docs/source/tutorials/classical_shadows.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Classical Shadows in Pauli Basis"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Overview"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "[Classical shadows](https://www.nature.com/articles/s41567-020-0932-7) formalism is an efficient method to estimate multiple observables. In this tutorial, we will show how to use the `shadows` module in `TensorCircuit` to implement classic shadows in Pauli basis."
   ],
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    "collapsed": false
   }
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   "cell_type": "markdown",
   "source": [
    "Let's first briefly review the classic shadows in Pauli basis. For an $n$-qubit quantum state $\\rho$, we randomly perform Pauli projection measurement on each qubit and obtain a snapshot like $\\{1,-1,-1,1\\cdots1,-1\\}$. This process is equivalent to apply a random unitary $U$ to $\\rho$ and measure in computational basis to obtain $|b\\rangle=|s_1\\cdots s_n\\rangle,\\ s_j\\in\\{0,1\\}$:\n",
    "$$\n",
    "\\begin{equation}\n",
    "    \\rho\\rightarrow U\\rho U^{\\dagger}\\xrightarrow{measure}|b\\rangle\\langle b|,\n",
    "\\end{equation}\n",
    "$$\n",
    "where $U=\\bigotimes_{j=1}^{n}u_j$, $u_i\\in\\{H, HS^{\\dagger}, \\mathbb{I}\\}$ correspond to the projection measurements of Pauli $X$, $Y$, $Z$ respectively. Then we reverse the operation to get the equivalent measurement result on $\\rho$:\n",
    "$$\n",
    "\\begin{equation}\n",
    "    \\rho\\xrightarrow{measure}U^{\\dagger}|b\\rangle\\langle b| U.\n",
    "\\end{equation}\n",
    "$$\n",
    "Moreover, we perform $N$ random measurements and view their average as a quantum channel:\n",
    "$$\n",
    "\\begin{equation}\n",
    "    \\mathbb{E}\\left[U^{\\dagger}|b\\rangle\\langle b|U\\right]=\\mathcal{M}(\\rho),\n",
    "\\end{equation}\n",
    "$$\n",
    "we can invert the channel to get the approximation of $\\rho$:\n",
    "$$\n",
    "\\begin{equation}\n",
    "    \\rho=\\mathbb{E}\\left[\\mathcal{M}^{-1}(U^{\\dagger}|b\\rangle\\langle b|U)\\right].\n",
    "\\end{equation}\n",
    "$$\n",
    "We call each $\\rho_i=\\mathcal{M}^{-1}(U_i^{\\dagger}|b_i\\rangle\\langle b_i|U_i)$ a shadow snapshot state and their ensemble $S(\\rho;N)=\\{\\rho_i|i=1\\cdots N\\}$ classical shadows."
   ],
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   "cell_type": "markdown",
   "source": [
    "In Pauli basis, we have a simple expression of $\\mathcal{M}^{-1}$:\n",
    "$$\n",
    "\\begin{eqnarray}\n",
    "    \\rho_i&=&\\mathcal{M}^{-1}(U_i^{\\dagger}|b_i\\rangle\\langle b_i|U_i)=\\bigotimes_{j=1}^{n}3u_{ij}^{\\dagger}|s_{ij}\\rangle\\langle s_{ij}|u_{ij}-\\mathbb{I},\\\\\n",
    "    \\rho&=&\\frac{1}{N}\\sum_{i=1}^{N}\\rho_i\\ .\n",
    "\\end{eqnarray}\n",
    "$$\n",
    "For an observable Pauli string $O=\\bigotimes_{j=1}^{n}P_j,\\ P_j\\in\\{\\mathbb{I}, X, Y, Z\\}$, we can directly use $\\rho$ to calculate $\\langle O\\rangle=\\text{Tr}(O\\rho)$. In practice, we will divide the classical shadows into $K$ parts to calculate the expectation value independently and take the median to avoid the influence of outliers:\n",
    "$$\n",
    "\\begin{equation}\n",
    "    \\langle O\\rangle=\\text{median}\\{\\langle O_{(1)}\\rangle\\cdots\\langle O_{(K)}\\rangle\\},\n",
    "\\end{equation}\n",
    "$$\n",
    "where\n",
    "$$\n",
    "\\begin{equation}\n",
    "    \\begin{split}\n",
    "        \\langle O_{(k)}\\rangle&=\\frac{1}{\\lceil N/K\\rceil}\\sum_{i=(k-1)\\lceil N/K\\rceil+1}^{k\\lceil N/K\\rceil}\\text{Tr}\\left[\\bigotimes_{j=1}^{n}P_j(3u_{ij}^{\\dagger}|s_{ij}\\rangle\\langle s_{ij}|u_{ij}-\\mathbb{I})\\right]\\\\\n",
    "        &=\\frac{1}{\\lceil N/K\\rceil}\\sum_{i=(k-1)\\lceil N/K\\rceil+1}^{k\\lceil N/K\\rceil}\\prod_{j=1}^n\\text{Tr}\\left[3P_j u_{ij}^{\\dagger}|s_{ij}\\rangle\\langle s_{ij}|u_{ij}\\right].\n",
    "    \\end{split}\n",
    "\\end{equation}\n",
    "$$"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-08-08T12:07:59.787548200Z",
     "start_time": "2023-08-08T12:07:59.689077500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "('complex128', 'float64')"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorcircuit as tc\n",
    "from tensorcircuit import shadows\n",
    "import numpy as np\n",
    "from functools import partial\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "BK = tc.set_backend(\"jax\")\n",
    "tc.set_dtype(\"complex128\")"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Construct the Classical Shadow Snapshots"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We first set the number of qubits $n$ and the number of repeated measurements $r$ on each Pauli string. Then from the target observable Pauli strings $\\{O_i|i=1,\\cdots,M\\}$ (0, 1, 2, and 3 correspond to $\\mathbb{I}$, $X$, $Y$, and $Z$, respectively), the error $\\epsilon$ and the rate of failure $\\delta$, we can use `shadow_bound` function to get the total number of snapshots $N$ and the number of equal parts $K$ to split the shadow snapshot states to compute the median of means:\n",
    "$$\n",
    "\\begin{eqnarray}\n",
    "    K&=&2\\log(2M/\\delta),\\\\\n",
    "    N&=&K\\frac{34}{\\epsilon^2}\\max_{1\\le i\\le M}\\left\\|O_i-\\frac{\\text{Tr}(O_i)}{2^n}\\mathbb{I}\\right\\|^2_{\\text{shadow}}=K\\frac{34}{\\epsilon^2}3^{\\max_{1\\le i\\le M}k_i},\n",
    "\\end{eqnarray}\n",
    "$$\n",
    "where $k_i$ is the number of nontrivial Pauli matrices in $O_i$. Please refer to the Theorem S1 and Lemma S3 in [Huang, Kueng and Preskill (2020)](https://www.nature.com/articles/s41567-020-0932-7) for the details of proof. It should be noted that `shadow_bound` has a certain degree of overestimation of $N$, and so many measurements are not really needed in practice. And `shadow_bound` is not jitable and no need to jit."
   ],
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    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N: 489600\tK: 16\tnumber of Pauli strings: 97920\n"
     ]
    }
   ],
   "source": [
    "n, r = 8, 5\n",
    "ps = [\n",
    "    [1, 0, 0, 0, 0, 0, 0, 2],\n",
    "    [0, 3, 0, 0, 0, 0, 1, 0],\n",
    "    [0, 0, 2, 0, 0, 3, 0, 0],\n",
    "    [0, 0, 0, 1, 2, 0, 0, 0],\n",
    "    [0, 0, 0, 3, 1, 0, 0, 0],\n",
    "    [0, 0, 0, 0, 0, 3, 0, 0],\n",
    "    [0, 0, 0, 0, 0, 0, 2, 0],\n",
    "    [3, 0, 0, 0, 0, 0, 0, 1],\n",
    "    [0, 2, 0, 0, 0, 0, 3, 0],\n",
    "    [0, 0, 1, 0, 0, 2, 0, 0],\n",
    "]\n",
    "\n",
    "epsilon, delta = 0.1, 0.01\n",
    "N, K = shadows.shadow_bound(ps, epsilon, delta)\n",
    "nps = N // r  # number of random pauli strings\n",
    "print(f\"N: {N}\\tK: {K}\\tnumber of Pauli strings: {nps}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T12:07:59.787548200Z",
     "start_time": "2023-08-08T12:07:59.701762Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Then we use random quantum circuit to generate an entangled state."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "nlayers = 10\n",
    "thetas = 2 * np.random.rand(nlayers, n) - 1\n",
    "\n",
    "c = tc.Circuit(n)\n",
    "for i in range(n):\n",
    "    c.H(i)\n",
    "for i in range(nlayers):\n",
    "    for j in range(n):\n",
    "        c.cnot(j, (j + 1) % n)\n",
    "    for j in range(n):\n",
    "        c.rz(j, theta=thetas[i, j] * np.pi)\n",
    "\n",
    "psi = c.state()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T12:07:59.893829700Z",
     "start_time": "2023-08-08T12:07:59.708422100Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We randomly generate Pauli strings. Since the function after just-in-time compilation does not support random sampling, we need to generate all random states in advance, that is, variable `status`."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "pauli_strings = BK.convert_to_tensor(np.random.randint(1, 4, size=(nps, n)))\n",
    "status = BK.convert_to_tensor(np.random.rand(nps, r))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T12:07:59.919435300Z",
     "start_time": "2023-08-08T12:07:59.908781200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "If `measurement_only`=True, the outputs of `shadow_snapshots` are snapshot bit strings $b=s_1\\cdots s_n,\\ s_j\\in\\{0,1\\}$, otherwise the outputs are snapshot states $\\{u_{j}^{\\dagger}|s_j\\rangle\\langle s_j| u_j\\ |j=1,\\cdots,n\\}$. If you only need to generate one batch of snapshots or generate multiple batches of snapshots with different `nps` or `r`, jit cannot provide speedup. JIT will only accelerate when the same shape of snapshots are generated multiple times."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(97920, 5, 8, 2, 2)\n"
     ]
    }
   ],
   "source": [
    "@partial(BK.jit, static_argnums=(3,))\n",
    "def shadow_ss(psi, pauli_strings, status, measurement_only=False):\n",
    "    return shadows.shadow_snapshots(\n",
    "        psi, pauli_strings, status, measurement_only=measurement_only\n",
    "    )\n",
    "\n",
    "\n",
    "ss_states = shadow_ss(psi, pauli_strings, status)  # jit is not necessary here\n",
    "print(ss_states.shape)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T12:08:03.113557900Z",
     "start_time": "2023-08-08T12:08:00.167642400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Estimate the Expectation Values of Observables"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Since the operation of taking the median is not jitable, the outputs of `expectation_ps_shadows` have $K$ values, and we need to take the median of them."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "def shadow_expec(snapshots_states, ob):\n",
    "    return shadows.expectation_ps_shadow(snapshots_states, ps=ob, k=K)\n",
    "\n",
    "\n",
    "sejit = BK.jit(shadow_expec)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T12:08:03.189105100Z",
     "start_time": "2023-08-08T12:08:03.113557900Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "It can be seen from the running time that every time the number of Pauli strings changes, `shadow_expec` will be recompiled, but for the same number of Pauli strings but different observables, `shadow_expec` will only be compiled once. In the end, the absolute errors given by classical shadows are much smaller than the $\\epsilon=0.1$ we set, so shadow_bound gives a very loose upper bound."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "observalbe: No.0\tnumber of Pauli strings: 1000\ttime: 0.9208428859710693\n",
