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<!DOCTYPE html>
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<title>aliCaffePython</title>
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<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="c1"># Make sure that caffe is on the python path:</span>
<span class="n">caffe_root</span> <span class="o">=</span> <span class="s1">'../'</span> <span class="c1"># this file is expected to be in {caffe_root}/examples</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">caffe_root</span> <span class="o">+</span> <span class="s1">'python'</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">caffe</span>
</pre></div>
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<h2 id="-Launch-the-python-shell"> Launch the python shell<a class="anchor-link" href="#-Launch-the-python-shell">¶</a></h2><p>In the iPython shell in your Caffe repository, load the different libraries :</p>
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<p>Define a network model</p>
<p>Let’s create first a very simple model with a single convolution
composed of 3 convolutional neurons, with kernel of size 5x5 and stride
of 1 :</p>
<p>simple network
<img src="aliCaffePython_files/simple_network.png">
This net will produce 3 output maps from an input map.</p>
<p>The output map for a convolution given receptive field size has a dimension given by the following equation :</p>
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<p>output = (input - kernel_size) / stride + 1</p>
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<p>Create a first file conv.prototxt describing the neuron network :</p>
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<p>name: "convolution"
input: "data"
input_dim: 1
input_dim: 1
input_dim: 100
input_dim: 100
layer {
name: "conv"
type: "Convolution"
bottom: "data"
top: "conv"
convolution_param {
num_output: 3
kernel_size: 5
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}</p>
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<p>with one layer, a convolution, from the Catalog of available layers</p>
<p>Load the net</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">net</span> <span class="o">=</span> <span class="n">caffe</span><span class="o">.</span><span class="n">Net</span><span class="p">(</span><span class="s1">'conv.prototxt'</span><span class="p">,</span> <span class="n">caffe</span><span class="o">.</span><span class="n">TEST</span><span class="p">)</span>
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<p>The names of input layers of the net are given by print net.inputs.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span> <span class="n">net</span><span class="o">.</span><span class="n">inputs</span>
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<pre>['data']
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<p>The net contains two ordered dictionaries</p>
<pre><code>net.blobs for input data and its propagation in the layers :
net.blobs['data'] contains input data, an array of shape (1, 1, 100, 100)
net.blobs['conv'] contains computed data in layer ‘conv’ (1, 3, 96, 96)
initialiazed with zeros.
To print the infos,</code></pre>
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<div class=" highlight hl-ipython2"><pre> <span class="p">[(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">net</span><span class="o">.</span><span class="n">blobs</span><span class="o">.</span><span class="n">items</span><span class="p">()]</span>
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<pre>[('data', (1, 1, 100, 100)), ('conv', (1, 3, 96, 96))]</pre>
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<p>net.params a vector of blobs for weight and bias parameters</p>
<p>net.params['conv'][0] contains the weight parameters, an array of
shape (3, 1, 5, 5) net.params['conv'][1] contains the bias parameters,
an array of shape (3,)</p>
<p>initialiazed with ‘weight_filler’ and ‘bias_filler’ algorithms.</p>
<p>To print the infos :</p>
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<div class=" highlight hl-ipython2"><pre> <span class="p">[(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">net</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">items</span><span class="p">()]</span>
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<pre>[('conv', (3, 1, 5, 5), (3,))]</pre>
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<p>Blobs are memory abstraction objects (with execution depending on the
mode), and data is contained in the field data as an array :</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">print</span> <span class="n">net</span><span class="o">.</span><span class="n">blobs</span><span class="p">[</span><span class="s1">'conv'</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span>
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<pre>(1, 3, 96, 96)
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<p>To draw the network, a simle python command :</p>
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<div class=" highlight hl-ipython2"><pre><span class="o">%</span><span class="k">run</span> ../python/draw_net.py /home/ali/git/caffe/examples/conv.prototxt my_net.png
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<pre>Drawing net to my_net.png
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<div class=" highlight hl-ipython2"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span>
<span class="n">display</span> <span class="p">(</span><span class="n">Image</span><span class="p">(</span><span class="s1">'my_net.png'</span><span class="p">))</span>
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6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%0AEARBI9EsBEEQBI3+H5ylRj3D2xfrAAAAAElFTkSuQmCC%0A">
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<p>Compute the net output on an image as input</p>
<p>Let’s load a gray image of size 1x360x480 (channel x height x width) into the previous net :</p>
<p>Gray cat</p>
<p>We need to reshape the data blob (1, 1, 100, 100) to the new size (1, 1, 360, 480) to fit the image :</p>
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<div class="prompt input_prompt">In [9]:</div>
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<div class=" highlight hl-ipython2"><pre><span class="n">IMAGE_FILE</span> <span class="o">=</span> <span class="s1">'images/cat_gray.jpg'</span>
<span class="n">input_image</span> <span class="o">=</span> <span class="n">caffe</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">load_image</span><span class="p">(</span><span class="n">IMAGE_FILE</span><span class="p">)</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">input_image</span><span class="p">)</span>
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<div class="output_area"><div class="prompt output_prompt">Out[9]:</div>
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<pre><matplotlib.image.AxesImage at 0x7f6c7ccf8310></pre>
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i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%0AAAAASUVORK5CYII=%0A">
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<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">Image</span>
<span class="n">im</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">'images/cat_gray.jpg'</span><span class="p">))</span>
<span class="n">im_input</span> <span class="o">=</span> <span class="n">im</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span>
<span class="n">net</span><span class="o">.</span><span class="n">blobs</span><span class="p">[</span><span class="s1">'data'</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">*</span><span class="n">im_input</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">net</span><span class="o">.</span><span class="n">blobs</span><span class="p">[</span><span class="s1">'data'</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">im_input</span>
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<p>Let’s compute the blobs given this input</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">net</span><span class="o">.</span><span class="n">forward</span><span class="p">()</span>
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<pre>{'conv': array([[[[ 0.34817484, 0.41027504, 0.20904401, ..., 0.74670231,
0.75402164, 0.98378962],
[ 0.24743864, 0.35323229, 0.26290619, ..., 0.87386566,
0.82398188, 0.93434209],
[ 0.25423867, 0.29808408, 0.2523751 , ..., 0.82616019,
0.91159248, 1.07081389],
...,
