demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed to import tensorflow.\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "import torch\n",
    "import yaml\n",
    "\n",
    "from easydict import EasyDict as edict\n",
    "from pytorch_transformers.tokenization_bert import BertTokenizer\n",
    "from vilbert.datasets import ConceptCapLoaderTrain, ConceptCapLoaderVal\n",
    "from vilbert.vilbert import VILBertForVLTasks, BertConfig, BertForMultiModalPreTraining\n",
    "from vilbert.task_utils import LoadDatasetEval\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import PIL\n",
    "\n",
    "from maskrcnn_benchmark.config import cfg\n",
    "from maskrcnn_benchmark.layers import nms\n",
    "from maskrcnn_benchmark.modeling.detector import build_detection_model\n",
    "from maskrcnn_benchmark.structures.image_list import to_image_list\n",
    "from maskrcnn_benchmark.utils.model_serialization import load_state_dict\n",
    "from PIL import Image\n",
    "import cv2\n",
    "import argparse\n",
    "import glob\n",
    "from types import SimpleNamespace\n",
    "import pdb\n",
    "\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FeatureExtractor:\n",
    "    MAX_SIZE = 1333\n",
    "    MIN_SIZE = 800\n",
    "\n",
    "    def __init__(self):\n",
    "        self.args = self.get_parser()\n",
    "        self.detection_model = self._build_detection_model()\n",
    "\n",
    "    def get_parser(self):        \n",
    "        parser = SimpleNamespace(model_file= 'save/resnext_models/model_final.pth',\n",
    "                                config_file='save/resnext_models/e2e_faster_rcnn_X-152-32x8d-FPN_1x_MLP_2048_FPN_512_train.yaml',\n",
    "                                batch_size=1,\n",
    "                                num_features=100,\n",
    "                                feature_name=\"fc6\",\n",
    "                                confidence_threshold=0,\n",
    "                                background=False,\n",
    "                                partition=0)\n",
    "        return parser\n",
    "    \n",
    "    def _build_detection_model(self):\n",
    "        cfg.merge_from_file(self.args.config_file)\n",
    "        cfg.freeze()\n",
    "\n",
    "        model = build_detection_model(cfg)\n",
    "        checkpoint = torch.load(self.args.model_file, map_location=torch.device(\"cpu\"))\n",
    "\n",
    "        load_state_dict(model, checkpoint.pop(\"model\"))\n",
    "\n",
    "        model.to(\"cuda\")\n",
    "        model.eval()\n",
    "        return model\n",
    "\n",
    "    def _image_transform(self, path):\n",
    "        img = Image.open(path)\n",
    "        im = np.array(img).astype(np.float32)\n",
    "        # IndexError: too many indices for array, grayscale images\n",
    "        if len(im.shape) < 3:\n",
    "            im = np.repeat(im[:, :, np.newaxis], 3, axis=2)\n",
    "        im = im[:, :, ::-1]\n",
    "        im -= np.array([102.9801, 115.9465, 122.7717])\n",
    "        im_shape = im.shape\n",
    "        im_height = im_shape[0]\n",
    "        im_width = im_shape[1]\n",
    "        im_size_min = np.min(im_shape[0:2])\n",
    "        im_size_max = np.max(im_shape[0:2])\n",
    "\n",
    "        # Scale based on minimum size\n",
    "        im_scale = self.MIN_SIZE / im_size_min\n",
    "\n",
    "        # Prevent the biggest axis from being more than max_size\n",
    "        # If bigger, scale it down\n",
    "        if np.round(im_scale * im_size_max) > self.MAX_SIZE:\n",
    "            im_scale = self.MAX_SIZE / im_size_max\n",
    "\n",
    "        im = cv2.resize(\n",
    "            im, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR\n",
    "        )\n",
    "        img = torch.from_numpy(im).permute(2, 0, 1)\n",
    "\n",
    "        im_info = {\"width\": im_width, \"height\": im_height}\n",
    "\n",
    "        return img, im_scale, im_info\n",
    "\n",
    "    def _process_feature_extraction(\n",
    "        self, output, im_scales, im_infos, feature_name=\"fc6\", conf_thresh=0\n",
    "    ):\n",
    "        batch_size = len(output[0][\"proposals\"])\n",
    "        n_boxes_per_image = [len(boxes) for boxes in output[0][\"proposals\"]]\n",
    "        score_list = output[0][\"scores\"].split(n_boxes_per_image)\n",
    "        score_list = [torch.nn.functional.softmax(x, -1) for x in score_list]\n",
    "        feats = output[0][feature_name].split(n_boxes_per_image)\n",
    "        cur_device = score_list[0].device\n",
    "\n",
    "        feat_list = []\n",
    "        info_list = []\n",
    "\n",
    "        for i in range(batch_size):\n",
    "            dets = output[0][\"proposals\"][i].bbox / im_scales[i]\n",
    "            scores = score_list[i]\n",
    "            max_conf = torch.zeros((scores.shape[0])).to(cur_device)\n",
    "            conf_thresh_tensor = torch.full_like(max_conf, conf_thresh)\n",
    "            start_index = 1\n",
    "            # Column 0 of the scores matrix is for the background class\n",
    "            if self.args.background:\n",
    "                start_index = 0\n",
    "            for cls_ind in range(start_index, scores.shape[1]):\n",
    "                cls_scores = scores[:, cls_ind]\n",
    "                keep = nms(dets, cls_scores, 0.5)\n",
    "                max_conf[keep] = torch.where(\n",
    "                    # Better than max one till now and minimally greater than conf_thresh\n",
    "                    (cls_scores[keep] > max_conf[keep])\n",
    "                    & (cls_scores[keep] > conf_thresh_tensor[keep]),\n",
    "                    cls_scores[keep],\n",
    "                    max_conf[keep],\n",
    "                )\n",
    "\n",
    "            sorted_scores, sorted_indices = torch.sort(max_conf, descending=True)\n",
    "            num_boxes = (sorted_scores[: self.args.num_features] != 0).sum()\n",
    "            keep_boxes = sorted_indices[: self.args.num_features]\n",
    "            feat_list.append(feats[i][keep_boxes])\n",
    "            bbox = output[0][\"proposals\"][i][keep_boxes].bbox / im_scales[i]\n",
    "            # Predict the class label using the scores\n",
    "            objects = torch.argmax(scores[keep_boxes][start_index:], dim=1)\n",
    "            cls_prob = torch.max(scores[keep_boxes][start_index:], dim=1)\n",
    "\n",
    "            info_list.append(\n",
    "                {\n",
    "                    \"bbox\": bbox.cpu().numpy(),\n",
    "                    \"num_boxes\": num_boxes.item(),\n",
    "                    \"objects\": objects.cpu().numpy(),\n",
    "                    \"image_width\": im_infos[i][\"width\"],\n",
    "                    \"image_height\": im_infos[i][\"height\"],\n",
    "                    \"cls_prob\": scores[keep_boxes].cpu().numpy(),\n",
    "                }\n",
    "            )\n",
    "\n",
    "        return feat_list, info_list\n",
    "\n",
    "    def get_detectron_features(self, image_paths):\n",
    "        img_tensor, im_scales, im_infos = [], [], []\n",
    "\n",
    "        for image_path in image_paths:\n",
    "            im, im_scale, im_info = self._image_transform(image_path)\n",
    "            img_tensor.append(im)\n",
    "            im_scales.append(im_scale)\n",
    "            im_infos.append(im_info)\n",
    "\n",
    "        # Image dimensions should be divisible by 32, to allow convolutions\n",
    "        # in detector to work\n",
    "        current_img_list = to_image_list(img_tensor, size_divisible=32)\n",
    "        current_img_list = current_img_list.to(\"cuda\")\n",
    "\n",
    "        with torch.no_grad():\n",
    "            output = self.detection_model(current_img_list)\n",
    "\n",
    "        feat_list = self._process_feature_extraction(\n",
    "            output,\n",
    "            im_scales,\n",
    "            im_infos,\n",
    "            self.args.feature_name,\n",
    "            self.args.confidence_threshold,\n",
    "        )\n",
    "\n",
    "        return feat_list\n",
    "\n",
    "    def _chunks(self, array, chunk_size):\n",
    "        for i in range(0, len(array), chunk_size):\n",
    "            yield array[i : i + chunk_size]\n",
    "\n",
    "    def _save_feature(self, file_name, feature, info):\n",
    "        file_base_name = os.path.basename(file_name)\n",
    "        file_base_name = file_base_name.split(\".\")[0]\n",
    "        info[\"image_id\"] = file_base_name\n",
    "        info[\"features\"] = feature.cpu().numpy()\n",
    "        file_base_name = file_base_name + \".npy\"\n",
    "\n",
    "        np.save(os.path.join(self.args.output_folder, file_base_name), info)\n",
    "\n",
    "    def extract_features(self, image_path):\n",
    "\n",
    "        features, infos = self.get_detectron_features([image_path])\n",
    "\n",
    "        return features, infos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_batch(batch):\n",
    "    return [tokenizer.convert_tokens_to_ids(sent) for sent in batch]\n",
    "\n",
    "def untokenize_batch(batch):\n",
    "    return [tokenizer.convert_ids_to_tokens(sent) for sent in batch]\n",
    "\n",
    "def detokenize(sent):\n",
    "    \"\"\" Roughly detokenizes (mainly undoes wordpiece) \"\"\"\n",
    "    new_sent = []\n",
    "    for i, tok in enumerate(sent):\n",
    "        if tok.startswith(\"##\"):\n",
    "            new_sent[len(new_sent) - 1] = new_sent[len(new_sent) - 1] + tok[2:]\n",
    "        else:\n",
    "            new_sent.append(tok)\n",
    "    return new_sent\n",
    "\n",
    "def printer(sent, should_detokenize=True):\n",
    "    if should_detokenize:\n",
    "        sent = detokenize(sent)[1:-1]\n",
    "    print(\" \".join(sent))\n",
    "\n",
    "\n",
    "# write arbitary string for given sentense. \n",
    "import _pickle as cPickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prediction(question, features, spatials, segment_ids, input_mask, image_mask, co_attention_mask, task_tokens, ):\n",
    "\n",
    "    vil_prediction, vil_prediction_gqa, vil_logit, vil_binary_prediction, vil_tri_prediction, vision_prediction, vision_logit, linguisic_prediction, linguisic_logit, attn_data_list = model(\n",
    "        question, features, spatials, segment_ids, input_mask, image_mask, co_attention_mask, task_tokens, output_all_attention_masks=True\n",
    "    )\n",
    "    \n",
    "    height, width = img.shape[0], img.shape[1]\n",
    "\n",
    "    logits = torch.max(vil_prediction, 1)[1].data  # argmax\n",
    "    # Load VQA label to answers:\n",
    "    label2ans_path = os.path.join('save', \"VQA\" ,\"cache\", \"trainval_label2ans.pkl\")\n",
    "    vqa_label2ans = cPickle.load(open(label2ans_path, \"rb\"))\n",
    "    answer = vqa_label2ans[logits[0].item()]\n",
    "    print(\"VQA: \" + answer)\n",
    "\n",
    "    # Load GQA label to answers:\n",
    "    label2ans_path = os.path.join('save', \"gqa\" ,\"cache\", \"trainval_label2ans.pkl\")\n",
    "\n",
    "    logtis_gqa = torch.max(vil_prediction_gqa, 1)[1].data\n",
    "    gqa_label2ans = cPickle.load(open(label2ans_path, \"rb\"))\n",
    "    answer = gqa_label2ans[logtis_gqa[0].item()]\n",
    "    print(\"GQA: \" + answer)\n",
    "\n",
    "    # vil_binary_prediction NLVR2, 0: False 1: True Task 12\n",
    "    logtis_binary = torch.max(vil_binary_prediction, 1)[1].data\n",
    "    print(\"NLVR: \" + str(logtis_binary.item()))\n",
    "\n",
    "    # vil_entaliment:  \n",
    "    label_map = {0:\"contradiction\", 1:\"neutral\", 2:\"entailment\"}\n",
    "    logtis_tri = torch.max(vil_tri_prediction, 1)[1].data\n",
    "    print(\"Entaliment: \" + str(label_map[logtis_tri.item()]))\n",
    "\n",
    "    # vil_logit: \n",
    "    logits_vil = vil_logit[0].item()\n",
    "    print(\"ViL_logit: %f\" %logits_vil)\n",
    "\n",
    "    # grounding: \n",
    "    logits_vision = torch.max(vision_logit, 1)[1].data\n",
    "    grounding_val, grounding_idx = torch.sort(vision_logit.view(-1), 0, True)\n",
    "\n",
    "    examples_per_row = 5\n",
    "    ncols = examples_per_row \n",
    "    nrows = 1\n",
    "    figsize = [12, ncols*20]     # figure size, inches\n",
    "    fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=figsize)\n",
    "\n",
    "    for i, axi in enumerate(ax.flat):\n",
    "        idx = grounding_idx[i]\n",
    "        val = grounding_val[i]\n",
    "        box = spatials[0][idx][:4].tolist()\n",
    "        y1 = int(box[1] * height)\n",
    "        y2 = int(box[3] * height)\n",
    "        x1 = int(box[0] * width)\n",
    "        x2 = int(box[2] * width)\n",
    "        patch = img[y1:y2,x1:x2]\n",
    "        axi.imshow(patch)\n",
    "        axi.axis('off')\n",
    "        axi.set_title(str(i) + \": \" + str(val.item()))\n",
    "\n",
    "    plt.axis('off')\n",
    "    plt.tight_layout(True)\n",
    "    plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_prediction(query, task, features, infos):\n",
    "\n",
    "    tokens = tokenizer.encode(query)\n",
    "    tokens = tokenizer.add_special_tokens_single_sentence(tokens)\n",
    "\n",
    "    segment_ids = [0] * len(tokens)\n",
    "    input_mask = [1] * len(tokens)\n",
    "\n",
    "    max_length = 37\n",
    "    if len(tokens) < max_length:\n",
    "        # Note here we pad in front of the sentence\n",
    "        padding = [0] * (max_length - len(tokens))\n",
    "        tokens = tokens + padding\n",
    "        input_mask += padding\n",
    "        segment_ids += padding\n",
    "\n",
    "    text = torch.from_numpy(np.array(tokens)).cuda().unsqueeze(0)\n",
    "    input_mask = torch.from_numpy(np.array(input_mask)).cuda().unsqueeze(0)\n",
    "    segment_ids = torch.from_numpy(np.array(segment_ids)).cuda().unsqueeze(0)\n",
    "    task = torch.from_numpy(np.array(task)).cuda().unsqueeze(0)\n",
    "\n",
    "    num_image = len(infos)\n",
    "\n",
    "    feature_list = []\n",
    "    image_location_list = []\n",
    "    image_mask_list = []\n",
    "    for i in range(num_image):\n",
    "        image_w = infos[i]['image_width']\n",
    "        image_h = infos[i]['image_height']\n",
    "        feature = features[i]\n",
    "        num_boxes = feature.shape[0]\n",
    "\n",
    "        g_feat = torch.sum(feature, dim=0) / num_boxes\n",
    "        num_boxes = num_boxes + 1\n",
    "        feature = torch.cat([g_feat.view(1,-1), feature], dim=0)\n",
    "        boxes = infos[i]['bbox']\n",
    "        image_location = np.zeros((boxes.shape[0], 5), dtype=np.float32)\n",
    "        image_location[:,:4] = boxes\n",
    "        image_location[:,4] = (image_location[:,3] - image_location[:,1]) * (image_location[:,2] - image_location[:,0]) / (float(image_w) * float(image_h))\n",
    "        image_location[:,0] = image_location[:,0] / float(image_w)\n",
    "        image_location[:,1] = image_location[:,1] / float(image_h)\n",
    "        image_location[:,2] = image_location[:,2] / float(image_w)\n",
    "        image_location[:,3] = image_location[:,3] / float(image_h)\n",
    "        g_location = np.array([0,0,1,1,1])\n",
    "        image_location = np.concatenate([np.expand_dims(g_location, axis=0), image_location], axis=0)\n",
    "        image_mask = [1] * (int(num_boxes))\n",
    "\n",
    "        feature_list.append(feature)\n",
    "        image_location_list.append(torch.tensor(image_location))\n",
    "        image_mask_list.append(torch.tensor(image_mask))\n",
    "\n",
    "    features = torch.stack(feature_list, dim=0).float().cuda()\n",
    "    spatials = torch.stack(image_location_list, dim=0).float().cuda()\n",
    "    image_mask = torch.stack(image_mask_list, dim=0).byte().cuda()\n",
    "    co_attention_mask = torch.zeros((num_image, num_boxes, max_length)).cuda()\n",
    "\n",
    "    prediction(text, features, spatials, segment_ids, input_mask, image_mask, co_attention_mask, task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.bn1.bias                  loaded from backbone.body.layer1.0.bn1.bias                  of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.bn1.running_mean          loaded from backbone.body.layer1.0.bn1.running_mean          of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.bn1.running_var           loaded from backbone.body.layer1.0.bn1.running_var           of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.bn1.weight                loaded from backbone.body.layer1.0.bn1.weight                of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.bn2.bias                  loaded from backbone.body.layer1.0.bn2.bias                  of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.bn2.running_mean          loaded from backbone.body.layer1.0.bn2.running_mean          of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.bn2.running_var           loaded from backbone.body.layer1.0.bn2.running_var           of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.bn2.weight                loaded from backbone.body.layer1.0.bn2.weight                of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.bn3.bias                  loaded from backbone.body.layer1.0.bn3.bias                  of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.bn3.running_mean          loaded from backbone.body.layer1.0.bn3.running_mean          of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.bn3.running_var           loaded from backbone.body.layer1.0.bn3.running_var           of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.bn3.weight                loaded from backbone.body.layer1.0.bn3.weight                of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.conv1.weight              loaded from backbone.body.layer1.0.conv1.weight              of shape (256, 64, 1, 1)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.conv2.weight              loaded from backbone.body.layer1.0.conv2.weight              of shape (256, 8, 3, 3)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.conv3.weight              loaded from backbone.body.layer1.0.conv3.weight              of shape (256, 256, 1, 1)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.downsample.0.weight       loaded from backbone.body.layer1.0.downsample.0.weight       of shape (256, 64, 1, 1)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.downsample.1.bias         loaded from backbone.body.layer1.0.downsample.1.bias         of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.downsample.1.running_mean loaded from backbone.body.layer1.0.downsample.1.running_mean of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.downsample.1.running_var  loaded from backbone.body.layer1.0.downsample.1.running_var  of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.0.downsample.1.weight       loaded from backbone.body.layer1.0.downsample.1.weight       of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.1.bn1.bias                  loaded from backbone.body.layer1.1.bn1.bias                  of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.1.bn1.running_mean          loaded from backbone.body.layer1.1.bn1.running_mean          of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.1.bn1.running_var           loaded from backbone.body.layer1.1.bn1.running_var           of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.1.bn1.weight                loaded from backbone.body.layer1.1.bn1.weight                of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.1.bn2.bias                  loaded from backbone.body.layer1.1.bn2.bias                  of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.1.bn2.running_mean          loaded from backbone.body.layer1.1.bn2.running_mean          of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.1.bn2.running_var           loaded from backbone.body.layer1.1.bn2.running_var           of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.1.bn2.weight                loaded from backbone.body.layer1.1.bn2.weight                of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.1.bn3.bias                  loaded from backbone.body.layer1.1.bn3.bias                  of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.1.bn3.running_mean          loaded from backbone.body.layer1.1.bn3.running_mean          of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.1.bn3.running_var           loaded from backbone.body.layer1.1.bn3.running_var           of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.1.bn3.weight                loaded from backbone.body.layer1.1.bn3.weight                of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.1.conv1.weight              loaded from backbone.body.layer1.1.conv1.weight              of shape (256, 256, 1, 1)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.1.conv2.weight              loaded from backbone.body.layer1.1.conv2.weight              of shape (256, 8, 3, 3)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.1.conv3.weight              loaded from backbone.body.layer1.1.conv3.weight              of shape (256, 256, 1, 1)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.2.bn1.bias                  loaded from backbone.body.layer1.2.bn1.bias                  of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.2.bn1.running_mean          loaded from backbone.body.layer1.2.bn1.running_mean          of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.2.bn1.running_var           loaded from backbone.body.layer1.2.bn1.running_var           of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.2.bn1.weight                loaded from backbone.body.layer1.2.bn1.weight                of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.2.bn2.bias                  loaded from backbone.body.layer1.2.bn2.bias                  of shape (256,)\n"
     ]
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    {
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      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.2.bn2.running_mean          loaded from backbone.body.layer1.2.bn2.running_mean          of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.2.bn2.running_var           loaded from backbone.body.layer1.2.bn2.running_var           of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.2.bn2.weight                loaded from backbone.body.layer1.2.bn2.weight                of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.2.bn3.bias                  loaded from backbone.body.layer1.2.bn3.bias                  of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.2.bn3.running_mean          loaded from backbone.body.layer1.2.bn3.running_mean          of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.2.bn3.running_var           loaded from backbone.body.layer1.2.bn3.running_var           of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.2.bn3.weight                loaded from backbone.body.layer1.2.bn3.weight                of shape (256,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.2.conv1.weight              loaded from backbone.body.layer1.2.conv1.weight              of shape (256, 256, 1, 1)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer1.2.conv2.weight              loaded from backbone.body.layer1.2.conv2.weight              of shape (256, 8, 3, 3)\n",
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      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.0.conv1.weight              loaded from backbone.body.layer2.0.conv1.weight              of shape (512, 256, 1, 1)\n",
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      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.0.downsample.0.weight       loaded from backbone.body.layer2.0.downsample.0.weight       of shape (512, 256, 1, 1)\n",
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      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.1.bn3.bias                  loaded from backbone.body.layer2.1.bn3.bias                  of shape (512,)\n",
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     ]
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      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.1.bn3.running_var           loaded from backbone.body.layer2.1.bn3.running_var           of shape (512,)\n",
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      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.1.conv1.weight              loaded from backbone.body.layer2.1.conv1.weight              of shape (512, 512, 1, 1)\n",
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      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.2.bn1.bias                  loaded from backbone.body.layer2.2.bn1.bias                  of shape (512,)\n",
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      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.4.bn1.bias                  loaded from backbone.body.layer2.4.bn1.bias                  of shape (512,)\n",
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      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.4.bn2.running_mean          loaded from backbone.body.layer2.4.bn2.running_mean          of shape (512,)\n",
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      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.4.conv1.weight              loaded from backbone.body.layer2.4.conv1.weight              of shape (512, 512, 1, 1)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.4.conv2.weight              loaded from backbone.body.layer2.4.conv2.weight              of shape (512, 16, 3, 3)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.4.conv3.weight              loaded from backbone.body.layer2.4.conv3.weight              of shape (512, 512, 1, 1)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.5.bn1.bias                  loaded from backbone.body.layer2.5.bn1.bias                  of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.5.bn1.running_mean          loaded from backbone.body.layer2.5.bn1.running_mean          of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.5.bn1.running_var           loaded from backbone.body.layer2.5.bn1.running_var           of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.5.bn1.weight                loaded from backbone.body.layer2.5.bn1.weight                of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.5.bn2.bias                  loaded from backbone.body.layer2.5.bn2.bias                  of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.5.bn2.running_mean          loaded from backbone.body.layer2.5.bn2.running_mean          of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.5.bn2.running_var           loaded from backbone.body.layer2.5.bn2.running_var           of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.5.bn2.weight                loaded from backbone.body.layer2.5.bn2.weight                of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.5.bn3.bias                  loaded from backbone.body.layer2.5.bn3.bias                  of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.5.bn3.running_mean          loaded from backbone.body.layer2.5.bn3.running_mean          of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.5.bn3.running_var           loaded from backbone.body.layer2.5.bn3.running_var           of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.5.bn3.weight                loaded from backbone.body.layer2.5.bn3.weight                of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.5.conv1.weight              loaded from backbone.body.layer2.5.conv1.weight              of shape (512, 512, 1, 1)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.5.conv2.weight              loaded from backbone.body.layer2.5.conv2.weight              of shape (512, 16, 3, 3)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.5.conv3.weight              loaded from backbone.body.layer2.5.conv3.weight              of shape (512, 512, 1, 1)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.6.bn1.bias                  loaded from backbone.body.layer2.6.bn1.bias                  of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.6.bn1.running_mean          loaded from backbone.body.layer2.6.bn1.running_mean          of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.6.bn1.running_var           loaded from backbone.body.layer2.6.bn1.running_var           of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.6.bn1.weight                loaded from backbone.body.layer2.6.bn1.weight                of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.6.bn2.bias                  loaded from backbone.body.layer2.6.bn2.bias                  of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.6.bn2.running_mean          loaded from backbone.body.layer2.6.bn2.running_mean          of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.6.bn2.running_var           loaded from backbone.body.layer2.6.bn2.running_var           of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.6.bn2.weight                loaded from backbone.body.layer2.6.bn2.weight                of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.6.bn3.bias                  loaded from backbone.body.layer2.6.bn3.bias                  of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.6.bn3.running_mean          loaded from backbone.body.layer2.6.bn3.running_mean          of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.6.bn3.running_var           loaded from backbone.body.layer2.6.bn3.running_var           of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.6.bn3.weight                loaded from backbone.body.layer2.6.bn3.weight                of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.6.conv1.weight              loaded from backbone.body.layer2.6.conv1.weight              of shape (512, 512, 1, 1)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.6.conv2.weight              loaded from backbone.body.layer2.6.conv2.weight              of shape (512, 16, 3, 3)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.6.conv3.weight              loaded from backbone.body.layer2.6.conv3.weight              of shape (512, 512, 1, 1)\n"
     ]
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    {
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      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.7.bn1.bias                  loaded from backbone.body.layer2.7.bn1.bias                  of shape (512,)\n",
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      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.7.bn3.bias                  loaded from backbone.body.layer2.7.bn3.bias                  of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.7.bn3.running_mean          loaded from backbone.body.layer2.7.bn3.running_mean          of shape (512,)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer2.7.bn3.running_var           loaded from backbone.body.layer2.7.bn3.running_var           of shape (512,)\n",
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      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.0.downsample.0.weight       loaded from backbone.body.layer3.0.downsample.0.weight       of shape (1024, 512, 1, 1)\n",
      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.0.downsample.1.bias         loaded from backbone.body.layer3.0.downsample.1.bias         of shape (1024,)\n",
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     ]
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      "01/13/2020 22:59:04 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.1.bn2.running_mean          loaded from backbone.body.layer3.1.bn2.running_mean          of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.11.bn3.weight               loaded from backbone.body.layer3.11.bn3.weight               of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.11.conv1.weight             loaded from backbone.body.layer3.11.conv1.weight             of shape (1024, 1024, 1, 1)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.11.conv2.weight             loaded from backbone.body.layer3.11.conv2.weight             of shape (1024, 32, 3, 3)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.11.conv3.weight             loaded from backbone.body.layer3.11.conv3.weight             of shape (1024, 1024, 1, 1)\n"
