docs/source/tutorial_notebooks/Parameter_Estimation_Workflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameter Estimation Workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "sys.path.append(os.path.abspath(os.path.join('../..')))\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "from numba import njit\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bayesflow.networks import InvertibleNetwork, InvariantNetwork\n",
    "from bayesflow.amortizers import SingleModelAmortizer\n",
    "from bayesflow.trainers import ParameterEstimationTrainer\n",
    "from bayesflow.diagnostics import *\n",
    "from bayesflow.models import GenerativeModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulator settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "code_folding": [
     23,
     42,
     52
    ]
   },
   "outputs": [],
   "source": [
    "def prior(batch_size):\n",
    "    \"\"\"\n",
    "    Samples from the prior 'batch_size' times.\n",
    "    ----------\n",
    "    \n",
    "    Arguments:\n",
    "    batch_size : int -- the number of samples to draw from the prior\n",
    "    ----------\n",
    "    \n",
    "    Output:\n",
    "    theta : np.ndarray of shape (batch_size, theta_dim) -- the samples batch of parameters\n",
    "    \"\"\"\n",
    "    \n",
    "    # Prior ranges for the simulator \n",
    "    # v_c ~ U(-7.0, 7.0)\n",
    "    # a_c ~ U(0.1, 4.0)\n",
    "    # t0 ~ U(0.1, 3.0)\n",
    "    p_samples = np.random.uniform(low=(0.1, 0.1, 0.1, 0.1, 0.1), \n",
    "                                  high=(7.0, 7.0, 4.0, 4.0, 3.0), size=(batch_size, 5))\n",
    "    return p_samples.astype(np.float32)\n",
    "\n",
    "\n",
    "@njit\n",
    "def diffusion_trial(v, a, ndt, zr, dt, max_steps):\n",
    "    \"\"\"Simulates a trial from the diffusion model.\"\"\"\n",
    "\n",
    "    n_steps = 0.\n",
    "    x = a * zr\n",
    "\n",
    "    # Simulate a single DM path\n",
    "    while (x > 0 and x < a and n_steps < max_steps):\n",
    "\n",
    "        # DDM equation\n",
    "        x += v*dt + np.sqrt(dt) * np.random.normal()\n",
    "\n",
    "        # Increment step\n",
    "        n_steps += 1.0\n",
    "\n",
    "    rt = n_steps * dt\n",
    "    return rt + ndt if x > 0. else -rt - ndt\n",
    "\n",
    "@njit\n",
    "def diffusion_condition(n_trials, v, a, ndt, zr=0.5, dt=0.005, max_steps=1e4):\n",
    "    \"\"\"Simulates a diffusion process over an entire condition.\"\"\"\n",
    "    \n",
    "    x = np.empty(n_trials)\n",
    "    for i in range(n_trials):\n",
    "        x[i] = diffusion_trial(v, a, ndt, zr, dt, max_steps)\n",
    "    return x\n",
    "\n",
    "\n",
    "@njit\n",
    "def diffusion_2_conds(params, n_trials, dt=0.005, max_steps=1e4):\n",
    "    \"\"\"\n",
    "    Simulates a diffusion process for 2 conditions with 5 parameters (v1, v2, a1, a2, ndt).\n",
    "    \"\"\"\n",
    "    \n",
    "    n_trials_c1 = n_trials[0]\n",
    "    n_trials_c2 = n_trials[1]\n",
    "    \n",
    "    v1, v2, a1, a2, ndt = params\n",
    "    rt_c1 = diffusion_condition(n_trials_c1, v1, a1, ndt,  dt=dt, max_steps=max_steps)\n",
    "    rt_c2 = diffusion_condition(n_trials_c2, v2, a2, ndt, dt=dt, max_steps=max_steps)\n",
    "    rts = np.concatenate((rt_c1, rt_c2))\n",
    "    return rts\n",
    "\n",
    "\n",
    "def batch_simulator(prior_samples, n_obs, dt=0.005, s=1.0, max_iter=1e4):\n",
    "    \"\"\"\n",
    "    Simulate multiple diffusion_model_datasets.\n",
    "    \"\"\"\n",
    "    \n",
    "    n_sim = prior_samples.shape[0]\n",
    "    sim_data = np.empty((n_sim, n_obs), dtype=np.float32)\n",
    "    \n",
    "    n1 = n_obs//2\n",
    "    n2 = n_obs - n1\n",
    "    \n",
    "    # Simulate diffusion data\n",
    "    for i in range(n_sim):\n",
    "        sim_data[i] = diffusion_2_conds(prior_samples[i], (n1, n2))\n",
    "        \n",
    "    # Create condition labels\n",
    "    cond_arr = np.stack(n_sim * [np.concatenate((np.zeros(n1), np.ones(n2)))] )\n",
    "    sim_data = np.stack((sim_data, cond_arr), axis=-1)\n",
    "    \n",
    "    return sim_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An example Bayesian workflow (with BayesFlow)\n",
    "\n",
    "Towards a principled Bayesian workflow for cognitive modeling:\n",
    "\n",
    "https://betanalpha.github.io/assets/case_studies/principled_bayesian_workflow.html\n",
    "\n",
    "https://arxiv.org/abs/1904.12765"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prior predictive checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train an amortized parameter estimation network\n",
    "Here, we use an invariant summary network and an invertible inference network with default settings.\n",
    "\n",
    "We connect the networks through a *SingleModelAmortizer* instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_net = InvariantNetwork()\n",
    "inference_net = InvertibleNetwork({'n_params': 5})\n",
    "amortizer = SingleModelAmortizer(inference_net, summary_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We connect the prior and simulator through a *GenerativeModel* class which will take care of forward inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "generative_model = GenerativeModel(prior, batch_simulator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = ParameterEstimationTrainer(\n",
    "    network=amortizer, \n",
    "    generative_model=generative_model,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Offline training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using pre-simulated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-simulated data (could be loaded from somewhere else)\n",
    "n_sim = 5000\n",
    "n_obs = 100\n",
    "true_params, x = generative_model(n_sim, n_obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "losses = trainer.train_offline(epochs=1, batch_size=64, params=true_params, sim_data=x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using internally simulated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating 1000 data sets upfront...\n",
      "Converting 1000 simulations to a TensorFlow data set...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "197cd565e9f145ceac470a99d633ada0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training epoch 1:   0%|          | 0/32 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "15a91f5e6e2642908612d9d8a973ee78",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training epoch 2:   0%|          | 0/32 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.63 s, sys: 1.57 s, total: 7.2 s\n",
      "Wall time: 5.42 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "losses = trainer.simulate_and_train_offline(n_sim=1000, epochs=2, batch_size=32, n_obs=n_obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Online training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fixed n_obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df7c840ea2ec4c4f86cf88d908ea1f71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training epoch 1:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b8058f8dfeb4ddaa5f2085e37012cd6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training epoch 2:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18.1 s, sys: 4.84 s, total: 22.9 s\n",
      "Wall time: 17.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "losses = trainer.train_online(epochs=2, iterations_per_epoch=100, batch_size=32, n_obs=n_obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variable n_obs\n",
    "def prior_N(n_min=60, n_max=300):\n",
    "    \"\"\"\n",
    "    A prior or the number of observation (will be called internally at each backprop step).\n",
    "    \"\"\"\n",
    "    \n",
    "    return np.random.randint(n_min, n_max+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9021e60c0404e25a42780042c2075e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training epoch 1:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa76930bba4545359515e6870fe1ff2a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training epoch 2:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 22.8 s, sys: 9.69 s, total: 32.5 s\n",
      "Wall time: 20.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "losses = trainer.train_online(epochs=2, iterations_per_epoch=100, batch_size=32, n_obs=prior_N)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Round-based training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating initial 200 data sets...\n",
      "Converting 200 simulations to a TensorFlow data set...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8f49b1f3a4346f39d9ed841d8e98519",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training epoch 1:   0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating new 200 data sets and appending to previous...\n",
      "New total number of simulated data sets: 400\n",
      "Converting 400 simulations to a TensorFlow data set...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b67924293cc4593b3b74c753c6be30b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training epoch 1:   0%|          | 0/13 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating new 200 data sets and appending to previous...\n",
      "New total number of simulated data sets: 600\n",
      "Converting 600 simulations to a TensorFlow data set...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8011e1f3962847e28d2902376a2c1f9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training epoch 1:   0%|          | 0/19 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating new 200 data sets and appending to previous...\n",
      "New total number of simulated data sets: 800\n",
      "Converting 800 simulations to a TensorFlow data set...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "edf737cdcaa44e14986533ca5585d7e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training epoch 1:   0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating new 200 data sets and appending to previous...\n",
      "New total number of simulated data sets: 1000\n",
      "Converting 1000 simulations to a TensorFlow data set...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc510227d5b94759a78e67a47ad8155e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training epoch 1:   0%|          | 0/32 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.28 s, sys: 2.36 s, total: 10.6 s\n",
      "Wall time: 7.97 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "losses = trainer.train_rounds(epochs=1, rounds=5, sim_per_round=200, batch_size=32, n_obs=n_obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experience-replay training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "67512671200548689a24ea0499357617",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training epoch 1:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fd3352fa33034bfe85107e9c3d434173",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training epoch 2:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5648e9a88644c7e8e7cb1b31e4c2fb8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training epoch 3:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 34.3 s, sys: 14.3 s, total: 48.6 s\n",
      "Wall time: 31 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "losses = trainer.train_experience_replay(epochs=3, \n",
    "                                         batch_size=32, \n",
    "                                         iterations_per_epoch=100, \n",
    "                                         capacity=100,\n",
    "                                         n_obs=prior_N)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_meta = {\n",
    "    'n_dense_s1': 2,\n",
    "    'n_dense_s2': 2,\n",
    "    'n_dense_s3': 2,\n",
    "    'n_equiv':    2,\n",
    "    'dense_s1_args': {'activation': 'relu', 'units': 64},\n",
    "    'dense_s2_args': {'activation': 'relu', 'units': 64},\n",
    "    'dense_s3_args': {'activation': 'relu', 'units': 64},\n",
    "}\n",
    "\n",
    "bf_meta = {\n",
    "    'n_coupling_layers': 4,\n",
    "    's_args': {\n",
    "        'units': [64, 64, 64],\n",
    "        'activation': 'elu',\n",
    "        'initializer': 'glorot_uniform',\n",
    "    },\n",
    "    't_args': {\n",
    "        'units': [64, 64, 64],\n",
    "        'activation': 'elu',\n",
    "        'initializer': 'glorot_uniform',\n",
    "    },\n",
    "    'n_params': 5,\n",
    "    'alpha': 1.9,\n",
    "    'permute': True\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_net = InvariantNetwork(sum_meta)\n",
    "inference_net = InvertibleNetwork(bf_meta)\n",
    "amortizer = SingleModelAmortizer(inference_net, summary_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compuational faithfulness\n",
    "(Via simulation-based calibration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_sbc = 5000\n",
    "n_post_samples_sbc = 250\n",
    "params_sbc = prior(n_sbc)\n",
    "x_sbc = batch_simulator(params_sbc, 100)\n",
    "param_samples = np.concatenate([amortizer.sample(x, n_post_samples_sbc) \n",
    "                                for x in tf.split(x_sbc, 10, axis=0)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1728x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plot_sbc(param_samples, params_sbc, param_names=['v1', 'v2', 'a1', 'a2', 'ndt'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model sensitivity/adequacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick and dirty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABZQAAAEUCAYAAAC8rLP6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xl8VNX9+P/XyWTfyCQkISYgkIRVQgAVEPmAsmmtgriDRawUi3srfvXTn1artUrBta10+aAiAlK14tLKkhhWETdIEEwIQSEbSchClplJMjP390eY20kySSbJZCG8n4/HPGwmd+aemfS+Ofd9znkfpWkaQgghhBBCCCGEEEIIIURbvHq6AUIIIYQQQgghhBBCCCHODZJQFkIIIYQQQgghhBBCCOEWSSgLIYQQQgghhBBCCCGEcIsklIUQQgghhBBCCCGEEEK4RRLKQgghhBBCCCGEEEIIIdwiCWUhhBBCCCGEEEIIIYQQbpGEshBCCCGEEEIIIYQQokOUUouVUtaebofoPpJQFt1KKfWyUmq/UsokwUYI0R2UUmOUUuuUUj8qpSxKqR/OxqKwnm6bEKLvUkotVUqlKqXKlFKaUurynm6TEKLvUkqFne3fHFZK1SilTiml3ldKjejptgkhzj9KqceVUj/2dDtE15GEsuhuBmAD8FpPN0QIcd4YD1QDS4BRwC+Ba4GNPdkoIUSfFwh8BjzS0w0RQpwXYoAhwG9p6Pv8FAgGPlNKGXuyYUIIIfoeSSgLj1FK/UIpdUYpFdDk+UeVUvlKKS9N0+7XNO0V4LseaqYQoo9pK/YA6zRNW6ZpWoqmacc1TdsK/D9gjlIqtEcaLYQ45ymlZimldpydgXxGKbVTKXWp4/eapr2sadqzQGoPNlMI0Ye0Fnc0Tfte07S5mqa9r2lalqZpXwMLaEg0ywoJIYTbzsaZ/1NKPXF2tUOZUupNpVTQ2d8rpdQzSqlipVS1UuodwOj0+sXAM8CFZ1dpaUqpp3rkw4guIwll4Un/BHyBeU2e/xnwtqZp9u5vkhDiPNCR2NMPMAOmLm6bEKLvCgb+AkwCLgOygS1KqYgebZUQoi9rb9zpd/a/p7uhbUKIvuVGIByYTsPg1DwaJuUAPAD8moZVWOOBb4EnnV67CVgB5NEwqBUDrOqORovuozRN6+k2iD7k7MhUP03Trj7783jgG+AiTdMOOx23GPg/TdO8e6ShQog+xd3Yc/Z3A4CvgE2api3v9sYKIfokpZQXUArcp2naeqfnBwM/AFM1TdvTM60TQvRFLcWds78zAP+mISE0SSb3CCHcpZTaAYRrmpbk9NxfgbGapk1WSuUBazVN+/+cfv8eMM+R41FKPQ4s0TRtcLc2XnQbmaEsPO0tYNbZhA00zBD8pmlCRwghPMyt2KOUigK2ARnA/3ZvE4UQfYlSasjZDT+PKaUqgUoaZgNe2MNNE0L0Ue7GnbPJ5LeAYcB8SSYLITrgYJOf84HosyUDY4HPm/xeBs3PM5JQFp62FSgBFiqlvIHbaOjMCCFEV2oz9iil4oCdwAkabq7qu72VQoi+5BNgEHAvDcvPk4FiGkrwCCFEV2gz7iilfGkoBzYRmK5pWl4PtFMIce6ra/KzRkMOUTn9LM5jUm5AeJSmaTal1AZgEfA9DUusNvZsq4QQfV1bsUcpFQ+k0FDf61ZJJgshOuNsvdJRwE/ObvTpGLSK6tGGCSH6LHfijlIqEPgXDTOW/0fTtIKeaKsQou/SNO3M2Y3PpwD/cfrVlCaH1gGGbmuY6HaSUBZdYS0NBdqfBT7VNK3E8QulVAINm0kMOvtz8tlfHdM0rbq7GyqE6FNcxh6l1CgakskZNGwgEaGUY2CdEk3TbD3QViHEua2chlURv1BK5QARwB9p2OwT0Ou1DwAuOPtUglKqGjiladqpbm6vEOLc12rcUUqF0JDciQPmAnanUmBnNE0zN39LIYTokBeAZ5RSmcAXwHXAzCbH/AAMUEpNpmEDUZOmabIheh8iJS+Ex2malkFDvZ1kmpe7+D/gAPA7GkarDpx9XNydbRRC9D2txJ6badhZeA4NOw0XOj0GdnMzhRB9wNl6pDcB8TQMVr0JvExDXHH4JQ19nH+f/fmNsz//stsaKoToM9yIOxOAy4HBQDqN+zu3dG9rhRB93CvAq8BLNNx/TQaebnLMZuBdGvpBJcD/684Giq6nNE3KngghhBBCCCGEEEIIIYRom8xQFkIIIYQQQgghhBBCCOEWSSgLIYQQQgghhBBCCCGEcIsklIUQQgghhBBCCCGEEEK4RRLKQgghhBBCCCGEEEIIIdwiCWUhhBBCCCGEEEIIIYQQbunVCeWrrrpKA+QhD3n0sseePXu08PBwbcCAARp9jMQdecijdz7y8vK0MWPGaD4+Php9jMQdecijdz4sFot20003aUopjT5IYo885NH7Hpqmac8//7ymlNLOXqN9isQdecijdz62bNmiBQcHa4MHD9ZwU69OKJ8+fbqnmyCEcOHll18mMjKSffv29XRTPE7ijhC903vvvceJEyf49NNPe7opHidxR4je6dtvv+Wjjz7ihRde6OmmdAmJPUL0PmVlZbzyyivcdtttfPjhhz3dHI+TuCNE76NpGitXriQxMbFdOR7vLmyTEKKPMZvNBAQEsHbtWiwWCxERET3dJCFEH+eIOw8++CDz589n0KBBPd0kIUQf54g7l112GdnZ2RJ3hBBdzmKx4OvrS0REBF9++SWxsbF4efXq+X9CiHOcpmlYLBYCAgJ47733MBgMhIaGuv16iVBCiDbZ7XYee+wxLr/8cqqrqwkKCpJkshCiy7377rskJCSQlZWFUkqSOkKILpeRkcHw4cP54IMPACTuCCG6XFlZGTNnzuTRRx8FYODAgZJMFkJ0KZvNxj333MNVV11FbW0tRqOxXclkkISyEKINdXV13HHHHaxYsYJLL70Uf3//nm6SEOI88Morr3DLLbcwZMgQIiMje7o5QojzQFpaGlOnTsVut5OQkNDTzRFCnAdOnDjBlClT+Oqrr7j00kt7ujlCiPOAyWTihhtu4K9//SuTJ0/Gx8enQ+8jCWUhRIsqKyu55pprePvtt3n22Wd57bXX8PaWSjlCiK5jt9t55JFHeOihh5g3bx7bt28nPDy8p5vVbkqpRKWURSn1dk+3RQjRtnfeeYc5c+YwcOBA9u3bx5gxY3q6SUKIPu7gwYNMnjyZU6dOsW3bNm666aaebpIQoo87ffo0M2bM4KOPPuJPf/oTzz//fIdXRHRLZkgpVd3kqQDgNU3T7u+O8wvRG2RnZ5OSkkJ+fj6xsbHMnDmTxMTEnm5Wq37xi1+wY8cO3njjDRYvXtzTzWkXiTtCnJv+8pe/sGrVKu69915eeeUVDAZDTzepo/4CfNXTjRDC087F/kxbvv76a2677Tb+53/+h82bN2M0Gnu6SUKIPq66uppZs2bh7+/Pnj17GD16dE83SQhxHliwYAEHDhzgvffeY/78+Z16r25JKGuaFuz430qpIKAIeLc7zi1EV3L3pio7O5s1a9YQHh5OXFwclZWVrFmzhhkzZnDs2LFee1O2YsUKlixZwqxZs3q6Ke0mcUd0p3MxwdJb27xkyRLCwsK4/fbbUUr1dHM6RCl1K1ABfA7IunnR63T0+m+pP3PXXXf1ivjRURMmTGDt2rXcfPPNUtpLCNEtgoODWbt2LUlJScTFxfV0c4QQ54mXX36ZsrIyLr/88k6/l9I0zQNNascJlboDeBKI19o4+cUXX6x9/fXX3dMw0Wv11qSH801VaGgolZWVlJWVubypWr16NcePH6egoICKigrCwsLw9/cnLy+PGTNmtPn67rR3717efvtt/vKXv7i79KHXZ3wk7ghPahqTEhISSE1NdSsWdPQcno577Ylf3SE/P5+HH36Yv/71r4SFhbnzkl4bd5RSocDXwAzgLiBB07TbW3uNxB3RnTpz/a9evZqqqqpG12lFRQUmk4no6Ohe11drjcVi4Z577uGhhx4iKSnJnZf02rjjTCmVCBwC3pPYI0Tvomkaf/zjH4mNjeX221u9PJ11a+w5W6prBhAEnAL+qGna/7Vw7K+AR2lYCfo+sEzTtNrW3l/ijhDdb+vWrWzfvp2VK1e6O2HHrYN6ohjqHcBbLSV1lFJLgaUguyqL3jUTpmmCp7i4mPDwcP2myvHflJSUZm3LyMjghx9+IDAwkLCwMMxmMwcOHCA0NNSt13eXDz74gAULFjBo0CBKS0v70kZYEneER7iKSatWrSIpKclj13J2djarVq2iuLgYi8XC4cOHOXjwIMuXL9ffr7MJ55SUFLfjV1c7fPgwV199NRUVFWRmZjJp0qRuPX8XeAZYo2labmsdNok7oqd05vrPz89vNpPObDazbds25s2b1+N9NXeVl5czb948du3axcSJE91NKJ8rpNyOEL2QzWbjoYce4s9//jOLFi1qT0K5uz0H3KVpWq1SagSwQyl1QNO0b5wPUkrNAR4DrgQKgA+A3519TgjRQ5reJ1ZWVvL4448zevRoqqurCQkJ8di5ujWhrJQaBEyjYcaOS5qm/R34OzSMXnVT00Qv5emkh/PF5e3tjVKK+vr6NhMyrpJIW7duZc6cOY1m6YSGhpKfn9/s9