5c/zwww+i9VVCQgLefvtttG7dGh4eHpDL5fD19UX//v1x6NChCu3jF198gd69e8PDwwMSiQQhISFmPyb6zJ07F7169YKPjw9sbGzg5uaGNm3a4KeffkJ+fr7O9FVRhgkhhBBCyiNhjLHnvRGEEEIIIf9FJSUleO+997Bs2TLY2dmhb9++qFevHrKysrB//37cuXMHAQEB2L17N+rVq6c1rxBYjI+P11luVFQUunbtitDQUGqt/xIRzptEIoG/vz8ePnyI/Px86Ps6zhjDkCFDsGXLFtStWxd9+/ZFUVERtm/fjpSUFCxevBjvv/++1jz6ysPt27exbt06NGzYENeuXVMPT0tLQ4cOHXDr1i106NABHTp0QFJSErZs2QILCwscPnwY7dq1M3ofk5OT0a5dOzx48ACDBg1CQEAAjh8/jhMnTqBPnz7YvXs3pNLSdkWmHpPyXLt2DR07dkR+fj6GDh2KGjVqYO/evbh8+TLefPNNLFu2TGv68PBwTJgwATKZTH1sfHx8RK87Q57VNdm+fXucOXMGgYGBaNeuHeRyOU6fPo1jx47B3d0dx44dQ4MGDbTmMeWYFBYWwsbGBnK5HCEhIWjatCkKCwsRERGBW7duITAwEEeOHIGNjY16noMHD2LIkCHo0KEDateuDUdHRyQmJmL79u3Izs7G999/jy+++MLofZw1axZmz54NKysrBAQE4MqVKwgODkZUVJTZjokhtWvXhru7O5o2bQpPT0/k5uYiKioKV69eRfPmzXHy5EnY2tqqpzd3GSaEEEIIMQojhBBCCCFVYsaMGQwACwwMZA8fPtQaV1JSwr7++msGgNWrV49lZWVpjff19WW+vr6iy42MjGQAWGhoaBVtOakKycnJ7MiRIyw7O5sxxlj9+vWZoa/j//zzDwPAOnXqxPLz89XDnzx5wnx9fZlcLmf37t0zat3vv/8+A8AWLlyoNfy9995jANhHH32kNfzkyZNMJpOxRo0aMaVSaeQeMjZ27FgGgP3+++/qYSqVio0bN44BYCtXrtSa3tRjUp4uXbowAGz37t3qYQqFgnXv3p0BYIcPH9aa/s6dOyw6Olp9fOVyud7rzpBndU0uXryY3b59W2f43LlzGQDWr18/nXGmHBOFQsG+//57lpGRobUMhULBBgwYwACw+fPna40rKioSLSOJiYnMy8uLWVpa6izPkCtXrrBz584xhULBkpKSGAAWHBysd/qKHBNDCgoKRIePGTOGAWBLlizRGm7uMkwIIYQQYgz6tkEIIYQQUgVu3rzJpFIpc3V1ZcnJyXqnGzlyJAPAvvrqK8YYY/fu3WMARD9CwFAzgHju3DnWq1cvZm9vzxwdHdmgQYP0Bnrv3r3LJk2axHx8fJiVlRXz9vZm48aNY/Hx8TrTCoG0hw8fsnHjxjEvLy8mkUhYZGSkwf0ODg5mAFhhYSH76quvWN26dZmFhYV624XxYoTAr+b2h4WFMQAsLCyMHTx4kHXq1InZ2toyV1dXNnbsWJaamqqznMOHD7M+ffqwatWqMSsrK1atWjUWHBzMli9fbnDbn7Xygn+jRo3SCcYKFi1axACwr7/+utz1FBQUMBcXF2ZlZcVSUlK0xtWoUYNJpVKWk5OjM9+gQYNEA+H6ZGdnM7lczurUqcNUKpXWuEePHjGpVMo6dOhgcBmVCYjeuHGDAWBdu3bVGXfq1CkGgI0YMcLgMioS1A8NDdV7zWqW5dTUVDZ9+nTm5+fHrKysmIeHBxs6dCi7evWqSesTU1JSwmxtbZmdnZ3WcHMcE8HJkycZANa/f3+jt+u1115jAFhsbKzR82gyJqivj75jUlHbt29nANj06dMNTkdBfUIIIYQ8CxbmbPVPCCGEEEK48PBwqFQqvPXWW/Dy8tI73VdffYX169dj5cqV+Oabb+Ds7IzQ0FAsWrQIADB9+nT1tGXzSsfExGDBggUICQnBlClTcOHCBWzbtg2XL1/GlStXYG1trZ729OnT6N27N/Ly8jBgwADUq1cP8fHxWLduHfbu3Yvo6GjUqVNHa/lCahZXV1cMGzYMCoUCjo6ORu3/66+/josXL6J3795wdXXVWbapdu7ciV27dmHAgAF45513cPToUaxevRp37tzB8ePH1dPt3r0bAwYMgLOzMwYOHIhq1arhyZMniI2Nxbp16zB58uRKbcez9PjxYwA8HUhZwrDDhw9j9uzZBpezdetWZGRkYPDgwfDw8NBZh7u7O+zt7Q2uo2vXruVub3R0NIqKitCzZ0+dnOLVqlVD06ZNcfr0aRQWFmqVTXMR0rP06tVLZ1zbtm3h7OyMI0eOmH29ISEhiI+Px6pVqxAcHKx1nTo7OwPg11L79u1x+/ZthISEYPjw4YiPj8fmzZuxe/duHDhwAB06dKjwNkgkEshkMq3URoB5j4mlpSUAwMLCuJ+QaWlpOH36NGxtbSt9/VeEvmNSUbt37wYANGnSxCzLI4QQQgipDArqE0IIIYRUgZMnTwIAunfvbnC6Bg0aoHr16khMTMSDBw/g4+ODWbNmITw8HID+HOkADzJt3LgRw4YNUw8bO3Ys1qxZg23btmH48OEAgOLiYgwfPhwqlQoxMTFo3ry5evrjx48jJCQEH3zwAXbu3Km1/CtXrmDChAlYvnw5ZDKZKbuPR48e4dKlS3B1dTVpPn127NiBqKgodOrUCQCgVCrRo0cPREVF4dSpU2jfvj0AYOXKlWCMISoqCs2aNdNaRlpamlHr2rZtG2JjY43etpCQEIMdeVaUEIC/d+8eGjZsqDXu3r17AICbN2+Wu5y//voLAEQfaHh4eODx48fIzc3VCeybsg6Ad8YKAP7+/qLj/f39cfHiRdy9exeNGjUyapmmMLR+iUSCevXqISYmBvn5+Vo50StLOPerVq1CSEiI6DX7ySef4Pbt2/j888/xww8/qIePHz8effr0wbhx4xAXF1fhAPTmzZuRk5ODIUOGaA035zFZuXIlAPEHBADv/yM8PBxKpRKPHj3Cjh07kJmZiT///BMODg4V2a1K0XdMjLVo0SJkZmYiMzMTJ06cQExMDHr16oWxY8eaeUsJIYQQQkxHQX1CCCGEkCqQnJwMAPDx8Sl3Wh8fHzx69AhJSUlGTS/o0qWLVkAfACZOnIg1a9bg7Nmz6qD+rl27EB8fj2+//VYroA8AQUFBGDhwILZt24bs7GytlvhWVlaYP3++yQF9AJg9e7bZAvoAMHLkSHVAHwBkMhnGjRuHqKgonD17Vh3UF2h25Clwc3Mzal3btm3DqlWrTNq+qgjq9+3bFxs2bMDcuXPRrVs3dev2tLQ09ZscmZmZBpdx7949REZGolatWujZs6foOlauXInZs2djwYIF6uFnzpzBrl27jFqHICsrCwDg5OQkOl4oW8J05mbK+s0Z1C+PQqHAhg0b4ObmhpkzZ2qN6927N3r37o2IiAicPHkSQUFBJi//wYMHmDZtGmxsbPDtt99qjTPXMdm3bx+WLl2Khg0bYtKkSaLTxMfHa701Ym9vj7CwMIwePdqk/TEHQ8fEWIsWLUJCQoL6/6NHj8Yff/yhfmOBEEIIIeR5oqA+IYQQQshzxhgDAJ2UJeVp1aqVzrCaNWsC0A7Enjp1CgAQFxcn2oo4OTkZKpUKN2/eRJs2bdTDa9euDXd3d5O2SdC2bdsKzaePsfs6dOhQbN26Fe3atcOIESPQrVs3dO7cGZ6enkavKzw8XP2mxPM0YsQIhIWFITIyEk2bNkWfPn1QXFyMbdu2qVM6lffARXhzYcKECaKtwGfPno29e/fixx9/RHR0NNq3b4+kpCRs3rwZjRo1wqVLl7TWIbRe1jR+/Hj4+flVen+NIVZ+p0+frk5z8yKKi4tDQUEBQkJCRAPnISEhiIiIQGxsrMlB/fT0dPTr1w8pKSlYvXo16tevb67NVouJicGwYcPg5OSEf/75B3K5XHS6kJAQMMZQXFyM+Ph4LFu2DGPHjsWZM2fw66+/qqcLDw9HfHy81ryDBg1CixYtzLK95R0TY8uwsI3JycmIjIzEJ598gnbt2iEiIkJd9xBCCCGEPC8U1CeEEEIIqQLe3t6Ii4vDgwcPyg20PXz4UD2PKcRa3wr5rpVKpXpYeno6AGDdunUGl5eXl6f1f0N9AZSnMvOKMXZfhw0bBktLSyxatAhLly7F77//DolEgpCQEPz0009mCxw+CxYWFti7dy/mzp2L9evXY9myZXBycsJrr72GGTNmICAgQCdHviaVSoXw8HBIpVJMnDhRdJqaNWvi7NmzCA0Nxd69e3HmzBn4+Pjgm2++gZ+fH4YPH661jrKtlwEezPXz81OfI30t8bOzswHobzVuDLH+A8aPHw9nZ2ej129svxDmIqxX3zUhXPemvsGQkZGBHj164OrVq/jjjz9EW8RX9phcuHABvXr1gkQiQUREBBo3blzudllaWsLf3x8LFixAfn4+Fi9ejL59+6Jv374AeFC/bB5/Pz8/s1ybxhwTQ2VYjLe3N0aMGIF69eqhbdu2+Pjjj7Fp06ZKbyshhBBCSGVQUJ8QQgghpAp07NgRUVFROHToEHr06KF3uri4ODx69Ag1atQwKfWOKYSA3c6dO/HKK68YPZ+pbw4YM6/QWrykpESnw01zpWV5/fXX8frrryM7OxsnT57E1q1b8ddff6F37964ceNGua26X5Sc+gAgl8sRGhqK0NBQreFCB6iab1aUtW/fPjx8+BC9e/dGrVq19E5Xo0YNrFixQme40Cpecx1lW1hrEvK2C3ncy7p16xakUmmlOk0V3moxdf2MMdy+fRvVq1eHnZ1dhddfEcL1J3R8XJYw3JSHDenp6ejRowcuXLiA3377DVOmTBGdrjLH5Pz58+jZsyeUSiX279+PwMBAo7dP0KtXL/z++++IiopSB/WFsmtuxh4TQ2XYkMDAQLi4uFTZ9hNCCCGEmIKC+oQQQgghVWDcuHGYO3culi9fjo8++khvi+rvv/8eAHRaUstkMigUCrNsS7t27QAA0dHRJgX1q4KLiwsAIDExEb6+vurhKpUKFy9eNOu6HB0d0adPH/Tp0wdKpRIrV67E6dOn0bt3b4PzvSg59Q0R3roQ+k0QY6iD3PIolUps3LgRFhYWeOONN4yap3379pDL5Thw4AAYY1oPdpKSknD58mW0a9dO3TeAuQnnYP/+/fjss8+0xp05cwaZmZnqwLK5CSmKNN8aETRo0ADW1tY4e/asaIe0Qqt1Y1uqawavFy9ejHfffVfvtBU9JufPn0ePHj1QUlKCiIgIdR1iqkePHgGAzgM8czPlmFRUbm4usrKyTH6jihBCCCGkKugm1iSEEEIIIZUWEBCADz74AGlpaRgwYACSkpK0xqtUKnz77bdYu3Yt6tatixkzZmiNd3V1RWpqKgoLCyu9LQMHDkStWrXw008/4ejRozrji4uLcfz48UqvxxhCq++yOet/+ukn3Lt3r9LLP3TokOgxS0lJASDegW5Z4eHhYIwZ/RHL824uQnoUTZs3b8bKlSsRGBiI119/XXS+J0+eYOfOnXB3d8err76qd/nFxcUoKCjQGqZSqTBjxgzcuHEDU6dORfXq1Y3aVkdHRwwbNgx3797Fn3/+qR7OGMPnn38OlUqFN99806hlVURAQAC6dOmCyMhI7NmzRz28uLhY3UFtVa1f6BRaSKWlycrKCiNGjEBqairmzJmjNe7gwYPYu3cv6tWrp9URtD7p6eno3r07Lly4gF9++QXvv/++wekrckyEgH5xcTH27t2LDh06GFzHmTNnRK+5hIQE9f5W1cMUwPRjYkhCQoJoS/7i4mJMnz4dKpWqSveFEEIIIcRY1FKfEEIIIaSKzJ8/H1lZWVi5ciX8/f3Rv39/1K1bF9nZ2di/fz9u3boFf39/7NmzRyf1Rrdu3RATE4MBAwagc+fOsLKyQlBQkMkdaQI8hcvmzZvRt29fBAcHo3v37mjSpAkA4P79+zh27Bjc3NwQFxdnlv02ZMKECZg/fz5mzZqF2NhY1K1bFzExMbhy5QqCg4N1cm2b6uOPP8b9+/fVObIlEgmOHz+OM2fOoGPHjkYFTqvS+PHj1f8WHvRoDvvxxx+1Oidu164dfHx80LBhQ1hbW+PMmTOIiopCnTp18M8//+jtKHf16tUoLi7G2LFjYWVlpXd7Hj9+jMaNG6NXr16oXbs2FAoFIiIiEBcXh/79++sEocszd+5cREZG4r333sPBgwcREBCAY8eO4cSJE+jduzfGjRtX6WNiyB9//IGOHTvitddew9ChQ1G9enXs27cPly5dwuTJk9G1a1et6VNTU7UeqBUXFyM1NVVr/cZ0mtygQQNUr14dGzduhK2tLWrWrAmJRIJ33nkHTk5OmDdvHo4cOYLvvvsOJ0+eRLt27RAfH4/NmzfD1tYWYWFhoh0Zl/X6668jNjYWDRo0QHp6ulEdB5tyTIQW7xkZGejTpw8OHDiAAwcOaC3f2dkZ06dPV///hx9+wLFjxxAcHIxatWrBwsICd+7cwZ49e6BQKPDhhx+aVG/FxcVh7ty5AKB+4BQXF6c+J+7u7vjxxx8rdUz0uXDhAt544w107twZ/v7+cHd3x+PHj3Hw4EF1/yjC21WazFmGCSGEEEKMwgghhBBCSJU6cOAAGzJkCKtevTqztLRkzs7OrEOHDmzhwoUsPz9fdJ6cnBz25ptvsmrVqjGpVMoAsNDQUMYYY5GRkVr/13Tv3j0GgI0bN05n3MOHD9kHH3zA/P39mVwuZ46Ojqxhw4Zs8uTJ7NChQ1rTAmDBwcEm72twcDAr7yvm+fPnWffu3ZmtrS1zdHRkAwcOZLdu3WLjxo1jANi9e/fU04aFhTEALCwsTGc5Ysdh48aNbOjQoaxu3brM1taWOTk5sRYtWrD58+ez3Nxck/fH3AAY/GjuO2OMhYaGsqZNmzIHBwdmbW3NGjZsyGbOnMmysrIMrqdhw4YMALt27ZrB6bKzs9mYMWNYnTp1mLW1NXNwcGAdOnRgy5cvZ0qlskL7+OjRIzZx4kTm5eXFrKysWL169djs2bNZYWGh6PSmHpPy3Lhxgw0ePJi5ubkxuVzOGjduzH799VfR/RGuF0MfY506dYoFBwczBwcH0W1/8uQJmzZtGvP19WWWlpbM3d2dDR48mF2+fNnodfj6+lboeBl7TIw5Hr6+vlrz7Ny5kw0fPpzVrVuX2dnZMUtLS1ajRg322muvsT179hi9bwLhujZ2/RU9JmISEhLYhx9+yFq3bs3c3NyYTCZjTk5OrH379mzevHl66xBzl2FCCCGEkPJIGDPQ2xQhhBBCCCGEEEIIIYQQQl4YlFOfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIf+vzJo1CxKJBFFRUVrDJRIJQkJCKr0ccxo/fjwkEgni4+OrbB3mEhISAolE8rw3gxBCCCGEEEL+8yioTwghhJAXxogRIyCRSLBx40aD06WlpUEul8Pd3R0KheIZbZ35hYeHQyKRIDw8/HlvygvhZXqIIUbYfkOfbdu2qacXzr/mx8bGBgEBAZg6dSqSk5MNru/u3buQSqWQSCRYsmSJ3uni4+MNblN515umo0ePYsaMGejatSucnJwgkUgwfvx4g/M8fPgQU6ZMQa1atWBlZYXq1atjwoQJePDggc60Ysek7Kd79+46850+fRoDBw6Eu7s75HI5AgIC8PXXX6OgoMDofdMUERGBkJAQODo6wsHBASEhIYiIiDDbMSnP+vXr0bZtW9jZ2cHFxQX9+vVDTEyM6LQ7d+7E1KlT0alTJ9jZ2UEikWDWrFkmr/NZXH/FxcXYsmULxo8fj4YNG8LOzg4ODg5o164dfv/9dyiVSr3zGntM0tLSsGzZMrz66quoU6eO+l7Rt29fvedw69atGDx4MPz9/eHo6Ah7e3s0btwY06dPR2Jiosn7uXbtWkyZMgVt2rSBXC43WM9X5piISUhIwNtvv43WrVvDw8MDcrkcvr6+6N+/Pw4dOiQ6T1WUYUIIIYRULYvnvQGEEEIIIYJJkyZh48aNCAsLw/Dhw/VOt3btWigUCowZMwZWVlZmWff169dha2trlmWZy5w5c/DZZ5+hRo0az3tTiAkmTZqEmjVrio5r0KCBzrDu3bsjKCgIAJCamorDhw9jyZIl2LZtG86fPw8PDw/RZa1cuRKMMUgkEvz11194//33DW5X8+bNMWjQIJ3hTZo0KWePtNe5atUq2NraolatWsjOzjY4/Z07d9CxY0ekpKSgZ8+eGDZsGG7duoVVq1Zhz549OHnyJOrWrauevkWLFggNDRVd1ubNm3H16lX07t1ba/jWrVsxbNgwyGQyvPHGG/D29saJEyfw7bff4vDhwzh06BDkcrnR+7hu3TqMHj0a7u7uGDduHCQSCf7++2/06dMHa9euxahRoyp1TMrzww8/4Msvv0StWrXw9ttvIzc3Fxs3bkSnTp3UDxs0LVy4EEeOHIGjoyOqV6+O27dvV2r9VenOnTsYPHgwHBwc0K1bN7z66qvIysrCzp078d5772Hfvn3Yvn27zls/phyTf/75B++88w5q1KiBbt26oUaNGnj48CG2bNmCffv2YcGCBZgxY4bW8v/9919cvHgRgYGBqFatGgAgNjYWv/76K1atWoXjx4+jcePGRu/nzJkzkZCQAHd3d1SrVg0JCQlmPyb63Lp1C5s2bUKHDh3Qvn17ODo6IjExEdu3b8eePXvw/fff44svvtCax9xlmBBCCCHPACOEEEIIeUGoVCrm5+fHpFIpu3//vt7pmjdvzgCwy5cvm7yO0NBQBoBFRkZWYkvNs5ywsDAGgIWFhVVqW14EwcHBrLJfLceNG8cAsHv37plno54xYfujo6ONml44/3PmzNEarlQqWb9+/RgA9vXXX4vOW1JSwmrUqMGqVavGRo0axQCwc+fOiU577949BoCNGzfOpP0Rc/bsWXblyhVWUlLCoqOjy11u//79GQD2yy+/aA3/+++/GQDWu3dvo9ZbVFTE3NzcmIWFBUtOTlYPz8/PZ+7u7szS0pLFxMSoh6tUKvbee++JHl9D0tPTmbOzM3N3d9eqgx49esS8vb2Zs7MzS09P15rH1GNiyM2bN5mFhQULCAhgmZmZ6uFXrlxhtra2rG7duqy4uFhrnqNHj7KbN28ylUrFNmzYwACw0NBQk9f9LK6/hw8fst9//53l5eVpDc/NzWVt2rRhANjff/+tNc7UY3Lo0CG2a9cuplQqtZYTFxfHnJycmKWlJUtMTNQaV1BQILq9K1asYADY4MGDTdrPAwcOsPj4eMYYY3PmzDFYz1fkmBhSVFSks++MMZaYmMi8vLyYpaUly8jI0BpnzjJMCCGEkGeD0u8QQggh5IUhkUgwYcIEqFQqrFq1SnSac+fO4eLFi2jbti2aNGmCR48eITQ0FO3bt4enpyfkcjn8/Pzw7rvvIiUlxaR1i+XUf/DgAUaMGAFXV1fY29sjODgYR48eFV2GQqHA4sWL0bt3b/j4+EAul8PT0xOvv/46Lly4oDXt+PHjMWHCBADAhAkTtNKLaE6jLx3GqlWr0L59e9jb28Pe3h7t27cXPWZRUVHqdBznz59H79694eDgACcnJ7z22msmp9o4fvw4goODYWdnBzc3NwwbNkw0jQoAk86Nn5+fevtr166tPhaa5+Tff//FiBEjUK9ePdja2sLJyQmdO3fGli1bTNqHF51UKlWnvjh37pzoNBEREUhMTMTIkSPV5eivv/6q8m1r06YNGjduDJlMVu60hYWFiIiIgJeXF6ZOnao1bsiQIWjRogUiIiJw9+7dcpf177//Ii0tDa+88gq8vLzUw0+cOIHU1FQMGjQIrVu3Vg+XSCT47rvvAAB//vknGGNG7d8///yDzMxMTJ06FT4+Purh1apVw/Tp05GZmYl//vlHax5Tjkl5wsLCUFJSgi+//BJOTk7q4Y0bN8bYsWNx584dHD58WGuezp07w9/fv1J9Whhz/QHAyZMn0b9/f7i6usLa2hoNGjTArFmzkJ+fb9R6atSogXfeeUfnrSg7Ozt89NFHAIAjR45ojTP1mHTr1g39+/eHVKr9U7d+/foYNmwYiouLcfLkSa1x1tbWots7ZMgQADD57YcePXrA19fXqGkrckwMsbKy0tl3AKhevTo6duyI4uJinTcHzFmGCSGEEPJsUFCfEEIIIS+UCRMmQCqVIjw8XDQQFxYWBoCnOAF4LuCFCxfCy8sLI0aMwNSpU1G3bl388ccf6NChA7Kysiq8LUlJSejQoQM2btyItm3bYtq0aXB1dUXPnj1x6tQpnenT09Mxffp0FBUVoV+/fvjwww8REhKCPXv2oGPHjjh79qx62kGDBmHgwIEAgIEDByI0NFT9Kc+HH36I8ePH4+HDh5g0aRImT56MxMREjB8/Xh0EKismJgadO3eGhYWFOtfztm3b0KNHDxQWFhp1PA4dOoRu3brh9OnTGDx4MN566y3cu3cPnTp1QkZGhs70ppyb6dOno3nz5gCADz74QH0sNPM6f/7557h69SqCgoLwwQcfYMiQIbhx4wYGDx6MxYsXG7UPLwuh7FtYiGfLFAL4Y8eORdeuXeHj44P169cbPJePHj3CH3/8gTlz5mDVqlV4+PCh+TdcQ1paGkpKSuDr6ysacK5duzYAIDIystxlCfs7efJkreGPHz/WWpYmZ2dnuLi4ICEhwagHBwDUHV/36tVLZ5yQ9seUAKupntf6jbn+tmzZguDgYERFRWHQoEGYPn067O3tMXv2bPTo0QNFRUWV2gZLS0sAumXenMdE3zr02b17NwDTUlSZk6nba0haWhpOnz4NW1tb1KlTp9LLI4QQQsjzRTn1CSGEEPJC8fHxQc+ePREREYGjR48iODhYPa6oqAjr16+Hra2tOud+t27dkJycDHt7e63lrF69GuPGjcOSJUvw5ZdfVmhbPv/8cyQmJuK7777TWsayZcswZcoUneldXFxw//59nRz4V69eRfv27fHFF1/gwIEDAHhQPzMzE9u3b8egQYOM7pTw2LFjWLRoERo2bIjo6Gh1y9XZs2ejffv2+Pnnn/H666+rc7QLdu/ejY0bN2LYsGHqYWPHjsWaNWuwbds2g30YAIBKpcJbb72FkpISHD16VL18xhhGjx6N9evX68xjyrmZPn06YmNjcfHiRUyfPh1+fn46y9uzZ49OMCo3NxcdO3bEV199hUmTJhnVL0J4eLhJbygMGjQILVq0MHr6FStWYN++faLjPvvsM72tggVKpRIrV64EAJ3zCABPnjzBzp070bRpUzRr1gwAMHr0aMyZMwdbtmzRyfkuOHDggLr8ATxQOG3aNCxYsEC0ZW9lubi4QCaTISEhQZ37X9O9e/cAADdv3jS4nISEBBw6dAg1atRAnz59tMYJ/Q0Iy9KUlZWlfth08+ZNrdz9+ty6dQsA4O/vrzNOGCZMUxVu3boFe3t7eHt7P9P1l3f95eTkYPLkyZDJZIiOjlaXO83rf8GCBZg5c2aFt0Eo82WD9+Y6Jjk5Odi8eTOsra3RuXNn0Wm2bduG2NhY5Ofn4+rVq4iIiEDt2rXxzTffmLo7ZqHvmBgjPj4e4eHhUCqVePToEXbs2IHMzEz8+eefcHBwMPemEkIIIeRZe565fwghhBBCxGzatIkBYGPHjtUaLuSLNibXr0qlYo6OjiwkJERruL5c+ABYcHCw+v9FRUXM2tqaeXp66uRbViqVLCAgwKSc+gMGDGBWVlZMoVCoh5WXU18sx/XEiRMZALZp0yad6YXjM2nSJPWwyMhIBoB16dJFZ3ph3EcffVTu9h85coQBYAMGDNAZFx8fz2QymdE59fWdm4rm9F64cCEDwKKiooyaXsj/b+zH2D4PhO039NHMZS2c/+7du7PQ0FAWGhrK3n//fVa/fn0GgLVv357l5ubqrOfHH39kANiCBQvUw65fv84AsK5du+pM//jxYxYaGspiY2NZdnY2S0lJYTt27GANGzZkANhnn31m1P6VZUzu7W7dujEAbMmSJVrDt2zZoj4mb731lsH1CNfszJkzdcbl5OQwR0dHZmlpyc6fP681burUqep1rF+/3qh98vf3ZwB08tYLZDIZCwgI0Dt/ZfORW1pasho1aoiOu3//PgPAevXqpXf+qsqpv3r1agaAvfPOO6LbZWFhwerWrWvyOgVLly5lAFi3bt10xlX2mAhGjhzJALBvvvlG7zRlr+E2bdqw27dvG78jIsrLqa+PoWNiDKF+Fz729vZszZo15c5HOfUJIYSQlwO11CeEEELIC2fQoEFwc3PD5s2bsWTJEnWrQqHV4sSJE7Wm37p1K5YuXYrz588jIyMDSqVSPe7Ro0cV2oYbN26gsLAQ3bp102lZLZVK0bFjR9EWxrGxsZg/fz6OHz+O5ORkFBcXa41PTU1FtWrVKrRNANS5+cXy/wvDYmNjdca1atVKZ1jNmjUBAJmZmeWu9+LFiwAg2sLV19cXPj4+oq3fzXluUlJSMHfuXOzduxcJCQkoKCjQGm/s8oR0HlUlOjoa7du3N3r6Q4cO4dChQ1rDOnTogMOHD4u26l+5ciWkUilGjhypHtagQQMEBgYiKioKd+/e1XqjwdPTE7NmzVL/38HBAQMGDEBgYCCaNGmCn376CZ988glcXFxM2Evj/PTTTwgKCsL777+PnTt3olmzZrh9+za2b9+OZs2a4dKlSwbzeKtUKoSFhUEikehc9wBgb2+Pn376CZMnT0aHDh0wePBgeHt74+TJkzh37hwaNGiAuLg49TqE1suanJ2dMX36dHPutl6xsbHYtm2b1jA/Pz+j39R5XgzVOz4+Pqhbty5u3LiBnJwck1uB7969G++//z58fX2xdu1ac2yuji+++ALr169Hnz598MUXX+idLjw8HOHh4cjKysKFCxfw5ZdfonXr1ti6dSu6desGgNeXixYt0plX8xqrLEPHxNgyHBISAsYYiouLER8fj2XLlmHs2LE4c+YMfv31V7NtKyGEEEKeDwrqE0IIIeSFY2VlhdGjR+OXX37B33//jUmTJuHBgwc4dOgQ/P390aVLF/W0CxcuxIwZM+Dh4YFevXqhZs2asLGxAQAsWrSownmehXzvnp6eouM1O+sUnDx5Uh346dWrF/z9/WFvbw+JRIJt27bh4sWLlc47nZ2dDalUqk47UnabpFKpaD8Cmh1MCoQ8zZqBdn2MOR5lg/rmPDfp6ekIDAzE/fv30alTJ/To0QPOzs6QyWSIjY3F9u3bK31sn5c5c+bgs88+g0qlQnx8PGbNmoU1a9bgzTffxJo1a7SmPXXqFK5du4aePXuievXqWuPGjRuHs2fPIiwsDN9++2256/X29ka/fv2wZs0anD17tkIpPsrTvHlznD17FqGhoYiMjERkZCTq1auHpUuXIjMzE//73/9Ey7LgwIEDuH//Prp37y6aNx/g/WtUr14d8+fPx/bt26FUKtGmTRscOnQI8+bNQ1xcnHod8fHxmD17ttb8vr6+6oCocJ1kZWXBzc1Na7q8vDwolUrRa8lYsbGxOusPDg5WB/WdnJz09gOSnZ2ttY3PkrBusXoP4GXpxo0byM7ONimoHxERgTfeeANeXl44fPiw6APPyh6T2bNnY86cOejWrRu2bt1qVGewTk5OCAkJwd69e1G/fn2MHTsW9+7dg6WlJTIzM3XOIWC+oH55x6S8MlyWpaUl/P39sWDBAuTn52Px4sXo27cv+vbta5btJYQQQsjzQUF9QgghhLyQJk2ahF9++QUrV67EpEmTEB4eDpVKpdVat6SkBN9++y2qV6+O2NhYreAgYwzz58+v8PqFIFFKSoroeKGDTk3ff/89ioqKcPz4cXTq1Elr3KlTp9St3SvD0dERKpUKT5480Qmwp6SkQKVSwdHRsdLrKcvU42Huc/PXX3/h/v37Ov0bAMDcuXOxfft2o5dV1Tn1K0oqlaJOnTpYtWoVEhISsHbtWrzxxhsYNGiQehqhw9gDBw6Idj4L8P2bPXu2UXny3d3dAQD5+fmV3wE9GjRogE2bNukMFwLZbdq00Tuvvg5yy9IXpBwzZgykUqn6TRWh9bI+/v7+iImJwa1bt3SC+oby7Rtr/PjxBlvl+/v7Izo6GsnJyTo55M2x/ooS6hSxek9zuCl1z759+/Daa6/B3d0dkZGRejtvrcwxmT17NmbNmoWQkBDs3LlT/VDRWI6Ojmjfvj22bduG27dvo2HDhvDz8zNYhirDmGNSXhk2pFevXvj9998RFRVFQX1CCCHkJUdBfUIIIYS8kJo2bYrAwECcPHkScXFxCA8Ph0wmw7hx49TTpKamIisrC927d9dp7RsTE6OTnsUU9evXh7W1NWJiYlBYWKiVBkWlUuHkyZM689y5cweurq46Af38/HycP39eZ3qhxagxLeUFLVu2xIULFxAVFYWhQ4dqjTty5AgAVEkAunnz5gB4R73/+9//tMYlJCTgwYMHWsMqcm4MHY87d+4AAF599VWdcceOHTNhT3jQWzhWxvDz83smQX2BRCLBL7/8glatWuHzzz/HgAEDIJPJkJeXh02bNsHW1hYjRowQnffUqVPqDj6NCdqdOXMGAEQ7Jq5KOTk52LlzJ1xdXdGzZ0/RadLS0rB9+3a4urritddeM3kdJ06cQHx8PPr162d06/bg4GBs2LAB+/fv10mhFBERoZ6mqgQHByM6Ohr79+/H2LFjn+n6DV1/LVu2BADReicxMRF37txBnTp1jG6lv2/fPgwaNAiurq7qtzf0qegxmTVrFmbPno3g4GDs3r3bqE60xQhpvYQ3m6qKKcekop7VvhBCCCGk6pXffIcQQggh5DmZNGkSAN5K9+7du+jXr59WKgJPT0/Y2Njg/PnzWi2NMzIyMHXq1Eqt28rKCkOHDkVKSgoWLlyoNW7FihWi+fR9fX2RkZGBq1evqocplUrMmDEDT5480Zne1dUVAPDw4UOjt0t4qDF79mx16gmAp6EQUjJoPvgwl6CgINSuXRu7du3C8ePH1cMZY/jiiy90AoEVOTeGjoevry8AaK0bANavX489e/aYtC9RUVFgjBn9eR75zlu0aIFBgwYhLi4O69evBwD8/fffyMnJwZAhQ7BixQrRzw8//ACgtIU7wAP3Zft2AHi++xMnTqBRo0bqhzbmVlBQgJKSEq1hRUVFmDRpEtLT0xEaGirabwAArFmzBgqFAqNHj4ZcLte7Ds3rQPDo0SNMnjwZFhYWRqUiEgwdOhROTk5YvHix1oOqpKQkLFq0CM7OzhgyZIjRyzPVhAkTYGFhge+//14r5czVq1exevVq1K1bV53iy9wMXX8DBw6Ek5MTwsLCtOo3xhg+//xzFBcXG32dCMFrFxcXREZGlvvmQUWOSWhoKGbPno3OnTuXG9AvKirCqVOnRMeFhYXhzJkzqFevXpW+IWHqMTHkzJkzKCws1BmekJCAOXPmAAC10ieEEEL+A+gRPSGEEEJeWCNGjMBHH32EEydOACgN8gukUineffddLFy4EM2bN8eAAQOQnZ2NvXv3wtfXVyfnuKnmzp2LQ4cOYebMmTh+/DhatmyJ69evY8+ePejVqxf279+vNf3UqVOxf/9+BAUFYejQobC2tkZUVBQSExMREhKi00Frhw4dYGNjg0WLFiE7O1vdov2zzz7Tu01dunTB1KlTsXjxYjRp0gRvvPEGGGPYunUrHjx4gGnTpmn1OWAuUqkUy5YtQ79+/dCjRw8MGzYM1atXx+HDh5GUlKTu9FRzelPPTbdu3fDjjz9iypQpGDJkCOzs7FCrVi2MHDkSY8aMwbx58zB16lRERkbC19cXly5dwsGDB/H6669j69atZt/nilqxYgX27dsnOi4kJES0s1Exs2bNwrZt2/DNN99gxIgR6kC9WIexgn79+sHLyws7duzAkydP4OHhgU8++QRxcXEIDg6Gj48PCgoKEB0djQsXLsDFxQVr1qzRm8qnrOPHj2PFihUAoH5Qdfz4cXVAt0GDBlrl99y5c3j99dfRs2dP+Pj4IDs7G7t378b9+/fx5ptvGnz4ZmzqnV9//RVr165FUFAQPD098eDBA2zfvh35+fn466+/RDuJ1sfFxQVLlizBmDFj0KpVKwwfPhxSqRSbNm3C48ePsWbNGp0OhU09JoYEBARg1qxZmDlzJpo1a4bBgwcjLy8PGzZsQHFxMZYvX67Tynrbtm3qznfv3bunHiakmAoKCir3GAKGrz9HR0csX74cI0aMQLt27TBs2DB4eHjg0KFDiImJQdu2bXXe4BETFxeHQYMGoaioCCEhIdiwYYPONGU7Djb1mISHh+Obb76BhYUF2rZtiwULFuisQ/M6LCgoQIcOHdCkSRO0aNECNWrUQFZWFs6cOYPz58/D3t4eYWFh5e6bphUrVqgfQF6+fFk9TLgHDBo0SJ1WqyLHxJAffvgBx44dQ3BwMGrVqgULCwvcuXMHe/bsgUKhwIcffoigoCCtecxZhgkhhBDyjDBCCCGEkBfY2LFjGQDm5eXFiouLdcYrFAr2/fffM39/fyaXy1mtWrXYRx99xHJycpivry/z9fXVmj40NJQBYJGRkVrDAbDg4GCd5SckJLBhw4YxZ2dnZmtryzp37syOHDmidzmbN29mrVq1Yra2tszd3Z0NHTqU3blzh40bN44BYPfu3dOafvfu3SwwMJDZ2NgwAEzz65m+eRhjbOXKlSwwMJDZ2toyW1tbFhgYyFauXKkzXWRkJAPAQkNDdcbdu3ePAWDjxo3TGafP0aNHWZcuXZiNjQ1zdXVlQ4YMYQkJCSw4OJiV/Wpp6rlhjLH58+czf39/ZmlpqXNOYmNjWa9evZiLiwtzcHBgwcHB7ODBgywsLIwBYGFhYUbvR1UQzpehj+Z5ELZ7zpw5epf5xhtvMADszz//ZABY3bp1y92Ojz/+mAFgCxcuZIwxtnz5ctanTx9Ws2ZNZm1tzaytrVn9+vXZBx98wB48eGDSPgrbrO9T9hpKSEhgQ4YMYT4+PszKyoo5Ozuzbt26sc2bNxtcz+nTpxkA1rZt23K36dChQ6xHjx7M09OTWVpaMm9vbzZs2DB2/vx5k/ZN0969e1mXLl2Yvb09s7e3Z126dGH79u0TndbUY2KMtWvXsjZt2jAbGxvm5OTE+vTpw86cOSM6rVAX6fuYcn0buv4Y49d/3759mbOzM7OysmIBAQHsq6++Yrm5uUYtX6iPKnK8jD0m5R2PstehQqFgs2fPZiEhIaxatWrM0tKS2draskaNGrHp06ezhIQEYw+fWnl1geb6K3NMxOzcuZMNHz6c1a1bl9nZ2TFLS0tWo0YN9tprr7E9e/aIzlMVZZgQQgghVUvCWBX18kMIIYQQQgghhBBCCCGEELOinPqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYQQQgghhBBCyEuCgvqEEEIIIYSI8PPzg0QigUQiwbZt2/RO16NHD0gkEoSHh2sNDw8PV89vZWWFtLQ0vcsoKSmBp6enevpZs2aJThcREYHXX38dNWrUgJWVFZydnVG/fn0MGDAACxcuxJ07d7Smj4+PVy+zvE98fLyRR6Z8165dw5QpUxAQEAAbGxvY2dmhdu3aCAkJwVdffYWTJ0/qzCMc77LHUZ/x48fr7IOlpSWqVauGV199FXv37jXb/pR17tw59TpHjRpV7vSa27h48WKD03744Yfqaf38/My0xYQQQggh5L/E4nlvACGEEEIIIS+6WbNmYeDAgZBIJBWav7i4GJs2bcK7774rOj4iIgJPnjwxuIz3338fv/32GwDAzs4O/v7+sLW1RUJCAnbt2oVdu3YhKSkJP/74o+j8bdq0gVwu17t8a2trI/fGsHXr1mHixIlQKBSwtLRErVq14OrqipSUFBw5cgRHjhzB3r17ERMTY5b1eXp6wt/fHwBQWFiImzdvYufOndi5cyc+//xz/PDDD2ZZj6Y1a9ao/71t2zbk5OTAwcHB6HmnTp0qOk6pVGLjxo1m2UZCCCGEEPLfRS31CSGEEEIIMUAmk+HixYvYsmVLheb39/eHRCLRCgSXJYyrX7++6PgNGzbgt99+g1QqxS+//IK0tDRcvXoVZ8+eRUpKCq5evYovv/wSnp6eetfxzz//4Pjx43o/3t7eFdo/TfHx8Zg0aRIUCgUmTpyIhw8f4vbt2zhz5gzi4+ORlJSEJUuWoFGjRpVel6Bv377qfYiJiUFqaipmzJgBAJgzZw6io6PNti6Av1WxYcMGAICzszPy8/OxdetWo+atX78+zp49ixs3boiOP3DgAJKTk/WWA0IIIYQQQgAK6hNCCCGEEGLQiBEjAACzZ88GY8zk+WvVqoUuXbrg1KlTuH37ts74nJwc7NixA7Vr10anTp1El7Fq1SoAwMSJEzFt2jSdFveNGjXCd999h08++cTk7TOnjRs3oqioCPXr18fy5ct1HjJ4e3vjvffew+rVq6tsG6ysrDB//ny0aNFCvU3mtH//fqSkpMDHxweff/45ABh8YKNp9OjRAIC1a9eKjheGjxkzxgxbSgghhBBC/qsoqE8IIYQQQogBEydOhJ+fH65cuYK///67QsswFMzdvHkzCgoKMGrUKL3pfe7evQsA6kD1i0rYzqZNm0IqfX4/NSQSCYKCggAAt27dMuuyhQD+8OHDMXLkSEilUkRGRuLhw4flzvvGG2/AxsYGa9eu1XlAlJeXh23btqkfAhFCCCGEEKIPBfUJIYQQQggxwNLSEl9++SUA3lpfpVKZvIwhQ4bA2toa69at0xknBPqFwL8YR0dHAMCZM2dMXvezJGxnbGwsiouLn+u2VOStivJkZ2dj+/btAICRI0eiZs2a6NKlC1QqFdavX1/u/A4ODhg4cCDi4+Nx4sQJrXFbt25FXl6ewYc7hBBCCCGEABTUJ4QQQgghpFzjx49HnTp1cP369Qqlc3FycsKAAQNw+/ZtrRzvDx8+RFRUFNq2bWswj3qfPn0A8Fbi77zzDs6ePQulUmn6jlQxYTtv376Nvn37Yu/evcjPz3/m28EYUwfN69WrZ7blCm9VNGrUSP3WxKhRowAYn4JHSK1Tdnrh/4Ye7hBCCCGEEAJQUJ8QQgghhJByWVhY4KuvvgIAfPPNNxUKqAvBWs1g7rp166BSqcoN5H766ado3bo1GGP4888/0bZtWzg6OiIoKAifffYZYmJiyl1/7dq1IZFIRD/mSuvTo0cPvPXWWwCAQ4cOoV+/fnByckLz5s3x9ttvY9euXVX+MEKhUOCTTz5BbGwsAGDo0KFmW7Zw7kaOHKkeNnjwYFhZWeHKlSvqdRrSq1cveHp64u+//0ZRUREAICkpCYcPH0arVq3M2okwIYQQQgj5b6KgPiGEEEIIIUYYM2YM/P39cePGDdE0OuXp27cv3N3d8ffff6tT06xduxYWFhYYPny4wXkdHBxw/PhxLFy4EA0bNgQA5Ofn48SJE5g3bx4CAwMxaNAgZGZm6l1GmzZt0KlTJ9FPy5YtTd4ffZYuXYotW7YgODgYMpkMJSUluHTpEpYuXYoBAwagefPmuHz5stnWt3fvXgQFBSEoKAht2rSBu7s7fvzxRwDAhx9+qM6tX1kPHjzAkSNHAJR2ngwAzs7O6NevHwDjWusL5zszMxO7d+8GAKxfvx5KpZI6yCWEEEIIIUahoD4hhBBCCCFGkMlk6tb63377LUr+j707D6u6zP8//jqAbApUoiwJQm64lYqpaNpigjqVpqWZUoxLMpqpNH6T3NHSylF0UpPGJMdUcjTbNMUpU8KsHLFFLTMNE0ixEVwSBM7vD36c6cgi64FzeD6u63PJuT/3532/P9B1de73uc/9ycur0PUNGjTQsGHDdP78eW3fvl0pKSn69ttvFRYWpiZNmtzwemdnZ0VFRenIkSM6c+aMtm7dqqlTpyowMFCS9O6772rIkCGlXr9582YlJSWVeKxdu7ZC93IjQ4YM0Z49e/Tbb78pMTFR8+fPV7du3SRJ3333ne6//36dO3euWsY6e/asPvvsM3322WdKSUmRs7OzBgwYoHfeeUdLliypljEkmR5u26NHD912221m54q24Ckqzt/I9d/a+Oc//yl7e3uzDwsAAACA0lDUBwAAAMrp8ccfV5s2bfTjjz+Wew/1Pypaib1+/XrTA3Irszrb19dXDz/8sJYsWaIffvhBzz77rCTpk08+KfYA1trk7u6u+++/XzNnztSBAwe0efNm2dnZ6ezZs4qLi6uWMZ588kkZjUYZjUbl5eXp7Nmz2r59uwYPHlwt8YuUtPVOkQceeEDu7u7KyMjQ7t27bxjrzjvvVFBQkLZv3669e/fq8OHD6tevn7y8vKo1ZwAAANgmivoAAABAOdnb22v27NmSKrdav0ePHmrVqpXef/99rV+/Xu7u7nrooYeqlJODg4NefvlleXt7S5K++OKLKsWrSY888oiGDh0qqW7neb2vvvpKR48elSQ988wzxZ5J4OLiouzsbEnlf2DuqFGjlJuba/pQh613AAAAUF4U9QEAAIAKeOyxx9SuXTudPHlS8fHxFb5+5MiRysnJ0a+//qqhQ4fKxcWlyjnZ2dmpefPmkgofFFuXFW1dU9fz/KOiQr2rq6u8vLxKPIq2UHrnnXd06dKlG8YcNWqUDAaDUlNT1ahRo2r/ZgEAAABsF0V9AAAAoALs7Ow0Z84cSdKCBQtMD70tr/DwcPXt21d9+/bVuHHjynXN2bNnyzx/4cIFHTlyRJLUqlWrCuVTnW6UpyQlJydLqt08KyIvL0+bNm2SJK1YsUIZGRmlHs2aNdOVK1e0devWG8Zt3ry5xo8fr759++qvf/2rXF1da/pWAAAAYCMo6gMAAAAV9Oijj6pjx476+eefK7yH/W233abdu3dr9+7dCgkJKdc1AwcO1MiRI/Xxxx8X+xAhJSVFgwYN0sWLF+Xj46OwsLAK5VOaiIgIGQwGRURElPuaF198Ub1799bGjRt18eJFs3Pp6emKjIzUvn37ZDAY9OSTT1ZLnhUVEBAgg8FQ7m9Z7Ny5U2fPnpWLi4tp66CS2NnZmR50W94teFatWqXdu3ebPiQCAAAAysOhthMAAAAArI3BYNCcOXP0yCOPKD8/v8bHy8/P14YNG7RhwwY5OzurZcuWcnZ2VlpamtLS0iRJN910k95++201bNiwxBiPPvqonJycSh1j4cKF6t27d5XyNBgMSkpKUlJSkuzs7NSiRQvdfPPNOnfunE6fPq28vDzZ29tryZIlCg4OLjHGpEmT9Ne//rXUMfbs2aMOHTpUKc+KKCrQDx48WG5ubmX2HTVqlF555RV9/PHHOnPmjG699VZLpAgAAIB6hqI+AAAAUAlDhgxRp06dlJKSUuNj7dq1Szt37tSOHTv07bffKi0tTRcuXFCjRo3UvXt3hYWFaeLEiWratGmpMb766qsyxzh//rzZ64yMDElSp06dyp3niy++qH79+mnHjh06cOCAzpw5o1OnTsnJyUmtW7dWnz599Je//EW33357qTEuXbpU5p70FX048R/l5+crMzNTknTHHXfcsH92drbee+89SYUF+xu5/fbb1bFjR33zzTfasGGDpk2bVulcAQAAgNIYjEajsbaTAAAAAFB3FBQU6JZbbpHRaNQvv/xywxXq1uI///mPgoODde+99+rjjz+u7XQAAACASmFPfQAAAABmvvvuO2VlZempp56ymYK+JNPzD5599tlazgQAAACoPIr6AAAAAMwkJyfLwcFBzzzzTG2nUq2Sk5MVFBSkgQMH1nYqAAAAQKWx/Q4AAAAAAAAAAFaClfoAAAAAAAAAAFgJivoAAAAAAAAAAFgJivoAAAAAAAAAAFgJh9pOAAAAW1ZQUKC0tDS5ubnJYDDUdjoAAACoYUajURcvXpSvr6/s7FhLCQCofhT1AQCoQWlpafLz86vtNAAAAGBhp0+fVrNmzWo7DQCADaKoDwBADXJzc5NUOKlzd3ev5WwAAABQ07Kzs+Xn52d6HwgAQHWjqA8AsBkrV67UK6+8ovT0dLVv316xsbHq3bv3Da/77LPPdPfdd6tDhw5KSUkxO7dlyxbNmjVLJ06cUIsWLfTCCy/o4YcfLndORVvuuLu7U9QHAACoR9h6EQBQU9jcDQBgExISEjRlyhTNmDFDhw4dUu/evTVgwAClpqaWeV1WVpaeeOIJ9e3bt9i5/fv3a/jw4QoPD9fhw4cVHh6uYcOG6cCBAzV1GwAAAAAAAGUyGI1GY20nAQBAVXXv3l1dunTRqlWrTG1t27bV4MGDtXDhwlKve+yxx9SqVSvZ29tr27ZtZiv1hw8fruzsbO3YscPU1r9/f918883auHFjufLKzs6Wh4eHsrKyWKkPAABQD/D+DwBQ01ipDwCwerm5uTp48KBCQ0PN2kNDQ5WcnFzqdWvXrtWJEyc0Z86cEs/v37+/WMywsLAyY+bk5Cg7O9vsAAAAAAAAqC4U9QEAVi8zM1P5+fny8vIya/fy8lJGRkaJ1xw/flzTp0/XW2+9JQeHkh8xk5GRUaGYkrRw4UJ5eHiYDj8/vwreDQAAAAAAQOko6gMAbMb1DyMzGo0lPqAsPz9fjz/+uObNm6fWrVtXS8wi0dHRysrKMh2nT5+uwB0AAAAAAACUreSliQAAWBFPT0/Z29sXW0F/9uzZYivtJenixYv66quvdOjQIT399NOSpIKCAhmNRjk4OGjXrl2677775O3tXe6YRZycnOTk5FQNdwUAAAAAAFAcK/UBAFbP0dFRwcHBSkxMNGtPTExUz549i/V3d3fXN998o5SUFNMRGRmpNm3aKCUlRd27d5ckhYSEFIu5a9euEmMCAAAAAABYAiv1AQA2ISoqSuHh4eratatCQkIUFxen1NRURUZGSircFufMmTNat26d7Ozs1KFDB7PrmzZtKmdnZ7P2yZMnq0+fPnrppZc0aNAgvfvuu9q9e7eSkpIsem8AAAAAAABFKOoDAGzC8OHDdf78ecXExCg9PV0dOnTQ9u3b1bx5c0lSenq6UlNTKxSzZ8+e2rRpk2bOnKlZs2apRYsWSkhIMK3kr4iUlBQ1atSowtf9kaenp/z9/asUAwAAAAAAWDeD0Wg01nYSAADYquzsbHl4eFRLLBcXFx07dozCPgAAQB1W9P4vKytL7u7utZ0OAMAGsVIfAAALePDBB+Xj41Pp6zMzM7V161ZlZmZS1AcAAAAAoB6jqA8AgAU0btxYvr6+tZ0GAAAAAACwcna1nQAAAAAAAAAAACgfivoAAAAAAAAAAFgJivoAAAAAAAAAAFgJivoAAAAAAAAAAFgJivoAAAAAAAAAAFgJivoAAAAAAAAAAFgJivoAAAAAAAAAAFgJivoAAAAAAAAAAFgJivoAAAAAAAAAAFgJivoAAAAAAAAAAFgJivoAAAAAAAAAAFgJivoAAAAAAAAAAFgJivoAAAAAAAAAAFgJivoAAAAAAAClmTtXmj+/5HPz5xeeBwDAgijqAwAAAAAAlMbeXpo9u3hhf/78wnZ7+9rJCwBQbznUdgIAAAAAAAB11qxZhf/Onv2/10UF/ZiY/50HAMBCKOoDAAAAAACU5Y+F/QULpNxcCvoAgFrD9jsAAAAAAAA3MmuW5OhYWNB3dKSgDwCoNRT1AQA2Y+XKlQoMDJSzs7OCg4O1b9++UvsmJSWpV69eaty4sVxcXBQUFKSlS5ea9YmPj5fBYCh2XL16taZvBQAAAHVMwbz5Um6u8h0KC/sF80p5eC4AADWMoj4AwCYkJCRoypQpmjFjhg4dOqTevXtrwIABSk1NLbF/w4YN9fTTT2vv3r06evSoZs6cqZkzZyouLs6sn7u7u9LT080OZ2dnS9wSAAAA6ogjI+bLbu5szVKMHPJyNEsxsps7W0dGUNgHAFgee+oDAGzCkiVLNGbMGI0dO1aSFBsbq507d2rVqlVauHBhsf6dO3dW586dTa8DAgK0detW7du3T0899ZSp3WAwyNvbu+ZvAAAAAHXSkRHz1W5TYUF/gQq33FmgWTJIitk0W0cktdvIVjwAAMthpT4AwOrl5ubq4MGDCg0NNWsPDQ1VcnJyuWIcOnRIycnJuvvuu83aL126pObNm6tZs2Z64IEHdOjQoTLj5OTkKDs72+wAAACAdcrPlz76MN+soF9kvmZptmK0c3u+8vNrKUEAQL3ESn0AgNXLzMxUfn6+vLy8zNq9vLyUkZFR5rXNmjXTuXPnlJeXp7lz55pW+ktSUFCQ4uPj1bFjR2VnZ2vZsmXq1auXDh8+rFatWpUYb+HChZo3b17VbwoAAAC1bt8+6dmLc0s9P1+zpGyp8z7pnnsslhYAoJ6jqA8AsBkGg8HstdFoLNZ2vX379unSpUv6/PPPNX36dLVs2VIjRoyQJPXo0UM9evQw9e3Vq5e6dOmiv//971q+fHmJ8aKjoxUVFWV6nZ2dLT8/v8reEgAAAGpRenr19gMAoDpQ1AcAWD1PT0/Z29sXW5V/9uzZYqv3rxcYGChJ6tixo3799VfNnTvXVNS/np2dne68804dP3681HhOTk5ycnKq4B0AAACgLvLxqd5+AABUB/bUBwBYPUdHRwUHBysxMdGsPTExUT179ix3HKPRqJycnDLPp6SkyIdZGwAAQL3Qu7fUrJlU2pc/DQbJz6+wHwAAlsJKfQCATYiKilJ4eLi6du2qkJAQxcXFKTU1VZGRkZIKt8U5c+aM1q1bJ0lasWKF/P39FRQUJElKSkrS4sWLNWnSJFPMefPmqUePHmrVqpWys7O1fPlypaSkaMWKFZa/QQAAAFicvb20bJn0yCOFBXyj8X/nigr9sbGF/QAAsBSK+gAAmzB8+HCdP39eMTExSk9PV4cOHbR9+3Y1b95ckpSenq7U1FRT/4KCAkVHR+vkyZNycHBQixYttGjRIo0fP97U58KFC3rqqaeUkZEhDw8Pde7cWXv37lW3bt0sfn8AAACoHUOGSP/6lzR5svTLL/9rb9assKA/ZEitpQYAqKcMRuMfP2cGAADVKTs7Wx4eHoqIiFBAQECl46SlpSkuLk4HDx5Uly5dqi9BAAAAlEt+vrRvX+FDcX18CrfcKWmFftH7v6ysLLm7u1s+UQCAzWOlPgAAAAAAwA3Y20v33FPbWQAAwINyAQAAAAAAAACwGhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQA2Y+XKlQoMDJSzs7OCg4O1b9++UvsmJSWpV69eaty4sVxcXBQUFKSlS5cW67dlyxa1a9dOTk5Oateund55552avAUAAAAAAIAyUdQHANiEhIQETZkyRTNmzNChQ4fUu3dvDRgwQKmpqSX2b9iwoZ5++mnt3btXR48e1cyZMzVz5kzFxcWZ+uzfv1/Dhw9XeHi4Dh8+rPDwcA0bNkwHDhyw1G0BAAAAAACYMRiNRmNtJwEAQFV1795dXbp00apVq0xtbdu21eDBg7Vw4cJyxRgyZIgaNmyof/7zn5Kk4cOHKzs7Wzt27DD16d+/v26++WZt3LixXDGzs7Pl4eGhiIgIBQQElP+GrpOWlqa4uDgdPHhQXbp0qXQcAAAA1Kyi939ZWVlyd3ev7XQAADaIlfoAAKuXm5urgwcPKjQ01Kw9NDRUycnJ5Ypx6NAhJScn6+677za17d+/v1jMsLCwMmPm5OQoOzvb7AAAAAAAAKguFPUBAFYvMzNT+fn58vLyMmv38vJSRkZGmdc2a9ZMTk5O6tq1qyZOnKixY8eazmVkZFQ45sKFC+Xh4WE6/Pz8KnFHAAAAAAAAJaOoDwCwGQaDwey10Wgs1na9ffv26auvvtJrr72m2NjYYtvqVDRmdHS0srKyTMfp06creBcAAAAAAAClc6jtBAAAqCpPT0/Z29sXW0F/9uzZYivtrxcYGChJ6tixo3799VfNnTtXI0aMkCR5e3tXOKaTk5OcnJwqcxsAAAAAAAA3xEp9AIDVc3R0VHBwsBITE83aExMT1bNnz3LHMRqNysnJMb0OCQkpFnPXrl0VigkAAAAAAFCdWKkPALAJUVFRCg8PV9euXRUSEqK4uDilpqYqMjJSUuG2OGfOnNG6deskSStWrJC/v7+CgoIkSUlJSVq8eLEmTZpkijl58mT16dNHL730kgYNGqR3331Xu3fvVlJSkuVvEAAAAAAAQBT1AQA2Yvjw4Tp//rxiYmKUnp6uDh06aPv27WrevLkkKT09Xampqab+BQUFio6O1smTJ+Xg4KAWLVpo0aJFGj9+vKlPz549tWnTJs2cOVOzZs1SixYtlJCQoO7du1v8/gAAAAAAACTJYDQajbWdBAAAtio7O1seHh6KiIhQQEBApeOkpaUpLi5OBw8eVJcuXaovQQAAAFSrovd/WVlZcnd3r+10AAA2iD31AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEg61nQAAAAAAAPWB0WhUXl6e8vPzazsVVFGDBg1kb29f22kAAOopivoAAAAAANSw3Nxcpaen68qVK7WdCqqBwWBQs2bN1KhRo9pOBQBQD1HUBwAAAACgBhUUFOjkyZOyt7eXr6+vHB0dZTAYajstVJLRaNS5c+f0yy+/qFWrVqzYBwBYHEV9AAAAAABqUG5urgoKCuTn5ydXV9faTgfVoEmTJjp16pSuXbtGUR8AYHE8KBcAAAAAAAuws2MKbiv4pgUAoDbxjgIAAAAAAAAAACtBUR8AAAAAAAAAACvBnvoAAAAAAFiB/Hxp3z4pPV3y8ZF695bYzh0AgPqHlfoAAAAAANRxW7dKAQHSvfdKjz9e+G9AQGF7TTAYDGUeERERpn7btm0rdn1ERIQGDx5c6uuzZ89q/Pjx8vf3l5OTk7y9vRUWFqb9+/eXmtPcuXNN49vZ2cnX11cjR47U6dOnzfoFBAQoNja22PWxsbEKCAgoFi8yMtKsX0pKigwGg06dOlVqLgAA1CaK+gAAAAAA1GFbt0qPPCL98ot5+5kzhe01UdhPT083HbGxsXJ3dzdrW7ZsWZXiDx06VIcPH9abb76pH374Qe+9957uuece/fbbb2Ve1759e6Wnp+uXX35RQkKCvvnmGw0bNqzSeTg7O2vNmjX64YcfKh0DAABLY/sdAAAAAADqqPx8afJkyWgsfs5olAwGacoUadCg6t2Kx9vb2/Szh4eHDAaDWVtVXLhwQUlJSdqzZ4/uvvtuSVLz5s3VrVu3G17r4OBgysPX11fjxo3TM888o+zsbLm7u1c4lzZt2qhp06aaOXOm3n777QpfDwBAbWClPgAAAAAAddS+fcVX6P+R0SidPl3Yz1o0atRIjRo10rZt25STk1PpOBkZGdq6davs7e1lX4VPNBYtWqQtW7boyy+/rHQMAAAsiaI+AAAAAAB1VHp69farCSNGjDAV6ouOt956q9T+Dg4Oio+P15tvvqmbbrpJvXr10vPPP6+vv/76hmN98803atSokVxdXeXj46M9e/Zo4sSJatiwYaXz79Kli4YNG6bp06dXOgYAAJZEUR8AYDNWrlypwMBAOTs7Kzg4WPvKWLK2detW9evXT02aNJG7u7tCQkK0c+dOsz7x8fElPhju6tWrNX0rAAAAkiQfn+rtVxOWLl2qlJQUs+Ohhx4q85qhQ4cqLS1N7733nsLCwrRnzx516dJF8fHxZV7Xpk0bpaSk6Msvv9QLL7ygTp066YUXXqjyPSxYsED79u3Trl27qhwLAICaRlEfAGATEhISNGXKFM2YMUOHDh1S7969NWDAAKWmppbYf+/everXr5+2b9+ugwcP6t5779WDDz6oQ4cOmfW7/qFw6enpcnZ2tsQtAQAAqHdvqVmzwr3zS2IwSH5+hf1qi7e3t1q2bGl2uLm53fA6Z2dn9evXT7Nnz1ZycrIiIiI0Z86cMq9xdHRUy5Yt1b59ez3//PPq1KmT/vKXv5j1cXd3V1ZWVrFrL1y4IA8PjxLjtmjRQuPGjdP06dNlLOkBBgAA1CEU9QEANmHJkiUaM2aMxo4dq7Zt2yo2NlZ+fn5atWpVif1jY2P1f//3f7rzzjvVqlUrvfjii2rVqpXef/99s35FD4X74wEAAGAp9vbSsmWFP19f2C96HRtbvQ/JrS3t2rXT5cuXK3TNrFmztHHjRv3nP/8xtQUFBZW4P/6XX36pNm3alBpr9uzZ+uGHH7Rp06YK5QAAgKVR1AcAWL3c3FwdPHhQoaGhZu2hoaFKTk4uV4yCggJdvHhRt9xyi1n7pUuX1Lx5czVr1kwPPPBAsZX818vJyVF2drbZAQAAUBVDhkj/+pd0663m7c2aFbYPGVI7eVXW+fPndd9992n9+vX6+uuvdfLkSW3evFkvv/yyBg0aVKFYt912mwYNGqTZs2eb2qKiorRjxw7FxMToyJEjOnLkiObPn6+PPvpIzz77bKmxvLy8FBUVpeXLl1f63gAAsASK+gAAq5eZman8/Hx5eXmZtXt5eSkjI6NcMf72t7/p8uXLGjZsmKktKChI8fHxeu+997Rx40Y5OzurV69eOn78eKlxFi5cKA8PD9Ph5+dXuZsCAAD4gyFDpFOnpE8+kTZsKPz35EnrK+hLUqNGjdS9e3ctXbpUffr0UYcOHTRr1iyNGzdOr776aoXjPfvss/rwww914MABSVKPHj20c+dO7d69W3fddZfuuusu7dq1Szt37lT37t3LjDVt2jQ1atSoUvcFAIClGIxsFgcAsHJpaWm69dZblZycrJCQEFP7Cy+8oH/+8586duxYmddv3LhRY8eO1bvvvqv777+/1H4FBQXq0qWL+vTpU+oKrpycHOXk5JheZ2dny8/PTxEREQoICKjYjf1BWlqa4uLidPDgQXXp0qXScQAAgOVdvXpVJ0+eVGBgIM/msRFl/U2zs7Pl4eGhrKwsubu711KGAABb5lDbCQAAUFWenp6yt7cvtir/7NmzxVbvXy8hIUFjxozR5s2byyzoS5KdnZ3uvPPOMlfqOzk5ycnJqfzJAwAAAAAAVADb7wAArJ6jo6OCg4OVmJho1p6YmKiePXuWet3GjRsVERGhDRs26E9/+tMNxzEajUpJSZGPj0+VcwYAAAAAAKgMivoAAItYvny5vvjiC7O2s2fP6uuvvy6x/7vvvqvRo0eXO35UVJT+8Y9/6I033tDRo0c1depUpaamKjIyUpIUHR2tJ554wtR/48aNeuKJJ/S3v/1NPXr0UEZGhjIyMpSVlWXqM2/ePO3cuVM//fSTUlJSNGbMGKWkpJhiAgAAAAAAWBpFfQCARUyZMkUfffSRWduqVavUuXPnEvunpKTozTffLHf84cOHKzY2VjExMerUqZP27t2r7du3q3nz5pKk9PR0paammvqvXr1aeXl5mjhxonx8fEzH5MmTTX0uXLigp556Sm3btlVoaKjOnDmjvXv3qlu3bhW5dQAAAAAAgGrDnvoAAJsxYcIETZgwocRz8fHxZq/37Nlzw3hLly7V0qVLqyEzAAAAAACA6sFKfQAAAAAAAAAArARFfQAAAAAAAAAArARFfQAAAAAAAAAArARFfQAAAAAAAAAArAQPygUAWMy3336rt99+2+y1JG3evFlGo7FYXwAAAAAAAJhjpT4AwGK2bNmiESNGmI4tW7bIaDTqscceM2svOgcAAABJc+dK8+eXfG7+/MLzNSQiIkIGg8F0NG7cWP3799fXX39drO9TTz0le3t7bdq0qdi5y5cv67nnntNtt90mZ2dnNWnSRPfcc48++OADU5977rnHbKyiIzIy0tTHYDBo27ZtJea6Z88eGQwGXbhwwex1hw4dlJ+fb9b3pptuUnx8vOl1QEBAiWMvWrSoAr8tAAAsg5X6AACLmDNnTm2nAAAAYJ3s7aXZswt/njXrf+3z5xe2x8TU6PD9+/fX2rVrJUkZGRmaOXOmHnjgAaWmppr6XLlyRQkJCZo2bZrWrFmjxx57zCxGZGSkvvjiC7366qtq166dzp8/r+TkZJ0/f96s37hx4xRz3f24urpWKf8TJ05o3bp1+vOf/1xmv5iYGI0bN86szc3NrUpjAwBQEyjqAwAsgqI+AABAJRUV8v9Y2P9jQf+Phf4a4OTkJG9vb0mSt7e3nnvuOfXp00fnzp1TkyZNJBVup9iuXTtFR0fLx8dHp06dUkBAgCnG+++/r2XLlmngwIGSClfGBwcHFxvL1dXVNFZ1mTRpkubMmaMRI0bI2dm51H5ubm7VPjYAADWB7XcAAAAAAKjrZs0qLODPni05OVmsoH+9S5cu6a233lLLli3VuHFjU/uaNWs0atQoeXh4aODAgaaV/UW8vb21fft2Xbx40aL5StKUKVOUl5enV1991eJjAwBQEyjqAwDqhJSUFC1dulRLly7Vl19+WdvpAAAA1D2zZkmOjlJubuG/Firof/DBB2rUqJEaNWokNzc3vffee0pISJCdXWFJ4fjx4/r88881fPhwSdKoUaO0du1aFRQUmGLExcUpOTlZjRs31p133qmpU6fqs88+KzbWypUrTWMVHW+++WaV8nd1ddWcOXO0cOFCZWVlldrvueeeKzb2nj17qjQ2AAA1gaI+AMAi9u7dqyeeeEKff/55sXMzZ85UcHCw/vrXv+qvf/2revTooUmTJtVClgAAAHXY/Pn/K+jn5pb+8Nxqdu+99yolJUUpKSk6cOCAQkNDNWDAAP3888+SClfph4WFydPTU5I0cOBAXb58Wbt37zbF6NOnj3766Sf9+9//1tChQ/Xdd9+pd+/emn/dPYwcOdI0VtHx8MMPV/kexowZI09PT7300kul9pk2bVqxsbt3717lsQEAqG4U9QEAFpGQkGDaa/WPPvnkE7344ouyt7dXeHi4IiMj5enpqZUrV2rbtm21kywAAEBd88c99HNy/rcVjwUK+w0bNlTLli3VsmVLdevWTWvWrNHly5f1+uuvKz8/X+vWrdOHH34oBwcHOTg4yNXVVb/99pvWrFljFqdBgwbq3bu3pk+frl27dikmJkbz589Xbm6uqY+Hh4dprKLD3d29yvfg4OCgBQsWaNmyZUpLSyuxj6enZ7GxXVxcqjw2AADVjQflAgAsYv/+/erevXuxSdnq1atlMBj02muvafTo0ZKkyZMn6/bbb1d8fLwGDx5cC9kCAADUISU9FLekh+daiMFgkJ2dnX7//XfTPvmHDh2Svb29qc+xY8c0cuRInT9/3mzv/T9q166d8vLydPXqVTk6OtZ43o8++qheeeUVzZs3r8bHAgCgJlHUBwBYRFpamrp27Vqs/ZNPPpG7u7siIiJMba1bt9bAgQP1xRdfWDBDAACAOio/v+SH4ha9zs+v0eFzcnKUkZEhSfrvf/+rV199VZcuXdKDDz6o2NhY/elPf9Idd9xhdk379u01ZcoUrV+/XpMnT9Y999yjESNGqGvXrmrcuLGOHDmi559/Xvfee6/Zoo8rV66Yxiri5OSkm2++2fT65MmTSklJMevTsmXLct3LokWLFBYWVuK5ixcvFhvb1dW1Wr4pAABAdaKoDwCwiP/+97+mfVaL/PLLLzp37pz+9Kc/mR60VqRly5bavn27JVMEAACom+bOLf2cBVbof/TRR/Lx8ZEkubm5KSgoSJs3b1bbtm314YcfasOGDcWuMRgMGjJkiNasWaPJkycrLCxMb775pp5//nlduXJFvr6+euCBBzS76JsG/9/rr7+u119/3awtLCxMH330kel1VFRUsfE++eSTct3Lfffdp/vuu0+7du0qdm727NnF8hk/frxee+21csUGAMBSKOoDACzCzc2t2P6lBw8elCQFBwcX628wGOTs7GyR3AAAAFCy+Ph4xcfHl3r+2rVrpZ5bvny56efo6GhFR0eXOdaePXtumI/RaCz3+XvuuafE/jt37izWdurUqRuODQBAXcGDcgEAFnH77bfrgw8+0OXLl01t77zzjgwGg/r06VOs/4kTJ+Tr62vJFAEAAAAAAOo8ivoAAIsYPXq0fvvtN919991avny5nnnmGa1fv15+fn665557zPrm5+dr79696tixY+0kCwAAAAAAUEex/Q4AwCJGjRqlf//733rzzTd16NAhGY1Gubm56fXXXy+2n/6HH36ozMzMUh9iBgAAAAAAUF9R1AcAWMzatWs1ZswY7d+/X7fccovCwsLUrFmzYv2cnJy0dOlSDRo0qBayBAAAAAAAqLvYfgcAYFF33XWXpk2bpjFjxpRY0JeksLAwTZ48WY0bN65Q7JUrVyowMFDOzs4KDg7Wvn37Su27detW9evXT02aNJG7u7tCQkJKfGjali1b1K5dOzk5Oaldu3Z65513KpQTAAAAAABAdaKoDwCwCQkJCZoyZYpmzJihQ4cOqXfv3howYIBSU1NL7L93717169dP27dv18GDB3XvvffqwQcf1KFDh0x99u/fr+HDhys8PFyHDx9WeHi4hg0bpgMHDljqtgAAAAAAAMwYjEajsbaTAADYvrfffrtS1w0bNqxc/bp3764uXbpo1apVpra2bdtq8ODBWrhwYblitG/fXsOHD9fs2bMlScOHD1d2drZ27Nhh6tO/f3/dfPPN2rhxY7liZmdny8PDQxEREQoICCjXNSVJS0tTXFycDh48qC5dulQ6DgAAsLyrV6/q5MmTpm8UwvqV9Tctev+XlZUld3f3WsoQAGDL2FMfAGARjz32mAwGQ7n7G41GGQyGchX1c3NzdfDgQU2fPt2sPTQ0VMnJyeUar6CgQBcvXtQtt9xiatu/f7+mTp1q1i8sLEyxsbGlxsnJyVFOTo7pdXZ2drnGBwAAAAAAKA+K+gAAi3FwcNDAgQPVqVOnao2bmZmp/Px8eXl5mbV7eXkpIyOjXDH+9re/6fLly2YfImRkZFQ45sKFCzVv3rwKZA8AAOor49XfpWvXLDdggwYyOLtYbjwAAFAjKOoDACxi8ODB+vDDD/Xee+/p559/1ujRozVy5EjdfPPN1TbG9d8EKFrtfyMbN27U3Llz9e6776pp06ZVihkdHa2oqCjT6+zsbPn5+ZUnfQAAUI8Yr/6ugr2JMl65bLExDa4NZdenX4UK+xEREbpw4YK2bdtW7FxAQIB+/vnnYu0LFy7U9OnTderUKQUGBqpJkyY6ceKE3NzcTH06deqkwYMHa+7cuZKkn376STNmzNCnn36q3377TZ6engoODtYrr7yi1q1b69SpU5o/f74+/vhjZWRkyNfXV6NGjdKMGTPk6OhY4d8FAADWjKI+AMAitm7dqszMTK1bt07x8fF65plnNG3aNA0ePFijR49Wv379Kh3b09NT9vb2xVbQnz17tthK++slJCRozJgx2rx5s+6//36zc97e3hWO6eTkJCcnpwreAQAAqHeuXSss6Ds0kBpYoCh9LbdwvGvXpGpcrR8TE6Nx48aZtf2xeC9JFy9e1OLFi0v9NmNubq769eunoKAgbd26VT4+Pvrll1+0fft2ZWVlSZKOHTumgoICrV69Wi1bttS3336rcePG6fLly1q8eHG13Q8AANaAoj4AwGI8PT0VFRWlqKgoffHFF1qzZo3efvttvf3227r11lsVERGhiIgI3XbbbRWK6+joqODgYCUmJurhhx82tScmJmrQoEGlXrdx40aNHj1aGzdu1J/+9Kdi50NCQpSYmGi2r/6uXbvUs2fPCuUHAABQqgaOMlhgQYBRkvKqf6sfNzc3eXt7l9ln0qRJWrJkiSZOnFjsW5GSdOTIEf3000/6+OOP1bx5c0lS8+bN1atXL1Of/v37q3///qbXt912m77//nutWrWKoj4AoN6xq+0EAAD1U7du3bR69Wqlp6crPj5eLVu21AsvvKDWrVtr9+7dFY4XFRWlf/zjH3rjjTd09OhRTZ06VampqYqMjJRUuC3OE088Yeq/ceNGPfHEE/rb3/6mHj16KCMjQxkZGabVYJI0efJk7dq1Sy+99JKOHTuml156Sbt379aUKVOqfP8AAAD1xYgRI9SyZUvFxMSUeL5Jkyays7PTv/71L+Xn55c7blZWlm655ZbqShMAAKtBUR8AUKucnZ0VGhqq/v37y8fHRwUFBbpy5UqF4wwfPlyxsbGKiYlRp06dtHfvXm3fvt202is9PV2pqamm/qtXr1ZeXp4mTpwoHx8f0zF58mRTn549e2rTpk1au3atbr/9dsXHxyshIUHdu3ev+o0DAADYgOeee06NGjUyO/bs2WPWx2AwaNGiRYqLi9OJEyeKxbj11lu1fPlyzZ49WzfffLPuu+8+zZ8/Xz/99FOp4544cUJ///vfTQs4AACoT9h+BwBQK/Lz8/XBBx/ojTfe0I4dO5Sfn69OnTopOjpaffv2rVTMCRMmaMKECSWei4+PN3t9/WSzNI888ogeeeSRSuUDAABg66ZNm6aIiAiztltvvbVYv7CwMN11112aNWuWNmzYUOz8xIkT9cQTT+iTTz7RgQMHtHnzZr344ot67733ij17KS0tTf3799ejjz6qsWPHVuv9AABgDSjqAwAs6siRI3rjjTe0fv16nT17Vo0bN9aECRM0evRo3X777bWdXp139OjRKsfw9PSUv79/NWQDAADqO09PT7Vs2bJcfRctWqSQkBBNmzatxPNubm566KGH9NBDD2nBggUKCwvTggULzIr6aWlpuvfeexUSEqK4uLhquQcAAKwNRX0AgEXExcXpjTfe0JdffimDwaDQ0FCNHj1agwYNUoMGDWo7vTrv0qVLMhgMGjVqVJVjubi46NixYxT2AQCARXXr1k1DhgzR9OnTb9jXYDAoKChIycnJprYzZ87o3nvvVXBwsNauXSs7O3YUBgDUTxT1AQAWERkZqQYNGujBBx/Uk08+afpa9qFDh8q8rlu3bpZIr867evWqjEajhgwZIk9Pz0rHyczM1NatW5WZmUlRHwAAlEtWVpZSUlLM2ooeUHvx4kVlZGSYnXN1dZW7u3uJsV544QW1b99eDg7/K0ekpKRozpw5Cg8PV7t27eTo6KhPP/1Ub7zxhp577jlJhSv077nnHvn7+2vx4sU6d+6c6Xpvb+/quE0AAKwGRX0AgMVcu3ZN77//vt5///1yX5Ofn1+DGVkfT09P+fr61nYaAACgulzLldFC41TWnj171LlzZ7O2J598UpI0e/ZszZ492+zc+PHj9dprr5UYq3Xr1ho9erTZ1jnNmjVTQECA5s2bp1OnTslgMJheT506VZK0a9cu/fjjj/rxxx/VrFkzs5hGo0V+gwAA1BkU9QEAFlE08QMAAICkBg1kcG0o45XLUt41iwxpcG0oVXDbw/j4eMXHx1dqvICAgBIL7qtXr9bq1atNrz09PbVs2bIyY0VERBR7IC8AAPUVRX0AgEWsXbu2tlMAAACoMwzOLrLr00+6ZpmCvqTCDxKcXSw3HgAAqBEU9QEAAAAAqAUGZxeJIjsAAKggivoAAIuYMGFCpa5buXJlNWcCAAAAAABgvSjqAwAsorSHpZXEYDCYfqaoDwAAAAAA8D8U9QEAFvHJJ5+Uq19qaqpiYmJ04sQJs+I+AAAAAAAAKOoDACzk7rvvLvP8f//7X7344otasWKFrl69qpCQEL300ksWyg4AAAAAAMA6UNQHANSqq1evKjY2Vi+//LIuXLigoKAgvfjiixo8eHBtpwYAAAAAAFDn2NV2AgCA+sloNOof//iHWrVqpeeff16urq6Ki4vTt99+S0EfAAAAAACgFKzUBwBY3LZt2/T888/r+++/l7u7u1588UVNmTJFzs7OtZ0aAAAAAABAnUZRHwBgMUlJSXruuef0+eefy9HRUVOnTtWMGTN0880313ZqAAAAFpeamqrMzEyLjefp6Sl/f3+LjQcAAGoGRX0AgEU89NBD+vDDD2VnZ6cnn3xSMTExatasWW2nBQAAUCtSU1MVFBSk33//3WJjuri46NixYxUu7GdkZOiFF17Qhx9+qDNnzqhp06bq1KmTpkyZor59+yogIEA///yzNm7cqMcee8zs2vbt2+vIkSNau3atIiIiJEkBAQGaMmWKpkyZYnr9888/m3K87bbbNGnSJI0fP94UJycnRzExMVq/fr0yMjLUrFkzzZgxQ6NHj678LwQAACtFUR8AYBEffPCBDAaD/P39lZGRoaeeeuqG1xgMBn344YcWyA4AAMCyMjMz9fvvv2vIkCHy9PS0yHhbt25VZmZmhYr6p06dUq9evXTTTTfp5Zdf1u23365r165p586dmjhxoo4dOyZJ8vPz09q1a82K+p9//rkyMjLUsGHDG44TExOjcePG6dKlS4qPj1dkZKRuuukmDR8+XJI0bNgw/frrr1qzZo1atmyps2fPKi8vr4K/BQAAbANFfQCAxRiNRp08eVInT54sV3+DwVDDGQEAANQuT09P+fr61nYapZowYYIMBoO++OILs+J8+/btzVbJjxw5UkuXLtXp06fl5+cnSXrjjTc0cuRIrVu37objuLm5ydvbW5K0YMECvf3229q2bZuGDx+ujz76SJ9++ql++ukn3XLLLZIKV/cDAFBfUdQHAFhEeQv5AAAAqBt+++03ffTRR3rhhRdKXG1/0003mX728vJSWFiY3nzzTc2cOVNXrlxRQkKCPv3003IV9a/n7Oysa9euSZLee+89de3aVS+//LL++c9/qmHDhnrooYc0f/58ubi4VPr+AACwVhT1AQAW0bx589pOAQAAABXw448/ymg0KigoqFz9R48erWeffVYzZszQv/71L7Vo0UKdOnWq0Jh5eXlav369vvnmG/3lL3+RJP30009KSkqSs7Oz3nnnHWVmZmrChAn67bff9MYbb1T0tgAAsHp2tZ0AAADVZeXKlQoMDJSzs7OCg4O1b9++Uvump6fr8ccfV5s2bWRnZ2d6UNsfxcfHy2AwFDuuXr1ag3cBAABQNxiNRknl3xLxT3/6ky5duqS9e/fqjTfeqNBDbJ977jk1atRILi4umjhxoqZNm2Z6UG5BQYEMBoPeeustdevWTQMHDtSSJUsUHx9v0QcNAwBQV1DUBwDYhISEBE2ZMkUzZszQoUOH1Lt3bw0YMECpqakl9s/JyVGTJk00Y8YM3XHHHaXGdXd3V3p6utnh7OxcU7cBAABQZ7Rq1UoGg0FHjx4tV38HBweFh4drzpw5OnDggEaOHFnusaZNm6aUlBT9/PPPunTpkl5++WXZ2RWWLHx8fHTrrbfKw8PD1L9t27YyGo365ZdfKnZTAADYAIr6AACbsGTJEo0ZM0Zjx45V27ZtFRsbKz8/P61atarE/gEBAVq2bJmeeOIJswni9QwGg7y9vc0OAACA+uCWW25RWFiYVqxYocuXLxc7f+HChWJto0eP1qeffqpBgwbp5ptvLvdYnp6eatmypXx9fYt9M6BXr15KS0vTpUuXTG0//PCD7Ozs1KxZs/LfEAAANoKiPgDA6uXm5urgwYMKDQ01aw8NDVVycnKVYl+6dEnNmzdXs2bN9MADD+jQoUNl9s/JyVF2drbZAQAAYK1Wrlyp/Px8devWTVu2bNHx48d19OhRLV++XCEhIcX6t23bVpmZmVq7dm215fD444+rcePG+vOf/6wjR45o7969mjZtmkaPHs2DcgEA9RIPygUAWL3MzEzl5+fLy8vLrN3Ly0sZGRmVjhsUFKT4+Hh17NhR2dnZWrZsmXr16qXDhw+rVatWJV6zcOFCzZs3r9JjAgCA+iUzM7NOjxMYGKj//Oc/euGFF/Tss88qPT1dTZo0UXBwcKnfiGzcuHFVUi2mUaNGSkxM1KRJk9S1a1c1btxYw4YN04IFC6p1HAAArAVFfQCAzbj+q9pGo7HcD3YrSY8ePdSjRw/T6169eqlLly76+9//ruXLl5d4TXR0tKKiokyvs7Oz5efnV+kcAACAbfL09JSLi4u2bt1qsTFdXFzk6elZ4et8fHz06quv6tVXXy3x/KlTp8q8/vpteq7vf6PrpcLFFomJiTfsBwBAfUBRHwBg9Tw9PWVvb19sVf7Zs2eLrd6vCjs7O9155506fvx4qX2cnJzk5ORUbWMCAADb5O/vr2PHjllspb5U+J7J39/fYuMBAICaQVEfAGD1HB0dFRwcrMTERD388MOm9sTERA0aNKjaxjEajUpJSVHHjh2rLSYAAKi//P39KbIDAIAKo6gPALAJUVFRCg8PV9euXRUSEqK4uDilpqYqMjJSUuG2OGfOnNG6detM16SkpEgqfBjuuXPnlJKSIkdHR7Vr106SNG/ePPXo0UOtWrVSdna2li9frpSUFK1YscLi9wcAAAAAACBR1AcA2Ijhw4fr/PnziomJUXp6ujp06KDt27erefPmkqT09HSlpqaaXdO5c2fTzwcPHtSGDRvUvHlz076uFy5c0FNPPaWMjAx5eHioc+fO2rt3r7p162ax+wIAAAAAAPgjivoAAJsxYcIETZgwocRz8fHxxdqMRmOZ8ZYuXaqlS5dWR2oAAAAAAADVwq62EwAAAAAAoD4oKCio7RRQTW60OAQAgJrESn0AAAAAAGqQo6Oj7OzslJaWpiZNmsjR0VEGg6G200IlGY1GnTt3TgaDQQ0aNKjtdAAA9RBFfQAAAAAAapCdnZ0CAwOVnp6utLS02k4H1cBgMKhZs2ayt7ev7VQAAPUQRX0AAAAAAGqYo6Oj/P39lZeXp/z8/NpOB1XUoEEDCvoAgFpDUR8AAAAAAAso2q6FLVsAAEBV8KBcAAAAAAAAAACsBEV9AAAAAAAAAACsBEV9AAAAAAAAAACsBHvqAwBQDx09erTKMXJycuTk5FSlGJ6envL3969yLgAAAAAA1BcU9QEAqEcuXbokg8GgUaNGVTmWwWCQ0WisUgwXFxcdO3aMwj4AAAAAAOVEUR8AgHrk6tWrMhqNGjJkiDw9PSsd5/jx4/rkk0+qFCczM1Nbt25VZmYmRX0AAAAAAMqJoj4AAPWQp6enfH19K319ZmZmtcQBAAAAAAAVw4NyAQAAAAAAAACwEqzUBwAAtao6HtrLA3cBAAAAAPUFRX0AAFArqvOhvTxwFwAAAABQX1DUBwAAtaK6HtrLA3cBAAAAAPUJRX0AAFCreNguAAAAAADlx4NyAQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQAAAAAAAACwEhT1AQA2Y+XKlQoMDJSzs7OCg4O1b9++Uvump6fr8ccfV5s2bWRnZ6cpU6aU2G/Lli1q166dnJyc1K5dO73zzjs1lD0AAAAAAMCNUdQHANiEhIQETZkyRTNmzNChQ4fUu3dvDRgwQKmpqSX2z8nJUZMmTTRjxgzdcccdJfbZv3+/hg8frvDwcB0+fFjh4eEaNmyYDhw4UJO3AgAAAAAAUCqK+gAAm7BkyRKNGTNGY8eOVdu2bRUbGys/Pz+tWrWqxP4BAQFatmyZnnjiCXl4eJTYJzY2Vv369VN0dLSCgoIUHR2tvn37KjY2tgbvBAAAAAAAoHQOtZ0AAABVlZubq4MHD2r69Olm7aGhoUpOTq503P3792vq1KlmbWFhYWUW9XNycpSTk2N6nZ2dXenxUTFHjx6tcgxPT0/5+/tXQzYAAAAAANQMivoAAKuXmZmp/Px8eXl5mbV7eXkpIyOj0nEzMjIqHHPhwoWaN29epcdExV26dEkGg0GjRo2qciwnJydt2bJFPj4+VYrDhwMAAAAAgJpCUR8AYDMMBoPZa6PRWKytpmNGR0crKirK9Do7O1t+fn5VygFlu3r1qoxGo4YMGSJPT89Kx0lNTdXOnTv1wAMPVDknFxcXHTt2jMI+AAAAAKDaUdQHAFg9T09P2dvbF1tBf/bs2WIr7SvC29u7wjGdnJzk5ORU6TFReZ6envL19a309ZmZmdXy4UBmZqa2bt2qzMxMivoAAAAAgGpHUR8AYPUcHR0VHBysxMREPfzww6b2xMREDRo0qNJxQ0JClJiYaLav/q5du9SzZ88q5Yu6raofDgAAAAAAUJMo6gMAbEJUVJTCw8PVtWtXhYSEKC4uTqmpqYqMjJRUuC3OmTNntG7dOtM1KSkpkgr3ZD937pxSUlLk6Oiodu3aSZImT56sPn366KWXXtKgQYP07rvvavfu3UpKSrL4/QEAAAAAAEgU9QEANmL48OE6f/68YmJilJ6erg4dOmj79u1q3ry5JCk9PV2pqalm13Tu3Nn088GDB7VhwwY1b95cp06dkiT17NlTmzZt0syZMzVr1iy1aNFCCQkJ6t69u8XuC9br6NGjVY7BA3cBAAAAANejqA8AsBkTJkzQhAkTSjwXHx9frM1oNN4w5iOPPKJHHnmkqqmhHrl06ZIMBoNGjRpV5Vg8cBcAAAAAcD2K+gAAANXo6tWrPHAXAAAAAFBjKOoDAADUAB64CwAAAACoCRT1AQAA6rCq7s3PvvwAAAAAYFso6gMAANRB1bU3P/vyAwAAAIBtoagPAABQB1XH3vzsyw8AAAAAtoeiPgAAQB3G3vwAAAAAgD+yq+0EAAAAAAAAAABA+bBSHwAAC3CzM6ihCip9/U0O9vJu1FDuNhSnLuViq3Hc7QzybtRQJ7/5Wg5Xf690LpJ0yy236NZbb61SDAAA6gPjxezaTgEAYOMMRqPRWNtJAABgq7Kzs+Xh4aEdk8eryc0elY7z398u6OfUn9W6dWu5urrYRJy6lIutxsnOuqifTv5U6Rz+yN7eXg8PflgNGzWslngAANiq7CtX1HjkWGVlZcnd3b220wEA2CBW6gMAYAHXjNIVGSp9/cX8fJ2/8rsuFxglG4lTl3Kx1TgXcnN1/srvatWqlVxcKv8Bw++//67jx4/rdGambqniehAXFxe5uVHgAADYsNxrtZ0BAMDGUdQHAMACco1GXatCgfdqgVGXcq8pxyg52kicupSLrcYpimHn7CLHRo0qncvFnFxdyr2mt999r9Ixitjb22v4sGFq5OZWpTj5eXmyd6jaW1lXV1d5eFT+GzQAAJTIwbG2MwAA2DiK+gAAAChTXl6eJKlt27ZydXWtdJysrCz9+OOP2rBxY3WlViXV9QEDHw4AAAAAsCSK+gAAACgXV1dXublVfsX/lStXJFX9w4Hz58/r1KlTVYpTnR8w1KVvH0h8yAAAAADYOor6AAAAsKjq+nCgKnGq6wOGuvbtA4lvIAAAAAC2jqI+AAAA6i1b+vaBVL0fMjg4OOjpp5+msA8AAADUMRT1AQAAgCqqC98++GOcqn44cOXKFR09elSpP/8szyZNKh2H1f4AAABA9aOoDwCABTgaDGogY6Wvd7YzqJFjAzkZZDNx6lIuthqnLuVS1+LUpVxqIs7NDV3VsFHDSscxXLumRo4NtOvDDyodQ5Ls7R00evSf5ebmXqU4AGBV8nJrOwMAgI2jqA8AgAU0MEiuVSjUudnbq7GrixraGWwmTl3KxVbj1KVc6lqcupRLXYyTI6Mau7qouX9zOTk7VS7G1Rz9nPqzrmVdkBzsK50LAFidvGu1nQEAwMZR1AcAwAK2X8yV/83Olb7+aMZ/tX13kkZ5B8jL9WabiFOXcrHVOHUpl7oWpy7lUqfjjAqQ1y2Vi/Nr9q9avztJNw1+TK2a+lc6F0m65ZZbdOutt1YpBgBYin12dm2nAACwcRT1AQCwgIsFRl2WXaWvv5CXr4xLl5VdYFQjG4lTl3Kx1Th1KZe6Fqcu5WKrcdKyL+rXy1f0SMSfK51HEScnJ23ZskU+Pj5VipOTkyMnp8p98+CPPD095e9ftQ8qANguQ+W/KAUAQLlQ1AcA2IyVK1fqlVdeUXp6utq3b6/Y2Fj17t271P6ffvqpoqKi9N1338nX11f/93//p8jISNP5+Ph4/fnPxYtRv//+u5ydK7/qHgDqg6tXr8poNGrIkCHy9PSsdJzU1FTt3LlTDzzwQJVzMhgMMhqrXm1zcXHRsWPHKOwDAACgVlDUBwDYhISEBE2ZMkUrV65Ur169tHr1ag0YMEBHjhwpsehy8uRJDRw4UOPGjdP69ev12WefacKECWrSpImGDh1q6ufu7q7vv//e7FoK+gBQfp6envL19a309ZmZmdXy4cDx48f1ySefVDlOZmamtm7dqn379qlt27aVjsNqfwAAAFQWRX0AgE1YsmSJxowZo7Fjx0qSYmNjtXPnTq1atUoLFy4s1v+1116Tv7+/YmNjJUlt27bVV199pcWLF5sV9Q0Gg7y9vS1yDwCA0lXHhwPVEefSpUsyGAwaNWpUpWNIbCkEAACAyqOoDwCwerm5uTp48KCmT59u1h4aGqrk5OQSr9m/f79CQ0PN2sLCwrRmzRpdu3ZNDRo0kFRYvGnevLny8/PVqVMnzZ8/X507dy41l5ycHOXk5JheZ/OgNACwKdWxrVBd3FKouj5k4MMBAACAmkdRHwBg9TIzM5Wfny8vLy+zdi8vL2VkZJR4TUZGRon98/LylJmZKR8fHwUFBSk+Pl4dO3ZUdna2li1bpl69eunw4cNq1apViXEXLlyoefPmVc+NAQDqrKqs+K9rWwpV54cMfDgAAABQ8yjqAwBshsFgMHttNBqLtd2o/x/be/TooR49epjO9+rVS126dNHf//53LV++vMSY0dHRioqKMr3Ozs6Wn59fxW4EAFAv1JUtharrQ4bq/HCAhxEDAACUjqI+AMDqeXp6yt7evtiq/LNnzxZbjV/E29u7xP4ODg5q3LhxidfY2dnpzjvv1PHjx0vNxcnJqVr2NgYAwNLqyocDRQ8jzszMpKgPAABQAor6AACr5+joqODgYCUmJurhhx82tScmJmrQoEElXhMSEqL333/frG3Xrl3q2rWraT/96xmNRqWkpKhjx47VlzwAADamqh8OAAAAoGwU9QEANiEqKkrh4eHq2rWrQkJCFBcXp9TUVEVGRkoq3BbnzJkzWrdunSQpMjJSr776qqKiojRu3Djt379fa9as0caNG00x582bpx49eqhVq1bKzs7W8uXLlZKSohUrVtTKPQIAUJ8cPXq0StezLz8AALBVFPUBADZh+PDhOn/+vGJiYpSenq4OHTpo+/btat68uSQpPT1dqamppv6BgYHavn27pk6dqhUrVsjX11fLly/X0KFDTX0uXLigp556ShkZGfLw8FDnzp21d+9edevWzeL3BwBAfXHp0iUZDAaNGjWqSnHYlx8AANgqivoAAJsxYcIETZgwocRz8fHxxdruvvtu/ec//yk13tKlS7V06dLqSg8AAJTD1atXq7w3f9G+/Pv27VPbtm2rlA8r/gEAQF1DUR8AAAAAUOdUZW/+6lrtL7HiHwAA1D0U9QEAAAAANqU6VvtLrPgHAAB1E0V9AAAAAIBNqspqf4kV/wAAoG6iqA8AAAAAQAlY8Q8AAOoiivoAAAAAAJSBFf8AAKAuoagPAAAAAEANqmsr/nNycuTk5FTp64vwrQEAAGoHRX0AAAAAACygrqz4NxgMMhqNVYoh8a0BAABqC0V9AAAAAACsQHWs+D9+/Lg++eSTavvWQGZmJkV9AAAsjKI+AAAAAABWpCor/jMzM6scAwAA1C672k4AAAAAAAAAAACUD0V9AAAAAAAAAACsBEV9AAAAAAAAAACsBEV9AAAAAAAAAACsBA/KBQAAAAAAlXL06NEqx/D09JS/v381ZAMAQP1AUR8AAAAAAFTIpUuXZDAYNGrUqCrHcnJy0pYtW+Tj41OlOHw4AACoLyjqAwAAAACACrl69aqMRqOGDBkiT0/PSsdJTU3Vzp079cADD1Q5JxcXFx07dozCPgDA5lHUBwAAAAAAleLp6SlfX99KX5+ZmVktHw5kZmZq69atyszMpKgPALB5FPUBAAAAAECtquqHAwAA1Cd2tZ0AAAAAAAAAAAAoH4r6AAAAAAAAAABYCYr6AAAAAAAAAABYCYr6AACbsXLlSgUGBsrZ2VnBwcHat29fmf0//fRTBQcHy9nZWbfddptee+21Yn22bNmidu3aycnJSe3atdM777xTU+kDAAAAAADcEEV9AIBNSEhI0JQpUzRjxgwdOnRIvXv31oABA5Samlpi/5MnT2rgwIHq3bu3Dh06pOeff17PPPOMtmzZYuqzf/9+DR8+XOHh4Tp8+LDCw8M1bNgwHThwwFK3BQAAAAAAYIaiPgDAJixZskRjxozR2LFj1bZtW8XGxsrPz0+rVq0qsf9rr70mf39/xcbGqm3btho7dqxGjx6txYsXm/rExsaqX79+io6OVlBQkKKjo9W3b1/FxsZa6K4AAAAAAADMOdR2AgAAVFVubq4OHjyo6dOnm7WHhoYqOTm5xGv279+v0NBQs7awsDCtWbNG165dU4MGDbR//35NnTq1WJ+yivo5OTnKyckxvc7KypIkZWRkVOSWijl37pwkKT09Xbm5uTYRpy7lYqtx6lIudS1OXcrFVuPUpVzqWpy6lIutxqlLudS1OHUpl+qMc/78eUnSpUuXlJ2dXek41aFofKPRWKt5AABsl8HI/2UAAFYuLS1Nt956qz777DP17NnT1P7iiy/qzTff1Pfff1/smtatWysiIkLPP/+8qS05OVm9evVSWlqafHx85OjoqPj4eD3++OOmPhs2bNCf//xns8L9H82dO1fz5s2rxrsDAACANTp9+rSaNWtW22kAAGwQK/UBADbDYDCYvTYajcXabtT/+vaKxoyOjlZUVJTpdUFBgX777Tc1bty4zOsAAABgG4xGoy5evChfX9/aTgUAYKMo6gMArJ6np6fs7e2LbXFz9uxZeXl5lXiNt7d3if0dHBzUuHHjMvuUFlOSnJyc5OTkZNZ20003lfdWAAAAYAM8PDxqOwUAgA3jQbkAAKvn6Oio4OBgJSYmmrUnJiaabcfzRyEhIcX679q1S127dlWDBg3K7FNaTAAAAAAAgJrGSn0AgE2IiopSeHi4unbtqpCQEMXFxSk1NVWRkZGSCrfFOXPmjNatWydJioyM1KuvvqqoqCiNGzdO+/fv15o1a7Rx40ZTzMmTJ6tPnz566aWXNGjQIL377rvavXu3kpKSauUeAQAAAAAAKOoDAGzC8OHDdf78ecXExCg9PV0dOnTQ9u3b1bx5c0lSenq6UlNTTf0DAwO1fft2TZ06VStWrJCvr6+WL1+uoUOHmvr07NlTmzZt0syZMzVr1iy1aNFCCQkJ6t69u8XvDwAAAAAAQJIMxqKnAgIAAAAAAAAAgDqNPfUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUB2LSAgAAZDAYZDAZt27at1H7333+/DAaD4uPjzdrj4+NN1zs6Our8+fOlxsjLy1PTpk1N/efOnVtiv507d2rIkCG69dZb5ejoqJtuuklt2rTRgw8+qL/97W86ceKEWf9Tp06ZYt7oOHXqVDl/Mzd25MgRjR8/Xq1bt5aLi4saNmyowMBA3XPPPZo1a5aSk5OLXVP0+77+91iaiIiIYvfQoEED+fj46KGHHtKOHTuq7X727NlT4lienp4KCgrSiBEjtHr1amVnZ5ca449/i+t/1yXdy/XH4MGDq+1+AAAAYHnMLyrP1ucXX3/9dZn9O3fubOobERFRZt9ff/1VDRo0kMFgUK9evW6Yyx//u3z22WfL7Lts2TKzvAFYJ4faTgAALGXu3LkaNGhQpd+4XLt2TQkJCZowYUKJ53fu3Klz586VGePpp5/WihUrJEkNGzZUq1at5Orqqp9//lkffPCBPvjgA6Wnp2vx4sUlXt+1a1c5OTmVGt/Z2bmcd1O2t956S6NHj1Zubq4aNGggf39/3XLLLTp79qw+/fRTffrpp9qxY4e++uqrahmvadOmatWqlSTp6tWr+uGHH/T+++/r/fffV3R0tF588cVqGadI0Rtjo9Goixcv6pdfftGmTZu0adMm/fWvf9VLL71U6t/5Rv54L9dr165dpXMGAABA3cL8ovxsfX4hSevXr9fLL79c4rnvvvtOKSkp5Y61ceNG5eXlSZKSk5N14sQJtWjRolzXbtiwQS+//LLs7e1LzROA9aOoD6BesLe31+HDh7VlyxY98sgjFb6+VatW+vHHH/XPf/6z1Dfd//znPyVJbdq00ffff1/s/MaNG7VixQrZ2dlp6dKlGj9+vNkb6CNHjmjDhg1yd3cvNY/NmzcrICCgwvlXxKlTpzRmzBjl5uZq9OjRWrhwoZo2bWo6n5GRoS1btujAgQPVNuaAAQPMVt/k5uZqxowZWrx4sRYuXKgHH3xQISEh1TZeUlKS2Wuj0ahvvvlGS5cuVXx8vCZOnKjTp09r4cKFFY59/b0AAADA9jC/KD9bn1/4+vrq4sWL2rBhgxYtWiQ7u+KbYtzob1la/5tuukkXLlzQ+vXrNWfOnBteVxR/9+7dCgsLK3b++++/11dffVXuPADUXWy/A6BeGDFihCRp3rx5MhqNFb7e399fffr00eeff64ff/yx2PmLFy/qvffeU2BgYKlfj3zzzTclSaNHj9YzzzxTbEVMu3bttGDBAv3f//1fhfOrTps2bVJOTo7atGmj119/3ewNtyR5e3tr4sSJWrduXY3l4OjoqJdfflmdOnUy5VSTDAaDbr/9dq1du1ZxcXGSpEWLFmnPnj01Oi4AAACsE/OL8rP1+YWLi4uGDBmiM2fO6JNPPil23mg0asOGDWrYsKEefvjhG8Y7cuSI/vOf/8jFxUV/+9vfJP2vyH8jo0aNklT6avyiOOHh4eWKB6DuoqgPoF4YPXq0AgIC9O233+rtt9+uVIyy3iD961//0u+//66RI0eW+vXbn376SZJMbyTrqqI8O3bsWOIqE0sxGAy66667JEnHjx+32Ljjxo3ToEGDJBUW9gEAAIDrMb8ov/owvyj6W5ZUfN+zZ49Onz6thx9+WA0bNrxhrKIYDzzwgB5//HG5u7vrxIkT2r9//w2vvfvuu+Xn56d33nlHly9fNjtnNBr11ltvmT6EAGDdKOoDqBcaNGigGTNmSCpcTVNQUFDhGI8++qicnZ311ltvFTtX9Ea86M1cSYq+9vrFF19UeGxLKsozJSVF165dq9VcKrPqqTpERkZKkj755BP9/vvvtZIDAAAA6i7mF+VXH+YX9913n2699VZt3bpVV65cMTtX9Lcsz+r4goIC038Pjz/+uJydnU0F+PKs1jcYDBo5cqQuX76sd955x+xcUlKSTp06pcGDB8vNza1c9wWg7qKoD6DeiIiI0G233aajR49W6uuWHh4eevDBB/Xjjz+arZL45ZdftGfPHnXr1k1t2rQp9fr+/ftLKnwz9pe//EVffvml8vPzK34jNawozx9//FEDBgzQjh07ir0xtQSj0ajPPvtMktSyZUuLjt2zZ08ZDAbl5ubq8OHDFh0bAAAA1oH5RfnUh/mFnZ2dHn/8cV28eFHvvvuuqf3q1av617/+JR8fH/Xt2/eGcYpW9d98880aOHCgJGnkyJGSpISEBOXm5t4wRtGHB9d/CMDWO4BtoagPoN5wcHDQrFmzJEkxMTGVesNb0tcq33rrLRUUFJS5ikaSnnvuOQUHB8toNOq1115Tt27d5O7urrvuukvTp0/XV199dcPxAwMDZTAYSjyq62u3999/v5566ilJ0r///W8NHDhQHh4euuOOOxQZGakPPvigxicLubm5+r//+z+lpKRIkoYNG1aj413P3d1dHh4ekqSzZ89W6No333yz1L8RAAAAbAfzi/KpL/OLkorp7777rrKzszVixAjZ29vfMEbRtUOHDpWjo6Okwm8BeHt767ffftP27dtvGKNdu3bq3Lmz/v3vfys9PV2SlJOTo82bN6tp06bq169fhe8NQN1DUR9AvRIeHq5WrVrp+++/L/FrrjcyYMAAeXp66u233zZ9dXT9+vVycHDQY489Vua1bm5uSkpK0t/+9je1bdtWknTlyhV99tlneumll3TnnXdq8ODBunDhQqkxunbtql69epV4dO7cucL3U5rVq1dry5Ytuvvuu2Vvb6+8vDx9/fXXWr16tR588EHdcccd+uabb6ptvB07duiuu+7SXXfdpa5du8rT01OLFy+WJE2dOtW096UlFe13efHixQpd17Rp01L/RgAAALAtzC/Kpz7MLzp27Kjbb79diYmJpoVBFdl65/fff9eWLVskFW69U8TOzs7030J5H5gbHh6u/Px8bdy4UZL0wQcf6MKFCxoxYoQcHBzKf1MA6iyK+gDqFXt7e9Nqmvnz5ysvL69C1zdo0EDDhg3T+fPntX37dqWkpOjbb79VWFiYmjRpcsPrnZ2dFRUVpSNHjujMmTPaunWrpk6dqsDAQEmFKznKemjR5s2blZSUVOKxdu3aCt3LjQwZMkR79uzRb7/9psTERM2fP1/dunWTJH333Xe6//77de7cuWoZ6+zZs/rss8/02WefKSUlRc7OzhowYIDeeecdLVmypFrGqKhLly5J+t8eoOU1YMCAUv9GAAAAsC3ML8qvPswvRo0apby8PG3cuFGZmZn66KOP1L59+3J962Hbtm26ePGifH19dffdd5udK9qC54MPPtB///vfG8Yq+mZA0YcARf/e6NsfAKwHRX0A9c7jjz+uNm3a6Mcffyz3Soc/KlplsX79+gqtvLier6+vHn74YS1ZskQ//PCDnn32WUmFD2ct2uuxLnB3d9f999+vmTNn6sCBA9q8ebPs7Ox09uxZxcXFVcsYTz75pIxGo4xGo/Ly8nT27Flt375dgwcPrpb4FZWVlaXs7GxJhSvvAQAAgNIwv6gYW55fjBw5UnZ2dlq/fr02bdqkvLy8cv8ti/7beeyxx2RnZ16u69q1q1q3bq3c3Fy9/fbbN4zl7e2t+++/XykpKdq7d6927NihoKAgde3ateI3BaBOoqgPoN6xt7fX7NmzJVVuNU2PHj3UqlUrvf/++1q/fr3c3d310EMPVSknBwcHvfzyy/L29pYkffHFF1WKV5MeeeQRDR06VFLdzrMqPvvsMxmNRjk6OuqOO+6o7XQAAABQhzG/qBpbml/4+vrqvvvu01dffaVXXnlFdnZ2plX2Zfn111+1a9cuSdKSJUtKfMbBDz/8IKliW/AU/Zubm8sDcgEbQ1EfQL302GOPqV27djp58qTi4+MrfP3IkSOVk5OjX3/9VUOHDpWLi0uVc7Kzs1Pz5s0lFT7IqS677bbbJNX9PCvrtddekyT17dtXzs7OtZwNAAAA6jrmF1VjS/OLoi1uUlNTdffdd6tZs2Y3vGbDhg3Kz8+Xk5OTvLy8Sj2kwgVIP/300w1jPvzww2rUqJFSU1NlMBjK9eECAOtBUR9AvWRnZ6c5c+ZIkhYsWGB6KFV5hYeHq2/fvurbt6/GjRtXrmuKHpZUmgsXLujIkSOSpFatWlUon+p0ozwlKTk5WVLt5llTXn/9db3//vuSpOjo6FrOBgAAANaA+UXp6tv8YujQoQoNDVXfvn31zDPPlOuaotX306dPV0ZGRqlHSEiIpP89gLcsrq6uevbZZ9W3b1+NHz/e9AEPANtAUR9AvfXoo4+qY8eO+vnnnyu8x+Rtt92m3bt3a/fu3aY3VjcycOBAjRw5Uh9//HGxN/kpKSkaNGiQLl68KB8fH4WFhVUon9JERETIYDAoIiKi3Ne8+OKL6t27tzZu3KiLFy+anUtPT1dkZKT27dsng8GgJ598slryrKiAgAAZDIZKrYIqidFo1Ndff63Ro0frqaeekiTNnDlTvXv3rpb4AAAAsH3ML0pW3+YXjRo10s6dO7V79+5y7eH/3Xff6dChQ5Ju/CDbovPlKepL0ty5c7V7926tWrWqXP0BWA+H2k4AAGqLwWDQnDlz9Mgjjyg/P7/Gx8vPz9eGDRu0YcMGOTs7q2XLlnJ2dlZaWprS0tIkSTfddJPefvttNWzYsMQYjz76qJycnEodY+HChVUuRBsMBiUlJSkpKUl2dnZq0aKFbr75Zp07d06nT59WXl6e7O3ttWTJEgUHB5cYY9KkSfrrX/9a6hh79uxRhw4dqpRnVdx1112mny9evKjTp0/rv//9ryTJzc1NL730kv7yl7/UVnoAAACwQswvSlYf5hdVUbRKPyQkRC1btiyz7/DhwzVlyhQdP35cn3/+uXr06GGJFAHUQRT1AdRrQ4YMUadOnZSSklLjY+3atUs7d+7Ujh079O233yotLU0XLlxQo0aN1L17d4WFhWnixIlq2rRpqTG++uqrMsc4f/682euMjAxJUqdOncqd54svvqh+/fppx44dOnDggM6cOaNTp07JyclJrVu3Vp8+ffSXv/xFt99+e6kxLl26pEuXLpV6vqIPD/uj/Px8ZWZmSlKlH2JbtHLKwcFB7u7uatq0qUJDQ3Xvvffq8ccfl5ubW6ljF3F0dKzU2AAAALBdzC+Kqw/zi8oqKCjQW2+9JenGq/QlqXHjxurfv7/ef/99/fOf/6SoD9RjBqPRaKztJAAA1a+goEC33HKLjEajfvnll1IL1dbmP//5j4KDg3Xvvffq448/rpWxJeny5ctydXW16PgAAABAbWF+AQB1B3vqA4CN+u6775SVlaWnnnrKZt5wS/9bZf/ss89afOyvv/5akuTv709BHwAAAPUK8wsAqDso6gOAjUpOTpaDg4OeeeaZ2k6lWiUnJysoKEgDBw606LhpaWl65ZVXJEkPPvigRccGAAAAahvzCwCoO9h+BwCAMmzevFkLFizQ999/r5ycHDVp0kSHDh3SrbfeWtupAQAAAACAeoiV+gAAlOHMmTP67rvvdMsttyg8PFyff/45BX0AqIdWrlypwMBAOTs7Kzg4WPv27SvXdZ999pkcHBwq9FBJAAAAoCys1AcAAACAMiQkJCg8PFwrV65Ur169tHr1av3jH//QkSNH5O/vX+p1WVlZ6tKli1q2bKlff/1VKSkplksaAAAANouiPgAAAACUoXv37urSpYtWrVplamvbtq0GDx6shQsXlnrdY489platWsne3l7btm2jqA8AAIBq4VDbCViLgoICpaWlyc3NTQaDobbTAQAAQA0zGo26ePGifH19ZWfHrpX1VW5urg4ePKjp06ebtYeGhio5ObnU69auXasTJ05o/fr1WrBgwQ3HycnJUU5Ojul1QUGBfvvtNzVu3Jj5BwAAQD1QkfkHRf1ySktLk5+fX22nAQAAAAs7ffq0mjVrVttpoJZkZmYqPz9fXl5eZu1eXl7KyMgo8Zrjx49r+vTp2rdvnxwcyjflWrhwoebNm1flfAEAAGDdyjP/oKhfTm5ubpIKf6nu7u61nA0AAABqWnZ2tvz8/EzvA1G/Xb9a3mg0lriCPj8/X48//rjmzZun1q1blzt+dHS0oqKiTK+zsrLk7+/P/AMAAKCeqMj8g6J+ORW9YXd3d+dNNQAAQD3C1if1m6enp+zt7Yutyj979myx1fuSdPHiRX311Vc6dOiQnn76aUmFW+kYjUY5ODho165duu+++4pd5+TkJCcnp2LtzD8AAADql/LMP9gcFAAAAABK4ejoqODgYCUmJpq1JyYmqmfPnsX6u7u765tvvlFKSorpiIyMVJs2bZSSkqLu3btbKnUAAADYKFbqAwAAAEAZoqKiFB4erq5duyokJERxcXFKTU1VZGSkpMKtc86cOaN169bJzs5OHTp0MLu+adOmcnZ2LtYOAAAAVAZFfQAAgErIz8/XtWvXajsNVJGjo6Ps7PjyKso2fPhwnT9/XjExMUpPT1eHDh20fft2NW/eXJKUnp6u1NTUWs4SAADYMuYftqG65h8Go9ForIZ8bF52drY8PDyUlZXFnpYAANRjRqNRGRkZunDhQm2ngmpgZ2enwMBAOTo6FjvH+z/UJv77AwAAEvMPW1Nd8w9W6gMAAFRA0Rvqpk2bytXVlYeoWrGCggKlpaUpPT1d/v7+/C0BAABQ5zD/sB3VOf+gqA8AAFBO+fn5pjfUjRs3ru10UA2aNGmitLQ05eXlqUGDBrWdDgAAAGDC/MP2VNf8gw1EAQAAyqloD0tXV9dazgTVpehrr/n5+bWcCQAAAGCO+Yftqa75Byv1KyglJUWNGjWqUgxPT0/5+/tXU0YAAMDS+Mqr7eBvibqO+QcAAOA9q+2orr8lRf0Kuvvuu6scw8XFRceOHeONNQAAAIAyMf8AAADA9SjqV9CDDz4oHx+fSl+fmZmprVu3KjMzkzfVAAAAAMrE/AMAAADXo6hfQY0bN5avr29tpwEAAKxYfr60b5+Uni75+Ei9e0v29rWdFYC6iPkHAACoKuYftocH5QIAAFjQ1q1SQIB0773S448X/hsQUNheEwwGQ5lHRESEqd+2bduKXR8REaHBgweX+vrs2bMaP368/P395eTkJG9vb4WFhWn//v2l5jR37lzT+HZ2dvL19dXIkSN1+vRps34BAQGKjY0tdn1sbKwCAgKKxYuMjDTrl5KSIoPBoFOnTpWaCwAAAGDLmH/Y5vyjThb1V65cqcDAQDk7Oys4OFj79u0rtW9SUpJ69eqlxo0by8XFRUFBQVq6dKlZn/j4+BL/I7p69WpN3woAAIDJ1q3SI49Iv/xi3n7mTGF7TbyxTk9PNx2xsbFyd3c3a1u2bFmV4g8dOlSHDx/Wm2++qR9++EHvvfee7rnnHv32229lXte+fXulp6frl19+UUJCgr755hsNGzas0nk4OztrzZo1+uGHHyodAwAAALAlzD/+x9bmH3Vu+52EhARNmTJFK1euVK9evbR69WoNGDBAR44cKXEPyIYNG+rpp5/W7bffroYNGyopKUnjx49Xw4YN9dRTT5n6ubu76/vvvze71tnZucbvBwAAQCr8yuvkyZLRWPyc0SgZDNKUKdKgQdX7VVhvb2/Tzx4eHjIYDGZtVXHhwgUlJSVpz549pod5Nm/eXN26dbvhtQ4ODqY8fH19NW7cOD3zzDPKzs6Wu7t7hXNp06aNmjZtqpkzZ+rtt9+u8PUAAACALWH+Yc7W5h91bqX+kiVLNGbMGI0dO1Zt27ZVbGys/Pz8tGrVqhL7d+7cWSNGjFD79u0VEBCgUaNGKSwsrNjq/qL/gP54AAAAWMq+fcVXyPyR0SidPl3Yz1o0atRIjRo10rZt25STk1PpOBkZGdq6davs7e1lX4UZxaJFi7RlyxZ9+eWXlY4BAAAA2ALmH6WzhflHnSrq5+bm6uDBgwoNDTVrDw0NVXJycrliHDp0SMnJyaZPa4pcunRJzZs3V7NmzfTAAw/o0KFDZcbJyclRdna22QEAAFBZ6enV268mjBgxwvRGueh46623Su3v4OCg+Ph4vfnmm7rpppvUq1cvPf/88/r6669vONY333yjRo0aydXVVT4+PtqzZ48mTpyohg0bVjr/Ll26aNiwYZo+fXqlYwAAAAC2gPmHOVubf9Spon5mZqby8/Pl5eVl1u7l5aWMjIwyr23WrJmcnJzUtWtXTZw4UWPHjjWdCwoKUnx8vN577z1t3LhRzs7O6tWrl44fP15qvIULF8rDw8N0+Pn5Ve3mAABAvebjU739asLSpUuVkpJidjz00ENlXjN06FClpaXpvffeU1hYmPbs2aMuXbooPj6+zOvatGmjlJQUffnll3rhhRfUqVMnvfDCC1W+hwULFmjfvn3atWtXlWMBAAAA1or5hzlbm3/UqaJ+EYPBYPbaaDQWa7vevn379NVXX+m1115TbGysNm7caDrXo0cPjRo1SnfccYd69+6tt99+W61bt9bf//73UuNFR0crKyvLdFz/NGQAAICK6N1batascO/KkhgMkp9fYb/a4u3trZYtW5odbm5uN7zO2dlZ/fr10+zZs5WcnKyIiAjNmTOnzGscHR3VsmVLtW/fXs8//7w6deqkv/zlL2Z93N3dlZWVVezaCxcuyMPDo8S4LVq00Lhx4zR9+nQZS9pAFAAAAKgHmH+Ys7X5R50q6nt6esre3r7YqvyzZ88WW71/vcDAQHXs2FHjxo3T1KlTNXfu3FL72tnZ6c477yxzpb6Tk5Pc3d3NDgAAgMqyt5eWLSv8+fo31kWvY2Or9yFVtaVdu3a6fPlyha6ZNWuWNm7cqP/85z+mtqCgoBL3p/zyyy/Vpk2bUmPNnj1bP/zwgzZt2lShHAAAAABbwfyjbNY+/6hTRX1HR0cFBwcrMTHRrD0xMVE9e/Ysdxyj0VjmwxKMRqNSUlLkU5vfLwEAAPXOkCHSv/4l3XqreXuzZoXtQ4bUTl6Vdf78ed13331av369vv76a508eVKbN2/Wyy+/rEGDBlUo1m233aZBgwZp9uzZpraoqCjt2LFDMTExOnLkiI4cOaL58+fro48+0rPPPltqLC8vL0VFRWn58uWVvjcAAADA2jH/KJ21zz8cLDZSOUVFRSk8PFxdu3ZVSEiI4uLilJqaqsjISEmF2+KcOXNG69atkyStWLFC/v7+CgoKkiQlJSVp8eLFmjRpkinmvHnz1KNHD7Vq1UrZ2dlavny5UlJStGLFCsvfIAAAqNeGDJEGDZL27St8KJWPT+FXXq1xhUyjRo3UvXt3LV26VCdOnNC1a9fk5+encePG6fnnn69wvGeffVa9evXSgQMH1L17d/Xo0UM7d+5UTEyMYmNjJUnt27fXzp071b179zJjTZs2TatWrdLVq1crc2sAAACATWD+UTprnn8YjHVws9GVK1fq5ZdfVnp6ujp06KClS5eqT58+kqSIiAidOnVKe/bskST9/e9/1+rVq3Xy5Ek5ODiY9jEaP3687OwKv4gwdepUbd26VRkZGfLw8FDnzp01d+5chYSElDun7OxseXh4KCIiQgEBAZW+t7S0NMXFxengwYPq0qVLpeMAAADLu3r1qk6ePKnAwEA5OzvXdjqoBmX9TYve/2VlZbEVIyyO+QcAAGD+YXuqa/5R51bqS9KECRM0YcKEEs9d/yTjSZMmma3KL8nSpUu1dOnS6koPAAAAAAAAAIBaUaf21AcAAAAAAAAAAKWjqA8AAAAAAAAAgJWgqA8AAAAAAAAAgJWgqA8AAAAAAAAAgJWgqA8AAAAAAAAAgJWgqA8AAAAAAAAAgJWgqA8AAAAAAAAAgJWgqA8AAAAAAAAAgJWgqA8AAFAPREREyGAwmI7GjRurf//++vrrr4v1feqpp2Rvb69NmzYVO3f58mU999xzuu222+Ts7KwmTZronnvu0QcffGDqc88995iNVXRERkaa+hgMBm3btq3EXPfs2SODwaALFy6Yve7QoYPy8/PN+t50002Kj483vQ4ICChx7EWLFlXgtwUAAACgKph/1Oz8g6I+AACApcydK82fX/K5+fMLz9eg/v37Kz09Xenp6fr3v/8tBwcHPfDAA2Z9rly5ooSEBE2bNk1r1qwpFiMyMlLbtm3Tq6++qmPHjumjjz7S0KFDdf78ebN+48aNM41VdLz88stVyv/EiRNat27dDfvFxMQUG3vSpElVGhsAAACwOsw/qpR/XZ5/ONRodAAAAPyPvb00e3bhz7Nm/a99/vzC9piYGh3eyclJ3t7ekiRvb28999xz6tOnj86dO6cmTZpIkjZv3qx27dopOjpaPj4+OnXqlAICAkwx3n//fS1btkwDBw6UVLgyJTg4uNhYrq6uprGqy6RJkzRnzhyNGDFCzs7OpfZzc3Or9rEBAAAAq8P8o0rq8vyDlfoAAACWMmtW4Rvn2bP/t2Lmj2+o//hGu4ZdunRJb731llq2bKnGjRub2tesWaNRo0bJw8NDAwcO1Nq1a82u8/b21vbt23Xx4kWL5VpkypQpysvL06uvvmrxsQEAAACrw/yjSury/IOiPgAAgCX98Y21k5NF31B/8MEHatSokRo1aiQ3Nze99957SkhIkJ1d4VvC48eP6/PPP5P0U2YAAKaeSURBVNfw4cMlSaNGjdLatWtVUFBgihEXF6fk5GQ1btxYd955p6ZOnarPPvus2FgrV640jVV0vPnmm1XK39XVVXPmzNHChQuVlZVVar/nnnuu2Nh79uyp0tgAAACAVWL+UWl1ef5BUR8AAMDSZs2SHB2l3NzCfy20Qubee+9VSkqKUlJSdODAAYWGhmrAgAH6+eefJRWukgkLC5Onp6ckaeDAgbp8+bJ2795titGnTx/99NNP+ve//62hQ4fqu+++U+/evTX/ur06R44caRqr6Hj44YerfA9jxoyRp6enXnrppVL7TJs2rdjY3bt3r/LYAAAAgFVi/lFpdXX+QVEfAADA0ubP/98b6tzc0h9eVc0aNmyoli1bqmXLlurWrZvWrFmjy5cv6/XXX1d+fr7WrVunDz/8UA4ODnJwcJCrq6t+++23Yg+satCggXr37q3p06dr165diomJ0fz585Wbm2vq4+HhYRqr6HB3d6/yPTg4OGjBggVatmyZ0tLSSuzj6elZbGwXF5cqjw0AAABYJeYflVZX5x8U9QEAACzpj3tY5uQU3+PSggwGg+zs7PT777+b9qk8dOiQ2QqTzZs3a9u2bTp//nypcdq1a6e8vDxdvXrVInk/+uijat++vebNm2eR8QAAAACrxfyjyuri/MOhthMAAACoN0p6KFXRv7Nnm7+uATk5OcrIyJAk/fe//9Wrr76qS5cu6cEHH1RsbKz+9Kc/6Y477jC7pn379poyZYrWr1+vyZMn65577tGIESPUtWtXNW7cWEeOHNHzzz+ve++912wlzJUrV0xjFXFyctLNN99sen3y5EmlpKSY9WnZsmW57mXRokUKCwsr8dzFixeLje3q6lotK3UAAAAAq8H8w2bnHxT1AQAALCU/v+SHUhW9zs+v0eE/+ugj+fj4SJLc3NwUFBSkzZs3q23btvrwww+1YcOGYtcYDAYNGTJEa9as0eTJkxUWFqY333xTzz//vK5cuSJfX1898MADml00Kfj/Xn/9db3++utmbWFhYfroo49Mr6OiooqN98knn5TrXu677z7dd9992rVrV7Fzs2fPLpbP+PHj9dprr5UrNgAAAGATmH/Y7PzDYDQajTUW3YZkZ2fLw8NDERERCggIqHSctLQ0xcXF6eDBg+rSpUv1JQgAAGrc1atXdfLkSQUGBsrZ2bm200E1KOtvWvT+Lysri1X+sDjmHwAAgPmH7amu+Qd76gMAAAAAAAAAYCXqZFF/5cqVpk8rgoODtW/fvlL7JiUlqVevXmrcuLFcXFwUFBSkpUuXFuu3ZcsWtWvXTk5OTmrXrp3eeeedmrwFAAAAAAAAAACqXZ0r6ickJGjKlCmaMWOGDh06pN69e2vAgAFKTU0tsX/Dhg319NNPa+/evTp69KhmzpypmTNnKi4uztRn//79Gj58uMLDw3X48GGFh4dr2LBhOnDggKVuCwAAAAAAAACAKqtzRf0lS5ZozJgxGjt2rNq2bavY2Fj5+flp1apVJfbv3LmzRowYofbt2ysgIECjRo1SWFiY2er+2NhY9evXT9HR0QoKClJ0dLT69u2r2NhYC90VAAAAAAAAAABVV6eK+rm5uTp48KBCQ0PN2kNDQ5WcnFyuGIcOHVJycrLuvvtuU9v+/fuLxQwLCyszZk5OjrKzs80OAAAAAAAAAABqU50q6mdmZio/P19eXl5m7V5eXsrIyCjz2mbNmsnJyUldu3bVxIkTNXbsWNO5jIyMCsdcuHChPDw8TIefn18l7ggAAAAAAAAAgOpTp4r6RQwGg9lro9FYrO16+/bt01dffaXXXntNsbGx2rhxY5ViRkdHKysry3ScPn26gncBAAAAAAAAAED1cqjtBP7I09NT9vb2xVbQnz17tthK++sFBgZKkjp27Khff/1Vc+fO1YgRIyRJ3t7eFY7p5OQkJyenytwGAAAAAAAAAAA1ok6t1Hd0dFRwcLASExPN2hMTE9WzZ89yxzEajcrJyTG9DgkJKRZz165dFYoJAAAAAAAAAEBtq1Mr9SUpKipK4eHh6tq1q0JCQhQXF6fU1FRFRkZKKtwW58yZM1q3bp0kacWKFfL391dQUJAkKSkpSYsXL9akSZNMMSdPnqw+ffropZde0qBBg/Tuu+9q9+7dSkpKsvwNAgAAm2O8+rt07ZrlBmzQQAZnF8uNBwAAAKDOYP6BOlfUHz58uM6fP6+YmBilp6erQ4cO2r59u5o3by5JSk9PV2pqqql/QUGBoqOjdfLkSTk4OKhFixZatGiRxo8fb+rTs2dPbdq0STNnztSsWbPUokULJSQkqHv37ha/PwAAYFuMV39Xwd5EGa9cttiYBteGsuvTr0JvrCMiInThwgVt27at2LmAgAD9/PPPxdoXLlyo6dOn69SpUwoMDFSTJk104sQJubm5mfp06tRJgwcP1ty5cyVJP/30k2bMmKFPP/1