{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "619f6aa7",
   "metadata": {},
   "source": [
    "# Trajectories and adaptive integration\n",
    "\n",
    "The {doc}`ODE tutorial <ode_integration>` advances a state one step at a time with the `ode_*_step` functions. To get a whole trajectory, `braintools.quad` has two drivers:\n",
    "\n",
    "- `quad.odeint(f, y0, t0, t1, dt, *args, method=...)` runs any registered fixed-step stepper over a uniform time grid as one compiled `scan`;\n",
    "- `quad.solve_ivp(f, (t0, t1), y0, *args, method='dopri5', rtol=..., atol=..., t_eval=...)` adapts the step size to meet an error tolerance, like `scipy.integrate.solve_ivp`.\n",
    "\n",
    "Both are differentiable and work under `jit` and `vmap`. This tutorial covers:\n",
    "\n",
    "1. `odeint` with fixed-step methods, and how their accuracy scales with the step size;\n",
    "2. `solve_ivp` on the stiff-ish Van der Pol oscillator, checked against SciPy, with and without `t_eval`;\n",
    "3. gradients through both drivers (`checkpoint=True`), used for a parameter sensitivity;\n",
    "4. the method registry: `list_methods`, `get_method` and `register_method` with a custom stepper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c9bf9f0c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-19T16:27:22.936715Z",
     "iopub.status.busy": "2026-10-19T16:27:22.936367Z",
     "iopub.status.idle": "2026-10-19T16:27:27.449593Z",
     "shell.execute_reply": "2026-10-19T16:27:27.447742Z"
    }
   },
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "import brainstate\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.integrate\n",
    "\n",
    "from braintools import quad\n",
    "\n",
    "jax.config.update('jax_enable_x64', True)  # float64, to compare errors down to 1e-10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec3251b5",
   "metadata": {},
   "source": [
    "## 1. Fixed-step trajectories with `odeint`\n",
    "\n",
    "The harmonic oscillator $\\ddot x = -\\omega^2 x$ has the exact solution $x(t) = \\cos \\omega t$, $v(t) = -\\omega \\sin \\omega t$ for $x(0) = 1$, $v(0) = 0$. The right-hand side takes the state first and the time second, `f(y, t, *args)` (SciPy uses `(t, y)`). Extra positional arguments after `dt` are passed on to `f`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9834bf37",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-19T16:27:27.453011Z",
     "iopub.status.busy": "2026-10-19T16:27:27.452562Z",
     "iopub.status.idle": "2026-10-19T16:27:31.059956Z",
     "shell.execute_reply": "2026-10-19T16:27:31.058923Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ts: (201,)  ys: (201, 2)  ys[0] == y0: True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "save_every=10 -> ts: (21,)  first times: [0.  0.5 1. ]\n"
     ]
    }
   ],
   "source": [
    "def oscillator(y, t, omega):\n",
    "    x, v = y\n",
    "    return jnp.stack([v, -omega ** 2 * x])\n",
    "\n",
    "\n",
    "omega, T = 2.0, 10.0\n",
    "y0 = jnp.array([1.0, 0.0])\n",
    "\n",
    "ts, ys = quad.odeint(oscillator, y0, 0.0, T, 0.05, omega, method='rk4')\n",
    "print('ts:', ts.shape, ' ys:', ys.shape, ' ys[0] == y0:', bool(jnp.all(ys[0] == y0)))\n",
    "\n",
    "ts_s, ys_s = quad.odeint(oscillator, y0, 0.0, T, 0.05, omega, method='rk4', save_every=10)\n",
    "print('save_every=10 -> ts:', ts_s.shape, ' first times:', ts_s[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d37ebd4",
   "metadata": {},
   "source": [
    "`ts` holds the `n + 1 = 201` grid times including `t0`, and every leaf of `ys` gains a leading time axis. `save_every=10` keeps one state in ten, which saves memory on long runs without changing the steps.\n",
    "\n",
    "`method` selects the stepper by name. Below, the forward Euler (order 1), midpoint (`'rk2'`, order 2) and classical Runge–Kutta (`'rk4'`, order 4) methods integrate the oscillator to $T = 10$ with step sizes from 0.04 down to 0.0025. The left panel shows trajectories at `dt = 0.05`, the right panel the final-time error. Looping over a few *configurations* in Python is fine; each `odeint` call is one compiled `scan` over the time steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dffc6fed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-19T16:27:31.065712Z",
     "iopub.status.busy": "2026-10-19T16:27:31.064961Z",
     "iopub.status.idle": "2026-10-19T16:27:33.452158Z",
     "shell.execute_reply": "2026-10-19T16:27:33.451120Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "euler  errors [2.15 0.88 0.4  0.19 0.09]  observed order [1.28 1.14 1.07 1.03]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rk2    errors [1.93e-02 4.81e-03 1.21e-03 3.03e-04 7.60e-05]  observed order [2.01 1.99 2.   2.  ]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rk4    errors [6.39e-06 3.83e-07 2.42e-08 1.52e-09 9.49e-11]  observed order [4.06 3.99 3.99 4.  ]\n"
     ]
    },
    {
     "data": {
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GgAY9AwAqKiqs68+2/5n2PfX4Z/Kvf/2Lp59+mjVr1jSYqU8IIYRoTNnZ2RQVFZ1zSExycjI9e/ZssLxuqHpycnK9Y5z65RP8/aHy1B5CkyZNYt68efzwww/cdNNNlJSUsGLFCu644w5rUqIukXCmBsfOzs71ntvZ2REQENAgdrVaTWhoaL3l4eHh9Z5nZ2dTUFDAhx9+yIcffnja82VlZdV7fq7rrOt5dLbX98iRIyiKwtNPP83TTz99xvP6+/uf8RhAg347ycnJAA3uu7RaLW3atGmwbdu2bRv0Izr1/b1YCQkJFBYW4uXlddr1da/pmeL19PRskHC7EM8//zzXXnst7dq1IzIykhEjRnDLLbfQuXPnM+5T9zPzz58RHx8fXF1dG7we4eHhDWaha9euHVCbPPPx8SEhIYG9e/da+3L9U93rcPfdd/PVV18xcuRI/P39GTZsGBMnTmTEiBEXfO2NbenSpTz11FPMnDnztMNFz9cDDzxAnz59uOGGG85r+7qf7Uud6U+0fJKUEkK0anXfRJ2Li4vLWRNDvr6+AKSnp9frt1C3rK431Nn2X7duHYqi1PvjmZ6eDtR+m3Y2RqMRd3d38vLyzrqdEEII0VKdOsvsqU5NnPTq1YuQkBC++uorbrrpJn788UfKy8uZNGmSdZu6BtBLliw5bd+ZUyckgdoq54tt9Fx3rilTpjBt2rTTbvPPRMb5XOf5nvfhhx8+YzX3P5Mjp3J3dwdo0DS8pbBYLHh5efG///3vtOvPlKRpLP379+fo0aN8//33/Prrr3z88ce89dZbvP/++9x2221n3bcxkyAWi4Wrr76af//736ddX5fE8vLyYvfu3fzyyy+sXLmSlStXsnDhQqZOnVqv8XxzW716NVOnTmX06NG8//77F32ctWvXsmrVKr799tt6lW41NTWUl5eTlJSEm5tbvYRz3c/2pfSwEq2DJKWEEE3K09MTo9F42vLpQ4cONVh2oTcCdcmkc1m4cOFZZ/KLiYkBasveT01ApaWlkZKSYm28frb9P/74Yw4cOEBERIR1eV3T07rjn0ld6X5T36QJIYQQnp6eODs7ExcXd9btgoODT/u3+uDBg9b1F2PixIm8/fbbFBUVsXTpUkJCQujVq5d1fd0kJ15eXhddER0cHIzFYiExMbFeFc4/Z8nz9PTEyckJs9ncaNXXdfHHxcWd8Zh1VUtarfaizhsUFITRaCQxMbHe8rr3JCEhoV6lWXV1NYmJiURHR9fbdu/evVgslnpJvX++v2e7NzvTurCwMNasWUPfvn3P+qXgqfGeWsmVnZ19yQk3Nzc3ZsyYwYwZMygpKaF///4899xzZ0xK1f3MJCQk1Ju4JjMzk4KCggY/73XVbqe+BocPHwawNuIPCwujpKTkvN5jnU7HmDFjGDNmDBaLhbvvvpsPPviAp59++qwJyqaydetWrrvuOrp168ZXX33VIBl8IY4fPw5gbZR/qtTUVEJDQ3nrrbfqDdus+9n+5yRC4vIjPaWEEE1Ko9EwfPhwvvvuO+sfJKidUeSXX35psL2DgwMFBQXnffzG6inVqVMnOnTowIcfflhvOuoFCxagUqkYP368dVlhYSEHDx6ksLDQuuzaa69Fq9Xy3//+17pMURTef/99/P39rVPrVlRUUFxc3OD8L7zwAoqitIgybSGEEJc3tVrNuHHj+PHHH9m+fXuD9XUVP6NGjeKvv/5iy5Yt1nWlpaV8+OGHhISE1PsS5kJMmjSJyspKFi9ezKpVq5g4cWK99cOHD8fZ2ZmXX36Z6urqBvtnZ2ef8xx1f/dP/bsMMH/+/HrPNRoNN9xwA998881pk3Tnc65/6tq1K6GhocybN6/BPU3da+vl5cXAgQP54IMPrFXVF3JerVZLt27dGrx/3bp1w9PTk/fff7/e7HWLFi1qEMuoUaPIyMhg6dKl1mU1NTXMnz8fR0dHBgwYAGCdzfB092cODg6nXTdx4kTMZjMvvPBCg31qamqs2w8dOhStVsv8+fPrVZrVzUr3T0ePHrUOjzybup5bdRwdHQkPDz9tq4U6o0aNOu25//Of/wA0mLkxLS2t3ox8RUVFfPrpp8TExFgr/CZOnMiWLVtOe89bUFBATU3NaeNVq9XWCr2zxdxUDhw4wOjRowkJCeGnn346ZxuKcxk8eDDLly9v8M/T05Nu3bqxfPlyxowZU2+fHTt24OLiQqdOnS7p3KLlk0opIUSTmzNnDqtWreKqq67i7rvvtt7wdOrUib1799bbNjY2ljVr1vCf//wHPz8/QkNDT9vPok5jfasJMHfuXMaOHcuwYcOYPHkycXFxvPvuu9x22231vqVZvnw5M2bMqFd9FRAQwIMPPsjcuXOprq6me/fufPfdd2zcuJH//e9/1lL/jIwMunTpwo033kiHDh0A+OWXX1ixYgUjRozg2muvbbTrEUIIIc7k5Zdf5tdff2XAgAHcfvvtdOzYkfT0dL7++mv++OMPXF1deeyxx/jiiy8YOXIk999/P25ubixevJjExES++eabix4y17VrV8LDw3nyySeprKysN3QPantGLViwgFtuuYWuXbsyefJkPD09OX78OD///DN9+/bl3XffPes5YmNjueGGG5g3bx65ubn06tWL33//3VrJcmp1y6uvvsq6devo2bMns2bNIiIigry8PHbu3MmaNWsueGi9Wq1mwYIFjBkzhpiYGGbMmIGvry8HDx5k//791gTFe++9R79+/YiKimLWrFm0adOGzMxMtmzZQkpKCnv27Dnrea699lqefPJJioqKrMOetFotL774InfccQeDBw9m0qRJJCYmsnDhwgY9pW6//XY++OADpk+fzo4dOwgJCWHZsmVs2rSJefPm4eTkBNS2GIiIiGDp0qW0a9cONzc3IiMjiYyMJDY2FoD777+f4cOHo9FomDx5MgMGDOCOO+7glVdeYffu3QwbNgytVktCQgJff/01b7/9NuPHj8fT05OHH36YV155hWuuuYZRo0axa9cuVq5cedphW0OGDAE4Z7PziIgIBg4cSGxsLG5ubmzfvp1ly5Zx7733nnGf6Ohopk2bxocffkhBQQEDBgzgr7/+YvHixYwbN45BgwbV275du3bMnDmTbdu24e3tzSeffEJmZiYLFy60bvPII4/www8/cM011zB9+nRiY2MpLS1l3759LFu2jKSkJDw8PLjtttvIy8tj8ODBBAQEkJyczPz584mJiTlnpdCGDRusDdOzs7MpLS3lxRdfBGqHMfbv39+6rUqlYsCAAaxfv/6MxysuLmb48OHk5+fzyCOPNGjwHhYWRu/eva3PBw4cyO+//37W4atBQUENerFBbUN7b29vxo0b12Dd6tWrGTNmjPSUuhLYYMY/IcQV6Pfff1diY2MVnU6ntGnTRnn//fcbTMWrKIpy8OBBpX///orRaDztVMBNbfny5UpMTIyi1+uVgIAA5amnnlKqqqrqbbNw4UIFUBYuXFhvudlsVl5++WUlODhY0el0SqdOnepNs6woipKfn69MmTJFCQ8PV+zt7RW9Xq906tRJefnllxucRwghhGhKycnJytSpUxVPT09Fr9crbdq0Ue655x6lsrLSus3Ro0eV8ePHK66urorBYFB69Oih/PTTT/WOs27dOgVQvv7663rLExMTT/v3UlEU5cknn1QAJTw8/IzxrVu3Thk+fLji4uKiGAwGJSwsTJk+fbqyfft26zbTpk1THBwcTrt/aWmpcs899yhubm6Ko6OjMm7cOOXQoUMKoLz66qv1ts3MzFTuueceJTAwUNFqtYqPj48yZMgQ5cMPP7zo6/zjjz+Uq6++WnFyclIcHByUzp07K/Pnz6+3zdGjR5WpU6cqPj4+ilarVfz9/ZVrrrlGWbZs2Rlfl1NjtrOzU5YsWdJg3X//+18lNDRU0ev1Srdu3ZQNGzYoAwYMUAYMGNDgGDNmzFA8PDwUnU6nREVFnfb92rx5s/U+DlCeffZZRVEUpaamRrnvvvsUT09PRaVSNbiv+/DDD5XY2FjFaDQqTk5OSlRUlPLvf/9bSUtLs25jNpuVOXPmKL6+vorRaFQGDhyoxMXFKcHBwQ3uA4ODg5Xg4OBzvjYvvvii0qNHD8XV1VUxGo1Khw4dlJdeeqnevdbp7kOrq6uVOXPmKKGhoYpWq1UCAwOVxx9/XKmoqGgQx+jRo5VffvlF6dy5s6LX65UOHTo0+NlQFEUpLi5WHn/8cSU8PFzR6XSKh4eH0qdPH+WNN96wxrNs2TJl2LBhipeXl6LT6ZSgoCDljjvuUNLT0895rXXXcbp/de9TXRyAMnny5LMer+7n+Uz//vmexMbGKj4+PueM83TqXsd/OnDggAIoa9asuajjitZFpSgX0JFPCCGEEEIIIVqp3bt306VLFz777DNuvvlmW4dzyWbOnMnhw4fZuHGjrUO5ooSEhBAZGclPP/1k61DO24oVK7jmmmvYs2cPUVFRjXLM4uJi3NzcmDdvHvfcc0+jHBNqK6g2bNjAjh07pFLqCiA9pYQQQgghhBCXnfLy8gbL5s2bh1qtrjekqTV79tln2bZtG5s2bbJ1KKKFW7duHZMnT260hBTUDh309/dn1qxZjXbM3NxcPv74Y1588UVJSF0hpFJKCCGEEEIIcdmZM2cOO3bsYNCgQdjZ2bFy5UpWrlxp7aUkxMVqjZVSQrRU0uhcCCGEEEIIcdnp06cPq1ev5oUXXqCkpISgoCCee+45nnzySVuHJoQQ4iSplBJCCCGEEEIIIYQQzU56SgkhhBBCCCGEEEKIZidJKSGEEEIIIYQQQgjR7K6onlIWi4W0tDScnJykk78QQgghrBRFobi4GD8/P9Rq+c7ufMm9lRBCCCFO53zvra6opFRaWhqBgYG2DkMIIYQQLdSJEycICAiwdRithtxbCSGEEOJsznVvdUUlpZycnIDaF8XZ2dnG0QghhBCipSgqKiIwMNB6ryDOj9xbCSGEEOJ0zvfe6opKStWVlTs7O8uNkxBCCCEakCFoF0burYQQQghxNue6t5KmCUIIIYQQQgghhBCi2UlSSgghhBBCCCGEEEI0O0lKCSGEEEIIIYQQQohmJ0kpIYQQQgghhBBCCNHsJCklhBBCCCGEEEIIIZqdJKWEEEIIIYQQQgghRLOzs3UAQgghhBBCCCGEEKJ5mC0KfyXmkVVcgZeTgR6hbmjUKpvEIkkpIYQQQgghhBBCiCvAqrh05vwYT3phhXWZr4uBZ8dEMCLSt9njkeF7QgghhBBCCCGEEJe5VXHp3PXZznoJKYCMwgru+mwnq+LSmz0mSUoJIYQQQgghhBBCXMbMFoU5P8ajnGZd3bI5P8Zjtpxui6Yjw/eEEEIIcdHiUgtZtiOF3NIqSitrGB3ly7gu/jbrSyCEEEIIcSWyWBQKyqvJLakkp6SK3NJKckuqap+XVpGQWdygQupUCpBeWMFfiXn0DnNvtrglKSWEEEKIC1ZjtrBg/VHe/i0Bb2cDQW72KCg89PUePtp4jBfGRdI9xM3WYQohhBBCtEqKolBaZf47yVRSSW5plfV5Xunfiafa55U0RpFTVvGZE1dNQZJSQgghhLggNWYLMxZtY9ORHO4eGM79Q9qis6vtCLDzeD7P/xjP1P/7i6/v7E2kv4uNoxVCCCGEaBkqa8y1yaSSKnJKTlYy1VU0lZ6aeKpdX1ljueBzuBi1uDvq8HDQ4+6oq/3noKeoopqFm5LOub+Xk+EiruziSVJKCCGEEBfk9V8OsfloLp/e2pN+bT3qresaZOKLWb2Y/OEWbl20je/u6Yufq9FGkQohhBBCNB2zRaGgrDahZE0ynUws1VUv1SWcckoqKa6oueBzGLRqPBz1uDvq8XA4mWRy1OPu8HfCyd1Rh4ejHpO9zvpF4eliXRWXQUZhxWn7SqkAHxcDPUKbt9JdklJCCCGEOG8r96Xz4YZjPDW6Y4OEVB2jTsNH07px3XubrYkpg1bTzJEKIYQQQlwYRVEoqayxVjDVDps7pYKprprp5Pq80qoLHjKnUatOJpT0eDjqrI/d6x6fkmRyd9Rhr7u0tE1NTQ0WiwWdTse0DmpeXpuPxsFESfzv6Pzao3X1oa4T6LNjIpq9L6gkpYQQQghxXtIKynn46z2M7uzLzH6hZ93Wy8nAx9O6cc38P1i0OYk7B4Q1U5RCCCGEEH+rqD5lyNwpzb/zTlYz/bMheNVFDJlztdf+I9F0SpLJWtVUu87ZoEV9gYkfs9lMeXk5jo6OpKamkp+fT2RkJFu2bKGoqIjhw4fz2WefATBlyhSefPJJ/Pz8uOeee5gwYQIDBw7kgQceYN2X7zO119X8VmFgf0YCdq4+4OqDj4uBZ8dEMCLS94Kv/VJJUkoIIYQQ5+Wt1YcxaDW8en0UKtW5b6Y6+jozpWcQ7609woTYANwd9c0QpRBCCCEai9mi8FdiHlnFFXg51Q7tsvUMu2aLQn5ZVb1EUr0k0ykNwXNLqiiuvPAhc/Y6jXVo3KlJJjeHvyuY6taZHHRoNacfMncqRVHIy8tDZXSjvLyczZs3M2TIEDIzM/nqq6+477772L9/Px988AHvvPMOa9asYf78+Xz//fcsWbKETz/9lLVr17J69Wq2bdvGe++9R0pKCtnZ2QD4+vqiKLVlWzfeeCP29vYALFy4EKOxtpXC0qVLAZhjUfhrYkyLeF9VSl3UV4CioiJcXFwoLCzE2dnZ1uEIIYQQrUZCZjHD523gmWsimN737FVSp8orrWLA3HWMi/HnhXGRTRjhpZF7hIsjr5sQQly+VsWlM+fHeNIL/56NzbcJKmoURaG4bshc3UxzJ6uX8kobNgTPK6viQrMYdmpVg/5L9auY6j8+25C5wsJCtFot9vb2bNq0ibZt2+Ll5cX8+fMZN24cgYGB3HLLLbz00ksEBAQQHh7Opk2bMBqNmEwmCgsLKSkpYciQIezfv5+UlBReeOEFPvroI1JSUli5ciWzZs0iOzubpKQkunfvTkVFBRaLxZpoag3O9x5BKqWEEEIIcU6v/3IIf5ORm3oGX9B+bg467h/clldXHWRq72Daejs1UYRCCCGEaCyr4tK567OdDRpiZxRWcNdnO1kwpetZE1MV1eZ61Uo5JyuZck+TZMotqaLKfOFD5kz2Wmsi6dTqJTdH3cmG4HrrLHTORrt6Vd6lpaWUl5fj4eHBoUOHKC/PJaZjDKtWrUKtVjNs2DDmzZuHr68vkyZNYubMmfTt25dbb72Va6+9lltvvZWpU6fy1ltvce+99+Ll5UVSUhKlpaUA9O3bF6PRiFqt5tNPP8VkMqHX6ykpKcHe3h5nZ2cOHDgAQFBQEB999BEAAQEBzJo1CwBPT088PT0BMBiad0a85iRJKSGEEEKc1Y7kfFbHZ/L25JgzzuhyNlP7BLNocxLv/36MNydGN0GEQgghhGgsZovCnB/jTztDW92yx77dR0p+OQVl1ac0BK8bNldFyUUMmXPQaU5p+H1y2Fy9/kwn/+uow81eB4qF1NRUgoODKSgoYNOmTYweOppjx47x/fdf8q9//YvNmzfz9pdf8s4777B06VK++uorvvnmGz766CP++OMPli1bxtq1a8nKyiImJob8/HzU6tp7naioKFxcXAB45JFHMJlMAPzyyy/odDoAli1bZo3/zTfftD6+8847rY/79ev39zU6OFzw63K5k+F7QgghhDir2xZv50ReGSsfuOqCG3PWWbD+KG+tOcyfjw/BzUHXyBFeOrlHuDjyugkhROtTY7ZQWF5NflkV+WXV5JVWUVBWRV5pNQVlVRzMKOL3wzmXfB6tRnVKIkl/snqp9nFtb6baRFNlQRah/t54urnw448/0rt3bzw8PHjmmWe49957cXNzY+TIkXz55Zeo1WrCwsJISUkhKyuLDh06UF5eTkJCAnfffTdr1qzh8OHDLF68mJdeeomkpCR27NjBDTfcQHZ2Nvn5+bRr146amho0Gs159cgUF0eG7wkhhBDikqUWlLP2YCYvjou66IQUwKTugby15jBLt53groEyE58QQgjRGKrNFvLLqij4R3Ipv6yK/NLapFNt8unv54Xl1Y1y7phAV6IDXHB31KOtKcVZq9CuTTCpCXGYHAz0792dL7/8Eg8PD66++iqeeuopImJjuW7AdYwaNYqpU6cyePJkYscN4cUXX2TkyJF88sknBAUF4eHhQUVFBdXV1djZ2XHTTTeh1+uxt7dn7dq16PV6goODKS8vR6VS0a5dO9asWQNAu3bteOmllwAICQkhJCQEqD8czs5OUiEthbwTQgghhDijL7Yex15nx7Uxfpd0HDcHHWM6+/HZn8nc3r+NzWfuEUIIIVqayhqzNblUm0Q6e3Ipv/TiZpar42yww81Bh4uxtjeTo50ZirIweofwv3V7qEo7iH27PlRlHaMiaTfOPa6n7MhfVCTtxm3o7bTJ2kjCpr3M+fxzXnjhBeKzsrh5/ny2/LCbdK2WAX16YLFYrDPCDRkyBF/f2j5U7733Hm5ubgBs377dWrG0fPlya3yvv/669fGMGTOsj2NiYi76mkXLI0kpIYQQQpxWVY2FL7cd54au/jjoL/2WYVqfYL7ZmcLag1lcHeHdCBEKIYQQLVNFtblhcukfj2srm/6ucCqtMl/UuVQqcDHYYTJqcXMyQP5xAgJD8HZ34eCGHxlw9Uh8PUx8MvcZnpzzAs56O268fiwbN24gNTWVqKgoSkpK2Lt3LzfcMJtDhxNYuX4zxw5vwb5dH1CpQa0BQOsRhFprwNfFwGO33Ya5prbq6umnn7bGc//991sf33zzzdbHgwYNsj4ODf17Jl8ZQndlk6SUEEIIIU7rl/0Z5JRUMaXXhc24dyadA1yJDnTl0y1JkpQSQgjRKiiKQlmV+TTJpSryyqpPDpf7x/C5sioqqi98NjlFUdBgweRoxFiVj4vRDv+AIIqO7sLTw43IzjFs/flzIiI6MWjQQF594kEmTpjA9WOvoWuXGP796quMHDmY6Oi7eOzjj+nevSPXf/wkQ+6cSHCwP38E+RDq7oCDgwOPPPIwKpWKwMBA4uLiAIiOjubIkSMAvH7HtdzlEAiAzjMEnWdI7WNXH3D14dkxEXh5ejTGSyyucJKUEkIIIcRpLfkzmZ6hbrT1dmq0Y07qFshT3+0jp6QSD0d9ox1XCCGE7ZgtCn8l5pFVXIGXk4EeoW4tcpi2oiiUVNZYk0t5ZfUbfJ+aXLImn8qqqao5/wSToigoNVWotXpUVWUYK3MICI/AriSL6qxjdBs0ivyju8lNjGfSzHvYvno5qccO8cLLr/HeGy9RmJfDh699yBNPPIG9xZ6npl7PG2+sJ8jbhYkDwnBMaUPbtgHEBrtx/52zCA0NRaNWsXLlSutwuD179ljj+fbbb62PX375ZevjCRMmWB+fWrVUZ0SkLwumdGXOj/GkF1ZYl/u4GHh2TAQjIn3P+zUR4mxk9j0hhBBCNJBaUE7fV9fy1qRorusS0GjHzSutovtLa5gztlOjVWA1BrlHuDjyugkhVsWlN0hc+DZD4sJiUSiuqDljcqmu59KpyaWCsiqqzef38ddSXYFKo0Wl1lBxIg5H/3aYnIwU7/iJqKHjcdGr+POzuUx97DWUkjzef+pOPvlhHekJ+3ng1kkcO57K7m1/8vTTT7N+/Xq2bNnCt99+y9y5c9m1axf79+9nypQpHD16lMLCQrp27UphYSFqtRonp8b7MuhStZaEo2h5zvceQZJSQgghhGjgow3HmPvrIXY8NRQng7ZRj33L/22l2mzhy9t7N+pxL4XcI1wced2EuLKtikvnrs928s8PlHUpiwVTup5XYspsUSgqr66XXPpnQ+9Tk0v5pVUUlFdjtpz9o6xSU4VisaDWGajKTkatM2Dn4k31kc14+AbhHx7BiTWLaBfbj05durPs5XsZe8ud9O53FbcN78ZbH37KwP59GdavB99/t5ywsDAmTpzIBx98gIODA//5z3946KGHsFgsbNy4kauvvpqamhqKi4utVUtCXKnO9x5Bhu8JIYQQooGf9qYxqL1noyekAK7p7Mtj3+6zfusqhBCi9TFbFOb8GN8gIQVYlz25PA5FgcLy6r9njyutn1zKL6tNMJ2pVEIx12CpLEVj74K5JJ+awgz0/h2pyjxKdW4KXjGDsSRtQ12SRdfRU0ha9yVUljL+jkf4ccHzuLq48ODjz/HxGyuJaN+B++4ewfx5++nSpQ1XX30Vn3ueoEuXLnTs2JFR3q/Stm1b3N3dGXj4II6Ojmg0GuL3x1njWbZsmfXxE088YX08bNgwALRarSSkhLgAUiklhBBCiHpO5JVx1evreOfGLoyN9mv04xeUVdHtxTU8MyaCqb1DGv34F0PuES6OvG5CXL7qqpcKymuHvRWUVVNQXvvf/LJqDqUX8Ut85nkfr6YkD43RGVQqyo/twBjaBaWmmqJt3+HSdzLmkjyK131M9PTnUeclse+L15j6+pfkHtrGLx+9wptfr+PYrs38sXIZb3+wkAM7/+RQ3C4ef/RRtm/fTlZWFqNGjSIxMRGz2Ux4eDjl5eVotVrs7KQWQ4jmJpVSQgghhLgoP+1Nx6BVM6SDV5Mc39VeR7+2Hvy0J73FJKWEEOJyda7kUuHJSqWCkz2X6h4XVTSsXjKXF6NSqVAbHKlIOYDG0YTW1Yfi3avQ+3dA5xlC7i/v4hg9Ar1POGmf3EvkjY8R06UrS+4cz93zlhEeHs4bdz3G6w9MwNPVifcyfmDeI/0xqCx8vbSUu+4aSGlpKQduiKR79xhqaiJRHp+OVquFXsFw140AdBw9DEbXVid169bNGuOpTbuNRmPTv8BCiEsiSSkhhBBC1PPT3jSGdPDGQd90twmjo3z59zd7ySyqwNtZhvAJIcS5nC259M+E0tmSS5aqCiwVxdg5e1JdkIG5OAdDYCQVx/dhLsnDIWIAJXt/xVJVgXO3sZRsWIS9gwOdx84k7rN38AvvyOAJM/np/c9IK22D1tUHS0UJSk0VAIagzmjsXQDwGD2bN24byeCoID64uag2sQTclxBvjWfwF0usj++++24AHB0d6d69O4BUOQlxmZP/w4UQQghhlZRTyv60Iu4ZFN6k5xna0RsVsP5QFpO6BzXpuYQQoiVpkFw6Ncl0nsklRbFQk5eKnckPxVxN2eEtOEQMxFJWQNH2HzANmEZVznEK//gfnuMepzx5D8UbFtPlvgWUH9pO6ubvufbZj8nad4TMrANMHT2B5L0FlBcYmHxzb04cdkSvVrh6UH/SU6PQarX4+vpiuWclarUagDljP6ffa2vJKKzApdd46/U5dOwP1DY7D24XwYBOgQDWhJQQQpxKklJCCCGEsFpzIBO9nZpB7Ztm6F4dk4OO6EBX1h/KlqSUEKJVuqDk0inriiqqsVgUVCoVNYVZqHQGNEZnyo9uQ+sRhJ2LN4VbvsK+XR+07gFkffM8pgEz0HoEcuLtybS57R08vH35660Hue7V5bg6ufDLyrXcdfs09HizNs+VB6Z2g4p2xPe058bJQ9GY+5GXM5Hw8HAUZRAq1bMnr6Lr3xd0VRvrw24hA6yPg4L+/h1dl5AC0KhVPDsmgrs+24kK6jU8r5t979kxEWjUKoQQ4kwkKSWEEEIIq3WHsugd5o5Rp2nycw1s58XHG49Rbbag1ajPvYMQotUzWxT+Ssyzzr7ZI9TN5kkLs0WhuKK2v9Lpkku1M8dVNUguFZZXAmrM5UVYKkrRmnypyjqGpaIEQ1Bnyg5vQTFX49CxP4Vbl6HW2ePUZRQ5P72J1jMU//4TSd/4CQEx/YkaOJY9O+KJ7hxETI9wthb50m9weyIiOrI79B4G9O9PaIAvmVP/ol14GDqdDp6q+PsiZu20PvzXmO4nH3kztGvbk4/1uLnWDqlTqRrv9R4R6cuCKV2Z82M86YV/x+PjYuDZMRGMiPRttHMJIS5PkpQSQgghBAAllTX8lZjHU6MjmuV8A9t78taaw+w6XkCPUJk+W4jL3aq49AbJC99GTF5cTHIpv7iCoiozirmGqsxj6P3aYy4vpjxhC46dh1Gdn07Jnl8wDZxORcoBird/j+e4xyg9sIHinT/hc/PrWJK2U5W8m5hbn6MwrxCzJZPBPa8h0ykdg8rMgGGdyepqh7uLI926dKbitkh83JzxcDPBnOF/X8BtX/39ePir1odXR0y1PvaM6HjJr1NjGxHpy9URPi0u2SiEaB0kKSWEEEIIAP5IyKHarDT50L06Uf4uuDnoWH8oS5JSQlzmVsWlc9dnO/nHZG5kFFZw12c7WTClqzUxdbrkUmHZ3wmlBsmlknLyS6sorlaozEpC42BCY+9CSdxaDEGdsXP2IG/NBzh3vw6NsycZn/4Lz+ueQm104sR/biDg/s/BYiFn+Yt0e+xLjPZaEjbsYfSUaahLdRwzB3Pd6I4oZb7kDw5h8ODeaJWu2FnuoW1wADq70adc0VV/P7wu6u/H3QL/fuzl1Oivr61p1Cp6h7nbOgwhRCskSSkhhBBCALVNx8M8HQhyt2+W86nVKvq39eD3w9n8e0SHZjmnEKL51JgtFJZXk1taxZPL4xokpODvPkT3/m87Pg4ais125GWlYamqQOseSEXKfjCbMQR3pmTvalQ6Iw4d+pG39mN0HsE4dr6azKVPY9+hH07Rwyn4fTGePcbg37kvKQXH6NQ1htAOvhzKaEevfm0ICQrkqN9T9O5zFb4eLlRNO0R4cCAmBz26+Tf/HdgzN/z9eNbVp0R8Sg8mIYQQl0ySUkIIIYRAURTWHcpiTGe/Zj3vgPaefLc7zTrkQwjRsiiKQlmVmUJrw+6q2ube1iFwtZVLheVV5JdUkptfQCkG8gqLyE8+iCEoipqiHMqPbcMpZiRVmUcpPbAB08AZlB3ZSmn8BjzHPkL+jpWkHduO1/hnKUvYSnVBBkEj70RblY9RYyEq3J1CSxiuzo5Edw+juN1MvD3caBcehmry1/i6u+Dl6oTLiyPR2Z3sUffY4L8v5KbTN/QGqe4RQghbajVJqVdeeYVvv/2WgwcPYjQa6dOnD6+99hrt27e3dWhCCCFEqxefXkRmUSWDOzTP0L06/dt6olLB74eymXDq8BYhRKOqMVsoqqihoKyqNsF0ckjcqcmmwpPLsvPyKaqCkho1GYd2onILRGN0pmjb9zhE9Edt70rOD6/jNvQOVFoDaZ/cg9+Md7BUlZP63+kEPfI9NYXZ5P32IX4z5qNTVVOYlQiASu+A1s0fAJ1PWzSOtUkhh6ih/Pu+O7ihZxtcjENxMWpPJpdO6blEr1Men1pd6d2kr50QQoim02qSUr///jv33HMP3bt3p6amhieeeIJhw4YRHx+Pg4ODrcMTQgghWrV1B7Nw0GnoFtK8vZ3cHfVE+rmw6UiOJKVs5LrrrmP9+vUMGTKEZcuW2ToccRaKolBRbaGg/O/eSrX/rToluVSbWMotKCKvuJQylZGsE8coKa9E5xlC2ZGtqHVGDEGdKdy6DK1bAPZte5H941zsw3vi0LE/6Yv/hUuvCdi370Pupq9w7XsjhiAX7EoyCHLW4BvgRmpsD7p0CcTbw50c/zeJ6RaDm5MRw8xjeLu7YHLQ4/qfaTgZ7NiWlM+NH9X2i9K6+qB19QHAztENO8fa3zlqrZ7+EQGEX4b9loQQQpxZq0lKrVq1qt7zRYsW4eXlxY4dO+jfv7+NohJCCCEuDxsTcugT7vH3sJdm1KuNGz/tTUdRlEadqlycnwceeIBbb72VxYsX2zqUK0ZdI29rEunkTHCFpwyHKyirJr+4jMysTKr0JnJyc8k6Fo82uAvVuSlUJO/BqetoKpL3Up64E9PA6ZTsW0NF8h48rnmIws1Lqc5LweOahyiK34JisaDzDEFXXYqj0Y4wP2fKOnfGw8uL8I5BVITMxs/Hh0A/H+wn/IKHixOu9jpcH9uIq70Wo1aD6qVRf1/EXX1OuaJOZ73eHqFu+LoYyCisOG1fKRXg42KQCQ+EEOIK1GqSUv9UWFgIgJvbmf94VVZWUllZaX1eVFTU5HEJIYQQrU15lZldxwt4YpRtmo33DHXno42JnMgrb7Ym6+JvAwcOZP369bYOox6zRWkV08tXVJ/Sa6lulriTlUoFp1QvpaWmUKGxp6RGw/Gdv2Px7YhK50DBhsW49BwPajuyl7+E5/VPYqkoJX3R/QTcu4TqnBNk/u/fBD64lKqsVAr++h6v4C6oFTP6mmLaeDpgZwjF7Kkluos/6o7j0Csjadu2LY43PI+boxFXBx2uDw3AxajF2ahFqznDTHFE/fPyGo1GreLZMRHc9dlOVFAvMVX3rj47JqJFvsdCCCGaVqtMSlksFh588EH69u1LZGTkGbd75ZVXmDNnTjNGJoQQQrQ+O5LzqTJb6B3mYZPzdw91Q6WCP4/lSlLqHzZs2MDcuXPZsWMH6enpLF++nHHjxtXb5r333mPu3LlkZGQQHR3N/Pnz6dGjh20CbgSr4tKZ82M86YUV1mW+LgaeHRPBiEjfRj+fxaJQXFFTL4n0d3Lp72FyGVnZFJZWUqF1JPXwXkprNOAeTEncb9i5eGMIjCR/3ScYgqMxtokl44vHce4+DvvwnqR+dDfuw+/FEBRF5tYfcBvqi9bdETtzFT6OWtzd3ci9agTRnXwxOeop7/h/dIjogMmhM64PjMPVXoeLfT9c7W/DxajFQadBpbq70V+LpjQi0pcFU7o2eG99mvC9FUII0fK1yqTUPffcQ1xcHH/88cdZt3v88ceZPXu29XlRURGBgdKvQgghhDjVlmM5uDvoaOftaJPzuxi1dPRx5s/EXCZ2l7/TpyotLSU6Oppbb72V66+/vsH6pUuXMnv2bN5//3169uzJvHnzGD58OIcOHcLLq7ZpfUxMDDU1NQ32/fXXX/Hza97ZFs9lVVw6d322s8EQr4zCCu76bCcLpnQ9Y/KissZ8skKpul710t9D42rILigiIy0NxdmHjPRUspMOoQTFUpF2mMq0QzjHjqH00CaqMo5gGjCNwq3LqMlPx33EfeSvXwiAaeAMCg/sQGPvgqN7MCrFgpNOTaCHA5U9+uHtH0RQiB/mkFfx8/HB28OEy4QtmBx0uBi1uMzegKu9FmeDFp3dqVVLA095HNuYL2uLMSLSl6sjfFpFFZwQQojm0eqSUvfeey8//fQTGzZsICAg4Kzb6vV69Hp9M0UmhBBCtE6bj+bSK8zdpv2cerZx49f9mTY7f0s1cuRIRo4cecb1//nPf5g1axYzZswA4P333+fnn3/mk08+4bHHHgNg9+7djRZPU7ZGMFsU5vwYf9qeQwq1Tb5nf7WbNQcySUo4iOLkQ0k1JPzxE3Zt+1JRYyF/3ULcBt+GpaKE7O9exvvm16nOSSbzyycJvO9/VKQcIO+Xd/Gb+R4VKScojduEe2AsqNTotHb4uhiw6xiBNiyAdp180LadhpNeTUCAHy7XvYmrUYervRaX+/rhYtTiaq/FUT/qlP93Bp4SdZdGe20uJxq1it5h7rYOQwghRAvRapJSiqJw3333sXz5ctavX09oaKitQxJCCCFaveKKavamFDI+9uxf9DS1nqHuLNyUREp+GQEmGcJ3PqqqqtixYwePP/64dZlarWbo0KFs2bKlSc7ZlK0R/krMqzesC6Bw6zfofdthCIoi7aM7cB95P8uqLKR+MBvP659C6xFE1u51uPtFozE6o9EZcDGocXX3xGf4jXRs64FTpCfqnl8SGhKKi30ELv+ajKtRi6t9b1zt78DZqMXFOAK9naZJrksIIYQQZ9ZqklL33HMPn3/+Od9//z1OTk5kZGQA4OLigtFotHF0QgghROu0LSkPs0Whj436SdWpm3Vr67E8AmIlKXU+cnJyMJvNeHt711vu7e3NwYMHz/s4Q4cOZc+ePZSWlhIQEMDXX39N7969T7ttU7ZGyCquaLDM2CYWtdEZAO/JL6Oxd+Gazr70WrmltmLJqMX1vrW1w+GMWpz0Y1Fbh4INb5S4hBBCCNF0Wk1SasGCBUDtDDGnWrhwIdOnT2/+gIQQQojLwJajufg4GwixcYNxNwcd7b2d2JqYyw02rtq60qxZs+a8t23K1gheToYGy3SeIdbHds61idObewbL8C8hhBDiMtFqklKKcroOA0IIIS5EtdlCTkklFdUWtBqVDJMSbD6aSx8b95Oq07ONG78fzrZ1GK2Gh4cHGo2GzMz6vbgyMzPx8fGxUVQXr0eoG74uBjIKK07bV0pF7UxtdVV1QgghhGj91LYOQAghRNMrrzLzyR+J9HttLb1fWcugN9bT77V1jF+wme93p1Jtttg6RGEDheXVxKcX0auFVJ10C3EjObeMnJLKc28s0Ol0xMbG8ttvv1mXWSwWfvvttzMOv2vJNGoVz46JAGoTUKeqe/7smAiZqU0IIYS4jLSaSikhhBAXZ19KITMXbyO3tIpxMf6M7uyDUWtHTkkln289zgNf7mbpthMsmBKLi1Fr63BFM9p5PB9Fge4hLaPypGuQKwC7jxcwNML77BtfIUpKSjhy5Ij1eWJiIrt378bNzY2goCBmz57NtGnT6NatGz169GDevHmUlpZaZ+NrbUZE+rJgSlfm/Bhfr+m5j4uBZ8dEMCLS14bRCSGEEKKxSVJKCCEuY1uO5jLr0+2EeTnyzV19CHSrP1xvTLQfW47mcudnO5jw/mY+md5dhvRdQbYn5eHhqLN5P6k6/q5GPJ307DqRL0mpk7Zv386gQYOsz+uajE+bNo1FixYxadIksrOzeeaZZ8jIyCAmJoZVq1Y1aH7emoyI9OXqCB/+Sswjq7gCL6faIXtSISWEEEJcflTKFdSsqaioCBcXFwoLC3F2drZ1OEII0aQ2HclhxqJt9Ax14/0psTjoz/w9xJGsEmYs+gtFgZ/vuwoXe6mYuhJM+qB2BrMPbulm61Csbv90O8UVNXxxe69mPa/cI1wced2EEEIIcTrne48gPaWEEOIylFlUwf1f7KJnqBsfT+t21oQUQLiXI1/M6kVxRQ0Pfb1bJpe4AlTVWNiTUtBihu7V6RJkYk9KAWaL/AwKIYQQQlzuJCklhBCXGbNF4cEvd6NRq3hrUgx6O8157Rdgsuc/E6NZcyCLDzcca+Ioha3tTyukotpCbLDJ1qHU0yXIlbIqM4czi20dihBCCCGEaGKSlBJCiMvMe+uOsDUxl7cnd8HDUX9B+w7p6M2dA8J4/ZdD7E8rbKIIRUuwIzkfg1ZNJz8XW4dST+cAFzRqFbuOF9g6FCGEEEII0cQkKSWEEJeR5NxS5q9N4K6BYfQOc7+oYzw0rB2hHg48/2O8DOO7jG1LyiM6wBWdXcu6FbDX2dHBx4ldx/NtHYoQQgghhGhiLetOVAghxCV5ZcVBPBz13Duo7UUfQ6tR89TojmxNzOOX/ZmNGJ1oKRRFYUdyPt1CWtbQvTpdglzZdaLA1mEIIYQQQogmJkkpIYS4TGw5msuq/Rk8OqIDRt359ZE6k4HtvRjQzpNXVh6gssbcSBGKliI5t4yckiq6tbAm53W6BJo4klVCYVm1rUMRQgghhBBNSJJSQghxGTBbFF74KZ6YQFfGRvs1yjGfGt2RlPxylmxJbpTjiZZje3Lt0LiuQS23Ugpgd0qBTeMQQgghhLgsWcyQuBH2Lav9r8V2X0KffY5wIYQQrcKquAzi04v45q7eqNWqRjlmW28nbujqz8cbE5naO6TF9R4SF2/3iXzCvRxxMWptHcpphXo44GSwIy61kAHtPG0djhBCCCHE5SP+B1j1KBSl/b3M2Q9GvAYRY5s9HPmEIYQQrZyiKCz4/Qh9w92JDW7c4Vi3929DRlEFP+xJO/fGotXYfaKAmEBXW4dxRiqVikg/F/alyAyQQgghhBCNJv4H+Gpq/YQUQFF67fL4H5o9JElKCSFEK7fpSC5xqUXcOSCs0Y8d7uXEkA5efLjhqMzEd5moqDZzML24RSelAKICXNiXKkkpIYQQQohGYTHXVkhxunv6k8tWPdbsQ/kkKSWEEK3cgt+PEOnvTL9wjyY5/h0DwjicWcL6Q9lNcnzRvOJSC6mxKC0+KRXp70JqQTl5pVW2DkUIIYQQovUpz4fUnRD3LZbf58IXk1m75zhH8iwAzPqhnD+O15yygwJFqZC8uVnDlJ5SQgjRiu1NKWDTkVzevakLKlXj9JL6p+4hJmICXflgw1EGdfBqknOI5rP7RAF6OzXtfZxsHcpZRfm7ALBP+koJIYQQQjRkroGiFMhPgrxEshP3U5J5jFBtDt9sjEdVXcb1HbXM/L6cdu5qHu2nZ2VCDQNCNIS7qbmuox3BLqepUyrJbNbLkKSUEEK0Yp/8kUiQmz0jI32b7BwqlYqZ/UK574tdHM0uIczTscnOJZre7hMFRPm7oNW07GLpYDd7nPTS7FwIIYQQV7CKotqkU34iFRkJGMpSSTwUx464BMaHFvNdfDlrjtXw7igjn26u5Fi+hfdGG9FbqkEDOHrzwhQfHLzCwFHPXL6wHnpU2zNMeOPo3SyXVkeSUkII0Urll1axIi6D2Ve3Q9NIM+6dybBO3rjaa/lq+wkeH9mxSc8lmtbuEwWM6ORj6zDOSa1W0cnfWZqdCyGEEOLyZTHXNh3PT4L8JKqyEvjzr+1c5VNOWtIRnv8lkw/GGNl8ooYbvykn+UEnMlNq2BBfw/hgAzF+Brz9vKBtFA/1CAFTKJhCuMYUAqZg0Dngd+q5En+vbWp+2r5SqtpZ+IL7NNPF15KklBBCtFLf7ExBURTGxwY0+bn0dhrGxfjzzY5UHh7WvsVX2YjTyympJCW/nJggV1uHcl6i/F1YsS/D1mEIIYQQQly8yhIoSIb8JEpTD2JXfAJ9SQrvfr+ViW3KcNdXE7WglDVT7XHSqbhlQQlxdznioECwqxrF6E5sbDA7+/lBcEd6mULoZQoBUyghTr6EqM/zvlytgRGv1c6yh4r6iamTX3CPeLV2u2YkSSkhhGiFFEXhi7+OM6yTDx6O+mY558RugSzanMT6Q9lcHdG8Zb2icew+XgBAdICrTeM4X1EBrny0MZH80ipMDjpbhyOEEEII0ZDFUtuHKT/RWvG0Z+d2nKoyaWOXxbM/HWdQqB0DQ+zo+d8SXhuqZ3Q7LfsTyyn00eHloOfdSaG4duyIvU9bkq8JAVMIuIXyhGswGJzRA41yxx8xFiZ+WjsLX1Ha38ud/WoTUhFjG+MsF0SSUkII0QptS8rnaHYpL1wb2WznjPBzJsrfhaXbTkhSqpXak1KAh6OOAJPR1qGcl1ObnfeXvlJCCCGEsJXqcsivrXYqTz9MUdphvMlm/da9FGSnM66dwtNrK9BpVDw9QM+nv1QQ5a2mTYyOzt4avEwu4NeOLW/44+gbDm5tWDA1pDb55OzPYE0zpmYixkKH0bWz7JVk1vaQCu7T7BVSdSQpJYQQrdDnW5MJcbenVxv3Zj3vxG4BPPdjPFlFFXg5G5r13OLS7T5RQEyga5PN1NjY6pqdS1JKCCGEEE1KUaA0G/KTUPISUeUnkZV4gB179zEyoJTf951g+cEa5o0wsHBbFb8ereG7yfYUZ1VTUK6AysCM/qHo3AMhrCNvDq3t7YQphBtMIWA0AdBi5j5WayD0KltHAUhSSgghWp3iimpWxmXwwNC2qJu4wfk/jY3x54WfDvDT3nRu7RfarOcWl0ZRFPamFHJr39bzvtU1O49LlWbnQgghhLhENZVQcALyE7HkHuPg3h20dyiiOP0YL3wXzxtD1BzIsTBsSRkps51ISTfz8cZKRk60J8hFzeC2juDTkdtvDuYu91BwC2XMLSeTTy6BtNGcYTY7cVaSlBJCiFbml/2ZVJktjIvxb/Zzuxi19G/nwc/7JCnV2qTkl1NYXk3nABdbh3JBInxdWHsw09ZhCCGEEKKlUxQoz4e8RJS8Y1hyE9EUJbPs1z/p61WCr5LJsCUl/He0kWAXFb3nFhN/tyNuRhWWmmoqzQbCggNZ87g3SscIug4O5Zt7Q8EUSqgphFB7N1CpJInSyOT1FEKIVub73an0CHHDz9U2fYFGd/blX0v3kFZQbrMYxIXbd7LaKNK/dSWlOvo6sXBzIqWVNTjo5bZFCCGEuKKZq6HwRG1D8bxEjh/ag6owhUBNFv9duZ8OrlUMDrVj8OJS7ojVMTlSy4rN5YR21+Hrp+HuXk64B7VF6x9OwfJgVO5twBTCW/8KBddAsNPTwdbXeIWRuzshhGhFsosr2XQkhxfHRdkshqEdvdHZqVmxL53brmpjszjEhdmbUoiPswFPp+aZrbGxdPR1RlHgYEYxscEmW4cjhBBCiKZWXnCyt9MxytIP41CRzu5du0k9kczogCLe3VpOXrnCMwP0zPulAl9HFY/01eNqV43RTgVOvvzvXh/cAtqCdzifXBcCbrXD7MY5eMLJ3pqto8Pm5U+SUkII0Yr8vDcNjVrFyEgfm8XgZNDSv62nJKVambjUQqJa2dA9gLbejtipVcSnF0lSSgghhGhuFnPjz9JmMUNRKuQlQn4SxamH2LNnF/28y9kbf5glf+Uyd5iBz/dW8/6OKjbOcODEoWrisy2M9tcztK09Zkc/aBfBf3qGnGwoHspNd4eAKRi0Rvwa4dJF85CklBBCtCLf7U5jQDtPTA46m8ZxTWdfHly6m9SCcvxlCF+LpygK+1ILmdkK+4Dp7TSEeTpyIL3I1qEIIYQQV5b4H2DVo1CU9vcyZz8Y8RpEjD37vpXF1pnschPjcK3JoiYnkTeX/8UjXavIKa2i/8JSDt3rSEquhX99V862WY64YKGtuxocPLluSBDjbggCv3aMGRfCmJPJpw6O3qBWN+mli+YjSSkhWihFUUjIKiGvtAqzRcHTSU9bL8dWM5W7aHzJuaXsPlHAOzd2sXUoDOnohc5OzUqplmoVTuTVNjmPamX9pOpE+DlLUkoIIYRoTvE/wFdTAaX+8qL02uUTFkNAt9reTvm1FU8bNm0h3FCIn5LOzC9OMLu3jghPNaGvFvPnTAc6eKg5eKKC0k4GPB11fHKLL4RH0rFnG7bdHAKmEILdQrndNRj0jtjb4LJF85OklBAtTF5pFQs3JfLjnjSScsvqrQt2t2dEpA939A/DzcaVMqL5rYzLwKBVM7Sjl61DsQ7hWxWXIUmpVqC1Njmv09HXiV/2Z2CxKKjVkpgXQgghmpTFXFshdUpCqrBCoaJGwdtRzbL4atxev4nBoXZM+bacQSEaZnbV8dHycqZFa/FrY8fgUDtcXU2o/MPJ+iQQo084mEJZMiOkdridsx/9L3UYoLgsSFJKiBbk573pPPN9HFU1FkZE+vDc2E4EuzugUak4llPCL/sz+XzrcZZtT+G5sZ24prOvVE5dQVbFZTCgnSf2upbxq/vqCC8e/3YfeaVVkiRt4falFuLr0vqanNfp6OtMWZWZ5LwyQj0cbB2OEEIIcXmwWKAkwzrMTlWQzLEDe0k8uIchbmksjavmUK6FZwboeXljJTUWeHO4gfJqhQotgJpnx7bB3a8NBLZjydWhJ/s7hXCzKQQMtV+GSaMHcTYt45ONEFe4GrOFx7/dx9c7UhjRyYfnx3XCy8lQb5sgd3sGtvfiX1e35dnv93PfF7vYdCSHl66LQiOVA5e9jMIKdp8o4K1J0bYOxWpQey8sCqw/lMX1XQNsHY44i32pBa22Sgpqk1IA8WlFkpQSQgghLkRVKeQnQ34SVVlHOBS3iyiXEhITDvHJ+qO8MNCOVUdqeHpdBdtmOXIooZpfj5oZMsJAJy81fk61nzNeHqK3fua4Jfrkl5HXvU/b6Em2ujJxmZCklBA2Vm228OCXu/llfwZvTojm+q7+Z61+8nIysGBKLF9vP8Gj3+ylqsbC3AnRkpi6zP0an4GdWsXg9t62DsXKy9lAdIALvx2QpFRLpigKcalF3NYKm5zX8XDU4+mk50B6EaM7+9o6nDOKj4/n3XffZcuWLWRkZADg4+ND7969uffee4mIiLBxhEIIIS47ddVOeYnUZB9FVZCMqiCZ//txMze2q8RSms3gxaVsmOFAdqnC0I9LSZvtiLZEwQ4zqPT0jQrkm1hfCI9g5NAQRlaXw++vEen19/C6037WcJY57sSlk6SUEDZUY7Zw7+c7WXswi//e3JVhnXzOe98J3QIxaDU8uHQ3FkXhrUkxMpTvMrYqLoPeYe642GttHUo9gzt48/HGY1TVWNDZySwoLVFKfm2T88iA1lspBbXVUi252fnKlSsZN24cXbt25dprr8XbuzaBnJmZyerVq+natSvff/89w4cPt3GkQgghWp3KEihIPtlUPIn4PdtxM+fgo2TwzLJ4ru+gIsZHQ5u3ilk63kjvQDtWbitjhLuBAGcVjw92Q+3bgWCvNmSODgFTCAGmEJ41hYBLAE4aLU6nns9ihl1Lapua/7PROQCq2oRUcJ9muHhxuZOklBA2NPeXQ6w5kMVHU2MZ3OHCK2DGRPuhUsG9n+8iws+Z2/uHNUGUwtbyS6vYmpjHnLGdbB1KA0M6evHWmsNsS8qjb7iHrcMRp7E/rTaR08nP2caRXJoIX2d+2J1q6zDO6LHHHuPRRx/l+eefb7Duueee47nnnuORRx6RpJQQQoiGLBYoTof8JMw5xyhNP4xzVQbrtuxEVZLBQJ8S/r26gjCTmju66Xjp2zLGttMyKVJLoJMFg50OTCH89YIPHkHtwKMN304IAVMomIK5wWi6sHjUGhjx2snZ91TUT0yd/BJ8xKu12wlxiSQpJYSN/Lw3nQ82HOOp0R0vKiFV55rOfsSlFvHaqkNEB7jSs417I0YpWoI1BzKxKArDIlrO0L06nfyc8XE28NuBLElKtVDx6UV4OOoa9KlrbTr6OvH+7xUUllfjYmxZFYMAhw8f5uabbz7j+htvvJHXXnutGSMSQgjRopxa7ZSXSFZiPEcOHaCPZzGrtyfw14lKnuyv55UNlRzKtbDkOiPJR6rQqAAfHVO6ueHiHQThHfhfv78bis96IAScA0Bjx/mPuTgPEWNh4qe1s/AVpf293NmvNiEVMbYxzyauYJKUEsIGjmQV88iyPVzT2ZeZjdDn5eFh7dh9Ip97v9jFivuvarUzbInT+zU+k65BJrycW15SQaVSMbijF78dzOTpazrKENIWKD6tyNoovDUbFuHD3ueG4WxoeQkpgJCQEH7++Wfat29/2vU///wzwcHBzRyVEEKIZmOxQHEa5CdhyT1G2pF9BNjlk5V8mP9bvZ/He9awLdXMvSvL2XqbI3uP1fDhjir6TLDHx95MRy8tmEKZfWMAWo9Q8Axj+oSQk8mnYDpfaLVTY4gYCx1GQ/JmKMkER+/aIXtSISUakSSlhGhmZovCw1/vxcfFwGs3dG6UD/F2GjXzb+zK8HkbeOGneN65sUsjRCpagsoaM5uO5HDv4HBbh3JGg9t78fnW4yTllsnMaC3QgfQiroluuc3Bz5dRp8FIy70Jfv7557nppptYv349Q4cOrddT6rfffmPVqlV8/vnnNo5SCCHEJaksts5kp+QloipI5offNjPApxyHshOM+V8BS64zolapCJ5bTOFjTqiqFZKyKlEUAx0C3Vgw3RM6RTG0XwhDHwgBUwhRplCinP1BY4e9ra/xn9QaCL3K1lGIy5gkpYRoZos3J7EnpYBld/bGQd94/wt6Oul5clRHHvp6D+NjA+jfzrPRji1sZ+uxPMqqzAzu4GXrUM6oV5g7dmoVfyRkS1KqhSkoqyK1oJyIy6BSqqWbMGEC/v7+vPPOO7z55psNZt9bv349vXv3tnGUQgghzspitvZ2Ij+JjKP7sCtKwcOcwYIVe+nhWU6sn4Y+/1fKcwP1DAuzY8GPpYQPMxDhqWFCJwNatxCcfdtQviwInXc4jqYQPpgdAq7BOBld6WrraxSihZGklBDN6EReGW/8eoipvYKJDXZr9ONf39WfZTtSeOq7OH79V38M2pZbVSDOz9qDWfi5GGjv7XTujW3EUW9H12ATGxJyuKV3iK3DEaeIT788mpy3Fn369KFPH5mJSAghWrTKYmvSyZxzDE3RcXbv3k1Jdgr93PP4zx/FGO1U3NVdx0PfltE7wI57e+gwV1ZhVjRgNLFwVgcCQtuCb1tWjgmx9ne69WS1E4DOltcoRCsiSSkhmtEz38fhatTyyIgOTXJ8lUrFi9dFMnLeRv677gizh52+t4loHRRFYe3BLAZ18GrxvZr6t/Xg/d+PUW22oNWobR2OOCk+rQiDVk2oh6OtQxFCCCGah8Vc25j7ZOKpJO0QiYf2E+VcxLa9B1l/MJdH+ur5YHsVq47WsHySPbt3V5FbptCvj57+IXp0rr4Q1pHPXgpG5VbbVPzeO0LANRiMrsgdthCNR5JSQjSTjQnZrDuUzftTuuLYiMP2/inM05GZV4Xy0cZEbukdIk3PW7Gj2aUczytjSMeWO3SvzlVtPXnj18PsOVFAt5DGrwIUF+dAejHtfZzRqFt2UvNKcODAAUaPHs2xY8dsHYoQQjQ+i7l5m2FXFFlnsitLP4yxLJWStAQW/7qbe6LKSMipZOYPFWyc4cDu4zU8srqSLTMdMJrNOOlVYHRj0lB/brguEII7MH1MCJxMPnVz8rNWO8lfTyGaniSlhGgGFovCyysO0i3YxPBOjTpZ62nd2T+Mz/5M5r11R3hubKcmP59oGusOZqG3U9O7jYetQzmnSH8XXIxaNiTkSFKqBYlPLyIm0MXWYQigqqqK5ORkW4fRQEFBAUOHDqWmpoaamhoeeOABZs2aZeuwhBCtSfwPsOrR2uqkOs5+MOK12tnbLka9aqdEyE9i61/baGcswFSVym1fpvDcQD2e9iqcXi7m+IOOOOtVrIkr57YORvycdTwx0hulTWf6dQtly8wQMIUQaQoh0hQMBhdcG+HShRCXTpJSQjSD5btSOZBexDd39WmWYVgu9lruHBDGvDWHue2qUAJMLW4eD3EefjuYSZ8wd4y6lt8bTKNW0S/cg40J2cy+up2twxFAVY2FI1nF3NQj0NahXBFmz5591vXZ2dnNFMmFcXJyYsOGDdjb21NaWkpkZCTXX3897u7utg5NCNEaxP8AX00FlPrLi9Jrl0/89MyJqYoi6xC78ozDVOck4lyZzrJ1uwjV5RProzBpWRlTorSMaa/lyU9LeX6Qnj6BdnT2VmPnYEIfFE7GRz54BLdH5RbKd7eH1PZ3cvZnZFNWagkhGo0kpYRoYhXVZt789RCjonyIDTY123mn9wlh4aZE3l6TwNwJ0c12XtE4iiuq2Z6Uz7NjImwdynm7qq0HTyzfR2FZNS72WluHc8VLyCqm2qwQIU3Om8Xbb79NTEwMzs6nf71LSkqaOaLzo9FosLev/eKisrISRVFQFOUcewkhBLXVTKsepUFCCk4uU8GKR0DvBIUnID+JxIP7yEk9RneXPD7bmkFxJdzVXcf9P5Tj46jihcEGTqRW4uSpAbWRR0eGERgSBsHtWXN1SG3CyS2U+x8LBkPt71uZb1qI1k2SUkI0saXbTpBRVMEjw5umufmZOOjtuGdQOC/8FM99g9sS5C7VUq3JlqO51FgUBrRr+f2k6vRr64FFgS3HchgR6WvrcK548WlFqFTQ3keSUs0hPDycf/3rX0yZMuW063fv3k1sbOwFH3fDhg3MnTuXHTt2kJ6ezvLlyxk3bly9bd577z3mzp1LRkYG0dHRzJ8/nx49epz3OQoKChgwYAAJCQnMnTsXD4+WP2RYCNECJG+uN2Sv2qyQVqwQ7KrmcK6ZVUdquL9nBt8+MZpFu6v54UZ7/thTxZ5MC92HGQgzqam0cwb/jrzzUAAG73BwC+Vf00JOVjv50VWqnYS47LWqpNT53JgJ0ZJU1ph5//ejXBvjT6iHQ7Off3L3IOavPcL//XGMOddGNvv5xcXbkJBNiLt9q0omBpjsCXG3Z/PRXElKtQDx6UWEuDs06cQK4m/dunVjx44dZ0xKqVSqi6pAKi0tJTo6mltvvZXrr7++wfqlS5cye/Zs3n//fXr27Mm8efMYPnw4hw4dwsurNqkdExNDTU1Ng31//fVX/Pz8cHV1Zc+ePWRmZnL99dczfvx4vL29LzhWIcRlyGKGolTIT0LJq+3tVJ19lGW/bWdScD7ZJRZuWV7Or1PsicuyMPrzMtIecsKiQG5Z7e+8/hG+RHYLg4gobhkWwi2mEDCF0Nv172onow0vUQhhW63qTvVcN2ZCtDTf7kwlo6iCewaF2eT8Rp2GW3oF8+GGYzw4tB0mB51N4hAXbmNCDv3btr6C9F5t3PnzWK6twxDUVkpF+EqVVHN58803qaysPOP66OhoLBbLBR935MiRjBw58ozr//Of/zBr1ixmzJgBwPvvv8/PP//MJ598wmOPPQbUVmmdD29vb6Kjo9m4cSPjx4+/4FiFEK3UKb2dyE/iaPxu3M2ZuFal8ey3h5gWrSHUVYX3GyVsnGFPG5Oa//5axvDJRlwNKqZEaTErEO2jJmW2IwAdPDTMGVRb5eQxdREeoVfZ7vqEEC1aq0pKnevGzFbS09PJyspCp9Oh1WoxGo2YTCZrjwZxZaoxW/jv+iOMivQl3MvpvPZRFIWSkhIKCgqoqKjAbDaj1Wqxt7fHZDJhMBguOI6pvYN5//ejLPkzmfuHtL3g/UXzS84tJTm3jKtGXfoQGkVRKCoqorCwkIqKChRFwc7ODkdHR0wmEzpd4yYqe4e58+W2E+SUVOLhqG/UY4vzpygKB9KLuGNA4yfELRYLBQUFFBUVWZMwWq0WJycnTCYTdnat6tai0fj4NP3Mqv9UVVXFjh07ePzxx63L1Go1Q4cOZcuWLed1jMzMTOzt7XFycqKwsJANGzZw1113nXH7ysrKesm3oqKii78AIUTzsM5kl4iSl0h19lF0JSn8/ucunKqz6GoqYfYvFfQN1HBDhJbbFpcyu5eOMe21GO0sKCo9KvdQ/njOg5C2EWi9wvjjphBwCYQvJjMtJoPT95VS1c7CF9ynmS9YCNGaXJl3jo0sPz+fQ0cOkFeajJ02GJXGAVcduLs6ERgYSHBw8BV7k34l+2FPGifyyvlgSrdzbltRUUFiYiKpqamUl5ejMVdgqSmi0pyPnc4PtV1tUstkMhEUFERAQABqtfq84nB31DOhWwCLNydxe/82GLQyNr+l25CQg51aRe+wi5/9qqSkhKSkJFJSUqiuqsRQkUG1UkG1ygxaL9R2LqhUKtzd3QkODsbX17dRZobs1aY25q3H8hjdWYbw2UpqQTlFFTV09D2/hPj5KCgoICkpibS0NKgqwVB+giqlgmo7B8xaTzR2zqjVary8vAgJCcHTs/VV+rU2OTk5mM3mBkPtvL29OXjw4HkdIzk5mdtvv93a4Py+++4jKirqjNu/8sorzJkz55LiFkI0gYoiKEiGk0PscpIOkHE8gUiHPFZuO8Lx/Gru6KbjsdUVVNTA2yMNbN9biZ+Tmq4mLdfHuOMfFAxtI1j7UTAqt1AwhfDYgyG1iSW1htPOrTvy9ZOz76mon5g6eU8x4lWQvlBCiLO4rDMlzfVtXlVVFScKdrHIbgVUganGQkCFEeORWIz7hzIoOIHYyA4EBwc3yoc+0fIpisJHGxMZ2N7zrDNfmc1mDh8+zLFjx6AkgeNFq9ltOc4unYXyk0knVbmCnxnaWhyJLh5IXl4fDh8+TEREBH5+fucVz2392vC/rcf5blcqk3sENco1iqaz8XA2XYNMOBkufAa7qqoqDhw4wInjydjn/c6Rsj/YrSpkl96OKnXt7x91mUJIjYowlRuRZaPIyYnAycmJyMjIS25w7O1soI2HA38ey5WklA3Fp9X+vYvwdbnkY5WXlxMXF0fh8Tgcsn/hcPUedulq2KfXU61SQXXtz1R4jYYwjT8dKkaRkZGBm5sbUVFRZ5yNTrQMPXr0OO/hfQCPP/44s2fPtj4vKioiMDCwCSITQtRjrXaq7e1UlHIQl+oM0hIPsWLrYW6LrGJ9Ug1vb61i+SR7Vu+r5sfD1Xx+gz3OWjMejnbgFsajU/zQeYSAf3semhRS21DcNZh+hr9/V1/Qp5WIsTDx09pZ+E5peo6zX21CKmJs41y/EOKydVknpZrr27zq6mp8nTtxS0EVlUoGpUo28bpM9jluxr3mD3LjuvHz0Ru4MTqFfj1jMRqlld/l7s9jeRxIL+LJUT3PuE1BQQG7du2iMvcIh3M/5WttHiVaFZ3MesYSjLPKDYPanrLqbHKVNHapclhf/TNB2Su4xjiC8vJyUlNTiYmJQas9e/IixMOBwe29+GxrMpO6B0pytAWrNlvYfDSXOwe0ueB9s7Oz2b1rF4a0XzhQspKvHVSYjWoiLO5cq2mDi9oVnUpNZVUG6ZZk9miyWV3+GRElRgaVX0/xlmJCQkLo1KnTeVfinU7PNu5skb5SNhWfXoSbgw5v50sbQpmSksKBXVswJS9kIwf4wdEBvUFHBAFcaxeKi8YFO3MFNdXpHDcfY7sqiZUlH9BN8aRP9UTy8/Np164dbdu2ld87TcDDwwONRkNmZma95ZmZmU02nFCv16PXy9BcIZpEZXG93k6W3ER+++MvrvIqoTrvBLd+V8T/rjdyvFCh039LKHvCibIChT+PVXJbpJFOQR484uMBkdHceFUIN5pCwBRK35Mz2aHW4NYUcUeMhQ6ja2fjK8kER+/aIXtSISWEOA+XdVKqub7N8/HxwWg00qEqkqqqKgoLC+lbVUVRyS42VP7AZt+dtC/fwxsbb+VodgkTh/TAza1J/iSIFuKTTYm083akb/jph1+lpKSwZ88eqrK+5yPzRtL0GgYpAXR3uQknOzeMRiNOTk7Y2dlRWVlJYWEhPaoqKCxYzRrzRv5bvZKrsv9kQM3tFBcX06NHDxwdHc8a0829grh10Xb2phQSHejaBFctGsPuEwWUVNZw1QU2OU9ISOBo3DYsqe/zkjGHEkcNA+yi6eE0Bnu1PQ4ODjg6OqLRaKioqKCwsJD+lYXk5C5jpSWe/5Z9xtUVHeir3ERBQQHdu3e/qB5mAL3auPHFX8fJLq7E00k+vNpCXZPzi00EKYrCvn37qNjzLeVZS3jGzYBGZWKYsT/djf3Qq/Q4Oztjb2+PSqWivLyctkVFDCtPJy37U77VpvHfovcYoe+P+aCZgoICunTpcs4EurgwOp2O2NhYfvvtN+tsxBaLhd9++417773XtsEJIRo6pdqJ/CQKThzArigFx4oU3l+5lyH+FbR119D9oxI+uMZIFx81jywu5bvJ9gS5qOgZoKPaOZTQ8DZkf+6Hxr894aYQPn48BEwheBqcsdnAabUGpJm5EOIiXNZJqeb6Ni8wMLBesktRFPLy8jh2zAfnjC50L1jFUu16Kvw/4IcDYziSb+bhsbE2aYoqml5ybilrDmTyynVRp/1AePjwYRIO7CMz/T3e02cRrLbnAadb8dQHExgYSGhoaIPhLhaLhezsbI4c8eKG7KH0zl7IEs0xjuS/zhTzLCo3VtKrVy9MJtMZ4xrQzgt/VyP/25osSakW7I+EHFyMWiL9z2/YVV3yIDf+dxKy5vORs46OKh9muc7EpDURGhpKcHBwg4kXzGYzGRkZHDnix7ScZI5kzmeR8SCHc19hsuUBKv+opHfv3jg4OFzwNfQ+2Vfqz2O5jIk+vyGmonHFpxcxKurihk+azWZ2bN+Occ8H/Fy1jm89HImx68AYxwk4650JCwsjKCiowd/X6upq0tLS0B4J5aHMXfyV9xGfqTZwtPow45VZlJeX07t370Zvrn+5Kykp4ciRI9bniYmJ7N69Gzc3N4KCgpg9ezbTpk2jW7du9OjRg3nz5lFaWmqdjU8I0cz+Ue1EfhJ79+yBolQ6O+Tw2u/FtDGpmdBJy4QlpdwUqWVGFx0ZeRWUeGrB6MaCaeG07RiByq8tu68NqR1iZwrhYWd/a/WRDIwWQlwuWlVS6lw3Zi1FXfNgd3d3cnJy2LPHnjvzY/g2fz4Hgn7CIT2DF75VeHJc1/PuCSRaj0Wbk3A1ahnXxb/BusOHD5MQv4f96a+yxL6K/oo/Q9zvwNPkSUxMDE5Op29KrFar8fb2xtvbm7S0NHT7HHgkdysLS7/g/eIPmGqZCH9Cz549z1iFp1GruKlnEPPXJvDkqAhc7KVioSXaciyXXm3c0KjPXeGiKAp79uyhKH4N6/I/4DtnA8P0fbjKYRQ+3j5ER0efsdpJo9Hg7++Pn58fycnJKPvdeCn9S95Q7eCT/De5WbkHZbNC7969z1mF909ezgbaeDpIUspGCsurSckvJ8L3wj+yWCwWtm39E7edr/G2Jp5Njk7cYH89XfVdCQwMJDIy8ozVTlqtluDg2uR6QkIAXeL9iD4xjxcc01hU8A63cA+bN2+md+/el/3wry+++IKxY8fi4OBQ7/HF2L59O4MGDbI+r6sAnzZtGosWLWLSpElkZ2fzzDPPkJGRQUxMDKtWrWrQ/FwI0UgsZihOr0045SVSln6YzKRDhOpy+XP3QfafyGNmVx1vbankYI6FD8YY+XVzJToNdO6pp1uADg9vPwjryE9vBKL3CgdTCM/dEQKmYDC4cO4pcoQQ4vKhUhTldPN3tkjr16+vd2NWp+7G7FyKiopwcXGhsLCwWRuvVldXs3PnTjLSk1mX/SbrtKWEp/XCzXkcL03sgZeXV7PFIppWaWUNPV/+jWl9gnlkeId6644ePcqhuF3Ep77CYocqrlN3oZvLeMLCwoiIiLigYTbl5eVs27aNoozdfJv/Pnv1ambZTybIKZa+ffue8ec7q7iCPq+s5cnRHZnRN/SSrlU0vvIqM53n/MJToyOY1ifknNvHxcVRuPt7fi5cyM8ORibYjyXa2IuIiAjCwsIu6NxFRUVs374dS/K3vFuzhmw7PTNc7yHEqQ39+vW74KF8Tyzfx1+JeayZPeCC9hOXbuuxXCZ9+Ce//qs/7bzPf/Y9RVHYvm0bzluf51XtIfYa7LnJaSodDB2IiYnB379hov1scnJy2Ln9LxwS3uAp+wzs7FyY5noPga6B9O3bt8XNStuY9wjOzs7s3r2bNm3a1Ht8ObLVvZUQTaqyGPKTIT8Jc85RNEXHyU85zKotcdwYVsLu1HJe3ljJVxPs+e5gNf/ZUsWGGQ5sPlHDngwLd/X3IV3ti8U5AP+wTnByJjtMIXBKtZMQQlzOzvceoWXdEZ7DwIEDaUU5NCutVkuPHj3Yu9fAINVjmLNe5g+/P6lJNTD3ezVPT+onN3KXiR/2pFFWVcNNPYPrLc/IyODA/n0cSX3VmpDq7jqByMhIQkMvPDlkNBrp27cvO3YYGK92wJL3JouUL5mldmLrVjuuuuqq0yYRvJwMDO/kwxd/HWd6nxBpPNzCbE/Oo9qs0Dvs9L3ITnX06FGy49azrmAhPzsamehwAzH23enatSu+vhc+bMvZ2Zl+/frxp50dDyYamV/5A58VLOA29UNs3br1gpMIPULc+HzrcfJKq3BzkOFazSk+vQidnZo2HhdWmbN//36cdr7NfLuDxBkcme48k7b2benR4+L6IHp4eNCv/0C2aDS8fuBVnnTI4LOC95mpeoDt27fTo0ePS2qo35Kdeq/SGu9bhLjsWSxQnFZ/iN2uHYTr89EVH+f+b1N4daiByhqFgLdKKHjUidJyhcV/lDM52J4AFy039faEsBiujQ1m3H21Sac+plD6nKx2kvlnhRDi/LSqpFRrplKp6Ny5M2q1mqHKo1Rlv8wOv/WUJHvy8QoN914/SPpsXAY+33qcge1rezfVKSoqYufOneSnLeD/HCq5RhVJd9cJREVFERISctHn0mg0dOvWje3ARMtdLCz8L4uKP2aW+gH++ktPv379TvuBb3y3AGYs3EZcahFRAZc+XbxoPFuO5uLhqKOt19mHy2VnZ3Nsz2aOZ77Dclcj441jiTZ2o1u3bpc0ZEen09G7d2/+VKm4P6GYlyy/83n+u0xnNrt27aJ79+7nfaxuIbX9zbYn5TGsk/TPa07xaUW093bCTnP+CZ/jx49j2fo+31RvYqOTE1OdbiHcGE6vXr1wdXW96Fjs7e3p068/W4AX45/nAed8vipcxM3MIj4+nsjIyIs+thCilbKYm2eWtsoSa8LJnHOMmtxj6EtSWL5+F50c8mlnMjP+qzJm99bRJ9COSe+V8OUNRqJ9NLgbVVRrXfEIaUP8W+7o20cS4N6GVfeGgFsoHk5+jNPUfoySr/eEEOLSSFKqGalUKiIja2foG8Uj5Oa8TEHA13weF0Sg21+Mv7qvVK60YvtSCtmXWsjHU//uBFBTU8O2bduoyfiW+foUell86O1+Ix07drykhFQdtVpNt27d2FJdza01t/B22RK+LXifqXZPEhcXR+fOnRvsc1W4B55Oer7ZmSJJqRZm89Fceod5nPX3QFlZGbu2baHm+FzedtUxSNudrsZedOnSpVF6yGi1Wnr27Mkf1dU8cSiDJ/WH+a7gYyap7uHIkSOEh4ef13ECTPb4uRjYnpwvSalmFp9eRCe/86++zc/PJ2XzMo4VfseX7iautb+WDoYO9OjR45ISUnWMRiM9+/Tjz6qHefngE/zLPZkVpd8xJvEG3NzcpLeiEFeS+B9g1aO1M9DVcfaDEa9BxNgLO5bFcrK3U6I1+XT8cBxl2cl0MGSzaFMq9loVEztpmfx1Gb0DNMzurWf7oQpc29jRzt3APYODCe/YDoLbc+C7EOsQuxceDQajKwAXNhheCCHEhZKkVDNTqVR06dKFiooKJlbdyvySjwnzf4f/bHmWjoGHiezY3tYhiov0+V/J+LoYGNj+78l49+7dS3XWdt6zbMJX7cAIj7sJCgo67w/250OtVtO9e3f+qKzkzuRjvMCfrMv/P+xUd+Du7t6gD4ydRs24GD+W7UjhiVEd0dldnsNnWpviimr2pRYysVvgGbdRFIVdu3bhlPR/zHYxE6UJZYjjtXTo0OGC+/2cjU6no1evXmysquSxhOd5wjmdoNJf0RzU4Obmdt5DubqFuPFXYl6jxSXOrcZsISGrhPGxAee3fU0NcVvXYX/8Xd70MtFH34uehp5ER0fj7n7uYaTny8HBga59BrKnfDaPHX+Jl9x3EFwZjt0eO1xcXC66CbgQohWJ/wG+mgr8Y0hrUXrt8omfNkxMVZZAQW1vJ0vOUfJPHMLdnMmBAwfYeeg4N0eq+WxvFRuTzXwwxsiyLZXklCm8PMSAj6Mag70T+HXgg4d9cfQJB69wXpoaYu3tNEgjH4WEEMLW5DexDdRVt5SWlnJr5RDmqdcS5fQhb/x6N/N8vRvlm2nRvIorqvl+dxp39A+zDplJSUkhPfkIvxYupkhvxxTXe/B28z5t9dKl0ul0dO/enY3l5dx5/DDv2CcTULIOu712uLm5YTQa621/Q2wAH21MZN2hLIZLFUuLsC0pD7NFoc9Z+kklJCSgPvoL/9UcwKhyYZzzNPz9/Gnbtm2jx2Nvb0/X2G7sLLuXaalzWOK0gQB9e4y7jAwYMOC8+kt1DzGxYl865VVmjDpp6tocknJLqaqx0N7n/Bqcx+3bS2D8G9zpbsBX48MI+1GEhYUREHB+Sa0LYTKZaNNjOMayFEYWL+F75RsC7ALZtWsXfftKpbAQlzWLubZC6p8JKfh72Y/3Q/puyjOPsfbP3YwOLOFYSibPrq9kyXVG1h6r4e6fKzh8nyOFmTUk5NSA2pEBnf2J6eYDUZ2ZPTTE2lR8hOvf1U4X3hVPCCFEc5ESCRvR6/XExsbiaRrG9TXe7Hc7TnL+Thav3o7FYrF1eOIC/bgnnYpqM5O611a5lJeXs2/fPrIyF7DWqGG8YQSeRl9iY2ObrLGvk5MTnaOj8fO5l+FlFr6tWE1uZSZ79uxpsG0HH2c6+Tnz7c6UJolFXLgtR3PxdTEQ7G5/2vUFBQUcj/uTHfn/Y69ez/XOU3FzdCMmJqbJYvL09CQ0sju9TNOIrqxkWdFi8krziI+PP6/9u4e6UWNR2H2ioMliFPUdzCgGav8fP5f09HQ0OxexQJ9Brp2eiU434+nmSceOHZssvqCgIPSdr2O60gn3mgq+Kl5CTl4OR48ebbJzCiFsrKoUdn9uHbKXXmyhrLo2EfX875Vkl1qorFHwffE42avmUr7nW574ah+VhVmYDCr6tHFC8Y1hwMgb2LfkcRjzNr2eWMFzyw/Dk5kEPhdP5BNrYcw86PcgRFwLvtHWhJQQQoiWTSqlbMjd3Z127dphrrmDPTnPU+n/NYvjOtKv/UFiO0fYOjxxAZbtOEH/dp74uNTOeLd3717IXsuH+iz6Kv60c+xP586dG1QsNbaAgAByctoxtGIqu8oWsSr/Yyaq/83x48cJCgqqt+0NXQN4ZeUB8kurMMnsaDZX20/K/bTVIhaLhV07d2KX8gEfuRgZYriKEF0oXbt2vaAZ8S5G27Ztyc4eyJ37dvMvJZ7fipZhSJ6Cr68vnp6eZ923nZcTTgY7tiXlndeMguLSHcooxtNJf84ZD6urq0n4azXlectZ4eXKRIfr8dZ707Vr1yavWIqKiuKP7DuZs+df3O6Rycby9WgPafH29sbJ6fwqvIQQLYjFAiUZ1r5OSu4xVAXJ/PHXbryULNrZF/DAygqu62jHwBA7Bn9axn9HGRgUakdBhUJ5DXg6qPhhsj0uHQagaz+UPSeH2OlNIdx1MrmkteU1CiGEaDJSKWVj4eHhmNx8GO8wgXStQoTpA95Zd4yCggJbhybO05GsEnYeL2BCbG2VVGpqKrlpiXxb/hNGxY4hptsICgpqtma+nTp1QuPRldtrwtmjLmRfyRr2799PeXl5ve3GRPthtiisjMtolrjEmRWUVRGfXkTvNqdP3Bw9ehTjidW8Y8whUGWiv/0w2rVrh8lkavLYVCoVMTExFAXczD1FCpvM8RytOsKePXswm81n3VetVtEt2MS2JOkr1VwOZhTT4TyG7sXvjyPoyHu86u5CR7t2ROuiiYqKwt7+9JV6jUmj0RAV25sq/zuZVljE2vK1ZFRnsHv3bhTldEN7Wp+VK1da+7yd+liIVquqDDLj4eAK2PJf8pfeT9K80fBud36aauLrmeGwcCT33zGDF158EfZ9xY9/7GVfUjYAw9o7EOxS+7Fj310ODAqt/ULlP8MNBJ1c3t1fg27wo7XVTp3GgV+MVDsJIcQVQCqlbEytVtd+4CsqYnzpZr5yTcUlaS/fb3Rk6jUDpMdGK7BsRwouRi1DOnpRVVVFXFwcuVmf8KfBjpmGsbjam4iIaL7KN61WS3R0NNvKpjM67Rl+MKwjpLIH8fHxxMbGWrfzdNLTJ8yDH/ekcVPPoLMcUTS1P4/loSictpqotLSUxPgdJBZ8zVEXI3c7T8HN1a1J+kidiYODAx0iY3AonE634o/4UfmcQO1jJCQk0KFDh7Pu2z3UjffWHqHGbLH2WxNN51BGMcMizj4LY15eHuxcwiJdFiUaV2Y6Xoevr2+T9JE6Ezc3N9yiRzBy3R+srjnAD8XLuFV9JydOnGhQ1dka9evX77SPhWixLBYoybRWO1VnH0FbfIITRw/y174EbmhTxqojNXx/sJoF1xhZsrWSnekWFo0zorJU17YmMIXw7xu9sfcKhaAOvDYhBEyhYApmtN4Z5kVCUTqnn19FVTsLX3Cf5r1uIYQQNidJqRbA2dn55DC+WwnIeR6d71d8tCuS/lHHCQ0JtnV44izMFoXlu1K4NsYPg1bD3r37UeXt4RO7FHpYvAiz70FkZCRabfMWnXt5eeEfHMbg0pvYUvE/NhQsxllzP8HBwXh4eFi3GxPty2Pf7iOrqAIvZ0Ozxij+tuVoDkFu9gSYGlap7Nu3D+cTi/nI2cBVum74a/2Jjo5u9oR1SEgI6elXcfvO37lPSeaPkpUYjhoICAjA0dHxjPt1C3ajtMrMwYxiIv1dmjHiK09pZQ3H88rO2uTcYrGwf/sfaHO+4RtvZ8baj8Jd505UVFQzRlqrQ4cObEqdwRO77+curxS2VW5DH6/Hx8cHnU6GFAvR6KrKoOA45CdCfhI1OcfYtmMnPU1F5KQl8fTqQt6/xsDOdAvXfFFG+kNOZKWZWRVXxQ1tjHT0c8Hg5QERXbivTzCqkw3FR5tCwCUQNHacNbU94rWTs++pqN/w/OTfsxGvglomxRBCiCuNJKVaiPDwcFJSUhhfPJg3Letpr1vGJ+u0PDPFr9kTGuL8bUjIJrOokvGxARQVFXE8OYnt+UsoNWoYbJqBj48Pvr6+NomtU6dOZGRkMDXxV+bZZxBdcQCnOCf69+9vbbY+opMvT30Xx4p96UzvG2qTOAVsOZZ72ln3MjIyqEr6iy81CTipTAxyvIbQ0FBcXGyT3ImKiuKPzKlMPf4UC1Vbia7uR1xcHL169TrjPp0DXLBTq9h1okCSUk3scOa5m5wnJSXhm7CIR01GgtV+9ND3oEOHDhgMzZ+UVqvVdIjpxfG0yVxb/CW/sZLOus4cPHiwSWYpFeKypyj1qp0qM49AfhL6khN8vGoPo4NK8XFUEfthKd9OssfHUcX4d0rYe6cD9nYqfJ00mJ2DiQoKZmcfbwjrRKwphI9OJp+CjSbqviq9qK9FIsbCxE9rZ+E72fQcqK2QGvFq7XohhBBXHElKtRBqtZqoqChKS0sZmLGF3V47OJF0NWPjDtC9i9yct1Tf7EihnbcjUf4ubN26FSXnZ5bbK1xn1xOTztMm1Qd1tFpt7SxapbcSlfsKKyxf4advR1JSEm3atAHAxV7LgHae/LhXklK2kl1cyeHMEu4ZFF5vucViIX7/fqoyF7HR1cjNDuNwNjrTvn17G0UKjo6OBLfvjFveKH40/8rq4q9x19xJRkYGPj4+p93HoNUQ4efMruP53NJLKj+b0qGMYtQqaOt9+sq16upq0nf9SlrFVg47unGX47WYXE2EhIQ0b6Cn8PT05HinG7hlw1pWW0r5rXw1xmQjwcHBNku+CtGiVZfXVjvlJVqTT/H79mAoT6eNJoNX1hXQ3V/D0DZ29P2whMf66RkfoWVnUjm9PXX4erjy+sRgPLp0wuDXjtRxIWCq/fecSyBoar8IbbKv0yLGQofRkLy5NoHm6F07ZE8qpIQQ4oolSakWxNPTEx8fHwaWT+PP0vdp77qYTzY/SES7MBwcHGwdnviHksoa1hzI5IEh7cjKyiIv4wQ/Vv6Oj85AtPM1hIeH26T64FRBQUEkJ4dyY34sT6r2sLfkVwyHDQQGBlor8MZE+/HAl7tJyS877fAx0bS2HMsFaNDkPDk5GceU1bxsX0o7AojQRREREdHks+2dS9u2bVl3YiR3x6/lGdNxulYdwiHeAS8vL2sF3j91CXRlQ0JOM0d65TmYUUyIuwMG7ek/3B0+dAj/pE94zGSiiy6GQLtAIiMjbd67MKJTJ7YnTuP2lFd4R7WVHoZeHDhw4KwVeEJcthQFSrKsCaeqrCMUph7G05LJ5l0HyMzK5rqOWl7eWElljcKcQQYWra6gjUnNnd10tPPQ4OntC6EdWfOGL87+bcG9Df+dVVvthNHEUFv3K1VrIPQq28YghBCixZCkVAvTqVMnsrKyGFPkw/euGegTk9i8az9X9+th69DEP6yOz6Ci2sI1UT7s37OV4uzP2WLUMkM/CkejI2FhYbYOEZVKRVRUFJvyxzMybRerlT+IqBrMkSNHaquogKEdvTFo1fy8N507Btg+5ivNlqO5hHs51uvpVV1dzZEDcWTkf8sxVz13O0/AZDK1iBm87OzsiOgURVL2zXQr/j9+5RvCtW05fvz4GStuugSZWLwlmfzSKkwO0iuoqRzKKD5jP6nS0lLK9nzHz9pMyjVuDLcfgZ+fH25ubs0cZUNGoxGfmKvxy/iZr82JrCz9Ea9sL7Kzs/H09LR1eBfs+eef5+GHH24wk2F5eTlz587lmWeesVFkosWorjjZ2ynJ2t8p78Qhtu05wHDfAv5MLOLzfTW8M9LAkp1VfBVfzS9THMjPrSazVAGdE5MGhqNxDYT2Ubw+OsRa7XSDSyDY1f6edbXhJQohhBDnS5JSLYy9vT1t2rShqnIqvxW8jr/X/1i0LYTukQW4urraOjxxiu93p9E9xIS5OJvqvCS+5AARZlfC7XvRsWNHNJqWUYpuMpnwDQhiSMFw1lhWs71wOcZjRkJDQzEYDDjo7RjQzpOVcRmSlLKBLUdzuKpt/Q/eCQkJuKb9wAvOdvSwi8Dfzp9OnTrZKMKG/Pz8OBbUl2m7f+Y+YwG7K3ZgPGwkICDgtJVcXYNMAOw+UcCgDl7NHe4VQVEUDmUWM7X36YdIHoiPwyXtC770dGaAYQCudq7WxHRL0KZNGzaF3sKDBx/hEc+jHK0+issBFzw8PGxeyXWh5syZw5133tkgKVVWVsacOXMkKXUlUBQozbZWOyl5iRw9sJcQuxwqspN4/uckXhmiJ7lQod8npaTOdiQt28Jbv1YwfIoDvk529OvgDiGduSUykBkeoeDehtFuobWz2RlNhLWy/y+EEEKIM5GkVAsUHh5OcnIy4wra8YnTETyTdrFhhytjh8i00i1FbkklGxNyePaajiQkJJCS8xlHjFruc5jUYipaTtWhQwfWpQ3ihhNr+M64l87Vozh06BDR0dEAjIz05cGlu0kvLMfXxWjjaK8c6YXlJOWW8e8Rfw/dq6io4MSR/SSUrqXExYGBTmPw9fVtERUtdVQqFREREexNv5mrc+ayTllBlCGGo0ePnrbnVaCbEXcHHTuP50tSqolkl1SSV1pFh9NUShUUFKDZv4xPjWUYVB70NfYjNDS0QdLEljQaDSGd+1CWOphOlX+yWr2CNnZtSEtLa3G/T89FUZTTJtL27NnTov4/FpeopvKUaqckLDnHUBcm8+OGnXR1zsffWMF1S8t4ebCe9h5qur1WzM47HAl0VlFapVCuciCwbSirnnJFie5CpHsoq2aHgCmUYJf/Z+++49uqr8aPf7S3bHnJ25Zn7AwnTuLEGSQhDhmQsFehrEJpaQtt2j6MPj8o7dNCofB08RQKpUDZZQYSMsl0nOk4zvC2vPdesqz1+8ONqckOTmTZ3/fr5dcLX13de2QUSffonPONIubf1U6itlQQBEEY60RSahRSKBQkJCRg77+N6JYn0IV8wjv508ic4putDGPRuiP1SIDJ/g6aast5X15PhttMmCqB1NTUUffNvk6nIzomFnXnDawdeI89ne9hrPYjPj4evV7PogkhKGQSNhxtEAPPL6F91jYAZlm+ulAtKSkhsP5TnjCqmaucjkluGlUVLScEBgaii03nxqZ4vqSOfbZsNOUa4uLiTloxVCKRMC3an0NVHd4JdhwoahhceS/5FCvvFR8/gq7xIz4z61mhzUKv1JOYmHipQzyrqKgodsbdyA8ObeMBVT0FjgJ0hTrCwsJOO69sNDGZTEgkEiQSCUlJScPeB1wuFz09PXzve9/zYoTCefF4oK912EDxurJjuNqriJI08Or2CqKMEpbEy7ny7T5uSpVz51Qln+61EThNQUSUgjtmhxGUnIw0Jom29dFIA+PBZOGFR2JBGwASCWIpG0EQBGG8E0mpUcpisWC1WrmqfRr/pzhMaPMeduQauH7pAm+HJjDYujcvIZCmaivW1reoV8u42XgTwcHBBAYGnv0AXpCUlERNzUxuqfiMV7XlzHC0UFRUxPTp0/HTKJibEMQXIil1Se2zthEfrCNQrwIGZ840lB3hsH03LpWBubqlREdHj9qFDlJSUthbdTPXNPyGDXzJdM1sysrKmDBhwkn7Tos28ddtZbjcHmTS0ZW0HQuKGrpRK6REBwyvfmpvb0dT8C9eNoC/1ECGKoPExMSTEoejgUQiIWnyDNorriDDto3Nki+YoJhATU0N0dHR3g7vrP7whz/g8Xi45557ePLJJ4etHqhUKomNjSUzM9OLEfoYt+vir9DmtENH9dBsp/7GUtS9NRw7dpyKyiqujHPySu4AlR1ufn25mj9u6kerkPDEQhVaBSjVGjAn89JDIZgiEyAsiVdu+/dAcf8orpWrhk41+tOqgiAIguAdIik1SslkMpKTk7H1XUdc8yEUwev417HZzJ/WREiIaH/xpuq2Pg5UtvPIglDoKOFfimbmeCIIVkSdsnVptFCr1cTFxaHquBFj/+vs6/oAU10gSUlJGAwGlk0M5bGPj9DSYydIrzr7AYVvbJ+1jQzLV0nMkpIS/Bo+4l2DlvmqTAxyw6isaDnBYDAQEDeNK5smsYZy9vXtQG8dHPL/9aTHtGh/euxOSpt6TjuMW7hwhQ3dJJkNJyX8io8fQdn6OVvMOm7QLkWn1p12IP1oEBoaijXpZh7Yu4m7NK3kD+SjL9ETGRk56qul7rzzTmDwS6W5c+d6faVMn3Z8Dax/GLrqvtpmDIdlv4PUVed+HI8H+tr+Y6C4lb6GEvIOH2VOUBcFpZX8/dAAv79CzUcFDp7eZWfffXqqyh0cqHVzZZyaeSkRTNeEwtQ0frcodjDhFGDhFlMsaANBIiFyZB+9IAiCIIwr4hPTKBYVFUVpaSkr2qfzF8Uhwlqy2Z1n5JorRFLKmz7Lr0OtkBLqaqKk/R1a1TLuMN6M2WzGZDJ5O7wzio+Pp6IijRvLtbyirSDD2UpJSQnp6eksSTXz2MdH2HS8kVszRn9Vgq9r6x2gpKmHBxYNDpfv6+ujoewoBxyHUKgNzNEuJiYmBrVafZYjeVdSUhI51uu5tu5J1rGLWdoFp6yWSov0RyqBQ1XtIil1ERQ1dJ80T6qtrQ1t4Qe8opcQLPEnTZlGQkLCqFmE4XQSUtNoKlnO/L5NbJdsZopyis9USwEsWCAqmr+R42vg/TsAz/DtXfWD2296Y3hiyjkAndVDq9h11RSh6a2Bjkr+sPYYP0j30OfwMOfvfRx9QEd9p4d73+nj2AM6DCqICVBDyESWxUay+OYoiJzAclMsy02x4BfFBMXofg0WBEEQBF8nklKjmEQiITExke7ua0lszoXg9XxcMJe508RsKW9ak1fH7Egdmr4jfKRoZ64nkgBF6KiukjpBoVBgsVhQtN2AX/8b7O36gIC6IJKTkwnU68iwBPDF0QaRlLoE9lcMzpM6USlVUlKCqfFTPtJrWKCeg1auJSEhwZshnhO9Xk+gZTLLGibyEVb29u1EZ9URFxeHUvnViF6dSk5yqJFDVR3cIp5fI8rl9lDc2M3VU8OHbS8+fgRN6+d8adZxgzYLrVpLTMypV+cbTYKDgylNupF79qznbm0bRweO+ky1lPANuV2DFVJfT0jBV9s+/QEUr2fvwcNESluJkDbyo7W93JuuJC1URtwz3Wy4XUt6mJTDtf30TFIRZI7k/+42IEmbRnxwPMfvjQWThUhTLD/SBYFEwugZ+y8IgiAI44v4dDfKRUREoNcbWaaYQYnGSUPXHg4eLfJ2WONWUUM3hQ3dpOr7sLa9T6tMyky/GwgLCxs2P2Q0i4uLY8CUxk39WrI9FbQ5WigpKQFg6cRQcspa6O53eDnKsW+ftY0Ifw0R/hpsNhuNFYVsH9iPVCJjlmYhsbGxo75K6oSkpCS6Qq/j2u5esvt30Ovopby8/KT9pkX7k1vV7oUIx7bK1l7sTjcT/mPIeXt7O9qiD3ldLyFQYiRNmUZiYuKor5I6ISFlMlrTFWTa+tlm20xPbw+1tbXeDku42Cp3D2/ZA9YWO9hc7gTg/s9svJTdAnlv8dfP9nOopAo8bmbFaPGLSITkFVjf/AnTv/M8kts+4M0tRwn5bTPSnxWQ9ft9KG54CRb8F0y5CaJmgj4YRtnCJIIgCIIw3ohKqVFOKpWSmJhIb88qLM37UQeu55Pjs5k5pXXUDtQey9YcrsWgkjFJXsUf5U3M8oQRqAgb1XN/vk6hUBAbG4uq7Tr09jc51PUpATVBJCUlkZVi5snPjrOzpIUVk8O8HeqYtr+ijYx/r7pXXl5OYMMa3jdomKOahU6u84kqqRP0ej0mSxrL61P4mCr223ajr9CTkJAwbK7OtCh/3t5bRafNgZ9m9A3a9lVfrbz3VfteaXERqtYv2GTWcq1mMTqNzieqpE4IDg6mNPF67t37Bd/RtHDccRxDiYHIyMhRt7qpMIJ6Gk/a1D0AA67BKqmfzlHir5ZAytW8du2VEDA4VPx23VfJJdEcLAiCIAi+RVRK+YDIyEi0OiPLZJMo0Nkpac0n/3ixt8MadzweD5/m1TEtyEN9+/vUKeTMMl5HcHCwz1RJnRAfH0+/aSrX9CvZ5Sml19VLaWkpUQFaksx6thQ0eTvEMa3H7uRobScZlgAcDgc15UXstO/BLZExS7uI6OhoVCrfGjZ/olpqRU8fe2w7sA3YqKqqGrZPeszgzLX8mg4vRDh2FTZ0E6hTEmwYfM709PQgKfyMd7UuTOiYpppGXFycz7W+xadOxc//cjJsdrb3baGnp4f6+npvh3VW99xzD93d3Sdt7+3t5Z577vFCRD5Ebz5p0y2TFFybMpjETgqUEaKTQsZ9kHYzRGWAPkRUOwmCIAiCD/OtT6jjlFQqJSEhgUjT9YQ53QQHrGHt8Ra6urq8Hdq4klvVQU27jdmGJj6R1zDDZSJEGeNTVVInnFiefJpuKR6Pm8Pd66iursZut7M4xczWoiZc7lPN9BBGQm5lO24PzIwNwGq1EtC0iff1amYrpmGQGYiPj/d2iOdNr9djjEljhSeWNomdfPthysrKcLvdQ/tYAnX4aRTkVnZ4L9AxqKihe3iVVEkJhubPWafXkaldgEap8akqqRNCQkJoTbiRu7v6qHU3UuYso7S01NthndXrr7+OzWY7abvNZuONN97wQkQ+JGbO4Cp7nC7JJAFjxOB+giAIgiCMCSIp5SOioqJQafxYLkmkQN9DfnMZxSWj/8P5WPL54Tr8VRIMAx9SoVAwy3A1JpPJZ9so4+Li6A+cx5U2Dzsch7C77FitVrJSQmjrHSCvWsz+uVj2WdsI1CmJDVBTUV7K0Z4v6ZVKmaXLIjw8HK3WN0fuJiQk4Aq+mvl9Nvb0bcJmsw2bAySVSpga5c8h8dwaUUWNXyWlbDYb/YWb+FzRgVKiYKZqJrGxscPaKH1JbMo0QvVzSR5wssO2lc7OTlpaWrwd1il1dXXR2dmJx+Ohu7ubrq6uoZ/29nbWrVtHSIhYPfeMpDJY9rt///L1xNS/f1/29OB+giAIgiCMCSIp5SNkMhkWi4U40034uT0EGT5h49G6U34bK4w8t9vD2vxaZphsbPIUM8GlIUKd5JNVUieo1WoiIqOYp55Ht8TN0Z5tVFRUMDncSIBOyWbRwnfR7LMOzpOqrq7GvzGb93RS0mXx+Mv8fWqW1NeZTCZkUTO4bsCfGroocZRQWlqKx/NV1V16tIlDVR3DtgkXzjbgoqK1lwn/TkqVl5cT3PAZ7xuNzFRlopFpsFgsXo7ywoWFhdEYez33dHRR6rRS66wdtdVS/v7+BAQEIJFISEpKwmQyDf0EBQVxzz338IMf/MDbYY5+qavgpjfA+LW5hsbwwe2pq7wTlyAIgiAIF4VvfnU6TsXGxlJaWsoVbjOfGpvoqG3n6vJyJk6c6O3Qxry8mg6aehzMCfyc/1UruFu1GIPB4PPfesfHx1NbuZTFddvZ5dlF2sBiamuqWZQcwpaCRh5eNsHbIY45/Q4XeTUdPLw0mbLSUro7PqfWX841+mWEhIRgNBrPfpBRLCEhgYqyq5jY/zY5kg0kKZNobGwkNDQUGFyBr3Ozg/KWXuKD9V6O1veVNHXj8UByqBGHw0FbwU7KPVbskgAyNZk+OZ/sP0kkEiInziKydAoRzip22rYT0RxBZ2fnqJvlt3XrVjweD5dffjkffvghAQEBQ7cplUpiYmIIDw/3YoQ+JHUVTLhycDW+nsbBWVMxc0SFlCAIgiCMQSIp5UOUSiXR0dHYe6/j454XMSo/IPt4IElJSSgUYiWri+mTA5UYFS6Ospcwl5J47WwSEhJ8fhUog8FAcFgUi5umskl6nCLbAfTlehanpPBhbg3VbX1EBfhmK9lolV/TyYDTTZzBjbI6j3+obSQRSYQ8wqcr704wm80URF3GLXnv8f9U9dQ6awkoCxhKSqVF+QNwqKpDJKVGQGFDNxIJJJn1VFVVElm3hl/6+ZGmSsNf5u+T88m+LjIykr1R13J3+ZM8JT9Gt7ubsrIy0tPTvR3aMAsWLADAarUSFRXlc4PlRx2pDCzzvR2FIAiCIAgXmfjE5GPi4uJQaGJZ5NDRYCpjc1U/lZWV3g5rTPN4PHxxtIEb/LP5UiNlnnImGrVmzHzjHR8fjyzoajJsdnL6NtLb20ui3oVCJmFzwcnLcwvfzP6KNgwqOfKeBvpbPyVfrWK2bikmk2lYZYUvS0hMIsZvCREOJ7t7N9HW1kZHRwcAfhoFiSF6DlWJuVIjoaihm+gALRqFjPqiAxyxH6ZJLmWe+jKfnk/2n2QyGcGTFjGXGP7RAgaJHrvdPmpbQGNiYpBKpfT19VFYWEh+fv6wH0EQBEEQBOErIinlY7RaLWFhYczUL6NVLsHp+JyjxeXDVrgSRlZuRSvNfS5c6o1oPBJSjUuHLjrGgsDAQHRBUawgnlJpH5UDxTTUVDA7LpAtYq7UiNtrbSMt0oCj/hifyeoJ8+hIUk7w6bk/XxceHk5b+BV8q2eAw85i2l3tlJeXD90+Ldqf3KoO7wU4hhQ1dJNsNtDY2Ii58mNe9zeSJI8nVB46pp5TsbGx1IevYoLbxfz0CWRmZo7aStXm5mauuuoqDAYDEydOZNq0acN+BEEQBEEQhK+MjavqcSY+Ph6tYQYz7RLsAQfYVtVPfX29t8Mas/61p5QZygI2age4TJqIWqr2yeXVzyQ+Ph5j4PUk2QfY2/0ZHR0dZMYY2Gttpbvf4e3wxgyny83BijYseheapjV8qdUwW3s5GrWGsLCwsx/AR0ilUmISJpCmmYPB7SbHtpW6ujr6+/uBwRa+4sZubAMuL0fq+wobupkQaqC65BiN3bsoVsqZp1mAn58fJpPJ2+GNGIVCwdTrV6P56RH8I5O8Hc4Z/fjHP6ajo4O9e/ei0WhYv349r7/+OomJiaxZs8bb4QmCIAiCIIwqF5SUstvt7Nixg3/+85+89NJLfPTRR1it1pGOTTiNEyv8XKbKpErloapjz7AqBGHkuN1uNhe3MSXwE2wSCZP9riU8PBy1Wu3t0EZUWFgY0gALq5zBHJa20exsJE7Vg8PlYUfx6Fx+3RcV1HfTO+AixlnNFncBBhRMVc8YU5V3J0RHR9McuoKbu3vZb8+l19VLRUUFAGmR/rjcHo7Xd3o3SB/X2mOnpcdOjL8STdFH/NOgIVwaQrw8fkxVSZ2gUCrBB/6dfPnllzz//PPMmDFjMEEbE8Ptt9/OM888w1NPPeXt8ARBEARBEEaV8/p0l52dzU033YS/vz+XX345P/7xj/n1r3/N7bffTkJCAomJiTz77LN0d3dfrHiFf4uLiyPAfzkWhxuZYTN7KzpobxczWkbazqMVKGyNZOtayfSE4ifzJzY21tthjTiJREJsbCzR/lcT4HJxuHstzs4mEkN0bBFzpUbMXmsrSpmEyb1f8Kleyyx1JkqpcsxV3sHgwgwhlolcJp+EGxe5/fuorKzE5XKRHGpAKZdyuFokpb6JoobB91rtQBuu9i3s1qqZp1mISqUaMzPvfFFvb+/Qyqwmk4nm5mYAJk+eTG5urjdDEwRBEARBGHXOOSm1atUqbr75ZmJjY9m4cSPd3d20trZSU1NDX18fJSUl/Pd//zdbtmwhKSmJTZs2Xcy4x73Q0FC0Oj1LJMkU6frIbSwX1WoXwccHKrjM9C/qFHKmG6/GaDSOmWHUXxcdHY3NL5WVNjk57lJsbhvpZgVbi5pwuUfnQGFfs7+ijUSDk1z7HtwSKTM18wkLCxtzlXcnWCwW2kJWsqynj322nfTb+6mtrUUhk5IaZiS/psPbIfq0woZulHIppupNfKruxw8Nk5WTiY6ORiaTeTu8cSs5OZmioiIA0tLSeOmll6itreXFF18cU226giAIgiAII+Gck1JXXnklVquVZ555hvnz56PRaIbdHhcXx5133sn69evZsmXLmGtFGW1OVLbEBFyPv9uDR/Exx61fzWwRvrmenh4OVLVT5V/OJJcOszJ2TLbEnKBQKIiMiiJdtwAHHo72bCVG0U17n4NcsVLaN+bxeNhb3soqRQ4f6uVMlyejl+rH9HPKYDCgiUpjuSuEFomNIkfRUKtxWqQf+TWiUuqbKGroJsZfSVDjF3ym1zNDnYlcKh+TlXe+5KGHHhqa8/jEE0/wxRdfEB0dzZ/+9Cd++9vfejk6QRAEQRCE0eWcM0f3338/CoXinPZNTU1l8eLFFxyUcG6io6ORq0wscZopNbaQXd9BZWWlt8MaM7YeKmGSdD1H1QoytItQKBRERER4O6yLymKxYDMt5PI+B3vsewhT2fHXyNgsWvi+sbLmHjpsTvylG2mUy5muyxrTlXcnxMXFoQpYxpR+O/t6t9Dd3U1LSwuTI/0pb+ml0yYG6V+owsZuUmR17HaX4ZBImanOwGw2o9VqvR3auHb77bdz1113ATB9+nQqKyvZv38/1dXV3Hzzzd4NThAEQRAEYZS5oHKmuLg4WltbT9re0dFBXFzcNw5KODdKpZKIiAgm+V+DUwJd9k8pt1bgdru9HZrPczqdrMuvRRGwh2CXFIt21rhoiTEYDASaI1gon0id1IF14BgTAyRsL2r2dmg+b2dhA6mSSjZpukgggHB5+JiukjohODiY3oh5XN8nochTR6OrkfLyctIi/QA4WiuqpS6E2+2huKGLq5zreddoYJIyFaPUOC6eU75Gq9WSnp5OUFCQt0MRBEEQBEEYdS4oKVVRUYHLdfJS3na7ndra2m8clHDuLBYLco2FOXYlzcZCDtTbqKur83ZYPq+6upre1nxydG7myicik8jG5IDzU7FYLKgCVpJqHyC3ZwMJugEKG7pp7BKtod/E9uO1XGdYy16Nmum6y8dF5R0Mthpb4hJI0F9GoMvFvr4dNDY2EqqTolPKRAvfBapu78Pt6EfuyaZaIWe2eh56vV4kPgRBEARBEASfIj+fndesWTP03xs2bMDPz2/od5fLxZYtW8bNhftoYTQaCQoKIrNjHjvcWylu2UV5eQCRkZHeDs2nZR+1Eqxbh1siIdV45eBg+XHSEhMSEoLMFM2KpmB+r2ojU9uEhFC2FzVz08wob4fnkxwOB2X1TcQFFeLnMTJROWVcVN6dEBkZyTbzEq63buJ16WGWuFdSU13FpAg/Mez8AuVXtbJCupePDHIiJEFEy6NFlZRwwWJjYzEajUilUkwmE1u3bvV2SIIgCIIgjBPnlZS65pprgMFvvu+8885htykUCmJjY3nuuedGLDjh3FgsFpqbLie+aQv96h2UNCxgUlvbmJ9Vc7G0tLRwuKKOY36tZLjD0MuM4yrZKpFIsFgsyBquIsD+BsW2dcQY7ubLggaRlLpABwsrWOj+krV6NXNUM5FL5OPqOSWXywmNSyWoKpW/U8VB+36MVUamREax7kiDt8PzSfuKalih2cBPNRqu1V42uFCB+DJC+AZ2796NXq/3dhiCIAiCIIwz59W+53a7cbvdREdH09TUNPS72+3GbrdTVFTEVVdddbFiFU7DbDaj0xtYKEmkWNfHgYZqKioqvB2Wz7Jarchtn1CrkDPVsBy9Xk9wcLC3w7qkoqKi6POfzFU2GXvcJST697OrtAWnS8wruxBbDpdj9ttGv0TKdO1l43IYdWxsLF3By7iit4/9th302/uJ1Lqp7bDR0mP3dng+xeVyYa86SL6xDY1EwRTlFKKiopDLz+t7JuEicDqd/OpXv6KmpsbboQiCIAiCIPiEC5opZbVaxdyKUUQikRAbG0tcwDVo3R663Wuorq2jv1/MADpfNpuN/PI6mvTHiHEoCVclj6uKlhMUCgVR0dHM0M7HjhuNbjs9A25yq9q9HZrPaW1tRVq3n01+kCaNwU/qNy7brPR6PYromVw14E8TvZQ4SjA42gBEC995qqurY7ZtPR8aDExXzUIpUY7L16nRSC6X8+yzz+J0OkfsmDt27GDlypWEh4cjkUj45JNPTtrnhRdeIDY2FrVazaxZs9i3b995nUMikbBgwQJmzpzJW2+9NUKRC4IgCIIgnN05J6Xefffdcz5odXU12dnZFxSQcGGio6ORq0O4bMCPCmMNR1rsVFVVeTssn1NRUUFbw072a2VkqmcNJmeixmfLWmxsLLaAy7m8b4Aiz260chdrD5Z7OyyfY7VaCXZ/QblSwXRdFjqdbtwm9WNjY9EGXEGq3c7evq3I+zvx18g5XC2GnZ+P8oI8HLp8eqQSZqlnExwcLNquRpHLL7+c7du3j9jxent7SUtL44UXXjjl7e+99x6rV6/miSeeIDc3l7S0NJYuXUpTU9PQPlOnTmXSpEkn/ZxYGGXXrl0cPHiQNWvW8Nvf/pb8/PwRi18QBEEQBOFMzrnW/69//StPPvkkd999NytXriQlJWXY7Z2dnWRnZ/Pmm2+yadMm/v73v494sMLpnVjJa0bHMtY5/kVF21oqKgwkJCQglV5QQdy443a7qaqqoomt6NwSJhgXj+uWGIPBgCkkgoUNE9gktRIXVMSOEoW3w/IpNpuN2tJ8jhgbCHEFY1HEYbFYkEgk3g7NK0JDQzkWehk3HPsXv1JV0epuweIXJSqlzkN7ezvyks9436gjRhJPgCxAVEmNMsuXL+eRRx7hyJEjTJ8+HZ1ON+z2VatWnffxli9fftrbn3/+ee677z7uvvtuAF588UXWrl3Lq6++yiOPPAJAXl7eGc9xYiXQsLAwVqxYQW5uLlOmTDnlvna7Hbv9q5bbrq6u83k4giAIgiAIw5zz1fb27dtZs2YNf/7zn3n00UfR6XSYzWbUajXt7e00NDQQFBTEXXfdxdGjRzGbzRczbuEULBYLVVXTmFb3AU3aXJq6r6W+vn5cLDs/Empra2lrLCXHYCPNlSBaYhh8TvXWrCSx4/fI9Os52pBKWU0j8ZHi3/e5qKysRNawjm3+GpYoFyCXy8f1MGqJREJMXCIhFXPxcx1kvy0bs+Ja9td04vF4xm2y7nxYy0tx9WyjOFTFt7Tz0Wq14v12lHnggQeAwWTR10kkElwu14ida2BggIMHD/Loo48ObZNKpWRlZZGTk3NOx+jt7cXtdmMwGOjp6eHLL7/kpptuOu3+Tz31FE8++eQ3jl0QBEEQBO9xuV3kNuXS3NdMsDaY9JB0ZFLvrAx+XiUgq1atYtWqVbS0tLBz506qqqqw2WwEBQUxbdo0pk2bJqpyvMhoNBIQEMCc5mm8IMvlYMNhLBUBIil1jqxWK5Udn9LpJ2OG8SrREsPgEP2jJgtXNvnzJ10TEnk7a/aX8hORlDort9tNdXkJhZI85B4ds/UziYqKQqEY39Vm0dHR7AxZwrVVO3hfepArdctprYa6zn4i/DXeDm9Us9vtuAo38LneicoRTKoqgdjYWJHMG2Xc7ku3IERLSwsul+ukxKTZbKawsPCcjtHY2Mi1114LDA7Rv++++5g5c+Zp93/00UdZvXr10O9dXV3jts1dEARBEHzR5srNPL3vaRr7Goe2mbVmHsl4hKyYrEsezwX1JR05cmToA8zXvfTSS9x///3fKKgzeeGFF3j22WdpaGggLS2NP//5z2RkZFy08/kai8VCW/NVmFv30+TZQGtrGp2dnfj5+Xk7tFGtvb2dnrZGDqqrSOgPwBwQNu6rpOCrIfqKuqXonB8SGrKe3eW38oDdjkql8nZ4o1pdXR3+jVtZY1AS3D8ZlUQlnlOASqUiIGYii6otvE4z3arDwCwOV7eLpNRZVFZWomtaz0aTDmPXfOQyuUgGCN9YXFwchw8fPuf9VSqVeP0XBEEQBB+1uXIzq7etxoNn2PamviZWb1vN8wufv+SJqQsqa1q2bBk///nPcTgcQ9taWlpYuXLl0PyCi+FchnmOd6Ghoah1/sx1RFCoa6e4o52KigpvhzXqWa1W2pq+oFClIE62WLTE/Ifo6Gh6AmaywuamU3+Ego4BKiorvR3WqGctL6e8axPtMhlJikUEBQVhMBi8HdaoEBsbS3/QMubb+jk2sA0/pZs9xfXeDmtUc7vdNBXuZbukCjwyEqTTiYyMRKlUejs04RS2b9/OypUrSUhIICEhgVWrVrFz584RP09QUBAymYzGxsZh2xsbGwkNDR3x8wmCIAiC4LtcbhdP73v6pIQUMLTtd/t+h8s9cqMGzsUFJaW2bt3Kxx9/zMyZMzl+/Dhr165l0qRJdHV1nXWY5jfxn8M8U1NTefHFF9Fqtbz66qsX7Zy+RiqVEhMTw9SAVXgkUNL5KTU1NQwMDHg7tFHLbrdTX1fHQed+AhxSZgXOEC0x/0GpVBIRGcVs5Qx6ZW5k2v1sP2K9pC0qvqajowNJ7QHWqO0Yes1MNYZgsVi8HdaoERAQgCN8Fqv6lVTTToipityKVm+HNao1NDQQXLuW9w0GBjqnEa1Vi8q7UerNN98kKysLrVbLgw8+yIMPPohGo2Hx4sW8/fbbI3oupVLJ9OnT2bJly9A2t9vNli1byMzMHNFzCYIgCILg23Kbcoe17H2dBw8NfQ3kNuVewqguMCk1Z84c8vLymDRpEunp6Vx77bX85Cc/Ydu2bcTExIx0jMBXwzyzsr4qJTvfYZ7jRUxMDEpdHLNsKso0xfT0O6iqqvJ2WKNWZWUl8q48dmg8hNsmo1OKlpivs1gsuAKXkWnrxxSwlcONAzQ0NHg7rFHLarUy0LaWfLWKnq4sokxqUXn3NbEWC6HGxUQ6nMj1OyhtG6Cnt9fbYY1alaUFlPTl0CKX0t8+j5RwP9GWPUr95je/4ZlnnuG9994bSkq99957PP300/z6178+7+P19PSQl5c39KWf1WolLy9v6H199erVvPzyy7z++usUFBTw/e9/n97e3qHV+ARBEARBEJr7mtlQsQEAj3uwKqr+nXr6SvtOue+ldMFTyYuLizlw4ACRkZHI5XKKioro6zv5AY2UMw3zPN3Fsd1up6ura9jPeKBSqQgLC2O6ej6NCsht2k5FRQUez8lleuOd2+2msrKS/M4v8AAW7QrREnMKfn5+6M2xLHFH0qru4UhPtWgLPQ273U5LZQFbPBXonSri5alYLBZRefc1kZGRNJkXcWOPjQZFAXZ62Z1f4u2wRqWuri40Zev4QKck2BWBZMDM7NQ4b4clnEZ5eTkrV648afuqVauwWq3nfbwDBw4MLSYDg0moadOm8fjjjwNw88038/vf/57HH3+cqVOnkpeXx/r160UiXBAEQRDGMZvTRnZtNs/uf5aV765k3l/n8V7Re9T9s46WL1oA0MZrkRlPXnEvWBt8SWO9oKTU008/TWZmJkuWLOHo0aPs27ePQ4cOMWXKlFFVtfTUU0/h5+c39DOeql8sFgshAQtJsLuxsgubzXbSzAlhsCXG2VXPJkUbET2hpAcaREvMacTGxhLifxXhDidS9RdUN7aOm0Tv+aiqqsKvaQMbdBr62uaQ6CcjOjra22GNOjKZjHBLMjMVacg8blT+e8kprsPlurQ97L7AWl6Ou20jBzVqNLbLCNNJiIoM93ZYwmlERUUNa6c7YfPmzRf0OWThwoV4PJ6Tfl577bWhfX74wx9SWVmJ3W5n7969zJo165s8BEEQBEEQfIzb46agtYC/H/k79268l5l/msmtT9/KG8ffYP9H+2lZ20JKQApRV0YRsDAAAL8MP1QhXy1eIkFCqDaU9JD0Sxr7Ba2+98c//pFPPvmE5cuXAzBp0iT27dvHY489xsKFC7Hb7SMaJFzYMM/xvGyxyWTC3xRAel0S/9KUYO2sILgiWAw+/Rqr1Upr+yfUK+TEdV5JWHCAaIk5jbCwMI4FTuKqVgWvGio43NHLBKuVtLQ0b4c2ang8HiqtZZTb9uBSKunqmMucmSGi8u40YmNj2R+8lOVNz7IhYBflHQupq6sbN6/T58LhcNBbtI0tym78CKa3YyJJIXqk0gsudBYusp/+9Kc8+OCD5OXlMWfOHACys7N57bXX+OMf/+jl6ARBEARBGCsaehvIqcthd91u9jXso6GmgeY1zUTcE4Gt3Yan1MO1t1zLrN/OYk7kHExq09Dqe8CwgecSBrs6Hs54GJn05Oqpi+mCklJHjhwhKCho2DaFQsGzzz7LVVddNSKBfd1/DvO85pprgK+Gef7whz885X3G+7LFFouF1qbrWN/1NEe71mBpjqWnpwe9Xu/t0EaFrq4u2lub2e4sIMylY6opQQyjPgOpVEpMbCyaugVI2cqxvu3U1hpJTU1FoVB4O7xRoaGhAX1DDh/ppEQOJGCX6LgsLcHbYY1aOp0OdXQ6y+oC+URuo8RZiNXqL5JS/6GqqorAxi/4zKAnU5XJhj4Z18eJtqzR7Pvf/z6hoaE899xzvP/++wCkpKTw3nvvcfXVV3s5OkEQBEEQfFWfo48DjQfIqcvhy6NfUuOqAQ+UPFZC/BPxGPwMBCYH8v3p32fuNXOxGE8eIZIVk8XzC5/n6X1PDxt6btaaeTjjYbJisr5+2ovugpJSX09I/acFCxZccDBns3r1au68805mzJhBRkYGf/jDH8QwzzOIiIjguCGEjEYTezT1ZDltWK1WJk+e7O3QRgWr1QrtO9ijkRPckMmMKSpRSXYWMTExlBUuJKtmPbtUexlwLKOqqor4+HhvhzYqWK1WWjq+oNZPTmDzFSQHKggwmbwd1qgWGxtLTdFSJvf/ixLNNto6Umhvb8ck/m54PB5qi/OodBYwIPEnUTKLT10SJkUGeDs04TScTie//e1vueeee9i1a5e3wxEEQRAEwYe53C4K2gqGqqF27tiJ1E+KKlRF8S+Kibw7kllzZ7H0uaXcuOhGZkTMQHHv2YsFsmKyWBS1iNymXJr7mgnWBpMekn7JK6ROuKCklLfcfPPNNDc38/jjj9PQ0MDUqVPFMM8zkEqlREdHM7FhBVs8b3Oo6TMMagMpKSnI5T71v37EORwOampqOGjbhl4JWsnlJMfHipaYs1Cr1YRGWZhRmcI6hZU93UcxVOiJi4sb94O8u7u76a89yjpFG7GeUArborgqM8TbYY16ZrOZY2HzuPbou/wqsJqCnhZiKipEUgpoamoiqHodzxp1TFQk09VvBCA51ODlyITTkcvlPPPMM9xxxx3eDkUQBEEQBB9U11M3lITa27CXql1VeFwe/Gf703m4k6hJUVx92dVM3zad+bHz8VNd2OgZmVTGzNCZIxz9hfG5zMQPf/jD07brCSeLjY2lrGwK06reI1+RT6bjRqqrq8d9m1pVVRXyXisblHYCOhOYGawkJibG22H5hNjYWJpLVjK541mOSTcxt28KTU1N4z45bLVaUbasI1urYaFkIUfcErLSYr0c1egnkUiIiUsksGwu/q6D7HPuYkpdCKmpqeO6/RrAWlaKvWs7FcEa7tXMo7pDjVHtJsxP7e3QhDNYvHgx27dvF4tmCIIgCIJwVj0DPexv2E92bTY7CndQL62nc38n3XndRN4XiU6vI9GYyLdmfYs5184hyhA15ooBfC4pJZwfjUaD2WwmsTKDXOUerJ0HMFQMrjA31p7M58rj8VBRUYG1/RN6VBI6O67msTnmcX8BfK4CAgLQhCWTWePP34JaaXY1Y7Vax3VSyuFwUF9ZyiHnUQwePa6+dLQKCZMixND8cxEdHc2O0CVcU7Wdd4y59LtWUFVVRWJiordD85re3l5kZRv5SOshTGLCIrdw2K1hQphs3L52+4rly5fzyCOPcOTIEaZPn45Opxt2+6pVq7wUmSAIgiAI3uZ0OznWeozddbvZZd3F7i93o5+up7ekl5oXa0h5LoWZGTNJmp/EzYtuZtK3JyGXju20zdh+dAIwOPA8uXIZke3ZHHJtIq5nJi0tLQQHB3s7NK9oamrC3tXMRmkNcbYg0IeQmiRmIp2P2NhYivKWYnJ9yIGujQTLgsf1EP3q6mr8m7ezRqdipnIaxQ1KpscEIJeJdtBzoVQqCbJMJqkwjgG/Zg7b8/Cr9CMhIWHcJmAqKirQtGxgm0nLSs1lKJVKqjqdzE3w93Zowlk88MADADz//PMn3SaRSHC5XJc6JEEQBEEQvKi6u5qcuhxy6nLYU7eH0rdLCVoWhEQhofHzRhIzEpm7bC4z75jJ/Nj5GJTja1SDSEqNA0FBQQQHBTKxNpbNflUsHWjEarWO26SU1Wqlp20dJUoF6oYruClJI+bXnKeIiAg0kbO5oupfrPE7zuUeOxUVFUyaNMnboV1yHo+HCquV2p4v6TFKmapZxIYeKStnn35BCOFksbGx7NEv4zLby+yXbGeGaiaNjY3jcvEBp9NJa1EOedI6VAQyVTWV0IhIyrdX8p35cd4OTzgLt9vt7RAEQRAEQfCiroEu9tXvI7s6m12Vu2hwNlDz9xr8ZvphmGJArVYzN2QuS6ctJfO2TCINkd4O2atEUmqciI2NxVJ5LUr3HzjY+gl6pZm+vj60Wq23Q7ukenp6aG5qIttxkDCPAqttKlfPTPB2WD5HJpMxPSWOmmMZ2P1zOWLbg65ax4QJE8bdEP3m5mYUjYf4VD3AFEksPTYT/U4PsyyB3g7Np/j7+6OOyWD+sdfZrm2nyllFsDV4XCalampqCK1fxwcGA9NU01FJVDi1wbg9lUwQQ85HNYfDgUajIS8vb1wm6QVBEARhPHK4HRxpPkJOfQ6b8zdzrPIYqmgVtf+oRe4vJ+K6CGZdNYtF0xaxfOpyJn57otdWuhuNxtfV4zgWGRmJJTiCaWV69mkrmOseoKKigtTUVG+HdklZrVZkXfvZrpZgaZ/OlEA58THjOzN9oWJiYqjVL+Wyvt3s9+xkuuYyampqxt1wX6vVSm/bWooMSu7SL6GmS41G4WKymCd13i5Li+f1vYuJcmxiv20HMS0x47IttKrkKO39B2jX+5GhySQ0NBRr+wASCSSZRVJqNFMoFERHR4sWPUEQBEEYwzweD5VdleTUD66St+PgDprzmwnMCqRtWxv2ejuZkzK55rFrWJi4kNmRs9EpdGc/8DglklLjhFwuJyYmhrDyLHbL11DesRFNlY7k5GRksvGRpXU4HFRXV3O4ZyNKFRxqXs5j84PHzeMfaVqtlskJMXQdiWabrhmroxyD1TCuklK9vb101JawRVJFqCeABEUiB/pVpMcoUcrFPKnzFRsVyV7NZdzY9Rl/kReyzN0z7tpCm5ubCaj8gpf0GhJl0YTIQoiNjWX93mZiArToVOJte7T7xS9+wWOPPcY///lPAgICvB2OIAiCIAgjoNPeyZ76Peys2Mn+lv1UVFZQ91odMT+Nwe6wI++Tsyx2GZmPZZIZlkmYPszbIfsM8el2HImNjSUpdBYTW9aQI91HkuMqamtriY6O9nZol0R1dTXS3lrWK7tJtcfQgZob5qZ4Oyyftnx6Iqv3XEnswMsc6t1InDKelpYWgoLGxzwlq9WKsWUjm3UaVqjno1AoOdpo47754k3oQkilUuLCzahq0pAFlHGgfy/+1f7jqi3UWl6GrGMz+cFKbtPMR6/XExwcTGFDGRNCjd4OTzgHf/nLXygtLSU8PJyYmJiTVt/Lzc31UmSCIAiCIJwrh8tBXnMeOXU5rNu5jmplNSig8MFCEn6VgCZQw/QV07k17VbmrZxHSkAKUon4UvpCjI9P+QIAOp2O+Cgz4VVT2BSYT2vfUaxW47hISnk8HqxWKzUdn9CmlCFtu5oZ4WoCjOOrLWikRYWb6fdPIatbwauKarLcnVit1nGRlHI6ndRUVVDSvweZUk2aZjYuQxjd/bXMsojqiAs1Z0IEb5YsZUXPs2yX7Ga+ZuG4aQvt7e3FU7KZT9UDmDAxQTGBuLg4PB4PBfXd3JEZ4+0QhXNwzTXXeDsEQRAEQRDOk8fjwdppZXfdbnLqc9jw2QbwB41FQ/lfyzFfayZtdhqr3lvF8qnLmRE6A61ifM1nvlhEUmqciY2NJaxgFSHOQ+zv/IJA7STa2trGfItBU1MT/T0dbPGUM8nhR05XFA8sEhd4I2FBspna/YtQm7aQ17sN/0Z/bDYbGo3G26FdVNXV1fg17+ETnYwZ8mS0Mi01Di1KuZS0KH9vh+ezZsQF89+eGB7pD+Rjg41CRyF+Vr9xkZSqqKggoHk9a016FqrnoFaqiYyMpLnHTlvvgKiU8hFPPPGEt0MQBEEQBOEctPW3saduD9k12exp3EPRxiLcNjeBSwLpbeolSBPElXFXMvuz2WSGZ2LWmb0d8pgkklLjTEhICFMjA7EeCWOPfyMLnR1YrdYxn5SyWq30t2/gqErOnN5FqGRw1XSxtPpIuDojgZW7F3JL7xds4QBz3VdSUVFBSsrYbY08UXnX1rWBJqOcm3VLCA0NZc3xLqZF+aNWiDllFyohWI9aLqVCtpRp/e+zV/olE5UTx3xbqNPppKV4L+VU4CKAGeqZREdHI5PJKKxvAxAr7/mQjo4OPvjgA8rKyvj5z39OQEAAubm5mM1mIiIivB2eIAiCIIxLdpedQ02H2Fm5k80HNlNnqKNzXydt29uw/NyCPkJPkjGJa6dfy5yVc0g0JYqWvEtAJKXGGYlEQnxcHPrjV+OR/I3C9k/QKEz09/ejVqu9Hd5F0d3dTXNTE3v69xCqlHK4JZP5cf5olCJxMBISzEb89TrCbVPpMBRS2H8IbZWWpKSkMTtEvqmpCVnzcT5TdpBEOGHyMGJiY9m/LpdvzxYVeN+EXCZlcqQfG/oy+XbPuzysrqfWWTvm20JramoIq/+CXxuNpCmnoJfqh6rDChu60ChkRAeIEnFfkJ+fT1ZWFn5+flRUVHDfffcREBDARx99RFVVFW+88Ya3QxQEQRCEccHj8VDSUUJOXQ47ynaw6YNNGC830l/ZT/WL1SQ+lcjUuVOZeuVUlk1dRnpIOmr52LwmHs1EUmocioqKYlJEPC11KnZoi5niclJZWUlycrK3Q7sorFYrsu48tqjdZLnTeL9Pxm/mxHs7rDFDIpGwaEIIHx5ZxQzDYQ66tzBJM5O6ujqioqK8Hd5FYbVacbZ8Sq5ezW26KzAajbQ5VbT3OZgVF+jt8HzelEh/1h7uISZ4ERGObHL6thHREEFvb+9JQ6PHAo/HQ1Xpcbr69tGoM3KT5jJCQ0PRageTUIX13SSHGpBKJV6OVDgXq1ev5q677uKZZ57BYPiqum3FihV861vf8mJkgiAIgjD2tdhayKnLYXft4Gyow385TMjVIchNcjqOdRCzMIb5l88n8/ZMMsMzCdKM3S89fYVISo1DCoWCtIRI9pRdRoNxMzU9X6Kp1JKYmIhUOrbKEx0OBzU1NRzuXotCBU09q/BTS7ksKcTboY0pWRPDeedAHd9yhvN7TSv1jhr8rH5jMinV09NDV10xGyXlmD0BpCgnYbFY2GZtRSGTkB5t8naIPm9KpB9/32WlJGUp36rcxPPy4yxxd1JeXs7kyZO9Hd6Ia2lpwa/yC/7PqCZJFk2YPAyLxTJ0e0FDN1Oj/LwYoXA+9u/fz0svvXTS9oiICBoaGrwQkSAIgiCMXTanjdzGXL4s/pKcqhyqqabqL1UYpxvxz/THL9mPjMgMlkxeQuZ1mST4JyCRiC/6RhORlBqnLBYL5sCFpNg2sV2+k2j7Eurr68fcrIuqqiokPRV8ruxmPhY2tehYOTUchWxsJd+8LTM+EIVMQjPXYHa+RG73Z4QpImlvb8dkGltJGqvVirZ5HZu1Gq7SXIZKqSIiIoK9Ow8zJVK0hY6EtEh/ALoMcUxXpqP2lLDXlk1AdQDJyckolUrvBjjCystKGejYzLEgJXdqFmEwGIZaFR0uN6VN3dwyc+wleMcqlUpFV1fXSduLi4sJDg72QkSCIAiCMHa4PW6K2orIqc9h3Z51HC45jDpVTd0bdciNcszXmMm8K5OFkxayKGkR026fhlI2tj47jjUiKTVOGY1GFiYFUZWdzr6wQzT1HsRqNY2ppJTb7aa8vJzyjg/oVUoxSq6lc0DCDTPFzJ+RplXKmWUJZFt7MtdJNLysq2aeuxOr1TqmklIDAwPUWEs4PHAQjUrLNM0cYmJikEql7LW2ctMMkTgYCTGBWvw0CppdGprMK7i+7jd8KNnDQm0WlZWVJCYmejvEEdPV1YWkZAMfaAYIJZBEReKwKqny5l4cLo8Ycu5DVq1axa9+9Svef/99YLDFuaqqiocffpjrr7/ey9EJgiAIgu9p7G0kpz6H3XW72bJnC9U7qzHfYKYrvwtblY2YGTGsenwV86PnMytsFib12Ln+GA9EUmocS0qIQ3JwFVEDB9jjWE9I+3Q6Ozvx8xsbbSJ1dXU4uxv5XFrPLHcIxzpCiTKpSYscG49vtFmYHMzv1reyOngZGvenHOheh3+dP6mpqWNmiH5FRQWmli/5RK8kUzkdlVSFxWKhvKWXlp4BMixjexXLS0UikTAl0o/jDb0smjKdK2qjeZM2cvsPYrAaiI+PHzOtxuXl5aiaP2NrgJZrtJejVqmJjIwcur2wYbDiZkKo0VshCufpueee44YbbiAkJASbzcaCBQtoaGggMzOT3/zmN94OTxAEQRBGvT5HHwcaD7AhfwNH7UcptBZS8WwFif+TiEPuQBOgYUHkAjIzBudCWYwW0ZLnw0RSahwzm83MjTGwuySBvSHlLLBbsVqjmDp1qrdDGxFlZWU0tn1AjULOKvUq/lgu4XsLo8QL1kWyMDmY/1lbQK4sk2t6PuYD6VHmumxUVVWRlJTk7fC+MZfLhbW8jIqeL7EbZczQLSYiIgK1Wk1OXiUyqYQZsSIpNVLSIv15d38VcdfM5FjhSrLa/8oeyTYy1LOoq6sblrjxVf39/fQWbWW7vBWdJJCpqqnExsYOW7WysKGbcD81flqFFyMVzoefnx+bNm0iOzubw4cP09PTQ3p6OllZWd4OTRAEQRBGJZfbRWFbIbvrdvOvz/5FnbEOdFDwwwLin4hHbVYz78F5rEpbxdzIuaQ9lIZCJj4bjRUiKTWOSaVS5k2K44OjN2Fy/5ZDnR9jqo1nwoQJPl/Z0tTURE9HC5vcRUxyGmjqS6LfCddMHTvtiaNNfLCeCH8NVoeMmzXzeJcccvu24lfhR3x8/LALbV9UXV2Nvnkv/9J5mC6Lxyg1Eh8/uIrj7rIW0iL90KvES+pImRLpx1+2DtCLCknMHK5rfJONdFPkKMK/zH9MJKXKy8sJbljDx0Y9c9RzUclUw1r3AArru5gQJqqkfNHcuXOZO3eut8MQBEEQhFGprqeOnLoccupz+PTtT3H6OdFP1FOzrgbTAhNJ05O4eu3VLLAsYFbYLPxUottlrBJXUONcdHQ0aWHFtLaHsj2giVn2ZsrLy0lNTfV2aN9IWVkZvW3ryFfJuU+1mC9rFUyLMhAbNPaWkx8tJBIJC5OD2VncxJXJS1hRu5Wdnhwy+pdQXV1NbGyst0O8YB6Ph/Lycpo619JokHOTbjnBwcEYjUbcbg85Za18e7aYVTaS0qL8Aciv7mBafDx1pSuY0v8he6RbSOlKobm52aeHRjudTpoLcyh1l+KSBDBLPZvo6OiThrgXNnRz7TSRTBcEQRAEwbf1DPSwr34fW4u3kteTR+6nuThaHZhvMNMv7ceoMnJ51OVk/j2TOeFziDKIDpfxQiSlxjmVSsWqaRE8/PENSAJe4FjnhxgqzSQmJqJQ+GZJZGdnJy3NzewY2EuUQk6QYjZHWl08sVJc2F1sC5KCeWtvFQP6CBbLJvOptJQj/fswlhmJiYnx2TeWhoYGpE3H+EjZxUTCCVOEk5CQAMDx+i7a+xzMSQjycpRji9moxmxUcbimk2WTkimIWMQt+f/iMXUdNc4aSkpKfDopVVlZSUTdGn5lNJKmSsMgNRAXFzdsn46+Aeo7+0WllCAIgiAIPsfpdnKs9RhfFn/Juu3raIlsoXVPK62bWon77ziMqUYSDAksT1vOnOVzmBQ0CblUpCfGI/F/XWDyhEQiAmswdhvYorcyZaCHiooKn13hqqysDE9nNjtU8C35TI52KgEPV04O83ZoY96chCAUMgm1bj+0wdeysOlX5Li3kKaeTV1dnc+u7lhWVkZf60cU6JXcpV+Bn58fQUGDSajs0hbUCinTov29G+QYNCXSn/yaDiQSCXGJyXRbLyfKsZNdfZuJbI2kra2NgADfm+PldrupKcyl1Z5Lk8GfW9WXERYWhk43vJKzsKEbgBSx8p4gCIIgCF7icrvIbcqlua+ZYG0w6SHpyKSnHstR3V1NTl0OO8p28PGLH2O62sRA6wA1/6gh7rE4UuenMuumWVwWdxkZoRkYlOIzjiCSUgKg0+m4fkowf9u+km7jO5R1fIq+3EhcXJzPzQHq7e2lrq6Ovb0b8VdKSDIs45UyJQuSjATqVd4Ob8zTq+TMjA3gQG0faYlxLG+K52FpHSUDx/Ar8SM8PNznqqVaWlqwNxTyqbSaKE8QiYrkoVlSANllrcyMDUAl961/K74gLdKPl7aX43Z7iIqKYnvkCu4q2Mj/yIu53NVEaWkpGRkZ3g7zvNXU1BBSvYZn/fRMlCdhlpuHPadOKKzvQimTYhFtx4IgCIIgeMHmys08ve9pGvsah7aZtWYeyXiErJgsuga62FOzh+zqbPa37mfXr3dhvt6MKkJFv60fjVvDFRlXkHlNJplhmUQafH8mqDDyxsaa2sI3tmRmCg5VGjP6FHzpPIytv4+qqipvh3XeSkpKoOsQ61UOFspS6XFpON7UzzViJsslszA5mJzyVqJi41AFXk9av529PV/Q3d1NU1OTt8M7b8XFxbibPmC3VsM83TJ0Oh3h4eEADDjd7Le2MVe07l0UaVH+dNudWFt7kUqlxCRPYZJ2LkEuF7v6ttDY2EhXV5e3wzwvbreb8sJ8ynp3UqmQc5l2MQEBAZhMppP2LWzoJtGsRy4Tb9W+5LXXXjvldqfTyaOPPnppgxEEQRCEC7S5cjOrt60elpACaOxr5Af/+gGLXljE/Hfnc/211/PKa69Q3V1N8OXBpCek86P0H7H17a3s+c4enl/4PDcm3SgSUsJpiU+6AgBGo5GrJhjpbF5MrRysXesoKyvD7XZ7O7Rz1tfXR01NDQe6PkUFTPa/joL+wRXRlqSYvR3euLEgKYR+hxtrrxxZSBJXu0IplHRQ5bAOJg19SGtrK731JayRlBLmUTNRNY2EhIShaq9DVe3YHC7miaTURTElwh+A/JoOAGJiYmiIvIY7uno5NHCEDleHzz2namtrCaxax2sGFUnSaKLkUSQlJZ1y34KGbiaEinlSvubBBx/kxhtvpL29fWhbUVERs2bN4p133vFiZIIgCIJwblxuF0/vexoPnqFttgobHXs6AGjf1U7B9gLcHjeZP87kgfse4M+X/5n8p/N575b3uD/tfqYETzltm58g/CeRlBKG3HZZKkXOBczsk7BpYC+9vT3U1NR4O6xzVlJSgqT7CGtVNi6XJqGR69laYWNlWjgapXhBvFSSzHrC/NTsKG4hISGBoIAbSRgYILvrY9rb22lubvZ2iOespKQEadPHbNNqmK9dglajJSoqauj27LJW/LUKUsUg6ovCT6sgNlDL4epOAGQyGVHJU5mpmone7WanbQt1dXX09vZ6OdJz4/F4KCs6RmPnBopUSi7TXYHJZDrlwHaX20NxQzcTxDwpn3Po0CFqamqYPHkymzZt4oUXXiA9PZ0JEyZw+PBhb4cnCIIgCGfUYmvhT/v/RGNfI7ZKGzV/G7wedNlcODucAJivNRN8ZTDPXvYsG+/eyC/m/IKFUQvRKcTIAeH8iaSUMMQcHESWRYOtOYtquQdr1xeDrUs+UC1ls9moqakht/NjpEiY7HcjbUozDV12bp4ZdfYDCCNGIpGwMDmYbcVNhIeH4wpK4VZ7EEckLVQ5rBQVFXk7xHPS3t5OV10JX3iOEuxRMlmdQUJCAlLpVy+b2aUtZMYFIpX61pwsX3Ji2PkJsbGxNIVfw51d3eyz59Lh6qC4uNh7AZ6Huro6Aio/5xWDHIs0DIvcctoFJSpae7E5XKSIhKfPiY+PJzs7m+uuu45ly5bxk5/8hFdeeYW33noLPz8/b4cnCIIgCMP0OfrYUb2Dh954iGs/upZ5L83jZ4t+htvuRmFSYEgb/IJMn6InaNnw7gC3Z/RfJwqjn0hKCcPctyiVvX2LmGmDzQM59Pb2UFlZ6e2wzqqkpAS6j/OZqpfFknj0CiPZ9R4mhBpIixQXAZfagqQQypt7qe3oJykpieCgW4ZVSzU2Np79IF5WVFSEvPFfbNSqma9ZjE6jIyYmZuj2HruTw9UdzBGtexfVlEg/jtV14XANfuhRKBSEJk9nniIdg9vFdtsmampq6O7u9nKkZ+bxeCgtPEp9xxccUym5XLcCf39/zOZTtxYfqxuclTUxXCSlfNHatWt59913yczMxN/fn7///e/U1dV5OyxBEARBwOl2crj5MC8dfonL/usyZvxxBg9sfoAX/+tFjhYdRe4vZ/4z85EoJciNcvxmnf5aKlh7crW3IJwvkZQShkm1hDMzXIW0bTGV/66WKikpweVyeTu00+rt7aWqqooDHe8hQUKa/034myP5sqiZm2ZE+dxqb2PB3IRA5FIJ24qbiYyMxB2cyi0DJ6qlykd9tVRrayvdtUV8TAEhqJmmmUN8fPywKql91lacbg9z4wO9GOnYlxblj93ppqjhq6RTfHw89ZE3cldnDwfsebS72iksLPRilGdXU1ODf8VnvGyQkyCNIF4Rf9pZUgDH6joJ91Nj0ikvYZTCSLj//vu58cYbefjhh9m5cyf5+fkolUomT57M+++/7+3wBEEQhHHG4/FQ0VnBW8ff4qEvHyLxzkRW/nIlf8n7CwUFBdg77UQYInjkw0f48y1/ZsctO9jywBZCdaFIOPV1lAQJodpQ0kPSL/GjEcYiubcDEEafey9L5L53s1gS+CVfyHcTY1tKRUXFKZcsHw0KCwvxdOWyRtXPCmkyeoU/+V0aJEi4Vqy65xUGtYLpMSa2FzXx7dkxJCcnI2m+lcTuP7O960Oi5Bbq6+sJCwvzdqinVFBQgKfpfbZp1dysWYpWrR1WJQWQXdpKuJ8aS5Donb+YJoYbkUogv6aTSRGD39QplUoiJswgtDaDN9zH2da3EVODiY6ODvz9/b0b8Cm43W5Kjudj6/yCokAt39VdidFoPG2VFMDxui4mRogqT1+UnZ3N3r17SUtLAyA0NJR169bxwgsvcM8993DTTTd5OUJBEARhrGuxtZBdkc3G/I0Uy4o59skxbFU2Ir8TCaHg7+9PVkwWs38/m9lhs4kynPxF/iMZj7B622okSIYNPD+RqHo442ExyFwYESIpJZwkKy2WqHVFqLtWUB/8OSUdH6Et1REdHY1CofB2eMN0dnZSV1vL7q6P0agkTPG/idjYWJ5bX8cVE82iysCLLp8Qwv9uLqbf4SIiIoIS80Rubw3hCWU7ZQMFGIuMhIaGjrpKtoaGBvobivlAUkyEx8RkdQZJSUnIZMPfdLNLW5iTEDTq4h9rtEo5SWYD+TUdfGtW9ND2uLg4dhZdzz1l+3leepi5rkUUFhYye/ZsL0Z7ahUVFYRUfswvDAqSZdHEKmJJTU097XPH4/FwtLaTO+fEXtpAhRFx8OBBVCrVSdt/8IMfkJWV5YWIBEEQhLGuz9FHblMum49v5vO1n2OfZqfrYBctG1uIezSOwFmBTFoxicWTFzP7ytmkBKScNaGUFZPF8wuf5+l9T9PY99XoDbPWzMMZD5MVI97ThJEhklLCSSQSCd+eHcNTG+dxjWEj65R5JPSvorS0lJSUFG+HN0xBQQGurhy+UDu4XpqGQeVHiyyQ8uYKnrp2srfDG9eyUs089UUhu0payEo1k5ycTEHzt0lv/z1b3B8Rp5xAVVXVSRVI3uTxeAbLmBv/SY5ezW3aKzHoDURHRw/br6XHTmFDN/cviPNSpOPLlEg/Dtd0DtumUCiImTiT0Lo5hLry2NTzOSHNIbS0tBAUNHrmfDkcDqzHDtLSs5nyQAMP6K4iMDDwlCvunVDf2U97n4OJ4aJSyhedKiF1QnJy8iWMRBAEQRirnG4nx1uPk1OXwy7rLtY+s5awu8Jw2VzU7agjOi2amZfPJPPbmWSGZzLNPA2NXHPe58mKyWJR1CJym3Jp7msmWBtMeki6qJASRpRISgmndNvcRP641Yq8/wY61W9zpO0dVOWDg561Wq23wwOgpaWF5qZGNvd+ToBCwkTTdSQkJPBsTg3JZgMZlgBvhziuxQfriQvSsbmgkaxUM2FhYZSYk7mlNZH/klZx1LYXdaGa8PDwUVOBV11djaQhj9cUdSR4gklVTWPChAnDZkkB7C5rBWBO/OhJfoxlaVH+fJhbi23AhUb51Yeg2NhYdsTcxA+O5/CLoFKsDivGY0Yuu+yyUVPBVlZWhrn6ff7HT0u6fCKR8sizJvfFkHPf98EHH/D+++9TVVXFwMDAsNtyc3O9FJUgCILgqzweD5VdlWwp2sKBxgMc7j7M4ScPY77BjDZRiypahVlp5rJJlzF71WwywjIIUI/MtZBMKmNm6MwROZYgnIoYdC6ckk4l51sZUaxpnsKVdj1rKaHb3kBBQYG3QwP+3d5y9Cg9rR+zXQ1XqhZg0BpRmULZdLyR2zNjRs1F6Xi2JNXM5oIm3G4PEomEiRMnIjXfyqI+O1v61tNr76W0tNTbYQKDFS2FBQVUt7xJoUpJlvEm/P39Tzn3andpCwkhesxGtRciHX/SIv1xuT0crx9eLSWTyYhLnUaM/5Wk2gfY1PMpnZ2dVFVVeSnS4fr6+qg7vodtjv10yuQs1q0gNDQUk8l0xvsdq+vEpFUQ5ieeX77oT3/6E3fffTdms5lDhw6RkZFBYGAg5eXlLF++3NvhCYIgCD6i1dbKuvJ1fPe17zLrd7NY+clKfnL/T1jz0Rq6Hd3E3xHP8rnLeTzzcfb+bS9b7tzCL+f8kmWWZSOWkBKES0FUSgmndd+CRP6RU4XbcSdqxZ/Jbn8dnSIEi8VCQIB3X+gqKiroba/nX859JEs1JJiWkJSUxPsHa9Eq5WLA+SiRlWrmpR3l5NV0kB5tIigoiICoZK5pncFOST45PWtRlauIjo5Gp/PuwPDi4mLUjTv5h7aPmRIL0QoLKSkpp0xuZpe1sHjC6YdUCyMrOdSAUi7lcHUn02OGv/ZER0dTYbmGBw5t5oeqJg7b81AVqkZFBd6xY8cIrn6d14165qozCZAHnFML9LG6LiZF+InEuo/6v//7P/72t79x66238tprr/Ff//VfxMXF8fjjj9PW1ubt8ARBEIRR6sRcqD11e/j8y88pOlxE0NIgWre0ggdCI0K59lfXMi9mHpnhmec0F0oQfIGolBJOK8So5pqpEaxtiuZGl4Vt8naaevM4evQoHo/n7Ae4SOx2O0VFRVS2vEGJUsYyww34+/kTGh7JO/uquS49Ar1K5FtHg/RoEwE6JZuOfzUcMTU1FXvojdza4+TLgX20DrRw/PhxL0YJ3d3dVJYVsb/zQzpkchYabsZsNp9y7k9lay/VbTbmxAd6IdLxSSGTkhpmJL+m46TbJBIJE6dMRRNyC0t6+9jQ9xld9i6Ki4svfaD/oampCWfxZt6QV6KUqpivWYzFYkGv15/1vsdqO0kVrXs+q6qqijlz5gCg0Wjo7u4G4Nvf/jbvvPOON0M7paKiIqZOnTr0o9Fo+OSTT7wdliAIwpjncrvIb87nuW3Pcc+Ge0h/Op1lWct4/fjr1NhrkGllTAiYwE8f+ikf/O4Dsm/N5vWVr3PflPuYFDRJJKSEMUNcuQtn9L2F8XyQW0uL506SBn7JF84PCeqYgtVqJS7OO0OeCwoKcHQW8K6skstcwZg1k5g8eTKf5dfT0mPnjszRMzh7vJNJJVw+IYTNxxt5eNkEAHQ6HdFxSejab2CD40O+7HobU8MPaWhoIDQ01CtxHj16FHXDu7xpkLFYMZNAZRATJ0485b7bippRyCTMSRDzpC6ltEg/dpS0nPK2oKAgrMkruGf3JnZqOvmydwMaq4bIyEj8/C79sHC3282x/Dzcjf9kfaCOG7Wr8FP7kZSUdNb7tvcOUNfZL4ac+7DQ0FDa2tqIiYkhOjqaPXv2kJaWhtVq9eoXOqeTnJxMXl4eAD09PcTGxrJkyRLvBiUIgjAGnZgLlVOXwzsfvUNjWCNdvV0UrS5iwp8nIA2WMuGWCaxIWMGcy+aM6FwoQRjNRFJKOKOEEAOLJ4SwqbqZH0Qv5Gl2crz9fRSFCkJDQy/50PPm5maqqyrZ3vEaTqWU+f73EBkZiclk4uXXj7J4QggJIYZLGpNwZlkpZj44WENFSy+xQYMteklJSXxZPY97yzbzW30dafZjqI+oCQwMvOQtV1VVVfRV5/NPTx5BHiNzDFcRHx9/2nbCbUVNzIgJENV4l9iUSH9ez6mk0+bAT3PycyR14kT2VX+H+6ue5M+mvaSrZ5GXl8f8+fNPGlR/sRUVFeFf8SH/z+gmURrFVNU0UlJSzum5LYac+77LL7+cNWvWMG3aNO6++25+8pOf8MEHH3DgwAGuu+46b4d3RmvWrGHx4sVeb6cWBEEYK1ptreyt38ue+j2898p7uCwu1NFqKt6uIOy2MIItwXzv4++xIGEBmeGZRBmiRPu+MO6Iq6pTcLlcOBwOb4cxavxwQQw/fLuVevlVfMtZxW5FE4nOJvLz85k6depZ769QKJDJvnl5qdPp5PDhw3S1fMQGtZtvyzLxU4eQkpLCjpIWihq7efLqU1e3CN4zPzEIpVzK5oJG7p0/WF2nUCiYNHkyBd3fYXbrs3zqfo9IRTwFBQVMmTLlksVms9k4dvQozU2vcNCg4l7dzRi1RhITE0+5f7/DRU55Kz/JOnvFizCy0qIGK4eO1HQyL/HkKjWdTkfklPkEtS7mM8duPu1+l3tlP6K8vJyEhIRLFmdHRwf1x7Ip6NlAjb+BHxluwmQyERUVdU73P1bXiVYpwxIokgK+6m9/+xtutxuAH/zgBwQGBrJ7925WrVrF/ffff97H27FjB88++ywHDx6kvr6ejz/+mGuuuWbYPi+88ALPPvssDQ0NpKWl8ec//5mMjIzzPtf777/PHXfccd73EwRBEAbZnDYONBxge+l28rry2Pn6TmQ6GYGLA+l2dGOUGJkVNouH3nmI2WGzSQ1MFW14wrgnklL/wePx0NDQQEdHh7dDGVXUwNNXhOJ0uTHKHyfT040MCQqpgpKSEuTysz+N/P39CQ0N/UaZ/+PHj9PfUck/3fuZ7NExwbSSlJQU1Go1L+84zJRIP2ZZRInraKNTyZmXEMSm418lpQAiIiKojprM7Z1z+S/28WXnO6gr7yYsLOyUs5wuhsOHD6NpWMNL2l5mS+KIU01gypQpp02i7rO20e9wszA55JLEJ3wlLkiPXiXncE3HKZNSAPHx8eysvpVHDuXwvcAmdtm2oyhSYDabMRgufgWl2+0m71Au6poXedlfzwL1fEIVoaSlpZ3za9+xui5Sw4xIpeJbUl8llUqHVefdcsst3HLLLRd8vN7eXtLS0rjnnntOWWn13nvvsXr1al588UVmzZrFH/7wB5YuXUpRUREhIYOvVVOnTsXpdJ50340bNxIeHg5AV1cXu3fv5t13373gWAVBEMYbl9vF8dbjbC7czJYDW6gLqqN+XT195X1EPxCNYYqBBHMCSyYuYXbWbNLN6WjkGm+HLQijikhK/YcTCamQkBC0Wq0onfwP5gEn1W19mFQSwumghX780aCW+6HVak97Ee/xeOjr66OpqQmAsLCwCzp/U1MTlRVWdre8QItKym2GOwgODiYmJoZjdZ3sKm3hz7dOE//PRqmsFDP//ckR2nsHMOmUQ9unTJnCttbr+X55Hs8ZS0juP4bqkIoFCxagUqkuakwVFRV0V+Xz7sCXyFVasvy+TVRU1NBF3KlsK2omzE9Nkvnsw6qFkSWVSpgUceph51/tI2XKtBkcabqfuxr/lzfYQpIyhYMHDzJ//vwRqdg8k8LCQozl/+JJXQ/hEjMLtUtITEzEaDz3VryjdZ3MF/PKfF5/fz/5+fk0NTUNVU2dsGrVqvM61vLly1m+fPlpb3/++ee57777uPvuuwF48cUXWbt2La+++iqPPPIIwNDMqDP59NNPueKKK1Cr1Wfcz263Y7fbh37v6uo6h0chCIIwNng8Hqq6q8ipy2HLsS1seG8Dflf60VPQQ+uGVmJ+HEPK8hQyozKZFzuPjJsyCNSIxXEE4UxEUurfXC7XUEIqMFC8cHydWg0dduh3uTGqgzE56uiS2NFIXbjd7jMm8TSawW8DmpqaCAkJOe8LQ5vNRm5uLk1N/+RzjYPbpRkEauOHqg9e2FpKpEnD8kneGZItnF1WSgiPfQxbi5q4Lj1yaLtWqyUldSKq3u8zq+33fOx+lxBlNHl5eWRkZFy0JGNnZyfHj+ZT2fR/7DOouFd3KwHagNMONz9hW3ETC5ODRfLTS9Ki/FmTV3fGfUwmE4GTl5DVmccOxx4+7n6L+2QPcezYsYvaGtrY2Ejj0e3s7d1AtVHPA37fJsAv4LxaB3vtTqwtvXzvsviLFqdw8a1fv5477riDlpaTB/NLJBJcLteInWtgYICDBw/y6KOPDm2TSqVkZWWRk5NzXsd6//33+e53v3vW/Z566imefPLJ845VEATBV7XaWsmpyWFP3Z7Bn1/tIeoHUUhkEnqaewiXhpO1NIvZd81mdvhsog3R4rOiIJyHSzv9dRQ7MUPqUg/u9iVmo5p+p5sB5OjlISg9HtpdrThdDmw22xnve+Lver6zutxuN7m5ufR35PJ3aQGZTiMpfteQmpqKVqulsKGLdUca+NHlCchl4uk8WoUY1aRF+bO5oPGk2ywWC4bYqXxbuRiJe4BP2/9GQ2MDpaWlFyUWh8PBwYMHkda+xst6J4ulE4lTpZKWlnbGQdTVbX2UN/eyIOnStBYKJ0uL9Ke+s5+mrv4z7peSkkJb0h081qOh2d3K5z0fUlFRQW1t7UWJq6+vj/wDu+mr/RNv+elYqllCmCKMadOmndeQ9cKGLjweSBVDzn3aj370I2688Ubq6+txu93DfkYyIQXQ0tKCy+XCbDYP2242m2loaDjn43R2drJv3z6WLl161n0fffRROjs7h36qq6vPO25BEITRzOa0kV2bzSMfPcKV/7iShe8v5MasG3lzzZs0DjQSfHkwM8Jn8JO5P2H7+9vJvi2b/130v9w84WZijDEiISUI50lUSn2NeBE5PZ1KjlGtoK3fRbhWSYDbRIOnnW5HExJCkclkp225utC/6+HDh2lrKObt7jfRyGQsCXiA8PBwYmNjAfjj5hKiAjTDqm+E0WlJSgh/3VaG3elCJR9eLTd16lS2tV/Dz0qL+YWukV3dHyMtlKLX6y+45fNU3G43+/fvx1P9Bb+XFxDl8eMyv5uIj48/Y9seDK66J5dKmCtaq7xmWrQ/ALlV7SybdPrnhVQqZfrMDPa3P8ijFb/il4H5RNotyPJkaLVaTCbTiMXkcDjYu2cPuvI/89/+UqbKEsjULGDixInn1bYHg/OkFDIJSWaxgqgva2xsZPXq1SclikYzPz8/GhtP/tLgVFQq1UVvrxYEQbiUTsyF2lO/h483fkxxYzGaSRpqXqlBl6zDNN/EoscXsWjSIuZEzmHat6ahVYhCBkEYKSIpJZwXs5+aksZu7KhRyvUED9hoktiRO1vAFoREIkGpVJ79QOegqKiImspS1rf8AatSygO62wkwDg4NBjhe18UXRxt45vopKESV1Ki3JDWU328sJqes9aRB4Uqlkmnp6ezv/z7fqX6Slw0HCbRFIDskQ61Wj0gSwePxkJ+fT2/1QV7rX4NDqeZG0w8ICQhhwoQJZ73/5oImZsUFYFCfvppKuLjC/DSE+anJreo4Y1IKQK/XkzAzixpbAze2/Y2PPWsIkYWi2K9g7ty5I7Lkvdvt5uDBg+jL3uJJTR0maQBXG28bljg/H8dqu0gyG1DKxeuZL7vhhhvYtm0b8fEXvw0zKCgImUx2UkKpsbGR0FDR0i4IgnAqJ+ZC7a7ezb6mfWzcspGmQ02E3hRKp7UTl81FnC6Oa5++lszwTDJCxVwoQbiYRFJKOC8ahQyTVklzzwBxgRo8niACHA20SexIXR3QN1gVdaY2qHNRUlJCcVEhuY3Ps1Pl4n7FEsz6ycyYMWPo2H/cUkx0gJZr0yNG4JEJF1uSWU9UgIYNxxpPuXpdcHAwyZOmoRx4kKuaf897njWoJQYkeyRkZmbi7+9/wef2eDwcOXKExpL9bGx7gUKNku/q78asC2X69OlnbbHqsTvJKWvl0RVnT14JF1d6jIncyvZz2jcqKoqOKVexam8l1v4t/JO/8x3J95HsljBnzpxvlJgaqroreI8/uXfSqtJyv993CTIGMXXq1As65rH6TiaK1j2f95e//IUbb7yRnTt3Mnny5JPeDx988MERO5dSqWT69Ols2bKFa665Bhh8bm7ZsoUf/vCHI3YeQRAEX9dqayWnNofPsj/DqrNSfqycqheqSHomCYfRQWByIIujF5M5K1PMhRKES0wkpYTzZjaq6bB109Hvxl+txmRK4eXX/sScKy8n0CWB3sEZUhdSMeXxeCguLqa48DiHG57lA3UPtzCZGL/FTJ8+HT8/PwD2WdvYcKyR529KE1VSPkIikbBiUhgfHKzh11dPPOUMsPj4eHp6eljieoCO9hd4i7e4W6pGskfCjBkzCAo6/9Y5t9tNfn4+DaW5bGp5ni0aObdrVhGlTSIjI2NoEP+Z7CxuZsDlJivFd9pxxqr0aBO/W1/IgNN9ThVFkyZNYm/Pnfz0UBO/HjjK650vca/kh+zevZtZs2add4sdfDWXzFH8Oa/aP6dIreEu432Ea8OZNWsWcvn5v7UOON0UNXRz4/So876vMLq88847bNy4EbVazbZt24Zd1EgkkvNOSvX09AybsWe1WsnLyyMgIIDo6GhWr17NnXfeyYwZM8jIyOAPf/gDvb29Q6vxCYIgjEc2p43cxlxy6nJ4+7W3saXakEglFD9cTOJTieiidKx8aiVLpy0lMzyT1MBUZNKLu0qvIAinJpJSwnlTyqUE61U099gxafUAaGT+GDwSWugh0OmGvsEVDdVq9Tl/y+B0OsnLy6O+top9jc/wibqXW5nI5IBvMWnSpKH5HG63h19/fpwpkX5cM1VUSfmSFZPDeGlHOfusbcw5zWymyZMnc8Bu58aSXvp6XudV/s6trutx7nEyceJELBbLOZ/Pbrdz4MABemoPs6HtL2zVyPi2ZiXJukzS09OHkpxns6mgkQmhBqICxPwAb0uP9mfA6eZ4fRdTo/zPur9EImH6jBnkDDzEY0ef53FJEa90/IVvee7BscvBtGnTzmtuWW9vL/v27UNe8TGv2tdzVK3hTuO9WDRx55zkPJWSpm4cLo+olBoDfvGLX/Dkk0/yyCOPnNeg+9M5cOAAixYtGvp99erVANx555289tpr3HzzzTQ3N/P444/T0NDA1KlTWb9+vU/NtBIEQfimTsyFyqnLYW/DXta9uA5NigZdso6aIzUERQaRNjGNuzbdxdzIuUwLEXOhBGG08Jmk1G9+8xvWrl1LXl4eSqWSjo6Oi3Yuj8dz3qvEjTSFQnHOyRy3283vfvc7/va3v9HQ0EBSUhL/7//9P66//nqWLFmCTCZj/fr1SCQS2tramDJlCvfccw+/+tWvcLlcfPe73+XLL7+koaGB6OhoHnjgAR566KFh53j11Vd57rnnKC0tJSAggOuuu44f/fdTxMfHAXD7t+8AIDIqnPW5G/FzOvAQjNPpPKeLtMbGRo4cOUJPezHr2l8kR+Xh20wlJeAmJk2aNGw+y0eHajlS28kH38tEKhVltb5kSqQfkSYNnx+pP21SSiqVMmPGDPa53dxVLueTzr/zBh+wytmI64iLhoYGpkyZcsbWK4/HQ01NDceOHcPZvJ03bB9TppZzh3olSbq5zJgx45znrThdbrYWNnHbrJgLeszCyJoY7odSLuVgZfs5JaVg8PU0c84cdnvcPHnsjzzvPsbfO1/iOt31uA64iIiIIDU1FbVafdpjuN1urFYrRYUFyGv/ye+k+XSrNNxpvId4TSKzZ8/+Ri2mx+q6kEggJUwkpXzdwMAAN99884gkpAAWLlyIx+M54z4//OEPRbueIAjjyom5UDusO9heuJ3j7uOUvl2KRC4h5JoQFOEKzMFmliQsIfN1MRdKEEYzn0lKDQwMcOONN5KZmcnf//73i3ouh8PBhg0bLuo5zmbp0qXn3P721FNP8eabb/Liiy+SmJjIjh07uP322wkODub1119n8uTJ/OlPf+Khhx7ie9/7HhERETz++OPA4IVWZGQk//rXvwgMDGT37t1897vfJSwsjJtuugmAv/71r6xevZqnn36a5cuX09nZSXZ2NuH+Gt78bAuLpiby6quvsnDhQlwuJwaPm1aJg35HHf7uQFwu19CP3W5HrVbj8Xjo6+ujpaWFqqoq2ttaaelYw5vOvTjkEh6QX0GU3+VMnjyZmJivkgG9difPrC/kyilhzIgNGPk/vHBRSSQSVkwO48ODNfxq1alb+GAwMTVz5kwOyeVcZQ3A3PS/vCPZRXFrPstdd9Pa2kp4eDgRERGYTCaUSiUej4eenh6am5uprKykr72e2pbXeV1ejVau4vuGewjXJJKenn5eA4Bzqzpo73OQlSqqDkYDpVzK5