gnet8j_encoding_multisubjects.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:95% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:95% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stimulus-driven predictive model of brain activity: Joint training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import src.numpy_utility as pnu\n",
    "from src.file_utility import save_stuff, flatten_dict, embed_dict, zip_dict\n",
    "from src.config import *\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#device: 4\n",
      "device#: 0\n",
      "device name: NVIDIA A100-PCIE-40GB\n",
      "\n",
      "torch: 1.10.0+cu113\n",
      "cuda:  11.3\n",
      "cudnn: 8200\n",
      "dtype: torch.float32\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "print ('#device:', torch.cuda.device_count())\n",
    "print ('device#:', torch.cuda.current_device())\n",
    "print ('device name:', torch.cuda.get_device_name(torch.cuda.current_device()))\n",
    "\n",
    "torch.manual_seed(time.time())\n",
    "device = torch.device(\"cuda:1\") #cuda\n",
    "torch.backends.cudnn.enabled=True\n",
    "\n",
    "print ('\\ntorch:', torch.__version__)\n",
    "print ('cuda: ', torch.version.cuda)\n",
    "print ('cudnn:', torch.backends.cudnn.version())\n",
    "print ('dtype:', torch.get_default_dtype())\n",
    "#torch.set_default_dtype(torch.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time Stamp: Feb-04-2022_1818\n"
     ]
    }
   ],
   "source": [
    "timestamp = time.strftime('%b-%d-%Y_%H%M', time.localtime()) # 'Aug-10-2020_1638' #\n",
    "\n",
    "model_name = 'gnet8j_mpf_evc'\n",
    "model_dir = '%s_%s' % (model_name, timestamp)\n",
    "\n",
    "output_dir = root_dir+\"output/multisubject/%s_%s/\" % (model_name,timestamp) \n",
    "\n",
    "trn_subjects = [1,2,3,4,5,6,7,8]\n",
    "for k,s in enumerate(trn_subjects): \n",
    "    subject_dir = output_dir + 'S%02d/'%s\n",
    "    if not os.path.exists(subject_dir):\n",
    "        os.makedirs(subject_dir)   \n",
    "print (\"Time Stamp: %s\" % timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the stimuli images\n",
    "From the subjectwise image preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------  subject 1  -------\n",
      "block size: (10000, 3, 227, 227) , dtype: uint8 , value range: 0 255\n",
      "--------  subject 2  -------\n",
      "block size: (10000, 3, 227, 227) , dtype: uint8 , value range: 0 255\n",
      "--------  subject 3  -------\n",
      "block size: (10000, 3, 227, 227) , dtype: uint8 , value range: 0 255\n",
      "--------  subject 4  -------\n",
      "block size: (10000, 3, 227, 227) , dtype: uint8 , value range: 0 255\n",
      "--------  subject 5  -------\n",
      "block size: (10000, 3, 227, 227) , dtype: uint8 , value range: 0 255\n",
      "--------  subject 6  -------\n",
      "block size: (10000, 3, 227, 227) , dtype: uint8 , value range: 0 255\n",
      "--------  subject 7  -------\n",
      "block size: (10000, 3, 227, 227) , dtype: uint8 , value range: 0 255\n",
      "--------  subject 8  -------\n",
      "block size: (10000, 3, 227, 227) , dtype: uint8 , value range: 0 255\n"
     ]
    }
   ],
   "source": [
    "exp_design = loadmat(exp_design_file)\n",
    "ordering = exp_design['masterordering'].flatten() - 1 # zero-indexed ordering of indices (matlab-like to python-like)\n",
    "\n",
    "image_data = {}\n",
    "for s in trn_subjects: \n",
    "    image_data_set = h5py.File(stim_dir + \"S%d_stimuli_227.h5py\"%s, 'r')\n",
    "    image_data[s] = np.copy(image_data_set['stimuli'])\n",
    "    image_data_set.close()\n",
    "    print ('--------  subject %d  -------' % s)\n",
    "    print ('block size:', image_data[s].shape, ', dtype:', image_data[s].dtype, ', value range:',\\\n",
    "           np.min(image_data[s][0]), np.max(image_data[s][0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x576 with 24 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 1003\n",
    "plt.figure(figsize=(4,1*len(trn_subjects)))\n",
    "for k,s in enumerate(trn_subjects): \n",
    "    for i in range(3):\n",
    "        plt.subplot(len(trn_subjects), 3, 3*k+i+1)\n",
    "        plt.imshow(image_data[s][n+i].transpose((1,2,0)), cmap='gray', interpolation='None')\n",
    "        plt.gca().get_xaxis().set_visible(False)\n",
    "        plt.gca().get_yaxis().set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the voxel data\n",
    "- We load a subset (a range of voxels) of all voxel for the sake of the example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.file_utility import load_mask_from_nii, view_data\n",
    "from src.roi import roi_map, iterate_roi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta_dir = '/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------  subject 1  -------\n",
      "(81, 104, 83)\n",
      "full mask length = 699192\n",
      "selection length = 4627\n",
      "1348 \t: V1\n",
      "1415 \t: V2\n",
      "1188 \t: V3\n",
      "676 \t: hV4\n",
      "0 \t: V3ab\n",
      "0 \t: LO\n",
      "0 \t: IPS\n",
      "0 \t: VO\n",
      "0 \t: PHC\n",
      "0 \t: MT\n",
      "0 \t: MST\n",
      "0 \t: other\n",
      "--------  subject 2  -------\n",
      "(82, 106, 84)\n",
      "full mask length = 730128\n",
      "selection length = 3765\n",
      "1097 \t: V1\n",
      "1083 \t: V2\n",
      "1081 \t: V3\n",
      "504 \t: hV4\n",
      "0 \t: V3ab\n",
      "0 \t: LO\n",
      "0 \t: IPS\n",
      "0 \t: VO\n",
      "0 \t: PHC\n",
      "0 \t: MT\n",
      "0 \t: MST\n",
      "0 \t: other\n",
      "--------  subject 3  -------\n",
      "(81, 106, 82)\n",
      "full mask length = 704052\n",
      "selection length = 3778\n",
      "1251 \t: V1\n",
      "1160 \t: V2\n",
      "945 \t: V3\n",
      "422 \t: hV4\n",
      "0 \t: V3ab\n",
      "0 \t: LO\n",
      "0 \t: IPS\n",
      "0 \t: VO\n",
      "0 \t: PHC\n",
      "0 \t: MT\n",
      "0 \t: MST\n",
      "0 \t: other\n",
      "--------  subject 4  -------\n",
      "(85, 99, 80)\n",
      "full mask length = 673200\n",
      "selection length = 3027\n",
      "871 \t: V1\n",
      "883 \t: V2\n",
      "800 \t: V3\n",
      "473 \t: hV4\n",
      "0 \t: V3ab\n",
      "0 \t: LO\n",
      "0 \t: IPS\n",
      "0 \t: VO\n",
      "0 \t: PHC\n",
      "0 \t: MT\n",
      "0 \t: MST\n",
      "0 \t: other\n",
      "--------  subject 5  -------\n",
      "(79, 97, 78)\n",
      "full mask length = 597714\n",
      "selection length = 3685\n",
      "1115 \t: V1\n",
      "1088 \t: V2\n",
      "934 \t: V3\n",
      "548 \t: hV4\n",
      "0 \t: V3ab\n",
      "0 \t: LO\n",
      "0 \t: IPS\n",
      "0 \t: VO\n",
      "0 \t: PHC\n",
      "0 \t: MT\n",
      "0 \t: MST\n",
      "0 \t: other\n",
      "--------  subject 6  -------\n",
      "(85, 113, 83)\n",
      "full mask length = 797215\n",
      "selection length = 4024\n",
      "1130 \t: V1\n",
      "1191 \t: V2\n",
      "1215 \t: V3\n",
      "488 \t: hV4\n",
      "0 \t: V3ab\n",
      "0 \t: LO\n",
      "0 \t: IPS\n",
      "0 \t: VO\n",
      "0 \t: PHC\n",
      "0 \t: MT\n",
      "0 \t: MST\n",
      "0 \t: other\n",
      "--------  subject 7  -------\n",
      "(78, 95, 81)\n",
      "full mask length = 600210\n",
      "selection length = 3201\n",
      "1148 \t: V1\n",
      "958 \t: V2\n",
      "721 \t: V3\n",
      "374 \t: hV4\n",
      "0 \t: V3ab\n",
      "0 \t: LO\n",
      "0 \t: IPS\n",
      "0 \t: VO\n",
      "0 \t: PHC\n",
      "0 \t: MT\n",
      "0 \t: MST\n",
      "0 \t: other\n",
      "--------  subject 8  -------\n",
      "(80, 103, 78)\n",
      "full mask length = 642720\n",
      "selection length = 3490\n",
      "1081 \t: V1\n",
      "1040 \t: V2\n",
      "879 \t: V3\n",
      "490 \t: hV4\n",
      "0 \t: V3ab\n",
      "0 \t: LO\n",
      "0 \t: IPS\n",
      "0 \t: VO\n",
      "0 \t: PHC\n",
      "0 \t: MT\n",
      "0 \t: MST\n",
      "0 \t: other\n"
     ]
    }
   ],
   "source": [
    "group_names = ['V1', 'V2', 'V3', 'hV4', 'V3ab', 'LO', 'IPS', 'VO', 'PHC', 'MT', 'MST', 'other']\n",
    "group = [[1,2],[3,4],[5,6], [7], [16, 17], [14, 15], [18,19,20,21,22,23], [8, 9], [10,11], [13], [12], [24,25,0]]\n",
    "\n",
    "brain_nii_shape, voxel_mask, voxel_idx, voxel_roi, voxel_ncsnr = {}, {}, {}, {}, {}\n",
    "\n",
    "for k,s in enumerate(trn_subjects):\n",
    "    print ('--------  subject %d  -------' % s)\n",
    "    voxel_mask_full = load_mask_from_nii(mask_dir + \"subj%02d/func1pt8mm/brainmask_vcventral_1.0.nii\"%s)\n",
    "    #voxel_mask_full = load_mask_from_nii(mask_dir + \"subj%02d/func1pt8mm/brainmask_nsdgeneral_1.0.nii\"%s)\n",
    "    voxel_roi_full  = load_mask_from_nii(mask_dir + \"subj%02d/func1pt8mm/roi/prf-visualrois.nii.gz\"%s)\n",
    "    voxel_kast_full = load_mask_from_nii(mask_dir + \"subj%02d/func1pt8mm/roi/Kastner2015.nii.gz\"%(s))\n",
    "    general_mask_full  = load_mask_from_nii(mask_dir + \"subj%02d/func1pt8mm/roi/nsdgeneral.nii.gz\"%(s))\n",
    "    ncsnr_full = load_mask_from_nii(beta_dir + \"subj%02d/func1pt8mm/betas_fithrf_GLMdenoise_RR/ncsnr.nii.gz\"%s)\n",
    "    ###\n",
    "    brain_nii_shape[s] = voxel_roi_full.shape\n",
    "    print (brain_nii_shape[s])\n",
    "    ###\n",
    "    voxel_roi_mask_full = (voxel_roi_full>0).flatten().astype(bool)\n",
    "    voxel_joined_roi_full = np.copy(voxel_kast_full.flatten())  # load kastner rois\n",
    "    voxel_joined_roi_full[voxel_roi_mask_full] = voxel_roi_full.flatten()[voxel_roi_mask_full] # overwrite with prf rois\n",
    "    ###\n",
    "    voxel_mask[s]  = np.nan_to_num(voxel_mask_full).flatten().astype(bool)\n",
    "    voxel_idx[s]   = np.arange(len(voxel_mask[s]))[voxel_mask[s]]\n",
    "    voxel_roi[s]   = voxel_joined_roi_full[voxel_mask[s]]\n",
    "    voxel_ncsnr[s] = ncsnr_full.flatten()[voxel_mask[s]]\n",
    "        \n",
    "    print ('full mask length = %d'%len(voxel_mask[s]))\n",
    "    print ('selection length = %d'%np.sum(voxel_mask[s]))\n",
    "    \n",
    "    for roi_mask, roi_name in iterate_roi(group, voxel_roi[s], roi_map, group_name=group_names):\n",
    "        print (\"%d \\t: %s\" % (np.sum(roi_mask), roi_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x2304 with 48 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slice_idx = 35\n",
    "plt.figure(figsize=(12, 4*len(trn_subjects)))\n",
    "for k,s in enumerate(trn_subjects): \n",
    "    subject_dir = output_dir + 'S%02d/'%s\n",
    "    if not os.path.exists(subject_dir):\n",
    "        os.makedirs(subject_dir)\n",
    "    \n",
    "    volume_brain_mask = view_data(brain_nii_shape[s], voxel_idx[s], np.ones_like(voxel_idx[s]), save_to=subject_dir+\"subj%02d_mask\"%s)\n",
    "    volume_brain_roi  = view_data(brain_nii_shape[s], voxel_idx[s], voxel_roi[s], save_to=subject_dir+\"subj%02d_roi\"%s)\n",
    "    volume_ncsnr      = view_data(brain_nii_shape[s], voxel_idx[s], voxel_ncsnr[s], save_to=subject_dir+\"subj%02d_ncsnr\"%s) \n",
    "    ##\n",
    "    plt.subplot(len(trn_subjects),3,3*k+1)\n",
    "    plt.imshow(volume_brain_mask[:,:,slice_idx], cmap='gray', interpolation='None')\n",
    "    plt.title('Mask')\n",
    "    plt.colorbar()\n",
    "    _=plt.ylabel('Subject %d'%s)\n",
    "    _=plt.gca().set_xticklabels([])\n",
    "    _=plt.gca().set_yticklabels([])\n",
    "    plt.subplot(len(trn_subjects),3,3*k+2)\n",
    "    plt.imshow(volume_brain_roi[:,:,slice_idx], cmap='jet', interpolation='None')\n",
    "    plt.clim([0,7])\n",
    "    plt.title('ROI')\n",
    "    plt.colorbar()\n",
    "    _=plt.gca().set_xticklabels([])\n",
    "    _=plt.gca().set_yticklabels([])\n",
    "    plt.subplot(len(trn_subjects),3,3*k+3)\n",
    "    plt.imshow(volume_ncsnr[:,:,slice_idx], cmap='hot', interpolation='None')\n",
    "    plt.title('NCSNR')\n",
    "    plt.colorbar()\n",
    "    _=plt.gca().set_xticklabels([])\n",
    "    _=plt.gca().set_yticklabels([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load voxels\n",
    "Only run one of these cells\n",
    "## Quick load (Load the voxel data block for these voxels only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys([1, 2, 3, 4, 5, 6, 7, 8])\n"
     ]
    }
   ],
   "source": [
    "#voxel_data_set = h5py.File(root_dir+'voxel_synth_data_V1-4_part1.h5py', 'r')\n",
    "#voxel_data_set = h5py.File(voxel_dir+'voxel_data_nsdgeneral_part1.h5py', 'r')\n",
    "voxel_data_set = h5py.File(voxel_dir+'voxel_data_V1_4_part1.h5py', 'r')\n",
    "voxel_data_dict = embed_dict({k: np.copy(d) for k,d in voxel_data_set.items()})\n",
    "voxel_data_set.close()\n",
    "voxel_data = voxel_data_dict['voxel_data']\n",
    "\n",
    "#voxel_data_set = h5py.File(root_dir+'voxel_synth_data_V1-4_part2.h5py', 'r')\n",
    "#voxel_data_set = h5py.File(voxel_dir+'voxel_data_nsdgeneral_part2.h5py', 'r')\n",
    "voxel_data_set = h5py.File(voxel_dir+'voxel_data_V1_4_part2.h5py', 'r')\n",
    "voxel_data_dict = embed_dict({k: np.copy(d) for k,d in voxel_data_set.items()})\n",
    "voxel_data_set.close()\n",
    "voxel_data.update(voxel_data_dict['voxel_data'])\n",
    "\n",
    "voxel_data = {s: voxel_data['%d'%s] for s in voxel_roi.keys()}\n",
    "print (voxel_data.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slow load from raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------  subject 1  -------\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session01.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session02.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session03.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session04.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session05.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session06.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session07.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session08.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session09.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session10.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session11.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session12.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session13.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session14.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session15.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session16.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session17.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session18.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session19.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session20.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session21.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session22.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session23.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session24.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session25.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session26.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session27.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session28.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session29.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session30.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session31.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session32.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session33.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session34.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session35.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session36.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session37.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session38.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session39.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj01/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session40.nii.gz\n",
      "int16 -32768 32767 (750, 81, 104, 83)\n",
      "----------------------------\n",
      "(30000, 4627)\n",
      "--------  subject 2  -------\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session01.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session02.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session03.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session04.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session05.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session06.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session07.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session08.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session09.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session10.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session11.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session12.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session13.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session14.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session15.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session16.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session17.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session18.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session19.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session20.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session21.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session22.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session23.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session24.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session25.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session26.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session27.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session28.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session29.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session30.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session31.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session32.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session33.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session34.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session35.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session36.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session37.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session38.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session39.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj02/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session40.nii.gz\n",
      "int16 -32768 32767 (750, 82, 106, 84)\n",
      "----------------------------\n",
      "(30000, 3765)\n",
      "--------  subject 3  -------\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session01.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session02.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session03.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session04.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session05.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session06.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session07.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session08.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session09.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session10.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session11.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session12.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session13.nii.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session14.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session15.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session16.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session17.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session18.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session19.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session20.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session21.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session22.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session23.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session24.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session25.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session26.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session27.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session28.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session29.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session30.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session31.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj03/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session32.nii.gz\n",
      "int16 -32768 32767 (750, 81, 106, 82)\n",
      "----------------------------\n",
      "(24000, 3778)\n",
      "--------  subject 4  -------\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session01.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session02.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session03.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session04.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session05.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session06.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session07.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session08.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session09.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session10.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session11.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session12.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session13.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session14.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session15.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session16.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session17.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session18.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session19.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session20.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session21.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session22.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session23.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session24.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session25.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session26.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session27.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session28.nii.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session29.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj04/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session30.nii.gz\n",
      "int16 -32768 32767 (750, 85, 99, 80)\n",
      "----------------------------\n",
      "(22500, 3027)\n",
      "--------  subject 5  -------\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session01.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session02.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session03.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session04.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session05.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session06.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session07.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session08.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session09.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session10.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session11.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session12.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session13.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session14.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session15.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session16.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session17.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session18.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session19.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session20.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session21.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session22.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session23.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session24.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session25.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session26.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session27.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session28.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session29.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session30.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session31.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session32.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session33.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session34.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session35.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session36.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session37.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session38.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session39.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj05/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session40.nii.gz\n",
      "int16 -32768 32767 (750, 79, 97, 78)\n",
      "----------------------------\n",
      "(30000, 3685)\n",
      "--------  subject 6  -------\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session01.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session02.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session03.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session04.nii.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session05.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session06.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session07.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session08.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session09.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session10.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session11.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session12.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session13.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session14.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session15.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session16.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session17.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session18.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session19.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session20.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session21.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session22.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session23.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session24.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session25.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session26.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session27.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session28.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session29.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session30.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session31.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj06/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session32.nii.gz\n",
      "int16 -32768 32767 (750, 85, 113, 83)\n",
      "----------------------------\n",
      "(24000, 4024)\n",
      "--------  subject 7  -------\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session01.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session02.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session03.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session04.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session05.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session06.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session07.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session08.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session09.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session10.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session11.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session12.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session13.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session14.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session15.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session16.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session17.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session18.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session19.nii.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session20.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session21.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session22.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session23.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session24.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session25.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session26.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session27.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session28.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session29.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session30.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session31.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session32.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session33.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session34.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session35.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session36.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session37.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session38.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session39.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj07/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session40.nii.gz\n",
      "int16 -32768 32767 (750, 78, 95, 81)\n",
      "----------------------------\n",
      "(30000, 3201)\n",
      "--------  subject 8  -------\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session01.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session02.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session03.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session04.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session05.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session06.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session07.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session08.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session09.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session10.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session11.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session12.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session13.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session14.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session15.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session16.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session17.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session18.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session19.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session20.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session21.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session22.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session23.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session24.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session25.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session26.nii.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session27.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session28.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session29.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "/export/raid1/home/surly/raid4/kendrick-data/nsd/nsddata_betas/ppdata/subj08/func1pt8mm/betas_fithrf_GLMdenoise_RR/betas_session30.nii.gz\n",