[ 0.48195004, 0.36952889, 0.53944957, ..., 0.26091671,
1.04394865, 2.69910812],
[ 0.54389817, 0.509866 , 0.64406353, ..., 0.43723965,
0.70883918, 1.71184325],
[ 0.78313452, 0.83525568, 0.67195249, ..., 2.27120996,
1.53877044, 1.86886072]],
[[ 0.70632005, 0.55720198, 0.55999583, ..., 1.02132368,
1.03369617, 1.14927959],
[ 0.62712717, 0.58100367, 0.63543195, ..., 1.10538316,
1.19613838, 1.2529341 ],
[ 0.5902034 , 0.51536572, 0.59892392, ..., 1.26755846,
1.30553865, 1.29252529],
...,
[ 0.67748165, 0.5960933 , 0.66291642, ..., 4.34516287,
4.46954393, 4.66408157],
[ 0.66167545, 0.72956038, 0.69478631, ..., 4.6348424 ,
5.59340954, 5.08595848],
[ 0.59308386, 0.45403275, 0.58611327, ..., 5.06368732,
5.32388783, 4.24856853]],
[[ 0.31358588, 0.24499926, 0.20097062, ..., 0.65039307,
0.78225982, 0.94156921],
[ 0.25801808, 0.1644496 , 0.14180589, ..., 0.78234446,
0.84004575, 0.93789238],
[ 0.2381517 , 0.18440831, 0.24513158, ..., 0.79006296,
0.79629505, 0.76895088],
...,
[ 0.2167387 , 0.43156868, 0.40656167, ..., 1.24292529,
2.91519475, 3.00049376],
[ 0.77026498, 0.61816972, 0.54285932, ..., 1.9081943 ,
2.44406176, 2.05345726],
[ 0.9933151 , 0.85817611, 0.9370411 , ..., 1.37574124,
1.25703228, 1.36115348]]]], dtype=float32)}</pre>
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<p>Now net.blobs['conv'] is filled with data, and the 3 pictures inside
each of the 3 neurons (net.blobs['conv'].data[0,i]) can be plotted
easily.</p>
<p>To save the net parameters net.params, just call :</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">net</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'mymodel.caffemodel'</span><span class="p">)</span>
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<h1 id="Load-pretrained-parameters-to-classify-an-image">Load pretrained parameters to classify an image<a class="anchor-link" href="#Load-pretrained-parameters-to-classify-an-image">¶</a></h1><p>In the previous net, weight and bias params have been initialiazed randomly.</p>
<p>It is possible to load trained parameters and in this case, the result of the net will produce a classification.</p>
<p>Many trained models can be downloaded from the community in the Caffe
Model Zoo, such as car classification, flower classification, digit
classification…</p>
<p>Model informations are written in Github Gist format. The parameters
are saved in a .caffemodel file specified in the gist. To download the
model :</p>
<p>./scripts/download_model_from_gist.sh <gist_id></gist_id></p>
<p>./scripts/download_model_binary.py <dirname></dirname></p>
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<p>where is the gist directory (by default the gist is saved in the <em>models</em> directory).</p>
<p>Let’s download the CaffeNet model and the labels corresponding to the classes :</p>
<p>./scripts/download_model_binary.py models/bvlc_reference_caffenet
./data/ilsvrc12/get_ilsvrc_aux.sh</p>
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<div class=" highlight hl-ipython2"><pre><span class="c1">#have a look at the model</span>
<span class="o">%</span><span class="k">run</span> ../python/draw_net.py ../models/bvlc_reference_caffenet/deploy.prototxt caffenet.png
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<pre>Drawing net to caffenet.png
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<div class=" highlight hl-ipython2"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span>
<span class="n">display</span> <span class="p">(</span><span class="n">Image</span><span class="p">(</span><span class="s1">'caffenet.png'</span><span class="p">))</span>
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A+xEjRgQGBn7wwQcrVqwYMGCA0olQ9o0fP75Vq1Zvv/32fc8OGjRo+PDhFy5c0B/a2dkZ3Vbe1qZN%0AGxH566+/iivMtm3b0tLSwsLCatSg9uamFi1atGjR4pdffvnss8++/vrr4ODgP//809jYWF8bpm8g%0Adpf169fHxMToH6elpRWNZ2dnF1WU5eXl5ebmPk0wfZM3Q0NDa2trETE1NdW3S7WwsDA1NRURa2tr%0AQ0PDu2ZaWVlZWFhYWlo+zdaPSaVSiYiF3c2X2ksnLzm5OY0+NLpYFt8ybUud5nUa9W5ULKsBAAAA%0AAAAAAAAAAAAAeJ5RJwYAwH+Jm5ub0hEUVqlSJf17xMsglUpEslMu2VX20A+YW1cUERNzayVTlW7m%0A5uYUiT2ctbW1s7PzxYtlvGgQpcH69evNzMzGjRv3oAmGhob29vYVK1bUH7q6uh4+fFin0+krcMqX%0ALy8i5cqVe9DlT8DKyooisXvZ2NiIyNixY/Uf+bsaiN1OpVKtXLnSxMREf2hra6v6p0S5XLlyxsbG%0A+sdmZmbm5uZPE0lfgaZWq7OyskQkPz9f38fsxo0bBQUFj7zc1tbW3NzcwsLC1tbW0tLSwsLCysrK%0A2tra3Nzc0tLS1tb2abLdV352fkpcSkFOgYmFSdGgVqM1MPzXPdvPbD5jbGr80oiXijUgAAAAAAAA%0AAAAAAAAAgOcUdWIA8F9VqJE/YsSvTknvm5wp4efl4lUZ06mkt0Zpdv369cOHD8fGxn7wwQdKZ/mv%0AqlSnWeL5/ZfO7iiqE7uRFi8iVTxaKxkLKE0Ktdo/0tL8HBxKeN/k/Pzw69cvZmePKa3FunE5OTUs%0ALB4975nZtm3blStXxo8fXzSyb9++Fi1a3D4nISEhISHhww8/1B++8cYbe/bsOXXqlI+Pj4hcu3ZN%0ARBo1oqXSM2dvb6//PzU11cTERK1Wa7Xa+840MDD49ttv+/fvX7IB7yMzM1Oj0eh0uvT0dPmnoiwz%0AMzMnJycnJyctLS0nJyc3NzcjIyM7OzsnJyc7O/vy5cv6s/pLipeTu1NhXmHY3LCOn3XUjyRFJkXu%0Ajmz1Xqu7Zt4sxssvNDY1FhFNoUZEisoj/w77Oz0hvcPIDkXzow9FOzd1LvbAAAAAAAAAAAAAAAAA%0AAJ4T1IkBwH9P6g35Zqd8u0uy80X3fYlu/XeizA+XBeHi5vjv6sQaT5OWrjKz2zNJdSxORq2VIzFi%0AZSadvWXa61K+OJuR4NEuXry4cuXKH374wdnZuahOLCkpae/evXv27ElISFi3bp2yCUsVTWG+iIhO%0Ap28gVpiXLSJadUG99h/FnNwQsWexS+M3LWwcRSRizxJH58aerQbqJ4iIVqs2MLj585tG/c86IiKi%0A1ahFpDD/hrGpZdF8nVajMjAs2lpdkCMiort/PQCUwjfL40gtKPjm4sVvo6Oz1Wpd164lufXfWVnz%0Ao6MXxMS4lSv3r+rEGu/Z09LBYaaX17NINS86+qPTp4sOB9SoscTH51ls9Dh27tw5Y8aMbt26zZ8/%0AX0S0Wm1kZKSNjc3u3buvXbs2ZMgQDw+P3NzcwYMH9+rVa8SIEfqrAgMD586dO3PmzJ9++kmlUq1b%0At65ixYpFZ59nWq12+fLlv/76a3x8fJ06dd59993OnTsXNfJ6epaWliJy5MiRvLy8TZs2rVu37siR%0AIwYGBiqVSq1W3z7TwMBA39dLcdbWN1tr6ovc/q2n/+ipC9QiolVrDYwMRMSrg1eF2hW2z9yenpDu%0A2tI16XzSpeOX/rfyfyJSkFsgIur8mx9JR3fH5AvJO2buaNS70bld53Izc0Xk3K5zbq3dLuy7EDY3%0ArP6r9fct3iciOq3u6sWrZtZm1IkBAAAAAAAAAAAAAAAAeGLUiQHAf4+9pUx+TRbtk+z8kt7ao5J8%0A010WhP/rCx2txf7ZtDk5dVkmb5YvXhVLU/l6hyzZL4kZsuk/1dEqIyPjxIkTLVu2NDQ0fPTsUqlO%0AnTpjx4794Ycfbh90cnLq0KHDyJEjnZ3/2293PnjwYK1atSpVqlQsq0XsWZqdellETm79um6bwPOH%0Af87JSBKRoxun+L7yWZdPt5/c+vWeVUPsq3iqVIZmVuU7DV2nVRf8GTYnK+WSiPwRMs61SW+HavVy%0As66d3jlPRLJSL8ef26PTafTLHtsw+YVOI6KOrdXPPxMW5Nr0LbNyDiKSeH7/hSNrRCQr5fKZsAWV%0A3Vo4VPUulpsqdkeOHKlUqVK1atWUDlJCysw3y+0uX76cmJhYjL2h7E1MJnt6LoqNzS6uFR+bh5XV%0AN15eC2Ji/u2Fjqam9iYmzyJSoVb7y5Ur0zw99YcqleqtqlWfxUaP4+DBg6+99lpubm54ePjt41FR%0AUWFhYWvWrFm2bFn37t0tLCw++uijdu3aFU0wNTU9dOjQxx9/3K9fv5o1a8bGxh47duxxqoB27drl%0A4+NjZ2dX7PdSSkyaNCktLa1fv34xMTE///zzBx98kJ2d/eabbxb7RnXr1q1bt+6oUaOuXbu2devW%0A1atX79y5U61WGxkZFRYWSmmqE1NQQU7B7qDdqZdSRWT9uPWNejeqWq+qoYnh++vfDxkVcmbzmYgd%0AEV4dvfot6mdmZZYSm7Jn4R4RSb2cGv5deKPejbpO6pqbnrvnuz2R4ZG9ZvdycnOyr26fm54bdTBq%0ASZ8lhXmFF/dfvH27cSfGKXOfAAAAAAAAAAAAAAAAAMoE6sQA4D9JpRIHS7le8m+WFzEzfpKrNrxf%0A3Dn+sStSfh4oFiYiIivekc1nZM/5Z7XXM7Js2bKPP/7Yzs6ub9++vXv3btKkSTH2DCkxpqam9w7a%0A2NiUfJJi17JlS51O16JFi4CAgDfeeMPW1vZpVvNsNcCz1YCiQ68273m1ee/WaWNp9PrEe6/y6fip%0AT8dPbx8xt6rQ+I0vG7/xZdHIgHlXb9tloL4L2e0qufpVcvVr2ffbp8lfMl5++eX09PQmTZoEBAT0%0A6NHDwcFB6UTPXNn4ZrndhAkTli9fXq1atbfffrt3796e/1Q0PQ2ViIOJyfWCgqdf6t8ye6I63g1N%0AmhR7Er1f4uP7Vqs2pFatZ7T+v9KsWbMHlRI5OzsPGjToIddWrFjxxx9//FfbxcfHt2vXzsjI6KWX%0AXurXr1+XLl0sLJ5NJbpCrly5kpKS8u23N5+r27Zt269fv0WLFj2LOrEiFSpUCAgICAgISE1N3bJl%0Ay++//75ly5acnJyCggLqxEwsTDqM6NBhRIe7xm2r2A74ccBdgw41Hd6Y/sYb098oGrGwtXj/91s/%0AB3+05aOixzMTZj6DvAAAAAAAAAAAAAAAAACeawZKBwAA4Kl87H+zSExPrZW3GiuX5omo1WpjY+O0%0AtLQFCxY0a9ascuXKn3/++ZkzZ5TOhZs0Go1Wq927d++gQYMqVKjw6quvBgcH5+bmKp2rLCssLNTp%0AdIcPH37//fcdHR1feumln376KTtbidJYPCmNRmNgYHD58uVp06bVrVvXw8Pjq6++unTpktK5ygKt%0ATjfj/PlRf/3lf+DAuL//jr5xQ+lEJUqtVuv/37ZtW58+fRwcHN58883Nmzfr+1+VAYmJiePG3eoo%0A5efnZ29vn5ycXDK729vb9+3bNzg4ODU1ddu2bcOGDXvppZdKZmsAAAAAAAAAAAAAAAAAwNOjnxgA%0AlAVf75DP18v7reUNH2nhItn5MidMoq7JmXixNZfZPcWzkuy7KOtPyfpTcnCk9F4i0dflyy5yKFo2%0An5GTYyXwRwk7J+5OsjRAvKuIyH0W0Y8/XESiDFstDWtKgUa+2SkZc8TCREJO/J+9+w6rsvzDAH6f%0AwV4yRXAriiCJOxXFgebAkQrOnIkVZYkVjjRzpWmuHKGWE0pcVGJOTBFFxk8QHKAsEVQQVPY5nPH7%0A4xiSIqKMI3p/Lq+uzvM+53nu93DO+2LX+fZFYAySsnD2SwAw88L7baGnBQC/hSEzD6Gz0anxc3cM%0Auo4J2+E7Fc4tyttaocSCP/HjSHh0r+zrWfOEQiEAuVwO4O7du6tWrfr++++trKxGjhw5adKktm3b%0AVuFeSqXy0qVLR44c+fvvv/ft2zdv3ryIiIimTZvOmzevU6dOAHJzc3/66SeRSCSVSuPj41u0aDFj%0AxgxVs6NyDr3xlEqlUqlUKBR///33kSNHRCJR3759R40aNXLkyDeslc3rQ/WaAwgKCgoKClIqlc7O%0AzpMmTRo+fLi+vn6V73Xy5EnVRkeOHJkzZ05ISEjz5s1XrFhha2uL57z59fX1w8PDjx8/fuzYsYMH%0AD3722WepqakHDx4MDQ0NCgpKS0v78MMPFy5cWK9evdWrV0skku+///7SpUtNmzb98ccfbWxsqvYU%0AXkOq1oiqK9v169fnzZvn7e3dokWLMWPGTJw4sUmle2Gtunlz7tWrnk2aDLey6m5qmieTrU1ISMjP%0Aj8nJqaOhscbBwc7AIDgrK+DOnYA7d8736DEmPDyxoGBRq1YXsrMD79691KvX9KioU5mZtgYGv7Rt%0A62BoCODZRVTj5buam/tFTEzHOnWkCsWPN28+cnXVFYkOpKcH3r2bVFBwtnt3AGZHjrxfr56eWAzg%0At9u3MyWSUGfnTsbGz9sxKDNzQmSkb4cOzmZmT22XI5O9Z2ERk5NzITv7VGbmDzduzGvRYoGtbSVf%0Az1pHoVAAKCoqOnDggL+/v6am5uDBgydMmNC/f38NjVdqfvoihYWFe/bsiYuLMzQ0jIyM7N+///Tp%0A04VCYZnXB0NDw3KuKkFBQTNnznz06NFnn302a9YsAHv27FmwYMGSJUvGjh371L5SqbRdu3bVcUbl%0A0NLSeu+999577+kmWkRERERERERERERERERERERERPQ6Yz8xIqJaLzsfUbcRMRdr3NHdBkolPvHD%0AyHbYPhERcyEUwGUNsvOhKYLPWaRkYVco5g9CPzt0aYrfwpD+ED5nsdYdv09DeDI+8gVQ9iI5RS8O%0A4+aDyFtYMhQrR2DIOygshkgIl1bYFYqMHACQKfBpL2z9AGvd0d8eGbn4qh86NS5vx1wJsvNfsPuh%0AS+j5I5YfxfdH4RMMpbLyr6s6qZqipKenb9q0qV27di1btly4cGFqamqVLK5QKB48eLB79+60tLQd%0AO3Z8/PHHS5YsuXnz5tixYxMSEvLz84cMGaKrq+vt7T1//vy1a9cGBQW5urrm5OSUc6hKgtUWqvZi%0AxcXFJ06cmDRpkoWFxQcffPDXX3+pimGoOsjlctXLHhwcPHnyZFNTU1dX13379lXta966des///zz%0A3r17vr6+33777YYNG6Kjo+fNmwfgeW/+Bw8eaGho+Pr6pqWlHTx4cMaMGT169DAwMHB0dPzrr79u%0A3rxpYGDwxx9/REdHT5ky5cyZM5s2bdq3b9/ly5cXLeeHuIwAACAASURBVFpUhclrC1UbqBs3bixb%0AtqxZs2ZdunRZt25dRkbGq62WLZVGPXoU0bPnGgeH7qamSuCT6OiRVlbb27WL6NlTKBC4hIRkS6Wa%0AQqFPcnJKQcGuW7fm29r2s7DoYmz82+3b6UVFPsnJax0cfu/YMfzBg4+iogCUuUiOTPbCMG5hYZEP%0AHy6xs1vZuvWQevUK5XKRQOBibr4rNTVDIgEgUyo/bdp0a9u2ax0c+ltYZEgkX9nYdDI2LmfHXJks%0Au7i4zN3raGisdnA40a1bWv/+i1u1kiuV316/vjU5+dVeyTeATCZTKpUSieTPP/8cMmSImZnZhAkT%0ATp48qazS3wZkMpmHh8fVq1d/+OGHBQsWjB49esWKFadOnSrn5ljOVaV3794zZ84E4OjoqFq/V69e%0Arq6uzxaJRURESCQSVS0ZERERERERERERERERERERERERUfnYT4yIqHZLvI+lR7DGDeYGj0dCErA7%0AFLtD/zPtYhJc30EDY9zIgEd3mOjBpRUAWNVB/D3MHwQADU1Q1xARKc9d5Gw8XN95QZ6MXGTnY10Q%0AZvTG4qHQ1gAA41LNlkQCePcHgAcFmLoLra2waMgLdhzaBjnrIS63tLlnS7S0RNB1fH0AH/tCQ4Sp%0A3V4QtXw3b97s27dvpZaosJSUlOd9l72krGLJkiWqxikFBQWV3E4kEvXp08fKyiopKemrr75SNV3J%0AyspatGjRtm3bTExMkpKSSr6nbmpq+umnn86aNWvjxo1isfh5h+bMmVPJVC9FqVTu2bPn/PnzNbnp%0As1TlfPn5+Xv37t2zZ8+b3Vetxj4OKqrX9lmqT4RUKj127FhgYKCenp5IJKqSHQUCQb169SwsLJKS%0AkmbMmAHAysrKzMzs8uXLADZt2lTmm3/r1q1z5sxRfZrGjBlTp04dJycnAHp6egDMzMy6d+8OoF69%0AeikpKR9//DGAVq1alSxbk9LT02v4h5iQkFDmuFKpVP18w8LCLl68+PXXX7/C4on5+Uvj49e0bm2u%0ApaUaCcnK2p2auvu/9bQXHzxwtbRsoKNzIy/Po3FjE01NF3NzAFba2vF5efNbtgTQUFe3rpZWxMOH%0Az1vk7P37rpaW5efJkEiypdJ1CQkzmjZd3KqVtkgEwFhTs2SCSCDwtrEB8KC4eOqlS60NDRe1alX+%0AjkPr1ctxdRULBOXsa6Sh8U3Llmaamh9HR29KSprWuHH5OUtbsmTJ1q1bKz7/9VFU9NzCcalUCiAn%0AJ+f333/fvXu3iYlJFTYW27FjR3BwcFBQkKoFqJubm1gs7tSp0/OuD5s2bZozZ87zrioAxo0b5+Pj%0A4+vr26dPHwC//fbb9OnTn9pUJpOtWLFi+fLlJeVkREREREREREREREREREREREREROVgnRgRUe02%0A6Ce0qQ8z/Scj4cmwq4crC8uYLBQAgInek5HSXz8XCFBHB/dyXrBI+TaPw6QdmOmPPRexYTQMtR+v%0AXHoXHQ0AmPE7MnMR+Cm0xC/esfwiMQDGujDWhV09GOlgwnbsDq1snZhIJDI2Nq7UEhX2Uu10qqo7%0AikAgAFDyBXoXF5dFixZdv35dU1MT/1a5qHTu3BlAZGSkqiCnzENVEumlaGtr19gP6IVUPxRBueUc%0AtV0Nv9rqejFL7ysQCAwNDe/fvw8gIiICz3/zq55Vp06dMtcBoKOjU/qhoaFhYmJideQvh1AorOEf%0AYkXqc0peqJe9rg0KDW1jaGj2b5EYgPCHD+0MDK706fPsZNUNxKRU1dZ/7n1AHQ2NexJJ+YuUb7Oj%0A46TIyJkxMXtSUze8846hWPzsLjoiEYAZly9nSqWBXbpoCYUv3LH8IrES0xo3nhkbeyMv76Uy6+np%0AvT5X0ZdSkXrpqu0kpqIqTrb8t2hQLBa7ubmhYtcHldJXFQAaGhpTpkxZtmxZcnKytbV1YmKivb39%0AU5uuXr26c+fOI0aMqPLTISIiIiIiIiIiIiIiIiIiIiIiojcS68SIiGq3VSPhugGODTC7/+ORPAmS%0A7iNfAr0n35+HXAHRi0qtSnvlRUa2Q5v6+NgXp67DaSW2jMeUsuq1Dl7CnotYNgyODaoyNoChjgCg%0Ap/mieS/SpEkTf3//yq5SMStXrpw/f36Zh1SlWS4uLmPHjrWxsenatWvpb6JXobp16wLQ19dX9Wu6%0Afft2y5YtVYfMzMwAGBgYqFq4lHmoOiKVQyAQjBw58rvvvqux7cocF4vFMpnMyspq2rRpbm5uV65c%0AGTVqVM1Eqnk19nFQed6bSvWam5iYjBs3buLEiSEhIUuXLq2BPKr2Qa/Dm/+VWVpa1vAPceLEiWW2%0AFFO9mJqamsOHDx89enTfvn11dHReti5wlb29a2ioo5HR7BYtVCN5MllSQUG+XK5XqsWcXKkUvUzN%0A4SsvMtLKqo2h4cfR0acyM52Cg7c4Ok5p1OjZaQfT0/ekpi6zs3P8t/1glcQWCQQmGhoWparmKmLm%0AzJnu7u4v9ZTXREpKSmBgYJmHVJeIxo0bT5w4cdSoUX/88cfPP/9cVfs+ePAAQHJysp2dXenxylwf%0ARo8evXbt2l27drVr127gwIFPHT1+/LiWlpaqFxkRERERERERERERERERERERERFRRbzkt++JiOg1%0AM8gBcwdgbgCOxD4esauHwmKsOPZkztU72HD65ZZ95UWWHoGNBU7OhN+HkCsw/88y5mTk4iNfdGmK%0Ar957PBKV+oIdZYqKJk9/CACD21R0/utJLBYLBIKOHTv++OOPt27dOnr06IQJE6q1IkX19ffOnTur%0AuqAEBQWVHLpz5w6A7t27l3Oo+oK9hlTFe8bGxh9//HFwcHBqaurChQufbQJDVUgkEgkEAj09vcmT%0AJwcHB2dmZq5fv759+/Y1FoBv/soTCAQikUgkEvXt23f79u1379719fUdPHiwtrb2K6w2yNJybosW%0Ac69ePXLvnmrEzsCgUC5fER9fMudqbu6Gl+zb9sqLLI2Ls9HXP9mtm1+HDnKlcv61a8/OyZBIPoqO%0A7mJi8pWNjWok6tGj8neUVawpVnpRUXpR0Shr64pMfiOJxWIA5ubms2bNio2NTUpKWrhwYatWrap2%0Al3feeQfAhg0bFIrHv5Tcvn07MDCwMtcHAwODUaNG+fv7Hz58uH///qUPnTlz5s6dO59//nlJrXJY%0AWFgVnQoRERERERERERERERERERERERG9sdhPjIiotpLKAUChxHdDcCERY7fh9Cy0bYDB78DGAosD%0AkfYQvVvi2l2EJWH/RwAgkQGATAHxv2XCRTIAUCqh+hJyrgQApLLyFimQPnnis9achEd3mBtgVAd8%0A4odGJgCQWwQAeZLHe33si3wJdk5+HCM7H7+HY9GQ5+54+DJGb8M+DwxoXcaOP56AiR5GtIOhNgqL%0A8dUBTOmG6bWtdkP1pXNNTU2pVGptbT1hwoQxY8Y4ODhU975yuVxV9RQSEtKsWbMPP/xQqVQeOXJk%0A586dI0aMsLCwALBr164OHTpMmDChuLj4eYcAFBYWApBIJKXXLygoAKCsWKnDa071QgmFwgEDBowf%0AP97V1VVHR6eSaz7KSLh1+W+Lph3jzvvGX/AF0Kbf543buJo3bld62p0bIVHH1qRdOw2gnk03APLi%0AIr06Vo4DZpnWf/wmSYw8FHVsTXbalTr1Wg77+pRY83G2tOtnLp/8Ke3aabOGbd5x+axx28ERfyyx%0A7+WhV8eqkuFrgFAoVBVI9OvXb9y4cUOHDtXX16/WHVVvYKVSqdo3Pz8fQHFx8fTp08t580ulUgAy%0AmUxVrFIyUvLOV7Xpy8/PV/UDVB0t+fSpvEkflhKq01Fd2Vq1ajVx4sTRo0c3bNiwMmtKFQoACqXy%0Au1atLmRnj42IOO3k1NbIaLClpY2+/uK4uLSiot5mZtfy8sIePNjfqRMAiUIBQKZUiv+ttylSKAAo%0AAdXjXJlMtXI5ixTI5SVPfNaahASPxo3NtbRGWVt/Eh3dSFe3ZNk8mUy118fR0fky2c527VQxsqXS%0A32/fXtSq1fN2PHz37ujw8H2dOg2oW/ep7RZdv54plX7SpEkrA4NCufzjqKhR1tYl5WdvD7FYLJfL%0AtbW133///bFjx/br109DQ6P6tvP09AwICDhy5Mi4ceMGDBiQmZkZHR39888/9+7du5zrw/OuKiVR%0AJ0+evGPHDnt7+5ILCIDg4ODNmzcPGDBg586dABQKRWJiooGBQadOnarvBGujqVOnxsbGDhs2bODA%0AgW3aVNn/JCAzITPm75iGjg1Prjl5/fR1AM2dmgMoLiyuY1Wn35f9rB1eUJaZdDEpdE/oRd+LAPp8%0A3qfN4DYN2/3n0ncz5Ob/9v/v/M7zAN4d/25Pz56WLS0BJJxPOL3xdOzfsU27NO31SS/79+wPLz7c%0AY3qPOlZ1qursiIiIiIiIiIiIiIiIiIiIiIjozcY6MSKi2ic7Hz+fRdJ9APj+b0zsgkldcToOzqsw%0Apz8+dsYpL3z2Gw5dQmAMhrSB71SIBFgciOQsAJi1D5O6om0DbPwHKVkAsDgQn/fB9vOPm3HN+wOL%0AhpSxiKE2Eu9j7UkASMnCmpOY1BXGuv/JlpWPTt9jcldk5sHZButHI1+CZX8DQNpDrD0FQ20cvAR7%0AK2z8BwByi3AmHp/0hKa47B0BaGnAUBtaz7llZedj3Sl8fQDjO0NDhHkD8G7Tqn/Nq5VIJCouLjY2%0ANh43btzYsWPffffdkuYh1W3v3r2qBiYZGRkBAQGqxj6HDh1av379rFmzWrZsKRKJTExM/Pz8xGKx%0AWCx+3qFbt279+uuvANLS0n755ZeRI0caGRlduHDh0KFDAFJTU7dt29a1a1c7O7uaOa+qJRQKATg5%0AOU2YMGH48OF16lTNd7Xv3Ai5fm6n8wcbhGJN0wZt4i/46pvU7zh0wbMz69l0M2/UdsfMBgamjQZ9%0A8SeAYkl+sO/nAct7u0zb2ajNQABN27/fwN5l56zGD+/Ehe6f6zR2jeq51rbOhuZN9i5o22vSFqO6%0AzQG06Tcj2PeLzsMXGZg1rpITqQ4aGhoCgaBTp04TJ050c3MzNTWtgU137dqVlpYGYP369VOmTNm3%0Ab9+9e/cArFy50svLq8w3v1Qq3bRp0+3btwEsXbp05MiR9vb29+/f9/HxAXD79u1z584pFArVsqtW%0ArZoxY8aff/6pmr9161Z3d3cTExMAb8yHpTSRSKRQKBo0aDBx4sQxY8ZU/oyypdKfk5OTCgoAfB8f%0AP7Fhw0mNGp2OjHQODp7TosXHTZqc6tbts8uXD6WnB969O6RePd/27UUCweK4uOSCAgCzYmMnNWzY%0A1shoY2JiSkEBgMXXr3/erNn2W7fSi4oAzLt6dVGrVs8uYigWJ+bnr01IAJBSULAmIWFSw4bG/61H%0AypJKO505M7lhw0yp1NnMbP077+TL5cvi4wGkFRWtTUgwFIsPpqfbGxhsTEoCkCuTnbl//5MmTTSF%0AwjJ3BKAlFBpqaGgJy+j/XE9b2z8t7deUlJFWVrpi8YxmzfqYm1fy5a1FVAWWYrG4X79+H3zwwZAh%0AQ3R1dV/4rMqzsLA4fPjw999/Hx4evnr16j59+qxZs0a19fNujuVfVVT33AYNGkyaNGn8+PElG0VG%0ARk6bNq2oqCg0NLR0gLNnz9bAadYucXFxYWFhERERc+fOtbCwGD58uKura+/evStTyH3z3M3zO8+P%0A2zhOpClq2K7h1/W/Nm1k+umfnwKQ5Et+n/H7ql6rpuyc4jCovGL+Jp2b1G9T/6LvReP6xoO/Hfzs%0AhObdmjfq0Oj8zvMmDUxGrx9dMt6sazPzZuaxf8dO/GWikaURgD6f99n7xd6hi4aaNq6JWyERERER%0AEREREREREREREREREdV2gjesawERUa3g7+8/atQopY+6c9BzuG8Bmrr5+/vXzHYPHjyIiorq0aNH%0A6eZCz4qNjXVwcDhx4oRNVTSN6d27d2JiYnJycuWXqnn9+/d3d3f/7rvvama7c+fONWvWrF69euVP%0AU32uP9yYVZE1H96J+3uj2/A5/2jpmahGtnmaGtVt7rbg4vOe8tSE3KyUvQvaWds6D/jsYOk5ls27%0A3L15ofeUbU3bv68aVMiLf51hOWXdHaFYUzWSnRZ7evv0IV8d19DSe2HUxP8FBP0ytYZ/YwwNDbW2%0Atm7QoEH509avX7906dKwsLCaSVV7LVy48ObNm8HBwTW5aUpKyr179yrS/kggEOzt2NHd+gX9eagK%0ACQIC9u7d6+7uru4gr0KpVAYFBbVr187Y2Lj8mcuXL//555/PnDlTM8FqkZiYmMGDB9+8ebNZs2bq%0AzlIprq6ugYGBJQ81NDRkMpmqzHjYsGEuLi7t27dXHRIIBBN/ndh2WNvyF7wbd/fnkT9/deYrPZPH%0A98cvTL6waG4xN2yu6mFWStbitotbOLf45NAnL4z31HMrOEEhV3iZe63OXC0UPS4TTYtN2+2xe+aJ%0AmVp6Wi/c9FLApZ1TdvK/8xARERERERERERERERERERERvbXK+P/TExERUU0yNjbu1atX+UVipEZO%0ATk4vLBJ7KUql4p+dH7XoMrakSOwVaGjpAyguyntqvPeUX3QMLYL9ZubeT1aNCEUaAEqKxACYWLc2%0ANG8cdujbV969ur377rsvLBKj11yjRo0qUiRG9LIEAkGfPn1eWCRGbwMjIyNhqZ57xcXFSqVSoVBc%0AvHjxm2++6dChQ8OGDadPn/7XX39VZDWlQrnnoz2dx3UuKRJ7lra+NgBJnqTy4Z9HVR5WUiQGwLq1%0AtVkTsz8X/Fl9mxIRERERERERERERERERERER0RtDrO4AREREpAb5+fkAiouLNTQ01J3lrXMr5tj9%0A1MtdR/1QmUUSIwMAWLfq9dS4rlHdPlN/DVw3NOjXDwd7HSldHlaadaveF/bNcXDxNDRrUpkYRERU%0AS/n4+DRp0kQsFtepU0csFhsYGGhra+vo6Ojp6WlqahoZGYnFYiMjI3XHfEwul+fk5ADIycmRy+VF%0ARUWFhYVSqVQkEikUiqcmK5VKmUwGIDU19ZdfftmyZQsAuVRe/haxR2NvR98e+cPIcuZcOnQJgG1v%0A25IRSb7kzOYz95Pv37l6R8dI5/2l79ezq8rachXb3rYHZx/s9WkvsyZmVb44ERERERERERERERER%0AERERERG9SVgnRkRE9HbJz89fv379vXv3AMyePXvs2LHt27dXd6i3S2LEQQBmDR1f9onyYklGUkRh%0A7v1bMX/fuLjX5t3Rju95PTvNsnmXzu9/F3rgm/A/l3QevqjMpeo26ahUyJP+90ebfl+8bAwiInoD%0A/P777wUFBTKZ7NGjR+VM09DQ0NfX19HR0dbWLj3+VC8vVaVZJSMVFBRIJBKZTJabmwvg0aNHCoWi%0AsLCwqKiozPna2toCgaCcBTU1NaVSabNmzRISEkQaL2jceungJQANHJ/uZimTyJIjkvMy82L/jg3f%0AG95pTCcXLxfVIaVSuf/L/S5eLnVt6gLYPHzzxvc3fhPxjbaBNqpU446NFXJF1B9RLl+4VO3KRERE%0ARERERERERERERERERET0hmGdGBERUW0SFxdnY2