Ag/cqva+Q7nXjU3mJiay36FkgeL3ueD3s95T/IBJf25XOG+ifr6eiIjIwkPD8dkMiGXy3G73XR3d9PY2EhlZSWetiJKW17hTa2DEIzc7/8AZm0oGRkZ33gY//G6LixBOnQqn3lbFk7jzjvv5L333uOxxx7zdiiCIAhjSlt/G3vr97IpfxNf7voS1yQXrZta6SvtI+r7UUQujmR61HQWT1xM5rWZYi6UIPgIn/n0++STTwLw2muveTeQUcZut/Pb3/6WzZs3k5mZCUBcXBy7du3ipZde4u233+all17ijjvuoKGhgXXr1nHo0KGhmScKhWLobwtgsVjIycnh/fffH0pK/c///A8//elPh1VPzZw5E4DYiMG2F6OfHxaLBbvdjs1mI9TVQ5ung0Z3KzqXFKVTi91uZ/fu3bhcLmDwGw63q5fmzi1sGdjDUSVM86i50v9+/HTRTJ8+neDg4GGP93frC+nud/LIMjFw2letmBzG387Swgcgk8mYPn06RXo9jkIjKQ1v8JK8jD93/oHZRDDVvoLa2sFKPalUitvtBsA90EBDx3rWeQooV8mY4wphccD3CNAHkZGRcd4zhDYXNBJsUDEl4txa/YSLb3qMic8P1533/ZRKJZmZmeTrdKw4HsPMplf4k7GEP7b/jjnyKUwaWIi5ajD5+J/PKfqKqOr4gs9lddTrZCyQTeQyv5sJ8gv6Ri17/+lYXScTw8VzbCxwuVw888wzbNiwgSlTppw06Pz555/3UmSCIAi+xea0cajxEDn1OWw7vo3sV7MJvyuc/sp+WnNaiZ0SyxW3XkFmZObQXCi51GcubwVB+Lcx/a/Wbrdjt9uHfu/q6vJiNBdHaWkpfX19LFmyZNj2gYEBpk2bBsCNN97Ixx9/zNNPP81f//pXEhMTh+37wgsv8Oqrr1JVVYXNZmNgYGBo9aimpibq6upYvHjxKc8fYRq8GGvtHcDj8aBSqZDJZNhsMoKcGgac7XTSTzdddLk72NjyFzz2dtweNx30c1zuxC6VkCqR8iP5YkJNCwkODiYtLQ2tdnifd05ZK2/kVPLLlalito8PS4v0I8Jfw9oztPCdIJFImDBhAgEBAeTnB/DTlsMca3uXtaoqdvS8QpBLQoxHQwBq7J5+Ojw2jivcOOUSpji1/FhzAyGaiYSHhzN58uTzHr7v8XjYeKyBrJQQ0So6iqRH+/O3HeU0dPYT6nf6lrtTkUqlTJ06lZqgIGz5STxdv5Yd3Zv5XJfLFtdhIlwKwtBg8qiweXpo89goVHiQqCDdHcRNxtsIUkRgsVhISUlBJvvmQ1GdLjdHa7u4IvXcK/iE0evIkSND779Hjx4ddpv4xl4QBOH0XG4XBW0FbCncQm5bLvlt+RT8ooCYn8YgN8pRBChINCQyd8VcZt87m/SQdDEXShDGgDGdlHrqqaeGVQGNRT09PQCsXbuWiIjhQ79VKhUAfX19HDx4EJlMRklJybB93n33XX72s5/x3HPPkZmZicFg4Nlnn2Xv3r0AZ60AOLHyXZ/dRYfNgUmrRC6Xo9frsdvtSOxygt1ubM42ej0yZG4XtZ5uZEjQoeBGTwwRqpkYTYMzgpKTk4mMjDzpPH0DTh7+MJ+M2ADuyIy9oL+VMDpIJBKunDLYwvfkGVr4/lNISAgLFy6kpCQChzWFCX0NtHSso8JVQzW9VEt70CLDDyXf8liINGShV4ZjNBpJSUkhJCTkgmI9VtdFRWsfv7p60gXdX7g40qMHW+Vyq9pZMfnch5T/p8jISIKDgyksjGJy1TLmdORR3buTYk8T9dJ2KqUeDCgIRc8MSTzRfsvQyvwIDAwkJSXlG7fr/afS5h5sDhdTIkWl1FiwdetWb4cgCIJwybncLnKbcmnuayZYG0x6SPpZV7PzeDxUd1eTU5fDJ19+QmFfIc5gJ6VPlGK+1oxhqoFJ35vEkqlLmB87n1m3zRJzoQRhDPJqUuqRRx7hd7/73Rn3KSgoYMKEC2vVevTRR4dWqYHBSqmoqLMvt61QKFi6dOkFnXOkfL3c/3RSU1NRqVRUVVWxYMGCU+7z05/+FKlUyhdffMGKFSu48sorufzyywHIzs5mzpw5PPDAA0P7l5WVDf23wWAgNjaWLVu2DFv95+uxauQS6jpsaBUyVAoZEokEtVqNSqXC4XDgcrtRyjtZHPIQTqdz6L5KpZKgoCAiIiIICQk57WDYX39+nKbufl6/J0NUrIwBq9LC+duOcnaVtrAw+dwSRnK5nJSUFOLj46mtrcVQl4ypo4O0Ey1W/6bRaAgKCiIyMpLAwMBvVJmw7kg9/loFmfHiA9BoEmJUE+GvIbfywpNSMJi4T0tLIykpidraiVB/OcbOzpOGSut0OoKDg4mKivpGw8xP53B1B1IJTBItooIgCIIP2ly5maf3PU1jX+PQNrPWzCMZj5AVkzVs37b+NvbV7yOnPofP1n5Gc0sz/nP8afyiEVW4iqjwKO75v3uYHzef2WGziTHGiCpTQRjjvJqU+ulPf8pdd911xn3i4uIu+PgqlWqoWuh8SCSS827z8RaDwcDPfvYzfvKTn+B2u5k3b97QIHKj0UhQUBCvvvoqOTk5pKen8/Of/5w777yT/Px8TCYTiYmJvPHGG2zYsAGLxcI///lP9u/fj8Xy1QDhX/7yl3zve98jJCSE5cuX093dTXZ2Nj/60Y8AiI2NJW/vTiamZ3C4q5PpSZHI/p1cOvG31Ol0aDQaZs+ejVQqHdp+ptWuTnhnXxXv7Kvmd9dPxhJ0+hXXBN8xMdxIfLCOT/PqzjkpdYJSqcRisWCxWHC73dhsNhwOB1Kp9IL/zZ+Kx+Nh3ZF6lqaGDlUECqNHeoyJg1XtI3IsjUZDQkICCQkJuN1u+vr6cDqdyGQy1Gr1OX9JcKEO13SSGGIQQ84FQRAEn7O5cjOrt63Gw/AvdJr6mli9bTVPz38af5U/W4u3kteTx/7t++nY3UHU/VH0OHqQK+XMDJ3J7N/MZnbYbDEXShDGIa/+iw8ODj5pkLVw/n79618THBzMU089RXl5Of7+/qSnp/Poo49y880388tf/pL09HRgcGD8xo0b+d73vsd7773H/fffz6FDh7j55puRSCTceuutPPDAA3zxxRdDx7/zzjvp7+/nf//3f/nZz35GUFAQN9xww9Dtzz33HKtXr+aVV14hJDSMXYcKiA3UnvJbDY1Gc06JqBMOVrbz+KdHuX12NDfPjP4GfyVhNJFIJFw7LYIXtpbxP9c4L/hiXCqVotNdnETl8frB1r0nReveqDQ92p8NRxuwO12o5N98rtMJUqkUvV4/Ysc7F/k1HaJ1TxAEQfA5LreLp/c9fVJCCsDldNF7tJf/8vwX/dX9VDxbwYQ/TkAdpWbq1VO5KvUqZmeJuVCCIPjQTKmqqira2tqoqqrC5XKRl5cHQEJCwiW/gBhtJBIJDz300LDV8U5oaGgY9rtCoeDAgQNDv6tUKv7xj3/wj3/8Y9h+Tz311LDf77//fu6///5Tnn/lypWsXLkSgO5+BxUtfdS224gwab5RuW1JYzf3//MgU6P8efyqiRd8HGF0unpqBL/fWMym441cMy3i7He4xE607s0RrXujUnqMiYF/DwifHjNy850utX6Hi8L6bpF0FwRBEHxOblPusJY9gKY1TZjmmZAZZDR+2IgmTkNYQhjXvX8dC5MWkhGWQZDmzAvdCIIwvvhMUurxxx/n9ddfH/r9xMo2W7duZeHChV6KSvg6g1pBpElDTXsfbiDSpEF6AYmpwoYubnt5L8EGFS/ePh2lXLRPjTVRAVpmxJj4JK921CWlBlv3Grgi1Sxa90aplDAjaoWUQ1XtPp2UOl7fhdPtYWqkv7dDEQRBEIRz0t7fzp76Pbx75F0A6t+uRzdBhzHdiMflwT3gRqFQkPDrBAAemfUIV8Zd6c2QBUEYxXwmKfXaa6/x2muveTsM4RyYdEqkEqhqt+F2e4g0nXkFv6/LKWvlgbcOEu6v4c3vzMKk8435XsL5u2ZaBE+sOUZLj50g/cjMghoJx+q6sLb08sTKVG+HIpyGQiZlSoQ/uSM0V8pb8qs7UMqkJIcavB2KIAiCIJxSv7Of3KZcNh3bRHZBNvV+9dS/Ww8eCLs1DMM0A8qgwc/r5mvNJ90/RHthqyALgjA++ExSSvAtflolsVIJVW19lDT1EKw9e7WU3eniuY3FvLyznFmWAF66fQZ+2os7YFjwrisnh/Grz47zyaFa7p1/4YsajLSPcmsJ0iuZlyDKy0ez9BgTHx+qwePx+OzKPIdrOkkJN4pqUEEQBGHUcLldFLYXklOXw6ZDm8jZkoPf5X60Z7fTV9RHxD0RzL5tNpmxmayvWQ8ppz6OBAlmrZn0kPRL+wAE4SzcbjcDAwPeDsPnKRQKZLJvPttVJKWEi8agVpBkNlDTbqO2vZfObjuVRU0smhgxbDBxU3c/7++v5u29VbT0DPDo8gncOy8OqdQ3LzKFc2fSKbliopl391fznXmWUZFYcLjcrDlcy9VTI5CL1r1RLT3anxe3l1HX2U+E//lVZI4Wh2s6mC+Sn4IgCIKXVXdXk12dzd7GvWQXZVPwQgExP4lhoGWAvoY+ErWJXH3H1WSGZzIrbNbQXKhZlbNYvW01wLCB5xIGP9M9nPEwMunILUgiCN/UwMAAVqsVt9vt7VDGBH9/f0JDQ7/RdZxISgkXlUImJTZQS4vMRWcT/Prz4zzw7hEiTBpMWiU17TbaegdQK6RcnRbBvfMtJJpFG8t4csvMaG7/+15yq9qZHhPg7XDYUdxMS88A16dHejsU4SzS/z1LKrey3SeTUl39Dsqbe/nBwgRvhyIIgiCMMx39Hext2Mvnez+noL+ABncDRauLiP15LKowFSGZISwIX8DczLnMvn82FuOpvzzMisni+YXP8/S+p4cNPTdrzTyc8TBZMVmX8mEJwhl5PB7q6+uRyWRERUUhlYovoC+Ux+Ohr6+PpqYmAMLCwi74WCIpJVx0EokEg0ZJsEHFy3fMIL++D2tLL+19A2SlmIkJ1LIgKRh/rZgdNR7NiQ8kKkDDu/uqR0VS6qPcWiaEGkgNN3o7FOEsgvQqogO05Fa1szIt3NvhnLfD1R0ApEX5ezUOQRAEYeyzu+wcajrEnro9fPT5R9S56tDEa7A+YyVoaRCmqSauevYqsqZnMTdiLpPunIRcem6XilkxWSyKWkRuUy7Nfc0Ea4NJD0kXFVLCqON0Ounr6yM8PBytVuvtcHyeRjP4pXBTUxMhISEX3MonklLCJRUXrCc1SrSqCF+RSiXcPCOKF7aW8fjKVAxq780R6+xzsOl4Iz9fmuy1GITzkx7tT26lbw47z63swE+jIC5I5+1QBEEQhDHG7XFT2FbI7trd7GvYx5a1W+hr7iNoaRCtJa3I/eVMnj6Z2169jTkRc5hhnoFWceEX6TKpjJmhM0fwEQjCyHO5XAAolaIYYqScSO45HA6RlBIEwXfdMD2K5zcVs+ZwHbfNivFaHJ/l1+F0u7l6mu9V3YxXMy0BfJ5fT6/diU7lW29puVXtpEf7i/l5giAIwoio7akluzqbL/Z/QamylOr91TSvbSbu0TgkJglmvZkr469k9rzZzA6bTbA22NshC4JXjIY5tmPFSPwtfesTvCAIY1Kon5rLJ5j5Z04l38qI9sobhcfj4e29VSxKDiHEoL7k5xcuzCxLAE63h0NVHcxL9J0qTLfbw6Gqdu4bRatOCoIgCL6l097JvoZ97KrYxYfvfAgZMNA4gPUZK8nPJxM4IZD5afNZNnMZs6+eTZxfnLgYFwRh1BFJKYFf/vKXfPLJJ+Tl5Xk7FGEcu3tuLLe9specslbmeGE1styqdo7Xd/Ffy0Trni+JD9YToFOyz9rqU0mpsuYeuvqdQ8PaBUEQBOFsBlwD5DXlkVOfw566PWz+82ZMi0wozUqq9lYRlRrFrLRZPLj5QeZGzWVS0CQUUu+NRRAEYfQYzdf8IiklCMKoMCc+kAmhBl7NtnolKfVGTiWxgVouSxSl7L5EIpGQERvAXmubt0M5L7lV7UglYsi5IAiCcHpuj5vi9mJ2lO9gV/kuCgYKKHu5DG2CFtNlJpThSmJMMSyeuJjZa2YzwzwDvVLv7bAFYcxzuT3ss7bR1N1PiEFNhiUAmRjHcMFEUkoYEQ6HA4VCfBMjXDiJRMI9cy08/FE+1pZeLJdw+HNzt511R+p5eNkEMd/HB2VYAnh6fSF2pwuV3DdW+smt7CDJbEDvY3OwBEEQhIurvqeenPoc1h9cz56je/Akemh4rwGP20PYrWHEr4xnTvIcFqcuZtYNszDrzN4OWRDGlfVH63nys+PUd/YPbQvzU/PEylSWTQrzYmQX38W65peO+BGFS87tdvPUU09hsVjQaDSkpaXxwQcfAPDaa6/h7+8/bP9PPvnkrP3kr7zyCikpKajVaiZMmMD//d//Dd1WUVGBRCLhvffeY8GCBajVat56660Rf1zC+LNqajgBWiWvZVsv6Xnf21+FTCrhxulRl/S8wsjIsAQw4HSTX9Pp7VDOWW5VO9NF654gCMK41zXQxZbKLfzPnv9h0QuLSLs1jSd2P8HGvRupO1CHRq7huh9dx/PPPc9Hqz5i30/38ceVf2RV/CqRkBKES2z90Xq+/2busIQUQENnP99/M5f1R+sv2rnH8jW/+Ip2DHjqqad48803efHFF0lMTGTHjh3cfvvtBAdfWBvSW2+9xeOPP85f/vIXpk2bxqFDh7jvvvvQ6XTceeedQ/s98sgjPPfcc0ybNg21WgyGFr45tULGbbNjeGVnOQ9lJRGgu/jLtQ443by1t4qr0yLw04pqP1+UEmbEoJKzz9rGzNgAb4dzVp19DkqaevjegnhvhyIIgiBcYgOuAQ43H2ZL4RYOdx0mvzqfst+WEf/LeFxOFzK1jClBU8i8I5PM8EymBE1BIROfTwThYvB4PNgcrnPa1+X28MSaY3hOdRxAAvxyzXHmJgSdUyufRiE7r4UHxvI1v0hKnYFtwEVZc88lP298sB6N8txaUOx2O7/97W/ZvHkzmZmZAMTFxbFr1y5eeuklrrjiivM+/xNPPMFzzz3HddddB4DFYuH48eO89NJLw56gP/7xj4f2EYSRcmfmYFLq5Z3lPLxswkU/30e5NdR39nPvfMtFP5dwccikEmbEmthrbeMHi7wdzdkdqm4HEEPOBUEQxgGPx0NxezF76vfw0aaPqJRX4tQ7KXyokNifxqKOVpP2nTSuTLmSedHzmPn9mRiUBm+HLQjjgs3hIvXxDSNyLA/Q0NXP5F9uPKf9j/9qKVrluaVjxvo1v0hKnUFZcw9X/XnXJT/v5z+ax6QIv3Pat7S0lL6+PpYsWTJs+8DAANOmTTvvc/f29lJWVsZ3vvMd7rvvvqHtTqcTP7/hMc2YMeO8jy8IZxOoV3HnnFhe313BvfMsBOpVF+1cTpeb/9tWxvJJoSSaxQdAX5ZhCeQvX5bgdLmRy0Z3Z3puZTsBOiWxgVpvhyIIgiBcBA29DeTU5bCnfg+fffQZfbI+DFMM1HxYg98sP2JmxvDDd3/IwsSFzA6bTagu1NshC4Iwio31a36RlDqD+GA9n/9onlfOe656egYrudauXUtERMSw21QqFVu3bsXjGV5k6HA4znq8l19+mVmzZg27TSYbXr2l0126QdTC+PLd+XG8sbuCv+0s59HlKRftPGsO11HV1sdfb0+/aOcQLo0MSwC9Ay6O1nUxdZSvaLevoo0ZMabzKtkWBEEQRq/ugW72N+xne9l2cjtyyduQR391P6E3hdJt70Zj0DAvYh6zX5zN7LDZJJmSxHuAIIwCGoWM479aek777rO2cdc/9p91v9funkmG5ezjJDSKc1+cZ6xf84uk1BlolLJzrljyltTUVFQqFVVVVSxYsOCk24ODg+nu7qa3t3foCZWXl3fa45nNZsLDwykvL+e22267WGELwhmZdErumhvLq7squG9+HEEXoVrK5fbwl62lZKWEMDF8dP87F85uSqQfOqWM3WUtozopZXe6OFTVwc+XJns7FEEQBOECOVwODjcfZkf5Dr7Y8QXN4c105HXQ+EEjCb9OQBenY2LyRFZMXkHm0kzSgtNQyi7+nExBEM6PRCI55xa6+YnBhPmpaejsP+VcKQkQ6qdmfmLwOc2UOh9j/ZpfJKV8nMFg4Gc/+xk/+clPcLvdzJs3j87OTrKzszEajaxcuRKtVstjjz3Ggw8+yN69e3nttdfOeMwnn3ySBx98ED8/P5YtW4bdbufAgQO0t7ezevXqS/PAhHHvvvlxvLG7kj9sLuZ/rpk84sf/+FAt5c29PHdj2ogfW7j0FDIpGZYAdpe28sDCBG+Hc1r5NZ3YnW5mxwV6OxRBEAThHHk8Hko7StlTv4ddlbv4/G+f47/CH1evi4q/VJDwZAKps1K5/crbWZSwiJlhMzEqjd4OWxCEESSTSnhiZSrffzMXCQxLTJ1IQT2xMnXEE1Iw9q/5RVJqDPj1r39NcHAwTz31FOXl5fj7+5Oens5jjz1GQEAAb775Jj//+c95+eWXWbx4Mb/85S/57ne/e9rj3XvvvWi1Wp599ll+/vOfo9PpmDx5Mj/+8Y8v3YMSxj1/rZKfLEni12uPc/OM6P/f3p2HNXXl/wN/Z2FfZUuIrAKKuIDIjnZAsW7VWsdap4rb1NpWaxHHijPf6tQuLrX+mKrVtk+rTlc7ndJFW5eiuLIjdUdRVGQLuICAbEl+fzgyZbSKmuSG5P16nj5tbi73vnOeNLnnk3PPQT8P7Y1mutHUihU/n8bo/u4Y4MXJpo1FrL8L3tlZhKZWFSwfYEi0PuWUXIWdhRS93dlZISIyZFUNVcgsz8Th0sPIrclF4T8K4ZTgBCtfKzTVNcFOZYfHBjyG6L3RiHSPhMJWIXRkItKxEX3dsWFKKF7/8SQqapvat8sdLLF0TBBG9HXX2bmNuc8v0vzvzYdGrK6uDg4ODqitrYW9fccOQVNTE0pKSuDr66uzpQ5NGduXHkabSo0n1h6EhZkEaS/GQKylXx7e2n4Sn2VdQvqCP0DhaKWVY5LwTpTXYvR7B/HlrChE+xnmSKTEj7MhFYuwaUaE0FHof9zrGoF+H9uNjEV9Sz3yqvKw8+hO5FzMgdJGidKNpbDytYLLcBc0/dqEqIgoDO07FFGKW/NCiUWGvbAGEXWkrT6pSq1BTslVKG80wc3OEhG+TjoZIdUV3KtNO3uNwJFSRGSwpBIxlj3ZFxM/yMTWvFL8KcLrkY9ZrLyBTYcuICkhgAUpI9Nbbg8nG3McPldjkEWpNpUa+Rev4eUhAUJHISIyea3qVhyrPoasiiz8nPkzjh49Cvsoeyh/UELTooF8ghxxL8RhcK/BGOw9GMFTgmEh0d2KwETUdUjEIoO81uyqWJQiIoMW4euEP4Z64O3tpxDdwxk+Lg+/AkSrSo2Ufx+DwtEKzw3uocWUZAjEYhGiezjjUHENFjxueBOJHy+vQ2OLCpE97r8iCxERaZdGo8H52vM4VHoIOcoc7C/Yj9LvSuHxvAcayhrQVNmEPnZ98PSCpxHdPRoR8gg4WHAhFCIiXWNRiogM3t/HBiH/4lW89HkBvn0p5qHnC1q9qwhHSq9j6/NRBjvnED2aGH9nLPn+BG40tcLO0kzoOB3klFyBlZkE/Qx8VVciImNR3ViNzPJMbM/ajiIUQVmnxJlXz6Dnqp6ADeDS1wWPez+OmOgYRLpHwsPOQ+jIREQmh0UpIjJ4dpZmWD85FE+9fxjLtp3E2089+Gp86aeq8MG+81g8MhBhPhypYqxi/Fza7/Mf2lsmdJwOss9fxUDvbjCTcA4SIiJdaGhtQH5VPjLLM/Ft2re45ngN5jJznF18Fp4veMLRzxHj1mSnPx8AACfbSURBVIzDiKgRiOkeg8AXAzkvFBGRwFiUIqIuoY/CAa+P7YPF3x6DzM4S84b6QyTq3ISCxy7XIvnrX5HQ2w2zeNueUfNxtobCwRIHi2sMqijVplIj58JVvv+IiLSoTd2G4zXHcbj8MLIrsrH3m73QmGngGO2IqhNVsO1tiwF9BmDGDzMQ6xGLENcQWEq54A4RkSFhUYqIuow/RXjhSn0zVu86g6Y2FV4d3uu+hans81fw5y15CJDZ4t2JIVpbwY8Mk0gkwmM9XbHvTLXQUTo4WlaLG01tiPV3EToKEVGXpdFoUFJXgoOXDiL9aDqKREW4vOcy6k/Ww3O2JyTOErjZuWFUz1GIei8KkfJIOFo6Ch2biIjugUUpIupS5g4JgKWZBG9uP4WLVxrw2hNBcHe4cxW9NpUaX+WW4s3tJxHq1Q0fTQ2DjQU/8kxBXC9XfJVbitKrjfB0shY6DgDg4Nka2FlKEezB+aSIyLSp1CoUKAtQ3VgNV2tXhLqFQiL+/Xkea27WIKsiCxlnM/Dztp8hDhOj/ng9KrdWwv8Nf7iHuqP/0P4YMXAEosZHwdPOU4+vhoiIHhV7aETU5Tw3uAfc7C2x7MeTGPruPkyP8UG0nzMC5faoqmvCyfI6fHTgPM4q6zExzAPLnuzLic1NSKy/C6RiETKKlEiM9hE6DoBbRanoHs6Qcj4pIjJhv1z8BStyVqCqsap9m8xahpSIFCR4JwAAGlsbkVeVh6yKLBy+fBj7U/dD/owcAHBpzyUEhAQgfkg8oiZHIdYjFoFOgfcsahERkWFjUYqIuqSxwQrE9XJF6u6z+CzrIt7PONfh+Rg/Z/y/Z0LQlyudmRw7SzMM9O6GjKJqgyhK1Te3oeDSNSwd20foKEREgvnl4i9IzkiGBpoO25WNSszPmI8E9wRU1VXh1M1TOPfuOTg/7gzbPrYwl5nD39YfcUFxiBoXhQFuA2AlvXOENBERdU0sShkhkUiEtLQ0jBs3TugoRDplb2mGJWOC8H+je+N8TQPOVN2Au4MlAmR2sOWteiYtrpcb3ks/i6ZWleCj5LLOXUGbWoPBnE+KDNTq1auxadMmiEQipKSkYMqUKUJHIiOjUquwImfFHQWp5opmqG6qYN3DGpuWboKVtxVcRrog6Jkg/GHAHxDfKx4Rz0TAyZKr5hIR/ZYx9fl5H4GJaW1txaJFi9CvXz/Y2NhAoVBg6tSpKC8vFzoa0UMTi0Xwd7PFqH7uGODVjQUpQlwvV9xsVSH3wlWho+BgcQ08ulnB29kw5rci+q1jx47hiy++QH5+PnJzc7Fu3Tpcv35d6FhkZPaW7m2/Ze/mhZuo2VkDAGgoakD98XoAQPcZ3ZE0Pwnbn9qOzJRMrByxEiN8R7AgRUSGR60CSg4Ax7659W+1SuhEHXS1Pj97bkampaXlns83NjaioKAAr732GoKDg3Ht2jW88sorGDt2LPLy8vSUkohItwLldpDbWyKjqBqDA1wFzbL/bDUGB7jcd6VIIiGcOnUK0dHRsLS0BAAEBwdjx44dmDRpksDJqCu72XYTBVUF2FO0B7/W/4qjJ46ibEsZfFN8IZKIIPrPSrhOcf8tOIktxOjn2g9e9l5CxSYiur+TPwA7FgF1vynw2CuAESuBoLF6iWBsfX6OlOri4uLiMHfuXCQlJcHFxQXDhw+/Y5+lS5fC3d0dR48ehYODA3bv3o2JEyeiV69eiIqKwrp165Cfn49Lly4J8AqIiLRPJBIhrpcrMoqUguYov34T56sbMMhf2MIYdV379+/HmDFjoFAoIBKJ8N13392xz/r16+Hj4wNLS0tERkYiJyen08fv27cvMjIycP36dVy7dg0ZGRkoKyvT4isgU6BSq3Cs+hg+OvoRRq4ciahPojBr2ywsHb4UJy6cgJmLGVxHuwIawNLTEs7DnO96HFdrflYSkQE7+QPw9dSOBSkAqKu4tf3kDzo5rbH3+TlSyghs2bIFL774Ig4dOgQACAwMBABoNBrMmzcP27Ztw4EDB+Dv73/Xv6+trYVIJIKjo6O+IhMR6dyQQDd8lVuKc9X18HO1FSRDRlE1xCIg1v/uHTCi+2loaEBwcDBmzpyJ8ePH3/H81q1bkZycjI0bNyIyMhKpqakYPnw4ioqK4ObmBgAICQlBW1vbHX+7a9cuBAUFYd68eRgyZAgcHBwQFRUFiYQrmdG9aTQalN4oRVZFFjLLM/HjVz8C7oCVrxVKPi2B/Gk5fPv4YtT3ozAkcAgGygZi8k+ToWxU3jGvFACIIILMWoZQt1ABXg0RmSyNBmht7Ny+ahXw86vAXT7Dbm0T3RpB1SMO6MyKoGbWwAOMojfmPj+LUvfS0gjUnNH/eV16Auadn3skICAAq1at6rCtra0NU6ZMwZEjR3Dw4EF07979rn/b1NSERYsW4U9/+hPs7e0fKTYRkSF5rKcrrMwk2HmiEi/F3f0LWtd2n6xEuI8THK3NBTk/dX0jR47EyJEjf/f5NWvWYNasWZgxYwYAYOPGjdi+fTs++eQTpKSkAAAKCwvveY7Zs2dj9uzZAIDnnnsOAQEB2glPRuVa0zVkV2bjQMkB5F/Nx9G0o9BoNHB53AWNjY3opumGIZ5DEPVNFKLdo+Ft793htuWUiBQkZyRDBFGHwpQIt/ZZFLEIks505IiItKW1EXhboaWDaW6NoFrh2bnd/1oOmNt0+ujG3OdnUepeas4AH/5B/+d9fh+gCOn07gMHDrxj2/z582FhYYGsrCy4uNx9xafW1lZMnDgRGo0GGzZseNi0REQGydJMgrherth5XJiiVENzGw6du4JXh/fS+7nJNLS0tCA/Px+LFy9u3yYWi5GQkIDMzMxOH0epVMLNzQ1FRUXIycnBxo0bf3ff5uZmNDc3tz+uq6t7uPBk8JramlCgLEBGcQb25O5BlUsVru67irr8Ovgk+8Cupx16ufTC6JDRiBoVhT7OfSAV/37XIsE7AWvi1mBFzor2Sc8BQGYtw6KIRUjwTtDHyyIi6pKMuc/PotS9uPS8VSAS4rwPwMbmzgrrsGHD8OWXX2Lnzp2YPHnyHc/ffnNevHgRe/bsMciKKRHRoxrRV45XvipE+fWbUDha6fXc+89Uo6VNjWFBMr2el0xHTU0NVCoVZLKO7zGZTIbTp093+jhPPvkkamtrYWNjg02bNkEq/f3Lw+XLl+P1119/6MxkuNQaNU5dPYXM8kzsO7MPe7buQbdR3XDz3E1Ufl2JHn/tgQEJAxCRGIG4gDiEycJgbfZgq4omeCcg3jMeBcoCVDdWw9XaFaFuoRwhRUTCMLO+NWKpMy4eBj6fcP/9Jn8DeMd07twPwJj7/CxK3Yu59QONWDIkY8eOxZgxY/Dss89CIpF0WEXn9pvz7Nmz2Lt3L5ydOdcJERmn+EA3mElE2HWiEtNjffV67t2nqtBTZgtv584PzSYSwoOMqlq8eDGSk5PbH9fV1cHTs5O3KpDBuXzjMg6XH0bm5UzkVOXg2KpjUExXQGIjQWN5IwLEAYgdHovoGdGIco+Ci9Xdf4l/EBKxBOHycC2kJyJ6RCJR52+h8xtya5W9ugrcfV4p0a3n/YZ0bk4pLTCWPj+LUkbsqaeewqefforExERIpVJMmDABra2tmDBhAgoKCrBt2zaoVCpUVlYCAJycnGBuznlPiMh42FuaIcbPBTv0XJRqU6mx57QSkyO5tDnpjouLCyQSCaqqqjpsr6qqglwu18k5LSwsYGFhoZNjk+7VNtciuyIbu47uQn5lPmosalCyogROQ53gEO4At1g3DPIahPje8Yh+Ohq+Dr4d5oUiIjJZYgkwYuWtVfYgQsfC1H8+J0es0FtB6jZj6POzKGXkJkyYALVajcTERIjFYoSGhuKHH24tVRkSEtJh37179yIuLk7/IYmIdGhEXzn+lnYMV+qb4Wyrn8503sVruN7YioTevHWPdMfc3BwDBw5Eeno6xo0bBwBQq9VIT0/H3LlzhQ1HBqFZ1YxCZSEyyzPx0/6fcPbyWdj2t0XZ5jJYKCwgHy7H4wsfR3y/eAz2How+iX1gJjYTOjYRkWEKGgtM/OetVfbqfnPbn73iVkEqaKwgsbp6n59FqS4uIyPjjm0aTcfhhBMnTsTEiRN/93kiImM2LEiGv6Udw44TlZgc6a2Xc+4+WQU3OwsEezjq5XxkvOrr61FcXNz+uKSkBIWFhXBycoKXlxeSk5Mxbdo0hIWFISIiAqmpqWhoaGhfjY9Mi1qjxplrZ3C47DCyK7Ox/9B+1GTXQD5JjtrztWi92orgwcF4dsWziFHEIEweBhsz3mJMRNRpQWOBwNG35piqrwJsZbfmkNLhCClj7/OzKEVEREbNxdYCgwNckVZQppeilEqtwfajFRjeRw6xmLe90KPJy8tDfHx8++Pb8zlNmzYNmzdvxjPPPIPq6mosWbIElZWVCAkJwY4dO+6Y/JyMV3l9+a2RUFk/4az5WVReqMSFdy+g56qeUNuq4ervijE9xiBqUBSi3KPgZu0mdGQioq5NLAF8BwudwmiwKEVEREZvfGh3vPJVIS5eadD5xOPZ56+gsq4J4wZ01+l5yDTExcXd99fOuXPn8nY9E1LXUofcilxkVmTi31//G00+TRBbiVE0vwj+b/jDQeGAp958CqMjRiNGEQM/Rz/OC0VERAaLRSkiIjJ6jwfJYWshxbcFZZg/rKdOz5V2pAxeTtYI9XLU6XmIyDS0qFrwa/WvyCzPRHZFNvZ+thcWnhaw7WOLy3mX4eroioH+AzE7YzZiPWPR36U/zCScF4qIiLoGFqWIiMjoWZlLMKqfHGlHypCUEKCzUQNNrSrsOF6JGYO4YhURPRyNRoMz187gUOkh7D21F0WqIlz6/hLUN9WQTZBB6iSFh5sHRgaORNRnUQiXh8PO3E7o2ERERA+FRSkiIjIJ40M98HXeZeRfvIYwHyednCP9lBI3mtswLkShk+MTkXGqbKhEVkUW0k+mY++BvRD1FeHa/muozauFT7IPvGK9EOoeihGhIxA1IQpyG7nQkYmIiLSCRSkiIjIJET5O6O5ohW/yL+usKJV2pAzBno7o4Wqrk+MTkXG40XIDuZW5yKrIwr4z+5C3OQ/uU9zRXNGMqvQq9AruhVETRiE6KRox3WPQs1tPjr4kIiKj1CWKUhcuXMAbb7yBPXv2oLKyEgqFAlOmTMHf/vY3mJubCx2PiIi6ALFYhEnhnlifUYyUkYFwtNbu94fyRhP2nVHir6N6a/W4RNT1tapbcbT6KPaf34/simycunEKZ984C48/e8BcZg6pjRRBdkEYFDwI0TOjEewaDHMJr3GJiMj4dYmi1OnTp6FWq/HBBx/A398fx48fx6xZs9DQ0IDVq1cLHY+IiLqIZyO9sHZvMb7MKcWLcX5aPfbnWZdgJhFj/AAPrR6XiLoejUaDc9fPIasiC9sObcPJayehkWlw/u3zcBriBMcoR/Sf2h/DIoYhzj8OYZPD4GDhIHRsIiIivesSRakRI0ZgxIgR7Y979OiBoqIibNiwgUUpIiLqNGdbC4wLUeCfmRfw3GBfmEnEWjluc5sKn2dfxB9DPeBgzVWviEyRslGJrIosZJVn4ef0n1FdXo1usd1Qtb0KZo5m8PPyw3NrnsNjAY8hWhENhS3nniMiIuoSRam7qa2thZPTvecEaW5uRnNzc/vjuro6XccyCCKRCGlpaRg3bpzQUYiIDM6MWF98nXcZO09U4on+2ukU/vhrBWrqWzA91kcrxyMiw9fQ2oC8yjzsO7cPBdcLcDT3KK6mX4Xni56ob6qHFFLEKGIQ9WYUohXR6NmtJ8Qi7RTCiYjItBlTn79LfjMWFxdj7dq1mD179j33W758ORwcHNr/8fT01FPCruOFF16ASCRCamqq0FGIiPSit7s9Yvyc8fHBEq0cT6PRYNOhEsT1coUfJzgnMlqt6lYUKguxPn89hr09DLGfx2LWV7Pw+sjXUXylGJZyS4SMCsFz/Z7DV698haINRfhg2AeY0XcGAp0CWZAiIjISKrUKuZW5+On8T8itzIVKrRI60j0Zep9f0JFSKSkpWLly5T33OXXqFAIDA9sfl5WVYcSIEXj66acxa9ase/7t4sWLkZyc3P64rq7O6AtTLS0tnd43LS0NWVlZUCg4fJyITMtzg30xc3Me9p+pxmM9XR/pWNklV3GivA5bZkZoKR0RGQKNRoOS2hJkVmQiqyILP27+ERb9LWDmZIbiDcXwTvKGXw8/jPtmHBL6JCDSPZLzQhERGblfLv6CFTkrUNVY1b5NZi1DSkQKErwT9JLB2Pr8ghalFixYgOnTp99znx49erT/d3l5OeLj4xETE4MPP/zwvse3sLCAhYXFo8Y0aHFxcejbty+kUik+++wz9OvX7459li5dig8//BA7d+5E//79Adwq7r388svYuXMnRo8ere/YRESCiu/lhnCfblj+82kM8neBWPxwS61rNBqs3HEaQe72eCzARcspiUjfam7WIKsiCwcvHkRuTS6OfX4Mlt0t4RDhgMa6RnRTdcOQHkPw9/S/I8o9Ch52XNiAiMhU/HLxFyRnJEMDTYftykYlkjOSsSZujU4KU8be5xe0KOXq6gpX1879Ql1WVob4+HgMHDgQmzZtgljMIdC3bdmyBS+++CIOHToEAO0jyzQaDebNm4dt27bhwIED8Pf3BwCo1WokJiZi4cKF6NOnj2C5iYiEIhKJsHhUb4x//zC+KyzD+NCH61huP1aBI5eu44vnIiESPVxhi4iE09jaiPyqfOw5uwf7j+2H0kEJ5fdKtF5rRffp3eHY2xH9/fpjZOhIRD0Rhd5OvXkbHhGRkdBoNLjZdrNT+6rUKizPWX5HQQpA+7YVOSsQKY+ERCy57/GspFYPdO1ozH3+LjHReVlZGeLi4uDt7Y3Vq1ejurq6/Tm5XK6z895su4mSWu3MOfIgfB18YSW16vT+AQEBWLVqVYdtbW1tmDJlCo4cOYKDBw+ie/fu7c+tXLkSUqkU8+bN01pmIqKuJtSrG0b1k2P1ziKM6ucOS7P7X0D8VnObCit3nMbQQDfE+HOUFFFX0KZuw4krJ5BVnoX04+nI/CUTjnGOqM2pRW1OLbzneiPyqUhEeUch3j8eAyYPgKXUUujYRESkAzfbbiLyi0itHa+qsQoxX8V0at/sZ7NhbWbd6WMbc5+/SxSldu/ejeLiYhQXF8PDo+Ov2RrNnZVKbSmpLcEz257R2fF/z9YntiLIOajT+w8cOPCObfPnz4eFhQWysrLg4vLfzlJ+fj7+8Y9/oKCggL/qE5HJWzg8EMPW7MN76Wfx6ojA+//Bb2w+dAHl15uwaXq4jtIR0aPSaDS4WHcRh8sOI6siC1klWTi9/jQ853iira4N9efq0XtUb4x/djyiF0YjUh6JbpbdhI5NRETUgTH3+btEUWr69On3nXtKF3wdfLH1ia2CnPdB2NjY3LFt2LBh+PLLL7Fz505Mnjy5ffuBAwegVCrh5eXVvk2lUmHBggVITU3FhQsXHjo3EVFX4+tig+THe+KdnUWI6uHc6UnPj5fV4t3dZ5AY5Q1/NzsdpyQyPSq1CgXKAlQ3VsPV2hWhbqGduh0CAK7cvIKcyhzsOLIDR2uPolpUjbOLz8LjeQ9Y+VrBZYALhnQfgsGDByPquSh42nl2iYt2IiLSLiupFbKfze7UvvlV+Xgp/aX77vf+0PcxUHZnAelu534Qxtzn7xJFKaFYSa0eaMSSIRk7dizGjBmDZ599FhKJBJMmTQIAJCYmIiGh4+Rrw4cPR2JiImbMmCFEVCIiQb3wmB+yzl/F/K2F+OmVwZDZ3/tWnasNLZj9aT4C5XZIGflgo6uI6P4edGWjm203UVBVcGuFvN0/orSxFNb+1riw5gK6xXaDS7QLnlj2BBJCEzDYezB6T+3d6QIXEREZL5FI1Olb6GIUMZBZy6BsVN51XikRRJBZyxCjiNHbd4yx9PlZlDJiTz31FD799FMkJiZCKpViwoQJcHZ2hrOzc4f9zMzMIJfL0atXL4GSEhEJRywWYc3EYIz6xwE8/888fDw9HC62d1+5talVhXlfHsHNVhU2TBn4wPNQEdG9dWZlo3jPeJy6egqZ5ZnIqsjCvl/2oaG0AS4jXXDl1BWILcQYED4AUz+aipjuMQiVhT7wL9JERES/JRFLkBKRguSMZIgg6vA9JcKt0baLIhbp/UcPY+jzsyhl5CZMmNA+875YLMb48eOFjkREZHBcbC3w8bRwzNiciyfXHcLH08MQKLfvsM/56nrM/eIIipX12DQjHN0d2ckl0iaVWoUVOSt+d2UjjUaD+f+eDzu5HSqPV6LqX1Xo8X89ADvAzdMNT/o/iajBUYh0j4SzlfNdzkBERPTwErwTsCZuzV1H8y6KWHTX0bz60NX7/CKNLmcKNzB1dXVwcHBAbW0t7O07djaamppQUlICX19fWFpylRVtY/sSUVdQfv0m/rwlDyU19UjoLcPjfeRQqdUovHQd3+RfhszeEmufHYA+Cgeho5KW3esagX6fNtsttzIXM3fO7LBNo9LgetZ1OIQ7oO1GG4r/rxiB/wiErdgW/mp/jBk8BlHuUfC29+a8UEREdE/a6pM+yryHxuZebdrZawSOlCIiIvoPhaMVvnkhGpsOlWDHiUrM+/IIgFsToj8d5omFw3vBxoJfnUS6UN1YfedGMVCbXQubnjYwdzVH4NpAzA2bi1n9ZkEq5v+LRESkfxKxBOFyrr6sLfw2JyIi+g0bCynmDgnA3CEBqKxtgqWZGI7W5kLHIjJ6rtZ3rn4pEongk+zT/lgsFSNMFsaCFBERkZEQCx2AiIjIUMkdLFmQItKTULdQyKxl7RPG/i8RRJBbyxHqFqrnZERERKQrLEoRERERkeBur2wE4I7ClJArGxEREZHusCj1P0xo3ne9YrsSERHR/dxe2cjN2q3Ddpm1DGvi1gi2shERERkP9k21RxttyRvy/8PMzAwA0NjYCCsrLvOtbY2NjQD+285EREREd5PgnYB4z3iubERERFolkdz6HmlpaWGfX0u00c9nUeo/JBIJHB0doVQqAQDW1tZcWlgLNBoNGhsboVQq4ejo2P5BQERERPR7uLIRERFpm1QqhbW1Naqrq2FmZgaxmDeOPSxt9vNZlPoNuVwOAO2FKdIeR0fH9vYlIiIiIiIi0ieRSAR3d3eUlJTg4sWLQscxCtro57Mo9Ru336Rubm5obW0VOo7RMDMz4wgpIiIiIiIiEpS5uTkCAgLQ0tIidJQuT1v9fBal7kIikbCIQkRERERERGRkxGIxLC0thY5B/8GbKImIiIiIiIiISO9YlCIiIiIiIiIiIr1jUYqIiIiIiIiIiPTOpOaU0mg0AIC6ujqBkxAREZEhuX1tcPtagTqH11ZERER0N529tjKpotSNGzcAAJ6engInISIiIkN048YNODg4CB2jy+C1FREREd3L/a6tRBoT+klQrVajvLwcdnZ2EIlEWj12XV0dPD09UVpaCnt7e60em+6NbS8ctr1w2PbCYdsLQ9ftrtFocOPGDSgUCojFnN2gs3R5bWUIwsPDkZubK3SMR2Kor0HIXPo6ty7Po+1ja+N4/H40bob6WSIkY2gTXb6Gzl5bmdRIKbFYDA8PD52ew97enh/CAmHbC4dtLxy2vXDY9sLQZbtzhNSD08e1lZAkEkmX///cUF+DkLn0dW5dnkfbx9bm8fj9aJwM9bNESMbQJrp+DZ25tuJPgUREREREdzFnzhyhIzwyQ30NQubS17l1eR5tH9tQ3ydkOPgeuZMxtIkhvAaTun1Pl+rq6uDg4IDa2touXy3tatj2wmHbC4dtLxy2vTDY7kREho2f00T0MDhSSkssLCywdOlSWFhYCB3F5LDthcO2Fw7bXjhse2Gw3YmIDBs/p4noYXCkFBERERERERER6R1HShERERERERERkd6xKEVERERERERERHrHohQREREREREREekdi1JERERERERERKR3LEppyfr16+Hj4wNLS0tERkYiJydH6EhGb/ny5QgPD4ednR3c3Nwwbtw4FBUVCR3L5KxYsQIikQhJSUlCRzEJZWVlmDJlCpydnWFlZYV+/fohLy9P6FhGT6VS4bXXXoOvry+srKzg5+eHN954A1wrRPv279+PMWPGQKFQQCQS4bvvvuvwvEajwZIlS+Du7g4rKyskJCTg7NmzwoQlIqKHUlpairi4OAQFBaF///7417/+JXQkIhIIi1JasHXrViQnJ2Pp0qUoKChAcHAwhg8fDqVSKXQ0o7Zv3z7MmTMHWVlZ2L17N1pbW/H444+joaFB6GgmIzc3Fx988AH69+8vdBSTcO3aNcTGxsLMzAw///wzTp48iXfffRfdunUTOprRW7lyJTZs2IB169bh1KlTWLlyJVatWoW1a9cKHc3oNDQ0IDg4GOvXr7/r86tWrcJ7772HjRs3Ijs7GzY2Nhg+fDiampr0nJSIiB6WVCpFamoqTp48iV27diEpKYnX8EQmSqThz7yPLDIyEuHh4Vi3bh0AQK1Ww9PTEy+//DJSUlIETmc6qqur4ebmhn379uGxxx4TOo7Rq6+vR2hoKN5//328+eabCAkJQWpqqtCxjFpKSgoOHTqEAwcOCB3F5DzxxBOQyWT4+OOP27f98Y9/hJWVFT777DMBkxk3kUiEtLQ0jBs3DsCtUVIKhQILFizAX/7yFwBAbW0tZDIZNm/ejEmTJgmYloiIHlZwcDC2bdsGT09PoaMQkZ5xpNQjamlpQX5+PhISEtq3icViJCQkIDMzU8Bkpqe2thYA4OTkJHAS0zBnzhyMHj26w3ufdOuHH35AWFgYnn76abi5uWHAgAH46KOPhI5lEmJiYpCeno4zZ84AAH799VccPHgQI0eOFDiZaSkpKUFlZWWHzx0HBwdERkbyO5eISIvudys1oL3pS/Lz86FSqViQIjJRUqEDdHU1NTVQqVSQyWQdtstkMpw+fVqgVKZHrVYjKSkJsbGx6Nu3r9BxjN5XX32FgoIC5ObmCh3FpJw/fx4bNmxAcnIy/vrXvyI3Nxfz5s2Dubk5pk2bJnQ8o5aSkoK6ujoEBgZCIpFApVLhrbfewuTJk4WOZlIqKysB4K7fubefIyKiR3f7VuqZM2di/Pjxdzx/e/qSjRs3IjIyEqmpqRg+fDiKiorg5uYGAAgJCUFbW9sdf7tr1y4oFAoAwNWrVzF16lT+yEZkwliUIqMwZ84cHD9+HAcPHhQ6itErLS3FK6+8gt27d8PS0lLoOCZFrVYjLCwMb7/9NgBgwIABOH78ODZu3MiilI59/fXX+Pzzz/HFF1+gT58+KCwsRFJSEhQKBdueiIiMzsiRI+85GnjNmjWYNWsWZsyYAQDYuHEjtm/fjk8++aR9+pLCwsJ7nqO5uRnjxo1DSkoKYmJitJadiLoW3r73iFxcXCCRSFBVVdVhe1VVFeRyuUCpTMvcuXOxbds27N27Fx4eHkLHMXr5+flQKpUIDQ2FVCqFVCrFvn378N5770EqlUKlUgkd0Wi5u7sjKCiow7bevXvj0qVLAiUyHQsXLkRKSgomTZqEfv36ITExEfPnz8fy5cuFjmZSbn+v8juXiEg42pi+RKPRYPr06RgyZAgSExN1FZWIugAWpR6Rubk5Bg4ciPT09PZtarUa6enpiI6OFjCZ8dNoNJg7dy7S0tKwZ88e+Pr6Ch3JJAwdOhTHjh1DYWFh+z9hYWGYPHkyCgsLIZFIhI5otGJjY1FUVNRh25kzZ+Dt7S1QItPR2NgIsbjjV6ZEIoFarRYokWny9fWFXC7v8J1bV1eH7OxsfucSEenJvaYv6eyt1IcOHcLWrVvx3XffISQkBCEhITh27Jgu4hKRgePte1qQnJyMadOmISwsDBEREUhNTUVDQ0P7cFbSjTlz5uCLL77A999/Dzs7u/YvQQcHB1hZWQmcznjZ2dndMW+XjY0NnJ2dOZ+Xjs2fPx8xMTF4++23MXHiROTk5ODDDz/Ehx9+KHQ0ozdmzBi89dZb8PLyQp8+fXDkyBGsWbMGM2fOFDqa0amvr0dxcXH745KSEhQWFsLJyQleXl5ISkrCm2++iYCAAPj6+uK1116DQqFoX6GPiIgM36BBg/jDDhEBYFFKK5555hlUV1djyZIlqKysREhICHbs2HHHrwekXRs2bAAAxMXFddi+adMmTJ8+Xf+BiHQsPDwcaWlpWLx4MZYtWwZfX1+kpqZysm09WLt2LV577TW89NJLUCqVUCgUmD17NpYsWSJ0NKOTl5eH+Pj49sfJyckAgGnTpmHz5s149dVX0dDQgOeffx7Xr1/HoEGDsGPHDs5xR0SkJ5y+hIi0SaTRaDRChyAiIiIiIiLDIxKJkJaW1mFEamRkJCIiIrB27VoAt6Yv8fLywty5c9snOici6gyOlCIiIiIiIqJ297uVmtOXEJG2cKQUERERERERtcvIyOhwK/Vtt2+lBoB169bhnXfeaZ++5L333kNkZKSekxJRV8eiFBERERERERER6Z34/rsQERERERERERFpF4tSRERERERERESkdyxKERERERERERGR3rEoRUREREREREREeseiFBERERERERER6R2LUkRkkuLi4pCUlCR0DCIiIiIiIpPFohQREREREREREemdSKPRaIQOQUSkT9OnT8eWLVs6bCspKYGPj48wgYiIiIiIiEwQi1JEZHJqa2sxcuRI9O3bF8uWLQMAuLq6QiKRCJyMiIiIiIjIdPD2PSIyOQ4ODjA3N4e1tTXkcjnkcjkLUkRERESPiHN2EtGDYlGKiIiIiIiItMrHxwepqalCxyAiA8eiFBERERERERER6R2LUkRkkszNzaFSqYSOQURERNQlNTQ0YOrUqbC1tYW7uzvefffd9ufi4uJw8eJFzJ8/HyKRCCKRSMCkRGTIWJQiIpPk4+OD7OxsXLhwATU1NVCr1UJHIiIiIuoyFi5ciH379uH777/Hrl27kJGRgYKCAgDAt99+Cw8PDyxbtgwVFRWoqKgQOC0RGSoWpYjIJP3lL3+BRCJBUFAQXF1dcenSJaEjEREREXUJ9fX1+Pjjj7F69WoMHToU/fr1w5YtW9DW1gYAcHJygkQigZ2dXfuiMkREdyMVOgARkRB69uyJzMxMoWMQERERdTnnzp1DS0sLIiMj27c5OTmhV69eAqYioq6II6WIiIiIiIiIiEjvWJQiIiIiIiKiTvPz84OZmRmys7Pbt127dg1nzpxpf8xFZYioM1iUIiIiIiIiok6ztbXFn//8ZyxcuBB79uzB8ePHMX36dIjF/+1e+vj4YP/+/SgrK0NNTY2AaYnIkHFOKSIiIiIiInog77zzDurr6zFmzBjY2dlhwYIFqK2tbX9+2bJlmD17Nvz8/NDc3AyNRiNgWiIyVCINPx2IiIiIiIiIiEjPePseERERERERERHpHYtSRERERERERESkdyxKERERERERERGR3rEoRUREREREREREeseiFBERERERERER6R2LUkREREREREREpHcsShERERERERERkd6xKEVERERERERERHrHohQREREREREREekdi1JERERERERERKR3LEoREREREREREZHesShFRERERERERER69/8BFB1zgIue580AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exact_T = jnp.array([jnp.cos(omega * T), -omega * jnp.sin(omega * T)])\n",
    "dts = np.array([0.04, 0.02, 0.01, 0.005, 0.0025])\n",
    "methods = ['euler', 'rk2', 'rk4']\n",
    "\n",
    "errors = {}\n",
    "for m in methods:\n",
    "    errors[m] = np.array([float(jnp.max(jnp.abs(quad.odeint(oscillator, y0, 0.0, T, dt, omega, method=m)[1][-1]\n",
    "                                               - exact_T)))\n",
    "                          for dt in dts])\n",
    "    orders = np.log2(errors[m][:-1] / errors[m][1:])\n",
    "    print(f'{m:6s} errors {np.array2string(errors[m], precision=2)}  observed order {np.round(orders, 2)}')\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "t_fine = np.linspace(0, T, 1000)\n",
    "axes[0].plot(t_fine, np.cos(omega * t_fine), 'k', lw=3, alpha=0.3, label='exact')\n",
    "for m in methods:\n",
    "    ts_m, ys_m = quad.odeint(oscillator, y0, 0.0, T, 0.05, omega, method=m)\n",
    "    axes[0].plot(ts_m, ys_m[:, 0], lw=1, label=m)\n",
    "axes[0].set_xlabel('t')\n",
    "axes[0].set_ylabel('x(t)')\n",
    "axes[0].set_title('dt = 0.05')\n",
    "axes[0].legend(loc='lower left')\n",
    "for m, order in zip(methods, [1, 2, 4]):\n",
    "    axes[1].loglog(dts, errors[m], 'o-', label=m)\n",
    "    axes[1].loglog(dts, errors[m][-1] * (dts / dts[-1]) ** order, 'k:', lw=0.8)\n",
    "axes[1].set_xlabel('dt')\n",
    "axes[1].set_ylabel('max error at t = 10')\n",
    "axes[1].set_title('convergence (dotted: slopes 1, 2, 4)')\n",
    "axes[1].legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3563662",
   "metadata": {},
   "source": [
    "RK2 and RK4 converge at their nominal orders 2 and 4 from the start: halving `dt` divides the error by 4 and 16. Euler approaches order 1 only slowly (observed orders 1.28 down to 1.03), because on an oscillator it multiplies the amplitude by $\\sqrt{1 + \\omega^2 \\Delta t^2}$ every step: at `dt = 0.05` its trajectory spirals outward, and at `dt = 0.04` its final error (2.15) exceeds the amplitude of the solution. RK4 at `dt = 0.04` (error 6.4e-6) is about 10,000 times more accurate than Euler with a 16 times smaller step (error 0.09 at `dt = 0.0025`)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "444fb8e0",
   "metadata": {},
   "source": [
    "## 2. Adaptive steps with `solve_ivp`\n",
    "\n",