RUVFXh5eREYGAhAYGAgdXV1WK3WRse19PqWPoMnZwH9+c9/5oEHHmDixIl8/PHH9O/fv9Pv2RtI3BGevGZcxSSr1Up+fj6hoaFkZmZSXl5OWFhYh2twbtiwgaysLCIiIggPD8disZCVlcWGDRt48skn3R5oa+1zu0oKuRN/PG3Xrl3MnTsXf39/du3aRXJycree39OUUsnATGBcW8dK3Dn39NYVU+3VmevfcWPi3Pc5ePAg0dHRHe6rdff3mpuby1VXXUV2djYbN27k1ltv7bJzdTcptyM6o7fEuN7SDk8ym80sXLiQDz74gOXLl7NixYqeblKLNE077Pzj2Uc88E2TQ++gYQD9MIBS6hlgPZJQFqLHON8nent786c//Ynvv/+eIUOG8MYbb3g0mQzQsa38Om4RsEfTtB+6+bziHOVI0jgLDQ2loKCg3e/luLiqqqrw8fFhx44dpKWl4ePjQ1VVFWvWrCE7O9vla52TSF5eXoSFhREdHc3BgwcbHVdZWckFF1zQ7PVGoxGbzYbZbEbTNMxmM76+vlitVnbu3MmHH37Izp07ycnJcfl6V58hLi6uzXa765lnnuH+++/n2muvJTU1tc8kk8+SuNOHZGdns3r1ah5//HFWr17d5v/3PX3NuIpJUVFR/PDDD+zduxeLxYLRaKSiooJjx4516DxffPEF4eHhBAQEUF1dTV5eHoWFhfzzn//Ub7KaxqPw8HBSUlLc/tyOpJCzluJXV/n000+ZNWsWAwYMYN++fed8Mvms6cBg4KRS6hSwHLhBKfVtTzZKdF5X/fvb2vnaE+vaozPX/8yZMykrK6OiogK73U5FRQVFRUXNrl+z2czHH3/cZvu7+3v94YcfmDx5Mnl5eWzdurWvJZNDgaeBh3u6LaL7dTZmdPe12Nvb4Um1tbXMmjWLzZs38/LLL7Ny5Up3ywr2GKXUa0opE5AJFAL/cXHYaCDd6ed0IFopFeHi/ZYqpb5WSn1dUlLSJW0WQjTkrWw2G7t37+bll1/m+++/Z8CAAYwZM4ZNmzZ5PJZ2d8mLRcDz3XzO89a5Nrrrqr1NZ8IUFRXx7bffYrFYWL16dbs+k3MSJj09nfDwcJRSHD16lGnTpunHuHo/V7N5kpOT2bp1KxUVFY1qEF5//fXNXj9mzBjq6upIT0+nvLwco9HIoEGDyMrK0mczlpeXk5OTw+zZs936DOB6FlBH/u5Tp07l/vvv58UXX8Tbuycq4XQpiTt9REdK4Hh6lYOr2XlxcXGkp6djNBrx9/fHbDZjt9tJSkrq8Hk0TaOqqopjx47h6+tLQEAAZrOZNWvWUFlZSXR0NOnp6XpN9mHDhlFVVeX2505ISGDVqlXU19czYMAAYmNjMRgMLuNXV0lKSuL666/ntddeIzw8vNvO28X+Drzj9PNyGhLMy3qkNcJjurNMTFeX+5o5cyZr1qwBaLP/0lRiYiJ33XWX3s+44IILmDNnDgEBAfoxRUVFpKWlYTQa22x/d5ffiY2NZcaMGSxfvpwxY8Z4/P17mJTbOU+5EzPauj/w5LXYmXvQ3lSSy1P8/PyYOXMmDz74IDfddFNPN8ctmqbdo5S6H5hMw2C5q7rIwcAZp58d/zsEKG3yfrIqS4gOaG88dZRaLSoqwmg04ufnR1hYGBUVFfoEJE/G0m4bGlNKXQbEAu921znPZ+fa6G5L7U1ISNBnwhQWFpKamkp5eTmTJk1q92fKyMjgwIEDfPjhh6Snp1NfX4+/vz8VFRVA6zOfXc3mCQgIYPbs2YSEhJCfn09ISEiLN3sJCQl89913REZGcumllxIZGUlWVhYXX3wxRqORM2fOYDQamTp1KseOHdO/k6YzDdqasd2ev3tlZSWbNm0CYPr06bz66qt9LpkscadvcWdmblOeXOUArmfnGQwGkpOT6devHxUVFQQEBDBlyhTi4+M7dJ6JEydSXl7OyZMn8fHxARpm+w0bNozw8HBOnDhBWloaZrNZr8melpaGwWBw63NnZ2eTmppKUlIS0dHRFBUVkZGRwYwZM7r8Zs1ut/Pmm29is9mIjY3lnXfe6UvJZDRNM2madsrxAKoBi6ZpMh3nHOepWOLOLMKOxLr2cCSF3em/tPT6ZcuW8cwzz7Bs2TIWLFjQKC5++23DhPzx48e32X5Px+iWbN68mZKSEnx9fVm7dm2fSyY7ldt5qa1jNU37u6ZpF2uadnEf2ivjvNZWzHDn/qCz16Ijti1btoz77ruP48ePd+ge1J17na5aveFpBw8e5KuvGsqZP/XUU+dMMtlB0zSbpml7gDhcD4xXA85/LMf/rnJxrBCinTqS0ysqKuLkyZMUFRVRW1tLYGAgVqsVk8nUJf2r7swe3QH8S9M0CTDd4Fwb3W2pvceOHdNnwjhmu4wfP57o6OhGr23rM2VnZ3P8+HEMBgNGo5GCggKysrIYPHgwkZGRbc58bmk2j7s3YMeOHWPq1KlkZmaSkZGBY284s9nMrFmz9OPsdjv5+fktzjQICAhoNjvSeZmqu3/3goICfvKTn3DkyBEmTpzI4MGD2/wM5yiJO31IR+p+uppR3JnSDq5m511//fWkpKRQVVXV6DwVFRUdOs+UKVNISUnh5MmT+Pr6EhYWRlRUFJdeeimhoaFUVVXh5eWl79Db9L9tfW7nOBEaGoqXlxenTp3i9ddfZ/DgwV32b0RtbS2LFy/mnXfeITAwkJtvvrlLztObaJr2VE+34XznqdVanogl7s487o4a54mJiR651h3fb2VlJSdOnMBoNGKxWLjiiisa9dVaar+nY3RTmqbx4osvsnz5cu6//35effVVj7xvLzSd/5bbgYaZgwal1ChN08b3YLtEN2grZrhzf9CZa9E5tjkG2g8dOkRoaKgeB9avX090dHSbsbi1drS0MXJMTAxWq7VXrchNSUlh/vz5DBs2jK+++orWVg2cA7xpqKHc1GFgLPDPsz+PBYo0TSt1cawQop3ak9PLzs7mnXfe4d///jd1dXUEBgZiMBgwmUxAQwnWrihv2G0JZU3T7u6uc4me3XDJ1c0b0OoNXWvtddz0OI5xrjnl7mdKSUkhKSmJQ4cOYbFYiIuL4/Dhw2RlZVFfX8+ePXvw9fXl6quv1kd+nG/wWkoiudthyc/PJzg4mPr6eoYPH46/vz+HDx/m0KFDTJw4Ue9suUr4wH+Dh8lkoqysTP/sTZepuvN3//7777nqqqsoLS3l448/7svJZIk7fUxHbnY6s7S7LY6BIU+cxxE3MzIyOH78OCNGjMBisVBRUYFSSh9Iy87OprS0lIiICE6ePElgYCChoaEEBwezfft2oqKimDlzZqvtWbt2LXFxcRQVFbF3714CAwP1mcqeXFbv7MyZM1x//fWkpaXx/PPPn3OzdMS5yZOlIzwRS9y9MYiNjSUnJ4eCggK9TNYFF1zA0KFD29Xmrub8/SYlJenfycSJExuVwICWY7UnY3TT/ufQoUNZsWIFaWlpjBs3jqVLl3bsg54bpNzOeayt/pE79weduRadY1tlZaU+sJSZmUl0dDRms5lt27Yxb968NmOxq3bk5OQQExPDXXfdxZkzZ+jXrx8DBw7UV3wWFxczZ86cRu/raFdPlH58++23ufPOOxk5ciSbN28+p5LJSqko4ErgE8BMw8qH24AFLg5/C3hTKbWehjrLjwNvdk9Lhej73M3pZWdn8+yzz/L+++9js9kIDAwEwGq1YjAYsFqt5OTkkJqayvLlyz3axt5dDb6LKaV4+OH/7luxatUqnnrqKaBhWUpsbCzJycmMGjWKjRs36sctXryYwMDARvUqH3zwQZRSnD59GoBnn32W0aNHk5SURHJyMvv37wcaSgsMHz6c5ORkkpOTufHGG91u79q1a/Xk6tq1a10e8+677zJ69Gj+8Ic/cOTIEf15m83GP/7xD15//XVGjhzJc889BzTsdH3FFVcwcuRIRo8ezSuvvOJ2e1xxNS1/1apVrFy5stWp+u5sENOZTWTy8/OJj49nypQp+Pv7Y7fbGTRoEHa7nYKCAoxGI0OHDuXo0aPU1ta6XJrZdIlnezolsbGx7NmzhxMnTvD555+TlpZGZWUlSim+/fZbfel8WVkZM2fObHG5l81ma3WZalvf0d69e5kyZQq1tbXs3LmTOXPmuP0ZhGf05bjj5eXF119/rT+/fv16/ZzJycl4eXnpG1l+8803jBkzhoSEBB544IFGydmWuCo34bhmWtLZpd1NtbT0COjweZzf0zGzJy8vj8svv5yEhAQGDx5McXEx2dnZ7N69mwsuuID+/fszcOBAampqOHLkCJmZmVgsFo4fP95mexxxIjMzk8DAQAIDA6mtrWXAgAEeXVbvkJ+fz9SpU9m9ezfr1q3j0UcfPadurvqK8yn2QEOZqzlz5vDGG2/wyiuvYLPZ9Fn5d955J8OGDWPEiBG8//77brXHE7HE3SXlCQkJ7N69u9EeC7t37yYhIcHtc3WHlpbZK6XcjtWeitFNY/PRo0dZsGABaWlpXHHFFdx88828/fbbvXp5fGf01nI751Pc8XSfpz3a6h+5cw/VmWvRObY5SnE5lxU8ePAg0dHRzWLFhg0bXJav8Pf3Jy0tjY8++oi8vDw0TaOmpoa8vDxsNhsFBQV8++23vP3225w6dYry8vJG77t+/foeKf2oaRorVqzgZz/7md7vaZoMOgdoNAxE5QHlwCrgIU3TPlRKDVJKVSulBgFomrYF+COQBpw4+3iyZ5r9X30x7pSVlTFr1iwSExOZNWsW5eXlQEPcSUpKIikpicsuu4z09IY9Ej2d4xE9w93816uvvsrGjRvx9fUlNjaW4OBggoKCMBgM1NbWYrVasdlsJCUlkZqa6tFY2LcKpraTn58f//rXv/jf//1f+vfv3+z3v/rVr1i+fDnZ2dlMmDCBG2+8Ua9nmZCQwIcffsjtt9+O3W4nLS2N2NhYAPbt28cnn3zCt99+i5+fH6dPn6aurk5/3/Xr13PxxRe3q61lZWX87ne/4+uvv0YpxYQJE7juuuswGo2Njrvooov417/+xaJFizhz5oy+YdyuXbswmUzs2LGD2NhYRo0axW233Yafnx8vvPAC48ePp6qqigkTJjBr1ixGjRrV3q8TcD37pri4GED/zK5m5LgzKt6ZkXPHxRgdHa3PBv7000+5+OKLqa2txWg0opTCZDKRmZnJ1KlTPTqbOyEhgfT0dDRNw8/PT+/sxcTEUFpa2mzWc2szDRzfmWPU3ZH8SUxMbPM7OnToEJGRkWzZsoUhQ4Z47PMJ9/XluHP33Y0nhC9cuJCFCxcCDf/fmzt3LsnJyQAsW7aMv//970yaNImf/OQnbNmyhauvvrrV9nR0pYCnlnZD6zMM2zvQ5Oo9Kysr8fb2Jjc3l7y8PIYMGYKmaeTl5QENG2iGhoayd+9e7HY7J0+eRClFREQEERERHDp0iDFjxrTaHkecOHXqlD5zqKamhuTk5C5ZyZKfn09xcTH/+c9/GpX4Ed3rfIo9VquV22+/nRkzZjBhwgRMJpNeY3zv3r0AHD16FLvdrq/6cUfTWOKo5enuDDh3V1k4ymQVFBRQUVGB0Whk9OjRHDt2rFddQy3NnKmqqmoUqw0GAwEBAaxdu9bl9+SJGN00Np88eRKLxcK4ceO45ZZbUErh5eXVa0u/eVpvKbdzPsUdT/d52qOt/pG791AdvRadY9uIESPYu3cvFotF3wiqqKio2SQWx6zluXPnNipfoWkaCQkJzJ07l5ycHD755BP69euHzWbDz89P37TY19cXg8FAZWUlXl5eFBUVER0dTWhoqD6Q1N2lH+12O3v27OHWW2/lzTffxM/Pr8vO1VXODkJNa+F3J2kop+P83IvAi93QNLf1xbjz/PPPM2PGDB577DGef/55nn/+eVasWMGQIUPYuXMnRqORTz/9lKVLl7J//368vb09muMRPcPd2P3dd98RFRXF/fffz1tvvYXBYKC0tJS6ujqCgoL0fbJCQ0Px8/PzaCw8r2coe3t7s3TpUl56qfX9KxITEwkMDNRHggBuu+02fUOzHTt2MGXKFP0PVVhYSP/+/fV/RPr379/pWiVbt25l1qxZhIeHYzQamTVrFlu2bGl23MiRIxk+fDgBAQHMnTtXH2UOCAggJiaGIUOGYDab8fX1JTQ0lJiYGMaPbyitFhISwsiRIzuVTHA1+8ZisVBb23hj2KYzctwZFe/MyLmrkfuioiKSk5P1ZVnQsNFeRUWFx+vLHDt2jPDwcKxWK6WlpZSVlWGz2aiqqiI2NrbZrOfWZhq0Vpy9pe/I8f/FX/7ylxw4cECSyT2oL8ed1mzcuJHbbrtNb2tlZSWTJ09GKcWiRYvYvHmzW23qzEoBT+iKDaSc39PLy4vMzEz9d76+vlRXVzNz5kzi4+OJj48nOjqaKVOmUF5eri9lSkxMJDIyksDAQPLy8lptjyNOREVFUVRUhL+/P1OmTCEW1onRAAAgAElEQVQ6Otqjse/kyZMAXHrppRw/frxXJcLOR+dT7Nm2bRtJSUmMHz+eyspKgoOD9XJZe/fu1WcOeXl5ubzZdEdHNkpxd5WFY1XVtGnTmDt3LtOmTevwJp+ttb+zG1u1NnPGEasXLVqExWIhICCgxe/JE21xxNEzZ85gs9kwm81cc801xMTE6CsiumIzGtG68ynuOPNUn6c9WusfOd8fHDp0iAMHDnDmzBlSUlI8MlPNObZFRkYyZswYbDYbRqORkJAQ5syZ06wMzsGDB4mKiiIsLIySkhLS09PZsWMH+/fvp7a2lpKSEg4dOkR9fT1Wq5WqqiqsVislJSV4eXlhs9nw8fGhtraW2NhYve/kiEndsdmng9lspri4GIPBwLvvvsv69evPyWRyX9EX486HH37IHXfcAcAdd9yhx5DLLrtMTz5PmjRJn4Di6RyP6Bkt5XYAXnvtNe6//35Wr17NtGnT+OUvf0lYWBhDhgyhurqagIAA/P399T5w//79yczM9HgsPK8TygD33nsv69ev58yZMy0e8+2335KYmEhUVJT+XGJiIiUlJZSXl7Nx40ZuvfVW/XezZ88mNzeXYcOGcc8997Bz585G77dw4UJ9OcQjjzwCNF8m1XS5RH5+PgMHDtTfIy4urs2gMGjQIL1j8be//Y2oqChiYmIYNGgQy5cvJzw8vNHxP/74IwcOHGDixIltfGstc3Vz4e/v3+wfVVdJC3cSRW0d09JNiauLcfbs2QQEBDBixAhqamowm82YTCZ8fX3bXEbfkpbOn5+fj7e3N2azGR8fHwIDA9E0jaKiIqqrq11+zpaS502XmNbW1pKVlcUDDzzA6tWrAfTv6Je//CWvv/46o0aN4vvvvwfQa+qIntOX405LNm3apN9cNZ3V1pn37WgioqOva2/pHXfO0/Q9lVLU19frcQIallE6HxcdHU1kZCSDBg0iOjqakJAQoGFQrLi4uM0ObmJiIk8//TSXXHIJY8eOJTIy0q0SIu567733GDZsmL6UUOJO73C+xJ6jR4+ilGLDhg28+OKLbN68GbvdTmFhIXa7nfT0dMaPH89NN91EUVGR2+/rrKVyD62VjHF3YLwzJb7c0ZFkuCvuJMjb+p481ZbY2FiysrL4wx/+wD//+U+MRiPV1dVdttmfcN/5EnecdVWfp7OKi4s5cOAAFouFAQMGeKwURNPYNnToUP785z/zl7/8hWXLlrFgwQKXE3sGDhzIxx9/zJtvvsmRI0ew2WzU1tayd+9evvzyS4KCgvRruV+/fvr9pGPFiZ+fH0opTp48ycGDB8nOzqasrIxJkyZ1aQx1VlZWxuzZs7n66quxWq34+/s32u9H9Iy+FneKioqIiYkBGpLFjhXgztasWeNy5YMncjyi+znuIV966SV27dqll1358ccf+cc//sG6det4/fXXOXnyJAUFBZw8eZKKigouvvhifHx8sNvt+Pr6YrPZCA0NJTExsUsmTp7XJS+gYbRy0aJFvPrqq81GTl966SX+8Y9/cPz4cZcjRfPnz+edd95h//79/O1vf9OfDw4O5ptvvmH37t2kpaVxyy238Pzzz7N48WLA9XII52VSrriqtdWeGpRffvklBoNB3+Bl6tSpzJw5U9/gpbq6mhtuuIGXX3652Yhue7ialh8VFYWmaXr5DU9uiuWsrc13XC1VXbVqFcXFxZjNZgoLC/Hy8mLu3LksXLiw2bHr16/X6yRNmjSJBQsWNDumpfPHxsZSXl5OcHDDKiGr1YpSCm9vb77++mtWr17t9hJQ546p86ZaSqlGGwpeeOGFLFmyhHXr1nH33XefF0s8zxXnS9xx2L9/P4GBgVx00UUefd+ObrjVmY26Wlt61HRDqISEBFJTU9s8j/N7Wq1WBg4cSEFBAYGBgQQEBHDFFVdgtVqbndvPzw8/Pz98fX31eoW5ubmUlJSQkZHhMq446+xmoy159dVXeeihh5g8eTKzZ8/u1HsJzzpfYo/VamXPnj189dVXFBQUMG/ePMLDwxk7dixVVVX89Kc/5Y033uDFF19k+fLlrFu3zu33dujo5sfuLCnvys1EoX27hrfGnRji+J6KiorIzMzU60I7ZlR5qi3BwcG89tpr+oqL2tpacnJyGD16NHa7vcv6nqJt50vcceiqPk9nOPo9WVlZDBgwAGhYvj9lyhR9gMdxvbnaXN2da7G12OYqVkyYMIGMjAzOnDmj33tWVVVhNBoJCgoiKyuLsWPHEhYWRk1NDWFhYVRVVWG32wkMDCQyMpLCwkKGDx9OdXU1FouFjIwMli9fzuDBg7s0hjqcOHGCq6++mpycHNatW6fPZBU973yLO2lpaaxZs4Y9e/Y0et5TOR7RvRwx22azkZOTg8Fg0HNJ77//PseOHePEiRMMGTKEEydOEBgYSGFhoV4KcdiwYfpmfCUlJcTGxmIwGDAYDB6PhRL1gIceeojx48dz5513NnreUV/HUZM4JycHf39//fe33nor48eP54477mg2EmkwGJg+fTrTp09nzJgxrF27Vg82rqxfv56VK1c2ez4hIYH33nsPb29vPvroIywWC7GxsRw+fJi5c+fqxzX9x99sNjd6nw0bNnDVVVfh4+NDVFQUU6ZM4euvv2bo0KHU19dzww03sHDhQubPn99iG93pYLjqMDh2kvR00qKpjtyUOIJ4UFAQ4eHhREZGukwmr1y5kqNHj+q1ltPS0igoKGD58uWNaho7n9955vCkSZOora3Fy8sLPz8/rFYrlZWV+Pn54ePj0ygR3Nb34lynLDMzk6CgIDRNw2g06uf++OOP+fTTT0lJSeH3v/89v/nNb2QTrF7mXIg7cXFx7NixQ38+Ly+P6dOnt/uzvvPOO/pMHWgYfXcsyXK8b0dGSjuaiOhMAqOlBAqg/8Ofn5/P559/zqlTp5g8ebI+cOfqPI64WllZyYkTJ6ipqSEiIoLbbrtNr/deUVFBSEhIs3MnJycTGRlJv379yM/P5/jx45w6dYorr7ySsWPHupUo92R9abvdzmOPPcbKlSuZN28eGzZsaNaJFz3vfIg9cXFxTJs2jf79+9O/f3/uvPNO/P39Wb58OU899ZR+zd5000160qG93K2H3BFdNdjjkJGRoc9ScdQ8jYyM7NCsybZiSGxsLDk5ORw6dEifbVheXk5paSnZ2dkdTsw727RpE0uWLGHgwIHceeedWCwWhg4dyuzZszl27FiX9j2Fe86HuOPgqT5PRxO7rjj6Pc77xgDN9o1xHnD38fFh27ZtrFu3jtmzZze7P2qvprHid7/7HdBQLiI0NFRfmVVfX4/dbkfTNMrLyzEYDMyaNYvTp0/r9ZJ9fHyorq4mPj6ekJAQQkJCmDJlCn5+fnqt+a6MoQDp6elcffXVmEwmtm3bxrRpLssOix7Ul+JOdHQ0hYWFxMTEUFhY2GhWdUZGBkuWLOHTTz8lIiJCf97dHI/ofRwxOz09neDgYOrr6zl58iRHjx4lNzcXq9VKbGwso0eP5vTp02RnZzNgwADuuOMOKisrOXbsGEop4uPjMZvNHDx4kKKiIo/E8qYkoQyEh4dz8803s2bNGn7+8583+/38+fNZu3Yta9eubbQBw6BBg3j22WebLQ/OysrCy8tL/0MdPHiQCy+8sNU2tDZ6lZ2dzY8//sjhw4eZN28eRUVFbNmyRd+91NVsO8cIhWOUbNCgQXz22WfcfvvtmEwmvvjiCx566CE0TeOuu+5i5MiR/PrXv26xfe2Z0dfSzUVXd+Kbztx1zITRNM1lJywlJYWEhIRGI4kVFRXNkkopKSmUlJQQHh6uL9tWSlFcXNzo2NZmDgcEBNCvXz+sViv19fV658lxc+VOQsvRsczIyOD48eMkJSVRVlaGv78/ZrOZcePGAQ03Yps2bWL37t288cYbrf4jJ3pOb487AHPmzOE3v/mNXlts27ZtPPfcc25/RmhIMr777rvs2rVLfy4mJoaQkBC++OILJk6cyFtvvcX999/frveFjs8Q7GwCw1WMW716NTabTU+axMTEcPLkST7//HMGDx6sJ4cd53Gseti2bRvR0dEkJyczePBgQkNDG23e2XRWjauVFikpKfj7+6NpGpdffrn+++7ahMZh165drFy5knvuuYdXX31VX5IqepfzIfbMmTOHP/7xj3oZq507d/KrX/0KpRTXXnstO3bs4MorryQ1NbXDG9R09SxiTw72OMvOzub48eMYDAaMRiNms5m9e/cyZswYffDLk2bOnMl9992HwWDQ+yt2u52kpCRSUlI6nZgvLS1l6dKlTJw4kc2bNzcr5ya123uH8yHugOf6PJ1ZSeWKo99jMBg4cuQIVquVgIAAAgMDG11vzonnffv2ERQURHR0NOnp6Vgslg6f35X6+nquuOIKPvnkEyorK/H19SU4OJjKykqOHz9OQEAANpuNpKQk4uPjqaysZODAgXr90AceeEC/xxo3bhzR0dHY7Xa9L9dVMRQaJiTdfffdGAwG9uzZo89GF71LX4o71113HWvXruWxxx5j7dq1+sTCkydPMn/+fNatW8ewYcP0493N8YjeKT8/Hx8fH/bs2UNpaam+H5ndbsdut+Pt7U1ISIjen/P19cVqteqlxRISEjCZTISEhFBVVcXs2bM7NSjZGinwc9bDDz+s1yVx5be//S0vvvgidru90fN333038fHxjZ6rrq7mjjvuYNSoUSQlJXHkyBGeeuop/ffO9XXcqVXp6HBfe+21rFixgtdee41Zs2bx9ddfA7B48WJMJhNhYWGkp6fz3HPPUVBQwOLFi/Udde+9916qq6u56KKLuOSSS7jzzjtJSkpi7969rFu3js8++0xv03/+8x+XbWhvrcDu5rgpcSRzHUvA/f39XdYHc3dzrfz8fGpraxvNtPP396e2trbRsc41Dx0zhwE9YTx9+nS8vLxITk4mNjaWiIgI7HY7l112WYvndnCuMTh27FiSkpLIyMjAZDIB6BtqaZpGZWUlN9xwA59//rkkk3u53hx3oKEj9sQTT3DJJZdwySWX8Nvf/la/WV+yZIkegz744APi4uLYt28f11xzTaOdvHft2kVcXFyzRMXq1atZsmQJCQkJxMfHd2i3847WGe2K+qT5+fnk5+cTFBREQEAASinCw8Opr69vtMleZWUlBoOBNWvWkJ6eTnR0NF5eXuzbt4/a2lri4+OJjY11e/NR57ryjk37nHXHJlSOlR7Tp09n7969/PnPf5Zkci/X12OP0Wjk17/+NZdccgnJycmMHz+ea665BoAVK1bw1FNPkZSUxLp163jhhRfcalNTHd0o2BMb0HVGSkoKSUlJ2O12fbM8Ly8vMjIyPFI/vanExESGDh1Kv379qKioICAggClTpuibDLq7UWFTjrhTVlbGPffcw2WXXcamTZu6/fsU7uvrcQc81+fx9H2XY6XAmTNnqKmpwdvbG5PJRH5+PseOHdO/I8e9keM+xpF0rqur8/h9X2xsLAEBAVx33XVERkZisVj0FZdGo5GEhASGDx/Od999x4cffojJZGpUxvC6665j9OjRAHzxxRfs3LmTnJycLq+TrmkaSik2bdrEvn37JJncy/WVuPPYY4+xfft2EhMT2b59O4899hgATz/9NKWlpdxzzz0kJyfrE+XczfGI3snb25v3339fL81qtVqxWq3Y7XZ9U9KCggJOnz7NDz/8gNVqbbRfTWhoKDabrVs2sleu6rb0FhdffLHmuIjOZ48//jhxcXGNllw4RmCfeeaZNn/fHW3oDZzrg0HD7u01NTX6EqiQkBCWLVumH7969WqqqqoazYpxLC1vety2bdtQSukXqmOGzezZs/VjnWcTpKWl6TNxHMleu93Orl270DSNr776iqCgIKZPn653hlydu622mkwmLBYL4eHhlJSUsHHjRubNm8eDDz7Yncs6+1QtDYk7Pae9yzudrznnGYLtqaHcnte1ZvXq1bz77rvExMToS0lLSkrIzc0lJiaGm266ST9PQEAAAQEB7Ny5k7CwMJRS+gCYY+lpR+KquzHNk/Lz85k/fz4vvPACl19+eZecowUSd8Q5pytiT3vjpqM/V1JSQmZmJhUVFfTr1w+j0chDDz3ksSX2ztqKTY7PUFBQwAUXXNDmeWtra1m0aBGJiYlYrVaPfp9t6FNxByT29Faevu/Kzs7WVwp4e3uTm5tLZWUlQ4YMYerUqTz55JPAf6/VHTt26KUxTCYTAQEBneqftNQmRzzctWsXP/74I2azmVGjRhEfH8/BgwcxGo3MmTPH5bW9fft2nnjiCT3x7hiMeuaZZ7pkZYKmaaxcuZIDBw6wfv367t54r0/FHok7QrTud7/7Ha+88gpmsxmLxdLs9456yOHh4dTU1NCvXz+Sk5O59tprAY/d/7kVd2SG8jmgrdl07Zlt19GZMV2947gnOGYLWSwWamtr9Y1ZoqOjXc7Sc3dWzMyZM4mMjKSsrIyamhpMJhOlpaVERUU1OtZ5tpJjgzwfH59Go+ZTp07l6aef5mc/+xlBQUGcPHmSwsLCNmfktDSb2mazcdddd5GTk8PLL79MfX098+fPlxqB4pzjPAs/Li6uxZ3HnWNYSkoKM2bMaPcMwY7OLHTVZkdbiouLqaur08vsmEwmlFJMnjyZqKioRuepr68nNDSUsLAwvd69v79/p3fe7ehMv446cuQIkydP5siRIy47O0KIxjw169ARe5YtW8Z9993H8ePHW42bzhz9uejoaKZNm8bcuXP1euzuxOCOaCs2Oa+0aGsWTUVFBXPmzOGf//wnR44c6fWr54ToCE/fdzmvFLDZbIwaNYrFixdz4403YrVa9eMc16qfnx9msxmTyYTJZGLEiBEev+9z7otVVFQwZswYFi9ezLXXXquXGqyrq2vx2j527BhTp07FaDRSUFBASUkJSilef/11j69UsNlsPPjggzz66KNomtboOxNCCE+rr6+ntraWurq6Zr9TSuHl5YVSigEDBhAbG4tSCovF0i33f01JDeVzQFt1+tyt49eZelxdXSvQUxxLoJrOhHHVCWptc63Vq1c3mqHzyCOPsH79evbv349SiiuuuIIFCxa0WD86ISGBJ554Al9fX8LCwigvLycnJ4dRo0bpf4M5c+Zw8OBBtm7dyuzZs1v9O7RWY3Dbtm384x//4NJLL+WTTz6hf//+nvo6heg27myU5yqGpaamdigZ3Nnaeq7a4th4p7a2lqioKOLj4zEYDM3a57ieR4wYwd69e/XnfX19OxVXm8Y0g8GAyWTigQceAGDixIke24hh9+7dXHfddfj7+7Nr1y69hrsQomWe2IDOOfZUVFRgMBg4dOgQoaGheq32pnXTnWcxe3t7U1hYSHx8fKP+XEBAQIc3K22rvSkpKZw5c4YTJ04QHh7ORRdd1KENsnJzc7n66qs5evQo69ev58iRIy4H2zuyuaAQvUlX3HclJSW5XCngfH/k6Eds2LCBbdu2ERUVxeTJk/Hz8+uS+z7nvphz28rLy/Hz82vU1qbXdn5+vh7HSktLiYiIwM/Pj6Kiok7Vm27KbDZz++23869//YuHH36YP/7xj909O1kI0Qe1tMIsOzub3bt366VNXdE0DZvNRm5uLmPGjCEpKUnf6Li7NyGWhPI5oK3dvt3dDdydhE1H29ATWroI29MJc7XBVUtJ94ULFxIdHU1+fn6jnVVdcYyaFxQUUFFRgdFoZPTo0Wzbto1x48YRFhZGWFgYMTEx+pKE1r7Llj5TXV0dTzzxBNdddx0bN25sVDtHiHOJO4mWzsQwT3PVlvHjxzNy5EiioqJaXLqdnZ1NUVGRvhnfsGHDyM3Npbi4mNmzZ7scqGoPR0zLzs5m1apVZGVlER4ejqZp7Nixg4KCAh555JFOnePAgQPMmjWLwYMHs2XLFgYPHtzh9xLifNLZDeigceyprKzEaDRisVjIzMzUV2Q5x01X/RpN0zCbzVRVVen9ubVr1+oJaYfOJmedzz127Fi979KRUhrV1dVMmTKFM2fOsGXLFq688kpWr17d6e9TiN6oK+673L0/SkxM5Mknn2TBggV6KZqQkJAuve9r2jY/Pz/Ky8sZP368fkzTa9sRT53rPZtMJgYMGKDPZvZEe2+55RY++eQTXnrpJR566KFOv58QQrSUc5oxYwbvvvsu33zzTYuvdcxQjoiI4KKLLsLLywtfX1+uvfbaLitx2BpJKJ8j2ppN585su87OjOnK3XLbq63Z1h3thLWUsNqwYQNms9nt2d2OUXPn39ntdvbt28e0adMaHevO36Clz+TY2O83v/kN3t5yOYtzlzuJFk/M7ussx0DW+vXrGTRoECNHjtSTMKGhoVRVVbX4j7lz3HKsUPjmm2/cTiS3p1ZqSkoKxcXFRERE6BuKKqUoKSnp9E3W2LFj+c1vfsO9995LREREh99HiPONJ2YdOsdBR9mcgIAAKioqgOZx01W/JiEhoVltPU8ku5tydxDQndgWHBzME088wcSJE0lKSgLOndVzQnSEp++72nt/5InzZ2dn6ys8ASZNmtTiCk/ntiUnJ5Ofn4+fnx92u12/tseNG6evIvXx8SE/P5/CwkIGDBigl+cYN26cR/uGjzzyCLfffjs333yzR95PCCFa6h/9/ve/p7CwkPr6ery8vBptFunl5YWmacTGxqJpGqNGjaJ///6Ul5eTkZHB0qVLe+SzSAbqPNIVNwvt0d6NY1rT1k1KRztBLSWsduzYwfTp0/Xz1NbWkpWVxQMPPMB1113X7LO09F0PHDiww38Dx2eqrKzkueeeY9CgQfj5+fHb3/623Z9TiN7GncRAb4hhjoTwwIEDycvLIz09nf79+xMXF8cFF1zQbGd3Z85xqz0rFJqe291BLceGnQ4BAQGUl5c3qyfvDrvdzh/+8AcWLVrEoEGDJO4I0QGemHXoHAcdZXMsFkujTakccTM7O5uPPvqImpoazGYzgYGBxMXFMWzYMKqqqhq9b0eTs6317dwZBGwrtm3atImIiAhmzpzJL37xC49/n0KcT7pzclB2djYrV67k6NGj+gZ/aWlpFBQUsHz5cpdJZaBRPDGZTPpKinHjxpGamtooViilCAgIoKioiAEDBjBu3Diio6OblfJor/T0dHbv3s19993H1KlTO/U9CCFEU037R0VFRWzdupXPP/8cPz8/bDYbBoMBHx8fAKxWK0ajEavVypQpU4iKiuL06dONNlbuqb6PJJTPIz05k6Mz9Ztd6aqZii0lrDRN0+v0FRUVsX37diorK6murmbbtm0cPHiwUeeope/65z//Oampqc2ed/dvUFhYyE9+8hMOHTrE7NmzueKKKzr1eYXwBE8MFrmTGOjp2WjOCeGoqCj279+Pj48PNTU1ep302bNnt/h6x4ya9PR0KioqCAsLc5nYaevc0Ha5j9jYWA4fPozFYtFnKJ8+fZpTp06haRqrV692++9UW1vLnXfeycaNG/H29uaxxx5r8zVCCNc6m9BxjoORkZGMGTOGjIwMjEZjo2Xpjn6X1WqlsLAQPz8/ampq8PX1JT8/n+nTpzeL3TNmzODYsWOtJmdbqsfsqm/nziBga7Ht448/5uGHH+aaa65pcXOZ3rR6TgjxXykpKfrmeo6SfEopiouLXfZdXN0rlpWV6fFk9erVhIeHU1tby+7du/U6y/Hx8QQGBhIeHk5oaGizgbX2Sk1N5frrrycsLIxFixY1q9MuhBCd5dw/Onz4MJs3byY3N1ff9NPxXx8fH72kRXBwMD/96U8ZOHBgs1r4ISEhPfZZJKF8jmtPIqcnZ3J4uvZpV81UTEhIYNWqVdTX1+u7Zubm5nLy5Emef/55IiMjMRgM+g7IRqMRLy8vsrKy2LBhA08++STQ9nf9+uuvk5uby8CBA/n5z3/e6g2b4+9qs9m46qqrOH36NJ988okkk0Wv4MnBIndK+/TkbDTngazTp0+TkJBAeXk55eXlDB8+nNGjR3Ps2DFmzZrl8vXe3t6kpaXpsdBsNpOWlsb06dPbdW4HxyCaq3gxc+ZMDh48qNdQrqqqIisriwEDBjBp0iSqqqrc+judOXOG+fPn89lnn/Hcc8/x6KOPuv+FCSE8rmkcHDp0KEuXLm1x3wx/f3+UUvj6+qJpGqdPn6Zfv36UlZW1e5PTpvF+69atlJeXExcXh5eXV7O+natBwJycHGJiYnj88ceJjY3l0KFDegkLh+DgYNauXcv+/fu58cYbWbduXRd8k0KIrpSfn09paSkWiwWTyURQUBBRUVHYbDaXK6Xauld0DMrv27ePoKAgjEYjZrOZb775hqeeeqrNwTB3rF+/njvvvJPhw4fz6aefSjJZCNElHP2jkpISPvroIz2ZbDAY9ESypmnY7XZsNhuRkZFMnjyZBx54oNeV+pKEci/Q0dl9HUnk9NRMDk/PKO6KmYrZ2dmkpqaSlJREXl4eRUVFZGVlUV1dTXR0NJqmUVlZycmTJ4mKisLHx4eYmBh9Z3RHfTAHV9+14xzjxo1j2rRp+g3c4MGD9WNd/V2ffvppPvnkE3x9fdm5cycTJkzo8OcUwpO6e6O8npyN5jyQVV5eTmRkJMHBwQwbNoxp06Zht9tbjWlKKaBhc6nc3Fyqqqqw2+2UlZW1et7s7GxycnL4/PPPCQoKAhpKUPj6+jJw4MAW/x1Yvnw5GzZsYP/+/RQUFOjtdN54q7W/U2FhIVdddRVHjhzhrbfe4mc/+1l7vzIhRBdoz74ZNpuN4cOHU1xcTE1NDQBXXHEF+/fvb1TKy53Y3TTe19XVER4erm8ICI37do7k9/r160lLS+P06dOUlZURGRnJkCFDqK2tJScnh6CgIEJDQ8nMzKSsrIxDhw6Rn5/PAw88wIsvvojBYOj8lyaE6FY+Pj4UFhbi7+9PUFAQ9fX1ZGVlceGFF7qcAOSIWUVFRWRmZlJeXk5YWBhGoxFo6INt27ZN34DPIVRvdtIAACAASURBVCoqimPHjnV6M6pVq1bxyCOPMH36dD744INGk5aEEMKTHP2jpUuXcuLECQACAwMxGAzU1NRgtVoxGAz4+fkxYMAArr/+eoYOHdrjk6tc6daEslLqVuBJYBBwClisadru7mxDb9OZ2X1NO/Zt1fXtSZ6eUdwVF5Pz95mYmEhRURF//etfqaur0+udVlVVoZSioqKCsWPH6iPXmqahaVq7zgGub+BcHRMZGUlYWBipqamt1mgVzUnc6Vq9YaO87uI8kOVIKtvtdsaNGwe0HdPq6+sZO3YsKSkp2Gw2QkND6devH9988w3bt2/XZ9c4Dyw6/o2Ii4sjLy+Pw4cP4+3tzYUXXkhNTQ0lJSVMnjzZZUxZtmyZvmri8ccf12cROrT1dwoKCqJfv3785z//aXHWtRCid3L0u4xGIxaLhcTEREwmEwEBAQQEBDQq5eXQVkxoGu/DwsIwmUz6hoDgOg5aLBYuuugi/v3vf+Pt7U1lZSWnT5/WZzfv27cPb29vjEYj/v7+WK1WJk6cyL333ivJZCHOUZqmYTQaqaiowNvbW1/CbTabm5Ww2b59O1u2bKGwsBC73U58fDwXXngh5eXllJaWkp2dzcyZM1m3bh1BQUHk5uZSWVmJl5cXM2fO7NDeEE2FhIRwyy23sHbtWvz8/No83pN7Awkhzg+OuHHo0CF9XxsfHx+Cg4Px9fXVN1o2m80opQgJCWHatGkYDAY9bva2