Uv/32mzw9PRUcHKxXXnlFrVu31qlTpzR//nx9/PHHysjIkK+vr0aNGqUZM2bI0dGxwr8LAAAAoD5h/sH8Q6qDRX1JmjBhgiZMmFDiufj4eLPXkyZNMluVX5pHHnlEjzzySHWkBwAA8D/XrhW+oXZoIDWwwJvCa7mF4127JlXjapmYmBiNGzfOrO2Pb54l6eLFi1q8eLHmzZtXYozc3Fz169dPQUFB2rp1q3x8fPTLL79o+/btysrKkiQdO3ZMBQUFWr16tVq2bKlvv/1W48aN0+XLl7V48eJqux+guq1cuVKvvPKK0tPT1b59e8XGxqp3794l9k1KStJzzz2nY8eO6cqVK2revLnGjx+vqVOnWjhrAABgc5h/mNTn+UedLOoDAABYnQaOMjg51fgwRknKq/6v2rq5ucnb27vMPpMmTdKSJUs0ceJENW3atNj5I0eO6KefftLHH39s+pZl8+bN1atXL1Of/v37q3///qbXt912m77//nutWrXKqt9Uw7YlJCRoypQpWrlypXr16qXVq1drwIABOnLkiPz9/Yv1b9iwoZ5++mndfvvtatiwoZKSkjR+/Hg1bNhQTz31VC3cAQAAsDnMP+r1/KNOPSgXAAAAddeIESPUsmVLxcTElHi+SZMmsrOz07/+9S/l5+eXO25WVpZuueWW6koTqHZLlizRmDFjNHbsWLVt21axsbHy8/PTqlWrSuzfuXNnjRgxQu3bt1dAQIBGjRqlsLAw7du3z8KZAwAAWC/mH6WjqA8AAAA999xzatSokdmxZ88esz4Gg0GLFi1SXFycTpw4USzGrbfequXLl2v27Nm6+eabdd9992n+/Pn66aefSh33xIkT+vvf/67IyMjqviWgWuTm5urgwYMKDQ01aw8NDVVycnK5Yhw6dEjJycm6++67S+2Tk5Oj7OxsswMAAMBWMf+oGor6AAAA0LRp05SSkmJ2dO/evVi/sLAw3XXXXZo1a1aJcSZOnKiMjAytX79eISEh2rx5s9q3b6/ExMRifdPS0tS/f389+uijGjt2bLXfE1AdMjMzlZ+fLy8vL7N2Ly8vZWRklHlts2bN5OTkpK5du2rixIll/ne+cOFCeXh4mA4/P79qyR8AAKAuYv5RNRT1AQAAIE9PT7Vs2dLscHEp+UFYixYtUkJCgg4dOlTieTc3Nz300EN64YUXdPjwYfXu3VsLFiww65OWlqZ7771XISEhiouLq/b7AaqbwWAwe200Gou1XW/fvn366quv9Nprryk2NlYbN24stW90dLSysrJMx+nTp6slbwAAgLqI+UfV8KBcAAAAVEi3bt00ZMgQTZ8+/YZ9DQaDgoKCzLYpOXPmjO69914FBwdr7dq1srNjnQnqLk9PT9nb2xdblX/27Nliq/evFxgYKEnq2LGjfv31V82dO1cjRowosa+Tk5OcLPCwOwAAAGvD/KM4ivoAAAD1RFZWllJSUszaih4QdfHixWJFS1dXV7m7u5cY64UXXlD79u3l4PC/t5MpKSmaM2eOwsPD1a5dOzk6OurTTz/VG2+8oeeee05S4QqZe+65R/7+/lq8eLHOnTtnut7b27s6bhOoVo6OjgoODlZiYqIefvhhU3tiYqIGDRpU7jhGo1E5OTk1kSIAAECdxPyj5lDUBwAAqA7XcmW00DiVtWfPHnXu3Nms7cknn5QkzZ49W7NnzzY7N378eL322mslxmrdurVGjx5t9tXVZs2aKSAgQPPmzdOpU6dkMBhMr6dOnSpJ2rVrl3788Uf9+OOPatasmVlMo9Eiv0GgwqKiohQeHq6uXbuavrKdmppqesBadHS0zpw5o3Xr1kmSVqxYIX9/fwUFBUmSkpKStHjxYk2aNKnW7gEAANgY5h/1ev5BUR8AAKAqGjSQwbWhjFcuS3nXLDKkwbWh1KBBha6Jj49XfHx8pcYLCAgo8Q3v6tWrtXr1atNrT09PLVu2rMxYERERioiIqFQeQG0ZPny4zp8/r5iYGKWnp6tDhw7avn27mjdvLklKT09XamqqqX9BQYGio6N18uRJOTg4qEWLFlq0aJHGjx9fW7cAAABsBfMP0+v6PP+gqA8AAFAFBmcX2fXpJ12zzBtqSYVv5J1LfogUgJoxYcIETZgwocRz109YJ02axKp8AABQI5h/QKKoDwAAUGUGZxeJN7kAAAAALID5B6z/Ub8AAAAAAAAAANQTFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPUBAAAAAAAAALASFPWB/8fevYdVVaf9H/9szngA0614BCw1z2mYikiHMWG0aSxrtFLM1NQHx0I6SZYplXZUdBLTKSXHIjQxsyyjRlPTmmKkqdSyPGC6GcVSRAUE9u8PfuxpCyjsA+wN79d1rWva3/1d97oX9VzPuu/9XWsBAAAAAAAAgJvwqusEAAAA3F12drZyc3Nr7XhGo1HBwcG1djwAAAAAroP6AzT1AQAA7JCdna2uXbvq/PnztXZMf39/7du3r8YX1jk5OXr22Wf1wQcf6OjRo2rVqpX69OmjuLg4DRkyRKGhoTp8+LBSU1N11113We3bo0cP7dmzRytXrtT48eMlSaGhoYqLi1NcXJzl8+HDhy05XnnllZo+fbqmTJliiVNYWKjExEStXr1aOTk5at++vWbNmqUJEybY/gcBAAAAGgjqD+oPiaY+AACAXXJzc3X+/HmNHDlSRqOxVo6Xnp6u3NzcGl1UHzp0SBEREWrWrJleeOEF9e7dWxcuXNDmzZs1bdo07du3T5LUoUMHrVy50uqi+osvvlBOTo4aN2582eMkJibq/vvvV35+vlJSUjR16lQ1a9ZMo0ePliSNGjVK//3vf/X666+rU6dOOn78uIqLi2v4VwAAAAAaJuoPaw21/nDJpn5ycrJefPFFmUwm9ejRQ0lJSYqMjKx0bnp6upYuXaqsrCwVFhaqR48emjNnjqKjoy1zUlJSdN9991XY9/z58/Lz83PaeQAAgIbDaDSqbdu2dZ1GlWJjY2UwGPSvf/3L6uK4R48eVqtUxowZo4ULF+rIkSPq0KGDJGnFihUaM2aMVq1addnjNG3aVK1bt5YkPfPMM1qzZo3effddjR49Wh999JE+++wzHThwQM2bN5dUtroGAAAAQM1Qf5RpqPWHy70oNy0tTXFxcZo1a5Z2796tyMhIDRs2TNnZ2ZXO37Ztm4YOHapNmzYpMzNTN910k2699Vbt3r3bal5AQIBMJpPVRkMfAAA0BL/++qs++ugjTZs2rdLVLs2aNbP8c1BQkKKjo/XGG29Iks6dO6e0tDSbb0/18/PThQsXJEnvvfee+vXrpxdeeEHt2rVTly5d9PDDD9fqrcMAAAAAnIv6w/lcrqm/YMECTZw4UZMmTVK3bt2UlJSkDh06aOnSpZXOT0pK0qOPPqrrrrtOnTt31rx589S5c2dt3LjRap7BYFDr1q2tNgAAgIbgp59+ktlsVteuXas1f8KECUpJSZHZbNY777yjq666Sn369KnRMYuLi5WSkqJvv/1WQ4YMkSQdOHBAO3bs0Hfffaf169crKSlJ77zzjqZNm1bTUwIAAADgoqg/nM+lmvpFRUXKzMxUVFSU1XhUVJR27txZrRilpaU6c+aM5ZaKcvn5+QoJCVH79u31pz/9qcJK/osVFhYqLy/PagMAAHBHZrNZUtkih+q45ZZblJ+fr23btmnFihU1WiXz2GOPqUmTJvL399e0adP0yCOPWF5UVVpaKoPBoDfffFP9+/fX8OHDtWDBAqWkpNSL1TIAAAAAqD9qg0s19XNzc1VSUqKgoCCr8aCgIOXk5FQrxssvv6yzZ89q1KhRlrGuXbsqJSVF7733nlJTU+Xn56eIiAjt37+/yjjz589XYGCgZSt/phMAAIC76dy5swwGg/bu3Vut+V5eXoqJidFTTz2lL7/8UmPGjKn2sR555BFlZWXp8OHDys/P1wsvvCAPj7JLzjZt2qhdu3YKDAy0zO/WrZvMZrN++eWXmp0UAAAAAJdE/eF8LtXUL3fxrzhms7lav+ykpqZqzpw5SktLU6tWrSzjAwcO1NixY3XNNdcoMjJSa9asUZcuXfS3v/2tylgJCQk6ffq0ZTty5IjtJwQAAFCHmjdvrujoaC1ZskRnz56t8P2pU6cqjE2YMEGfffaZRowYoSuuuKLaxzIajerUqZPatm1b4fotIiJCx44dU35+vmXsxx9/lIeHh9q3b1/9EwIAAADgsqg/nM+lmvpGo1Genp4VVuUfP368wur9i6WlpWnixIlas2aNbr755kvO9fDw0HXXXXfJlfq+vr4KCAiw2gAAANxVcnKySkpK1L9/f61bt0779+/X3r17tXjxYoWHh1eY361bN+Xm5mrlypUOy+Gee+5RixYtdN9992nPnj3atm2bHnnkEU2YMEH+/v4OOw4AAACAukX94VxedZ3A7/n4+CgsLEwZGRm6/fbbLeMZGRkaMWJElfulpqZqwoQJSk1N1S233HLZ45jNZmVlZalXr14OyRsAACA3N9elj9OxY0f9+9//1rPPPquHHnpIJpNJLVu2VFhYmJYuXVrpPi1atLAn1QqaNGmijIwMTZ8+Xf369VOLFi00atQoPfPMMw49DgAAAFDfUX9cXn2uP1yqqS9J8fHxiomJUb9+/RQeHq7ly5crOztbU6dOlVT2WJyjR49q1apVksoa+uPGjdOiRYs0cOBAyyp/f39/y/OS5s6dq4EDB6pz587Ky8vT4sWLlZWVpSVLltTNSQIAgHrDaDTK399f6enptXZMf39/GY3GGu/Xpk0bvfLKK3rllVcq/f7QoUOX3P/i22Qvnn+5/aWydx1lZGRcdh4AAACAiqg/qr+/VH/rD5dr6o8ePVonT55UYmKiTCaTevbsqU2bNikkJESSZDKZlJ2dbZm/bNkyFRcXa9q0aZo2bZpl/N5771VKSoqksv8AJk+erJycHAUGBqpv377atm2b+vfvX6vnBgAA6p/g4GDt27ev1lbKSGUX8sHBwbV2PAAAAACugfoDkgs29SUpNjZWsbGxlX5X3qgvt3Xr1svGW7hwoRYuXOiAzAAAACoKDg7mIhcAAABAraD+gEu9KBcAAAAAAAAAAFSNpj4AAAAAAAAAAG6Cpj4AAAAAAAAAAG6Cpj4AAEANlZaW1nUKcBCz2VzXKQAAAACXRP1Rfziq/nDJF+UCAAC4Ih8fH3l4eOjYsWNq2bKlfHx8ZDAY6jot2MhsNuvEiRMyGAzy9vau63QAAAAAK9Qf9Ysj6w+a+gAAANXk4eGhjh07ymQy6dixY3WdDhzAYDCoffv28vT0rOtUAAAAACvUH/WPo+oPmvoAAAA14OPjo+DgYBUXF6ukpKSu04GdvL29aegDAADAZVF/1C+Oqj9q3NRfvHixBg4cqP79+1vGjh8/rpycHPXu3bvC/A0bNmjDhg1asWKFfZkCAAC4iPLbJXlkC+AaqFEAAEB9Rv2Bi9X4RblxcXH66KOPrMaWLl2qvn37Vjo/KytLb7zxhm3ZAQAAAMBlUKMAAACgIalxUx8AAAAAAAAAANQNmvoAAAAAAAAAALgJmvoAAAAAAAAAALgJmvoAAAAAAAAAALgJmvoAAAAAAAAAALgJL1t2+u6777RmzRqrz5K0du1amc3mCnMBAAAAwJmoUQAAANBQ2NTUX7dundatW2f5XH6RfNddd1WYazabZTAYbEwPAAAAAC6PGgUAAAANRY2b+k899ZQz8gAAAAAAm1CjAAAAoCGhqQ8AAADArVGjAAAAoCHhRbkAAAAAAAAAALgJm56pfylZWVnasmWLJGnw4MG67rrrHH0IAAAAAKg2ahQAAADUJzVeqb9t2zaNGzdOX3zxRYXvnnjiCYWFhenhhx/Www8/rIEDB2r69Ok1Tio5OVkdO3aUn5+fwsLCtH379irnpqena+jQoWrZsqUCAgIUHh6uzZs3V5i3bt06de/eXb6+vurevbvWr19f47wAAAAAuJ7aqFEAAAAAV1Hjpn5aWprWrl2r7t27W41v2bJF8+bNk6enp2JiYjR16lQZjUYlJyfr3XffrVH8uLg4zZo1S7t371ZkZKSGDRum7OzsSudv27ZNQ4cO1aZNm5SZmambbrpJt956q3bv3m2Zs2vXLo0ePVoxMTH65ptvFBMTo1GjRunLL7+s6ekDAAAAcDHOrlEAAAAAV1Ljpv6uXbs0YMAABQQEWI0vW7ZMBoNBr776qlJSUrRkyRJt375d3t7eSklJqXb8BQsWaOLEiZo0aZK6deumpKQkdejQQUuXLq10flJSkh599FFdd9116ty5s+bNm6fOnTtr48aNVnOGDh2qhIQEde3aVQkJCRoyZIiSkpJqevoAAAAAXIyzaxQAAADAldS4qX/s2DF16dKlwviWLVsUEBCg8ePHW8a6dOmi4cOH6+uvv65W7KKiImVmZioqKspqPCoqSjt37qxWjNLSUp05c0bNmze3jO3atatCzOjo6EvGLCwsVF5entUGAAAAwPU4s0YBAAAAXE2Nm/q//fabjEaj1dgvv/yiEydOaPDgwfLwsA7ZqVMn5ebmVit2bm6uSkpKFBQUZDUeFBSknJycasV4+eWXdfbsWY0aNcoylpOTU+OY8+fPV2BgoGXr0KFDtY4PAAAAoHY5s0YBAAAAXE2Nm/pNmzbVsWPHrMYyMzMlSWFhYRXmGwwG+fn51egYBoPB6rPZbK4wVpnU1FTNmTNHaWlpatWqlV0xExISdPr0act25MiRGpwBAAAAgNpSGzUKAAAA4Cq8arpD79699f777+vs2bNq3LixJGn9+vUyGAy6/vrrK8z/+eef1bZt22rFNhqN8vT0rLCC/vjx4xVW2l8sLS1NEydO1Nq1a3XzzTdbfde6desax/T19ZWvr2+18gYAAABQd5xZowAAAACupsYr9SdMmKBff/1VN9xwgxYvXqwHHnhAq1evVocOHXTjjTdazS0pKdG2bdvUq1evasX28fFRWFiYMjIyrMYzMjI0aNCgKvdLTU3V+PHj9dZbb+mWW26p8H14eHiFmB9//PElYwIAAABwD86sUcolJyerY8eO8vPzU1hYmLZv317l3PT0dA0dOlQtW7ZUQECAwsPDtXnzZltODQAAAKigxiv1x44dq08//VRvvPGGdu/eLbPZrKZNm+rvf/97hWdVfvDBB8rNzVV0dHS148fHxysmJkb9+vVTeHi4li9fruzsbE2dOlVS2WNxjh49qlWrVkkqa+iPGzdOixYt0sCBAy0r8v39/RUYGChJevDBB3X99dfr+eef14gRI7RhwwZ98skn2rFjR01PHwAAAICLcXaNkpaWpri4OCUnJysiIkLLli3TsGHDtGfPHgUHB1eYv23bNg0dOlTz5s1Ts2bNtHLlSt1666368ssv1bdvX7vPFwAAAA1bjZv6krRy5UpNnDhRu3btUvPmzRUdHa327dtXmOfr66uFCxdqxIgR1Y49evRonTx5UomJiTKZTOrZs6c2bdqkkJAQSZLJZFJ2drZl/rJly1RcXKxp06Zp2rRplvF7771XKSkpkqRBgwbp7bff1hNPPKEnn3xSV111ldLS0jRgwABbTh8AAACAi3FmjbJgwQJNnDhRkyZNkiQlJSVp8+bNWrp0qebPn19hflJSktXnefPmacOGDdq4cSNNfQAAANjNpqa+JA0ePFiDBw++5Jzo6OgarYApFxsbq9jY2Eq/K2/Ul9u6dWu1Yt5555268847a5wLAAAAAPfgjBqlqKhImZmZmjlzptV4VFSUdu7cWa0YpaWlOnPmjJo3b17lnMLCQhUWFlo+5+XlVTtHAAAANCw1fqY+AAAAADQUubm5KikpUVBQkNV4UFCQ5dGfl/Pyyy/r7NmzGjVqVJVz5s+fr8DAQMvWoUMHu/IGAABA/VXjlfpr1qyx6UCXuoAFAAAAAFvVRo1iMBisPpvN5gpjlUlNTdWcOXO0YcMGtWrVqsp5CQkJio+Pt3zOy8tzaGN/7969dscwGo2VvkMAAAAAtavGTf277rqrWhev5covdmnqAwAAAHAGZ9YoRqNRnp6eFVblHz9+vMLq/YulpaVp4sSJWrt2rW6++eZLzvX19ZWvr+/lk6+h/Px8GQwGjR071u5Y/v7+2rdvH419AACAOmbTM/W9vLw0fPhw9enTx8HpAAAAAEDNOatG8fHxUVhYmDIyMnT77bdbxjMyMi75st3U1FRNmDBBqampuuWWWxyaU00UFBTIbDZr5MiRMhqNNsfJzc1Venq6cnNzaeoDAADUsRo39W+77TZ98MEHeu+993T48GFNmDBBY8aM0RVXXOGM/Ootbn8FAAAAHMPZNUp8fLxiYmLUr18/hYeHa/ny5crOztbUqVMllT065+jRo1q1apWksob+uHHjtGjRIg0cONCyyt/f31+BgYEOyammjEaj2rZtWyfHBgAAgGPVuKlfvjpj1apVSklJ0QMPPKBHHnlEt912myZMmKChQ4c6I896g9tfAQAAAMdydo0yevRonTx5UomJiTKZTOrZs6c2bdqkkJAQSZLJZFJ2drZl/rJly1RcXKxp06Zp2rRplvF7771XKSkpduUCAAAA2PT4HaPRqPj4eMXHx+tf//qXXn/9da1Zs0Zr1qxRu3btNH78eI0fP15XXnmlo/N1e9z+CgAAADies2uU2NhYxcbGVvrdxY36rVu32nQMAAAAoDo87A3Qv39/LVu2TCaTSSkpKerUqZOeffZZdenSRZ988okjcqyXym9/tXWz5wcBAAAAoD6jRgEAAEB9ZtNK/cr4+fkpKipKJpNJP/74o44dO6Zz5845KjyqwLP5AQAAgMpRowAAAKA+srupX1JSovfff18rVqzQhx9+qJKSEvXp00cJCQkaMmSII3JEJXg2PwAAAFA5ahQAAADUZzY39ffs2aMVK1Zo9erVOn78uFq0aKHY2FhNmDBBvXv3dmSOqATP5gcAAACsUaMAAACgIahxU3/58uVasWKFvvrqKxkMBkVFRWnChAkaMWKEvL29nZEjLqH82fwAAABAQ0WNAgAAgIakxk39qVOnytvbW7feeqvuvfdetWvXTpK0e/fuS+7Xv39/2zIEAAAAgEugRqk9vNMLAACg7tn0+J0LFy5o48aN2rhxY7X3KSkpseVQAAAAAHBZ1CjOxTu9AAAAXEeNm/r33nuvM/IAAAAAAJtQozgf7/QCAABwHTVu6q9cudIZeQAAAACATahRag/v9AIAAKh7HnWdAAAAAAAAAAAAqJ4ar9SPjY216UDJyck27QcAAAAAl0KNAgAAgIakxk39V199tdpzDQaD5Z+5YAYAAADgDNQoAAAAaEhq3NTfsmVLteZlZ2crMTFRP//8s9WFMwAAAAA4EjUKAAAAGpIaN/VvuOGGS37/22+/ad68eVqyZIkKCgoUHh6u559/3uYEAQAAAOBSqFEAAADQkDjsRbkFBQV67rnndNVVV+nll19WaGio0tPT9fnnn2vw4ME1ipWcnKyOHTvKz89PYWFh2r59e5VzTSaT7rnnHl199dXy8PBQXFxchTkpKSkyGAwVtoKCgpqeJgAAAAA34cgaBQAAAHAVdjf1zWazXnvtNXXu3FmPP/64GjVqpOXLl+u7777TbbfdVuN4aWlpiouL06xZs7R7925FRkZq2LBhys7OrnR+YWGhWrZsqVmzZumaa66pMm5AQIBMJpPV5ufnV+P8AAAAALg2R9coAAAAgCuxq6n/7rvvqkePHpoyZYry8/M1b948/fTTT5o0aZI8PGwLvWDBAk2cOFGTJk1St27dlJSUpA4dOmjp0qWVzg8NDdWiRYs0btw4BQYGVhnXYDCodevWVhsAAACA+sUZNQoAAADgSmy6qt2xY4ciIiJ0xx136ODBg5oxY4YOHDigmTNn2rX6vaioSJmZmYqKirIaj4qK0s6dO22OK0n5+fkKCQlR+/bt9ac//Um7d+++5PzCwkLl5eVZbQAAAABck7NqFAAAAMDV1PhFuX/+85/1wQcfyMPDQ/fee68SExPVvn17hySTm5urkpISBQUFWY0HBQUpJyfH5rhdu3ZVSkqKevXqpby8PC1atEgRERH65ptv1Llz50r3mT9/vubOnWvzMQEAAADUDmfWKAAAAICrqXFT//3335fBYFBwcLBycnI0efLky+5jMBj0wQcfVPsYBoPB6rPZbK4wVhMDBw7UwIEDLZ8jIiJ07bXX6m9/+5sWL15c6T4JCQmKj4+3fM7Ly1OHDh1szsHV7d271679jUajgoODHZQNAAAAUH21UaMAAAAArqLGTX2prMl+8OBBHTx4sFrzq9uQNxqN8vT0rLAq//jx4xVW79vDw8ND1113nfbv31/lHF9fX/n6+jrsmK4qPz9fBoNBY8eOtSuOv7+/9u3bR2MfAAAAdcJZNQoAAADgamrc1K/uRbItfHx8FBYWpoyMDN1+++2W8YyMDI0YMcJhxzGbzcrKylKvXr0cFtNdFRQUyGw2a+TIkTIajTbFyM3NVXp6unJzc2nqAwAAoNY5s0YBAAAAXE2Nm/ohISHOyMMiPj5eMTEx6tevn8LDw7V8+XJlZ2dr6tSpksoei3P06FGtWrXKsk9WVpakslXnJ06cUFZWlnx8fNS9e3dJ0ty5czVw4EB17txZeXl5Wrx4sbKysrRkyRKnnos7MRqNatu2bV2nAQAAANSYs2sUAAAAwJXY9PgdZxo9erROnjypxMREmUwm9ezZU5s2bbJcqJtMJmVnZ1vt07dvX8s/Z2Zm6q233lJISIgOHTokSTp16pQmT56snJwcBQYGqm/fvtq2bZv69+9fa+cFAAAAACjDO70AAABs53JNfUmKjY1VbGxspd+lpKRUGDObzZeMt3DhQi1cuNARqQEAAAAAbMQ7vQAAAOznkk19AAAAAED948h3em3fvl3dunWzKx9W/AMAAHdEUx8AAAAAUKvseaeXo1b7S6z4BwAA7ommPgAAAADAbThitb/0vxX/ubm5NPUBAIBboakPAAAAAHA79qz2BwAAcGcedZ0AAAAAAAAAAACoHpr6AAAAAAAAAAC4CZr6AAAAAAAAAAC4CZr6AAAAAAAAAAC4CZr6AAAAAAAAAAC4CZr6AAAAAAAAAAC4CZr6AAAAAAAAAAC4Ca+6TgAAAAAAgLqyd+9eu2MYjUYFBwc7IBsAAIDLo6kPAAAAAGhw8vPzZTAYNHbsWLtj+fv7a9++fTT2AQBAraCpD4dhhQsAAAAAd1FQUCCz2ayRI0fKaDTaHCc3N1fp6enavn27unXrZldOhYWF8vX1tSuGRF0FAEB9R1MfdmOFCwAAAAB3ZTQa1bZtW5v3d2Q9ZDAYZDab7Y5DXQUAQP1GUx92c/QKl9zcXC4+AQAAALgFR9VD+/fv15YtW6irAADAZdHUh8PYu8IFAAAAANyVvfVQbm6uQ+IAAID6j6Y+XA7P5gcAAAAAAACAytHUh8vg2fwAAAAAAAAAcGk09eEyeDY/AAAAAAAAAFyaR10nUJnk5GR17NhRfn5+CgsL0/bt26ucazKZdM899+jqq6+Wh4eH4uLiKp23bt06de/eXb6+vurevbvWr1/vpOxhr/JnSNq62fODAAAAAAAAAAC4Mpdr6qelpSkuLk6zZs3S7t27FRkZqWHDhik7O7vS+YWFhWrZsqVmzZqla665ptI5u3bt0ujRoxUTE6NvvvlGMTExGjVqlL788ktnngoAAAAAAAAAAA7lco/fWbBggSZOnKhJkyZJkpKSkrR582YtXbpU8+fPrzA/NDRUixYtkiStWLGi0phJSUkaOnSoEhISJEkJCQn67LPPlJSUpNTUVCedCQAAAAAAdWPv3r127W80GnmcKQAALsqlmvpFRUXKzMzUzJkzrcajoqK0c+dOm+Pu2rVLM2bMsBqLjo5WUlJSlfsUFhaqsLDQ8jkvL8/m46Nu2HsRK3EhCwAAAMC95Ofny2AwaOzYsXbF8ff31759+6iHAABwQS7V1M/NzVVJSYmCgoKsxoOCgpSTk2Nz3JycnBrHnD9/vubOnWvzMVF3HHURK3EhCwAAgDLJycl68cUXZTKZ1KNHDyUlJSkyMrLSuSaTSQ899JAyMzO1f/9+PfDAA5dcUAQ4UkFBgcxms0aOHGnz+8Zyc3OVnp6u7du3q1u3bnblw0IpAAAcz6Wa+uUMBoPVZ7PZXGHM2TETEhIUHx9v+ZyXl6cOHTrYlQNqhyMuYiUuZAEAAFCm/L1fycnJioiI0LJlyzRs2DDt2bOn0mu837/3a+HChXWQMVBWg7Rt29amfVkoBQCAa3Oppr7RaJSnp2eFFfTHjx+vsNK+Jlq3bl3jmL6+vvL19bX5mKh79lzESlzIAgAAoIwz3vsFuDJHL5TKzc2lFgIAwIFcqqnv4+OjsLAwZWRk6Pbbb7eMZ2RkaMSIETbHDQ8PV0ZGhtVz9T/++GMNGjTIrnxRv3EhCwAAAGe99+tivNMLrsjehVIAAMA5XKqpL0nx8fGKiYlRv379FB4eruXLlys7O1tTp06VVPZYnKNHj2rVqlWWfbKysiSVraw+ceKEsrKy5OPjo+7du0uSHnzwQV1//fV6/vnnNWLECG3YsEGffPKJduzYUevnB/fDhSwAAEDD5az3fl2Md3oBAACgulyuqT969GidPHlSiYmJMplM6tmzpzZt2qSQkBBJZS+dys7Ottqnb9++ln/OzMzUW2+9pZCQEB06dEiSNGjQIL399tt64okn9OSTT+qqq65SWlqaBgwYUGvnBQAAAMB9OeO9X7/HO70AAABQXS7X1Jek2NhYxcbGVvpdSkpKhTGz2XzZmHfeeafuvPNOe1MDAAAA0IA4671fF+OdXqjP9u7da3cMo9HI40wBAPj/XLKpDwAAAACuwFnv/QIagvz8fBkMBo0dO9buWP7+/tq3bx+NfQAARFMfqDX2rk5hZQoAAEDdcMZ7v4CGoKCgQGazWSNHjpTRaLQ5Tm5urtLT07V9+3Z169bNrpwKCwvtviuG2gwAUNdo6gNO5qjVKaxMAQAAqBvOeO9XdTX1MKixSm3OvZmXp1o3aayAehTHlXKpr3EcncuVrVoqqLXtj6vyPH9ObZo20cNTp9gco5zBYKjWI3wvxcfHR6+99prdj+AqKiqUj4/9j91q3ry52rVrZ3ccAEDdMp/Jq/ZcmvqAkzlidYojV6awqgQAAKDmnPHer+oY3tRHLVVg8/59Wl+hQTcPVpcAHzWqJ3FcKZf6GseVcpGkPlc0UtiQCIUEh8jXz/YmeF5ennJycuyKc/bsWR09elTZq5Yr+/LTa4WHh4duuvEmNWrkb1ccX18/NW7S2EFZAQBqquTcuWrPpakP1BKj0ai2bdvatC/PogQAAGiYLpilczLYvP+ZkhKdPHdeZ0vNUj2J40q51Nc4rpTL7+O08/GRb6NGNscpOnvO7jjn/3+Mzp07y9/f9ib6b7/9piNHjtgdJy/vjA4dOqg1779vc4xynp5emjDhPjVtGmB3LACADYouVHsqTX3ADbjisyhZ8Q8AAOB8RWazLtjRDC0oNSu/6IIKzZJPPYnjSrnU1ziulIurxSmP4eHnL58mTWzORWfPOSROyf+P061bNzWy4wePc+fOae/evTp7oVgBdr5zAABgIy+f6k91YhoAHMye1f6SY1f8+/r6at26dWrTpo1dcXhRFQAAAADYp1GjRmra1I4fGf6/3BMnHJJLYGCg3XEAAFWjqQ80II5a8Z+dna3NmzfrT3/6k905OeJFVY76gYEfBwAAAAA0REVFRZKk9PXr7Y7l5eWlv/71rzT2AcCJaOoDDZC9K/5zc3Md8uPA/v37tWXLFrviOPIHBt43AAAAAKAhKi4uliSHPcbn3LlzNPUBwIlo6gOwmSN+HLA3jqN+YOB9AwAAAAAaOkc9xgcA4Fw09QHUC670vgFW/AMAAAAAAMBZaOoDgBz3voHyFf+5ubk09QEAAAAAAOBwNPUB4HfsXfEPAAAAAA1d7okTdu3fqFEjnskPAJdAUx8AAAAAAAB2KyoqkiSlr19vVxxPT0+NHjVKTZo2tStOSXGxPL3sb33xIwMAV0NTv4aaehjUWKU279/My1OtmzRWQD2K40q51Nc4rpSLq8VxpVwkKcDDoNZNGuvgt/+RV8F5m+NIUlFRoXx8fO2K4ag4jsqlefPmateund1xAKA2mM/k1XUKAAC4leLiYklSt27d1KhRI5tinD59Wj/99JPeSk11ZGp2cdSPDPw4AMBRaOrX0PCmPmqpApv379P6Cg26ebC6BPioUT2J40q51Nc4rpSLq8VxpVwkKc/fQ11vHqxT776tr2yOUn95enrq9ttuV+Mmjes6FQC4rJJz5+o6BQAA3FKjRo3UtGkTm/Y99////689PwxI0smTJ3Xo0CG74zjyRwZH/DjA3QcAJJr6NXbBLJ2Tweb9z5SU6OS58zpbapbqSRxXyqW+xnGlXFwtjivlIkmniop08tx5de7cWf7+/jbH+e2333TkyBGXiOOoXM6fP6/9+/frrNmsxv409QG4gaILdZ0BAAANlj0/DEj/+3HAUXFc