    "A fixed step must be small enough for the fastest part of the trajectory everywhere. The Van der Pol oscillator\n",
    "\n",
    "$$\\dot x = v, \\qquad \\dot v = \\mu (1 - x^2) v - x$$\n",
    "\n",
    "with $\\mu = 10$ is a relaxation oscillator: slow drifts alternate with fast jumps. That makes it stiff-ish, and the right step size varies by orders of magnitude along the cycle. `solve_ivp` uses an embedded Runge–Kutta pair (`'dopri5'`, Dormand–Prince 5(4), by default): each step also yields an error estimate, the step is accepted when the error is below `atol + rtol * |y|`, and a PI controller picks the next step size.\n",
    "\n",
    "Without `t_eval`, the solution holds the accepted steps. The output has a static length of `max_steps + 1` (here the default 4096 + 1), padded after the `n_steps` accepted steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a716d345",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-19T16:27:33.455900Z",
     "iopub.status.busy": "2026-10-19T16:27:33.454999Z",
     "iopub.status.idle": "2026-10-19T16:27:35.435594Z",
     "shell.execute_reply": "2026-10-19T16:27:35.434753Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accepted steps: 859, rejected: 15, success: True\n",
      "output length: 4097  last time: 40.0\n",
      "step size: min 5.00e-04, max 8.32e-02\n"
     ]
    }
   ],
   "source": [
    "def van_der_pol(y, t, mu):\n",
    "    x, v = y\n",
    "    return jnp.stack([v, mu * (1 - x ** 2) * v - x])\n",
    "\n",
    "\n",
    "mu = 10.0\n",
    "vdp_y0 = jnp.array([2.0, 0.0])\n",
    "t_span = (0.0, 40.0)\n",
    "\n",
    "sol = quad.solve_ivp(van_der_pol, t_span, vdp_y0, mu, rtol=1e-6, atol=1e-9)\n",
    "n = int(sol.n_steps)\n",
    "print(f'accepted steps: {n}, rejected: {int(sol.n_rejected)}, success: {bool(sol.success)}')\n",
    "print('output length:', sol.ts.shape[0], ' last time:', float(sol.ts[n]))\n",
    "h = np.diff(np.asarray(sol.ts[:n + 1]))\n",
    "print(f'step size: min {h.min():.2e}, max {h.max():.2e}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9cb65ff",
   "metadata": {},
   "source": [
    "SciPy's `solve_ivp` with its own Dormand–Prince pair (`'RK45'`) at the same tolerances takes a similar number of steps. The counts differ somewhat because the step-size controllers differ (braintools uses a PI controller, SciPy a plain one):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7492a421",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-19T16:27:35.438023Z",
     "iopub.status.busy": "2026-10-19T16:27:35.437845Z",
     "iopub.status.idle": "2026-10-19T16:27:35.469264Z",
     "shell.execute_reply": "2026-10-19T16:27:35.468285Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scipy RK45: accepted steps 691, function evaluations 4490, success True\n"
     ]
    }
   ],
   "source": [
    "def van_der_pol_scipy(t, y):\n",
    "    return [y[1], mu * (1 - y[0] ** 2) * y[1] - y[0]]\n",
    "\n",
    "\n",
    "sp = scipy.integrate.solve_ivp(van_der_pol_scipy, t_span, np.asarray(vdp_y0), method='RK45', rtol=1e-6, atol=1e-9)\n",
    "print(f'scipy RK45: accepted steps {len(sp.t) - 1}, function evaluations {sp.nfev}, success {sp.success}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "083a96de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-19T16:27:35.471603Z",
     "iopub.status.busy": "2026-10-19T16:27:35.471367Z",
     "iopub.status.idle": "2026-10-19T16:27:35.743231Z",
     "shell.execute_reply": "2026-10-19T16:27:35.742263Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(10, 5), sharex=True)\n",
    "axes[0].plot(sol.ts[:n + 1], sol.ys[:n + 1, 0], '.-', ms=2, lw=0.8)\n",
    "axes[0].set_ylabel('x(t)')\n",
    "axes[0].set_title(f'Van der Pol, mu = {mu:g}: {n} accepted dopri5 steps')\n",
    "axes[1].semilogy(sol.ts[1:n + 1], h, lw=0.8)\n",
    "axes[1].set_ylabel('step size')\n",
    "axes[1].set_xlabel('t')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb81a59",
   "metadata": {},
   "source": [
    "The first step is small (the starting-step heuristic picks 5e-4). After that, the step size grows to about 0.08 on the slow branches and drops more than tenfold, to about 0.005, during each fast jump of $x$.\n",
    "\n",
    "### Output at requested times: `t_eval`\n",
    "\n",
    "With `t_eval`, the solution is returned at the requested times. `solve_ivp` shortens any step that would cross a `t_eval` point so that it lands on it, so the outputs have the accuracy of the solver itself rather than of an interpolant. This costs about one extra step per output point. Below, both solvers return 401 points; the reference is SciPy's 8th-order `'DOP853'` at `rtol = atol = 1e-12`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1be699cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-19T16:27:35.745881Z",
     "iopub.status.busy": "2026-10-19T16:27:35.745573Z",
     "iopub.status.idle": "2026-10-19T16:27:37.152601Z",
     "shell.execute_reply": "2026-10-19T16:27:37.151851Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "braintools dopri5: accepted steps 1449, rejected 3 (compile + run 1.1 s)\n",
      "max |x - reference|: braintools 5.0e-05, scipy RK45 1.7e-04\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_eval = jnp.linspace(*t_span, 401)\n",
    "\n",
    "start = time.perf_counter()\n",
    "sol_te = quad.solve_ivp(van_der_pol, t_span, vdp_y0, mu, rtol=1e-6, atol=1e-9, t_eval=t_eval)\n",
    "jax.block_until_ready(sol_te.ys)\n",
    "print(f'braintools dopri5: accepted steps {int(sol_te.n_steps)}, rejected {int(sol_te.n_rejected)} '\n",
    "      f'(compile + run {time.perf_counter() - start:.1f} s)')\n",
    "\n",
    "sp_te = scipy.integrate.solve_ivp(van_der_pol_scipy, t_span, np.asarray(vdp_y0), method='RK45',\n",
    "                                  rtol=1e-6, atol=1e-9, t_eval=np.asarray(t_eval))\n",
    "ref = scipy.integrate.solve_ivp(van_der_pol_scipy, t_span, np.asarray(vdp_y0), method='DOP853',\n",
    "                                rtol=1e-12, atol=1e-12, t_eval=np.asarray(t_eval))\n",
    "\n",
    "err_bt = np.abs(np.asarray(sol_te.ys[:, 0]) - ref.y[0])\n",
    "err_sp = np.abs(sp_te.y[0] - ref.y[0])\n",
    "print(f'max |x - reference|: braintools {err_bt.max():.1e}, scipy RK45 {err_sp.max():.1e}')\n",
    "\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.semilogy(t_eval, err_bt + 1e-16, lw=0.8, label='braintools dopri5')\n",
    "plt.semilogy(t_eval, err_sp + 1e-16, lw=0.8, label='scipy RK45 (dense output)')\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('|x - reference|')\n",
    "plt.title('error at the 401 t_eval points (rtol=1e-6, atol=1e-9)')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1ddeb5d",
   "metadata": {},
   "source": [
    "The maximum errors are 5.0e-5 for braintools and 1.7e-4 for SciPy. For both, the error jumps at each fast transition of $x$, where a small error in the timing of the jump becomes a large difference in $x$. SciPy interpolates its dense output at `t_eval`; braintools instead steps onto every output time. That costs extra steps (1449 accepted steps with `t_eval` against 859 without), but the outputs are as accurate as the steps themselves.\n",
    "\n",
    "The `method` argument selects other embedded pairs. A higher-order pair takes fewer, more expensive steps at tight tolerances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6754f93e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-19T16:27:37.154744Z",
     "iopub.status.busy": "2026-10-19T16:27:37.154507Z",
     "iopub.status.idle": "2026-10-19T16:27:38.498916Z",
     "shell.execute_reply": "2026-10-19T16:27:38.497947Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rk23    accepted  4096  rejected    0  max error 3.9e+00  success False\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dopri5  accepted  2632  rejected    0  max error 3.6e-07  success True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dopri8  accepted  1173  rejected    9  max error 8.9e-09  success True\n"
     ]
    }
   ],
   "source": [
    "for m in ['rk23', 'dopri5', 'dopri8']:\n",
    "    s = quad.solve_ivp(van_der_pol, t_span, vdp_y0, mu, method=m, rtol=1e-8, atol=1e-10, t_eval=t_eval)\n",
    "    err = np.abs(np.asarray(s.ys[:, 0]) - ref.y[0]).max()\n",
    "    print(f'{m:7s} accepted {int(s.n_steps):5d}  rejected {int(s.n_rejected):4d}  '\n",
    "          f'max error {err:.1e}  success {bool(s.success)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d27dbc93",
   "metadata": {},
   "source": [
    "At `rtol = 1e-8`, the 8th-order `'dopri8'` (DOP853) needs fewer than half the steps of `'dopri5'` (1173 against 2632) and is more accurate. The third-order `'rk23'` would need far more steps than either: it uses up the default `max_steps = 4096` attempts before reaching `t1 = 40`, so `success` is `False`. The outputs after the last accepted step hold that step's state, hence the large error. Raise `max_steps` (a static size, which costs compile time and memory) or use a higher-order pair."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "081116c1",
   "metadata": {},
   "source": [
    "## 3. Gradients through the solvers\n",
    "\n",
    "Both drivers are differentiable with respect to the initial state, the extra arguments and the times. A common use is a **parameter sensitivity**: how much the trajectory changes when a parameter changes. Below, `jax.jacfwd` gives $\\partial x(t) / \\partial \\mu$ at all 201 output times of a Van der Pol oscillator with $\\mu = 1$, checked against a central finite difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a9175b87",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-19T16:27:38.501272Z",
     "iopub.status.busy": "2026-10-19T16:27:38.501049Z",
     "iopub.status.idle": "2026-10-19T16:27:40.757106Z",
     "shell.execute_reply": "2026-10-19T16:27:40.755894Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx(t)/dmu: (201,)  max |AD - finite difference| = 2.4e-07\n"
     ]
    },
    {
     "data": {
      "image/png": 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FJsvgS1DdlsEXJSvXyMyt59i/YTFPM59fjP1xajeeF/o3xMdFB4pJZsCEqCA+XnmCbzeexcPJgX+e7k4dv7wqy4oCv9wD5zdBYHN4aHWxFZht4Xp6Dl+uO82cnRcxmBSc9Vqe6tOAh7vXw9HhpufJC8fD8WXqcbMRcMdb4BNqn6CFqIbSsw30+3wTl5Oz6Ns4gOnj2xVfgT3f8b/UmhjNhls/yJucuJrCu8uOseNcAgC1vJx5Z0hT+jetWfyFxlyInAubPoaUGPV7gc3Vh4jhd1g5alHdFbZ0OiPHQJO3VtklnmP/ubPUrdJ69eqF0Whky5YtABiNRry8vBg+fDi//PILAFevXiUoKIgdO3bQqVMnZs2axdSpU9l69CImRSHIy4Vt61YwbNiwYh8UXLx4kdzc3CJ/7uLiQu3aJW/bzf/9FXkZfOVoVCfszlmv4/HeDbjS5mk+/Kcffx2KQdkVxYojV5nR/ARtomej6fcftR2TFGkRwm42nYrj241nAfjo3hYFiTrAqVVqoq5zglGz7Z6oA/i4OfLOkKaM61SHN5YeYee5RD5ZdZLF+y/x3tBmdKmft8T2zv+qe0gPLoAjv6uJe8dHoccL4Oxl3z+EENXAV+tOczk5i2AfF74Y07p0iXpyDCx9ArKT1Qf6je+2fqB5GtX0ZN7DHVl55Crv/3OcmKRMHvl1H3c2DeSdIU0J8iqiha9OD20fhBajYdd02PI5XDuszrBLsi5EsVq0aHHjWKfT4efnd8tS88BAtVh1bGwsoK4aMClgUhTcnBzwd3cs1e8JDa0+D+slWRdmCfJy4av72nB/p1DeXnaUE1dTcN4/A432IiwYC3V7wID/QWATe4cqRLWTnJHL84siAbXy+6AWQQU/NBoK+hp3ehT86ts+wGKEB3gw/+FOLI2M4YN/jnM2Lp37ZuxiTPsQXhvUGE/vEBj2vZqgr34DLmyB7V/Bwfkw4CNoPsLefwQhqqyTV1P5aet5AN4d0hR3p1LePm78UE3Ua7dTuzvYmEaj1sro1TCAr9afZsbmc6w6eo1tZxJ4oX8E4zuHFf3QQe8C3aZCmwdg86fq/vZ8aXGgcwCXUlTAF6KcXPQ6jv3nTrv9bnPo9beustVoNLd8L792jslkAiA9x4SiqL3UQ3xc0Gg0xc6Y52vatCkXL14s8ufdu3e/ZW98ZSbJuiiTjvX8+Oupbvyw+Rzj173NZNNSHtKtwOn8ZpTvu6Hp+Cj0ellmvISwoa/XnyY+LYcGAe68MehfD8yOLYX4U+Diq/Yyr4A0Gg3DWgfTp1Egn6w6wZydUSzYE83Gk3F8MKwZdzQOhFqtYMJf6iqB1a9DwhlIuWzv0IWoshRF4Y2lhzGYFPo3CVT/PyyN+NPqbDSovdTtuFXOxVHHywMacU+rWry2+DD7o5J4569j/H3oCp+MbEld/2Iqv7v6woD/3vq9Va/C2fVqXY02E0ArJaCE9Wg0mlIvRa9MsnKNaF29SE9Lw1NvvNGiLTIyssRrly9ffntSr5ggOw2cPHBxtf/KQUuRVxdRZnqdlid6h/PH1AFsqfMEd+R8ykpjezSKEXZOg6/bqTfUQgiri0rIYPaOCwC8MbgJzv9+Gt50OAyfoc5CV/CHaF4uet4f2pyFj3QizM+VqylZPDR7L1MXHCApI0fdatNwADy2AwZ9rs6254s9ARmJ9gteiCrm932X2HPhOi56HW8PaVr6Czf8Vy1kGTEQQjpYL0AzNKrpye+PduG9oc1wd3Jg78XrDPxyMzO3nsdkKmUJp5x0uHoEMhLg76nwUz+4csiqcQtR1SiKQsz1TJq3aouLiyufvv8OZ8+eZd68ecyaNavE60NDQwkPD7/1o5Yv4b4awn0ocb96VFQUkZGRREVFYTQaiYyMJDIykrS0NAv9CS1HknVRbnX93Zj3cEeeHNaHF7Qv8kDOy5xXgiA9FpNj9S3KJ4Qt/W/lCXKNCt0b+NMzosbtJ2i10GIUtBxt++DKqGM9P1Y804NHetRDq4GlkZcZ8MUWtp7Oax3p4AjtH1I/g9rHedF4mNYBDv0mHSuEKKekjBw+XHECgGf6NqC2dxH7vP/tykE4uhjQQJ83rBdgGWi1GsZ3CmXl1O50DfcjK9fEf/4+xpgZO4tu83YzRzd4dIv64NPRA2L2wg89YeVrkJ1q/T+AEFVAZo6R9BwDPr6+zP7lF1asWEHz5s2ZP38+77zzjvkDKiZIu6Yel2JC4q233qJ169a8/fbbpKWl0bp1a1q3bs3evXvN/91WJtXgSyDV4M1z6XoGL/52iL3nrtFde4icev35dGRLano5Q9QuqNW64MZaCGER+y5e597vtqPVwPJnutOo5k2vVekJan9jJ3f7BWgBB6Ku8/yig5yLTwfU3vEvDWh46wqC6xdg7iiIP6l+Xa83DP4cfOvZPmAhqoBXFx9m/u4oIgLd+efp7uh1pZzjmTsSTq+G5iPh3h+tG2Q5KIrC3F1R/Hf5cTJyjLg7OfD+0GYMbV1yFWkAUq6oS+KPLlG/9qgFY+er23WEKIPiKohXFYqicDo2jaxcIzU8nIou9miO9HhIjgatAwQ0rRBbUyxVDd7+fxJRpQT7uDJ3ckdev7sF23Xt2HomnoFfbmb7nj1qy6jp3SF6j73DFKLKUBSF9/85BsDItiG3JuqQ1yu4DZxabYfoLKd1HR/+frob9+f1dZ657TxDvtnKiaspBSf5hMGjW9WZPJ0TnNsA33aBHd9Kn2QhzHQmNpUFe6IAeH9o89In6gDtJkFQS+j1qpWiswyNRsO4TqGsmtqDdqE+pGUbmLowkmcXRpKaVXKRKzyDYOQsGPeH+voD4FvXmiELUeklZ+aSlWtEp9FQw92p/APePKvuHlghEnVLqlp/GlEhaLUaHuxal3+e7k7TWp5cz8jl6yUbSMMZ4k7AzP6w+k3IzbJ3qEJUessPX+VAVBKujjqe7x9x6w+vX4TDi9Q3MTd/+wRoQa6ODnwwrDkzH2yHv7sjp66lcc8321iwO6qgH6uDI/R4ER7fAXV7giFTnfn6eSBkXrfvH0CISmTahrMoCvRvEkiHur7mXdxwIDyyqcJ1nShKiK8rCx7pxLN9I9BpNSw5EMNdX21hf1QpXzPC+8LjO9WkPX8JrqLAuU2yHUeImyiKwrWUbAD8PZxwMOchYFEyroMxR51Vd/Ur/3gVjCTrwmrq13Bn8eNdeKhbXXaYmtI17X+sdeyjPgHb/pU6y36p4u0NEaKyyDYY+WjlcQCm9KhPgOe/lszt/kH9/61eb6jdxg4RWkefRoGsnNqDnhE1yDaYeGXxYaYujCQt21Bwkl99eOBPGPyFuq9U7wLO3vYKWYhK5WJCOn9GxgDwVJ8GZRtEU4o+7BWIg07LM30bsGhKJ4J9XIhOzGTU9zuYufU8pdoxqne5tW1t5Fz4ZQj89qAUvRQiz/WMHLINRhy0WvwtMquuQNpV9dg9ALTmtZqrDCRZF1bl5KDjzcFN+PnB9ji4+TI5ZTJPKC+R5VxDbSP1Uz9Y/4G9wxSiUvpjXwzRiZkEeDjxcI9/Lb3MToX9v6rHnR63fXBW5u/uxM8PtuflAY3QaTX8GXmZIV9v5fiVm5bFazTQbqI6y37PtILkIScdkqLtE7gQlcC3G85iUqBXwxo0Dzaje8SfT8CWzyr1Kpa2ob4sf6Y7g5oHYTAp/OfvYzwxb3/plsXfLCNRnek7thS+7Qxn1lolXiEqC5OpYFa9hocTOq0FHuiZctX/z7QO4Fr5VxAWRpJ1YRO9GwWw4pnudAjz5Z/sVnRM+oAjfgPUWT8hhNlMJoUft5wDYErP+rf3YI2cD9nJ4BeuLtGsgrRaDY/1qs/CRzoR5OXMufh0hn27jWUH/9V33TsEvIILvl71GnzXFQ7/btuAhagEYpIy+WP/JQCe6hNe+gvjz8CBObDuPchMsk5wNuLprOeb+1rz9t1N0Os0LD98lSHfbLu1RkZJuj4ND60B/wh15m/OvfDP8+rDQiGqoYT0HHKNJvQ6LX5uFio2rXNU/x/zb1glZ9VBknVhQwGezsx9uCMPdatLMu4MjnmAD30/IL7tMwUnZSbJ/i4hSmHN8Wuci0/H09mBMe1Dbv2hyQS7vlePOz5a5Yqt/Fu7MF+WP92dHhE1yMo18fT8A/x3+XEMxkIeBuZmwrWj6oOMPx6CPx6GLDNuwIWo4qZvOovBpNClvh9tQ83Yq74nr+p7g/5VosiaRqNhYte6LJzSmVpezpyPT2fYtO0sP3yl9IPUbgNTNquvw6D+N/qht/oaJEQ1YjIpxKWqs+qBnk5oLTGrnk+jqdKdpqr2HZyocPQ6LW8ObsI397XG1VHH9Mt1uee73Ry7nKL2SJ59t7q/KyvZ3qEKUaH9sFmdVR/XKRQ3p3/Nql/aDYlnwckLWo61Q3S25+PmyM8PtuexXmpBqx82n2PCz7u5np5z64l6F5i4Uq1SrdGpBfh+6Kn2hRaimotNyWLBHnWLyJPmzKpnp0HkPPW4wyNWiMx+2tTx4e+nu9O9gT+ZuUYen7ufz9ecwmQq5cSC3gUG/g/GLwH3mpBwWvawi2onKTMHg8mEo06Lt6uFEuvMJDAZSjytspNkXdjF4Ba1WPZkV+r6uxGTlMmI77ezZ8tKiD2m7u/6oRdcOWTvMIWokPZdTGTfxes46rQ82CXs9hPqdFJbmA35stL3VzeHTqvh5QGNmHZfG1wddWw7k8CQaVs5E5v6rxMdoNcrMGkleIVA4jn4sR/s+UlW9ohq7YfN58gxmGgX6kPnemZUVT68SF2t4lsP6vexXoB24pv3MHByN3XFwFfrTvPY3H2kZ5uRKNTvA49tg+EzoG73gu/La46o4hRFIT5NfXDu5+6I1hLFJw05cP28ukrFaGY9iUpGknVhN+EBHix9vCvdwv3JyDEyarUDv7f8EcUzOO/muS/smy1vZEL8S/6s+rDWtW+vAJ+vZnNoOsyGUVUcg1oEseTxrtTxdSU6MZNh325n25n4208M6aAuUY0YCMZsWP8eZCTYPmAhKoDE9Bzm7lL7qj/ZJxxNaW+oFQV25y2Bbz+5ym67cdBpeWNwEz4Z0QJHnZZVR69x73fbuZyUWfpB3Pyh+YiCr+PPwPQecO2Y5QMWooJIyzaQlWtEq9HgY6m96vnv1XpX0OktM2YFVTVfUUWl4eWq5+eJ7XmgcyiKAi/scOTNmt9iCu+n3jz/9TQsfUzdZyqE4FxcGquPXQO4vQI8QE6GjSOqmBrW9GDpE11pF+pDapaBCTN3s3BP1O0nuvrC2PnQ/wMYNr1K9KMXoizm744iM9dIs9qe9IyoUfoLL26H2KPqTXOr+6wXYAUxsl0I8x/pRA0PJ05cTWXYt9vUrXxlseZNuHpInZw4stiygQpRQSTkzar7uDniYImHeYqpIFkv43v2Bx98QJcuXXB1dcXb27vE83Nzc3n55Zdp3rw5bm5u1KpViwceeIDLly+XeG15SbIu7E6v0/Kfe5rx3tBm6LQa5hxK48Gs58nu+QZotHBwPvz9nL3DFKJC+HHreRQF7mgUQHiAx60/zLwOnzeCJY9J0o66dHXO5I7c06oWBpPCy38c5qMVJ27fa6rRQJcnIeLOgu+dWQenVts2YCHsxGA0MS9vVv3BLnVLP6sO4FZDrY3Rejy4+FgpwoqlbagPS5/oSkSgO9dSshk1fQebT8WZP9CQb6BuT8hNh98nwuo3wFj19+CK6iMr10hKXttDf0vNqmelFLRsczajteRNcnJyGDlyJI899lipzs/IyGD//v28+eab7N+/n8WLF3Py5EmGDBlSpt9vDknWRYUxvlMoP05oh6ujjs1nErn3SCeuj1gEfg2g18v2Dk8Iu4tPy+b3fWpLpUd61Lv9hCOL1eKMVw+pRY0EznodX4xuxTN3NADg+01nefH3Q+QWVik+3/WL6o3zvFGw+VPZiiOqvHUnYolJysTHVc/gFkHmXVwjAoZ9rxZRq0Zqe7vw26Nd6FTPl7RsA5Nm7WHR3mjzBnHzg3GLoetU9evtX8Oc4ZW6T72ovnr16sVTTz3F1KlT8fHxITAwkK+mfU9GRjrvvfgU/r7ehIeHs2LFihvXzJo167aZ7aVLlxb/wDA9b1ubq586qVcG7777Ls8++yzNmzcv1fleXl6sWbOGUaNG0bBhQzp16sQ333zDvn37iIoqZNWeBUmyLiqU3g0DmP9wJ/zcHDkSk8KQf3ScG7UOfMIKTpK9XaKamrcrihyDiZYh3nSoW0hLpYML1M8tx6qzxQJQWzA92y+CT0e2RKfV8Mf+Szw2Zx9ZucbCL/AIgqbDAUXdx/7HQ5CbZdOYhbClX3dcBGB0+zo468vYq7gavuZ4ueiZPakDQ/NW77z0+yG+3XjGvEF0DtDvXRg5G/RucH6Tuiw++ZJ1ghbCimbPno2/vz+7d+/miSee5LUXnuHFRx+kR/eu7N+/n/79+zN+/HgyMsq4+s+QBTmpNO09AvdaEbi7uxf6MXDgQMv+wQqRnJyMRqMp1TL68pBkXVQ4LUO8+eOxLoT6qcWhRvywm6OX81q5nVoF33WBdf+R2S5RrRhNCgvzWio92CX09qfO8WfUlm0aHTQfaYcIK74RbYOZPq4tTg5a1h6P5YGfdpOcWUgVWQdHuPsLGPyFuszuyB/wyz2QLsXnRNVzJjaVrWfi0Wrg/o51Sn+hyQTr31fbHlbj92MnBx3/N7rVjbaRH688yccrT6CY+9+k6VCYvAY8g9UZQ1epnyH+JSe96I9/P1Au9tzM0p1bBi1btuSNN96gQYMGPPz0czg6OePn58+Tj02hQYMGvPXWWyQkJHDoUBk7PuWkAxqWL/yZyMjIIj9+/PHHso1fSllZWbz88suMHTsWT09Pq/4uh5JPEcL2wvzd+OOxLkz8eQ+HY5IZ+8NOfnmoI62uHQUU2PKZ+tR5yDfqjbUQVdzm03HEJGXi5aJnYLNClqkeyptVD78DPAJtG1wl0rdJIL9M6sDk2XvZfSGRMT/s5NeHOuDv7nT7ye0mgm9dWPgARO+En/rC/b+DX33bBy6EleTPqvdpFEiIr2vpL7y4FTZ/Art+gBdOgb6IzhTVgEajto30ctHz0YoTfLvxLGnZBt65uylarRkrDgKbwsPr1IeE1fi/pyjCf2sV/bMG/eH+3wq+/iQccouYvQ7tBhP/Kfj6i+aFd0J5J9nsEFu0aAGASVFIyjTi7eND61YtbkwwBAaq9yexsbFmjw2oD7KcPAmtYbTb/yO5ubmMGjUKRVH47rvvrP77ZGZdVFj+7k7MfbgjbUN9SMkyMO7HXewOflBN0DU6OLQQ5t6r7tEVoopbsFvdEzW8Te3bl6maTDctgR9j48gqn471/FgwpRP+7k4cv5LC2B92EpeaXfjJ9XrBQ6vBq47aUnLX9zaNVQhrSss28Mf+GAAmdAk17+LIeernZsMlsczzaM/6vD+0GRoN/LLjIi/8dhBDcfUxCuNR89YK1+s/gC2fV+vVC6Ly0OvVNmqpWQZyjSY0Gi2ergWvD/lJu8mk/n+h1WpvW4WSm1tC33Sdnqat2ha5BN6ay+DzE/WLFy+yZs0aq8+qg8ysiwrO01l/YxZsx7kEJszczYwHBtLt/iBYNAHOb4aZA2DcH+BZzBNHISqx2JQs1h1Xn0KP7VDIMtWL2yA5Gpy8oOFdNo6ucmpay4vfHu3M2B92cjo2jbEzdjLv4Y4EeBSSdAQ0gslrYevn0O892wcrhJUs2X+JtGwD9Wq40bW+Gcuus9Pg2DL1uBq0azPHuE6heDg78Nyigyw+EEOO0cQXo1vhoCvD/FjULtj8sXqckaC+/lTRPvaiFF4rpk2Y5l8P8V8spnbCv4uyTT1c9piKkJiutmvTaii2WFyNGjVITU0lPT0dNzc3ACIjIws/2WhQazwAy5cvLzapd3GxfJHd/ET99OnTbNiwAT8/P4v/jsLI//GiwnNzcuDnie3pGVGDzFwjk2bvYavSCiauAPeaEHtMTdhlP6moon7bdwmDSaFtqA8RgR63n+AfAX3fgS5PSRV4M9T1d2PBI50I8nLmTGwaY37YybWUIgrJeQSq1a7zt92YTBC923bBCmFhiqIwO28J/PhOoeYt1z72p9puzLc+BLe3UoSV1z2tavPd/W3Q6zT8fegKL/x2EOO/W0aWRp2O0P999XjHN/DnE9LarTpzdCv649+rW4o916V055ZRjsFEal67tpJeVzp27IirqyuvvfYaZ8+eZd68ecyaNev2E425cO0IxJ8Gk5HQ0FDCw8OL/Khdu3axvzcqKorIyEiioqIwGo039rqnpaXdOKdRo0YsWbIEUBP1ESNGsHfvXubOnYvRaOTq1atcvXqVnJwc8/4DmUmSdVEpOOt1/PBAW/o1CSTHYOLhX/ayOytYLcbiW0/dq+NaSHVsISo5002F5ca0Dyn8JI9A6PYs9HzRhpFVDWF5CXstL2fOxaUXn7DnUxRY+TL81B/2zbJJnEJY2o5zCZyJTcPVUce9bYPNu/jgfPVzK+k8UZT+TWvyzX1tcNBqWBp5mZf/OISpLAl7l6dg6HfqzOnBebBw3O0FwoSoQK5nqMmru5MDJb06+Pr6MmfOHJYvX07z5s2ZP38+77zzzu0nZiYBCigm0JaxY8VN3nrrLVq3bs3bb79NWloarVu3pnXr1uzdu/fGOSdPniQ5Wd1qGxMTw7Jly7h06RKtWrUiKCjoxsf27dvLHU9xNIrZ5Sorl82bN/PJJ5+wb98+rly5wpIlSxg6dGipr09JScHLy4vk5GSb7EsQxcs2GJny6z42nozD3cmBOZM70srPBM7esjRMVElbT8cz7qddeDg5sOv1O3B1lN1L1hCVkMHYGTuJScokItCdhY90xsetiOKVJhMsfx72zlS/7vcedH3adsEKYQFPzz/AsoOXua9jHf47rHS9hgG4fhG+bAFo1OWz3kU8RBQALD98hafmH8BoUhjTPoT/Dmtu3iqGfCdXwG8Pqq2r6veBMfNkJVUVlJWVxfnz56lbty7OzpWvFoSiKJy4mkqu0UQdX1e8XS1UBDrupFowz7M2uAdYZkwrK+7v0pz8sspnN+np6bRs2ZJp06bZOxRhAU4OOr4f15bO9fxIyzbwwE+7OJqkK0jUDTnwx8NwaZ99AxXCQubvUQvLDW1du/BEfd1/4NBvMtNSTnX8XFnwSCcCPZ04dS2NB2ftIT27iOWmWi0M+lxdzQCw5k3Y9pXtghWinK6n57DyyFUAxrY3o10bQOJZcAuAuj0kUS+Fu5oH8X+jW6HVwII90bz711Hz27oBNByo1ufRu8HZ9eqHEBVMfmE5nVaDp7PeMoPmZhVUtnfxscyYlUiVT9YHDhzI+++/z7Bhw+wdirAQZ72OHye0u1ElfvxPuzkTm7fHZOvncHgRzBkGV8rYw1GICiIhLZvVR9Ub6jEdCrkpTo5RqwQvngypV20cXdUT4uvKrw91xNtVz8HoJB75dS9ZucbCT9Zo1DoBvV9Xv17zJuz41maxClEeS/IKnzUJ8qRZbTNXDdbvA88dh+E/WCe4KmhIy1p8NqolGg3M3nGRaRuKKf5VnLBuanuuwf8HjQZZNkghLCC/sJyPq2PZVpAUJjNR/ezkCToLPQCoRKp8si6qpvyic81re5GYnsODP+8mNjULOj8JIZ3Udm6/DoXY4/YOVYgy+2P/JXKNCi2DvWhay+v2Ew4tBBSo00XtBy7KLSLQg1kTO+DmqGPbmQSenn+g+NZLPV+CHi+px6tehd0zbBOoEGWkKDfVwegQUmyl5iLpHNQWY6LUhrUO5u3BTQD4dPWpG+04zRbWFdpNKvg6I1FWVokKIddoIjVLXZHmW9Q2MnMpCmReV4+raW0qSdb/JTs7m5SUlFs+RMXk6axn1sT2hPm5cul6JpNm7SENZ7h/EdRqrbY5+eUeSDhr71CFMJuiKCzYnX9DXcgyVUUp6K3eaqwNI6v6WoV4M+OBdjjqtKw+do03lh4pftlq79fUJfEaHbjappWLEGUVGZ3EyWupODlouadV8RWTbxN7AkxFrDYRJXqwa10e71UfgNeWHGbNsWvlGzAjEX4ZorayNZbQm1oIK7uenoOCgpujA8768heBAyAnDYw56vurUyGTFtWAJOv/8uGHH+Ll5XXjIyRE9mNVZH7uTsye1AE/N0eOxKTwxNz95Oo9YNxiCGwGaddg9hC1II4Qlciu84mci0/HzVHH3S1r3X7C5f0QfxIcnKHJPbYPsIrrEu7P1/e1vrHPdPrmc0WfrNHAHW/DlM3QbLjtghSiDPJn1Qc1D8LLxYwlpbmZ8GNf+L9mkFTGWWHBi3c2ZGTbYEwKPDlvP3svJJZ9sISzaiur06tg2dPqQ1wh7EBRFBLzqsAXWZy1LPSu4B2qruSppoWkq+efuhivvvoqycnJNz6io6PtHZIoQaifGz892B5nvZZNp+J4bfFhFBcfGL9U7T+dcgnmj5HZAFGp5C+RHNKqFu5OhRSWy59VbzQYnKvn02Zru7NpTd4YpC5b/WjFCVYcvlL0yRoN1GxW8HXqVenDLiqctGwDyw5eBmB0Ua0gi3JyBeSkgtYBPM1s9SZu0Gg0fDi8OX0aBZBtMDH5l71EJWSUbbCQ9jByVkFbtzVvWTRWYT+VrVlXWraBHINaWM7bnIeAJdHq1OXvlaQC/M0s9Xcoyfq/ODk54enpecuHqPhahXjzzdg2aDXw275LfLP+DLjXgAf+hJot4K5PLdKXUQhbSMrIYXl+pebClsAbcuDw7+qxLIG3qoldw3igcygAUxdGEhmdVPJF1y/Cj/1gzr1w9Yh1AxTCDP8cukxGjpG6/m50qGvm/s9Di9TPLUZW2xkuS3HQaZl2XxtaBHuRlJHLQ7P3kJpVxmXsDQfCkK/V4+1fwfavLReosDm9Xk10MzLK+ADHTvILy3m7WLCwXCWX/3eY/3daVlW+YW9aWhpnzhRU3Tx//jyRkZH4+vpSp46Z7UpEhda3SSD/uacZbyw9wmdrTtE4yJO+TWrBI5vkxkJUKov3x5BjUCs1N69dyKx52jWo0Qiun4d6vW0fYDWi0Wh4a3ATohIz2Hgyjsmz97L0iS4E+7gWfZF7IHgFQ3IUzB0BD62RFleiQliQtwR+dHszC8ulJ8CZNepxi9FWiKz6cXHUMeOBdgz5ZiunY9OYuiCSHx5oh64siU7r+yE9Dta+DavfALca0HKM5YMWVqfT6fD29iY2NhYAV1fXshWBtCGD0URyWgaKouCmcyArK8syA6deVVfyOHurRS0rCUVRyMjIIDY2Fm9vb3S68k0WapTKts7CTBs3bqR379tvZidMmMCsWbNKvN6cpvWiYnhj6WHm7IzC3cmBpU90JTzAveCH147BqRXQ/Xn7BShEMRRFof//beZ0bBrv3dOU8Z3Dij45KwWc5XXJFtKyDYz4bjsnrqbSqKYHSx7viotjMW/Amddh5kCIOw7+DeGhVdWyP6yoOE5eTeXOLzbjoNWw/dU+BHg4l/7i3TNg+QsQ1AqmbLJajNXRwegkRk3fQbbBxJSe9Xh1YOOyDaQoaqK+4xvwrA1P7QO9i2WDFTahKApXr14lKSnJ3qGUSmqWgeTMXBwdNOa9rhTHZISUy4ACHkGVsmWbt7c3NWvWLPRhizn5ZeV5TFFGvXr1qnT7PkT5vDW4KaeuprH7QiKP/LKXJU90VYvopCfAzwPUtm6O7tBxir1DFeI2+6Ouczo2DWe9lntal1CpWRJ1m3F3cmDmg+0Z8s1WTlxN5fUlh/P6Jhcx4+HiA+N+V5fDx5+E3yfB/b/LdhxhN/mF5e5oHGD+DfWhhepnmVW3uJYh3nw8ogXPLIhk+qZzRAR4cG/bMtQE0Gig33ugc4R2EyVRr8Q0Gg1BQUEEBASQm1uxq/wrisKDs/ZwKTGDZ/tG0LFuIQVxyyJyPmz7DGo0gdG/WGZMG9Lr9eWeUc9X5ZN1Uf04Omj5dlwbhny9lXPx6UxdcIAfJ7RH5+YHXZ+Bdf+Bla+oy4jr9bR3uELcYt4u9YZ6cItaeDoX8iT56hH1KbObtAiztVreLnw9tg3jftrF4gMxtA71YXyn0KIv8ApWW0n+1B/Oroe170D/92wWrxD5sg1GFh+4BMCY9mZuAUw8D5f2gEYLze61QnTinla1OX0tjW82nOHVJYdpWNODZoVtgSqJVgt937Z8gMIudDqdxRI+a9l1LoFdF1NxddQxoFUdnAsriFsWB3+GtGjo/jQ4W2i2vpKSjbyiSvJ3d2L6+HY4OWjZcDKOL9aeUn/Q7TloORYUE/w+EZKk2r+oOJIzc/nnsFqpudDCcgDLnoLPIuDEPzaMTOTrXN+Plwc0BOA/fx1lf9T14i+o2RzumaYen1wB2WlWjlCI260+eo2kjFxqejrTI6KGeRf7hMHk9TDwY/AItEp8Ap7rF0HfxgHkGEw8MW9/2QvO3ezEP7DkMTCZyj+WEIXIr4MxpGURnWvKIvYEXDmo7leXB4SSrIuqq3mwF/+7twUA32w4w9bT8eoyscH/p1aIz0iAReMh10KFMIQopz8jY8jKNRER6E6bOt63nxB3Uu2vDhDcwaaxiQIPd6/HwGY1yTUqPDF3P/Fp2cVf0Gw4DJsOk9eCk3vx5wphBflL4Ee1Cza/gJlGA8FtocPDVohM5NNqNXw6siW1vV24mJDBK38cLt82ztSr6vabg/Ng00eWC1SIPMkZuSzPa2k6pqgJhrI4lNeaNryfrCJEknVRxQ1tXZuxHeqgKPDsokj1plrvAqPnqHtKLx+A5c+rhVmEsCNFUZi/W72hHtuhTuF7oQ/OVz+H91NbEwq70Gg0fDyiBfVquHElOYtnFhzAaCrhNaTlGHDxLvhaZrqEjUQlZLD1TDwaDYxsJ10JKjJvV0e+ua81DloN/xy+wpydF8s+mEdNGPS5erzpf3BmrWWCFCLPkgOXyDaYaFTTg5bBZdi2URiTCQ79ph5LRwNAknVRDbw1uAkRge7EpWbz/KKDmEwK+ITCiJnqHrzEC2CQ2XVhX4cuJXP8SgqODlqGFVZYzmSEg3lFnuQNzO48nPVMH9cWV0cd284kMGPLudJdqCiw83uYP0YSdmETi/aqDwG7hfsT4ltMy8HCrH0X/nxS7aQibKJ1HR9eGdgIgPf+Ps7hS8nlGOx+aJ+3ImLxFEi5YoEIhVAnGPKXwBc5wVAW2ckQ0l5tgRoxwDJjVnKSrIsqz8VRxzf3tcFZr2XTqTh+3Jp3U12/D4xfAg/8KVVThd3N3x0FwKDmQXi7Ot5+wvnNkHpZ7TfacKBtgxOFahDowdt3NwHgs9UnORJTipvqlBi1F/LpVbDzWytHKKo7g9HEb/vUG2qzC8sZc2H/bDjwq/raI2zmoW516dckkByjun89pTz71/u/r9bOyIiHxQ+rD36FKKcD0UmcuJqKk4OWoa1K6FxjDhcfGDkLph4BffUuLJdPknVRLUQEevD23U0B+HjlSSKjk9Qf1OsFupsKYshyeGEHadkGlh1Ub4bHtC9imWr+Evhm94KDk40iEyUZ1S6EO5sGkmtUeGbBATJzSrgR9gqGO/+rHq99By5HWjtEUY1tOhXHtZRsfN0c6dskwLyLT69Wa7u4B0LdXtYITxRBo9Hw6YiWBPu4EJWYwft/l2Nlg94ZRswCvRtc2AKbP7VYnKL6WnDTBIOXqxV6oDsUMmlRTUmyLqqNMe1DGNQiCINJ4en5B8jIMRT8MDcTVryitnQTwsaWRV4mI8dIvRpudKjre/sJhhw4tVI9bjnWtsGJYmk0Gj4a3oIADyfOxqXz3+XHS76o3SRoNBhMufDHQ1IhXlhN/jLV4a1r4+RgZguoyHnq5xajbn2oLWzCy1XP56NaodHAor2XWHf8WtkH8w9Xi+uCOsMuExOiHFKzcvnroBUKy105pBbSFbeQZF1UGxqNhg+HN6e2t/qk+uOVN70gXNoDu76DXd/DaSnCImxrwR71CfXY9kXs+3JwhKcOwJBvILidjaMTJfFxc+TTkS0B+HXnRdafKOGmWqOBIV+DRy1IOAMrX7ZBlKK6iU3JYv2JWABGF7Vipyjp8Tc9ILzPwpGJ0upQ15fJ3eoC8Mriw1xPzyn7YC1Hw8Pr4a5P1NcgIcpo2cHLZOYaqV/DjfZhPpYbeN27MK0D7J5huTGrAEnWRbXi6aznw+HNAZi1/QK7ziWoP6jbAzpMUY+XPqbeqAhhA0dikjl0KRm9TsPwNsXs+3Lzgzbj5SarguoRUYOJXcMAeOn3QySWdFPt6gv3zgA0cGAOHFls9RhF9fL7/ksYTQptQ31oEOhh3sWHfweTAYJaQWATq8QnSuf5/g0JD1CL5L7555HyDVa7bcGxzK6LMlpQUueaski5DGfXq8f1+1hmzCpCknVR7fSIqHFjX/BLfxwq2GPa712o0RjSY2HZU/JGJmwif1b9zqY18XMvZC+6/DusNF4e0IiIQHfi03JKt8c0rBv0eAG0DpAcbf0ARbVhMik3bqhHl6Vd28G8JfCtZFbd3pz1Oj4f1RKdVsPfh67w10ELFPtLvgRzR8DJleUfS1QrR2KSORyTjKNOy/A2wZYb+OB8UExQpwv41bfcuFWAJOuiWnptUGOCvJy5mJDBp6vzlsPrXdSZLp0jnFwOBxfYN0hR5WXkGPjzgHrjNbaofV9bPoOZA+DUahtGJsrCWa/jf/e2QKOBxQdi2HQqruSLer4Mj26Drs9YP0BRbWw9E09UYgYezg4Mbhlk3sVGA0QMBL8G0GyEdQIUZmkR7M0TvcMBePPPI8SmlLPd7J6f1L7rf0+FzKRyxyeqj/wJhv5NA/F1s1AROEWBA3PV49b3W2bMKkSSdVEt3bwcfua28+y9kKj+oGZz6JVXZG7Va5CeYKcIRXXw96ErpGYbCPVzpXM9v9tPUBS1yFPUDkgvReIn7K51HR8mdA4D4PUlh28tZFkYnR4CGlk/MFGtzN11EYB72wTj6mhmcTidA/R+FZ7co26/ERXCk73DaVrLk6SMXP5TnurwAD1fAr9wSL0Cq163TICiysvIMbC0pAmGsojeBYln1Y4FTYZabtwqQpJ1UW31ahjAyLbBKAq8+PshsnLzlsN3eRoCmqi9SK+Vc3+YEMXIb30yun0IWm0h+74u7c17A3OFJkNsHJ0oqxfubEgtL2cuXc/k/9acKv2FVw7B/LGQlWK94ESVdzU5i7XH1cJy93Usxw211MeoUBwdtPzv3hZoNeqD3s2lWblTFL0L3DMN0EDkHHWWXYgS/H3oCmnZBur4FjHBUFaRebPqTe4BJ3fLjVtFSLIuqrU3Bjch0NOJ8/Hp/LD5nPpNnR5GzIQnd0O9nvYNUFRZxy6nsD8qCQethhFti9j3lb9vtPHd4GRmgShhN+5ODrw/rBkAP209z+FLySVfZDLC75PULTjr/mPlCEVVtnBPNEaTQocwXyLMLSwXsw+O/6W2ixQVTrPaXkzoEgbAW38eKZhkKIs6naDjo+rxsmfkIaEoUYkTDGVhMsG5TeqxLIEvlCTrolrzctHz+iC10u20DWeITsxQfxDQGDxq2jEyUdX9ulNdpnpns5oEeDjffkJOOhz6TT2WIk+VTp9GgdzdshYmBV7+4xC5RlPxF2h1MOhT9XjPjxC10/pBiirHYDTd2FN6f6cyzKpv+woWjoMN71s4MmEpz/WLINDTiQsJGXy38Wz5BrvjTfAJg5RLsOl/FolPVE2nrqWyPyoJnVbDyKImGMpCq1W33IyZrxaXE7eRZF1Ue3e3CKJzPT+yDSbe/6eQfWCnVsOuH2wfmKiykjNzWXogBoAHOoUWftLRJZCTCj51IayHDaMTlvLW4CZ4ueg5diWFX3ZcLPmCer2g9ThAgWVPgzHX2iGKKmbDyTiuJGfh6+bIgGZmPnDOvK6u7AApLFeBeTjreXOwOsnw3caznI9PL/tgjm5w12fq8alVYMi2QISiKpqfN6t+R6MAAjwLmWAoDwcnaHSXmriL28h/FVHtaTQa3r2nKTqthlVHr91awTlqF8wbqRabiztpvyBFlbJ4/yUyc41EBLrToa5v4Sftm6V+bjtB3sAqqRoeTrw8QC0e9+XaUyX3Xgfo/z64+kP8Sdj7s5UjFFVNfmG5kW2DcXLQmXfxwQVgzIHA5hDUwgrRCUsZ1DyI7g38yTGaeOvPIyjlafHZoC/c+xM8ulVNmoT4l/RsA7/vvQTA2PLUwfi33CxpT1sKcgcoBBAR6MGDefvA3ll2lGxD3j6wkA7QoD+YcmH1G/YLUFQZiqLcWAI/vnMYmsKKOCkKdHoc6veBVrKHqzIb3T6ExkGepGQZSldszsUHer+mHm/8rzrbKUQpRCdm3HjYbHalZkUpeDjUdoKFIxOWptFoeO+eZjg6aNlyOp6/D10p34DNR4DewrOlospYciCG1GwDYX6u9GxQw3IDb/sCvmpVsOVPFEqSdSHyTO3bgBoearG5n7aeV7+p0cCAj0DrAKdXw9kN9g1SVHrbzyZwLi4ddycHhrWuXfhJGg00Gw7jl4B7gG0DFBal02p4K2/J6txdFzl5NbXki9pMUDtSZF6HvTOtHKGoKubvjkJRoHsDf8L83cy7+OJ2dTWH3g1ajLZOgMKiwvzdeLxXfQA+WnGifMXm8hkNcGCOLIcXNyiKwi87LgDqBIPlCssZ1X9r1y8AMrteHEnWhcjj4azntbvUJatfrzvDleRM9Qd+9aH9ZPV49ZvqC4wQZZT/pje8TW3cnczsfywqpc71/RjQtCYmBd77+1jJS1Z1DjDwYxj8BXSdaosQRSWXbTCyaG80APeXZZnqvrxZ9eb3grOnBSMT1jSlR31qejoTk5TJ7O0Xyj/g/NHw5xOwY1r5xxJVwo6zCZy6loaro46R7SxYWO70akiOVleTNZbWtMWRZF2ImwxtVZt2oT5k5hpvXbLa4yVw8oJrh+HQQvsFKCq1K8mZrDl2DYBxRRWWO7kCNn8CKeVc1igqlNfuaoyjTsvWM/Gsy+uBXay63aHdRLVKvBAl+OvgFeLTcgjycuaOxoHmXWwyFbzetJtk+eCE1bg46ni+fwQA32w4w/XS1MUoTvOR6ufNn0LK5XJGJ6qC2TdNMHg66y038J4f1c+tx8sWjBJIsi7ETTQaDa8NagzA7/sucfpa3pJVNz/o8bx6vO49yM20U4SiMpu/KwqTAp3qFdP/eNtXsP59iJxj2+CEVdXxc+Wh7nUB+GD5cXIMJbRyu1luJiSUs0WTqLIURWFm3tat8Z1D0evMvLXTamHiP/DEbqjV2goRCmsa3iaYRjU9SM0y8PX6M+UbrPkoCO4Auemw4QPLBCgqrUvXM25MMDzQOcxyAyeegzNrAY36UFoUS5J1If6lTR0f7mwaiEmBj1fdVAG+wxSo1xvu+gQc5CmgME+OwcS83eoy1fGdwgo/Ke4kRG0HjQ5ajbNdcMImnugdjr+7WhcjfztEiS4fgG/aq72vZQuOKMSu84kcu5KCs17L2PblqNRco6HlghI2o9NqeO0udZLh150XuJhQjlZuWi3cmZekR86Xh4TV3Jyd6gRDl/p+RU8wlEV+LZbwvuBbz3LjVlGSrAtRiBfvbIhWA2uOXWPfxUT1m3pneGApNB6sFgATwgz/HL5MfFo2AR5O9G9axDLV/b+onyPuBM8g2wUnbMLdyYEX8pasfrvxLOnZhpIv8gmD7FSIPQZH/rBugKJSyp9VH94mGB83R/MuToqCjEQrRCVsqUdEDXpE1CDXqNw6yVAWIR2gwZ2gGGHjR5YJUFQ6WblGFuxRe6tPyOuWZBG5WWphOSioByWKZbNkPTs7m82bN/Prr78yffp0Fi9ezPnz523164UwS3iAByPbhgBqldVCC0LJLJcoJUVR+GGz+no3oUtY4ctUc9IL3sDaSOukqmpE22DC/FxJTM9hVmkKQrn4QJen1ONN/1OrNQuRJyohgzXH1WWqE8tyQ736Dfi8sTqLKiq1Vwc2QqOBfw5d4UBUOVs+5rePPPwbXDtW/uBEpbPs4GWSMnKp7e3CHY0s2JXGwQnGLlRXqzboZ7lxqzCrJ+vbtm1j1KhReHt706dPH6ZOncp7773HuHHjCA8Pp0GDBnzyySekppainY0QNjS1XwOcHLTsuXCd9SduKghlMsK2L+HLllKARZTKtjMJHL+SgqujruhKzQfnQ1YS+NSVN7AqzEGn5dl+6uz69E1nSc7MLfmijlPUpD3hjMyui1vM2n4BRVFnVhuYu0w19Rqc+AcMWVCzuXUCFDbTOMiTEW3Uat0frjhRvsFqtVIrdId2RdpqVT+KojBr2wVALYbrYG4djOJoNFCnI9z1sRRQLSWrJutDhgxh9OjRhIWFsXr1alJTU0lISODSpUtkZGRw+vRp3njjDdatW0dERARr1qyxZjhCmCXIy4UHu4YB8PHKkxhNeW9YWh2cXKm2nNj2pf0CFJXGD1vOATCqXQjeroUsUzWZYOf36nHHR+UNrIob3KIWEYHupGQZ+Cnv30axnDxkdl3cJjUr90a7tkl571Vm2fczmAwQ3B5qNrNscMIunusfgaNOy+7ziew4m1C+wYZNhwf/hsCmlglOVBpbz8TfqIMxpn2IvcOp9qyarA8aNIjz58/z8ccf0717d1xcXG75eb169ZgwYQIrV65k3bp1aLWyhV5ULI/3DMfT2YGT11L5MzKm4Ac9X1Q/75ulzk4IUYTjV1LYfCoOrQYe6la38JNy09UnzW41oPX9tg1Q2JxOq+G5vNn1n7aeJ7E07ZY6PAIuvpB4Fo78buUIRWXw295LpGUbqF/DjR4Naph3cW4m7J6hHnd81PLBCbsI8nJhdF5y9eW6UyWcXQJHV6nPU01N36Q+RB7Tvo75dTCKs/I1+Od5SJRt0OawanY8ZcoU9PrS9eRr0qQJd9xxhzXDEcJsXq56Hu1VH1B7mN6YXa/XW52NMGTBjq/tGKGo6H7cor4pDWwWRIiva+EnOXnAPdNg6hH1WFR5dzatSdNanqTnGJm+qRQVl508oOvT6nH0busGJyo8o0m5UfNgYte6aLVmJlWHFkJGPHiFQJOhFo9P2M9jveqj12nYeS6RnefKObsOagHCNW/J6041cfhSMlvPxKPTaoqeYCiL9AS1CvyeHyH1iuXGrQZsNpVdr149EhJuf9FISkqiXj3rlu2fNm0aYWFhODs707FjR3bvlhccUXrjO4Xi5aLnXFw6K47kvcBoNNDjJfV4z0+QHm+/AEWFdS0li2UH1RUZk7uX4k1PLy0BqwuNRsML/dVWWbN3XCA2Javki9o/DA+tgcGfWzk6UdGtOHKFqMQMvFz0DG9T27yLTSbY/o163Okx0DlYPkBhN7W8XRjVLm92fe3p8g+4/n11y9/GD8s/lqjwpm9WHx7f3aKYCYay2P0DGDIhqCXU6Wy5casBmyXrFy5cwGi8vXp2dnY2MTExhVxhGQsXLuS5557j7bffZv/+/bRs2ZI777yT2NjYki8WAvBw1jMxbz/gN+vPFFSGb9APglpBbgbsmGa3+ETFNWv7BXKNCh3CfGldx6fwkw7MgSuHbBuYqBB6NaxB6zreZOWa+HZjaWbX3dW2SqJaUxSFaRvUfy8PdgnD1dHMZDv2GCRdBCcvaPOAFSIU9vZ473D0Og07ziWw+3w5W/N1fRo0Wji7Hq4etkyAokK6mJDO8sPqpNQjPepbbuCcdDVZB+g6VbZXmMnqyfqyZctYtmwZAKtWrbrx9bJly1iyZAnvvfceYWFhVvv9n3/+OQ8//DATJ06kSZMmfP/997i6ujJz5kyr/U5R9TzYJQw3Rx0nrqay7njegx6NBnrmz67/qL4YCZEnLdvA3J0XgWJm1dPi4O/nYHp3uHrEhtGJikCj0fB8P3V2fcGeKBLSskt/cVqs3DhXU+tPxHL8SgpujrobD5LNUrOZuuVm5EzZdlNF1fZ2YWQ7C+1d9wkr2CqxXbb9VWU/bjmPSYGeETVoUsvTcgMfmAuZieq/pcZDLDduNWH1tU9Dhw4F1JuSCRNu7R2s1+sJCwvjs88+s8rvzsnJYd++fbz66qs3vqfVaunbty87duwo9Jrs7GyyswtumFJSUqwSm6hcvF0dGd85jO83neXrDWe4o3EAGo0GGt6lVmluMQYc3ewdpqhA5u+KIiXLQF1/N/o2Diz8pH0/gzEbarWRirvVVNdwP1oEe3HoUjKzt1/gubyl8cU6uRIWPQABjeCRTTJLUY0oisI3G84AakulQrtLlIZHoPohqqzHe9Xnt73RbDuTwN4LibQL8y37YF2fhqOL1daRd7wFXsGWC1RUCPFp2Te6Szza04Kz6kZDQW2nLk/JtpsysPrMuslkwmQyUadOHWJjY298bTKZyM7O5uTJkwwePNgqvzs+Ph6j0Uhg4K1vSIGBgVy9erXQaz788EO8vLxufISESMsCoZrcvS7Oei0Ho5PYeiZvj7pGA/3fl7Y34haZOcYb+74e61m/8OJPuVkF1Zg7PS4JVzWl0Whu3BjN3nGR9OxStGULbq/+e7lyEC5ut3KEoiLZcTaBA1FJODpoeag0dTD+LfmS5YMSFVKwjysj2qpJ9Zfryrl3vVZrCOuutvrb+Z0FohMVzS/bL5BtMNEy2ItO9crxYOffji2FpChw9YdW0u2mLGy2Z/38+fP4+/vb6teV2auvvkpycvKNj+joaHuHJCoIf3cnxnaoA6h71wtlur0ug6h+5u66SHxaDiG+LgwrqvjT/tmQHguewdB0qE3jExXLnU1rUtffjeTMXBbsKcV7jpsftByrHku9jGolf1Z9TPsQAjzMLEiZFA1ftIDZQ9TWbaLKe7xXODqthi2n4zkSk1y+wbo+o37eNwsyk8obmqhAUrNymb1D3bb3aM/66spRSwnrDt2fVz/0LiWfL25j1WR9wYIFpT43Ojqabdu2WfT3+/v7o9PpuHbt1j7Y165do2bNmoVe4+TkhKen5y0fQuR7pEc9HHVadp1PvLVoS+pVWPo4zOgD+QXoRLWUmWPk+7xWXE/2DkevK+RlNjcLtv6fetz9OdCVrsWlqJp0Wg2P9FC7ovy45Rw5BlPJF3V6XP18cjkklKI4naj09kddZ/vZBBy0GqaUZZnqjmmgGAFFbpqriRBfVwY1DwLU15ZyCe+rzrA3HwnGXAtEJyqKn7ddIDkzl3o13OjftPD8qMw8AtWtE50ft+y41YhVk/XvvvuOxo0b8/HHH3P8+PHbfp6cnMzy5cu57777aNOmTaGt3crD0dGRtm3bsm7duhvfM5lMrFu3js6dpW2AMF+Qlwv35i0r+27jTbPrehc49idciYRzG+0Sm6gY8mfVg31cGN6miH19B35V+4x61obW42wboKiQhrWuTQ0PJ64kZ7Hs4OWSL6gRAQ3uBBRZllpNTMtb0TWsdW1qe5uZbKdcUXscg1qNWVQbD3dXHwT+fegKV5LLsaJCo4HJ6+DuL8C9hmWCE3aXnJHLjLwHOVP7RqArbNuesCurJuubNm3if//7H2vWrKFZs2Z4enrSoEEDmjdvTnBwMH5+fkyaNIk6depw5MgRhgyxfIXA5557jhkzZjB79myOHz/OY489Rnp6OhMnTrT47xLVw5Qe9dBoYMPJOM7GpanfdPaCVvepx3LjXG2ps+rqm16Rs+oAju7gXhO6PQsOTjaMUFRUznodk7qqe5CnbzqLyVSKFTqdn1A/R86FjHK2ZxIV2tHLyaw7EYtWA4/1KsOs+tbP1WKWdTpD/T6WD1BUWM3z9iAbTAqztl0o32BanUViEhXHj1vPkZploGGgB4PzVmFYRPQe+OUeOLvBcmNWU1YvyTdkyBCGDBlCfHw8W7ZsISoqiszMTPz9/WndujWtW7dGq7XeM4PRo0cTFxfHW2+9xdWrV2nVqhUrV668reicEKUV5u/GHY0CWXv8Gj9vO8/7Q5urP+j4qNpH8vQqdVmqnwWraYpKQZ1Vzy5+Vh2g1Vh1n7pGbnxEgfs71eHbDWc4HZvG+hOx9G1SwvtU3R4Q2BziT0HMPmjQzzaBCpv7ZNVJAAa3qEW9Gu7mXZwUre4zBuj9mhSzrIYe7l6PnecSmbc7iqfuaIC7Uzlv/2P2q5Xh+78v/54qscT0HGZuPQ/As/0iCi+GW1br/wPnN6t1eer3tty41ZDNCswdPnyYYcOG8cwzz/DKK68wefJk2rZti1arZfr06Vb93U8++SQXL14kOzubXbt20bFjR6v+PlH1TeoWBsAf+2JIyshRv+lXP29ZKrDLuv+mRcWTlWtk+uaCWXVHhxJeXvUu4FDGtkuiSvJ01nN/p1CAG3UPiqXRwNBv4bljkqhXYTvPJbDxZBwOWg3P9Yswf4Atn4ExRy30VLeH5QMUFV7vhgHUq+FGapaBhaUpYlmc7DSYNQh2fANRhbdBFpXD9E1nSc8x0qy2J3c2teAk5rmNaqKuc4ReL1tu3GrKZsn6gAEDePHFF8nNLShKER8fz913380rr7xiqzCEsIjO9fxoHORJZq6R+btveuPr9Kj6OXIuZJWz8qqoVObsvEhcaja1vYuZVT+2TJ2NkK4BogiTuoah12nYe/E6hy+V4jUkqAW4VfxOK6JsFEXhfytPADCmQwhh/m7mDWDIgfOb1OPer1k4OlFZaLUaJndT967P3Hoeg7EURSyL4uQOze5Vj/f8ZIHohD3EpmYxe8cFAJ7rF2G5CvCKAuveU4/bTQLvOpYZtxqzWbK+YcMGlixZQvv27Tl27Bj//PMPzZo1IyUlhcjISFuFIYRFaDQaJnUNA+CXHRfIzX/jq9cbajSCnDQ4MMd+AQqbSs7IvdFS6ak+RcyqG3Jg1evw+ySInGfjCEVlEeDpfKN686ztF8y7OPG85QMSdrXm2DUORCXhotfxdJ8G5g/g4AiP74LRcyG0i+UDFJXG8Da18XNzJCYpkxVHrpZvsPYPqZ+P/QlpceUPTtjctxvOkpVronUdb3o3DLDcwCdXQMxe0Luq7dpEudksWe/SpQuRkZE0a9aMNm3aMGzYMJ599lk2btxIaGiorcIQwmKGtKqFv7sjV5KzCt74NBro8SL0fgNajLZvgMJmpm08Q1JGLhGB7oxoW8Ss+t6ZkBwF7oHQfIRtAxSVyoQuYQD8degyCWnZJV9gNMDPd8FXrSD2hFVjE7ZjNCk39qpP6hZGgKeZfdXzOThC48EWjExURs56HeM7q/fbP245h1KeNrO1WkOtNmDKhUiZmKhsLidlMm93FADP92touVl1kwnW582qd3wU3C34EKAas1myDnDq1Cn27t1LcHAwDg4OnDx5koyMDFuGIITFODnoGJe3vzS/QAegJmI9X5SlqdVEdGLGjQq7r97VGIfCKsBnJMLGD9XjXq9Kj2NRrNZ1fGgZ7EWOwcSC0uwv1TmAi496vH+2dYMTNrN4/yVOx6bh5aLnkR5lKFh6frP0wxa3GN8pFCcHLQcvJbM/6nr5Bms3Sf2892c1SROVxscrT5BjMNGxri9dw/0sN/CJvyH2GDh5QdenLTduNWezZP2jjz6ic+fO9OvXjyNHjrB7924OHDhAixYt2LFDClSIymlcp1AcdVoio5PYd7Gcb3yiUvp41UlyjCa6hfvTK6KI3rObP4WsJAhoAq3H2zQ+UTnlz67P2XmxYJtNcdo+qH6OnAe55eilLCqErFwjX6w9DcDjverj5aI3b4BLe2H23TCtI2SnWiFCURn5uTtxT6taAPy642L5Bmt2r9q2NukinF1vgeiELeyPus7SyMtoNPDm4CaWm1UHaDQIhn4H/d4teIAsys1myfqXX37J0qVL+frrr3F2dqZZs2bs3r2b4cOH06tXL1uFIYRF+d/0xjdz202z64qiFhL7dbjs56rCIqOT+Oug+qb36l2NCn/TSzirtvQD6P+eOgsqRAkGtQi6sc1m9dFrJV9Qvw94hagPhY79afX4hHX9suMCMUmZ1PR0vvHgptRMJliRV4E5pCM4eVg8PlF5je8UBsDyw1eJL802m6I4ukLL+9TXnZw0ywQnrEpRFP7z1zEARrYNplltL8v+Aq0OWt0H7SZadtxqzqat2wYOHHjL9/R6PZ988gmrV6+2VRhCWNykbnUBWHXkKrEpWeo3NRrY/g2cXQcH59sxOmEtiqLw33+OA3Bvm2Ca1iriTW/t2+q+vvC+6ocQpeDkoGNsB7WK7uzSFJrT6qDNBPU4v6e2qJRiU7L4Mm9W/bn+ETjrdeYNcPg3tcCTozv0fdsKEYrKrHmwFy1DvMkxmli0t5xt3Pq8Ds8chKZDLRKbsK4/Iy8TGZ2Em6OOF/o3tNzAmUlqSz9hFTZL1v39i96/27NnT1uFIYTFNQ7ypF2oDwaTcmv/0rZ5N877f1Fn2kWVsvrYNXZfSMRZr+X5/sX0Pu74KNRuC/3ft11wokq4v2MoDloNuy8kcuxySskXtB4HGp3a+zj2uPUDFFbx0YoTpOcYaRnizYii2kAWJTtNfUAI0P058Khp+QBFpTc+r97O3J1RGE3luD9x8lAfFIoKLyPHwEcr1AKkj/cOL3vBysKseRO+aQ+n11puTHGDTQvMCVFV3d9JnQGbv/umN75m94LeDRJOqzfPosrIyjXyQd6s+sPd6xHkVUzBuLBuMHkdBDS2UXSiqqjp5cydzdRkq1Sz655B0DBvBZus6KmU9lxIZPGBGDQa+M+Qpmi1Zu4n3fYFpF4B71Do9IRVYhSV3+AWQXi76olJymTDidjyD2jMhWPLpKBhBfbD5nNcTcmitrcLD+WtCLWImH2w/1dIvQxO7pYbV9wgyboQFjCwWRA+rnouJ2ex8WTeG5+TBzQbrh7vkwrNVck3688QlZhBkJczj/YsokrzzUW+LFnARVQrE/P2Ky+NjCEpI6fkC7o9CyN+ht6vWzcwYXFGk8Jbfx4FYEz7EFqGeJs3wPWLsP1r9bj/+6C34MyZqFKc9TpGtQsB4Ned5Sw0pyjwQy9YNB7OrCt/cMLiriRn8v2mswC8dldj87fWFMVkguUvAgq0GAN1OllmXHELSdaFsABnvY6ReW98c25+48uv0HxsqbqnR1R6Z2JTmb5ZfdN7++6muDkVUjAuLRa+bAWbPpGZBlEubUN9aBzkSbbBxNIDMSVfENxOfUjo4GT94IRFzdt1keNXUvB0dijbflKTAUI6QFh3aHy35QMUVcr9HdUVgZtOxXExIb3sA2k0UDdvO2vkXAtEJizt3WXHyMo10T7Mh7uaW3BrTORcdWbd0UOtAC+sQpJ1ISwkvxjUxlNxRCdmqN+s3RYCmoIhSy36Iyo1RVF4fckRco0KdzQK4M6mgYWfuPIVSLsKx/8EZFZdlJ1Go2FMe/VB4II90Sjm1L+QWhmVRmJ6Dp+uPgXAC3c2xM+9DA9b/OrDA8tgzDxZzSNKFOrnRs+8dqNzd0WVb7BW96mfT66AjMRyRiYsadXRq6w8ehUHrYb/3NPMcq3aMpNg7Tvqca9XpD6GFUmyLoSF1PV3o1u4P4qi7l0H1BumNg9ASCe1vYmo1Bbvj2HXebWo3DtDmhb+pndqtdq2T6OFIV9LqzZRbkNb1cbJQcuJq6lERieVfIGiwOZP4Ou2cP2CtcMTFvDRiuMkZ+bSOMiT+/Ie/JaayVRwrNGAs6dlgxNVVn6huUV7o8nKNZZ9oJrNoGYLtfPJ4d8tFJ0or9SsXN7O21rzSI96NA6y4GvD2ncgIx78G0LHKZYbV9xGknUhLGhcXqG5RXujyTHk3UB1nAIPrYKGA+wYmSivpIwcPliuFpV75o4IQnxdbz8pOw3+eU497vQ41GptwwhFVeXlqmdQ8yAAFuwuRasljQbOb4HEs3BwoZWjE+W15XQci/ZeQqOB9+5pioPOzFuzf56Fv5+DrFJ0DBDiJr0bBVDb24WkjFz+PnSlfIO1ul/9LEvhK4xPVp3kakoWoX6uPH1HA8sNbDKqK0YBBn0GOr3lxha3kWRdCAu6o3EgAR5OxKflsPrYVfWbshyxSvhoxQkS03OICHRncvciKqlu+C8kR4NXHej1qm0DFFXamLzZ1r8OXSYt21DyBfnLUg/Ol+XwFVhatoFX/jgMwITOYbQL8zVvgHMbYd8s2PsTXDtq8fhE1abTahjbIW+bze5yLoVvPhK0ergSKf8WK4B9F6/fKB7432HNLVdUDtR2fcO+h8d2QN3ulhtXFEqSdSEsSK/T3ripnvPvCqvpCbB7BhhKUdFZVCibT8WxYI86o/n+0OboC5v5itkHu75Tjwf/n7QwERbVPsyHejXcyMgx8tfByyVf0PhucHSH6+chaqf1AxRl8tGK48QkZRLi68JLA8wsKpeTDsueVo/bT4bQzpYPUFR5I9uFoNNq2HvxOqeupZZ9IDe/ghWEUhXernIMJl5bfBhFgRFtg+ka7m+dXxTYxDrjiltIsi6EhY1pH4JWAzvPJXI2Lk39pqLADz1h+QtwerV9AxRmSc7I5aXfDwEwoXMoHeoWMfOVeF6dVWg2Ahr0tWGEojq4pdBcaWbAHN2gyT3q8cF5VoxMlNX2s/HM2an+Xf5veAtcHc2sb7H+fUi6CJ7BcMfbVohQVAeBns70aRQAlHKbTXF6vQaPbYeuT1sgMlFW3208y8lrqfi6OfL6XY0tN/CZdTBvDKSUc8uEMIsk60JYWC1vF3o1VN/4Fu3Ne+PTaG66cZ5vp8hEWbzz11GupmRR19+NVwYW86bXfAQ8shHu+sRmsYnq5d42weh1Gg5eSubY5VLsT245Vv18dCnkZlo1NmGejJyC5e/3daxDF3Nnvs5vgZ15K3nu/kKKyolyyS9q+Mf+S+UrNBfYBAKbWigqURaR0Ul8tf40AG/f3QQfN0fLDJyVrK7kObUCdnxjmTFFqUiyLoQV5M+A/bHvErnGvEJz+XtIT62S1iaVxIrDV1hyIAatBj4b1RIXx0L2fN28HziwCbiauedUiFLyc3eifxO1Pc6CPaWYXQ/tqtZPyE6BE/9YOTphjo9XniQqMYNaXs68OrCReRenx8PihwEFWo+DBv2sEqOoPnpE1KCWlzPJmbmsPHLVMoPmZllmHFFqGTkGnl0YidGkMLhFEENa1rLc4CtfhZRL4BMGvV+z3LiiRJKsC2EFvRsFUCOv0Ny647HqNwObSmuTSiQuNZvXlqgzX4/2rE+bOj63n5RyBX7sC5f22jg6UV2NySsGteRATMkzYFottJ+kVmn2t2AlYFEuG07GMmv7BQA+vLcFHs5mVlJOPA/GHPCPgIEfWz5AUe3otBpG5U0yzC9vobncLPhjMnwaodbqETbz/j/HOR+fTpCXMx8MbW65nuqR8/Oq/Gvgnm/VbVbCZiRZF8IK9DotI9oGA7Dw5hmwmys0iwpLURReXXyY6xm5NKrpwTN9C0l0TCZYMgVi9sKKl6TitrCJrvX9CfZxITXLwPLDpdg32O1ZGPotBLW0fnCiRLEpWbyw6CAAD3YJo2dEDfMHCWkPj26D0XPkpllYzKh2ar2dXedvqrdTFnpniDsJ2clwdLHlAhTFWnvsGvN2qfebn41siZerhdqpxZ0saEnb61UI62qZcUWpSbIuhJWMaqc+pd50Ko7LSXn7RZuNAK0DXN6vvgCKCumXHRdZe/waep2G/xvdCieHQpa/7/gazm8CBxcY+r206BM2odVqGN0uv9BcOYtBCZsymRSeXRRJQnoOjYM8ecXc5e83PxD0DIIaZlaPF6IYN9fbWbinnK8t+fUyDi4oZ1SiNOJSs3n5D7UQ7sPd65pfA6MoORmwaALkZkDdntDjBcuMK8wiyboQVlLX341O9XwxKfD7vkvqN91rQHg/tWp4zH77BigKdTA6iff/OQbAKwMb0ziokMJN5zbB2nfV4wEfQo0IG0YoqruReTNguy8kcia2FDNgigKXD8DmT2QFiB19v/ks284k4KLX8fXY1ub1Pc5OhR/vgON/WS9AUe2NzSs09/u+S2QbylForvkI0OjUlWfxpy0UnSiMyaTw4u8HSUjPoVFND16404IP8dKuqVtu3APh3h/V/urC5iRZF8KKxrRX3/gW7onGZMq7Sb7zA3j+JLQaa8fIRGGSM3J5Yt5+co0KdzYNZFLXsNtPSoqC3yeCYlRnD9o+aOswRTVX06ug1dKNjhPFyU6Bn/qrrb6uHbFydKIw+6Ou89nqUwC8O6Qp4QHupb/YZILFUyBmH6x4RSr7C6vp3bAGgZ5OJKbnsObYtbIP5B4A4Xeox4cWWiY4Uaiv159h48k4nBy0fDGmiJWAZeVbF6ZsgnF/qH+nwi4kWRfCigY0q4mnswMxSZlsPROvftOvPrj52TcwcRtFUXj+t4Ncup5JHV9XPh7R8vbiLLmZsHAcZCSoe4AH/58sfxd2MTrvQeAf+y6RYzAVf7KzF0QMUI/lxtnmrqfn8PT8AxhNCne3rMXIdsHmDbDxQzj5D+icYNRs0LtYJ1BR7TnotDe28JW70FzLMerngwvVB07C4jadiuOLdepDwPeHNqNRTQu1cMzJKDh28oCazS0zrigTSdaFsCJnvY5hrWsDRewBk0qpFcaPW86z9vg1HHVavr2/DV4uhRRnMRnAsza4+qnFneSmWdhJ/gxYQmlnwFqMVj8f/h1M5VjeKsxiMJp4av6BGw8BPxjWzLwKzUeXwOa8iu93fwnB7awTqBB5RrULQaOBbWcSuJiQXvaBGt4FTp6QHAVROywXoADg0vUMnllwAEVRty+MzHvIUm4pl2FaB9g1XbZNVRCSrAthZfkzYKuPXSUhLVv9ZkYi/HQnfNFM3Yso7GrXuQQ+WnkCgDfvbkKz2l6Fn+jkAaPnwuS14F3HhhEKcSsHnZaRbfMKzZWm53qDfuDsDalX4Pxm6wYnbvh41Um2nonHRa/jhwfa4mlOm7Yrh2Dp4+px5ydl65SwiRBfV7o3ULsULChPoTm9C3R5Gvq+A37hlglOAJCVa+TxuftJysilRbAXb9/dxDID52TA/LGQHA17Z4IhyzLjinKRZF0IK2tSy5MWwV7kGhWWHIhRv+nioy6lzs2AY3/aN8BqLiohg0fn7MNoUhjSshbjOhaShMedLHjCrNWCbz3bBilEIUbn9UXecjqe6MSM4k92cIJmw9XjQ4usHJkA+DMyhh82nwPg05EtzVuimnkdFtyvvkfU7wN937VSlELc7r4O6mvLb3svkWssxxL2ni+q7SM9Ai0UmVAUhXf/OsahS8l4u+r59v425hWrLIrJBEsfgyuR4OILYxfI6sEKQpJ1IWwgv9Dcgj3RKIqi7nPO388VKT3X7SU1K5fJv+zhet7T6f/d2+L2JaqX9sH0nuqbmDHXPoEKUYgQX1e65bXoKVWhufyl8MeX3bonUVjckZjkG62UHu9Vn0EtgswbwMkLGt0FvvVhxEzQOVghSiEKd0fjQPzdnYhPy2bd8XIUmhMWN2v7BebvjkKjgS9GtyLYx9UyA2/6HxxbqnYrGj1HLS4nKoQqnax/8MEHdOnSBVdXV7y9ve0djqjG7m4ZhItex5nYNPZHXVe/2XIMoIGLW+H6BXuGVy0ZTQrPLIjk1LU0Ajyc+GF8O1wc//V0OuEszBsJhkxIj7dPoEIUY0zeDNiivdEYSpoBC+mobt/Qu0KCtFOylvi0bKb8uo+sXBO9Gtbg+f5laKWk1cKAj9QtNy4+lg9SiGLoddobhRDn7y5nz/WcDHU1z4b/WiCy6m39iWu897faWvblAY3o1dBCFdr3zYJNH6nHgz+HsK6WGVdYRJVO1nNychg5ciSPPfaYvUMR1ZyHs57BeTMrC/Lf+LyCoW4P9ViWpdrc/1aeYP2JWJwctMx4oB01vZxvPSEtDubcW1D5feQs0Jmx31QIG+jXJBBfN0eupWSz8WRc8SdrNPDAMnjuuPpvWlhcRo6Bh2btISYpkzA/V74c3RqdtpQF5RQF9v4Mhhz1a40GXH2tF6wQxRiTt81m8+m4krfZFCflMix+GDZ/CmmxFoqu+jl2OYWn5h3ApKh/N1N6WGg73rVj8Pez6nG3Z6HNA5YZV1hMlU7W3333XZ599lmaN5eWA8L+8mfA/j50hdSsvOXUre5TPx+cL1U3bWjB7qgbe0k/GdmSliHet56QnQrzR8P18+pM5H2/gZMZfZGFsBEnBx33tlE7TpSq0JxvXVlSbSUGo4mn5x/g4KVkfFz1zHywPV6uZjzgW/s2/D0VFtwn7wfC7kL93Oga7oeiwG+l2WZTFP9wqN0WFKPajUKYLTYli4dm7yE9x0iX+n68N9TMrhLFCWyi1sRoMwHueNsyYwqLqtLJuhAVSZs6PoQHuJOZa+Svg1fUbzYaDHo3SDwH0bvsG2A1sfroVV5bchiAp/uEM6RlrVtPyEmHuaMgZp+6/HTcYimOIyq0MR3UmhjrT8RyJTmzdBeZjJByxYpRVS+KovDOX0dZe1xdrfPjhHbUq2HGA77tX8O2L9XjpkPVWXUh7Cy/3s7C0myzKU7LvE4GhxZYIKrqJT3bwORf9nIlOYt6Ndz47v626HUWTt+6Pq22hpTXnQpJkvV/yc7OJiUl5ZYPISxBo9HcWFa2MH8GzMkd+rwB9/4ky1JtYPf5RJ6ary4jG9UumGf7Rdx+0uVIuLRH7Q87bjH4N7B5nEKYo34NdzrW9cWkwKI9l0q+4OIO+L+m6gyusIjvN51jzk616NOXY1rRNtSM5es7v4PVb6jHd7wFrcdZJ0ghzNS/qRnbbIrTdDhoHeDKQYg9brkAq7hsg5Epv+7jUN5qnZ/NXa1TlGvHYOF4yEou+J4k6hVWpUvWX3nlFTQaTbEfJ06cKPP4H374IV5eXjc+QkJCLBi9qO6Gta6NXqfh4KVkjl3OexDU+XFoPkJaZFjZiaspTJ69h2yDiTsaBfDfYc0LX0YW1hXGzINxf0DtNrYPVIgyuC+v5eDCPVEYTSUsofYLV/eOXt6vtiUU5fLHvkv8b6V63/HmoCYMaGZG5fddP8DKV9TjHi9Ct+esEKEQZXPzNpv5u0uxzaYobn7Q4E71+KDMrpeG0aQwdUEkW8/E4+qo4+eJHQj1cyv/wNeOwuzBaleQla+VfzxhdZUuWX/++ec5fvx4sR/16pW96MKrr75KcnLyjY/o6HJWwRTiJn7uTvRvUhO4aXZdWN2l6xlMmLmblCwDbUN9+Oa+NjjcvIzMkKMWwckX0R9COtg+UCHK6M6mNfF21XM5OYvNp0qYAXOvAQ36q8eR86wfXBX2z6ErvPj7QQAmd6vLpG5mtDvaOxNWvKged3sOer8us1uiwsnfZrPhpBnbbArTMq915KFF6jYcUSRFUXht8WFWHLmKo07LD+Pb0erftXXK4uphmDU4r3BuK+j/XvnHFFZX6ZL1GjVq0KhRo2I/HB0dyzy+k5MTnp6et3wIYUmj85bCLzkQQ1Zu3htWegJs+Qz+ecGOkVVNV5Izuf/HXVxLySYi0J2fJvyrRVtuproc+Md+aqs2ISohZ72Oe9uorZbmlWYGLL+45aGFcuNcRuuOX+OZBQXVmV8f1Ni8AQKagqMHdH1GXf4uibqogMzeZlOUiAFqHRifUGmFWoKPVp5g4d5otBr4amwrujXwL/+gVw7B7CGQmQi12sADS6XbRCVR6ZJ1c0RFRREZGUlUVBRGo5HIyEgiIyNJS0uzd2iiGusW7k9tbxdSsgwsP5xX4CnzOqz7D+z9CVKv2TfAKuRaShb3zdjFxYQMQnxdmD2pA96uNz3My0qBOSPgzBr1SXOSrHYQldfYvI4T60/Eci0lq/iTIwaAiy+kXoGzG2wQXdWy9XQ8j83dj8GkcE+rWnxQ1Laa4tTpCI9tUysxS6IuKrCxebPri/ZGl7zNpigOTvB0JExaKUVbi/HF2lNM36R2q/loeAvzttUUJWY//JKXqNduC+OXqA9ORKVQpZP1t956i9atW/P222+TlpZG69atad26NXv37rV3aKIa02o1N26qf915Uf2mfzgEdwDFBIel57olxKZmMXbGTs7HpxPs48L8hzsR5HVTXYCMRPjlHri4VS0mN34x1O9tv4CFKKfwAA86hPliNCklt1pycITmI9XjyLnWD64K2X0+kYd/2UuOwUT/JoF8OrJl6XqpKwqsf1+9cc7nEyqJuqjwBjSriZeLnpikTDafLkehORdvi8VU1SiKwudrTvHF2tMAvDGoMaPaW6BulskIS6aok0K12+Ul6t7lH1fYTJVO1mfNmoWiKLd99OrVy96hiWpudPs66HUaDkQlcSQmrxpnyzHq50jpuV5e8WnZ3DdjF+fi0qntrSbqwT6uBSekXIGf71ILbLn4woRlENrFfgELYSFj8h4Ezt8djamkGbD8pfAn/oHMJOsGVkVsOxPPhJm7ycw10iOiBl/f17p0bZRMRlj2FGz+BOaOlP/eolJx1usYnldobkF5Cs3lS4+HuFPlH6eKyE/Uv1qnJuqv39WYyd3LXn/rFlodjPoFmtyjLn139rLMuMJmqnSyLkRFVcPDiYF5S5t+2XFB/Waz4aBzgtijcPWQ/YKr5K4mZzH2h52ciU0jyMuZ+Q93IsT3pkQ94Sz82BfijoN7TZi4Amq1tl/AQljQXc2D8HR2ICYpky1nStgXGtRSLWo2aaXcwJXChpOxTJy150aiPn1cW5wcdCVfmJsJv02AA7+CRgv93pWZLVHp5C+FX3s8ltiSttkU59Ai+KwhrHzZQpFVboqi8NnqU3y9/gygzqg/3MMCifr1CwXHAY3VhN3Jo/zjCpuTZF0IO3mgcygAf0ZeJikjR90/1HCg+kNpbVImUQkZjJy+ndM3Jep1/FxvPck9QP1v7ReuJikBjewTrBBWoM6AqYXm5uRvsymKRgM9X1JbFMpS7GKtOnqVR/KWvvdtHMiMB9reWqiyKGmxavXl43+BzlG9YZY+6qISigj0oG2oj7rNZl85Cs3VbgsmA5zbqK5yq8YUReG/y4/zzQY1UX9zcJPyz6ibTLD2XfimA1zYaoEohb1Jsi6EnbQN9aFJkCfZBhO/7c1747tRoXkRGHPtF1wldOpaKiO+3050YiZhfq789mhnwvwL6Unq5AH3/wYPrQFfM9osCVFJjOukPghce/waUQkZdo6m8lt28DKPz91PrlFhUPMgvhvXpnQz6rEn4Mc7IGYvOHure0Ub3231eIWwlvzZ9QV7okreZlMUv/oQ0rHa1+gxGE28+PshZmw5D8DbdzfhIXNaPxYmJ0NdxbP1czBmw6U9FohU2Jsk60LYiUajuTG7PmfXRfWNr/4d4B0K9fuolcpFqRyMTmLU9B3EpmbTqKYHix7tXLBH3WSC9R/A1v8ruMAzSFqWiCorPMCdnhE1UBSYtf1CyRdcOwp/PgGbPrZ6bJXNzK3neWbBAYwmheGta/PlmFal26MOsO1LtcOEbz2YvA7Culk3WCGsbFDzIDycHYhOzGT72YSyD9RyrPp5/y/VskZPVq6Rx+bu5/d9l9BpNXw6siUTu5YzUU+5Aj8PhOPLQKuHod9Dt2ctE7CwK0nWhbCje1rVxtPZgYsJGWw6HQc6B3j6ANw7A9z87B1epbDhZCz3zdhJUkYurUK8WfBIJwI8nNUfZqXAwvth88ew9h24esSusQphK5PyZmgW7Y0mNauEVTqJ5+DAHNjzExgNNoiu4jOZFD5ccZz//H0MRYEJnUP5ZGRLHEqbqAMM+gzaT4aH1qodP4So5FwcdQxrrRaam7e7hG02xWk+AhzdIeEMnN9koegqh5SsXCbM3M2aY9dwdNDy/bi2jGgbXL5BrxyEGX3gSiS4+sGEv6DVWIvEK+xPknUh7MjFUcfIdnlt3HbkvfFpS7G8UgAwb1cUk2fvJT3HSLdwf+ZM7ljQRz2/kNzJ5WrhvqHfQ81m9g1YCBvp0cCf8AB30rINBdtsitLgTvUGL+0qnFppmwArsFyjiRd+O3ij1/FLAxryzpCmJbdny82E3TPU1TwAjq5qwi4PXkUVkr8UftXRa1y6XsZtNk4eBR1w9vxoocgqvstJmYz6fge7zifi4eTAL5M60K9JOXvOxx6Hn/pD6mXwb6iu4gntbJmARYUgyboQdpa/v3TDydhb95dePQJRu+wUVcWmKAqfrDrBa0sOYzQp3NsmmJkPtsfdyUE94fQamNEb4k+CR5Ba8V2eMotqRKPRMLFrGKAuhTcWt7/UwRFaj1ePd0+3fnAVWHJmLpNm7WHxgRh0Wg2fjGjB473C0ZRUgC8pGmYOgOUvqPtFhaiiGgd50jXcD6NJYebWC2UfqN1D6uezG6rFtr/Dl5IZOm0bJ66mUsPDifmPdKJTPQs8yKvRCCIGQIP+MFlq8VRFkqwLYWd1/d3okbe/dHZ+G7fIefB9V/XGrxru5ypOVq6RqQsjmbbhLADP3NGAT0e2wNEh7+Vs0ycwdwRkJUNwB3hkIwS3tV/AQtjJ8NbBeLnoiUrMYP2J2OJPbj9ZbSt2frM6U1MNXYhPZ/i329hyOh4XvY4fJ7S7sfKpWGc3wA+9CpagBre3dqhC2NWUHvUBtdBcUkZO2QYJbALDZ8DUw+DsacHoKp7VR6/eqKvTMNCDpU90pVntcrTLTLkC2anqsUYDw76HsQulBWcVJcm6EBXApLwZsHm7okhMz1Gfkjq4qP3WL26zb3AVyLWULEb/sJM/Iy/joNXw8YgWPNsv4tZZL4+a6ud2D8GDfxd8LUQ14+Kou7FkdebW88Wf7B0CjQarx7uq3+z6jrMJDP12G2fj0gnycua3RzvTu2FA8RcZDbDuPfh1GGTEq33rH9kI9XraJGYh7KV7A38aB3mSkWMsuUVkcVqMqtLFXhVF4cct55gyZx+ZuUa6N/Dn98c6U9vbpeyDnlkL07urRUHzJ3P0LqCVlK6qkr9ZISqAnhE1aFbbk8xcIz9vO6++eeUv294xzb7BVRAHoq5z99dbORidhJeLnlkTOzAqf9YrJ73gxNbj1LZsgz8HByf7BCtEBfFA51B0Wg07ziVw7HIJS007TlE/H1oImdetH1wFMX93FON/2kVSRi4tQ7z5szSzXskxMPtu2PIpoEDbiTBpFXjXsUnMQtiTRqPh0Z5qP/BZ2y+QlWss/6A3v49XAVm5Rp5bdJD3/zmOosB9Hevw84Pt8XDWl21AYy6seQvm3AvpcWpdnmr0Ol2dSbIuRAWg0Wh4srdaLXjW9gukZOVCp8fVH55cAfFn7Bid/f2x7xKjf9hJbGo2EYHuLHuyK90a+IMhB1a/CdM6QUaierJGAyEd7BuwEBVELW8XBjRTV5fM3FbC7HpoVwjvB92eU5fEV3HZBiOvLj7Mq4sPYzAp3N2yFgsf6USAp3PJF6fHqj2MHT1gxEy4+wt1dkuIauKu5kHU9nYhPi2Hxftjyj7QlUNqgbT5YywXnJ1dup7Bvd9tZ0le7Yu3Bjfhg6HNzOsmcbPrF9SaGNu+VL9uPxkmr63SqxJEgar/bixEJdG/SU0aBLiTmmVQK8P7N1CXw6PAru/sHZ5dZBuMvPXnEZ7/7SA5BhN9Gwey+PGuhPq5QdxJ+PEO2P4VJEfB0cX2DleICumhvDZuSw/E3FrE8t80Ghj3O/R8scrvfYzJq8o8f3cUGg083y+Cr8a0wllfTDeO/CrvALVaw/DpMGUTNLvX+gELUcHoddobry0ztpwrvohlcVx81Adf5zdD3CkLRmgf28/GM+SbbRy9nIKvmyO/PtSBSd3qllyksjCKApHz4fvuELNXfV0e9avaZUIeDlYbkqwLUUFotRoe760WbZm59TyZOUbo/IT6wwNzC2aOq4noxAxGfb+DX/Ja2j3VJ5wfxrfF3VGntkea3kPd0+/iC6PnqE+ahRC3aVPHhx4RNTCYFL5YV/lvhstr25l4dUvNpWS8XPT8/GB7nrqjQfE309F74NtOcPlAwfea3Qt+9a0fsBAV1Oj2IXi56Dkfn86aY1fLNoh3SN7EBLD3J8sFZ2Mmk8K0DWcY9+MuEtNzaFrLk2VPdqVLff+yD5qbAevfh+wUtWDulC3QZIjlghaVgiTrQlQgd7eoRR1fVxLSc5i/OwrCukPN5urT1PjT9g7PZtafuMbgm26mZz7Yjuf7N0SbEg2/DlWr5BuyoH4feGw7NL7b3iELUaG90D8CUGfXz8SmFn+y0QDHlqlbTKoQo0nhi7WnGP9Twc303091o1dxheQM2erN8sw71VaQa9+1XcBCVHBuTg480FltP/vdpnMoZe1e0z6vjdv+XyAtzkLR2U5CWjYTft7NJ6tOYlJgeJva/PFYF4J9XMs2YP5/R0c3GPot9HlDbUHrE2q5oEWlIcm6EBWIg07Loz3VmZrpm8+SbTSps8ZTD0OdjnaOzvpyDCb+u/w4k2btJTkzl5bBXvzzdDf6NApUT9jyGZzbCA7OMOAjuP8P8Ayya8xCVAYtgr3p3yQQkwL/t6aEB3+pl+G3CeoWkyrSxu1KciZjZ+zki7WnMSkwsm0wfzzWhRDfYm6mY/bB9J6w+RNQjNB8JIycZbOYhagMJnQJw8lBy8HoJNYdL6FFZFHq3wG12qgzydu+sGh81rbrXAJ3fbWFLafjcdZr+XhECz4b2bL4LTVFyUiEJY/C3pkF36vXE3q8CDoHywUtKhVJ1oWoYO5tW5uans5cS8nmj30x4BMGDo72DsvqzsSmMezbbfyw+RwAEzqHsujRzgTf3OKk7zvQeIg6m97pMWlVIoQZnu/fEI0G/jl8hSMxyUWf6F0HGg1Sjzd+aJvgrGjNsWsM/HILu88n4uao4/9Gt+ST4m6mc7PUqss/9oW44+Dqrybp9/4ILt62DF2ICs/f3YlJeXvX/7v8ODkGUwlXFEKjgd6vq8d7foTUMi6pt6Fco4nPV59k7IydXEvJpn4NN/58ohuj2oWYvz9dUeDoEpjWAQ7OV1fwZJewAkpUG3KnK0QF4+Sg45EeakuUL9edIi3boP7AZIRTq28tclQFKIrCvF1RDP56C0cvp+Djqmf6+La8O7ghTju/gkXjC5aEufjA6F9ln6gQZdCwpgdDWtYC4P/WlLB3vddrgAaO/QmX9lo/OCtIzzbw+pLDPPzLXpIycmle24t/nu7OsNbBxV94fJladVkxqbPpT+yGpsNsE7QQldDjverj7+7Iufh05u4qY9/18DvUfdmGLNj/q2UDtLBzcWmM+G47X60/c2PZ+7Inu9Gwpof5g6VchoXj4LcH1ZZsNRrBuD/AqQxjiSpJknUhKqD7OtYh1M+VaynZfLXutJqo/9AT5o1Un7pWEbGpWTz8yz5eW3KYrFwTXcP9WDm1B3d6RqvLT9e+A8f/grPr7R2qEFXC1L4R6LQa1p2IZX9UMT16A5tAq/vU4zVvFTwwqyT2Xkjkrq+2MHdXFACTu9Xlj8e6EObvVvgFNz8EbTZCTc7HzFdn0938bBCxEJWXh7Oe5/o1BOCLtadJysgxfxCNBvq/p7ZC7P68hSO0jPzJhUFfqTV1PJ0d+Hpsaz4f1Qo3JzOXqRsNsONb+KY9nPgbtA7Q82WYshlC2lvnDyAqJUnWhaiAnPU63hnSFFArw5+Ky1BneADWVf7lUYqi8GdkDP3/bzNrj19Dr9Pw2l2N+HVMAwI3vQI/9YPYo2ql96HfqYXkhBDlVtffjRFt1JnlT1aeLL4gVK9XQecEF7fB6TU2irB8sg1GPlpxglHTd3AxIYNaXs7Mm9yRNwY3wdGhkFsekwn2zVK7S+S/rmq16rL3RnfZMnQhKrVR7YJpGOhBcmYuX607U7ZB6nRSuyxUwC1uMUmZTPh5D68tOUxmrpEu9f1Y9WwP7s5brWS2hNOw+g3ISYPg9vDIJuj9Gjg4WTZwUelVvP8bhBAA9G4YQP8mgRhMCm8uPYLSYQr41IW0a7Dlc3uHV2Zxqdk8OmcfzyyIJCkjV21v8nhnHnHZiHZaW9j3M6BAy7Hw5F51dq8s/UmFEIV66o5wHHVadpxLYMmBmKJP9A6BjlPU47Vvqyt8KrB9F68z+KutfL/pLCYF7m0TzMpne9AlvIjWSdG7YUZv+OsZuHYY9lTetlFC2JuDTssbgxsD8MuOC5yLSyvfgDkZFaJlrcmkMGfnRfp/vonNp+JwdNDy+l2NmfNQR4K8zOx1npNRcBzQGHq+BHd/CZNWQ81mlg1cVBmSrAtRgb11dxOc9Vp2nU/kzyMJcOd/1R/s+AYSz9s3ODOZTAoL90TR9/NNrDqqzqY/1y+CpU90pXGgG+z8DjKvQ2AzeHA5DPtelp8KYQXBPq4807cBAO8sO8rV5KyiT+72LIR2hb7vgqZi3jKkZRt4+88jjPh+O6dj0/B3d2T6+LZ8Nqolns762y9IuQKLp6greK5EgpOn2l2i85M2j12IqqR7gxr0blgDg0nhoxUnyj7QiX/gq1bqQ0I7uhCfzv0/7uKNpUdIzzHSNtSHFc905+Ee9dBqzZhEMOTAtq/g/5rAtaMF3+/1CrR9sEKuJBAVh0Ypc1PE6iElJQUvLy+Sk5Px9PS0dziiGpq24QyfrDpJDQ8n1j3XA8/fRqrtyxrfrbZ1qwROX0vl9SVH2H1BfUretJYnX/T3okF4w4IlX+c2QdxJaDdJWpQIYWUGo4l7v9vOwUvJ9G5Yg5kPtje/gnEFsPbYNd788whX8h44jGwbzOuDGuPtWkgHDUWBzZ+qraFy0gANtB4Hd7wN7jVsGrcQVdXpa6kM+HILRpPCD+Pb0r9pTfMHidoFM/urDwgnrrR569qsXCPfbzrLtxvPkmMw4aLX8dKAhjzQOQydOUm6osDJFepy98Sz6vc6PAJ3fWKdwEWlYU5+KY9yhKjgJnevSz1/N+JSs/l8zWm480P1Dez4X3B+i73DK1ZGjoFPV53krq+2sPtCIq6OOt7rX4u/GvxDg0V9YNf0gpPr9YSOj0iiLoQNOOi0fDqyJY4OWjacjOO3fZdKd2F6gnUDK6WLCelMmrWHyb/s5UpyFnV8XZnzUEc+Gdmy8EQd1O008afURL12O3h4HdzzjSTqQlhQg0APHspr5fbCbweJTswo4YpC1OkILcaoHRmWPGLTOj1bTscx8MstfLH2NDkGE90b+LNyancmdq1rXqIevQd+vgsWjFUTdbcAuGcaDPif9YIXVZLMrJdAZtZFRbDldBzjf9oNwHf3t2Fg1KfqUqq7Pq2Q+5wURWHZwct8uPwEV1PUGa9BDT34b+3teO37FrLzejw3GwEjZJ+oEPYyfdNZPlxxAg8nB1Y924Na3kXswVQUtZ7Eqtfh/t8grJttA82TmWPku41n+H7zOXIMJvQ6DZO61WXqHRG4OP6rb3r+rFZAY/BVkweSotS96k2Hy9JTIawkx2Bi1PQdREYn0TLYi98e7VJ4gcfiZCXDd90gOUpdAXPPNOsEmyc6MYOPVpzgn8NXAAjwcOKtu5swqHmQ+auO/pqaV38HcHCGjo+qFe6dJY8QKnPyS0nWSyDJuqgo/vPXMWZuO4+zXssfk9vQtE5AhSy8duhSEu/+dYx9F9W2UHW9Hfi24UEanfkBTXqcelJAU7VFS/gddoxUCGE0KYz4fjsHopLo3sCf2RM7FL4XU1Fg6WNq60j3mvDoFnAPsFmcJpPC0sgYPl11kst5S967N/Dn7bubEh7gfnusZ9fD+vfh8n41MR/5s81iFUKo1dMHfbWFpIxcHuwSdqPDjVkubINZgwBF3fbX+G6Lx5malcu0DWeZufU8OUYTWg1M6BLGc/0i8Cis5kVp7PgWVr0Gre6H3q+CV7BlgxaVniTrFiTJuqgoDEYTk2bvZfOpOIK8nPnzia4EeDqrP4w/A/7hdo3vQnw6n605xV8HLwPgotfxZJ9wpqROw2H/TPUk33rQ+3WZ1RKiAjkbl8ZdX24h22Digc6hvDukaeEzSTnpMOMOiDsOdXvA+KWg1d1+noVtOR3Hh8tPcOxKCgC1vJx56+4m3Nm05q1xKgpc2AIbPoSo7er39K7qrFafN+U1RwgbW3/iGpNm7QXg2/vbcFfzIPMHWfO2WmfCxRce3wEeZdgDX4hco4lFe6P5vzWniE9T+8J3C/fn9UGNaRxkxv1+UjRs+VRtMdvkHvV7hmxIOAuBTSwSq6h6JFm3IEnWRUWSkpXLsGnbOBuXTqsQbxY83AHnTe/D9q/Vpal2mKm+lpLFl+tOs2hPNAaTggtZDG/my1N3d6aml7O6XH/uKOj5ovqUWVfGJ9VCCKtZeiCGZxdFoigwpUc9XhnYqPCEPe4k/NAbctOhx0vQ53WrxXQg6jqfrznFltPxAHg4OfB473Amdg3DWf+vhwRn18OG/8KlPerXOido/5Bazd6GKwCEELf6aMUJvt90FncnBxY/3oWIQA/zBjDkwI99IKgVDPgQnMy8/l+MJoWlB2L4ct1povL209fzd+P1QY3p0yig9Eve407B9i/h4EIw5YJ/Q3h8pzwUFKUiyboFSbIuKprz8ekMnbaN5MxchrQI4guXGWgPzgMnL7Vgkn8Dm8QRk5TJjM3nWLAniqxcEzVI4o3AbQzKWo5Dk0G37i8zGqRwnBAV3LxdUby25DAAU/s2YGrfiMJPPPQbLJ4MaOC+hRBxp0Xj2HfxOl+uO83mU+q2Gb1Ow7hOoTzVpwG+bkUUj9v6hdrmSecEbcZDt+fAq7ZF4xJCmM9gNDF2xk72XLiOl4ueHye0o32Yr3mD5KSDo1u54jCaFJYfvsIXa09xNi4dAH93J57sXZ/7O4Wi15Uyyb60D7Z+rraXIy+FqtsDer0GoZ3LFaOoPiRZtyBJ1kVFtP1MPONn7sZoUuhe14OZmv+gv7wHfOvDhL+sepN6JjaN7zedZemBGAwmhUaaKF7yWkevnE1oTepSMvwbwqNbwaGIG2shRIX009bzvPf3MQBeHdiIKT3rF37i38/C3pmg1cNTe8EnrFy/V1EUtpyO54fN59h6Rp1J12k1DG9dmyf7hBPqd9ONelqcWrwpsBk0ukv9XlaKulS2wxTwCCxXLEIIy7qensOk2Xs4EJWEo4OWr8a0YkCzMiyJB3Wmfcun0OVpcHIv8fTMHCO/7YtmxpZzRCdmAuDtqufRnvV5oHMoro5mTCSseh12fFPwdaPB0HUqhLQ38w8hqjtJ1oELFy7w3nvvsX79eq5evUqtWrUYN24cr7/+Oo6OpU8gJFkXFdXKI1d4btFBMnKMNPHMYqn+TRzTY8DFB+75tuAm1gKMJoWNJ2OZuyuKDSdjURTor93Dc+6raZRztODEkI7Q+QloOEhm0oWopKZtOMMnq04CMKpdMK/f1QQv139tXzFkw5Ip4FMX+r5d5t+VkWPgj/0xzNp2/sZsl4NWw71tgnmidzh1/FzVExUFLm6DA3PgyGIwZkOtNvDw+gpZaFMIcavMHCNPzT/A2uPX0Gjg7cFNeLBrXfMH+vMJ9XWgdjt1+59r4bP0l5MyWbA7il93XuR6Ri4APq56Jnaty8SuYaUrHpcUrda9cPNTvz61GhbcB81HqEl6QCPz4///9u47LIqrbQP4vbv03ntXFEVQwR4L9hbBbkxii1FjiS1+MXlTNPF9YxKNGjUxGmti72CJiQXsFbBgoQnSq9Kl7c73B7oJERQQ2F24f9e1V8LsmZkHxtndZ885zyECk3UAwIkTJ7Bnzx6MHTsWTZs2RVhYGKZMmYJx48Zh+fLlVT4Ok3VSZpGpuZi2PRgP0/PRRJKOPaYbYJbzLHnutgDo/cVrHT8p6ykOBCdg17U4pGQXQAwZSqGGvi0tsdj4T9gGfw+IJEBLX6DTTH67TNRA/HgqEitPRQAAzPU18bWvOwb+uziUIJQ9ns/RzEsDxGqVfnh+TiYTEBz3BIdDE3HkVhJyCksBAHqaahjpbYfJXZ1hb/IsSc+KB27tBm5uB57E/n0QGy