05_Tensorboard使用.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Tensorboard用途"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① Tensorboad 可以用来查看loss是否按照我们预想的变化，或者查看训练到某一步输出的图像是什么样。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class SummaryWriter in module torch.utils.tensorboard.writer:\n",
      "\n",
      "class SummaryWriter(builtins.object)\n",
      " |  SummaryWriter(log_dir=None, comment='', purge_step=None, max_queue=10, flush_secs=120, filename_suffix='')\n",
      " |  \n",
      " |  Writes entries directly to event files in the log_dir to be\n",
      " |  consumed by TensorBoard.\n",
      " |  \n",
      " |  The `SummaryWriter` class provides a high-level API to create an event file\n",
      " |  in a given directory and add summaries and events to it. The class updates the\n",
      " |  file contents asynchronously. This allows a training program to call methods\n",
      " |  to add data to the file directly from the training loop, without slowing down\n",
      " |  training.\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __enter__(self)\n",
      " |  \n",
      " |  __exit__(self, exc_type, exc_val, exc_tb)\n",
      " |  \n",
      " |  __init__(self, log_dir=None, comment='', purge_step=None, max_queue=10, flush_secs=120, filename_suffix='')\n",
      " |      Creates a `SummaryWriter` that will write out events and summaries\n",
      " |      to the event file.\n",
      " |      \n",
      " |      Args:\n",
      " |          log_dir (string): Save directory location. Default is\n",
      " |            runs/**CURRENT_DATETIME_HOSTNAME**, which changes after each run.\n",
      " |            Use hierarchical folder structure to compare\n",
      " |            between runs easily. e.g. pass in 'runs/exp1', 'runs/exp2', etc.\n",
      " |            for each new experiment to compare across them.\n",
      " |          comment (string): Comment log_dir suffix appended to the default\n",
      " |            ``log_dir``. If ``log_dir`` is assigned, this argument has no effect.\n",
      " |          purge_step (int):\n",
      " |            When logging crashes at step :math:`T+X` and restarts at step :math:`T`,\n",
      " |            any events whose global_step larger or equal to :math:`T` will be\n",
      " |            purged and hidden from TensorBoard.\n",
      " |            Note that crashed and resumed experiments should have the same ``log_dir``.\n",
      " |          max_queue (int): Size of the queue for pending events and\n",
      " |            summaries before one of the 'add' calls forces a flush to disk.\n",
      " |            Default is ten items.\n",
      " |          flush_secs (int): How often, in seconds, to flush the\n",
      " |            pending events and summaries to disk. Default is every two minutes.\n",
      " |          filename_suffix (string): Suffix added to all event filenames in\n",
      " |            the log_dir directory. More details on filename construction in\n",
      " |            tensorboard.summary.writer.event_file_writer.EventFileWriter.\n",
      " |      \n",
      " |      Examples::\n",
      " |      \n",
      " |          from torch.utils.tensorboard import SummaryWriter\n",
      " |      \n",
      " |          # create a summary writer with automatically generated folder name.\n",
      " |          writer = SummaryWriter()\n",
      " |          # folder location: runs/May04_22-14-54_s-MacBook-Pro.local/\n",
      " |      \n",
      " |          # create a summary writer using the specified folder name.\n",
      " |          writer = SummaryWriter(\"my_experiment\")\n",
      " |          # folder location: my_experiment\n",
      " |      \n",
      " |          # create a summary writer with comment appended.\n",
      " |          writer = SummaryWriter(comment=\"LR_0.1_BATCH_16\")\n",
      " |          # folder location: runs/May04_22-14-54_s-MacBook-Pro.localLR_0.1_BATCH_16/\n",
      " |  \n",
      " |  add_audio(self, tag, snd_tensor, global_step=None, sample_rate=44100, walltime=None)\n",
      " |      Add audio data to summary.\n",
      " |      \n",
      " |      Args:\n",
      " |          tag (string): Data identifier\n",
      " |          snd_tensor (torch.Tensor): Sound data\n",
      " |          global_step (int): Global step value to record\n",
      " |          sample_rate (int): sample rate in Hz\n",
      " |          walltime (float): Optional override default walltime (time.time())\n",
      " |            seconds after epoch of event\n",
      " |      Shape:\n",
      " |          snd_tensor: :math:`(1, L)`. The values should lie between [-1, 1].\n",
      " |  \n",
      " |  add_custom_scalars(self, layout)\n",
      " |      Create special chart by collecting charts tags in 'scalars'. Note that this function can only be called once\n",
      " |      for each SummaryWriter() object. Because it only provides metadata to tensorboard, the function can be called\n",
      " |      before or after the training loop.\n",
      " |      \n",
      " |      Args:\n",
      " |          layout (dict): {categoryName: *charts*}, where *charts* is also a dictionary\n",
      " |            {chartName: *ListOfProperties*}. The first element in *ListOfProperties* is the chart's type\n",
      " |            (one of **Multiline** or **Margin**) and the second element should be a list containing the tags\n",
      " |            you have used in add_scalar function, which will be collected into the new chart.\n",
      " |      \n",
      " |      Examples::\n",
      " |      \n",
      " |          layout = {'Taiwan':{'twse':['Multiline',['twse/0050', 'twse/2330']]},\n",
      " |                       'USA':{ 'dow':['Margin',   ['dow/aaa', 'dow/bbb', 'dow/ccc']],\n",
      " |                            'nasdaq':['Margin',   ['nasdaq/aaa', 'nasdaq/bbb', 'nasdaq/ccc']]}}\n",
      " |      \n",
      " |          writer.add_custom_scalars(layout)\n",
      " |  \n",
      " |  add_custom_scalars_marginchart(self, tags, category='default', title='untitled')\n",
      " |      Shorthand for creating marginchart. Similar to ``add_custom_scalars()``, but the only necessary argument\n",
      " |      is *tags*, which should have exactly 3 elements.\n",
      " |      \n",
      " |      Args:\n",
      " |          tags (list): list of tags that have been used in ``add_scalar()``\n",
      " |      \n",
      " |      Examples::\n",
      " |      \n",
      " |          writer.add_custom_scalars_marginchart(['twse/0050', 'twse/2330', 'twse/2006'])\n",
      " |  \n",
      " |  add_custom_scalars_multilinechart(self, tags, category='default', title='untitled')\n",
      " |      Shorthand for creating multilinechart. Similar to ``add_custom_scalars()``, but the only necessary argument\n",
      " |      is *tags*.\n",
      " |      \n",
      " |      Args:\n",
      " |          tags (list): list of tags that have been used in ``add_scalar()``\n",
      " |      \n",
      " |      Examples::\n",
      " |      \n",
      " |          writer.add_custom_scalars_multilinechart(['twse/0050', 'twse/2330'])\n",
      " |  \n",
      " |  add_embedding(self, mat, metadata=None, label_img=None, global_step=None, tag='default', metadata_header=None)\n",
      " |      Add embedding projector data to summary.\n",
      " |      \n",
      " |      Args:\n",
      " |          mat (torch.Tensor or numpy.array): A matrix which each row is the feature vector of the data point\n",
      " |          metadata (list): A list of labels, each element will be convert to string\n",
      " |          label_img (torch.Tensor): Images correspond to each data point\n",
      " |          global_step (int): Global step value to record\n",
      " |          tag (string): Name for the embedding\n",
      " |      Shape:\n",
      " |          mat: :math:`(N, D)`, where N is number of data and D is feature dimension\n",
      " |      \n",
      " |          label_img: :math:`(N, C, H, W)`\n",
      " |      \n",
      " |      Examples::\n",
      " |      \n",
      " |          import keyword\n",
      " |          import torch\n",
      " |          meta = []\n",
      " |          while len(meta)<100:\n",
      " |              meta = meta+keyword.kwlist # get some strings\n",
      " |          meta = meta[:100]\n",
      " |      \n",
      " |          for i, v in enumerate(meta):\n",
      " |              meta[i] = v+str(i)\n",
      " |      \n",
      " |          label_img = torch.rand(100, 3, 10, 32)\n",
      " |          for i in range(100):\n",
      " |              label_img[i]*=i/100.0\n",
      " |      \n",
      " |          writer.add_embedding(torch.randn(100, 5), metadata=meta, label_img=label_img)\n",
      " |          writer.add_embedding(torch.randn(100, 5), label_img=label_img)\n",
      " |          writer.add_embedding(torch.randn(100, 5), metadata=meta)\n",
      " |  \n",
      " |  add_figure(self, tag, figure, global_step=None, close=True, walltime=None)\n",
      " |      Render matplotlib figure into an image and add it to summary.\n",
      " |      \n",
      " |      Note that this requires the ``matplotlib`` package.\n",
      " |      \n",
      " |      Args:\n",
      " |          tag (string): Data identifier\n",
      " |          figure (matplotlib.pyplot.figure) or list of figures: Figure or a list of figures\n",
      " |          global_step (int): Global step value to record\n",
      " |          close (bool): Flag to automatically close the figure\n",
      " |          walltime (float): Optional override default walltime (time.time())\n",
      " |            seconds after epoch of event\n",
      " |  \n",
      " |  add_graph(self, model, input_to_model=None, verbose=False, use_strict_trace=True)\n",
      " |      Add graph data to summary.\n",
      " |      \n",
      " |      Args:\n",
      " |          model (torch.nn.Module): Model to draw.\n",
      " |          input_to_model (torch.Tensor or list of torch.Tensor): A variable or a tuple of\n",
      " |              variables to be fed.\n",
      " |          verbose (bool): Whether to print graph structure in console.\n",
      " |          use_strict_trace (bool): Whether to pass keyword argument `strict` to\n",
      " |              `torch.jit.trace`. Pass False when you want the tracer to\n",
      " |              record your mutable container types (list, dict)\n",
      " |  \n",
      " |  add_histogram(self, tag, values, global_step=None, bins='tensorflow', walltime=None, max_bins=None)\n",
      " |      Add histogram to summary.\n",
      " |      \n",
      " |      Args:\n",
      " |          tag (string): Data identifier\n",
      " |          values (torch.Tensor, numpy.array, or string/blobname): Values to build histogram\n",
      " |          global_step (int): Global step value to record\n",
      " |          bins (string): One of {'tensorflow','auto', 'fd', ...}. This determines how the bins are made. You can find\n",
      " |            other options in: https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html\n",
      " |          walltime (float): Optional override default walltime (time.time())\n",
      " |            seconds after epoch of event\n",
      " |      \n",
      " |      Examples::\n",
      " |      \n",
      " |          from torch.utils.tensorboard import SummaryWriter\n",
      " |          import numpy as np\n",
      " |          writer = SummaryWriter()\n",
      " |          for i in range(10):\n",
      " |              x = np.random.random(1000)\n",
      " |              writer.add_histogram('distribution centers', x + i, i)\n",
      " |          writer.close()\n",
      " |      \n",
      " |      Expected result:\n",
      " |      \n",
      " |      .. image:: _static/img/tensorboard/add_histogram.png\n",
      " |         :scale: 50 %\n",
      " |  \n",
      " |  add_histogram_raw(self, tag, min, max, num, sum, sum_squares, bucket_limits, bucket_counts, global_step=None, walltime=None)\n",
      " |      Adds histogram with raw data.\n",
      " |      \n",
      " |      Args:\n",
      " |          tag (string): Data identifier\n",
      " |          min (float or int): Min value\n",
      " |          max (float or int): Max value\n",
      " |          num (int): Number of values\n",
      " |          sum (float or int): Sum of all values\n",
      " |          sum_squares (float or int): Sum of squares for all values\n",
      " |          bucket_limits (torch.Tensor, numpy.array): Upper value per bucket.\n",
      " |            The number of elements of it should be the same as `bucket_counts`.\n",
      " |          bucket_counts (torch.Tensor, numpy.array): Number of values per bucket\n",
      " |          global_step (int): Global step value to record\n",
      " |          walltime (float): Optional override default walltime (time.time())\n",
      " |            seconds after epoch of event\n",
      " |          see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/histogram/README.md\n",
      " |      \n",
      " |      Examples::\n",
      " |      \n",
      " |          from torch.utils.tensorboard import SummaryWriter\n",
      " |          import numpy as np\n",
      " |          writer = SummaryWriter()\n",
      " |          dummy_data = []\n",
      " |          for idx, value in enumerate(range(50)):\n",
      " |              dummy_data += [idx + 0.001] * value\n",
      " |      \n",
      " |          bins = list(range(50+2))\n",
      " |          bins = np.array(bins)\n",
      " |          values = np.array(dummy_data).astype(float).reshape(-1)\n",
      " |          counts, limits = np.histogram(values, bins=bins)\n",
      " |          sum_sq = values.dot(values)\n",
      " |          writer.add_histogram_raw(\n",
      " |              tag='histogram_with_raw_data',\n",
      " |              min=values.min(),\n",
      " |              max=values.max(),\n",
      " |              num=len(values),\n",
      " |              sum=values.sum(),\n",
      " |              sum_squares=sum_sq,\n",
      " |              bucket_limits=limits[1:].tolist(),\n",
      " |              bucket_counts=counts.tolist(),\n",
      " |              global_step=0)\n",
      " |          writer.close()\n",
      " |      \n",
      " |      Expected result:\n",
      " |      \n",
      " |      .. image:: _static/img/tensorboard/add_histogram_raw.png\n",
      " |         :scale: 50 %\n",
      " |  \n",
      " |  add_hparams(self, hparam_dict, metric_dict, hparam_domain_discrete=None, run_name=None)\n",
      " |      Add a set of hyperparameters to be compared in TensorBoard.\n",
      " |      \n",
      " |      Args:\n",
      " |          hparam_dict (dict): Each key-value pair in the dictionary is the\n",
      " |            name of the hyper parameter and it's corresponding value.\n",
      " |            The type of the value can be one of `bool`, `string`, `float`,\n",
      " |            `int`, or `None`.\n",
      " |          metric_dict (dict): Each key-value pair in the dictionary is the\n",
      " |            name of the metric and it's corresponding value. Note that the key used\n",
      " |            here should be unique in the tensorboard record. Otherwise the value\n",
      " |            you added by ``add_scalar`` will be displayed in hparam plugin. In most\n",
      " |            cases, this is unwanted.\n",
      " |          hparam_domain_discrete: (Optional[Dict[str, List[Any]]]) A dictionary that\n",
      " |            contains names of the hyperparameters and all discrete values they can hold\n",
      " |          run_name (str): Name of the run, to be included as part of the logdir.\n",
      " |            If unspecified, will use current timestamp.\n",
      " |      \n",
      " |      Examples::\n",
      " |      \n",
      " |          from torch.utils.tensorboard import SummaryWriter\n",
      " |          with SummaryWriter() as w:\n",
      " |              for i in range(5):\n",
      " |                  w.add_hparams({'lr': 0.1*i, 'bsize': i},\n",
      " |                                {'hparam/accuracy': 10*i, 'hparam/loss': 10*i})\n",
      " |      \n",
      " |      Expected result:\n",
      " |      \n",
      " |      .. image:: _static/img/tensorboard/add_hparam.png\n",
      " |         :scale: 50 %\n",
      " |  \n",
      " |  add_image(self, tag, img_tensor, global_step=None, walltime=None, dataformats='CHW')\n",
      " |      Add image data to summary.\n",
      " |      \n",
      " |      Note that this requires the ``pillow`` package.\n",
      " |      \n",
      " |      Args:\n",
      " |          tag (string): Data identifier\n",
      " |          img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data\n",
      " |          global_step (int): Global step value to record\n",
      " |          walltime (float): Optional override default walltime (time.time())\n",
      " |            seconds after epoch of event\n",
      " |          dataformats (string): Image data format specification of the form\n",
      " |            CHW, HWC, HW, WH, etc.\n",
      " |      Shape:\n",
      " |          img_tensor: Default is :math:`(3, H, W)`. You can use ``torchvision.utils.make_grid()`` to\n",
      " |          convert a batch of tensor into 3xHxW format or call ``add_images`` and let us do the job.\n",
      " |          Tensor with :math:`(1, H, W)`, :math:`(H, W)`, :math:`(H, W, 3)` is also suitable as long as\n",
      " |          corresponding ``dataformats`` argument is passed, e.g. ``CHW``, ``HWC``, ``HW``.\n",
      " |      \n",
      " |      Examples::\n",
      " |      \n",
      " |          from torch.utils.tensorboard import SummaryWriter\n",
      " |          import numpy as np\n",
      " |          img = np.zeros((3, 100, 100))\n",
      " |          img[0] = np.arange(0, 10000).reshape(100, 100) / 10000\n",
      " |          img[1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000\n",
      " |      \n",
      " |          img_HWC = np.zeros((100, 100, 3))\n",
      " |          img_HWC[:, :, 0] = np.arange(0, 10000).reshape(100, 100) / 10000\n",
      " |          img_HWC[:, :, 1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000\n",
      " |      \n",
      " |          writer = SummaryWriter()\n",
      " |          writer.add_image('my_image', img, 0)\n",
      " |      \n",
      " |          # If you have non-default dimension setting, set the dataformats argument.\n",
      " |          writer.add_image('my_image_HWC', img_HWC, 0, dataformats='HWC')\n",
      " |          writer.close()\n",
      " |      \n",
      " |      Expected result:\n",
      " |      \n",
      " |      .. image:: _static/img/tensorboard/add_image.png\n",
      " |         :scale: 50 %\n",
      " |  \n",
      " |  add_image_with_boxes(self, tag, img_tensor, box_tensor, global_step=None, walltime=None, rescale=1, dataformats='CHW', labels=None)\n",
      " |      Add image and draw bounding boxes on the image.\n",
      " |      \n",
      " |      Args:\n",
      " |          tag (string): Data identifier\n",
      " |          img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data\n",
      " |          box_tensor (torch.Tensor, numpy.array, or string/blobname): Box data (for detected objects)\n",
      " |            box should be represented as [x1, y1, x2, y2].\n",
      " |          global_step (int): Global step value to record\n",
      " |          walltime (float): Optional override default walltime (time.time())\n",
      " |            seconds after epoch of event\n",
      " |          rescale (float): Optional scale override\n",
      " |          dataformats (string): Image data format specification of the form\n",
      " |            NCHW, NHWC, CHW, HWC, HW, WH, etc.\n",
      " |          labels (list of string): The label to be shown for each bounding box.\n",
      " |      Shape:\n",
      " |          img_tensor: Default is :math:`(3, H, W)`. It can be specified with ``dataformats`` argument.\n",
      " |          e.g. CHW or HWC\n",
      " |      \n",
      " |          box_tensor: (torch.Tensor, numpy.array, or string/blobname): NX4,  where N is the number of\n",
      " |          boxes and each 4 elements in a row represents (xmin, ymin, xmax, ymax).\n",
      " |  \n",
      " |  add_images(self, tag, img_tensor, global_step=None, walltime=None, dataformats='NCHW')\n",
      " |      Add batched image data to summary.\n",
      " |      \n",
      " |      Note that this requires the ``pillow`` package.\n",
      " |      \n",
      " |      Args:\n",
      " |          tag (string): Data identifier\n",
      " |          img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data\n",
      " |          global_step (int): Global step value to record\n",
      " |          walltime (float): Optional override default walltime (time.time())\n",
      " |            seconds after epoch of event\n",
      " |          dataformats (string): Image data format specification of the form\n",
      " |            NCHW, NHWC, CHW, HWC, HW, WH, etc.\n",
      " |      Shape:\n",
      " |          img_tensor: Default is :math:`(N, 3, H, W)`. If ``dataformats`` is specified, other shape will be\n",
      " |          accepted. e.g. NCHW or NHWC.\n",
      " |      \n",
      " |      Examples::\n",
      " |      \n",
      " |          from torch.utils.tensorboard import SummaryWriter\n",
      " |          import numpy as np\n",
      " |      \n",
      " |          img_batch = np.zeros((16, 3, 100, 100))\n",
      " |          for i in range(16):\n",
      " |              img_batch[i, 0] = np.arange(0, 10000).reshape(100, 100) / 10000 / 16 * i\n",
      " |              img_batch[i, 1] = (1 - np.arange(0, 10000).reshape(100, 100) / 10000) / 16 * i\n",
      " |      \n",
      " |          writer = SummaryWriter()\n",
      " |          writer.add_images('my_image_batch', img_batch, 0)\n",
      " |          writer.close()\n",
      " |      \n",
      " |      Expected result:\n",
      " |      \n",
      " |      .. image:: _static/img/tensorboard/add_images.png\n",
      " |         :scale: 30 %\n",
      " |  \n",
      " |  add_mesh(self, tag, vertices, colors=None, faces=None, config_dict=None, global_step=None, walltime=None)\n",
      " |      Add meshes or 3D point clouds to TensorBoard. The visualization is based on Three.js,\n",
      " |      so it allows users to interact with the rendered object. Besides the basic definitions\n",
      " |      such as vertices, faces, users can further provide camera parameter, lighting condition, etc.\n",
      " |      Please check https://threejs.org/docs/index.html#manual/en/introduction/Creating-a-scene for\n",
      " |      advanced usage.\n",
      " |      \n",
      " |      Args:\n",
      " |          tag (string): Data identifier\n",
      " |          vertices (torch.Tensor): List of the 3D coordinates of vertices.\n",
      " |          colors (torch.Tensor): Colors for each vertex\n",
      " |          faces (torch.Tensor): Indices of vertices within each triangle. (Optional)\n",
      " |          config_dict: Dictionary with ThreeJS classes names and configuration.\n",
      " |          global_step (int): Global step value to record\n",
      " |          walltime (float): Optional override default walltime (time.time())\n",
      " |            seconds after epoch of event\n",
      " |      \n",
      " |      Shape:\n",
      " |          vertices: :math:`(B, N, 3)`. (batch, number_of_vertices, channels)\n",
      " |      \n",
      " |          colors: :math:`(B, N, 3)`. The values should lie in [0, 255] for type `uint8` or [0, 1] for type `float`.\n",
      " |      \n",
      " |          faces: :math:`(B, N, 3)`. The values should lie in [0, number_of_vertices] for type `uint8`.\n",
      " |      \n",
      " |      Examples::\n",
      " |      \n",
      " |          from torch.utils.tensorboard import SummaryWriter\n",
      " |          vertices_tensor = torch.as_tensor([\n",
      " |              [1, 1, 1],\n",
      " |              [-1, -1, 1],\n",
      " |              [1, -1, -1],\n",
      " |              [-1, 1, -1],\n",
      " |          ], dtype=torch.float).unsqueeze(0)\n",
      " |          colors_tensor = torch.as_tensor([\n",
      " |              [255, 0, 0],\n",
      " |              [0, 255, 0],\n",
      " |              [0, 0, 255],\n",
      " |              [255, 0, 255],\n",
      " |          ], dtype=torch.int).unsqueeze(0)\n",
      " |          faces_tensor = torch.as_tensor([\n",
      " |              [0, 2, 3],\n",
      " |              [0, 3, 1],\n",
      " |              [0, 1, 2],\n",
      " |              [1, 3, 2],\n",
      " |          ], dtype=torch.int).unsqueeze(0)\n",
      " |      \n",
      " |          writer = SummaryWriter()\n",
      " |          writer.add_mesh('my_mesh', vertices=vertices_tensor, colors=colors_tensor, faces=faces_tensor)\n",
      " |      \n",
      " |          writer.close()\n",
      " |  \n",
      " |  add_onnx_graph(self, prototxt)\n",
      " |  \n",
      " |  add_pr_curve(self, tag, labels, predictions, global_step=None, num_thresholds=127, weights=None, walltime=None)\n",
      " |      Adds precision recall curve.\n",
      " |      Plotting a precision-recall curve lets you understand your model's\n",
      " |      performance under different threshold settings. With this function,\n",
      " |      you provide the ground truth labeling (T/F) and prediction confidence\n",
      " |      (usually the output of your model) for each target. The TensorBoard UI\n",
      " |      will let you choose the threshold interactively.\n",
      " |      \n",
      " |      Args:\n",
      " |          tag (string): Data identifier\n",
      " |          labels (torch.Tensor, numpy.array, or string/blobname):\n",
      " |            Ground truth data. Binary label for each element.\n",
      " |          predictions (torch.Tensor, numpy.array, or string/blobname):\n",
      " |            The probability that an element be classified as true.\n",
      " |            Value should be in [0, 1]\n",
      " |          global_step (int): Global step value to record\n",
      " |          num_thresholds (int): Number of thresholds used to draw the curve.\n",
      " |          walltime (float): Optional override default walltime (time.time())\n",
      " |            seconds after epoch of event\n",
      " |      \n",
      " |      Examples::\n",
      " |      \n",
      " |          from torch.utils.tensorboard import SummaryWriter\n",
      " |          import numpy as np\n",
      " |          labels = np.random.randint(2, size=100)  # binary label\n",
      " |          predictions = np.random.rand(100)\n",
      " |          writer = SummaryWriter()\n",
      " |          writer.add_pr_curve('pr_curve', labels, predictions, 0)\n",
      " |          writer.close()\n",
      " |  \n",
      " |  add_pr_curve_raw(self, tag, true_positive_counts, false_positive_counts, true_negative_counts, false_negative_counts, precision, recall, global_step=None, num_thresholds=127, weights=None, walltime=None)\n",
      " |      Adds precision recall curve with raw data.\n",
      " |      \n",
      " |      Args:\n",
      " |          tag (string): Data identifier\n",
      " |          true_positive_counts (torch.Tensor, numpy.array, or string/blobname): true positive counts\n",
      " |          false_positive_counts (torch.Tensor, numpy.array, or string/blobname): false positive counts\n",
      " |          true_negative_counts (torch.Tensor, numpy.array, or string/blobname): true negative counts\n",
      " |          false_negative_counts (torch.Tensor, numpy.array, or string/blobname): false negative counts\n",
      " |          precision (torch.Tensor, numpy.array, or string/blobname): precision\n",
      " |          recall (torch.Tensor, numpy.array, or string/blobname): recall\n",
      " |          global_step (int): Global step value to record\n",
      " |          num_thresholds (int): Number of thresholds used to draw the curve.\n",
      " |          walltime (float): Optional override default walltime (time.time())\n",
      " |            seconds after epoch of event\n",
      " |          see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/README.md\n",
      " |  \n",
      " |  add_scalar(self, tag, scalar_value, global_step=None, walltime=None, new_style=False, double_precision=False)\n",
      " |      Add scalar data to summary.\n",
      " |      \n",
      " |      Args:\n",
      " |          tag (string): Data identifier\n",
      " |          scalar_value (float or string/blobname): Value to save\n",
      " |          global_step (int): Global step value to record\n",
      " |          walltime (float): Optional override default walltime (time.time())\n",
      " |            with seconds after epoch of event\n",
      " |          new_style (boolean): Whether to use new style (tensor field) or old\n",
      " |            style (simple_value field). New style could lead to faster data loading.\n",
      " |      Examples::\n",
      " |      \n",
      " |          from torch.utils.tensorboard import SummaryWriter\n",
      " |          writer = SummaryWriter()\n",
      " |          x = range(100)\n",
      " |          for i in x:\n",
      " |              writer.add_scalar('y=2x', i * 2, i)\n",
      " |          writer.close()\n",
      " |      \n",
      " |      Expected result:\n",
      " |      \n",
      " |      .. image:: _static/img/tensorboard/add_scalar.png\n",
      " |         :scale: 50 %\n",
      " |  \n",
      " |  add_scalars(self, main_tag, tag_scalar_dict, global_step=None, walltime=None)\n",
      " |      Adds many scalar data to summary.\n",
      " |      \n",
      " |      Args:\n",
      " |          main_tag (string): The parent name for the tags\n",
      " |          tag_scalar_dict (dict): Key-value pair storing the tag and corresponding values\n",
      " |          global_step (int): Global step value to record\n",
      " |          walltime (float): Optional override default walltime (time.time())\n",
      " |            seconds after epoch of event\n",
      " |      \n",
      " |      Examples::\n",
      " |      \n",
      " |          from torch.utils.tensorboard import SummaryWriter\n",
      " |          writer = SummaryWriter()\n",
      " |          r = 5\n",
      " |          for i in range(100):\n",
      " |              writer.add_scalars('run_14h', {'xsinx':i*np.sin(i/r),\n",
      " |                                              'xcosx':i*np.cos(i/r),\n",
      " |                                              'tanx': np.tan(i/r)}, i)\n",
      " |          writer.close()\n",
      " |          # This call adds three values to the same scalar plot with the tag\n",
      " |          # 'run_14h' in TensorBoard's scalar section.\n",