Ul/dllBWSs0CVgC3AF8CMd117t6sM7P7nDv2RUVF7N27l4CAAJRSbi9n7i5dMaPY0xeTq+/TYrHg5+dHfX09J06cIDAwkKCgIKqrqzGbzYSEhGA2mykvL+/0snVXxxw9epTExESGDRvGwoULJZncThJ3ul5Pb5TXnZwHsoxGI6WlpSQlJREZGelWzT7H7JoRI0bos2tKSkooKyvj7rvvZuzYsSQnJ1NVVcWqVauIiYlh//79+Pv7M378eMLCwvTYU1FRwXXXXUdaWhp5eXl6LCwqKuLIkSPk5uYC6Dc47fk7ffXVV4wePZrQ0FB27typz6wWQvQO7iQyHP2uCy64gIyMDMxms56oKSsrY+LEie2O3U3jyIgRI0hNTcVoNGK321327Rz93PT0dOx2O2FhYdTV1XHmzBni4uIwmUx4eXnh7+9PeXk5sbGx3HPPPfj7+5OamsqwYcM8/O0J0fe4iglAjyY8rVYrkyZN4rPPPiMvLw8fHx+GDx/OyJEjG7Vj+/btPPHEEwQGBmK326mvr+f7778HGsrfJCUl6YPkEyZMYMuWLRgMBkJCQggLCyMjI8OtezBXLBYLGRkZXHrppdx9990sXbrUrT6Pp/cGEkL0Dc6x2FG2wmq1EhsbS0JCAqmpqVitVnbv3o2maZSWluLj40NdXZ2+yajJZMLHxwd/f3+uvPJKLr744l49YNWdM5R/BzytadoXZ3/ue9PXOqAzs/ucO/aZmZkEBgailMJoNHb5svP26o3T8505LymPiYmhsrJSX1Jls9mwWq1UVVVRW1tLv379sNlsnDhxgtraWiIiIvSEb1taS+o4AtA333zDd999R1lZGbt372bp0qXEx8cTGxvblV9BXyVxp4v19EZ53c15IMu50+BOTHPMrnGU0Dl9+jTZ2dn4+vrqmy7s27ePESNGkJWVRXFxsf7avXv3YjabGTVqFNCwAUN0dDQDBgygqKgI+O9AmJeXF4MGDWo0sOju3+n9999n4cKF3H333bzyyiuSTBaiF8nOzmbDhg1s3bqV6OhofQDKVSLDud9lMpkoKyvDaDQydOhQPdnU3tg9c+ZMVq5cSUlJCaWlpZw5c4a6ujqCgoI4dOgQF110UbM46Ojn5ufn64Nh/v7++Pn5kZiYyKlTp7Db7Xz55ZeEhIRw2223oZRqs4SQEKKBq+TmypUrUUq1uGFmd/Dx8SEjI4Phw4czduxYLBYLpaWljcpuQcO+MuHh4YSHh1NYWEhdXR0mk4nc3FyWLFlCZGSkHgv69+9PbGwsERER+Pv76+/Zkb5KWVkZ8+bN49tvv+WHH34gMjLS7ffp7nJvQojezzkW+/j4kJaWBjSUGXNMFrrooot4//33OXHiBIMGDcLb25u6ujq8vLzw8fHBbrfj4+PD6NGjmTNnjr7StDfrloSyUsoAXAx8pJQ6BvgDm4FHNE0zNzl2KbAUYNCgQd3RvB7Vmdl9zgmC8vJy/P39qampITk5Geh9y849NaPY00uMnJeUOzbYyszMJCYmBi8vL86cOYPJZMLf35/a2lp8fHy49tprqaysxGKxMHv27Dbb4Ly8IScnh6SkJOLj4/UbuHHjxukB6JJLLuHvf/87RUVFTPj/2Xvz6KbuM///dWVZi2UbS8aWN5Z4wTbghSUJFAxhABNIQ5MZJtOGTKZJpp3SpEnakm/ntClJSTvNSZhOk3SG37SlLW1hsnQmlGYFU0ioYyAJMXYJNrIxxguWjS3ZsrUgWff3B9WtbEu2vMnC3Nc5OYmlq7sp963neT7PsmQJc+fOndYBusliNLojM3YifbFoMhmtpuXk5FBaWsqZM2ekjObs7GwuXbpEfHy8lLVcXl7OrFmzcLlcKJVKLl68iMPhwOFwoNFoiI2NlX4z0tPT6ezsxGq18umnn6JQKPB6veTn5w9pfzHS9/TSSy/x2GOPsWzZMnbs2DGBd0pGRma8+GwV33BNgIqKClasWIHBYAgYyPBplL/d5P+eTxOqq6vp6uoiISGBsrIy6f1ACIJAb28vzc3NxMTEkJiYSEpKCnV1dQNaf/k+n56eTn19PR0dHahUKtxuNw6HA5fLRVNTEw6Hg+rqalQqFffff78U0JmulS4yMhNNoOBmR0cHgDRzZSoCnj49CPZvH01NTZLPr9frcbvdGI1GLBYLRqMRq9UqaYHb7WbNmjWcP38eq9VKQkICa9aswePxjOrcLl26xO233059fT2//vWvSUpKGtXnp3O7N0EQ1MB/AesAA1AHfFsUxbcDbPtFYA/g71d9VhTFY5N/pjIykYW/Fp85c4bExEREUeSjjz6S5kScOnUKq9VKTEwMnZ2dKJVKYmJiJNuosLCQ6Oho+vr6EEURk8kU8T51uDKUjUA0sAUoAdzA74Enge/4byiK4k+BnwIsXbp05Ka0k8SBAwd48803aW9v5+GHH6a0tHRSjjOe7D5/Z8DXw3fFihXSyu9YjfFI7gk1ESVGg6+vvb1devh9D7soipw/f57i4mL6+/v58MMP6e7uxmAwsHnzZubPny9lz4w0BML/nAsLC9HpdFRVVdHX10dBQQF33333gGnse/fuxWw2k5+fz8yZM0lISGDLli0R8x1cR4SsO5GykBUu3ZloIq2X01gIl+5t3boVp9OJwWDg6NGjaDQaFAqF5Oz5yr59/Y67u7vp6+sjJiYGp9PJuXPnSElJ4Y477sBqtRIVFcX27dupq6uTnLL8/PygA7ICXZPX6+Vf//Vfef7557nrrrvYv3//gIE3MtOf61V7biR8doLL5UKv10uB15qaGubNm8exY8cC6lcodlNjYyNz5syR7NBgdlVZWRlZWVn09vZKi2AdHR1UVFQwd+5crFbrkIzpdevW8cgjj5CYmMiVK1eIjY3FZrMRHR3NhQsXaG9vZ/bs2axfv564uLigrTNkph83iu5Mtn0RKLjpcrmGbBfOgKfJZOLkyZP09/fT1NSETqcjPT09YPB31qxZWK1WDAYDKSkp1NfXY7fbmTFjxpB2Yunp6dhsNlavXi193mq1EhcXF/K5nTlzhk2bNtHX18e77747pnYZ07zdmxJoAlYDl4BNwKuCIBSIongxwPYVoiiuDOP5jYsbRXdkwo+/FlssFvR6PTabjU8//ZS8vDw6Ozvp6+uTqlIVCgUulwuv10tsbCz9/f1cunSJoqIiVq5ciVarvS5a6ShG3mRC8K1avSSK4mVRFK8AP+KaQE0p//3f/01KSgpFRUVkZWXx61//GoC77rqLn/3sZ/zqV7/ilVdeCXl/77zzDrm5uWRnZ/Pss88G3c5qtbJlyxbuvPNOXnvtNTo6OmhpaSEuLo4vfvGL3HPPPXz2s58d8JkHH3yQ5ORkFi5cKL2Wk5PDtm3bePHFF8nNzUWtVuP1eqUf4MFDD0bC53jYbDYyMjIkx8BkMo1qP5OF/8qPLwjjy8yBa+e/e/dunnzySXbv3j3kvANd37vvvovDce1/UaPRyOrVq1mwYAExMTHEx8eTmppKdnY2aWlpLFmyRCo5D9VwGHzOOTk5rF27loKCArZt20ZOTg4tLS3Ex8fT0NDA+fPnuf/++3n00Ue5+eabpW1kRk3IuiOK4k9FUVwqiuLS0WYpjIWp1p28vDzy8/OpqKiQ3uvv72fRokVDdGek96YD4dA9nzbt3bsXjUaDw+HAbrdz4cIFYmJiaGlpoaOjA4fDgU6nw2KxAJCUlER+fj6CIBAbG8u8efNISkrC4/EQFxfHQw89xPr169m2bRtbt26luLh4QDlpKDrV1tbGL3/5S7761a/yu9/9Tg4mT2MiTXvmzp1LQUEBxcXFLF26dEz7vlHw2Qm+IXsAWq2WlpYWjh49ilqtDqhfI9lNI70Pf9Wvffv2UVlZSXNzMxqNBkAaTqrX6+nu7h7y+ZycHBITE3E4HPT29uJyuZg1axbLly+Xhu+dOnWKb3zjG8TFxUm2cKQ7UDKhE2m6A8HtmuE0aSyEw77wBTf9UavVQwbLhSvgefjwYR555BEuXLggDQbVaDTS/IjB5/Dggw/S1dVFV1cXsbGxJCYmcvXqVXJzc4dowbp16+jq6sJqtY7Z3927dy+CIPCnP/1pzL2XJ+I8IhVRFPtEUXxaFMWLoih6RVF8A2gAlkz1uY2GqdKdF154gYULF7JgwQJ+/OMfj/i6j+nua90I+Guxz1ZrampCpVJx/vx5qeJdp9PhcDhwOp2IoohKpcLpdBIdHY3RaGTjxo2kpqYGtMcikbBkKIuiaBEEoRmYsozjYFRVVfH000/zla98hVOnTrFp0ybuv/9+6f3vf//7PPzwwyHtq7+/n4cffpjDhw+TkZHBzTffLGWzDuaxxx7j9ttv53e/+53UK8q3yvmjH/2I/Pz8IcbBF7/4RR555JEB5+djosrOI70n1HAlRqFk4QS6PqPRSGVlJampf53XZrfbWbhwIRqNBqvVSkZGBjqdDrvdPursmWDnXFVVxe7du2lpaaGmpgaXy0V+fj7f//73panI02Sle0qQdSc03fHxwgsvBNSdkd6bDky27gXSpvr6eoxGo9T3vre3lwsXLkjTfJ1OJ3V1dcTFxSGKIkajkRUrVki9BJ955pkhxxltxYsv8zktLY3KykrS0tLknsnTnEjUnqNHjzJz5sxh911YWCj9/kZa5VS48DkqeXl5lJeXA9eqC3xZMIsXLx5Q7eDTr5FKswO973A4OHr0qDRUpqWlhezsbCmT8MqVK6jVapKTk+np6SEuLo6Ojg4sFgu///3vpcA3XNO/zs5OkpKSmDdvHg6HA5vNxuzZs/nmN7/J1772NTQaDQaD4Yb7Tm8UIlF3hrNrAmnSWAmHXxXot9/XD9hqtYZlvoV/a7+jR4+SlJREbm4uNTU1XLx4kTlz5nD69Glyc3OHnMP69euBa72Um5qayMjI4Lvf/a70uv/+W1papKFVNpttVP5ub28vsbGxPPfcc/y///f/pNZBY+FGavcmCIIRmAecDbLJIkEQrgBdwG+AH4qiOLoeJJPAVOjOn//8Z372s59x6tQpVCoVt99+O3fccQculyvg6/7/v0x3X+tGwF+LZ86cydtvv01raysajQa9Xk92djZ2u12qIBEEAaVSiVKplAbyDa7guB5a6YQrQxngl8DXBEFIFgRBDzwOvBHG4wekurqa3NxcAG666SZUKhVwrb/Tt771LTZu3MjixYtD2tepU6fIzs4mMzMTlUrF5z//eX7/+98P2a6np4f333+fhx56CACVSiUZGM3Nzbz55pv88z//85DPrVq1CoPBEPT4vmzlZ555ZsxZrb4MGH/i4+NpbW0d9b4mg0Cr8L4V91CybAJdX3FxMWazecAqc3R0NPPnz2f16tV87nOf484772T58uUkJyePOnsm0DnX19dTX18vDfp7++23+cMf/oDJZBpQ4jUdVrqnGFl3/sJYdWe496YLk617gbSpvb0dt9vN2rVriYmJQaVSsXDhQjZv3syLL77IE088gdFoxGw2o9VqpXZGw2UY+RycULL8Wltb+cxnPsPTTz8NXNMpOZg8/Yk07Qll342NjRiNRj788MOIrJwKF76MOLVazfLly/F6vZjNZuLj41mzZs2AygR//RrObgr0vtlsltrxZGRk8Kc//Yljx47x5ptv0tvbi81mIzExkebmZrq6ulAoFIiiSF1dnaRz3d3d1NfXS0GgwsJCvF4vvb29fPzxx3zyySdUVlZy1113SZnOMtOXSNOdcNo14fCrAv32P/HEE2zfvj0sWf/+WdhWqxW3282VK1cAyM/PR6fT0draitPpDHgOJpOJuro6srKyuPfee9m5c+eQYLJ/lrdWq8XpdHL//feH5O+Kosjzzz9PQUEBbW1tKJXKcQWTfUyE3x3pCIIQDewD9oqiWBNgk/eBhUAy8HfAF4Anguzry4IgfCQIwke+Ht+TyVTozrlz51i2bBkxMTEolUpWr17N66+/HvR1HzeCr3Uj4NNih8PB+++/j9PppK+vj97eXiwWC7NmzUKv16NQKIiKikKtVkuzcZKTk4mNjSUqKmrAPq+HVjrh6qEM8AwwEzgPOIFXgR+E8fgB8YmNKIr85Cc/4Qc/uHZKL730EmVlZXR3d1NXV8dXvvIVAEpKSrDZbEP2s2vXLqxWK7NmzZJey8jI4OTJk0O2vXDhAklJSTzwwAOcOXOGJUuW8MILL6DT6Xj88cd57rnnAh4jHER6T6jhMvD27t074oCEQNen1WopLS2VjK60tDS2b9/OkSNHBqzsR0VFsXPnzlEbDIHOuaqqisLCQi5evMjPf/5z9Ho9JSUlUhnpdF7pDjOy7vyFserOVGvSSBw+fFjKapk1axalpaU4nc5R9SqcbN0LlAHodDoRBAGj0SgFgrxe74DKhby8POLi4sjOziY+Pn5IL8FAhNLP+tNPP2Xjxo10dXWxYsWK8V+gzHVDpGmPIAiUlpYiCAL/8i//wpe//GXg2jPj23dZWRmpqamYzeaAGbiTQSTOkvDPiLPZbNJAYN/f/vjr10iVC4PfP336NACLFy+mo6ODCxcuSBmBiYmJAMTFxUk9TouLizlx4gQKhYKWlhba29tRKBQYDAYeffRRAJYtW8bixYvZs2cP7e3tFBUVkZ2dPeX3VCY8RJruDGfXBNOkQISiE+Hyq4L99ofjGfNfNPf1QnY4HLS1tTFv3jzmz59PW1sbmzdvDhhMHkt1qe/1YNfn+26ampqoqKjg2LFjbNy4kVdffZX29vaI0fVIRhAEBdcyjq8CjwTaRhTFC35/VguCsJNrAeUfBtg2rHOypkJ3Fi5cyHe+8x06OzvRarW89dZbLF26lDvvvDPg6z4i3deSGRmTycT+/fs5ceIEJpOJK1eu0N3dLQWPff2R3W43breb2NhYqccyXNPq5ORkmpubw1ZZMlGELaAsiqIb+Opf/okImpqasNlsbNq0iZaWFgoLC6VsrUcffVQyhP05fvx40P299tprQ14LlPHl8Xg4ffo0L730ErfeeiuPPfYYzz77LLfeeivJycksWbKEY8eOjfm6xsN4hgROBoGMtWAlRqEYbcGuL9CK+dy5cyeklClQWVRWVhbNzc288sorzJkzh0ceeQSdThe0lF1mbMi681fGojtvvPHGlGvScBw+fJjvfve7GAwGZs+eTXNzM9/5znfYuHEjN998c8hDOydb9wJpU6CsvPr6eqqqqrh8+TJOpxONRoNarR5VaedIDu7x48fZvHkzGo2G999/n0WLFk3INcpEPpGmPc888wzl5eWkpaXR3t7O+vXrycvLY9WqVYjiX31NX4mzP5NZAjgRw38ni2BBo+H0a6TS7MHvO51OKeP5vffeQ6VSYbVaaW1tpaOjA0EQiI+P52//9m956qmnMJlM3HPPPYiiiMfjwel00tvbi8FgQBAE1Go177zzDhcuXKCzs5OHHnoIvV5Pc3MzTz75pBzYmeZEmu6M5GsF06TBhKoTkeZXTQb+i+Z6vR6Px0Nvby89PT2IoojFYkGpVA6ouvTZKgcPHkSj0QRt2TN4/z6G+w3wfTdxcXG8++67nD59moKCAjIyMnA6nRGn65GIcO2h2sO1Aeeb/uJPhYIITHm521TpTn5+Pt/61rdYv349sbGxFBUVoVQqg74Oke9ryYyMyWRi165d1NbWolQqaWxsxOPxoFAopIxkh8NBc3MzeXl5kl9ot9tJSkpizpw5lJSU0NXVxec//3nq6uquq1Y64cxQjjiqqqpYtWoVf/zjH7FYLCxcuJCKigo+85nPBP3McKtXGRkZNDU1Sa81NzcHXIHOyMggIyODW2+9FYAtW7bw7LPP4vF4OHjwIG+99RZOp5Oenh7uu+8+fvvb307A1YZGJPWE8hkE/f39tLS08MEHH3DgwAG2b9/Otm3bhmwfitHmG4jnn9H44IMPBl3VD+W6Q8lQGLyvr3/967z88ssUFBTwpS99CbVaLfdMvkG4nnSnvLx8yjVpOH7xi19gMBikVkAul4u4uDiqqqq49dZbQ85kHI/uhfL8B9Km5ORkRFEcsApdUVGB2+2WsvucTidtbW3k5+eHtNA0koPb2dnJHXfcQVpaGu+88w5z584dcZ8y04dI0x5A2j45OZm7776bU6dOsWrVqgH7Tk9P5+OPPw5b5VSkz5IYzHD6NVif7r///hHtnd27d0vfeUtLCx6PB7vdjtPppL+/H7fbTVdXF2fPnpX2P3fuXBQKBVqtFpPJhFKppKOjgwULFpCbm8uuXbtwOBw88sgjREdHc/z4cUpKSuTAzg1ApOnOSL5WME0aTKg6MV6/arzVEuGotvBfNM/Ly6Ozs5OZM2dKWcpKpZLt27dLx/W3VXyUl5dLrb1CqS4d7jfA990cPnyY06dPs2XLFjweDx0dHVJWaKTregSwG8gH1omi6Ai2kSAIG4HToiiaBUHIA74LDI2+hpmp0h2Ahx56SGq18+1vf1taDAn2eqT7WjIjU1ZWRlVVFQ0NDVgsFjweD4IgEB0djcvlQqvVolKppF7+V69eZf369SQmJnLu3DmampqIi4uTfhv8W/5cD9zQAeXq6mopO0uv13Pvvffy5ptvDis2w61eeTweTCYTDQ0NpKen8/LLL7N///4h26WkpDBr1ixqa2vJzc3lyJEjzJ8/nx/+8If88IfXKkSOHTvGrl27pkRMQg2kTjZlZWX09/dTXV2NTqcjNTUVi8XCrl27mDt3bsCg7UhGm8lk4siRIyxatIjVq1fT09PDkSNHAu4vFMaaybRt2zbq6+tZvnw50dHRIZWyy0wPrifdiRRNCkZTUxOzZ8+W/rbb7cTHx2OxWKTXQs1kHIvuhfr8B9Km7du3Awx4TaFQkJ6eLmVjarVaDAZDwLK6QIzk4CYmJvI///M/LFu2TCpdl7lxiDTt6evrw+v1EhcXR19fH4cOHWLHjh0A3HzzzdK+V61axVNPPcV999036qG4Y2G02XCRQCD9ClWfBgecsrOzOXLkCHBtiJXH48HhcODxePB6vSiVSrRaLe3t7ezbtw+Px0NxcTEVFRXAtWGfSqUSm81GXl4eRqORrVu3Ul1dTVxcHHV1dZSUlEjnIAd2pjeRpjvD2TXDadJgRqMTY/WrxlstEa5qC/9F86SkJAoKCqiqqmLhwoUUFBQMCWL72yq+xfP+/n4OHjxIcnIyKpWKoqKigPv3LcDX19eTmpoasMqhqqoKq9WKIAisWrWKwsJCSZ/8iXRdnyoEQZgD/AvgAtr8MnH/BTgOfArMF0XxErAW+JUgCLGAGfgt8G9hP+lBTJXuALS3t5OcnMylS5f4v//7P+n/vWCvR7qvJRMY/5aLjY2NdHZ2Eh0djUajwe12I4oi/f39qFQqRFHE4XBIC+/JycnU1NSwYsUKiouLKSkpCZgseb0QzqF8EYe/2ADceeedvPXWW2Pen1Kp5Cc/+QkbNmwgPz+fe+65hwULFkjvb9q0SRrC8NJLL7F161YKCwuprKzk29/+9oj7/8IXvsDy5cupra0lIyND+nGdrrS0tNDS0oJOp0Or1SIIglRK5T9ozx//AQm+3oJPPvkku3fvlpymkQb3jYbR7M9ms/EP//APnDt3jnnz5vHv//7vJCQkTPqwDJnI4nrTnUhm1qxZWK1W6e+YmBh6enrQ6/XSa+HKZBzp+Q80vGXwazqdbkCpP1wbHjL4tWAEGv4TGxvLb37zG6lc74477pCDyTcokaY9ZrOZlStXUlRUxC233MIdd9zB7bffPmTfmzdv5r777iMnJ2fSfi9NJhO7d+/mySeflIbm+hNJsyRCJRR9GjzsymazceTIEdauXUtcXBwOh0PK2IqNjUWj0SCKojSx/OTJk9Ii2IoVK6RWPh6Ph4SEBMrLy4FrA5G2bt3KM888Q1ZWFllZWQPONZKGP8tMLJGmO8MxnCYNZqRhlxPBeH2WifZ5gjF4KGBmZiY/+clP+K//+q+Aw+r8bZW8vDza29tpbGykp6cHtVqNxWLh8uXL0uDVwft3OByIokhMTMyQQa0HDx7kwIEDdHV1kZSURGJiIuXl5Vy9ehW1Wj3gPK5HXQ8Hoig2iqIoiKKoEUUx1u+ffaIoXvrLf1/6y7bbRVE0iqKoE0UxUxTFHaNojzFpTKXu/N3f/R3z58/nzjvv5D//8z8lnyTY6zLXH7/85S/58pe/zIcffkh3dzdms1myl1wulzRYz+v1IggCLpeLxMREli1bRkpKCrGx08Oh3gAAIABJREFUscTExHD69Gm6uroGtAO6HhFCdVSngqVLl4offfTRVJ+GzCQTrBxr9+7dvPbaa6Smpkp9iux2O2q1muzs7GFLwP1X5f3bX3R3d1NUVIRC8de1FK/XG1Lv4kDn6RsEONL+Ll++zB133EFVVRW//e1v+fznPz/W2xWJTHmvrIlE1p3rB/8eygkJCTQ3N1NXVzegh3KwHukTwZNPPhnS8x+MwZpy7tw5qqqqJIeqp6cHr9fLhg0beOmll0bcn69U3Zfx5/F4+NnPfkZlZSWPP/44//Ef/zH2i408ZN2RmRAG2wv19fVSS4asrKxJ15HJIhR98tcMs9lMTU0Nly9fxmg0snPnTnbs2EFLSwtnzpzB6/WiUqmIiooiJiaGxYsX43Q6eeGFF4bcv1dffZVLly6RlZXFAw88gM1mk+7fYJ0CsFqtxMXFXQ8ZOtNKd0DWnrESzM+YSJ0Yr40x3s+PhtG01hisAX/4wx9oaGgAoKioiLy8PNRqdVBNCKRbbW1teL1ePvjgA1QqFYsWLSIpKQmNRiPZUwsWLCArK2vSvq9JZlppj6w7MtcrJpOJzZs34/V6UavVtLa20tnZKb0fFRWFQqGQ2l7Mnj0bu91OTk4OGzZsAKCmpgaLxYIoirz44ouRrEEh6c4N3fJCZuoZrhxr3bp1HDhwAIvFgl6vx+FwYLfbycrKGnFFOVjpt28FfLS9GIOdp1arHXF/tbW13H777bS3t/OHP/yBjRs3jvo+ycjIDMXXY8pXcpSRkcEDDzyA0+kMSw/48UxvD6QpTqeTmJgYTCaTVBbl67llMplGvA5fWWhHRwcNDQ1S77gHHniAbdu2sXv37kntoygjcz0y2F7wPRfNzc1oNJpJ15HJ6HFqMpmor6+noqKClJQUqfXEYH3yle2bzWbKy8vR6XSkpKRgNpvZs2cPgiCg1WpJS0ujq6uLqKgoRFFEoVDQ1dXFmjVrBrT0aW5u5pNPPuHSpUssWrSIDRs2YDAYuOeee4BrgaDq6mrq6+spLCwcELCXW37JXE+EY+bMeGyMifh8qIy2tcbgFhZ2u5309HRWrlyJ0WgE/hr4DsRg3YqJicHhcHDixAk0Gg1f+tKX0Ov11NTUYLVaSUhI4KabbuLxxx+f1O8rHP2qZWRkppaysjKcTidKpZJz587h8XgGvO/LSlapVKhUKtLS0khISGD+/PmSvhmNRmkhfTpohBxQvoGIxB+64Xp+btu2je3bt7Nr1y7a2tpITk4mKyuLqKioEUsDgvU2MxgMdHV1SX+H6sgEO0+73T7s/s6ePcuqVatQKpW899570jAIGRmZiWH9+vUTPrwgVK0cz/T2QJqSnZ1NS0sLCxcu5OrVq9KAG7VaHVJ/Ud/Q0WeffZZTp07R19fHxo0b6evr48EHH0SlUpGSkoLL5ZKHYMnI/IVA9kJWVhYajWbUWXyjsbNMJhP79u3j0KFDGI1GiouLpdLt8TybvuBORkYGXV1dWCwWysvLKSgoICoqaoA++QJONTU1Unsxu91OSkoKBoOBxsZGCgsLUalUUpZydHQ0cXFx5Obmcu+99wJ/7RH7xS9+kSNHjvC1r32N//iP/5DKPv0DToWFheh0Oqqqqujr66OgoOC6mGIuIzOYyZ45k52dza5du/B4PCQnJ5ORkTHkGR6O8dgoo2G0g0x9tsqPf/xjamtr6evrIzMzc8A2wQLf/otlvmB5a2srJ0+eJDk5maVLl9LQ0CAtogEDAjeTuTAYjn7VMjIyU0tVVRU2m21AVrI/KpVKCijPmzePX/3qVwDs2bNnwCD26bSQLgeUbxCm4ocuFMdqpKEW69evZ+7cuZSVldHa2kpaWlpIgfBgq/ILFy6UeiuPZoU62Hn6yjgH7w+uZeI0NjYyb948vv/974ccTI7EwL+MzFQynmditJ8djVaOJ0MpmKZ0d3dz3333BSxRDeWa6urq2LBhA2q1mnnz5pGYmMivf/1r1Go1RUVFOJ1OqqurKSgokIdgycgwcVl8o9EO37a1tbUYjUYUCgUVFRWsWLFC6nE61mfTP7gTHx8vlYM3Nzezc+fOAfv1BZwuX75MSkoKdrsdu93OokWLpEX4qKgoSkpKuOmmm3jvvffo7OwkIyODLVu2DDnHz372s8yfP58nnngCv0FOAbPAk5KSrpc2FzIyYcc3RLywsJCWlhbMZjNdXV1s3749ZG0IRxY1DLRnfC0ofOXcgWwuk8nEa6+9Ji0o9fb2UltbyxtvvMGmTZvQarUBgy2BFsv6+vrQ6/VkZGTw4IMPolAoePfdd8MeuBltUF1GRub64vDhw/z4xz+mvLycvr6+oNu53W5mzJjB3XffzdKlS6XnPxxaPFXIAeUbhHD/0IXqWIXiyA23ohwssDLcqvxYVqiHO8/B+zOZTHzzm99k8eLFzJ07F4PBwOHDh5k9e/aIxzWZTOzatYv29nacTidnz56lsrJyVAakjMx0YjyLYWP57GCtdLlc1NbW8uijj7J58+YhztFYM16CacqsWbOG1cThrslsNnP69GmWLFnCXXfdBcB7770nZQn6ytfhmgPoG6AlI3MjM5YsvkC2x2jsLN+2vkoEX/C1pqaGkpKSoKXeoeAf3DEajRiNRmlRKtiiWGNjI2azmZSUFBYtWiSVY/oW4V966SXefPNNlEolRUVFzJ8/nyNHjgDw4Ycf8sknn/A3f/M3rFu3ji1btgx7Tj78kwdkZGQG4q8nvufWarVSV1c3YlXWcIvOvgGko1lkH2lR3mfPuFwuqQWFb4BnIJurrKyMjo4ODAYDMTEx6HQ6BEHAbDZz8uRJ7rzzzoDBFv97otVqqa2txeVySRnOdXV1pKWlUVpaKg3wC1fgRtY4GZnpy+HDh9m+fTtmsxmPx0N/f3/A7XyZybm5uSQlJQ2oqJ/sipapRA4o3yBM5g/deByr8ZRjjRQsmsiVoFDLzkRR5NFHH+Wdd97hwoULzJ49G71eT1paWkjB+/3791NbW0tiYiIGgwGn00ltbS379+/nqaeeGtO5y8hcz4xnMWwsnx2caVNeXo5Wq0UQhAkpR/cRTPsefPBBKVATSBODXdPzzz/Pr3/9a1JTU7ly5Qoulwu9Xk9LSwtqtXpAxrNGo6GtrY2SkpJxXYOMzHRgtPZCMNvDN/TXn2B2lk9nEhIScDgcUgDGarWOu8dpoMWq+vp6mpubefLJJ4cEhXJycti5c+eAAWNWq3WA7pw+fVpajGptbaWnp4d58+bx9NNPc/bsWTweDwUFBUH1MVy9XGVkpgtj9duG842AIe89//zzpKen43a7Awaf9+zZQ39/P83NzVRUVHDgwAG2b98+IKjts2dqa2sle6mvr48VK1YEbNnV0tIi2Sg+kpKSUCqVLFmyJGjVgu+e9PX18T//8z80NjaSkpKCTqcjPT0di8VCfX09zzzzzIS3QhsJWeNkZKYXJpOJ/fv3c+LECcrLy+nt7UUUxYDb+pICBEEgJiaGuXPn3lDtbuSA8g3CZP3QBTNcenp6KCwsHLBtIENoPIHfQIGVjo4OduzYQVZW1oQOtwml7MztdvOlL32Jd955h/T0dHJycoiJicHpdFJVVYXdbh/xWCdOnMBgMEiOm1arxWAwcPLkyXFdg4zM9cp4FsPG8lmlUsm7777L1atXaW9vx2AwoFAo0Ov1E1rZMZz2+dr8BNLElpYWoqOjOXPmjDSw1GKx8Pbbb7No0SIWLlzIpUuXpEGmra2taDQaYmNjsdvtaLVaLBYLSqVyxF70MmNHEITfAmsBHdAGPCeK4s+n9qymN+NpjTOazJGJGPrrs8ny8vIoLy+XXlepVOMuzx68WFVfX8/x48cpKSkJWqkxnB5973vfo6mpCYPBIA0JNZvNNDc309zcjFarZdWqVZw4cQK1Wh1wATxcvVxlZKYLo/XbfPp38OBBNBoNixcvRqFQDLBbgCEVWOfPn6ejo4MNGzYM0YaysjL6+/uprq4mJiaGlJQULBYLu3btYu7cuUP049FHH0UQBPR6PcXFxQOqIwZf29mzZ6XFNACn04larR7WL01PT6exsZG9e/fS3t7OZz7zGaKionA4HHR3d6PX61mwYEFIWdwTjaxxMjLTB1/FeG1tLVevXsVmsw27vS9pR6PRsH79+iHtxaY7ckD5BmGyfugmwrEKxZEL5CgODhaZzWaqq6txu92sXr16wvpEBys7++CDD6irq6OlpYXExERef/11jh8/zsKFC8nIyECn0wHXgsIOh4OLFy+GVGY2ePVLFEV6e3tHVaImIzNdGM9i2FgcssuXL2OxWDAYDPT09GC1WklJSaG0tBSY2BLGYNo3nCZGR0dz9OhREhMTSUhI4KOPPuL8+fPk5ubyT//0T7hcLnJycqTp5nPmzMHhcLBs2TKam5tpa2tDqVTKbXQmnx8CD4mi6BIEIQ84JgjCJ6IofjzVJzYdCeeciIkY+uuzyQwGA8uXL6eyspL29nZKS0u59957x3XOg4PDzc3NlJSUSPsMtjAWTHdOnDiBXq9HEAScTqd0XT09PcTExLBo0SK0Wi0ajQaHw8GhQ4eGXEO4ernKyEwXRuO3+eufIAgIgkB5eTkrVqzAaDRKdosoigO0q6amBr1ez9WrV4cEn3NyciT9iImJkQK/er2etra2gPqxefNmbDbbiDbXunXr+OSTTzh//jyiKCIIAl1dXeTm5gZd6DaZTFRVVbFnzx5EUeS+++7j0qVLqFQq1q9fLw3gCxTADgeyxsnIXP/4hiX/9re/pbW1FUEQuHr16rCfEQQBURSZMWMG2dnZPPPMMzfccy8HlG8QJuuHLphjpdfrQ3asRiKYo6jRaAYEi2pqalAoFKSkpAQ0jEI9lu8eRUdHI4oiZWVlzJ49m/z8fMlg8TlNn/vc58jIyKC5uZnq6mp+8IMf0NTURENDg5QN6HA4sNls9Pb2YrPZhnV2b731Vo4dOyb1OvVlGEZHR4/4WRmZ6ch4FsNG+9mysjKysrLIyMigpqaG6Oho4K8BmPfee4+2tjaSk5MxmUxT8vz5FpxEUUQURaxWK6mpqdxzzz10dHSQkZGBQqEY4FxVVVWRmZmJRqNh1apV8oJUGBBF8az/n3/5JwuQA8qTQDjnREzE0F9/m8xms1FaWjqhz6UvOGwymXj00Ufp7u6mtbWVvLy8AQGmUElKSuLixYv09PSgUqnwer0oFAoSExNRq9VSVRVAcnJywPs+nfsHyshMNKPx2/z1z1edpNPpqKmpwWg0Dgjq+muX1WpFrVYP0DJ/bUhPT6eiooKUlBTpfafTidFopLW1dch5hGpz5eTk8MQTT7Bv3z5OnjyJIAisWbMm6GKazw90Op3ExMSwePFiTCYTCQkJzJ8/X7J3fNc3VW0mZI2Tkbl+MZlMPP/885w4cYKmpibcbjeCIOD1eoN+Jjo6mjlz5qBWq9m4cSOZmZk3pAbIAeUbiMn4oQvmWBUUFITsWI1EMEfR4XAMCFr7Mu/y8vKkz47GafIPXPuyAAFmzJiB1WodsNpfWVlJcnIy/f39iKLI7Nmz2b59O3q9Hr1eT2xsLK2trVitVhISEtBoNMycOXNEZ3fr1q20trbS0dGBxWJBrVYTHR3N8uXL5cnBMjckwzlVI5W4j3YhzbdA5gvI+srROzo6KC8vR6FQoFQqycjImLJFHY/Hw/Llyzl//jw9PT2sWbOGvLw8+vv7h9XjYD0JZSYPQRD+C/gioAU+Ad4a9P6XgS8DzJ49O9ynN60I50CkiRr669vOp0++kvTRDiEOhs+m0Wg0CIKAw+GQ7JiRSsv98S10R0VFIQgCbrcbrVZLXl4e7e3ttLe3k5SUhMPhwG63s3z58oDBJhkZmdERqp7465/PbvG1txrcD91fu3wtdtauXSvtyz8gu27dOg4cOCC113I6ndIAvGAVp6HaXDk5OTz99NMh3YeXX34Zg8FAZmYmS5cuJTo6GqvVit1ux+l0YrVa5TYTMjIy42Lfvn2UlZXR1NSEx+MBhlaNDyYhIQGdTofH4+H48eNSNeuNhhxQlhkXE+VYDUcwR9Fmsw0wXHzD8oxGI2azmZqaGi5fvozRaBySTTjSIMEzZ86QmJiIKIq43W7cbjcKhYJz587R09PDmTNniI+P5+233+bmm2/mH//xH0lMTKSlpYX777+fPXv2UFRUJN2TAwcOsHLlyiHXEKin9BNPPEFZWRmtra2kpaVRXV1NVlbWiJ+VkZmuBNKSUEvcR6NDgwOyRqORgoICjh49itvtJiUlRcrws1qtk7qoEyx4pNPpeOmll9DpdGzfvh1BELBarcTFxck9/CIMURS/KgjC14DlwG2Aa9D7PwV+CrB06dLhrVaZYQnnQKTRBE2GCwKPpk3HWFp6+GyaxYsXU15ejk6nQ6vVcvr0aXJzc0PWha1bt9LU1MTp06ex2+3k5uZiMBgoLS3lxIkT1NXVSYvnixYtQq1WExcXN8q7KjMRyL3bb0z89c9oNLJixQpOnz4NQFxc3AB98teuoqIiLl++jFqtxuv1DrEZcnJy2L59O7t27aKtrQ2j0UhmZiZRUVFBW1OMdqFsOERR5Ec/+hFPP/00jz32GAkJCVLlWCA/UG4zISMjMxZMJhOvvPLKgGByMKKiovB6vWg0GhITE7HZbMyfP5+8vLwp6d8eCcgBZZlxEY6eUcM5iv6Gy5UrV6iqqsJisdDU1ERUVBQqlWpINmEogwR9q/FwbWjFihUrpCmfUVFRuFwuqqurpWnGZrNZyvgJdE82bNgwoCTU/xoC3VP/+7d79255crDMDUUomXiTUeLuH5B1OBxUVlZiNpuJiYlh1apVpKamSttO5qJOMI1avXo1P/nJT+js7GTTpk2Iokh3d/eARTzZuYosRFHsB/4kCMJ9wDbgxSk+pWnJVC2mDJe9MlIQeDQaNpptffq5b98+Zs2axfz581mxYgU1NTVYLBaAoIHoQNrb09PDH//4R2w2GzqdDpfLJR1//vz5eDweVq9eLS9iRQZy7/YbkMH6p1aryc3NDficD/Yx/J/5QDbD+vXrpUHBvkSX4aojJqqfvdfr5Rvf+AYvvPACixcvJjExccD7/n6gbOPIyMiMFZPJxOOPP05DQ8OwwWSlUkl0dDRarRa3201ubi7z5s2TEo2mqn97JCAHlKeQ8UwkjyQm+8d8OEfR33ApKioiNjaWN954g4SEBDIzMwNmE4YySNBX2iWKovR+X18fCQkJKBQKKisrUavVLF26FL1ePyTjJ5DBFq4+sDIy4WSidSxUZ2QyStx9Adl9+/Zx6NAhjEYjGzZsoLKykqNHj7J27VqpV99kLuoE0qiGhga2bNlCXFwcr7zyCpcvXw7oAMrOVcSi5FoPZZlJIJyLKaFqlO85drlcHD9+XGpjtX//fp566qlRaVio2/qf2+zZswe068rLy+P06dM4nc6AWYOBruvpp5/m7bffpru7m5ycHDwejzSc78iRI8ybN4/t27dLA4rlRaypRe7dfmPhb3/5hmLabLZRPYeh2AyjsStGWvwKxWZ0Op3cf//9vPbaazz++ON85Stf4Ze//CXR0dGyHyQjIzNh+PdNdrvdQbfTaDQsWbKEwsJCCgoKpGQjOdnvGnJAeYoI50TysZxbJAW6h3MUd+/ePcBwycnJITU1FaPRyOrVq6V9+DteoQwSnDdvntRDec2aNVLpmFar5aOPPiItLY28vDxpyjkMzPgJdA/H6uz6X//7778vTUVubGzkwQcfvCFLK2Qig8nQsVAz8SarxD0nJwej0chdd90l7VuhUHDkyBFOnz7Nhg0bJt2Z8WmUr3WPxWLhgw8+IDo6moqKCm666aZJOa7MxCAIQjLwN8AbgANYB3wBuHcqz2u6M9bFlMG/19nZ2VKANJANFKpG+Yb7VlRUoNPppGFZhw4d4t577x2VhoW6rf+55efnS73fT5w4QV9fH3DNprHZbEO0OtB1ffDBB9hsNm677TaUSiWXL1/GbDZz9uxZFixYQHp6OuvXr5ftkAhipN7tMtODQPZXV1fXlPuRwy1+hWozvvbaa7z22mv8+7//O9/4xjcARu1DRZovKyMjE3ns27eP8+fP43a7pVYWgxEEAY1Gw1NPPSXZOuNJFJyOhC2gLAjCMWAZ4MslbxFFMTdcx480wjmRfDREaqA7mKMYyHBJSUnBbDYPeM3f8QplkKDNZmPNmjWIoojH48HhcLBmzRrOnz+PTqfDaDRKArN69Wri4uJC6os41sFYOTk5XLx4kb179xITE4PdbufDDz/kxIkT7NixgwceeGBM+5WRGQ+ToWOhZuJNZub+4HMwGo2sWbOGEydOhCULLz09nfr6eqmtjl6vp7i4GEEQRuzt5UN2pqYUkWvtLf4/QAE0Ao+Lovj7KT2rG5hgz8Pg3+v6+nr27t1LSUkJWVlZAW2gUDUqPT2dQ4cOSQNbfL2GNRoN+/fv59577w1Zw0LVO/9z8/VSPXfuHKdOnaKgoIDFixdLVRYwUKv9P+v1elEoFOTk5JCRkYHH45F6rc6ZM4eenh4UCgXt7e2jvucyk8tIvdtBHgg6HYhEP9JkMlFfX09FRcWAmRM+HyzYOe/fv5/k5GSam5vJyMhg7dq1fPjhhyxdulTa71gGkkaaLysjIxNZnDx5koSEBDQaDf39/Xg8ngGtzBQKBenp6aSnpw/ojyy3GBxIuDOUH5GHQ1wjnBPJR0MkGijDESg4nJ6eTmdnZ9Cpv6EOEvQZMBcuXODMmTN4vV5KSkro7OzE5XLh9XqlCcn+Tl2ge9jR0cGOHTvIysoas2P1i1/8ApVKRWdnJyqVisTERKxWK8899xwrV66MyO9nqpEXsiaXydCxUDPxJvPHPNA5aLVaNm/ePKpFIZ+GVFdXY7FYSEhIoLCwcMTnf926dTz88MPU1dUBUFxcjEajoaCgICQtDuZM5ebmcujQIZqampg1a5Zc4TBJiKLYAawecUMZYPIDj8MFFwb/Xre2tmIwGGhtbSUnJyegDRSqRq1bt47f/OY36HQ6WlpaiIqKIioqiuTkZClLOVQNC1XvAg0WVavVWCwWNmzYgEKhkLYdrNW+z9bV1VFWVsZjjz2GTqdDp9PR3NyMQqFApVLhdruZMWMGUVFRUlXXaO65bKtMPiP1bpcHgkYuoephpPmRvmc+IyODrq4uLBYL5eXlFBQUEBUVxd13383evXulc/ZVYNXW1nLx4kXS0tK4dOkSd999N42NjTz00EOYTKYBLciKi4sDVlcM5nrzZWVkZCaHkSrQrFYrJpMJlUo1ZCaGUqlEr9eTn58v2YX+yC0G/4rc8mKKCOdE8tEQaQbKSAQKDkdFRQ3b0y8Ux8xnGEVHR/PKK69gNpvRarUUFRWxfPlyaVhXaWkpW7duHba3q9lsprq6GrfbzerVq8fsWDU1NeF0OlGpVKhUKgBmzJhBR0eHbCQNj7yQNUlMho6NJvN4sn7MJyL72ach/f391NfXExUVhcViITY2dsTnf86cOVy8eJGGhgZmz56NWq1m9uzZtLS0UFFRIZ3jaJyp8+fP88orr5Cfny/1Vv3ud78LIAeVZaaMUAKP4w04DxdcGPx7bbVaSUhIwGq1Sq8NtoFC1YecnBxKS0t5/fXX8Xq9xMbGkpKSglKpJDY2lrKyMrZt2xbytYSid8HO7dZbbx1Rq9etW8dXvvIVjh49SmZmJhaLheTkZERR5PLly3i9Xux2O/39/SQlJdHT04PNZuPJJ58c8r3IAZ2IQe7dPkGEI+N+OD0EBhxfqVRGlB/p/8zHx8dTU1NDW1sbzc3N7Ny5k5ycHMlmdLlclJeX4/V6uXTpEv39/Zw9e5bo6GjOnz/PzJkz2b9/Pw6Hg9raWoxGIwqFgoqKClasWIHBYBhWS643X1ZGRmbiGakC7eLFi5w5cwan04ler8fr9aJUKqVeyhqNhnnz5iEIAhkZGVMeo4tkwh1Q/qEgCM8CtcB3RFE8FubjRwyROmgtUgPdgfAZdz09PTQ2NqLX6ykoKJCCw8MFSUZyzMrKyujv7+fnP/85NpuNhx9+GLvdztGjR9FoNMyaNYvvfe97AY8x+B7W1NRgt9txOBy8/PLL9PX1ERUVRWNjo2RkhcKsWbP48MMPB0w6djgczJw5c8iqmYxMOAikY/X19aSmpgYMMoSKRqPh6NGjCILArbfeGvastonIfvY5V2fOnCE2NhatVovdbqe1tZWioqIBzpC/o2owGPjf//1fTCYTGzZs4O6776a9vV3qhTpr1qwRM3QCOVOVlZVERUVhMBgApH//4he/kAPKMlNGKAOcxpvpOlxwQalU8u6773L16lVp6K7VakWv10vbDraBRqMPW7du5dChQ2RmZhITE4PD4aCjo4P4+Hj27dsHDL84NBYG6+fatWv54IMPOHDggJTlp9Vq6erqYtGiRezevZvm5mZOnjzJH//4R4qLi9mwYQNGo5GtW7cCsGPHDhoaGujv70en06FSqbBYLKSnpwf8XuSATviRe7dPHuHKuA+mh/v27cPpdA44/uXLlxFFkezs7IjwIwe32zEajXi9XlpaWqR75LMZa2triYmJoaGhAbvdjt1uR61Wk5ubS0pKCi0tLVitVm677TZJmwVBAK75VCUlJcNqyfXky8rIyEwsPp/q4MGDuN1utFotPT09XLx4Ebfbzd69e/F4PFitVgRBQK/XS4l6Xq8Xt9stBZddLhfLly8nKiqKdevWTfGVRS7hDCh/C/gUuAp8HviDIAjFoijW+290o/T1itTeK5Ea6B6Mv3FXWFgonWcofbUGZxgAQ16rq6vjpz/9KYIg8M1vfhOtVkt5eTkzZszg7//+7+np6eHIkSPMnTt3yPEG38MLFy7Q1tZGWloaHR0dCIKA1+vl4sWLozJIH3zwQU6cOIHVamXGjBk4HA76+vq45ZZbZCNpeEZcyLpRdGeiGaxjSqUSURSJiYkhPj7kKZntAAAgAElEQVSe+vp6HnnkEbKysqQe5aH2vfvc5z4nPdeTxXAZR+PNfvY5VxaLRQpOabVaqRWPzxnyv+b09HT+7d/+jZaWFpYtW4YoilKlhUKhwOv1Mn/+/BGz/QI5U1euXCEpKWnAdgkJCTQ1NY35GmVkxstIgcfRZroGeqaDBReUSiWtra1YLBYMBgN2ux2z2YzL5WLBggV4vd5hs49DHapbWlrKmTNnsFqtUssJURSZPXt2wMWhsWZCBtLPuro6fve735GVlcWGDRuorKzk3XffpbS0lLVr13LkyBEMBgMff/wxR44cYfHixezbt4+8vLwB+965c6e07/j4eN59911iYmJYvHgxCoViyPciB3SmBLl3+yQRroz7YHp49OhR1qxZQ0JCgtQqoq2tDY1GQ1paGhcvXpRsjbKyMoCw+5OhPPM+m/HRRx9FEAS6urro6+tDp9ORm5vL1atX0Wg0tLW1oVKpiI+PJyEhgStXrmC1WqXBomlpaWRmZgY9l+vFl41UBEFQA//FtUUpA1AHfFsUxbeDbP91rsV5tMD/AttEURzSu11GZrLxt4P6+vpoa2uTBu7ZbDZ6e3ulDGRBEIiJiaG3t5eYmBiUSiVxcXFkZWWh0+mor69Ho9GQmZkpz4AYgbAFlEVRPOn3515BEL4AbAJeGrTdDdPXKxJ7r0RqoHswYzHuAmUYPP/88wiCQFZW1oCsg/j4eNavX8+SJUtISkrivffeQ6FQkJKSEtB58mfwPXS73cyaNQuXy4VarSY6Opq+vj48Hs+IZVv+rF+/nh07dvDcc8/R0dHBzJkzueWWW0hKSpJXzYIT0kLWjaQ7E42/ju3evRutVis5PdXV1URFRWG1WoNm1foHT+rr68nIyAhLmfRkZxz5nCu9Xo/T6USr1dLe3o7VauW1114jOTlZunaflpnNZlJSUkhJSeGmm24iPT2dqqoqOjo6yM3NZf78+dJAreGy/QI5UzExMWi12gHbWa3WIc6rjEw4GSkIMZpM12DPtC9w6vusL7ig0Wik3/6amhqsVivp6enMmjWLzMzMCbOBtm7dKmUXfvLJJ/T39+P1esnPz5/QjOxg8xsAlixZQkJCAqmpqVitVuLi4qirq5O2v+2226Thn0ePHh0SUB5s1zidTtasWTNgwJ//9yIHdMKP3Lt98pjIjPvhFoyC6aHveGazmfLycmJiYjAajZjNZlpbWxEEgUWLFknP2lT0Kx9NK6DNmzdjs9mIj49HqVSiUqm4evUqWq0Wi8WCUqmU2vTMnDmTiooKKeDj8Xg4fvw4paWlQc/levFlIxgl0MQ1PbnEtXjNq4IgFIiieNF/Q0EQNgD/yrXqiFbgdeB7f3lNRias+NtBDocDlUqFw+HA4/HgdrulYLJCoSAqKgqXy0V/fz8Oh4ObbrpJWtDKyMhg3rx5lJaWjmp2zo3KVPZQFgFhCo8vE4RIDHQPZizG3UjOFkBVVRVxcXFS35zo6Gi8Xi9tbW0olcoBTtZwx/O/h1euXKGhoQGz2cyMGTO4evUqXq9XyuIcjUH6wAMPsHLlSsrKymhtbSUtLU1eNRuGUBeyZCYG/+eypqZGCmL6+pLCwODw4ODJBx98gMViIT4+PqTA6Xjw6YHL5eL48eNYLBbUajX79+/nqaeeGvf+fc5VWloaVVVVNDY20tDQgE6no6+vj5tuuok9e/bQ09ODTqfjzJkzwLXWNr57dtttt5GUlMQnn3zCokWLQs72C+RMPfHEE/z3f/83XV1dUo/Yrq4uvv71r4/7WmVkxspIQYjRZLoGW2iuq6sLGFzYu3cv8fHxKBQKSW98JdoT6UD4P49NTU3Mnj2b/Pz8gBo3nkzIQHaRyzU0SczhcHDs2DE6Ojqw2+3cc889pKamsmbNGun6g12H/+LhhQsXeO+996TMSP+sQTmgIzOdmKiM+5EWjILp4bJly+jp6ZHsKl/7nJSUlCF+zGT3Kw8WEB/pmfd9rrGxkcOHD6NUKlEoFKjVamJiYqR2gP39/Wzfvp25c+eyZ88eLl26RGZmJp2dnfT09DB//nzy8vKoq6sbV2tDmeCIotgHPO330huCIDQAS4CLgzb/J2CPKIpnAQRBeAbYhxxQlpkC/O0gn65cvXqVq1ev0tvbO2Bbt9uNIAgIgoAoipjNZoxGI1FRUTQ0NHDbbbfJCXshEpaAsiAICcCtwHuAB/gHYBXweDiOfz0TjiEQ1yNjMe6Gc7ZEUeTgwYO89dZb3HLLLZSWlg4wjJKTk8nIyBiQjROqMVlYWEhsbKxkDMXHxzNz5kySk5PHZJDKRtK4kBeyJhH/59IXRHY6ndJzOjg4PDh4kpqaisVioaamRnrWRvuMjGZCenR0NBUVFeh0OvR6PQ6Hg0OHDnHvvfeO+hkLdFyfhtTU1GAymRBFUerX1dzcjF6vp7q6mhMnTpCYmEhxcTGJiYlD7pnBYJBaf4Sa7RdIJzIyMvjFL35BU1MTGRkZfP3rX5f7J8tMKSMFIUaT6TrcQnOg5yGcbRn8j2+z2SYkI3swga5HrVYP2MZsNnP06FGioqL49NNPcblcHD58WOqZHOr1Z2dns3fvXkm/LRYL9fX1A7IGZVtFZrowURn3wRaM9u/fT3JyMi0tLdK8BZvNJukhwJ49e2hra8NoNEot74qLi6VBvf5M1kJ8oID4rl27SE1NxePxBLW5fJ/TaDS8/PLLNDY2kpWVJSXpdHd3s3jxYlauXDng8772GCqVSgok+/dmlgkPgiAYgXnA2QBvLwD82+qcAYyCICSKotgZjvOTkfHhbwfFx8fT3t6OzWbD4XAM2M7r9UrBZKVSSWJiIjNmzKC7uxu9Xk9KSgpPPPGEbMOESLgylKOB7wN5QD9QA9wlimJtmI5/XRKuIRDXI2Mx7oI5W16vl71791JRUcHKlSvZtGkTCQkJA5wh33fh6386GmPSd6633XYbVVVVREVF4fV6SUtLk0tAJxF5ISv8DH4uLRYLXq+X4uJiYGiwZnDwJC8vjz/96U+0tbUN2780GMHa2qSnp2M2m7FYLCQkJFBYWEh0dDSVlZXodLoBrSCSk5NHndkznFavW7eOAwcOkJSUhMFgwO12Y7Va0el0HD16lI8//pjU1FS+9KUvcf78+YD3bOHChaxbty5g0G00i47r16+XA8gyEcdwgcfRZLqONkA8FW0ZsrOz2bVrF263m5SUFNLT04mKigqYke3rlXr58mWMRiMmk2lYXQp0PUlJSQiCINkup0+fxmq1Ultbi1Kp5JZbbiE+Pp5z586hVqtDvv66ujpKSkpobW2VhhguWLBgxKxBGZnrkYnKuA+0YORbyP7c5z4n2Q9dXV1DfL2HHnqIxsZGqTVWcXExRqMx4KLR6dOncTqd7N69e0ITkQYHxF0uF7W1tbS3t7Nhw4agfmpZWRkKhYKf/exntLW1cfPNN5ORkYFWq2Xz5s1SG57BlSH+7TFcLhenTp2ioaEBt9tNdnb2iJooM34EQYjmWsbxXlEUawJsEgt0+/3t++84YEBAWZ5XIzPZrFu3ju985ztUVFTQ2tqK1+sNuq0vyUcQBPLz80lJScFqtbJ69Wri4uJkbRkFYQko/6Wv183hONZ0IlxDIK5HxmLcBXK29Ho9hw8fprm5mTvuuIOSkhIsFgtbtmwZ1fFGGu7l+6zdbqerqwu9Xi83eZ985IWsMOP//7per6erq4vCwkKSkpKkFgv+wYrBASCj0UhhYSHNzc1jctoCOTvnz5+noaFB6pdlsViIjY2lu7ub2tpaoqOj6e/vJzo6mtjYWNatW0dra2vQYwR61ofTarhWVqXX63G73dIk4T//+c9cvnyZwsJCfvOb3/D666/T0tJCc3MzWVlZeL3eAfcsUNBNXnSUuREINdN1tAHicLdlMJlMHDlyRNI4s9lMZ2cn27dvH5KR3dHRIS1Aq1QqMjIyRny2g7W6gb8OHr548SJ//vOfSUpK4mtf+xr9/f18+umnNDU1UVJSEvL1t7S0kJWVNWBbOWtQZjozERn3gRa9KisrSU5OHtHXy8nJGTIc02q1Dlg0cjgcHD16FIA1a9YEnV3hz2gWpf0D4mazmYMHD2Kz2bhy5QqLFy+WKssGn3tlZSWvvvoqTqeThQsXAlBbW4sgCOTl5ZGUlDTsTIhdu3ZRWVlJV1cXarUaURTp6Ojg+eefl7MIJxFBEBTAb7g2h+aRIJv1AvF+f/v+2zZ4Q3lejcxkYjKZePHFFzl06BDd3d1Bt/O1uIBriYWZmZkkJCRgt9tRqVRyst8YmMoeyjIjMJFDICKZsbb1GK1x5+9sVVVVSdmKer2eRYsWUVRURHx8PH/7t38bcL/BjhdKUEcu/Qw/8kLW1DA4s3+4YE2gAFBUVBQ7d+4c0/MyWDNramrQ6/WYTCZyc3OlUlJf/3GXyzWgfxZAb29vwOnhJpOJffv2cejQIYxGI8XFxZKz1t3dTVFR0YDtfVotiiIpKSkolUpJu6Ojo7l69SoZGRm8/PLLKJVKHA4Hn/3sZ3E4HFRWVnLo0CGp9U6wezF4oJ8vm7GxsXHM91BGJlxMdEuvsQSIR/PbPNbz9X3u4MGDaDQaFi9ezJo1a4BrgzH9s3p917Bjxw48Hg8zZ86UyrytVuuICQXBrsf3mt1up7e3l69+9avodDrgmkO1atWqUfWNDme7EBmZ6UIgm8dsNrNhw4YB2wXz9UZaNDp27Bh6vX5AcNf33lj9F398z73L5aK8vJyenh60Wi2CIFBeXs6KFSuGBIdNJhNmsxlRFLnllltoaWmhv79fWlwvLy+noKAgoN3lu+bU1FROnDhBVFQUMTExZGZmolQq6ejokJOsJglBEARgD2AENomi6A6y6VmgCHj1L38XAWa53YVMOPFpWVlZ2ZD2Fv4oFApUKpU0HPkLX/gCs2fPprKyErPZTGlpKVu3bpU1ZZTIAeUI5kYw2MOdYefb5yeffMLcuXOZM2cOc+fO5f9n793jm7rv+//nkWRL8lUSloVvYHwBc7HBhIQklJDEDow0l6bLY1tDmy2kS5suTdtHyWNtvyzJlq7rb/Gybr9+62VbklGGm3T5pRlJGgJ4kFKHQFIC9iA2srnZMr6AJd90dLF0fn9QnUi2ZMt3A5/n48EjQT7n6HOO+bzP5/N635xOJw8//PCEvlNEkgsE0RlLrJnqCMHhNtPlcqnpoAaDAUBteBcMBsnMzCQ9PZ3k5GQMBgNOp5P6+noee+yxiOuG7FRTUxM2mw2NRsPhw4dZt24dFouF8+fPj2qrvV4vPT092Gw2Ll68SH9/P1arlX/6p39i6dKlVFdXqzbE6/WSlJSEwWCgsTFaduFnhAT0UOf35ORk5s+fT2dnp4hUFsxppuvdP13O24mON/y8ECHhxWazRQhH4YK1y+XitttuIysrSz0vnoCCaKJ3YWEhBw4coKKigq9//evodDr8fv+EygqFmI1yIQLB1U60Nc/GjRsjym7B2E13YzmNQmsCjUajfj6a3Rjv/iU075uamjAajWrTq6VLl6LVamlsbESv16tj37lzJw0NDRQXFyPLMg6Hg6GhIQYHB0lISGDJkiUEAoGo665whoaGyM7OxmKxcEXnvJKu3trayu7du0WfoemhGlgKVCqKEluhg58D/yFJ0i7gIrAd+I/pH55AcAW73c7TTz/NuXPnOHv2LD6fL+axGo0Gne6K/Lly5UrWrFlDe3s7GzduFPZjEghBeQ5zPSzYZ0OM/bd/+zd27dpFbm4u3/3udzGbzUiSNOZ37tu3T21olZeXx9atW7nrrruum0hygWA6mEoBaLjNDKUuLVq0CI/Hg9FoRJZlTCYTHR0dLFq0iKVLl9LY2Kg2ETSbzSPGE7JTPp8PrVZLe3s7vb29XL58mXvuuUdtmtfd3Y3D4aCjo4OEhISITuWLFi3i1VdfZWBggPXr1/P9739fjUoM2ZCTJ0+yb98+gsEgKSkpuN3uuKKFGhsb1VrQbreb+fPnY7FYhFNLMGe52hyxEx1v+HkWiwWPx0NSUpLaeDQkHA0XrE+ePKmKwPE2KI0mer/44os0NjbyzjvvcPjwYW6++Wa++tWvTtqJN9PlQgSCa4Xha57QvIXJ7/XGCkQa7nBqaGigrKws4hqj7V9C8/7JJ59EkiRyc3NxuVzodDr0ej0dHR3k5eXxwAMP8P3vf58f//jHFBYWsnbtWkpKSmhra0Ov15OYmEh+fj7BYJD09PSo667h93Xy5El1HQfQ1dVFR0cHy5cvFyW/phhJkhYCXwO8QEdIxP/9Z4eAU8AyRVEuKIqyR5KkvwcOAEbg/wOemflRC65H9u3bR1VVFU1NTSiKQiAQGPOcxMRE0tLS+NGPfiR6PkwRQlCew8zmgn2qU1FjMZNirN1u54UXXuDFF18kMTGR9PR03n///VHrd4Wew6FDhzh8+DB5eXksWLAAl8vFX/3VXwHXRyS5QDDdTIXNGW4zV65cycWLF0lLS6O+vh5ZlgkGgxQWFnLp0iVycnKw2WyqYBNqDDOc8KifxsZGUlJS1I3fgQMHuOOOO7j11lupqqpiaGgIm81Gbm4utbW1PProo9x+++1s2bKFwcFBvva1r/Gtb30r4t5ycnJoaWlh3759aDQaUlNTGRwcRJZlAoHAmNFCFy9eZP78+bjdbtxuN+Xl5eo9V1dXRzxTYEZsu0AwGlebI3ai4w0/r6SkhLq6OpKSknA6nRE10ocL1qtXr6a2tpZjx46pza7GEpmGX0Ov1/Pmm2/S0tLCj3/8Y9auXQtMnRNPlPISCCbPVO71RgtEiuZwamlpITk5OeK7xtq/hDfKCy+31dHRgcFgQK/X8+CDD1JfX4/FYuGGG27A4/HQ1NREQUEBCQkJeL1e7r33XiD2umv4fR0/fpympiYsFguKonDu3Dm1vIdGo5nzTsmrCUVRzgPSKIekDDv+BeCFaR2UQDAMu91OVVUVsiyjKArt7e0MDQ3FPD4xMRG9Xk9CQgI//OEPhZg8hQhBeY4zGwv2mSxDMVNirN1u55vf/CZ79+5V07EGBga4dOlSRP2ucFFLp9Nx8eJFCgsLsdvtJCYmcvnyZcxms5q++vLLL6tNMuDajSQXCKaTyaSTRxNHi4uL1Z91dXXR1tZGRkYGwWBQbYi5ceNGamtrcblcY87bkJ2Cz5o5+Hw+NVJGURSam5upqKiIsGUul4sXX3yRV155BZ1Ox+HDh7nhhhtGXL+yspInnngCn8+HxWLB7/ejKAo5OTk4HA61XMdwQhvR8M7v5eXl2Gw27HY7LS0t5Ofnq8/0+eefR5IkCgsLRUSPYFaZbUfseB1YEx1v+Hk2m41169Zx7NgxFEUhNTVVFY527NgRIVjbbDbuuOMOjhw5ErfIFC5eO51O/vEf/5Guri6Ki4tJS0ujubk5wjYKp5JAMLsMn4sTLb0XYjRxOry0FlzJsigrK6O+vh6r1Tqu/UuoUV5XVxcejweDwYDVaiUpKYk333yT+vp6Fi5ciNFoVMt9Afh8PrUx+XhK7hQXF7Nt2zZqamo4cuQIiqKQlZUVkcEBc9spKRAIpg673c63v/1tfvvb3+J2u0c9VqPRYLFYWLZsGdnZ2ZjNZh555JEZGun1gRCUBSOYyVTUmSrrsWfPHhoaGsjKyqKkpISOjg4kScLpdJKRkUF9fT0bN26MELXee+89nE4nubm5OJ1OVejp6OggNTUVk8lEa2urSP0UCCbJRGzOaCI0oP6srKxMtSvDhdP8/Py45m3ITg0ODlJcXExbWxv9/f0sW7aMNWvWMDQ0FDOC8bXXXmPevHns2bNn1KYzhYWF9PT00NfXR3p6Onl5eaSkpNDR0cH69etjPrtYnd/r6+spKyuLeKbd3d0AqqgtInoEs8VslvSaiANrouMdfp5er2fJkiUjviuaYG00Grn33nvVZnl2u31ExkGsaxw9epRLly6xZs0aioqK1AaiFRUVvP7666oQdPLkSY4fP862bduEDRAIZpDprCMPn2Ui7d+/H4ieZVFYWMjg4CCpqalqs3Kz2ayeM9o4Qo2MQ+UQ2traKCoqoqmpiZKSEsrLy7l8+TKtra2kpqZiMBhwuVwsWbKErKysce+XiouLeeaZzyopVFdX09/fH3GMyA4VCK5t7HY7NTU1/Pd//zd2uz2mmKzVaklISECv11NaWsrGjRvV5sZjZUQIxo8QlAUjmIpU1HgjYKZbjPX7/fj9fjo7O/nOd75DbW0tFouFlJQU2tvbcTqdFBUVYTabaW5ujhC1QtGCjY2NmM1mZFkmKSlJNV4ul0t9TiL1UyCYOBOxOaOJ0EBcAnW88zY8Erirq4vly5dTUlIyYnESLgjJsozX6+WRRx7hS1/6EvPmzYt67ZCtPHv2LOnp6Wqkj9FoxOl0otPp1FIVY40v3I4WFhZSWFgYcZzX6x1xrojoEcwGs+mInYgDa6Ljjfe8sQTr4eJTS0sLTzzxBIWFhZSWllJZWUllZSU/+9nPgCupnRs2bCAhIYGlS5eq9/iTn/yEwcFB5s2bp9Z0/uSTT/j2t79NeXm5iFgWCGaI6QreiSVUGwyGqFkWIftx/vx58vPzVfszmri9f/9+ioqKWLNmDXBlvbN79258Ph8//OEP+eSTT/B4PGRkZOD1evH7/Zw8eRKdTsf69evZsmXLpG3M9dBnSCAQfEaoXvK5c+cYGBjA4/HEPFaSJIxGI4qicPPNN2O1WiPKjAmmFiEoC0Yw2VTU8Xrdp0uMHRgY4MEHHyQhIYHNmzczMDCAxWLh0qVLuFwutf5XTk4OBQUFI0Qtk8mE2+3G5XJx66238tZbb+Hz+UhNTaWnp4eenh6+853vxHwGYwnqIu1UILjCcJvT2dnJsWPH8Hg8VFdXR50bo4nQiqJMeX3WWJHA4YuTl156CUVROHDgAB999BFf/vKXeeKJJ0YVk0PXu/nmmzlw4ABerxev10tvby86nS7uyMHhdrS6unqEHdfr9SPOExE9gtliou/+yb47J+o0n+h44zlvLOE5XHwKNe8MpY8nJyfz0ksvkZ2dzSuvvMLXvvY1WltbWbBgAUuXLlVTwtPS0mhqaqKsrEwt1+P3+3G5XPT29nLvvfeKMjgCwQwxXXXkYwnVsizT09Ojfk+4ADtecdvhcJCQkMCJEye4cOECv/vd71QHdlpamlovXpZlkpOT6e/vJz09HavVqvak2bZt26RqmIrsUIHg+iFULzkUeSzLcsx6yZIkYbVaWbt2LZcvX1Y1HmEjpg8hKAtGMFmv71zo3t7R0cHnP/95Tpw4wYsvvshtt92meujtdjsJCQlotVqMRiOHDh1i48aNQGSEYUlJCbW1tZjNZpYuXcrAwAAffPABiqKQnp7Od77znaiLoXgE9alMdRPCtOBqJ9zmyLLMgQMHALjjjjvUVO14UsTDxdFYP5vMfBlrA/OVr3yFrVu3cvToUT73uc/x9a9/fdRr79+/n0AgwIkTJ9RIZ51Oh6IoPPjgg5Oay9HsuNVqRZKkuOpGCwRzkal4d852/eZYjCY8h8Snzs5OtXmnxWKhr6+PhoYGfD4fBw8e5NZbb+Uv//Ivee2111SneYhQHfgzZ84wNDREcnIysiyTmJgIIBpbCQQzyHTZoXBb0djYiNPpxGQyYTab+fa3vx11/TK8hjuMLm7rdDoOHDhAIBDgyJEjaLVaFEXB6/XicrmwWq2UlpZSX1+P2+1Gr9erf0wmE06nk6qqKvLz8ydd3kPYKYHg2qempoZz584RDAbp7u6mt7c35rEajYb09HScTid33nmnWjZMMH0IQVkwgsl6fWe7e/vp06f5gz/4Azo7O/nv//5vPv/5zwPw6KOP8vTTT5OZmUkgECA5OZmcnByys7Npbm6OWutw8eLFamOsNWvW8L3vfW/M5xCPoD5VovtMNlAUCKaLcJtz4MABtXN3eLOV4XNj+HxtaWmhvr6egoICbDab2lAzXDgtLy+f9HyJtYHp6+vj29/+NkePHuWHP/whP/jBD9TaghDd8VNfX8/Zs2dJSkrCZDIhyzJ+v5/8/PxJL4Ci2fGnnnoKQET0CK5apuLdOddTpaPZipD41NjYSDAYJDU1Fb/fr9q+5uZmlixZQnV1Na+99hr19fWcOXOGsrIyCgsL6evro6WlRc2sSE9Px+fz0dbWRlpaGqWlpcCV7JBTp07R2toKIBzUAsE0MV12KCcnh5aWFhoaGkhOTsZsNuN0Orl8+TJA1LXFeMVtSZLo6uqiqamJ1NRUbrnlFmRZpqysjNTUVBwOBwUFBTz22GPs2LGDlpYWvF4vSUlJAJjNZjo6OmLabREoIxBc34TbgHPnzvHOO+/g8XgIBAIxI5NDJCYmkpuby6JFi9iyZcsMjfj6RgjKgqhMxus7E9E/sRYbwWCQBx54gIGBAQ4ePMiNN96onhNqfLVhwwY0Go36eTAYxOFwxBRgxvsc4hHUp0p0nwvR4ALBVBCyOaG5ET5Ho82N8Pna0NBAc3NzhHiiKAqyLNPf368KpxNt/hdrYxP+s4MHD/Lhhx/yyiuv8Gd/9mcjrhFNyL5w4QIajUbdZCUlJeHxeHA6nVP6TKN9LhBcjUzFu3Mup0rHshUVFRXU1tbS0dFBSkoKg4ODKIqCoig0NzeTk5PDH//xH7Nr1y4sFgsrV64kJSVFjRBcsWIFWVlZVFRU8OGHH9Lf38/Q0JBaBuemm26is7OTuro6NBoNCxYsiJkdIhAIJs902aHKykqeeOIJtFotBoMBWZYJBoOUlZXFXOuMV9zu6OigsbGRjIwMVq1ahcViYfHixQwNDY0QrHNycjh8+DDz589XP/N4PNhsNtrb20dce6yGy0JoFgiuXUJN99577z2SkpLo6OigqamJYDCIJEkEAoGo50mShE6nQ6vVsmbNGr74xS8K+zCDCEFZMCFGE1mm0use7XuAUaMMf/7zn5Oenk5RUdGI6/zud7/j5MmTEdGP4eRRyYMAACAASURBVGL3VKRPxSOoT5XoPtvR4ALBVDOeuRGar9XV1SxcuDBCKC4qKiI1NTViczPetM7wjU1CQgJvvPEGzz//PLm5uaxcuRKfz0dhYSG5ublUVFSQm5vLunXrRlwnlpDd39+P0WhElmUMBoPqfbdYLON8agLB9cFUvTvnaqp0LFvR3NysNgZ1u93IskxOTo5anz1Uzib83OLiYqxWq2oHt2/frmZtNDY24nK5WLhwIe3t7ej1ej755BM0Gg3BYDCikZ9wUAsE08N02KHi4mIKCgpwuVy4XC5MJhPl5eVYrdaYa53xittlZWV85StfYeHChbS0tOB0Ojl+/Dh5eXlUV1dTX1+Py+VCkiR6e3s5ffo0HR0dFBQUkJiYSFdXFwaDAafTOaJPRiwbWFNTg8PhoLu7G6/Xy8mTJ/nkk08mFPgjEAjmHqE9V1NTE8nJyZw+fZqLFy+iKAo6nQ6fz6euUeCKiBwqYbpw4UK8Xi/Z2dm89NJLwibMMEJQngWullSeWOOM5j2uqqoiKyuLoaEhcnJyqKiooLm5OWJhAlcaRcV736N1Kh6+2Dhy5Ajf+ta3+PWvf80NN9wQ8zqhxle1tbXccccdGI3GmGJ3LDF7rN9dPIL6VInuc7UWpEAwUSYyN+J1rIx3voQ2Nl6vl/3799PZ2YnRaKSvr489e/ag0+n44IMPePzxx8nJySE5OTmq+DK8pmGohjFc2Zi1t7erG7+CggIKCgrif2ACwRxkutY5c71cxWQZzZaFGoP+5Cc/Yc+ePSQlJdHV1UVmZibbtm3j/fffV+3K8HPhM/tns9lUh7rL5cLtdpOamhqzkd9oDrerYS0rEFxvlJWVjaih7nK5Rt0bjCVue71e/vzP/5ytW7dSWVnJ8ePHee+99/B6vciyjNfr5dNPP2VwcBCHw4Hb7aa1tZW8vDxsNhuXLl2ivr6eRYsW4fP50Gq1fO5znxuRCRHLBr799tt4PB58Ph9DQ0PodDocDge7du3i2WefnfQzEwgEs0v4nsvlchEMBlEURf2vJEkEg0E0Gg0ajYaUlBS1QV9PTw/FxcU888wzYh0yC2jGPkQwlYTEzf7+fnJzc9UXqd1un9ExVFdXs337dqqrq6N+92jjDPceazQavF4vTU1NnDhxQj22traWyspKnnvuOTVCcLz3Pfx7TCYTFouFI0eOqJsmRVHYvXs3b7zxBna7PWpdnfDrhFI+zWYzR44cITU1VV3EhD+XZ599lqqqqojxVlVV8fzzz495DyFPf6iOWPh3jOeYeH5XlZWV9PT0qIbX5XLR09Ojit8CwdVGvHMjnJBQEk40oXi888XhcKjRfAMDAyQnJ5OSkkIgEMDtdtPU1MT58+fp7+8Hrmx6oqVwhmoahrqem0wment7CQaD9PX1sXLlSu69915WrlyJVqulqKhoTBstEMxVpnOdMxH7MJvEs94KZyxb5vf7+dWvfqXapgcffJCf/vSn3HXXXWOeG8v+bdmyhccff5wtW7awatWqiNr1sRxuc2EtKxAIojOVewO73c4//MM/sHjxYnbu3EltbS3FxcXo9Xp6enrweDykpaVhNBrx+XwcPXpULd+VkpKC1+vFYrGQmpqKwWCgqakJrVZLamoqR44c4cSJEwQCAfbv3w/EtoEhx7skSSQlJakNhmtra6fkmQkEgtkltK4xm81qWa5Qaa+QoAyoovKiRYvIyMhg9erVrF69mldeeYW77rprlu/i+kREKM8ws13zNt4mbqONc7j3uLGxUfUoxeoSPpH7juWlliSJvr4+3G43O3fu5MyZMyxcuJAvfelL6HS6EVEz9fX1rFy5Ur2GzWZj06ZNOBwOVewe/lzee+89nE6nWsvVZDLR1dUFwJo1a8a8h3jS2MY6Jp7f1VyuBSkQTJTxpoHGG7U43vkS2ti4XC78fj/Jycn4/X76+/tpb2/HYDBw0003YTabgdjiS6imoSzLXL58mf7+fjQaDbfccgtZWVmqOJadnU15eTm1tbWi0abgqiXa+767u5unn36awsLCSUezztVyFRAZtavT6dTmoPHO5dFs2aFDh7jvvvswGAwcPnyY8vLyuM+Fse3feKK/Z3stKxAIYjPZvUHIjtXX13Pq1ClOnjyJy+XiT/7kT/D7/djtdux2O6WlpWoPiBMnTqDX63E4HJSVlSHLMkajEafTycDAAIFAgBtuuIH333+fixcvkpKSgtVqxePxqLXeIbYdgivNthITE9X/9/v96s8EAsHVTSj4pq+vj+7ubvr6+tSayeF9deDK/Pd4PKozPTU1Vaw9ZhEhKM8ws13zNt5NwGjjHJ427nQ60ev1EalVU9GELlZ6+tq1a7Hb7fzqV7/i8uXLlJSUkJmZSWdnJ/v27Rshxpw5c4aUlJSI+xsu/Ax/Lj6fD4vFQmNjoxqt4/F4VO9YvPcwGeL9Xc3lzbVAMBOMtnmKlpYdrct5NEIbm8TERLRaLYODg3R3d9PV1UVGRgbp6emkpqaqkcaxxJfi4mLmzZvH8ePHCQaDqgfe4XCg1+sj0jWrq6uFUCO4qhn+vu/s7KShoQG/38+GDRtm3EkyFaUZ4rlGPI5pGH0ux7Jlra2t3H333eTn57Nnzx7y8/PjPjfe9cJ4RKjZXssKBILRmejeINyOtbe3c+TIEQKBAPfffz8ajYajR49y/vx5BgcHMRgM6nlGoxG3241Op1PFZFmW8fl8JCYmkp6ejsfjQafTkZiYSG9vL5mZmepxIWE4lh364IMPuHjxIomJiSQkJOD3+9WaqQKBYO4S7xqsqKiIHTt2YDabCQQCDA0NIUmSGswzNDSE0WgkPT2d9PR0SkpKuHDhAkaj8Zope3a1IgTlGWa2a95ORa3R4d5jvV6P0+lk9erVI46N53qxiOWlfvTRR6mpqSE/P5/s7GxKS0spKSlBr9fz8ssvU15eHiHGlJWVUV9fj9VqjRl1M/y5mEwm3G43DoeD999/H6fTSXt7O5mZmRFjnM7fndiwCQTxE23zFG9GxmjXfPTRR9m1axfnzp1DlmUWLVpEamoqOTk52Gw2li9fHlcEkKIo5OfnRzTc6+npwel0Rox39+7dSJKE2WympKQEm80m5r3gqmL4+76xsRGNRsP8+fPjFlYnS2gD09DQQEtLC2VlZRQWFk5IzJ5oZlc0x3Q8czmaLbNarTz00EM8//zzaiO+WOeGxuJwONQ08njvNV4RarbXsgKBYHoIt2N+v5+FCxeqdqykpIT58+fT2dlJMBikvb2dnJwcVeTp6Ohg0aJFuN1uDAYD3d3dBINBgsGguq/KzMxkcHCQ3t5eFEVBlmWCwaCa6QXR7VBFRQW//vWv8fl8DA4OkpCQgMVi4c4775zpRyQQCOIknr5boVI8L7/8MpIkcenSJaxWK4WFhfT09OB2uwkGg8yfP59Fixbh9XrVcjuKoogMzjnAjAvKkiQVAw3A64qifHmmv3+2me2GMmNtAsLTnM6cOROxCQuNc7j3eNWqVWqkXaxIvfHcd7gnK+Tx7u/vJzs7m1WrVvHpp5/i9/v56le/GpECEQwGaW1tZcOGDRHXKywsVJvOxBJ+hj+XkpIS3n77bZxOJxaLBb1ej8FgoKurC7vdPuKZTAdiwyYQTBy73c7TTz9NZ2cnWVlZqjgLYwtZw73p999/P1qtlkAgwNGjRzGbzaxdu5YtW7bEvYgxm8309PQgyzIGgwGPx0MgEFAF5tCiy2AwIEkSsixTV1fHunXr0Ov1Yt4LrhqGv+87OjrQ6XSUlJSox0ynkyR8A+NyudBqtTQ0NJCWlha3DQhnopldIQHF5XKpn43nHR4MBnnxxRd55JFHMJlMvPzyy2OeMxEn2kQiuGd7LSsQCKYHh8NBV1cX+fn5mM1mli9fTmtrK4FAQN2TJSUlodFoOH/+PB6Ph/T0dObNm8fNN9/M8uXL6ezsxOl0kpeXR1NTEwkJCWRkZFBSUkJjYyOtra3Isqw2Iy4sLByzGfFDDz1Ee3s7XV1deL1e9Ho9mZmZPPTQQzP0ZAQCwXgZvn4K9d3q6uoiLy+Pt956i2effZaUlBR0Oh05OTmYzWbmzZvHunXrCAaD7N27l8zMTDVL3Ofzcd9996HX60WpiznCbEQo/1/go1n43jnBbNe8HW0TEL4RWblyJSkpKWpdqxUrVkSMc7j3OHxDEivNMp77jrYZCkUlX758mXvuuQej0chTTz0VVWzNy8uL+vmKFSuiprmHxn3o0CE++eQTTCYT+fn55ObmkpCQwMKFC/F6vZhMJu655x76+vpoa2vDYDBM++9ObNimjuvdkXWtMZYAErIjXV1dzJ8/H4/Ho4qzVqt1VCFruA1qa2tj8+bNXLp0iePHj0+4m3hpaSnJyclqYxmTyURBQYG6iQotulavXk1dXR3JyckYjUaOHTvGkiVLxLwXXDUMf99nZmaSm5sbV7O3qSB8A+NyuTCbzciyrEYKj1fMnmhmV0lJCbW1tZjNZoLBIC0tLdTX11NQUEB1dfWowq3X6+WRRx7hF7/4BXq9nq1bt4773mHskjkTzeKY7bWsQHA9sm/fPl5++WVOnz6NJEksXryY9evXT6om/XA++ugj9u7dS25uLqtWrcLlcuF0OjGbzbjdbrq7uwHIyMigoKCAxMREWlpaUBSFyspKHnrooajrMYvFou5lTp48idVqJRgM4na76evrG7NhYHFxMdu2bWP//v20t7erGbPC5ggEc5dYfbfa2to4ceIEycnJJCQk0NPTQ29vL62trXzhC19Qj125ciUbN24EUIXlW265RW0KKvZGc4MZFZQlSfoTwAV8ABTN5HfPJWaz5u1om4DhtTuLi4uxWq2kpqaOWXN0PPekKErMn8XaDL3wwgvs2LGD7Oxs9uzZg6IoUcXWrVu3qh1/44mEfumllwgEAvT09GC1Wrl06RKffvopx48fR6PRkJeXx7Jly9SNsNVqxWAw8Nxzz8V1r5NBbNimlOvakXUtEY8AErIj8+fPR5ZltWlMY2PjqNG+w6Oa582bR01NDX19faxbt45XX3113DVYo6Xer1+/XrVNoU1UaNGl0WhYt24djY2NajkMkc4luNoIXxOE5qzL5ZoR52j4BsZsNuPxeDAajWqk8HAx2263U1NTw4cffggwIvsg3myhaOXAFi9eTE5ODg0NDTQ3N8dVeqO3t5cvfvGL/M///A9/93d/xyOPPDKhe+/s7KSxsZGenh4kSYpqt/bv308gEODEiROqaJSdnR1XBLfo3yAQTD+hNcRvfvMbPvzwQzIyMpBlmUAgwOHDhzEajRw/fnxECvl452YwGOTRRx9l7969ZGRksHr1ajQaDT6fj2AwiE6nw2g0YjKZSEhIQJIk0tLS8Pv9lJSUkJ6ejtFojNo8vKKigpdffpnW1lbS09PJysrCYDDg9XqB0feF4QibIxBcXcTqu9XZ2UliYiIDAwN0dXXh9/vRaDRotVq6u7uxWq10dHSQl5en2pOHHnpIdSilpqYKTWQOMWOCsiRJacDfABXAozP1vYKRxHohT2fN3nARKCEhgb1797Jz5042bdoU4c2ONoYTJ07wi1/8ghtuuIF33nlHrWMcS2zNz8+PS4QNiU4nTpwgKSmJjIwMDAYDbW1tLFq0iK6uLnp7e9XIRpvNNuMlJ8TiafIIR9a1RTwReCE7UlJSQl1dHQAGg0FdnIzmYApFNTscDv7rv/4LnU7H6tWr0el0467DHG73ysrKSE5Opr6+nsHBQUpLSyNsk06n47333sPn82EymdS68CKdS3C1M9PO0fANTMgGyLJMeno6LpcrQsy22+1UVVXR1NSExWJBURQOHjxIe3s7Tz31FMXFxRQVFVFVVcXQ0JAaba3VakfYkWj3GbpGdXU1CxcujKtsRkVFBXa7nXvvvZf09HSam5vjflahe/d6vWqmQ6hxVjS7FXJ0paSkqOJ7KDNNIBBMD/GWmQlfQ9jtdhITE2lra1PXQG63m6NHjzJv3jy6urrYtGnThOrEe71eHn74YX75y1+ybt067rrrLk6fPo3L5SI7O5u1a9eSlJSExWLh4MGDJCQkqHWMQzbG5XKp6exPPvkk9913n+owr62tpby8nA0bNqjNSm+99VY1WMflconGwwLBNUho/eT3+0lKSuLs2bP09vbidDpRFAVFUfD7/UiSpAb/OBwO+vv7WbRo0QjnlLARc5OZjFB+DnhJUZTWUA2UaEiS9BjwGMCCBQtmaGgCmN6avSERyOv1cvjwYZKSkpg/fz7Hjx9HlmXVYEQbQ2trK8uWLePAgQOkpKSon8cyLPEanJDoFEo/hyues1BziFAReI1Gw6lTp0R6xVWIcGRde8Tj+ArZEZvNpkb7dnR0kJmZGXWTFYpM7urqoq+vD41GQzAYRK/Xs2jRIiwWC2azedwNxXbt2kVTU1OESFxRUTEi68Nut3Px4kW1Zrvb7aa2tpbFixfz1FNPTfaRCQSzzmQ3AuOp8xseKWy1WiktLaW+vh6z2TwiqmX//v10dXUxb948jEYjAJIk0d3drTa0q62tpaysDIfDQWdnJz09PWzbtm1c6494HfYNDQ1cvHiRRx55hDVr1oxbHArde1NTE0lJSWrTq1At9uF2y+l0otVq1Xs3Go3IskxPT8+Y3yUQCEYSb0mueMrMDC/fY7FY6O3tZXBwkJSUFIxGI2fPnqW4uBiv1zvhpqd+v58zZ85QUVHBH/7hH6LVasnKygKuRC47HA4efvhh9u/fr4pA69at4/DhwxgMBmRZRqPRUFdXh9FoRJIk+vv71b4Qw2uoms3mcTcrFQgEVxd2u11dP506dYqGhgaGhobURp2hP5IkYTAYUBSFxMRE8vPzCQQC/M3f/I0QkK8SNGMfcgVJku6QJOlFSZLe/P3fV0uStGGs835/7CqgEvjHsY5VFOVfFUVZoyjKGqvVGu/wBFNAZWUlPT09uFwugsGgGskzVl2reHA4HKSlpdHY2EhSUhJJSUkYjUa1C3po4xYaQ2NjI2+//TavvvoqBoOBqqqqCDF5KggXr2VZBqC/v5+0tDQ8Hg85OTmsW7cOk8lEW1sbqampIvX86kN1ZI12kCRJj0mS9LEkSR+H6sMJ5iaheRvOcMdXuC2zWq2sXLmSG2+8MeriJLS56+zsxGazodfraWlpQa/Xc/fdd9PX10cwGBzRUKy9vX3Ucdrtdvbu3Qug2pi6ujra2trYvXs327dvp7q6Wt18FhYWUlFRodpFs9lMTk7OuO2N3W6nuro64voCwdVMaI729/eTm5urChWx/m2HIoVDjXgLCgr46U9/ys9+9jMef/zxERHBHo9HjeKFK6Kq1+ulvb1dFXSKi4u5/fbb+eM//mMqKipobm4ec8zh81Cn041qt06fPo2iKJw9e5a//Mu/5KabblLFofA10liE7t3j8ailPkIZVtHslslkiqhjevLkSRobG3E4HMJ2CATjJB5bFS4SjzXHQ3snQA10CTX1BZBlGZ1Oh6IoEYE48axRQtcfGBggJSWFuro6/vAP/5D+/v6IY/r6+tDpdKpIvnbtWjIzM9Hr9ZhMJpxOp5rREGrWZzab1fs6cuSIeg+h+5AkacLNSgXTjyRJT/x+T+SVJOk/RjnuzyRJCkiSNBD25/aZG6lgLhOydWlpaXR1danOJqPRiNFoRFEUNTJZq9WiKAoGgwGTyURRUZHQW64i4opQliTpG8A24GXgT37/sQ/4W+BzcVzidiAfuPD76OQUQCtJ0jJFUVaPb8iC6WI601JDIlB4NLDH48FkMkV4pouLi7n11lvZunUr/f393HPPPRQUFHDw4EEKCwun1LiEInmys7NpaGjA4/GoXrLBwUFWrVqlCkzr168fs460YG4R5sgqH+tYRVH+FfhXgDVr1sRXzE0wK8TbrNJoNHLgwAHgSk3UWM6g8HrLv/vd77Db7dx0002qYy0jI4PS0tJxNxTbv38/NpsNjUajLpgGBwd59913WbFihRqZVFVVxaeffkpycjIWi4WSkhJsNpsaFTQeJtpgSyCYy4y30RzEHxGdk5PDyZMnVfEVrog0oVrrEykFFm0eXrx4EUVRKCoqGmG3Xn/9db785S9TVVVFR0fHqN8XT6R2cXEx9913H/39/WNmnJWVlZGSkkJjYyOnTp0iLS2NgoICUlNThe0QCMbJeEpyhRPLpoQHvtx666289dZbhLJ8XS4XPp+PRYsW4XQ6Wb36s+10PGuUhoYGNm/ezOc+9zleffVVEhMTo66vQg33jEajas9CmQ9ms5nLly+Tm5vLhx9+SCAQQKPRcNddd6nXkCRp1GalouH4nKQd+CGwCTCOcexhRVHi0YIE1xkOh4OEhAT279/PhQsXCAaD9Pb24vf7AUhISCAYDBIIBNDr9ZSVlZGfn8/KlStJTU2d5dELxkO8JS++C1QqinJGkqTv/v6zT4GlcZ7/r8CrYX/fxhWBWSh0c4yprk8zvCFVX18fbW1teL1e/H4/GRkZfPzxx+h0OnQ6HRs3buSb3/wmTqeTLVu28LnPXXlHTUd9rXABfXBwUF2QXbp0idLSUqxW64h6i4KritsRjqxZZzyp6vEwluMrXMy5//771c1KLBwOB/Pnz+fTTz/l9OnT5Ofnk5+fT3d3NzfeeCMVFRXU1taOu6GYw+Fg1apVHD58GLhSw7mzsxOfz6c2uwnVG+zt7WXevHl4PB61Znt488B4n+FEhDeBYK4znf0dKisrOX78eEQNZafTyeLFiykqKuI3v/kNtbW1DA0NYTQaycvLIzs7m4KCgqjXCy+fM3/+fNVBVFhYiCzLatR0yG7t2bOHb33rW9xyyy186Utf4pe//GXM0mP79u1TaxHOnz8fr9cbU/SN1/EWOk6n07Fy5UoA3G43q1evjloiQyAQxGY8JbniKS8YPo+XLl3KwMAAH3zwgVq3eMmSJSxevBiHw4Fer49boD148CBf+MIXSE5O5gc/+IH6ebT1VVZWFklJSRElK7q7u2ltbeW+++5jzZo1vPrqqwwNDSFJEoqi8NZbb3HmzBlKSkpYu3atugYLNStdsmQJWVlZouH4HEVRlDcAJElaA+SOcbhAEJWcnBzeeOMNLly4gMfjYXBwkEAgAKBGJOt0OhITE8nIyCAlJYXs7Gyhu1yFxCsopwLnf///oeg9HVeilMdEURQ3oHb4kCRpAPAoiiJyy69hhjek8vl87Nmzh0AgQHp6Oh6Ph8bGRkwmE4sXL+add97hH/7hH5Blma9//evq5gamr75WNAE9XLwRC52rGuHImmWmK2J2NMfXeEXVefPm8c///M/Y7XbuvPNObDYbnZ2dEfWWwxt96nQ6DAYDO3bsGFXczcnJob+/X63h7HK5kGWZFStWqNHOjY2Nqogly7JaDujYsWMsWbKEBx54YFzPcDqFN4FgphjuQElISJi2/g7FxcVs27aNmpoajhw5gqIo3H777axbt47a2lqSk5Pp6OggMTGRwcFBAFpaWti4cWPUcYfK58yfP18tc7Nu3TqsViv9/f1qplMwGOR73/sezz//PF/4wheoqanBaDTGbABYXl5OVVUVWq2WjIwMPB4PDQ0NlJaWRrVt8WachY578sknkSQJs9lMeXn5hLMkBILrmXjE4nidPTByHq9Zs4bvfe97k9q3vPbaazz88MMUFhayZ8+eiH5F0ZzXO3bsUEtWdHZ2jqiT/Oabb1JWVsbSpUvZvXs3SUlJpKWlcebMGbq7u3nuuedGNEuPVYNecFVSLknSJaAH2An8naIoQ7M8JsEcoLKykr/927/F4/Hg9/tVMRkgEAhgMBgiSl0UFRVRUFAw6cAjwcwTr6D8W66IMf9P2Gd/Abw/kS9VFOXZiZwnuLoYLuzIskxZWRlarZaOjg6cTifJycmkp6djs9loamrC6/Vyxx13sHDhwohrzWR9LdFF9NpAOLJmn6mOmA3f7Oh0OiRJwu/3Rwi74xVV+/r6aG5u5sEHH6SiokLd3EXrLDyRCEGLxcL69evp6+tjaGiIZcuWqcc4nU70ej25ubmUlJTQ2Niodj4OXbO6ujruZzidjVUFgpkgmgPF4XAgSRKFhYXjyhKIl+LiYp555hn1+/fv388LL7yAwWBgaGiI4uJient76e3tRZZlbr/9dpqbm9W07hAhe5eVlYXH41E7ljc2NkZkHAAcP36cF154gW984xv88z//M1qtNqKBzfAGgM3NzQwNDZGRkaHWIIQrTqTw+s8TyQgZT4kMgUAQm3jE4vGWF4xnTxLvvuX48eM89thj2Gw2tm7ditfrVX8Wy3ltMBjUdUWoD07I+WQymRgaGlJtdFFRkdo0UJIk1q9fr9pKsa+6JvkNsIIrQYfLgdeAIeDvoh0sSdJjwGNAhCNDcO1gt9upqanhww8/BGBwcJCEhIQIWxOO0Whk2bJl3HbbbTz33HMzOVTBFBKvoPxN4G1Jkv4cSJUk6SRXopPvnraRCa56hgs7TqcTk8lEb28vVqtVjcgLNXO4+eab6ezspLi4OCI9KtqCbKrT6AXXPsKRNT6mYo5NJmJ2+PcXFRVRW1uLxWIhISFBrY98xx13qI1vHn300bhF1UAggFar5emnn2b16tW0tbXR0NBAT08PJpNJbZATXkojPEKwu7ub48ePk56ezvnz50c0/Iu2ady2bVtE+Qy9Xq+W2rHZbNhsNlwuF6mpqROqtzieyCeBYC4wfJ53dnaOcKAUFRXhdrtHlIuY6nd+uKAiSRKSJHHq1CmWLVtGcXExiqLgcrkoLCyMOv9Cc7WkpIS6ujrgSqmbjo4O8vLyeOCBB1S7s3r1at544w3a2tp45plnRtx76N5cLhfNzc04HA4yMzPVdVP4tdevXz9i/OPNCBG2QyCYPOPJDJjJPUswGKS5uZlXX32Vxx57jAULFiDLcoR9iBUAIMuyuidzOp0YDAa6urowmUy8+eab9PX14Xa7MZvNWK1WMjMzYyr0KwAAIABJREFUcbvdGI3GmLZScG2gKMqZsL82SJL0N8BTxBCURb+aq4vx7gP37dvHX//1X9Pa2qrag0AgQCAQICkpSY1SDpW6sFgsaDQaLBaLcF5f5cQlKCuK4pAk6QbgFmAB0MqVIuyB0c8UXM8MF3bMZjNOpxOz2QzAxYsXaWtro6+vjxUrVhAIBLBYLKxYsYLKysq4aqSKxlMCwdQzVXNsohGz0b6/qqqKsrIyTCYTJ06cUEWf06dPs2HDBuBKhGA8wsjhw4d55JFHeOuttyguLubee+/Fbrdz/vx5Fi5cqJ43fLMVihDs7+/H4XCg1WoZGhqiq6sr6vOJtmkMT/1ctWrVmLUPx/MMp7OxqiB+JEnSAz/jSlNQC9AM/EBRlHdndWBzjGjzfO/evWzatCni33taWlpEuYixrjlRR1i4oGI2m5FlmbS0NNra2khPT0eWZUwmU8z5F5qrNptNLXXT0dGBwWDAaDTy05/+lF/96ld8//vfp7Kykg8++CCuew/di9frpaGhAbgS1eN0OtHpdFRWVo4YP0RmM4T+G+u5CNshEEwNcy3L0ev18qd/+qc4nU7Kyspob2/n5MmTmM1msrOz1WynWM7rc+fOYbPZOHDgABcuXCA9PV2te2owGBgYGKC1tZWUlBRkWQau1GEvLy8XWQ7XHwogzfYgBJNnvPvAUNBNR0cHwWCQixcvcubMGXw+n5rtFWooKkkSCQkJ+Hw+EhISyMzMVNcxgquTeCOUURQlCNT9/o9AMCbDhZ3s7GxaWlpYvnw5ycnJHDx4kIsXLzJv3jzcbjd9fX0sWbJE3ehMVY1UgUAwPqZqjk006i3a94fSKouLi3G5XOrPXC6Xev3Qz0cTRt58802+9KUvkZubi0ajifuewyMEQzVVExMT6e3tZfHixVgslriez3DbNlbtw/E+w7m2mb1O0XHF8b4BuMCVbK5fSpJUqijKudkc2Fwi2pyz2WwcP36crKws9bh4RYnJOMLsdju7d+8GwGKxkJGRQVNTExkZGbS0tNDT00MwGKSwsDDm/Aufq1arFb1eT3JyMpIk0dfXx3/+538yMDDAgQMHokZi22w2fvvb35KZmak630MNAEPXLi0tpa2tjY6ODnQ6XUQt0vr6elwul+qAKikpwWq1Ul9fz/nz59Xsjr1797Jz5042btzIli1bRpT2EQgE1wYul4sHHniAgwcPcvPNN1NfX09KSgpmsxmPx0N9fb2aJRrNed3S0kJLSwvJycmYzWZcLhdNTU0sWrQIg8GgZkzceeeddHR0qP0nbrnlFvR6/bizHET26dxAkiQdV9YxWq40MzcAQ8NrI0uStBk4pihKpyRJJcBfAf814wMWTDnj3QfW1NRw+vRpdW3i8/nw+XxotVp0Op26HnK73bjdbgwGAykpKSxdulTUVL8GiEtQliTpLJ8144tAUZTora4F1xzjfdEPF3YKCgrYuHEjTU1N/Pu//zvt7e0UFhaSmJjIqVOnWLJkCQ8++OCYRkU0nhIIppepmmMTjXqL9v2ZmZl0dnYCVxY2siwjSZK6yAkXnWIJI9XV1TzxxBOsWbOGt99+G6vVGvc9h0cI9vX1kZaWxuDgIFqtlpKSkgnboLFEHBE5ePWhKMog8GzYR2//fh11A3BuNsY0F4k251atWsXevXvVsjDjKb0wUUdYSIgO1SL2eDw0NTWxZMkSLly4QF5eHunp6ZjN5lEbxkSbqzk5OXR0dPAv//Iv6HQ6nnrqKdLS0jhw4AD3339/xPkLFizg9ddfJyEhAZPJhNPpVBsAhl/bYDBw2223RYzDbrdz5swZtFqtGl1dV1dHaWkpTqeT/Px8vF4vhw8fJjk5GZvNxokTJ/B4PCK7SyC4Bmlra2Pz5s00NTWxa9cu3nrrLXp7e9X660ajMaKcRTTndX19PXl5eTQ0NJCcnExBQQEOhwOHw0FycjK5ubmUl5djtVpxOBw8/PDD7N+/n/b2dlJTU8e1VhHZp3OK7cAzYX//MvDXkiS9DJwClimKcgGoAP5DkqQUoBP4T+BHMz1YwdQz1p4oXBNKSEjg5z//OR0dHXi9XjweDwCJiYlotVoCgQDLly/H7/djs9lYtWoVRqNxRL8awdVLvBHKXx329yyu1FX+xdQORzBXmeiLPppY8qtf/YoTJ07wF3/xFyQnJzNv3jx18VJbW0t+fv6o1xSNpwSC6WWq5thEo02ifX9ubi49PT24XC4WL16s1lBeuXIl7777Lp2dnWzcuBG73R7Te/6Nb3yDFStWsGHDBl5//fWI8Yx1z+ERgpcuXaKnpwe9Xs9dd92l1j6eLhsULaq5urpaRPFcJUiSZAMWAydneyxziWhzzmg0snHjxgnVS461Aaqvrx91voSE6NWrV1NXV4eiKHR2dnL27Fny8/PZvn37iAZ8sRg+Vx9//HH+/d//nXnz5vHkk0+SkZFBMBhUo5bD7/3ChQssW7ZMjQQ0m80sX748oqnV8OcQsrG7d+8mGAwiyzJGoxGj0ahGIIaaGR46dIjk5GSMRqNaDzrezAqBQPAZczGSNnxMmZmZVFVV4XK5ePfdd6moqODQoUM4nU61vrEsywSDQbUM4XCHmE6nQ5ZlDh8+TGJiIgsWLECSJObPn09PTw+5ublqubHQ+mc8WQ7Dn2FXV5fIPp0j/L7nzLMxfpwSdtw2YNsMDEkww4y2JwqVt+jq6sLj8dDY2Kg25lSUz+JPg8GgWhrnzjvv5KGHHpqww0kwt4m3hnLt8M8kSaoFfg38ZKoHJZg6pmrRM5VlJr773e9y0003IctyRFfxeK8pmscIBNPLVMyxqW4QpdVq2bZtG83NzfT393P77bfT09PDxx9/TGZmJps2bcJoNMb8jhUrVnD77bezceNGzGbziPGMdc/hm63BwUFaWlooKyujsLAQl8s1JTYoHnstoniuLiRJSgB2ATsURWkc9rPruuN5rDk30X/Lo6Vs5+fnx5wvISFao9GwZMkS9u3bRzAYRKfTUVZWFpejezihuXz27FmWLl3K3XffTUZGBnBlU7Z27Vo1MlCWZY4fP85HH33EypUrKSkpwWazAVc2ZLEyH4Y3EUxNTcXj8eDz+fB4PGot6BUrVtDX1xfRwyJUD1pkdwkE42MuvoOjjWn16tVs3bqVBQsWUF1dzblz5xgaGsLv9+P1ejGZTBQWFlJQ8FmicUgQ3rdvH1VVVXR2dqqOp+bmZoqKijCbzfT396u1Uqdqffjee+/FrCEvEAimhnh1odH2RDU1NTQ1NTFv3jwsFguXLl1Cq9USDAbRarUoikIwGGRoaAir1crSpUsZGhoSZbWuYaRwT8K4TpQkE3BeUZT0qR3SZ6xZs0b5+OOPp+vy1zzhL+yJbtZChmfXrl3k5eWxbNmyERud5557Lq7rvPzyy/zoRz9Si7Jv3759RB3TeK8ZGld7ezvZ2dmjCuXjEdXnYtTBVcA11YBB2J0rjGeORePZZ5/lxIkT+Hw+TCYTGRkZXLhwAY/Hw3333Tfm9eL5/urq6ginFFyJlElNTeXxxx+nv7+f//N//g/PPfccNTU1ox473nue7POJdr147PVY93wdMeftjiRJGqAGSAPuVxTFH+vY69XuxJpHE3kXR5tDtbW1lJWVRZw7fL5UV1dz5swZ2tvbOXHihNokJiMjgw0bNox7fjU1NbF161bWrl1Lenq6mk1xxx13RKR5AuzatYu9e/dis9nw+/0Eg0GCwSDr1q1TMx9ifXe4LXj//ffVMkAGgyFi3KGNYVNTk7r+crvdrFu3Dr1efz3ajskw5+3OeLlebc9Emcw7eLr2I6ExNTc3oygKN954Iy6XC1mWkWUZi8WCLMsxbdHwDKgnnngCrVaLVqvlyJEjBINBrFYrBoMBm81GTk4ObreboqKiCa1/oj3Dd9+90rN28+bN6mfX6domFteU7RF2Z+YZry4Ua322efNmjEYjSUlJALzxxhsEAoEIMTmk7WzYsIHS0lIKCgrEPL46icvuxFtD+elhHyUBnwf2jnNQghlkslHF4YZnwYIFuFwu6urq1I1OvCnwR44c4Z577gHg61//OgsXLgQml1Yfr5drPJEEczHqQCCYLSbjSbbb7apIYjKZ6Orq4vDhwxQWFpKQkEB/f/+Yc2u09O7QBquhoYGysrKIY0IRLR0dHdx9993U19fzB3/wB3HVhR7PPYeODY1px44dk84CCQQCnDhxQm06GN6BPYSoIX91IF1R7l4CbMDdo4nJ1zOx5vlES2wNr2FcWFhIYWFhxHHD50tRURE7duzAYrGgKAp+vx+73U5JSUnU40fD5/Pxla98hY8++giz2cx9993HypUrOXjwIDt37uTGG29k69at6n3YbDa+8IUvYDKZ6OzspK6uDo1Gw6lTp8ZsahVuC0pKSqirq8NoNOJ0OiOyJkLPpaamhr1795KZmcnixYs5duwYnZ2dbNq0KWapIIFAEMlE38HTuR9xOBw0NTXx+uuvU1JSwpo1a0hLS+PgwYPcfvvtmEwmTCYTFRUVHDt2jCNHjnDvvfdGTTnfv38/fr+fjIwMJElixYoV/O///i89PT1kZGRQWlqKVqud1N4oVv389957L6J+fnNzMzk5OWzfvl0E+QgEk2S8utBoe6K+vj7a2tro7u5GlmUAta66w+FgaGgIs9ms2ovKysppuivBXCDeGsrD/zUNAv8X+I8pHY0gJhOJ1pms8BBueJYuXUpdXR1ut5vdu3eTmpqKz+dj9erVo77o3377bf7oj/6IrKws9uzZo4rJMDOlK8ZjPKeyrIdAcD0SXs9zcHCQwcFBkpOT6e3tJSkpiUuXLrFs2bIJza1oG6xQ9/Hwa4TKY9xyyy10dXXx1ltvsXnzZs6fPz/ltddH2/SF7i9em11fX8/Zs2dJSkpSmw42NDQwODgYcZyoIX/VUA0sBSoVRZFnezBXE5N5Fw/fAFVXV485X5qbm1m/fj3t7e1cuHABnU5HUVERly5dinp8LPr6+vjiF7/IRx99xH333ccNN9zAW2+9xalTp0hNTSUnJ4fy8vKIEhrD12kJCQmcOXMGr9fLkiVLRhVtwm2BzWZj3bp1HDt2DGBEfcLi4mKeeeYZHnrooYio6LFKBQmuHiRJ0gM/AyoBC9AM/EBRlHdndWDXGBN9B0/XfiQYDPLhhx9SW1urlrmQJIne3l76+/v55JNP1PGWlJSwadMmHA5HzGhBh8PB/Pnz8Xg8GI1GsrOzSU5O5vTp0yxcuDCiMelU9skYXj9fq9UiSRJGo1ENYhJ2SiCYOJNxhoXP84SEBD788EMCgQD9/f1q4z2/309vby8Wi4VAIMCGDRtGbWQsuHYYU1CWJEkL7AN+qSiKZ/qHJBjORKN1YtUTbGtri8vb29DQgMvlUpvD2Gw2PvroI2RZJjMzk0uXLrFnzx6ysrKwWCwcP36cbdu2qdd75ZVX+OpXv0p5eTnvvPOOWiojRLRooqku0D4e4xnr2IaGBtEASyAYg+H1PDMzM2lubgaupFfrdDr6+vomFPUH0TdYZWVl1NfXY7VaVafUyZMn2b17N4mJiezcuZNz586xfft2EhIScDgcFBUVTZkDK9amb9euXXg8ngib/fzzz5OTk4Pf749qR1wuF263G6fTqQrxer0ep9MZ8Z2ihvzcR5KkhcDXAC/QESozAHxNUZRdszawq4SpisK32+10dnaq4ml4Z/EHHnggoqTXggULWLp06aiRvqPR3d3NXXfdxcmTJ3n44YfJzMzkgw8+oLOzk7S0NPx+P93d3Xi93ohGeKF1mtfrpa6ujuTkZBYvXgygdkqPxXBboNfrxxShi4uLI6KiATo7O2lqauLJJ5+MqxyRYM6iA1qBDcAF4G7gl5IklSqKcm42B3YtMdF38FTsR4YfGwgE2LJlC7W1tdxwww08+OCDaLVaXC6XWv6it7cXs9mMLMvU1dWp6eexyMnJwev10tDQAIDBYCAQCJCfn88//dM/qbZhqvtkDE+9r66uVh3sIIJ8BILJMhFn2PB53tLSwpEjR5BlGa/Xi0ajQavVkpqaik6nw+fz4ff72bx5M08//bSYq9cJmrEOUBQlAPy/QkyeWux2O9XV1Wzfvp3q6mrsdnvMY8NFC41Gg8lkUjcko1FZWUlPTw8ul4tgMIjdbufQoUPk5uaSm5urpp1H++59+/Zx4MABjh49Snd3N93d3Xz88cdkZGRw6623otFo8Hg8JCYmIssyGo2GpqYmampq1GssXLiQe+65h4MHD44Qk0MUFxfz+OOP89xzz/H4449PueEJGc9wYhnPaMe2tLSoTcDGemaxGM/vWiC4Wgm3U2azmdTUVIqKilRBdGhoKKIG+3ijah0OB2lpaRGfhdLZQxEtqampPProo6xatYqamhqOHj2qzl2j0YgkSbjd7ohjJ2Nzoo0pLS2NI0eOYLFY8Hq9HDp0iHfeeYeDBw/y29/+NqYd0Wg0tLa2Mjg4SFJSEoODg7S2tkbUmIfPHHHD71ks2uYOiqKcVxRFUhTFoChKStgfISbHwXje27EIbYKSkpLYtGkTAHv37kWWZTWD4KWXXqK/v5+8vDy1pBfAunXr1OvEO7/S0tJYuHAhb7/9Ntu3b6e+vh63201HRwft7e1cvnwZi8VCY2MjaWlptLe3A5+t044dO0ZSUhKKoiDLMqtXrx5znTdRWxBut0JlNiRJQpKkCa1xBHMDRVEGFUV5VlGUc4qiBBVFeRs4C9ww22O7lpjovBtu1zo7O3nvvff4+OOPR+wN4rWBWq2W/Px8/v7v/56amhrS09PVMeXk5HDLLbcQDAbVaGONRkN9ff2o6eeVlZVotVpKS0vR6/V0dHQQCAQiAoZg4nvTeJ9hrPVVyHYKBILxMVwXCjnMR7MHoQZ8Bw8e5NChQ3z88cd4vV4MBgNJSUkkJSWRmJhIeno6aWlpbNiwgdtuu42BgQGxlriOiLfkxTuSJN2tKMqvp3U01wkTqY01kcjZ4RHAbW1trF+/Xj3GZDLR3d3N008/TWFhoXoNgKqqKqxWK5cuXUKWZQYHBxkYGEBRFO666y5ee+01kpKS0Ov1uN1ujEYjFouFw4cPs3fvXjZu3Midd95JXl4eO3funLXo3vFEEkQ7tr6+nrKysimpQy3qMgvmIlPViNLx/7P3/mFN3vf+/+NOAkkIIEEgIAaVgFgtINZOHbXKULGt0/0+rW5tr3Xr1h27s12nu3bOWdd16659d6bXZ+d012bX1a7urHY7W9vNbW21ULo5qs5qFaaCIVbkh4QICcT8giT39w9O7hEMPw2I+n5cl5eG3LnzDpfvV17v5+tXezsJCQmcPHlS6as1Z84cMjMzWbJkCQcPHmTRokWTngo+UmS9uLiYRx55hDfeeIP169ejVqt59tlneeKJJ+jq6iI7O5tFixZhMpkU8TlegyFGWhNAW1ub0hfZ7/crpewOh0MR1YfakXA4jNlsJhAI4PV6MRgMpKenEw6Hr3hfMSlZcCMznu/tsezWULEjLS2NnJwcZcBTYWEhu3btUp5fvHix0rv4zJkzLF26dMxM3wiHDx+msLCQ2bNn8/vf/175+ezZs3nvvfcIh8MkJiaSkpJCT0+PUqkREYYiftqXv/xlJEnCaDRSVlaGyWRShhSPxmRswVC71djYiMFgQJZljEajyAK8gZAkyQQsBE7FeO5h4GGAvLy8aV7Z9c9k9t1QuzZ8QN7wuRLDbaDNZqO+vh6LxcKuXbu49dZbSU1NpbS0lO9///vKe0SqG2Bw8LnFYiE1NZXGxkZlNoPRaBx3L3qdTsfq1atj+oVXW0ky1u9QtPcSCOLLeCvDI/5VQ0MDb7zxBhaLBZ1Ox9///ncaGxvRarUkJiZSVFREa2srarWa7u5uLBaLEhh3uVxR1ViCG5vxCsoq4BVJkv7KYDmVHHlCluXPTsXCbmQm2h9wpNYVzc3NzJs3b1ShcugX9uOPPx715W+326mvrycYDLJmzRrlHnq9noGBAcxmM0ajkc7OTnp7ewkGg3i9Xg4fPkx3dzdGoxGVSqVM+RwYGODYsWNs3LiR+vp6tFrtNRdTJ9JWI9a1+fn5Yw70GQ3Rl1kwk4lnwEOj0VBbW6vcS6fT8f7775OdnU1+fj4bNmygubl50u1tRhKZPvKRj/CNb3yD733vezz77LOsXbuW3bt3Y7fbyc7OVso8y8vLyczMjOvwupHWVFhYyOuvv45OpyMlJYWWlhZ8Ph+zZ8+msbERk8l0hR1JS0vD6XSSkZGBXq/H5/Ph9XoxGo1xW69AcD0w1vf2eOzWWGLH0Ocj/YdPnz5Na2srq1evHtU+RQ5bNTU1/P73v2fTpk28+uqrUdfIssyCBQtYuHAhzc3NJCYmMjAwgNPpvEIcLywsZPPmzbjd7km3KJsIQ+1WT08POp0On89HWVnZFb8nwfWJJEkJwIvAHlmWG4c/L8vys8CzAMuXL5eHPy+IX7A9wlC7Vltbi9FoZNmyZVEVnJGzwdBr6+vrsdlslJSUYLFYOHv2LF//+tfJyMjg7NmzaDSxj/KRs6PJZFLeIxJUG89ax5txPVWCr2jvJRDEn7H29lD/yuVykZSUpLQvdDgc9Pf3I8sygUAAm81GOBxGkiRCoRBmsxkYbNeVlpYmfImbiPEKylZgx1Qu5GZiolHdeGXODv/yb2xsxOfz4fP52LdvH0ajkTlz5nD48GFlIENKSgopKSn09fXx7rvvkpiYiFarVdabnp5OcXExPT09HDx4EI/Hw09/+lNuvfXWqAyg8a5xIozX2ZtIJsFkBvqMRrx6QQoEU0E8Ax6RPrGRv5OTk5kzZw4VFRVKRvD69esnvdbCwkIqKyt5/vnnaW1txWw2c//99/P1r3+d3/3udyxdupRAIMDevXtJT08nJycHv9+vBLwiUfV4ZrdEDn179+7l7bffRpZlVqxYgcfjob+/H4PBAAwO2vL5fCQkJOByuYAr7UhJSQnJycl0dHQomUQWi2XUXocCwY3KaN/b47FbY4kdw583mUxotVruvPPOUSsYIoetxsZG9u3bh9lsxmw2Y7Vao9YbCRCp1WosFgutra14vV5yc3NjBuxiZSQePHiQ1atXxz0gP1Ssitjr8vLySbcjEswsJElSAf8D9APbr/FyrkumqrowYtciZ4OhLa2Gnw0i1+7atYv58+eTlpbG2bNneeaZZ9BoNNx3330jiskwuiAbD7F8qgXf6ZizIxAI/oHVauWJJ57AbreTk5NDe3s7CxYs4J133qGvr49QKERycjJer5eUlBSlgtLj8ZCRkYFGo1Gq2pcuXSp8iZuIUQVlSZLuk2X5JVmWvzldC7oZmGhUN16Zs8O//M+dO0dnZyeFhYUYjUb8fj/19fX4/X5uvfXWqIEM586dw2AwcPfdd+NwOBTBWafT0dvby9GjRwkEAmzcuJFgMIjVap1SMXW6WklcrcMkSrYEM5l47tGBgQEqKio4e/asIohWVFQQDAbjslar1UpNTQ1lZWWsWbMGh8PB5z//edrb21m/fj0f/ehHcbvd7N+/n6qqKmW4FgzasM7OTsxm86QPO6MdwHw+H2vXrlVsxIEDB8jPz1cG7RmNRqU90KxZs2IO+orYmtLS0ihbM1pvM4HgZmQ8dmus7+7JfrcfOHCAw4cP8+c//5nS0lI+97nP4fV6rwjCDQ8QLV68WPHdxlMhFatFGcQvIB8RqyK/B61WO+l2RIKZgzQYIdgNmIC7ZVkeuMZLui6Z6urCiZwNIvbu3Xff5ec//zkZGRls3759zKGdIwmywKTPT8P9oMrKyquqPBsL0d5LIJgeIrrK+fPnCQaDdHR00Nvbi8lkore3l3A4zLx585S5WcnJyQQCAebOnUsoFMJoNGK32zGZTKxatQqtVit8iZuIsTKUfwq8NB0LuZmYzEEmHpmzw52LSFuLrKwsAKXMOjk5WRnI0N7eTmdnJ16vl02bNrF48WLlfhcvXuTw4cN4vV6CwSD33Xcfd9xxh+Kc6HS6mGvUaDQ8+eSTHDlyBICVK1eydevWCTkN09VK4moj5KJkSzCTiUfAI3LAOHbsGDqdLqqEc7zlleNh+J73+XzY7XbKysr4xCc+AQzaAZPJxIkTJ7jrrrsoLy+nsbGRzs5OsrKyJh1wGi2AFcsWmUwm3G43JpOJpKQk9Ho9ra2tOBwOZWDhcDsisnEEgvExHrs1nv2k1+uVPqYrVqwYl304d+4cf//731mzZg333nsvKpUKjUZzRRBuMgGi0VqUwdRUNwm7c8OxC7gFWCfLsu9aL+Z6ZaqrCydyNsjNzaW3t5fDhw8zb948/vmf/5mBgQHS09NHvP9w4ff+++9X9vRkq0dj+UE1NTVRdjMyhHy0zOd4txIRCASTJ7If9+3bRzAYpKurC5VKxcDAAIFAgNOnTwNQUFBAcXExJ06c4JZbbsHpdCrD1hcuXEgwGOT++++nurqajo6OmOccwY3LWIKyNC2ruMmIhwM/WaFy6IHl0qVLvP/++8pQPZ/PRzgcJj8//4qBDHa7XSkdj3Dp0iXFEVm3bh1FRUXKpF8YFHx6enqi1miz2XA6nXR1dWE0GpEkidraWjo6Oq6YIDwaE3H2rtZ5uZoIuTisCWYyVxvwGHrAWLlyJbW1tdTU1FBRUYFer49r8CSy571eL0lJScyfP581a9aQmJgYdd3SpUvZv38/LpeLzMxMtFotZrP5qqoXRgtgxbJFS5cu5cCBA3zwgx+kra2Nzs5O9Ho9Tz/99KhtP0Q2jkAwNuO1WyPtp6F2a8uWLcrrY10X2eOzZ89m48aNFBQU8M///M/MmTNHaRcRKwh3td/9w0Vzu93O8ePH8fv97Nq1K64ijLA7NwaSJM0DvgAEgM7I/0/gC7Isv3jNFnYdEs/qwpHOIOOxD+FwmBUrVvC///u/fOpTnyI1NRW/3z+qbzVWBedkxfKhfpDdbqexsZGLFy/S0tLCd75EYU0CAAAgAElEQVTzHWDszGcxqFwgmDkM3Y+SJOFwOPB6vfj9frRaLSkpKYRCIWRZRq1WEw6HueWWWwiHw2i1WqVV1tCBx2If35yMJSirJUmqYBRhWZblt+K7pJuDq9108RAqs7OzsVqtNDU1IUkSCxYsoLi4OKpnpywPzuooLy+npqYGGHQ8Xn31VWpqavj0pz+NJEnodDpl8BWgDLdZv349Xq8Xt9vNnDlzyMnJoauri/T0dEWgliSJrq6uK6LjownB43X2ZoLzIgysYKZytXZk6AEjLS2NyspKjh8/zuHDh9m8eXNcgye5ubk0NDTwi1/8gk9+8pOsXLkyZvazXq9nw4YNpKSkxC2IM9oBLJYtiqwhKysLnU7HnXfeqdiv0eyayNwRCMYmnnYLYmfoDfUdUlJS2LlzJ7/4xS/4wQ9+QEtLC729vWMG4a7mu3+oaO7z+ZRM6oqKCtxutxBhBFcgy3ILIhEoLsSrunCsM8ho+zcQCPDggw/S3NzMz3/+cw4ePEhHR8eY9m4s+zbW+WkkPyTiB9ntdurq6jAYDGRnZ2O325Wq1FAoxMmTJ3G5XKhUKnw+HydPnmTz5s2sW7dODCoXCGYAkT3+0ksv0dvbS1paGl6vV6kiDwaDio1IS0sjJyeHjRs3YjKZ+Pvf/05zczMlJSVkZmbGbOEnuPkYS1DWMtiLayQHRQbGNTFIkqRfApWAAegEfiDL8nPjXKcgBuM9rMRyDmBQJAkGgyxcuBBJkujp6aGvr4+CgoKYZU2VlZVYrVb27t3LwYMHmT9/PsuWLePIkSP4fD4MBgN/+9vfGBgYQKVSkZeXh16vx2azKc3djx07hsfjUSaBwmB/U6fTSUdHR9SaR3PCxuvsCefl5kbYnbG5GtFjuNBqMpmoqqqivb1dOTzs2bMnLuKoJEk8++yzzJo1iwULFmC1WpVo+uuvv87SpUuVrOh4Cy2jHcBGskWx1jCaXYPJ9zUUCG424mm34MoMvYjv4Pf7efrpp7l8+TIbN26kubl5VDE7HkGhyD16e3tpaWmho6OD9PT0qHZCkTUK2yAQxJ94VRdO9gzS29vLRz/6UWpra/nP//xPpRIrkuQzGkPtWyST2Ol0Issy69atG3NYXyw/pLKyEpvNxjvvvIPb7cZoNKLX6/F6vWRnZ5Oens4f/vAHDAYDSUlJqNVqGhsbgcHkpUgQrK+vj5KSkqj1ikHlAsH0EdnjoVCItrY2gsEg58+fJxwO4/V6kWUZWZbR6/XMmjWLgoICMjIyCIVCysDioX6OqLwWwNiCskeW5XiNmP//gIdkWQ5IkrQIeFuSpPdkWT4Wp/sLYjCSc6DT6SgoKMBsNtPY2IjL5cJoNJKTk0Nzc3NMB6ipqYkTJ05w8OBBSkpKePjhh0lISFCGX+n1es6dO8e8efOUsohAIEBTUxNdXV1UVVVx6tQpmpub0ev1ZGZmAiilFUOziyNOWCAQ4ODBg7hcLhITE3nxxRd58sknx+3sxTo4+nw+3n77bZEFeHMg7M7/MRXZryMJrRqN5qrE0eFrvXTpEk8++SS33nor27Zt4/z589TX16PX61Gr1Zw7d47z58+zZcuWKRFgRzuATeTgOdrhEhDBL4FgGhhPht6+fftwuVwcP36cxMRE/vVf/xWz2Ux7e/u4WmlMNig09B6lpaX09fXR0tLC0qVLo8RkIcIIBFNLPKoLJ9Ne4uDBg9x77710dnbywAMPUFZWxvPPPz9uuxKxb4FAgLq6OpKSktDpdMiyrLxuJJ8lVn9lh8PBzp07KSkpwel04nQ68Xg8SJKEJEmUlZWRmppKT08PKSkpJCUlYbVaMRgMXL58mZaWFv785z+TmJhIf38/8+fPF4PKBYJpxGq18uKLL3LkyBEuXLjAnDlz0Ol0AHR1dQEQCoUIh8PKaxITEwkEAvh8PnJzc6+YUSHOJYKhjCUoxw1Zlk8Nffh/fyzATSfsTJbJCEIjCRi1tbVs2bIFlUqlHFLC4TDt7e0jOkC/+93v2L9/P9/85jfJzs7m8uXLygCq8vJyjh8/TiAQIC0tjVtuuQWTycSf//xnRRhWqVQsW7aM9vZ23n//fZKSkpTM6KKiItatW6d8xhdffJFZs2bR19dHZmamUo5x4MABtm3bphizsT5/rD6EtbW1GI1GkQV4EyDsziBT1fplJKFVp9NNWhwdvtbTp0/zox/9iLVr1/LHP/4Rg8HAt7/9bTQaDampqeh0OjIyMuju7kaSpDHbSkyGsUTj8TpXox0uZVmeliFcAsHNzngy9DQajSIml5WVKUOGRxM+4lER9eKLL9LU1ER/fz9paWksWrRIGTSak5OjXCdEGIFg5jPRXsxWq5XPfOYzdHd3s337dnJzcxUxd7x2JWLfmpqa0Ov1SJKEx+OhvLwcrVZLdXU1jzzySMzXtre3k5CQoLStSEtLo7e3l2AwSGFhIampqXR3d+N0Ounp6WHz5s1KD9XZs2cTCoWU+Tlut5ve3l4lYxmgo6MDm82GxWIRg8oFgmnAarWyY8cOzp49i9FoJBgM0tLSgs/nw+l0EggEkCQpqvpBo9GQmJjIwMAAoVAItVo96kBhgWBah/JJkvQT4EFAD7wHvBbjmoeBhwHy8vLi+fbXNZMVhIb2vIqUPaWlpeHxeLDZbHR0dChOw5w5c5T+ybEcoE2bNrF9+3Y2bdqkrAcGD2RarZaioiKl7DzyWqfTiVarVR6bTCbuueceqqur8fv9SJJERUUFW7duBf5R8p2Xl8fJkyeVScaSJCni90QOZ8MPjsePHwdg2bJlUQMERRbgjct47M6NzlS2ftHpdNTW1iJJEitWrOChhx5iz549pKamRl03XnF0+FpvvfVWHnjgAZYvX47BYADg8OHDpKeno9frgcGexenp6Rw5cmTKxPN4ROTHOlzGawiQQCAYmdECRJEMvQ984AN0dXWRmZlJQkICx48fp6ioKEr4GB64amhomHQ5dySD6LnnniMjI4O8vDx8Ph91dXUsXLiQd999F5fLJUQYgeA6YqK9mKurq7n33ntJSEhQWgMGg0GlMiJCamoq9fX17Nq1i/r6eqXKtLi4mHXr1vHQQw/x5S9/GUmSMBqNSoVDJHFoJDQaDbW1tYoP5vP5qK+vp7i4GBg8w23evJm6ujoGBgaieqh+6EMfwuPxKGfNgYEBUlJS0Gq12Gw2cnNzmT9/Pjk5OXGdcSEQCEamuroah8OhzK6KaEDd3d14PB5UKhXwj3Y6kcqD7OxsAoEAvb29IulOMCajCsqyLF858egqkGX5S5IkPQqsAtYyOIl4+DXPAs8CLF++fOxmUTcJkxWEcnNzsdlsNDQ0YDAYMBqNSrnSgQMHyM3NJS0tDafTic1mY8OGDcyfP19xgNra2vj1r3+N2Wxm06ZNVFVVASMfyIArhGan08myZcuUNen1erZu3ar04okwtNTqlltu4ciRI+h0Ojo6OsjLy8Pj8bBq1aqoXstjMXydfr+fiooKUTp6EzEeu3OjB7ImO9V7KMPFk4KCAmpqakhPT2fLli3KQQkmPyE9UmoeCoU4c+YMGzdu5Pbbb2flypVXrHV4L8FI36+Z3Dd9rMNlPIYACQSCsYkVIJJlmV/+8peYzWY+9KEPsWnTJkUcAaIOVbECVzabDYPBEHXf8dq9SEbh7NmzCQaD2Gw2CgoKSEpKorW1Ne6DRgUCwdQz3pZYv/nNb3jjjTfIzs5m/vz5ishjt9vp7e3l3LlzSJKkVCzYbDbF3rz//vuoVCp6enowGAxKAH3z5s243e4J+WGSJF3xt06no7+/X7nGZDJRXFxMW1tbzPOfRqOhrKyMY8cGCwGNRqMiZH/qU58iGAxecf4TCARTQ319PQ0NDfT29tLf349arUalUuH1eoHB6vRIBUEElUpFKBQiLy8PjUYjfA3BmExby4sIsiyHgL9KkvRp4BHg6elew/XIZAWhdevWsX37dtRqNTqdDp/PRzgcJj09HRj8oo9EtpcsWUJzczPr16/noYce4gc/+AEvvPACKpWKj3zkIyQlJV0xnTiWkRnqPC1dupT29na0Wi3hcHhUkWToZzSZTCxevJjW1lZcLhcLFy5k6dKlaLVaUlImFucYus5du3bhdrujnhdZgDc+Y9mdGz2QNVmBN0Is8WS0MszJTEgfWvVw9OhRent7OXLkCHl5eVf0WF+xYgVvv/02kiSh1+uV0q21a9fGRTyfKsY6XMZjCJBAIJg4/f39fO5zn+Odd95h1apVwKAfEinnTklJidqLsQJXJSUl1NfXk5mZic/n48SJE9jtdjZs2IDVah1xL0fu1d/fz7x587DZbEiSxMWLF5k7dy5dXV1Kqy+BQHB9MVZ10+OPP873vvc9cnNzWbFiBf39/RQWFmK325UeyF6vF6fTSV1dHcXFxdTX11NSUkJHRwdJSUkkJSXh8/no6OigtLR00n7YwMAAFRUVnD17Vqleveuuu66okFCr1XznO9+54nNFMqMTEhLIzc0lGAwiy7KyRr1eP+EznGD6kSRpO4OVncXAS7IsPzjKtV8Fvs5gFejLwCOyLF+RuCOYfqxWKw0NDXR3d9Pf348kSQQCAWRZjgoShUIh5d+yLJOamopKpaKlpUWpIBcIRmPaBeVh7225hu9/XTFZQaiwsJD8/HxcLpfiHJSVlXHo0CEA1qxZo1w7tBTq7Nmz/OIXvyA1NZWvfOUrE5osPtx5Gu800OGf8QMf+ABut5sFCxawevXquGTsTcbBEtxQ3JR252r/38cSTwYGBmhra7uiDDNSmjlRcbS6uppQKMSRI0dwu92sWrWKrKysmKXm27Zto6OjA4fDobTVWbhwIdu2baO6unpGt44Y7XApBl0IBNNPX18fH//4x6muruarX/0qCQkJo7aXiFRSwOAgzUjWoMViwePx4PP5OHDgAFlZWVRVVaHX60dtuxMJgkVKzAsKCujo6MDpdJKbm8uGDRumpDe8QCC4doTDYR5++GF2797NkiVLePjhh2ltbeXgwYPAYIWoSqUiKSmJ22+/ndOnT+NwOLh06RJFRUVYLBZOnz6t+Do6nU6Zx3DhwgUAKisraW5ujumHxbInubm5uN3uqLOhy+UiPT19XBUShYWFSmb00KGAMChUifPWdUMH8F2gikGhOCaSJFUB/wZ86P9e8yrw7f/7meAaU11dTTgcJhAI0N/fHzV0D6IrEGRZJhwOK4k6KpUKg8HAtm3brtHqBdcT0yIoS5KUxaCx+SPgA9YB9wEi7DFOJpvxV11dzfnz59HpdKxcuVIRhrVa7RXX9/X1odFo+OIXv8izzz5LSkoKn/3sZ6+6PcR4RZLhnzEiEuXm5sYtY28yQpfg+kTYnX9wtf/vY2X9ZmdnY7fbo342VLidqDja0NDACy+8QDgcpqSkhP7+fqWEc7gQU1hYyNe+9jWqq6vp6Ohgzpw5UeKKCBoJBILx0N/fz9q1a2loaODnP/85Dz744BVB8LKyMqqrq9mzZw8JCQm0t7crE9L9fj9vvvkms2bNwuPxKFnNW7ZsiQpqwcjB+EgwfdGiRYoAYzabmTt3LkVFRWzdunXKesMLBIJrw5e+9CV2797NqlWruP/++1GpVMpebmtro7W1lby8PLKysmhsbCQvL0/JXL506RI2m00JQiUlJeFwOHA4HIr9cLvd1NTUxLQRI9mTyspKampqgGj/aSJ2JnKWS09PZ9WqVZw4cYKuri42bNjA1q1bhb26DpBl+RUASZKWA3NHufQBYHdkALokSU8BLyIE5RlBfX09drsdtVodlYUMg2KyRqNBr9ej0+kIBAKEw2EyMjJISUkhPz+f0tJSsV8F42K6MpRlBsvMnwFUQAvwFVmWfz9N73/dM1FBaKizsHLlSmpra6mpqaGiooLLly/jcDjweDy8/vrryiA9m82GLMvk5ORwxx13MGvWLI4ePUpqaqoiKk9lpl+sz/i1r30t7sZMZAHeNAi7M4Sr+X8fq0IiNzeX7u7uuA2KyszMJD09nSVLlpCRkYHf76e7u5sVK1aMmlEz/DOJoJFAcOMw2p6PR8ZuYmIiDzzwAMnJyfh8Ph5//PGoew0XXvbv34/T6aS0tJSmpiZkWebixYv09PQwd+5c5ZqqqqooezlaMH48AszQGRMws3rDCwSCKxnLPt17771YrVY+8YlPKD2TASwWCzqdjjvvvBO3283JkycxGAzo9Xq8Xi/Z2dnMmTNHaXvR0NCA3++nra0Ng8HA+fPnycjIQKVSMWfOnCtshNVq5YknnsBut5OTk0NGRgaXLl2is7OTlpYWPvvZz46Y1TwehvpgbrebDRs2iGqKG5clwNAz1UnAJEnSbFmWu6/Rmm4qRrMzLpdLaXMRGbYXyVLWarUkJiYqf7RaLfn5+cybNw+73U5RUZHIThaMm2kRlGVZdgBrxrxQMCoTEYSGlqinpaVRWVnJ8ePHqampQa/Xs2rVKpKTkzlx4gQHDhygsrKS8+fPs2bNGjIyMvj0pz+N3W6npqaG48ePU1VVhc1mo76+nvz8fHbt2jUlDoIQewXxQtid+BERPBwOB+3t7XR2dpKQkMC9996L3++/KuF2z549VFZWIkkSBQUFGAwG4B9D9yJ/v/nmm+zcuZOBgQFl+vBIGXrCjggE1z+jZeUCV5WxW1dXx8DAAGvXruXuu+8e8V7D2/0EAgGMRiOXLl2ivLycffv2KQNtysvLMZlMNDc3c+LECXJycpT3Gy0YPx4BZib3hhcIBNGMZLvuuecebDYbDz74IGvXruXMmTMjDs6L+F0XL14kOzsbr9eL1+ulrKyMzMxMPB4P+fn5eDwenE4nKpWKy5cvYzabyczMxO/3U19frwzfGrqurq4usrOzcTgcHDp0iMLCQkwmk3Luu9rKB+GD3TQkA71DHkf+nQJcISjf6APQp5uxKpckScLpdDIwMAAMnqeGDtzUaDSsXLmSu+66C7vdTmNjI52dnWRlZYnqJ8GEuJY9lAVTyPDDh8lkoqqqin379rFkyRI6OjqUnsrLly/nlVde4dSpU8ybN4+MjAzlNRUVFRw+fJiGhgaam5spKSnBYrFcdbnldPYCFH0HBYKro7CwkMrKSnbu3EkwGMRkMjF37lyamppGLKcca8/JsswTTzzBd7/7Xb7yla9gMBiuGAZTUVFBMBjEarWyc+dO1Gq1kr3c0NBAcXEx1dXVAGKPCwQ3GLF6t0d+Dkw6Y/fVV1/lvvvuIzs7m7vuuoujR4+i0WiwWCxKP+TIvYb7UkajEZ/Ph8vlwmQykZmZydy5c0lKSlJet3TpUvbv3z+h6o2xBJirHawqEAimj1i2q7Ozky1bthAKhbj77rvJysoas52hXq/HbrfT3t7OwoULlaCVy+WiuLiYRx55RHnP++67j97eXmXoemRgcU9PzxXrSkpK4vTp03R1dQFw8eJFkpOTyc7OJhQK8cQTT2CxWIQ/JRiLy0DqkMeRf7tjXXyjD0Cfbkbzkf7617/y1ltv0dvbG/UaWZaVjGS9Xk9WVhYul4vMzEy0Wi1ms1mIyYIJIwTlODHTRMuRDh9ut5uGhgaSkpJIS0vD5XLxxz/+EbfbzX333UdBQUHUffR6PZs3bwZg3rx5cSm3jIhDXV1d+P1+Tp06xYkTJ3jsscfi/jsTfQcFgvjQ3NxMZWVllE1xuVwxyylH6s0XKaPMzs6mtraWV155hc9//vPs2LGDn/3sZzGHwaSkpFBdXU0wGCQjI0MZGAGDgTOv10tLS8uIe3wqbPNMs/cCwY3IaFm5sixPKmP3xz/+MY8++ig5OTncc889NDc343K5SExM5Pz58/ztb38DBlthzJ49m8rKyihfatGiRdTU1GA0GgmHw2i1WpxOJ8uWLVPeQ6/Xs2HDhnENsRovYqCwQHB9EGtoZ29vL8888wxqtZpf/vKXvPzyy4r/EGtwHvyjAuNTn/oUtbW1uN1uwuEwLpcr5t5PS0vD6XTi9XoVMTkcDmM0GpVr2tvblaGjXq+XUCik9IWfNWsWt912G/X19QSDQdasWSPOTIKxOAWUAv/7f49LAbtodzE9jOQj/elPf+Kdd94hEAhE9U6OVFPpdDoSEhL42Mc+xqOPPipaBAquGiEox4GZKFqOdPhITU1FlmWSkpJwu9385S9/wev1cscdd/Dtb3+bHTt24HA4CAQCaLVaMjMz+drXvsaePXviVm65d+9empqamD17Nunp6fj9fpqamti7dy/f+ta34vL5I4wWvRMGUyAYP+MtuY615xwOBzt37qSyspKMjAx+9KMfcfbsWb785S/zX//1X0iSNKpgsmfPHrKyspThMzDoEHV2diLL8ojBLri6svhYzER7LxDciIyVlTuRjF1ZlvmP//gPvv/971NSUsInP/lJzpw5Q3JyMunp6bhcLs6ePUswGESWZTQaDQ6Hg+TkZMxmMxaLRRkWXFRURE5ODu3t7SxdupT29na0Wi3hcHhSQ6xEb3iBYGqZqiDw8PsWFBRQU1MTNbTz5ZdfVhJ5brnlFn7yk59EVXvGajER6ZkeCAQ4e/YsAE6nk5qaGu67776Ye7+kpITk5OSoClSLxUJ+fr5yTW5uLgcOHFBmVpw8eRKPx4Ner1d8tUglmEqluq7OTCLQHz8kSdIwqBGpAbUkSTogKMtycNilvwBekCTpReAi8DjwwnSu9WYmlo9ks9k4evQowWBQaXcTaXMRDoeV7OSKigoeffRR0Z5GEBeEoBwHZqJoOdLh44c//CE2mw2fz6ccnJYvX05xcTHwD6MTIfI4NzcXm80W5ajMmTMnylEZL4cPHyY9PV3JMtTr9aSnp3PkyJGr/NRXIvoOCgTxYSTHpa2tLWqQVaw919bWRjAYJC0tDa/Xi8/n4+Mf/ziLFi1SbMxogklubi6BQICGhgZg0GY4nU40Gg1paWmkpqYq/b8iZeaRrJx42+aZaO8FghuRsbJyJ5KxK8syra2tfPGLX8RoNJKRkYHT6cRoNJKdnU1raysej0cZjqXX60lOTsZqtVJQUBCVbTy8mmqokDFRoXciASpx8BMIJs5UBYFj3Xfnzp2UlJSwbNky6urqSEpKwu/3k5iYSElJiVLZ0NDQEDXwfLj/EMkkPnToEAaDgdzcXNLT07Hb7SMKpRF7WVpaGmUT161bF3XN//zP/5CdnU1ycjILFy6kubkZi8VCKBTi4sWLJCYmsmjRIuU118OZSQT6487jwNAMr08D35Yk6XngNLBYluULsiy/IUnSD4BaQA+8POx1gilk3bp1fOMb36ChoUEJBiUmJiLLMnq9HrfbzaxZswiHwwwMDBAMBjEajRQUFPDd735X7A1B3BCCchy4lqLlaBHZWIeP4uJifD4fgUAAv9/Phz/8YebOnUt+fj7V1dVYLBZuu+025fpISXtBQQF79uxRhBSn04nNZmPDhg2TWndk0NbQx8N/Fg9E30GBID4MF3dsNhsHDx5k9erVUQ68Tqe7Ys91dXVhMBjo7+8nKSmJf//3f0eSpCts5EiCSeS9i4uLaWtro7OzE41Gw2OPPUZzczM2my2qlY/T6aSnpwdZliktLY26l8/n4+233550FosIUgkE08NYWbnjydh1u924XC7MZjMvvPACarWaZ555hr6+PoxGI36/n9TUVHQ6nTIRPTExkczMTMWWNTc38/TTT4+6ThGgEghmJhPZYxPJco1134GBAdra2lizZg0LFy6ku7sbtVqN2Wxm48aNHDp0SOnD3tjYiMlkiuk/RDKJDQaDknwDkJWVNerciLFsYmFhIVVVVZw4cULpm3rLLbdw4cIFAoGAMh8D4M9//rPSDmi4HzXTEHY0vsiy/CTw5AhPJw+79v8B/2+KlyQYhtVq5emnn6ampgaPx0MoFEKWZUKhECqVilmzZpGUlKRUXIVCITIyMvj0pz9Nfn6+2BeCuCIE5ThwrUTLyURkXS4Xe/bs4ROf+AQf/vCHoyLYY7W1WL16tZKhbDQaWbJkCc3Nzaxfv35C616xYgVvv/220gvV5/PhdDpZu3btpH4PoyH6DgoE8WH4YaWtrY3Vq1crtiZi/4YOgYnsucuXL3Po0CGcTief+cxnUKvVuFyucdvIoe+t0+m48847lQPU/Pnz2b59O2q1Gr1ej9/vJxwOU1JSQltbW5Rtttvt1NbWYjQaJ53FIoJUAsH0MZpYO5aQe/HiRe655x76+/s5ceIEGs2gyxvxC/R6Pe+99x79/f34fD40Gg1JSUlkZ2ej0+no7+9Hq9XGJdg9klAlAlQCwdQy3j020TNVrPtmZ2dz8eJFnn/+eerr63niiSfo7OzEZDJhMpkwGo04HA6lDzIQs9ozkklsMpmQZRmfz4fX62XVqlX8/e9/H3VuxFi+zNatW/H5fKSnpys+ml6v56GHHgJgx44dHD16FKPRiFarpaenh4sXL2K1WmesCCXsqOBmImKr/vKXvyDLMuFwGEmSFF8lHA7T29uLRqNRspMlSaK8vBy1Wh1VtSAQxAMhKMeBayVajtSrNNZ0XlmWeeqpp/jWt77FHXfcwapVq2KWlY8klLS3t2OxWKKciXA4PKkv623bttHR0YHD4cDpdKLValm4cCHbtm27+l/KMETfQYEgfgw9rDz++OMxHXi32x215y5dusTJkyfR6/WsWrUqqs/oRGxk5L0jwsyePXsUG2exWHC5XEo7HrPZTHt7O2fPnqWnp0fpV3j8+HEAli1bNun+gCJIJRBcW8aTRdjY2MjGjRu5dOkSv/nNbxQxGQZtSWVlJTt37mTWrFkEg0EMBgMtLS1IkoQkSfT39+PxeEhLS2PFihVXvd6RhCoRoBIIppbx7rGJZrnGum9GRgb79++np6eHLVu2oFar0Wg05ObmApCZmcmhQ4dISEjAaDSOWO1ZWFjIhg0bOHnypOLXlJWVKQLv1QxJH3ouamhooKenh7S0NKqrq1m3bh25ubk4HA76+/tJS0ujshng4WkAACAASURBVLISrVY7o7N9hR0V3IiM5OtEbJXH48Hr9aJWq+nv7yccDiuvlSQJnU6nVGGtXLmSyspK0VtcMCUIQTkOXCvRcnhE1m6309DQwMDAQNR03gceeIAf/vCH/OxnP+P+++/nueeeIyEh4Yr7jSaUVFdXx+3LurCwkK997WtUV1fT0dHBnDlzptTAib6DAkH8Gc2Bj+y5n/70p3zve99j2bJl/OhHP+K99967Khs5kjBjMpmYP38+aWlp2O126urqUKlULFy4kLlz51JfX4/X68Xv91NRUaH0LYSJZ7GIIJVAcO0YTxbhr3/9ayXbbvv27RQUFCivjexbm81GSUkJhYWFSv/1SBue3t5ekpKSyMvLIz8//6qD3aMJVSJAJRBMLePdYxPNch1+37a2Nn71q1/R29vL/fffT15eHqmpqTz22GPU1NTgcrno6upi7ty5XLp0iaSkpFGrPbdt24bf74/KJI6Iv6mpqTHXOd6WHZGftbS0MG/ePOX+u3fvpq+vj6qqKqWfPEw+gWi6EHZUcKMxUo/2nJwcqqurycvLQ6/XK60uImKySqVCpVIprXYGBgbYunUr3/qWaG0tmDqEoBwn4ilajtchGC7oNDY2olKpyM7Ojsq+e+6553juuef4xje+wVNPPXXF4L2hn2E0oSSeX9ZC5BUIrm/GcuC7u7v5xje+wcaNG/n1r39NcnIyK1euvKr3HEmY8Xq9SvnomTNnUKlUhMNhFi9ejMlkIjMzk5SUFG699VbcbnfUPScTGBP2SyC4NoyVRXj27Fkee+wx9Ho9X/7yl9FqtezevZvKykpqamqUw9mhQ4fo6elhYGCAxsZGDAYDixcvRq/Xk5qaisViobi4OC7B7tGEKhGgEgimlvHusYlmuQ6/79GjR/F6vbz22mtXZBzPnz+f6upqLly4gNlsZv369UpgeySxdqR1j5Tgo1arJ9SyYyRb2tLSct1l+wo7KrjRGL4/A4EATU1NdHV1YTablf7mAKFQSGl5IUkSKpWK2bNnU1RUhFarJRgMXsuPIrgJEILyDGMiPbwKCgrYuXMnwWCQrKwszp8/T2pqqjKdNxQKKSXoDQ0NLFmyZMz3H0koEV/WAoFgKCPZhAULFiDLMrNnz6auro78/PyYFRGTYSRhxu12U1lZyfPPP89f//pXsrOz+eAHP6gc2CLizf333y+yWASC64yhQfZjx46xcuXKKLEjsr+DwSA1NTU8+OCDpKWlkZKSolzz/PPPU1ZWplQx9PX14XQ6aWxspKioCL1ej9frJT8/n9LSUlJSUnjkkUfGva6JBP8hWqARASqBYGoZzx6bTJZrYWEhCxYsQKPREAwGsdlsFBUVjfr+586do7GxURnQF+mhPJI9ibXuWOvU6/UTGj64b98+JEnCaDSyaNEiZUCg0Wi8Yg7G9eAnCTsquJEYft5pbGwkPT2dQCDAsmXLOHjwICaTiUWLFnH27FklU1mtVpOUlMT8+fPxeDzk5+fP6GCQ4MZACMozjPH28LJardTU1FBSUkJ7ezt2ux2Xy8WSJUswmUw4HA5+/OMfs2rVKhITE3nppZfGnFg8FuLLWiAQDGW4Tbh8+TIf/vCHWbVqFU888UTMg9XVMJIwo9FoqKmpoaysDIDe3l6amprIyMjAZDJFteIQgTGB4PpheJD91KlT1NbWUllZqQSMent7OXr0KJs3b6a0tJR58+ZFlWunpqbS2trKmjVrlJY4kf6D3d3dtLW1KQcxs9nMiRMnuHDhAsCIPtNEgv+iHFsgmPlMxj94+eWX+eY3v8lbb71Fdnb2mD5PQUEBe/bsUc55kR7KixcvHrc9GWmde/bsiWrnBaMPH9TpdEiShM/no66ujvLycrRarVKZIfwkgeDakZCQwP79+wkEAqjVaqUKPTU1FbvdzrFjx5Qg+Re+8AVsNhstLS10d3djMpmYN28eubm5YgifYFoQgvIMY7w9vIYKz5EveavVSn19PXq9nhdeeIFQKERjYyN33333uMqfBALB9ct4s+WmCrvdzj333MOJEyf4+Mc/PiXvMZIwo9PpFHu4ePFipYfykSNHSEhIwG63s2HDBqxWq3KvyDRkgUAwcxkeZF+2bBk1NTUcP36cqqoqenp6eOGFF7BarZSVlZGSkkIwGIyyfX19fcyaNYv9+/djtVpJSEggLy+PuXPn4vF46O/vx+Vy8cEPfpCmpiZUKhVmsxm32620y2hubo6yrRMZ4CUCWQLB9cFEEmd+9KMf8S//8i+sXLly3FVYzc3NrF69mo6ODlwul9JD+cCBA4o4BGMP2ou1zokOH1y2bBl1dXUYDAb0ej3Hjx+nqKhIsU3CPgkE08fQM1xCQgKnTp3C6XSSkJDA6dOncTgcaDQa+vr6OH78OBqNhnA4TGVlJU8++eQV95mOGVUCQQQhKM8wxusQxBKeLRYLp0+f5tlnn0Wv13PnnXeSk5NDR0cHp06dUkqrZvKkXoFAMHEmki033vtNRJw+e/YsGzduxG638/vf/5577rnnaj7OiIyWmRMZUmMymSgvL+fw4cOcOHGC22+/naqqKvR6PTt27ECSJCwWiwiyCa47rnXQ6Fow3NcxmUxUVFRw+PBhzp07x8svv8y5c+fYsGEDH/nIRzh27Bi//e1vMZlMLFiwQNnnycnJ2O12gsEgGo2GM2fOYDKZ+OhHP8qZM2cIBoM4HI6o/utpaWk4HA527txJZWVllM3o7e2ltLQ0aq2jDfASAo1AcGMQDof5t3/7N3bs2MFHPvIR9u7di16vj7pmJFvd3t6OxWKJsgXhcJi33noLSZIUkXnRokVkZmZOaBDeRIcPqlQqysvLaWxsxOl0AghfSCC4Bgw/w+3fvx+n00lpaSlvvfUWfX19JCUl4fF46OzsRK1WM3fuXMLhMBcvXsRqtSr7VvgagmuBEJRnCBHno6GhQZk+brFYRnQIIsJze3s777zzDk6nE0mSsNlsLF26lNdee43vfOc72Gw2kpOTMRqN+P1+6uvr8Xq91+hTTpyb8QAtEEyUiWTLjcVY4vTwPblq1SruuusugsEgtbW1fOADH4j75xvKeDJzTCYTiYmJ3H777Sxbtkw5MLW3t5OVlcVtt90GXN3vSSCYTuIdNLpeiBVk1+v1bN68mV/96le8//77LFq0iIyMDBobG2lra8NsNuPz+ejq6qKnp4eFCxdy2223EQgE2LdvH263G4PBoFQ0JCQk0NbWpgzMigzzBGhrayMYDN4Qg6sEAsHV8/3vf58dO3bwpS99iaeffhq1Wh31/Gi2OpY9s9lseL1eRUz2+/3U1dVRXFxMfn7+uNc1meGDJpMJk8mEy+UiJSXlhv4uEQhmKsPPcD09PbhcLmpqauju7iY7OxtZlrl06RKJiYmkpqbi8/morKxEq9WKM4zgmiME5SliNCF0+HMFBQXK9PGSkhIMBgP19fV4PB6Ki4sVMXnXrl1Rr3nuuec4dOgQqampJCcn09vbS05ODt/61rfIzs7G6XSiVquVyLler8fn8ynDFsaz1mvJzXqAFggmynhb5YyH0cRp4Io9uWvXLu644w4yMzM5duwYRqNxRFs3VbZl3bp17NixA4fDQSAQQKvVcv78eVavXq2UdBqNRs6dO4fNZsNut18xsE8gmMnEM2h0PTFS1l1ZWRkLFixQZkf4fD7efPNN5s6dq0xA37JlCy6Xi9raWpYsWYJKpWLz5s3U1dWh1+sJBAK4XC7UajXf+c53qK6uxu12R4k9XV1dMfuSpqenX5eDqwQCwcSI5cekp6fzhS98AUmSrrh+NFsdy57V19fzwQ9+kPb2dnw+n3JWq6+v5+GHH57QWqdq+KBAIJg6hp7hTp8+zdmzZwkGgyQkJNDf38+lS5eYPXs2qampFBYWEgqFGBgYwGQyEQ6HxRlGcM0RgvIUMJoQClcKMjt37qSkpERxOgoLC8nMzFSmjMe6X01NDXa7nZSUFDo7O5kzZw4lJSUMDAzwq1/9ii1btigDH7xer+KghMNhjEbjuNZ6rQ+pN+sBWiCYKONtlTMeRhOnh+7JP/3pT9hsNjo6OtBoNNx+++0YDAbFfpw/f56dO3cyMDBAdnY2gUBg1EznqxWbhx/sDAYDp0+fZtasWUpQTafTodFoaGxsVEQikVUouB6IZ9DoemJ41p3P5yMYDNLc3MzixYsZGBjA7/eTlJREKBTC6XSSnJxMWloadrudM2fOYLVa2b9/P8uWLVNa4hw/fhyAlJQUJYtvuM3Kzc1Fo9GQm5sbtSabzUZPTw+yLNPS0oLRaFSC/5O1YTM1sC8Q3MxEzkgajYaDBw9SVVXFK6+8wkMPPRRTTIbRbXWsLGKLxUJJSQnz58+nsbERl8vFrFmzooLz8UT0dBcIZhaRAXw9PT2cPn2acDiM1+tFlmUGBgbQaDSEQiHy8vLw+/309/crA0DFGUYwExCC8hQwVobf0OcCgQDnz5+nra2NpUuXsmjRIkwmU9RBcaT7tbS00N/fT3d3N/PmzSMlJYVwOExraysAJSUlJCcnK8Mf0tLSsFgsUSVUM1m0vVkP0ALBRIlnxslo4nR9fT1Op5NDhw5x4cIFEhISyMjIIBQKYbVa6evrY+XKlbz44oscOnQItVpNRkYGfr+fhoYGiouLR8x0vppAVnV1NRaLRWllAYMHwZdffpnS0lJkWcbn85GSkoIsy3R2dhIOh0VmjuC6IZ5Bo+uNSNbd7373O+677z7MZjMJCQlYLBYWLVpEXV0dMGj7nE4ns2fPxmw2K8M5i4qKcDqd1NTUUFFRweXLl/F6vVgsFuU9rFYrNTU1lJSU0NbWht1up7u7m3vvvZempiZcLhepqanYbDYOHjzI6tWro9qSXY0APJMD+wLBzUx1dTUDAwP87Gc/w+PxsHr1atLT00c9I41lq4dnEe/atYu+vj6l/QSgtKCYKkSfVYFgZmC1Wmlvb8fpdNLW1qb0M48MDg+Hw4TDYYLBIFqtVrEty5cvx+VyiTOMYEYwLYKyJEla4CfAOiAdaAb+Q5bl16fj/aeb0YRQWZaV5+x2O3V1deh0Ovx+Pz6fj7q6OsrLy9FqtYrzEet+kiTR2dmJ3+/n9ttvZ+HChcCgExK5NiIylZaWRolM69atG9darzU38wFaIJgIY2WcTCT7bbQS81deeYUzZ87Q3t6uZP3Ksoxer8dgMHD58mXa2tpwuVwEg0EyMjKQJEm5tr29HZ1ON+lA1miDbmINKY38LBJQ27BhA319fbS1tYnMHMF1xc1Ypjx0vzc3N/Ob3/yG22+/nT/84Q/89re/VUSYyGApjUZDQkICxcXFtLe3KwP2ysvLATh+/Dg1NTXo9fqoORW7d+9Gr9crNiliD1wuF36/P8q2trW1sXr1auWaeAThZ3JgXyC4kRnLNzp06BC//e1vSUhI4LHHHiMvL2/MEvOJ2uqxrhfVCwLBjUdkX+/btw+dTse8efM4evQoarWaUCiELMvIskxqaioJCQnk5uaSmZnJpk2bkCSJYDAYVWElEFxLpitDWQO0AmuAC8DdwP9KklQsy/L5aVrDtDGWEBp5rrGxkaSkJEwmE+3t7Yrwcvz4cYqKihRnYvj9enp6lLLMwsJCMjIyCIfDSqTqq1/9KjC+sqaZLNrejAdoQfy42QJZI2WcTDT7bSS78cYbb9Dc3Ex7ezuzZ88mISEBr9eLx+MhKyuLxMREPB4PXV1dJCYmkpWVhc/nIykpCRhsN9HZ2cnq1asnFcia6KCbvr4+Kisr8fl8pKenKzYk0i9VOGCC64mbrUx56H5/7733eO2118jLy6OyspL//u//RqPRcPHiRSwWC5mZmfT19XHhwgUMBgOvvvqq0nNw8eLFwOCgzqqqKvbt28fatWuvEG9ra2vZsmVL1BqGlqlHfs+PP/543IPwMzmwLxDcKIw2vyaWb/SHP/yBl156CaPRyFe+8hUyMjKw2+0cP34cv9/Prl27Yoq7EVu9d+9e3n77bWRZZsWKFSOuazTbLqoXBIIbj6H7WpIkJEni3XffRafToVKpuHTpEjDYug8Gkwg/9rGPEQwGefLJJ6/hygWC2EyLoCzLsgd4csiP/ihJ0vvAbcD56VjDdDKWEBp5zul0KsZj/fr1OBwOpdRhaJ9Ru93OgQMHMJlMLF26FI1Gg9Fo5Jvf/CY2m42amhrOnj1LUVERTz31FOvXr1fWMlZZ00wWbW+2A7Qg7txUgayRmEz2Wyy78cILLyi9/jo6Orhw4QJ6vZ7+/n7UajX9/f0AaDQaVq5cyeXLl2loaAAGB4I6nU40Gg3r1q2juro6pgCsVqujho9GDmtWq5UnnniCrq4usrOzldZAkc8xkh2L9K0XNkRwI3AzlSkPtVtz5sxh+fLlJCUlcfr0aZYuXcqJEyeUdmGRAH13dzeSJOHxeJQMnkAgEFX5Fcn4GUrk8XiC67m5uUrveKfTidFoZM6cOVGtxCbKTA7sCwQ3ArGE2eHza4b7Rvn5+ZSXl1NaWqoEsGprawGoqKjA7XbHFHetVisvvvhi1LlNr9ePOUPikUceuWLdonpBILjxGLqvfT4fDQ0NOBwOgsEgKpUKSZLQaDSoVCoGBgZYtmwZer1+StvgCARXwzXpoSxJkglYCJyK8dzDwMMAeXl507yy+DCWEBp5LlLOUF5efkXfrOGR6aqqKt58801efvllzGYzH/jAB3jttdcoKSnh0UcfVQSU+fPnj7m+4Y5MZWWlknl4tYJLvEuzbqYDtCC+3GyBrJG4muw3q9XK3r176ejooK+vj7KyMgoLC5UgV3t7O2lpaYTDYZxOJ2azmccee4z58+eze/duiouLOX36NKdOnWJgYIBNmzYBsQNZzc3NSJJEUlJSVCZOZWWlMoQ0Ozs7qjVQZmbmiINuhtoxYUME04kkSduBB4Fi4CVZlh+8pgu6Djl37hwDAwOkpaVx++2309nZSWtrK+fPn6epqYm5c+cq/k5LSwvd3d0kJydz+fJlAPr7+7Hb7YTDYRYtWqRUfq1YsSKmeLtixQp6enqA0YPrBQUF7NmzRzkMOp1ObDYbGzZsmPRnncmBfYHgRiCWMDswMEBbW1uUf5CcnExtbS1f/OIXWbJkCW+//bZyrqmtrcVoNCrDPYfee2h7sd27d/Puu+/i8Xg4c+YMTU1NrF+/ntzc3AnPkBDVCwLBjcFQfeTYsWOsXLlSaed1+fJlgsEgsiwTDAYVMTkhIYHMzEyWL18ufALBjGbaBWVJkhKAF4E9siw3Dn9eluVngWcBli9fLk/z8uLGaEJo5LnIIUKr1cYcEDXUATp16hSHDh0iOzsbi8WCLMuo1WoaGhpITU2NytYbqwfpcEempqYmLuVTojRLMJO50QNZIzHZ7Der1cpTTz3FK6+8Qnp6Op/61Kc4ePAgMNifeOXKlRw6dAiVSkVycjIf/ehH2bp1qxIM0+l01NTU0N7ejsVi4Y477ojK0hkuAOfm5qLX66/IxHn++ecpKysjJycHv9+vtNBobGyM6jUvgk+CGUQH8F2gCtBf47Vcd3R2dvLrX/+azs5Ovve97+Hz+Th16pSy99VqNefOnVPmT/T19REKhTCZTDgcDvr7+5EkCVmW6enp4b333mPBggX84Ac/ALiqaobm5mZWr16tDDs2Go0sWbKE5ubmqOqwiSCqsQSCqSWWMJudnY3dblceB4NBnnvuOd577z0eeughqqqqgH/4FpF7qFQq5TXDxd3q6mpCoRDvv/8+qamppKam4vP5ePPNN/mnf/on3G73hLKORfWCQHD98+abbyqtSrOzswkGg7z22mu0trbi9/u5fPkyoVAISZLQarWo1WoSEhIIBAKkp6djMBjYtm2b8AkEM5ZpFZQlSVIB/wP0A9un871nIsMPERqNBp1Ox549e8jNzaWhoYHi4mL+9Kc/sW/fPlJTU0lKSsJqtZKRkUFubi4+n4/GxkZMJhOpqak0NDTELBmPMJXlU6I0SzBTuVkCWbGYbPbbD3/4Q371q18xa9YsPv/5z2MymZAkiba2NnQ6Hfn5+Tz88MNX7O3hgaWUlBTC4TAqlSrKJjzyyCNRr3388cejsn4i621tbWXNmjUsWrSIuro64B/9mM1ms4jYC2Ycsiy/AiBJ0nJg7hiXC4bQ1NTExo0buXTpElu2bCEcDnP69GmSkpLo7+8nMTERSZJwu914PB7MZjNutxuv14vb7SYYDBIKhQgGgwBkZGSg0+nweDzA2OLtWL5KJEA29LqxhnSNBxEQEwimjljCbG5uLt3d3bhcLhISEvjJT35Cc3MzX/3qV2NWHAy9h91up7Gxkc7OTrKysrBarYro3NbWprTRiVRd9fb28te//pWMjAxqa2sxm80sXrxY8XlGyjoW1QsCwfWN1Wpl586dqNVqMjIycDgcNDU10dPTg8/nAwZ9CBiskAgGgwwMDGCxWEhLS+Oee+5RqqcEgpnKtAnKkiRJwG7ABNwty/LAdL33ZBirdUM8WjsMvYdGo6GjowOLxaI4DVarlbq6Ourr65kzZw7Jycno9XokScLhcJCYmEhmZiYulwsAm81Gc3Mz8+bNGzFDeCrLp0RplmAmcrMHsiaT/fazn/2MZ555hry8PLZv364cjpKTk3G5XMjyyJr70MBSX18fRqMRv98fFfgabhOsVis2m4133nmHnJwcpUdyX18fZrOZvr4+TCYT5eXlUYc4Uf0gENw4HDp0iE2bNqHRaPjLX/7CrFmzqK6uprW1lfnz59Pb28vly5e5ePEiPp9PmXIeKRVtbW1V+rmHw2G0Wi0GgwFJkkhNTVWC21cj3oqMQYHg+iOWMNvb20tRURGvv/46x44dw+Px8P3vf5+vf/3ro97D4XDQ0NCASqVCo9Ewd+7cqAHBhw4dwmw2Y7PZol5/+vRptm7diiRJuFwupXVXxNeJZUNE9YLgWiFJUjqDus0G4BLw77Is741x3f/P3ptHR3Wf9/+vO4tmkWY0M1pGywiENgQGgQHbGAyYw5bEtZ2kbuPi2E3s7y8taZo6jdPEiZ16Sdu04XvaJk7V5HzthqYQJ3FS23hHitjEYscskmWERhIGabSNmBlppFk0y/39gedmJLSCEAI+r3M4tmau7nyko89zn8+zvJ+ngO8A4aSXK2RZbp2Jdc5mEvNfPvjgA4aGhvD5fEqyezQGBwfRaDQYjUZKSkrQ6/WiOE9wTTCTFcqVwAJgoyzLwRn83CkzkXTDdEg7jLzH22+/jdfrVdqpLBYLpaWlHDhwgLKyMhYsWMDZs2cZHBxkwYIFBINBJRCdnp6Oz+ejrq5u1AETO3fuxG6343K5aGlpIRwOD1vndB2GxEFLMNu41hJZ083IxNdDDz00oY2Kx+P88pe/ZOHChXzuc59Tgsnd3d2KhuB4di85sZQYOGEwGJTE10ibkLCFDocDr9eL1+vl4MGDVFRUoFarefjhh6murgYgKysLnU5HQUHBuMNtpjv5JxBcCa5nqZ3JMHJv1tfXY7PZ+M///E/ee+895fVNmzZhMBgIh8PU1NRw6tQp1Go1KpWK9vZ2YrEYJpOJWCymDAdNSUkhIyMDk8lEeno6mZmZdHR0XPaaRcWgQHDtMTIwq1arkSQJh8NBLBbjyJEj3HfffXz2s58d9x4bNmzg8ccfp7OzUzl/eTwe1Go1Z8+e5eGHH0aj0RCNRikqKqK9vZ3+/n60Wi0LFy6ktLQUs9lMbW0tKpWKDz/8EJ1ON64NEd0LgqvEj7lQiGMHlgKvS5J0Upbli2QDgV/Ksvz5GV3dLCdRmXzkyBF6e3sV32Q84vE4Q0NDpKWlMTg4yNKlSwFRnCeY/agmvuTykSRpLvAXXDBIXZIkDXz874GZ+Pypklxhlwju2mw2ZZjCRO9fymcMDQ1hs9lobGxkcHCQeDxORUUFDzzwAEVFRQwNDeFwOLDb7UrbhNlsJhaLYbVaMZlMFBUVUVxcDFwI/uzbt4/du3fz3//937S2tuJwOHA4HBw4cACn00k8Hsfn8+HxeNi4ceNl/942btyIx+PB5/NN+70Fgkskkci6e7YnspJxOp1UVlbyxBNPUFlZidPpvKR7PP/88/j9fhwOhzKRfKx7RSIRfD4fKpWK//3f/+UHP/gBBw8e5MUXX2Tv3r3s378fgGXLlo1r9xKJJYDy8nICgQBer1dJfI20CQlbWFpayurVq7FarUSjUdrb23nkkUfYtGkTjzzyCCaTCZfLhclkuii5N9bPONXfgUAwk8iy/FNZllfIsrwiKyvrai9nRknemxaLBb/fj8lk4l/+5V/Ys2fPsD2bSIbrdDpycnKw2+3KUGOdTkd6ejqRSETRYNfpdCxYsIA77rhDSdLn5+dPS3I7EZgazR4JBILZS2lpKdu2bePZZ5/FbreTk5ODxWJhyZIl/OM//iPLly8f9xy3Z88enn76aZqbm/F4PLjdbpqamjh79iy9vb189NFHVFdXc//99xOLxXC73Wg0GrKzsxkaGmLBggUASreVxWKhvb1d2BDBrEOSpFTgj4EnZVkekGX5IPAq8ODVXdm1w65duzh+/Dher5dIZPxaJkmSUKvVSqI8Go0q3QsgivMEs58ZqVCWZfksIM3EZ00HE0k3TIe0w8h7WCwWAoEALpeLqqoqKioq2LRpEytWrABQDl3Jul2FhYU888wzihNSWVlJf38/4XCY2tpaUlNT6e/vx2g0KsP7EtcmdFCns31KtGYJZhNJiawwFxJZibf+QpblnVdtYRMwXcMtp6JpPjAwwJ/+6Z/i8/nYv38/XV1d7Nu3j4qKCtrb2+nu7ubMmTPcfffdw3SOR7N7yRV8WVlZLF68mLq6OiXxNdImJNtCu92O3W5XNEmTdU1H+9kn+hmFrrtAcIHZVqlfVVWF1Wpl7969HDx4kO985ztkZGTwq1/9iptvvnnYni0pKSEQCGAymTh37pzSNREKhdBoNIqGsl6v55ZbbqG9+A9WZQAAIABJREFUvZ3z58/T2tpKRkYGsViM2tpa0tLSFK3Ty0FUDAoEs4ep2jan08lPf/pT6uvrWbt2LevXrx/md4z1PU8//TQdHR3EYjFisRjxeBxJkgiHw/h8PgwGAzabjVAoxGOPPcb27duJRqPY7XbS0tKoq6sjKytL8XN0Oh1r1qxh27ZtV+pXIxBcKmVATJblpqTXTgLrxrj+bkmSPEAn8Jwsy5VXeoGznd27d9Pa2srg4OC4MoEJUlNT0el0xONx1Gq18v+iC0pwLTCjQ/muFSaSbpgOaYeR9ygvL+fll1+moaEBjUZDaWkpLS0t5Obm0t3dTWtrKxUVFRQXF1/U8p0gEcg5ffo0RqMRWZYZGBjgpptuQqvVKhqmxcXF6PV6nn322cv9VV2EOGgJZgvXWiIrwXQFQSeb+Oru7uauu+7i+PHjVFZWotFohq0h8Zlvvvkm586d46abblK+dzS7NzKxNNbwvgSXY09nIvknEEwVSZI0XPCv1IBakiQ9EJVleWzxvCvIdCWpppNz585RU1PD0aNHueOOOzCbzUiSpAzhTCYYDPLuu++ybNkyCgoKGBgYwGw24/F4CIfDyuArSZJoampi/vz5LF++nCNHjtDW1kZGRgZ33nknBoPhqv/cAoFg+piqbXM6nXz5y1/mxIkT2Gw2DAaDomWs0+nG9Duqqqpwu92kp6fT09ODSqVSBmkNDQ2h1+sJhULD/IsNGzYofk13dzfV1dUcO3aMLVu2XPEg0WxLIAquOdKAvhGv9QGmUa79FReGmncDtwG/kSTJJ8vyL0ZeeKPIfDmdTpxOp9JxPhEqlQqtVotarWbu3LlkZ2crXVCiOE9wLSACyqNQUlLC9u3biUQi5OTkkJ+fj1qtVh7806GhN/IeZ86coa6ujrS0NP7sz/5M0Tw2Go0sWbJEyW4HAgEWLVo0qnFJBHK++tWvIkkSVquVhQsXotFo0Ov1Y2qYXm2E4yMQ/IHpCoJOJlDrdDr5xCc+QWdnJy+//DJ33333mGtYunQpb7/9Nj6fb0K7l0gsJfb2jh07lL0NDNvvJSUlikbyVO3pTCT/BIJL4Ang75O+/jzwNPDU1VjM5SSprsTz2e/388orr3Dq1CnuuecePvWpTymDqhJDOJMDMcna7Z2dnZw5cwabzYbJZFI0B7VaLRqNBkmSGBgYIBKJEI1GueWWW1CpVJw+fZrMzExFpkf4GILrDUmSvgJ8AVgM/EKW5S9c1QXNAFOxbfF4nL/6q7+iqqqK8vJyHA4Her2eeDzOsWPHmD9//ph+R2JmDaDoL8fjcSVYZLVa0Wg0in8x0oey2+2sX7+eo0ePXvEg0WxMIAquOQYA84jXzIB/5IWyLH+Y9OUhSZL+HbgPuCigLMvyT7kQfGbFihUTl+1eo+zatYtAIEAsFpvwWkmSMJlMzJs3D4PBwNDQEI8++iibNm2agZUKBNPDjGgoX0s4nU6qq6upqKjAbrfT3d1NXV0dGzZsGNZ+ndDQq6+v5/jx4/T19VFVVTVpbc7kezQ3N/OLX/yCsrIyTp06xX/8x3+QnZ1NSUmJorGcGAaxaNEitm3bNqZTUFpayj333MOdd97JunXruPXWWyfUML2aCI1TgWA4yRrECS4lCDqWpnlJSQmVlZV85zvfYfPmzXi9XmpqapRg8lhrGBgYQKvVsmPHDrZv386+ffuG2cWRjLa3f/CDH7B9+/Zhr1VXV7Nhw4ZL0iSdSLdd6LoLrgayLD8ly7I04t9TV2s9LpdLkYlIYDabJxxSd6Wez0899RRNTU188pOfZPXq1ciyrOzNhx9+eNiePXbsGPAH7fZQKMScOXOUazQaDVqtllAohNVqpaysDLfbrVT8GI1GDAYDqampNDY2TurnFgiuUTqA7wEvXO2FzBTJti0xO6ampobdu3dfZKdeeeUV9uzZw9q1a/mbv/kb1q5di16vJxwOEw6Hx/U78vPzsVgsuFwuotEokUgEjUaDSqVSElu5ubmKfzGWD5Wenj6p9vfLYTrm/AhueJoAjSRJyRtiCTDaQL6RyFyD3aHTyWuvvTapyuTU1FQWLFjAqlWr0Ov15Ofn8+yzz4pgsuCaQ1Qoj2C0Vm+fz0dzc/OwDZ547+zZs8ydO1eprJtKFjhZHuLee+9l6dKlmEwXuklGqxAMBoPU1NRMWCk0VQ3Tq4nQOBUIhjMdHRAwuqb5zTffTHV1NTabjYKCAv7kT/4En8+HzWYbdw0tLS2888476PV65s+fjyzLeL1efv3rX1NYWDhpfWO32w3A8uXLldcAmpubL0lHcCLddqHrLhBceqX+lXo+P/PMM9xzzz3k5eWNujcLCwuV10OhkKJxChfmPwQCASRJQpZlgsEgarUao9GorCkSiRCLxZg3bx7BYBCj0ah0aYkOBcH1iizLvwWQJGkF4Jjg8uuChG1Lnh2j1+sBLjqPffrTn+bLX/4yc+bMQaVSKVrGPp8Pk8k0rk0rKSlhcHAQnU6HJEn09fURiURIS0tTpDNWr17NAw88oNxnpA914MAB1qxZM21Vw2N1jwipL8HlIsvyoCRJvwWekSTp/wBLgXuBVSOvlSTpXmA/4ANuAb4KfHsGlztrSOzJDz74YMxrDAYDKSkpmEwmlixZwty5c/nxj388g6sUCKYfEVAewVQexJdz2IpGo/zVX/0Vt956K4888ghr1qwZ9v7IA+DIts/xnJGpapheTYTjIxAMZzqDoCM1zSsrK3E6nbjdbh544AFKSkrw+XwX2ayRa2hvbyc7Oxuz2YzBYAAutGm53e4x7d1oezscDl903eXu94l024Wuu+BG51KTVJf7fE4OeITDYY4cOcIbb7yByWQaFkwemSBP3rOVlZX4/X/osvV4PPT29iLLMhaLhYGBAaLRKLFYjP7+foLBICUlJSxcuBCz2Uxtba3yvSkpKWK4jUDA7NYynYrMzmizY4LBoKKJ/NJLL1FVVcWPf/xjysvLefTRR3n++eeRJGlKtjBRVNTY2MiZM2cwm82o1Wrsdjv333//RWsczYdas2aNcs3lJufGk7UQUl+CaeLLXOh26AHOA9tkWW6QJGkN8KYsy2kfX3f/x9fpgHbgn2VZ3nE1FnylcTqd7Ny5k6NHjwKwcuVKVq1aRXNzM/X19bS0tOBwOIjFYsM6ERIJcIDMzEyWLFnCrbfeik6nUwoJBYJrGRFQHsFUHsSTOWyN5hjl5eVx//3389prrylVNyMZeQAc2fY5kTNyrQRRhOMjEFzMdOzfkbZnw4YN7Nq1i4MHD7Jo0SKi0SharXbMAFHyGp544gl8Pp9S+QMXsuxer5f6+noqKysvOvyNtrd1Ot1FnyP2u0BwZbnUJNVUn8/JNker1eJyuSgpKaG3t5fnn38ek8nE+++/T35+/pjBEBhfY93v9xOJRDCZTIo9GRgYIB6Pk5WVxTPPPANcqA7U6XTcfvvtnDhxgp6eHjZv3szWrVuvCd9IILiSzFYt08no/47m25w8eVKZHXPzzTcrc2j+7d/+jXg8TkdHB+Xl5QDo9XpqamqQJInbbrttUlXCLpeLtLQ0zGYzc+fOxWKxUFZWRjQaZdu2bTidzlH9oGQfajqLZ8YraJquLjfBjY0syx7g06O8foALQ/sSX//ZTK7rauF0OvnBD35AU1MTVqsVSZJ4/fXX+d///V+WL1/O0aNH6e7u5uDBg0pAOaG3Lssy8Xic9PR0Pve5z1FcXCz2peC6QgSURzDZB7HT6aSlpYXDhw+Tk5NDeXk5drt92GFrNMfoRz/6Efv376e+vp7/+I//GLPNe+QBcGTbZ2J9l+qMzJZBeMLxEQgun5H7ORGESdger9fLfffdR319PStWrODhhx9GrVYD0NLSQnt7O0888cRFtiBx3/fff5+Ojg5isRjZ2dnABQmeoaEhWlpaKCwsvOjwN9rezsrKUoZvTXa/zxZbJRBcy1xKkmoqz+eR/s7bb7+N1+uls7OT119/nby8PCoqKvjnf/5nLBYLDofjomDIzp07CYVCw3ym6upq5s+fzzvvvENbWxvhcJi0tDRleI1Op1O0QktKSpSfMeE/+f1+Nm/eLOyGQHANMFHn52jnqurqalauXInBYBgmo/Xcc8+h1WrZt28fy5YtG/a99957r2LPJoNGo6GmpkZZW0KC8M4775xUEHy6i2fGK2gSUl8CwfRTVVWF2+3GZrNhNBqBC/Jag4OD7N27F6/Xi06nIxgMIssykiQhSRIqlQqz2YxKpeL73/8+oVBI7EvBdYcIKI9gMg/ihPPgcDhoa2vj5MmTHD58mKKiIoqKivjGN74BDHeMuru7+eCDD9i9ezdDQ0P86Ec/mlAzdLy2T7h0Z2Q2TQAWjo9AcHmMtp+3b99ORUWFcnj57W9/S319PWvXrmXlypX4/f4Jdf0A5b4rV67kjTfeoLGxEVmWSUtLw+v1Eo1Guf3220c9/G3bto1HHnmEnTt3UlNTAwxvD5vMfp9NtkoguNGYyvN5165dnD59mnA4jNVqxev14vF42LdvH2VlZRQUFKDX6wmFQnz00UecPHmS7Oxs8vPzKS8vJysri5qaGhYtWsTJkyfxer1YrVYMBgPHjx9nw4YNrFu3jpdeeon33nsPuBDkicViisxFsj90rXRpCQSCPzBR5+dYAedAIKAEh91uN//2b/+G2Wzm17/+NcuWLRv3eycjOyFJ0pj/ncx9J5Ocm0ryfKIAtbB/AsH0kpDuslqtymuRSETpkNLr9fT09CDLMiqVCr1ePyywvHnzZr74xS9exZ9AILhyiIDyKEz0IE44D+FwWJkkrtVqcbvdFBcXK9clHKPu7m5qa2sxGo3Mnz8ftVrNuXPncDqdk37gj3RG3nvvPQ4dOoTRaGT//v08/PDDk54KOtsG4QnHRyC4dEbbz9FoVKlUAVi1ahVlZWUUFRXx0EMPTUrXD1Dua7FY+KM/+iP2799PR0cHc+bM4c4776Snp2eYzYOLOycS3RWJQ1R1dfWkA8KzzVYJBDcak3k+O51O3n77bXJycrBarYRCIdxuN1arlaKiIpYtW8bQ0JDSAtrX10csFiMQCBAKhaitrWXx4sUMDg5SV1dHWlqacp/jx4+Tnp6u7P1FixZx4sQJBgcHSUtLQ5ZlotEomZmZbNy4cSZ+JQLBrESSJA0XznVqQC1Jkh6IyrIcvbormzwTBUrHCjj7/X4l+eX1ernjjjv4/ve/z2233aZcN9aw8717944ZxE0Eeffs2YPFYmFoaIhQKITFYmH9+vWKrzWRnMVEybmpJs9Fd6dAMLPk5+dz9OhRPvzwQyKRCKmpqcRiMUU+MDMzE7/fTywWIxaLIUkSBoOB5cuXEw6HLzorCQTXEyKgfAkknIcDBw6QlZXFnDlzkGUZn89HcXGxEuxIOEa/+93viMfjZGZmUlRUhF6vx2azDQuKTJSZTnZG9u/fz+HDhykoKMDhcODz+XjyyScBJhVUni2D8EQru+BGZrr+/kfbz9nZ2Zw5c0bREL3pppuGTTKfjK6fLMvD3rPb7fzxH/8xLpeLZ599FrjQOTHe4e9yA8KzxVYJBIKxqaqqUuS4otEoZ86cIS8vj7Nnz1JRUaHorweDQbRaLfn5+bhcLvr7+5XX6+rqMJlMyLKsDP40GAwMDAwwODjIK6+8gsViob+/n5tvvpmPPvpI0VDOzc2lvLxc+A+CG50ngL9P+vrzwNPAU1dlNZfARIHSsQLOOTk5vPbaa3zxi18c9l4yUx12nhzkLSgooK+vj3g8zurVq7Hb7YpPlVjDRHIW4yXnpuorie5OgWBmKSkpoaenR+nwPH/+PJ2dnQwNDaHRaJBlGbvdTldXFykpKYqme2ZmpqK3LhBcr4iA8iiMNsUzeZBLwilJtGTChSy3xWIZFuzYuHEjjz76KPv27SM7O5vMzEwGBwdZunTpsOsmm5lOOCP79+9nwYIF2Gw2AOW/L7zwwqQCyrNhEJ5oZRfcyEz2738yQefR9rNareb999/ngw8+oKKigkgkMmr1ykS2YCI7MdHh73IDwrPBVgkEgvFxuVwsXbqUvXv3cuLECfr6+lizZg2pqakUFBRQXV1NNBqlrKyM/v5+HA4HkiTh9Xrx+Xykp6djtVqRZZkzZ84QCAQwGAz09vYyODhIamqqolva0NBAUVERt956K+vWrQMgHo+LJJPghkeW5ae4hoLHozEyUKpWqzEYDOzYsWPUIZ39/f243W5qa2t59dVXUavVfPWrXx313lMZdv7RRx/x+OOPc/78edLS0jAajfT29qLX6zl69Chr164d5utcbrXwpfhKortTIJg5mpub2bx5M42NjTQ2NtLb20s8HicajSr+zNDQkDJnJj8/n82bN1+UfBIIrkdUV3sBs43EFM+9e/ei1+sxGAzU1NSwfft2nE4ncCFL9eqrr/L+++8rA6vcbjfl5eVKsEOWZX75y1/yxhtvkJ2dzfz589Hr9Upme6wqvoRTk6hgHo22traLMvAWi4X29vZJ/YwbN27E4/Hg8/mIx+P4fD48Hs+MtotO9WcWCK4nJvP3v2fPHr7yla/w0ksv0dLSQmtrK88//7xihxKM3M9vvfUWv/jFL0hJSWHt2rU0NzdjMplGTdaMZwsmYycShz+TyYTL5brocxIB4WSmEhCeDbZKIBCMT35+PgMDA5w4cQK/38/y5cux2WzccccdGI1GPvWpT1FYWEgsFsPtdtPW1oZKpeKee+7h3nvv5eabb2bx4sVUVFSwePFiDAaDstfnzZuHyWQiFAphMBgwGo2cPXuW8vJy5fNFkkkguPZxOp1UVlayY8cOANauXavse4fDgd/vp7q6mg0bNig+h0aj4dChQ7z66qv8wz/8A5/4xCeorKzkiSeeoLKycpi/NNJfGWvY+YEDB3jyySfx+XykpaXR2dlJU1MTmZmZaDQaTp48SSAQUHydifygyXC5vpJAILiy1NXV0d7eTjweJz09HbVaTSgUwmw2s2nTJubNm4dGo0GlUpGXl8fKlSvJysoS5xbBDYGoUB7BaFM8JUmip6dHCfb8+te/JhKJYLPZ6O7uxu12YzQa6e/vR61Wc/fdd7Nt2zZ+8pOf8OCDD/LNb36Tn//859hsNsxms2JcLrWKr6CgAJ/Pp1QmA/h8vovuMRazoVVKtLILbmTG+vuvr6+nsrKSuro69u3bR2ZmJgUFBQSDQerr61m8ePGwFsg9e/bwwgsv0NTUhCRJRCIR6uvryc7O5m//9m+BC1n1np4epcInucp5IlswGTsxXpXM5er8zQZbJRAIxu+WyMjI4Otf/zoqlYrHHnsMm82Gx+PBYDBgMBiU7q3GxkY8Hg+9vb3cddddww5byZV+S5YsUQZqaTQaFixYgNvtxufzUVhYiMvlQqfTEY/HhXaoQHAdkCjmcbvdhMNhGhoa2Llz56hDf5ubm9m2bRudnZ188pOfpKGhgf/6r/9i9erVE3Z+TWbYeVNTE9nZ2ciyTEdHBwaDgVgsRnd3N8uXLycej2O320ftIL1UhCayQDA7SXStv/322xiNRubOnUtTUxN+vx+LxUJpaSlWq5Vly5bh9XpZvnz5sFk14twiuBEQAeURjDbFU6/X4/V66ejoUALO+fn5lJaW4vf7OXfuHAMDA7S3t/PMM89QVFSE2+3m8ccf5x/+4R+QJGncoMhU27offvhhRTPZYrEoB7Kvfe1rk/45r3arlGhlF9zIjPb339LSQnNzM3PnzsXn8zE0NMT58+exWq1Kq1R7ezt6vR64EEx+8sknsdlszJ8/H5/PR21tLenp6SxZsoSTJ0+SmZlJU1MTbrebLVu2THjAGsnl2onpCAhfbVslENzoTCTRk52djcPh4POf/zyRSASTycRnPvMZduzYoVT/2e127HY7a9asoa6ujqKiogmTWIn7Ju9/n89HMBhUqgHFYU0guPbZuXMnTU1N2Gw2rFYrwWBQ6cZM3tvJhSfxeJxIJMJrr73Gli1bqKysnJIO8VhBXFmWsVgsaLVaWltb0ev1aLVa/H4/g4OD3H777XR0dEzrzy+S5wLB7CPh+7z//vukpKTQ2dlJd3c3sViM1NRUNBoNubm5wIUB5Dqdjry8PHFuEdxwiIDyCPLz82loaCAYDCoVyslGYmTA2WQysXDhQrxeL3l5eRiNRtRqNb/61a9Qq9XKfaezii+hk/zCCy/Q1taGw+Hga1/72qT0k2cLIhsvuJEZ7e+/rq6OiooKZfCUzWYjEAjQ1dWFyWQiGo3S0NCAz+ejsrKS1157TTk89ff3o9VqgQuDrFJSUmhoaKC9vZ3MzExkWb5II3CmnB3hWAkE1zbJEj3d3d00NjbS1dXFiRMn+NGPfsSdd97JqVOnhvk8MHbiePHixWzbtm3Uz0q2F4nDnM/nG+YniFkLAsHsZqpDh48ePYrValXOXUajEYvFcpHEV0IWIhaLkZ+fT11dnWJ3ptr5OFYQ9+zZs0oXaG5uLl6vl0AgQFpaGqtXr0an010RPVThKwkEw5mu4eWXSlVVFbFYTEksWSwWent7iUajGI1GJElCpVIRCATweDzMnz9fSFsIbkhEQHkEGzdu5Pjx4zQ1NSHLMpIkDTMSVVVVowaco9Eo//M//8OBAwc4evToRQer8ZhqZtrpdNLc3ExxcTFr166dcQM7HYhsvOBGJvH3v3PnTnbv3o3H48Hv96PRaDCbzVgsFqLRKIODg/T19dHX10dDQwNqtZqVK1fi9/s5efIkixYtYt++fbjdbhYtWoTJZMLv99Pa2kpKSgpqtZr+/n5UKhUNDQ309vbi8XgYHBykp6eHSCSiDLppbm6eNqftajuBAoFgenA6nbz66qtIkoRarcbn85GZmUl3dzcNDQ08+eSTPPvss1OqABSSNwLB9culDt2WJGnY19nZ2bS0tAxLKB05coQ33ngDrVbLd7/73WFnrUvpfBwtiJvcBTpnzhwGBgaQJIl7770XnU4nil8EghngUu3IdFJfX8/+/fvp7OwkGAwiSRIazR9CZzqdThncuX79erZu3Sr8E8ENiSTL8tVew5isWLFC/v3vfz/jn5vQyzl69CiBQABJkojFYqSlpVFaWkpXVxc9PT1YrVYkSeLs2bOcOnUKnU7H66+/zqpVqya8/3jBlvHeTzawomJHMEuQJr7k2mGm7E5CM7CpqQmr1cqZM2fo7+8nPz+fZcuWcfr0aQKBAIFAAL/fjyzLfPKTnwTg0KFDNDQ0EA6HicfjLF68mK6uLgYGBgiHw2RkZJCTk6NU1ixYsACPx8OCBQvo6+vj3Llz5OXlsX79egYGBjhw4ABr1qyhuLj4sm2KsFGCGULYnWlmpO9RUlJCdXU1p0+fRpIkzpw5w8DAAKFQiPb2doqLi3nkkUewWCxjVhwn7tnR0UFeXt60JZdE0kpwlbiu7A5Mv+1JaBMnB3Z9Ph8mk2lMO/H0009TU1NDRkYGer2eUCjE+fPnKSgoIBqN0tbWRjAY5Pjx49x2223s3r2bzMzMYfeYTt8jMZ+ivb0ds9lMcXEx6enp02rDBIIpcl3ZnonszqXYkUthNL+nubmZgwcP8tZbb9HX10csFlOuV6lUqNVq1Go1paWlfPazn+Wpp56atvUIBLOMSdmdGatQliTpK8AXgMXAL2RZ/sJMffZUKS0t5amnnhoW8MnMzESSJOrq6sjJyWHx4sU0NzfT29urDMGqqqoaNnl8NCbKuE30fnLrKUysESYQCGYnVVVVnDlzhr6+Pnp7e1GpVMiyjNfrpaenh8WLF1NXV8fixYtpbW1l5cqVeDwedu/ejUajIRqNEo/H0ev1uN1uJeicmpqq6A/abDb0ej0DAwPE43FlyMy8efNIS0ujqakJAJvNRkdHB6WlpZdtU4SNEgiuPUbzPbZv305FRQXLli2jtraWwcFBuru78fv9FBcX89BDD2G1WscdppvY84kDW2K48eV2QFztyiWBQDA6lzJ0e+vWrXR0dNDT04PX60Wn05GTk4PBYKCoqAiPx0NtbS0lJSX85Cc/uSiYDNPb0bBp06ZrSkZQILjeuBQ7MlX27NnD9u3biUQi5OTk0NXVxY4dOygoKODAgQP4/f5hwWRJkojH46SkpGCxWJAkiaNHj07begSCa5WZlLzoAL4HbAEMM/i5l8zIgE9qairp6emEw2EWLlzIv//7v3PLLbdw00038cYbbyjC7BPdc7xgy0Tvz4SBFQiuF2ZzIqu+vp6WlhbS09NJTU0lEomgVqsJh8O0t7ezdu1avvSlL1FaWqpk6g8dOkRqaio9PT3E43ElmHL+/HlMJhOyLBOJRFCpVGi1WvR6PZs2beLNN99Eo9FgMBjIyspSDmM+n08ZQOPz+ZS1JduU5Oy9RqNBkiRFKmO0Sh1howSCa4/RfI9oNIrL5aK0tJTVq1fjdDrx+/3k5eXx53/+59jtdnw+37gt5dMZ/E3YoldffRW9Xs+yZcuumja8QCAYnUuVnrjvvvt44YUXOHfuHJIkIcsyc+bMwWAwUFNTw9q1a/nEJz5BbW0tFRUVY95H2ACB4NrnUuzIVHA6nWzfvh21Wk1KSgqnTp2ira2N1NRUTp8+TSgUIhKJDPuehOSFRqMhOzubUCh0kVSPQHAjopqpD5Jl+beyLL8MnJ+pz7xcEgEfQAn4uFwuzp8/T1tbGyqVitdee419+/YpwWSn00llZSVPPPEElZWVFw2UcLlcmM3mYa+ZzWZlYvBE7ycMbDLTaWAFguuMRCLrhau9kJF4vV4Mhj/k1hIBYKPRyNatW9m2bZtyMNq4cSMej4eenh70ej05OTnMmzePZcuW4XA40Ol02O12cnJyMJlMRCIRBgYGMBqN5OXlUVhYyKc+9SnWrVuHw+EgGAwSCoWwWCxYrVZ8Pt+oTlsiGOT3+9Fqtezdu5eamhpl4vnzzz9/kY2bThs1kT0VCATTw2i+R3Z2Nt3d3USjUex2O1/4whe45Za7LIaSAAAgAElEQVRbuPXWW8nKysLn8+HxeMYdQpMcqE4Ef202m1KpPFmSbVGC2tpauru7geF+kkAguHok/BWfz0c8Hp+UnXA6nVRXV+NwOMjMzCQ7O1uZLXH69Gm2bdvG1q1bsVqtYp8LBDcAl2JHpkJVVRXRaBSNRkNraysAarWa8+fPKzIXarUaleoPoTJZllGr1ZjNZmKxGLIsc9ttt03LegSCa5kZCyjPVsYLWCQHfEKhEC6XC6fTSW1tLc899xxvvfUWubm5yiEs+cDjcDhGDbhMFGyZ6P0rbWAFguuJ2ZzISgRWBgcHGRoaIhwOMzQ0hEqlumg/l5aWYjKZ6Onpobe3F71ez8KFC5VJ4wlNL5PJREFBAXPnziU9PZ1AIIDJZOKxxx5TBmqVlZXh8Xg4f/48ZWVl5OXl4fF4yMvLu8imJAeDmpqasNlsZGRk0NTUNGZgKNlGdXZ28uabb/Lyyy/T09MzpYDwZOypQCCYHkbzPYxGIy0tLXz961/nN7/5Df39/dx8880sWbIEl8uFyWSasNJ4oiT5ZEm2RTabDZVKhdFopLGxERCJdYFgtpCQnjCZTJO2E4n93dHRQVpaGnq9nu7ubpqamjAajfT29iJJktjnAsENwqXYkangcrnIzs6mra2NlJQUUlJS0Ov1BAIB5Rq1Wo0kSUpQOeF3aLVahoaGWLZsGVu3bp2W9QgE1zIzKXkxKSRJ+hLwJbgwXfdKMl4rJkB7ezt+v5/BwUHC4TChUEjRIZVlmW9+85uo1WpFZ2u0llG32813v/tdiouLhw25gdGnnk80FV1MPRcIrg8qKipIS0ujsbGRM2fOEA6HUavVpKWlsXPnTkVaIi8vj1OnTvHcc8+xYsUKADIyMkhNTcXj8aDRaMjKyiIUCinTh2OxGMXFxSxZskQZXlFYWEhVVRV+v58777wTSZKIRqMUFRWxefNmmpubL7IpO3bsUOQrkquY29vb2bdvH16vF1mWh0lfJGzUzp07eeedd7Db7WzZsgWDwTClVnehxSwQzBwjfY+WlhYOHDhAW1sbWq2WhoYGzp07x9/93d/xxS9+cdL3TQSqw+EwjY2Nij7q0qVLp7S+ZCmd8vJyamtrMRqNeL1eJQmW8JMEAsHVZarSE4n97fV6UalU/O53vyMcDuPxeGhtbcVsNl+0z8VgToHg+uZKStjk5+cTDod59913MZvNRCIR3G63UoUMkJKSQjgcRpZlJEkiKyuLkpISTCYTt912G1u3bhU2RyBgFgaUZVn+KfBTuDAB9Ep+1lgBi507dxIKhcjMzESv11NXV6cETgDS09MpKipiaGiIF154QQkoJxyi7u5uGhsbaWtro7e3l/T0dNatW0d/fz/V1dVs2LBh1OANTC5gLDTCBILpZSYTWQkSAZw1a9awYsUKampqgAuB5r179wKwdu1afvazn/Hee+/x2c9+lhdffJG9e/fywgsv0NbWhsPh4Gtf+xqHDh3i4MGDdHZ2AjB//nzKy8spKipSPm8iuzHaAJpkDTOLxUIwGGRgYAC3261MY5dl+aJAcWlpKXa7nU9/+tPDpDRg8gFhocUsEEwP4wVekt8zGAwEAgH8fj979+6lubkZs9nM6tWrsVgseL1eXnzxRe64445J+yAbN25k+/btnD59GpvNhk6nw+v1Kh1fk71Psi2y2+2sXr2aY8eOIcsyJpNJJNYFgllCsk3RarXIskw0Gh036JvY39FolJqaGmRZprCwEKPRSDQapb29nUAgMOkB5gKBQDAeiTPYvHnz6OjooKWlhaGhIQoKCjh//kJTa6JyWaPRUFxczIMPPqgU6QgEgj8w6wLKM8lYAYuamhoWLVpENBqltbVVCSQDWK1WCgoK0Gq1yvCsBPn5+bS0tFBfX09qairBYJBYLEZ/fz9utxu73Q5Ac3PzuAZJBIwFgpllJhNZCZKTR3v37sVqtbJs2TIaGxux2WxIksRLL73EqVOn2LBhAxs2bECr1Y46fbywsJBgMIjNZhvW2XC5UjjJVYtlZWXU1NTgcrkoLCxEkiQGBwdZvXo1Op3uokDx5QaEr/RADoHgRiAReInFYrS3t3P48GFefvllHnvsMQoLCy8Kyng8HgoLC/n9739PXl4ey5cvVyQrrFYrXV1dU+oSKC0tJTc3l56eHsLhMBaLhWXLlo1qM8ZjZAW1Tqdj/vz5IoAkEMwikgO9fr+fN998k1AoRFFREVqtlp///Ods2bLlosq+jRs38q//+q/U1NSgUqnIz89Hp9ORm5uLSqVi8eLF2O125Xsm28GUCG7X1dXh8/mwWq0sXrxYVDMLBDc4iTNYSkoK27dvJx6P8+CDD2K323nttdcIBAIYDAZ0Oh06nY5FixYJeVGBYAxmLKAsSZLm489TA2pJkvRAVJbl6EytYSRjBSwGBwepq6sjLS2NhQsXcvz4cUWjtKCgAL1eTyQSQZKkYQGTjRs38pWvfAW1Wo1er8fv96NWq8nPz6exsRG73S4q7ASCG5zRqgUTwVeVSsWRI0cUm5SXl8fKlSu59dZbx7UbV0oKJ/m+CamM3/3ud8oAwaVLl2K324nH4xetbyz7qlarqaysnLBNdSL5H4FAMDFVVVXEYjHq6+sxGo3k5OTg9XrZvn07q1atwmazEQ6HOXDggCJHkZKSwuc+9zm6u7sVnXa4MEvCbrdPSv842c69//77rFy5UhleDIxqM8ZDyH0JBLOfRKA3HA5TVVVFSkoKarWaU6dOYTQa0el07Nq1i0OHDvHYY48pyfHS0lK+9rWvMTg4yIkTJ0hJScFoNJKVlUV5eTlZWVnD7MVkEtbJybQzZ86gUqnweDykpqaKamaBYAIkSbIBzwObgV7gcVmWd41ynQR8H/g/H7/0PPBNObkab5ZSWlrKM888Q0lJCY2NjajVavLy8vjhD3/IoUOHOHr0qDJ474EHHhD2QiAYg5msUH4C+Pukrz8PPA08NYNrGMZYAQuTyYQsy3i9XrxeL8uXL+fo0aP09/cTi8UIh8P09/djNpvZvHnzsOCIzWYDLuiNmkwmbDYbmZmZ+Hw+QFTYCQQzyWxLZI3VpqnX65Xgq1qt5tChQ0qwduXKlfh8vgntxpXqbBjtvidPnsTn8ykDsXQ63UXrS9hXt9uNy+Wiq6uLoaEhHA4HRqNxwjZVEUASCC6dREA3IeFltVoxGo3AHyqNjxw5wsqVKzl8+DA6nY6mpibKysqorq7mqaeeYvv27Xi9XqxWK6FQiMHBQYqKiia0RSPtXENDAzU1NWzYsEHp1LoUX0h0bwkEs4+Eramvr2ffvn1kZGQQDAYJBoNkZWXR3t7OwMAAAwMDaLVasrOzUavVbN++nblz5ypVy3fccQc7duygsrISv98/LBmd7AM5nU5aWlo4dOgQubm5lJeXY7fb6e/vR6PRKGeylpYWHA4HHR0dGI1GjEYjwWCQjo4OlixZIuYxCK4K15D294+BIcAOLAVelyTppCzLDSOu+xLwaWAJIAN7gFbgP2dwrVPmpZdeoquri6985Ss89NBDF70/mgygQCAYnRkLKMuy/BRXMXg8GiMDFmq1GoPBQGdnJ11dXXR1dWGxWJgzZw4lJSWKjrLX68VkMpGfn8+LL75IRUUFxcXFSkC6oqKC0tJSuru7qa2tpa2tjUAgwIsvvohWq+Wxxx672j/6mFxDDzqBYDLMqkTWWG2awWAQj8dDW1sb1dXVDA0NkZWVxV133TWrBk45nU46Ozvxer3YbDYCgQDV1dWUlZXxjW98Y9i1paWlbNiwge3btxONRrHb7fT29tLV1UU4HEalUk04aE8EkASCqZMc0J0zZw7vvvuu0r5pNpuVSmOv18uJEydQq9UcPHgQr9dLRkYGJpOJF154gczMTI4dO4bH46GoqIiioiLUavWEbZ8j7dyyZcuorq7m2LFjbNmyRXQbCATXCclVwC0tLQB0dXURiUQIhUKcP38en89HPB4nJSVFCejqdDqMRiMPPvgg7777LuFwmDvuuAMYvzsp8XmJAX5er5eDBw9SUVFBf38/siwzMDCAy+Xi8OHDGI1G0tLSFD9Cr9fj8/lEt6jgqnCtaH9LkpQK/DGwSJblAeCgJEmvAg8C3xpx+Z8D/1eW5faPv/f/Av8fszig/MMf/pBHH32U1atX85d/+ZfKQHOBQHBp3PA7KBGwSBh5q9VKLBajs7OT1NRUFi5cSFpaGmVlZRQVFSmOjs1m4/jx4/T19VFfX4/ZbMZut1NRUUFdXR1ZWVlkZWWRn5/P7373O3JycrDb7TgcDqqrqyksLJxVDw+4dh50AsFkmQ2JrOQkzf79+zGbzcTjcaxWq9LK+dFHH9HX18cvf/lLtFotS5cuRa/Xc/ToUW677bZZswerqqooLi7G4XDQ2NioaBLm5+ePur7m5mY2bNigBJZeeeUVTCaTIgEEYtCeQDDdJAd0FyxYwMmTJ4lGo3R2dqLVapVK47KyMl555RWampoIh8OsXLkStVqN1+ulra2NoqIiHA4HwWAQi8Wi+EBj2aLkquiCggIWLlyI3W7Hbrezfv16jh49KroNBILriIStOXnyJGlpaRQXF9PY2KjMkDl37hySJCFJEqFQCEmSMJlMuN1u/H4/gUCA733ve3z7299W7jled1JlZaVi28xmM42NjXR1ddHe3s78+fMZHBxU5thkZ2fT19dHW1sbVquVrKwsQqEQFotFdIsKrgqT1f6eBZQBMVmWm5JeOwmsG+Xamz5+L/m6m67g2i6ZeDzOt771LX7wgx/w6U9/ml27dolgskAwDYhd9DEJI//mm29SV1dHbm4uFRUVqNVqAoEAtbW1pKWl8cMf/pD29naGhoY4e/asMmgiESApLi4mEAhgMplwuVwEAgHuu+++YQ8Kn883Gx8e19KDTiC4Jkiu3vnwww+pr69HrVazePFiDAYDtbW15Ofn895779HQ0IDdbqewsJCUlBTWr1+PwWCgubmZXbt2EYlErnrXQLLWcyIgPJ4W6kidQ4vFQiAQUCSAQMgACQTTTfK+s9vtbNq0iXfeeUfRPtZoNNTV1fGZz3yG06dPE41GmTNnDv39/QwNDREMBsnMzMRmsxEKhYhEItjt9nGHCY+sivb5fNTW1rJ69WrsdjsGg4G7775bTEgXCK4jErYmIY1jMBhwOBwcP34cSZJQq9UYjUa8Xi8qlYqMjAxUKhXd3d3EYjHy8vKw2Ww0NzcP82vG6k4aaduSZzhEo1FcLhepqakYDAZyc3MVmY22tjbUajWxWIyioiLRISG4KlzusOoZJA3oG/FaH2CaxLV9QJokSdJIHWVJkr7EBYkM5syZM32rnQSyLPPQQw+xc+dOvvzlL/PDH/4QtVo9o2sQCK5XbpiA8kRSDgkjP3fuXD7xiU9w++238+6773LixAluueUWtmzZwsDAAL/97W9ZsGABdrsdl8tFY2Mj8+fPJxQKAReCI4sWLVIOTU888cS18vBQpiAntFxHG4QhEAgmh9Pp5Lvf/a5SfSzLMjk5Objdbj788ENuvfVWVCoVhw4d4pZbbkGr1VJcXEwsFkOWZZqamigvL6epqQm32620il/NroGxBu2NFRAeeX15eTnV1dVYrVbi8bhofRcIrgCJfRcOh5VOAovFQjgcxmw2YzAY8Hg8/NM//RMajQaTyYTD4cDhcPDOO+8Qi8UoKytDkiQMBgM2m42jR4+O+5kjq6Jra2tRqVR8+OGH9Pf3U1dXR1FREZWVlUJKSyC4TkjYmoTOusFgwOv1MnfuXLKzszl9+jQGg4FoNEosFgMgEomgVqux2+2UlJTg9/sVvwYY96w2kQ+S0FUGMJlMFBQU4HK56Ovr4+zZs2RmZpKWlsbWrVuFDRLMOFP1oa8iA4B5xGtmwD+Ja83AwGhD+WRZ/inwU4AVK1bM6NA+SZJYsWIFixYt4pvf/CYXZgkKBILpQHW1FzATJCpn/H4/DodDcV6cTicAvb29+P1++vv7WbVqFZ/5zGfIyclBq9Vyyy238MlPfpLc3Fw6OjqwWq2cP38eSZKU7NqZM2dIT09XtE6T9QUTD49kZuPDw+l00traSl9fHxaLhWAwSG1tLS0tLbNurQLBbCdhc7q7u4lEIqhUKnp7e9Fqtcp+am1tpb+/H6PRyKpVq/iLv/gLBgYG0Ov1GAwGZeid1WplaGhI0Ry22Wzs2rWLyspKnnjiCSorKxVbdqXZuHEjHo9H0UQczeaNd71Op6OsrIylS5ficrkwmUyzRs5DILhe2LhxI83NzVRXVxMIBNDpdLjdbjIzM1m8eDGtra243W5MJhMmk4mUlBQ6OjpwuVxotVoyMjIYGBjA7/fjdDppaGjg7Nmz49oZl8uF2XzhTGm321m9ejUqlYra2lp+85vfEIvFyMnJucj/EggE1y6JZ3xeXh4DAwN4PB76+vrIyspCpVLxmc98hsLCQubNm4fJZCIjIwNZlikqKuKmm27C4XAM82vGO6slf95oPsjGjRvRarXKvJtAIEAgECA1NZUVK1bw13/919x1110Eg8Gr+BsT3MhM1Ye+ijQBGkmSkp3zJcDIgXx8/NqSSVx3VXC5XBw4cACARx99lG9961simCwQTDM3REA5uXImOShTVVVFa2srq1at4mc/+xmdnZ3DjHx3dzdLly5V7uP1eiksLKS/v59gMIjJZGLu3LkEg0EkSVI0lauqqhQH6Fp5eFRVVVFRUUE8HleqDFQqFXV1dbNurQLBbCdhcxItl6mpqej1erxeL2q1GofDgdvtpra2FpvNpiSdElU+Cc1Sn8+HJEnDqhmCwSDvvPPOuIeuS8XpdI4bqE5oGyYkfSYKCI92/Te+8Q3+/u//nmeffZZt27aJYLJAMM2UlpaSn5+vJKMMBgN5eXnk5eXxq1/9ilOnTimBlkgkQmZmplKlvGLFClQqFW63G6fTyeDgILIsk5ubO66dGS15PjAwgNVqZcmSJaSnp3P48GHC4bDifwkEgmubxDO+qKiIkpIS0tPTyc/PJy0tjfnz5+N2uwkGg8pAXpfLhcFgQKfTEQwGKS8vBy50bh49enTMs9rIzxvNByktLeWxxx4jFovR1dWFTqdTEvTLli0b854CwUwxVR/6aiHL8iDwW+AZSZJSJUlaDdwL/HyUy/8b+FtJkvIlScoDvg78bMYWOw4ffvght99+O/fff7/SSS4QCKafG0LyYizNot///vc89dRTRCIR3njjDbKzs4cNgdi8eTMGg0H5HqvVitfrZeHChcqk4LS0NO666y6MRiM2m02ZSJzclj7WcInZhMvlori4WBlykWiRtVqts26tAsFsJ2FzysvLOXHiBIODg1gsFqUC8Pz58/T397Nlyxa+/vWvU11dDUBZWRk1NTUArF+/nhMnTuDxeNiwYYNy7xMnTpCdnT3tWueTHco5lrbhWEz1eoFAcPlEIhG2bNmCSnWhbqCmpoZDhw7R2dmJxWJRKgpjsRgdHR3E43EGBgbIy8sjHA4TCoXQarXodDqsVivr1q1Dp9ONaWcSA4vhgn917Ngx4IJ9MhqNSkVQY2Mja9asEVJaAsF1wshn/J49e3j66ad5//33FR12l8uFXq9n6dKl9Pb2Eo1Gh1UJtrS0cPbsWWpqarDZbJSXl2O320eVCBzPp9i0aROFhYVUVVXR0dGBz+fjjjvuUGY+wOyVHRTcGFxDPvGXgReAHuA8sE2W5QZJktYAb8qynPbxdT8BioD6j7/+fx+/dlU5cOAA99xzD3q9njfeeAO9Xn+1lyQQXLdc1wHlhG7y+++/T0NDA8uWLVOcivfee4+dO3dit9vZu3cvCxYsABhm5BMBFrjggOTl5dHS0sKaNWsoLi5W9D/1ej1Go3FYgMftdvPd736X4uLiqz5IazIkqosSQy7gwvBAk2k0/X2BQDCSZJ32lpYWwuEwpaWlbNq0iT179hAKhcjMzKS9vZ1wOMz999/PM888Q2lpqXIA8vv9VFRU0NLSwp49e0hPT8dut6PT6RTN4e7ubrZs2TLss6fjgCSGcgoE1w/JWo2hUIjf//73nDt3juzsbHJycujr68Pj8SidEIlOq8LCQiUxHgwGMZvNiu803gDOkcnzUCjE+vXraWpqIhgMYjQalUT8bJT9EggEl4/T6aS6uhqDwYDFYqGtrQ2Px0NGRgbLly8nMzOTjRs3Khrrp06dor+/nwMHDpCbm4terycUCikDPXU63ZRtRXLArrKyEr9/uOyrsD8CwcTIsuwBPj3K6we4MIgv8bUM/N3H/2YFL730Ep///OcpLCzkrbfeorCw8GovSSC4rrnuAsqJoE59fT0tLS1UVFRQVlbGyy+/zL59+7DZbMybN4/u7m7mzZtHdXU1eXl5Yw7tSz4gFRUVsXnzZpqbm4dVG+/YsUPRDgTo7u6mvr6eSCTCunXrpnWQ1kTDBS+VkdVFYliWQDB5Rlb3hsNhRbOrvLwcrVZLXV0dAJ2dnfziF7/gT//0T5XvTxyAEvdZt26dsg9bWloIBAL4/f5ROydg+AHpUm3ENTR9WiAQTEDyMz0QCNDZ2cmaNWtYunQpBw4cULqQNBoNoVAIWZaJxWJ89NFH5OTkIMsy0WiUaDTK7t27aW1tpby8nKKiogk/W5ZlLBYLAwMDlJeXU1tbq7yXkpIifAuB4DolkZhOSUnB4XDgdDrR6/UMDg5SX1+PVqsFYP78+fT09NDW1oYsy6xZswaz2UxtbS1GoxGj0cixY8eYP3/+ZdkKcbYRCG48Xn/9dZYvX86rr75KRkbG1V6OQHDdc10FlJODOj6fD7VazZEjRwgEAkiShE6nw+fzcfbsWW6++WYef/xxJZg8Xqv3yGDMpk2bhn09cmprY2MjKpWKnJwcRbMLZq4l/VK4VqQ5BILZyMjq3sS+aW9vR6/Xk5mZyXPPPUdpaSn9/f3DElDj3cdisVBcXIzJZGLbtm3AxZ0TyQeky7ER19D0aYFAMAGlpaXce++9HD9+HL/fzzPPPMPdd9+tJK4efvhhOjs76e3txWAwkJubi0ajob29ncLCQrKzszl27BgOhwOz2awM8tu8efOonzdWUm3NmjXcfvvtnDhxgp6eHjZv3szWrVuFbyEQXIe4XC5l8N6pU6dIS0tDpVLh9Xrxer2kpaXR29vL+fPnWbx4MWvXrlWS2SqVitWrV9PY2KgM1rvc84042wgENwbxeJze3l6ys7P56U9/SjQavaj4RiAQXBmuq4BycjCmo6OD8+fP89FHHxGJRLBYLEQiEfr7+7ntttsYGhri8OHDLF26dEqt3qNV/43MgHd1daHRaJRhE4nXZ3tL+jWk6yQQzCpGq+4tLi5Gr9dTWFjIX//1X7N69WpKS0vHDCaPdZ+RtmO8A1JlZeUl2whRySMQXD8cOXKEu+++m29/+9s8++yzw94rLS1l69at/OY3v6G3t5eenh5F4kqlUuHz+YhGo+Tm5pKamsrg4CCSJLFmzRqam5svSqrD+Em1kpISNm/ePOulvwQCweWRlpbG9u3bicfjxONxtFotgUCAlJQUZFlGkiS8Xi+ZmZnU1dXxpS99iaqqKiWZnZDdS9gjcbYRCAQTEQ6H+eIXv8i7777LsWPHMJvNSjeEQCC48lw3AWWn08mrr74KgEaj4cyZMwQCAeLxOENDQ/T09CDLMlarlfT0dHw+Hx988AGVlZXs3LmTgoICFi5cqOgHjxYAHq/6LznAk52djcPhGDYEYjoq/URLukAw+3A6nbS0tHD48GFycnKUYTJ9fX0cP36c733ve2zZsoUlS5ZMeK/JVgmPdUC6HBshKnkEguuD3bt387nPfY68vDzuueeeUa8pKSnh1KlTWK1WMjIy6OzsZGBggMzMTKU6cOHChaSnpxMIBDAYDBQXF49pS8ZLqo0MaAsEguuPt956i+9///v4/X5yc3P5/9u79+iqynPf49+HhITcgARDSEIFDUFQVKBYxcKRYSHeoFCsrY0tjl6ghXppT2mPu4NTEd329NTRY7WWI7toS7e2eKFQteoRtbUVpVtrxVFKCSoUEoiJJCTkQmCt9/yxLg2BwCJk3eb8fcbI0KzMtebz5vJjrme+850DBw7EOUdnZyfZ2dlkZmZy6NAh6uvrGTNmzFE3/dbJ7N7Fa6lDES84cOAA8+fP56WXXuL73/++7v0kkgSeaChHGr2RO3ju3LmTgwcPEgwGOXz4MKH14iEjI4OMjAwaGxtxzrFjxw5GjRrFmWeeSXNzc/QmECUlJcdt4pxohvDixYuj/8BH6mlubu7Xg6NYm006+BBJjMjf+siRI9m/fz9NTU28+uqrlJaW8sILL1BbW8sll1zCj370o5gOck53lvDpLluhmTwi6e3BBx9kyZIlTJ48mWeeeYbhw4cfd7sdO3Zw7rnn0tjYyJEjRygrK+Pw4cN0dnYyaNAgRo4cGZ1d2N7ezqRJk06YJVoyR8S/fv3rX/PFL36RzMxMvvKVr/D666+ze/duhg4dSkFBAQMHDiQjI4NAIEB+fj7l5eXR9dh1Mrt38VzqUCTd1dbWcvXVV7N161bWrFnDF77whWSXJOJLA5JdQH+INHonT55MR0cHHR0dZGZmcuTIEYLBIBCatZydnQ2EGs4dHR1ccMEFDB06lOHDh7Nr1y527NjBhg0bqKmpYf/+/cycOfOo/dTW1h5zufrgwYOpq6s76rHIwVFBQQG1tbUUFBT0yz/+M2fOZP/+/TQ3NxMMBmlubj6mzsjBR2trKyNHjqS1tZXVq1dTU1NzWvsWkWNFsqeyspJp06ZRWFhIc3Mzzz//PLW1tVx11VXMmzePNWvWxPQ3eLrZEUtGJEtNTQ0rV65k2bJlrFy5Upkk0s+2b9/OkiVLuPLKK3n55Zd7bSZD6Hhm2rRplJSUMHbsWM4//3zGjx9PWVkZ9913H9OmTaO+vh7nHFOnTiU7O/uEWZLK2SMi8dPV1cXXvvY1MjIymDJlCnQiGV0AABmOSURBVAcPHmTGjBmMGTOG1tZWhg4dSiAQoKOjg9zcXIYNG8aWLVuOyobKykoWL17MnXfeedQEHb/rPpEpck+eoqIiNm7cmOzSRJLu1ltv5b333uN3v/udmskiSeSJGco9b+jw4Ycfsn//foLBIKNHj6alpYW2tjaOHDmCmVFaWsq5555LRUUF9fX1bNu2jfLycpqammhsbGTLli0sXbr0mAOaU5mBE4+ZfrGcxY/3Ossi8i/dL/MuKSlh+PDhBINBcnNz+exnP8v48eMBMLOY/wZPJzv6Y6ZPPK5w0Cwb8RszKwJWA1VAI/BvzrlH47GvyNqkY8eO5cUXX2TatGlkZp748K68vJzW1tboTbCam5vJysqiqqqKWbNmMWvWLKqrq9m4cSN1dXUUFBScMEs0y1DEX2pqanjhhRd45513yMvLY8yYMRQUFLB161ZaWloYOXIkmZmZZGVlMXjwYAYNGsSwYcMoLS09arkL6Z2WOhQ5VuSYZ+XKldTV1cW0pKCIxI8nGsoDBw7kiSeeYOfOnbS1tREIBDhw4ADZ2dk0NTWRkZEBhJo6nZ2dAIwYMYKWlha2bdtGXl4eOTk55OfnM3bsWC688MKjbjwTabBs2bKF9957jwsuuICKioqkrPN1smaTDj5EEqf7SaaGhgYefvhhiouLOeuss6LNZEjs3+DpNKTj1fjViS7xoQeALqAEmAg8Y2ZvO+f+1p87OXjwINXV1SxatIjZs2czY8aMmJ4XWV6nqKiI6dOnR49nqquro9ucapZoyRwRf9i+fTs33ngjeXl5FBcXU1hYyK5duxg4cCD5+fkMHjyYxsZGcnNzKS0tJSsri8LCQsaNG0d2drbWOY2RlhISOdqTTz7JmjVrePzxxykuLqa4uDjZJYn4Xlo3lGtqanj00UdZu3Ytu3fvJjMzE+ccBw4cICMjI3oDiEAgwIABAygoKCAYDLJz5062bt3K0KFD2bt3LyNGjDhqncDuzZ/uDZYLL7yQ/Px8tmzZQnt7OxMmTEi5GTg6+BBJnEhTZvfu3fz85z8nGAwybNgwcnJy+MMf/kBzczNDhw6lrKwsul5gd7HMBk7kmujxavzqRJf4iZnlAdcCE5xzB4E/mdlvgS8At/XXfurr67nmmmt46623mDdv3ik9VzOKRaQvurq6WLBgAZs3b2b69OkcOHCAs88+m9dff53MzEyKioro6uqKLnexa9cuJkyYQEdHBy+++CLnnHMOS5cuBXTPl5M53ftqiHjJ/fffz6233soll1xCe3s7WVlZyS5JREjjhnKk0fuPf/yDrKwsBg0aREtLC11dXWRnZ3PmmWdiZmRlZdHU1MSRI0fIycmhoKCArq4uurq6KC0tpaWlhfr6ekaMGMGkSZMoKSmhubk52oDt2WCprKykuLiYgoICFi9enMxvwXHp4EMkMSJvhN555x2ef/55cnNz+fa3v82oUaN48MEHo7nR1NTEu+++S1VV1THPX716NYFAgNraWjZt2sT69etZunTpUVdHJHKpiHg1fnWiS3xmLBBwzm3v9tjbwGXdNzKzRcAigDPPPPOUdlBTU8OVV17J3r172bBhA7Nnzz7lIjWjWERORWtrK9deey2bN29mzpw5XHPNNbzyyis0NjYCoX/Xd+/ezZAhQxgyZAhjxoxh//795Obm0tzcTGFhIaWlpQDccccdPP/885SUlDBx4sToPV+0FNa/6MSfCASDQW677TZ++MMfMm/ePB599FFycnKSXZaIhCWsodzf6wlGGr2HDh3iyJEjdHZ20tXVRV5eHuPHj6ejowPnXPQMeV5eHhA6s15QUBCdubxixYpow2bw4MHRG8lEGrDpNrNOBx8i8Rdp9DY0NPDcc88xYsQIPvWpT3HDDTewceNGpk+fTl1dXfQN1HnnnXfUMjoQyrBAIBBdf7C0tJSmpibuueceRo8eTWVlZcKXiohX41cnusRn8oEDPR47ABx1nbdzbhWwCmDKlCku1hffs2cPl156KQAvv/wyF1988WkVKyJyMoFAgJkzZ/Lmm2/y+c9/nvPPPx8z44wzzuC1117DzBgyZAhFRUW0tbWRn5+Pc47y8nIuuyx0Li0YDLJly5bohKARI0YA8Nprr/Hxj388esM5vWf5F534E7/71re+xb333suSJUu47777okuZikhqSOQM5X5dTzDS6C0sLGTv3r3k5eVhZuTn5xMIBBg4cCBnnHEGe/bsAeDQoUOYGV1dXRQXF5OdnU1ZWdlJG7DpOLNOBx8i8RVp9JaXl3P55ZczZ84cOjs7ozlSUVFx1N9gMBg85iRUbW0ttbW10TXcAQoLC9m3b1/0DVWiT2jFq/GrE13iMweBwT0eGwy09seLl5eXc9NNN1FdXa2/IRFJiIyMDG655RaKiooYM2ZM9FihoaGBkSNHUltby+HDh8nIyKCiooIPPviApqYmJk+eHH2NlpYWmpqaGD16NIcOHaKwsBAzA2Dbtm1Mnz49ZSfsiEhy3HjjjZSVlbF06dJoXohI6khIQzke6wmWl5ezd+9eGhoayMvLIzs7m0AgAIQuySoqKuLss8+muro6usZpZC3Trq4uzjrrLGbOnAmcuAGrmXUi6au/r4wAOHLkCI899hhz584lOzub6667DoCsrKzoOoCxnIQqLy9n06ZN0cs/ATo6Ohg+fDh1dXXRbRJ5QiuejV+d6BIf2Q5kmlmlc64m/NiFwGndkO+hhx5i6tSpjB8/nttvv/20ixQROZlNmzZRX18fvQorInKs8M9//pOPfOQj0Suwtm3bRlNTE0OHDmXs2LFkZ2cTDAaj758KCwsZPHgwhYWFdHZ2kpOTQ05ODs3NzSk/YUdEEqOuro61a9fyzW9+k4kTJzJx4sRklyQivUjUDOWY1hM8Feeccw7Lli2jpaWFRYsWkZOTw9///ncGDRpEWVkZl19+eXT2zrRp03jkkUfYvHkzZsa0adNintmjmXUiaa1fr4xoa2vj+uuv5/e//z3Dhw/nE5/4RPRrkTdCsZ6EmjlzJuvXr6epqYnCwkI6Ojpob2+noqIi+oYqGSe01PgVOT3OuTYzWwesMLOvEMqeucClfXw9br/9du68804WLlzIqlWr+rNcEZHjWr9+PZ/73OeorKxkzpw5ZGb+621j92OFyPKCQPReNAUFBcycOfOY908bN26kpaWFcePG8eqrrwKhq7iysrI0YUdE2Lp1K1dddRX79+9n/vz5jBo1KtklicgJJKqhHNN6ghDbTWr+8pe/UF1dTSAQ4JZbbiE3N5f58+f3enfgyspKli9f3ufi1WARST/9fWVEQ0MDs2fP5o033uCOO+6gvb2d5ubmYxq9sZ6EqqysZOnSpdxzzz3s27eP4cOHU1FRQUZGxlFXT+iElkhaWgI8BHwAfAgs7suJrMOHD/PVr36Vhx9+mC996Us88MAD/V2niMgxVq5cyU033cRFF13EU089dVQzubsTnfju7f1T5N41U6dO5a9//Sv19fVUVVVxww036PhGxMf++Mc/8slPfpJBgwbxyiuvqJkskgYS1VCOeT3Bk92k5oUXXmD+/PkUFRXx8ssvM378+HjUKyLpL+YrI052Iuvdd9/lyiuvZM+ePaxbt465c+dSU1PTa6M31pNQs2bNYvTo0WzcuJG6urroDOfuz9UJLZH045zbD8w7nddoa2vjuuuu49lnn+V73/sey5cv1/qBItKr/ljmyznHsmXLuPvuu5k9ezZr164lNze31+1P9cR39+1bW1upqqrqdUKQiPjHunXrqK6uZvTo0Tz33HOMHj062SWJSAwS1VDut/UEnXOMGzeODRs2aJ0tETmRmK+MONmJrMzMTHJycnjppZeYOnUq0H+NXjWMReR4MjIy6OzsZNWqVSxcuDDZ5YhI6jvtZb7MjM7OThYuXMhPf/rTXmcmd3eqxzE67hGRngKBABdddBHr169n2LBhyS5HRGJkzh3TO4nPjsx+DTggsp7g74BLT3SQM2XKFPfGG2/gnOPNN99kypQpQGitrQEDBiSibBE5uZScMmdmk4BXnXO53R77FjDDOTent+dFcgfgzTffZNKkSQwYMEC5I5JaUjJ3+qp77uzYsYNhw4ZRWFio3BFJLSmbO+FlvpoILfO1PfzYL4Fa51yvy3xFsqe1tZXa2lrGjRtHMBjEzHRFhEjq8NQfYyR3gsEgb731Fh/96EcB9XhEUkxMuZPIv9glQA6h9QR/RYzrCQYCAb7+9a/zsY99jNdeew1AQSMisYheGdHtsZivjHjooYe4+OKLuffeewHljojE3+bNm5k6dSpf/vKXAeWOiMSst2W+zuu5oZktMrM3zOyNhoYG9u3bx2WXXUZVVRWdnZ0MGDBAzWQRHzKzIjP7jZm1mdkuM6s+wbbLzeywmR3s9nF2rPvq6upiwYIFXHLJJWzduhXQMY9IOkrUkhd9Wk8wGAzy6U9/mvXr1/Od73yHiy++OE7ViYjXOOfazGwdsMLMIldGzAUuPdlzV6xYwe23305VVZUuNReRhHj66af5zGc+Q2lpKT/4wQ+SXY6IpJc+LfM1YcIEN3XqVD744AOeeOIJBg0aFP9KRSRVneqyOWudc58/1Z0EAgGuvvpqXnzxRe6++27dE0skjSVsyYu+yM/Pd+3t7fz4xz/m5ptvTnY5InJ8KTuNJXyDmoeAWcCHwG0nu0FNcXGxa2xsZMGCBfzsZz9j4MCBiShVRE5NyuZOX4waNcrt2bOHyZMn8/TTT1NSUpLskkTkWCmbO31d5iszM9MNGzaMZ555Jrq0oIiknLhnz6kum2Nmy4ExfWko5+bmusOHD7N69WoWLFhwmpWLSJzElDsJm6HcF+3t7Tz++ONce+21yS5FRNJQX66M+PDDD/nud7/LXXfdpUs+RSQh9u3bxxVXXMFjjz1Gfn5+sssRkfTTpxugZ2ZmsmnTJioqKuJeoIiktN6WzbnsBM+ZY2b7gb3AT5xzK2PZ0aFDh3j22Wepqqrqe7UikhJSeoaymTUAu06y2RlAYwLKSSUas3+kw7gbnXNXJruI/nKS3EmHn0c8aNz+kS5j9lPunEi6/LxOhdfG5LXxgPfGFOt4Ujp3+nID9B7Z47Wfa3deHZvGlV76Oq64Z4+ZTQced86N6PbYQuAG59yM42x/LtAM1AMXA08C/90596teXn8RsCj86TnAP/p1ALHz6u9WLDR2/+rL+GPKnZRuKMfCzN5wzvnqGi2N2T/8Ou5U5defh8btH34cczrz4s/La2Py2njAe2Pyynj6ssxXj+d74vtwPF4dm8aVXpI5LjP7Pb3PNn4VuJk+LJvTbdvbgIuccyl9ablXf7diobH7c+wQ3/Gn9JIXIiIiIiIicmJ9WeZLRPzheLOMuwuvoXzKy+Z03wUpvM68iMTHgGQXICIiIiIiIiIiieecawPWASvMLM/MPg7MBX55vO3NbK6ZFVrIx4BbgA2Jq1hEUoEXGsqrkl1AEmjM/uHXcacqv/48NG7/8OOY05kXf15eG5PXxgPeG5PXxtNXXv4+eHVsGld6SfVxLQFygA+AXwGLI2uwm9l0MzvYbdvrgR1AK7AG+IFz7hcJrrcvUv1nEE8au3/Fbfxpv4ayiIiIiIiIiIiIiCSGF2Yoi4iIiIiIiIiIiEgCqKEsIiIiIiIiIiIiIjFJ24aymRWZ2W/MrM3MdplZdbJriiczyzaz1eGxtprZW2Z2VbLrShQzqzSzTjP7z2TXkihmdr2Z/T38O/6umU1Pdk1+5be8AWUOKHeUO6nLa5lkZjeZ2RtmdsjMfp7sek6XV/PTzP7TzPaaWYuZbTezryS7pv7gx6zvzmt5EuG1XInwar6AdzMmwu9Zk0xezblYeDULY+HlvIxFIjI1s79fMIEeALqAEmAi8IyZvR1ZON6DMoHdwGXAP4GrgcfM7Hzn3M5kFpYgDwD/lewiEsXMZgE/AD4L/BkoTW5Fvue3vAFlDih3lDupy2uZVAfcBVxB6IZA6c6r+fl94MvOuUNmNg74vZm95Zx7M9mFnSZfZf1xeC1PIryWKxFezRfwbsZE+D1rksmrORcLr2ZhLLycl7GIe6am5QxlM8sDrgX+p3PuoHPuT8BvgS8kt7L4cc61OeeWO+d2OueCzrmngfeBjya7tngzs+uBZuDFZNeSQHcAK5xzr4d/3rXOudpkF+VHfswb8HfmgHJHuZO6vJhJzrl1zrn1wIfJrqU/eDU/nXN/c84dinwa/qhIYkmnzadZH+XFPInwWq5EeDVfwJsZE+H3rEkmL+dcLLyahbHwcl7GIhGZmpYNZWAsEHDObe/22NvAeUmqJ+HMrITQ98HTZ9XMbDCwAvhWsmtJFDPLAKYAxWa2w8z2mNlPzMxvZxRThe/zBvyTOaDcUe6kPGVSmvFSfprZT82sHdgG7AV+l+SS+syPWX8cypM056V8AW9lTISyJumUcwJ4Ly9jEe9MTdeGcj5woMdjB4CCJNSScGY2EHgE+IVzbluy64mzO4HVzrndyS4kgUqAgcCngemELsuZBCxLZlE+5uu8Ad9lDih3lDupzfeZlE68lp/OuSWEftemA+uAQyd+RkrzY9b3pDxJY17LF/BcxkQoa5JLOSeezMtYxDtT07WhfBAY3OOxwUBrEmpJKDMbAPyS0BpANyW5nLgys4nATOD/JLuWBOsI//d+59xe51wj8CNCa/5I4vk2b8BfmQPKHZQ76cDXmZROvJqfzrlA+JLhkcDiZNfTFz7O+p6UJ2nKq/kC3siYCGVNSlDO+ZyX8zIW8czUdL0p33Yg08wqnXM14ccuxONT183MgNWEZpJd7Zw7nOSS4m0GMBr4Z2jo5AMZZnauc25yEuuKK+dck5ntIbTGjSSfL/MGfJk5oNxR7qQ+32ZSOvFJfmaSvuubzsCHWX8cypM05JN8gfTOmIgZKGuSTTnnYz7Ky1j0e6am5Qxl51wboenaK8wsz8w+DswldNbBy1YC44E5zrmOk23sAasI/cJPDH/8X+AZQnco9bqHgZvNbLiZFQLfAJ5Ock2+5OO8Af9lDih3lDspzouZZGaZZjYIyCD0RnuQmaXrpIcIT+VnOBeuN7N8M8swsyuAzwEvJbu2PvJz1kd5MU8iPJorEZ7KF/BkxkQoa5LMyzkXC49nYSw8l5exSFSmpmVDOWwJkAN8APwKWOyc8+xZJjMbBXyV0D9E+8zsYPjjhiSXFjfOuXbn3L7IB6HLVTqdcw3Jri0B7gT+i9AZ1b8DbwH/ntSK/M1XeQP+zBxQ7qDcSRdey6RlhJZduQ34fPj/03b9bo/mpyN0meQeoAm4B/iGc25DUqvqI59nfU9ey5MIT+VKhEfzBTyWMRHKmpTh1ZyLhSezMBYezstYJCRTzTld3SoiIiIiIiIiIiIiJ5fOM5RFREREREREREREJIHUUBYRERERERERERGRmKihLCIiIiIiIiIiIiIxUUNZRERERERERERERGKihrKIiIiIiIiIiIiIxEQNZRERERERERERERGJiRrK0mdmNt3M/pHsOo7HzGaY2Z5k1yEiIiIiIiIiIuIlaij7lJntNLMOMzvY7eMnJ3mOM7Mxkc+dc390zp0Tp/p+bmZ3xeO1RUREREREREREpG8yk12AJNUc59zGZBchIiIiIiIiIiIi6UEzlOUoZjbGzP5gZgfMrNHM1oYffyW8ydvh2cyf7bmsRHjW87fNbIuZtZnZajMrMbNnzazVzDaaWWG37R83s33hfb1iZueFH18E3AB8J7yvp8KPl5nZk2bWYGbvm9kt3V4rJzyrucnMtgIXxf+7JSIiIiIiIiIi4i9qKEtPdwL/DygERgL3Azjn/lv46xc65/Kdc2t7ef61wCxgLDAHeBb4LnAGod+3W7pt+yxQCQwH/gI8Et7XqvD//+/wvuaY2QDgKeBtoBz4BPANM7si/Fq3AxXhjyuAG0/jeyAiIiIiIiIiIiLHoYayv603s+ZuHwuBw8AooMw51+mc+9Mpvub9zrl651wt8Edgs3PuLefcIeA3wKTIhs65h5xzreGvLQcuNLMhvbzuRUCxc26Fc67LOfce8B/A9eGvfwb4d+fcfufcbuC+U6xbRERERERERERETkINZX+b55wb2u3jP4DvAAb82cz+ZmZfOsXXrO/2/x3H+TwfwMwyzOx/mdm7ZtYC7Axvc0YvrzsKKOveACc087kk/PUyYHe37XedYt0iIiIiIiIiIiJyEropnxzFObcPWAhgZtOAjWb2inNuRz/vqhqYC8wk1EweAjQRamYDuB7b7wbed85V9vJ6e4GPAH8Lf35mfxYrIiIiIiIiIiIimqEsPZjZdWY2MvxpE6HGbiD8eT1wdj/tqgA4BHwI5AJ39/h6z339GWgxs/8RvgFfhplNMLPIzfceA/7NzArD9d/cT3WKiIiIiIiIiIhImBrK/vaUmR3s9vEbQmsVbzazg8BvgVudc++Ht18O/CK85MRnTnPfawgtS1ELbAVe7/H11cC54X2td84FCN3kbyLwPtAI/IzQzGaAO8Kv9z6hmwr+8jTrExERERERERERkR7MuZ4rC4iIiIiIiIiIiIiIHEszlEVEREREREREREQkJmooi4iIiIiIiIiIiEhM1FAWERERERERERERkZiooSwiIiIiIiIiIiIiMVFDWURERERERERERERiooayiIiIiIiIiIiIiMREDWURERERERERERERiYkayiIiIiIiIiIiIiISEzWURURERERERERERCQm/x9l/WgL7pXq4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Validate (quick and dirty)\n",
    "true_params = prior(300)\n",
    "x = batch_simulator(true_params).astype(np.float32)\n",
    "param_samples = amortizer.sample(x, n_samples=1000)\n",
    "param_means = param_samples.mean(axis=0)\n",
    "true_vs_estimated(true_params, param_means, ['v1', 'v2', 'a1', 'a2','ndt'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Bayesian eyechart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simulate\n",
    "param_names = ['v1', 'v2', 'a1', 'a2','ndt']\n",
    "n_sim_s = 500\n",
    "n_samples_posterior = 1000\n",
    "true_params = prior(n_sim_s)\n",
    "x = batch_simulator(true_params)\n",
    "\n",
    "# Sample from posterior\n",
    "param_samples = amortizer.sample(x, n_samples_posterior)\n",
    "\n",
    "### Posterior z-score\n",
    "# Compute posterior means and stds\n",
    "post_means = param_samples.mean(0)\n",
    "post_stds = param_samples.std(0)\n",
    "post_vars = param_samples.var(0)\n",
    "\n",
    "# Compute posterior z score\n",
    "post_z_score = (post_means - true_params) / post_stds\n",
    "\n",
    "### Posterior contraction, i.e., 1 - post_var / prior_var\n",
    "prior_a = (0.1, 0.1, 0.1, 0.1, 0.1) # lower bound of uniform prior\n",
    "prior_b = (7.0, 7.0, 4.0, 4.0, 3.0) # upper bound of uniform prior\n",
    "\n",
    "# Compute prior vars analytically\n",
    "prior_vars = np.array([(b-a)**2/12 for a,b in zip(prior_a, prior_b)])\n",
    "post_cont = 1 - post_vars / prior_vars\n",
    "\n",
    "# Plotting time\n",
    "f, axarr = plt.subplots(2, 3, figsize=(12, 6))\n",
    "for i, (p, ax) in enumerate(zip(param_names, axarr.flat)):\n",
    "    \n",
    "\n",
    "    ax.scatter(post_cont[:, i], post_z_score[:, i], color='#8f2727', alpha=0.7)\n",
    "    ax.set_title(p, fontsize=20)\n",
    "    sns.despine(ax=ax)\n",
    "    ax.set_xlim([-0.1, 1.05])\n",
    "    ax.set_ylim([-3.5, 3.5])\n",
    "    ax.grid(color='black', alpha=0.1)\n",
    "    ax.set_xlabel('Posterior contraction', fontsize=14)\n",
    "    if i == 0 or i == 3:\n",
    "        ax.set_ylabel('Posterior z-score', fontsize=14)\n",
    "f.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Posterior postdictive/predictive checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "307.2px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}