6ccBR+DuA8C90dSvoSKzWRfsaPgVlJqVX3RBhWbJp57EcaVc6mscV8rF1eK4Ui6/j+Ph5y+fJrZfOOrsOdeJ46BcCs1SftEFHf/tlEq9vG2OI7E6BYBtDCn/kDw8ZB43puJ3q96USktlHh/zv0Evn1rMDgAAuDJX+HGgvt59IFGbATVFUx8AUCsc9dIsR/Ly8tJf//pXLh6BhsLDQx4r/6FSyaqxb1j1Ztn4fTFV7wsAAOAAjng0kSv8wCA59scBajOgZmjqAwBqhSNemiU5bnXKuXPntHfvXp07d44LR6CBMI8bo1LJqrH/+4Z+ZSv4gbrmYzDIW2ab9/fzMKiJj7d8Dao3cVwpl/oax5VycbU4rpRLfY3jSrm4WhxH53JF40Z2ve+stOC8mvh4O+xRrQV5pxXg52dzHMDtFRdVeypNfQBArXKVZ2OWyz1xwq79uU0UcC+/b+ybV78tw4ULNPTh0rwNUiM7GjdNPT3VopG/GnsY6k0cV8qlvsZxpVxcLY4r5VJf47hSLq4Wx5Vy+X0cY+NGatTI9qb+OQ+Dfm3kr/zjx+VVVGhzHEny9fWz64cKoE4VV/+dXi7Z1E9OTtaLL74ok8mkHj16KCkpSZGRkVXO/+yzzxQfH6/vv/9ebdu21aOPPqqpU6davk9JSdF9991XYb/z58/Lj18AAaBBctTjgBz1DEneEwDUHvO4MZaGvtnbm4Y+XNqmM0UKvsL2mmVvzm/a9MkOjW0dqqBGV9SLOK6US32N40q5uFocV8qlvsZxpVxcLY4r5eLIOAdMx/Tup5/L/MkOm2OU8/Pz0/bt29WuXTu7YwG1zTMvr9pzXa6pn5aWpri4OCUnJysiIkLLli3TsGHDtGfPHgUHB1eYf/DgQQ0fPlz333+/Vq9erc8//1yxsbFq2bKl7rjjDsu8gIAA/fDDD1b70tAHgIbLEY8DcuQzJB2FHxmAyzOsetPS0DdcuCDDqjdp7MNlnSk166w8bN7/VHGJcvLPKq/UrCb1JI4r5VJf47hSLq4Wx5Vyqa9xXCkXV4vjSrk4Ms5/z56T6Uy+Ro4cKaPRaHOc3Nxcpaen6/i582rfNMDmOEBdMdTgxhmXa+ovWLBAEydO1KRJkyRJSUlJ2rx5s5YuXar58+dXmP/qq68qODhYSUlJksqaM19//bVeeuklq6a+wWBQ69ata+UcAADuwxEvqnKV9wS44o8MvPAKrubiZ+hbPks09gEAAOqQ0WhU27Zt7Y6zd+9eu/OobGEx4EpcqqlfVFSkzMxMzZw502o8KipKO3furHSfXbt2KSoqymosOjpar7/+ui5cuCBvb29JUn5+vkJCQlRSUqI+ffro6aefVt++favMpbCwUIWF/3uOV14Nbn8AADQsrvKeAFf7kYGXEcPVVPZS3MpengsAAAD3k5+fL4PBoLFjx9oVx9fXV+vWrVObNm3sisOPA9CcOZKnp/TkkxW/e/ppqaSkbI4NXKqpn5ubq5KSEgUFBVmNBwUFKScnp9J9cnJyKp1fXFys3NxctWnTRl27dlVKSop69eqlvLw8LVq0SBEREfrmm2/UuXPnSuPOnz9fc+fOdcyJAQBQi1zlRwbA5ZSWVvpS3PLGvkpL6yQtAAAA2K+goEBms9mux/hkZ2dr8+bN+tOf/mR3Pv7+/tq3bx+N/YbM01OaPbvsn3/f2H/66bLxxESbQ7tUU7+cwWCw+mw2myuMXW7+78cHDhyogQMHWr6PiIjQtddeq7/97W9avHhxpTETEhIUHx9v+ZyXl6cOHTrU7EQAAIByT5ywa3+e7w9HMY+Pqfo7VugDAADUC/Y8xic3N9fuHwbK46Snp2v79u3q1q2bzXEkVvy7tfJG/u8b+79v6Fe2gr+aXKqpbzQa5enpWWFV/vHjxyusxi/XunXrSud7eXmpRYsWle7j4eGh6667Tvv3768yF19fX/n6+tbwDAAAQLmioiJJUvr69XWcSRme7w8AAACgOux9vr+jHgUk8Tggt/f7xv4zz0hFRXY39CUXa+r7+PgoLCxMGRkZuv322y3jGRkZGjFiRKX7hIeHa+PGjVZjH3/8sfr162d5nv7FzGazsrKy1KtXr5rnaDDIWzV4FfFF/DwMauLjLV+D6k0cV8qlvsZxpVxcLY4r5VJf47hSLq4Wx5VyccU4XqUlauLjrc6dO8vf39+mGL/99puOHDliVwxJOn/+vPbv36+CvNMK8POzOQ4amOKius4AAAAAbsgRjwKSeBxQvfHkk/9r6Pv42N3Ql1ysqS9J8fHxiomJUb9+/RQeHq7ly5crOztbU6dOlVT2WJyjR49q1apVkqSpU6fqlVdeUXx8vO6//37t2rVLr7/+ulJTUy0x586dq4EDB6pz587Ky8vT4sWLlZWVpSVLltQ4P2+D1MiOBkdTT0+1aOSvxh6GehPHlXKpr3FcKRdXi+NKudTXOK6Ui6vFcaVcXDmOsXEjNWpkW0Peq6hI5+yMIUnnPAz6tZG/vIoKpfNnbY6DBqb4Ql1nAAAAADdm74p/Rz8OKDc3l6Z+XXj66f819IuKyj7Xp5X6kjR69GidPHlSiYmJMplM6tmzpzZt2qSQkBBJkslkUnZ2tmV+x44dtWnTJs2YMUNLlixR27ZttXjxYt1xxx2WOadOndLkyZOVk5OjwMBA9e3bV9u2bVP//v1rnN+mM0UKvsL2FX57c37Tpk92aGzrUAU1uqJexHGlXOprHFfKxdXiuFIu9TWOK+XianFcKZf6GsdRufw377RWf7JDzW67S51b2XcR27x5c7Vr186uGHAPnnl5dZ0CAAAAYPePA+X27t1rdx78KFBDFz9Dv/yzVH+eqV8uNjZWsbGxlX6XkpJSYeyGG27Qv//97yrjLVy4UAsXLnRIbmdKzTorD5v3P1Vcopz8s8orNatJPYnjSrnU1ziulIurxXGlXOprHFfKxdXiuFIu9TWOo3I5lndG/z17TneOv8/mGOUc9UzLwsJCu9/fw0W1cxlsv1EFAAAAcBmOesY/j/CpocpeilvZy3Nt4JJNfQAAAEdyxWdaGgwGmc32dY25qLZNSYm0fbtkMklt2kiRkZKnZ11nBQAAADiHI+ohHuFjg5ISKTFRJY8/qe1bf1d/PP6kPMu/txFNfQAA0GC4yjMt9+/fry1btnBRXQfS06UHH5R++eV/Y+3bS4sWSSNH1l1eAAAAgLM56jE+qKY5c8rqj9DK6o8n7ao/aOoDAADUkCN+HHBEHMn+52KW59EQfhhIT5fuvFO6+AaJo0fLxt95h8Y+AAAAAMdwZv1BUx8AAMANOeq5mJLj3hPgyj8OlJSUrdCv7IlHZrNkMEhxcdKIETyKBwAAAIB9nF1/0NQHAABwQ674ngBX/nFg+3brW14vZjZLR46UzbvxRoceGgAAAEAD4+z6g6Y+AACAG3OV9wQ48scBZ7wA2GRy7DwAAAAAqIqz6w+a+gAAAHCZHwec9QLg6t48YOdNBgAAAEC9xju9qsfZ9QdNfQAAADiMI17+6wyRkVL79mUvparsuZYGQ9n3kZG1nxsAAADg6hz5Ti9n3Jnrapxdf9DUBwAAQL3n6SktWiTdeWfZBfTvL6wNhrL/TUriJbkAAABAZRz1Ti9n3Znrapxdf9DUBwAAQIMwcqT0zjvSgw9av7SqffuyC+qRI+ssNQAAAMAtuOqdua7ImfUHTX0AAAA0GCNHSiNGSNu3l72Uqk2bslteWaEPAAAAwNGcVX/Q1AcAAECD4ukp3XhjXWcBAAAANFyOeOFuYWGhfH197Y7j7Bf3OqP+oKkPAAAAAAAAAHA6R75w12AwyFzZW2hryB1f3EtTHwAAAAAAAADgdI564e7+/fu1ZcuWBvviXpr6AAAAAAAAAIBaY+8Ld3Nzcx0Sx1151HUCAAAAAAAAAACgemjqAwAAAAAAAADgJmjqAwAAAAAAAADgJmjqAwAAAAAAAADgJmjqAwAAAAAAAADgJmjqAwAAAAAAAADgJlyyqZ+cnKyOHTvKz89PYWFh2r59+yXnf/bZZwoLC5Ofn5+uvPJKvfrqqxXmrFu3Tt27d5evr6+6d++u9evXOyt9AAAAAPWMM2oUAAAAwBYu19RPS0tTXFycZs2apd27dysyMlLDhg1TdnZ2pfMPHjyo4cOHKzIyUrt379bjjz+uBx54QOvWrbPM2bVrl0aPHq2YmBh98803iomJ0ahRo/Tll1/W1mkBAAAAcFPOqFEAAAAAW7lcU3/BggWaOHGiJk2apG7duikpKUkdOnTQ0qVLK53/6quvKjg4WElJSerWrZsmTZqkCRMm6KWXXrLMSUpK0tChQ5WQkKCuXbsqISFBQ4YMUVJSUi2dFQAAAAB35YwaBQAAALCVV10n8HtFRUXKzMzUzJkzrcajoqK0c+fOSvfZtWuXoqKirMaio6P1+uuv68KFC/L29tauXbs0Y8aMCnMu1dQvLCxUYWGh5fPp06clSTk5OTU5pQpOnDghSTKZTCoqKqoXcVwpl/oax5VycbU4rpRLfY3jSrm4WhxXyqW+xnGlXFwtjivl4sg4J0+elCTl5+crLy/P5jiOUH58s9lcp3mgbjmrRrkY9Yd75lJf47hSLq4Wx5Vyqa9xXCkXV4vjSrnU1ziulIurxXGlXBwZx23rD7MLOXr0qFmS+fPPP7caf/bZZ81dunSpdJ/OnTubn332Wauxzz//3CzJfOzYMbPZbDZ7e3ub33zzTas5b775ptnHx6fKXJ566imzJDY2NjY2NjY2tga+HTlyxJZLW9QTzqpRLkb9wcbGxsbGxsbGJlWv/nCplfrlDAaD1Wez2Vxh7HLzLx6vacyEhATFx8dbPpeWlurXX39VixYtLrkfAAAA6gez2awzZ86obdu2dZ0KXIAzapTfo/4AAABo2GpSf7hUU99oNMrT07PCLabHjx9XUFBQpfu0bt260vleXl5q0aLFJedUFVOSfH195evrazXWrFmz6p4KAAAA6oHAwMC6TgF1zFk1ysWoPwAAAFDd+sOlXpTr4+OjsLAwZWRkWI1nZGRo0KBBle4THh5eYf7HH3+sfv36WZ5VWdWcqmICAAAAgOS8GgUAAACwlUs19SUpPj5er732mlasWKG9e/dqxowZys7O1tSpUyWV3ZY6btw4y/ypU6fq8OHDio+P1969e7VixQq9/vrrevjhhy1zHnzwQX388cd6/vnntW/fPj3//PP65JNPFBcXV9unBwAAAMDNOKNGAQAAAGzlUo/fkaTRo0fr5MmTSkxMlMlkUs+ePbVp0yaFhIRIKnujcXZ2tmV+x44dtWnTJs2YMUNLlixR27ZttXjxYt1xxx2WOYMGDdLbb7+tJ554Qk8++aSuuuoqpaWlacCAAbV+fgAAAADcizNqFAAAAMBWBnP5G5sAAAAAAAAAAIBLc7nH7wAAAAAAAAAAgMrR1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AcAAAAAAAAAwE3Q1AfQ4ISGhspgMMhgMOjdd9+tct7NN98sg8GglJQUq/GUlBTL/j4+Pjp58mSVMYqLi9WqVSvL/Dlz5lQ6b/PmzRo5cqTatWsnHx8fNWvWTFdffbVuvfVWvfzyy/r555+t5h86dMgS83LboUOHqvmXubw9e/ZoypQp6tKli/z9/dW4cWN17NhRN954o5588knt3Lmzwj7lf++L/45VGT9+fIVz8Pb2Vps2bfTnP/9ZH374ocPOZ+vWrZZjXOzGG2+85L+zctOnT7fEyMjIcFhuAAAAcF3UFLarbzVFuZr+/X+vqKhIS5cu1c0336zWrVvLx8dHQUFB+sMf/qAlS5aosLCwWucaFxdX5bxnnnlGBoNB48ePt+MsAbgKr7pOAADq0pw5czRixIhKm7rVceHCBaWlpSk2NrbS7zdv3qwTJ05cMsZf//pXLVmyRJLUuHFjde7cWY0aNdLhw4f1/vvv6/3335fJZNJLL71U6f79+vWTr69vlfH9/PyqeTaX9uabb2rChAkqKiqSt7e3goOD1bx5cx0/flyfffaZPvvsM3344Yf6+uuvHXK8Vq1aqXPnzpKkgoIC/fjjj9q4caM2btyohIQEzZs3zyHHsceFCxf09ttvWz7/4x//0NChQ+swIwAAANQ2aorqq681hT1//++//14jRoywNP1DQ0PVp08fHTt2TFu2bNGWLVu0YMECvffee+rRo8cl81i2bJkeffRRtW3b1iHnBcB1sVIfQIPl6empb775RuvWrbNp/86dO8tgMOgf//hHlXPKv7v66qsr/T41NVVLliyRh4eHFi1apJMnT+r777/XV199pePHj+v777/XrFmz1KpVqyqPsXbtWu3YsaPKrXXr1jad3+8dOnRIEydOVFFRkSZMmKBffvlFP/30k/71r3/p0KFDMplMeuWVV9S9e3e7j1Vu2LBhlnP4+uuvlZubq4cffliSNH/+fO3atcthx7LVRx99pNzcXDVr1kySlJ6errNnz9ZtUgAAAKg11BTVV19rCnv+/j///LMiIyP1888/66abbtJ3332ngwcP6l//+pd++eUXffnll7r22mt14MABy7yqeHp6qqCgwCUWPwFwPpr6ABqsu+++W5I0d+5cmc3mGu8fHBys66+/Xl988YV++umnCt+fOXNG7733njp27KiIiIhKY7zxxhuSpAkTJuiBBx6osDqme/fueuaZZ/Too4/WOD9Hevvtt1VYWKirr75af//73ytckLZu3VrTpk3TqlWrnJaDj4+PXnjhBfXp08eSU10rL7CmTZumHj166OzZs1q/fn0dZwUAAIDaQk1RffW1prDn7z927Fj99ttvGjJkiD766KMKK/H79++vrVu3qnfv3vrtt98UExNTZR6jR4+Wh4eHXnvtNf3yyy92nxcA10ZTH0CDNWHCBIWGhuq7777TmjVrbIoxduxYSdLq1asrfPfOO+/o/PnzGjNmTJW34h44cECSLBeVrqo8z169esnDo+7+X4fBYNDgwYMlSfv376+zPCTp9OnT2rhxoyTpnnvu0T333CNJl1xlBQAAgPqFmqL66mtNYevf/5NPPtEXX3whb29vvf766/Lx8al0XtOmTfXqq69Kknbt2qVPP/200nndunXTXXfdpcLCQj377LM1ygWA+6GpD6DB8vb21qxZsySVrawpLS2tcYy//OUv8vPz05tvvlnhu/KL8vKL9MoEBARIkv71r3/V+Ni1qTzPrKwsXbhwoU5zsWUFlDOsWbNGBQUFuuaaa9S9e3fdc889MhgM+vTTT2Uymeo6PQAAANQCaorqq681ha1///IfgYYPH66QkJBLzg0PD9c111xjtV9lZs+eLU9PT61YsUKHDx+uUT4A3AtNfQAN2vjx43XllVdq7969Nt16GRgYqFtvvVU//fST1fMYf/nlF23dulX9+/ev8tmXkvTHP/5RUtnq7v/7v//TV199pZKSkpqfiJOV5/nTTz9p2LBh+vDDD3Xu3Llaz8NsNuvzzz+XJHXq1KnWj/975Svyx4wZI6nshVaDBg1SSUmJ3nrrrbpMDQAAALWImqJ66mtNYevff+fOnZKkG264oVrHuf766yXpku8BuPrqq3XPPfeoqKiI1fpAPUdTH0CD5uXlpSeffFKSlJiYaNPFb/mqmd8/duXNN99UaWnpJVfUSNJjjz2msLAwmc1mvfrqq+rfv78CAgI0ePBgzZw5U19//fVlj9+xY0cZDIZKN0fdgnvzzTdr8uTJkqRPP/1Uw4cPV2BgoK655hpNnTpV77//vtMLh6KiIj366KPKysqSJI0aNcqpx7uUQ4cOaceOHTIYDLrrrrss4zyCBwAAoOGhpqie+lpT2Pr3P3r0qCTpqquuqtZxyudd7nn55av1U1JSdPDgwRqcCQB3QlMfQIMXExOjzp0764cffqj0ltfLGTZsmIxGo9asWWO5jXT16tXy8vKyavhWpmnTptqxY4defvlldevWTZJ07tw5ff7553r++ed13XXX6bbbbtOpU6eqjNGvXz9FRERUuvXt27fG51OVZcuWad26dbrhhhvk6emp4uJi/ec//9GyZct066236pprrtG3337rsON9+OGHGjx4sAYPHqx+/frJaDTqpZdekiTNmDHD8hzMurB69WqZzWZFRkaqQ4cOlvFRo0bJ29tb33zzjUP/FgAAAHBt1BTVUx9rClv//mfOnJEkNW7cuFrHKZ9Xvl9VOnXqpJiYGF24cEFPP/10Dc8GgLugqQ+gwfP09LSsrHn66adVXFxco/29vb01atQonTx5Ups2bVJWVpa+++47RUdHq2XLlpfd38/PT/Hx8dqzZ4+OHj2q9PR0zZgxQx07dpQkbdiwQSNHjqxy/7Vr12rHjh2VbitXrqzRuVzOyJEjtXXrVv3666/KyMjQ008/rf79+0uSvv/+e9188806ceKEQ451/Phxff755/r888+VlZUlPz8/DRs2TOvXr9eCBQsccgxblT/btHxlfjmj0aioqChJrNYHAABoSKgpqq8+1hS2/P2bNm0qSTp79my1jlE+r3y/S5k9e7a8vLz0j3/8Qz///HNNTgWAm6CpDwAqa85effXV+umnn2xqxsbExEgqa/aWN3zLx2qibdu2uv3227VgwQL9+OOPeuihhyRJW7ZssTz30RUEBATo5ptv1hNPPKEvv/xSa9eulYeHh44fP67ly5c75Bj33nuvzGazzGaziouLdfz4cW3atEm33XabQ+Lb6l//+pd++OEHeXt76y9/+UuF78ufsf/WW2/Z9KI0AAAAuCdqipqprzVFdf/+7dq1k6RqN93L55XvdykdO3bU+PHjVVxcrMTExJqeAgA3QFMfAFS2smb27NmSbFtZM3DgQHXu3FkbN27U6tWrFRAQoD//+c925eTl5aUXXnhBrVu3llTWTHZVd955p+644w5Jrp2nI6xatUqSdOHCBbVo0aLCM0fLV+8fPXpU//znP+syVQAAANQiagr71Mea4lJ//0GDBkmSPvvss2rF2rZtmyQpPDy8WvOfeOIJeXt7680339SPP/5Yk7QBuAGa+gDw/911113q3r27Dh48qJSUlBrvP2bMGBUWFuq///2v7rjjDvn7+9udk4eHh0JCQiSVvdTJlV155ZWSXD9Pe1y4cEFpaWmSpBYtWigoKKjSrUmTJpJ4BA8AAEBDQ01hn/pYU1T19y+/63fTpk06fPjwJWN88cUX+uabbyRJo0ePrtZxQ0JCNGHCBJWUlLBaH6iHaOoDwP/n4eGhp556SpL0zDPPWF5QVV0xMTEaMmSIhgwZovvvv79a+xw/fvyS3586dUp79uyRJHXu3LlG+TjS5fKUpJ07d0qq2zyd7cMPP1Rubq4aN26sQ4cOKScnp9Jt3bp1kqT09HSdO3eujrMGAABAbaGmqFp9rSls/fsPHTpUAwYM0IULFzRx4sQq/1s5c+aMpkyZIkkaMGCAhgwZUu3cZs2aJR8fH6Wmpmrv3r3V3g+A66OpDwC/85e//EW9evXS4cOHa/y8ySuvvFKffPKJPvnkk2rfEjl8+HCNGTNG//znPytcxGVlZWnEiBE6c+aM2rRpo+jo6BrlU5Xx48fLYDBo/Pjx1d5n3rx5ioyMVGpqqs6cOWP1nclk0tSpU7V9+3YZDAbde++9DsmzpkJDQ2UwGGxaEVVd5Svvb7/9dstq/MoMGTJEbdq0UX5+vtavX++0fAAAAOB6qCkqV19rCnv+/v/4xz/UrFkzffrpp4qOjtb3339v9f1XX32lG2+8Uf/5z3/UrFmzGt8J3KFDB02aNEmlpaWWO44B1A9edZ0AALgSg8Ggp556SnfeeadKSkqcfrySkhK99dZbeuutt+Tn56dOnTrJz89Px44d07FjxyRJzZo105o1a9S4ceNKY/zlL3+Rr69vlceYP3++IiMj7crTYDBox44d2rFjhzw8PHTVVVfpiiuu0IkTJ3TkyBEVFxfL09NTCxYsUFhYWKUxpk+frocffrjKY2zdulU9e/a0K09nOnXqlDZu3Cjp8i8s8/T01F133aWFCxfqH//4h+XluQAAAKj/qCkqV19rCnv+/p07d9a2bds0YsQIbdmyRT179lRoaKhatmwpk8mkX375RVLZjw0bNmyw6Q6Gxx9/XCtWrFBBQYH9JwvAZdDUB4CLjBw5Un369FFWVpbTj/Xxxx9r8+bN+vDDD/Xdd9/p2LFjOnXqlJo0aaIBAwYoOjpa06ZNU6tWraqM8fXXX1/yGCdPnrT6nJOTI0nq06dPtfOcN2+ehg4dqg8//FBffvmljh49qkOHDsnX11ddunTR9ddfr//7v/9T7969q4yRn5+v/Pz8Kr+v6YvEfq+kpES5ubmSpGuuuaZG+0mSt7d3ld/5+PhIktauXavCwkK1bt26Wre8jh07VgsXLtQnn3yinJwcy8uxAAAAUP9RU1RUX2sKe//+vXr10t69e/Xaa68pPT1d3377rY4ePapmzZrpxhtv1MiRI3X//ffLz8/PpvNq166dJk+erMWLF9u0PwDXZDCbzea6TgIAUDtKS0vVvHlzmc1m/fLLL2ratGldp+QQ//73vxUWFqabbrpJ//znP6u9X3p6uu644w61bNmywrMwe/furW+//VZLlixRbGyso1MGAAAA3BI1BQDUPZ6pDwANyPfff6/Tp09r8uTJ9ebiW5LlWaUPPfRQjfb7z3/+I0nq1q2b1XhBQYH2799f6XcAAABAQ0ZNAQB1j6Y+ADQgO3fulJeXlx544IG6TsWhdu7cqa5du2r48OHV3mfv3r1avny5JOnWW2+1jJvNZj3++OMqKChQ8+bNFRER4fB8AQAAAHdFTQEAdY/H7wAAGpTFixdr2bJl+vHHH1VcXKwuXbro3//+t8xms4YOHar9+/dbnhm6fPly3X///XWcMQAAAAAAwP+wUh8A0KAcOHBAP/74o9q2bavY2Fht375djRs3VnFxsb744gsVFRVp8ODBeuedd2joAwAAAAAAl8NKfQAAAAAAAAAA3AQr9QEAAAAAAAAAcBNedZ2ArZKTk/Xiiy/KZDKpR48eSkpKUmRkZKVzt27dqptuuqnC+N69e9W1a9dqHa+0tFTHjh1T06ZNZTAY7ModAAAArs9sNuvMmTNq27atPDxYC4PaRf0BAADQsNSk/nDLpn5aWpri4uKUnJysiIgILVu2TMOGDdOePXsUHBxc5X4//PCDAgICLJ9btmxZ7WMeO3ZMHTp0sCtvAAAAuJ8jR46offv2dZ0GGhjqDwAAgIapOvWHWz5Tf8CAAbr22mu1dOlSy1i3bt102223af78+RXml6/U/+2339SsWTObjnn69Gk1a9ZMR44csfphAAAAAPVTXl6eOnTooFOnTikwMLCu00EDQ/0BAADQsNSk/nC7lfpFRUXKzMzUzJkzrcajoqK0c+fOS+7bt29fFRQUqHv37nriiScqfSRPucLCQhUWFlo+nzlzRpIUEBDARTUAAEADwqNPUBfK/7uj/gAAAGhYqlN/uN3DQXNzc1VSUqKgoCCr8aCgIOXk5FS6T5s2bbR8+XKtW7dO6enpuvrqqzVkyBBt27atyuPMnz9fgYGBlo1bXwEAAAAAAAAAdc3tVuqXu/gXC7PZXOWvGFdffbWuvvpqy+fw8HAdOXJEL730kq6//vpK90lISFB8fLzlc/ntDwAAAAAAAAAA1BW3a+objUZ5enpWWJV//PjxCqv3L2XgwIFavXp1ld/7+vrK19fX5jwBAED9VlJSogsXLtR1GrCTj4+PPDzc7uZVAAAANDDUH/WDo+oPt2vq+/j4KCwsTBkZGbr99tst4xkZGRoxYkS14+zevVtt2rRxRooAAKAeM5vNysnJ0alTp+o6FTiAh4eHOnbsKB8fn7pOBQAAAKiA+qN+cVT94XZNfUmKj49XTEyM+vXrp/DwcC1fvlzZ2dmaOnWqpLJH5xw9elSrVq2SJCUlJSk0NFQ9evRQUVGRVq9erXXr1mndunV1eRoAAMANlV9Qt2rVSo0aNeIlqm6stLRUx44dk8lkUnBwMP8uAQAA4HKoP+oPR9YfbtnUHz16tE6ePKnExESZTCb17NlTmzZtUkhIiCTJZDIpOzvbMr+oqEgPP/ywjh49Kn9/f/Xo0UMffPCBhg8fXlenAAAA3FBJSYnlgrpFixZ1nQ4coGXLljp27JiKi4vl7e1d1+kAAAAAFtQf9Y+j6g+D2Ww2OzCveisvL0+BgYE6ffq0AgIC6jodAABQBwoKCnTw4EGFhobK39+/rtOBA5w/f16HDh1Sx44d5efnZ/Ud13+oS/z3BwAAqD/qH0fVH265Ur8uZWVlqUmTJnbFMBqNCg4OdlBGAACgtnHLa/3Bv0u4OuoPAADANWv94ah/lzT1a+iGG26wO4a/v7/27dvHhTUAAADgApKTk/Xiiy/KZDKpR48eSkpKUmRkZKVzd+zYoccee0z79u3TuXPnFBISoilTpmjGjBmWOSkpKbrvvvsq7Hv+/PkKK7Iuh/oDAAAAF6OpX0O33nqr2rRpY/P+ubm5Sk9PV25uLhfVAAAAQB1LS0tTXFyckpOTFRERoWXLlmnYsGHas2dPpdfrjRs31l//+lf17t1bjRs31o4dOzRlyhQ1btxYkydPtswLCAjQDz/8YLVvTRv6EvUHAAAAKqKpX0MtWrRQ27Zt6zoNAADgxkpKpO3bJZNJatNGioyUPD3rOiugYVqwYIEmTpyoSZMmSZKSkpK0efNmLV26VPPnz68wv2/fvurbt6/lc2hoqNLT07V9+3arpr7BYFDr1q3tzo/6AwAA2Iv6o/7xqOsEAAAAGpL0dCk0VLrpJumee8r+NzS0bNwZDAbDJbfx48db5r377rsV9h8/frxuu+22Kj8fP35cU6ZMUXBwsHx9fdW6dWtFR0dr165dVeY0Z84cy/E9PDzUtm1bjRkzRkeOHLGaFxoaqqSkpAr7JyUlKTQ0tEK8qVOnWs3LysqSwWDQoUOHqswFDVtRUZEyMzMVFRVlNR4VFaWdO3dWK8bu3bu1c+fOCo/Jyc/PV0hIiNq3b68//elP2r179yXjFBYWKi8vz2oDAACwF/VH/aw/aOoDAADUkvR06c47pV9+sR4/erRs3BkX1iaTybIlJSUpICDAamzRokV2xb/jjjv0zTff6I033tCPP/6o9957TzfeeKN+/fXXS+7Xo0cPmUwm/fLLL0pLS9O3336rUaNG2ZyHn5+fXn/9df344482x0DDk5ubq5KSEgUFBVmNBwUFKScn55L7tm/fXr6+vurXr5+mTZtmWekvSV27dlVKSoree+89paamys/PTxEREdq/f3+V8ebPn6/AwEDL1qFDB/tODgAANHjUH/9T3+oPHr8DAABQC0pKpAcflMzmit+ZzZLBIMXFSSNGOPZW2N8//iMwMNBhjwSRpFOnTmnHjh3aunWrZZVySEiI+vfvf9l9vby8LHm0bdtW999/vx544AHl5eUpICCgxrlcffXVatWqlZ544gmtWbOmxvujYTMYDFafzWZzhbGLbd++Xfn5+friiy80c+ZMderUSXfffbckaeDAgRo4cKBlbkREhK699lr97W9/0+LFiyuNl5CQoPj4eMvnvLw8GvsAAMBm1B/W6lv9wUp9AACAWrB9e8UVMr9nNktHjpTNcxdNmjRRkyZN9O6776qwsNDmODk5OUpPT5enp6c87agonnvuOa1bt05fffWVzTHQsBiNRnl6elZYlX/8+PEKq/cv1rFjR/Xq1Uv333+/ZsyYoTlz5lQ518PDQ9ddd90lV+r7+voqICDAagMAALAV9UfV6kP9QVMfAACgFphMjp3nDHfffbflQrl8e/PNN6uc7+XlpZSUFL3xxhtq1qyZIiIi9Pjjj+s///nPZY/17bffqkmTJmrUqJHatGmjrVu3atq0aWrcuLHN+V977bUaNWqUZs6caXMMNCw+Pj4KCwtTRkaG1XhGRoYGDRpU7Thms/mShaXZbFZWVpbatGljc64AAAA1Qf1hrb7VHzT1AQAAakF1e3l12fNbuHChsrKyrLY///nPl9znjjvu0LFjx/Tee+8pOjpaW7du1bXXXquUlJRL7nf11VcrKytLX331lZ599ln16dNHzz77rN3n8Mwzz2j79u36+OOP7Y6FhiE+Pl6vvfaaVqxYob1792rGjBnKzs62vPgsISFB48aNs8xfsmSJNm7cqP3792v//v1auXKlXnrpJY0dO9YyZ+7cudq8ebMOHDigrKwsTZw4UVlZWRVepgYAAOAs1B/W6lv9wTP1AQAAakFkpNS+fdlLqSp7rqXBUPZ9ZGTt51audevW6tSpk9VY06ZNderUqUvu5+fnp6FDh2ro0KGaPXu2Jk2apKeeekrjx4+vch8fHx/LsXr06KH9+/fr//7v//SPf/zDMicgIECnT5+usO+pU6cUGBhYadyrrrpK999/v2bOnKnXX3/9knkDkjR69GidPHlSiYmJMplM6tmzpzZt2qSQkBBJZS97y87OtswvLS1VQkKCDh48KC8vL1111VV67rnnNGXKFMucU6dOafLkycrJyVFgYKD69u2rbdu2Vet