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.12.bn1.bias                 loaded from backbone.body.layer3.12.bn1.bias                 of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.12.bn1.running_mean         loaded from backbone.body.layer3.12.bn1.running_mean         of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.12.bn1.running_var          loaded from backbone.body.layer3.12.bn1.running_var          of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.12.bn2.running_var          loaded from backbone.body.layer3.12.bn2.running_var          of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.13.bn1.bias                 loaded from backbone.body.layer3.13.bn1.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.14.bn1.bias                 loaded from backbone.body.layer3.14.bn1.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.14.bn3.running_var          loaded from backbone.body.layer3.14.bn3.running_var          of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.15.bn1.bias                 loaded from backbone.body.layer3.15.bn1.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.17.bn2.running_mean         loaded from backbone.body.layer3.17.bn2.running_mean         of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.18.bn3.weight               loaded from backbone.body.layer3.18.bn3.weight               of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.18.conv2.weight             loaded from backbone.body.layer3.18.conv2.weight             of shape (1024, 32, 3, 3)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.19.bn1.bias                 loaded from backbone.body.layer3.19.bn1.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.19.bn1.running_var          loaded from backbone.body.layer3.19.bn1.running_var          of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.19.bn1.weight               loaded from backbone.body.layer3.19.bn1.weight               of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.19.bn2.bias                 loaded from backbone.body.layer3.19.bn2.bias                 of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.19.bn2.running_mean         loaded from backbone.body.layer3.19.bn2.running_mean         of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.19.bn2.running_var          loaded from backbone.body.layer3.19.bn2.running_var          of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.19.bn2.weight               loaded from backbone.body.layer3.19.bn2.weight               of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.19.bn3.bias                 loaded from backbone.body.layer3.19.bn3.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.19.bn3.running_var          loaded from backbone.body.layer3.19.bn3.running_var          of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.19.bn3.weight               loaded from backbone.body.layer3.19.bn3.weight               of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.19.conv1.weight             loaded from backbone.body.layer3.19.conv1.weight             of shape (1024, 1024, 1, 1)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.19.conv3.weight             loaded from backbone.body.layer3.19.conv3.weight             of shape (1024, 1024, 1, 1)\n"
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.2.bn1.bias                  loaded from backbone.body.layer3.2.bn1.bias                  of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.21.bn1.bias                 loaded from backbone.body.layer3.21.bn1.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.21.bn3.running_var          loaded from backbone.body.layer3.21.bn3.running_var          of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.22.bn1.bias                 loaded from backbone.body.layer3.22.bn1.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.24.bn2.running_mean         loaded from backbone.body.layer3.24.bn2.running_mean         of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.24.conv2.weight             loaded from backbone.body.layer3.24.conv2.weight             of shape (1024, 32, 3, 3)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.25.bn1.bias                 loaded from backbone.body.layer3.25.bn1.bias                 of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.25.bn1.running_mean         loaded from backbone.body.layer3.25.bn1.running_mean         of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.25.bn1.running_var          loaded from backbone.body.layer3.25.bn1.running_var          of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.25.bn1.weight               loaded from backbone.body.layer3.25.bn1.weight               of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.25.bn2.bias                 loaded from backbone.body.layer3.25.bn2.bias                 of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.25.bn2.running_mean         loaded from backbone.body.layer3.25.bn2.running_mean         of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.25.bn2.running_var          loaded from backbone.body.layer3.25.bn2.running_var          of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.25.bn2.weight               loaded from backbone.body.layer3.25.bn2.weight               of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.25.bn3.bias                 loaded from backbone.body.layer3.25.bn3.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.25.bn3.running_var          loaded from backbone.body.layer3.25.bn3.running_var          of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.25.bn3.weight               loaded from backbone.body.layer3.25.bn3.weight               of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.25.conv1.weight             loaded from backbone.body.layer3.25.conv1.weight             of shape (1024, 1024, 1, 1)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.25.conv2.weight             loaded from backbone.body.layer3.25.conv2.weight             of shape (1024, 32, 3, 3)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.25.conv3.weight             loaded from backbone.body.layer3.25.conv3.weight             of shape (1024, 1024, 1, 1)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.26.bn1.bias                 loaded from backbone.body.layer3.26.bn1.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.26.bn1.running_var          loaded from backbone.body.layer3.26.bn1.running_var          of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.26.bn2.bias                 loaded from backbone.body.layer3.26.bn2.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.26.bn2.weight               loaded from backbone.body.layer3.26.bn2.weight               of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.26.bn3.bias                 loaded from backbone.body.layer3.26.bn3.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.26.bn3.running_var          loaded from backbone.body.layer3.26.bn3.running_var          of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.26.bn3.weight               loaded from backbone.body.layer3.26.bn3.weight               of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.26.conv1.weight             loaded from backbone.body.layer3.26.conv1.weight             of shape (1024, 1024, 1, 1)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.26.conv2.weight             loaded from backbone.body.layer3.26.conv2.weight             of shape (1024, 32, 3, 3)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.26.conv3.weight             loaded from backbone.body.layer3.26.conv3.weight             of shape (1024, 1024, 1, 1)\n"
     ]
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    {
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.27.bn1.bias                 loaded from backbone.body.layer3.27.bn1.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.29.bn1.bias                 loaded from backbone.body.layer3.29.bn1.bias                 of shape (1024,)\n",
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     ]
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.29.bn3.running_var          loaded from backbone.body.layer3.29.bn3.running_var          of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.3.bn1.bias                  loaded from backbone.body.layer3.3.bn1.bias                  of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.31.bn1.running_mean         loaded from backbone.body.layer3.31.bn1.running_mean         of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.31.bn1.weight               loaded from backbone.body.layer3.31.bn1.weight               of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.31.bn2.bias                 loaded from backbone.body.layer3.31.bn2.bias                 of shape (1024,)\n"
     ]
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     "name": "stderr",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.31.bn2.running_mean         loaded from backbone.body.layer3.31.bn2.running_mean         of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.31.bn2.running_var          loaded from backbone.body.layer3.31.bn2.running_var          of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.31.bn2.weight               loaded from backbone.body.layer3.31.bn2.weight               of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.31.bn3.bias                 loaded from backbone.body.layer3.31.bn3.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.31.bn3.weight               loaded from backbone.body.layer3.31.bn3.weight               of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.31.conv1.weight             loaded from backbone.body.layer3.31.conv1.weight             of shape (1024, 1024, 1, 1)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.32.bn1.bias                 loaded from backbone.body.layer3.32.bn1.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.33.bn1.bias                 loaded from backbone.body.layer3.33.bn1.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.34.bn1.bias                 loaded from backbone.body.layer3.34.bn1.bias                 of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.4.bn3.running_var           loaded from backbone.body.layer3.4.bn3.running_var           of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.6.bn1.weight                loaded from backbone.body.layer3.6.bn1.weight                of shape (1024,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.6.bn2.running_mean          loaded from backbone.body.layer3.6.bn2.running_mean          of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.6.bn2.running_var           loaded from backbone.body.layer3.6.bn2.running_var           of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.6.bn2.weight                loaded from backbone.body.layer3.6.bn2.weight                of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.6.bn3.bias                  loaded from backbone.body.layer3.6.bn3.bias                  of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.6.bn3.running_mean          loaded from backbone.body.layer3.6.bn3.running_mean          of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.6.bn3.running_var           loaded from backbone.body.layer3.6.bn3.running_var           of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.6.bn3.weight                loaded from backbone.body.layer3.6.bn3.weight                of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.6.conv1.weight              loaded from backbone.body.layer3.6.conv1.weight              of shape (1024, 1024, 1, 1)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.6.conv2.weight              loaded from backbone.body.layer3.6.conv2.weight              of shape (1024, 32, 3, 3)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.6.conv3.weight              loaded from backbone.body.layer3.6.conv3.weight              of shape (1024, 1024, 1, 1)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.7.bn1.bias                  loaded from backbone.body.layer3.7.bn1.bias                  of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.7.bn1.running_mean          loaded from backbone.body.layer3.7.bn1.running_mean          of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.7.bn1.running_var           loaded from backbone.body.layer3.7.bn1.running_var           of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.7.bn1.weight                loaded from backbone.body.layer3.7.bn1.weight                of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.7.bn2.bias                  loaded from backbone.body.layer3.7.bn2.bias                  of shape (1024,)\n"
     ]
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    {
     "name": "stderr",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.7.bn2.running_mean          loaded from backbone.body.layer3.7.bn2.running_mean          of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.7.bn2.running_var           loaded from backbone.body.layer3.7.bn2.running_var           of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.7.bn2.weight                loaded from backbone.body.layer3.7.bn2.weight                of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.7.bn3.bias                  loaded from backbone.body.layer3.7.bn3.bias                  of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.7.bn3.running_mean          loaded from backbone.body.layer3.7.bn3.running_mean          of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.7.bn3.running_var           loaded from backbone.body.layer3.7.bn3.running_var           of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.7.bn3.weight                loaded from backbone.body.layer3.7.bn3.weight                of shape (1024,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.7.conv1.weight              loaded from backbone.body.layer3.7.conv1.weight              of shape (1024, 1024, 1, 1)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer3.9.conv2.weight              loaded from backbone.body.layer3.9.conv2.weight              of shape (1024, 32, 3, 3)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer4.0.bn1.bias                  loaded from backbone.body.layer4.0.bn1.bias                  of shape (2048,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer4.0.downsample.0.weight       loaded from backbone.body.layer4.0.downsample.0.weight       of shape (2048, 1024, 1, 1)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer4.2.bn1.bias                  loaded from backbone.body.layer4.2.bn1.bias                  of shape (2048,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer4.2.bn2.running_mean          loaded from backbone.body.layer4.2.bn2.running_mean          of shape (2048,)\n",
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      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.layer4.2.conv3.weight              loaded from backbone.body.layer4.2.conv3.weight              of shape (2048, 2048, 1, 1)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.stem.bn1.bias                      loaded from backbone.body.stem.bn1.bias                      of shape (64,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.stem.bn1.running_mean              loaded from backbone.body.stem.bn1.running_mean              of shape (64,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.stem.bn1.running_var               loaded from backbone.body.stem.bn1.running_var               of shape (64,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.stem.bn1.weight                    loaded from backbone.body.stem.bn1.weight                    of shape (64,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.body.stem.conv1.weight                  loaded from backbone.body.stem.conv1.weight                  of shape (64, 3, 7, 7)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_inner1.bias                     loaded from backbone.fpn.fpn_inner1.bias                     of shape (512,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_inner1.weight                   loaded from backbone.fpn.fpn_inner1.weight                   of shape (512, 256, 1, 1)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_inner2.bias                     loaded from backbone.fpn.fpn_inner2.bias                     of shape (512,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_inner2.weight                   loaded from backbone.fpn.fpn_inner2.weight                   of shape (512, 512, 1, 1)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_inner3.bias                     loaded from backbone.fpn.fpn_inner3.bias                     of shape (512,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_inner3.weight                   loaded from backbone.fpn.fpn_inner3.weight                   of shape (512, 1024, 1, 1)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_inner4.bias                     loaded from backbone.fpn.fpn_inner4.bias                     of shape (512,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_inner4.weight                   loaded from backbone.fpn.fpn_inner4.weight                   of shape (512, 2048, 1, 1)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_layer1.bias                     loaded from backbone.fpn.fpn_layer1.bias                     of shape (512,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_layer1.weight                   loaded from backbone.fpn.fpn_layer1.weight                   of shape (512, 512, 3, 3)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_layer2.bias                     loaded from backbone.fpn.fpn_layer2.bias                     of shape (512,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_layer2.weight                   loaded from backbone.fpn.fpn_layer2.weight                   of shape (512, 512, 3, 3)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_layer3.bias                     loaded from backbone.fpn.fpn_layer3.bias                     of shape (512,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_layer3.weight                   loaded from backbone.fpn.fpn_layer3.weight                   of shape (512, 512, 3, 3)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_layer4.bias                     loaded from backbone.fpn.fpn_layer4.bias                     of shape (512,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   backbone.fpn.fpn_layer4.weight                   loaded from backbone.fpn.fpn_layer4.weight                   of shape (512, 512, 3, 3)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   roi_heads.box.feature_extractor.fc6.bias         loaded from roi_heads.box.feature_extractor.fc6.bias         of shape (2048,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   roi_heads.box.feature_extractor.fc6.weight       loaded from roi_heads.box.feature_extractor.fc6.weight       of shape (2048, 25088)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   roi_heads.box.feature_extractor.fc7.bias         loaded from roi_heads.box.feature_extractor.fc7.bias         of shape (2048,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   roi_heads.box.feature_extractor.fc7.weight       loaded from roi_heads.box.feature_extractor.fc7.weight       of shape (2048, 2048)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   roi_heads.box.predictor.bbox_pred.bias           loaded from roi_heads.box.predictor.bbox_pred.bias           of shape (6404,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   roi_heads.box.predictor.bbox_pred.weight         loaded from roi_heads.box.predictor.bbox_pred.weight         of shape (6404, 2048)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   roi_heads.box.predictor.cls_score.bias           loaded from roi_heads.box.predictor.cls_score.bias           of shape (1601,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   roi_heads.box.predictor.cls_score.weight         loaded from roi_heads.box.predictor.cls_score.weight         of shape (1601, 2048)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   rpn.anchor_generator.cell_anchors.0              loaded from rpn.anchor_generator.cell_anchors.0              of shape (3, 4)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   rpn.anchor_generator.cell_anchors.1              loaded from rpn.anchor_generator.cell_anchors.1              of shape (3, 4)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   rpn.anchor_generator.cell_anchors.2              loaded from rpn.anchor_generator.cell_anchors.2              of shape (3, 4)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   rpn.anchor_generator.cell_anchors.3              loaded from rpn.anchor_generator.cell_anchors.3              of shape (3, 4)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   rpn.anchor_generator.cell_anchors.4              loaded from rpn.anchor_generator.cell_anchors.4              of shape (3, 4)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   rpn.head.bbox_pred.bias                          loaded from rpn.head.bbox_pred.bias                          of shape (12,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   rpn.head.bbox_pred.weight                        loaded from rpn.head.bbox_pred.weight                        of shape (12, 512, 1, 1)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   rpn.head.cls_logits.bias                         loaded from rpn.head.cls_logits.bias                         of shape (3,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   rpn.head.cls_logits.weight                       loaded from rpn.head.cls_logits.weight                       of shape (3, 512, 1, 1)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   rpn.head.conv.bias                               loaded from rpn.head.conv.bias                               of shape (512,)\n",
      "01/13/2020 22:59:05 - INFO - maskrcnn_benchmark.utils.model_serialization -   rpn.head.conv.weight                             loaded from rpn.head.conv.weight                             of shape (512, 512, 3, 3)\n",
      "01/13/2020 22:59:05 - INFO - vilbert.utils -   loading weights file save/multitask_model/pytorch_model_9.bin\n",
      "01/13/2020 22:59:12 - INFO - pytorch_transformers.tokenization_utils -   loading file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt from cache at /home/jiasen/.cache/torch/pytorch_transformers/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# =============================\n",
    "# ViLBERT part\n",
    "# =============================\n",
    "feature_extractor = FeatureExtractor()\n",
    "\n",
    "args = SimpleNamespace(from_pretrained= \"save/multitask_model/pytorch_model_9.bin\",\n",
    "                       bert_model=\"bert-base-uncased\",\n",
    "                       config_file=\"config/bert_base_6layer_6conect.json\",\n",
    "                       max_seq_length=101,\n",
    "                       train_batch_size=1,\n",
    "                       do_lower_case=True,\n",
    "                       predict_feature=False,\n",
    "                       seed=42,\n",
    "                       num_workers=0,\n",
    "                       baseline=False,\n",
    "                       img_weight=1,\n",
    "                       distributed=False,\n",
    "                       objective=1,\n",
    "                       visual_target=0,\n",
    "                       dynamic_attention=False,\n",
    "                       task_specific_tokens=True,\n",
    "                       tasks='1',\n",
    "                       save_name='',\n",
    "                       in_memory=False,\n",
    "                       batch_size=1,\n",
    "                       local_rank=-1,\n",
    "                       split='mteval',\n",
    "                       clean_train_sets=True\n",
    "                      )\n",
    "\n",
    "config = BertConfig.from_json_file(args.config_file)\n",
    "with open('./vilbert_tasks.yml', 'r') as f:\n",
    "    task_cfg = edict(yaml.safe_load(f))\n",
    "\n",
    "task_names = []\n",
    "for i, task_id in enumerate(args.tasks.split('-')):\n",
    "    task = 'TASK' + task_id\n",
    "    name = task_cfg[task]['name']\n",
    "    task_names.append(name)\n",
    "\n",
    "timeStamp = args.from_pretrained.split('/')[-1] + '-' + args.save_name\n",
    "config = BertConfig.from_json_file(args.config_file)\n",
    "default_gpu=True\n",
    "\n",
    "if args.predict_feature:\n",
    "    config.v_target_size = 2048\n",
    "    config.predict_feature = True\n",
    "else:\n",
    "    config.v_target_size = 1601\n",
    "    config.predict_feature = False\n",
    "\n",
    "if args.task_specific_tokens:\n",
    "    config.task_specific_tokens = True    \n",
    "\n",
    "if args.dynamic_attention:\n",
    "    config.dynamic_attention = True\n",
    "\n",
    "config.visualization = True\n",
    "num_labels = 3129\n",
    "\n",
    "if args.baseline:\n",
    "    model = BaseBertForVLTasks.from_pretrained(\n",
    "        args.from_pretrained, config=config, num_labels=num_labels, default_gpu=default_gpu\n",
    "        )\n",
    "else:\n",
    "    model = VILBertForVLTasks.from_pretrained(\n",
    "        args.from_pretrained, config=config, num_labels=num_labels, default_gpu=default_gpu\n",
    "        )\n",
    "    \n",
    "model.eval()\n",
    "cuda = torch.cuda.is_available()\n",
    "if cuda: model = model.cuda(0)\n",
    "tokenizer = BertTokenizer.from_pretrained(\n",
    "    args.bert_model, do_lower_case=args.do_lower_case\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xsMLQZGShhDLnzs2bjMdj9vd2CCOfN9+6TxJHOK5Fs9VAU1Us06DX7RAGPmJZ4VkWg34Xz7FR1VcCq6qSoigoqwLHszk82EcSBeqyhKrGdRziIEKWREzdIIlS6rJEUVR63S6BHxFFGarmIgqwWC558eKIjc1NirKi2WgRJRmO6xAmJQf7Nzi/POPk+BjHdhmPZ7SbHRAkhDpHV3VWi4i6EonSmEF/B0FQETDoNDcZXo0xDRNRFJFkmY2dHqGfoKk2Da9Ho9EmzzJOT8+w7Qajq4i7h2/x8vkJUq2zWmS0nB3SUEbVQZRBlg3isECVDKgL3nn4iOOTr4j9kirTWa8C1usJaRwgCgZxIFICTtOjqAuqqmK9DtENE8v00HSB9WrN4cE9RsMpnueSFz5V7XN2PiUvRVTNRtUsDMNFUQx2dvd59Pb3uLj+nDSq8cxdxsNLRhc1b97/FkV9wbf/2SNEuWRnew/X7vLkqyOSNOLf/Jv/nv2DLfqdHsdHL0nSlCCMMXWD0fUQcglyjWCZkecRG5sWhpmR5iGKKHJ5fsa927dpODZlXnB2fIqhOiiahFgCWY2tabimTrickKzWtGwbUYKD3V1sy6QUckxHJ8ljLq8uaKsa2/0OD+7e5tGDe+wO+vQbTYSqpBQLWo0GrufQb3dQFJG7tw/41nvv0FRV2o6LIat0Wi2oBaq6ptPrIYigizGGoZDmCZ1+C1kD01IwTYUoTnEdC9PUqcqC5WpOluWomkZe+6iKwnrlY6gmdV2xmM+oKVFpspgvGQ2HCIKKokrYjkCv56IoAuPpCE1TOdw/oOm5SKJAWeTomsp7D79hCfnpz/7qg7quMTSTJM0wDIs8K3BdmygMkBUJ09BQZJWG66LKMuvFkuOTKxRDRtYValEnijJOz7+m1enz4mhEq+/itR00TWA+ueb0+JirqzFBmGNaFScnL3l2fIZommze2mH/5g7b+y2GlxOmswmma1JTcX1xxWK5RNUM0nxFEqa0Wj2CKOX48gzLc3jw8C1mqxXh2me5WnJwaxfL0zg5eYmmGVxeTDAcFUWVSZMYTVMpy5rh9YQwTAmSiAdvPmTQG3B9dY0AxL8pGWfjMUkQIlQ1UlWTJykyNWWesV4sKNI1q8WSnY1drq+vOTjYpchS1ssVtqGxmEwQEVFEhbIoyIucJEmoS4E0SSjylLqq+Xf/7n/l1x9/ycbGPpZhUVSgGBbz+RJJVBAEmfOrSzRdx7DajMcztje32Rhscnl5iWs1oZJRZQNNEzBNk5bXJc1Cmi2V+w/eYrkeMpmMqauCJFojihnnl0c8+q23CIIFiqJwfT2mrirqqub6asTB/m3arU1mk2uWyxVHxy9QVIXlakWj0cRxXLKkJE6WBKsAVWkwm67Y2eywmK6pcxFdbnJ9vmJzsEOVVzS9LtOrgPnCR1ZEGk2Zy+tnNFsGRVxSZxKxnyPUcOPgFidHZyRJSMMzyLI1QTTj1q13ME0LXdexLJPxZEjD03BdhaIek8c6/eYeq0nC3cMfgOCzTp8yX4XM5zO6nQ5/8ef/kZOTCf3ONoc3t+kPGoyH55DneLbH5dUl/sqnKkq2+wNUbU2YrkgJ+Wff/w5xHFLm0GtuMx1NEEqJ1XRN6PvIioBhmuRZTZ6nqIKOWIjoskydZbiGxr2bd4iWEa2GhaappGJJWr3qjw0aTZqaznK15NGDe8wvLmgoCl9/+ikvTl7wez/8fd68cYv9zQE3djep64SDg026HY8sDNhsd38TP6hQRAlJEGi4FlHgI1GhpzZZpKCbWxSpSZFZPH86QRaaVGVOHCS4ts16vWJra4s0TQnCEE2RcK0GZVEShzG6olAVJdPxjCrTMAyRnb0edVWgqjJuowIB5rMQ23NYzhcItUhZFqzXS1azCdPxNT/85//qmwns+sVHHwiAZVookoxpWORZQpZGyIZBXoCkWESJT7tjMp1eUpZgOW3myzFB4vP482c8fPgQx5N58vQIr7WFoha0200sSyUOfKgFLs7HdLu73LyzTy1nTGYFtaZx//0DikKg2anZaO/T6XZxGy6e5yILEqIgI0kampni+zHHRzN6/Rs8Pz2n1moW/oK8SHjnrW8xnE2o5RLLMRjPJ9hWA8duM/NnzBZLsixnsVpzcTXk0bvf5uT0krwoOXp5zMsXR7SaHYbDEZeXFxi6zlv37zOdTDBVnaooMQyNpudR1yWe5zIZXmHpDmlaYZomsqIgSwoCIrauYOgGlmby8a8/ptftsV6vXzXdbZvNzQGaJjMaXvPhr36JJGk8/uwJL5895+snT3n3/W/x9NkLxpMJFTVZUTGZz9nbv8vZ6QW24/xmiqrhuh5ZWtBp72KYErPpBAGBWkyZzI85Pj5jc9eg0dQJwhk3bmyT5isePrpPEC2Zz6fMJjOm0ymu41KUGZPhhMlojoiEatW02m1+/w9+H9O18aMV7V6byXyKWKkoSonjOGSxwGK+QJIK4jBjOoqZTVbs7+0xHc2oK4Xz0yGSIGPaMlWREUUpVSZiyE3aTp88yVhMJnh2nxfPj1gtxzRaJnmZYtsWjWaXsqhYLCa0Wh5XV6cYWo0s5egaOHYHf1WxWIxotzROTk5RjIha8rG0XRpekyIvEQWNX374JbKoMhyd8r3vv0u0XKNrKkVesFgs6fV7FFnO3Vt3GF5OqdUKs2nw/PSIrKjQ1RrDKhCqJqpq8/Zb79HtN0nykCRL0QyLLCvY397H0cxXkYCqoOnYeIbDbDzBajpYjRZXqyW2Z5NHMdPzawhyDEVF1TWMpsvx8JKoyvjOe99BE2QUII9DTo+eMxxe8OVXj5lNJjQsB90wqcUKwzTxXBfbMjAUmdVsSuz7JMGUWizIypLxYkIQrJCkmsCfY6gOsqyiqSqmZXB8dIwoyQw2NgmCkGAdUFdQ5QXL5ZqN3jbDqwmO5VALERubLoqqATWaXqNIOmWuYZg6nU6POM5QRBkqkESB1XLBv/zD//abCezLX/3dB7KsEGU5sqrir3yWkxmKIFHFIev5BFURGM+mJHlOlucIYomqrRlsthB1hYvZNesiIYkFqqKm61mYLkTpjOnkAts02Rxsc3E5BVHFdVPOzoYIQsHNw12CmY9c5Wx1dsjyOWm4Rqrgx3/z92xv7FLV0Ot1IZZRJJPj8yMMRySvAsoiedUgpKbSCwabKqoak5c5QZSSFSWz1YiGZ+BaOtPpCElXmYchP//oU1brmDBdk1UVRS1wfHFFmBUYlkuUVnz59XNWQcoqSkFWMW2XvMy5uLwmznKSQiDOC8IownIsdN1AEETqusZzG4RRwni64Feffs5P/vFDLi6n3HnjHc5PTgCJ/+1//z8oJHj43vsUgohu2ey+8YBPnnzObDmiP+hy6/YbTOcJmuGhmx6yolOyJE1i7OaaqppTZio7W5vI8oLnz77G9ZpcXQ9RFZNoXTC6GuE4N/ji1ye0nR3KSuD0fMhilvH4l0fs9AfMRz7+sub8bMWtm+9yejZm73Cf+WpBa2CwXgU8e3ZMEKxI4pAojBBqGVWyyaqSo9MTVFWnYXXotXf44otPKescyzYIg4j9nQNW8xWWYdFsteh0XcpMY70oSJMYWSoYj5cYho5py1y/jLHtBk7DJkoi/PUSz7UR65r1dIZrqrQ9hyQIsXUPRVIpi4KLkyt0rcZfz9g7GGBZGaamUQQyurWFKpiEqwhDthj0BqzWS1quze2DmxRpyO7ePl9++hkd16MuMi5HZ8RCDFrOWzdvst3osrOxz3zlIwoSPafJXreFLObMllfUusjZ1TVVWUFd4bgGklxzcGOb6XxEp9VgMZ0hqRKiqgCQJjme3kDKFRTBoN/fptnp0uh10a1XgxlJUHBNj/FwTFEURGVGpIoc+XPsjS5vvHGPB7fvoWoGulGBWCJJAoaho+kGz54dY9stPvn4MwTPYREn5EKFZukImsjF6BK71aDMc6bTCY7tkCUlqqRTxzVarVFVJsOrMZ5rURQFeQprP8A0VSarZzieTVGIlIhcXo/oDDZRDRXkjKJIoaxRShDSmtLP2O/s4EkW7//2H38zgf38R3/+wfV4QlmWFHmJpMh0Om0EQURRVBzHQ5Akiho0TaHIczzXxbBtWt0+cVLQ620iCgrROuLi7IJ7b9zj5OSU+WzFjf1D+p0+i+mK84shhm2zs93h8Wef886jd3Fsm/Vqiee6XJ6dUpclw+EYz2vjuR0mkxm9/oAojkiTiHUYsLm5hdO0qSm4dfsmy+WS4dUQ23Up0xrH2KDILJqNAbJcY5g1vW4Xoa4wDI00TRhPJjRbLcIgpK5z8ixHUVSqqsa1bCaTMbqhcnFxTV1DVZUsFgvGkwlRGLHyfRbzJUEYEUYxNRWy/Opj1DSN6XTK6Pqa58+fs3d4yGQ8pq5rvvfb30eSJLLQ5/r6incevc3Hnz7mZ//wc8q8oiorkjjiX/7xv6AuEsoiQ5IUNNXgzt07yLJIb6OFIMTcu3cf2xVRZImGu8XV9RVFWaGpJtejEQcH+3z44S9I05hOt4miWbRbLfrdDp2Ox2Q2xfOaNN024+EYXXVotTaYzpZsb25x541Djl5+jaKItNs2vh/jmA0URaLdbrGztcnJ8TFR6CMqIMkStuWynq34yd/9mIMbe2xs7HF+fsF8NicKI1RdQ1YlLq4uubg8pa4UVFUjyyPixEdAZblYsljMSMKCW3cOKcnY2dtmPpsDIoZiYNoyjZZNlkeEUYhh2Bh6l+U8I0tUNjcOqSuNYP0qglKk4FhNyEEqMzRJYD6Z84tfPCaISioqbNfj0y++4vTyms2tfSpB4HI0xOm06O1ucrC7gyJK9Dptnh69IEwSDna20RDZ6O8wnS958uI5ve1tlisfTbNYLtaYmsKNg0OOXrzEMU0W8wXz2Zw4SRAlGadpo2gaZ1dX7OztoCgKsiKCAFVVYWg6V1dXOKaFpmmIgoBlWXRMm8hfo8kim+0GPcNCTBOycI2u6AQrn6qGuoQ0yV79lVHjNZvUZYUkytS1xN7eLq7rsru7S6vRRBQrDEMnLVKGoyGL+QLHdjk6eokiO4RBQBIFKIpG0+vgmBYCFYOtDtQieV4hCTK24zCfTcnzlLIoEEWJLMlouk1W6wBFVrAcG0ESePfb33AKef7y0w8sy0bTdCRFI4pigjBiuVqxt3ODdZjQ6fbIi/I30zMdURC4mCyQJQ3T9NBkE6GSuHv7Lu+//z7Xl1f4i5D79x7QanT47OMvKUtIshRV1xlendNudui12whUKJKIUIJreVQVNBtdJEFlNJ5x49YdJrMZqmmg/ualaoaOKImYpsblxTmaqr3KHq3WNByLIFhQVAmbO216A5dWS2ej1acuc7rNFr7vs7O3zXA4xNBVJAR0TSXPMizjVWNSU1XarRanl5fkZYEoK6iqTp7npGlBkpWUFURxgChLKJpGw2tSUbG1s0MUx+R5hm4afPbZ5+i/WfObb77JaHzNnRuHaJrG3/7dT6CU+e53f5u7t+/xr/+bf4VjyGThmn63RafZII1TOr0+YeCj6SqmVWLbCkItEMRzkiyjrmUESaDIBBRFJi/z3wQhZbyGy3g0xrA92s0WF6cXLJZznIbD1dU14SpGU1w+/uQL4ijFcUxcR2d4fcL9N27SabuswwX333hIv72Bv/YxdJUnT75CEGoUpWBnZ5c4yUijGFkSaLgeoPLkyXOCIGJjMKDdaaAZCrPljP6gT5amSILB3t4hqilz49YBWSygyhpZluE0bLIqwg8Djo6O2drcQ6xURMFgNB8xW8wQpZrdvR2owDU3yWIVz96m6W2g6y6ffPIZnXYPCYk8LUjGY85fviALI/7yP/wVBzfuMVqEZEXF86OX/OEPf8jhwU3KquTk6AV37tzg5t1DKnLqqsZUNYoi5cbNQw5u3qTOKgxRIU5quv0NZkuftKqZL33SJKdC5P7Nm8R+RJZmbPT7UAG1gCirWG4DRZeJixzDNFksZmRZTByHrzZ+XuD7axzHRpElJEmkLHPyPENOwdRMGrZNPF/x7PFj8ihAVxWmoxV5VmCqJrphIqsKtSiCIKAYGqas4zoO29u7gEAap8RhQNNroOgCRZWjKDJhGLC1vc2zZy9wHRdHb3NxdnrOyc8AACAASURBVEqv02W9WnPj4JDVcs5ofE2r00SWVepKxPcD+t0eYRCQJSkCArIkIIoSaVJiW/arrORsiiiJvP+dP/xmAvvR3/z7DxyvSX9jizQrERUV2/N48PYjvj465+jiErfXQ9VlloGPIIv0NjZQbI+irKhzsHUTsar48MOf8eLF1+zsb7GajBn0e0xHM773/nf5yd/+FF03OD07xdAM3rh1G0USkAVQJRlLNhFKhd5gi6bX5upixP37b1FUFc1WC9O2UFWd0WhGr7OJLCp0mi3KvERCoi5EvJYFVY7nKYTxnDjxmY4nTMYL0jAhWoeogoQoSkiiSLvVoNNs0Gl1aDY8mg2PqizZGPSpipIiL5AMkV6/R7/fpRYqgiiipkZSZCzXoeFZiJKEYZos1yvCMCJOUmoEGp6NJMt0e108z+XWrdskSUwUhaz9OV8/fU4YZnR6W3z5xRMG/QFlmdB2HVzHRBAERElFM1zGsyWCLJAVKcOLU7LsVYA1T1PKuiJO13gtjTKr+OrZYyohJU58siIjTUpct816teTb3/o2nm3zD//4U0zTRpF0vv/d3+UffvIRzUabNIswTRlNg27Do0hyYj+hKFMo4fPHTxiPJhwe3CCKfBqeg6oVnJ6OOD2Z0G62uXGwy5PPn/Lzf3xMu9Pnd3/v92i1G4TxnOH4isvrKxRDY2tji05nkydPX2DaGog1y0XCZDzDcR0yCtI8QRTh8vIK23CwLY/5fE1RWOSxgly7TIdr5pM5WeSzvdmliGOWyzH9XpuqjNgcNFnPxlweH/Hl46fs7x9Q1yJXozkvz0fotkuahvxnf/JHHLQkXj75lDqcc39/m+2ORxUGaHVJKki4rsFqPUNRJc6OTmgaHlIJp8fnrNZrijxD1zTGwxHdVpvZ9Zi+Y+PZDqaqkUQZ09mCje19dMfl7GpIUuQkaUGv22Uxm+GvfTRVpaorPNti0O9h6ibdbpe6qlBVFct0ULw2dqtDECasVhGW12a0jkgFg9HVkKoWcRoNyrIijmKEqubs+ITz4xPcZovxeMLKXyEKIuvQp6prrodDkjwhCCNqBLKsoCx5FZBGYT5bIcsy49EITVcYTa+opYLBdocXz88oy5qqEpiMX/VOxRosw0QSJFTdQBAU8rxkMp8RJ6/iImmZ8/3v/sk3E9hXn/3sg5///CMm0ynHJ6cUdcX/9X//DePZjJAK07WRNJnJYkItCQRpxPOTI2br1atAqghFkTMaXVHVOdPpmLouGfQ8Gk0XQ1UYXV3z8sVLkjSj2e7w3m+9jShAlkQUeUbk+6iyRlkUCKLG3u4B49GrGtwwDJarFdPpBFnQSKKchtNAQGAyHqPIElma0Ww0ifMlTbuLPxPJE4P5NEaVLG7u32WxWJJHKbPxAlXWOD45BQSKPKfbapPGMbqqMptMEQFZknBtl97Ao9NqUOQJogANz2YwGOA4Lu12C1NXUFWVOI5J44S6rtE0jSdPnpBnGbqqk2U5mq5yeXnBcrlgf3+HOI3ZOzhg7+CQ0XhKt9cljFYsVzOyNMFtNgmTFK81wG50EBSVMA7Y3dsiXGfYpkdWrBlPJmi6TpavmS+n5FmM5WgMNrqkRcpkMkOVbapKoj/o8PTJE8q8RBAFVqsVpmkjVBKLxYLpdMiDB28QJxGrxRLL8DBUB38RolkKeZbT8JqvGujzBYosMRoP2dnZ4smzcza3DnBNj+fPXvD4ky84OLxDTU273UKU4Or6gr39XfzgVVTi29/6Lb56+oJer49mimRZxGyypuZV0NhudKjKEtex2NzcYD4bc3Z2hqYpdNouhmqjCiZtd0CwDCjTlNlkQrJa0mm3mM/m/OjHf4uh6mz3t2mYDWZhwu7hDeK85OXZJc3uBvPFHF2tuXNrl8cf/QO379wFQSZNC45eXiDKFr5fUGk1WRoyGg5xbAdLs7BVE8c0uHv7FsvFhDgK6HUa3DzYpWU7/OA730YTJZbzOUWaUeYFruNSCwKz+RzDMDANC01SePPuHao8p8hyAj9EFlXiOKSuBNrtNifHxwhIVGVNo9EgKlYcv3xCEq5ptz2chku716PZbLO9t43baCApCpqmYxo6dV5QFSViBcPVkuF4yPbBLqqpgiggawpREqHpJnleksQpZVVTFjUCMpqmU1EgKiIbW5usgwWdfpNOr8kq8rENlyAIqaoaTVe5urpE0zVUVaYsK9brAFl61abRdQNRgLquKKucH3z/P/9mAvv4w//ng1c1cJOXL1+i6gab29tcjYcEWURWpfjhCsczERWJxWqFalo0mk0My2a5XpGXBUEU0O32GQ6HtFttAn/F5188ptlwabgOL58/xzAtFqs1b715C8swEOqKnY1Net0uSRIhyNDubPDll1+yt7eHLEkEvo9hmlRViW15RL5P6EfMxiP29zapiphGwyXPMhALZFGgzH0aTZV2y+HxJ19w9uIau+UiCTJVWlCXAoPNDW7evIGuqKiKjCSJNDwXgYqyLAGRPMu5eWOH+XjC/s4OvXaLOI5peg1sy2C9WpDFIRuDPlEY0HQb2KaF57jIosTe7i5ZlhFFMQf7B9i2hfGbUvXwxgG+H5KmKbb9apo42OiwsTFgc3ub+WoJosx8GRLHJYois7014OrimJ2tQ07PTukOdNrtPrs7u5xdPMNzPUbjEa1Wi5OTc3w/QZY0iqzG8xqoqogsyVyfX6AqKt1BH1XROD46w3EsqqokL3J++atf03AbfPn5EyzTo9vdYDIf0Wo1MEyVyXjK5vY2i8WSLEs5v7hmPFkgCDIgICJzdnbJ+995n4urM0RJJE0SDg4O+Ok//CP33rhHnCRcjy44Ob7gjftvcHz+lHUww9Bd2u0mmqEQhBlFVTIZXdJouBiWwd7BHtfjIXkxpd3skiYVkiiytblBWVTs7dzk3TfvE4QpsmLRbA/o9TaJg4xOo0ch5li2RVVX/OwXv8IPc0RBpMgCvv3eQ9791vsIqoFmezheh1Zni9EiQrUbhPGYbq/NYrFAqiVc06XhOJimynh8hSTWbG/32eh3adg2py9e0G91CFYLbh4eoEgKzYZHq9FAVRSC9RrHNKmLkt2tAf2Gi2voSIKApVu4XgPbsuj1epR5hWXZvHjxEkmSXqXjxycEyxlNyyBYz1kFK3RVxlFlCgTKqsS2LGRZYr1aMZ8vsC2LW7dvs324j+O51EJNWZUUlFRVhWkZBOsARVVRVIUszTA0gzRNXw1tFFBVjfl8RavZ5Hp0znB0RZwkUIlYloXvr1EUmSzPqOqSSqgo6wKhVKEWCIOI2A/QVQ1NlfHXK/757/1X30xgw/MvP6hrUFWVwWCDbq9PQU2r0+LRt95mOLzkwYO7XF9dYFkmgiCSpjmW5ZGVJUEUoVsWJ2dnvHhxzKA3QBYUrq5n7B8c0Ou16bYbfPLxrxAEGbfR5ObNTeIwQFM01mufbrdLGPsglcxnPm8/fIv1ao4ogiiKjIdDijxntVxwc/+AzUGP7c0u69UEwxK4vr7AtHXiJCdeh6hSwWo1RtNlwiDiv/4v/jXNjQ6eZZPHOYf7+yiy9GoiKdScnZyzu7MFdYWmaHS6PUbXQxzHZTUdkScp56cXaLJKt9khiWPSOKbdbOHaBqvlq+tI/V4XVZExVB1VUdA0C1VTaTUalGVBlqa0Gh6tZhN/5SMJAqamU5Upd2/f4Oz8lLoq+cUvPsRxHURJRlUMZFlFFGqq3GdvZ4DbaXF89gLTSbg4H/Py5RFlESOLOudnU2oU5lOfRmNAXcqomsZ0OkbTRbTfxEHW/po0z3j27DlSLXOwf8j/+R//mjBMyLKCt995xGCwgarKXFyeUVbwq1/9HFkRCKKQq8vRK1mJMls7O5iOyTpc49gejz95QrfTwnJebTTTNIjChGazQ54WjIZjNjb6rMMppu3wxRdfcP+tW2i6SJHV+IFPVaUUlYhj6UCObqgEQchyvUYzLWRJ5MXRGVVVols1YTLFsk2KSuT8+DndwQb//j/8BX/zdz8mq1JaHZf79+8SrafsbPa5vrji6npCWUoIgoRrG/zxD39IFq5RdZUsi5AF0FWZn/38p2RFQqdnIooSruOymK843DmgSGOieIXXalBWBWmeItQ111fXHOzs02m2MUydJIrJ4hihrlnMZ+iKDFXBbDqm5bk0bQupTEiCNaIg0Gh4VGWNrKlMJ3OiKCKOE7rdLl999TVVKSBKJtQKRS5QCAK5UJMkKbPRGFMzkAWBqsgp65IagY8ffwqqTJDGVHVNHEcIQkWcROiqSlG8ChNr6qt7tEHoU1Q5CBVxGAAViqpSFbxK3ec5Ql0hCBJ5WjGeDhFFAdMyqKsay7UYT4d0eh2Ojl8SLXKW8xWyIBLHMZqqQFmRxCG//wffMAf2xSd//0FV1ZiGSRQnVDXcufcGmq4Rpz7dTovlfIZIRZFnlBUkSUYlyERRjKrprMKQTqdPp9GiSEuOj464ffch88Ucz9FZziZ88fmXmJbFYGODLF28upicZmiaThSFIJZUlOxsH3J1dcn+/j4Xp69O716vT6vZJPDXbG70efnyObJUARkXV0dYjokkg+u0cIwOm527yJLL5eWE+3fv8/Of/Qyz7RKuAv7LP/1TTk+OybKEe2/cQVNlJFHi8uISRVURRJiOJ6zWAWEY8eith4iCxObGJoqssvn/cvZmv5Il9n3f5+xb1al9u7fu1n17n56NlEjOiCKHm2XKsWzHTpDEToBEcZD/YuI3I0EAw4GBKIiFBIEC5MGURC0WZVLmMiSHnOFM90zPdPfd97q1n6qzr3moht6kh3mqlwLqpc7v/Jbv9/Pt9ZjNHERE3vvFe2xurNFpdzg8OMB3fcqlMvV6DceZc3FxxdJZkqYps9mMbrtDkkR4noeCCFlOloY0mnXSNKbIMoQCuus95s6CVrtLGMRYZpnJ8ApZTDk/O+RyMsIwVebuEYGbYVk1ZCXHWyYslwnLRUSzuY4gKAiCRJz43NrdYTi6xnUWiIKMaZoUgCCIeAuf2XRJ6EdUazXW1tfJswJVkXjy6ceIFJydXfLKqy+RCwl2uUKaQpYXyIpCWhQg56i6jKFaPP7gCa+++hJWRaHX33zhVcx4+skzlguPrEgYj4ZkQki7vYbn+YTJkjjx0RSLyWSCKOTUGy2Wiymj8RVBEJDmOTkiSVpw796riIJKRopmZbjBFUkWUm+2UCkYjK+Z+z6lRhlBzpk7A27eWKdfqZLHIR8+esTFxZAokVE0i/5aly984dcQ/QmmJuFMBuxu99Ckghs31tjZ6pIUOR8+fky3u8aNzZu0a20UpSAvYjJRoFwpY1kGIDCfreQiRV5QskwkSULTVISioFatYhoGVsmiVq2ytb6GoYrkkcdyPll1PklCkibUm10EQUAQRMIwJAxjREHm9PQULy4xHi0oBJVpELJ1+y7NTgfbqtC2y5iaga5pqKqKbhls3dql2mpSrleR8gJJECmVDHzPJc1SirzA0AzCOML3XSQJwjCgyFPSNAEKilxGV0zSKMNUdUQBBAEq5QYpIa1WkyzL0UwdTVvZyOaLGbIiY4g2AgIXFxc0ajV0TWXhzCmVSnz1a//4sxWw073HbwsIKIpEs1mHPGE+GiOmGbmXMD4bUFEMDFnBkDWGlwOknBWhwbCYD+d4sznr7SaB71Mu13AWIbtbPcQkpKJbjK5mzGcBdqXO+noP0pQ0zajUqnR7HWbzGVmcEzgBSeKxWMyRZJHne0dYeonz8wvKlQqiKKAq+ooSkeZomk6nvcl2/w5kFlYhUjNKePMppAm2ZVFt2Ny5fwPP8Xh4/yGffvKMn/34x3zr62+xf3jIG1/9Jq1KhfH1gPVOi41WnbpdYndnEyFPePNLn+cvv/dn2CWLcskgS2Ncd8k7P/kRv/V3vknJWJET3IXPYDjk7OycIPL5jz/6AaP5kvF8SbPV491332c0GTEbzzg/ueCnj5/xg5+/xw/ffZ/T8xFn5xMuxj7vPH6OZdfZunmXq6shSRQRxwHj8ZisUJi7OYEX01/r40w8ijyjZJnEoUKzsYGsatRqNQxN53B/n16nzXLus793hKroDK5H3Lp3hyCO2N8/pNls0+/3SYIMWZKxyzZJkjCdDqnWyhwdnlEya4RRjKhI5KREYcTmxgahH1MgokoStl7FmwbMJlP8ZEl7s0GQxXjeBbKSIUgZ5UoF0zLY3togTjw0w2I0ukbTJMqlEr6bMJvOEGQZs2STpSLT0ZSyVqbXWUNVdFTFoNFsMh0OyPKAatkiCUVMvYtlNhmPr2k2bhB6MQ2jjCTIhGHByfEVf/anf8EXX77JZOLw//3hX7JMFDJVp1QSeOuN12hXDPw4QFV0fDdGUSz8IOXyaoRdqWPIBhvtPhW1hO94TEYj2vUu9UqLXrOJpRoYqk6W5OxsbWOWS5xdnPHa/ZfodTp0ex1q9RqNdptqo8fGxi7Vahe9opHmBaqos3dwTJwIhGFGmkISxUwnqwZClAQMXaXdabG11afTKWHbMv1+k52NHqYk0rArCIBiWiDLZBI4oYuXRqRFjiYraEgUUoKoCswWc5I8xa5WifOM69EYRRWwrBJXFyMMrYyulsmzjPVeD1FQmU8mLBYz1vt96s0Gsgp2VaXRqVOp1/j4yadUKjWazTYVw8Z3PEZnVzTqVVqtGs12kziG6dChpJQRY/jatz9jATv85GdvL5wZuqaQZylZGpNnCYu5Q71uU7FLOM6MRqPC0nEol8rIksRo4eF6Ac1Gi5JZRtcNNElla+0GH33wmJ3NNpoqQpbx7NkeIFKxbSqVMoZmIQoyumFyfHSEIorsbPUhy7gaXtNottANkzSKyNKEtW6byWSEqZU5PztHESTiKIQsw116HB9d8fnXvsh8OsYyylCIlO0qBQXzhUO1akMhIhYCWZrx+dde5fT0iDCO8cKY9975CRvr63RadULPxdQ1vIWDYej89Gc/Z319Hcss4bkeiizz/PlTlos5uzd3kOWM6WRElhe8/PAl+pvrXF6e89bXvsru3ZtUq1UGg2vu3r3D9XjExFkgaRbdtR2+9s1v0dvawi8yLqdT/LwgVWQstcTz/SNAYuIsWCwDVKPEYDzB80PiKOFycIUTecxcj8F4hqqVmUwc9g6eQiGQxCnb2zt89OFjKnYFVdZIk4Tr4QhJlHEWDvfuPSCJYpZLF6Eo8AOfIAzYvX2Ty6szHGdOrVJBlhSKREUxCuLEoV7uQqyTpgm54CPkMsPRNcPZgOF0QG+tzeZmH89ZgBQSJiFplpBmOe7SRUAmS0SCaP7XfrjZbI4kvhhxk4yls6BZb5DFCWHgs9nfJAwiZjMHSVRJwpBGvU6SpMwmDiIyvhewcJbMpgvscon3f/Uep+cXLDwfNwioVSp0qzVu3nrAH//Z91H0EpphIJOw2Wtw8PRj3Diiv7GJJMmIskwYxwRxjKSq1CoVet11avUWiqJSKVeYzx3MkoWpqXhLD1M3OT+/pFFrYGircf307Iwnn3y8cmrIMkHok2c5aZIyGg1J4iWaotJb22D/4BjNtAAQRcgFAUEQiNKYVquFruvouoFlmpiahK6rWKaOIBZopkxSxKQkeK5HlAYIsogX+/S21/EiD0EqiPOIPMtAFMmKHEmRSdMMs1TipQcPcRYzojClXq8TBCG2XWY0HjKZjEjiDFVTMXUD13EYTYZIkggUyJLM2fE5mqwxHkwQ4pyKYWIbJvVKhdODCw73D1b/zyBlPlmysd5nY2OD19/4jGbu93/y52+LYoEkifS6LS4vLiiyAoqcQEiYuwsM22QZekiaSkqBbhkIQkoW+TRqNovFhOHwnDyPefr0E7rdNpKYoaoSWZqyWHhkGaRFjiAJKFJBu91cyQxEAV1VGQ2vGQ6HNHs9xtMZM2dOf61P2TAYXg9QVJnlcsJsco2uS2hqRrmsopsKvXaHq6srrgcDqnYFu1JlNBpiWDq6LpMXOdtb26yvrSMhUOQpmqpSqzX51aOPWM5mXF1d0un0yNOUkrXy2HW6bRrNLnme0Wo3qddrLzqIPl/60hcQhQJFLDANiyiK8TwfRVN58OA+49EQWVLotde4desudqXE53/9czSadXJRQgxFSpbJ7u4twjDE1A0kQWCt3eXWrbsUCLz86qus9df5wQ//I2EUkbx4GAwr52p8jt7Q8dMA0zaxKyaTyRBNM5jPpiyXHteXAyzDYjpx0BWVHIE0SV9cljLyLKfd7jIaXGNbJucXF+imTpFnyIpEtWYjChK6rlPEAooKuqHiTF2mozlZFhHGLgoGQRwgKSJGWSeMIrIo59nHzzm7HtLp9tns30BVdKZjB1210bUKlWoJdxkiSTKGppMmKYEfcHV5QafdIg5TZpMpkiDhuh5+ECEJMpKooCkyqqwQBRG2XePurXscHhyxXLqIoszCcdjf26dcrXM9dVBklVcfvkxZt/ijP/kLpnOPkl0nL3IaNZPf+Ttv0apXqLfazGYz/DDAWS7QDQPTNPFcl43+OkHg43s+l5dXKyG0v2RwfcnwagAIxHFCEIaMx2NmsxmLpct6v8fV4Ir9g+ccHO5j6jqHh4d0Oy1MU8dURaIkw/UjRFVHt8zVy6HI0SwLq1wiikIqlQpxHBMHEb7r4c5miKJIEPgomkKQROQSBEmELMq4/hJZkwjSgHm0ICXFDVfjoixqiNIL7WCaYugmfhgShgGLmYPnelxfXbPW63J1fUXFtonjCFXRVnow0yJJIuIwYmd7E03TOT484dOPn7GzvsWNzR0GZ2fUS2WEJIU0ZffmHbb6fRbzORIyZ6dnJGkCAnz9737GK+T18aO3x6MRsrKasWVZodFqUbZtBEVE0zT8wHuhyreRJZU4SjBFgW6rTaNWQ5IEms0aS3fJbDmn1WmjqRJpnnF+eYEgKtSbDdY2+6xvroPgcX19jqJIQIbneSRJjiBKiLJGEEU4zgJVlMjihDzLWCyXBMGc23duU7VNkiQCIUESRWRFQ9dUDg4O6K+vEUUBT589QTMU4jggDF2O98/54FcfEIYRSRRxeHDAf/j+X/HP/4f/kSBKECSFQoBmq8V4OkVRNH753q8oV20M06QoClRVJQx98jwliVayitCPyHMBUda5uLrC9wMm0ymteoPDvSMuz65IooQoDHCXcwoyHj16n9HlkPsP7vKdP/4Ohq5yc2MDIYzotRocnB4iCBmz+YQwCZFVGVkSSZIIXVcR5YjPf/HzuLHP2voasiwxm0woGQaT0ZyiKLh/5y63dndJkgyyHKEQWLgO9XoDd7nEmTn0Oj2iIKTZaPDJk0cEUcR0MiUvcrZ2NkmjiOl0hut6ZElKVgCCjO+6hMGSpecgyRrNeocMePM336TV6dJud8mDnOvzEYWis7V5k3qtSskyqVVXRXFnaxvfTUmTlVfu5PgMXdO5tXuDXrdLHEcUmUy1WmM6mSEIEoEXEUYpb33lGwRBwNHxMZVylTQuGI/n+F5Eu9FGUhWyJOd6MCApRPwkxfV8SrrJ4f4RZ+dXZLmEVbbJ8oRXH96hUbGQiow0S2nW6xRZRq1SoWSaSIJAu9lEkgUWiwWGoaEoEpomE8X+yqyt6SAKRElCtVrFtm0KUSCMQrqdNr1ej06nvSIx1Gr02h00VcH3XPI4oigEPnq2h65bdLtdPvroMQIFt+/eJc9zKhUbwzCYzWbIkszx8TEfP/4YcoHJZEK91UTTDZIsJc0yLN1E01eXxKkzY+xMkVQFCtA0FXcaEEYxiqyQZTlJkuB7LggiSZisvqeuHCVVu8Ji7mCZFkmUYpdLLN0ltl2mbJeYzx1G10OiMCFY+tTsKhfHp9QrNSLP5c0vfQmhKHCcJXfv7iJS0Gw16K9vsL+/z2K55D/7p7/72QqYLnhv7+3tI0jyiurpLKjUmgRhghylqMgQ5aRBglLINO060TLAmYW4TkSaSGSpiO9F9De2uXXjPl/+8teplE2q1QoHh0coqk5KQavdIAiX2La2mudNE/LVZWO2dDEsk6UfcH5+jqorRC9AaWmao5o65XKF+XSJrtvIosF0usAqrxC5V4Mx2zt9XM9lcHXJ9fWAbqeNKOQ0G3V+/s4HpBm88aU3qZQstre2SLOcf/k//y80mi3+3t//+yuMS8nCD0IKUUJRVbzQR5Qkwjji9PSY8WRCr9ulyHPSNGVtbZOsEHm6d0CS5Lz88su8dO8eSZLyy3feZzgYcXFxxXw24XOfe5Xp5Ir7d+7w4bNn2M0Ky8jF8+aIecF8MeXw+ICbt7dotKpolsbSW5AWCRsba5yeHCFJ8PDh68wdD9CIvJTF2EMRNYqoQCigVqnguT7T6Yw8iaEA2y4hyyJpErG+vo7vB4iCgOe6ZHHCYjEnTwvanR6e76PIKlEckmcikiDTatlcXI7Q9DLtTp0odtEMg5nj4rkuQeQzmkwZXg2plGsspx6Vco2bd27guQ7TyYBaVcGyZKo1k9Hogjwzef31z1Eul/B9D7tiY1fKPH78EaqkYhh1JuMZmmYSxymuG6CpBqPRlLOzCx48eIm/+N73mM8cwiBFklYMN8Oyee+994nCmLSQcJY+qqpj6QbXwzFpJqBoGpVqhSiJqJY1GpUSLz24B3lGr9PhenjN+voakiQyG49J4xjEHFEoCHyfosiQZQlVFVEVCcMqU2/U0XQdTdd58skTOp0Oa2trlMslBEHANA067Q6SJP01jyxJIibTKZquI8kqiAV7z59xfHJElhWsra9hWRbz+YwwDEmSBFFe2XR2tm9QrdVpttrs7R9SLpfxPQ9RWK1uiqxgsViw9DziPCUIQtIoQUHBmXsYhsl8PkUUV9wKyzQJ3AChEMizgsVyQcmysUwLZ77AWSxp1RurhiOO0VQVVVEoWSZpktPtrOG7Pi8/eIhlGuiyxHAwoFavYVklfN+nXDYZDq9ejNaXbO1sM57N+M//q7+5gAl/WzL3d//fdt0oJwAAIABJREFUf1UMh0P6GxsEQYDjeuwfHHF0dMyd9T7Pnj1jupjT6q1xeHhIp91FkiS27t7h5+/8nN94401Ojg/53CsPcOcz3EWA6yyJhYRarYxZtul21rCsMtPZkAcv32HphQh5wdXVBX7goRoqGRmKruDPVw/D2toakiDiTKbY5SpHF1doqkiaCDQqPSp2FbtigRjguA6TyYhuvU8cp6RpTuj5bG6tIQkCnW6D559ckgsCvbU2v3j3HV579SHT+ZLTy2tu37pJt9tFEAr+r3/7+7z04B7/5J/8Y/b39ohJefL4I+7eu72SVlglFvM5w8E1tm1zc/cuJbtClGacnJwgkLHWbrGcjylVazQbPT784DHn5+ekWcRvvvVVFs5y1VWpCt/5zh9SrzXZ3NxEsnQOzk+5eWuNZRBSSDKSYRL4CUkQ4s0XK5aYG+C7AZqsIgki6/0uo9E1SRKxXC4plUrIskxRFFiWtbqaiuIKBqgZHB6dgaiQpjmDi8Gq6DVsRFFlNvcRZQXLMggjF0VSEZBwgymKqbLe3+bG9i6zyZy9o6esbbcpmysarO+lVEo1fD/k+9//K3q9HusbXTx/QaVikRU+mrKKaNje3sWPIhaLBd1ulzhOmU8cREFjufTw3YDZLMAsGZTLZZbLJdVqnYuLK8bDEY1mi+HwnG63zXQ6pWI3WDoucRJSbfVIwwB/sUA2LJZxjKqqEPoIAhRFQZ7m1Os1CiFHKkJurnf5B9/+Nr/6xc+oNRsUAnieR6fTwVu6aLJCqawRxymyLKNbJo6zIjg02y0CL2Y0GqEoCmtrXXzfRxRXv9Xvb+P7PlEU4DgOZcsgSSKKIkeSJIIIBKEgz1NKJRtdNylykTjJGJwdoeoauq5SqdcAKBBXuQlHJ/zoxz/kK1/5CqWSCeQUWUKWpSTxSpO18BZ01jvsX5xyfHKGpVmoooRml4mjiM3NdQoypuMJg8EATdMQcokojAniBFXRieMUzw/xPB9DkVguHTZ2NhAkWC5XuKFWvcXGzi4//cmPkQURTV2RRkqlElEac3l5yZtf+E0WzphyyeTi8powFBAUg/cfPeYvfvje38jE/1sL2O//b28XsixTrVaZLRw63TUOj06p1+soEqiaxne++13WNre49+A+f/AHf8DJyQlf+dqXaDXaZFFMo1zm7PA5o5NjRpdjLMMiElUkWVh1L1GGKIo0Wzb1RoVKcxvT1BHEnIcv3+PdX/6MZq+GrMo4wyHD8QirVCJPYkpGCd+P8OMESY4xjSqtep80AVnMQQlRdDg5OcRknXa7y2zqYOo6W1sbBP6Cd975CWdHU9rdDm99/StoGvjeglqjjeOFRP7qlGsYBhvrfTzP5cnHj+n3+0zdOaZpcnF2hqlbdFptyHJURVmJWlttxpPpCgGdZSsMERklXWe4nBJ6KUUus76+zng8BkQCP2GzbhPEEccnp8RximGXaKytcbGYMg0uGU+mdLe2kPQSV9dD8iDl3u5d4iigosn4rg9xjjtz6G/0EKSco+MD7OoarutSFAVJkmAYBtP5jOVyiV220DWT6+GUMMiI45RyuYK7WHJwsEel2kBWSziLJWW7hKII+G5AnoNd0ynVVeIcTKXF7ZsvMZyfo1cSnn36Sy5OrmlV19hY22Y2maMYOs5yTre1yWBwiSSDpkvEcYSm6fTXN5lGB0hIbPQ30TSL6WiBqTc4Ob5kc3Ob6WzJ8+fPybKEOI4pCoHt7W2iMGZv74BS2SAMltTrddxlCMByMUcrNxCSBCFLCbKCXFHQNIWWXcJZzMnTlVylZJdZLh00OWO9WSeYOyiigGGtbGG2bWOaJmvdHrIo0aqXaLSaJEnCwcEBtUaDjc015vM5pmbSaDQ4Ojpgc3OTpetwuH/AgwcPuLwaoaoqOzs7PH/+lHanyeMnj7l9+xaCIKCqNrqmUGQRgecjKwb7h2ckKbz64BZRFJGTgbSinExnDnfu3cV1XZIoJox8VFmiyBLqtSrHB/ukmc54PELTNJSSRi6LfPTkU+7cuMV0NKW21qHeqAIZge9iGAaB6yEIAnkuEvghum4iqzoff/wJF+dDut0uuiqs9JmqyOXgnF6vR61SYzqaImk6o+shjVqF+XSGoqz2p71+j8vLS7JIwTIk9p9/yvVgTJxKqEYF07b5zr//4WcL9Th99u7b52fHK40KkGbFiuopKExHMzzPo1qxuXNrl7ptc+/WLdaaHap2jW69zr3tLQ6ePGOxjOls3iOWVGJB4fx6wjJI+PJXvs7773+Iqpm4TszCiTk9OeHy+Jzl2CHwQu4/fJUH9x6gShqmUcL3YpJU4Go4Zx7GeFlCWKQ07Q2SOKXIfMqGQBEHqILOS7c/z+TSo253uHPrFqYl8enTj7k4P+Xu3ft0O+uIosjNew9oVtfoVzYwtTKXw2tY+FjlKtfD1YUwp2DpzDFUFT/0qJXKKMiUrCpbW7cYjRxESUKSJA4ODjEV0BSVwI0YXl7TbfUw9RKz2ZKSbCIXImkYUrVtyqUScbzC/oZ5QEpKqWqh2waFImDZBtPpkDRMadp1qppF7gdE0xliEtFr1Rmcn3L31jb+zCWcOvT7LU5P9/EWAWKqk4sZslBQ1k2WExdJUHF9l9l8sqIzBAG5DGHuk4kFSZKiaWV8d7GixBYFoiQgCClRtLJGtdttyhWJ3Z17PPvogDB0ubo4YGdjnY9+8QlKJHF14mBXuxwdHzG4OmF7vYszcygSnzhakWFDN8WdLcnyiJsPNykSAU218JcxumpjlxpMRysT9MJxqbZEksTH0CssnRRn7pPGCeWyhiZlFGmKoel4S5/FcoGqaLQ6XYrIQ9VUDNMgL1KaVYsiDZDFnIppIuQZmrzaB1XKJSRVI0xz1EoF06yySDKUko2zDEjSgonjUqrXuDq/xLRsxsMxrWaLVrPB9eUFeZpRqpQRZZHL6wFJlnJ8dk5nrc/c9znZv6BcrvDJ06ccHh2T5gU7G1vksYAu6YSuRxanSIWMKMh4nk/JNBgNLzk7u8SwDEajCXbZhhzWe1285RxnsoC8QBYlZFEiSTKSJCcrREzLWuUxaCqiIGKbZcqGBXlOvV6lZIpMxlcsliOSKGRwcYVdrlG3mzjugtl0yvnhMaG7xNQ0tLJOIRdkUUCc54RRjqnazK4dZuMZ9Xqdy8tL0ijGNA1azTrrm+ucXJ8xWswIiphUUnl6eExaqFh2nVqrRa1ZptOt8Vt/7zMq8X/4/X/3dtku89FHj1bhF8fHtNtdfvazn3JxcY7neUymE84vztnbe85yuUSWZAbDa157+AqTwSW+77LWW+P6+pqdzQ2iYEmj10UQ4eDolChNcf0QN4xBllmGKwRzFEUEQcjV5SWKKLMczVB1jd944zeoVRt4S49bN26QximtRpPpaIYiy5SMMnlcUBQSV4MRJ2dnNFttAjFla3eLn//8XW7u7CBLKgoio8EVVVsnSEP+n//7/2R4dcQnex9w77VbOPMxtUaLLMtoddqQFTTqDQRxtRzVFYUnHz/h00+e4i2WHB/uU7YMFrMp/bUesmKi6wah73JxeoYsCcRRSMW2CUKfSrVCs9UkSmNESUZRFPJ8tUsxDQPfWz2U3U6T+WyCbRoEXoguK+RxSr/TQ5cUuq02QprRqtYJJg5aJjMbjoiTEEEUWLo+J6cXKLpJq9Gk2WjR6XXZvbnLB48+wAt81jpbRGGEoZsMh2Nq1SqyIGAZCpWyzfnFJba92gutXBcwmYzpb/QxdYk4zpjN51imRqtd52B/n2q5TJHBMghIspwiTdje2kJWVM7Oh4ym139txp9NhzRaVdI8ZDC5JopjvMBnsVii6Trj0QhRLkjSgErFoNm10DSZ/toai8WMhw/v0O5UAJ8sydENbXVFbNTQFJVa1YY8x7INrJKJYWhYmo6YF1SMEt1qA6tir4CZlTJJkiDLMp1WC1EAQ9PwggA38inZNu1WgzROsKslTs/P8IOAZegzns1RTRNRlhEVlawoEHM4Oz1jfb3Pwllwa/cWvuehiKsDy9HpMY1GDVES0DQF07DI0pSLy0sq1SqiIJDlOVEUoakqnu9T5Dl3bt/G93yyNCOJY0SxwPc8kjhCU3TyLFsZtYVipdb3fcJw9Z8oihzT0KlWbJbecqV8L3LKJYu6vXKEDAcDAtej1+6hSTIyEoqiYCgar77yMlubfbZv9pE0ge5ane3+TZ4+f85wNOLs7BzyAlGSOLu8wAtD5osFum7SarYZTx1mc4dGq8N4Mif3QrqNFlIGzUoFQ1UoGRqWrvHW3/1PP1sBk7LR27IsUm82sCtlnn7yCcPBAFPXcBcuqq5SKlnIsszg+prPvfYa4/GIi6tr6tUKeRwyn04YDK5oNuoMLk8J/SX7p+foL5amRqlCikhciHztW7/F//qv/iXXgwGB6zMZj5lNZ1yenuGMJ4xGU55+8hxLs/jNL3+VPM4IgwBnNuflhy/RrLdY7/V58OBVoiRnb38fZ+mwDFzOhudMphN8N+Tl+w/Z3bmJ77p86UtfYHAxYHNnh3/0D/8BTz76Fbv3d/n48DmaqvP9//BDVFVFfGGUzdKMi8tLdFXn6uKK9f4KF91ud7hz+zadZpNqzeb48IiDswt++ctf0Gs1adZqyKJIEIT83u/9H9zY3eXyakC91aJaqxO9kDDIikroRyyXHhtrG+jKKuxAk2VMQ6XX6NCp12k36pQNg1s7O5Ck9HtrmKrGzfUbKEhIgsjGxibNdoc4ziiXKlwPxzx88BJxEHD//m0kseDe/ftsb93gkyefIooisiwjCALuYomQZ+RZTKfZolKtMBpOyIFypcz19SV37tyl0WggFAmBHyBKIv31DtWqTRR4VCsVTs8uiJIYUZYxdJWyaRDFGdOZw2u/9iq1Zp3jkyO+8fWv0O02KchotBo829tbUUGaLQqhQFZETMNAkgAyiqLAW7j4Xky/2yfwPIQio1GvYBkasiyR5wmqomAYKiXDwDI0Wp3aCg1j2yznM8hyNjprFGkOmsBi4bB0F8iKiFUyiKMAQ9cx9FUupmGZVKs2uirTqFW5vr6ibJewyhUMq0SYRFwNh1wMrnFdDyQFQ9O4uXub8+Mz7HKJ6WRCkWa4S4d2p40gCezc2MKwDErlMoIoEYUhlmlhlS2KooCiIMsyfN9HVRQkeZVXalkmlmURhiGiKFAUGVmWIQoyWbbKdxRFCU3VQShWO1BpVYiSJCFNU4SiQFVULNPE0HXyKCWJEzRFZWdzh5JuEIcRrjNHFCQ6rSaWpRH4S07PT9AMGVkWiP3VBd713RcSim3KZYsbt27g+RFZkhEGIQdHxwwGQ6IwJQ5TVEVHT1OChUvsR8RhSOgH6JqMLAl87bf/5g7sb92B/fh7/3uhyOKLzEOJ8XiKKMrYVpnDo0s+efop/a1NXN8jDEM0RaXdbrOxfYPU96iXdS5PT/H9AN00Ic9YW2/zp3/xI2aOx/7xKY4TYNdbzB0Xy65w/0YDuZA4PTzCMCzCdDW65HEERU6r1SZNUzRdp1arYZZMHrx0j2rb5Mc/fofRcIqAgmEYPHjlDqWygaSIDM8GfPzoCRtrNzAMg1qjSqVRYupcU1N7fHy0x+3bu9QsDcGS+OjkiK/8+m/w+//63xC6Ea1ah7wQWd/eZH29SxK6BGFKf32TwPfZ2NhgcHmFqokoisTe3h71VpcHd+/w6N2fYhkmURKj6QZ+FDOZu1iWxcnJCd/+9re5Hg35kz/5E4IgYHf3Ni+/9ICSqaNrEsoLmYSsyUR+tHq5qMrKROv7lEolPN9H13Vkzebq7BRTVUiLnDjPaFQbzKZTsiLl4vyKN978IvP5hKU7ZWdnl9FgRn2tyfsfPmKjv83zvROiJOXzr7/G3t4zWs0qaSbwr//N79Ptb9JsN5jPJ9RrFV5+8DLv/uQHvPnlr/KrDz/gq299mcViyuH+Eaqk884vfoUgF9x/cI/7t28xHc/46MlT3DDCKsvUm022bvTJ45BqyULTDJ4dHBAXMaqqIyCiaDpxkFGvNjFUg7OT0xVRww9IogRTq5AkKWkasbHVZTofcXl+wdraGp1Oj/lk5RcUCpjOxuRFQaPdYTiaomoW8/kCQzVI8ogHDx7geUum0ym6piG/IHOkaYqYF1SbDchyZGBrs48kSewdHZIiEvkBirp6AbQaTfb29hGQ0HSRTqcDeYqqSIgUWCUTZzrBrpQYjUbc2N0lJ0MQpBeoGZEkSWk0qoRhSBbFSNJqHMwpEEWRKE0A8H0fy7LwfZdWq/VCzmEQhjGqogP89TVRlIAsxff9lWXsRWFUVXXlddQ0wjgnigOWrotpmiDktJsNfN8n9n3iNMEslZF1A9000HSTydzBFFIWvsfS9fC8iLJlc3Z2xtL3uJ7MqNoVBEGiWWuS5zmz6YIsywiCAFkSGQ3HZNnKf2Ra+ovjRca//Xc/+Gw7sMHJ47fjKEGWFSxrRUvor20Qxwn1Rp3XX38Vd+nSajV5/bXX2NraZLPfQxQV0jhkMRsjUhAEIX4UsXNjhygIqVXrhL6PkBdUqjZZmlK2TF57+JDHv3oXMcu5vroGCXIBfv03vsjMdbize5O19XWur4d4nkeer1hG+3v7XA4v+OY3voXremiaxubmBk+ePML1XD7+6DHBaImYi8xmDotwSaVtE4khmQZLd4LZLWFUNJ58+iFn0zNCKUI3Vep2lfl8Tr+7jutGPDs4YDgaMhsPefTRU57v73N0eMQv33uPn7/7c1TNYDAc0t/eIfB9trd3+KM//i7vffAIs2TT7naZTKfc3L2L5/k8e/acJ59+ShCEHB+f8PWvf4ODw0P+8vvfJ05W9pmne0+pNRqIooxmGFxcDZBlheFwRKvXw7BKfOcP/4jjk1OOL6/RDIl6vcaPf/pT7ty5R6fVQspz6vUSr7/yOkGQohsauiqTxRGGYuF4ExRZQhFlLs7Oee3V1/mT7/4p49EERc4YjsYcH1+gmSV6vXWyPCEMA06OjnjzC58jiVOa9Sq1WoXQcwn9kHazw/uPHrO+0eGtr3yRIo5o1Vr88Ic/otFq8o2vvEG9aqPpCnkck0UpzXqDhbOgZlmst9rcvXOPsllGEiRubO4Q+wk3t27w0oMbGJrA7sYaa+0aG2sN1no1VCnFUBTqlTKmqjMZXpOnMb12k2qlzHZ/jWa9jiqJKLqKIAj013s0Ww2kLCUOPcQiRxZyFAFkUcAydDb762z11rAtE0tXMGWZfqdFmqxSel66cwdNFlHFgq31HoOLC1q1JoHngSzgLBaEUcDF+TmSIrG1tUWpbJEn0Yp0IoAoSSiyRMlaJRcBZHlMliQUeUZR5MRJTJalpEWGrmvkebbCQmvqiyV7ThzHiKJIkqw+ozBB11fAzTRLyAtAFFFkBRAoCjAM8wXuHJIsIS8EckHg8PiY3Vu3iKKQwPfJshgv9NEsi5njsFgGlEs1Ai/GGV9wfnaOpmtoskQaRUgCq2LtLBCLAlkU8T0fd+FALhBFIVEYEWUFsqrjBhFRkhKEMVlRsNbv883f/kef0Ur05Bdva6oKBbjLBa1mh0qlimGYzKYTZEUmiEL6G5soioJp6QSeS14UdJo1rs5P0RSVvYMjOp11EAQOD4/IipQbN3dQNQ1NUxkNBwhkhMGCctVmcD2g2WwhyjJf/cZX+enPfsq9u7c53H+Kqik823tGFCcs3SWCKCJJMt/61rd4/70PiMKIu3fu8KMf/Yj/5r/+ZyznS85OzjjcuyRDJCpilvESL/Po3lxn7/yAFI/D63MU06AgZ+hNUWoWjz/+gCRMOdrfZ3w1QpZ0pu6CvYPnBEuHOIqJo4AoDEiTGNPQkWSRpefx8SefcnL4nO/95fcZTOaUKg1Or654/nyPKI758IMPcZw5v/u7/x03bmxy5/YupqnzwQfvc/PWNv/8v/9vuTg75ZVXHnB79xZhEGJaJnGaYZgWYRTxwYePsKtVptMZ9+8/oNVu02p0qJYszk6P2ds/oChESpbF+fkRparJn/75XyFIFrpVJifH0A10xWTuLLCtVbJUr9dmNLxehVVcDbi+PuHmjdv86J1fsnv7PodHxximijOd8sUvfpEi8lg6C7a3tymKhGa9Qa/V42DvkL2jc/7ZP/0vMDWZjU6Psmnzk3d+zptffoN+u0bVLuPMHbI45db2jZXqPkt4+cY9bu3sspw5pEmKriromsJ6r0XNNokClw9++TN6zRqfPH6EoSq0mjZx4tGstKiWyxiqwu3dm9iWSdUuUzIN8hfdRrVmUymbbPd7lDSVtVabTqNBvVKmZlvcubnNje0NSqZGo15b6blUBVESqFdK2IaB7y5ptxogZOiCiKmKpLGPaarc3r3BbDrltdde5+bNbdZ7PYaDK5z5lCxNuR4MUFWFRqVMEscYhsly4RAFAcuFgySI5HlOybLQZIXj42MMw1gVoSJnOp2SJymKLJPEMUWewwsTtiLL6LpCEiUIhYAsyisIZ5pQZBk5BYIgkKQpcZKQJqtOTnhhTRJEUGSZLC0YD0dMJ1NqlSqet0TSVgj586srFp5HlMSYhoEii6iKwmzpkhWQJClRECCKMjkwHs8RRQlVM5hMp6RZQRCumGJxEiOpqxBsP4rJyFEUBVURuXlzhzff+oxE1k/e/9HbC2dBnmd8+ukzOp0Ox8cnWGWbeq1KFCe0mm2KIkdWZULPp1SyVgA0ERpVG2fu8LnP/xpRuoL5NRotzKpBo9WiWqvyjW98nYcvv8Tdu7t865vf4Hhwxde+/jXmsxmKKnN4fIila9y9tUtGyvVowNe+/g08P1iJXZG4Glzz9NM9ojDl3r17aKrGK6++jKVb/Ps//0tMvcwbb32Lz33h8xyfH/LGV99g6o15/OwjWv02nh9y88GrvP/BE5qNNSZLj0fPPuXhg1dxZ3OmwwmmZPHyK58jLjL+xb/4n9he71Etm6y1mxR5QrfdxjJ0zi8uuB5fEwQeobug1uygmFX8OOU/+Z3f4fatW0hCsUqDzmN+8pMfUi5bPHv+CXmRUqtX2N5a4/EHv0IoUgbn53juAtMwqFQqFIKEbVeQZYVut8fR8TEbG5uIosTJySkN3UbOV53ESy+/gqZbDEcDKhWL08tzGq0tpk7M3PH48KOPaLda/OLdD4nC1d6kXDI5OtpnNL5m7swwdIOvvfVldKPEo4+fMXM8EESKIuXmzjamYVC1NC6vrnCWDq1mA3/poqo67iJgMHLRDZlPPvqQo70D/GXE9XDKr7/xJZajMxaOw8XFFbs7uzjTOb7nkmUp7/3VL7m6HNBut7mxs4WsQBTOOTvZI4tdpKLCfOLww+/9gDwWSKOMD95/xOHhMbd3dgmDALtUQmRF1SjyAss0KVerKKqMrqvokoxaFHz6wWPmgwGvvPIyaRiiKzK+73F1eUaj0VhBLEUB3dBQNJk8TVnMpytxZ+ihWTr+bILrOUhyQRT5KKrKo8cfUa3X0FWVRq1GvVrh5o0b3NzeZmO9j++51MsVkjjCNM1VYny1iiyJiMJqn7WYOXiex1q3hx/4iMrq2JNkKfWqTZLGJEmM57mU7RJRGGOaq9BZRVWQJZUwiBBFGVEUyfMUQVJIk4w8LyiKFTkkiRPiOFm5UYJg1YmFGc/39nHnDgtnTtm0UCwDP4qIi4xchCxPGF1f4PsOziJiOJmCpFAUAmEYo8gqU2dBXsgEcYooyaSFQLlSwQtCskLALJURJVbUCwREUSBNI7QXUYe//Q//y8+2A/vBH/9eYRgGo9GIoigYj8fUajUEQcAuaRiGwdXVFZ3WCuvhef4LJbHGcrlc6UwUlVqtRuCF5DmrmZocP/QQZfCXC4osQ5ZUJF0lySXSLEBXZZ58/ClBELG9vc10NuTnP31ElqR4nsf/z9mb/Eian3d+n3df431jj4yMzKzMylp739jsJntIcdQURY5I0R4DhjCC4bn4ZPhgGAYGBgzCB8Ow4TnbAjyYg2csidJIGg0pbkNSFLdudldXV3d1d61ZlXtExh7vvvrwJmvu/Q/kEvG+z+/5fdcgigmiiDjNaHd7GJLL/fv32d7eQpUlXn75RRQZtre3uXL5Mt5kSqxl3B59zCwcE2chURZzcHRIs3WBluHywbvv0d/aYBZ67O7uIOUl0cxDsU1ufnCLYBTyldfepGM6nI1P2HtwjyxJMVQNL/RprXW5fv06Bw8fsZhMURsG48mCUlA5PhkiSQL/1X/9RziOSTifYxgGN35zgyzJcZ0G3bU1BuubTKenFEWB49jEcYwsqyiyxjvvvINsNjFsiyhOUA0T13WxdRVTlXhw/y433vkVly9frVrR5ytUVSeKIsbjKYKm8PT1a8xnMy5dukQY+gRxxCrwWc5LJEngmWevcnpwny/+o8/x/R/9kC9++Xf53t/9EElR+bsf/BRRsdGtJqqqMlhvsbO9iamUvPveDb76+2/y6N49+p01bn5wl/du3aHmqKy3atRtizCVOZ4FdNZbmHqGo9QIgoDJbIrt2jz73NPIYoltm0gCNOt17n5yj5rt8mBvj0a9xcNP7vLR+7d58TMv0usO2Lt/D1UTabZcFEWjSAu8IOTFF5/HrdukWYiu66z8Clrw0xzbtMiDCBkRQZH4ZO8BHz+4RxEXXLlymc2tPp63pNPp8Itf/ArPC3jts59DLwR0XSdL4nOsKCRKYsIkZLWY43kenuchyzKmUcNtdblx831qtRpxGHDx4jbXrl3EXy6wLQvf9/GiCtMsyxLTNDk4OMB1XTRNQ5ZlsrgS2sqyXDGIgkAQBDSbTeI4RhAEoig+f69A13WiMEGRZKIkxbAcLLfqegiWlZbNck3GwxGGoZGnGZqmYZomS696Z8PIQxQURNXg0f4Bw7MRoihg1Ux6m11WcUCt6eCtZgSLFaooUQoS/kyilEoaLZfF3GM2WVIEKboiE0sKYehjmOoTzaOhm4hSSc0xiFZkJ5YQAAAgAElEQVQCRZFhOVU5yWKxwLJqZGnBv/y//t9PJ2T97v/3v5eCIKAoCpIk4Xketm1TliXHx8coisJgMEBEwPMCZFmu8qwUiX6/fx69Mj1PiZRotVqUZcmPv/8jnnv5BWo1i+HRMVd2L+GFAafjCTXbQRALlvMpdbcJiMRxTJ7nTBfz6sPWdIqixAsCZos5w9GYyTjg8PAQ319VdLOm4tQs6vV6daoZCqla4KzXEMQUihRRFBmOzyg0G1NWIcupt1ukQsne3h6aJLNzYYvT0ZDZYk42zWlIdR5/8oBWp0kSxdRdF9swycqC45MTxuMxlqZjawZZGlOUEoKkI4gyURyQZTG/++aXsFyZZrPJ8HjIpYu7AKR51W6U5zlraz10XUc8NwEHQQSlSKPbZDpbcPf+A25/co+vf/3r3P34Q7a3NimLjEf7j6nVanx0+xNmsxn9fp+XXnqF7e1tvvu97/DKSy8zPY8rllWNg6Mj3n3vJrKq4bg1dFNjrdlkrbPGvQePCLIMXRNx6i737u8xW8bEiUCSJPS6Lhd3NpiOh6wCH6HI0VUR13LJCoUf/8Nb/Iv/4b+lbqocHjzmz//qP1AoFlefuojrytQ0m9lsxtmoArO/9IUv8tHtWwhFThhn9Hs9Lmxu8cknd3m4t8dnPvNZwsUKQ1Y5OTth//Exzz77LJPRkEePH1YbRi6SFBGvvvoKG5tV6KKqyiRRhCJrJHlGFicIJciyjGboJHkGskSwiEjTBE2X8LwlzVYDRVYZj6dYVo3ED7EsizDwKAHXddFNAy+slPRZklTbUZwgqwoF1bY0ny2xayZZlqCrEmmaoqpqJTRN4nOmUERRlMpCVOTYVo0sy8iSGFGsiLQwDKnX6wiC8OQl1zQd0zTPB1mErplEUUSSZ0RJjKQYvP/BR/S6XRqug2XozKczWq0WtmlW0idBRpQlln5FLMmKgKYaHByeACJOwyFOI/I8I0pXmA2X6XJO4CeQFHRb6wxPJ3TcBpPZmFW05OHhIapiVm1eeYKm2sznUyS5Yrpdp04YhrTaTaIoIE0K/MDDtS2yLGF9fR3HcQCR/+5f/J+fboD97b/+X0rbrlTkjuMwGo2YzWY888wzpHml1RJFkTiISZKKJTk7O6Mkp92uapTKsmJMNE2jLEtWqxUbawPCOAaxxDEtpuNJRUFnKVEQVmyIriBJlUHW0mtYpk1G9XtMrfrgS1GouihlicDPcV2XledxdHREGIaUQsFwOCTLE/wi4eoLT/P3v/wRX/+Dr3Hw8CGyJKEbBssyZ/fCNo8ePOTg+IinX3weWVX4zVvvsLbeAaEgiWIef7CPK7n8/u9+lfv37/Obd25QZDnbG5t013r82be/zR//8R/z7//i31EzTCRZIyvAsOuVEj9JaDQc/tkf/xGths6f/MmfEPorrl+9hmVZXH/6Kc7OztB1natXr/Luu+9y9fKViv2JC4RCQDEgL0sOj09ZBdXDb+oaB/tV9HNrbYDjOBwdHVEUxZMB/u1vf5svvPFatT07dZqNLkgSb739NpbjomoCcZpQAnmSk4YZXpRyMp5Sd2RMw0ZQVN658QFZKgAlzz5ziShcMTo7QZIVPv+51+l32ixmS370439g6Sf8s//yG4SrBcPhCfcePsZPCl585WnyfIUhG6xWK1RF58UXXuBo/4DJ8JTf//KXufdoH/LiPGbJJUlTRqMR/mxB5Pm8/+FN4jgjiTNqlnGuuA9YLkKuPrvLF774Bnfv3mGt1yFNYy5f3CWJqqiYoixJshRZUymocCPf9wmXBaIE9UaNNA1J0spfWLNdPC/AMLRz2UHF6qXnRmfLqaFqGt5qRRZHSCJV9LggYRjGOaCekqQRZVmcK+xVJEkmTyJKqvckiiIkpWKXBalaGoQy+09+RMsiSRKg+vmCICJJUsVS5nlVyRdnWJYFSnWFO5vMqNUb3Ltzl1azjq7KGHoNXdVI04zID7CsGlGagCChaRrTxRTPCygLgTzP6Q/WCMJlRSLkIVFZkhYlYqmioLLR3SZKctSy2kY/vvcRo8WMQpBI8xxZEdAFDV3X8TyvknmcD+zfiqPLssTUVAaDPq5jY5o6hqahKAr/9J//T5+OhXx465ffskybMIiIo4Q4SnAdl5PjE6IoZHx2BmWJCJiGTpLENBt1GvUWNavKipJEmThK8AMfgJKcxXRCWlYFmavFCk3VCIOIoixZeQum8zmyIhNlCbdu3aC3vsZwOKYoMyQkbt++jbf06bY6yJJIzTDxgyVFnqIqIrWaTc2x0A2Vq1d3eeWVlxns9Ilin35/ncnojNloSpok59ntGUJRMJlM2NrY5N7de6RRRpaX6K5Bp9NhMhxycXOb1XzF+mADCrh0bYdvfOMPuPvJHZI85Rt/+E0e7t3nD7/+dSgLTr0VuSwyWS1YJhGz1YL2ep8oT1nvdPi9N7/CV776Vd678T7jyYS1fo9fvfUWO7s73P74Iz73+uskacRivmA2n2JbJj//h58gKxKTyZhLu7u0W23GZ2e89NJL/Oxnf4/b6nFhe4d7D+4jShLLYIUoS/Q3BuzubCJJKlkBN26+z+nwjEf7R8RJiqXKWKZJKUCeFcRhxHQ6R9FUZvMVXhgwGo1ZrnwkSaEoMzYGfRQZkrCylgAcPj7g8OCIo+MRJTJ5GvDw0SNWgY+qm3zu86+j6TJpFlEiUArQarZ4++13eP6Z5+j31jg9PsVtNmi4dQ72H9FqNTk5PaLf72GoCqoisLW9zrXr13j11Vfp9XskSYgkSXzzm99kODlh++JFOr0OiqwgyzJ7D/e4d/cO4/GclV9t+4ML24zOJpSCSORH1Mw2slx1T+Z5pTV7+OARqmpgGCa3bt1ElmUkWaIoIcsy6vUm0+mcP/vzv+D6tafodjqkWUaRl+iaTlGWSCJVRDPCOekkQSkiICIJAoqsIIkS09kMgcqTqqkqkixjGhqi+J8WgSyr2EnDqNJd4zhmMpkwm83QVANdr9JOTV1DEAQajQayINLvdXEdG9s0yMucMPTJ8gynZnF4fMTB0QGj4Qg/ChBkhawo+c73f8Cjg8ecjE6pN+vEWUSSFsi6gahoHOwfk6UFUZxg1Cym8wn3Hz1AM3VG4zMURUOVJdpNlySJicKYtbV14jih22sTxwGiIFGzXVy7hm4YNOsNHNtGkSR0TaXp1tl56tVPmcj64dvfytMcURBRJAXLtBARadabGJaFoRmMhiMcp1p3yyynyHPiICONE7K0YDyZUqu5qJpKmiZIkkitafHJ/bu4jUplfDY6o6TKA7NsG7fZpJQFUhJ2r13k5vu3UHST4rw78t7d+0zOJoyGlW3DsExuvPcbnrp+vUpm1TU2NgaYpg5iSbvTYDYbosgyjx4+ombVKLIC262Ti1CTDcajM1RF5eH9hzx95ToffPARg8EWR6NTfG9Fr91BQqTT7XN8eMztD95HFDMe3rnDarng6aevM5mM+fKbX+JsdMprn32F3/8nX6Pfa/PqZ19hOZ/y9PWr9HsdfvHTn7L3YI84SRmPp3z1q1/lM599lXavy+bWBaIopN1uMjwbMj6rCAFBLOl2GnQ6bdYHG2RJxvHxEY5dQzd0/uGnP2Wwvs6d+3ssFwt+885vOD095eL2NkEUsrW1wccf3KYsQVENEATm8wWIsLW5wd6duwz6A2zbquq7FnM21nusb3Y5Oj5jufLwfZ8syShKocKBdInBRh9DqjLpiywnTzNMTed0OGawcYHAn5FkKZKqYddqZEmCpJQUZcFsPsd16giCzGK+5Pbtj6jX62iKhqyL6JqKrmlVG1MSE0Yem+t9dFXFdm0c18XzVkSxz1q/x3vv3yAKfNYG69y5exfLspFkBVVRiQIf13FpdvvYjkshihyeDslLGI/GnB4PGY6WCLJEnKbU6i5RGvNo/4DZfMXm1kXceo16s8lkNiMIYybTGQgiw+MTnOYasqywWCyQJYW8qF5sp9EgiqqhnxdVg08UJaRphqpqeN6SOElAEGg2mwhIIAiVKVzTSJIYSarM9yCQ5wWKopIkKZIkVZHQmoksV9uMJEmIoowsChRZRpGmlEWBKBTosoIkgKKJ6IaOa5uIItRsA83QcBpOlekWBjzY26PVbnFhe5ONzTUUTUYQC0RRY7JYoOga/cEaYbQiiOfIao6f5hi2Rq/fJliuWGt32FxfxzV17JrJ5mCD1WLJa6++Sqvp4jgGzXqLZr3JYL0PJeRJhmkYpHGCqiiIlFx+/vOfDsT/4b/5l6UgVKFmvzX/Vh+QiCiLRFFAFIYYRpUKEAchcRyiaRZJnBLEVU6323AoywzNUhiPzwjKFfOVx2Z/B1PSyILKmT6dLZAVkTiDQipx2ibTxTFxVPDZV77IjV/8kl5njSTMEEoJRag2gSAOEMTKatFbWydJUgpAVODw8BGqqmK4CsPJlNFoStNt8PjxY5rdNsP5GfFZQHfQQ1QlVoslWxtbfPc//oRUEHnlH79Gr9Nk78HH1YlW67AarfBPp1ze6jMYbDAdzbn61NPMVyts28LSNWI/4N133uOFF15ga/sCtz68zQ/+7gc4Tp3VwsNod7l54z0MTcE0DbZ3tmg263zhS7/D6595iZ///OdkaUSehpR5gSDC2emQja1d0jQlTfJqO5rNCYOYnZ0dptMpf/Od7/G1r32N2WLO2dkZzU6To6Mj2u02B48e0+n1mc6WmKZdscJNl0ePHjIdTSjKklwsCP2A9bUurVYLPwk4OPF47oUX2D94RBLnvH/rI0xTR7Nkvv6NrzI/GXJ4dEoURXiLOf3+gPdufshg+xI7gwZ7R0eEaUYeJ9RtA8c1sBs1ojCh1+txdjbh8PCY9d4ay9mUes3h2rVtkjBCKKHb7aLqCkEQsDibcP3yFURNIksLPC9AEgtct4bj1InjlNOTMzRDJ4gSRqMRTz11jTJNmQxPCTNQFIXPvPZZ3vrN2xXmI8uEfkCvu8ViMWexmCGIOY5jY1kWglCJuI8eP+RsMqbb7dLr9VhbW8NfeYglzIOcLInQNAVJhHrdYXWOKe09vMfGxgZlIeB5XsUmlyWrxQzHcc6vgJUqXpKqa6csy9V1VayG0m8B+9/CMfk5hKMqFUlTkT0yhmFVePViSl4WyOeKfUmSKrWALFMK1btimlVFmmVZSKpCGMWESUwU50iyiqKorHwf3VAIwiWKplAkCka9xuHpCbIscfj4Abomo5sKcVljrdtieHDAVmedKIioWXZVlNywOD2Z4NhdoigiKzw2N9fxVil5JpImIWtra6Rpynw6Q9MVHMukJOc//2/+5095hbz9q2/lRY4kS5SUCKKArMgggOf5mKaNqmqViboERVNIsxxFLsnKBE1TyPIUSYalP2XhTXjv/bc58EeczibMF0sODw5pNZuouooXemhUX0IYhaCmfPjJDRbLOYulz8NHd3j9jddY+R5FCUESsvRXHJ4cYVsOjtNkOl0CIoEfEsUhYRBw//4dJFfh4OgYsZQhKbi2ewldUTBUlbpU5969B7z+2ueIVgFZmBFFOWFccHR6xnwypxTAatXQLRtNlHjhyhVefeo5siDh+qXrbKxt8vFHn3B6MsSQVfb3HiEYMpsXN7nx/g0uXtpmc3ub7Z0dLl29QqM34PkXn2d75yJhHPHqq6/xs1/8AlXV+c7ffp/joyG/8zv/mOVqxXK5wHUcmo0m02XIbLFA07SqDEKptHSz2ZzxeIwiCTx19Qo//tEP0XWVOAj4/OdeR5FEdNMFRDTNYH19Hd9bYKjgWCpuu0G91yAtci7uXqHd7PL++x+TJCVRGDGfj1nO5xwc7NNqtcjyAlmVOD09rhrRTR3TtjEsE0VXma9WLMMQrUypNdoI4rnSWxRwLIs0gzhMmS88sqzg4u5lrJrNfLkkEwqErGTQ30BAot3qEsc5vd6AptNidrYgTkoUuYbnhRR5zvHJEZ7nEQQhg7UBZ2dn9AcbdHpdbr5/i2ajgaJqqCrousx8PGR8fEivWWf/wT0oEn7zm7ewTIW1XgNZKmjWbVaLJcPjE0Lfo97o0euusXFhG8+vDhZN05EEiQINRdMoc3i49whBlvHDGEkzmS/nTCYzgiihyEvmswWTyYQ4ivmr73yX9a1Nak4dx3VQVIU8S9BUBd/3EEQR9bwVSBAqFrQ4z5uTROUJnmTbNqqqY9s2AKUkkmQ5SZxg2SaCWFWpJUWOIlbQTp4VT+KrV0E1bJM4pqGrWLIMeYImC9RtA4mCJPJxTRNFFknjkLV2i8Faj41eG8e26HU7jIcHvPTcdVzLwnUckErWBh3KIqLIC2zTwTR1dEPA9+cosoYkqri2DGWKSIGhK5iGRlZmIBQ89fKXPt0G9t1/83+UuqLieR71ulP1MBoGSRIxny9pt9vEQYjj2AzHZ9RcBz8MEdSMPM9pNiv69sLFC0RlyOHxAWmR8uBkRJqmtOsNoiAkWHm0Gk2KouCll1/lww/ep26bfHDrJs8++yyjYZW6udF1GI/ndFobNJ1N0qRkvphiGBrhZEJW5Oi6SUE1sDVFZj6fkkQxfhZiGSYP7t7j+vXrFfMkCgiSxGoZsX94wPHpyfkVYc507mGYNmKR8srnX+XByUPsjoNcFLz+zEsIXkq7VvUAHp+ekpcl3bU1tnYuMp3O+bd/9qeIgowiCfwX/9k38VcLvv+9H7K+vs4bb3yBWJBZLhe0Gi6L2ZSD/Ue89srLjEYjgsCn3W7zve9/l6eeusb1q5fPm18KojRDFioQWRJFRKESCiZxgWFbrOYrjk9Paa/3q5M6zQi9kMl4jF5r4nke0+m0EnPW6wRBUOWEuTLD4ag60ZOCF156mV+9/RZJEhGuMizLIKckjAMESSAKIhRJptfrc+fxfeqdJoqhUwDBKuZo/4i6U6dnddnYaDM8fkBJTt1tMlzM6fbXuPn2hzRbDv31JromEkUx3c4GZ8MlyFDkOSdHx+iKyrNPP8NT1y7zw598n0bDxVQk+t0enVYbTVHw/RWFAMPhkNHIw3IdJrMpQRxw7fIldgY9xDwHUalA9SKl0Wiw8gKSJOXw4Jhup+r2VJRq2+t1OlV58rxKss0FgXv37pGlBWdnZxiGwQsvPkejUWc0nTMZzzB0h3q9SafTYeVNKYqMK1e2Wa1WeJ7HbDrn6OgIq+Zw69YtXnvj83TbTQxdYTI8xvc9QKDZ6DJbrjg9PWWt0yYvYoQSmm6TOE4p82oQF5SE5zoy+7yirWbZLJOYOI5RpKqiLUtSkihGQkBy7Or5KSqxbBQFyIYCikgm5OBlaLKGIJYURYpumVXml5fQrrdIi5T7D+9xaXcbRRHwco+pv0JTFFzXRSoEsrisulYRaNSbLIaP0Q0L0bQJ0wzDsggWKwyp6tvIMpk8T5HkCtCXZRlZUknTnC//0X//6Tawezd//q3frpphGJ0LzKq7u2maqKqKKiuEYVRVnddddMPA0HREJJIoJUtyPrz1EYcHR3SabUI/ptlscfXiJSbDM56+co27H33CZn9AkeYkSU7o+dXL0e7i2i5ZWnB6PCQOci5euMxivuLoeB9NE7l+7Qrz+QxJqBgTUVbQdQ1RlMjyDEWWUDWNerNemY03NsiSFM3QqTkOlCXr65tkecazzzzDo0d7JEnClctXECUJgZzFYka708LQFdaabWq6iWvY2KrJYH0dURS4fv0pBAE0Vefh/YfUHZc33nidzY0Bk9Epq9WK609dQ1Ykvv0Xf45u1Pjym29y+4MPsC2TzfV1KAv0c/B1f3+ffn+N7e0LpEmM562QZQVRlkiTFNM0KluKJDOZTinLkpPTIcPTIUma8sHtD7HtGk6tRs22sDSTUgTD0NjcHOD7KwShZGtrg/5al/l8yqufeZUwiumvb7CYzwh8D1EQMDQTRZXwAx9FlXEdhziOsUyLS5cuk2VV+UeWZuRZxnwyp+E2aNebNN0abl3nc69/hldefInlKuDjO/ewDJtmq0G32yVLS5bLAFnUcd0WRZkjKTLeKkASZa5fu858tuCnP/sJuxcvMZsvuLizwwe3P8JbVvhSEMaEQYjbaDLY2uLZ557FrtlsbWyiqQpxFOGtVvh+gFWrIUqVFCROUprNJqqiUatVGiTLsojjmE63y+nJCYIgVPIhoZJOXLy4y6VLl3Bdl8lkXF1B45gkTvG8FfP5jJKC/f3HFdl1cowsiEzOxrz99ttsbmwwOj3h5Zdf4ac//TllVkJRMp/OODk6RhAUHj58RBRETKez6mDWZGq2jaQoDNY3KUsQZfEc09RABOX8fwiCgCTKIM+RJYmizImTGEmRMWoWolw1xlNURISiyCR5iiCLWGaFP3mhj6KpFEKGooqIikIhAGmBrCogVEbxvCyY+EsEXaFhN5AFGVlUEVDIcwjDBEU1UISSMM0RFZXReIZuWuRRjqFqUAhVg5WqUOY5lNXhJUoyaZp9egzs3/+r/7Usy5KyLCjLkiypMr3TNH1yL5dlGdvUWfpeJbhTKiCzUa8jCnKlVdEVZFVCUiX8IEA0JI6Pjrh69SoP7t57omuhKCkVlY2NAW/9+pd4izlREFJvdOh2ewzWdhHFkqU3ZG/vNp1OB1WxUJUaOhq6ZRL4EaVQfTFpnCCIJUf7B2xfvczWYIPRcIgqyUynU9Y3Nyp6OwfD1KsHOo1YzFcoWlVBdvPme2imgduwsc6vFB2niYyMqxoUBdy4cYNXX/ssf/1Xf8Orr38O13UZjUaEeUSj7pDGIe1mi6Io8DyPKIq4v3dKp9Ph/RvvsrOzw9aFDYSyfIKR1JyqA9FxbMajU9rtFqvViiTP2Fwf4HlLbMuiyGHl+8ymC2r1BmVW4tTrRHmKaZo82nuMY1rkSYrpVumlplmVUXiex8cf32ZrawtFN0iShA9vf8Qrr7zKweFxlbGuyZweViUUGxc2SPOMjQtbPLj/qBJ1ZgXeYk5vvc9oOuZsMmNrsMXo9Iy6U2ew0Wa1GGMaGuSw//iE7d3rRGnKyegR29u7HDw+pdMecHJ8yHxxxiuvPstwNME0bEI/YHRSyXf66z1st8KlapaOpqjMzsb83ptf5v79u/T7fSRJohAz7t69z1q3VwHYInQ6LWbzKQpVkF4YBhSUQEEcpyjn24NQcl4UW2m+puMx0nl6g+lYFAW4bp3Aj85DBaoIpLX1Kizy9PSUtbU15vN5BYWEMW///Kd0Oh12d3fxghBBEJAkib29PdprO4iSgOuYpFFIlsaMRiMGg002BluUckG70+LRo4ckUcBy4bH34BHr6wMs3aDRbnL744+QNZmNjQ2iKKBmWZRBJb9YrJbUXJu0qA6FQijQFb2SZhQC5NXngwRxkVIUGZqhoigacRiBkOEFK4pSIs0FarJOIcDZeMz2zgVWnkciF4iWTj7xq4EoypimTZrkdPvr3L/3gM1+kxKRuCwRJAUv8GkaNnIBiiSTZiFJkhBFwflzlSHLKkmS8Af//FNiYB+/+/ffKsvySeKBpmuEQVgp68MIXdcAqgiYMMbzV1VQmlBFePhLD0moPihZUEiTHFO1cK0ajuEwPhmzub6FIigogoJtVrVqy9mKa7tX6XZ6XN65zGb/AoZi4PsJZ6MhlmVgGCqH+4eEfshsMqdWczBNC0XTqNWqPCfLrlqaL+xsUW+2CMIQXVV5fLCPoev8+le/ot1qIWlqFesb+FiGRrfdpOG43Hz3XZrNBk9dvUyn0cJSVJ658hRpkFBkJd5qSZImlAj8+te/5vnnn6PMcyRBQJVlLNdCKHNCP8DQFNIkJk1iVE1lvbfOWrdNv7/G7u4OcRQhSlCr2RweHqAoCs1mkyDwq5LP5YpWq4Vhmuiqhiwr6JpKkZcYpokiq4RxiKxo51fhBrPZDFWWMfSqZHT/0SOuX71atZknMbIo4NgmzbqLbdvkac5Xv/IVBEFgOj5jtZize3GHsigxTR1VVciLDIGysubYNRzHpVN3cR0Hz6ukLceHh7zx2usc7h8wPDvh5ZeeR1MV+mt9FvMVmqzTatb5nS++UTX2lCV1p4Yklrzw/DXaTYesBFWWqDk1Ll+6yBe++AZpXtDqtigBWdUQRJHJdMrR0SEnwyFHx8cEYcj9/Qfoqkqz3sBQNWRJRlRlprMFl3d2mUxn+KFPve6iaTrD4Qm9XpfFYsnKW6HIClEUsbm5WcXWnA+bbr+NqlbfhaopmJbxRHxKWbJczHEcnbJMgILQD3HsGq+89CKyLLPyfbIsZTyd4tQckiTh3sNHXL18hW63y2Q8pt1q0WhU0MutD94HWeIv/vIveP7551mufNyai6woWFaNMIo4G0/Z2BhwNpmgqBpJliKqEhu9dVaBjx/4tHs9JFnBaTTIsozg3JVQ5CUCVfksIqRJQpqnIFWJEGman9sEFUpRZDpbEGYJsqWTizCbzSgFgRyBKE5wDYt6vcFy5VNz6qxvbPL4YJ9ms0UplIRpxsL3sBwHSZQqD2ZZkkYxAimyJJIVWXW4lAUUBaois/v8Fz4lBvav/7cSIE1TdNNguVzyW2ErBeR5hqxWJxdFSRj5lRpaFAhWHrVaDcqSh/fuM9jaJEsL6s0G3nKFqqosVssnBlVVVSsxm24ShiG1mk2WZSwWCzrNNqIoksseAgqBn+NYTbIsw7I14sR7EiEyX6xw3QaqquK6NU6OD1ksFrTaXZIkIUtTwjDEsWwMTef4+BjLrVOSV96z5RxJkqhZVf+eLKl0Oh0ePrh3fidPSbKc7vqA+WpOGFZWFcMw0GSN+XzO2toaq9Wq6uwLI7zl8gneZLs2RVGgK+p5k1GErusMh0N0y3yi+VFVlSSJsG2bIksqPENRECSp8ubJEt6ywiQPj4/pdDpESYZuWOciR4GyqEy/5AU3b97kzS/9HmVZslwtnrBTmlZlqqclfPjhhyRJwvpgDdtyyPOcpbdCUqr/23VdHj58yGBzg5PTcQUaOw7Hx8eVgjwvyPOcmjSeDlIAACAASURBVO2gqiqmaTI8mXJ09Jgo9mk2m2RZzmoxpdV02N28Sm+9S7df52T4iLIscaw65BLvfHSbZrPJWrdHUcCtW7fYvnCR6XxBKQrnW0ofVZLRVJnHDx+gqSpXrlyiEAUUQeav/vKvubZ7lRdeehEv8gmikG6riSxU7g7D1KjXXcLQR5Qgjipmz3GcSkBtmPi+f477JmR5hGXVKHKeYGVRFNHuNMnSgtHZMZ1Oi7zISJOM+/f2mUymTGYTvvK1rxKEIUuvwsL6/T6Xdy8hlAJhGHJyMuRP//Tf8odf/zqaolRCbkqiosKGLMtCVVUODw8RgU67R6NeR1FVPvroI1rdFlbN5sM7H7G23qfVatFqNNnfe8xqsWT3wjYnR0dMxyMG2xsA9Lp95rMZqq5Qq1kcD0/w/RWKbWCqFnlckucpiiazCiM0w+SDo4+xbQchL+nWGqi5iKYaTFcLrlze5uDggNlijnuOaTcaLfb399nYGhBEIaquU5Q53mKJlBa4hoUiSvS6bVYrnziv8PM8qbLcyjLni//0f/x0G9jjj3/9LUkWq3JKAUzTQJaV8+mdVypjSTrXKQnkWXaeiSRRFDlZGiFSUdzGea7QZDKh1e5wOjzFtqshpWnauf1HQ4IK1MtzVqsVtVqNLE2Iw5BSjFktfRRJJ/AzFFHh5PgQyrSqqC9yJFFiuVoyHI7Is5S7d++wu71DmmVPcpMANFUlzVLqbh2zZiHLIqHn02zWEYBarXY+dEL29/cpihLTsjBMm0a7xWI+I04TNjY2ePjw3MZCQaPZ4G/+9m+qenTdQFN1nn36WY6PT+j2uiiqhqIqzCZnSJJIHEeMxxNc10WRZSRBJCtSXNcBqCj2LH2i3halKuwOSmp2rRoUllmluZYFecG5IrsCces1m7IsuHTxIpbjVCdtliJKEpqu8/c/+3u+/e1/x3y5ZDAY0O/3cdwaJQWaqrOYz5nMZtQbLookcXFnm8VsxubmBgIleZ6c9wsE1dVIFMnSBFkWOTsb0nCaRGnA5auXmc8XBGFIr9tE02QkFO4/vMvDR3eJU58wqK5Iq9kKp9mk22yhKyoHj/e4unsZURC4eukyWRKzs7lJr92i7lhkaUK308I0NMZnZ9hGjTwr+MIXvnCeSCqgqDJ5lpAkESUlpVBiWxaj0YgLF7ZQNYXTk9GToWSaJtPZlGajSZqmFYhf5OeFHJX+Ks1iRKkkjiMkScI0DfK8wDQsikLgwtY2uq7zzLPPEsYRzz33HOP5lMHmBpoi88knH/O3f/sfWHkrPvvaZ/nxT35Ks93k+rXL1OsOYRygKBp5lmGZJqcnJ0zGY+7du8cb/+jz3Hj3Hf7jj36IQHVLUhQFRVHQNJ3j4SkfffIJUZSwtbHNZDxBU5QKD7UtSiBLqxTdIA659/A+giwiazLLKKDdXsN16iRJjBcHGLUaml1jEawo8gJFVjAVgyyKadQbBGFIVqaopkGz1aq6AxYLwihEkSWm8wW6obHyV0wmZzQcp9Kq5VU8+cnJCUleoOha1U1aFuTn1/+dp974dELWvdu/+JaqKiyXCyRFIk2yqlSzyIGyqtYqi0ohnCfkWYpp6ERBTE23oKi+3DzLyPMMEZE4qbADw9BJ4ghVkdE0FVVVCAOf1XyOqin4oY+mq5RlQRrHBGHAYhFSdzukaYptSswXZzQaNp6/rFT/gkhelji1GoPBOmVZ0nRdxuMxRZJiGDpxGpMkMYIkMFjvs5jPGQ1HqIqMYVYmdKtWI04y5osl3XanAnFdhyBOKtYnDFEVCceuMZtMEMqSQb/P2XBYFdCur2FZJqIgEicJy2U1XNM8QTMVVudG4TCOMC2LmuuSpSllKWCaFrIi4fv+E2xQFKgwQiBOMkQBsixFliqtEEKV6aTpOvV6s8Ir05R6zWZ/f59ep4tlG9x5cIeDw8dcuXqFo5NjGs0GGxtbXNjZ5tKlqziOw+3bt9H0auNaLlfU6w26631M0yBLU6aTM7qdNof7B/R6HQxNY//xY65duczRwSEXL1zAtkwEMnYvbpMmMe12k/3DfRYrj7X+AEmUsW2LRsvhws4FhuMpWa5ycDhm+8JFev0+gR/z61++hW1Z7N17gFurIQsSpmGy2d/Eny+ggDiOkWSRhuvi2DZZmpKG2XlTtcW7N27w3s2bXLt8GbEscVt1XNchimIEUUSgYi7vfHKHze3tJ4fX8nxr9laravMVBDrddpXYUashiCWCUCLLIo5r46084jjFMh3m8xWmYbFYzEnSiJ/84Md8+c03ebD3kPv371PkKd5qRU2voqefe+EFHj8+4PhkyDe+8U2iOCSOA8LA5/R4hKaqrPfXKcqS2WzKc889i+97NBsNdncvEgU+ilg9CxcuXCAKQwRRoNvqYFs1fv3LX9NdW0NURJrdLpOzIZsbG/hByNl0gulY7F69hGbqHJ0cURoCj/cPmC0W+KGHbGikFHzy4B69Rhv7nMQKPb/qhQw8/LTaTpMsJ45TBFEmDIOqO9YPcOrN8xtQQhLF2LaFrqhEfoCuVdIbRdNRTAMkkcVyVeWdUbD7aQfYh299/1uSJOK6DrKiYlpGNShUBaEsabfbRHGVFCqJIkWeEwUhhmKSpQlFmrBaLFBEmThNyLOi0rGc9+ZFUUiZZVBUQ6oscvIsQ1ZkZFVGUTXyIkVVFSzDwHU38BYrbFsjTuckqcdyMcUybSbjKa12iySuVM3L5ZIyy1EUGcuokicR4MPbt+n3+4iiyNHBIXGSUKtVqQ81x65aho6P0TQdw7CYz6b4UUgQxmzsXODBoz1sy6DdauPNlnRb7fPiDh9dV1FkqaoCi2PCKECShfNTUUXXq5p3SZXwvQBD188rziDPMhRFPb8m8sRAX5EboKhVV6AoyVAWVUenU+Eos/mcdruysKRZcW5xcQh8/5xRCwiCgN1rF6k5DpPpGLfu4Pk+RVlSbzRQFQ1JEhkM1rFsE8dxUBUN3/dpdtrMZlMMTafZbCBRIksSds0ijVO63Q5FnlOzbKaTKTXLZDQa4dQs4jii228RJymqYaJpFr3uWqXzkzPCJKHRWmexzBBlnSzL6bSbGKrN6HQIeUGeFjy4c4/Pv/4GZ6dnVcAhAkcHR+wfPGJjY4BQwHK54C+//W10SePw6IQ4TXjmuWd588tv8vGH71d4mK6wf3BAu9Umy4rKilarsbOzjR+GlbcWGI/HFSDfXyOO4irRYlklnp6cnJz79wo0TWUynaAoCo5Tr6r7UpAkBUmC5WrB1vom88UMQRSxazU0TaGmmxzs7/PK668zncyRJIVnn38R27J5//0bPN7bq6LcbRfnvPna9zzWemt8+MEHLOZzGk0XRRJZW+vTajWxDJN2u1t1PKYJj/cekcYJly5fqxjGms1wPOThnTu0Wi3yvEAzDQQRbt3+gHsPH7DWXyMS4nMIwEJRFZIyZTifImoKa2YT13Epi5Iyy2g1W8yDBYUsQC5ULoEs4+ysSr5dLVa06k2iOCMKE6IoZH29j1AK5ElGmZeIkogsS8RZxnSxIIqrd74iGnIuPf2PPt0Ae+sXf/ctWanU84IAFAUnpyc4toMXhQiShCrJBIsllqojFGV1DZIFVE0lyRMa7RZpkSMIUBQZSRzRbLTOM681QMS2axRFiShKGLpBGETMZ3OKLKswsTimKGEyPEIQUlRFJgpjTN1GkfVqpZerxIEkjVBlEc9bIksC5Dlh4BPnCaqmcHGn2gpMQycvqmTScLaAMuXRg6qmyzI1xuMJRVYSpSm7uxeZTc/wZhM0SayAeiApUiRVIi8z3EYdBAHdtIiiGNuuTuler0OaRqyWC2RBJI1zFEEjLTPSPKuiohWRnByE6uShKMizHFGWKYocUZFBlMiKAss0SdIMSVbIS/CCkNl8wc/+4ee88867vHfrI0zLxrZq3L+/hyLLaLpJEMbMJyNsy6ZI4XD/lG6zR911iTyfdquDpmqIiJwej2g32yiyBGWKqRroisp4dIZbcylLgaIocR2XIs/x/QCBiunrtJvomsr6Wo+G6zIY9MnihAuDPi9cv4KllCTBhK1Bl8U8JFj6pHHEfDZhtZiSFwVBlHA6PqE3GPDg8BC1ZnPmebx/+0NETeJvvvPXLKMYt9ckzHzCyCdMUprtPr/7u/+E//v/+VeMxmOOTw6IvCU/+N7fsbGxQbPVQhYVmg2XC1sDvNWK/toGRV5yenJCGUakUUQShqiSRK/bY+/eIRcvXCdJJBQqT2UhFpyOzzBth4bbpVFfI0lT8qLEci1KuSApIooSbMtBVFSipEpurTsGnWYLWZBwnTqnJ0eYhki37RAHc+7c+ZjdK1dorncYL6astetM52NEZARBJIwjXv7sZzBqNo5tsfI8FElCECAXClbeAlUXCRcTBhcGLPOYrY1NpDimiGOSPMRudDg4HYKmcHB8TEpJZ63D2lofUVGIcx/LsJnMFthuncHWRc5GC9zaGlERkiIQpCkJGQ+O9wjiFEW3MTQBUa6KgU3bpcxzNgbrFGlSJa4qEsvVDN9fousqgiCDoFKKKqPFmCwMsDWFokzIixxN1VFkjZ1rr3+6AXZ0/8a3mnWXTqdLft7SIokimq6Qnkd9lJQ4rlvldMsSpSgRhQGaXjFlUD3stmNjmCaqpiFLCsPhsHK+T2cVtnPuTkeAvCie5I4VRYFl2xVIrypVxIwEUHUb6rpWTWtBJEkSjo6PK72LYRCFEVEYnut9MpI4RaAy0XY6XYbDEbKkUBQill2j2WqDdP73mjXSOKHVaOCvFjRcl7LMmUwneN6yst0UJb7vV3jIeQRwEPiIokCeZ8xmE3zPI4oCNF0l8D1mswkIOaIgYep6hVVFMQICRZZAWZAXCWWZs1rOKYscSQBV1QmDKrKoVqtV1xihwjZt2+b69evs7Ozw1P/P2Xs8y5XeZ5rP8Tbz5El3PXAvbAEoz2JRpETTpERKoVbEhBQxJsYsZjH72c2mI+oPmN0splvRywm1Wq0OTZDTkrpF06SKrCqyPKpQcAXg+nvTm+PtLL6sy5W0qAUiEIELZJ7Eye983/t73+e9dYtWs0FVZtimOJoPzs7Ispj++gZRFOE1WwIxXGRUVcmVq3ukkljgzwanXL1xjYqC8+E5uqnz4QcfsLGxjueJncB8PqPRcBmNhiRJTNNrEQQBw+EQz/N49OgRV65c4ezsDNdxePjgIUkSMR6PUBS4fv2KKCqRYWu9y/N3brLZ67B3aRPPMWi6JuFySsdvcn58SMO2sA2NXrfNw/v3+cEf/gFlVjA+O+WFG3dQkCjCmHix4N5HH/A//6//C6+99ipZlnD12jVeeOE2nudj2y7T6QzHcXn6+ec8/vwJZVHxySf3sB0Lx3ap6pqiLFcNQSaHB0fM5nMeP3xAkkY8ePyQV776Kn/x7/4CVdG4+8mnnA+HSLKCoqrsHxygKipZmhAuArxGg0ajyVq/LwzXq6FPEgsm/Xg6I0szJGpkSabb7lJkOXkSkacJSZZhuw0kwDB1HMciTWIUuV4F5EWTfZynBFFIIVfUiky31+XJ0QFNvyWmow2b9z9+l7wuGC2GOJ5FVia0e03WNnuMJyNKqSLNklWztosqa4RRSF7kNJoe5+dD3IZNu90nijKKskbTHNbXdkkiCVl10EyHRRxwcv4M0zGRZI2y0MiJGAyHVChohk2WSXitNpIsMZtPyeIl/U6XqiwIw5iqKETRSl5y/aVvf7kp5N2f/T/14eEhbd8jjmPKUjC0agmQVfI0JQhEA4lp2qiahmFbqAg0bVEUlGV+EX/4ogmlzHIMw7hgdyuKKIL94u98AWezLIv5fH5RPNBoOORFuqq8UijLHMOwxFS0qC4WrtPzMwF8ixNUVWUxm2OuCgyiKALEFGl9fZ04jkESqfmqKgijJf1+jzROSdMcQ7coqxxFE8crVVVRNJXFYsmDBw/wfZ8wDGk0GqIZRlIv3m9JjWUZyNQXHKcwjGg2m0Rhwnw+R1bEdRbFb7Ep4Wp4oWgqZ2fn2LaNqorWalUXbPMv/j0Qor0wJGqMRgMsy0JTFFGw6omfK8uSht/m/Pwcv+lRFIXQesKAN998k4bncPnyrpgcV5LwMekCgVLUFZcvX+bs7EwsgJ7HZDKh1RJ9AZ7roes6g8GA+XxOVVViKmsYbG5uM59PL9rNkzRCloVRV1NUgiAkWEbsHx6RJAn/zZ/9KWka02o1GY+nyIq4p87Ph4wnU2zbJQxDEqkinIudwtHBM1zHYrmc82d/9qfEcUYcCrCgsorfTGYLqgpajRbDwQnz+QRdN2i12qiawd1PPhBQyV4HXdd4tv+Enc0d1tc2+PCDT9jZukS76zMP5/z4v/6EF156gTt3nodK4v79+/zDP/yEP/mTP+bq1T1G4yF1UaLKCnEY8rd//5/pdjtcurwtkEedDoeHhxweHuM0fVRNYWujR6vVJAxjmo0WWZlRlCmu32M+mdJrd1AUifl0iqkbRFEAurjXdPO3R/3FMsRyHeS8ZBlHyJbKw3v32Vpbo99tM1xMyOuSPM/Z2NggjmOCSOjSiipgg7JUc2XvOlGUsLa2xqOnT0DWMC2xkdjZ3qPIK2bzCZ2OT5bk5FmF1ZAIw5irV67z7gcfcm1vlziYc2l7g4ODxwxHYyTFotnocHY64PLOBlkaYeoyyXLKRq+PJEmMZ1PKosZzmxRpxr/8n/7Vl5tCfvLrH7+RZZlgv2e5YFoVhRDdNf1icri5sYnX8snznDCOMDTRshzHEb7vk2UFIM7Hy2WA65jUdXUBbVMUefVLxbKEFpNl6aqUQELT1NXwoEKRVdbW++R5jixLpGl2IXBnWSZw1qpKVQmbYl3VKKqCZRuomoKmKSDV9Nd6fHb/Hq1Wk6pSSLMUVVNoeT7Hx0doqtiyS5KGhIxumIzHEyazOc+eHdBwW6iqTFmWF6ZQ13VJ0piiyNnZ2caxPGRkGo0mwTJErPwSWVZiaBqu49BYmUsFCFJjuVxi2RbD0UgscF+wpDJB5jw+PKbX6VLkgp2mSDJREJKlKcPBAMe2SGKBlmm32zi2Q1VVLJdLigqaTY8sSWj5TWoqkGTu3LnDC7dvrY7sJuFigalr+J6PqRsswuAiPPzFrvgLiF5ZlpSFQM+89957vPrqq6SpCJe3Wi0ODvbZ2tpisRARmixPGY1GZFmGJCvsXNqhKCuSPOfmrefY29sjSWKGpycEizmGoTIZDoijiKtXrrG3t8d3/8V3uPHibV597SskUcz2zg7Xb96g1Wmxf7TP+ckpnU6bg2fPVmbkD7n/4BFvvf0OqqShairXr1/h8ePH3Lv3GZqusbmzxbWrN9B0HdMy2dxYJwgDsiTj9OyUxXLJX/2Hv0TVFE7Pz2h5LS7vXiYKY1555RW2t3c4OzvFNg2mkwlFnjIbj+l0urzy6iv0ej36az3SNMGyLN566y0cx+b+42d87w++x3Q+oek10HSDlt+mrEv6/T7D8YzlfE7LF1nJ6XSK73usr60xnI3RbYswjkRLVVEwXSzY3tlmtgwIkwi/7ROnIddvXGU4GZGXFWmR02g2GY2neO02mmZSlDWe1+bp0338hodlCD3TcizSNKLbXyNPS0zbR1cF20/TVdIsIMsjgmDGxlafKE158PAJwTJla2MbWZbYP3iCpkj4fpeqUmn5fSynQRyHGIbKYinM7pUE8zCilhXh/C8hXAS88NXf/3JHyN/8/Edv1LWEaVgUecloMqXT65GkOTWiLEDXDM7PTqnKAmRouA4SwmlcVQIoKMbM4ousKApVma/cyDKGIVzBgvyqImsqNcKd+4Xn7ItpnEjay0ynk9WxUwFEgYBjO2iaTg0YhkleCrRPsULmhtGCNE04OztlY2MdRZHx/RaKIqOqppgy9bpQ1+iagWkK8sZ8EdPtdtEtkzAKybKcbrfHZLbANjRMw2Q2naEqCoZuUFc1XrPBdDJBZpXzKiviOKKuJYpCVGfVdUZZFQzORrRaPlVVQy3hOC6qqqNrBoZpXKCGxTE1R9NUyrIgy1JkWSJJY8IwQNPVVReiWFyWy+UKO5yT5wWD4Ygsr+j4TXRNQUFA8SxTFIVkcUyapNiWTcNxsUyTKIqhlsmqnF6vx7Vr17As68LY/IUFRlMUwjDgxo3rxLHYYXlek+VSQPCqqrzY8cWxcFp3Ol0Ojk6I4gRNN7EtF9O0+OUv36TZ9Gg1Xa5fv45pGBi6vhJ1xfW98+vfMB0MMCSJ2dk5LdPEVlRUJHrtLjXQaXmMR2Jiqqs6e1ev8M1vfZNLu1dAqhkMTmk6Frdv3RJ2mlaTwWDM5uYGWZYQBEvRoahpKKrC67/zNdbXevTX1/jBD37AdDZjPpvjODY/+/FPGYwGXN7ZQgaKIsNQtVVNmS4MmnmG03BRFBnHcbhz5w737t3j1kuvEAZLWu0GURxS1zXrG1v85r3fMBgO2bt8lX5/DV3XePDwvgBA6ib7h0fUKlRVTXd9jVqWKKqagprBaIzndzkfDUjSmIZnswgDNNNid+8as8WMqq5xXU9Ur+kGdaWQJCl+q0u/3WY+XaJqKs2Gi26ajMcz8rxGVg0c2yVa2aWWiwmyXCFRMRxGXN27jusYxOEYWa5IopjB+YQiWeK6Hi2/i2G5WK6DREWcLBmNBiiqQhQlKIrO1uU9ZoslWZKhIPP8V7775Rawx5/+6g3dMCnLiiAQObNWu4vX9qEGQzP47LN7tJstdE1lNDxHkiAKljRch2C5YPfyJXRVR9c00jhG11RkRWBBkiQhCmOKosA0RX9dGEUrQ6v4okiSCHfKSGS5MErqmk6WZxSFKPFUFZ0wiITmJslUktAvbNMmT3NM00JSZEEi7fSI4pQgjCnLGsOwqKoCqKAWtI1gueTR48fYjoPb8mi2PJ7uPxGIYV1jOp8K35Yis1gs2NjYIMsygiAgTYWDO45FhZbbsIiiAEkWObr33/+AK1f2yLKYyWp6mGUVs9kCp+Fx/cZzLJYhtQSSrAqGVp6hqip5nqFpOgCtVuuCUmAYxkX0paggCCMsu8FgOKYoKxRNYzgec3h4xGI64eqVXeoyJ41jxpM5NQpyLTDCo9EI07bRdANJ0xnP5rgNlyzL+eSTT1EUlcFgyK1btzk5OUVVNY6PDoU3sKpot9t0u12iSPQjeJ5Hnud0Oh3G4zFlUYlavSRla2MTryGKXzrtDoauc2l7m47foqzEwydJRMGEaRhUJciyeLB1Gk3C6ZTNbhtNkUiTRPQuFDWdbhtD17i8vUO5CjpvbG7y5NlTJFknjOZoCvheU0gM8zmOa3Hp0i6z+eRCxzw9PSXNcl566VU+/fRTTo8ORV5vBTOUJYmd7W2QatIkErvX5RzbNCjLgsVCBMwX8zme51EVJc4qqzifz7nx3E3u3b3H3u4lKDKCxVIkV9DwvRamaREuQmQJ2p02eVnhNBpomomsanh+k6zIOT0bkGQ5tayINqAkZ7JY0um0UVWFMF4Q5zkbmztMxgFllRDHMUmSUlUVYRjRarWYTmd4XhND0ZnN5vgtj7zIRWeB26LX2yCKAny/jWM55FmGKgu5qO11yDMIljPKImE6OsUydMq8xHO7qBLYlss8jMmLioePH+G4BsFyjq6pVGVJv7uGrtscHB4L0KHdoMpL7rz6T2tg//wO7Jd/+0aapPTXNjEth4bbYjpfsAiEX2s2n3Pl8h6KLK3C1KL+amtzkygUzcxpkiBJCGFfM5CQhEVCE6RMTVOxTMHxFnhZBUVRxIQuFYxxGVFkcHo2QJJkms3mygNV0mx6yLJKnhVohsHR0TFBFFEUJZPxhChOaDaaBFGO1+qi6zaSrCPLOpbdxHVbFEWM53nMF3NavoesqPjtNi3fRzZVSjKSOKC31mY6HaHpCpZlUGUV49GIhusiSzIN16XltZCQWMwXNJsmuqliGCqSUpPnKds720ynY6I4Ik0zJpM5TqNJmuXIsspf/tVf8uFHH3Hr9h201THdtCyqulrVx8sXn50wVcqrZvD+aqeZoqoaEgpX9q5ydHxKp9tD0w12Ntd5551fcXK4z/6zJwwHI7YvXaEsKuI8x216LIIQzbGYxxFpkRNlGWUuPmfDMFFVjU8/vUen28N2XM7OB1zaWsf3RZuOIksYusbt27e4e/djUGUazQZlVTObz7l2/SZRlBGEMaYqIQN7l3cJFnN8r0kYzNnbvcxwNCHLcoGUkaSV7qkznky5fes2esPhwecPODjZx/EbfPTwM97+6C7vffoZbddmPBgwGQ1pNhqEQcDTg32KMqfR6VEWGZcubdB0LNI44dZztxiPRvz8F7/AazYxDYNOVxSXKLpOEMbEWc79jz/g2rXrDAdDNjc3WcwXqIrC8dERpqmx3uuI5nrbIApj1re3kBUNmYrxeISuG7z3/nurPgKNjY01Njob/ONPf8rx4T7P336OdtPHMV2oEL+3XM7OTnn/ow9xGy61JGO7DYbjKaYmUVZQKxJn50P2rlxhsQzp9/okZY0kQZpFVFXBxvYOcVyg6w51HQE1hqGtcN0pRZ7juhb9fhdNUmn7HU5OTzBMHb/dRlZ0Prv/OZKSMZvMaLgNbNMlTQtODs6pch1ZGVAWMVIuYes98gSarksYjGg6Hrpp8/jxY0pKdi5v8+jhPVpek8uXdijijPOTc9JENJ0tpwENwyEcz3jp61/yCPn07s/fSLKYWoL9gyecnR2TRXM8WydaphiqxZNnR0iqRZDmSJpKWhaE8zl1LTSpk9MBTa+FpluCFaZb5GVFlpdQ1cLfEgbICsR5AmVJURYomkKap8iqzGK5wHYsEU+gwtB0oihG1w2iIOJg/4D++sYqD2mx1u9jGhotv0G/3yVOQnRFASrm8xFhOKftNzF0jSJP0VWVqqxY6/dZzOeUZcFwcC52laMhaRTTajQp0oK6qGm32qRRgtPw8DtdFnFIlKZIqkYp18R5hmaZpFEmia3JCgAAIABJREFUEM6FRJ5VSJKO6zRpNDzieEG73Vot/jVJGGKbBjeuXufO7RuEyzlSXVIVxcpjVwlXNhqqalLmNWUJmqLht9uEccQiXGIYNqoi8fTp55yentLwfObLGMdr0281+OGP/g5Zb1BiMZiE9Hs9sjylv95mPB1SS8IbRVFQJSkG0Gv7xMESRRZI8J3tTR4/fkjDMtnd3iJKMlqtNrZhkqUZf/5v/pzPPnvIN7/5bSxLI88yoiDCXvHIxDTLA0ViGWfM4ozzeUwqa+SySlZLhKVOqVgYXpeklLn+vHCzG7rKR3ff5+z8nLqW2dna5fR0yGZ/je31Pl994Q6KXPPLX77NaDSl0+3T7fc5PTpFrQ02Nj2+/jtfFVNt12UwHXNwckSj1eSFF55nthjjNi08r0GW5piawXI6IE8XfOv3v49mGnzjX3yLyXJGZ63PYDbnys0bLOMZbtPB95qs99awNI1HDx4TLkMkWcXzPE4Hxzz/wsuYdpMgzpnMAsaDE44HA1JJ52SeUpoOy2COqatMx2fElUQty9x58SWQVQZnQ7bW18iiiDCVGU3HvP76V6mrmsODA6o6JU7n7KxfFvgcxUBRDNI0JwoD8jIkyWJqYDQeQw0tr81oNL6IUZW5RJJFFJXQocu8xtJMen6bMAg5PnpKs2EyGg6QZQPN0iilAE1WqWuJ6XRAt9OgKGJKagzLRdZrDo/3afs+vfYa4TTh1s2XyNKCs/MzJsfH6KZFKcF0NkWWJLI8pVZqXnn9e19uAXv3F3/3xmgwxnGbdPwelBVXdq+Qpzk/+9mbvP/+h/T760iKwmw65dGjR/i+T6/jE4QBRVngt9pomsYiDFE1jbIuicJE2BeoqWpppTPov03IyzLUrGBuBl6zRZEVNL2WQPusJolVVdFsNOj3+5iWJTJtK+Z4ECzJchGncV1XlO6u/lzXNWazKUWREUYBiqKTpBlRnKBqOq2WjywryLKCqoqdVVVVzOdzml6TZbBEVhRRuGoaFJUoSzVNE9MyLqialCVlKdpWkiRle3uLVssniiIs02A4GGHbzuo9qzSbHoeHB3it5oV43263V2bXmjRNQZJoNFzUFWZYN0yOjw/xOz6mIegQrZZPs9nA81p02h2QRJV9kC2588pLbO5dYmt3h8vX92i2GyRFgiZr5HlBr9dHkVWowbEs9FVYOc0z1rc2iVcDAt/3OTo45NnTp6AqnJwdi1JURaaqa9Ky5P0PPmJjfR1dt0A1COKURZzQWt+g0nWq1CZMJJCbyJqHLLsg2USx0EDjOGM8ngIyR4fHjM7HmKaDbTe4cnmHuiy5du0qmxvrv9UIdZ1//x//iobnk5c13/7udzg9P2FzZ5vxdMr7773L+ek5YRByZe8Kw8GQSzuXCIOQx4+fcO3aNVRVFgMSRcNtOPTWetiuxXB6xnh8yicff4hpWGxs32Bt+xb++i1u3tgjz+H49IzFPODe/fuMFzNa7RbP336eJIlxbBPf95lN5xRpQVFkrPdbvPTKq6xvbmLaJm2vhW1oGLpCmeeURSWatuIITZUp8pQkilBVBbPZJMsTPrl3j7qGYClYYtQVaVZfVLxpukoUhGiqSpIJj1uWZJiazXIRXET5yrJksVig6xbLcInTdFiGAapmMFssCaOEtMio6pqKCk03cZwGqqJQFhlRnNPt9PD9HpIkoxoGB0eHbF6+xOBkTsvrMJ7MyfNcxPGWZxQsSfIZO+t9NENBVnVURUOWVNZ7m/S76+zdePnLLWCHn733xquvfIWb124g1QhzZQ1vv/0Ow9GEuob5fMHN555jPp+x1uvQ7/XY3Ohx6dIlVF24csuqot9f4/TsDE3XadpNkjTlww8/4tq165Sl+HL6fgdqQbcQmTKbNEmZzxdoikZRlkwmExzLvrAcpJlo0pGk3zYLJ4nIhQ2HA3GRisJkMsIwhPAvrZjjeZ7SaLjMlzGyohInKefDIbbjohkGmm5g6yZpnCIh43ktwSRXRaNMHIsGpTTPqGvRdpylCUWek6Up8uq1e/0epmnx8ccf8zd/8/9SliV+SzisNU2jyKsVZVNkIE3LEqw1XVAyqqoSFVSdDrIqUxQ5hqZTFCLu4zgOWZHTbHooyJi6wXIx5x9+8g+89pWvQF2haiqObTGbzNhY20CRZLIkxjJU+p02VSFjWsKBr6oawXKB3/JBktENg/F4wmyxoN3uoGkqQRDQdBu0fJ/NrU0xRVVVSmp2966yvXOZm7fvYOgOaQ6zZUyQlljNFmfTGVGWo9QWp6MpizDi+HxAGCfMZlOG4zFRGIOkcu3aDQ6OjonClMu7e5RlTV0pnBw/Y3//gOOjQz766CN++MMf0va7okQmTdnY2OKF519kPp9h2xZpmrCxucmd288JamldMx6NWF9bw13xv1p+h+UyIIpC0iSlrldZ1DIjL3Ik1cK2mkKjlFUGkyWS7tDs9PCbFmtrG2ysb2JZDllZ8Nzt2+xcvkSRZEwmY2zb4KMPPxRN2XWFDFRVSg2cDYbs7e7y5s9/QRKH5EmKY9m4to3r2ERJgKrJnA8G9Nd6Im6WCeR0kiR0Oh0arkBdddpdzodDVFUR2WXdIM8yXMclWASoisJsOmfn0iXa7Q5xHLNYLGg0RE9ny2+LhU5X0XUDRTcwLJe8hMH5OZ7XoK5qJpMZ1BKe20BTFUpUlsuEOEpI0gRVVVBUgdEqi4QoWbK+0UNSwDR15ss5QbCgrkvKLETVdbZ3djFUHcdyePutX/P40SN+/4/+7Mv5wP7rX/6fdZqmq3KIPWRZJo5jDMNgEcRUq12Qoml4XpMoCKnqAlUVPqt2u4tlWUynU2RVwTRsfvGLX7DZ26SqKoazCa7rcuXKFZbLBR2/ffEaRVHQ6XTIsozhcHhBZSjL8qKd+AuukljMhM/McRwWi8VK2A4vuGVxHKMrOvP5Uni3lgsRp6hyakW/WIzeeustvvvd71KWIrirrsgQjUaDCmFcXd/aFJaG42OB87Vs0iy+aI4RC6SgRRiGQZrnwrvltQW2J80v+GCLxeziOvI8R1ZA002azSaz2ezitQ8PD4UfTJNJEyHYxnFMnpXohoptmCSJqMh69uwZcZby+uuvkyQRv/rVr9jb2+Pvf/hfiNOMr73+dRqNBm+9/Uv+4LvfwTAVZLOJaQg/nu/75HkGVcV0Oqazto5hGIwGwsgYxzEN2yHPcw4P97lz6zaT8ZjhaMTGxhZZBVleI6s60yAhihI+vXcfy7JJsoL19XURms7moh4PmSRLV1VjCttbG5wczml6Lp7fQpJqNEUmz1JajSZ1XdNyJSbjIa+/+rIo0lhpgkEQsLneZzKeI0kKWR6jaRKyrDIaTdjY3mA6FUOYJEk4OTmh2+0SBAHdzhphGDCenKNpGpPJjPF4zMsvv8hofE4UGFx/4UXs9Q3SPGVw8pS2JRPOBlSqw3g4QqprNtc3+NpXX+edd96hqiq2N7ZFKiCe02o20BWTRRBimiZPnjwhL2ucRoNlKFhge5cu4zVdMUldTImzFEWT0SyTJ8/20U2Lre1L9NfXOD45YH9/nz/54z/hnbffoy4rLFvDbXvM5wsBHczEzqrZcIjjGNsVzUWz2YwkF/Yc3/fw/BafffYZdSY4Z77nUUsSVa0jyQZPnxzSX2vhNBsMRkM6HcGo8902cZSiqSo3blzjrX/8BbJU8OTpQ37wR9/nbHxGGAe4ri04/KVClko0m10GwzNUVSYNJmxvX0KSFMJFiFSDoatYusEf/en//uV8YM8++dUbWZ5weWeH2WxCWeaomoqiyrTbPrZr4fseiiZBVdJwLBRZEgJkS+gyX1AUbMsmiAJUVeHRZw95tv8MZIkkzxiNR7RbPm+/8zYbK7d4HMc8e/KE+599hqwoOI6D13QIgiUNr7Ga8syBmtlsKooCVjXrWZaKwcFqMFDXNaen59hOgyiIcewG3U6PoihIkgRjZZzd3d3FcRw0TbuY8HkN4VULw/CCT/7o4UOyJCXLhUlXUbjA4BiGjiQJBJHriOIMVVPRNMGtNwyDoshXnPkZVVWjKPJFHVZZVqvOgYCtrS0URWE2m62ouDEgrcgX4mfafgfLNskzca3Hxyd4bZ9Ot48kyTx8+FBQW8cjnp2OSKqaR88OOJuM+IM//ENyqcDzfVRNaFW2bXF8eCimemmKrKhM5gsUVaQgqIRtpcgz5vM5g/GY7Y0NfN9nfW2bKMmwrCbLKGM2X/L0dMDZYMT+/hGG6ZDFKWmc8Pmjz5nO5+RZyeB8SBTG2JbD5tomeVZSyzI18PDxY46OTwjiiK2tLZAk0iwTYeZ2jyhYkiYJy+WCqqooy4o4CIjCiLbvo8hisDCbzqkKyMqMMAzxfZ8nT54wHo958803WVtbI8sKqjrn5s0bdLtdVMUASebjjz9me2cLXSv5m//0I1753W9j2w1sTebeO7/EKRK+9q1vcO/uhzx5/Ij9/Wf87Cc/Za3f5+aNG5yMxuzuXeb28zf593/xF3zjd3+PJElBken3N/D9Noqs8fydF9h/+gx9RRU2LAvZ0FhbX+N8MKThtzAsG1nTycqKyXhEmsaYps5sOieOUhpug9l8wnQxIwxDVEmmLmvquhSamCzjuo6ABwCqrOA1m7jNJkVeYNsOhmYI7tl8iiaraIrG/tN9VFUjSZaMRxN83we5Is9jjo/P6HfXCeZDzo6PuHZ1lzSMWM5nXNoRTVe21UdVRGeAbYusdNOzyYqQyfQU1+2iKhp5VqBrwn5UlylZFnPnpS9po3j40ZtvmLpBtdJ4xJevFLVaRUFdVYwnEyREYDmJU3RVv8D1mrqBIsniaFFXKLKChMStm8/x3K2b7F6/SlEVXLq8Q6/f42uvfxXD1NE0Fb/ZJI4jNE2l2+/S8lucHB0JX5gs895777G9vc18uUBWZHRNtIK3WiLWIjw4BovFAt/3cRwbRVG4vLPLv/43/5rlYkkULUmTSPRCgnDuywJXYxkmjm0TJ+JL3PRaqyeaRrvdoShK1jf61HXFpZ2di0btNE6oV5nQ+WLKchEIWaIGWZFIkwzHcXAdh7IUON8vArhVVbNcBiBJ4jioqCCQc8RxQrvdQVFU5nOhU+iasdI4ZCbjIZqmYtgOjusyWy7FkbDRZHNrm73dXe4++pz1zS3++//xfyBKEvyOT7/vY9oGSRihqjKKKgtonqriuA0Mw2A4ndJwm2RpQlmUaIqMYejYDRfP8wgWAZIkswxjNMvl4HTALIg5G05ZBCFZlqFqGoPhkKyoWERLZssFk0lAGKbcuHGbIq8AheUy5tGjJzx88pSDwyMqwLId8rwkyypu33qBNM8pyposz4mCJY6lMxoNV4mGkmUwxvfbDIZDwihE1WSWQUhVQVnV1BW8+eYv2Vjf5Lmbt7h8eZfbt+9weHhIksTcu3eXoij58Y9/yuXdPW7cuMnh4RG7u+s8d+smhqHi2QZfuX2Tn/3nv6fjd1B1nbIs2dzYBEnm9d95nZbv0ep6qE6bT+7d5d1332G5mFNVIMkKQRgyHI14+Ogh21tbzOdTGg2HF1+6Q5onyEqN4zUJgjntjsdgcM6DBw+Jk4xr164DJc/2n+F73iqiZNHyPfYPxOZgPp+jqRqyLJElKUWWIlFR1SVJGpNnKZqqUFU1URKLZrE0pbe1RRgELGZTojBCUTSWy4C9K1dJ42DFnkswHJ2z82MuXdrl4cOn7O1tUtclhmHSdBucDQZs7lxCUTSyWCWOEiRFQpZk5osF89mEJIlpNCwk2ViVNNuiExMFWZKRJJlbL/7TQMN/fgf22btvSJJCHCckcUJRVERxRpKIrrrFYkmcpgyHI7I4R1d1gmWELNVkaYYiKyyDJUmScHRwiCLJCIBJQVUX5FVFy/dYX19jPBqhqrJw/BcF6oq5tLW1xc7lS2i6TrslRHzTsnjuueeEwdU0WF9fv6BM/vVf/zVf//rXCUOxRU+SRFyMJAT1g/0DlssFV/Yu8+zp51R1zkcffky300FVFE5Pjml5Hk8+f7yitRooqroaM7voqsZidWOMp4KHfnx0tGIxqaiKiPzkeY5lm2ir3ZcsC2G/rsTOSSCGylWjsroCGGarQYDD558/odn0CENhCZFlheUyQKollkGIaVkkScLV61dIk5g4CpHqGsOxGZwP2btyjTAWhRXBMiBYBrz80stoEnz03vusd9vYmkIWTBmdnKAYKruXLxMtl6sIWEVelKi6hqyqhFEkoh1FTrPhkqYJkiwzmUxo97rIqk6OxNH5gM/3Dzkbz0nrinC+IApilkFEJUuEaUKtyRQSxFFMVuY0Wi5JEnB6fsLZ+QnjxQRqg42tbTqdLnGacXY+hFpiPJ6ShCmqKXbIDdtAU2UWq9TC4eEhfrdJf30T3bTQTY2nB0+Jk5yylti7sktRFmxsblDVNZPplCRNCKOIhm1iaCq2Y3P//gMkFCzLZXNrk16vTxjmzMZThgcPmZ8/IwrG9Df73N9/ysnhkMH5hI2NbUzLwnZt1rd6vPmrn7O+c5N2x+fGzWtUK90yjhNMx0XXJL7+ja9xfn6Opik82/+cDz56F89zsWzhdwyDOdQlDddma2MDQ9N5/PnnyLJEsFyytbXJ0dExiqySFyIBswxDbNtmfW2dKAhZzISDv8hS4jwSD+osxbYdJtMJumny9Mk+29s7zNKYNEuQJdANnSTN2dzeYTydYesKWZqzd+0q56NTDFNhMllyZfcmB8ePURSVyWiCqurIisbGxjbjyYwkXtBuO8znIxRZotNZo+F2ca0Wlu4RZTPyNMVreFDWGLqFoVlMp0te/Mp3vpwG9jf/97+qXddFVWVmsxmtVouyrAEZTRO7rLqu8TyPKA4u6KpKIRGGIYYtkLtRmKCbBqqqI0kSi2B6EUXJ8xzgou9uuVxS1MUFpdU0TbKVudX112i1WgyH58yWYmcl4H+e8Izpwqn95MkTwThv+iuKbBPPb3K4f0DDcfhPP/r/GA7Pef7553m6/4xut4tlOVy/eYe230GRIAsWOLZJLdWMx2MajYbo8lsuaTZFosBxXObzGZZtkGUJWZZRFaVgeMkykiYRpTWyahHM5lzd22E0maAZJpPzMRsbG6SF6AOs6hpdN2m2fEZnCyxHYhkMMQyTJCxpNn3iOBQaoFLjujbb29t88sk9ru5e5ezsiCSNMBqCVCuCsTnRMhBdnZpOUedEUcTulSt89uA+zWYLTbWIohhVVTg/P+fmjWuEC7EYzKcLHKeBrBSrna8KikrD9ciKAtcROl1RpjiNJvceHTKeBcRxRpLmBEHAbDGnLMVnkiQJSZKg67ogk2QZsqzSane5f/8h3a7QCN2mg20LxNF0KrKwluOuKBhC63ztK6/z8ssv47ouvuewmA5Q6xylLliMjqAUdpBut83JyRH9jS6LMOD0ZITjemxs7qGoEuenB0SLKU3HxnRtdF1hMj7HNi3KUsI0mxi24NJFSYmumYwGA95//30kReGll16iqEq8jsNwILA6pmkiU/HRx+9z69ZN3n3rN7TbPe4/esprX/8alqki1QUb3T6TcMnOzmU8t8XTJ4figavVqJpElIRUWYGqWDRbG9S1gIiWRcH52ZCyjLl2/Tqf3L+PYZlsXdrm5PSI5XKOprpMZmNs12QwFfBQTTHo+z00XeL09JyG6zGfi7IY27VXNgoXVbHI8iVNv8lguKDb26aocvIyQpMNBqNz8iql3+4iVTVxXnM6HvOVm69QVyXHB4ekVcaz46d85bWXUeqCCpksTlCR0BSVsihwvRa22+DpswPW2uucD45puLZg5+cl129eI4qX/OD7/9s/qYH9swvYL//j/1ULUbpeHR8Lqgp0XWcyGl5QJIqioGblilfVC+TuFwuTrApSZFWJYk3LEFVthiFCqIZh8MX7SPOMje0tTk5OaLfbQscqc2Qk4rxalXSK62m2PCaTEbquE8cpUK1c3+1VKNzBMERQupYkyjynyFKW8zntdpvjsxOuXbvGo6dP0Q0LRdH49Tvv8s1vfpPZ+JxOxycIBK66KAp832c8HrOzsyPgbFEiwIUK5HlKFAXYpoXrNpnP5xRFhqzZyJKBKisUuRgU2I3mxeTo8ORY6F+mvTK1esRBimUJkbvdbjMdLQEZyzKI4pQ0i3jhhTu8+96vSaOCra0tgnBBq+WiqDaz2Yxer4ehqyRJwnQ0FlYMWcKyLB4+vE+r1cJ2Gqiqjqpqv82WJhHdbpej/QOazSYdv0ucRoRhyM/f/EeODk/43ve+BytDbVEUfOd7P+Df/eVfoxgWSVYRRjGj0YiyLKklkVZQVfFeZFm+uGc00yCOUlH8YohyZEkREaflcinydppoacrLChAoaFmWoazIyoJr12/y0ovPEy1mUKTYlk6n2+PVV17mwacfc+XyJgdPHpGmS65c2eWjex/RdETT1enhAYoMr7zyCqbmkJY5uiHjNRqClqLblKVEkqWUZU6Si7TF1voWZVkynk7xO23iWNwHX+REXbfB2dkpzYaDaeqocsnaxjZPnwktT9Mkiixme2uL07MzlssAXTc52D9ic3MHz++gaCpr65u89/47VLWowGt3mtRknJ0ccHRwyHe+9ce8/et3QFaxmi7n5+dICly+tI2tWcRpRCEVTIMRjuMQLBZkSU7HE/dns9FC0ww+f7rP2tqawGi7FllesrnT4/j0lF53i0azQxBHlGWKquo8O3hKmkVIZcHWxiazeUhnbQ1TF7Gyw/0D5mHAtZvXqOuccD7DMVvYpk6ZF/gtMYhRVJ04L4iSmFvXb/Do4T00TSMMUsqqJkpj1jfX+O/+2//jSxbbfvjmG/LKGpAkIXGUECwXKLKyEhBTPrl7V4AN45i277NcLFkGItNlmiaWY6+C2PUFh10EpUUQ2F15rL7YcaWZyFl2Ol3yXJBbF7MF1BJFluFYFnEcCCZYFCHVkEQRtu0I9DIiERAGC8qiJAoDRsMhuq6RpTGaLFPmOTX1iuOfsLG1RaPpYZoWd+7cJi8yDMtE0zU03cRtNOn110jSjHanS5YXhFFM03WEtyaNmUzGdDptNFWjqkrKskCSFFTVYD5fUhU5lq5T1TW/euttMf2kYrFcrHRF2N3dI8srvKZHWeY0Gg7z+YJ+b40sy6nrCs002dreYjIbo0gKvf4aWV6wvb3FBx+8x09/8nOWiyXXr13n/PwM3/dJswzdMEjiHGXV5ei4Dln2RTSpJo4TNE2j0+kyHk0wTAvbEs3NcZ5TSzJbm1t8//vfp9PpEMWCStHr9fgPP/wvdHrrjCZzNEME9sMooKxKkkSE7YMgEDecImwsInZVEUUxsqxQlhVJmhGEochgIq1wTKyms8oqRyoJCqsEsiIznS347P4DdMNAUXV+896HDJYJyyTl8yeHNDwfSdYx7QZlLdHqtZlOZxwdHPJ7X/8qly9t8/HHd5mFCfPFkt3Ll5AUhGiJyjKIhKM9L8gL0SReVzW2KwY0H3z4Id/89rcvuGhxGFEWBd1Ol7/9279jMpwwm40pK4n9/SN+75vfQjd0JuMJRV6ytXWZZlPgw2/cvI5hWOxeuU67vY6EymSxwDAcTs9OuHr9Ek+f3qfhGpRFxt2PH2HZDm6jQZrmnA+H3Lx6jcs7u6TLANexRdNPKnbYDcfFti3kSqB7DMMky0SZhu2s9MyVkyBNY9rtNpKiEEcJqiLj2i5pmdNb6+E1GzRtG6UGv9mgqkrspk5Z5GRpjN1wQKqxVtlaDRVKhM4KKLIMq4dVmiXEwQJNU8jzDL/VJo5TDMsiikNe+8qXdOI/u/vWG1VVkWUp7bZPUea4ToM0FfiNdrvN9vY2s9mMhw8foKxiQF+Iv3Vdo2satmVj6BplkbOYz1derYSqqi6eyGEYUlUV4/kc0zRZ39ggjCKKIse2LEzDRNXkC66WBKiKIgCKskxdgWkIIqqmyBR5jlTX1GWJRE1eZORJwtnpkRB1FzOQxKIYBBGTkRiBZ1mKqihkWUJZluRZgeu6TKfTVQGGQAP1ej1m4zGSLELgRZGTJQlr6z3iMCKOIvobG0RpTqfbv+Df206D3sYGnbaH6zpYtoPjuHQ6XQ4PTtAMgzha4rUajMcTfL9NGAQ8fPiQwWDAc7dfIMvEUaxCeOWEDUPCbTr87u98g0s720iKJHZhpolpmvR6PVqtLkVVYJkmRSFwKnVdEaUp3U53tUtKKcoSr+kRhKHQvtpdJFkhTROCMCBJE6IoYhGELJZLMlyiJCOKIgaDoUAmVYJeURQV0UoCEO9TI4piyrIizYqLBU1a+frqqkaRFUxLfM5VVVEWFazC/2UpWuDrukDVdPKiAkmhquDh4ye89trvMJxO+fzx57R8n8OjU+K8Ii2gUkze+/Aukmyy/+QZv/eN15jNp7R6Pda2LtHvtpnMRhwd7rO23mc4HlLXMotwgeOafPDBh6ytrfHRBx8wmc7o9nts7Vzi7qefkIYhe7u7Ykcuy8RxxHM3biBJEpZr0Wz6HOyf8Of/9t9y8OwZa2trVFXNaDgXKHHLwnUdfvS3PyKKI4GPngvCw9nZOTs7Wzy4f4+25zI8H1DmJRI6mqaw1u/z9q/epOHadLwGg7NTlouIZqPBfLmgyAvyvMB1REuX7Tbo9IT5W1JVFE3Dci0kRSFME3rdFsvlAl03CZbB6v9GNBUdn54QBksUWcE1dG7s7TIdj9FVhUyusWyLLM0YjqaYlo3ntWhaTWzdpCpyqCuCYEme5xwcHbK21ufo8ABTt1AVCcdukCQFuuaSpAWO2+TFF//pXsh/dgG7/95/faOsxNSirAR7fToTBlZRL7WgBmzHob/ep+k1BYhfki9wyJZpUq/6DqMwxHVtojikLAuCYMnBwT6yItH0xLb7+s2bKIpMq93i/oNPBdk1TZBksC2LuhaB7izLmM1mmKbggdVlzXw2RUIY+bM0RlVAlmrklS+rrnI0XSZNYyQQeUoOAAAgAElEQVQk3IZLGItWoG6rTZnnApcsIYy7eYamaERBgO+1qIpCjKIlMdVxbPF+NF0TC0EYkOUplmkIOwcKqmkiKRpFnqGoMlGa0u6vI1VCj9INA01TuXv3U+4/eEBV19y79zE//vFPeOGFF1a6YsKTp59TljW/fvc9zs5OeP1rrwk9DPEAWAYzNjfXiQMxuZVWUMXBaEBZFoxnEwzbXi0+AYa+oloUGUg1ru3iOII75nkeTa9Jp9thNp+SpBmSJGFbInDfaDSwXQ8kldPzAcNZQprlopRjMSdNU7KiIC8KgiBcIbUNyrIWDy1J5F/TVVmGthpgwMqOIktoqljYsixDWplpv9BcdV0DSYAnZUVQKkzLYTAcMZvOGQ+PoC6Jg4jT0zOchk+UlUyWKVeuPkens8G3v/kdjo6e4fd8SgnCLEWlxPNcbMdClsDzfAzLxHUdZFVib3ePKIr53u//PuvrG6RZRlGWtPw2rmMxm85I/3/O3iPIsvQ803uOt9e7tFWVWb66qy3QJBqmG4YgCZCakRgiQCliYkKamFBoI20YDC0U6pBZSVppIQlDzsSssKAoSuAQBNHwBCCgG43ualMuKysrfV5vzrn3+HO0+G8luJEWnRUVN2qTlXnN/3/mfZ83Djg43OfS1kXu371LrVbFcW1qtQZJkvM7X/oSl7YuMZtN0TWTcq1JnmaMJ2OOTo9od5qouoHhmvQHXZJUaMNkSXQ9ju1QKTUxNBfPmyHJChIFuqEznQ6plksYmoxllYnjhDCIuHv3PttbW0RhzHg4RDYsti9fJcpy/MAno2C6mLO2uc54NqFRqaAqBoqqiVCdPCVNUtJYSHzqtSpxEHB5a5sizUiiCN00ORuMUSWVjbULLOYRnc46luFQthyG/T6L+ZxKpcy1a9fp9bpcvHSJXm9ApVxFSiXiKEFTDGTJxLYqSIrGyekZr776MSuw6fHDNyzLFPmPkgjoUFUV27YI4ni5fVKQNRXbdUiyDMMyKQpEWRvHSIoMsoRp6bglB7fkYlgmWZ5hWhara2sgSWR5xqWtS3T7Z2iGhmnqrK2tMpkOWV1doXt6gqKqKKqCrCjUGw2qtdo5ukfXDTRdpd1u4XkT4jg838hourK04RRoqiQqyCLDtG1msxmnp10araao6BSVKBT4H9EehjQadYJgQRSFS56TKW6oZTpTlqVkWYxl6WiywmAwEHggq4Q/D4nTlPW1VbIspdFqCRJGHNJqtUizjHq9TpwmvPvr95h5Ho1GidFwiKKorK2toqkSL7/8Eq5b5pnbt3EskyQKmc3G2JZNGCxIk4jxeMBkPCbOE5I4Fvoyy2AyGSNR8NOf/5ySa7FYzNjYWAVyoODi1iVmnk+SZcwXc/ICzrpdxiMxO1tbX0fTFTRFwbUdVE3Ddis82H3CaOIRxAWKBMNhnzwXFZeq6QRxjGlZDMcjTEt4YbM8x18szt0LeVGAVPD0D4pYnUuFRJblqKoQJyOJ+LGiKJaVmdD3SUv93X/0J3/CP/mn/z4vf+Jlrl5a4/e+/LtUq2UuX97mwYN7zLwZURSw8AOC+YL/+X/8n/gv/4v/HMs0QVFYv3CRtWaDe/fvYhiKoJ1ksJgHaIYYfVBIGIZOnhfcu3ufJM/5va/8Ppbt8J2//Q4bFzaoVmtEUcjR4T7tTptuv0tWpMRxxvra5nIZJHILTMsiI0fRVWqNBkfHp3RWN7DLNTIkTrpdyqU6iyClVGnSaK9il2qUyy3Gk4goTVFUg87aBofHZ9y6+SyyLJFkGbVak8FghOW6ZGlBMA+FNGI242zo88EHH2GYJoZlkQGGZfDg4QPGswmRH1CtNuiPxuRkOI5Fvd7irDfk2uVter0zLMMiixNcW4hT94+PSAuFiltBLmTu39+hVq1SpBnT0QhFU7BdlyRJWYQLJFlhOByhyhqKrKBJBrqmM5styHOFUqlKGGfohskLL/72xzRz33vrjVKlRJhEnHZPMW0L3TTQTQM0jbTIkTUVRddwyi7eYg6KjFEtgyqj2iZRnlJoEmGaMJ7P0B2LLMnQdMHuKgDHddGWkWGymjMaDxiNeownA0olhx/96HtsrK+g6DaVWpUwjnFdF89fECUJeSZuIM3QmHkTpt4Uz58QpzFxEot2JMswLYP+4Azd0FlEIUmcUKnWsas1kjzDX7Y6o+kEt1QiQ/CV0iLHm/vopiFin4pcZDgWYpEQhQFRFGDbFv5McMdMQ8fQXaIooVauokkSUpGTRTmaaiBRiKyBpXL/xs2bNBoNPvva57h1a5vXX/885bLAatu2ynQ6xrIcojBB1xTq9Qq+N6XVbFKybVSlQFUk1jfXRSRXGNBo1NBUDdd1aLdaNNcaBIGPqhQUeYbnTZnMphyfHFFyy+S5oML6vk+9UcctOeSkFHnC8dEBpi6i24Iw4kc/+SWKUWLqiTTpNImZzaZkaQbIRHGK5wWMx0Oq1SphGGKZNotFuKysxFxL4JJyIEdS5XPGm1QIUq25rOAX4W+WQqqqIMkakiRDUSBLMOgPqNUq3LhxA8+fcbi/j67KdOo1bl29xDNXNmiUVMqOQb3qcmXrIt3TE7pnXWaTGfNJyKPdh9imTZrmlNwqYZDw8//nbTxvgWk7qLK0JBPnZLkIQ9k/OELXdbYu3cCyy/T6fbI0YfvyFUH0NSxWVlt84xvfYGfnEZ1Wh/0nj5dYdIkbz12kIMfzFuhahW5vQZgY1Bub5IWFatRBrTKYhPRGM8qNVXS7xq/uPOTWc7/F5vYzXNi+SWvlIoVmcefDe7TXLhLEQmOWpgWOXUYqJOrVOqqiYtsNHMtFyiW6ZyfomspsOuHCxYvkaUar0aFSbaPqugiTSSP29vZJUyiW44YoFKh223Z4fHiI4TqMBhO2L26xt/uYK1cuc3xygKoU5HGAH4QkaYw39ymQ0FQdqZDJ4gxyBV3TkJe2KJCZenPiOCTNIl7+xMfUgR0/efcN1dBJsoRqo4LpmMRFzNHZAXmS0mk3SZIQqUgZ9Lo4tolEDklM4PvLANGA6aRPGntoskQShCAJfngUx7TXOiALE3CYRDzZP2Jr+yrVWouZF3B4dIrp2PjBQphu5z7jyQhJBqScJIvIpYwwL4iXw8A49MmLiPlcaJosw0DVMuq1CkeHpxSyTnNlk8k8JC4kSiWVRTDDsgymszGWaUCR4U1GaJqB6zhMRiPKrkuRZWxdusTZyQm2YzMeD5G1gjSLmE5HRMGCJE4xdJNyq0Gp4lLIsEgiZov5Mp0pxTAUJBRMw4RcxtBtihySKOXo9Bjf84Q/Mokpcgl/PifLYwzHZTYdo0oSlXKJnIxFPEORoFGri5ZWVTk9O6VWE5Xj5uYFZlOPKEqouA5B4PPuR++RaRCEERfXN9EVCxkRhlutlOn2uyiaiuPayLJOuVJDNQ3GU49mZ5WHj45Y+AlyoWG6BWmacdodUxQqkqwx9xckcYzlmEvldY6q6mRFvgQ6FkRpjKQoFEjohg2FDIgKzHIcDNMiTlPmy0NDbEvFckQctqKNVCWZl158iUatxuGTA/qjEa9+5jPcuP0MP/zpj7Bcm+PjMwzNIvKnOKZGpWSRxWLet4hzDs+GKKrBSW9GIdUYzBRytYlZX0cya0xCkKR8qUcs0aqVIckwVRNvssCPRAZiv9/nnTsfMhh6hJnM+uZVJmHO9WdfYveoy/s7ezz3269R6VwkN8sMBguCRGMaQG/mo5fqHHcHeLOYyWROd9ij0axTrzc4Ojqh1VrhV+/cYePCBaq1Or3+iPHEY//glJkX8tInP0WGhmXVcWtthmOP09NTKrUqk+mYvIAPHr1LEExor9QYjXpcvX6VRRTTaHcYTKesdlYYTfs4JZMsLzA1FynXyFOJslMhCCNW1jeJs4xFFBHGEYPekNXWBov5lPHwCElJmYcLoqKg0HSsQqC9e4M+8yhkZXOTUW+MlErIGQRpwHzusYh8wizEdE0kJWc0POPTn/3KxzvATh/ce2M+W+BPAyZDD8csMZ+GbG1eZu7NkAqJyWiC65QYdvsoyMzGU+aLOZVKFd2wQIJqpcJkPML3FrhumbPeCNM2qdaFTms6nQmrUKVCs9kCwNAtgnDBxYsXqVYqXLlyBd/zSeKM1ZU18rwQynRZxTQtjk8OaNTKDPqnJEmIP58RJym6qkNRoALT0RRVNalVhKJdVo3zFOlarYFrl6nXmkRhRKVcQ5FUNEP4JNttgbF+KieJoghZAUmGPE+YjAcUecF4MiZchORFgWbZOK7NyekprmNj2w7BYo6qyMRpjKzKlCsV4lTgrGezKfJ53qCKof1GXkKRIysyhaLhlgSIT9U0JEUY2If9HmEQChNyLkJp4zRjMp6x/+SQvJBFaneSsP/kkMtXr5AVMhW3SrSIME3RGuqWyXAyxnEdZEXB82dCbKsqol2TVd5+5z0OjrqEUUJndQ1/MSEMErKkYOb5S62X8KrKqkSWFsvFjTgwkkTw+zVdxMjJsoqiKOecM8PQlhuy+NwOpiji0HtK5pBladlOFkjAwcE+v/d7v48/92ivCILpzqOH6JqGqqhLTpuMbZmC77UQZJLpzMdyShQoUBRIkszK2oYg77aa2LaJBNiOjaSZ9EdTDo/OGHohx4MZh70Jo3mMa1m41Ro5CrVGm1qjTZRCtd5AtxziuOC1177AxoUtVlZW2XtyRKvV4cG9h4RxxsybkxYSFy9eEubzJGLuz/ja17/O8cnZebjujRs3ODw84NVPf0Yw96oVZjMPVRXwSMh5//07uOUy12/cFJQL00TWVUaTKYPhmCs3LyPJMpevXObihU2mkxHVeoU4ClhpNRh2p7Q7HbyFT06Bv5gjA5ZpUK9VKMg5Pj0U0NL5jHAhch5a9Q6BN6XkCKSTW6oIe1aScvv6LcIwxF2y4Q7395kOp+iKxsH+EaqlUxQZaVFQqTU4PDnD833ICz772lc/3gE2OvzgjWA+o1kvoyqQBAG1sst8NmPh+3jTGWQFp8cnbKyu4898/JnPPAr57t+/iW6Y2JbNoNejP+hBIeN5c9YuXGBtfQMZgZFJ04QizRkOhlimzaA3oHfW59qV62RJTp6lOJaLH4RIiogXkxUFbSlLUDWNX7/zU/JcpLkURcZkKrYgWQp5mhP7AWkGwSIEWaVUqUMOhmkTRjG26XJ8fMpgMGSlvUqj3uDJ/gGSUuDPBd1yOByQF5nwFe7vYZfEC5WTMxwKkudkPGM2n7N5cQtUCd/3MCydcqVMUWQkcYikFLglF8e10ZZM8Jk/wSmZRIlQz1dKJeI4xlBVLl28gOfPMAyNeZbz0gsvMhj0yXMhM5nOJlRdkZzdOzljY3WNqeehqfoS/62So3J2eoY3m9PutHHcMo7t0mqsUqQSfuQjawpZnhIuA4tn/pyT0zPm4Yx5MKdUbeD5C54cnJKkMPVnnPXOoEAMiEeesIssRcmOa7EIwiVx1yaMIwzdXB5SBoUkXn9F1tA1Y5l/oCw3tsmyXcvON5VP/abiaznEl0GRJMbjEVtbFxkO+6xtrJPnOcfHR7z++uf5yU9+xnO3X+Dx4ydsbK6zmAfC1aBoIlnHdJA1A9O0mc58Tk7P0A2N8Xi4TOgx8KYesyBhEcRcvnKd096IeZjx6de/zJXrt7l//x7d/phFlNIbTUkljUzWiDPICx3bqXJ03GMRpeQo3Lx1myTNcZwaTqnOytoGbqlKkibYjsX9B/dotZu4parI9Lx8mWq1yi9+8QuqVYFCL5dLOI7Lhx99wHgyIk0ToODWrZs82D3glVd+i+/8/fd4+bdeIS9Unnn+Ra7dvM3hyRNU3UReaiNb7QaBN0WVMvzJiKIwmM3nSJpKLkGRZzSaVeb+aOnIiZn5MxRFOCCyJKHilrAUi4cf3eXF559BKgp6gzGVUhXynIUv+H1pFCNlUC1X6Z70GI7GxGlGBuiGSr1eQ1Z0Dg5PUGQF27R49bO/9/EOsDu//Ks33IrBdNJjOhnQalf56MP3cF2TKMrpdnsUOTy4/4DhcMSdO+/z/PMvkBY5zzxzmzwrKJcqUOR88Ytf4PS0S5HLlGp1Tk/PxA0/m2LpJs1miyITG6lWq8Vbv3ybR48eU6lUqVWbBEHIIogwDIuzsx7VSp3j42PyrOD46ATLgiQMWMznDIcjPC8kjiFNCvq9Id2zM/JCQlJ0Xv3MazhuidHUY/vyFSRkGo0W1XKNWrWGoRuMxkNW11ZRFZVarU6eFUwmU8rlCqqqoaoamqEynkwZDYYEQcSTJ4ccHXdRZIN6o4NmmqytbVBIgtoRhiFFnpNlCbVGS/hIZZksz7EtAyiYzwWWWswYAnxvImYzroWqSki6jjeb4E8mmLqG788YdE9YazU5PToinM+YTMesb2wynk4YTT2QNSRVpcgyLl/aptfvEccplWqd8XhMu9kWrDRJIs1iZAkWQUi9VqXT6lDIKa5bJk0KPvpwh/k85vj4FNMyKZUcKm6Z6cxnMp4JvVqeI6tC3OuWqsLnSYFlPk1XkskARV4+j5oIMdV0FShI04Tp1BNpU3A+uH8qFH36mOdiCSEjYZg67XaLfr8nqBNra/S6fXRDR1UFGmk4GrO3u8sijNB1E900CcIUVJ0bt27jOGU2LlygXHGp1SpARr/Xx9QN5v6cLPKRk4T+6SFKkVOvVjjYf8JsMmIwnJCmEmGckuSQ5bB5aRtVs5j6EU6pSnt1HQlhy5pOPTx/Qb21gm5YTKYz0ixFklVkWSJNUm4/dxvHrTEej9A0jSiKuHLlCl/96ld5sreLPxdk1eeffw7LMvn0p1/FcSz29/d57oWXuXv/Pkgy48mUvf0DDMvlmeeeZ2Njg1ZrhQ8/vM9gMCSc++RxSJEmdGo1QEVWZGRNJY5TFv4MspjFdEKBjK7pLPw5URhy8/oNZqMZJbfM6f4xvdMTLqyvcXpyylpnXYBC60I7maeZuGhlBeKCs+4ARTdwq1XmiymuqaEokrDNTT0ub11k1Dvjc1/8px/vAPuHH/ztG4+fnKAbJcJURtVc/tf/7V/zYOeQMI0oVavcffCAZqdDEMfcfuEFFlHEk8ePqJarfOLlV3i08xDTMPjg/fdZba+z0lkFRcU0dWaTKZ986RPUqjUm47EAsC1JEOvrF5ZRYwpxHBPHMeVqDZBQFJUwDKi4ZbIsoVGv43tTJiOfQX/KdBIxDwvuP9zHNqvsPNznmRduc9QdsHppi/3DE1TTRtUN8kIii4VA1/NmLAKfeTBHUiQKCcFSSoTk4fr16ySJUOQLR4BFHOWkccHl7esoss3Vq89iO1Vst0qc5shoaIrFwg/IUwlV0VB1C8MsMxxOmY5nTCYemmHS7w4oCpko8pBkGI+HKDJIcsFoMmTmz9i6dIGdux8yn0xwLBOpyNjcWOfDO+8y7J1SbTTYebSD5ZgUpHiLBZevXaXebrHarOLNJtiWjqQWxGlIrVYm8GYUeQpFhqlpTMdj1tsrpGGClMmoskocZIz6M9ZXLzAdTHHdEu26YJ37i4i9x4d0+300zUQ3RNsmKQpICgWgKEK8nEuibRZtqbb8K4JcAJI0JYwD0e7ZT/lk6rk9CzgPjBFfhUBtK4IU4vs+n3vtdQ7293FLFb70xS/z/e//gNWVNT54/wP+6D/8IyxHVPP1ZgcUlavXbjBfRLQ6a5yenbGzu8vMnwmMtaKha6KdffLoIc8///zS5pTgumXKlQq+75FnMvV6nUazycWtS6ysrFFQsNJpU65UWSzmxMsc1Uq1Sl6klMoug3EPWYUffP/7Qme4DNi4vH2N/f1jPE/oIp9qJev1Ov1+n2pVjF8mkxHD4QDLMlEUmbt377O1tc2w38OxDCxDw7Utrl7eJgwDjg9PiFOV8dDnyuUbfOlLX+XsqM/GyiaWUUZXHDbXm4SLOScHRyhphprKlA0HAx3DcogjgYYKgxhvFlB1KmRRxttv/YJ/7w+/QskxlqOWgrk/ZzIeMw9CFBl6Z6d0T09Jk4TJdAqqQqfTYb1TYbVZw9IkLE3FtXRaZZeLnRaXb3/m4x1gH7z7d2/UGmV0SxVzHm/E737ly1y/dYXVtQ1RObgO165epbS0X1i2zc3rV/H9OW//8i1cxyHPYzRVZbEIGQ5HjMcjXMsWpm1F4fHjR8iyTMl2SPOEyWRKu9UmTTNc16FUcrAsk3qjfo6rqZZddFWlUa+SJjFxnPHd73yP8WiO7VQoVJODw2PKpRLNRpPmaoup73P9xi1OTk9ptdqsrKxQcct89MEdLmxuUq2IAWUcJQz6Qw4OjrBMi9nMY2/vCaenZzQaTcbjiQAjFjK2ZdPprHD1yg18f86FS1vLaKwazWabAokwCEmSjDAImQfCjvF3f/ddrly+yvb2Fa5du8F4MKLZaHN0eIwX+GiqjucvODs7I05yqrUG7dYadz98n1qlztbmFsPBCNkwGE3GBFGAv1hQb61x1uvhOjaNpcfTLVeIwpgkFJsdyzIZTUaUymIpkWcJaSTIDmXXpcgzfM9DKiSiOKWzcpHvfPt7RLEQuBbIjCcT0ixDQmY089l5tAuySiFBgURGQVbkFLm0JNuKraGsKOcGe21JOpGe0jpkaQnhU5Cl33DcgHPtX5qmpGl6Dq8UifEFFHDr1jNcurTF40ePidOEL3/py/w3b7zBC889z2Q04taN6/zwxz/Asi1G4zGf/vRnmc8XvPbaa4zHI077PY6OT7hy9SrlcpV2awXHKTGdTlkECy5ef4YEDbexytqlq+yfDpgGCWgGtVqVme+zurZGkuZ84xvf4NqVq5yenWGaOooM5VIJQ9eYB3NarRbT6ZRWp8Xjx4+5evkyq50OsgSGIrO7s0O1XAIpZzweEcUJnucRhDG1Wp13332Ps9MTms0Wk8mYbrcLyFQqNXq9PoamQQF5lqGpKienJ0hAGAZ02qvM5wsWQUilUsVySwSLCM+LKFfbzLwhtXINsozNzgpSlgk1vaLjlktIBSRpIoTYhsnpiTho19ZXmUxHjMZDFFUlzoRKQVJkZrMJ88WclZUOYRgI4KEm5rxnp8cgJ5AlZFksLi63RLNe4+TwCc/+9v93C/n/64X8X/6Hf1YAnJycsLa2wbVr19A04V+s2JXzlft8Poe84NatWzx69Ih6tUQQhCRhThLFpFmIqquUyjVct4zrupydnZHnwrRdLpfxPI9Gs0awDMut1RrLbZVoI2QZNMNkMpngOA5SkSMVwhs5HU84HQ4EjrpQ6fVHzKOYVrtGp9EgzxKCdIFtuRydHLPaWcE2bJ7s7fHC7eco1xuYpvjeii6yGcNIkCG88ZDhcMjBwQEAq6urrK+vi3W+IqEt2Uks7TGGbZ0PnR2nJMzqicARZUWOtxDIn4/u3qVRrzOfz2nVG5TLZR4/2mVzc5NFGpPEochhLDLyDJxyhTTJULQUspzxcEKj2Wb34AlhFqDKAvqXRzKdVpPppEeaply/+QymW8awSoTemMV8zt27d9m8cAEv9Ck5LkkQimxBRaHf79JutymX6oAChcrPfnGPtfUVsixj98kubqnCYDTD8+dcvLjF9378Q4aDKZZdQpZFdS1rYl5lakKZnucpiq6dAx8lgdAlSRIkSUFRtKUzQ7zWLN+XT5ltglgrDOAiBzRfLlMSlGXoy5/92X/FN7/5Tba3L1FvNfG9BV/4whf4/ptvcvuZW7Sbde7v3mc0mrC1tSUQNIYlZCP1OlEu3CVpkqPKGhc2NtjZuUcSi8SnXNVot1Y4Pu6iyBrDUZ8rV64wHg9ptVrkec54OCJbavuyrKDT6XB0ILhjpWqF0XDCjVs3mUxm+PM5mmlRsi0xG81SppMRMhnt1Tbz+RzfF4d2vdWm3VnFnweUqjV6vR5pGFCplPirv/orPvWpT3F4eEi10qRWq2PaOupy8TGfz5Ekia9+9av89V//NbYtROi1RpMkzpAVeO7ZZ3jv3XdQJZnZ5JhbNy5TBBOmkwGapqHoBigqcRCi6BpHJ8fUm02CIKBZrzMYDLDdElkS4uoqM98jziWa7RWm4yGWoZ+b+WVZJssK5ELBMgUqyc/nkEast5siwNgqEwcL6q7D57/+Zx/PCymH0zeee+4T3Lr5IqZR4sH9xzy4v8vW1g1UREWhqSrVSoU4CAkWC0xdBMgmSUrZreDNZqyudjBMgzCK8TyfYL6g5DpEsUgdWiwWZLmgluqmjqbpOI7LZDLm6OgI09SJE0EmEHaXBZqmUOQij86fe/yrv/jXVCs1vv/9H9Lt9Wl0WoIF75hc2b6EbTvkWUapVKbsulTLVTY6qyw8n4k/X6KbC6IopdXqMJvNhVNfkSgKWF1do1arY1k20+kMRVExDYWDgwN0XSMMFkxnEzE7UAoMU1vyzH0m4xFZnpKkCbIi0+2ecevmjaUtJidLEgb9HqZlMPdmnPYmIMlIKLi2y2y64Kw7QNUsHj95SJ5LqIqBNw9YxAmLNCGjQNF0Nle3iaNYyDws4ew/6w6YTnwsUyXLMlZWVpYhxAG97hmOZdMfDTntntDqtImSGNtxUBSdXn/I1MuZeT5BHGCYBqVqmRxwSlXu3X/IaDomiiMkSQVZQZKVpUVMQpWF4RtZQZFV0kzQQ4IwPK+k8rw4j8gTgcYCmJgsSbZPB/lPv8Sh9zSRPENVFCzLodPpQCHz4kvPs7a2ySuvvMJPf/pTvvbHf8yv3/4FRZaR5CnXr1/j4cMd/pN/8S/41v/9f+E4Nv1+j1yGRqPJ3A8F/icv6J6dsrq2ymDYRZEkuqenxGHI51//LCdHB0SBz8Zqh48e7XHnvfe5cvUaTqnEBx99RJoKf2fv9JgwCPj0pz/Ng4c7xEmKtpzLyYWKbdsM+0M2Vjs8enAf3YBe95Ao9DEMYSlTNLF9rjeaDEYjJFlBXyKjq9UKkiTxta/9CWEYEYYRnbV1HuzskGdC9JskKXt7j4UjIg3w5x6HR0c83H3EYDgmK+DrX/sT3vKkzYcAACAASURBVH7n13zytz7FeDSif7KHruQYlg5yQa5KnBweMBkPkRSJMJizdfmSqNzLLrolUuwNTcGwLRTdQFZkTEvH1FVMy0BSJNxKCdu2cSyH3tkZmqLS2FwniSMqlTJICjN/QbvV4sGDB3zic3/48VrIwaN33xh2u0wGfSxN4da1qxzv7/H+r3/Jb73ySShybNOm3+1h2wIYeHh4LBhOixDbNWitNJn5HoqiY1mWWHsrGYZlkKQxu48fIUky9XqDZqNFf9hjMp7gzT2yPKXZahGFCY5Tptc/xfNmmLrBeDxhvgiEwVdW+OM/+hp/9X/8n9SaLfqjEXGccvPmTS5evMiDR7uoiNvzwc4jchR+8dY77J/2uPnSJ3m0+4C333mXhzt7GIbJ7t4uw3GfOA3xJ3PiJKZer7O22iIOPDbWOgzOTjg6O0E3TJGqMh5hGAaGZvLRB3dpNdpkaUqR5efJ4VEUcWF1jSJJeXJ0jJTnVMplVE1mMplhmw6qZmDrClKW4ZgGaRyTRCGdZh3bUKnYLlJeYFoGhqbiujaGouKNPS5uXKLi2JRKJTY2L7G1fY3p1KfVbLHwffE9LJujg0Ns08LRbOJFTLiIIYUXnn2e6XBEu9Umz+DgqMfP3/oAzXKwHAdF1XArFTx/weMn+4zHY+I4ptvzAJVCEkN4SZJQJRG1p+iasNHJUCC8r3EcI0sSkiS8p0ksuPNpEpImS/JDlArfZRxQ5ClBMCcOA/IsEdmJhUS4CEX2YJaiaQrtdpNmq8a1mzc5PDzgJ//wY0pll9F4RH885vUvfpmbt27S7fWJk4RHj3ZI0pR6o0Gz1RJZpL7PaNSn026CVFAqVRmOplSqLTYvXeaDj+7x3AvPsXX5MigF3mLO/tEhhlygUDAe9IjjgO2tC8tQmQWtZhMvmCMrCnmRoygSe493qJQd0kIiS1NWOh28uYftWCz8OWQStlliMs/YvLCF65Q563YZjacomk6SFZRtE9txqDYabF+5yrf+3d+gqgqua5PHKeutFo8f3UPKc4wlXTbLU7I8YzyaoKkam+vr3Lpxnbd+8XN+/et32Nq6xLvvvE9W6BSKTZgqaLJKSVdpmULm4poWrUqTxXhB6keYskIRhSRzHzUvUFDQJAUlBSnKiWYBcp6jZAqxH2JKOv7Iw1A1HMuiVq1QBCkmCuF0gaGoVB0XQzPZvnKNlUu3P14L+Td//l8XhiFIiY1Gg9Fwcm7E9r3J+e1pGIbA5WQiSs0puZQrIrn55ORYJByrqsAlA+12m9lMDEkty0LXBHiwUqkwHo85OztDNcTNVCqVSJaJKVFSUK/XmU6nrK2tMR4MRcJJrcYi8smyglK5Ss6ydF74JKGwn4T+XCTX5BlZDv3hiNFkiu8tOD054Pbt50XI6WxGURTcu/8hvV4PQzf5+te/zo1rl3FLNmkscC5xGOIvREBoqVQiDMOl0Rv6/T62bQstmCoAh0+TX6KFSPVZxBEApimEnq7jUK81ReKxIvPRRx+Rpimbm5tLH2G2TC9XmM8F6z9Y/ixplp3z+DVVPVev7+zscPHiRbIsYz6fU7ItHMc5TxYqimKZt6gSpWI2J8sq9+7v4C1iUA16/QHePOXk5ATdMNja2uLo5JSsEJz5R492GYw9Cgls2xWYYkVC11WMpc4LQFumGymKgnpOIxHVVRRFS5yP/I/M3YIZVpCRLQNuhU5M6MuSMFn+7PI5N25jYwPTMvjDf/If0Gw2+da3vsXa2hq7u7tkWcalS5f41Kc+xV/+5V/yp3/6p/zbf/tvqFarGIYhfj9dw7IsDMOg2WzheR7NZhtFUbh/7yGf+eyrAim+s0Oaprz55t9z48YNqtUqJdsBYOfxLhcvbtHpdHj06JFAThUyGxsb9IdDao06p6envPTCi+w8eMDjg32y5Rzy5ZdfFhvBLOb4+BjynBxFIKDTlE6nw+lZl1c/9xpxmnG4f3RezV65coUf/OAHzH2f1dVVLNtld3eHVrMKuUQUZTQaDQajLkkScfnyZY6OhIvg8uXLvP/++6yurvLBBx9w/cZzbG9v8+abf0+7XUErcqQ8oFFxcbRsmQIvk8UJtiP8sZZhM572l/NNiWazed4xxVG61KxVz7MLTNM858TleU6cgWOZRFGApqp4vi/GS7U6v/37/+nHayHv/fK7b1imiaooRGGEREGRFXTabeIkWtpcrGV8fE6lWqZSLVOtVpAloeq2LZsoWgiNmOcJ3LIuAltFMlCCvQyI8DyPyWjM5sYmvj/HNPUlnVXGtiwGo8m5Hy6N4nOSRRiGzMM5bkkEemRFjiorVMsu5bJL2XEYDXuEcUClVObDjz7klVdeIQoCfvrTnxIu5pRLLoau0e91+YOvfoVapcKXvvBFvvoHf4CiyjTrQuU+noxFUtLMp1QqMR6PiZZzu8FggKKotFotMYhc8vSfijHjOKbkioNdWkbMP22PBoMBEjLdbpc4EmnSjuMsMcnp0u+pMx6PRMudZVimSRgEpHFM2S0tDejCcN7v9phNp3TPzphNp/TOuiJhRlGRJJnRaMhoNAIgjBbImkpRyMz9mImXMRz55JLCg51ddMMhjGLW1zd47/0P0U1HRNFHMf3+iHkYADKWLWQSWZ6em7GfBpb8ZugunW8Tn2q5TNM6p9YKjyOo6tOWUQzoy2VhTk6ShDgWYAFV1wijmCTL+Jf/2b/k6OiA1dVVut0Rx8cnTGcTbt++zc2bNxmNRjz//PMM+iPCKODLX/4yZ2ddgiCk3+/RaDRIEmFaL5VK9Hp95vM58/ni3Ec7m8149OgRi4WP44gN6Wc+8xlOTk5wXJf+YESlUsXzPFzXpXvWx7YcyqUSR0dH5IXAYNerNT744AMMXefylYtsb18iCBbUKxXGkxGyorK2vsHx8TGO47K7u0u57HJ21kXVNKI44Rv/+79iZWUVVVUZDnrcu3eXw8MDPvGJT4rL1HG4/fwzmKbOyckpSRxTIOG6Np43O3/fPZ2PPZ3zrq6uMpvOhBi93mDv8T71dgdFM1nEBY6po5kGpXIJWQXPn4q0I29OnITCdZEmlEol+v0eUSQwTY5VXoIXhPRFILQiDEPgnCzHJctzNFXBX3gi/coUGRGb117+eC3k+z//9huz2Uyk9y5JAIYhVrq2ZVKv1bAtC4qCPMuZ+z7ezOP4+BTbtvC8GXESYhgG9UaNzY0LQmUcx3ieSOyRJOlcdR3HMc1aiygO6aysoGoKUBDHAZPRiFqtSr1WwXUsDEMjyxKQcgxDI8kSwjBCVRXSJKFaqeJPxnjjKVEYUq64VCsVbMfl+rXrlEpl2q0Wv/M7X+L27dtcu3aN9957l5V2G8s0qdfq/MWf/wWKIuPagngxmY5J04TDg+NzqOFTQqjneXieBxSMRkOgYNgbcHx4RJakaIrA/siaUPebpkmaprRaLbIkp1qpLYGRIusyyzJs22Y+n58DFX3fZ+rPKJYxa0+rMkVRmE2m5FnOnfc/xHVLlMsVLMsWOY+KiuuWMAyTyWRKt3tGuVyhVquTJDGWaaPpFt3+iNEk4PhsijePQFZ47/330QwLy7bZ3X1CECUMBxM0w+T46IxClgUJYeZhOjZuuUQSJSiaisRTEoiouIDzWZYkSeTLwJU0y4SuqyiEpGJZGYqkdglVU4mThDRL0TQTRRWEkjASVeOtZ26h6qoAZioSG5tbvPjii7zwwov87Gc/5fT0dAmj7FOrNuh0Vnj33V/jui6PHz/mc597jbOzU1zbwjJNRsMhly5cIoljbt28wenJMeWSy70H98jzTFCHl79HFEXcu3dPyHzKJVFBL3Vtvu8RhTG3n32W6XSKrCgslqjzLMtY6XQ4PTmkUi7Taja5e+8urutyeHjIzs4OrXaHzto69VqdyXSGpKqYtsPX/+Q/pt5oMRqN6HQ6WIbBxsYqcRxhOyVWVlbQbYMsTXn44D6T8YiXXnyZh48eUi6XkJe8/FKpxGQyWfL0RRKWYRgs5h6rnRUUWWZrS2y7m80O3f6AuTcjKwoMU2f/cJ88Szk5OmaxCHmy/5hms4mmacv5brFkzqnYpksYhiwC/5wFmKbiecyyhKwosHSdJA4pua7wxxYSqq6yuv0xcyF37/z4DcMwz5EoQgktXjRvPgMJMUvY3WU8mWCYJo7rUBQwGU8xTAOQkBWBmB4OR8znc5I4oeS6xFEEBYRBhGM7rHQ6zBcCfDfzBYrY9z1azQYzbyZ480VBvVbj4PBAYHI18YYPgojKMlnFLZVYzP1lFShaqzAS4LzBcMSbb36Pbr/L9uUt/vv/7r9lb2+fH/3ohziOw/HxAS++9AJFmvK5z36GzQsbNBuN5dIgRVV0XNfl9LTL48d7FAV4ns+VK1dZXV2h0+kwnU5pNBrkaUq9XlsGdzSAAkVVKIoc3dQZ9AfLdlDl7OwMQ9PI0lS0robBYhngq+s6IIbXuqFjWxbtdnu5NewjSRKO6zJfzNna2sayLCRJBK+UllXg0/JdVcVhpuuawBAVEIYRfpgwnS146+33ODjp8+DRYw6PT2i2WnT7Q4ajMaqu488D5osAVTdZBAHT6ZSNzU0GgwGGaYtbdjlkN3QNRRHyiWI5CJOeVlkIE/c/rtBUVT2vqlVFOX+TZ1kmNmGytmxzZZGSZLmUyhU03aRRr1MulRgN+0y8Be++9x63nrmF7dh86Utf4qx7whe++EV+9atfoWnCurS7u0ulUuLOnffY3t7m8e7u+XPueTOm0wmVcoXFYsFkMgFZVIRChyjGB0mScO3aNbGlXApru90zqtUKt289y/6TPU6Pj3FLLv1el929XVFdZhmtVpODgycMBgMmk8m5Q8AwTK5cvkIURgxGE9xyeclDUxmNp/z5X/wbrt28xVd/93f5wfe/RxDOufPrd3Fsm6tXr/K3/+5veekTn6DIUkbDHr7nk6UF1VqVGzeuc3R0iOu6xHG8tB/B1atXef/992m32ziGTqvZZOZNKIqCRqPOw4cPxOdfghyJj+7d5Re/+CX7jx+jaxqQUatV6Pf7yy1jRq1WZzabEYYhmqphWmJjr2kaSRIRx/EykDgljiPIc6bjIYosch+iULD51q9+zAosGOy9oWk6jUYTXTeoN5oUFOiGQZqleL6PrChcvXZN8NU1nbwAJFkcIkEg/HsgECmyjGmZUMCDBw+4fv26kAogYsVms5mgE8hi2Ht0eky9VsMwDcrlMrIkgjYHgyGVapVSSdwY3W6PcqmEBBimyWIuaKEUQoVfrdaIlo56wxABpA8fPSSOY/7gD77Cxe0rtFfavPjCc1y/eY233/ol165cYzqdkqcJo+GAYX+A69iUnBIyCoqkstJpCwN1lvNkb4+jwwMW8znlUok4isjTDMd22D84wPdFy2lYJkEQ4M08Wq029VoTSZJYWVllMh6T5ynb25eZLrHXmqZRq9WYLkGQ2hIaGUWR8Bxa5rmaX9U1KuXK+XZPbOkEkPFpxfD00XVL4jWRZEqlMl6Q0B9MGE08zgYjsgKCOCZOsvOLK0lzxuMptlvGcmzyIkNRxfdYLBZLwgC4roNUCNaIqmq/OZRUUSX9Zu76G4X901mZoFMUorqEZaKT+PciDAmjmDCMKbkCh12q1MgKETZ88cI6G6trVOvtJf4o4sUXX+Cb3/wmvu/R7XZ55ZOv8POf/5x//s//GX/zN9/ii1/8InfvfsTZ2SnVSoXnnnuO3d1dwlBQM7775nep1arYjs2rn/oU3bMzojBg4QtgYJakjAZDUCTGI5FzevHiBUzTZHdnB0mCIFjQ73VRVIXOSoetrUvs7uzQ657hLRbcu3+feqNBlqbnhYLrOJRcl5nn886vfsX2xQvsPNghTXNef/3zTIYTZtMJb775XUq2zSc/+TK6rvFod4+rV6/RajYZj4bkacLZWZdbt57F9zx+9c5b1Ot1nn32WSzLYm9vj1qtxu7uroitA2y7TBBGpGnCeDJmf/8Jvi+6C7daZTIRtONrV67xhddf59lb17h54+pyDrnJwcEB7XZHpJBPJoI4K4E/9ynIUTVhA6w3GqiaJhiCSKiyoJC4jkO0CLEtE1XTWLvy0scUsv7yh2/opiHIl1mGP/cJopBFOMc0DGRJJUtzily88Vy3TKVSxXbLzBcB9WZDZCkaYn3aaLSIkxTLMNnc3Dz/oM3nIpA0jmMKOSeOYw6OD+l0OuzsPiIMQrrdHkmQc9bts7q6jlTI+P6C3Ud71JdQuDzNcRwXt1w6x7cYpkEYCBLqxYuXKFeFWvrylW0syxDDZsulWqui6Qpz3+fG9esi0ivNWO10RI/vTzF0nd5Zj7d/9Ss0TScMA8rlMnfu3GF7e5ssSxmNRst8gJST41N2dnZQZEWkowHTJR/e930URaXX7XN0dMx3/vbbvPzyS6iqwnQ6o9/vn1cmx8fHVKtV8jynVHKRZXHgO+WSyAMIQzqdDu12W0hBYoFmRpLwfJ+8EOjsOApIkpiiEEN1CiGybdQbvPXuRxweHjP1faKsIMlzwigligUOPIxi8hySrFj+nwvSNEbRFJq1FrphYNsCUy1RoKoKeSroDbqu/6PBfXb+CIL5lefClB3HyXmUnCKLikxZztIASqUq1Up9+VxAmhfIqhi8hwuB9fngzns0O2tMpzOuXbvKm29+l9dff51/+IefiKojEjMvTdNYW1vl29/+9nkwzaAv5l57e3tLvLLPq6++eh5+++DBvWW8XZ1+f8BoNKLdbnP//n0MW+fw6ICTk2MURUaWhFxm78kepycn9Hpd2ittBsMBGxsbPN59xPVr1/CDBFkWB7s/X+B7PjKi/fz1278iicWmdTocoasKp0eH7O3uUnIdfvTjn/D51z7HfD7j/Tt3WF1Z4fL2FRZByGw0Zv/JLof7+1iGxf7eAZsXLpDloho/PDzk6tWr/OxnPztf6Fy6dEksU3SX014PTdX55dtvoagKe0/2aLbqVGpNJtMZr7zyKX7yox9Tr5ZZX61DkTIeTxkOR2iaznQqcimLoiCKInTLIMszkiwhLyDNxDItW1bj8+mYIhOSoiiMmPseFCKCbfPmKx/vADvbefcNU9fpdU9JkxBFllAkhSROiNOCcrWKquvEUczTwNXJZEIUedimxnzmYxkmaZwSRwm9wYg0K5h7C5I0pygkJEl8uFVdZnVt5Xwbtbq2Tp4XNJstTMPAdUuMxgM2LqyzCAIWwYIgWGC5NkEUoSqgaSpBsBDbmyxnb+8xcRyhGzqWrROEc7pnx+zt7eB7U2QJTMMgDxbUyi7NSp0kjJAlifXVdTF/8sdUahUM08BxSvhhRLVeY3v7CrZrsggWNNtNPrhzh9P/l7M3C7LzvM87f9++nX3pfUdjIwmAFAGKEkWJ4mIrVkybcmwnipMax/ZMlSYVx9LMVGrsmmFuUuOUy3GsmvGF43EmtuPYphgntiSboiSLpEmBIAES+9aN3vt09+mzL9/+zcX79ZF9kVyQN6gigAZw+pz3+7/P/3l+z+4uy0vHMXWLjbVNHmzvoJkWfhQzcH2iOKLXbtNuNdAUi06jTdbJUCmVyGQcDNOk2+0REVAdGxM5PkUWHHNkojDCNEz6gwGNRhNv6GHohkgFIIlvfLeLBCiq2OJWxgQ0sd3vki9kWJg/RrvTwTQ0PK+Pouns1PbZOmjTGQzpdLvEJARRgB94ZJwMzXaPMIpxPSG65vM54ijG93xUWcWwLA4PD5mfmaF1KA4HQ9fRDB1JkZFlbSTOC96X8NaJ7oBoNHUlEoRRQBD6RHGIFwR4XkiSqNi2OLQ0TSdKCz00XUPTFCQp5sSJJZ555mmcjMP773/IZz7zNJubm/z4j7/E6uoKYRAxNTWNLMnU64eMj4tOz+vXr/PpT3+aP/mTVzh98hTDwRBNU2m1WmSzWWq1HYLAx3VFI30Q+Kys3GdhYYF2u8VubZfHzz+OrmpYlsPm9jb9/oBWu80LL7zA7Vu3+Oyzz7K6ukaxUOT61RsEvtA6ozjGsHXqh/vohsbuzg5z83Nc+fAKtf0DitUqK/dX2T+o86nPfpY/+8Y3eeTcOd774Ar79X0Wpmdpt5pYpo1p27z2rde5dOk91h+sU6wU6Q/7RElEvpDD9QcMvD69Xp+dnR1OnTrFq6++Sj6fp1gsMjk5Oar526/t4g0H2JZJtVLFUA0eeegRkgj+6nuv4/su9YNDPvHk01y9eotBpw1yKDTWZh3dEEs/XTVRZB3X88hninhDH9uy8AZDfHdIFLrIUgxxSOj7uJ6LnbGQVZm+72FnHJAl5k5//KMdYIP9+y8Ph0NhlJNF03UUh5SKZUzDJApEFMhxHLHO9wNMw8T1RVRAU/WRGTGMIkrFErm8qLBXFJlKpZpqMg6qqrC/v0cxWyKfK2Aapvgzwgjf9dFklbn5OYbpmlbTNKrVKoqqUiqVyWfFNelIHHc9n1wuJ0pkMxn6A1H7dgTJy+fzo3yd54ro0I0bNyiWS0RRNCrpmJubpd1uixr67V1cz6dcFhifOPBJ4hhVUTh58iQnT57EdjIsLi1i2TZPPfUpzjzyCA8/8jBLS0ssLMwzMzVFxrExHYfZ+VnxIY5DxifGabSbhFHI1Pi4yGD6ogXHdV2GrouqabRbbVxPWDDCtFtSTvWiIAhSMJy4todhyPbOthBsnQySnLC2ui4E8ygUsR3NYP+gydWbd3E9D9f1kRWNXm+IH4R0+n1ATkPYjH48IkOEYYiiyrQaDZaXj7Gzu4dl2fQGA0zLRkIe9SMcoXB+sI1MSJI4DcergswbiXZtsXU1yTg5Mpkcum6kGy6xkfXDANu2hUXGHTIzPUWv12F1ZYWPX3iSu3fu8JUvf5lf+qV/xi/983/O22+/TbfbpVar4XkeS0uLvPvuu6KrM0kYK1c5OBA8/7m5WbrdLu12G9u2KBaF831hYYG7d+/SbotqveXlZfb39/n+OxchgcmpKXRNJ4wiNjY28D2fdrPFG2++wZkzZ/n2t7/Liy++yPe///30va/QG/YB0v4InT//8z+nVCoxMTHJvXv3OPPIIxw7dozf/f/+PU899RQXL16kUChw/MQJtjY3uPLBFdbW1ymXi7zwuc8RxQmTU9PUaruUykVc12Nvr5ZOtsKDNxgMGAwGVKtVPM+jWCyysLDAgwcP0hRMiSAIOHXqFNeuXSOfy3H58mW+8pWv8OZfv8WP/J3P8Vff+y6KJNNuNnEcnXKljJyESAoE/pBBf4BhmCiaShB4NNotbMsUdqskotVsMRj00RWVfq+TotGh1W5zeHiIZRoMB0PmZqepLHxEEf/yG3/2cqNxSBQHozfP3t4ekoRYC8cRrjuk02oR+D6SJJ6qB406hmkSBCHDgXhylUslgjCi3WqREJLNZtja2kpxME1yuRy9Xo+tjS0A+oM+/b7wbvmeh2kI42s2J97Mwjkfk8sLOkQcpj2ErRZ2xkGWlNTPU0nr3DRURUt9ayaaqqNrBkkClp3BDwIyWYdms4njOCgydDttdmu7bGxssFc7YHZ2lqnpGXH9khJUScKyLYbD4Q8Y/Y5Dp9sll8+zv7tHnCREYYiERL1+MBK4gyRiOOgz7PdTjIzK2PgYc3OzOIaFH4jXulAokMlkyOTyQlMkoVKtoOuCtKqkbDHD0JFlCSlJ+zrjmP2DAyzTJJvLMTExgaqJshPHtFEUGUiYnJnj6rXb9N2A/YNDDMMEWYjFSSKBJKBnRyhvJAijkCiOkWRJpClUGUMXD4XBYIjl2CiqTgQkqdfn6L+jg09oX0mqb4nQvqIqOHZWIHd0HdNyUFJdNQG6vR6ZbI5Ot0s2K0Roy7Lo9br8/M/9E+7cvsnW5ibD4RBJgt/4jX/DL//yL/PlL3+Z7a1dfuTvfJ6V1RXOnjvHxXe/z7WrV3npx36M1ZUVFhYW2a7tggw3b93i2eeeQzcM3r30Hptb28zMznH95nVc36NUqnBQr3Pz1i2Wj59gbmGe+3fvsbe/L3oONI3xsXHeffddHjv3KAPXZeAOufDEx/mvf/ZnnL9wgdn5Ob7+za/j+T6WZXP79m0WFxZ54okn+PznP893vvNd1tfXIU64fPkyx5aXuXnzJuVymfn5ed566y2yToYf/uEfZnJmEtOyuH37DseWj1MslYgi8fCzbWGbyOfz7OzWME2BM3rxxRd5++23efHFF7l58yarq6vMzs6ys7NDGEaMjY1RKBRYW1vjsUcfZTAYMBwOmZiY5Nvf/haqqnDt+lX+4c/8QzY2tvCDGIUh7nBA4LtoqkKj0RTSiSKTyxcI40DoprJMkkTYhkmn08KyLGRJYegOIImIoxBJBidjIcsy0yf+2xPYf78X8tV/k4AwWx6t849aedqtrugzNAxIK9MajQa6rhMqMTNT0yRhwrA/GFkNZFmlVCqxu79JkkCn3WNmZoZm6xBFUeh2OwS+KO3I5HPptiLAUDWxbg19ZFXBHQqktCRJDDyXcrmK22uTz+dxfeFPUxWdlbUHjI+PMxwOyWeyoxCxqqocHh5SKpWwLAsv/IEuk6Sr3V63TbfbZWx8El3X2dnZodPpMDE9QzabFf+e9PojSYJcoes6ra7IhhaLRRQkfD/EP/JxGSq2odM8bNDsdZDihPnZOba3t9FSIkOUWgoURRQqtDqi2FXRDDKZDEng4/oenU6HXq9HkiQUK+VRiYqcCH+Z53lYjoPrih6DbrcLSiQgjbJGu93Ej3wk1eHNt97FzBZpd4Xv6bDZ57DdxQsDFFX/W+1RQRCMOj6PDKi2ZZDPFdOilx66YRFJErKqYOvq6NA62iweHVyyEuN7Yfp1I1TtB+XHhm2IItsoETID0O/3abUaZLNZVE0hl8ulxS46uYyN7/UZq1Solio0Gg0+/elPY9sZfu/3fo8vfvFneOONN5hamOFrX/sap04eZ25mhgcrK5RKJfrdPoqhMT4+zka6dKlUKpTLZTqdDpcuXeITn/gEYRiy5tuFYgAAIABJREFUsLAgUE6xMAsfP35cLGa6XcrlMmtra+TzeTKZDAC93oDp6Wm2t7eZnJwkDEPee+89vvCFL3B/9d7IjH339h2GQ9EdqusmU1NTXL58mfPnz3PlyhUeeughJElibW2Nc+fOMTU1xWuvvcbf/bs/Irx1ssTagy06HUEtuXv37sg4eubMGSYmp7l37x7j4+PcuXOHL3zhC3zjG99gfHycXC5Ho9Hg+eef591332Vvb4/FxUWazSahH3BwcEAulyOTy3PzxhUOG/tMTo7TbYlz4eHTpzg+YzI9VWV97R5S7EOiMhh6LBxbwg0CTF08yHzPIw4CNEn0g8qkmqgk0OK+74IsUSgUsCyHZ37qlz+akfXele++7Hm++LB0ByiyMCnquk6xUkFSZIr5AmEUUT+s0+31UBWVcrVMu90mCiJURR2RNjXDQE3Z146TGY2qwlwpFgGaqZMr5vHjgN6ghxd4WLbFzu4OY5UKlm2xv38waseWZBnX9ZBl8ZQ+8qu1Ol0R0PU88efLCt1ul52dHVRVxXGE6VXXdfbrddbX15menqQ/HJDEMc12E03XiCWJMIqwHYfZ+TkylsW1D6/RajZpNRscNhu02i26XYHujZJkxICPghA/Cuj0ejRbLXTDpNvr0XcHKEgYms5hvY6uqPQ6XZIoJolinGyGRJLY2d0hl8vheR79wRDbtnEHfdbW10XpbFmEwIWAbgscUeo/UhSZJBa9mHEUosgSmqFRrYwhS6ApMqphsl9v0+n7qJqC57okSNTqTfr9AVEiMXCHAjioG6nBVAMkQZaQRRYxCAJkSWIwcEmQRbELkM1kkNJfmyRxul4PRlpLEiep9UBG183Upa+lE64ozpUleXSllGXBVStXSqmpt8nY2BiapmKZBr1uG8e2WXnwgPGJCT7z2Wf40//6p5SrVS5/cJlKtcL21ja2ZfOpp59if2+PJI65evUqFy48QbPdYnV1lbGxMfb29tna2qJer7OwsEgQhOzuivfOrVu30TSNjY0Nzp07x/r6+ogeu7m5yfLiEtubW2iGsAxcv3aLzc0tPvWpp0Y2g3a7ze1bd1lZWaFcqmDbDvfvr6DrBqZp8clPPsnFi9/nmWc+w95eDdM00u2mwd7ODuVikfm5eQxdJYhCbt26hSxJfPDhh/zIj3yee/duc3CwL9rjW13CMGJza1PIHHaG5557nj/6oz/mJ37i76FpOqZpsbm5xbPPPsfFi9/nRFoJ12g0MHSdCxcukMvleO/SJVyvD1HAfm2PbmfAucceB1lIOe9/eJ1hv4fvDlMLk4KTyYhaRFVF13UxgaeI8MP6IYPhkGqlKrh8lTL5QglZEjQL1ws59bHnPtoE9r0//tdJt9tDVXTiGGzbRlVlDg8PyZXzNOqHI8/RkaFOPCVbIl4ki79svV7HtC2SREI1dPrdBp1OF001sCyhn+npkzoIPYJI0Bs0Q6zlG40Gpq7jWDZ2xqFx2BrFTrqDPoZhUSpkWFtbo5Im5A3LGb1gURThD33GxsZG19L19XUsy0onMjFdxOnKXtd12u02s7OzI5F5f3+f/f19pDhhcXFxBNs7Sgb0+wJRXCyWeeONNzhz5gz3Vu9RLJY5f+HjGLaF7/tEvkf9cB9iCX/okstk6bSERcJxHBIJ2t1W+qHWR9OL5wW4rkulVKTVERGNI7R1rljANEWkwzbEj+12G1mWRRV9FIkrt5JQyhbTqTjEjSJurWxTKE/Rah/QbHSIUXnjncskyPhxgqKp6Jo50q9AbDCPyAKmaQq7SRRhW2JDWiqV6PZ7mI6NqekctbtLQByHACRJlLLtGcEJ4yQhm5p4B54vfGsjSoX4fvuBSCnEcUwuJxqmx8YqEIeMVYtcuniRz33u87z11lscP36ML37xi3z5y1+mUqlQLBbpdgVTKwg8oiik2xXgxH5HEGnPnDnDjRs3WFo6xs7ODrOzs/z1X7/DsWPHmJoaZ2trizCMeOmll7h27Rrdblf8G5KEsWqVm9euY5qmsLkEPkEYUsyXUo0vZmdnizNnzlGv16mUxwjjiEuXLvGP//E/Jo4Cvv1tUad39eoHPPvss3xw9UN6HaG5LS4u8vbbb3PhY49jGIYwu6Y61vjEGO++9z4zM3Mix7q5wic/+RTf+96bZJwCL7zwAt1+j9dff50nn3yS3d1dfu3Xfo1f+7Vfo9Vq8cILL+D7PteuXWNmZoowDEWs6ORJTp04yauvvkomk2Fyqsq9OzfpdFo88+nP4g4j3rn4fSamJxj2eyzNTxAM6qihKJyplKu4vkch1buH/QGLi4uEvj/ivA16fdz+QHxeLX2UElBVnb39Os/+1C99tAnse1//g5cty0JThfjW6fSQZZnx8TF6wz6KqmCYJqVymcP6IY1mk0azyVi1Sr/bo98f0Ol0ACiVSySyjOf7yIj8ZLPRodlsIkkS09Mz1OuHRL7LoNvjsH6ANxjQabVwe3363S737t8nm8vhDj2WlpaEfpLEZLNiMWBZFplMBi8IMAyTZrMprj6qQpwibY6MdbouwuW6rqNqomotn80Lm8hgwNj4JLKs4A5dXNcb2SYq5TKHh4cif+fYrK49QFYVfM8TBw4yC/MLzM3OEsQhqw/WmF1YYHNrCyeTIYojNN3AzubQTJNmu8345BSWbdMfDvGjGFVXkBUVRVUJfF9kxjRD0CWSUHRl+h7ZbAZd19AMjV6vS6fTpt1s0+uJhcXfLA/O5/PYGZthf0gYBJimQSJJXLt5l8P2gMGgI7KafkSj1UU1TZBkEmJ0zRyJ8EcmxSNrRBAEBHGSLlxCIQk0m2SyGQLPRdP0EbtelkDMyXG6eZT/Fl5HGV0xJSRFvLklWUbTdaI4Yntnm9m5acIoQNd0ut0uSZIwPz/H7Vs30VSZcqnErdt3+Z//6T/lL/7yNVYfrDA+McH/+r/9L1y/cZ0kJrW+fMDzzz9HrVYTNopCgSAKWVldRdN1VE1j/+CA3V1xlfI8j1KlyNVr18nlc6xvrNPr9+j1BqNOy3arxfHlZdrtNgsLC3T7Pc6eO0dtr46ma9T2apx/4jw3b95mbX2d4G9wzd577z3u3b3DI488QhRFbG5uiAesBItLi6yvrZEkCU9eeIIbN27QbreZn5tjfn6e+sE+V69d4/zHHocEVu6vMDU9wfXrN3j00cdYPnaCmzdvcu36NX7/93+fP/iDP2BuTkgXq6ur/PRP/zSXL1/GTxMyqqqMpsSlpSX+4pvf5LHHHmNnZwfT1CiXiuiqzre+9R2+/85FPvPM03Q7Tfb2u8iSRLWaZ26yypmHHsL3PAqFAraTYaw6xvT0NEEgAAztZpswiMjm8kiygmEJK04M9PsecSKTJBJzp85/tAnsj//v/z05igPIskyhUCAMQyFyS2IC8X1/JHxHkRD/jkyb29vbI9EQGH2owjDEchwGgYdhGNTrdRbn5vGGLsOOm3qGEuqNQyzbSPNaiNW5ptHr9knChPn5eSzboF4/QLdMnFTzCb2AZrPJ/JxgPmmKipcaKCvFUppZlAiTmCSJmKhOIEkSrVYLzTTEVUwXCF9Dkei0+4yNTaAqRkpUiNnZ3UTBIJvPoWiG6L8ECnmHvd1tuu0OiqyRLxZY39oml89jZRxiImRVwZAUEVSPElZWHgjnfhChayahLDBDY5UqvXYHN10SZCybiADX9TE1nV5vICZMAgzLIExCQi9hfGySDz8QLdKSJDHsDZmamqLR6dBq1ol8j5mZGbpDnw9urZErjhPFHp12j5u37rJba2BYGcI4wg18LDtDkkjomslgMCAMo/QgEjlKVTNQFIVWq8Xs7LR47efnBQPLdvDDQCxjfF8sAiD1HkkjumoY+qPD8ei9dPTrXNdlYmKCnd0tUapr2wz7sUAwuQMGgx6Li4ssLC3y4Ycf0u22GR8f5/DwkE996lPcvn2bqakpTp8+ze/8zu+Qy+VYWlpibW0Nz/N46aWXeOWVV+gPupw/fx6AS5cu8fDDD2NZFpubm7RaLdyhz8/8zM/w6quvUizlR6HkQYqRKpUE5rxSqbC/v08uJ9IP5UqFldV7TE1NsbOzQ7VaRZZU1tbWMEzx4J2anGFzY4NyucxgMODRR8+mE16fXC7Dxx49x+7uLru7u5SLZdFolbVoNpv0egMmJibY398X2dkgpt/vjxrRH79wnvfff59/9a/+L371X/9rFuZn+fjHP87NmzeJ45jjx4/TbDY5PDxMI3VXgZjp6Wn6/S5JHNPtikP5YHeX3kDABDqdDmPVcdY2xNXa83vIiYSj6fyjn/ocvc4WlqYQhdB2xXtVShLRyzA2JpZzpomcQG8gPvee5wmtVxKuB0VR+OxP/rOPNoH9xav//uWZmZlR3GAwGKBpggZQyOU5PGxAIpHPFZBlUS7b7w3odfuEYcSgL+I7nuuL0K5hkc3ksGyTMIqYmpyk1WlDFLO9LarEWo0muVyWUqUsqA9egB8FTExOoSgyxWKRQr4ACTiOw+rqitjAqcoIlmboRursltIDr8sbb71Ft9sln83hui71+gHrGxvMzy8I7E26GfODAIBsJks2k8HUzVTT8mi12+mvC4mikGwmTxRHtLttpEQiTmL2ajtkMw66ptPrD9jZ2eGRs2fFAe7YmJaBIitEfsygP+SgXiefF5tGGYlur0Ov3z3yvWJbFnEciw91HNPr9djY2MAdivU3gGGZbG1voWoaw4HL/v4B8wsLgtoQCPSx64lGoFwuw/SUCAEblkNn4OMHMcVSiXv3Vxl6AW4Q4YchcQy6YaKoGrlcjn56YOq6cM0fbaclSTytQXxwyuXyaIN6dAX20yvD30wEyCk3TESaBKlE2CLc0YHX7Xbp9/vs7++TL+RG3yfX9UWTuAwLCwvie3p4yOzsbNp0fo1yuUySJDz77LN87Wtf4+BA0BKOHz+OLMs0Gg2azSbXr1/n/PnztFsdGo0mcST4WVtb23Q7PZEVVXUqlQoXL14UU2ajRZgezJlMBk3VaLVa6LpOv99nOPSYn5/n9u3byLLMWHV8FIsSMa+IarXKYNhncXGR/f0DyqUSmUwmLe94B03TOHv2HJU0SvfuxYucPHmS4dCl1+3RTDd45XKFDz74ANu22dzYplgs0uuJ21IYhtT2akxPT/Pmm2+wvHycg4P9kaXowoULfOMb3+CHfuiHuH79OuPj41y4cJ63336bc+fOsbG+xmDQE1vMnR0yto2Uft0oipifm2fvYJ+N9U3+0f/wj2gc1MlYDsW8iSbHJFHARHUC1/cop8kS2xLoraPBqNPtkrEcEvhBIgPSBquA5bNPfTQbhTTcf7lUFhuuTCZDNpejUCxSHRtDUVUmJqewbNHf52SywrUNTE1OkclkmZqaxvP8UWSl3e7Q6XRp1huQQK8/4M3vvcnkxCTZNIA8Pl6hfnhAvdlkb38f3TRQVIO5uQV0TWV9bZ2JiQkKxRKyJJHJOkI7SiDjZCiXK4TpIfTuxUs4jsP7773P5Q8/JPR86vsHHNb3sWwL0zBAVtjcWOf+/ftMTk6ys71N67CBrqi89hd/SRwrXL9+g+XlY9hZA9PW0QyDSnUCkogoirEdh8AXlpEkSTB1kTwIopiJyWnqBw36gwH9bhcFGVWW8IYBe/sHzE7PUigWCYIAU9dT2GOWscoY/W4fkDAtW+TFghDDMMjl8jh2hl6vjywr7OzuUq83qZQrqLpOt90dTbt22modhAGNRoNCIc/e7i6dTo+Ll97Hi1VcP+HNt96m3e/TbHXQDQdZ1TFME1nVUDVjVInm+T6KqhIlPkHo0h90sUwL0xC6oSLLeK7LxPg47lAEm5U0/pQkCaRXpjAMEcsACdu2CQI/JbeKLfSRPqLrOoVCQSQTVJlsNiu2rK5Lr9clDFxMS8PJWOkT3B0dJKdOneLu3btsb2+PNtAA6+vryLJMs9lE0zQmJye5evUqDz98hv39AwaDIbmcsISUSmUGgyGqqrG3t8/YWJVer8fExATbWzsUinm2trbIpZNhq9XBtoX3cGe7RiaTZbe2Iw74RKLT7jLoD2k2mzz22GPcvn2LOI75+z/9D/j269/l4088yd5+DVVVePjhh7l9+w4bG+tIkkwhV6BSqXL//n0kWaE3EJ2fN2/eZHZ2ltXVVSqVKisrK4DMuXOPsrGxyYUnznPp0iV+9md/lg8//IDl5WWuXLmCLMtsbm7y0ksv8Vu/9VucPHmS6elpXnnlFc6cOcN7ly5y+vRpikXh3F9YmGdzfZ3TD53m/v37nDp1ip3dHbK5AoZh8s7F71MdmyQKI65fu8KJ48vsbG+RhCGh55OEEc3DBt7QE3idfB5VFwu+0A9xh8ORx3EwGCBLMkEYcOLRpz/aFfKvXvm3ydEG8Wi9e0SmCHyfw8NDAGHsTEf9OI4p5QsinDoYiFr49C90FC4uFwtCWA+D0c9lC3n6/T6aJr6OZQlPVrvTGwWWo9hDU1SKxTKu63Lu3Dlq2zt4/pB8sYhlWdTrDSaqY3S7Qvi8du0aM1PTTC8s0Gt3Rmjb3d1dSpUqc4sL7G5t02qJ4GqpUOT69etkUsTPQaNDJmOTEPKxJ87x5htv0Wy2yTgFJsfLnD59Gj+KicWNh1w+Q32vxsWLF3nqmU9z9859+t0Btm0zPT3NYNij2TxEQsO0LZFrDAMmx8axbYEu8r2QZrNJt9vl5MmT3L59m+UTx7Esi06jSRzH1Ot1LMdJCSEG/aEwJ6oayJKI7fR6PQaDAcvLJ3BdV+B5TB05iYUOoVp8951r7B62uL+6RhCFSMi4gQit66aBJCn4QTSiYei6Ti6XodUW1/A4jokC0iufPCrbSJKEarU6Mooe/f8jzStJEtyh/wNemC4mM4FZCZBS5I6qqiM5YmZ2SqCQi0WazaawwegKOzs7IvBt6KiKzv7+PjMzM0iSxL1793jooYeQZZmdnR1xpR4OmZ6eZn9/f3RdFYsYceCGodi0H3HYLMuiVquRyWTEB0sW4ed+X7jai0VRsOGleo8wShdHS4ZWq5ESNqLRYby4OM8HH15mbm6One0a1WoVdyDyrWPjldR6pOD7IefPf4wHDx7gui5ra2vMzc0xPj5JrVbj1q1bPPfcc3znO98R19DNTVRV5cSp01y7dk34/1SZZ555hkajwbFjx/iTP/kTfuM3foOvfOUrfOYzn+HGjRucPXuWc+fO8dWvfpWf//mf59VXvsbs3LR4r0oSy0uL+L7P4UGdw2aDyclJLl9+j2eeeZZ2t8fFi5dYOLZMu9nCcwd8/NxD5E146Pg8GUsl8oXlZui6SKl8omhHRS8Q+dHI8BzDaCnl+z6f+LH/6b95hfzvHmBvvvrV5MiLBdDrCVKEbdujET+TyQg3eNqacnRwPHjwgPn5eTKZzOiNf5T0T9KDq1arjd5oO3vCZHckzM7OzqZP6hhV08jniyCFIzhft90S28dSidr2DoqukSQJh4eHFLK5NA1fFIduNofriy3enVs3R2/gdrfH5uY2P/zDLzAcDsUVR5JHWsbu9g6KbovMpKHQHbTp90Vxh2VmiSOPbrdL4Abcvn2bM2fOceXK+xw7cZw4Drlx8zadToe//1P/gDAMxSaPCF1RkRThmG+0mgJUt7tLEsUjvUdkJYX14+hDns1mIWXDS5JEjCBUxHGMYYgEgqolKKouDsuBCDSHYZw+HDQMXUUhQVZNtvcO+S9/+Rab2weE6eHi+z66JRzukizc2/Hf2LIeBavjxEdVZTzfRcEYAQuHQ28kyJdLVZCT0XtETUPoui68Xkn8g3o01xNbqKPJUU9TEkEQUKvVBJgxDiiXy9RqNWF+lGGsXEpRNjk+vHqdSqXCwcEB09PTKW1CyB9jY2Osra2NDpsgCJiZER/4fD5PHMcUi2V2d3eZm5tjd3d3dM1JkoRcLsdgMBgBD/v9Ll7Kbev1epw5c4ZCocClS+9jGAa1Wg0QkkcYulSr4ymRpUkmY7O8vES31+Zg/zBF/TSZGBtnamqK9957jx998fN89atf5XOf+yGuX79JsVhka2uLT37yk6km12AwcHnooYf44MpVCoXC6LNXrY5z9fo1zp49y+zsLBsba8zOzvLB5StMz0zy8z/3P/KVr3yFX/zFX+TXf/3X+ZVf+RX+3k/+JE9/6lP8+q//Or/6q7/K9MwkJ06c4K233uD/+OVf4d/9zm9jWRaNegPdFA+xzc1NJiencf2AjY0Nzj52nts3byHLUMo6LE2N8eT5R4jcFqYiNK0gDNF14SWVVWU0nft+OEIoASjpuRPHMc9/8csfTQO7/+EbL2uawAQ3m6I4c2pqkk6njW0YKJJEr9OhtruTNtl0iMKAMA65fPl9ZmdnODjYx/c9dnd3ME2DarVCgsggFotFDMOg3etSqVQolIosLyxTyBc42DvAsp2ReXY46GOaFtlsjp2dbbIZB9u2aDZbZPMFUQabLwh3euoharVaQlvrtVlb3yaJIianxlBlmT955Wv4Xsj+vriaVKtVpqam+POvf513vv8ODz/yCHbGIeNkSIjx/YA33nyLmdl5JEnkEy1TE0H0jnA8r64+4Pz5x5manmYw6PPppz/NxPg4i4tLAoqHaIVxMhkUwA9CWu3WiECQz+UwdZ3dWm0U+DZNwUorl8v85Te/yfFjy9iOQ0SEqir4gY+u6WlXgEvku+zt1mi2WjQbDWGKlVWGrouh6YRhkPZetmi0Orz59mUGQUgUQxCIAoneoIckQRgEBJ6Posq47hA5TVqEgZ+aUgNURU3NrWoqFwAI7S0KYzJpxKvX6xH8DR3M80T70xE91rLNv8VXO9KKqtUqURSJ6JcppuejySibzVDf32d5eZkPP/wQ3TARzBsBH8xms3/L4mKaJnNzC2xv72AYJvX6IaVSmSNfm+cNkWWRmFBVBdcdEkUhmYxDt9sBJLGp9TyiKKZQyI9IHxsbW+zs7I4sHrIsLDwiipdjOHSJIrF4UlWN27dv0u12OLa0zNWrV3nkkTOoisL771+hVCoShgGnT59G01T6/QG+FzI3N4+Tdbh//z5OxmZ+fpGbt+6wdGyJ+uEhkixhmDYbm5ssLhyj1Wpzf+Uezz77HH/95pt86Utf4hvf/DpvfO9NvvrVr/Kbv/mb/If/8B946623+M+vvspTTz3FK6+8QrkiTMnZrMPy4hJ/+l/+M8vLy7z77rvMzsziZBwePFjl9OnTJEnM+sYmFy48wQcfXKVUKlCuFLl+9SbFUoXdnR0eOX0cTQHX81KN0xPOgFTTrdfrREE0amrP5XJ4rovnC8DksbOf/Gga2NbNiy/fv3uPvd0aw8EAQ9MhSYRvYzDEHQ4J/IAwCLAti163S7lUQjcNqtUjJG8FWRZI3TiO6XQ6OJkc3V6PKIwIw4hEksjm8nT7Azr1Fp12h8mpSdbX1pAlib2DGkHg0+2J3Nhg0Md2LOoHB8zOzqGqwrU+GAwolyt02136fTEthpFPr9tD00zCwOXw4IBsNkuhUORHf/THiOIYyzZHfKjHzj/O/MIC12/e4Oyj5zB0FVlWyGZyXHji4ziZrKiKkhNWVu5DElPMF8jnC1RKFWzHZm1jnSCM2F7fIJfJ02w0qO3tp5VWE3iey8baOpZjsb29xezsnCCNegGO5TDw+gyG/bSUVaaYz+F7LhcefxxI8HyPoTdAVmUcxyZOJLF0sB0c20RVVLqdDqqs4AU++/sHZJwMFy9epFgqIEsSumGyvXPABzfv4vmCFJok4hDTNAUJiOIoFeCH6TVQ1KhFYYiuayQxaKpKFEZEYYipm5AkJDGoioqqauiGnka5VDKOM6pFA4gjQSpIkgTPd0cLoiAIBI5b09je3h5t5sIoSNHkUTrxBYxVqty8eQtV1UgSiW63K+Jp7fZIzC4UCiO6xPXrNxgfH6fX6zE/P08jPeTFh6lLoVAYERSOAH++LwpGdN0aBdBFjlbkd4Wl5KhxSRTaWOnyJZctMBwOWFhYACTm5xbZ2dlGVWUefuQ0qqpz/vx57t9f4e6deziOw/nzj/POO2/jOA73798bBdjDOOL27TsYhk4Q+NTrzdGDOp/PC1P2/gFTU1McNpromsaLL/4o3/jGNzj/+Mf4j3/4++TzeX7+536Bl19+mbNnz9LpdPjDP/xDzpw5Q71eJwxDmq0GTzzxBFevXGFtbS1l2CX8xE/8BLdv3cZLLTzPP/88tVoNTTfY29tjanKKDz64zPrGBp/45NPMzczTbtSpVLLIcoRuCatNqVweLRmIRUJDlhXiKGKYbh77/b44c0yTpTOf+GgH2Ob9917OZLNUKyUmxsbQVIVOpyvQN0mCahh0By6V6jjIKr7v47kuzWYbXVORpJhyoYDvuoS+DxGoskY2n8MxLXx3SBj4DPodcjkHWU6wTBNZkUQPZKlIqVoeaSbzc6Iyfn5unl63x9j4lGj8TSTu3LtFo9Gk1WxS29nD8wKm52YZn5pkdmGOhbklMk6GyYkJbNuhVCqhygozs7OMT1dJpJixahlVVhj0eoKyoGj0vR6SIqFbBm769L1z+zampjNeKqDIGqadZ3p2ka47IJYTdNNkcX4JzVTwfZeb129w+9YNFhaXUBSF2v4+Ej6WaZDLZAVBIoopFwvIEuimgWPbTE1O4g36hGFAEgcM3T5uGJFIInUx7PTRUbF1A02VGQ76dAYB+/U6cQKV8TF832NhcQE3dCmOT/DIQw+TtXNUxmb446/9ObuHLaI4IIyStLLMBEnD9UKSWIFEJomHSMQosgRJSBJHREGCoRmiuFfXRabUsmi326i6jqqpKVHCJZvNMBj26fd6DAbdNDupIiGhaAqOk0FKG4Z83yOOQyzHQtUUHMfGcWwkmVHcyzAM4sjDHfZRVWGqjZMkpUZ4SJKov1tfXx8xqer1eor6dmi1WoyNjY0yrEfre9sx2NzcIJN1AGn0IROZS4fhcDDimmmaluplEYZh4rqY/vATAAAgAElEQVQDHMem221jGBrdbpskiTBNjb2DA8Iowh0O2a1tE0UCJFgtV7l29Sr1AzHxPf30U4yNV9jcEhV+h4eH6KZGqVzCsm3u318RyQvd4ORJASZ0HBvf9+j1+jiOk6K4EwLfxbZNrl27KhYb7TaSJPPjP/4Sb3z3u0xOTNBpt1k6dox6vU6xXODylcucOHmcp556mt/7vd/n1EMPcebcOR45c5bXvvUtdMNgfFwsCXTdYHx8gu9+53tMT04RhxFLJ46haQZLi8e49O5FVDlJX4shxbyEJqkQygx6ggBrOAYyKlEQkyvmQZJGMtLRQiefyzF7+sJHE/H/6P/5F4llmOiqjJTA1NQUQ9dHkhSGfh9VFVeXfn9IIZsDYiQ5odsZ0u0Kt7hp6cRhROBHvPbaazz33AskKjimRW1nV7jFMwLy54ch8zPzo7H8/v37SKrCxMQEnueJlLplsbq6ytLSMrphsLa2RhiGPHLmIZH76g3RZAXbziBrMkHkc//+fSwzy1i1TLmQ586t28KCIIu7dyZvCtJnIkLq/b7gfEkJaLa4BkiySrvdJQxDcrkcWSeDnLhoukNtv8nY+CSGreFkTAbdHnt7eyREmLrIJyqKwnZtl92dPaZnZ8jldCRkAjdgMHA5duwYtZ1dXNelOFbhzp07lMtlJqoVWq0WnU6HQi5PO9W1Ti4fp9fpjj7U6+vreIFPrlBld3ebxcVFJqcnsW2bWk1EkiJJgTBkvFTFi2R+6//9fe6u7zBwXYZulCYidMJYkCyOUgmK7KdsMmU0OcWxPAppx5Cig4VukUjyKNPZbDe4cOGCsKi4wvfTbrbE5ikWZArLFplB3/eRpIQw8AiikHa7zfT0dGrVkEaHied5GLoQ+XtdoZ3l83kODw/x/RDTFBpbvV5nfHycJEmo1WqMjY2RJAKnfLQd1TTh93McB0mORp2ljp2l1+uNyKVhGI8KKOAHr4/of4xIkmiklx3xy3I5YfvwQ/FzvU43LREReu1mGkXyPI+DgwM0TRslO8TEp5PJ2bTS8pnx8UmazSZLSwtcu3ZttIAQvQk/SCYITVQ0yvf7fSYmJjAMg3w+z+rqKrZucOLECV544QX+z3/5L/nSl77E7t4Op0+f5nd/93cxDIPz58/z+uuv8+mnn+av//pNfuEXfoG3336bcrEwaqOKoojZ2VlarRY7Ozucf/JJarUaD1bXmZmeRopCep0mh/u7/Itf/PvEXoImaQRBCHJMzxuioKCiMPCH9Pt9DMMYRfGOJvXP/9xHzELeufJXLydxjGUIGL/nB6yuruFk8+hqjK6pAjynSNTr+2xvCxKAppkkSSxEeykRRRh+wEH9gPGJCTx/SBj6lAqioeX1b3+H+bkF9g/qZB2x6Xnw4AGTk5NMTQsul23bGLrD1tYOExNT7O3X+ODKFeIkYWZmhlbrkG67zWAwYGNtgzt37nLj5i3m5uZIEomACCSJG7duoNsmmmnQbHewMw4SsLa2RqfdZWxsHE3TqNVqTE5M02y38DyfXn9IEMXkC0VhAel2kRPYrR1w48ZdFhYXcBwT3+sz6A/TD4eO7wcUcnk8b4hlGszMTeNkbeIoJI5iojhB1XXcwZCd3V1K5RKtjgimV6tVVFmlWh0THC1JRtE0ioUCcZKwMD+P7/lESSyoBhmHqx9eZ3p6RlwzfB/X8yiVRGFFjEQplycIfK7fuMPm7j6DQAAko/iobCES20hZ8NUkCVSFEdFDSjeNQpsTyxhJAcfJjGJaSIL7JZDQEZZpk8lk6Xa6SJIsGrnTa5rtZIVmpKkomkoUBOiaim2ZeO6QcqlIGIgaNt9zsUyTJI6wbSvFrth0Oh0URUm1OOFTGw6HjI2NiaB8Oi2JQ8EYCfJHKJl+vy88Z712egW18D3hcfM8b+TbOtLrjpYqRxtWodnFHDWQg8Rw6IocoJNBTgtc2q02Y2NjuO5QhNANA8dx2Nra4ty5c1QqFaIo4vDwkDAMOXv2LLl8jhvXb2HbmRQFpfNgbYVCIT/qp+h2uxQKRaGB/o2yDl3XGR8fJwwFaPPITfCzP/tPePOtt3jtW9/i+eefZ3t7Gz/w+e3f/m1+8id/kna7zclTx/EDj5X795mamsL3Pd5//z3GqlWazSYnTx4nisL039JkbKxKFMV0Ox0+/vEnePBgjevXrvKpp59GkmCikqNUKKHIsqBREBNLCE9kFBEnMbVajRs3brC2tiZyv6kf8OTjn/loE9if/rt/mWQci06zlT7BJvjw2g0sy2ZxvsLu7i6FfInZ2TkkFAZenzCOCb0Y27axbRMkoX/s79VZWlpOSxxcwUvyfQI/SlfzObrdPpnMDzac1WqVlbUHo0qyZqNHLpfjYx97TIyZCuzt7RHHMY3WPpZh4ThZrrx3hbGxCbrp06c/HHDs1DL1eh2312VsbIyMI2gX9WaDUk5A3bzBkPfee4/Z2fl043KIkTG5e29FFKjqOtlslunJSRHdqdd5/dt/heXkaXXafPEf/hQJPmOVKuvrm0xNTZHJZGg3DikUCuzs1QjSLJ+dcVBVnShMRNmHpGI7YhKNfA+QR8x7XRcrZcMwWFlfJQgCHv/Yxxj2+qysrJDJiyfv8vIy7ZbIOZ45cyadEkQQut1ukysX0ZBJooj/+Ed/yv2tOut7DXqDPu5QVLRpmka71x3lSH3fp5BzkCRhOj06wDTNYDAQulWYCKIE6aTZG/TTCSCi0+2iKEo6aQQM+8IMG4ahOCglCcMS7d2SDKHnMnT7qLKYuI6YX0eb7qNmbtcbUKlU2N7axXGE5UZcr0wajTqlUolWSxg9jwpQwjAcXSmPtK2j979t2wyGQi8TfjZtlKM92pIdWYqOWO5HlhBBjxAxtVKpNJrcKpUKt2/fFlnAQoGsk8E0TXo9UdKSxGLrPD09LZYcgUiQWJbFsWPHuJI+oB0nO2py6nRaWLZBJuNwWG9hmibFYpG9vYNR9tV13VGr1dHhbFkWBwcHPP3007zxV9/j0UcfpVKp8Oabb1IoFPixl16kVquxs7NDf9BlfX2d48eOsbi4+ANMlSle24WFBRRFodlsUigU6HQ6HB4e4DjZkQXqxPHTvPXWW1QqJXrdDstTFi88+1nkUPScWpaFHycYmkBhGZZJFInvxVG86sje8skvfOkj0ig+fOvlaqWKk/qVxscnUFSNyelpMpbBxMQUlpnhtde+y6VL77O2vsX6xjZPXrhAxnEYugMO6w0URaXV7RBEEXbGwfcGzM3NC1zN2Hi6dhdvGN0yMCxTOMNX7pOxbBqHh0RBiGVn0kymYOevrq5QqVSQpIRut83GxiazUzNMTk5xcFDn7JkzRHHMsaVj7B3sMT0+ScbJsDAzT33/AFVT2drYIAoTNFVn7cEapmlRLpd555132N3ZRTdMFhcW0Q2Dm9dvYegatm0zMzXFrRu3sB2HwdBjYmKMEyeXiSOPMIw4deq0QPgMh8iaRr6QJ5fLkc9mkCWJmERsw3STXLaQWlLEG87QNBwnk66RGekuhXyRqdkpHNsmSjWVbDbL/OKCyFL2+xi6kULr+vT7PXq9HsPhkMFgQOD57Nd2+PrXv4FmZag3u+zUhZfvaGJKkgQ7LagQTP0AJ/XEiQNMSk2pYiMpy6LeTEw4PoEfkkBqlxCTibDFzKddmbFYxkgSSkqdCKKQMIowTANFVRj0eyRxNApFHxV+HB0UgmAi0+l0MHRx6B+VRYjcqjoS0Y8OhWw2O8IP5XK50Wt61JFp2zaGIQ4hwxD1c4OB8O8NBgNyufzoNbAsS2xR057Low3zEWnhyEby4MEDpqenidOUwf7evsDThMIuYFmWyLsGAc1WS1zDdR3Ltrlz545o5lJVXFcsFA4ODjAMXRxeh4cU8sIA3Wg0KBZLo21rJq3u293dHb12pmmi6zobGxucv3CBm7duUj88RFFVHn7kYf7Tf/rDERKr12lz8sQJ8vk877zzDt12G1mSqNVqBEGYcv/ikRtfkhKmp6dRFJUHDx4QBCGvv/46S0vHkDUNZIWluWmWl5bQNZlyMSe4fJZNEseCge97ow026fsnDENkSWHuof+2BvbfPcBuv/e9l7e2Nmm0GjiWjaTKFCsFCuUSB4eHqJpJGMTks0UsK8vQj5EUnUoxz9bWJu9eukQ2m6HZaaPrJq7nUavt4w/6dHtdYmRsx0HTdYGmOThAVpXRC29ZFlMTk6OncCaXoVIt0+/3KBdzLMzNsbW5gTsY0myJDsBqpcr9u/eRJYHa2TuosbW9iSpLbK1vsr7ygJs3bnGwv4/vekShj2HaOBkLXVPxfQ/XHTI/P8PiwhzNwxbr6+tIMdiOjSLL5BybXNZhcWEOP/Q59dAp5uZmSJIAWYHYj7BNm+3NHWwnw9zCHLdu3WZ7e4dGvYltOKxtrFOtjBF4onRDUxRqOzus3l+h3Rbluo1Gg+npKQEqVGV293aEkTKB4WBAIZen0+2wtr7O3t4ex44dw3Es0YwgJbSbDdqtJsVCHtsyaTVbjFUqTExOcufeA7YPGvgRDAfuyBBrmiZKml2NolBcFdNDRDtqBEoF96My3SASWoWohYvSwy1OvWrCgtJsNtJyGB1F00gAWVWQZBU/jFA0UYcXxxGWaRBFfircg6LKmKbBcDgQtXlhkE4yPaIoFqbI9ApZKpVpNkXf5RGxI5PJ0O12xUSSBt2PqKTZ9Oc0VaXX741+j2OLQ+Bo8omieJTp/UHJsDC95vP50SRmmmYKWeyN7B5hujVdmJ/H8zxx6KdB/YN6Pe1Gtf8W3SMIAsIwRNMF562T6p2yLKEoMqZpoSriKisWEcJ+UKvVRtOmpmkjj9vR17UsC9f3MC2T6alp6vU6e3t7OI7No48+yr27d/E8cRVttzs89eQnWFlZ5XP/P2lvFmPZfd/5fc6+3HP3W7fqVld19d7sZpPiJlK2JUu2pdgejcfA6MHAaLIANrIASV4meTMQ5iHINi8DTF7ykEwwGS+Th3iRJS9jWRIpmZJINsWt2d1kV1d1bbfq7tvZz8nD//xPkwHEAAwBguhmd9Wts/z+v9/3911+/TeoVRuljVUUxUyns+IgmXJwcIDleDzcFUTbNM24despzoYDPvzoI06PT6lVPWqega5m6JpJEGWoSkYcr8hzmReaE4YCX0ticZ0v3PqMltKH9958udNpYxg61ZpwPuif9vng3h0qNQ8/CJhOp7z+0zcZjSZcuHiJ995/n88//wxZltJZW8NxbB7u7ZGjFKNDynwyYTAYYtoWjx49ArKS15XmeTl6eZ7Hcr4obanXum2BJTkWoR9yenrK8fExk9GYZrvN5nqPMBSUjieeuEGcRDz3zOfY2Nzg/IUdOp01sjQXHt2NOijgRwFuRbjNOrYNZAyHA0QiuUKWZFy+fIVeb4PJZIyh62xt9sjShNd++iN0XeXw+Jhmq8blyxd5cP8+s9kCx3LY33/EcrXErXi0Wm0820NTdSzTZn1zg0ajwXK+YDQcEoYhWZJgmQbtVoflYkG1VuX4+JAg8Dk5PUHTVOIgwtQN4igWXme6SC3aLMYQ09Sp12uYpkFvfZ1KxcU0DcbDId21NUI/YDge89pP3iRVDUaLFYqmkSUiQUhRVFRdjJKyEOWp+H+SgCpemrCQiCnEyWP+V5IkKKpSdl4r30fXBbWgUnHprK8TRiF5lqOZNkbBeFcUkSqepjGuYzM6O8U0DBzbQUEU7IrrMptO0VQNP/BFKvUqKAtNGIZomk6eZwyHw5LEmiRJmUNKUXRlNqP8RzhfUFIoDF1E2Ukyq/CsU0osTXao7XabwWAgrKJ0o+B7CTzMth18PyAuhOqLYpxWVYV6vc58Pmd9fZ1qtcrx8THdbrfs8jxPWBMF4YrT0z4KCo1Gk2q1xnQyRdP0cuwUFCLRZcvwF9M0yxT2NE0ZDAaFjtUsuh2F3Ye7bJ47x2w6o1JxeVCYO4LoDtUc7t//iG9+85/yF3/xbV555RUOD49EBN2DXVrtFnGcFDQXHxSN07MznnryFr//+7/PG2+8zt179+msrfGN3/4Gf/PX3+HGjQtkSUgcJuimjaErRHFQdMRGwQOr49gujuOI8JVrz362Anb3zR+87AerktLw7nvvcOPWk1SrHpopWt+fvvZTgqWP4zj82le/yls/u82vfPlLtNsdWu0WruvwxM0b5IgAD800MBQV23Gp1WtCrd9uiTDRnfOMJlNs2xbaQNsmWPnlgzaZj1E1cRrPptMSj9ns9bh09Qo1T+TS6ZrAWO5/eA/fX5GTsX94xI9f+wnXrj1Bq93Gq3mMxmO2d7ZpNlocHR1BLjY6tVqN+WLKo/1HeG6V/skJD3Z38SoVtrfOFbbBOteeuEyj0WSj12NtbY3pbMzhowPWO+tYls31609w+63b/Mmf/im3b7+F6zjs7u5x6eIl/u2//UPUAleybZvQD1hf75KmCdOpYIqvdTqc2zpHGIZcvHRBECcdT3CXEiGGbzYa2K6DURSXNBUi52C15K23bqOooGvCfHA4HJGmEee2z3P7nfcZL3ziTAhzyVRM0xI8p1x41UuxLYWVjhRiy6IgbFc0slx2J0WUWypGyyzLcWwLVdNJ0pjxaEK1Ljh4URTjegLAz8iF+Z2uYhk6q+WCNIlKTaRk+cuC4nni8IzjGK1w+ZWwgqpq1Os1gELI7pWAfVaMK6vVqrQCAtGpTadTdEP7mBtGVhZrwzCKGDph1y0zIOT3MAwDx3HLTkd+FulWoajC6221XOL7gizb7/dLWZ5MxJbCdemiqus6frASXnu2y2AgDjrLspnPZ8IVprgneQ6tVovxeFzy2uI4RiaNr62tidT6NKXeqJdhtvP5nJ3zO0RRSFhkXW5u9jg4OMBfBmxtbfGtb32LyWTGF178Aq12k1df/SG/93u/y/7ePgcHjwgCn0ajwWg6o9PucPf+PcZDkSC/3hPGAX/0h3/Ms8/e5JlbV3BMFdf2GA0nZHlClsWQi9xLQW2JiaO4oMw4bFx5+rMVsNPdd1/WVJ13373Dhx8+xDQc9By67TZ7d/Zpek2effoZ2mttnn72c7zx5utce+Iqe7t3SNKQk/4xjUad3Yd79HobtNtNNrod5vMx6+tr6JrKaDjAMmzyHCzLoVp0XWQ5SSQejuVySbVaxbZrqIoIN614HjWvSqvVIUnh3p33WS7m1Gs1lss5x8eHXL1yGU0VXmAHe/s8deMGg/4RaRRwcniApasMTvucP7/Fxvqa4CClOc1mE7MQMFebbXYuXeLCpYtkKnj1OtVGAz/LsFyPPFdJUhiNJjQaHeqdLm6jRkzG+HREmmasb6zjuBYXLl+k2alxfHbI+vltOhtrdDptFFWh1WrixxFRklD1PDprHU76fXw/IMtShoMhWZLx8GCfTMnRdJV2u0X/9ESE/hadl6HanJ4MCIKQbreLUgQlmJaOrqu06lWWixmJYrF3NGY0CVBz4ZwpqBKqkCtloouAnChdYdo6uZILN69cJOrkikoYx8LxVROBvXGSloC776/QVJUoDCCDPBMurF5NdL+kGUkUoqkiJ1BXFKpuhf7hMZAWWJTItnQchyQTzrij8QjbEiG6IgA4wCkE75YlQHvXrTCbzUmihFq1jqqoJHFCHAflGJbnGcvlShgwzufUG6KgaKpR0jYEa0LGwGWoqgzkpfRkS5K46P5U0jTBcexieaKiKGBbLuPRmDyHRq3GeDyiVhN/bzAYCtvq6bT0WZOFcbVa0Wq2CYOQ8WgkQjI0YaWdZXmRAm7ieVVs2y5tn7MsK0KfjcIqeiCCT7bOkSQx08kYXdNYLua0mg2m0xmVikcUJ5iWLSx72h3CQnFx+fJlms0GnbUOP3jl7/iHv/V1/viP/wg/EDZN4/EI3/dJwoS93YfEUcTGxgaGYWCbJu++/TZbO5e5e+cDfvOrX0XNcnRNBUUcErbhiOjG4jBQiw57PB6xWi24+uzPF3N/agH7Z//pN1/+3ve+T6fTYWdnh+c+9yxJmnD//n12dnaYzea89tprLJcrPnzwgK985Su0Wx2iMMYPYizL5fRszBuv32ZjY5MwjImTjLpXI4kzqtU6V69cR9N06oUcyPdXdLtdDg4OuHHjBsPhkPlckGffePMtFstZsVWxmU4ExeHo6BC36JzkhikIAnGKFCL0re0tVv6KNImpVColh8m0LBRVYX9/nzxT+PDDD4miiNPTU+I45dLFS+zv7XHv/n2qnidSxWtVtre2WBSnYKPWYGN9g6pX5fWf/pTLly9hWxa2YeK4Do16lc89+4xgHCuwvbWNZdnYuknV8wSm5fv85Mc/Jk4T0V0WQLDoRh/zi9qdDlVX+J75q5WgErgu08mEk36/cHeldHiYjKdCLqNAEmUslws0zeDbf/M9FrHCZOFjm2J0lN0GmlJiOr4foGk6WQqW1LCpAm+KohjbskiSuNQtKihkH/OQSxKBo6mK+DuL5Yrnn3uB8WSCvxLge5yIIlj1Ktx+83Wm4xGeJ0YITdPwfV90HrbwjqvVamiqAJxF8XkMzi+Xi7L7iKKI+WwmOHBpKvhenlMGwggXXNHRGoaBpsufQS3Z/tVqrdSjWpZZ8tDyXGg8J5NJ2QEpilLIjNISt12tVuU4resqx0dHXLi4U4SqQLVaK7XCgMinHAxwHIf19XXm8znz+Zxzm9usfL/c0JmmTa+3AVBsAYe4rtjmdrtdplOh2w3DENsWW/zFYkGlUilVK61WqxDpzwlD0VkKvaddOnUMhkNmE0EvefjwIb/927/Nd779HdbWuoRhwIMHD2i3OwwGQwJfsOhXqxWHh4c4jsPOzg67u7vs7h+w3esyHRzRrDqcHB+JuEPHgTwnSQuu3GJVmnBWKhVc1/1UIuunFrBguPdyrVbjjTfe5NaNm2iaxv/8P/1zzm9fKCxo7vPiiy/x/R/8gCiI2X24x/e/9wqPDo/JMoXxZMJyucC2Hd55713efe894jghT3J6m5tMJlPiOKFWq2MYJqDgOBbvvPNOSUCUYts8z3nyqVucP7/FtWtXxY0+PWNr6xw3b94QB3oBXqqqih8G9DY3abZaHBwecO78DlWvSpqkpZf77t6uyP7rn1CvNTEMg0qlUiQZQafTYbVYMRwM6bQES/+tt96if3xMs97gtR/9iPFwyA9f/SE//tHfMx2P8SoVTk6OSZOU2F9iGhprnQ5KrtA/OsbUTcIwIvFDPMfl9PiE6WQinCubTa5evgJQssQFudQijhPCMCIKIsFcz5Xi4V+ysd4jSTOajSbnzvXIslTkYwJhKLhP+3sHImbL93ErNX78xjuMFiLfU1dFcrphGKiGWl7DJBHpQ5pmoSg6UZSSpRlhGBXM+wJYV1SiArvJcwEqU6gncjnOJBFaUST39x/ywgvPkcSp4HfFEaapsXv/LrPpiJorUokkdCAJtmmWFtikXhYFSe+Q2JQMoGk2m8KSxzR5sPshjWYdyBkOB6XBgChsGfP5HM/zsGxxqGma0JVWq1WCwMe2LSoVtxxpJe4kt7ZiM2mzXC5IkhjPqxCGgcgtdcTnSdOkGE9FEI4IwNVYrfwSW5OmkFL2JLerUSiEztLa6ujopChQCwzDoNFoUKlUME2Tfr8vlhPVajlK6rqGruvUaoI6NJ4MMUxdeO0VtJTZbP4xI8kUwzBLedJiuSSIQuqNBrff+hk7Fy+i6jrLVUAYhYRRhGnZrHyfOE1EqLKqcHxywt179zBMkzjOaDaq/NZv/hob3RaGoZUONoYu1BwC+6uW2908g8HZgCc+/5XPVsDGBx+8/ODBAxq1Gt3uGlEYce3aE6RxyqOjfUzL4gevvMI/+ke/zWQy5YUXP8/zz79Axavw4ksv8OVf/iLPPP0Uzz7zNPuP9vj13/gN4TJBim1bpGlCHEcslzP29naBjNPTU0Ak0NRqNbrdLvV6XaTuFCfJ0dERp6enGMXG6Pbt2/Q2eiUA6ocB58+fJ0kS9g8PuHz1iggRaTS4d/c+nleh3mjQarbpdNa4cP4ClmlhW8JscD5bYug6uqZzcnKMosDmZg/IqVU9GvU6aSI6sdlkim05XL9+nU6nQ8WrgAq6ppEVK/zl0uev/uqveOqpp7hz5w6L+QJbN7hz5wNq1SqttggfqRUPruu69Hq90gjQdd2SVyR9/DfW14UrwWiMpouW27IsLFtnPBGja1zY4AiblxatVouq52EYNu/f3WPveIBhuyRRgGbqKMV2DEAt/LuWyyWg4boV1tY6TEYTNE2AzMvlgmC5ElhRnqMbBcsayAvGepbnZGmK57hkRchImqU0G3V2di6wWs5o1Kq8/pO/x9AVsjgGcqwia0HaWKuqWgTZGoUiwyMqcBJJ3JQWLJIXlmUZUeSXSVpZEUgjLXUajQa+H5T4XhyL7WOWJyL+S1dJs4ScjMVyXiSHa2VRFRtFFd/3AVG4JTNf3rcwDHEcG8hRVQXLshgOhxiGXuhC09JySqaxyw0mQBJnZRd7ejbA9wVvbH19HV0XFI5pQeCWxU8mRjmOU7wTSsmlnM/n1OuPKRmSgNxo1ImiEMdxCfwlmqbi+4LnJ5UQo+GEVqvNg492sR2rwAM94li8x+X75/vFBBCX4StJppCEK4LVmPObXbJC1xoXSdxZriD94arVKv5KcMW63S7rlz8jBnbnJ//u5Z3z25w7t1muVvd3H3JwcMgvfvEL3Lx5i/l8zq2nbvG5p59mvbtGq1mn0qgSBCveeectzk6PWc7n9E+OaXfahGFUZCGqHB8fCrX9ao5h6NQbNWrVJqoqXhhdN0rXgHv37mOYBu32Gvv7j9hYP8f9e/dK48R33hYM9CiKOb+zw/vv3yEubGbSNOO9d+9w2j9le+sciqKiaTqbmz1+9s7buI7L7u4uhmHQanUYDoeoqsrGxga1Wo1ms8loNKLT6TCfzhicDTi/vc1gNKTqeVQqHo2aSO8ejcfkWUoYR6yvrWyNAhUAACAASURBVFOpeNTrdZ555hl83+f8+fM0anVOjo/prK1hmiZZngtwuuJhGgaWbQl7a8MoiJYqJyd94jjh0aN9QaJUFFaLpRAfk+O4LrZt8+DBh+zs7BCGUbGNFB2LrguAOgx8yDVeee02izAnSFJMU4D8Mug3hyLU1QOUcgM7GAzQDZ0sTVBziApnCBBAeJakhMVDDGKjJ0N3LUOYNTqORRyFPNrfYzQYsP/wAa//5DU67SZh4KOogq6haGpZAGR3lRYSGRCdped5hGFY0Bhy4bRhCeGz2MAtqRd5o8vlvKQ1SDmQZObL8Vf6gKVJjKEbBWtc0DuEaaNNngmSdpaK7lEBrCIfwLFtIYGJY/RilJIvqTRqDHwfEGC/6PJqJWVGOuzKDaRt26yKBVkQRNQb9ZKHdnx8TKXiltFttm2XCVaShCz9+zRNLa2sJDa5sbEhpF7jERXXQ9f1ggNnEkdhIWlCLEsS4VZSbzRIkwTHtYo4O6EECAJhXS0lUNJ2SXLkFEWhUm9iGyrne22uXNqm1RDkcdM0sS1bZB8UXehysSoXFHEcc+76c5+tgE0PP3jZ932m0zGeVyFLUnTDYDQecuHiJe7du1fyVwaDPnfuvEer3aR/esZkNKLVqNM/OuKN11/H82qcnY64/+GDwqnT5NGjR6yvb3Dx4kWiKCbPwHFc3nvvPdbW1uj3+/zZn/1ZmbDjB6FIRp7MWS5XrLW7xEnCZDLm+rXrzOdz4TOfxLz99ttUazWevHWLarVKEiec29pCycH3BRv80eEht249Rf/khJs3bxKG8SeKV5rmLFYLzgYD9OJielUPv9hqWY5NrVrF9wO6613eeustNF3n3PktptMpYZAwmc6IImEGeHLSZzA4IycvvcjjNBG2PdUq49G4NH1brVYcHx9TrT7GRERXWqFZrzMaDQvnCF2csKZJEAaslj6TybT0H5sUiUdRFBElGd3OGu1Wlz/4v/5vItUkVzQUUihoLhL7Eqd5Q3QBWcxytWC5WGAYhR1OEGAYetkhgcCAoiBCM/TSxDBHRddUojDEK4JKTdMgzzMWsxHnehvomsJ8MRVuJ4qCaVoikapwa5WYSKPZKKyroVFvlX704/GYXm+Dfr8PgGlaIljEq7D38CGGIfzbPU+4YcixSWBCwqtMkGO1QkEguidBoRCEXDHWPPbGk51fnufl55DcKzm6ye2p7VrFM2cQRyHtdrvsGBVFZTKZCC1msbmUvLD5fE6eCcPPbncdr+qVY6ttuSxXc87OzkrGPVA6aMgUKbF5TMokqUqlgm5oZXpYr9djOp0QhTGdTof9/X0MQ6NWqxNEghLieeIQWKyWqEpeJpENh0PyPC11l5IjGARBuTUW3MCMSrWBoebcvLrDtUvnUckFu991icKQtLhe5EqBN1plwe1d/YzJ3B++9cOXQUS3K2pOpeLgeVW63TXuf/gh9XqdbrdLs1nHsgzCMOD+/Xt89+9e5datJxkPRly6eIknrl7nxz9+nfv3dolDsWIfj6f86q/+GrblcHh4xObmVintkB7ne3t7PPfcc8Xcf4QfBBw8OipW3BqWIYIUer0ei7lIGzo9PeX07IybT92iu77OZDIRm47C/LBWq+LYrrAjTlMB+ptG0W1QtsvLpV84g4pOIgdURRGe7502tWqVk9M+W9vbOLbD6ekpvV6POIlxPZfBYEC12ub8hR3SOCbLclqtJqZpEUUhwyJqznLsEu+KQoETORWX9fX1wgwvLcM5HNslSUIqrsAq3CJVSYiNE07PTrlw4QKmafLlL3+Z4XDIpUsXCQKfCxd2CMKYYLXk6LjPhw+PiRQTzTTJUpHsbRZgvnxZheA6JU0joZGMI9IsoVatEvo+pin0kFlhRyvThzRDL0HyNBOjk21Z6JqKa1lYpo6uqTiWyfVr17BMneFwgGk55Cjopo1eWLhUq1WmRRZBXAioXdcljnJGI7HBE6PRjGvXrhVawlG5/KjXaoXDRVoQOd2ygM3n87JDleOqEBQ/5rtBXm4k4zgt5UNyfJRFR46BH8fFQIyz88WscIrQoYAFBB3CYj5flPjVfP44LFqoKVZUXI9qtc5oNGLli3Ts0XCC53lUPKck5bZarTI9e7FYlJ/Ptm0MQy8pHVEUkSMWGhcuXODg4EAUJNTS7VZRchaLJY7r0qg3GY3GVKqe6CKThNVqheWYWLZFEAZEcSz83OIY27FZLBcoqkqapaRZSrVWwzAdTF1hc63OpfObRIFfjrgV18UP5MiblNQX3/eJ45TzT/58EF//udULmE+G5HnO5rpwCA0CsfW6efMm9z66w0avhWnY/OVf/jWXL18liiKeuP4Un3vueXFDtwUuValU+Pd/7z9AURRmsxmOY4nEFCJBVjQ1+keP+N7ffpeLV25w9doVZpMpVy9folKp8NqP/h6Aq9dvMRzM6PXOsVotyJSMd++8LzRwusJkNBZYUatNf/+Q+XLB+vo6Bw/36DS7pEGMbmilVce1K8IWOEkESDoYDGg2m6WX2Xw+p+JYNGrdwtHUYjSaMJ8vOZyesLm9yXg8pVqt0umukSUxnuuQ+CEXzm1jWQZaHuI6Oo4tTkCRSVil022L0WDlk6cJcSwCQVqtFrbnMZnMyvFJ+mTZjkmw1Bieic1XqsNyOSutYhpei4O9E7rra/zhv/kD+v0+Fy5c4JVXXsE0bS5fuc6tp27w3ge7zBY51XqV0/kpiuKSo5DlWhFtlpfOowJ/qUKmERoCiI4TyFSDOIdUUTEQ5NGciJwEJVchTTANCyPNhI2SpmGqJhXL5D/7j7/BWtvGUFw82yJPEv7l//qvuXs4YbIKIRUdjGS0y86wWW+UrhzD0YRazWM0GuB5Lrqu0+/3mU6n5fbq8PCwAMgtXBdWq5BlQblYrgLqjRbDofDPMgzJccvKLkoWJsMwxLPgCizWD5aFtCkpRPuPxdOyUMxmM9rtNuPxGNe1yZK0fCmbzTauqzGZzOh0OiRJUk4ZWZaxXImoNtu2WQW+GKc9p+jUqqRWShQHzGcBmqY8jhMMQwxDYzAaoilihK3X6yRJiqrqVCpVZrMZs8mM9fV1To761Ks1lsulyA/VFCbjAZWKJw6JMGKw6GMaBnkS0qiKaWuxWLCcC6yWLC84bitsLOJVgmmrZL7GWkthNFiipQ5JEEJqEyx8FCXg7HTEYvkRtYZF7Ct4tSrD4Zk4RFaLEkdUFe3nVCfxz6eKuf/on/9XeaVo+6XS/cqVS8KjXtUZj8cEQUCvJ/zFn3/+8zx69Ij9gz3effddfud3fofpdFoKXSWw6TgW3W6Xn91+u3TbJMv56KOPOOmPODk54rnnn8WyLBzH4XOf+xxJnDIcj0p9WrvTKk+e4+NjPLfwXk8EGXU0GglbZkMvYt7qZWKOeBAc9vf3S6sRoLRwFp1Pguu6GIZWxsZXKlUsyyl9opb+Y4yjUqlQr3qFL/+ARqNBmsYsFquSMCi4QlrxtT+ZtShHDl3XmcyW5Sls2zb9fr/Qoq6zLAI7pgVvyPeF9Y/rumxsbNA/O+XevQ+4fl0cKMLRVrzgtltB11X+/C++z7sfnrJCYRHPITUxTb3ETpbLZWmlMx6PS6xjNBqVGy55wotNY1T8GQVNUWi1WgzOxOG39HMsI4VkwT/9nX/MV7/yFQ4ePKDhOXS3N1hNxpDHeO0N/pv//l9wf/8UzTDRTasINxWfS9rayECQNKMcXaSIOo5jarVa6U4h7ckpsJnVasViuaTT6TCdioNnMZ/SbrfLTjZN0zIKcLValRiT6KweC7fFiJY+Zu4bj8coyWeS9zsIVqXfuyhui/LaeZ7H2dkZnueVxFM/CEoCr+j+REe7trZWBLO0isPVLvWd86W0/gnKbeLGxgYPHz6kVquVRpK2bWNbVikrGo+HpR21+H6wKLBVqUeUPDxhuCA2tv1+v1BQPKbcaDGEWcY8GlGz2nSbOXGkcjYBt27jYPBPvvFlfuHzlzEVD1XLyQkIVzlBFJbXX9OM8h2M45iXPsUT/1NHyLd+8O2X260Oi8Uc0zQLb6ljoiji4e4jLFPwe6qex1pnjf7JCZYpqAhrnTWSOEFVVPonfXRNZ7lYksQxgb/itH/Ko0cHqKrKD37wKvuPHmGYIgF8Y6Mnshl1wYM67Z8JjZ6moqrC7PDe3Q8wDR3LNFjMZ2JVHYsTcT6d4q9WwsI2y1nf2MAwxOcH8Dy34PiIVbPk7KytrZVEwTAUIQSTybh0LJhOZyiFVYwYL8XNq9dFRqBebJAe4wPiJZMFKM9TwjBA01SS4mGVaTlxHAvrniiiUqmyt7dXkHdFqHC73SaOBU4xnc2o1Wu4lQqmZdJstQTwPpsxHPaxbasEpMUoJIzikjQhyxLcWod37uwSZQm5lpFnGp5XKblPsvOTL6ckcIZhwGq1LDfIShFUW29USFJBschTiMKQ5WJOGASYToVf/uJz/LP/4j/CM0JWozNalTpqrqMZOY2Gy3R6xmw5pd3p8O77H2AYNqhaWVDlNktynQzDIC46GrFVq5feUfP5nM3NzSKXsVYkZ2elg0RYjNsSZ9FUpSxI8mWUL6T0pZOgv2075ddJkugTQSWWZZfr/4+7VCiKQrPZYLValaC0IAhTyonkYSWDRIIwLA8JYV4wLVnqhmEwGAi7Hb/A3cIwxHFdskyMXxIzHI1GQm5VyIm2traKyDe/PAjkJlMK0dM0Kblpuq5z7ty5kpoir6UUvEszSHmPDEUjzXNQcxzd5fMv3MA0HEajgCQPMRWFz926zPpalXCVcHj4iDAK8JcxWS44cSJqD0ajYSFXg/M3f74W8lNHyE6nI/AC08Gt2CXIGYYh1693iOOwIOqlDEdnAhg/2MWyhIZpuRA/aK1auBuo0KhXGQwGXLlyhc2NcwRRSKfTxXYdVEVjOhG2KK1WAxH/nher8zEqedndbJ3rCbB5PCRNojIsM43FetZ13QLDyTg6OBQmhNUqpmmVJ6tjCbeDKA6wLZP9vV1UVWXv4YNyhFIK1wLLcqhW6+UIbJomQRSWWrkLFy4wHY8+sc3KMoQu0nFIU8FQl4JbXdNKbZqUeDRazXJzIxOu5SkubZn39/fLXwdBUAL08/m8PCnl+CW8qqwSGPY8lw937/PwYIbrukxnE7JchMrGcUqWUTorSJpAmsbYjkOWJ2i6gqLm5b+aLjSP8+UCTdGxdRvLsJkMB9imieYo/IPf+GV2zrUZHx+wVquSxSmGraPpBv5yRegLYq1tW+RphJJG6EadsLDQUQqXTkVR6Ha7hYNJl4PDYzY3N0uSalzcd6swudzZ2eH+/fvCmaHQ0opCY5X5prLrlSRhCcxL/CtJkrJzEp1zRJ6nJElUumMkSVJ2exJ4L4NJCi6bTJ+Xf2e59MuuR352WZhnBfFWduPz+bzMBRiPhcyu1Wo8xhjTlGq1yunpSYm7LRYLzp8/X4TeLsrvK1OZHneNBrqultdYjM1L4jguC9/e3h71utgYyudMNjPy2UsKXExzK6ApJFFCpWrx0gvPcvvN93n99h66l5fYoGVZxKmCZTkkSYih2CJyrsi/kMVdhNy6n1aiPr0D++j2Ky/LFyNLRXUGGI/HgABtG40Gy9W84A+JNjfyQ5bzBY5tYxmm2DrqOhXHFQx1yxJuCk3ROXhehcV8JsA+DUbDPvPZmCgKmEyGfHj/wwLnEILio+ODYkuYFtiDjlcRLHyKmyNX5qomIpxkgKrclMitXlL4uz948AAZ4VWv10tfJ9t0SJOM+WxOnuUEvk+aJLiOQ6fbLQW0y+USvWDO+74AKN3ChsZ1neKm1MouMCyKz7wQ5BqGge2IUU06wvZ6Pep1MfqKdHIFBaUEt5MkIQgC6vV6GerrVFySNOPS5ctUaw0MU2flrzg9O8PzbEzL5Ke33+N0HBJkGagpWaqgG6YgVvoBuqkTxREoOUmaEHxsVJYPoTTyC8OQZqNFFESYaBjkeI6FoeVcu3yRf/z1L3Fxo4Oj6TRrHUzDJdMSlsmc2BdGe2maEyUx3U6P7//gx6wi0EyjtI8GyoPSMAz29vZotBpEUUiaJswXMxzXxvdXVGse1WqNg4MDut2u4IMV910SY2UCFYBRLAvkr4Hy+8giJH9PFg1Z4OS4GQQBuq6VxNX5XNARfH9FteqVh4I8qCzLLl9+VaWcLCaTsXAAcW3iJMIPVtS8GpPxuBCOt5jPFvgrn9VySVh0QEkioJk0TYlCn4rrMDgbiIWAKXIstKK4UUwTj7MF9FIrats2SSIWTpKLJn9+3/dLgrM8DCTGJq+LqWsEUUyapbi6xS+9eJP337/L0dEco6JTsSzOrTdIogmLWUC3u4ZpaZi6g6IquBW3+H55qaB59GifJ1/62mfrwFzXKzcuMpZL0wXeYdniAfODZXlCyuKmWyZGYpEpMFuKZJ2Fv6LZbOJHIZbpYFo6Z4N+UUwswmiJoTcJ8pT19W6JBwgf8yr3799HVXQuX77MpYtXWC6X5dbEdUUSS61Ww624TCYT/Mjn+OSYVruNU7GZz2asra2J8SMSrpeypT89OxEe+YWoN4oiej1h4+NVquX3kqJYeeIen53RarXY3d0VuEMsZniJ1/j+Ek0ToQvyxksHAc8T4HCW52XGoYrCdDzBqzVLt4TFYkGv18MwjOJlF9jGu+++y6NHwjTx8PiILMu4f/8+nfY6qqryF9/6X3jxxRf56KP7PPf855jPp5yd6aQKDAcT0qwivO/9BRWrXtIIKpUKqia8raZTISeRluB5ppClEIXCiHA8mlKr1RgcD6lXLL765Wf56q/8MsFiKsJbalX8UZ/6xgbD4ZC1bo/FZMp8GWC5OpYLTmWN/tkYNUqxwgVkKZlCyQmSxE7P8wiCoCQ4C0hBMNdFdNh+WdRP+wPa7XY53qTFmCVJqPKllI4SsqBJSyFJg/g4SVUUirgoYI8LuNw6JkkiXFen09IRQlXV0gViuRQTgqQ7yI2jpHjIDWGj0cA0TY6Pj4u/f0q98P0SgTgOcSQUAbqplR2g7/uEQUC9Xi38zWxWBRguD20pzzk7G5b0FPk5Pc9jNpuR5xmViodpmiXXq91ul1IsSR2R6oHlcll+nTQLhX8dKioKvY01VFIMTScMQoJU4xd+4ReoOgFa5rBcLUBJmE+WmLZBlseFBCwplxIXLlz5tBL16R3Yz175y5dN0wLEpsFxHCoVD9u2UAu+h2lZ4qYoCpYtRNnypFMU4T8utFsmR0dHhb99xNHRMe12C1UVUWOe57BczgvZioaiwHK5Yn//gIpbZX19k6effgrpAe551RIjC4KASlWQQMMwhIKD1Gy1hBRiJVKAv/vd72KaJru7D8uNUbVaJScrgc80TQspkQiP9SrVUnYiOUjy4afoRLrdrhglC1KlfCGyTIRUCIqC3Oi5bG6ew3YtHuzusrG+geu6HB8f4zqOwDTi5GNSloA333yT6VRwu+7du8f3vvc97t69y0svvSR+zmYT3/d5+PAh9+4+YDqZ0ettsr29haapvH/nPa5evSJCKXSNDz58hGI0GM3nJEmAoTlohoGI0APHtQu9n8B4ogIPlNdHjl1ytNKBL3/pJf7Df/JbnFuv4ligaynNaoU0VonSmO65Nf7Vv/5XfPs7f8nnn/k86+1NomRKs7PGyo84Oxvy5htvcHQ6INNtMsQLslgIuYw05ANR3FaFSaPruhwdHZVpQqvVCk0VBn6DweATshrZVcnRVNd1Kq5TdFB64S2flIUNHvO95GEnC4F8zuM4LgnA8vrIZ1921tJsUfK9pHwoz3N8X2w6pa7TcRyWq2XZ6dmWVVBYxFLJ932yPGW19PEjH/K89NG3LaP8nvLzVyqVUgMJgqBs206JAy4WAkMcjUYl1CG7H/kZpHOF7LiDICgxZLnQEsuKJapmCTJxEPLVX3ueLIHXfnIXTLA1i6986QUMLWIx9ZlOR0xnYxq1NkkalaoI162US6Qszdn+rIaGr/3VH7y8WM7Ii4d6vpgRhkvIEgzTwnUcPLeKrlkkcULVqzGZjKjWGjiuiHoXD1bKtSuX2FhfY7WcE0Y+09kA17VYLJYsFktU1SQOc1BS8jwreC0Bm5ubzOczwtBnOV5Rr9bI0hzfn2MV87KiKFRsj7PTs/KlkxjObLqgXhU+8Nvb2yRxRqPRwHU91ta6BEGI63oEQYRp2LTbXWazJYuFj645xInwWRde53lhiR3RbDbQVIU8S0SEmaaW46PYTmVluz4cDknTRFjMkOIHKx482Gd7e4dGs0EUx6x1OzSaTQzLpOZVydIEf7VAURI8z0FTFCbjCaf9My7sXMCxbdIsIwhWLBZzut01bt68znvvvstsNubgYI/5bMqtJ58kCiOiMEE3TTrdDX5y+x3iTCGIVziuSxDrGLp4YMhF8G7gL1HyjCgMUOM5iWoxCVNqhoqtwSDUqVZqeGrMzlqTL730DB++e5vpcMDeg0foagXDqNJq1ljMlhwdnvBLX/gltjbPcXJ6yI/+/lXe++Aef/1X3+Pqhetc3O7xhV98nttvv8Ng5GNYFmkS06jXiIuiMh6PBRk0Fcz2j9tBy0NSYDuPt6e9DSF4ljiNZMSDWLCoCiWp1DSNYlSHNIvJ8pQ0jVGUnCyPIQPXccizDMs00RSVMAgKJTfM57Oy2M5mU0AUeBm0IW2ddd0oOxlVfYy3SfJs/+REpHMpSvGZFlQqDiJ708eyTLIsRs116rUGSRIVh52QLQmyrUqW5SRJKvII5mJakUVLVRXi+LGjbBk+rBsomgqKiNWreBXmizk54mf0i2UEaYamKcKJRFVRsow80sm0CFXT2Kiv8+999Sr37x3x0e4S2zawjZTnn76CrUAYrqhUGiRJThj5eF69IGVT5HOKGjQejz/VE/9TR8gwiNAtyhNCMost3eD4+JA8V0gTsRquVCoMR2fEcUi/L1KN7QKHarUaTGZzkjASRmWFn9JqGRCGYbF9UIGUw8NjkiRh7+Eh29vb+KuQWlVgTI5tc3h0RLsjhLqiHT4TmsEwodfrcTbolyfs5uYmSSzA7jgMGY/HrJZBuY6WIanzxZh6rVlsuWwcRzzMUpYxHo9FIpO/LAH0g4MD7IKI12p1SjwwCILyBO9tnGNvb0+40QYRluWQ5TmDwYjt7W3yPOXo6KgYVZbcu3dPEIPrreIEFVKPOI5xHY/19XW2t3fo9/s899xzJFnK0dEBlmUVxnqnfOMb3yjW48JGGeCLX/xioQ1MqDUaJEmKHwnngCD0MU0XzxOdSNVzWS2WBYlQxMgpccwsXmCYLrmakCQxpqpiG5D6Af/7//YHHB7scfDRexw82hUE19gnyyOiKKdScbFdu/Ca2sCwdJ588klOh6d02hskoeD8vPPOOzzz9C0eHv/08TNY4EdAqQ2NkhhFUT8xAhqGQafTodPpMByMS8nNaDQqaUCaphEWZGmJ58hnRQLgkvtGpqDpStmpaJrGKlyVDPP5fI5XqZYdWVJ8HemEIukfsjDJjrVaraLrZvkZLMssFwCSxf7xaUCMtXYhko9pNltFIc7RVLHtrtW9cpsti7oMHpkXJoqOI8jWshuU2KIImhXGpOVCJBQJUxXHxV+usE2L6XhSWnFnWUYUR7gVB5lXoOSApha0lZTz2+doNdrlWO/qOkGwEMlQ/kQ4YWQK2lJjsViiaXO2twW/UyadVSomvV7v00rUpxewZmetkJXMmBUY0quv/oivf/3rhLGw30iziMVqySoQba+mK3hVYdMbJbCYzUsawWq1YnvzHGESFgxiXxBND46oenXq9SbrilYKQdM0ZbValLiB57r4wZzDwwULXwSTDgYjumsbnA3OODs7ww+WrK+v43keb7/9NmuddTH+TeZcunSZfr9faNNEwMN4PGY6G+P74qVd+QuBR6kqXtUmTRSef/75orVelsCyqqqMpyM2NjaYz+ccHR1hmjZbW5vMZiIi6qP7D0RBajVLvtZoMqbdbrN/uI+lG1y9epXBYIBrV+h21ljOxUZPmOOZOHaFxfyMdsthPJ7iOII3961vfYsXXny+fDgPDw9ptVpEQYjvLzEMjclkVOjTQup1oU/Nkpw8VrB0jzQNWAULHCstJTIPHjwgTzNQsvJnJkvRTEdIfbIEXVepagoXem3+xf/wL3n48H1i36dW87hx8xbvv/8+5y+cQ1V1TNVktVowmU1LiYui5Lz77ttiMRCkLOZLTk8ecf36VTY3eoSrOdVavRxVDU1jOByW1AJhA5N+YoMnxcyTyQTTsFlfX2c2m5Uk1EajUepLpctHFEWYhhwJhZWx8NxKHnfySkYUJpgWnwj5kPiPxIgN2yosiRSWS8H7MgyT5XJVYk/ShkdSQoT9z7IcUcMwLPGwZcFXOzkR+Kw8bAeDQSnuNwxBZZDbUNGJijFaUQQ+1ul0GI2EPZEsNq4rcGIpYpfFVo6aUoP6cfZ+o9FgsVjQbDaZz+cl5i1hhDROyBUN07BxTI2v/uqXWS7EMkvRhNC9VhGE21wTGCopoknx5+S5xnA4ZrGYlW4is9mCavXnUsCA/48R8vYPvvPy22+/w/Fxn05njVqtzo2bNwnCiHq9Wrgc1DGLDValUuH4+ATTNMqtTr0u4p7OTodsbp7jvffv4NXE1qzbXQdFxTBt6o0mumGgqDnL1ZL1jQ5Lf0a17tFZW8OremR5LPhPxc1AVQhDseb3isSXrS3BWzk+PhaC1TQvb5LEbyRukWUp87nQKt68eatgKs/RVOEuOp8L7d/R0WF5UkrynqZpLFcLFEXSIUQXmmUpnc6aCF+o1HDdCkmakefw3nvvYzsOum7gFC6qEl9azBeoqka7Kby7BIirAcKqWFW1ApcQW9tLly7iVatlfJjEbrJUdEhCwLuk02lzetovRLsune4Gf/3XPySIdFZ+yCIcU/PqxElGv9/nxo0naDaFK8Z8PkdBR1UNTEMnjUMcQ8fSVf7rhGuMbAAAIABJREFU//x3eenWVT668zZVT6NaqfDgo33CImg4jmN+9KNXmU0Ew3zl+2iaUoxnAvRdL7iC7bUWtmWiaSrjyYwbN57kzu5B+WJKKosUR88Xc+IwEkaIugCI67U6URihazp5TvlCJol4WeQ2XeI40is/z9ISyFZVIXqX9AThsiBIynGUYBcUIsMwCt8st8zDzBDb2fF4XDqtym5NJiOZpll0x0IDKLhreVmAPy5DStOUIAioFWJ0SUaVhUTTRMh0p9Mpt5uie/MKJxIhdh+NRgV+F5WFNwiCT/DFZHGUfDrZrUqKD1CmMAmPOB9VUUordoA0SUDTUPOcRsXkv/xPfpcgGJHmBn/3yptohkbds3j6yUtE/hQ/WBFFKbPZBMjRdbPc3MvnXeJ252+++NkwsHu3f/CyVxHrfH8V8MHdDwR4mafEUVAypHVdo+rVME2TZrPNleuXqddqaKpR0hXWOl1M02RnZwevWiXL8sI/KSmSjX0RTFB3yLKE8WSIaWnoulqA5zlZBpPprAitEILeer3JeDyFXDzsy9WCk5OTsmhVKh6+75dmbYvFgvv379Pr9UriYau1hu+HgEKns0YYRjiOi2GYzGYioPf4+LgkuMpTUzf0ApwXp3WtVmM8nqAoIozCqxREylx0M+12m1rhbaZrUHEqtNttsiyj0WySJkUyT0H9qFarJTtZEm7TNCZJBDXj0cEjXFck1EjNp6oojMfjckQBWF9fL4mZx/0B733wkDgzCbOYMFmhaza+H7G1dQ6v4jKdjBmNRkVMmtC4/o//3X/Ld//yO3TWOjx960nadsrWRoso9Lnz7nu8+cZb3Lv/EefOnWd7e5u9vT1s2+b81jbNZpMgDKlUipPdXzGdTrF0G8u2GE+E8Hpra5ssFUG59w/Oym4nSdNyayfHNatYikgypbBqFp2UU2hFe70eeqF8kMXE/5jQWHR4WdmBiJQlMUpKrMyyzHLcVIrfl04PURSXuFtevORS+iVTiwQ1IfkEZyotpFKmadJoCG7hx4m0ktMm/4zkrfm+X/LSROiNWxYYabMkC6ws0o8DRsxPqANms1l5veSSQz5D1Wq15DPKkVimOM3ngjJlGSaqJjzj8sIyKUgiWvUWRDP+4dd+jTRbkWsWf/Ktv8P1HEhDfucbX4dEjKzT2bIQmuvops1ytUI3DGp1kdJtmCY5sP3EC58NAzs8PBRYQuZi2SbXr19HM0QVXhTp0xsbG3S7XRbJio2NTRQlpn8iSK2VqkeeZly7do0giERKtSpSbISXULM4VVWiKCuIsQn1eg3dEBfy7HTI9tYOcZziVDwePPiQMIqo1ercvn2bBw8ecv36DZ6+9aSQZlRd2u12yYSOo7QgAubs7u5x48Z1bty4UbTZShnEK+PloyhmY2MDRRHR8pcuXSo1jHKl7nk1giBgtphRqwlrl1arxXQ65dKlS4zHQ6bTGY8mB7Tb7TJ81bIqxHEoXFEVSOOEIFiVmIhuGgRRSBwGuK5X6ON0VFUnjqOCBGiWkh6RpReXaTbyhez1eqUjqZQ2VatVxpMZJ2dDwjAgjHIgI88VdMPh4PAhL774AsvFrKSSqIp4PDqNOhd7XboNl9lkxMZmj6qjEvtipPjZ7bfZ2OwRpjl7B3tEUUCv28MuPmsQBKiaiut61GoNdFPjo48+otVcAxXWNjdQspwsV/n8Cy8ymI75P7/9ajnyoAprHTn6ybF5MBiU90WOhFph31Or1YQ7ReFpJlnzEiOSbhmGrpYLAdnxSUmNU/DyZrMZXtUtMTd5bWvVeomhSRdYiYHKF14y3OX4mOc59bqgrYj7+zjlW3b3Ev+SiduGYTGbLQqgPSdNi8R0TUFVIS86Xvk9JY6WJElpxx5FQcl3i6KoLFKyO5Xuv1mWMR2NhYojy4migHZD2EnVarXy8+Xa49IhC6pVcQjDmKeeuMxiMsSP5qhWjbygRzQqQj5XqVSK2tElyzJOTo5Y06zi0MgYDIZllyuJrZ+pgG1f2Ma1XNrNFv3+GR988AG2a5GrOTtbOzTbHdrtZgEO2hyd9FnvbnI2GAmm93ROvVorRaazxbQ4ZTRs28S0dFTNpdXsEAQB/f4Zr776Y772ta+RJnD/3gMCP+LP//RvePjwAN3R+OY3v8m3/uxbfPnLX6K91i1E5EnJ0/r4RT06OmJ7awff9wmCiEuXLgEUfBfBJveDJXEsWvdazWM+XzEcrsjyhLOzMxzHEXa6u0I3eevWLXxf5PSleVKs3yuF2Nfj7t279HrrgoekanQ6HXTTKBUMliNGzbpXYVQQgqfzhbBKbjYIgxjHkCnTVllkWy2RyGSYGlvbm/irEFXXSqxQ0zRGoxHtZovj435Z0BaLFc1mu9g01Xhqc5M/+pN/R5orKLqC41YYTZY888wzLJd+aZAnuwFNM5mNTkmDBfNRH7vVo9frsVGLaNQ9BvOAF198CVS4cPUio9GA1XyBruQcP5pz8cplLMvidHDGj3/8U5588gauV2Fr6zzhImFtvYMfiyDWPMoZjkeYtlV2LIZhEBZOq6ZpigKBIFpKH3i5gRTZjhaLpV+C95LULOkT8gWWHZZSiLeFH71RYF/ihQwjYcyn6aJA6YpehsbKYidlQ76/LJyDM+I4KvCypHBmDUpmuSh4AnCXuJf0/pc0i8dOqjrj8WPsUOJNEnjP87QsRGGRJJ7EGZb5cSG+KACSiCwL8MepIv9vhQLFqPmY6uGXWZhSH+s5bombflwVkOY5/+A3f53An1Nt1Dnzxc+cJELJcffuXcx8BYgFRLPZpLe5znzh0+v1UBS1KNoaWSZ4kJ+5gFmWhe8vef/4BIDz57fQDJVmp42aq0XLqWGaNoeHhwR+zHf/9vtcf+JKyRB3r7kFgClCWAfDUyyzIm5oJFri6WyMqujUah6/8itfI0kykjij3VqjXm+zde4qgZ8QKyLR+5e+9MtcuHARz3NRUej3z8jThO985zu8+NIL1Ot12u026+vrZbtsWU55QklSXpyI9fFqGWA7JpPpqLA8ScizlCeeuEaSZI/dHuoCTJUyENd1i2TiBcdHJ3zhC1/Ask2Wy7noiiyDB7sfCt99RaHTEfKrJElYzWeF3Yx4gXZ2dlgFEesbmyxGA9I0F2EVtltynIIgwDCVjz3oj0NLJWaRpmkJWIdhSK/XY1W4G4RRiuZYxHGIprlgqKxmAZZV58knn+Tg4KDETABUpXixDYN2s4FjaETFhsqxxckdp4I/FacxuZLQWWsSVhzGZ2PWOh2SJKXREP72t27dKruM1WpFvdYuk6WCICJeic7GdIxS0iOTfmTBieOYar1WOMMK3arszBRFXBvHFaRMGS0mO6HBYEBadGJSC6igIh1fpQRM4G6PDfnkIsS0zU+QYaVbhfz68s/KIiGtq03TLAwFqkWatUu/3y8+uxhp5YZPjv1SjiR996XAXB7Uwq0hKMdjAXfM0FQR5CGzAyT2JuVCQAnMy0PCtu2yi5JQgxSJy4WC/K9q6GWxUosQ2izL0BQV0zGoKBUWszlnyxlnYxVfM0iVtLyum5ubuFpEGEYMRzPG4yGb5zao1QWO6/tBMaInBSH3/4eU6I2//fOXdc0othkt4lRU3MViyulxHwUYjwdkWcJau8NqteTyxcvMV0X4ZxZxfHLC//Fv/pjLT7zAzI/JMAmClCQJmM8nTIYL3nnrLm+//S67+w+5fPkq0+kcy3KZL1YEUYBh5fjRGMuxqVZdWq0GjivCOEaTEX7k0+1s8Au/+EskaY5hO+So9Da3ePVHP+Kd994jyVLqzRqmbaAbCmeDPmdnA0bjKadnfSFwVVVe/eEPcSsejUabn77+Jp31NbobPQZnQ2zHxnVcgiAsbmKIa7ukScLOhfMEwYrT0zMsy6Zeb9Boiviw+XwBKKxWPo7tcHR4jGE5tDtdavWGEC6bOqom0nL8ZUS90YAcTFOk/YThCssUbq26rrOxvsl8tgRFMMTFgxzg1mosfLH52uhtMJ5MmEyn2JZNnCe0rRav/fQtxmmCH6WYucXXfuNXCaZj9v8f0t40yK70vO/7nf3cc/fbK3pDAxhgMAsGGzmcITlDihoqIkVSjCJV7NiKbamiL84mJ04YxXExH5Q4TpWTUixb5TgxI1pOrEixJJtaOKRIzgxnOPsQs2EANIBGA73f23c9+3lPPrznPd1QxVRljKopoAZA4/a973ne5/k//+XWB2xv3SWKYwzdRtN0XNfiV3/lr2OS8Nyrb5OYHg8fn2J6dhasKmY4pjk9RaNRp7fXo9OcwR+HNJttHK+CYWoYpk616lGtegV/TicXOb3RgP6gj+tUaNaqDAZ9HNvGH014/rUPyFJBtVonipPCcmaIZdloWY4oRjEZZV8pQe4sy2Rwi1dB8vYEQmT4/gTIMUyrhA9AynjUj4rtkCUSWLdNkygIyTMhA30xECDF4FmGput4VQ8/DHC9CmmaFW4YTfr9ASrJSI5MkqMVxwnVqnRyUEL9IAiZmpqm1zvA82ScmxSGS/6hUiGoLlsVF4nbWcSxFIcrvErTIQj9cnOpQHvDMJGp2nr5sxAyjSqKDsde27ZJClxRLbzUKC8XEGMMU0N62mtATpbmmLpLlQYfe2yGzz59CavawHN1tm/v8vwP3sKrGlTtKgvHHNau3abTadDbj2k2LQa9sDB7jJmMh+jGoa32wcEBZy8//eEwsG73gGazXsypu/SHA06cOF60tzau66FFkvsi7WFlZe80mlRrNroO3VHAX/uly9zcDbn0+Me4dWuNij5iOOjSrpg02i7LTz/B7bv30Az4W//Nf83jjz/O6vETTE1NgZYThnLr0qjWmCDtqPtdacHrOS7VwiWg1+uRpin3tjZxXZe1tTWazSatlnQW9X2fnZ0tOq02udA4ceIEQkCaKnypwuXLHyWKJP/p/PmLTCYT9nevl9rKrZ1tGo0G/nhCxZNr7clkwmJhbTI3N4cfBoz9CUEghbHKuE2JfS9evMibP7xSdhVzc3PlTRrHMXGWsrO9y+LiIqZlsLl5l83NbYQQnH3oATRN4+rVq/T7Yx459yiTyYS5uXnpzjCcoOsGM7OzhbLBYGlpCds0mAQ+lVqF4XiE25omj3P+27/9Fa68+xa7gcXe3l45ZlmmJbGVKKDdbDEeDDE1HZElXL58mc31a3T3Bzy0Msfa2hqNRoPjx49TrzdpNpulH9Zw2C8DSnu9Ht1ut8QUMaV4OYoi3n//fdrtqfuSpev1OoJDIbUCycMgBEMvx4vhcFgC0OWfKTZpyq1BAeiG6ZbjGYAQUDRVhRwmxXYq5WhuWQ4ikwuRRuE+HEUysk5xI2W3wn18M/XQKzmS2iyq4BHlRKJe41E7ZvX6fN8vLYMUqC6EKLdzQKlEUVCDcrFVVkJqIaAWAHL0zMtpRC64KiXfLooi9PwQl1PdmerCRJ7i2m6hA7WJY/maMARhMOTpT30ZyEmSlHAy5OTJ1QKrk1K6paUlso4crev1Or3+PRbnThKHUTlJaLmgVZfa5tFo9KNK1I8uYKdPny79vO7cuVMKjZvNJpWKnL1zTWd3v0uvf1CKoS3d4rnvfQfDNOn5CU7nFAN9GuvWHksrj7Bz/TUa9Wk0bUDvoMtwNGA8HtJsd/iVX/mVEohdX19na3uTdltSNjbW7xZ+YhXm52eJi05IxTEtLy9Tr1cZjGxarUYpo5A2QNsFidDEn/jFlma/BCdN02R9/YfMzs7yne98jy9/+cvcuvU+c3NzDAYjPE/eQEtLS+zv73JscZ7trS1urN3i2DEZBGoYBpVqrRQB5zmsrp5kY2MDy3KwbRdNS9jYuFeK0UsMJQzKh3x6ao7vf//7/NP/85/xS7/0HzAcjvnMM59lNBowGQ35vX/xB5w4cYpqXQLVf/D7/5JJGPDAA2c4ODjgS1/6QmHnI3G9LMt4//o1NF0w2zyGW6kyFDmXP3qJ7377jwniEVeu3AbNQDNMDMNC5Bq6luM4FuPxkHZVLgwc0yb0J0W0mYPruiwsLJQcJt8P6fWkI6osKjm7u7ulDbEatYbDIVMzs4g0JSqkKmrpIF1D5Np+PB5hOnb5wJmmSSgEtmOX2IsMGJGGgmpTedQaR3VbCmiXUjXtSKGRC420mDCSRD7MMsnbQMbemxwc9ItxUSpIskxQr1cKfpmDCgaxLKcE7G1b2k2rAiaEKCU4qqNSEWbK2QQOOWdqOxnHMSsrK+UWXL0Xf9a659Cy59DeWYH3ajOt8C212Y6isPx9oHztijisfk9hYnmuFb8uRkldvkbb0UjigLW1LpbbYn/3Do41JIkmxFkDr17j2MIcZmoxHO3R3U2YnZ2l2+0yNd1id3dXQiK2A1rO9PR0Sbj9UAVM6bMGgwGPPvoo/eEAwzA4ODj0XF9fX+cTn3iSbrfLjRvXeOCBB9ifhKyePotlWcwk8N7NPjuTHrPLE9zegEp7lh++/iIXjtep12qQxszNz2BaFfJMIFJBGEQ8ePpMAWZrJfCocAD1ISncotPpcO/ePdlyN+rl2lndKhKrGpaMbUUo7HQ65a/n5+ep1RpcvHiZfr9Pu90uV9lyFhf0ejLEc3N7C8e2OX/+fHnoNENnZ2eHY8eOyQ3QSFI2er0eZ8+eRaVDJ0lCtTi8W1tbRTGN2T/o8ZGPfIQPrq1x8fIlPv7JTxD6Pq+/+SaVapWK4/C1//1/5dTpB5iZm+WNN95gfX2D/+Ir/xX9/pBWq4XjODz77LM882OfpnfQpdGoMZlIk8b5YzOsrV3n2Nw8Fx45R388YXt7k417N9jdTzEMrTy09XqbNAwIAx+v6nLQ28N1LOJEo9/bx7Pl1qpRb7E76pd5nKPRpKQOOI5DtVpncVF51Muw1dnZ+UOzwaKwhGHA9HSdLJV+U8rMznEc0vwwaVqC7YdMdjjUK6rPNwzjosMJyuKl7ILUqKQeQqOgEgihkRRfO86knlAzLAzDJMtyJsMJpiWpF6oTUt2VouwoYqtSaxzSLeLyDClStgLq1etX4nAFqA8Gg5IwHccx7Xa71CEqQP1oMVLaTvXn1TZSiaKPeqCpjbsqTOpzUAXP0o2CplEpsbBD/zBVzHRElqNrBlkuL+JWy6VeryL8nN1en1a7jUGl7OJGoxHXrl2jatSwbMHdrQNOneqUZqPtdhNNM8qtbaInhFHw4QvY/Lx0hZibmymIdtJQb39/nzzP6XQ6nD59unBpbPDYYxeks0TTwLFNbNviO8+9zNrNW6TVJd6+8iaLC3M4bo1UmISxRtW10K2MqXaHvf0Brm3R70ugUj4UFqJw31S3UavVKlpcrQQnbdvmxIkT3L59myiJ79s6qdtuamqKNBXYlgRg81x+6Pu9bolJ7OxtF4C5ZB97ha+WaZqgy5a8gjS6MzRKdnKSSefKpaUlQLbg7akOTsXlxKmTpdOFYZnMNRqksVxhrywtk2QpIKTj6nDE3NysFO4WkWU///M/T78nWfW//J/952U73ul0MGyLXq+LYZh0u3Ij9/DDZ7m3eZcw9Flbu87s7CxRFLG3t4dIcy6ff4wb/RG797a5d+MGk3iMrtUQ5OToaAW/x3EcahUdUxeYukazUcXKLUQW0R9EVKoNtna36BYLDV3X5fdQ8OTUA6zrOtPT0+VD8M4771Cr1Zg7Nl8SSisVR+YKzC8gNDmOWZZFKrLDC6KQ6jimRZjEZUegPO6VAaHi5VWr1XL8OjQqPBRbg34fo1xaL2domo5p2OUWU9dNHNeiVvOk20rhN6eE5kd9ruI4Lsc2ZYvs2JXiZ8lUH498LNMp/e0ts+B2YUjNqpFR9aT9c7vTLN1k1cJGLS0Ur0sVTSXEVlIkNSKqIqc2sOr/qXOqzpMyVMyKs/lnR1rlfJGmKa4l/e7iOC0LVLVisnVvAyPLmAQ6/qTHwuwqtmViGBqmKbtC07aJogHnz59nY+MKJ1cfIU388nPyw6h8XUfH5f/fBWziDzANmziWqcLNZpMsS1hZWSoPk+/71GsyGsqyTcbjIdWqBSKGLKW3d4+DvW3iicRg1u+s0a6Y1KtN+kHKyVOL2HqAicb87By6TnEQj/JA5IH0ownkOpMD6S3u+1LXaNoW416vlEekBSGxVquVxadW80gS6d8kEoFtFX5husGxY/Khq1YrRUQU6DoFP81HCFksTUOa4BmWIzuDcCIfkiy9j0dUrdcxLIvJSPJ84jhECCnElZiFT+hH5XbItm1SIcpDqpsGupZTb1Sp2A4ijalUnEIm4xBGYyzLwHRMms0a3e4B1ZpHGFLaHum6QZ47LC4uSrLi9CxREoKR8MKf/gm3BiPGUY6lOyTUMDUNTdfINQPDqmAZJv6wz8MPrhJGI9ASQr/Pf/pf/m2sSGJlSaajRQEzMzOMx+MilEUrnVAHg4HUvuV5qc+s1WosLy9z+/ZtVlZWMBpNslySKDudDpMwKHlZWZZhWiZ6ohOGR5K+LelKoRjs6oFU1jtZlpc4k9I5qi4jy/L7x8oM9CKJXNdM8lwjz0WJJwHouih1lSo8Q8lwFMgd+FIeNxlLvCtNBHEclthprSZ5fbVarZwMqtVq2ZnU6/X7nE6B+zCr2dnZkmOmLgXlPKGKjer6FCamis7RhCCFxR513ZDLgLj8GpWCJKxoF6qjO2qnozo9NcLalsXjH71EvepxeuU0a3f38NwOItbotKfoRZKLZxbuHBryfZmenef111+n2ZKuIs1mE9OxMQ2bhYWFf7MRUn2jhqlhmQ6pkCGc8kBkgI6upyXHJI5jKVHIY/xRSJxlGJqgUa+xmwiiRAKpWibf3B++9SZPPXmOYBSQRAFVu8723j1q1QaaJtnoe3t7LCzIUJHp6Wnee+89pjozhcNqs2TY26ZRtvOtVqNYacvb1nXtQuyqk8QhaSxvSLfilJ5mpiVlIAqHOXasTRTJkUUtMlCtddUpP0zLcqhUPQaDQdGl6eWtaNuFaZ6lfPMrZdw7Qh6MxcVFeZM7VlnQhCbw/TFkgoOi6Lqui6lLHzLTMkqvsNFoUC4BsjylWpVJNa4lxyz50DSYjMaMA5+pSpvJeIiWQ55mJEaOME1sy0BoolhqpOhCgud372yQZRmLi/P893/nV9nYHWLnEEcBUWpQISUJ5Z+dmZkhDOOSaJqmqdSaHnFjmEwmdDodnnjiidLfLIokr84wDMJYgrvKkTSMoxLsBkrvejU+qgANWZyyQ3F1mpYXGlB2a4d+XkWyt65DroFWjEmJICfDtk0sS4HcdtlVKR85pYGUn6+MA1NuF4pFrzoexTMDSkqE2hAqfSZQFm5VeOR04dy3hDjq7XV0lFWvSS0SlPZSUUQUMVuNlMq9Vo69Vvl3FUVCvV+qAxNChuWajlt2nRI7PExOf+ihBxnsrLG9vUvVa0BebIhrNYbZoed9mqZgSIXNlXdeZm9zyOrJOXkZFBbfAEEUSmeMD1vAbKeK42olSzgIgpLrMuwPDmUIaVIC0mGakOkpljWD64ScemCJ539wnanVKfx0wrg7xO8KVmYrBIkDRhXiHo7osHNwQGdqBpDzeI6g1++yce8Op0+fJvR9VleOS2M9QbltkQUqpVKpl12hUYCqenmYJL4RBBNsRxYPQYZh6WSxja7p5HpGrdYoQzU1zSDSM8JE3sau7ZJmicyxS1MsR3YVWzvbzMzMEUUBtZojsQbbJU4ORyjXpWA7eyVD2rIsDga94lYzyrGru7cvb2x/QKMhwx6SSCbQeEXIShzHzM/MShFtLNDynHQcEOc5Zq6RahH9vuRKpYXk5eL5c2xcX+OnfuYL/MY/f5ZYz6hYGY5pEkQJlpHjVWySFGxLuoJ+4omLpJOYreQAXR/gVSqYpo2hO1hawsLKyQK890vdoPIya7VaaFrO0tJCebt3u12iKODu3Tvl4Y+iiHqzUW7Adnd3EYkgRTCJQjrTbUaDIZqQoPhwNEFkOZqmQlFMbNst8CWdWk12S5KtbpQdiXwQ06JDPTz6QoChmRgY8hygYWpyRNIMnThNyPOMLNPRDQu78K0XuUHFk1yrPJPdiOoKXdctLzK1DVQY16HluEAzdDRdpslbji27b8Mgz1JpYWOY+NGIildD000ykYJygDAKh440wTTkhRUnGY7rSdNDw0bTc4RIEUI+w3GUohuys1Td3dFuTAiBrpnliB1nKXaxYTdsC6GBnVtoCIQeYVg1RGJQrYasdjy2wlnJ8QxGjIKUrKJhey7pvofl5SzML2NnOkkMgTXgqaeewtBdRJqU4dK2Jd+bcDwhi+IPX8BKMa2mHTqyFrN2u90uLZkVeKqKnG7lOIZOHI85/cADVLwXCX0fP0158803me60uPLKTWamlhhP5Jo+OtBot9s4lnbfLbC6ulr6bIWhvPGSOEPTjDJkYDLJ8TxpI9zpdDAskyyWG5aw4PwkSVJIJrTyIGlaTpYl2LZJGBaiXCFKzEymBtdKEbi0SXHJ9Ry7eJ1KdmRZkkC4u7t7JN49uE+gqx5w5devmM8KGN7Z2aHT6RwC/dVqOV6oC2I0GpV2La1Wq/y6eZ6DphEVWj9FSFTcKKl02Cm7Odkl2YhkLBOmNQ3DMgtKiU2axUxGAz7/kz/BaGetwAzzElhX1ID19fX7AHTLmhxKSxyHNI3vGzsajcah/XWlWgDcbkkKrVQqTOtTsks2wLKNkoyZRnHJ/lb4DFCqDtRoqF6L2kq6rltmSx7dpgGySCDzLFMOx1TTlM6iFdMhTOLyeVBbVAVoq83iUe6UurQOGf72fWx2VSzG4zGmZZYOJ3A45h2dalTxlV2bga0cNXJpMZ4JjVyTMIvIEgzLJskzHNcqPPqdEl8yLP2+jaO8qCk7MMMwyFP5e9IHjbIGqPdPfo8Btm2Sa5ClEbVak9FoUF4Y/YMDTp05BXaDPJexeiIPGQwO2Fy7QxzlaI50xhj0fRbm50rvf/UZqfryoQtYGIb3OTwqEDbLskLHF5YtusIhHU0yAAAgAElEQVQsHEfaQ4fpBNcxGQwHrCzMsj5MybKcY4sLnFxaJF+e49b6He5t7qK1fGpGg8F4TGqbpeWHTEiWD2G1WictOqHEkgVMFVFZQBweffRRebBNo8CvJHYUxzFuwcRP0hjTlHHoURThuBaGqdFs1gtAOC22IAlRNMEy7SOcIAvbpjyUaRaWzHJpGWQxMzNTHg4l8ygtRzKZpDOZTDB1Datoj5NISkvmZ2dK73Hf98tbWnUTSZJgu04pmRkOhyRRXBYxNbodDTWN47jU3vV6Xc6srPKv/sHXyfNiq1SMCJWKQ5qF8rZOBToGmi6ouBZzZ06XRUMd5nrWKOPdLMui1ZGhD74flq8jyzJc3HKkAoqRvCLdS7waOUJ+hoWXlKbJjsFxLeIso1Jx2e91sQwTz3HJdYNm02FQbL6Ojk+qs1HjWVR4wKmxTBY4+fXV31W/zqFkmWvocvOZpBiJUfwbFqZFySuTGGu9wNwy0iyVEX7+BN00iJK4fH8qVY84TajoHrpp4BTBv+1Wh263W+oW1Xt09JlS8h3f9wszgaC8oFSBVOM1UPybByXhVV28sgBTYmyqYMrCRdkhy+9fQwhZoNNclJdFlmWYhkaOdBrWHQ3TNqjbDp948vHC0NFlPJYcwJ2dPRJtLBdnhs7xpWV0YHFxkdEwxGvJDjmLdhkOhyVGqtw71Fn+0AVMpBkH3Z6M9BKCLBdl94A49ARX8/b+/r48zI0mQkvQDJ2pziw/9+98jl//2h8wEjp7+9tE4z5GlqNZgs2dPi0rxTDHZHEOBW40HA7pdrulxYdUzB/KLISA4XhU/Pt1NF1nNJHExmh0mPorBcs5hi5besuWflSVinSMTbMYXdeIE9m5+JNQhodmWblA6DSaJfAqA1CL+PQwII2lo2utCPAYDYblLeWLQzM8kUvLkWpFbutykZZ4gFN4po+LbkttR9UDr6ylVceVZRmInEatTv1YvfxzR4MX1Ph0lPyoJDpnzpzh+otX0TTZMcS5RpSEmBZouUa9VkcD/tLPfp5chMTx4RpdUQaUcFlZ+ShA13W9sthrmkYSxaUOUGFdSZLIxJyJjEWzbLnV8zy3VBTkRQqS0vNNRmMcU3Z0lutIRwrTJBOCTAjiJEHksp9S3cLR16rwpUqlWvK8JGs/pV6vyvcuK7IdNJ0klQ+z5cqxyqkYJHFWFhPP88r8SUUeVWC9PCeS4KokQTMzMyXf66izQ6VSKbEhNXkcZcCjup5io5hlCXnRwWXxoRWQ6ghFklL3qkyOXICGYRUdPCUWqb6+fH+iErPL85y82NQKIRAFhUVdwK7rkPkSkhB6gqFDmoQ8/pHHSEbb1BstpmcchoMDTM1icWmV3f198sziZ770BZr1KqmZMNWZReghaSJonDlNlqdl165Gb8V/+9AFzDYtlheX5ANYJIWMRiN5I2mHo4t6aBYWFhgOh4zHAX7UpznVwTV0bENw5sQym2/dYjjYw2aKhakFNvbX2e31uXh6DtKwTEFRB1BiKHJeV2OaaqlBlA4Fe3vd8k2uVCoYhtTpea5DnoPIBH40YTweI0RaFi+p5M+Jw6i8faoVD8cyuXtnowgMcbFNg2pHKvKTQnZRr9UIdOmYodjCR2/0KAru2wwFQXCfUWOWylvQdd1ylFQPnHoYpBZPHiqVN6nM5tStOxqNqDcb5Rpc/RtHmd1q1EnShGEy5Cd/8if5xotXi4Mfg2GjGbrMdcylYsE1LXIhGA0OoOKUwK96ONvtNnt7e4zH4/tcA9SDpF6DP56UD7Lv+wyHw3I7KciZnZtBelD5+L7AtR06rSnGowFuzSM3rRJbKg6C1CUWWY4KHAcOWe3FnxO5tHkxC2cDkedkhd2yaUqyc79YMriuSzQayEBVAaZj0hv0yXHQDB0zkxtK1RlHR/IPFO9NCFE6f6ixT11mw+GwBP6PklRVl6SeIaXOUIUJuO+z1HWpnzVNk0QU8EQOGdKhNc3k+GlYOsYRvhfF8wOUBVOdlaNF3vO8MpxGahMDjKPOE5l0MNEygWHLQluzbEgTbKfC3m4X0Kl6cqwPExmu3Kp2qFWrDA56NDx5IQeJ9IobDyK8mlzw7O3tcevWLc6ePUur1eLu3bt87Kc/ZAEbDQZQkPVM28LzvLIbsAyzZAMrG5O7d2VQres0aU1PEUQxGCZ6FjDXbmLrGrZjkmUJW1u7BASs3bxDcnkWz9IxMoMgmJQr6ixL7mMaSzJiWo4algWu67G4KLsrZWDnuh55nklW+0yH/W6XHLPcoKobSN06YTAiibOysCnr49FoxK1bW+VmZ3p6ukwj39vbY3FxsVz9gsBxvLLlrdVqpQxEOViqdXSpwzNtojjFNC3SLMetVOkPRmVqdK1Wk3KLXo/p6Wl2d3fLv6vEsXmes7W1JePs4pgkissDr1bfClivNjzyVPDss88ebp3EYZejkeIHIe1qC388plmrY2g6FcdFpBkaBZcoirn+wTU50gQhzXqjXEy0Wp0y9VkKhe1ilOuWuN7s7DRBMKHebBSfsVbSCJIoLlLQK5iOQ5AVq35djoS6qZFDOZaqcV5hY2rEPWrJrIqLTJ+W46vvD6XguV7FMCwsy+F7v/d/8du/83/z+//yG/hxRM0GoUvL+8loQpbm5dlRDsMKmxTkRElMe0ra+2hJjGboGLrOxFfJ3ALTtjBtCxHnBdRxiKMpjEnBNLID0wqagvy8JVetsMLGQNNESZlQE0ccx1i2RZyEZJlA182yg9L0nDhMSjWA7NKyUlc6mUxwLL18LTIdS5TPuGwKc3TDIBMZUZRi2BYVxyYXgpmZGkEQkaWyi+719nErNmkcEYcBliNpI3qRNxEFIVW3VV7wKysrPPjgg9KuajjkoYce+lEl6kcXsDzPGfR6aEUHtr+/z/zCMTmupPLWHY1GZRDniRMnCkpDm97kDo1Wi7pVhyTn9o3n0TMJfnuuy6ibYFQNxn7I9Mwxot5NaSqoRaWHUa/XY2ZmpnzYZAGQmIckxsnuJEsFtuvSdhwQAhnE4GAaGtvb20wmE2r1JgcHXcZjn8XFRQYDyU7XdckFUjo1kOTUZquOaemcOnWa3d3dYi0/LLSV8pBub29TrVbZ398v9H+9UngbBNJWent7m16vx4ULF8quRMlqVJHxi4RlNYIclcOYpsHcnFwxLy4uEkcRwyMFsVqtUq1L94Vms0meCRzHKYNtSxPAgtJQ0QzefPNNTLPKOIqp2LJApWlKxXXwkOTELMu4cOEC8eBOaU2jxkilXFCHXOEXsrOQfDi1KNB1yq3k0WSbLMvIyEjzFLuQojRq9eJhll1eFIdEqSTl5VlRtMjkZkw7jDRTttEKQFY0A9XJq9HXMAw0DLI0L8YnG9tyyUQhhh/c5seffITPP/MkvaHPL//Nv0VuuwzGAZpu4Hn3awkdxy5j8vQiIUvpWdVFo3BiBUbHhSe/WtQg8vLCVmlA96U+6Ye6StlhZuXvZapLO3LRawXnTRVW15WfiTxrOqahl/Qo9e+oggwUP+fF1jVDLwqs+p6zOMEwXDSk2aOmy5Ac+b0IsizC9SpYustwPKDebqKCS0xLJ44iueHNU3x/QpZkpNGAJJf+aMvLyyUxdm5uroRuPlQB0yydarPG9vY2eS43bv44YDwe0+o0pdDWMhHkpCJjZ29XbleCfQLHppVnHPS6pFrG4vIS3795Hf1gyOzxRR765HGG/pi762us3RvStlqYxoBRpmHqOb39LWmIqOckmsDzbPSR1F1JgbS0xN3d3ea1117h81/8PEmYkKaCOE7p7u1LBrqf8sarP8R2GiwsLpPm8N57L+PVquT6OkmScOncSYRIORj0qdU9XMfDMG1sp0qWJczOTrO31yX0Aw66PdI4wTA1EAZvvn6FRx97hL29Pfb399nr9uSNp5usr2/gpxrjTOe3v/kyP/7pJ/n0k+cRyQjdcBF5TJaDYel4NdnZpiLBqzTkAc9Scgzp/GlWsDQdw3PkejzPMdGwHJswzdjZO6BRnyJOYuI0ApGU/KjRaETF8ZiZa+CYFsdPnefmW7eo1CAPcwQaNccmFiGRSPDMCkY6IU92EHFIrV4IesdBEVPWLAuXphlkWYSmGRiayWgg7Y03795DiBTbtQCdbveAVqtFrVbDtk1c1yYNCka8ppGmsvCMR5Ls2a64DALAsPATgVkxyJOYXGi4hoQJDh/qtByJ5IE3MU27LLhJkhcjmY3I4wIu0Elzk0x3sXQTogF57GKT0jBSMId87de+wvdev8av/r3fYGp2kQyBrgkMPQcRMBmOaTWbPPXUp3jo0fN89atfxbY9bNshCQWWY5NkMaahkxWF3zAsaT1ThMbYho4fRWiGiW47pEUB7DTqTEZjLFs6rSZRiu065LkhBee2LAJK1mOaNmkaY+pWKQIXQqCbkGVJmasZRymed+g4IXFhq7y8dV0nERmabpJkMZZ+VGqko2mQ6yl+klPLqniGYHGujq5DVLgqj/p72I5Bo1nFT2S6e8W1cC0b22kyPhgjiCUnMxGYmsGwSPx++aVXOHPmTLlF//N+/LlEVt/3OXXqVKlXkqQ6qbFSrN7JZFKEGEiMytIdTMMiCEIG/YjZxVkGgxFxlKHVK7z97hVu3r3NX/j3/hLheMLbV97lxz9+juHeGNP2GPdHeJUKmlZhMIz44MY1plt1Pnj3BleuXOErX/kK7713FcMwcByHEydOsXZtjTAMGY0mzM8vMBxL3dXS0gKPnHsUPwxYXp7jlddex6tZLK/MMp4MEcJmMBgxHPaZmZliMg6o15pcufIOq6snEUbOW1fe5vjyCnPHForvMaTfO0Cg8cCZ02iaRq3R5LvPPc8Xv/Bl3rzyNmfOnOEbf/xdbLdKtz+i1WrwJ//qj/B7u/zY009i6ilJkuH7cgsVBVIvF0xCkkgSgjVdkKQRmcjoHUjRuW5KlrSh5XJrpufYlsGpE8uYWk610yo8x+IST1P8M0PPcG2T6zduymzAbIxlGGiGIX2p8hjLtsmSlC9+6ae4dWONmWaT/b1JOaa4rleKiRWrvOJWqVUbMs8yjiAXeLUqnucSRQHt9hRTUzIgRrLLffb395mamqHb7RLHMlm6XvNKysRj5x7iOy/8gMwAXT/sFORomh4ZafKSwqO2g+ocKlxOkTcHgwG6YUi/7zzHMjSCyQi75mLoGr55wMj3qTgn+P3f/x6vvPYOOHUePfcUtlvh0uVzHDs2V34tPwqpenXu3dvk1dde5oknnmA4HDOZBNxYu4WdyPE5c6JDikWeEgQZpik7poEvqFSkoDrLJcdLZDpBrJFqDnqW3kffkEU6LbE3Bf5LAwFZkNR4qN4ThbUBJbCvpifZSYsjRcogSZNSciWEnE7k1FBQj4TAdR1AgEj58pd+mjt37kCWMj+3QL0xjUZM/2BCPxiDZoPI8f0x+8Nt5jpz7O73abQaBH6MY9apNaRkaGFhgYODg3Jj/+f5gf1oDKwA7KMoYWfnRmHId5gCo2mHFh6KLRwEAaeXVxlGPpppl+LrXMD07AyOlfDZT3yCV998kxvXb3PQGzPYusv5BxeJ/AnaQBrRmYbLiz94nc7MMbRMRyRw9uxZHnvsMW7duiU3FrZbullu3l0nihLOnDnDb//O75ackpm5WTyvglt38KMx5x57uPywhLAxTR3HrBRMcAnS3rx5G9MssgWrLksrq6RZxrvvvU+e5xxfWSbXdKpFFFn3oEez2ebn/t2/yIsvvswDp87wT7/+z3jwoUd54YUXOH3mFE888QR6nvPqay/zqU/9mGR+Gznt9tQRfMxG0wySdEKchISxdOvM9Ry36jAORlLWkyboWi6LlG3helIEH+cakzTCMS0pyWpIMHk8HHFwcIChw8zMtHyo9Aq6ZUIst3WGaZIJE8M0iYOY9Zs3+fiFz6AJQZrKwjMe+6XudDweouu1YilSePcL6dGVloc/LR8my7LwvFq5sDh58gHiOGZubo44jllfX8cfT8iR2OXDZx/i289/H40ckadkUVYKlBVmpSRCkn6gk6YJ1Wq9pMIosPqo8DvXTHQtR6SHTiA6gr/7P/4dtnsjnn/uVdZu/RGa0cCbPoUg5eTSFO1OFX8U8sH7V8lyQa5r1GtNoiTh+OpJvKrL+u0Nvv713yLN5Hiq6AppGsuwlURaWEt6hNwK6kaFNE3QSIknQ0BQrU/JWLsUTNvEsl0m/hj7iE2QGhHVWVZLLqAsXiBhIOXWobBQdQGBxAzRBKZu3Lc8yDl0tjBNu/xapmFQqVQBOU6auoEQKa1GnX5vwHA45vbb7/HII6fJc43pqTnyXPrcGTpUClVMq9VCGAHz8/MkocZwLE06ncLKKAxD0iRD49+AiT+/sMj+/j7D4ZBGqynz+AydRl1yTHZ2dhBCsLKyUib01mo1RuMDKvU6g0Efy6jjuAZRFpJlKeNsxA9efYWZ2WOsrBxnqt7iztp7XHn3OqdWW3TqLnWvyu07m/R6PR557BIH+9vkSYzmym2O0vc999xz7O3tcerUKUSWcvr0aV5//U0+/vGPMxwOidOU4ydOsLGxQSJCOq029XqTzQ25bGg36owGQ/wooOLl9Pt9ajWvSLqOOLF6kus3b8jI+NGYlROrhBMf3TDZuH2Lrd0tLMvis8/8W/zmb/4mi4tL3Lhxkw+uXmd1dZUw0/jlv/Efsre1yerCNHc3t3n66aeJkpwom2BoEAQBNa/CYNCl3Z4iywWuU6O732PsT5iZmSER8uF13Bo5CZkOvd19dAM8U2cwHuLoJnEmGA6GVKtVajW55lddT6vdYdg/YHt3lyTLiUWKpiU0bY8oSTHyQtgsMhApzzzzjBwNhZQXDYdSyhVFcoxst9soEbGmabz6+qs8/PCjJJkcbwajERXPwbBMDEvqOzUDqnWP3d1dRpMxrusVmaJ1PM9jfn6eY3PzBOGEO5sDsiTAdqukmcRnTF3ZXJulLlDRTBSm1Gw28X3pDaYA96O4XYaJlmeALLJxHNFanMGrVvm1//lrtDszuPU53GqD5ePLrBzv8OZrz/PDN35Irye/zsLSItV6k4NsQLs1hR9GfOmnv8Dc7DHeee990kRw9+4mg4M+iByEGpMzciGwTQuj2DBaToZIYj755GU+8+mnSJOEODf57/6Hv4fruGXnqfiYsuPS7wP71RJDWaWrLktRSUrM8YjPmiLMapqGbhhoaOXX0XWNNE1QXvuS2pOVG231NTUhP88sSXBtg+WVRcaTgAfPnsE0DUQuLds1TaPVrNLptJkMIsgtTLPCYDJifX8dz2nh1dyycCVJSrPZREMSnz90AVtfXy+/AelxPyhcCqwSc5iamiroCYfM77prc6u3QzToUa0kZIYcJ9xKDDjUWtPEYcSw32e6M8Xmhk1/HODUpkjDAwbjEUkW89kf/zRbuzsYBri2ie1W2NjYYDwe881vfpNf+IVfKFvjNE54+QevcOHyhXI7GkQRV69eLTzKKpCb3L61wdz0DDMzM6zduE4cp4wnY8IoYHZ2uhxJ3nnnPbY2t4mykMWF5XLLVnUroOWcPHmSix+5yHjkc+vWLT7xiU+ysbHBuXPnWFxc5Bvf+AZ/5Rd/gXeuvMWJ1UX6/S5nHjzJu+9d4x//43/Ew4+c4dGHz2IZBu+++0PmZmbpDweM/Ak1t0m/32c4HPL8cy+S6xqPnHuUPM9pNhvcWrvJJz72BFmWMPDHWI6JV6uRJSl6Jhntt2+vs7iwzM7eDg89eJp79+5R8+pUqzXGvo/R6mC44BgOlqmT+jEimZDGKUmUcPLEcURwj3atgR8mTCYTwijCrXjUGvWCPiFv+t3dXWbn59ja2abZbGIUOkZFK1C3v3qwGo1GAQqbpR+bsm7ZzLckyx+dmek2e6OIVBiI3CDVCkE2Wcl4V2RP1XEoSouyolEP7lEirY7AKrbBQteJ0oRf+/u/zsVLH+X0g2fY63cZ+iPmFjyuvPUar7z4uqTa6A5OxWV3v8e8adOemsKyHBzXo1Zt8N57V3EdD7dTo1qt0z/octDtMRpNJNXEcTDQcGyrLP4Pnp7j6ac+SZaGDHpbVKt1js1O87X/7Tf4j/7j/4QgkZQZkSUksRJpV8vRTm3oy26yKDJq86p4aIrqoaAf9d4B6LqBzqGnmCyMsvNW3Z7clqZYlZok+KLhuhaGluHaFhoxw3Ef05BjayYEhmVy4/ZdQODaJt29HaJggGPX5ELBNFlZXkXHJoz9ojBKvzbJEND/XBD/R1pK33jr+a+qD155jAeBlIrcubNBrVZjd3e3PExqZT8+6BECs1MdvEqL/d4u67e3uDcQxPGY2ZklhqMRJ1dXWVpaIheCStWjd3CArqWYlo1rm+zv7dFut7AcE8vU6fb6CCHZ2YtLi+i6UfLG/CDkoYfP8s4773LmzGlm5mdw3QoffPCBZK0PhjQabfyRNKIbjYb09uV2zat5VCrSrmdmZgpN03nwwTOyYLSb3Ly5xvz8MSbDEYapF/QIGaU16Et+z4Vz5xFZxkNnH0TXc1aPr/DHf/SHWJZOmqWADD0dDsfcu3uP/ihgcWGZax98wMlTp2TwbWuKME648s5Vbty8Tb3epNWekpvQnX2GwzFxGtPd20dkgvff/4CD4RCvVieKEvqDETfW1khSwfTMHD946QfMzx/jhe+/hGnZhEHEzu4ua3eH6LU2WR5i5xphEpOmkqLSbDeIJyE//bnP4NqC0A+Ik5RGo1FuEUejMb1el0rFYzQa05xqY9o2tXqDeqOBY1ncWV+nUauxubnNsWOSkjKZjNjb22V+/hhpKspMw+FwyHA0xjKNUj5SqXrs7u+z1x+TYqLpJkKRPcVhN6CwOVWsJPZVOaLQsEpqjCQQ5+h5jp4LHLdCs93i8ccv8/DDZ9DMjO29De7eu83C4hzfevZbdHeG3F3vIhIHrJy0eOgPjQsBpBXPU09/ml/79b9Ps9lCMyBLYqbbbVrNOtPtFo6lMz3VYmZmissXzvPkEx/h5MlFOp02QuQ029M4bg3NMHn8Y08yPz/HC899Dx2BYZpoSMBeUiMkJ+yoK4WuH459R0M5DnWgopSVqWInsbWwlJOpbjXLZEKQzDy1DzeggK6BbZnoOfijIT/12U9S9SzqjQaWZWMWiUk5MA4y3njnBucfPMny0ixROMS2PXLNIkx8RJIxGUUF7na4kVU0oeHwgNOXPvXhLKXHYx+APE+klYttsru7y8WLF9E0o5RjDIdDCToXYKKlu2RJhGPVePONqyyfXGL99iaDoM3Jk8dBaHzk0gWmphtcvHgOIVLpha3n2GJIrWaRDPcY9+/RajQxKhXG4wjT1FlYmKdSkbKgvb0ut27fxPM82tPH2O0dsHrqJAeDAYPhAXEQc+nCBZI4xWgbxIFPrSYdXVvtJlPTrZJYGEVRYZEbS5b4eMzy8iKbm3d54MQqceiztb1Jlk4h0oRms44mNL77p3/KcDDirdff5GMffZyD3p4ksYYTnnn6aa7dvMn3v/8qUzPH0HOdYX/IL/78L/J3/+E/4bvPvYZBSrs1x2uvvsJjly7zveef4xd+8S+zt7NLu9Hk29/6FguL8wxHfWZmZ5mdneXCuQvEYcJ+f8LV62u8fX2NyxcuMjvVIRHycjENh5Xjp3j/6g2kY6YUPb/w4nfJsTFtB0uzEUFBoMxlwOp4OKLTavDSSy/xyY+d5s7GbeqNDv1+r3A9lcRbwzDY3ZHODJu7O7iuR7PWlBswy2JpaYko8Gm1WoUJnwMc4jL9fp8slQ9Vv9+XVsNJim1LDKe3v8NHLl7g9Xf/BULTyLVDexhNl9Ix+ZkpQb+L41RKiZSiCqjxS2FlJnKTpus5i4vHWFheYePeJpapEUQhk4lPvzvi3StbBEHIfncLDEGtBoZZxbQsslwQRCH9gy69riRRv/Tiq/za//IPSZKM/W6X6ak2nXaTxfk5Vpelv9tg2EdHK9+/LPEhr1OtTfHAI49z8fKT6KZDMBnxe7/7u3z5Z/4iod+n1+vz9d/654wnPrrpYjomyZ9xopCEXtlsHO2aQG4WgyAo3z9FmFW0D8dx0HKO2OekJRPe0C1JPSmgAdM0MQ3Ji2zVqpxYOCkLXiJDOgzLJgpTLFNDMwx2dw6Iw5zTJ09QrbhUK1NkiYUfhnRmO2RhSoYh4wTztJzm5JIjL8m8/7ofP7IDe+flP/zqsWPzxFmGZbtcvb7GhUsf4ebtOwg04jQFHU6cXMb1HMgzKhWHd956lZnpBb71wuusnlliplXn5s0+XrOFXq3S6dQlc9iwmPgTvvCFzzE3O8/25g7VdodJaqBVO9iNGeIso2ELaobAqTUwNNDynDgVuLUa9WaNTrsqvctFxvT0FIapsb6+zvyxRaq1BqbpIuJIOikYOlEwIY1j0iRiNBhQ9Ty2treJ44SwoGIILWc0HqKZOWmWIXKNer2FV2vSG4y4trbG7dvrXLx0iSeefIKTp09Ijy7LodHoEMUZbtXl4sVLRbHdJQh9xqHPuYsX6O7egdzErDR5+/2rmJ7F21fe4os/9SX+n9/9U9rtKYJwyBe/8Iw0KsQgDGK27qyDpvHe9ds8/8rb7PVSjs0uE05GNBo1nv3m8yRJxvnHHqXdqbO4OM/s3Aw3rl/DtXRu3AnpxzmVSk7s+2i6TZIE2LmPV28SJYK/8df/KjOe4LWXfkClPsOrL7+K59VkynoQMjU9Rb1aY3ZmRupVLQdHN0mjiDwTaLqOPwk46A/wbJfAj6jXmmi5gaab+JOAKIzYPejiOC7tTofBcIhpGmRCcNAfkDDm9t0t7tzZA9MlQ8bgGbpJFuXEaUijUaffP5DyliIgVdMgiuJyHFI/KwG5yHJyDQSCT33645w8sUSzLkXl+3tdBv0+pmVKkbeucdAf4DgepuFgmCaWZWNZNoZuIIRGnAjiJGM8HmFoOfFkyGc++TFm2h4VV6PV9khDnzQJSKnQMzMAACAASURBVNKQIBgThBF+FON4deYWz3LmofM0mx3ee/8dgsmIl77/ApPJmLt3bjM/v0C3e5eFYx2uvX8T122R5RBmEwxNKzhWOUlyuHWVjAAH23aIY9nZWJZZFjvb0iUNJU1k95ik9xkcRlFBmM2hVvWIo1B2v1mObeakmSSA1x2Dpx4/z0yrgmlZmIYjdZ55zLGVeZIM6o0GL333OzzzzCXaU21sp4JhmViWRhQmhFGM0DJM20TTNcIoxK1UcJwKQmj4fsTDj//Yh0vmfu/lb3/Vch1GozG3bt2m1W7R7w/Y39+n2+0yPT1FEkfs7+/KFr4AFVePr6BbHqunHqBaMUjCCddu7jERBrPLS7TqNUzdYG5+nmq1ysadO9zd2KDiOqysrtIfjMg1aDWbWLZFGvqYhsUkilleXiHwAzY2t6jU6iyvrFCrVWk2OyUb2dBNpqamqVQqdDpTQI6ua4wnEzA0VlZXybUcy3aoeBVur68zNTVNq9XC9wvNoyNbcss0UePfeDSh1WqyeuI4SZJw/rHHuHr1apF7Kb28jSLafn19nZpX5bXXX+fipUs0my2e/tSnuXXzFi/94CV+4pnP8MYbb8nU6eGAv/ZX/zJLC3N8/Z/8JhM/5p13fsjP/dy/TZYkvPP2e5x98BHu3LnDl7/8Bb79p98mERrb2/tYls3y0hyXL53j7bdf52d/5me5cP4caRZx9+5dtBy8IsH55OpxXnvrOjhVJpG0fs6SnDgOIE2JhSBJMx45fZyalWNoYDkututgqTGtkEo5trSJ3t/fLW/1/f19rt24IW/xJKbVajIZT/A8r8wADKOwlD1Nz0gahVzRg8gO1RCuY2LbVTY2e/iRTN5I0wRyqHlVdMsow0EUzHGYuG2XALfC4uxCb+rYDoah8dBDZ7l48Tzj8ZiowErV95GmGf4kKBnoEq8Dx61I0XuaousaYSjTxk1T0jwCf8Lly4/heRaOpbOyvCgTjISUaGVCIHI5ftpOBdCYmj6GW3G4desmo/GYuxsbrN24Tp4L7qyvo2mSe6ZpOY7lsrW9XZJVjT8zQiqyrmLsqx9SW8ihjjY/BPh1/bBbk1tbOXK6rovjutI6ShFZDYNao0YucryKi2NofO6zn8JzDMbjEWkisF2ZZi8QZGlG1WtQqzdYXZ4jTTPiKCkNKG3bOYJjxiVZW/LV4rJTPPvRT3+4Avbq83/41WazJf2qWx02723i2A7NRoMHz5xhNBzSaNSlDMCVPLDRcIJpmaS5Tq/b4+7t67z0wgvYtQVCw8adauBq0gqm0Wpg2RaWYRBHEWjgVFvMzMyys7ePV62XAQpBnBW0ApeaV+VPvvVtrrxzFa/aQDcsNm7eQoiUJI7ptNv0ul2+9c1nuX3zNsHEZ+j7LCwtcW3tOk6lwsa9e6DrxGmKqRssLCxw+/YdpqammAQBcRxhWXYZCOtVJBHw2Wef5ZVXX2V1dZVOuy3zIltNskwaN+7t7XPj+g3pESUEFcfj5s2bJHGCH/icOrnK/NwcG7duIkReOJHWsW2N/e0tvvi5z+F6NW7e/ID1mzd44bvPoeUG9WqN8XhAv7fD2Uce4pFHH+HevXt4VY8vfv4nmJ1psrpyjDTNGA4H7O5scerkKdbW1uQB1XWSNOWdDzZJdYsoi4rXqIEm0HIQhdbvr/yFL7N79xbXr73Hex9c5/Lly/ihlJOJLGMwGJCJFNPQSw7W/v4+pmkyNd2hXq9hGDqNRp2pzhRJmuC4Dm+89UaBXWrEccTe/j5BELC2tiY32IU55ebmJlOtJmmaY9p1btxcJzekts/QDZIsIycvBdFKjnMYjBEV+JCG70+wLBPfn2AYOmkSMzc3y+kzp1heXmJvb59333236D4SbNuRD1qcoGnKUlq6p+qGSa0mxeAyXMYrHDAcDM2k3azyxBOXSKIxaRYw6A/IRU6SCCZ+SK7pjEYTBqMJluVQrTXQNJvrN65jmjoHgwMCf8LBQVdSK3KZR9moN9nb3aUzVePC+UfZ3d0m9GO04rKU/Cz5n64bxa8PLamkfM4tlStpGhdZm0fthbQiok3CCaZloRsGfhBI/M3QsRwb2zKLjk1Hz2POP3KG8fAA162QpBkHgwMs28QyDdqdaUbDIXOz89SqDtIlGFqtNkmSlqOuNLzMy6Biy7KoVWvlhXL60lP/2gKmqU3E/9ePr/1PfzN3LZvAl5qp0XCCbTsloS5NUzY373HqxHGq1Qq9Imw0IWZ+7jhpnBFO9hFpxjeeu8nxC48z0TO8RB6+hYWFMoXGLyLAdKvGZz/7Wf74j/8Q1zYRScTOxhpVz8bMxtzbuEXVMSWnKMqwHBvXsTBFXIZ/KvcDXTO4evUqt26ts9Xrcf78uSLsdY4zZ87w+uuvc+bMGZLJCCEkdrCzt8/y8jJJErO3v8P8/HxZvF544fuycE1Psbm5yUFvwINnHqBScSXGcdAlCGQ81O7WLsvLywSBTBy+efsWAJMwoFarsbe1xZkHH8ZyKnIBEgdMRn1cy6U5M0eSROhaThZnmLrFJBhjOwauo7G33yti6DziJOeTH3+Sg94mjmtQ9ySuNxgeULEdrl27wdzcnEz5sW1+/4/eYKsfME4KP/fUIEl90iAhERlZEvDv/9xPsNjxsPUcw3WxLenS0Ov10AHDOEzCHgwGZImQBMSB9O2X0iOYX1zg9todpqen2dnZYXl5mclkVForL62cKO1kwjCgWpGs81arhQhG/B+/9TsMY42eLxjFGZmmAzqWUdAyjgDPR0NcFPZVun04Dr1eT7pyeC7nzp3j4YfP8uabbxY4mosQ/y9nbxYr2X3n933OfqpO7XXr7vvtvn177+YiLhJFUQs12jyjGSszToTEnkzmyYYfkgcjAQLaBhwggBM/GHAM24jtcTJJFM+MOCOJoiSSkriz2Wyy9777VnVr30/V2fPwr1uUEWAeSIAgSKCaffve8zu/5fv9fMG2u4ShIFbUayI1vdkUWPB0Oo2sasLcr6tj8a3ALnkszS7i+12mJpPETInBsE8ikUPX4/S6g7HlbOA66Jopilmnx/TsIrPzgsobIjBV1UqFqakCvXYHz3e5fuVxHty9gR+00TSFKDD59//+z4nlc2Pz/Ol4eHpQ+09zAMKxBU+SJIYDYdU7tTyJ/eCnNidZE6Jbu9cfH8kArLjwGCfiMTQp5OKZJb77zS/TaVbYPdin3+mzvLxMs1lnb2+HmZkZ5uaW0FR9zIGLx+Ns74gufWqqMO72wpCxENm2bXIjfWS73eb3/u4/kj5TB/bg1q9f8j2PM2urBK7HZGGSTDZPPJbk+LjEmTMiNWgwHHJcPGFpdZXjYoWzF67g+RLF40MajROm5xdoOTEm5pdEnL0koWsqpqFTLp9g93s8ePAARVWpl0v0O018d0i33eJwf5dOp8u5CxewzBgH+/vMThWIqxKddoOtzYeYioI5esN8+OGHtFsdKuXKWCC6sXGOleVlcpkMzmCIqRvc/vgTAs8nbsY5s7YKskyn2+Pw8JjhcMDM3AwPHjzEHbocHR2xvb3N0tLSyJ6jYFnCfzgzM8vh0SHJRIK33vw1G+vrWPEEk5MFBrbQTG3ubJLOpInFTAxd56ObN9k4dxbPten1m9y7d5tcNosqK1iWSbNbRVUidEOm37PxAw/D1Ahw8EOBjPE8FytucH59nXv37rC2usjA7qMbQji8ubnJ3s4+6XRajGsTE+wXK7z+649QYwkiKSDwXIgUPG+IH8q4zpCYCt/8ynNMZJPU6zVy+Tx7e/tjr6Uky+iGSfmkNLarmGacn/zkFRYXF1FVjexEDjMWH12tJKq1GqmRFzIIxKK2WCzSqDWFHiqMsO0+1Xqdw6Mj7MEAt+fw+S98AWSJh9vbRLKCpAqtUBgFgDSWT5zKJE55YKdSmNNr22mXIUkS165exHGG7O7ujg9PwgfrjoqhJ8Yh38fzfIbDwTjtCYkR804IO4UbQMXzXHzHJpNN4LldgkCsMfxAplJrjpbUHtV6k0xmgpNyjWa7Q4Q06uhcnnvuCxRLJSQknvrcExSLRaanpjgulajVmsQMlWariu/2+PY3vk2lVKHUaI8pLKdexlN71WlxOv0borH/Vtc/9ZGedl6nxV7XdZBHO0NFIfCDsZZQkiQsM46ha4SuzR/9V39A5PaIGTqzC0tkszmiMETTdGZmZigUJtF1DdPQ8YNwDFPQdYO4FR9RLgQ2u9+3SSQSyLIs8F2jMONYLMba1Wc/2wj58Xs/f6ndbBB6/mhX4PLyyz9mc3sHWdZ441e/5JPbd7l85Sr3N7fo9Ry2d/Z4sL2PFAkT8vrGMpv7hyRyZyiXa7RbNQq5LA8fPkBXVRr1JmbM5MzZs2xublPIpilM5Nne2iSVTAAhtXqdna0dktkCUQTppIUq+fiey+zsLOl0ioPjA6anZ8nmcvRte4zmVXUNx3U53DvA0HVBVYhgenKK6akpyicndPpdMpmsCMewLAqTU5ROSjz++ONkMknuP7hHOp2iWCyxu7vLhx/eYjj0WFtfo9Nqs7A4T6/bZePcOnbfJp3O0Kw3iBSwh/YYUX1qQXnyySeRZIVqrUIsbtDvD2g1OyiKynHxgEw2T7Pd5KRUolFrCFxzEJJMpej1HRYWF3Bdh1QqzXDoMDFRoN3tkk6nOTo6Zn9/n7mZeTbOn+edd99l4/x5kok4XqSzd9jADQElQFNVXMcnIkRWNGKGitNv8vRjlwkClyAIadYb9AYDNF3n5oc3KRQmOTw8IB6LY9sDTDOGLCucO3eOXrdH8eQEIrEvNM34yFgep93p0Gq3WJhfIJvNsLy8zNLSyvjBi8djSLI8LkipeALH80jms+zsHzB0fAJJQ9MUgQKX1TEf/7TrOHWGCLCiPR6fTrn7yWSSZNpi6AwJo4h2p8NgOERWBOhyYNtjYmq/byNJ8shBEEc3xLgWBB5IIacUVwSBjImJNIoSIY0Ka7/v0Gj1SCVz9AYDDo+OQZKpNZoMBi6qqohfVzNJpdN8/PEtfue732Vrc5MwCMlmMwztAUPXo1GvMT83hSzJGEYcz3H43n/2Xf70z348VgKcmsBPO67T4vwp5eLTGDfhpIpG2jF5DICUUNBUnYEzEKp7WRnLUE7/0lUVAp+F2QLry7MokUOlUsUNItxBH1XVSCaTKLJGMpkWV0tNQ1FkWu0WnidIGfLoey0S1YP/BEkUhiGGbowZc2vXPv/ZCthrL/+Hl86srdCs19FVHc/32T864UtfeZF33nqbcq3Ok597isLkND/56asUiyc4rke9aTMcejz77OcYBn0CVeO45NDqdNGUiEq1SDxmUCnXSKdTZLJZ7j94hBGP0WnUMU0diHj++S/y8OEmVjLFytoZ+oGGETOJfJe4IZHMCP1TOpNhfnGe1994nTCKWF1bo9Focf7CBh/dukUmm2boOkxOTxJJEctrK/SHNq1um0w+S6Pe4Ac/+AH5/AQTExPcuHGD69evibez77G4uIiu6xQKk2iaTiqVoVZtUpgq0Gq3SCYsPNfF91xkScLu9chmc+wXD5idm6U/6KPpGqqqk0ylBJtJN5idn2bg2KysrnL+whVc38eIGTQbNtPT00zkJ5AUmXq9Q7lSZ2XlLHfuP0A3YqQSSX7w//y/ZFNpokjhjTfeYO3sWe7dvcf58xfI5fPs7O5y+dIlkELanQ63H+zQ6vp4oUwk+/iesLd4voMRT9BpNShkLdIJjUcP71OYKJDP5TBjCebnFjh7dp2pKSFjmV9YZDAcokgy9XpddEGSzMzMLJIkYxoWmmrguC6e5zM9PUWhMIkzHOA4rlgqS8qYlZbLZfF8n2w2y9LSEsOhx8AZUKqVMeIWW/tHBJGK73vCfOwG4x0JMF5CG4aBYWhEUTjetcmyRKfT5uLFC3S77THWp1ZtoKriOOB5zmhsVxgMhBpclhVSqRSGeYqrFmEgknwqTRiOPYKK7NEf9BgOHTpdG02PoesJghDq7Qae75PJ5Gi1Oni+T9yKo2kaphkjjEK++tWv8Wd//ufMzc5gxUX3avds6vUW09N5hs4AVTEJPYlicR/dCHjj7bsEgfBSimIqjbRbBsPhp3BLsaz/tBsT+0EVz/PHnSuR/BvcMQlFVsYHAqJIQBRlGVVSMVSFb734JaayCULPxjTj6GacQi6HpupIkSQIMYbJyvIyfbuPaeqYpoGua0DEuXMb4zH8NEjnlKIiilg0DvdYvfLMZytgj279+qV+v0fKSuB5Lr3+gG//zvdIp3NMTEzyhS88x/7BPrt7+1y6dJmnnnma2bl5ZDnOzvY29foJmQmL3tBl96CLrhpEgYOiSQwHQ9LJJJ1uj0cPN3n6mWe5cuUahYkc9mDI0HEplk4YOg7xRJII0BKTRGFATIsI/b5Y+roOuYk8x0eHTE9Ps7CwKOgNusH9+/fY2NggnU7x4m99AySJoSMEqIdHR8zOzXFcLHL+3Abz8/MYhslg6DI5WSCby4zeACHtdot2u0U+XyCZzLC4uMITT3yOSApYXV1BUxWGgwHpVBKiCM91qVYrKKZKGEbEYzEWFxfZfLTFwsICruvS6fXo9tpoqkKvN+T+vYf4QYDrDbGHEkkriT3oo6kab7z+Jq4bEiLTbLeYmJig223xwpdeIAolWu0eE4UCnV6XdDZNp9sjlUizu7PHx7c/Zn5hjsPDfUrVDo5n0Oz1QfbFgtkNGDoDbMdDjjx+/7e/zcbqPNeuXSeTzqBIMg83d7h37y7xeJyjoyNmZmbZ398jCAI+vHmTiXye4+MShikyDxNWknq9zs7ePufPn0dVFQzdoN/voagqnXabeDwGAfRGIIAgDFBUlY8++ohHjx7R6wyI5AhJlVlYW+PmrXtEki4W8aGLquicIsx/k0EvOqhgvBv7TZa7YKYN0bRTFBCE0alS/5SIGo6uZDoSwhqnqqcjmjTquEZ4G1XG0E16vT6aKpwaqqKJRB4UBkOXrt3HcQWRtVatI8vKKE5NdEkJK4Gm6xwdHWElLFrNNv1+T1jzJAlF1Xhw/xMKhQk02aTb6eP6Ns89/wQ/evX9cYjN6R7sdFw+RdicClsV5VPhr+eNfKFRNEY5hcGnZnkk8blwVLROx9J4PI6hGfiezZnFOTy7hS5DpVZH0eO4dh9Jlun3+ui66Mz39vZG46r4PbRaAmJYLB6Pr7yGblKv18YF1Pd9Ws3WuLiduf6FzzhC/urnL/XtAapp0uh1eOa553jw6D5HB4c0OmV2dh4yP1fgwsULzC0soqkyTr/JZFLnmc9/jsuXz2NJCrubR5gTS2xvbxKTAxodG0VVabWbZLIpVlZW2d3ZZzAYcGH9PHt72xi6yttv/Ypz62fIpnL0O3129h+hShGqqtGoNUkmU5iGStzUyaYLDAYupUqVIIxIpEWYQNISac9v/OqX7O/vj4JDNIaDAWurq+iaRiJhkkplKJ1UWVpdwIirON6AbD5Hq94h8CGTyXNyUiKVjmMYIZ3uCUQh5WoZz/PJ53MkLYtWu81OsUyoxogrBivLCwSRT6VSZWpmlkq1yqs/e5XPPfEkrWaHTHqCWMzCMHQURUVCYevhNt1Om1ajxcLiIleuXCYeN6iWywxcn3a7y8WLV3Adh0azRij55CeytFtN5mZmqVWaPHywwxNPPEW5XObixQtMT03x9of71LstAilAUVQCP8Q0VYIwYOD4yJHDH/3t7/Hw7k0G9oC//KufEEQhxeMa+0cl7j54wOWrV4mbGooUErMMLl4+x3GpwtTMPHfvPGL93HkOjw6Zmp4EycPxArKZNJtbW5gxA7tvM1GYoN1t0x8GHBwdYiVTmHETRVW4dPkSC4uLJNMxCANMPcYP/q8f4voStheKIhJBEAgpg+e5QIRh6AwGNmEYIKPgjewwQRASSRLzCwvIirhixuNCqT8Y2ILHpSjIksLQCYhCMSFqI8GmYcaQZIEEl5SQIAw43Sgrska33aPdapNOZrD7LppmomomHdvG80N8L0BWR4LddhNZVlBVERnoBy6O76HpKvVGnf/y+9/n1kc3GQ6HZFMix8DQIrqdHsOBR6/TwbJUosglZli8+dYn2LaNrIjLoaSpmPH46EqojzpGRt2XLAgTskTM+JQRZ9s2qqYhyQqariHJp7IhMZa6roeqaOi6uDbHFYmnrp7n7MoURsxgZnmFS1evk7DiGKYOEpgxAz90QAqICOj1uyiKjOv64xeOKLyDkdndG+c8CMS6OfZAuq77114h/9oC9slbP34pQOGnP/81xUqL6dklDFXFc7oEvsJjV69RKVcoVWoM/QArmSKbzVGulHFD6NtdVAl+8dqv+MU7n+B5Q9xhh3JdmIwzmRT9TpeBM+RgXxAgbt+5zS9/9SZ+AIl4mlqtweHBASelI9aWp6lVjnHcocjui2m4jo2shBzt7RJFEUcHB7SaHey+Q+i5xC2TWr1CNp1ndXmVjz78iHQ6w9079+j0+qQzWZxhRKlY50c//hmeD5NT07iOixVPYffEfsz3fSYnJ1FVDUmWOTw4Zn52gddffwMrkSKdzFA5OeHkpIxlJUmkMtRqNQ6Pj7hx4yYzM3Ps7R5RPD7h2Wef5t133uPkpMz9+w8YDkWe5eRkgVarwbXr10GKaLYaOO6Ak/IJhckJPvfUEximimVpWDGVcqXE1s5DsaRVJKbn5oibMfL5LBOFNLohc+HiBmEYUi5X+OjOHrFEAj8KxFs4CJEViU6vjRVL8T/8g/8Or9/izOICrXaX8+cvicg6x2Hj/AaTUwWQQizTJGEl6PZ7lMs11lbWmZ6eolw+RlUlUqkEQeCTTma5ceMGb775a2ZmZhg6A5qNJp1Oh63NbRYWl2k06qNuQGilXvnJq7z91nu8//4NJqdmSaVzbO3tE0oag5AxCkfXTXw/+P/JHkAYp0+v677vYyUSTE1Njc7zFr1un263hwxje43nCWJEEPjIqizErlEoBNpyNBobdUzdYDjaEaXTKXZ2dlBkhdmZGRqNJigKgxHEMAh9fN9jemaa/f19NE3H8/wx0NJ1HRR1FLoRRvzsZz8jnU7T7XYplwSIs1Kt0u+LFHk/8HAcWwRI9zs8fFRCM3ThTNB10S39RjjI+CFXBDr8NFZOkZUx/BEYWaKikYdYQRsFB8uyQJiHQYimjcjLoc/lS+tM5FI0GjWajQbdTpdmtYqux5ienmYwGKKq2tjqJIqTSBY7xW6ByGE9jWmUJQXbHiBL8hjs6bhDPM9l/bEvfrYC9m//+T96aWvnEEm36A/huFRmYWaCduOEWx8/ZHtrC8Mw+ODDW+zsH7Kzs8uVS5eICEhkspROTtjZfMDQCSh3AxzXQZMC3EAFQnrtFo7jcO/uHQbOkIcP7vPkU08Qj8VZWVllsjDJxEQBu99ibWWBe7dvoWsah4fHVBtN/DDEipkU8jmyqRgnxRNm5+Z55+33WFtbY3p6EscdYFkxup0+BwcHPPbYY/i+T7lWFVmTwyE33rvFW+98wO/87u/SaNRYXlnEtnsMBiK4IwxDDg4OaLVEW7u7s8fU1Ay9VpsvvfACyUQGTVO5e/s2F85fYGFphR//+CdcvHKFo9KxwBD5EIWQz08QhgG5TJ5cNk8mmyYet3j06CETE3lSqRS9Xp9+v8elyxeoVCrIskQ6neL27dsYuip+IcDQVVbX1khl8/TtAaXSCe+++yFPP/MUihISRuLrzGQKyKrJrbs7JLJi1+Q53uhr7BO34gSuRzquUy8dsLf9kDCUaLXaVKtlPvfk4zzafoSsiB/oYb/P0sIi6UyG/eMjGtUW09MFiqUDZuemMXSdbDbPrVu3+cJznxfCxjBkenqGg8N91lbXaLVavPPOO3ieQCBtb+3wi5+/xsbGBdbWzpLO5kmkkvzspz8nPzmFH0l0HY8gCAWKCPk/scqcyiVOzd2nl8kgCNBUDSuRELKHoVjyO45Do14bfzbyhbbsdNcFoGqG6GRkURhjhoUsC1NzGETs7uyNUEQKngeB2O0ThYyW5j5hGOD5PrY9wIonEZ1NRBD4SJKMYYqjhe8J4W2lWkWRZaxEglanjRRJYg0Rj5OfyOF6AxxHpJFv71cBUWg/FabKY1S6+LMZGdhHPklD10asL5EbEYUSxijF/PRzhmGOP+M4DhKi07Usi6X5SZYXZogZErl0msLkJHHDxDQNhkOP8skJ8bhFvVEXhvAoEDtfRR0j1odDgbo+lTud7ryCIKDfF8LnwdDmNLLx0tNf/WwF7P6Nn730YGsfSYmRm5imUq7w+3/zOwTegN29EhP5HIoio5lxdvb2eeaZZ7j98S2+8pUXSGVyrJ9bY21hgc2tPQ5qNqqq4Dl9ZudXRZucTlCpllFVDUM3mJicoG+3aTQbdDttnnz8cYqlQzKZBN1OA9mXcF2fdLZA13bptDskrAT37t4jm7KYmZkHFGZn57ASCVKpBOlcGs/3OD4sUigUxuRUVRc5jrVajXqjyRNPPE670yGdSfJo8w6ZTJJhv89EocDm1hbZXI4wCpmankJRdOJWgolshg/e/4AbNz9ia2ubVrvF3MwMR8UTLl2+wsHhAc8++zR2f8AnH99lYW6Rza1HbGys07cHnNs4h+u54kqYThKLx4iAZDKBYYodz9mzZxFmYZ+pqSmWllZ49OgRtUoNK56k3elx8+YnNJtd1s9u8PwXv0KpVCIKQ5rNNt2ujWGm2T8o0R4G9AdDWp02iwvz+EMHTVHoD/o4/R6XNs5wtPeIyklJLKINg2Qizu3bH3P16hWCKMTu9bh+9Rqbjx6RzudQVI1Bv8/Pf/4zLl7awPNcBrbDBx/c4Ny58/zyV6+xtLREpVLBNE3m5xYoTBVIpVNsnDvPwsICt27dIpFIMDMzw/Fxkbt37/HJnfvsbG/z/Be/yNXr12n1bQ6LVYIRa0xCGivIT3c/vynK1DQd2xbggfX1dTx3SOALT2+n3UbXNBzHcCMP/gAAIABJREFUJYpCNPk0vcgl9ENUzUBCxTAtImR03SSMQmJGAtdzxhF1p0ZnVdXRVFOMr7IiDgiKNLI3adi2MxJuauM9VBSJ4jqwbXzPp9vtinFwZFIvl8viAe52sawUqUyKZquJRES73cEwdLZ3y8SsuBCajgqQKNgqMqJj832fSApRZIXAd5HCiCAKBUAxCNE1Az/41GokUrfkEYZbJpvNEjNj6IaGZVl4dpPf++63iFyHeq1Crd4gHovTajUJwgjX9ej3e7iu0BWGoTC6I4PjucQti25PjK3tTocgjDBMkzAS/8zl8+gjyYpuGCSSSZYvPvVZl/i/fOnipavMzy8xkc+xfmaVmAGqKpPPTzE3O83NmzeYnJ6m2Wrx7DNPc+nSeaRItO2VkxMWp6e4d/8RW6UOeixGJhVHVnVMU6dRq+D5HqZh0O11icfiRPjY/T6e5/Deu+9wdLRPPp9DUVVuvHcTTTepVZsMHY90Oosiy5hGnERcR5E1/CBkfmEeVRUBIu1OF98LOH/xIsl0inxhgrNnzpDP5djf3WN5aYm11WUmp6YJAw/ft1lenkNTJOq1CqZpcXx8TLPZZHp6CoBKtcxw6PDg7iesnTnDytlz9Pp9Hrt+ncFwwMUrl9nc3kJTFQ72d7CsBFcvXWb/YJ/Dw30ifHTNJAx9isWiuLxEwejNFOA47lig+MorrzA3Nyc0ZIaB68PZtTPohsnhwRGqqtPt23zxC8+zunaW27fvUq1U8FyXxcVljopVXnv91yDrnNQ6tLsdkTbtODiDIb1+h9xEloXJSVYXZ7lycZ3lxUXW1zeYmZ0lnY4zkc+RSCawkkmuX7tGPB6jdFwEWWJ6ZoZ0MsHXv/4ijutQmJjGcVyWl1cIgoBXXvkxkiTxne98h5NyGUmSqNXqgNDodbtdnn76aY6OD1heWiGTyXL//j1CZDRVoVQ84qc//Sk9e0jP8TGMOMOBM75Q/aZ04JQ+KskKrufhemJPlsvlcF0RRuu7AX4gugDHcVAlwYBXVRUZ8D1x3LAHLtlsHk3VcF2HmCnGoJNyCdvuk0ymkGVlJCNRGQyGKJpChNgBiQ4sGl04Q0wzJsTEsir2clFEGAbjPZRt2+Ovxx3p24R5Xufpp57h/Q/eR9VVen3RNYNMvWGPu69x9uXo2fU9B0UVMgjXc4iiAEM3xgUsisB1PXxP6ApN00CSTvHRp2lIo0g1WUFWxAUzn9A4u7KIrkhk0iniVgJNUVlbXeXoqDhG4Nz46Cbdbo+5hQWQJFzHJYoYyywkSSKZTJFMilhAVVXGlqcgCLl37z7VapW9vX0+/+LvfjYl/l/87/848ryAZqNDIpGi0+mQTI5CSZMp+t0OcdPEC2V0w2Lo2CgEeAMbD8EwYtjn7fc+4VE7RiwRI5tUebS5TzabpNOsYcXiIyxxgqHroRoyiZjAFp/qeRKJFJKk8PWvfImd7T3CEM6cE9zswHWwEjGSqs/MbIFmo8re7iZf/9qX2d7eYeB4bGxcwB4Ky4k7dEglEty5c4eFuXlM02RzexPTsJA1nWTSwnf7TOSz3P3kLnosRSIZH+lqGC8gfT8gHdO58fHH9IcBq2tnmZmeoNtuUW+3WVhcpnx0SBD4pFJpFFkjl5sgDH3qjRPu3d1kY2ODWCyGpouUccdxKBQKdHo9LMtCksQP4NbWFoah0e60SKcL5LJZpgsTlEolJODg8JiNC5fEtUcSlyNdUen0exRLVSZnZzkuVvjBX75GfmqSYrHImbU1Ht2/x9d/60X+7X/4d5ydmuQPvvfbVIrbdFptzq5foj8YUCru8Nvf/A6lWoXkKCC33+2wuryCqmt07D6mqtFqtVAUlW6nj+d5TE9Ps729TcISLwDDMDg6Psa2bZ599lleffVVNjYuIMuIQBBDdACzs7MszC9Ra/VZXJhBiXz+6f/yzziudyBewPUkEdNmJcaaoU/JoSPY3ogM7Ps+pqGxsLBAr9fB87xxpkNEgDmyium6jmFo9JpVjFgcP5RYWFwmRMX1BUJdliROSg1SqQQxS5imw3CkApMUer0O0YjwILoYBPk1DAkjCcOIja+m4jr6acDuqeD2VMt1uuiOoogL58+TSqe5d+8e9qDH3Nwce7tbGKrGUaklRuDRlTAMQ6LRGCid6r5kCeSIwHdF4IuicO2Jx/nJT17BiqeRZRVF1VFUkfQl7EYKp5F7sViMwPMxY4JEce1MgW9++Yv4Tg9VVgTH3w9QJNjfOxhFEcYpVarMz8/T7fUYDBymp3KUSiUmJydpt9tYljWWSqRSKcJIMOcUWaNYFClbnU6Pra0t/vG//LPPpsTff/TRS57nsrIwTSIm4w36ICnEExkGwx7lcgnPGeA4Dq1Wi163Q7/TJHAdfD/AskxmJgqclGoctjxavS7tZo1MLkvge8hEEIFlJVBkdbQ/UDBG3KmrV6+g63ESyUn+4T/8n4knFM5unOHzzzxFNpXk+HAfx3XYPzzG1GK4vk88pnPl6nmqtRKBH+IHUK+1cYMBjusQM0yKx8cszi8gR3BSLLG/v0+tVkeWZNrNJoQh9WqVRDzJm2+/w+7uHo4zQFVlbLtPq9Xk5OSEod3h4eY2tWaT41KRBw/vU6mekMnn6PR7WIqKpsgYpk6z0eS4WGRmdhLDVEc7oo9othqCJx4JH1u/b6NoCpVKhSAIse0hyWQC13XI5bIkkxmMmI7rDjFNHQmw+10SCYtGvcow6GMYOjc/+JBe32Z1dZV0Ns1PX3uFng39gRDWBr6PM7BptRvoMY0/+v7fYmh3WF9bxnVdLl++wpn1M5w7f5Z+u0uj1SSZSlGplrH7fVzHZWd3l3qzgePavPbGL/nFL96iUm3yaHOL/sBmaXmBuBnj7NmzAu89Ocn7799A03SeevZZfNfDNA1q9QpPP/3UOFarXC7xL/63f8Wg1yGfTaMbOqlMnnJDPAyKqo8X9aZpjs3hp37IMGR0kRSCUEkaUU3dIY1GSyjONYPA98Zi18nJKf6b//oPuH71Ms8//xzHpSJBFOC6A1QNut0W8VhS2HFkRlhrDSR1dK0b4vmCaa+ObE6M9FCKKrpEXdeRUEZOAtGBhSOGWRSGhL/Bpe92RWhzu9vizV+/xeNPPgGSzNzsPEeHR3h+SL8vlvDqb+RKigg6oW0TgSmCpmIaOoaioUoSK2fPcHBwIBKZghBZUdE0ZaSxUwgCRmO5KIIS4nYiSRJ//P3f4+zqEtpIiBqzEmiGjuMOiMfihFFIq9Vmdm6ere1d3nvvfXITk6STcXq9HtlsdpRELlLATpf4VsIkmUwgSQq5XB7LSpJMpFlbPcPypSc/2wj5xk/+75fK5TL7B8c4foQeS/Lg4ZaAovWGRIHEzPQsiqTQalZJJURuZCyhky/MEoTQG7bo2T0O99r0hwM82UEJIrqtOjFVR5MNFNVAVmWS2QSB79No1DHiMbp2j2Ho8sLXXqQ1sHH7XXLZCR5s7XBULLF+/iKeF5KykiTTGlOTGQa9NnIQ0Cw36XRsLCvGRx9/wHReuOEdz2N6dpad3T229/c5u36Oe/cfEYsJpPHFi5dotbo0Oy0ebW/heCHdXh9ZNdje3cMPQ/YODgjCkHffv08qXUCR4zQbNrOzKxzsF/GGHpP5Ag/vfUK2MIFpxWnU6gSOy4c3P+bNtz9ga/uYubllhoMBU5N5kFysRILiSZXj0hFLC2eYm1zl7uYdyq1jYqkY+7tlVleWSVhJev0e/YHNSbVCIp3BHjjMLy4iSdBpDpCQuX//FhPTGWoNm25XI5Qk2p0Gc/PTREQkUyka5TLPPHadtBpwcnzMzQ9vsX7uHNFo3xWEEXt7u9y9cw/fk/j1G+9wcFTm5sf3+PJXv86f/Mn/yc337jAYBtSabeyBR6Xa4MaN27zwpW/Q6ZbpdgfMzS/QajX5+m99heWVRX78Vz/GslLYtk2v12NxcYGDAxHhdnxc5IUvP8fU9Cyv/vxNPB867SadTgsHmYEnISOWzOEoIf7UR+e6LkHkYRg6nuuQz+cBiSiIcF2fmGEQ+D6KqiKrcHF9iZf++7/PdEbm9Vd+zsnRAcNuGykIGHZ7RFFAv9cimdAI0dDNBCi6YMEH4jIThRG+P8RzxQikqiqKzGgnJhbYiiwKWhgGyJJEFIZAiOMNR+QIWWB6FFWMloFLEHj0u8Lf2aw3aLfb1GoNfD9kMHDwg3BshVJHPkdVVXE9D0nRCUIPVQHPC4kiBSWKUAKbq2szPNqpEWgWijJECWUkM44kRcQkE5GFJTpE07JQTQPPHZBJxnnsTIGBLV4ER0cVdrcPefvd9ykWqzh9G13VmJmZIgp8JiayrCwtkkkm0A2TYrFEKpUeJ7QLJUKGTqeDoeo0ah3qzRbxeIxqrYQf+phmjJWLn/tsBezm2z99KZfLocinIZ5Ch5PNZhnYPRxnOE6WjkJIJKxxsEe/28f1fJxBH8tKcu/BEYMgwLA02vUWU5MT9HstpAg0VRMR9WHASfEEXTdpd7r0bZuubbO9vce1648TNzTCCCYnp8jlc0wW8miqSq/XotvvcPfObfb2dtjb3SGdyrC5vUMinSKZThH5EQd7B/zoRz/m1s1PiMUslheW0TSDTNaiMFHg8PAI27ZJp9OEgdDt9NsShmrR7zvoeoLicQ0p1EjGsly7coWjYpF0Jk+v0+fw6BjTNPBDn3q9zrXL1+n0bQ72j1g/ew7XDQhCUHQTWfbAl3jisS9wdFBjYHscHO6BBGtnLvHw0QMarTLzS2usrF3m5i2Bxh50h2xu7hAEEc1mm4SV5oMPPiSXK3B0VKRWOyGXmaDVbDI7N02lVufgqMrNj+6RzGSwhza1WpWpyWl2NrcY9DusLC8ymY0zHA74xje/Qb8vWPzlWplqtYKp65w/f4Efvvwy2UyeXr/P13/rRTzP4dlnn+Vgd5/1jXOsrqywsrZMu9NmdXmJ//hn/5FnnnqcZFKsACqVE8IwoF6vMzc3Tzye4PXXX2N9/SzzC3PMzMxQq9WYn1skkbLodHpMT89RKpWYyKV57HNP88ndTTxk1BHnTpaVsXH5dA8UhtDt9ohCcY4Xa5JQXNJiFuVqdURoMEjETConRZ587DqXLp3j2vXLLK8ssbO9Q7lco9FoI0UQM0w8T0LVDWRFExKMUWKQNPo9COKFGL9UWR25AcQFU4htxWgplu2+6MQi8EZiTiLB5hJFjhH2Wvgaxf5MoH4+DSoJ+M0V0Kl8Qvw3iSgKkAiJkInHLWR8sgmDl/7Hf8C/+Dd/imxY6GqAFMqEioKEhyapoGricyH4QUSERBQE+N6QyG4RtxIEXogsKdRqVZaWFkGCpflZZmdnBaMtHhuNxmKyeLT1kETC4tGjB3ieSJQ6PDyg2+2Sy+XY2d1lMHRYWFhga+sR+UIe2x6gyCrrn9VKdP/mr1+qlCuUy2UmJiYpl8tMTk4JtrYVp1AoMBgMxrxzWRbucYH4UPD8ACtuksvleO2X72NYYrcVU00GdodUwmRubpqJwiTvvPse21vbxBMp2p0eYSSLC5dtU623WFtZ59KF8ywtr7C9vUMynaDX72FoCqosU6tVuHblCjNTE5xdWyUMAr7xjW/S6XbQdI2pXIGN8xtcOH8RVdMIvZC333qHRr1BMh3D9VyuXb3GYDBkeXmZqalJyicVquU6ccuESPzQSUB8RLLs93tMTU6yv3+AYRqYpkEkBaRTKQ4O9um0+rzz7vtcvX6d0zzLM+sbxKw41y6fJQwDev0OqVSMxeUZpmcm0XSDvf0jms0yx8V9NrcPOS62ebS5i6ZB9aTOpUtXCKMITdfp9YXn0IzFSKczJBIxAk88XJEMi8urfHx3C6Q4A3fIzNwMILG3t4eExEQmzXe+8w2m82lS6TQHBwckUxbVeo1CoSAwNwmL4dAZoXo6lCtVvvq1rxFFIdVqGV3TeOLJJzguHbG7s8vc7Ay+7/PNb3ydWvWEO3fu8+MfvcL8/Bz7+7t0u132dg/RdYOVlWUWF5doNVs0my3u3LnL0tIK+4fi+LG7s086maBeryFrOsVai1gsheuK8Uk8ICPOFWLMCUf01SgUgR9BEBAGIorNill0uoI3p6oq6VQSu9NlZ2uTodPh1q2bHB4dCNW8JASupqYiRxF910c34siKhjsUydOCHR+MLn7BmFyqjNhpYkT9VMn/qTNAaC5O5R+KImCLQXga4HGatC2NC7Tw1I6yHDUNx/2UOAGML5ySJImiMypgkqQQ+iFxXeX8+jILc1P88p1PCBQFRfLwXR80DU0JkYKIUBKjaGyUs6nKCnFTJ2nqfOnzj5HPF9ANA1M3uXr1KiEB6VSS+fkZ2p02rudRr9cpFAo0220iSSIeF5kHq6trWFaC4+NjCoVJVldX6Xa7NJuCp3/3/n3CMOLk5ATHcdnZ3ueFb33vsyGl2+0uhcIUqqpjGAbLy8skkyJwAkWi3euK5bPvsbu7i2VZzM3NsTAzTaPZJpIlHHvAO2+/RzoZ0R72MJQYrt0lkzb44nPP8N67H/Dx7XuEoST+X7pBq1QlFotjDwfEYgZJK8a//lf/kn/+z/4pf/Inf4JiGCiGiRS4hGGAlUqSS2V4cO8Oi7OTZKdydBp13n/3HerNGrqhkk+n6NttfH/IxvoqzWabbC7J7OwsB8VdlpdW6Np9AiK2dndoNptMzswyNZVlcnIS1/Mo16oMhy5L8wukEkmSqTytVgsvdMlN5IknLBYW5hgO+jSb54kcmUvXryCrEYl0jJ3dR5QbJ5SqNb77rW/hRwesLE0TRh7dboWkNcUXn32SH73yf7C6sMGNDz5m7dwlqu0uzz19jcWFaUIvwvMHeN6QIPSYnMrheUmCIMKMabzz1gc8fv0xFhcXub/5iI9uPQDFYOj3sAddZO009CHg3Jmz/OH3v0ercUKr2+P27dtcvHgRRZVJp8X32fM8ikdHhAFcvnyZp55N4LkB5ZMj2u02S0tLqIrEo8175LJpVhbmabRbTBayTE3nsGIqZ8+e59LFK0xOTdBoZMnlcrzyyqvEYiLpul6vjj15L774IvV6k3wmC5LEzOwUnWaL8xvrpCZm+Muf/hJPjo93SqcYmNOrbbfbFdx2WSYa8c1Ow0Qcx+GodEQkCa2WLKk0Gm1WZwU5ZGHuDKoS4/Lli7z66s954UtfE+sSR4yS/+u//nM8zxFCUFXCHQYEEfhhNJZyDIcuqjoi00oSMdMY0yEsKzlO3w7DgCD0kFCQJaGLDCNh8ZFlxnoozwvGl1bB9ncAkWYlK4wLtyxJqKPINllW8HyQUIhC0aUFoUff7vPUk4+h6yq6rtIZ+MhygKYpBPIIua0a+FEkXCGSRC43QRQFqJGLJknoRoqjoyJSBGfX19jd2yZAgCj7fSFbyeTyqLrB0PVotTocHN4ll8mLOMa+S7lcZXl5lbt37/LG6+8yOTnJc89/gTCQkOQYP/yrH7K4uMD8/AKLS+f/uhL113dg773xo5disRiNRmPEElLG1T+KfJJJobhWVXE5OX1jnxyXUXWZk3IFVYsxPzdHu1tn6IBth0S4bJw7w8lJkcPDIp4XIakaA8cVZuyVNerN5mghG9ButYgCYfl4+eWX2dnbIZIkLl++QrvVptvu4TsOigT2oEsykURVDS5duiQuXDGT/ESeBw8fkkol8Dwfy4pTmJ6k1WmRzuZAEoz9RqOFrKhMTU8JbVJhDkU12ds9ZHnlLNNTs7SbXarVBpEcUSqXWFlbZejaghvf6wpjsOMwkS9QqZXp9TuoisTC/DyO63D23DqVSoOJiQVqNRdFTtPvhWw+2uZ/+if/BFkb0qy3+eDdmxwcH7OyOsfiwhTHB4fMzkxx//4dNjbWCEMXVRWanVdffYX52TlmZ+dotxt0+12y2QmGLjzc2Sc7OU3cNKhUKyQSCa5evcaN996jXi3SapTpdjosLa/wye2PMUyTg8NDTCNGu90iFk+Qy+fp2T1KpSJWwhrxpWSGwwG5XA7LsigdHaEqMvlcjnaryaDfZ35+jlarw71799neeUQqbRGLmwR+xNraCq7rkExZ6LrGYGjTarXJZnMUpjJUKzU0RScRt5idKlBtNDk4LmOls9iDoQAyRox4+IJT5/uCdhqGwSjkwyGRiIvxzHcFwhjE9U2RMTWFP/7Dv83Xvvwl3vzV20Jsu3/ACy98iYeP7nPn7i02t+6TyVrc366g6CauHwiDcxCMKatRFI4psIqioCrayLysj8GCjEQO4t+FD/O0gxRSh0h0TZKE74djRfwYjx0JAsZponYw8oBGkTiGnV4uReoQSFKELEUQjfyMkcsf/+F/QeD7vPbmLTpDF0OLUGWdQFKQ8VFRkVRNmNyDQMhDZAkFn5gKtz58j0q5wmPXr9FsNpienaJcLtPqtKlUqmSyOeGLNUzu3nvIM888LVYfQw/DjBGEEYeHx7z8l3+FouoYhtB+vf7a6+ztHbCzd8CZM2eo1WsYZpzDwxLf/Ju//9lGyAcfvf2SYZiI9B9hGs3lciM43Gl6SUA2mxuPkb7vY2gGrjdk/dw6w4FYMNpum4ODCoGns3xmnmTC5MH9h/QHLooaI5AigXFWFdod0WqWSkWiKMSyEsJq4gvLx0mlzIOtTY4Oj5lfWObb3/wbfHjzFn4A3b5NpVrDDyT2D4qgaKTSOba3d1FVg3g8hawqBGGEH/rs7u0SM5Ooik693qLd7lAslpiZmUGS4M4nD3CGHhOT02zt7BIEEeVylempKQ6Oi/T6A+wRqE2SZKQQ1pbWqFWqBEHEUfGIXD4HIUQjh/6bb77Nw719zHiW6ellNjau8pc/fJkf/sUPiJsyA1fm9p17fPH5z9Oxe7zw5a/RavaoVU5QVTi/sY7nDTk6OmR2ZgYJmWQigWnGUFQN33eYn5+hUm3ycGcfVINAkui12ziuw/FxUeiowpDf/RvfJJONk8nkaLYa4nsZs0gkklRqFY6LRa5fE2SOVCqF67qkUsnxG1fTRCx9p9liYX5e7HyiiOFwyHA4HGuUEskE58+vjwWgly9d5rh4yPz8HN1uh35fkBxiMZPjo2MSyRiqqpNIJMlm01QrJ+QKk3SHHnfvPSSSTgF9ooCL4JDYCNTn4Xm+CMUNA5LJBGHgiU4tFCOfJIMiSxiqQqdR5f133uTa1esUCnkmp/KcnBRxHJenn34Gw9So1WrcfniIGbfwApCIiAhHIk0VXdEYjlT+ooApI3+rMn6edN0YjYXCyxkEPqYWA8IRtDxEliRcxyEKIjRVJRhdAk+jAqenZ0ZMOvBHOzhFUWCExzktdkEoRlSxy5JQJImLG2d47tlrZDM5/t2fvkyomDjDNpaZwo0iotBDDhUCQjRt9GcbBagyJAyFr73wHN/99je4fPki1eoJ+Xx2hHry0YwYScvCdX3sgcOHH95idnaOUqlM1+6zfvYM+XwOTVN57PHHePyJx5iZmaVYPOb4+Jh0PsE3v/UtBkObtbOrHBePGQ4GdHs9fu/7f+czIqV/9cpLpVKJbDY7PtOecsdNUx8D5RzHpV6vk0gk2N7exdRjdHot7MGAeq3DcGAzvzjB9s4Jkpwi0obU6lW6HRtZ0giQCYIIWRPamoQl6I3T01P0el2IhGLY1LRRuK6CE4TcuXOX99+7wYP7D/m7f++/Zf/oCEmR0WMWiVSGeDKDYVo0uz167R6ZdJapqSlazTazM/O02h3mF5dIJtI0G21MwySfy1MoTIw8YSqWZZGbyJFIxajVT3Bcm+nZCSzLZOhElEplpianyWbzxGIWUQAnxTKry2doNhoYZkyA3CSZtZU1dnYO+PpvfYu/+NFPeOErz7Nydo7Xf/Uyf+/v/x1+9cZPiekG0/OPU2+UWVjOk8nlSaUX0DWL2Zk8ugKB71GtVDB0Hc/16LR6KLKGrulkszlczxaiVy9i6MPW7gGSqmF3u6iaSjab5aMbH1IoTHBubQGFgL29fdrtDnfv3mPj/AV8z+XkpMz01AyaKjjr1WqVKApoNOo0mw08z6Xb6qAZOkkrQafdxh0MmSwU6PX7rKysEIQepWKZSqVMv9/D94Ul5c1fv8Xi4jy23RdjXxSMXlQRmUwWw1QZDlyajRaGruN7Lrbj8PGdR/QdF22U4KwoQvslYIlCDOyOdkOnoRUCQDhKY49EwZMUBUWSSMQN/vPf/z1e/OoLvP/umyRScR4+vEssZpLN5gkDgQh67Prj/OLNGyiawXDojNX0ruuBrOC7rggTGXVQhq6PDNXiWTplb52CA4NALOtVWUwZwgt66sv00DR9lEHwaQGLxWI4jjvqPKOxK0Hw8ZXxWC2eVWXEvg+EA0CR+Fvf+x3uffwBpWKFe1snDPwIGQcpVAhkGUNTMRQdSZNwnQGO56IbJooU4f9/nL1nkGXnfeb3O/mce87Nt2/Hme6Z7p48AGYQByBIACQIUEyioiWtaNlb1moludYryd4q135A7Qe5ylUqyaW1XF5JK5FaqyQrrEiCEhMABuQBBpNT90zndHO+Jx9/eG83dj+YHzAfu2qqwz3ve/7heX6P1+fMsXniwENXVeIkol6vkcllSSSFUnmMXqdHtdbAcTKUxya4eeMWqXSa+/fvE0UDNE2h221Tr9cwDI1Go06xWOTsA2cojeUAuHP3DpmMzanTx1AUlYcefJBzT37EUI/rb7/ykmFZSCiQiLglEemuMvQGWCmboReiaAbZXI5GvUk6bZOybObm5rn03iVOnzjO0PUIQwMSmUZtjdXqkDCGKAgII/GmlJCRJR1JFuwi27YolYqj8jpA1RRiRSUZCf7C4RAFSMKA5eW7/OlX/oLHn3qShRNn6Q5D+n6CmS0QqQap4jhWukg/SLhxcxlJs1la2abW8Xn/+hLvX1vDSBfoDgO8MCSfz1GrbLOzvUlxrEx/0GdnZ5vy+ASyrKEoFoaZptqqMj09RcY22VhfJZtxkFUFI53CKebRDAXufUXnAAAgAElEQVTH1tleX0VCIV+aplbr8Z1/eoUv/w+/zmxpmtUbr2IHbf73f/u/oRkqqjHF8tIHpBwdN9HQncNEOCSyRGEsy7Dv0ht4zC+eot0Zks0Xafda5IoZUmmT+6vLlIpFKtU29zYqdAYhsaww6HcYDAfk8zn6gy5zszPMzU1z9fJlbly7xXalzVNPP8detUkUCbpGLpPC0KDa3CFbzLC8tkp34HN08QSmJTAw7W5bpJ3v7ZDLZ0GTGPoeA9cjBnrtNpOT4+SyaaIwQEkkVEnj0PQ0G1u75PNZavUdVMUgjjS2t/eQlZjtzd2RKVomjDyslE6rPWR27hi3bt7GlVSSOEFBIok/PMyyoiDJgnFGIpEkjPRX4oIIE4Uw8JHiCF01KOTzTE1OkE+bHJ6dEdYZy8Y0U4ShT6NRZXn5DvV6jZvLOyiyjBdClEjEiUwcxUhJSJxIBGGI5ws6hqbrmCkLZJkkFkP5MPQJAo84CUWmgKoiS/txaPuhvRJeEGCYJrKqEMeiHdQ1E0mGMPJGtNIEWRKWKhIOcgH2BbFK7IEsE6CTxCF64vHisxcoZtM0fYkbN6/S73dRVAckGVOOkSUZSZPRdEFFCUPwXR8l9jkyXeBjj50hbRrIkoRlpEDSMA2bH/7wdbotAdQ8MjtHLpthd3ebYs5h2GuTS9sMBi7NRpNsJieCQsIQSYrJZGwUOaGQLUEccv7BM5iagiqBrsr0OnUef+4LH+0Cu/zOd18KQx9VU9jZ2abZqnP48NTogRO2CEO3aLc6RGGCk7LxXRcr61Bv1ClPTGCnU3i+j27oDN0Bu7tbwsYwGKLqOp7vIysaKBKyDJIiAjvzuQJBEDA+Ps729jayrIIM+sh9r456/ygWce1BFPLaa6/y9a9/je9+99vs7e3x/POfpt1u47keTqGMZaeZPbJANlfk0Nwi49OHmT50lLNnz3HyxClOnTrL3/3d31OemKbf98gXx1EVhWqtxqnTZ4nDiDAIGA6GpMwUsaJgpxxazTYp02Jnd5uJiXHarSaObdFp9LBNC8NMsdPs8KN3L9KPfOZPLTI96eBkGvzpf/h9SoVxrJzOrbVb7Na6/NIv/RTXri5RGlvk4cce57NffJbvfO/rzB6aZ3NthSiGixffIwwj2h0xj3PSWTY2tigUx3nrrXfZ2q7ihwqybrGzU2MwGHLuwbNICSRxhJ2yODQ5AYHH8cVFZCmhsrtNrbLL5OQ4l69cQVE1Njd3cKwiq/e2aVR73Li6RHW3iSYZvPrd79PreKRSWd5+6yILCycgFnyzfLoAsYKTc2i02oyNT6HqJosnTzEMAiwniywJYXAmkyFlpdndqXHyxClB69REBWSns4RBQBwlpOwciWzQ7XnsVBu4rouTdpAA0zKRR2hlz/WJwvhgwB3HCYaujfx/gs6gaxpD1wMp5uTiURbmpmk0qqIqUhXiOEJXVMbHSxTyefK5HG99cIdEPKQEUYSsyHhDFzslKiNhhZEOLEL7m0NZ/pCvtf/1/dYvGXHI9vVcfvBhWyiqNgXPDXAcZzTXS/4rlv1/uYUUv6uQUshSTBRDksiYuoytS5w6Pk/GtrDz43z/h2/gRRKabhJHMbIsyB2qpoIkKjpVM0in02Rsk9npMcqFNK1+j0a7jWoaSIpKpVHnzANnKU+MM+z3BcDSD0hnbNLZDBPj4xQKBaampykWixSLxdFCQyjxM5kssqwwdIdIkkSv38U0bVzPx7QcJFnn3FMf0cz9wduvvNTrD0hn0kxNT6LpGiQxTtomDGIqezWcdBpJkmm32/S6HaIwIpBiEZGVEkGmpmXSaNSYmBjjyNwca/fu4Q6HBImEZtgw+iAVEhJkSETybzJKTWnU60RhLKobw0AbRUEdfIgjO8V+Eg2A67r85V/+JZ7rcfjQYZxSGSedpdPtc+jwEfZqDayUw8KJk/znv/17vvoXf0F5rMyJk6cJwgTVsugPfJxMBtvOMPR9Ot0+uUJpRO0MsHNF+n2P6akpwjBg0B/g9vs46RT1WhXLcuh3B5h2hkvXb2FmbDRL5tnnP86gucPf/fX/w7Ejx6nVd7h87X1On3mBz/3kF7l7a4Xt3RpnH3iQSArxfJdctoCdypCxdO7evStCVrr9UXycSRjGXLp0hUqtxcPnzpPJFGl2h/Rdn1a7g2PZrK3fJ2VbOLbJ4twc48U8lq4yWR6jVHAoFnLIkqgkxiemAAVF1wlcj6npGaanZ3AcRwx2FYlCMS8O+FgJSU7IZtPomko25zDod1E1GctxUFWNjc1tUrZDr9cXUgDfJxoFqTpOijBM6HR6B3onP/BZXd9kbX2DOE743ne/R75QZnu3wpWr12n2XZHzqaqi6hrx3veBeK7roymaYILFIZZhIskSXiBGIKoiWFmmYeC7XSxNQVWgVquRyWTY2ljD9YYYmo6EhGPbvHNtmSAMUTUTzw8wNX205dTwA58g8FFVTWjAiNFVRTh5Rmk8cZQQhUJfJY2mXqoiHQzioygCST7QdkmSROxDEAWCyZ9IhCPG2b51al/7puv6QYBvItjXJJIseps44lMff5JDkyWSKCBdnOKti+/hBTGKImam8ggzrema+BlGrWuSxKhSwqPnzrB4ZIbtvV0GQ1e4RgYDVtZWuXf/HpIsQRRg2ylStoWuaSiKJL4ucUDIjaKQhYVFOp0OmiqSzQd9D9XUUFSZwdBjt1IlCBJy+TF6PY+Hn37uo11gX/vr//RSNlugWm2yu1vFStmYhoWsKOiaRsq2aTZFEo2uaxw9OkfKNAnjhH6/h6ZoJMQQJ0KNqyrsbm1y/oGHSKVS3FnZxAvFmlhTJRQlGa2kxcZG1zWB5qjX0VRdlJ0JB2+cfa63LMuougokSLKIng+CEF1V2dup8Pabb/PtV17lMy++yNG5OZbu3KHf7/Pd736Xyx98wGde+DSHD89w6vRpFF1n6Av8NapGs9UlkhTCRMHJ5IlihRCV5fvrjE/PoqjikGxtbmMaBtMz06ISKhRYurdCvljg29/5Djdv3eKLX/wCFy6c59K7P+L/+N3fxUgyVHd7/De/8DliZLbWs/zcL3yeP/+zv0dSEv77X/153njzhzz33GcoFsbZ2llnarxEJpthfHISRVWYnpnmR6+/Tr5Y4MzZs2iGwcAdsLm1zeTMYdq9PtlcjiAYIskR4+UirXqN0HeZmR6nkE0TxQGaKhMGAROT01y/eZe5o4tYTgYzlaHZrpPIElOHDpEvlTjzwBlyxRxTh6aQdYWxcpkjC0fI5bPIijD7I8W8/e5b3LhxB0mSmZ6e5vLlS+TzOaamJmm1mhRLOVJOCklWCKOQar1Kq9McGfst8sU8Y2Nlwijk8UcfRTM0CoUi7196HyNdOJgNaZpGPHoWVFUVl5emCcx3GAotl6kjyYhItigeBVuIA3rk8AxzMxPYKY3BoE8qZVEsFMiP4vk0TWN3a5sP7m4gySqeLwzfcRwThwIOGYTieyUJBH5A4HkcmZ0hDj1U3fywwhoN+UEYt8PR9nGfJBGNqqr9hKFeZ4hpaGiGwNwMhoI4q2kK0UiqsU+fBQ4G+bIikSQycQIqEb/6332ZQs5BliO8xOIb3/wWiaTgeoKUKkni8lRVHVVTURWFOBaIHqKAC4+ex+22mJyaJp/LE0cxiqIyXh5nrFQiCn2mJyc+DBiRJKI4BEkSOCIJNF0nTmJqtQaZbJ4gjJAVBc/3UAwJSVZRVJXbt5cYH59iefk+J06c4diDD3+0C+zGtasvJYmKZWeYnJ4lmy0RRgnNdpc4DimPj5PJ5iiWJ0atUwc77eC5IYV8kWJxjGFviCQp3Lx6g2KxSNbJkEgxrU6f1c09vEAkOSuy2MIkkjJaBfv4foDvB7TbbeErG62NP4SicbCdQUpGH4R4iLWReFBKRm84Ir79zZf5f//yL3n3nTf50he/QKmQJ59PoxsasiKzsrpCsZRn6LpMTExyZGEBzUwzNnmIIJLYqVRJF0pMzczR8yLu3LlNZa9Ovz/AslIEUYzrh+RyeUpjk2zU9uh0OywuLnL0yBHGS0XmD03wf//B75FzHELfR1U03r/0LoXiGL/8z/4Vv/BLP0l+vEw66zAYdimPj6EpElEUcPXKZc6fe4A//pM/Fpxxy6TVbmFZKW7eukWv32fo+/iey4MPnefmrTsMfQ8/9FFkGAzb3L51i8D3ePzRR1EViUq1imVZWLZDkij4MZQnD3F76T7VRofyxAx2tsShueN4kYxhZ2n1XIJYQdZT2JkSg0FIt+fTbg+RJIMgVCiUJpk+dJTZ2VnyufyIDCqjyBLdbhvHsej1O8LLORiyublJLpemWMwLfLGTot3psrm9zdH5o9TrFaE501Vef+NHdF0ZeYR72Q96bTQagr4wSrSJQlGl64aKnRLSit5gQBzFGIZ5oIjvdtp87MlH2VhdZm5ujmq1SiFXZNAfgiSTy+VZ39jg3l4HPwhxh0My2SyD3ocECc/rEwl1hUjyiUK+8LkX2NtaJ5b1A3nD/iWzr1uTEC3fPr8sCKMD4GEcx0iRjG0bWJaGFwQEfkIYhaiqTBTFB9mKiqJ8WH2NXu5xnCBLMnoc8ZOffYHA7QIxHVfmvUsfkMga/igzlCQSxFpVGwm2JVRVI05CVDnhsfMPsDAngJmmbpHEoCkKuqph6ir5bG70QhAgRFGZGkQJaLpGIZ8XZnXVGF220ofPhKIgKSKQhURYqr71T99ma2uXN998i1/+F7/+0YSsQ0/0qKqs0Op6NNe2mJ09hOOUMC2J7b0G6XSW9qAHcUgUJQybXYoFkbg8dBtsb1fIZrNMz86zu9skk8lQbdZptlskcYihSiijZJdEUgAh8pNQDuBnJPLIHBsThj4gVO+qKiOpwuak+PuWEoU4CMXbMEqQdPEgGFKMJMkUx8f4zKdfEBmMuoKhWnQ6LYbDIYvH5g+EhtVqVfCMUGl1ukiKysmzD9FoNNjYrjB1aJaZyRkGA0E2kAiBhL2dLdrugLsb1zl+5gFCd8D0WFHQXBWZ3/13/45wGLFwcpHimMn42CzbWw2+9o9f57U3L3Pq5HE2GjvMzJ5ja73N/FGb99/9Ea1Ojzg0+OM/+SqqZtMbeFCrMz09TaW+xrGTp5iamqI3dIkDn9XVVYJIvJUty6RZr5LNFJkoTzIzOUWnO8C208iagy8ZqLFBpCrYKZuZ0iTnLjxDs9Gm1Wrh+ypuoKJpacbGp9jZu8rx46fZ2NwkCSLSqXEymnAnWIZGt9uh0Y0JQ4tKZZvKzg6lYp75hXm6rRatZpUkSfCDPo16E11zWFw8zt7e1kj5n2FycpLNnQpDL+AHP3qL8WKOUqnF0vIqzzz9NN+/uEJvOKBSrZLJZEYBxM7o8wuJouAg8CMMQ5BiEXemKIRRMNpgagcVi4BVOrz//gc88MADVBsiMbzvehiux169QTgShcZxTLfTIQg9RrsDfD88YF/tt3WFjM3c7BTLm+2D+ZeiKIK+YgmrjaZ8eOns67j2DeqtVgsjNolifxTkKwS5hm4RhoMDCct+os/+xQUiBVySRGuddhz2drex9ARNl2jUW/heiDtaJERhgKGrBxdKHINlWYRxxLDnY1oauq6ztbWDbul4XgARBzFprjs4qCwlSaLveuiGSYQ02pia7PYqB4ggVdVHGZQSzWaTyclJKrUacRJhOhZjxRI/8ZkXSRKYnJj+cVfUj7/Apo/OUS6OoaoqN65fp1ieoDvwuHb1MouLi0iyyd989e/46Z/+GbLZIsN+n0w6zduX3xKhBIrKxz/+DJqmUchlcYcDAtdjUp3nzPmYIH6ZKzdukyACFlA0DDnBTyCKEkT1Kpz6+zJACTHf2u//9+PojVFArCzLRH4g3q5BiGkItXcSCDTIY489TqFQ4MaNG0xMlnnqqQu88r3XaXe7NOptzp8/Tyfp4w58mlET2zFH7KghR+am6DRFEIWqqpw7fZZ6swlSwuraGqXyGLKZ49Sp09y4dYtevcHubpWrV69Sa9bIZ/JIqTy5CYUrSxX86zGqucywH5MrPwBShWqjj2FOUC7PMOh2yTpjHD9ylkuXLhGZPnZ2nImJGCtl4DgpLl58h5mZGVw35oc/fJvx6cMMOm2a9QYT07OEbsBw6FMqjrO922ByfIZG0+ORc+eFgyFJsBybIPJxGw1i3War3iVUU9SbHQqFAlvbG1y7dZdzDz7E9s49iqUUlfomvWGDiYkJFudnWbm/hqLJeEmIp4kKodcd4ozNM3X0AbrtOlvVNnEQ8c77V5ifP0o577C2uoOmpggDgUX23IQkTvibv/kGp06f5fz5U+iqRm1vk83VXRbnj/PdV1+jXq+j6BqWY9Pp9zBNk2SU9qyoEkEAYSTmXVEUYJpjDAa9D5+RKCKMQmQpIVceQzcschmbOIhZX98kimPOnHmAIJboDF3u3F8hitOjNjEhGsECfS8U1VwEIBEGoCoqWdtmceEIbq/C0kaHIIgOLiDLskcZjCbE7kEL6Hke7V6fwWAwshcpWKbG5z77LC9/6xuoakb4E0OxQNgPnR0Oh/+V3gxA1Q18LyQOPJ7+5MdJopitrU1sx+TGjV1002DQF2dLlvcpGBqaZhCGIs5MU+TRzyFaWiVWGEYRw8BDQaHfauA4DpAQEKJi4g5dNFMnDGL8QHh/wyjBSWUIQ1/8zv/FGVYUiUajhmU5pNOOKCbcPlZKI5ctoOvyR7/AdNJsrdc5Mn+UBx+8gJNNs7y8zH/7xDP02y1cz+NfnTwr7ASSjGlaDLp9Hjj7MJl0DklR0FSDoe/RjyRcZNq+ixZbrK4scfqRh7m1vE4Yy6M/oEyYBKMtUACSRL5QoNXp4kc+mpKgqZqwR8Tx6I8dEgaCK6SOQl1NXeh+NNsim80yOTnJ4pE58tkcly+9i23IRKHP8tIq/UHIr/7zX+H999/n9ddfxzJlQq9H1tGQpIRWq0GpVGI47HPj2nVSqRTHFo9w+/Zt6s0mCwsLmCkbw7QpFArcunWDDz74AN/3SGVMTo2fY+mujedrSInMoZkJXv7m17GdDLIa0233MVIW7aGLYZbpBT3MtI3kjPHZz3yBW9eusNqsMHn8GLVWn3rLxbEtpsoTNKs7mKZDvxeQLRUxUkN03WTx3DxSEnP58lUUWSF0Y/RCnk88d47JyakDTV9hYoIoEroiXTM4ujBPrztgxhGeyKc/9iTf/MbXOX3iLMfnjnP16mUkVaFUKjA7N8OxY0ep1Su89dZbHDt2jDgCPwwIQ8E/LxXGaTXatDs9LCdPJBtomsKjz2Xx3D6V7WUeu3CBv/qrv0JPqZw6dWp0wC0eee55EmQWHniI77/2Az7xiRe5cf0K1X6XF7/0M9zf/hNkTaPeGZIybTzXxzJ1VEWi1++MDqZ8kNAt9GEeoacgSyEoASkrjeeGRB5MjI8x7HdQnSyHJg7RqDZot1wSSUVSVeq9gEiWCIKIhIAECdfroKsGgd9HViXcnosUxKQUnelCGrffZPbQNKH/ATISkmIQRgGqoiIrEhCTyMJKFEQBvUGIrBhCfppIo9YuYP7oKYhfIYllEjxMw8b1wQ8ExcIwU/gjGgexoNS6SSIkT5KC7egkyhA9paHqFsubu7S7Q1RNIHOQZZF6rkREkociSwyGAsstxRGqqlHdrmCpCSnbwFFMBq6LlCS4vQFDTwi5/chHNVSq1Sq2bWM7FoEfQ5JQa7XRNI1utytYbYEw2mcyOUGm7Xdpd9sUCgW8dgfdMBgOPdrt7o+9wH78DOzW0kuaabJT3aMwVuLeygp2NoudcXjt1ddAUSiNlcjn8yiKRjabp1ZvomoqH1y+zLWrNzBNk+2dPZrtNinb5oNLV6jXG9y6dRNd09ne2SWJJfwwwLL0g/TjOE6IE0RIbLOFoqoCS6KILEExHBxFP6mCyTQYDLAMA9/3sK0UadtmanKcFz79aSbGx9lY3yBtO2iawdATSvF6rcba2n3q9RqplCUsNKaAt9VqNXRdpGkrssyJ4ye4c/s2+VyO48eOsbx8jygUrH/P84miiFwuy+zcLAsLCwRBwOuvv8nM9Ayzs0ewrBSGblIslajUmni+sFeQSIRxjOeN2nA/pFGtcf/eMovzR0RAbMohlytw7vw5XnzhBXyvj6HLzB+d49HHH8NK2URxxOe/8HmuXr1KvVonjCOCMObpjz/DyTNnKZVKdDodJiYmkGV5ZAtTKJVKVGsVDF1s9fZ2K+zu7rK2tsqFJ59kb2+bza0NHn3sUZrNBjt7Ffb2qnS7fZJYJpsrUK83KRRLeG5It9MfbZiE2LPXH8AIBhmGAdmMI0TCqRT1VpdDc/M42QKNdo+dSp1qo02uVCabzXPr5m2efPrjXPrgMuNjYzz9iY/x1htvECCxsr6JlXLoD4YjlrtOf9AbRYWJsFRB/wTkBHc4JIokTENF1SQ63R6Ok2EwaHFosog36LNyf5UEmTgMcWyLN996k6X7azRbXbxIGkkZgoM5WxAkSLLK0A9IQkiZNkkUMDU9xsbaPU6fPMmbFy+L1mzUWeyLYJMkIkGE6HqeJ5Ki4nhER5VQVYXJcpn8yAaHooy2qDJIEoahH7SdB0KK/Vg1VSUOY6Q45me/9Fl6vSaaptBotHnjvduC2pGIhZkki0G7qmqj7amOphsjPLVELpPiqQuPUtnbQZJl2p0u+zp/SVbQDZM4TvB8XzzPkjQ6k6J99/0A1/UYDkXKuTC7K+TzeWRZotlsICujCLvREkCShL6v1WrxxLOf+WhD/Eq7/9L4oRnGp6dIFIXx6WlUyyQiYW7uCE42y63bd5manMEPY0zHAU0nbWcYK49z9sGzrG9u8PLL3+D7P/gRlz+4yd2lVXb3drlz5w6lUplPPvc8b198D1kV8DUxnJdAlkbJJgqu62FaFvqIfy6rygHaJ4oiwjgkjEIRCwVkMw79Xpu0ncJOpdjb3sLzPfKFAmEEE1NTOE6atbUVDs1OsbG+zpkzp1lZuc9gOGBqaoqlpSWBDXIFrFGSJPb29g7MwVEUsbGxwd7eDmvr6/T7ParVCplMGk1XaTYbLC+tsbiwSLk0wc7uHr4XcXhuDt1I8fgTT3L27INomkGv79EbeiTISLJKKmUz6PWp16pk0mnW1zeo1VuoukEmkyWby/C9732Le/eXyeaz/OM/fYsr164xNjGBblhceOICYRRhOxlOnjqDk82TStl0u11yuSztdgfTNCkU8oShaLd1Q2NhYZFarUYUxszNicSgVqtFEA1pNGvkCwUmJic5c+Ys7U4f03AoFssUSyV6/QFhGNNqtg4sPVEUMRj0RqrwhInJSXw/EOJPVCw7jZ0pIGsWu9Um5x55gjBReOPt9/iXv/4vefedi+hWinarSxTFlIoF1tc2qNbqaKbF7l4FXU+RyBKaoSOrMr1+D0VSxRA/jun1+9gpi0K+QG/QI4rBMIWSXFIUOt0e05Nlzp45zqDV4dVXXuXjH3+KKPDpdhqcOHWGEJnllU38SMxgRVpOMBJ7JiSxRKIovPD8Z/jZn/l5fuHnfoaxsSy6KuGHHqtrOwRhQByFIrZMEgc7jKPRvG5fsgCyIqoibZRmPXd0kWanTb3RJAFc10PVtZFs5MNgWHX0EjdM/UDSoSmCjV/K2diOQbvdIpcr88OL19mH8EiyIpQCkiS2omEkBB4jKUUYeBTSDo8+/BC2qRPFCaVyWYxRNI39MJFmp03KSotEdFnEvIVRhGlaB9KQ/TPkOA6yIosLe4QGsmxB5PBcd7SMg8FwgGEanP+oOrCbKxsv9QYD2t0+ru8x9Hz6rkuz3WEYeERxwtjYJCD8i5plY2XS5CyLJ558gt/8zX/BysoShUKWRqtLGEMYQL/fI05i7t1b4frNOyiqimUZKJqMrAggWxjEpNNpEVUl9MbE+9ovELFgkuB6Z5w07nCIqWvYKYMoCrnw2CNU9/bIpGySKCadzTA2VkaRdGYPz5EkEWvry7jDLvlCnvWNNYZDsdnb3haoj1arRRJHhIHP3Oxh4ihk/ugRlu7eoVat4KTTdLsdVE2lWCowPT1DOmNz6dIllu/d5djiSdbXNxgrC2uNHwrbSTafp9lqk85myWRznHngQZ586ilOnDjJE09c4NDMIXLZLI7j0O20kTWNTC6H54U42QyX3ntP/IzlMcIg4syDDzExOUM6k2dldY1eb0gmnaHd6ZJICopmkEgyiwtHkCT54MF33SGGoVOr1RgOXF57TcyWMpk01WqF6elpqtUqa6urnD51hr1qbbTinuTUqVMMhn1WV9fY2N6m2xPp42fOnKHVbgtP3yiVu1Qq0W63yWayJLGI0EKSSFBx/QhZMchki6xtbKNoJkM35D/+2Z9y8+ZdbCdLrV5ncmoazwuo14SXUlJUbt2+i6oZwqjtB2iqJr5vFBKHH4beFgpFqpUKURiRyqRF5TMiL6TSaVRVwUmZFJ0Ms3NHiJOI9y69g6Yp3F66z/WbywSxguu7IytQROCL6ktTDaIk4Tf/x99ie3OHYr7AzZvXSFkaxxaPYloWiqpzb/keuqEjqzoJMQnx6PBGlMfG6XQ6jDzZBwlCkPDIY0/w6vdfIyZGVmT8QCyLDF1oLPeR2Amgaxr90TxQN0TupyqpfOlLn0dTQbdS5PIl3rh4TSSij7yqksyoAhslGSHmaZIkoUpgmRpjpRzDfg9N02g1m0xOTTEcinQkWZYxR6hsx3HodDpEUYRt23S7HVzXpdsVrWChWBRh1akUQRgQxWKe1+8PiKNk5BcNabXauK5HKmVz7smPqANzA+klx3bIZjLYKRsn5WAaOrlMlnwxj21nSKezOBmhSzJMnSiJqGzf4+K7b6DrCQ89eIK7d28QJzGGaeL5LoqiYhomcQKZQpFup4ftpPA8F103UVWNMAo5PDvH5SuX8dxRIEMUESdCWS1JEooskyASVUiEETadTuHYJp12U4Rx6pqIOcoegawAACAASURBVDcN9vYqPPzwY7z8zZfxvQGV6g6T4yVq9abYPFWrWCmLsdIY3W4H0xSbItu22dzc5NixY4RhyJUrVygUCsQIE3E6LS6te/fucf/+fVbW7tPtdjl2bIFbt29QLpewbYu9yi6aoTG/eARJAeRkpNHyUFUZ23YoFApEQcyxxUVOnT7NsePHmD+6wNT0FNlcDl2xcByHhYXjOE4G28nR7Xg4To4wBieVFjl/soxmWKiaTqPR5NbNG3TaDXZ3d0lZNkkcs7uzQxxH3Lx5k5mZGfq9HoauUyqV2N3dZWJykkw2i65l6XQG+H7A/PwCFy++w87uKqurS9TqWwRRxNDtsruzziuvfodHHj3H1Mwkg36fp556mqWlJTKZNOPjZdbXN2g2m7TbHcbHx8nn8yKhKp+nVCoRRREvvvgiDz/8GOfPP4Kq6eiGiWlatDtter0hrueTJDL1vRoTY+P0O100TSPjpGm32nQ6XXTdoN8f4PseuWxWROT5Ia4Xo8gaKTONbWdIEolMOsfM9Cwzk+PImoGsm6Ldt1I0u0NWN/cIUAhHWq8wjJFkDUXVmZya4tSZB9jd3ePq5atcv36Vz7z4PH/793+NrMpMTM9y5Mg8Fy+9j6YbyIqKNFoiaKoBRPzWb/02b731Fp7nj4bz2kgHprK2uXOQ2BNH8WhMkYNECHfHxsYOMDaaphElkWDkSxJxDKZpc/6R8ySKgqyZNNo+V2/cZTh0hfeSBEbQwSQRujI5UVA0DUWWkOQERZbJ57IYRophr0e/P2Bne5tet4ckQa8nliOmbuC5LnEUocgCgphJp+l1uyia6KbE5xAcbGKjKGJra4tKvU42kxW/QxyPAIwyhm7w0IVnPtoFtr5TeymOxCFVRtHthqqiSDKtWh3fc9E0je3dbYaDLu6whxwHuP0GVy6/z6Db5sb1K8wfPcLOboXeoIuqysRRiCyDF8VIKNTqTXLZHHEcjiwGMbpuUq1WaTabKLJ6oHE5iDwfBTkABw+DLEs4qVG4iKpgmRZOJkuj3UKWVRqNBqurq5iWhu+59Dsd3MEAzbBYWloiDCMGg6H4fRXBCHeDAN0w6A8GNNstavU65x95mPXNDTRNpVarMj09Q73ewPeD0VxJpzw2jq4pTE1NMhwOePfddzh79gy5XJa7d+6gGzqWZaHrGkmcYNs2nu9ipUyclIPneXi+h+sPCaOIXq9HNpPGcz22t7ZwbJtur4PjOBw9Os/m5iaWmeJjH/sYtVqNdrtDJp3Fth3Gxgo884mPc+/eMkeOzBGEATs7u5RKJWRFOtimua4nyA1xiJ22iaKYQ4dmOXXyzOhQyURBSCaTolLZJpdLs7p2D2/gkcQBrVYDP/AoFAo0m22iKOa99y5zaGaan/qpL/CVP/8qKcsil8uxePw4e7s7mKbB2FjpoP2+evUqV65cIQbyhQJBHDFz+BCD4ZBioUS+kCOXzWNaNg+dO8+Jkyc5ffI0cSJyBpEkJsYn6HW7xFGEMfo7uwMPTdEJkxhVUdE1nWKpRDaf5YnHH6VcHmd2bob5E6eRFJ3SWJkjRxc5NHeEH7z5LgnyiDsWEQQRcQwTE5NkcjlUVWNzcwMJGPZ7BP6Az37uRYrlMraTIQYGXsDuKDU+SYTcwfd8SmN5nvnEs/zDP/yDYHCN3CTCSiQRxgJNbY7aRtd1cYe+MOv7Pp1OB1kWG8ler0eMSBlyBwNMwyEMJf7Zl7/MybOnkDWT8sRhfvD667S7HRRVY9+DuU/2kCRIpRwkSRrNdj0c2+EnPvd5vCBgZmqG8sQUqmYRxAnF0gS6ZpBKpTHMFKpmkLLTqJqJpMgMhi6KpiOrBgkyqqaRzeXRdAMkmWa7SaFUJJctsLm1TRIneJ7P+voGhWKJOEp4+GMfsQK7c3/9pTAMR1B/ibW1FVRFpVKpkHhDep0O3V4PWZWRSahsr7N66zqr68vcvnGTY/OL7G7v0m60abbbWCkLlIQoiNB0FVXRcNI5UilREaiKimrouJ7Pwvy8eJBHsU5JkqCo6oGYdX+dvQ97S6cyYkOZREhSAiT0+n1y+QKDgYc6Gv7LmoRlanQ6bXRVI44kmq02g/6QTqeLH4gE40ajIRJ4Ruk2w+GQbrcrdC6juKtmU3DSdnarJInExMQE80cX6fY6LCwssHJ/hVJpDJDwhsJcbJkOURiTLxSwUw4bq1ucO3eeu3eXGPSGHF88Qb5YIJNN02jW6HTaBIGHREIUBiKENgoII5dUymRvb4+xsTEG/QGPPfYor732fQA2Nzf5xV/8Rf78K/+RlZUVVlZWuHXrOtVqjWq1hqbqvPLKK8zPHyVJEhbnFxkMBlSqewyHAwzT5MSJk1QqVd588wd88MF7NBt1hsMelz94n3arhSIr7O1WMVWDKI5GQ/KIZrODqhgsLBwnimJazQbXr93AskSM/PhEmXq9gawo5PJ52p0OS8tL2I6DYRpMz8wwOzvL9u4u4xPj3Lt3j3Q6TbvVYjjoE0UJoSwxGLpCDS9BoVRi6vBhpmZmaNTqHJmbZXV1lTgKhW7KE/F9iRRh6ia2lcIwVX7ypz+HpoKmCttUrdnm/uoGsmphWim+8fI/0h0MCeKIYOgSRjFBKBwAR+YXMAyDpXtLhJ7HscV5fvu3/ydOnj7O0OszMztLsTRBOpujNxhw7/4KkiTaNAkYDlzGx0sUCyVeffVVdN3YDxZH0wSTK44SsXHXxVxPVXQiJPwwRB4tvDRNo9lsImvqAdFC03QM08ay0vz0z/8slVoNJ5PHDyS+/o2vHejGFEU4WPYH5+IHkMVFKEmknTTpTIb5o/PYTpZGo0uEimKYZLJFao02kmbR7PaQFJ29WpMIhU5/QBBJdPouqp7CGiV5WZbNfnhwrVYlm82RzxXo9QYoioqmaFhWiiiK6XS6dLs9nvnM/7+Z+8fKKBrtFkkYUSgU0E0Ryrm6voYkSThSIt5siRhEtntdVpbu0qnu8J3Xvs3D587j2Gle+NQLbGxs4MfXqLQaKLqGqoHvu2i6zWAwII5lUoaOJAUMBgNhM1paOvC1Sco+RXQf0SuL9jERxlbTFGt70zKQJBXXddF1DTOVot5oIKsKqqJTKOTYrW6jKUKEt7q8yrGF45hOGs/zWFtbodcb4LouYRjS7XXIFsao1euUy2U8z2OvUqEz4nhXKhWRHagLL+LnP/dF3nrrLT7xiWfZ2trgsUef4Pbt2zTqdY4dO8bK/VUG3SG6rrOxsU2SJLSbIo5scX6Rer3J9es3CQmEHq3TYW5ujuXlu4KbrmqMj4/h2gbIEkHooekSKytLtFodNjZXDqrSxcVF/uAP/oCzZ89y49p1jBEaSJZlTNPk8uXLnDx5kkuXLlEqlXj/4iU++clP8u7Ft5mfP8L169ep7NWw7TQnT81zeHZC6IqiCN8bcvrESS5evAiRRLvZotNr4wYehVKJQqHEiROnWd/cZbI8jkRIp91iefkuR4/OEQQei4vz3L13nzffeZtcLkcmn2PguUwdmmF5eZmx8QkuXLjA3/393zI1Pc3NW7cYLxaEliry6MYBgecjJ+CkbFRdwx8OkA2NZz/xDJ7nkcvlmJmZInADhkNRuZg5jWHfxUml0U2JVqM6Qru02a3uMr94koceeRxT0UhCl929ilgauTEjnzSKojAzc4h6vX4Q/bdweJZnn32Gl156iZ/9uS9y585NjjfbHD92mkqlgmHZIKsMh32UEVrasiw+9alPkU6nR2btBFXTME19BA2NUGTwPUFhdV0XVRGQQ9FCemi6LqRDpokfhiiK2EeGYUiz2eS3fvvX8YMIWdEIwpi337lIOAoEPjCUJ9HB/0mSRFRSqip4ZH7A2Ng4Dz96gereDhNTc6MQDg3D0Jg+IiQ7u7u7pDM26vY2s7OzFAoFWq0WnueK7MdBlzgRVirx9SGZTI5UyqFSqZHLFdB180DgGsdg24I79+P+/Xig4b2VlxRJod/t0esPUQyTdDaLqWtoRppap4qdGuP4oRJf+T//kKUbH9BqreEaE/zKL/9z1la2iRyLKG1iEtFrVpmcKJG4AUoCkSQTazLtvrAmGaouwg1iiZX7q8QRxJEww8Zxgq7JSLIgWaqaQhRHSML+jWYoJEToo8FtnMTEYciJE8doNKo8+6lPcfnSe+hJgqnqHD9+nHavh53PidYoDJCJ0VUZ3dBRFB3NtA6U+WEg+vbhYEAQhhhOCkOzWDx2nEajRTabZWn5Lg888CDVSpWzZ87yh3/473n22WepVnZ5/PFH2NpYBQTMrtfroUgSp04d5+q1K0hKzLsX38D1e0yUCrz66it0e30SFGRZo9Fq4KQdVtfuj97SYqOzMH8Mz3VZOLrAm2+8ybNPPcMHly6xt7PFWLnA5cvvoyoqN27dJJ/LcvPmLcoTZRIF7q8vs1vdYnPrPrKss3TvFpXqDu1Om0atgWHobG+vUxqbIggjmq0Gx08c49bdm/zVX/8V61sbeH5Az/PwQg839tCMFAkazz33SdbWVmjU9nj22Wd4++23SdkOU9OHuH9vlc2NLULPRYpjioU8s4cPcX/5PuWxMYqFAvVmjYsX3yFtm1y59D4yMe5QJDoHYYDkBziGRr/bJfA9uu0uYRgjJwodz6Mz7LNw/Di94ZBISpiemWRsokS+PEexVMKyLXqtLrl0gavvXSOMfcZKkzRqNcqlIqah0e706PdDdCVFq9Zl6A1FKrZukrJSZLMZ6rUqsgL/5n/+X/naP/xnyqUc3/rG1+g32txfWefr3/on7l69jEZCdXcPSZLRNIMgjnHDgOef/wRnH3yAl1/+Jt7oZYwY8RNGoZhRyQpRJIuw3VyewHOR5QDbztHr9VFVQXMRSeX7BnYLPwj51//6Nxj26yzdvc2tGzfZWFtjbWWFKPQFVxtRycWhmCHHYYKqqchSQhiKzTiSwqde+DSbu1sMhx6JKmOmbTquuBMGQUQqm8Ow0uw22qSyOZrdPt2hx7GTJ9na2WOn3iZbnuDW3Xu0egOOzC8iqykqrS71zpBr166ScgpodoFQMsmUJpg4fISNnT2e+dTzHy3Y9u1rdxJFEtqrKE6QDRXCgGDo0V3Z5Xuv/S3fe+X7aFafQn6CYn6a69ev8W9//9/zw3/8DsV0lpPnzvKji68zJivcX7rFtVvXMXJZNFlD0VIMw4SeH9BudRnLF0g7