NTmRX09PTmzJkzZ86cqopUw+7cufPgwQN1p6iU%0Ae0nhmjqGQtFLd3IrluSlXj15+cR6oUjT7duLBqaNnjezda+P7iWGxZzaaNWyewP7vs9O0DG0AHD3%0AZmibfi+b4vVSWFgYHx/fokULdQd5feXk5CQmJqo7BZF65ObmpqSkNGr03Kvl2yk2NhbA6dOnmzVr%0AphopLi7Oy8tTVWTl5eUVFxc/fPhQLpc/evRIKpXm5+c/W6ylKuIqeVhS3FUZurq6WlpaJSVnhoaG%0AIpFI1eVMKBSqmpsZGBiIxWItLS1dXd0NGzZs2rSpzKWEQqGGhsaECROmT5/evn17gUCA8grKACA5%0APFnbUPvZcrKivKJrJ68FrQsSaYrmhs01bWRacijpYlL43vDwveGl5yecT7B/z/7lz748BhYGABIv%0AJILF3UREREREREREREREREREREREVC7WiREREdUOTZs2HTFixGeffXbgwAFvb29bW1t1J6ppOTk5%0Amzdv3r59u4WFRc+ePdUd59UV5mToGtV9hSdq65u2HzTbwKTB2T0zQg984zJtp0AgLHuqQNBj/E8P%0A0q+d2eU5fO7ZZ49r6hgBKMy59woxXh8uLi4+Pj79+/fv16+fp6eng4ODuhO9XrKzs3/55Zfdu3eL%0AxeJFi8ruLEf0Buvfv/+2bdv69OmjunvWr19f3YnULzg4ePXq1VFRUUOHDjU3Ny8Z19DQMDY2NjY2%0AVmO2V9CgQQOlUll6RCwWy2SyFi1azJo1a9SoUarSsgrKycgxrGv47Li+qf6A2QNMGpj89tlvAd8E%0ATNk5RSB8XHN269Ity5aWsy/MfrX8QrHwqfwAFDLFs7VqOkY6qoSvthERERERERERERERERERERER%0AEb09nvPdYiIiInrN6Orq7t+/PyYmRldXd8CAAZ6ensnJyeoOVUPy8/NXrFjRpUuXgwcPrlu37ubN%0Am7169VJ3qFcnEAoVCnkFJxfm3lfIi0uPtHh3rE3nUSnRR6KOri7niRra+i7Tdsqkhae3e5QVQgDg%0A6S+n1zZ2dnZXrlw5c+ZMUVHR4MGDR44cefLkSXWHei0kJyd7eXm9++67AQEBK1euTE9P9/T0VHco%0Aoprm6OgYHx/v5+cXFRXVo0cPT0/PpKQkdYdSD6VSGRgYOGDAgEmTJrVu3To2NjYgIMDQsIyaqNrF%0AwMCgpKeZWCwWiUTu7u4RERFxcXEeHh4vVSQGQCgUKuSK5x3tNLZTx9EdYwJjjv94vGRQkifJSsmS%0AFkhLzyxnEZW8zDx5sRyAcX3jwpzCp44WPCjQM9Z7alAgEAC1/7ZNRERERERERERERERERERERETV%0Aj3ViRERUNR4WqDvB28He3v7w4cPHjx9PT0/v27fv3LlzMzMz1R2qGslkMj8/v969e/v6+s6fPz8h%0AIcHDw0NDQ0PduSpF19BSWvjoP0PPtBMpGT//+5cCoeg/EwSCbqNX1anXMjJweWrsk2+rKxXykn+q%0A1KnXsscHP925EfLswtKChwB0DV+lrdnrxsnJ6fTp08HBwXXr1v3www8HDRoUGBj4bIeWt0RCQoKX%0Al1ffvn0vX768devWxMREDw8PLS0tded6Yz0sLn7xJFIfoVDo5uZ29erV33///caNG/369fPy8kpJ%0ASVF3rhp18uTJ4cOHf/rpp61bt46Ojt61a5ednZ26Q1UNfX19mUwGwMzMzNvbOyEhwdfXt3379q+2%0AmqGlYeGj/1Rtlb6VCAQCt1Vuli0tjy4/evX4VdWgpa1lcVHxqXWnSqbdjbsbvDX4qec+tea+L/cJ%0ARUIA9d+pn5eZl5WcVXpCQmhCs27NnnpWwcMCAGW2OyMiIiIiIiIiIiIiIiIiIiIiIiqNdWJERFRR%0Anb/HVweeHiwqxtIj6LICpl6VWlyhxJqTsF8I/RnouAx7I55bOEMAXFxcIiMjfX19L1y44OzsvGLF%0Aivz8fHWHqmKq5if9+/f/7rvvJk2alJCQ4O3traf3dIeN2sjSpktxUV5xUV7JSLG0AEBxUZ5S+aQJ%0AibQw59xvXiINbYFAKCsuBCAvlqgOiTV1XaZuF2ton94x/dG9m6rBwtz7Jf8s0bTdsNa9Pno2Q1He%0AfQB1m3Wu2lNTIycnp8OHD4eEhDRp0uTTTz8dMGDAgQMH5PKK9m17A8TExEydOrVv375XrlzZtm3b%0A1atXJ0yYoKmpqe5ctV7nM2e+io19arBILl8aF9fl7FnTI0cquX5aUdGvKSnu4eFdzp6t5FL0PKpq%0AsWvXrvn6+l6+fNnFxcXLyys1NVXduarduXPnRowYMW3atObNm0dHR/v7+9vb26s7VFVq3779iBEj%0A/P3909PTlyxZ0qhRo8qs1qxrM0meRJInKRkpLiwGIJPIVA81dTUn7Zikoa2xy2NXxs0MAK3fa23e%0AzPzYymO/ffZb5L7IwKWBh+Yc6jyuMwBVkzFJnkSpePIbbVFOkf9Mf7G2WCAUABg4d6BIU7R90vYH%0Atx8AkEvl8WfiDy863Ner71PZ8u7nAWjSuUllTpCIiIiIiIiIiIiIiIiIiIiIiN4GrBMjIqLHUh+8%0AYEJdQ5joPj2orQGvvoi7C0Xlyrpm+iPyFj7piandEJuO0VvxSxk9kOgJgUDg5uZ2/fr1NWvW7N+/%0Av0ePHr/++quqq8YbICwsbMSIEZ9++mmXLl3i4uKWL19uZmam7lBVxqbzaAAZSeGqh/cSw87v/QpA%0AwaO7+xd3CVw7JHDtkH3fddrj3eJ6yC5r254ZyZGh++cByMtOjTz8/cM7cQDq1GvZbfSP0sKcw2sH%0AXz+3IznqcLDvDADBfl/cvXmh9Had3l9Yt+nT9WD3EsMEAmHT9sOq/3RrVNeuXf/6669Lly45ODh8%0A+eWXvXv39vPze2M+F88TFRU1derUIUOGZGdnBwQEqCrEanvbvRqTWlhY/oS6Wlomz5TbaYtEXs2b%0Ax+XmKipd02ytrf2+ldW+tLQHUmkll6LylfQW27ZtW3R0dJ8+febOnXvv3j1156oWFy5cGDly5Acf%0AfNC0adOoqCh/f38HBwd1h6p6NjY2+/fvd3Nzq5IrXsdRHQEkhSepHqZEphyaewhAdmr239//fTfu%0ALgDLlpZuP7oV5RRtcN1wfsd5kabIM8DTYaBDTGBMwDcBeZl5H2z5QNtAOyksaf9X+wE8uvvo+3e/%0A3zBkw4YhG5Z2XDrPZt6FXRda9myp2qJui7pfnfnKuL7xT4N+WuS4aFnnZaG7Q6fumWplb/VUtuSw%0AZIFQ0Pb9tpU/TSIiIiIiIiIiIiIiIiIiIiIierMJlGzXQkRU4/z9/UeNGqX0UXeOUpLuY8J2BH/1%0Aik+3XYC4e3jlM0rOwtxD8Pvw8cPjV/HeOrSsi+uLXnHBSnLfAjR18/f3V8/2Ly8vL2/VqlU//PCD%0AlZWVl5fXwIEDBQKBukO9ori4uMWLF587d65nbeFUsQAAIABJREFUz54//PBDx44d1Z2oolSf6w83%0AZlVk8tGN7nXqNn935LLqTvU8xzeP1TE07z5u3QtnJv4vIOiXqbXxN8bY2NgffvjBz8/Pyspq8uTJ%0A48aN09LSUneoKhYSErJmzZqIiIgOHTosWLDA1dW1Nn72BQLB3o4d3a2ta37rpIKCCZGRwd27v9rT%0AbU+ejMvLUw6rgnpLQUBAS3396y4ulV+qgtvt3bvX3d29ZrZ7DRUXF//2228LFy5MS0sbOXLkF198%0AYWFhoe5QVePixYsrV66MiIgYNGjQN9988+6776o70WtBIBBM/HVi22EvqLPycfOxsLF4f9n7NZOq%0A4raO2WpgbjB6/egXzrwUcGnnlJ218a5NRERERERERERERERERERERERVgv3EiIgItx/AdQMyc9UZ%0AYHWpL6v3bQUzfaQ9VFueWkdfX3/hwoU3btzo06fPjBkzhg8fHhYWpu5QLy0jI2Pu3LmDBg3Kzc09%0AceLE6dOna1GR2MtynrDxVuzxwpwMteyekRT+KOPmuyOWqGX3GtO6detdu3bFx8cPGDBg2bJlTk5O%0AmzdvLnxR86ja4uTJkyNHjhw3bpxIJDpx4kR4ePjgwYNrY5GYGt0uLHS9cCFTIlF3EFIDDQ2NCRMm%0AXL9+/aeffjpz5kzPnj2/++67+/fvqztXpURHR48fP37UqFFGRkYhISGHDx9mkdjLGrtp7JXjV3Iy%0ActQd5D+Sw5MzbmYMW/qmtQAlIiIiIiIiIiIiIiIiIiIiIqLqwDoxIqK3ztU76LcW8wLw1QEIP0Ju%0AEXZcwNU7uJuDj3whV+CfeHzhj8Zzkf4QzqvQcDYyc+EfgYnb0WPV40WkMsz/A56/PV4nX/pk/TwJ%0AlhzB5J3osAwuaxCT9ng86Drqe+NMfBmRnJrD0vA/I1IZujarjrN/k1lbW/v4+MTExDRr1szd3X38%0A+PHXr19Xd6gKycvLW7FiRc+ePc+fP+/n5/e///3Ppaaa6qiLjoG5y7SdofvnyaQFNbx1/sM7l46u%0AHjjjkIa2QQ1vrRZNmzb18fG5cePGmDFj1q1b5+TktHbt2txc9dXFVo5SqQwMDBw0aNCHH35oZGQU%0AHBwcGhr6xn9eqsTV3Nx+58/Pu3r1q9hYYUBArky249atq7m5dyWSj6Ki5ErlP/fvfxET0/j48fSi%0AIufg4IbHjmVKJP5paRMjI3sEB6sWkSoU869d84yOVq2TL5eXrJ8nky2Ji5v8v/91+Ocfl5CQmJzH%0AdSZBmZn1jx49U8sLkN5UmpqaHh4eiYmJa9asOXr0qLOz84oVKx49eqTuXC8tNjZ2/PjxQ4cO1dDQ%0ACA4OPnnyZNeuXdUdqlYyMDeYsnNKwNwAaYH0xbNrxKM7j47/eNwzwFPbQFvdWYiIiIiIiIiIiIiI%0AiIiIiIiIqBZgnRgR0VvHzQeRt7BkKFaOwJB3UFiMbwYCgKUhfh4HmQKaIvicRUoWdoVi/iD0s4OO%0AJlxaYVcoVP0VFEoM/AmpD7BhNJYOw0c9cOff71QrlfjEDyPbYftERMyFUACXNcgpAoBcCbLzH/97%0A+UISUCTDoiHV9AK84Vq1arVv377z588rFApXV9e5c+dmZKinaVVFSKXSzZs39+jRY9++fWvWrImL%0Ai3NzcxMK34rfT0ys7dsPmXv1zLaa3FQhL74Ztrf35C16xtY1ua/aNW7ceN26dfHx8ePGjfPx8ena%0AteuKFSsePqxNXQvlcvmBAwcGDBjg6enZpEkTVSmIk5OTunPVGm5hYZEPHy6xs1vZuvWQevUK5fJv%0AWrYEYKml9bOjo0yp1BQKfZKTUwoKdt26Nd/Wtp+FhY5I5GJuvis1NUMiAaBQKgdeuJBaWLihTZul%0AdnYfNWlyp+jxLU0JfBIdPdLKanu7dhE9ewoFApeQkByZDECuTJZdXKz6d3o9qarFkpOTV69efejQ%0AoW7duq1YsSIn5/XqKPU8V69enTp16uDBg+/evXvmzJmgoCBeFirJyt5q4LyBwduC1R0EAOTF8vC9%0A4RO2TqhjXUfdWYiIiIiIiIiIiIiIiIiIiIiIqHZ4K76HTUREpWXkIjsf64KgUGLxUGhr/Oeolhhd%0Am6GBMQB4dIdLK2ybAH0tGOs+mbPrAk5dh5cLBAIAaGaOpmaPD4UkYHcoWn0LwXQIpuPENWTk4mw8%0AAAxtg5z1GPzOC+LJFJh9EFvGo3OTKjrht1KXLl3OnTvn5+d38eJFVYOU162BkqotUv/+/devXz9t%0A2rT4+HgPDw9NTU1156pRhmZN3uk7oyZ3FIo02vT74i3pJPasBg0arFu3LiUlxcvLy8/Pr1u3bt99%0A993rXEipIpPJ/Pz8evXq9eWXXzo4OFy6dOmvv/5iKcjLypBIsqXSdQkJCqVycatW2iJR6aNaQmFX%0AE5MGOjoAPBo3djE339a2rb5YbFzqorQrNfVUZqZX8+YCAEAzPb2menqqQyFZWbtTU1udOiUICBAE%0ABJzIyMiQSM7evw9gaL16Oa6ugy0ta+pE6RWpqsUSEhKWLFly8ODBHj16vOa9BxMTEz09PV1dXXNy%0Acj799NPk5OR58+bFxcWpO9ebwKyJWZ8ZfdSdAgBEGiKXL1zYSYyIiIiIiIiIiIiIiIiIiIiIiCqO%0AdWJERG+dzeOgp4WZ/uj0PfIlMCzrq6dCAQCY6D0ZUZWEqRyOAYDmFk/PBxCeDLt6UPr854/rv7Vh%0A4grcdhb8CecWmNjlJc6IyiQQCNzc3K5du7Z27VrVV943b94slUrVnQsAQkND33///RkzZri4uMTH%0Axy9fvtzY2FjdoehtYW5uvnDhwoSEhC+//DIgIMDJyWnu3Ll37txRd64ySCSSX3/9tWfPnvPnz3d2%0Ado6Ojvb393d0dFR3rlpps6Ojnkg0Myam05kz+TKZoVj87BzVPcqkVG1YqVsfDt+9C6C5nt5T8wGE%0AP3xoZ2CgHDas9B/Xf2vDxKXvoPR609XV/fzzzxMSEhYuXOjn56eqFsvLy1N3rv9ISkry9PR0cXG5%0Ad+/esWPHwsLC1q9fHxUVVVxc7ODgMHv2bIlEou6MREREREREREREREREREREREREpB6sEyMieuuM%0AbIdL36CPLSJT4LQSv4a89ApJ9wHgUWEZh/IkSLqP/P9+P1muqOjKAVHQFmPJ0JeORM+joaGhapDi%0A7e29cePG3r17HzhwQKlUqivP9evXx48fP3r0aBsbm5iYGB8fn/r166srDL3NTE1NFy5cmJKSsmjR%0AoqNHj/bo0cPLyyspKUnduR4rLCzcvHmzk5PTsmXLevXqdfny5V27djk4OKg7Vy020srqUq9efczN%0AIx8+dAoO/jUl5WVXSCooAPCouPjZQ3kyWVJBQb5cXnpQrr4rLVWSnp6eqlrM29t7x44dqkLroqIi%0AdedCcnKyl5dXv3797ty5c+zYsfDwcBcXF9Uhe3v78+fPb9iwYePGje3bt7948aJ6oxIRERERERER%0AEREREREREREREZFasE6MiOits/QIbCxwcib8PoRcgfl/Ph6XVbiaS9VJ7NiVMg7Z1UNhMVYcezJy%0A9Q42nK7QFkev4PYDLHB90rss+EZFI1H59PT0vL29ExMThw8f7u3tPWzYsAsXLtRwhrS0NE9Pz4ED%0ABwI4f/68v79/q1atajgD0VMMDAy8vb1TUlJWrlx54cKFPn36eHp6JiQkqDFSTk7O2rVru3Xrtnbt%0A2jFjxsTHx+/atcvOzk6Nkd4MS+PibPT1T3br5tehg1ypnH/tmmpcVuFqLlUnsWMZGc8esjMwKJTL%0AV8THl4xczc3dkJj4slvQa0VfX9/b2zs1NfXrr7/++eefnZycNm/erK5WXbdu3fLy8urbt29iYuKB%0AAwciIiJKKsRKCIVCDw+P6OhoS0vLrl27Tp8+/XXrhEZERERERERERERERERERERERETVTazuAERE%0AVNPWnIRHd5gbYFQHfOKHRiYAUM8I6Q8RfRtt6gOARAYAMgXE/xYU5xYBQJ4EAL7siwP/w9cHYW0M%0Ap+a4kID0RwCQkInB78DGAosDkfYQvVvi2l2EJWH/RwBw+DJGb8M+DwxoXUaqE9ew4ihGtHtcVKZQ%0AIu4ejHTQ3aY6X4u3jLm5+bp16yZPnjx79uyxY8e2a9dOR0enxnaPiooyMTHZsWPHuHHjhEJWqtNr%0ARF9f//PPP58yZcrGjRvXrFnTv3//du3aaWhoqCXM9evXHz58OG7cuDlz5rRs2VItGd5IaxISPBo3%0ANtfSGmVt/Ul0dCNdXQD1tLXTi4qiHz1qY2QEQKJQAJApleJ/65VzZTIAeTIZgC+bNz+Qnv71lSvW%0A2tpOpqYXsrPTi4oAJOTnD7a0tNHXXxwXl1ZU1NvM7FpeXtiDB/s7dQJw+O7d0eHh+zp1GlC37vOy%0AqRqRVbhYm2qUqlps8uTJP/zww08//fTbb781bNiwhjMolcpLly7VrVt3y5Yt48ePF4vL+1t806ZN%0AT5w4sXv37pkzZ544cWLLli3PVpQRERERERERERERERERERERERHRm4p1YkREb52sfHT6HpO7IjMP%0AzjZYPxoAlg2D1z7si0Rzc6w+ieQsAJi1D5O6om0D5Euw7G8ASHuItacwpRuCvPDNHxjxM4x1MbUb%0AHBvArh6Ss9DYFKe88NlvOHQJgTEY0ga+U2GoDQBaGjDUhlZZd57zCRi6EYXF+Cf+P+MJS6v3pXg7%0AOTo6Hj169OTJk7/++qtMJquxfYcPH/7JJ59oaWnV2I5qcf3cTnVHqGL3U6PVHaGGGBgYzJ49e8aM%0AGVu3bg0JCVFXjA4dOsyYMaNJkybqCvCmypJKO505M7lhw0yp1NnMbP077wBYZmfnFROzLz29ub7+%0A6ps3kwsKAMyKjZ3UsGFbI6N8uXxZfDyAtKKitQkJUxo1CnJy+ubq1RFhYcYaGlMbNXI0MrIzMEgu%0AKGisq3uqW7fPLl8+lJ4eePfukHr1fNu3NxSLAWgJhYYaGlrPL449ff/+ntRUAMkFBatv3uxtbu5o%0AZFRDLwpVmIWFxapVq7788ssff/wxJSWl5gNMmjRp4sSJFaxfFQgEEyZM6Nu3r6enZ79+/caPH792%0A7VoTE5PqDvmau3HmRuGjQnWnqHa3o26rOwIREREREREREREREREREREREamTQKlUqjsDEdFbx9/f%0Af9SoUUofdeeg53DfAjR18/f3V3cQqk1CQkKcnZ3lcrm6g1S9+g0apt5SQ10EvSUEAsHejh3dra3V%0AHeQtIggI2Lt3r7u7u7qDULXbt2+fp6enWCzeuHHj+++/r+44atOgYYPbqW9LAVWDhg1updxSdwoi%0AIiIiIiIiIiIiIiIiIiIiIlIP9hMjIiIiqgLdunWryf5sREREL+Tm5ubs7Pzll18OHz7czc1t48aN%0A5ubm6g6lBqm3UtUdgYiIiIiIiIiIiIiIiIiIiIiIqCYI1R2AiIiIiIiIiKqFhYXFrl27Dh8+fOHC%0AhZYtW27ZskXdiYiIiIiIiIiIiIiIiIiIiIiIiKi6sE6MiIiIiIiI6E02aNCg2NhYNze3jz76aNCg%0AQamp7K9FRERERERERERERERERERERET0BmKdGBEREREREdEbzsjIyMfH559//rlx44aDg8OWLVuU%0ASqW6QxEREREREREREREREREREREREVFVYp0YERERERER0VuhR48e0dHRH3300SeffOLs7BwfH6/u%0ARERERERERERERERERERERERERFRlWCdGRERERERE9LbQ0dFZvnx5cHBwVlaWo6PjihUr5HK5ukMR%0AERERERERERERERERERERERFRFWCdGBERVZmHBepOQEREVLMeFherOwLRq+jSpUtUVNS33367YMEC%0AJyenq1evqjsRERERERERERERERERERERERERVRbrxIiIqLKKirH0CLqsgKlXheYrlfglBG4+mBeA%0AD3fBL6ya8xEREVW1Irl8aVxcl7NnTY8cqch8hVK5JiHB/tQp/b/+6vjPP3vT0pTVHZHoRTQ0NLy9%0AvSMiIuRyuaOj4+zZs6VSqbpDERERERERERERERERERERERER0atjnRgREVWWtga8+iLuLhQV+877%0A4kAsDsSWD7B0GFaOxNwArDtVzRGJiIiqlLZI5NW8eVxurkJZoZvfzNjYyIcPP2nSZGrjxrG5uaPD%0Aw39JSanukEQV4eDgcP78+ZUrV27YsKFDhw7h4eHqTkRERERERERERERERERERERERESviHViRERU%0ABXQ0YGFQoZkpWVgciOndYawLAMa6mOaEuQHIyq/WgERERFVMRySy0NKqyMzkgoJMiWRP+/aeTZuu%0Ac3D4o3NnAKtu3KjmgEQVJRaLP//88+joaDMzs65du37++ef5+fzNjIiIiIiIiIiIiIiIiIiIiIiI%0AqPZhnRgREdUo3zDIFOjT6slIb1sUSLHtnPoyERERVafbhYWrW7cuedjXwsJMUzOtqEiNkYie1axZ%0As1OnTm3cuHH79u1t2rQ5ffq0uhMRERERERERERERERERERERERHRyxGrOwAREamZUonQJOyPxIFL%0AOPslPvZFSAJa1MWqkehhAwDx9+B9ELaWSH2A1GysH4029QFAKsPiQGQXoI4OpHLkS5+sGXQdE7bD%0AdyqcWzy93bmbAFC/zpORBsYAEJ1anSdJRERUihIIzc7en55+ID39bPfuH0dFhWRnt9DXX9W6dQ9T%0AUwDxeXneV67YGhikFhamFhSsf+edNkZGAKQKxeK4uGyptI6GhlShyJfLS9YMysycEBnp26GDs5nZ%0AU9s5mZo+NSJVKLqamFTzWRK9NIFA4OHh8d57702fPr1Pnz7Tpk1btWqVgUHFmsYSERERERERERER%0AERERERERERGRurGfGBHR206hRFYeNp1BShZ+Og3v/tg0FtfuoM9qXL8LAK4bcPk2vn8f2yci+jbG%0AbHv8rIE/IfUBNozG0mH4qAfuPHqyZq4E2fnIKatRSvpDADDWezJiogcASVnVdoZERET/pVAqs6TS%0ATUlJKQUFPyUkeLdosalNm2u5uX3OnbuemwvANTT0ck7O93Z229u2jc7JGRMRoXrWwAsXUgsLN7Rp%0As9TO7qMmTe6U6gmWK5NlFxfnyGQv3D0kK6tIoVjUqtULZxKpRaNGjY4ePbp3794DBw7Y2tr+8ccf%0A6k5EREREREREREREREREREREREREFcI6MSKit51ICNd3Hjf1WjoUPWwwthMWD4VMgdUnAWBWX8zu%0A/3imqR5u3AOAXRdw6jq8XCAQAEAzczQt1T1laBvkrMfgd8rYzlAHAASlRlQrSF/8vXoiIqKqIRII%0AXC0tG+joAFhqZ9fD1HRs/fqLW7WSKZWrExIAzGrefLaNjWqmqabmjbw8ALtSU09lZno1b666izXT%0A02uq96TueWi9ejmuroMtLcvfWqZUzr56dYujY2dj4+o6PaKq4ObmduXKla5duw4bNszd3f3+/fvq%0ATkREREREREREREREREREREREREQvwDoxIiICAKEAADTFjx8OaQMAMWkAML0H3Dtg3SksDoREBpkC%0AAA7HAEBzi6dXKCF+zh3G1hIAHhY+GXmQDwBWdSp7CkRERC9FdafSFD6+Yw2xtAQQk5MDYHrjxu7W%0A1usSEhbHxUnkcplSCeDw3bsAmpeqDXvqXicW/PdeWJYF1645m5pObNiwSk6BqFrVrVt33759f/75%0A5/nz51u3br1v3z51JyIiIiIiIiIiIiIiIiIiIiIiIqLysE6MiIjKYGUEAAZaABB8A/YLYVMX37pC%0AX+vxhKT7APCo8DnPfz67egCQ/vDJSPojAHBqXpm8RERElWWlrQ3AQCwGEJyVZX/qlI2+/re2tvri%0Ax1XUSQUFAB4VF7/yFgF37mgLhUvs7KoiL1ENGTx4cGxs7NChQ0eNGjV48OC0tDR1JyIiIiIiIiIi%0AIiIiIiIiIiIiIqKysU6MiIjKkJUPAM4tAGDyTggEGNgaAORKAFAqH3cSO3bluSuo2o49a2Q7CAUI%0Auv5k5HQcNEQY26lKghMREb2irOJiAM6mpgAm/+9/AoFgYN26AORKJQDlv53EjmVkPG8FVdux5zl6%0A797twsIFtrYlTceCs7KqKjxRtapTp46Pj8/ff/8dExPTunXrLVu2KMt9txMRERERERERERERERER%0AEREREZFasE6MiIiekP9b3HXyGmwt4dUXALLzkf4QIQnYdu5xA7GwZIzqAJEQXx/EiWsoLEbQ9cdt%0AwRIyAeDwZdT5An/HlrFFfWPMGQCfYOQUAUBOEXzO4puBaGBc/adHRET0DPm/5S4nMzJsDQy8mjcH%0AkC2VphcVhWRlbUtJeSSTAQh78GCUtbVIIPj6ypUTGRmFcnlQZmZ6URGAhPx8AIfv3q1z+PDf9+6V%0AucuJjIwVN24A2JCYuCExcX1Cgmd09PMmE72e3nvvvatXr06fPv3jjz8eMGBASkqKuhMRERERERER%0AERERERERERERERHRf7BOjIiInth2DvfzcD8Pdx4hdDZ0NABg1UgY6cDTDy3r4rvBMNbFgj/RwwZB%0AXrC1xIifYbsA527CsQGm90ByFuQKaGnAUBta4rJ3WTwEs/vD0w/zAjB1J7zfw/xBNXmWRERET2xL%0ASbkvld6XSu8UFYX26KEjEgFY1bq1kVjseflyS33972xtjTU0Fly71sPMLMjJyVZff0RYmO3Jk+ey%0AshyNjKY3bpxcUCBXKrWEQkMNDS1hGX/DOp+dPfTixX/u3//s8mXVn89jYjYlJX3YqFGNny5Rpejq%0A6i5fvvzs2bMpKSl2dnYrVqxQKJ7TQ5aIiIiIiIiIiIiIiIiIiIiIiIhqnED57/8+n4iIaoy/v/+o%0AUaOUPurOUYrtAsTdw2sVSY3ctwBN3fz9/dUdhIjozScQCPZ27OhubV3zW9uePBmXl6ccNqzmt1Yv%0AQUDA3r173d3d1R2EarGioqLly5cvW7asY8eOv/zyi62trboTEREREREREREREREREREREREREfuJ%0AEREREREREdHL0NbWXrhwYXh4uEQiadu27cKFC4uLi9UdioiIiIiIiIiIiIiIiIiIiIiI6G3HOjEi%0AIgKAXAkASGXqzkFERFRTcmUyAFKFQt1BiGqrNm3ahIaGLly4cPny5R07doyMjFR3IiIiIiIiIiIi%0AIiIiIiIiIiIiorca68SIiN52eRJ8fQDpDwHAYw/OJ6g7EBERUTXLk8m+vnIlvagIgEdU1PnsbHUn%0AIqqtxGKxt7d3ZGSkjo7Ou+++O3v2bIlEou5QREREREREREREREREREREREREbymxugMQEZGa6Wvh%0AhxH4YYS6cxAREdUUfbH4B3v7H+zt1R2E6A1hb28fEhKybdu2WbNmHThwYNu2bc7OzuoORURERERE%0ARERERERERERERERE9NZhnRgRERERERERVYpQKPTw8Ojbt6+Hh0evXr2mTZv2448/6uvrPzXtr7/+%0AatiwYZs2bZ5dISgo6ObNmzUSll6FjY1Nr1691J2CiOjNl5ycfPz4cXWneOv069evcePG6k5BRERU%0ANqlU+vvvvxcVFak7yNuFfwsmIiIiIiIiIiKi2ot1YkRERERERERUBZo0aXL8+PHdu3fPnDnzxIkT%0AW7ZscXFxKTkaExMzfPhwU1PTmJgYc3Pzp5773nv9ZDJ5zeallyAWi4qLZepOQUT05pszZ87vv/+u%0A7hRvnTFjxvj5+ak7BRERUdkOHz48ceJEdad464hFomIZ/xZMREREREREREREtRLrxIiIiIiIiIio%0AaggEggkTJvTr18/T07Nfv37jx49fu3atiYmJXC6fNGkSgKysLDc3t1OnTolEotJPlMnke6fBvYN6%0AYlP5/CMwaiur+IiIaoJcLneztvbv2FHdQd4i7uHhMn4LnIiIXmOq+5Ry2DB1B3mL+KeljQoPV3cK%0AIiIiIiIiIiIiolckVHcAIiIiIiIiInqjWFpaHjhwYO/evUePHrW3tz948OCqVauio6Nl/2fvvsOi%0AOtoogJ8Flt4RUVFROnYs0SixREyiWOKngj3GhprYC9ixiwbFIEaJ3cQCsQS7IoKViAVrFKkqTQRB%0AOmz5/lhFbGgEdgXP78mTZ++9szPnIrADzLsjEolEonPnzrm7uys6IxERERERERERERERERERERER%0AUVXD/cSIiBTG76yiE9A7xDyBubmiQxARfTZOpaZmFBUpOgURlb9+/fo5ODiMGzdu0KBBIpFILH6+%0AIZVYLPby8mrTpk2fPn0Um5CIiIiIiIiIiIiIiIiIiIiIiKgqYZ0YEZECmJqaKisruf4hUXQQeqeO%0AfesqOgIR0WehjqmpX1ycolN8XpSVlU1NTRWdgj4XNWvW3LdvX4sWLW7duvXapSFDhjRs2NDW1lYh%0AwYiIiIiIiIiIiIiIiIiIiIiIiKoe1okRESlAu3btRCKxolMQEREp3oNHjxQdgYgq1oYNG65fvy6R%0AvPIWCVKpVCQS9e7d+8qVK5qamorKRkREREREREREREREREREREREVJUoKToAEREREREREVVNDx8+%0AnDp16mtFYjJFRUVRUVEjR46UfyoiIiIiIiIiIiIiIiIiIiIiIqIqiXViRERERERERFQhpk6dmpeX%0A966rIpFo165d27dvl2ckIiIiIiIiIiIiIiIiIiIiIiKiqop1YkRERERERERUIZo3b25hYQFAWVlZ%0AW1v7rW1Gjx5948YN+eZSmJRn2HMZS44oOgcRERERERHRpy0+N1fREYiIiIiIiIiIiIgqJRVFByAi%0AIiIiIiKiqsnd3d3d3T07OzssLOzcuXOnT5++ePFiUVGRmppaQUGBrI1IJOratatic8rHv0lYG4J1%0AIbAxwexuACCRYs0pbDyH+HTY1cC0b+DcAgKBooMSERFVsMsZGW63b196+lRHRcXJxGRZw4bVVFUV%0AHYqIiIgUzCcmZkKJ95EZYWa20d5egXmIiIiIiIiIiIiIKinWiRERERERERFRBdLW1nZ0dHR0dPTw%0A8Hj69Om5c+fOnDkTHBx8/fp1sVispKRXu0tsAAAgAElEQVSUmJgIQKronBXNria8+mJdyMszk/2R%0AloNxHRGZAr+z6P87svIx0kFhCYmIiOQgIjNz8b17C2xttVRUfrl/f2N8fFJBwaE2bRSdi4iIiBSp%0ASCLZ9ejRsgYNZIcCgWBQ7dqKjURERERERERERERUSbFOjIiIiIiIiIjkxMDAoEePHj169ACQnZ19%0A4cKFs2fPnjhx4tKlS5m5ig5X8dSFLx/HpSE1CztHPj90aoxv1+CXE6wTIyKiKi44NXVny5aaysoA%0AtjZvfjglJfTJE0WHIiIiIgXblZAwuE6dcfXrKzoIERERERERERERUaXHOjEiIiIiIiIiUgBtbe1v%0Avvnmm2++WbRokUAg0NdUdCD5evQUq5xfHnaxQzVtJGQoLhAREZFcTLG0LHkokkgG16mjqDBERET0%0AKZBIpZ6RkQ/y8vYlJn5paPhj3brmWlqKDkVERERERERERERUWbFOjIiIiIiIiIjoFVIpwmLx1xXs%0AvYYz0zD2T5yPhrUJfumL9lYAkJmHxUegLEChGLcS0MgUc51goPmeSyU5WL5+plCEthYVf29ERERv%0AkysW/xYbe+vZMz2h8GJ6eu+aNWdYWSkJBJlFRYvv3VMWCAolkltZWY10dOba2uoLhYFJSYdTUg4n%0AJ1/r1Mk1IuJUaqqtjs4me/vGurqHk5OHXLnytKhojo3NIjs7AL/Fxv5848ZvTZuOrleveESJVDrv%0A7l2vxo1LniQiIqJPinxmCM6mpt9Wr37z2bOL6emnUlNX3L8/29p6nq2tou+eiIiIiIiIiIiIqFJi%0AnRgRERERERER0SskUqRlY10o8ovgcxpu3+HRU7j+gc6rcHM+TPXRaikGfgGPHgDwOAsOK3AgAldn%0AQ1npnZdK3zDtfDTyRVjYUx53R0RE9BqRVPr9P/+YqKlta95cSSD4PS5udESEnY7O18bGrUJDB9au%0A7WFrC+BxQYHD2bMHkpKudOrUQl9/8JUr2SLRhrg478aNb2dlOV28OCYi4nz79k41aiyws5tw40Zr%0AAwNZ/91MTFxMTUvWg+1PSlodFXU2Lc1MU1MqlbrWry9QyJ0TERHRu8lzhrCqcWMAmUVFPjExHnfv%0Azr97t6a6+igWkxMRERERERERERH9d0qKDkBERERERERE9GlRVkL3JqhjAABLeqG9FQZ+gUW9IJJg%0AVRCWH8P9x3Bt/7xxdR3McULsEyw9WtqlUogkcN8Hv8FoXb8i74qIiOgdfo2OPvn48RwbGyWBAMCw%0AunU3N2/evlq15ZGR97OzXV8s0a6upjbHxiY2N3dZZGRtDY1a6uoA5trYmGlqdjUxMVFTu5yRIWvp%0AWq9eHQ2N9XFxssPf4+OnW1mVHLFjtWrrmzXzadLkcUHB2OvXN8fHy+teiYiI6EPJf4agJxTOsbFZ%0A26QJgHWxsXK6TyIiIiIiIiIiIqKqhXViRERERERERERvoSQAANUXe7H3bAoANxNwPhoAdNRftmxv%0ABQAXoku7VIp5gehgjR++LKfcRERE/1HwkycAamtoyA6FSko/1q1rIBSeT08HoKOiUtyyvZERgAvp%0A6QBK7gAmAPSFwkKJRHaoqqQ00cLiSHJyVE5OoURyLyvLXk+v5IgGQmEDHZ2fzc03NGsGYMfDhxV4%0Ae0RERPRR5D9DkBlVr566svL97OyKuS0iIiIiIiIiIiKiKo51YkRERERERERE71dLDwB01J7Xj8Wl%0AvbxkogsAehqlXXqXAxFQV8HiXuWel4iI6EOlFRYCeHM1tuzvB3G5ucVnTNTUAOiVWBf+LiPNzLRU%0AVNbGxOxPSupnavquZr1q1gSgpaz8EbGJiIioQilqhqAsEBgKhVba2h+Zm4iIiIiIiIiIiOjzxjox%0AIiIiIiIiIqL3S8sBgA7Wz7cIO3zz5aWH6QDQpUFpl97q2G08eop53SF48Y7rZ++Xc2wiIqL3aqmv%0AD2DJvXsSqVR2Ji43NyAhoX21agAOp6QUt3yYlwegS/Xq7+1TTygcaWa2JT7ePyGhd82a72qWmJcH%0AoEeNGmW7AyIiIip/ipohJObnJ+bnu7y7zpyIiIiIiIiIiIiISsE6MSIiIiIiIiKidxJLnj8I+he2%0ANTClC2Z8i4a14BOMpMznl3xD0M4CP3cq7RKA3EIAyBc9v3TyX3geA4C1p7H2NH4Nxk+7cPS2vG6M%0AiIjohVnW1gZC4V+JiY7nz/vGxMz9998xERHdatSYYWXVUEfHJzo6KT9f1tI3NradkdHP5uYA8iUS%0AANIXnWSJRAAKJZLibidYWGSLxfZ6ekKll3+J8IqK2vLgwTORCECeWDz99u3hZmau9evL506JiIjo%0Aw8lthrDw7t3xN278m5UFIE8sHhsR4WJqOt3KSm53SkRERERERERERFSVqCg6ABERERERERHRp2vj%0AOfRpDgBJmQhzh4YQAC66YdFh/LAFjU2hrARjHZyaAhUlqKi+81LME3gHAUB8GlYHwdoE/TYgrwgh%0Aka8MF71E7ndIRESfvZrq6lc6dZpx69a59PR5d+/2qFFje4sWWsrKAC526LDo3r0frl5trKurLBAY%0Aq6qeatdORSDwjYmJz80FsOju3YkWFlsePEjMzwcw+86dhXZ2GsrKAOprao43Nx/7ag1YemHhmujo%0AGbduDa5TR6ikNNvauo2hoSJumoiIiN5DbjOEmurq/gkJm+Pj+9aqpamiMsHCorOxsYJumoiIiIiI%0AiIiIiKjSE0il0ve3IiIiIiIiIiKqMAKBYM8oOLdUdI5X2c7DvRRINyg6h6L5X4bL7+BvkIiI5MDZ%0A2RkXLvi3aqXoIJ8R5/BwtG3r7++v6CBERERv5+/v7+LiIv3+e0UH+Yz4JyS4hIfzp2AiIiIiIiIi%0AIiKqpJQUHYCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiMqEdWJERERERERERG+RVQAAhSJF5yAi%0AIiIiIiIiIiIiIiIiIiIiIvoArBMjIiIiIiIiInpFdgFm7EViBgCM/gMXohUdiIiIiIiIiIiIiIiI%0AiIiIiIiI6H1UFB2AiIiIiIiIiOjToq2GFX2woo+icxAREREREREREREREREREREREX0w7idGRERE%0ARERERERERERERERERERERERERERERFS5sU6MiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKhyY50Y%0AEREREREREdEnKuUZ9lzGkiOKzkFERPRfpBQU7ElIWHLvnqKDEBER0aeC0wMiIiIiIiIiIiIi+VBR%0AdAAiIiIiIiIiov8gMgWB19GwFvyvYOsFAAiegk42rzc7Hw2HFQAw0gE/tkVbi9cbiCVwWInTU6Au%0AfOV8aCR8QxBwBQCa18WkzhjSBgCC72LlCRy7jR5NMLg1nFu+8qxzUVh6FEdvQSBAZ1sUiFAkho0J%0AJnaGfZ2PvNN/k7A2BOtCYGOC2d2en2y9DO2tsbLPR/YpkmDWfkz4GrUNPrIHog8UHBwcFRWl6BTl%0AT11dvX///qqqqooOQgQAkdnZgcnJ0ywtAYil0o3x8QEJCcn5+XU1NdWUlGpraJiqq6cWFno1aiTP%0AVP9mZa2NiVkXG2ujrT3b5o1X6PdpHRra3sho5cdmFkmls+7cmWBuXltD4+N6IKoyCgsLd+/enZ+f%0Ar+ggcvLNN9/Uq1dP0SmIFI/TgzdxekBUOs4ZiIiIiIiIiIiqGNaJEREREREREVGlERIJvzPYOgyq%0AKuho87xObHXQW+rE1p6GhhB5RfDp/3olmMzBGwiLwR//YKTDK+c7WKO9FdRU8Mc/UFbC4NbPz39t%0AizXBmPEtlveGQPB6bw6WCBgN7QmwMMbJSQCQ8gwDN6H1MgRNRnurj7lZu5rw6ot1Ia+cNNGFoebH%0A9CajogS37zBqB37pC/NqH98P0Xt98+23YpFI0SkqhLa29v/+9z9FpyBCyJMnfnFxW5s3BxCXm9sr%0ALEwKbGvRopmengCQSKU7Hz2aePPm9zVryjmYnY6OV6NG62JjP7D9w7y8OiUWbZuoqRmWoRRTRSBw%0As7Iade3aL40amWtpfXQ/RFXAoUOHfvjhB0WnkJ8BAwbs3LlT0SmIFIzTg7fi9ICodJwzEBERERER%0AERFVMawTIyIiIiIiIqLK4U4Shm7GtblQVQEADSEAtLXAoZu4/xhW1V+2TMpEeg7qGuJeytuLxABs%0APo86BlgVhOHtoPRq3ZdAAL8huJOE8Djs+AdD2wDAzksw0Hx7kZiMlhoAKL+4aqKLNS5ovAArjn9k%0AnRjwlvCBP31kV8WMtDC/O3r6Iswd2mpl7a0KKCoq8vPzW7JkSVJSktwGrVmz5pw5c0aNGiUUvuMT%0AtPITi0Rfj9hk3vx7RQcpZxt/MhJV0fq3N0VHRy9btmz37t05OTkKCaClpdW/f/+ZM2daWLyxKeRn%0A705W1tArV6516qSqpFQgkXx34cIzkSjS0VFb5fnv/JUEgsF16tTW0Fj/wQuyy5G6svIHtozNzR16%0A5crZr74qPhPYpk0ZRzdSVZ1va9szLCysQ4fiDwiVr7CwsHnz5p06dUoikch5aHNz81mzZg0dOrQK%0Av4aWF9kLlne6t6KDyMPWH7d+Pi/QRO/C6UEpOD2Qg5SUlOXLl2/cuDE7O1vOQ2tra48aNcrNzc3E%0AxETOQ1cNnDMQEREREREREVUx/B0oEREREREREVUCEimGbMaP7WD06nt/T+qMC9FYcwprB7w86XcW%0AYzvAfd87e7v+CJbV0dkWk/xx7Da6NXq9gYYQf7miyUJM3IPOtkjIwPozOD7xnUVib2VmCABPc//D%0AU+SjaW1YGGP6X/htkKKjKJRUKv3rr79mzZqVmJj4448/NmjQQG5D3759e9q0aatXr162bFmfPn0E%0A/+kTi6ji/fvvv0uXLt29e7ehoeHPP/9ct25dhcR48ODBli1btm3bNmDAgJkzZ9rZ2SkkxidIIpUO%0AuXLlRzMzI1VVANsePLiXnb2+WbM3Fz13rFYtrbBQERk/yKO8vO4XL4ql0nLvuamenoWW1vTbt39r%0A2rTcO//MhYeHz58//+jRox07dvT29lb+4EX/5SUiImL8+PFLly6dN2/eoEGDVLjWn4gIAKcHH4DT%0Ag4qTmpq6YsWKdevWGRsbz5gxw8jISM4B0tLSNm3atGHDhp9++mn69OnGxsZyDkBERERERERERPRJ%0A4d/PiIiIiIiIiKgSOHgDVx+8Ugwm09sedQ2x5QIW9YKBJgAUinD8NuZ0K61ObF0IZneDoRYWHILX%0AybfUiQGoXw3eLhi5HQM2IisfgT8938Hsw12KA4CvLAEgMw+Lj0BZgEIxbiWgkSnmOj0PXMqlksQS%0A7L2KwzcRm4bQqQi8gcM3cfgmrs2B6x84dRe2NbBpKBqbAoBUitWnEB4HPQ1suYDCF2+ULN3w/MG3%0ADTBhD6Z9A4vPdfVUQECAh4dHVFTUiBEjRo8ebWBgIM/RnZycRo0a5efnN2jQoPnz53t4ePTr10+e%0AAYjeJSwsbO7cuUFBQRYWFp6enr169VJsDcaoUaP+/vvvdevWNWjQwNHRcdGiRW3KvJtEFXAwOflq%0ARsbaJk1kh4eSkwG0MzR8a+M+tWrJHmQWFS2+d09ZICiUSG5lZTXS0Zlra6svFAYmJR1OSTmcnHyt%0AUyfXiIhTqam2Ojqb7O0b6+oeTk4ecuXK06KiOTY2i+zsAPwWG/vzjRu/NW06ul69t3Zo8MYWTxvi%0A4sZERACQfv/9M5Ho97i4abduyQ63PnhwJytLTygcExGxvlkzsVS6NzHxcHJybG7uma+++rjMxeN+%0Aa2Iy4caNaZaWFlpaoPIQEhIyZ86cCxcudOvW7fjx4zY2NgqJ4eTk9NNPP23dunX8+PFz586dOnXq%0AmDFj1NS4RyoRfe44PeD0QCESExMXLFiwdevWOnXqrFy5smvXrkpKSgpJMnjw4KNHj3p5ea1Zs2bY%0AsGHz58+v9eLznIiIiIiIiIiI6HOjmF/SERERERERERH9J7vDAaCl2evnVZQwvhNyC/H72edn9l3D%0A/+yh/O7feaRmQSxBXUNoq2FsBwTfRcTDt7cc3hbdGuHsfTjaoc6HlRFJpBBLkJaDAxEYvg0GmnBt%0Aj6x8tFoKLVUs/x9W9cMfI3DoBlosQUZuaZdeo6wERztsD8PjZwDQoi52XUJiBjacgbczdo9CeBzG%0A/Pm88a/BmP4X1g7A+kFY3Q8AJju+LBID8KUFxBIEXPmgm6pirly50rlz5/79+zdr1iwkJMTNzU3O%0ARWIyBgYGbm5uISEhzZo169+/f+fOna9evSr/GETFgoKCHBwcvvzyy5SUlI0bN548ebJPnz4K36hH%0ARUWlT58+J0+e3LhxY0pKypdffung4BAUFKTYVAq3OyEBQEt9fdnhg7w8APVLXe6cJRK1Cg3VUlFZ%0A3rDhqsaN/2jR4lBKSovTpzOKilro6+969CgxP39DXJx348a7W7UKf/pUtnTbqUaNBXZ2AFq/+D7Z%0AzcTExdR0dL16pXT42tCu9eqZv8imq6Iy1dKy+HCOjQ2AGmpq65s1A6AsEDgaG29/+PBxQcFHZy72%0ApaGhWCoNSEgow0eanvvnn38cHR07deqkr69/9OhRX19fRRWJyejr60+aNCk0NLR79+4zZ860trb2%0A8/MTiUTvfyYRUdXF6QGnB3KWlJTk6upqbm5+8uTJ1atXnzx50snJSVFFYgCUlJScnJyCgoJWr159%0A4sQJKyuriRMnJiUlKSoPERERERERERGRArFOjIiIiIiIiIgqgYsx0NOAUPktl0Y6QEsNPqdRJAaA%0AzecxwqG0rvzO4udOzx+P/xpqKvA6+c7GhlpQF2LNqXfWkr3m/mOojEVtN0zcg28b4vJs1K+G5cdw%0A/zFc2z9vU10Hc5wQ+wRLj5Z26U3Fm4wJBKhtgFr6ADDXCWZG6NoQJrq4HP+8wdHbkAI6agDQtwUA%0AXIx5pSsTHQA4e/+DbqrKiI6OdnZ2btWqVX5+/oEDB5YuXarw9xevVavW0qVLDxw4kJ+f37JlS2dn%0A5+joaMVGos/QwYMHHRwcunTpkpOTs3HjxsDAQEdHRwUu8XyTkpKSo6NjYGDgxo0bs7Ozu3Tp4uDg%0AcPDgQUXnUpiL6el6QqHwxb+RbI+OlPz8Up6yPDLyfna2a716ssPqampzbGxic3OXRUbW1tCopa4O%0AYK6NjZmmZlcTExM1tcsZGbKWrvXq1dHQWB8XJzv8PT5+upVVKR0ujYx8c3ShQFDKYUkGqqplzyxj%0AoqYG4GxaWikfFnqv8PBwR0dH2T5++/fv9/X1tbW1VXSo5wwNDd3c3M6fP+/k5DRx4kRLS0tWixHR%0A54zTA04P5CY5OXnixIlWVlYnTpxYunSpwivESiquFlu0aFFgYKCsWiwlJUXRuYiIiIiIiIiIiOTq%0Ak/htHRERERERERFR6ZIzX1ZJvUZfEz+2xaOn2HsV1x7C3PidLQEUiuAbAvvFELhC4Iqa01Egwu5w%0APHr6lsbep6AuxPYfUSTGoE3Ie/090N/CxgTSDchbi/hl8BsM82oAcD4aAHTUXzZrbwUAF6JLu/Sm%0A1xbOCV69pK+BwhdLo9uaQyrF4ZsA8DQXALrYvfJcfU0ASH72/juqGlJSUlxdXe3s7O7cubNjx46d%0AO3c2adJE0aFeatKkyc6dO3fs2HHnzh07OztXV9fHjx8rOhRVfVKpNCAgwN7evmfPnrLHBw4ccHR0%0AFLx7ka5iCQQCR0fHv//+OyAgQCqV9uzZ097eXvZY0dHkLTk/X7b4W8ZWRwfAvezsUp5yPj0dgE6J%0ADeLaGxkBuJCejtdeUAB9obBQIpEdqiopTbSwOJKcHJWTUyiR3MvKstfTe2+HH61kko/OLKMvFAJI%0ALigoS57P2ZUrV7p06fLFF1/k5+f/9ddfmzZtsre3V3SotyiuFuvWrduECROsrKz8/PzEYrGicxER%0AyRunB6VnluH0oIweP34sq8329/efPXt2UFDQp7AF8ZtkmxIHBQXNnj3b39/fwsLC3d09vWyfh0RE%0ARERERERERJUI68SIiIiIiIiIqBJQVoJY8s6rE76GQIDVQfA9jQlfl9ZPwBVM7QLphpf//TEcIgl8%0ATr/e8sQd7I/A2gHo1wL9W+FOEmbs/cjwSgIAiCvxluUmugCgp1HapbKY64Tfh2DEdkzfC/d9WNYb%0A87u/0kBWBvI51Fbk5eV5eHhYWloeO3bM29tbtm+SokO9nWx/JG9v72PHjllYWHh4eOTl5Sk6FFVN%0AYrF4+/btTZo0cXZ2NjIyCggI2LlzZ6tWrRSd60O1atVq586dAQEBRkZGzs7OTZo02b59+2dVFqIs%0AEIhLfAfvYmwM4FypO2PI/hIQl5tbfEa2n4beB6zrHWlmpqWisjYmZn9SUj9T07J3+IHKOIRspfhn%0AWEZYdlevXu3Ro0erVq1ycnICAgL+/PPPli1bKjrUexgZGbm5uYWEhHTq1Gn8+PGNGjX63L4tEBFx%0AevAhT+f04KM9ffrU3d3dwsJCViF29uzZgQMHfoIVYiWpqKgMHDjw7Nmzs2fP3rZtm5mZmbu7+9On%0Ab3uXICIiIiIiIiIioqqFdWJEREREREREVAnU1EPGqyUzsrIx2f+tqqN7Y1yKQ0IGGtR83uDNlV9i%0ACVacwJA2r5zs2wLVtLHhDLLyX56MTMHPu7BnFNRUAGDtAOhrYu1pHL31zoSlrDSTbREm291L5mE6%0AAHRpUNqlshBLcSsRYe5Y2Qf7x8L9Oyi/+kugpzkAUFOvTKN84kQikZ+fn6Wl5dq1a2fNmnXq1Ckn%0AJyclpU/6t2FKSkpOTk6nTp2aMmWKt7e3paUlN0Wh8iUSiWQVYsOGDTMzMzt8+PCmTZsqUYVYSa1a%0Atdq0adPhw4fNzMyGDRsmqxYTiUTvf2blV1NdPaPo5R6XvWvVamNouC42NrbEmmmZfLF424MHANpX%0AqwbgcEpK8aWHeXkAulSv/t7h9ITCkWZmW+Lj/RMSetd8/ir7nzqUbVKX/+K7mWxnj+KXTdE7XkHL%0AkhnA06IiADXV1d/bkopFRET06NGjZcuWjx492rFjx65duyrX94eaNWvOnz//9OnTzZs3HzFihOzb%0AgkTy7ncaoHKVlZp1bf+1k14nFR2E6DPF6cF7M6PyTA/i4+PfetLHx8fT0/P+/fvyDJORkeHu7m5m%0AZrZly5ZZs2adOXNm4MCBwhKb133ihELhwIEDT58+PWXKlC1btsiqxTIyMhSd67NWljlD+kPuC0dE%0ARERERERE9H6f9MoYIiIiIiIiIiKZ9lbIyn+llOtx1sv/A5jsCADjOr5skFMAAHkvV8ph5yUYaqK6%0Azis9q6mgexNk5mFV0PMzCRn4Zg2mf/OyjMpICzO+AYBhWxGd+vaEuYWvD1dsxrdoWAs+wUjKfH7G%0ANwTtLPBzp9IuFfeZ/6LqQXb72QXPD2Xni5fPZRUAQKEIAJYewcHrOBuFY7dxIRr3Up6fL5aaDQDt%0ALN9+L1VAQEBAo0aNJkyY0KtXr+Dg4Eq3jm348OFnzpzp1avXhAkTGjVqFBAQoOhQVYpUKrkV/Ntf%0Ai9tunVzngGfnmCv7P4fN9QoKCtasWWNlZTV8+HBbW9ujR4/6+vo2bNhQ0bnKqmHDhr6+vkePHrW1%0AtR0+fLiVldWaNWsKCgre/8zKrL2RUZZIlPWiKE4AbGveXF8o/OrMGf+EhCKJBECuWBycmtojLKyB%0Ajg6AGVZWDXV0fKKjk/Kfv5T6xsa2MzL62dwcQP6rK7NlPReWqK6ZYGGRLRbb6+kJX1Tblt5hrlhc%0A3C0AWYZF9+7dz85eGxOTKRIBOJ6SIpZKa6qrJ+bnX8/MLDl0tkhU9sypBQUA2hkZlelj/VEuX77c%0AuXNnHR2dWrVqjRo16smTJ7LzUql006ZNPXr0mDlzZpcuXSZMmJD54sZL8vHxkS2dl6e7d+86Ozu3%0AaNHiwYMHO3bsOHDgwCe7/eZ7mZqaLl269PTp082aNRs+fHiTJk0CAgK4dUxFS4lMOb7i+LYR28L3%0AhH9I+4fXHvp+7+tWx22e3bzdE3fnpOVUdEKiKo/Tgw/JrMDpgUQiWb16dcOGDbW1tVu1arVnz56S%0Ar02yV/9iixYtKvncnJycqVOnOjo6Nm7ceMaMGVZWVvLJnJmZ6eHhYW5uvnnz5smTJ4eEhAwcOFBV%0AVVU+o5cvDQ2N4cOHh4SETJ48efPmzebm5h4eHm+diVFF+69zhjN+ZyYZTir+78TKExWdkIiIiIiI%0AiIioClBRdAAiIiIiIiIiovcb+iW2XMDFGHzTAAD+vo6N5wBg1A5MdkRnW3S0Rm97dGsEAHeS8Oc/%0ASMgAgPG7MLwd2lpg3zVM+wsSKdafwZj2L3vefw03EwBg+TGoC1HPCPMCEZ+GGwm4/ghNawNAeBwe%0AZQDA4yx09MLULpjU+ZV4l+Kw5hQAPEjHzP3o0xwtzV5e1VTFRTcsOowftqCxKZSVYKyDU1OgogSV%0Ad1+KeQLvIACIT8PqIPRrAd8QAEjIgPcp5BUiPg0AFh3GxM7YcgGJGQAw+28s7IkvLeAbgpHbX2Yw%0A0IRPfwxq/fzwQjSUBHBpWeZ/mE/PuXPnpk+fHh4e7uLisnXr1pov3tu+0tHT03Nzcxs6dKiPj8+A%0AAQNWrVq1cuXKyrtq/5MS9tfsgpynDb4akfk46u75bcGbRxYVZNu0HaLoXBUlLy/Pz8/Py8srKSnJ%0A2dnZ1dXVzMzs/U+rVGxtbVetWjV27Njffvtt2rRpXl5eU6dOHT16tIaGhqKjVYihdetuefDgYnr6%0ANy+2zrDW1r7x9de/Rkcvi4x0jYioo6GhpqTUo0aNgC++0BcKAWgqK1/s0GHRvXs/XL3aWFdXWSAw%0AVlU91a6dikDgGxMTn5sLYNHduxMtLLY8eJCYnw9g9p07C+3sNJSVAdTX1Bxvbj62fv3iDKV0GJOT%0A4x0dDSA+N3d1dPSwunW9GjV6WljoHR0dlJq6oVmzBjo69TQ1nxYViaTSpQ0aTLl5MyAxsameXo5Y%0AvDQyEkBCfr53dPRwM7OyZL6Qnp5+2q4AACAASURBVK4kELiYmsr3HwcRERGLFy9esGCBlpbWL7/8%0AsnHjxqSkpEOHDgFYv379uHHjrl271qxZs5SUlDp16jx69Gjfvn0lnx4eHu7m5ibPwJGRkXPmzNm7%0Ad2/Dhg39/Pw6d+4s/yq1ilCnTp2lS5cOHz583bp1AwYMWLhw4bx58/r27Vs17u4TZGJt0mtxr3Ob%0Azn1I44SbCSe8TnR176qqqXp67emwHWHPUp6N3j26okMSVW2cHnzK0wMAkydPTktLGzduXGRkpJ+f%0AX//+/bOyskaOHAmgqKho165dy5Ytk7UUCASDBg0qfmJGRka3bt3S0tIuXrxYrVo1+aR99uzZqlWr%0AfHx8xGLxmDFjhg4dqqWlJZ+hK5Smpubw4cNdXFy2b9/u7e3t4+Mzfvz4KVOm6OrqKjraZ+Q/zRnE%0AReKre692n9dddigQCFr0a1GR6YiIiIiIiIiIqggB30ORiIiIiIiIiBRLIBDsGQXn99Usdf0VNjXg%0A7SyXTJXcH//gSfbzYjaJFIkZOH0Pk/3xZNXzBj18YaKDjUPf35X/Zbj8jkrxG6T79+9Pnjz58OHD%0ATk5OkyZNktubrMtBZGTkmjVrZLe2evXqSnFrAoHg6xGbzJt/r+ggr8tKe3A5cHGnH/1khwn/nj66%0Atq+eiWW/ef98yNM3/mS0Z88eZ+fK8Z0oNzfXx8fHy8vr2bNnAwcOHDFiRO3atRUdqsI9evRo06ZN%0AO3fu1NXVnTp16vjx4zU1NRUd6kM5OzvjwgX/Vq3e27LrxYs22trejRvLIVUl1SMszERNbaO9fenN%0AnMPD0batv79/eY27atWqMWPGyD7rioqKjI2NxWJxVlYWgLZt2168eDE5OdnExARA7dq1MzMzZZdk%0Anj596uXlFRAQEBkZKYdX3vv378+ePXvv3r22trZTpkypMhVib5K9jB45cqRVq1Zz5szp0aPHR3Ry%0A/vx5VVXVVh/w5fnp8Pf3d3Fx8U73ltuIkwwnVbesPuvSrNKbhawLaftjW1UNVQDiIvFsq9lSsdTz%0AoWdZht7641YrVaty/Fom+nTIvpal379/Xs3pwXt94PTAPyHBJTy8HF+L4+LiZs2atXPnTtnhiRMn%0Avv32Wxsbm7t37wLYvn17dnb2uHHj3vrcfv367du37/z5823atCmvPKWQ/QSxcuVKkUhUlSrE3pST%0Ak7N9+/b169cLhcJp06Z93E8NUVFROTk5TZs2rYiE8vTJzhnCd4cX5BQ4jCjPd8zhnIGIiIiIiIiI%0APgdKig5ARERERERERPRBtg7D4ZtIfqboHJ+8hYcwZDN++PL5oZIAtQ3Q1gIWxs/PXIxBZApWVY46%0Alw+SnJzs6urasGHD+Pj4P//809fXt1JUUn04a2trX1/fP/7448GDBw0bNnR1dU1OTlZ0qMoqJyOx%0ATZ/FxYemth3VtY1yM5IUGKkiZGRkeHh41K1bd8GCBX379j1//vz8+fM/hyIxALVr154/f35ISEj3%0A7t0XLFhQt25dDw+PjIwMRecqZ1ubNz+cnJycn6/oIJ+oi+npkdnZqxSxUH7KlCklFxmLRKLiLUEM%0ADAwABAYGAkhPT09ISOjYsWNxS6lUumjRounTp8uhWCs6Onro0KENGza8evWqj4/PoUOHHB0dq2qR%0AGF68jB49etTY2LhXr15t2rQ5ePDgBz738ePHXl5eFhYWDg4OkyZNqtCcn4+O4zrKisRkJCIJ9wYh%0AKhecHpROgdODR48erVq1qviwS5cu1apVS0hIACCRSDw9Pd3c3BwdHefOnRsTE1PyicHBwX/99de3%0A334rhyKxvLw8T0/PevXqLVmyZMiQIaGhoWPHjq2qRWIAtLS0xo4dGxoaOnjw4CVLltSrV8/T0zMv%0AL+9DnltQULB79+727dtbW1v379+/oqN+tqQS6ak1pw56HFzXe92RpUfS4tIUnYiIiIiIiIiIqNJg%0AnRgRERERERERVQ4mutjrisn+yClQdJRP27koAFgdhPwiAJBKER6HmfuxYzgAJGRgyREETYauuiJD%0Alpdnz565u7tbWloeP37c29v74MGD7dq1U3SoiuLg4BAYGOjt7X38+HFLS0t3d/dnzypz3aRUGn/j%0A6Lmdk3fOapiX9eSk35BtU83+XuGYnnhHdr0w79k/++eH/70wbO+coz59wvbOKcjNkErESffPh/01%0Aa/fcZrmZyYdW99g1p0nO04SoS/6nN48K/OW72GuBO2c1+HuFY0bK/bRHN4/69Nk2td4Bz84ZSfdk%0A3dawaKOhW71kELGosLr5F/K+/QqTnp7u7u5uZma2evXqIUOGnD9/3s3NrVq1aorOJW81a9acP3/+%0A+fPnhwwZsnr1ajMzM3d39/T0dEXnKjcmamp7W7eefOtWjlis6CyfnIT8/CWRkUHt2umqqCgwhkQi%0AmTdvnpeX12+//SY7s3r16vr160+ePPnSpUuzZ8+ePn36rl27itv7+Pi4uLjo6elVaKqYmJihQ4fa%0A2dmdO3fO29v75MmTTk5OSkqfxZ+KbG1tfX19jxw5Uq1atZ49e7Zr1y4oKOhdjSUSybFjx3r27Fmr%0AVq0ZM2bIVu2X3PztcyARS6LORe2ftX9h04WZyZk+3X08GnvkPs0tyCk48cuJnT/v9Praa13vdUl3%0A3lJrfWHrhUmGkyYZTgKQn5V/2vd08WFJUon06LKj3y/+vp9XP3ncElFVx+lBKRQ7PXBwcKhRo0bJ%0AM4WFhW3btgXw7NkzWRlYWFjY4sWL7ezsFi5cWNxs27ZtAExNTVu3bq2jo/Pll1+GhISUe7z8/HxP%0AT08zMzPZe0yEhoZOmjRJX1+/3Af6BOnr60+aNCk0NLRv374LFiwwMzPz9PTMf3ex5b///jt58mRj%0AY+OBAweeO3dOKpVW7p/Ky0kFzRnys/Jtv7Y1a2kWFx534pcTS9ssPb7iuLzvjYiIiIiIiIiocvos%0A/vhHRERERERERFVDk9pY3Au+IYrO8WnbMRzjOuKPf1BrBr5aib4bcPUB/hgOaxMUibEjDDtHoo6B%0AolOWWWFhoaenp7m5+aZNm2bPnn3q1CknJ6cqvBeKjEAgcHJyOnXq1OzZszdt2mRubr5mzRqRSKTo%0AXB+pWt2m0Zf35mYm3z239cu+S78evjE1/tr5XVMBFOVn/72is1BVs1WveW36LO44bMPDW8cPLP86%0APyddSVn477mt2ekP7/+z277rtNp2nVQ1dI3rtYy+si8jOVJVXafX9JOp8ddOrBvw6E5w55Fbekw5%0A9ORBxMW9s9+aITnmH7GooEX3mfK99QqRlJQ0ceJEMzOzDRs2jB079sKFC5MmTTI0NFR0LkUyNDSU%0ArfscPnz4hg0bzMzMJk6cmJRURbaPa6Kru9jOzvfVTSeoSCLZ8eDBzpYt62hoKDDG/v37O3bsuHz5%0A8mXLlm3YsEEqlQKwtrYOCwtr0qRJhw4dVFVVV6xYoa2tLWt/8eJFkUjUunXriouUkJDg6upqZ2d3%0A5syZ5cuXf1YVYiXZ2dn5+vru379fQ0OjS5cuDg4OwcHBJRvcunVr4sSJxsbGXbt2PXz4sFgslkgk%0Asku5ubmKiKwwEpFEWVX5wpYL6Q/Tw3eHfzv9W9tOtspC5b+m/dW0V9OBawdODZ4qEAh8e/vmZ72+%0Anr7tsLZG9Yxkj9V11Dv91Kn4sNiNQzd8evgEeQcFrQ66sPWC7MuEiMqI04O3+kSmB8XOnz+fn58v%0AqwfT19dftWrVyZMnExISFi1aJBaL58+f//vvvxe3BNCqVaugoKCTJ08+evSoc+fON2/eLK8k+fn5%0Aa9assbS09PDwkO1C7Obm9hn+BGFoaOjm5nb+/Pm+fft6eHhYWVmtWbOmZLVYenr6mjVrmjRp0qBB%0AA19f36ysLKlUKnvl+sAtyKq2CpozaOhpfL/k+3H7xy28s7DbrG5SsfTo8qMXt1+U670RERERERER%0AEVVOinw3TSIiIiIiIiKi/8rCGDO+VXSIT5uJLnwHvP2SUBnu38k3TYX53//+d/LkycGDB//888+f%0A2zo2FRWVgQMHfvfdd2vXrp0xY0ZQUNDBgwcVHeq/Ewi09Gtp6tXIfBxt33UaAG2D2ho6xqkPIgBc%0AP+Gd+TjG1mGYrK2GTrVm300L3T7uZpDvF709tA1MMx/H2Lb7QU3LwNS2AwA9NW0AGrrGpnadAGjp%0A13r2JLbpNxMBGJo20tAxfhJ/7c0IEoko/MDCrwasrl6vhZzuusJkZ2c3adKkqKhoxIgRw4YNMzCo%0A/MWg5cfAwGDSpEk//PDDli1btm3btnPnztjY2OL6nErNQktrhpWVolN8WoRKSu7W1opOgY4dO9rY%0A2AQHB8+YMWPs2LFCoXDEiBEAcnNzDQwMOnbs+OuvvwqFwhUrVigpKaWlpW3cuLF4RXhFSEtLa9So%0AkYqKyowZMwYPHqyuXiU2FS0De3v7TZs2hYaGrl692tHRcefOnT169Pjzzz/XrVt3/fp1oVBYVFQE%0AoLhCTOZzWwiuoqZS/4v6+rX1U6NT2/7QVtNA07qDdUxYTPie8PA94SVbRl+Ibvhtw9eerqyiXMoh%0AAEsHy+pW1e+fuR84PzBgaoCyULnN4DYVcSNEnxtOD970iUwPZEQikbu7u5+f32v14Xp6enPmzKlW%0ArdrYsWPXrVs3atQoAAkJCTVq1Bg9ejSANm3aLFu2TLZZ7ubNm8slzODBg//+++/evXtPmDChTp06%0A5dJn5SWrFhs4cOCaNWumTZt29uzZv/76KygoaP369YGBgRKJRDYxkE0SipWy+djno6LnDOq66t9M%0A+0bLSCtgasC5Tee+HPplRdwFEREREREREVFVwjoxIiIiIiIiIiKqfKKjo8eOHTt58mRFB1EYQ0PD%0AefPm6ejoHD9+XNFZyqDkFnACgaqmXl5WKoCUmEsAhOovK3lqWH5ZfB4CJQBqWgZv7wdQUdMseaiq%0AqZeZEvXm4FcOLatp1daqTf8y34biZWVlPXnyZPfu3W3acJH92xkYGEyZMqVt27b9+/fPysqqGnVi%0A9MkyMDAwMDBo0KCBnp7e0KFDd+zYMWLEiLCwsO7du//22289e/b8+uuvvby81NTUlixZMnbs2LFj%0Ax0ZGRsqeW1BQAODu3btCodDCwqJc8mRkZGRkZPz9999NmzYtlw6rhg4dOnTo0KFNmzZr1651dXXN%0AysqSbUz62vrvYhkZGS1btiw+zMvLK7k0PD8/vyyFZMX1vR/4QFtbW0dHR1tbW1dXV09PT1tbW3ZG%0AX19fR0dHRaXc/vwn+5hoGjx/YX1w7UENmxruF93L3rOmvqamvmYNmxrquup/jv3z8p7LrBMjos/B%0AvHnzOnTo8MMPP7z16qhRoyZPnnz//n3ZoYGBQclv6Z06dQJw+/bt8goTGxs7fPjwWbNmlVeHVUCd%0AOnV++eUXHR2d4OBge3v7iIgIFRWVUjbxzs/P79Kli+xxUVFRdna27LFUKs3IyChu9uzZM7FY/NGp%0AhEKh7OcXDQ0NWcG/tra2UCgEoK+vLxAIlJSU9PT0AKiqqmppaclaampqyiYJGhoaOjo6urq6mpqa%0AmpqaBgYGxf2Uo4qbM8h8OfTLA7MOpEanlleHRERERERERERVGOvEiIiIiIiIiIioUhK8Whr0eaqy%0AHwSBAEB22gODWnayExq61QGoauiW1wjx14+oqKjLtjKrMpSUlBQd4VPHDxHJWa9evQDIVuvOmjUr%0ALS2tY8eOampqu3fvrlu3rp+f35IlSwIDAwMCAl57op2dnYWFRVTUW2pcPxo//98qKyvr/PnzssdS%0AqbSUlhKJxNHRsfjwtQXWampqmpqab3veB3n69OlbHxSvMpdKpTExMcVnsrKysrOzs7KysrKy3uxN%0AXV1dW1tbWfn17TjKriC7IC0+rTC3UFVTtfikRCxRUv74z67G3RoDKNkhEVFVdeDAAXV19blz576r%0AgbKysqGhYfXq1WWH1tbWYWFhUqlU9nNftWrVAJTv+x1UxItFFXDlypX4+Pj4+HgApRSJAZBKpWZm%0AZrIPY3E1FwCBQKCvr1/crIxV3IWFhTk5OShRpp6VlSULlpGRIZVKi4qKZPOE4pa5ubm5ubnPnj0r%0AbvkaWWmZtra2pqbmu4rky6Lc5wxKykqaBpraxny/DyIiIiIiIiKi92OdGBERERERERFVekVi/BML%0AB0t5j5vyDCGRiHqM2d3kPTR9yp48eRIWFhYXF/fzzz8rOktlVdOybVLkuQe3ThTXieU8TQBgatex%0AXPp/dOdUTkaifbfpxWeSoy7KtiwjqiyKJJJ/nj51MDKS87gpBQUhT55EZWfPtrGR89CVUWJiIoAe%0APXrgxS5Vsp0f6tSpU7wEvOSeVABsbW3v3btXesESlSMjIyMXFxd1dfV9+/YlJydraGgUFBRIJJI3%0AW4pEouXLl8s/4XtlZGTIasays7MzMzOfPXsmO7xw4cLOnTvLd6watjWK8otOrTnVdWZX2Znke8n3%0ATt/rMKbDay2fb85WUCRUEwIQF4kBFJc6lJSZnAmg4XcNyzcq0WeLM4RP1rFjxx49ejRv3rziM2fP%0Anv3qq69KtklMTExMTBw/frzs8H//+19oaGhERIS9vT2A1NRUAF988YUcU1cOCQkJpqam5djhF198%0AkZmZ2bx582PHjuXn52tqasqKr95q5cqVxXt+fppkG51lZmbKiscyMjJycnJyc3NlBee5ublXrlyR%0AlZmVo3KfM2QmZ2YmZ341+isQEREREREREdH7sE6MiIiIiIiIiCqx9Bx4ncSvwcgugHSDXIf+Nwlr%0AQ7AuBDYm/61OrPUytLfGyj4VkupyPNz24VIsdNTh1BjLeqMa32pZvqKiorZt27Zjxw5zc3NZnZhE%0AItmyZcvu3bsTEhIsLS1Hjx7t5ORUZfcB+4/ERQUAIJXKNhArys8GIBEVNukyIfZa4J3Q361a99fU%0AMwFwJ3SjiXnrBh1GyhoAkEhESkrPf70pFr3oBwAgEYsAFBXkCNW0ittLJWKBkjKAhLsh10+sqW/f%0A807oRgBSqSQzJUqoocM6MYXjF8sHSi8s9IqK+jUmJlskkn7/vTyH/jcra21MzLrYWBtt7f+0Crx1%0AaGh7I6OVjRpVRKrLGRlut29fevpUR0XFycRkWcOG1VQVtjGRl5eXoaFhnz59dHV18/Lypk+fPnz4%0AcFdXVwCDBw8+d+7cwYMHhwwZ8ujRo8ePHxevAqdS3LhxY/ny5devX9fS0vr6669nzJhhaGhYjv0L%0ABAIrKyt3d3dfX9+YmJiDBw/u2rXr0qVLSkpKAoGg5P4bIpFIJBKVZTOQCqKvr19yx5JixsbGZa8T%0AExWKAEhEEiUVJQCNvm1kbGF8fOXxjMQM6/bWyZHJD648+HHbjwAK8woBiAqef8RMbE1S7qecWHni%0AiwFf3A2+m/csD8Dd4Ls2HW1C14dq6ms27dlUXUe9KL8ocF5g60Gt2w5rW8aoRMQZwms+qRnCyZMn%0APT09+/Tps3btWgASieTevXt6enqnT59OTU0dN26cnZ1dXl7e2LFjXVxcpk9//n4Wrq6ua9asWbly%0A5Z9//ikQCPbv31+9evXiq5+zrVu3enh4FB+6uLh4enqWY/9CodDIyGjfvn1isfjixYuHDh0KCAiI%0AiYlRVVUViUSv1ZPn5uZ+4nViQqHQwMCglJD+/v5///13GUcp9zlDXHhc7tNchxEOJtYmRflFAVMD%0A7Hvbfz3+6zLmJCIiIiIiIiL6HHxyf80iIiIiIiIiIvpwhlpY3At+Z5FdIO+h7WrCqy/WhfznJ5ro%0AwlCz/PMAiHiIxYexoAe01PDLCWw8h6RMHKpUO1oVFhZOnDixdevWvXv31tPTU3Scj2FpaTlnzpwd%0AO3YUn1m0aNHTp0+HDBkSGxu7c+fOn3/+OTs7u3///goM+dH27t0bHh4+YMCApk2blr23O6GbstMf%0AArh29JeGnVwjw3bmZiYDCD+4pEX3mT2nHb929JfQ7eMMTRsIBMrqOtW6TdwvERVeP+WdlfYAwD97%0A51q3GWBUp0leVuqNkz4AstIfJtwNlUrFsm4vBy5u3m169OV9svY3T/lafzko83H0yfWDREX5SffP%0AlwzjsuBK2e+oIhw7duzkyZMuLi6fw74BVemLpaTExMT58+c7OTl17dpVTU2t7B0aqqoubtDALy4u%0Au+x9/Ud2OjpejRqti439r080UVMzrJiV2RGZmYvv3Vtga6ulovLL/fsb4+OTCgoOtWlTEWN9iPT0%0A9DVr1syYMWPw4MFCoXD27NltXoQZPXq0QCBYu3bt1atX4+PjZ86cOXfu3DIO99tvv6Wmpg4cONDS%0AUu7bqsrFnTt3fHx8Jk+erKmp6efnt3v37sePH2/evLmChjM3N584ceLEiROjoqIOHDiwf//+sLAw%0AJSUlZWXlgoICAHl5eTo6OhU0+qemMLfwtO/p9AfpAA7MPfDFgC9qN6mtrKr804Gf9rrtvXn45p0T%0Adxp1bTTEb4i6jnpaXFro+lAA6Q/TQ34L+WLAF98v+j4vIy/0t9B7IfdcVrvUsKlhWNcwLyNPIpLk%0APs0NXR8a6BHY0rmlsopyl6ld6rWsp+C7JaoSOEMo6ZOaIVy4cKFXr155eXkhISElz0dHR586dcrf%0A33/z5s19+/bV1NScMGFC586dixuoqaldvHhxypQpQ4YMqVevXlxc3OXLl99bLx0REeHn59e3b9+O%0AHTsqKSlVxB0plkgkCgwMdHNzKz7zfYUVRiorKzs4ODg4OCxbtiw8PDwwMHD//v137txRVVWVSqWy%0A3WLz8vIqaPTKooLmDNpG2tcDr//zxz9NezVV1VBtP7q9dQdrRd8rEREREREREVHlIJC+eJNdIiIi%0AIiIiIiKFEAgEe0bBueXH92A7D/dS5L2fmIzAFTYmuLtQAUO/aVUQxrSHpioAFIlhPBViCbJ+LVOf%0A/pfh8jvk9huk+Pj4evXqAVBVVXVycho0aJCTk5O6uvqbLe3s7L777rtJkybJJ9h/Va9ePXNz8+Dg%0A4EePHq1YseLXX5//M5w9e3bIkCGyS+UykLe397Fjx/79999y6e29XFxc/P39AVhbWw8ZMmTAgAEW%0AFhZvbSkQCL4escm8uVz3MZCDjT8Z7dmzx9nZWW4jjhkzZsOGDQDq1asn+5jb2dm92SwpKalWrVr+%0A/v6Vt5ysor9YZC5duuTs7JyYmFizZs1y7LZ0Bw8e7NmzJwBtbW1nZ+eBAwd27NhRWVn5zZbOzs64%0AcMG/VasP6dY2KOhedracdwuRERw4YKOtfdfRUf5Dv2lVVNSY+vU1lZUBFEkkxkePiqXSrO7dP/Dp%0AzuHhaNtW9p2tMrK3t4+IiJA9GDJkiIuLS61atd5sFh0dbWlpefDgwcaNG8s9Y5ls3Lhx0KBBGhoa%0AAEQikb29vUQiuX37djkO0aFDhzFjxri7u7/1ampqqmxF+MmTJ4uKip4+fVqJ6tj9/f1dXFy8070V%0AHUQetv641UrVqvJ+LROVQva1/OGv+JwhyJRlhuCfkOASHl5511H88ssvsj3HqlevPnDgwAEDBrzr%0AZ4QWLVq0bt26ZMFVpbB3797c3NwhQ4ZU3BCenp7//PPPlStvfzOR2NhY2fTg3LlzYrH4zp07b/0Z%0ArRLhnIGIiIiIiIiIqIqpgm8fRURERERERET0eZri+LxITEYkwaDWiktTNoWFhQcPHnR2dtbT0+ve%0Avfv27dtzc3MVHepjJCUlldwuxsHBwdDQMCUlRYGRykVkZOTChQstLS1tbGw8PDyioqIUnagqEwqF%0AAOLi4jw9PRs0aGBlZeXh4XHv3j1F5ypnVfWLpaTs7Ow///zT0dHRwMBg6NChBw8eFIvFig5V6U2x%0AtNQsUXQnkkgG1a6twDxyVryEPSIiws3NzdTU1NbW1tPTMzk5WbHBysvIkSNlRWIyYrG4V69e8gxg%0AbGw8YsSIQ4cOpaen3759uxIViRERfeY+8xmCqqoqgMePH69bt65169Y1atSYOHHiuXPnFJ2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3btUlRUfLfP0qVL79+/X6NoRIzP569Zs2b6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%0AyXO2X+Fh75xNO9L/N0bdloi6y+oW1uU8r4lumE8pv5CI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<p>This model has been trained on processed images, so you need to
preprocess the image with a preprocessor, before saving it in the blob.</p>
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<div class="prompt input_prompt">In [18]:</div>
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<div class=" highlight hl-ipython2"><pre><span class="c1">#load the model</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">caffe</span><span class="o">.</span><span class="n">Net</span><span class="p">(</span><span class="s1">'../models/bvlc_reference_caffenet/deploy.prototxt'</span><span class="p">,</span>
<span class="s1">'../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'</span><span class="p">,</span>
<span class="n">caffe</span><span class="o">.</span><span class="n">TEST</span><span class="p">)</span>
<span class="c1"># load input and configure preprocessing</span>
<span class="n">transformer</span> <span class="o">=</span> <span class="n">caffe</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">Transformer</span><span class="p">({</span><span class="s1">'data'</span><span class="p">:</span> <span class="n">net</span><span class="o">.</span><span class="n">blobs</span><span class="p">[</span><span class="s1">'data'</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">})</span>
<span class="n">transformer</span><span class="o">.</span><span class="n">set_mean</span><span class="p">(</span><span class="s1">'data'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'../python/caffe/imagenet/ilsvrc_2012_mean.npy'</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="n">transformer</span><span class="o">.</span><span class="n">set_transpose</span><span class="p">(</span><span class="s1">'data'</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="n">transformer</span><span class="o">.</span><span class="n">set_channel_swap</span><span class="p">(</span><span class="s1">'data'</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
<span class="n">transformer</span><span class="o">.</span><span class="n">set_raw_scale</span><span class="p">(</span><span class="s1">'data'</span><span class="p">,</span> <span class="mf">255.0</span><span class="p">)</span>
<span class="c1">#note we can change the batch size on-the-fly</span>
<span class="c1">#since we classify only one image, we change batch size from 10 to 1</span>
<span class="n">net</span><span class="o">.</span><span class="n">blobs</span><span class="p">[</span><span class="s1">'data'</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">227</span><span class="p">,</span><span class="mi">227</span><span class="p">)</span>
<span class="c1">#load the image in the data layer</span>
<span class="n">im</span> <span class="o">=</span> <span class="n">caffe</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">load_image</span><span class="p">(</span><span class="s1">'images/cat.jpg'</span><span class="p">)</span>
<span class="n">net</span><span class="o">.</span><span class="n">blobs</span><span class="p">[</span><span class="s1">'data'</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">preprocess</span><span class="p">(</span><span class="s1">'data'</span><span class="p">,</span> <span class="n">im</span><span class="p">)</span>
<span class="c1">#compute</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">forward</span><span class="p">()</span>
<span class="c1"># other possibility : out = net.forward_all(data=np.asarray([transformer.preprocess('data', im)]))</span>
<span class="c1">#predicted predicted class</span>
<span class="k">print</span> <span class="n">out</span><span class="p">[</span><span class="s1">'prob'</span><span class="p">]</span><span class="o">.</span><span class="n">argmax</span><span class="p">()</span>
<span class="c1">#print predicted labels</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s2">"../data/ilsvrc12/synset_words.txt"</span><span class="p">,</span> <span class="nb">str</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">'</span><span class="se">\t</span><span class="s1">'</span><span class="p">)</span>
<span class="n">top_k</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">blobs</span><span class="p">[</span><span class="s1">'prob'</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">argsort</span><span class="p">()[</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">6</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="k">print</span> <span class="n">labels</span><span class="p">[</span><span class="n">top_k</span><span class="p">]</span>
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<pre>281
['n02123045 tabby, tabby cat' 'n02123159 tiger cat'
'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'
'n02127052 lynx, catamount']
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<h1 id="Learn-:-solve-the-params-on-training-data">Learn : solve the params on training data<a class="anchor-link" href="#Learn-:-solve-the-params-on-training-data">¶</a></h1><p>It is now time to create your own model, and training the parameters on training data.</p>
<p>To train a network, you need</p>
<pre><code>its model definition, as seen previously
a second protobuf file, the solver file, describing the parameters for the stochastic gradient.
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<p>For example, the CaffeNet solver :</p>
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<p>net: "models/bvlc_reference_caffenet/train_val.prototxt"</p>
<p>test_iter: 1000</p>
<p>test_interval: 1000</p>
<p>base_lr: 0.01</p>
<p>lr_policy: "step"</p>
<p>gamma: 0.1</p>
<p>stepsize: 100000</p>
<p>display: 20</p>
<p>max_iter: 450000</p>