      "observalbe: No.1\tnumber of Pauli strings: 1000\ttime: 0.001764535903930664\n",
      "observalbe: No.2\tnumber of Pauli strings: 1000\ttime: 0.0013713836669921875\n",
      "observalbe: No.3\tnumber of Pauli strings: 1000\ttime: 0.0013463497161865234\n",
      "observalbe: No.4\tnumber of Pauli strings: 1000\ttime: 0.0014214515686035156\n",
      "observalbe: No.5\tnumber of Pauli strings: 1000\ttime: 0.0013613700866699219\n",
      "observalbe: No.6\tnumber of Pauli strings: 1000\ttime: 0.001367330551147461\n",
      "observalbe: No.7\tnumber of Pauli strings: 1000\ttime: 0.001378774642944336\n",
      "observalbe: No.8\tnumber of Pauli strings: 1000\ttime: 0.0013740062713623047\n",
      "observalbe: No.9\tnumber of Pauli strings: 1000\ttime: 0.001354217529296875\n",
      "observalbe: No.0\tnumber of Pauli strings: 11000\ttime: 0.9906811714172363\n",
      "observalbe: No.1\tnumber of Pauli strings: 11000\ttime: 0.012621402740478516\n",
      "observalbe: No.2\tnumber of Pauli strings: 11000\ttime: 0.012280464172363281\n",
      "observalbe: No.3\tnumber of Pauli strings: 11000\ttime: 0.012493133544921875\n",
      "observalbe: No.4\tnumber of Pauli strings: 11000\ttime: 0.012433767318725586\n",
      "observalbe: No.5\tnumber of Pauli strings: 11000\ttime: 0.012764692306518555\n",
      "observalbe: No.6\tnumber of Pauli strings: 11000\ttime: 0.012403726577758789\n",
      "observalbe: No.7\tnumber of Pauli strings: 11000\ttime: 0.012158393859863281\n",
      "observalbe: No.8\tnumber of Pauli strings: 11000\ttime: 0.012058734893798828\n",
      "observalbe: No.9\tnumber of Pauli strings: 11000\ttime: 0.012238740921020508\n",
      "observalbe: No.0\tnumber of Pauli strings: 21000\ttime: 1.0218532085418701\n",
      "observalbe: No.1\tnumber of Pauli strings: 21000\ttime: 0.024202823638916016\n",
      "observalbe: No.2\tnumber of Pauli strings: 21000\ttime: 0.023923397064208984\n",
      "observalbe: No.3\tnumber of Pauli strings: 21000\ttime: 0.023802757263183594\n",
      "observalbe: No.4\tnumber of Pauli strings: 21000\ttime: 0.02439141273498535\n",
      "observalbe: No.5\tnumber of Pauli strings: 21000\ttime: 0.024312973022460938\n",
      "observalbe: No.6\tnumber of Pauli strings: 21000\ttime: 0.02409648895263672\n",
      "observalbe: No.7\tnumber of Pauli strings: 21000\ttime: 0.0239260196685791\n",
      "observalbe: No.8\tnumber of Pauli strings: 21000\ttime: 0.024311542510986328\n",
      "observalbe: No.9\tnumber of Pauli strings: 21000\ttime: 0.023871660232543945\n",
      "observalbe: No.0\tnumber of Pauli strings: 31000\ttime: 0.9866394996643066\n",
      "observalbe: No.1\tnumber of Pauli strings: 31000\ttime: 0.03965640068054199\n",
      "observalbe: No.2\tnumber of Pauli strings: 31000\ttime: 0.040810585021972656\n",
      "observalbe: No.3\tnumber of Pauli strings: 31000\ttime: 0.040923118591308594\n",
      "observalbe: No.4\tnumber of Pauli strings: 31000\ttime: 0.03870081901550293\n",
      "observalbe: No.5\tnumber of Pauli strings: 31000\ttime: 0.03658175468444824\n",
      "observalbe: No.6\tnumber of Pauli strings: 31000\ttime: 0.033846139907836914\n",
      "observalbe: No.7\tnumber of Pauli strings: 31000\ttime: 0.03284430503845215\n",
      "observalbe: No.8\tnumber of Pauli strings: 31000\ttime: 0.03468632698059082\n",
      "observalbe: No.9\tnumber of Pauli strings: 31000\ttime: 0.034407615661621094\n",
      "observalbe: No.0\tnumber of Pauli strings: 41000\ttime: 1.014129400253296\n",
      "observalbe: No.1\tnumber of Pauli strings: 41000\ttime: 0.053369998931884766\n",
      "observalbe: No.2\tnumber of Pauli strings: 41000\ttime: 0.05180001258850098\n",
      "observalbe: No.3\tnumber of Pauli strings: 41000\ttime: 0.05307412147521973\n",
      "observalbe: No.4\tnumber of Pauli strings: 41000\ttime: 0.05329442024230957\n",
      "observalbe: No.5\tnumber of Pauli strings: 41000\ttime: 0.05172109603881836\n",
      "observalbe: No.6\tnumber of Pauli strings: 41000\ttime: 0.052023887634277344\n",
      "observalbe: No.7\tnumber of Pauli strings: 41000\ttime: 0.05315876007080078\n",
      "observalbe: No.8\tnumber of Pauli strings: 41000\ttime: 0.05295825004577637\n",
      "observalbe: No.9\tnumber of Pauli strings: 41000\ttime: 0.05689072608947754\n",
      "observalbe: No.0\tnumber of Pauli strings: 51000\ttime: 0.997788667678833\n",
      "observalbe: No.1\tnumber of Pauli strings: 51000\ttime: 0.06549453735351562\n",
      "observalbe: No.2\tnumber of Pauli strings: 51000\ttime: 0.06295180320739746\n",
      "observalbe: No.3\tnumber of Pauli strings: 51000\ttime: 0.06317925453186035\n",
      "observalbe: No.4\tnumber of Pauli strings: 51000\ttime: 0.06182670593261719\n",
      "observalbe: No.5\tnumber of Pauli strings: 51000\ttime: 0.06398820877075195\n",
      "observalbe: No.6\tnumber of Pauli strings: 51000\ttime: 0.06252193450927734\n",
      "observalbe: No.7\tnumber of Pauli strings: 51000\ttime: 0.057137250900268555\n",
      "observalbe: No.8\tnumber of Pauli strings: 51000\ttime: 0.06129312515258789\n",
      "observalbe: No.9\tnumber of Pauli strings: 51000\ttime: 0.06306266784667969\n",
      "observalbe: No.0\tnumber of Pauli strings: 61000\ttime: 1.0299479961395264\n",
      "observalbe: No.1\tnumber of Pauli strings: 61000\ttime: 0.06776976585388184\n",
      "observalbe: No.2\tnumber of Pauli strings: 61000\ttime: 0.06797552108764648\n",
      "observalbe: No.3\tnumber of Pauli strings: 61000\ttime: 0.06658697128295898\n",
      "observalbe: No.4\tnumber of Pauli strings: 61000\ttime: 0.06751775741577148\n",
      "observalbe: No.5\tnumber of Pauli strings: 61000\ttime: 0.06934762001037598\n",
      "observalbe: No.6\tnumber of Pauli strings: 61000\ttime: 0.06731104850769043\n",
      "observalbe: No.7\tnumber of Pauli strings: 61000\ttime: 0.06712603569030762\n",
      "observalbe: No.8\tnumber of Pauli strings: 61000\ttime: 0.07176804542541504\n",
      "observalbe: No.9\tnumber of Pauli strings: 61000\ttime: 0.06982183456420898\n",
      "observalbe: No.0\tnumber of Pauli strings: 71000\ttime: 1.0498173236846924\n",
      "observalbe: No.1\tnumber of Pauli strings: 71000\ttime: 0.07942628860473633\n",
      "observalbe: No.2\tnumber of Pauli strings: 71000\ttime: 0.07740139961242676\n",
      "observalbe: No.3\tnumber of Pauli strings: 71000\ttime: 0.07828497886657715\n",
      "observalbe: No.4\tnumber of Pauli strings: 71000\ttime: 0.07857322692871094\n",
      "observalbe: No.5\tnumber of Pauli strings: 71000\ttime: 0.07705187797546387\n",
      "observalbe: No.6\tnumber of Pauli strings: 71000\ttime: 0.0785682201385498\n",
      "observalbe: No.7\tnumber of Pauli strings: 71000\ttime: 0.07775306701660156\n",
      "observalbe: No.8\tnumber of Pauli strings: 71000\ttime: 0.07622480392456055\n",
      "observalbe: No.9\tnumber of Pauli strings: 71000\ttime: 0.07927823066711426\n",
      "observalbe: No.0\tnumber of Pauli strings: 81000\ttime: 1.05106520652771\n",
      "observalbe: No.1\tnumber of Pauli strings: 81000\ttime: 0.08436226844787598\n",
      "observalbe: No.2\tnumber of Pauli strings: 81000\ttime: 0.08675956726074219\n",
      "observalbe: No.3\tnumber of Pauli strings: 81000\ttime: 0.08600664138793945\n",
      "observalbe: No.4\tnumber of Pauli strings: 81000\ttime: 0.08568215370178223\n",
      "observalbe: No.5\tnumber of Pauli strings: 81000\ttime: 0.0852055549621582\n",
      "observalbe: No.6\tnumber of Pauli strings: 81000\ttime: 0.08647036552429199\n",
      "observalbe: No.7\tnumber of Pauli strings: 81000\ttime: 0.08586692810058594\n",
      "observalbe: No.8\tnumber of Pauli strings: 81000\ttime: 0.0845186710357666\n",
      "observalbe: No.9\tnumber of Pauli strings: 81000\ttime: 0.08591985702514648\n",
      "observalbe: No.0\tnumber of Pauli strings: 91000\ttime: 1.0378658771514893\n",
      "observalbe: No.1\tnumber of Pauli strings: 91000\ttime: 0.09274506568908691\n",
      "observalbe: No.2\tnumber of Pauli strings: 91000\ttime: 0.09148311614990234\n",
      "observalbe: No.3\tnumber of Pauli strings: 91000\ttime: 0.09451770782470703\n",
      "observalbe: No.4\tnumber of Pauli strings: 91000\ttime: 0.09089279174804688\n",
      "observalbe: No.5\tnumber of Pauli strings: 91000\ttime: 0.09438014030456543\n",
      "observalbe: No.6\tnumber of Pauli strings: 91000\ttime: 0.09279298782348633\n",
      "observalbe: No.7\tnumber of Pauli strings: 91000\ttime: 0.09514856338500977\n",
      "observalbe: No.8\tnumber of Pauli strings: 91000\ttime: 0.09679627418518066\n",
      "observalbe: No.9\tnumber of Pauli strings: 91000\ttime: 0.09592223167419434\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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Yvnw51dXVjB071m0bN24cmzZtyum3adMmxowZg67reY9jWRZf//rX+e1vf8vzzz/Peeed16vjPlMkPFdI2s+Yk7wmQRAE4Sxg0qRJzJs3j4ceeshtu/POO9m4cSNLly5l165drFmzhtWrV3PXXXe5faZNm8bq1avd11/72tf45S9/yRNPPEFJSQk1NTXU1NQQixUmOf5UiGgqJHmdJkEQBEHo/yxZsgQzKxd38uTJPPXUU6xdu5aJEyeyaNEilixZkhPC27t3L/X19e7rH//4xzQ3N/PJT36SYcOGuX9PPvlkX15Kl5HwXCFpL5JENAmCIAj9kMcff7xDW1VVFYlEIqdt9uzZzJ49u9Pj7N+/P+e1ZVk9Mbw+Q5ymQpJdERxENAmCIAhCP0ZEUyGR8JwgCIIgnDWIaCok7UVSexElCIIgCEK/QURTIengNMmivYIgCILQXxHRVEik5IAgCIIgnDWIaCokhsyeEwRBEISzBRFNhaTD7DnJaRIEQRCE/oqIpkLiiCRn/R/JaRIEQRCEfouIpkJhmmDZlVR9jmiS8JwgCIIg9FdENBWK7FCciCZBEARB6PeIaCoURh7RJHWaBEEQhH7I/Pnz0TSN5cuX57SvX78eTdNO+7g//vGPufjiiyktLaW0tJQpU6bwpz/96UyH22uIaCoU2UngPslpEgRBEPo3wWCQFStW0NjY2GPHHDFiBMuXL2fr1q289tprXH311Vx//fW88847PXaOnkREU6HIDsXpgY5tgiAIgtCPmD59OpWVlSxbtqzTPuvWrWPChAkEAgGqqqpYuXLlSY/5+c9/ns985jNceOGFjBkzhu9973sUFxfz8ssv9/TwewRvoQfwocUJxXl8oPvUcxFNgiAIHyosy8KKxQpybi0U6lZoTdd17r//fm666SZuv/12RowYkbN969atzJkzh8WLFzN37lw2b97MbbfdRkVFBfPnzz/l8Q3D4OmnnyYSiTBlypTuXk6fIKKpUDiJ4LoPPN7cNkEQBOFDgRWL8d7kywpy7rHbtqKFw93aZ9asWVRXV3Pffffx2GOP5WxbtWoV06ZN49577wVgzJgx7NixgwceeOCkountt99mypQpxONxiouL+e1vf8v48eO7fT19gYTnCoVTDVz3gUdXzyWnSRAEQejnrFixgjVr1rBz586c9p07dzJ16tSctqlTp7J7924Mo/P729ixY3njjTf429/+xq233sqXvvQlduzY0StjP1PEaSoUTiK4x6f+QMJzgiAIHzK0UIix27YW7Nynw1VXXcWMGTNYuHBhl8Jup8Lv93PBBRcAcNlll/Hqq6/ygx/8gJ/85CdnfOyeRkRTocgbnhPRJAiC8GFC07Ruh8j6A8uXL6e6upqxY8e6bePGjWPTpk05/TZt2sSYMWPQdb3LxzZNk0Qi0WNj7Un6RXju4YcfpqqqimAwyBVXXMErr7xy0v5PP/00F110EcFgkEmTJvHHP/4xZ7tTTyL779prr+3NS+g+TnjOkxWekzpNgiAIwlnApEmTmDdvHg899JDbduedd7Jx40aWLl3Krl27WLNmDatXr+auu+5y+0ybNo3Vq1e7rxcuXMiLL77I/v37efvtt1m4cCHPP/888+bN69Pr6SoFF01PPvkkd9xxB/fddx/btm3jkksuYcaMGdTV1eXtv3nzZm688Ua+8pWv8PrrrzNz5kxmzpzJ9u3bc/pde+21HDt2zP379a9/3ReX03Vcp8mb5TRJTpMgCIJwdrBkyRJM03RfT548maeeeoq1a9cyceJEFi1axJIlS3JCeHv37qW+vt59XVdXx80338zYsWOZNm0ar776Ks888wyf/vSn+/JSuoxmWZZVyAFcccUVXH755a7yNE2TkSNH8vWvf53vfOc7HfrPnTuXSCTC73//e7ftYx/7GNXV1TzyyCOAcpqamppYv359l8aQSCRyrMCWlhZGjhxJc3MzpaWlZ3B1J2HfX2HN52DQWBh2Mbz9NMxYBlNu653zCYIgCAUlHo+zb98+zjvvPILBYKGHc9ZysvexpaWFsrKyXrt/F9RpSiaTbN26lenTp7ttHo+H6dOns2XLlrz7bNmyJac/wIwZMzr0f/755xkyZAhjx47l1ltvpaGhodNxLFu2jLKyMvdv5MiRZ3BVXcRJBNf9ktMkCIIgCGcBBRVN9fX1GIbB0KFDc9qHDh1KTU1N3n1qampO2f/aa6/lP/7jP9i4cSMrVqzghRde4Lrrrut0yuPChQtpbm52/w4dOnSGV9YFHIGke7NKDkhOkyAIgiD0Vz6Qs+e+8IUvuM8nTZrExRdfzOjRo3n++eeZNm1ah/6BQIBAINCXQ8ytCC45TYIgCILQ7ymo0zRo0CB0Xae2tjanvba2lsrKyrz7VFZWdqs/wPnnn8+gQYPYs2fPmQ+6p8gpOSB1mgRBEAShv1NQ0eT3+7nsssvYuHGj22aaJhs3bux03ZkpU6bk9Ad49tlnT7pOzeHDh2loaGDYsGE9M/CewC05kDV7TkoOCIIgCEK/peAlB+644w4effRRtyT7rbfeSiQS4ZZbbgHg5ptvZuHChW7/b3zjG2zYsIGVK1fy7rvvsnjxYl577TUWLFgAQFtbG3fffTcvv/wy+/fvZ+PGjVx//fVccMEFzJgxoyDXmBfXafJn5TSJ0yQIgiAI/ZWC5zTNnTuX48ePs2jRImpqaqiurmbDhg1usvfBgwfxeDLa7sorr+SJJ57gu9/9Lvfccw8XXngh69evZ+LEiYBahfmtt95izZo1NDU1MXz4cK655hqWLl3a93lLJ8OdPSc5TYIgCIJwNlBw0QSwYMEC1ylqz/PPP9+h7YYbbuCGG27I2z8UCvHMM8/05PB6BzcR3KuEE4jTJAiCIAj9mIKH5z60uCUHsp0myWkSBEEQhP6KiKZCkVNyQHKaBEEQBKG/I6KpUOSUHJCcJkEQBKH/Mn/+fDRNY/ny5Tnt69evR9O0HjnH8uXL0TSNb37zmz1yvN5ARFOhMKROkyAIgnD2EAwGWbFiBY2NjT1+7FdffZWf/OQnXHzxxT1+7J5ERFOhyFcRXOo0CYIgCP2U6dOnU1lZybJlyzrts27dOiZMmEAgEKCqqoqVK1ee8rhtbW3MmzePRx99lAEDBvTkkHscEU2FIic8JzlNgiAIH0YsyyKVMAryZ1lWt8aq6zr3338/P/zhDzl8+HCH7Vu3bmXOnDl84Qtf4O2332bx4sXce++9PP744yc97te+9jU++9nPMn369G6NpxD0i5IDH0ryVQSXnCZBEIQPFemkyU+/8UJBzv3//eAT+AJ6t/aZNWsW1dXV3HfffTz22GM521atWsW0adO49957ARgzZgw7duzggQceYP78+XmPt3btWrZt28arr756WtfQ14jTVCiynSap0yQIgiCcJaxYscJdxSObnTt3MnXq1Jy2qVOnsnv3bgyjoylw6NAhvvGNb/CrX/2KYDDYq2PuKcRpKhRuRXC/1GkSBEH4kOL1e/j/fvCJgp37dLjqqquYMWMGCxcu7NRB6gpbt26lrq6OyZMnu22GYfDiiy+yevVqEokEut49J6y3EdFUKPKG58RpEgRB+DChaVq3Q2T9geXLl1NdXc3YsWPdtnHjxrFp06acfps2bWLMmDF5xc+0adN4++23c9puueUWLrroIr797W/3O8EEIpoKh9RpEgRBEM5SJk2axLx583jooYfctjvvvJPLL7+cpUuXMnfuXLZs2cLq1av50Y9+5PaZNm0as2bNYsGCBZSUlLjrxjoUFRVRUVHRob2/IDlNhSJfyQFxmgRBEISzhCVLlmCapvt68uTJPPXUU6xdu5aJEyeyaNEilixZkhPC27t3L/X19QUYbc8gTlOhcJ0mr9RpEgRBEPo1+coGVFVVkUgkctpmz57N7NmzOz3O/v37T3qe559//jRG13eI01Qo3IrgfnGaBEEQBOEsQERTocgOz+mS0yQIgiAI/R0RTYUibyK4OE2CIAiC0F8R0VQo8pYckJwmQRAEQeiviGgqFOI0CYIgCMJZhYimQpE3EVxymgRBEAShvyKiqVC4ieBSEVwQBEEQzgZENBWKfOE5qdMkCIIgCP0WEU2FIm9FcAnPCYIgCEJ/RURToXBCcbo3q06ThOcEQRAEob8ioqlQSEVwQRAE4Sxh/vz5aJrG8uXLc9rXr1+PpmmnfdzFixejaVrO30UXXXSmw+01RDQVCiOpHnPCc5LTJAiCIPRPgsEgK1asoLGxsUePO2HCBI4dO+b+vfTSSz16/J5ERFOhcMNzWaLJMiFrxWhBEAThg41lWaTi8YL8WZbVrbFOnz6dyspKli1b1mmfdevWMWHCBAKBAFVVVaxcufKUx/V6vVRWVrp/gwYN6ta4+hJvoQfwoSVfyQEAy0C0rCAIwoeDdCLBQ1/6h4Kc+/Y1/4kvGOxyf13Xuf/++7npppu4/fbbGTFiRM72rVu3MmfOHBYvXszcuXPZvHkzt912GxUVFcyfP7/T4+7evZvhw4cTDAaZMmUKy5Yt49xzzz3dy+pV5O5cKPKVHAApOyAIgiD0W2bNmkV1dTX33Xdfh22rVq1i2rRp3HvvvYwZM4b58+ezYMECHnjggU6Pd8UVV/D444+zYcMGfvzjH7Nv3z7+7u/+jtbW1t68jNNGnKZCYFmZ8JynnWiSZHBBEIQPDd5AgNvX/GfBzn06rFixgquvvpq77rorp33nzp1cf/31OW1Tp07lwQcfxDAMdF3vcKzrrrvOfX7xxRdzxRVXMGrUKJ566im+8pWvnNb4ehMRTYUg201q7zSJaBIEQfjQoGlat0Jk/YGrrrqKGTNmsHDhwpOG3U6H8vJyxowZw549e3r0uD2FhOcKgdleNGWpbylwKQiCIPRzli9fzu9+9zu2bNnito0bN45Nmzbl9Nu0aRNjxozJ6zLlo62tjb179zJs2LAeHW9PIaKpEGQ7TR4faJqUHRAEQRDOGiZNmsS8efN46KGH3LY777yTjRs3snTpUnbt2sWaNWtYvXp1Thhv2rRprF692n1911138cILL7B//342b97MrFmz0HWdG2+8sU+vp6uIaCoE2SE43acepcClIAiCcBaxZMkSzKwyOZMnT+app55i7dq1TJw4kUWLFrFkyZKcEN7evXupr693Xx8+fJgbb7yRsWPHMmfOHCoqKnj55ZcZPHhwX15Kl5GcpkLgOE2arlwmENEkCIIg9Fsef/zxDm1VVVUkEomcttmzZzN79uxOj7N///6c12vXru2J4fUZ4jQVAqcauO7PtMmivYIgCILQrxHRVAiyq4E7OKJJ6jQJgiAIQr9ERFMhyK4G7iDhOUEQBEHo14hoKgTZ1cAdnOcimgRBEAShXyKiqRC4TlN2eM6uYSE5TYIgCILQLxHRVAhOltMkdZoEQRAEoV8ioqkQuLPn8okmCc8JgiAIQn9ERFMhyBuek5wmQRAEQejP9AvR9PDDD1NVVUUwGOSKK67glVdeOWn/p59+mosuuohgMMikSZP44x//2Gnff/mXf0HTNB588MEeHvUZ4IbnsmfPSU6TIAiCIPRnCi6annzySe644w7uu+8+tm3bxiWXXMKMGTOoq6vL23/z5s3ceOONfOUrX+H1119n5syZzJw5k+3bt3fo+9vf/paXX36Z4cOH9/ZldInWeIqDDVGa2iKqwXP6dZp+8fIBbvvVVpJp89SdBUEQBEE4YwoumlatWsVXv/pVbrnlFsaPH88jjzxCOBzmZz/7Wd7+P/jBD7j22mu5++67GTduHEuXLmXy5Mk5CwACHDlyhK9//ev86le/wufz5T2WQyKRoKWlJeevN/jZS/u56oG/8Ps3DqmGvBXBuxae+8kLe/nj2zW8caipZwcpCIIgCO2YP38+mqaxfPnynPb169ejOcuBnSZHjhzhH//xH6moqCAUCjFp0iRee+21Mzpmb1FQ0ZRMJtm6dSvTp0932zweD9OnT2fLli1599myZUtOf4AZM2bk9DdNky9+8YvcfffdTJgw4ZTjWLZsGWVlZe7fyJEjT/OKTk7Yr0JwZspeqyc7PNfNOk1tCdWvOSaz7QRBEITeJxgMsmLFChobG3vsmI2NjUydOhWfz8ef/vQnduzYwcqVKxkwYECPnaMnKahoqq+vxzAMhg4dmtM+dOhQampq8u5TU1Nzyv4rVqzA6/Vy++23d2kcCxcupLm52f07dOhQN6+kawRt0WSk7dlzndRpammIkYieXAxFbNHUGhfRJAiCcLZiWRZm0ijIn2VZ3Rrr9OnTqaysZNmyZZ32WbduHRMmTCAQCFBVVcXKlStPeswVK1YwcuRIfv7zn/PRj36U8847j2uuuYbRo0d3a2x9hffUXc4utm7dyg9+8AO2bdvWZcswEAgQCAR6eWQQ9jmiKU9FcDs8F4+m+dV9L1MxvJg591ye9zjJtEnKUP/YW8RpEgRBOGuxUiZHF20uyLmHL7kSzf4x3xV0Xef+++/npptu4vbbb2fEiBE527du3cqcOXNYvHgxc+fOZfPmzdx2221UVFQwf/78vMf87//+b2bMmMENN9zACy+8wDnnnMNtt93GV7/61TO5tF6joE7ToEGD0HWd2tranPba2loqKyvz7lNZWXnS/n/961+pq6vj3HPPxev14vV6OXDgAHfeeSdVVVW9ch1dJeSE51ynqePac60tYKYtGo9FOj2O4zIBtMSlRIEgCILQN8yaNYvq6mruu+++DttWrVrFtGnTuPfeexkzZgzz589nwYIFPPDAA50e7/333+fHP/4xF154Ic888wy33nort99+O2vWrOnNyzhtCuo0+f1+LrvsMjZu3MjMmTMBlY+0ceNGFixYkHefKVOmsHHjRr75zW+6bc8++yxTpkwB4Itf/GLenKcvfvGL3HLLLb1yHV0lI5o6d5qMtCo5kE6ZGIaJrnfUtZFkRihJeE4QBOHsRfN5GL7kyoKd+3RYsWIFV199NXfddVdO+86dO7n++utz2qZOncqDDz6IYRjoekdXyzRNPvKRj3D//fcDcOmll7J9+3YeeeQRvvSlL53W+HqTgofn7rjjDr70pS/xkY98hI9+9KM8+OCDRCIRV+DcfPPNnHPOOW4M9Rvf+Aaf+MQnWLlyJZ/97GdZu3Ytr732Gj/96U8BqKiooKKiIuccPp+PyspKxo4d27cX146QHZ6z3IrgHWfPmelMjDkVM9CL84imRKaWU0tMnCZBEISzFU3TuhUi6w9cddVVzJgxg4ULF3Yadusqw4YNY/z48Tlt48aNY926dWd03N6i4KJp7ty5HD9+nEWLFlFTU0N1dTUbNmxwk70PHjyIx5MRDldeeSVPPPEE3/3ud7nnnnu48MILWb9+PRMnTizUJXQZZ/Yc6XwVwW2nKZURRMl4mmBxx3IJ2U5TizhNgiAIQh+zfPlyqqurc8yIcePGsWnTppx+mzZtYsyYMXldJlBO1HvvvZfTtmvXLkaNGtXzg+4BCi6aABYsWNBpOO7555/v0HbDDTdwww03dPn4+/fvP82R9SyO02Q6i/LqHXOajKxilYlOXKTsnKZWyWkSBEEQ+phJkyYxb948HnroIbftzjvv5PLLL2fp0qXMnTuXLVu2sHr1an70ox+5faZNm8asWbPce/63vvUtrrzySu6//37mzJnDK6+8wk9/+lM3etTfKHhxyw8TTk5T3rXnbAFlGlnhuU4EUW4iuDhNgiAIQt+zZMkSTDPzQ3/y5Mk89dRTrF27lokTJ7Jo0SKWLFmSE8Lbu3cv9fX17uvLL7+c3/72t/z6179m4sSJLF26lAcffJB58+b15aV0mX7hNH1YcJwmzUwruZo3ETwjmpKx/OvQ5eY0iWgSBEEQepfHH3+8Q1tVVRWJRCKnbfbs2cyePbvT4+SL/Hzuc5/jc5/73JkOsU8Qp6kPCfuVMPLhLNibTzR1ITyXlPCcIAiCIPQ1Ipr6kIBXvd1ebKcoXyJ4l8JzWU5TPNXtqq6CIAiCIHQfEU19iMejEfLpp3CaMk1dSQRPGRbxlJm3nyAIgiAIPYeIpj4m5NeznKZ8s+eycprineQ0JXPFlBS4FARBEITeR0RTHxPy6fg0WwzlcZqyJiKQ6oLTBDKDThAEQRD6AhFNfUyO05RdEdwWUDnhuc5ympK5DlSzVAUXBEEQhF5HRFMfE/Zn5TTlhOdUOQIjSw91XnJAwnOCIAiC0NeIaOpjgj4dH52H53JFU34HKZrIFVMtUnZAEARBEHodEU19TNiv43WdppPnNCU7EUNtttPklDCQApeCIAiC0PuIaOpjQr7snKZs0WTnNBma29TZ7LmoPXtuWFkQkAKXgiAIgtAXiGjqY0L+rNlz+XKazCzR1El4rs0Oz1XaoklmzwmCIAi9yfz589E0jeXLl+e0r1+/Hk3TOtnr1FRVVaFpWoe/r33ta2c65F5BRFMfk1vcMmv2nJvTlO00dZLT5DpNIUDCc4IgCELvEwwGWbFiBY2NjT12zFdffZVjx465f88++ywAN9xwQ4+doycR0dTHhP2dheds0WRmPhIzbZFO5YboTNMimsx1miQ8JwiCcHZiWRbJZLIgf91dgmv69OlUVlaybNmyTvusW7eOCRMmEAgEqKqqYuXKlSc95uDBg6msrHT/fv/73zN69Gg+8YlPdGtsfYX31F2EniSUPXsuOzzn1Gkyc23OZMzA69Pd19EsETVcwnOCIAhnNalUivvvv78g577nnnvw+/2n7mij6zr3338/N910E7fffjsjRozI2b5161bmzJnD4sWLmTt3Lps3b+a2226joqKC+fPnn/L4yWSSX/7yl9xxxx1nFPLrTcRp6mNCfm9m9lzeiuC5H0n7EJ1To8mjweCSACDhOUEQBKFvmDVrFtXV1dx3330dtq1atYpp06Zx7733MmbMGObPn8+CBQt44IEHunTs9evX09TU1CWBVSjEaepjQj5P1tpz2aLJSQRvJ5pi+UVTUcBLaVDtL+E5QRCEsxOfz8c999xTsHOfDitWrODqq6/mrrvuymnfuXMn119/fU7b1KlTefDBBzEMA13XORmPPfYY1113HcOHDz+tcfUFIpr6mLDfi187SSJ4B6cpN6cpYs+cK/J7KQ2pf/ASnhMEQTg70TStWyGy/sBVV13FjBkzWLhwYY+5QgcOHOC5557jN7/5TY8cr7cQ0dTHBHMSwbNLDjg5TSd3mtpcp0mnJKj2b5G15wRBEIQ+ZPny5VRXVzN27Fi3bdy4cWzatCmn36ZNmxgzZswpXaaf//znDBkyhM9+9rO9Mt6eQnKa+phwdnHLvBXB1T8s3ac+mvY5TU65gezwXCxlkDJMBEEQBKEvmDRpEvPmzeOhhx5y2+688042btzI0qVL2bVrF2vWrGH16tU5Ybxp06axevXqnGOZpsnPf/5zvvSlL+H19m8vR0RTHxPKXrBXz5PTZKnHULHa1n7RXtdp8ntdpwkkr0kQBEHoW5YsWYKZtfbX5MmTeeqpp1i7di0TJ05k0aJFLFmyJCeEt3fvXurr63OO89xzz3Hw4EG+/OUv99XQT5v+Lek+gISyw3M5FcGdnCZbNJX4aWtMdAjPOTWaigJevLqHsF8nmjRoiaUYWHR2xcUFQRCEs4PHH3+8Q1tVVRWJRCKnbfbs2cyePbvT4+zfv79D2zXXXNPtmlGFottOUyqVwuv1sn379t4YzweeTiuCO3Wa2jtNnZQcKAqofjKDThAEQRD6hm6LJp/Px7nnnoth5F9MVjg5YZ8Hv3aSiuCWegyWOOG59qIp4zQBlIbsZHCZQScIgiAIvcpp5TT967/+K/fccw8nTpzo6fF84AllReSsfAv2uk6TcqE6lBxwEsH9ql+J7TRJgUtBEARB6F1OK6dp9erV7Nmzh+HDhzNq1CiKiopytm/btq1HBvdBJKRnkuYSpoeg88LjxbLAtJQICpWcKjxnO012MriE5wRBEAShdzkt0TRz5sweHsaHh2zRFDOyRZMPM+vjCJXYTlNnFcH9TnhOClwKgiAIQl9wWqIp35ozQtfwkiuaBjgvPF43nwk6LzkQSebmNGUKXIpoEgRBEITe5IxKDmzdupWdO3cCMGHCBC699NIeGdQHlYOHfs7+fQ9TeX4Ro/dGiWabSB4dEyWUdg/zccCvpl92vvZc7uy5FgnPCYIgCEKvclqiqa6uji984Qs8//zzlJeXA9DU1MSnPvUp1q5dy+DBg3tyjB8YLCtNKt1IyqeRxks8leUi2U5TU9jD2qtKeKmhjlvIk9OUzKw9BxKeEwRBEIS+4rRmz33961+ntbWVd955hxMnTnDixAm2b99OS0sLt99+e0+P8QODVy8BIO3VSKG7hSoB0H0Ylo/GYvWRHEulsVCz57KLfjlOUzjgzJ6T9ecEQRAEoS84Ladpw4YNPPfcc4wbN85tGz9+PA8//DDXXHNNjw3ug4bXWwxAWveQRifW3mnCSySoRJMBJL0QSFukkyY+WyRFbdFU7M6ec4pbitMkCIIgCL3JaTlNpmni8/k6tPt8vpx1aIRcvN5cpymWzHKHPDqG5SMS1NymRKDjor3O2nPhDuE5cZoEQRCE3mH+/Plomsby5ctz2tevX4+maZ3sdWoMw+Dee+/lvPPOIxQKMXr0aJYuXdpvl1U5LdF09dVX841vfIOjR4+6bUeOHOFb3/oW06ZN67HBfdBwRZOukcLbzmnyYeCjLZj5SNIlShg5yeCWZbkhvWKZPScIgiD0IcFgkBUrVtDY2Nhjx1yxYgU//vGPWb16NTt37mTFihV8//vf54c//GGPnaMnOS3RtHr1alpaWqiqqmL06NGMHj2a8847j5aWln57of0BPctpSlvtcpo8XttpyhJNRY5oUv0SaZO0qdR3uMPacyKaBEEQzjYsy8IwogX5666bM336dCorK1m2bFmnfdatW8eECRMIBAJUVVWxcuXKkx5z8+bNXH/99Xz2s5+lqqqKf/iHf+Caa67hlVde6dbY+orTymkaOXIk27Zt47nnnuPdd98FYNy4cUyfPr1HB/dBw6urnCbDq5FEJ9ZONJmWl0ggY3OmipQwcsJz2SIrM3vOrgieSGOaFh7P6dukgiAIQt9imjGef2FSQc79yU+8ja6Hu9xf13Xuv/9+brrpJm6//XZGjBiRs33r1q3MmTOHxYsXM3fuXDZv3sxtt91GRUUF8+fPz3vMK6+8kp/+9Kfs2rWLMWPG8Oabb/LSSy+xatWqM7m0XqPboimVShEKhXjjjTf49Kc/zac//eneGNcHEic8Z2kaKU970aSr8Fwo4zQlQ7ZossNzzsy5oM+Dbosjx2myLGhLpt3XZz2bfwixRvj4HRAoLvRoBEEQBGDWrFlUV1dz33338dhjj+VsW7VqFdOmTePee+8FYMyYMezYsYMHHnigU9H0ne98h5aWFi666CJ0XccwDL73ve8xb9683r6U06Lbosnn83HuuediGMapOws56HoRoAEWKa8nN6dJ0zAIEAlkRFMimJsI7izW6+QzAQS8Hvy6h6Rh0hr/gIimRCv8+bvq+fZ1MPMRGDWlsGMSBEHoBTyeEJ/8xNsFO/fpsGLFCq6++mruuuuunPadO3dy/fXX57RNnTqVBx98EMMw0HW9w7GeeuopfvWrX/HEE08wYcIE3njjDb75zW8yfPhwvvSlL53W+HqT08pp+td//VfuueceTpw40dPj+UCjaRpeTa02l/K2y2kCUlqw3ew59dzJaYq0mznnHNMJ0X1gksFbMhMMaNwPP78Onl0E6UTBhiQIgtAbaJqGrocL8ne6s96uuuoqZsyYwcKFC8/4+u+++26+853v8IUvfIFJkybxxS9+kW9961snzZsqJKeV07R69Wr27NnD8OHDGTVqFEVFRTnbt23b1iOD+yDi1YKkrRhJ3ZNbERxo8pdhZuUkxf25TlNbInfdOYeSoI/6tuQHSDQdUY8DzoNRV8Ibv4JNP4Ddz8LcX0LF6MKOTxAE4UPO8uXLqa6uZuzYsW7buHHj2LRpU06/TZs2MWbMmLwuE0A0GsXjyfVvdF3vt+WLTks0zZw5s4eH8eHBqwUAMHStg9NU7y/PeR3zOk6TnQjurDvnz/3HV2qXHWj9oNRqajmmHgeeBzN/BBd9Fv77dqjbAX/5HvzDzwo7PkEQhA85kyZNYt68eTz00ENu25133snll1/O0qVLmTt3Llu2bGH16tX86Ec/cvtMmzaNWbNmsWDBAgA+//nP873vfY9zzz2XCRMm8Prrr7Nq1Sq+/OUv9/k1dYVui6Z0Oo2maXz5y1/ukDl/ujz88MM88MAD1NTUcMkll/DDH/6Qj370o532f/rpp7n33nvZv38/F154IStWrOAzn/mMu33x4sWsXbuWQ4cO4ff7ueyyy/je977HFVdc0SPjPRN0WzSlvVpuThPQ0E40Re1PxxFNbe5ivbkf2wdu/blWOzxXMlw9XvRZMJLw9HxoPlKwYQmCIAgZlixZwpNPPum+njx5Mk899RSLFi1i6dKlDBs2jCVLluQkge/du5f6+nr39Q9/+EPuvfdebrvtNurq6hg+fDj//M//zKJFi/ryUrpMt0WT1+vlgQce4Oabb+6RATz55JPccccdPPLII1xxxRU8+OCDzJgxg/fee48hQ4Z06L9582ZuvPFGli1bxuc+9zmeeOIJZs6cybZt25g4cSKgMvZXr17N+eefTywW4//9v//HNddcw549ewq+mLDrNHm13NlzQEOgNOd1xDaUknHVz3GmigK5TtMHrsCl4zSVDsu0BcvUY6Kl78cjCILwIefxxx/v0FZVVUUikZtrOnv2bGbPnt3pcfbv35/zuqSkhAcffJAHH3ywB0bZ+5x2RfAXXnihRwawatUqvvrVr3LLLbcwfvx4HnnkEcLhMD/7Wf4QzA9+8AOuvfZa7r77bsaNG8fSpUuZPHkyq1evdvvcdNNNTJ8+nfPPP58JEyawatUqWlpaeOutt/IeM5FI0NLSkvPXW3gt5QoZXvI4TaokQXlKxXIjmio81sFp8rdzmtwCl2dJeK7pEBgnEXittmgqyRJNAVs0xUU0CYIgCIXhtHKarrvuOr7zne/w9ttvc9lll3VIBP/7v//7Lh0nmUyydevWnAx8j8fD9OnT2bJlS959tmzZwh133JHTNmPGDNavX9/pOX76059SVlbGJZdckrfPsmXL+Ld/+7cujflM8aIEjqlDNJYrmhr96n08J2XS5PPQii2a3OKWH4Dw3JGt8OjVUD1P5Svlw5k9Vzo80xa0XThxmgRBEIQCcVqi6bbbbgPIW7FT07Qu13Cqr6/HMAyGDh2a0z506FC30nh7ampq8vavqanJafv973/PF77wBaLRKMOGDePZZ59l0KBBeY+5cOHCHCHW0tLCyJEju3QN3cURTZbX6jB77oQtmkakDN7BS6tlYgEJt+RAJ+G5gBOeOwucptp31OOhk5TIz+s0OaKpFUwTPKdlkgqCIAjCaXNadx7TNDv96y9FLz/1qU/xxhtvsHnzZq699lrmzJlDXV1d3r6BQIDS0tKcv97CaymBY3kzzpFDk18VGhuRVu+hAaS8kIrnVgTvzGlqTZzaaXr0xff52hPbSBsFms4Zsxd6bD6kypi3x0hBm/055XOasCDZ2qtDFARBEIR8dEs0feYzn6G5udl9vXz5cpqamtzXDQ0NjB8/vsvHGzRoELquU1tbm9NeW1tLZWVl3n0qKyu71L+oqIgLLriAj33sYzz22GN4vd4OJd8LgW4pl8jSzQ6J4E1+VfhyWNrArjZAzOfJLKOS7CSnKdR1p+nh5/fwh7eO8faR5lP27RUc0ZSOQ1ttx+2tNYAFHh+Es5xBb1C1geQ1CYIgCAWhW6LpmWeeycmUv//++3OqgqfTad57770uH88pB7Bx40a3zTRNNm7cyJQp+ZfNmDJlSk5/gGeffbbT/tnHbZ/lXwi8ph1a81odEsEbA2pmXYWVpsyrhFDcr5FMGFimlRWea1fcMtC1nCbDtGi2Z9jVtsTP7EJOl1hT5nnTwY7bs0Nz2SE4TZO8JkEQBKGgdEs0We3CKe1fnw533HEHjz76KGvWrGHnzp3ceuutRCIRbrnlFgBuvvnmnETxb3zjG2zYsIGVK1fy7rvvsnjxYl577TW3UFYkEuGee+7h5Zdf5sCBA2zdupUvf/nLHDlyhBtuuOGMx3umeE31lmteg5RhkbLDZKZl0eLzA1BhGZR7lbiK+zWwIJUwMuG59sUtQ12bPdccS7kRsZrmQommxszzxgMdt7tJ4MM6bgvKDDpBEAShcJxWInhPMnfuXI4fP86iRYuoqamhurqaDRs2uMneBw8ezCmxfuWVV/LEE0/w3e9+l3vuuYcLL7yQ9evXuzWadF3n3XffZc2aNdTX11NRUcHll1/OX//6VyZMmFCQa8zGa3rAA5quxFIsZeDTPTSmDEz7OitIUebTIZZZtDcRSxNJ5neaurr2XGM06T6vaSmQ65Ytmpr2d9yeLwncISBOkyAIglA4uiWaNE3rsMDf6S74l82CBQtcp6g9zz//fIe2G264oVPXKBgM8pvf/OaMx9RbOE6Tx6sEUDxpUBr0UZdUgieUMAloBmW205QqUo/JeDorEbz9MiqZ8JxlWZ1+Jk1ZoqmuYOG5bNGUJzyXr9yAgxOeE6dJEARBKADdEk2WZTF//nwCdu5NPB7nX/7lX9w6Tf0hZ6i/47UnrTmiyanyXe8kecdNPB7TDc8lQ7Z4ihud1mlyKoKnDItE2iToy78wYmMk40TVFEo0xZsyz/OF57rkNBUoiV0QBEH4UNOtnKYvfelLDBkyhLKyMsrKyvjHf/xHhg8f7r4eMmRIjy2v8kHFa6cdeXQTXTPcZPDjKbWhOG6hawZlPiWEHNGUiKU7rQhe5Pfisc2lk4XocsNz/SERPF9Ok7OESj6nSXKaBEEQCsH8+fPRNI3ly5fntK9fv/6MIk6tra1885vfZNSoUYRCIa688kpeffXVMx1ur9Etp+nnP/95b43jQ4NuZJLnQ96Y6zTV2TWWiuImenHadZoSAaVr49E0cXt5lfZOk8ejURL00RxL0RJPMaQ0mPfcTdGMoKorRE6TkcrNR2o+DKYBnixnzF2sV3KaBEEQ+hPBYJAVK1bwz//8zwwYMKBHjvlP//RPbN++nV/84hcMHz6cX/7yl0yfPp0dO3Zwzjnn9Mg5ehIpq9zHeEwDjy2cQt6YWxU812lKuzlN8YBS8G1tGZET9ncMv7mL9p5kBl2209SWyDhXfUY8K6zm8YGZzuQwgSp2mW+xXgfJaRIE4QOGZVlEDKMgf92dAT99+nQqKytZtmxZp33WrVvHhAkTCAQCVFVVsXLlyk77xmIx1q1bx/e//32uuuoqLrjgAhYvXswFF1zAj3/8426Nra8o+Oy5Dx1GCm/aJKnrhLxx12k6nsxymjRDzZ4DYj4lmlI7NjIULw2eCgLejlpXJYPHThGey91W0xzngiHFPXFVXcMJzQXKIDwQGvepEF25vWRNrBHSMfVcnCZBED4ERE2T0S++XZBz771qEkV6/hzYfOi6zv33389NN93E7bffzogRI3K2b926lTlz5rB48WLmzp3L5s2bue2226ioqGD+/PkdjpdOpzEMg2AwNzoSCoV46aWXTuuaehtxmvoaM43XdZrimZwmJ8k7YeLRMuG5mC1rtfdf5ivePxH263njx27ZgZM4Tdmz56AABS6dmXOhchgwyh5U1gw6Jwk8NBB8oY77i9MkCIJQUGbNmkV1dTX33Xdfh22rVq1i2rRp3HvvvYwZM4b58+ezYMECHnjggbzHKikpYcqUKSxdupSjR49iGAa//OUv2bJlC8eOHevtSzktxGnqa4wU3nQmPBezxZIjmopjueG5qK4EUtIKM0Q7SHEg/0dWEnQKXHYtERwKLJrKbdGUPYPuZEngcHpOU1sdPLsIPvIVGHl5t4YrCILQ24Q9HvZeNalg5z4dVqxYwdVXX81dd92V075z506uv/76nLapU6fy4IMPYhgGeh5X6xe/+AVf/vKXOeecc9B1ncmTJ3PjjTeydevW0xpbbyOiqa8xkrlOU/vwXMJEJ0W5PXsu4lF9k1aYMiKEOxFNbq2mk6w/5ySCDykJUNea6PsZdK5oGgDl59qDyhJNJ0sCh9Nzmravgzd/DckIzP1F98YrCILQy2ia1q0QWX/gqquuYsaMGSxcuDBv2K07jB49mhdeeIFIJEJLSwvDhg1j7ty5nH/++T0z2B5GRFNfY6aznKY40ZSBaVnUpzJ1mrKdpjbNwgKSZphKLdJh5pxDJjx3aqfpomGl1LUep7avl1JxajSFBsCAKvU8Ozx3siRwULlQ0D2nqeWIeozUd30fQRAE4aQsX76c6upqxo4d67aNGzeOTZs25fTbtGkTY8aMyesyZVNUVERRURGNjY0888wzfP/73++VcZ8pktPU17QLz8WTBidSBk4lgiJ79pyT05QGUjokrRClRDqsO+dwqvCcZVluIvi4yhKgALWacpymPOE512nqJDznOk3dKG7ZWmuf+8TJ+wmCIAhdZtKkScybN4+HHnrIbbvzzjvZuHEjS5cuZdeuXaxZs4bVq1fnhPGmTZvG6tWr3dfPPPMMGzZsYN++fTz77LN86lOf4qKLLnLXn+1viGjqa8xUTngumjTc0FwoYaJb4CFJke7BTmci7tdUeO5kTpNTcqCT8FwsZZBMqzpPY23RVNvXtZoc0RQsz4TnWo5A2s61OqXT5OQ0tYJpdu2cbTXqMdrQ7eEKgiAInbNkyRLMrO/iyZMn89RTT7F27VomTpzIokWLWLJkSU4Ib+/evdTXZ5z/5uZmvva1r3HRRRdx88038/GPf5xnnnkGn8/Xl5fSZSQ819cYKXRbvDiz545nLaECoGspNE2jzKtzImUQ83tIJsOU00aRL7/OLQ1l1p/Lh+My+XSN8werMgOFSwQfAMVDwBtSJQZaDsPA87vuNGFBsjVTIfxkOE5T9ISqA9UDayUKgiB82Hj88cc7tFVVVXVYPm327NnMnj270+Ps378/5/WcOXOYM2dOTwyxTxCnqa8xsp2mGLEsp6k4rtp11Otyr9K0jtPk1UwG+PKLIsdpau2k5EBjRLk55WE/lXbF8LrWBIbZveJmZ4RTpyk0QIkXx21yQnTuYr2dOE3eoCqKCV1PBnecJsvoXlhPEARBENohoqmvMVM5ieB5nSZbNLlVwX1KNAFU6FH3UNnVXDOz5/KLKmfm3ICwj0HFfjwaGKZFQ1sfhuiynSbInUGXTmRCaJ05TZqWcZu6kgyeiuUKJQnRCYIgCGeAiKa+pl0ieDRpUJfMLKECoFtKyJTbVcHjfo20FcS0PAzQIgD84Q9/4MEHHyQWUxW0y8JKNLWvxeTgtJeH/Xh1D4NLAkAf5zVl12mC3AKXTmFLPaCqhXdGoBtlB9pqc19HJRlcEARBOH1ENPU1eSqCH09lllABE83KdZpifqfAZYhyWzS99957NDc3U1urhMFQO+RW35Z0E76zcaqBD7DFldO/T2fQdXCasmbQZSeBnyzvKNiNsgOt7USTzKATBEEQzgARTX2N0S48lzSod50mVdhSs1TBS0c0Jf1KBCXNMKW2aHKS71K24BoY9uPX1cdZ19pRCDW64Tk/UADRZFknD8+dKgncoTsFLp18JgcJzwmC0A/o7kK5Qi6FfP9ENPU1Zgqv4cyeixFLGdS5i/WqGk2YSkQ5VcETjmiywhSbrZim6YqmZFI5SB6PxpBSJ+SWTzTZTlPAg9Ha6iaD91mBy2SbSsaGjGjKDs+dqtyAg1t2oAtJ3e2dJhFNgiAUEGcafTQaPUVP4WQ4718hyhJIyYG+xkih2yo56E0QT6ZoSeZWA8fMDc8ls0WT1eYKJcg4TQCVpUEON8Y4lkcIOYngf/foUvYs2sOIJf8O9GHZAcdl0gOZxXid8FxbLZx4Xz3vbAkVByc8d1pOk4TnBEEoHLquU15eTl1dHQDhcDjvAuxCfizLIhqNUldXR3l5+SmrjPcGIpr6GiOF1xZNHs0ibUZpSOWG51ynyZk9pyJqJM0wIaM1py5GtmgaWmaH3PKIJsdpKj20FzMa5ZyIcl36LDzXPjTnPPeXqJpLh15RbZ0t1uvQnUV7HafJqQclTpMgCAWmsrISwBVOQvcpLy9338e+RkRTX2OmUGvweoE0hh7DsEADwgkLjycFpp3T5M6e8wAGSStESbolRzRlu07DnJBb3vBcCiwLT1zNthvkMzvta1kW8ZRJqJMlW06LfKJJ01SIrna7+oMuOE2nkdM05CI4+rqIJkEQCo6maQwbNowhQ4bk/OgVuobP5yuIw+QgoqkvMU2wTDRA14swjGYsu1hlmceDboFOGstIoZE9e05HiaYiAukWGjtxmiodpylPGYGmaBKfmUazS94P8Chhlq/kwC//dpB712/nZ/M/wtUXDe2JK88tbJlNuS2asBP7esNpGjLBFk0SnhMEoX+g63pBb/7C6SGJ4H2JmRE4Xl0tZaIHlFgYqKmPQtNS/H16D9/563fc8FzUTghPmiF8qZbOw3POjLjmWIdTN0aShNIZV6pMU6KpOZYinjJy+j792iEAnn/v+OlcZX7a12hycGbQOfSG0zR0vD0GEU2CIAjC6SOiqS8xMqLF51M3f4+dEz1QUwIp7UmznxT/c/B/KLPFUsTnwwISVhHeRFPn4bmy/GUE0oZJSzxNMJ3gxICLOHTOJ/GnEoTs8F92DlRzNMXbR9TMtAMNPTjDI194DjIz6BxOJZq66jQZaYjYi0IOGaceJTwnCIIgnAEimvoSI8tp8pYAoAXURzDA/igsTSWBx9IxQvbztK6T1uHt6GfZf/zcUzpNtS2JnDoWzfbSKqF0gnfGfYndF95A84l0VjgvI5pe3teAs+vBE52LppaWFlavXs0f/vAHDMPotJ9LZ6KpPEs0hQeB13/y43TVaYrUARZ4vDBojGpzFu0VBEEQhNNARFNfYmYW0/XaTpNpJ1uXo6adWpqR1b0N3VI5SFb4CCkrxIYjX2bX1qNun3yiKZk23WKWkClsWemBlF+dN9aWZGieuk6b99S7zw83Rjtd0PfNN9+kvr6eV199ld/85jenFk7xJvUYLM9tzw7PnSqfCSDQxYrgrXZormiIEmMgi/YKgiAIZ4SIpj6kLp7g7eILiXhL8HpVTlPaLs41wLJFkycjdlqSLZRZKvwWG/Ayl4T/G4CD72WmqmaH5/xeD4OKlVNzLCuvyVlCZbgnk/efjKUzBS6zRNOmvZkQVsqwONrUMT8K1DIuDu+88w5PPfUU6XQ6b191AZ3kNGWH57oimrrqNDnrzpUMBV8QfEXqtYToBEEQhNNERFMf8rsTUT592b8zeurvmVc/mxV8lxPhIQCUm0o0mVpGeDQlmigzlWg57BvKx0t/zvSyB0E3OFpWwZvnjCaZzJ2yOjSPEHKcpgotWzQZWYnjCXefPXVtaBoMsRf0zReia21t5fDhwwB8/vOfR9d13nvvPdauXdv5FNrOZs8FSiBkL9B7qnwmyM1pMjuusZcZpO00Fdu1PMIV6lFm0AmCIAiniYimPiRpGAxMNQFQY4R5S7uUqE/lNg001EdhZIXnmpPNlJlKtDR4y4kTYGzoBc4dF2DjuI+w5YJJ7DFyP8LKdkIIMoUti80s0ZQwOwisLbbLNGF4KROGK3HiJIPvbtzNqzWvAhmXafjw4Vx22WXMmzcPn8/Hnj17+NWvfpXjfrl0ltMEmRBdd5wmLLU0S2dkO00AYfu8MoNOEARBOE1ENPUhtw6EHZuv553X5/OT4Tv4J+tHVLf+jc+FipiQtkWTJ+M0tSRaKE8rYdDiLSLiUQKrwWcRCahpdye03DofQ/MkdzvhOb+VSbJOJk03EdwRTZvsfKapowcxqkKFsw6ciGBaJl/981f5pz//Ew2xBlc0XXTRRQCcf/75/OM//iN+v5/9+/fz9ttvd7x412kq77hteLU9+Al53rV2eIPgsdcbOll+UqdOUz8Iz1kWvPwI7PtroUciCIIgdAMRTX2JXaepwopzWTF8io1Mj/6Ja30hSKuE62zR1JxopizVCkCbL0zUru30rh50+7R4cuuTDstTq8kJz2lmZr9USsuE51riWJbFZttpuvKCQZw7MAzAwYYo7ze9T0O8AdMyOdZ8jPffV+vEjR071j3eqFGjqK6uBqCpqanjtZ/MaZpxP3zlORhzXcdt7dG0jNt0smTwDk5TPxJNdTtgw7fhd7cXeiSCIAhCNxDR1JcYtiDy+NySAyFvjFjKwDSUaEq1D8+llJsS9YaIe5VY2OMvdvu06p05TZnwXFM0iW6BaQXctmRac2fP1bUkONAQ5UhTDJ+ucXnVAEZVKNF0oCHKG8ffcPfb//5+DMNgwIABDBkyJOfcO4+rc+491k6YpBOQiqjn+USTvwhGXg6eLv5zDHQhGbw/5zQ5Y2vrweKhgiAIQq8joqkvcSqC694s0RQnlkxjpFRSc9qTmwhenlA3+Zg3SMIWTfvC5W6fiNeXcwp3RlxWwcrGSIoBpgZkVtNOGR6GlNglCgyTP7x9DIBLRw4g7Pe6oungiShv1r3p7nf4fZUAPnbs2A6rcx9pU9dwtL5d2MwJzaFlSgacCafjNDnJ5v3BaXLCisnWjJAWBEEQ+j0imvoSpyK47s91mpImRloJjpQny2lKNFNmi6aEL0DKX8qRwGCaAkVun4jPn1PI0qkKfiwnPJdkoJErcFKmN6dEwbptSgxdeYFyZEYMCKNp0JZIs7X2dQA0S6P+kMp7cvKZsonZiebRaLsZd26NprKuu0knI2gLr86cJtPMiKZiJzzXD0UTdG0NPUEQBKFfIKKpL3Eqgnt87tpzIW+caCrtiqaEJ80lDZdwXst5tMQbKbfDc0mvH8Nfzt/KLsk5ZMwfyKmP5ITnWuJpYkklwJqiKQaa6qP22mGylL1Ws5PX9P5x1X7laFUIMujTlWulRzjUdgCAingF6USaUCjEyJEjO1xezD6HkYznCLmT5jOdDm7ZgU4SwWMnMoVEi+wQohOec8ZSSBwR2f65IAiC0K8R0dSXODfyduG5eDKNYec0pTWNC1ouYGLjRJrjTZSlVSK44fNCsJwtZRcDMLRZOSZRXyBnin9JwEvYrjLuzKBrjCapsJ2m0lYlgNKosJ4jmgBCPp3qkeXu63MHhtFDh9zXw6OqJMCYMWPyrs7dmlZtXitFXWsmp6rHRdOpnCYnZyhckVmWpb86TVKhXBAE4axBRFNfku002aLJ6zGIJ2NuTpOhqY/Eb/ppTjRTbosmy+eBUDlbyqoBGH1cLaUS8+eKJk3TMmvKNSvHpymaYoDtApW27Acgpakk8KGlQfyk0bD46HkD8Xsz/yRGVYTRQ0pkYWVEU77QHECboURTgDQ7jmaJgV5zmjoRTW3tksChf82eE9EkCIJwViKiqS9xE8F96HoRlp2YnTbaMO3wnKVlPpJoLE6ZXafJ8nloKRrCniK17MjoBpW4nda9NCdyi0m6BS5bYkSTBsm0mXGabNGU1pVoGuI3mBt4g0/49nLl6Iqc44yqKEIPHQRgOMMpSheBB0aPHp338pqS6hweDXYezhInJ6vRdDqcaimV1nZJ4JA7e67Qi/a6ifGIaBIEQTiLENHUl7glB7xomgeLsN3c6obnPFZmNpyZgrBdpwmvh3fDyjkZ2NbMgLSB185lOhbLzJSD3KrgjdEkxRb40dAsk5I2FW4z9QDpeIJSLYquWVRoUaZeMCjnOOcMCLjhuclMVo0V4Pf7yUc0ZZG0lNu052i2aOoHTpMze64/LNorTpMgCMJZiYimviRr9hwAHjULzjTbMNImFhbeLNHkN/14LLu+ka6xzadEx7DmBnxeP2E7LFebaLf+XFal76Zoyl2iJZRqxJfKLD0Sb2yjNKC2BTwG44eV5hzHE6hB8yTBDFLSrMKJyYo8S6QAlmURSaaJWyrB/GBdU2Zjj+c0nYbT1J8W7RXRJAiCcFYioqkvyQrPAWgeNYPOMtowUiamHseT9ZH4DT9pK47HUqG7LYYSQ8Oa6/H5MqKprp1oGpaV09QYTVJhLwZcFKvDY5nohkrSTjRFOX+gCtP5NYN2ZZdoSO0CIB0dSapZnaOtOP96b4m0iWlBwp6VV9/YQtIOOfaW05SORThxLNJxez6nCfpPgcuc2XMimgRBEM4W+oVoevjhh6mqqiIYDHLFFVfwyiuvnLT/008/zUUXXUQwGGTSpEn88Y9/dLelUim+/e1vM2nSJIqKihg+fDg333wzR48e7e3LODVuIrgSFh5bNGFFMA2TtDdXAPhNP60eD+G0qrnUYGuQYc0N+H0+wnYu0/FkO6fJDs8da4nTmOU0hdtUHpRuHy/eFMFI2/taVk7pAoBdzdvVpugoLFOFD9vIL5qidnkDx2nykWLvcbuvW6epPO++3cZ2ml7c+0l+/W9/4+judmUE2urUY7bTBP1n0V5xmgRBEM5KCi6annzySe644w7uu+8+tm3bxiWXXMKMGTOoq6vL23/z5s3ceOONfOUrX+H1119n5syZzJw5k+3b1Q0+Go2ybds27r33XrZt28ZvfvMb3nvvPf7+7/++Ly8rP27JAeU06V4lmjQrgpG2SPtyBYnf9NOsewilMjlLw6O1FCUT+L1eimyx1OA4OjbZVcGbokkG2k5TqPUIAD5DHS/REiOVygiuRCKRc5w36t4AwBMb4ba1mflFUyShri2lqWsLkObdGjt81uNOkyo5cDyqajA11rQrptl+CRWH/jCDzrJENAmCIJylFFw0rVq1iq9+9avccsstjB8/nkceeYRwOMzPfvazvP1/8IMfcO2113L33Xczbtw4li5dyuTJk1m9ejUAZWVlPPvss8yZM4exY8fysY99jNWrV7N161YOHjzYl5fWkaySAwBeXeUJeYhgpDNOk4FybfyGnyaPh0A6I2bGt6hrCHg1wrZQaS+anPDc8bYE9W2ZauBFUZXr49XUOBKtiZxyBdmiqSHWwOG2w4CGHh8GQEpLETXaCRSbWEqN2fSofK2glubdY3YSey/lNEXTdp5VLFNFHcvquISKQ38QTalYJrcNRDQJgiCcRRRUNCWTSbZu3cr06dPdNo/Hw/Tp09myZUvefbZs2ZLTH2DGjBmd9gdobm5G0zTKy8vzbk8kErS0tOT89QpuIrgSTT6fuvl7tWiOaGoKqPCR3/TT4vHgT2VusmPsEFvAC0W2aDph5IqmiuIAukfDMC3eP9ZCqWWH56K14PPh89jVx9sSOU5TPJ5xtN48rtabK/eOwGeH3FKeFJFUnhwiMk6TZReTVE5TL4mmQCmm5SFm2KIpnhVWTLRCyhZ2/TGnqb1IEtEkCIJw1lBQ0VRfX49hGAwdmusIDB06lJqamrz71NTUdKt/PB7n29/+NjfeeCOlpaV5+yxbtoyysjL3L98SIT3CuM/D7MfgsvkA+H3qpq9rUVLpJKZXiZaWgApN+g0VnvNmCZvzI2rtt6DHJBxXDktju7JDukdjSIlK8D56UAkXzQe+dBRPOIxPUyIjEU126jS9cfwNAM4rnoBfU+dJe9KdiiZnyRbNq84b1FIqPGeaGWHQg3WaYmYZFqq8QTKWJZoclylQCv5w7n79YdFeEU2CIAhnLQUPz/UmqVSKOXPmYFkWP/7xjzvtt3DhQpqbm92/Q4cOddr3jBg8Fib9A4z4CAABvxJxQT1G3FDuVsKTJO1Vz/2mn2aPx63HdE7AR7EdigvpaddparLaTXsjkwwea1RCyF9k14EKh/Hp6hjJaLrTnKY365TTdMmQS/DZ4cKUJ0UsHcMws8JhNhFbNHn86rxBLU1tS4LGxgawZ//1WCK4N0jEGuy+zHGa3HymdqE56B9LqbQXSdmFLgVBEIR+TUFF06BBg9B1ndra2pz22tpaKisr8+5TWVnZpf6OYDpw4ADPPvtspy4TQCAQoLS0NOevLwjZoinsjRNHOUKt3jZ0TTlOjmjSk0oUfKy8mKSmREnIk6Q4rsRIM57cBXLJ5DUNTCtBFQ5nRJPfa4fn4kZepyllpHin4R0A/m7kR/BniSaAaLpjXlPUHqPXp85bpKt93ncEqC+saiX1BJpGVD/HfZmT0+TmM+X599MfFu11ZxI66+eJ0yQIgnC2UFDR5Pf7ueyyy9i4caPbZpomGzduZMqUKXn3mTJlSk5/gGeffTanvyOYdu/ezXPPPUdFRUX7w/QLHKcp5I2R1GzR5GvFi+0OGX6adJ3KI0c5p9XgG6OGktBUzlCIKEV2eC6labS2y2tynCZn5lxZSIkaTziMz66fmUyYeZ2m9xrfI2EkKAuUcdmwMYRsZ8rwqPPlC9E5JQf8wYzTBHDYKfXQUy6TTUTLiKKz0mkqV8vhkIpkJggIgiAI/ZqCh+fuuOMOHn30UdasWcPOnTu59dZbiUQi3HLLLQDcfPPNLFy40O3/jW98gw0bNrBy5UreffddFi9ezGuvvcaCBQsAJZj+4R/+gddee41f/epXGIZBTU0NNTU1Oa5Kf8BZtDfojZPS1VT+Zn8Lfk3VUXISwYmbfKxVY0xRkIRdMTxEG37Dg8+us9S+VpOzaG+FXaOpPKSu3RMO4/crIZVKmnmdJicJ/JLBl6DrHgaF1TFMXblV0VQ+p8nA46/lqE/VzPKYaTRM6upsEdNTSeA2EbLCczk5Tfb5TuY09QvRdG5WWy9NPBAEQRB6FG+hBzB37lyOHz/OokWLqKmpobq6mg0bNrjJ3gcPHsTjyWi7K6+8kieeeILvfve73HPPPVx44YWsX7+eiRMnAnDkyBH++7//G4Dq6uqcc/3lL3/hk5/8ZJ9cV1fw2nWawt44ad2eOedvYpRdsdtreWnVfKStIOcE1UeVMHXAJGi24fF4CSUTpLw+jifTjM7Kex5WFkSzYIDtNA0KxjGxRVPAA22QTJLXadrduBuA8RXjARgY0CABpv0x5HWaEmn8FS+wj21MZjYAQQwaG+x6Wz0smqLmQPd5Mp4VnnOWUMnrNLVbtLd9CfS+wMlhCg8EfzEk21TIrqh/uqGCIAhChoKLJoAFCxa4TlF7nn/++Q5tN9xwAzfccEPe/lVVVR3ye/orjtMU9kbBY4Kl0epvIRRLomkalmURJUCaMMUBWzTZNZcC6Wa8updwKkELxdQlc6t5Dy0NUmpqeNFIY1GuxzgBeEIh/CE16yyV1vI6TY4oKvOrvJtSe6m8tCOa0h1FUyRpoHnbQAPLZ6GlNAJaimjTcdDpuZlzzvmMzPG67DS1X7S3h8fUJbJzmoLltmiSvCZBEISzgYKH5z7MOKIpZJca8KaLSHtShC2TkC2S0gRps3wUO05TWgnCYLIJn9dHOKn27RCeKw26+UzNXtATKuTnKQoTCKkQX9LQ8zpNTqJ32KesqyI7pymFOl4+pymWTKPp6hwpXR2zzGtS5FQQ72GB4hS2hPY5TSdxmvrDor2OQAqWSzK4IAjCWYaIpgKi6yo85/emABNvqghTMwhZFuGAvRyJEaBNtygJ+jBNk4RdciCQasTr9RFKKqFT385pqiwLupXAowENM6KEkCccxh9Wx06bel6nyclZCnuVaArYxTDjtoGXL6cpkjTQPEo0xezHqgE+yjVHNPVweC6RiUWmkyamkwh/MqcJCl/gMsdpEtEkCIJwNiGiqYA4TpN6nkI3wqBB2LQIeZXg8Zt+YrpFScCb4woF4vX4vF7Ctmiqa+c0BX06g3XbrQrrmFEldLRwmECxKkCZsrx5K4Kf+8YxVv8oTekue4Ffu+RA1BZhbamO68/FkgbYTpOzqO+IYg9l2K5UD4omy7SIJvw5bcm4AcloRoDkc5qg8Iv2itMkCIJw1iKiqYB4PD4MS938dW8S3VTuScgyCdu1lPyGH0tPUBL0uk6QBwNvrAGf3+8u5nu8ndMEUOZVuUveYEY0ecJhAna1cEPzYBiZJGrn+Be8eYIhzVD02rtqgz0lPmFngucLz7UlU254Lqkr92pwiF5xmmJtKUx7aRiPXa08GUvDnmdVh9JzMoKkPYWeQZddHV1EkyAIwlmFiKYCY6BybLx6Co+lygSETYuQXRPJb/pBj1Ic9LpOUIAkmhHD79Xd8Fz7RHCAYnvWYSDkbSeaQmCZWFpuZW9HNPljSiT57FlpSbs9aa9Bly8815aMoml20UyP6l/msyjTbIHVg3Waoi3q+CFPM0E7HywZN+DNtarDxXM6nxlXaNHkzJ6T8JwgCMJZh4imAmNZyvXxaRbYidYhyyKsZQpcanqM4kDGaQqgnByfV3PDc+0TwQFCtmgK5YimIvSiMLqR6FQ0BaN2de+YOo8j1pL2ZMt8TlMklak15DhNQS1NuR2qa/P0XJX1SLM6fthzAr9uC7rGBtj9Z9Xh4i90vnPBc5qc8JyIJkEQhLMNEU0FxjKVu+TXdLfidtg0CZEpcKnpUUqCvoxossVOQCcnEbx9qYWBfpXwffF5A3KcJk8ohDcdw9Jyq4gnEglM0yRoL0viiSWwLCuzvIqpQol56zSlWzPHsZ2mVCJGha7O+/aJnquJFGlSxy/yNOLX1PGT770IZhqGXwpDLup850Iu2muakLDFpeQ0CYIgnHWIaCowRloJG7/HwrCXHglZFiF7qn5GNGU5TXYJgIBuELZzmhKWRUs61zkK2SGqSVW5okmzRRO2+PLZ66qYpkk0GSWcsNepiyZyZtcl7fyrfGvPxdKZ5PCE7f5Eo1HKbKfp5aMdF/k9XaKO06Q34rPDf8k9L6uNl9x48p0LuZRKsjVr8eJsp6mp78ciCIIgdBsRTQUmlVDJ2j6PhWEviBs2LcKmcm6c8FyuaFJiyKcl8RoWfmcplVRuXpNTv8gf9GJGlbjwFGU7TUrIFBUVufs0RZooUjoMLRJzz2mhkTY6d5rijsjz+N3wXDQSwWep538+2HMFR6PNGacpYKn3KXniOHi8MHH2yXcu5KK9jqOkB1TNKHGaBEEQzipENBWYVErlCfm8SdIo8ROyTEKpJkA5TR49SsinZ0STT31sXi2BZmUlgydyRVMq7iyiq3cMzxkZ0RQIBPD7lSBqbmnEXqYOMxJx85nQfWAnqrcvOWBZFklLtV1QPtYNz0XtczZaxeysT3O0KXba71M2kRY1wCLPCfyWCnclzRBceA0UDTr5zoV0mrJnzoGIJkEQhLMMEU0FJpVUokn3JUhrGacplFKJyn7Dj9cXR9O0TKK2zxZaRNEsT6YqeFbNJcuyXNHkC3ixIu1zmuJuTpPP5yMYVIKoteG4ewyzrc09p6b7sEyVtN5+9lzSMDHtRYZHlowkEFL90oZBEi9NXrW47qY99WfyVrk4OU1hvRGfx85pssJwyUkSwB0KOXsue+Zc9mNnoulP34GffELVnxIEQRAKjoimPqTtry9Rs2QJzfaCwirJ2naNvHGSmnJQQpZFOKHEi+M0QWZ2WyCgXCGvFVFOU8qZQZdxmoy0iWmqkJgvoGHG7GVUsnKaHKfJ7/cTCCihEzmRERPZosnj87uiqX14LpY03BpNA4JljCgbgWEfO0oIq2Q40HOiyclpKvK2EHASwT0DYMy1p965/aK9fUn2zLnsx3yiybJg23/AsTfg6OudH7N+N/zxf0PL0R4d6oeBQ4cO8fjjj7N///5CD0UQhLMEEU19SHzHDhqf+DWRLSppOZlMkk7ZxS39cUw7ETxsmoRSSmB48OCzxVRGNClXyGtGcsJz2aIpGcskXnvNlCsQPOEwmseDl5Qrmnw+nyuaos2ZXB8zEiFuiy2vzw+diKZIlmgqD5YxsnRkJkRHiKLB5wLw0p6GM15M2bIsInadpnAojc9esiVZNha8gVMfoP2ivX1JdjVwyITpUlFIJ9v1bQLnfW450vkxNz8Er/wEXvt5Dw70g09TUxO//vWv2b9/P7///e8xTfPUOwmC8KFHRFMfog9UVbGNRiVMEokE6bQtmnzRnNlzPgx0O8fJZ6lHt7hlMKTazVbIDs9l1WpK2flNvoCOFbdziTQNzQ7D+bS0G57LdppirZnSAQBx+7Xf788Jz2WLn1gy7a47V+ovZVTpqEwyOCEqhlcR8unUtyV4rzb3+N0lEU1j2osWF4XNTMmBcFXXDlDIRXvbO02BrNpViZbcvs1ZQqn5UOfHbNyvHhv2nPHwPiwkk0nWrl3r5tzV19ezc+fOAo9KEISzARFNfYh3QK5oisfjrmjy+GIYnjQBPOh2/xBKDHkti5SZyjhNIbuKuNHcudPk5DNlJ4GHQmh2wUu/buZ1mpKRdqE3WzQFgkFXNKWtNEkz44xEEhmnqdRfysiSkSQ9GdHkLR/BR89TDs9Lu88sRBexZ84Firzo4aJMeI6SvP1bW9/hpZeu5OjRpzKNhSpwmb1YL4BHzwin9q5Xdrit+XDnx2yyBdWJ93tkiB90LMviv/7rv6ipqSEcDjN58mQAXnjhBXGbBEE4JSKa+hDdFk3ppo6iSfdHMLQ0IS3zkRTZBS4DRoDWZGtGNBWpG6033YRm6YRTHRftTeWUG7AX6y0Ku9u9XjNvTlMyGs8Zc7xNzYoLBgJgZhbJzQ7RRZO5omlU6ahMrSZCUHoOH79AzWo707ymaJOdz1QWgHBFJhE8nr8OVEPDCySStdTW/THT6MygixzPu0+v0X72HHReq6klSyg1dxKeM81M6O7Evr7P0SoQJ06coKWl5dQd8/DSSy/xzjvv4PF4mDt3Lp/+9Kfx+/3U1dXx3nvv9fBIhb7mxInNRKP7Cj0M4QOMiKY+RHedpiagnWjyxTA9acKa1+3vOE1+009ToqmDaPKlG9EsDyE7PFefx2lqX27Awe/T8obn0onc5VjitvNUFA4BHlc4RZLZoikrPBco5dySc12nqdkWTVNt0fS3fSdIpnN/0Tc3N/Paa6+RTndcP89h+5FmvvXkG7y5W4XUwqV+uOp/4x//aft68+8bT9Sqx3iWc1MxWj3WvdPp+XqF9uG57Oftnaac8FwnTlOkjqQnxeFhQdLplsKtp9eHRKNRfvKTn/Dzn/+82/lxu3btYuPGjQBcd911jBo1ilAoxBVXXAEot+lMc+6EwhGLHeT1N77IW2/fWuihCB9gRDT1IY5oMltasFKpduG5BKYnSUjT3f7hLNHUkmjJiKZidRxvskE5TXZ4rjae5OA7bwGZGk2+oI7plhvIFLH0+8kbnku3W/g3HlNjKAqrXCg3GTyd6zSR5TSVB8vBr24+xz1hKB3GRZUlVBT5iSYNXj+YW1jyueee4/e//z1vvPFGh/csljRY9sedXP/wJn77+hFe2q5EUFF5AEZNIXD17QAkY/lFUyJRo64jfjRzQzznMvV4ZFvefXqN9iUHsp93CM91QTQ1HWL/yDDvXVjMkcrghyJEd/jwYRKJBI2Njd1ym+LxOOvWrQPgsssu4/LLL3e3TZkyBZ/PR01NDbt27erxMQt9QzS63358H8vquRUIBCEbEU19iF5aCvbSJkZTky2afJnt/lh+p8nw05xozoimUpWT403W206Tak9rGr97/FGgfTXwPE5TwOMuo5LtNBnp9uvRqTGU2vuaeWbQRRKpnPAcQLGdcN7oKYVACR6PxpWdhOiampoA2Lcv11bftKeeGQ++yE9efB/DLp9gRNR1FZUpsekL6vb15v+STNhOk2nGSKVsseaKpq19G9LqjtOULZoSzRDPIxCaDxIPqv/C8aDnQyGajh075j6vq6vr1n6JRILS0lKuu+66nG3hcJiPfvSjgLhNZzPJpAq3W5ZBItH1fxuC0B1ENPUhmq6jl6mbZLqx0Z4N5yGdVkJE98YIeXxYlhJW4axFe3PCcyVKfDh1mryWSdBQYqLWDq8l4wbvjPTzrQtMXkwqIZQjmkJ6TnFLRzS1z4V1zllarGbsWUZH0dQUb0Ozj1VqJzYPCCpR0+bJuFt/Z4uml9qJpogdAty/f797w1r83+8w79//xsETUYaVBfnGtAvVexhX5wmXqnH4g0pkGimzg+BT4691n8cTdoiu8mLQdGir7dv6Ru1LDkDXwnOQv+xA0yGSdnX4lO/DIZqOHs18XsePdz0nrb5e/ZurrKzE6/V22O64TUePHmXPHpmJeDaSTGa+V9z/64LQw4ho6mOy85qcEgKmYddd8scoM0PUJB+lIfntHKepKdLkHiNYMgA8PnyeBJqlPsJwQgmsZt2HkU5Tm0jyh4+EafbCeo86fq5o8mF5lDuje7xuRXDDFmwOSbvKeHE4hF/3YNk5TdlVwZvs6fIevAR1dZwhdtXyhL30CsDUC5VoevNwMy3xTO6UI5oikQgNDQ1Ek2ke37wfgC9+bBR//tZVzLr0HAB8KSWMisoDJPbto+0/12bGGk+zefNmXnvtNfW+mumcL9KEk9fkD8OQ8er5ka30Ge1nz2U/d0J3oNwvRyQ5s+vyheiaD5P0O6JJ655o2vs/sObvoWFv1/fpB5ypaBo0KP8yO8XFxXzkIx8B4Pnnnxe36Swkmczk9CXix07SUxBOHxFNfYw+UM3cMhobXRfHMJSY8friDEtVYliVxM3LcxLBGyMqtOTxePD6fBAqx4taew4gGFPCIxoqIhGN8BNvjIR9Q33HLvqYK5r8WCjRpFmejNNk/5NIDSgGIGGoPsFgkJKgF0wlgnKdJuWS+LQiNDv8OExX4sYwM+HHc8pDnDeoCMO0eHmv+oJLp9Pu+wBw4MABmmNKUHk9Gkuun0BJ0MeAsBJrITsKFy7zU/f9Bzi+7H68urrBNZ9o5c9//jN/+MMfSKfTtl2fcZ9yksHPUVPNOdqHeU1dDc/FGiFtz2J0Qon5ajU1HyLpU+93srtO05Yfwb4X4J3fdH2fAtPW1kZrVh2xnhRNAFdeeSVer5cjR47khAGFs4NEMvPvIef/uiD0ICKa+hh9QDkARlOj6zRZaVs0eROUmUqsWATx2b92/Yaf1qhdLykQUMIkWI5XS4LtNIVspykSLmFDTQNbAgaanQe03xckGgjmiCY9HAS7mCamnslp8igRZhQpseGIpkAgQEnQm3cpldakcpqCnmK3rcqyl20xfDm/2qdeoPKxnLymSLu6UAcOHKDVzscqDflcEVYS9KJrGkWmel1U5idluw5eOzerqUmNw7IsIpEIiWRuXkOuaMrKa+oLjDQk7YWOQwMy7flEk+MqhQdBxQW5bdmHbD6I4T1Np6nmbfXYWnvyfl1g+Z/eZenvd5zxcU6F4zI54bXjx4932RHqimgqKSlh1KhRACKazkJyw3Py+Qm9g4imPia7wKUrmkwlZny+OMVmJgfIQrX7TT+RqBIXjrghVI6mWXjtuk7ODLqm0oEsqVEC62PvxRlkebA0jT0jR+WIJi0UcmfPWUbGaUrr6oZkeZsASNk3JSWafPlFU8oWTXpGNI2OK2dMw5MTWry8SjltO46pffKJphbbaSoNZnJPPB6NwUEffux8r7IAaXudPK+lrr2tuc3tH4lE3JlzDnmdpiOvd0zk6g2yRVF2JfB8oskJzZWdA2Uj1PM8tZqS0YyQSvo8WLHGrhXsbKuDNvu9aTsz0XS4McojL+zlsZf2Ud+WOPUOJyMVg7+u6jRk6AiZsWPH4vF4SCQSXZpBl0wmaW5W7+/JRBPAkCFDAKitPXMxKfQtOaJJnCahlxDR1Mfo5XaByyzRhKHEht+bpMjIiCZsAeU3/UTi7USTnUzstSt8O7Wa3hh/OTWGxaAkfOKdGGNtEfTeuefjySpu6QmFXdFEWsuIJq8XC/AUGZiaRtp2ejLhuTyz59JKpIW9marcg9pqSGtK/Oyty9wEK4rU/k4IzhFNAwcOxOPx0NzcTF2DuvGXhjKhPYDKgHqtBzx4vRrGCSXMvCnlarW1ZY0pEnGTwD0eFdrLSQ4dPA68IUi2QsNueh0nn8lfDHpWIvLJnKbSEVmiqZ3TFG8mSSavzPJoGLoGjV0o7Oe4THDGoulv7zcwxbufj/vep7Y5fuodTsaW1bDx3+A/rs8r/hynacSIEQy0w9xdCdE1NNi1vcJhwlk/HPLhiKbuzMwT+gc5+YviNAm9hIimPiZfIjhpJZp83gSBdMjta1q2mDL9mXXnspwmAB3lBDlOk2nfkL9wwMBnwDif6r/r3PNznCZPOOTOnjOzRJPl0TE9HrxFBilfRrRsfOxdqncn8NhJ6zl1mtLK4SnyZZwmWo5g2ov2Hmw46DaX2UKoxa6r5Iim8vJyhg8fDkDNYZW/UxLMneU0WFehQ0/Ii9HcDHboUE+o80faMiIiGo26oqmkZBLQ7ten7oXh1ep5X9RryjdzLvt1jtNkjzPHaWqX05Q1c85Bhej6VjS9uucYY73HuUBv4MjxxlPv0BmWBW/ZS900H4LffLWDA+iIpuHDhzN48GCga+KmK6E5h6FDh7rHlWTwzrFMk9ibb2LaC3oXGtNMZ0qKAHFJBBd6CRFNfYwrmk6cyITnbHfJ700RMDKiCSsTxjGimdwiwL3Zeizb5UlnQiOfNKJcUKf6Twg5oum8HNFEMOikQ2GlNPz+zBIpCb8P/8ABrmjSLJ0D2xspajEYFFM3nmynKW6oMRT77PEmIxBvwqup6zvamBErpSElhNo7TUVFRW4+yYm6o8yM+Ll4TxLDyNw4B3jUvmbQg3Ei40R4oipE4yzA6hzXCc+VlV2qhpU8jmlmhZCGOyG6PshryjdzLvt1vvBc6fCMaGo5misimg+R9Leb6ejzdG02XO32zPPW2jOqVfXu/szNqab+JKFB0zh56LDmLajfBXoAvEHY8xz89f+6m7OTwCsrK11HqCtOU3dEk9MnGo3S1tZ2it4fXlqffY79c79A7YoVhR4KAKnUCcDKeW0YZ+h8CkIeRDT1MW4ieHZOU9IJz6XwpwJuX8MaQNL+iLSYukG2d5osO9m5zA7PBeNR5rcccdeeu7hYCaVDQ4cRzaoIbgSyzpP04PF48NvhupYiH4ELprL/3E+pc5uZKuUDE+q82SUHEqYSPk5hS1rUjTSoKYFS35yxzUsCXiYkdPxxk2TadIVOtmiKNRzjwpROcVOa3a9mnJBSO58p7feQbshML/baS7rEYu1Fk9q3uHgcHrvsQjyeled0Tl+Kpjwz57Jf54TnHNE0AoorVU0pMwWRLFel6ZCqzZRFl5PBs52mdAwSp7eOW11rnNbmJvd1Q2Nz553//F34vxfC+8/n3/720+px7HXw2VXq+V/uh71/ATIu06BBgwgEAq7T1NOiye/3u6E/CdF1TuytN9Xj1j4s2XESnNCc3z8IXVffeRKiE3oDEU19jJMInmhuwrDDS1bKFk16Cl86U9fIpJQUyl3xJZXr095pMuzKtwNiMb5dv4eb1v+UYFszqYQ6dmXYz5DWFiyPh3dDmfCZ4bOdJQvSCRMaD+BP27WeSkp4pfbzHB4xRZ0qGGDc1GEADEyqvKVspylp2SG2gC0AbKek2C470Niasc2b9rbwmZifaTEfLfFUjtN07rnnqrHF2zDs0N62Zw5i2bMAnZlzcS85TpNuqHHHE5lfltmiKRAYSjCoQn/xeFZCtTODrnY7pM8wiflU5FusFzKiKR3LjMFZrLfsHBVGLFHvfU5eU/Mht6SEQ5fKDqRiytEBJcZAJYafBq/ua6TEk3nfmptPIr52PQNmGv7n/3R0tkwD3lZLnDDpBrh0Hky+GbBg3VegOVMCYNgw9V5ki6ZThdG6I5ogN0Qn5Ce5/wAAiff3YSaTBR5Nphq43z+YQED9G5FkcKE3ENHUxzjhuXgk44qYthAJeNP40pkwmWWVkLaTmItSyiXKOE3qOMm4+mI3sZiiG1Q01ROPtJGM2UukhLyMrVE323f8mdBf2m/nK1keUnETtqzGZ6ovv9rKj1N3PAh2nagBZRqDR6oxDrDHkS2a0rZoGhByRJP6shroVzfltkgmzNFar45ZZmq0xHJFUzAYpLKyEoCUX4mMxmMR9r2lbnqBtLo5RjSLdJZo8to1jRLJTH5FtmgKBioJBmzRlJ0MPqAKQgPBSOaGrHqDfOvOgT2Tzg6zxVvswpb2GEtVQc+8eU3N+ZymLoimuh1gmVA0GAaep9paa06+Tyf8bV8DpVpGqEYjrfk7pmKZcR1+Ffa/lLv9wGZoParemwvVAsxc94Cq3B5tgKfnc/SI+jfs5L1VVFSgadopZ9CZpukmgndVNMkMulOTdJY8MgySewtfIDXbaXJ/IInTJPQCIpr6GKe4ZXb9IyOphEhQT+M1Alm9i7Fs0RS2azk5lbvTgRDHPQGMLPHit8Nv8bY2d0kRn9/DmMP7AdiuZRK7DbvWjWbpJFvbYNsv8JoqzyhWpL50htW8qMaYambAMHXsipQSXtmiydCUABwYzHWahoSVs2UkDGK2ixVpVs5E2NJobieaAKqqqoCMaALY9swBLMvCm7SLWGJiNOQRTamsG3j0BIbhzDjMdpqyvkg1LStE18vJ4J2F5zyeTAmCeDNE6pWIQ8s4TPlm0DVlcpqaE0rQpnwaROs7LsmSjROaq5ykQn9w2sngr+w7QYmWcZqS8Uj+jsffIzvfhJdW5W53QnPjrwe7ECu+IMz5DwiUweFXOLb3HSAjmrxeLxUVqubXyUJ0zc3NpNNpdF2nvLy8S9fVazPo3vktPHABvP9Czx63j7HSaZKHMgI+/t57BRyNIiOaKgi6TtMHXzQdPvIEW16eTix28NSdhR5BRFMf4ykqAp/PTbIOBoOk40oIBXQjx2nSKUKzbyI6yrUJBAKYZpKtJ37IW1NKCJZm3BU9qARNLKtqsv+HYxiz910AthuZxOGUPRNNs3QSx96HdAyv7XgYdkK3P6V+oQeidQyoVKKt1PChm94c0WTaoqkkDq/813+SqFdfqAOLy9VxTD91UXUDijYrNytsaTRHO4omJ68p5WvGNzSI7vVQu6+Fo7ubwF6U94RhuDWa0DQ3PJfKCrGl0+pG6vWWoOthVzQl2lv2bpHLvhJN5R23Zec1OaG54iHgtf8tlNmOU3atpubM7LmjESV+WhzBcbIZdDmiSYmD0xFNTdEk79a05DhNJDuZSVW3Uz0OHK1Cgnv/B46+rtrSCdixXj2fdEPufgPPgy/8kjbfEFoMP2BRGc4kw3clr8kJzVVUVODxdO3rzgnPHT9+HLMna3i9sx4ix2HXhp47ZgFIHT0KqcwySIn3dhVwNPYYXNE0mEBn/9c/YFiWyb59PyQa3Udd3dn9b+psQkRTH6NpGt7yclL+jGhKxZXYCXpMdCPzkfisEB5/IGf/QCDAoUM/py15EHQYNL4R7Jwf3Q7dxe1ZP7rHQIs3cuEuJZreT5u0pZXwyDhNHpINStDoPiVcLI9BycAglkfdMPyxesLaCfSAjgeNstgQomkllFKGCR47F+r5l/nrE4+zY4cSTUVlygkIGAGOR9WNzXGaAE6ciHUQTW5eky9KSDvBuCvVr8ZtzxzAjKjk9rp02nWa/Oef7zpNKTOTW5FO24IvUGm/z47T1JloOnVCa2L3bpp+89vTm4remdOU3RZv6hiaAygbqR6d8Fw6AW21rmhqiKu+rT4lvlP1J1lw1hVNF0PJ6TtNr+5vJIBBwKn1BejpTmYr1dnVwkdfDZP+QT3/q+027XlOvTclw2DU1I77nncVR6c/DMAgThD4j89AjQqldkc0dTU0B6pmmK7rpFIpGhvPoIxCe5waWk1ntyvghuZsEv3KaRqUcZo+4OG5lpY3SdoTgSLRwodIPyyIaCoA+oABJO1EbOU0KdGkaaBn3YT8BPG2E026t5n39/3QfT3gghY8duhDt/sm7Bwiv57EMmFAayuDTzRgAW+3KYHjFK3ULJ1EOgADz0e3w3emZlB5filpu0SB30iivf8XwoPVTXlAbCixdAzDNIgk0mi6nYh9TN18W+2E4PCAofb+furj6kst2pIRNs0NbaTTSgg5oqmoqAjTXouP3Zu4eOogNA0OvnMCI6lEXE0i5TpNoYkT8RpxLExM0pn3SVdjCPjVGAJunkO7ytpO2YH6XScPawFH//W7HLvnHqJ/e+Wk/fLSWcmB7LZ4U9bMueGZ7e3Dc82HMbHDcQBelZuU8CshvOXV1/KPwTRdwZHjNJ3GUiqv7GugxHaZHAcnRJJIItWxsyOaho6Hj39LPd/5Ozi+KxOamzgbPHrHfYFjcfXvelggrnKffn4dvP/CycNoh16BTT+g/rja1h3R5PF4ulUHqktYFpzYr56f7aJp/34AvMNtcbKr8E5Tbk7ThyM8d/z4s+7zSOQkP5SEHkVEUwHQBwxwnSYrmSTa8DJm2q6J5M2EOIL4CfiDOfu2tT2GacYoKbqYVMSLN2QwsEJ9OWh2raVEVIkmnyeOmVY31jEHVSLuW63KIcoWTUmrCGb9BN0ukGlpBkPPKyMdVmLObyRhz3OUDVViZkBMORTRdJSGaAuaZqKZ0GonzsYi6hrCFerGHzAD1EftteaaMk5T6wklbLxeb06dKE9CnScaThNONXLBR4a625JYnEilSdtOU3DiRLzpGKZH3aw1TUPXdfwBdZ2BgNrXTQSPH811iooHQ9m5gAVH33CbjzTF+NaTb/D24YyQSr6v3sP4O91LGm+Opog7Mwjbz56D/OE5RyhlP3dF0yElmDQN09IoLVHr0wWKlGiq3b+DNw81dTxP4z5IRVQl9IoLupbTdOhVlaDejlf2naDUzmdyZrR5NZMj9XmEpxOeGzIehoyDsZ8FLPifpfDen9S29qG5LNyillO/AKM+rkokPPVFBpernLkOM+hMA576Ejy7iPr3lbPWHdEEJ59Bd+TIEX72s5+54+oSsUZI2O9NvsWXzyIStmgqmT4dNA2jvp50ff3Jd+pl8iWCJxLHPrAFSi3L4nj9n93XkcieD+y19jdENBWAbKfpxKH9JCNbMJL2grm+KKAcFR0PRYGMaBow4AjR6CY0TafYuokTu9TNdugw9SvD41XCK51MYFkGfi2GlVYf8djDylJ/q7EJgFSt+vWvWR6S+kAY+VF0p26UJ03l+WWk7XCfP52Cvf9DRaUSUeVRdbONpCIcj6rjlUQDmLZrFEuo8YcHq/wkv+nneOQ46ZRBIppxg6KtuaE5UF8Ggag6T2upTqqmlskzRrnbIx71xeDUaQpNmohuxLFs0RQOhykqKiLgV8LNFU3BSkDDNBN2IbwsnGTwo5m8pv987TC/ff0IP3lR2d5GSwumHfZMdOOXdcowmfvTLRyusX/1ntRpas5ymrLCc87zaL2aidZ0iKRdbqAtVcTICltUedV7cK5Wy93/+aYKnWZT85Z6HDpeuTolthjtTDQd2AyPTYcn5+U0tyXSbD/a4jpNQ4YMIYn6t3eott17G2vKFOscfJF6/Ls71OPO/4Z0HCouhGGX5B8DmTXnhp07Gr74GxhwHsSbqah9yZ1B15qVx8fevyhHCqhvUu3dFU0nm0H3P//zPxw8eJDXXuvE0ctHdp5ZrBESnc00jMNv/hm2/6Y7w+1Tkvv2AxAcPx7fuSp03J3/E72BI5oC/sFuSN4wIqTTp1eDrL8Tie4hGt2HpvnRNB3DaOuw1qbQO4hoKgD6gHI3EdxMqJu7YddhMr1RPLSi2dP9S+08FY8nzegLVFho5MhbOL6nlRPvqZvtgIGH8PliWNnhDSuBjzbXabroiC2a6o5A4wGSW38F2E5T2qt+pdgpKZaWYtDIYlK2aPJZGsQaqQyrG4jrNKWi1NuL8Za3ZIRP1PCBL0xoQKXbVt9S7yaBW5YKQaYimcKWDom0SVFcvW4rDtBy5DCDRhQzaqLKj4p7NbxmGsueZu4bNQp/0IvpUWLMEU0Zp6nSfv8C+P0q5NKVvKYDJ5Sg23tcPaayXIX4rq6vVbf21UO8W9NKGeo4hlMANJscpylrCRWH0ACw881oOQrNh918ptZkCaMrlWhKW3FMDc731LCrto2/vd9OwGQngQMUn0I0HbZFwb4XcxLltx5oxDAtKoPqPa+oqMDwqn+ntQ3tcoCOq3w6Ss/JuGwjPgLnXZXpM+kGFZvOQ1tbm1tSYNiwYWp23aX/CID3zV/lL0T5xi8BiBIgQsgdY3foLPTX2trK+7bjeOLEiQ77dUr7NQGbOnGbdm2At9bCn/533ywkfRo44blAVRXBMWMBiBcwGTx7CRW/vwJdD+HzqX8XZ3NeUzrdSjqdX1wfP65cpoEDpxAKVQESousrRDQVAG9WeM6w3R0joT4KwxfBo0XwoG4U5faMqBEj3yEUasPnG8J5Vbdz5N13SDQHsBrCaB6LIUPfxzAM/CEV2rKsOH6r1RVN41qaANjjKaPtP/6BlD37RbN0LAtSCYO0XdbA9MbQvR5X2PnC6gYyJPY3Nab4IDTLQ1uqjQa7/tCAtowjFkv7oHQ4Hl3HV2SvNdfcQqQ5iWk0kGh6mFTsrxh2Mcps0dQST+GjGG+yBDSNHQdV/scV159P8YAANaUeShP2zD2PB72sjOCQAVj24sAxUydueQn4HdE0xD12pn5Le9HUsezA4RNKzO6rb8M0rRzRlNyzByudJmVafPGt91n+fv4v5tZ4igefVTeTUntx3f/eFe3Y0RET2eG5bKdJ03JrNTUfImU7TS2JEsYOOwfnv3LKpzGYJsLEeW5nOzHUQTTZojbaAOk8BQqzb/R/e8R9+so+5fIN8av3fODAgXjsGmANJ5pyj+HkMw0Zn9v+8Tsyz53k8Dy0rwQOQPVNoHng4GaGlKl/s24yePQEvPsHNZaP3A1AKa0Eop3cPFvytzvhuYaGBjfvDuCdd95xwyD13QlJtZ/R2FmI7oSd0Bs5Dsfe6Prx+wgzGiVdoxwNf1UVgbFKNBXSacosoeLB51P165xk8K7MoLMsi1+/cpDXD/Zg0v8pznf8+HPU1/+FZLIhZ5tpJqmre4Y33/pnXvzrR9jy8vSchYgdnHymwYOuoahIhecjURFNfYGIpgKgDxjohudIqxuPkXCm+0fQaMOjKdFUqgcJBlsYOVLl0YwevRCvt4jmOrtw4yHlUlQO3UsikSBYrGr2YMXxa1HMlDpuhe5juBXF0jy8bQZJepXjodlVtpOxNGl7rTvLp26gKXuGnS+kbiAlx/5EWrPQLS+l8QoiqQiNdvL0gLZMTlLM8LqJzEWldjHMlgjR5gRm+giQxkwdwEp1FE2NDa2Y3jDBuDrnu3ZIbPDIEr60bCqN5wQos5dN0QcORPN4CFUOcnOa3q2Ls+1orENOE5xkBp0TGmo54q6PdqhR7R9PmRxriZM6nEkgt1Ipkvv382ZrlGcbWnjk4FGi0Y7T/B95YS8NkSRjK3wEbFG38oUamqPtkqUdpynWmLmJZ4smyBJNR6DpIEk7CdzUygkHfPh85QAki9VNY5RWy3M7a3PzHLJnzoFysOz1/HKWaHHIvtFvX+eO7ZV99sxFewblwO0/4wJT3exbWtuFQ2od0TQut/38T8K0RXDtcqgY3fHcNu0rgQPq39YFqgjm4JQSH65o2r5O1bkaOon6c6YDatYdG5fkHjidgHX/BKsugo1LO5y3pKSEYDBo3+Ays/PefjuzBE1bWxuJRBcryXdwmjpJBs8uTrr72fx9CkjygKoErg8YgF5eTmDsGKCwM+gy1cAr0Owq94FuJINv2tPAwt+8zZd+9krH/5u9wInGTbz19j/z5lv/xF9f+iibNn+Ct7d/nZ3v3sNfX5rC29tvo77+OSwrTTJZz969K3P2j8eP0tr6NqAxaPD0jGiKdN0BF04fEU0FIDsRXDPUr9i0nQdkeqN4tIxoKtf9DK3ci8dj0thYiVFUjWkaxOyQRVldCMPwEi5qJp7YSbBIJcdaVgKfFsNE/RL3hMNcXKHCU2+VjCd14XVqLHYIIBFLk/So7XhV+Cxpz4ryBVS7dvQ1Ij7Vf0BsKNFUlEY7ubUskgmvJEwfRrESKAPK1U08HUkTaU6CqW60lhkFe/HcbNHU9KYK5wSiFWiGSb2m5eSVDAj7KE8oIeUsSeMfPgywZwWaPqKWht+vBJkTngOyit61E02BEihXpQ6o20EybXLUMkhcOQRjcJD3j7flOE2gflm/06Rcobil85eXP8uWlz/N7j3LaGp6jSONUf79r+pGufBTdo4FHo7EdP7fc+1+lTuiqX6PWmNO82QKWzq4tZoO59Ro8gdUro4TjkgNVPtd4K3jcGOM92pte7/tOLQeA7SM6+PxnDxE59zog+VqCZRX/514yuDNQ834SWOk1Oc3YMfjXBBX+VKxrOrv6v3MSgLPRtPg7+6Ej93a8bxZuEngw4fnbrBDdIPrNgFZoun1X9rb52XKDdCoxJQTfo01wi/+V2bm3qYHoT73hqNpWocQXUNDA0eOHEHTMgtcNzTkOgWdcmIfKXTeD07ERDuJ05Qlrnb/OX+frpKMKve0BxOEndCc3y5CG3ScJtt9PRWWZbFpTz2RxKn7dnlMWUngDu4Muvauch7+8p76fFviaX78Qu9P3W9pVjXKPB7lzsbjh6mr+yNHjz5JOt1EwD+UUef+f4wfpxasPnrsaVpaMmLdcZnKyi4j4B9EUdgRTeI09QUimgpAdk4Tpl03KaG+2AxfFA9t6CgxUmr5CAXVjeh401D2Nu8l3taGZSnxUkyKxtoLAUgk/odgsS1AHKfJo0SUpyjMJWXKhXrrI98gGVRCSLcrh0ca4yTtnB9LV2NJ2XkmXktXN3HLxBNQ4qQ8VkkkHaEl0YJmQWkkN/8iZh9rSIW68XhiHlqbYliWHZ6yonjsukrZoim6U31p+VMphh9TX3hvvfWWu31A2O+KJt3OU/ENHw52UnLa8uLxJdE0C8vy4PdnclmCQSU88q5J5dzUa3dwtClGenAQq8SHMSLM+8cjGdFku2/xXbt4ryWT09KiVRCNvs/Bg//O1m1z+c1fV5JIm3z0vIF84lx1gzX9JVh4+MXLB9hVm5Wr4Iom+9d6caVacy4bp1ZT00FoPuImgpfaoVO/3xZNperG8YlB6vjP7bDFUK39pVsxGgKZNQg7LTtgpDJ5N59cqB5f+xlv7qshaZiMKnL+/UUJkKIU9ZmkYllVwS0rKzzXzmnqAqZpcsB2NkaMGJG7ccy1EB7E4IRyZurq6rBqtquQlscHk+ZkRNM556t9/rxIvX8/uxYOvAT+Ehh+qRKEz9zT4fztZ9A5LtP555/vLvfTZdHUuI8X+Bj/Ef80f+Dqzp2mhqyb9pGtqkL86fLMQnj0U8pR66G1FduLJt+IEWjhMFYy6bpQJ2PN5v3M+/e/8f+e7blwXl7RFHAKXJ7aaXpxV8ZJ/PmmfdQ0d1JvrIdosx2h88+7nav+7nUurf4PRp9/NyNGfInqS37O1Kl/5YILvs2wYbOoHHo9YLFr17+5rrGTzzR4sHJbi4rU97/MoOsbRDQVAG/W7DnNcESTspVNr8ppQlM3oVDaIhS2lyBJBtnVuIuovbJ8wAN+LU79YRVuMYyXCRSr3CLLTODzxDA1JUi0cJiLS5Tr9FZCc3OavPb5D22vA/z2vuoXoZPlokej7k07HLTDcbGhRFIRWpMtFEe96CboaPjs4pkxXblAlYPUzSWcDtN0olU5TOpK0ejoNKUOOIImRZU9S+ett95yKzMPKPJT5jhNdhKw75zhmJoarWn68NvCzjSKXbse6LwqOGREU907KjRnixKr2Me++oxoCl/+EXWMXbvZE8vkAZ1/yW+YOOEhBpR/DIBoRP2a/NfPjENL2EnrRQO4ZvxQDNNiye92ZL7gHNFk2McrbeeqQCY8d3QbmCkSttM0uEy9v04uR7JYHau6WOVnPLfTDru1z2dy6KzsQPMhsAzwBuHyr6iyDLETtL7yBACTBqv3dSCN4C9xRZOVyqoK3lYHsRPKORs8tuM1nYJjx44Rj8cJBAIdnSavHy75AoNoRMNSM+jssTH2WiiqyIimj81V13HgJfjxx1Vyeslw+PIGmP2YElm7/wy7cp2d7Bl0lmW5omnSpEluYnmXRFMqBq3H2E0VAFu5mPeO5VlyJhmBNnsG1IAqwFLFP/Px5lp4+ZH820AJVqecw/b/VM5a7MxzdhJ2YUv/eao2mObxELzQ/tHWhRDdU68pd/a1Az2XP5TIWkLFwQ3PnSIR/GhTjN11bXg0mHhOKYm0yQ829m5+lhNGKyq6EJ+vlIEDp1JV9S+MHbOIioqrcr6zRl/wv9H1MM0tr1NT+1+kUo00Nb8KwJDB1wAQDp8HeEinm/PmPwk9S8FF08MPP0xVVRXBYJArrriCV145eeHAp59+mosuuohgMMikSZP44x//mLP9N7/5Dddcc427oOcbb7zRi6M/PXLCc67T5CSCK6cpYU9lC6QsAgF1Q2pN+tndtJtYixIu3mgSs9WgraGKeLwIiBEaan/pWnH8WhzTnj3kCYe5uEQ93xNN0JJS9rhu51Qd2tGIZtrCzbBIJpPuamHetgiUK9E0IKhuwgOiSjS1pVopa1PXUoyHgCOaNJUfVTFQfZEVpYtobopmnCYAraNosmrVTSgZ8DHs2DH89nTyffaXdWdOk1NyAMtLwK9uSMlU5riQEU2xeLsClwBDJ6jHup0cOhHDskWJFdLZVd9G6ojap+STnwRUeG5fIvPl1mj4GTr0s4wc+WUAhoTqub56OJeMLM9ZrPdfPzsOv+7hpT31POu4QMEyTMtDXep8TMuTO3POwRFNtnMTtcNDI+xyA34nPBdSn/G5lrpZvHGoibrW+ElEUydLqThhogFVoPvgin8GYOz+XwAWk021FtxArRXmPeWKJr+RJZocl2ng+eDLLBbtvmeRJM2xznNInFlqVVVV6HqewpeXfhEvBgNpAuD4O2qtRKr/kXQ67c5uGzRqXCYMmGhWAvmfnoPKicp5+9i/qG3P3JOTEJ/tNB07doyGhga8Xi/jxo3rnmhq3E+MALUMdpv+q3E0bW3tQpnOex4aABPt5Ph8Ibrju+C3/wIbvg217+Q/Z/1u9ZnqfrW24YGX4LEZ0JjrBsVak/zuoTd4/43Oq6pnk9yv9vdXZcqAOMngp5pBt7u2lR3H1A+IPXVt3XJFLMvi9YONJNJGh23JrCVUHDrNX2zHS7vVvpeMLGfx59V3wFOvHWbv8baT7XbamGbKzX8sKhpzyv7BQCVVo24DYM+eFdTU/h7LMiguvohQSKUU6HqQUEh9P0syeO9TUNH05JNPcscdd3Dfffexbds2LrnkEmbMmNFpFd7Nmzdz44038pWvfIXXX3+dmTNnMnPmTLZvzxQbjEQifPzjH2fFihV9dRndRi/PhOdcpynpOE1Rjgyq5r9MdXPzJOJ4vUoEtCZ97GrcRcRxmtIGVtRAs7zU1qhkWn2A+uKyrLjKabLskgXhMIP9Pob6vVjAEUt99F5bPDXUJtAsOyRk4i5vopkmnrY296Zd6Ve/FMtjQ4kmo0TSrZTboqkkmcJvf6lFTXWTHGDnHYXTYaLNcTenCcDSM2UCQBX6pFWJxWRpGF8oxLn27Lk333xTHS87p6nCdpqGDcsUtzS8BG3RlEjk3qidL9JUqgHDaGfBu07TTg6fiIDfvklrGu83tWDYy2kUf+IT6vqOHeOIPdsQoN5+H7fXqhDo4HADd35afSm2vvQ3TuwOYwVKGVVRxD/9nfqVvmLDu+rGESzjrejneLphJW9Hr4PSdqEo6JAY7jhNwwaoX9Q+JzxnVwX3txzgkhHKdfqfnXUdk8AdOltKxclnGqDGyuQvYvmLGZk+wD/oLxKuV8cbOPpSGHUlocGqnxeTWNwOBZ0kNBdPGUxf9QKf+cFfMcz8N09HNJ1//vl5tzPkIhhxOYNRN77jCa/K0bpgOo2NjViWhd/vp6SkRFUiH/VxGD9TOUzZwvSqu6FoMDTshlcfdZudquAtLS28+qr6dT927FgCgYArmro0g+7EPg4xHNAoLytlCMeJWkH+a327JXmcJPCBo+FC5SKwZyMY7fJ/Nv8AdwHkzpyo/baAHHmFut6S4Sr8++/TM+v+Abtfq+XgjhO8+oeTrFdoY1lWh/AcQOAiO6/pFE7Tf7+ZETBtiTRHuxEG+3/P7mLWjzbz0xfe77Atf3jOnj2XqHVLnOTjhd1KLF514WA+UjWQ6eOGYJgW//eZ3klsj8b2Y1kpdL3I/T46FSNHfplQ6FySyTr27LkfULPmsskO0Qm9S0FF06pVq/jqV7/KLbfcwvjx43nkkUcIh8P87Gc/y9v/Bz/4Addeey13330348aNY+nSpUyePJnVq1e7fb74xS+yaNEipk+f3leX0W0sv5+0I5rMNOBxw3OGL8LbxdW8bNlrsBm1aBoYhk7E1DjQcoDmo0q4+NMGxFJolofjx6vsYx9E81hgJVROk6UcCU9YuS4jgnZRTXtxXl8q88taszK/5p3aOL5UCqutzQ3PnaPvxcTEbwaJNCWJGW2uaCpqi7qiKWaom3dxcTGWx0JDIxqJYVlK0FioNe4g4zQl9uwh5bWFSGkR3spKN0S3c+dOEokEA4r8lNuFAXU7PKdXVGDqKnwXSpoUBdQ5YtFAzk3J6y1H19XxE+1t+4oL1EyyRAttx/dj+TP/NeL24sSe4mJ8o0bhHTyYo4OHYmb996lPKtG2ab8f09IIeeMMNms5cscdHP7+E9RuLadVXQq3feoCwn6dvccjbDvYBMEyGlLql3tDelR+p6n0HCwLGveGiTb4MP3qugJ2kr4bntPtG0TLERYW/Y7Pezbz/ut/UcvEQOdOU/ucJsf1GGiLpmAZNefNBuD7vp/SaKq8qIGXqAkFofHX4LfDrYdst7DTcgPA3uNtNESSHGmKUdvS8eaZTCY5aAvm0aM7n13Hpf/IENT5NnMZu0fOBd2bs+acpmkqBHrLH2DOmo4FRoNlaiYfwPMrVNI8EAqFKC1VjqnjWE+apN6/bKfplI5J4z4OoD7T8847n9ne59FJs3vPXrZuzVrz0BVN56taVqEBammdI1lFNJuPwJtPZl7v2Zj/nPv+qh7Pu0q5qP/0HAydqGZJ/nK2ChkCTbXqseFIhFSic3EBYJw4gdnSApqGf1TGaXKSweO7OhcalmXliCYgN6/vJBxtivGTF9V7887RjsUq84kmv38ImqZjWalOQ1aGablO01Vj1L53z7gITYM/ba/hjXxV9c+Q7NCc1kltsvboeoALL/hXQJUkgEw+k0NmBp2Ipt6mYKIpmUyydevWHHHj8XiYPn06W7ZsybvPli1bOoihGTNmdNq/qyQSCVpaWnL+epOcacqGiaYPJW2H50xvlGPxJM32L8mkoW7uiXgxmteDaZkcO6hugP60gRVNg6UTi5VimmHQDIIVceU0eWKYhhJCHtvNGRZQAucEqt2XFY7wmAYeO1yXLZqMSMQVTRVGDU0+e7HeBo1Etmhqbss4TXH161jTNOwIIelkHCz72j06tm5zRVN8xw5SPuXU+Ip9+IYOZeCJEwwIBEilUuzcuZMBYT9lScdpUjcuzePB8Kr3qyRpUBxUY4/HgznvtaZpBAKd2PZePwxSzpC/fqcbngOwdRa+4cPVMcaM4eDQ3F+Jx5Pqeo82GzQl1E157+030vLHP7l9Wt5W4yoOeLl2whDA4j+3HgZ/CVFTiZ6IMTB/TpMvSKRpKDWvlnP0rTIljMnkcfh96jFltrkz7z524BF+6F/NPcduV4lqRYMzs+UcOstpatyvHh2nCfhT+O8xLQ0PFicoB2BghbrZ6GOvdUN0R+wyAZmZcx2dpn31mZyew42xDtsPHjyIYRiUlpaevDDlhP/FZd49lNNMCyX8aqfOunXrXMHV5Urg1fNU6YlEM/zl/7jNTojOsiyCwSAXXKBuTgMHDkTTNJLJZMcwW3tO7OOgLZrOHTWKoQOKmc5LAGzYsCHjVjk1mgaer/5/jJ6mXu96JnOsLQ+rGZYDbSF5cIvKhcrGsmC/Oj5Vf6cey86BW/5/9t47TK6rzPr9nVA5V+fcauVkSZYt52yDM8HkAWOGIZsZGIZhGGCGOMAMySRjcjAmGWMbG+csS1bOudU5d3V15XDi/WOfquqWZOO530e61/t59EitrrDPPvvsvfZ617veB7Bc9ViZZJV5TE1VslltpodeeN2rsEyu1lbkimcW4FkinhtjbBzzedbOPSNpBmcK+FwKlywVQL938sWFwL76yFHKTsLK+CkBtgC5njnhOVlWcbvFgeD5bAf2jqRIF3VCXpU17VEAljaHePU6wfR+4YFD/9eF1fmcWLsrzNCLbfX1lxGPi3vp9bYTDM5/pmoZdC/ZDvyp218MNCUSCUzTrC5KldbU1MTExKnt4CcmJv5Xr3+x7fOf/zyRSKT6p6Oj4//o8/5YKzmGlpJpIGEjq63zmKahgkaqApoQm1mpHCDgE+Bidkroa9zV8JwMSGia443UWMS2S+zzWjyQdEqsOKCp1SOYppSTneXSa6AilBtBtgRoSqfTzu91UT7ECc9FtEmSLkdoPeuibOZqmqayhtsJNxbnlLVwB5zsMaW2oNpOFpqiuFArGWkHD6K5BIPhCbpQm5uRgKUOK7dnzx7igVp4rsI06bqO5czkoGYS9Yi+lzV/NcxYaS9YzNNhRMK53qoQHMAMiv67HDGyZ8kShlrnWwJUwnNTiSyuKXHvyq5Z3AsW0PJ3wnE8eziBMTtLoVCgvv9RLnId5769Y5RMm7wtgEHBip06PAfkpwWgLDrO7xZ+FEWEXytMk67Pwpt/C5d+Anvtm9gjLWfKjooPWHXDyc7bzxeeO5FpAh4Y9/OgdSYl3OQdK4uKIzcta/A5TFOuf6dws55y3MAbV550LX3TtfsynDzZ8HNuaO4FT+TeMJFVL+O9/JSzAyNIksS+ffuqB6kXDZpkBa50Qvo7flLNYquIwQFWrlxZnauqqhKNRoE/rmvSE/2MItatrq4uiHRwFrtYUO/FMAzuuusukehQHXMnHLnk5eLvil9TIQk7fiz+fdUXhU2GqdUAUqVNHRIld1z+mts9oGdKHL8nwPE/NGAPCfYqNVkb+4m+Fwea5obmAJRQqPpsPJ/J5b27xSHlihVNQufHi2OaDk9kuHPnSK2P6ZMBdsUgci7TBH/cduDpowKsnr+oHlWpPe8fvGIxbkXmub4kv915Cv3j/0GrZM4FX4SeaW6TJIllSz9NLHo2C3s+dNIz8bfANL1QmPRvqf3FheB/De2jH/0o6XS6+md4+E9bULMCmmQHYEhKHYZTe85yFRjIlWpMk+JsZnacWKfYGAuzQuDqMUysvFUNq5WKYpHwNwpGZ4ffRtHECe1EpimtOG7fWu3kFs70i1Ms85mmuaDJq6dIqWKhlVJe3KUiLlMGScZf1nE7J8JiupYdUzG4NOTaRmk536/ItRNr6cBBdLcABlsbZX62SmSq9eRyKIqGy307WvppIhVHcMcDqlCo2BhIhJRR4qEBMXaa7xSgqcI0nUoMLkBTgzUGcm1RKjiFYeeCpsEOwRy024LRSJTEYr7+2XtoGhabgeuy01hw12+Jronhiepg2mQffJDx8XG0UoEuJUW+pPHIwUkKVhSAvBU7dXgOyI86thR+xwpCjVd/V7Ec0PSkCMdc+C9Ir7yVu9Z+nw3lb/OxlY+LjfbENkcIrpUTAkza9klMU0Ez2DWU4l/1d9J7qdD9+P1+vF7HCV6S0FTnPk8ehvSQKA6suGsgYE6byzRVjETntj+qZ5rbLv4o7hXXcuUb383b3/72eUDnf1VzruscoXvCFqVjmA+aKqG5SnuxYvDRRBoLhaDXBQ89jBVoRQZe1aPhcrkYGxsTPlNzw3PgME2SsIvIjMHW74kxbVoNiy4Xf+DkEN2AE5rrOEswqIBtWYx95CMYGR2joKLt24yhm2STted/sv+PgKYTMufmtpoY/OQQnWnZ/H6vAC7Xr2llSZN4xo9O/XGm6YsPHMa2YUO3mN/T2TLGnJqKlqXPK6Eyt/0x24FnHD3TBYsb5v1/e8zP287rBuBffrOHzz9w6Hl1d//bNjc8979tPl8np5/+c5qbrz/pd4GAYB51fQZN+1+U9/lfNtu2KJXGKBQGyef7yOWOks0ewjRPBrNz26FDH+XxJ5ayceO5bN/xOg4c+BDH+75Kcvb/LEr0l2h/MdBUX1+PoignFcScnJyseqCc2Jqbm/9Xr3+xzePxEA6H5/35U7a5TBOAJHnRKm7cskHSLFZBk+4VC/KZZ17J8iWCktUL4v1uw8QoyKgI8JXPiwXe31jEtkpMuXW8TvRNDgjQZEyLtOysw964tdpkD2f6sW3Rp5OYJl9UZOEAuuoAqkyAYFa83h0IIjt9AijMqcsVjUUBUZ6l0ipMkyQ5i7phUDpyBM0VJO2XuT9g8tWWBSRDEXwTkyxblqOpqY/xoY/i8QpGI+8X/amAJtly4TfcRJ3CtVr5FExTJTx3qtOnwzQ1eBzNlIObMpEQJZcbV5sAM54lixlqEZ+zyhDZS9P5DJqmc/7RzSgJ8UblzC5knw9KaSLdoo/pu++pjq2MTUQq8dsdw5RMAcyKVgTLV9uoK82Ynqbs6E8sx/Td76st9nOZprkhhctXNOFVSjTwecbH7zn5mp1wnW1qbN9+A1u2XoOe6hWbsyRXTT+3DcxiWDaRaB3EayGqua3od8KKmfGaE3j90pM9p4C+6Sw3rvglr196F8PJ+QtuLpersscvCjRFO4RWqf0M2tvbeec738nll1/O6tWrq+G0E9vhiQyf/v1Brv/mRp7rmwN6OoVlBCNC+N3e3o4kScTjcTo7O+d9xosCTabBYE4capqKJSY/9SlmnhWvD5dGqqAulZiqFTauOKQH6oS2CeDgPbVSNud/QDCGlfDdiWJwB/Cx4ILqf8187/sUNj9X/Vk7epD0VLGqJwfoO5zkueMJCtqpjSfLz8M0AXOcwU9mmrb0zTCdLRPxubhwSQNLmsRc753MvmD4a1NvgieOTKPKEp+/YTWqLGHZMJ2rseOVEiqSpFSfgWqfqqzyyc96pqSzy9EsVfRMc9uHX76Ud14o5t5tT/Vx04+2Mps/Ramh/0WzrDLF4gAAgeB80LTpeIL33L6D0dQLg4/na4rix+sVB9t84U9n0Llv/808u+kCNj93Kc9tuYItW69i67Zr2b79hue9l5qWZHzit4BNWZsknd7BxOTdDAx8k5mZp/5kff1Ttb8YaHK73axfv57HHqudkizL4rHHHuOcc8455XvOOeecea8HeOSRR5739X+trQaaHKZJ8qIToRJjslwFTKAog+4VFLLX28bimHjQLE28z22YmCUZ1aE9cxmxiXqjGoorz4Rbx6uJiSz7/ZS1BEpapDDnHcbJMwc0RTL9WMxnmlTdwNZ1LE2rsk2y6vQpEybqbAgBb6jaJ4DCHKapYnBpKmLRkWQZW6mYe4q/tf5+7FIJzR1msKG2yY41NKJPTNDSUmE08mSuM9FklRTidRXQJFkqHknG68zqctlfY6GcVjG4LObmmwuO9aaYscRmFXTcxFs9boKSBJLEUHMrrjYBCtw9PYzExb/X5IU2JKGbjD36FPWlNNqsYM+KRec7iikiXUWQJIp79jA7VPvumFRg+5EEdvVRlCnkTqax889tqf7bCol76vPUFnuXo2mybWNekc+zFtSxoeUQq+t2cbj3lpM+F9UD3iglj0yxPIJhpJkdf1D8LtxeZSo2Hxcb/TkL65h1MglP1BqVYwKglCxFeAPBKfVMtm2Tyg1xUfsmXtb1JBOp+aCjYi/R1NTEnokSH797H0Mzp6jZ9zxNVVXOP/98brjhhqpzN0C6qPOz5wa5/psbufJrz/DDZ/vZO5KuOrcD0LFB/D28tXqN73jHO3jrW9+KLM9fLl9UBl1mhCFbbN710+I6cwcdOUFquBriS407LJM3IgTglVbJonvsM8LzKtYtMgBBiLxlVWihKqE9y5qjZxJFkQu7djH99a8DoMSdhIGxCY4fEf2Yli1MbCiZvOO2raz+5MNc/82N/HLrECW9NhefLzwHc5zBT8E03eOE5q5e3YJblemqC+BSJPKa+bwgwbJsPv+ACO++6axOFjYEaQyJ52qu+WRF5O1yxef5G8HcWpMnM02behOYlk1PQ4D2mP+k36uKzL9fvZxvvHEdPpfCM8cSXPfNjRwYS5+yvy+m5Qv92LaJqobxuOfLTL75eC8P7J/gU/c+j4XE87SZXJnHnHJJf+pyKjMzTzE9LfR1ihJAVcPVcc/lj5DJ7Drl+0RJGGGTcMb637Jq5S0s7Pkwra1vIBbd8Cfp65+y/UXDc//8z//M9773PX7yk59w6NAh3vOe95DP53nb294GwI033shHP/rR6uv/6Z/+iQcffJAvf/nLHD58mE9+8pNs376dm2++ufqaZDLJ7t27OXhQnHSPHDnC7t27/491T/83WwU0VdzAkX3InihKtWCuk/2lSOg+B6D42lgUXYSEBLZgMoQnkoTquIMXSyoetwAF/sYs46qOt2KD4/ORTG4kjmCAsq4ANuBx9EEeqYy3lMSUTg7PAfNCdAFZWEJ49AB1GdHniFNYuJo9N0cc29rQ6lyX+F2spa3KNGEK0FQ6eBBLUjFVH4ONrup7x+qbMCYm8Hprp8vC+RazPR5SRXEinss0eXwOy2S4sCzX84bnsse3VUMJxazGPV/ZxT0/mESTA2TcgsGKuxQW+sR1DbS0ozr1z2Zklbw7gGSbnDYrFvZZyUvq7rsAOOwVYZwqaCqlUX0WgXXLAEgcOlTtz/Kohd+cr08oZE52b87PSXYwwxUReA00KYoHRRHhMXH6Fs2typzdKUCOqQ1jWadwhg41U/DP8ZyadQBavLv6f5uPi3l47sK6qv9RhWmyLBND1wnHBIjIEIQDvxNvbDo5cy6R0wgqtY0sk5//bM4NzX3xwcPc/twQL//a0/xgY///6zDJ0EyBS7/0JJ+4ez97R9K4FIlzekR/tw0ksSqf2+YwOzPHqnUIW1tbiUQiJ31mhSV6IabJTPQ5dgMQc1iYUv+4SPxID1c/NzXtsCHxnvm6swpocjI4Ofcfa8ydNyxCcADHncPk5D6RcecOQutazEyGsQ/9C5gm4auvJvaGNwKgZRTuf1jomkpBBSUu5vkKtwfTstk7kubf7trH+V98gm890UsqW0IfFPP51EyTE547dgzbqoXPyobJH/aLe339GjEOLkVmQb2Yq8eeJ0R3375x9o2mCbgV/vEycVhsjoiD06lA04l6JphbtPdk0PSUo2e68ITQ3IntujWt/O5959IZ9zMyW+Q1t27m2d7/dwaSc0XgczVJtm2zf1SAsYcPTrLzf1E4+ON37+ftP9nOQwcm/qS6JssyONb7eUBYIFx80V4uunAXF16wjabG6wCYmLzvlO+dmhaHsMaGq4hE1tLUdC3d3e9m+bLPUV9/6f/1vv6p218UNL3+9a/nS1/6Ev/xH//B2rVr2b17Nw8++GBV7D00NFQt2Alw7rnncscdd/Dd736XNWvWcOedd3L33XezatWq6mvuvfde1q1bxzXXXAPAG97wBtatW8d3vvMC7rl/5lbJ6KqUQpEkL5I7iOyAJq8jtC65LAyPeIC83jb8Lj8d/nZs50RVASiq5dSv0zUCfuHD428oYVhUw3NZRSc58wwxZpFsC1NWKbncxJPHaSsdZqm5AwnQ5flAxO3w9+OpDH1RsQG2SimynhlcEtRlRWpc2JlKlT6VigVsyyJvmMTrhbDeVsCWZRq6FlSZJluvicB1l1hI5zNNTZipFJJTVkbX3SCD9poCyVxpXl8l24XLLy44r4sFNpfPczRfQnMW8gpoMmM22acFNZyeLmJZNsWczoC6lhlVbGR1LpXVjsP6QGs72YhYYI854dFGpmgs+pBsC1uSye0VXlKHukQ6sKYlMIrTVRYgctUlAKTmAPgun4bfng+a8qn5wMa27SpoUv1mNTznOkHDUQvRzdc0LIyJkLYkWfxh1ynMY4ON5OeAplTZYQscPVOmpLPPWdTPWVhXBQnxeBzLMvnph9/Pjz74bmJe8RkZgiJbD05pN9CfyNPkr3mx6foUmqOFs227CpoWLOjhmJNhVdRNPnPfQV77nU30Tr24VPVKM0yLD/56NzN5ja46P5+4dgVb/v1yfvr2DfjdCumiztHKZwbqaplpI9tP+XmWVSY5u5lYTNyI2dlZTPPUIteJwSNouPFIJqFKKR7bpjDphswo0YgA6GlHp3iS/qv5tFrGY6BRZPnNbYsqIbrHxd8Vq4HOc7BllfH/+E/0sTFc7e00f+qTVVNKLacSLhYwlSLx2G7iHeL+vn9NJ09fEeLH0w+xQi2RyJX5n4eO8Kr/vBNb15HcblwnJEEAuDs7kTwe7EIBfaQm3H7qyDTZkkFz2MuGBXEKO3fSe8XLuCohGJVjpxCD66bF/zwkDiPvumgh9UEB6FoiYq2ZmJNBV64W6z0FaKoyTfPDc7ZtV0unnCo0d2Jb1hzm9zefzwWL6ynqJn//4208Naf0yott+fypM+eGk0UypVpIVOi4/vjhwLJsNjkM8Jb+JAG/+NzCnwA0jY3/mnz+GKoaZUH3zfN+19R0LQBTU384Seyt6xmSyU0ANDZe+X+9X3+J9hcXgt98880MDg5SLpfZsmULZ511VvV3Tz75JD/+8Y/nvf61r30tR44coVwus3//fq6++up5v7/pppuwbfukP5/85Cf/DFfz4lo1PFdhmiQvijuI4jhY+x2mSQtlQLaQUPE46bPLvGJRlWwbj0ds6KqjjdJNA59HsBz+piIeQ66Cpik7RXJ2IyoGEQeAlLx+FNtg9dT9NGbEhm8q82vIuWWZnM/PNcMpLvK/gu3hlbTbKVp9OleGVa7wCnAa1cRG73I2D9u26ZtNc96Ww1y6Z5TxkGAlVE+YJdpaPB4BTCQnfFg6cBDNHSLjk5gN1TbwsWax8GnOaXGgfx2UJZTOMsWMONnMZZqkoBjbrAPGNmtw4dbDfPa4eL/b3QgW4IL80Z3itXPEsL3GKmbcUQDiLpWlji9KX0sHA6YI9fTmnfAdo7hSYeJOwZlUIEhfuAWzZw2qKj6juOf7Io091k3o1W9DDgTIzwnz2IU0kRMyYfLp+doJfXAQY3wcyeXi6SVXVMNzJ24UFVfwYmIAbU4yQ0AeqP77J08/yVcePlJjVgCCzeT9NaCas5PoqlTNnNval8Syoac+QEvEN49pSk9OMDMyRGZ6ksyTgl3KE8CoLC2ntBvI0RyogaawO8O4kxWVTCZJp9MoioIn1khRN1FliU+/YiVBj8rOoRRX37KRn24eOOlzn699+8nj7BicJeRR+fk/nMXbz19APODGpcis7xJAc2v/HKBZCdGNnLo6wdDQj9i1682k03ehqiqWZZFKpU792hEx79qUMvKcjTA36QfbIuoSz24q54SpKoCt0mS5Fo4775/A5Z3/+4quqf8p4WZeEYEvuIDt372d7IMPYskKh97xr/QVwGwVBxgtqxKxXJR8ExTKaUbSh7CxmexPY/3wuzQ9+wjfPPBzvvaKJSxrDhGfFcC72NSKJJ+8bUiqiscpp5J75pnq/9/jeDNdt6YFWYKJz34WfXiY9YfFIeDoKWwHtvQlGU4WiQfcVSNYgKbwqZimU2fOAXgcpknTEvMY1v5EntFUEbcic3bPC9hZzGkRv