      "int16 -32768 32767 (750, 80, 103, 78)\n",
      "----------------------------\n",
      "(22500, 3490)\n",
      "saved voxel_mask.1 in h5py file\n",
      "saved voxel_mask.2 in h5py file\n",
      "saved voxel_mask.3 in h5py file\n",
      "saved voxel_mask.4 in h5py file\n",
      "saved voxel_mask.5 in h5py file\n",
      "saved voxel_mask.6 in h5py file\n",
      "saved voxel_mask.7 in h5py file\n",
      "saved voxel_mask.8 in h5py file\n",
      "saved voxel_roi.1 in h5py file\n",
      "saved voxel_roi.2 in h5py file\n",
      "saved voxel_roi.3 in h5py file\n",
      "saved voxel_roi.4 in h5py file\n",
      "saved voxel_roi.5 in h5py file\n",
      "saved voxel_roi.6 in h5py file\n",
      "saved voxel_roi.7 in h5py file\n",
      "saved voxel_roi.8 in h5py file\n",
      "saved voxel_idx.1 in h5py file\n",
      "saved voxel_idx.2 in h5py file\n",
      "saved voxel_idx.3 in h5py file\n",
      "saved voxel_idx.4 in h5py file\n",
      "saved voxel_idx.5 in h5py file\n",
      "saved voxel_idx.6 in h5py file\n",
      "saved voxel_idx.7 in h5py file\n",
      "saved voxel_idx.8 in h5py file\n",
      "saved voxel_ncsnr.1 in h5py file\n",
      "saved voxel_ncsnr.2 in h5py file\n",
      "saved voxel_ncsnr.3 in h5py file\n",
      "saved voxel_ncsnr.4 in h5py file\n",
      "saved voxel_ncsnr.5 in h5py file\n",
      "saved voxel_ncsnr.6 in h5py file\n",
      "saved voxel_ncsnr.7 in h5py file\n",
      "saved voxel_ncsnr.8 in h5py file\n",
      "saved voxel_data.1 in h5py file\n",
      "saved voxel_data.2 in h5py file\n",
      "saved voxel_data.3 in h5py file\n",
      "saved voxel_data.4 in h5py file\n",
      "saved voxel_data.5 in h5py file\n",
      "saved voxel_data.6 in h5py file\n",
      "saved voxel_data.7 in h5py file\n",
      "saved voxel_data.8 in h5py file\n"
     ]
    }
   ],
   "source": [
    "## Long version\n",
    "from src.load_nsd import load_betas\n",
    "\n",
    "voxel_data = {}\n",
    "for k,s in enumerate(trn_subjects):\n",
    "    print ('--------  subject %d  -------' % s)\n",
    "    beta_subj = beta_root + \"subj%02d/func1pt8mm/betas_fithrf_GLMdenoise_RR/\"%s\n",
    "    voxel_data[s],_ = load_betas(folder_name=beta_subj, zscore=False, voxel_mask=voxel_mask[s], up_to=-1, load_ext='.nii.gz')\n",
    "    print ('----------------------------')\n",
    "    print (voxel_data[s].shape)\n",
    "    \n",
    "save_stuff(voxel_dir + 'voxel_data_general_raw', \n",
    "    flatten_dict({\n",
    "   'voxel_mask': voxel_mask,\n",
    "   'voxel_roi': voxel_roi,\n",
    "   'voxel_idx': voxel_idx,\n",
    "   'voxel_ncsnr': voxel_ncsnr,\n",
    "   'voxel_data': voxel_data\n",
    "            }))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset split and unpacking or averaging\n",
    "## Reduce the stimulus set to those of which we have responses so far.\n",
    "- It just so happens that images with index less than 1000 are shared among subjects and are garanteed not to be shown in the remainder. We therefore chose to reparate our training and validation set along these lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------  subject 1  -------\n",
      "Total number of voxels = 4627\n",
      "--------  subject 2  -------\n",
      "Total number of voxels = 3765\n",
      "--------  subject 3  -------\n",
      "Total number of voxels = 3778\n",
      "--------  subject 4  -------\n",
      "Total number of voxels = 3027\n",
      "--------  subject 5  -------\n",
      "Total number of voxels = 3685\n",
      "--------  subject 6  -------\n",
      "Total number of voxels = 4024\n",
      "--------  subject 7  -------\n",
      "Total number of voxels = 3201\n",
      "--------  subject 8  -------\n",
      "Total number of voxels = 3490\n"
     ]
    }
   ],
   "source": [
    "from src.load_nsd import image_feature_fn, ordering_split\n",
    "trn_stim_ordering, trn_voxel_data, val_stim_ordering, val_voxel_data = {},{},{},{}\n",
    "\n",
    "stim_data = {}\n",
    "data_size, nnv = {}, {}\n",
    "for k,s in enumerate(trn_subjects):\n",
    "    print ('--------  subject %d  -------' % s)\n",
    "    data_size[s], nnv[s] = voxel_data[s].shape      \n",
    "    stim_data[s] = image_feature_fn(image_data[s])\n",
    "    \n",
    "    trn_stim_ordering[s], trn_voxel_data[s], \\\n",
    "    val_stim_ordering[s], val_voxel_data[s] = \\\n",
    "        ordering_split(voxel_data[s], ordering, combine_trial=False)\n",
    "    \n",
    "del image_data\n",
    "del voxel_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[0.47611815]]\n",
      "\n",
      "  [[0.45324636]]\n",
      "\n",
      "  [[0.41308543]]]]\n"
     ]
    }
   ],
   "source": [
    "trn_stim_mean = sum([np.mean(stim_data[s], axis=(0,2,3), keepdims=True) for s in trn_subjects])/len(trn_subjects)\n",
    "print (trn_stim_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pz3yGUnEQLzBMT08y2j9GSkyaOAz2F5lfqrJ21Sqm5qbpLZXRqaUddmmFEWCRMiDwA7qdOkP9g4wWBQWZZ2JF/5O9ZA/DRDB9eJrpI7OARVmDUC7KVXhGkmgJ0kFIy+DwIKtXb8YkHtNTcyhHU12YQTmgpEciVGY8SpKaLjqSSMchiVJwLXguYZywMDuLMSlCSUwicD0H1w9odVuMjKyg2dIkQwb1KHKPpzS0u+7dwQc//03O2LCWgaFhwnadhcQn9QJyvkePF3DZhRfwtZuvpz26krXNOvnBPtrdmGLBww9ySOXyple8hB3338bu6+9G+gUKA6PkhoYxXpludYmJNQPMLc2y2IwQ1pCXbUS3xUSfT9xsIPuGKAYBOhWQSpYWatRnDyNsSn+Q4vU67LjvHsrDKx/Nvv5E0JqpETU0xiZI5WSuXaGIVyxRyA0QeGXywkV2YqwDUd4hBUq5HP2uhxBQr3Zp16tMrFqL0ZN85nP/QV9vH416BWkCentKeFLj4tGRUKk38VwfR/l4bp52VyOEoFAcJEq6CFtAm4g1QyUG/B5cz2EgX6TSboI+fW40YVKMTVHCAyVBeQiVZUVTBMJKjE4QiWD26DyzR+fAWLRJ8ByF8nwc10WpFBMbeofKJB1LHKZIV2CSmEa9ynkXXk6cpJhmGysECIWxIG1MGDoYGzE8OEB/bwE/yGc/8yhwSkO75IpL+L8f/yKOo/AdnyOLLaTv4fsF/JzH9u3n8fVbbyLoHWPU83D6fKI0oVjMEcddrOPw5le9ghvvuJNElugfLFMslSj3D4Hr4uaLHL33fnoHJN3I0mnFLC7M09s/yKq1G+kfHKbdjelWZolsyt23f5tWq0U+cFgxvgKrJd2oRRwrrFDse/AeLnjOCx/VYjzuSDRR0sIRPggXx3jYAKzWCBziTkx1pkrfhgFeee461udzD/v/J79edM5a9uzYxyc/9UVazZDLr3gRd9/xHcZX9jE5fRDPzZEr9zOzMEmp1EuSapqNRQbGNnHNC17Di1/3HK7augoHuODMpzOa1xRVwMjIMJ12iECR2JjA8entH3kSF+v7YazEJAZLhOsqdGqxIsEqgfKLWQyaGBzHw6JR0kE7GpccUgqsFegoRTsWTErl+ALGdQncgDhKCVsh5YE+Uhui2126UYwSdrlhwCdOE3KlHJ6Q9JYLCOUQRxEm8Hg0pdpTGlre9TGxptWNGSnmSYwm5yiueubTGR0bIe22ec1rXocUFieJqDbqWBQOHaxX4Oy6z+e+9AWsl2NgMI/vezhBDqzAaIGnBK1Eo5c0kfQ4cWwGYzVWNIjbDzI8vobhifUcP3gfIlzCjSskiaHSTalOHaSQ84njmDBsUlmsMRO6P/4q/ISQxG1UnJCamNS28PwSTlJElXy8vMsVl2/gV6/ejJSKH3ZQuq7H29/2It7wU89jZq5BBPz8r72WoueQDzzUcrYSvjdjabPirQVtNH/6Mz/N5LFJrtjYh6c8UIqeIM/xKGVwYJhWGlFrpSw12k/MwvwI6HZaxFEXIRTGGrAOnueTaonVDXQa4zh5EhOTmohCrockThE2XO59FNSrNTpdjZ8v018u0Jqbodw3gkm7uMphbGyMNIwx1iCVQuBgBeg0IfBczt6yjoVmiFAKYRWNRpNS0acg/B/b2E5paFESkzgCUBRzJQB+4VUvQbk5hJRYo7E6xSCZXKqT812MgVZX4nRarBryeM3Ln0OkPbQXcPDQCaIUDApHWPomVjM7NYnWbdJuyFC/R5IIDh47xorhQcTcNM12G1NboFOfZ83GMwj37iLqnOCWnYdZPVBkbHwlrWbIQ5N1LnrmVY9mX38i6F+9lbPO3M4F12zmgi09DDguaE2P7zLq+kghEPKRm3pOGk/O91i3avCUv/Nkm5Axhhs+8gEeuOV2iFLGR/sxcUJiPSKbECEZHhmh1uzi+GXa4SyjfadRC5YOSXWCIiWxCuVawqgDUkCkcBxFGlZxHB9jDM2kghWG+dk55uYWaNYbDA+W8HJDTO45gIybuMUC55+T7cHqc89Haku706JYyCGUhxSSVCf4vkPYCZmcXcLP51mqzLFixQQ3XH83L3rplfT1eT92K9aPUEfL8cfveQvVSpviwGrO27SayEhk1KBzbA6tDYVSD4WeAcqlAmG7w/zUJBqJFJJQGwq9IxibkgtKbN2wBikkjpLE2nL8wD58ZbCuR73ZIk0F+47PsnHlGJ1Y0+3Uqc2fgKTJ+JozOLbzDk4cOsQ39syCEVRDmO9MAfDH7341E1vP5EkZpPoB+NpH3o3vff/p92hSw6eCtQZjDNVak6/8/Z8jdQepExSKQt4jyBVYXFwiXyzh4ePpmEhLPJvSUygSCZfXveNdPO+lV7B3z97H/fkeDeJEo9MUoRxsGqFTjZIiO0REik4sUiqSNAabUqsucsu370YIwejYGAuLVSJtaDWmKfYMkvMdCkGBZtuw9+BeBseHcb319I2uxJEOKvDRcRcL6CRkeMVKPM9HWFi1ah2HDh3hoss2cf3XvsRbX/vqH7uH5pSG9q3bH6CUL1MuCGjPoz2HZrVCJwrR3SqdRAAepWKRkdXriY1iaPN5mE6bZm0WT0jSziImbDM3dwTl57BC4ukWjShPzle0XBfp92ALI1SOnaDs55iaqbJQr0N3lisv3c6OXVVmTlzHzNQxtm1czbte+iyOLTV44Ytfymg5zxc+/xmE7/OVr3ydq15/ejQWi+Ub6/HEyUZlY1KajQZf/s9/Yu7wCYwSeDKivxhQLhUwYUyz1UUJgfIcRsZXMTWzQD4QFP2ApK3B7+Pat/80SSqJhOVzX9jD1k2nRyFSWI3UGqzAzQdEYQdrHRzlYE1Mag1KW4QI0DblG9ffhOf45MtD+Pk89VqNTqvDmedeyp4ddxAZTRzuxnXv5eUvfRG333InPUN7+KP/80GarTa1ao1OnJAqw2DfShzXAylIkogdD97H2IqV9PaUufGrk2jLj53iP6WhKeHRrFdwHEGtukSuUKI8NILTaZO0A+LKLJqAdmyYObybsBuhcjk8x6KERyQdHBdGVp+JXZwkX+zHKQ7QbS6hTxxgarGJkJbtl17N0sIcg/2GRi0kjjS1eottW9aze+9hovoct+09wfPO28hCpYVXqCKFTyFf5MA9d1Fw8xzYt4/j0emTov5BsDbrCv+vN9vJ7xljEEKQ6BQdpXS6XbqVWWozs9SmdhO2GtQqVbRWaBuTz3mMD+XQKfheAd3uEFbaBIUcxZxCUKQyX6EZxgwWcywsNVkgRQYjTE9Xue+3/4zBNdt53luv5ZUv28bOnTufpJX5fkhZYqlZJ4yWEEJQDjxypRwSH6kcdKpJkRjd5u57H2BwaJBOrcPS/ByLc8dItUSqmD0P3kEYpwwODhEVI+ZnFrn1tlvo7R2m0urye7/68/zRX7yPYnEMq4ezZFK3tTynBwuzNfrLfZTyAUmU0m3HGCw/bpB2SkNLjcGkMXGUUMgXwFharRYmDcn3lKl36jiOS+DnEWmICnIEgU/UaWCSDsJaVH6IpFNHej7CCzi2/z4mtl6KM3mAgXKeY1Mn2H3/XQQ9QyjlIITHlrO2sDT3RfYcOMT5F17AsakSF8YxtXaHXVNL5As5js21+f0/+BNecfWFlIf7CbsNrjlj86Pd28cdnU436543hlajgUCiXIVODTZNadbqCJtgkggTJ6Cy+KrTqKE7TaQGazWVxRms1gSOJO+XcQYlaTek3dHoKCXsdMn7eWSi8f0CzWqVbhpBqkErGvWQqUpIIjXV0BCaFu1ulZnKEn5QotEJed+vfYe/ViXe/ze/8GQvGwBRq0KnukijVScO20xZMA4EQZ6hwSFWrh3j7G3buP/Ohzh0eIbePg9XuUgsYSqwQtNuJUCI57nMzkzjOg5Sao4dmyaf7yEQhh337uX66z7FNde+GitchKvwEo80TZmdnqavp0xvXxmQhHEKrnpUw3qnNDRJjMQSGzA6JlfoQQRluo0KMkrpHVpN4AVIx6XZmmPu2CSlvhzG+kgnoJwPkCah023TadawURtfdHGCPI5MabdrlDyH5vxhIiNYtWKMqZlFDuy6i7A+SzVy2LN3D7Y9Szvs0KxGDPqSIydmsTLgZ978Wn7/T/+GX//pn2LjuvUcX5pm/FEsxE8CzVodHaQYo0m6EVIprFWksSZqd7DGIJRD1FjE6ATX8XF8Adogog6gSOIuJtGEcYRfUphUo6OIpJsitETGCT2FPkwSknbaNNoJUhuMIzlRdTg6OUezU+PQksa4EcY61Juaam0OY2OSeJ7Dcj+udOkZPH1asPycx4pVQ0zYMYzjIlMDUmed9MKSNg3f/NqN3H77Di4+fwt33rcbYdsIKXGVREqPfFEy3JOVMZSEmVaLcq5Mt9NhbvYEo2OrcB3BwT3HeN6LHYzVuDh4QiCkZPv27dSqtSwjrASFfABhRMp/90ZONXt4SkNL0gQXSSkf0Akj3HyZyvwsru/RbCcUCi75/glcV1EaWEX/8EbC8Ahh3SdNDdaRNJoLTKxcjbQGdESt3sAc2EXR90k6go40tBsNxkbaVA7fyaH9sxTzitlml4KfZ66WMpQb4vjiLJuGS1RaXSYXW1zwtO1sv/hp/NKrpukf7OHgkQPsSyc44zFt8eOHUm8PvuPR7XYxiSEOO8QhdFsdhNXEcQdPZel515FgY5J2F0KNFQLdaRGGTQQpOQkmEcRRmzQ2CG0IhEMriUhsRL5/hMEtl3Lu08/F9z1qnYjf+vm/xlNNSjnLVPUEQkpy5SGM0w+ighIS6ULJSRlUedpu8GQv2ffAYE2KVQp0ghUgkGSdUYrUdKnMTtPttLnngT0YE+F7Pp7r86JztjKYd1k5NIwTBPTm8ljXYd+xSa67bw+HwjbNRhNrsg6kuZmjCGsyCgQEUoHUAqVAWovEAWNBaIZXrMGSkpmO+JGMDH4EQ/O9PKHpoKRHGLbYuOYspqeOUygWKPX0sfehe7BuwPbznsH01BFMouk2srGEMEpQsUbiILCsP/dy7vjyv9BtNSk0Jql352l1O+QcycqxQTxibnrwMJdc+jR279xBzi8SC0narpMbXsUf/t6vgBVoHXP34S6HHriZxtxRhCdIwy7HWnl6h0+foms+n8P3ffKl/MPfsybz8K21GY+FzaaqjTHEUUgaJxitsdpiRJJlaP0AKZ0sQMcipUAI+T3FbYiThJe/5c95SSfPwWPH+de/+HPCbpOCa3GUodVO+NVf/TPe8gtXM7vU5k3PewdUDnPhmkFSz6OdSqri9DE0bRLcNCBBZ/eHMFgrEMLJGo4RHDg4S0+hyEJlESEUNtVcuH6ES7ZsYPPEBI40ID1cx0EIzebxMZ594VZ2Hlrgzz//VTpRTBjWOH7wGLFO8JSbxV9CYYXGERIrsiynRKGkw9JCHpMarANCfvc2e8w3WhgZ0lQQdltUmx3uvf1GgkIPvYMTFAuSpaFRjDZMHdtPZeYY7XYdNyhQXZxGCUUSdZB+idrSHA/c/Fl02AHXpdOcRZqERq2D0SHlvjFiDZtW9rMwd4xiMU9vucjB+TaDeUWrcoQPfmA/L37BM7nxnp3Mzc7zlne8h5wvswyUcHFESj6dPdUrPakQ8r/MgC//QSlFLl+A/Pf//A9KnPwguI7LX/3ZO1g7Nkg72kIyW+MrX/40C/NzdBpVJB2++vVP4/eN0Vk4Rk9nkq2+w5ZiiWrgUdES354+Q7OeNaROm7wFRIC0IQkeCovjOVTqCe1uSJg0MTohlyuhgN5yibO3rKZUKJAkFk8EGf2BScABR5W47JxRXrL/MJ+4fweBn2NkzRowgCuXuUQygigjBELa5cSIwAp42esvo1JZoGdkRdYsQDbSdKo77dQ3Ws4HNMePHGFwuI9ckGNxbgqDz4UXnsf6rQ6VRoPZqSlM0sX3AhoL0yRxRJS0cZ080hhqM4eJU0WcWvqKLp6QSM9j9/F52u0u1151Jscmj+K5isnJObaesYV2J2JDn6Zr4fO3HuaCDcNsOXMTZ523jW/efBd33vJF1l/7DKT1KA70MrgUscTpE2f8MFhr0VqTJAm+7yHED6c6+FHrbkII1q8YRkpJ0Re84x3PwfM8HrrtBg4dPc7s0gn27bid9+3+Dq6QjKiATr7MHcerLKQRPcUyg+vWPJ6v+ZgwvHIdYdRFCoGJDEnSAesQJy2StEulUqVYLmIaLVJhEDZlrK+XZ29dQ6EwgLQWR3doLc6QWvALAaWBHsqFXnTg8LSLtvGpHbvAGDr1Dh/50D/yjl/8+Yx/xICVy/wtBqQEa8DEhlygaHW6pN/TxW+FwJwiC3lKQ9M46DhCuA7NdkwcztJptzn07a8j4zkaLU2xnDVczkwdQwloN1tIZUmSFD+QREsLVOpN+nvKFPN54jCkRYIwhiiOUBKETRguu0wvdFi7bj133PMAvuMQRSE1rdg8UiRQcNett3HR0y7l5S98Hr/9N/9Es9Fiy4XbqB8/zNLsQdR4/lSv9KTDGMNtH7uBnXfdyOT8LDJX5Jf/6n/T01fO2of+C36QW/K97spJIhljQacaYw3r14zwjjc8g7/61jeYSlqkSYIQglBLGqllUUbM+UVygU9CyGwXVg+dPtPpliy/rhwXIVPcwhDWhuRED9YmfP2W+0nThCjqIEWAq1x+9dqrWbVqjHsP7GNxqcbaNRsodhP6RvoZLo8yWz0GaZehVSso9fWRxhGpsTy08wH+6kN/hzAii7+kROnMNZdCYABBxi/iKpdysZcYg79ctk5Fdts9Ek7Z799tLHH8+HE67Rb1aoVDx6dZnJ/HcyX79+zBpE1qlXkWpo/juS6NeoU0jalXm0TdFp1WjcVGm4GeAgP9RerNBq6jqNXaxKml203YuGKAwEmJw4SVI71EnQYjfYN04gQZ+GitmJxvMNJXYu7EMSb1Kh6at/zcm19Dah2anZi+lcN8a8cxtvafPnGGMea/fc9ay9JClfvvvIejR44Q1uvMzc3y4M13EscxxpjMaIx5+N+TRqW1/r4/IzIjk0IgESgp8B2Fr1wcpVi5dpTazCwnqnMkiSVJNZGRWFyUCnBzAf1jGyn1r6A0MMLWpz/ziV+kHwIhFEpZUp3iOjmM7eC5RSwdAicg7DZRwsFxsmzhqv48E2uGwfOYGF5Nj1/kVz7wQf7PF7/MsUOT7N9/nMVag4VOm5sPHEDl8iRWECcaKRRBIQ9YtBUIR2XuotEYLAaBETbribQCqRzS75l0+FGGZk75M2G3QxS28IiJ4ojBss/4WB/KpiwuVdm7Zy+Tx45QW5ilXquCjcnlPLJMaEIcxRRcF2MVJ6YqxIkl1QLHLzBfDdk8PoCUkvd+/CY+fOMOIreMb0LWjQdsXzvMQK7AppW9rFq9gpzvECUROz/7l1y6eYgNqyeQCDxPU5mc5dff9nr2HdzxGLb38cX+Q7N0oxjzPTdSqjWf/sc72HFoD9V2EysSwtoCd1z3BWam5x82spP4XtdRqWzYVQiBlBIlFY5USCGzIdBlAlHXyX7GdV3e9dm/wzrg57t4foojDY4n6etdSaozPpGhkT6K5RLHj5x4QtfnkVCfnWHuxBTzU/uZn91Ho7pIp7NEsTSIW3B4+qVPI4w6aA25wOONL34pRT+gU5/mo5/4OO/78nVsW7eRstU8tGcX1coh7r7lVj7wyU9T33cAx/FApziey+DgEFKqjL5AJziOg5UQJgnayuX9s2Ak1mokFp2m342fl4vbj4RT19Ecj0I+T7fTZaCnTDdq4WqJMZo0TugrZ/1gYdjFcQSedIjaTbqtNsoqqpUaQ2MDOK7EN5IodUiMpd2JmFpssG64CE4+Y5DVCf/+2Rt4zZVnIazFEQY/5yCF5O49M0z05Cj6mjCM+cJXb2ByapEXXnk+jWoF5Ug+f90Xqctern589vox48Gbv8M910u2X3o25124FbAcOjzNnkP3EaeaknKQJkbalFw+T7GUe9iIvhePFKc9UlwnhOCSs9Zw9hmjzC3UWaq3kKKXYmkUxyuihAWR0G52cP0c1cUnk6D4+xEmzYw7RljCFFzRJmm0qM3OogX05V1y+TwFBEJZVuUsXuCzYng9L7/CcuGJaaqVLitWn8fTnnYJUjo8MD3J0FKdWlylOb+TnnIJ3IAP/tuHsdpm624trquIBURhhOu46CQCDEZqQCCsIdXfPQylECcJOX8oTmlojqNQDvQUi9RqS/j5AiaNsBh6ekpUGw3ygUWnFj/wcBwfh5SFekhbS/LC8I279vGcC88gCHIErqTdqOMol8B36YocrVoTaVLyrkIhuG3XJIMFj7lmTKMbYWUArsONu45w4boh8o5g1679TM7VueqSLSTKxyQJbW+I51609bHu8eOGcy/aTr6Up3ewlyRKcD2XTRtW8tf/9Jvc/bVbuefLX2R65iBxM+Ta9/wig4ODj3vTsRAS18khVEJf7xgIB8f18TxBT88A2qRZNw4SKf+7q/tkQViF1hbPEUhjsUKTCIHCIIUDJJy1eYx3n7uJL3/xfvrLJZQMMD5MjG9kZX4DcZKQuHV6B0aQWvDut72b/XfdT1uF9E+swHNvZN3GdYysnEAJtRyLSRQCIV3iKCSf66GZxFhrkVpjhUILS6q/2x9irF2mmP7hOKWhCamIWyFSRDhKYnWIFJJiLuDAsVmM0DS6EQiXfGoJnAQrwboOOWGYXGziux6FYok4jOl0umgNYZzgO4p7dh+k0epihQQDjquYrDRptB2KfsAF557BN269jwu3buTuXfvZN1ljYrgElSo6TUmikJm5Gv0m5d2vv5oHH9z9WPf4ccOm7Vt+ICW4lJJLXnAF5zzrYnbet5fzL9uGlD858tJ1Z76cjrmdKErodDq4jkOu2EO32yRNU4QIyElBErV+Ys/w48LPBViboqQCFIgEz0qMcnGsAGk566zzME6X8y6eILGCKAwRnotuRySVDjsffIBrX/dSzFKbYP16kriGaVVZs2I9FavYfOYW3vv3/4ASEr2coHccFy0sRhsQCj9waLQ1wgoMEiGynxQmebjjUQDmFOfjKXd3cfoYnW6Tvr4+Wu0KNrIMDAygI4eeQpFq2KXbjXEcQb1taKIpFXuo1bv4jiQyMDHYQ16mVNuNZd48DRZqnYjFZpvUGHLSxVqI0whHuURGcs0Fm3j7T/8061d9ni/ddCe4Dj2lIo7rcv7ZG7nrwQMsNJp0Ox1WXXA+e/fs5aGpNuc8xk1+IiCEIJfPccHTzvmht9jJrOLJr4vViK/dd5jXP+vksOgj7661lmYY0QlDioU+ut0lyj0+Rgva7TlcUUCIFLQBktOK5RkrMnEKa5Cuiy/zSNfD93yUhMRoUgdu71jqK17LWucAYbuFb/N0uxEnZo8wGVb5/Oe/RG8xT+62WxkcGmTPjgMoGdDpW8U7f+k9IMX3UIYDSqCsxlpDkiQIJTDGIkXWWWAAx0KqLRqbtWKIxyFGK/UPk7QbzM3O43mKVCd0OhH5QoGhwZjOvKbdjoiiGCMFfaUy03Nz2VCj8Ahch5lqi3qrTRTGuJ6LsgmlQsD+6To5L6AehVlFXgJaIF2JloovfuchXvEmj2tf9RaeefULeeWbf5ZOHDNRGGbzRD+FnotJky6tTsj0sUN8/GvfZNOmsx+XfX6i8MOMxRjDp/7pI1x/3a3M1jvUYkO7ExKGkr9wClx+1ZV84K/e/IjGlhrNK1/225TLBaq1Bo7M5rrCboirFNKRKBUgHIO0Gb3C6QKpwBUSL5fHVS5apeS8Aqs2bMakCVEUUavWCOMFrlQ3g1xFbBOwKa1Gg2q4xLbt59DWbWr1Gu24Q6fmEDkJdaE5ujjNmi1nZ8kknQmuWGERFrQVWXlBCqzO+P+tFRgynskEjU0tsTG4ygFrH3vB+sGHdrF6qECxUCIOG4TthJ4yWLfEUL9AC8HCUiO7erWlWq0grMXzXBQGg0UjqdRaDK5YSatWQylLX2+ZbniEOIzBaLSyeMJFZmNAWJ2SWij1O3zp05/CJJaLz97KzOwifuAwM1djYu165qcOE4Udhof7+e23vw6C04dt91Q4mab/r8kPay1HD51g8dBeZLKE1DGm06FerRLHWRvSVz69j70P3cIN3/jn/zbzZq1FW8vFl74T35FUKyeIohBHJiilKOT7yHmg3BztsAsIrJCY00j3z0FigwLFYh6UwlEu5d4eVo6vYXbxBK2om9XRpMPNusQ5UY3RYBAig6GDzRVoVzrUlxYo4OCVfEoll1pfCWfI5fixOVanNuspXT6spCWrTGMRZAk/KyXWLHsWUpAu1ywlJqOvy5UQ4tS8RqdM71e6EStXb6DUU6LS6BJ4gNVMTk9Ra3U5PNPgojNWsn7FAHnPQ0qF57okSUqiLRN9BVphlxONiHptCUGKqzzmlqoIadFSIqUgtZYXXnPl94yIZ+SXjlW86KUv5gUvfTF/+We/hR8o2q2Q3dP7efCh+wi1ZGFukt9774f45T//O/74b//l0e/uE4wflGEEMMZy4K4D5IIia1cOMtobUA4UQ8UCnhMBIVpXObD7fl79svd8X0HbWI22mquf92vkfIUxVSTQU+yjlB/Ed4sZSy+KJDV4novvOuRchzA6XWbTASTSASvSbKrB9Rhfu4VtF5zLc573Us657NlsuvjpjG/extyDd+EXekgdgzES4zk06vP8/pc/w/5eSXD1OsS5/bznq9+gvxTgBUUSoZGei9UGmRqyikqm4yesxJIirYQUEiFJrMZoDdpkbqWGMEwAgdAapR957U5NoKoU3779Xi44bxtrkpBCvsDs7DxLjQ5hGLPr+DyHp6v82pufw+xim0CmXPvCFxGnKY3qPL/2px/E9QsMlwpIFKWiz8JSnWq7A8JBKpvpWSWaa599OTfefDupTZdTrZn7oDyPnJMSxVls14liCqHiNW99HdJxee5znw8KJA5apyydPsmzHxvWWv76Nb/LzOIMq7eNUSzlMJMpjkxxlcZ3LEkSZ21bwnDv/d9GW40jHIzW/Mlff4Obvvw1UB2sbaKki6MC4jBCoBEipdNt4HkuSJfAV/hBEWMT0vQ0itGkROsUxy3yjBe8nNXr1hLFKScWlliar1KvLXL8yAHCrmZVb56oE+GXC0gRolstxnLDnFca4L5v3svdN9xLB8HGUi+NMKIUuGx/xosoBHnm5uYYKBQpjwyyd8dutp51Bo4SrBgYJDGZxJgrLFK5pGmIthKkZFxNUvJy0JpFAW70GLOOI4PD3PKdb2PTiNWj/Zw4fgSnNMRC9Tj3LlZJE0NoUibGhukptBke6ufE0QMMDg1QXZzm19/2cjpacv/9DyKVR7PZIPAsrarg/I2jfOuhE8SpQSnF5FKSpZvd5VPeanY8uI/eQDCycgwvn2d2vkpf3whL1SXa9RrSc3EcFyEV2ITUWPjxtP9OK7QbbT5x82doWpeN6WaefsYK8nkPrysZcXuoNusoxxLHEmSHOE04/8xrKJdHiFHkcnmisJOpqnRSpLQ0wi6uK/C9bNrD9XM4Mk+sE1AOrW4K0lJ6tJpEPwFsPvtyrnjRNdnUeWpo1BpUak1a9Qrz80s06lWCoEhzcZY4P4BLRNxxcW2K8h2KQY7XXfNspk9MYrdvpnrbQ2xaN8qKn/1Fbvj6N5nwm2wYTkl0nqVmB1Wrk8/lWFyqkvMdAi8gstBT8Fk1NIhoHyfw4sy/FApsHmsirJZYIxDJYzS01UMBylU0I8Pq1eOcOBIxOTeTiQdISWoSeopDeEGZYqLJ54ukeYsRgnLfENZKHrjlPvx8MdO7Mop615CGLc5aeyZ3HqyQtJoI4Jx1Pj/zmufyoU99E+W6aGLybsKs6WHpRI3eXIM3vulN7N65Cz+QdJOIE0dm6A0EQU4SuB5h1MZbd2q2qCcCP6gF65FgreUz/+c6mjqkOLGBJBhgeikmCFwK0iPVCQU/R6VdRyKQ0idJIpK4QbUWg/SJwgK+42O1wlUCjcEPAqTQCOmhU4GRKUYZXC9HogXlcolSTz8vfvXlP6GV+PHxjOddhU4MxqR0u20WF5eIopTEatApmIT+kUEmjx6mfMYlCHuIxeoCRd/H8xxU2RA2Q7zxIvHOPQxP9NKWMVNf/yQ9z/5NXjt0LyrnYGaO09/XB31lVvQPZI0YOiRNGvT5ZA3GFigGYDQkCcLEWBMitIFOExCYjgLKP/R9Tmlo287cCl/4FrVWk3zvAGs2KKQ7ycGFJp0oRihJJ+oSpRqjfDrdBK/YA06eXLnAl274GsJ28ZRPpdkgtYLpSouNK/oQwiM2mWbwO591MXlXsnGiDymX6xjKwYQLjORatGpNTJjjGet97rknoZ04RHNHGMwFyNwYkbXkC3mSMOZ0GfZ4NMXneqvJ0Pi5tEiZqy4RNUKevnEAqSxhGNOKQlItUEojEAipaLQ79Pd4SMfDUw5RojHECCtQro/renS6NXwh8bwAq1zKvXm2P+0S3v2OF7Oyr4gQgjiOTxvOkIXZKr0DBZrtJnGUolMNJDSqdYyNyRcLxKlBSIFyHb72zZ1s37KapYUaYz0+/X29uFZTn50izRm8vMPgwBC76ecX1h8F1Y+ZnSauVvF7y9ioDY6HlA6e5+E6CpaHQRExNm4iuhE2jbFJB5t0MHFKGHVJE0PsDD/i+5ya1zGK2bBqgLlKF4xlYKCXysICgZMxCCrhUC6VuPXeEwz0F1g/UUR3KpSDGouTx7nmkgu5/qabM0EB6xG4sFjrcu3TzwIMUjl4jsPZV76YtjdGbkWRZ12bQzkCqw2ibyteLk//oEQnKe2ow/ZLC/QEgiXTh4g10fwBiiWf2W4vN9x+L69ef9Hjs9uPEScbgH8cg6u7Ep0rESjD7OxR2jYmWl3GVy65fImBnpTZ2jSJBs/qrInAZlnDTrdFsx3hB3mMtTiuhxAByvN45Wt+it/8+ZeglFwey8nKrT8J+rvHA/O1o+TLm5hfqJILfBr1Bp7vg7H4uYCwFRG1u/T1DVGv1Ji44sXsO7qbHbdcT2+5n9de8ywKBRgYGUBGJby+AW7RKX/4/LMR9QpCOszddTvDL30dzM9gq3uxfi+y3IPN55AijzExIolJZ6aYXzgM5JEiIYkTklSgkwhtIE1DLG2YOPeHvs8pDS0IPCpLDZLUoOMIVSxR6B1hYmycHccWMBjm5hb45I23cPbGrdx0737ynkuiQ3J+jvpNezhndZF2u0N+YIyd999LmiY40iBVgRe9/C186iPvp3rkfv7zY/+BzXs8tHc/5WKZVrPC9L5NbFu/ipGRAVr7TzC+ZZB7vnYXm9atoNBcycqxXnwhEKFApXUWpg8/nvv9mPDjfogt8LEvfZbp6SOsXrMZ3xug25mkGSUoTxG1ujhakg88tLEIkUObjA8jcHNoJMUg4NXv+jl+8S1X4jvfZR/8789yehrYSYwOTtDphkTdFnGUEkcJjpvDao1GkiQJriPoG+4j1lmSrH/lei562dtpt5pcd3SOc3wQUYqWMNluEwwOc2TPPkZXrcTfdjYDV7yA6oMP0Z05RDdqM7r5LIp9/dhmm6h1gnazS3OxRq25RL25lP1u5aO7zUw2SqZo7SGMxcmpR3AcfwRDM4nm2Rdv5av3T+N6LkkKmA5DZYVULsbE5PM9eMowvuUcpuYjhNOlWzd0TZvyyrV43gGqTYHXqdE7PAEnFlh96Sv5zif+L5+790Y8L8eeI4ts37SB+6cbIB0ajTpSQi5X5Nh8i2OLIbv37uT1q5+JUx7h4JHjpDLPjsNLDPcqNq1ewdTCAq968Usfv91+HPDj3mgDw30cO7abONQYuqSxoRXGBC4IXISvKeaCjIMw0QgLRlte8NKX8Lu//9M/UsfI/wtoNJs0Oh2ElSRRhONmNN9SSfKFAIGk1W6Q8wpEzSP0Dg2yMN/E9Vyk51DoHeS+TpPh4XG6xuBoGAwC7rjjPl4wNkL73vtonzjEbKfFpa/5KWgs0m3Wqe7bRUNDq9mmuTTPYqWB1hHtToS0EivbCBVn+mlCIEzmwufSzmMzNG0twwM9RNEBjBLUqrNErSb95RzFfClLwXoKLyhwfK5CqdiLjmqMr54gas7RbdZw+4cZ6l1gqdrl4mtfwx0P7eGGj70fkyaESZeeXIF7du7kT//wT/n0H/w+geug8iXQMePj41x95cXYJOUP/vQhbrrnCJPH9vHXv/+LaCAXKIR2sCZl1USE9E6foutJI/veOtcjduIDn/nCB3jG2c+n3VzAIMgXC6RhipKZl+BJQdEr0WjWscvqYNoYfurnXoNSp09nx2NFEsd89QvXcfmVl6GkQbmFZWHChI4DcZwglCTnF+jt78Ua6OvtRTkulaUFJA55P4ebCwgbLaZnZ8iVC8z2jvG12++iL0057+wzuei8y5m6/S4O791LKCw6SWhrg7EQWMO3liy5ICDWEmMsNjVsdtuk0pAYieO5uI5Dov1HZF875c7Mz03T7XTI5wvMTVfI5yWJlQyNbma4Z5rEKoYGBxlctYV6s0riFpiaillfdjl64Ah+ukCnPMy6S1/Idf/3g9z9L5/i4g0j9PcN0FuUvGN8NTfvnUIh8GyEUpZmO6SAJi/hmzd+k8n5NtvP2Mo1Vz+TS7avQ9urCa3FVQGdGEQaYlWOnqFxOrXFx3G7HztOGtnJwvQj3XBCCIZ7irz6ylfxn1/6CE55kEKQo1TIEydVutoQRZpulKKtxZCClSjpM9zz6CRfT1fkgoCevkxCKWx0GRodwMHDDXJoHeNJhcHSijr0DQ9z/OgUpYKH0Smr125g8uhxCqUe6vUG1gh6+/pQ0scqwe62YlN8nL0LVdr3/ydxqcxhd5hjBw8yOz9Lf6mHlEzvGs8lqUd4+QKdsMugkxDlS6y54FKCvpU4JuGSiy+j0mgzf+zQD32fUxpaT6kfaVIuXNvhyInjnL9tK0tpg263ztuuvZh/v+k4VjmoNKS/t59SURN1LCQVVmw6g4tGY9ZsuoC53Tfwluefxfs/8S0ufOGFrDnzSpYWj5DrNHnlign6RtYiheLlV2ynUPQp+pK142uIkeR9l+/ct48HDza4d/cUv/C65yN0C2VaGQNUN8JxOzQnK5jg9OEM0Vp/X2Pwj+rSvfZXXsodN96A6u9jbHgFObFAjRau8ukQkYoUhEDHAuWCkg4F73TJtT4+2L9nP1c8/SruuW8HEyPDRDZCdzWOqxCxJFYQt9uUyn2EiSFwFDoVhFGLTruNEJpcsY80SQk7HQaGR4mSCKxlcGgUZ2I7jaO3YX3Nhz/+Ra54409z9NiRjGnMVQSOS6vTJu8qEqkI6zX8IMd9u/fwv/7kvZl2mzaEYUSo04wS6xFwSkPrG19Nq7XE8GiJmcUQL/AZ6C8QxSml/gHeevUYGzatQ1qLWxygZXKkcQMTN8iNnsd9X/8EU/tv58ynvZidt32NX3vz1dRqdYp+i8G1I9QbAUmakM/FdNtNztq6jerCYXr6emiGEV6Qo0aZ/jXncclwSNpus3uqydkTFtfLobstpCNwHYelyiLD5d7Ha68fM6SUP9B9/K9d+UJ8N/snhGDz1lHOPfMSxtasxXFTbnvgs7STLs1uQjuEbpRgbEoQ5NAm5ZrXvvNUc4f/z2Hfzgf4zCcP4bpFRl50Fbad4BRcOgk4gUdrZpFcUERIied6BIUi6ITU5hBRih9AfaGC5/kUS0WMMThKUSqXKJQKaA218ctI2x1e+q5L6JqY2ekZtmzbxurNq7j2FW+gulgliS2DQ0NMT09nw50iwqYpAouxljhMiOIEcYpuxkcyNAUwMjpAOXcuW61GC4f+/j66tSqHDuxjxYBHy0zQt2Ij3XaXRjUTQc8PrUN2q8S1XZxxxkaqlTq5dJ7zz9nMwnyNM84epdnsUOjpxesZhDjBcX1ai12GxgYYGhlYTrSkSMAKS5+XoJwc3dAhTl2EC0I5+OU8Nmrhei79XhE3V/y+53+SoACSJPk+Y3tYv+x7DOx7b7qTX40xvOkvX83H/ugf6aQVRMFFhwHCKgJHUHJcijaP1oogl+Mv/vA1xHEKpI/poeM4/r7nf5KgAFZuWMfKDWt4/oteRm1uibUTY2AtsbXML1XZs/Of2LLpEtrtNkIpPF/heQV6XJf+dou5yVmEMCjPx5MKpCIoBPiOR19PH0ZaTAqimMemGpPC6o2r+JXf+j2MEuhQkyQxnY5CJ0kmHG81RgisjcCC1YBJSbrh995oP3DtxA8jfrz33nufDnz7J7CQTxSecf7553/nyfjFT63do8f/1LV7pBvtbuAZwAycRvMTp4YCxsie/8nCU2v36PE/cu1+6I32FJ7CU3j8cBr1az+Fp/A/F08Z2lN4Ck8AnjK0p/AUngA8ZWhP4Sk8AXjK0J7CU3gC8EPT+/fee68PXMj/w2nW888//0lhm3lq7R49/qeu3SPV0S7k//HCIfCkFF15au0eC/5Hrt0jGdoMwAf/f7/P7kPHAMNAKc9IIc+l529keKyAwKJ1JgzQX3aQy5StGc+gyrpSrEbhYKVGIbBCZeSUVmCtRlsD3Sa777mLz93Z5Xf/92b+5jcPMVAOWdEnqJsShxshexsJgYFxV5JXhoJ0ufoFvYxN9OA5CikT3v9vR+j6g/zmH7//4ed/kjAD8K2/+wVEcwFXgO9bpGNpJoZq22GxrpjqKBqpxQiQ1nD2CsVAWXHLbs1MGGUqlDJr9bFkf0wSjdaZnBPL2gpGZ1+/t9XLGPNwi1d/OYfWlvGcYG81xmidtXsJWDeap9pKaXVTevsG+dCH/uG0WLs/ft9HqdRbCCFQQmCFWG5zsgjpIqwGwTJXcMbrIWTGnp/1HVqssEibEaNiFFKxPFiusVZiBCgk5iTNsBVkl6jMGPjFMqe+sFgrEWhY/r4RZBR1AoQ19JRL/OY7X/Lw8/9XPJKhaYCd+4+y0kvZsnGEQ0dr7Du+yPhgiXJxHOkYrLYoAWlX4Eon0+xaXpyDRw6x70jI3GLKXKvOQ0dn+OgfvBrlGKwBax2sTUlqs7QWF6ASck71Wupzt7IBTXnEMD/X4YGjTRY7mlW+Q8u1JBJq2hB1YmycgDRgLFovsW2N+33P/yRBA/zM712F64bYNMLW2ghlgS4idNHakHRT0kiTphEmEZg0BsfhLXGC1ZBiOHCwwtvff5BUpxgBzU5CmqZYSyYvZC2+72GxRGGUyTc5DlEUY0WmdDI3lxmdN5xjbr5Dqg1SKfryHk3b5kQjRghLkprve/4nCRqgVmtSqTWxJ8UATSZZJZVGWhetlo3IsCwkn506Qpw8nTK2YSUyFmGpZKYlZ0EjEVYgRcYJIrGkFuQytYOxAiVSLBIrLeAgbJr9P4VGoTA207dWQpNaUN8lsf2Ba3fK7v3nnrOaRnWJqekmlU5IHBvqjRCrU7QQKJNRJwtUJmtjMpkla1O6ccyteyb59Tc+g8OTC0wtzmUqHEpgrcxeWmQUa+0IPBd+7s8/y+B4D/t0k/kpSZ8HM7UYbQ3KzwT3MIJuIhgfLaETTYIhtUs0OpI9h+CFj9OOP1bI4Z/BCYaANkwoBClYF0OKEimeFRlZpwRBdugIUhDLtxkRn3j9+fTmHFKtCVxF74CHclJsCt1EsBBbpisxyfLxmmhNkmR77TiSONU4Mutzncgp9kpFbyHA9wRn9bgc7WoEhsDzGB0qPVlL9d8hBGKZ194qkXHfW4G2mSCFY7JbTsqMlVlIhbWZcQgMGokjDRYFArQ2WOkgDAihkUJkg7M2Y9N2cNFosBohZPb3MFgjsSJBkSnYCJsJygssGoMwoH4ACe5/xSkN7dBcjU61SStOCXWKMZa5ehNtBTIRGMcuK22ITGNKxGjjIKyl3dGctWGEXbsW+M+bv029m2K0Jkl8FIZERFjj0GnN0w5jQLAYtRDKxcQG4bk4DiBUxhJrFZEBTwhW9WkOHp9n/doR4jimWAwoeg0C7/RhT9W7P4DOB+AESK+EzfVgnX6kyoNysUphpY8VCqSHEC5GdJE2BiGxNuLEAsRpjCMcrIGlUNDNvCYCCZ6AVf0FYp2QGoE2meBCuuz2hIkhNZm44Y0zEUHgIKWlFPi0RMJAj0MnVDRjTaXRfbKX7GGITMwWsUzHjQRBmrmFQmOFRBsJ0mRsYDYTnrZZYIIjDCkSJQzSZAYrrUaTHdbW2kwI3iisgHTZtJACY0EYi5UGBVgrgBRhHKwwy66jwMnuyow6/BRyMqc0tHo7Za7eIo40CEszSqi12lggRSNTSGU2m4NRaKFRaCSKMNKct6GHXM7n1970Yt7zt58nNQaZdjFaZssmLN1GSCtWtEXEYFCiG0VsWN1DQadIFMZkHA1aWLQx4ErOWCXYdsYYtVpEpRay1km44gJB15w+FYsvffrrRN1ppE2RJo/RCbFJSSNLI4YkMjS7lm6i6XQVkda0o+xWes+LR3nfZ2eYbwvyrkdHG9JYc8e3hxDaUDkRM/9gTGWqw9R0F6UtXqoJrcQNLEkoqLTgoZbihsOKWjPFSks3Bs/1WGyEhKFLsahQrotvwHVPn+FRLck8QKOy28xksZSDQWsnE6SQyx9wYbE2C7+wBoGDFgalQUj7sK6SWb4lMRarTHYoiUwvWwgwRoHJhC4MIIzAWIMQTvY8YnlsC4WxNrs9LRihHrtYfK3dYaHZREqJLx0Elkqznd1iVqCtRmiPJBEIGyKVgzYSR1ouPvdMOt2QequLNfAnb7sGayFNNMYkoCERKcePNAlliYEVQxRLPj3KJ20cxTiKMMncAaRGWQ8hMjWPoUGNRVDIlyn1JvzjRxZ5YE6xekJwxWPf58cFaZqSd/LkfPB9B+X04PkxrpTk3DzKSVE4OJ7FlQopMz1lIT2M1Xz0T9bxO393J3uPg0GBtLz7FfM4gO84meSStZRdn6JnKAUxAzmHorEUSoZNA5bjD1nG8i6rih5lVzPfERgJVhisVeg0pehaBvM+5fKTOYb2/RB2WexEarACITJ32FiBEBYtzHKiQ+Ba0DIlKwtLLA8HbhgrkehMAkqY7D4TLIcuAoRZNja5rAm+rP4gltVlTPYz0liEUBiRCccrm92sRgmEyeK/R8KpeR2xFNyAoXKBFYP9jA33csamFVihM0MxAim7dJsRyoFKu0VPfojAs3iOi+e6DPU7GWuQ8REmJk4SrEkwJgtec54g7ECqsoVSvuXWw3Wu3TzMjEmyE8cKtDAYAbHxkL2WbrvD3FKNLVt8ds5I4lTi9q57zJv8eGHH/pgxv81Qn8v4gEtfKaHklJFOjBdIXHyEUqAyDTDP9TPlEmkQlEh1xB++7SJwUowFEoHWMTpJMBh0kmIwRLHO3MPYEKaaJBGQpnTShGv7FVelCZ2OpBPFNEKfVlczWY2Yr1kW25ZEuKAEJec0MjQhsCaLuKwQaCMRyqK1kx1IyzeKIzSJVVnsbrMcgVAWsRxJISQGgRISYzWptUgJCIG0GmtklhSxZFRH0gAqcx2FwCqb3YAoJBaxfGMibOayGoWw9pSTsqc0tOc97ZwsIxi3cR2Fkw/QRmNsxi1oSLCJwBAiZI5PfPZuhGN5+yufgyxIjElRbqZzLaTNFkxkrqY1Tpbx0ZYjc1Ws6NLyBD0ln7V9LkYKpivNTO5UCjQiO4WN5sR8gW/fNUNhwOPw0Tl+64yUocDyzoeOPh77/Lhg+0aPszesZMVgnp6RIqLQs5wyjrDaAi7EIXgeQlmsDhBRK9Pi8kCYPCJMwMkCeKEtOnQQEoS12FhidIpUMULmM8JWI0nizLU3Nku+xEmMTkNSCqQ6Qi/HFMYojNB0Wh26oSCSvU/yin0XxpxMOSxn5IUBbVEyyWRwhcTo5XhKWpQRaAFIizEyMyaybHQW4FnkcvLE2sxNtNJmyRabeUzWqOVflpWorBFIvZzhFykGicSAzJRmrDDLruT3U1X8IJzS0HzlQM6l2FOityzwgjyOlwmzCQzorJ5gjIPSKdVmJofaDSNyOQ+bgvIky+cFEktCJo0jSNFkMkOVTooVHWqRYrbdYSQnAMv+xQhrLZ6T0appx2CRtJsx6zcLCiWPotukVoW/vKuHNHf6JEOabUmj1qIsoVzMIVQXIwXCWnAd0ALheNgkYzRGZsmhzE12EK7CRB2kFFjrYx2LdFNwHEi7CCNROBiRKVNKaTGRRiiLJMAkgI0zOgVZxHcMpquxyiE1WcydJgYpJI6XIpR7ynd6omBZjo/kSbqHzNVLDViZRURSWIRVCGvQUjxsJMZaBBpjFUpk2VgrLNpKlDFIqUDoh5MoBgkmi9+EyH4HGKwVaAlKyO/hZMniuASLYxVWaIxZrrk9Ak5paGdtLBHXUpKoijZgtcXY7MNshUQKh4RMZskKi+cGWdZH5UBIYp2QVwUsGqEl2s0khmIsJtZIa0AqKt02gaPwHM1SBJ1IsXtpiVYCxqS41iG1kFiFa+DIlOU5Z4/w2x+e4nkbC3ScEgeaLXpPI15HT7lZ7szPYR0HAj/LJkoQ7vItlsYIsyzSKgXGS8B4CBFjhIfs78VEESzXxPAcDAqZ5rGeRhiDikpYTHbCCoVv8liZIkOfOBIEvsAp+nSTDqQB3TjFUQ7aRgjhYI2Li4M5ncTil6OlkzrsApOVk05Wna1FkalwWiGwRoNws4NdZrHcScPLCtEGR2iUzOpfEos0NhMWlJnrZ7UmlRYpHYTNoi5pBVKlGe26XY7nrMjqczZTsJbSwCmScKc0NCVEVo9wFDbRgM40okTGTmIwIF3cgg9Ji8vP2cpSo0FQEDjKJVEqKzgis44R62Q+MxZs9sFwpEQJhZWSRFukgNlWSM5TnDU2zJ2Hp7MTw2SuRM4TiFRz8GiF11/pcNFZLkuzayjv2ckNOj7VKz1h8J0E30spuhqlFBjDsroUQneyAFzHkGhI29ktHxskKUYYpG5hU4mwMYgUIhejMslpTYLAwegwc32yTwUGb9lwLalOsVqQJALbbSOERFsNJiYRHsZa0jRFohEo1OkUo6EQxmCkxBGZG2eFwGqQwmS3vFRgU562eYTb981hRIp0Ms9JGLmclQREihJZPe3c7YP8r7e/hff87w8xP1fPygXWonV2a7pWoNHZ/0Nk/TjZTZdlJ63NbjeNQUiQJvO8ssL2D8cpDc1xAN+i8LFpl5QUjIcROgs0TRa0piiElQyPS/LDE7iOh+MYXD+HFCorcKcmY3vVCUZnJ7CQBrShnmj6paStU8LUUAoCnr92FZVEZwVxkWV2jDEEUjLQa1k1BlhF2LXgO/Q85wq8+3Y8Hvv8uGByIaVAk3YzYmyxy0BvgOtCu91lqdbm2KLhtgNNjkxbmt2UTRNF1owqUm0wWvM7n1pCZhKUWGMxxi4Xcsk0ucRJ70Ihpc06PgQIJbEahMgULE9m0rLWJI2vFDnfMlqUrB50WD/qMNarmBh32HDpk7li34WxGmNt1vSDORlmZe1qQmEtKDRrV+Q4Z7XP/mmfVier9Wb0VA7GLNfKRBZbOQp+9g1vJe6k/Oo1z+YDN32Hw8emM/cTi8FkHSLLmtRiucVNG4G1KUo6gMDY5WqcyW7TzNN8rIamJLgOqQ5RypJEBisyYXijXYxOiTW02wk5YfF8xXiPlxWmY4HQAtyEfJJw5kaLK9rcePsCeANYq4iTBtI1SGmZb3cxBsYKPm9cN06Sl3Qb8JyzV3H3vunsRYWlp6AouCk2O97Ye6DAyEqXVKrsljhNcKwO9XqCV7BctMUhmW4z0JfnxFQdbS3tNKZIwDnrYmzqYd2I6SWDjCAVMjMSo8mab5aZjk0WgyiVfdg42d1gTspWCITNPjb25KfFZmXcky5/12o6iWWxmbJzJoaHsuTD+HjA517zpCzVf4O0J3tmszqYtQ7CahyZlTb+90+Nsn8qR2fyCFvlcX7+jDaqUOCrx3u59WA9EzYzYjkXAEN9Lnd88zP8r7c+wIqRbdy1905+7ld+h4PHp4E08y5t1jfKcj+uEhaWO0Qy2kCBkAZhxMPFbiMs0mTu6CPhRzK0k/FXVq+PSGOFFmBERDMx1KancEZcmhqOz4asGbeEdCgP9+KW+zln6Ciek4kvmLmUq9aPct2xLhpNp1ZjZiElZx36Ci5vfMF22pGgPtekkfg80Jqm1NvL5ZeeSzS5h1oH8kVFqBNm5gQPHIXtayyVjosrQfwI7TBPFI5WNONuwrhneehgg/F+Qag1WhicwOC1XNyCJe+4zCeWelUTJi6djuXIUpeTXcVSgtHmYY7IzMgykXmtNUJ8l6xVSgVkTcOOozAGfFczMVRi/1QLgUDrLI79XmLXH6Sl/WTCSJHVz+RybCoMVkBPMeAt16znhqmtiKU2D03WSDsPsGnzZt73iZt51/O28cCJEu0ofrheJqRhvhqz9qzLeOcFPYwMObx226W8/Q/fw8ozn4VWXuZp2eXb32auoM4a4zASHCswwiJOEuKSeQySrHAu7SPHt6c2NKHQWW4eJTsIG2BNk4GyYLImqEwep9Nu0U5iNl9wCRNbyqSTVVwnojLZ5FBtno3naA5WatzyYIPIBrhqBWNrAc+htdRlug4dm/Bbb3kWzXaBXL/mCwc7XLKhn5X5XvpyLrVWlyHPwVMxCy2HXU3Lgb1tckjWDAyzdaXm+LzM2plOE7ieIPAl0kpKvgDpU6vH3HdQ02ylzIQJncglRrBYi7OOG5vdYlK4D5dABXyPsHwWn6Rp1sHvOPJhF2nDaI4jCzELd70ZJRqkHYFnNDu/c4Qztw2wWE1Y98ZbkVaglw1XCEGapv+NTflJx3LmL7VZh7202Qe/m1ic6l4uGVnDF+ZW8q4Lv8lXbqnx9Tu+xvZNK3jvZ+6htOUKOpFFW5BCL7dmAW6mp1ePQ3YtLFEo5OhUjuEPrc+8AJvp9aU6xRFqubM/azbWGJRUWCMR8uSkRJZPEMvF7kfCqeVHTApG48mEhw5NYyOYn4957cs2sWVVmQ/ujWh3LP963zzijq8yUlR4rmTLsGL1gKHTFfzsbTAXKbSEM1aUWNurmL+7xtmXlagsVukYyx+/9UqKPcOEaZcwFZx95kpOzC/RUy4ThQYtoDK4ne3ug+hYstDMU6ymHF5K+Pdbj/Ab470M9/Vi0s7jsMuPD3YcrvP1uSqQZai2rx/AkYYjc222ryoTGTCuImpF+K5BKQetNZ7no7VGLhdbneWbeuvqXn7mjZtYWuzyS287G2FSwiRl9Gmf5lu/t4VzrtyE7Ro6+6eJbESukENsPZNt73glyf5PMfyM5/GKq6f57PWHs86H5bhCKfXwWM3pguxCk4jl3kVDVliOOjH/5wZBb+EbDNb30nj2Jp615Qx6hma48e6DdLspgwqEyGIoBAhHYtIEpSTvPaB5QxByzsYRivcdxR2dwOgUg8BIg04sjlJoa3ActZzAyw6l5cA3ez6jkHI5I/ojSCifWh8traNUhzvuPchHb1wgcCUDnsPttx8jIeElz1zJP3zuOPXYUnQS5lsGT8BC1XK/p9g05nGinXUuPGdwGKMkRF28Yp5YQ9SJaaQJZ21Zw917F+lqzWJdMlZ2yDsDpNpjPolAd2nrmEQJgpxmxDMM9flsG/e4YX+Xj950nNCewPX7HvMmP16wsDziYVFSsPNolYvXlRkouowM+iQzXTyrSZTGcxW+6/K7v3Qe175wIyvO/zeGfcntH7qSwU09mAhEpQF9BWwi0McW0Y7GVQ43fPY36D3yFfTEVui5nPCejzK76wGO7Fzg6v486UO7yY8HVL/8Uf71V87kH9+5gbHXXk8Y6SzBAg/fbqcLjAVls1tJiKzB1wBKZiXsy1eUGe5fyYBwiHMpl64dIk0Shssu98Q+ptFBLfcsolOkkFz1nlGub7+K5vV/wNB5W/j7+59Dtxrw66//NGlkEFriCpsZmXDQQuOqbDIFkx2WjhRZd4ljyblZV68+Od/3CDh1el9pWrWI9315mkaUjVQ4UnH/V9tcs6qPSy+wnLPSoVXPcWQpBmvwlERKRUGlbF6do6EdppY0qW8J2zFhWsELPLTupdlOMdIhjCPqzQq7Dje44JzVVFuSTpQ1jPb2OExXodOscCy3iW3uAZTjIVWKNj5FL6YuJFJIentPn8ZY4OGbQgiB6zrUu4qSSPjIP78Y0g4mhrnbDjB+2UasSbCxQTRr3PUvV/HyX/gWjbkK/ZddhCycCX4ZGndB+RJse57w5n/i6zcd5JLnp/SeeyZ6906SpTsZ2DxM/4VnsP74PLLZJpCS6t4qhQ1nsdCBg5M7uf4vLuY/vjHJ3193nOWQ7bQyNGuXG9WtxQiJNQpHCITVSCn40v4Gv3nOMEuVDtpaescdlEkZ8AdIGgZXSFKVNQtjMjd+23MdFt53N5edsZE7XnwJ6+xtDPat5jf+9WL+6LW3o22KEApH2qwHEg9r0+XkSHYzpmnWdPyH73wpG7duxqRZ78HP/+4HHvF9TmloRdflm/fuIEw1qcl8eYlGC4VXyoGBZ126lnWrD1H2ezl0qEqaSFJjSWNFcfVKbrt1B81WzDPXrgBZZmwiob8vyDrxpeJtb7iMw7MduqnLVKUCD0q8Uj9rejWp10e9ZljV5zKn+ukmcnnADxIE+YJmvC+g2VR0Qk2rnTxOW/3Y8a7njiHbLh+6ucr73jbBupVFRgZKdGKNnZ0j6oQEQY6xsyZIOwmyZwNi7dOxrmTDNs3bP30fYZzyrfd/nL7NY2w4b4JC4JF851bcjSsoTYzwylf1o6VFmYiZSoV4vk2nHTL4kl/k6NEPUz4R03fmIH3lPioHdzMwUMAf8qjVW7z1OQO85ZWrufStdxA4MtMDO10gs2ypWG7uVTbN6oWYbNRFKI6mlqQV4egudx6Z58pLVrN4YoE/O28FxW3P4NX/95u04+w23PycIgu75/Bf8Xw+bZ/Bz+QP8AUOMkqNreVzwMYoazNFW2uzLhsrEMZFWbBSINJMoNBTin3z4BcWKBZclo4fQ57Ckk6ZaioUEr50W4UwTVEqq2flHJecUtiOS7XRJXA1o6MFyHWZrUbMVkO0m+PiK8/ingNLvP6qDVgsR2NLPW5TKCQMFAzYhLwS3LW/xXV3HeOm2x7ATS1PP3uQp28dQssSUmtG+x1a1sXREb2FCMcBRwpcqVBuzO4lSasTZVX/0yieT1KBTiWvuahEddFy/84ljk9WGX3mmRzeMY1/2dsw57+DRX8j99y0iy9+8GMc/OJfYA58GHXgI/zSbz2Lh/bPsWJFiTNLOYo6xlRreP150oMnkN4Q9pw3ES90+fbX7iIoBYxuW8e+40vMfuFv2TiUY3TEp7F3hmN79mJcjxNLDb7z0Als4NJsGQYR3Pa+8/ngz61GPzYxmscXy6UJK0DZ5RZhYZbLStmtcs+cwIk0B2YW6SnnmF/sUCyVOHbwMPrYHj716rP44MvPQlrYdV2NtLePF43fznnjH+Er9Zs4w67ixK88SPPPb8AmMXGaIIizIdFYY1ILaVa+Jk3RFpSFLUMuqwLFunXjfObPfofWtz9F2X/k1zm164jgirV5fuPlq9iwtoe7HzjOp+6ok5f9jPW5YARKaYyEJJXoFGRPnkkbs77WpdONONAMGSoV+Maew7TDmJ9/5bMZGc7xz5+9H2e4jwu3FLhmdAOd5maW6gto0ce3b9/BqlWrqSceBxaaSOGxZqxI3OogyhbHaIzV6NjBUZK+QoHFehMvDh+nnX7siBPBbDvFE5LrH6zzmuf1sGK8nw/9zhcpBkXqjb9gzbZeVFVz8XO3ZapLSQLNFJtohEl4+Yu38R+fupeJkX7au+Yov+iXeegTf8wZa0dpz0+Ssx8nN1Lmaedvph1qZNIklwuYna0Qh5oVL/k5HvrHP+WCrUMUSh49sYezURB1YvIFh/v3zfL0n3odE8/x6PnS3z/ZS/YwxElpK7KYSNiMpwNAiKwN67UDEe+9e4Fnre+lp+jTabU43u6ymYDi0SkGRzuMeT5nDebYVemQL2lqto7BMtbrc5RFPrqnzNlb2xQciEODthopQ7TwUdqQANIsd+mKzBXdPdvlxl9/EypNee+1V+LFmn/qDrD3Ed7nlDfa7773bi45c4gVxZgP/+duPn9Xk1bisKq/QLvTjx8oOl1LvZZirWXTdgN+P0tVzexSk5n5DoO5nix7mFocx8UPJLv2LbBQ0URJwreuv59PfeFWOt0GV1xwFuepWX7jstUIJ0TYFnF9iZxtk2/WOGdinsCXKNeQ83zKZYlVitSkpBq64emTddQIfKEpOZL1Yy737Yr45FePIoXLlvUBY4PgNQyFnCSdqlE/OElnvkbaswp7/rs41hjgzrtO8LTnbGPH/uN4Y4NUPvOXrF8xSLvexo0TOgeWmN0zRScWuFrhXvVHnPOL72epEXPw+AyzX/q/bF/ZQ2WxzT13H+PoiXnE6BpuuneeBx+aZ9vGFRy//kvc99GP8pdvnniyl+z7YIzJqAXIOmLsyflpm/LsMcGxpVl6mxY/aRPEXdotuH9Kce9CyFf3Heee3Ye4yxb4ja1w1lABIyAVGkdIBihwx9ffTPz832D/whK/cHYpazHUKSZd7qE0GmGWjRyytsHlWbdbxq7mNRefReok3LnrALuO7HzEdzmlodlykfuOh9y8Y4ldi5aDlYTt/T3kvJi5Vj9aWyZn6oRxQtyJaMUT7Dy8wP37Ouw5UmOhkvKpW/dzaHqWvr4eHCk5eHiBD359joVqhwdOtDhSizlwaI6/+Nfv8It/+EkCnRLoCHGky6bxfs7bsgo1dZiZTsLOuwsIDJ4rcLwIk4rlIqxAp/a0KryW3GxYcK6qiXSCTCOiruHg0SYzs4L5pYjgzJU0V67lc1/eRe7Mi0lbMbZ6DLH7n1k92OSyZ65lfY9PsafIx//tBnoH+0gqET1X/TLe1f+LXNEw9vrfoXTtb9PpdbHH/w72fZhnPX09Ow9WOXiiSbm/yF07ZhgeCSiXFEu772fLeA/lfp/rbj5CvZ5wbMHlwNHWk71kD0PbbEoDm+2v0Xp55iwzuo6FqUYbjw7jpRwqKHJ8znLJItTDmE4cE5ZWMDD3IK1ui9/b5mDCjNFKCUlHGPYWNmHjJnXt0K21sVZnxk2CTJe5SFie6jd2uXtEoa3g1tkjyP0NPvnt3RyPW1TiR6bBPDU5z2UraFUM37htlkRqyoGH9HP4+QLCar59yzh943cyPBIglOAzty4ws9BhulbnzoMSKR2GBnpIUstitcpwucS+EzWWOgbbMfS6irIv2VFt09KWg4nlHZ87zGBviW4M+Tub3P3QCRxXMhHDQr3NFmvAKBIcrNWEsSIVCa57ehlaagw9geK+Qy0u2tTLrQe7uDJTAd0zuUArKnDkn25lqD/P1U9bi9l3L8pRTO2YY2ysSBqnpCLFO/tFHLjug4xPDHPbPQ9x+bteh/VWMH/LXzK1s0bv8T8kMZq5+ZBOtJd2W9JT9njxszdy/54pPvOFChvWD4OxTM1pDs62aXdyNObajE8Uma02GCk79A+PPdlL9jDkMmvIyRKVFBKtl0eBrOW2ac3FypLvEXzzlgq3OREX6HlauRxRVOD26QXOfMFW9t7+RdaWiviBy4s+m+M778x6JhWGABj2l4hSzW3VdjaPBssN8A+Tq2XkOyLrL7XS4ilBEicMVwVyrMC+vxnggXdWuPoR3ueUhvZnH99FqzLLW567iW67yhfuaBJqy9RizCvOh7+7Y4lrNzu4jkOlpdhzYo4kNZRzHgfna1i53HScGsZGh6DV4pb7mkRW8cCxFp7v8uevWMuznyb4m08fx8m7FPI5VvSVSYXg8GyDdqdJsRAwUxXkA5cvPejxwjPjbIrWQGRSWq0OjnJJ1ekT0R9fSgmXLBdsCDhciVk/6LHreJvLz+hnsZswOR/RV1DMLC6RdjQjawuMXf5Mdn70y9y5Z4bnPGsdvfkyet/Xed4zJzKOkXbCJ37nQ6xZ90VWDBWo1JpMThsirRFG8KyLV9MM29QaMTffP8vkpMOVF/XylZsnufbKlRSLsPdAyLPO6SEeLlIsuPT2lOgt5MkPr+Tgk71oy7DWYk9yU/IwIUiWIwGMtqwd8binusRFZQ8vyrHP5jmSjFKIyvzDh36Dn/3536I+P8M7L5hgotHirE4Hv7OJOCcp08e1q7p8qv0spr/019w7bdE2G8MRGISIEVLhujmMzvpN1XKpQOuUwEgWSDnyTMm2Vy/SXx58xPc5paG1wphGaPjUzcdZN5yj5OTYPNJDt6u5eXeBguzSPr6NcO1hlqY0fX7AQtqhHmo2DJYI8cCVNDsRC4tVckpwcKHNu1+wnj//1RcSi4A77z5I/diDBIEkEIZuaBBKs6qvh3YHjLV4jsrGcrTDbENmLUg2m6EakiHdZQJRpU6fG23tkKCuXXSaZHN7yrB9TZm7Di4yWvDo6/E5XunSU1CsnOjlxpuP8Yz0Rs7b0IPv9mM7XRbqIZ0oIgwjOh3DyDlnkju6yNRsnb0HK/i+m024y5RLtq1iarZKM4w4Plkn55ZITJUb75jh4nMHWKqH1JqSizeUUZ5FtD22nbea4PKLIVckDlPY82Sv2jKMXaZ9W55LE8txjrZYCZqUMLbkXY9/9zdwweRuEtWDRPPvf/wSPv5v/8xSzaBlifffeYx3XTjB5rE+zvtImzvf2UPF1lhZfBC/9HT8IEHjLk+/Z6NbmTlL0jTJaAyWa2uWbBYt7ypKiUP3U4ZbSh4vf6yDn44jQUpmGwkmVWwZLLJ/rk4gHWY6bSIT8q29IX9zxUauveRyfuHFD7Kj63Lj/ZN88JO3E8mQQuBRrbZIkoQuEJTzfPL6aS6cuYtd2qfe9VhpBbE2SNeQl13q3TzDfSmrRku87bJhPnrvEqVcQBiH5Fyf/Qv9jJfrOJ5gbb/D4WqKFQm5fPFUr/SEQWrFnZNt3nh5mf0nmtSbsNiJmWoqVg45HF2I6R9wiUOXr95ylEKP5Vu3LTE+mtUYhwcE21/ycu79j69xfLbC8y8fR84c5szxAs1WnocOLtLpJKRCoVLBzfceo6fgMjxY5shMyiXnpFw5NIiUgr6ePFhNUFCsGu7lb/7lPjatLxNc9TR2/vs3+cxndzO6epRL3vr0J3vZMgiL1gYhl9mXjc2mGKTN+gwtJMZmH2BjuGfFVtYuHWRw0wo+/J+f5ks370KqAkpa3nHhAK85f4KUgIP7TyAo0hGarb0tYm0Zfd6L4ANfzpIuNhuFyQhXTUZ3Jy0GmbWsCXCFIKcCwqjJYNTi6NaLufNlDS5+hNc5paHFsabZDvF9h7aWNDoRQ4USkY2pRlOcqE9y3nrB/QsjnFu6n1bDcNOO/fztdTt50fgAxcDS1+ujbD+dNCGJDL7vMNg/RG1pjgsLw/zL9AyFXoPv+7gyIW8Sjk036cv5WM9htq544bljfOWBSQIvx//H3n9HWXac573wr6p2PLH7dA6TMwYYhEEOBAECzFEMEklJpkTRtrKtK9nX4VKWZcv6dEUlS7Iky6JISRZzECMAJhCRGCLOAJPzdA4nnx2r6v5xGjDJj5q5S+AFxlp41po1vbpP96569353Vb3heRq9iP3LZYaUITSCrxzr9AtrhSS7iML7R5dyRguGB57pYDLBzbtr7D+zwuYJlwDBxIgPxiHRGcpY0lVJo9fj1HyXoQHFcHWAQ3/3CcYHfUYHB5k5G9Ho9eh0EybWT/DWX/olPvj+32Kg5lFvptx65QirDYN0M1510wTlqouwMD4eUhivcejbp9n6o+/C2hT54Sf5q7uaPH3o9xgYcLjnaIfm0QbX/+SLbbU+rLVIYVk6+mB/bZGyTx0Ha5U2kqy6nsyk1E8/wjd+69/zuvc/yY/f8Gr+5kP/HeUG/Mmb1rF7XY1umnJ6rsvRxgwzXZeH//lr+Xd//CCL8hj/+cbj/Prqe7D682hhcYTFWkOuNWotnC+ExQr9HINWW2v+za5xrk3rOINT6BbMfm0KztPLd+FaR9PnsxgqhEgkm4cH+KsnDuErwWhYIc7hhj0+eXqcd/ynRfasG+LI8gq1UpF7OxnrO5poKaZUctlYCekkCptoWrbL+qnd9ObmadVXeLoXcPmQoVir0Ily9Moq+w7lhEGLmY5DcWWFHaODHF3qImRKpgVhkLJUF/iuSzdKUIqLqgJ9oiqpKYdQekjP8NlHV7h5m6ITgfUkWZLxrWMRr7x8kJW4R7fVYutUkU7k4DqawyciHJmBSqj4ipf98M187I+/RKYV5ULM/R/6U3ZtHUS6muXVHmkKvgdbNo4yUHFxN49z+oH9FF/9Bkze4+E/eYq//vxvUg0UzQhm2zn3fr2H60ha3ZSp6YuHBsJaizaW35veQ7Vbx1ECbQUtBEoKUsdh6ua91Pa+jPf+ym8xFAa8541v5q3VE7z2Ry/noUMnuffwPJ/bP8NjM11ONQxuMIJf3cD6So/f/5U38C8+8Dn+9Y1/y3u/0eB3/32Otn6fxMdaHGv7vWdWrzWvKyy6f46zoK2lVBH08j678cv3bDnvfC5cvQ9sGh3mhg3TFGzM3SdmUVKibV9gYCSs8m8+fJTxgTalQPCJJ45z595NbCpVeOjASeLhKhvHCrz91XsZHArwPIdmJ+ctP/cXbB2Z58RyiyvHBnm62eFYw7K1UqadCcKwzHI3wZGa3VXNXNfnz3/tfcR5wtcfOcRdDx/l754UHFvo4Qce1lpyownci6eMyFGCxAj0GheHFIaZRZ9mkuH6KZ5xuWqLoJe0qUqP2ojLAwdz3nyDx6l5y9kopdXNqASGqWHJn/7Ol7hqW43cajw/o1RwqZQdqqUi1162gV//431cdWmNa1+7C7H7BrA5X/jVr3L8r/9/mDijXAm5/2TKocUevlIst+J+SZ0Cx1VcRKWOz/E6/ted1/MTlzY41zWMVEKyeszWsmI2ztAiYGzuDJV33sFv/8XHKPuWT57R/OnRGp3VqK91IMpYW8b1DUKG6Dyh266Ta4ff+Jkd/B8P38n9Hz+EMfQT0mu0EI7jIoxAu7rPukWfR1KZfi5NW8GxVZgo9xm2r917yXnnc0FHGy2XqOoe7V6X4kCVVtrnWXSUomd6lEuS67auY6be5vd+4XZ+91NP0oo1h+bOEiWamZUu5WKB//6px9m1ucZTJyLefPtOrIXjS6tYYP9qG7CMVQc4dKbBtVsGSGPNeKlIbARZrtk1kfJnH/0UX398lfXrh6mWS7z/n7+LrhG8+1/9JghNLQxodi6ereNgxeXASYtLRuhatk4EdLs91k8EuFJz8HSXm68YZ/+xFdySphmHVCqaTz/UouJppkdLTA04gCKKDQMDAefqXbaMVXB82LRumMFNQ8grX4EQguqHnuGJg3VOvf+bHJq9GyMcqkXN/cc6tDIXVzSYa6REWb4mhiFQysHzFAVPMFK5eAqyjc2xOiPPIv7ontO8+tJBVnWLe/a18Jw+B2gvXeBXLlmB2HDOrXK6q0l1TpamaCEg6qLzRcLBHRRKYziVzXjFGqBoNU+QRFWULfDKd+zi7l8/i9U51iiEkmSJplIL6UV5n9tR9iOd/TJEhbEZZ7xN7PRnkWlGvb5CbXLq753PBUN0ZddBSUmWpaAzjOkzCaV5TjtJmSpN8bnfeQ2Hv/TPmbn3KKcWOjRbCZeuG2Xv1mma7TYHzyziu4ov3H+U+eU5PvbVw7xsQ4Vfum0Sqby17Z6gWrV85NdvZ31FM1LwkE5OkuUstiMOnMv55X/6Lj74H36YkaLixHydZjvFs3Djji1cunUb3cxSKBZ/gLf7+aHRztg16rNt3KWXKbaPK4YrPq7VuEJwxcaAj92/hOs4HJrpoqxm/aDm0umQri0yUDak2mByWGhKsl5OoCT7T9a55dW7Gb59J+ry3RiT8qFf+APuPdxCS8FcOyYIJA+fifiLR1YRwuXcSodTKxGJznFdhVISz3OplR1GK4rhEHx18Wy7rTaYLCfwKiAkX32mzV2Pd8mVoh5byqGP6yk+eKLOE6uL1F2XGy8b4RVXj+OXJghK68l0DyslgVdAWgnLj+PrLrp9is7hLzL76B9z138Puftzp/rSV/RJd4zu/2s1kr5ajbH0t3AWYyDPcqyWZEYReB7/8Wd/GHGBguwLh/d1RiFPKBdKdJIWSjn9C9m+l+8ubudtP/9VtpQNB+s93nnHdj7y1Wc4s7jKtslB3nTDVpJccfe3D1IulIkySTeeIWr2+LFXVNFf1+SZJggDFpoBn/nU40zXAqZHJcvP5MRxTuhJikGAtQ6lgUl++SfeTT1KMUaireTUwhxpJrlqxzpqg0M/qHv9vGGBXCT4wmXriOJrj3W5YmvA0+fAkeCplMs3hyibs2WsgqRfHZ4Jw8Zhlwee6XL5ekkrUcw0E/Ydj/gnt1WQWHpJwr97+99yaC7GC3xKgWW45vDRfV2WWglbR0JOrnRxpOXAbAvX7XOMeJ6P60pcaRkKHAKnT7k2PeDhFS6eM5oQGi00V0wljG2/grJvGVA5MltkrJb1xUzyJkp6GOHx2myZzG9Rcmp86PM5nhtQW3cHebdONnMfj+7cQG16CGu+jHQDViba/OW2EsP79/G7f3QKnZu+7loOwgVrDNJapBb9aKQEz3HI10qw+nUrhmoxZOHUSbyR8/dBXtjR0pwsthxenmNHrYwvIAbSNMdYy5dO383N120htZqdG4Y4ORdz+ZYNdJOU2dUGq80MbXL8ICQzmjzL0DZCG8u//fgcg75DPTYoBGnUop6PEi10GB8scvMOh49/O8VzXeLM8sefeBDPLzE6XGXHxk2UAhdlLNo4TE+U6HQNltYP6l4/f1joRnCq0ad6Gxl0iLOcraMecSxYbBhcnZFZH2yMMRIrNVIrrNTUE8OZJcX+xbgv1SQ9PviVDgu9nP9531cpeoJiWOSJhQ7LzZyNgwHzqx0QglOrMUqCdJx+t4MjKfsOUmhyK5EIktzS0ZY0tczWM2qjF88h7dmSq0eOxfihRSqJziWerKEzSK2haIfomZwstYRK001Tfnsz/Jf2Pn6VEUbqJ9ijNK978xXEV2xkeWycdH6OiXuOsZIa6s0e68s1ip7PStxBkGOtwmrT50nVIKXGaIHrOCS55lnNDIllR1EhraAdd5n2p887nws62rpykW+cPI2UksfjBlsGi1jHoRJ4LDU7LPV6PPLYMTbVCozsWI/nQrniss4pE6UFltsZDz5xijzNcDyfXBvSNKfsu5RDh7NRFykll64rc3yhwyNHm1SLCkdHZFYz5EuWE005hCeePsZKt0ucWjSKiYLP9q2T6EyTdGOMMUhxgX6FFxA9oyn6UJnw1jgIDdr22/PdMKPbNGSiTJSmSDxWE8Mjx3rEwhBaD+tAd67P9zjXSkmRDAUO1aLLqeWEejtmetBjuZ6Qa8PxxRSpHKQQaGtwPQclBGXfwXUgVBLl+Az5mnOtjOWoL2horCXPNKXs4qmqAUueJwgpsVLQjH2qoYf1FKnQBH6MkAGuhqqT0YkzdvoZkXuG4TdfxU+fnSPbX8Y2Wuy5bAx3cpKsl9Cz8JUrx/mFTx3i1VXNyuoKt20Z4sOP9/qJadvn6+9XoRislqAMeZoiZX9X0BcIEVQ6B9EvewOcOsJxI9my/e8PiFzwjBb4AsfpE7ekJuNYo8uems/8apc81yx2e5xodHlmtsVT3zrEJeXTHDo2y/FzSxw61eabj52iWCzgqP4DliQxUkIvSTi93Eabvt5UyevnL/7dm8s4pse1V/gYpZmuaYZ903+96B7DBYfJisNgKHhmZp6TTz7DTUNQyOGm3TvZOHDxkPOEnoNk7Q2ZgdCSLBd9IQqtGBtQfOPgEvV2wueeaPDNw01SK3ByQyfpsbza43Qrp7cm4tCLNQdnWshMstqMMDrn9HKEpU/c47ourufguOB5DiXPpVIUoDTbhj2mBzxik3JgKWGlZ8nzfp40SzVa98X6LhZoo4G+3l6mHRwJr7tmkl9/+x6UiIkTSSfp8guvuYR6K8OYnPdOtvFMP/QudUZnfYVXve92/LENWCmJW3XmFlo8MrdCbCWfPp7TSQ3XbCj39/m2T10vrMHpE2r2t4hKAfK5JlRrAGs5IjbQfPxBjp09yRcfPHze+VxwRSt7Pm/YsY6FTsy3ZpbJreaRuTaTgyELrQhjDc1eTCdNubM4wk/9yOt5369NYfM6d//nL/ETx3LqzTalSoFWs8VAuUy71+tTY1uL6ygKvsTVgvUDAaiALWMBnuOw2hRMTvncvGOIlZVVnKDAclOx//AynhBsGS2TRh3um89IsoSZhx/l+l3nz2e8kNBakom+AkqfAs5yYtnw9FwPKyyOcEh0zkpkafX65DFKamJtGa44CGFZbCccXF3h+i0DnFlaJTXwyKkVhOpHDJ/lbew/DBZHWkLPwVqHkbJl3A843cvYPxdjrKCbZH16a6PXhEbWBiuelfC7OGCNwRhNr91jcGqaQKRcvXWS3/jQl3nt1jE+dzLlqo2DTNTKGFnkhrEU386jC5LZM/N9/epAMjRWRvo+aavN0soqq3mCzfM++SwQiYxytYqQCiENqq9Bs0YnafE8jzzP8TyHPNcIucZGZi1byznGZpQ9H3uBYPcFVzQlNKlNGAkd3nPpCK7r0ktSSr7LhlqJG3du5PLNo1w1Mczv/vwWRM1AHKEfmOdVb7mZM4c+wG/9H9eRpZKR4UFCn7WtTZ8GNs0zdoyX2TBdZdeGYT77QIuxWsAXv7XKG68RbFjvk3QMvlcGG6CEZNeOGqGUlDyNVwzYWHDQ5JyrR3zz+OIP5Eb/IOCKnMxYmj3DF5/u8fn9PZ6aTVBIhHFo9DSdJizWM3IhCAPBQFGRGc2Rs21anYyhoiQzlvuOrJLpvuCCciVS9SkdpJC4rsL3XKQCTzq4QjJcsBgjeHolodmxdGNDO0oxxmK0Ic+f5aT/Xw2WFxdniMZogxAJJo+YbeZ87cgs73/v6/nYlz7FoB/yzht3kaMY8uq8bWAGJSRJbplptekawZuv3oVXHkTnGb3mKq00J1IFhkuDfR0JY5CuQ5boNUZoB7PGzW9Nn3ksz/qRxzw39NnM+gErbTQbwphqGDJQ8PpCGefBBVc0z+YcWckInYxTPfinl4zxVydazHYSXrV9hIqxdBDgOtz7UJOvf/Nr7L2kyo510zjVAnf99uf4L588QJxGpBqwlvGhAU7OLvT51ZVkOHRYnO8wPKR485t28Ykv7ufGS4a565k2gcypjiRooVha7dDrxVCscv3mAved6JFmhk2ex2M6ASz/L5i/XjB86WCHlcVuv4SHfh5mpZ0glcARAgcYG3DwlOTESsqJuYRq0eBLw3Kes9TKWWz2CVNZUzTpJ5j77Lm+66Jkn1ewHEiGC4p6JOhlOfMdg841ue1LyWZ5tsYv3z8G9CsvWNMbtyjlXETr2RpvojRkUYdGu0upUOQ1l03yn/7kD/ipd/wcqRfxb9//f/H+X/n3ZO0YJ89J04jlhTl6wsV1FANFCV5A2miy3GqybyVjY1XRiRPAgJQ4xiVKU6xwsQbUGlUdGKTVGCtRss/haLR4jj1ZCEE7SxhQisVGhBLP09He9o4reVVzgrDgYXOPxw+fIju8hJQOXzleZ+fmaQ6dXWSw4rFr8nIKgc9nuxEffv/XWe5EbJoa4NDJRYLQw1EST3nknmbz+BBJmjIUKrQxFB3JlnHDV+49yG1X1Dh1ps3xxQSlDetsxtlVQSF0cFwXlSWcczSuAx1hcVVf+inNNKXg4pEeksahlxi6cYqSgoLnUAo9Jise7ShloZ1zcLZNpeCTJjnaZKy0+iIdcm11UWqN4dkYhJJg+sIjynGwFgaLHuvKktWuZbZlaMcJQkCa9ZUy9fcQgTxLjfbsP2P7umFZlj1HO34xQKxpjqVpwvrqIFnWQoZF0ur1XH7lFP/nX30FURsgqPn82iVNBJZWo83pTo8wDHjjrq14OGA09dYST6/m/OHdR/jdt+5m/7kljBFIJTi5GrN+3GCswZF90lpj+iS1mRX9qhBt1wIkz9Ks99uzjrcKDIYp356x6JHzB5IuaNnf/+RBykUfKQ2FUPDw4Yh106OMjpZI05Snj55h2+Yx3vmmG1hZ6fHYwXM8fXCVdVvWk2Q53dhyzZ6N9HpxXycYTaPbIzegXIctIwGvvCLkh24rsGfjIPcdalLyLE+eSij7hrfdNsK20Ql2bShzx9YKG8dd9u7cyvjwGFeW477qhyOQUhH4HnF+8eSCGr2YzFgKnmTraBElJZ1exmMnFlnsaXq9CJ3ltLsxSZbDWlBDCIFSfXWXZ2s3leOsbe3625dK4FDxFc1uztGllJlWSjtOSFNNmpo1ZSP7HEW61vo56rtn/6a1hmfZjMQapfjFAm1zpLEorekkTdrtDr/50Uf47ffcyi/+u39NQZYJa5eSZylKW9JE044jMk9xX74ZkQrcwSpJfZVDc23e/8nHMRbK5SH2zUT9njYLB5Yy6u0erNHeO1Ih1ZqKge4nzvsC8hpj1+gU1rSvF3RAwS/QJrhg+doFV7TcGH71vx9goOxx6MwKUZISBgHDQyU2b5nk+PEZjp9tUH78LI7nYLRhZnaVHMOOrRMs17ssL3fAQrPZf9ukaUac5LhKkI56TAQe004RkUpuWV/hwSN1WqlhZFiwUk+Ya+WcradEHThVj+k89SSlQkC9IygGHvecbvKy0RJjIaih8g/iPv9AMD1YYHVlmU5iePzEMqViSLfX1/Nu1Nt9Z1KqL7K3Jrj3LIf+s6tYn0V4jV9fCMJigBSCJDckmUZbS5z1VyS7xj787Nbw2VXrWQd69uvv3D5+l+NdRPtuCViRo9M2otUgLJRYPX2Ef/p/H6G04Xaac0cIBsb4/Ce+zNsHE1qNJrO9mFgGJCrk2NI5VFlQHC7yHz73JBaLJwU5KV0tQAmktVw1PUCW98UalVKYfE0fTVocX6KtwRixtqVc0wVEgIFCocpBp4wNz3Ehcd0LO1puWWjWWW57RElKs9VlcnKYerOH6zlcsmsjR46epdVo87otPnNdj0OdFklqMWtSplEOr755G/c8dII4TigWfULPYf1QQDeW7DtSZ7/osGNDhVwKbt45yP2H59nouXz7UBetDAtNS7sYI5Fom+M4FqMVibZEQrA/tuxrJmx3L56H5fBMnW6UkyQJWhta7T5x0LN0C886Vt+pNKwJUDzrZP3PCgqFEAfQol8OFKX5c10VxqwxsyEw3xGefzawobX+jmt8N+33s19/5/UuFpR9Q+pa0t48px58Gmk0g8JlSCrGPYdtMqTqCe585+3MzUccbTTQQZFbHznD7azy6au2os7UWWdTvvjTL+e2P7iH0dGA//Cpb4GWOK6LloKvHjzOA36IEn6fn0RZpOgreOa5BqGQsq8fJ6TCrsn1To8M8Z43XYsZWIdSd/H0mfMH4S7oaFEvphfltDtdsJapiUFmZ1dRjoPWhhXdxhrDcr3H+265hHOH23zxwNn+apXnWATaGOYWE377R6b5j5+ep96JCQYdpiser93q4cqA2FGcbcTs2eWw/1gbYx3OLku00QjlYWVCnAmkK8hzmGt2wApybSkHAevGSqRJhdpQ4Qd2s58vtDb0er3nVpbvXK2klBhrv8MR/pfzWWtxXBfXgUpB0YkM3TxHSodM6zVFE4m1Zk2yyXwXV8p3rmDfie/3GeCiczKAuNuk01vFd33W71xHnhl6vYjZPOdY1OM+o/GEwxWrdQ4uLHHW5nz6scMkcQ9tFuEbp9n+ymupdV1KhQI/feu1PCMle3fsYfO2Lbz7p97H0GCV2tRGtu/ezic+9dl+Z7VyUK5COgGu8lFugFI+TuhhrUIKCPyQD/yXn8Nxi+jU0F68hMMzy+edzwUdrdVN0FowUCniuJKZuRWmpsZpt7pkqcBJMmrDNe50JK/7y0NIIWnHOVIYcmMYKBQoFwtMTg7xyf0dfvzaIeYaOSfqGUIp2sJSzgU37hzmP901j1zIGfAzMgx37i3xp3evMlYVdOIUITWh75MLGC1XWFxtMlj00bb/xmmnMU734qlu+M4H+7vPRs/KxYLneRjTdxYhBb7nIeQax7vRNDoGYy1G0y/sFhJrdJ+qeg12zWG/nyLMd1/XPreKPju+7/3ZxYIf/ZdvJ0l7fRlcbdEiR6L6OS7Tz/ntPDLPI0+fxCs5TN18HT/9Ng9p+mkNITW95ZRjx48Q+Io3XV7AFCZp0+LRxX380n96H716B4OkUA75xX//42gM2vT1zqQQ5FZj7RpJj+kHQk6fWqK3Cu//T79Pc/EYb339G7n7sX1ccsnAeedzPkdTALXhIeIkQ+ucUqnI4OAwvV5KrRb+rzyO4/DVJILIMDRQYqA2iO/2lSsd6VCsVIiNw7ZLt3NqpcGOa8oMLyzieg53vmqaNLU0egl3vnGUpJsw0/C59doSy67mN963yN/dt4z2E2oVF20cWlHK0GCFTu4iAp+a51P0S4yPQCP3v2v8LxIUwMjIyPf9oeM45HnfUZTqb0dc5VIMHCSGWPfFHYwx6NysaSV/tzN9rwPBd69g3+9c9p0/+345s+8Y74tuu8FijcQvIGy/srCvq9nXlbaAsILcLuANVHCLHgNT65/jGDH0e/+oSc4ePsMG6+I4IdsHizT8AqbfwomcklhsX7kTF7AYYVDW7Z9116KMlv45TVjN9MZpwCKswJjtGKO57c7L8FX4XeP/XpzP0SYA/uD3//AHY76/B8+y4RWAK/+ez/z0q/9Bf3oCOP4P+s3njwmAP/uzP3uRLv+88aLbbvf03gt/cvNNbLjAR678F7cAfVWZrc9zYP8v8X1tdz5H2wfcAswBF0/M/MJQ9Ce770Ucw0u2+4fjH6XtxMW0L38JL+EfKy6eUoCX8BL+EeMlR3sJL+EFwEuO9hJewguAlxztJbyEFwAvOdpLeAkvAP7e8P6jjz7qA9fwv3GYde/evecXrfr/CC/Z7h+Of6y2O18e7Rrgvv+vRvUC4Bbg/hfp2i/Z7h+Of5S2O5+jzQF87hO/jMiX8UwZREaUSQ6eCtm4sYMjNPQUkXQJVQyBS9bLiTuCsAKO8oiTGKSirCDLDK5yMF2H5dSnWhF4SqDTnCEfzrRiTi9l/QLONOUXfuYn+PznP06WZPSSEkZ0qHoa6ZUpFVNS65B3IxItCXxL3HEZGB/gDe/41efG/yJhDuDBz/0NpXIVN/BRUqKkQ5IlNFaWqVarOF6AyXPajQYFzyPB4jke880mT377W7z+LW9h8eRxiqFLODCKzg3KdbFZhPJCHKVoL89g8hwDpFajckt1YiNnjx2jMlAlCH2iLKfoBPTymEa9zrqp9aAUWZrguB6uK9FW4PgB2/e+7KKw3d2f+EtOnpnBUR5PPLYfz2RUNmxm64YRqoFmuFThZW2BsJLu6jKZEawsL9PKch6uL9PLDYd1Rie3RNYSChjAEgPaQNlRrKff8lJ0HAJHMCAkYaHM+LvfBeUiAoHrujhCECcxSS9Gel6/NCtNaScRzTOznPnyF1ksVfm1//bf/l7bnc/RNICnDKvzdQp+l9wzmMiixASmvUojswhPURCGNJO06xprDalWyAxmFjXdyDI6KJmPJMVyRsENKRbh6BG448phfN9n4+YJlhotwvlFdgYZh3uKo6d6tPOUZqeJMV3a9Q6FYhfcAkmvQxqD5/i0mytkwidTisWGJSx/9/hfJDx37QcfeoR2q8NNN17H4FCBLLVIoUEq0AlSOZBlJEoSRT1iN6RYKvPAt4/xQ2+xTExME+cpSRTRbjYoBAHSdXG1JTcG1y2iA1BCEQgLVmJMxuFDj7Lr0psQEjqrDdzBARSGSlhESk2uU6TV6EwjpAdW0FpJ/v/G/yJAAyRZRrvZop1p5mbnCB2BNznGRHWK1tIcU06JYrkGQZnm4goLM6dZ7cXU66u4OuNgu0PD9mtGV9IcgaXiB5zQGZd7IUMCPAWh7MsxJYDxfJ5YXWXnH/1XJn/2F5GBRFlIlUBozVJ9icHKAMYaOr0uVhsKlYDO4gLdJP2u8X8vLli9317KWJpXlCsQhAEy6BJ32/QKCYWij44lysnotCHwHLRJiCOQNiBwI4Y8Q0bOcNlFKkU31bTqGq9gObx0ipr1SdKMoVqV0Y0TLNUjtqzMMzkJh4+epb5i6WaKWklhPYdUCISUGJMTxQlBUITIYDHUBgVZfvHQzZ2dPYfjBezcM8S+R/dx5513omTCo/ueQLoGKyVKK5JehFcaRBrQnkXKgOnhIX7/jz/Erq2jtJpdRoaHiXpNSuUSChctwckyzp05Tlgoc2K+hZPkDI1VqVUH8fxpzp46iZAKTwpOn15ESsvCyjyTE2MYNDLXXH3DLZg04ff+8K/58Xe95cU22XNYXllBOYru0iIFB1a1prhUJ4rarPdKFB54nKWJKdxKCd1qIrtdZLvDqCNZ6qQkuCibsAIoJdjuOsxmGXeUKiQ6Y0opXGvwpcT6imauKSjJbscyEEU8+omPcNnb3opf8DDSIbeGwA/A9Cv6i4Ui2mgyJRHC4Dxfcp6xgmV8skCUaoSUzM4KyoHExRD4PsJkdLuCUV+QxBm5LuBWLXFLEwjJatp/e5cLktzkpEmGK6HXcqnTp/taaJ9krDZEmkk6kSbwKgxt8Dh25BxbyxkPHtcMBDkriw7VzQMUwgrLKw2sbuJKjXUkwhOUhMK7iCrKlCqxcb3H7/zFxym7Ibu2bWFsbIytl2ynNlShuRIz4BmOnDxLbbgAzTZOMAADAyyfPc6+40vcevVuThxbplDI2bBxI+eOnkU5XQK/Si+rk4qQU6eWKRRCjtdb6AHF9vEtzJ0+ydTmSwnDAlm7ywAWbSVOYLniiitxPEXWi0AJHM8lyixOELzYJnsOcZzTixMWziwQWFi3ZQu7JmvoKOKquQw1NkJh+zYWDx8m6bbpxAmRzQmFw3i1RLjcwFqJI2GHI9kc+qwTKdsHqrSaTaaUxHouRQHdPesoPHmaCpIzUcLZosvEDTeQa8vS8jKDg4MsLSxQqlYxUuI7ikxrSASZcBCORy7O39N3wfB+AYfcsQwPBYwPKK7bMchokFNSHnmS4oSalcWMVk8SFVyiPCNpZYjEkLmWnQVFteSSxqA7Lmnm4RiwnkepqrDSUhqpMr/SJG7XefJkxMxSysMHGywtLLMiBnn5LbsZHhlFaMnCUot6fZUd2zaxYfMltBKPc82ceqw422iw0Mx+YDf7+WL23Dnqq23edtst3H7jpfi+ixAw6Bf42w9/gd/+4MfQToHJsQnqPZhZijh4/BRRI+KGV9xG4Aq++e0DzC3Pc89Dj/Lhj91NaaDIk8dn2LJtPd947DTjZcXEYIWTcw18z+JplwNPPY0Iy0zVxvryvMvzxL02I4NlJobHCMICrhMglIewoK3gnW+7nT/+xOdebJM9B+m6dFsd4iSmYwRDhSK7d27kup5DoVZDhkUaZ2fQvRhtcoxSFMISXWM502xRwpDZlKuDgJ3FIq1ml5unN7I1CLihVoPhMic3DqGQuE/N8EQW85mKw7FLL6GzeTO1oSGUEISFAnGW4gcBnufieS59QnXQaJS0aN0/+50PF1zRSgVJNSziSY0fFpAm4aZLS+R5hVJR8djhZXZvE/2VKnd4otFieLBGeaRErTJE5lTZVi5y7swMvV6dvLdIpDXNesRITZBQZOF0TtIOWVrtMT7scfBMs0+BoJfZfMUgzflzBI7kkhGXTpRwZmaJ5eVVwqC/fPkqQSQagYtbuHiWtB0bJvnop+9hcmqQQ+fqTI5XKRcLCEfwpje9DPl391EMXLLMZ+fQRspX7iU3hu5KHbdc5M7r9rD/wGHGRobYWtC0GxlfeuAxGr2E//ynfwNIPrfvJEODPrs3lRkIfMrFCmNTY4xNrOfMzBm277qUocl12Czn7JkTDNRGsMLDCI10HGKTEmjJscNHKV1EBeaelCzPzlFQLqvAcn0JmU6x3jhQChF+SLY4T6fVRGtN4Hg0GnUaUZdaGBIlLcYKZeq5ZqgX8fr16xgshWSlMkHcYXMvprYQs28oIJrYQq1UYc/WLbQWF4jSNmG5jDaCLEsIXR/hOGgjQGtMkpFJTdxpc/bkKQoYqs+Xbq6TBAR+F13cyGowwpmzi4TdeVIT43sQDoScWWjgBZJWp8fEBkm1BmlnhbnFVXpJTKEAVvt4IieUEYFfoTZQYmOtR2JjepkgSzN2b1UY02Ko4OGGPmmU0WxEBKGhnRgC18f6himhcEjpJRLpZJR9yfHlHC9QjF5AtPuFRFdnvPcn38WHPv5p7rz+EkJVJM0yXOXzzJGj5IEDjovGogohvXaTykANO1jFWsPeq69mcqzEw08cJnY9pjeNMNLr0uqsEKcenvKZHAxpJDkRipbS3HzNXorlGtJqwmKFHMsj99/H9TfdjOO6FIohUmRoYzl5+iyHD55m59YRPFcydQFFlBcSsdastDqUhGTXJbvYPj3IjasaiSJJc/I4Jo9S0jzrR27ThCUsXQQmy2hpTTXL2OaFXFoICYTEF4LmwiyF2hj+q3fxsXue4pLL9lAulSi6Lp4jKQ4OYGeWKZWqpBm022dxhCQMijiuS6773C5OlrI8t0i2sIDUkiHv/NpyF3S0zMlJ2l1s5ySd+CkGHYWWEuE79KKEZsNQUpbJcsihVg/b81hOUgYqOZVQ4AgPaTNineIrRRh4GJkR+j7HZ3rkRjAxDEkGPceiBKwmUHMs66ZG8W0H0YvJuoagkqNTwWpX0okdlCtR1mG+nWNyTaFS4szZH9Stfv7YvnM7MrfcfsOV7N6xmeXFJVYaLaoFRZ4LXrZ3FxhNwXMI/IBeFFFfmKUyOkGaRhib8eSx0yws9shUyqH6zJpWd5+6QVvLk2csJb/AQNVDY/jU1x9kwhvilW+4hc7iHKVqlSuuvhrXVVSLZZQQGNvnNJwcKiK3DCOkZOfm7SzE7RfbZM8h6bVxjSVVllKhwJ7N6yicaeCt30D71Am69To6jnClS6PXQecZXaOJdcq8kdR8j2uDIueaDQbGRnGlIGo1GHFDDmdL7Hu8wO49V1EcqOAXC/hSEqcZPgnDm2pYC6405HmGcAWu5/ZVZITCKkGWRriuh/V9jCO4EMfzBR3t2EKTksxoxTGuzmiHVVYay7h+lUDA+rEKjeVVTi8JpqujBCXLr//lAjqPKZXKDA4UueEqhTEpMvFJhMPiomBgKKTYWyK10FjUFIIirVWD7wsaDYHSES+/cZpnnjmMUUUKA5JenmBVxkgRCgHkoY8/WCVvx9RnV3GSLju2VH4wd/oHgLzTwXEsk2NjGANKQohHq2uYGp3CkiJRHDt0jD3VGo32KgMDZQwOn/m7r6LzFqtRzNxKE+H3CX0qgWT7JTuZqpaILJw8cZynji+hjY+yikq+RDqe8aG//lv+yY++E6kEM2dPUR3cTaFSwKYGJQU6Tzl5ro6Sio3DQ0i/yKAYe7FN9hyW5laouB4zJmdxeYFd54ZYPnGCr339G+RGkxnwXI/NYyMYpTjdXKGdQ906JDrjhmKR4cClHrm4jiRBMn/VdmoPP8nB8jDrN21gdGo9wvWxWURy+hmKJsKRLr20Q+Pw/6AYeBRRtExAeMkVHPjsl/mlyjAnWys8kqW4V+zBGRmipxTyAjupCzqasB6tWFMKFIePxxw9t8glW8qUwxLt3grNmTbdriV0I6xO+cuPt4h7MZ7rI5Xl//rpW/m9jzzDruF5nKKBxGXrqMOcsOjYMhsXuXracqye4aBIrORluyQnZjXfevIwGyfLdLWhnUu6jiLuRRTyjAIOZStw2222WM1qwaE84lCulH5gN/v5witW0FoTZU2GiiP4yQBPP/o4D+4/jU0jrrx0M7u2bmbXnkt56onHUNLDL9X48qf+lpMnZ1jspDiexPcE73rHWygUq3z9K1/l9ptfSZJoPN9n9+6rmXzyAAWnyOzs0zx+dJ7V7hJeweN/fvoL/PCbX0Pc0Tz58CMMlAuMbtoAWtPudhgZGyTq9jg1s4jrFZmcuHgcrdlokgHbtm7jqulB3NlF7jp4iFuuv4mjBw4wMlDjxPIiD585RyPPCIWia3PSzFD2XYaUopkkXF0dIM0yQtfn8jNLiF27WBmHVm+Z8MwKniNw3RBnICCzAcJmWFHAEeA4Acsrq8wfPcL7lnOujCIeXTlEJizbrYv7yLeJ05xzfomj5nkqfnqFAcrlrTi+ohLNsavaZPO6GlHaZn5umWrNwcgeLeuy2HIoFx16HUuWpfR6HX7+1z+DEJZLx9fR6UZUnZRWpBjfPMx8tMrWIZeV3OL5HuXREhs3D3Josc5KXOf0iTq1gkO7m4KUXL6+worNaRlBK5b02pZKISdUOQhDIBVjw+GFpvSCQbkQJwmB61KfOUUvEWSuw+tefi2f/NLdrEZdMgHSEWycWsfw1BSf/dynODWzQr2TMVwKeOVrX8bwwCbSJKJWG2LP7p0U/RDlWhYXFpkaG+ama65HZzG7Lt3GdTe0+Ov/+XF6vS7puXN88KOf4Kd++E0I49LtdcBKWsvLfPvJJ/nkvY+xeWSAqYESV1y9l6B88WjLuUKyqjM2lAu8/MwCbam4bHojx5/Yz9U3X4f7zvdw6l//MsUw4MypY5zME966cxeblCRBUC6XKY6OkrQbFLdsp3H6FGZ1GZlrXv2en+L+v/o4Mo7ROcRRF08oUgUl6dJutckRWCVxHZfNGzdxVxZj3QpnD6/g9HI+s7rKz09M4iEo+Q4X8LMLh/df/erbeGjfIS7bvo4ff+stvOftt/Gym/eyaWqcQeHidH1EVINmgXjO8kNXj6JkXykm7iac+ZMf4xM/dxNTYx6V4RJHWn26uJXVFrMLMQ/sX+Srj81ScQVxfZWvfuUwzNV5244hiqQst2PqaYC1kkLRY7CgGB2QbBrO2bvBsGNC4ekuAyqGuMXY9Lof1L1+3nDcIkFQpFAq4VVGCP0CSaPDvgP7cIKQ6bERyEEojyDwOXb4GLMzDaI4QhV83vL61yPUGLWxIb75rcc4e+YkWy/bSyYlR46eoOBaHvjmV3CDkC/c9yCDwSBjtRHuuOM6qmFIM0px0pS/+fBnwFWo0CdLcx5/5jC9qMevvPetFAsel19xBei8rzBzkcAXDgPjo6iVFbJ6i0JYZPvmjQyMDPH4whIf/eVfYviHf4R9J45xw+gEv3bnnVy3YxuljZuoDA9SHZ9Aui5BWCJvtsiabY4kCSeuGqRz5iBzMzMsLK2w0myTdmOido9eq00vSdBYVufnWF1cZGVphrmzZ5hfWGI26hJPDNIeL/JL69bTjFLO4PApJUjN+WkOL7ii+V6IkoI4jrDkDA5U8FyPUqXKpskApEYLD20kjioQhD4bxpc5vtCmFIQceeQhrv3Vj/Pwv303yqbs3VYmTg3fPrVKOVDsvXqEbbu386WvHeTYnGZyrEiv1cb1Cihr2TnlE7WajFYdam6bJ8+0cGVKteCxtNJCBi4myvsBhdoAs0cOMn35D+x+Py+snjtNnmlkGOIFHst5l6GpUQ6dO8focIkv3v8k1+29HGVdgoEhHvnqPbSNJo7hra+/HZyQHdPrOPD4Qd75jrcwM3sSN/CZO3GEyy67BGNgU6uLEPDW176GDMv84jyXX3I9A16Nj9x1F6cWO2zfNMTi7DI2j2lGXZqrS3x9/wkaqyuM1gZBaDrdGPciitjmwKZ1G7gp6aIbbfJej2/PzJKNjrLeGja95R38we9/gGuGamybnKK+tMRyFOMqReC4dHKDVx3g6/vux9lYw50eoZMpek+cZHKwxkonohY42FSTZRqEpBSE5MRIR1GeGCVJMp4+cpxzSy2klVitEZ5LwXOZb/fY/crXkQ1VmTpyEnXyxHnnc+GoY5YxMFSiWgpZXJrnmSe+RWu5wRU3vJzi+ASjo4PMzEUYYLjiMDIxyjvcIc588C7WFTwm9rwFE7VZXs5oxRFB6LPUSsnSnMW2w3yry+BUh2IouG6HSxQnVBwPq2O2ra8xOlxm34lZoq7HuZmI6apD1OpHGTMsr73jUg49cYyTpxYQKxEz3YvnYSkO1uhFLbJuGxUM4Dkun/vKfezdMEYjS/hXP/ZGjLZkcUqSWeJmzNJSi9C1uEGRlW7EtCO55LIddLOYdeu2YZKUbz30TbbMN9iwcRPjm7ausR7m3Pvlu3j5K9/AwtwCXZ3xnre+lv/xkU+zMN/hnm98nbe85lU4gcOpYolNY8M8cHiO1986xsj0OJs8j0Jl4MU22XPo2Byv3aJwbpazvQ4jg1WisWG+eWA/P/aGH+KDf/b7jDkuQ0ag4x7WGqSw5FnMcpJzWvWo7izgjgxSncsYv20LJ548wPpDdbgqpxN1IYHADVDC0k5ypO1QDANslqCVJAwCdk5PMj0+QqsdI7x+ZNLkOStFj8KjD8Ob3kKpWqV9gcqQCzraA1+/iw3lhA//xScplDw2TA3iF0rM1pfoNhPOdpZRvkMSxRxb6HLu7AILvc38t8UzSOnwwX/5c2z66f/Mv9iwibe6HSqVHM+TlD3Lug0uk1WX06dXqamExXqHPLUsG03a6zBSKVOtVhkMfRxXcONtl+N3Vnjo4RP0TIdq0eW+r91HGmt8x8EakObiaWFyPR+n56EDAbZfTbBhuMZyo8XJHux77BFuffkrSPKEL/7dF1lNNIGS3Pn625ieWod0Qw4efIbxkVFKtQpf/tgnufyq9Uxt3gMVj66pU3AC/uov/pwtl23nxttfwUprlShucfllV5CkCVYoyp6l1eiirSXTcODpw9xwzR6eOjXHPQ88w8uuvx5jFd1268U22XOQlQpjecxEuYhXrbA0MMgzM3PcODHNRz/7SfYMDpDmGsdz6QnBMwuzFDcOM7JnG+fufZKtTR++epiCkdQxLH38YTrdNhXX5977HiJTDlGe00xaFKSPFpbc5CTttE/Vqg2m0SEsBigJmTTkvQxPQCPNEapM6847sO0Iz3f72n/nwQUdjVyRxQmTk0UcK4njjDzLaZ5ZwE9zIlJUpsiilKBQIOpFbNu6EZEnEITc/8Ahrk41zWrOj143yoP759mzvsrybIuqY/jyE11KhRxX5pSkQiEwbs58u0vQ6lB2UtrtFQbH4PEH76XdtPjSQL4mXZsapFRI2xflkxePaCUrsyfoJoJer4fEoDyPo4tL3HbFDpoHz7Fx1+WkeU6hUKYR9XCUQ2QsjbkmX9z/Bd7wQ29k544dCOmgk5SbbljHZ7/+NAdSwdD0Btp1y+YvfYwrLtvK2XPzmEssg+UBhipFGs0Gf/uXf8Wgp1jsWVIb0+h0kTrjVXfezP4nn2HDQIVzzRZJp8NKvU21Nvxim+w5bFy/gWtDw+xXHmB6pMbZM+dYXlygPL2B9aUiw45HO+/huw6HuotMB0UqKxnBPYcZdIo04ggrLblyCKXgmeUlBj2PZZFw5IkF2DhE1zgYIBuq4DVX+7JaWY5jJZnSKFehjCaxgjTLSNOc2chSGBvnmtteRR736CYJjZUF8gs8eBd2NKsxDpCmiKBAL+7gSMXZM022jwhMpsmzDIEkirqUfZ/7v/FNfvGXfoJP/df/wkp9lZ3hIJ+4bAMry7O4JubphVkGK1B3LS+7QhN6LvWlPuWz1hJPeoReTpw6ZJ0OkwMu6apLhMaVEoxBYcjyDF95JHl/JgJJJ7p4to7BwAjJ8gqTk2MUC2Uay8u84xU3s2PHNlYaXyPw/DWJV02c5wyUPYZKPrsv28t4rUiKQWdd7v3yXaTtBq963Rt4srrMP908SK+1wtRYxqHBa1g6epBXvf4V/OX//DAvv/4GpjZvp1wq8OPv/VEazRX+/G8/xUgFGqttqiXB1vFp/vsnvsrPvvMNTI+OEsc5k5NlvnXoMC97sY22BpNEVHaME4UBJ5ZbPJb0+OG3vp0PfeqjXF+tkhmNtNCOuoyuKZ9mWUZiDAaXdpZipGIlalF2PXzfxwpIrWXaK7H59tv5xuMnKdablEcnUJNbcG1G0FmmtdJAt5uk2tDtJeRoqiPT3PSqW3ErFWaOHObYYw8zMTnF1ocfpjhSIrnppvPO58KyTUi0Nhih6EUZvbyES49ulOC7HuMVnzNn61y2vQaO4q4H5/GMxw+/5u302oLxwUFOXDbMm97/H/kPv3gz69cJ0kygM42JBNYVRKkgSwIcqwgdSR5r2p2QVEYIA54vQWh0bvCkRKcCPwjotDNSmaIcgU4FuZJUihd+d7xQWFpsoExOq9tFZ5rmaotyaYBz8wtcfsVlKJMDmpX6EkGuCQLJ1GSNu+65j4FKkW7a4ode8UrueONbaR67n9R1uVo3ODkb841vn+COq6YJC02eWGqxvW75yff+PEpKjh07gzGasVoV6ZYoK0OjqVleWWVq3Q7O1pf42bfdyUDoszS/zImzx4hy2LzuQgTbLxzKoSDPE9LQ59so1gv4zGc/xZjvM59mDBccBII4SckN5EoTIXAcxXKrhVESoXMKBk722gSOxAiFFIKCEnz5w58kGiyz46Y70UKjcUkaq0xv3Mr6XSWC2jAjtXHSNGLl3EmWmy0a++6jfnyG8dCj4ksOH3+apDZEq9lGzjxz3vlc8KksuBbjQOhX6HYbVN2YgzMRt64r0dOCZ460sXGXb+xr4hciRmqGglPk0UMBldIkW64aJnrdrRSGpwi1pL0ocZVDwYVMO6hckqVQUpLMGkLfIXNyshgcW0J6FpP3RQ0EhiyX+AqwOUJYHMfpy54KgxSCbnLxnNGUD53lLl/++hO84uq9jJRdvnT3Q9x04w2szp3k4w8f46aXX8bNu/bQzFIqxiHpxiTNRd749h8DPHKnX/Zz+sw8l2y8lKwwxuaJjHjcMDos+Pq3ThH1JB/++CfZvW0rb3zVHWzbthFyTZq0cFCMlcscWW2iXMny0gr1Rps87vDM4VU2rB9k285LGB2oooKLp6rmDZvX09ZNvrlQZ95L0JFDajTDboA1hrZOUWh8qViKI0LHwxMGE4OnFHGa4CmPlrDgSBCKNhapBNp1ef3bXkErTrGVUXpxj9xaJge24Jar4AYUVIDVGUopWs0GvhWw4zJWWiuca3dRGQwWi5xrrnJmqcW1I7XzzueCjlYNBMWSoN2uYzJYrLfY4CU8cXSFSi3FSoFf1dRqkDVBKE23tcp8d4xbr6iwcbqGKy153KbiTULURmegkZBLjDA4jkICSTumm1mSHEqBhxWCJEtJcgfP0Ugl8IXFaI00ijBQfbleCygHV/WTvxcLPveFBym4EW++5WXUakXu3/ckWSb4xNfuZcpx2bFjknW1GrmweMphZblJvRVz262vwFrLV+75En6QcuMtr+L4csrGbg9x/HEe747ghSW+9XSX6Y072SZiHj42w+njx/j0J5fo1Lu89s1vpqAk84szdKzByy3Cd2guLnHXQ0+wYXKcwyfO8Mo7/jnFSoHFhTkq8uLR/z792a/TXFnmZ379/+Rvf/tPmajmmJEq7cMzCCvBKnLXIYpjfCvomATHgOe6JHkCSrBgckByIkmZdyy7L7kcrxJS9j2SXhfXkeSBh2c0MsuQbkZQKmOVix8WMIAjHfKs3+xZkIbiyCShO0urHaECB996TAwPcHauft75XNDRFpfrROk5olULaCrFhDSGsaokcA1RqmjWXVYPxPxIlPKp1NLYWOHySya58bJpfutPv8x9f/YI2dRneN3v/A5//e/fi+sFOEAuNK7nE0cx2vZF0ZM8J3QdDDkYB185KNmXz3Gkh5UGYSHOBc6aJJGxGdZ6RFpT9C4iBj2d89YffSPzh89y+vQyr7jpak7Mz3Dr9GZmzpxlebGOe6kH1hL4DlooCuWAqy7dggx8XvG617Iws4BfGOCt7/4n/M2f/j4/9r73sv+pb3H/A0/T1Tmvf+WtPHDXF/ipH3oT4xPrOHvyFKVqjYHBAnfd82WeeHI/1cAjyg1OL2Gu0+baK7by1ceOIJXg8KkjbFs3jY+l0ey+2BZ7Dpacbp7xiV/7v5lvNVlFQdJhfSVERDGdOCE1kKQZjhQ4GjypEGmKJwWtSDMU+jyc5RQsbL1sLyMTk5i0x+hwgTDskSPBURglya3AcwOU5+IoF1TfyVACt1AiS7okcUJQnWBx9gy5MSy0upQdhbCCseHz7wYu+FTqvE75WM68u5vK5jt51Q/9C+47OsjXDpf47CMennaYLjlMbfb49t5Rpq4YZvdwQLvt89lPPMSOgoLHP03wtY+zc+9NZLnEaEOWW7qRptWO0QZMmuI6Lp6jiNKUKDHkWtOOEvI8p+g7ZDYny1JSY4kSgTaaNM9IjUIoSyFU2IsovO/VQn71N/+crVddQrnokWQJk4Ml1k9P8uo7buNn3/NOqqUCS8srrN+wjpV6jzSP+ZtP/i3S8eg0WoyNDWOl5vOf+hjv+sl/xpfuvgfbjXjDK24m0wmPfe0rvOrN7+DUsadxXAfhFRAKlmYXuXTHpQwVSpR8F6sEbjmksbrC1dftZc+WUcqex+LSLEOjU8R5gjDphSf1AsH3SwxXBnDjBN9xqHsSOdtiZW6VuTilOVLmHIY5k3M47vF0nPB03ONIlPF4L+NIlrKgDW6UMHjdNYxPrqdSG6I4MECuAgRg3SGU4yIdxfqNGxCug3R9UH1nk1KQtNv4QYlqqUIrMsRJB+0UUY5kuFggH9tED5cLqcVfcEVzk4BzYYWFg48zMO/wrRP38rZdLkoKFCWMFGSpxVogzlC+h00h0ZKRasAHPvKHeH4AQvHVv/5jyoWATpLheZaCq+jlUPAcUqvJLHhKooRLmhu0FHiOjzE5K80UqSSu62BzQ+BopHRwpCTKDI4rMUZS8C+eYIg1cN3OTeSdmNpgjfsfPMj00ASp1ZSrA7gFj1JexvF8rhsa4tGDJ5geHkQagdWWoeExjDT8z7/8c979k/8UnSte+dq38fjX72Hi0mt4k1th9uw84eA4N9/+Rvbv+xo7L7uOJI0oDNX40mf+kjhtcaIecd2uzZw4eowNk9OMj02zZ9s2dk+uY7HVZvbsWTCS4OIpE6WbxNRbbTppgjaaASUIhQWT4/cynLl+O4Sthui2YiZN8VCgIHMCTLfJiV6EqwTjI+OMTU+gXUHYdclVTqQqyFINV0gyIek26pQHRwiURAuFciSukZxeWSUROZ6QuJ7PytI8zXqLiapPp9tDJacZDiy+e35Hu+CK5rqKqtvipvU+taJPqVggiXLijiHJDUm3H54ulQNQHlFkyITCpE20VEg/xEgP6xR4xY/9Im/6N3+I67roDFIsnhJESYobCFxHYIwizQ2B8nCQGKFZ6lpwFJnuq2CGgUOiLVoJoljjeR6dxGB0TuJcPBrWWydHePr0aU6fmSF3Au4/cIAtO7dw8tgJzpw+jrA5WbdJuVyk225R9QWHTy8jZM5v/NZvsLIyy+/+zgd4x4/+BCv1Bq3mEo6Q3Pz6tyBwue8b9/PtJ54ijxM+9/m7uXTvrRw+9BhPfXsfzeYqp+ttQtfHxWfP7t1MDQ4yPDzK3Z/9KCJVLDSWGB0d4Hf//GMsrHTp9F5si/0vxEajpSC3Bl8KCsaisBQ0CG0hzynFCcWeRoYuY6HPlB/iOT6BMbzisisoC8XkjVczvG49xWqVWqFKODxAsTyIW64RFAKMFBij6UQxZ8/OUiiVCJWDzjWp0TgCRoZGaUUpuYSo3qK+2iB1AjIE1mpyIfvd1+fBBR1NaIEviijlohzL8mKCsWCkQyeGRhKRGIdWx5Dl4AqFMQZPOcSZZP+hkwj6krB5GnHZjS8n9CVWCBypUK7CGkue52RJRi5S/JJLO04xQqMNOEIjrWTjlmm6iaTetRR8RZxqnEK/41ZYTZ5ZTBL9wG7288Xhk7NY6fHwM09z+thx/uW738IT+w/wyttvZ2rDFnpRhgxKLNcbKEdx3Q03Y4Th6dk6Xljgq9/4Im989euQ1uI5mv1PHibLI4Rw8D3FbTdcTU8Ikk6Ta/dezoPfepRtO6/k6NlT/M3HP8Km2hCnlpvcef0euu1VyqOjTIwV2XP59Tyy/zG+eeAU9z70KGke0+4u9av7LxIYa1GOQ2L7ssMWgSckBoOQYPIcaTQijinHGSUksa+I85yRguLYwafoKUNtYiOlUgmlVL9O0S9Q9At4QYjjBkgLjqMIrCAMAuJWF3wFVpDpHCEVRcdHSZc0SlntNRnQhsdPnqPeilnuRHRaMUfmG+edzwUdLY6h281Icwta0ktzEAptDHmsqfg+SvbzF3lm6WQpJjeMeR1e+9a3srQw2w+/6x5KOdBd5lc++igWRWYkUZqTaYFRHo4vyLQl6uTkrkOWWFzXYaAUEPiCZ54+CSZHWEOm+9viPAVHSLA+GoGnLp7IWZ5pFpdbfO3hgyBdSrUBDh05zV989JN87Stf5bEnnqDVaFFyPYrFEmmvw1tf+wrIBFnUJWrG/MXHPkFqDKFX5sZbruX+u76AJidJc7ZdejXvfuMryKzEWM3qwiKfv+cLDFdDqgMuTx0/y7rRGtPrpxgYmGCgUiXJBYsLZ3B9n8B1yVNLJgym20X4F0+bTJqnxL02bQs6zylZi7F9WrcAwFqEkCAEKjc4aUyhGzHmWg5HXc4Kwbrr9jIxtp6gEFAKQwoFH60UwnVxgxDpKIwSCCnITUylPESuJO1OFxUoGo06fhiyUF8h0ynt1SVUmtI1BifOONFoc67eoycT3nLN5vPO58LBEJ2hgf1zKQPbNlEuhSAt2lhK5QDrKM4uxURJRiPWSOmikci8w5c/+2WWTp7E5glYl4WDj5I2lvCkYeuea1DGUCl6BIFExwlKKkxm0UZSUKCFReea3GqMAKEgLBaxyuJ6koILvhTYVCOkYaWX92nALhLs2LQR14FyoHjs4CFajVWmp8Z58x03ceedL2fbpq0IYGBkkMn167l052bGfMFP/cgbwbqcrbfZPF3i41/4OL/9R79Hr7HK5PQWWq0G+596jIceeZCJ6fWcOXma2XNHOHj8Sco25+jxOebOtKkMFHndK29lyHcxOqdcK9NsNzl64iwzS6tE3RwT+AwXQp46eAIdvyh0+98Xco0gdhAYlOBaTSAFSmm0BSnBIHEFYDQ+EOoMJ4crrWJauYwNTaMKXv9FrByU8vBdDyfw8BwHx3VRFtACk8RUh2tIIXGQzJ2Z7VcgeR5Rt0tzaZFExwSNGIngysBnPPB5xd4pQgt/ff/B88/nQhMWQuB5isunQ84+fYokSnCkx/KS4cdbBpNljAy4LLVhYiSg4EhcBzxhiOIOvU6OsQohLb/5Kz/HoaNHiFfP8d7f+BOcYok0NxhtkF6A4zqUSwHKMSRaoDMLwsVqSaebk5iAhXqXWLss1jXtdk6c5ZSHPPLMMjXo4LsXTwnWxGCZ4UqJ8kAFlWt67Yh2o81ffPYuigNDTIyPEOUpzZVVHrjvAbqrDe574HHmjx/iNbffQMlTHJutc/jYCbYNl/jyN+/i8Kn9fOyv/xvf/No9HHzsQT7x4T9jefUEJ08exHUk9z36NK0k5abrdvMjb3sL9979NeZmTrL/iSdQKFwFh0/Nc/XWScqeYaAQkJiM3PNoti+erWO+1tMYSoGxFk96OAhc2yd7k1bhYJEIHAWhkDhWMuIIBi/fiSMsBz/7WQ7/4e/TrdeJjSa3ApQC5SJdH1c4SKkwjsXEZ4nzFGslcZ4hfQftSFqtBr0owhEpQ0OTtDtd8jTBZBkbM8VHHjjFYyspee/8NIcXDtHJgF6ksUJQKCpWG5pu2mVs3OfPjUHgsdrqMlYN6HYyejkUfYUgY2txhS2XbEd6PsbmlJycj/zWf+RHfu5fsvu2V/H+Tz3Gz9+2jVrRR2DptnOs65LrPj+GkT651lgFlcDFVymJ9XBcRT1N8T2HTDosrmZUfXBcF3ERkYDqokfg9iv3V9st0szgEHHb7vXc+7W7+dS9T/Ke11zPvU8e5OTcDNft3s7bf/xHOH3gUSa372B0YoKEnLs//2WOnFqiqxdwXAffAes66MQSxSkLK8/QSXJC33DTVbvZe/U1rCwvsnD6KO98+5t54KGH6EUNDjx9BB13CHzJnsuuYrlVp1CocPT0PEnS5rGnj/FjL7bR1uC4LrE2BELgI/rnMguZApFapCsQNiNH4QkHbQ0i8CFNaD36NML3qeUxot1h/wc+gClXuPrn/yXBcAUlBEiBwMEag7QOpemrEbkhthEmTpEGgkKB0zPznP3M31K47ErQPRZ9h/HEEhlNSUjeVCqiPA9RrZ53Phdc0aTKcISl1YvQiWao6qIQRDnguEhlGKuVqfc0hVJIwQOhFMXQQ+YJGy+7kU6ri7AWQY6ix0f/+ANIm6K6C1x36ytpd2LSFIKiSxqnuEoRdbuErqUUeLhW0+zkGKkQVuOYhEooCIsOE7WAkgfd3OnTAlwgn/FCYnV2noVuxsxKHaEkD3z7IfYdW+HQmTlOHp9ltCgYKJepjI8ivIBrb7mNLOowOjKGPzhI4Ei6yyu85o2v4X3/7G288/W3sWnTCI5VuEnGko4IfMG2jdO8511v4S2vfjXjw0PEK2fZMDFFgIsQiutvuYVSMeT+xx/FKxUYDgtEWc5N199CN44R0sFIw3L74gk7hkoh6TNOKUeCtRgJjhAIAcIYhJUoATkWIw0WS4YmMwYbePhC4ElwbA6dNt/6wG8wu28faZZjEBjpYKVAS8HQ5HoscOypfcRZF+V7nDl+nOUzBxi97U4GNu6keeAAN95xOx1ryXNB16ZYDVGaEdvn2Y8Wp6A8h1Iq6cUJXh4Qhj5WShqtHoOVAo1WTKWoaHZSGj2foh9TDX2M43L4iW8z+vKXYUWNTjvH8VySrEN7tUt5rMyP/sLP89i+h+glLZrLhqGyR6o11vFJjKHXTZBSUQoUSIOrBK1OhlEOJemy0owJCw5Vx6Xe6dJrXTwPy+ToBMNFl7nEMFR0aNQTwHJ6vk0rbrBn50ZOLq7w8FMHefMdt3D66FOMjYySeAGDjsPw+BijY2MY1+HUM4fodJvcdt11jL5xGBOWiOpNCAIOfPtJTh07yO5dl1KuTpAuztKIe2zctQOZahaP7Wf92AiPHFvAWmhmCXfdu4/X376X/cfP0eqmBI7CGb54cpA+CovGQYAFqwTCWmxu0AKw/TM8xmKFxCqJ1AYpXFKbQS/GsRZhDC4grcYkcOILn0cWq4xdehmumyO1wEWRaxDKYf3uq8iSlLhVx+QppaHN+GFInmUYYWm161hhyAVI4RG5HhmWAXl+2104Ye1KhOeS+SlhuUyeW1Y7moECFDxBL0opDxSJ1pIw64YEv/wnX6JQHYAgwFof4QTkaYQXCFwXAsfhX73vn/NHH/pDVDjAWGmQlolRTk4uFVGakwqXDSMh8ysZQZCTxlAOPJCSRq/HcMUnTnJ8BHEqWKzHNFKX0yspb/8B3OgfBL74wBNM+iGRbxiuDrBhfJCzjz7NDZdM87XHT/KKW6+hNbvKZVs3c+zwAe583Zv7FQdSksUdHOkiHIESsGPPbjYlW+l2OqRpxkAlQAUxjXaTrZsmCOU6hkdGEK6DLQ2S5RFHHt3Plst2UJ7YQCs6xmuv3sTwYIVNOzbz2L4nKZTKhMWQFNBZTsk9PwnoCwmlJI4ApS1GWMCgjEBLhWcyYmsJhEOiDcIVGGPB5iy5As91cKMYKQ0gcKzAKEFmLF4v5vjffBj1vp+htmE9qdREwIjp9665jgtG4gU+vdySLyyRC0Vv7hQtp4jbixFDg/Q6Ea7nIV2XousywPPk3k8yBz9wcD2FzjLiKGa04qGsYscr38FjK4JalFLauJdf/fBn+L/+6gso38e6BbI44QM/83ae/ta9/Mbbr2W56+Co/iQG5CJLzR4GwS/82cdJtSEo+CiTYbVl+4RH1IlIshxSzUClv71Jkpxi2Ff38F1B7sFSlOE5hqInePWWi+hhEZrX/tAbGRwICSoht7zmNfyzH3ol6zdtJQhdPCMYHapy2zVXcN3ePRw78hTNxSXqK8vkaYqxGTrJyHsJOk0RWAqeR7lYJO40cZRLrVxmdKAGSjA7O0t9eZm42yHpdZjeOMXS0hKLCyvglxif3sUXvvoQu9dvpZM7FKo18sSwvjZAwXP5mZ9854ttsufQTOM+/6S0aGkR9ElfIceRCgeIrUYBVlikFAgFpczgG42UAoOHlQqjwPR9jsRq0qjH/v/xJ5w5dACTGpTWWM9HCoujHBorS3TrLXSagStxpGXl8SeZvv5KMq9KWqnQGq7RMZoYQ8FzyZ7vitZJUjrtCKQgUJIzDUMxjij5hle+5kaG55p87fHH+ckffguV8Y0YnUE+S3txgS998L+St8/wyd/4WVwPNo1JrDF04wzlKD74m7/ObXfcwnXv+FmGqiFS5qjUYbjq025muL5kpGwI/JDVWECSMTbss9xK8B1Bnhqwko0DHnmuEd2c1d752YheSLztTa/CWstP/uS70XnOqWOH0FbSyDU/8+63MTwywsryAtJVxG2PndsuxToOruOB46KEQHk+aIsUFiMlqqBIsghHJ6AFTqlA1O0y4DjEfkwQhhid4zoeflBAdnxGBieZmT3GzMI8r7z9RhzdPwt34x7veeurmVi/niyNWVpdZteLbbQ1rMoQ6Jf2KdPPmQoBEonVAovCs5Zcgsg00nOQWAIlybTGlwJDhrISIwUOgizXOEpgHBfdbXPsr/6GHT/5U/ilENKI40eOUB2qEfoBcZ7S7nWRyiU3msTmLMx3OLjY5YpNO1icO8uC2298rtXrtDasP+98LuhoQehyri4J0RSrgnXDAZODPjqDL3z+Xu7+9Kd5Ivf43TteQ7Qywxf/7Pd44qGvYnIouRknlwxBwWXdeECWZCQ5TIwFGKMgnWXskpuxOfgiI0ogUZKCFHi+RCgH15GkaY+FxYhL11dZXOiSC0FtyKWVa3QU01Me2vo4BRgYuHhWtE59lSjuUSsX8P2QaqVClKVU5AC91QZPzcwwNDJIMDKBqwUHzs5ibMrI4DChlEipsFmK1RnC8VGhIqvHqJJHOSyC65K3YqwUdNsdpLV0c4Nf8rE9i/IcHD8kCEOGhqbwyjWszWl2u7zpVTdSLZcwvYTF2XOUpIdXvXj60UTeQ9F3EK0EMut3cEgBOQYlDcKAEha7ttJZI8AahLDk1iIF2DW1HCEsjqvQ2mKyPi+ITdosPr2fkcv3MLswx9DoGMZq0iwjS3PINcpTNE+eopMlDI8OsS6TnJnrstp0kDpk0umSCsOcd/5CiQs62tcOteisxlw7ZWl3PdI840AjolCQHPrzj/OW0KEew5U79rDYyftvXwR5nnHluMd0WWN6mscOtakVPCquYnbWkMYxSSb5mX/yXu6Yj7jWdfij0QwXzdaRADd0WFztcP/JjLdeXWXraAEpLZUBH2MNVkiqRYdvn4m4dDrAZj0OLsMrJi+eypCv3P8w51aWKBiFcROc1MVKCyls3DLBTddfi69yRK+LJwRbJkfwBfQaLXzfJUfgFQKC0hhZ1MWiyGsereYKC8vLhKFPklkKgUexXCHPNJ4v6HUjQtel01igVB7CKklldIiVw0coFwssL5+GVoN/95//gKECaCEJhUtpdIzrX/eOF9tsACiTY4xFS4EyBqH6QRFtbL9a24r+aiclVoO2GqkURitcafusWAgcIbFWY7EoFLHVOFICisTkLO3bR3nr9j6vvraAxWKRViP8ACkly08/RW1wiH2PP0VpeAwpHYTVaG1ZVoqndMBw83lGHa/bGPKZecMXDqdIFWNtP1koJPydFnxzUHJnkLM6KhGiX1JV9QXLbTjW0RxYtZypR2S5pRAIQpnQTPrG2jsVcmCxySFjqBV8JlXA9cMZRnnoOGK65vGmapFWLyO1Ei+FKDV0ZUA5jhkedrlsTJEnEYUw4IpJQ5RfPJUhnnQYczwyR1KQIXmQIgxoz7A4t8DffOKzJFlOL9Fs2zbdrxG1BoHoN70aixAK6bi4eUSuIUJjU4PJ+xXlRloC6dBTCk+BcF0KUoNWaCRWgauhZTQqSTk128BxLL61FITE9QJcLUAJ/IuI2UgAjhVYs+ZMtr91NJnBWIt1LEJDpgUWjcLBZiCFRVhLavouo9Foa5FG9J3VcRAYjNEU/AJxlpBlKaQWgQbpoqQhtwbHGvI0J0CSJwnFICVrncT1CpQ9aGY5J5IMKSUZz5NA9RtHWiRphNEarMTi8OYdCqxm5kCbR4Zr7BpUpK2EPBMMlwXdTOF4muumfEyWICZdDIrlXkbJKVDyc4SUZEiunXZ5fCZDOzl713mURUaGoZ4Iyq5iIIAwVLiFgE4zZbDqMOIIktxjuR4zNFQizgzd2DJcckjMxROidkOPMHXYND6Ik2c02x71OKOXJ4wWCox6klhnaCMI04xmlGFcSPIcI8DiYfMOI75HLCQhBmkgVNBVHiUyIulBnlIUOZ5xGQ0UPaPAJvQSDZFGYqklKVFu2FSxuOUycSZBwOVbNjLfWKS52uL6bZMvtsmeQ0G4tIXFABLR5wdB40lBDDhakEmBlGBz2Xc+BK6yoDWelFgjsWvvDqMk2hqkFGS5wCpI8hgxMYVrMlxVBCH7VHO2nzbAQre+TKPXpeAHOCLASEOeZjgSakUNqoDN+ypI58MFn8qXrQtxh0KeXDQcmE8QVvPFEzlv3wb37vGZMAlJpKkEPngCYwwFR1N0HZRIkaGiF0s8B4ZLLr5ryXKLNTAy4lPvaq7fHtLrRHQ6Ec1IMjxsCUuKZjdDegHWCFxjCAKX+YUeUkUM1kJ6uWJxIWfzmEucGDJtKXgXz1t5ZrnJlKN5aP8pfuJdr+erX/4mpxd7XDZaJpQwNDFI4AoePHCK6fFxhoxBC8VgWKCe5gw6gpVUIxPNgNvXl+/lhkHpIRxJM8sJ0pxEezjGsmnjBBboRAk6ThkJPESkscIQpwlL7S5Jo0voC3yT081S2nnOXD2lguWRY7O84cU22ho2Xr6HxxbO4gqJsCCwkFu0lITWJe+v1/3yLKHIMBhp0FqghNfPcymD1qa/PGLBgFECoyDNc6Yv38vimTN0GnUGShWUUhibQabJhCVGc/See3B9D4EltgFD4xs4eeIUg2GOcizK5iRKUCk9X7o5YSn7klu3uFw/CQkSz+a0IkmhPEBBxMy0YP2QjzQW4Xn0YoPrOgSuw8SmYaa3bEJiydsNxtZv4Ngzz9BtxZybWWZoKODjD63w8q1lBkOFKiuUspyZaXOuYdg6BY50sSKjkynqpsimUsbKckQhKGB1TCcO6cU9FtuGXbWLZ0WreZJyEGDSHn/yF19g87SH72rG1lfZtn4LrSRhqFRm4Pg8l23dSKI1pbCCoxSOzZCeQzfpVx8ILLnOcYWLF0gcR/VzPlJiogQKAYVCmTTtkXUTkjzpi/YlEVmekxvJuk6Lu/cfYWpkil4WsdGBxbgDxrBh03YeOTnzYpvsOZQrVTTgWAtWYAQoKTA275/PpAUjcekHR6wFlMUCQlmUVeRaI5Ts89NkOcJ11nzOIByP1ZVlqI1g4oQsz9DCoAQ4SvUduxkzGoR4rk+SpzjC5cSxU2AyltopOo/xjcAPHEYvwLdywadyaqxEkEe4jqC+6lB2FHOrEWMTHqHr0Iss2zaX6dUT7j+TcOO2Kuu3DzA1PkJtqIowFhUU8UOfNChy5uwsfqnM0OgEgxMjLMwu8K6XeazUI3IBRxciJks+uA6LacIO4VApCtzAp1fP2VDTSEdR8zw6kcFzJa1Oh5KvGKuGFNyL54xmAp+VXpNCWZFpwbhbRYQxQRDgVMqMMoAjBdXQZ3zDVqzyENbgBgFK+lhX8a2Pf4TK4AiNThNHCG6742VYJ0Rm/e23SWJWuqusnJqjm55h0/Q2osxQq5VxgyLFXpteu4nWMa4JmSj4jAz7dOoJuD5+3GZhoYHOOsjw/ExOLySUH2KMwJUCg0EKiQQEikxZjAasIZMCAwgrEdYgHYeM/vZPOGtxk1xjpUDKftLbEQ7O8Bi93JLRZahYxSiFJ/vnYQCTJ2RWo6yhqyKcVJNGbdTamTA3GeSQCYijhIWlxnnnc0FHGxsKmDllUEIgHQ8jDcWCSxAGRHFMqerTy6BcDbl6Cmq1Mnkc89RjB2l1cjzPZWqyxjMnG2wYL1IILL1eShJplrua0bKgmYU40jLfMeg05+xCyuW7qriejxGaJDG0kpRes0e3o+nmhmdmDK/aXUa5knKhSLPVphlBKbt4eC+STsxsmoKQeCblZLNDL9GUyxV0khJWa2AtxYKLkiFpFvfzZ8rFkf2G2Guu2Etl/QQmzVCuh/JCTjz9FJv3XIVOE5QXMBQUqY6PEVZqHHv6UTbv2kPcaSOCAhKJ71YRaQNLi8FSEc/xEYFLwQ9YSQ1SGTpJTvEC258XEqpURglJJizKQGoNyvQj2kJbHCnIlMQxkNKPMBoNiBwhnLWcm8BiEdIQhC5jEwMcOVMn1wbX5tTn5ikODiLLJaR0EY4DUpLlOcYonDhmNepRDgYRuksUt9FOkcQm/fIuV9Inlcz7PZfnwYUT1p0Yx1EUShKdgYtLqeqRxRqpCjQTjTIOmY0ohg77j80xNlJhKPRZaebk3YiDh2dRrqTZ0BzpSHq9jDSPKfoBj57tsaWWUPIlxBk1VxA5DgcOr5LYtcY+ZakFHsYXdGPFlpEKQ9UcJ3Q5uxizaTxnLhZcublMqi6e6v1i4NBraAqeZN4oqtYgXUW5UKJcKaJ8B1JBKB0yT2ENeJ7br99T/RC1CgpYFbC4usjU+s3MHT/K9I5L0DpDKZ9Gs06pXEQJhbaadVt20ulEOFbjOApdLKGiGGNKyDCn4EgCx8X3CxjlUZYxwnNwhKJ4/gj1C4rWwiy+7G8FlZQ4FmKpMcJitQDRp7VwtUA6gsTmSAnS9sl2AXJtUI7CFYKwUGJhqdk/qglFvd0BV5H2uggMQki0tYg8RSCxUnDgkW9h85xypw1WIcnJre4n0I1B6xwpBIYUYc+/k7pgCVYvSjjX0NSbmrAQoIXAU4KFpmV+JaIqM7KkzX/7Vodi0WViqEK3m/LY4QaVgkM3MrhBSL0ec3w5x7ER5xoxvcyyeaKIsP1G0WrZwfeLDI36FCs+fqHAUCXESIeedahHOe2OwTGW+WaOTjI6rZiJwYClhmHrcEivlRDVLx7KNFyH1BE08hxfWRYlIHKU4yNdDyldXMcSVEIUAjcoIaQL9Gv3pHQpjY7gGMvU1AZMkrBuyw5c5fbfkNJQLZcRBlzPRUmFlJJyIewX2vY7t0BJVBgSlqq4hQJ+YQwhPZw+TwSxNSinSOfiaeXjvge+iUTg2X7lvgUkqn/2wpJr1ipDDNJYilJSLRax1iLXuuyVktjcYKTl2lfvIYp0v2O0XEQLA6UQQp9me6kftczBaEmqc0SeIVsdUmEh17hev21H0j8ramGRSvYrobREiH94CZYCGF2/CSdcYnGhB27Yf8MEHiOjEXGekuZQGsj58RslJ7qGqiso1gIyN4OSRacpQyNlRoeHSJVkoBIyMpEyOOjQbGted/0giysx1ncZHHeJkx7r1hVZWOniK8XGzT7tSPSp0PIE4Qb4rqQbxQRhkShNEI5gNk6pFYtMT9S+a/wvEhTA5olprpicZNumKQ4uzLJ+ZBKhDa20R96IGJ8eQPkhpaExvCDESomiX2skpUIoheM6SNFv58AahJTYJEM6CovAiD6PoZR9fkIhFRJBwRoc3wcr8f2A3Gh8HTI8uQknLNDTksLgMFdevpVWQ1F0BLOZ+a7xv0hQAOsmxtBCgZL9AoXc4AuBMTlKCjwr+sESIUiNRggHi6E6MNAPltDfdoo8wwl9Du+fpTg2iS8F/tgE52ZmEb5LbbRAsVTFc0W/SFgIcm0QWcy2qQlmTcZoBqJcYqIdYcMiOtPoNMaYDER/DMNDle8a//dCWPv9X2OPPvrozcB9P3g7vmC4Ze/evfe/GBd+yXb/cPxjtd35VrR9wC3AHHDxhPIuDAVM0B//i4WXbPcPxz9K2/29K9pLeAkv4QeHi4io/iW8hH+8eMnRXsJLeAHwkqO9hJfwAuAlR3sJL+EFwEuO9hJewguAvze8/+ijj/rANfxvHGbdu3fvi8Jx/ZLt/uH4x2q78+XRruF/88Qh8KIkXXnJds8H/yhtdz5HmwPYsmULnve/CG/Empzt9/v6++E783TPfv7Zz37v714op/ed1/ve7z/7+2macvz48efG/yJhDuDrd3+O+x69j0oYYDCMDRU59cwCV95wAzuuuoVqZYi4scRTD38Zx2+T5BXK5YAkEShWiboZN77+F9BJilUSsAgpEUpgMsPxh77I5S9/MzpL6H3uI7gFhQkHqL7qTXz0k59jw+AwR06fY8u2jRw7dhJfam66+Ubu/dr9lIIAYR3CwO9fE4+BQsi1t15+Udgu8Is4juwLaQpJ/7/+SUdKgVhrm0GItfsv1j7Lc7QHos8EAmLtmVkjXgXbLxxe+2a/EafPSWJMnzekX1/ZZ+EyGCT9sVhh17oCvvNPGrTWNOr158b/vTifo2kA3/fP62jGGOSaAf4+xzmfg32/r7/zWt+LZ//O9zrwd/7sO8f/IkEDtOqLdDur6LZLN44gHQQVUV88ypP3HiWKcpqtJQqFMaxIkHKGxVnoxClhEOIFAUlziXKpQm5SCmGBZjchTgyu55LmGaEURJ5LrlziTsyA76NzzXVXXcEnv3gX51bbfOvpZ3D8AJ0lDA2OcbLRYOcllxAdP8GcEoz2qojxYc7OL3Itl18UtlOORCkHgURIi5AgRb8jTUoFos9yJaSzRnRA/6lfu//956BPR8jat63od/ZLVJ+AR9i1iUqU6TuksQZrbZ8bB4G2ln7nbV/eSRgBwiBRGDR9p3QQpN81/u/FBdtkvt+K9J1fSym/7yrznQ7xvc7x/Vaw7+dA3zuG8znq9xvvi40k17QWW5hyGYGhGEhK/w97/x1uV1Xt/+OvOVfbfZ9ekpPeSELvvXcpIigKdlREVGwX27VeFdFrw66IgCKIIooI0pFOSKghIY30nJy++96rzfn7Y629ifwg8V79kHzv43ie5Oy+9ypjjTHf4z3eI5Wl5hUp1Hza0u3U/ZCcBM93GWnUyCQSmBY4lsJ1a9zz5+8xe8FRVIubmTL3cFK5XgzbIfQCgkaZwDGRriZz6lkYpkltdJg2wyBXrfC2/fbCMm1kwqZamMBrNHASSY5/1/modBLjpKMJNZQLEyjTZMuWXafDOo7f8UkdNXyCxBBRd7WMydci7lGIvK4ZxeLzRBjb3AM0UVDQGogaRqUGIQVKRt8qdVMHi9gZo8+R8X0h4/lhGoTQaGUgZAjhP9mP9mq2vRP6ZZGlZS93vFd6bnvf8Ur3Xx4tdyUbG5ugpy2N69eZPK2DUPlUvQaOY6IbGldWMEQQdQUHGoMQHYBlJQnIMNA/Ba/hs3jJYwShz9oNq0km2tjroNfxyH03M7kjj1upRKkSAuUFyESamuvRcAym7H4YlUoBFYR0zpwF0qA4NkRNmiTsKJ11Gx75fDtu4DOlu3tn77KWaRE5m4iVU4WUSCERInYlER1zKQ1ARB3YsZiPEE23UAhpREMLiR0XEQv2xJ+jIWoRDaM7kWdF4jwiekhFL49/T+SkWoJQJhghKBm77avb/8jRthdZtr3/8ueazvXyCPhqzvhK73+l2y//zl3NTFPQP202vZ0djBcmqJRK2GGktOvSgNEJGqagXCxjCg8lJFXPRdaKCKfK+LK15HIpElKAMCGUOCYsfvjXtGdNVq1ZxnNf+wizp+/GvPl7M3e/wygVRvFCl0CHDG9cjec2CFXU4h9UqyTbu3AbVSrFMTp7JiGFplarUZ4YxknsOvO/o7WVQmBFziUiN4nGLWmkMOOIFnuDUGgiSTnRvPBqGa2phIjXYCpeXUUS4xodL7LCKLXUKhrlJFRrvRf9EokmShe14O8+I9RGdF7uINv+H0e0V1tvvdprX+m9277v5ZGs+dnbOujLv3fbx1/+PbtSZCuUSzh2jZEJj1JhM7lMlkq5RrlYpb+3k4YJnYkMoa8o1wPSKZOS61GveqRlJPrS8ASOCeVamY5slg1Da0mbJo26QXtXGwEhG0YHWffwRv7yt5spj0+QcIxIOSxp0tnRRqVUIBQhOjRIpyShb7Bu8wQDA21oP6RRt8h3Zklmbd7zvi/t7N0GNJdbTTECELGWqBAqSiNFHF1E/DopMGKAQwjBX2+5nVNe/zpasuIiSjKVVlF6KDRCxykgIUpImmVliYim0+gYJBEhQhiRAEkc0QTRmk2i0aHeJj99ZfuHCtbbAyW295qXv+6VHK/5t/mv+Z5tHa752LZ/t33fto63K0W49pxFsVSgMFGkXGvg1hqUJsp0taWo1XxU2Wd8cJxquUZXZxv4Hnge+YSF5ysadZ8NG7YSBA3cumDryAiOZbN5ZBzwI/SNkEKpgGGmMA2XqbP7mSg1mDSjn6QlsaSPMA20CkBqGlXF2ESJZD7J+JYSrq6TyHgUhwfxvF1n5BVCIpEgo3VZ5GQyEgkX0aBKISIksHVbSkxhIAzJj3/2Xf58660Y0sAwotTTEBJDGFEa2kxFJWhpRMimEAipYsSSyMnQSEwMBFKK6P1IBEYrFRVS7XAu3z8U0V4N6Xs1MOOVkMlXAzq2fX/z9dve/0ffsyuu0wISyFQPKTvBtCnTEVKQaqug/BJ4AcVGkXyunYZbp1atUm3U6UolKHohpgqoaYNcwmK83MA0JL6rqdXr9PdkSWBiNsYpKklbDrJ2gXHPw6sWWDB/Mo6oUxKRBollNfCDNIYV4HkhHW0mWkPBUxBILEcQZiWF4cbO3mUtE0IT+1nUbR6jjiJS3WmhjEIayGaaJ0DLaMyVIuRXV/8EoQVnnXU6kWqdxCDSGtGxTohGoFGoVuQ04zQwgv21ECihMURUMxDaQAgVJY9CILVEIRBi+6JQ/3Dq+D858V8NZWzefqX0cUfftT1H3ZWi2LbmNSoMbtpE0NnDxMgGGqUKU2dMolAokLEymJ5HkNCIeoArPSaGfTxbUXMDcu0p6nWXpGNRqym0sPArHqmUjxumUGEa25YUiyUGpkyl2mjgJKBRrSKtGq4vSJoKv17DlApL2AjpYSYDivWQhGmQzNg0XBctTWo1l2QyvbN3WcuaEUqKSI1YNEtiQsTpYbT+MoSMTnji2poArxEQ+CEThQlWrHqGunsiqWQiVjNuZnkxSKKj96EVqgVoRErcWgtap6aQcdTTEc6pFVLHUnciRiO3Y/8U6vhqEeyV1lCvdn9wcJB58+a16nHN5w488EBOP/10zj//fLq7u7cL6W/7m3YlG3V9kimLVMJGhgI/YTAxMoZUmqHxUUTWxFINalJjeT75nEOowDahPFHHEIpyzSXpGFgJgd2TYqLiIUcKJBMNwvZ2GvUGy55ZgeFoVCCRIqRRC0hnbVzPp+6BaYX4tYDKRA0hJU4iy8imYbQpESEIT6NKCtWx60j1CRmBELK1DmvWy+JlRhzhouEVkVMKIZESnJRJPp9mxco1PP7oQ9w4ZRbvfOv5LYcSUW0a3QRctEShkToGQgCEgTQUSkfjmgXRX00TwWyWIJplhO1r7/+PSMX/CPtjR9Z0wrGxMdrb25kzZw5KqVZ6GQQBQgieeOIJPv/5zzNz5kyy2Szd3d1kMhlyuRxtbW10dHQwefJkLr300v/JJrymVqk1SISgG2UKriLr2ISGAcLAzjvYJjR0gqS0wdcEXoAfeAQuVJUCB3JtNso28eoBw4MV2tKCVDqNYWtyqSRewyVphdSKLo4EFZoEFpSrHkqDYTuIUGKaIZZwsKRFMpkmk01hAAlbUix4eLYkndt1Rl4ZwkBKQJitQnWT6NFyPkQEUsgImEBG8t9SmnR0DuB7PluHhnjg3r/yl1tvw5AiipBGtDZ77rmlSCmoVhoYpkCaIA2jFUGFiBSvpBQxK0XE9byYWSI0y596kf13P4Lz37L9KTw7jGjbAg5NZ2g+vu1rXi21fPlz1WqVKVOm/J1zSSnJZDIIIXBdlzCMoNIwDNFa09nZiWEYZLNZgiBASolSCtd1ueqqq/jGN77xjx2919g6smlGC6N0dZiURj0aRohpSaqhgWNrpEyggjraSpIxfUYLddIm1GxBNpSYpo0tQ1QYUg0sTCPA9TWq4dKQGsPJs8/+R9LuTiBy/aTTWYrlEbraMvgKfE+j3QJ1kSJsTFBoBIhQIYTCDlLUMg6d7Q6G5dCoFmnv2JVSRx1NkYl01+II16RZiZcK1s1/vFSwNqTBKScfx4oVz7Jx0yb6+kd59rnFnH7m6UgpCTyFsCQP3Xsvl7z/q9z7yLVIohnpSgHSQGjQOvoOjY7hf0BrLv3w1/j173/AwvlTMQ2HTN5Fyn8hvL8t1eqVambN26+05nr++ec58sgjW85jGAZKKdLpNFJKXNdt0b1c10UIQSKRoKMjko/zfZ8wDDFNsxX1TDP6+bNnz2b16tW7HBhiGRIpDMarLoYl8AKTdDKBrV0KtRBTethmBsMMcEsKxxGMFz0CrcnZNhMFFyNhkHFStKU9giCDX1M0lAuhZMvm9WxxiySMgMrEc7ihwhQW0pQ0BGhMTCFRpoWTSuM26mBaHL/nPNoTKexylURSMTpcxLCTVPxdSEE1KpoRiZtGjiXlS6lbM+SImPcommCFjNK8k1/3Bq688md4oaJeLrP4icW8uHoVs2fNw3IkK5Zu4tEnHuL1b96HjRtfZMaM+RHtSmiEkoQ6BB1RvVY8s5bjTjwOLWvsvXAmm7dO0NPTgcBEacVJp5/EJz/xebZs3viqm/M/LlhvG+GajzWL0K8E9z/99NMcddRRKBUdxObf5vtqtRqO4yCEIAxDgiDA931mzJiB70ezp4IgwHVdfN///4uYUkpKpRLj4+N0dHTsUs4Woqk16iQyaYyGItQhoxVIyoDO7jzFiQoNUSdppghTIKuaZFaifRitB6STkqDeINQBCJOyW2OgO89EPSSTylARCj8MSIQ2HVMcCgUXjcQywA8Eji0IfY/Qb2AnkwSVGoOZXp56dgkJ0yObTTA0GNDTbVMsu7j1Xac9UcSwOZHSJbTQvxiej2lX0XotcjKawIYQtLXnybe343uSRYuXc8SR+/Kzn/+Mb37j22jtMX+PqVz4/ov50Q+/wyc+/rnW92oUvlKc9boLePCxW+jtyzHQ38Oee0wm1CGG7dDb0wbAH269nVQiC1ri+9vvKvqH9+y2Na2/+4CXOVnzdUop8vk8p556Kl1dXfT09NDV1UVfXx8zZ84knU4zY8YM0ul0y6Hq9TpKKbq7uxkZGWF0dJRNmzYxODhIuVymXC5TrVap1Wo0Gg1832857sKFC//RTXnNLPQDdCCiE1+EZLIimtulTbYOVdGuBGWiGz5huYHAwPcMzKQDYYBlCCZP7iaVz+Dks9iWRd0DKxSowAUfOtIZ0hnF+OYGMoj4lEE1xBHQqAS4FYEODSzbwW24JC2LaslFhdBwG/giZGyoTjZtknJ2pm7q35sURowuQlxVpslcFBAzNJoX9Og0/sPv70EA7377JSA08+buQV9fO/k2h3qtTrlUiArbhgMCjjv+RCbGRxndOoQKFMceeS65fAcDk3spVZey156z6O/tQUhJ3W3QaNRJJVP88vobuePuR0g5eSBOb3dwgf9fX8JeHtmaprVm6tSpTJo0iXQ6jWmarddIKTEMA601XV1d1Ot1Go0G+XyeIAjQWjN58mTS6TRTpkxh5syZzJkzhxkzZpBKpZg1axbz589n+vTpLeCk6XSe59HX17dLIY+OFBy5735U053MmDWPhgY7Z1Ov+di+JpGGNtvB9TTSMgmxMITB2Fid7vYuhJklme3H8zzKjTpz5u1O6PjM2edg9jrkNGzbYKLi4VY1vq2QtkWgfJShqVY8LK3AV/T397HFg8AQNMIQjaIR+NTrAd64y/CYS2EsIDR2nYgWOVaMNMom2ZeWqzXXZULAlo2b2LRhM8NbXuCs084nlzFZvWwxl33j68ycOol63WfFC2sZHR3mht9cgwaqhRqfu+RylFL85xc+TS7fxvDoIqZN7aK/rwvLMTElFAslgkCRSiX5+bXXcv3vbqWvewpaq21+qo5YI9uxf3qY2CtFuSAIsCzr75xRa92KWL7vk0gkSCaTTJ06tZU2+r6P7/ts2bKF3t5efN8nn8/jOA6TJk2KCo1xyjljxgy01oRhiFKKDRs24LouBx54INdcc80/u1n/EltVkSweHSRseAxlHGZ3z6CuQvwUmG1Zikph+QojlUSToAgYXgrdDXXfp2xaVIa2oJLtpARsbZQxkj1sHFrLpq2DlLWJSOaomQ5Jxin5Icg0TpsJCgp1l/aeDDXTocP38Tu7mVCKMJDYNYutOiTtJEgkDDZVFV2JXSftljGtKsoQRcRZjCH8iHAcOaBAMzB9gJHhEfbZ70i+8+Pv8qfbX0TK6NRua2vD9cGyFG69xvPPLyPpZEhlLPbZYy7Tpk5h1coXUEqxcfMY7fk0XqCo1Wv09HQxb/5sfvXrP0YRS8Vz2EQM0IgArWM08v9VHW17ti2JuIlWVqtVwjAklUoxZ84choaGKJfL+L7P9OnT2W233ahWqzz//PN0dnaSyWQolUrU63U8z6Onp4dSqdRKFZugimVZKKWYPn06WmtyudwOft1rZ1bKYZpjM5FssLA7R2l8jL58nvysXoqNCkoqtlarzOrsY0NxnLmpDFtLE7QLqJgORuAyuX8aGyvjdCbSpBMZ8p1pCAO8MCBlWxD6NNwaXkcneqJIOpem4XqkHQPLBCyToluhZKUxEKjAICUF442ANsdC+B7akmRsG8PYdcAQLTTNMBZdsKMB70KE0KRhoeMU0qC/fzL9fZPo627nhqt/zFve/UECL6RRdjlk/wVs3LiJ8fER1q9bywnHHYLn1kHDxo2bqFRdhCHxfZ9avUY6leaDH/0QF134MbTSKN3EFnRcPIsG/mptAkF8+zV2tGbq1gQ2mpGsv7+fXC7XSiXz+TyGYbSAkvnz5zM8PMy0adNwHIdiscjjjz/O+Pg4Qgg8z6OzsxOA7u5uwjBkcHCwtU6zbZswDP+uSXVnm6UMNpRGaXiKQgJqfoP2wCHXlqCjqxOkYN29T5CaMpOz58xgeKyAUIpyo0J7Oo2qCh5/9Ck+/Y6zuGPZCvBrFMo1lOeDNKm4DSypIqaC7+I4FlppkqaIWjoSSeq+j/A1eUNRrdWRholjChxbEoQKrxGSNh0myi52bvtTK19Li2peRitSSBnzroQRzYsWGt3stI4LykjB/Ln7cf1vr+euex7kd3+6ldedcAiB4aCxGB+vUKlX6Z8yjRdXLWNiooLjmFiWhVaKRDLJ+s1bsAwHrUOUijqulQqjfSxA6Ch51SK6CGghYmR0+9uzw6T8lepj21sH1Wq1VhRzXRfP85g/fz5tbW2tdVW9XqdarbZqYoODgzzxxBM0Gg3S6TSFQoFLLrkEIQQzZszg61//OpZlcdRRRxEEAX19fcyZM4fjjz8ew4hGFfm+TxAEfOpTn9rRJr1mtvd0aHM8Dp+ZYeXYCL2ZNJtGJ3h00fMEbgPHFBx62N5sGNrEoy+sZsm9dzK2agW6bU/q1QrrNg9zzmELSSNImJJQ1UmaJp5hUBcQmgI/DPB1gDAMrGQKKTSWFJiGgfLDqMZraHxDIEwblCIMoFQNqQIJ26EeBkgFytspejyvaE0miBQCwzBiUrAR3TYk0pBIYSKlwdoXNpPN9GLbNjf98QbGizVq9SoH7LmALSMTTOmfxNQpvRTLZUqFMUypCJWgozNPOpngJ7/8BQnH4dEH7scybZQOCbVCEUaMESRSRPVHLVSrdo6IWmUkZlzve3X7h0nFzb+vBuc37bzzziMMQzzPQ0rJtGnTCMPw74CTJlSfzWZZuXIlq1atoqOjg7GxMaZOncrAwAAdHR2Ypsl3v/td5s2bx+LFi7nlllvwPI/HHnuMOXPm4DhOa833auDMzrSk2Mqe+SrpULBvt2bylBpDY5oyivsXv0AQKBzbJN/TDpUaE1OmsHXxCjLJQb578euolib4+VU3QsmnnjJBm0y4LlUFSSmpBYoU4AYapUMkISESQyhCrSAMaOgAqQ0SIgJClHDAjNIwI9Q0RIjUJiJhY5i7zvxvCDEMm5h/hRFHtIhALNFByJmnvJe7H72BXMahvz/HxESSUrmO6weEXiWacx0KFj+xhN7+PuyExcOPPEdP7wCHHHok77zgbcyaMR+lFLvNHeCOO27n/fMWvqQboqIqtUYTdXrql9p2RLxUQ4AMkTuIWTvcs+Pj4yQSCZRSmGYUZk3TbBWctwU7giDg4YcfxvM8pk+fjmmaLacMw7DlgJ4XceoOO+wwOjs7WbduHYVCgaOPPpp99tmHPfbYg7a2Nq6//nosy+IHP/gB73znO7nrrrtazJEVK1YwMDDQcmitNZdddllrDbcr2MhwksVPr2fGLJPBkQK+4dDdk2CkapIxGwhLMimRJTBgvDJKPt3G3CPmkxaDLH3qCebuvi8HLJzJSrdOsVzBTnTg+mNIX1Oy8wShoqZ85nsuq50keHWUaTLkazq0oqIVXWmHYrWKdAUJadDZlsOsFmj4CjOTxEkl8SeKNBQEu1ANstXYGbfBSKF56rEXOP7Uk9GyzOyZkzAMkz3mz6Ber1OrNqjWXDSaZ55diV+bSnt7hmQyhZNM0j8wldHxAhYm1WqZpG0we+Z8tBKgFQ88vIRvfPVL6Gg4dsQQQcXtZzpKVdFRmw5qm0AT65KI7XMdd+ho9XodKSXDw8MkEgkMw2jB6tlsFiCCn8vllrPNnj27dfI312lNypXv+7iuS1dXF48//jhDQ0N85jOf4Yc//CGVSoVZs2axYMECisUi3/nOd/jsZz/LBz/4QUqlEpVKhUwmg+u69PX1kUwmo4MiJRdddBHJZHKXKljXfMGkvh4ymQT99JFP5hkqVmlLJ/BKYGjJJqU4cOHeTFIhIgyQwqSnvpHBdSvJ5tuYv+++LH1sMZ3taZRboi2dpeq5JKyAEX+YWi3P04FGlEdwkgnGKwGhVJSFxHBsBl0XEfjkfI9ivYE30WDG1D46VEgjlWBq0mF9JoMKAjLZXQdIiriFih994zd85rKPMG1aF22ZDPN36yBQHdEQeSEYGZtAK4Xn+yQsIypheAFrN27h3e/7MJd87OuAoFqpcubrjmNsbILxsRHWvPgiS59ZzII99o2+z4CPf+Zz6FC1anQ6FgbScZlMYAJh67koyEXNo82egFezf4jrWK/XIzmtQgHDMLAsC8dxWkXjdevWobXmox/9KLNmzfo7BwNaqaPnebiuSy6XQ0qJaZqYpsnnPvc59ttvP84//3z2228/isUiqVSKjo4OisUiW7duxfM8Fi1axIYNGzjppJMIgoAtW7a0QJAZM2ZQLpdJJHadGdYJx8RJ2hTH63R0JWhUyiSEgQwCckmbYqDAh3YrRSrfgW1ZaEIKy1bSPTCTVDJNIpmmXKwRSslYrUHKBCUEoW4wUmrgDhfp6bcpqwC1uYiTsai4IXVl4gchuT6bUilgTEssL4TpU1i6ahkJR5BtMxApSbXk0mgIMPt39i5r2QknHE7gNhDSYOGC6Ugktm0zNjFBKpUg1AKhoau7g4V77cUnP/Vp5s9eQFOaznFy3Pqne7jko1GxO51Okc/l8EPJk0+voq9/gPsefJSFe+wXsfgVNAleusVeUnGaqGMRkzAGQKKLgESDFFE7zT+bOpZKJRzHIZFIEIYhrusyPDyMEIJkMkmhUGihfrfccgv//d//zbJly1o1r2aIDYKAIAjwPA/HcdBas2nTJtra2gjDkOnTpzNjxgyUUqxYsYLnnnuORYsWsf/++5PNZlm3bh0rVqzgox/9KNlslnw+jxCCcrnMF77whdbasUlI3hXM8KGtzWRkIqQ4IcklkhSqFXqTNkpIUkrjqQA/DBFK4XkRx1N5VbK9fdRqFUzbIZfJsbUwQnsyi6cajBaqpFM5iltdTM9jw0aPnGNRwSAMAgwtcQOPiZKPkBoz1KR7U2warLNveztPP++RtxNs3VAn6LQI6hI/CAg27DqNn+lUik0TBbKpBNJKcMYbz+bi910IQFu+K+pybq7XiHrDNLRkChqNAv297VE0Igo6b3nrRQyPfY1MUlCuVHj6iUfQ+uIoCxJEziK2lahrqmER/a/i79Q66hogBN1U5PonC9a+75NMJhkZGcEwjBabwzRNqtUqhUKB/v5+urq60Frz9re/nXK5zJe+9KXWZzTXTc1CdbQRgo6ODt761rdy6qmn0tfXR6PRoFgscuGFF2LbNkopPvShD/HNb36TGTNmYBgGV111FW94wxvYc889EULg+z6TJk3C8zyUUrtURFOGz/BQhYxts3V0jNykDtxKnS1Ck09YyNDFk3YkvBT6YJqgNEqbjE+MkU7nSKXb2ViuMVr2mJLxsWSALTW2DPGFwrDBtCwCDbmMw8hIiWzapFoLMA2JZUkCqRkda5BIWtjCxEwYFKoenWmT8rjCzioMIUlldp20+2e//CUD/VPiwnPcqiJEtHaLKVlKg9AqZthH7qR0XHtD8Jtbl7bqW0LC2W88k5/97L8ZLVTYuugpjj3+KO697y6OO/akFjwfRSdiWpVCaBNa8H2ze0VEazItWsRj8c+mjs1+sVwuh+/7rF69mnw+T7VabTlck9UxOjpKo9EgmUzyrW99ix/84AesXr0aKSWe5xGGId2xpJmUkiVLlrRYIa7rkkgkOPTQQ1FKUSqVALAsi1NOOYX169ez++67t0CYP/7xjzz33HNks1kajQaVSgXHcRgaGqK/f9dIgcqeiVCCihtQaXiMl8v4CHTDZcNIlZ72DFTHCMIw6tlHk2zr56dLivzXF/+TwaV/QQuJX63SbhqEso7yIGPbEHr4xYCEY2CampHRCrZlk0ta+IEgk3RwBOgwavrEtqgVXDzTRLiatqyJmZIYYUC5CKbWpHadLhl6OidjYMUZURP1jiQKhNCgjDi6RERgjUboKKVrErWOPmBSfF+iVZRZtbV34STSqKBMpTjBs889y/HHndh8S9wUGhWnhTbiaBY7kXjJEZvO3Yx6zSj4arZDR7vjjjvI5/MsWLCASZMmMXny5BaK+HKFYvh7dv5hhx3WQhODIKC9vb2V3i1atKjlUI7jEAQBExMTTJo0id13351bbrkF0zSZPXs2b3zjG7nttttaTvb444/jOE5rPbd06VK6uroolUq7FLwvPZ+klcQ1FPn2LAhNXthIDAIZ4oYKMBHKRwU+ChjfuJa3vvFc2rp66Trx/Wxc8QwHmffyyIRLsVAnlUlhWCncQo1kwsQINfVqiGMkQCs8y0BXAoysweahGqYhsBMmohoiDcnmsWEwNHUfxodcEg7YKUl9tIHedbJuIJLibkHoKmaBiAiciNK2iBXSJEEqraK+tfikVyqqITY5v1prvvu9n3L+uWewfEWDRr3Cc08tAQSGMNEyQGtBqERUV4w5lcgQoUUcHaPfEf0XS9xpUP9s6nj66adjGFGhEF5qlWme9E35gSZj37IidkET9u/u7uaxxx5jzz33bD13xx13oJRqQf2O42BZFqVSiRkzZvCGN7yBP/zhD3R0dLBlyxbOOOMMvvGNb/DZz36Wq6++mu7ubiZNirQ3Ojo6OOOMM1pO77oua9eu/R8f1P8XJkObwCogpaQ3lyKVybPHsYcjFUjTQqkANISVMcIwQKkQwzT41o9+xM2nvZH1y5cjzRS5A4/jRB2igTDwQWlqtSor16+nNj7BqrVrUVrT1ZNmbGuNuuuTa5h0mA4yAZW6TzJl0igF5A2LjYWQRlLjWIJKMQRHk+nOxPWjXcOEVnFkkdH/UiGUETkTMfVwm341VNwuo0XLQSPMQkad13EdbNKkHrLtbVSqdcYmqnR2V3n2uSfZc4/9aKpqGULG+iFxZAuNiAnSTBtbcc6I12aRSuT27B9CHZsncROyb0L19Xq9BfE3n3+l9+dyOS6++GKOP/545s2b10oXm2CJ7/vYto2UkoGBAZYvX45hGCSTSQYGBpg7dy7nnXcen//85/nQhz7E+vXr2bx5M93d3QRB0EJC8/n8LlVHy3b4vLhGkc5IGspj7z33xKtXMKSFFQRoIfC8OtWRjYxs3Ug66eAk0nzsHeex4dnnmLZwAS8++xTJdNR9HiqN0BqtAjLZNlJdXWTsFCeoEMMw8Rt1pGXjVStYyQSeUlimQej7SKXwNDy67EkueNub8KoF1q1dT6E4jlepU64rEoldp2CtIXISEYunqvjcirH2KHN5iY8R6TRu8wEi0snXIiICSyFiSTjNL666gTedfTKNWoNyaYKHH1nEXnvujxQSJWMQT4SUxrfwHx/9OD+9+oYoXdSRhEHkuM0aGvFa7Z9MHbdlgDRvN6PYtk62rW0rMde8/4lPfKJ1v1kuaDLvS6US7e3tdHV1ceihh/KpT30K27aZOXMmo6OjJBIJbrvtNs466yze/va3M3PmTM4555xWyaHZPLrPPvvsaHNeUxMacimbQrlCLpVgt+kDBD7Yjonvh+gw4MV1o+S7evEbZRw7iZFMEoZlbrvlKo5bNxtn7pFUq2UMJ4FtSDAMQiIxzzVPP8U++x+GmUpRKxZxkikMy8IQglCFpEyTEI2dSOGrgDQGbVLS2T0JOWkak2buQRj6KCSWAMvZdYAkVETMkEIhdaTToWUMOmjicAVNmR3RksoBhIoIv1JFKlfajB+L2CXd3R10d/WzZWgTfqApFUZ46rHbOetN72Z8vMr0GZPJZkxUqHE9P7q4AcR1tViXLk5hI8RR/7Op48s1Ql5NIm5b25am1by/7eu31QSRUlIsFnEch2QySXd3N1dccQVXXHEFkydPZtOmTViWxfDwMLNmzaKnp4d58+ZhmiYTExMtTZHe3t4dbcprbi6aWsOnqytHWA1iQqpPudygWCxgmKAaFbaWC7h1l0QiicCnERokgjL/ec8IpzVepN/QJKWgUq3g+W7EWvc9Ort78X2PUmGCRCpNrTyB4SRRgUvoBSAN7EwGH4VpWgRegK6OIy0bFQQopTBMC6lUhHjuQutb4gjV1F2MzmcBMnYnrWMkUGzzLzYtkahIWFgKNAFSmyAivUY0XPfb3/O6k45kZGiETRvW81/fXMS83ebiu3VCFVKrVjnlzIv4xMfe3YJXQEdMkia/OXZw1XL0V7d/KFfYkUDpqzWANv++PCJu20IjpWTBggWtz9m6dSu9vb1cffXVQFTHu/HGG5k+fTrLli3jvPPOw/d9tm7dSqlUore3d5cDQZo2o393ZrZXqIaaaqnIM88sIZ1KMV4cJ4lAG4KxekCHbaKkiTIkOcMipxVG+yTcFUPsVl/GhsJWJrTAU5qG62HZJq4PoVsh0dNJZ97C0BWUCFFSo6RPyS9TGB+h6DYQgaJcq+IjMULNuifuQgpBqHyk8lG1OgnHJN/WBUcdv7N3GxA5WMuZhIwaLWXElI+WSXEfmIhXSCKMpQyisnMkrNN0AoHSYdQoqkJmz5xNNp/hwL1m4dU9xkaGMITA8z08P+CzX/g+Rx+5T7Rm1dF7X/pdILUGTHRLYi6+GGzH/qHU8ZWcaFun2zZNfPlrXkl5uLmuaq7TtgVVFixYwEMPPcTAwABaazo6OqhUKhiGwfj4OIODg9TrdQzDYHR0FIDdd9+dRCLxir91Z1quvJXhcgkpNO0CgppLrSpIAFVpoGoe0nWxrXZoVFEIZMLC1xrbACOR5/p1NgfKAna2jazQ5JMJPK9KMqiS6R5ADa1hwvewDMg4abzQx7Rs8jokYYG2HFTgY+a7QQiC0EfJiAKrvAZCJFBtuYgZn37lpcDOsOgwNh0mgtUNRdQa03pNdI6pCOOPiL1aR82hEDmljqbHvLjqGQ478mQW7jaVadO6sSyTzcPjHHn8MTz+8KPk2idxy6034thm1BqjIwEoFdfkmrC+ECriaxFGYAhh1IS6g+35X61+Xx7hXi3ivZpoajN1bAIh295XSjF58mRWrFiBZVkkk0kuvfRSli5dyu23306j0WD27Nncd9997LbbbixcuJDu7m7S6fQu5WQAaBkdMCUwTBtH+Sgrg3DrtBGiaGAkJFL75GiQkAYy9DGFDbUy75wX4CSKOCKJlD7SdFBhgGknMIwMqBoojeFIbKlRfo2UIZA6qiyZIiRQYaS9bwi0DkmbFgqJaTv4lkUY+hiGiVYhchcq9oNCNQvB+qWCsGhC7DSzI16qpwkVgyKR3qOOEcqrr3mEX175YaYNdOJ7IW19e3DuW9/J0UfsR3dXmlNPPJZPfPxDJByzVbcTKpLciNLWCISKBwBEazJ46TG9I8zxf5g6vlp6+PLU8ZXWcds+32Tch2FIpVJh69at9Pf34/s+lUqFZ599loMOOoixsTHmzZvHfffdx+TJk/noRz/KpZdeyqJFi/jsZz9LoxFRhmzbxrbtXYpQDJC3FbmUjWUaqDBEyiSGqVAJB5OIAaKUxjQlOtlOECqkIUGFbB0bxXVdOtM92CKagmKLIC7YCoSKJlb6KkBICzdQGMLAc11M045OFg1hEGAaoEIPKQyUqQiUxvcboASe38C2LECjaoWdvcteMk0M6sXTNoUgJJrWGTOmouiCEZ9b8TotTjeVDmPhVXjnOw7lXe9cHK/ZXkINtVIorbn1jrv58uc+wd77HtRap+omvB8DHi2wJSqaRfwCmn6xLf75yvY/6kdr3m6ihc1o1LwPURrZFOBpvq/ZnNm8/fTTTzN37lwMwyCRSJBKpRgZGcF1XTZt2sS0adMolUpUq1WuvPJKMpkMlUqFfffdl6985St8//vf59Of/jRKKX70ox9Rq9UolUqvWF7YqRb6hIGPVBGUbxgaz40cIhAaX4WEocYKo1xfKo0XNBBmAo2mUami2gOUFARug9CLdATDMCCRSKIDhTAlquEitEBaDjIM8YJKzJ4QCKFo+DWEFnHdSSCNqJlKhwJTggoU6ADL2nUimka1nKU5XFA0I5kSqBgciQZ1vnwp81L6CE3xUwXCjKJcGCPyADGFa9aCE1E6pnABkRMZgEJrhRIBUjclCyIWihZNHlgMkW7HdnhmFotFRkdHW/JvhUKBQqHQ0vvwPK/lUE2Vq6bTGYaBaZo0Go2/SxsPOOAAhoeHqdVqrZqabdvk83nmzJlDNptlfHyc22+/PRqSVy6zdetWrrzySkqlEpdccgkdHR2cc845/P73v8fzvFZBPQi23xf0WloQ+NQqZaqVCXyvSr1SxauVqdaKVCslQrce9eZ6Ho16mSBoEAQaHbrctrIEbVN4bMLBr5dRKEwpMYUkYdooFYL2IPQxtcSUgsCrQhhiGRYSGbX8a4Vh2ggZ9xJKhVBxzUkHqCAgDDx839vh6KHX0prQgoqHB2odnfDERPpmANGiiQnGmh4xQIKK3gOxHEHzNXFarXSIbhLfleYtbzoher/WMQskTht1c1kDoY5KDRGhGIhJBEqrbX7xK9s/FNEsy/o70GLbyNEUx3Fdl1QqRb1eZ+3atWzatIlVq1axZs0aVq1axcTEBJVKhXQ6zejoKHfccQebNm2is7OTarWKbduMjo7yyCOP8NRTT+E4DnfddVeruXOPPfbgrLPO4te//jUnnXQS733ve9m8eTOnnHIKy5YtwzAMJk+e3HK4XcGUW8eQISiN79ZBCwzLJKj72EkbrRSupyIUSyiq1QZOMkGoBBNjRQZOOZW//OZ3HHTwJCwh0SpEoAjDKCpKw4lSmLhVwxBOrGEBUlgIGRL6AaZugXRoaUYnivJRloMRhhG1yRAEuxIHq9X5YcQMfA2x3j0qLqO11kbN1FHHLHwRi6tqoqEW4qU1XDMiEUXF2LfignT8Mar5qSEIGZcZ4iWS0AhCNAYvObiManTbsR062vXXX8/y5csZHx+nUqlQrVYpFot4nkepVGoxP0qlEtlsFs/zSKVS+L7f6jfr6+tjt91249JLL20BF6Zpcvnll9PZ2cnkyZNbzP4LLriAL3/5y1x77bX09fVRLBaRUrJq1SruueeeFkiSz+dbDnvwwQfz3ve+F6UU7e3t3HTTTf+rY/uvNsM2UXUQpkGoApTvY0iBaUWDz5UU6DDAbzYbmhLPc/EaFeZ1JVnx6F1c9uWvcMtfbmQ/tRW/EbMbpIAwINRedPqJ6CobhiGiOXgvFiA1jUSUZsnoao6M+rVCYSK1wg9dTMsm9DS22oUcTcQ0qmarioggdBGvl3SMBkaF4ybxWEYCVUITj4KPH4v5t0TPQbwcimUKomXONqln7IhSmHGyGkVNraPoGCHk8US15tTQHSQDO3S02267jeHhYXK5HI1GgzAMaTQavOUtb+H9738/bW1trYgHkdevXr2aWbNmMT4+TmdnZ2s911zTNVtgPvaxj7W+pxkZlVKcdtpp3HHHHcyePZvZs2eTTqdbqljr1q2jWq1SKpU4/PDDGRgY4JhjjuGqq67Cdd2WHv8uYTqi/0Sd0wLDsWNtD0mtNEEylUArgQ5CsA1MYSINgZ21OHG3NBrBptt+wH5Gcxg6JJ0UyrTQQYhpSAIVI2AqarSVIkR7dbSZJEa3o5NORi040tAIaSNlQBgoEnYC32tgomAHc5hfS9PKQBmRw0gtIoYwRlyw1hE0EdOsovUmIF/qf1StKBidlwqNVM251TJGJJs1tugVkWOagB+VoAWoiJ6CDnWsCxI7dAyUaKJjvAP91B2v0W666SaOOeYYstksUko6OzuZMWMGv/vd71i5ciWGYVCr1SgUCggh2Lx5M7Nnz2ZoaIiOjg6WL1/eqqdprVtNn0BrqIXjOK2UVErJwoULSSaTrc7pfD7P7NmzOeGEEzj22GMpl8tYlsUzzzzDjBkzmDVrFrVarcUU2VXM0BJDhggZJRqGVFiWgykEqXQWKQykkDi2g0Us/qI0IgiisgA+BgFSB0it0dqnXJ7Aq47iNspUqyXcepGgUY6BFw8V+ijtoDyXIIzuE4YEvovnNvDdkEatgtfwCIIQrQ3yvQPYHf1IexeqozVPZB3VyXQz9QO0iNdZKthmVicx4ijiDLK5zoqdL16bRdErjOKUjteAorm+iy42zWipCBHRuGykFC0nR0d+r3XssK2889Vth5f/yy67jGXLllEqldi0aRMzZ85spYSf+cxnmDx5MpdffnkLGZw0aRJbtmyhv7+fJUuWsN9++7WUi5u0qVQqFe8X/Xd/lVIEQUAikWi93rZtyuUyyWSSTCbDpEmTWkKsl156KdOmTeO8885jbGwMy7J2qTWaMCQIK77IKKSIOHvSsrBMgVAQBiEIjQwloSHRQYAhNL6WKOKpkzRHwJqYtkDpaP2h0FiGjdKgDRvDiBdivofGgqCBlBZayoivJ8Po6i2iaSlaS0K3TGNzEVSAyOR38h57ybSKBt6KUIGM1mYyjl7RCR81j0VAiUSJaCEqWoU1FZ/6cU4XF66laEaj5rqt2cdGlIHQaiFtteBErTnNmCQIVRB9roj+6h2FM/6BiNbd3U2tVuOiiy5qtbPU63UWL17M1q1bGRwc5NprryWfz5NMJmnq6mut2XfffQnDsMUCmZiYoKen51U5kBAhlcVisfW453mtrun29namTZvGPvvswwEHHIDneXz729/m4Ycf3uWY+0B8FY7QLYHAVwGhHxB4Hl4joFH38MKQUEWtHTKM1lGhMkil07T3zqBn+jzyk2dhJtNkc11ku3pQhCANpDAIlUKpAM/30L5Ce3VCP0SFPhqDQPkEvk+owmjNIeOTEokKg0j1SSmUNHbUjf8am0ArEaGFOiYzxMhjRNzXrdsvRaiQEP0S0i4EOgShYsQjXm/pJvNeNFn4uqXhGB2zyIkjFn/Mb2TbOrKIv1dGIqvEwMl2bIeO9vjjj3PjjTeydOlSbNtmfHy8JW2wbNkyisUid911F+9973sBGBsbwzAMCoUCQKsoXSwWaWtrA2jV2LbVFNnW+YaHh3nxxRepVqutoYOe5zEyMkKpVGpFulKpxKpVq1qRMJVK7VK1ND8M0K7CtGw6+6eSSOcw0lkCoQi1D6YRRSEhEIaJME3sdA4rlUGjqZeLlEa24E0MYYQBjUaF6sQYlmVHtUkRnz/CiIjBhGhhxidKjMw1cx8dRQk/cNF+gB9G0g9h4ONrHa0T5a6TDWgdggjjTq84VYzTxAiWjxo/mwVl4jYagYAwqodFyt3NzjIdXUhU03GJHTUk1CFSi5jpL2InjmTntIpWhFo3OZSKZouOUpFjhkr9U6ijAfD617+eTCbD9OnTyeVyTJ8+nUqlgm3bPPjgg9x6661ccMEFDA8Pc+yxx3L77bezZcsWOjs7qVQqmKZJsVjEtu0Wk2Nb9r5pmn83Vhdg2rRprV6zbDZLvV5voZuO45DNZnEcB9d1WbRoEQMDA+RyOcrlMl1dXX/3+3eSGQCpTDtuBAtSrFYw7CSGNEhmOqOrsAqiRs5QgyExDAulNYYKWokPJACNtpMY0Yo/Rg+jYqqlNUJKQi2QhhnJowUaRMSgUGFAKEGqgCBU2DK+GisdUZxitE1KgUzl/+737yQzAIIwAF8gtELEI3GFbIq9Rdaak4aIpTviNLBJ3Wo1ajYtwu91XE+LgKIIwUSFUU0uwvHjl0f4Y5SNRpGwyUJROhZTjareBEEror3ivhOvBh4sWbLkcODB/+3e2gXsiP322++hnfHF/953/3v7v7rvthfRngCOAAbZlXDfHZsB9BP9/p1l/953/3v7P7nvXjWi/dv+bf+2f53tOsjBv+3f9n/Y/u1o/7Z/22tg/3a0f9u/7TWwfzvav+3f9hrYvx3t3/Zvew3sVeH9JUuWOMAB/H8YZt1vv/12yqzYf++7/739X91326ujHcD/xwuHwE4puvLvfffP2P/Jfbc9RxsEyOS7kKZB4PlYto1AUygWyefyGNJsJZ8SAYaMulObPEaIe3YiRnXUlr4NhUb8fSsDujkfeBuxH90cYxrPzVbNHqCIPCrirtjm0DitFG611Pr9O8kGATQWy9e9wOaCy2+++y0Guvp4y9vfxKrlz3P0yacwunYVK15cwcL5Czn0iFNY8+KzTFnzIsuv/RMJDJ4PLG5NS9Kdnbzx+CN5avEips2ezcN/uZsuM8W7v3EZGz/9H3TUQyZmT8F8YSNdl13IYJhk9z/dw8dXrGe8NMyxJx3LlNPexsobrkUhmDnboHfG4Rih4vpfX017PsfA1F7SXf0cvPfCXWLf/eTOdVRqYZPCGLXAxATj5inU5OgHYcgHjpB84/KvsH5zjXm7TeWLX/4OP/njk0yEmfjF8Tim5kds203doly99FgsakxTgKdpkQSEwlPw44+cTKhDHn5okA0Tgxy7b2fr97/ctudoIYCUBoa0MJIWtXKFdDZLZ1cP9WqVsleho70LGasRCSHAiAnRSkcDyyFu2os7geONbjJdBS9tmI669f6ufwii+cXNKYtaxhsuJEJH445EzE+T0YfjbvP7d5KFAJuH1lKeGGPy7OM4/8S9+NM9z2KOvsBxp72B2vgwTk+aydUpVAZXMrr2EPSHL2O5H1CuNxjp6qJ3Zi/vnjGHKx74G/cuf57LLv8KQaDZ/3Un8ZXLb+Adb383B/Z10G6kqI95pEKfwzZtpnvSbIIjzqJ3/Fomz+qiNuzgLH+AOXvuR8I2+Pn3vsekycMI2yVh5eid1kO2ezrhS3orO33flRohpUZE7EVED5rbiIUoiBtd4cz8H/jvry5nydJNhFqQSKU49OBD+OJHT+Ve/5z4Y6NzyICWAE9EQ31JF7LJhGzKHDQJzSKi9MeBQxMqzc//41T8UGGMDXPispvZVK3g7Xtu6/e/3P4xAdWY9JtKp6MfrMC2HdxGIxIm0SJuPY87WFUUbXQQvtQT1IJdXtJs0IJW/5BoNeXF0SmaMhe144U6/nwd92jF3ymBbWaL/T2BdOebUavTkUjyUCXJ4T29KCvNi6UUna5LLh+was0wheIEBx1zGg99+EK6xwp0tuchaZF0SxjLS+gVK/nSVz7J135wC28+58N09+ZZt2o1r3v9EbzvvM/yzMrN7D3ZJGybT27mbBgZxZ/UyaP3PcIZ572BursVt1DFNxpMmTeDA2b3ce/jx7Lq8VsJ3Dq/+tPvWf3s04hEB6VydWfvspZZUmJIFenva9ESDlJaI9EYaEItor9ByDPLN9HwIo0P1wtpy+f4y90rOf6ER3nMP5QgigFRV7aOPjPiZ0e9fSagdHO4Li0REUNFs6pDoTDi0U2eUjyzdBkH7DGP5eufYeXgnThpxd6c+6rbs+O+/5e1s6hAYTQFeqJe7yjaRH13kZOhUWHUpaPikC+akxS1RstYsbyVKr70eFOZSBGpNUXBMG5D2KZFATSE8QQbFWsciWbf0a5hE65gaXUeWzoS/PKelRz1ujew+dkH2X+vubideXrapzBvwWGkhwaZXq/zpyQ8qhsYCDozaTYPFyD0+OJ3f8PZrzuIvnl7YO2+B1lT8sDVv+Zz//Fljjz2BIbIMmv6wQyEI2zxx/jjj27i7j/+kbb2Hg45Yg/27OlnuhUyXhvmQ/9xA73hei6bvg/Tv/lxlj33JGs2Psub33Ixvuuxbt36nb3bgLjFxwRLg44J8U0tq+iCLjBRkRSEkuxmOzwVuoSBYtOGDSQsyQF7zGGtPxUMMJttLEIgYt0UrTVIjUE8J0JEyxYEGLGUXSSnIFqXcSmgh2GWf/vT3DmeIud5FPyQiuHGzgABAABJREFUyR2T2Pstr749/5CjAc0gGo8PiloXHMuJ5b3NyFuCbToHlW7lwlrolvSd0CDCl5xCCBEr0hJlCXF/kYgVj4TQaNVsGd9Gi31bGXIh4wF0/J1O+s62mzOHcvmRCfo6UrzvFx2see4ZNqwf5e29OaSVIz99MsVqhfWf+yadoWBRIsUXv/hOlq0Yo+7amDLFtb/6MT+3Haq//QuB93su/c43ENPnMFZL0NbZyaR0ihcnAu687Gts3bIV7ZgIt0HfpD5OOOIgjjv1ZMpf/Q7J/Bz++5ef4utX/oBUENDw6zy3scTYuMYY9Qn8YJeSgZBSYiBARrJ50UQXGckZKIkQkeRAKDR3BmeTyS3m1K4sL9QU2cBjY73Oqq7T8WUSQ+h4Mk0UyeJOGgJDYsbnmyKaRR2lowIt44GDCKSmJTE3o/A3LCtJYryDmZ7PVlegpYmLv/3t2dEGR82sOhYweanLVCmNZUf6gkortApb4jo6bEp86fhLmnlwnBuLqJVP6eastbhRr5UrRyq8zXlXQsbZYTwyKoqoZkuLZJuv2qWSx6uPbmdSRwoDRT2UvPuDH+cLb3sn1eVj6LVjeMMjzJ8yjd4Dj+FTpsF/X/afLHpgKZWyw7oVK/j9zdew575HMLT2eS668E10dXcz8ewLfPvdH+CI44+kf+psXn/BORQRPLtsHX1z9+a73/wi77n4Ytpsk/dc8kEGb7qF0E2j3ncyV1x9Hc/d/yRuZQsbtwxy3TcvZ/Gj99C3YG/KRZdauOtcpAxDYxkGloSp+Hw5OcQlag1SykibEokQ0blgmPC5RQfw5ScP4cblR3HxDVOYvWABwnRImZCyJJYEI8YQIixBYIr44i/Ais+xpjaIZYAtwJI6kqSQAkPAxo6jWdd2BIvSHdhJSUcmgUCwpb59PdF/aI3WXIBqFXe+ttSAdGuYd2ukLtEVYVutEhVHwPGhLdx07U94z398mWWPPMYND/yV/7r0cyxb/AR/ue1XXPqFH8RilDHCSAR6IECHAkOKbZwr0nF4eXd2uAvJGQhCtoyW0QouvPCtJFc9T6KzE7Mjwxe+9wPaJs1l3oEunWedxX3f+zS/vvkhBscbHLD7FA46+VhmPfgwt93yRyzb4bkXNyCtHH1Te9jv6OOhMkJC+Lz1zHdx1PHH8f2ffpfFg2Mso5cXX7iFj332EoQQdJ9zKn9M3stBjQSrnvoboS7x/IYKj979JHN3351HH/sbbzr3Ap5bv4GxiSq7T9k1dEOkkEgZcm7heWZsWom1x0LE8meZMq+dLU47AsVeZomjL74VUys2bl7JktEShhSkkx18/uapNMJfsY/8LcVikbGxMb76x5VEnqZRQoIOsIVBU/HqJW2WSLsRFa0NDUG8KowCiCCEQy4gvOVSakJQrEs8e/vbs2NHi8eUqji4tmRidYiQseyyimXV0C+tt7bRAxFCoBW0d/dzwSe+hNCaBYcexCcWzEUjWbjfQSzc76BtJLvi6CZoZceR8MzfmyEE4TZOtiulPgCbhsoIU+G5AZ1deUqySpjK8vkvf5XR0QYNq0a46Emy63rISpe+TIOjTz2OWqVIdevT9HaHnHf+8WScBFU/JHvsgaxetZIpWbAqQwSeg9PZQRCEbBgd4dYf/xRDhrQFJse94U3ccuddHHP4kewzbyullWtZ/exmjj5qOg898BiPLXqS3unz6W3LUGuMsm7YZ1Kbs7N32UsmBXOly4K+PNLrZez+B1jZvRlxz2q+9tNJ3PfMW3jX1u/xaGMyJd9lSt9UBvoDPL9CqWRRK1ZJpyzu/vOfGXF9Jvd3YMiAUEgMITE0aBGd/hHKKJEvAQYIYSCFIhKli5DvKMAItBTYbV1s7pzO2OAmKlrRCLZ/gd/xGk1pQhG05Bmao3BEc7Zw3O4diZ0YLZmCRqOOaVk4loPSKtb+iFPD2FdTqdQ2UUjRnCHcRCCjdnQi+Wt4aU3WTF+liNLSZhRTL6Wnu4ItWr6ZUw6bTRjCIyuXc/yxR9DwXGqFEsoDt96gUCng1oe441eLmDN9Kk7fbhTXLMORHpMnD/DQQ0/x3redheXEUy9TkylXq5SV5Pj3vg27vQMlJX/5xud571tPIeXkWDnS4HtJl68cfDhf+ORn+OilH6Uj30ZiVg9f/uBF7HXQvhimZHTzaqYP5BgcXo07VGKg74CdvctaZglB/qpPclylTsMLEKHP+VOncMyVM9l6z24kp3Xy2BlpBrdWwawxUStSrfp4tRo93VPQQnPAjVsp7LkveusWsrtdzgf3b+MHP38zRqzsLEVTGkHHsH9TWUtECLdwQIQYMaqOiCKeRBD4DZ6cfhLWup8wkc5w/GlnbHd7drhGC1U8xCIMI0nqaNEGxOpJGlo/VyukNHAbNWzbQgLlSpmWJFecD0f/JFJG4RjRjH6RI4bxWi8Mw0hHRCl0fF8pFT2vI2Rz20L13V9410sTV3cBc10PLUwSCZOrr72Kyvgwl1z0Xho+JLsGcGzNvP4UYb1Mqm2AQii45YYrWTc0Qa5vgJ//9i9UyjXMVBvz9t6HPQ46hH33251jjzmCA/ecxev2msYCOcHJU9J85zuXcfgxx3DBwbO567H72Pyez/L9L1xGuZ7j7W97Dx/4wMc4cPYk3vHms9i4Zgt77bs/6aTE8+D3v72NOfPmMWSndvYue8mk5Ln3/4TpmQwH9/YxKZPn0WKJZE+NTb37k3QMNlQDipVxBrdO0KjZ7DZ9OgvmzqQwUWd4qEJ6nY8wTU675yLGKlVcT2MaMtIgkTHgIsEUEiEltiExBdhSIg0DQ4aYTZQtLngbRADd6KY1PHPL9znmwo+zatMQ53967nY3Z4cRLQybNasoN1VxHhuhhBqhQ5pqylEEUiScZKTEBKTSseM5CZpLtwjyV5imQRA05VYiZwxjUAUZD5YDQh2BJbIpLKNpDS4MowyTC9/zZtCaOz5xMV/60tf/l0f3X2v7zEmwavXzFIvjyMDn7LPfgZV0aHgBQ2uewfXqPPsUnH3ee9j7hLOY26449bgD+epXv8PNGweRVpLrfvdLhLQJVR1DWARhnZuu/w3X3XA3HV09YDm85Yy9Wb5hlA4a3H/ofNSFJ1OrBaxdt56xQpXjT/sExVKZm2+6GVNUGR4fpqM9jV13SHWncUt1/vrbW7joY5fgefWdvduACJQQQmEh2WtWH6PPlxiYs4A1a9aRNBNot8roeBkpQ9x6SEdO8OBjz5Fty2NoiyAIqKteqsUJ1OAQo8M5fvaVLZx6ostTEylivlIcvYgVxTS2jMTEo0FWkQakoQ2awuCGiJSwTvSGqc7bjR/9+LuseOBjbJH/JBgS1a6i0rwimnIvjebggagAKInoVa3lWySzBGhc18e2zUgxV5iRKmxcP5NStFJNXgZkiEATbquTToR0IkWr7hGtA6PfNb2/l2KpxMw99/4fHdD/l/bU40+gLHhxxXr8RkitVkXUXSzLYlLfAIZto/yAJx+6hyP3n8d4YhqDy1dw/Q1X8bMfXk2tUePyr30bPwjo7e7iDW9+Izf8+nruvW8Rbe2dqMBn+dKnmfqew5ncleOevz3KhR/8Dtn2JKWKiy1ldMHKbkThMql3MqaVYOZAD1vHxgjyKTAt7ESSzaNjqNDb2busZfN6aoy/sJTePWdy59PPcey8Bcx80zsYG/4SE88/xqQ99sWu+dj9AmUqhotlpk7L8eIal55eh0Z5gvtPvB7f91jfIVny1CYm1s/AL4+i5bTWtJ0IY4g5FzTXZ8SMEJqafQDRug6BoTXirBG+9Nggc753Nhvm5dn9sh+z/PU/edXt2aGjDW/aSM/AVDTQ8FxSiSQ6CKJh8CLKZUMRoTNKxZNJlGpRZExTEgQBQRBgGgYI0VIT1n6A77lR7tv0XHgJsWxlk1FKKSTE2mkIFQ+T07DihWUMjxcQWmNPlP5XB/b/hT358N/YuG6UrkndDK3fRKleASOBVjUcs45jW1iGiTQUP//5r7n44vfx1ne9i+9f8X0++skPgQDf9fnVb65h8vR9uOGWv3H/fU/R09vPksWPMX1gCtOmdnDBJZeTNhwCI6onFgtVtBCEVpoDjz6R8shanGQ3n/7Mx1i+ZjPf+cpSOnIJ2jM2R556CMp1WfroKv561x0cf+yuMcPaGt7MOR/QnHHQMnQj4K41Kzh143qeWdnBNe4v+OO8+UzxPTZ1mdiWRblYpyINCAWhF9A16jKWLJN1DH68l2bLSo90vp0Nm7fykdfvRf+DP+Ur2TfS8E20oeNgQZRWamLKn+al0/KlyTXdbffw5Dc3oT0X49g+qpVN7J4Y2O727NDRFi26n/3FcXT19hAqzY++diEXfeanqDCC7AUgDSOugwH6JR5aqyYmJbZtR0MLgwCtdPReIfBDH0tGeobbDsqQUsavi5gAzfVdi6miIn0+rTQzZs1ixqROembuj7Ph6f/1wf1X2z777cd7zh8gNFOMj23gjvue4dFnVrNmzSpUIh+tLS2F9gOMYpFf33ArYxNjXHTh+9iyfh2PPPYoh594OiI/h1/89GqkaWM4aR565EFSts3E6CChr9CGQblR4UMfvwRfKNLpNJ7r0ZZN4oYBW4ZNZk/p5Zlnn+Gu2+6hWhjnU9/+LsXhdVz/05/zlW9cxoq1G8k4u84C10xmadS28rZP7ssNX3qEzYUa5WKBxkSWLb0BhcI4h45XGZzTzuiojxs2SCQsch0OTqrO1I2Kx9tHSCz+E/4CSV93wIpnPFAh9z71Ah8R63nr2VfzixvfiyEibUcjIj/+HSG+yRbRSqFFpOd40gmd/OXJKRx246GIDStZMbEGqtvvKtqho20Z3MQNv/4hqlHDcQxMp4MffO2jTOrr5oCjz+L2W//AxR//IjoMWw5mxD/oJeL1NorEaFy3wde/9lnefcGFPP7Q47zhzWcjRSJm+MfSzCoWs1TEDi1jJ4PAj6JjM/CZQjA+PESmcAe6s+9/fXD/1fbQA49z0823RtNaRJIPXHYFDzx6MflcjomJcRyrg1pDY2NTFmWcjWu4+ffj3HLTn5GGiTQdbrtjCY5p05lLUKqUWfLU3/ACH9e1EWRBS9JCkckl+d3Vv8DTCttM4FgGlUadUi3ESZocdeRBKE9RG91KqquLRqHKqscXYQSCzVvXc/jpb+P5v+wa464ARosFVl49xNFv34dffvVJBjo6KJeKWKbBov2TDG6ZIDsYice2dWmkyDA8XMNI22hRZ+4WWD8nx0HpQe6qe+ggwLENNq1fT3dPN6PTp3LrH56h2hC0p2XrIh8KHQ2lFxFGEBKj4MIAHSJDmCg9xh6bB/EdyXh+Fa9/ykSL5Ha3Z4eONj5awiDAsGwwHYIgxJQmQxMN7r7tJsLQ41tf/DC2k8AwE0hcDCmYvnBfDjv6dJTnYZgWYRhgWBZCgDQNctk8k6bO4vQ3DVAe34iT6sRM5Ldpl4mcMvB9hGFgSAPCZijX+HH6CoLKyAiW51NFYnq7zjpjxcb1NMaLCGmihMc3v3AFb7joA/zmu/9N0BjESHTQKG4kNCDpm1TdCrrgYlk2tpOivSODZQqqXpl1a1ZiOW0EymX/3faka8oAq19YzeaREWqeZrzeIGmZtOcyTO1rw7IgUzGxzRquqylsGSIIPNrz7bzzvHN46v5bOOuc1/Geiy7iW9/6Cj27n8w73v9+Nm/dmR0yL9mCgQZdcyajTZ8jTtubfWefwopVzxOENbZkFNNmzcYxk5FivhY0/AYd3QmELpLNdLBsZYnyPkUmClXCLkndlXi+ix94ICR+/1YevsElP3c1Hdk5mDpa65totBEli1GJiZjYrCIVZK0IOjRHXLEn47ZBd/ccujYtppabud3t2aGjOQRsHS/Q1paNJKuFQGsLMwzwSg0saZJMZ2nUagSBwrZMKm7Ik48+xtOPPsIHPvk1fvKDb7LHHruz6vknMSwbKyxzwslnIYCxrYN0dU9ibHiEdN5C6RDDtNiwbAnTd98f0zSj9Z9W8eTFZqYMKgwRUvDMo49S9UM2jw/TLXdQon8N7cxMF10Fk6Sw+C9/C4n6i9z5u2h8bs+0WQwNrWTB3EPYsGkllp3GiOd9KRXgBx4qbFANNM8+sxikZHjDat517rksOORoHv3zbXR1tNE/Yy4T4xvIOUmq1SptyRTJZJJ1IyUqlTL4JjIpWbpuM1JpumYoPnvYPqx74klm7rYnDz34IE5dMfTs3/jW2ud48xvP3Nm7DYDlW37GhqfL9M5p46wL3sCvPnsLCw84lFWbnqEeNGhXPuMH5EmlAoY21ZBJh6AGbZ0pahXIpBXVmsfYyi3UJmk2DyYQQhOEAYaEtXmPwhafz1wyj9tXNtvQNGDGBF0d18yiclNzHJSnDbL5Xh4ZLZIYnqDRcDk4M4W629ju9uzQ0VJt7RjlMlppnl+7ie58CstJkkylSFoJEJqGWyc0wUBQbbgRTUVqQgXf/erHmTF7PhPj43T0zWDT+hVkUllmzpiFDnzybTlsJ0GoA5ykHa3hwpA7f30N53y8H4nAME2U7xMSgR9ShARaoLyASdNmMrHhcXryaXLJFEZ61xl4fnu9ytZgDNMwSDgpMNsYmDOfXEpjumW6DtyXLdqgWhulpyPLwpl9PLZ4FdJXbCkMsviZJWTSGWb0DbBwz335671/xhASJyhz+jmncfMf72DylB46Zr2O2T2S0cmH0CiO8PS1V9LTpenI53jD2SewcaTB2W84g/++/DI2bxjlF9dfx/s++h6CwOfAg/bnF9//OUOjo2Ty+V3G0fY9ZhaHpAKefrrGyueWsWliBOOvv2NOh8ukKZ14T4+yYfeQoU1gYTN1ap6x4Rpd+TRrXixzaIfB6mQS5XrstrAH0xqm8vxW8rVx1j67CXvOJto6UnR1tiNlAVM0wQ7Rav2KifyYUrQ4v1KGlIYneNPvN+BkZ6FUGkFITvts3s727NDRavUaA93d7H/wgawYrXLE3nNQSrF6/SirV73AQ4/+jdHhcQwpSOZMpk+axYXveCv3P7KYfedP56vf+zmbhwsce+QxWJZBb/80TMvgul/9gp7eSYSBZqIwRnF8jLm7zWPGrAWMb1rJRBBw1Te+Gs1rNiJWxLx5c2F8mGUjRSbbAiFM3vZf3+Z7Nz+EDhVBGNLf38/F/5JD/c9bqKN+KtcPWDg5j2EOc88ff4gf+gT1gIHJ01Ch4IgTj+P2P97E2GgFFdQBxdyp0xBzdsNxMpz7xjN47OH7ueTtb+LP9z7EA08soVau8ZYLPsQhC9vw3Bq/uOZOps9YgmHYfOY/P8SqdVv55S9/Q8F3aJdbuO+OW0mn07z+7H143cmn8skvfJNL3vVWvvL5z2Om8wSDg6Tap+3sXdYyU9qseX497nDAqAtt/R7pYQPV0KjQZsvIMJlSQMoWZJIpaiWbWrHEZr+CVwnpGcjjqzq793j8ad0o0XQqF1fbdBt1pszanXWbnuCwQw7jou/8BcOMkA+Nippy4oEWrdG+KnrMweDc7y5lyNIsnR+StTTPTAxz0uwDt789O9rgrYPDnHD04XT39jIwLRm3gmtmDXQyfdKh/OkvN2M6gkbFQ9Yla9atI5PLs9fe+zJlajfpRJpJ/X1UGzWs0MJ2HCYmCpQLRWbPnU86lSFUHgcsmE6pUGLLpnXc/+db2X3fA3n+iccwpMRXITqZ4MnVq+lNOkzt6mDt8AiOGXLND7/KnNmTacskWbFuCwftM/tfcZz/JRbG/XNKQjaXoljzyKU7qdRqiESF//zI5xkqPMu9i9bhq5B8R5oTTzmHM04+BEt4rFi1Ad/zWfzscnIdXfzm5r/S0dXL3jO72Lh+K/XiGI0qbBkrc8gBu5FqzzE2USGXMTlgvwVc8a11PHn7ONiSqZM6eehvS/j4f3wUw5QEQ0Mse+Qx9l54ONfe9AvaumZy3EHzdvYua9mfb1nPxMgoDTNBd3fA4Se0s+qhMtZmk8bSIT7KPXT/2mXNBk1QK5E1NH2dFvemXbacmGaoIBF9gvy8HsreKPkOhwm/hplKM1Gsc/3Vi/DGLXp6ell539Xse+q7W2h2VEiLp4DqqHasRdR7meh5gVtOOQ03GGGsMEZXV45JiSnc8OiDnDT91RvS/oGBz5rVa9cxc948vMDDMKKpmqlsmnK1TKPqIgDLMpAipFpyeejxh3nuhSWccsKZfOD97+C+v91PqVQglczgJDT5XIY5u80nDANCJZg6fQ4jpXH22GMhi396FUd2dvNCZYiZeyxg7QurCCxB1rYJgE2lConeARbgYi7Ym7E1a7EF9Ha2Y1oWhtx1uI4SgaEjeNj3QxavGMWQAbP6Olgx6DNw8Dz2EO1894fXMLV3KieeeBRH7D+L4vBGlBJ0Z8H1fPZf2MfV199N1VeceeoZ3HPzr9lrr91ZseJ53v2WdzCzXuWJ58aQC4+gY/m9/OiK6zj/Xafx8Y9+iN9cezULZw/w7LKtHHrYAdi2TeD7fOWyz9GQKb73xtfTN20mbzjteOQuxF9buMdUHn5sglmTu9iwqUE2E9K9W5mRDp/xwgX8vK2N9MwexuaNU52YYNbcOZhCYlgWtXqDR+f7HLH+DhqbKnxhueCZQpWr3TTpVMDtT5RYqI5Hi7/x6U9/muUvrMA0gTBqkVEiIslL3ezqjjtEpGDRXzYzMG8IS0m6u9oYHR/DdiS5ju2PJd6hoyVyGW57ZDEbNo/x9NJnedfb34RhJMh091KeGCObTTA+WsaQgsAzMKTiyquuZ/q0Pr7zvR+TsU2Ep2h0byaTcCh5DdZurZDr7ueDH7gYKUyKpXGGB7fw+Ruv56hMiqStGdlQYlxKQtsgY5hIrXAcm6wKeeaZZzj19KPYMFjmsSefwkczpbudmueS34UcLZRRT/BhC6bw+Avr8AOXslul3qjyze9fxf1/+CUbhxR2wmFkYoRJHVmSiTQaB7cyQaVSoFysMnlyPyed9jo2//xXzFownwf/2sa6DSN0tCd5dNHzbBmqsvjRp3Du+BuTejp541tO5dZ7F3PLtddx3e+v4oGHHmTaHgv5w29uRUoL05Z857JPMuuk12PKJGecdCSGEvh6+zSi19KWPbsRU9bYa88BXnjuBcRAnq3Do4QqQb3W4MBFv+SMqVWefWqcQi3kiNxUlt48yLMnvgux4DDWT9QYGTgW3X8sdjKL8l2CH38VYYUYdobC8Hpef9brWbd+HeVyic7SGLVSnfykfrxGA8M0EKYdMY/imWgohSyO8dZfPQ3lMnRPgdIoIjsNN+Ww/IRX354dOtq8+bN5YcVKnESSdCZDrVajs6udTDoHWhEKgWmauG6IaflNxQVWr9mEaZlYHe0EDZ+NExUSZgOPiE/WkRins7MLKSWZdIINq5djmg6/WbWKs2fNxZMCy5B4gcuyF9djOprpHT24SGbvNo+e7pksXfkE82ZNoVhr0N6RZWu5guHsOsTYPad0UsuZ3LNkKb4P3e0ZjjvrPK67/pc8v/hBpOXw5z/9Aq0lve1tPPCn25nem6SzfyZWWwfSCCMmjWOwdumTnHDKqVipHIHSbB3cSMrs45uX/4IZcxbQ2T0ZX1dZunYL/PEOXE9z6FF7s2LlSrSW3HHLw0yZPh8pNG7DZfPgEFMG1/P2d5zPLX++FW2YHHPEITt7l7WsJ+Fj+ib33bkEaaXxXI1fd3Asj1n7380qeQxf2/tPVPY0eW6NZOKcSUy9pJ/E4B2se/wGTvlljarpsPuAxbOjkhVT9iTXk+WkPYa4+8+atS+u5Zhjj2Xvffbnzlt/x/jTt1CpuYyv0hRGJpi68HDq5VHqxa24SkLgUiiVwfO4ekLSfsnBiFRIKjGLFcufp768xHb8bMeOFjQ8vGoNqSUq9BkeG2Pdhg0MbymwbM1GZqdyrLUsarUKoR9Rr0IVjWpVCAYHh8ilUuiqoIqHDkOqvk/JS0TtdVogTZtsOMLgls3MnDbAokYZt+HiuiGpdIIjDtiLTVvHkJaFU68x9OyT/GrVC0gBfgCd3TnGKlWmzpiOq7YPs76W9sDTK/jABe/hmNedScLRpPNdNDA45fBvsCVw+dLnv4nQkEkkOebYk5hYuwwMQeC72Ol2nJTClALLMEl39XDT7/7A7H33IQgUlXqDFzduYq8Fc+ic1onTMZdH7ryRXC7Lww88QjbXxje/9gnu/uOtZE6/hInaTXz641/ilj/9lkmmw9aJGoWyYOHU6SxfvYrd99yDar2ws3dZyy7sOBCvvJLfL9pI32mTuXXFILZj0xgfZmLVTPr0FqYdvCd/uvE52owcV/2njTAG+dLVM1i6ZIJct81Nh/8H7X+9jJ++ziJsLKZ7aY7+jx9A4s6l+ImQBXvsyY++dzmH7r83DS+gLZ9BaLB7HfzxdZw/8wXMgZD7Kkcyas8mVaxQW38nVzy+mGPfWcFBMSVrsmRzheXpXk44+NW3Z4eOtmzFi+Q7O1mzeRVTpk1lw+YtTExUMEwbUwasro1j2SZChPT3TaZQKqGUwjRNfN8nDDXSNDFtSWdnO5lcHs9zyeY6yWQzKKWwjBQrRhR9fb2EYYhl2pgpi3QqJJ1pw9cWc6bPwvUbrBkvkk3YjA2P0tWZI6gFBMU6Tj6HqdZTI/evPN7/lJ1+8klc8dOfEfoaz6vzxnPfBpufZSLZR0/3ZAqjw0yfMY/CxBhPPLWUSnGYZas30NZeYcGcyXR3tfHs8x7PPruM0tYqvucRVCbIpTOsXlckY7Xz4JInOb3/DB6/54/k29s44IC9Wf/Ci6xcs5L3vPtTzNp7Dz6Shz+Uqwwtf4iBdpv7b13E0NgYq1Y/yVvPfQMLZk1j1szJ3PHXv3HKGefseMNeA/vJ1jnUxDRWd73I9JHZFIeu5cjBBs/NSOMYC7ln8GncTxhodufoE87i7rV3I8IUn3pjESs5lRdmjTJ3xh8Yv3gWe7ZpFj89zkiH4JMfvYd63eBDB+7OTz71EY5905sIwoBEwkYKiet7dOTzHN63nn0nlWg0Kuw+6SY+dveBpLN5lq4rMXU0yXVeBdtOEoYByCQ9ye2fdzt0tPdf9BECNNf+8CdceMlH+PoV3+BNx72eq669ive87Z386Y7b6G7r4MhTT2T2tFkEGp5c9Dhz5i9g/rx5fPgjH+QH3/sBYRjynve8i3PedALHn3AiqJBKuYIXhBgoKuUykwcGSCbTGKYVoz4RqzgMAoRh4XsB3b292NUJZk6dxobhrQxt3UjvzAHMRJGRiRqp9K6TOv75r3czNjJKMuMQasX111/Fdz7/acZ9zdcu/xaOlcZ3FW9645vZvHEV690GnT3zGW+E3PpEkaTexF13PMKchQuZNiWHJKDqKYpBNHu64QZ87OMf569//gsjQ0MYTOHu2x5i/6MOYw/H4IX1o+x15ttYctuv6cnmmLffUbiNGh/9+NcpNBqseWENtXqZIICVz7/IyccfubN3WcvWb3qKd5aXcduKGql1j9Ez/zBeHOjnuYfvYZ+jD0U9X0CJGplUisWPP05PVw+bN29EksWt5qkW+tl9zslcetNv+eDHLmVgjs8zlSfpyN/M+iUT/OLhZ+nJJTn00P0QQtLe0cboyAiZbBYVhsxcdyebt4xhm4LVgxXO6rmdjZM+zh9u/iul/GSchocKPEJhEroefrj9sd87hJkM00S7AaYh0VLwyQ9fyq+u+QVCKU447XV05NtwbBupBL7nMjI2zJlnnsE3LvsvUIoL3vluQgQhgl9cfQ2bN24gDFUkgyAElmkhpYmdSmNbTjwHPRqcjoxuC2kwVizQqFdJpdJUChMsefo5qrWQzp4eVq3dwtIVg2wZrrJh/cS/7GD/s9bT3sb8OTPZd+EepBIJprT3cdvfFvOn31xHwrHYe+4U5k/N8OSSR7jsk+9kt3mzueXWO+nryrLblE4WzJ7Oe957NvvssxfX/e5hJvf3MfjMo9RG1pJPZalowcYVS3lq2VLOe8tZvPO95xJaWTLZTu5/fAnDWzcxcf9N/OTKP/DdK38WdaprQU9bJ46TI5dMcvttf+SIow7BJGTx0tU7e5e1LFAb+AaaxwaSjBw/iaUvPoAd/JZGYZSkbWBYSYS0KZZrNOp1PM9FSEEQSgwR8qa3nkdpbIz3f+ASvHqVv9zyB66+6if8+YdD7L/XPlhd3WxomCStJI5hsnHpMhLCQLg+ph8wWvCpBlnqfoLxUZPCSMD0yd185JB5nL9gMkI4pBJZ/DCk5vkviQW/iu0woq3buJ6BSVOwDAsV+Hz5c5/nssu/yRVXfI8rr7mKT3/hy/zmml/S2dFBb28Pq1atYnXyRX5x1TW4oc+CPfcg8H2khKVPLeXcN0e1Bt8PsEwLISPmcMJ2yGQzmNIkCAK8RgOQVMoVytUqlfIIgVsjpQIMBQ3l4RUm8Nw62UyWTRvHGJiUpRbsOqhjR3s7G9dvZu26jZx81Em8/Z1ncvb5F0acw2wvE57Dshc2oJXms1+7hu5pC7nrr3/g0UcepB54XHzhBbzh5L1I59r5iaxz2tnvIpVS/PnWO+np6WDfAw+hXCpxyYc/ydknT2PR00McddqpPHTnzXTk+rCdgCdWjfKWs9/I3fc8ylFH74dpOxwwp5fK+iwHHnQIp5x5Jl//7Ff4wHsuYGD+bCo7UHN6rWzxUzZDgw20huef2hiJ9lpJfn7l13n7uz/O6856C089V8BAk0oKAq9B0rbxRUjHpJnYpsXY2DifvvSDuK4bLVEsi3Pfci4vLF3GUUcczp//cheVWo3A96hqja42mDb8U3Jsxk7YiEQXgVtizixBqdagtOSr2J3TWL85ie/5+EAq4dAx/WByPV3b3Z4dOtqk3n5kLJSjEXz2S1/Aa7j4boM3vuk8Lr7ovfhBQKI9z/d/cAXf+u4P+e9vfI3i+Dhf/trXUUGIkCZhoJg5dy7SNDBkpPYqpIFpSkKtadRrBL5PMpUiYSdIZtLoIEQaknw+R63WzsZNG6kOj5LJpGhr66FWKeEiGdo6xM+v+hVCC774hU/9q471P20j41sp18cJEPzl/tuo1IpMnjyTA486hgdvu4WBzhRDo4LTTz+TpcvXkk6spFyeYMGsuSxdtZIrfvwDfvPrSZjS4NSzTqOzPcG3v3EFFa9GdbNLbs16ioVxFj/7FA88MItao0Z5fDNves+HKQ0WyHSbPPPQQ5x8+qEsW7Wau+98iIMXTKVgtlPcsoL7H0/x5rccx/Nr1pDMCa77/bWc+brzdvZuA2C3+XsyOjwSt2NJDANeXAMiCKhrm19fdw2+76NUwMDANAYmT6Ved1nz4kqKD93Pn278VSwTL0BLhCGQ0qC7vRNjwTxeXLuW2bOn09GRRStNd28vQRASTP4MX/3Eu1DVEfKJQXqzUWd/HZsFp7ydDU8+wTObNyClBUJgGgajqx/myDf8aLvbs0NHW7r4byxZ8gxKCq6/5icknSSmY7DPXgt5/J7bOPf1p5NKJsnk29n34g8yPriRt557LtKyaZTLCENiGoIgVC1xStOyME0TrUNUoFBaESgJocaWEi/wkKHC9wKEEJiWQzaXYdasmYSBIgg9fD/A7+wi9H3qnosRKkIUXd39/7KD/c/aeacfS8r0sFNJbMcBFAdt7Wb6kW9g74WzWe/38d7Ocb72nSuoF6v4M2bT1zePC95xEpZzBnVX49LG/EOO4TPvfyfdbR18/ic/IyiMsWHTZpY8uRTZ0cv0nnak0FQ9zbTDPsjGxx/ksccf5X3veydV1+DDl34V20jy+nNPQ6RMHn7oEbr7Opjek+PRJeOcecyZfP07v2ThjIU7e5e1TPkNdlu4J8ueexIdgpYShKRUD8FM4AeFuP1KsGH9RjZsXB+pWalYmwaBkOC5LmDS3T+LwtgaFj9wN2vWbuHnX3gzqzcPobREaQ9d9zANcEsTHHPeRfzx2qtQqoKjJCkp8aXJ7kccwx5HHcu7Ejann/cfFAolTMuAoMq9P/oIHzrhxlfdnh2z9zt6cFJJhDBJ2zbSEOhQ0fDrEHiUq3UMw8BKbibhJKnVXaRhUKlWSFhOhMoQ9flELS6CIPTp6Ohkt7nzsZwE9XoJU2pUGFCuVGlry1GtNsAwCJRCqBBLSEQYknBMvMDCNk20isSDkr5HdtV5+O3H0dfT8y872P+s/fIP91OvlPEDaEtJTMchk0qSuP/DbB6eoFytsHLhXuw2bRbF0gRJs49jjpjBsrUN2ttt8m1tdHXmGFz2OBde+DaWPv0CT/78h3hxw60fSqZP6kUIi3VrJtgyuJolDz1AJpsll0pz8+9vAmWQzXYwMH0SPVMm8cWv/Iprb/wVDzx4Dw/c+xS/ufa76ECTyrZz7JmH7uxd1rJQCIRWSGlSrVQxDYNsNouTaYcQEukeKoXNkby3bDYZx2prOiK1B74f9fUZJsXR9Qhh8MLaQdxKmWUb1vPGE47gjufXMWXaNBSR/ujXf/I7crk8zrxDOOCQQ5g9fCV3/22Yghewecsg+bYOTF+DDrAsG79RRRoW0w9943a3Z4eOJusV+rvbKZbqGJZFpV4FYZBKJXDSKXyhMKWF77mEhosUKh7xZOEFPolEAqE1lmViGAKv0SDE4OBDDqOzsxfQ+GHA4kVPkErZ6FBTKBQo1xuRBro0EFRwLJN8OkO1ViHUkEwkGC8XSCWTZFNJJqZ8mzBMkss+/C861P+8HbrPfG69536Ettk8XgMVpcoKn7SZpD2TorM9w9SpPew2Z38OOWZfrvjv6xjdPMZ73nE+3/rWb0FKbCsZ9QEagv/62JmMDA2R6uwnCA16Bvr4yfeuY9HgUiwBHb1dDPT1Mjw+RiNw+eLnPkOjWuLZZx7ipEP34dh9d8MPPObNncVZZ7+Rt51zHgnHwBKKq35xE5+49MM7e7cBkEplKZai41urVBAyuqhKfMx0J9XhtQgZZUVCy1hNLZKREyhUqBHCwrAcMu2TqU5sJN89k1ptE14Y8tUf3cnolgZJJ8AsTSWTyvLFax8kmXJYuWIZtZrHipUr+cxped59rsMee+5NrXQvT65IYu5+Om7DxUARSBMpTILS2Ha35x8AQ7aw17QZ9M+Yyk1/vhPbdkjaCUxDUSmW8AMfMykplCoIwyRlmQjbAs/HsWwIYwZ73cVwbJS0sKWkrb0D27bwPJfAa3D2ueezZcNG+vr6cN0G6bb2WK3WRIc1hjYM0jVlcoxK2hC6eIHCTlgEnkdhbCO20eCghXP+ZQf7n7WK67H33HkEWhP4AV7gEgYhCdPESjik01mU1mzYOMr6dYP86fbHUIFH0knwta9eh9CCQrFAMu2QSiYo1QP+84vXUC5VCLTH7BnTsTIJZKB4/XH7c9xJB9HbkeHWOxbji5lk8l08/vijXPCOs1j24jqu/e0tHHXwXvR2dbJwj73x3DK/uO4qLnrb+9i8aS3pfGVn77KW1cpl8rl2wjCIpL9NC3TEQqoVN5PI9RGMromEo0RTSS0SOQ3CENMwUYCdzKDcKolMF+NbXiCVlAgEw8UaDz7yGI1CBWElGHVhMLQ57rgTOPb4k/nbAw8wMTLMT+9TvGefEtX6Y5h2O6usUzi+dgv/8aEL+Mq3r8SQIUraFDc+td3t2SG87xgGVirJ9X+4DddzOfXEYwBFreFTcT1KtQZbRguEQuJ6LhPVGhOFEhgSwzSwHQtDShLJJLZhITU0XBfDtAhCRRBEwqkIg299/ztsGR7m0k9/GikkW7dsYrw4wRXf+yE/+PnP2TQ2gjAMtPJRwmLL2BA6BKEkt911D7/705+5+Z57/gWH+V9jFg5OIoVpOVi2TTqRoquzg87OXhLJFLYRUddqDRdfCWzTJJm0cT2fsbFxNg8NoXVAuVJmeGSMarXEurWr2TK0hXK1SqA1fsOlVK8xOjHBTTfdx8+uu5e6MtAywTmnHEp/bw83/eUhLMvCNJI8tvhppIhSLMtOoYMKV/zwq+y+20JMuf1BDa+lbVi/jmo5YgghdHzR1bz9g98GTGqFTSSyXQgp45aWSO7i9t98D8OQhKEm2zEVvzJBEAZoHSJNiWM75FMJerIpnthYo3v2HI458RAqCqZPm8a+BxzEDdddz+wZs6hXKzTqNQY31nng/q3cd/9mHrn3flbXD2avuf0kk0m0cCCMx5dtx3YY0YrVOvc+8gQicKm7Abc98DDF8QIfu/g9/P62u9iybgNSCNwgOkiWNMjncxQKBUpUSCQT2LZFtVpDACGa+bvtHqkKWwYgo7pa6HH51y5HSoMrfvRjAqHpmzSA5/t84CMfQaAxtCAMI1k7wzSY1j8VVIgf+oTaRFmQ2IVEQDOpFPVag0zOQOgoRbax0EDGTuHVfWzLBGmQSUmqNU2oAQPcmiKVcLClTYBHEIaUyxX8MJLyq1SqlApl2tozSCDwAqTjYNsO9ZqL1i7fvfJPEXsmNHnnOcdy420P0dWeRIU+YRAgCdm4apQbb7kLVxnMmbv7Tt5jL5lAI02HeqOGadixQK9gfGglSBPLTuBWxkEHLdmLn37jM9TrNebNmcELK9ZRHn+RbPsU0u2TadQKZLqms/mFhxnozoHpkLUcFq/ayooXB0k6JsueX841v/gx8xcu4InHHyHwXd45r0LZlyQzOfZ773cwn17OuGszpydL6LvRlCPDiuq+27EdRjQvCKnUXAquT2BaHH3YYew+Zw5f+/aP2LJ+I/1dXXz8Yx9ij332xnEc/DBgolhECwM/CKjW6oyPFajU6lTcOoHr8/SzT6I1eL6PNCSu2wApufHaa3lh2fNc+eOf8KmPfAQFmNKgVqqw6IlFkWoxAsMwufZXvyTwfIIwRAchtmVgGTZtHduvZ7yW5jg2mVyapJMklc6TdGwSmSTCNJDCJN/VR7YjTyqbxPVNTMcg6aRIplNMmjqJdD5PKp8hm28jkXDobOtit/nzsBMJTMOgs6OdZDZDJpWiEXhMjJYoFQooFZDJ5xCGpFZ1yeaSBGGDNcvXcNDC6dz3wDPccMvDJJJt3P/UM3R15egdGCCV3nXoa/sdeiSBWyUIIqVsz/eYNqUfKW2EtPBqJcLQw051QExlb+vIU3E1bzn9aISUBIFGWgmk4ZBMdzO2/klSmSwZx6a3uwtpWvQ4EkNp+rJpTn/9Gfzom1+kPWmSH3mB1/eMsXEsoFALGK8FjFcVPb1ddGUNlj+/FNOIZq2BReD9k1IGb3r9mdzw298TVAOECrjxt39AIzAtk2KlQrlW56uXfwu04H1vfSsdk7v54c9/QXFkIkYZA4Q0CH0PQkFDNaLxOULgN+qYpkUYhAjD5IFHH+Pst76dP99yC7lEEuLu6i/81+cpF0vs94N9CP0A4cOxJ52I5zXQOqRaKDJeqmEYBvZE8V9zpP8FJi0LaZlRl7jQZNI5QgFtqQxK+ziORRAYJBIGNT1OvR4gEyY5M4OVcHBNj7GxMTLZFF3dfdQbHoZlctSUyYQoSqMFRjdPkMtlSGQyaNeno7uHMKijAx8LQaXmcsFx+yKEoOG5XHvVHYw1fHwd8ps/3Y0KBdKxyRgapXadoVeB70crLmHEcm+aVDLJAXvP4oFHnkArkIaFDl6SeTOkJGmGdE+ZQTJhEgQ+xZENKD8gCOqYhkWoAlLpDCjNlKkDdKoaI6Mj2LbNBW84klp5lNOPP5hvP3Qno3WXbAqaKjRJVUQ7FqlkkkmTZyBNC3wPrVSEG2zHduhoN9z0B0aLBRzHxDQdkimD0A8IwzCqhSHw/QCJ4MfXXI1jWyglkEJz8OGHcuThh/L9n11JYXgU23Lo6Oxg2tzduOyr/8Wpx52I8kNuvPW3ZHNdzJ0/lyt/9gP6+7oYK3tcd81P0Wh2mzmTUAX84cZfk3SiiSd1z6fWqKOUJp1OkU0nQUqc1K4jzhNKE8dJsPDAA1j+9JNYEiwvQJsKqW2QEm1oGo0agWcgBPg1F5kx8X1NgMnshXOolSrU/BA7Y9PWngENq599nraOdmbOnE25XMY0HdId7dx/z72ccPIJFItjaGEjkPi1CYY3b0T4LvsdMp/f3vY3/EbAouEhcpks1UqJQIVMmzljZ++ylm1YvZRaXZPNZRAIqvUGX/nEO/jDXQ/wt4cXkch0gQ5o1AoA/Pp7l4IOyWRsBIrfXfUtTn7TRXT2zqFWHSXTNoBbG0eKgMD3SGazuJU6IqVBRbPYyuPDdPZNw8pIGm0DvJhIMtt9AY1i+qFnIw2NKhd5/sU67VaEsAsCPM/Fd7cvpb5DR2t4HlooajUXwwhQ1WghPWlgGtNmz6I9346pYNPataxbt57h8WEMy6Red3ngwUd5/NHHkELS3ZbDUxrD95g5uZcfP/8sS5YsiQYHmAaHHTqAYdoING4QYicy1FyXIAixLBtp2IwVykCJ0PdIJFOtMW2NahUZTx0N/V1HnActSKQTbFmzkpTjYAgTbTUQGJEYqhlNSTXasoyPFKjXGxhJk0Q6iZFMkE+m0RKSSYtcLgPSRusANwiYNm8eXf2TGR8ZItWeozZaIrAtdps7hzWrV5NJt2GaAsMy+NF193P2sbuzev0Qs+fmcX/nEgiNKQxKpQoNt45ERCn8LmLjYxVOPud8fvD1z/P/Y+89ozS7qjP/3wk3vbly6JyVU0sgkBAIgck2JmPjBDiDIx6Px3hmjI1tBocBR8CAbWwyBpMzCCGUc6vVOVd15ao333DC/8PbyMOscfd/GQb1eOlZqz50VfVb5+57993n7PA8Q8NDbNwwjFCW9/3L/dTHtpB1V8j7/6q5/ZUvf4Mbb7wOXxkmSmI+/8WbGRrfQbs5Q3V4E2l3jbS7TFKqonSA8IK4VkW7FoGWSCFoFyFha5X7v/YlkIry3L2c3LCby7Zvo5EdZu/XjrHj2meT5228l+S5wxYGrSP0OeYgz+loSmmkkASxAu8plyrkRcb87Am6a8t0exlKWsIoxhSGWrWEcZZ6ZQhjDdY60sJQnFGk6WvNh97/QYQI8D4faE3JQbSMygm7n3AtgQz40le+ipIBMlRY9221mYEYoQ9CjPN443FSkmYZuB5SSarV8ycZonSIcz0KJ1FhQkmDkzFZkQOCUAU4MrRQjIyNEiUJMghoLi/jhSTNDZV6CVNEOCmItKTb99x9x11ce911rC0uILzFOcXi0hp9kxOXypiswDhBnuZoE1BTMaVYUYsk3czR7mX0ix6TQ0PIWFFSdVbWVmmutR9rkz0KrS3v+JM3IQQ0W02edNUu7r/7AbzL6a7M4LzFMzizewQ3PPV6iqLg1MIKtVhyen4O5wpqo1sxWY8gKiHUJHn7JOH4NCCQUqDP0BRYa8mtBdNHJSUSUbClromyhwkP7ecWtY7LL9tBUWRICdZYfv/XXsxv/I+PYM9sH896Pee84jPySEUxqFWk6QpCSHSoaXe7FMaiFaRZByUl1g+aN9N+hlcwsW4dG7ZsZaQ6RN5sc/LUKU4uzrLzgi1s2DiNLxwT9QbdvIcQgj233UypkrBhJMK7QaZu16Z17D1+mlocIoRgtdWlksRYW+CVYN3wNCeXmtSTiKGhoe/Jjf5eoNoYot3KKCcxQkI/zQgU+DxneKRBu5vivBrMmVmFCHJ6y8uEpRgdBmirCcOEsF5ieXWVXq9HqVzmSdc9mTAMaGUZUVLHpR027tyCsR4lJZ6AKFL0eg6JpN1u86fv+gLVSszySpckiej1+6hSiTAMKSclVCD4oadd91ib7FFccNEVHD56fNCCJxWvesGT6PQM7ZVTeOdQYYTJB7JdF23byMT4KCppQNqkl1luu3sPyoWknWWkjnEmxztDGJTJs5Sh8UkcksWlVZxQ7D+1wLrhiLzfJ4lCLr/8SsRDx8GDlp5nXGA41cuIaqMUywtkRcHY6BTVSom1tewMme+/jXM62gXbponsKK3CEAUBzXYP/GC71+mlSCmJ4oAsNQxVSsRK08679PsFSgVkeZ/OqcNkSmG9wmRtpofKDCdbqVcq9IucvF9Qi0ukpqCPRZqcqYkJjs8vs23dKMdmFiDP6XjP5qkhRuoJPjccm0/ptts0m22U0piigPj82TpWShDoYdqrK/jCUx2qEZQqJHi6aY61cPL4CcaG60xv30av1aQ6Oo4OQoS0RFEy4KkIA8KuIkkapOkqtfowC6fniCsNhDfUakP0MoN3jmMPP0RQKlOpTVOrKAySvCgYHhnm1Eyfj3386+yYGmVkaBgCSblaolopMzY9xUNrLW56rI12Bq1OwdDwGIvzp7npyZchlaZSb1Ab3kDaa5H1VoABYdUbX/cyGvU6x07N0BgexvdbvOWNr+cNf/BeOulAX10qjfMGazNOza7R67ZwXhLbnL41aBXwrfsO8dynPZnO/En8xDqWHwYlHYlUhHSZ7wdsWZ0niiO8cCgl+LM3/iS/+paPUx7bfNbrOWd6v8j6HJldpN/t0ex0KKmBBFOtFDHVqJAay8LsAj7v0Gz1WOm16TQ7eOdJjUV5R9rrM7fcpNlu0u72MbklSwtSC0srncG8WadLp5+jgxgvE47MLRHqkMMzS6x12gyNNAhDzYn5FU4vdtlzfJZOP0UmAaUwJogirrx4Oy47f4quCElhPbWRMepT48TVGkIIolqNpFqlVCuzeecFTG3dRNYzhKXGmaSTJ5ABSgqy3DBzep7Z46eYPX6UoeFh9t+3hwMP7yNrdzEixFqPs56h0QZBJcb6giSRDA+PUY5DaqUSYUmjpefUQotnPv0q1rpt+lmGUAoRxgyPDlNNyo+1xR6F8Dnbd12GVoqf/pHnc2phjQP79pKnbbLuyncou6pSjU6ao1VAr9ViqdllaLjOlRdtRAUJUkiszXDeYV1x5mw/2DqiJZEOcTj+6cOfJHzkfQz3F1leXCDxOVVhaGhH1uwRnribu+9+gPvuuZ9GtU5rbZlOL+PVP7CT3sqJs17POSNaq5cjhCQKE9baTXRNIoOQ3Fge3neUylCFqBRhXIDwhl7fEwYxa/0egdD0rCHQmm6aYyJJmhYEUUi5UqG5tjaIjnFEohS9To9Ya5ppj3p5kD2y1mO9xBYFy2ttSoGi6zK0Elgv2bVpPQdPzDA2UuW+PfspVxrf5S3+3kFpSa1WpdNuU0pKCKmJtGZ5cZbh0Qm869IYLiO8xYqcUA9IYbXWOGdxDsqViDAcY8f2zTjvybOUK657As3mMjKuovGsLXcIopggVlz11BtQQpH3M0zfUB4qY7MC4T0Tk+vopQWlagRnIl3hPEkU0+vlBPr8Se+naYdTs02klOzb8wCbLrqGn/urd9HvLPNtolPwaKUZG6rSzzOmptdx+sRxQmUZGW6wtLqMMX3CsIrNUoQHfUYFGgHGeRQC4y0WyR+/9kJ6yw9x2YXTzC7v5Ok/dgEjIwF67Dl4H3PRgUcQdom784sIy1VG4xI27/H2PfCvWmP/Z5zN0RRAuVylEoZ0TcHUxCTtXsqmjVOEgabwgxpRlASYNMN6x4axERaaHdAhY6MNFhdWqQ3X2bphnP1HTtLvdCmHAUGUMDk+SS9PKdI+gQ6IlKLV63Lxjq2sNjtMjdWYWWxi85zCwWUX7SBLC3JjKAWSrDD00pyxoTrkjjAMiJLwO9b/GEEBREJAGJGUK2ipMMagFEzGGxBSkZRjvB90jQ/HpYFclRiMesycWKTXbrJh61aGR4awvkABOpBEUYQSkuLM+MimzXW8lXR7XSqNOi5PwUFQigisxEpLqVpCyYC0yPngF+9nev0UxjmkVywuLjM02qBSGvqO9T9GUABFkaNJmZ6e5Ak3/gBvfOvf06hHxGISzqhuCuH52z/+z7Rzgc8L/ssf/CUrqx2ecNUl1Iam+NEXvYD/+Z5/QYQJ3umByGAuUN6ilaKRJEhXDNSLvCAvXcQHvnSQi061eSQ8ydOfPk3H5Xzx07dw+c4yR2dTjszkjD15Gydnl9i+fRsUq4xtv5itl136Hev/3yG8/z974j333HM9cMv/DUt+n/CU3bt3f/Ox+MOP2+7fj/+otjtbRLsLeApwGrD/t1b1fwEKmGKw/scKj9vu34//kLb7NyPa43gcj+N7h/OHbP1xPI7/wHjc0R7H4/g+4HFHexyP4/uAxx3tcTyO7wMed7TH8Ti+D3jc0R7H4/g+4N+so91zzz0RcA3/D9czdu/enZ3rl/9v4HHb/fvxH9V2ZytYX8P/4xV64DHpbuBx2303+A9pu7M52mmAz3z9m/T6PYTzODzCS5wvzrR1SrQQmGLQtAqA9KytrFGplgczOt6dabQ+83Pv+XaRXODx1oEUYAfqls47nBt8GTOgTOj3+jjnGBouE+oIyFFBBakESgxYaQMlQUC5EnLTjS9+dP2PEU4D/MsHPsI1z34Kdm6W93/hbxgbeSEUkJr72ffwYZ503RN46KF9tDstojih1++hg5D1m8d52g2v5gMf+mO6zRZhWZC1QASCeqVGmnXZ/MQSK0cMl05u4N7ZRazrMj65jXoYsG/uMEMqYWGxiS0yNo9P40djlpaOsyEYIQxqXHLxlXz5vi/x5B+Muf19BSJUXHTjel765F85L2z312/9MzrtNv3VNRwD/T3vHUmtfGbI0vOMH3gOt916C9Zn6LBKIiQ+EEinEMpj0x5GKEIfIHQAwiGtp5U3kVaADMlMD4o+Xkic0wSxpsh7/NzP/wTOQ6QlXsCf/sFf4YXC2RyHBRGinELFnrBUpzE6xGt/+df+TdudzdEsQJpbuv0cwRktX2uRZ5q8/bfboJ3BOYt04KUYsBQXBd5anB80gHrvBk2dDCZaJYNmWn/GsaQUWGsfdUJjDM450jQlz3OKPKPWqNBsLmGKnEpdILAEoUbiMVojJShdfMf6HyNYgBMzMzyxv8rR/iozR5r87M/8DG/9g1cwOXURC/O3cus936AcbOCGp/8ie+7/MGtrS+R9w0RjF3c98Ce02qfIU8PYBTHH7rYoa+n3mvgRQ+lmTbY5Yq6dUCtn7Du6zOVbrsFGOa//sTfy+295PfmKpzJRodPpsvfgA4yOJqyFikRkFM4xGpc4+OWM6aTE6dYKZT/+Het/jGAB2p0uK3NzmMxSFAUeQxhF9LOUqBIjnWbzhim+0OphTY9SmGNCSxTXMKlDSE3hMoT3NPMcspQ3v+sdvO5lP0Z9tEzeE3iZg5BYNxDRcC6HdkBhclyRokKFVxrf9/zKf3oNb/zVNyMCMA4EEikkQkiqY4IgFN+x/v8d506GCFBBSKAUSisCrRBaI6RACTXgPHceZwxeeby1nBGfwUuBlANmWIFCKT1wsjPaaEJKhBCDyPZvtIJ56/DesmnTNHnaw1hHtT6CwBOEIYGSBFKipTgjQ3v+5HeMz/jMl77InvvvYsPGETZMCcJ6yubt65ncXiFd6yB9nxueOIYPUoIkJ6mFfOnLn+JbtxxFlTyT2xJm7hFIU6LoCEYbm1CLIf2e4ZL6jbSyKrOtNd78W29n64VXcOUNVyF0E7NcUB5SqCKnMzbL2EiVyrimk7Sojg8zN3sMOal51nOv4NDSAt7B4X1HH2uT/S+wOGvIiwGBahzXiYISpaiByA1hoKkPBfTTLkjInceHFXLTB2MxwvK0pz+VSmOE2nCJ2tatKFVh4oq/oTp2NY2pBiNT0wxNjzI0PUJ9rEJ9eJTa6Aj1kWGCJEGrAC0DVCgQ3vLWv3gT0muSch2vHB6NDMD7PsadfeLs3EKEYYBUAhGEKKUgCEEIhBQDCSYtUTpESoV0EnFGVECgUEKekdxR/8qdrvWj4yDAIBJKifz27wmBOOO81ho8llBrnAOlPdVqHaUkYSTR0qO0RJ5RrNESxHnkaGEkSHuLNJdaVDdIKrWEoU1w7/3fpHXCEZfrZLbF7//Bf2f+5Clay4KIKrWKQKUh6ZqiOV/mgsueQm20xJYLtlCuO4IoYfOVN3Dbtz7HS196BXEasWvbdi6/8Ar++VNv54v/cjej0+MEYQ1VEghpyVzB8lybXjtjYXaZh/cdYOWOWf707Z+lnxqmpzfRFb3H2mSPQusI7zReSsJSGSEH3Poi1gSVOl4BOqLIMnRYIc9WgYAgGiUYLTM5PcrOiy/GKYgrJWphhVAKQi2Q4St5zetfT9+kBKqCoILUNcIgJgxjSo1pvApAVZBK4aTG5y1M0aXotwmEYXxiHXE5BDS2L1Hq7LN853Y0qc+ob8oBc5OSA+EJpfBioMzpvSGIY1AKqQRSejwD4ku8x8MZzWuFEoOto0CgJEgh8YAzBdbkCBwei9YCrSSlOGZ8YhSJIYoSpBp8P1SSUClC71DSoxRoNaBYOF+QhJadW5/EC579PKIwI4ju589/5wvoZDvlUgnpIkyqSXuGWnUr6zZUeeL1T6I2tpHhnZqtG6/g8l1Xs3XDOp797FfyLx/6Aq//hf/Kjt07WNaHecLVG0lKO7jwmqfyJ+/6Ez76mfdQ7u1kdvUennDds5AVSNe6dPd4qs2CWId0+55qrcGrXvXTDF9xEbJWMLquhJCG8fjsbLvfTxSpQeiAkclxpBfoKEGXyjifo5RkdHSKJKkNpL6ExYkY6QTeWopU8fKXv5Q/f+vvIoA4msSJApTm139jjE3bYGxsjP/+u2+i2ZxBa0OgBE4bHAVDo5JyXAZhEEojlAEvUVLz2t/8ZXrtDqEOqA+NkZTL6NKZIHQWnJt7P4iJogpBEKLlYPxbBYMIJqTES0UQJQMKNSWRKsTLQRjVWg+2idZgsz7e+8G5zA4OtsYMONHx9gx3+eDnMIh0SggqtRKmyAiiElqFhFoTBmecTXqkGjCLa3mGsesxnVn8TqSpY2RylJmZUzRPh3zu43sRzvOR9/wxhU/4xV/6W37jv7yDMAyJVIlq+Vp+/BW/wLOe+0omas/gD970J7zi536QxpYqt999G694zcv4y795K1fueCrPuOLnmZ0peNvf/wHPff5V/PBzfpQTy4coxyWG6hexVlhEJrjiwsvo5Zqh0VFcUWF7ZTPbL9zGRCWgkG1e8BPPp3ASS0gen5ur6fuFMIkp1ct49GAyPU4IE9BaoYKIZz772Uxt2ATSU4gAJT29og2ho1qVICHtadAKqwqEtARhwNhIiVf+SBmlFSIICFB0m00cOVIHOOcZqgxjpCcuV4mimDCoUZ7YjIqqXHX55QQqwWQGLwxxNUZLx2Bs+9/GuSNaoNFaEUYxUVyiVCqTxDFRXCYKQ4JAIJRGaY0KIqRSg20kg+2jVgEqCM9QJ4OQCqk1SIlSeqASItSZbaNEyQAhBEqpgZM6h1SDn2slCAOFlpJQCZQWBIFGS4XWGiUl8hwh/PuJXMBDD9yKiGr0+z2uvnGcd3zid1nqfpULt27k5S+6jGrJcsMNLyLN1/jA+/4CE3RxWcGb3/Sj/M0H/jOT9U185p8/TZp1+KFfLvHB932QYzPHWFg9TIplcmQLX7v5Hzgxu4eX3vRyvBU869kv5ND+W+k126y0UkpVh5XTNHYvMjw2ytrMKYaHJvBDD/CFz3+IifVjdDsrlCbWHmuT/SukRYoQkRlkJBDBgIk4KkXgBU9/5g2UayWipEEUBXhbYApD4Tw/+VOvJtQhQSzBZWiVokrD5EWf4yeO0m2lWDyokFQapMqxtiBwEhlKtuy8AC1DnPMIXSKIaogwJIhicpMTNiI63TUQhjhOqJTrBNF3yb0vhEAqjQokSmukDAgCTawHEStUAVEQorRCK4VWAweS0cDpnPMDp1GDt6XgDEej94N/PZrqH/DReyzfLgUIIShyRxyXUHJwDguEIJCg1IBaTZ8530kEUkj0OUL49xO6sJxoC5ZTx/p1Ezz80AmuvvACVlc0q53TzM112X35hfze7/0G5ahGFGliHXDxxQm33XyI3/qFt9NpKpxJWOksYmcu4OF99/GGn/9lRifq1GtDbB7fwbe+Psvfveud7NvXYcfFV3Ly6DzLCwts2NrgyPFjuF7IwvQdqJUaU9Ob2XXJVRR5k8WHNNXqGO3yMaQIOPbQ+fOS0jpE4vCklOpVwjBEyRLKB0hVUK4mWAtZbqDQSBVgs1W084Oze1JCyxpBOIETnnXTI+hQkJTKyDjAGom1llgGCBdhjUVGEc5qtu3YPlCv+fbRCLBG4YUmDEI8Cqk9wgU4JyHQhMF3uXXsLJ/CmBycQqoAKTxSOqK4RKQlWmkCJQfyTgKUhECKwZfWAzZeGDipDgbJkUeTIR4hBk4ixLfLBYOMpUQODr9CkISOQAkCLRDaDc5pQhLoQdQLpETqwWcgzp9kyI7NF/GGn3kd6xs5q4uK9UOXsGP6ubzvHz9M2unwqY+/jztu30+gA6anxgHPob0nOLwv59ILr+Xlr3o53i9j8ox1I2Pc8flTfONDH+bhT36Ihx64j7VTjo/83edoLeeUpgtWinv49Kc/zOLs3bz4h1/NcDLNyOgkW6/YhTsRMrS0i4ceeIgrrryGhx66j3UTW1jaV3D8QYcSlmwmf6xN9iik0HhlUUkDKQzeZlivMYVDMtBBD2ONRuA1OAFCBbzu9a9HKcU/vuev8UHGyJAgCkf50R/7KZQMKJeqCAdSOhpDwzgcIpYkcRkZWGLtmZ6aRCg9oPtDoHRAnJQBh5AOKSBKIrwDbIaX4Px3mXV0WZ/W7H7y9jwejwhiRFDCC08YRYThgNcxjEKCKBokPM5kAYUCKSVaS5TwSDz6jPN8u67GmeK0deZfv/e/FLilFASBHNA244mUJFCDyKXlwOBKgxaDvyvOn5cyQbXE2om9XPOk57Nz+wV85J/fQW2owqVPfQqtLpQnFJdfPYX3hpMLbQ4fOkS9McmG9et4y1t/l50Xj/Led3+E5fZx5k8cZv/B+1GnF+gc38sFTct60eQ/P+96Gg1Na6HNg7fdzc//zO+wsLiPl73gRxgdGeaFP/FKfvh5P8mI3kTqekxP1Th1+CBz/RbZ6jDjkxvYfMUOnA0YGxl5rE32KKRU6EATxwpXBBCV0drhFTjhyYuCXruPE30gJ1Ih1SREq8HO5ltf/grCO352+xN4w2xI83Nfx9uBMlKpUqY+NDwgAHIFhUnJ+wqtwkFxPBBIH1BY/+2NF1oo4rgEziOkIjcWVIjTA23ywJ+d5vCcjrbuwicS6Iiss8rqiYcxaQuwWDTWS/j2mUpLAiWIQk0YqjPp9oFzCHdmwdYxf/oYRZ5iihScxbkCGBSsJQy2gI+m+yGMy8SBIFCOQIEUgigI0FKeiZ4Krc6UDKQAcf5sHRfnTnI6Pc3Rowc5dfowL/rFq1loHmZHYytCOW687tncdscXWFxYpddp8/Vv3smp0yfAK7yXPLL3Qe689fO84PqXMN4Y54anvoDCNOm2WuzrdXjy5RfT6a5QqzWgKFMZiZmabLA8v8pv//7P89wX/RzaB/zze/4UERpqcYlSpcbFl1/JzIJn66VX4tKEchCS5x0qw+ePbJOTKdKFSGEHsszODOqzXiBEwMyxUyzPz+G9oegVeGn59d9606ARQoUMj24lQCD3zhMtdln/T/dTee3/YMvr/pLhv/gMyjqU1iBjymGDMJboALwZ1GOF9INEYBgR6hAZKFAhOtBEkSAMYpQWCFcQyAgXJGe9nnM62vLcDOsuupoNF11HuT5Ob+UUK8f3U6RNnMvxZ9qmstyQ54bCGIrC4L3HOztwNiForS2RlENGJ7eR9roINF58O80v8dYNHFYKlAAlBHFSGVBCK0WgJKEenGEkg0g3UIEELxUIOZDR4fwJaZfuvpoNG57IFVdcz4bxjRzes0SgYj71zQ8zMS554JHb+cIHPk4/zYB5Pv+Zv+WD7/9T/v7v3kFYSvilX/wpWplnct0W/uRvfo88bdItHE940hVsMD0yv8CPvPld7Fh3BWTwqle8gS/d90cMjY5w9fbr2bj5IA/efTPLzpCpgqGhOi992auYPTlLpRHzgme9hK3TGzm59zAuM0xVzh+WZ2xIYQc7HOcE1lsQ+UCgwkqyoo/JUuIopt1aJonLaDxCwanZWcKSJBUhnyxm8aIHQQvR0ygfEezdR+01b6H+ijcShQlKQSAUPo9xusB5iUVjnAEpESrEWYFiwLvpZIhwOc5YgjNBJgq/y2TIpp27WZ6bp9PuEJRiJndeyfDkenrLcwSuS+I7lMjIu0e551tfYe7kcdqrK/S7XWxRDPjxvUcKxdzMDAcfvgshBWm3OaC7xiO8+1+i36D9pVSpoJRix5ZpAu2ItCaQg2glvi0iL8/EdQ8gBqy78vyJaA/ffwsrK0d58ME7aGxICDfAgRN3c9HkVSgh+cgn34u5qM1vvfm19DopO3Y8nfGJS/jZ1/wae/bczh+85e+591t3se/A3Xzs1/+WbSc7bNRb+eZXjrO9toNadwPGWH7nt94M5ZzPfupD/MAlP0UiBYdmHuDPfvcL+K7nM//8VV7/ircSqYgT999DEipGKmNcfeF2/vj33041nObapz6d9nmkLYcocKZDmmmkckifYJwa7HhUiLcW6x1gwCpe9+u/hvUW6wT/5adfjVYhU+MNTvkmLpVIX0NGGT7MEZnkjnHP3LvegHQO49r03RqWAufLBEFAGEq8M1gkKpSD40sYIlRIIEEGJXpLCyBCijxF8F1uHV90wwX81199Dc+76Vl0O4LWwhzTYyNMDI9Sq0SUE00QaQIVMtpQLMyeJO116bSadDptOp0OedonCBVpbrjggotIojOpUwb9k945bJFj0h5ZmtHrdtBaIAWcml0g0NGgeViAUAPRAwQIofDfrucJaHV7oM6fWtDFl13J7OwpwlCzcrggPD7FzR+7md3XbkNIyfQWxaYtT2Tj9ip/8nt/wrOvfyKvev4zueVrHyCKy1xz6TP4y/d8lt0XXcfyyhIGwVOurdBut1CB5vTyImmek8Qxw6UJyqOCD3363ZhqwqFH9uPUEENTm3nTH/8Rn/vUhxmdajA6sY6l3hoPnrid17/5VUDO6NY639jzcaLpx9pi/wohFcYboIuTDh0YsAXeO6wtBs8NUKs0ULEGk2EALQRhGGMtjI9tQpgSQSCwpo/xCj80ipeSDb/zOoSAzKcEeog4UqTZKv3VefAOYyxxpUYUqQE9u1KIQAxyD4TIICQ1PTQgoxhnvstkCAwaKC/YVGFi4hK0jqiOjFI4h1AhVoRYL86ckSRDFY/J+zhbUBR9iiyl3+9gsoKh2kDzzFpHqZTgCoMwGd708TbFFB2KrItzhkQrYh1yx533EQchWkkCNaiheeEBiROS2fllbr3nGG952wdp9isoHX239/h7hkdO7GWkvp5777iL0u4eIxu28KTX5tx7/21MlhSt/ZIv/vPn2BZcw+zpWWyecdu3bsFnGc943rW86uUvoprM8fIfeyHjV17OsYkx3vqAo3X55eyfOcb01jGWZk8SJwm7L30y++4/zOjwNi67+AlcsWGcP3nLH3HT9c9ntHQx62LFK3/8l5i66CqyXHPjdc9naX6M3/3jN8PBNeRSian8/JG8wgcERiJsSKQS8twTSoW1fRB9lLAIY9m4eSNPuv5ajJdIArwMmVi/kTAUWFnGqQwnHDLRaGXxrsBbT5b2kUJhTIFNm6S9QYdT1hk4eahjTJqipEYq0IFGyxApQ8JEo3CEUoPQlIhQQeWsl3PO1//b3/M+KuO7WLd+K2GpRjeLKE9eRHbfQzTW74CsR6+9RpoOth1SSbLuHGGpgRKVf20axiGcB2/I8z5aa4RQOGEQ3iCFQ1MQqIEjxqHiEx+7mW5vFVcMxA+/nQFaWutz10PHeeDBB0mShDTNUBL6mUdH5885w696djzzUkpJneOtb3Bk3x7umVvhwCf+gWuvu55Dx+7hx1/yYvSpHjavcOCRB5lbWiLPW1zeuJyDRxaZOdHnrW99G0G5TL08/OgbXV97HUXzBO//wzcTvvBpfObTn6Y8EjE/m/HIbR9n/RVXsNZZ4qlP2c3HP/GPDKXL/O1fvIUrrroBAZw40mbHLkeeHeDSqy7gK188SmbPn92AlDlWeZQGZ/toYqw0aFlBhQXWggwEOy+8lB07NiC9Bef4xVe+ko0XbsUEHi/XCEsx0MWVHL6o0984hV6YoVyu4pyltbbKxEQdFWqyrMXqygmcdVjvCXSClALvBlLQwnukkpSSkMX2Kk4rnDegPIizz8meM6K99GUv4vJduzhx7DBpdgjnIfchhZHUGpPUxzcwPr2FxvDEoFNfSoIgGfQ2qoFgg9SKMAzRoSYsN9DlcYK4Oih4BwodBoRBQBQq4qhEfKY2NjwUMNpI8F5zfH6Jd3zwS/zpuz/JO//hk9z3wENY4zDeE5fKNJtN+tYR6PPnrRwnwzT7TR568DY2Vi4iO7WB0e5Ah/vwww9hQ8XRk3fwyMFTaJtxamEJYwS6MDz4yBx7Hn4YKcA4iwxiTNHBeui028ycnOFeG+KHSjgrKFUC3vG+X2NtbY0kbnD68Dz/8rGvcHJxHz/56h+nrUo8+bobcXmPcqXO3ocf4P3v/BgXTzyDT99yF97ltNYWH2uT/SuMpuiDlAYtPDJyqLCCc4qR4VGcM0jnmJioUNgCHWhEFFAYKDJLOSgjxRA260OSQKuAUoloZZFAxwg80glG6iN0+i26nSZYcDLDA8JbdKQQSoBwBCrAiUEL2NZdFxJFwwijyQtDP8+wxpz1cs75CquVYg65JdatS1ho76TXvYts9SRoQVrkaCRehvjgjEgD4KOEMKmeEWcTZ+bQHFoOitblkqTb7KOiAKxESIt0DhlI8l6X2YMHueCy6xiJBPsWC/7wrz44KK95R1QqkZuC4UadS6+4higpcfL4ISant2P9IBt5vmDvgXvZdvHlXHvdc/no+/6Jv373O4hCzX03Xcfb/vIPYf0c/e5G9jyyF2MNi82Uqy/ayt2HCjbXq/zMLd/k0C/8KPOtHsdmT3F6ucvTL97MF/ecIIwMKyurNGrT7D16kunhGnd8SXLy6Cm8XSQaHeWKpy+ycepCRusdfuW3/4j3/sZLqD7h2bQevJugvMBLXvxMPv/5T3Do2AEubsT0zPmTSEp7BUGiECicSPDWEmDJXI+h+jR4wcnZWYIoBuexTvEnv/2fUGFIZgoaukxAC+2qCNtFOIlc6iLFKoXMyYsChWJhboGpTRtwARQ9CXmEYDDPlnYH+n9xfEYgA7CmYPcVV3HPbbdQ5E1MlhD4Gq3W2lmv55yOZoVgdTli3eaLOHTv7URJmdrYeoKSJgkDnLXkxgGDFD1KkpQaGFOwMx40DRtnaHfbdKhy5OD9CB2CCJDSP9qKtXnXZQQagnKZC659Es12k+b8cWpByNjEerwYzLL1ej0mxkbQWpP2CkbGahjjEMqBUEh1/ojFr5uYpNVtcdcdt3LlNdto9zIePHQHnfYce+4/yQs3PZ33f+ZzbKlu4six40ipOHEsQaQ5amWeh1/9UnZsmWD1vgfYMFrnyq0T/M/3fJCrn/xUHt53gjCuMj83z3/7zF08+5nX8blPf4yXPuUibpk9wsJck/XVJ/PQ0bfhkyEeXniQff2YF01VUJuuZf8nPsbGXVv56IPf4vInXYw7NIOy58+YjLU9IhEShhrIkSJGhZZIhZQrJZSGzRs20OtnrK02MYXl0J4j7LpqN63uCkki6XvJtrrCdTqoUGO1w267Ei96dNo5lZJmeGoU6zTCJ1i/ilAWKRXOe4ZHh0BY8IMGiyIdlLBGJsZQMiCpNeg1u5TKKdZ/l+n93EmOz3RJ4gRMDxnGKB1SGhpGhzFhGBHFCTpKBucxIQmihKVTx9CBphSFlAKNKk9w6MBDyChEa4UXjjhOiKKQUqSoxQFBaRilFEGoGR8fZcuV11OuN5Ay4NSpUzz88MM8+NBD7HnoYbyQeClYNTEPPfgg1g+mvPHnTwuWLRwXXHo5k2PTzJe+yc17/gdri0forbX59f/0i1TGyjzzuqcw0hjG+QyXO5JyifmVeSYny5w6fZo9t95JI1BcuG2ad3708xyemefgocNI4cnSDtY6bnr6jbyx/SAhkoOrPS6/bBvKO9705r/hYx+5k3f82SfYf3ePMFZ86tNfpVFJ2DQ2ztaN2xieKmO7fdphcF69pPLM4oQfOIESg/k0LwiCkLHx6YG4oHeDYWI3aM2z1hAlmlKUoENNYAuuqm1B6giiEr7TRb1oN611Q7jcU5zp8lDCUWRdwiCiKBzOeBCSteUm3W4OYtCllJsM5wX1ehW8QIQeiaMwXcR3273vLRw73cEWDmcjgiBAIGmMjKNVjNIxQZigomQwda0kYVSiXBmmH4+Sx0P0VJU777yZIFboIAZp0dqRFf2BiHkSkIQB5cYo/VaL2SNzGBHwwc8dJDUOoWLQAdV6jeGRYcan11Ou1ykyA61Frn/aDyLVIM3PeVRHCxuT5N0m+w/cz/UbX0WpHFAa0fzpX/wV//13/5wH7z3GwfmjzM2dokgdXZOxZ98+Vttt7nh4H/WKIaiViKt1OmtLtJdW6HZa7H/kAbavGyXUAo8izTK+2GnwmvwkF+7axC+84jdojCq+ecsd3PaN/Wyc3MTnPnUr/bVVwlgQVCSlSsJff/JdrJ1e5NChU6x1uzQ4+znj+wnpBHmWoQnxXmOxoB1edli3fhJXOIoiJ1ARedHm7971HqojQ2TWUYuryDDGW8XoffuwJqfotSFwSA/FjnG8lWTdDGskxkOkNM6H+KzACXB5TlSKqFRCtJZYb0niEGPMoIleKSKtSVOLzwQiP7srndvRgKX2YJbMixSdRAMSnMYwyEGlXKmAMIiRSmOcwwuPDGLyZIiiNIqoTCGjAb+CUCHCS/AKKQRJGLC41CIsR2gdEY9Ooxp1dJDwshuqSBQrzTY6iEAECHGmo0QNRhrWul2GxsZwUhIEIeIcyovfT3hrmZ87TliKgAbZkd2Uit1cdvE2nvWMJ9Fba/OUS36A+ZU+i+1V+kWXlV6TakkxM7fG/Q8dRHrN3PIqe4+uUKpFPOmaJ7Dzggu5eMsOrrviElrtJjs3T/LgzmsIF0/T+PrtfOGWv2Ik3kSAZ9PGHXzt5vuoNDQz2TKugD233UffSibj9Tz9WZcwNKzI0xylzh9HE1JguhmI4kzHUICkjDOaRCt81idsp9isg7SWowcPsW7rVmLhUTokkSO0F9okgaUIDLYQuE0X4IRBhSFL/S793ONdD2MgNV3SNEVGAlcUCKmwhSHPGPQ66oAojOl3Mrw3IB1BWCdIHBk9vDo7zco5z2iphdnFPsY4ZBBSrlXx3lMpD/GlD72bNG1jspSPfuLLXHvBLiZ2bkAJT1RqMDK2mTxtkYsORw4usm6zoCI1Iu1z9+HTbK6U6EYhbSFRaESQkFSGmNk7z7WVGkJHjI5H9BclKjCILB8M7PlBkiVMYgqvWVlZHNAECYmQ58+UcKNRoRTX6K3MsWffPi7dtpt9p25nfHyaO2+/h1//3bfw0S+8nU3bxzh5ZJb+qiEINbVag4WVGe5wDisTKuUSYRzjspxub56pyY185Y5bqMUVNqyf4sCJ09Qb43zt8hex5eaP8Pd/aXjuj1zDbbfvZf36zdz78H6qpQoy6LM8P8vhk2We9dyXg1b86Tt/j6dccR0f/pevcni+w1Mfa6OdgXUFMizo5X0SVUKEHm9yQqm58kN34U4eQ2gPpTrHn30hru2QEwPH0GVotuZor61wOEtYW13kKiEpHjhN+UefQb7UxAw3yHDk3S4iEkS1hLiIEUaRO4s1hszkeO+IozLCO/CWei1BycEEgfdQdMGZNnGy7qzXc86Itrhc4JQmy3KedMVVxHENYw1xVMW5AmEM3jli7Xnlf3orUXWEPMsIdEjuNSJqIKMSwhbseWSObjellsRsChWn84CvzDgin2II8UrRzgV33n0bKihjLFTikCAp0xjdyNLyEkIGOARCarwD6T1BWCOII7zw+PMn6cjCwgIf/eB7ed6P/DTKxiwXBzh+ah8LCwtMDo+wthBw6YaX85Ln76bdzwd9onlKu7mCpWCt2+S2h/dzz56DLKyucODUcQ6cOs1tD9zH2so8hxdXaK61iKMa0lp65YCljVt4aneZm254CVo6VvOUsnQsrJxCx4IwqRPohAMnHuLYwYOUyhV2XHAdYrwPwflTGvFOkqUg+h7n7JmWq4yf8SNwdD93/adX8/mfexlteuz68/ezMnOY5ZMzyAJMq83SzCrdXo/w4EF25RYnahRFSld5stYyaZZj8ozm8hp5X5F1Cjw5VvZRQuNxlOs1KtXoUdrDIFSkaY5EgPRY14fA0et30cHZX/DnjGhBJFERFDbn2OwMw/URXOFQgebePXsZTxIaQ1We/pQn8sn3/DfiuExSrrLtsqdAEONshpMlCqt47W/+Vw49cjc7r7qWXc8wg+OUEKSdNku9DrZzkhMnT/G0m34AIyUf+qbnRdd76kNjzK0sUy7VcM5gTI5HYp0DL+n1VgnCAGcHn3e+YH7+GDdcdyWt2eMU7jipHeXAnkP47ipFkfC3H3gr11z9UvqzS3gLhTQIGxIri1IhKyuL9Pp9GPMMrXSwbsAopqUi9xHOeOYXT1GtlVhXKtEtAj6xYnnepU/j5lu+Qm20zgtu+hFOL9xLrSXpLHTJ6YCXzB+a5TWvewNpZZa3/+XvEw7luOL8yTqaogdKYqxHebBm4GwXWgnC0ur3cYXj0MZpJvYdBDw4jyQg6+Y0eweojIxTSSKCMEIGOb5dwpiCYHISXJ/cljCpobN0nPW1i/Eqo1Sq42yBd5bO2iotVzA+OYanIE09o40ywnsEAXiLLAba6c51zno954xo//LlA1x3eZn7Ds8xvxITqBLWGISXXLxtC1GQc/eDj9CYWs/2K57AziffyK4n3URj3TCFCChkggvKtAxkJFxw5U2IuI6qjqErw8S1MUanN9FdnidrL1JTfYJ8GYfgJTeNQmkEa5sMJZDlPVaXFmmtNREOtBQ4HDqQhHGEQJxXg58+7/DIvkPc8+BBbJ5ytH2Y1732jQyPbwU9QdZf5FUvfT5Dk3Vq1cE0unc5PZPT7/Qpl8uk/R5JFLOwtsrw8AilcpnCeVaabcrlMo1SzEXrJ1BRnU3DIzSN58+//Ak+/YFPE3jF77zxZ1CBoRpM4/BsufAyhkZrVGoht33zC9x952GuvmE3Y1xAOTx/bIezeJNSFH2ky3G2T2LB9buILOWa930ac+e3uPLhPfTbqwgPUVyhl6+wtHBycObJIEwipOlTdFNIPPnPvBHzht/FecCmKCkoepD2lvBBjDCWmZOzCAvGGUbGG0jhBhMjThDEEo9DK4XL2jip0GGEPMdzd86IduM1o8gwQSuJ1IrVFYNpL5IuLHH4wBGqsWXnxnF84eh2uqgkJW22ON1aZrFdxmR9Vk/cz7YdO3nyNc/hn/7hDxmfWs/U1h1EYQ0RhZgiY3LHZSydOkqpIhA64R8/PcO6osWJliLt9VlZXmLHlm04b1BBRNrP0BE4AqyBShgy4N46fx6Wxrox6vEwrbTDoYdz9KrhY/vewolDsxBkfPDvv8yv/+4PMy6qXHL5Nm655RHSwhNYj7EW323jPRw5upd105tRRU6tNsrC/ClwBpPnhMLRXFlk565LGa41WF6coz40xPzMAht37qCyzTIuJunXJQtHJYt7D5Ksj6lO78J0VxitDrH/0B421oZIbf+xNtmjsGR4F2LyNv0ehEmVd54Csb2GWzpN48AMP3S0ja9PsmbbFMYicbgiI80y0jyjrKrEoxeSzh5HBRl50yI2rsefPkbe71KvNFABZKttOms1hsuGNM3Zu+chLrtwO5VaidMnFti4eQwt1KM0GyoQWG/odtawHrRLwJy9BeucjtYvLM3VNgvHjnD51ddy260f5MTBRV76k7/I6/7HPw5owp1CCE8QWvbc9g0+93d/zMimbUgfUK9UicaHiX3Km//41xku1Ylzy5jxyABc4chyy+jmK1maO4U3Gb00x819mn1rc6x2HJXqELXhYQrraXZ6NCoKay3SZJgiQyhFGNZw55WbwfyJGZ7z0z/J3m99lvsXTiH8IdTiJWRLM7zn43fyT+//OJVohH6rw7UvupCWmOfhW1Yw1hB4j+1nNFRIEkCvs0y/cFS9ZXx8gixLKUzGhtFhdm7YyrZNmwicJO122LplC71ui9Vum+1DU5w4lmNMGwLJBVc/h3zlfowzbNmyi2R8BwsLyxw9OkNp+PzpdcSA9xk2t3jnGfWgdB/iCKtyvFaIsIz0GYe7OYHWLM3NUWkMUaR9dBCRpXBkqMyGcplWcw0Rh4TbJ+nffzchAuMLev0M7y151sTZBlobmq0OrvA051bYvHMdWg9aCY3xaBViTUbey9FCU7gCIbpk5+B2PudzOb8WMFyuUKuXuOXLn6LRuIon3fhsFudnmJ9fxhSaIAxZXF5i721f4ZE7Ps0TX/gThKUKKlD0Wku0VpcwaZuVowcJKiMsLS7hERRG0M8cRe4xTrP18ichZEClWmfXlvU8tP8kYZJw5Mh+SkpQHRqh3hjBioROmjNz4jTG5witUDrEFwPKuvMFT3/+C+l1+pD1Gapr2o8knJg9wNDwEO/64J+yFt7OvQ/s5dd/5+14YdggI264chejY0N0nSQXmkZg6agqvb6h2epz6uQxGvUqgdY4VxDrgC3DQxS9Pp+/81a2br2AucVFxiYm2TbUpTWXMFqJeOUrfplKVOHqa66mIy0/+OJX0RgeY3iyw5Nv2MFLX/aiQTvTeQLjBb7IsYXBpvDHh5t85vlXgooQ+aBlT1jwWY9LhhoYa2gvr7J6eo7CZFjXw3nJu4/eSb+1QigFemgM/dBR+pFHOEesFT7PQYPJHcYFXHXdsxhuVCispVdYVtZWOH16jsNHjzI/O8exY4c4eXyGdRsmsU4ipccUGpO1zno953S0kw98kiAKKHzEZU94JsOjMdt2XMjWrdtYNz1BOYaP/9Ub+drfvYl7b/kUUkdEpQreOk7PtWl1uzgBPQthEhJXYk4fPULhJGma0+v0WF5o0neewgsmNmwhKleIkxI//rLngc+5+gnXc+rESe659Svk3SY6LmOMRSQV2l3DyYMHUQKyPMOfR2nHL3/iE7TzNS656YeYPbGCdhGuHxNT8LnPf5hbPvkQq51lfuV1L+Cr79vHE576YioM8UPXP4mhWoVSrDliFFYo0sKgtcB7w7HjR2i3mxw6uI8Dq33e+/mv8V/e+ma+8q3bqTUaZL0+uy8c5oqLp+kuLfLMm57B6sp+olCz5567oHB88aP/hI49SRxwZGUvN3/uX0hK1cfaZI9CeUdhU4zzfKy+BRVpFkyfZvMwRBqfKzAZ0iguWr+DHVEF6xxp2sc7R9bLsEWPGdvDAsZ3ELPHiSY30ikswX/+zxz6sz/H9R3CCPIsR5gu/WyFO26/mXf/1Z/x33799YgUirkuvWMLLB44wumHjjF73z6mkiECrTBG0lltYszZBz/PuVdYt/VKDhw8gJCKVmsWqQOaa02SSg2Ttdl7ywfprZzEhyE2L/AJOGv51s03Mzq1gaCbDhivfMHI8AihVvSbp3jgi//E7HIKMmSkbBmaej2J6RJWhpBqDq8VSsckpWHuv/cOpjdsJ65U2P/Ig1z3tAkoj9BstjHWoqvD5IVBKcG/QeH/mKBWbzA2Nk5SGmZ0uIzTih968Q/w9a9+kh1bNlGrDHP8Q0cJJmNqVwd86Z//lMs2PJtGeYJqpFk/pCGHkwT4PggREEcxQ40GWdqk2/Xcd/8dXLF9A9WkjDlDq95qNXnONau8+V2L5KLEvuMrjDcCrr/yKu588GuUfZfy1h3c8dl/4daFPcSJZn65x9iGx0QS7f8I73M0AZ8b3g6njuGEoOjlfOHScV7WVfiFuQELtjIIG/COrVdy08O34K3HZRYVh/RX2wRJTOH71P0k83Gb+2//FH85XeVXT0vG9h7EyhCDQeSO3uoK9ZERZBbjRIRJLZXP72ffQ/cgvUOGGlkE5HaBC8IhvuAKhChQcYA8x3N3zoi2a+clbNl2MRs3bWd4ZD216hBhGHD3F9/P4vHbOHDPN/BKEYQh0oOxOf00p5s6xmplnOkiTAeR55QpaJ/Yw6bxBqH0bBwWbBr2VGONs4YoqROEZUamNg0oyIOAMNSMjoywvHSKSrnC5btv4Jtf/gKriyfBdJEyQEpBnhU4Yc6nnSOF7dE5epgwgOF1YyQS3vmZDzAT5ywdzjm5/zA7r4vIspxv/uMD3D0Xcnjtbj7w9dv5mR/8ASq1SfIzJLNrayusri6SZ6scPXqY1bU2XgxqTMIZrty1GYeg10u54upredmv3M9CW3DVtRdw0wt3cHLuEQ4eXqLTbKLDCvUkoej0yPoVTplj7L56B65//pzR3pGs54vVzajl0xRFioszVhZX0B/6Ov+09xtQbyC8HfQ5WkclLPGb9RHwnuBM95GQlggDqWOmd5hPLZ/gzyPBm7OI4X/8q4EEU24gNwgBa4sd2nOGTncF5yVCao4/9ACTE2NMTNQYCmGq4pkoDTNRUlDEOBtQZOngc86CczcV20HdytoC4wq0Vhy561MsHbuXu77yKcJqFWMMYWkIIaMBg5z1eNMhiAKETXGmAOUIQ0WkGPToGYMOYnq9jHa7Q14YVtbW6KcpOikzMjKBDmPK5TLz87OEGrr9Fnvu+xbDUxuZn51n74P3UC15QiEQWhAG509TLIAvDGpsI8YqTj28B9N1vPvtH+eP3vi3PO/GJ9JrG048mBJLTyDhSU+7kGf/1E8xMeX46OG72DMzx1wuODE7R24MaWY5Pd9CSkWvWzAUaGLtWc3g2EqfXMdIkTM3+wjr148yOjLB/Yf384kPfYhqqcxCe548c4gkYq3Zod9f49IrNzG+bgsHF45xsr3yWJvsUZS850RdsRp5TKxJU8f2b+yl2cv4p+4yz7z/S3iZQJZC0UbajBdtv5ibSkPkRY5zln6nTXN5CatL5CLgBjnEHwVV+j/5MuZf/XoWfur5GJuDUhR5H1Ur0eou0lnLaTVPs/XySyD1+LxFvrqE8QXZ83bjCgdpSrezist7aBVh3Nnf8Od0NFPkWGvRQcC++77B8sn7ePCOL3Hw5Dzx2Fb67RVM0aM5f4K86OALh7GGpz/jWegwRqoQIzxKn5FtMjlSKsIoIkpixqcnGZ2cxJoUawpSD9ZDNDJOfd0GdKCw1rEwv0xndYVLr7qakfEGw1OTXH3djdx169eo1cqUdMiGDSPnU0AjLTLyXpei6FHHsv/UEkGwxsqJFk9/6ov5lZ9+I5/5yMfZctkooyNVRqMRRNDn+T93EQfunaPvoFRWOGvPiCgYSkrR63YpnOHg7AKV6jDNzDLf6aGyFs4XTI1vwouYbr/N0uwK2td4zjOez4apzbR7K5RCTZpllCsxn9n3WZZmTuFWHCY5fwhUf6yY5WeXjvOZX3gNB9/4SxS+4KKwRKEKlkxOqh2Hu02EMbgMKHKkjfi9C64B5zB5Ttbt00tzHioJuuOj6CsuYrXXov3nf8biK2/kM1+7Axhwjuq4hBYBzhT0ez1sV5L2U/7CHOLOe+6lFI/RynL8aJWuchSdHInCWo/N00GL1lnw/ysb3u82ueuL7ycWKV/58N/QDcfYetEVlKvD6NIUzg7mzrxzGO+whcM4x71f/xydZosiTcmygl63T7ezOqAFx+KLjLzXx6U9Fo88TJYXZFlB4TzOe8pDwzgGc2hKSRbnTjN/8iTKgc1SvPM84fpnogNNr7VKWmTn1RmtFFdZPH2UWnWI8pZdrBua5N7Pf4RtO3cgvGDjhin+6h1/yg8//0d48Y/eyFNvCNkydAknHpnlbW/7Ra68scLhU0v0+zk272GMo+cs3X5GkRc0ajGF0EgtWJw/Nej1RJ/htzS86CeuRff71EcqjI1P85IXPA8U9Nsp9UoJlffZUt6EWLNMbAgYteXH2mSPwnZzWovLvOsP/wg3vY6sgMQVvHN1BikVyIBXHX8QFzSQ3oEo8Nkqqu/4uw0X4awbMF8r2LdhE8H8Msv33swjG9bzW0tztOvDnDpwEC8MxjsqtUmsyemlLbA5JuvhHRQC/m51iZOnF4n7Ob3/+TFKXUkhOxRFD2sdwgvc2f3s3I42c/g+8uYJ5o48wLvf9gfEU5czOjpKY2SS1mqbIu1hncV6Bh3+xpDlDu8MYyN1RCjIrKPb62HTNqtLKxRFQXt1mW5rFWf65HlG99QDIAfbzsJ5jPd4ETxKH+ecQ2rN7Owx6pPbSZKE/Q/dO+DQ8J7cGuJyMlAlPU8QJwnl2hgLC7MMTU+w2Fnmr95/F/v238o3vvFp7r/3qzznKT9AJ1/ikl3TnDwWUKkkbJvczKEHjvCsZz2fIsuJYkUYN0AISoFkuBYT6AChS4NMq7GUokFnjBCeOI4plTRhU7H7CU/gC7ffy4t+5kf53De/ST0poSLNt756K1GlQqeZ0k0Sxms7iIfOTgL6/YTF4jw445HaU1KSf1hbRjmJUiHWGlCG31x8CIxAFAofSKxrsX10lN+ubCDPDCa3fPYb/8IDL38mv29T/vb+O3jxL7yGD77tr1ARKBlQGR5CehAIpAgxqUAlChjw1whhWGguUS/XEbkjy5YRfQ3OIUREgcPZ73LrWEpivvHx9/CNr93ME571SoJA0hib5uieu+mtHiOq1PHIgca19RhTkOY593z5C8SRRuuAOAoIggF1clyK6HW6FNayutal28nIej26nR4rMycpjMMUEmsHwnP+DIW4ONPDGIURd3z5o0iqjG7cxczMMTzghSSOSvjziEC1tbrK3IkTmE6HUw/v5Rd+6bdR4Qpf+NI3OHJshvGRYZLyTo4f3M/cTJv9B47zxS+9m7v2PMKuy8dZmFlE64CJ4Rr9fhelFcYrMqMIAsj6bVzeJ88NWgV4PFEUY33KK17zLO5e/SYn9u/lion1hLLEgeMHWFldJYoSHDneV5jpzhJay8ET99Hqrz7WJnsUJs/BCwqXDoZcTY9PZx1QQKgJdIgTglvW5nC+QxEaPvPal3H7f34tfZtx08ZRpqUG69CB4n3v/Bt+6hd+gtf+0o/zyXe9F+synAOnBtntnAwvy+RFDkFB1uthnCXrdSh8MVBOCmv005Q89+RmwKaFyiE3g0nss+Ccjnbb5z/EydWCp/7wq0migKGJDawsHB8IWCgFYiD47hEYZ/n6zbdy22c/w/T6UbLcY4ynyHO0FgNhAOtIKiWiJGZicpRKrUpjbIpytcbykfsGndqA8XJgCGcHozFn6Oy890RhyJ57v8TmTdsZGV3P3d/4KtZ5gig8x5zr9xdPfs5L2LZrI6vtFk+88Qb+8eO/z8ZtG9i6vcT+o8f4zJe/jCkO8oH33U19qEyaRqyeanPlBeN87gvfYu8dHWo1SaWSECchgdZUS5rhqmasXKaWJNRLMd5ZvNQEQUAQhCgRcWTvt4g6Zaq7JvjWoQfZuTNmfL3AOkm5Nkw4NEicvPzGZ9LKWrCuTdE7f7KOWmu8t/zZP/wT8T9/mt+cm0WowXSGtzkukGAFrgBRq/Oh51/OiYUFDs4c56uv+wkeftb1/PWuHZi8oNfPcR7e87Z38pEPf5ZouEJpaIi4VqM8PErW62JNhi2aRIGiyBzOgcn6eBdQFBajFTIYdO17lVGc4ZbM04zcGsQ56rfntGwvGOXyay+j02oyPD7F6UP34cMqYTTgC7FuQGRp/UC7eromqSXLoAJ05BDO4JQmUiCw1JKBYKGQgsWFJWq1KiKIEELSXl1EKE1hDEqLwRiMDNi041KO7nvgO8TkgyDgK594N43REa548o3Mr82diZrnz9bxzi9/nksu3M1C8xSRdoyNVZldPcYtf3svlWQS45v82bvew03PuYG3vu0jPPGSjRw8tswlV25kaqzG01/xEqY29vjsJ/aghEJoSTczKBzzvRWMl4RBSBiV6Pf7BIEkSRICIdm0cz0zR9qcOHUPI2KSwtZ54q6ncCcPc//tN/OMZz2Hpd4yn/nSPVy28SLWT13KF2dufaxN9iiEjHny855NtRSy+t4P8IDPEEQ4k6FUSN7vIhQIqRECHjw2x7e+/kGypR4oQViv8qa3/Al/9MY/4jdOHxpQzUcKhB1QFWiPFBKXpwOuxyLESfB4pHcIBFkvx2Z9HIZOp4nv9YmVoNdJ0Sok8JLUFkitCRtn53U8Z0Sbnt7E/Pwiw+PrOH3oQVRpGN9v4a0dqHPagQqMyQr23nM70nmsKXCFwxUe5weCAOgQ49xAocM5XFFQrVRA+MEihCNJSqzNnRyQWtoBp/8V111PGIQ4z6PiF+6MAo2UkubqGh6PDs5kNc+jkNbqLBKXFNc/9XnU6gl7b53F25Th8hiZXaVlDUdOnObmW7/JpRc9kdBPsHXXNu67bYU3vOEDbNpsKFZy+nlOEAQkYUy1UqJdeNLUIG2O9BCcefsbmw9sIwW1cIIXvPiZZHOK573oOuJGgw9/7SOoIKQ+MkySlKgMT7JpYhte1vjbf3w3vcXzZ0zGCcOP/PhPUn3bX/CrvVWEUuAygijAnRmbccYSO8+DFzT44kc/Q76a4YVEAsoJfve3fwv9h7/MJiEJy5rCFvS7TYKwgcPjfY6S4FWCUNmgFct4MleQZxk2daggQomAQli63TZLzTWsLbDeMqwCcI4oDh+VJ/u3cO70vhckkeLU4fsxpiDvLA4SEM5hjcHisbag02qBc3S7fbJeC2/7mKIgTwtMUVDkfYQUIDxOOIIkplRJSJLKwEmQxHHCifu/jins4PPFgJ0Y57lw99P+ddFn1EO990gpMCYf6Ft7xfkU0cIwJAzKLK0sU6tNccNFV7EumqAa5IwN5YxMD2GVZKltuOfhr/PN++5l/yML9Jjhvs99luats/zED7+Op+xaj3cGvEFKQSmO8TKgmwsMAqUEzhi8FXgMSRKy5aIrCEfu4Ndf9VT2nT7E0SP7WJ1pkfVSVBJz9113YJ2Bost9+25hw3SV6fXnTzJE4IhcxtGbb6GLoygMKIW1ksKYAf2bhK9Pb+cPjxxDajHY8ThLYR2d1Sbt+UWy0gQ/NDxCZ7WP7VvytqUwHWxhcV5RGIcWHnyAzQdjVsIOoqQpUnKXoaWiqkqkeYqSUHiHwDBGgFCKfrtDa3bprNdzTkfrtRbJuqvgHAiP8AOlROsGX8J7vNfsue9ejPXUh+uouIKUMe1eSpoWtDopq0td5uebdNOM9lqP5YUl1lZWWFtbotftkxcpnTSnyHNUGOCsp720inVw00tegnc5I5ObHk2KwJlM5Bn1UB2FSPFtmoPzA0kYk4mEdRs34a1ifFiwdLzHkCpz5djl7CrK/M6rf5CJ0YSZIylh2XDo8H7uvX+Zv3nvmzjZXubonm/w6le8iFCHeCFxzoNwg62193Q6XXq9Pp4BS5O3BVIo/vIv/5h3/NkJHtp7gisnLiGqNPmxH/xBqrGmt7TIpi1bMU7Ra6/w2le+lkuv2c0lF1z4WJvsUbzhv7+F0Vf/An/uCqK4gpIe7wYqr8IP7vvttZ10KlX23/sQSgRYATIIUEGA0JIgioCAihikyKQcEKI64/FO4xBIJbEMstxSg3N9rOnjABnHaAJkFKLI6HQ7WK9wQpIXhl216oBiUZ/7mTvnb9isj7OWot8FMSgefztqeAYPe7PVpV6JqVVLg490BYV1hHFA1CgRlRNqIwlD9YhqrUq5XqVaaxAnCd56NAKXQzkuE5fLdJqrOO/odJcZnt6MzQ3OFIxNbcA6O2BEPqNzrYIAUIQywMFg6vo8Qd7J+OJnPsHaQpNjx/bwzT2HGRkfpRqHrCz1yFPP1z7xLZ61YTvPv+6JXJGs55lbdnBJbYKJpMqxQ6f55y98id7pg3hvCMKBUqp3EiXko3YorEEJj0KQG0ctKXHTustI96zxjKc9j+LeY7z5yS9m9shxwsYwlVIwIL3J+qRo/unvP8Sxu07g0/OnYF2pSI6nfWakxkuDTspIpciLLgA7ZAIbNvPFDQNRzDzLUELiEbgzOmrogDCGub4785KyBEEw6InFYM1AeinPMqQU2KKDcwGcGYBVYRmvPVFQIiSga3qkpkfXGfq2R8kXgxqyEefMdp+7BcukuKKPTkp4Z3De4/zgjOS9x3vP3IG9aKkI42SglyYkzdaAc1BaAVhUUELqkCx3pGmBO2OQuDoEYUhcqyLCAK0CTj1wC6aw1OuT7LntZpwTPPX5P4hzjgt2P21wNuTM1lFJ0BFeDTKTyPMnogkvkOWcU/v38tznvopmc8A92OooRFJlrbnKydkWX7vrAEf3zdIvDVMENXypzHyqODa3Qm1klC/cuYehegVTOJRWBFGAlBqtFeVyGcFAP6ywGcI5+kXBwZOeSy7bzSMPnOTKZz6Fz95yFzdMTuOLlB07LiKqJLRaq1wUTnJ142LWiToLh2cfa5M9iqSX8cUiAwqcsQg/iFZBVEUGmhdWKsgTeziaN4nKFaQSg11WUSC8wEqBDjUN63hvZ3nAJyMlTmu8kBS5w1tJYQ04T54XeF8BCVonyFKEdwXCiUGyQ0HmDcZYTF7gMthcHsd5Bd7iz3FkOVvWUQHUkohAjZ5xLItwFutCvDUI43HCMrlumnq9ASIcEEFKz1BDYkxOnlmkhDQDxIBjf2i4AUJS5BatJc4P9KrLSUwUhmRZShwGRIFi3aYt4B0CR61aQ4cBW3ZcTLe5OBD7rlUYG65TqlaIg5hIye9Y/2MEBVAfbbB6soMuK5bnjrJz5xTNtQ4GSckUFIWiPjpCv99nbHiIorPG5M4d3HvnHgIpWFleIakOs2Y1F21S7JlpEkiFQ9CoNej1upRKCW/62Rfw++/9AlEU0mjUqCQJmzdWUdax1FnjW1+9HaOnuX+xSVyqc3JxgQvrYyQT44jFiJMrpxkZrqMqje9Y/2MEBRC+893cMTnGkAS+rQUrHN4LpPSUVJWMkJelAXuvvIzjh4+j9YC62xrD0PQ0P/+bv83Cf/lvDE9NIaREx2VUAM47pBhorSE0OlA4L9BBgNRuQEPucoQA6zVKwMVPeiEPPvBZ+v0Md0YE0+IZnRxHCEljePg71v+/Q/h/o5PinnvuuR645Xtuxu8fnrJ79+5vPhZ/+HHb/fvxH9V2Z4todwFPAU4D5xjUPq+ggCkG63+s8Ljt/v34D2m7fzOiPY7H8Ti+dzh/MgeP43H8B8bjjvY4Hsf3AY872uN4HN8HPO5oj+NxfB/wuKM9jsfxfcC/md6/5557IuAa/h9Os+7evfsx4U973Hb/fvxHtd3Z6mjX8P944RB4TIquPG677wb/IW13Nkc7DbDy0M/i00VWVy3DQ5NEocL6HOEVJ08t8dRnXo5wAz7HPCvARyyvtullGXEQEYaaTrtFvTZEt99m3bopvM3o9woQg26VxeUOSgmEFFQqJYq8xVCthnEZReFprq4xNlFBCTGQZRKG6liNer1BZgxBdRuq8V8BSZalHD585NH1P0Y4DfDV+z7Ecm+Bbr9NEEREgUYWgkpdkhqPKTKKzBEODWbsymGVtL9KagyVYBhj1lDeUysPk/uCOIjp91OW28s4nSGVQwnQskykApSOwfTIbJ9AS7wBpSOkTkizNdbaLapxQtcYJsrTNNMmTimSooQuCdLC8cNP/M3zwnZT0xPob/evckaNy4vvUOXyeM4MbwweCwQIBt3u3/4B8H/s9/Xf+TkgeNe738kdd95OY2SE+UOPkAYlsl6P0bEJLrvsEt77t/+Id56pjSNs2rABpTXGerSW1Ot1fu41v/To+v93nM3RLIArFrDZaWzfYhM5WJYNMK5L3mkRySV6nR5ShSwvtJmcGCIUHbrFYAivKBSR8qytHgefUvQlShSYNCNONAhPvVQAhkBXKbJZQkKwOROTJVbnV+n7ZYIiA5EjpEBo0K6Nqt+IWvoUsY4QYQQInGt9x/ofI1iA1ewYzV4T6x2drEOZKlI6Flc61ONpkH06eUq5q8mLPmFlgkg5rOnT7O0jsxGhgGpVkXcty50jg64/HVBLamSmSd92aPZXsRZ8kDMSjyAJObk4z0hQR4QrDJUmObZ0mKobYcV1CJ1iQcyxVsxREWX6UhC3Kljxnet/jGAB7r//Ifq9lCgKMMbgrEdpDQwGflvtLsYYyqUSURzirMMYy/zSMqONGr00o1atDMaKAGsteWEoJSFZXmAKQxSFCCGRUvDlr3+O06dmWGsvD1i1h6ps2HUl7/2Hf2RxcYVPfeozYAVCwchUldVWk1MnZ9i4fj0ikEilv2P9/zvOSWUgncALh5TgpcPYEEeOMQLrPUU/p1xOWFruMD4SUeRdyhVNr9/G5ZIgjsEX4FKSMMKmXaJqQn0oIEsLAh1QLgWkmSFUZ7rynaHf7VIemmRlboE4toDB2QK8IpQOX4SIyhTmaAe/8QqsO4AQ03iWv/tb/T2CN4Z6KcAaidQhsQ4pTMFEtQ5SYUyVSqNGN+uhtCO3ORZNGFfxQhB7ifQJJ2aWSL2homMqlSpNt8ZCd5YwSpBijLKzJLFjxaxheoOm46TUwIkyo9EWmu15puIxUAmn11o0klFq1S3kTUMvb6MJmBrfxlzz8GNtskehpKQoMkqlmCjSeC/p9fpUq1WsNZTMQADQOz9wvFabSqXC5NjwGboLQWELZmbnqVerNBoNYPC73W4fJSVRDH/9rrfRT3s0W202bViHFIo4SXjik5/G+//+bxgervLIw0dACmr1mGqjzsTYCNbDkUMnGB0bQdrB8PHZcE5H88KjRIBUORqJkDlYj/CeONAcOz7PhvUTtFsp69fXyIyhyBzlRGKMJggM3bYlDhVaOwrrKPIeSmuCIMKREUVyEM1Mhi9isqxPrSJoLi5QigQ+KSGx4AXWu0HndFHg/SxpD4JDHybe9SQgRnD+zFQ5UnLTIQpisDnWglMpqfKI3A+kh5xFaYP3FVKT01ldZHhoksysoVSDfrGA15JAlnBhwOnuKVpZzkRjmnZzifXVKYq6odvuokyZsB5hfItpO82aWkHkOWNsYT4/Tt7J2VAfpWt7mJkZ+nSoywniEhw4cjcXrd/9WJvsUSwsrlKvlQYd+YD3jjzP6XY7mMKy1l4jDAbUDs1Ol7GREYyDOInw1mPsYNp/emKKxaUFoijGmAKlJOs3TPHLv/qLNFtttm1Zz1qzzdZNG1hrrhCGIa1Oh8NHDnB6rceJYzNUG1W08KgwYMPmdQgE3lgm142DgDiMWF05u5rMuWmPvMBLCAKolAaDdWhFUE3Ii5R3vm+eWC9RqTuGhgTWKOLEUIoC1k9FTIxXcV6Q9wy5cjinWF7OGB9LSCoJS3M9ojBHBzE61JSSAJv36PcSZu86weatNUqxxhQWLwRaODSaPDPkez+MVpB1DSVh8cIDBedL1SLNPEoKXN8iA0UcBZjco41A6pjUZIzVh3AuJ05CjFGEU+PMrbZYmxNsWN9gR2MHne4iXZOjowAbVlmXh7igycTkVrr5CoIapSgm1W1KPkYnJWbbx/CrHl0qUW0MM5wl9IMaSeQoXEojkvheiVKUUPTXuGB6J3n37Ioo30+Mjw1xamaO6emAKIzQWjEy3MBYh5A5W4Y302y3sNaRRBWW19aoVCpkHYO1BUkcMdSoYSyUK/FghtFZpI6IwwChBBNjDdatm2ZhaQ9p1kMKhXOOldUWDzz8MKdOnqZSTgakR6UIVVfUSiWstay2mgwNVyknMUtLKzQqQ2e9nnM6mjGOpdOGemWSxZWCPOuzutannznSQjDf1KwbH2idOSOQTmMywZE5S7ff5M57+wgFcSjQgSHPVygnUHioxoLltmO4Jokiz0hVU68ECK9RvkNScUgG/I46GIyhKxWjwohOZ5XFxVXWbZ4mSGJ852O4ym7gaiD9Ht3u7w7bxjYjXI/VvIfE0+qsMdYYRochaa/NUGmU1bxFUEQUhWa5s8BkYxTlNRdtvxhvDEY6WnRptleoMc7J+Tm2rt+A68Hs4irjYyNordAVTyK2oIISrXaByBo41Yaq4/TifsJKg5Zt0Sk0E/WdzM4doFrbwNHZPWxZP0rLLGOJHmuTPQqtQ0pJDB5Onz7N0soqWimyrCAMAxqNIVqtJlEUUSrFdDp9tNREUYD3Eusdp+eW8Bi8l+R5ziP77uX6Jz+dmdlDFEVGrTbMkSPHSJL4zMCwoF6rM7e4wv5HDlKpVCmXYoYrCVkApfESuTEESrG0sMzwcJXFpSWKwpJUS2e/nnNd8PysZXUBHtm/CC6k389xVlMYSZobKoFEefCFotnJSbQnSwsadUlhNMYKStpRYCgKgc09QkqksKwZDzag13HEgSDSEIiQSilESYdUAWuLBoRHS4XUhkALeipD6xLSKeaPtwmCDkF0Kyq6CxtsBt7wvbnb3yW6ps1s8zSBjMB5Oj3HhuEawktK5RKn24sMVdbjfY+Ob1OKIzp5ShhZTNFmse9ZPHGE9UMVkmSEO+5/gKc9cTdKSMqNUVywSKA8zX4bLRVZENFdWKDXb5EkFaJkjKvWP5e95rPoSBMoaKdNBB2GKjERMDk0xKHVRbrtgiduetpjbbJHobVkamocpQYU51EUo5QkjiOMMXgPUnlOnphl545trK216PY7tNqWMI7ptrsMDw3hvCbPMj7wkfeQJAkPPPQgJ48fIe0XtMMecRjQbrdoNGo4Z2i1ekjv6bZbjI2Ng1AU0lBPKkyVp87Q+gUYZxFK088tY/U6lfLZRRzP6WiNIUkoPVHJIZSi2VT0U0uWC3RfsDDjWG0HVMKCupIcn4MNG0JMZolLmkoV2msMZJ2EotfNB+ctBDoMEE6SFpIszSkKRV8ICuvAe6RPQQzEuZEWJUHqPoE06KCCUgMRb0WI9QXOZQjdhIu/Nzf7u8W60WkW144SuohquYbIuzxy5DAmgJHyFhY7HVaae2hUYrROGK2O0u/lLHZy2t0uq2vLOBWwkqdsXD/NM665kmqlxunllYFaTxBSs2PIUgtpY4IwIxgpGLUbsIUhKsfce+ifiYqAvjEE2iBcmdOLM5SSEjOtQwzLUTaXquShJ4jOHw3rZrPFIwcOMTRUpVKuUi2XkFJijKVerw9oMqTkSddegzUFtVqFLMtY67QJhEDWyiitufkbXyTrd9m+bROLS01OHD9BlJTp91fptZu0rSUv3EBIJAixePYeOEC9ViWMY9qdFlNTU+TG0Gp3EFJQ9PsYZ4mjkCgSlCslWu2zU/WdO+soATyhhpUVT557vFUo4QkVA8rvwBLGgBKMj0G3K/C5IisyJAFKafp9S5Y5krKh1w2xZES5wruMakWC07jCURuOSUpl8NDsrtGoJUitEdIhBUQ6AClRgUTqwZi5R4EN8LZHuxefN+mQtbU1XE8QjUQcPHyIrdt2sE6PQOCYmz/OjvFpCt8Hl4OyLK0sEccxE0OaDY1h5pMOQ40Juu0ea6dXyAvNgSOHsarN9OgU/W7EPZ09rHZmKemAC7ZcQEyZrl8gzxRFsUI5qdNzS4zKcXoyobCnqI4OI3tNhmPPSnOJxAb0lSNYOrvG1/cTK2trNOplxkdGKZdLIGBtrcnE+DgeS7vTJUt75HlOGAYD9dh+n1q5RLvbp1ot089WGRlqcO/Jw5w8OUujNkSe9+l3Mwoc1gqUVBRFZ0Daaw1JtY5WiqGhBlmWo6SkVh38v3aviwRanR7eWnq9PuPjE5ycm6dRbZz1ev5/cEB75OBpptfvs7AksBhcLukWYIDcKsJC4KQltaCkpVSWRErQSyHLBO1UECro90Ki0IPTSOFxQoASpDmkqSC3EmkyDhxeZd+BNi/5wS1oz4B/XwqcEwPCVmchs0hZIH0JIXIG7Knnz4G+FI1Tr3fod5fZfdlVdIqC1VaTKBGElSqpbQPQTy1TlXH69OgVXSrFJKf7C2SyOjicp3OcmF3m2gt3s2PLFHGQ0PGOg4cOs3HjVtb3ArRsMFQtYVzI3ME+kxvHibzif/zap3nJG7bTL9U4PnuIscmEsF1npL6JJJukUXL0sw5h1qSUTD3GFvtXbFg/jTOW46dOcejIETrdLhfuuoAoCuinFiU8UgVUazWssXiR0u70EEIThJqlpdMcmznE+z/4IdJezsYt0+RZj7wwrHWaxGGJpeU1GvUKoyNjCKWJIsXB/QcYrtdRgaYaBOS5pN1rY01BHEV45yjyFJynXC7TancplxLGxsfOej3ndLQBK+yA1rFe95TLnjSHPHVkueLQHETSoZUbVPKDiFKYEscDarowAa09qz3IckvJKpJQYoUlzzVBBGurnnAEsJ4sn2Vx2XHgkGAtlXzhyyd45o0R2kqk8iDVmUq/RgiDsR5jDEVmyHNH4Uepbvve3OzvFq18lThw1OtbsEVBSWk6Uc7h+QXWjY3TNw5jBLV4UGw/Nb+ElJZIVeiuFmxZdyG9bA6rxkl0xvJKk1BUWVw4gIliTpxaoBqVqFUaTA2v49a9B1hrz7NrZ8wn//owz3/xC9my9SGmRyZYa59kw9A0sRYEwnJk7m46y2WiRspkfT2VSszBU4d5zmNttDO48+77aFTLrF+/ni2bNtDt9gDFvv2HaXe6lJIEKQUzM7PUG1WWV9p00y47t6/ntjtv5cCh/dx5+52YzFBkKYcfOc7m7etYXloiCmK6/Q4ykDSzgk3VMtKD0gGuyBmaGCfNU5ABmzZtwhSGjjUUec7i8jI2H9SkO50OGzZuwuSGrHf2bfe5s45WYI3HWsAPaLq9FTgBDo+1nkKAdoo0s6BS+sohMkFmNGBRCkYqnrWWplo39HMIFfTSjKqICQOLdznfuA/0vQGFh9RJpuqW1Y7ikT09xmqCdRdEaB2DACE9UgaAILKCItGEWU7uHksCp+9Epgy1kVGa3dPMrKyxms4h81EuWbcN6RSV4QaVKGCl2cO5HrVShDQ9Omt9GtWQ5fkuI6OKNK1xw7ansJYu0VtuMzFW5+jxJSLnsZknjz337j/AUvc0capYPCCIm13u/sTnuOYJNxGEK5RqBZIR1joniFyJSmMH6zZIVvJVKrVJTOqo1hYea5M9it1XXMHxE0fp9lKKokApjQ4E66anyPMMYwuEl1QrCXFcYmxkmGMn9vPRj3+Yvfv2sbq2gsUP2LEROGc5cvA43nm8h0BKgkDRyexAs8FZFmZmaQwNIZVACYUxBh1EgyOAM2T9FJtbjHVcddVlWOsx1tDt92i3zu5o5yw4eQtFDsaA9QM1EuHBu4HTZc7jCk02eAGQF4a8H+JsH+ctWlq0z0lTi3SWuVMwvyBZbQqGAol2KRVhqbU9q60AKw2ZFcSBZ7YXMDbiWOt5lpuOuaMW63KcN4AFO9Bh8y7AW7CF43ziQIlXy5RlgE5H2T4yzeZ4A9ds3EGz1aedCrQyIAXtTodtG57MNTuuY3J8Jxfs2kkcNiiN5ASyyvDUMFZ2GY4VE+vGOTU3x9Jqm3W7IsYnA6bq21hb7bBrdCeXX3wlPzwS87UPX8gn3h7xwP13cuj4LCeX5jh2bC9FN2Pj5Ci+m3PsxCyL+wrmZ05hTJfo7Bnq7yuSJGD79q2USzFjYyOk/ZS9+/Zx8sQp7t/zMHff/xDNToe1ZptH9u9nqDHOyNBWsq4kTftY70jTnG4nxXmPFw7rLIVzdHKDMzlSab71tZtx3pPmKbWhEYJAUxSGLO+xuLRIv9tEAN1On3a3Q1oUWFPw8J69zM3Pk/dSDh86RhiePWaduzPED6KW9RJnz0Qw7zFuIBronKOTOoaritxAGCkUjjyF2GUEoWJp2dFPJYWXQEGn0Ew3CuZWYpLQUOSO0fpAYeZ0U+C8IrGCsUbG1nVlRoYjTp9cI/DBGRpwCVbihcB7CQx0AByDut/5EtNStUwnDbAUFH04sdxm/4nbuXD7TrZPref04kla+RprnVOIk5qlpXmGqnUW2nO0llO87DBnEqLKEPiUCzZfy217vk4Y5mzfMkQSlun5Yco+JqzVmJ6a4NTCCk8un8avSGSR0WuvsnmqRiR2sJBBUcxxermF9gVT9a24ckFXtqFSEBbnz/n2wx//JL1uSrmSIIRifGyU4zMnmYkWqJTLGJNz4uQMT7jmKrawjU6nRxgEPPnJ12Ncl2/eeSu9bkbfFmgg1Jr60Cj5/El27bqYn/vpn0FJxb4DB/HOkqZ9ypUKaWEoKY2UisZQldw48jQjzfsUWYEwjnUbpzh29ARZP2VxddDBEkVn108/t6M5ME5ijccYMRBScCARVCr/H3v/HWTZlZ13or+993HX3/SuKssbFAoebdDoRnvHJilRdKIoKUTp8VH2SQqNRqOYmRg+hoZDSSHxDUVRFCVakSLZZDe7yW62b7RvNHyhUCjvsip95vX3HrfN++NcQENFsIoh9DRqFFj/ICJRefOcfe46a61vrfV9kkBYBDlpphglgoZyhJ6jFBkaVcVEZLm+5SG8gqFYWTC55tqmR0lqKoEhTh0uhUTDUEsCJfEVpLnCF5JOP2ditsy1tRHRhKTerKKlRQiHdB7GFXJONrf8ifHu19i6SYdRYmhW6mzutjh5cB+d4QBpFaMULlzfIs2H3H30AKGrsmcKpLNsDa5xcN9ddNpdwnoN53x6Q8fNzed49J572e5tMuwP0DpksNLi1M0LzC01eeK5Z1mcrbFypcXBoxV0vsSP/tgBypFgq9umUhsS1g6wvnMVKyyi36M13Gbf4mGunrrEbPPoa31kr9j73/lO+oMhYItUz1fcd/dxpFSEYUi/3ydPE7pbLWoT06yvr4PQtNtt5ueW6bU+QxCVMMMhgQrRDnJt+OVf+k2eP3ue7Z0WlUqFT3zqD0jSGJNrWjstavUagScRShD3egxGI3qDPmmc8d73vIckixmOYqJyDYyjVAkZDAZ4t2HIvj0YYmWhwonPqfMhUUVx/EDK3Iyg2VTcd4/mX/+2x6RzKCvo2pyyL8lHHnHfo7yc8b4HNAcfnuBnf2GANTkT0iGEh7GS3GX4UnLBVjAiw5OCqjT4TuIMfPOlhBPHPdZ7lnLocXbFY/PUgO96LEJJD2dTXtaA8/yCZvxOsUDWOHv2EhNzfe49th+bCTKpILL0RqvMTkqqtSUm6jUajQZnL6wyUWmyOH2EnfYQ35P0um2kdQzza9SaB7i4foETy/fQKQ3xdM5msMq+0CGlYWMz5Zf+fy/xlbLPT7VW+atfv8zf+ccfIHOThGGfNG+ye2OToGGpqCZLS8foJys0q9Pkc3NcvLryWh/ZfzFh8QKfTrfD7OQ0c/OTICBLc9I0Iwx92jev044ztp/4KkHUIKiGrG1s0u91qdfr7HR38FSZ/+1/+ymazSnStBBdmZmY4NKVayzMzrC5sY4TjlK1TKVcZWdnG2MsWRxTn54gHY0AwQ/98A8wikecvXQJnWsEjlxrrt24RpJoJhqvcgQrTS1YSbtr2B0ZFqRkbU2yd08xIW1zx1tPOp5+1pBpWGh4GOdAGpyLubIasrWt2dpqEadlKp4pOONDw3bXYrXigeMeNzaHWOvjCNhKcwIMByJLkhlOnyl0hBsTCh/N+99axdrxRL8TWFM0HJ0ppHjvFJuoVDhybB6L5erqTY7vv5/OKKWpZnCiz4HF+7i5vor2FJdvrmEIGI0Ew/aIvsno9ddp1KoszC+wGLyd7rDHVDXk3MXTLO17Ay+df5LGQplessO+uQVWLw9QKM4NUr56/6P8j2+v8exLLyE9Qe61eHjPu9i3NM356xuIwLA6Ok2kItaSS/Q3Y6rl2df6yF6xre2ih7q0sIDvCXSuccDW9g4OgVI+V86eZmlxGVWugIBTp55mc3MD6YX8s5/8aUqVCqMkQWKp16sYW6PX69GsVXj32x6hVqvxn37r3yGkw5cldtsdEMXLWvkhSgZonfG2tz7CV77ydQZJSikMGI4GhGGFzc0t0jhFCHnbTOrPMOsoCgi6JAgCiJ3FiyWnTmsePKlIEsnXTltWO4KJsiV3ju4ISl5ALTAkPZguK+p1iRECjYcvLDp3zFSKRc7ECurTkr09uDk0CCeohpAYDxFbutZjsgKDnmCqDtZoBAJrHMJZrHVkmeD8swlH9ltunS1/5ywbjDi6fJQkjklGbYQIWZyYxFeOJ09tUi5nHDo4ydrmDZb2LGO9ErVKnYtX20SDIYcOv4Es2aG12sLWR9hsno38BgfnJ+n2T3HPsWNsd3a4sdLnY7/yOQLfJyz7/OO/9yPooI0sWe697yBuOKAxey82KbN+4xzV0JA7wwN7HuJLp79EHnsMleHhmf2v9ZG9YnOzU4ziIWEQsLmxSX80KmSVfUWaZlxfXSWYWODn//n/wff9xE+wsrHFpUuXOXDgMD/yI3+Jdq/HtZtr3HX0IMYYLl1eodmsUavWCTwfax3D4QiJIAyCAtkEMu1w2rLvwF4mJ2q0ujGf/vzjYEzRKtAZAsgNSOlQysdpjbiNitFtHc0YAdpiESSxJEstvnX0Ap+dbk534HFlI8dTIFE0AsOeqmWQCda7IPBwQ8M7lj3K11LyTDEcCiq+T7mU4TnJ5Ys5e44WU/9H+gGXyhZhBS7XJFYhZE6eKqoVhzXFTWZJQhYbksSRJeAyS3VS8EfPCH74fd+mp/0qzS/ltAarbLc22dNcIEu3SAeWrd4Oe+fmEZWU61srhIGi095lKIcMWi2GeUo/dQivx43eOlFQo1SqUV/QbF/aYbursdrSUxdZbC7y2H138xfe+S42Rzu8dPo6VwfPMFGaQWYDqv4im70t2r0trNzCD8uMRjcRvs96r8eM3MOG3CEfGIbZnSMWnyQpzhheOnueOI7Zv28vZ85fYJjE7Fvay/LyMqUo4pE3PswT3/w6Swf28NBDD5LlljhNiMctgJcuXCJJE+ZmZpDSp9vvorWl1+0wMTGBlHDvyQd56pknKFWqHD5yiIcefJCP/eEnefHMeaT0sU4zGAwJI59IRRhri3auECRGEwpVKJLewm7vaNphpEBkAl9q/FJIUIJYO/pDSzLUCOVx9wxcbjkGuV9MjhhLK/VZqgmOzChu9ixCSFJjyFEkaU61pFBCsHDIkiSWuw4JhnsF7Qs+vZ4mliC1JYoUeIZcW+5/WNHvZuS5IRlZ8hSyrGj85qYAbO4Uq1eX0bJFrTJJZkPqyqen2uxfmKXd6bPV3gHpIXJLn21cUiWLYiajBpMzZTIrOb50Ehn4xHHMVmuHrQ3HzIkJ9k0v0RsOiGo+fSRtvcbK9XX2LAdMzpa5eG6HhcUFLm1dpelPshF3aFQcHk2CUoXACeJkm0G+RblSp1avM4jvHGndldVVzp+/iAPm5+cphSHHDx8CKbi2sobJMwajmJtrN/jZ3/wdEJpf/KX/xLvf+27arQ71coVWu8MLL76EChR5mvF85zR5DrVKCW0d95YilB9SKVf44Ps/QLVe5SMf/QRf/caTCMBoDTisS4l8D5wi1Xo8+lfQJlT8Ep6UJPrWiO3twRAxHn8Cjixanr5u0LlmrhbSjxWPPy9wDobaEXmWJ9ckC1VJ4BkWpxUTDCk1FLNVyVvv1UxMCM6dzRnlHk+sKObrjq3TkszBQwcs17YyJiLHNgqpZQF0WIWSkkBCNkgw2pFrSBNHlkGuBXEK1gkSfecIEa4O1imHlmrQRFjNys4KkV8lcWUyNWS6PM/FzjXKeUhUadAetTgytUCSjfC8kDyJybyYTi9hoVInTWPe9+i93FjvoU1KZ7hFJ825drnL3n1znLj7BJduXGLZLnHk4AQruzcoR1VUM6XZhUZlmjQeMVOeIfEMw96I40ce45sv/QFTzTqN6txrfWSv2BPPPks1KjM1MUEp9HniueeJRyn3nDjG8tIcaxvbIATv+2t/k5WnPsX5Uy8Rj0I2N3eYmmqyvrVNvVzh5PGjaAs6z1D4BIEgyzJq1RLbWxu8973volYu8Y0nn+GFF15iZmqC2elpktEIbS3WOXxZhjF/iZQSbQr97FIUMhqOaPX7hZ71Ley2jqaEQNhC8G960lBZ9QqFRWMR1nFxq1gZv7AbcO8en+VA40sI/QCE49p6metdw5fPQGx9gsgiHJRCmG1atAiQ5NRKgucuK3optAPDKBOoUOF7GqcdeWLoGEkaCyyQpoYsLVJbqwUYsEYgzJ2zU2XjmCCq45dKrG/fZGn2PnqjTfqtTaanaozyEY/MPszG7lU8FbFUm0J4OUf3P8ZO6yI73ZuE4SR6vc3V9haDeJ0wKJEywsp5drb7NCbrNKcjquWQrz5/mrnqIT75/JMcXz7E5uqAk/fv5/nL36Qiq8hkhvpUBKEj2cmJByXyiQqdrSrLjQMQ3xo5+07akX372GkV6KGx0Oq0mWxOkGU5SRZz+MAy1gniJOFqp83+dxym0U+5ubHLKB6yf98y11ZuYJKUyabPscPTzC0+xuNffYojh+/GWkcQhPz2R/8jH/3YN3HWMj8/i3OGarlCtVwizjOsyQlUQKINCkFYjogHQzp5n51Wh263UCCl8iprtJd1fhEQ+gKlUmTmI6IcYwKO781phh6eMgihqASCwLdkzhJYS3TYobRHK3bMCBDSIowiKgnizILJaFZF0eCuaioljyy3lAgYaEsNiZYGax2l0GJyi8ZhtMAVwCPGCLRzbO1KhtmdM4GeJo560OTq5lU87TNqX6Okymx0WjTCWY4uH2Zl8xKT0R5u7t6g23QwiPG7V1i5sc3ElMezT7/Imx96AI8J+iyCzZn1p/jmi08jnEc+ypClEpe6AyJPcPrsc8wuzGG1xogS11euMulPMqMa7CQbTJSXybuOUn2WXnqdZ859hkYzYt/yQXb7117rI3vFBoOYfr/HtWsp1UqlmPrROZevXaMUBPT7I+q1Kr3hkDTOmJ+qsX7jGnOL+7DOEY+G1CJHaBOStbN86WyL+998kxMn3snpp79KVF9idnaG1ZV1lpeWSJMM7XJ0qkmSnOGwT6VaRQgf4QF5hpOSrc1NRoOEQZ5gEg0GxJ+hpfRn2rDOdfFJEujHkonIYJSk1XVMl2FxWmA1IHO6IwgrOQ3fo9O2lJRPKXTkuaNaynFIsAov0AQCRCDxRECcJ1QjiYwFzUgyzGMEATt9h5MOFUQoErQV5NphMkGaF+CIMw5ymCoZ8juExgBgYmKC58+foj7ZZOh6ZFaxWJvlwOFj9NJdnrmwiqpUUSpjFOe0WiPu2rvEdvsqzamAQW/EseP76bgdRtuWRNxgFCeMDDi/zESlzmhbM5UbLsUtoiyiXoo4cFBSmwxpdfrMTi/hRMag12PSm+aZb1zn2N1LPHDvQd5afReXL1zn7I2vMDSbtIev9Yn9F7vv3hPkWU4UhezstsgzjRSCiakG1oEnFZtb2wSBT6Ne5eZWl2rVZ3KiQb+9xsd++1f4J//0n/Ph3/x5qh6cP3+Nyp5HuG9ywMqFs/jVVb70lSGHD97Ds89/hUq1CoToQDOIY6w15LnBWU3aTxnECSbV9PMEE2uctgWjnQSkwN1mHOm2jpbEklFskVaigUbJYoSg0/cYjiSVckgcj/CETz9zxAPDZOSRG0u5JEHljGKfcmjwfUmOI1IFH185FAhPIkgRykGuKJVTrPOoCJ9QCRoly9q2h3Ip2km0LbYJ9HhiBQfaWJyVaAmt+M4ht3VeSBD6bHc3iIfQH2WMlkKcssRpzEJ9ka31K8hyA+VppN9jtWvwQkk1aVIql5ieXMLpIe3qOk3/bnbsBvOVCsOBodvrcfKuPbx49iZ5J6WTDjlx5ADDdYNJdziyNEfcrnLDXaFZnsavwbGFOheuv0T5tMdoIeGbm7+LbC/RHw3JxfZrfWSvmJSSMAgIA59quUI4EaB8j9Zum2ESs7O7y749i5SiMkmmaa2dY2F+knPPfZLH3v193Htkmi987BeJO1f48rMrHDp8kG999VP4niKMQlr9IcN+l1Zrh512j9QYet0uUVQmSYaAot1po1OLwzHMYrLEEAgw2GKBRAk8AUaBlK+yj2a1Q2fFB2ln0dYnCh3n1lLuXvZJUsd622ex6pNiqUSS7Y6jVArR2tHpShYXJCIv0MaNlmG6WVysR0iWZ0SRj801RhgEHqEnEaFCugzf91mchc5QUBMKk7kiVdQOmzmMK0bCjDY4q0juINTRWUt1qkzdn+L6VpvjE01sacDpJ1dZOlzh6soqy8dryKzE2miAb6cYpQMWq7M0/Bqtfo/h1ot4fo1+u0/kSwLlozyIahqCgN52zGC0y1uPvpH5pQYvXn2W8lyT/k7IzajDcHWde+87zlZm2dlp0R3tsjA/wYsrz3Klcw6/O8fRfQe5sjYkjO6cl1QkMs5fvcHygcNEUUCtVgMcE4f2kKYZi/PzNJsVnvvGV1i9eZl4NOL61RLtzojr13+FmzfafNeH7uMbz16h3NxLa2DpDXf59Gc+gdvcxGs22BjErG1uMzFZo99qE0RlRvEIrQ15lmLyjJHR6NiiQmhEAZnNqAalondrMiwOrEB4r9LRnLXFpAcWNPjOsdnKSYxgp6NoRpayBxv9HGMcnjSU/JBRZoiTHClC1tZzQl/QTwDjaCWWqQCGOsO3DpTGaKiUQElBIh2DxDJRERgMtUiRJ4IDRwsIP8/Baoke9/dy6/CcQFBsYd8pJnAMRoIk3WBxdp6poM4gC3nf+w/SGa0Q55JrFzd5aP8sw6QPdUFry2NltMKbjr+R+oTi86dvUKv1GAxHvOnEPkyesbaziaoKyjSpT9R4uHEPkLOyuY3v+0zWm/iqx80VxdKxKTa2W7S9Xe47eBzr17l88zQfuudHefbsc1zqX0Osn2JrmLCnfueAISdqdfYf3sPcPYe59vzTHD/xIJur58nyAZ974kkefeejaL/M3NIsp555ilqzSqfTYWOzxdVrmnLJ4xf+4x8wHLTJtQGbMzUxw9yUR88T7GxtkTpDtRQQpylTjRqd4Yh0FGPynKF1mCQnqiiMNOgU+jolVBIv8lDKo+xKCCkxRhP5r7KPVqopnPbxpUeWG4Za4HkezjjagxzPgfIkzoHBR1pHL8lQqYcQHp5nkblmaAI8p0BYQhyJ8akIh1UGKQUiMuTaoYUClYOE1tCjHnoMhppyWVEJUkLh4TlHCuA8nHX4xkOIYpuAO2gfbXWwxqDdod0ZcmNzi8P7T5K6EYEZUYmmWd+6yMLyAud2VsApymmV3O0Sqgm+eeHrLM8v0Sg3GcZb3H/kbp4+/wIiNMzW9hKUfNjVXO6dxk+aTDQ0m90UP/LZ7l7CFzM0Jy3r2xeRLmS4O2SzdIN9sydYCE7ytbOfYpANObpnjvUrGislzdqds2FdmYlQfkx85SLXW0M+/DM/xerVG7RbLSKlKKU7fPTrT5Nrx3d/3w/xBx/5PfI8IU8TMp3Q6UniNKVaqrJnoULSH5CaETYfsJXm1PyAnVHCMO7hByGtjqE1ijGxplmvUDE5WeSRxpqix+SwwCjT5HaEE45yWKIUlAiDkDCs3fJ+/gxgSMBuGyIhSbTHMNY0qwGel+IrgbEKpy25loyMZjLw6eY5E5FHILOi54UkswZPWRzgGY/cpYCjm0gmyxaQ5M7hkWOMIlSFz2ghGcSOuZJhOHB0tByniZJca4yWGCsQDjJjMXdOGw2fGkHZ4A8tew7tR+aSTithmO3ikk1aoxYrmxtM1yoc3H+cQWeVjJR6NWQmXKREg+mJIQcmTnD+wjXMqMRcVGWmFtHqZfREl7RXplYPma7vpxkF3OxcRadLiHJKo1plqraHldYNputNJlyJr379axy/9yDHl+7j6cvPovWAuYOKkt9kZ/POmQz5qf/zP7Jy/Rqj3BAnGRjNMEmpO0FLWn71o1+k3AiIEfzGr/173vG2N7NnYR/PP/sEkRrx1VPX0FrT6Xfo99oYCb4SHK6GRB5c7fSYqFaJoia5LWr++WYJNeMY9GJ6WY6zDlRBDuWhcKIYuvACQZpDplNynWGVo5y+ShYsSVCkc1qjtaNa8akEhiNNj/bIEGtDZgRNT1OSIQOTUA8kQisyKRESlFWgHP0kpuFHpAKs9Yi1QQnBKFdoYal6ILWHQZOMB5Z1bvCEYtDTZJkm1z7GFtQKaS7Hg8QgjEEbQe7unDpDipTeTof5+VlOP3mVe47PESoPu20Z1DOG16A2l6PrjmsXbjIzIajKgL1zS0SiwUsr59C5oz+A2ekl2uUh9XCanf5lnjt3kyNLhxFSstFb58WVa+yt1Thx5CHObZyjfT1lcqZEq3MVEweMphTCW2bvoXm++vg5Hn4kJk8SROkAJhmy7l2n3blTpkRhdXOLTm9Amgw4PDPJjVYf2R/wprtPsoXhrskan7l0DRlnhL7Ps0+d5uJUi2DU5WqnxzuOH+CuB+4h7w1IdUxjZhZ/cpbVlQ22Wyn3HDhKa2uD3Z0tNKKgGffL5Jkl1xmBVIhxgDAWlOcQKIQoNrTDIMTHYp0lFD5SvMrFT+tyfE+SZeCc4N45wbWLBmscPSHxtaMSWHpGEMoMRYjOLInMkbkmE4rIt3ippuyXaCUpge9TVoZKzVFWHkoY8tzhjyR33+cRVixCKDxh+OSX4JFGBk6Sa4k2GmG8Yt7MKnIc0jrwJaEfIft3TpG2s9tlerKOjXvUajmD3DDqW3ZFwnzYpHaPoJtV2L9njudffJEsnMB3gnOX2whzjVplFhvGnH72Rd795x6mfdlwvnWJ2elJDiwqdk2LNI3p7kjKcyOuXoNLW4/zwbe+lRdWr7G51uYd97+d1Y1NrnY22OyuszQzxTseexM3uitIr0a3v4ktwXJtibnSHdTsb23jZyPefGiZu/fPoeKEer3O8kP3ocoSJwM+VJ3Aw9Coz/HSC9/i4ANvw5IzWL9KPOgiq3WSYY/K5BJ+VGZ6eont1fN8//e+k/7WBpfWhnz8D3+f565u4wufcr1Kq93DIgi8kFznKKFoRhVilyCMpAC6C55Row1S+QipkPJVOhrWRyqJ1QasBGGYCjSTDrZHDozgbfdByZcEoUJIR+AZPM8QeCA9gacAVyHPM3o9aO2klKKAqUmFDARI2NqG9bOaasPhkBidkupiAc84ge8seeZwFBwm1klSZxBIlKeIAo84yyn2rO8MqwSOp59+icZkg/c8/AbapsO2iZmdX8DFGf1RyrHZPZTJmZiqojqKe+47TDJYZXHiDZzfvsKgl/K2tz7E5vUeH3rwB/idL/4Wrd4aR2buxWWrbPY2eMuJY7y48SLTSwH7F+/iWy8+xWyzxlRQ4amrTxCZKvum5pHG0dabZDuG0XCEUwOmlw4zGO2wNtxhOTrxWh/ZK/b/ef/b6V+5jL9/AbvTI2+NOPbDP4QXlti5dpGpA0tI49DasnH9HPuPncAOd/ErdebvegSdp5g85af/159ifrFONNHkzPMvcPjAPg4eP0S7NeTM5VXa/RinHSJwDHpDnDFI6aF1ikEyUamDkDRExCDuUY0qWGuI85goKHj+jU7R9ta6jbd1NC80BIGPUgbjLAjwnEQjEc6AhIkmKOFAaiLPoXyBCkJ8oVGehxQKXI7DUoogCj2EMmTCUZIeQkm0TdGALzMcAiElvrBgBUYLopqgVNNjdRvAGlIt8H3wRI4Vhnodbg7unIZ1JZjhDW+yLDcP8Onnn8UXI+5bPsLpazd4971vYG2zwtrGGq31jKAJ03M11q61iOY0cbRLoBwLEzW62Ra7ccwfP/s7nDy6B+f5dEZdws0ae2oRm6PLvGHvQ5y+cJab2RqqO4mZgLXrjuN3vxUv2uTCjTUiv8pUrcnJex6gtaX52vmvsbF5jSvnYvaeLCGm/wzsg98he+Pf+oegE4TnY0xGr9XGl4Js2GXP/Y/g+QEuHfL5L3yW9e1VqrXLXDx/jpXVDU4cX+SBN76bnd0dDpw4wuNf/hKZdmy2hxx75Ac5dbXNFz/3OcqViJXtmCCMEArSPMM4jXUGjSPyPKwotrvzPKNaqpNkBaVd4BUCHGHgkSQp1ah6y/u57cnevGJobWn6I4FEoB3UihoR5SQ4y7XLPsIKkJZQCpQCY3MCHzzP4kmNsBmpLn6nO7BIoenuSsqBwUifF1YE00pz+rTHMBVoDb00IB0ZCC3nu5CdKlMvG9p9RSwNEo9RUuwElSNNbyg5fOAOSh27u8w1KlxJV7n/0BGu3rhOS3eJqPLJrz/OwtIS7ZHhobfcRbStuOGvUwsnqFf38/SpM5TqA8q6Qe71qZUrZM5ws7tGvVRmsbHIpYmbNGZSbLLE9toqS/M1/MjHTWoqnk99rkKzLDm706FWg9FWREuN+Pyzj1P3FXONRUzYZ2pxh7W1HqXsaqG3eQdYNkyJAg+b5lxducKTX/sU1pVZb20y6myzfOAQptNm5vh92FaXrz3xLa6udhilmstbl/jMEyt0uj2UsMzOLeLQzM1UKUURcnICL/Bp1KfwNraxJsVkhqhUI89zqlEZ3wsx1iCEQGcZ2hg0BiU9QFCplMisRSCplmuM0lcZ0QJtKecWD42wAi1BOx8rJD45CI/1bcOeskVIB1KglMWXCpVLfD9DiGKQuGw8cmeY9AWeK5RhttoCT2jWh47pcsSpDU1FGbrOMR8ZjC04I3Qo8ELLEI8jh1Iu3yjIglIVkOuEwdAn1Zb0zskcmVxY5NzGKfKrKToQVEseN2/2mZ+c4ca6x3TSY3pmmmeePsPy8hJuFZ7Tz/Hu6hvprnQpH5tice8851YGuN2cykxAmnURlQYvXnuBibkpXjx9g+mZaXa7uyxHh+h1LZEBEyUoSuh6zkx5gYPLy3y283nqYZXuWoPGTM7O6gaNmQo/9sEf4+c+8rM0qrcmAf1O2q/+2r9i3+HDTDTqrFy/xotnztEexZw6t4oXhsinL+EFAfrzT1EqRfR7PTylqFRDgqCMkB61ZhPf85lZPESjMU2zXmdjfQ2jzVguS2GtRikPJT3yPEUbU9AoKFfIMwGWgtwHKdFZglI+aV5QgidpRpwMmJuevOX93NbR1keOzsgwND7SWZS1TAqDtRp8RS+3BLng3ACEk/hCoDwfJTS+lHhKgjH4nocvIckysI6qHxL6lsDTSGV440FJOzaInqJUNgw6kj17NdvbltwqdjOYkorluZSVLcXeWcFmV1COEra7oK2jUXJsdu8cT9tY22LnRs7ePfMIcrYvDJk7qqhV6nz3eycQaYMnL5xibk+JjZ3rvONN74Qrmt/99Fd4/ztO8sKN85jcsXrWUJ9LObZwmAsrF5CDBs35jLNntzhx/wmS1jXEtGRzdZf9d00RMcddhx7gw1/8bUalAQcWTnB+7QZH9u6nP9rAD4fs6hzqPufOblByv8/x+YdI4ltrfH0n7ROPP8fgk1/F6JxyqUSaxFgHYRhgrSGKSsztWaTfabHT6nD4+L3obITyFHmeFxRxvXYxUjUYMjc9T61aZn5uDmMy9iz/Lf7oo7+BFBJhNVZ6WGOw1mKMQXgKqzXOWIQxZNaCMwRhCFLhez5JVkz359ag7avcRyvo3BQellFeMFzhBFIKlDFMe4JAQGY8nNVoTyITi/IVvgPfy/GcwjiDdRrpinpvuqyJAvA9RZIZFsqWZ3shb9s75NSax/IkDNvgO8Bo9tUltbJgbcdn/5ThuWsFZ0krcZSEYLKec3nb4+TEnVNnbK7s0NctBmmd3d4q+44vMep38GSbizcz/OgS9ckak9EEuRP8/kc+z70PHOK+I3Osb/fZ11hkYm6SNO0zWZ/n+voNVq522Jj7Bp3zjsFuTKOsOf90mxOPHOH+BxZRQRmvEvMHn/0Ic4s1rp/pcWRvRD+DzvYOzVqNxX3TlMMSX/zEixw/sJfqQoW79y9g08ZrfWSvWL1RIc9SgnKV7/meH+DFc88yGo1YuXKR6ZklwlIZpyRhVKMapoRhiTDw0VmGFymk8gvp53KZcjliamoapfxC7NJJMBlBZQJrrqONwQ9FQU8vJUrBaDSiXC5jbIaQCt/zkFJSKpcRzpGmKSbXpHmK5wSBepWoY813ZMri4QhLYPEYJZaSg7lQkFhD4HlFAWkBLMKTaIqIJp1CKYESGXUVYIUhQIDRTNQcClBKMmg7GhMJ1/oejTL0rOBqX1EWRUd+T9PwyYuGfRXJc4lguioYkHP/AkQqIDGGQDrKpTsnolX3wUx+NxNVn3iwSxAEnDhxkEsXdpGjnMZcmQeWj7Kys8vSbIMD79pLO2ux0+1zT/MQu/FVTl+4gHARjx7Zw3OnrxIGHnvtMosnusxG83RY5x/9j3+d3/noH3IJy3sfeQunnjrNnqX9qMWbeGuWsy98hdqRCktTe5mpKsp6gcdPPcVdb5lld6sFPXjxo6ewnuX+n/j+1/rYAPjJ/++/ZHt7m82tNhPNOk8//XjBqwhjWWKDtA6pPKIoKEAx6WN9gRCSXOdMTc2ibcby0jR/66//JZzTfOHxL/PxT/4hazdvUqrWOHr0JO3WJt1uD5zBWIczDitzhv0BQeRTqlZwxoCDXGckcYq12Vgofgz23YZN9PYsWLkhMwZr1XgSxZEJqEhBrmFkJFZB5gTOuvFcsynouIREGIfwMpTzSKVFOk0tlAgBwxgCr0AJM6G4f0bw0bOOtx1wvNiC9x01fOHrxZ5bGgZ88D5NN4aZhuXiuoKBx9lrDhysJYq9ZcW6uHMcre5P8syFM0w1QoQHU+VJTp27yeL8FK42IKDOp7/xJA8ePoZf9vnGVy9x4u4GJ5aOcLH9AvWgzN17jyCl47OPP8/b3nI/R08cJBxO8cWzn2fgtvBmMs7vPs9AD8lvRvzW73+ZzZUNRDDk8KOLfPC7HqWzk3F14yp7jk/wraeeYW3zSzx45ASXdm/y5nveRGf3BofvnWGqdOC1PrJX7FOf/zJ5muIHAWHoU63XkaOMJNUYo7F5ipOCICzhe4pGY5I87iOiCmEQMhx0aQ2GNCaaPPPcN/mRv/xDlKtNkiTGDwJK1RqeH1LyfMrV/eyTHs8/+xQAWudoYZHGkuaKwXCAc44gCPClhwE85WN0jM0EqdC3pRO9fZ4lBQ6Fr8C4YoRKeyFYixI5ge+IJHhWYj2Bs4ZYC3wlCaVBSIdzAk9ZpJOEysc6h/Adg0wwGeZUQkGnK3j+quA9J+BSS3B4zrJjFBXPQ8liQXSl5WOc5uaOpNPTrA4UFV+wlVr2NywG2Ne8c4gdk75hcalJXc4SzeR884XnOXqoyZWrl9hTX2YzbTOzt8QzN85wiOPUpi2f/9R18G9y8EiJ2kyZ7nCHs+fa1A7mfPPqNzCtMmphwK7ocnxhD9lwkucev0hnbcBj77yXxfkpvnGmy7Drc7R5D1s3z5DkIUrAqZee5/LKKrWJhNMrLxEPJb9/7rMsLTaJqhmHJm+9jv+dtDc8cBJnLYNRhu9JqmGJIIjIdAoCDBKrCxrC+sQUgS+QNsQLQqw1pFlCo1FDG8Pl89e496E3ESd9cI542CPLUoJSjbBcxuQGIQUnH3gjhoKd+8wzz9IedLBoPKmIQg+Tx2hUMSnlBNIajPIIpEd2G0LRP8OaTOE8uSgmQzwpinrLWDzlIYxDU4hOGOOwVuFJSyAlzo2dDFMoNEqHUpLQd1hTqNHkmUBaQcm3VCXEPclszTITSvAc7ZJPKTDMKc3eZiFM6KtCaMOQYa1AeQ4pBCMt0f6dM4LV72g6iWHyyIC6mGff3jb7ZuYol2bJBx6jQZfDU/OsPaNZETdpr/R54NhRPv3pJ3jgHYdxniYKJbIUM1musnpZs3Jxg6WdJpPNPWSbU6zv3iTyG/zoX3sHv/7vPsP3/OUHsb0SUwcSnvzWaWpLhtGVjOzgJkl7gpIQNMoLhPUKK1/fRQyHDCs5qysx+dLl1/rIXrGpesi//YVf4Pr1S+R5xoH9h4hqNZqNCZJ+n95wwOTE1HiYXOMHESZLUVLiScHkxARCCIa9DpVSRJblaG0xxlEq16nUPLJ0xKjXQ2cZUa2GGY3o9FsIoVC+pVqLKJer9Ed9rAVji9FizzmsCPB9j1BKfD8k9G89VXNbR4sNpFaiJPjCYS2EMidzBUAibTH5Ya0jkAJLQdUtXIYUCocjlArIEEJhHARYygH4KkcgwLdUSoUaUznMqUcS6Tk8IbimLUvHJFMNi18gIxgDJnVIv5CVyhGY3CG1w95BQg0f+oG386v/9vexukKlOUOcKKxXRtiUjl7j8L6jXF/dZO/9e7j5/C7Wi7jrzYvM7n87W4Mtdre6iIVp9hxosrueM7UsmJ5fwAK9zZxtNmlWSrz9h9/E2cdXGPQHXH5mlwPLdV48N2Lf0Ul0WxPPdYhGS6xtXkMi6ZsB88vTHH4sZH9lL8988yKTeytM1G7ddP1O2k//9D+j3e2QxClxEtPptJmrVtjZ3WX/gQMs1OpYBA5HEIaYLKPemGZ3e4N6o4mSOb7nETQnEE7iBT5J4sizEeVSGa0TnHNMT0zQ7rRYO/cC9zz6AZr5EspXtLd3iHUPPwtZmt/DaDhkMOrjxvTzDot0AidVAfubV9tHU8UHjjT4QuE5V/DYOYO0EmstuTE4FKEzRX9NSqwQCCdR1iCVwBnFTCRAGhq+JQxEscDoC0IffE9gDNQqjsRZyAXWOvxA0qwa0rxguypEeATGOVwGWjt0LnChwFhDcAeNYG33Vtl/f4MXv7jCyb95iM+eOs3+5TdyafcKb7nnCIePHuOXf+45kp5PYgymn/Hr//7THD7RoFQNmJgLKDWGxLuCxekmo56kfaOPne8zObvI9d3zNJv7OffZJ9jqlHj/jz1Ir7tLd6DoXhry0s4l9h1Z5si9VXZeiplrTNK6JNDCEq5PEw936R81NGeXWD6aESXTr/WRvWKjJEOIopZ3DjrtNlG5SpZqEII4LghVg6iEkIog8LFYGs0m/c4uw+4NJuaOUKvWmV2cQ2tNuVxja3Od2sQsw6HEj6rI6iTHlg7zjnf+uULmyVmshS989o+wuWAg+lSSKuVKBW3GC6HWorBYK7F5Xoi+mFcpFl8PBEPP0VCCUWYYUYAPPlDyLBUgswLfk2hjEU6SGYcSglBZvMDiYQiVT5ZpnJPsakFkIJSCNDEEQ4WHJioJwkxhnGO3Jxgkgi0J5aoj61pK1YIBqxBGdCgfnBDEscDmMLIKxZ0zGXLj6ibaKB793pM88cTzPPLgcT75uaeYb86R+xn//Gd/k/vuu59Rt0/n5gb9fMieE0ucffom9z14iLVrA07WpthsrbHWWme6UacVjvjAoTfzjbOX0GtNvIkK27HH1FKES6+x8XXN0gMR9x9cwF+e5dOf+CaHBxNIEpbUHKM9qywv7KFZifjk79+k8dU2975tP1//+jbHJpvw9tf61AqrVCvoVopUPsrPya1FSsHi3kV0pvHDEibL8KUP1hL3R1ghqdQrhGFI7tfY2tqgvbMFQnHg0EmGwxHV5gKdQZ+onKONZmdrm25rd6yeupfJ5iRpllIvldGRY5gOWNte58Ce/aRpSqVcJkkSrLOFUyIxeT6mqP/T7VaOpgCazWl8bMHMowv6OSMcNweSim+ZVJJAOPSYA9+Ma0KlJAqHLwFnCcbyvMZA2ZMYWQwdKykIfFDCohTYimA4EJSbUBeO+4+BCxzhLPglicksnlNFFM0dIpKUSoI4M0wEkqA++yeu/zUyBTDoZoxWNCe+5xgLxyXn/2iEP2ygJsv0b9T4wIOPcW24gzOWw2+6i6vPnafpJjl8EOYXJ7nv4EEub66x78A+Zusz7I4SlhtVbqy3+NBjj3Hq4mUmmyGdTpvOyoher8y+E7NEI48XNy8xpUa88z0n+fofXeWv/NNHeOLZi/zIO3+IK5dvUPVr/Jt/8S6uX7rJle5lRrEjqk78iet/jUwBlKIy3oyHtZZOr0sljKjVmsxl4AXF61RKiRWKar3CvuW97Ha2KQWSiflF4ol5uu0NGs1JarU2M1NT1Gs1jLEcUh5JlpLlOTrPCIOQ/qCPdY5Wq4Wxhrvvf5jNzVXcmNsxz1P2Le/DWoMxBqTC6gxri/ZTbaL+J67/vzbxpwn3PfPMM28FvvrtP8fvmL3toYce+tpr8YdfP7v/dvvv9exuFdGeAt4GrAN3DpR3e1PAAsX1v1b2+tn9t9t/l2f3p0a01+11e92+fXbnLG+9bq/bf8f2uqO9bq/bd8Bed7TX7XX7Dtjrjva6vW7fAXvd0V631+07YK872uv2un0H7E/toz3zzDMhBVXL/2P7GQ899NCtJz3/b7LXz+6/3f57PbtbNazfwP/DO/TAazLdwOtn92rsv8uzu5WjrQPsqUwzXoLG4SOEYzRsUy5P4hBI6WjtbDIxNYdAIpzl5rWnWDzwRvJkSBDVWLnyBPsOPoQU4GzBYY5zeIDDIAgQpBRz+bbYfREOIQzCWZwFIQ0uh0LmM2c8XgwSnMwQFCs5mYXLSemV63+NbB3gF59I6Q9ByWLbPBSuoJfGYYVAFnrjFOwnoBBYKfBE8VMpCt53IQXOKYTMAYkUAuUECImUxWC1EK6oA5wHqphCl0IW/16OVSnl+G86sELiSYcQCgQIJyiHjg8cGr1y/a+RrQM899J5tDaAGE/xW5QsWKmUUBRqSbY4MweSYvDCUdDDO5vhnMVZh7E5whbKSFonCFMsMEvnsAJkQYODc8UZWgfFl01hrUFYW8w02gwlDc4YhB/g+SFKKYQfgTU88Oh7X7n+/9pu5WgGwJfgyVrBOS41AoFfmyUeDSnXamAlczP7QLriZp1k0E8IZURUCbA4ultbBEfLCAdCOYQraJXl+IAcDiFCZLEkDq5wIiHU2BEtCAlBAsKAkDhpCk2qUCG8Ms6TOAkid3Ahe+X6XyMzAKME+pkoONuxJDiclCAEhVCkQDlwsjg3TwhyUZDRFjwUAickUhgkCidBCVkM1AqHEw5pJVJ4OCzOWZRyCBcgnUZIAUIgbOFg2GIQV1kBniAzrmB0Aox0+P/V9b9GZgDSLGF9Y4vJyQmUChgOBtQbNXZ2W8xOTyFdwYEPDiEEGxtrzM/PI6zGaEOaJGRpTKlUIhkO6PW6TDUmwOSFWIUr1m8sAiFAUDgUCCyOzY11LDA9NYtNWgiTghD45RpCegiXkKdpEQgQhJXKn7j+/9puuyYjCJGyYCgGH2FBSEW1GoAzaJcjlcfO1hazs/MIB3fd/XakEmxtbxFFHv3cFl825RCFcAzy5QVRC6qQT6R4tY7VFIXCoZFSF68cwCoLsoQQAuEbhOfhlEKM2YskAZgUaP23P+Zvoynl4UuHk+CQSCFRCFyxbl5EeGzhRKrgD/SEQzhV0DfgARohCxdQUhSj4aJwIB+JFaY4SyRIhRIeFoMnZBHBkEVEhLFzA55ECoeQqtgYlh6+LIhu7xSzzjI5OQHOMYpHjIYjyuUSlVKEscXyrxT2FWebnZ7FphnOgXMa31MoInrdNqPREJcbtMkKdm1rX+H4cM6RpgkYixOSwPPZ3NykWSsjGaD7K1gLpdoEUno4AdJToA3Kkwin0Ebjy1sLhNyeM2ScfjhR5BxS+mgMwikkHr6SZC5mZn4JnCUDZFTh2oWvsnzsUTZuXuDeux5GSIHCkphC/UNJjXAKoSRCGsAWbxrsWInKFlFLhDili8gmSsXlCIWxEmXBCYeVEqzDuRy4c74s+BIXOJQVOFGQyzoJyozPEotTPsIVe3yyeOMgnENIiZMWZIDC4iGQMiAHPAxSKITgFUcSuPHPXLGTJ4uoiRAoZBH9VJFBCCxCFkuVnpCAK1R//DuHM6Tb6+OcIyqV8H3F5MxE8QIJIkyukUi0M8SjEcYYsjxDOUuWxsxMT2GtxRlNnqWEyqdSrZOlA5I4LohStaHRaGBtQbMRRRGtVovA9qj5lu31DWZmp9FWEYQ+IFCBh3Mw6HWpVRtYHKEf4Hl+UQ7dwm7vaMoipSooCpzECocQHkJocAFC+PgiApPT7feISg12Nq9y8fmPM7fvQRb2HMRmOUoKzrz4DPeefBiHxDmDVEUOjhBFneEUDgHCIKRFuKj4sjmvyNMp3mDFl8rhTJFG4fJxo0Lg9J2zYe1Lge+pMVOzwjmJkLrgXRm/Uv2CP63gE3QSIQROaFSRzyCkRLoi+lkpiBwgPKR049pl7JxQkIEWgQzhCo0CKVVxnsLhO6+QL5ZFMiulRAmFEg4nBKG8cwbMlZT0+gOiKGI4HBKEIUEQEI96RFHIYDTAaAMCpLM4qymVy1QrZYy2WKfZbe0wUa2x29rGV47d3Ra1WpVGqUFmCsLTLMuQNqXbuUYgYJgVJKoLiwukcUyl1sDzQ6wQdHZbWEBahzfp45wlSQpKhPDVyjY5AqxUyHHKZwTjQlxh5BiowDAaCwQM2utMNKdo6wWUUqzevMb83B6GvTZHjxxFCDB6gPJLICwOA6540EiDw4wLUn+cRgqE8HGk4BQ4UzillDgy0AqhDM74CPKiRrtDzFcSf5zaFmkhCBTaWQKhEEIhxtGEAkoa06r7COeQUiKdG+d7Dm8MCsjxin+RYSjAYjH4UozPS4FwKNy40pf4qoALAuchpEQJUaTgwiHxAIu6jQ7zd9KiUojwirMqVco4KbHOEoRhwU/j+SRpjLGmiC5RANZgTLHtbI2hXq6gdUatHGFdTq1RQwpI8xxcSr+7SZqMKIVl/ChEWJiqRUgZIKSiXJNIz8MJge97TM/NAY7Ai8h1gq8CSrUQlKJca97yfm4f0ZzAYTFSwjiqITTg49vC+5y01KISu9vXEU4wWV7kXd/1o6yvX0G4mFbrKrunfw49aPPQD/4OhjHRDxKJxYx5SRCCrN8lqDTGtCtQxC8NUuJcUaziHM6NJU9F/nL5XDjtHdR5kUqifInnFFrowlGQ+MLh3LgW9VRBBCpFoaJDgVI6BJ60uLHTFNXWOIIJhyclyHF0R4L0xkzxAiUp9OWcHH+uw1P/Bd2UrmAtk2LsmNKhhMRTd87h7X/gYVKTY7QGLEpKbJaRDgfsrm9iMYS1OsIZcp2DtThTIILWWYxOC4pvnWBNPkYnDZ3WakFNJwSer/C8KhKBEgrhCZRUOFswtjknC6Y3IQs+R2PxPYV1Y5UkpXBOoCy3pdC4PRgiHJ6zGFHArEXqNobox8QpyhZw8f7lu9Djh9jfuEJUm+Dima9RKZU4+42rvOd7/gbE14izJkHoI/CxTiK0BKVxVhBUppDogqxVSF5GT4QVOFGkiM6BcMWlC1c44stKjM7dOTWaLyWBkoUjjWF0KYqUzhs3N4QQCOVwsqilpHTjbLqow6RgjFIK7NjBCoIixmnj2OnGKaCgkH+VongODov/spOqMZ27eNnR1Phzilr3NqzW31FTSmGzDD+IyPMUYwxSeMggZH55L3maIK3F2ByXZ+Ac/a0dOr0u+SjBGI3QGSYb4XD0Wyt4wuKFIUoWIu9KBjiTIZXC8wK0LYAnqQRSFmCcUIU+IMpHSYfA4QVRkTEIDyEV1tyadx/+TBHNYsdfEuMK1EyIlwGLIg2xzoEoWKyGWYf21ecYpQnLR05y+QXNjfNfJNh7H2L+zWSpQGYthC1jhaHT2mFyagInwNgMKcfe617W8lA4m2KUQSIKaM4VElHCycIh8YGsABHGEO2dYMorkEFpLc4rUsiXF209USR8QtrCCaUc975eBkpUEY2kQGJReONUz+CQ+GKMZFL8jpRFbepLhaWocXi5Tzfuzcnx31RCvlLzClmIkygBpZcbpneAWaeIKlVMmuOrCOcVqbN1FqyhHPgkvR6eF+CcIxsNUZ5kcqJJ7PfRWcqwPURgaK9fJAhD/CgikAJUEemVVAU/o6dAOnzp4/kejAl/hLUYZ/GFV7C+eQHO6ALhtRYZFMCV1ppc3/oFf1tH+xf/y5+DXobwHcjCc6XvgfAR0scZjfN9fBtgfY0zBpX5dNKck3u6rO1GbGxZZpZiNn71HwAZSA9PWPLMEnkGH0Fiwfc8lJZIpZEmRgYBu8MhFZPxXR+8j/3v/F+LekKAMAVogNXFl885hDUIU6S1d4L5gcSTBuzL4IWhwAkBYbGqkA8BxtFIIqV8JTpLacdpXoEMqnHzduxDSMn488Z1myxEhpQqlFm9cXMbHGrcUwMPKUA6MUYti1a/kOqOSh29qIRB4nkWh8PoHM+T5EZj8gxjDVZ5xVkGCplrlJ+i0xSlfLTQRJUyg90+yhMFR76SCOHjKb9o5isxFoGXCOGBFBg8pM7Gkaz4HescxhqqUamIZhKstiAUWTxgffUSfqnBXW94x59+P7e74aybMuoOUJGh4lm09AhGFTzfIqK4eOJaoHEom+JMgAwlE17OqUsZSgVknsaKMs4OKDf3kg02Icvx0CRaockgd5iki7MhiYkJM4lSKU6ndP0G7e2EA7aoV4qgoHm5fhPOjb+cBsyd81b2FIQO8AqqNGcLKF4Jg5QKM55KEKJIIZUrACGJwCmHcgrlLE4ViKJ1Bh8JyuExjuoUwNDLKaAU3jhFLX7gO1GkPGNkUWIKtHHcQsCNUUhr8eSdg9h6SLQQhXCFs+AVSLWnfDzpofOUwI/QOicIS9gsRSgP4RuUVnh+ANoDTCEj5nkI5eMrhVQKpXwQAmtzPKFAKnAaazXOQdkLsLao7XzPoxKEJFmOFwqUiHDkXL18lsFoyFZ7QHPiVVKCS6moRIowyJFKUvUVqYvxVAUrDVI7wnKNnnX4poxVI2Ip8PM6CklmLJkb4eyIlasxS0fWCV1CHtawqcOaAf2kxsy0T97aRBqBsEXzVQQxbhQx0TDstLbB+QhjQKVFWmklzuU4EQAGJ0rYvAOUvi0P+9Va4AvsuCksKCSHxRh5FIJiKkQVdaZ0gCymYcQ4kklpC6cTY2dkjBLKYjLkZWi/mBwp6jhkjkQhJHgIhBTIsczWK+NecsyJJtwYYHFjxZ/X7qz+awvDCKdNcV5SIbVGOoWRCcaMKen9sEjFPYW0ReXqSYVGgLN4yicolYn7HtJz+EKgZTGBE4jirK0QCFU4lVAh11t90ixn75ShWSrhewWMbx1EoT9Guw03Vm/QHo4YJTGz81NUyrVb3s9tHc33Ewa9HM945BVNJKAShcTOILxCJseIIeXQK9Q5RIleZ4RfqaBiQb87Yn3TcniPYmYhQPZ7uOYi2bCD0j08vw5xj8GOJvJVoRZqHe1dQz0NKc1UiI2hvdWj85kP0V5dQ9khtbrEx1J60w/i7/1fiijngHQX2PNtediv1qJxT6RwrpdhfFfMjroiSiEBZ8a1GUihi/E0UYAdYlxfvdwtU8XwTNF8ftkxhBo7zrjGG9dtBeDxX8a2Ckj///J71o5beAIpbPHzOwRL0lrjeT5jrBstCiBCSg9nwQ9K5PEAhCrqJFsoGFlHISQoJU4WIJCUoPBBSnwoxuCkX9S4yiteYhJ2csnD73gvuc548emnqPopkS1hpUSMG9vWOTzlkWQjjDNMzy2hZEBYfpXc+6FRoIo38kSlgRdIjFL4cYYRjnJpmmoAnjJkzmGdplGrYQOF7NfJ0i6m36fdHnLXg8eQvQQV5dyIBVe3QxbnKywcniLEkPe3SPsJsa4S1LvYsMpga0h1YT+Xt1eov+WXaPghwnQAA94MwvMBVwzdagP5HfJNAXxpsSJHyKLCUg4QFqXGkc3lBdjh7DjyjMsoWUQricX/v6CVuDEaKSmimxJFk1sWtZV0rkDMxm0CJwVKFF9VTxbIokSgnCnaJaJwRDmeIlF3kORV6Hvk45aPN27tOBzWUKCwqhie8MMIJQqhQKsCBALP87EIMqsRSuF5BWgnhcKqgiFbCFWg6BisA+087nnjm8YIZImTDzzM+Se+xLHGNNLocVZRjNHduHGZvob6xAxCgB9443P+0+22Bc1GVmUtyxGlMof/xhdY+tC/4EY7wlWbTD/2Mxz+yx/mRuLo+RXObGqO/fDvsKvnybXl2PQZ/vaHuvzjvypoddcIRJVL/Rk2y+/kxD2Psv/gEtKzBHf/A0b1R0m8BXZUnc2gyeQ7/iXt6rvRB9/HYOcK9x2qIEsTCNmEYAGC/QgV4KyPcUVJb+M2cOcINYRKE2LwbErgEiKRUJaGyOWUyfGUoSw0ZeUoofFFTiAMJQwRloqwRNIQyZySyIlkQqQMkdQEMiciI5ApoUsoy4xIaUKhCaUmlBlVkVMSKSVpCNBEThMJgy8MocwpecW/jaQlkpbwDnI0ISS+7+FJhecpIj9ECYFSBfqKkyjpo9MMrxRhrMNagxKgPK9Ql7UU423Il/shxTysK15Uzhl8L0I46OaCIAjH/VrwA5/NVh8AYx1KeYWzS0GeZ5QrZZSQKBWMe2ivso82V81oDSL8yLB/1uPS9oByzSGk4ZE3PELgKZ6UMBhJDizvYb7RpFGLaEaKsN9m84plpmo4ODmFZ7bYeOZr3PuOX+eFr3yOqTBAhBPcc3iWp9fryH0THDIltKyxkH+D+9/9JpRwPPHVr3Jlo4PTGUKJAvE0GqSPxGGNhxMScgP2zologdFFIT/Oa5WS+E6N1zEkgRtLkTuQ0lC0PU0BjChZIIOOAsAYp5YKAfblXph4BT1UTozT0ZfrtqIp+/L0hxDFyJUcj7wJyyv/X1qDLHAm7pS5GmcgN5Yw8JBCkjtDGITEaoQxssgKpET5HliHVxqnktYDN0RKDxkUKaYRFA4r3CsD1hZQSLTRgMQvV9E4hCmelScl5WodrCnqW6nwBEXaaCzCE0hv3EcTRb14K7utow2yKsgOce7x4tf/M73hNfYePcTo+vNFCpNtgq1hshH1+b0EdOhrS9yZ4tpLd+E3K0x2Lf00o7qjOfr2d/C7P/0P2HcoYOFYk6h6gI/8wt9jqpbRCCSeN+CRQzm9qb9Ct7NJnt1gvjLHdifg1Fc+wZGTbyasT+H5VTBF7YOzMOyBTSkWjL4tz/pVm9UpLrVF208I8MA6gRSugPcp1mSKUYTxG/Fl58BhxyWcEA4jZAGYjBvcL39RhCzaihZemRqBYkfNCoGSHk648RCxRCiJtEXdJv1il0sJhzPeePLmzjApBIHvFbJfQQAix2pLEIRIKYoX2AiisESxo6fw/AhncqRSSARxHJObrNgLkaDxCqkx4SGFwLoiUhmTUW80ij4dGuuKqSQVhMW/kUWqqtMcqyDHJ0COv38USPhtmta3B0Nkxm4QYZMUYXbp7V5j4/wVTG75vV/6i4jWBtPz89xcl3R3d9jevELkK1Kbsne5zk4+Rz+9wI3ru6xdbjF1ZJ7mvoCHH36EZqNKT/ZwWc57Fp5mmM0RDwd8fuW70ZcvsaDOEYyeYWZyiuo+y/1v/0voQQedtPFdAysStBVI5TPq7FJqBpiRhFtvLHzHbNjPyXJTjJpJgRYaZQVKOIx1pGmGcaKoE6wrdloF5NYU4grG4QkwQmCtxhOgXob6jcWMpzvUOGoZJ/Acr0RRIQV+4OOsKNRObIFYClX8DrbY7RMInDHUyhGP3b/wWh8bAMZpfBEU+FaaFRHekxij0TpHSQ/f90iSjHLFL+otm6NEIXubihiBAZ1hxm0OxnWWdkXEsq5Yrk1tobfunMNSLKoJ53BWEwYROo3J8xyJRecpCIUUFuUV/3VW4OyrnN6fme0wu2yZijy8QLPT6rG206bZmObkI3+NZ574XZ5/aQebbBOUH6BarlMtTTA/JVi3AdsvvEQtKhGVa9x99yJn1oacWDoIpHz6C09QGpU58tABPnu1SuvmRWrVeT7w//7rNCb30R92ODC7xP/5Pz3KIyd67Jz+19QO/01EvsDa2kUWl4/ilAACMs9HdLokpZlvz5P+Nti5S9cYpcl4BtPieYparcLOZgetNEYLpFDkrtgAxhmMsThbgE/V/lni8jFU5JNlKdbmBFKROYvS40nQlydA3HjlxXOUQ5801/hBgFI+kSfwvQDfD5BoUB7lKMJaxW6rVzibLyl7Fu4QR7PGjtM6ikFqPCwO3/MxaUKej3C5JQwCXJbhjMbzQrJh/5WJGKNzPL9ctEicLZaMLShZpMi+H2C0RkrFoNejWNsGZx1CSbJhH2ML5RjhBEr5GAkKjcPDYouWgzO3zaJu62i78RzbZ67R63d59K841q+l6ESxtL+JJw1vfei72J34Go2oyU6vw40Lz7F++UVmD7yJ5859iXKtQW+wTWmyTjBhsJcd+xfLfOFrZ3nbwyfp9n3e/wM/xer2iE//6t/n0B4FwzbDqEqtPk2/s8HIzPG1046Z5c9y796/hMkd84vHWb9xloWFYwWsnWfEwx2IE5g59Oqf9LfBDi1UOXDgBFLKYvRH2nERpJBSI0SR3qy1+5w9t87u7i79JCbPcr72B7/CnrkJZu+fRFiHkJpACIaiGBi2xha7fNrgPEXkSYzzqZUVvbhowj52/yF6iWR6doJnX7zGxMQsZRLOtQRuqNhb89hzcI5aqYTLNXPTd0b/EcC8PAJlTDH36Io6M9c5QqliWNgVKLN2Foslz2KEkBidgxOFqHwWU6DSY7KD8ZqXFHLcsAZnDMNeDyNKGOkjw5AoKlONAqzWKE/heYJ0NKTSmEDhyPOMKKphdTauh2+tdHVbR2t1RrQz8G2Je974j1Bxly9/vselbpd37n0zX//IP+Hg0cN87nPXuPeeCX7vN36Gg294E6ee/jjHH/5envvGp6lXmnR3t5kr3YvonuHqC8+zd3IPFy5cZXLS8eJLp5hbOEh5qoLLh8halXIA0kiGo4REGhI9zT3v/88MWheYmDhMnjtm5k4wiHeIgkkyVWI4HPH1Z/4DJ37o5789T/tV2tFDy+TWFVwVuGKiwWhWb67ihGJtfYNP/u5v8f1/5a/RWr2CSXM2Vze4fOqrOGnYf2SG7qhTgPXC4KyibxSNqsM3jsfe/CaiyiS1UsCVjZydJEG5nFGcM19VnNoq8bajPlc2U+4+tJ8ta/n62RhPaHxpeWEY40yCk218LPsmSxzbs++1PjYAWrs7HJyfoi8UVy+t4EtJPOwVGnt5QuAHeFGdUa+NM0PyUb+IbHnOYNAmTUYMVtfIsiGiXEJaD2eLqRjnDChVoIu+j7WGioCg1ER7Gozh8oULLM/PYvN87EgepXKFLI6RNmOUeAyH25w4cQ+761fZ3Nq+5f3c1tG0CglzTXmhyca1r/LUU1epTYbMKMHXPv6L3Hfv3Xz241/nve89wtlzXe669wTPP/sUKg04/dXPkWjB3PFF+u2Y5sIe5NQ600fu5loL7O5VFhf2MzchyXrblDzF1U7KG2bvolRqEAlDN16lEpUJZQeyYnRpMBpRLk2SpD20KZOnmj/48Ef41Jd/nTe/+0Oc+HY97VdpP/cL/4F2u4tCkVofpUY4A1IZ3vPB78WpJh/80Z/gs3/8+1jnYZxmYiYmzwyVko+npthfFey/71GO7q1yZjPF82pcvHYNZImXunUWpOQbK30mfI+Ri7B4aL/KxcThJQm/80zKZKR5cc2R6AwnyjRrIVv9DGzRED7c1IQyQonea31kr9jBgwfYO99gu5exuTZA6xjPLxEEIaN+GxGUCaIqo0EXYX3Aw7oMnKFSrqLTBL9UJo77lP0IK3xUdRKnNXgeeZKBTnBSEFUaeHnM05/7KPc99gGc0Wyc+QrHFhfwwxBw42hZTJyUAo+doUbiqE3N021tMlt7lYufnoJwQjDKAw4cfxPO/zmOLy/gKlXCzkucPx3y1vceQVZLdHeu8YZ3/0WeuTRiZ/08211YWghJ8wFzzSluXG3x6Pt+nAcfeYyrv/6zHLn/Xby0fZb7VcQLl04TlhY54hm6vQ5eaQqb9Yiah7iW7CHvtfn7f/P72DObcs/Rk5Sqk4SRR+4ytra7JPE6z1/3iL/wIu/7y9+mp/0q7cB8jUakgYCJagmtphj0O5SUR6U2ze7ukGHc4YF3/AU+8MYFBv2Ej//xHyPl86Qx/PzP/Qs++aVTPHe9xUwMO32PQdrHRk08Af/oLz7CS2eeY9gdoUXA1vkX2bt8P7tSoESOJwKcLUbVQhz57iZT88vYwYi6qhLbnMNTgmEi2U5Twui1FPr8kyY9j4oSXB05tNU447DOotME5QUEnk+ajgiCkFwnCCFBeZisIH/ylMJZh/IE1hiE8nHWkqYJZVUmzS0TUUSSDVF+wRAwE+Q8/enfBmM5NDOFEmB1ThBFeFLhBPjKZ/++Y4yGp+mmkslmnTU53pa4hd3W0d71YM4CHea8Fv3H9zJdeys9nXD9mSvc/fY/z2zYZ3WtzZlLV5gqedy8+TzHZjyCvYf41qkRg84NFuOYzY0LtA7fxcRiBeVPo8uH2E4Evr8fUTvIsQOaZzbOcnkQMrH6As3pvdRLk+zcvM4oS7mx6vHvPvoJvvzRv8dvfvTr5Fqy24fH7jZ87/d8iJmG4Bf/XhNRuTOKeYBS1CQq+UzP1anVm+ysb/O29z7GN791ilF/C+X71GQTZzQf//Dvsdt4gK31FmGpyWjYoTOQPHVujeWlaW6uDYlHhhN7Gjx57ho/+K5HOLZvhkN73svjZ/+Yqhsxte8E85UR6BrdWJKicULTTQRYBfVZMukYCg8XD8lkwJlNTUlqRrlgObx1L+g7ab1ewqlRTreXYuIhxmREfkiajorJD5PQ21ylVKsXS5iegiRH4MjjIUmSoJNeMWXvwPN9nM3ROiPLFGmaIsOi0b27scbMVA2XJexrVBFOg81BRgglyJIYrywxWiN0satWq4akLkHnGdIT+H50y/u5raNNyxvsrGa0pUMJMN2XqC68j5N3zSITmD72Dja2PsGjbzpCezSP659BlC2rq5aSukFtvkTe38aJPlevnyYKemRveS/HDh9Dxy3yOGO3t0486pB6k+R+xmCkGbbWmFhYwis38KRABB7WOt7zA/+aB9+5yc6lT/KVP/pdvvjCgGd+5hN0Zc5s3fIPf+w937aH/Wrt+JE9dLa2qc1NEg81vWqTNMuYW15ChtMkoxghFPP1jCvlPZRGm+zZd4jJhbv53If/De9977to1Ks89s73o6tHafc26ZVyji03aFQUZy5eoTfU/Jt//L1sbrX4V7/2OYJKibCT8T13N3j+2g4qKLM9sCRW8L4TEeHkMlIpTK7ZbcVsDxIkKWu7OYEavdZH9opdOf0ctdm9pMMeOhviBxFpOkIpic4Ttm5exlMBXhCCzlFKoZRHrjVZnpIlCUm/T6YNfg3IR1iKdaosywgCiTUpwjk2ttpEvkfk+0gMgV9A/8o6fE9glSpoIFQwZn1THD58L+LC0zz9uQ/jwiZ91b/l/dy2tftrX5zjw1+Z5bnWW+i84dd4+JH3c/apNTLV5+7HPkTeu87stOGFLz/JjHqeuN+ns7bB/ELAyZPHOXlihkTtsDg5waNHB2QrXyDdPU9zZhlZmUCV97O07346OhizhUTsufthhrurpKN+QVhJjJQ5RsPf/dvfz+f/499m58l/xWP7r/EP3jXA7jvMaDjkwpriX3547dvzpL8NNtGsse/Ewzz+9Rd48caQq6s7fO5r32Sm6vGlD/8av/Iz/wO/8jP/gIpI2DtlyfsbNDxNZe8B5qfr/JP/+ScZDUas3rjAB940wQ++616OLU4QJkPa/QEXr21TigKurdykVlb86//ph5mZWeChI0vY0iT75xr8+Pc/wo9/4ASoiC9egc2dnI2tHrm21JshB+ZqLE5Ocs9yE0H99jf1HbJ40GHU3kFnCeiEPO4jbFrwMpqM1tY21UYDcOTJiN7mCjurV9lYuUiexEzPz7HnnjczvXyA9vYWg16XwaCDLx2+EugkwWYZWZayMFNHmBydaZzzGQxSpPMxeUY66GKzEfmwTZr2CcMyyknSeMT+/Sc5dvQkVQY0g1vv8t1+qDisUV8Q7MQBJ9Q0g+Yidz9m2bzYIawd4czNX2V6+ghLBy5SndjHMbXLC9efpr9aQlYHNKNJHnjkJ3j88/8OdWnAl093OfuT388P/tOvUa9OM1AhndYmew88xI2XzjDKHb6WLB19I7urZyk1lvGDBsLdJEHwv//rP+B//pvvYKtV5v0nPDZ6gjzvUDl4jOTqNp1++9v1rF+1bbUNQq7yyIP38PWLO5QXj3N4UvLFlYT9D7yLZ089gRSK3/2jz3H84ccYLewnlo5Kq0UcZ0wv7+Wv//W/wTPPfoVTTz/H6vom7/3uD2K6+9k70+Ds2ojPfeR5MgHS8/lrHziCn2/QtSUOTlZ58ORDKBly9OgCRz/3eZ66mvFLn/hlFpoBP/73/ilGRmhr+Nqzl2llmr3NO6TTD2xfP8faxRcKSgFXrPEEfoiKQny/zMzcHMIJgqiErzyCcpVSdYqp2QWc1njK0csNE/MHmFpaZrR6HpXFVGWfRqmMjASJ1iTDDsP+iCcvbXDf8f3k5RpRGJHHQ4IwAimwWU6326JSKiOzIbt5ieX5Jp//2G/zfT/2d6iUy0ivfMv7ua2j5Tqm39qiPDTk3gTnv/EE7bUhD37Xn2Pn0m8ys/g2gqiPqL8BVzlAqhe40fkMU40+YaLZzUfc+OqH6Wchn3rxKnsadb7nx/85w6SHV5pjtH6TSm2OYb+Npdgz0tYglKQ8tY+16+dQZkggPEBgdcbRN/8gVz7zn/mFrw15y2LE89vbHAssM2VJru6cOmOHCfKtbVbNDNXJKRA5F1sSVJ1urQbW8b6//y/57M//D8w89EMIUjwp+KPf/Oc8/PZ38LFv3USaOSYbTb54qsvsPY/x8VMp4PiVx69jM1tQSODhMsG/+6OLWAU6H/GVc5cRj1/G8y2eFEi3THU+4/6lv0GcjvjYk1co9gOKbeyaJ8jMnTMneui+t2FlWOyC6RQpBDpLsDohi4fodERv4yo6H2HSYkfNWYcY50Vh6JFritTSSp59+nne810fxOxeR0V1Zhf2kKWOYDggjmMqlctUQ4uwI6Sz2BxUuUwy6lOrTzI1s4gwhlw7Vs6dQvdmOXVzxAe3rpNkIGuvcvFzpgQ3nU+qB/hqh3JtlhV7hY995JP84N/53/HEGb7x1JAlL6G33ePC1euMshF+ss10tcbcxAT1SoetTNFzAYN0ksHIo+FdZhTdzdTCYUYmY+XiM2z1R8hsiMkTtAYhQybmjqNkmcykGJ3x5//8vQgCEt8SO8HnrvVZDHKsP0mlPkn7TpmKBabrHrK5RDMW1KqSi9e6rLRTpHDc+MqnsFgSrRHWsXn1W0wv3UOWFYOt4f63kKQJZ37v57nrnT/Mwsk5pFfMdUqlyLUZb1b7BX+LUuQmByvwpIcVhtTm2DwkEQbpwPMjhmlOokOElIyyFGEsQoZIkROW7pyX1Ob5Jxn2CxLVglhW4mzBuSidwzhwzhTABQ5rLXgRvlIoT1KqNrj7yH20dtdYO/8CSxNNVlfb9G9u8sBbD7J69XmMEWS5RTjN9EQAmSm2qqVDSIcdrCGTjExZytVp8CTnLl1n755FQgzTJcvazTWCSpWSf+utkdvD+9VZlg7ME6dDhlstWpdukPc61EoBzUqT7e0ON899hZkjx+n2BnjpVVq7Bq8W0BtlCO88n7yyybKQ5APLvSeOMjE/y6Ubu8wvwWZ7F+wBmjNHaA8/TlU4Uh2T5xkrNy4wPbOHXHiUpMdHPvb79JOcycijt5US+RojSmwmOc3SgAf27WO0Mvw2PepXb5MVj29dGnCjNUKMhTuMKx7shVOf4Y0f+lG0lTz6V/8pX/qt/4NHv28Wqg3q0wcY7I4Ynnuee37g7xYDjspHiWKZ0VEsO7oxWU3xbinYe4vdqmJoWeIhlSDPLMr3yFINKKIQtLUFhZoQOK0xwpFnd85bKs01BofwFH4QoXwfIYrlzbA+yfTCPvYfPExUnSRzkMQxn/69X+ehNz/CZ37355nLcnY3PoGO++TxCF9AdvM8IDlzfo3O2jXe+u53U+1exfPKmDzFSoETBROyothq8MqKQMQwWCPONEsVy7nz53jw7uPMTU0SBh4mz0njwS3v57aOtrXVYv3yTeqVgM/84v/KKDdUg0nCmuRbH/1JXrp4jsBOUfMS1ldeIrm5QT/PqY0swlgu7EI1L/O+Y0d47tyT6HCRc5eHrD/zn9hcfAtZv8VvP/frqJ01JkROp93lP/3kjzPKRiQZxAPNaOpuYhT//pd/FpcaKlHOFz/7DN/1ofuRTiGVR2Ainj3TZbJ850ygK8/n+GKJ+w5UkU4xSFPSRPOtS7sgffrBLKGOya1kdmYfOqgRd0bUlu9luHaVuUceK8h6ZLFvp6UoZvXEePjVFtvUjoIE1QpXkBQBzkokjhyLNyaYQQmkVWidvbJpbbEIBTiFVXfI2gPwvf+vf0hYKeGsIE7AOoMCwgj6rTbr169w5skvkPT7DLsthsM+2WDA9Rcd3/W9P0KWpPQHPbrtXc5/6Y/wfR+pFBXA9jdpBGV+65d/ne/789/HdMUhTQ5BFU8Z8tiijUUpRaXSIIoq9LoD8myHkrAcOHgClW4S+R6eUjz77GkOPfDGW97PbR0t3WkRqRRZ20tvcJOJcombO9c5euAEa1sXuffkYf74U99i+eoEb/zgu/ijP/gIWS+mO2sQgaVbMcTZiP9w6ilcZnjhC79G+6XPYnRI4+IKeT5gbnEeJwJ2rp/H6ZiZxUUWDx7B2oDO7pDTl68xMBZ0TFN5nDjcQBvFH378FM9+6t/ykz//y7RFgh1u0azMfpse9au3WuRTLftoA2cu3sRTPjdbKYfmm8wePEEgLO3tHmmWUTnyJoSA2mQFzztEpVnH98OCbUCqMb9lQW2AVQipyXSGrzwQspgNlA45pgI3tljX9qzAOIGSBR2gEwanRCFF5Ao25cwWtV7g3TkvqetnnmflwtmCEm88Va9zTbkxxf0Pv5EjR44yM7+I0Q6tLUJYslHCqL+LzjLitINFMeh1ydMMP5BoGDNuG3zP8eDRA1zb3OH8s5d507veRcW32NE2pTBi3/6jOBFgtSG1gqAJTaPZvnGO6VBgsiZHFg1isM6+pQVma7eeE739CJbw6PZy+qMzVJqT9OMeU80pKrWAe+95Cy8+/VXuW14m6bZZu96lfW6DPaKC7mSMtgxeoJA9g2cjfvzeGZ6/CvXmJINul0m5zupI0mrvgNUYnSKczygZ4W10EYHF1xmzLma5BOtaEumQQwdO4pBYl3Hv+36Cf9R5kf/wsVM4qbk9leV3znaGOc9ebHFja4Cxhf7bXQsRn/z8Vzj06PcVNAZCFbJNzBUbxJ4i8H2c9AvJpoJ2GOFEkUo5gRYG5yy+HxSszVZix/TpuQSMLRZBFWhdMBLHVqOcwLcC5YoYaIXAaoMbi25k+Z0T0WqNCe57yzvxoyoCSNIU3yt0c1LAjPKCrEk6IC0AkjzHIcnzDKMNWZIQlEuM4h7loIG0Bid9tNFoHMlwRCW9gHSCF599nqsvPsN3/8iPEgWwfv0cCMni3qME0qOvLaEX4h88SW4kGzfOM7+nSihjGtMNuqOdW97PbR0tCgzL81Os7bRB91G+or27TffxHcK3DNm+PGKzs83BvUdJWiv04zZTXkiQC5I0xrcTDLwy3sjx0oUtpmolRCLJ4h7X+prl/XU6gxHS5AgboWSOMBab9ZFZSJ7FNEqaNIG9kz67u5bNjRWMS3BOgHUcOvweZuvP0+4EjBlV7wiTJufNRxvMV+HiDUhdTkrEe97+BlpDQytOEdbHOIvEoIXFcx7S9zDa4EmF4WV6vWJfyo1XPpTw0fZlbY9CvNEIUGMhEIsopIjsmMrOCnxvzCwtPHSucU4jrURJgTYaeQctfkaVCbS2pHGC8sfoo/MBi+d5ZGP9M98LyZMYrCU3WTEL2dNo40B5DAdDylOzOB0DAmEyhJUocsLpvXRuXEDkMUmvRVBt8uKlNdpnvsUb3vdBpmoeu2sXsdYxuXCA1Ep8axAS9uw7ihGC0Sil014rqNtvYbdnKvYc3UGPyNOUUMSx4/jJw1w/c4XLp7cYpjFVFIP+OmeeHBDKAKvAlw6NQrqUprVEnkR7IMKEpOdTsSOcUnQ3OpSCOkluqRiLinwClyNiRy4GSJ0TSo0XWXTs4Ss4c3GNv/sX38zP/sY3sAaunX2G2ckSnZ7GcedMN1SCkAzL4cVZHjq5RJZp4sySj1KCckA8snztpTU2BxqHwrPFVDnGoGRA7nTBwS8EZoxTCBymKK6KTeNxiuikw+YG6Y0dVzikVWTKYHWx8BnnEEjITYaHIJAeGoPJiy1ic8cQGTBGQyWBX0JnMcKZsd6CIx3FhOVasUdnCuDHK9XxnSTpdjBOEpQq5N0O09Nz7DTn8XevF0IZxiBtAkbjdW8Shoq0eQizeobJSpX22W/RN5ZTX/g0CHjrd/8F6iXFcHelqHOlj9dcRkoPvXOFJjGNSNGxt0Ydb5srVH2PWsnSKAcIIWhEkp3L11neU0fYEaGyRGXN/GwZnRqUc0yiqTifCSGpkVLzLJGvaXgCfxAQ2A41W/AtkAnieBdHgihnBCJFaYdUHcomphLARAmm6z7zdc1MqKlIyURo+Sd/403sXP0aly4/TZpUCKQjuHOyHyrVqHiz+pZ40OMTXzmHy3OCKCQdpgSBYaoaoXHkWiOcxDiDkwonDLkp9A0K6dhiIbGIOQU1nB1v9TprMVYivGL1o5CFGtOtWYERYGwhW5xZjRMCbRyJyTG2EOAzL+uq3SHmxtQOzll0lmKNxWo95r2X5GmCs5bcpKigwjAeEg87EISUy1W0Ay8MgKJ+/cb5i7h8hM1GKKvRecoL567S2elQ6V5HScjzhMg55jyHsppWkvOJj3+Mj/zGr7Or5umYBj4a2b6Cap2jKnPa7RFrN7c4/cL5W97PbSNatVZCOB+nBYKQXhIzqQK6SYqMFM2KwBpHNL2fSS7RLIUI49E40KBz2cOoFOlyJGWsTPC8gJKqMxz1aTiD0B4EDp8KKjTgwJcGPyjhSNAWgtAV/PA6JPdHyK5EyzLKOH76n/0TAuezZ0mQmISmuXPAkJurOxw5usjqZocz13Z48MgCQanC5751nj//9sNs7iZ040JDwFOSTGvCsaqksB7KL9iLNXbM8ViwFXtOYgRo4fCtgrH8sDCAMlhb0NBJYQpimvHSo6ZAHYXNyYTFsxKNwxcWZzzuIMUr8nREEFTJswxrLaVSBac8dBajgqggSEVgrcLkGUoIpIywOsGkI5xOMa6QBTPZCGvg00+e5z1370E7h3SGfbMNcunTbvWohGCznBRHIAVKCZZLkpXegMbyAZ78yG+wGTu+5y/9FaZUTHvQo7O9xVc+9WXKnsHU5m95P7d1tG5P0GopcqGYKVUYZQLnh+SuhhKCRFaRaAa9PkMxM9brlPTWFNKvYcVUwUmRxyhXIcscgU2IXZPAFPINJs8IrEWkMQE5uVWQOYQMyaXCOE1ic3xjGVLDJJo891AVRzlU6NRyreVIM7/YSbpDbHo64qVLm7R7Q+4/vBfpg8kSHrxriW4/YZgZVra64BeU1EHgo5QkzXMkgkAokjxFSb9QocEVb3NTOJ5CYJwu9NAwBYe8liBfbupacAJtHUqM1/QpIqZvBbl2KOUwViC9ghX5TrEwKGGVRCc5UbUBUqHTIUpKdFbUW35QQiiJGSVIPyjWpkYwHLY4/+TncUlMmo5w3R0OLUzyXLzJ1aFhr58QADVpGGQ5Xd+nRoobE/NYI/EKPjIO1hVZ6wariWZ6foE//I1fZc9EBL0uSa6peZZY8wpr9J9mt+fery3h3CxB3qXnh/j1Co4SYalMUKkitEUIg/U9GkJgSiVc7iFdRpYPkcbhpMWOYqSsEdkYKzQlW4z/mCAgdylet02mc3Sck2cxealN4gKUskjjFaQsKkTGI/7/7Z3LjiTXcYa/OJfM7K6u7ukmTQ1FWgYtQoTMjQRD0EKQ4JUMPYcfxC9hwO9iwRvDG0IwBImwhhTEkTgkTWou3V3dVZWZ5xLhxUnJoGHPwNZgpkH0v6xF1alzKiojIyL/z530uPkK2weG08D45JzttKdSEZuf01E/BznPD773DX77yUP+8u4xv/zNObupcBQDTy4uufdgJh4esB1HvIfgHHNt1UPDkbQiofXR0IZzMgMXBKm2ePf7pUHtcVaoEjCT5mMhrgEKcVRkoce0DARxiBfE2mP/JSv1BuXdIs0QdnVyChhpGkEcIQT61Yo8Z1JKWNrxq/f+iWl7DTXhTchpZN0FZovtj6f3uNJzdz2wm2c+TI53hwkxOAxGb206p6g1c1mBUpqRs9bC0Hf8xTpSr5/wjXXkSIzLurQKaNaA+oz722fPOs7CbhKoAyaRtRQqCXnlzxGfcH5Fmc5h39MdR5w7g8OefCCc+VP24xMs9ETrGI6OUJ2J0pFFufrst3TREZNQT5WVC9jlJeX690jNrDuHs45gI7sn1wwnB3zx8D/oUSYr7Osll59dIKmCh9klRG7OYOwHH37KdjtycnTMLz58xNXumg9+d8nffO+bjLrm/uUFNQt9FwnOk3JurlUVTJcCSPO4Rp1QgTwnOt9h3rUCgSyMbHVU0YYT4g9WdkoVawazVjC39M/EIZTm/Y+joDhbOOU3RKVkxBsyV3IteC8EZzz49b/x6MH9ZsdXlVoKvmRWfce4S83anHbf2VMJg0dTRycr3n3zjMfXI59s9vx8b/zVUUubg284rQZ2bn7tbqHZmkRSKkSz5r1vUIq2uVBrQEmtrj1g+xQ9M9A++fQTLr74HKMSLPNQjgmd0t2/hw0dXgLzvCNoS1PwEfN9wxAVI/SB7GaO3BmpTsxuwpLDlSvmCmGa2arhu4PFySmTxwxByGLIWJid4tVAJ4RISTtSF+iysrKOPTPUyODhqk7P66z/ZB2fnbA+6rkYKyfHPY+uM9/51mu8//GWX97/PeNcOV0fcTXOpAXVGkS4rs3aTCuE4CiqiHq8A/WOfoiMY8JJO/z9lFCriAaKq42VXUGDx6o1bjURsdL8H1UXE9EF8GhCsYqrN+cmzTnHo08/4MFHHzDEQB7n5nimf3CzqohWAgpBmPYjQxT2U25MawrFwFIiqDJpK4685ozBK/fPZ+a736R+fo8Vhbpw64oqUVhSRyPn3ABFplhVDg8H5mlGTVFr1gbNWPNPTB3314853z9hsECxRAxXMHoCAb/QK80148menqQzzvV4zc30UiLFCRf6MX3pyK6Z1aifsKUvYZrZ+EhnjqiZqTointln0MxkA8eS2HmwMmEKdZqWyfVEEYc6Yyodb9SbMxj7zltf47PHGz5/tOHhNfzr+4/5ux+/w+lZ5J9/9TsOpON6tyc6T8GhamxTbofiGuJpqb0RzZiXsaDz6w2H/YBmx2Y78srJmsvNluJbOqOqdC6Qa8XhMCmINRtH89rgDhqotAqkieGqW/DJN0P33vsp2+1VI8AsPpemBS0ZH3qoqTlkGcxpbv1DrfQ1UyxDyfQB5KBDslCmTDFjCJG4OqALkc2457O95+1OWUtBqy19SGGqpcEbUUx9q+p6XZrhS7XWoDpHLvZf4JD/Rc8OtFoZU2WWjDojluXGumQm7+ilECVQqoAVdgbe76ji8CXS+QlzgVKMreyIRPYYMRtBKhuUToQxz+xVMAcOZZTEXIWeAdd7kCM2WvAHmT52dH1okO8MOSc2m0sET9/fnEc9Pv70EZf7wrtvfY3zy5mi5zx4uOWn//6Yg9gTxFExam2M5lor6oxUjU4ivirSeZxCcc0Lo6rSxw7VuqAsHNv92MaviqDecMDIhNfY7KrFUW35MdQO6ds8X3PZXWzHRW7UFa1q/mPKLLX1NzTNYFCmLU6ay3NOCbRSrDJdX3B4eEjMjYwq4iip0gePHB0wj4LWQucjJ+tDLr64YiD7/EoAAAMZSURBVD1EPtoWvr1yBJ+ac7M1ClCuFS8tZVVTNBvZjFrqciETUlJKdS3jeoqeGWivrBzrtWeiR4NxKo4JxyDCyidSXVG6wIllNvPAUT8S3DF7GUnJcWYDe2842WPuANRIJXLgrpjsmBCNgyLsmaA6JmmNo96UIsqheK4tsZfK2zXjrRBdYRwjZT+irnJv54nVcC7x6g0KNBXH6uiAYT1wmEd+/N03+JdffEGqzbMwL/bdzi2jUE7wtSFfnSmx75imqdlR50ahcWaoOVwVqigimTx7zk7XPLq4XoaO41L2b/22XBuxxoVAqhXGSgngszQTUG0gyRpvTqBpygigZaJM00LXMUo1YgzkOS9XuoqVRJn3BB8ZtxtO7pxSp8vm4OyFapneMhVjOIzkUahaOakzXz/t+fk88+sR3vCRO1JQL1hVAkaqCuKIBsXA1Y4xZ4o2jFT0wq42/tzT9LRA8wDrs1OSGocSsM6jVjnxkV5bg7N6o6tt/OSMSnWnBK8NRNgN+LTjbjxgqxVXwUXHVDI+vEY3NWjFViKBTKrQGRQRrorQucxF9ajMRIOpQBIIZngbUEmUaLx+FPGMTDjeefP0S+t/SfLQONWrzrM5v+Q3Dza8//Ee10eOnRKRBnJY4BLONcCFaUtbnMHQQR8i3nmqLk1qg6BKEkfUgMWuGdCQODsOtOe2ylKFXCB6oogE1CrHGnCuY8xKHKDaMsGvcNKFL63/Jamh3qh03oFEXC945xBNuKx4lG61Yj9e4/0BJ/3AOK/YXF7QD0fMuyuOT+6Qxh2qHadff5N5rhzliZRmhlUbWbvz6l2mqw0/ee2M9x5s2FRHjIm7URknRwyVfsE3BmlnId7BmJiT4nwrWHWpcnjn1S+t/7/raYH2OsDf/8M/Ps9NfJF6HfjoJX42333rzh9fePvPDvjJX7+k1fzf9dL37jvf/9EL/dC/fX5v9T/u3dMC7WfAD4HPgZtDEX+2PO3L/uwlruF27/7/+krunZjdnLz8Vrf6qurm1HNvdauvsG4D7Va3egG6DbRb3eoF6DbQbnWrF6D/BGPAeIsP8UUvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x576 with 24 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 1006\n",
    "plt.figure(figsize=(4,1*len(trn_subjects)))\n",
    "for k,s in enumerate(trn_subjects): \n",
    "    for i in range(3):\n",
    "        plt.subplot(len(trn_subjects), 3, 3*k+i+1)\n",
    "        plt.imshow(stim_data[s][n+i].transpose((1,2,0)), cmap='gray', interpolation='None')\n",
    "        plt.gca().get_xaxis().set_visible(False)\n",
    "        plt.gca().get_yaxis().set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.torch_joint_training_unpacked_sequences import *\n",
    "from src.torch_gnet import Encoder\n",
    "from src.torch_mpf import Torch_LayerwiseFWRF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model instanciation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "_log_act_fn = lambda _x: T.log(1 + T.abs(_x))*T.tanh(_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "shared_model = Encoder(trn_stim_mean, trunk_width=64).to(device)\n",
    "rec, fmaps, h = shared_model(T.from_numpy(stim_data[trn_subjects[0]][:20]).to(device))\n",
    "\n",
    "subject_fwrfs = {s: Torch_LayerwiseFWRF(fmaps, nv=nnv[s], pre_nl=_log_act_fn, \\\n",
    "                 post_nl=_log_act_fn, dtype=np.float32).to(device) for s in trn_subjects}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------- subject 1 ----------\n",
      "block size [4627, 25, 25]  \n",
      "block size [4627, 13, 13]  \n",
      "block size [4627, 512]     \n",
      "block size [4627]          \n",
      "--------- subject 2 ----------\n",
      "block size [3765, 25, 25]  \n",
      "block size [3765, 13, 13]  \n",
      "block size [3765, 512]     \n",
      "block size [3765]          \n",
      "--------- subject 3 ----------\n",
      "block size [3778, 25, 25]  \n",
      "block size [3778, 13, 13]  \n",
      "block size [3778, 512]     \n",
      "block size [3778]          \n",
      "--------- subject 4 ----------\n",
      "block size [3027, 25, 25]  \n",
      "block size [3027, 13, 13]  \n",
      "block size [3027, 512]     \n",
      "block size [3027]          \n",
      "--------- subject 5 ----------\n",
      "block size [3685, 25, 25]  \n",
      "block size [3685, 13, 13]  \n",
      "block size [3685, 512]     \n",
      "block size [3685]          \n",
      "--------- subject 6 ----------\n",
      "block size [4024, 25, 25]  \n",
      "block size [4024, 13, 13]  \n",
      "block size [4024, 512]     \n",
      "block size [4024]          \n",
      "--------- subject 7 ----------\n",
      "block size [3201, 25, 25]  \n",
      "block size [3201, 13, 13]  \n",
      "block size [3201, 512]     \n",
      "block size [3201]          \n",
      "--------- subject 8 ----------\n",
      "block size [3490, 25, 25]  \n",
      "block size [3490, 13, 13]  \n",
      "block size [3490, 512]     \n",
      "block size [3490]          \n",
      "\n",
      "1034944 shared params\n",
      "1341 approx params per voxels\n"
     ]
    }
   ],
   "source": [
    "for s,sp in subject_fwrfs.items():\n",
    "    print (\"--------- subject %d ----------\"%s)\n",
    "    for p in sp.parameters():\n",
    "        print (\"block size %-16s\" % (list(p.size())))\n",
    "        \n",
    "param_count = 0\n",
    "for w in shared_model.enc.parameters():\n",
    "    param_count += np.prod(tuple(w.size()))\n",
    "print ('')\n",
    "print (param_count, \"shared params\")\n",
    "total_nv = 0\n",
    "for s,sp in subject_fwrfs.items():\n",
    "    for p in sp.parameters():\n",
    "        param_count += np.prod(tuple(p.size()))\n",
    "    total_nv += nnv[s]\n",
    "print (param_count // total_nv, \"approx params per voxels\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load prefilters value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try:\n",
    "    from torch.hub import load_state_dict_from_url\n",
    "except ImportError:\n",
    "    from torch.utils.model_zoo import load_url as load_state_dict_from_url\n",
    "    \n",
    "state_dict = load_state_dict_from_url('https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth', progress=True)\n",
    "        ### Rename dictionary keys to match new breakdown\n",
    "pre_state_dict = {}\n",
    "pre_state_dict['conv1.0.weight'] = state_dict.pop('features.0.weight')\n",
    "pre_state_dict['conv1.0.bias'] = state_dict.pop('features.0.bias')\n",
    "pre_state_dict['conv2.0.weight'] = state_dict.pop('features.3.weight')\n",
    "pre_state_dict['conv2.0.bias'] = state_dict.pop('features.3.bias')\n",
    "  \n",
    "    \n",
    "shared_model.pre.load_state_dict(pre_state_dict)\n",
    "#for s,m in subject_models.items():\n",
    "#    m.pre.load_state_dict(pre_state_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checkpoint reload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#joined_checkpoint = torch.load(output_dir+'model_params_encft')\n",
    "#\n",
    "#best_params = joined_checkpoint['best_params']\n",
    "#shared_model.load_state_dict(best_params['enc'])\n",
    "#shared_model.eval() \n",
    "#for s,sd in subject_fwrfs.items():\n",
    "#    sd.load_state_dict(best_params['fwrfs'][s])\n",
    "#    sd.eval() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss function, grad function, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer_net = optim.Adam([\n",
    "       #{'params': shared_model.pre.parameters()},\n",
    "       {'params': shared_model.enc.parameters()},\n",
    "   ], lr=1e-3, betas=(0.9, 0.999), eps=1e-08)\n",
    "\n",
    "subject_optimizer = {s: optim.Adam([\n",
    "       {'params': sp.parameters()}\n",
    "   ], lr=1e-3, betas=(0.9, 0.999), eps=1e-08) for s,sp in subject_fwrfs.items()}\n",
    "\n",
    "#subject_opts = {s: [optimizer_net] for s in subject_optimizer.keys()}\n",
    "#subject_opts = {s: [subject_optimizer[s]] for s in subject_optimizer.keys()}\n",
    "subject_opts = {s: [optimizer_net, subject_optimizer[s]] for s in subject_optimizer.keys()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _model_fn(_ext, _con, _x):\n",
    "    '''model consists of an extractor (_ext) and a connection model (_con)'''\n",
    "    _y, _fm, _h = _ext(_x)\n",
    "    return _con(_fm)\n",
    "\n",
    "def _smoothness_loss_fn(_rf, n):\n",
    "    delta_x = T.sum(T.pow(T.abs(_rf[:, 1:]    - _rf[:, :-1]), n))\n",
    "    delta_y = T.sum(T.pow(T.abs(_rf[:, :, 1:] - _rf[:, :, :-1]), n))\n",
    "    return delta_x + delta_y\n",
    "\n",
    "def vox_loss_fn(r, v, nu=0.5, delta=1.):\n",
    "    #err = T.sum(huber(r, v, delta), dim=0)\n",
    "    err = T.sum((r - v)**2, dim=0)\n",
    "    # squared correlation coefficient with 'leak'\n",
    "    cr = r - T.mean(r, dim=0, keepdim=True)\n",
    "    cv = v - T.mean(v, dim=0, keepdim=True)\n",
    "    wgt = T.clamp(T.pow(T.mean(cr*cv, dim=0), 2) / ((T.mean(cr**2, dim=0)) * (T.mean(cv**2, dim=0)) + 1e-6), min=nu, max=1).detach()\n",
    " \n",
    "    weighted_err = wgt * err # error per voxel\n",
    "    loss = T.sum(weighted_err) / T.mean(wgt)\n",
    "    return err, loss\n",
    "    \n",
    "def _loss_fn(_ext, _con, _x, _v):\n",
    "    _r = _model_fn(_ext, _con, _x)\n",
    "    #_err = T.sum((_r - _v)**2, dim=0)\n",
    "    #_loss = T.sum(_err)\n",
    "    _err, _loss = vox_loss_fn(_r, _v, nu=0.1, delta=.5)\n",
    "\n",
    "    _loss += fpX(1e-1) * T.sum(T.abs(_con.w))\n",
    "    return _err, _loss\n",
    "    \n",
    "    \n",
    "def _training_fn(_ext, _con, _opts, xb, yb):\n",
    "    for _opt in _opts:\n",
    "        _opt.zero_grad()\n",
    "        _err, _loss = _loss_fn(_ext, _con, T.from_numpy(xb).to(device), T.from_numpy(yb).to(device))\n",
    "        _loss.backward()\n",
    "        _opt.step()\n",
    "    return _err\n",
    "\n",
    "def _holdout_fn(_ext, _con, xb, yb):\n",
    "    # print (xb.shape, yb.shape)\n",
    "    _err,_ = _loss_fn(_ext, _con, T.from_numpy(xb).to(device), T.from_numpy(yb).to(device))\n",
    "    return _err\n",
    "\n",
    "def _pred_fn(_ext, _con, xb):\n",
    "    return _model_fn(_ext, _con, T.from_numpy(xb).to(device))\n",
    "\n",
    "def print_grads(_ext, _con, _params, _opt, xb, yb):\n",
    "    _opt.zero_grad()\n",
    "    _err, _loss = _loss_fn(_ext, _con, T.from_numpy(xb).to(device), T.from_numpy(yb).to(device))  \n",
    "    _loss.backward()   \n",
    "    for p in _params:\n",
    "        prg = get_value(p.grad)     \n",
    "        print (\"%-16s : value=%f, grad=%f\" % (list(p.size()), np.mean(np.abs(get_value(p))), np.mean(np.abs(prg))))\n",
    "    print ('--------------------------------------')\n",
    "    sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training\n",
    "IDEA: Use the more stable estimate from the holdout val cc to weight the voxels\n",
    "Use a contiguity cost instead of sparsity for RF weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'src.torch_joint_training_unpacked_sequences' from '/export/raid1/home/styvesg/code/nsd_gnet8x/src/torch_joint_training_unpacked_sequences.py'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import src.torch_joint_training_unpacked_sequences as aaa\n",
    "import imp\n",
    "imp.reload(aaa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subject 1 masked 4627 of 4627\n",
      "subject 1 training/holdout 24300 2700\n",
      "subject 2 masked 3765 of 3765\n",
      "subject 2 training/holdout 24300 2700\n",
      "subject 3 masked 3778 of 3778\n",
      "subject 3 training/holdout 19466 2163\n",
      "subject 4 masked 3027 of 3027\n",
      "subject 4 training/holdout 18280 2032\n",
      "subject 5 masked 3685 of 3685\n",
      "subject 5 training/holdout 24300 2700\n",
      "subject 6 masked 4024 of 4024\n",
      "subject 6 training/holdout 19466 2163\n",
      "subject 7 masked 3201 of 3201\n",
      "subject 7 training/holdout 24300 2700\n",
      "subject 8 masked 3490 of 3490\n",
      "subject 8 training/holdout 18280 2032\n",
      "/ : 0.1 %"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/raid1/home/styvesg/code/nsd_gnet8x/src/torch_joint_training_unpacked_sequences.py:90: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  islices = np.array(islices)[ordering]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\ : 100.0 %"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/raid1/home/styvesg/code/nsd_gnet8x/src/torch_joint_training_unpacked_sequences.py:94: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  islices = np.array(islices)#, dtype=int)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/ : 100.0 %\n",
      "  Epoch 1 of 50 took       197.753s\n",
      "  training loss:               0.968096\n",
      "  holdout loss (batch):        0.960699\n",
      "  Subject 1: median (max) validation accuracy = 0.168 (0.552)\n",
      "  Subject 2: median (max) validation accuracy = 0.193 (0.544)\n",
      "  Subject 3: median (max) validation accuracy = 0.169 (0.461)\n",
      "  Subject 4: median (max) validation accuracy = 0.170 (0.582)\n",
      "  Subject 5: median (max) validation accuracy = 0.195 (0.509)\n",
      "  Subject 6: median (max) validation accuracy = 0.191 (0.617)\n",
      "  Subject 7: median (max) validation accuracy = 0.127 (0.467)\n",
      "  Subject 8: median (max) validation accuracy = 0.149 (0.491)\n",
      "** Saving params with joint score = 0.170 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 2 of 50 took       198.057s\n",
      "  training loss:               0.953047\n",
      "  holdout loss (batch):        0.951304\n",
      "  Subject 1: median (max) validation accuracy = 0.194 (0.631)\n",
      "  Subject 2: median (max) validation accuracy = 0.219 (0.601)\n",
      "  Subject 3: median (max) validation accuracy = 0.186 (0.479)\n",
      "  Subject 4: median (max) validation accuracy = 0.184 (0.619)\n",
      "  Subject 5: median (max) validation accuracy = 0.207 (0.528)\n",
      "  Subject 6: median (max) validation accuracy = 0.212 (0.656)\n",
      "  Subject 7: median (max) validation accuracy = 0.134 (0.483)\n",
      "  Subject 8: median (max) validation accuracy = 0.156 (0.505)\n",
      "** Saving params with joint score = 0.187 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 3 of 50 took       198.447s\n",
      "  training loss:               0.943658\n",
      "  holdout loss (batch):        0.943798\n",
      "  Subject 1: median (max) validation accuracy = 0.226 (0.668)\n",
      "  Subject 2: median (max) validation accuracy = 0.249 (0.648)\n",
      "  Subject 3: median (max) validation accuracy = 0.196 (0.493)\n",
      "  Subject 4: median (max) validation accuracy = 0.197 (0.633)\n",
      "  Subject 5: median (max) validation accuracy = 0.219 (0.542)\n",
      "  Subject 6: median (max) validation accuracy = 0.228 (0.663)\n",
      "  Subject 7: median (max) validation accuracy = 0.143 (0.491)\n",
      "  Subject 8: median (max) validation accuracy = 0.164 (0.514)\n",
      "** Saving params with joint score = 0.202 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 4 of 50 took       198.850s\n",
      "  training loss:               0.934365\n",
      "  holdout loss (batch):        0.937341\n",
      "  Subject 1: median (max) validation accuracy = 0.258 (0.692)\n",
      "  Subject 2: median (max) validation accuracy = 0.279 (0.684)\n",
      "  Subject 3: median (max) validation accuracy = 0.206 (0.508)\n",
      "  Subject 4: median (max) validation accuracy = 0.208 (0.643)\n",
      "  Subject 5: median (max) validation accuracy = 0.227 (0.553)\n",
      "  Subject 6: median (max) validation accuracy = 0.241 (0.668)\n",
      "  Subject 7: median (max) validation accuracy = 0.149 (0.495)\n",
      "  Subject 8: median (max) validation accuracy = 0.168 (0.517)\n",
      "** Saving params with joint score = 0.214 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 5 of 50 took       198.665s\n",
      "  training loss:               0.926716\n",
      "  holdout loss (batch):        0.930607\n",
      "  Subject 1: median (max) validation accuracy = 0.282 (0.703)\n",
      "  Subject 2: median (max) validation accuracy = 0.301 (0.705)\n",
      "  Subject 3: median (max) validation accuracy = 0.212 (0.514)\n",
      "  Subject 4: median (max) validation accuracy = 0.213 (0.648)\n",
      "  Subject 5: median (max) validation accuracy = 0.232 (0.554)\n",
      "  Subject 6: median (max) validation accuracy = 0.249 (0.673)\n",
      "  Subject 7: median (max) validation accuracy = 0.153 (0.496)\n",
      "  Subject 8: median (max) validation accuracy = 0.171 (0.521)\n",
      "** Saving params with joint score = 0.222 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 6 of 50 took       198.967s\n",
      "  training loss:               0.920529\n",
      "  holdout loss (batch):        0.926477\n",
      "  Subject 1: median (max) validation accuracy = 0.298 (0.711)\n",
      "  Subject 2: median (max) validation accuracy = 0.316 (0.711)\n",
      "  Subject 3: median (max) validation accuracy = 0.217 (0.522)\n",
      "  Subject 4: median (max) validation accuracy = 0.219 (0.654)\n",
      "  Subject 5: median (max) validation accuracy = 0.235 (0.559)\n",
      "  Subject 6: median (max) validation accuracy = 0.258 (0.675)\n",
      "  Subject 7: median (max) validation accuracy = 0.154 (0.499)\n",
      "  Subject 8: median (max) validation accuracy = 0.173 (0.524)\n",
      "** Saving params with joint score = 0.228 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 7 of 50 took       199.583s\n",
      "  training loss:               0.916636\n",
      "  holdout loss (batch):        0.924613\n",
      "  Subject 1: median (max) validation accuracy = 0.308 (0.718)\n",
      "  Subject 2: median (max) validation accuracy = 0.324 (0.717)\n",
      "  Subject 3: median (max) validation accuracy = 0.221 (0.529)\n",
      "  Subject 4: median (max) validation accuracy = 0.222 (0.657)\n",
      "  Subject 5: median (max) validation accuracy = 0.237 (0.559)\n",
      "  Subject 6: median (max) validation accuracy = 0.262 (0.677)\n",
      "  Subject 7: median (max) validation accuracy = 0.153 (0.502)\n",
      "  Subject 8: median (max) validation accuracy = 0.174 (0.525)\n",
      "** Saving params with joint score = 0.232 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 8 of 50 took       202.297s\n",
      "  training loss:               0.913659\n",
      "  holdout loss (batch):        0.922196\n",
      "  Subject 1: median (max) validation accuracy = 0.316 (0.724)\n",
      "  Subject 2: median (max) validation accuracy = 0.329 (0.722)\n",
      "  Subject 3: median (max) validation accuracy = 0.225 (0.531)\n",
      "  Subject 4: median (max) validation accuracy = 0.226 (0.660)\n",
      "  Subject 5: median (max) validation accuracy = 0.239 (0.562)\n",
      "  Subject 6: median (max) validation accuracy = 0.267 (0.678)\n",
      "  Subject 7: median (max) validation accuracy = 0.155 (0.499)\n",
      "  Subject 8: median (max) validation accuracy = 0.174 (0.529)\n",
      "** Saving params with joint score = 0.235 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 9 of 50 took       202.109s\n",
      "  training loss:               0.911291\n",
      "  holdout loss (batch):        0.921598\n",
      "  Subject 1: median (max) validation accuracy = 0.321 (0.724)\n",
      "  Subject 2: median (max) validation accuracy = 0.335 (0.725)\n",
      "  Subject 3: median (max) validation accuracy = 0.227 (0.534)\n",
      "  Subject 4: median (max) validation accuracy = 0.227 (0.663)\n",
      "  Subject 5: median (max) validation accuracy = 0.240 (0.561)\n",
      "  Subject 6: median (max) validation accuracy = 0.270 (0.680)\n",
      "  Subject 7: median (max) validation accuracy = 0.156 (0.503)\n",
      "  Subject 8: median (max) validation accuracy = 0.178 (0.528)\n",
      "** Saving params with joint score = 0.238 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 10 of 50 took       201.990s\n",
      "  training loss:               0.909430\n",
      "  holdout loss (batch):        0.922505\n",
      "  Subject 1: median (max) validation accuracy = 0.323 (0.726)\n",
      "  Subject 2: median (max) validation accuracy = 0.338 (0.724)\n",
      "  Subject 3: median (max) validation accuracy = 0.227 (0.537)\n",
      "  Subject 4: median (max) validation accuracy = 0.229 (0.666)\n",
      "  Subject 5: median (max) validation accuracy = 0.240 (0.564)\n",
      "  Subject 6: median (max) validation accuracy = 0.274 (0.680)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.503)\n",
      "  Subject 8: median (max) validation accuracy = 0.176 (0.531)\n",
      "** Saving params with joint score = 0.239 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 11 of 50 took       201.935s\n",
      "  training loss:               0.907877\n",
      "  holdout loss (batch):        0.919696\n",
      "  Subject 1: median (max) validation accuracy = 0.327 (0.728)\n",
      "  Subject 2: median (max) validation accuracy = 0.343 (0.729)\n",
      "  Subject 3: median (max) validation accuracy = 0.230 (0.540)\n",
      "  Subject 4: median (max) validation accuracy = 0.231 (0.669)\n",
      "  Subject 5: median (max) validation accuracy = 0.240 (0.565)\n",
      "  Subject 6: median (max) validation accuracy = 0.277 (0.679)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.503)\n",
      "  Subject 8: median (max) validation accuracy = 0.176 (0.531)\n",
      "** Saving params with joint score = 0.241 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 12 of 50 took       201.804s\n",
      "  training loss:               0.906701\n",
      "  holdout loss (batch):        0.918402\n",
      "  Subject 1: median (max) validation accuracy = 0.331 (0.730)\n",
      "  Subject 2: median (max) validation accuracy = 0.343 (0.727)\n",
      "  Subject 3: median (max) validation accuracy = 0.229 (0.540)\n",
      "  Subject 4: median (max) validation accuracy = 0.232 (0.671)\n",
      "  Subject 5: median (max) validation accuracy = 0.241 (0.568)\n",
      "  Subject 6: median (max) validation accuracy = 0.279 (0.681)\n",
      "  Subject 7: median (max) validation accuracy = 0.156 (0.503)\n",
      "  Subject 8: median (max) validation accuracy = 0.177 (0.531)\n",
      "** Saving params with joint score = 0.242 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 13 of 50 took       202.103s\n",
      "  training loss:               0.905685\n",
      "  holdout loss (batch):        0.919210\n",
      "  Subject 1: median (max) validation accuracy = 0.332 (0.730)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Subject 2: median (max) validation accuracy = 0.345 (0.732)\n",
      "  Subject 3: median (max) validation accuracy = 0.231 (0.540)\n",
      "  Subject 4: median (max) validation accuracy = 0.233 (0.671)\n",
      "  Subject 5: median (max) validation accuracy = 0.243 (0.568)\n",
      "  Subject 6: median (max) validation accuracy = 0.281 (0.681)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.504)\n",
      "  Subject 8: median (max) validation accuracy = 0.178 (0.531)\n",
      "** Saving params with joint score = 0.243 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 14 of 50 took       201.711s\n",
      "  training loss:               0.904611\n",
      "  holdout loss (batch):        0.919127\n",