+g0/Sy5R85R5RIJZRKZVgUcBc7rsYBAAZ7WGNe32YvrujwMvHXgZ2jgKdPykbx+a4pW5cdQFGpFKfupWHPjXicj0zH8wzHwUQH73dzxihv+xeXePy3whzgfkDZ607shbIl155/lpLJgPx0jt6h18ZkvRLLli3DunXr8PDhwyrvw2SdlF1uYQn+b99tnLibAnWUYoneAbxV6o+C4b9Bx9Ov2sd7mJ6HE3dT8OfdVNyNz0BH8X0MFF9Df7VgXHaehVaDpsPFXA/ITQFu7igrGldL1VmJSHnciH2MhQduy3u8+7tbYoZPU3jaGb5YhKmkENj2JvD4IdDuPaDdZMDg7+S+RCrDncRsnLyXioCbSUjMeip/ztFUBxO7OGGktx30NdXK95Tvfgd4cLTs/zX0APdhgNd4wK49e9SJVJAgCPg5KFo+ekcsAoa1tcPcPq5/f0n3Kmn3gd+HAblla6KnWXTFLp13sTXOVN6LDgCdXEwwrpMTBrSygqSiJSmfy0kGok4CkX8BkaeA0r9fn+A+DBi1tbq/JtFLMVmvxOeff44TJ07gxo0bVd6HyTqpAkEQsOlCDFafjkROYSmaihKQrO6IYV62aOdogi6xP8FYXAB1r7cBK09AXQtSmYDcwhJEpObhdkIWbiVk4358OuyyrsNbHIF2ogi0EUdBW1T894n4pkXUqBSWSPFzYBR+DopGqazs44K1oRb6u1uhn7slmlvqw1BbHWrZscDvw4EnMQAAQayGdPuBuGbYH2cyjHAyQYLc4r8/buhpqqG/uxWGtTJCF+0EiJNDy9ZEj78GjPcHTJ/Nlb+1u2zIa5t3ykbwvGaRKSJSDveScrDiZARO3U8FUDb9pZurGbwcjOHlaAxPO8MXhqoXlkgRnZ6HyNQ8JMVHw+3BWnTPPwk1kQwAsKBkGi7o9sdIbzuM8raFo1kFPfYlhUB2/N/FeAUB+MENyEv5u42pK9D6LcBzNGDkUCe/PzVuTNYrEBUVBW9vbyxfvhxTpkyptF1RURGKiorkP+fk5MDe3p7JOqmEguJSHAxJxNZLsYhKywMAaKAE1zRnwEiUL2+XK2gjTTBCJgwQLGuG70rHAgB08RS3Nd+HRPSPlwUdU8BtMNDCr6ziKYvGETU6D1JysPZMFAIfpCG/WPrC8/qaajDWEqNjyWWMLD2GjuIH5Z7/vmQMdmqORGcXU0yyiEa7u0sgLswGirJfPFm//wJdPqyrX4WIlMjN+CysOBkhX/3hOZEIMNRWfzbbRoAAIL+oFLJ/ZS32olT8R/coegjXETYiEN5uTcp60U8tBsJPAOragLS4rM5GaRGQk1i27ZP4v6fQ+M8q66137Qc061e2TBxH7lAdatDJ+ieffILvvvvupW3u378PN7e/iz4kJiaiR48e8PHxwcaNG1+67+LFi/HVV1+9sJ3JOqkSQRBwMSoTx+4k42FaNszSrqBXSSD6iYOhL3paru1tUXOsdf4ZnnaG8LQzQucbc6CubVg2B8y+E2DWjHNCiQhAWc/WxagMnAhLQWB4OjLyiips563xCO9rnkYbhMOiNBlJvdfAtstYiMWismGmO0b83VjfumwOum3bsv86dAY0qjgclogahLtJ2bgW8xihcVkIjX8ir9z+b4ba6mhmqYdmlvpwszZAD1fzskKUhTmA1j8+p28bAsScq/hk2sZla6I/n8Ink/FzDtWrBp2sp6enIzMz86VtXFxc5BXfk5KS4OPjg06dOmHr1q0Qv+JmZM86NVTZBSV4lJkHjdI8GEifQK/0MbSLM6GuZwa49FB0eESkgkqlMuQUliKroBhZT0ugpSaBrZE2DLTV/p7XLpMCggyQPBvS+jQLyIgEtI0AbZO/Ky0TET2TnluE7KfPp+GJIBKVjeAx19d8sWZGRXKSgNS7gKwUkGgAapqARBMwsCl7sOecFKhBJ+vVkZiYiJ49e8Lb2xvbt2+HRPKKCpAV4Jx1IiIiIiIiqg3VyS8b7ELIiYmJ8PHxgaOjI5YvX4709L/nwlhZsXI1ERERERERKa8Gm6yfPHkSUVFRiIqKgp2dXbnnGvBgAiIiIiIiImoAGmw1hYkTJ5ZVj6zgQURERERERKTMGmyyTkRERERERKSqGuww+NryvCc+JydHwZEQERERERGRKnueV1ZlxDeT9VfIzc0FANjb2ys4EiIiIiIiImoIcnNzYWho+NI2DXrpttogk8mQlJQEfX39qq3rqCDP14OPj4/nEnNKiNdH+fEaKTdeH+XHa6TceH2UH6+R8uM1Um6qcn0EQUBubi5sbGwgFr98Vjp71l9BLBa/UE1emRkYGCj1P87GjtdH+fEaKTdeH+XHa6TceH2UH6+R8uM1Um6qcH1e1aP+HAvMERERERERESkZJutERERERERESobJegOhqamJRYsWQVNTU9GhUAV4fZQfr5Fy4/VRfrxGyo3XR/nxGik/XiPl1hCvDwvMERERERERESkZ9qwTERERERERKRkm60RERERERERKhsk6ERERERERkZJhsk5ERERERESkZJisExERERERESkZJutERERERERESobJOhEREREREZGSYbJOREREREREpGSYrBMREREREREpGSbrREREREREREqGyToRERERERGRkmGyTkRERERERKRk1BQdgLKTyWRISkqCvr4+RCKRosMhIiIiIiIiFSUIAnJzc2FjYwOx+OV950zWXyEpKQn29vaKDoOIiIiIiIgaiPj4eNjZ2b20DZP1V9DX1wdQ9sc0MDBQcDRERERERESkqnJycmBvby/PM1+GyforPB/6bmBgwGSdiIiIiIiIXltVplizwBwRERERERGRkmGyTkRERERERKRkmKwTERERERERKRnOWa8lUqkUJSUlig6DGgl1dXVIJBJFh0FERERERHWEyfprEgQBKSkpyMrKUnQo1MgYGRnBysqqSsUpiIiIiF7mcX4xTt1PRWRqLgZ72qCNvZGiQyJq9Jisv6bnibqFhQV0dHSYOFGdEwQBBQUFSEtLAwBYW1srOCIiIiJSRdlPS3AwJAF/3k3BtZjHkAll2zdeiMG7HR3xfwOaw0BLXbFBEjViTNZfg1QqlSfqpqamig6HGhFtbW0AQFpaGiwsLDgknoiIiKolKesp3tl4FTEZ+fJtLa0NYGOkjVP3U/H7lUc4cTcFi4a0xGAPa3ZIESmAyhSYW7x4MUQiUbmHm5vbS/fZt28f3NzcoKWlBQ8PDxw/frxWY3o+R11HR6dWj0tUFc//3bFWAhEREVVHXGYBRq+/jJiMfNgYauHzwS1w/uOeOD6nGzZOaIed73eEs5ku0nOLMGtnKJb+8UDRIRM1SiqTrAOAu7s7kpOT5Y8LFy5U2vbSpUsYO3YsJk+ejNDQUAwdOhRDhw5FWFhYrcfFbxpJEfjvjoiIiKorOj0Po9dfRsKTp3Ay1cG+6V3wfjcX2Jv83fnUpakZ/pjTDbN7uwIANp5/iLDEbEWFTNRoqVSyrqamBisrK/nDzMys0rY//vgjBgwYgP/7v/9DixYtsGTJEnh5eWHt2rX1GDERERERkXJ4kJKDMesvIyWnEK4Wetg7rTNsjbQrbKulLsH8vs0wpLUNZAKwOOAuBEGo54iJGjeVStYjIyNhY2MDFxcXvPPOO4iLi6u07eXLl9GnT59y2/r374/Lly/XdZgqYeLEiRg6dGidHX/r1q0wMjKq1WNevHgRHh4eUFdXr5XYY2NjIRKJcPPmzdc+FhEREZEyS8spxLhN15CRV4yW1gbYPbUTLAy0Xrnffwa5QUdDghuPnuDwzcR6iJSInlOZZL1jx47YunUrTpw4gXXr1iEmJgbdunVDbm5uhe1TUlJgaWlZbpulpSVSUlJeep6ioiLk5OSUezREP/74I7Zu3Vpnxx8zZgwiIiJq9Zjz589HmzZtEBMTU6exExERETUkpVIZZu0KRXpuEZpZ6mHXlE4w1dOs0r7WhtqY1aspAOCb4w+QW8haOUT1RWWS9YEDB2LUqFHw9PRE//79cfz4cWRlZWHv3r21ep6lS5fC0NBQ/rC3t6/V4ysLQ0PDWu/5/idtbW1YWFjU6jGjo6PRq1cv2NnZ1WnsRERERA3Jsr/CcS3mMfQ01fDLu94w1KnecmyTuzrLC86tORNVR1ES0b+pTLL+b0ZGRmjWrBmioip+wbCyskJqamq5bampqbCysnrpcT/99FNkZ2fLH/Hx8bUWszL55zD4EydOoGvXrjAyMoKpqSnefPNNREdHy9v+9ttv0NPTQ2RkpHzbjBkz4ObmhoKCggqP/+9h8NHR0fDz84OlpSX09PTQvn17nDp1qtw+RUVFWLhwIezt7aGpqYmmTZti06ZN8uHqmZmZeO+99yASifDjjz9CIpHgxo0bAACZTAYTExN06tRJfrzt27eX+7Ll2rVraNu2LbS0tNCuXTuEhobW+O9HREREpAr+upuC9WcfAgC+H+kJF3O9ah9DU02CL4e0BACs2bILk6bNREBAQK3GSUQvUtlkPS8vD9HR0bC2tq7w+c6dO+P06dPltp08eRKdO3d+6XE1NTVhYGBQ7lFVgiCgoLhUIY/XKfiRn5+P+fPn48aNGzh9+jTEYjGGDRsGmUwGABg/fjwGDRqEd955B6WlpTh27Bg2btyIHTt2VHnZury8PAwaNAinT59GaGgoBgwYgCFDhpSrOzB+/Hjs2rULq1evxv3797F+/Xro6enB3t4eycnJMDAwwKpVq5CcnIypU6eiTZs2CAoKAgDcuXMHIpEIoaGhyMvLAwCcPXsWPXr0kJ//zTffRMuWLREcHIzFixdjwYIFNf6bERERESm7jdv34u3JM1AQeRXvveGMQR4Vf26uip7NLeBa+AAp+5dg28Zf4Ofnx4SdqI6pKTqAqlqwYAGGDBkCR0dHJCUlYdGiRZBIJBg7diyAskTP1tYWS5cuBQDMmTMHPXr0wA8//IDBgwdj9+7duHHjBjZs2FBnMT4tkaLll3/W2fFf5t7X/aGjUbPLOWLEiHI/b968Gebm5rh37x5atWoFAFi/fj08PT0xe/ZsHDx4EIsXL4a3t3eVz9G6dWu0bt1a/vOSJUtw6NAhBAQEYNasWYiIiMDevXtx8uRJeWFAFxcXeXsrKyuIRCIYGhrKR0f4+PggKCgICxYsQFBQEPr27YsHDx7gwoULGDBgAIKCgvDxxx8DAHbu3AmZTIZNmzZBS0sL7u7uSEhIwPTp02v0NyMiIiJSZvsPHsaUcWMAkRgQDqHVOC8ALV/rmA6FMYBIDEEmg0QiQVBQEHx9fWsnYCJ6gcr0rCckJGDs2LFo3rw5Ro8eDVNTU1y5cgXm5uYAgLi4OCQnJ8vbd+nSBTt37sSGDRvQunVr7N+/H4cPH5Ynn/S3yMhIjB07Fi4uLjAwMICTkxMAlOv1NjY2xqZNm7Bu3To0adIEn3zySbXOkZeXhwULFqBFixYwMjKCnp4e7t+/Lz/HzZs3IZFI5D3hVdGjRw9cuHABUqkUZ8+ehY+PjzyBT0pKQlRUFHx8fAAA9+/fh6enJ7S0/q56+qpRFkRERESq6odtB58l6mWJ9YVz5177mH6D+gGCDBCJIZVK5Z+ziKhuqEzP+u7du1/6/PPh0P80atQojBo1qo4iepG2ugT3vu5fb+f797lr6vmIhV9//RU2NjaQyWRo1aoViouLy7U7d+4cJBIJkpOTkZ+fD319/SqfY8GCBTh58iSWL1+Opk2bQltbGyNHjpSfQ1u74jU+X6Z79+7Izc1FSEgIzp07h2+++QZWVlb49ttv0bp1a9jY2MDV1bXaxyUiIiJSZUHhaYhWcwIEGcQSSa0l1r6+vvjyxy1Y+fthWDb3wqDBb772MYmociqTrKsCkUhU46HoipKZmYnw8HD8+uuv6NatGwDgwoULL7S7dOkSvvvuOxw5cgQLFy7ErFmzsG3btiqf5+LFi5g4cSKGDRsGoKynPTY2Vv68h4cHZDIZzp49Kx8G/ypGRkbw9PTE2rVroa6uDjc3N1hYWGDMmDE4evRouV76Fi1a4Pfff0dhYaG8d/3KlStVjp+IiIhIFWTkFWHBvtvQce2It/6zBtZPH8LHx6fWhqt/NmM8jmTb4nF+Mc5GpKN3C8tX70RENaIyw+CpbhgbG8PU1BQbNmxAVFQUzpw5g/nz55drk5ubi3HjxmH27NkYOHAgduzYgT179mD//v3yNp9++inGjx9f6XlcXV1x8OBB3Lx5E7du3cLbb78tL2AHAE5OTpgwYQLee+89HD58GDExMQgKCnrl0nw+Pj7YsWOHPDE3MTFBixYtsGfPnnLJ+ttvvw2RSIQpU6bg3r17OH78OJYvX16tvxURERGRMhMEAf+37xYy8orQ3FIfWxZPx4oVK2p1XrmGmhjD2toCAPZcb5irJhEpCybrjZxYLMbu3bsRHByMVq1aYd68eVi2bFm5NnPmzIGuri6++eYbAGW94N988w2mTZuGxMREAEBycnK5Oe7/tmLFChgbG6NLly4YMmQI+vfvDy8vr3Jt1q1bh5EjR8qXhZsyZQry8/NfGn+PHj1eGNrl4+PzwjY9PT0cOXIEd+7cQdu2bfHZZ5/hu+++q8qfiIiIiEglbLsUi8DwdGioibF6bFtovcY0yZcZ075sadwzD9KQnltUJ+cgIkAkvM6aX41ATk4ODA0NkZ2d/cIyboWFhYiJiYGzs3O5wmWqYOzYsZBIJNi+fXudHH/9+vVYsmQJEhIS6uT4pNr//oiIiKh2PUjJge/aiyguleErX3dM6OJUp+cb+tNF3IzPwqcD3TCtR5M6PRdRQ/Ky/PLf2LPeyJSWluLevXu4fPky3N3d6+Qc8fHxOH78eJ0dn4iIiIj+VlgixZxdN1FcKkMvNwuM7+xY5+d83ru+90Y82PdHVDeYrDcyYWFhaNeuHdzd3fHBBx/UyTm8vLzw6NEjDjMnIiIiqgff/vEA4am5MNPTxPcjPSESier8nG96WkNbXYLo9HyExD2p8/MRNUaqVbqcXlubNm1QUFBQp+dIT0+v0+MTERERUZkzD1Kx9VIsAGD5KE+Y6WnWy3n1tdQxyMMaB0ISsOd6PLwdTerlvESNCXvWiYiIiIhUUFpuIf5v320AwHtvOMOnuUW9nv/5UPijt5NRUFxar+cmagyYrBMRERERqRiZTMD/7buNzPxiuFnp4+MBzes9hvZOxnAw0UFBsRSBDziykqi2MVknIiIiIlIxGy88xNmIdGiqibGmDpdpexmRSISBHlYAgONhyfV+fqKGjsk6EREREZEKuRSdgW//eAAA+HJIS7ha6isslkGtrAEAgQ/S8LRYqrA4iBoiJutERERERCoiOfspPtwZCpkADPeyxdsdHBQaj6edIWyNtFFQLMXZCA6FJ6pNTNaJiIhUSKlUhtzCEkWHQUQKUFwqw4wdIcjML0ZLawN8M8yjXpZpexmRSIRBz4fC3+FQeKLaxGS9ERIEAVOnToWJiQlEIhFu3rwJHx8fzJ07t8rHiI2Nle9bH5ycnLBq1Sr5zyKRCIcPH5b//ODBA3Tq1AlaWlpo06ZNpduIiFRNQEAAps38EAt/2ISZO0PgteQkPBb/hUE/nsfSP+7jUlQGiko59JSoMfjvsXsIjcuCgZYafnnXWyHz1Csy0KNsKPzp+6koLOHrEVFt4TrrjdCJEyewdetWBAUFwcXFBWZmZjh48CDU1dWrfAx7e3skJyfDzMwMABAUFISePXviyZMnMDIyqqPI/5acnAxjY2P5z4sWLYKuri7Cw8Ohp6dX6TYiIlWycfteTBk3BhCJAUEG8+FfQMe1IwDgXnIO7iXnYP3Zh9DXVMM3wz0wpLWNgiMmorqyPzgBv11+BAD48a22cDDVUXBEf2tjZwRrQy0kZxfifGQG+ra0VHRIRA0Ck/VGKDo6GtbW1ujSpYt8m4mJSbWOIZFIYGVlVduhVdm/zx0dHY3BgwfD0dHxpduqq7i4GBoaGjXen4ioJqQyAVsvxeLjNbvkibpILEYLxGHF9HmwN9bG5YeZOBuRjvORGUjPLcLs3aHILyrFWwqev0pEte9SdAY+PVi2nvrs3q7o6Va/66m/ilgswoBWVthyMRbH7yQzWSeqJRwG38hMnDgRH374IeLi4iASieDk5AQALwyDd3JywjfffIP33nsP+vr6cHBwwIYNG+TP/3MYfGxsLHr27AkAMDY2hkgkwsSJEwEAMpkMS5cuhbOzM7S1tdG6dWvs37//pTGmpaVhyJAh0NbWhrOzM3bs2PFCm38OgxeJRAgODsbXX38NkUiExYsXV7gNAOLj4zF69GgYGRnBxMQEfn5+iI2NLff3GTp0KP73v//BxsYGzZs3r9Z+y5cvh7W1NUxNTTFz5kyUlPw9r7SoqAgLFy6Evb09NDU10bRpU2zatEn+fFhYGAYOHAg9PT1YWlpi3LhxyMjIeOnfioganuj0PIxefxlLjt6Dmp0HIMggkUggyGT4aMJweDsaw8JAC35tbLFidBtc/bQ33unoAEEAPjl4B5suxCj6VyCiWhSZmotpvwejRCpgsKc15vZ2VXRIFRr8bCj8qXupnJpDVEuYrCuJgIAAzJs3DwEBAXV6nh9//BFff/017OzskJycjOvXr1fa9ocffkC7du0QGhqKGTNmYPr06QgPD3+hnb29PQ4cOAAACA8PR3JyMn788UcAwNKlS/Hbb7/hl19+wd27dzFv3jy8++67OHv2bKXnnThxIuLj4xEYGIj9+/fj559/RlpaWqXtk5OT4e7ujo8++gjJyclYsGBBhdtKSkrQv39/6Ovr4/z587h48SL09PQwYMAAFBcXy493+vRphIeH4+TJkzh69GiV9wsMDER0dDQCAwOxbds2bN26FVu3bpU/P378eOzatQurV6/G/fv3sX79evnw/KysLPTq1Qtt27bFjRs3cOLECaSmpmL06NGV/t5E1PCcvJeKwavPI/jRE+hpqmHlx5Nx+PBhzJ49G/7+/vD19X1hH7FYhP8ObYWp3V0AAEuO3sPaM5EQBKG+wyeiWpaWW4iJW64jt7AU7RyN8cOo1hCLFVtQrjJeDsaw0NdEblEpLkaxs4GoNnAYvBIICAiAn58fJBIJVq1aVekHstpgaGgIfX39Kg1jHzRoEGbMmAEAWLhwIVauXInAwEB5b/NzEolEPozewsJCPme9qKgI33zzDU6dOoXOnTsDAFxcXHDhwgWsX78ePXr0eOGcERER+OOPP3Dt2jW0b98eALBp0ya0aNGi0jitrKygpqYGPT09+e+kp6f3wrbt27dDJpNh48aN8sqpW7ZsgZGREYKCgtCvXz8AgK6uLjZu3Cgf/l7V/YyNjbF27VpIJBK4ublh8ODBOH36NKZMmYKIiAjs3bsXJ0+eRJ8+feR/i+fWrl2Ltm3b4ptvvpFv27x5M+zt7REREYFmzZq95EoRUUPw+5VHWOQfBpkAdGliimWjWsPWSBuAI/z8/F66r0gkwqcD3aCroYaVpyKw/K8IqEvEmNajSf0ET0S1rqC4FO9vu4HErKdwNtPFhvHtlKagXEXEYhEGtrLCtsuPcPxOCnq5cSg80etiz7oSCAwMhEQigVQqhUQiQVBQkKJDAgB4enrK/18kEsHKyuqlPdz/FhUVhYKCAvTt2xd6enryx2+//Ybo6OgK97l//z7U1NTg7e0t3+bm5lYrRetu3bqFqKgo6Ovry2MxMTFBYWFhuXg8PDzKzVOv6n7u7u6QSP5+E7W2tpb/vW7evAmJRFLhFxTPzxEYGFju7+Tm5gYAlf6tiKhhkMkEfHfiAb44XJaov9XeHr+91+FZol51IpEIc/q44pOBZa8dP/wVgai0vLoImYjqWHGpDDN3hOB2QjaMddSxZWJ7mOgqfw2dQc+Gwv91NwXFpTIFR0Ok+tizrgR69uyJVatWyRN2Hx8fRYcEAC9UhxeJRJDJqv7Cm5dX9iHx2LFjsLW1Lfecpqbm6wdYTXl5efD29q5wDry5ubn8/3V1dWu038v+XtraL//QnZeXhyFDhuC777574Tlra+uX7ktEqqu4VIaP99/C4ZtJAID5fZvhw15NX2vd5GndXXDlYSaCwtPx6cHb2DO1s9IOmyWiF5VIZZi5MwSB4enQVBNj44R2cDLTffWOSqCdkwnM9DSRkVeES9EZ8GmuXIXwiFQNk3Ul4OvrC39/fwQFBcHHx6fOhsDXpec90VLp3wVFWrZsCU1NTcTFxVXao/xvbm5uKC0tRXBwsHwYfHh4OLKysl47Ri8vL+zZswcWFhYwMDCo8/3+ycPDAzKZDGfPnpUPg//3OQ4cOAAnJyeoqfG2JGoMcgpL8MHvwbgUnQk1sQhLh3tgVDv71z6uSFQ2h73fynO4HvsEO6/F4d1ONV8Vg4jqT4lUhtm7QnHyXio0niXq3o7VW7FHkSRiEQa0ssT2K3H4404Kk3Wi18Rh8ErC19cXK1asUMlEHQAcHR0hEolw9OhRpKenIy8vD/r6+liwYAHmzZuHbdu2ITo6GiEhIVizZg22bdtW4XGaN2+OAQMGYNq0abh69SqCg4Px/vvvv7JnuireeecdmJmZwc/PD+fPn0dMTAyCgoIwe/ZsJCQk1Pp+/+Tk5IQJEybgvffew+HDh+XH2Lt3LwBg5syZePz4McaOHYvr168jOjoaf/75JyZNmlTuCxAiahiSs59i9C+XcSk6E7oaEmya2L5WEvXn7Ix18H/9y+qLfPfHA6RkF9basYmobhw6fBjt3xyHA4f8oSERY8M4b3RzNX/1jkpmUKuyEYF/3ktBiZRD4Yleh8ok60uXLkX79u2hr68PCwsLDB06tMLK5P+0detWiESicg8tLa16irhxsbW1xVdffYVPPvkElpaWmDVrFgBgyZIl+OKLL7B06VK0aNECAwYMwLFjx+Ds7FzpsbZs2QIbGxv06NEDw4cPx9SpU2Fh8frfzOro6ODcuXNwcHDA8OHD0aJFC0yePBmFhYUv7TGv6X7/tm7dOowcORIzZsyAm5sbpkyZgvz8fACAjY0NLl68CKlUin79+sHDwwNz586FkZERxGKVuU2JqAoepORg2E+X8CAlF+b6mtgzrTN6NKv9D+TjOzuhjb0RcotK8aV/WK0fn4hqz8HDhzF82DDc+nM30g8uwTibdJXtle7gbAJTXQ1kFZTgysNMRYdDpNJEgoqs7TJgwAC89dZbaN++PUpLS/Gf//wHYWFhuHfv3gtzjJ/bunUr5syZUy6pF4lEsLSsenXKnJwcGBoaIjs7+4XErLCwEDExMXB2duaXAFTv+O+PSPVcisrAtN+DkVtUiibmutg6qQPsTXTq7HwPUnLw5uoLKJUJ+OVdLwxoxRoYRMomv6gU7YaMw4NTewFBBrFEgjmzZ2PFihWKDq3GPj14B7uuxWFsBwcsHe6h6HCIlMrL8st/U5kuuxMnTmDixIlwd3dH69atsXXrVsTFxSE4OPil+z2vYv78UZ1EnYiIqLb430zEhC3XkFtUivZOxjgwvUudJuoA4GZlgOk+Zcu3fXXkHgpLOK2GSJlk5BVh7K9XkKnvKk/UZUpUbLimBnmULZv7190UlHIoPFGNqUyy/m/Z2dkAIF/fuzJ5eXlwdHSEvb09/Pz8cPfu3foIj4iICAAgCALWBUVjzu6bKJEKGORhhd8nd4SRTv0swzSzZ1NYGWghObsQu6/F1cs5iejVHmXmY8S6S7idkA3b1l2xcuMOzJk9G/7+/ipbw+i5Ti6mMNZRR2Z+Ma7FPFZ0OEQqSyXLTstkMsydOxdvvPEGWrVqVWm75s2bY/PmzfD09ER2djaWL1+OLl264O7du7Czs6twn6KiIhQVFcl/zsnJqfX4iYiocZDKBCwOuIvfrzwCAEzu6ozPBrWo16XUtNQlmNWrKT4/HIa1gdEY094B2hqSejs/Eb3o6sNMzNwZgoy8YtgZa+O39zrAxVwPwNuKDq1WqEvE6NfSCntuxON4WDK6NDVTdEhEKkkle9ZnzpyJsLAw7N69+6XtOnfujPHjx6NNmzbo0aMHDh48CHNzc6xfv77SfZYuXQpDQ0P5w96+9qrzEhFR45FbWIKpv93A71ceQSQCPh/cAl+82VIha56PbmcPO2NtZOQV4fcrsfV+fiIqIwgCNl2IwdsbryIjrxgtrQ1wcEaXZ4l6wzLw2VD4E2GpkMpUokQWkdJRuWR91qxZOHr0KAIDAyvtHa+Muro62rZti6ioqErbfPrpp8jOzpY/4uPjXzdkIiJqZOIyCzBi3SWcfpAGDTUx1o71wvvdXBQWj4aaGHN6uwIA1gVFI6+oVGGxEDVWBcWlmLP7JpYcvQepTIBfGxvsn94ZFvoNs0hslyZmMNBSQ0ZeEW7Ecig8UU2oTLIuCAJmzZqFQ4cO4cyZMy9d+qsyUqkUd+7cgbV15dVwNTU1YWBgUO7xKjIZC2dQ/eO/OyLlExAQgLfe+wA+Hy5DRGoeLPQ1sXdaZwz2VHwV9mFtbeFiposnBSXYciFG0eEQNSpRabkY/vMlBNxKgkQswpdvtsSqMW2go6GSM1KrRENNjH7uZb3rx+8kKzgaItWkMq8QM2fOxM6dO+Hv7w99fX2kpKQAAAwNDaGtrQ0AGD9+PGxtbbF06VIAwNdff41OnTqhadOmyMrKwrJly/Do0SO8//77tRKThoYGxGIxkpKSYG5uDg0NDYhE9T+8kRoXQRBQXFyM9PR0iMViaGjUT5EqIno5f39/DB06FBCJAUGG9lOXIuDTubAyVI5eMzWJGHP6uGLO7pvYcP4hxnd2gqGOuqLDImrQZDIB2y7H4ts/HqCoVAYzPU389HZbdHQxVXRo9WKQhxX2Byfgj7AULBrirpBpQESqrEbJes+ePV+alJ45c6bGAVVm3bp1APDCUhZbtmzBxIkTAQBxcXEQi/8eLPDkyRNMmTIFKSkpMDY2hre3Ny5duoSWLVvWSkxisRjOzs5ITk5GUlJSrRyTqKp0dHTg4OBQ7t88ESlGXlEpFq/fK0/URWIJOmmnKE2i/twQTxv8HBiN0AunMOTdXVgwcbjKV50mUlbJ2U/xf/tu40JUBgCgm6sZlo9qDUsD5XpdqEtvNDWDvqYa0nKLEBz3BO2dXr6KExGVV6NkvU2bNuV+Likpwc2bNxEWFoYJEybURlwvEIRXF6YICgoq9/PKlSuxcuXKOonnOQ0NDTg4OKC0tBRSKdevpfohkUigpqbGkRxESuBeUg5m7QxBonaTsnWSxRLIZFL06dVL0aG9QCwWobNaDP46uATpIjHOH9rWIJaJIlImgiBgX3AC/nv0HnIKS6GlLsZ/BrXAuE6Oje59W1NNgr4tLXEwNBH+NxOZrBNVU42S9coS4MWLFyMvL++1AlJFIpEI6urqUFfncEIiosZCEATsvBaHr47cQ3GpDE3a9cDCAc2ReD8YPj4+SpsAZ0WHQiQWQ5CVjQAICgpS2liJVE1Eai4+PxSGa88KqrW2M8SKMW3QpAFWe6+qEd52z5L1JHw+uCW01Ll0JFFV1eqc9XfffRcdOnTA8uXLa/OwRERESiU5+yk+OXAHZyPSAQA9m5tjxeg2MNbVAPCOYoN7hV69euHHH38ERGIIMinadOii6JCIVF5BcSlWn47CxvMPUSoToK0uwdw+rnivqzPUJY17ulpnF1PYGmkjMesp/rybAr82tooOiUhl1GqyfvnyZWhpNZ55OERE1LgIgoADIYn46shd5BaWQkNNjP/r1xyTuzqrTOEkX19fHD58GB+t3oUco2aI1nZTdEhEKksqE7A/OB4rTkYgNacIANCvpSUW+brD1khbwdEpB7FYhJHedvjxdCT23Uhgsk5UDTVK1ocPH17uZ0EQkJycjBs3buCLL76olcCIiIiUSfzjAiwKuIszD9IAAK3tjfDDKE80tdBXcGTV5+fnB3P3N/D2xqvYdS0eU3s0YWJBVA3+/v74/eBxRKk5Isu8NQDA3kQbi950R5+WlgqOTvk8T9YvRmcg4UkB7Ix1FB0SkUqoUbJuaGhY7mexWIzmzZvj66+/Rr9+/WolMCIiImVQWCLFhnMP8VNgFIpKZdCQiDG3ryumdnOBmgoPb+3S1AydXExw5eFjrD0TiaXDPRUdEpHSEwQB32/YgU8+GCdf/cHxrcX4fMY4jOvsCE01zseuiL2JDro0McWl6EwcCE7EnD6uig6JSCXUKFnfsmVLbcdBpJKkMgFhidm4GpOJpKxCZBUUI+tpCZ4UlEBPUwJ3G0O42xgg5fYF3Au5jN69erGQE5EKCXyQhsVH7uJRZgEAoKOzCZYMbYVmlqrXm16Rj/o1x6hfLmPvjQRM694ETma6ig6JSCkJgoBzkRlYfToSJzfvL7dM42CzJ3i/m4uiQ1R6o9rZ4VJ0JvaHxOPDXk1VZuoQkSK99pz1vLw8yGSyctsMDAxe97BESisttxB/3EnBhagMXHmYidzC0krbXozKREHkVaQfXAKIxFj944/Yve8AxowcXuk+RKR4t+KzsOzPcPn6yJYGmvhscEsM8bRuUEsvtXcyQY9m5jgbkY7VpyOxYkwbRYdEpFRKpDL8EZaCjecf4nZCNgBA37k1cm/4QyKRQCqVon9f5VumURkNcLfGl5p3Ef/4Ka7EZKJLEzNFh0Sk9GqUrMfExGDWrFkICgpCYWGhfLsgCBCJRFxvnBqcwhIpTt5LxYGQBJyLSIdM+Ps5fS01dHIxhauFHox01GGkowFDbXVkFRTjblIOdl77DenPvoGHSIxZP+xAmrEHJrzhBAMtLvdHpEyi0vLww1/h+CMsBQCgIRFj0htO+LC3K/Q0a7Umq9L4qF8znI1Ix6GbiZjawwVuVvzCnSj7aQn2XI/D1ouxSMou+6yrpS7Gux0dMfU/vXHlXW8EBQUp9TKNykZbQ4I3W9tg17U47L+RwGSdqApEgiAIr25W3htvvAFBEDBnzhxYWlq+0MvQo0ePWgtQ0XJycmBoaIjs7GyOGGiEotPz8PvlRzgQklCuB72tgxH6trTEG03M0MrWEJKXDOUKCAiAn58fxBIJZFIpzId/AR3XjjDUVsd/h7bCkNY29fGrENFLRKTmYl1QNPxvJkImACIRMLytHeb2cYW9ScMvhDR9ezD+CEvBG01NsX1yxwY1eoCoOsISs7Hjahz8byaioLis88lMTwPjOjnh3U4OMNXTVHCEqi007gmG/XwJWupiXPusDzstqFGqTn5Zo2RdT08PwcHBaN68eY2DVBVM1hufw4fLKrxmGjZFrG5L+XZbI20Ma2uL4V62cDHXq9YxAwICEBQUhO7de0Dk1A5rzkQhKi0PADC+syM+G9yCRWmI6llAQAD2BPyBNL2miNT6e/myvi0tsaBfczS3ahjz0qsiLrMAfVacRbFUhg3jvNHP3UrRIRHVm4LiUhy7nYwdV+NwMz5Lvr2ZpR7e7+oC3zY20FLne3RtEAQBfVeeQ1RaHr4Z5oG3OzooOiSielfnyXrPnj3x2WefoU+fPjUOUlUwWW88nuQX47Mft2DdZx/IC8dYjPgCQ4b4YnxnR3RtalZrxVBKpTKsPBWBnwKjAQAetob4+R2vRtGDR6RoJVIZvv7pN/x3zqRy9/rwoX6Y7tMEnnZGig5RIb4/8QA/B0XD0VQHf83rzi8QqUETBAE3Hj3BvhvxOHY7GfnPetHVJSL0d7fCu50c0dHZhKNM6sCv5x7if8fvw9VCD3/O7c5Cc9ToVCe/rNEEvI0bN+KDDz5AYmIiWrVqBXX18kNYPD25/AupjrDEbPx2ORb+N5OQ/Ocf5Sq8+lo8wa8T2tX6OdUkYvxffze0czLBvD03cScxG4NWn8fqsW3Rs7lFrZ+PiIC0nELsuhaPHVcf4cHhw/+q5vwY6971VnSICjWjZ1PsC07Ao8wCbLkYiw96NFF0SES1LjYjHwG3knAoNBExGfny7Q4mOnirgz1GedvDXJ9D3evSmA72WH06EpFpefjrXioGtOJIHqLK1ChZT09PR3R0NCZNmiTfJhKJWGCOVEZeUSmO3ErCrmtx8uquAODapiNC/lHhdciAvnUaR8/mFjg+uxtm7QxBSFwW3t92A98Ma4Ux7TksjKg2FJfKcOZBKvbdSEBQRDqkz6pDWjb3KlfNeeigfgqOVPH0NNWwcIAbFuy7hbVnojDcyxYW+lqKDovotf22Zz+2HziObOPmSDVuJd+uoyHBIA9rjPK2Qwf2otcbAy11jO/iiJ8Co/FzUBT6u79Y/4qIytRoGHzLli3RokULfPzxxxUWmHN0dKy1ABWNw+AblrDEbOy8Fgf/0ET5kDcNiRgDWllhQhdHeDkY48iRI/Ve4bW4VIZPDt7GwZBEAMCc3q6Y28eVb15ENSAIAkLisnDkVhICbiXhcX6x/DlvR2OM7+yIga2sceL4UVZz/heZTMCwny/iVkI2Rrezw/cjWys6JKIaySkswZ9hKfhp226cXftxuSkv/Qe9Cd/WNhjYygq6DXSVB2WXmVeEN747g8ISGX57rwO6NzNXdEhE9abO56zr6uri1q1baNq0aY2DVBVM1lVfflEpAiroRXcx08XYDg4Y4W0HE10NBUZYRhAE/PBXBNYGRgEARrezw/+GeUBdIlZwZETKTxAEhCXm4OjtJBy9nYzErKfy5yz0NTHcyw4jve3Q1KJ6xSEbo+BHTzBi3SWIRMDB6V3Q1sFY0SERVUlxqQxB4Wk4fDMRp+6nobhUhsenf0Vu8BFAkEEslmDK9Jn4Ze2Pig6VAHx15C62XIxFR2cT7JnWWdHhENWbOp+z3qtXr0aTrJNqksoEXIrOwOHQJJwIK184ZkAra7zdwQGdXJRryJtIJMKC/s1hbaSFLw6HYe+NBKTmFOHnd7z4zT9RBUqkMlx9+Bgn76Xg5L1U+VrIAKCrIUHflpbwa2OLbq5mUOOXXlXm7WiMYW1tcSg0EfP23MSx2d34GkRK6/lImkOhCTh6OxlZBSXy55pa6KGJ3wBs+MeUl0H9eiswWvqnqd1dsP3KI1yNeYwbsY/RzslE0SERKZ0avfsOGTIE8+bNw507d+Dh4fFCgTkOJyRFEAQBN+OzcOx2MgJuJSEtt0j+nLOZLsZ2sMcILzulXyP1nY6OsNTXwqxdITgbkY63NlzB5ontWfCGGrWAgAAEBgailXcXaLl2wPmIDFyMzkBuYam8jba6BL3cLDCktTV8mltwqaXXsHiIO64+zERsZgEWB9zFslEcDk/KJSYjH4dCE3E4NBFxjwvk2y30NeHXxgZ+bWzhbmMAkagHBnvacMqLErI21MYILzvsvh6PnwKjsGVSB0WHRKR0ajQMXiyuvIeioRWY4zB45Xb4sD/2HjkBWLsjVrdFuZ41Ix11DPawxrC2tvB2NFaqXvSquBmfhclbryMzvxj2JtrYOqkDmlRzfXciVZf9tAQrN+7AV7P/XmbNfPgX0HHtCAAw09NAbzdL9HO3xBtNzZig16KrDzPx1q9XIAjAmrFtMaS1jaJDokausESKI7eSsPNaHELjsuTbdTQkGNDKCsPa2qJLEzNIuBSYyojNyEevH4KQF3EV/U0yMGrIAH6hQg1enc9Zb0yYrCufvKJSXIhMxy+/74X/93PLfYA3d++Cnm4W8Gtjix7NzKGhptpDX2Mz8jFxyzXEZhbASEcdG8e34zAxatCyCooR/OgJbjx6gsvRmbidkIWMU3/POYVIDNdeo/Dhp0vQ1dUMnnZG/GBeh374KxxrzkRBX0sNf8zpBjtjHUWHRI1QXGYBdlx9hD034uXD3MUioJurOYZ72aJvS0voaHCqhqp6c8EqHPthHkRiCQSZFP7+/kzYqUGr8znrRPWpVCrDrYRsXI7OwKXoTNyIfYJiqQyPzwSWWye5m14qdnzRt0H1rDmZ6eLA9C54b9sN3IrPwthfr2DhADdM7uqsciMFiP5NEATEZhbgRuxjeYIelZb3Qjtnjw64fcMfYokEMqkUy2e/Dd/ergqIuPGZ3dsV5yMzcDM+C/P23MTuqZ355QjVm4jUXPx4OhLH7yTjedeSrZE23unkgJHedlxasIEwy40ERGIIMinEEgmCgoKYrBM9U+We9dWrV1f5oLNnz65xQMqGPev1TyYT8CAlF5eeJefXYh4jr6i0XBtHUx3YZt/Frv99KC8a05C/iS0oLsWCfbdw/E4KAKC3mwWWjWqtFFXsiaqqqFSKsMTsssQ89glC4p4gI6/4hXYu5rpo52iMdk4m6OZqBmtDbQQEBHDOqYLEZRZg0OrzyCsqxeSuzvh8cAt+WUh1JiAgAAeP/Yl0vaa4p9FMnqR3b2aO8Z0c0dPNgl8YNTABAQHw8/OTd8Ds2X8Qo0cMU3RYRHWmTobBOzs7l/s5PT0dBQUFMDIyAgBkZWVBR0cHFhYWePjwYc0iV0JM1uteflEp7iRm41Z8FkLjsnA1JhNP/lHNFSibf97ZxRRdmpiiS1MzuJjpQiQSNaoP8IIgYPvVOCw5eg/FpTJoJobASz0RY4cOavC/O6mW58Xg2nfpCstWXXE99gluxD7G7cRsFJfKyrXVkIjhaWcIbydjtHM0gbejMb+EUkIBt5Iwe1coAGBen2aY04cjG6j27dx7AO+MGVluetuIYX6Y3dsVLaz5Gawh23fgEOau2oESi5aYNfEtfDmkpaJDIqozdT5nfefOnfj555+xadMmNG/eHAAQHh6OKVOmYNq0aXjnnXdqFrkSYrJeewICAnD69Bk0a9sJJi274FZ8Fm7GZyEiNReyf/0r1NWQoIOzCbo0MUPnJqZoaW0AMb9JBwDcS8rBW5+vwZ0tn8s/0GzesReT3h6l6NCoERMEAfGPn+Lnbbvx3UeTAbEYkJUvBgcAJroa8HY0ftZzboxWtobQVGs4U1cass0XYvD10XsAgM8Ht8D73VwUHBE1FIIgIOBWEqbO+BBpV/zl09vGv/8Btq5fq+jwqJ6cjUjHhM3XIBYBh2a8gdb2RooOiahO1Pmc9S+++AL79++XJ+oA0Lx5c6xcuRIjR46s02T9p59+wrJly5CSkoLWrVtjzZo16NCh8qUe9u3bhy+++AKxsbFwdXXFd999h0GDBtVZfFRGEASk5xYhPDUX4Sm5OH70CA7Ji8H9+MIHeGtDLbSxN0JreyO0czRGa3sjqHNd5Aq1tDFAd700hD0rxAKRGPN/3Il0Ew9M92kCfS31Vx+EqBak5RbifEQGzkak4/LDTKTnFuHx6YCy+1xWVgxOM+M+xowd+azn3BjOz0bFkOp5r6szCopLsfyvCPz32H3oaKjh7Y4Oig6LVFzCkwJ8fjgMQeHpEKxbAcIh+fS24YP7KTo8qkc9mpljaBsbHL6ZhE8O3kHArDf4WZAavRol68nJySgtLX1hu1QqRWpq6msHVZk9e/Zg/vz5+OWXX9CxY0esWrUK/fv3R3h4OCwsLF5of+nSJYwdOxZLly7Fm2++iZ07d2Lo0KEICQlBq1at6izOxqSwRIr4xwWIe/Z4mJ6P8NRcRKTmyiu2AihXDA4iMUxyIvGez9tobW+ENvZGsDRgkZjq6NenN35as1r+gUbdzgM/B0Vj9/V4jGpnh7HtHeBkpqvoMKkBikjNxZFbSTh9Pw33knPKPacuEcHduzOu/KMY3E8fjYOvr6eCoqXaNrNnU+QXS7EuKBqfHb4DsQh4qwMTdqqZS9EZmL49BNlPS6AhEWP+jHGwfdcLF8+faxTT2+hFn7/ZEkER6bifnINNF2LwQY8mig6JVMjzaXg9e/ZsMK8fNRoGP2TIECQmJmLjxo3w8vICAAQHB2Pq1KmwtbVFQEBArQcKAB07dkT79u2xdm3ZkCiZTAZ7e3t8+OGH+OSTT15oP2bMGOTn5+Po0aPybZ06dUKbNm3wyy+/VOmcjXkY/OHD/vjj5Cl4tO8Cjy59kJZTiIQnT8sl52m5RZXuLxaVVTNvbqmP4ofXsHnRjEZRDK6+PJ+v36NHD2g37YilfzxATEa+/PkuTUwxpr09erlZsLedXkvCkwL430zCkVtJeJCSW+45D1tDdG9mhm6u5mhjbwQtdUmjqiXRGAmCgMUBd7Ht8iMAwDsdHfDlkJaczkDVsud6HD47FIZSmYDW9kb4YVRrNLXQU3RYpAT23YjH/+2/DQ2JGDumdER7LllLVfC8UKEqLAFY53PW09PTMWHCBJw4cQLq6mVJQGlpKfr374+tW7dW2Mv9uoqLi6Gjo4P9+/dj6NCh8u0TJkxAVlYW/P39X9jHwcEB8+fPx9y5c+XbFi1ahMOHD+PWrVtVOq8qJOs5hSXYt/8wLl04iw5vdEPv/oMglQEyQYBUJkAQAKkgoLhUhvyiUuQXl6KgSIr84tJnP0tRUFSKnMJSZOQVIT23CFHXgxC9c1G5Ii//HLb+T/qaanAw1YGDiQ4cTXXR3EoPzSz10cRcr9wyavwAX7dKpDKcvp+G3dfjcDYiXV5BV00sgpeDMbo3M0P3ZuZoaW0ANQ4royq4FZ+FDece4o+wZHldCXWJCD2amWOQhzW6NzOHmZ6mYoMkhZDJBPx4OhKrz0RCEIDWdob46R0vrsNOrySVCfj+xAOsP1dWjHhIaxssG+nZoJZdpdcjCAKmbw/BibspMNZRx6EZb3C0IL3S+x/MwqYN6wBBBrFEgjmzZ2PFihWKDqtCdT5n3dzcHMePH0dkZCTu378PAHBzc0OzZs1qcrgqycjIgFQqhaWlZbntlpaWePDgQYX7pKSkVNg+JSWl0vMUFRWhqOjv3uKcnJxK2yqL/6zcgp/+Mw0QibF5/U8vTayr6nFESLlh6zqZD9Bt0GDYGWvD3qQsMX/+MNRWr9IcVF9fXybpdUhdIsaAVlYY0MoKCU8KsPdGAgJuJiI2swDXYh/jWuxjLP8rAlrqYrS0NoCnnRFa2RrCzUofzma60NVUa5DDh6h6BEHAucgM/BIUjcsPM+XbO7uYwq+NDQa0soKRDqu1N3ZisQjz+jZDGwcjzNtzE7cSstFt5jJ4qydh/IjBfP2gChWXyvDhrhD8ebdsyuSc3q6Y28eVdSyoHJFIhJVj2iBpw2XcTsjGe1uv4+CMLnzvoUoJgoAIieOz4pRiyKRS+Pj4KDqsWlGjZP05V1dXuLo2rOVbli5diq+++krRYVRL5K2r5RJrIfkuTNt0g0gkgkQMiEUiiEUiaKqJoaupBh0Nify/eppq0NFQg65m2f+b6WnCXF8Tt91y8eEkf/mw9dXz34Wv7+t9AUD1x85YB/P7NsP8vs0Ql1mAc5HpOBeRjsvRmcgtKkVIXBZC4rLK7aOZGIKI7V9CJJZg1apVWPLzNowfPRK2xtpc07aRSMx6ii8Oh+HMgzQAZaMyfNvYYGp3F7hZKefIIlKsns0tcGRWVwz/ZDVu7FqMWJEYB37bgPW/7cbUcWMUHR4pEUEQ8OnBO/jzbio01MRYNtITfm1sFR0WKSltDQk2jm+HoT9dxMOMfEz7PRi/T+4IDTWODKQX7buRgDj9lrAd/SUGmDyG78C+DeZL49dK1uuTmZkZJBLJCwXsUlNTYWVlVeE+VlZW1WoPAJ9++inmz58v/zknJwf29vavEXndm/m2H/7as1meWG/6ZAJ8ffu+1jG7N3sLDiY6HLbeADiY6uBdU0e828kRMpmAhxn5CEvMxp1nj+i0PGTmFyP5QXDZlz3PKsx/v/kgNj4yhYZEDEdTHTib6cLZXBcuZrpwNtODs5kuzPQ02COi4gICAnD6zBnIrNxxusAeBcVSaEjEeLeTIyZ3c4atkbaiQyQlZ2+ig07ayQj+xwoVH6/djTRjD3zY2xWG2qyZQcCPpyNxICQBYhHwy7te6OVm+eqdqFGzMNDC5kntMXLdZVyNeYxPDt7G8pGtuZQvlZOaU4glx8qWFf1y5nhM7d6wihLWaM66onTs2BEdOnTAmjVrAJQVmHNwcMCsWbMqLTBXUFCAI0eOyLd16dIFnp6eDa7AHOeD0+vIKijGll37Mf/9d+SFOdq89w3yrduguFRW6X76mmpwNtctS+SfPVzM9NDEQhc6GirzXWCj9XcxFjGEZ2ui9+g7AEuHe6Cphb6iwyMV8vzf0vMvjZ9PxzLUVsfYDg6Y0MUR1ob84qexel4wDAC+GebBJf+oWs5GpOO9rdchlQkY7GGNH0a3Zo0Dkpv2+w38eTcVnnaGODi9i0rUZarzAnOKsmfPHkyYMAHr169Hhw4dsGrVKuzduxcPHjyApaUlxo8fD1tbWyxduhRA2dJtPXr0wLfffovBgwdj9+7d+Oabb6q1dJuqJOtEteHfX/pIZQKSsp4iJiNf/niYkY+YjDwkPHmKyl49RCLA2VQXbtb6KH14HZmRoRg8oA8mjR3JnnglMmbSB9i77Vf5FJqBYybh6I5f2WtBNfLP1w9Dt85YcvQeotLyAJRNqRjkYY33ujqjtZ0hXwcakfOR6Zi05TpKZQJm+DTBxwPcFB0SqSD/m4lYsO8WSqQCvB2N8ev4djDR5Rz2xu6PO8mYviMEamIRjnzYFS2sVSNXq7NkffPmzfD19YWZmdlrB1lTa9euxbJly5CSkoI2bdpg9erV6NixbC61j48PnJycsHXrVnn7ffv24fPPP0dsbCxcXV3x/fffY9CgQVU+H5N1oooVlkgR/7jgWfKej5j0sv9Gp5cNqweAgsirSD+4RF5TwfGtRejg0x/uNgZo62AMLwcj2Bpp84O7AuwPTsDsbzcgef8Sec+6Mi9zQqpHKhNw+n4qNl2IwdWYx/LtrhZ6GNrWFn5tbFg9voGLycjHkDUXkFdUCt/WNlg1pg2/DKQauxydiWm/30BOYSmcTHWwZVIHOLNKfKNVUFyKHsuCkJ5bhNm9mmJ+v+aKDqnK6ixZ79WrFy5dugQvLy/4+fnB19cXLVq0eO2AlRmTdaLqS88twoOUHHz12cc4c+B3+TxWfe8hMOk9pVxbc31NeDkYwcvBGF6OxvCwNeTwtjokCAJWnorE6tORAICWJRFoKn2EPr16MVGnOhOWmI3NF2Jw9E5yuak1HZxMMKCVFfq0sISDKRP3hkQmEzBmw2Vcj32C9k7G2P5+R2iq8bWdXk9kai4mbrmOxKynMNZRx/cjW6NvS9Y/aIw2nn+I/x67DwcTHZyc312lXl/qdBj8kydPcOzYMQQEBODEiROwtLSEr68v/Pz80LVrV4jFyj9PoDqYrBPV3L/nsa7ZsgvWHl1xJzEbIXFPcC8pB6Wy8i9BamIR3G0M0LmJGbq5msHb0ZjJey0RBAGfHQ7DzqtxAIAZPk2woF9z9nRRvckpLMGJOyk4FJqIKzGZ5abSNLPUQ58WlujdwhJt7I24CoWK+/1yLL7wvwsdDQn+nNsd9ib8MoZqR1puId7fdgO3E7IBAMPa2mLRkJZc2q0RKSqVovv3gUjNKcK3wz3wVgfVqoNRb3PWi4uLcebMGQQEBODIkSN4+vQpBg0aBF9fXwwcOBC6uqo/NIXJOtHreVnxw6fFUoQlZSPk0ROExD1BSFwW0nOLyrXRUhejg7MpujU1Q1dXM7hZ6XPYfA2tOhWBVaciIRaVFXlStTc3aliSsp7i+J1knLqfiuuxTyD9xxd3Znoa6NncArqpN5EREYJ+fXpz5IcKSXhSgP4rzyG/WIrFQ1pi4hvOig6JGpjCEilWnorAr+ceQiaUjdJbOswDfdjL3ijsuPoInx0Kg7WhFs7+X0+VW9JPYQXmbty4gYCAAPj7+2PkyJH44osvauvQCsNknaj+CIKAxKynuB77GOcjM3AhMgNp/0rezfU1YZ0VBiTfxdtDB+KtkcMVFK1q2X0tDp8cvAMA+N+wVnino6OCIyL6W3ZBCYIi0nDqfhqCwtOQW1j6Qs2LD5duwLz33+YcVSUnCAImbrmOsxHpaOdojL3TOnP0DtWZkLgn+L99txCdng8A6NPCAh8PcEMzS65o0lCVSGXouTwICU+eYtGQlpikgl8GKkU1+JKSEqirq/7aqkzWiRRHEAREpuXhXEQ6LkRl4OrDx8i8d6ncB3ifD5fh/XdGoV9LK5jrayo6ZKV05kEqpvwWDKlMwIe9muIjFSrCQo1PiVSG6zGP8dFH83HlyA75agXPa144merAp7kFerpZoKOzCafJKJkDwQn4aN8taKiJ8cecbmhirqfokKiBKyyRYuXJCGy8EAOpTIBYBIz0tkPL4gjcun4JPXv25MicBuT5a4yprgYuLOwFbQ3Vew9QimS9oWCyTqQ8ikqlGD9lJvb9tvGFonViEdCliRl829igv7sVDLVV/8vC2nArPgtvbbiCpyVSjPS2w7KRnpxGQCrh3zUvus/8HkmG7iiR/v2xRVtdgjeamqG/uyX6tLCEMZdyUqj03CL0WXEW2U9L8H/9m2Nmz6aKDokakai0PCz/Mxwn7qbIR+aIxBIIMilXO2kgZDIBfVeeRXR6Pj4e0BwzfFTzNYbJei1isk6kXP79AX7af39BsqE7bj0rNAMAGhIxerlZYHR7O3R3NYeaRLXmMtWW9NwiDPzxPDLyitC9mTk2TWgH9Ub6tyDV9O+aF3lFpbgYlYGg8DQEPkhHSk6hvK1ELEJH57Lq8v1aWsHKUEuBkTdOnx68g13X4uBuY4DDM9/g6w0pREjcE7w1aToiA/fJR+a0HTgWOzb+rDLrcFPFjt9JxowdITDQUsPFT3pBX0s1O2aYrNciJutEyqeionVxmQU4cjsJh0MTEZmWJ29rZaCFUe3sMLqdfaOqRiyTCRi/+RouRGWgmaUeDs14A7qaaooOi6jWCIKA+8m5OHU/FSfCUnAvOafc823sjTCglRX6u1txnns9iH9cgJ7Lg1AqE7B3Wmd0cDZRdEjUiPn7+2Po0KEQicUQZDKYD/8COq4d0c3VDBM6O6GnmwVXnFAxgiDgzTUXcDcpB7N7u2J+32aKDqnGmKzXIibrRKpFEAQ8SMnF/uAEHAxJwJOCEgCASAT0am6BSW84442mpg1+KPhPgVFY9mc4tNTFODKrK1xZbIcauLjMAvx5NwV/3k1BcNyTcsvCedgawq+NDXxb2+BK0F8IDAzkPNZa9smB29h9PR5dm5ph+/sdFR0OkfyLfadW7RGu1Rx/3EnG80Un7E20Ma6TI0a3s+eSbyriUlQG3t54FToaElxc2Eulpz3VS7IeFxeHR48eoaCgAObm5nB3d4emZsMr7sRknUh1FZVKcfJeKnZfi8eFqAz59maWepj0hjOGtbVtkMWprsc+xlsbrkAqE/D9SE+Mbmev6JCI6lVaTiH+upeKP++m4HJ0JkqffUJ/GnUVaQeWQCyRQCblPNba8s9e9QPTO8Pbkb3qpHziHxfg9yuPsOd6PLKfln2Rr6kmxtA2tpjQxQktbfg5X5nN33sTB0MSMbaDA5YO91B0OK+lzpL12NhYrFu3Drt370ZCQgL+uauGhga6deuGqVOnYsSIERCLG8Y8JSbrRA1DdHoetl2Kxf7gBBQUSwGULQM3tZsL3u7o0GCGiD/JL8ag1eeRnF2IoW1ssHJMmwY/ioDoZR7nF+PY7SQcCk3EqS3LkBt8RD6PtZPvu9i2fi2XeXpNH++/hb03EtDN1Qy/T2avOim3p8VSBNxKxNZLj3D/H9Nn2jsZY0IXJ/R3t2K9BSWTX1SK9v87hYJiaYP4QrBOkvXZs2dj27Zt6N+/P4YMGYIOHTrAxsYG2traePz4McLCwnD+/Hns3r0bEokEW7ZsQfv27WvlF1IkJutEDUv20xLsuxGPLRdjkZj1FABgrKOODuKHUEu9j/59e6tsT5sgCJjyWzBO3U+Fs5kujnzYFXoN5EsIotqwcfteTBk3Rr704/N5rO2djPHeG87o29Ky0RakrKnYjHz0XnEWUpmAgzO6wMvBWNEhEVWJIAi48egJtl2KxYmwFPkIHEsDTbzT0RFvdbCHhT4LVSqD58u1OZvp4sxHPVS+E6JOkvVPP/0UCxYsgKmp6SvbnjhxAgUFBRg+fHjVIlZiTNaJGqbiUhkOhybi56Ao3LsSWG7t9n0HDmHk8KGKDrHatl95hM8Ph0FDIsbBGV3QytZQ0SERKZ2AgACcCQyEWbO2iNNtidMP0iB99iHd1kgbE7o4Ykx7By7/WEUf7b2FAyEJ6NHMHNve66DocIhqJDWnEDuuxmHn1Thk5BUBANQlIgzysMb4zk7wcjBS+QRRlb396xVcis7ER32b4cPerooO57WxwFwtYrJO1LCVSmUYPmEajuzaDMjKhsZadxmGNT+uwtC2tipTLfZheh4Gr76ApyVSfD64Bd7v5qLokIhUQmpOIbZfeYQdV+PwOL8YAKCrIcH4Lk6Y3NUZZnoNrx5Pbfl1+14s+HEnNB088deq+Whjb6TokIheS3GpDH+EJWPbpViExGXJt3vYGmJ8Z0eI4oJx8fxZFqisR4lZT9H1uzMQBOD8xz0bxMo+dZ6s9+rVCwcPHoSRkdELJx46dCjOnDlT3UMqLSbrRA3f87Xbnxedej40toW1Ab7ydVf6JYhKpDKM/OUybsVnoUsTU2yf3BFiFfmSgUhZFJZI4X8zEZsvxCI8NRcAoKUuxjsdHTGtuwssDDgc9p+ev24+H5HEYn3U0NxJyMZvl2PhfysJxaUyFEReRfrBJRCJJRBkLFBZX56vbtPJxQS7p3ZWdDi1ojr5ZY0mZgUFBaG4uPiF7YWFhTh//nxNDklEpDC+vr7w9/fHnNmzse/AIXw9ewIMtNRwPzkHo9dfxuxdoUjJLlR0mJX6KTAKt+KzYKClhuWjWjNRJ6oBLXUJxrR3wIm53fDr+HbwtDNEYYkMmy7EoOv3gVj6x315BWkC/vjrlDxRl0gkCAoKUnRIRLXKw84Qy0a1xpVPe2PhADeopd4DRGIIMikgEuOzn/fgUlQGOEi57giCgAMhCQCAEV52Co5GMapVeej27dvy/7937x5SUlLkP0ulUpw4cQK2tra1Fx0RUT3x9fUt9w356Hb2WP5XOHZdi0PArSScup+KD3u54v1uzkpVJTY07gnWnIkCACwZ2go2RtoKjohItYlEIvRtaYk+LSxwLjIDa05H4sajJ1h/9iH2XI/Hh71c8W4nB2iqNbxlH6vF2h0QZBCJxZBKpfDx8VF0RER1wkRXA9N9msB6wTgMG3YAIrEYgkyGVN0meHvjVbSwNsDsXk3R392KX5bXspvxWXiYng9tdQkGelgrOhyFqNYweLFYLC+uUNFu2traWLNmDd57773ai1DBOAyeqHELS8zGl/5h8rlrLa0N8P1IT6Uo3lZQXIrBqy8gJiMfQ1rbYM3YtooOiajBEQQBZx6k4ds/HiAyLQ8AYG+ijS/fdEfflpYKjk4xpDIB3b8PROT1IHTQSsbUMUM4HJgahYCAAAQFBaFZm45IMnTHgeAE5D9bDraZpR4+7OWKQR7WKlPvRtl9fvgOtl+Jw7C2tlg5po2iw6k1dTZn/dGjRxAEAS4uLrh27RrMzc3lz2loaMDCwgISScP6ppnJOhHJZAIOhibiv8fuIaugBBKxCFO7u2BOb1doqSvmNU8QBMzZfRMBt5JgZaCFP+d2h6EOq1cT1ZVSqQz7gxOw4mQE0nLLqkX3bWmJxb7usG1kI1pO3kvFlN9uwEhHHVc+7a2w10EiRcsqKMbmCzHYcjEWuUWlAABXCz18PMANfVpYsIL8aygqlaLD/04j+2kJtk/uiK6uZooOqdawGnwtYrJORM+l5xZh8ZG7OHY7GQDgYqaL70Z6or1T/Reg++1yLL70vwuJWIRdUzopfRE8ooaioLgUa89EYcO5hyiVCdBWl6Cn9iOop95H7969GkUP87hNV3E+MgPTerjg04EtFB0OkcJlPy3B1oux2HThIXIKy5L29k7G+GRgC3g7Gis4OtV0IiwFH2wPhpWBFi5+0qtBjVaokwJzV65cqXIABQUFuHv3bpXbExGpAnN9Tfz0thfWj/OGub4mHmbkY/T6y1jkH4a8Z9+o14eQuCdYcvQeAODTgW5M1InqkY6GGj4e4Ibjc7qhg5MJMu9dwrrPPsCatWvg5+eHgIAARYdYp6LT83A+MgMiEfBuR0dFh0OkFAy11TGnjyvOL+yF6T5NoKkmxvXYJxix7hI++D0Y8Y8LFB2iyvnrbllttCGtG/e0gion6+PGjUP//v2xb98+5OfnV9jm3r17+M9//oMmTZogODi41oIkIlIm/d2tcGpeD4xpZw9BALZdfoT+K8/hbER6nZ87M68IM7aHoEQqYJCHFSZ3da7zcxLRi5pZ6mPPtE5or5n0rEK0DBCJ8fPOgAZdHfr3y48AAL3dLBrEesdEtclQWx0LB7gh6P98MKadPcQi4MTdFPRdeRZrz0SiqFSq6BBVQqlUhjPhaQCAvi2tFByNYlU5Wb937x4GDx6Mzz//HEZGRnB3d0ffvn0xZMgQdO3aFWZmZvDy8kJMTAz++usvjB8/vi7jJiJSKEMddXw30hPbJ3eEnbE2ErOeYsLma5i/9yae5L+4tGVtkMoEDP9kNe4dWgODtJv4fmRrzocjUiCRSIRpb/nKq6JDkCGkxAYTtlxHao7yLvdYU3lFpTgQXLaM0vjOTooNhkiJWRtq47uRnvhjTnd0cjFBYYkMy/+KqLcv9lVdSFwWsgpKYKSjDi8HI0WHo1A1mrN+48YNXLhwAY8ePcLTp09hZmaGtm3bomfPnjAxaVjDMTlnnYhepaC4FMv+DMfWS7EQBMBMTwNf+bbCIA+rWkumS6QyjPxkNQKWz5Ovbezv798o5scSKbuAgAAEBgZCatUSf+baoahUBjM9Dax92wudXEwVHV6t+f3KI3xxOAwuZro4Nb8Hl6kiqgJBEBBwKwn/O3ZfXpxyeFtbLBrizsKwlfjm+H1sOPcQw9vaYkUDqgL/XIMrMBcbG4slS5bgzJkzSElJgY2NDd5991189tln0NDQqHQ/Hx8fnD17tty2adOm4ZdffqnyuZmsE1FVhcQ9wcL9t+XLOzUrfACn4li8OaDvayXVhSVSzNoZgr1r/4fc4COAIINEIsHs2bOxYsWK2gqfiGpBZGouPtwVigcpuZCIRfhsUAtMesNJ5UfBCIKAAavOIzw1F4uGtMSkNzgFh6g6cgtLsPJkJLZeioFMACwNNPHdCE/4NLdQdGhKp9cPQXiYno+f3vbCYM+Gt756nRSY+6cHDx5U+tyff/5Zk0O+8nwymQzr16/H3bt3sXLlSvzyyy/4z3/+88p9p0yZguTkZPnj+++/r/X4iIgAwMvBGEdnd8Wc3q4ojr6Kkz8uwK+//Aw/Pz/sOXCoRsfMKSzB+E3XcOp+GvSdW8sTdalUCh8fn9r9BYjotbla6uPgjC7wa2MDqUzA10fvYe6em3harNpzVUPishCemgstdTGGe9kpOhwilaOvpY4vh7TEvg+6wNlMF6k5RZi45TpGfLIas2bPafDFKavqYXoeHqbnQ10iQvdmDWe5tpqqUbLu5eWFn376qdy2oqIizJo1C35+frUS2D8NGDAAW7ZsQb9+/eDi4gJfX18sWLAABw8efOW+Ojo6sLKykj/YO05EdUlTTYJ5fZuhn3GmfA4rRGLMXrEdO64+QqlUVuVjJTwpwFvrr+Ba7GPoa6rh4Lez4e/vj9mzZ3MIPJES09FQw6oxbfDlmy0hEYvgfzMJI9ZdUul57HuuxwEABnvYwFCbQ3eJasrb0RjHZ3fDpDecUBB5FQe/m4OfflrbKFaTqIrT98sKy3VyMYW+Fl9rapSsb926FV9++SUGDRqE1NRU3Lx5E23btsWpU6dw/vz52o6xQtnZ2VWaH79jxw6YmZmhVatW+PTTT1FQwKUTiKjuDX+zPwSZDGKJBBBkEKzc8dmhMHT9LhDfHL+PsMTsSitG34zPwoe7QtFjWRDuJefATE8Du6d1QkcXU/j6+mLFihVM1ImUnEgkwntdnbHz/Y4w09PAveQcDP/5EqLSchUdWrXlFpbgyK1kAMBbHewVHA2R6tPWkGDREHf0Mkwrq0PzbDWJX/ceUXRoCnfyfioAoE8LSwVHohxqPGc9ISEBkyZNQmhoKPLz8zFx4kT88MMP0NGp+2U8oqKi4O3tjeXLl2PKlCmVttuwYQMcHR1hY2OD27dvY+HChejQocNLe+SLiopQVFQk/zknJwf29vacs05E1RYQEICgoCB07d4duRZt8OPpSDz+R6X4Jua66OhiCnWxCGKxCGpiEULjsnDj0RN5m04uJlg63BPOZrqK+BWIqBbEPy7AhM3X8DAjH4ba6tg0oR3aOalOQd4dVx/hs0NhaGqhh5Pzuqv8/HsiZREQEAA/Pz+IxGXLP5oP/wKT3xmJRUPcoaUuUXR49e5JfjHa/e8UpDIBFxb2hJ1xw1wesjpz1tVe50TFxcWQSqWQSqWwtraGlpZWtfb/5JNP8N133720zf379+Hm5ib/OTExEQMGDMCoUaNemqgDwNSpU+X/7+HhAWtra/Tu3RvR0dFo0qRJhfssXboUX331VTV+CyKiivn6+pbrAX+rgz2CwtMRcDMJp+6nIjo9H9Hp+S/spy4RYYinDd7r6oxWtob1GTIR1QF7Ex3sn94Fk7ddR2hcFt7ZeBWrx7ZFf3fVWD9497V4AMBb7e2ZqBPVIl9fX/j7++NMYCDyjZvj1FN77LoWj3vJudg0oR3M9DQVHWK9CopIg1QmwM1Kv8Em6tVVo5713bt3Y/r06ejWrRs2bdqEmzdvYtKkSXB0dMTvv/8OFxeXKh0nPT0dmZmZL23j4uIir/ielJQEHx8fdOrUCVu3boVYXL1R/Pn5+dDT08OJEyfQv3//CtuwZ52I6kNuYQlO3ktFbGYBZDIBUkGATCbAUEcdI7zsYGlQvS8/iUj5PS2W4sNdITh1Pw1iEfDD6NYY1la5i7WFJWbjzTUXoCER48p/esNEt/JVeIjo9ZyPTMeHu0KRVVACexNtbJnYAU0t9BQdVr2ZuTMEx24nY1bPpljQv7miw6kzdb50m66uLpYvX47p06fLtz158gTTpk3DiRMnkJOTU/2oXyExMRE9e/aEt7c3tm/fDomk+kNDLl68iK5du+LWrVvw9PSs0j5cuo2IiIhqS6lUhs8OhWHPjXiVSNi/OByG3688wpue1lj7tpeiwyFq8B6m52HS1ut4lFkAQ211bBjnjY4upooOq84Vl8rgteQk8opKcXjmG2hjb6TokOpMnS/dFhISUi5RBwBjY2Ps3bv3hSrxtSExMRE+Pj5wcHDA8uXLkZ6ejpSUFKSkpJRr4+bmhmvXrgEAoqOjsWTJEgQHByM2NhYBAQEYP348unfvXuVEnYiIiKg2qUnEWDrcA2M7OEAmAPP33sLBkARFh1Whp8VSHL6ZCAB4q72DgqMhahxczPVwcHoXeDkYIftpCcZtugb/Z/dhQ3Yt5jHyikphrq8JT04BlKvRnPXmzSsfljBu3LgaB1OZkydPIioqClFRUbCzK//t8/OBASUlJQgPD5dXe9fQ0MCpU6ewatUq5Ofnw97eHiNGjMDnn39e6/ERERERVZVYLML/hraCSATsvBqHj/bdAgClW7/82J1k5BaWwt5EG12aNPyePSJlYaqniZ1TOmH+3ps4ficFc/fcRFGpDKPbNdzVGE49qwLf280CYjFrYzxX5WR9/vz5VT7oihUrahRMZSZOnIiJEye+tI2Tk1O5ZZDs7e1x9uzZWo2DiIiIqDaIxSL8168VgL8TdjWJGL6tbRQc2d92XytbW/2t9g788ExUz7TUJVg71gtf6oZh+5U4LDxwG4IgYEwDHeVyLjIdAODT3FzBkSiXKifroaGh5X4OCQlBaWmpvJc9IiICEokE3t7etRshERERUQP0QsK+9yaMtNXRvZniP6zeT87BjUdPIBGLMNJbuXr8iRoLsViEJX6tIBGJsO3yIyw8cAcyARjboWEl7AlPCvAwPR8SsQidm5gpOhylUuVkPTAwUP7/K1asgL6+PrZt2wZjY2MAZQXmJk2ahG7dutV+lEREREQN0POEPedpCY7eTsYH24Oxc0onhRdX2noxFgAwoJUVV6cgUiCRSITFvu4Qi0XYcjEWnx68A5kg4J2OjooOrdacj8wAALSxN4KhtrqCo1EuNSow98MPP2Dp0qXyRB0oKzD33//+Fz/88EOtBUdERETU0InFIqwY3QbdXM1QUCzFpC3XEJWWp7B4HucXywvLTeripLA4iKiMSCTCl2+2xOSuzgCAzw6F4citJAVHVXvOPxsC391V8aOKlE2NkvWcnBykp6e/sD09PR25ubmvHRQRERFRY6KhJsa6d73R2s4QTwpK8OaClZg640MEBATUeyy7r8ehqFSGVrYG8HY0fvUORFTnRCIRPh/cAhM6l/Wof7T3Fq48zFRwVK9PKhNw4VnPerdmHAL/bzVK1ocNG4ZJkybh4MGDSEhIQEJCAg4cOIDJkydj+PDhtR0jERERUYOnp6mGzRPbwyDtJsJ//xIb1/8MPz+/ek3YS6Qy/H75EQBgUhdniEQsLEekLEQiEb4c4o4B7lYolsow9bcbiEhV7Y7S2wlZyCkshYGWGpdsq0CNkvVffvkFAwcOxNtvvw1HR0c4Ojri7bffxoABA/Dzzz/XdoxEREREjYKpniY6aCYDIjEEmQwisRhnzgS+esda8tfdVCRnF8JMTwNvtraut/MSUdVIxCKseqsN2jkaI6ewFBM3X0NKdqGiw6qxcxFlvepvNDWDmqRGqWmDVqO/iI6ODn7++WdkZmYiNDQUoaGhePz4MX7++Wfo6urWdoxEREREjYbvwH6AIJMn7LGa5ZenrUtbLsYAAN7u6AhNNUm9nJOIqkdLXYJfx7eDi7kukrILMXHLNeQVlSo6rBqRz1dXglUwlNFrfX2hq6sLT09PeHp6MkknIiIiqgW+vr7w9/fHyPFTYDHiC9wUNcXq01F1ft47Cdm48egJ1MQivNuxYS0NRdTQGOtqYNukDjDX18SDlFx8vP9WvX2pV1tyCksQGp8FAOjalPPVK8KxBkRERERKxtfXF/u2/oIfFkwGAKw8FYEDwQl1es6tl2IBAIM9rWHB5dqIlJ69iQ7Wj/OGukSE43dSsPF8jKJDqpZLUZmQygS4mOnC3kRH0eEoJSbrRERERErq3U6O+KBHEwDAwgO3cSkqo07Ok5ZTKF8KatIbznVyDiKqfV4OxvjyzZYAgG9PPMBVFaoQ/3wIfDdX9qpXhsk6ERERkRL7uH9zDGltg1KZgGnbg+uk+vO3Jx6gWCqDt6Mx2tgb1frxiajuvNvJEcPa2kIqEzBzZyhSc1Sj4Nz5Z0u2cb565ZisExERESkxsViEZSM90d7JGLnPqj/X5ofx4EePcTAkEQDwxbMeOiJSHSKRCN8M84CblT7iQs+h67AJOHj4sKLDeqnYjHzEPS6AukSETi6mig5HaTFZJyIiIlJyWuoSbBjXDi5mZdWf39t6Hfm1UP1ZKhOwOOAeAGB0Ozv2qhOpKG0NCUaYpSD94BJEBe7HiGHDEBAQoOiwKvXj5p14fPpXWD4Jg66mmqLDUVpM1omIiIhUgLGuBrZO6gBTXQ3cTcrB9B0hKCyRvtYx996Ix53EbOhrquHjAW61FCkRKcK94MsQSyTypR93B5xQdEgVCggIwKqFU5EbfAQX1y1U6i8VFI3JOhEREZGKcDDVwcYJ7aClLsa5iHRM/T24xgl7dkEJlv0ZDgCY27cZzPQ0azNUIqpnPXv2hEwqhUhclrDfLLVRyvXXT50+DYjEgCCDRCJBUFCQokNSWkzWiYiIiFRIWwdjbJ7YHtrqEpyLSMf7227gaXH1E/aVpyLwOL8YrhZ6GN/ZsQ4iJaL65OvrC39/f8yYOQtu45agwKot/nv0nqLDeoFty/by3n+pVAofHx9Fh6S0mKwTERERqZguTcywdVJ76GhIcCEqA5O2XqvWHPYrDzPx+5VHAICvfN2hLuFHQqKGwNfXF2tXr8LmRdMhEgG7r8fj1L1URYdVTqmdF8yHfwGvQW/D398fvr6+ig5JafGVmYiIiEgFdXQxxe+TO0BPUw1XHj7GuE1XEZuR/8r9DgQnYPyma5DKBAz2tEaXplzjmKih6dzEFO93dQYAfHLwNjLzihQc0d8Cw9Oh49oRX33zHRP1V2CyTkRERKSivB1N8PvkDtDXUkNIXBb6rTqHlScjKpzHLpMJWP5nOD7adwvFUhkGuFth+cjWCoiaiOrDR/2ao7mlPjLyivGl/11FhwMAiH9cgKi0PEjEInRryvXVX4XJOhEREZEKa+tgjCOzuqKbqxmKS2X48XQk+q86h4BbSbgYlYGzEen478+/oe3gd/H9hh0AgBk+TfDzO17Q1pAoOHoiqita6hL8MLo1JGIRjt1Jxun7ih8OHxSRDgDwcjCCoY66gqNRflzUjoiIiEjFOZnp4rf3OuD4nRR8ffQuHmUWYPauUABAQeRVpB9cIq++/PHyjfh4wGAFR0xE9aGVrSEmd3XGhnMP8aX/XXRyMVXouuZnw9MAAD7NLRQWgyphzzoRERFRAyASiTDY0xqnP/LB1O4ucLXQQ3NLfehmPoBIXJaoiyUSlCQqx3BYIqofc/u4ws5YG4lZT7HiZITC4igskeJiVCYAwKc5h8BXBZN1IiIiogZET1MN/xnUAifn98Cf87rjx/nvQpCVrWcs4zJJRI2OjoYa/ju0FQBgy8UY3EnIVkgc12Mf42mJFBb6mmhpbaCQGFSNyiTrTk5OEIlE5R7ffvvtS/cpLCzEzJkzYWpqCj09PYwYMQKpqYqfq0FERERUX56vvTx79mwuk0TUSPk0t8CQ1jaQCWXV4UulsnqPIfBB+rNYzCESier9/KpIZZJ1APj666+RnJwsf3z44YcvbT9v3jwcOXIE+/btw9mzZ5GUlIThw4fXU7REREREysHX1xcrVqxgok7UiH35ZksYaKnhblIOtlyMrffzB0WUzVfvyfnqVaZSybq+vj6srKzkD11d3UrbZmdnY9OmTVixYgV69eoFb29vbNmyBZcuXcKVK1fqMWoiIiIiIiLFMtfXxH8GtQAArDwVgeTsp/V27rjMAjxMz4eaWIQ3XM3q7byqTqWS9W+//RampqZo27Ytli1bhtLS0krbBgcHo6SkBH369JFvc3Nzg4ODAy5fvlwf4RIRERERESmN0e3s4e1ojIJiKf577H69nfd5r7q3ozEMtLhkW1WpzNJts2fPhpeXF0xMTHDp0iV8+umnSE5OxooVKypsn5KSAg0NDRgZGZXbbmlpiZSUlErPU1RUhKKiIvnPOTk5tRI/ERERERGRIonFInzt544hay7g2O1kjG2fga710NMd+ODZEHg3DoGvDoX2rH/yyScvFI379+PBgwcAgPnz58PHxweenp744IMP8MMPP2DNmjXlEuvasHTpUhgaGsof9vb2tXp8IiIiIiIiRXG3McS4To4AgEUBYSgurdtic7mFJbgUzSXbakKhyfpHH32E+/fvv/Th4uJS4b4dO3ZEaWkpYmNjK3zeysoKxcXFyMrKKrc9NTUVVlZWlcb06aefIjs7W/6Ij4+v6a9HRERERESkdOb3aw5TXQ1Ep+dj88WYOj3XH2EpKCqVoamFHppb6tfpuRoahQ6DNzc3h7l5zb5duXnzJsRiMSwsKh5K4e3tDXV1dZw+fRojRowAAISHhyMuLg6dO3eu9LiamprQ1NSsUUxERERERETKzlBbHZ8OaoEF+25h9elI+LWxgbWhdp2c60BwAgBguJctl2yrJpUoMHf58mWsWrUKt27dwsOHD7Fjxw7MmzcP7777LoyNjQEAiYmJcHNzw7Vr1wAAhoaGmDx5MubPn4/AwEAEBwdj0qRJ6Ny5Mzp16qTIX4eIiIiIiEihhre1RbvnxeaO1k2xufjHBbga8xgiETC0jW2dnKMhU4lkXVNTE7t370aPHj3g7u6O//3vf5g3bx42bNggb1NSUoLw8HAUFBTIt61cuRJvvvkmRowYge7du8PKygoHDx5UxK9ARERERESkNMqKzbWCWAQcu5OMcxHptX6Ow6GJAIAuTUxhY1Q3PfcNmUgQBEHRQSiznJwcGBoaIjs7GwYGBooOh4iIiIiIqNZ8feQeNl+MgYOJDv6a1x1a6pJaOa4gCOj1w1nEZOTjh1GtMcLbrlaOq+qqk1+qRM86ERERERER1b75/ZrBykALcY8LsPZMVK0dNzQ+CzEZ+dBWl2BAq8oLfFPlmKwTERERERE1Unqaaljs2xIAsP5cNKLScmvluAdDygrLDWxlBV1NhdY1V1lM1omIiIiIiBqx/u5W6O1mgRKpgM8OheF1Z0oXlUpx5FYyAGC4F4e/1xSTdSIiIiIiokZMJBLhKz93aKtLcDXmMfY/W26tps7cT0P20xJYGWihcxPTWoqy8WGyTkRERERE1MjZGetgTh9XAMA3x+8jPbeoxsc6EFJWBX5oW1tIxFxbvaaYrBMREREREREmd3WGm5U+nhSUYM7uUEhl1R8On/CkAEHhaQCA4V5cW/11MFknIiIiIiIiqEvEWDO2LbTVJbgUnYnVpyOrtb8glM15L5UJ6ORigmaW+nUUaePAZJ2IiIiIiIgAAK6W+vjfsFYAgNVnInEhMqPK+x4MScTZiHRoqInxv2EedRVio8FknYiIiIiIiOSGe9nhrfb2EARg7p5QpOUUvnKf9NwifH30HgBgbh9XNDHXq+swGzwm60RERERERFTOYl93uFnpIy70PLoOm4hDhw+/vH3AXWQ/LYG7jQGmdHOpnyAbOCbrREREREREVI6WugTDTZORfnAJIgP3YfiwYdi4fW+FbU+EpeDYnWRIxCJ8N8IT6hKmmbWBf0UiIiIiIiJ6wf2QKxBLJIAgA0RiLFy7G3uvx0MQyqrEP8kvxuHQRHzhHwYA+KCHC1rZGioy5AZFTdEBEBERERERkfLp2bMnVq1aBYlEAqlUColtK3x84DaO3E7C02IpQuKe4Pnqbi7muviwl6tiA25gmKwTERERERHRC3x9feHv74+goCB0794DKcat8MNf4Tj/jwrxblb66OlmgUldnKClLlFgtA2PSHg+hoEqlJOTA0NDQ2RnZ8PAwEDR4RARERERESnM3aRsHApJhKOZLnq5WcDWSFvRIamU6uSX7FknIiIiIiKiKnG3MYS7Deel1wcWmCMiIiIiIiJSMkzWiYiIiIiIiJQMk3UiIiIiIiIiJcNknYiIiIiIiEjJMFknIiIiIiIiUjKsBv8Kz1e2y8nJUXAkREREREREpMqe55VVWUGdyfor5ObmAgDs7e0VHAkRERERERE1BLm5uTA0fPkSeCKhKil9IyaTyZCUlAR9fX2IRCJFh1OpnJwc2NvbIz4+HgYGBooOh/6F10f58RopN14f5cdrpNx4fZQfr5Hy4zVSbqpyfQRBQG5uLmxsbCAWv3xWOnvWX0EsFsPOzk7RYVSZgYGBUv/jbOx4fZQfr5Fy4/VRfrxGyo3XR/nxGik/XiPlpgrX51U96s+xwBwRERERERGRkmGyTkRERERERKRkmKw3EJqamli0aBE0NTUVHQpVgNdH+fEaKTdeH+XHa6TceH2UH6+R8uM1Um4N8fqwwBwRERERERGRkmHPOhEREREREZGSYbJOREREREREpGSYrBMREREREREpGSbrKuSnn36Ck5MTtLS00LFjR1y7du2l7fft2wc3NzdoaWnBw8MDx48fr6dIG5elS5eiffv20NfXh4WFBYYOHYrw8PCX7rN161aIRKJyDy0trXqKuPFZvHjxC39vNze3l+7D+6d+OTk5vXCNRCIRZs6cWWF73kN169y5cxgyZAhsbGwgEolw+PDhcs8LgoAvv/wS1tbW0NbWRp8+fRAZGfnK41b3fYwq97JrVFJSgoULF8LDwwO6urqwsbHB+PHjkZSU9NJj1uS1kir2qnto4sSJL/ytBwwY8Mrj8h6qPa+6RhW9J4lEIixbtqzSY/Ieqj1V+XxdWFiImTNnwtTUFHp6ehgxYgRSU1Nfetyavn8pCpN1FbFnzx7Mnz8fixYtQkhICFq3bo3+/fsjLS2twvaXLl3C2LFjMXnyZISGhmLo0KEYOnQowsLC6jnyhu/s2bOYOXMmrly5gpMnT6KkpAT9+vVDfn7+S/czMDBAcnKy/PHo0aN6irhxcnd3L/f3vnDhQqVtef/Uv+vXr5e7PidPngQAjBo1qtJ9eA/Vnfz8fLRu3Ro//fRThc9///33WL16NX755RdcvXoVurq66N+/PwoLCys9ZnXfx+jlXnaNCgoKEBISgi+++AIhISE4ePAgwsPD4evr+8rjVue1kir3qnsIAAYMGFDub71r166XHpP3UO161TX657VJTk7G5s2bIRKJMGLEiJcel/dQ7ajK5+t58+bhyJEj2LdvH86ePYukpCQMHz78pcetyfuXQgmkEjp06CDMnDlT/rNUKhVsbGyEpUuXVth+9OjRwuDBg8tt69ixozBt2rQ6jZMEIS0tTQAgnD17ttI2W7ZsEQwNDesvqEZu0aJFQuvWravcnveP4s2ZM0do0qSJIJPJKnye91D9ASAcOnRI/rNMJhOsrKyEZcuWybdlZWUJmpqawq5duyo9TnXfx6jq/n2NKnLt2jUBgPDo0aNK21T3tZKqpqLrM2HCBMHPz69ax+E9VHeqcg/5+fkJvXr1emkb3kN159+fr7OysgR1dXVh37598jb3798XAAiXL1+u8Bg1ff9SJPasq4Di4mIEBwejT58+8m1isRh9+vTB5cuXK9zn8uXL5doDQP/+/SttT7UnOzsbAGBiYvLSdnl5eXB0dIS9vT38/Pxw9+7d+giv0YqMjISNjQ1cXFzwzjvvIC4urtK2vH8Uq7i4GNu3b8d7770HkUhUaTveQ4oRExODlJSUcveIoaEhOnbsWOk9UpP3Mapd2dnZEIlEMDIyemm76rxW0usJCgqChYUFmjdvjunTpyMzM7PStryHFCs1NRXHjh3D5MmTX9mW91Dd+Pfn6+DgYJSUlJS7J9zc3ODg4FDpPVGT9y9FY7KuAjIyMiCVSmFpaVluu6WlJVJSUircJyUlpVrtqXbIZDLMnTsXb7zxBlq1alVpu+bNm2Pz5s3w9/fH9u3bIZPJ0KVLFyQkJNRjtI1Hx44dsXXrVpw4cQLr1q1DTEwMunXrhtzc3Arb8/5RrMOHDyMrKwsTJ06stA3vIcV5fh9U5x6pyfsY1Z7CwkIsXLgQY8eOhYGBQaXtqvtaSTU3YMAA/Pbbbzh9+jS+++47nD17FgMHDoRUKq2wPe8hxdq2bRv09fVfOcSa91DdqOjzdUpKCjQ0NF74AvJV+dHzNlXdR9HUFB0AUUMyc+ZMhIWFvXJ+UufOndG5c2f5z126dEGLFi2wfv16LFmypK7DbHQGDhwo/39PT0907NgRjo6O2Lt3b5W+Jaf6tWnTJgwcOBA2NjaVtuE9RFQ1JSUlGD16NARBwLp1617alq+V9eett96S/7+Hhwc8PT3RpEkTBAUFoXfv3gqMjCqyefNmvPPOO68sZMp7qG5U9fN1Q8SedRVgZmYGiUTyQnXD1NRUWFlZVbiPlZVVtdrT65s1axaOHj2KwMBA2NnZVWtfdXV1tG3bFlFRUXUUHf2TkZERmjVrVunfm/eP4jx69AinTp3C+++/X639eA/Vn+f3QXXukZq8j9Hre56oP3r0CCdPnnxpr3pFXvVaSbXHxcUFZmZmlf6teQ8pzvnz5xEeHl7t9yWA91BtqOzztZWVFYqLi5GVlVWu/avyo+dtqrqPojFZVwEaGhrw9vbG6dOn5dtkMhlOnz5drmfpnzp37lyuPQCcPHmy0vZUc4IgYNasWTh06BDOnDkDZ2fnah9DKpXizp07sLa2roMI6d/y8vIQHR1d6d+b94/ibNmyBRYWFhg8eHC19uM9VH+cnZ1hZWVV7h7JycnB1atXK71HavI+Rq/neaIeGRmJU6dOwdTUtNrHeNVrJdWehIQEZGZmVvq35j2kOJs2bYK3tzdat25d7X15D9Xcqz5fe3t7Q11dvdw9ER4ejri4uErviZq8fymcggvcURXt3r1b0NTUFLZu3Srcu3dPmDp1qmBkZCSkpKQIgiAI48aNEz755BN5+4sXLwpqamrC8uXLhfv37wuLFi0S1NXVhTt37ijqV2iwpk+fLhgaGgpBQUFCcnKy/FFQUCBv8+/r89VXXwl//vmnEB0dLQQHBwtvvfWWoKWlJdy9e1cRv0KD99FHHwlBQUFCTEyMcPHiRaFPnz6CmZmZkJaWJggC7x9lIZVKBQcHB2HhwoUvPMd7qH7l5uYKoaGhQmhoqABAWLFihRAaGiqvJP7tt98KRkZGgr+/v3D79m3Bz89PcHZ2Fp4+fSo/Rq9evYQ1a9bIf37V+xhVz8uuUXFxseDr6yvY2dkJN2/eLPfeVFRUJD/Gv6/Rq14rqepedn1yc3OFBQsWCJcvXxZiYmKEU6dOCV5eXoKrq6tQWFgoPwbvobr1qtc5QRCE7OxsQUdHR1i3bl2Fx+A9VHeq8vn6gw8+EBwcHIQzZ84IN27cEDp37ix07ty53HGaN28uHDx4UP5zVd6/lAmTdRWyZs0awcHBQdDQ0BA6dOggXLlyRf5cjx49hAkTJpRrv3fvXqFZs2aChoaG4O7uLhw7dqyeI24cAFT42LJli7zNv6/P3Llz5dfS0tJSGDRokBASElL/wTcSY8aMEaytrQUNDQ3B1tZWGDNmjBAVFSV/nvePcvjzzz8FAEJ4ePgLz/Eeql+BgYEVvq49vwYymUz44osvBEtLS0FTU1Po3bv3C9fN0dFRWLRoUbltL3sfo+p52TWKiYmp9L0pMDBQfox/X6NXvVZS1b3s+hQUFAj9+vUTzM3NBXV1dcHR0VGYMmXKC0k376G69arXOUEQhPXr1wva2tpCVlZWhcfgPVR3qvL5+unTp8KMGTMEY2NjQUdHRxg2bJiQnJz8wnH+uU9V3r+UiUgQBKFu+uyJiIiIiIiIqCY4Z52IiIiIiIhIyUGHKeEAAAHXSURBVDBZJyIiIiIiIlIyTNaJiIiIiIiIlAyTdSIiIiIiIiIlw2SdiIiIiIiISMkwWSciIiIiIiJSMkzWiYiIiIiIiJQMk3UiIiIiIiIiJcNknYiIiIiIiEjJMFknIiKi1+bj44O5c+cqOgwiIqIGg8k6ERERERERkZIRCYIgKDoIIiIiUl0TJ07Etm3bym2LiYmBk5OTYgIiIiJqAJisExER0WvJzs7GwIED0apVK3z99dcAAHNzc0gkEgVHRkREpLrUFB0AERERqTZDQ0NoaGhAR0cHVlZWig6HiIioQeCcdSIiIiIiIiIlw2SdiIiIiIiISMkwWSciIqLXpqGhAalUqugwiIiIGgwm60RERPTanJyccPXqVcTGxiIjIwMymUzRIREREak0JutERET02hYsWACJRIKWLVvC3NwccXFxig6JiIhIpXHpNiIiIiIiIiIlw551IiIiIiIiIiXDZJ2IiIiIiIhIyTBZJyIiIiIiIlIyTNaJiIiIiIiIlAyTdSIiIiIiIiIlw2SdiIiIiIiISMkwWSciIiIiIiJSMkzWiYiIiIiIiJQMk3UiIiIiIiIiJcNknYiIiIiIiEjJMFknIiIiIiIiUjJM1omIiIiIiIiUzP8DX/zSAekEBnEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_sens = jnp.linspace(0.0, 20.0, 201)\n",
    "\n",
    "\n",
    "def x_traj(mu):\n",
    "    s = quad.solve_ivp(van_der_pol, (0.0, 20.0), vdp_y0, mu, rtol=1e-10, atol=1e-12, t_eval=t_sens)\n",
    "    return s.ys[:, 0]\n",
    "\n",
    "\n",
    "sens = jax.jacfwd(x_traj)(1.0)\n",
    "eps = 1e-5\n",
    "sens_fd = (x_traj(1.0 + eps) - x_traj(1.0 - eps)) / (2 * eps)\n",
    "print('dx(t)/dmu:', sens.shape, f' max |AD - finite difference| = {float(jnp.max(jnp.abs(sens - sens_fd))):.1e}')\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(10, 4.5), sharex=True)\n",
    "axes[0].plot(t_sens, x_traj(1.0), label='mu = 1')\n",
    "axes[0].plot(t_sens, x_traj(1.2), '--', label='mu = 1.2')\n",
    "axes[0].set_ylabel('x(t)')\n",
    "axes[0].legend(loc='upper right')\n",
    "axes[1].plot(t_sens, sens, label='jax.jacfwd')\n",
    "axes[1].plot(t_sens[::5], sens_fd[::5], 'k.', ms=4, label='finite difference')\n",
    "axes[1].set_ylabel('dx(t) / dmu')\n",
    "axes[1].set_xlabel('t')\n",
    "axes[1].legend(loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0c6bcc7",
   "metadata": {},
   "source": [
    "A larger $\\mu$ lengthens the period, so the phase lag accumulates from cycle to cycle and the sensitivity oscillates with growing amplitude. The automatic derivative matches the finite difference to 2.4e-7.\n",
    "\n",
    "Reverse mode (`jax.grad`) is what training and fitting use. It must keep the intermediate states of the forward pass, which for long rollouts can exhaust memory. `checkpoint=True` stores only some of them and recomputes the rest on the backward pass, so memory no longer grows with the number of steps; the values and gradients are the same. It is the default for `solve_ivp` (whose loop always has `max_steps` iterations) and opt-in for `odeint`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d4724e28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-19T16:27:40.759625Z",
     "iopub.status.busy": "2026-10-19T16:27:40.759371Z",
     "iopub.status.idle": "2026-10-19T16:28:05.187721Z",
     "shell.execute_reply": "2026-10-19T16:28:05.186848Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odeint    grad (checkpoint=False) +0.12332256   (checkpoint=True) +0.12332256   finite difference +0.12332254\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solve_ivp grad (checkpoint=False) -0.10313317   (checkpoint=True) -0.10313317   finite difference -0.10313316\n"
     ]
    }
   ],
   "source": [
    "def loss_odeint(omega, checkpoint):\n",
    "    _, ys = quad.odeint(oscillator, y0, 0.0, 50.0, 0.001, omega, checkpoint=checkpoint)  # 50,000 steps\n",
    "    return jnp.mean(ys[:, 0] ** 2)\n",
    "\n",
    "\n",
    "def loss_ivp(mu, checkpoint):\n",
    "    s = quad.solve_ivp(van_der_pol, (0.0, 20.0), vdp_y0, mu, rtol=1e-8, atol=1e-10,\n",
    "                       t_eval=t_sens, checkpoint=checkpoint)\n",
    "    return jnp.mean(s.ys[:, 0] ** 2)\n",
    "\n",
    "\n",
    "for name, loss, p in [('odeint', loss_odeint, omega), ('solve_ivp', loss_ivp, 1.0)]:\n",
    "    g_plain = jax.grad(loss)(p, False)\n",
    "    g_ckpt = jax.grad(loss)(p, True)\n",
    "    g_fd = (loss(p + eps, False) - loss(p - eps, False)) / (2 * eps)\n",
    "    print(f'{name:9s} grad (checkpoint=False) {float(g_plain):+.8f}   (checkpoint=True) {float(g_ckpt):+.8f}'\n",
    "          f'   finite difference {float(g_fd):+.8f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eca58dc",
   "metadata": {},
   "source": [
    "`solve_ivp` treats the accepted step sizes as constants when differentiating (\"discretise, then optimise\"), so its gradient is the exact gradient of the computed solution, and it agrees with the finite difference to the accuracy of the solve.\n",
    "\n",
    "## 4. The method registry\n",
    "\n",
    "`odeint` and `solve_ivp` look their `method` up in a registry of steppers, one table per equation kind (`'ode'`, `'sde'`, `'imex'`, `'dde'`). `list_methods` lists the names, and `get_method` returns the stepper; the full function name works too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bffe995f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-19T16:28:05.190005Z",
     "iopub.status.busy": "2026-10-19T16:28:05.189809Z",
     "iopub.status.idle": "2026-10-19T16:28:05.194306Z",
     "shell.execute_reply": "2026-10-19T16:28:05.193577Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ODE methods: ['bs32', 'dopri5', 'dopri8', 'euler', 'expeuler', 'heun', 'midpoint', 'ralston2', 'ralston3', 'rk2', 'rk23', 'rk3', 'rk4', 'rk45', 'rk45_dopri', 'rk4_38', 'rk87_dopri', 'rkf45', 'ssprk33']\n",
      "SDE methods: ['euler', 'expeuler', 'heun', 'implicit_euler', 'milstein', 'srk2', 'srk3', 'srk4', 'tamed_euler']\n",
      "get_method('rk4') is ode_rk4_step: True\n",
      "get_method('ode_dopri5_step') is ode_dopri5_step: True\n",
      "KeyError: \"Unknown 'ode' method 'rk5'. Did you mean one of ['rk45', 'rkf45', 'rk4', 'rk3', 'rk2']? Available methods: ['bs32', 'dopri5', 'dopri8', 'euler', 'expeuler', 'heun', 'midpoint', 'ralston2', 'ralston3', 'rk2', 'rk23', 'rk3', 'rk4', 'rk45', 'rk45_dopri', 'rk4_38', 'rk87_dopri', 'rkf45', 'ssprk33'].\"\n"
     ]
    }
   ],
   "source": [
    "print('ODE methods:', quad.list_methods('ode'))\n",
    "print('SDE methods:', quad.list_methods('sde'))\n",
    "print(\"get_method('rk4') is ode_rk4_step:\", quad.get_method('rk4') is quad.ode_rk4_step)\n",
    "print(\"get_method('ode_dopri5_step') is ode_dopri5_step:\", quad.get_method('ode_dopri5_step') is quad.ode_dopri5_step)\n",
    "try:\n",
    "    quad.get_method('rk5')\n",
    "except KeyError as e:\n",
    "    print('KeyError:', e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a8b1c7b",
   "metadata": {},
   "source": [
    "`register_method(name, fn)` adds a stepper. It must follow the convention of the built-in ODE steppers: `fn(f, y, t, *args, **kwargs)` returns the state after one step, with the step size read from `brainstate.environ.get_dt()` (`odeint` sets it from its `dt` argument).\n",
    "\n",
    "As an example, the function below builds an explicit Runge–Kutta stepper from a Butcher tableau, and registers Kutta's classical third-order method under the name `'kutta3'`. `overwrite=True` lets the cell run twice; without it, registering an existing name raises `ValueError`. Once registered, `odeint` accepts the new name, and the convergence study of section 1 confirms the order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "df96fd20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-19T16:28:05.196683Z",
     "iopub.status.busy": "2026-10-19T16:28:05.196445Z",
     "iopub.status.idle": "2026-10-19T16:28:05.531400Z",
     "shell.execute_reply": "2026-10-19T16:28:05.530567Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'kutta3' registered: True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kutta3 errors [7.55e-04 9.59e-05 1.21e-05 1.52e-06 1.90e-07]  observed order [2.98 2.99 2.99 3.  ]\n"
     ]
    }
   ],
   "source": [
    "def make_explicit_rk(a, b, c):\n",
    "    \"\"\"Return an ODE stepper for the explicit Runge-Kutta tableau (a, b, c).\"\"\"\n",
    "\n",
    "    def step(f, y, t, *args, **kwargs):\n",
    "        dt = brainstate.environ.get_dt()\n",
    "        ks = []\n",
    "        for i in range(len(b)):  # loop over the stages of one step (unrolled at trace time)\n",
    "            yi = y + dt * sum(a[i][j] * ks[j] for j in range(i)) if i else y\n",
    "            ks.append(f(yi, t + c[i] * dt, *args, **kwargs))\n",
    "        return y + dt * sum(bi * k for bi, k in zip(b, ks))\n",
    "\n",
    "    return step\n",
    "\n",
    "\n",
    "kutta3 = make_explicit_rk(a=[[], [1 / 2], [-1, 2]], b=[1 / 6, 2 / 3, 1 / 6], c=[0, 1 / 2, 1])\n",
    "quad.register_method('kutta3', kutta3, overwrite=True)\n",
    "print(\"'kutta3' registered:\", 'kutta3' in quad.list_methods('ode'))\n",
    "\n",
    "err_k3 = np.array([float(jnp.max(jnp.abs(quad.odeint(oscillator, y0, 0.0, T, dt, omega, method='kutta3')[1][-1]\n",
    "                                         - exact_T)))\n",
    "                   for dt in dts])\n",
    "print('kutta3 errors', np.array2string(err_k3, precision=2),\n",
    "      ' observed order', np.round(np.log2(err_k3[:-1] / err_k3[1:]), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6a8a2c9",
   "metadata": {},
   "source": [
    "Halving the step divides the error by about 8, as expected for a third-order method. The stage loop inside the stepper is a plain Python loop over the three stages of *one* step: it is unrolled when `odeint` traces the stepper once, while the loop over time steps stays inside the compiled `scan`.\n",
    "\n",
    "`solve_ivp` additionally needs an error estimate and the order of that estimate for its step-size controller, so it accepts only the built-in embedded pairs, which the error message lists. A registered stepper such as `'kutta3'` works with `odeint` but is rejected by `solve_ivp` with a `ValueError`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f0ef0bf4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-19T16:28:05.533582Z",
     "iopub.status.busy": "2026-10-19T16:28:05.533427Z",
     "iopub.status.idle": "2026-10-19T16:28:05.537140Z",
     "shell.execute_reply": "2026-10-19T16:28:05.536579Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ValueError: Method 'kutta3' has no embedded error estimate. Adaptive methods: ['bs32', 'dopri5', 'dopri8', 'rk23', 'rk45', 'rk45_dopri', 'rk87_dopri', 'rkf45'].\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    quad.solve_ivp(oscillator, (0.0, 1.0), y0, omega, method='kutta3')\n",
    "except ValueError as e:\n",
    "    print('ValueError:', e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a37cfeb7",
   "metadata": {},
   "source": [
    "For the single-step functions behind these drivers, see the {doc}`ODE <ode_integration>`, {doc}`SDE <sde_integration>` and {doc}`IMEX and delay <imex_and_dde_integration>` tutorials; for fitting models with `odeint` and `solve_ivp`, the {doc}`model-fitting tutorial <../fit/01_fitting_models>`; and for every parameter, the {doc}`API reference <../apis/quad>`."
   ]
  }
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