FpVKhfX19YPVq8CKSOgj9XGESJmxLAsZkJFGsegiqy+dFulIigzpdBpFkckV8uSzOXqtpri5NZWIhNm5o3jDAH/oEkcCW7K5uSnyL1Mp1re3sEyTfqfL/Pw8S0tLGIZFGMf0By0KhQLl8ji+73P06FE2NjbI54sjvUuG3d0dzpw9xeVL73P+/Hnu318lSRLSmRyV6i4PPfQQH7x/icGgRzaXRldUdna2sJw0cQSHZue4t7JMEoXk8hnGSuP0ej0Astk8Fy5coNPqUqvVDtKdO50WhqkxMzlBHMd89atfZXHxOG+8/TpBFNLpdFE1g1KpJCphVcADDVMgZ0ROoSwIvMWckAtEQuem6xq+6x28sQW0TyHBR9FkHDtHNlPiS1/6aVIpkzu373HixAkcxyGKIlZWVqhUKjz99NO8+85bLBxb5Pvf/z7l8Uksx6aYy9NqtbBtoUnb3FijVt2jWq2Sy+VIZzKUSmVymSz1RpVDhw8jKSqqotNotLh+8xYnH3qUvb09HnroQa5fu4aTsjAMg1arQbk4wWd/4tPs7GzxZ3/8J4yPTxJFEdmsw9h4mVpTjA4MQ8O0bIG3HhtneXmZtCUWQRcuXOBXvvxlTEun2axTKOT41V//DcrlMkkUsHFvjSiIUQ2HUw89xObabbLZLL/3e79PkiR4vmjfuv0Bf/Kn/xdXL1/mj/7oj0gioZVTNAEoSOQEd+gjyyqqImiuKdvCdQdAjKIYB0sm4esUL9koipDlFCnb5Hd+57co5DNi3imr/Jvf+V/Y29sdYadDZEVUlLKkoijaAeFFVkS5qcgaZ88+yK/92q/hpE2GPZ/7qyuUJydwHAfP96lWKriuiywpTE5OCrR3HBIGgoIrj7Dfe7vb9DpdoihAUxTyxSK9Xg/TTDF9aIpmq0O7OyCdzlIulzE0hSgKee7Zpz9asO1mpf6S74lBYRAEeGGALsvoErTWl6lXV9hYucfA79HqhlSaLr/6G7/JwsmTPPbQed57511KE2VypSJ7q+usrq3R6vQxMja6ohO4LhKCrT41PU2cxOiqysrKyoHlA/jwEpMVkXhAArKgJ+xj4PbXyuJgie1Mp90afaAxzVZTaHES6PQ7RHHMcDgkCAPyuTy5bIaZQ4cIwpAjc3OosoqVsiiVxul1+5THyrgDlzCIkJCQElAU+PjTT7OzvU0uk+Xdt99BlRWkJOH44nE2t3aZnpnkRz/6AY88+ghvv/M2mq4jKwqqpvLFL36Rr3zlKxxbXKBarZJ1MkyOj3N36Q6WYVFr1OgPBqSdFPfuL5FKWdQbddIZh7STIY4j7t+/R2WvwunTp+j1uvihi66rHD48ze3bt7Adm/PnztPvdXn+hU/zo9ffgEQeLQN2CL0Izw0YDoSwd9DvMegP6XWHSEgHaOg4SnCHHp228PURgaGbhL6QLMiShK6ohF5ENpv//zh7syDJ0vM87zn7kntmVVbW1lXV1XtPTy8zmBkAg54ZAMRCEIQAigRl01xASSRFWpZIOWSHgmFYF5YshWxLDNKWRJAUF9CUAXIAgiAIzGCZfd9632vfsnLPc06e3Rf/qRz4wrqYvpmLiaiuzjzn+7//+973eTl96j4kQFWFZu7IkSM4jsPly5f55Cc/ySuvvIKsqNTrU/SHQ5aOLDMYDBj0xGJCzEVjRiOPNE1YuXMbK2cRBGHGaw9RNQ0/8Lh7+w6arlEsFpluNHjgkQ8QhSFvvvk6jXod0zSxDI365CQjd8Dld95ifW2FeyurnD//ILad46VXX8bQLIaDPpqqMTk1Rb1ex/U9HNeh0+2QMwR7/7d/+7dxXId+r4uhK5iGxqGlI5w5e5aB43Lq1P14rs+D73uEQqXK8ZPHUXWDr37l/wFA0/QsNdxg+cgyu9u7vPXm29nLbGbxayLOTBA3NBHEO9ZVSZmnUiybOp2O2AKaBpYlttRSJnZ98MEHmJ6Z5cqVa7z22usMhw7Nvd0xl19VtIxcAQepSQApcbYZLzA5WUfTFMIw4NI77/DwIw/jOQ4TkxN02i1M06AxOYkfxAwGQyanRHJZoVBEV3UmajWmpqbElTGX5/DSMpphYdl5FE1nfmGRUq2G6/rY+QIz8wtIyGxsb2GaNkcOL7xHoOHVG180dEMQHwwd3dBxe100JP7od/8NQdTG0AuMYplQqfCJz/xtfuTHnkA3TPr7bZz+gBdeeZlDhxf4+lf+HMdxObS8RHVigsOHFjh8aJ6NzXUa03Xa+/uYhsH29g6O4wC8y/uGsdte0zRhFI0jJBlMw6RcKuNn8exppi4+iHdTFBXX9ahUK3TafVrtDvNz85BCu9kkiQK8UYhm6JCmVCo1ZEXFyKLTCsUSDz74oOhS/BGHl5ZJU5iaqON4Lq7jYZo2ly5dxrYLbG3t8Gu/9g/5V//qXwu8ta4zNzfLnTt3aDQa7O7uIUsqW9tbfO3JJ7lw4QJrq6uUi0XiMOLmzVvMzs3S6XRoTE3RH/S5desGE7UqKyt3qNYqeJ5LisAQq5rC2bP38fR3v0MY+aytr5GzBekhZ1tcunSZl156kVdefoXvPfMCJBJRGIvNp5HDMiyiIEFK0izaXULXLGTFQFX1bMhLtqFSSRMZU7UgVTEMG9suiJQeWca2C1hmEUXSeeDCg5w4fpI33n6dQwvzPP/Cc1y+colKpczzLzyH57ksLR6mN+ij6zqlcpmdnR2Wlw6/+z3rGjnL4vXXX6Pf6xNEAvYnwmsjRiMv60bF5i1Jkuwz3sb3hhAHpEnIaNAn9F0i30NVQuqNCiPfYWFhgVZrH90yKBRtCAX6aaJWxR+5SGlKu7XPRLWGBFRKZVzX5diJkzz/3LOCoyWn5G2LXjfg57/wd5lozIEs9Fr/7nd+izsrt6hPzXDjxnWuXb8uaLCkmdI94Sc//3lefvVVVu6uoGWJ3rIsEyPkQgdIG0kWUoeEFMM0AEmo3sk0XIoQd3ueK5qNUYgzdPjJn/o8hVKZhcUlnn7qaVbX1hj0utlnnMWwIWcOF0PM0oKAJBFb6ShMOX36Pi5cuMDMTAPLyOONRnQHAzFXHPkkScpwMEQ3cszMzLK2vkaxKKQrrf02uqbSH7oMhg5xkiIpKrpp0e0PmJiskyLR3N8jikIKxQKuI9Llkyhk5DqcOnX8vRWwjb3WF31fxLarukYcBeR0HYWUtc2b9Icdtre6BLLN+Uc+yuMf+wRB3MU2bZqbW3z9L/4Cx/cxCwV+47/7x/z45z7LRrPJ2TP3881vfANT1/ilX/q7fPvppzh//hyba2vs7O2TpqkIl00SDMNAhHhGKAiUiD/y0A0dVVaJgwh/FKDI0ngIGQQiT0+RpSzCKmE4GIKkjKUOvuNw+tgyGyt3cLyA1n6TrZ0ddNOk2dpnenaGcrHE0WPHxelmWTzw4EMYlkUSC26ZYZiEQczly1ex7TyeF3D0yHH++I++zKd+9NP0B32OHzvK7Vu36bQ7XL1yhYnaJGura3Q6bS5evMg7b73J5uYmh+bn2dvd4/jx41y9dp2lpcOsrNxjf6/J/OwsA2dIEPooqrCYKKqgAIShz9rqPZrNXfb2dhgM+ty+c4sXX3yRy5cu0Wq1cR2PVruNZVfw3BGnjp+AJOazf+sz1OuTnD93P5/82MdYXlzgZ372Z2jUp5icmOLs/feTkKCqCpaVI45jZmfnuXD+AX7+575AfarO3//7v8TS0jI//Xf+DouLhzl0aInTZ85yePkI33nqO5TKRUzTZHV1lUKhgGmanDp1ikKhQJSkDDKNkOu4lCtl9nZ3aTQaqKpMGEWMfI/Z6VmuXLtC4EeAQrvTw3Ucer3ueOnz1ptv4wcht2/fwh302N5Yw9Akhr0OSTxi5PQgCXH9Hp12k263RZLA3NwcQeBhWyoTpSrvf+QhIOLEyWOQxly9coU0DvnWN7/B3t4OpiW0dS+98hKSJEimmiLheQrViTphCl4Y0R8MeeQDDyNrCh/4wIcAhb/6xl9m/DBxm/D8gCSR+N53vysKUJIiHyysMsyFrqhZcRZbeE3TkDVNFC1NE7+PrFAoFsjZNooiAkYOtplnLzzA5NQMf/LlP+Xq1WsMB32kjJ0mSeL2I+RJGlEkxguyLJOzbUzTolgsY+gWc/Nz9Ho91je2hNOiWEKSZTE+GQUgSdh2fqy+32s1WTh0CMu06Hf7jHyParWCqmrYuQKmaRFFMZ7v0e/1OLxwCF3VSEkpFvK09napT04QBSHHjh95bwVsp937Yi6XF6RHoFwu4TsD/uQP/pB3rt1ifn6OzbUmThSxcOQ07/vA+0nCLvfurZJTLR553yN8/4UXeeiDH0RJFTQrzze+/Te87+zDHF0+ztuX36I97IMiMvwsw2RtdVNsGOOEnGVTKhaFCj+KSdIQRZbQdANVUUlCET0V+iG+740JFQeYEVXRkBAerygSXHJD15hr1PEGbVq7a5w/fZSbdzZoNlv4I5+V1TVu3rrDyuoK6xsbrKxkL56dYxT4DF2PBx9+iEc/dJFmZok6fvw4tm3T6wlNTqlUolqtksYBly9fwnOH9HtdphrT3Lu3gqpqWKbOm2+8QbVa5W9/7nNiGzg5QX2qzskTp1ldW0FGRlNFhp/jCLqHHwo1+P7+PpIksbW1RbfTodvt0ul0GHT7RIE4uYI4ptcbCGW8ovHoo4/z2c9+hl/71V/izp0bfOoTH+Phh88TBi5bGxsM3C5vv/U6H7p4EdPQuXHzOqdOHuPsA/dz7txZnnj8CT7+8Y9TLJdxHYeZmRk2NzfJFQpcvX6FRz/0IRKEr84bueQLeR5+5CGuXLmCLMs0Go2x7WV6eprqRIMUcVgdP3mClXsr5GzBwuoN+6RxwqDfE0y1I8e4t7KB7wekiQRSzObGFpqusrKyQmu/jSKrOEOHcrnMnds3GfS7gqxAgkRIGHj0h33qdRHI3O0N0BQDQzfI2wa7O9s8//KzrK6t8M47b3Hv3h0ef/wi3U6L8+fO8b3vfofz5y9w594KV65cIYpjNAUkGdJYpt3vg64RIdHc77B87BiJJFMqlPn3/+E/MOz3IIlJEbmLmq5z6fI1Yb723AzkKea+QrQtIaUJfhhkBUzJ4tUkNFVH18XMl0wfecAUS6KYMA4xTYsjx07wg2ef5+q1a1nCt0rkj36IqhFlHtUYy7IpFouYdp6EFElWcFwf3bAoFIsCZFmZwDAtapOTrG9usb2zi23b2Lkcu7t7dLtdTEsnl8/T3NujVC5mc1OPnd1dkkR4h++srBD4Ho1GgyRNaTWb2PkCrutlUW4yruuhazpHjh5+b0P8H7z2TkoqBopRmgAJRhyhxQn/1+99g4cemOOdl19i399jpyezdPgUv/5rP8vP/uIvcWbpKHqqMkwS9oYDzhw5zo995sdxE5+yUuR//Rf/M5t7dzi0PE+UhKzfXaNWqLG+tjmGvcmyzOTkJKVSiTiO6fb2sKwcaxvrhIEQE2qKJkJE1RjbtomiiEqlIq5Dkpx5yhQ0NSFfLHLsyBEKlsrT3/o6P/W3PoGGz+9/9UWCMEY1TRTDxvNHGWVWRZM0kCV0w6IyWUfTNE6ePIWpGzx47iSDwYDWfofBYIBhGExPz7K+vs7p06d57umnidIIy7Lo9Xr0BkN+7FM/zle/+hccWpih0+ngeR7FYpFz587R6oig2CRMMAyNF194AV2Bta01mq0mummyvbtDPp8nn89n1+QUGWHbMUyNwPEwbYsgCOj0e+iayUc+8jEsO8/q6jrFgsWHLz7KoNekubeNv1WP5QAAIABJREFUndM5ceIEupZne3uTke/yJ1/+zwSRsMnIqoRdKrC706RQKGFbBaanZjBNG9cV1pJf/W//IX/0R7/P9PQ0u3ttjh49TpJEqBpsru8gSRILCws0Gg22t7cxTZOrV68SxOL70U2DVqtJHEZEmRH/6q1rlMtFdFXluWeeJwgCjh09ydDxGAwcylWD0cjFc95Fz3S7fTEQnptD1xT6/S62peMHDjONSZrNJlPTM4xGI2qTE+RzNSYnpum12ty9c4Ot9g6eJ1BJUYa7jqNUqPWLJT77qR/jzuo6t1Y22dndZW31HroKxYKNEoNWrvHAo09Qn17k8qUbNJstGo0G+5t3CP0RRAGRP0SSUkZhRIpEu9Xn2NGj3L17E0USaV9RKiQbI99HiiM00xAYGkkikSRIZYIoRpJj4lA8X0kYkcRRRpAQBa0/dDh68j6iVKLfH+K5AzxnSOwNMQxhuYODWbNAXJumCYpBGImGgFSlWCzz+OMX+dxP/Di6aqGoKs5IPGe+77O/1xTvmCIxHA7RDEN8J5KI14vDiGe//zT5QoFP/Oin2drZJchsR5oisbO1zezcYRRFIkoT5BQhoQpD1u6t8Cu/8nPvbYi/ttn8YpqmDIdDVFUSTnFNY+gHzM7W6DseH/7EJ7l86SbVnM38ZInrl6/Q3lynVC7SHvZYXD7KqRMn+ea3vskrr77E9SuXePWl74IUUKtU6e4PsOQ8S/NLyLLCoOdhWToL81McWZplenqC4yeOcfFDF/nZz/w03f1dHj5/mn6rhTfwWJyd5+TJI7iOw6A7pDE5Ra8zoJgvYRoGYRCiKBKVSpU0jnHdHmE4QjMUElnl63/9FIXGIqMgAknOZkKhKIZxMtaaWZaJO+jiDXrcvnGZjbU73L5zl2efew6kFNdzOH/hPN956tv8yMc+yiuvvszkzCwfevwx9rtNjp8+zsLiPLXJCo2ZSeYbUyiazMMf+hDTM3OoqslsowFSiqmZqJLEwvwMihKzs7XOcCD8hd5wiJQk9Aa9cQBEFMckcUyv2ydMI/xQWGaWl09gW3lazX0GnTad9j4fefwxJqtVlpYWqVaq7Le6gEped+l3dpmuTTBsDzm6eJS93Rahn1I2ypxYPklrt8VUo8Hxk8e47/QyjakKywvT/O7vfomdnTa7e/skScTKyg0a9Qn2d9uEkYBT3rxxi3KpyuUrV3G9EY7rEaURnufQajVpTE7iDoacPn2C4aDPqeMn6Lb7TNTq3LxxC83Q2NvfZr+1get1s0DfEEVVSJKIdrtFvmAjk6Cp0O22UJQUpBQpgThKaUw1cAY+2ztNVFWj325RtnXu3brOxvY6gQ8yGv4oIk0U/CAhlVWCKCZKUu6t3WPp8BGOLh/nqW99G9vSiSKfOPJBsiCJKZsqXnePvKEgo1EpTtLt7iErEqPAA0R6t6FbBEHM+y9+lHurK0hpgpSG6JpCFEskksHM3BKKDKqmQZygSDK2ZmAoGpaqYuommiwjpQmVchHfH1GrTRCHMYpmk8sVaLc6qKpCHIVUCkWSOCIM3s3r1HWRxB2nKYGUksoqimaJjFElJp8TurR2p8MHHn2CSE4JowjHdej1eqiqip0tDrwo5My5c3jeiHyugKxouEOPXK5AuTbB0eMnuXb9OpZlMRwMkECAC7wRjdkZen2xwNFNA0VTMW0bK5/j6OFD760D+8unnk+ljFjpuAOxGUSimBd+uyAYiYF7EpGzTPrdDpoqmEVRmOD7YeY/8/n+s89xaGGBSqVCvVrj0qW3QUqQU41isYRpGiRpRGMyT7Fkk0QR7c4+O9t7LCwep1Ke4PP/1Y/xu7/9b1lYnGZza4cwEj42pAQ/EGv6D3/4o7z22ms8//zz3Lp1i1u3bjA5OcnM7JRw5leqyAr84HvfF7IAVUPOlZmfneXa1Rv4figEulGEpmlM1ss4jkO322VmpkEQjuj1ekgS1Kp1MVDOkL6C8FCl0+kwMzNDHEssLMwThC5pmjI3M4uiKIxGAUcW59jrOJRqdcrlMkoa4PshGztNKpUKqgRp7DPstlm7t8Ibb7zBcOgwjAVs7mDRIRgtCYHvk7dsSrkSm1s7VCo1Hr/4GKVCjvnZOV5/41U+/rlPsr+3g20avPTyiwwGA8FKO36MQfMmhmGwtrbJg+97lHvrm7z06hsYVg5ZspmsNzh34SEOLx9lfXON+kSB7a17kEa88/Ydhm5Ap9cXPPw0REpSJElhe7fFwsICnj9i+fBRgY6uVlFVlZNn7mPl7j3m5ua49PbbnD17P3du3WZ3dxdiX4gYU5kXX3lZbELTAD8I6A+6FHMVDFMw2y4+9kE6nRaDQY/d3V38ANGFD4bi2mSoWLqRJfbIYhlASqWQp1YqEvoB7UEPL0jHynbPH2Wbb5kkC7ywDJ2lxSNUinUqlQp//Cd/iKqCLKWkiGCNYrHIseMn0awCZy48wtrGDt3mOnfv3oUkwHecLMdBQUKh3Jjn1s1rKPEIKREgyCRVKNWmKBTLOL0+jtMjTX2kNIEkEonbsqBFpGmCpECSRNRqkywsHaFcrrKwUKJcrfPss69y9eYWjpuQpAqtVosk6qKrikg3kgWFdeT7SKqKpMgi6HbkimfasAmTlFJ1gv/+n/yPxNKI48eP4/QHwqniOMQZabbVbuO6rrgWxjGaov7Q+yGCkQ+E1pqmYVkWriuE45IkUSgU6HQ62LYtfkY2Az+xNP3/24H9FwvYk3/zTBonCUgJpmkK3Yc3IgpCgsxYWizk8JwhtVqNtZW7mLqBhEsUhgx6PQxFFlIGSUa3LGIkkkBGkmMKhQIyCmEQUyzl2d3ZwHXWkKQU29Szoa1MsTTFtWs3ed/954mTkMnJKt9/5llu3rhDpVbFcQb0XT/zScJ9p8/wyiuv8PgTj/HBD76fa9euYWTojm//zVPcvXsPRdYI/Yh6vY6WNwj9gDhOGQwc2q0uKQq9oUMQD5mYqAromucxPz8rXjApwTKFgt80zbG9KIqEn/NgHlGplDBNM8PURMzOziKhUMjr6FYRxTCRpJQzp48TxAnHTp6lUqsy6PXZ3Vwj8lzWV1a5cukq3tCjH7pjd78sg6aqeP0hH3j4EbY2Nzl29iTf/OY3KVcrkMacOHoERQaZlInaJHu7O2xvbjExUaVQKNBoNFjb2CRvpJw+fYa95j6liQlOnrqPty5fYWp6honJadrtHt2uh6ToXDh3jlrN5LXXnuPS26+z3/JIYgVn6PG+h87Tae1z6e23KBfKOL7QApENi13PZ+SHTE1N8/Nf+EX+7M/+DEVRKOZzfPazn+Wpb3+HfMEmCRyCQKDBz59/gD/4gz8gTWN838cZOQw7DmkaEyY+iiJh53TypQKWZTAYHITBvrvBVmUFb+RAJGHmLNyRR9G2yJsG1XKFVr+NF0hjNLIfiv8evB2iW4HJiWkCX2CZb92+QbVaxnEGGKaGZeaQJA1Nt1hYWuaf/uY/w/NH1AsmL730Ei+/+AJJ4BPHqVCdRwmPfeyTDHpdBt0WK7dvoCoShl1EVgzyxSK2mWNhfo67dy4xOVni8OI8WxvrgjbrR4wCD8PUUTQdP4yw8hUq5UnisM3O7j59J+GpZ97CLlY4tLBIr98lDV067RaOM4AkyjyeEXGGy7Y0lSRVKJTqBLGMXSgTJSk/9wtfYGIyx/7+PtVqlUI+n/G+grFP07KE+FuTFXxvlDkMEvxghK7rY7Q2MA4vAZicrLGzs5OhvhVyudz45/7Io+97bwXsey+9mXr+CN/3x5KGJIpxBkPQdXK5HJZuoGsKw+GQ9n4zG0h20GWJvKGhEhP6I0JJQbfzeGFEzihhWiqeM8TzxMwjJSZJQrbWLmEYGp7bHxekXL5MPl9ATQp0Oi2ee+H5jMhwDMMSBUA1xBrZccSDPz09jesOqdfrjEYeTq/P0HFZWlpib2+fUqlEu92lUCjgun3RJXg+77x1iVu373Hj1iqKquMF3pjomZDiui7FUikzuVpZWrI/xppomjYWlOZyYhbl+6EQbr708jjvz1Q1fvGXf4E/f/KrbK5voyomdqFIosSQ6hSKOWanZ3jisQ+hqQamYeAMPX773/67TOCbIqkKd+/eFl7Go8fpdDrkyzL9fh/HG3L2zBniaMT5s/dRKuYpmFOs3rvL3OwsOzvb+K7H0pEjXL92k0KhIoSjm5s88qEPUiyXaLba7O03+dxPfJqnnv4BOavKnTsr/JNf/w3++T//H2jurOAHDvNLR5iqz/DSi6/zz7/4m3zxf/pn2IZJGouNXJIkpBKk2aY4TiRGfogz9LFzIlyj0+lgGSb1+gSHDh0iDDyWlpZ44YUXhV1L0wiCgL29HYbDIWkGLJRkcDxPnN69tshmREfTFUajEaYptFtCT5ZiyBqJBHbeRk5iLE1HVxX8JCBGaNYO8iSB8TZQURSCcEChUGR1ZYcPP/FR3nrrHTqdFgCykmLoNrpuE4UpuUKOX/+n/5jF5UX89jabm5tMTU6ytbGBIqucPXueVFLojXwmajW++fUnURWJciGPYRWQFJVUkinlC0hywo3rl0hjH1WBJA5JoxgVEfaimQZhFKEYNnaujOdHVCt1wjAhV5xgFKQYpkaYjHjmue+wtdYkiUKRvu25yDIYhkaxWKZQKFCv5anUGkRpgcXlU0iGSbFaI04TVEMI272hQ6EgDnDXddFVjaHTH4ft6LpOHITjxHTXdcf/z7aFVemg6PX7fSzTGHdntVpNZGEgOrXHHjrz3grYN55+LlU0kfpzUMRkxGmWSKL1dF0XQxex52kqupQ0CfEGQ7xBlyj0USUZzbLZ7/bIFwvYmgVSxKDfJs4e8LxtMPKHJEFAGLhiCJxFdJUqRdrdHkePLDAa+fS6DlEUoKgQBEEWw54XKvx8iX5fCAIHgx5xIoB9hqaTIOLfkiQiCEeiCEgSUSRQIAC+H1IsVJidO8yNW3eZm1riuRdfYGdnjxdefJm+kyWSjwIUJR4L/wRBQ2dqaoqNjQ2OHz/O66+/Sq02KV6EKMLQdCEbUCS8jseZc0vcd+YoT/3193D7Meg6qZWgpRayphKnCblSkSiJqdUnOXPmDB89/0FGYcBf/dU3uLtyh43dbT78sY+zsrGB5/pIo5jbt28LGmkxh+cOWDw0w+b6GseOHePsuTM8cfEi/+kPfo8LFy7w/R88y3333UcubzD0RHHe2N7ivvtFF7u2tsbiwmHq9WmcoU/eLrG5tsrbb7+CZauUizmKdZuJ2iw3rq/w6KMfYKJaZvXeCm+/8SbIOidPHWd/fxeASq3KrVu38UchCRr7+/vcf/8Z7t69K/x/iMLU6/dZWlpiZ2t3vNm9ffs2J04eY39/H0VJKRbL7De7OI6LN4pQVZ1upw9plv6jCmGm7/ssLCzQ7uxTzpUYhQFRElIrlUS3EYWERIz8+F3PbbbxO/DfAqhaiKZZPPS+D/LNv/oW65ubKLIIBrEsC0PXScKEWq2Gbev86//9f+HK9UsUdYn9/X1IU0I/ELKgWGDQE1UUZkWSkUlQZY3G9DSe56MaOu3OLrIMpqYjAVGYEAXiZlIoFPA8h2ariR8GFIslhm6AhEqqyDRmZpmZPoSlF2g1m6yt3uGDDz1MZSLPXnOXm9evoSoSpiYOak0z6PV66EpElKrkKrNodhkvSNlttTl95j4k1Rpf/4zswBai1cLYQwygye8eIAeOmjE99ocyPG3bBiAMRCiL7/s4jjNG7eRyOU4uTr23AvYX3/peWp2ojQd+juOQs/KMXA8rZ445RLZt4w4HY9W2LIVj02mxKLqVNBVf7L21VWI/YOT2UZUEVVbY3tzEMCUMXWFqosbde7fRVZU4CgjjiHKlgmEYSESkiUYhXyWKA4LQQVUhTRUUVR9bWw7uzmJVLISPQBbmEP5QIG4WQZVIzExPs7p6D9O08VyfmdnDTE3PEbhD9pstapN1/uOX/pDdvX06PUdghEeO0JgNhywvL9Pr9ZiamuKdd94hl8uxvLzMq6+8LjqKMGTQ7zI3N0Ov38HpdUliGSmJ+fJ/+hJ/7+/9HKg5Yi3PsN8mShIRTa8Ke8bBlvPY8gmeeOxxphqTfOlL/5Hl5aOkska3JzrPWNYZjUYsLS1hGAbDQQ/fczF1jV5zlVqlRLlcZOHQPM3mLp1OB8Oy6fX38H2fm3duMzFZ59atW5RKJaRUxrRLHDq0iDsU85uJWo319RVa7SZSkjIzV2Rrp0M+P0kcx3zh53+By++8TbFQ4vvf/QFICZKUYFrquEOOoxTXC8bC1DAMuf/+++n1u6yurjI9PTWWikxUJ7Mg3TL9TpvFxUWu3byOpgn7kKroOI44yXN2AccbZcTTZHz45gq2APShYOZMBsMhukI2Axsxin1UTSBqKpUKvV5PxLvZtuj4UtHF7Le7nLnvAs8++zyaauCO/CwAQ4UkopjPoasSC4uzfPxTHyUIXLFZzQnKrq6JhJ8ojtFNjW5ffKa2YTPo96nVarjOCNO2sqTsd5OGJBQRvKGa+H5IrpgjDANMSyU5COJQLSRJIcIhSSAII5xhSBpLJLGMbVjYeXGgDvpd0TkNxfY2IcUfhTSmJuk7I6YXj+EFKWa+hOsFHD5ylFQSszNT08cOnX6vl9mc8mNisaZp4+9W10UKUT6fJ5fLZTF0P3SrSxKiUDh+lpaWxl2ZsLoFfOD8ifdWwH7vT7+SaoaObefFL+FH6JqQLRQKOcLwXU+cob3ro3K9Dokko2omqmYQpxJJFBAGIqSztbOPKieEgYNtWmxtrOEMO6SESIzGV9U0lYShG5idmafV2qRcmiSNdaLYR9XjjK6poJsaum5mFgk5u19ncWwkgtIZJmLrEooC53uBYDsRQxoThiLIIJ8r0+25LCwd5c61FymUK6RIpKnBG+9cwQ8Ten2X5l4H1xXK54OTaDAYYB0kRsti/qfrBpBmBtmAX/+Nf8QLLz3Fs9+7ihRqEO/x5JO/w+f/61+mO6yiWI4INfXEw2BqOnKaUCtXoFxjYX6Ohy6c5/0PP8Kf/+evMhz4XL92O4PwKTgZANLSDY4cXqZcKnDp0iU+/qNPsLh4CENXuHr5HdbWVgjiiJ2dHUjFwHXouvhBxLDvkEQxmqzhEBCHERceOMfdu7dZXlpkZWUFWVKYm5vDUB0kOc/29oCN9S10Q4VEQtd1PvLYRzl7/2muXH2T7Z0NFuZneen5l4mimCCRUFV1zEovl8u0Wk0RPW8qlEqlMealkMszPTPJ+soKc/MzXL52k1KpRLlcRlNVnn32GZIk4dyZ+/nOs2+J2UwocDuSopDP5xn5LsSgaBpB5GMbKqWcTRKFqJaGNwrGAbQH2PCDTkC88CP6/SF+mOI4HvlcgYEzFCx34OMf+zCf/cyPcu/udVxvQC5vCLSzLkYJlXKZUqkkDvrAI44jiuUacZzS73S5//5zREHI3t6e4HUpCsVCjeGwL7rGKKJWq2HoFkkCcRoRhC79XhvbEIEmiiQ+zzQKSRUwbYOB5yLJKmGYMFFrQJIShCMG3R6VchHbthkMXUHuTSFNdOxigdWtXVJFJ1+qcOzoadH1FU22N7colYRfMQgCoiBkf38fK5cfd2BJtr0/mAunqbgtHRBcS6XSmO/n+z5hGDAxMYFpmqyvr1Ov19nc3GR6eppHL5x8bwXsT5/8qxRZysB7YpZQLJRRVZVbN68iSRK5XA6k5IeqrYaSCC/V/v4evu8RhiFzs4fJFco4jkdRV2h39vBGfVr7O8xOT9Fv9YhGAUO3ief5An1iW+TyFu6oj6YrDLviamiaNoViBdvKE0ewsrbKwtwMvUGfzrBLqTxBlEjUyjUsTabbaaOo8Ti6SpIkSqUKhWJRfOCpKD5+FNPt9Jmbm0PRRFR7rVbDdV06nRYbG2tIJMikyKQYmsVrr77B5uY2juNhmXmGg1G2oQ2YObrE+soqU5N1PvLEY3zta18jSSLCJEZRcnjDGG/o8Nff+gr/5n/7TRbmG1x66wZvX9nDcVyQVVQzh22bSHLMYNCnZAWUKmUeev8jnLn/HJKqIKsS1WqFwWBAziowNTXF9evXxTbN81hfX+cjH/kI927fQlVV4Tns93npuZeolsp4Q4edliNsOamM6wvmeyolSHLKwuFDPPjgg3zta1+jWCxCnDA9M8WwP0BVVQJvRByHFMoVXC/EcyMe/sDDXLz4KB//kQ/zyMMPo+s6/+Jf/kvura1yd+UeZ8+e5dKbr2Xx8wmH5husr68zMzVNrTbJxvYGwHiou7u7i67r1Ot1dnd30aw8hgah56KrBp12l/1Wk36/y+bagHa7TZwmDH0P084TZV6/WBGjhP7AoVCqUjAsvF6TmUmbXn+IhIahWUgSyEqCpiTZHDPg7AMXuHnzNqZp05ia4emnnyaMfBqNBr/6y/8NlUqFmflZXnvjdaamJlHShGIpj58VRdvOj/WFSZIwGPQgUbJcyj7NZhPbtinm8sLGVijQ70CpWsG0DaLQJWfKaHLI1tpd/OxK5vu+ACcmCbppUSwWcYZ7pKnE9PQst2/dJU0SkiSiWq3S3hWRdLIM+WIB3w+plGsZ9QWQNOIUZEmnWKoiYRAlCrKsUq3VkRWRxXr73l2CMMYsiNtGSIbjyeZbB4fPYDDANK1xXuaBsyZJkiy8RB1DRA/mZAeC5yiKuPjgqfdWwJ59451U/FDhhwyCgCgUgQOaKmeD1dE4piuKsyj3KESSUhFvlSnjW/sd+v3hOBtPlqHb24MkJAkjhr0hw/4ATY2QkDEMi2q1iusOiaVAiBYHopXXdJPBQESBFStVjh45Tq/Twgs82t021YlJphpzHF06gmXKjFyHVreF4zjcvbOCYZmMRv74Pq5qeUajEZNTgvSgqQYT9SkMw8BzXDRDz7LxJEaei0SC0+9hGTYTExP4fkgYRhh6jief/Dr93pDZ2VmWTx3lwQsPcOGB87z83PNcvnyZUqWYMY5kep0Rhmayvnadw4frlEtFZMni//it38IZeuRLZdbWt8kVc2iKxJEjiyzPH+I7Tz/NoaVFHnzk/ciKwvTMFJqmsbu7QyFfFmw00xx3x2JRIcJHD0ieqqJwaHaOzfUNAm+Ebtk8//zz3Lx5m+FwyH6rxfKRJfr9LrXJKe7evUspW14kUcSjjz7K1atXcQZDHnrgQX7tH/0D/s/f+ffs7nWZmp7h4sWLxEnE/aeOk6Ypv/Kr/4Cf+Zmfod5oYOVsdnd3mZuZ5dbtG0SBR7u9R5yELC0uk0QpJ06dFOHCYUij0eDKlStIksQDDzzAzs4OzVaXMHKZa0yxt71Hrzfg4sWL3Lp7i6JZpTpR4cUXX+Rr3/hLWu02qaQxcIYkqYJuWtx37jy6VSKNEz588QMkQY8//fIfc/nSVUp5kQ6uajBRK+F5HosLh/ETIZJWZIPt7d1xPiSyxE/9xI+SJAm5YgFVV2g2mygZorlUbRDHMb4vcDm1Wo0oCknTmKEzQpYkJBQMw2B/fx8ty0ssFososkEqgTtyiCOfeq3A6688yxMXH+WPv/wnzM7MMxgMBGM+43wJPt4wS7G3iGOx+TMM8R4P+2IcIcsSumlk4bT+mGwcSQ6GYdAfOAwcD9vOIys6pVKF7c2Ak/edpt1uUyyVyRfLTE7VkVWFMEjHBcjPYt60LPhDz1KODm5pwFh+FGWSJc/zspm2mCsehAg/fGb5vRWwH7xxOfU8j62tLUjEDGtmZmY81DyonuMCFkXouk7oC4SzN3QY9B2SOMb3BuPAzK4bEoQuo5GLIqXUJ6YwDRtNNiiUc0iSgpyK2C9I6HaamJYQ1Amao8KhQwuYhs309Cwrq+uoisIzz32PWr2Wtf8pedMgCVya+zs0Zg8TRjH9/pAYUQR13SRMYiRZDFJL5ar4/UPxIKhZ2IiXUVldV2xsdE0TV9wsOFVRFAI/GhvOVVUTm0C3K2wdvoAY7u7uCtqAplIq6EShhIyBpkkoaoIsa+hqEVXpi2t3AoPBAF3XefKrX+EXv/DzXL1ym9def4NjJ0/xkY9+jJ293XG4qaZpY1X+gRzAdYWexzAMFEtsejzPQzMNLN1ARrDLZ6dn2N/fx/dDFElGksTGtZCzuHnnJo1Gg1ptkkKhwNbmDo899hidTo+dzS3m5htcv3GFgeOgaSaFYpWLFx/lK1/9v5GTBNu28Xyfen2Ky1evkM8V2dnbZbq+QK6Q49y5+3nt9ZcoFApcvnaVQ/OLxGE4fgkOBsQ/fJ0rl6oMBx2Ggx7FQlW8ZKqCZqg4/TZTU1MkUcCdO7fI5XLsN5uUihUmyxM8+/xzXLt7hyAxuHrzDnEcE/pDJssanuvzE5/7aR577DHWN+5x6NA8z/3gWT75yU+x3+2wublJuTzB737p95mYmEDRNU6cOMahuQaJJBKtJQT913E8FCRMQyOOhcZMVcSGFMR8rtvvMRgM0DSFer1OGPoM+n1kWdjEDE0nV8iL67Sa0u3s02/vEgU+vuuN31VZlpmbm2OvuS8kPLGKoqnk7Dw7e22KxTLTszMoioShi+L49puvgxSNY94Mw8yue0UA5uYXWT56kjCScD2Ro7m1f13Mt2WJanWCVqePrGgMh0MkSXRQcRyTL5TGMy7LssZFKUkSfN8fN0RiJhqgKAK4KWCkB7h1EW94arH+3grY8+9cTw9WoFKaYhgGpqmLlF7pXZjrQfE6qPKymhIEEeurG+TzRTqtNsFoiESccZbUTIwn0Wq1KJdrBH5CqVShPxqiKBqGZmCZOdxhnzAajcV7Is8wJGeLFW4ul8e2bZJEiPkkTVR23/UIXIcodJGIUDTRvjuej6SoKJrYOsqqim4Jk7FlWbTbbUCmki0Okiggza4AB+JRVZYxNB1NF6fJgeHc932GQ2HTAOh1W6yurnL08LIYiEsSfhSKzygJ2d1pEcUpSAmKkmYDWgvXcUQ7rRvEYYSmCERPt9NiaWGadq9LEMb5SuUoAAAgAElEQVT4QYJuiZV0uVymtdckTWP29vbGrXo+n8/mcDp9ZwiysIvolomcitRqGQldMbOgh5S8nSNJYkrFPL1eB9UQnZxlWUiSTN4usLmxTbFYxDAsJDlka2sTO2+hKDqWmePm7RvUJqrkzRxD10FV9Ey4KAbUcRwjKznhYdUUMYcERmEgDjNJzg4MYW4+6MYOZid5y8Rxe2iqTBKDJOuohk6Q6Y1qExXu3b6FpiqEwYib167z6U9/mu5OkygJkXSVp7//HKurG+RyOZrNHdJkyMTEFIW8uI5Xq2XqUxPYVpGHHnqEnZ0tpqammV9YxDBtrl+/zve+/zRxktDtdSiXKszOzpLLFSgUSkThQXZDL1uESRllQ8+kIy0maiXmD83x3e8+harKeCOHem1i/O+uFOt0+z06nQ6SlJImESN3QKmYxzZFwvfGxtq4eYhjERir6RXm5uaoVGrEqUJzv4WVz3H37l18t4NMSuiPUNSUNImxcua773LsYJgWo0DGdSM0Nc/U9CE8N6bV2cMwLOpT0xSLJfLFMikqhmHijPp4noeqG+O5piRJBGFMo9EQEpof6hIPrpBhKMTjB8s2XdezuViILMs8eGrpvRcwKU1RFPGiOoMhI98VkeCy9u5LmW0UDlq+MBUYGQXxJVy7cpUoHNHvd5EBJRVbi1zeolqZYGpqBt3Isd9sY02UkWUVd+iKvL00RVMlUkIU1crAbXHGoHq3+9MMm1zOwM6ZostwXHRFYTDYx/ddCnZZdHSySioh/GRxBEhIqjYW1B048w/+RIFQhKcS48JULdeEtmU4GIsAXddFVqBer2ddQoiVCVo1WR//nrKqEKcJsZ9mIlgdz3PoDjpUqxUxG+kLFXicJniOi6kb6KosuqVogKJo+EGEZlokB19fIuKxNE38Ow6+jx/ODlAllUQSOO+DohuMXCHy9IfZA5YyGrloqoplmYSBi6xoWSdUOnhsSBMhUahUauy3d5meabCzs0UYJNi2AACgpEiJmPeEYUiavAuelGUVP7tSxHEEqvidLTPHqftO8/Zrb4wjug4sKwcxaYqi0GxuCGQOEeXyBIV8mVRO6fa7GGaVJBX0klLRhjhBAnxvxKGZGVZWb5PLmfieg6lb7OzsoKlg5AySGBzHp1KpkM+bmLk8b715ieHQ5fOf/0l2dvZ48sknOXX6NI7jZM9bgB9G2KaJrpmZvMEXm/A0YRR4JDGoqkoci3dEeHQlhp0mSRoRBCNIQmSZcfKREOAKAsUoEN1omgmkU+IMZR5lqOkDRpgj4tqkAoZhMjMzR5zAwPGo1WokxEyV8/S7He7euYXrDpHSBLsgSBKqrpEmEQlCNKzqpjjMdRs7n6NWOYTj+bTbbVTVoFAso2S4artYJIxF514ul0EWgtThcIhp5caLLlUV2+gwDMdCVwFclMc3hoN30TRNTi68xw7sqRdfTZMkgTTONGBiOJ+EEWZOGKcVWcNxnB+qmAqSrI7NxTnLEsO8RNyDgyDi3rVXaLf3M+qkgh9EwhZUrhIgOrFyuUIhX8IwDPIFC2fYx7RFWyql/H+GgYokE6MIy04SZaktFoam4wdDJDnG0i36veF4DgAQZ2EGfqYmVmQZCWV8R1dlgam2LAtVF0WDJCUMQ0auCDVRFInJyUkBkZOkcS5fkiTEkmifkygWqvJE3Pk1Q0eVTCzLIEUMVw3DYH3jHqZpMPKDTDjpMzk5yaDXF3q5KMBUNLzAxw/EZmdiqi5ONE3PPo+Qzc3NcaE/+F0URUFJxXU3SsVDP+h1UGUFRQY3bCPLCoami3Sngi20eKqMppQFY8oPSdOUXCHPyHt3A913hlQq5eyalyfwE2zbBCkhChmLd3/4z8zMDPdWV7KuTiJM4rGINJ/PE2Tdru8LfZDrumOxcBRFSHLM0eVFrlx+C0XRRPhtKgijk/VFkd5OzKDXwfd9ep1uNljWKBYshoMmpZxNryNyB5q7O7jBiIlanTCWyOUt8nmbNE1p7ndRFSHY7nR6SFkAszcaCsO/prC72wISZhvT9PuCcWblhci57yCugLJGvT6VjUag3++xv7Uutp6GgmUZqIpEa7+DadpUKhXixGVzcwtFUZiYqNPt9cazpCgDEM7MNghGPq47FAnrhoHrJkxNNZidmefqjetiWx5HAnbZmEYGLENHN0QxMXJ5vFGAXcgTu0Vcb0AUD3G9Hq7XI5cXXZVhFbLvqChyJ3WDOEoplSq0euF4rlUsV4WuKxRX1Fy+OO6wKpVKBmA06ff7YyuR+M5EXkUulxMhPLLM6aXGeytgr125meqGSugHAqIXh4y8AWmacnh5md3dXYJAVFTxkAq7zqljy7z51mtAhKoJtbCq5HCGI3w/ZuT02TiwQ0gKhplDMWxKlQmOHj6M74sC1OkNaLVa2VUtYZBpTOqTk+MH2zbFQ9LtCY6RokpoqoFlFQDQdZkgHJEiukJJklAVaTxsjMMIFHW8Po+CcMybF7MggSnRDNG5ybKMImUvYyrSiqRxiKtgpxlZAEqaCFCcJivoptAhjQJf4KxDQffQDVVcgSQFTVFptZoZ01+49d2RR+j75EwLVVHEYZGtrSu1SRoz06RZAQxHoyz2Swg5kyQZB3l4nsf21iamLYa29Yka66urmJpKpVjCC1xxRc4YamHoEyc+cRqjybnMFlUhiEJGoxHVahVJUYQdTNHp9duUyxXSRBH6NkkiSUXBFyfrUFynIh/LEp2Un5nlfdfLhspZUIvvU6+L7a8kSVnwS8JoNBoPhTudPoWcjmUq7GztCO1TFBBGI9IozRwYLr1+h6npaSEtSCUm6vNYpsLKyg0xQ0pVjh6/D900sEyNMIjI5QpEUUCcBEiKTBIrROIjRVEkgbje3ebu3Vs0pmokaczc7AKe5zDodYmjAMcbkkiCKqJrRQzdZHJySrzorVbWZQXIkth2I0UiH1SWiCOQs6794hMXeeaZZ1AVnUKhiKKKEU6r26M+NZ0dnCnDQY/NzXVs06BUKpDIOtVqld2dZmbl8bBtoSTY22mKa5rnkMYQxTHTC0vkSiVanR6Lc0eIk5CR1yONI9QsQCYMQza39tA0jXxOvF+maVOqVrL0dKFYCEKxONJNm6Hr0e12s0WYMj6wDm4FhUJBLBG8IJsbCt/xwRzXNE0+8yOPvrcC9uadO6kmq5kANIZEGicFmabIyYuTBM0QQrU4jCBNUbO/TmxpUqI0IUXOBvASUSiCMpIwgVRgPFRD6MwSJURKZdIUDE0HUtLYExYKX7yIUehRLOaQEDmGjtuj7wWUilXKxRobG5s8+OB53rr0GpppkM/nUeRqhiFJs04ipj8UHraoH2FaMn7kZi+IJNbWUYBtVcRwMYiytlecMJKiYhgK3XaP0SggCQNSQobDAaZdQJZ1Rn5/LAMoZUnbUWZ8lVOhxPajkIOI9TgT3QZeSC6XG8+2oijBUDXBHHOH405FlkVOo+/740KaEo5nC8BYk+b7Pt6gR5yIq7AQ/EKc/r/tndmPZdd13n9nHu9ct7qquqpHilPTlFoWYVtyjCh2ZDmwYRtOYiAP+X/yLwR5TB4CBIgTG3mQAyeIjJgSJZEUm2Oz5+4abt35nnvmc3Ye9tmnmg92AD5ZQG2AYLNZdavuuXuvvda3vu9bAtf1ZYAWon3toihaeKDXlQzpsizazM42dZzGNkVUdQO+muim5PUIXd6odSV9zU29hmYaVFEUrKMtlvKxKgW64YAh95ntOog6Z7FYkG5jut0Qz7U5PzumqjOKLGNn/5uEHZfVYkJe5Vy5coW8rEAz0AzZ3c3SHF2DPI0JA5PF8pw8cVks5vSGXXxnjON1yfItnb7FR+9+gB/YJOkayxQSr3VDihxcx8fUIzZRyq3X7pBXNbblUjVs8qrhOW3XG6q6ZDmfsV5PZfe6ytg7uIqGI91a05ST44dk2ZI3Xv8DxrtdTiePePjgQ2y7pipqtEpnONildrrM50vczoDhzhWcoIvpuFSVQOBRlhllHtPpyGdmGRJDWs23LOYrbNvF0ARCFKw2E7bRjCD0ybMC3+9i6DaL5Zphf0SSJCwWC4Kgy3A0otfvcnp6iml7LBeRPBedHqPRiNFojOVI0nSRy/1y44bkB5qWju/76GhYlmTk94cjNptNS7dSwako5F73vbAtLTVNaz3OPM/jzq2vKeb+yb1PhBphZhgGmpCbvygKqmbgreO52I2OSRMSZ6AumUwmEgvIM+nqsHOlkdFIUa1hGOi13nBAMvKqOdi2gaHJobVlXlAUOXm6RtNreu4Qx3E4Pz+j23GxbIPlcko3DJivpnS7Q1zToygz6rrAMAVhr8twsMMvfvEZb775Fqapyg05BMF1XQKvC1rBer1EM3SWi42cX2dqBG5AFMWg67K54DigSe1nnm9IkxxLl9IKy9boj4ZUlck6SkjSbctnWa/XmKZ0LBBCUBayC6UZsssmhEA08wC3q01rULdYyHJiNBpJ3WWRtyoDZV6ntJqyW5q3HVQlKE9TSeg0jUrSYFynDZqeH1JVgjxPG66S3wbA9h/qJrCVLT1jfj6l25MEZ8f25JAIjdaSuKwLlsslvuUhqMiyBEMHISq2cdp2r4MgwHV9MEw5v7EumiETGqenpzJrSxM6oc/Z2TGmXuP5DgcHr7HezCmymKgxxewPRxKD6gbkWSkde6sK2zR4/OQLDE1rHSyevXiOaw8oK2kK6IcGo8Bu984mmjU4ms1yuZYcLjKibUJWatK7ff8QhGAyOcU07dZ6aTQYUtclYcenzDKizULOBqgNdkZ76EbNJx9/gGkUuN4Aw9CYL6ZYlo4mKqhFozMs6PavEoZdat3C8UKSrML1fWzXpyx0bBOyfMtmtWK5mjLsdyUeqhkMBzvQmCW8OH7Kaj3Dcw36g4DxeJeyqMnzEk0zWC5WFJl0l6k1+V76wxG9bh/bDfG8AM8L2KR5C0m4ro/QLjhbva6kn2wi2XQwNL2h7aRsttI5VzEXVENOXeqW6bTuyUIIzAZ2iKKI731dIuu7H34i7Ea8aRkmdV1B1Sj9G0C7qEpsx2lIkALf9aiqC16Hqm+B5gPNyZvBoHVR4nnywKSFrI81Q6MsK6pSyOwijymyLbXIuXX4Gru7O/iex/vvv0eWJ4RhQBh42GbFYj5nvV4z6AUsFxNpDldpmKaDJgRX9vfRNZPVSuoR4zhmFW3QDB3HsYjjLTdu3CYIh9S1NJf78ovP6ff7LJdLdNPC9y/kEp1Q53yy4PDwOpZl8f6HH8ppQlhoukunN8TzmsPdSI6UuNgwXVkepUk7109lS0m8RtM0XMdnOpXC87KUgejsTMp/ut0uvV6vOSy11NoBnU7QdkTVUj+zqvO2I6k1JEPX8ZuSbCFB6WaTHR0dkSSJ1KW5dluWZlnKYrFohbq1KKmFheO57evEccxwOMQLfNLVvMnYbRzLYDI5Jd5uGfRHPJmcMB6Peeutt7l37xMs2+bg4EAqMapmhuBs3rjblmi6xAGfPXtC6Fj0+10++fhDgtDH9XwOr91gm6Q8efSYfn9IVUKSJNy6dYuTF08pioxtMsGyDcIwwDJDRGUzGIwQomK1fiEdLtIMP/B4+vRxY6MjFR6W5xPHCbtXrrKOYoqmeXXjxg1cR2J9T58+o6rkmdJ12CYxZZ7jeiG93ghDt9luIzQtx7J01ps5daXzxutvM59tiaKYvChYrqY4js2oG1BUJUFnwHyxxPECylrQ6w6oc4k59ftdLFvKns4nM0nLsDWSJMN1QrICiryi0w2oqoxoM0HTdPI0o64lHarjXxDSC81mf+8AgUOnO6TbH1IJuf+6vXErdtdNo+mWyu7nNk5bbMu0dNzGY7+qqlYUry7fKIpkY6cht6psq7XoaTTSnufxvW/f+XoB7OefPRS6rkMtJTB5Jks/UZZYji/xoEZ0bFkWmoA8zZgtp/IGadrymgZFmrG3vwvAZiuV5h0/aN+AbsmoXFQlRVGQZyU6GmWVU5cZWZawf+UQx7Q4Pz8nz1PSLJYTm1cLDC3nO3e/zV/95X8j8HXKQgaZLIXDqzeoRYKm6ZydT1pw+/bt2/zyl7/kT/7kj/mb//Uj0HUOD6/h2CGgs1rNWM5OOTw8BHQs2+bLLx9y584dNMPk849/huv65LnGdD5HN21Mr4Nm+piWj+cP2/JxvV63mZZlWS3TX+lM1aBRkB2mVkiM3CRCkx5gaZy0av4sy9is1jJdb8r8MJR/XiwWLd9GbzLouswac0qLg6NDCaKuovaCUcHr+Pi47SiXZYnnO60iQbp4zPAdF9u2WS6XxI0bh8zypA7u2bNn0pzO1BBULOcLyjLHcxzqUrptGp2wCR6C/YPD1rKo3+/z/NEXDAYDirxqjCgjnjx+wOuvv4ofuLz3t/+TIPBJkw26aRBtYly/QyVA0xIc2wNM6kqwTWL6fSlLipO1tOiO1+wMd4hWMRqSP7fcruRF63VecrIwOdgfU1Y553OJ473yyqtSwO366MB7771HUZwiMMjSAteT+6fTl02nJF2jaybXrt3k+MWEKIrYPxizms9I05ROZ8DZ6Zxef4+bN17DDwN6gwFpFjN58QGTyaSBayQksF5HGLqFoettOWY7Dnle0e3v4Do+uiUIgy6V0On3dtE0jfHuCMPQePDwM148fSaneWsaBqIxN5DlZ280Ynd8iO2OqGqTbZJSVAXCEPSCvlSS6NaFBGw4kKB+TevMYlqya64u0m7z/BVZVe1XxRsrmhFsqhtp6kZzHuA7d175egHsw/vPRV3XpNlFB0jiIgnxWgKrumnQG/RleZOk6GhUomx5YVL/VzI9O8NxHJ4/f86NW9+QNsuLBZ1O0HTGZOqYpBIMP9jbl0FRlwHQ9Wy0Sqaki8UCw9DbrEb5kt2+cZMf/5+/wfdNku2yscR1qErYO9ptiZFpmrJcLqnrmrfeeot0s6YWGWUpcZdXXnmVk5NnoFV4ls75+Tnj8ZjhcIfj41Pmi4UElA25SbK0RjNMMC0q3aaoLUwzoNZlt1OJghVIbRgGlLKsUtwdGQAkR2awu9M0RaTpXVlXpKncqL7rteC8EIJovWGxWLS6QE2Tn6cyihNCtCXkMPT54ov7clxYKqkat27dIs9Lal1rZVaqJFAdzOVqfqHEUHyjxqhOTsCR3KskSeRMSlOOOBuNRnSHO2SlzPx8x2Ubb/j4ww/QBFRNEyArKnRNtthv3rzJo8cPEPGc1WqF5/nIiTk2bkMA9TwP1zH56N6HdAOftMjx/ZCw06c/3OHpk0+aFrxHkhWtljdJEm7d/AbPXzxjOj3FNjWqMqff6dLp9Lh++xvomsl2m1IWNa7j8PjJfdI0QtNrTNOmzORk6e12y87OGNM0JVSynUq8t9KwHZ/9q0ccXD3iwYMHiEripEmctU4pIMtpQ29oLZ6NoMawbMCjqmC13GCICMs22iaHY0tZn+RKNaRk2yUvK175xhsIzcaxA4SmMV8smM0WmKZJGPpoei07naaNEBV5khL60qsuSWIW64WEh4qM1Trl9u27vP7GW3SHI/IqR9c1tBpZwTTNFMMw8MNOY45gtlmUTEISyQszTcpKXNB5GkoMyAwZwPOdlmqkaFKqK/kbb7/2NbuQH7wvdF0Cs5phU1aCoqrlUIVmGIBpW1TiwiuMWrT6R9d1WwaubkiC6Yunz5gvZev0zddfZzwey1HjRdWKd1XbXQLJshxaLpf4fojruqjx6aK5MbI8wbJ9XNcljTOqIsWxpJNmVUIUpeiWieNapPG27czJySwGvqazjddUdUZdSlLdfHlCLTKyKGVnZwfP89gmqfQna7gtWSbN6epKk/ypPEEzXRy3Q9gdkpcyYMpx6aJtIxuGgVY1Qb4R6Zqm+n0EWVFy+xuv8eDBA2rAsT28IGyItVVLHPY8D0PTW0lUFEVt2i0Jv1l7Q8dxjKdJRxHHc6UVsO+jGdJdQJgX8wPU7ylH1OlUZeOGoGmgSTys4wfEsTRxPNgdSz8vVwqKbdtkdj6R7rOVoKgEfig7mfF2g2NorWJACEFe1Hzy6X3qWg7yPTg4INues15vePjwYTOC3kfT5BCK58+fUwuTbjfk2tGBHPSBztH1GywXa7JMCopHo1FLu3EcR/LuAh1N6KxWGzbrKWUd4VgapycTeqOQR48eI0rJZQrDkOGwR7RZIISg1w/odrs8eShdS2aLFVlWUNXgu7sYlsO3v/1Ok00KRMOD1Iiajn2Obdikec7JyQmGpZMlJp5vsbvXQddyou2a5TTC93p0wj6L1QmLxQzHlZkKQqcsa3w/YDpf0u12yfKSq0eHFHmNZjhous1wcAXP87h1+wbz2YT5fMK7P/kxoW/zL//83/K3P/4x2/UK3/PwfR8BeJ0uk8mUYccnSUuStObw2vULBwrfI442TKdziXU2Z8VxpKW0adnYts3JyQnXrh21QHxRyAnfyknYtu0GFinb82BZVksUL8uSumlSua7LW7cPvl4A+8//6d8Ly3axXA/b8dFMFzfs47h+y8TXTYOiAeDrUg4ZMDQp9zg7OyOK0zbVHAwGeF7A+fSEj+99yt7uLkeH17ly5QpRcmGcqOsXuI0KZKoEK+tGStLvkpdNvVwVuIGHqRmYpts4IegNu1tHMyyqvEJDlmgqvUXIbMz3Q9I0xrR0bMMkywo0vQSREa0ieeO5Lnleyi5N2JUBIk8QtYZpWgSeTylyNMOirjS8oEuaRu2Ns9lsANpSUXUR5QAJ2eGbn0/JixRqget7pElOrWl0wh5Ck2WgAucVDy5L0jZjaonEL7ljqAlNQgh6gcSpdNNoJTm1JgOT1jBi1euo15Id1wsZiGXLLqOpXejckiRBE5DGG0xDYBoao9GQNI4wDY04SbA8n8n0nOGgx3a1YDTsI8qCFy9OCLsDbKfDg8fP2N3bb8nAQgjyssZ1/ZbFvVotmgtAfpbT8xO6/Z4E7W2HopJzDuNow2Ix4+rBnsx4RMX+/j5lvebF8wlFZnLr1g2ePf2CxVIacWZJwtHREV8++AJLl7SMTqfDs2fPGI1GFAL0xsCgKgVeEOAFPWzHQ7ckITMIe4zHV6QLcfMM8yLF1OTz0xBUdY0XBsRpguP0EXUFyJmURRIT+i4Pv3zAzs4OplVRi5Kqygm7HRAGz5+fUlfQ7ciLNE63iKbxFXYGTGZTxsMddnd3+eLzzxgPBywXM0xDMJtN2T885NGjR1zZGXN2dsb3v/99Pv7sPp3+Do7voVc6ZV21ErDxWHZ4XcfjydOH8jmWsgrQdF06vLzksqrrOkHgN5bXkjeZpGkLZ+R5ju/7rVvFdrtl0+icDcNgOBw2ahCJyd1949rXC2D/8T/8O2FaLmgWmukgDAfN9LEdH9HonkzbwrAaQ8OqxjJMikyCv3Vd8+LFCZ1ej9feeLPtxtWVtHvRasnoriqBbplo6NSiaqgHkpd14esl0E2LMAxZLGaMxjs4jjykuqVTFhtMw0XUJq4tx68n+RahSbM5W9joOpiWhiaqthNYVTWlrgM1nuuSZzWaZjTYTYmB3HhpXjamha78EMoKoZV4XkBdCjRRU4ucmhpds6hrnaJIGiHrhT+S+pDTUjTUk6xVOqSJ7FqKLOXu3bt88tmnOLYvSyzTagm4cJEd9bu91tJHBSClGxRCsFqtpOe+YTCbn7WTmyzHbi+Huq6x0NsL49GjRy2NQ+JvRotbmKaJoMJAawmVNSadToAuSga9gOn5iQSqqelZGus4YpOk+J2Q+XxKtl0jigJDh/U6Yry7j98ZEvSGFGVNnKQI3W9siKASWiv6lTbPFR3LJEm2TM9PWK/XfOvbd7l+4xZnpzOKcsuXD+5jGTpv3nmdF8+fcGVnRFEUfPr5BxxdfYUssSjLGt2omE1PyfOczSyh2w3Ji4Q0jTk5eYFjyWbLzs4umtthf3+f+XRGnGS4fofx3lUsx6M2czRNRzcs6dCCTp7LSzkpBB0/wHFs4u0G07bIyxLTtijZIoSBXlu4hivnRlJgW6AjvbzyPOV48hzXkYM6DN0mCPtk8art8pmOlN/YjrR09j1pFjmfTinzgtAPKDLZzY3yBB0NrakKqrLmzrfeYR2noJs4ps/VqwfklZz/4Hg+cZxSVxqdrk+/32c+n5MXBb4fNBw70RJPJVRSEwZBq30MOx3yPGe1WjEYDJqKym+ddh3nwvhQCEGZF+1r/kMBzPz7/geAZnXQLEdKbUzZObOcANfxqAyrbYXXZU2yTZvbPsM2HXoDX9rv9GX5pQko84Jhf8B0OiUMpIPqZrPFbljaogbT8VrXAyXsVOxeFWx3dnapyop5tG6B6m4nQAiZ3WQiI8sK6rLRTkY5SX1BE1Cv1bpHWgaaZpJnKjMSJJlobhOBpsnJgkLTWUebllWuaxXrheyyXbt2jSiSuFCvJ/ktmu22AH1RlO3vL431wHJsNNdpv2aNASKmwsRyO/T6u7LNXKuulmwrKy2dYRgsVkt5YHRNzoaczSmKgp2dIXt7e+zvS/qKEAae23x2ZsVyuWS1WEhdpG1jdAOKumI+PUe3TPKqpBTSN63Ik9biVw1mUFYuYRiSZwmbuKTIcibzWRP4JEZ3luUIvQ9uRYnGYHCVGB3PNfjRX/0Vd3/9O3QCn2vXD0nzit0r+8yWKx49eU663XL16hHz2ZI0LjAsD8/x6fX6TCZfUhUJJ48/53u/9ZuU61PqKOTs8T1u3r7FH//z3+bTTz7i/OFHvHj4JU/KnGfPnvHbv/dn3P/8Eb//wz9kcjbj/v37TM9XTM/OeOfud/jZz37G9ZvXuXp4gz/4oz/h84dfEHY6bLdbTo+nvPFrb3I6XZBmBRUSw6nFFtdwG+PGgjSJiLYSGrh6dR99viCLlhSpSacbEEUb4vUG17HIaslU393dxbalXrIszCY4dOiYBYtpjl5peJaLazjYjs+g0+dcQFZV6I5FlCSAhSY0LC9AdxzCnr87ztAAAA3wSURBVI6m26znMyzXxfZCvCCgXC9J0pTxeEzVlPKrbYbtyiBj+i4n0wmfffQRIFUTt199jQdfPpQE57ImjmOZgTXUKcdxyNLkK2dsuVzKTLSZY7q7u8vOSF4kjiOZ/SrLyhtuKEiIZnI+lQB/VAHX/v4Y9Q/ywN57V+RVSV2BYdnopkOelQhN3sSKIe26biv16PV6xNGWzWZDlkkpjOM4rZ9TEEgcwfM8JpMJs9lCttwb9bnry0klihaghokURUGnieKKT1U2rXbJG9FavE15EalSSHU+VJaSJJL5rbCiunHuVM9CkewU50353qubRvLgKga9LtPplP2G6f3ggUz7l8slP/jBD3jv579o/Mvc9vdRgUf93LYl3WgE67omS+I2xa5rCey+zF5WqgEhxFccGizLIkvkrXl8fMxiMcPzPK5fv87nn3/O3t4e/X6f8XjMkydPmE+njEYj+cwa7FGJa9VABSVWV2Wp6o6q1rfv+2xW8/Y9qKtS8XmEJucOakLSCvJkQ5VvcB0Ts64pqhJdM3n49DmabvJHf/xnJFnGoycfMxqOeX78gt3xPpomp4MHnS5B0OHZw8eIumRxfsZ//4v/wuTslOtH13j11Vf4Z7/7A376k3d59OUD7n30EVVZs1xGHB5d5w///E/RdZOiqjk6vCnnCPgu/V7Io/tPuXv3Lg8ePOBses5vfvc3OD2f8OjRI+7cucPk+Dknp2e8/ubbxGlCmmdURcl2K/Wpw1GX8/NzBv0RN66/yv0HXzKdnRJ2pQ437HZwfI/zifTR7wYhRVGyWq04Pz9vbY/kRKuKNE24ceMmvW6Hs+MXHB+f8uZb32S1jjk9PWOwO8JoGiHJNmrxJ03TOD8/Z29vj2S7QVQl49EQo7E0V1lPFEvbnIODA/KSllJh2DamoaMjEGXF48ePuXrtOtFmy/7Vg8ZhWbTOquu1HOjsuHZrUKm0jm+//TbPnz9vO+GmabbZPdrFEJU2KWhwr+Vy2XbHf+PXvmYX8n+/976Q5ZuUl/i+34p4/UYOotrNoITQRqsFUyWKbduNul+OHBsMBk1WZbUH8WKQQv0V7CsIgpYnpY5HjWjxMsuRD82x7DajUkGmLMu2I6JAbRXlVTB9uWmgDqcCv9VhBhonDrftJCp6CcgbY7PZtF/fulBqfAW3UqVwljUjw6AtjVRgnUwmaKJuwFHZce0PR216rWx21feoAKayVMuyOD09bYcBe97Fs+x0Om1qrro9281GtukbzFJJrBzHaYObYs+rTupsJrlGBwcHjEYjku2GKIpYzhftUBMV3Lo9mb0sFyscV5bxeRqRJFs0IflAV3b3qYQgy0v29vaohEaVb6kq2WW2bJsoiltfuvF4zHazxvUDirwiS2NsXbCZT4jXS7yezfXrN5mez7l2eJ3/+hd/yXh8xHf/ye/w8PhZQ4GQ4uK8SBn2e8wXU0wt4J133kEIwY9+9CNs2+To6IgsThqQeU60idlsIzmN2vebLmJGtFjxr//Nv+Jv/vqv2Wy2/OD3/wV/939/wjZeM7pyhdu3b/N3P30P15dM+tFwh+VyjWNapKksNbM8aQ6yaC5vm/l8RV1XrNeyq25YLssoYTjcoaikZMdxHFxbXjKf3vtYnh9LFlf9Tpcb148IwpDZbCbL+OWKTqeDYWgtIZpaOsh0wi7r7RrLMJlMThkMBg2Xq6kCzIuLbrFomhtNxWQ3n4/neZLk7jhtBWU33v9yLoXNbDbDtOWFuFqtCJqp4CrQKUeKuq759pvXv+ZUop/+QigG/sseYCogpOkFoxpoMxnQv/IG1MFSmNZ6HbUZkeu6LS4jO3Vp2/4vy5Jer4fv+1LzJWjZ5bp5kWEBXykzFYtdBcWXmekqwCmwUQUc9f3AhXNEkyWpr1P/X9IF1q18RwnEVVaifpZqAyuwW/1MJad4uduqfvckSaiKvH1dJVtSm0YJ51V2VBRFa1siKQ4upqmzWq3aQG3bsomg2te6LkmGWZaxWCxYLpfSP6vJppUwfzqdtnSYOI5555132vd8enqK67rs7Oy0Gakyo1ONBABDkzMTlssly6UUv6epbJ0XheyaWqbT7huQHmie6ZIVeRP4qwaL09B1s8m4C3qDEXGSE3g+oWfT8y2mp8c4fY8gCFgtFjhewGq1YbneEvb6hOHowp/dl4L1OI5kmV0Krl+/ThiGfPjBB9y+fZOfvvsTbNvmz/70T1lHMb+89xHXrt/kO7/+TcmB2254+ughH773c97/8H2C0KPIKx49fMrvfP+fcuPmEaOBQa834OmzF+imw63b36Db3wFdp9eRQ2B2xkPu3btHEHhEUdSaeeZpxbfefotf/vIDdvf3sGyXTz69j2HZVIbTyskcx2HQ7VEUhayGSrmnlLtrUWTUmqTXmLbTcPo6Lc5ZFSVFIfmR8jzXLcYshNZkRv5Fc6WpDtR5y7KsrZCyTDpxKIG24n+pWRWi4YfJPW3g+h4vnj3FdaXzhbpsVSl65/bXFHP//N4XQgUHgQTXfUdKRTCttp3/cskm/9tqo7AKXhcBKm+zIlWe6fpFUFHliRyFlbZB0nEcAs9vMyUhBEmWtkGjKIqv/FlZ2aogqd6nSn1f9h5SgUZ1CFWAVu/9ZQ+jl6cQqRJQBaaXA5TjOBRV2T4TdbDV14hG0XDReb1woLQMvS07sywjK8qvlMeqpFNpunrPMntqdGg67czKKIrodDrMZuetPCjPc0RVc+3aNU5OTkjTlMFgIJssdd2KajudDteuXWM6nRLHMaPRqA3WT58+5cqVK5xOZObWHw5aqZS6EOq8ZDqdys/BtvHDUGarho5p0O4JSzfY2dlhPp/j+z6L6al8z6bZBq31Rgbvg4MD4jhmvd5guz6dTgfPMolWcwLPJRweMDk7wbIMTMugaBwzvCCQ7ia+NM7brjeUZUEUReztSYmPa8t9V2SyueJYMhNwbBvdlgf++fEJYeiTxFteeeUW6TbmxeNHuK7ZwCZXGI+vcP3mTX701/+Dez/9KUVR8PDhYzy/w6uvvcH9R485unGTK/tD3nnnHd599902M1YDYT799FPyOEHXBLdu3eSjjz6i1x8wGu9TCXjlzl3Oz88lPlnVbbDwfZ9VtJEk7/kC05DSN82Q8wDOz2cMBj05aCSJcB2/4Za5kt+oG+iWTpJssRy74WVJeZLqHL68jxSnS+1jx3HaLruCO4zG7kee6xzdNMiz4oIv+hKxVcWT1WpFGIb83m/f/XoB7IPP7gu1GRslQHuw68aKRi0lHSqKAseSkTqKovbgqk2sSgD19/KA522HrUa0nBB1qMMwbLucknt1YXHzckaj63pbvqng+rJAWfqQb74SpHq9Ht1uty0tVZb4ckamXkMFWfV3qvxTP1cFq7abovGV72l1XqaJaGx+1Xw89ZplWaKJupHfSIC9bBwdVLaoPjPln9SK7QFRXeAIcRy1m0o+26oR60oMwtB0ptNpc9uFHB8fSyJtM5VH4XBxHMuWvmm23c3ZbNba3JydnRDHMVePDtssTg2lOD4+pdfp0gnCFmoYjIa8ePGCqira92UaGvEm4oc//CHPnz3l8ZMvENB8TjLL98IOZdFcAoZLEAQsZlO2sSxhHdtDNw12BgfYtk0aR5yfTxC6YHxlD4D1RmKr/W4P25Cvu90mFFWF711chDqCKIrYGQ4ld86xELgIDYY7u6xWzci+osD3PNbTCdF2zbVrhzx5+pzx+Iqk/2zmaPmmsSPKEDXcefubPHr8lL3DqxSlxDHPz2fs7u7Kydk17ZRqEx3XNvEDKdf75tvf4suHT8nLijgr6XR6Ug1TVdSArswXapmlR6uIfrdH6Pmst2tqUdENwhbPVQabtdCl2UEDndi2jeldDOqRrlp1W2XZtv2VS/blCdsK5225hpomZ702pNa6lmdBM0yKvEmATL2NI8phWMEsv/Wtr0lk/fCzh0Id/ry8wFikrYnRBhiVhakPqRsOAFkKqOxDlXsSR4O8kCmnzM7yC3sbcZGWvvzvNE1JN1u2adJmdkEQEHY7LddKgZjqwb6c9agHq8odVUqpwKWCi0p9FX6mbpKXbwZ1q+it+VzZZnRt5lHXVE3J2+1220ytvQRq0Vrmqp+tPsCyMa9raReG2ZaXqhRXz74oCubzeduNnZ3P8TyvtSeWA35lx2gyPWsxM/V9oR80gVJqMVfNiCz1+VmWFDsr5YLKAHd2diQ+1+8TrzdE2y2DwQDLdVrMUwiBFXhkSYKoanqdLlG0JitKonjL/v6+fD1LWjbpjWg83kZouo5hmJSixtClJXO326co5XPUbJM8SRCVVDNYtkutG8R5Sb6aYpoG2/WmzXh1y8L3QizPoq4qTN0gT2S5U+QVQtdwLNoRakAzLkwGgixO5LPMcjrdHkLX8BoOk2npPPzkU4LQQYiaKI5bjamgxDSlBfl4PMazHTbxFt00cAIfXejN5S9x0U7Yk+4mtbz4LEtnG60x9Jp+v8/u7h6Pn7ygLGpqYO/goMFJBxiWI5sLaYphSj+7zXJNlZbYjsl6vcIwNFbzGUEQ8Fvf+y5PnjzBdjyeH5+haYYcDlJVOK5LpddtpaFpch6sAukVpUVl41mTsSr52cuQSVVViKogzlJELe2sbdvFC8KXKiNZ5aj9J4RgOBxSFAW/+91f/3oB7HJdrst1uf4xL/3//yWX63Jdrsv1j3NdBrDLdbku16/sugxgl+tyXa5f2XUZwC7X5bpcv7LrMoBdrst1uX5l12UAu1yX63L9yq7/BwSLVrxz6xGTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VQA: in water\n",
      "GQA: water\n",
      "NLVR: 0\n",
      "Entaliment: neutral\n",
      "ViL_logit: 1.139376\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x7200 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1: VQA, 2: GenomeQA, 4: Visual7w, 7: Retrieval COCO, 8: Retrieval Flickr30k \n",
    "# 9: refcoco, 10: refcoco+ 11: refcocog, 12: NLVR2, 13: VisualEntailment, 15: GQA, 16: GuessWhat, \n",
    "\n",
    "\n",
    "image_path = 'demo/1.jpg'\n",
    "features, infos = feature_extractor.extract_features(image_path)\n",
    "\n",
    "img = PIL.Image.open(image_path).convert('RGB')\n",
    "img = torch.tensor(np.array(img))\n",
    "\n",
    "plt.axis('off')\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "    \n",
    "query = \"swimming elephant\"\n",
    "task = [9]\n",
    "custom_prediction(query, task, features, infos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}