      " |      \n",
      " |      Expected result:\n",
      " |      \n",
      " |      .. image:: _static/img/tensorboard/add_scalars.png\n",
      " |         :scale: 50 %\n",
      " |  \n",
      " |  add_text(self, tag, text_string, global_step=None, walltime=None)\n",
      " |      Add text data to summary.\n",
      " |      \n",
      " |      Args:\n",
      " |          tag (string): Data identifier\n",
      " |          text_string (string): String to save\n",
      " |          global_step (int): Global step value to record\n",
      " |          walltime (float): Optional override default walltime (time.time())\n",
      " |            seconds after epoch of event\n",
      " |      Examples::\n",
      " |      \n",
      " |          writer.add_text('lstm', 'This is an lstm', 0)\n",
      " |          writer.add_text('rnn', 'This is an rnn', 10)\n",
      " |  \n",
      " |  add_video(self, tag, vid_tensor, global_step=None, fps=4, walltime=None)\n",
      " |      Add video data to summary.\n",
      " |      \n",
      " |      Note that this requires the ``moviepy`` package.\n",
      " |      \n",
      " |      Args:\n",
      " |          tag (string): Data identifier\n",
      " |          vid_tensor (torch.Tensor): Video data\n",
      " |          global_step (int): Global step value to record\n",
      " |          fps (float or int): Frames per second\n",
      " |          walltime (float): Optional override default walltime (time.time())\n",
      " |            seconds after epoch of event\n",
      " |      Shape:\n",
      " |          vid_tensor: :math:`(N, T, C, H, W)`. The values should lie in [0, 255] for type `uint8` or [0, 1] for type `float`.\n",
      " |  \n",
      " |  close(self)\n",
      " |  \n",
      " |  flush(self)\n",
      " |      Flushes the event file to disk.\n",
      " |      Call this method to make sure that all pending events have been written to\n",
      " |      disk.\n",
      " |  \n",
      " |  get_logdir(self)\n",
      " |      Returns the directory where event files will be written.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.tensorboard import SummaryWriter\n",
    "help(SummaryWriter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Tensorboard 写日志"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.tensorboard import SummaryWriter\n",
    "writer = SummaryWriter(\"logs\")  # 创建一个logs文件夹，writer写的文件都在该文件夹下\n",
    "# writer.add_image()\n",
    "for i in range(100):\n",
    "    writer.add_scalar(tag=\"y=2x\", scalar_value=2*i, global_step=i)\n",
    "writer.close()"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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1jn9m5H7cOVdttY4rSZrQmTMT2lIyoYHIpH0zZZWU8dxL02w/S7MO/sBCuR4hec2aNTp06JC+973v6ezZszFPAZjNkwyyRfAHAABYOLkEf8maT+ALX/hCdCK/Dz74QP/1X/+ljz7Kfi786xv8b9X9e76sj65D6LcCfLEmzgzo7eLqhAAdE6onzujMxBZtKbHmBDiu+1W9pTiyuWNcv/3Iv4UK4Rsq5L9rJvjfCAj+WLKuV0jetWuXnnjiiWjtcnzPlflE8AcAAFg4uQb/+XDdW/wRsUpbfXfrD/2xvQOKEyYjNNtsP0v5+SkB8uill15iln4AAABgSfpYp49PyOf3a1tkSeITCBCP4A8AAAAAWDriJxNERgWZNwEAAAAAAEsVwR8AAAAAAIMR/AEAAAAAMBjBHwAAAAAAgxH8sbi48l2ABWDiNQEAAABYMgj+WFzC1j+lrnB+yzEPVthfLP1LAQAAALCELauoqMh3GQBJUuj1gArCVkr+9bJPtPQTc2xTPz9rAAAA4G9C5INr7dq1Sz1dwRBthVP6nwXX8l2MBXE8XKivX7sp38UAAAAAcAOiqz8WjYbp5fp5aJljiQmD4106GS4g9AMAAADIG1r8AQAAAAAwGC3+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMFc4XCYyf0AAAAAADAULf4AAAAAABhs2auvvprvMgAAAAAAgAXiqqmpoas/gHl14sSJOR/j2rVrKi4uVlFRkSYnJ+ehVADypaioSN/+9rf1k5/8hJ9nYJGpqqrS4OBgvouBJYrPz9JBV38AALBgioqK9C//8i/63e9+p8bGRhUVFeW7SAAA3HAI/gAAYEHYof/w4cP62c9+pmeffZbwDwBAHhD8AQDAvHOG/ldeeUWSdP78ecI/AAB5QPAHAAAL4mc/+1k09Nvs8P+5z30uT6UCAODGsyzfBQAAAOaZnJxMOdHn+fPnr3NpAAC4sdHiD+C6+9u//dt8FwEAAAC4YRD8AeSkomqHtn75/lnvZ4d+wj8AAABwfRD8AczaZ1ev0WdXr9HtG9yzCv/xYT+/4d+nlkC36j1ZbOqpV3cgoECgRT5JvpaAWnwLXT4AABYDj+q7Y39fzvr3oK9F3al+4UZ/x6Z6Wb97k547elyfWuL24/f0IuFrUcDx95avxflZivtbzNeS/DPQXS+Pr0WBFp+sz2PsZwLZYYw/gFn57Oo1uq+yKvr97RvckqTTrx9Pu58z5P/85z+Pafn/+c9/vgAlTcenlkCjyiWVtwe0O27tWM9+1XaMylPfrfbdpZKG1er1ql+y/kApH9bzzde5yAAA5INnuyo0pKHabtV31+qV7d1qLJdUHlCgMbLNWI/213Zo1NeiQGP5zL7DrfI298tXuU5D3aOpzxHZLsnJVd9dO/NdfZJzq1zd6tFFDavV26x+WZUDlXO5Zsyf/mbt39it9uZ6vVLb4Vhh/S22rqdV3Y5tvf0+tQQqdczbrH5fiwKVx6zPhq8lD4U3C8EfQEafXb1Gf7H2dknSxs13adny5THri1etzvpYdsh3hv/ryQ7zw61eeeP/xvDUq7tZaumw/jgZ7aiVt8P5R4dH9c27VSqpMRBQo2NXu7IAAACTeLZXSEMtOr++QpW13WovtyvEm9UfCW96vkOjUiS42TvWy/r16VNleanKy+Mq2lOG/dRGO2rVuj6gymNeNVsJX90bu1Xb4VbL7vVzv1gsCOvvKetrd3Rpv5oT/hDDQiL4A0jr5s8UqaJqR9J116amdOLYoP5weTyrY8W37F/v8O+p71Z7xZD2ezvkbulWfbBW0azua1Fg7wWrxcLaWvXd7dpdGlkfDfozLQqK7hpQ5XlCPwDAND7V7i6VeqzvLnTXqrnZWt4SCKhRY+rZ71W6em9P/V6t69kvb2QjX4sjuOeovHGmxX+sx24vLo+plB8+lvvxMT98LQE5O4BExVcCSRrradVQRWP0767y6HtZrkCgUcPDwwta1hsBwR9AWlf+NKnTrx9PGMs/19CfaflCcNY4jzY/r5ZAQC2tXh2rDKhRrfLWOv8KGVVHrVcdcnQ5k0/19UH1y6pEaFaLal/Zrr3retRCpTUAwDCe+r1aNzYW/X59bbcC5aWSxtSzv1Vqb9Rue8hc0hb8jdpeUapSbZenY1Sj8mjjujFdCMZtVt6oQKBRyQ2rNX5Jq7PF37EdXf0Xlf7myDDJaLf+/Xp+fbtVGZDs89LRrw6796U9dMTR1T/AmzonBH8AGb3/XlC3fO626Hh+STr/zttZh/7FqV/dPXvV3hiQknX7t/kqVa5ylQe6tXF/rTo64taPdqi2NumeAAAsaW4N6fmhCu2NfG+3+Hvqu9Xevlc9+71qlkee0VGrt5ynXt3t1pA4W0VPq3oqKuWWNCq31mtICcP9sxzjb0ve4o/Fpr47EGnBn5kryVM/puFjtWpWS2xljz0fRO1uaWi/EjqRRIeRZDMrM5Ih+API6PYNbt2+wa333wvGhP+lKr7rmfMPCMk5Xt+j+kppeGxYx2qPqbK7Xp7n16u98pj2X5Akt+q727X++bl1WQQAYDHq7+iQp77CsWSm5dbrtSbBDeyWevbXatROavZEf556ddeeV21Hvzzaq+Z6j4Laq3VDLYmhbpaSt/jT1X+xsXpOxnKvly68Imm0ObHRxddi9cBMOnZkZmJmjfVo/0IU2HAEfwBp2Y/sO/36cb3/XlAfffBbbf3y/frdf7+f76LNmh34x8bGNNbTknwyPscfEZ76ZlVceF5D6yol9au51qeWwHq1evul+r2SguqobbUeReOcLwAAACNFJmTz1Ks70C717JfXK+txfy21sSFvtEO1zfaXz+tioF3tGlPP/tx/WUYr7uNn9V8/PFPhILr6LxYzLf5xko7x36/ajrjKgMgTAVp8/WruZzLAuSL4A0jLGfolRcL/73TlT5N5Ltns2WPNrPH5mbnXX9TzzUFtrKiU1d2wUWqNHGPmqGpurVSguV4dtfH12gAAmKG/uVbRiW/Vo/1erzUGO1ButcBbHeVmJEyaK2lsSK/MoZLc+j1udf8/33JBe9vX6/noY9/Oz7knAeZXQou/r0WBves0VnrRet+S7JN8QsDYnpnOSh5kj+APIK2jL/w0YdlSDP3xSne3KxBf3Rxhjxfsb26W5FG9JHuyv6T6k3RXAwDAOJGJb30tCgQC1rhsb1xVeulutQd2R8OZ6rsVsB+jqxYFAt3q2R/XSy7ryf08qrdmflP/qNTf2qJAS702rlunHmbZXbx8LQo0lkc+E80ajT4VIvFJSTMTAlo89d2qPV/LkMp5QPAHcEOaGccfJ2a8YBL2Ly9Jw63UNQMAbiD278BkgV+S3OtV6hzjHwhEhgPYvy+b5e33qL47oMBFx4R+WU7u52uJzKtjH66/WV61qHtji2pHI5MO7i6VNKzWbLr2YUFFu/qP9Wi/t9nRQm9327crACSNDWtY5SpPNjQgvsVf1jG99LScFVdNTU0434UAYJYTJ07M+RjXrl1TcXGxioqKNDm59HsYAACwGFVVVWlwcDDfxcASxedn6SjIdwEAAAAAAMDCIfgDAAAAAGAwxvgDWPIeDF/T/xOeVLErLIVdkhjBBABIwSVNhl16xrVSz7hW5Ls0AHBd0OIPYEl7MHxNneE/qljhSN4n9AMA0ghLRQqrIXxVjeEr+S4NAFwXy15//fV8lwEAcvZUeFKufBcCALAEhVUX/lStrpvzXRAAWHB09QewpN3saOH/sWulfuJaoQ+oCgAApHC/rumnoT9KkgroJQbgBkHwB7C0OYb0t7tW6A+EfgBAGse1TDO/PPidAeDG4Fq7di1VnQDmVSgUmvMxrl27puLiYhUVFWlycjLldsHQ5ejX7oLVcz4vAMB8/O4AcKNhcj8AAAAAAAxG8AcAAAAAwGCM8QewIO67776c9z1x4sQ8lgQAAAC4sdHiDyAn333kgGr8e/JdDAAAAAAZ0OIPYNZuW1sSfUlSX++hPJcIAAAAQCq0+AOYldvWluibdd+Lfn/Pvdto+QcAAAAWMVr8AWR029oSbfl8mSTpK3/9N1qxcmXM+r8suT0fxQIAAACQBYI/gLRWr7lF333kQNJ1n1y9quc6n9Zv//vSdS7V3LW0tCgUDiscCikcDisUCiscDkWXhcJhXZuaUnd3t373u9/lu7gAAABAzgj+ANK6PP6R+noPJXTnX8qhX5JC4bD2/o//Ef0+HP2P5Wc/69EXv/hFTYdC+v/+9/8m/AMAAGDJYow/gIxOvXFSp944GbPs1//x70s29EtSOBSSJE2HQpqeDml6elrT09O6FnlJ0q233qoqr1d79/7PfBYVAAAAmBOCP4CM7rl3m+65d1tC+F/KwuFI835YCjub+u2Fkp577jkdOXJEKnBd38ItWWVqCgQVDAbV5c93WQAAwNLlUX13QC2+DJv5WtRd70lYFsi4442H4A8gLXvW/r7eQ9GXJJ15ayTPJZubUMgK99HQH47p6a+vf/0b+t7/+X39r//1qMKh+IoBJFPW9JTqzjXI7XZrX2++S5Mtv7qCQQWaypKs6lIwGIy8upRYlxG7b1lTwLH9zMuuBElYH1mRab+05SxrUiC6T0Cxq6194suf3fkkZ0VO6vuQaRtnGWLP4e+KO6d9LV1++bsSy+e8Z4nXnuz6E8uWvkLK2j5hm7ImBSLX5O+KK4PzngaaVGZdWJoyzdyThPcyxX7ZvV+Ra81U45bzOZJ/lpIfN/PPSsK9y+rziLzxtSgQCERe3YrNWD61pFqXbr+0x4zw1Ks70CJfwnnsZem2n9knIRR66tUdPVYWwTLVcdJde8zpuh3XGogNpNnchyUs4dqzvueSNKqO2lapMf198VWu09Aro9bXLebdw/lE8AeQlh367db+U2+c1I/+75Yl3c1fksLh0EzQjwv98d+HwqHrVq70UgSTvJwvcd1dd7oVfPft61W4tGXJdp9gcIc0mGS1v0vBh99Vjdstt9stt3uf4usyypoeVpXj+5GD3si2kVdNp4Ia1NHIjnfd6Vaws2ZmfaR2JP1+6crpV1dfnc41RPZrOKe6pyLhU2VqCuzQ0cgxGwar1Ba5QZnKGW+wwbFtg9SWJJTFbBO9V351Bduk6LoavftwslAYdy37etW7z96nQYMaVEPcPZO/S8G+ag3UOK9jQNV98WULqrNm5jrvaEsWxLPXe3RQqtqRUPlRU+3W4DMHNSK/upyfm4ZzquuzrznDe5liv6zer7IaVSuoYELZ5uMcqT9L1mFn/7PiNNvPI643n1r2XtB+r1der1fe1ova3W6Ha4/quxu1rmd/knXp9ku3boZne4XU061+eVTfXaljke1bh8vVmCQ5xm4fUCBQKQ0nuZ723brYap97WOWNKSoS0h4n3bXHcq8v1Zi9ndcrb3N/FvfWDKMdtTPX7fXKu79HYxrWsf74Le17Hf9qVLlKtbs9bnl3vTyS5KnX3nVDsnK/T5XRr5EMwR9AWj9sOpDQxf/y+Ed5Ks38CYUj6T4+9Md0/I/0CqDF30AjOui1QsrRhHVlanr4DnU+clAp+7WUNemp6gF1Jqs0iPA/WqdzDbEh6Nw7mXvKxO6Xppz+HaoKdupJ+wS9T6pT1aops/ebOXfv0UHpjs1KlnmTlTOl3n2RAJ0qwDuUbdYdzvLFlclRAnUF23RHZ02WPUX86mqr0mCDVwedt3PkoLwNg6pKVbaRg3pm0K3qmjklfw2qSjtieirUqNptB9Ve7fM6Pje9RzWoO7Q5+p6k+syl2y9WsverrKZaGnhEzwzGlW1ezpHuszQ/PyuZrg/51K/m2g5Fs1T/MQ1rnTZ6JHm2q6J0WM93RNb2d6tnrFyVvgz7pV1n86l2tyItuaPqqG1Wf3TzYWndRsU27MZv75XX26xjSa/JETyTntuW5jhprz3RxfPJ0mg298Esvtrdutg6817OsO+1V/t7xjTc6o2tMIhU0kQrUHMyt40AACAASURBVCL3zbO9QqUXz1v30Fep8tLdag8EFGgsl8oboxUFib01bkwEfwA3JHtyv/jQ7+wGEJ0GIJwp+Md2Z7a6s1qte86urTHdgZPuYy8PqKmpK26dX13BPtW5paq2YPQ4sd1k07SmpuqK7ejGHPt98vPNHC92XVNXUG1Vkruuz3H8xX5fUiirUbUGpEeTlUPWNTxVp3PPHNQ7qY7h71LbHc7QW6bNd0TKmK5MCfvNllt33pXksDuqFBzoSwxnuZxvpE8DwXQB097uHZ1z1+nRjN3rrdDvPZjl8KH4Cg+nNGF2fvTKavSfuaiymmq5B48mD6r+HarSOWVR35PdfknfrzLVVEsDfSPqPTqoqoebklbwzO0czt0cn6X5+FmZxbmxCPgqVa6LOj8qyb1epcPHHCFuVOcvSuuSJVfnftms81WqfCx5662vslxjQ69oVNltH6tf3T3rtNcOgr5KlQ8/r47ZthKnu3ZPvbqj3fY92rhOKm/MMEzBLkuqe2QCX4sa1/WoOzH1xxh9ZUjrktSgOLv0R5aodndp5GuP6veuU8/+mUoCDbdGKw1qZ/0Gm4ngD+CGFIqGebvZ3wr6zu7/0W1D6br6+9UVfFjvRrsc12iguk9d/hEdfGZQ7uqaaMtYTHfgpPvYx3Sr7s6j0W6vqntKTWW92ueuUWcw0q3ae1AjZU16qu7cTDfoJF1sna2psd21M4XhJOdLs+7gPrcaBhXpyr5PvYv+vqRx151yu+t051F33Lkid7SrT9UD6Vqny9T0cFXkmmx2a6/1qum8Q22B+ICWbL803n5XQWew9j+qOrfzcDNj4HccdScJ1rM8XxozFRrOcea92tcwGFmX/PNW1danOnXqkWxDf0Zv691g8soPlTXp4aqgBvrSnyvmWoJBBfvq5LytvU92OrrURz6/yfqllzUp0Falwdm2XqfcL8X75X9UdRpQ34isig+33etjHs+R6rM055+VLM6NxcNTr+7Gcg1HWmw9G9fltF8263yV5Rp+3tEa7hiXX3ksMcglbJ9B6e52K4g3lmvsQjDLvRzFzvbaHS3ZVmv2OjXa3dRjDpjmHhnBo/q9Wb5Ho69oaN3e2LH6MV367UV7tW54WGOS1QPjYg4VODcYgj+AG5LVfT9sZ/7Y7v3h2O7+aVv8/TtUJbfq+uygYLU+37G5LPaPcGd34HT7SJKC6rSbvEb6NJDqb5KRd3ROVWpLOoGYo3zBuGDV+6Q6s2mxnYvFfl8ycbYoO7uIR1ok0wbVmK7fKYp48JnEgJbFfnEHiXRtj9yvh99V52BQ0WkWRg7KG6loOLojvtdGDueL4TiP4sb4O1Ne776Zio74yfki+zWcq1NfQiXIXDjL5vg89VVroMYeHuDsWRL7OYmdr8Aec+7g7PGQ4h6WNQWicxDMZqLLtPulOFdsb45eHc0wnCGXc6T9LM3lZyWbc2NR8NR3K9BeoaH9XtlD1EfPX8xpv4zrPPXaWx43Dny0Q7WR8Hys0jHGO9X2qQuk5t2aaRn2turi7vYsJ5tzFCfdtY92qNZbmzSEjnY8r+HSCm13hNp098gYkaERWb1HGlVHy5Aqmu332KeW9goNtcRWGrg1pJbuC5FdOlRr7M2bP8vyXQAAyIdQZHK/hPH9zgWRCoCMk/sFO1XjTdZKNaKjg216uKZMfaqWOh+ZaV1LuU+ypspUerXP3avopGHuoDpr4sY9py60FnweviV5X2S1pCddcZeaHq6S3FXqC9bNLK7qU7B6ptz+R+ukzppZj1HOab/efXJHd/CrK3inju5LtlmDdgQfVk3ZQY2MzOF81o6q04BqZtEsO3LQK/dBq/dJl79XsXUDNdoc6FNf1zuxlQap9B7VYFubHvUfTAyu/kdV5z6nhmjZUr339mckFyPqGwiqb4dfZZur5R58JuYe+ruCalOD3LM8fqb9kr9ffu2oktxVfXJ+JFX1qPwHE3sa5HaOWDGfpTn+rMz23MgPX0tAjWqV15skWEXG2luBzOrWfvHYaMb90q3zbK9Q6fDzKVu++5tbVRnYq+2eDo2OZt4+2bFnQnm/jg03au/GmavIWpprz1bae2sQX+1uqWd/9r0ZRjtU+7z1xANpTD37EytS+js6JE995AQt1rj+GOUKBBqtL8d6tL82+x4hpqLFH8ANKTwzgD/SzT829Icdrf5pJ/frParBuHHM/q6Zbs29T3ZK1Y/q0cgY3Gz2yVpZk5r8ktWNvEaddhfnsiYF7FbMyLmecjZpOrsGS5JjTHRZTXVMt+b48wWybUVf7PclnZE+Dchxz6JdxHtjuutbM5xHhjdEg4xfO5J2Jy9TmfMt6GpTVdD5HqTaL3v+rjbd0fmkFZzKmhRwtK6XNT2c3fky3SN/l4JtGSZzcx4r4VELySqcRnTQ26DBqrbMj6OTJPXqyc6gquJn6M+1a30ORvoGFKx6WE9Va6YXSqQMD1cNqmG2z7PMuF+K98u/Q1XOpx5En4Rg90hwvJ+5niPdZynXn5WEz9ncP/9YIJHW9NZkran93erRbtXareW+Wu1WZAx3uv3SrZNH2ytKNexsGvbUq9vRJO+p3+sYz59k+zRGXxnSWLmzG7lPleWpJt9LI8O1O8f4exyt+76Wxpmyp70PJvGpsnwsbnz+bJRqfco/TCL6mxMmAnSO8fcS+iXR4g/gBhUKhZPM6C8ltPrLOR9AMr3aV7NZgb6ggm3WksEGt6KNriN9GlCf6s41aN9IlvukFGlpbAsq+LDVavbOozPH0GCDIg3dseVzS11BZ4vgoBrckeA2clCPdFarry+oOknBwUFHC17c+R5JX5aldV/SH++gt0F3Ou7ZYIM7ux4DZZt1h87paJJta54Kqs/+4yW+Z0Oa/dKcTE2Bvui4/qBzgryRg3rk3YCCMzdh5j2f5fmq2hz3MtipGve+hNAfs020lT2+DOnuo/05bVMwsDlFr48ZIwe9cvc1KRD53M6cN8v3aa5G+jQQrFOdOhWTVe+6U25VqS0YVJtj8WBDhi7/mfZL8X75d1RJgw1xFR29OjrYprYdfslZyZLjOdJ/lubws+KU0+cf14V7vUpVrsZAQI2OxcOtXjX3j6qjpUfd7QFZDavDavVGAla6/ZRmXXC7KtSjlrhu/i0Xumdab53n8STZPp3RDtW2rlegPaDdMdeS9R2xD5T62uNsbw6o3Z6DztnynPbezrY8i5hno9bporLtDOFrCaixXJHg3hxdFmiUxnr2M1HfHLjWrl3Lc6oAzKtQKKT77rsv5/1PnDiha9euqbi4WEVFRZqcnEy5bTB0Ofq1u2B11uf4vw4c0N/v/3tNXbtmLYgZ1z9j+fJleqy1Vc92dWV9bADA4pbr7w4sLF9LQHsvZB/uZrs9FiuP6rvbtbs0fcWHp75b7btLZypQPPXqrj2fOL7f16JA5TF5japBmTta/AHckELhsMIKa9mywszbpuvqDwAA5kGkS3h3tiF+tttj8bKeftCRaauOWnk7YhaotjnJhv3NMnzahJwQ/AHckMKhsP71Rz9SOGSN7w+FQta/4ZDCobBVMRAOKRTKMKs/AACYB/1qnlVam+32wI2N4A/ghvT/PvlEvosAAAAAXBfM6g8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AHLy3UcOqMa/J9/FAAAAAJABk/sBmLXb1pZEX5LU13sozyUCAAAAkAot/gBm5ba1Jfpm3fei399z7zZa/gEAAIBFjBZ/ABndtrZEWz5fJkn6yl//jVasXBmz/i9Lbs9HsQAAAABkgeAPIK3Va27Rdx85kHTdJ1ev6rnOp/Xb/750nUsFAAAAIFt09QeQ1uXxj5KO4V9Uod+V7wIAAJYcfncAuIEQ/AFkdOqNkzr1xsmYZb/+j39fHKFfksLWP+sUym85AACL3gr7i3A+SwEA1xdd/QFkdM+923TPvdt06o2TuufebfkuToyQZmowXwtNiL/kAADp0dQP4MZDiz+AtOxZ+/t6D0VfknTmrZE8l8zyM9cKx3eEfgBAJjO/K05oeR7LAQDXDy3+ANKyQ7/d1f/UGyf13rl3dXn8ozyXzNLkullF4bBq9GlkiUtUAAAA0nPpDRVqd0FRvgsCANeFa+3atfyFDGBehUIh3XfffTnvf+LECV27dk3FxcUqKirS5OTkPJYOAAAAuLHQ1R8AAAAAAIMR/AEAAAAAMBjBHwAAAAAAgxH8AQAAAAAwGMEfAAAAAACDEfwBAAAAADAYwR8AAAAAAIMR/AEAAAAAMBjBHwAAAAAAgxH8AQAAAAAwGMEfAAAAAACDEfwBAAAAADAYwR8AAAAAAIMR/AHk5LuPHFCNf0++iwEAAAAgg2X5LgCApee2tSXRlyT19R7Kc4kAAAAApEKLP4BZuW1tib5Z973o9/fcu42WfwAAAGARo8UfQEa3rS3Rls+XSZK+8td/oxUrV8as/8uS2/NRLAAAAABZIPgDSGv1mlv03UcOJF33ydWreq7zaf32vy9d51IBAAAAyBZd/QGkdXn8o6Rj+An9AAAAwNJA8AeQ0ak3TurUGydjlv36P/6d0A8AAAAsAQR/ABndc+823XPvtoTwDwAAAGDxI/gDSMuetb+v91D0JUln3hrJc8kAAAAAZIPJ/QCkZYd+u7X/1Bsn9d65d3V5/KM8lwwAgMXLH76sNhXJ7Vqe76IAAC3+ANL7YdOBhC7+hH4ASGVSwfCk/PkuBvJsSjskKTyprnwXBQBE8AcAAJg3ZZKCmtLDmp7FPhMKhi7PssJgISoYptQVvqxg+KrKct43dZns61ycQXhynsu2XPtcRRp0SVU5v0+Zy1SmCSqaAGSFrv4AAADzZEQrNaAp1YWnVOYqVOxsKFcVCF+VOxy7T7CgWO6CQknTWQduv0IKalpt4at627VSs591ZVLB0JSCBcXyqnDWeyeUJzypKhWq01Wk3qRbTOnR8LTkKtK+rI44rabwhOrCmbdM4FqpmpzuyXxbriddhaoKh7Q5h7394SnJtVxHo0si90SL5foALCUEfwAAgJxNqSs8qaqEgHpVfeGr0e+sgG0ZLFidIvzaFQVWKM9O7HmSShKE7VD5zJxCf3w4n1Zd+LLqouddroZoRcCnkXs0qWCyMJ8qrMccI5PIezHLq8iFP3xZbbOolIi5L8m44ucCiAwV0E2Oaw/pTklB13JCP4BZI/gDAADkbLn2uVbLr8va4YoP9FYwvjO6PNswXyR3wmDMKTWpQAfTBvVplSm+l0EyV/WwpNhQOQdJQnuZJtTnqBBoCk+lqPCwW7EXUvYVKVWhywpm2GawYLX2uVar1zWzzJrIL5uWeLtyItOkf1ZFyWDB8thlKlTnPPTQAHDjIfgDAADMybTedi1XW+iyuhzh1h+eUJ0KFdSUpLnN7O4PT6ouLN2ZsreAdb42FarTVayDacraFB1ukKL1PUMvgtkODyjTn1Sn5WrIeo/5lqwiJZ5VOZC6N0Z6b7sKpfCUapQp+FuBPliQrkBWRYlcK/WkY6k/PCWFk/UemFZb+LLanItm1VMCwI2A4A8AADAnhRpRkRpcl9UWvqoy10pJ2YTw7PW6irVZE6oLT6gp6TEn1RaWggWfyXC+q6pToToLkh3Dbo2ezzHkV/VUeFpB12eMDqEjWq6grqpa02l7ZZQpJLkKNZC24mRK1UmW7VDiMBGrZ0UBIR9ARszqDwAAkCN/+LI1I38oMuY7fFV9ocvqC01L4WnVRdbNfeb1Qh10rVRQBXonYZ3dQpxpzP5VBcJTCroyVQ7Mn82aklvpymWNW1/6lmtAkjs8lWaCxmnVhKclLVdfmiP5k0wAaXXzd070BwCzQ4s/AABAjnrjxnqnZ42tTzuOPO2M9CvlTXKuMv1JdWFpsCBdq++UusJX5dZK1VzHMeLvqFhul2QNK7gpw7j2paxQB13LVZe2u//VyPuUrjfFZJJJAyMVO6JVH0DuCP4AAADzJvPkbYMx8wCknhSuTBNWz4EspapQGCxYrSd1VVVaroY8PQbOGp8+pYAr1fwA2UxKOBfZPR4w90oZSbpJg5pUXXhKBxPe++Tj9pNvU6igpuWOLo8MzzC20gTA9UDwBwAAWKzmbZI2u+V9AYRTTAbomhlR2utaLemy2kITMRMgZj72VOLEdZkkvc5CHXSt1sGk66bUFf5UR+d8n5drn2u5gqFJdbnix+L/KRLeU1cczGzzGSnmSQcr1amptMMDACATgj8AAEDOriqQdEx27Iz51kz4i0Gq8sZuk3JW/2QVERkf52eZmaBwUv5sQ/asKj7s3haz9amqwlOq0lW9PeceESvV6ZqKu0Z7gsPMEz0Ouop1UNNqillaqIMuHuEHYG4I/gAAADlbKa9rpaOVOV1X/6tyq1AD17mEseLL67QQs/o7WRMUVoevqi08HyF7NiJd/SPXdlf4stpkVyoUyV1gVYg8peVJhiJcVSA8rWeyqoAo1EFXke4MT6otPCm5btKO8FW5VSR3hrkVRlSctCeEP3w5ybj/2GtL2ivCtVJu18qMJQZwY2BWfwAAgDmZUlf4srrilpZpQgFlP0Y/3l3h3PddvFbqEVehBq/7XAPWI/KCruUpJ058xFUod2gi4X20928LT2V5ruXa51qpoKbUFrIrUnIfn9/rWi13QfJXTUGh1Ssi2XpCPwAHgj8AAMAclOmqqsLSHUlCvjNIlikkJX0cXzqFenvuRVxUUrVsL6QyTUV6W6RudR9RsRpchbpDdsCfjDyGcaW8ruVSeHIWFTnTM8MpwlfVN+fHOQLA3BD8AQAAchZ5NrtrpR6JC5VWkJSqwpPyR5/hflOW49WntVnSws92n2/TukNS0LWQf5La9355xgnyel3F8spqnbcqakKR96FIDS7JHf5T3Pj7eFbvj2A4pM5o6/tya5LC0OU59QABgLlgjD8AAEDO0j+bvddVpB3hT2V3NR/Musu33TWdNpq5S/8epWINtVgZrSyw3surulPTUkLPgcj8CGFrIsfY8fxFchfYj2ecUFD2ZI9M2Afg+iH4AwAA5KhMIclVlKbruvWIN3/4stxaqUeyPu6U3FquBuPDodUlfnBBZ623grfT265CKWy35idzVQ8rfk4A6710ssK81Yo/WLA67SMTR1ScUAGQ7IkIALAQCP4AAAA5GlFx0rAX21JvhcjsJ7SzH//2mTk+V36GM6Bmc/6Uj/OzuYrktq8xnGL7jL0VptUUnpJcy3U01SbhqeQz1qctW+ZNRvQZdWpCdaHLqkt5nJWqSVHxEp1p37VSNQXFswrudgWA3UugL3Q1cj9znwAQADIh+AMAAMxJ5FFx9mRurtiWen/kcW7eLI9mbZ86dOZiJmzOp0hFQpJW6zJNqC8cu23MPbK5CtWZ7jF5LvuRe9mwH0eYjUIddK3WwSwqCZLpda1Wb477zliufa7VWVVUAMBcudauXZv2yaAAMFuhUEj33XdfzvufOHFC165dU3FxsYqKijQ5OTmPpQMAAABuLMwYAwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABiM4A8AAAAAgMEI/gAAAAAAGIzgDwAAAACAwQj+AAAAAAAYjOAPAAAAAIDBCP4AAAAAABhsWb4LAMBMJ06cyHcRAAAAAIgWfwAAAAAAjEbwBwAAAADAYAR/AAAAAAAMRvAHAAAAAMBgBH8AAAAAAAxG8AcAAAAAwGAEfwAAAAAADEbwBwAAAADAYAR/AAAAAAAMRvAHAAAAAMBgBH8AAAAAAAxG8AcAAAAAwGAEfwAAAAAADEbwBwAAAADAYAR/AAAAAAAMRvAHAAAAAMBgBH8AAAAAAAxG8AcAAAAAwGAEfwAAAAAADEbwBwAAAADAYAR/AAAAAAAMRvAHAAAAAMBgBH8AAAAAAAxG8AcAAAAAwGAEfwAAAAAADEbwBwAAAADAYAR/AAAAAAAMRvAHAAAAAMBgBH8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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Tensorboard 读日志"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① 在 Anaconda 终端里面，激活py3.6.3环境，再输入 tensorboard --logdir=C:\\Users\\wangy\\Desktop\\03CV\\logs 命令，将网址赋值浏览器的网址栏，回车，即可查看tensorboard显示日志情况。"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "② 为避免多人使用端口导致冲突，也可以在后面加上后缀，使得端口独立，tensorboard --logdir=C:\\Users\\wangy\\Desktop\\03CV\\logs --port=6008"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "③ 输入网址可得Tensorboard界面。"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Tensorboard 读图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=768x512 at 0x7FEB781F8820>\n",
      "(512, 768, 3)\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x332 at 0x7FEB781F8A90>\n",
      "(332, 500, 3)\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "img_path1 = \"Data/hymenoptera_data/train/ants/0013035.jpg\" \n",
    "img_PIL1 = Image.open(img_path1)\n",
    "img_array1 = np.array(img_PIL1)\n",
    "print(img_PIL1)\n",
    "print(img_array1.shape)\n",
    "\n",
    "\n",
    "img_path2 = \"Data/hymenoptera_data/train/bees/90179376_abc234e5f4.jpg\"\n",
    "img_PIL2 = Image.open(img_path2)\n",
    "img_array2 = np.array(img_PIL2)\n",
    "print(img_PIL2)\n",
    "print(img_array2.shape)\n",
    "\n",
    "\n",
    "writer = SummaryWriter(\"logs\")\n",
    "# gloabl_step=1:表示该图片在第1步; dataformats=\"HWC\"表示当前图片为HWC格式\n",
    "writer.add_image(tag=\"test\", img_tensor=img_array1, global_step=1, dataformats=\"HWC\")\n",
    "# global_step=2:表示该图片在第2步\n",
    "writer.add_image(\"test\", img_array2, 2, dataformats=\"HWC\")\n",
    "writer.close()"
   ]
  },
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+9SmOHDkCQCqV4v/+3/+LruvcddddlMtl7r///maY6cOHD2MYBpOTk/z1X/81Q0ND7Nq1i0ceeYSuri5+5Vd+hU2bNnHs2DEuueQSHn74Yfbv38/nPvc5+vv7+fM///OQ+BsQiMSPPvooe/fu5VOf+hQf//jHOXDgAB//+MfZt28f3/ve97j22mu59NJLO74WV111FdVqlT/6oz/i4osv5oILLuDBBx9kfHycTZs28Td/8zdcc801mKbJI488wszMDPfeey/VapX/8T/+BxdccAEXX3wxL7/8Mhs3bmTVqlX8yZ/8CQD33nsvF198cex1S6fT865VEE43gudzMVFv0ZDJ84iLraKudxyhjpdCq8AV5HSeX9Q8PoevT3te2Gg3rcLj/KK231YnWm+hfoJqvhYV4Ba6j0E47eMLpBG9XvOJ3cFv81/ftgvW0fie5+E4jVy7wb5Np9PNedhWteFGBnQN0qkUx2amyOVyjRDZlhW7uwKROqA9Z/jc2ubCqbeLq5oWfUEgXnCNcx/PfTptvylPzYW39n1cP5wLuRF2OghjPSsou+2CckiU9rymg7t1PWpu8LZrM99eju5ZAG+JhvB9eoLvpLq5r5oHYEwzWLHEvMoB26wKD9k1vpnq5hupHqxTLNe3IAiCIAiCIAiCIAiCIAgCvA85lOdjxYoV2LbNyy+/jOM4vPTSS2zcuJEVK1YAUCwWGR0dpauri1qthmmasf0cOXIE13WZnJzkuuuu4+abb0bXdWzbRtd18vk8hUKBu+++m9HRUX784x+za9cucrlcbH+u61KtVrnrrrvI5XIopdi/fz8DAwOkUilefPFFHMfhrbfeYuvWrR2v99133+WCCy7g937v93jjjTd48MEHKZVKbfUmJyfZs2cPAIVCgX379lGpVCiXy02BYN26daxcuZKvfOUrzXaDg4NLum6CcDrSKp4tLAIuHvJ6IXzfJ5ruV4/Jd+p57YLtUokKdu8l3HGbGKja56cIx0ppFR5bHbuRnjuoQ1udpj01WNJxaMOxjtIO6nTST9ShfKII+kokEui6TjqdplKukUwm8TyPmZkZsukEqUSSUqlEuVymUqnw9jvvcs011+C6buPvsETcX9HxQnDcHJq/Re8DtO3vjhy80Toq/Lvv+w3Tcoug7Ct3zkE86+j1nLkw157nga9C34Nw1jAnMmv+3FjzieQL7c3gmrQeN78fx63/y1QPt9dLDHgOT5g5fpDM8tXqNNuOI4x10vf4rWqe2+olvpYZ4IVkeukTEgRBEARBEARBEARBEARBOImcVEG5UqmQSqXo6upqupQvu+wyBgYGOHjw4Akd6/LLL2dwcJA/+7M/w7Is7r33XgBs2+b//J//w8jICOeffz533HEHzz//PG+88UZsP7lcjmuvvZZXXnmFZ599lquuuopNmza95/m9+eab7N27l02bNnHZZZfxa7/2a3zzm9887v5ee+01HnnkkdC5u+66671OUxBOaTQjrEhqLSJvJyJSXF3f9wnMyK2CqBERkL0Yv6jS5/pQgHLbx1rIIdk40XBIzolbqt1GHFRchFYRcb6QyX7EeY3y8f2gbXSOswIdLqrpGG58ukoL1TV8FdtPELpaKYXvhF2jnhYW+ELzbF4Dp+13PyYM9mLE7QPVen18H6W1XzA9ct29OGHamxNDE4kEruc1RORKhVqtDq7HsaNHMU0TwzAaOX9NKFds8oVpCoUCa9asIZvNUi3PoGkGmVSKmmO3jRVduT/ryg2tT4s4ybXgPs6d81yvLYd4VFRuXp9AEJ51vAf9aLOuYVrDUEedxWrOfRyIw8oNi8e0hq4OiuuFvrd522PmHf2tsffm1qmUmnMjK9V0m2tLyKUeYCnF1zL9/M/SMaDhWv73uSG2WVW+Wp1mrWstqT+AFZ7DX5SO8kwyw9cyA0xqkihHEARBEARBEARBEARBEIRTg+OLJdohe/bswTAMPvaxj6HrOsPDw1x55ZW4rsvY2BiJRIJLL70UwzC47LLL0DSNsbGxRfsNwoe2YhgGtm1TrVY566yzyGQyAKxfv57f/u3fJp1O8/TTTzM2NtbM4Rz3D8i6rqOUolQqYZomK1euBBrO4VqtxpVXXolhGJx33nlt7t9SqYRt26xYsQLDMFi7di2JRCMv4mc/+1luvvlm3njjDV544QV0XSebzQLhvLCdcPDgQc4++2w2btyIrut89KMfZWRkZME2J9J9JwgfFEppsQVU81jT9FBpiKLhEm2H0kHp+Mwdo2mhEjeuphmh0ipczVfi17X0vM+LX6vOx48S51aN5sdVPqFyPH0pz0d5DfdpcBwtmk/z9+Zxh2NH5xEtJ4qQgOn7JBIJCoUCqVSKdDrN/v370TSNdDpNNpsll8s1x5+cnCSRSGDbNt3d3Rw9ehTf95vhshdbRyfMt/b5zkXbNj9ni2qcWHA+IXd0pARicqsbubU0heZF1hAds3WfB3+vKrRG2HRfgd/4rik9XDR99s+DpfFMMsMLyUzo3AvJNL/Us2JWED6+d/ausyr8v8Ih7qsWjqu9IAiCIAiCIAiCIAiCIAjCieakOpSnp6f53ve+xy/90i9xxRVX4Lou27dv57nnnsOyLJ566iluueUWrr32WvL5PE888UQo33KUQqFAvV7n3nvv5Vvf+lbot9dee43NmzfzB3/wB+TzeTzPI5lM8vrrr3PgwAG+8IUvoOs6o6Oj/OM//iOlUoliscgdd9wB0MwtXCgUePXVV7npppu4+eabmZiYaP7D/lNPPcWdd97JH/7hH4bOB/i+z89+9jNuvfVWtm3bxtTUVLPOT37yE+6++27+63/9r1iWxbPPPsv4+Dj9/f187GMf44tf/CK7du3q6Lq+9NJLDA0N8fnPf765poXazrdWQTjd0FT4j6xmiFtihKxmm/lF1KCdqzdj97b8GslV68WIdyqoM/ubfpwipQqLyfFz7izUcae/z1cvGgba9338mJDXajY3rmq6mPW2fhYLS6xFxoqdX4sDN5iHioTyjl1pxMUc61SPtuwkvHbMUO6s21fTtJA7d//+/SSTSTZt2kS9XsdxHDzPQ9M0kqbJ+Pg4IyMjDA8PU61WqZQrHD58mBUrVlGtVknEpCxY7K4qpdryLC/0IkE013AofLUfqdP6G40Q1vOJyU03ckteZM91Q78157kEgTs095DDXcNr3UeBuIwWfqlC10IvACz1ZYsoX0/3cV1MmOtHUl380MxyX63IPbUiST/qsV6YpO/xu9VpbrfKfC3bz8tG6rjmJwiCIAiCIAiCIAiCIAiCcCJQ27ZtO3E2rTOUm2++mZUrV/LAAw980FMRhNOGv/u7v+POO+9cUpvbb74i9vxC4lMnYppDu5g2JxY30Nz2vgMBcb42QEfiXkioVaop3EVWsmC/0f7mI9qstWY0x+zcl/awyioyZ1rE/rjcxK0Cc3BOm2+80AS1tt+joanjVxv/8kGoXTQceUzI6+gpL2aaiZbQxLZtc+DAAdauXYtSCtu2GwJyMtmMWFGv16nVS4yNjbF27Vrq9TqJRIJdb+/GcTwuuOAiyuVys34r7jwvALQeR8Ozx4b7Viok3LbtZWgTlDXPDX13faf5PSoCzwnKcyJyqyO5VVDW/Pg+FluDp7eLwVGHsu+H6yitXVDWlMIwDH747KttY3TCgOcuGJ56hefwlcpUrPDcKY+bOf403SdhsAVBEARBEARBEARBEARBCPHYY4/xhS98IXRuuuBRmhk/oeOcVIfymcBdd93F1q1befrppz/oqQjCh55WMS8sMKnmf9vE1AUE5eAX3QsE4VZhOexw1Y0Y96rnhRyRrW2aolxEAIqdTTStstYuFkfXpSf0mBDDEae1r7UJdXNVYtYzjxAduDzxW8I7t7ixW0YO9RMNjdy8VsG5TsZWQfsWodqLhJmOEdcVYRezFyOKB63m5tUu1nkRZ3oyk6ZcLpPJZKjVaiQSCbpyWY4ePcr4+Di+77N16xYA8vk8mUx6du0GluVQLs+QSOhMTU2xfPlyLMvCsixs28ZxLXK5bnzfBTw8LybsdUSUj7wRAMTkWdbb/6r3Z/sOrobnum11lD/nRoZGDuVQuOyWnMo+Pijwm3mVPWgRj0MicyT/sTs7Ec/3m279tnczjHbh3Ii4jz3aQ177RJ3IeoxDWV/U+b0Qi4m8Y5rB7+eG2OrU+Wplmq1Obclj3F4vcZ1V4a/SvXwn1X28UxUEQRAEQRAEQRAEQRAEQTguRFB+jzz66KM8+uijH/Q0BOGMYiHX8fGGro1z0IaIETw1TYtU8UN9dBp2+kTNeaHhWkNWt352MsapmIN9KWuIa7fYufkoFAp0dXVRrVZJpVKUSiWU61AsFslkMqxZs4bx8XFs26a/v59MJsP09DSOYzE4OEgmk+Ldd/eQTCapVqvU63Vs22ZwcLAR5towlzSfTurGudnVEiXUaFjr+erEfS6lr2g+8ebatHZB2W+oz82i/HAI64VCW7ceR5/jk8Xrhsmvdi/npnqZr1anGYh7YWABunyPr1SmuMmq8PVMH68b7SHRBUEQBEEQBEEQBEEQBEEQTgYiKAuCcNqglD77eWIFTk3rQAiLEZ1UtJqvQi7pIADxkolbXkwI4IaYGPwcPm4chB3K0es2nyA7n6gXbr90T+dSxd+gTVwI7cX7jYbFjllny6lGtzHO8Mj3vt5eKpUKuWyWUqmEphQHDhxg8+bNjI+Pc/jwYQzDoKenh8nJSarVKpZl0dXVx+TkOAcP7qentwvTNMlmsxw5coShoSHS6TTVahXHbrh4NU1bNKx5a9jqaI7rMPHu48Xw5hGIm5+05z4ORl8w93HUNR/MSbULwlHRN+RMV1qonU97OOsgz3KzL19r61vp728Y6R+aWZ5JZrinVuS+WoGuJeZX3urU+NviYR5JdfOn6V5m1PsjiAuCIAiCIAiCIAiCIAiCcOYigrIgCKcNCn0ecXOOTnKvtrVR4dyw8YJyXMjryFgk2kQ1jbALMS4Hb/ucY/5ojtTRUCGRtBFy2Av3FxPyuhNnb0eCcvS6vpeYwQsQFcM7EZgb8wuLdF5LePJmu6ibPG4NWrj/mfw0mUwG16rjOzZvvvEGt99+O0ePHuWcc85hz549KKUYGxvDsizy+Tye53H48FG2bDmXjZvWN4XmgwcPctFFF7Fv3z6SySTr169n9zt78TwPx5nfvdq6jrjr1baEOL0xshFj+4q8oBAnKAfHTfHbX9yhPDev9okFoaqDPNBBzmNNM0L3WimFmj0XFNdX7Xs1KigT/t44d5I27wJYSvHNdA+Pmzm+XCtwd6245D7urhW5qV7ijzP9PG7mTsIsBUEQBEEQBEEQBEEQBEEQGoigLAjCacNiYvJiv72XNnFCsJEIOxs9V6FoES1RaH4kLHaMuBft2o8bjKh4HRXC/BjbaViAi4rKwXHbfDoQlI9Hgjteh3J0bp2FFY+6PtvbKW9OWG7kYp5fVG1+9xxcx6JQKJDJZLjs0ospFosopdixYwfpdJparUa9Xueiiy5iz549ZDIZNE3D9Wyq1SqmmaS/v590Os2RI0fo6ekhnU5z9tln886uPY28wq67aCjmxRzmzSsRkx9Z8xfP7e0xj5Ac41AOPlv3/3xo2tyLDtGQ6oGgHOj4mqY1zmnJtvX5WtjJrLdlj56rM9tw7obOhsn2AU3/4By+k5rO1zL9PGLm+EplikvtpeVX7vI9/qg8wd31El/L9vO2nly8kSAIgiAIgiAIgiAIgiAIwhIRQVkQhNMHLcid2qCT8MedCcxBKO3ZNoDvuzE1ooOFxbO4sTyViPTTXseNhLzVtJh1YYe/+3M5k33fB6XQvURoPo12/pzwhw9qzlnq+z7K0NuEQi8Q+YJww77XJir7UUEyZs5e270It4le40Y/ESEyKl4C2uzdmLvucU7dSL+tEnhw3VSLMO6DpiVxZ8VXTdNwXZd0KkG1WqWnp4djx45hmibHjh1jeHiYcrlMb28vr736KmvWrEHXNKx6na5cjmKhwNEjR+jt6QHAcuqUSiVs28bzPOrVSTRNw9B0apUqe8YnOPfcc1lz1ioq5Uae5lK52n59VDhXttfcsy2irBfdizFCK4vn7/W9iHM/cCwH7uHAjdxSfOXPzk+bvdBuKGy17/v4eCgUKgjVPPsZiMlxJXiOogJ0a53gurTuVS+6L1XMCxJxb3m8z7ytJ/nNruVss6p8tTrNWtdaUvutTo0HC2N8M93LN1PdEgZbEARBEARBEARBEARBEIQTigjKgiB8qDhR+ZWX0s9iIaRPxFht+Y9RzXGb42tz4jK0HPtzxw1Bb1ZIbc1Hu2D46IYcq1SLLBupG7f69v4W+97eU+ycTlKE4pAD2/fxvEY+Y13Xm47hgwcPsnbtWmzbJplMUiwWsawa1Wq56aZ1HIfBwUEqlUojPLbr4nkNAdd3PfzZkNaZTIZ6vY5SinK5jO/7JBIJbNvGsqx2VTxuzk0Rde5cjLwe0zKSYzrW6Rx2H/vR+lHRvsPw0VrLiwVKKZRuNI8DUbl1byqlmvu99Vw0nLXvRfZwRHyem2P0+MTmZH8vvJBM80vJNPfUivxWNb/k/Mr3VfPcXi/x9XQfPzSzJ2mWgiAIgiAIgiAIgiAIgiCcaYigLAjCGUdnYnFnIm84jHIH/cbobQuJXs1mEUewpoVF4Lk8ya3i9qyA7IeP58qcGB1dS8j5GYSP7iDkeGgdbSGkOxCUo0J1ByGdT5TCXK2VSaVS+L6PbVuk0inq9ToApVIJ0zTZsGFDMyS167r09fUBPomEgW3bgM/U1BTr1q3D8zxqtRrZbJZ61WnkcfYb+a/HRg+xbt06HMtm2bJl7HjzLZ566ikuueQSLMuiXC5jaIm2OUZX6qs5oT+4Ll7k5YC426XaQoK34/te2L2uze216GSi+amVariRvdlxVIuHWkUEZZTePG7mUJ6rDEphqLkcynGCMoRzRTevRWRNgUs6ejVONb6T6uZxM8d9tSL31IoklyAsD3gO/708zu12ma+n+9int+8jQRAEQRAEQRAEQRAEQRCEpSCCsiAIHyqiYmNngmRsTx2NFXYnL57fN3bsaFTe2PlFg27PCUzNcTpYVycO5SB3b/Dpq7A7tFPaI1EvXVCO7ZfoPe54SguiaWDbdTRNwzQTgIdhNP6arFarTE9Ps2bNGur1Oo7TcBwfO3YMHxfLrjXapRIYhsn+/ftZvnw5O3bswLZt0maSWq3WFEyTuoGOoiuTZeLoMS7cej7/+vyzVCoVfN9vhtxuW3t0f6vweaVUm6Acn4t5cYcyeOHzfrhu9D7MzaNRGqKv3nI+6EcLn5t1KIdCXrf0p5RCV0boO4CrRVbit+9T5bULscF+n1vbSbK8v0dmlMafpnub+ZWvsypLar/NqvCQXeM7qW7+MtWDdYKiNwiCIAiCIAiCIAiCIAiCcOYhgrIgCKclUUdt6+d89eejVUydqxebNTlCWKxSym/qoY0+aXctx4ZwjjhK/RYxd9YNS0suWN/3UZoONMIyB8JssI7Avex7TkjyU0qhgjy2QWn5LVSvVUQOwhC31FNE8yG3r0vTwoKoF8nl23qt5tYeFve0ee5DKBRzrC69+IsFreeVUiTMZDNMteXYZDIZfN9nbGwMx3FYe/ZasulGmGpd1/E8j1QqRSqR4djhKUZGhimV8niuia7r2LbN+vXrqVQqWPUSjlsnm8qilGLZUC+WXaW3t5+XXv4ZN3ziRq7edhWu7eA4Drlcjqpba1uLF1ms1lSUZ9eD38hn3fpigzeXL3m+vNNxz1NjrJZ95wb7dPZFCuWF2gcTaZ2vjt5exzdCe6yZB7p13806kgOR2Q1c9I2KDZF5nnsZuj4RMT0u33mrE/tUZEwz+P3cEJc6Nb5SnmLzEvIrJ32P+6p5bqqX+FpmgBeS6ZM4U0EQBEEQBEEQBEEQBEEQPqzE2ZYEQRDOKE5U3uUThed5zfy9MOfcbKWRlzfsUg7qB+0D5ltfp7mbOxXs20Jla+ESVycqXvtKD5Ug5HFrabqrZ4tCX7zEjBfkPG4N19zT04Omac1Q1++++y5btmxh9erVDA4OMjExQW9vL/l8vhHKul4nn89z4YUXMj4+jq5pJJSPU6vg1CpMjx/Fs2qkUimy2SzFYpHdu3fT09ODUoqxsTFc1+Wf//mfSSQSKKVwHAfbtueE3dn76bpu+GWAljzPwWdw3Hq+tUTbx/U3X724lwoWKpoy2o41ZYCuhYphGG1F13V0XW/eo+A4+DyRz2ywF051XjZS/ErPCv4gu4xJbWnvA67wHP6idJS/KB1jwGt3vguCIAiCIAiCIAiCIAiCICzEqf8vqIIgCO8T0RytnQigre3m6zOuTSfzCIflDf8eCG2tTuaAQHjrZI4drdHXQqXdGzoPkXbtaIBGI6du47OT69WoN3+buHad3M9UKkWt1nAF63pjjYODgxQKBcrlMrquU6vV6OrqYsWKFRSLxYZ71nUZGRpGR9GdzaFp4PsujmNRqZQAD8+2wHUwDR1cB8uyqFar/PznP2fjxo2hPNaB+zl0KRdw0Ibc2vOUxYjWm2/fLbT/FxPto5/NEidGt9RtLUu9x50QCOmnCz80s9zSs5K/SvdiqaX9b9w2q8L/KxzivmqB5CnsyhYEQRAEQRAEQRAEQRAE4dRCQl4LgnDG06k4PF+7VjFwKWMFRNsZhhES+Bohf73QGEqpNhezbdu4rtt0ccbNcbGxY+er2vPgRkMmx+ZD1iMhrtvGmhPDlZqdSzREMQ5t+HpzSN/30ebN5dt6IqaflrpKKer1Oul0Gsdx8DyPgwcPsm7dOnRdJ51Oc+zYMbZu3cr+/ftZs2YNhUIBTdMYHBxsuGo1jeLEFEZSJ6E52LUi5eIEfd0pKmUL27bp6emhr7ebmZkZzjnnPGzbZWRkhLfe3NHMy5zJZBpO4ZbcvsG9b1tXjHPYnycncMgJ7rttvzXO+y11oyHKIw5l1R7Oej5He+s5L+pW9/VQW6VUM8R7XF9xYatbzy+VpYjPpwqWUvxlupeHzS6+XCtwd63Ycduk7/G71Wlut8p8LdvPy0bqJM5UEARBEARBEARBEARBEIQPAyIoC4IgzBInXi1EoF8tJCYfj1AVuCUDYdgwDBzPDTlRW+sYhkEqlcIwDAqFArVarSG0diB4L+b0nH9N7Tl4o7iR4ebzNbeKpV6b4TIu53SL+KkUKi6JcuycwwTXUCmFrutYltUsiUSCkZERZmZm0HUd3/fp7e3l0KFD1Go1jhw5wsjICBMTE6xcuZJ33t7Fsv4B/u3FZ1m+chDTNAGoVmbwPZtkMkkymaRWqzE8PEzdcnj44Yf5whd+lb//+7/ntttuY+zoEZLJZOPeaRqe57bdi/Zc1e3iajObcUTQbS1RR26sIDxPGOjotYxzKEf7CrXTtVDIcmP2f0Va67u+N7vesIi+kOP6eF6YWKzPU51JTedrmX4eMXN8pTLFpXat47ZrXYv7i0f4oZnj6+k+JrUOIw8IgiAIgiAIgiAIgiAIgnDGIYKyIAinDy0amI/fDNrfKrRCvDjWej5aJ9o+EPQWR2tpA63i51J05EAUdm2HhK6Ty2Uol8sNB7LrYiQNyjNFggW7ro87K3AWi0WMbJa0mULpWjNscyKRwHWsZt7dQDQFD103cBxn1hHb6LMhYM7mzlX67Foai9BarlXr2hcT4KOXcD7h1/d9FLPhnomE8FbJUD2Yc+DOOXLbHdTB0E2BP+xJTAAAIABJREFUEi10Tz3PQ9cdXNfFtnzOu2Arzz33r2zetIFSqUS9VkXHo1ytkkgk0DSNo4cPY5opkskklXIJHAvl2hhmksNH9+I5Fudt2YjmufQN9POTF19k1VmrKRSm6e0ZpFqtsnffu6w5ex3DwyN0d2V5/PHvY3s2+VKBhJlorE3Rtp7mNYx+19rrxN+vYE8G/au2Z0ARfYnAC4VU1/xEuF+t/XnzMOY2v1IhUTqoZ7SKzqjg/QB8/Ob6og575c++R6AUbszz2voZZb5Q3q3Hp5tDOcrbepLf7FrOdVaFr1SmWOHN78iPclO9xDarwl+le/lOqvskzlIQBEEQBEEQBEEQBEEQhNMVyaEsCMIZz/HmXg3aztdHp/1YloWu6ziO0xSXgzy6juOwb98+uru7MQyDXC6Hpmls2LAB1204WC3L4q233uLFF1/k8OHDzdDXQKi/xjx1bNtF0wxSqcyc4DobKjuZTDbn3pqjeSEX6ntloXDJC52br2iahq9UqATrD9B1nWQihWN7KOXz+OPf56w1qxgdHaVYLGDbdarVKulUEtex8Fwbx65TrZTRlIfme5TLM3hunf17djE02M+xI0cYHBwEzadQmKavp4tMMkHWTJKfnuTo0aMMLxtmZGiEv/mbb3HTJz9FtVrnhhs+iet4GEYCXTcWLNGcwnFF1/VQ0ZSBriUW/Ywrrb9phh4q6EZb0QwdpWttJTivGfq8uZAX21OtTuJofuhO8kfHHZ+uzuT5eCaZ4dbeVfxxpp+ZJeRX7vI9vlKZ4sHCGFud+kmcoSAIgiAIgiAIgiAIgiAIpyMiKAuCcNqykHj0fnCiRFVd17Ftu+kk3rlzJ67rUiqVyOfzlEolDMPANE1SqRRdXV3s3LmTarWKaZokk0k+9rGPsWXLluY1cByHY8eOcejQIaanp3Fdl2XLlqGUaoTQdhyqs+7baM7mgODcfOvsVOBdrET7XuxcR/1ojTy8vmocx4Vi1jQDwzDIZrPkchmq1SqZTJpUKoWu6xQK03MCddP97oPr4Psuhq4auYg9BzORoFAooJQilUpRKpVIJU0MTQfPZ6Cvh6NHjtDf20d5pkJvTy+7d+9mzZo14Achv3UaeYk1QENTRnuJiMdx4m0w59Z1KqU3P4Pj1tL6+3y/+ZoCXWt+Kq1Rmk7kiKi/2MsHSxWT447jvse1PRPE5Fa+k+rm1t5VS3Ycb3Yt/rZ4mP9cmaLL9xZvIAiCIAiCIAiCIAiCIAjCGYEIyoIgnPF80A5l0zTxPK8Rptp1eeGFF+jq6mLFihUsW7aMrq4ujhw5AjTczIZhNJyws1SrVXbv3s3U1BTDw8O4rotpmnR1dbF27VrOOussstksR44cYWJ8ikOjh5meKlCt1PE8j1wuR19fH6ZpkkgkmrmbF1tn6/n3QicC8nxziDqT5zsXhDEP1ub7PtlsF8lkinw+Tyab4txzz+HY+BGKM3kqlRJDw4N4tkVCU+j4+I4NnoXr2XhuHTwbx6pTnJ7EtmrkpycxEiblcompqUl6u7tI6BqmrjM5foxzNm4gnU7znW9/l/POO49nn32ejRs2YVkW2WxX2FUc4zSOKyqmtDmUO3AxL1Y0TUPT9XCZFZx1PdE8RtdQht789BRtpfW+BJ+d7KUT4VCOE5M9z2vLKf1hYEZp/HGmn1t7V/FMMrOktnfXivxTfpS7azMnaXaCIAiCIAiCIAiCIAiCIJxOiKAsCMJphB8pMTU6cCwflxiqa013pq8pvJamC4nHUYEzaN9aAneyh0/NsfnoR6/Ett1Zh/FBLrroAvL5Ivv27WN6ehpdVyjNJ9eVIZHU6e3rxvMd+nq7yWZSLB9eRm9PF0NDQ0xNTQEN0XliYoJbbr2ZoeFBzj1vMxs2rqNWqfPO27vRlcFg/zI0dHzXI2kkSCVNErqB7znYVg1dA3wXTfmo2eJ5Dg1zqmpbs+YTKgvdq4XuU6jPGNE4cOYGruQ4R7KuJcAP/srzSCYNCqVpjAR05dIkExpvbH+d8847jw3r1uNaNsWpIgMDXXh2ha3nbaRSmMKvVdFsC811qVeqpBJJdF2nVqtQLFbAd0llujASGppuYTszlMpTjI0d48ChPH//0OOsPW8TP/7JT9lwzjk4SpE0s3iqIdjqhoFuGBizwn5biYSkNlSyrWgqiSLRLLqeCIm+USFY1xMoPQGa0fwkIhTHtVd6o6DpzePW+xPd/837Pvv8eApcfJRqye88W4L6vu83BN+W5135/qyHu1GU7zfPBcfK98HzUP5c0fBRvgee2zyH56LcRvmwMqYZ/H5uiN/sXs7benLxBrN0+R7/uTLJ3xaPsNm1TuIMBUEQBEEQBEEQBEEQBEE41TE+6AmcCvzGuv3cMDLBMrORN3C8bvLU4UG+sXfNBzwzQRCWyol0zrayUMjd90p0rus2bOCZ557joosvYGhkOe8e2M8555xDMpmkWq3y05/+lE3nnoNlzYk8iUSCarXKO++8g+u6pFIp1q9fz4UXXsju3bu56KKLePbZZ3nttdfo6+ujUqngui6Dg4Ns3LiR119/ne7ubsbHx+np6SGdTmOaJrVajaSZwXGckADszYbCNgwDz/NmQzW/t3XHEXetj8dB7nleI9S365JMJiiVSnR1dVEpFUklk9TrdQw9ya5duxjo62fdunW89tpreF6NVCLJjh072LBhPYdGD1CrVZou32KxhO+7TE8XSKcTTEwco5ifoiuboVKvkTEzjE9MUi5Z7Nz9JqmMyWs/38Xwim6uvvoaijNlzHQK23ZxfGt2zc3Zt6+n7QLF1IleH7/93THf9wMVF5RCi3luold+rvr8oc+jEzze5zDqJF4oF/J8zuPG+XbncbSOdoL/rDhVedlI8Ss9K7i7NsOXawUGPKejdludGg8WxvhOqpu/SvcuKTezIAiCIAiCIAiCIAiCIAgfDs54Qfl/XvwGPQmbIbPe/MfzIbPOVcum+EjvDL//8498oPMTPnycf/753HHHHSQSCXzfZ2pqikceeYRCocAXv/hFBgYGmnX379/PAw88wPXXX8+VV15JMpkkn8/zxBNP8M477/ClL32pkYN1lsnJSd58802uueaa0Ji2bfP973+f7du3v2/r/KAIQjW3OhxPFO2i1skRosYnJ9i4eRNDw8OMHjrAO++8w2BPH8lkEtu2ufrqbfQNLuOZZ56hUCiQSCQAGB5exsaN66nX67iuy65dOzl8uIuzzz6b1177Bbfe+inuv/8BbrrpJvbu3Ut3dzddXV3k83kuvfRSCoUCF110ETt27GBychLHcejp6SGJQTKZbIqyALbTEKOCcMG6vnRBuROOV5CMupt1Q6dSqdDdk8MwNM4++2xGR0dRSlGvN0J/+77P4OAghqZTr9e54oorePmlfyPZnaBYLLL7nbfp7e4inU4DUK9bKOXjui69PQNMjNvouk69PEO2J42ZzFKtOew7cJS6neD8CzZzcHSMT11xBXtG9+P5CjOdwfE8jKSJa8+Kn818zZ0I7u11onteaTH9RHJjK7/9unkRSVkRFo/9mDZtAnfs0JEcyO1VOsqZPF+469Z6nQjKnvfhzaUcxyOpLh43c/xGrcB9tSLJDnMl31MrcrNV4evpPn5oZk/yLAVBEARBEARBEARBEARBOJU4owXl31i3n56EzVnZaugftH3grGyVA+VGHXEqCyeaiYkJvv3tb1OpVLjtttv4xCc+wWOPPYZlWTz88MMh4Xf58uVs3bqVRx99lJ07d3LDDTdw4403Mjo6CsCTTz7J888/H+r/ySefZGRkhM9+9rM89NBDHD58+H1d3wdJq8h0Il3K76dDWdM0hoaG2LVrFx+94jJGR0fp7e1ldHSUoaEhdu7cSVfvUW655RZ27tzJ5OQkvb29eJ5DuVymXC5jGAb9/f0kEgksy6K3t5fDhw9z4YUXcvDgQdLpNAcPHmTVSsXZZ59NPp+nWCw2X1BYvXo1R48eJZ/Pk0onyWQyKKVIpVJomobnN4RUx3Ha8i0f77rj6MShvND9COoGjmIAdza8sWmauDaMjY6SNA3q9TpmKkG9XsfyXIaHh3EcB9M0MQyder3K9LSDbdtomka9Xm/kYs6lSafT+D5Ua2U0HQyloZROrVrGR6daKzOdHwfl4HoWa9esY6ZcwkxnsW2HhKm1uLwXX88iVy3SZuG+lFKttuhFx1r4fHQvvLdQ0ktxKMcJynH9nakO5VYspfjLdC8Pm118tTrNTfVSR+0GPIf/Xh7nbmuGr2UG2KcnTvJMBUEQBEEQBEEQBEEQBEE4FTij4xbeMDLBmmx13t/XZKvcMDLxPs5IENoJxKs1a9aQSqV4+umnefDBB6nVah/01E5Z4vK2nk7oyQSF0gwrVq/i/gce4NO33874xFE2blpP0jRIZ0zS6TS/+MUvOHz4MLlcDgDfd/E8h0wmhWkm6OrKMjk5zsxMgVKpyKFDB5s5bnO5HJdffjkTExO8/PLLAFQqFfL5PK7rks/nMU2TZcuWsWzZMkzTpFKp4DgOnudhmiZKqdNmH5bLZQYGBnAcB8MwKBQKKKXo7+8nm82yfPlystmG6zKXy2GaJvv27ePTn76dJ598kmPHjrF27VrWrF3NsqEBlg0NMLx8GemMyQUXXMDo6BiGAaOjoySNhjD89tvv8OaOnUxMzXDlVZezctUQX/7yr/Ozn77Ibf/udlKpDPW6he061KpWW27tIM/wgoX2onQ9VFz8ttKaD9zXVLxwG5lPW+7qSK7kOAIHe2uJE4EXK8Hc4o6b841p18nYrus2XzI405jUdP4gO8ivdo/wciLVcbtL7RoPFQ/zu9U8yRP8go0gCIIgCIIgCIIgCIIgCKcep5RD+cnrX3jfx1zon0F9YJlZ57/9t//2fk1HOAX4L//lv5z0MQYHB/mP//E/AjA9Pc13v/tdAJLJJJ/5zGf4zGc+EwpT/d3vfpdPfvKT/Kf/9J8oFos88cQTTE1NAXDjjTdy4403AvFu5Q8Tyg+LPn5c3l7Pxw+e7EjY48CFGaSUDVzM/mwu0bBjM/y+TateFhz6tAtabdOJ6mwxIYqjYYETmo5pJLBrNjdefxMv/Oin/PLn7+F7jz5Cb3c3ZiLB1PRh8tNlrr7qOg4c2I+mgWX5pNNZCoVpNF0xNDzAvv2NvLyaZjQEQM/lrJUreP7551m+fDlDI4MUi0VqdhllePQNdrP/oEF/fx/T05N4nottN5y4Y2OjpNPrse06iUSCnu4uSjMFRpYPMTExheu54GvNq+IrvyP3cig8tc9sTubGuWSiEWrbsix832+4jFW4rWmaeJ6H4zjNvM5BfmmlFJ7nkTANlObT3Z2jXqniuTa6lqSnuw+l68yUKijAc6FaszCMJAqdn7/6Cpdcdhl1q4aRTJBMp9F1HQ3QLRvPV/z81TcoFsv09qfY8dYezvn0tUwXyuzacwRNhy3nn8+ON9/i1n93Cy+9/HN+9b7fZN/oWGP/6RpJLdnYO7N7QxHsw7jcx+FnQIsLZx1B19r/qlete9UHTdebW7O5ZyN71Wup3z6vIEy3F/4e896aTzjEcpyrWJu9Br7ntVZs1okTl4MQ3fPlVJ5vrBMdceB05HXD5De7lnOdVeErlSlWdJBfOel73FfNc3u9xB9mBnghmX4fZioIgiAIgiAIgiAIgiAIwgfBKSUo3/j0tvddVFbMLyor4FjdfF8ERuHMIgh5vXXrVs477zyOHj1KKpWKDXmdTqcplUp84xvfQNd1rr/+em644YYFQ15/WGkXh9pdhR6tAuWcMBwKgR196OMEpQ7yvC42v9h2cUPF9FOvN0Rb27bp6enhf/3Jn/E7v/1bvP7qaxweG6Wnr5f+7mU89S//xLZt28jn83Rnc6RSKVy7jue5HDk0hqE00maSmZkZNE3Dd33qtQqrVo5QKBTYsHkT5ZkSx44cxbIsdu96B991mZ6cwkjomJkU9WqZnu5uzISOXa9iGAZ60qBWKXFkbBQ8h76BZYCO6/hommqIoVojvHQg6nYaGjvIL+55HvV6HaVU6JymtV5rH89zZkNaN8Ypl2dIJMyQQ9UwNIrFIsmkgQaN66EnqFZ7mmGtHcsilUrNidCJBLUSrFq1ild+/jJnrVyBZiYavzsuKI+61cgzPTNTYd26dVTKRWxf4/U33mbV6lUcOnSIer3MR6+8gp+/+jpv7tjJRR+9kUJ1ztkdOH07y2R7atNJaPj2FzHa23sLhLCez8Hs0S4yt83Bi+nvPYbl/jDxTDLDTxJp7qkVua9WoKuD/MoDnsNflI7yQjLD1zL9jMW8wCAIgiAIgiAIgiAIgiAIwunNKfevfjc+ve19G+s31u3nqmVTnDVP2Ov95TQ/Ge9/3+YjnHm8+uqrbN26lQsvvJCdO3fG1tm4cSPXX389TzzxBHv37qVUKqGUauaDPZNoE4fi9Cql5pzHvt9Uq1T0fMs5Ws5H27X2u+h8ThBBaN5WIfbyy6/gqaee5tqrr2LnjjcZGFiG7zn09/Ww/bVf8PnPf55//ucfsmnTJlJJk3x+uhEqffVZKDwMXaHrGjWrjsInm0lTrZR59ecvc+GFF/Luu++SNhN4jsVAXx+VSgnHsXAVWLUaVjKJ8n0MTSOh6/iu2xC863U8x6FYLJJKpfBReB5oWsMZG7iGO71WgejvzTpTc7kcjuNQqVSAhovfsmpNcdp1XRzbxjAaf50lEgmUaeK5gN94MUj5NHI9K0WpVKM7m2u081yOHjvcENo91RQgo+GbU6kM+ApN03B9Ba6H74MyEszMlFm9eiWvvvo6524+h+GhAd7csYtzz7uQJ5/6EaZpMD5xhCs+einPvfgi5194KdMzJTRj7q/fuRcdFr9G72cY98Xu2UJzWeqzERWOFXPhtIMXA4LjpoAcc47Wz5jnWGsZSwXH4lAOYSnFN9M9PJLq4reqee6pFTtqt82q8D27xl+mevhOqhvrNE05IAiCIAiCIAiCIAiCIAhCO6ecoPx+8o29a/hI7wwHyo18ycE/KSsaYnLBTvCNvWs+yCkKH3IqlQrbt2/nkksuYf/+/aGQ1wD79+/ngQceYNmyZXzuc5/DNE1mZmb4wQ9+QKlUAsIhrycnJ3nggQeYmZn5wNZ0MomKVMprd8/5WiBCBaGrw6KG7/ugzQlVQSjsVjG5NTz2XMMO5nOcAkqc+BaEbjZNEwDDMFi7Zh1/9Vf38+9/58s89OCjDA33MDTcRyqV4f777+euuz7DSy+9hOvaLFs2SLlcpqenh0Nj+ykUCgwNDdLX10OxmKe7O8fo6AGUZmBoGuvWruXAgQPseecdPnnjrbzx5nZMM4HrWHT35Bq5mbMpPN9phIdWinRmOQODfWRzaZSRQCmfTCaDZTnYto3yNDzPI5lMomlaUwBciMDRDGCaJqZpkslkGBgYwLIsKpUKrl0FryFop7IZlFKzQrOF67rUylVMMxu6rjXbJmEYeJ7XdH1PF6aZnJwgl+1h8+ZzeX37K1iWhWEYGEZDDE+lMuh6gr6+ATxvds9oGprvg+czUynT3d2N67qsWLGCmeI0y4ZXs+/gETZtPAczpbHpnNVMTI1z5VXXcsHFl/Lu2AStwYE/TCGXj8eh7MW4hr2IaBwVj+NCXqvId4iEzAbcmLFQi4d3PhOZURp/nOnnEbOLr1an2WZVFm2T9D1+tzrN7VaZr6f7JAy2IAiCIAiCIAiCIAiCIHxIUNu2bfvw/Ev2cfIb6/Zzw8gEy8w6AON1k6cOD4qYLAgnkb/7u7/jzjvvXFKbT3/yisiZOIGy4Vxtirt6+HsoX2/ghm0JeTvnFg2/b+PH5KrVFwgHG/TjEB7PiMuh3Na4vV/l+SQSCZLJJAcOHGDD+jXoSuPN17ezYsVy8HzyxWnWbTyHvsFBXnzh39CVhaE0rt72MbZv386RsTH6BrswTZOxsTEKhQIHDh7mgksuYMPGjRhJk6mpKYaXLceyLA4ePNgY09BZvnw5e/fuxfM80uk0SilWrl7Fu/v3Ua/XMZRHX/9yLAuy3V2AR7lYxnEccrkcmUyGUrXWDGGtaY1cy7quN8TnWde9Nysop9Np9uzZw0s/e4ViscixY8dYtmwZ5XIZpfmsWLGCjJnknM0bOTx2iNHRUTKZDFu2bOHSSy+lVK5hWRa1uo2eMMH18XwXpXwsq4aZSlCr1chPF1m+fAXnnruFV15+oSlom6bZCB/u2PRlDfbt3Uu2pwff0HBdl0zKpFQssGvXLj553Y38wz88zuWXX873v/84v3LPr/HX//svMA1YtWY1V137Uf7lyRe551e/yL6DY2T7luG7dts9jt51P3avhGvpMS8xuDGu3CjtL0PEVIoQfWHCbZqB40Vk3/ebb621nvd0FTqnue1CMBEB2VXtIa8VXlhgdtpDV0fD4vu+ausncLs//9O3Fr8IZzDbrCq/W51ms2t13OaHZo6vp/uY1M68iBqCIAiCIAiCIAiCIAiC8H7w2GOP8YUvfCF0brrgUZoZP6HjiKAsCMIHwvEIyrfe+NHImQ4E5RaionJTUJ4VcENiM4lQ2zhtL5r1Nlas9sNjaSyu3Cmt/Y9lQ2nUajWUUmQyGSbGjzI9PclXv/If+KfHn+Dgwf0MDAzgeR5Hxie4+OKLyaYSvPLKK9hWjXM2bsKyLN58azszMzOcddZZdHV1MbJyFf/y1L8yvGKEc849l77+Hl595XW2bNmCbdscPnyYcrnMwMAAY2NjTaF1ZmaGVCqFnjDwFQz29TI9XcGyQU/qJJMGGTNDsVikt7cX0zSp2Q7VahXP83Bdl2TSoFwuk06ncV2X4eFhLMvhX//1X9mxYwe6ruOjYRgGQ0ND+L7PoUOHWL16JcNDQxw6sJ+z161h9MBBHMdhaGiIfD5PqVTCsiy6u7v52DUfZ2TVagpThcZ1NAzGx49iGBojK4ZwHIcDBw6Cr7Fh/dmMj49TLBbJ5XKN+6Ugl/Q5evgwiXQa3zDQdYWOwnVsDh06yMjgciYmJunr62Ns7Ai73t5LSodEQuPKq7Yxnp+gq28Fud5BMBKUazZmItF2j9t3c5wU7C2xRmeCcidZwqNttFmRsFWgbavb4hBu1ouIw6pFmA5KIJQHzmQ3xpEciPLz5ktu9BCZn9Y2z0BQ/tG/7Vj0Gghwd22GL9cKDHidObtnlMY3Uz18M91zkmcmCIIgCIIgCIIgCIIgCGce75egrK9evfr/O6E9CoIgdMCdd97JQw89tKQ2G85eju97LcWPKY3foHGs8GG2BMe+5zbqeS74Hr7XCF/MbBhjNZt7F99rFh8v9J2Y8YG244Z6RiNPc4fr9KPOy9k8r7quo+s6lmWRzeZYuXIl999/P+edt4Vbb/s03/7mt1i7Zi292TTF6Sl27HyTyy+/jPzUOKMH9tPbm2Pj+rM5emQMz7XRNTj//K1ccuGlPPkvT2FZZQ7s28vmTefw8ks/Y3hoGYMD/ZRLM+SnpxqRwn2PaqWMbdWZnpqku6cLQ9eplGvYtsfw8lUkzEaI60q5TCKRYHx8vJEL2XVwXWfWDetTr5ZR+AwO9FMs5PnmNx7gn/7fD5iZmWmGnk4kkhSLRbq7u8lms2QyGaYKBWzLoVKt4Pk+mtLJZHO4nk8qlWZkxUo2bVzPwEA/O3ftZHxigvPO3UI2m8VxHPr7B3AdmJoex7Yd6vU6uqHR3dVwb/u+j+M46LqO6zh4dg1NKRzPw0NHN3Qcy6FartDd0wOuR6lU5uDBg6xZs5Y9b+8mn5/gjl+6jUQiyTPPv8C2j99IqVzFMMyGUB4jfnpt5+J2jL9oDRUpsXvsOATlNjwa4eJnn53Gpzd77DVDyQc5kJu5kOceydnY9LPPK95cCZ5R38Pz3Mb3loLv4btec1zfazjQw38+xP0Z0XKdlAq9BHLg0MTSr8EZyFuGyffMHCjFFtdCX2TvmPh81KlxnVXhbSPJMe2MzrYiCIIgCIIgCIIgCIIgCCeUz372szz22GOhc7W6j9VBCrulEGdcEgRBOCWJF5CPv8zXZ0P8ckOloZ5FS+fzW8r851t7IMppmobjexw7NsHw8AiP/+MTjB48xGc/+1l+8cpLVMozFPJTlEollO9iJg10w2dy4ggaHh857xzKMwWOHj7Ejh1vYiaTrBpZQU9XjlqljGPVWT60jLHRg+SnJunKZsimU1i1Kv29PSjfQ8MnmTTwfZd6rUIikcDzPCzLoqenh56ePgYHB/E8j4GBARzHQdO0ZgHo6emhq6uL8fFxHn/8cer1OsPDw0xMTKJpGsuXL6e7r5eRVStRho7tuVStOr4HHoqZUgVohKAOwoHncjlSqRSlUglN07j22msZHBxk9+7deJ6HYRjomkFPTx+ODYV8mZUrVpNIJCgWi3ieh2maoXzPjuNgGAau2wif7LkN52yxWCRlpjl8+DBTU1PMzMywc+dOUpkMQ8uHSGVMXAUDwysoV2okU5nmSwHHuw862XPHw/E8P57ntRffwfMdfNzG8ez51v3bPp67aPE8B89zQt87XVdAa2SC6PHx5j8/U5lRGn+a7uWWnpX80Mx11Gaza/G3xcP8UXmCrgXSBQiCIAiCIAiCIAiCIAiCcOohgrIgCKcNPm64xAldkTqubeE5dqgo3wsVfD/kqowtrtdW4gS16HyU76MBavb4eMXvILev67qNnMPoKENn1erVXHvtdTzz3LOUikX+95//Be/u2c3Bd/eyecNG3n57J65r85Et51Iul/jZv/2UlJnkkzfewEB/H3t37eSN7a9zz+e+wM633qBaLvL2jrcwEwaOVefI2CGSySS2bTMwMMD09DTpdJre3l48x6VcnCGdTmPbLoaRpFqpoSmDarXK5OQkrusyPT3dyI/sOSjVEKKz2TRd2Sz5qSl++sIL9Pf2kjQa7S699BI2bNhAT08PlmU1y8zMDLlcDjOdYnBwkOHhYTzfZ2hkBf3LhpipVKnULWo9//4GAAAgAElEQVS2g2XX8HyHUmmGkZFhBgf72b59Owf2HySfL6KUzrnnXEg200tP9yC5bC+2bVOr1UilUphmw0kciMuJRGI2/7PeuNcuHD06Tjqd5ujRo9i2zcjICG+99RZHJ8e5+KOXQFLjxZ+/xDWfvAmlJ6jUqsyUS9Rqtfj9faJelPDC5USJ167rhorvhEvwXITOh17SmD12HHzXbZZov67rtj9XccUNF1R7CdYW5O1uFZCDlxtEUD5+JjWdP8gO8qvdI7xupDpqc3u9xD/lR7m7NnOSZycIgiAIgiAIgiAIgiAIwolC4g4KgnDa4LtRV9sCLrdAIFKNYLpKqean63sNAakpNuntY3UgsinfbWsTtGq2nhWs5qbVLlx5Wnh8vW2dDdzZ85oGrlUiocB1LCqORVdXF+Waz82fvoMf/vM/ksmk+J0vfRnbrrJ+8zpefulV+vp6GOnv5/UXn+GKqz/GjZ+4lumpAj/4wb9QzI/ze//+P/DWW2+x483tHNz3LsuXD5FMpdjx+ls4nk0iYdDT38PgYB+VmQrnnreBN97aQTrXRd1W9Pb2YdWruPUaVrlOoVAgnUliphJMTeXp7u7FqdfRlSKX7uHhhx+kZllc/6mbeOqHT6FpCe6649P8+MUXGFm+knyxSDqdxnMbgnp3dw/5fJ5U2sSy6yhDx/FhbGyUVCpFX18vExMTFIt5TDPBO3v3c/bZJbLZLBdfdAXXfPzjlEoldu/eTd2pYdbSXHXNVXzn23/LnXf8O17b/gssy2JqaoqNGzfw9q4d9HdnqdoWlmuDX8fQbYyUSYU62e40CaU4eGAfF110CVa9xkBPN5u3nIeup3nin35ErqeXUr6Ab2SaOYdhLjdw6z6Lps72VbsLN5qD21ft+zS6d31UyBnc2EPhc7of1zY8RxXptzVEd1DHjXzXZvMch+cTzqHstaxzbnwt5IIm0qax9tnRVNBCD9UJ3PytbmRDm3uPLjjvx+QsF5bG64b5/7N35+FyXeWd779rjzVXnfnoTDqSLMuSLduyZTA2ngAzx8EQYhwSOpBOIEk33ZeQvjfdT9Lpey99n/sknb6XdC7k4UkT6EDHgUAwhGDm0eBZtizLmo+kM081V+1xrfvHGXxOnZJ0rBjbgvXxsyhV1d671jm1t/Dj337fxXty/bzBr/PBZpGBC1SQZ5XkPzQWuCuo8yepDg5a7os0U03TNE3TNE3TNE3TNE3TLoYOlDVNu2SsbUMM7cPZldeVUuveX3m+Wjm8JmRSa9qvrrxuGOsbOLQLmEXLNq37rOzXOpc2G6172vpztt9n4zbFSpF3/eq7+a3f+V2mJ6f47Kf/B/V6ld/57fejVMz1+67h5OQZto9u49HHHsR6+lGGhvbw+jtfywNf/ybEHv39/UyMj9Hd28eBAwfI5QoMjWxheGQ3QlgkUjlqtRpuV2ap2thMQByBjAn8GiiberPBZZfvwLQNqrVFxsfHuWLXlQRBiWTSxQ9Cfvj4w5w4vUA6naRaDvCCiL3X7+O7P/g++VwHi6USpUoN3/cBaDRq9PR2M78wx+7Ld9Lb1cnTTx7g+uuvp1ZdamU9NzdHHMeMjIxQLC6wbds2AHp6evjGNx/g9ttvp9lsctXePczPz1OtNfjKV77Me97zq3z+c/cxMjRIIpGg2aiDijGFge/7OJa9VMkuFQaCwPNJJZJEQcji4iLbRrYSej7jkxM0PI8rdl+Jk0zhHD3J7Xe8hsViGRW33HwgNgakG8PidqfKhQPlDS8ZYt3nwHM3JzwXFrPu+dpztnWez81lY6C84ec6T1a7+tms+czl99ZWNLd+Vrvre+176x5bAmShjA3bRUI3a3mhPOCm+Y6T4t1ehfd65Qu2tt4beXy6MsVnEjn+Mlmgqr8LTdM0TdM0TdM0TdM0TXtZ0oGypmk/c9qGv0JsCMCeC6Y2d4wLbdMuCF4byp0zAJcbK53bbLT+aZvjZPI5JqdnuP6VryL0A/7thz/M2bHTvOHO15PNZDjyzGFm5mucmZyhvzdDVjgcOnSI2ekpbr35FTz04x+iggp3vvZ2/uqvP8UrXnkTXjMgkUhw9Niz5LJdNLyzdHX20N3bRxxG7N27j7GTR7Btg8irk8p1sbg4TyaTQQiTZrNJoZAjjHyMOKZUnOPk2CTFWoDtCHq6O4mqDTpSOUrFGp1dPczMzGJYDvWGx/DQELVaDds2iaKIbDZNLEP6+nuo1sogJKVSif7+fkZGRojjmO7ubqamJti5cye2bROGIdu2bWV2dhrDMJiamiCZTNLX10NHIccXv/j3XHbZdibPnEEIQUdHnm9/4+vccMMNzM1Og4wQy9WycRDipBKoOCabStOo12nU65TLVSYnF/jle99OrenxwJe+wrv/xXuZXygSIzBpqdg0nquQX3mMW773djcoSNn6WpsK5dZTh3aBsGwb/K7fxlj32mYC5daq5vPFiatBulj/e1BKIdX6Cuq1ratXHluvgbadAFrWS1670sfK66axsUOBdvECIfhkMs/9boYPeGV+yatccJ93exXeGDT4aLLA/Ztck1nTNE3TNE3TNE3TNE3TtBePLgXRNO2SsXb907XVxptZD7a1wnHtWPv62scXQmvo1na+Kl432v9MrWOjSr1GvlCgUq5hmDa333EHe666muPHTzI1OcPAwBBBLJicKzM+WWZqqozjOBiGwczUOHt27+TMqWPUG1VGBod49pnDFAoFisUytuVSrVaRMsLzaxTLFVLJLNlUmjgKSLomSsb4TQ/TNFHLLZWr1SqlUgnXdSgUOnn0kcfJ57PMzEyTcC3y2RRevULaTRKHETMzswRRhBf4SCkRmMRxTD6fR6mYrq4Oerq6sQyTPVfsZmRomKuuuop8Po9pmqvrHm/duhXDMJibmyOVSpHL5Uin0xQKBSzLorOzEyEEUkpGR0c5efLk8rrPc9RqNQBS6QS2YaKW18tGLt8UIBW+75PL5Yj8AGKJ67p0dqZxXRfLsrATLrVa7bmbDFrWNV67FrdYfr7hO2+zbnfrNqLN2LCNvPC4qLWa25zTG87l1nXP2402lcjnCrFXnO8abb2+1/2dYWwceg3ln44Fw+QjqU7uzg/yoJO64PZdMuI/1ef5RHWa0Tat0jVN0zRN0zRN0zRN0zRNe+noCmVN0y4ZraFP+5bACsRyRTLtqzxXSpLVyjFXK4vF6uNmAqaVdsOroVebXZaOs7Z9sFwXYCmlUBs+q01dZyxX5y2EgaUEETFyucWwMgQCiyCIcBJJokhSXFxk55VXUqmWmJoY58jpZ5DNJr/4xlvJ5ToZOz3DgccOsP2KYQo5QU/GZHR4kOLMDHff/YvMVao8/sST7LthP8888wxXXHEF+Xyezs5OZot1Fqtl6o0S2/bs4szJYwipcEWSSEUce/Ywl19+OR0dnZwZG2PH6FYe+v4PmZurY6WK3H7LK3n4299ja3cnc6UKZtJl/vQ02UwXvf39PP7UATq6epDEjIwOEwZNdmwdoFIqMjjYC9LnxlfdQLVSo6vgopTD0NAQxWKR7u4cGTsmRpHPbMF2HGRsLrWpjpYqhY8fP7kaKo9s2046l2dxcZbSwiyRjOgqpJkZH0MYBpGMsCyLZhDS6TjkcwVmjzxLd0cnYzPHCWXMwvQMr771NiJpcOTYce583RuZmy3S1dVDEEbIljVlJfGGr7j1W293x5dqPU6bltcbTp12x2kJbMV51mJuF+4urU+8dEquVPmvVBYvWX5sMz3Zcr2tn9/KOb78bKUFfbui/ZZ21rGIN9woIqS1vqrZ3HgDiZI6TP5pGjNtfjfTy01Bk99vFhmNg/Nuvz/0+GJ5gk8mC3w8kSfQYb+maZqmaZqmaZqmaZqmveR0hbKmaZeM1mpDwzA2jPNVJ64Lmi7w/oU+e7NjZd/z/Uyb+SyEwBACC4WlQpBlTOUhZLy0/m0kEHG03D5YooQktg0aXp1MJsWeK3ZywzV7uerKrXR25Xns8R8jaPAPX74f31McHyvzzCmPuVqGwaHL+OxnPsfOy67k3nf9GiePjfGm17+FaqnOHbe+FtdKcsP+60inkziOg9cM2LF9F11dPSQSDl6zTiadwLYMbNOip7ubh3/8E5545hlyhSzbhgeI6h7v+63fIpCShWKZUqnMnquuJogU5XKNX733Pdxx6230dBcY6O9jeEsvjmUyNNgHcYRQITlbMlhwSOKTxKc4eYqoOk9l5gxhcwEjqJJzTHpyGbo78wgVkXBtgqZHT08XleIiQbPB2bFTDPT1UquUMU2TTCaD67pMnD1DHIbIOMRr1sll0zQaDWZmZlBKEQQBvu9TKBTYt28fo6Oj5PN5ZmZm8DyPjo4OKpUKvu8Tx/G60b5Sff1o3afdflLKDWOz1cVrtTsOnL9SeO3nbfZzV1q/P+/ryNg4lGDdc4GJwARlgDKW/ty6fZvrbuXvDu2n60Enyd35Af401cmCceH7Gd/bLPHV8gR3BI0XYXaapmmapmmapmmapmmapp2P/i+omqZdMtoFyBca7cKpdq+vfa3d++0831B5s++di4nAMUwc1cSNa7jKw459EiYkTRNbKAwhEUQoQhqeh5N0iGWAZSoGtnSR70lzfHKSkcsv56bbbuZ3PnQvV167lQ/+29+gFtV48vghvvClr+KksoydOg0I7rjtdh5/9DHu/sW38bn7/o5KqUzKtJg4c4LOzgKzswtUa01mpudIOi7bBnuJmhVOnTzG1sEtJC2H4uwiXb29xJFPX2eBhOsQCYsbb7mdbFc3QahYmC+TTqfp6enhqScf5+zpk+QySRI25DJJ8lmXTNIinTBxiTGCOrKxiGyWEUGNqF4kIULiRgkj9oi9GovTZzj97EHmJ0+RTTq4piCTTlIplkinEigZUS4tMjU5ztVXX03SdahXyywuzJFJJdizeydes0o65SCIkVEMUjI1Mclg/xbGTp1icXGRHTt2EMcxjz76KNdffz2WZdFoNDBNc7kqWq4bSsUbBnG0bki5cbTuc7GB8sW0tz6XC7WnvtjrZt0wjLaD5ZstEAIhTIQwMQwLw7AQwgTTWG1tjWlc1HWnvbA+k8hxd36ATyYLBOL8/xraJSP+rDbLX9RmGWipztc0TdM0TdM0TdM0TdM07cWjA2VN0y4Zmw2LN1uxvPJ+uzC51WaOc6EK5XYVy8+nQtNSBpYAK26QMgwSSmIpn6QlSaYkSdvAUBGIGCEkndksQaNBImGBI2nEdaxsJ3/wkf8XI9PPlx74Hvnufvbv38+f/5f/m9feuo9f/7Vf5JU330gyk+ToscP84PvfIo4b5HIu9933ad7xjrcShjXGz55m544RZman6O7ZwuJCjb1XXsMzTz9FyrUZ3tJHtTjPrh3beeapg+zYvp3KYokrd+7k5NHDSFPwrR/8kKmFRYIwxnISNBpNMrkUjWYR0wjY0puluyND0rXoLKQoZBLEfoOwUcZvVvHCkFogqYdQbkaUmxHjs0Xmyg0qFZ9qzaNWq1CvzTMzPsbhg08wO3mWaqVMKpXC85qAoqenG1BEoc+Vu3ex54pd9PV0Mz83SzadJOk4PP7oQ3Tks6QSCYoLiwhgdnoGSxhcddVV2LZNo9FASkkikSAMQ4IgQAiBZVlIFa0bbdcSltH60Xa94ZYQuo3ns/7x+c7D1nWSz3VNrL2OLvYmjwuNdmsfrw2KlXGOG0Fax5p5r/x5M6G59sKqCoOPJgvcnR/gO5tYX/mmoMEXy5N8oFnC0d+Vpmmapmmapmmapmmapr3odKCsadol61wB7ov5+c93m9Yw+fkyFBhImvUGcRThuDbZXJK+7q51bXuFEKggwnEcpJT4vk8qm2LL4DBSuOQ7e9m3/5VUqyHf/Np3+IPf/wO+/fVv8Hef/SxuKollGUxOjhPHMU8/fRDXddi2bZSPfez/4+6738bBJw+QTCYpl8sMbBmid8sACwsLpNNpTNMEIcnn82QyGaamJhAKert6MWRMGPoEYYjjOCwsLHDVNdfS2dlFf/8AzWadVCrBli19NOtVkskkCdfGsiyCIKBaLRM0mwRBQBRKolgRS0EYKaIY/CAGYTG/WGaxXMb3fZRaagsu45i5+Rl836dSqZBwXXLZLPlcjlQyydjYGHNzc3ieRxzHZLNZvvKVL5NILq3PHIY+AKVSCcuymJ6exnVdhoaGlr4bw6Cnpwff9zEMA8dxiOP4ottQb+b8PlcQ/Hw+62LD1HPdNPHTuB4v5lq/0LWmq5JfepOGxYcyvbwnt4WDVuK82zpK8v5mifsqU9wUNF+kGWqapmmapmmapmmapmmaBnDhRew0TdNeJjasc2osBUJrA7HNrIUqMFuOI1f/uHIksXyc84VOa98zDAOk2lD1qcS59znX8Q3D2LDerIEBZkwoFEp1InMRg7v20vQ9ZNhAeiWyVpNCukAyn6dSqVAp1QiVSaBcDMOlf/RqokjhmgbFYpkoith73U3c/bZf4D/90f/Km95xL13dHfzjF7+Eaye47obrOXbsGMcnS3T1dfKhD3+QwaE+7v2Vd/B//Mf/i8/87WdIpTsZO3yQweEBpqqCfTfdxhf+599w2y0346qIb3z9qwxvG+Xg0SPksikOPvE4Q0NDxF6IIKbZbLB911UcPPQsYyefZWSwj2wyiamgb2AQETdRMZiWy8zUWWwhAAvLdinVm5RqEdMziwBMTEyRcFN0dcH8whSNRh3LFGRTaRqVMn0DW4hiRXZ8hj179lCtm6hY0tXVQV9vN9uH+zh6/AjJpItlCxZmprnm6uuYnp1muH+AdCpJxRPETY8tPd2cPHGMfdftpRkGTE9OcXZihlfddCvFapUgCjFNCWL1NF1HyY0hbuu5QpugV0nZUl0bbziPxAXaCAMgFSt7CESbD4eVDVY/b+10hFi9WETL9u2Ose5nELLllQvPt3UPAKPlFytZul7U8ntq7ccv/y4NxZrfqzrntLUX10HL5T25ft7g1/n9ZpGu87S3Ho0D/qI2w3ecFB9JdbFgmOfcVtM0TdM0TdM0TdM0TdO0F4auUNY07ZLx02ilu9lW1RfaZmmCYsP458xh7c8UGSaBYRMaLqGVRtkdRFaebbv3k+/Zhi8yIEyGu1KkGzP0GiV2DnVjRQ3iMKC7uxfbTdHV08fp8YmlFsG2xa6rr8dMFbj73vdxbLLEJ+/7Kve8533c8+53cfLk0zz1+MPccfsNiAD+l9/+Aw78+GE+8Ov/gr/+7/+NHaMDEPlEgc/pU6cYHhpgcmKae3/lV3ngG9/iLW99E8VikR2XbePkqROcOnWKfCHLlVftxjAFwrBIpjNMTo5z/MRhtm/tJ2VLvPoiuYyLICaXy5BOJqjV6iQSebzQYK4YcPzEDOPjixx65gRnpuY5cOgoozt3M7R9BwuVGvVAIc0kykohnBSZQhfzC2Wm5+Ypl8s8/PDDTExMoJTCMAwWFhaW1zoGz/MIwxDLsih7FXq39FKtVjl25CiT0yd53WtfTcI1aTablBdrbBkY4sATT9Hf308QxViWhWma562kvdjq43Zt2i/q/G5zrl7o3N3MPhuHuWGsrHX83Li4TgMXqrpuV3l9setEay+OB9w0b84P8pfJAtUL3BhxR9Dgi+UJ3tssv0iz0zRN0zRN0zRN0zRN07SfX7pCWdO0S4Yw1wcMK8FTuzVhz0e1lDtupgr5XNusrSJWQsCaymKEQLTMaTNh2UrIufY5yKUqTSHAMhHSYOzMWa68+ho6Cll+NHmCQiqDqSSOm8QLDCbm5rGSGfKZLrr7BjHMBFtHd/L4448SRhEjIyPs3bsXgF17ruXWO95Af28/n/rkf6O6OM0ffeRPKC7M8NWv/E+eOfYT3n7XvRx88jg/+u7TXHPdMOWFRfbu3sPR46fJFgqcOnGMgeFtYBj8+m/8Jv/5P/+fjGzbhTINuro6CAOPa67Zy8LCHNPTc7jpLPOTAc0oYGt/irgxSTLbwc233EKlWiORSGEailMnx3CcBBPTRaanZ1lYLONHIZVaFcu2CaRieHiQUqlIR0cn23eMsri4SBAEGIBlgOeZdOUKWJZBIZ/BRDE5OUmlVCYMfVzHQhYKpNNpbNvk7PFjpNNpFmcWWTRKbL9sJ0YiQ6G3H2klkZbD4MhWrt5/HV/68v284c1vIZnK0AjkurB35VzYzLlpbCpIbb8O99pH1a7auPUca1c23TqflinLiyjlVXJjKGhsqAneWH98MQHv2t/JOffXwfHLXiAEH08W+Jyb5QNemV/yKufcNqskH2wWuSuo80fpLg5a7os4U03TNE3TNE3TNE3TNE37+aEDZU3TLhmqJXBrF8BtJoja2Hb6+W3f+t5qoL3SBXi57S6iXXi2Oa3r4ZrLrY1BIYRJFDdBWSwWZxjs7cS1YfbsDD25HdRkgoq0uOnOV/M3n/sHbtq3m97eXlQUMjgwzKc+9SluueUWtm7diiDCNG2y2SxKwfGTJ3jr2+4l6cCvv/ud3LB/H7/32/+Fa669mS996VNIy+dd734DD/3oAIcOHcH3FCNbt/HUM88yvP1yxsbGSO+5glJpkVe84hWcmZzBb/r09vYyfvYUW7eO8M1vfpNMtgPHNsgX0uQ6t2Ab/XRkHCqVgFzKpbu7k7n5IkeePUw6nebZw8coFussLBRRloHtOnS4BRzHoVIpU5yfIpPJUl6Ypbu7l7m5BZRSNGpVXMfCSeeI45h0ymFxcZGkY6OUolgsMj2dIJ/LkLAs/NAjCDxs22Z+YoLeziGwHB57/GluvP02Tk/O4zg5To7PsvOybYRCUa3WMEyTUrWGMOx139/KuRPH8Ybvd8N33hLytj+X29/gsNnW7CvatZC+kNbZtDvuhjm3C67btPu+GK2f1S641+skX7oWDJOPpDr5vJvhw41F9ofeObcdjQM+XZnifjfDR5Mdug22pmmapmmapmmapmmapr3AdMtrTdMuGYYy1g0lBUoKUAYoY2ltZMNALVcLS0AJEyVMJAYSg1gJlGGuG7ESG8bGttNsGCufhWEsDSkQylgdSLH6+StD0GYoAwMT07AxhAPKwARU5CEIwQbbTSHjEEsFmEERKxLYtk04P0fSq1MrnaVZPEtDmHRffhWBnSGUgv7uTvq7s3TkM0QKfL9Ks1pkcGCAdDJN5CvS6TSWYxILcDIZqtUGbmoL//4PP4oUPRyZOIzT3c+tb7yHuXKZv7v/Pvbuv4yEm2JxusK2gVG2b99KvT6PUnXm5he55tobuXb/9Rx+/An2btuGVy6zvbuL4tQUjXqV7o4UlgCTkLQdkXZNanUf01F0dvUxPVWkWlskn06Rch2CwEOZEiftkiukyHekyeXSeF4dYSYw7RR+IFDCwQ8lyXQaN5nEdBNI00GGdQwRI2VMJpdlemGB7kIvnYUekCaLCxWiWCKUQcpO0tnVTSQMPBXhZlIkUgkSjk3CcSnOTVOvLpJNJpiZmGXvvuuIhEAZAmkZCMNCYSCVWB3CsNaNti2mW84LS7gbhrBshGWDaa0b687plnMOw9owTMNYNwwhNgxMY90whXXB4RgmtjCwhYGFwBRLw4DVca5K4rXtpze0+lYgpFodxBJDsW5EcUws5bpBvH5Iee6hvTwdMR1+M9vP72b6GDOdjRusuWfgLr/GF8sT/JJXffEmqGmapmmapmmapmmapmk/B3SgrGnaJWNzax8bCGGueXxu37WPFzpum09vM4x142LXgjVMEyUksQyJZYNkKmLLlgRbt+bp609gmk2EiujKJXCkT1iaImVLhFfGalQ48P3vQc2jZ3Q7c/UGPVt6ufLaK5kuzXDNjTfQ2d9LI6ix/4ZreOLxA+zddwM9/f3YSYfO3h6q9RqGYdFshhBb5LZ0UQ6KXH395fzrf/Nevv+lf+Jv//Jv2HPZdfzL936Ie3/5/Tx24BD9QwMMbO3nQ//bv6NWq5FNpMinU5w5fYoTJ46Q6+7nulfdzKFDB6ktThIEHoYBuUyKm171SgYH+unsyINSnDhxiq6uHvp7dzA7UySRSOA4DhgxjWaNbCFNrCIuv+IybNslCCIaDY8okgRBQBiG+L5Po9GgUlnfIjeKIoIwxgt8JJBMZUgkk8zNLxBLhVSCsxOTHD1xEoSBYTtEkaSju5vZuTkymQyGYVJaXEQ2yhw/fIi+Qo5atcoDX/8aHV19yBgsy166sWEz6xi3u7FgE2sWX9R6yZtwMWs6t9Pafn7t+sSt6xW3e31ltIa951v7ePWYbcZm9tNrKF8aHnSS3J0f4E9TnevXV15pB7EsqyT/obHApyvT7I38F32emqZpmqZpmqZpmqZpmvazSLe81jTtZ4oQ5nKF43PrGwPrXrsoqs39N8LguUbAAog3brMJUoAhDFARuZxJNqvo7xFYtqDSUCyU6oR+TBjHuGbM9st3MleO6MwmkfUSGctkdGg7Ru8WSKSZX5zDsg0qp09zxatu5et//0VK83M8nHTx3Axvfuc91LwmnR05wijEcSxmp8YZ7h8hl0sSiTSKmGefPsj81DiWKLF9tJdPfOJjjG7bSm9vN7/37/8jf/Ppz/Dooaf4lfe+nYST4+gzz/Da19+J55+k0JGhq3+Ia/bfyKM//A7OnCJbSDM0vIVyvUTUrLMwM8XOy3dwevwk2WyesdPjbNt6Fel0Er9eolqtUq5VqVbqTE5Pk84WODs5QSaZJZvNUavVyGSyzC9WMAwDrxngugnS6TS+v9QeN5fLIYRAqoiR0RGUUoxPTpHO5Zk4Pcn45AS2bTM8PESt7nNybJxcJoVjCMZOT+BYLlEYE0WSerWBQ4StYkaGtnLy9Dg3vfoOnEQWP4pJJBLIZnOTrdg3nk9CrN+m3TrHm4qHN9MOexPbbJjzJi+ddUExG8PjteuKr6wP3vp57a7T1vc3zLnNPq1LOOvg+NL3mUSO+90M72+WeKdfw1GS9X8PL/15b+Tx6coUn0/k+GiysP5JQ40AACAASURBVD6E1jRN0zRN0zRN0zRN0zTtedGBsqZpl4znU225GjgtB8GC5RAKaF1Btv1xn3/wdLHrtUoUSIlpxHR3p+nImSSSIYiIbMqktyfP7HiFbDZP0nYY2bGL4mNP0dGZZ3p8mmq1yu3vfBuTs038IMBUkoXpSY6NHeXqm2+i0NPB+NkTHHjwIdIdOS6/6gp2XLEXEUu6O/r4xMf/K9/71lfYvX0HnYUOTo6Nke3u5obrr2Xy9Gn6t13O/suvpR7GfPn+z/HwU08ytdhk774bGRzZwTOHn+TxBx/gnXf9Al/9+y/whnf+Mg/++Af09PfRPzyKmc5R6Ouj2axz+vQpmrUqMvAxlWRxdoZGrcZIbx+Ok2Fm9gw9PT1U6xWq1TrFxTpnzo6Ty3ehDAfbMhk/O0kqlaK7u5uFhQVc18W2bRKuxLYdDMNYCnelxLIsgiDAMGwmp2ZIJpOkM1kazTrCsjGVZG5hDtO26SwUcJ0k9WaInUmye89eHnnoYZQwcCybcrHCT77/bd785jfjhyGzJY9rbrmWqu9hqHC10tgw2gSbG1oqt6sAXv+8dc3w9nu1cRHn4WaC1s2sT9yu+njD44Y213LD/hc6btv5tNlVtlzrOlD+2VAVBn+a6uSziRwfbixyR9BYfkfx3FWy9F3/klfhjqDBR5MF7nczL8V0NU3TNE3TNE3TNE3TNO2SZw4PD//xSz0JTdN+/rz97W/nvvvue1777Nm19YLbKForL9tVFrcGY202WQ4jngvNTDa0vF6z49J2Fw6r2kV9UoBQEiFiurttMhkbQ/igYhAmUWRRLTdwLQcwGdwyTKVYYnJuhsH+LfhhhNPdTRyZVKoVujoyuIZBureAmyuQzXdgWYqOTIJaZZ59N91BIpUhCkKCRkCzXmTfVZdjKB8Zepw48hTVWgnfq+A4kmtf8RrSuT6+/d0fc/ttt7JlywA//MGDbBkYZnBgGBTsGB1h/MQJbNvm2JkzpDNZojBm3/U38vrXvZYTx59hZLCfYmmRnt5uUBKhLIIooOE1wDAoFLro7ukgCDyKpSL1hs/8/CJKCYTlEIUwt1BExTGmaWKaJvl8nlgpXNcl4SZxXXfd2tcAnucRRhG+7+F5HrGMcBybM6fP0mw2SKWSxHGMbVlYponj2HTkc9RrVU6PnWZkZCsoQalUIvA9Ch0FiuUyA1t3EGDjuA5xLBGGgYxjjHbJJq0toze2xm49f1Sbc3dTUbHaxFat1dBtgtaNFcCbqHxW6wNcueawK58hWp7Tpoq5nYupUBZtqrzPZezs7Ka31V4eqsLgASfNY3aCXVFAt1rbJWLl72pIKckdYYObQ48nLJeSYbY9nqZpmqZpmqZpmqZpmqZdau655x6+8IUvrHvN8xXBahHGC0NXKGuadsmQm2lZKpcDpJVwak1ItxJASfVcWCyEgDheF04JIVAoxHKILIRJ3C4k3FAt2TYuXvcsbtPq2JQmCAkYTM/UieMYw5QkkglcJ0k2Y3L5riwz04vUvZAjZ8dxBgbIVbI8NTGH4ziU5uvkOrsxTZNEpod81xC9wsXzPCqixt7rX4N94xuQXoVcoYMzk1N0d3eTzie48663YJHl5LOPUi5PIGKF59XpGhqkWKnTWRjESDiEysdN5HjFja9FmkmOHz3CI088wh//0R/SPTDIN7/0eU48/SQzp08w8+wBjj7yIN/4xy+SSKeolYuMDvSwdWSQWq3CkUOHufNNb+Dpg0/QmxNYhs/81Fmk7EEhCL2Q2ekZag2Jm8jhBTGNIKDpR8QxxKHCiRSuMMilM/i+z2J5kVqtRjKZxHaTS2snBwG+7xOGEZZloeKYRq0JUjA0NESz2cTzPMrlCnGsCIKAWEkKnXmU49AzsJUf/eQJHMehXJxndLiferPJoUOHeMc9NxAIlzAIEEZALH1My0ZhrluXVwiBQUsg2iboVHL9/yWLTQTD7dtZb2JN4NYW7u1aTLdW8reExYLn2smvPKo1gZ5SavVsX6lMXmqDvX7d5LVt6VceW3+qjRXeIOX6fRVsuI6RrXPeGCRuJn/XXt4etRK8Kz/AXX6NDzZLdMmIc7XB/mJ5gk8mC3wykdNtsDVN0zRN0zRN0zRN0zRtk3SgrGnaz521odNKeLUSbF1s2+p/jpgYCxspDUqLIeWKj2VBrmCSzUJXVw4r5TG0fYRazWNxvkE+kaVnYCsVDxaKZXoGt9FoVhgcHsVJpBGGQYBNtjNHZJioWNLZ1c3cYkhDxlyx50qSiTSJbBaTGAuHb33t6xRyBlGjzpvuegdNM4kXKwJbYJs2t9z6Rgrd/VQ8nze/9R7m5+fYtnWYf/XB3+aRx37M3//tZ9hz5eXc9zd/jUzYJM6M4eRzvPWeX+P01Aw0Khw/dpid20YpZHspLRa5/tr9HHr6ccoVDyflMjs9SyKZZr5Yot5oYtkuhmkRqZByrY5E4XkBUkoyqSTNhk8c+jQaDRqNBkqppVBZtQSLQBRFWJaJEIJKpYJt22SzWbLZLM1mczW0LJfLTE9P09/XQ89QHwcO/RBLOOSyWUYGR/nhD77Ntu3DEM/Tke2g4hlIaRCr59qtX0j7bcQmtrmwtWH289mnzautG7Xdp10b6tbjrQ2Qz/X83PPY3JrKF9PqW/vZcr+b4WtOmvd5Zd7rVdasr7y+A8B7myXu8mt8JNXJd5zUSzVdTdM0TdM0TdM0TdM0Tbtk6EBZ07SfO2tDP6XUmjVv1UWsnPxCTMgkiJsA2IaLUILAiyktQOD5ZNKQy+WI4gYdnWks06VUaZJOZ9m1axfTs/PUajVs18G0Hbp7BqjXmzimhWMa2IaBRJJOmNDViyEsOrt6UVIQeTHSUViGweDIIKXFM6RzaY6OnaYYCK7ffyN/9Zd/yfHDR7nuuv3sueY6Orq7ELHi8ssvxw/gz/7rX2GYkg//3r/itlfsY3KqSr3mMTi8k/Ezp/ni3/0tv/D2X6bZ6KC31kQqk/7+fr7ylfu5YuflFOfrmG6CRrVGtemT7+haakcdReQ7cvheSLXWALVULe66ScLQJ4pilII4jlFKLVUgK0UURfi+TyqVIpVKLX/fEAQBURQRhgZSSoLAo1wOyWQyFAo5wjAkm81SKhWpVqv09/VgGJBwbXLZDlQs+do3v8OVV1/Gzbe+klpxjo5MAhlKDDOFVAZgAPF5v+4XUrvK3dZwdjM3SbQPbNcfu3Vp6PYh8LnXTj7Xa+dbF/l8NnYV0DQIhODjyQKfc7N8sFnkLr/G+mplAEWXjPiz2iwPOin+JNnBmGm/RDPWNE3TNE3TNE3TNE3TtJc/HShrmvZzaW2obCy3H5ZSrgZcxou4xqYUgCkxECgjQgURBhaRJyl7HjO2QdIcJpNLIFVIIevgewFerUxvVzfp1DDpVIZGFFGrNqhW6whMUmkL2zAQcUCtuMAPjx5i/w230zfUix8pME3SVoJQNYniiFfffivf/vb9nDhxjNGuDnJuji1buqjOTLO1u8DjP/gWn/0fH8dOuIxeto25+QrD23aydfQyRkav5Pd/748Y7MrxV3/+CYYGt/Dqt95Lb1Lwv//hH/D5T/81dsdWbr7xWmSzRKlU4gO/+Vt87GMfY/81N3D42DEeOvAofVuGKHR2k0qlMJWg2fAp16r4foidSLFQqmIbS6EyylgNj+M4Xg2UTdNELIdDvu9jmiaO4yyvrwzCUMRxTEdHlkQiQaVSwXEsHMchnU7TbDbo7u6mWq2Sz6S57dabefD7P6Kvt4d9t72JXVeOks64KBkyNTVDZKcQyaX5KNjUQsdt1yzexDabdaGK383Mp3VC7aqOWx+FuHDVcruQuXXb1hC83fwMw1jd9mKqsrWfbQuGyX9Md/PZRI4PNxbZH3q0C5ZvChrcF3p8MpHjvyfyBLrSXdM0TdM0TdM0TdM0TdM2sHbt2vVSz0HTtJehI0eOvNRT2MiIABOUBSpcelx2riBKtqmYtFa2Xf7fCEUcSyzTwIk84loRK9tLgEGAQ1JEtE8J11dwquU2y0rI5XlIDCnBsJeCTqFQQhLHCjCwTIc4lhD5CCUQhoGSMU7CRdTr2DLAdEyIAmp+mYToQSoH2/LJ5hI0m4IIC9OxiE3BmeMTXLZzJ2Hsk0gkiPwmnrBRArzqAgvHHuHhsMYvvuvfYVnzK78ZjFjgRQ2czl72334XMS6NyGekv4vp6Wlmz5zAtZNkMt1csbufmYVFxk5OYSjFqUefoHHmLGePP8WpQz/iN973Af7lv/49vva1r/MXH/1zfvPXf4U73/B6Hn7wezSnF3n64Qp79lwLqSQnp8a56vprqVSrzC40ENYgjzxylst330gikYEopu7VCSNJMpHBj0Jy2QQiEliWhWmay9XdEQgwLQvHMmk0GpjLAaNpmvi+Tz6fp9FoEMfR0jrTiQSu6WAqg45sAcMwWChXOHtmgt6+LmZmZhjY0oMfhThJh8HtW3n2mVOkBsbZMrqFbz7wLa67ei9NmSKZGiSWEmEaSAFGuNJGffXEWF03eOUcbLduryJa91zIjRuJNmt5twa18drdRPvjtIavQrSpqj5HRfKKdtXRouU6WdlmbWv51V9Mm7WSW9dTbn293fSkXLreVgLm8/wImG2u43brWWs/O46YDr+Z7eeOoMGHG4sMrFtfeYmjJO9vlvgFv8ZHUl086CRfmslqmqZpmqZpmqZpmqZp2suU9bIMjTRN09qIMCA2MVCszYDOt24ra8LjlVCrNVOyzCRx5KNCid/wUPUmbsIn6boABKFCtO3nsP7zVqqalVIoqQCFKQykijENgyA2MIkwhcAUCmQTUykiEmDECKGQRowZWSSckMrccVAWGWcXE2dL+LFDLp+hI5uio1OSaApMS66G1CMjI6RSKUqlEv39/WRzBar1OsNbtzEzPkb34Cg/eegHvOUdv4PhZql7dYxEAtM0sLGpVKZJOynSmU56enq4Yve1PHP4Sa7Yu4tDB49y8ugBapFi15VXsWvnTWSzWb72wD8RFRvUxyc4nDzGow89yvbt27nuuutoNPo5Mz5OJAWvvv21FBdnGDt2hu9+458Y3bWdy67cx+13vpUP/e6/YXDoMmqVUwyNDDO3MI+ZMHBdC8cPSbghYRQTBB6WKXCX1zx1XRfTNOno6FhuZR1imiau6xKFcjXMTCaTxHGMYRgYhk0y5eK6Lq699KV6nkej0WBk6ygLszPEcUQqmVwKMZUkk06yfXSUqfFZSqcn+ebsP5JMWPzkwYd49R2vQQKRlCAlEsXa5svnaue8mWra1nAW2oe4FzrmZgp3z9c6+/lYGyCfy0pF8fnWm97UWuZrjrPp9c/bBdPLx9B+tn3HSfEjO8k7/Srvb5bIrrZ0f65aeUBG/EVthu84KT6S6mLhRexUoWmapmmapmmapmmapmkvZ7rltaZplwxLxSAMDGUgMInaBMmt4bKx5vWVULl1peRatYplOiRSKZQMsR2bUIIlI5BgGjZyE+vihlGEIQRCKCwEQhgkzBgVh/h+TIyLaRm4FjiGIvaqJB2TaiCIZYQyBKZSiMAjmYVkfycpJ4MhDc7OzlNveOQ6cqR3byOdikkmDQwRYJgWjpui0NeJMAwqtSqNRoN8NkcqlSKfTXPnW97GJ/7i/yHX04PlgufFpOwkForF4gK+16DRXGR6ZhLXSRJHBolUnsWFEo889QQWGX7pnnv43kMPMj5xkqceewovDsl1dnH9vldQn59ByogDTz5KsbzId773Td7xjnfT3bGNo0ePcPXe3YhKmb7+fuanp3jwu9/l0LEZhoaG2HfjaxifmKR3aIhSucnEzDTDI/2UK2XqtTpREGIYSXKZNG7CROAghCCVSpFIJFAyJI5jgiAgjmM8z0PGPoZhIIQgjpe+O9M0EWKpVbJSiobvLb2vJEEccfToUQb6ejEtlzjysG2bRMJFyYhsJsHevbuIimWUDGjW66QKBYTlIsMYg6UQ1BAGKg7PeV6u/LldhXIrKaMNr50vqD3Xe3ITwXDresnQptq/JXRuG8Jucv3mFyTAXQ6R14bKF9SmGlmoF2g+2steIASfSeS4383w/maJd3sV1t8YJADFHUGDm0OPjyfyfDKZf4lmq2mapmmapmmapmmapmkvHzpQ1jTtkpGqT6OsHLGZJcBYFwOcay3WlY3OV6FcyDqEkaTYaPCK619FdyHPDx78AQiFiCKkbKKU3WZGrW15l9d0lUvtloWS4C0QeXUc20E5nUhpY0iwhEIFJXLJJE46pNoIkRgYZhIp6tQ9i1DmyfcNI1XIaFJQagZYcROCCCddAIelUF3GmAiklFjL7ZyDIKBSq5HPZqk3PAzD4Ffe9zvU6iUeevTHvPLqV/Olz3+Sg489yBvf9DYKvX1MzE9SLhfp6u0hncwSBE26+zup+ZLTx44RRjne+Ja3cvL0cR576CGUEAS+5B8+/zli4Oqrr+R1b7mb737vO2S7R/jsfX9HV0eWPbt2kCnMcOrYGCN9XVx1zbWEscnTh49x8vARTs01GN55Bc8+e5L+rgIFkSGUMYlEglOnx5AxFDoccrkCsYpw7ORqdXIYhljmUphomubq9+44DlJK4jgmDEOCIFwOHsEPlkJI27VW2yTXmw1UDKdOnaKzK09nR5Zms0k2l6Jea6IU9PR08KNnDrJj6xD9XYOUqg1STpJqGCKlRIqVdXzPfaPDyp83VfurNt7EoNok0a03UbR2xVbi3FXN7eb3z7GZYHZtW+t/VpC7Zt/NBsrt2lubbGyVrf1sqwqDP0118nk3ywebRe4IGsvvPHcdOErywWaRu4I6H0l38qiVeGkmq2mapmmapmmapmmapmkvA+bw8PAfv9ST0DTt58/b3/527rvvvue1z86OBmkrxjZjCt19SDxA4gUxAoGIo6VQD8lSMKBQdoBtQEoZiChCOHKpwtUAXwaYQtCUikhBo1nhzW95MwcOHmR+rohhGfihj2klUavrbj43lGodYEiBikGoABE3mZ09Rug36OzuoXNgB/WgSYRNw1eYhkkc+QQogijAIGbn8Bbmz54lqhXZks8RVsscPXCA+vRxervyJF2DXHcnE5OT9G/ZgpSKIAgZGhii5kXYtk0hX8A0TPzAJ5fPEUYhCEHT80hYaYrFeRZmnuXpJx9i/Knv8sPvfoM773wdTz7xOMiI7t4eTMump6+Tpudx5MiTCCV45PGncbNZ3vjWu+jq7icmxggajHSlSWdsZqYnOXt2EieVoxFEmLZFFNkcffYIzx49RF9PL5Zjcez4CXZs28VNN9/AyMgwY2NjzM8t0N/bx0B/nkajjmkZ2I5BvtCJYQjSmQRKRFimSS6bxTBtFhfKeJGPF4QEUUysFLVGk1hB0w/ww5AgioikpN5sYLsuGAYYJkEY4zV9ljtVU6s2qNUbdHZ1Mjc/z65du1hcLJHJpjEtgWGamJbF7v2v4O3veAdHjj1L6PnkCwWk6ayvcRRy+fx47lFYFoLlsHdpQeWlytg1A7nUol1JCXLpfNo4Ws+5eON5iUSt+WepTl+sG63Z69q20WvD2XVBs5DrJizOF8QKsW6sHndlneO1r7X5/JXq8tXnwsIQ5vphmhjG2iGW25o/Nzb8XKa54TULA0MIjo9NPa+/j7RLX8kwecBJ85idYFcU0N3mJo6CirnLrzEgIw5aLk2hb0DQNE3TNE3TNE3TNE3TXj7uuecevvCFL6x7zfMVwWoRxQtD/1cxTdMuGcpwKFbKOJagkDLoSCfJZRxcx8AwBLbtbtgn9iJSjk3KiWlUZxFRhLkmTIvjGBXHhL6PDCXpRBoZyuWq1mApUBOyTZC3saJzpQp6hRRgWg7pXA43naG7t4crdl/J8NZRhkdGSaYyZLJ5MrlOFBZS2IxPzfP0gceZODNGvbJAJmkxNXmS8ekZmr7H/Pw8J48cIW1b+I0qcRThOA61hrcaxDmOg2VZRFGE7/vLrZ6XfmjPa5DL5UhnMtx2220kswUSaZf5hQkWFycJ/DLZbBbXTTA7O086naazo5vOzk5sE8bHxzlw4Cn2XX89t7/mdSRzGabn5kk4FtlUEhn7PPvMCcJ6Fb/RYKCvl1ffejv1RsiRo8d55ugxhGnx+FMHePqZQ8zOz2DZBsND/SSSDo1GA8ey2dLbh2FAsbhAKpXANE0c01qtvq7X69TrdarVKnEcL1UIS4kQgmq1ShRFq2NlbeWV722lPbZt21jWUpXyylrMs7Oz5HI5Dh06hBCCKIpWf6+2bWMocF2bIAjwoxA3mWh7brSeH1JGS0MtjUvB2rbV5wqcL8a5jne+8dP0QlVna5euR60E78oPLK+bvKZ5z5qWFnf5Nb5Ynlhuk61pmqZpmqZpmqZpmqZpP190y2tN0y4Z+257HWNHn6W8OEd1fpJiAJabwc1vIVAuvgRY3963YKdQjTJjJ3/CyRNnue4Vb8JLBijTRhgmAFHkEwQhvhczMrKN6elpTIOlKtFYEYUehjQvOD8pQgQWSkAsAWHgdvQgTYvpcoPi8RMMj+6gp2+QjlweMxxBegtEscUV16YwLRcZKSJfceTZQ8TJFDONGtm+bnoGh+ns7yeOFbuvuILZidP8+FsHGd2zn62XXUWESTqdIgh8LMskkXCBLI1ana6uLpRSWIZJM/bZvn07jzz4LbYO9fPqt7yN7Zdt4cmnfkL/kE3gL5DPd1CuelQqFTo6OtixfQ+P/vhx+voKPPLjg1SKNXbvvZrbXvd6brnlFh748j8Q1xb48UMPcfjwYfbvLpDLZRk/O8/4qWOcPmMxuvNqTEIynVkmivNYhkmutx9iyVvf/FqOnTrN5FyJ6fE5unq7iKVPRz63GhZPTM5w1d5rkBIWihVKpRpSSpRQVMpVbNvG931c1yUMlgJbpdRq22ulFGG4tLax4zhLLa9tGyEEvu8TRRGNRoPurg48zyObTlCr1fC9PK7rABI3YSMMePBHP2JmZoZtWy+j4QdIWsPj56ocVwLRWEqEVGtuOmgfYp6rTfa5/LTD0LWh7sZG3S9M0LtatbzGhp+rTai8MWhut03Lcdt8vhK0+dm0n0efT2R5wE3zbq/Ce70KjlrpdrEkqyQfbizyhqDBn6Q6OGhtvIlJ0zRN0zRN0zRN0zRN034W6ZbXmqa9JC6m5fWeq0a46fbXkuvs5tkjRzCsJFKYhNIikktrCYNY1yPYsiIKHRke+8l3uPXVr6LerIBlIyXE0kQpiZOwcewE3Z097N6zjQNP/gDXNjGEQRRIDMy20dlqu15Woqx4OXsQS02HBaAsDDMJZoJGvcnE+DhzM7OcOH4MSwR4jUXqxQpnZ+ex3CS5ri2M7r2RN/3ye7DyvXQM7uR1b7+XXVddQ4RJttDJ3MIcs1OTCDvBK2+9k4HhHaQzvUTSw004q52GbcvG+//Zu+8oyc7y3vffd+dduXOY7umepMmjLCGBJCSEJSHA6CBjY4xtTsAY++JjbOPjgA0Hcy8Oy+uaYx8bOeCLDz72RZhrMEEgIwkJhZE0SCNpNDn1TOfKYef93j9qekJPKyBAIrwfrb26atdOVVPVM0u/ep7H87rzhbVuW19Ng3K1QhQH+J7H0Mgwx2YPUOjPECUmb3zzTzA330bXHUzLoVyuMDI0RKe9wPTJ/SRxStAOeGDnY8wvLvL6G3+MjRu2smHrdt7y1tu56tWvYs3kKqrVMrXFeZycycjwEJVKhQMHjrBh4zoOHz7C8RML3Hvvbr61ew+rR0oszM9gWiblxQUGBnpwXAvL1DFNg8D3GBsfI44lSZSyWKni+SGum8WwdNotD103yOcLBEFIFMWnXgNxeoYygK7rpGlKGIaEYYgmuhXqSZJgGAaWZRFHIa7rYOiCTMbFdbM4rommCQzDRBOCqSOHcSyb7TsuwQvPtJxeet3T9MyXGk5XLIsUTZx6rwhI5bltqrvLCm2mlzl/Vvj585FfStC6krOrg7u3zw2YNXF+++iXVH2sa6CJcxex7L52/jYay4+lI4R2zrK81fdKH+QUCUJwWLW8VoBQCB4zHT5n5xiSKeuT8KxHu2+goTTmtqBFv0x5wrAJv8dV9IqiKIqiKIqiKIqiKIryXF6ultcqUFYU5RXxUgLlSy9Zx8ETs5RG1mDmS1QW66SaiTQcLNNCRiFC6OdkdG1ZJwgjXn3FNQwN9zO0uoTllPDDmFTYSA10IQi8gMsvu4x9+x9jaMSislih2WhiaDk0TJDxefmf4Nz7UiSATnqqmjMVEiPWSKVBmnRHz+oiJQpDksDHb5UZGcyT03V8mZBqgqMnpnFtkzQK0YRGxslQqzSJMNFti0yhiOGY5LN9rNu4BbfYD5pFudLC85u0221KpRKWZSEQSClptVokScLi4iIyCYnTFNu2SJIQ29DJ5DUWGzUuu/yNJJQwNJ0wgiQB0zTYvnUTltWmsnCCo4em0XWLI8fn+aVf/mVsw2Lrth3YPYNMzy/iZjLkS3kuv+IK/st/ehdj4wM8+eTj1CsL9PX10Gk1GB+b5Mknprj51tfzgV9/Pxk9wbEsRlevZdu2LSwuzDM01IeTsclmXQqFArowSRJBs9Wm1ekAGgKNZrtBxs3S6XSI45g0TXEcB9d1sCyLKIpOVy7ncjkMo9uYI5PJYJ1qeR1FEfGp1uGuY2NZJsgUx7YwTZNs1sZxbDIZF01otJsNFuYWeMvtb2P/waMsVTCe3SJ66f7pKmkZn56PLGV6aqzx8iHK2rddobxSze13M1A+m6Ytb0P90kK05YFyIuT50fpSqHxqkfL8c+nntcVeuYr5nCprcf7rtXTOI0dnX9LzUX44eULjbivDg6bLeBozmp7dqr77XtsSB9weNClrOvsM65W5UEVRFEVRFEVRFEVRFOVHmgqUFUX5ofZSAuWh1YPccssteO02GzddRIDF0emTlAY1hKxhJiHzJ6eYK0+TKeYR6AjNZKHc4oY334ZZKHHgwHE2X7aZ2H8huQAAIABJREFU4bFRHn30Cfp7+0miBN8Pue7G12K7NsVMiZ2P7sI0MkS+BEsSNhuYrovQBDoJWtzEBOIoQDNNoihGExYSiZASIVI0BKajkzfA1iWhTIm1tBuCC40o9Ln80oupVivs2XuEwI9Yt34Lx2dmqbda9A4MIHUdYZokaYLrZtB0i737DvLM/mdx8j042RKBH1IoFqmWD1CtP4PnnWTq2F6q9SkswyTjZIkjDyFCDF3iOoIwaAESy9KIkxTXLjG5+gLq1TpBEJLKFISGQMcpFvj6l79EKdNh58P7yeUshHC4994HMHMuM5U5Lt56KYKQ8dUj9PWNYFgFMj0l2u0Wr7/+ai67aD3rJ0f41q6dTJ84wc233MDRI/s4cGA3V157GWHi8fl//QxTR48xNDzI6KohisUclmYhUvCCED+MCKOYttch8D3iJCGVkMtnaHdaCCGJk4i+vh6qi2Ua9TqmYSAA03KoVKrUanWkhDhO0AyBBBzLRiYpcRxgCA1T17FNG13oWKbB4MAASZyQyTgQx5ycmcPNl3jzf/hJDh89QZImxGmKLnRkIjF0gyRO0XWtO9eZlNFSjqIVQtgk1Z3TlezdHtgaqRCknGq/fGo5O1BdWnQpOOc/7dyKXCE0tFNh+9KCOFM93Q2FQUgNTXQf14S+4nGkpnXfA5oOmoZEA6GD0BGagdB1hHbuorHsWjT9vCWBbsXxqWN3r0E79Yy616RLgXbWogsNXYhzlrMDb7ms4luealWcCEnKmeW56sJlmnL02Nx38BtN+WE1rxl8wc6xX7fYHgfkT3cF6H6JwUZyfdTh1ZHPHsOirL3weARFURRFURRFURRFURRF+W55uQLl8wcXKoqifJ/atHU7zXaLp57ZzezsLNdccw2XXHIpzWaTYm+W3fseoV2bw9EEhoAoibuFoEKSSI3h8XWcrLTYv/8EtpUnjUIi3wMSUhlSLPQhYxOZOiCy6JqJpscQR+i6gZ4kyE4HI2rhyIBjzz7F1OG9BGEL3daRQgM0hFxaunW0xCEy8NGiAAAhUwxDo9VqUSj1ECUJlmWxUKkyv1imVCphmiZRFHUDSSHQdZ0wTonjlJGRVaxbt5Ejx47T6TToeFWajROkSZVWc5pa7Sh+cIKZmX00midJkiZCBOhaRBrHaBJMTcfUdJIkIg6762uVKpp2bmWspkO77TEwuArDztE3bGG7FkHYZmF+hvLCAj3FPBJJX98Inp/g+TGabhIEEXMLFRotj5FVa9iyZRvv/5VfYc3kKu695+u4GZM1ayZIkoSBgQF27NjBunXryOZcbMvBMh00rVtRHMcxvu/TarUwNA1LN5BpjJCSMAxPVxmbponjOKf30XWdMAxpNpsIIchms2QyGYIg6M5gPmu2ciaTAcDzPCqVCp1Op1vBHCYIoROFCY1GizSFjRs3I6Vk