      "  Subject 1: median (max) validation accuracy = 0.334 (0.732)\n",
      "  Subject 2: median (max) validation accuracy = 0.347 (0.732)\n",
      "  Subject 3: median (max) validation accuracy = 0.230 (0.540)\n",
      "  Subject 4: median (max) validation accuracy = 0.234 (0.674)\n",
      "  Subject 5: median (max) validation accuracy = 0.243 (0.570)\n",
      "  Subject 6: median (max) validation accuracy = 0.283 (0.681)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.503)\n",
      "  Subject 8: median (max) validation accuracy = 0.176 (0.533)\n",
      "** Saving params with joint score = 0.244 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 15 of 50 took       201.769s\n",
      "  training loss:               0.903929\n",
      "  holdout loss (batch):        0.917625\n",
      "  Subject 1: median (max) validation accuracy = 0.336 (0.734)\n",
      "  Subject 2: median (max) validation accuracy = 0.349 (0.732)\n",
      "  Subject 3: median (max) validation accuracy = 0.231 (0.543)\n",
      "  Subject 4: median (max) validation accuracy = 0.235 (0.674)\n",
      "  Subject 5: median (max) validation accuracy = 0.244 (0.572)\n",
      "  Subject 6: median (max) validation accuracy = 0.283 (0.681)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.503)\n",
      "  Subject 8: median (max) validation accuracy = 0.176 (0.531)\n",
      "** Saving params with joint score = 0.245 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 16 of 50 took       202.058s\n",
      "  training loss:               0.903145\n",
      "  holdout loss (batch):        0.918013\n",
      "  Subject 1: median (max) validation accuracy = 0.337 (0.733)\n",
      "  Subject 2: median (max) validation accuracy = 0.350 (0.732)\n",
      "  Subject 3: median (max) validation accuracy = 0.232 (0.542)\n",
      "  Subject 4: median (max) validation accuracy = 0.235 (0.675)\n",
      "  Subject 5: median (max) validation accuracy = 0.243 (0.571)\n",
      "  Subject 6: median (max) validation accuracy = 0.284 (0.683)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.503)\n",
      "  Subject 8: median (max) validation accuracy = 0.177 (0.535)\n",
      "** Saving params with joint score = 0.246 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 17 of 50 took       202.070s\n",
      "  training loss:               0.902466\n",
      "  holdout loss (batch):        0.916280\n",
      "  Subject 1: median (max) validation accuracy = 0.338 (0.732)\n",
      "  Subject 2: median (max) validation accuracy = 0.351 (0.734)\n",
      "  Subject 3: median (max) validation accuracy = 0.233 (0.542)\n",
      "  Subject 4: median (max) validation accuracy = 0.236 (0.675)\n",
      "  Subject 5: median (max) validation accuracy = 0.245 (0.572)\n",
      "  Subject 6: median (max) validation accuracy = 0.286 (0.683)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.502)\n",
      "  Subject 8: median (max) validation accuracy = 0.177 (0.534)\n",
      "** Saving params with joint score = 0.247 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 18 of 50 took       201.877s\n",
      "  training loss:               0.901993\n",
      "  holdout loss (batch):        0.916823\n",
      "  Subject 1: median (max) validation accuracy = 0.340 (0.735)\n",
      "  Subject 2: median (max) validation accuracy = 0.352 (0.735)\n",
      "  Subject 3: median (max) validation accuracy = 0.233 (0.544)\n",
      "  Subject 4: median (max) validation accuracy = 0.237 (0.676)\n",
      "  Subject 5: median (max) validation accuracy = 0.245 (0.573)\n",
      "  Subject 6: median (max) validation accuracy = 0.286 (0.683)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.503)\n",
      "  Subject 8: median (max) validation accuracy = 0.178 (0.535)\n",
      "** Saving params with joint score = 0.247 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 19 of 50 took       201.918s\n",
      "  training loss:               0.901408\n",
      "  holdout loss (batch):        0.917762\n",
      "  Subject 1: median (max) validation accuracy = 0.339 (0.735)\n",
      "  Subject 2: median (max) validation accuracy = 0.353 (0.736)\n",
      "  Subject 3: median (max) validation accuracy = 0.233 (0.541)\n",
      "  Subject 4: median (max) validation accuracy = 0.237 (0.677)\n",
      "  Subject 5: median (max) validation accuracy = 0.246 (0.575)\n",
      "  Subject 6: median (max) validation accuracy = 0.287 (0.683)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.503)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.536)\n",
      "** Saving params with joint score = 0.248 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 20 of 50 took       201.902s\n",
      "  training loss:               0.901081\n",
      "  holdout loss (batch):        0.916337\n",
      "  Subject 1: median (max) validation accuracy = 0.340 (0.734)\n",
      "  Subject 2: median (max) validation accuracy = 0.353 (0.736)\n",
      "  Subject 3: median (max) validation accuracy = 0.234 (0.541)\n",
      "  Subject 4: median (max) validation accuracy = 0.237 (0.678)\n",
      "  Subject 5: median (max) validation accuracy = 0.245 (0.573)\n",
      "  Subject 6: median (max) validation accuracy = 0.287 (0.684)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.503)\n",
      "  Subject 8: median (max) validation accuracy = 0.178 (0.537)\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 21 of 50 took       202.069s\n",
      "  training loss:               0.900551\n",
      "  holdout loss (batch):        0.915819\n",
      "  Subject 1: median (max) validation accuracy = 0.343 (0.735)\n",
      "  Subject 2: median (max) validation accuracy = 0.356 (0.737)\n",
      "  Subject 3: median (max) validation accuracy = 0.235 (0.544)\n",
      "  Subject 4: median (max) validation accuracy = 0.238 (0.678)\n",
      "  Subject 5: median (max) validation accuracy = 0.246 (0.576)\n",
      "  Subject 6: median (max) validation accuracy = 0.288 (0.683)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.504)\n",
      "  Subject 8: median (max) validation accuracy = 0.178 (0.535)\n",
      "** Saving params with joint score = 0.248 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 22 of 50 took       201.849s\n",
      "  training loss:               0.900342\n",
      "  holdout loss (batch):        0.916148\n",
      "  Subject 1: median (max) validation accuracy = 0.342 (0.735)\n",
      "  Subject 2: median (max) validation accuracy = 0.356 (0.735)\n",
      "  Subject 3: median (max) validation accuracy = 0.234 (0.544)\n",
      "  Subject 4: median (max) validation accuracy = 0.239 (0.679)\n",
      "  Subject 5: median (max) validation accuracy = 0.247 (0.575)\n",
      "  Subject 6: median (max) validation accuracy = 0.288 (0.684)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.503)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.536)\n",
      "** Saving params with joint score = 0.249 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 23 of 50 took       202.101s\n",
      "  training loss:               0.899624\n",
      "  holdout loss (batch):        0.915604\n",
      "  Subject 1: median (max) validation accuracy = 0.343 (0.738)\n",
      "  Subject 2: median (max) validation accuracy = 0.357 (0.736)\n",
      "  Subject 3: median (max) validation accuracy = 0.235 (0.542)\n",
      "  Subject 4: median (max) validation accuracy = 0.239 (0.679)\n",
      "  Subject 5: median (max) validation accuracy = 0.246 (0.576)\n",
      "  Subject 6: median (max) validation accuracy = 0.289 (0.685)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.504)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.537)\n",
      "** Saving params with joint score = 0.249 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 24 of 50 took       201.730s\n",
      "  training loss:               0.899200\n",
      "  holdout loss (batch):        0.913803\n",
      "  Subject 1: median (max) validation accuracy = 0.344 (0.736)\n",
      "  Subject 2: median (max) validation accuracy = 0.357 (0.736)\n",
      "  Subject 3: median (max) validation accuracy = 0.235 (0.544)\n",
      "  Subject 4: median (max) validation accuracy = 0.240 (0.681)\n",
      "  Subject 5: median (max) validation accuracy = 0.247 (0.577)\n",
      "  Subject 6: median (max) validation accuracy = 0.289 (0.684)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.503)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.534)\n",
      "** Saving params with joint score = 0.249 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 25 of 50 took       201.597s\n",
      "  training loss:               0.899105\n",
      "  holdout loss (batch):        0.914121\n",
      "  Subject 1: median (max) validation accuracy = 0.344 (0.734)\n",
      "  Subject 2: median (max) validation accuracy = 0.357 (0.734)\n",
      "  Subject 3: median (max) validation accuracy = 0.236 (0.546)\n",
      "  Subject 4: median (max) validation accuracy = 0.239 (0.680)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Subject 5: median (max) validation accuracy = 0.247 (0.577)\n",
      "  Subject 6: median (max) validation accuracy = 0.290 (0.684)\n",
      "  Subject 7: median (max) validation accuracy = 0.155 (0.506)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.538)\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 26 of 50 took       202.157s\n",
      "  training loss:               0.898646\n",
      "  holdout loss (batch):        0.915893\n",
      "  Subject 1: median (max) validation accuracy = 0.344 (0.737)\n",
      "  Subject 2: median (max) validation accuracy = 0.358 (0.736)\n",
      "  Subject 3: median (max) validation accuracy = 0.235 (0.542)\n",
      "  Subject 4: median (max) validation accuracy = 0.240 (0.680)\n",
      "  Subject 5: median (max) validation accuracy = 0.248 (0.578)\n",
      "  Subject 6: median (max) validation accuracy = 0.290 (0.686)\n",
      "  Subject 7: median (max) validation accuracy = 0.156 (0.504)\n",
      "  Subject 8: median (max) validation accuracy = 0.178 (0.536)\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 27 of 50 took       202.369s\n",
      "  training loss:               0.898417\n",
      "  holdout loss (batch):        0.915713\n",
      "  Subject 1: median (max) validation accuracy = 0.344 (0.737)\n",
      "  Subject 2: median (max) validation accuracy = 0.358 (0.736)\n",
      "  Subject 3: median (max) validation accuracy = 0.236 (0.544)\n",
      "  Subject 4: median (max) validation accuracy = 0.240 (0.680)\n",
      "  Subject 5: median (max) validation accuracy = 0.248 (0.578)\n",
      "  Subject 6: median (max) validation accuracy = 0.290 (0.685)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.506)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.539)\n",
      "** Saving params with joint score = 0.250 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 28 of 50 took       202.839s\n",
      "  training loss:               0.898123\n",
      "  holdout loss (batch):        0.913638\n",
      "  Subject 1: median (max) validation accuracy = 0.345 (0.736)\n",
      "  Subject 2: median (max) validation accuracy = 0.358 (0.736)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.544)\n",
      "  Subject 4: median (max) validation accuracy = 0.239 (0.680)\n",
      "  Subject 5: median (max) validation accuracy = 0.248 (0.579)\n",
      "  Subject 6: median (max) validation accuracy = 0.291 (0.685)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.504)\n",
      "  Subject 8: median (max) validation accuracy = 0.180 (0.538)\n",
      "** Saving params with joint score = 0.250 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 29 of 50 took       202.803s\n",
      "  training loss:               0.897844\n",
      "  holdout loss (batch):        0.913969\n",
      "  Subject 1: median (max) validation accuracy = 0.346 (0.736)\n",
      "  Subject 2: median (max) validation accuracy = 0.359 (0.737)\n",
      "  Subject 3: median (max) validation accuracy = 0.236 (0.543)\n",
      "  Subject 4: median (max) validation accuracy = 0.239 (0.682)\n",
      "  Subject 5: median (max) validation accuracy = 0.249 (0.577)\n",
      "  Subject 6: median (max) validation accuracy = 0.291 (0.685)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.504)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.537)\n",
      "** Saving params with joint score = 0.250 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 30 of 50 took       202.443s\n",
      "  training loss:               0.897526\n",
      "  holdout loss (batch):        0.912033\n",
      "  Subject 1: median (max) validation accuracy = 0.346 (0.736)\n",
      "  Subject 2: median (max) validation accuracy = 0.359 (0.736)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.544)\n",
      "  Subject 4: median (max) validation accuracy = 0.241 (0.681)\n",
      "  Subject 5: median (max) validation accuracy = 0.249 (0.580)\n",
      "  Subject 6: median (max) validation accuracy = 0.290 (0.685)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.503)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.539)\n",
      "** Saving params with joint score = 0.251 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 31 of 50 took       203.036s\n",
      "  training loss:               0.897219\n",
      "  holdout loss (batch):        0.913339\n",
      "  Subject 1: median (max) validation accuracy = 0.347 (0.737)\n",
      "  Subject 2: median (max) validation accuracy = 0.359 (0.737)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.542)\n",
      "  Subject 4: median (max) validation accuracy = 0.241 (0.683)\n",
      "  Subject 5: median (max) validation accuracy = 0.249 (0.576)\n",
      "  Subject 6: median (max) validation accuracy = 0.291 (0.686)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.505)\n",
      "  Subject 8: median (max) validation accuracy = 0.180 (0.539)\n",
      "** Saving params with joint score = 0.251 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 32 of 50 took       202.544s\n",
      "  training loss:               0.897223\n",
      "  holdout loss (batch):        0.917497\n",
      "  Subject 1: median (max) validation accuracy = 0.345 (0.738)\n",
      "  Subject 2: median (max) validation accuracy = 0.359 (0.736)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.544)\n",
      "  Subject 4: median (max) validation accuracy = 0.240 (0.684)\n",
      "  Subject 5: median (max) validation accuracy = 0.249 (0.577)\n",
      "  Subject 6: median (max) validation accuracy = 0.292 (0.687)\n",
      "  Subject 7: median (max) validation accuracy = 0.160 (0.506)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.539)\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 33 of 50 took       203.433s\n",
      "  training loss:               0.896995\n",
      "  holdout loss (batch):        0.913846\n",
      "  Subject 1: median (max) validation accuracy = 0.347 (0.737)\n",
      "  Subject 2: median (max) validation accuracy = 0.360 (0.736)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.542)\n",
      "  Subject 4: median (max) validation accuracy = 0.240 (0.682)\n",
      "  Subject 5: median (max) validation accuracy = 0.249 (0.576)\n",
      "  Subject 6: median (max) validation accuracy = 0.291 (0.686)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.504)\n",
      "  Subject 8: median (max) validation accuracy = 0.178 (0.539)\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 34 of 50 took       202.686s\n",
      "  training loss:               0.896723\n",
      "  holdout loss (batch):        0.913391\n",
      "  Subject 1: median (max) validation accuracy = 0.347 (0.737)\n",
      "  Subject 2: median (max) validation accuracy = 0.360 (0.739)\n",
      "  Subject 3: median (max) validation accuracy = 0.238 (0.543)\n",
      "  Subject 4: median (max) validation accuracy = 0.241 (0.683)\n",
      "  Subject 5: median (max) validation accuracy = 0.250 (0.577)\n",
      "  Subject 6: median (max) validation accuracy = 0.291 (0.686)\n",
      "  Subject 7: median (max) validation accuracy = 0.159 (0.505)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.537)\n",
      "** Saving params with joint score = 0.252 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 35 of 50 took       201.818s\n",
      "  training loss:               0.896591\n",
      "  holdout loss (batch):        0.913058\n",
      "  Subject 1: median (max) validation accuracy = 0.348 (0.738)\n",
      "  Subject 2: median (max) validation accuracy = 0.361 (0.737)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.544)\n",
      "  Subject 4: median (max) validation accuracy = 0.241 (0.682)\n",
      "  Subject 5: median (max) validation accuracy = 0.250 (0.578)\n",
      "  Subject 6: median (max) validation accuracy = 0.292 (0.687)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.505)\n",
      "  Subject 8: median (max) validation accuracy = 0.178 (0.538)\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 36 of 50 took       202.140s\n",
      "  training loss:               0.896121\n",
      "  holdout loss (batch):        0.914196\n",
      "  Subject 1: median (max) validation accuracy = 0.347 (0.738)\n",
      "  Subject 2: median (max) validation accuracy = 0.361 (0.737)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.544)\n",
      "  Subject 4: median (max) validation accuracy = 0.241 (0.682)\n",
      "  Subject 5: median (max) validation accuracy = 0.250 (0.577)\n",
      "  Subject 6: median (max) validation accuracy = 0.292 (0.686)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.503)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.540)\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 37 of 50 took       201.617s\n",
      "  training loss:               0.896100\n",
      "  holdout loss (batch):        0.915233\n",
      "  Subject 1: median (max) validation accuracy = 0.347 (0.739)\n",
      "  Subject 2: median (max) validation accuracy = 0.361 (0.737)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.544)\n",
      "  Subject 4: median (max) validation accuracy = 0.241 (0.683)\n",
      "  Subject 5: median (max) validation accuracy = 0.250 (0.577)\n",
      "  Subject 6: median (max) validation accuracy = 0.292 (0.688)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.503)\n",
      "  Subject 8: median (max) validation accuracy = 0.178 (0.539)\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 38 of 50 took       202.054s\n",
      "  training loss:               0.895951\n",
      "  holdout loss (batch):        0.915876\n",
      "  Subject 1: median (max) validation accuracy = 0.347 (0.738)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Subject 2: median (max) validation accuracy = 0.362 (0.737)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.543)\n",
      "  Subject 4: median (max) validation accuracy = 0.241 (0.684)\n",
      "  Subject 5: median (max) validation accuracy = 0.250 (0.578)\n",
      "  Subject 6: median (max) validation accuracy = 0.292 (0.687)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.505)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.540)\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 39 of 50 took       201.622s\n",
      "  training loss:               0.895750\n",
      "  holdout loss (batch):        0.914657\n",
      "  Subject 1: median (max) validation accuracy = 0.347 (0.738)\n",
      "  Subject 2: median (max) validation accuracy = 0.362 (0.738)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.544)\n",
      "  Subject 4: median (max) validation accuracy = 0.240 (0.683)\n",
      "  Subject 5: median (max) validation accuracy = 0.250 (0.577)\n",
      "  Subject 6: median (max) validation accuracy = 0.292 (0.688)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.502)\n",
      "  Subject 8: median (max) validation accuracy = 0.178 (0.541)\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 40 of 50 took       202.286s\n",
      "  training loss:               0.895632\n",
      "  holdout loss (batch):        0.913914\n",
      "  Subject 1: median (max) validation accuracy = 0.348 (0.738)\n",
      "  Subject 2: median (max) validation accuracy = 0.361 (0.737)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.544)\n",
      "  Subject 4: median (max) validation accuracy = 0.240 (0.684)\n",
      "  Subject 5: median (max) validation accuracy = 0.250 (0.578)\n",
      "  Subject 6: median (max) validation accuracy = 0.292 (0.688)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.505)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.541)\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 41 of 50 took       201.651s\n",
      "  training loss:               0.895352\n",
      "  holdout loss (batch):        0.913191\n",
      "  Subject 1: median (max) validation accuracy = 0.348 (0.739)\n",
      "  Subject 2: median (max) validation accuracy = 0.360 (0.737)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.544)\n",
      "  Subject 4: median (max) validation accuracy = 0.241 (0.684)\n",
      "  Subject 5: median (max) validation accuracy = 0.251 (0.578)\n",
      "  Subject 6: median (max) validation accuracy = 0.293 (0.686)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.506)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.541)\n",
      "** Saving params with joint score = 0.252 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 42 of 50 took       202.062s\n",
      "  training loss:               0.895109\n",
      "  holdout loss (batch):        0.914123\n",
      "  Subject 1: median (max) validation accuracy = 0.349 (0.738)\n",
      "  Subject 2: median (max) validation accuracy = 0.361 (0.738)\n",
      "  Subject 3: median (max) validation accuracy = 0.238 (0.543)\n",
      "  Subject 4: median (max) validation accuracy = 0.240 (0.684)\n",
      "  Subject 5: median (max) validation accuracy = 0.251 (0.578)\n",
      "  Subject 6: median (max) validation accuracy = 0.292 (0.687)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.505)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.539)\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 43 of 50 took       202.069s\n",
      "  training loss:               0.894958\n",
      "  holdout loss (batch):        0.914243\n",
      "  Subject 1: median (max) validation accuracy = 0.348 (0.739)\n",
      "  Subject 2: median (max) validation accuracy = 0.362 (0.738)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.545)\n",
      "  Subject 4: median (max) validation accuracy = 0.240 (0.683)\n",
      "  Subject 5: median (max) validation accuracy = 0.251 (0.578)\n",
      "  Subject 6: median (max) validation accuracy = 0.292 (0.687)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.505)\n",
      "  Subject 8: median (max) validation accuracy = 0.178 (0.540)\n",
      "** Saving params with joint score = 0.252 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 44 of 50 took       202.235s\n",
      "  training loss:               0.894886\n",
      "  holdout loss (batch):        0.912534\n",
      "  Subject 1: median (max) validation accuracy = 0.349 (0.739)\n",
      "  Subject 2: median (max) validation accuracy = 0.362 (0.739)\n",
      "  Subject 3: median (max) validation accuracy = 0.238 (0.543)\n",
      "  Subject 4: median (max) validation accuracy = 0.241 (0.684)\n",
      "  Subject 5: median (max) validation accuracy = 0.252 (0.580)\n",
      "  Subject 6: median (max) validation accuracy = 0.293 (0.686)\n",
      "  Subject 7: median (max) validation accuracy = 0.157 (0.506)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.541)\n",
      "** Saving params with joint score = 0.252 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 45 of 50 took       201.990s\n",
      "  training loss:               0.894629\n",
      "  holdout loss (batch):        0.913987\n",
      "  Subject 1: median (max) validation accuracy = 0.348 (0.739)\n",
      "  Subject 2: median (max) validation accuracy = 0.362 (0.737)\n",
      "  Subject 3: median (max) validation accuracy = 0.238 (0.543)\n",
      "  Subject 4: median (max) validation accuracy = 0.241 (0.684)\n",
      "  Subject 5: median (max) validation accuracy = 0.251 (0.579)\n",
      "  Subject 6: median (max) validation accuracy = 0.293 (0.687)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.505)\n",
      "  Subject 8: median (max) validation accuracy = 0.179 (0.540)\n",
      "** Saving params with joint score = 0.252 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 46 of 50 took       202.641s\n",
      "  training loss:               0.894671\n",
      "  holdout loss (batch):        0.915700\n",
      "  Subject 1: median (max) validation accuracy = 0.348 (0.740)\n",
      "  Subject 2: median (max) validation accuracy = 0.362 (0.738)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.542)\n",
      "  Subject 4: median (max) validation accuracy = 0.239 (0.684)\n",
      "  Subject 5: median (max) validation accuracy = 0.250 (0.579)\n",
      "  Subject 6: median (max) validation accuracy = 0.294 (0.689)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.504)\n",
      "  Subject 8: median (max) validation accuracy = 0.178 (0.540)\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 47 of 50 took       202.031s\n",
      "  training loss:               0.894366\n",
      "  holdout loss (batch):        0.915174\n",
      "  Subject 1: median (max) validation accuracy = 0.349 (0.739)\n",
      "  Subject 2: median (max) validation accuracy = 0.363 (0.738)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.544)\n",
      "  Subject 4: median (max) validation accuracy = 0.241 (0.684)\n",
      "  Subject 5: median (max) validation accuracy = 0.251 (0.578)\n",
      "  Subject 6: median (max) validation accuracy = 0.293 (0.688)\n",
      "  Subject 7: median (max) validation accuracy = 0.159 (0.502)\n",
      "  Subject 8: median (max) validation accuracy = 0.180 (0.540)\n",
      "** Saving params with joint score = 0.252 **\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 48 of 50 took       201.630s\n",
      "  training loss:               0.894186\n",
      "  holdout loss (batch):        0.913509\n",
      "  Subject 1: median (max) validation accuracy = 0.349 (0.740)\n",
      "  Subject 2: median (max) validation accuracy = 0.362 (0.738)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.543)\n",
      "  Subject 4: median (max) validation accuracy = 0.241 (0.684)\n",
      "  Subject 5: median (max) validation accuracy = 0.251 (0.579)\n",
      "  Subject 6: median (max) validation accuracy = 0.293 (0.688)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.505)\n",
      "  Subject 8: median (max) validation accuracy = 0.180 (0.542)\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 49 of 50 took       202.188s\n",
      "  training loss:               0.894206\n",
      "  holdout loss (batch):        0.912960\n",
      "  Subject 1: median (max) validation accuracy = 0.349 (0.739)\n",
      "  Subject 2: median (max) validation accuracy = 0.363 (0.738)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.542)\n",
      "  Subject 4: median (max) validation accuracy = 0.241 (0.684)\n",
      "  Subject 5: median (max) validation accuracy = 0.251 (0.579)\n",
      "  Subject 6: median (max) validation accuracy = 0.293 (0.688)\n",
      "  Subject 7: median (max) validation accuracy = 0.159 (0.506)\n",
      "  Subject 8: median (max) validation accuracy = 0.178 (0.543)\n",
      "\n",
      "/ : 100.0 %\n",
      "  Epoch 50 of 50 took       201.718s\n",
      "  training loss:               0.894191\n",
      "  holdout loss (batch):        0.913798\n",
      "  Subject 1: median (max) validation accuracy = 0.349 (0.740)\n",
      "  Subject 2: median (max) validation accuracy = 0.363 (0.739)\n",
      "  Subject 3: median (max) validation accuracy = 0.237 (0.543)\n",
      "  Subject 4: median (max) validation accuracy = 0.242 (0.685)\n",
      "  Subject 5: median (max) validation accuracy = 0.253 (0.578)\n",
      "  Subject 6: median (max) validation accuracy = 0.294 (0.688)\n",
      "  Subject 7: median (max) validation accuracy = 0.158 (0.506)\n",
      "  Subject 8: median (max) validation accuracy = 0.178 (0.540)\n",
      "** Saving params with joint score = 0.253 **\n",
      "\n"
     ]
    }
   ],
   "source": [
    "batch_size = 100\n",
    "num_epochs = 50\n",
    "holdout_frac = .1\n",
    "\n",
    "best_params, final_params, hold_cc_hist, hold_hist, trn_hist, best_epoch, best_joint_cc_score = \\\n",
    "    learn_params_(_training_fn, _holdout_fn, _pred_fn, shared_model, subject_fwrfs, subject_opts, \n",
    "        stim_data, trn_voxel_data, trn_stim_ordering,\n",
    "        num_epochs=num_epochs, batch_size=batch_size, holdout_frac=holdout_frac, masks=None, randomize=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24,3))\n",
    "plt.plot(trn_hist, color='r', marker='o', ms=10, lw=2)\n",
    "plt.plot(np.array(hold_hist), color='b', marker='o', ms=10, lw=2)\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epoch')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24,6))\n",
    "plt.subplot(2,1,1)\n",
    "for s,cc in hold_cc_hist.items():\n",
    "    plt.gca().violinplot(np.nan_to_num(np.array(cc)).T, showmeans=True, showmedians=True, showextrema=True)\n",
    "plt.ylabel('Validation history')\n",
    "plt.subplot(2,1,2)\n",
    "for s in hold_cc_hist.keys():\n",
    "    p=plt.plot([np.median(cc) for cc in hold_cc_hist[s]], lw=2, linestyle='--')\n",
    "plt.legend()\n",
    "plt.ylabel('Median history')\n",
    "plt.xlabel('epoch')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Final validation accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_voxel = {s: val_voxel_data[s] for s in val_voxel_data.keys()}\n",
    "shared_model.load_state_dict(best_params['enc'])\n",
    "shared_model.eval() \n",
    "for s,sd in subject_fwrfs.items():\n",
    "    sd.load_state_dict(best_params['fwrfs'][s])\n",
    "    sd.eval() \n",
    "\n",
    "batch_size = 100\n",
    "subject_val_cc = validation_(_pred_fn, shared_model, subject_fwrfs, stim_data, val_voxel, val_stim_ordering, batch_size)\n",
    "joined_val_cc = np.concatenate(list(subject_val_cc.values()), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best joint score = 0.253\n",
      "best joint val cc = 0.281\n",
      "subject 1: val cc = 0.361\n",
      "subject 2: val cc = 0.339\n",
      "subject 3: val cc = 0.295\n",
      "subject 4: val cc = 0.260\n",
      "subject 5: val cc = 0.287\n",
      "subject 6: val cc = 0.266\n",
      "subject 7: val cc = 0.239\n",
      "subject 8: val cc = 0.213\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print (\"best joint score = %.3f\"%best_joint_cc_score)\n",
    "print (\"best joint val cc = %.3f\"% np.median(joined_val_cc))\n",
    "for s,v in subject_val_cc.items():\n",
    "    print (\"subject %s: val cc = %.3f\"%(s, np.median(v)))\n",
    "\n",
    "fig=plt.figure(figsize=(12,4))\n",
    "plt.subplots_adjust(left=0., bottom=0.2, right=1., top=1., wspace=0., hspace=0.)\n",
    "_=plt.hist(joined_val_cc, bins=100, density=True, range=(-.5, 1.))\n",
    "_=plt.vlines(x=[0], ymin=1e-4, ymax=25, color='r')\n",
    "_=plt.yscale('log')\n",
    "_=plt.xlim([-.1, 0.9])\n",
    "_=plt.xlabel(r'$\\rho$')\n",
    "_=plt.ylabel('Relative frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save final parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save({\n",
    "            'num_epochs': num_epochs,\n",
    "            'batch_size': batch_size,\n",
    "            'holdout_frac': holdout_frac,   \n",
    "            'best_params': best_params, \n",
    "            'final_params': final_params,\n",
    "            'trn_loss_history': trn_hist,\n",
    "            'hold_loss_history': hold_hist,\n",
    "            'hold_cc_history': hold_cc_hist,\n",
    "            'best_epoch': best_epoch,\n",
    "            'best_joint_cc_score': best_joint_cc_score,\n",
    "            'val_cc': subject_val_cc,\n",
    "            'input_mean': trn_stim_mean,\n",
    "            'brain_nii_shape': brain_nii_shape,\n",
    "            'voxel_index': voxel_idx,\n",
    "            'voxel_roi': voxel_roi,\n",
    "            'voxel_mask': voxel_mask\n",
    "            }, output_dir+'model_params')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/export/raid1/home/styvesg/code/nsd_gnet8x/output/multisubject/gnet8j_mpf_evc_Feb-04-2022_1818/\n"
     ]
    }
   ],
   "source": [
    "print (output_dir)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}