5rwAAAI5A/WGtvtUfrNQHAACoBZ6e0qJFZf988fs3yz8nJTn2JVV1pXv37jp79myN9nnyySeVmpqqf//735axrl27Vvp8yq+++kpXX311lbFmz56tH3/8UW+//XaNckDDFRsbq0OHDqmwsFCZmZm6/vrrLd+lpKRo69atls/Tp0/Xd999p7Nnz+r06dP697//rf/7v/+Th8f/SquFCxfq8OHDKiws1PHjx7V582aFh4fX5ikBAIAGjvrj0ty9/qCpDwAAUEtGjpTeeUdq1856vH37svGRI+smL1udPHlSf/jDH7R69Wr95z//0cGDB7V27Vq98MILGjFiRI1iXXnllRoxYoRmz55tGYuPj9eHH36oxMRE7dmzR3v27NHTTz+tjz76SA899FCVsYKCghQfH6/FixfbfG4AAACAu6P+qJq71x88fgcAAKAWjRwpjRghbd9e9lKqNm3Kbnl1xxUyTZo00YABA7Rw4UL9/PPPunDhgjp06KD7779fjz/+eI3jPfTQQ4qIiNCXX36pAQMGaODAgdq8ebMSExOVlJQkqexW2c2bN2vAgAGXjPXII49o6dKlKigosOXUAAAAgHqB+qNq7lx/GMzmyp6qhIvl5eUpMDBQ48ePV2hoqM1xjh07puXLlyszM1PXXnut4xIEAABOV1BQoIMHD6pjx47y8/Or63TgAJf6d1p+/Xf69GkFBATUUYZoqKg/AAAA9Uf946j6g8fvAAAAAAAAAADgJmjqAwAAAAAAAADgJmjqAwAAAAAAAADgJmjqAwAAAAAAAADgJmjqAwAAAAAAAADgJmjqAwAAAAAAAADgJmjqAwAAAAAAAADgJmjqAwAAAAAAAADgJmjqAwAAAAAAAADgJmjqAwAANADjx4+XwWCwbC1atNAf//hH/ec//6kwd/LkyfL09NTbb79d4buzZ8/qscce05VXXik/Pz+1bNlSN954o95//33LnBtvvNHqWOXb1KlTLXMMBoPefffdSnPdunWrDAaDTp06ZfW5Z8+eKikpsZrbrFkzpaSkWD6HhoZWeuznnnuuBn8tAAAAAPag/nBu/UFTHwAAoLbMmSM9/XTl3z39dNn3TvTHP/5RJpNJJpNJn376qby8vPSnP/3Jas65c+eUlpamRx55RK+//nqFGFOnTtW7776rV155Rfv27dNHH32kO+64QydPnrSad//991uOVb698MILduX/888/a9WqVZedl5iYWOHY06dPt+vYAAAAgNuh/rArf1euP7ycGh0AAAD/4+kpzZ5d9s9PPvm/8aefLhtPTHTq4X19fdW6dWtJUuvWrfXYY4/p+uuv14kTJ9SyZUtJ0tq1a9W9e3clJCSoTZs2OnTokEJDQy0xNm7cqEWLFmn48OGSylamhIWFVThWo0aNLMdylOnTp+upp57S3XffLT8/vyrnNW3a1OHHBgAAANwO9YddXLn+YKU+AABAbXnyybIL59mz/7di5vcX1L+/0Hay/Px8vfnmm+rUqZNatGhhGX/99dc1duxYBQYGavjw4Vq5cqXVfq1bt9amTZt05syZWsu1XFxcnIqLi/XKK6/U+rEBAAAAt0P9YRdXrj9o6gMAANSm319Y+/rW6gX1+++/ryZNmqhJkyZq2rSp3nvvPaWlpcnDo+yScP/+/friiy80evRoSdLYsWO1cuVKlZaWWmIsX75cO3fuVIsWLXTddddpxowZ+vzzzyscKzk52XKs8u2NN96wK/9GjRrpqaee0vz583X69Okq5z322GMVjr1161a7jg0AAAC4JeoPm7ly/UFTHwAAoLY9+aTk4yMVFZX9by2tkLnpppuUlZWlrKwsffnll4qKitKwYcN0+PBhSWWrZKKjo2U0GiVJw4cP19mzZ/XJJ59YYlx//fU6cOCAPv30U91xxx36/vvvFRkZqacvelbnmDFjLMcq326//Xa7z2HixIkyGo16/vnnq5zzyCOPVDj2gAED7D42AAAA4JaoP2zmqvUHTX0AAIDa9vTT/7ugLiqq+uVVDta4cWN16tRJnTp1Uv/+/fX666/r7Nmz+vvf/66SkhKtWrVKH3zwgby8vOTl5aVGjRrp119/rfDCKm9vb0VGRmrmzJn6+OOPlZiYqKefflpFRUWWOYGBgZZjlW8BAQF2n4OXl5eeeeYZLVq0SMeOHat0jtForHBsf39/u48NAAAAuCXqD5u5av1BUx8AAKA2/f4ZloWFFZ9xWYsMBoM8PDx0/vx5y3Mqd+/ebbXCZO3atXr33Xd18uTJKuN0795dxcXFKigoqJW8//KXv6hHjx6aO3durRwPAAAAcFvUH3ZzxfrDq64TAAAAaDAqeylV+f/Onm392QkKCwuVk5MjSfrtt9/0yiuvKD8/X7feequSkpJ0yy236JprrrHap0ePHoqLi9Pq1av14IMP6sYbb9Tdd9+tfv36qUWLFtqzZ48ef/xx3XTTTVYrYc6dO2c5VjlfX19dccUVls8HDx5UVlaW1ZxOnTpV61yee+45RUdHV/rdmTNnKhy7UaNGDlmpAwAAALgN6o96W3+wUh8AAKC2lJRU/lKq8pdXlZQ49fAfffSR2rRpozZt2mjAgAH66quvtHbtWnXr1k0ffPCB7rjjjgr7GAwGjRw50nILbHR0tN544w1FRUWpW7dumj59uqKjo7VmzRqr/f7+979bjlW+3X333VZz4uPj1bdvX6vt66+/rta5/OEPf9Af/vAHFRcXV/hu9uzZFY796KOPVvfPhAYoOTlZHTt2lJ+fn8LCwrR9+/Yq5+7YsUMRERFq0aKF/P391bVrVy1cuLDCvHXr1ql79+7y9fVV9+7dtX79emeeAgAAQEXUH1Zz6lP9YTCbzWanHqGeyMvLU2BgoMaPH6/Q0FCb4xw7dkzLly9XZmamrr32WsclCAAAnK6goEAHDx60NP/g/i7177T8+u/06dOs8q/H0tLSFBMTo+TkZEVERGjZsmV67bXXtGfPHgUHB1eYv3v3bu3bt0+9e/dW48aNtWPHDk2ZMkULFy7U5MmTJUm7du2yvMDt9ttv1/r16zV79mzt2LGj2i9No/4AAADUH/WPo+oPVuoDAAAAaLAWLFigiRMnatKkSerWrZuSkpLUoUMHLV26tNL5ffv21d13360ePXooNDRUY8eOVXR0tNXq/qSkJA0dOlQJCQnq2rWrEhISNGTIECUlJdXSWQEAAKA+c9umfk1ukf29zz//XF5eXurTp49zEwQAAADg0oqKipSZmamoqCir8aioKO3cubNaMXbv3q2dO3fqhhtusIzt2rWrQszo6OhLxiwsLFReXp7VBgAAAFTGLZv6aWlpiouL06xZs7R7925FRkZq2LBhys7OvuR+p0+f1rhx4zRkyJBayhQAAACAq8rNzVVJSYmCgoKsxoOCgiq87Oxi7du3l6+vr/r166dp06Zp0qRJlu9ycnJqHHP+/PkKDAy0bB06dLDhjAAAANAQuGVTv6a3yJabMmWK7rnnHoWHh9dSpgAAAABcncFgsPpsNpsrjF1s+/bt+vrrr/Xqq68qKSlJqampdsVMSEjQ6dOnLduRI0dqeBYAAABoKLzqOoGaKr9FdubMmVbjl7tFduXKlfr555+1evVqPfPMM5c9TmFhoQoLCy2fuf0VAAAAqF+MRqM8PT0rrKA/fvx4hZX2F+vYsaMkqVevXvrvf/+rOXPm6O6775YktW7dusYxfX195evra8tpAAAAoIFxu5X6ttwiu3//fs2cOVNvvvmmvLyq9zsGt78CAAAA9ZuPj4/CwsKUkZFhNZ6RkaFBgwZVO47ZbLZaEBQeHl4h5scff1yjmAAAAEBV3G6lfrnq3s5aUlKie+65R3PnzlWXLl2qHT8hIUHx8fGWz3l5eTT2AQAAgHomPj5eMTEx6tevn8LDw7V8+XJlZ2dr6tSpksrqgqNHj2rVqlWSpCVLlig4OFhdu3aVJO3YsUMvvfSSpk+fbon54IMP6vrrr9fzzz+vESNGaMOGDfrkk0+0Y8eO2j9BAAAA1Dtu19Sv6S2yZ86c0ddff63du3frr3/9qySptLRUZrNZXl5e+vjjj/WHP/yhwn7c/goAAADUf6NHj9bJkyeVmJgok8mknj17atOmTQoJCZEkmUwmZWdnW+aXlpYqISFBBw8elJeXl6666io999xzmjJlimXOoEGD9Pbbb+uJJ57Qk08+qauuukppaWkaMGBArZ8fAAAA6h+3a+r//hbZ22+/3TKekZGhESNGVJgfEBCgb7/91mosOTlZ//znP/XOO+9YnoUJAAAAoGGKjY1VbGxspd+lpKRYfZ4+fbrVqvyq3HnnnbrzzjsdkR4AAABgxe2a+lLNbpH18PBQz549rfZv1aqV/Pz8KowDAADYwlxwXrpwofYO6O0tg59/7R0PAAAAgMug/oBbNvVreossAACAs5gLzqt0W4bM587W2jENjRrL4/qhNbqwHj9+vE6dOqV33323wnehoaE6fPhwhfH58+dr5syZOnTokDp27KiWLVvq559/VtOmTS1z+vTpo9tuu01z5syRJB04cECzZs3SZ599pl9//VVGo1FhYWF68cUX1aVLFx06dEhPP/20/vnPfyonJ0dt27bV2LFjNWvWLPn4+NT4bwEAAAA0JNQf1B+Smzb1pZrdInuxOXPmWP7FAwAA2OXChbILai9vybsWLgovFJUd78IFyYGrZRITE3X//fdbjf3+4lkqe1fRSy+9pLlz51Yao6ioSEOHDlXXrl2Vnp6uNm3a6JdfftGmTZt0+vRpSdK+fftUWlqqZcuWqVOnTvruu+90//336+zZs3rppZccdj4AAABAvUT9YdGQ6w+3beoDAAC4FG8fGXx9nX4YsyQVO/5W26ZNm6p169aXnDN9+nQtWLBA06ZNU6tWrSp8v2fPHh04cED//Oc/LXdQhoSEKCIiwjLnj3/8o/74xz9aPl955ZX64YcftHTpUre+qAYAAABqFfVHg64/POo6AQAAALiHu+++W506dVJiYmKl37ds2VIeHh565513VFJSUu24p0+fVvPmzR2VJgAAAIB6gPqjajT1AQAAoMcee0xNmjSx2rZu3Wo1x2Aw6LnnntPy5cv1888/V4jRrl07LV68WLNnz9YVV1yhP/zhD3r66ad14MCBKo/7888/629/+5umTp3q6FMCAAAA4KKoP+xDUx8AAAB65JFHlJWVZbUNGDCgwrzo6GgNHjxYTz75ZKVxpk2bppycHK1evVrh4eFau3atevTooYyMjApzjx07pj/+8Y/6y1/+okmTJjn8nAAAAAC4JuoP+9DUBwAAgIxGozp16mS1+ftX/iKs5557Tmlpadq9e3el3zdt2lR//vOf9eyzz+qbb75RZGSknnnmGas5x44d00033aTw8HAtX77c4ecDAAAAwHVRf9iHpj4AAABqpH///ho5cqRmzpx52bkGg0Fdu3bV2bNnLWNHjx7VjTfeqGuvvVYrV66UhweXpAAAAAAqR/1RkVddJwAAAIDacfr0aWVlZVmNlb8g6syZM8rJybH6rlGjRgoICKg01rPPPqsePXrIy+t/l5NZWVl66qmnFBMTo+7du8vHx0efffaZVqxYoccee0xS2QqZG2+8UcHBwXrppZd04sQJy/6tW7d2xGkCAAAAcAHUH85DUx8AAMARLhTJXEvHsdXWrVvVt29fq7F7771XkjR79mzNnj3b6rspU6bo1VdfrTRWly5dNGHCBKtbV9u3b6/Q0FDNnTtXhw4dksFgsHyeMWOGJOnjjz/WTz/9pJ9++knt27e3imk218pfEAAAAHB/1B8Nuv4wmN05+1qUl5enwMBAjR8/XqGhoTbHOXbsmJYvX67MzExde+21jksQAAA4XUFBgQ4ePKiOHTvKz89PkmQuOK/SbRkynzt7mb0dx9CosTyuHyqDX+XPnET1VfbvtFz59d/p06erXDEEOAv1BwAAoP6ofxxVf7BSHwAAwA4GP395XD9UunCh9g7q7c0FNQAAANAAUX9AoqkPAABgN4Ofv8RFLgAAAIBaQP0B93/VLwAAAAAAAAAADQRNfQAAAAAAAAAA3ARNfQAAAAAAAAAA3ARNfQAAAAAAAAAA3ARNfQAAAAAAAAAA3ARNfQAAAAAAAAAA3ARNfQAAAAAAAAAA3IRXXScAAADg7rKzs5Wbm1trxzMajQoODq614wH1XXJysl588UWZTCb16NFDSUlJioyMrHRuenq6li5dqqysLBUWFqpHjx6aM2eOoqOjLXNSUlJ03333Vdj3/Pnz8vPzc9p5AACAhoH6AzT1AQAA7JCdna2uXbvq/PnztXZMf39/7du3r8YX1jk5OXr22Wf1wQcf6OjRo2rVqpX69OmjuLg4DRkyRKGhoTp8+LBSU1N11113We3bo0cP7dmzRytXrtT48eMlSaGhoYqLi1NcXJzl8+HDhy05XnnllZo+fbqmTJliiVNYWKjExEStXr1aOTk5at++vWbNmqUJEybY/gcB7JCWlqa4uDglJycrIiJCy5Yt07Bhw7Rnz55K/29s27ZtGjp0qObNm6dmzZpp5cqVuvXWW/Xll1+qb9++lnkBAQH64YcfrPaloQ8AAOxF/UH9IdHUBwAAsEtubq7Onz+vkSNHymg01srx0tPTlZubW6OL6kOHDikiIkLNmjXTCy+8oN69e+vChQvavHmzpk2bpn379kmSOnTooJUrV1pdVH/xxRfKyclR48aNL3ucxMRE3X///crPz1dKSoqmTp2qZs2aafTo0ZKkUaNG6b///a9ef/11derUScePH1dxcXEN/wqA4yxYsEATJ07UpEmTJElJSUnavHmzli5dqvnz51eYn5SUZPV53rx52rBhgzZu3GjV1DcYDGrdurVTcwcAAA0P9Ye1hlp/0NQHAABwAKPRqLZt29Z1GlWKjY2VwWDQv/71L6uL4x49elitUhkzZowWLlyoI0eOqEOHDpKkFStWaMyYMVq1atVlj9O0aVNLI/OZZ57RmjVr9O6772r06NH66KOP9Nlnn+nAgQNq3ry5pLLVNUBdKSoqUmZmpmbOnGk1HhUVpZ07d1YrRmlpqc6cOWP5b7pcfn6+QkJCVFJSoj59+ujpp5+2avpfrLCwUIWFhZbPeXl5NTgTAADQ0FB/lGmo9QcvygUAAKjnfv31V3300UeaNm1apatdmjVrZvnnoKAgRUdH64033pAknTt3TmlpaTbfnurn56cLFy5Ikt577z3169dPL7zwgtq1a6cuXbro4YcfrtVbh4Hfy83NVUlJiYKCgqzGg4KClJOTU60YL7/8ss6ePatRo0ZZxrp27aqUlBS99957Sk1NlZ+fnyIiIrR///4q48yfP1+BgYGWrbyoBQAAcDfUH85HUx8AAKCe++mnn2Q2m9W1a9dqzZ8wYYJSUlJkNpv1zjvv6KqrrlKfPn1qdMzi4mKlpKTo22+/1ZAhQyRJBw4c0I4dO/Tdd99p/fr1SkpK0jvvvKNp06bV9JQAhzIYDFafzWZzhbHKpKamas6cOUpLS1OrVq0s4wMHDtTYsWN1zTXXKDIyUmvWrFGXLl30t7/9rcpYCQkJOn36tGU7cuSI7ScEAABQh6g/nI+mPgAAQD1nNpslVWxcVuWWW25Rfn6+tm3bphUrVtRolcxjjz2mJk2ayN/fX9OmTdMjjzxieVFVaWmpDAaD3nzzTfXv31/Dhw/XggULlJKSUi9Wy8D9GI1GeXp6VliVf/z48Qqr9y+WlpamiRMnas2aNbr55psvOdfDw0PXXXfdJVfq+/r6KiAgwGoDAABwR9QfzkdTHwAAoJ7r3LmzDAaD9u7dW635Xl5eiomJ0VNPPaUvv/xSY8aMqfaxHnnkEWVlZenw4cPKz8/XCy+8IA+PskvONm3aqF27dgoMDLTM79atm8xms3755ZeanRTgAD4+PgoLC1NGRobVeEZGhgYNGlTlfqmpqRo/frzeeust3XLLLZc9jtlsVlZWltq0aWN3zgAAAK6O+sP5aOoDAADUc82bN1d0dLSWLFmis2fPVvj+1KlTFcYmTJigzz77TCNGjNAVV1xR7WMZjUZ16tRJbdu2rbAyJyIiQseOHVN+fr5l7Mcff5SHh4fat29f/RMCHCg+Pl6vvfaaVqxYob1792rGjBnKzs7W1KlTJZU9FmfcuHGW+ampqRo3bpxefvllDRw4UDk5OcrJydHp06ctc+bOnavNmzfrwIEDysrK0sSJE5WVlWWJCQAAUJ9RfzgfTX0AAIAGIDk5WSUlJerfv7/WrVun/fv3a+/evVq8eLHCw8MrzO/WrZtyc3O1cuVKh+Vwzz33qEWLFrrvvvu0Z88ebdu2TY888ogmTJggf39/hx0HqInRo0crKSlJiYmJ6tOnj7Zt26ZNmzYpJCREkmQymZSdnW2Zv2zZMhUXF2vatGlq06aNZXvwwQctc06dOqXJkyerW7duioqK0tGjR7Vt2zb179+/1s8PAACgLlB/OJdXXScAAABQH+Tm5rr0cTp27Kh///vfevbZZ/XQQw/JZDKpZcuWCgsL09KlSyvdp0WLFvakWkGTJk2UkZGh6dOnq1+/fmrRooVGjRqlZ555xqHHAWoqNjZWsbGxlX6XkpJi9Xnr1q2Xjbdw4UItXLjQAZkBAABUjvrj8upz/UFTHwAAwA5Go1H+/v5KT0+vtWP6+/vLaDTWeL82bdrolVde0SuvvFLp94cOHbrk/hffJnvx/MvtL0ldu3at8PxyAAAAANVD/VH9/aX6W3/Q1AcAALBDcHCw9u3bV2srZaSyC/ng4OBaOx4AAAAA10D9AYmmPgAAgN2Cg4O5yAUAAABQK6g/wItyAQAAAAAAAABwEzT1AQAAAAAAAABwEzT1AQAAAAAAAABwEzT1AQAAaqi0tLSuU4CDmM3muk4BAAAAuCTqj/rDUfUHL8oFAACoJh8fH3l4eOjYsWNq2bKlfHx8ZDAY6jot2MhsNuvEiRMyGAzy9vau63QAAAAAK9Qf9Ysj6w+3beonJyfrxRdflMlkUo8ePZSUlKTIyMhK5+7YsUOPPfaY9u3bp3PnzikkJERTpkzRjBkzajlrAADgzjw8PNSxY0eZTCYdO3asrtOBAxgMBrVv316enp51nQoAAABghfqj/nFU/eGWTf20tDTFxcUpOTlZERERWrZsmYYNG6Y9e/YoODi4wvzGjRvrr3/9q3r37q3GjRtrx44dmjJliho3bqzJkyfXwRkAAAB35ePjo+DgYBUXF6ukpKSu04GdvL29aegDAADAZVF/1C+Oqj+c0tRfvHixBg4cqP79+1vGjh8/rpycHPXu3bvC/A0bNmjDhg1asWJFteIvWLBAEydO1KRJkyRJSUlJ2rx5s5YuXar58+dXmN+3b1/17dvX8jk0NFTp6enavn07TX0AAFBj5bdL8sgWwPmcXVsAAAC4OuoPXMwpL8qNi4vTRx99ZDW2dOlSq8b672VlZemNN96oVuyioiJlZmYqKirKajwqKko7d+6sVozdu3dr586duuGGG6qcU1hYqLy8PKsNAAAAQO1yZm0BAAAAuCOnNPWdKTc3VyUlJQoKCrIaDwoKUk5OziX3bd++vXx9fdWvXz9NmzbNstK/MvPnz1dgYKBl69Chg0PyBwAAAAAAAADAVm7X1C938ZuezWbzZd/+vH37dn399dd69dVXlZSUpNTU1CrnJiQk6PTp05btyJEjDskbAAAAAAAAAABbud2Lco1Gozw9PSusyj9+/HiF1fsX69ixoySpV69e+u9//6s5c+bo7rvvrnSur6+vfH19HZM0AAAAAAAAAAAO4HYr9X18fBQWFqaMjAyr8YyMDA0aNKjaccxmswoLCx2dHgAAAAAAAAAATuN2K/UlKT4+XjExMerXr5/Cw8O1fPlyZWdna+rUqZLKHp1z9OhRrVq1SpK0ZMkSBQcHq2vXrpKkHTt26KWXXtL06dPr7BwAAAAAAAAAAKgppzX1v/vuO61Zs8bqsyStXbtWZrO5wtyaGD16tE6ePKnExESZTCb17NlTmzZtUkhIiCTJZDIpOzvbMr+0tFQJCQk6ePCgvLy8dNVVV+m5557TlClTbD09AAAAALXEmbUFAAAA4G4M5ouvgh3Aw8Oj0hfZShVfcFv+ncFgUElJiaNTcZi8vDwFBgZq/PjxCg0NtTnOsWPHtHz5cmVmZuraa691XIIAAABwqPLrv9OnTysgIKCu02mw6mNtUR3UHwAAAA1LTeoPp6zUf+qpp5wRFgAAAEADQ20BAAAAWKOpDwAAAMBlUVsAAAAA1jzqOgEAAAAAqEvJycnq2LGj/Pz8FBYWpu3bt1c5Nz09XUOHDlXLli0VEBCg8PBwbd68ucK8devWqXv37vL19VX37t21fv16Z54CAAAAGpA6aepnZWVp4cKFWrhwob766qu6SAEAAABAPWBvbZGWlqa4uDjNmjVLu3fvVmRkpIYNG6bs7OxK52/btk1Dhw7Vpk2blJmZqZtuukm33nqrdu/ebZmza9cujR49WjExMfrmm28UExOjUaNG6csvv7T5PAEAAIByTmnqb9u2TePGjdMXX3xR4bsnnnhCYWFhevjhh/Xwww9r4MCBmj59ujPSAAAAAODmnF1bLFiwQBMnTtSkSZPUrVs3JSUlqUOHDlq6dGml85OSkvToo4/quuuuU+fOnTVv3jx17txZGzdutJozdOhQJSQkqGvXrkpISNCQIUOUlJRUo9wAAACAyjilqZ+Wlqa1a9eqe/fuVuNbtmzRvHnz5OnpqZiYGE2dOlVGo1HJycl69913nZEKAAAAADfmzNqiqKhImZmZioqKshqPiorSzp07qxWjtLRUZ86cUfPmzS1ju3btqhAzOjr6kjELCwuVl5dntQEAAACVcUpTf9euXRowYIACAgKsxpctWyaDwaBXX31VKSkpWrJkibZv3y5vb2+lpKQ4IxUAAAAAbsyZtUVubq5KSkoUFBRkNR4UFKScnJxqxXj55Zd19uxZjRo1yjKWk5NT45jz589XYGCgZevQoUO1jg8AAICGxylN/WPHjqlLly4Vxrds2aKAgACNHz/eMtalSxcNHz5cX3/9tTNSAQAAAODGaqO2MBgMVp/NZnOFscqkpqZqzpw5SktLU6tWreyKmZCQoNOnT1u2I0eO1OAMAAAA0JA4pan/22+/yWg0Wo398ssvOnHihAYPHiwPD+vDdurUSbm5uc5IBQAAAIAbc2ZtYTQa5enpWWEF/fHjxyustL9YWlqaJk6cqDVr1ujmm2+2+q5169Y1junr66uAgACrDQAAAKiMU5r6TZs21bFjx6zGMjMzJUlhYWEV5hsMBvn5+TkjFQAAAABuzJm1hY+Pj8LCwpSRkWE1npGRoUGDBlW5X2pqqsaPH6+33npLt9xyS4Xvw8PDK8T8+OOPLxkTAAAAqC4vZwTt3bu33n//fZ09e1aNGzeWJK1fv14Gg0HXX399hfk///yz2rZt64xUAAAAALgxZ9cW8fHxiomJUb9+/RQeHq7ly5crOztbU6dOlVT2WJyjR49q1apVksoa+uPGjdOiRYs0cOBAy4p8f39/BQYGSpIefPBBXX/99Xr++ec1YsQIbdiwQZ988ol27Nhh198CAAAAkJy0Un/ChAn69ddfdcMNN2jx4sV64IEHtHr1anXo0EE33nij1dySkhJt27ZNvXr1ckYqAAAAANyYs2uL0aNHKykpSYmJierTp4+2bdumTZs2KSQkRJJkMpmUnZ1tmb9s2TIVFxdr2rRpatOmjWV78MEHLXMGDRqkt99+WytXrlTv3r2VkpKitLQ0DRgwwL4/BgAAACAnrdQfO3asPv30U73xxhvavXu3zGazmjZtqr///e8Vnnn5wQcfKDc3V9HR0c5IBQAAAIAbq43aIjY2VrGxsZV+l5KSYvV569at1Yp555136s4776xRHgAAAEB1OKWpL0krV67UxIkTtWvXLjVv3lzR0dFq3759hXm+vr5auHChRowY4axUAAAAALgxagsAAADgf5zW1JekwYMHa/DgwZecEx0dzSp9AAAAAJdEbQEAAACUccoz9QEAAAAAAAAAgOM5ZaX+mjVrbNpv1KhRDs4EAAAAgDujtgAAAACsOaWpf9ddd8lgMFR7vtlslsFg4MIbAAAAgBVqCwAAAMCa056p7+XlpeHDh6tPnz7OOgQAAACABoDaAgAAAPgfpzT1b7vtNn3wwQd67733dPjwYU2YMEFjxozRFVdc4YzDAQAAAKinqC0AAAAAa055UW56erqOHj2qF198UcXFxXrggQfUtm1b3X333crIyHDGIQEAAADUQ9QWAAAAgDWnNPUlyWg0Kj4+Xv/5z3/0xRdfaNy4cfroo4/0xz/+UcHBwZo9e7YOHDjgrMMDAAAAqCeoLQAAAID/cVpT//f69++vZcuWyWQyKSUlRZ06ddKzzz6rLl266JNPPqmNFAAAAADUA9QWAAAAaOic9qLcyvj5+SkqKkomk0k//vijjh07pnPnztVmCgAAAADqAWoLAAAANFS10tQvKSnR+++/rxUrVujDDz9USUmJ+vTpo4SEBA0ZMqQ2UgAAAABQD1BbAAAAoKFzalN/z549WrFihVavXq3jx4+rRYsWio2N1YQJE9S7d29nHhoAAABAPUJtAQAAAJRxSlN/+fLlWrFihb766isZDAZFRUVpwoQJGjFihLy9vZ1xSAAAAAD1ELUFAAAAYM0pTf2pU6fK29tbt956q+699161a9dOkrR79+5L7te/f39npAMAAADATVFbOMbevXvtjmE0GhUcHOyAbAAAAGAPpz1+58KFC9q4caM2btxY7X1KSkqclQ4AAAAAN0VtYbv8/HwZDAaNHTvW7lj+/v7at28fjX0AAIA65pSm/r333uuMsAAAAAAaGGoL+xQUFMhsNmvkyJEyGo02x8nNzVV6erpyc3Np6gMAANQxpzT1V65c6YywAAAAABoYagvHMBqNatu2bV2nAQAAAAfwqOsEAAAAAAAAAABA9ThlpX5sbKxN+yUnJzs4E9fFi6oAAACAy6O2AAAAAKw5pan/6quvVnuuwWCw/HNDuPDmRVUAAABA9VFbAAAAANac0tTfsmVLteZlZ2crMTFRP//8s9UFeH3Gi6oAAACA6quN2iI5OVkvvviiTCaTevTooaSkJEVGRlY612Qy6aGHHlJmZqb279+vBx54QElJSVZzUlJSdN9991XY9/z58/Lz86tRbgAAAMDFnNLUv+GGGy75/W+//aZ58+ZpyZIlKigoUHh4uJ5//nlnpOKyeFEVAAAAcHnOri3S0tIUFxen5ORkRUREaNmyZRo2bJj27NlT6eKZwsJCtWzZUrNmzdLChQurjBsQEKAffvjBaoyGPgAAAByhVl+UW1BQoOeee05XXXWVXn75ZYWGhio9PV2ff/65Bg8eXKNYycnJ6tixo/z8/BQWFqbt27dXOTc9PV1Dhw5Vy5YtFRAQoPDwcG3evNne0wEAAABQRxxVWyxYsEATJ07UpEmT1K1bNyUlJalDhw5aunRppfNDQ0O1aNEijRs3ToGBgVXGNRgMat26tdUGAAAAOEKtNPXNZrNee+01de7cWY8//rgaNWqk5cuX67vvvtNtt91W43jlq2lmzZql3bt3KzIyUsOGDVN2dnal87dt26ahQ4dq06ZNyszM1E033aRbb71Vu3fvtvPMAAAAANQmR9YWRUVFyszMVFRUlNV4VFSUdu7caVee+fn5CgkJUfv27fWnP/3psrVHYWGh8vLyrDYAAACgMk55/M7vvfvuu3r88cf1ww8/KCAgQPPmzVNcXJxdt57+fjWNJCUlJWnz5s1aunSp5s+fX2H+xc+4nDdvnjZs2KCNGzeqb9++NufhCvbu3Wt3DKPRyHP5AQAA4PIcXVvk5uaqpKREQUFBVuNBQUHKycmxOc+uXbsqJSVFvXr1Ul5enhYtWqSIiAh988036ty5c6X7zJ8/X3PnzrX5mAAAAGg4nNbU37Fjhx577DF98cUX8vHx0YwZMzRr1ixdccUVdsUtX00zc+ZMq/GarKYpLS3VmTNn1Lx58yrnFBYWqrCw0PLZ1VbK5Ofny2AwaOzYsXbH8vf31759+2jsAwAAwCU5q7Yod/GLdc1mc41ftvt7AwcO1MCBAy2fIyIidO211+pvf/ubFi9eXOk+CQkJio+Pt3zOy8tThw4dbM4BAAAA9ZdTmvp//vOf9cEHH8jDw0P33nuvEhMT1b59e4fEdsRqmpdffllnz57VqFGjqpzj6itlCgoKZDabNXLkSBmNRpvj5ObmKj09Xbm5uTT1AQAA4HKcWVsYjUZ5enpWqCOOHz9eod6wh4eHh6677jrt37+/yjm+vr7y9fV12DEBAABQfzmlqf/+++/LYDAoODhYOTk5mjx58mX3MRgM+uCDD6p9DFtX06SmpmrOnDnasGGDWrVqVeU8d1kpYzQa1bZt27pOAwAAAHAKZ9YWPj4+CgsLU0ZGhm6//XbLeEZGhkaMGGFX3r9nNpuVlZWlXr16OSwmAAAAGi6nPX7HbDbr4MGDOnjwYLXmV/f2VntW06SlpWnixIlau3atbr755kvOZaUMAAAA4BqcVVtIUnx8vGJiYtSvXz+Fh4dr+fLlys7O1tSpUyWVLfY5evSoVq1aZdknKytLUtkjMU+cOKGsrCz5+Pioe/fukqS5c+dq4MCB6ty5s/Ly8rR48WJlZWVpyZIl1c4LAAAAqIpTmvrVvdi2ha2raVJTUzVhwgSlpqbqlltucVp+AAAAABzHmbWFJI0ePVonT55UYmKiTCaTevbsqU2bNikkJESSZDKZlJ2dbbVP3759Lf+cmZmpt956SyEhITp06JAk6dSpU5o8ebJycnIUGBiovn37atu2berfv79TzwUAAAANg1Oa+uUXwM5S09U0qampGjdunBYtWqSBAwdaVvn7+/srMDDQqbkCAAAAsJ2zawtJio2NVWxsbKXfpaSkVBgzm82XjLdw4UItXLjQEakBAAAAFTjt8TvOVNPVNMuWLVNxcbGmTZumadOmWcbvvffeSi/SAQAAAAAV7d271+4YRqNRwcHBDsgGAACgYXLLpr5Us9U0W7dudX5CAAAAAFBP5efny2AwaOzYsXbH8vf31759+2jsAwAA2Mhtm/oAAAAAgNpRUFAgs9mskSNHymg02hwnNzdX6enpys3NpakPAABgI5r6AAAAAIBqMRqNatu2bV2nAQAA0KB51HUCAAAAAAAAAACgemjqAwAA4P+1d6+xUZ33vsd/47FnsLklMBwbJ2AIIidcIoVCy61us9XGCJRzLAyK30ClBFJZVsXFiSoc4jTYQQgRwUAbcxGwEbu5sE9wWvUcS2C2KDVgVaob50UxldsATt3xhnGbGGM8Yw/rvHDGwfGQ4rWWPWtmvh9pFM/yWj//501Yz3+e9TwAAAAAgARBUx8AAAAAAAAAgATBmvqQJDU3N1u63ufzsdEVAAAAAAAAAIwwmvoprqurSy6XS+vWrbOUk5mZqatXr9LYBwAAAAAAAIARRFM/xfX09MgwDBUVFcnn85nKCAaDqqmpUTAYpKkPAAAAAAAAACOIpj4k9S+fk5ubG+8yAAAAAAAAAADfgI1yAQAAAAAAAABIEDT1AQAAAAAAAABIEDT1AQAAAAAAAABIEDT1AQAAAAAAAABIEGyUC9s0NzdbzvD5fJo+fboN1QAAAAAAAABA8qGpD8u6urrkcrm0bt06y1mZmZm6evUqjX0AAAAgiVmdEMRkIAAAkMpo6sOynp4eGYahoqIi+Xw+0znBYFA1NTUKBoPcoAMAAABJyK4JQUwGAgAAqYymPmzj8/mUm5sb7zIAAAAAOJQdE4KYDAQAAFIdTX0AAAAAwKhiQhAAAIB5NPXhOGy4CwAAAOBfYdwAAABSFU19OAYb7gIAACAeqqurtWfPHgUCAc2bN09+v1/5+fkxzw0EAnrllVfU2NiolpYWbdq0SX6/f8h5p0+fVkVFhf76179q1qxZ2rlzp1avXj3CnyQ12Dlu8Hq9On36tKZOnWophy8HAADAaKKpD8dgw10AAACMtlOnTmnLli2qrq7W8uXLdfjwYa1cuVJXrlyJeS8ZCoU0ZcoUbd++Xfv27YuZ2dDQoOLiYlVVVWn16tX66KOP9MILL+jixYtavHjxSH+kpGfXuKG1tVVnzpzR888/b7kmJhUBAIDRRFMfjsP6mgAAABgte/fu1YYNG7Rx40ZJkt/v15kzZ3Tw4EHt2rVryPkzZszQ/v37JUnHjx+Pmen3+/Xcc8+pvLxcklReXq4LFy7I7/fr/fffH6FPknqsjhuCwSCTigAAQEKiqQ8AAAAgJYXDYTU2Nmrbtm2DjhcUFOjy5cumcxsaGrR169ZBx1asWBFzmZ6oUCikUCg08L6zs9P038fwMKkIAAAkGpr6SFpsnAUAAIBvEgwGFYlElJ2dPeh4dna22tvbTee2t7cPO3PXrl3asWOH6b8JAACA1EFTH0mHDXcBAAAwHC6Xa9B7wzCGHBvpzPLycpWVlQ287+zs1LRp0zQ+zaWxume6jkfS3coZN1YTkijHSbVI0oQ0l3LGjVV6z10Zt3nCAgAAmDOc+wia+kg6dm+4W19frzlz5liqKRQKyev1xj1D4ukDAACAKJ/PJ7fbPWQG/c2bN4fMtB+OnJycYWd6vd6Y93qrxns0RT2ma3km51Et++F39eQEj7KSJMdJtUhS9wSPnvrhd5XT/LEiN1tN5wAAgNQW6e5+6HNp6iNpWV0b084Z/y6XS4ZhxD1D4ukDAACAKI/Ho4ULF6qurk6rV68eOF5XV6fCwkLTuUuXLlVdXd2gdfXPnj2rZcuWDTur15C6Zf6pgduRiDq67+rOPUNKkhwn1SJJd+4Z6ui+qz6PV8ocazoHAACkuHDvQ59KUx94ALtm/Le0tOj8+fOWcuzIkL56+iAYDNLUBwAAkFRWVqb169dr0aJFWrp0qY4cOaLW1laVlJRI6l8Wp62tTSdPnhy4pqmpSVL/JJBbt26pqalJHo9Hc+fOlSRt3rxZ3/ve97R7924VFhbq17/+tc6dO6eLFy8Ou76wYajXQsO5556hrnCvQobkSZIcJ9UiSSFD6gr36uY/P9e99AzTOZIU6euTO936MN2OnKysLE2cONFyLQAA4CGlex7+1BEsA0gKVmf8B4NByzl2ZAAAAGCo4uJidXR0qLKyUoFAQPPnz1dtba3y8vIkSYFAQK2tg5dUWbBgwcDPjY2Neu+995SXl6fr169LkpYtW6YPPvhAr7/+uioqKjRr1iydOnVKixcvHrXPhdETDoclSTUffRTnSuyVnp6un/zkJzT2AQBwIJr6QApqbm62nMHa/AAAIFmUlpaqtLQ05u9OnDgx5NjDLIm4du1arV271mppSAB9fX2SpDlz5igrK8t0TkdHh65fv+6InO7ubjU3N6u7u5umPgAADkRTH0ghdu4TwNr8AAAAwFeysrI0fvw409d3f7k5nlNyAACAc9HUB1KIXfsEsDY/AAAAAAAAEB809YdpfJpLY3XP9PWPpLuVM26sJiRRjpNqSdYcu2t54n9MUXZOtumcCWku5Ywbq/SeuzJud5rOAQDAyfg3DkCqC966ZTmDDXcBALAfTf1hWjXeoynqMX39MzmPatkPv6snJ3iUlSQ5TqolWXOcVIskdU/w6KkffldjGn6rW80fm86RJK93jMaOG2spAwCAkRD5cgkLAEg1dm7+y4a7AADYj6b+MPUaUrdcpq+/HYmoo/uu7twzpCTJcVItyZrjpFok6Z89YXV039Wvzp41nRGVluZW4f/+Xxo7zvx6n5mZmRo/foLlWgAAGCTcG+8KACAu7Nr8lw13AQAYGTT1hylsGOq10AztuWeoK9yrkCF5kiTHSbUka46TapGkO7296gr3Wr7J/+KLL/SXv/xF//Gf/8d0hsTsHwDACEn3xLsCAIgruzbbtWMZn0hfn9zp1lsYLAcEAEgGCdvUr66u1p49exQIBDRv3jz5/X7l5+fHPDcQCOiVV15RY2OjWlpatGnTJvn9/tEtGEhCVm/yu79c1sDKlwPM/gEAAACcyc5lfOzChCAAQDJIyKb+qVOntGXLFlVXV2v58uU6fPiwVq5cqStXrmj69OlDzg+FQpoyZYq2b9+uffv2xaFiAN/ErhlAAAAAAJzDrmV8Ojo6dP36dZYDAgDgSwnZ1N+7d682bNigjRs3SpL8fr/OnDmjgwcPateuXUPOnzFjhvbv3y9JOn78+KjWCmB08EgvAAAA4Ex2PeHrlOWAuN8HAMRbwjX1w+GwGhsbtW3btkHHCwoKdPnyZdv+TigUUigUGnjf2dlpWzYA+zjxkV63263iF17QuPHjLeUwWAAAAADsY9fYgSV8AADxlnBN/WAwqEgkouzs7EHHs7Oz1d7ebtvf2bVrl3bs2GFbHoCR4bRHeqOb/773/vumM6IYLAAAAAD2sWPswBI+AAAnSLimfpTL5Rr03jCMIcesKC8vV1lZ2cD7zs5OTZs2zbZ8APZyyiO9dmz+G81hsAAAAADYz45lfOxY/pMncwEAZiVcU9/n88ntdg+ZlX/z5s0hs/et8Hq98nq9tuUBSC1OWe8zWguDBQAAEpPH5VKGDNPXj0lzaZwnQ16XkibHSbUka46TanFajqu3V+M8GTr7//6v6Tqi3O50vfTSixo/foLlLABAEugLP/SpCdfU93g8Wrhwoerq6rR69eqB43V1dSosLIxjZQBgHzv3CrBjjX+7NhFmM2IAAIYnwyVlWWhijne7NTkrU2PTXEmT46RakjXHSbU4LSckQ5OzMpU3PU/eMeYnAoZ6QrrRekO3btxQ7yPW7mu93jEaO26spQwAgAP09T70qQnX1JeksrIyrV+/XosWLdLSpUt15MgRtba2qqSkRFL/0jltbW06efLkwDVNTU2SpK6uLt26dUtNTU3yeDyaO3duPD4CAHwju/YKsHONfydhvwEAQKqovR3W9EfHmL6+uf2fqj13UetyZig769GkyHFSLcma46RanJYzkLFuhrInma/l08Cn+tV/XZJx7qLpjCiPx6OjR49aXr1g0qRJeuyxxyzXAwAwx93Z+dDnJmRTv7i4WB0dHaqsrFQgEND8+fNVW1urvLw8SVIgEFBra+ugaxYsWDDwc2Njo9577z3l5eXp+vXro1k6AAyLE9b4t2sTYbtyovsNtN64Id+UKaZzJGb8AwCc7/Y9Q3eUZvr6z/siau+6o857hsYlSY6TaknWHCfV4rQcu2r57zvdCtzuUlFRkXw+n+mc1tZWnTlzRgVFa0xnRHm9Xp0+fVpTp061lOPz+TR9+nTL9QBAqnEN4yGyhGzqS1JpaalKS0tj/u7EiRNDjhmG+Uf0ACDRWflywO5NhK3mOG1pIr4YAAAAgFk+n0+5ubmmrw8GgzIMw7YvB55//nnTGVGZmZm6evUqjX0AGEEJ29QHAKQmpy1NxFJAAAAAiDenfDkQDAZVU1Oj+vp6zZkzx3ROKBSS12t+z4IonhoAkKxo6gMAEpITliaKLgXU3d1NUx8AAAAJz+qXA11dXXK5XFq3bp2lOlwuly0rLrCkEIBkRVN/mDwulzJk/h+WMWkujfNkyOtS0uQ4qZZkzXFSLU7LcVItyZrjpFpGIufRsVkaO26sqQyvSxrnydDn/92utGHsUh9LpK9P7nTr/yxnZmZq/PgJlnMASOoLx7sCAAASSk9Pj+UZ/y0tLTp//jxLCgHAN6CpP0wZLinLQhNpvNutyVmZGpvmSpocJ9WSrDlOqsVpOU6qJVlznFSL03L6IhFNzsrU7y/81nQddktLS9O/PftvysrKtJTj9Y4x/WUHkDQsflkHAECqsjLjPxgMWs6I5jhpSSGJGf8A7ENTf5hqb4c1/dExpq9vbv+nas9d1LqcGcrOejQpcpxUS7LmOKkWp+U4qZZkzXFSLU7LaW67ptpzF7Vq1SpNmjTJdC3Xrn2qS5cuW85pa2vTb//rtzLO/s50RpTH49HRo0eVnZ1tKSccDsnjsbYe6qRJk/TYY49ZygDMcHd2xrsEjJLq6mrt2bNHgUBA8+bNk9/vV35+/gPPv3DhgsrKyvSnP/1Jubm5+ulPf6qSkpKB3584cUIvvvjikOvu3r2rMWPMjyUAAMPnlCWFJHuWA2K/AQASTf1hu33P0B2lmb7+876I2rvuqPOeoXFJkuOkWpI1x0m1OC3HSbUka46TanFaTjQj/ZFHNS7H/I25cfOWLTmhm7cUuN1l26PKBUVrTGdE2bEeKmuhwjZvvim53VJFxdDfVVVJkUj/OV9yWV/KFwng1KlT2rJli6qrq7V8+XIdPnxYK1eu1JUrV2L+P+PatWtatWqVXn75Zf3yl7/UpUuXVFpaqilTpmjNmq/+vzlhwgT9+c9/HnQtDX0ASDx2LCkk2bccEPsNAJBo6gMAkHSc8qiyHeuh2rkWKgMXyO2W3nij/+f7G/tVVf3HKyvjUxfiau/evdqwYYM2btwoSfL7/Tpz5owOHjyoXbt2DTn/0KFDmj59uvx+v6T+Ddf/8Ic/6O233x7U1He5XMrJyRmVzwAAGHlOuMdmvwEAUTT1AQBATHYMXKzm2PUFAwMXSPqqkX9/Y//+hn6sGfxIauFwWI2Njdq2bdug4wUFBbp8+XLMaxoaGlRQUDDo2IoVK3Ts2DH19vYqIyNDUv9yDXl5eYpEInrmmWdUVVWlBQsWPLCWUCikUCg08L6T5Z8AICkl434DwWCQe2NglNHUBwAAjue0gQsbpSWw+xv7b70lhcM09FNYMBhUJBIZsn9Idna22tvbY17T3t4e8/y+vj4Fg0FNnTpVTz31lE6cOKGnn35anZ2d2r9/v5YvX65PPvlEs2fPjpm7a9cu7dixw54PBgDAQ7B6jx3V3NxsSy3cGwMPj6Y+AABIGU7aKI0Z/3FUUfFVQ9/joaEPuVyuQe8Nwxhy7F+df//xJUuWaMmSJQO/X758ub71rW/p5z//uQ4cOBAzs7y8XGVlZQPvOzs7NW3atOF9EAAARpHTNhGW+HIAqYOmPgAAwEOya6M0HlWOs6qqrxr64XD/exr7Kcnn88ntdg+ZlX/z5s0hs/GjcnJyYp6fnp6uyZMnx7wmLS1N3/72t9XS0vLAWrxer7xe7zA/AQAA8eO0TYQle74c4IsBJAKa+gAAAMNk16PKiIOvr6EffS/R2E9BHo9HCxcuVF1dnVavXj1wvK6uToWFhTGvWbp0qX7zm98MOnb27FktWrRoYD39rzMMQ01NTXr66aftKx4AAIdwylKZdn05wFMDSAQ09QEAAJAaYm2KG2vzXKSUsrIyrV+/XosWLdLSpUt15MgRtba2qqSkRFL/sjhtbW06efKkJKmkpES/+MUvVFZWppdfflkNDQ06duyY3n///YHMHTt2aMmSJZo9e7Y6Ozt14MABNTU16Z133onLZwQAIBE44csBO58aYLlNjCSa+gAAAEgNkUjsTXGj7yOR0a8JcVdcXKyOjg5VVlYqEAho/vz5qq2tVV5eniQpEAiotbV14PyZM2eqtrZWW7du1TvvvKPc3FwdOHBAa9asGTjn888/149//GO1t7dr4sSJWrBggX73u9/pO9/5zqh/PgAAUo2VLwfsemogutxmfX295syZYzpHYsY/YqOpDwAAECfNzc2Wrg+FQraswZ0yA4U335TU37uvr5cCAWnqVCk/X3IzQz+llZaWqrS0NObvTpw4MeTY97//ff3xj398YN6+ffu0b98+u8oDAACjzOpTA3ZuIsyMf8RCUx8AAGCU2XWT73K5ZBiG5XpSaaBQUyNt3iz97W9fHXv8cWn/fqmoKH51AQAAIHnYtYmwnTP+7ZgQlDKTgezy5puS2x17ic+qqv7ZRl9OPBoumvoAAACjzI6b/JaWFp0/f962gUIwGEz6G/SaGmntWunr34O0tfUf//BDGvsAAACwj5Nm/NsxISiVJgPZwu2OvXfX/Xt9mURTHwAAIE6srvdpNSOVRCL9M/RjjWMMQ3K5pC1bpMLC/ntvAAAAIN7smvFvx4Qg9gkwIdrIv7+xf39D38ISoDT1AQAAkPTq6wcvufN1hiF99ln/ec8+O2plAQAAAP+S1Yk8dkwIYp8Ak+5v7L/1lhQOW27oSzT1AQAAkAICAXvPAwAAAFKJ3fsEpMLyn1GR1yrkqnpLaeGw7mV4ZLxWIasPB9PUBwAAQNKbOtXe8wAAAIBUxPKfw1NTI336YpVe7Q0rJI+8vWG9PalKT/x7haX9vNLsKxEAAABwpvx86fHH+9fOj8XlkqZN6z8PAAAAAKyqqZGa1lTp1c43VKFKjVFIFarUq51vqGlNlWpqzGfT1AcAAEDSc7ul/fv7f/56Yz/63u9nk1wAAAAA1kUi/TP0K9Xf0H9L/Wvov6UKVahSlXpDn75YpUjEXD5NfQAAAKSEoiLpww+lxx4bfPzxx/uPW3n8FQAAAACi6uulrs7IoIZ+VLSx39UZUX29uXzW1AcAAICam5stZ/h8PsdvdlVUJBUW9t9kBwL9a+jn5zNDHwAAAIB9AgFph9584O+jjf7/GTCXT1MfAAAghXV1dcnlcmndunWWszIzM3X16lXHN/bdbunZZ+NdBQAAAJC67JhUFAqF5PV6LeeMxOSkqVPtPe/raOoDAACksJ6eHhmGoaKiIvl8PtM5wWBQNTU1CgaDjm/qAwAAAIgPOycVuVwuGYZhOWckJifl5/cv89nWJsUq0eXq/31+vrl8mvoAAACQz+dTbm6u5ZxUWcYHAAAAwPDZNamopaVF58+fd+zkJLdb2r9fWru2v4F/f2Pf5er/r99vfhlQmvoAAACwLBWX8QEAAABgjtVJRcFg0JackVRUJH34obR5s/S3v311/PHH+xv6RUXms2nqAwAAwDKW8QEAAACAwYqKpMJCqb6+f/PcqVP7l9wxO0M/iqY+AAAAbOPkmTIAAAAAMNrcbunZZ+3NTLM3DgAAAAAAAAAAjBSa+gAAAAAAAAAAJAia+gAAAAAAAAAAJAia+gAAAAAAAAAAJAia+gAAAAAAAAAAJAia+gAAAAAAAAAAJIiEbepXV1dr5syZGjNmjBYuXKj6+vpvPP/ChQtauHChxowZoyeeeEKHDh0apUoBAAAAONlIjC1Onz6tuXPnyuv1au7cufroo49GqnwAAACkmIRs6p86dUpbtmzR9u3b9fHHHys/P18rV65Ua2trzPOvXbumVatWKT8/Xx9//LFee+01bdq0SadPnx7lygEAAAA4yUiMLRoaGlRcXKz169frk08+0fr16/XCCy/o97///Wh9LAAAACSxhGzq7927Vxs2bNDGjRs1Z84c+f1+TZs2TQcPHox5/qFDhzR9+nT5/X7NmTNHGzdu1EsvvaS33357lCsHAAAA4CQjMbbw+/167rnnVF5erqeeekrl5eX6wQ9+IL/fP0qfCgAAAMksPd4FDFc4HFZjY6O2bds26HhBQYEuX74c85qGhgYVFBQMOrZixQodO3ZMvb29ysjIGHJNKBRSKBQaeP/FF19Iktrb2y3Vf+vWLUlSIBBQOBxOihwn1ZKsOU6qxWk5TqolWXOcVIvTcpxUS7LmOKkWp+U4qRY7czo6OiRJXV1d6uzsNJ1jh+jfNwwjrnVg5IzU2KKhoUFbt24dcs43NfUZfyRmLcma46RanJbjpFqSNcdJtTgtx0m1JGuOk2pxWo6TarEzJ2HHH0aCaWtrMyQZly5dGnR8586dxpNPPhnzmtmzZxs7d+4cdOzSpUuGJOPvf/97zGt+9rOfGZJ48eLFixcvXrx4pfjrs88+s+dGFo4zUmOLjIwM49133x10zrvvvmt4PJ4H1sL4gxcvXrx48eLFi5f0cOOPhJupH+VyuQa9NwxjyLF/dX6s41Hl5eUqKysbeH/v3j394x//0OTJk7/x7wAAACA5GIah27dvKzc3N96lYISNxNhiuJmMPwAAAFLbcMYfCdfU9/l8crvdQx5DvXnzprKzs2Nek5OTE/P89PR0TZ48OeY1Xq9XXq930LFHHnnEfOEAAABIOBMnTox3CRhBIzW2eNA5D8qUGH8AAADg4ccfCbdRrsfj0cKFC1VXVzfoeF1dnZYtWxbzmqVLlw45/+zZs1q0aFHM9fQBAAAAJL+RGls86JwHZQIAAADDkXBNfUkqKyvT0aNHdfz4cTU3N2vr1q1qbW1VSUmJpP5HV3/0ox8NnF9SUqIbN26orKxMzc3NOn78uI4dO6ZXX301Xh8BAAAAgAOMxNhi8+bNOnv2rHbv3q2rV69q9+7dOnfunLZs2TLaHw8AAABJKOGW35Gk4uJidXR0qLKyUoFAQPPnz1dtba3y8vIk9e963NraOnD+zJkzVVtbq61bt+qdd95Rbm6uDhw4oDVr1sTrIwAAAABwgJEYWyxbtkwffPCBXn/9dVVUVGjWrFk6deqUFi9ePOqfDwAAAMnHZUR3dQIAAAAAAAAAAI6WkMvvAAAAAAAAAACQimjqAwAAAAAAAACQIGjqAwAAAAAAAACQIGjqAwAAAAAAAACQIGjqAwAAAAAAAACQIGjqAwAAAAAAAACQIGjqAwAAAAAAAACQIGjqAwAAAAAAAACQIGjqAwAAAAAAAACQIGjqAwAAAAAAAACQIGjqAwAAAAAAAACQIP4/HjV4PtpZDu0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x1600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 16))\n",
    "\n",
    "vert_coord = 0.99\n",
    "fig.text(\n",
    "    0.5,\n",
    "    vert_coord,\n",
    "    \"THIS RUN = \" + case_name + \" \" + start_date + \" to \" + end_date,\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    fontsize=14,\n",
    "    color=\"royalblue\",\n",
    ")\n",
    "vert_coord = vert_coord - 0.015\n",
    "if base_case_name is not None:\n",
    "    fig.text(\n",
    "        0.5,\n",
    "        vert_coord,\n",
    "        \"BASELINE RUN = \"\n",
    "        + base_case_name\n",
    "        + \" \"\n",
    "        + base_start_date\n",
    "        + \" to \"\n",
    "        + base_end_date,\n",
    "        ha=\"center\",\n",
    "        va=\"center\",\n",
    "        fontsize=14,\n",
    "        color=\"red\",\n",
    "    )\n",
    "    vert_coord = vert_coord - 0.015\n",
    "\n",
    "fig.text(\n",
    "    0.5,\n",
    "    vert_coord,\n",
    "    \"Other runs = 1979-01-01 to 2023-12-31\",\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    fontsize=14,\n",
    ")\n",
    "vert_coord = vert_coord - 0.015\n",
    "\n",
    "fig.text(\n",
    "    0.5,\n",
    "    vert_coord,\n",
    "    \"Validation data = ERA5 1979-01-01 to 2023-12-31\",\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    fontsize=14,\n",
    ")\n",
    "vert_coord = vert_coord - 0.03\n",
    "\n",
    "ax = plotnmse(\n",
    "    fig,\n",
    "    nmse_cmip6[\"AM\"],\n",
    "    nmse_cesm2[\"AM\"],\n",
    "    nmse_dat[\"AM\"],\n",
    "    nmse_base_dat[\"AM\"],\n",
    "    0.3,\n",
    "    0.7,\n",
    "    vert_coord - 0.16,\n",
    "    vert_coord,\n",
    "    \"NMSE, SLP, AM\",\n",
    ")\n",
    "ax = plotnmse(\n",
    "    fig,\n",
    "    nmse_cmip6[\"DJF\"],\n",
    "    nmse_cesm2[\"DJF\"],\n",
    "    nmse_dat[\"DJF\"],\n",
    "    nmse_base_dat[\"DJF\"],\n",
    "    0.05,\n",
    "    0.45,\n",
    "    0.57,\n",
    "    0.72,\n",
    "    \"NMSE, SLP, DJF\",\n",
    ")\n",
    "ax = plotnmse(\n",
    "    fig,\n",
    "    nmse_cmip6[\"MAM\"],\n",
    "    nmse_cesm2[\"MAM\"],\n",
    "    nmse_dat[\"MAM\"],\n",
    "    nmse_base_dat[\"MAM\"],\n",
    "    0.55,\n",
    "    0.95,\n",
    "    0.57,\n",
    "    0.72,\n",
    "    \"NMSE, SLP, MAM\",\n",
    ")\n",
    "ax = plotnmse(\n",
    "    fig,\n",
    "    nmse_cmip6[\"JJA\"],\n",
    "    nmse_cesm2[\"JJA\"],\n",
    "    nmse_dat[\"JJA\"],\n",
    "    nmse_base_dat[\"JJA\"],\n",
    "    0.05,\n",
    "    0.45,\n",
    "    0.37,\n",
    "    0.52,\n",
    "    \"NMSE, SLP, JJA\",\n",
    ")\n",
    "ax = plotnmse(\n",
    "    fig,\n",
    "    nmse_cmip6[\"SON\"],\n",
    "    nmse_cesm2[\"SON\"],\n",
    "    nmse_dat[\"SON\"],\n",
    "    nmse_base_dat[\"SON\"],\n",
    "    0.55,\n",
    "    0.95,\n",
    "    0.37,\n",
    "    0.52,\n",
    "    \"NMSE, SLP, SON\",\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cupid-analysis",
   "language": "python",
   "name": "cupid-analysis"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "papermill": {
   "duration": 44.108364,
   "end_time": "2025-08-20T22:04:04.477165",
   "exception": null,
   "input_path": "/glade/derecho/scratch/hannay/tmp/tmphblainv3.ipynb",
   "output_path": "/glade/work/hannay/CUPiD/examples/key_metrics/computed_notebooks/atm/Global_PSL_NMSE_compare_obs_lens.ipynb",
   "parameters": {
    "CESM_output_dir": "/glade/derecho/scratch/hannay/archive",
    "base_case_name": "b.e30_beta06.B1850C_LTso.ne30_t232_wgx3.188",
    "base_case_output_dir": "/glade/derecho/scratch/gmarques/archive",
    "base_end_date": "0021-12-01",
    "base_regridded_output": false,
    "base_start_date": "0002-01-01",
    "case_name": "b.e30_beta06.B1850C_LTso.ne30_t232_wgx3.192",
    "end_date": "0021-12-01",
    "lc_kwargs": {
     "threads_per_worker": 1
    },
    "obs_data_dir": "/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CUPiD_obs_data",
    "product": "/glade/work/hannay/CUPiD/examples/key_metrics/computed_notebooks//atm/Global_PSL_NMSE_compare_obs_lens.ipynb",
    "regridded_output": false,
    "serial": true,
    "start_date": "0002-01-01",
    "subset_kwargs": {},
    "ts_dir": null,
    "validation_path": "atm/analysis_datasets/fv0.9x1.25/seasonal_climatology/nmse_validation/PSL/"
   },
   "start_time": "2025-08-20T22:03:20.368801"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}