/fr1+KcqoNM0BU1033eGThhFxElCJpelVa9xZO8empUyehideVPLpb+GtRfXHnpZ2+cXs82ScyqONdENrZd+PodzW16Lc9a9VOdWFZ9fRbyS063Dly1Ljy3f7vT6F7GIVKKpEcrKC7jHynBbcRUfd3toCo3lreq3xz7/VJ/mfV7trNBZURRFURRFURRFURRFUX44GK/0BSiKorxYqycm+My//C1Hj+7lVVdfxdTUMa659lrcgsbTT96LW3Io75slG0dkiz3ouT50oZNEEW6mSIzL6OSFBF6Nf7/rq1x50QUslhv4ImVouBdTN+kvDvDgfV/D0S08zyOTdZBJd/6uX5/D0iCKmuRKOTrNRaRtY1o6sZCnuxgjUgQJmkwx4pCpQ3uo1+usmrwAofeRipQ4iSn0lDhyZIZyy2PbRZeQKw3g5vsRlkaz2ezO+dU0TNNECA3D0EBKxsbWceGFV1Cv12k255hbOEK9FeLqOhnHQMYBQgqG+kdZvWo1XluiI4jjGEOPaLVaaJqGZemkiUBEEbXpGRyjxNDwKjwhiaMIyzaxbZNqtcxNb3w7//2D93D51ddy9Mg0M4tNms2EL3/+sxw+8DSf/+wX+MRffgJNWCTCI5QJTzz0MBm3QCJTml5KNpvHznn87u//N9Ik4ot3fYODBw5wrXMVMydOks3ncA2HTD7fDY87bZKIbvibSKI4wHZ0OpUOCImMIyQGc9MzaJqGFBqmo3No/wFyuTy2bXPixInua2i7p0Nk6IaYmtZtMR34PpZh0m63ifwAyzLo7emhWCxgWRa1Wo1SqYRtZzB6baTmsmpsNf0DQ0ys9Xho54PdgmNdwzBNhBC0Wi1yuSxRFHH8xAkuXD1MJQgwNBNDpkRSwKkvIAAgNcSLSDWXh78r7ZIu20ak+nlhcILstoBeei1WOvWp4ywPeZ8vUNa0c7+ntlLbbk0Y5z4mzvyZSHnqutIXH8g9V7AMpz6PL3gAuuGyoryAUAg+6Rb5vJ3jXX6dd/gNzg6VQfIur8abgxZ/7PZwl519Ba9WURRFURRFURRFURRFUb57VMtrRVFeES+l5bXTF3Jy4UlyPTH3P/AA119zI7Nz81yw6TLS1CGOJT/19p9j9+5n2XDBZjpRhJDQannc+GM3szi/wIY1E3z1i5/m2MEneefPvIkHHnuEgeFRtmzeiu04tJsnqS7uYfWqIr4XkqR5EnxazWn279/N3NxJtl10KUMTm9CyvRQGx9CsDHGQgA4aMYIEHR+diJ4M+O0FNENjcvPFbNi4hXXrJxlfPc7qsTEsw2LT5ksIUslceZFsMYeum2QymdPhmmVZCF3D0E0Mw8YwbMIoIevmMUwI/Saa3qFRmyEKPJIoRSbQaQtGhsYJfB8hYqKoTqM5QxC2SJKQOPZo1zuEzRonjxxleNUEUSKRQmBaNtlcDhBIPKqVEMvN8rW772FycgMDA3lmpk8QRxET46MMD2bZ89QTbNu+iSeffJg9z+xCI8Xzu+Gv0A1aYYRmOVRrLYRmUezNcuONr6PTjsjnsiASXMvCMC0KhSJBGFFeqFKvNU61gxYEYXC63bOum2hCIISO1+lg6N321q7j0my1qNVq2LZNu90mjLozkjWt24a6W5XcrVDWNY0kjpEyxdB1HKc7bzmfy5GmMWkqyWSyyFSAMBmfmGBizVoGhkeYm1/g+PFjBFGIbdmEYUgYRZiWRUpKb18f7/nF91AuL1CdnqHY04tZGCBMQYhTraKFBuJUK+oXqlBetmicv41c3iZb6Oe1s06FPH088RzHYYXza5p2+iecf73aCtXGy7dJ5dKM6VPBrzg/EF4eBD/XPOmVwuSz74uVelwvX05tf2Rq/tv6faT86PKExoOmy11WltUyYTxZ6jrQfW9nZMqNUYcdScge3aKm2mAriqIoiqIoiqIoiqIo3yMvV8trVaGsKMoPjIe++RWy2SxprCPjkCd2PcS2bRfSmJ3h+quuZ/3YWjphwOtvfydeu82x+d3ErkO2t4RupHS8OqvGNjC0ei0XX3EpndBgaHAM3XYYHVuNkTZYOH4/Wb3OwkKdWKaEGOiklHI6/asmuf7GW5lYu5W777qbnt5BemyLhfIiSRrhJi6BnpLKlIxIMIIOJyqLGLlhduzYzNj6rXz9wfsYtDOs37ieJh5BGDN38iC6XQCpEzZ8wrzOmvEBFuZn0DQdx7FIopRM1kVKgWHaiFQiNcmatWspFF0e/9Y3KPT1U6vVgBRNNwi9Jo3mIrqRpdao0/EXaFaO4od1dJHDsYvk8wMUSllOzM9z7UAfz+49yOZN2xCmQyIFC4tlbJHQiTxef+ttnDy5SKtZZ8/+A2zeuIrpqRm+ed9ODjyxh5/7L2/n6Sfuo7xQptWssVBrkcnnSJIMYRThdRIKfXnMfI5O6GNbBaLUQM9aOFmTVclqOp02pmnSbrZpN+oYZko2Z9Nqd8jlilTnF4mEThBEJFLS9gJsw6SQc2j7Pl7go2k6qUjQLY2FxUVMw8KysjRbHYIgQAiB67oEHkhLQ9gG7aBbEe15AZoG6dEZbNtksLfEhvVrcbyI0bEh7FyGsYl19A2N8tTuPTyx+yniSKIJkyBKkcLAdE0KhQLv+YV3027U+dSnPkU7CNFG1uAJDS+ErG3SibohuQZYIiFa4a/k5YHpUvB7OkDVzq8WPi+6OmuTpdhVpN2Zxd2HT4XQz7XbWVnt0mm7lc3nT81Ilt3XWKlqOD19MAEkK7Sv1tLnrhhe2iZdVsXcnUJ9rnSFIPrsCmshBFEcn1dZrSgvxlHd5Jdyg1wderzPq7IxCU89IgDJ1WGHf458Pu0U+CunSPgdtotXFEVRFEVRFEVRFEVRlFeKCpQVRfmB8cZbfopsNks2k6GnUESkBpbbQ3mhTbnRQXOz9BfybN68mU6rRRSEHJ8+SaPRONU2WuB5HqNjYyzOTSGsjRiWhd/x6CnmmT12mDCKKRR7mFoo4wUxGBq60Ki3PF59zXWMT6zm6NHDmKZgaGSYWq2GrutohkESh5iWToLENDLEXsiBw1Ncc8NWglMzfvfsepLjUnDo4H7ywz2sH12F12ngZGIGRlYxNNzP/uPHmNGhUCjQ8Tx83yeTKQAamtGtEHUclzSNaTY9Aj8iCiVhICmV+pg6dhxD03Fci6kTRxkcGKfTbmLaBrVaA8uGIOwQhTA0NEG1WiNXKmLbNnamOwvYNXSaXgeZ+MSxxtDIGK6Z4/bbf5KHH3yI3U88SrMcYVkWa9b0UJ8v87Wv3MWGLWtJU0kax1iug+1kMAwHzTAZHMgwuziHZkocTWBpOo1aDTfnkjEMQqOOJ0S3gjZNSdOUgYEBOp0OmWyejh/heR5uoY+e4T7K1SqtTgffTwANXddBdEPPIAgAjcGBISqVCs1mE8uyyGQydDodms0mWdcliiI8H8LQR6YCTWgYuoFhGxiGwWKlSueZPVh7DcrlKm944y3MzMzgZHN8/d57aDQaWIaJ67pkMhnq9Tqe53HrrbcyNTXF3meeZnZ2llIxi6UlaKQkUhKGGgjrVJApSZK0O7D6OZwTIv8Aeq4KYiklkhf/vL4Xr8HS7wZFeaketFwetFxu95u8x6/Tl8YsfSXDkinv8mrcFLT4aKaPBy33lb1YRVEURVEURVEURVEURXkJVMtrRVFeES+l5fWv/doHGBtdz7qJzVywbjsjPb2EfszA4AAtr03bbxP6CYODI7RbPuPjEzy5+1v4vs+WLVvodDpIKekd7GdmYZ7hoSHCMCLvuhzad4DBgV6EFvP4tw7QjoskZNGFjYgDtl5+FYPja6k1W9SqC3zzG3exZdMOSsUi09PTWJaJV6+hmQaaaTI6thY710uut5+Ts2VM02Fmepqjux6j17LJOA4ik2Hbpm1MnzjO3OIsQ0ODHDq8n/1PfYsdO3awUKkRxyljY+NEUmCZDrpuoekpumZhmA5RFJPPldANA89LSEKNrVsvwXUK+GGDoN3ENgSG3q02HhvZSLXaYfXqDfT1raLS7GBnXEZXj4Ew8L0O2WyeMGzy9JMPkoRVNAMW5mZ49pmnKC9Ok81aOK7g+NRR6vU6lmmRy1q0Ok0effRRLrv0EjauX0+im4R+xMjgKohTZJzQ299Po9EkCmO0NIFUsvvxxxks9eBYFlIziJKINE1pthqYpkGn41FvNvCDkGwmQ6lvBCklfuCTyJh2OyZOUoIwptXxabU7hFGCadnYdpZU6mi6werVq0mShHanw9DQEIEfEScxYRQjEVhmhkKhB9fNgdTQdYtUN7CcLLrpcHxqml3f2sXWbdtpNFuUSiU2bLyALVu2UCgWCMKArdu2cdVVVxFFEZ/73L9wYuo4lmUxYCWIxiztxZN4zQr5/hEkECQSgcS0TNIVqnKXV9MKOK+d9Eux1E79+Y7xXQlZZXpueCwlUqYs9ZuWMu1WEZ9a5KmfK80+Xj4j+fxw+cWFzctf0zhJSNOUYycWv80npyjn2mPYfM7OgRBsTUL0s+Yr52XKrWGbHUnII4aDt0KFv6IoiqIoiqIoiqIoiqJ8u16ultff20B53e184P1v5+YbbuCGG27ghmsvZyzaz56pzgqNKRVF+VHyUgLlN//468llXdIkJpfN8GvvuI2ndz7APV/7N37+597B3PQcZiaLH/iAxPM9HMfioosuIk1TLMsiDEPQBIODQ2RcB1dofOqOT3DZJZdi2wZhGlGuQpBYpKlGnERMbljD2o1baTbbGLrJ4onjzBw6gAlsXr+Ww/ufRcQhICkV84wNj1DIFVksl5GpIAgCZmdmueCC9Ty9635sw2T91i284z3vZWRkDcLWKWRyZBybmUMHmJ+b5r4HHuK117+efMalNnucvANprBPHMVbGRdPozulNuoFFKmFkeJxstkTWzVEs9RHECYZM+ea/f5UkCli3fgNJmmFychNS2sSxhobO9Nw0Hb9Nby6HaxgceOZJ5ueOMjJeIEoalMuHmD95GJIWwvCZOrmPtZMbSEnoKfVQX5ynWVtkZGSU0I945MFvct/X/50rL7ucsZ4C//S3f4Hu15GGSTbvInUQUiKFpNVu4TWaPLvnGTZt20Kx1EcURQhN4vsdfM/DsiwajQb6qUrgSr3OwvwCQRgBBmEEcZJSa7Tx/AghdCw7A+j4fkzHC1k1vgaEQRilIAzKlTpxlBIn3cAzjlN0TWN4eBjbtgmCAMMw0C2LKApJ4ohsJkvghzzw4IM8+uhj/OEf/yFRGuM4DvlCgfUbNtDb10ur2eLgwYNUqxUswyCKImaPH+fYoX1EoUc2m6HpR5hOBsPNgBDEUYSm6SvPTeasOcScu/6lWh4of6+qn2WanBUkL4XA3UB5KViW6ZlAmaXbK1zvSrfPJl5ioKzpOpqmceS4mqGsfOdCIXjEdPiSnaVfpqw/pw22YDyJ+KmgRUI3gE5UdbyiKIqiKIqiKIqiKIryHfjhCJR7t/DqiyLu/+Cf8Ml77uex6hDXv+kK7G89yuHge3ZWRVF+ALyUQPnKV72KyE8wdZvhoVX8jz/5KJVmiyuuey2XvuZmAlmg2ZmjVi0zONDHsaOH6e/vJ5vNksvlAFhcXMRvt3FMh9kTxzm+Zw+ve+01TIxNMLc4x8HjU5R619Co1rAsg4m1a9iwbTPzC4u06m0Ge3u5/ytfZf3IKO1GmX3P7MbUJBoJB48co7K4gKkLkqCD12iwMHuCtROTvOs//jyvueYaDk09TSZf4LLXvIbdR6YoL9QZWL2aQ8/u5Yv/8v9ROXaMnlWjvPdXPsD6DRuYO3GExaP7ePbJhxkZnsTN5pmvVjAsiIIITWh4fodsxsUPIoSAcqVMo9VCShcrjTiybzePP76Tq66+hhMLz7JYOUm1NkcqfWzyjIwOMXX8KNIPaVUqNBenmV2cIj+Qo9ausufJZ7nmyuuYnlqg7YXoukm5WiFfKPKqyy8n8Tts3LSemek55mfmEDJFyJjHHrifC8aHmD/yFK3ZwyRugf7hASzHQGiCSKS4rsuG9etoNBuYWQfbymIaBr7XBhmTxAm+75HLFUDoNBpNml6TMPRJU4N9+48gpUaaSmwnQy6fx3FddN0kSVLkqcruODYo5Hu49prr0YTJ7OwiptENcCUSITSCoIVp6iRpjNCg3WlhGBoyjhEywTQEfhATxhGO69DstNi48QKymSyu221jW61W6bQ7uK7L5k2bWDM5wU033cRNb76dE8eP0A4ipOnS09uHbjsEMSRpimU7yOSFv2q1VKH8nfpuVCi/qBBannlOZ7ZfFg6fKVg+FSxzztznlc638rmfv8J7pXXdP/8uFSgr301NoXG3leFB02V9EjOUxqcf05FcGfu8PvI4ZJhMa2oCjaIoiqIoiqIoiqIoivLSvFyB8sv4f7Bimrt3cfjm2yn1Axe+m49s2scH77gPgOve/RE27v0gd/BuPrIjYr+zmguKBnF5F//7zz/H/hhKr3on777lAvIa+DOP8Jk7/o398fOfVVGUHx6DAyWazSbHpsqUq3P8xf/7Bfr6+njssceYnZmhlHVIwhJlL6Q8N0t/qcjeQ3uRUjA6uop8rodstshQfx/FnM2f/N77Gelz2Xrxf6WThpQbizz79LO846evQrcl2Uye3v4BvFabx3c+yYU7tlKvV/mx225lzfgYf/xHH8UwDOzQQyPFCJskGJw4sZ9Dx3Xe+Mbb+fHtP02+WODE9AxPfeMBtl/2ZgbsGDONMA0fkdEwTZvC8BBaqcDgUJEb3/wWMoUBDh44QhR0iNMFcgWdxfI0l27YQKILbM0llTFh1CFJEirVFhnXQugplpkiJcRRg04QMDyxkfVbLyaIQ1av3sjJ6cPEqU/LizAKLscO1BkaW89gMcvOB79JvTLP5a/ewez8HIVSEcs1sM0YPajgGgXSVAPdxjc0rrzuBhbmWzy77zGuuf5VfPrv/5n+4THqzRYDPRnmFmYZ37iDwbHVjG+9hMVqBS2QJLFGqVDE932qrTbbLrqYJO7+Qrcsi0KhSKVSoVDKs3pygmMnpjDdENORPHPfMTrtCDPbx/otF5ExMui6wLAtUhLCOKTT9KlWq1RqdTI5l0SGzMxN88jOnRQKPYyMrKHdLhMEHoHXII7baFo3sLYsCyEEQRCeDl7TNCVuegz0lNCsHEJI/v5v/oZf/eX386Uv/ysf/dAHmZ5u8RNvewO/+hsfoFAonK7InVlYoHLwWSZ2XM74louYP3mSRsMnjSWmLhCGQcfv4AgLgDTthrCng89lOenZ7Z5XClaXh6jpSpW74qy1EnR5Zt/Tx5TLW/KeG3iLFXqNLL+epedy9mNL13fmOZx/eVIm5688+9zizPWeOdb54bE8a5vlld1n1j//uRTlO/GUYfOzhWFuCtq8z6syujRfWUgmk5C/bsxyl53jj90eys8zR11RFEVRFEVRFEVRFEVRXkkvY6BskN9xCWuzLXYtAqueZ9O+PNVP/SEfnFrN7e9/O6++5HPs33klt93Uw547Psy/zfVw8398N1de9m/sf/jlun5FUV5pQ0NDZDIZyuUycRwzPz9Pp9PBtm2OHTvCmjVrkEmMZVnEcYyha/QU8ywslEmjCNIY1zJpNhpk7BK5QpFs0cG2bXzfJ5ctUalUWSiXGR4YRNd1SCWHDxwkjlNM0yYIAxzHRrhZ1m/eTi6bJWy30ZA88a3HGezvZ3BohLG1m7j4qmvJ5nI8u/cZ2u022WyWsONRb1SZPnKQ3t5eRgp9mKZgfPUkb3jzW9mxZTPFngLCMOnp7ce1EqaPWkQpjE1Mki31MmA61Ot1kiQhSRLSNCZJEgwkmhDYhkEa+yy2QmSqc8mlVxJGHRYXFxgrTAIQBAHZbJ4gCGg2m0xOrsdyC1x0xTXUKtPojoFhdhA4PPzITi7euAMr49KJAmr1JtnecWbmZklT8IOIbCaP12wwONyH53cwNMGq1eO86rpbMbMu2d4sR4/OY+gOHb+N0CSNRgcdQTaTobxYZXR4BE238Lw2EoNisUStXMb3QvK5Iinw6K57kalBNp8nVxwil88TtDvdAD0OSWRCEsfddtW63g2C4xgpEzSh02rW8TsBmlgejuqkaUwcx+i6jmEYp17bbjtk4NRxJMVcHk2D48dPoBk6+/fsZdDWuPg1G3n4gfvZ/Hef5MSJE9TrdXzfx/M8NGkyMNSd/SyFRvXgYer1OplMBsIEx7KR4bIgmfOrcl9Me+rlj69YZ7xs5dnHPhP8Pv9xV6oIfjFVy+eH4SsEwWeFzy9UKf1iH3+uQBk1y1Z5GdxlZ7nHyvAOv8G7/Dr5sz4rNwUtrg47fMIt8Wmn8ApepaIoiqIoiqIoiqIoiqKs7GUIlMe58SMf4UaAuMn+L9/B3fUX2MWbZs8RH9jPQs2g1wHYw/6pG7n5P/8ma6f28fg9H+fuA9/ra1cU5fvJ4uIi2WyW0dFRoig6VdGaommgG4K9+54i67jYtk0URyAEg71FsraB69qkYYfenj7K1QaVSoUPf+xj6HgcPjSFZTrohsv7f+23u4F1FJAkGiePHKO8uEh/bw9xHJPN56jXqyRCZ+tFVzAwMMDC/Bz5fJatl17GfV/5Av3FAhdu3YZuuXzhrq/S31dCSkmrUadvaJCoLrGqC5hZl+mZI5SyObZtvZjJ9ZuIIoGfeOhBiCbAyfZy1Q1vpW+gl3u+sZNjc3Ve+9obcN1uCD41dQzP72CaJnHQ4tDe3URehTjs4PQU0AzByUoZXReYDpTLZSZWb+DAgYNYpkPQCdCSmN5CHtct4OZ7aMYS3TYZXTUIpPz3D32S2IsI45NsGJskn8+za/eTpNN1SvlBjpyc4ZLt2xkbXUXgpzz9xC467SZHjx/Ej3Uee3IfMpswaPXiRz65QpYkDUkCCYaOaWTYum0NM9NzfP4z/8rrbnwtpWIe9BZjq9dRrVbpKfTw9XsfolLXGJ3YimU5hKFAygjD0LrhoJBogGHozNeqRKFPHAbEhoFAJ4xjAq9DmqboukEn8E61XA7RNQ3btkiSmDju3s5mMwRBcDqITNOUucUFMraGpsHo0DAHDx5k49oLuKfd4OgzDW689Y3s2rWLVquF7/tkMhna7TaaZhJE3VmqA+MTjE5M8vBDD9FsNomCEM/zcA3nnNnJK1X3vphZwsuJF6j2hXOj4TPnOrcFyItptf3tBMpLt4VYqUT5zDlfKFR+vpB9pRD5/FBZVYUqL49QCD7pFvm8neM9fp3b/cbpx/Iy5dc7Fd4UtPhoto+nDPsVvFJFURRFURRFURRFURRFOdfLEChPcfcH7+A+40re9VvX4S3UXuJxmnzz7/6Q3QMXsOWyC7nyp97PBXd9lE/uVD2vFeVHRafTodPp9v23bZtcNkuz2aG/v59KdYHBoV46zQ4yDdFFQhwmNJsLpCnYhoHvR1i6RuBXSRMT0yrgeTGZXJZqpUlv/wizc4vk8i4H9x9ACgjChJ5CHreniONqZCydTE+J6vGT2LZLo9HCzWTJ9/TyD5/6FI0jezn+4P187bN38r4PfYxqPq52AAAgAElEQVShngxR2AFdp7evD2majIxsZ2ByLZ7nkc0XqTU9HnjgAbJuhqGBARLTpKco6CkUAI0oMmlHGW66+cdp+g2+8OUvMDI0jONYWLZJLpdDygQpPAZHenjioSc4fuwQay7YwsjaNWTcAkkMkRdxcmaK0eH1ZN1+3IyNLkLWZbOc2PcUd3728/T2DfAf3vlfWbfuAny/Q5wEHD0yRSwFhcFJZiot5modtu+4nEuvuI5KvcGGTVu57tpr+b0PfRhNJoyPjRJ2WmhJAz06Ro/VItUKdLwahm1Rq9Xo7x9k+8aNxHHM33/yH/iff/VPuG43RyzXW9xww9X09WSZPjnH4GA/ATH33f84GzZegW45xAnESUicdEjjBJCQpqRhQBQFJFGEkCmakMRhgG5apDJGphFCSDpeE81wEAJkoqMLMC2dJOmGr8GpsFnXdaIoOh3wmqZJq9GkJ5+jr1hiz769bL5oAx//zOe6FdESFipVTNMEui2fpZRomsQ1bQQpppAUzQyvvfJqqs0G33z4IdAEpOcGx8/X1np5++iVHluyYqC8rJ21POswZ/Zf3tJ6efD6wu22V7r2pYrvpapobYVAWVt2rqV9lh97xYrjZdezfFb0C82OVpTvtbKm89FML3faOd7n1bg67NCt1JdsTEI+1ZjhTqfAx90STVVBryiKoiiKoiiKoiiKonwfePlaXseP8LmdV/Jrt9zM3X/+FWpzFZqvXs0lDuwuvpq1fS+w/8CNvPdd4zzz//wD931tCgbex6sHBoCZl+PqFUX5PhDHCZYtSNMYy8pj2BYFo0S1Mo+p6XTqTcoLc6zbchHIFDOsU5k5RhT55NyUamWRVmOBTKGIZllEQYomBIgcfb1Zjh8/TjabZf7kPEP9gzQ7LbJFE9sqkmoZ+nvzTB/az5/93kdYveMifvl3PgRSI58v4Lg6r7v5er5xVxtbH6cddlhsHMUsrUG3TDRNJwxjzNjjwPwclmWTy5bIJRYjg/2sX3cBs9PHOPDoA1x66WZ2P/gYkxsvpR2ECFNSMMvMFDP4QYcd27Zy+MghMvQh/IQQn7ZWJzaySCNDLUkZ27qFfO8qZufniEOdkeG1XPua23jo0a9Sa1QxbYNWOyCTy+EagkqtwnjJ5cCex7hwxwbu/sYD6KZFvVImm8thGhn6+vqp1MpYlsXc3AxOpkCtVmPjxgn+zz/6EF4aYYQBjWP7WdVf4ObbbuXgEzvZ/dSzCMNmeNMljE5u4qorrufA3gNc87pbCMOUJIFMTzc09Grw0AOPcuzwXn7mp99KocdhaHKE97z3N1l/waW4bg9JGiJJ0ERCEgQ06gsYhnE6vG21WoRhSBQl6FIi0gRDl9imA0CaJNiGSRTHpGmKZgoMQydNAvJ5G13X8b2IdismkREy1ZBSYpgQhB4yn6Hueehhh/r8UaayAseyEUJHN2w0IdB1Hcs2MExYLM+yenSEtp8yOjRM68QR/vJvfp+njszxhW88Qr3Z4fHHHsQwbcLIR8YamjBBP39G8ZLTUag8P3xeyl6XAtNk+Uzm7tpzDyhXnmV8bvj63Ndz+jDLg25x5txLZ0/T7rmFtrTmTHi8tJ92Xth7fqimaSsHyGffXgqdlxbtOdprq3BZeSXs0y1+KTfI1aHHb3hVJpPwVK4suN1vcFPQ4uNuD3c6+Vf6UhVFURRFURRFURRFUZQfcS/jDGWo3fVvPHnhO3nbtd/kjm/czSNzv8Rtv/MRbvMXWIjAf76dF+7l7mfezU+89/e5UQN/5hE+848qTFaUHy0S0DHNbrvndruNZRtYhkmqg2UbDA2tYuHQk7Rmj1Gp17nihps5cOAAc3NldN0kSSPy+RKGbiGlBtJAyu7s3DVr1tBqtUjTFEFCT7GIH0ZIISAK2LDhEv7hE3/FfD3mV37m5+ntLdFq+iwuLtLuNNCGN/ETv3wJa8bHeHrvM8zUyhi1GrreDZQB6k2P3mKBVuCzffMa8vk8wjQo17rzoPsHennq4Sd4eNdDfOnL/8r7f/P32bz9WgzDYHpxFssy6C+UmBxfy7/+01+z86Gv0z88wuve+OP4XohrZbn1lp/F89osVmdx3DyddoTvpwhNQ9cshJD4fkgcp9hGhrGxMQ7tP4LZN8pb/9PrKFfb6IZL4Pv09g0QBAGdjk8m4+E4GQxDw8n20Nc3SMer8+d/+hfkLQ3RrNCulfmF3/gtLrzwIj7zla8ysO21vO26n+SKV11LJAP+77/8W951xevIFRzSjo6hW+SzNnHkg0hxzIgwaFJeaPLlL93J7374Dzh5YpZspkgcx3S8JrqWEgQeszMnSKIAw4REdoPLMAzxAp8kNUkRpHpEPmMjdAPD0AFBGHbnIXdbX+uYlo4QkM9kyGQtpEwIOm10ESN0kzQRJElCHCc4rkWj0SDj2DiOQSaTwdVN9ux6lFWj42zafjGJ0AjDkFw+Q6NRY3R0lERIcnmb8uIJ/ven/pJyeYaAlGq1yujoGN9KdDQtwkgTEJAkPujW+Z+A52ntvPRTrlCR/FKrcl+oAvgF95Pd9704K8jVzqq4fK7jn3+u88+9UtXy8v2XVyiLlfZRgbLyCnvQcrnNcnmH3+AXvBr5U1/eyMuU3+mUeXPY5qPZXvat8DtBURRFURRFURRFURRFUV4O39tA+dCd/NEHz15xmDv/6MOn7933dx/lvvN2uoMPfuPMvfvu+OCpbWL2f/F/8tEvfo+uVVGU73umYSBktx2upgsKxSJCgue1EeikqWBxoYxXnccMKuhRBMDg4CDxXELH95iYmERqOppmEIWSJE1ot5tYlsXi4jy6rhPHIbomcG0Ty7KIpEWmJ48g5U1v+XGKTpHLXnUVs7PTNJttfC8ilTGl/lVYpo5b6EW3M0RxmTROMKVA01JIUoZ68kwd2s/JqWP06TFhDFfe8GPEcYzrutSCiLWT6/n8Vz7LzqeP8tTuXTjuOL0DJUb6hwjjEND504/8Fk/vugsZNzh26AluueUmOklAFGsgHNxMHrNZAwRGXiOJu3OGs9k8UVjHNG2kDBkbHadeb3L51deS6xmgp6+PWrNDHCYkYYS09W61bdbBtlygRhjGjG2aZHp6mpMnj6DpgtAPqM5PMzk6wGvf8jaOHDiMNF2uuPo1VBcbfOKOv+Xxxx7iHz9zH2PjfdimQ4c6cRwSBw3iSJKmMDQMmzZv4NJLL2b71k2MrBrnGw88gmU5GLqOJiRh0MbrtEmiAF0X3bbVUpKmKWEYkiQJqexWLNuGjpsxEejEcUKa0j3nqUAZIA1idF0j0SUaJmgCQ5fomiQlRQjjVFDbbWGtGwaWZZEkEXEckwQ+e3c/wb6nnuKWN7yJo7PzaJpGq9XANLvvIT/0MHSJ5mjUWxW8JOENb34TYRxh2zbr1qwlaMzjtxt4YQRSLK8hXtHZFbhn1p27zXO1gn6xXmoI/WLaUT/XfkJ8+3ONn6+l9QsF6s8XTivKy+XTToHP2zne5Td4h9/AOvVh3h77/FN9mk87BT6h2mAriqIoiqIoiqIoiqIor4CXtUJZURTlO5KGyNQklSm5QoacY6Np4GZM4iShXq8zvnqYB/cE1GYreEFC9PBDXH/9DTTbAW4mYmFhnp7+kW6wJARx4mM7Fu12G0nK3PwCuVyOUj5Po1qh0Wix5fKrkInG0YP7WT85xoMiZveTO4liiaYZCCySJOXk/t1cc/U19BfydCoL2EmHThSTJBG60NDShCSpMbf/IdpzJ3k6nmVmocrUiUP0r13Pth0Xkh0cI9fbz8+/9zf4s8uvwLJzvO99v4sVl3nnL76Pq1/zOsBg71MPsWXdJIcP7CZstzm8dxf5VcPkCzmCNCCOY3r7h09VXHfD2kPH9tLXO8T0bId8rogfdNjz7LP09PRRn/co9o/jRRFhmFK0XGJiglaDTP8ojp2n0WjiOBls26ZRr0MUcv/XvoLw60TtBpbt8Ad/9tf82f+4g0svv4qiZfOx3/4AF6wZ49k9e8mEGm+4bIL7Hz1Gz7DJ2CqD8Ym1XHfDjUxcsImxiUnmZqaxbZd8oYgXJPT2DXPPPQ+iCRuSlDhuUK/O0el0EJpENwyEbpAkCZ7v0+50sCyLnKMRRxGWZtJfcAkjSblaw/MDvBCiWGLbdjcoJsFxHBw9xdJMDEMgCgVM0WG+6YPUEUKgCwshEoToViwXCjmGh4eZHF+NX1+gWCyyc+cDWIVBTNPEdV18v0OapvQUiug6ZJwSP/ueXySfz2O6fbS9NqWCy5/96f/FWC6LY8L4ujVorktuaM15HwGxLEh6vqB26efZLZ2/3Url7zRMXgqGn+s4Z7fSPjcAXv7Pkxdut312KPxcz1Ou0CZbSkhVgbLyfaIpND7ulrjTzvHrnQrXn56vDO/wG9wcdvhjt4e77Owre6GKoiiKoiiKoiiKoijKjxQVKCuK8gOj1WpQKg4iJSyW53lq9wn6BwcYn5wgTiRuNodpJFz31p+mUi5z7xfv5KGHHuLLX/4KH/7IH1KtV6g3KpQrc2QyOXTNptNpEXYCDMOg1WqRJAmtVotWpYKB5M5/vpPf2HIhjWqLbEnjzs/9M4/c/zVec9N1pGYRXTPwOiEACQZDE+uZLjeYrbVJoogoTdDjFE1ILCmpCI3SyBjl8gJmNkNUaXDx5tUU1m7gyNQUAyOTpDkHV7o89tQ0V1x+Cde97gra+7/Jb//qu/nTv/gbLrr8x3j/x/6U8d5VHD5wACdnsBjMUTRcekpjVOoekpRE+uSLxW6L5oxF269iag59vYMslI8BkC9kSEWKhmB2agbQ2LLpUp687z4++5lPMjN7nL/65y9TKvWzuHCAnp4SvX0lKjNl/vFTf8/i7EGSpM72C9bwjvf+N6Y9m1/5P36Tr931ZY4dW6BU6mN29jiabIFj8JPvvIW3/+cMuayL3elQbbZohBETk+toehI310cmU8BwihgkNFodUnSymQKeF9DuVIiDFqaukQpBKiCNIoIgoN1ud4P03l5G+nXS2KYyV8VKE0KZoAuwTYNU6EgRYpomQgiCMKbVaiGETqfVxrI18vk8GTuL3gmI04Q01dA0TrfJdhyHHTt2MDw8zMzcLCMjg2SyDroJlUqFiYkJ/KBDT08PR44c4ZH77mXn4zv5hV96L/3j65k9cZKJ4R5GBoZ44J6vYekhsQembrM4dxK7mCc3PHHeZ0A7L2g9M0N5qR32UrC6FKamK8xQ/l5WKJ+73/lB9vI5y2KFFtjLg/MXe77lz/HsUB0gWaFluJRyhZnNivLKmtYM3p8b5LLY532dGttjHxD0pQkfay9we9jko5k+jurmK32piqIoiqIoiqIoiqIoyo8AFSgrivIDoyCrhB3Juo2bOPStZ5g7+nWkP8HwcAHD6SOTyRCHPtMHjzM2Nsarr38L7WbMtRMT+InEKvTQM7Kedm2RKIpw8iZJK8Ip5LBNi1yhAGnK6PAIpuvw1LceY2Kgjy/9y//ibb/4W/TbCcfXrGfhwotYPDHH5GXraTRapLKNlAJdCJKgTXl+iv5iD7reT705TxwGaKREUYStSy685mauufWnCVodLhMpf/WxP0BkM9zytp9koNSPW+hhcvskBw4c4PFdu7jq6huJtkzw5//r/2fvzuPsrOv777+u/Tr7NvtkZrJvJEBICIR9lb3KVlTUAira21rvR6u01Ufa+rPcvbW9rVppS1wrVcAiKKKisoUdEpYsZM8kk8y+nH259uv+4yRDNhBsK0K/zzy+jzPnnGubMycDj7zP5/O5nzvvuJOTVl6IZsRZ863b6WpNc9zSJTg1mUh3K5NTBbwAFEWhMFWiu3s2qtwAu8GGJ3/JeZe+j0o9IBqNEng+iqZStywKpRLxTBrbtgkclx2bn2F83x4WLlhMxDRJJeLMmd1HJBJj06ZNPPXojylNvMKChYv5i9W3UnFkarU65YkRPvC5T9HR0Ybq15g5awZuPUG6s4tUsp3HfvVr0qbO+2/8GGu3P4UWiXLhldcxXnJQpCiK6mAHAa5dJ5dOgOwzo6eL0eECVaeErEvE4214tkO9XqdWLiNrKpZjgwy65BCjTkI3WbRkDs8/PUpXe5K9/RMYikyAROgGBD6AjKpqeJ6H69lYskMEGdmXwHOIJUzUogd6c45y4Ps4NYdERwuS5HP6Gady+9f/jSsuOptMXwcTExOcdurpfPrT/xe6KrFw4SJ+8fNfUypYrDh+Pi2pKLN6ZjBVtZFkhVTMZGR4Hyefeir/+M3vcfzCuVRLRX5yzz1Y9TqFchVFNzCicWzXxw9AlQ6ExzSraqXgYDjbXHAgPA1DZEUi9AMUSUJSVJAkggNBrXzETOJjVejKB7LXN9Mm+sgZz0eeBwDl2P/rcWjofGQ9soxy1PEl6fBzHVWNHIZ40+dv3spBcNT2YdhsRy8Iv4/WqyYfSnZwkV3jM40CucADJFa4FneXR/i+meTfzBSO+FCEIAiCIAiCIAiCIAiC8D9IBMqCILxthHoCI5bEbdSpFPIsnnkcnqQheWA1fObMnM1Xv/IFbrrpJh5//HG6uro4//KrqFQqtPfNJQglkjkb2S+zf/9+du/ewZIlSzDNGJZlMTU+QTyRYngqT6ioLDjhRB766b1gOjz04H9Smppgwfy5/OnffoH1G7bgeR65XI7e3l5s26VWbWBZFpVKBYBCoUCoKWhmFAmfwuQIkZY2JosVdu4fJZfOsGLpEkZHR8iYSR65537+339/Hw0vwNRVEpEALxbgWONMlD0+89d/R3f3PPzAYWJ/Ebs2zv1P/IiJ8QtYfuqlVCo1hob209HeiqJodLR3kYpq1CYaVEvjLJjdw+5XniHdNh/FlInFYhimydhoP8lYloblsWDuYr717X/h6g9chxNaICmcv+IU5s1rZcmS49E0DavhECg2N978cU495wr2TVk45UnisTTpZDuf/JNP02jUSMQjrH/kV+RSCj3tCSKJDhZ0ZfnHL/8DP330UW781GepVCq8+MpWentmEYQOASaGFkUOPZ558mGGdm1kXluEfVs2kjYzTBYruA0LoDkrWQLCEN8PsK06C2e3sOL4PqYGd9GWkjhubjfFcomI4TNRbdBwQxqOgiTrOI7TbEkdNmcoe56DL+sEgGbopDJJOi2bPYOTaIaJLkE0FyER1Th5xSmsPHkZ3/vuGr70T+u58ab3s3HDVhr1Mhk1j65FefmpR3BLo1D3GS9V+b8/81kUNYJpKCTiLdRrPtFIBkNL8qsH17Jk6TKqXpUPfOyT7Nixg4fvuxcZiXI+jx6NI6sRZMmfDpSRQOLoilvPdYlGVGKGjtVwcAJAlg+kzgeql4+as3z03zf5NdpGv7rP0Y8fGTKH4X+luvmQ+0c8f2hF9us7fBL1ofscWdktCL/PfmnEeFSPcpNV4iarjBY2u17c2CjyB3aVv47meFqPvNWXKQiCIAiCIAiCIAiCILxDvfmekoIgCG8RPZZCNyJE4wnKdRsvVNm2fS+dnd3EYjFGRkd49tnnSSSzhKGEbbtImoGHjKwY2E5ApVIjlW7huCUnMmv2AiLRBOuefY5tr2yhp6eHum1hey6F4gTPvbCeT/3Z51h1ymWsOGkZsUScQrlGMtdGteEwNTXF4OAg+XweVVVJJpNYlkUsFqO3t5dZs2ZBKKOZEdpa2nFrFf7jtq/RmYyydG4PfnUKI2hw+hmrCIKAdDqNoihomsGP7/she7ZtRPLqjA3uBWQuueI9XHjRpVQqFVKpFMuWLSOTyZBKpTBNE9u2sa06UuihygHbt65j88an8OxJfLdAvTaJrIRomoqqqs3WzYbRfG1VFU1V0VWN7VtfoeF66PE4k9USZ5+1lJZcluHB/Vj1BsXCFDd88NOsPPUiRibyRKLa9PkrlQpdnT2sWnU6y5etYsbMeQwPjVKvVCF0SSRinLh8BfF0hiefW0++UicWTR5oV+3guj6GYVCv19E0hVWnLGfurD4Cp47nNAgcF1VWkGm2u/aDgHq9jud5ZDIZemZ04DkVYhGNqKnhuQ0su46iK/i+Txg0w1TXs7GsOq7roqpqsxWzBJqmoJkG0USUdDpJSy5D6IdIYYAig2loZFJptm/byTe/cTs3fuiDbNtWIhHPkMnk2LFzK74aJV/zsOUom3dZ3PqVr3PK6WeRzrZSqzVQZQ3HcVBVlSAIaGlpIZ1O4/iQzLSQL9fomNFHItPCwMAAuiwhBx6+a0+3cT64ZFk+ammahipBKT+Fb1uvu+3rrSPP9dus8Bjrt3Uw+D309tAVBMFRC/+Idch+QRCIUFl4W3EkiX+LpLkk1c09ZhIIOdgG+7bqGLdVx+kKvLf6MgVBEARBEARBEARBEIR3IKWnp+dv3+qLEAThf5+rrrqKu++++03tc/Fl70I1IqAYdPTMYskJJ+NrEbpmzUOSZXTVIJGKoSgyK085hXQmgxkx6enp4aWXXqaro4veGT1s276VV17ZhWHEkWR45uFf82//ehtLlhyHoWtomkKpXKC9s5VnHl3LnFlzSbflWH7yKbS1d5LOtLB75w5CZFzXQ5ZlBgb2MWNGD47jUCwWicViaJrG4oWLOf6448F1ePKn/0kLCnfe9QMe+vWv+MTHP8YP1txO4Dtc/v73ce1Hb+LZdZuZ0zePWGjx3X/+e7AqVCZH6O2bQzydYduOPWzbvoWhyX205Trp7Oyjo7sXIxnBcQJmdLVRLo4zPLSHB374fYZ2buH5p59AkSW6Zs6k6qvE0+2oioypR6hWChQKUzQsi3QqTaGU55N/8kkKlSqleoM/uunDnHbWpZx38Xu49D3v5dyLruCq996AEs+Sr1Splkto+PjhgWBWlohGTWr1KhXLZsnxy1i79mG2bHmR8cIkYeiRaWll8bLT8FSNRq2G5Ic0LItIMk5UUanX63R0dhGGPhvWP8fM2bOx7QZu3aOUr1CzLMqVCm7g4wY+hqGhKipWtUEubpA0A1oyGYYHh8hmO0DLMFG1mCxaNOyQwPOZPaMN2TAwDJ1MJo2uqzTsGt0dbbTmsiRTMTRTp60lzd6BAVRFQlck4pEI9XKd/p17Gejfy/atmzhhxQJmdPehqjp7dm1Bibbz0T/5HDMXLOf8yy5mz+g4f/Duq9m9Z4BILEkkEsWya8RiEXp6ulm//nlCAtrauqg26iRSKVw/4LSzL2T7ts1ENI8w8IjFIgQHWj8jSUiyjHyMENfUdDy3zP49u9GQMBJpZEVFkmU4GBTDYftwjOPISNPB8hsNmI8MZkNZebUf94ElH3luDn5Lh7S8PiIs5oj7zfP85hC42Tw7PGRJR4XQYSgRhrBrz9Cb+n0kCG+VhiTzhBbhMT3KzMA9ECJL9Pgu19pVfGCLauCLNtiCIAiCIAiCIAiCIAjveNdddx333nvvYY9Zdojj1P9bzyMCZUEQ3hK/TaB8znmnoqoKqqqRTLXgSjK9sxeyYcMWctlWLNth6QlLmmEUEmPj48TjCTzPJ5lMEhLw7HPPkp/Kk2tJo8nQksnw81/+As00WHnqCmLxBOVSjZZsgohfwyvvwnOGefrpJygX9jIwsAmr4dHdOR/LtanXa2iaThCELF58POPjE9TqDoSgSDID+4dw7Ao7t7yMNzXOjrH9XPbej3L7Hfey9pc/R3fGsEOXhUuXUW14KEaCfQP9fO7Tt7Bq+UpWnXIGiUSCenkvu/aO09nZQybXQeeMDkrlBqppEoYu+YkBysURuju7UOQkiXgXq84/FyPTwaJlp9AyYzY1W8Z2PFpz7QR+s1mypno888xjlMvjZNNpFCmkUG62lE6lEjTqFqoZxQ8UEslW3CCkWq+h+y67dvezacsW+vf0UxopMD41QjIVIaJGCR1wfBvFVOns7qBqO2hOnZGxYbr6ZlO2ZPLlMmHg4/oOqXSKibFR6vU6sgzxaIJ4Isf8+SfROWMOi5Yu4cGHfoGuK7iWSzSiUS9XScZyTBTH8QMf04yAHWBXJPoWzsQJdGxXoVSoEsgGEdMg9BpIoc/SJUvZsWsrfuhSrVcpThQ4deUyYpEILW0tJJIJQi9A0Q3yE2Mooc+FF5zL8XO7iCXj1OwqquzRlo4wMDTE+Recx76hnQS6zKLjlpNqy/Ho2scplypcdO5FjBTLJHJZVF3HaVj4XsDKk0/hDy44l59893ZKm9dhhwoXX341+UKV3t4+tm/fSHtHG3v39DM2Oozk1IhGMyDJaLoJsoysqoRI+FKArEpIqoSBR1ArkR8dJhYxkaMZNENH0nQkFBRkQkUBWSGUZEJJQlWVw8JjWZYPBNDNyu1QAiSl2TL7wAqRCJEIQo75dUgzuOZgIBwEzVsOj3h9QgIgICQgxA9DkJrPNf8uh3BINfGrjgivjxE6B0E4fU0gNavUD6lOBggOzKHevXf4Tf4WE4S31pSs8FMjzg5FZ5Hvkgp9FOAUr8GFboP9ssp+RXurL1MQBEEQBEEQBEEQBEH4H/S7CpTFDGVBEN42Wltbyefz5HKtAMiyShiGNBoNIhETz6tSqVSIxWK4rksikaBcLhOGIfF4nEajQaPRoDWXIxaL4loWY2NjnHPOeUQiBj09fdRrDWRZpVAsE1hFzHiOVCbF0qVdKIZOwvOZPXsulZrKokWLsCyHnTt3Y5omiqIwsKefTDaNoRjM6GpHN3VqjTpLlq/CDGWu+sifEsvO4L77fsqLW7aTMyWOW7qYYmmCrlQCyfGY3dXGXXd/i/zEIMPDgwzkhwgaeWYvmY0Z9RmdGqQx7jKzdw6KBDIBGze8yCOP/pxkspdstgXXdwhshZZcJ+lEHMMw6N+9i0w2S4CEF7h4jk0ykuDCC99NuVKAUKHRaDCZHyMajeO4Ep4fQrVKsVBj06atLFgwB9M0+crnP43qFMl19bLqwndTcUM0Q2HHrp0o0gAzunrItraSjCaR0Fi89ESGtkjYfsiGTZs5ceU5hA2fSrGEGY2zf/8QfX19OAOiNr8AACAASURBVJZDrdpg+/attLd30p7Osmegn5ZckmQyTTQSgKJTq1XISUkCP2DuzAVUq1Vsx2Oq1iCfz/PuzktJZW1279iJHo/SokXo6DKYv2g+Qajy3LMvYOgR4pkUkiwT7zbZt28fpqZSrhRpbcmQTiaQXJuli+dg6DLFyd1M+T4nrTqPD338Y1jVGt/8+j/RIuvUalV0XaNhOZx+5rns3TdAV3uOJx9+lHu/+w3e/Yc38md/eQv5YoHR4RFkWSYIAjZv2U5nKkLXnIX87K7/4Lavf52VZ55Jw2nw/R/9ik0vvcCKVWfz0/vvY+fgPk7I9KEaJq7vIykyYRiiKAoq+nSFb92vMHfeIvp39jOZLzC7z0CWZbwgQKIZFvthcFiFsCQ1A9gjq4wPbzN9dIB7rK/f7DYc4/EgfLU99WsLXue5V8956PcUBMFhzzVvj66sFoS3k0f1KI/qUa63yvxxo0gshJm+w23VMX5pxPmHSIYpWXmrL1MQBEEQBEEQBEEQBEF4GxMzlAVBeNtoa2sjnU7jui6y3AzTHMch15LhxZfW09negu/71Ot1FEWhVqsBYJomnuehqiqXXnopf/mXf8n+/ftxXZdMJoMia/T2zmTDyxup1y0q5RoeMqGWQE504qkpTDNJEOooahJZjVCqlti7dx/Vap1Vp57OiSecRLFaIRHXico+P/7hf/Cu888mEVUxdQMfldb5Syk4OnuHxkgks5x/2R+Qm3cijh5nqlzm8cfX0po0+Mbqz/Kp69/L2gd/iVe3uO4913HBZTeQy86hNGkReAZdHd14noPjeFTrHqecch7/9OVvcNLyU5kq5NFNDceDdLqdQqnBzt0DzJ6zkEy2k2KpQqVWZXKqxPBYkYatUamB5YWEio6u63heQOBDGMjYtktXVxeXX34p1VqJfGEMP5rk8We28sJLLxOJxUmnFOxGiXg8hqKEDA7vwdAVfnzvPezatYv+wUFaZ83DzHQQiab55c9/wqzubmKGjmfZpJJpEokchh4jHo+zYsUK2tpaKFanOO74JXz2c3/DCy9splSp0tGZZe68XhYvnkkqLlEcGcWr1TBVhVgiyoknn4TnqaTSbaRyHXT0zqRYLrNl+zaeX7+ObTu2E4lFUVUd04jS0dnG5ld2cuGFF2IYBqqqYlkW0WiUjvY2JDyK+Qk0NcAnxAl8nn5+HV29syiWG7TkWrHserO6VzIZnygyMjzK5OgIO3dsYWJ4kgfvvYeFc2czOT6ObhoEAWiqyi2f/QsufPeVFDDYtm0CXXLpaIlRLoyDD7lcK71zF3H2BRdz/KmnIxsakqpgGMZ0FbEsy8020n4Ans/c2XMAGdWM0jt7LrquT/99OdKRraoPvT1YxXvkrOFjzS5+rRnGb2QReEctKfSRQh+ZACn0CQLvGOvw4xzr2g5+L2EYTlcni/nJwjvV980kl6Rn8H0zceARiYvsKveVhrixUXpLr00QBEEQBEEQBEEQBEF4exOBsiAIbxuVSoVsNotlNVsyu46P7/ukUim+9a1v8Oy6p2lpacGyLGq1GpIkoSgKuq4TiURQVRVN1ohGo/ziF78AYHx8nP7+frq6urjnnnsIgoBEIkGASqhoeJJBKEeRNR1FNghCGctuzgsOQwnbttm5cye27WBZDqHv8fOf/YQf/eedzJ87lz17+omYJolYHN00cfwA17OxGhWqlTonrjiTzr4FlKounV09lAoFquUyGzfu5drrPkCt7nLLZ1bz4oYdaJqJYcQwzRjDgyOEfrNRsGFo7BscZGxsjHK5THt7O6ZpkkqlKFbKdHR10tfXR7FcJggC0pkktVodWdJIplN4voSup1DVKLoRRVIMkGU000CPGGQyGYrFIuvWPUetVkHTFT72qT/nlr//Oy675noeeughRvbvYWZ3F77jEotGiEUjPPCz+8ikYlStMpqpMVYsMn/xcbhOgNVweGH9egLPxzAidHR0YZpRYrEY9brFxMQEQeiRbcngOA5PPfM8tUaDkZERgsAFyScW0Zg7t5fW1gSZTAxVDWjPJklEVWQpIPD8ZtiIjCSplMpVXCdElnQcL6BSqTE4OEipVCIel1EUBcuyaDQaKIpCIpFgaHiEZCrHrDnzSaZamTVzDo7jMTVVoF6vUioVWLRoEZIk0Wg0yGZbCX3wvIATV5zCuy66DCUCo+MjnHPOOXR3d6MoCr7vs2nzJvwwpKWjk+/+6Mc88Ni9nHvRhTz4q4eY0dNHLALV6iSZTIK5C+YSBhKKpr7aljoESWpWHUp4yJKLInv4Vpkd27dQqlQIZf11q4RfKxg+VsD8WutYxz3W8V97+UctDjbBPuTro9dvPteRjx90rNnNgvBOUJFk/jGa5bL0DNbqEQASYcCfNgrcVRpmqWe/xVcoCIIgCIIgCIIgCIIgvB2JGcqCILwlfpsZyh//4xtQVZVYLI6uG2iaRjQSY/uOLcRiEe686wece8751Go1DMPAMIxmOBwEaJqGZVkMjw5z3R9ey/33/4SbbriB3bt2kkllyU/lyWTShKGEZblEknECFECmtaUdAgfNiOH7CpISQVJVggCikTiGYdLW1sbOPXtpFMeo5if45u3/yoqVK/ECl1wmh++6FAqTNDwHNfCIGCrZlnYsXyaSbmHenAWkMznWPfs8fQvn8JFbPsWeYoVczyyKdY/z3nU2O3e8gqz41N0q6XgLAQ4hNsVynlg8xsTkKK7vggSKplCt14hGdexGg0jUxHFsJFWhWCqwaNGJzJq9gFe2bkRWDDQtTr3hYJgxZElD1VR0XUVWQmzLRVE0NF1B1SRkGYb27SfW2sfiBQu4/3v/yuaNL6EbUXpmzMRpNPBdi0xLAqtWZ6o4Rs2tkEglqJfr9HTMYHjfIGufeJqXXniRq6+5jhm9s4nGEqSSKRRFplDIoxsaHe0t3PYvt/HSi5uolm1ULeTGG9/HBRecy8jwALqiMDE1Tks2SeBbtKVMjp/fi2EoeK6Fa7loqsH+4SmKxQqGmcB2AxTZQDcMqvUGI6Nj3PRH1zM1OYkiSciyRF9fD6lkktauLi697N1Uqw7f/d59SI6LFyjksq3c+8M7sStTrDr9TCynhmbIqGoCAo2a7SArKte+93okReeWz/0FH/roh8mXikihRCKW5pVXNrJ95zaWLF3Gi5u2snnfXm7+449x/Q038Ucf/ji3f/1Wvnn711m8eBHHH7+EDRs2NatsJQnP9ZqVxZIMoYequERNMPWQx356H7lMkpWrzsKIJbAcDySJAIkwAIkDM5E5pBI58H9zUBweHrz+d7W8lkL/6P0OVhC/bnX0scPi17qOMAyn50S/2upbQpKaVd5ihrLwTlKRZB7UY7ygmRznOWRDnxZ8rrSrdAUeL2gmjvgwhSAIgiAIgiAIgiAIwtve72qGsgiUBUF4S/w2gfLCWSYTY3vZtm0zuVw7ruei6xqSBPl8gc/+1V8BUKmUiUSiOLaHpumABKGEaUZIJJIkUnEWLJhPvpBnaGgQZJBVhe6ePgIgmoghSTJIMtWGy8yZsylWa2hISK6NoUdwfAVF0QBIpZIEQUB+apTxfdvYs30TDz/8MCeuPJNUSxeKplGt1TDNCIsWLsCMRTFjMUICVCnE1KPIio4ZT9E9Zx7ZGfNIZTqolStomkJHZzu27dKwHVzfR1c1qo0qYRhSKJQplyqoikwul8b3PRRFwXN9pAMBYjQWJwgl4okUsqTQM2Mm4+Pj7N3bj2HEAQjx0PTmTGpJBkVRAYkwkJohJgGqqqKqBqBiRmNEklGSUZX+/t24xTFe2fQyF11yMZ6s4vgBjlNnanIMwzBxrQC76mJGYhiRCIqmMzUxTDqT4a677mPVmWfR2t5KtVqjr68Xq15BCkO+8tUv84Pv341neWhawD//863oukWjUSCR1mjrjnDGaatYuHgO55x7HvF4hERCw4gkKZcr1Bo2hWIJxw1xHBfPD2k4LkYszmR+knrDwjQNMskYhiljNSp0dOaYM6cHz2uQbe1gbGKcYqVMV08nTzy9kY72dvb272Z4eIB58xfS3tUJkoNhmPhegC+pWPUGV155NTt27iKUFNLZHLFoDBnwfZdNm1+mo7OVkZEhlixdyJNPPMx7LvsDYrEkYSjhei5K4PLCs4/w9GMPEo1GUGMtlPMFQMaMxpEUFUNXmMoXIPRYMr+PtY/+mgWLTyDd2svYvj2sW/8C3bMW4Ho+vh8QShAQIgchBAFSGCIdGI8shVJzHfjTfJ7pFTZ7oL+hdXDbN7SPHIAUEhIQEhKEzduweVHNx32/eZFhgETzgkNAkppLlpvB8MGw+GBwfNDBx4+1OHC83XtH/gu/0QTh99OwrPJDM8GIrLLcszFCWOA7XGtXqEgyW1Tjrb5EQRAEQRAEQRAEQRAE4b/gdxUoi5bXgiC8bcxqTTE1tJdzzzyTSCRCPB4nDENc1+X8889n48aNDA4OYhgGnucRi8VQFAVN05BlGU3TMAyD0dFRdF1nfHycVCpFGEhUyjVURSdixrAtF9cPmlkXEpLvEXUthnc+xc5tv0KSJ0ButtM2DKM5BxZQwiprH/sV27ZtIZNNkk5FkCSJ0dFRSqUS4+PjPPPMMwwMDDA5OUm1WsW2bXzfQ9VkkvEYTz/6CzpSJqoUsHnrVta/uAHXdckXi7S2txNKEq7vI0kQBD5PPvkE5XKJqalJJElB0wyKxTL1uoUsq7S2thONxonFEsRiCSKRGDt37qZaraNpBoqioChKs4WyLE/fh1dDuGaQfPgKJQhcn3LV4oqrr+XaG24m3daJZdfYt+tlCuO7mBobwvM8ylNjJJWAtojGuocf5P477+C4+Qs57fRT8V2HbCbORz/4QV566lna2tqpWQ6KEUVTZe6740dgBVQqLrd/8yskUjKuP4XtjRHKeWy3gB+WcbwSM3q76e6dS7UuoUciZNvaSaSS6IZGJpMiEjGJxSJEo1Gq1TL1en36Z7d582ZeemkdqXSCWbPm0NrWSTrbTqVWJ5RkOrtncPyJy7j+hquYu6CXmlUm19JGS1tX8/3mQ61u09XVha7KNBo1xsZH+MmP7+Wss85i/vz5TExMUKlU2L17NxMTY2zfvp1UKsW659dz0rLlZLPZ6fdUrVZjyclnMXP+CRTrDj974BdIVgXkZiWuVa2ghi5yCKqqc9EVV+PqKS6+6gN0zlsKCoyPDFDNjx42Z9j3fTzPe0Nzj33fP2y9sfbVh1c3H+u4x5rB/BvnLHN0S+sjHztWWHzwff1669D3vCC8U91vxLkwNYPbIyl8JOJhwOfqU3yvPCraYAuCIAiCIAiCIAiCIAi/kQiUBUF429B1vRmu5vNUKhXGx8cZGBggl8uRz+dJJpMAqKo6PTf5YGB08NZ1XYIgoFAoHBIiSfh+gOt6qKp2oDq3yTQMXNth375nUPUSiZiPY1sQKtPhmCRJuK6L73ksO2kFV15zLR+68SPUGnXy+Uk0TSGdTpLJpGhvb6e1tZV4PI7ruoRhSCwWQ9U0JDmkNDnKI7/8Gb3dXWRzrQwND+O6Lo7jYdsuqqrjeQGe5+E4DitXrmTevHm0tbUhSTKKoqJpOrpuEI3Gpu9rmk6lUqVSqSJJMvF44kD19tHVmwcdGQweGS4ShLh+gGKYLF52MiecfDKV0hTzZnfRkY1CYCOFfjMgLZZ44ecPsGf9M/RvfYkd+7ZzxZXXMHPeHDRdIvAt/uPfv8vPf/YA2WyWTCbDAz/+EQkjghpKLD6ulxUrTqJWq+I4DgCyLGPoUSqVEmEYgCzR3dNDS0c3th8Qi8UOrAie5zSDSSkgDAPq9Tq+7yPLzf8MappGOhmnp6+PXGsrASqyapLNZqlUKkxOTqKqKqeddhoPPfoIg4ODFAolkqkc4xNT6EYMxwkYGBxC0zQ621tZ99yzFAoFEvEo+/fvJxqNks1mGR4eJptN47o2uq7j+yHz5i0gDMPpkFvTNMYnplh15jlcdfV1fOBDH6JSqWBETRzHQQ4DHLtOwtDomdFFJpNjdKKIj0b/0CCO57J3oH/6+34js5DfSEh8pNdqj/2bWlAf6zjH2u/I+69XdXzoY8d6/rVmJR9ZzSwI71SOJPFvkTQXpWfwsBEDJJZ6Ft8rj/Dpep5EePRsckEQBEEQBEEQBEEQBEEAUH/zJoIgCL8fapLBomUrsTyPlmiUSCzO3Xffzfvedx2maRKNmlQqzfnJphlBURRUVUWSmm2bgyCgv7+f1rYMjUaDcrlMMh4lmUxjWRa1WoN0Ok0ioeCGHrKsYigyjUqZ/v0jnHrSCjTNoFYJyKZ8SpVmsNrS0sLk5CS2DaefdzkKEvGWWYxNFZHkGrqhIsugIhOJJqavRVEUJEnCcSyi0Sjjo0PENJ/dm19i757d9M2czfEnLGNwz05a29ppNBqESKQzWXKZNqLRKGEY0traSn9/P4Zu4DgO3V09BEGAruvT1ZeKorB/3xCyLGMaUfJTRVRVRVGl6VAcmP76YFUocFhV6XTFqAye7RCJ6jiywljVYv6SpXR1JNi7awOKDPbUIGqqD99XGB2fQsrGkRMRzlh2BqEUxfJNPvO5z/PVr36R8clRNr3yLM889xRTEyO897pruP/u79GVS7Jp6zDfX/s99gzuZXxyAk1r4Hgu5coUhpbGMHNEYxkCwA49KnaNVCJJICu4gUsiEWNisoqiSvguhKGPJIWYpkmIjOPZqKpKW1sLqXQC27YJQpVIPEO1VqKlrYMwDKnUakxs7ic/2UDVYsydt5C5c+ehGTpTU1PkcklOPW0VL6xfx7ITT+KH9/wnC+bNZdOGl2nv7GJ8fJx169bRaDSo1YtUq1VkSaO9vZ1UKjMdnh78sMDo0DCd3b0sXrKEidEJbH83qWwSNdTZ+sI6tm55Ac2H93/4Y/zLP3+NCy56F7VajUACSQ4wIjpdXbNxHAc/DAglqdlIOmy2lT748waOGage+djrhb3HCpAPfV+97nFf4/yHkg8JiaePc8g85Objr35GTjrG9se6D81A+c0G4ILwdjYlK3w61soC0+Gva5Ms9lyutypc6tT5SiTN/QdGIQiCIAiCIAiCIAiCIAjCQWKGsiAIb4nfZoayEclzwknLMc00ZixNMp7A0A1kSSaZSFGr1onoGqqqo5pJdD2LJoPVqBONxNiw4SWSySSNRpmR4WEcq048GiEVj+G4DsVqje6eblJJg9CXCYKAaCxOuVRn+cpzKNsedqDgBZCJpghC0FUJ/JDAdsll48h6jONXrGJkbIzAqaPqCuOTEyiqguf5NBoNXNfB81xkWSIWi2KoKo///G7Wr/05Vn4PC08+E8tVCCQD1/Pw8NDVOMlkhly2jUgkjht4aIaObhpYjk0kFkXVTFTdQFY1FE0nFkuCJJNIJmhYNqNjo6iqjOPaSHJz9iwcXWWqaBLIMoES4PkuiqxiOw6SLKOqzc8hqYqEpskEgY+umwR+SMTUURSIRiI0LId4roOWXCvIOjNmzufMy9+NnptBNNeKb5eZ05LCtWqcc9F7UHWdbdtewjR0Bra8iFotMLOrnY3btvHRT9zI0lPPQpIMlEBiMr8fz3GR8Ak8UJQ2otEMYSATBCFaRCNqakQ0HdnzKFTGUaSAeqNOGIZoWpx6zcH1QyYmikRNhY5uk8svfhceKlokAYqBrEioioaiN+dKt7V18pe3/C2yElKp2nzik58glkzieA5GxETVVLZs2ULddinXylh2nTPOPIOBvft49rmnKZeL+L5PS0srU2MDoCpc+/7rScSSyF6A54eMjY/iOBaNRp2x8RHmzp3LY2sfZ/eePdStBol4K4uPW0yjMUWtXmJg9wCbt2zhgx++gbJVx5dDli1ZwuDQOM+vf4WOrhnoyRRBGCCHIUoIgesiySGoCg3PQdYkCBz8wAHJJwhdgsBFVfSjWkofJEnSYSHswVD30O0OVn8f1YZ6ekrzgT9y+JoB8MHqYVmSX/36QLcBSTq8dfWh9w9uF0oKHJiHjiQjKyqSrBy2Dj62fefAm/p9JAhvd1Oywr1Ggh2KzkrXIhMGnOvWWelZbFQNirJoBS8IgiAIgiAIgiAIgvD77nc1Q/n3M1A2EyRwcETnPUF4x/ptAuVsq8eX/+mrdHb3EDFjpNNpUqkkhUKeeDyGbVt4IXS0dfLPX7yV2/+/v2PlGeeSy+UYnxhBUUKiMY0vfvGLqKrMrJm91Ko1grAZrrZl0tzznTWs+dKtnHbaGbTm0oxNDPPYY7+mr28GvmejaRqqrKDJOgEKhAGWa2H7LlokxYy+WUxM5YlFTIaH9uNYLj1dMyiXy6iaiqrqh7XYVRSFbCbLzp3byJcr+JLO7LmL0KIpQkUjCAJSyRiyrJFMJqfnQruei6Zp01XOAGHI9MxoTdNQFYUwDNm2fSuDg/tIpVI4jj1dIX0w/DuyStWp2/iWi44JjoJqNI95sGpZVVVkqRngaZqOZdnoqsHU1BQPPforHn78EbIdnUxONNg/MkGmrZ22rk4828ELJUZGx2jLZSlODVMtTvDAXd/iD6/7AAtXnMfO7RsxZJW+3j5GRsc44+LL+eRf/TXbd+1pBui6imc1iETjmIZG4IdEYxlisQig4LoOqizxr1/7Kk8+8SRWzSXT2k4qm8IPQNVUbM9ldHSE4ZES8bhBPG5y3HFz6evpxYgkCJCQpRBNAlcKKJcrJOJp/vW2NRQLU0hSSCqV4LLLLsO2bWRZmn7Nfd8nlELGx0do72jFNE22bt1KIpUgHosjyxITExO4XkAinmLHtl2MDo9hGBFUTUXXNXzfw/M8CoUie/bsxbYddN2go6OTiBGlVK2SSKdZesJJzJm9iPMuuQQ3DMi15MhlM0xMTGLoOjNn9pHNtTBRKKBrGookEYY+YRgQei46AYYEeigRHMhxD4bAB+4d9t448n1yrJbSh84ufq3nOGJJUnjUMQ9ey/SxOHwmsiRJzUD4sGMf45xHbHOs6zpIBMrC/1Z7FY07zSSWJLHMt5nh+7zXLmMAL6kGvmgJLwiCIAiCIAiCIAiC8HvrHREon/C+z/FH84Z4alsBgFNu+Buun7Ofp7a+ev/q9hd5brdz2H6n37CaS3ObeW73f+83+7aUO4UPfuIjXHvJBZx31jLSE8+wbQI462a+cFGcR18Q/wAuvD39NoGyzRiuH/KLn/2K//N//h+GhvaTTMbJF6ZQVJnhkSFiqQxxw+Rv/vzP8aujfPzTn6Vh1di6dROSHBCPR7n55k+yf3A35593DpbVwPMcXNcmZuh88yv/QEzyeeSJpznr7DMoV4osO/F4ZEAOQyQAX0JWVFzXIwx9CD1cp0FXSxddrTl2b9tMKhnDsuskIwaOZaHIEo7roajadOvogyYnJll64kmctPJ0Zi9YSm/vLCqWQzQWR1FkJCCZTE+Hx5IkEYSgKCqSJBOGIMvN0FdV1elws1ot09+/GwgwDA3HsQkOnYF8iEMDNjURJZdJEY3HcAOJRqM6PZcamvOG6/U6qtoMUCVJwg8COjra+fc7vsv45BSpbAu9M+ajmCYThTzRRIzCRJlsrp0TT1xOR3sHfmAzMrSX9ojC3qE851/1Ef7o+veTy7agKQrrX3qRP/vrW3l23TqyLTksy6Jh2ZiKThDKVGtVFAVK5RIhIbqsUK+UqZWLzOzqYGxolK1bB3j+ha18/BMfYdv2XUiqiuu4OJ5FJpclnUnQ0ZHl7LNOJx5PEcoqkgS6LDE1tJf+3Ts447SzeOaJZ3js0SeIxk3MSISbPvxhYvE4ruehGRqh1Gzd7HguhalxMuk4tl1janKCaCRKNBrBalhUymV8zyMeyzJrVg+pWBRZligVa/Tv2c3IyDD5/BRTU1NomgFIOI5LJpMlCEIUVQZJQfZh965dJOIxLMfB8Vwcx8e2HVzXAwk6u7rp7ulhxowZFAt5yuUisWgURZEJrSqKU6MwPorkhyixKHD4POFmiNvMfWVZaj7yBkLjw4LgYwXKitxsV31gyQcC5dc91iFh8pGB8pEVyofte1To/NpzlUWgLPxv5ksSL2kmd5tJMqHPQt9lmWdzrVNln6yyV9He6ksUBEEQBEEQBEEQBEEQjuF3FSjLv3mT396GgQkS7T0HBjWfwOxOlUTPYjoBmE9PW4PhXZWj9nvqm6v52i8nAJh99S3ccvXs/8nL/B06m5u/cDNnv5ldjjuB+dWnuHX1aj7/5W/z0I6jN3lnvUaC8NocNyQMJCRFxXEcgsCjXC6iqvL0XFyrXkbVI1QaNi9sHqVQHGdkZD+5lgye5zE4OMxdd3+bq6++mrGxMWKxCLl0ihmdHdQaVc67+BKUeIyzzj2LYrlANGriuja1eqUZtirNucSu1SD0fHzbQg08EpqC7tb4xj/9Pff8+xqKE4OMDA3y/DOPEIupyEqAaZqvziDm1UrPbEuOer1OPl/AcnyqdYdcSwvRRBxVVUkkUpimOT3buBmWNVv+BkE4vXzfR1EUZLnZrntkZATHsXBdG8dxkORjz7o90tTkGM8/9Qj92zZi1ScxDIMgCJqVyQcCO13Xp9tfy7KM57lopka5XGb2rFmMDQ5j2Q0ihokqK9SrNTKpNG1tHdiuh+dL1O0AxUwxOFWjvasP2Q/Yu3+ECy6/kh27d9PW0YqkKiQScUqFPL5nY+oGoQS6ZqJrEUJJxfUsHKuK41gEvoNn1YkZGhHDIJ8vs3egSoBGLJHEMCLU63XshsXk5CTj42NIckh7ezuuHzTnDfs+U2OjbNr4Etteep4ffPe7PPDj+9B1lXK1wXXvey8tba0gS+imgawo09W2qqYBAROTY0CI5zvUahXq1Sp2vYHvuCSiMbq6u9n08ss8+8yTeE6DUqWAaZrNudYHfoaWZeG6LtFolFqthqIoBD6kEglcq0ouGcezKnTm0kyMjAA05z8Tomoa5VqVqXyeaDTKvHnziEajlEolarUaU5Oj5CeG6N+19odtEQAAIABJREFUnbGp8aMC1mOFrr8pmH0jYfKbWcc61hu9jiPDYkEQ3piKJPP5WAtXprrYohqkA48vVye4vTpGV+C91ZcnCIIgCIIgCIIgCIIgvEXU/9Gjv7KPiQtns5y1PDdnHj2VLWyJzuaEFIwwm67IBM/tBs66mVsWg9fZQ/Xh1Wxf+AUWbGveXtADcCO38B2+9KN9zL/sZq5d2Ykpe1R2Psia7z1H8dBz5k7imusu4YROEwKL/U9+mzW/Hjnsss6++RaOCzw6+6o8tHoNTy24nJuvOYVOE7zSDh78zh08NwXpk97HTVcsJqN6VMYqaNF93Pmle+jPncIHb7yY+SkVrBGeu2cND+w+jvd95nL42Re5c2MvV376fbQ+fxtrHj9wdXOu4ZYbTiAB9HzhZjjqvP08dtcdrB085B/rzrqZL1zYA/TwuVvSfOfZLDcu3M7qNWsP+V7e4Gs05xpuuSRLMdVDett3+NKP+l/j9djOgi8sYPvqNaw9eA0Lt7N6zX6uueUa2sarZGZ1YmLR//BtfOfxIqjzufzj13JKuwlehR2/XMMdzx72UxGE/xbZXDeV0j5mzeymUq6TzaUZHh5G13Usu04iGSOVjFGpVHh+yx7AZt2LD6OqKpMTRVYsX8UTTzxFgM3y5cvIZVIM7R/EJGRofJxEroXLrr+eP7zpBvL5IpZjU67X0WoWqUQE3w1wfJfA9UAKkDUFmRCv5rLrlU08smMjL7/8EnXHZvDc07j/wYeYNzPOlVd+mW9++w7KjRqJZAZohrCapiHLMo7jEIuauJZNzDBIJHLUfQvTNNAVlZgZw/WavxuCIMD3fVRVnQ6YoRmyGUaznfbg4CCTk5MocjN89n2PIPSRFWN624Nh27EC5pd/8WvGd21g164dFGoOP3p2IwMDA9P7+b5PGEgEPkQjcRzHIZlTqDdK3HD9e/nRXT9gxbITCb0G6dYMmqYBCu3tnUiSQiKeQQ49Fi87g3gkgu85PPvs80wMbSPTMg9CDS+AmlVBkkI0XSKpGeSnipQbFrrqHwhyDbTQIBEzAJ96rYQh68RjBn41YPHCBbyydYLRcoGbb/4UN954I6bpsWvnXgYG8qimyuw5vcydOwfbdlEME7teR1dUJDxmdneQ8GqUS3lC20ZSZE466XgURaGzs5NSqUQQBChK8zWRZRnTNPE9BxnIZdPs3LGbTKaFWq1CNBrHUA1s22JwaB+aFvD9//gWLW0d/Plf/C2BL2NGdKrV8oEPDjRbTg8NDZHL5fB9n3QmR8IwePiZJxjas5OJsX2ccd67aGDgeR7tnV24noftNNA0DcdxGBkZpq21lWXLTsSqNwiCgLu+8TitKY3xwjizly8/KrAFDnxQ49VqeilUDnv+WI48zrHeX4F8+P5ScPTxjrwWJI4Oio8ImA9WUB92jCNO/0Y+UCEIQrMN9vXJDlZ5Fp+vTLLSsfiJM8y3Ikm+baZwxAc2BEEQBEEQBEEQBEEQ/lf5H61QptTPcKOVrlnQubAXb89jbNmfoPcEFY7rpXWsnxcObJpIVnjw71ez5vFXd1+7ZjXfeblC5eUDAeiSa3nPvBHu/PvVrL71DnZmL+bKlYef0kxrFNff2azo/ZcNmGddwelHXViCeOVBbl29hrUs5tp3z2fkB7eyevWt3LG7lYuvOAVYzCUXzaf6+Nf4/Oov8sCYinlg3wuuOxv16X/h86s/z9fWNjjhkovp9Dbwnw8PM/tdV3DSRZdzfPVJfvj4IaHq7nv40uqH2M9+Hjp43quW4z39NT6/+lbWbIhwzvvfw2F1xo+vYfWv98P+h1j9pXvo52hv6jXKqfR/8/OHhclHvx6vJ0Hce5Y1X1jN/8/encdJdtf1/n+d8z1r7VW9d0/3TM+eyTKZIQsESFgCAUkkYQ0qEsQfov6M6M+LernKNQEvP7xXUa/+HurPCz9BcxUEBDEsATKQkD2TSWAymaVnenp6r+5aT539nN8f1d3JzHQ2SIDo9/l41COdmlNVp05X8sgj73p/Pv/j9hpje1/GEDB01dW8qH0bH/m93+MjX5qi76KXIfvS0vPhz/77/8Rpdvibv/krjh4/TBj6VColhFAwDI1yuUipUMYwDJIk5LHD32PThq0osc4F553HUvUU5ZKBmsR4bYe5mXkURbDccXHCAN9t4zst/ukz/5ux0RHUNCF1HWxdRQidMPTptJvoIiVot9CTEDyXdmOOSp/KV775Hd7x/t/ik7fdzTmXX8W1b3kzd95+L+//tRvp6+sjTrt7i584uldVVUzTRFE1IhTQTdq+A0lKbaFKFIY02w2EphDFAX7gEkYuQgiEEGvjq7OZPJ22w6MHv8epqeN4botmvYVQU2q1edI4ggQUJUEISNMIlHCtAbr6fEII3vG+9/AH/+vT/OWXvs5ffPbzHDlyBNu2AbBtm3w+TyZrYpo2QggKxSxRK8ZvwxWvvpKBIRs3PMVjx+5mdmYG08hy3jm7UDQToYCShBiGgZ5aWEYG0zS57LJLePjAQzx44JskcYPAa1Exsxx76H4uGB+n4/ogYgwjwPMc2u0mQmgIYXb3O6cRUewTRDFxInBCgZ+aeIlKpWxx4lgDy0pJEoeR4TF2X7iBq1/3Sq546UvYu/tCwjgmjjxypk0aJWzZeT4jO87l/mMz3H3wEFgGmpXl53/+59m7dy+u6641tD3PI010SA2CICIIHXQ1YubEBKVChigJMS2BqibEiY/nd1DiiP6+QUZHRjBIeWjf12i3aiwvL+N0AsIIojglSRVGNoyRyeZRhc7xiaMsVmc4dPT71NpLXHL5S7AyCmnkkLUN8tkMqqJgaCaqamCaWYqVCm4YUujrJdtbAdvk3Tf+FnGuh1e+7o0UcgMQC1TNQtG7Y80tw8RWdSqZDGqioqg2uqaiaSoIlVRooHZvitBRhI6qGSiKQFEEqqqhKAIhlLNuKmLtpqQqqMZZNwWBgoBUhVRFUTVQBCnq2u3M9nKqCBLUtVucdkPm1VuadgPlM29xmhCnyfr/0pGk/+Du0ixeW97An9plYgV+yW3w+eYMlwXuj/vUJEmSJEmSJEmSJEmSpB+h57ehzGGmFmwu3T6EO65x8tZZDvTMcPWuF7HdKVGbOsxaH7c+w0HvqZ9t845R8j153v3BvWv31fqGgMcbyN5kDeuqd/Db11grb25q3TdZnz2IB7BlF6P5Mvlf+CBrz7rUx9D4MKPqYb76rUUi4OCDE7TGAfayeSjP6NCNfOj1K8dHMAbM3vspbtv121x3WYs7/2wfT9nRHd/BaPx9vrDy/LNfv5PDl7yaXUMwMftUD3xqT3qNlgB3gYn59ccVrl2Pp1E/+SCLEXByAfdiCxuYOHSc1iVX89u/+SIOP3ont/y/B/gh3oIkPalGzeHbt9/JxPHDjG7oxw/aCKGvNBENVKU7zrhUzDI5eQxVVVmsztPX38N3v3sHSRLR09PDtm1bCMOQ6elpGo0GrUadTCaDSp7acpUXX3Ipqgrbtm3h4f0OYRjTl8nipQppGKGrOiTQbDbp7a0wNXcSy85y66MPM73Y4vDCIkmSsnnnbj7+ic/hOA0ePTbJpo3baTveWeN4n7gX1jAMMpkMvu+jKCmdTpN8Pk+72SKTyaDoFmEY4nU6KIpCJmsRxzGTk0eYmZ7ENE1UJQUSwshHKAJTh6mTR9ix/VwSNSUOu81lBRXWGRO8sOxQrbsYhoEwC+iRQ71ep1wuI4QgiiLy+QKeG2HZGlHsMjQ8wtTMNCkqc60aqZXQqwd4QZVK6XxymTJNx1sb+a3rOtlMFs8POXT4EUbHhjhv9zZOTde4445vU+4tU/XrTB7fz6FDd1EoDzC2eQtJGuO0arTbbSqVCkKNCcMsmh4RBzGJAmmqsHnbdhz/KE6nRRQpqApcfvkrmJw8zsUX5bi8fgFuJyGIQpx2E4SKphlomoFlWQgheOihh1iY9zHzNq7v8+GP3oyiKLRa3VUNmUyGNE2JIkBJiOKYTNbk0QMH2Ll9K/l8niiIiRSVjG2iKALD0DFNhWpjgpPTTd5w7euYmZoj39Oz1p5d3YP9xKB/NTzt6+uj3lpm67nbcZtVUCNqtSVanZSecg+e45EmCkLVEepqSzfEsiyajSbFYpFyqQffD3nHu36J73z7u2TKvYROm1RVQU1RNA1N07j/jtsI/ZCLLrmcNFVRREoKpCikqJCkZzWJU3XlM6V2v6+WrNMIVni8Ha+sjApf/fnpGsRnNZefICVe57XEac+53vOnZ9aYJUk6yyftAv9s5fjNTo03+i3+or3AnYbNhzI9LKnix316kiRJkiRJkiRJkiRJ0vPseQ6U4eBsjdeNvI6x/FR3vPXkBIuv3sVLWzqz3372sWPr4U/xsc+ss0h4xeZrrmW3dwd//ZF9zHpX8N6bdzyDJz3Apz72WU571vHrIAxZv38xy21/8JfsOyubzWEZABqa9fQv+3xZ9xpteR5f8Njn+eP/9i027zyXXRddza+8b5S//Pi/ylBZes6dPH6cVmOZoaGhbkPVUNCExqZNY8zNVimVSgghmDo5SZLGoCSMjW3gvvvuo1pdQGgK8wuz3PXdGoODg+zZs4f+/j68dotarUa9toShqcRhwr333s3evXuxbRsVQaveIo0TbDPDzKkpatUlztm7GzSVvpFRhjaMM32ijmXZlLXuztvG/BJ+oUCSKoyNbmZmZpZiqXJWoOz7frcpGcfEcUwUdZvMg0MDzMyc4tBjj1DKFSkWymiajlBNVN0gm7U5NXWcdrs7GtrUVUi6QbSqqvQN5Ln7nu8QBB6GYXBs4iCbt56D74eoqsETRwQ/MdROUoVsNkeapoRhSE9PD81mk/n5eYaHhykWi2hCYOgpugGnpme6Y8A9hztv/QYby2WyGZ1GfQldS+jt6aG53EJkjbWR3X7goigCyzLYOL6FjaObmJqe5sLzNzBx5PtousrkqSmuf8ebOXr0KHd968tctPu3eHj6OM3lBUwri22Y1FsuQsuCEhKGDVBTkgiqjUViEeIGAYaho6jQbNaIYp8wNKlUemlpLq7vda+raaGqKjmrQKPR5NChR2m32xQrRUbGRnnz9W/DynRb2rqukyQJYRiutZR13SQIPHzfoVWtMqnA1h276AQddu25hDRR6HQ6KEqC4zh0nBA7W8aPVTZt34Vp5Lujz02LJH08WF0NkqEbhgoFrGyeSy9/LV67wSMP3YMXufgpaJZNs+WiKKfvHFZVHcdxyeUKhGGMphmoQiebr5Arlqi3GhR0g0RVUASoSvc9ep7PzKlpcsWD7Lnk5Xih+4Tgl7XzWj3P7outtIVXzhfl7KBJgbMC5DMD4qf6+ycfuX12yzhdecHV10nTdR4rx2BL0jPS3a/cw/+0S/xZe5GXBi5fDk7xV3aJT9jFH/fpSZIkSZIkSZIkSZIkSc+j53fkNdA6PIU7NExpdoIDANGDnGyNMVZe5OTDz/BJ1O7/sJ84Pou98xVcOWIBFptfdiV7e04/VNOAoEXN0+i7aIzS0z33sQlmzXN5xauGsABr/KVcubcExx9jSpzLK17Zh4bFrr2byQNwmJOLQ1x63W7yGmgDe7nyslE0IH/Z23hZ5gCf+kabF133uid5ba0b45/x/EOveSnbw5Mc/EFT2GdxjZ7aLMutPsb2WqD18dLNfU/7iN1v/wA3vmGYmYfu5LZbD7BY7GPsB3oTkvTUenqL5PNZfN9n+7ZdxJFAqGa3kSkUOp0WraaD6zkYpoYQgkOHDnHsWLet3Gw2CcMQ0zQJw5BqtUqj0UDTNHL5DL29vSuBbkjbabGwsEBvby9L1WU8rxv6BkGEoghyuRyVSoWFhQWqy3VGx7cTJSmNVgvTMoiSiHylSBwFOK02rutSLpfPCpPh8bBLCLEWtAkhMAyDYrFIEHrUGwu0nTodt0UYuURRxNLSEvV6Dc/rIDSFMPSJ4xBNV4mTkPvuuYd2s4HvOSwvVanXFkmSCE3THm+Hsk5Al8aEgUcYeGiie76FQgHDMKjVaszPz68F0GEYMjs3g2VrLM1Oc/utX8ZKEhTHQUEjDkOCIKBUqpAkMZCiKJCmCVEUEccxSQJhlLI432Ti2BHiuNs0dV2HJIIkStg8upGjRx6FNCIKPFrNBp4XYBgWlpnDMKy1QD5JFeqtGigBxZKC0BTsDGi6iqZpWFaGYrHMwMAAAwODDA4OY+jd0dtTU1M0m02WlpZWgtGAt1z/ZuI4xM5miKKIMAzXrp+iKGuhcprGBKFHzrapLizyyIEDzM/OkiQJrusRhiFxHNPX18Pw0GaGBscYHt6EptugaKd9Fs4MUNfCYQFJDEJkyef7MbJlglRj+67zWFiu4gSd0/Zjr+5A1jQN13XXvrgQhB6lfIE0Tekp508bHb36ux3ZsBlFaMzMzREmwWkjptcLk8/8bD9V2/ipmsZP5amOX2+c9Znnsd4xyspNkqRnZkkV/GxhkPfl+2mpghvdOrc2TnF+5P+4T02SJEmSJEmSJEmSJEl6njzvDWWOTzDFBVgH71m5o8WB4y1eykmeSZ58cmoW/Zp38oHkE3zsn2/hlqH38tb3fpArVPBm7+Ezd59+/OFv7uPke67jgzdfhzc7ixtp2EWg8WSv8CC3/NMw733Lr/DBVwLeLPd89h7gIJ/53MPceP2NfOhVEa351spI6Fm+8vf/St+7r+UDH3oLRC0Of/WviXqu4B2vLvHYP/w1h4+VuOeC/4ufv+pu/uyrTxx8fZiTi1dy5YfeC7/313zmcw/w3rfcyIdeBVFjgtv/4Qvr7kl+Tq7R0ybrj5/jbXfP8Ktv/CA3X+exuBjydLOwD9y2jwve/VY+eLO2dj3ueeqHSNIPxBYhjbkphoZGmJw8zNBAD0mSQBKhKAqO4xKELhk7R+B6HDt2lOFKhvO3b6E8OELL8bsN3LCFEIJOp0O73aYVRRiagaonVHr7CVyPwHE5+dgEe/dexJ6LdjB7YhnD0Ol0OgyPjeJ3XCZn56kMbuDi7edw/MQkCwvzXHjhHmYX5rFyBTpuAChYdoEUiBPQdXFa4NcN5zSiKME0bcJwJRRNUuIgxVAstozuYGHhOK32KQwNbvnsv/Cu97yXuaUGZiZPSkzkN0mSlI7rUGu4+L5Lu7OE0FT6ejfQ3z9Is9kkCRN0YaJpCkJouHG4MsY4JU0TFLpjt1eDUkVRCIMA0zQZ37SJU6dO0Ww0OEW3Wa1pGkMDW5hbWGbn7nOZ+/M6x6cXOO+8nbzrPTfQ25PDDZdouhZxahEnoGqCIO4WQ/sHB2nPTvPwg/uZPXmUu+/6Mpe+9DI6XpulaouJ6VnKw5uYb3rkewfoLJ0i01MiDFIc36NU7MNxW2SzGWZnZzFyGp7fwot8gsTl6msv5y//fB83vOuVaApkLI3+gSLVpSaWaSBUWKo2WKzN43bC7njpJOWC3XtotzvUW/fjBzHnnHs+8/V5NN1cC1UB4iRFVzWarWUWFua7wbrXZNcF5xLE0Nc/TK25iKUVMI0CumXSdLrjxONYADq6MLsfcEMnCGN0XV0JO7the5Ks7rlWSWIFXVOJoogkSdhxzsUMDjcYHBwmZxdwXZc4TVDSFN1QyGQyuK027Y5HIWPy4Le/zPcf2c/g0CjjY9uYOz5F38ZN6MJG6Cph5LF1fBzbNGm3O4hsgTDsXpdUaKRJgpIkKKSgPvl30p5yhPWZd6VPCKdX/3pGbrx+kKyslYvTFNR1viOXJsnac6ZnBMdr4b2qPetgW5IkuEe3eU1pA2/3WvyGu8zfNWf5qpnlj+yKHIMtSZIkSZIkSZIkSZL074xy2WWXyVrOkyjt2k3/1Pc53NLY9fYbeau9jz/4pIxKJem58OlPf5o3velNz+oxf//Zj5FRdRpLNW7/1rf59f/0YRq1ZVqOgxd4JKSo+Pi+Sxz6REFI220gFJVqtYptZ7EzGdxOtzHc6XSoVCrYRjckjMIAhYQ4DsmZNjMnpzhv6xb+5M//b955w/tYWq6hGSamnUU3LSq9fSiqhusHhEk35BsYGMSPQtqOSyaTIwjO/kbGalAL3bHBum6utXKFEGSzWeI4xjJ0Qt9ndnaaZnsepznH0ECZR++7hxMnZ7j05a9GzRTRdHOlORvh+x5JGhBFAf5KK3ZsbBO+320Kq6rFhg1jeG4EKCRaN4RbbUUDGKLbYBZCrIWnuq6jaRqZTIZGo0EURTiOg67rRFGEbgrCVoNixkC3BIVSAXf5BM3aNDPzdYTZw+DobvK5InOLC+iGhdeuohsWx04cx21WOXDfnWzb2keh3MfhYzPc850H6B3o5frrr2d04xh+3GZhcYp6dYm52QVypV52nnM+y4t1TMtguT7f3fssdI5MfJ9Go8YVV7yaUn4DvtdhoTpHmqYsLtUpV/pp1Fq4XkQ+V8aPXdJEYJommqKSJAlRGPKxP7yZt17/Ln7qp69hdnEWXTfPCh+rcwsEoUcUhZimji5SFGKcdhXLVvHcJglFFCWPaWRIFR3d1FauuY6qdH+Oksc/A90AWVnbo7zW6CVZGxu+Goj6vs/IyCiBH5Gm3XHnge9w5LGDBL5PsZjt/u5UlQfvvJ2Jw4eoLjYY3biZYv8g2867AFIbLWth2CZaqlBbWiQIPDRNI47jbvtY6476Xmv+JuvsMV656ynbyWf80XrHKunp+5B/2MD3ydrK3efuTgb48tfv+qFeQ5L+IzPSlN93lnh90CZC4ZcLgzyomT/u05IkSZIkSZIkSZIkSfp373Of+xw/93M/d9p9tUZCu7X4nL7O899QfsHSyA/s5dq3voW8BlHjMF/5hAyTJenHaf7kw2wf2cjn//ZP2X3uedxx+/9m27ZtuK7DQm2RuYV5TMXqtjuThMgPMLM5TKFiRy6pG9MippgtkMnkKBa7i9Cz+e5A+8DtoCrg+y5O4DG+ZSN/93d/xfLMNBvGRtAyFvlikU4IQRTTdr3unuE4xTAsSqUSQRCQkJLP51lcXCKfz572Hh5vJT8+YtiyukvXPc8jiiKEEPi+jxMG6Cshr+v7WFYW1/VJ4oDF6ROEbptK3zBxAq7rEobhSuCnIFSTbNbsjvcOUlRFR6iQEFOvL1Op9BMGMeFKqKYqKurKTuU0TdE0DcMwUNVuuKrrOkEQMDU1tfYeDMNYG6esqgpJ6DN1/BS9GzaimgoP3vM9KuWU7x3Yz1WvfysP3PevzC0sMjg0wvT0LE69w+zsHIu1Ov19JS7YuYVOp0Gpz8INUjByNJptvnPH3bxpaANWNkej2SFrGJy7YztLy02OPLKfTePnUi6XadYaQHe0uWnabNnSi6ZpzM6fIPB8NEOh47awsymuX2VkZJxMtsDJyTlCNyWTMbpBvtV9Xz2VCqVilurCLIHnoQsNVQVV7Yabrut2r3vkr12Her1JFENvT4EkiVicn8NxpsnmN2OadHcg24XTfv+rwaau693fxUrzN0ni00ZMq6pKEodr4f/qyGbTNKlWq2TsXLexH/ukoU/eVOkfGWZxeZHYadKOYi665BKiJGFu8QEuuuzF/Nu/fYWjRx6j4UT8pw9+kHrb5fDhI/RVetAtmyiKQGj4QYCuqGcEsmf/M5qu/dnZAe6qMwPl9ShntY1/sO++rRcgnylJEtlQlqQfUqAo/JdcL3+RlPldp8p7vAYP5vp/3KclSZIkSZIkSZIkSZIkPUdkoPykIqa+9Qk+9q0f93lIkrSqf3AIzTTY95070A2FobTK4tS9lAeGiFUDWwPTMgj9CDVVccOY2cPf45EHHqA6McEr3vh2trzkFRh6txGqZbt7lhPitRHPke9Rq9VoOXMM9m/FLmYQ2RyhnscqqEQoGDoYOoRpN2y1hYbv+8zPzzM+vplMPsep6VmGh4fpdNqnvYczdxfruo6iKARBsBbkrraBXadNBMzMzFDsydFoNBgZ3cBj2ve46k0/S77Sj6JqdDoOSRwSx+HKfuJu8KcJjUq5j6WlGopCN1jWE5xOCyF0bDuLJvS1c1kNL4UQZDIZ0jQliro7l8MwpF6vn7YzeLVVHYYhcbtFXJvD8lrc//WH2XPRi3nxlVdzzx3/RnloOyfmHDZvOZ80nWCp2mDjxnN45buvplTuwczm8XyH+ekTbBzdzCOPHmS69jXmvno3g/1Fjh2fpKe3n9mFk/QPDPPdb3+Vcq7Ayy56CROHDnP8yKO0evvp6+ml0WlwavYEu3buwTRN4kgFEoSW0O7UieOIIA7o6R3g0GMH2bhpC1u3buXo8eOEYbDW/s1msxiGQaWUp6+vB0MTKHR3PztOpxv4Ow5hGKIk3esECrlcnjTu0GnXMXWDFJWW22HTpn4ajZhWq0UcK9iF7o5sXddh5XMEnNZGBk5rKKuqujYeevV4gCiKmJ9f5MLde/F9n4xusO+2O9l9zhbqi7Okfkwa+WBouKbJr9/8h3hByvJSlXaU8OAdd/Gii7cxNzfBzGKdQrFImESkKcRx9/OayeYJfPe0Hc/rBbQpTx0mrx30tM4IlNdJoZ+qBf1sjpNhsiQ9d2ZVwY35gR/3aUiSJEmSJEmSJEmSJEnPsSdfgChJkvQTJlZNFCPDL/6fv8Yvv/8D1BYW0FQFHRVDMxFoxFGAH7hrrVqnWeOh++7FqdVxWi10y1oLbVd3BauqttbIFUIQpwmWZVCv19h78V5+9ddvxMjkMawcUQqqqmGa3VGeiqKgagLT0tF1nSNHjtBsNikUCtTr9bVm6ertifuTVx/f09ODrncfvxriRlFEs9lkaWmJyclJ+vv72bJtOy03IFPsJVfqwcjkCaJ4bQzxE8NqgGw2121QhyG+F2Ka5lorttFodPftxvHabTVMjeN4LchUVXWtNa0oCqbZHa+tqipxHK9ds1wuQxL4tJbniTotWsvz3P/Qw/QNjbBh4zZy+QpuR2XDhu28/OVX8cpXXU2Mzv5HHuV7jx5ldqGBYZc4cnyeVMlQKvfiBiHkIgUCAAAgAElEQVRzc3MUCkWSJO22v5OEkdFRhKHzvYcfQgkiFDWlWl1gYWGeOA7ZtWsn+XyRUqlCEkMulwO64Wyn0yZOQqpLi2zZsoWNGzfS6XQwDGPtmNVx36ufkTAIcBwHgE6nQ6vVotVq4fvdndy+758WBKepj2XqgIJmmOzcdS4bN46Tz+exDANIaDQadDqdtdHVZ4aaq8G++oQ9xavHPfHYIAjQdZ2TJ0/S29tLkiQ88MAD5PIZThw/RrvVZGFunjAMWVhcojIwTNPz8BWBG0dYmSzbt2/nRRftxjQEwyODJEn3mgZBsDby2nXdtc/YUwW0zzTk/UkiA2VJkiRJkiRJkiRJkiRJkqSnJhvKkiS9YPg1j6ZW4GWvu4F2VGXLzgvJFnKEKSRBjKoYJGGCKbLEEQihc9mrf4rX/PRbMewcqaozPbtInMuRoBBHq4FoQqFQwml4FCsWDxy4m/7elIbbIbZDPN3lwP7b6R8cwTAshGURRgpC0dDVhP33fJerr30jD0zfS9/AKFNTU2zctLnbZDYMfN9H0zQsy6LdqqNpBo7jMNRb4WM3/T71xSX+1z99kYOPHSURPnFso2Jzavo4U9NHCaOAxbkavT39ZI0KvYObccOIopnHXW6QBN0wWAHiMEEIDcM0yOWLdDodFAGqlhLzePCp6wLXbZEoCaVihXqzRSaTIZvJkREpn/zoB3nwe4d45TU/zVU/9SYc1yWOI3w/xbQtQj/EMAxc18U0TdRUJTeylUbbY+PWIseOPsZrr/9FIjNPa3mWmpcwNFxicX6ahaUFmm2PhBhV0XAadQCsrEnGEMwsLDLY2wthQqzqLNcaOJ1ldC0lTQy2bD4PXUDotnn0kUc5NbOIZVm84bJLWViuoawG/qlOITfI1PRj2JZKvbWMIpoEUYLfNLjkRduJY4Vs3qLd8SkWc7iui6aqtFpN8rkMfhjzzW99iz0XX0LLd/HdzlqAnyQJSQioCmEcoKIQhxEpKp7fII6a5PN5cmaWZqPDxrFhavUmk1MzZLJlfM8hTWOE0Mlm8+haFpSEIOiGt/rKFx/CIOheH8sCNUZRIA6jbktYU3GcFllT45u3/guaKhDuIpYWUm8v43oBkRKxPKewYetudp3/YharS4jUoSBMKj05xsdeRHP+MNMzUyhmGc0cQaQahhqRBD4aEEcRqqaufNmg21hXle5/Qpw2VvpZZrOKooCSnHX/mbF0ut7339TTH6ckzy7MfjxITpCZsiRJkiRJkiRJkiRJkiRJ0pOTgbIkSS8YG0a2IEyDRkNhoDKKZhdRzRyJkgAaQjVIlAglFQhVQdcsImEwV2uiOQG6ZVPqrRD73Qaurutks1lUTSFFQ9UF07PHaXYWsFyPpOPieQGEg7ztbe9nebmOKnSW621URaNSyXHs0QN881//kc1FjY9/+H9QTxS+/PV97D96jN6BftrtNqVSiSiKcByHSmWY+fl5hKIROg5TE4eZefgwqh5jVQxiJyGMI3pKJY48ch+eU+Wyi1+KJmLCsI3nedimhVkwSZIE13MIAheUgDiGJIZisYJlZkiSBMdxVlrF4dreXUVRSZNu03R5eZk0itk4toU0TjEMgz/+3ffx4o1Z7l+Y5v5vfJU3v/mdLC4tYQgdYejEYbwWIhpGd++wHybkKkO85HVb+c+//sv0VIrwxS/wmjdez7985rP8xn+5CVUx6R0YIgp83GaTFEE2myVRdIrZDMuLMywszPGaV7+a79x5J5XeCgN9Axw7dphSqUJ1abbbIEbFcT3UVGPvpVewR9UJw5C2G2GYGcI4xrRzWGYO1w3YOL6TO+66jYQYP1CII53Bwa20miFJkpLN5CmWMpycmujuhVYFQdBhubaA7zoMDgzgeR0WF2fXxn0Da21hTddRFQU1TVBI0DSFThiAkhKH3Ta3bWcIwxDT1BkeGWBudhHDtHHaPppm4HkddN2mUCiQsc2VZnCK67o0m03iOF65z18J/m0K2TxRrU67XiebMbnm2jfw1a/dSqc6R63jc96FL6FYGSLTP0Slt4/Aj2k3mvSWSoR+QL3eZm5+iXbWRMdHtSyyhRLtZoCmeiSoJGk3tFWETpCkoCiggKVr+OE6+4l/gGR23Ybwj7DoLBvKkiRJj7vGOcFNPuzLbeL9xo/7bCRJkiRJkiRJkiRJ+kkhA2VJkl4wQj8iSEN0s4zjx3iRjh4bKGqMJkyEauLHCaoiUFUNoVmEkSCT7yEMQ2wzg2WYpEJZC5Qty0a3NVRFw9Btvv2dE5R7hjjwyD6ymTxvf9vPs2PrpTz66CRO20UYOkLTieMERRGMbNjIlq3bue+h/ZDJcMGO81hutSiVy5CkFAo5kiRaCQx1lqpz9PX34jgOqqWw9bzz2Dw2yonpCTodH/wU3TDwgxb7bv8iYxt6+OY3l3jXr3yA6tIMTqeFbdvo+jC+HxCGHikhSeKhKDblcpnBgQ2EYczJU8e6O5B1a21Xc3cEdoSqaKiqiqaoqErKN2/7IuVCnle96hWEXg1zYBu//Ju/xv6Dx5k4doh8sYCumaRpgC5U4hja7TbDw8PdMeFxjBOkzC53uPjlr+BbX/8qnVqTc7eey4nbb+P8//ZpfvEDN/CfP/wR6q7H8sIipVIJ3TRJdR07o/H3n/wb6jMTPPjdc7nkiis5MTXF6PA4qqLTbnXQNI1Op4Od0ckXs3QcHyeIEIaNounEcYQiEgQxtXobITx6KgOkaYZXvOytGKaCZQqiKGF6ch5VFeiaTpoqCKETRQG2baLrgqHhAZQUhof6WFxu4bsthNJtEz8+9jkFFNIkQlEhTRIM06SQNygVBUePHKQZLpPGKVFgYNs2Hc8ljGM2DPex3GgiNAFpRKqkxBE06gGNOigIFKGujN4WGIbW/TJAquO0W5TyGeq1ReanT1CtLmHnStz74H7OOW8v3104iUpKcXgHdq5Mtd5B0SMyloHvd9CFTnVpHqfVpO2GxIrKtvE9tPxTBJ6KqRkkIQjFx9J1oigiTVPUyMd1fexiCVXVge4O7SfuTFb4yQ5n1937/AIc0y1J0k+4uMEX2vA7xSKH1vnja5wT3BSVecdpf97h47UFNlkjXGvrP9DLXuOc4KYgy++X+/jSD/IEwSI3+YDZ/xMYJof8emOaGzjzuv1gdrrT3OKGP/RZTdrr/b6e2bn+0L8vSZIkSZIkSZIkSfoRel4D5Ztvvvn5fHpJkp5Hv/d7v/fjPoWz2LaJE7VxwzZCy5KqJqnQUVWNBIGSgG7aKIpAqAamkUXTTIgTstkMlm6iJAqp6O4rNk0TQzeIlQjX6eC0Q4rFAd72tl/ixMTt5DID7H/gGFMTD5EqDn0DQ/i+j6UJrIyJZWYxyyrtWGPvpZfzix/8KGGqcfc99zM+Pg6BT8d11tqlSRozNFhhqdlEWCbNyOd3PvJRrKzF5NEJ8obNUqdJq9mkt1xgcKyXvpEKQz0bCAMPTQvo67NoNJdR1TFct9nd36vF6KagpzxIpdxHo96htlzHcRxKpRJpmqBpGlEUkSTdn0m7u4JVVcP3XUZHKtx1xzc4cvi7FAcHWMwMUcz18asfeAfXv/Nn+MQnPsFt3/wG37nzTsbHx3nL29/NOTvO4ejEUdI0pZzPUtQy1GoN3vCGNzDUU+Sn3n4df/bRP2J8Y4WXtha481//iVd+/Vb+7c57yeaLpEmI67Vx3Yie3AB33fENNpkB1YzKt/yA17z2NTx41wEqPWVuvfWr5AqC3t4Sdj5HlCYIQ2BZeaI0093zrAhUkZL6LorQCOOYxaUa+VwFQ0R0mm2mqvOcOH6EUqHApvKLV3YYCwYHRjh46AEUFYRQieOI48cmmJmaYs+lLyfwu6Own7j7enUvtqGpiJXRzYVchmLRIghS4qg7llrQ3Vmdz+cpl0vMLszTaNaoL9VQNQs/DBkb3USqCgI/IgwTFCXG0EzSNF4Zr50SxzGmbhGHHs16jeMnjpFRHdIkoFLZjGUWqVbb7NjzWupLVVI9R9tP6S2U0BJwHQ9V1ag3WqRpykMPH6B3YADD0AkVg7YbYmmCIKhz7NgRtm4YJ13ZM760tMRA3uDo0QnOu+glJOinj7r+IULZ9RrCP8qMVwbKkvTv02rTdo3yowvudgZtNsYhtzg6e7KZH8ErAoQc0ctMBjVuahgcebaha9zgC47T/dlfYL//1IfDk4WpHT5eq3G8MMKfiqd+/E53mls8+OQzOPZJH/8sQuGzWtfrfia6wT7Zp2tor3wB4FmdsSRJkiRJkiRJkiS9cD2vgfJPYiAlSdILl9A1lLCIQEHTNDTNxNAzJGmAoVuAIE1A04y1kc8in6NVb1Eq9hMHLkINMXWAEEWYJIqKGqeUCmWEEjMyvoWtu4uUBsZZWlpi7wXjOKFP9eRhWmGCYlh02i0UvYjjhqiqxh/8979k375vcuLkDH19A1x4/rkcPnyEwcFB8kIQktDfO8r8wiz1xRqV/l7aHYfW0jIH5xYolIoIIXD9AF1oxElAtXGCYk8FO9/H9vMvpON1cIMWUSowjBJpalBrepTyPXRaNUqmTqe5zNbxTTzyyMOomoadMUFJUFWVKIpJU1BRicMEy7S6+5UthVSoxKHKy654LeVykb6hHeSyBY4enWByfol8oUiSavT2beS667YwODjIOTvO49jxR8lksnhuilYo4TRbVMp5DLXES68aYnamxYbRLdx/5CiDO85lo7/MQ9NLZERM4HnYxSKaZePWanzvwXsZ67f5zm1TfOitv8j/97kvctW113DvHXcRRSaPPPwgF148TmJ2992qSo4oMdE0Cy2FCAiCCBIVBUEaB6ipiqFpkIRo+R4Wpo5Q1BN6FJ+ZIwfZc9GLabsxup0FNWXX9vM5cuQI+ZESipLSbHVoNjwG+oukeJiGShDGxAoIQxB6PoVMFkVNsbM6cRyTy5ukqUrsqvRme4niAF0TKOhkMhniNKHdcrAzBkuNBbZu3cpjjx2j3phk48hWhJZFFRZCMYmTAE3rBrpJkpDLFVAUQej7tGp1vGaDTEmjUChg2zYnTk4iVJ18KYNhZ1BVFVPTUTSdtttBaJAk3bZxq9XCdX1ApVDsQddyqBhoSsrC/DxLM3PoAYyPDXHyxBHGRoco5TMIEdLptFl2m1SKlbV/NtOVGdUJp+81TtdZqqyddle67jHqGTOv120Wn7Ezef1Y+MzzOXtfM8lPdqtakqRna6UdGp9xd+pwUy1g8w8YXj4bh+wR3sE0t7gLfFx/7kZHP+MANa5xy3LtSf/4rGA1bvCFZo2NKSCepgG8eiw63zLWaVIHDlekIVc0p+GMa/3DBshnOmSPsMfong9P2uxe+TwkWb7xDH4P1zgLXEGWT4oQ+MGa4pIkSZIkSZIkSZL075EceS1J0gtGKxK4votAwbYKCFVHVQVpLEiSbsAkdANN0xBCoGkGtt9kcLDM4tIshWKFXLGHlpsghMDO5MlmLFpOExRBtmBx2eiVLLeO4zttPvePn+Zrf/sJegoZ3nnj+9n54s1UqwsszEyybesuXNdF1w2OHz/O9u3nkKYpBw8+Si6XY2xsjJmZGfr7snzuU5/m0otezvbN43z45g9w8RWv4I1vejNNUyFbLBHEEXEUkiQJURTRrNVpNz12nbOH+fl5yqVBohCEluIHbTK2IFWbJHGDat1hz3m7+P0P/AZve/ubeMCrk8nmiVMgVQFOa9U67TaFQokgCFAUBV03iYKQfKGf0eERDF1nqdlmudbCsrPous7HPvbH9A8OM1Cts33nuXiex9dv+xqVniKjo5tYqjZwmq3ufueihaIK7GyWWm2J11/zFl77mtfzlX/7MlbSQj10nJrjEUUBbbeBnvioAtrtgEjJc/U7r+bN7/1VXvP2d7Nv3z4qPX309pQ4cehRXn/lBSgdj+PNU1hmifFNLyKOdSAhSVVSBCkqigqmIQAVXTNRVUG7s4iVE1QXZwg1F09t0Gx1EGYOVROEYcTYpq0cO3aM0G0TeC0u3D7GpxWo9I+iaIJO4CNETEpKGCSYpgnAhpERFuenicOQ3AaTmtMi1SJ6hnqYnpvm2PQEGzds44EHv0uj1US3TBbmQ/JZm9npKQb6+rAsi3q9xpZtA1h2gY7j4UUpURRhZW0sy0LXDXw/JJvJkykXsbMWE4cPkCtmcByXDRvG6OsboO35LC/VCKIEVUsJ3SZxHBGEMUEQEIYhURjS39/P7OwsO3bsIAxikjTFzuRIFB3dtMkVMhw6NsFStUaY6GzefSn6iWWsfIWy/nigcVrYe0bwq6wTFp85FXv9MdRPf8wzIdvHkvQflc4ni08MLVdD5pAb2g2++hyMTH46h+x+PhlMc4OzyDXGc9iMfq6b1qsBsVrmHUWd99UWuKXB+qFysMj+tvPU52D0saeS5eO1BW5oLjKxdlzIVUEIRv9zG+iLIr9jtbnFnebj4uzw/hqn++WCfbmnv2Y73emVVrvDDQ2HG9Y7SNGfs0BckiRJkiRJkiRJkl5IZKAsSdILxuJCjdFKgflTk+THRwENUg1VTdGECUIjVkBRNRIUdKHhpToiFGw953yIVbLZEn2xR7PVJGPZtJpt8sUe6q0655+/k3xRI2eUmZydZaivn95inr27NjFXa/JiO8sn/v5T3Ped27j1uut55PtzlEolLDMHKfiex/imbbTbTU6ePMmePRfyhS98gp2byhy87yt87TPHWZrfzzuvv4nvPXY/Wqafeq27WzmOI5I0Io5DshY4HZ+dW89j97kX03ET0tgjSDySNCQIPRrVZQQ2e/dcyLbxTVSXQ3rLGbAEdadDomoYunlamKxpGvl8HlVVKZUKdDodGs0OL3/5FbTqDeqNJrquoGkGhq7h+wGgsFxvMTKm0tPfz/HJCXK5AiMbhigWylQXl7EzJovVOn19fQComo4QguENmzg+eYKhoSGufMs70Yl4jWkwOXWS3oF+2k6NTqeNaWTYtutCfvumP0HPG5ycXSCbKXP5K67k8+d+lnvvupPNPVmMWDDx2DSbdu9laHATQuRRtSxOa3llLDQr71WgGRagogqNFB0Nm0gfRNgqzUUfxVQRRoEYBUjQhYFhZvC8gHq9zpGD9zM7NcFoj82A4fHAAw/gJirDO3ZjZQpouoll6jiNJf7oDz/K3Illduzs5f/4lV/Aq9aIOh0ANuQrDBUqLAcepXIeRUsRuk5iJuTyNq1Wi2I+hyay9G4ZRFXBtg0gZvbEPH19A4+P1jYMdGFhGhYZWzCycYzHHnuUXL4XTTcJ/YDl5SqZbAlNVXBadQJPRxMxaaoQxzG6roOmksQqo6OjBEGwMgo9JZPPkQrBzgsuZOcFF9KozVMe2kByeILXX/cmzr/4Uo5ON6m1XHKWSRSfPfJaPWuH8tmN4DMbyetFvs8kUD77vnXax0/7mCc5AUmSXsB0/rQ4cvZ9uTKvbNbYmARsg+c9UF57TQ+OxEC8EsaueZIWsTvNfrf74/ojpU/3lK3f+Gnau6eFyd0A+f2FMl9o1ril1j7tOddGiD9dgxmATPd52m02B4ABBPVuazw+e5z2DY0TZ4W3q+/97Fb249dt9ZhD9gi/n5zgpvYJPv6E9vXqOU/aI0/bEl97nScLy1fDdKMsw2RJkiRJkiRJkiTpPyQZKEuS9IKRTV3+5EP/le8feIQvPXgYVdERqkmysguYVCfVElShEycpmqKSyxSplPP8wltez+SBA7z8RRfRcZvUHZ8//OtPs2HXi/h//vbP+fBH/itWJqG6EKErBkvVJuM7zufDf/P39PT3MFNtsjC/xMThY8SuS21xkWKxuLL/VSUKY/K5EooKvb0W2ZzN/v376Sn3su9fPoeaRFx2+YW87OpR/vGLf8Po2E5wQwYqm4iTBAhI45DQa+F5dVA0dL1Es9mi3NOH73mksU2caMSBTbmUZSAzQK1WY0JJ+Jmf+1n23fsIV7zqKlLFxbazRGFKknRHXgshMAwDK2cSRQlJkpCmKW++7h08euggi9U5MjmTRqeNkmrEcczQ0DD9fYM063XmFpZAUchms4ShT09PD81Gh82bNzG/cIpNIxsRht7N5lQFPwoxMxYDg8NEcYSdMWnUPdzaEpalsVyroaoaUZhgWyqdKCI2TbQQLMui0WgwOjzA9NQUhYxJX08fU6ccfuY9v8vCch3PjxF6jmazRpJ0m9goglRR0TQNRRUrIboFqUBVIoxyP6WeYUY2X4ClG3ixj1AUEgWiKMHruKiKhh8m7LzgInZccAHFFL72L//E4uIiVSfi8quvo9kGTbdot+oUijkiz+eczT1kNB1TU3jk+CSmpvPwww/TNzjAlT/1Opz5RTqdDvlsL4ZtkTEEQdRifqFJFMWMDvdhW3kMK0Oaqqiqjm13m8ma0ND17t5vO5dDQcPxHBQ9pX9onErfMI12i47bxvM7VGfmqFRKDFSKNBoNao0muVyONFVBaCRRjKJ09zp7noeqqlTr3dHrvtsBVRDHMREmRj7H+C6bl1/1Or70z5/Htix0RcX3fITe/U+I1ZA2TVOUM8JZRV0vrT39/8T/oA3ls+5Tzpxvy1pLX5IkCaFzAtj4I33NItdmV3/uY0+l+8Wra5wT3BSdGcyu7OR90tHNj9vsTrP/jNHX64Wya54QUj+xYXuNd3qYvHbOZb3bMD7jOZ9JwL1GFLm2WHz8vTkOmP2n7ZR+JiOwD9kj7LFhrWXO+oH2l7KbgG6ovP8J95813nsdq8HzvtwmvhGe4KYa8IRQeS1sPuP8JUmSJEmSJEmSJOk/EhkoS5L0gvHYwQN4zTp7d58LgGEYGIZBFCcIoZMmKqnSbXPGcYKiKJRzNiVbJ6zN8/53v5Uv/sNnyPeU0DEo5Wwmp6a5+Q9vJo5CFEUhiiCMQwql7n7fvJ3jgWNHqWTzmKmCZZgkMZT7h2ifmkYIQRh2m5FBEFEoZhFCQTcUduzYxnJV5xuRj5qG5IomzbRN//AwbbfBji3nszRfw7QtgsAnjHw6nTbN+gK6kSeKBKqqUa0uMLqhF9cP8byAKDDxggb5SoF6o8lyzWXT5iGGN/WzWK2TyRVZXFykVOxbCby7FEWh0+mQpgrNRpsoinA9j8nJSSp9BTpum3w+S09pGMuymZ9bZH5+njSOUYRAESq9vb3Umw3+f/buO0yuu773+Pt3+pk+s72ot1WzLVvuRe42LsS5GIjzwL0Gp5EAhpQbOokDyYUkNzgkBEIgXAJXFBMcbFPc5YK75aZeVitp+04vZ06/f8zuWt2yMW73vJ7HtjQzO3PO7Gq1ns98vl/f98jl2rn//vv5hy99kZXLTuYLf/t3lKtlEsk0QWDTaDaJx+PYzRrVcgnTMMlPVkkkY2iaBih4XoDntRrYKCp2o4akGcTjMaxmnXXnn8tPbvkB9XqdTK6DUrWJEAappIbl2AhZQoTh7HkKIZAkCSFJCCEjy3Lr6yJ0EJKGF4SoikEoJOymi25qrdsHEvJ0izvEJ5QlxifG2bhlO0I10OMp1ixfRrFYpKdvGZoeY0thkv7eDjo6Ojht5XK2bn6OjRs30j1/Dru27uSsiy6kd+4chosl2nKdGHqdgJBEKkWtNE65VGL/3n20t/XQtiqHbiaoNuqokkSz2SSZTCLLrWBcll98pX2msex7IXPmzMW2beLxOIbR2ovt2halgovVKLF7905G8nnmzplPZ2c3nZ1dBEEItO7T9/3pxnqGXC6HqsqIEBqNBmP7Rxka3k9HVzf7R/axf2gPiUQCRZLxp4/nwDD5eB0eBL/c7wTH73iOS/w6DyASibxx+C7zAbT4qzcu+nWyezZk/dUayrfF5x/luYjxVUNl3YGhtZzlY8cbJh9iwCqyjjif+TWHsbepcW6yD2iBy1m++lJ7k/0yNzgHjEjX+ljoDXNToc5FiT4GrdbI7JcVps/e97F3WbfUualQ56ZDLn1FjxeJRCKRSCQSiUQikcivURQoRyKRN405c7voO3Etf/bxv6BULtPe1cFTD92DVy+y6pzL6OnqIl+qokgKhqagKAr5Wo1UJklHzxye2rSd9Ly5VCyHU8++gHRnD5lEH2despz9+4aQlRChuiiyRGmqRDLpM1pusGpgOeVKnt5ElquvvZZi7TwqJQsvCHGaNqZpIiSFcrGEpikEnkdPTydf/qd/YOV8iUvPO5/R0WE6+1cytXeIOelexof386d/9Lv805f/D6P5Ko5jMZnfQ6U6iaxq1Co2q/rnUa1WKVfGEeSoV3wcx4fQIRYzmJzYjKK4lGt1PMlH07pxfAnbCchmOlEUGcMwkYSComiARM+8fp594HZ+/n/+kWXLV7N+325OWXcZVqDSkelEVaBpezTtKkas9Wr1TBMVoF63kJAx4kkUzWT7cy+wrB1+9oN/5Qtf/DKKkUDCxZAkAlPCti1CAbKqIYc+iZiBZzcJQ5dQUlFVCSFCZElGApRUDlVVUWQZTUvy0COPU3EspI753PAnN9FwalRKVWqNKrUpCwKBFwZIQqBIMkHo44cBcqggBIShjySHuI6BphmEUogkCSzbIp5MtMJkESIZQKhQrJVoWBXk0KUzk2PVqWspT5W49NKLed8H/jtdKwZwg53EzBT9/b00bIv3f/ADPHz/HcxdvZSOvmW0t/fTM2eg9eaAmkMu2UtIgKq72LaNIhQCw6RSqbD5ud2su7AfzBiWZ2O7NgEBtt1E07Otr2U9MT2GXEdWJWyrQbkwytj4PvKlIgnTIB6LMVTM41hNYqZJRyJFNtXO6asG2LZnB7hV9m3ez9j+DhYvWU6us5sg8Fi+coCmY9He0YOu6wghcF0XPaawYGmKFSeeQr1e5cEND1FpekCNmCoTjyVo2K093EHQarzPjNTWNA2nadO06hjx2EFj18MwRJYlwjBsNZqFOOKgahG+9PhqQUgIBILW11hweBtZiIMvO2LTWbz0Y0UikTe5mdHOqHzL+PWGmoeOaP67o+MAACAASURBVH7TBIPOoSO5Dzh2Z5KNtQPC0aPsET7auW89IAB/1c18bme+vU+PrN5hDbPemj7mY+18ltNck00fcMH0yPTyMNfXhlnH8bWcj+i4xoNHIpFIJBKJRCKRSCTy5hAFypFI5E1DzszlmvcsZyRfYPv27XS1tfHCUxvJ79vKqeddQGFqN2ZyLrIsz+6c9ayAcqnOdb//x5x/ySXs270DX0hs3ryFW+/8Kffc922+8Pf/G7vewEypLF2c4Paf/4jli9cQhj7JZJLC+CRqXMIVOu1zlnLC3DOpO60XTBVFIWaY+EETPamhxk08D4qVKm+/4gqs2jaGdzbJdhnk2pZy1fJzePDOO9j89E5OnL+Ir33hJn7vf36Sx598iompIZYsWUzNlqm6Fjt37KCtLcvAssVMTu3H813iCRNDT1EuFwlCj3qjiud5pFI5VKkdVQ3R9DhCyHhuMNtwVRQFz/PR/ZD777mXk09dS6liccmVVzM4PImezhEKhSAQBAStsdHTrV9Zbo1bbt2HRxCA0CS8puC8886jNPEgq05bid0sYpgxXN9FU0xCQqpOGT/wSCQSFMfH2LFjB5qmkMt1Ek/FpwM+CXV63/NMGzcIAnRNp800SS9fSUf7PBxHxfEVkqkcQovR9GWmJjahKgJZ03DsJppuIAkZIeTpMLHVVE6lDWzbJiREkiXiCRNJVqcDUQ8hhQgFRsdHSCZ1FvR18cCd91Ac2k3Z9jllzUlceeV1zOufj2ma2E2XSmmKeqNKw3EZOOksJEnC0JPYns/o5Ai2bRGPJ0mkNBzHYu/wEJIE1bpFX98i+s/ooVYRLF9+AoowEJrK8P6x1v0YBpIkoaoquq63PgeKSrlS5Cf/dQsd7TGSKRNNlsll0wyP7AXfI5HSUVGp1SoUiq3z6+7tZ2TvHrq7sviOy/7NT7J1i8nlb7uKwb1jxM3cbNsfWg1ogCAIqNYa6LrBKWtPY9nCxTyy4R62btuGqup0zls6+7U1M1ZdFgpeo0Zck0lmk9jI06G9mP7aCQ4bZ/1694MPbPFHIpG3itb46HUHfr95jcK92fB0ph38BnXYbmK9c3Yk92G0jkOuc7mxvIeNB24a0DtZEz/CuR8hqD7U0cZ1HzuMnx6B7XPkz+2hLe7CnlYL+Bhjq2f3RDN9n7k0WMOsnxmjfZQgPRKJRCKRSCQSiUQikf8fRIFyJBJ50+jo7KVQnELXfVRdJp1OcuZZpzKySeOFZ57ngisuoVhphaGKoqBpGqmESSqV4bR1FzE0PMpzW7Zy6eVvZ+6CFdzyn9+nWrH4xEc/xsIlC/mNay4lnsyQz+fJZyfp6GxncnyEZQOL0HR4/vm9tHf20bAqGDEf0zSRJRVTV7ntv76PqvhcfMVvIaFBIJNNZyhU0kjpucxd2EbJMtn39ONMjI0TIjNaLvPe911PvjBJELpMTo2g6zpt7UtZtXwpllVHiBDXrmM7NRqNBpVKBUlUkEUShIpp9iNUGb8pE8o+oSTjum5rxDMaQeChKHKrEYrH+NQ4v/ehD1MZG6RnzlImK3US6RQNp4muteE4DsqhY5ZDCUIJTTUQuCiyhi5pBJrHypNPZ9/ob7LsVBvH87GqBVLJLE3XR1EPHoecz+dxHId0Ojk9bjmc3u2sI0ny9BsBWrf3vNaL3J7fJJlO8ZvXvgdJaKRjCgIN00wwMTpBRzZDKq5hNWzqQqLaaBKLJ1ERrT3KyEhCwXVtdF3FMAxc122NwqYVmvt+CCIgkTTxHQsNFbtYxJ4q0pvLUhwZ4dEnHmHpsjXIksKuXbtQVWl6P7WKpqjYHiiahi6p6JpC03bx/RDXa+30rtanaDgFFDWkXsmD7BD4Lqn2FEPDe8l19ZFK5sjl2imXy+i6iZgOdmdC/MAPUVWVRYvnETdbe5XTiQ7GJ4apFCvM6e8kHo+TS2axa03CUCA0hVrJpukFuEETt15HQqAZPo8/cj8Dq8/AMFNISisYntmtDRAgoZsaiiphGAqykFh34QV85enHGRsb49JFAwghECIkCDzCMMT3fcJmhepEnr75C2k0wtlAOQgCVFXl0Aj5iGuWf02OFB6Hx9GGjkQibwF+kfVlXp/G6KEtWgCOMg75wH3HHGc7NnSPuUN56AiXHV9r+Gh7nVVuTs/n5pf6cDg8jHYm2Vh3Dghmp8N/7fj3Ey+xhlnf1PhMdj5rjuOYj3WuB+6j3pCYz5r4ITc48GP9MrdW9nB9CBsSncd1rJFIJBKJRCKRSCQSibxVRIFyJBJ505B9G6deRk7H+dSnPsU//fOX+PmddzIvGeedF1/F2FSRdKYNTdNmW8o+Lnv276ZSa3L7Ld9nxYI+vvLFvyOeTPF7H/wgDz/2EFoo2PjYs3zmk3/BN7/9HRYtWERnTzvlYoHVKwe4+opLKRT28uiTIzz/zAs03CorBpYzVazgOR66lqJeK9CbdPjR+n/l6muuZ3S8xE9v/SEffP+7uOp3fwtJkvj+LT/hq3/zPZ58+lkuuPgyPvXF7zBcmqI4to1tu3Yi6wbIJu1t3eSynQxVdxGENmEtpNl0CAKQJZnAE/hhFd1Q8X0JSdIIQ4larYYb+MhqK0xXpASKKuEHHl6zQaE4RSLbTl9HJzEEFTmFF9jETQNNl7Asm2QqQxA6s21hIQSaahCG4WwLVZIknKpLJqdSb3qc/7Y/wJAq3H7HBq668hJCQAio1aqtVrPvYNs21WoVTdNIJpOoqoasKJimiWmaWJY1vRuY6aBWQiBQYwajk1MsX30C46P7+NEPvkStAStWnswll13GXZse5w8+8wnmzJnHZ276PHXbpVZrkE6++NebLLdGYadSKSYnJ0kmk9OBuTK9cxkQAfnRUdQwJBuP8/Mf3UISnf35AktXDZBpz6GbJnt27qKjM0u1VkSWwGo0yKbT+KqKJMtMTUwyp7+XAIHluPT05WjYTYrlKk5Qp1SeRJZl7PEJQhSsuiARz1Kv5YmZGcJQYFk2sViAIrUCWl/yAQlFFriOTXt7O7qi0rQ8ipMVNCnGwnmL8X2LibFJdm7aTn93H/F0Bt9x2PTsIFdeezG33/kjVHw629rBq5DPuyjSqTQaNeLJBNBq3Ktqq9lcqTUIQ0jEU1RreeRYkpyhsWTVKuKpNIraCrzDMCTwfYIwIHAD9DDAKhcoFpJgtM++ucO27emvq4P/XIsjBMrha1ganmlkRyKRt5IYH8nOP+D3M23WIuuLztHHH/+6HDRWefpYgkPHMB8tvH3RksAFjpAuH605+wZvSR+/AxvnRW6gjzXZV2eM+O5DwuaDmtuHjso+6PPY4EtHLV67LAiA6K+XSCQSiUQikUgkEom8hUSBciQSedNoOAIzm+Nr//YVCqO7kLe/QGn7Fpa/7b8xZ/EK2kMNQ3IoV2qMDA9jmIJiaRzX99hw/130dpnoSsjQpkcYKRR593vewcKFSxjdu5NkOoXrKYSuRTzmMDI6iKzoPPHIL1m3ciG3/HgrHhJ1DU5atoo7fnwr516wjkxbG0gGc/pXc+GlZ/IvN3+VHZt3MDk5TG3XvViNyxib0vjQh3+fhmXjFIZwfYc5S5YyMTVJSneZrFt0t2cZHx/nzLVnMzlc47lHH2bOoj4myzZNVJy6g5zI0GjaeNU6qmGiqBqyZOA4Ho1GhabjM6evE8mIUbUsLKeJJMuUJgvUqzV62zvpauvBbYbIsXYAYrEYge8TSySpWy61Rp1EIoZLg454GxOVidnx4SGt2Zau65LLdWB7DXTdR9AEN4uquq0wGVAUm3q9iiyB6wXk2rrx0Ykn2qnWPQzTozNtzo63lqRW49cPZDQjBM8AVBxPQtFTdHW18YkPXMdAj0J1fIofPHkfV17zdp64/24uO2UVU+U69VoTIWmkTIEkSXieh2marZ3A7RlG92zlth98l/Muu4quFSdj+gHIAjOuUxqfIqTOivlzMIREo1Am290FTYdzV59ELpWhqYAX2Gx64XHMmExv/2IsN04gFDQRQwoliMepFCZQhSATTxHXjVZ4jYuCSqPikkwJyvUKhqGh6wniZoxS0aYtZ7N58+N0dPYRiIDQA0mTEKGPLCRcp0YQBAhUavUSnufiug2CAFRVpVSu0mg0yaVj2PUynm8hVIXlKxaTi/dz6urLePzZ+xgpVOjI5JicLFO1XDo6ujEMKNdcQkmhrzvN0OBO4qqOL+sErk9cNwhlsBoObd39BAGETqvF7noWiqyiajHkmEd1dJRScYy+eX00qOG6KkFoIikaIR6h9+JOZeDIFeVDx2KLw+eLBtPNYils3f5I+5GFOLiVfGB4PXP5axleRyKR10urUbu7voeb7Do3WBlue932Gk+HjVr8lYXaksaOA357zKbxYfuBD3fQmOejOaQ1fZBjjJA+kgHfATR2H+fY6Kvre7jJifOZbB+D5WGuJ8vHZj53R2x+H+OYj2Ps+dZDG8nFPdwUHt/HHsY/Sgv9UMfa8RyJRCKRSCQSiUQikcgbxGsWKP/RHIXrutXDmknHEoawfszln/d5v74Di0QibxpdGYOG6/Oz2/6Lf/v613j0kXs564oL6F/Qztf+5a+ouT5u0yGZ6iCbbWNicoRcLoVt23R1daEFHnfedQdtbTEuO+NMfvbzH3Ly2vNZsmQZTz/9JIqi0d8/jz/54z/njp/eT6lax2naDJy2jkt8HUHA0v5ePnvjB9izbQexWIwLL7uaQqXAeZdewlRpgraOHvaPjvG2K69k8Jm7yC5Ywc/u3UDTLvOpz3+CL/7jv/ON793K73/wj7n3vl8gJ6f35QYS559xLrf/8CcUhraD5HPJ1VeQTOcYGt6LhMyTd9/O8uXL8WIqocghSxJO0yEMBRIy8/qyPPnQL0jHE3R296KJGIVGAyWVRpEFcxYspNlszjaNhRDIqo7l1IgpcNut/5fz1p1LJrkKzchRLlUxzSQwHdxJEkIR043OAF1J4LgBktDYuedRFi/OMTo6RE/PAmpVG8OIUS6WyGYS+I0y3W0p8sUCIRLZbJZEIoHrujQaDWRZbu0KVnSEZCOpKhMTw+iSy8TUJM16jb3jBTxXR4518Zd/+0V++cijdPR2sn1imBUnnowfBvhuSDMISSRbMyubzSa6rtNrxLjuxj/mknPP4rbvfo8/+9wpSDETy7HwbAe3WefLf/FpJodH6Mxl6chlKVaKvOs33sEDP/8pnT1zaFuwmKyW5uknnmDFqgEeef521l3yG9ScGoqiochxkqkc+cnBVvNZVjHMJH4o0FSTejlkyYITGRzajCppNEoOc+e0MX/OfDzb4I47vosRN/D8HKHnIrRw+rlv/SNEiCorSASUCmXC0CeQZBIxg8B2SMdjtCWT1KujNJ0GiquhyjovDN5H0R7npJMv4Mo570EzVGTXQ6g6XqjgeQ6Vqoska3TkFP7zO/+AY9c47dSL6F54ArYXIskqtUoZXVHItrXjNBqtMddSgFD8VigrO6jxNG3dPZy4qg9VFdihSaPpMTbVwPYgkATKYT8IHCkIfukfFl7ubYQQMN28P/Cy8AiPH4lE3pp2SCrgvuTtfq2cOutCGJJebqDtsnD2f4mOsCP6pRwltLwtPp/bDh3zPOulW9MvT4M/aLogJQ4KxQ/jl7m1UuO+VB83zx6fy42H3u6IgfmreMzHEcgfke8yH44jbJ9pq7/C44tEIpFIJBKJRCKRSOQ19JoFyi83TIZWs+i6bvW4AuUr2mXWpGR69NaDPF3x+dmUz6gdvVAcibxVvO+6q3nPDTfwta9+hS0vbOLq6z7EyMgIg3t2kVZlyuUyyZROsdJESDZzO9vIJHUCUoxOlcgXSnzgxo/iWAHJXDvZznnocoJSpcDpZ57L448/yvve/0esWXMizz77Ap09veTLNeRMN9f/0Ydx3Cbbn9nIpkd/yfhUkQsvuwqrXseIxShUyviewYIFCxibyDNZLNC/eBWgs2nHLi685Bw+9NFPsGmkwjvf8zts3r6LtrY2fLdIJpOjrMXZ+OjTXHD+29i8pYcdgzvpXb6WiakSp597Gh9+7zt4+wUXoTQd9pfG6OjVESHoehxZUjAMjScevpexwZ1UDJ3BrZvImll+8fCjfPHr/07D9giFhKIos6OsAVzXRlY1Nm96lu60xN98/IP88IFtPP7UJub1pkAoIMmzt58Zed1oNEgm0kiSgus2ef6FB3D9CgNLzyUMFEICUpkkkgx33vZjtj3zGCesXcu8patQ41ni8ST1eh3XdfE8D1VVCcMQ13XJ5lLYdZlnnn2IRNxAlX1qtQr/tv4nGLoMmPgo7Np/G+suu5SFSwdIpdso1SqEsoaqxab3SIfouk4QBEw2Snzirz/HL+64nd/+vd+hIVxkq46kq6RiBp++6dM09g4hOzbNqiDbkaU0WmfO0pV079xFzDAZ3Pg07/mzj7N57wtMNeoEporZEac6OkXg2CArqGj0ZEy27Rpn0Yo1rcculOno7GXP0C5uWf9TVq5cwAlrVlKv17EaHvgejzx6N0bMwWrUsBptiEwnQeATBD5hKBOGwfR+Yw/PcQm91j7lUNeRZQmhCexaHc92cQMLWQtxqeHYVdp6oNTYg+WWCO0MxbKDjE93d4qpyTH6+voQskrCNHnkoXswRINELODHt36Hv/hfX2fb0AihE5JKxCkWi8ydv5BnNj5Hb1cbjmOTzph4nseaNSdgJtq448ffobuji2YtTz5vESKjSFkCIeOERwqCj+eHg1deI555PCHE7N0cFjRHIpH/L7RGRr+eXG606iBU7tNeYdipqGydHek9HSxLR2vPzoz6hg3xl27ADljDrA+yRw9BZ0Zov6KwdvpYUflW4iWavr7DvNBlgQ8cZ5P5V3VgU/u49lYfi+8w7xW9aSASiUQikUgkEolEIpE3rtcsUH6lr9e+1Mdd0S7zyYUaG6sBT1d8NlZCunXBySmZG/pUPr/b4adT/it78Egk8oYyNrqfiy66gP/749u55jfexc4du0kkEtiBTGB5xJIZLNsh3dGJVS6x57ln+Y9vrGfB0n6++5P70TI59o4O4TVAi5m4korn2vT2zaFUKnHmWeexY+c27rv/Ec4//zzGx8dJ6BJtbVnqtouummzbuYvRWo0f3LuBfKEAsgShhy5pFK0So3sH0WMm+/buYu6ihTx438+5/PK389QzjxH6KsPDuzn/vAvYuWMroSxoNHwMSeaxR5+gXrfYnfe49IqrWbXuInbv24emaPzdX/0vrrziCqZqdRphyPIVp+AE/vRe5Zkw1keRY7z39z5E/8Kl7Ny7nz/+nRv43Q99EDOZpOYUcZoWkqS0msbToXK9XsVD5qLL387Xvvgo8+bNY3L3blasWMGuHXfj+ypz559IGLb2+Qq59cqu7/sIBEgWW3c8STbZyQmnnMX+vSW6u1JUa0X2DG5lYGAFzz29gerwdn6w7Vk++4V/JtXZhRQEVKslZFluNbQlqTVaWzdpWg7pZDcPPHA/O/fu421Xv52x8UlWds2jaNXJmAGEFm2ZDHvH96CnOyhaFqquoagmYfBigOh5HmEYoromNVfmTz/5V4xN5RGSgSL5OJ5HrVZjYt9uMgmFmBYjHo/z9Lad/I/ffy9P7N1Pcvkq9g8OEea6mXAFC088g6ZVo7Mtx/C+KWJGHE2VUYSP18zj5PcgrAJYZSDAaVRxhMcF516IIetsfGoD+Y5R5syfR0mq8PQTD6FpAYEHiiJRLecpGWmy7SZ+oOAHAilo7Zb2HBdFUWhvb0eEIDQoFyeoVMZwnBJB6JM0YrhOA094BFJIR6yNkXKRfYPPMWfuCSgipG/+Ih7YcB9Lly7FC+oIVKaKTU465Sy+/o/3IQuPBUtXUCy1xmNb9QoAixcvZnx0jEQqi+0IZEWn0fRYtXoFjuex4fbvMTY+QrG3na3P70bSVeKxNJmcRuiFGKqGf0gT6wiTqg8LeY/nNsdyUKh8yGVRQzkSeWs5Wig6GxiKON94ncZdD1gTXO8DevbwfccvxW9wQQAb1APPK8ZHsn3cWB5mfbF28B5lZ5KNtXqrmZx7iTD5wNHRosiNRuwoxxfjY3GH9bXpcdLHNe76xVAbYEPi0F3PKoMSrDvgkgHfAaEy+BqFyXBgU7vBl4p72Fg7+PqXEzLPHP8rftNAJBKJRCKRSCQSiUQib0Bv6h3KM2HyB7fYbKwe/Ar1N4c9rmiXeX+fSrcu+OZwNDY7Enmze9s1V2JZLl25XuLxJE7QpL17LsHWgFASNGwHhEw23sG60y/gC3ffxRknLWbXvjHMTJIXtu6gr6eXhmS1AswQZE2mWC6hqArVeo2BZSuZyhfZtGkTQRBQq1UwTB1Zltk9uJkrrrmK0845g0BRGJkaY+78BUwVyiQ1DU0OUFNxYjEdJSgjqQY1q8ro1D6GR8dYvHAR115zDR/4/ffzqU9/jNHRSRIxDVn2efr5Z/Ec+I/P3swDT9/PVH6MVDyLZMA5l1xIZ8pkz9BODEOnUsqzf2yUto5uOrv6cJ0AI57i7HUXULMCBofGcFz48je+i9AT7Ng1QiIeQw5shKmAJAgFCEmQzqXQ5Dj5yTz10GDZmRcxVRslPmwxuHMTmTbYsbvEornnoMgdhF4DP7BJZ1NMTE7R2ZFgdP8OEvEshpFgfOxBXHsvc+fOZfu27fR197Fs/jK2jA5S10yWLF5OwQ6ZHNmLqisI0RrrHIYhkiQRukVULUfgNtj03FMEoYzlwvwlKwGFrGlSrxZ5bvMWUpk0al7DcVygtc/X95poWiu0lCQJxwVDj0HTpqenh2K1RoiPGjhM5RvETI3tw7tZfdFFGMJjeM8WhifHWLp6KZLeTiyuYlk2J527jGqljjVeoD3bxZ5qA0lSSCkqhqFjqCGZtEZcVxia1DD0JEEQoqkqrlVDSRhsH9pHpWmxZGAhp5yxGqkZMrF3iKl9w/SvWkylXENIIa5jUa/nMWOdaLJBICSaro2QXDRNA7dBRpMJXIdqpUglP4SiB7iOR0KPIdUdkCW8wMd1m+y1LIQQOO44mzdtIBHPoWsKPZ0pnnn+fi668BqElMQ0YXhkjHf9jw9y3333ce27fhtfxBgbGyOdkHnugV/ysU/+Fd/93npK5Ty9PXMol6u845rfJKTELT/+Jt39Gc5YfCbtmbnkqjamoqAoEl7ooakydtNGkrXZNzSEYUjgSYRhiCwkGk6TWDaNbVsvBr5hiIw0myqLIGyF6bI0e/2hZi4TIiQMQ8LpHcvSAVW3mdsc6eMjkcibnD3BxqPsBT6sqetMstHSXv5+3JdwdbMV0A5N/37AGma95bYC3pexc3iW7zAPlfsOC1lb+6F/YQ2zvryH62cuFirfSs9/ieD6gLD3eHYEyypb5Q7W5DqmA+vp5/mQYPnQc9+tqBBoR9kTrHKzGef62sGfsyHz0OD5EDOB+dEcbe/z9Hke3Uz7+wB+mVsrrZD5pYPlmbHeWX7xGgbikUgkEolEIpFIJBKJ/Lq9cQPlC98N/Uvg25874tU9uuCTCzW+MeweFibP+OmUT7cuuKFPjcZfRyJvAWedeQ6lcpnVq1ezb98+MpkMhmGgqiq2bZNKpejvm0v/vEWIMCSWzGGn6/zhO67j4Yce5qxzLmH7jm0kUkmEgHg8Pr2/tzUK2vM8AgJOPvlktmx+DkVR6Orqomm3Amjf9ymXy4yNjdHR0YGqtfbK+rYFikIYCFRFRwhB4HmEkkN/RzcPP/AgubZOarUavu8jSRJ33XUXq1cupVavkErGyLa1Ua9WqTerTExMMLB0FYErGFi2jPvvv5vdxVEQUG9a1BtlHMcjlUrheR6u62PbNnpHG7rm02zWUQjxPUHDatLe0Ybv+2iKIDhg3LUkSQQ+IAmCMOS0088iZmooqoEfBpixNK5jU8uX6WrPYxopZFmQiiWwbBtFlZgqTGAYBmvXnsDOXc8S4iKkgEJxEgjwwgBkBU/RWL5kBZKiYJUqIILZ52ImTJYkCUmOQSiQVIXBwUH+4A8/wJz++biuS6C7fO4vP8l/e/vVNCplgrD1RqGZ86nX6yiKgizLKJIye45CCFzXxXGc1g5pWcLzXGRZwvMcRkZG6Onpwa4VmCyWKFaqnH7OhfgBlMtlDCPG5OQkbbkOqrUGXXO7SFYK5ItlkoZKrVIn0GQqkwWSMZVMLst4ocTevUO02Q4dne0U6lUURSKdTmFVSiAUDFMjRCKeSuLYrXMJgoDAc3GcJrISEhLgeQ6SLNAUncD1Z8eOq2aMqfw4CTOOE1jEjCT1ag1Dmg5RBSiKhqQqGIZOtVolmeyhXJlEVyUcx0aWWy3upl0mHo8jhKBWq2GaJkEIY+NjtOfS3POLW/nWzf+EpsC/f+ubLBhYSiFfZNWqE0gmkzz2+GOk01l8WyMZ66AwWUJVFFTVJQRkQlw/IBZPYdsHt4R1U8WxbSQBmUyKYr2Krqqz14cA06Oyw+kdyDP38FKh8HGFxSJaXhmJvJVsNfu4junw9kBHCUwHZI0NQZH1ZV6lUPmAMdN6nHXNCW7U+viFlmBD0+GeI4aqx3GfVv0YIeV0iHmg0OX6ZoObjxpez+zvPUaDeWbf85Gu01rB8oA1zPrmTKv5yOd+c7zvGHuaX7yvl+WoH3N8O5QHXs5jvYxdylfXW7utN5iv7hsUIpFIJBKJRCKRSCQSeb29MQPlC98Np14CX//UUW/ytnaZUTt8yeZxq6mssCYpMWpHo68jkTezbGcv5YrFKWtP4fs//AFrTz+VUrFGf998Ojs78X0fr2lTKVQwzAQf/vjnURWBJ0LytSr/sf7fufySi8m2tSOEoFAo43sCVZWwbRvDMLBtG9d16enpYWJignw+j2HqVMtT9PT0oGsKMVPHsS1625I888SDrF6xnL2Dw2TT3cRNE4ENBEzlx4gVKpy+fDV2ILANjcE9Q3z0ox/lH/7+bxhY0kcxP0KpqPHe62+gUqlQtcvoonfNeQAAIABJREFUqomi6vTM6WTHtk3s3voCShIMI4YkSXh+wNKBlXi+hO346LpJZ083ZduFiWEeu++naLJMcuFqTj7zPGxJwvY9XBQC10XXdRSl9e1fU000TWOqUGbuggHq1RqFqo2QS1x2+fsAlfGxvTzy2D2ceFKDns6lNGo+5UaJmBFneN9e5vbPY/fOrUgo9PUtoVGvcPfdD3HRZe+gVCmy+uxzuPLa32Df7mEc12Fk/xCELukDGkK+7+M4DmosQyqW4rN/8XFOWLuWgVWncO4557N37wjZlM7GR+5iUSbGRW+7kjt/+UuWrhygWi2jKIJqtYmiqHiejZAFCJkwlECo1GoNMtkUfuDiuB6yLGNbDXbs2MbQ3kGuvPJt3PHz27jsmncyvG8/RqYbSwhSqRwTExOkU1kSiQSDU8Mkm220tXVQnBylUq8ytmsbc3u6MRWNu+9+mBPOOY1yo8LA4vkks0nGpvIACNUlmdNwHQ03iLMvP85EtUQ8m6ZUqaAqIYoSR1LBceuEXgXfCfGRkYSCFMRpNJq4rk+l3iCRiJEwk+zfPUq2I8PI6F6y6TRCaiJEiAhlVFkg6UkK+Ql6+npA2CRSCrZToljcB6GPhIqMoDA5QS6bZsvmF0jETYaGJ/B9H9fy+bvP/z3z2lQWnbyCdevOZef+Yd593btRVY1Hn3iUsbEJXFdmTtdSTDnN4OgzJNIeUjgFQCrdye7dYyhJECLTej6m3wjQcG1UVUGEtN4YoeuI6eD4RS+GyAIgDA8Lk48cLh98WRAeITwW0ZvNIpG3mq1mH2vM47ytnOYjWZUvFSdelVD56vp0oDrdZB2QppvDeidrjhlKHqEZO8MpTd/nwcd24N7fIbOPNQcFqK1gdaNNq62cOnzU9M3p+dx82PG/eJ8AyFk+doxg9sDn+pjn/kpa2QdxWRAAmvrqBbVhnZsKdW76Fe7ioLDdmWw9d3rnr7aDORKJRCKRSCQSiUQikTeg1ydQ7uiDyeEjX3dgmFwYO+bd/HTq+MZYb6wGrEnJ0S7lSORNrmfOAlLJdvbuH+biiy+lXK2iyDqJeJpG3WbevHmtBqqk4HoBniyzf2SMVCaFphm8+93v5tmNz/GzX9yPqqosW7aMRYsWoaqtVquqqBRLRTyvtZe4s7OTsbERXNdFVxT27NpFEASk02kkReGD73svC3va+fE3Jvjzv/4y44URJOEjyQIpkLFqDv/xb//Cw7/cw4qTBvj+vXcyun8Uz61x7TvfwUMb7mLxgl5cTE49/XwKhRKjY2OcfNKphJLg3nvvJnQaqKFNh5whm+3GTKeQ40nCQEHVNUIh4/shqqYx9MIzbHnwTpIxmUazyQkLl1EqW6Q7M7hOgCQEsqpSLBZJpVIYhtEaE+27JNMJVNVEM9O446N4gcf2nVtIJnL0dC/ggnVX8PgTD1EpQibdjp6AppVH18B3XTQphaIYGGYcQYEzz+pB1+J4vqC9sxtfVVESGYqlMoocoqg6QRDMNpMVRUFRFFRZYvOmx/nJD/+Dm7/yr7R3dCCA+XP72LvredqzMe66+1aK9Uk6Fg4wNr6fMGx9b48ndKDV4AUPIUBIAUHokkoncBwHw9CRZYWnnnqKQn6Enr4++vp6aO/sIJbMkUymWbt2MYpmIBkG1XyRxYsGePLJx8nlcjTKDaSmRV86yeT259i2+Tk8J4+u2gQBnHr+2dz54P0EhGzc/jySIhNKIUJWUGIatu1ywRmXU7E8Nu7YQtktY6gmiiIhz77Q71OuThD6VcZG96JpBr4HiXiGWLwTz6kjCR/bF5QaFTLtHaRz7YyNTlJuWLTnEsiyjC5CgiBgZKLKRRddQ744wb6RXaRSCfYPb8ZxikiSSblQxw18YqZOsTCFjKC3vx+nUafp1NhdtLjw4rXM7Z9Pb+8izGSaq65ag2Vb7N4zyPDoMJl0jrNPPA8Cl4cf/CVCcgiEi2l04XkehbJLw5bQdBlZDg4aZ63KEqHvEnohpXyejt5unBCYCZVDCA4KkFv/Oig2DsPZfw68bGbk9YuXHf5zg4h2KEciEWJ8JJXl1kqR9XX1Vwo/bzOyXCTFZgPFVuDaau5uLLz8+9uQmM89bn02pJwdnT193ZqjNn8PDKhdbizvYePsHuOjj21+cZfwy/dqn/uQ2cc1cunF0dZC5VvGrxpMH0DEjzKG+3hMN6Fnfz/dIpezXPcrh+eRSCQSiUQikUgkEom88bz2gfK6d8DZb4evf+LwUPllhMk9unTcDzlqBy/r9pFI5I3JCyRqDRtNNajX68TjSSRJIhbzicViOI6H0E1cu0kqbTI2NkYiFsOuN+jsyjA5PsWaU87m1FPWAQGbtz3L4088SK3qoKoqqqqSy+WQZZnYdBsnHo/TtC0alTLpTBLLsvB8h5SR4MKzzmDLkw/TyI9QnBynWKuQzSQI/YB61aIt3UkqYbBkbo7BLVsZ37sfLZbkqScfZuOTD1Mr76WvK06oSchamnK1iKxlkUKJcsNi6cBSkprED//tMbbc9TCJnl5OPudcVp11Ls3Aw3Ec4qk4nV1dPP/8Jr7+l39FT1qhbd48jGw7q046m6rtsn9klGQ8Dr6DZGgYhkG5XMayLFIpn3Q6jiwHOK5LGAo0OcDUEpRL+xkZfpZHHy1j6Gn6eudSrQ3TtEdJ2Z2kEglsqzE9YrqJputYzSrZXIJy1cNqeKiyQuh6FKtFAllj5+Bu4vEY3d3dhNN7jmVZxfdDPM/jF7eupzE1zJI53aTMNIvn9iPh88tHHsPUfFavOZk9O58lnoshKyHdHZ3kS5O4rgsEPPfcczSbTZYsG2gF1IqOomhkUlls26ZcLrNp0/OkM0k0XTAxNkz//IUMj4yxdNFSfDsgpsdwPA+rYdPV2YvrNbn00sv51Kc/xs03/Q0fv/FDhI6F28wzZ1EfdUvnymtuQDHiWH7A9+74L3JtGWIJA01TcQMX1ZCoOU10XePscy7hK//8dwi1Qt2vQOAjCwNVUgnDEM930DSJQn4cTQ+ZN7+byYkpJsZ3Mj+eolSeRFZCCmUXoWpIegxHhJx4+lrCwGVqz14ajQaarOF5AaeuPYvCVJ35C1ZSrTWxnTr5qV3oMZOFC5ZTqzWIJXQqpTKe7yDLEvv27cNQVKpuDde1WXPqmWQ655LNdJIw01iWzY49u0jFE6xYtRzTMCjka0iqwpq1FxCPxSiX9mFVawSBx8TEBKl0lnrTJ2mGs6OrAWQ/QBESdqPGj7/3n5x+5iksX7t2+voQwpBguqEchmGrdByEh+1QPlJD+bDLwuiNZZFI5CjkNNfEHTbWZ8Y3v/L7+chh7egjt4GPmzZ/NvR8Oe3rV+3xj9ev49x5ueOwj9H0PsArex6P9Tiv5Dxfo89LJBKJRCKRSCQSiUQir4LXPlDe8KPWf3/3rw8OlV9GmAywseJzRcfxHf4V7cpxt5kjkcgbl66naTQa+IFPKAmatossyySS6dmGaxiGGLEkgQ893fN44snH6OjooOkKEvE0G+69m8997A/JxuN87/YN9PX1o8jQaDQYHBzEsS08z8OxZYLQRxYgC4EsC+xmhTDwaFouQSzG4hPXsmDxAPg29VqNjlSORr1M4DtAiOVY/O9//x6f+PNP8tUPf4R9+/ZQaDRYtHgea05aykP33E1bppv5J5zI5ESRarVKKGqUAhlFNkile6jWKvzBp7/ItRf9JnPVYXb96DsMnHUaDz+2gRNPPpOOZI6hwWFiksaSM09j/sIF5Dq7SCQSTE5OUWm69Pb2Ypo6vlOn0rCIxWIYhkGj0aBWqyFJEpqmAQ6GYSAUHU3Y+M29qFj0tXnUmwUmJ8aJJ/qIxbN4VpVis4Lr2AhNQzdzNJsNPM8jP1kBQFF8hATVWoVYPEmjVKK/twNV1cH1UXUN23bYtWMPyZRJELhcdeUVDG55nvPPO5t//eZX+FDXZyiXB5GFywubt3D5b76LF56eS9zUUGWZeq2Erpg4loMb2px6ysk8/vij7Nq2ie7ubmTVQNdNmg2L7p5OJneN0tGZpdGo0T93EfFYkng8jd0IcWwPVdWwQxdkCVMx0U2D8kQJqVzhne/6bZ7cvInLf+tdfO6zf8uqRRkqJZe8WyHRO4/dg0P0duQIkVE1A0WSEaGgXqrhBT5LVp7NhRdcxjPPPsVEaRBTlwCBhErg+jRcHyGa9Pctol5rUm+GCFXm3g2/RNdiTIxXSWWKoMg0XBdCDU02MHWdVMzkqSefYv/wXnKGhvBsOtq7aNgOp3R2UG34jOeLpFMJCvk6mq4jhQY4OpLUoFlt4PoeYehSrA8ztHc3Vm2cwpTNaadewsKBNcRSGYIgoFzJk88XacvkZvc5e56HUBRkoRKEHnWrhmrkUI0MsizTt3A1Qgh8L2TzpmcZHR1FVxUEAbqpUKvWKO3bRW+7QagEhIFHEAokoSCEhDQ9qnp2CLYkCILW3+uzoTH+EcPkg34dRG3kSCRyDFoHa6JRxZFIJBKJRCKRSCQSiUQih3l9Rl4fGiqvPudlhcnQGmP9/j7B+/uUY+5RvqJdpkcX/Owlxl1ffPHFfPjDH+aWW27h29/+9nGfSiQSee24rosQAlmWW+NtA4EQYrrlKiPLMkHQCp4kSUJVVTRNo1Kp0JbroFwusXLlcs4/6yyeefwJNj7+CGdccCE+PnEzTjweJ5/PMzo6Shi6CATNpoUsBLGYhu+BYWpMlstAQCydZKpWZcHceUwUS3iVMvG4jiRrjI2Nocg6m7fv5MY/+VOKhTKdXV3Yo2OMj49TK6mcfuaZbN+0DUKZzVt/yeTUBNlsEtNMks0YBKLVyO6fu5Br3/su9u3ezJlnnY7neWiKQjadxnEcMtksJDN89h++RLlcplgpE4Y+xUoBSY8zUcjTnssQN3RipkTgB8TMOLpmEPh5VEVDVbTWcysp5NIdbHryIUIa9M7tolgZwvUdfCSCWgHHaRI3M609zMKj3qi/2B6ltRtX0xTqjSamCJEkBc+1URQFx2liGDEq5QqTY/sRsorvgYyJaersGxnDTLWhKjB3wRKmpqZIJBJksn3s2r2d8fFJdFWiOjZMcXiYzMIV5OYsQJIkBAJV1UgkUq3PTyxGEIAIfUJ8JAl0XaVWs+js6qCnuw/PC/A8D8KDq2it56K1ezmbbcOy6gwsW05lqsDypct4z/XXMq+7g5HhIXY/dB/5fB7f99F1nQBBw3LYNbgfRZFYuHA++4anOPeci7EaDs16g4SZQNUCisUimqzgeR7tbV1kMx1YjYAwMFiwaAmjY3swjRSOHaAqJp4PkqwhhTK+FyJrKnXLJtfexVVvfwcAlcIULzz1CDFdI+mFbN+6BUVLYibihL6PoijIsk7MTEAo4fut58bzHDRdUK0VsJplzDholkrPnH5y3Z04TQ/f9ymXS+i6gq7rAARBQBiGs+PLZ/5MHvhn1XVdDMPATJgMDAyQy+XYs3sXjm0hhEoQhlQsCxSVefMXzt6nkAWE8iF7kVsOayZz9IbyzK8P3Mp8pNtEIpFIJBKJRCKRSCQSiUQikUjkcK9PoAwvhso3/iM0avDVPz/uMBlg1A756ZTHDX0qY3Z4xP3IV7TLfHKhxud3O4zax37B+O677+buu+9+WacQiUReW7VaDVVVW0EmECAdFGIJIVCUVjg3EyzLsoyu6+zavYO+vj4y2ThXX/dOKnYVX4VvffcbdHX0TodsMul0mmQyiW3X8QMP127OjiEWUojr2ggREosb/D/27jxI0vuu8/z793vu58mrsu7qqupT3VKrW9ZpbC3Y+BBgw/jC2IaBGQ8My8xAMAsRu8PAwMzOsLHDsF52OBYIwDAcCwTGEIAPbGyErLFlydbRaqnV6lvVXV1XVt753L/f/pFVpZbV9gy7Y7Pa+L0iKqoqqyuzKp/MVIU+z+f7HaQxIvRpxSOiWh1Ha7Y21smKjKwsmZufQ9k2X3jicSpRDXfUJ+n3kFZJP0t45JFH+f7v+wEurlzlyVOfZWq6QZZL5uYPYDs+Ugbcccd9PPHEWd78zu9AqJjtrQ2qzSlUCWQprnRYWblGoz7Js89kzDTrOEWJFBo7gPXuJnNLB9lqb1NUK3iOz8TE5M6I6JzJyWls20YIgdYapTRzU1Ue+fxfMjM74vylgtd8w1txPKhVZokqE1y+fJk06ZJQAgrHcdhuXcH3HHrtbaQYN75P3nkfq+urNJrzlKWkLDIs6XPp4hkc12JiYRrX8ZmanKdemSBO+wSuQ683RFgOr3vTW6iEEVprLMfj27/jfaytrfH4tXM89/gXYOM6z194gff+yI+Rpime55EmOQvz+3n22ceZm50lT1KKeER9YgJQDAY9FheXmZubI80UWZYhkEipAYHWAhBIYWFZDpLx4yoIIjzPY6I5w3qrxd2veR3t/havPnmS5vIBLMsiHo54YeU6V691uevu+/kXP/ZvGI1G/NWnPsniYoQj63R6m3zqk3+NHzkkw5jIq6FKTRTa1Ot1KuEMMqyzb+EAq6vnWd9ogSjx3JDXvPbVpBmoLEeWOUpqihImmtNst4cMBtcJghBlCRYOHOH86aeoVquoZEC7P0RasxTFeIS154ZIYRMnAxzXQiORlmI4GhAnfSqVAEfWefvbvpmlxRNsbHVwyoTzly5y9NZjJFlMWY7/u1sUxXgXsuOwUyRGaY0UgkKNd1o7rkupFN1+j6Is2Le0SKUace2FK5RlzP59x+hvXue1yweoNCdRRYFSGq3L8USCm7weaMa3/18z8nr3Y3mTSHl39LZhGIZhGIZhGIZhGIZhGIZxc393gTKMQ+XDd8CDH/pbhcm7PnitYC3VfO8+h7dO2zzeK1lLNXOe4O6axV1Vyf9yMbtp2GwYxitPHMfAOBiybRvXdfeayV/aiJRSIpE06k3anRZCaJ544rGdMdaSb3/P3wfbY3H5EAszs6RpyubmJsPhcOd2CoQE3/dBKfIsRZUarQVRWGO71UNKm/mFBc49e5oD8/tZW79GY7pBq52QJjkTjSarm2tMNSr0O20sQuJ+FycY/+xv/da3cXFljdXrV/i2t3z33n7mRm2eaqXJ1NQMZ8+e4dkzp7njruMI4TG5b5GVtTVOnribpz7/OVpXLxEFFZaW9/OGd3w3v/oLH+DrX3Mn6IJHn3yOb3n7e1m9fIXZuXkoQLiCdruN53kEQbDX6tZ6vNc2yzKurW/RnJthkD2L4zi8cLXFbcfuBB1QpIJmfZZed4Bt74w3HvaxpUWZlxRxF4Fmulbh8Uf/hun5Q6RJDSVKPNtna+Mao9E6a5cvs7GxRq3SwFcBTzz8KP1Wi3q9xpWtEb/8f/0JoR8hLEm0M6K7149pTEwzv+82Hs4/jvTg6O2votfv7O3QBotK1GBjfYs3feM8jz/2KBXfQymJbYVMT+3j0MHbWFtbA2GhtcCyxi1b1/V3RiwLpByPUJcIPM/DsgVlmTM9N01axghboKVic7PF1PwB8iQn8DyGg5hf/uVfY2VlhSeffB7LsrjlyJ1YrsfmZpvRKCPyqzx39inuvOMEvd517EDxj7/3e9jYGjLqu1iiRpbmPPn0o0xOh2xtrZOmBaMk5ujR+1BKMUqGOydUjH/vKAooy4I46bDduUZ3bYXmZITlWGxvXaefCizXwXFKbMemKBOGow5aSDyV49ghQrqUhWDf/BFs22Jm+haSfsYXHv5LgqCg0AH33XsvK2tbaOGgi2TvhA4pJVqPI14BSCHQaDx3PDs2LfLxJAHXQcgSy7GJKhWOHDvKFx/9zyTDAUFzglJJOv2YyGNnAsFOA/omma/eTa93XhNuLBp/uT3KpoxsGIZhGIZhGIZhGIZhGIbxt/c1C5S1hpuWgD74r/+L3/eVfHSr5Im+4i1TFnfXLOZcwRN9xUc3C37ojAmSDeP/T1zXR0p7J+xz90bu7o68llJiS0kpBEWhsaSk1blOo16n3xuwb2GBLM4YJj0yaZN0W1QrdXq9HpZlMTExQRRFrK+vU6lUiOMhqxvXmJysQ1bgOA5CQFALSNKUNE0J61VqswtstFZZXj7I1Y01Ulwm55r0Bx0GnS6rV84RhiFTziKCksBvUK01SHPN2vomUTSD51Vo1COmp2dJsz6dToczZ54hy1IOHTrE5maLWq2B6/jMzyzTjBpAA7t5BC9wOXbXXSTKZW37Or//wUeIbIk/scyFZ55k6tBRsmxAlqZoranPTIJw6I9yRqM+S/vmybMYz7FQZU6tMsl//89+gnPnn2I07FKrhGjlYEubfm+TUhXEowHDQcri4gK5nSG1xHV9ekPYv7yA7UA5WqPTWmVh+RD9vkTrkjgfMSpGjLICRMTaZheHhAvrLUa9BCue5J//yL+mMbmAW0kQqL3A2/Ms0jTlxL33snT45/n0Jz5CFNhIp0qe54S1iFHap8gzNrojcq14/FMfpVTw/T/5vzEYjFha2s/29jau4xPnKUIIFON2tiW9vXDUtu2998KSICSu59La7tGcnOXKlUtEUYS0IM9KhOUiLIdM9bm8so1t21T8BllaYDsO9WiKsxefx/Mcbj/5Kt745jfz0Y/8MVE14sDBfTzzzCWmZqbJEocLF7/IE089xMK+WbJ0RK1aJbFi8qxFt79KFDao1ny0LsnSlLywyXMfrVwcx6F1bQNpw9VWi+bEFMoLue3IYdI8w7IL2p0WlchmkCQUeY+Jpk82igmjGrbt47mzSGnTbXfwZUzN79Dbuk63sDhzSuCE+1C22ttZLoQAIcjLEst6cYKA1hrXFqRpjiVDbGnj2BaEgrLUjIqSZhSy3Y+pRhUEDg0XNq5d5npSorRm8cDB8fXazt7rwG6jWOwct/GNsfcHw+7JETcLlcVO8H3jZTcG04ZhGIZhGIZhGIZhGIZhGMbLfc0C5d9fy/nOOefmofKXofX4+/5LrqeaD14rvuIuZcMwXvl2g2PbtvcCP2BvXLPWmrwscR2XVGdICVEYkqYptiVReUG7vcXE1ATDYYzjOAyHQ9xSEQQBrusipWRubg7f9xFikolmjaLI6Lc7+EGAbduEYUhYlpRlSZJl1AKPM0+c41c+8LP8q5/+abqDjEajxmAwYjQacODgITzPI84VTlDF8ytobZFniqWl/QR+hGUL4jjm+XOnicI6RZkRRRETExMURUFQiXBsD601/f6Q1fYmx287zKnP/TUrF8+xpFKW3vjNSCIeeXoNCvihf/leDp68m0Fc4roVklHOMB7RvdBjptnAsyTx6gV+8fd+lff9w+8nas4iwymKokevl2NbHlOT+9BFTl6mZGmPbn8DVI4feizvP0y73cJyLCQ2aIuTJ+4mz1PWrq+CtEnTlNZWm0p1gYmJSaqNGn50konGJI49gUZz4dwl/tE/+Emm5pZAjk8EyouMeDhCSP1iaAmUZUmrHYO2ecvb382Dn/pLiiIDNO12F98L2De7CKrk+Qvn2ez0md+3hBCCNE13HkmCIi9QWu09hqSUCGnvBcq7JyhYloXjOHsNXMsuaUxEPPjgGU4cP0q/14dMI8ociWIwGFCW5bitW2rQ4+vYaG0wMVEnz1IkiutXL3DyxDGCSHHolv0EoU2SDClY4fkLj3Li5G0sLc+CEjxz+iyN+nhMeTVsIGSIbdsM4gFCpGRZilYdJiYmkbJgfmGRTneTSnUCAC90OHfpPMvLy+RlgWW7lEmBKz1s12djbZ0oqpJmBV5YJ7RshNQEYYAvLR781OPccmiJorTwgwjhWBRfEsrujpzffR7u7lHOSoXn+YSeQ2tjhQc/+2mqXg0lHU7edz/nL1wisATpqEs67HLpykXKNKHViRmMhgjPY35xiVK9PBzeHbm9e9nu7e9+frMR2EVRvOwyEygbhmEYhmEYhmEYhmEYhmF8ZV+zQPmXVgp+acUEvoZh/D9343jd3RHXX8qxHEbJiMAPiJMhvheM9+RaNsMsQQpNEAT4fsj6+sbOzlz2di7vXmccD5FSUuQlaZZTqzb2gkW0RCuFQqK1IPQDzj/3HKNBlyIbESdD0jQmTWPKMkfrEKXA8T3CapVarYFl2wjLJapWSJOSXrtHkozIspR+f4Vur4XjChzHocihNxgRx+nOuG+He7/uPvI854uffRgrz/j4h/6Qi+vb/A8/+i94+HOPk4xSvvGb3srWdo9X3XkPw2EMTZskHbGxdp2ZiRq/85u/zl/89i+hvQq3HbuV4/d+A9V6ne5gA8eyiKIqeZqBEpRlSVGmlPkIUKjSpdsZURaSIGgw7PfwQp+8AJD4QQWtarT6fYIgQCnodrtEtSqSkM52Tmd0iSwrGPZHHD54nDRJaLevo7RG2BYKvRf4Wpa1d4wt12PU7xFFAVGtSVmWOI6D4ziURcGzp88QjwbkeU6tOU1YnyRN072R6Y7jvixsFEIgdvYl777dGC4LMb4P/NCn19tGqYJOe4PItzhz7nmOHz+xc7xLlNptVZfYOyPFEQohBVmWYEvBZnudKysXOXnHUWanZ9jubrDd3iKq2rz1297ARH2OlauXaG11yXPF9OQk7XaLStQgCJso6SC7bTrt62RZhu8HxEkfx3Hww4ggT+j124xGI44dv43JyWmE0HTaPXSpkLZD4Hk4nk+eabIkxg9stCpQusSV44C21IJSOzQmF4i0C9Ilz3Ok82Jj+MYQ+cbLhBBkaY50BaWAD/3B75Cn2+TtAu1G3P/6N/HQ2bOUaYxnW/iujed5FBLK9hDLdajVauOA/sY28g238eXef7l9yjcfhW0CZcMwDMMwDMMwDMMwDMMwjK/k73aHsmEYxt+Cbdsv2Zm868YQcBgPiYKIkpzLV87hOhae65LGA/I0odmosL29jecF7Nu3jyuXV7Asi3KncVwUBVmWoVSxd5mUNv1hwtRUhWq1ihCCoigoigLHzYjcBk8/d5Gf/Ol/z3q7jXBsBsMuRZYxM93EdSK0kLiBB8KlN+ojpcRxHHr9FnHZaqsjAAAgAElEQVQyvi7bdgjDKvsP7OPadcXTpx+nXmtSFhbbnfa4galAxVCrTaDThO/7oX/O73zwg3zm1DO88R/+AFfXunz8wUfptNvkZCzvn+HayipeEBLnfZq1OsvLhzhz5jke+/yjvOWB+zl15hJXnnuKt739nTz55NNUZxrkStFut3FtC1sXDEct8mybeNTD81wWpg7Rbvc4ceIuLl85R70xTVlqur0ORTYkToYkRYEWgtEoYbIZYLuCLFVkWYbrughyBAWIlAc/+4lxIFxqJiYmxnuLLQvLcvYC3d0R1KEXMFGrosqcxaUDXF29ipQ2qBSdZ/zxH/wmBw7MU2vU+Uc/+CM0Z+bpDUZ7jx+tNUophCX2QlApJYIXT1TYfX9j63bcDh+gVcqtR4/w6U/+Bd/+jm/jz049xre86QHmpid49myM7fo7x3McRm9tbeF5Hjov6bY3WZqaZLJxC3fecSsPvPUB1tZWUVoQJz0cV9Fpd1i5cpqpyX3cduw2fOcKq9dWmJ2dp1GtUeDheSGNiSnmZqZ49tln6ff7ZFlMFEX4XpXJmSUmZxd4YeUirbVNotCjzGNqQRXLrxJ4IUprhsMhFS+i02+hVUGRxcRDjWPV6A8zKpUKJ++5H6/exNcBaamw7XFb27Htlz0HLWt82V4TmIJ+t8vQ83jDA2/lF//jz9CsTHLv3SfQ0uLatWvMTlWJ84LQC3n9N7+Vs2eeIWh2Aag26rRabYIwGl/fDcfiKzWSv1x4fCMTKBuGYRiGYRiGYRiGYRiGYfzXMYGyYRivGDcGyUopBOolgaCUkiiosLp2jdXrLxAEHsNewv79Szx16QJfePSzvP0d38ZCbZowrHD69DMcOHCA9c0tpJTkeU4cxzSbTYoioygKkiRjOByiFJw7dwGAZrPJrbfeShRFbLS28D2H97z/n9LDZu7wcW45epBPfvzPCSyxE7qNw8pSgGWVlGXKcJTQbm3TH3Q5cfJOpqf2g7aQIqDdGrE0/yqW9t1KFNZIY0lQdZmYmEQIQZ4VrG+ssdpt4c/v5/3/8qe45dgRuqvrSKfChfPPMTPVxLEDyjilUYlIVEF9ukpkR/T7OXe/+hv4jd+/n4989MO85n1L3HPPN7DR6rB49FV02itIC2wbNDmlSsnTPlk2RCmFI12WFzz+7U/+K7bbLV772vt4x3f/EEWesbZ+lZnpkJm5iAsXu9xy7Bhzc3Oo0gKR4zoBpYKNzRW2Nvvj4NjxaNQmyYocP5RoMT6+8oZ9tzeOn1bpCD8MGeYpzYkJLly6iOd5ZPkIR6e0Oy9wwN7H8oH9tNZ6FO0eUqu9UelFUY7fMw4Sd8NH27ZetkMZXjom2bED8rRE4PDmN76JC+fOcfstB/nLj/wZ73rfd5GmKY4XUBQFluWglOL69evMzo0bwlJoLp4/x9HDi2RJycrl66R5ysZWn153SJFDUbhMTkYszN7K2rUt2tsx1UqT+flJhsM+1Yk6uVbUGnVyx+XggYKNzWtMTDQYDAaM0oxC2Vh2SRBVsUYJ2Sgm7raJVZ/9+w+SJyVBEEAAeVngRw6brRaWZyGFGrfRFWy0tpmZ30+9PkFZjO+PsiyxHZ/yhgB3N1BWSu/df7ZtM1EPKQqFY1dYPnCM3/3QW0j1kDhOaW/3uOfuO/ErPo5lg9I8+sXHqIQRUzPjFvlgMMD3/b0x4jd7u/FnuFnAvPv57uvGjZ+P3788bDYMwzAMwzAMwzAMwzAMwzBeZAJlwzBeMbQSTExM8Dd//SAPvPlbKHeCoLLIcSyH3qBHp91ie3uLKPQRQhBONcgpiQKL4dY1/uS3fpW3f/cP89zpMwTVCqNsSKUy/rdJkhEEdaSw6XU75HlOlmWUZYkQgmqtgdaaNCt46tRpPCdgcnKCyeUJ3v7d30OpRjgioN8fcd+rv4k///Pfo1Yb8ao7FgmDKt1eBwpFI2oiKoKluX20Oxt86Lf/T8o85crmFgdvuZf7v+5+7Osuk81phNggy3Kk7XLt2jU2t9bpdNrcdcet1CbnsL0AqRTXr7yAH1SIkzZRvUKqBHaZUUpJWWos6aMHEHsxnmfRHQ2wbZs3vvl9lHrEQ5/9FJPNKSYnp3Fdi1HcwbIFlvAQtkY6kt7GJrbUpMOU85cj3vW+76Lbjbn99lehi5Jur02lWqXEZjACL2wQBDMUpcSyFL2kIEk20EoAksZ0Yxw+Wg4gqAZVNOMmuuu6O28+7Fy2G/JKyyEpFdLy8D0QWYylQkDyxccfZXmxxuJcHTnMeP7Mk+xbPszS4mGUilFKcfvtx3nyySfHY5uVQEoLS9gvGacOLw219xq4wgE3YnrWx7EU0/OHKLRLVKmw1t0iL0YMeymVSo0iUZRpQrNWH7ebewmh1+Dt738n//t/+Pe8973fgZAug0GH7fUWgb9MbXqBwWjEIO7w2BOfpb12kaMHFlhaPEhra8B6+xnmHJ+pufH+at+rMTm7QK/Xo0xj6sEkpYS0SClUiWUHVKabWBJqjQUsXTAsS3ypSbKYXr9LmqZYvs1sfYbL11Y4csc+chRpL6bSGD/mt9tt0lFCrdogzQv8QGOJcXNcWBJ2TuzY3XHuOA62bZMrhbAkuYrJk5xBPMBxxvvCLV1y6/Hb+PQnPkYYhmxvtbARxP3BXpN8HBLn6FK9LCAuv6RZfPNx1l95/LXWGsnLR+cbhmEYhmEYhmEYhmEYhmEYLzKBsmEYrxjN6SpSKv7oD/4Tg60XeOf7fhCAtMiwrJK19VXi4QBnZ7+rUiVxHLPd2sIPPNr9bS5trPL3dEartU6NjLrVwHMrOI5DmuaEYUiv36Eoir1W5G6YCLykBalUwer1a1xfu8rkVJ2w4uN7ClVoev0YP2ri+BZHjt4KCDaf2iJJB+SqRAqX5mSDu+55Db/9K/+Ro4tLvO8938lm7uJaLt1ul43NVcKwAkDoOhw8tMy+xRmee+45Vq+tMT1/gEJLbAGogtFohG0LPM9Fa4XWcmcMsYXgxV3Re7ugAaULwiBkfm6Bixcv0u/3iSKHuZkJLCEZjWKS0QjLcshLQZ7EhGFInMYsLe6nUolx3AoCD9sKqFQknm8jhCbNwQtCsrzEKjOSZNz6lsLGsiSWY49/PmkhxM4o852R5rs7kW8cQb37luc5tm0TjxLKsmRmskYYeRSly3NnT1MWCb7vM4oHCKHxHMmw30UpRb/fZ7UaUY0C4qykUAVgv2SE+q6b7egu9bgVr7FBCqKwQrU6SRh5KKUoyxzpSUqVURQCreT4a9Li1qNHqFarFEXK297xAK6vOXXqaba3O/R7QyqhT5bEaKkAD13mJHHM4uIig16H9fX1cQN5skc+mSKlPR7JLsQ4vI3V+D5UNo4bYDmSEoUnHQLPpRaF2EKSpTGtzasgFJbtkPT6iLLAdQIypej2h/RGAxbnlum0u6RpyShOCdw6PT1AIxHSxrXd8bHBQt4wJhxuCGxLjRAghEQIBbw4QjzPc1zH4emnn2Z5eRlbWqidgHf3ubd7XaiXB8NfLlD+cu+VUjdtMStTUDYMwzAMwzAMwzAMwzAMw/iKvqqBcrVa5dChQ1Qqla/mzRiG8f/SYDDYCxP/v6zb2cR1fSbqAb/1yx/guTOr/LMf/mGEH3Lm1FNELli2AKHQGqSE7fYWeZZQuIIjtx1n/g2vQ7igZUGvs4XWOfsWaqRJTKNeZf36KkII0iTbC6AE7DUwb/ywLAvCMCSKAsoyp9Ua0u+vUeQ5tx+/hYOHjzBRr/HIo4+Spin1RkhJTLdfokqLjc023W5CPIQnPvMFTj/9PA/8g/cTBCG1eoUkzgA13jcsFL3+NmVZcsvRg2ysbuM4HulghKLAswSDwTbV+gTdbne8a9iv7I0fRo+btq7rYtv2XvBnWTAcxiwt7ccPPK5fv0av3aHXXsOzLVzXxXNtHDtiYeEw/f4GvmuPRy9XQ/Yt7afXT6j4FRy3ytrGNkLmuK7LgQPHAB8/jMjzksC3SdOUstSUpcLx5M79KXAcByHkTXdk79odVwzjxnIQBJxfeYF/++P/I24Q8IM/+qNAwTAeMDM7S7/f5eKFZ9i30KRZbSAdSZHAuTOnmJubI/QbSN8nK0uyLMHzX74T+EtpPW6rowRxrMiEojYxDyJjMIw5cvgQ6xsrlGWO6/rYto9tuczv24fEQuuM8+eepjdY54WrV1hb7VPkgjCo09rqsr65QX3SR9pgSYdOe8izz17G9120ZeG4VfYtLJIkOZoStx6SZSnNZpPVQY84jrnlwD5euHYVVQqyPGGYZcRxTL/TRQgLpRSLiwtMTk5w+bkVFhYWiLOE9XaL246fYHNzk/lmkzweMFGL2Gq1GQwGOE2X0AqxbA+lM8D9kvtGI+WLgfJuwxtu3EmtKYpi7/6N43hvZ/ZoMMSWksDzSdN075hrrRH6pbcDLwbKXylIHo/hVjdtKn/pWGzDMAzDMAzDMAzDMAzDMAzj5r6qgbIJkw3jlaFSqXDo0CGeeuqpv+sf5Suq+nW0HfIzH/ggH/qtX+SJJx9F6g4Pf/YRlpaWSEYdLC9AqRLHsRiOBhT5EMuSbGx1uOPur0cpKJRk//LhcYjeGfJCdpH5+Xl67TZag1KgdvbmSms81lftZIs3Bo2WbWNZ4zYlSKSCQ0sLWHbB7/7Oz/HXn/oYP/MfPshwsE4QOrRamwipcCx3vBe2sGhtb/ITP/0Bfv2Xf45gosbS3H5GWUqSZDh2hMDC96qM0pjhIMVxLJIkY25+iv6gQxT45GnJ5QvPc3XjMm9+0zux/MpemLf7844booKiKPA8b6+pOYoHaCXo9RJsW3Ly5O3EvS6nn3mMQgissiTLPISW2G4N1xsxiHt0Wltgu3SGfVQpsANJtRHhRwcJw5AgCNG4aAW2EyKkQtgleV7ieS6qBGmN71PbsvF9HyEsxA07jHfHJwMvaSg7jkOe5zi2Ta/XoxjGVNyAMs654447OXU6od1uU+SSyWaNjbUr1MMZbNtmNOjiOQ6Skrjfw7Zd8Fxs1945ji8e5xsbt7uUGJ9IYAuJ4wryPCZq1NjavI7v+OjSpdsZsrg4SVlIGvUmWVZiCcFg0KPX2aLbvU53oMkzRb0xR56XbKy3CF0b19NYtkJREkRzfOOb3s3y4iKnnnmCiX1NFuduYXuU4HoQBSFKKYIgoB759NstNjY2UGnMdDWk3d0mHfTI8j4aQRBK0jQjzxJa3Q02tq/hRw5Xr18hzmMOHjzI449+hktPnKHMctyJiAe+9S0URcy+pRkurzyH0geYmjqAtF4auN9sr/H4frT2vi6ERjMOmi3LIi4KHnzwQYQQbG5uEng+eVkSD0d7Jz3sNpUtIV9yvVprtHj57X65sdc3+xn3mstmh7JhGIZhGIZhGIZhGIZhGMZX9FUNlE2YbBivHK+E52u33cGpWYRBhSeePs0nP/YXfNO3vIVacx+lygkCj6wYB43xsM/G+nWkK3BsF8ex0FJg2Taj0YhROkLpDK0UKotZu3qJxuQEtusyjFNs6bEbNyEdbAtcx6coCixrPB5bC4mUNsloQCUM6IzO8uu/8UvEcQvXFzhBD99XNKZm6fb6aBmB0GjLI9cgHQtKycXVqzzwru8a73FWgrRUWG5AnmdstzeQzhDf38/MTIMkHbG5dZXM7pFrQd8SuFZJw8943bd+Jx3toZBEgYtlOTvjvwuEVHieBzgMh8O9lmjNCUl0QlRxuHTmIltyjecvPcrMvEe90SQIbAa9AlVaeF4Vzw+olyXT0xnDYR9XaBzHIXBssjRGacEoSdju9UjjAYvLt+BYEoGFJzwKdxwsRtWQfq9HrVonzgS2E4AFtrBfMuJal+NgXCIQGiSCLC3BshnFMe2ta7SzHrPNJZxAUqvUmZ1ZRouEai2i0a1Qr9TJ85R+a4Va5LN/qcFv/qdf400PvI1O5iCrNXzbpxpWCaIJsrzE9SuMRiMC19q7/TxPETu7nG0hsXb2Uwt7PHY8z2IqtSqLc/spkoIkialFNerVCr1OhytXryCkpCiqVMOIldYK3c51LMsijDxcK8N2XDqdBC+MuPXWE4yGXZ5+7iKu3ySPoZ+XFHnJzFSFbrdL6EqUrJFqm+WDh5iZmaG7dpWpiQmGowTPL8hUSa+7Sa1ewdIOZDmXVy8zOTlNo9pgbt8yozymXq1hu2usjYYcPryfyZrNlZVncWs+7RdWmalMs3LpSVzL5+CBe+hlLaS0QboURUolchBKUBYl2hJoIUjTeG+EeZ6PG8Vxv09RZrRbW2SjIVJpVFGSqphGo0FrOCQX48vKPCf0A0YqRiUZrnAo0gxtS4StKcsS13JRSlFI62WB8Y0N5bEbguQbmsvjJrXzNX9NMwzDMAzDMAzDMAzDMAzDeCUwO5QNw3jFEJag12mjipQ0zzh51900p6ZILRtdFhQUKKXp97v0euM9yJ7rkWUZrutjWeOdvGVZUhQKKWw8z0NJTb+1zalP/RWveeMb6PsuNUcidwJZ6dhY0kFT4vk2aTYgTrtcfuFZhqMuTz35CJ32GrpMmJyuUpLSbStcEbG1tk6a2UxNNun3oFQS29pt24K2QJe7L8UCKWyk8BDCQqsCKTwkEZVKhTwft4tLlfDc2Sephz6TzRrPfOHzPPXIZ/jMwYf53h/9n+kME1wnwvPGLV+QKKWJ45yiGAd8URShtWY46qKEz9rlFZ67+HkaNZ+wUpIXI7p9h/4gx5YR9doERQFlWWI73nikdlRBa0WaxdiWT5EkaHLyIidLY2oNn+fPPcO+haOElSbVao2o4vJXH/sznn/2FN//Qz+GVuA449C21CVCihta1QJ2QkHbtvcaw57jkOuCq1euEQ/7/PhP/GuyLOf61VU8x+fE8dtBQR4XTDamqYZ1ZJ6xduUy57sbrF9scGypSZbGFLkmQJMlA1qjlEG/TxhVkBokEqVttIISKLERMG5OS2t8MoJSuMqi4nusrW8xymOE1LRaLe6882601hRFyfr6OmJnbrMQgrQYIh1FWWSUBXiBjfQ9nKDKbfPLVGt1Lly6SKe9TbXiU+gSx/XYXN+gHth8/I9+m4PL+1m+9QTSzcmLjMl6Dd/36W1t0R+N6I+G46a3Y1OfaGBZAt8PqTcm2O8toxQ4no9l23g4lIXNa+5/A/fd+/WsrKxQa1bodDd5/KnHWD64j82kQ1Sr8NgXHiIMfZyggmO7SM9CWZL2MKFZDbGw0EKghETrHK1BCBulivHIc5Wzvr7O2TPPcvvttyE5ThzHXL16lcFgQF6W6KRElwpLCNI0RZNRcWxOHDvCmaef5siR/Ty/uspglNMbjpC2ByK76TjrG0elf7mvj/+NCZQNwzAMwzAMwzAMwzAMwzBuxgTKhmG8YvR6PZS0We22uPvee/Ccu2hOTXFlrY3rVMiznN6gT57nJEmCkLsNRV4MJLFQ5ThAGo9VdpCuBULhlAX9VgvnwPI4SLYtbNtCWBaO7RAnKe12B8fVPPg3n+KJpz+J59mEgcbxh7iWS6uzgZQ2ca+gHkzxyOc+yz2veSOeK0kcB51JBLuBqUJIjUKidzMvKYjCOnEc02xOUatU8DyHoijI8xzbtqhWakjbIUkSOq2EwBacOHaE4/f/d4xGA8JwAs9xSUYxsR4hxDiwdV0X1/fwfX88cjiOKUROmUmeeuKL9AdXsa0ATQIywHVdiixjqjk5bqLq8WjvcaPTJS8yhNRoLIpCoVAURYZlK2xLkRdDhCixbZter09/MKRRDVle3sfpJz/LRz/6Ud7x9nfS6feJ6hUcy0EX+iXjpi37xdapYzloNO3uNq32NkUxbkk3ax5CFKAlnmMxTFM8y6dUJRYSlReUMmZueoKrgw3qocPGRothf4D0fRzHYjAcIEVJNuqzcuk8d7zqHqqNaUZFgesHWJaFa9kI8r2GsirH+5RFWbB2fZU4GSBdgeu61OoV4niIbbusr6+PTx7QGk2BxiKMfPZ588RxTJJkAET1GhO1KcIwot8dsNVaJwp8tFAUZYlC47s2m1ev0Nu8Ri906PeXaPoVigLSNMeWAjsIaG1vUK03yMqMvLeN49poXe6ME3ewbIs0zZDSRkob13fxXZfNVgffcVlaPojfaLB46BZsP+AzD38Kz/E5cvgolYrL+uZlDh0+iWsJtEpwLA8r9MbH37JQ5XhMfDXwybKMXqc9fr4JwbmLF3nyyce5eOEcw36XA8v7sW2b2dlZiqKg1+tRqVURQtDr9UBpVq9d5t4Tx3n0kc9z7dJFrl45T7SwRKkB4ZCVCklx0zHXNwbKux/feNKCUopiZ8S9YRiGYRiGYRiGYRiGYRiG8XImUDYM4xUjzYb0+31cP+TWE7czGva5uraOYzlsrq9gSYnSAiE0nu8gpUAhcV0Hx/EQjNu6SZIQj1JAkqUFkeuzuG8/D/3ph3jze97Nag5ZlpDHBUmSkeYlZVlSljmO4+D5Fl/36tdw5Ogs6xurPPyZT5LEKbOTEa7dpN3pIVOfbqfHh3/vd3nP+76Hq2sbWFgIocZ7msvxflm5szdYSDn+JYVFmqbUGxGqzNFa4zoVhNQ0Go1x47V02L90lPb6FZKsx/KxE5xOCw7edg+ZE+C5gnTUxfUdgmDcLrYsezye2bF3xnZLXNem1erSWV+nHL5AMdpi4xrUpgUMA7baMdPTy0TVBvGoAG2jsMjzAo3CD0Js22KUtrmycpGZ6RrCiukPtkjSEZnK8Nx5lpYXuHqtg+NF2I7g6PE7+NCHPsRwOOTUqVPc+3WvozvsYbkWjuW8ZOS1KsaBtO/69Id9Ll26RL/XwXJsqhWPbqdFNZgiy1IGwx62XVAJod/fHLe8C41ghOVWqAYOl58/y7WzT7OwsMD0AcnU1BTr/W18R3Dp0hm2rl6hvdXiT37v1/mWt72HE/fdz8T0HMJywVZIS+8FkZZlEQQBolA8/cTjXL96gXvvOYkKaiwszGNZkl63w3DYpygK0iLFdh0sGTAYjciyAtvyqdYj5uYWUGiSQZ8Lz5xCqYK6b+O4kjRN8HyfNIkRuSburFF1JVevnCOcXaI+OYOUHt3hCF0WOGGNtLNNkSboMiMbjLAjB6UKkniAZdlUJyeJwjqjLCdTJWWRMhym1Go1SqXY7A8JSosg8JifP8Y//r57yNOEhx56CLTFyqWLPPXkMzgClqZnuOXYUS6vbXDg8HGazSmk5eLIiO3W9t7zt7XV4dq1a0gpOXHiOHfdeZLQ8xkMhns7k289fhu3nzzBcDjEsWxuPXqU+dk5NAU//RM/xUarj/JqNBcPsjnojo+xzNGiQN+wA3vXjU1kIQSWdHYayZqyHAfQtm0jhflzyDAMwzAMwzAMwzAMwzAM48sx/wfVMIxXjFZrE9/3ydMh/bwYNwvLElX2EUCWFTiui2bcQrRtmzSHMAxAS5QGUDu7UwUTE5MszM1SppIDh+Z56L5XUV1aZGIjw7FSilJTqWikbZMkyXg8tgLLsknTlObEArcesbj/nr+HJRW/9iv/K6eeeQbHcZmOImQeo5Via/M6lrCZmpxhdfM6IBACBDZSOGiK8Q5XLVEKpqabnH3+NNUopBbVEELgOA6DwQghBPV6hXvvei1/8acvEJeSs89c5Mf/3c/x5BeeIwxDNBmNukNOQX+wRZaNCIKIKKyT7rRhW60unU4HRwv+6mN/RDNMUUoyO79ItzPEFg3uvfv1NBoLbG5ugnawbQtdSpASIR2yQuF4Hl5QYWZ+ktbWVbJ0gzzfYjDskmkLKRKklKRJTpqOyFyHLHf4n37qZ7l49gz7lw/QbrfxIh8t9EvCZCklUSVEo7n8wmUuXbrE5OQkQpcUec61lTWqlZDusEU6ilFlTpb26XV7wE7jVAnioYW/cISNtVXuffV9XL94hUZ1hiLNuL56FW+qymOf/zy1ussdx/fz4CeeZ34qpLVxhT/+g4tINyCMKhw9fpJjtx9ne3ubeDBEK8XExARRpcKxW45w9snP89iDn+at7/9+rl5dxbbH+6oHgxGVSoXGVJ2iVAwHOb4f4LkWnheilaDfS+h0tyhGHe687QiUGXEBZ8+fo9qYQABpqXFsOH/uLINuj1pzGlUUxMMR2hF4rouWFnFW0pye5YWLz+BoqAcRWTpEoECXlFnJxtomt9x6G521NbIkQZfg+z5xOhqPig98ynhIqkpKPR5dPeiP+Pr7H0BQUKuEDNIeRTxg/fnT9K+fZbSxxalOh9mZOdJcUyrJIElwHIcsy8jznMOHD++15fu9Lnkas7XdpVKpUKvVyFVJsVPX7/V6/MIv/AJPP3WK5lSTyKtQnV4k6w650tXY0kEKiVQ5WmUo/eKfNEKIm76GpGm+1353HA8hBEVRjBv4L8+jDcMwDMMwDMMwDMMwDMMwDMBaWlr6N1+tK19eXv5qXbVhGF8FKysrX7Pbete73sUf/uEf/q2+5zve/SaGwz7xaIhEYQkAjWWNRzpblsSy7L2drZb08IMKAgsh5N71bG9v4fses3NzrK6ts7i8QG8wYG7uIHkOjjMOdoWQ4+sWAttxdlqp49vxPAffdXAdmyAI8f2I247fxUc/+Wl6/RGha5ENh0w1G3z2oc/x6rvuZXs4oN6cosgK8rzA9wOEkEghdtrT4729tu/TO/8UK48/zKkvPMrXv+WtlKkmyRJ830WhKfKCY7edZHWjhRdEnD9/htuPH+XcFz/FH37w5/nER/6YbqvP617/RqRbHQfAec76+ibnnz/P9uYmw36P/+MX/x2HFmaws5LNzQ2uXFnhDd/89zl86NWsrY3ISyhKSZoVKK2xbJeiBNvycWwPy/KQwsWxKtx55/0MRjntTg/HrTJsdXj4M4/x7nf/Ezq9AZ7to5VGWoI4jZmcnkNLC2kLpABLSKQeH8skL6lEdTqtLZ49c4buqE8U+oy6XQpVUotCTp/6HLZbUIxaZOmIeNBHktPeWmM46KJzjYuHLUU0gl4AACAASURBVCx82yOqVnD8gJnFObxGxCNfeBDfV6ytrTK/PM/czK3MLs1Tn59kkHfp9jepeKC6Xa6ee5aVa8/T6mzQbAQ0Gj6zcxNstVZ54nMP8cQXP8FrXnsHV65e5PDxk3heSOBXiMIKcTJge3ONqWqFs6efZmZqGiE9hoMeG+urdDubWLIgSzu4Vs7q+afYeuF5hhsrDAcDJmbniPMM27Epi4LZpYMcPn6C5tw8I2mDdHFsB00O5IhSUyoB0qLV2qbSDGitbZF2Y5QtQQomZuYptU1alAgJSgmUlkjpYlseqpBoYZEXJXmeE49G2LZNkmWkmabbz1hd3+TcuQusr68xGPY5cOAgz585zcrVs5T0KcioRiFapdRrIbrMQeeoMufShXM8+8wzLC3ux/Y0QkKWJ6RpSpqmXHj+OZ4+fQpVFiwszFEqTeB71EKfshhRFF1EniLKEkvaZKnAc8aPoZ2XhfE4b+vFExTGI9THb9ICITSaEiE0Qmiy0v1v9fJmGIZhGIZhGIZhGIZhGIbxNfHe976XD3/4wy+5LEk1WTb6b3o7pqFsGMYrhm3bhGFIlmVo/eLoYWDc8AU07ATMNo7jUWr2wqTdPanN5hRRFNFud6lUaigk19c3sW2bQkGepjjOzihqIXb291p7+1cty8KyrJ1dwhJnJ2yeaE7xgZ/9eX7jg79Ge/USKs0Z9UfknZQf/Cf/lI995iFWNroszM9RFCXtTmfv99gdywuw3WpTqU3j+XXStW0qKqUrFNWqi+NIokqdbrdLux3z+te/gVOnHqfT3eTjH/kwor/BvqkKjdo8//nhR/mu78w5t/ICcZqyubmJsEvCwINS4TgW3/P+H0CPYtJel1NX/pLG5CyHbzlBpxtz24kTWJbk/Pnz2LaL63qEYbgzMlghhMRzQwCUF3H+wnVOHP9GtrcS/vwv/pRR/zIz08t02z32zc6wtdkHy0IKG9f2935fpdT/zd6bR9lxHeadv3tvrW/r1/uKxr4SBEhxlyiKNCWKtCRrs0XZlmc8jjMzyfjMJCdObCeTmWR84skyx0tsJ3OsKHYsO4ptWYu1mLJIwaZEiBtIEASxN5ZGo4FeXvfbX+01f9R7Dw0IsuyRlUg693fOO3hdr6vq1q2qBxx89X0fYRgSxzGGKXCEYiBn8NLhL2MrxfDQMFeW12kjGBkaoe0t8corX0NKj0pliaSzjuvkqdZWGB4cApEnSRzSxMGyB7GMlEjESKHoRD7r6+vkcjm27LqbielZgkBRHCpRbTf5xB9+Aj+sMTySZ6VxhXqjwsTgdn7w3vdw8L63sNyqEfkBS4tXuXLpIp7ncd+9d7G2+BrPfOXL3H7vbXS8KpZZJE0ltm2ze9c+lvLnaZy/zLbiEI1aFasM586/ge/77N27l1pjOYsUN0y2bL+NxGtz7twJQgzCTpsBx8FUCt+EtVrAWtsnXyzgShslLeIoi3pXShHHglbg4dh5BofHWaldYHrndupLq/hRSEiKUygQJxIpDOIkQkqFEN2HGoQCkUWyG93rPooiPD/Esixs20CgGBuewrXyMDHDhTPH+eSnvsRwsYDhKszExJEmUdxGKoMo7lAacGk2G7hEbNk6w+4926mu10mERIgUKQyUYSKEZN/+29i8dQtHX3mVC+fm8Nst3nTbbYwND7J98wiXLp/nyuVFpic2Ydk51qtNWlFEmgos08xi5VMB4np/co/e90YvDtswsn8KNf2/4S8sjUaj0Wg0Go1Go9FoNBqNRqP5PkELyhqN5nuGKIowDAPTNLuC5o2xtrLbodx7L4RAdp3JUkoMw6DT6WBZNp7nZ12yxRK1Wh3P8xkczBHHMSAA2f1TIKXInMRd0VoI2RXf0ky4SsCyTBwnx46de3nXu9/Hv/jHP8vW8TIqihCxYnTY4hMf/zhvf9f7ifwAVOZ+BrJu4+6xpCkkpmR8526ef/nr5CYmWGs3MOxB8GssXr7E5MwWhLBwXZd8rsjmzVuZ/9oF1q+t8PC9+/nov/0S9XqdsZm7qDaaDA8P02x7eJ7H6XNvkHNtDJHNZywMHn7wrRRsF2dwjEqlwsDQGG5JcO3aIqalsO1M/FXKwDDM7hxFpCmZaB/HxGlAqTREuxUxNrKZvDvG4IBg9559HHr6GT74wfcjRjPhfXV9LduGSPvxx1LKTNwrSi7PneSpP/hDzh99jU4+x45du/mBd74PgFp9hS/92R8SJwHjIyXKxQIr9RUaQRVTGbQ7Abn8EKa0cRyHYj6P79WIU0EqFEiDMIlJBAyNjmNaObzQx+t0mLtwFLcIZafM8vISbsFh/76HeeiBH8RQLpdXqjRbawRehzSJsJRkYHiIkYE89999D2998z185eVnme1em1GYQhojpcS2XK6s1zAMg1hKonab7du3Mzc3R6uVdQjHEYxPTLN3/x5Ovn6U4alppJkn5xZIk4hWrY49UGZwoIgZRHTCECWznuAoikBk703TxsLCljGVwOPyxcssxvOQpDSbTVIBMzv3kSaZe1elBqJ7LQqyazsTXRVp1+lrWQZxHGIYFlIY/Rh2y3JQpoFyShhOnkatgZM4tNseTj5FdB+6SNOUJEmwLIs4jrOYc9vudhpn95qQqn+flcpD5NwC73znE1zed5krl84j/A5JGtNpNRgdHcBvtRgqFwgimBgf5sLVlRvuJYG43k2+gZsF5Vt9l2g0Go1Go9FoNBqNRqPRaDQajeY6WlD+b84wBx65hyn7psXVszz1/BzkZjhwx26mSiYkIfXLx3j+5ErWEJub5a579jDqSkhD6vMbPpOj7Lv/ADMlE0mCv3KK54/M0wG23/84o8tP8fz5jcM4wCP3TLFxGNUzN/3OhvEWrhzi8Bl/w7IDcOwQxyo3bfMAHDt0jMrNm7nFPhNvhVMvHmG+/Y3buz7mbP/la4d59mQ9+3Db/Tw+tvKN85WG1K+e5tVjC3T+CmdC891PEAQopbBtmzAMbxCB+r27XZelEJmIJaToC26ZEAqGsvBCD4GiVCxz6fI8uXyRjhdgGAa2kyOKAtKu0JQJ2NejtQGSJIWeQ1pIUgSG5dBoh7zr3T/MR3/tVwn8KkG9SRyDZVv87n/8KDt27+e2/QdYa9TI5V08PyDzVWfjRUhylkUkTP6Xn/8lWu0G8wsXGB8bYn35Il7lKkcvv8aj7/mfaTY85ucvI6Xk6uIyaST402de5qOfeAbTcXn1xAnSXI5OK8SwTHZu3ca+2/fgtTuMDg/SbNZZWakQhIKGUAyPb2P7njtoeBGBFyGkRZykKGmSpikDpUFM06TT6dButxgsj6CUiZIWhqXwvRi3kGfT5lmSNGX3vjexurxA25C8+YHb2XfgPp544l1Mz8ySKxZJNriywzAkDEMqq0sQdPij//JxUq9FaDlEfpuf+un/ka8d/gv+8I9+m517hikWigh8gkBSdAeYO3+W2S3bSVMX1xlldGQEiaDRXMe0EmxpUq+sUW9WqdbXKJZtoiDAD1rkii4nz71BEDWoNdoMmSM8/q6/zbbNd9Kpt1itBcRxh3anSqNWI41jBgeKdJo1zl+a49VDn8EKTBqdDu/80R/j4uUFysUyedchTVPi2Kc8Os3utxU4efIk9973VpYqa1QqFfbvu6v/sMPstu0IafGJT/8ppoBQRMxMlxkYmaCxtsbC/GkeeXw/H/vd/8jUzBSlwTJGQZGkISkGSQppHBB2WnhhiDQFA6Zk3Czz+twJUlPx4A88zKZNm2h0fKSSGIaFMgQIkzhKUMpESqMbD210+8YzkdZU2YMFcZpgKJNOp0Xb6zA0PMqBu+5FGSnBWgWn4CBtE2kXwFCkSQrCII4kOTdPmoruNhWOnaPltRBCbbiHDer1BpZlUV9bZ2V5lVxxiHrnKtI0aHY6uDnF+NQkrU6bi5cWMEyX/OA4zWa7K2D3nNa3dij36B2bRqPRaDQajUaj0Wg0Go1Go9FovjlaUP5vToVjh57iGDB84BEOcIxDfVXWZucd+xluHePQ84uE+RnuvPtO7mo9zUvzJjvv2Eep+irP/MUSsT3FwTcf4OD6M7x6DcYPHGAmuchzfzZHS5XYeff93LlraYMIfAv8RV76ZuJvj4lZhr0q0dh2SmdOUP92D7+/T5Ph2x/grttmmX+p9S1Xy83ewb7FZzlRu3H58PadjHqnOfT8Ar4a584HD7J/doGX5r/dgWq+GwjDCMMwUcogDCOkygTJzGmYZG5ZYWAbika9Si6XI01N/NTn9IU3uPfO+6le69Bp1UmSlNkt26isr/XjsHtOWSklUgniOMEwDYQEUgPDzD6P45gkjjGNzMnZc0N7sSCfg+rKFX7+F/45v/wr/wTLT7AMMxOipeIf/9zf4xf/73/J3fffz7VKnSAOsaVBHMUIIBXgYiGBS/MXEEJQKgxipAntWoOvPfscd9x7L0qZWHaEkAmjY2OsrdV5+OGHeOKdj3NpcYEoWMXNjyOlhW2lKCW6fdAGpQEXlM307DjzV9Yw05BUdCiW8oyMjBCHCXESIGRMFEW4OZumH/Hi84cZosGmsSGmdt1BbLikQpIEEXbOQcmUVrvDylqFydkZfC9ieGQWpRT/4Bd+jihKCJMG586/RsvrYFs5hBAYhtEX9ZSyKZfLfPSPP4tpmqwuL+J5Hs985YsEXpMfetd7qDcqkMSIxCcMG/h+HcdQVC6vMLv9Nlqeh++FuI5Fs1kj50rCKMW0xxi0xsEsUG81oJiQ+gEyDJgZnwH7IE88tpN226NQLFKtrFGtN1hfXydJEmzbxjIMpGVRrTcZGRmnEwleO36SRrPD5MQmrl5NmN78JgD8KCCOY6LYQKQh1XbEjn13UGt5DI9OMzO7A8MwUEqxtrZGFCRUq8u84+3vxrIs8o5gefkaZ86ewrIM7LEJVtZWKBVzLF+bZ2QoR7vdRAhBMV8iCbsPPCQpxvoCx469AlJRnt6Mch3e++GP0Om0uHx1iXxukPraFcampql1YmxMTKvrTO53Dqv+udkYL9//2XYoW3b33snzprc8wb/5p3+fTWPDDA6X2XT7fuK45+jPIrmjOECZNqRgWiZIkL37SGWO6DAO6PgRbpIQk7BpyzSHn/0L2q0a7WiIYsGh1ejQqLSxbZuB8iitZgclLRw7cx1LlT2gkQrZH/PGPzUajUaj0Wg0Go1Go9FoNBqNRvNXRwvK39XMMFyoMHd4ER+gscDxxa08ODwB8273syVCAH+R1w7XsBOAYcYHQxaOzNFKgKTO2Ze/yqIMv83xSGZny1Qv/AWV2bcwOwDHa996rb95fJYWAyYP7mPh2RtF7TCMUINDjBdXWKgt8drh57pzovl+oJBz+/3JhkxJwt41LRGGgiTGzdk06i1sK0/oRQijwerF84x3Onzhdz7G237o/YjYolAokiQJzWYzixuO434ErhCCNMkcjkoa/eVxHN/Qo5ymouuEFqSpwLZt8jkXieJtD7+dn/nf/habyyWa7Q6GYWC4NlYU8s/+z3/K5i3b+LV/9/9y+fVLTE1ME4uYMAyxugKdbduYhtPvVm4GHtPb9/DekUlS02J1dZlGo8HU1BS/9Vu/xaOPPsK9995LGCcY7gCJjCjkXcIwwHEcTNMkjkNs2+4L4HEcE8cxSina7Tb79+/H932iKO7PeZqmiCilXa2ydXaSz//nPyEJ2wxveomf/Lv/kFQ6dJJOV1jPhMbeHNlODqPrcvX8EBJBKujOlUsSZ7/Xm/ssEjllfn6eK1euoJQiCDwcx2F8dIQkLtKsV8m5BXyvjTQEvtdmZbVKtd4mDXyKQ+sMjU8iEo/qWpUkikAWuHpthcmpzbTbHjlnCC8QmMpCCQPDsHHyBUrlYaIoIvAjTi6cJkx8fC+lUChQyLvk83nybiZ453I5lpeXGSiPsnXLLqIkJYxT4jjB8/yugJoJmEqaREGIUoparYZpmixeW6VUKmEY2fXlutn2R0ZGWF+vZNHsjsvVq1ewnEHaXptcaYKAAg89+l5q9bUsPlooLs9f5epChfHhSUBSthOSWh0nUZAq1qpVPvTkkyysrCClZKBYZu70cRYXFpiZmcHG7D9IAdwgIG98YOLmz3oIIbAsC8uyeOyH3sfciWNUanVGGk2sXGFDnHt2jmW3t9g0TcIgu6ekNPoJA2kqCIKIIAhIkgTHdhgbnWIpEUjhMrtpB3NzZ6m2q+yYmqVQKLG4eI0ojPtj7V+HUvw1RGT9l4VGo9FoNBqNRqPRaDQajUaj0dwKLSh/NzOcxw19Nvp1/TDEtN3sh95nG6Kjr8dUB/h1gO3c//hOyoC/+NIG9/MtsKe45/Gp7o5u4VaWW5lyVjhxLaFOk7duHYejS9/eMd6wzxVOvDAPDH/L1eLFV5nLPcgdexd4doPpun7qeY5EB9h319vYZ8bdyOtv7XjWfG/w8stfYXh4lMHyKEpdd06maUoaR5AYVNdXcZ0SSRjSbFQ4feZF4lqVpVeOc2mtwT3v+AHGRndQKBS5dPkKlm31BdCemJzFartZv6xSRFHUF9t64nL2mdkXsJQycC0X0hDXLVDIDXL/W97K6Ve+zpCTI/A8whAM16ZRa3H+4hxPPPEo/+pf/z/EokOSRpiWRIkApCJJEqLQQimFZTlI16RTCxjfsgfPS8nnLdI05ud//h+xe/du7rzzIFeuXOXuuzaxvFxlx44drCxfw3FcZDf22zS7EcdKoZRCiGw/jUaDffv2UavVCMMQKQykoi9mm6bNQL7A/tu2c+rYQY4fe5VryyvkCyXWqq3+3CilcByHIAgoFosYRtcVLa/PWZTE3V5ehVDXRcrePPp+SD6f78ea245DFEUsLF6lkLMpl4dQpk0+H9BqrmOaWZz0yPgsYTMgDH2Ghwb400//Mfe9+QEMw+DM6Qvcefc9BH7E5OQgQdDm5Kll0nSFgdIgllsiTU3iKMLrNAjClIN33kOzXUOkJq7rYhjZX5dBp8Napc7x109RqVS46667WK/XSEgQIiVIQmQikTLNkswFJCmgDDqBz2pllV27dmBYgvX1dXK5rLd7fn6eRqNJLufiui65XI7BfJ6tO/eQJAlhmAnSIkpI0ojiQAHXdYGI22+7l/HRCYJOQLVa5eSpwxx9fZ6JmQlSLKqNGi0/wLJd2q0WhgxZryxBElNbr+IWRkkE/fO4UZTtO/ZvISr35qS3LIoi9hx8E07OpdOsEPpR9zq7sbO49wCBEArXyZNdbAJSiJOUJEmzbmYlMFQWmX3b7Xdw5NWjPP7YY5w+fZFarY0XJtx24E6ee+4wrZaHpa6Ps3e9pYJvEL+/OVpQ1mg0Go1Go9FoNBqNRqPRaDSaW6EF5e9mKi065hB56Au7tmkS+h3ABdMmL6FSOcahp44xfOARdvZXtnAHgNoczz81B9vu55HCt9jft4i8tneMU86VePPjM9mCxGCWJb6tNOnuPtc338Pbp+sstf/KK3LxtTkm3nKQPVeuu1TtgkXz/BG+eg6kPcX+B/bryOvvI1LZIE5tkrSISHJdl3BKmkKSJMRxjGma+H4H17ZYXr3E4uJpNo1OcGnlMicu+uzZezutRva7nufh5FyiKOqLyTcLZ71+256o3HtlYprcIChn3c2GYYBQrFUbLC4tY1gWQcdDIkgRCEOhbEGjHVIq2/ziL/1z/uEv/BzTkzO0Gg2GioMkaQiJJEljRGqAIYl8sOwcSSqwbYtOp80nP/lJJiYmuOuuu6hWq4yNTlBdXyf22jTXK5imDSQIQdeJLBGo/itNBEEQYFkWxWKRa9euZW7tJCbluqBcq9eZnpmk7QX88I/9JB/+qb8DUUy90UQpE8e28EMP0kyMbLfbWJZFKiMSAQhBkqTI9LqglwJKmdn77jkEQS6X64uQtm0TJ0nXuRoTxwnr1TqObSNMgWFYWJZDcWCIOAgJWzGlUpFarcbVxcusrWynUmuDsMnnypx440XanTp7dm/j8vx5du3dRhj5hFGAky8T+gHlwTG8UNBqtRDKwrVspBREUUitVqPRqFGpVKjVagwODxImIUkSESVhPw5dSgVSAln/cApIZWK7DlESs15fZ3J0K9VqlTAMcV2XcrlMqTRAFIWEoU+n02L93CkGBwdRyqRYLFLMDaEcA8dxKA0UiKKoe90nHDt2lIvnz9Fut0mps3X7FpZXq0SkTExNEyYphmFiGSamaWLaeZaX1yiUiln8uJO7pQv5L3v16Am4cRxDaqBsB7+WuYM33k8b6d1HcRxnrmTSvuPfNGX/fhSpIk58hIJCocDQyAjnzp2jXB6kNFBg+7YtPPPlpyHN+p83uqGllCSkfw1BWaPRaDQajUaj0Wg0Go1Go9FoNLdCC8rf1SxQae5k+8Epll7POpT3T7nU564BJiv17Ww/OEPltQVa2ORN2V2vwpWK4q7b9nD1pVNUQpOSY36bYymxc9Jg/vBTnOhmTE/d+QjTmyXzl77NTQPJpdMsbL6LfZNzvHq1Sce3mZkZx6wsERe3M1oM8S/etJJ3kdcuTPDmXWWorgAws/+tTDeO8MLJFfykSRRKcvIb96f53uTa8jHm5mL27nqA6cl9CMvKYnTjFCEiBAkxgpxbJAqbvPTynzEyZFHvLHHnOx/kTmOC2noWAV2vN/pu2p6gvDEO2nXzAIRhSBhGWJZxQ9fvzQ7lXheu45hEYUy5PMHRY6e5bbqAjSJNEtIkRRVcRgZckiSh3ekgLPjZn/sF3nzvm3jowbdyz+1vIqKDkiZgEsUh7U5IIT9AmCaZYzrw+Y3f/HVyuRzvfs+7iOOYufPnePKHH+Tawnn+zk/+KFs2b+J3P/NllpaWsG0TwzCBFNPMvgt6rtdOp8P27dtZX1+nWq1imiaFfIkkTREiE+OsooMfRxAqlDvMwsI1LNdkoDxKs7pO3jZRShFHmQN1eXkZAEOZG+KsU6Ik6ouMmeDa601WmKbZjx43TbMvUqIMVJJimBIlUwwBnU5AEiWYhkNk5/H9kFRAECcor02wFnLP3QeII5+9u/dg2GUQDpZlEYSCc+eOMzlRIk06mFaRfN5GmhJXKX7jN36DD/zIj1MaHqXhtamuXKVeryOEyKKvCzncnEm+MEaj0cDzm4CHAJI4QaYCUtl9GEEB2UMPXqdNsTDAtm1beO7ws7z3XTuZnp7m3Llz3ahviyAIs7hzxyRNU0bKFlHYwlAFvE6VC2tLdFo+SZJgGlk0emlgDNOURFEd242ZmpngpS8coyVDnMEREitHeWgIZRhEQYLrusRByK7b7uLNb3kY080Rmj4yzgTevpB700MVG13LN3/ee5gijmOGByY5f/4UqWmQxkn/4YDe5719hGFImgoKQyWUYRNFCZ1OhzhOESgsyySKA+i6lf2gyQ++53EMU9But/Bkh4ffci/VlRVKuex+EsrqX9u9TvQouR7f3r+mNBqNRqPRaDQajUaj0Wg0Go1G89dCC8rf1ficPXoc9469PPLYAUhD6vOv8vx8AvjMHXkN9579vPWx/UBCWF/gWFd0rRx/kTN33stdj25BAklnhVMvXfcel3c9zuO7uj9Uz/LUWW6Mn2ZjfDYwvIXRdJUjGwqLF69V2bt1O/alNcBm6p7H6a3tL77EoSs3b9Nn8aVD3Dp1u86pi00e2bmH0tUTnD16gtK9B3n0cdk97mO8du0b1+qcf42LY2/tOrMT5o4dJ3/HAR55zAQS/JVTvKjdyd83vOm2d/Da64eJolW8zgppXOiKXQZJLIgIMWyDTitleHiAkZERRkcMGs0269U2f+sjH6JSWaPVNgn8CESCEAamaWMYRj/aWEqJH3rEcdZr7DgOpuX0x9ET2JQ0r8f/KgvXdTGljTRi1tbmGci7OI5Dq1aBKMWybQpIltfXGRgdxjEEURAyODrA8TOnmL98md/72G/zw+/7APfddx8IE9t2KeYdcvkcqZmnXVvl1ddeZ3hohIMHD1KvtUhTweTELADPfP4L/Mg738qxk2eQQiGVylyaXTE5TpNun3JMq9PGsm1sx+HUqVMUCgXanQ50O3Edx0FKgW0YGIaFEIpGvUoh7yAlRJ0Grm3jxwmW5RKJkFa7RhD6OI5DknRumDN6ccTQjYPuzqNhIFX2cl2XNBFEUW+cAdKQSGUTBB5+GCKNrnAYSEzTxDUMOo11MEOavuJH3/ff8ck//DgTMzMsr1SYmR5mdX2JVmMVW4WEYZvNO7ey3vSQRh6VGFgdj2e+8GlOvnwE84Mf5NQbRxmdGEUpQamUxTvEcUSn42dudWFgW3mGhyZpe1eIwwghJApQ6roImwmYKTl3AD+oY9sumzZt4tOf+zhPPvnj7N67gzfeOE3OLTIyMoJl52m21lEqzVzploshM8E+P1TAz4eEYdgXaW0nJkkUaWSQU5Ljh5/Dq11FjQxi5G2EYRFikYQpShhIaaDcPLaCIFFEnRRL2JmTHIFhqFuKxhvd+733SZL0O7KVUoRhSL1RwbEcTi8sMzk5ShBFmGY2/jAIMIQgCgNMqcjnLS6eOckf//4n2bFnJ+/98E9Qb7WxHUUUJkhhIIxMXDctA4SPkGDlXOIoYGh0iKOvvYyhYm675wAvv3AUIbLxm1b2zxtTWWQufQEiJUllP3q7d2xJEn2HvrE0Go1Go9FoNBqNRqPRaDQajeb7Ay0ofxdROXaIQzcvbC9w7PACx261QrjE8cNLHL/VZ0mLi0cOcbOpF2Du+aeYu8XyQ0/dci+9wXHo2ZuWXX2VZ65mb48deuoWY6x8621uOOBk/iWe6Ym/7XmO/PmtlOAKxzauRIe55//s+vH8ZfOl+Z7Ha8HenfewcOUicdkjUQZpIkiEQAhFFCniRKJkRK3qs23zNhBN4tikPDBCqxngugWWV9YzF2m3A3Zj76rv+5lLU0pM0yLnFrrR2oIkySKMs15jG9GNeDYMI3P+pjHSkASBz+c+/Ul2Tk9gNCq4pSIANb+JaFS5bcsM19ZWWWvUpzU0sQAAIABJREFUGZ+cpdJu0FqrYUewf2oLX/i93+Ezv/07fPhD78fzOhw5coQ9972T//1f/yYXfA8pJY888ijLy8sUiwUEiqnJGURq8vKrf47RWWf3vtsIoqDrEE2R8sao32azSa1W44EH3sLhw4e7HbhZfHLP3Ql0O5DlN4iMSmXOVIHqiokBQqYsLMzj+z7FYh7fAyEkQvRESXXDGJRh3uBwzdzmYJoGtm2SJAmOYwMQhj6maZPP52m32yQShErx0oDCwBAoAy9IecubH+K557/O/W++j06nw7XlJY4eO8rBA/tI4w4yZ2HYOartJiOTu8jlB2gHAakI+NqRE3zsP32cWqdDKQxptmoksei7bHviaRzHxGlKkiQ0m80Nsc7d+UmzaHEpFIhudHgaks8XEaLEvXc/wtYtO3jmy8/yEz/xPzAzvZUvfvEpkgRmN23BsXMEQYDv1zCU05+b3qvnhk/TlCAMAR9EjBeH4NrUOxGTo+OsNhuYbkrZtgnDGMNQSKlIYr7Bqds7xp4QDje6k2/lUs6Ec/rOcqUEoR9i23Z2jrpO4d457o3ZdFxc26FRb2EZNiMjQ6wureIYNqJgEvle1rG9Ib6aNHsgIomCfhx6s15hZnKIqbECS1fmbhiblBKBRIlM0E+SGNIEgYnquuPT7jlU4tax3BqNRqPRaDQajUaj0Wg0Go1Go8nQgrJGo/meQZIgEslAYRCRJigR90XIzDGZddeatoXvedimgx+2sa08mzfvolZt4bhWFo+bKX0A2HYmWgZB0Hdd5rpu5czBmJAk1+N9e8uVlH2hLBPLMkezMiUvPPdVjCgkDiPcYtZPK4IWfrPDT/3EjxMkAZ/78tM89+KryEKBzTMzmFHK+rVlSm4Oe8DkwO5dXLwwx47pcS6cPoXRFW9LpTKdTgfDsDBUdjwDA2VSIZjYuZUBuZk7736UdjsrJc/EwiwKWErZj7oGaDQa3W2W8DwP13VvEPJ6Eci9n79Zh26SJBhKUqvVMIxsHxs/v1U/by8yfGNsuGlaG5Zl4n0vKtmy3e66nRvEzjAOKBQKDAwMMjg4yKuvHEfQxLIcvKBDnEYkUUAQRwzlyygzRyFXxDRdPM9ncHCYK3Nn6EQpB+6+mzdOn+FabY0g7CCE7AvJ2bVhZWNAEUVRX4jdKCinQpDK7JXNGziG3Z9Py7TYNLOVuXOXqVarJHHK1q1bmZu7QL1RpZAfQEqF59VQygBSoigEkQCyfy4AcoU8tco6Is1iv0cnJ1gfv4pwHBzDpuNHN7iLe33VcsP5uPm8bIx1v5U7eaNDufd7QRB0RWVFEARAJjT3RNvetdPrWg7DECfn4nc6zF9eY2R8ANNyaNbq2KaJn9zYfdy7v64L34J8vshKq8HxV1/EzhVRKnfDQwtpmiKVIE0z4TslE6YRPTE9ey/QgrJGo9FoNBqNRqPRaDQajUaj0fxlaEFZo9F8z+B3VhkemoRogDQM6SRrXVdtTD5XxLZdJAadZgXHySFjsK0C+XyeUnEcNzeCH2adqlJKpFJ9N6WUEsdxKBaL5HI5/CAijhPCMOh2I7t9t25PULa6TtHecmUYpErgdzwOPfMlbt80wZotaacBAoFpKpZXY5qVdV49+jzDpmDH+CB+nKO+XCGNU9JmQCRhbLTIyTdOI4jZvW0Hl18+SZRE2LZLLlcgjAMGBga6nciCkZER3jh7lJ/5+7/IwpkTDI9tysTtrvtXStkXZpeWligUCjiOg2XZPPTQ26jX63iex8rKCnEc9DuWAcyuoHqzMNkjTVMs22BhYZ7llSWUymKEjW40NWSCZRxlzm+lsnlDKKSQmIbZ71FW3fl1HJM4CUlihWlaKJXH9zs0m83+vpMkoVKpEAVNBgYGGRgscfHyeUYmily5OM/Bg3dQLpcZGRnByDlMbduK41gULIeyXcAZHMYplBCWy/j0DP/hbe/k60eO4BZL7Np9O512nQsX5qjVapmgbVl9B7uhLFqtFuVymVqz0XX1ZmKv7Iri9IT4JCHwU9ycAySYpkG93iGfK/Ha0eOUSiX2799PGMD85bNMTk6SzxeZ3bSDa9cWaLXruK5NeXCYVjOL3O6JucurawzkbNr1JqfPnyJOE6Z276EVtnDdPFHSptPpYNtuX/RWSiK7DvPrQrG8QaS/WTy+2bUshEAZ2XkOwogoDsjlc1jSYHV1lVKp1B9jz6nciwA3pYVA0QkjyhMT/L1/8r+SoKg0ahiWiRcEGN2u7961JoXA89oYMnvw467776XuGbR8yfjkZlYq9ZseXMgcylHYQUqwbROlTDz/utMbwFCSNJF/k19TGo1Go9FoNBqNRqPRaDQajUbzfYf+X1SNRvM9w2d/70UOP/MCo2WbqFmnuXqZ1YWTePUFwvY6ZiJo1Ov4Xot2q4rrmNiqxIA7jIFJFAUgU3I5B8s2umKVoFDIRGfTNAmCgLW1tX5Ub6FQYHBwENfJPrdtGyHAMERf7DJMiWFClBr41Qq//+9+hQNbJzCSED9oEciU1XaDJJ/HdiCvLMoqTzlX5IGHH0KuN7GaAQPKZmB8klLJodbpcHpxFXNgktQe5u0f+BE6zVVGx4cYGRskn89TLpe77uSBLKZaGbRrjawzl5SUuNsTLTJXqlQ0Wm1yhSK2myNfLGGZDnGU4jgOpVKR0dER4jglDEOiOCBOwr4YHUVRX4xL4l7/cYpUCc3GOufnztCo1TGkSeCFRLFAGQ5hBIbpYtoOpu1gWDZI1e+rjqKsw1ZKiSRGmhZRlPDUJ34b6hW2T45DnNJotImiiHJxiMHyKM1OkyvL50lVRMuv0ugs4ydV7JzJlt27qPsdtuzeyezOnQyUc2zetpdCeZqhsUmefvozzL34NS4ffYGLxw7z0tef5qln/oBLV87RbjfJu3lsabFt+15uP/AmpqY3EUQhhmkSRgmtThs3V0RIE8twkNIgjlIMK4dIEyQpEoFMJaY0MVybRCqE6eDHWSf0Pfe+GdNyiBM49vob3HFwP/liASEh8tqcPXec3XvvYGhwKy8efo1f+Re/hGllIm4ch1nvt0g5e/Y0J86exHJNDFNQb7UpFobx/RBTGVRXlzBUCiLENAWobF0hU2xHktABEZFp4D0XvolEYkiFEhIlFFKkWay7iInCFoHn49oOfrvFgOvQqKzy0f/wr7g8fxLbFBgyJYw8kjTE8zvEcZy52UUCMs3E4ygmCjOBXpkOYSxw3AJSQRB6hHGANASGGaKMhJbXJAZ27T2Aa5oIYXLq/NXs2jJd4jjFcXLYtk3kB9m5zMfkcwEiqTA2PkSaxt2EAQtSu/9QiEaj0Wg0Go1Go9FoNBqNRqPRaG6NFpQ1Gs33DD/3Mz/J4rkTpIFHvdnA8zyUMkliwfpandOnT9Oq1/HbHsQpCgtTZUEMYeRn/bdR0N+eUgrXdfuOy54LNZ/P93tqkyTB9308v505O2XmNDbM7Hdt2+4LZUQB//7Xf5X/9LGPITshRivAW08ZKY0xOTLF+so6s6MlvvSZz7Jw5iztKyusn5wj7TQo5RziIGCtWiFxc7jDw3ip4vnX3uDctSWe+/rznDp1moWFBTzPI5fLYRgG+XyesbExGo0GQL9fttf13Iuj7sUM96Kl4cYe3Y0dt8ANjuRe3HNv+0IIDFOSJBFCpDQaNa5cmefKlcvYtonnBYyOjvfnxnXdftx233HadasKIXBdF9M0M8E6htgP2bppFsfO81/++I9ZXavQ9j0Gh4bYuX07jmNg2Snnzh9jYNDC8xuEUZs4CajVapimycjYOCNj4+TyRdxcAQybQnGA4sAgjpunUu9w9JUXePGFQ6xW52h6F0iSOp/9zO/xwkvP8qUv/ylLK8uYhoPrFJiZ3so9d78F07SvC+FxyPLyNdrtJvVqjWIpTxB4/TndGPfcm9MoiojjmEajwYULF7pdyT5SSi5ePM973/te4jCg065j+Gv8909+EClC/vb/9Hf5P37x1+i0M/E9igOuLc1z4twxYnyUaRAGKb6XkCQRSRJhWQaGIREq4OqViyRx13GuerHvCt+PMJSdnessMb479qR//UsFubzZd0Y3m20ENlJYNBot8vk8Z86e4B/87M+zurLG7bcfwPdCarUGrVarHyUfxzGmaRNFHsQ+KvJwwjZuzqBQdEmJsv3EHVptD5FCWF3jzz7xcQ59+WlKuTwySRnIFbhyaZ4Tp05yZXGRmU2bSRA0W20c1yUIAoo5mzvuvI0PfOhJxia3sLLusbTWptls9p3Z2XXIDY5ljUaj0Wg0Go1Go9FoNBqNRqPRfCNaUNZoNN8zfO1rn2cgZ/DSn3+NHVu3ks+Vsc0ScZijkBsliSWnTx6h3Vzi7OmXadYv4ToWjm1iSEmaxKRxJsj1upB73a9xHBMEQf/VE1ANw8CyLCxTYqiUKOxgGiBJUYZAyDQTmJWkXl3k9z7xO2zbsxnTtoiCFCtQvP78HJXFVUYGyqwHIfc+9nYYHkQOlti8ey+dJCE0BLnhMmnOoJoGtNKEtbZPNYh4+fQcDz3ydqZmN+MHEVEU9YXJ6elpBgcHWV9fxzRN2u1210UtuLkDGUAI1Y2ivh7R3JsPIQRBEPQFU9M0u/3BEZAgRAokpGmM5zVJ0hA/aHPoz5/mqS99Hse12LJlC7t3775BOO6J0xv7hnvifS8+vPczwqBcLpNz85w4e4kDd76JvQfvZGx8kmJxgDCIyefz1OqrLFw9zeK1OWLRwI/XMR1BQowfBijDwrJdoiRltbJGJEwMy8HN5ykNj/P+j/w0palZyOWwizbXVs+Ty0l8v4FppIxODLG4tMBzzz3HuXPnqVTWkcLkLQ88yMHb72DT9Cyh73F+7gwHD9zOyMgQoe9hm9ePcePLNM0N/b+SQqFALpejWCwihCCfz/PGyRN84XNf5LEfeIQHH7ibguHz73/5HxG0Fnjxha9wdbXC1i3babfbPPfcVzMB3zWoVtfIuw5GKhgtD6FESrtZxzIkSqSEQYsk7nDl8nmiqM1gOU8S+yRxiBKSNAYlgCQmTSIESdeNHGEaBlLEvPLK1/D9FdK0hW0JBBFJGhAEHufOnaVarfIrv/p/8cQT72awPIppulTXG92obUUcZf3PrVYHy3LotNosnp/jyFcP8fnP/wknThxHKUGz2QQSRkdHCQKPP/3sZzHikLW1NRzHYWFhgR07dpDL5ViurDK7dQum5dDxIqxcnunpacbHhzGMhLXVeb74+c9wbu4KjjtMcWCadivod3cLCUIm39AJrtFoNBqNRqPRaDQajUaj0Wg0mhvRgrJGo/me4aEn3sHA8DDbt+7g5NGjSGFjKJe8O4TrlBFYJKGk4JZIopj1tRV83yMKMndyFGZ/9sTknkux36dqGNi2jW3bfTG1F4VrKknoB3idFoIUQc/RmGAYknqjzq/9239Dvpyj2m5QDz0CBdtnZzAVrC17eI0Wy2sd/uhzX+BDH/lJ3PIwj7/3fdz/yGNcWa1xafkqwjFoRR0SUpQSNFpNzLyL7eaxLAfbthkZGsaysp7eXldtry85juO+09gwjP4xbOx6/mbiWZqm/ajvjSLbzX3JSZIgJJim4urVK5w/f64bl5wSxzG7du1i//79NzhBN3Y5b3Qqp2m2Tt+tXCwRp9Bst7j7gQd58KGHaLUaJKkgjlNEKgiCiN///f9MsZgHEqRKiWIf328RRQGO4+A4DkB/PtZrDerNJr7v48cpO/cd5J3v+zB3PfgOri43yFlDLF5e5OyZJd54442sA9pS5PI2lcoKc3NnmZ+fp1Vvsmlqmr27djM9OUmr0aCUL5BzHaIoQonrXcM39w9vnMve+enNj+d52LaN74ecfON1BAlnTp+EuE29ehmRNjg7d4xGs8b+/fu5774HGBwchkQwMjSMiBNc06C5voZpmn1nveM4iCRzpXc6LV4//gqXr5zDsQ1II9IkRIqUMPBRUmIZJnEYEXg+CgFxggJajTWef/5pLs+fwHVSHFtwZXGe5ZWrpGl2/q4urmAoqysgp/h+iNfxCfyQOE6BrB9bpgbF4gBrtSrNdpN7772fTZs2kyRg2zaGYdFoNJicnKY8NkYngfe+/32EUYRhmgwMllldq1AoFJicnO4nFQBs27GdA3ccZHFxnquLl8nnbCxlkEYpcdzr/s4eptjYC60FZY1Go9FoNBqNRqPRaDQajUaj+eaoTZs2/bPv1MZnZ2e/U5vWaDTfAS5fvvxfbV8f+MAH+IM/+IO/1jqlsYC7Dt7Fq199DouEkZkdGEYexxrGcQYZHhpjemwfh54+zNTEJnJujigBqUxct0iYCGzbAmkSxwlBGCF7bsVuFHRP6HQcF8MwNjhLI9bX11hfX2NoqJxFX1uZE9iUBi+8/Dxnzh3n5eNHqPlNyoNlklQwapvkShajYyWUYfDkez7E3Kkz/Oavf4qLl07zzifew9a738pD73iYhaVFTl6co1C2sKSJY7rc+aY7+OCHf4TBgTEmpmdwHIdKZQkpDTqdDltnt7FeXSeOYwzTIEkSHMchjmNsOxNVN0ZeK8NGStXvVJZk4qZUgigKWV1dJYoipBQoIxM7TUMhpUBKAaSkaUIUBywuLvAnn/sMhqGIYx8pFcowaNRbOI6L53v4vt8XtnuO5Z64LLjuTO65lVtxiuPYCJGyffcegjCgslZFGdlcE0fs3befZnuNI68eolCwqLdWCQKPfKFIu50yMTGLVHnCKCZJIYwTpGUTRjFCChauXCUSivzQLJt37ENgcOyVNwi8DmfPrVFr1PjRH/sI67VVDKUIAg/TNAijkLOnzlGpVDBNg527dnDq1AmGR4ZZW68iBCRpQip6Ar6BFLJ7HRk3XGdW1/leLBbJ5XL4vs+mTTNcuHCRlSuXOPTMF5kYGaRavUq1fhXPrxKJlKtXV6isrrFnz15uu+0g1ZUV1paWGRsqc23hPDs3b6LpBwhSbMukXqtSW69jmRZjE8MsLy/wJ5/9LIapGBsb7vZgi24UdYQf+P3xWpZBkghS4XNl8Rwj45IwanP+/Dk+89lPMbN5miBoEwYhxVwZzwsplgo06m3OnZujWCqysrrUjWF3EEIyNjrB9PgshmFhW5Kde3cwODSLVBYJJgIDmUqEiEFAK4x42xPvouU3WVxaotlpUx4e5trKCsWCw9TUNK8ff4OpyWkefOgR2q06X332Kzz2Aw+yvLLA8MAAoZdAKjCViZDXY+BNUxHHEUpm9/3Seudv+FtOo9FoNBqNRqPRaDQajUaj0Wi+szz55JN86lOfumGZ56cEQftvdD/fUUF5aGgoizDVaDTf9TSbTZaWlv6r7e//j6D88JYxXvj6c+zav5NyMQdxm8hvEKcxyi3iC5Pl1SsMj44xOjFBSsrw1BiWZSBFiu0WsfMjOKbCNB2SVBHFQVdIBqUMTMPOxNAwxDItisUSgRewXlvBMUJGB/MoqXALw1jKASH48Ec+xK/8xr/kiR98jJ/8iZ9m68wOPvfFpxjdPkF9ZY1mZQ2v1eT3P/dxdtw+xcCwyeBIhDIdXnzxDbbt301xZJRHn3g3rUadIy+8zqPveDtP/viPMzY5QTFfZmxiM7bhknNyrK2toQyB49j4YYd6fR0pUwwlSOIYJQ1KxRJSmqQJ+FFEFCegFAJ5Y/y0FEilkAK8dgev0yFMI1zXQglJ4LWRBti2iTIkcbc3+fVXX+GlF56nPFDE77TIWxZPf/kZPvvpL3L7bXfiBxHFQh4pBEqC4xh4vk+z2UIgMQ0byzIxTbPvBrdtG1tlDtk0FUhpYDt5TNNBCQPDsJCGS6FYouN1eOPEETAa5PIFbKeIbY4yPbmP4aFZpHKI4pROGBGlKSkJUhokCZiWSxwBiUDgsH377YxNTvHLv/lx3IKgsu5xbekyjz36ftaqqzi2S5LEWIZJQozlKJaXrrK6usTspim+9KmPMzpSxrUKrNVbFHIFlBAoqRBdh7JUN857nCn6JCmkQtBotkjTmOr6GspRSNfGyEnq9RDHHSFVeTyRIFOBYQhOnTzLmTOnmZ3ezTve8RCXzrxOp9ok8ROwLOIooblep7ayxosvvMKePXuo1qu4xQKbNk9z4fQxLlw8xcL8BUSaEnaaKDyWl05z+tTXWVk5Q7O2TODVWV6+QCIaEEYYRoxlw+jIGLEfYxkGw0NjCEMxf+Uix46dolAssmvPbnbt3su2bXuZmtrCxP/H3p1HyVHf995/117V++wzmhkJjZAEwyIQIGGxCINscMAGbMg1fgyJNyVxEschN87iy7UJxk5IghM7xomy2DG2eYixeTAYY4NtwIFoAAESQkLbaJ21Z+nu6u7af7/njx4Ni7d7c811cm+9zulzZmq6q6t/VRrp6FPf77d3kP7+ZVhWhqe2PYMfNslloTo3TWPeJZdxQLVQdZVECVCkxDB06m4FXYfIjzB0g1KhiEwEo/v3c+rwqdRqLvVanXXnnMN8tUKpLcuSvi4mpw+y6uQT2HfwGCgZ4qQVnCsKKAqo6sst2VsVyjA5lwbKqVQqlUqlUqlUKpVKpVKpVOo/l/9dgbL+c93ba4yOjjI0NEQul3s93yaVSv0vqtfrjI6O/qIP42eamTzIWWvPRM/qiLCJV58DqWDrWRwjplqfZfWqU6hVqjQbcxiaxez0GN3dvShaniDwcXIBuVwJULEzEsPJIJLj7Zh1VKVVSdtezKIkOpVyg2KHBXoRXcQ4to2TKSBUg8effJh/vvNL7N73Ap3dHXzxzi/xS2++gjPPXMtf//UWvnb3v7AjOcI3H/gimYzKjv3/RjabZeNl53Lymat457V/jKoc5TdLnYxNzOIYWd5y2VWcduoZtLe34zZ8pFRIpEKlMkep1I5EEIY+/T0DeJ5HpVIhk8m02ngbFrFoomgqQRSha8rivGUAXdcWW34DC21+W62mTdNYWAMVy3IIgga+FNimDULSrDcIgoCDBw+yd+9eEDEDAwNUKnPEkWRk27OMj03TP9DHX/31n2M6Du++4V2sXr0S4UWgKBQKBQqFEr4XUKvVcJxWa/Hjba9N08SyLJIkWQz6hBAL7bAlUkiazSb/fOe3+ZevfYUwnMNxYkr5PENDPay78HJmZ2romkMYJ8SxAKmiKBoKCorS+tyapmOZNn19/YRBwujBYwwPn8e/PfEsl11+MYV8wNatT3Huhq10tXehiFYo7Dd9bEcnDJtIYoTUmZsvU9Jjnn78e5xx1sV0LzmB+BXX7PF2youtwl/x/Svbk4dhiK7q5PN5pAiQMiESHkYxQyQlQlExdAUl8fHCBplCq3L+2PgBjhzbzqF9B3DnqqxcWcTUNEy7tZY9PT388N+eRTN0slYWP/SxdIPlK1biNSvIJGB6ci9NI8O0JrEzkvZCjGFIwuY+KuEUiqqjqxISiJsRUtEoZNqZrdQ4NnYYr/kSM3N1+vr6OOPMcygWi8RxTBSDYbx8XQVBgO/7nHTSSTi6YN/OJ7HUmGOzdbJtbWTsIjKWIFXMjN6qutd1qtUqS3qXtG6kWGhX37pmTYIgYM2aNQRBwKEDO3hm6zSmoWFogmeefp7urv5WqK8lwE9qbZ22u06lUqlUKpVKpVKpVCqVSqVSqZ/mdQ2UXddl+/btr+dbpFKp/4uc/sZ1eLGBUAwMJSZn55mZmWO+2iTWyqAY7Nm/hxNPWM7RQ2U02eTbjz7IiSet5opr3omTl7y4+18RwsY0TU5dsw5Elmw2uzDPVyGOBJ7n0d6Wx62VGezuBDvH1PQMK5efwEx5gmzOYPMH38ND37mPju4OpBrT3tbF3FyFr/y/X+Gee7/BTX/0cd77vs088XQ/2Z4sO7c/RXdPO41Kk+lymXrgUyiBKR1+8Mi3OOf8Tei6ztCK1WSyRWq1Gvl8Hsdx2Lt3L2ec3kMQeGiaQjbnEAQecRyiKJJGw6W9vZ2CY1LKadh2AaTO2NQsALEUi/Nij881Pv69aVq4rouu2rS3t2PbNgf2v8TU5Cj5bAZp2hw5WKNarXLkyBEsyyKOInbtfIG7vvJVcrkCUiqYmk4uV6DZbKKoCUIGfOkr/8iyZct417veTTaTw9QtGg2PQqFAe3snhqEtVu1qmoZhGMRxa7YwtFp1a6qBogqEEDSbVTyvwYUXnc+G88+lUa+RxCHFTIlarcbY2BhAKxCP3NZMY01DKiyeXyklqqJhmjZSKDiOg1tvMjEzR96xeOyxEbZvf57f/PAH+JNP/CV/f8dfUa82WvOdHYcw8lttxQ2bIAiZGBvDbTSQqoXQFMLQR2pmKwRXdViYE90qSFYXg2UhBPDyTGXDMKhWKySJJIkFhu4gpYKiQxyH6IoCIgbNWlwfRQUSDztjsf7CjYg4YWJiimwxRyQFxAlxlHDDr/4/gCBMEgxdhyhpBfmmRbMRYmkatVqTfN6hXkvI5iwsI4NKgCJtVEzCSBDFdWw7RxRFVGsuTz39bxQKbUShw1vfeg2Nukd7Vx+NRoNMJr9wvbXmctfr9cUAXZFQbzTw6wHPH3iJS3/peqyMQzP0MUwVVUKSKERRhK7rBKFPrVZD13XiOEbXdfr6+piZmUHTNEzdoFwuEzdibDWDChiaTm9PFgWdMGi1cFeU4/OTXysNlFOpVCqVSqVSqVQqlUqlUqlU6qd5XQPlVCqV+nlyXRdVLyFVna7OLvaPHgEsbDuDptv4oQAEs7NlxseOUsiqnDK8huFTT8dxMkxMHiXw55irVKjVG0R4LOk7hc7uFQA4ThbLdDBNExFGPPDNuxl9aQe/8/t/SqHUhe/HdHb189nP/Tn3/H/3cfJwP65bRTM0Rg8dobuji/7BAcrTk3znu9/i+ne/j3Vrz6ZWqdLe3s5MeYaOXDcokHF0lvS1U52O+OpX/pnLr76W0YOjdHR00L9kkFy2Snd3N+Pj45SKbVhWa4ZwFEWtUDeJsB0LzxNoqJiWweED+9j10nOcesqZrBg6iTAMWyGm2griZF6KAAAgAElEQVTMWrOS1cUqzeMVwJZlAa3A07Ztjh06yDfu+zKVuRlMzSFRMxQKBc4++2yeffZZdu3ahaHQqt5VNI5XfoZJhJAR3d2dVOsu9cDl6aefQgi4/vrrKWTbKBaL+F6AaZqoKosBq67rRFGElK1gWVGUxdBbVVWiKKBerxGEPtXDVcIwxDQMsk6GyclJbNumt7eXanWearW6EOa2qpM1TUXKhCSRi/tTVRUhWi2PszkbqST4oUelUuOsdet453XXc8cdn2Pnzp2cfsoaarUamq5RyrRTrc1jmjaa5mAYZUJ0egaXkSsWMHMOzUa0uLbH1yaK4sXPJRdC5uNfAwtheissRRigqGhxjFRa1dqaqhBHMbplE/jB4ixmSxP4vk/VbaBpGqXuThQpSKIQwzEJRJN6o4lhatimQaMRIKKYZrOJbeh0tXVTna9j5QxiJabmVlDMEpolUJRWK3iptiLXOFAIY4kXSaanp7Cd1pzlJFYXZo3HC5XnzuLs8WqlvHjtHZ9JrqJAovP8zt2sWnMq+WIniYxRZYAUIZqqkSQKSZIsVnQHQUAmk2ndsKAotLe3k7HsxeunXC4zP9Og0JZDCIHvBbS152nUo8WKfJTWLOUflQbKqVQqlUqlUqlUKpVKpVKpVCr106SBciqV+k+j1LWUaq2JCOt89tbPc9rQaoZOPhmj2EOUCJrVWQrdvUyNT9FoNFg6NIAuO3A6Oijk25memSabayeTd/DjCgePPIoQVU465WyqlRrl6VmyOQfPr2FrPZiqg/Th/nu+wPo3b+Ljn/4i257ZSmRV6Fli43n1VhCIQntXkSBq0pyLKJZKPP3804xNHuOOv/wIM3OHiYImWSeHmm2nVp1B0xT+9LZP8fgPtmKaBu7+nThBQlDIIpIEEUsq83MYhuTRx+7nkktu4/ChcfL5PEIGmIaNTGIabo3Ozk5qlXleeO4RxnY9x/zkMVaecgYNfxLHcbANG13XMQ0dVdVQFImyEFT6foJjGiASVFTm5+Z4+Hv3EfgVbD2DLh3mGyGa0sRSdXY+t51ly5bRaDRaIaHaakGs6waxO0+xo8CRsYMkxAgpkZbgud1P8/wfjNCoefz+hz/Cmy99K2PTc2R0G90wUEwd27EhjBGKiaoq+EETeGUlb8LE5DiapmAYGqZhtGbrJj4hOqZuI7WEdquLrKEzPTNHqa1AvdnESlQi3cC2NIIgQtMMdN0kjEOE3wrUWy2ZQ8rTsxzYf5Df/rXf5cL1m7j0iqv5009+mMHBpcyWfYTmYFl5gigmZ5icsHwlA0NDGIZBAszNT5JxSkRRq3pclSpS6vhR3KpwDsNWlbiugZBImaDICBH7SJng+RGKKtFUhUj45PN5mk2BBAw7gxQRGcchDCJEDNJ2SERANpNFxBG+6yGFj6aDSExUVcXMWshY4ocRiQhQ1AglEdS9JnWRYBgagVcj9BVyuSJ+vUHWMrEci1hIDN3Cq/uomkmCQtWt0PDqxJrADeucve5sGs0m2Vw7iqpjGBrNwKPh1SFqXWeGYaCoGnEiURUwMkXe/+H/jhACrxkihErWtkARuK6LpUAQeCRxTFuuSCAljaYPikYYxFiGQc7KI2XCju3P4XtNMnkbIWIURcEwDOquj65pSKkuXEMKKK0AH6lyvFr5eCvyVCqVSqVSqVQqlUqlUqlUKpVK/Xg/rvdjKpVK/Yc0e2Q79ckX0JrHaMuHRLpCz7IBIgT5thLNMECIVoWy7zdRNZPTzzwD2zHZvnMbXlSjUpvEbc4Qhj6hb/DNbz7Kmza9mU996s8WW+oeOniYHzz2AwZOWMIppyxnbPR5/vjD72fn8z/k8NFjZAs6qqGh2TpGxqLh1fGCJo2mj5l1cP0mnX3tHC0fYrYREsgsemaAWjNDeXqWRCi4rkcYhlyw8Q0cffFF/voTH+f2j/4Ba3t6OXhwP4loVbmOjo6yfv0bOHroILoq8Ro1TE0liiKEECxdupR8Ps/WrVsptvfw3K59bLjgEuYrVSzLwrIsTNNstRpeqPY8Xv2rqir5rI5lqOzZu5NHvv8torjBnt0TTBypMzc9T82toNLk3e98B99+8EGWDg4yOTGBrpsYhoWiqQgSwtin0FbgyNhRFEOj6Xm4oY9iaChSkncynLp0Od/52j38ypVXMbV3D72dHXSUimgCdMVioH85Ugrm5uZoNptMT08zMzPD9PQ0k5OTeJ63MFNZaYWDqKiqTlexDQ1Jey5P0nDZsfVJdN0k8CNQdBTLWah2VhersEXycrvp4+vR1laiv3+QCy+8kAsuOA8nqzJ5dBfCz3D7Jz9HVvNhdow2W6Ovo0gsBIphYxoZNNVCwQCp43sBmUyO7u5e+vsHWbZ0OaZh4TX9hXbWJoV8G5qqL7RZh7nZymLbb1VV0TSd++67j5mZGdrb23EchyAIyOUKCBFTKGbJF0wUTUG3dMLIw7RUdAMajYCpqTLT09OtVtMi4Hg4H0URvh9Sb8wzXZ6g0XApl6dIYpUwjAnDViVwkkiiKCZJEmq1GtBa9yhMEImKY+c5+8xNXHTB2+hsH1yYlZwgkoDZuWmq1XkMw6C9vZ1isbhYtWwYBrbdusEhDEPCMFy8To//PJvNEsdhq6JeVRAyQiQhcRIgRYSuSjQNZmYn2btnN26tCojFdu6vvMZfec23Hq3KcEWVi19rWuuRSqVSqVQqlUqlUqlUKpVKpVKpH0/ZsGGD/EUfRCqV+r/Pl7/8Zd7+9rf/T73mkzeew/jYUepuk4zTRkfbIKpm4CcaTjZP4DVo1AN0JWZs/CAnn34y7QMD5PN56l6dA4d2k8lqjE8dQQqdk07awMYLrkKqearVeaanp7Ftm0wmx6//2q9SLU9S0BoY0sWLBbNVweo1q4jtiN37xgiFxLZt1q07l907d2NZNuNTEwgpyRUdvDCgOV/Hr8NAbzsf/+itrDyhmwOj+yjmc0gZ0NXVRnlikke++U1WrVhFkBQYWnsu/f3LUBQF163SaFZoL7URxcFCy+gQ3Sqgaa3q4ImJCbLZLNXyOAf27mPTZVdTrjTIt7VaZR+fuauqKpZukXUcEmLCMGRy8hAHDx6kWXfpaCvy6U9/mvq8i2XGXPW2jYwe3M3kRIXIN6m4giCMsR0TVeqEcYjuGEiZEBEyNTNF3W9g5Ux830dqCkkU02nnKGgWOanRmHMZWDLIvoMHcRXoXTLAhk1vZmjoZDrbuxFKSFtbG3NzM8RxSKNxPEROkAgKhRyKoi22TzZNk5xZIE5CdBnw1X/4LFMHD/CP9zyML2J++PSzSFRURaJpBkEQUMiXiOMEJ9sKOY+v0fFAc/nSZXT3F7EcGF49zBWX/TJXvuVS/uT330NXtkDvqlV0LVuOdEpEiUqpkCUIAuI4XgiDNTTVYGZmFtM06e8fpOm32o+HYUgmkyGKJbZlAoIjBw9Rqc6BEiETk3q9zgnLl3L7Zz5FZ2cnlUqFUqnEOeecQ0dbkXw+R7lcRsiYRj2kr68bKUJmyq3QXYkVorhJEHioqk6mmMcxMiRRTLMxj+c1iMIGbq1GHIQ0Gg26u/rp7e1GUQWO4yzMn1aIJSRCQQoF08oiFBVF1Whr6yARBYQQZDIZwjCkUqmQzWSwnSzZfA5F11CSl1t8H78GQV0MfFsBurbwnFbLeiklM+UJgtAnDBoYmiRKWrOYdUXFtiw0TWP88Bj1motjWoRhjGFYSJkALIbF6msqj6WMePleOhXky/PEn3xh/N/52yyVSqVSqVQqlUqlUqlUKpVKpX4xvvGNb/Dud7/7Vdvmq4K6W/65vk/a8jqVSv2nse+FUSxd8sYNG7n/u0+yalUnI1ufJgwUam6Dt115BVGniiI8OnrydC7pRc+VEKh0dCyjo/MEdrywjYElPSwdXEGptJTntr9EW1svs3NllvR3MTc7T+AnfOPeBzh8aJRfve4qVp0whDYzhzRjnJxBrIb05bMEC7NZH//uw5y8ejWNZgVHidBMExlKCnaRQPPo7sriOFk+sPk3eOtll/HfP/rfmJ+fpdGYwQ8kQjN509sv58Wdu7ngjRchVQffcwn8hFwuQxSaIEIcC3bt3MaJK04miloVzGEY0tvbSxiGDAydTN8Jp9IIoVBow3acxarN4+GdrRvU3Apj40dxXRfTUNAUjfZiOx/9oz9C0zQypkbkxeze+SJVd5zI85BJCd8V9AwMEEQ+wmeholbj2Pg48+Ec+UIBXdHw4xAnn6Xh1snrFm2qRbvukOgKgdXg2NhBTEOiBXVqs4e55+4vcPOtn6bhuXR0FnHdVsWpWAwHFaQE0zSJ4xjLNFFQkQKkgGqzjqYpuDPHOHJoH0VVsGpgNaesXc7tW/6RI+OT+IHANDWkUBBCLlTU8qpqbRCEYUSCBAW6ekxmvGlu/7uPs2v3d1m/9gyqszN84/6vM1b1yHV1s37dBvqX9LJixQqcvLMQchu4rktbd4ZqxeXTn/1TxsamWLduHQMDA2zcuJHGbAXQ2bN3N36jST6fxfMCVEVF0wxq1TqJhOe2v4RhQNMP2Lv/AFOTx5ibraGpNlGUUCoVcOsVvMY8hq7Q1lZkSVcRO6OQz2fRDAvPc7GNVvtrXTMBj0rFpZhvo2dZD6ZhoxomihrQ9KqgqWiKhUwiQOL7HqZpIqWCaVroRpbAFyh6K0CvuVUymQwnnbyahlsjl8sRxZIoEURRq2X58esQQDPMxRnTC2OtUV5xDgAMU8et+yRJgKoIQj9utT9XNUJFEIchIvFxMgYyTrBMHVXTEeJ4C+vWe7Iwq/r4uRYiWfht8nLLa0hbXqdSqVQqlUqlUqlUKpVKpVKp1E+TBsr/R9vI5ltWs+emLTz2iz6Un6shrvnIJsq3/Z/2uVI/y4EXX2T86CTdpRJZw2D82Bi5TJZjs2Umx8bZvn07hbYuhpb1YmIAKkEQYVsOCiaWZfOGcy8iljGBD1EISZIwMzNDqa1IuVymkC9iGllmZuZYMbSSd777Br5+999jRwkDK1dg2zrZoo30DHYfOUa97nFC/xI+9Bu/Se9AiV9+5w0IPyRf6kZFx1A18vk883OzDJ+yjEcefpTLLn0bl1y0kZ0vTuP7EaVMgUrN5bzzLiAIAnTbwDAymIa9GHg2m01WrBzghZ3bsW2biFa4ahgGvu9TKBRIEsjkSiSxxDJNEkUstvo93k7ZrbmMjR2j5lYBmJt1KbUVuOfuu8lm82SzDp5bR1MdwkBi6BnKlWOsOPFEvKCJ67rEIsKQFlKRJElCxa3hhnUGlvZTHa/h+g2kCpqiYukGRAmKTKh6dcLIR4Qxqq6RsTUsx8C08uQLGXQtSxB4APi+TxSHhGG0sAZgGAa6rv5I+OdkMsRRQGdXB2eccTovPf0EZ50+wORsmYmxo6joSClRaN0A0Gql/Oq//hRFwbZtZmbmUVExTfCDkEZjhlwentr2JFf+xnk8/8zTCBI0E45NTFP97kNYpoGu6wtts9vIZE0yTo6pqTJBEFAuzyCFwoMPfhPLsti69V/JZIucecYZ1GoVCrkMTa/emlXcDHCcfCvYz2Rax+FDuTxPR8c0PT092FaBf3tiJ1EAdraMpkF7m4FlGeTzRVQNPK+BbkiiKMBwiq3PqLIYnosE4kgghUKSSFAl8cJNCnEck8QSFUEQJczPz5PNZunq6iBJJIqa4NgOQgXbNtF1nba2NsbHx5GR3wqfFbU1RxrjVe2nj1crH9+mLdyU0bpGVYSQi+dC0zTiWCy0d09arwUQMVEUoKoqGdsm9GLiWLwqOP6J+bAiQSocD67TyR+pVCqVSqVSqVQqlUqlUqlUKvWzve6BcmntNbzrLWvoswF/gpF7tvDAnvjlAzj3Pdy4/Fluu2v7wpYhrvnINfD127jnQGvLxs23sPqlm9jy+M/zyHTWv/dGhp65jbt2/Dz3++84kh9Zg9Zn3jTY+jpuTrDjvn/i3l3+L+gIF/RfzY1Xe/z93zyEe3ybvoqrf+s61nborz6/Heu5/j2Xsar4mu36IBtvuI6LlufRiXH3PcSWL41QP/c9fPT0UW7d8hitq0Nn/Xs/yvqxzzHS/97/EOcp9Yv30v5J0GAuUll5+gpeeP4FTjvtFKr1o1xyyhkM9C/BC2JMfGzLZnauilG0ECJBN2N0qeE4RZQowjJ16p5HnCQkSQPXbVWtBkGEonhIL2L/wVk+8IHfYf/+PUwf3MXJK3pZvrSXbc89zfnnrOHY2AShCp/4xCe475/+jtnyYd5x8Ua+dv9jNJUKthXSqIT0tmfo6y0yPnGYfIfB93/4PY6NjXHhxg3EcQ0vrlPo6CEWEil9ZKKj2znqDZ9EBLjBHL2dXTy3/QUKpXaCOAJCTN1EN2ws2wDFaAVycYy5MM8272RQVRXXdZmbneXo0aNU3Qo93Z1U5suoqkpvVxef+9znOHL4MIVsjqAZkLEdhBbTaKokwiZQ+9h/tIyh2piKgak5RArUG3XKs9NIO8GxCxw6OoGiaBTsEqZq0lG06BAGouEz23Txaj5OwSE2Y7yogTTyHJiscO8DX+PosSmyToSuq9TrdaSUxFFCkiRomkIu56AAddejvT2LkBFOxqHeCLGMmIztkLVznPaGS/nag49xzpnL2LT2Gvp6BxmfqdCeb1UNq7pClERoUkNXrcVrS1VVPC9g7Zo1jGx7Eitjo6gJihYhFItKFPDf/u6zWJbDXBhjZWyG+oq49TqOpuEFPmGiMFOTxNMBtm0Sxa022HZWQSaC0kLFeN0bZ7Z6hEIRlg+uxPebOFaGWBNopkGYhMgw4YSly4iiiMNHJxAqhBIM3WJ+/ginnLaEOI4pFNqZn5/Frc4ihMbsbBnZsOleUsDJF1rzj8MEz2tgmw6KomEaGebmZ+jsWMJstUZnVwEVFVXRCQLQjQQnY9H0IvaP7qWzs4vu7h6SpHUjQCscNtAMFYEkYxpMHdrN7PRRDNvi+w8/yDuv/zXCGCzTQAKJVFEVFUVVUDQVFgJlVAVD01uBsgKKpiOEoL+zlziMOHB4CqIGjWqT9vZONBSCIKJadYm8AFVI9u3aw8nDp9Js1tAMB92wQBPE0sc2Q0xDQUFDFSZBZOL7YeucK2qrmllN761LpVKpVCqVSqVSqVQqlUqlUqmf5vX9X9TiJt515RDTX7+NLTs82jZcz+a3X8vop+5i18JT4q3PcvTCtazXtzMS/9S9/ZzFjDxzlI1nr0ffMcL/1rd+7ZH8hDU4+vBNbHlcp+vsa3nPf3kvm26/g0eq/+vvN/SOj7CpfNv/fEA/9gi7+ACb+h/i3rHWpr5Lr+D02kPc+lfbyF+8mQ9echGP7XmE4bdeRtvuO7j5W/O0XbyZD16wngf2PMHwO27gIn2ELbc+wgRDXP3r7+Fdb9rFHT/YxZE3ncVFxcdan1E/i+H+Mru+PsPIsv8Y5yn1i/cXd3+ZbMZmdN9eatU5unp7eXb7Di7ceD6OncX3Eg7t3UHkdpJoDtmePmzDREiouHVmKg2UsSm6urpwHIe2tjY6OrupVCo0m03K5TJ9fX0A9PR0YZgwdmSav/rsP3DSsh6icI4jh9qxTYupsXlu/thfUGs2ObDvEGvOOgc7PBEPh6c6n2P3/hrX/OpVnH7+BczN1sjn82RzFp/57Kd4w4azqcxUee657Zxz9hkkqoYfzqCpMYYuSYRCs6GQz3UCNuXyNPNqDVDJOBk01cKyMyiKhmChdXASks/nyefzqKiEccjhIweZmZmhVquhKAqmaVIq5pmammCgfwmz02U+8fGPIaWko1jAazSxTRMRhSiY1OsCVbXozveRSAWkTiwkQgjmggpjs2MolkoiJbpjE3g+WcNETSRFy0HOurgxiDjBTyLMvEMgE4qlElpk4TYThID+/kGOHJ1ECEGUhERRRJIkrWM2NKIoRGDgeQHFQjvLewcJkpjJ2Skcx0GxDZpRiCoynHb2hTz4/adp+g1yuRzTk2Xa2tpQjFblbVP4RFGE4ziLVbPQao+sydZfiU9te4qOnm6q9TKmqaLbDigGQSxpenWybUViKZiZnUXVNRxdQ1HANDUMUyGOE+p+FdPUMR2NRESARtNrzYNu+g2y+TzPbd+G42Tp7uolFhGEEtMwiKIIGUacNLyS/fv34rmQy8LFGy6CpMnywaW4jTrZbBbPl+zYsYPpiTKaIoijhPrkDKedfjKTUxMIRSXf0YFtOeiahiJaVcAD/UsxTIVisa1VCRwplErtGIaD5zVIIgNDt1h54qnYtsnsTAXN0MkV2rFUFSkTkkhtVQsbEQcO7eH557ZhWW1c+8s3UHd9VPXlNtfHK6OPP17ZavzlGcsSdWH2ca3eYOnAEo4c2MHc9CxmtrhwrlSKhTyDA0sZn5qkWnXxNI2peo1CvoSRyaJrBk5GJ5M10GQdKZpUKnM0A5dCvgdVE4gEpDCIQtmqWk6lUqlUKpVKpVKpVCqVSqVSqdRP9PoGymuG6Dr4GHfsaNWzlp+8l3vVZcwDg1feyA32Q9x693a2H76U9WfrjGz9WXFhnjXXfoCrTm9DFz4TT32NLd/aS6yv4orN17K+zwZ/lEf+9gs8NruRzR9dT448+ktf4LatQ2z+1Y0M2hBX9/LQF+5kZMd2jr55PWfpI4zEr64U7rvyRja3PcHNXxyh7/IbuU6/i9vvK7Pq8s1cu64PW41x9zzE5788Qtc7PsI1/XWMjj5sXPZ+6/Pc+ZRL6dzr2fyWVeRVn/l50A/fxedrl3Hj4DZu/acR4hXX8JErE+68/d6fsgYx5Wfu4jsrP8rGc/M88p2F/V66irweM//sXXzm3r3ExTVc856rWPOqSuHj+9BZdfkHuap/O4/Mr+fq0/PALWzmJra8uJZrrnsLa3ps8Mts//aXuOfZCkPv+AjX9PsYXV3YwmXvt7dw59YKT+zx+cDZfTA2AcDSrjwT+0fwAf/xFymf3UUXMPLFmxlZeG8sHc+dB4YYXuax7c5HmPABRrn/nntZ7wDxCM/u38R56/I88rCLvmENS8e2cWcVeM15epWOH3/8dKzn+vddwao8+LPzYB7hrtvuwXvTZt57/mDr/C1UR1f+HZd26hdD+DqzFZclncvIaXlOPfEsJiaP8tTTT5DN5HjDG87jvCuvIow0JmaqRLpC0w9QFA1NB0WzUBSF6ekyuq6jTc9i2g6dne0Ui0WGhoZac4VNk4//3m+TLdp87BN/zXy1ye/+4R/zta9ugTmfemWMeuMl7rzne9gZhfe+732cOXwSTzwxwZpzzuGTn30bkTCYn58HQnpPOoE4jgmjBuvOughNNVmyZIBspsjkxBw9vW1YqkQhRIg6cRTQ3t1Fs+mCprNyxYl4C2Gk42QAbSGIawV1KBqaZlCpzHHkyCGq1SphGKLrOkmSYJr6QrvhBMs0MQ2d3/rgrxOHEUNLB5GJwHddSoUCQRAQxTGFQolQRCQiJhEGiRRINcETAXEccnTqMLqlI0iQQpA0A2yh0JfLo3kRohIQuxEeAtU0CKTEC5qtdQ98stkcx8bH+JUbfoUXXthJsVikMj9LtNDGO4oipJTYCzOJgyDgtFUreO7xx/nSX93GbNPj81/6MjPlOZREwdZtRNK6caAxOUVHRxtCKuQLJcIwJJYBjuMQRwLXbZAkyUKI2SKlxDA0fvDoQ9z+ZzfT3pklrnrkig6a0JESNF3Hb8RMzM8DkMvlmZt2UYsqhVIeL/SoVmtYloltOsRxiEhidF1DNxySpFV9G0UBftSgo6OTh777IEuW9HPxxjehouD7Hl1d3Xhek63bXmJ6eo53vuNSanPzfPUf/oFz1p3J6pNOoruzC6lAvTnH2678Jcy3v439e/dx2mmn8eB9X+fJkWe54IILGBhYiuv5aCjEUQCaRNd1BAqoEtM2SGIVL6ijNzXq9Tq2bZPJFmg0GiiKQqUyRxj5EGtk80UURaIoElM3kQimp8dZe84Ghk9bS1/PiRwbL2MpOrZtoS60tH5tkPzKQFlTWqGzIgUKonWDQ083tpYwuvtFBnuXkOvoolAogGxVKI+NTWBncvT2lVh24mp01cA0LUTUej3SJ5FNHKOd55/bz8z0JPlcG5XZiVZbcFUnCkMM3UEq6a1KqVQqlUqlUqlUKpVKpVKpVCr107yugfJQVwnPLb9iS4Vd/7oQ3913O7cubN31zChXXHoR+a2PvNxK+cc6nfXDMXu/dRePTrVx3sYhVrEX3nEVw+V7ufWOveQv3szmt67nsS8Cep0df3s7j0zFDF19DV1TT3DP97bDSZtYubqLkSd3MXLwCi57Y56Rh13iZ0apXDjMMNMM9YNrDrOebdiDOkcengD6cLxt3PWpEUZZy/W/dzZrGeEoYNS2s+WOO4gu+RC/d8ZaeKrCWy7p4+jdt3LXXoP17/oQGwH3B9s48vvDnKWPUD5jCP/FzzMBTPyMNXDDCDvXBcX1vOsSh5G/vZnHqqu47kNXcFnf7TywfD2niL08cNejlNvPY+PQKtgDoNN1+QdZ37+dLVseo8JjVMTLFcrD/+UtDJXv5da/2QXD1/GhK9/C8LN34QNG5V/5y88829p+xSaGtt7D6A9epH7jRQzTqjK3zVdcQjGvriC+cDO3vGkQmqM88E+7gI205+vsmXjFS449yxMLX29/YZQrLjqP/MOPsPakPo48v2Vhf68+T680vOnHH7/x5k30Hb6LW+/ei7HuOj50EcAQ567povzkPTyyA1a9aSWndI3wRJnUfxIJBqqtUo9DYttivHyQSAYMDnWjaRqJ1qA234adLeKYGjYhzUQipEARoKoSISFnZ9B1HaloqIrO9PQ0SZJQLpfp6urinnvu4dzVJ/LAI99E06Hu17nh/f+VT3/60zSDiIJjYxcdlpYEbqPOv9z9FT437rF8WS/fe+wZ1px1Dlddcy2OlaCoBTTVQKoK2UyRt17+bhQlwbEsQJBEonXsSUSUgGnkaS/kqVarBGFMJn/rG9IAACAASURBVJMjEgmgkcsVyGULJLGkWq1imjZSiYlige+HxHFIHMeoqophGAshqYGmaQghCMOQQ4cOkoQRt97yCfbufYn7v34vxVwep1ik6daZmJigu7ubKPZBS9BMie8LdFMjVmImpseo1V1UJYFYgpQ4lo6jOnQ6edSah+r6eI0moRBYbQUqkUeiqgglRKIiVI0gkiiKxqZLLmNssoyqG+RzNvVGq7K42WwipSRBkghBJpPhpt/9bYrSp1KvcGTSXagy76BerWFqOrGIUBAYpiRv64igiabqKIqKoRvk83mQrbnax9fkeKgshKDQluH973sXG88/h+FVy9i29Sme33uQnOZQzBS48LyNFErtnLB0KYZuYdsZEJLHnnyU+7/1TXTLwtAVFA2ElBhmpnXdigghJVIBVdewDYdE+NSbLn0DfYwdHeP7j36PdWdvoKOtn29/+0G2b9/OvOtx44d+k+rUOEf37WbF0l6mxybQNI3TzjyDpufR09tOvTEPiaRYyjI1PcYll1+O79Xw/RAvBpG0ZgorioZtta79rp4e4sSn6rp0dQ5g261Zx4qWp9FoMDM3Qy7rUCvP4QcNFEVSKLa3ZhvrCiitGcSaptDXP8DcXJWujgEqboiTKWLoFkHQIGOYi3O8jwfJr30I0Zp/rCoKSZwQxzFBEHHw8B66iiXmy3PkepcwP1chSRJsO4Oum+hoBEGMH9Vp1lyQOqVikVKpQDGXY2amwp4DY2hqjhNOWIMmTUTcqrCXQmLoIESMqv+kgcupVCqVSqVSqVQqlUqlUqlUKpUCUF/PnY+WKzj5rldsKTF8/hr6XvvEA0+wzx7mvOLP2uMI937rKPa6K9h8wyaGxDzjQFcxT/706/joLR/jQ2/sw+5cwhCAN83oVCuSHP32PYy4Q1zyrs1ctbatVbUKjG7dh736PPIA8bOM1gcZXncWS/0neOBoG8PrzmLIHuXZAwAe9sB5XP9Ht3DLR69mlf3ykZVHn6AcQ2X/+EIgXCIfjTKyy4fYpdyIWk+MR9g1sZQ1G1axdnmdXVvd/6E1yJsGfr0MnSVy9iCbfutj3PLR6xjOt7FkOfDUvdx/2Gb9FZu5/pIh4vnxhVf2sWZdG/HY3h9bidtVjBh9ZlerwnjXCKNJnuNnLGpUFrZv56jWziBA/ATbZwZZc3rrOX74ighZf80dCo9v4aabbuYzP4RN79hEnqPMuTnyr7gA9IG1nLcy3/pm56PsMldxXv9FnFLay8hTLz/vVefpf+D4S/nj22PcWY/W6o/y7a+P4K64hOvefxVnFXzK8z9+vVP/MZmZLKppkRAyPXOY8swETa8KekK1WcPMFdALedywSawKQkVFqiaoZis8VjU0FWIUIgmoCooqSWRMvlhAU0w6sllmju7nmw/fz4pTzkKio0rI6Dr1uk8Qq4SaA0aGQOjEikGiKXT06EzMTjI1eYSVy5cxPTUPZgdRFCBkhG4G6IbAMkO6OkrkchlUVaGtM4uitkJNXdGx9DxufQ5BgpPLgqai2xoYYNk2UhEoJkjdRioapmmgKBLNMpFJq720roFpgIqCoatoqkRVYsCnVCrQs6SHIInpX3YC51+0gWZcJ5ANDo/tR7NijLyNYRuowkSNbSzLwTAMGl6TuVqFUEatMNAyWhXQUqfdKWDEEq/uEugxvhET6IKKV8PM2TQSn3k/bM2s9mN6+/oZXH4SeqaEYecwdQstjEmSBMuy8H0fTdNoxglxIogaLmPTkxxxG8SZHt79vl8j9kPm5ubQLZ0ESRglOJkCUmgkqkkgFKSqYGdMspkiumaTyxUwjFZ4KuKErJMh8Hx0VWe2EtCYTTj/tFMRlXl6O3q44Vc+zH/9nZv5gw9/nA3nXsKKoWESYdLwYsqzFWarLhdf8kuce+75BJ5HWyGDo6kUcza+5xIFISI2kCQLVeISIUDXLKSIcCyFrq5OXtxxlGOTU0TC56HvbqVe91i1ehDDhFJnBxe+6VIwHBJNw8kX8P0QBQ3PDQmaIWEYEiUxbsNl7OgR5udqhEFEGIYoikaUCBRDxZMBia5g2gaaauFYFpXZcYIgQgiI4wTHztFW6uTwS/voai9SrVYptXXi2EV0LAzVASCIKggZ4jUTentPQNFULMsmk7FBS9BNi1gkJFKAqiCQoCokLLS4Fq2HrusLbdsFmm6hoGNbKlPlafaPT9KQTarVeTRdxbJNABRFIYiaiLiJV5lEkXVymZhTT11CZ6fKbGWMo+MTSBIcq0QS63h+A0kEikBR5eKNFwgFRb6u/xxKpVKpVCqVSqVSqVQqlUqlUqn/1F7f/0HdPkp5+UauGbYBna4NV3P1BcO0/cgTJ9i6R2f4oj6gTKWeZ2jtMDag92xkdY+POwcMXMa1GzRGttzGzX/2EOVla1gDuL7P/DNf4NabbuKmW+9gy533M/qad1h/zTUMTn+Nz9x6K1t2wPCpS1s/GNvKXn2YTf0ALiMHfFZfMoy3axt7nz9C20UbaZva19rf8jeysb/M/X9xEzff9gB7/Z/24cu42hDrh23Q83RljcWfjDyzl9K6qxiqvcijizORX7kGr2TTt+46Lj1pvhU+N+v47igP/c3NrbD27+7gS0/C4KXXcr4+wpbbbubPvl1m6PQ1C68/yiO33MWR1ddxzeqX415da33t+g5DZ7fW2h5ez5Dmcrxg18iWFravYTCZ4ygAMSM7ywydvR4dOFJ26RveRJeu03XhKXTVK5SB897/MT54cRc6C6GzaZNjlF2HHc5660a6dMAe4q3XXM2aE16xBgd0Tr9uLbnD2xfnbP/oeXrZTzr+ctVY2K6T73Borf56rn3HIONf/wy3fmoL29VhTl/2085h6j+aRIIUSmve8dwYiVollPPEsk5CAopBNptH0XTqDY8oUVotljVjsRLVMp2Fbdri/NxcLodMBMViHgGcetqZ3PjxP+d3/vBmjhwbx9Q1qg2X377x9zg4Nk/Va3JgbIxEjVm6YpDh01czeEIf5OGt115KZ69Jxmkydux5NELGRndRPvQS3/vGnYRhSLVapVKpEIYhjUYDIWJM06Szs522tiJhpJLPlhCBIGNm8RoCXQVF10BTSeImYWMPh/b9K9XpI2RUjfZsF7m8QS7voOsGmmaiGyqe12RmZnpxlnK9Xmd2dhbXdQmCgLPPfQMXXnwxu3bvYcXKVSwZGERBI0kkmtl6P6nEzLrz7D5ygEADTwgUw0Q2I5YVulme76YyNkXetDlx+RBxGOE1ImzbJFIS5hoVzLxNe3eJTL611o6EU4dPIJ+VqFoEtkZD0+nq6mJiYoJMJkMYhhi0QvJ8Wxe3/uXn+ORn/pHP/M3fcu0vv5OJqUmKxeLiDF5db/1e0zRt8Rwffwgh0HUd27ZxHIckScjlclQqFTKZDLquccuf/CG/+3vvpW/ZMsanXS67/Jc59dRTkVLiui6e5xHHMbqu4zgOxWKRUqmETAQXbLiIL/z9XeTsXpoNydixMplMBpSYXMH4kTbPIjHwvQQ/8OjuKZEvwve+8yhf/9q/cNJJPeQKGd50ySX4vk8sEqI45pLL3syll15Kb28vTc+j0WjQaDSou01ct0G9XqfRaFKv1ymXy9RqtYVAWVmcP6xrFpqmk88XsW0b3/fJ5/MEQcD09DTT061r5fnnn2fvgf0kEpYPrcC0c7R3lLAyFlESEOMRhiFh6CNESJJEIFvBsKqqWJaFbdvouv4jf940TXtVC+wwDF+etYwCQrJjxwv09w+Sy+eJ4hhNF6CEoIQYpsB2FExLYtnQ1VOgf6ATRXcZefoRRp7+Prtf2kkURaiKtViJbpkOIBZbditqa17z8eNJpVKpVCqVSqVSqVQqlUqlUqnUj6cNDg5+/HXbezDKPn+ITW+7mss2XcRZ3fM8/tW7eKYmGLzyRn77jCo/fLEVX9ans5z5xhUc27qdHaNN/n/27jzO0rOu8/7nWu7tLHXOqb260510pzudBdJZSAgJAyGEgAFEFmUEXEYZFfRxHx3XGZ3xmWdQn3HFZ1BUFnVkiYqjzhgEARMSAgGyd5Leq7q79jrrvV/X/HFXnU6D4+g8KhNf1/v16ld3VVf1Oee+7zp/9Pf+/n77b30Nr/2q27n1ebsYffaDvO+edUxvBXnodl7zNa/g9hftpXjgT7nrqXWWFofsfsFr+dqvvoPbbr6c5vrDPLQ4z/W31Dl2z2NsAstph5te/npedcet3DCxxN1/8AmWYoABZ5vX8tKLF/n0kQHZWodrb5F84fcfZGk9Yfcthxl9+oM8sgxsdYmuvJVX3HkHt14/g49P7+lPs7rnFvaP7uFzJ4HJK7ll/4h7PvdZFov93PY1r+UVL7mehUjD+kPc8/gmrIxYeOH1FJ/5HR5cNOPDdf4YPMHU9bdx7bW3cdttL+La+YQH//C3+fPFAgYnOOMf5s7XV8f08G7D0uef5Pia5LKXVMfsRRcXPPBnd/Hk+l6uv22a9b/4c/7sSZ8Xfv3LaDzxOY4Gl3HrHa/livLj/Nd7+iw8/9V87avv4EUH4Yn/9mE+cTahc+UtXLtvPze9/BXcdlXIyb/4IJ9Y3E7QlzIueum1RJ/5Ak8cXad54x28/pW38/yFAQ/8yYd5ZN1w+mTC5Xd+Pa//qtu45eKMz33k93lw3bB6ZJnmDV/F6191B7e98Ap46o/53f96kp1sfnCuxfW3TXH8ro/wxAXTrZ95njyuvGU/o3s+x32Lf/3zXz2Xsf8lr+e1r7iV63fV0azz0D0f52znJr7qa17FHS+5gdaZu/ngXy7xN94X4PyDed3rXsfv//7v/52+52vf+EqytCSMPJ48+gWE6pGZIZkZkSSS51x1E5YacZIzSnKQEqxESoXn+SjpobWPHwQXho5SY0xJvVEnLyx79x0g6syRFYbOxARpmpDlJQt7dvPH//UPyEWGkQVxljLIRkht6bRq7J2e5RXPvxE/GVJ2N5hp1uknOaGEj9/9p4y2Njlw9Y34Xojn+URRSGlylBKMRgM2N1Y5e2aRTnsGm+XsvWgP3e4A5TeQCAokfhAgTMrm0uPc+6mP8OCDf8ZH/uQ3eedv/AeWlwdMTU1hrSDLUjY2V0jSEWWxs1dWjAM/rTVSSnr9IZcdvIxet4cVUBiLp6NqZ7IQaF8SlzGPHD9C4UmkgDAMaciACXwahWTt+CK1MCQQiv7GJv3RgOnZSTbjAYkPulVDaMkwSdFSkY0G3PDc55KeO8NnPvYxpus19uzZS78fU5QZwDgkVloShgHKCwgabYZJwWA4QGoPzwuwnN+D7HkeWmvyPKfRmMAYixDV+TfGjM95nueMRiM8Xe1qDkKPJBmycu40V155iMEgZq07YuGiAwzT6h3CGEOcxJiiHDdrTVlSZDm+5yFQ9LsjXvc1b+TT99+HUBYhS5QSDEdDlFQXvC6Bxvc84lGPJBmwf/8uNld7ZGnGseN9bn3J85hotlFKU1qLFYI0zRgNY5T2qs8hkFIBAoFAquq1Yi1hGCKEoCgKyqIgTVOyLMNaQVFYtJT4XoApc5I4Jk4yrLVIqZiemmHfvn2EtYiszAmiJhPNSbQfkGYFhc0ZxFsMhyNGSY/19WXSJEfrkDwvq5NRzdhG6Sqs3QmUpZRIpZDbI66FEAhr0EqhpcKaEikFke9z8sQJlLK0OxN4vkYIS1FkIHJKk5DnKVkxojQpaTYiL3sgCowxrK5s0p6Y2f75N0hpsaZASQlU14UQIKRASEBYji/9dXM8HMdxHMdxHMdxHMdxHMdx/s/1xje+kbvuuuuCzyWpJctGf6+PI26++Wb7v/6yfwxNbn/725j55Dv4vUf+kR+6dTtv//YZ/vIdv3dhK/b/p/aVh5k89kWOFTPc/ta3s/+xn+FdnywgvI5v+K7DPPlzv8X9F3zHV/AYfIn9r/8h3sCHeMeHv7TrXbnyjT/ErRu/xjvv/pu3Xv+9+rucp6krOTxxjC8eL5i57dt4+4FH+Zl3feLCHc/OV9T73/9+Xve61/3dvue//CcoffJ0wFPH/oqTp+/F86EUCYNewHd860+RjHxOnDpFnGakSYa33ZDUykdKTRTVCWvBuCGplEIrH6UFcdzH92s0mzNgM6TNaUYBQb1Jd6tPvT3Bm97yRo6feYJza4vI0MeYnIvmOuyfneK2m24hTBKWT5/m3GqP3YcOs/fQc7ClpNdf4/LLrsJ6dXyvhjGWsixoTkQUZYIpM8osZTjcpNOZ4+RTj7KydJbrbrqFidlL0CJkhKDe8Dl7/It8/t7fo99f5emTS4S1WX7ip3+Fbtdy6tQp+v1uNRI4TbFWYK0AK6uA9hmtXSEEQntYY1g8cZyPffRuGrUaIhMUokQFmsGoz1Onn2Rt1CcMQ2pG0wxrBKOCbKuPKUqkr3nOlVeRxiNWVs/Rz0cIreiLgjRUeEFAPhih63V6vR51JZkNajz/0JUsLS1xbm2VN7/tuwk6swj8C/YbJ2nMxMQEM9OzVRhoJUlumJqaYjQa0Gq1tl/n+RB1OByye/de0jRFKYXneRRFTllWYWe322V5eZkoiFhYmCeqefzlJ/4Ckxbsv/Ri4ixmZXmNOE4BQV5U7xxCSXztjdusO41bK6rRyTMzM6yuLnNmbZHf+O1fQ2pDnAxpNFuYMh+35KEKv/M0oVHzEdLge4JdrT187vMneMWrXoP2BcL4KE+PdwwnSYI2kOV51VYHhNneCUyJlNVz8r1n7CWWEokYv/ZarVH9HQlaCkwZI6whTgwTE1UIb41gdXUVPGi1mzRrLbA+BpDaZ7O3ytrGaYoyRqnqMZr1BeamD2DLajy68jTK89C+92WBMkoiLYjtwzHVbpIkCYHns7G6xvT0NL3+FkoJ/uzPPszM7ASjwQApq5Hh2oOyLKnX69v/ZrXLWckIz1eUhaUWdRgNSwb9DYwZAQZhJGUmxzdXCFE9p51j8/EHTv/vvJU5juM4juM4juM4juM4juN8xdx111285S1vueBzm13DoL/6P/mO/z36f/0l/1j6fPSd7/jKPHT3o/z9P7TGa1/NG374DTQ1JGfv54P3FnDpG/ihbz7E1t2/+iVhMnxFj8Hf0WO//46/1/D9b+XvcJ607nD163+YN7Q0JGe5/0P3uDD5nwBjCupRC1NkLOzayxNHPoExErwCCKjX65Q5GAMCiTGmCrekNw6zfN8fjwDe+Zy1VWOxKAqims9gMKIeQbNRwxQpa2trTE/PgZDsXriIo6ceJQwVXmOCeNSvmqOjEU+fPMVlc9PsumQv85fWae25nKjeors14sDlVzG7cDGbWz0a9QZSKvI8I6p5lEaRxJY07lOv19m1axcffO+vs7a6SobkNW+6iuFmToZFp5L19XWGoy2G8Ygbn/cSrrvhDo4fTciKLkmSICQkyRCBxpgCYyQCxkFoWZbjUb9QhY9Vs9mSlyU+VYhpyoLuoE930CWq1ymKglBHBEJh0wRlBIWxhGFEURSMRiOMtdQnmiR5Rp6n5BakKVBAmqZE9ZAiSTmxtMFXv+ISVoc90vWziECQ2IQJv06apvi+T1EUBEFAlmUM+j2mO5PkWcYgzrCbgvmZaeB8sLuzo7g6n+KCscrj8Hf774QQRFHEYDBgqzvC8xTrq12UF7B+7izDeARIbGnG/wayCh+rJu/5kNSiKYqCY8eeZt++S/j19/waxhRoqfG9kCTO0dqMHx9gMOgxPTlFFCi0pwh8yeLSKcCglKDb7TLZmiPPczzPq67lMEAkOVprrBTkeY6ies2I8/92mmb4vj8eAV7mxXZAX51rYwy1MCLPRuR5ThT4tFrN8esyFubn5xmWMUHgIbWHFkH1Oq1hfn4X/eEK2aCPsQV5nqFEg7LMUaJ2wevcOR/PVH0OsNU5O3v2LPV6nfXVNX7v/b/Dm970JhZ2zVGWOdOzM/R6q0hbNYutNWA11hYUuRnffAASJRVROEGv16MoDFFYR4qM0SjBGKCUlOPrBKrx1/rLnp/jOI7jOI7jOI7jOI7jOI5zof+DAuV/agpW730f77j3Sz599EO84ye+Ik/o7+TYh9/BsyPa/usVy/fwvp+75yv9NJy/ZxONSbIsxQjDlVe8mPe+9zcpzRYX7YG1lS6CiLRI2OoOaDabBEGE1DWErBqkQggKawj92jh8lMpDSwUWmo0plJDU6pqyLClQvO+976auNlC1y3jDG9/C4edex8f+6qOY0iPe3EJiSWTGeiQ58tRDLJ6JWDx7ju98248j/BYLew4wu1CwtrbGA599kN4gYd++i5mfn8cPNTrw0YWgHtWYmpzFmJLpmQUee+wxbFHSXety5ug50B5Zqdkzt8AHHryXIos5dMWtXH/DK0EGLJ05Q5qNGA2HWCHBKvLCIkQ10rsKWDVCGYypWrVWSHzl42uPwPPJS0NNe4ySEa3OBH/1+U+zFQ8Q0hCUJZOqQTAsKTe7hCg2sxQ/CNi7+yKSMiVLRswvzHJya4WBiclDDyUE1kCuJUnZR2YRNQ2qWSdauIIf/+bv5eTJkwwGA4qiIM3Tcfjr+yFWbO9EDiJyIYnaHczWaQI9Ud0AEEUIsd3WFYI0TRBCURQ5WqvtAFiiBQRhQFEYunlBLQgJI8HW1ganTp1iOByycPFulldW2drsb4/KlpS2QFCF8F8aUO+Ey1acbx8//PAjLJ1+gqgRYcoCURZ0Ou3xnuCd7x+ONKN4wER9ikAF5ENDMvRI4wGUhkjXEAq01JRl1dYuC4uUojp/2yO8LSVSS4wBuR1wKwKUlkhdHUepqy3ytjQMRwmdTgdPGoZZjzQryIoCKRNA4nkBvu9jrCHyAwK/RllIvMDHCI20FmE9rjr0Ygajsxx58jHOrh5Byy6LZ56iPbGH+flLwHrV8y5Tas06fhQwGo1ohU2KMgdRtbSzLCONR1iT8fAXPsuZ04/wCz/3Y7z+zd/KC1/4ItLE4vu1qoVegJQeRSowpYcpDP04oV5rUPoWWc9ZWz2DEJYsHdCamMYPBGUZkeV9sjzFGA+lPISCsjCUZTq+fhzHcRzHcRzHcRzHcRzHcZy/nguUHcd51igNCKlpd6bY3OrxHd/2M/zMv/9RWvWAG66/gfWNGN+PUKraryulptxuKQdBQJ5fOHZ4p2WqpBqHmOMx2GGI8j0uu+IgP/vT38W//ql3cfbcKeYXJpmemqV3cpNuN0FKqKcKkeW0D3SQUYu53Q3+1Y/9G0YDiAfw/ve+m6uvvppQ12hNdggCjziOKcuS4XCILXKMLVBK4fsex06f5tu+/0d53uHrOX1mlUFsWT27TLvdZmNjhd6w4OqrXsY119zC0rl1ytLSHXQhG5CVBis9yrLE3w7OpXhmS7d6fVJUv6y1+H5As9NBoAj8iInZiMeOHkH4ilbQZFdrio2zy7RCRRqP8KVCNzzKfsG+/QdRGJJeF0TJqfVzbJBhQx+EpShLitKAgHrYor+VMshSFqbm6Q8GhFHE2vo6nueBEHjax0hTBeHKq/btSokUGik0ng4AGAwGzMzMkOc5Qpzf07tzDoUFjK1uFjAWaySb3R5zc3M0Gg2CIOCzD36aLMvo9XoEQUCn0+Hxxx8njmOiKKra68Zgdq6ZZ7Scd0ZKAyhVBbZhWGN2dp6Xv+w1fPCuD9Fs1ajVa5w7t4LnKYqiGD9HHVimpubp9XK6mxusrgzwJFx99WGsVDRadYqyHI9lFkKAAlMaQIyvYSW9Kljefo5VoCxQSqKkAFEFz2VeoLUgrEnIE2yo2eyuU294SA1YQZbFlLYAWTWVPVkDqcEqRkmG9gVhGFZBe1nSmdjHLTft4wXcyu/8zntQasDefZI9F+9iNOizZ/cesjhDas365iadTosk6+LLsDpP1hB6GtpVO/7Vr/s6XvZVdzI92WYYZ5w+fYpWq0WeeRizTpnlFEWBVoI4HrF7z8VEfoaRPmsbPUajEdrPULpE6hTb7xP602RZXl2H+flR7ztN7erY/b2/VTmO4ziO4ziO4ziO4ziO4/yT4gJlx3GeNfLCVCOrpcLYkmuvvZnv+4F/S797hiuuugojBKPekDwrKXKD5/n4gRqPeg6CAK01Ukq01uMgbqc1utMglVLiKRgOY15829fxoptei6ynPPbYY8wtzPDv/92Ps2/fxWxsjdBaEw+GPPLQw0i/RuR77N69m/nZBfbs2sMgHlDmBb1ej9nZBYSGssyp1WoMhr0q0PY0tbAaFVyaglq9yfNuvI0njxxnOMxQWjMxNUG7FZIVXTqz0xy89DpOnlxCCEGWJZRxTD3SqFKRGIkfRtiSLwmUJVIrtPLQutpVHAQRYRgy1WkzMzNLt9vl9NljLK+ewxOWKPRZWz5FK6qztbRcjayWsDVYo9mOCANFNhpiiphwosHAJOSjlKLMsQiSOMYYSxD4DNOUKGpiypK4SLjn0/dy2+0vRWpFXhbb58UHwFpRBche9Tyrz0vK0o7HYZ8PUuX4HFtrq5HQxoAQaM+rQkitmV+Y4fTp42xurXH33f+dufnd4+tiZmaGxcVF0jQlDEOMMcRxjLJVO7l6Dh5sj0h/Jk8HCCHwPI+ysLz2q7+Rm15wGz/0I9+DMdBsNimKbBwMB0HAzGyHxVOrrK+NANCBZjAs2HPJAUpjKIoCz/exVlAUJcYYytJWNwJs7xLe2R98fmz79hh3a6sAXgrAoIWm1W7yJ3/wYW656QZ2LezivkcfpjAjimGJ0hYpqv3TgSdIi7g6ljJAexaBpD8codOMra0toiiiVqtR5IJWq8XGWsL3fvdPcPToU5i0z1NfvI/u8iKfPHOO+QPPIaq3ed4tLyONMwLPA1uNXC+LbPtnrfqZXF/fRAjL2eU1lpfPsXTmJGnco1lvYO0kXkOQ5SllkVDmBU89+jhRo05zco6DBw9SGoFhSFEOyfI+aZIy6K1gTQEiRxOhVEiSZNV1onauDf8f+J3LcRzHcRzHcRzHcRzHcRzn2c0Fyo7jPGt4P0O+0QAAIABJREFUnofv++MAzZAztzDH8rkljNWsrCyT9gs6nU7Vwt1ure7scZVSMhwOx3tXd5rLwAV7dgHyQdXUzEVMKobUigYHLjvE8vISzz38cian4drrXsT+iy/hhTf/M/bu2UcQTlDmBfWgQW9jnaUypRABva0uxhjSUYyROTMzU2it6XQ65HmO1pI0jSmKglZ7Al8ojh49TRRMkqWb9LvrbJx4mn/3//0Cu+aneekrX0Z3a4UyT+j3u5RlSbPW5Bd+7j/y3T/ww4wGIyIh8dSF+6KrcFltv+4QpTzqtSZB6FGr1ZiZmePYsWP0Bn1aE03mowZJr4fRGpEW1Ot1hmQUNkMpwZ5L9pKnCVkck1Ow0d9g0xYoTyOkxNeayPNJ07Q6piIjL2KCUJLmMU888QQnTpwY73NOkgSrIQgiBOfbpDthP0BRFFUYPxjQ6/WIogilqkC52hNcBcpaa/I8H4fBo3iLj//lfezbdzH/8tu+gXf+2q/y1JNnxnuaG40GR48erZrSwHA4JAxDrLUEQTAemZ7k+fmbDjxvfIOCtZaiKJiamiJLBb5X5/nPv4X77v8UVpZIZcft5Hq9zvFjS/S7BWo72C/KmGEC9fYE2m7vR9YaOH+zAwqElSCqxrcQAiF3dhUbpKyOmcKitUIqEEKTpQnD7hZR4LN44ji7p6c5d+4MF++bZ6t3FktGozY1bl4HQTh+TK08lAwRIq72LUcRSZKQJAlaa4bDLkEQcfrUOTxdI1fTXH74Ij7wvl8mHQxQy09hRMQrXv11HD11jno4QZHH45ZwURSo0pIWBfVmjX6/z4nVFQbDdfqDTQa9VZqNfUw0p6Es8P2ces3jjDnNU5/9ItrXhJ1zXNeeII0VfqAQIqTdnMI2FMNBzGjYpTB9pK2uiZ0bBoq8JAxDisL8Y72FOY7jOI7jOI7jOI7jOI7jPCvJr/QTcBzH+duqRhtXIZuUmizLmZubI6r7bGxtYooAK6Ax0SQrcpCCer2B1tU+14mJFlp7CCGxFnw/II4TSiEohUAohZQKIST4giAKKJMMgSVJMiglSgXsv3SO9TW49957eeihhwj8kEZ9hvnd+3nu1VdQDwWPPnQ/f/LHf8CJ408xTGK68RC/ptizZzdKKfI8p8gNgR9RGskwGWJlSVHC0WNrxHHKKNlEaYOV8OTSKebbbbbOnOXP/9ufApIsLyhySy2ocfLkSW6+4lqG6wnNWgcpNIYSMd7bq1CqClp9P0RrH601hbB4vk9WpPTSLo8fewJPaSYKRf/MGsSGQkOiLANlSDVkQuBN1CHwwYAZxGRlRulJRBhhc02xVbC23OX0iS2SYYktNJ3aPL7wyIcCjGVxcZmTJ08AkKWGeq2FkFUYLRQIRdVeRWBLg7DVHuCdmwF2AmdPQ5HH+J5CS42wgiTNSJOC+Zk5+qvH+dQf/md2LSg+9KF3k/ZG4E+Q5zm9Xo+pqSkGgwGjJKa0hiRLUZ7GYKlPzTDYXEeMeozOLjJbj5BZigYsGl2bACRBEBEEEUmSob1qt/LBA1eAVWRZgTAWqQqihmZqZpput0BqiZAlRZlgLcjtfdFWKhqNFsIqhFVIFNJ6aOmhPI32q9+FkiAtQoHyqmNmKKswWUqsEVgj8KIGUbPN+kaf0bBg0E+46abbeeKJE2R5ibGWfr8PoiBJB1gjkCLEliFZyrjBK5WPRaF0MP5zXhrSrGAUpxSloFm3dKYm+Rdv+wEO3fhifL/NwsI+VtbWuWh3h/vv+29YWzIYDKpz50tGxRYZA5Y3TrLeO8mRp++j11+lLHPyTDIcpKyvL7O+sczy8hLHjx9HSkk4OUVzZp7Ab1DGMUUyYLi1xXBrQG+9Sz4a0a7VaEcRM80ZdAm1qESKmHok8ZRE2uq9pHpfcRzHcRzHcRzHcRzHcRzHcf467n9QHcd51lDqfGu1LEvOnl1mbnaS9cWTyDRh154DmHab4XBIu91GCMGpU6dot9vMzMyQZRmTk5NsbW1Rq1UjphuNBmb739ZaI2zV/LTbo461rFqxgfbw/YjPP/gFvu4Nb+FVr76TVmeKNIv55Mc/xrve9S4eeOABrj5wMa95+W0cfehBAiV5wY03sJn0abU7KC0ZDuPxyG3P80jTjP5og4lmhzCosbS4QppWgeTOzucoijh57GmKIsOPPJ73vOvIsowsy9BaU5Yl6+vrfPIz93DDna9kuduj1W4jRQm2xFi5vZ/Zx99u2+6M//aDCKjGJpdxiiwME4Uk7vfAGHJrMEaQy5LCGqwCqwUzrQ5yY5M8TxnVSpLc49xqjz2XX8ELbnoR+y/Zx/yeBbTW9Ho9Tp8+zdFjT/KhD32IMAzJ85Iwgt/9vffzb37yp/G227hheH4U+TPbyTufM8ZQq9UYjUYMBgPq9fr4eJZlSVHkRFHIxuZZIi35ye/9EYrNJab27OWd//pH2Njqc9VzD9Pb6rFr1y6KompeP/3004RhDWMsxoCUCmPAdFd5+vP3sLW2gsDw6SfP8X0/+K+YbHdIsrLaVb19Te48D2Oq0exXX301/+yfvZjPPPgpoppkdn4XG1tdHv7ik8hn3M6103D2PEOapkRBrRqrLQXY7db89phra863aatGvUIgLmjYVxO5z39c90K8WoPbbr+DldUz2Chifm4SpRTxEDqTLZqtBmmaMtWZZmV5i2ZTUQtFtR/aCqYmp+kPR+Pm/zN3NlePWZ2bPIOlxVVqkccLb7mdmQkfPwg5t7LBA5+6l+6ZY9yzucFtL3k5m5vrFMJSFglxHHNu+TSjeIt+v89kZwatxbiNLqWHFhJjwJqSJM44dOgK8jxnc3MTawV5no1b4MPhkG63i1aCiUZEWeYsLp0grEt63RHGWA4euIo8T5E6/Ad5v3Icx3Ecx3Ecx3Ecx3Ecx/mnwgXKjuM8a3zp+OZ9F+/lvr/6GP/9g/+Fy59zNV/zDQcYjEa0223iOEZrzcGDB7HWUpZltfd1e8TxTku4VquR5vkFwRgwHn2staA0GiEEw1GfRx59mE/fdw//4f/5eUoBjSYszE8R+j4zszW+6zu+hXvu/iidKGJ9eYm5qQ5+Yqm32mxsbZLnxXYoV+3k9TwfP1RMdubY3OzS3YpR263inTHdzWaTt3zj1/OLP/0TGGV54JGH2Xf4OsqyxBQlSMGePXt4/Te8hWHepxZJTNZHKw1Soz0f5Xn4QbS9O9pDSQ+tPKQFAUxPTfGJu/+C6ahJstElDH0GNqE0BZkwoCWFKJkIawS+Tw1FK5Qc663SvOxSvu4VX8vCxXtJcsswTtBSsbW+NR5nvWtuF4cOXsqb//mbePzxx/nBH/xRZmZbHHnyBAu7Zjm7tIHneRhTVEH+9thqKXYa1tVNBAD1Zp08z4nj86OTAfI8BVFgybGlJEn6nDjyEDcfupw//YuPk0qLF0bcdtvtFHGO5yk6nQ6bm5t0u13CqElZGqTURGGE53n0eyNe9Kqv46N3/xlnzy3xH3/+F4nTlCCsoX0w1iBVNQ5ba00YhtXz9nySfMgr73w1q5tn2NxY5JGHn6YoQatgvAd651quRmszDs7LsgQpYfvmBmsFIBCl+ZKfCT0Os8cBPBYp1fhnJosTfH+CielZapMddL3OT/7bf0e7Xacsy2psts1o1Fusrn6OG2+4kSuuOIAgIh4lhEGE1j5haMbHeifwBxCo8WtIkxwhFFiPMpe867fex6DfpV1X3HrzjXzk05/nsiv384Hf+232HbwGr9Eky1Kgap0P1wYYm22/JlUdT6lIkpwwCqnVakgEw/4Iz1MI6bFrd5PBMEEINf753blBxPcEo9EAhKEoMhZPd5mf38XMzAx5nuP7AYVxI68dx3Ecx3Ecx3Ecx3Ecx3H+Ji5QdhznWWMnCN5prL7nve/k3b/6C9x82R5e89rXoKdm6cQj8jwf70ZO05QoiqjVauNRyb7vY4xBSkmapnhBMA7EBFywu9eYEs/zyJOCP/rIH/DLv/LzLFw0xd59Pl69Q3djnbQYkqQj/s2P/RTrw5JXveVfEo8G3P/AvfRyySDNGK5voqQFJO3WJJ7nkWUZeZ4x0Z5mdaXHxkYXIe04aNwZ62ytJepM8RM/+/MsnTnNqMhI05SyLJAI0jTFGMNNL3gpjzzxWQJlyNIMW5siarSq4+YF263ogDCo4fvVx3XPJ5cljz76KOUgxigPowX9ZESmLFZJElHiB5rASuajCVq1CDFIeby/zK1f/Tqe98KXsbyxwumzZ2i3J9EeWFsgbA6A3W5Idze32FhbZ2Fung994Hf49d/8zyTJffzSL/0Cb3nztyLQaC0pimJ8jjGM9xNXLV5v/HFZlgyHQ6IwpChKsiwlLxKWV3oce+J+Hrj/4+x/7kV87IHPMdWe5onFE8RGcPia6+gNUlqtCdI05cSJE9TrdYytjrmUkiRJEEKwd/8+PFly+6tei/IiVs6tMtFuMRjGNBoN4HxTdydQLktD4IeUheX06dM88vBjaAla10jTBCFyGrUGo9FofG3v7PnudrvMTM1SFiXa01WQbAFThbc7Ddxn7gXfCZR3/qyw42tYSgmhR284IAxrxP0c1Qx523f+X/z8z/8y9bqgNBZhIUsTwmCSl770VeRFyqhfHYOiMASBGu+l3nm9O3utpdDj/c1CWDyvaplLBK989Wv59D0fJ+6d4dip09zx1a/m2ImjTE3O05qcYjiKCcMIsKRZl7NnzzI718Zai1Jy/HPgeT6j/og8L2lPdJiYmCTPYzzlVdeL9vGVHu/R3gmWi0LgeSGep5menqczOcvU1BRFUaB9vwrUn7E73XEcx3Ecx3Ecx3Ecx3Ecx/lyLlB2HOdZI80LtB8gdRVYXXLxZXzn9/wo7/+t3+DQNTfx5IklwijCFOV4HLQXVKHRTus1yzJQVVispUBLidwOl6WUWCzWGpTyyfKEehhVgW5T8+v/+dfIeyDPrnOw3aSbFGhZ4/SZHl/7xq/l+sMvJNgeId3t9rkpmmJls1+1bYWgMzlNuz1BlhXU6k3sIKPRiNjcWuXEiROkaUKSjvBDhSdrKKWIooA8HZLGMf18gAxCGqEkHaWUZc7SqaMsn1nC9xR33vpqPvrgA8wuNLBKkpeC6bldGBkgRUCZQ6MebY8Ol3iexviC6UaLb3/zm5mbmWfl3FmU1ehGjUE+BAG24yPSklm/ye7ONN3NLfzQcsed30xrYYFub5N6VCOOYwa9LkEQEEURVoYYY4jjGBWqcVt5NBoxGo34jn/5dm6/7WX83M+9g+uvv4E9uw9Qjxp4gaAoBwwHhomgCcIghEFKjyLXhF6TehihBXQ3Vynqk/i+T5JkGBIeffwzPPrQHyOjFBEtcHpzg0OHZ7gyuJQbn/8Szm2sMz2/QK/Xo9vtjkP7nca6tZa5uTn27t3LZq/P5mYf8DDGMjHVoixLrBTEWVo12T0FWpFjKCX4ShFnOZ1Oh7s/ehStPQpTYPMYzxfjsdjWWuJRidZVcJqnFowlyzKkUPjGgt1pKFc/A1Zs3+xA1SyXmPFx3Ql4hZDVGHepQEp8GeCF0XbY67G2toZX8zHA3MIsWWYo4pxv+qZvYmpyhjTNyHNDnBu0DqtHMYYgahIGQ7q9dYKgg5Le9k+mRIjzwXZRlEA1frrMBS9+ySt432/+CmdPPsSLX/hirr7senQUkOYJuTAokxNFHqNRnzKPEWYKg6AsbdUgLiFQHnNzC5RlyWOPPcLCwgIzU/MYY+hurZHFQ/y6jxQeUumqvS4NWJ/SatKspNZsI6UmLy0In6yobvBwHMdxHMdxHMdxHMdxHMdx/mbuf1Idx3nWiKIqrM3znCRJeMELXoAQglESc3rpLL7vA1UrOU3TcUgI26N/t0PmnfAYuKDl+cydvUIItPK3Rw1Dlsb4NuMF1+xlanKCtVHK6saAEyd7HD58DW9967fh+z5ra2ucPHmSlZWVaifzdtjneR5aa9I0Resq8NrZa7y1tUW83aw2xmDLKiSUQpGmOVFUJ01j8rTam2ytGDcwg7DG1OwctXqTYTri3Po6m70ho6SkNOB51c5k5WnqYXX8nrkHt1arYYXgM595GGstWVHST1NWNnp4kSSsazwjsVnJwuwMG+urxEmfvQevYG73RXiex2g0ZDAY0G63ATDG8JnPfIbV1VV6vR5FUWw/b3vB+dzc3OSaa66h2Wzx6Xv+iptvuYnnXH01lx96DnOzFzFR6wAaKXyEUOMQdueYVjuTCyaadZQEU+TY0nDkicdotlusrm3ygptv5+Dlh5id6zA5NYGlaq8LK8nznNFodEEgu9Nin5ycrMacD4fkeT6+5oqiALjg+tnZc12v1y8IiweDAUtLS+TbI9V3rqud7692WSuCICAIAsJQjY/TThN755g985p9pmc2k7909PXOr6LIx43uned27tw5ogjW1tY4dWqVw9ddSxDVeezIEU6fWWLp3PIFPxvWWmyRUxYZvhR4Sl7w/L70MXdep+cF5FnJocsuJ80zls6cJjMJSgnyPEVpQRB4413IZVlWNzwIie9X97wpKcnSgjyr/m737j0o5W1PGYD5+Xna7TZlWZJmCVmW4AeKRtNHyIKizEB86e5px3Ecx3Ecx3Ecx3Ecx3Ec52/LNZQdx3nWaNbrVaszjjHGsHj2LD/7n36BG68/zG/89nv4xn/xrYwGA4wx26OHS6wx4/AxTVOCIMDaqq3M9j7anbG3O78LIbClQEufLM6o1Wr8zvt/g2v2TuKZAS9563eyame482V30pmdY2VznU9+8qMIIQiDavduGNZQSox3uYZhuD1iO6csBVJoOp0aX/ziF1ldPUee5ygt8H1NGAZIwNeKMPR5/PEvYqmCTy+odgunKseUhun5XcypPZRFzlbe54bbX067PckgSQmCJmG9TUlUfa9W6O1gG6pgrSwyfvKn/m+GKTxxYol2u0YhYkosK1s5xuYUQ7j1+YdIRwlTM20OHjzIxKVXU0hFkWfMT04zSFK63T4TE20efvhh9u69BM8LMMagtQIk1u6MEWe8G/rhhx7hN9/9W7z6lV/Nc648RJFDnMDVV+/ml/7fd3J66Rxh5NNo1ACwIkXYjHqk6fdSsCUryyexRpCkI0ozIvJ9NntDJiYvJaxfxs0vfRmnTt3H1Fyba667mqjWxhaK1dVV0jQlDEOGwyGNRpuZmRkmJiYIgoAjR46A0uNroro+uGCctO/71MIIJSQSgUQglEJvZ+dnz1Y3OhSmuuaqtrxhMBiMz8NOg14IwerqKlNTM5RliVSqaiI/I6QtTYl4RrispbogTAZQz2gLCyHwPR9jCpQWaK/am6xCQfXlktaExxWXX8VoNGJqaorBYESzGYFVSKkRKKTQ1DzBH37gD9lYW+bbv/sH2RicD9d3btTYCWt3RnNLFMkopz05z8tf+SoefPA+Vo9scuiyK8mlphlNMRj0KE3OYBCzudHnwKV+deOAsQReFTY3Gw1Go5h4ZJmdWiDPc3q9DfI8p17z0Mqwe/fuqoGc55xafIr1rQ2k8Oi0LsIaA6jxe8kzQ2/HcRzHcRzHcRzHcRzHcRznb+YCZcdxnjXSOMFaiylKpjpTvPVffAMH9u1mc/UsJ44/jf6Wb2a4vWs3yzKklET1OgBZVo3gzbIMI6pmZaC987uTt3fg7gRxWtpqzLIWJHGXxdNHCOoBh/Yf5KN3/wX3PLzE97/1+8mAX333LzE9N4UVBiEUUgqgak/uBG3GGIqiwPdqDIdDtC757GcfYDAYYKl2FCslEMJiS5DKkOebPPb4Y9RrINFkxuLJkG5/hO+HUIIOQ/I0oTM7x1ac0pzbh/brTExAqzZBWij8wMP3FJ4QaK8aU9xoNLDWcteHP8DyylkeefJ+pqamGA0GpIUiSUbMzs5s7yseMtfu8Mu/+LM8fuRRWpdcgQwjlILQr7PV7YPSNBoNZmbmuOqqqkW+s3O3ajGPUFqjlMSUIIVkeXWF2dl5Fk8t8cKbbuSVd76UqBxx32c+RWwS7r//dwkbc4RZAy/YSy1qUpR9VtcKpISirEL2ZBhjSlsd4zJGWcP+Pc/lwKVXoJTH0ePHCIIOh6+8gUa9gx94HD9xkqIo8DyPJElotVrsvXg/YRgyGAxYXFzEWku5fT3tXB9KVdfLTpPW930C7dGIamitq8auEMRxjOd5NJtNpF9wbnmVsrBYKyhLg5DgeR6mZNw4L/KSBx98kEOHrjgfENvzwae1Fq2/pJHMhY1k+PI2c1ZmWEqsleRlQVkWvOe9v8XUVIcgqBOPChoTEwwGA7CSi/ZczNbWFlr4SFEN1w6CkMImnDh6glYUcnbxLI2ZXduPp9HaG4fIO8/BGIMpcrzAY2p2N+trizzn2htYW1/HKk1ZlIziHopqF3YtanH9dTehlKC/1SdWgpnJDoPeBjZM8b1qV/bK8hLNZot6vU4UBfR6K3zhi5+jPTHJ9NRsNVZ9mNCcmGBtdYMyX6PdmkJrdUGI7AJlx3Ecx3Ecx3Ecx3Ecx3Gcvx0XKDuO86wRBAFaazY2NrjrD++ipiH0JZ7waE/M8EPf+118y9u+j7m5OaIoGgdrO+FaURTbY7EtRVEQej5FUYwDup2vM8YQJ91qVLSSfPwv/5xHj3yBVqPJXz16nGSzRyvr89x9db7mjV9Ps6YoyoRavbndNK0am0Hg4Xkevu9vfxxQixo88cQT48A1zeLtccs7rUmoBTU8H+7+6EdYWnyS7mCJO1/2ZuKsRGUFUWOCZBTjBSFCaRqdGTJriUQNLwwIo6rJbYRHEDZQSgMG39d4QTAe3XzkyBGKLOHtb38bWZFzZnmFOI4JIx9syZkzx7BFSa58RnHOa77+W7i5u8mJ06dYOf5FpqdaFIXFqgApLAcPHuTYsWMopSiKgiRJxsFiEATk2+Oijama4Y3GBEmW4gmPb/nmb+THf/z7uUxO0JiN8KY0p04dQYRPomTA1tYVXLrvOdRqDYaDtBpTLQXGFliTY60hiVNKk7Lnol2E0QSd5gRHn/ocnXaDS/dfy8L8XrQOWV9fpSh6SCkpy5KZmRlmZ2exVI3iwWBwvq2+3ULeufFAKUkQBOPR0UopwjAc/95oNDh+/CgrKyscO/EUDz30KHv3z4yPw07oXJTnR7ELUT2GEoZ6vc4f/dEf8fI7XsFUp4McN423g2R9YSDqKW/857FnTBa31oK0gMAKyMtqfLdSgsFgQLc7ZDjI2Or1SJKMTmeKs8urzM7Okg0TQt8HU4X17clZzp1NWXjODGGjXe2PBoRQ23u5LwyUqxHqfWpRndm53Zw4dZJWq0mrHRCEHsqkbG6t0QgDwtDH9yLSbERUU/Q2Y3wv4Omnn+aJxx/lsoP7iaI6k50phPRJ0h5JWrKxmWPKEadOn6RxeZ08zwmCiHZrhsFwC0/XmZvdhZTVuPmddvmXHTPHcRzHcRzHcRzHcRzHcRznf8oFyo7jPGsEXkB/2OOppx/jA7/7m7SiBmVZkhuLFpp2s81v/PIv8tbv+i4OXXkVRZrRrNWI45jSWDrtKUaj0TjMq3UiNtZX2bVnL6PBkCxPwJRsrq+y1e9xdnGJ//6nf0R/a52W34ZE0gwbLA+7LI0Kvvf7foDLrrqC5d4mke8ThT5ZWrVXjS2p1T20CsjzkiCI0NpHiGrEse95DHt9fOlTUmJLgylK2hNNTiw+wvK5JToTAaNWjSho0RssUqvNoT1BkWcoLdFKoqSCssDTGqQgiqJx8zYvoKZKPF+gpCazmkBK4mTIk089gjGGQ1ceJsurEcxQhe5S2GoXsJWgJIGqRjVba2nXGlxz6EoeKWJWVhcJQgE2oV6bResao1GxHb5KLLqaKl4KtPbQWqKUIs9zrBV4RkAp0drnwKHLWTzX5/kvPcz9n/gkl1w6x3NvvY6N/hnqdQ9PpwwHa9TCAGENSipMkVNm+fnd07bEmpTpqQ5R2CQZJNiyZN+eQ0w0WqRZTJqmrK2tkWQlzXqNVqtDVKuTFSVra8vVDmMjUMob7xzO0nx7rLOPJz208NCBrtrJQUCr2WFmapJf+aVfZGPpGLVOg+/4nh/mm771W6jVfZaXV/F8Hynl9mu3aBWQJiW+H2CtZTQagbGkaUqvN+KTn/oEb/7nbyJNU7I8IYoilBKABSsJwxpVLl+F89U+4Sp0LvPqxgntyWqcdF6N1A4Cnz/9kz/mC184wtR8RG+YU6t5oOHE0RNcfvnl+EojaiF5GqM8j9walFZkGPq9hH//Kz+DsTm2DNkZIS2lOv/Y23uXEQKpNWHYpjQWzwflSdK8+vssLShNRj1qYPLqBoxaFOBn1fhxX3nYHC47cCWXXHQphU1oT+6lyC39zU1sNsRXFlAI3WD/JZdz6uQSaytd9u8/wMz0LnyviTEFUN0MUN1M8uV7qK0t/4HfuRzHcRzHcRzHcRzHcRzHcZ7dvvx/Vh3Hcf4PlWQZjXqDP/rwh2hF1S5kISxKVftgfV+z76K93PvxjzM/N8MojonjYTV615T0+ltYa6nVajQaDfr9PjMzM5w9s0h/0KUWBqTxkKeffpr3/9Z7eM+7381gq8fM5AxRrUmz1aLWaHH5VYfZfdFernjOVXT7PVqtFoH2ULZgqjNRfeyH+H6tCgSzrBplbaum53XXXUcYhlhrSdOEMi+IAo96PUTIguXlcwghxo3swI8Y9Pt4nrc9qnt7b64EqJqy1RhmixQKzwvwvIB2u40QCmvA84KqWWsLNjfXKcuS+vY48J0GNXDhOOXtkHBnFPjO3yul2L//AHmeb++mzmk2m/R6PYBxe9dae8H5q0Y26+09yue/Jooi4H+wd6/BlqV3fd+/z/Os676dW5++T899RtJIoxmEhIQASThGCBBgmXApMMLgxAT7BSknOCGJy4pDXiRlA+UAVXG5DDEpTAKizMVgnIAEGomRRqPRSCMx156Znu4+3ed+9nXdnicv1t67u6VGRgqx0uL3qTo1Z440GKnzAAAgAElEQVR2n7P2Wut0Tem3fv8/fMM3fAOVyfm6b3o79z7wWt7ypndx+sQDdLOTWOexrqIoCnxT0dQlvqlpqhnBT6irIU01pi5nNGVFXdfLRmqed6jqAl/XzIoJZTWjk6WsrKyQJAlxHHN0dMR4PF62p8uypK7bcHzRvo2idn91HLfN80XDeXd3l+nokE9+7BGYbPP4o4/w3NNP4gz0B53lOVl8j+vPx+IcLdrxbesZ9vb2+LVf+7U2aO10lud/sYt7NBpRFFOAa2Pa58eXdztMZmMOhwcEA6vrq3R6Hf7oQx/kiSef5tjxzvz1kOc5UQSXLr+Ci8zyfrq+tb/453Q6xtqIE8dPMZ2Pn18c/+Keadv21+9SjpbveXFuQwhtszxcG+W9eGChnQrgwFkaAo33pHnGeFwxHE0IxpKkHWySk8Q5kUuJo4zNzVP0eyucPn2WXm+wHFN+/Xv4s2ifsoiIiIiIiIiIiMgXpoayiNwy4iTh7/8XP87BzhZNMSLp9Ob/S8D7EgwMj/bBNPz3/+1P8t+87328+Ox5qqrCRDErq6vMpmMIbVAbR5amKrj97CmqsuR3/83v8C9/8V+wsbFBmkQcWxuQZu2O3W63y6gsSHAcDo/4xm/+ZvYmQxqXUM88O1ef5//+7d/g7/6XP0mc9uj2VxkfjLm6f5X1tQ2shbKc0Zm3qt/41V/NC88/zcc+9iivedWr2bpygaIcs3XlAp1uRDGdEUUpK4MNfJMwHh4RO0tRNsSRXYZ3i+eCnAmY4PFNjbWO4A11NW33/kYpvU6X9fV1fvff/ptlgGxMu883hMBsNrshPF2MBm7DOHdDoGytpd9bZdBfx4eawVqfzc1NLl26NN+53I62xoQbxojHcYr3kGUd6vlu4kXY99RTT/Ft3/Zt7BxsceHlj/Py5RfYPxpy351fT+3H1OEKo9GELI9oihJf1cymI2bFiN3dbZqmASx51mUwGDAdH+I9xFGCrwPBF0yrmtrDyc1jDAarVE3AuYgLFy4wmbVjtKuqwlnI8zZsL+pqGaIvdiZnWTbfp9yGp4eHh3zwDz9JqCYc629w7vQmP/zev8H6eoz3JWYePLfnIF6Gp+35b9uxURTRhPY89Xpd6rptjf/jf/yz3H//3dx111287W1voyxLinJInufz7xGIIrf8PQihYVLMMJGh0+mxt7fHI7//IT7wgUc4c3advAdZN5mPhm5HoVcVfPLJJ/jO7/wO9veOWFnZaC8f9oYHC1xmcXHK5St7rG1s4Otqfr0rIGCtwdhoOR4cwJrFfVYwHo8wNsP4sBzxXlUNoQk0DThnSJIEawFriIzDuJSq8nTzVeq6oWoavHXYqAt1ibOGqio4sXmWzfUT7Rj5ooDlsV8f4HuUG4uIiIiIiIiIiIh88RQoi8gt47/7r3+c3csX6CQOBgPgWuvRGMN4PCbr9jk6OuDc6io/+N3/Mf/J3/4x1tfXObWxgQfuufsusjSlrAomoyMe/ciH+flf+DkSZ1lbW2Pz2DpZGjGrZgQXSDs5l4aHXNx5hf39Q2wUESUJ7/ne76LEkOcdHrj7Pr7r7/0d+g3sX96hd9zx9PMvkFhD48xyh/Dq6irD4QhjYFZMOXXqOK954H5+8Z/9U17z2lcTZzGdXpe6GRNHXfIsZm1tnYO9irX1iLKY4lxO0zQ4a7FmMQLZtLFymGJNYDYpcc7RO3aGYxsnOH32HE996pO8/Rveys/83M/ivWcymZDnXZKkDSMX7dlFANw0zfLzRXC6CFWttVgTcffd9/PEE49z5x33c3R0xGQyoaraALaua4y9tks3hIBvFqGjJXIxo9Ehed7l8PCQODEMBgPGowOc6fHgG76Gzzz/Me4++yDWWtJ0ndMnz3JwsEduDXVdsn31ZepqyunNe+n3+1zd3mI0OmDnyhEuTbE2Ikl6eF+TpD2sszhjSWLHZDJhMis5PBziA9Q+kKZp2zw20bJBvbq6yqC/QpZldLt9QtO2suu6pq5rnHOcPnmS0yc2efnSZZrRLsXwRWwnwceQpo6qDiRZvgzbkyShqtogedGCXgTrfj722rl4/lrLxYsXef7553n/+/8d3/zOr+dbv+1dVNUM7z1JkjOZTDg8PFwG9JOy4MMf/jCf/OSncRGsruasbeQcDg9Y2xhQ1iVlUVBVDXFcc+7cJqPDIS+cf4aTJ25bjs/+XEmWUpQVcZJT+wZnAk1TzwPcdg9ygBveT1Oa9ppjmM7GRIknj1Oapm3rN03A0J6LWTGhKKcYY4iSBLynqhqci+nFhp3hAb6cceLUWbYvb5E6g/cNeZ5SNyUmRDS1IXIZxpjP25kcvCEYf5O/WW72NRERERERERERERFZUKAsIreMg91nyQaWWTnDWkseJwy6OS++/DRrKyfpRRk0gagfs3NwiTtvP8Fv/9b/2QZ5ZUVTta3YUAesjcjSnE6nwz2nT4Bt26nYmFlRsXd4hRcvXmB/fAhZRFVVZFnC9HDKD37/36KOE1azAVGUMCkrxo2lCBWkKSv9VR7or3Lx6kWKosIESGJLXc1ofMRwtM9TT13m53/qH7H30svc+1W308kfxKUdqqYkcl0aKqZVjYlSsv4Gaeyp64o8jgn1GBN3qGcF3X6PYGBSzmBWMj68xOOPf4iXL5zn4x98jtlWxLisyXIYlvDQgw/z6GMfI45j0qTdcdw0zXL0M7Acn9w2oCFJ0uU1uDYCu+L45imq0uO9Z2d7t22Cz8dZG2OwxmKNawNv49oR3SaACcRxRJYfpyxLqnJCRIxvGjbWT2Kt5elnn+C2c8c5ONxmdWUNXzo8U3IayukRh3sX2b1ynqRzAhs5huMRl7YuMx4f0u11MLMZm5ubDEeHbG6uUxYl3X6CNY7ZrOTwaMJkckQwKS7K6eYpve4Gg5WcJItxcQ4hxtiapm7D5YODA+pyRpZleO+XI6abEEjTjLf/1W/mwoUL/Ny/+t9YWVlhfHAwD21TsIa6qpcPQHTzTvve5+fMuQQfalzkqIuaLIkwoSTNwPuSNI1IU/jEEx/nj/74j/EenIOibK9L3omIY0cIgayb4H1Np2vbJrBtR0qvrq6yu7tHv9/DxRYXW5Is5mh0SN00/Mv//Vf4mZ/+BabTMXt7O6RRF2cdAY+xlrqpcJHFmHZMNY0hS3LKWUXsEkywYB3tzmIASxxDmqY0tcGXHhsaGl/O28/tXurFYHQDOAwmgCeAhcRF4OH585/h/HPPMpvN+P6/+bexWUJVFRhnqHxDU9fQ3DhiHVg2wJf3703+XtG4axEREREREREREZEvTIGyiNwyhm6bXq+DL2fs7u2QpZbJ3ohzZ05hZhN84TFhc9lK9N6TRAETILiUuOuIrMPTtLuFQ6CqGhof4WtP5QPej3nxpee4dHCZxnvylZSiKclcwvrKKi8fTnjLW74W5zOapiGKYDKb8uP/+d/jwde/itGk5vzFC9g0pvQlZ86e5Cd+4id47LEnefe738l6t8/b3/FW+v2cS7uf4VUP3s69DzyAD4a97W2cM/RXBsQuIrIB7wwmeFxod/AWoxkbqznGe5ooopwVeO/pZB22d1/m4sVncMkhr3voDA/edye/+i/+L9w+5P2Evcsl6xvH6a+uE0O7A9lDr9fDWtsG6rDc1Xxjc9YvQ+cQwnKX74kTJzg6OqIoiuVY54VFO3kR2BksbVfVwnx/bhRF4FOqssI6MAbOnLmNzc0NGl/TzVKqqsH4hro85Gi4zd7uFQ72r5LFA0KTMp3UVFXB5sZp0qRDHEcEP2P/4BDvPfv7lwghafdYG4s3CXHsuP2O20jijNpH2MgR2S6dboaNDEXlGY8OKcoJwbct7TTNl+9/0eJNkoTxbMLlq5dwkeG9P/IjbB5POBweEaUxWZYxHI1wPprvR25D+ul0ShzHy3B60VSOooiyrIiiiNlsRkMgYAi0obDDMFjrEUXt92tqj5/vEgcIoaGqKqLIkiQ37qs2xpBl6XK0eV3XjMdj+v0+03CEdfDCC8+RpT06+SpmfryL41q4dm+YZXN98QBCmL+/xdhri6dpGuI4bgN4F7f3wHyMeghm3rQH2z5rAKb9fNFsd9Zx8sQZrl7eoq49n/jEJ3jgoa9mf3ub2WxG5AzGG77UWPhzQ2cRERERERERERERuZECZRG5ZeyNDzmcjdqxw76mrkqCqUmyhOHBjKhJMcEvR/9aa4nsok0ZIDT4EKhNgwWsi4isoZp68jwliS0vvfIKewc7JHlC2ZSUzQyXRPhpTdM0pHEEjSeKY9I4wfvAeDzl3d/5HTz3wtM0BtY3j3Hh0kWMbbh48QL7+7usrOS8//3/luoAHnv0w3zbu9/Jyuo6IXGsHz9Bg6HT7WJNAB/AmTb4w0Ict23kJMV2DLPhEft7h5y6/U4aE5MkGePREVU5ptttR1LPprvEdDl1W5fxbERZNzgHLkA3yznY2yVNkmVovAzvrgsEF+dw0ca9/rWLQLPT6VAUs+Ve4GU7ef66z2WMWX4smsvOOeqywNg2hCyLmjjuEtHgnKEspzhrmVYzZsWoDbfTLk2Alc4aRd0QJznG17g4p/aeTt4jzEbs7G4znk0ZDI5T1R0CMcYakiwi1JYyeGpfstbbwFrH0dEBRVkznhU0NaRZhLM3hqmL97XYgRxFlv6gy4c+9EfkHXCxJc07jGdTZuWUKLa4eSC7OEeLkPf68wLXws26rq97zXxkc7g2frxpmvno7Rkw323dloNxNsJaqOtAoCG28fJ7hxAoyxJj2n3Ok8mUsixpmkC/n/H7/+53+ZEf/lGuXtmhk2U3PBCwuBcWxxtMAGswzoI17eeLa7s4Xz7gQ0XmOkRRDPNmcvs6R7t7ue0N2wD4gAXaT9tz5WKDdTFJktLvDxgfDamKgjRN58de4wjAvz8YvsktKSIiIiIiIiIiIiL/HgqUReSW0UQ5Vd2AD8Q2Jk1yTp8+SVk0VCEiz9fne1kbPG3TtmpqrLNEsWuDqtDgrMH7mqqpcS4m7+WMxkM+88QnqcKQKI0oQrvr1oWIqqkITc2L569wfKODrwNJnpIlGbOqZmNjgz/68EdYXesxLWr2X3yJe++7i099+uOUxYT/4afex3QyIwTHWvcEh7tX+ejHPsLb/sp34l1DaSKYNzHjeaAcxQ6DhwDOWpIsxzYVxfiIj3zgD3nu5Qv8w//pZzm/tY+vKiJniZxjfWWNsuOpmynTyYw3vu0+nv7s46yvdvmrb3+Yv/+f/hB3vu51PPimt3B0OGJ1fW0ZNCZJsgyRr9+pXFVNO7Z4/rq6run1Osum8v7BHk3tl0Hn9a3m68NXsFgb4Vy83BdsLe3Y46aC4AFPXbc/p2kaisl8Z3E5Zni4x872NpHLmFWO9bVTbGycYPP0aabTKfff92oee+xx8jxnb/cy0+KQohxiyoZATZJkuLhLnqeYUDKeDCmKKVtXtxhPS6I4ZWVwjF5/jf7KOr4pl+H5otFb0waxxhhq75mNxzRmyvve9w84/+LzrKynuDhiMp0SpzHGWabTKWbeaK7retlsXoiiiCzLODpsG9X9fq9to5clAXCRa89d5IhiR9WU1HU7vj2OFoGswZpoHvS2oX4UW0KAqirJ85yimBHHEXVdA7Tt3qgN9O+55z52d3e5eOl5PvXpx3no9Q8zPJjesDt78aDG4no2eGx7g4KzBNu2jRehchtGAzjKsqDb7RLHluDnr7FtOB+Z+SDqxet9wAYDHnzT4DLHpGw4ffYOjG84HI2ZjscMBivtQwCHB5jgIVRf4t8sGnktIiIiIiIiIiIi8oUoUBaRW4ZNGoytiAlEAR567avJOymPPPIhhoeBve3PErt2LHMcx+2IYNuOKV5fWSVLU4wP7W7YeeOxbXLOWF3v87rXv5ZXtl6irktmoaKuSybTEZFxjF1FN29DuLNnz3LxpX2i9ZjBYJXPPP0MxzaPs7rS4xOPPcEjjzzCD37fv+bESSiBNLEY4/iqh9/I69/8MK+9517ufs05BoMB6SBnPJ5SlR5rgOkE5xyxiwi+wjMf/9uUTEcH9JKYZz71pwzOrvPcS+fpnTjXtl5nRwz6G2TJOhcvTomjHk0+JYrGvOa+E7z6joc4/+xHWVs9xoPf+W3sb2+zefI2gmnbsov9yU3ThseLJqy1lk4npaqqZah8dHTE2toKeZ5jTLt72jdtu7mqqmWTNUmSz2kp2/mo47YFHUKN9wZnIY5TqqogBEMIDUU5IYoAX+E9HB0dce7s7aQxVCUMViNW10/STEbs7V3FOccH/+gPGfRXGQ6HPPmpzxJFNVtbL9HpWG7v9yHUGB8YjUbsH23z8sUnMbbBRDAcFbzude8iSePle3dRG6YuWrlx3I7Gbrwn+EAUYoKBH/2xH2M8OSDvxCSpYX9vSN7tMC1m+MLTHfTxZaAoCuI4Xo64hhv39y5+VlmWbSPctV1d4yzOtsc1nY3npzJACPNQvg1pcVHb/rUVgQZjmLeBA/1+l729PZyLAU8UtSOoV1fb8/X4Y8+Qd2B1rcuv/Ktf5OlnPsU7v/E9N4TD1zf/FyO/F2HzcuS15wY+1BAsl165zGxakiQ5xkTtsRHAtDvGQwiY0D5MEUIgog3Dr40WT8njdQye3soqo+GQJMnp91doqprJaPjnqh/f7CXeq7YsIiIiIiIiIiIi8oUoUBaRW0ZqI6bBk+VdUl8yGx9h/QrPP33EeBbwDgwlwDLgsnaC955Lh1fIsoyiKJjNamg8mUvYWNlgc/U43W6X48eOcfe5e/B1Q3CLHb+Ww9GQg4MDVteOsbuzTxb32Tzh2N/boZwNiaoRj3zgI3zkYx/l3NnTDPoZ3/vdb+c1r34V+3s7HB6MMDai1xtw9ekX+Z9/5f1k3Zzv/6EfpGsCqU3pdXLiLIb1guJoQvAlhhpCRV0UjEeXmB3sM3IR7/6+b2ewfgddl/DiEx9nY/MEK8fW8cEzndasrK3T+Am5CXS7X8ff+tXv4xd++p/w6vwhHn7rN8HqCdaTDlhDVbZBMVgMMdZa0qRLmrS7dg2GuvKsrKy1rWE3Y2vrFTaOHSfPU7z3zGYT8ryLbzzOxfN2saHxlsjEGDv/cBCMpwltS5fS0+11qOoRVTnGzypcN2E2K/HBgknYPH4G7z2bx8/hnONEdpqLrzwP5ZDpdItOntHp5CRJShyfoKw9xzf6fMvxd/GnT3+S1z5wJ0lsuLh7iIlyaqDT6bC6cRdxlnJwsMf5F59la2uX51/+55w5/lre8PDX0e90sc2MOE5pTETTBI6t9tgbb9PpDOj3Bvzmb/06P/OzP01nYOZhesC5jCTL8D4Q2QiPpxzPMNZiLXjf7koO3DjmejQa0elmTCYTkiRmMplAsMQuwhDwTUFdeSzXQm5jDI0vMA7i1NHtpsRxzN7e7jwAbn9v4jhhf/8QayPq2hNFCXGcUNc1u7v7xHFMfzAfPV7BmTOnGI+P+NjH/pB3vONbOTwY0e0ZcND4BhsiYhthYmiCJ89zHAZf1cRZuhzp7b0nCjFlWbK9c5m8l9IET2zNPOwGTELj2y3R3ntsEzAeyvl5aqoag8eFZv6ePI5AZD2HB1cYjXfZPHWagKecBvb39+n3eiTOtucyjZYPNYQQqOcPM1zPu8//moiIiIiIiIiIiIhco0BZRG4ZLgrEtSW2hm6SkRjHwd4+O7sBk0JTQZK0rc3aVySJITKWqm6oZwVF1e5BrmpPZB1laHhle4tp2bC6skK336MTJSRRDPMdwNZaNgYJ3Xyd7/me7+PNX/t1PPHkJ3nh+WdZG6xQm5pHH32Uq5e3uPts2xa2wbK+fpwXzr9CFFm6vVVCCBzuH3D6thOcPn2arJtjrWU0GuE6jsgFZuMZgYo86TGbHhG8w5iaxhdYG2GMYzQpOHHiHP2VNa5sbZNlGbPxhI2NDXwcQx3wdYwhYW+/5I1veTOjouYd3/JuLr3wFLfdfz+7pSeNDJEJFPORzmCIoog4jpcN5cX7d85hjGlHJMdtWF/Npgx6CWUxAa7t1TWG+Vhk+++/oJnDU3PhM09y5cpF3vB1b2U4qwje0O+ttA8AlM21UcvGgIk4c+Y0o/EeZXEV52ZE0QYEi/c1zjmapqGbdThx4iRVeUjejfHb++33ce246cGgy9ecvQ9jDNPplAsXLvDUi5/l6tYWn/rUxyjHRzz0wMOsrXWoyRiOJhwcHFD4gl4PHvnwB/hffu6nWd/ImJazZYP3+h3C17ezFwHw9a4fB74YJb5ogQPLfdbX721e7LO+fvR0kiR47ymKot05PL921z6aeRO/fXigPZ9QFO1I6yhKiZO2ZVzXFT7UxInj0599nLvvvpfbb7uP6bQgStr7wLr2Gof5dV+0luu6ZjqdEkURadoGy4lxHA0Pll8HPz/2AAScsWDaxveinbw4S8YY/HVhcAjhhoqx9YFyVlAUBf3VFV7Z22d9/Rh1VSxb5Tfb4/25rBrKIiIiIiIiIiIiIl+QAmURuWVYa+h1Mro24s7jmwzHIz7z1PMkPUtjM+pyRhy1zcuqqjE0tAloO946hEBRVhhrKXzDrGh3KB/WE/a2jri8vcW9Z29nY2WVbmfQhmZlg8VxfO0s/+zn/zm/8E//V0xk+Ma/8g5++w8+wJUrW5w9c4pullONx/QHq1RFRdl4+tkqdVNRTyvAs9LtcbC3z+rKCsEEOmnGeDZm89gas/GI/cN9TLDEKwZrIxosBMusGBNHGafO3k1T1pRNxOGoYGdvn9tvO0dVVRxubdM51iFNMpIsJ4oS7r7vbiYTw6Q4YBQsZx58AzvTmqTTo65KGu/p9XqEEOh0clYGa0TRtUbnYpzxYuzw+vo6O7uXKcuSl5/7DFsvNhztb7G2vsJkXEMwGGPnO5Td54erwUGwGBzWOEwG5WTEivH86u/8Fm/4ureQ5z06nXY/83Q6pTHR/NqD84EQHInZoNvtUlfgzRHjUQEUGGfJOymTyYxed5Uz5+5iNLzC/v5ler0es/GMKLYURcHW1pDaWzqdjNXVDdL4Ae678172tv+UD//x79MvXuTlx7b5B7/+u3z1N3wzX/WmtxBw/Ppv/jof+ZMPkWaGE6dWuXLlgCRrA+FFmLsY+714/845Aix3TFdVhZ03ZRfnxxjDeDxmc3OTsiyXO6TbDz9v9Lahf9NUy+/rQztyPIoiyrKkKAqsvbbH2BhDmkWsr6+zu7tLnud475lOp2R5u8d5OhtDCFgbEUVtMH/+/PPEzvNbv/1/8O53fRenT9wBZYmLInyAcv4gwiLYLstyPuK8ITQ1obYQAs+++Aw7Ozs0TUOStLunI+cIwWMCmABU7a7tqm4wPhDaRcoA873dDXXdNpbtPG0OIeC8wcUxVy5d5uTJ09x999088cTjDPpdgvX0BxmzWbnMoIM3BPv54bFToCwiIiIiIiIiIiLyBSlQFpFbRpx08bOa1d4q46uH/OnlXZ45v8+MBBcMmbVUdSCKUgIOjMGFiKZpKKZtSOdMhMXg4pS4k1LXnslkRBLHlAGefP6zZFnGSrdDvzvgzPGzrHRW2LnyMiuDLmVZYp3jIx96hF6nx22nz1DPZu0+YR9o6pLUORJT4/yYbtMwKiuwEd45sjSh6XQ5HB7y0T95lMFqn9/7nfdTznbodlJ8UXHXa+7lwQe+hswOgJhOts50csjeeMTKyhrrG6c4GhXcf99rsL5h5gO7W5ep7Dp33H0Xs8YRuQ5xlGOnQ0zwuCZQzQy9Xody6knTDs61QWi32yXPO8v9vYuduIvmaZZly0Bvf3+fTqfDM5/4AE8+9ihv+vp3cvWoJoktBAMsAmWLvW4P87XW8rU9yitpyrQs+P2nnuSt3/Id3Hbbw+wN95hOp0ynM6x1GNsGi75uqMuibfOmBoujPzhDmpzAlGPKakZZT8BU7O3tUDWGPM+x1hFc2zYvioKqNhwejNnZv8Jw+kGapmQ8nrC3U7Gxfpw3vP513Pvqt/AHv/c7lNMh3/qed3Pv697IaFwA8K53vZPt3Zc5f/5lRsMpeG5oBC9C3LaNy3L0s52/ZtGc/VwhtDuo4zhmOBzOw+d6Hg5fayND2+CNovYamaoNQ6uqWv58ax11XS/Pexw7jo4OmE7HzGYTvA9k89HUIQTyPAUflsF0XZftOPPKM5uMONzbZrQ7ZH11jdX1NZJOF4zBmHZPOT6QRu2Y7jyLmE4mXL50wHPPPUeWtXu0syzBuXakeggNFoNpAnVRYGYVAI55ATkEsB7MfAfzdQ145qOxvfd40z7wEcUx2xcuUa53+Jo3P8yTn3yc7iBnOL5KZPuAa3dMEwHN55372ihQFhEREREREREREflCFCiLyK2jrghFSYzjsSef4akLR0RRTB5ZiqZk3DSk1lAVs3a0rw/tqN4owrmYoijodjtUVUVde+q6HcM76K0wnU4ZTdsRwNV0xnQ6ZWtnl+3dHe68/Q7ObZxphzgbiE1CgqOezYidJY0TQlWzkucUo0Ooal781CeIQs3xk5ukx04S0i77kykRCftHR6ytrfPGt7yF4WTIidV1PvQHv0fHRgzNNi+9fJ47b38d3Y0T5EnKdFbibMErOxcoGs+J03di4pTJwQGToyPAc+LMSYbjCb1slWIyIeukDA8vU4WEKB/gsoY8NtSVw8WLocKWJM2wLsFFCZ1OB+ccZVne0D51saUpa7I0ZvvqZap6yuWXXqC3eoxxMSUGfMjnI5otLoqwJmpHIzsILhBcQ2w6xIkD2pHLIdSMi5K3fsv3cN9993PxyhZFMaMopvPw3+Ca9lgXI6OtsfjCMyqGYBpOp30uv/IC03LMsdOr7B1MWDl2CuctxbSiKAomU8eli0NWBhtYG9Pv56wMjnN1f5vt7W1uu3eTM28/w+bx01y8eJGjsuTN3/TXSdOY8azgyuWrGGPaPdF+wo/8zR8mz3N+4zd+gw984E/IQ9yOJg81NfW8jZvcMKq6qkPVxLkAAB2PSURBVKrlKPG6rvGmufaerG0D6OCpfUlRFcRxTDMfAR3HbbN30RRftMaLsgTTBtGL0L9pGryvGQx6HBwcAJBnG2xvb+NsCkCSRhTljCzL5mF3jHOBNE0Zj8dUVdUep6/p5z1CVeJMYHilZLSzTWd1lfXjx0lWV0icw5cVL51/hd3dXQ52tvHBMFjfIEkSrF38Z4YBa/AEfF3jMFAF/LQhUH/er/p0OiVNUyID5XQEdkowDrxpm+5E2DAP58sKLBzuXeazn/4YLoKiiLEukMWOyGUYE+Gb+qaD2NuN1iIiIiIiIiIiIiLyZ1GgLCK3jDi33HXiTp557FkOD2YEY6iCBw8++DaEK2sGg0G7vzVJcHHUhoqTCUmSMBqNlmEfcEOoZ+d7k0MIhLj9fHd0xOTZp9nausp9d97LIO/hCCQ2olqM0m0CzlqwhroJXLp4gXRllZeee4aJS3nPN7+bweYmTZpSO0ee51SzKXVTstFbZRxn/Ojf/Tvs713l2Wee4PVvfiPF2NHP+0wmEzbWNqjzlLW1DaIoYjKZUZU1iTGcv/ByG1QmMS5LGA6HpJ1Ou+fYOzqDAZNZg4tivHX4pj1Pi3HWSZIsx1xPJpMb2rVu3jD23pOmKU999inKsqSqS+56zevBpoyqBtu0u27bBun145YhjlKMiXAhJpiGECzGtAHriy9eIs9zVlc2uLK1i/e2HXPdVPNdyB7vb9xL3DQNZTljZTDA0fB77//XmMMX6Q66mPp2ov4xytkexvRpmkBVNUxnntG4YDBwWBcTxSlRlHDP3ffxqvtfw2g0Yjab8dyzLzArRlgL1gWmBRiXYmwblFdVRVFVdLt9dnf3+e7v/l6+9Vvfzfv+0T9kZX2Fvb0d0jghSRJms1l7/81HYBtjaJrmhveyCOxDaNvB6xsrHBwcLAPjxflftI/jOAba8dKL77FoPC/u3cV1G41GDAYDut0u0+l0OSJ78T2xYT5Ouv0zURQxHA6X19oYQ56mhHFDJ0vxZYC6wjeWva2rXH7lIjaO253ZGAgNsTUMBj2Kqt1lbZwhiq4dF74d4e28py4ryumMuAFvP7+1vRjN3TQNw+GQuq5xzmC8IdRh3nK/MQiObJeTJ85RVQV1MyVPE4K/ts/b/jnWeouIiIiIiIiIiIjI59P/vSoit4wschzfOIavA96kuDgi2DbMXATDSZZinKX2DdNixng8xhhDlmXL1iNww47Zqqra0b3XMwZjLTjLtC65vHuF85de4ur+Di41NE1BTTsG2sWWJgSOxiPyQY/jp09zcXub47ed4+w993Lx6g7PPv8C29tXODocYQPtvlobY3xgfHCFenqIqyccXr7MK+cv4EwgSSIia+j1esRx2jZVcVRlTRZFJNZwuLff7hueTJjN2mb1IqDsD1aJXIIJ0FQ1RdkGiIuW62Kn77VRyfaG87L4KMuSNGrbqwB1XdPtrxNcwnRW0/iwDDgX/1ye42DBGxwOZyy+rrEm4Jtrjd1FQ3c4HC+vxXKs8TxU9N63gW5RLENGFwx7V7bpRBHlaMxw/4hyWlBVFb6ZN1lDDCGhk/cxxtE07b0SxzHTacFwOCZJMtbWNpbjpvf395nNJpTljLou273AIbTvO8sZHw3ppBlXL2/R73R589d8LYeHh8RxTFnWNzyssBhlff2/L4Lk68/V4pja4NQt3/v153IRKF9/bYAbRmkvgmvvPZ1OB4DJZLL82uK1i2M0xizHgS/ugyiKSJKEOI6J4/ZYsjhp9ywTyOOIXpqTOEdsHRGBLHJkaQoh4IzFmnDDz2jb5QYDNGVFXVb4qr72AMfnfCzuRYDz588zHA7b6zp/n8uQ+jpV1ZBnPXq9FfJsQF2ZeTvZLndSi4iIiIiIiIiIiMgXTw1lEbllrEV9PvvYp3nlpYu8tFeRrOeEum1uxkC/32dWlYxn03mztsGGa2Fdp9NZjg2+PpCbTCbLRugiUAu+WY4ZroMniSwXdy5xdXeLq9uv8IbXP0RORlM1lJOKJMlYWTnG4d4u2eo63/ju72A8GTKeFhxMJphJwfMvvcJkWhFMezxf/TVfjYstu5de4sntZ9lY6fHAq+4nzjL2r16imk5Ik5wrV6+SWEeaRoTgKcua6WzM1iuXuf2eu5hOp6ysb8zbvQ3W2nYXcojoWgPFkKYKxN1VXBQRRdGypZzl3XlwGC+DzkXgDO25y7KMvcN2t3GSJCTpCnvbOxgX0+v1mBXFMpCGedvb2Hb/r21woaGTxjQmwpiAc4bZtCRNI6LIMRwdAAbvq3nY6anrmkCD8W1wuAgE2/fX3g+D9VVWjq0zGV7m5YtX2LjztVi7gXVdiqYmeEdVQ1kZoqRLMAnWOZpguLK9y2Cl0z5QUHtclDFY6bF5/PXzsHlM0zRsbe/QVAA1SRIxGU3pdDpURU3sEiajKX/9r30XgYbf/d0PceJEl52d/XYH8TwMv36fMlzbubw414uvLXYYL9rETX0tkF58ra5r4ji+1kaO7PJhimWoHwyDwYArV67gvZ+PfHc3BN3X730uigIb2utcluVy5LmxhmPr65jgGQ0PyZKsvQZ1iTEONw+jrQVHoJpN8aZtw0fGEqwlmHbnswlgGo8pK4rRCIshdRYTAjfbKl3X9XwPdPvv29vbxHFKlEbzNna7T/l6aZpSFCXgSZIc71MIlhDAGEsc25sG0SIiIiIiIiIiIiLyhSlQFpFbRr07Y+fFLSajing9wkSO4Bvq4KlmbajpzbUx1s65NtgKgbIssdaSJMlyZPD1Yd6iJbsYS2zrQCBgQiBLEvAVdVNhrOOl7Vc4+viYr7rz9aRpTprnNGVDWZZ45yhC4PLBPp1OhzhtaOoAviExGXknwztDYz21CYyrknMPfD3V7DJZv8twUnE8GnN0eEiaWSbTfeIsJxAzK2ZtCzvNKWee46eOMxpOCLEjRDEHB5c5Ojpi5eRJosjRiSN+7zd/g+effZa3/0fv5I771zDzfbyL97to3i4asNc3ja9vcF+8eJFOp8PVK68QJ4bR6AhjHDYE8tRRVNfaqEvBEnzFzu5lrtYFUXaC2267jdFoRFmWXN2+zGxWsr+/zz333EeedXHeUddtcOxDjfHXWquLsP/06ZNEacJHP/kJ3vT2r+ezjz3KubVTHD/3AFu7Y7IQE2UVbR02EAx0uittk9oaup0uZd2wu7e1bHO/9NJL3Hvv6zh27DjdTh9ruuSdLvfcdQZrPWU1Y29vm+KwIo6SNvAOgWJW4qIZ7/0bP8Sb3vQmfuqn/gm9tfa+6vV6hBDaZvd1I7vTNF3++UV4H8cxk8mEKIooiqptFwfT7luet5WPjo5I03R5L1/fMm/D1/bnrq0MODo6Is/bvdZF0e4RX4zODiHQ1G1bt9vtkmUZk+ERTdOQ5/myCb2+lvPwqx7i048+wYOvfj3TYtZec9uOIzdRhPeB4D02sgQbcM5SVAX1+Ig075N25uF37amLGbPplHaFd/u7FUJ7TT5XVZXEcUxRTDl37hxXrlxe3p9N3eBcjPmcP9aOSjfYeTgfxzHBO0Jo36vCZBEREREREREREZEvjQJlEbll7D39Mp+9sE2RwWRc0+nU1EVJp9MhHwyAttmYJ+ly9+9sNluGp9A2QhcNz0WT11qLwSyD6OA9wS7GDoNroGraJmbA46KY3aMhT7zwKc4cO8mdx+/A+ghbFXSto2xq4iihqTydrE9ZltS+JpiK2kXUoaFuPJFLSCJHEjuOrd5HUxd0s4q6jlnbtFTlBHAMx4F+J9DNE8qypmkCcbpKNEg4KC7T1IGeS+j2V5jNCnxZcOrkGf7Hn/ivONZPePmpz/CnJ45x3xveyHRWgA/YxoEx+NAGyYtm82Lc8iJ4dM6RZx1eevE5Tp06wRNPfpLbb78djyPMR1372rPYnbzYRW3nQb51Gf085fEP/yGGwPnHV3jLt/81zr/wWT76yG9w5x33sL3f8NrXvpYocdS1p2kW4XRM1bQjrDtZxpkzZwiNZ+vSZYqiICZm77Dg9N1vIoTAwaQh63VJsgjn4vl454o86zMcHVLXVTtGPIo4tnGKi5ePqIox5XiPQTJluPOnlOUeLumTpavE1pH3+/T7feI45ext93DPvRlXrlxmOBwynY7JOxnTyYTmUsNtx2/jHW99iM+8+BSHRwVV2VD7hiR1mHlze9FaTrOEYtZey0BFbxAzGl5rJBdFgZ1vpfBN04bHSbLclXz9wwAhBPI8X+7DHg4Pl0384D0ujjDWYoOjCe3XI2LSNCU0gaODIcaCjRyzsiCYtvX/0osX2b66Tz/uUltDsI5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