4vvv/UMLl/eSNmweMdPtvP44fn62rJh8pvtw3z0rn2nDDvWMufmg6b9TsivPebDrcps6U++KFDWP5OvOuofGM3UmKb/S5qmA2Np3v7jbWw6Nkhf31cBUS/P5ZrPusbj56GqUTRtmtnZLRiWzfZ0HsOyScw8jm3r+P2Lqv37W29/cdD0/8c2V9Mk2zKSpCC73Mi6k8YtO+GUoFjI3TQiSeJWdatiIXAbJj4nm8fleCsZho5bEpuUv7GIW5eq4bkp82iVyo47ZoBFvxBkWsWiMK0DTPf8E45HUfjB9a9jygIdmfcs+wRLrPVcSgeyJNHk7cTCJjxbocltVNNEV1TeeWSUCU0nZ1o8uPpskv4QbdG1+Gd8eD1is1I0E9uyKB06hOYKMdjgmvf94y0i3JhzxJy5XB3WRvFeX+k2dD0zDzTJQfHvjBPy6nNYnGdmxYnWzhSQHVlCbkzokXKztQV1oNRN0lkU6twqPZkUAP1tnfQ5upreKRGmaGEUa9ii3imJkwqFebTzDCLhcrXEQfHIL8UHb3gXciBI6GUvo+CvaSiKxQJr62q6G4BCej7TVGGZvOvWsVuprzJNJ2YLudxiXCa/91X6XvFK9MlJdD0zz8izNTjB1x/v5b0/31kT/J7ANCFBKuKqMk2V0+zZC+vm+RLF43Gmhwaqb8v1H8ZygEGOgCiIWyk0PKf1TedpnsM0RdyZqhj8+HGx4Hd0dDCUEuCxq87Pjed08/AHL+TipQ1opsUn7z3wokpd7B5OcctjYrP6zCtXnaRfqWRlbZkLmtrPFH8Pnxo0pR3tRqHQ+0fF4IPOnGl2DjZpt/j+/IQX24aok1GaKjmHh1Ant3zqezxy1+O1D7n8k/C2B+Gc9538Bc2ngb8etBwMbYJBcaqn+wISd90NwM+XXMb7d2lc8dWnufiXYnyNooJkhtEcJjtfymEqBaaHchSPideUDx1i/e238Iebz+VNjsZ/tx1+Xn1Z5JWvBCBx63ew8gKYPHZIAIvr17SRfeQRygdFaDo6I+bkqZim0efu5DH3h/hB4Fb87hqYb3HCc+MvMjzncsWQZfGeUqnG7lZYpjO6Y/M+/481j6rw7b9bz5Urm9FMi3f9bAcPH5ggmdf4xmPHOO8LT/DhO/fyi61DvPtnO04q+VLLnJtvN1AJzV2wuJ63niOYwC8+eMLB5hRt55z6fQfG0vh84kBdLk/M0zWeqtm2jWWdWvAPMJDI89YfbuWxw1M8t/er6HoSv7+HtrY3nfRaWXbT2CjsMSYnf88vJma4ducxPnB4iOkpJzT3/xGWCV4CTX+RVtU0mSaKYxfgktSqpqnOAU12wDlFmbVsqvaCADpuw8S7UvjfKEZt8tvGAmxLwuUzaQTczq+ypig1I0nuKmgqOb5PdrGInRUbkOWZPyVGG1u498IrnH7ZDPtauG3Zq6pJN341RMHvQXFAk6fRi8uweOTCV3CgbBB3KawN+Sm73Nx32rnknVppumPOqGoGI4d2YOXz6L4Yg41iETvXSfMfi9djSzaSJBbqsuYjc7iNwkwAlRR9/V+bIwR3IQfEJpR2WLyEUy7maKFEybQwxsdRkgJIlaVpjESC3GxtEZ4qNjDjiorrdal0TwqwNh2Lc2RaLETHZsWG38oo9r4x4o5RaDIcZmDlLHcl3knCGfeiPin0JetEWMV79dVVN/ioo2dp9zu6MWdUT2Sa8psEaCquPp0JVwizwjS55m8ULodpKucnsQsFck89RS4/X5h75dICbkXmwQMT3HDrZhK5MnawqQqa/B4BumcjrirTtNnJLpurZ/L5fPh8PqYHB8S1NLcgAYrj+5UhJMqcnMJjqS+RpylQO0lHvemq7cBcPdNxR++ysEHMhdaojx/ddCaXLWvEsuFbj4swhK6Z7H96lELmhHErG3zgl7swLZvrTmthcR5GDs8PXW5YIMZsa3+yFhKpME2jO2q6wzmtUBB9LJenXhA02bbNUE4A4siMuJZHO85Al1WMvMP2GOJ9BcuFhspzj/Tzsl98hfZ/fx+DN91EfvNmbJdP2CGcyq9Klmshuo1fhXIGPBFoWUN8SoTJXGefx4YFcXwuhYzqI+v2oas+CrYXQ60BTyuUxjQsMnYEFAXJ7Sb32GNMf+WrnOOUJhrw1/OBX+2al/ZfabHXvRZXZydmIsHkD37Iu362nZJusbYjysqWIIlv1Co3uKYnUC2DY1O5GjjITcNvbuL1xz/CQnmcdZnH5tXWa6pomjIvDjRJklT1aqocHGzb5rHD4vk90WrgxTS3KvONN63jmtUt6KbNe3++k3M+/xhffuQoiVyZ5rCXiM/FvtE0X3CE7ACmWaxqHAPBE0CT43K+sjXCey9eRMijcmg8U7VpeL62a45jeV4zGUnLeDwik/yP6ZqOHvsUTz+zjnR690m/m8qWuPGHW0nkNOp9M6yICIPexYs+iiy7Tno9QFOjE6KbfogdaTGn7pyc5ZEZcW2NDS+Bppfa/0GrhecMFKdIrkeSkJ3wXKwCmnxigXfpNdAUy4hbJtsmqqMtUOeUQikXoTgjqIhu15yFzRQPUUP9ZVXQlPc6wvNiEbJiUbS8NcbBBn664WIsWeZqPc+3ZiUUy+bhFhf/0dNHyRSbveyvx0qJ05Kno4Edq8/hwNJ1KNh8d2U3P1/VTV2mQMHj40und5NwS5QlB1WYOvf8/lYA0g2NVabpjS1iM0v6AuTrPEgSWDbompdsIMK+jesAGBn5GYYlGAHZUlH9oh+zpliIU44RpGnD4XwJfWIC14jYfPROm+K+ffOYJksLVZmm6WOHsQcHiGQz2LLM/nQBbJtjhgB2rcYoUlIn1CtOkMV2P3qTAKfDTv21oleGdW+ulu7QFwuK2l0u0+SI4WVTgLEZB0jOZZps0yTvFLH+WjJCwhfBchwxTmSaKuE5K+iAr2c3kc8J0FQRiUfdY9zxjrOoD7o5NJ7hK48cRQsEMFwytg1HHxULXoVpSuY1DjlFVs/uOVkEnnCYprUvu5Z1V15XdZQfCp8NF3yIU7WhRJK4t3ZKFkxTAdM0q5lzPT09HHcMMBc2BquvlSSJD1wuNp27d4/Sn8hz8JkxnrrjCPfeshtjTrbXZ+8/yMBMgdaIl7fU1fHk7Uf4w3f2YTr6KTOdpvOe22nWskxnywxUGJTGFQLoajmyQ/u4/psb+ehde8XYWhrFoihaW9YmXzCDLpFIULDdqBjYR4WdwLFYB/vrBBjNT3jwFUbxOO7aacJoj9ayJAvPbWHobX/PwOvfQPbxx0/6/GqrhOj6nhR/d52Lkc4QLor79uY3XMiv33UO+z75Mh78wAVEly6m6GtEd6eqrvwgQBNAOrwAT08PLf8l6pwlf/hDsk5h9JloEzuHUnzriZNDQJLbTeMHPwDA9Pd/wMjxUeIBN9980zpyDz1E+dgx5FAIyecD06S9mKSgmYzOFmDvb+BbG+DA7zBsmVHbAUF7fln9/JZTCsFPdgOf21zuNvpYyFR+AtOy+cQ9+3nGKZ1y2fKTrT1eTHMpMre8YS2vWNuKYdmUDYtVbWFuecNanvnIJXz5tWsA+NGzAzx0QDBcImRm43LFqu79MF8EvrotQizg5l0XCcboyw8frWr9TtV2DaWAmqXc/rHMHGfw5wdNxeIoo6N3YJoFjhz9ZFVbC0K/+NYfbmMoWaAz7udNy+/DJRtInjOpq7vkeT8zFjsLt7sBw0hzPFs7EP3Qfht4FxMMLnve9/6ttZdA01+gzQvPOSyFG7nKNEVUh1p17AZcpdopSnVi5ZZkkgo7JVJ0o5pRVywUKSTEhtbqd7ROXpuQJWjylpYb5oAmIay0y2UkZ5O3A7WTxOHmLo7VNeEvFvinQ2kWPpfhXb0CoD24cCmHvKIvIXcbplNod/fpF/DAxSKb4h1WlvNjIbwlm797bohQMU/C7+XmM3ykHVZc0otYTjZKf0OcZFgB2+ZldWFiqri+kU6xuBVMBZDJhYIcn+5hIH8uYNHYcBdLlz2DqmiovhQASWchSKu10Nf+XBF9fJx0xkm77rIp7dtPLllinU9hg19hiexm0jGNHDp0mK3JGbrHxYm9r6RRGNvLmLPodeYTSLZE8LhYoHKtAaZ84t6NF8TYFH0KbHhntQ8ZxyndVygScgBC1snSmnL0ZBOTtYy/0sFDWOk0ZY+PR8w4cmMM2ylLo0rz/cRcVdAkfs5v3kw2K7JIG+oFW1gsDnB6Z5Bvvel0AO7cPsKkJfqqZVykBsQGng0q6ApVD6OlTSHqg57nBU0NXd1c9Ja/R3eY0y1TDRg9J9d/NEyLUmkIWaqFHqKeDMMzOfp7eymXy3i9XlpbW+mdyrFAl6k/mEObI5Rd3R7h0grb9EQvkwMCHMyM5nj2TnEvHj4wwS+2DiNJ8JmLl7Lz92Ks9ZLJ+HGxSc18//ukbvsO7x8QmWfbKiE6WYE2MT57n3uEvSNpfrF1mKeOTlMsDlXFruXy1Atm0A0dEyxJmz2BOiaYjmxTOzsbBejLT3ggPUQ0KG7oZKmJjrE+DEnmgxe+n5mXvQLJ46G0dy8j730fqTvvPOk7AFh4QgbSggtIHRJgedIXpbVF9FFVZJY1hwn2LKDgb6yG5hZXtEjGDJZkkAl341myhMi111DvZCZbjrfYtdcIH6uvP36MHXPCQwDHd06xebSDVM8qXFqZvzv6CN980zrawh6mv/ktAOJvuwmPY465XhbPgvXop+Cuf4BikmRoKa/QPsMdsfeID933myrb1xyuMU0VVvCFmCaAe8xL+IT031x8rIsVj+zmR7NprEYv/3798qrJ5ott04nHGB7+CbZtoyoyX3ndWr76+jX8+l3n8Pubz+cVa9twKTKXr2jiH84X1/jh3+xhZLZAPlcLzc3NnBtPl0jmNRRZYmmz6M/fn7+A+qCHoWSBX20bOrkjCBb1yISY95ctF4kCB0bTL8p2YHjkx9U5nM3uY2LibkAU0X7nT7dzaDxDfdDNd18vs6Z+B5YtsT934ws680uSQmOj0BcPFsUe4UVnRmrgLtd7XnSdvb+F9hJo+gu0uUJw2Xb8klCqmqagQ5lLLoHYXYWaiWDBWZwN1WLI54SlNL3mCl4oU0wIkNEUEhtNbpmNItl4vW3EYmdVQVPWXXPjBiirCr6cmNwl1cWWHhGqeV3ytySX/Sv95/8r75+9jfNmd2LLXj6/LoomQcDTxZH2BTxx+tn88/KrsGSFFUd3c2XGOWWly6wxVK7d+yyBconekMITy9YJ52mzSMOk6PvednFajOUsIi6VLifdf7RNLApZQ0zXbChEyhPkwZF30N19M7Yt0dg4wNJLv4o7JL4zYZvYQM7jq17fvmwBY2KCAzHBUpW7JAoH9iKnSnR6ZFrcMhcHXMy4hBjXq5eZMQy6xgVLMI1F76EnsSWZoJ2h3ilKHMuKDWWgPk7ROfYN5cSGUgyFamaF1ExD/cUC/p1CG5PXxf9FWgUblJgucs9u8Z0VPdOOWA8ut4v/vtEpSWIAM/PrcFU0TRWmycpkyE7tBqCu7iIUJYBtGxSKg2xYEGd9VwzNtNg0Jr6rlPJgFFxYGRkkiXR6+zx/JmAeaNJKRVJTYrzrO7tRVBcpR8hcymfY/Jufc2IbmS1S7xUAvhIN65YH6Xj4v/n5z34CQNTjQpZljk/nOL/konQ0Q98u8SwY09MkvnMb/+QWff7drlEmhmr6jf1PjfLUY4N86NciseGd53Yz8eAIpmFVo1vDBx1N3y4xNkumRbhtvq5JhOgKfTWrh/+6/xDZXI1hMYw0sZhAqKcCTYPHRbZUCykCWhFLknjzay5kZ4MDmqbc2DNDOAQKI9Ninm9qWcXheBf3XPBGFj3+GNHXvgaA6W9/G1vXT/oegg2ibl6ldV9AYr8IDY1FWwh45ut23N1d5H010LRhwwbi8Ti2baG7U6Qd0ARQ/773EnYykQEuf9mZvGJtK6Zl88Ff7SZTEv3Jp8s8+pND9G6f4o72VwBw9eAW1stZMvfdh9bXhxKJEL/xxqplwQozhYRF8zFhuMuFH+bf4rdwwF5AcPXVwq8qOy5E7kBjWKwHmmExW3CsRV5ACG7bNn8oCYbDRiLtljC7g2jr6vi8nuN7w9OYL7K2nG3bHDz4IY4e+zSplGB+FVniVeva2bAgfhIo+Ncrl7GmI0qmZPD+X+wi4zC+J2bOVVimxY1BvC4nRO5W+afLBPi55bHek7RRAHtGUlg2tEa8XLZMrPf7x+aApsKpmSZdzzA2Jgo+19VdBMDx419C13N86Dd7eK4vSdCj8oO39JAYFnY/z4yew2PH/rjhc3PTdeioTFtizf0HSVQOuCvXxbZ0/oXe+jfVXgJNf4E2n2mqgSbFEJtmwClTYitio1Cyc0BTOgVA2W1yzC0WeVnTnfpzUCqWKSZEXDsWNrBlm/IKwWDE4+ejKH7qEBM443JhSRKHWup4emkHj63spm5XimBwhuNnBim5vHTYg1zaei+Wq4jun0bq6OObhz+HbOToiwW44PIgn3j5Wt790c/x6Xf8E0nZRcfYEC976m6KGbEg5PszdKshIqUCV+19Ftm2GahvZSYQRbKKNGbECau/WTz8zXnR326fYInGHAuKtMOmlb1ecl4/M3mLngUfYM+eqykUwrh8WRS3U73dtMmrCsYcN+r9uSKJ0VGyVhOmqSK7LTIT+/CUHJsE28awbdIu8R6frpFWXXSPi0w0M6BydERsRq2M4nWL8GgsK058h701Z/peTSzqJVXHsmobXcX/KtbZSdTJuNLJY2Py7leKBd5vwwd/uZufbxnk+INPALCnYTFfetVypp74sbjnWTBPcMd3n8A02djkNcGwBIPL5vm4SJLEey8WAOf4rGDSSrNivLNjTo3D2efYNMefCWDMSZtvaGhgZngIbBtfKEraqbfncrIkbZebfU88gnVCKn5/Ik+TkzlXTDh18/wGha6l2KobuVRAGjhCuqgxlSkTcwT9k4cnmPj0Z+i97HKmv/Y13J//Dy7vCmKbNhmn6OziM8U82XlnL1LB5KwFcc5ISqQmCwQibs57rdiwhg4msU2TouPlFpweI1LOsXVgDvBxdE09pYPUIdPgcXFkMsuWYzvmXU8oJOZqNpudX4gbGBx30uGzYk4W6lu4Yl0Ho/E28m4vtiFTODJIVBHvS+XFeIxdIvQhWweSqHV1NH3sYyj19Rhj46Tvu59TNWvhZTwQ8DMWiEPTKvKOJ9Rs48l1DN1dXaSi9VhORl9XV1fVOV3zJCn5GrA6xc+SJNHyX58j8orrid34FtRYjE+/YhVtUR9DyQJnfOZRrvn6M3zja9tJu3qZaXgOry/A4OJ1yKbJ1Je+zPS3vg1A/O1vRwkGq2VYOvMJlkojeIwsuAKUzvswz/SJNePC5W2w8tWiw3t+iWUZ5DNbaAgKYDGeLjolVJxkmRNC1QA7MgVGDS8eu8jn9X8icjDJywMBenweipbFJ3pHeeXOXnoLJxcBPrFp2nRVXD019cAfebXQPn3zjesIeVV2DaXYN7AbOFXmnFgPVrXNz0h7/ZmdNIY8JHJlNh47OfRbCc2t64pV33tgLIP/j4TnxsZ+gWnmCQSWsHrVrXi9HZS1SXYc/Cb37x3HpUjc9uY1GDMfp6xN4vH28Ksjr2LvaJp04RSAfU4Lh9eScZ+GjYxX0jnbeoxL5M3YwD8fHqJsPX+o8W+pvQSa/gKturhaJjJio/JJrqqmyePW8ZhFLFtsikoqWn1vMS8e3ILH5IA+jKQqyJZWY5pKGlq2EbMso6g2RquNucQRXQfEaTTu6InSiptEXZT+xig5r+iHGVLRu/NsdIkSDO8o3Un9gQvxZERWRzEcpEVL0D38cyTLwpQlJNumLpVk5eBR3tQY4Z9+/m1cpkFhxnnYd04StL1gQ30hy0V5cf07updj2wUMV4ixGEw4bsVtmjjZdTl9GqsTm2HE1YqnKDYgw+tmtqCh6zrZTJxdO69h4rhjV1FQyFoSs04afeUMeDBX4lA+C8jkcmJzzzVoRB12qE+RuD9rkHOJn716GUORaU6K61CCEgMOM9jKKL6YAB1RBzRNu2v3KWVJSBbYWPPKOFRAU9N55xFpb8NTKoEEhitPU2cYSQIZCZ8Fn7pzF97DIsNv3atexsJ8HyNHRCmMvOFh3zNPYM0RKruc+nNWyMa3Zg1mHCxFQ5Jc+P0LqqfcyoJ66bJGljaFCDth4HJKnORTowLETs88x/HpPJIknMULhULVJLarq6uaOaeVI9z1pZ08e+cxQkHxXsvtpZhJM3RgL3Pb8ekcTX4BJnJjgllVPSayatPZ0UlwpJfcxBgHDvXht8Hj3L2Rh7Yye8cd2JomxM+6zs3xDHFLQrJB9Sic+fpFJL3gtiVeq3v5t+UdHN08ARJc8fcrWXyGmEeJ4RypvUex57jFr5gdZDhZZKySKu5k0NVbFjdlPLxN94MNR0b2z7seSUrjd7Ihk3NKByWTSdIFHQkLbVx8T3TFUkJeF+cvaWRfo9Ct5HvTRC3xvoLPT3+kmVe9pkh3eJjBmQIT6RKy10vdTW8FYOZ735tnHllpz7Ys4V8b6/l0Rw/IMqYT+i21dZ30Wnd3N7Nx0edGK43bzFdDdLo7gY1Nxlurfyh7PLR+8Ys0//u/AxDxufjGm9bRFPagmRaJoSz+iSyFwCiWohEOHmf9pz8GskzuscfQh4ZQ4nHifycyryqgqX52gjNlRyzdsYGtgxmKukljyMOKljCsEUacHPo9I4M/YNfut/CW5YKVmsyUqoDpVCVUAH47KZi0NdYuOtURfvEyi59sWMzGs5bx30vaCSoy2zJ5Ltt2hG8OTmK8QLZa1agWYdb4YorPdsT9/M9rhG+eoQmG8vky51afAJrcqszVq8Wh7P69J5tzVkFTR5TFTUFcikSqoJMx2gGZUmmUyRPMJi1LY3hYsLmdnW9HUTwsXvRvAGQTPyHuTXLJ0kYa+SGp1FYUJci6Nd+hPR7HtmsJIc/XJEmiHBUhukZrFAn4UHOGBrfKsUKZWwYnX/D9fyvtJdD0Z26WZc2rPSc7tdd8uKuaJsVj0iGPAjrYEmomjO1kq5ScwrAZv8WxTC9qXRTFrIXntJIGko/CtKBIi6dbSI02lg1ZVSyEMWcj0iSViTqx0TZk8iyd6Md89XK2BtdjSzKr0we5uvcSGkdvwptyCrc6QGTJ2AE++Jsfc/fTOZ54OMsPP/cFfvDTz/CVlQtoLjsn6+QM5aEMrqkCki2hOCLhS5ITSLZFX0MzY/EAmjvErtVNpEKiz92SwtQ3dhHbKDbo8bDY7KQ9OWTHD0ryWqSKOtmcYM10S+LYgfM4/Jvl9N7Vjb8AKZ8Yk24FfLJM0bLYFxHXm82Kk2l+oUzUKTI3okLWLf4t2TYep7+xstj0dJ+HfU7mSyujhLrW41u3jo71ItynuQIEigoX7aojnHUjy1FxD+YsuJXwXCQWo/ULXyScEZ9tuXJ4/Cq+kJgPb1vfwYrkAG7LIB+K8dY3XMTAnp2oPrFY60WVjc89xR0f+xfGjwnqv8o0BSD6hjdgtIlNwO9dgCy7TtI7SJLEey5eSEtALGZZvZFyvInsuJMWnz+IVymyui1CxO9iyCn/Ul9fTygUquqZkERoZPejw0SPOyJrJ/R7ZFPNswcE09TsF9+XLrViOaWDRmUf5131atoXC2fpIzu2UTdHBJsLtOE752w6f/xjoq99LQDNvfs4r15sNjmvxHvv2MVvXCXKkk28BJvuEGzLGVd107Y0hj/spqHTYTU3zj+JX6iJjWnbgNiINXeUAVroL29AtsBOapwW8hH3zN/AytrUSWJw0zS55x5R56+TUeyUeG1kmZg7L1/VzOYGEWbNj6tEMgI45AMBhq9dwcz4F/nwmd8i6kmxpV9sVNE3vAE5HEbr6yP76Am15oBjVpHmQjMjjuDNPezcm+6Ta/epHZ3knAzMbmsYxvfQ3d2NqiiYqoGpFphOzw/plUxrHlNwemeM5z56GU9/+GLeGY6j+aZBcu69K8f2wYmqBQFA3TvegRwQh8JKeM47OcoGBzRZnefw5BEBpi9e2iDCXe1nCN8qvcDMsHDbXxl7lp5IP+Pp0guWUNEtm3umHM3WhLA5CEvCl02WJG5sq+fJDcu4JB6ibNl8tm+cDx99/kLtFfE/CNYpldrxvK+d265c1cLFi4M0OEk9weCpw3Or2uaHv2zD4DW3f46vPXkLT++dX87Gtm12Oa7h6zpjeFSlqs86NCnR2vEOyrg5eOgjVU0jwOTkfZS1SdzuRpqbhO60oeHlRKMbkCWNGxb/nqsXH2Bo+AcArFj+3wQCCzlvkXi+K6zzC7WMVxw2GhHP+KKmy/jcYsF2fmNwir7CKSoS/I21l0DTn7lpWi3TTbJMpArThKuqaVLcJh1uETJRS3EkW8UqCHao5GzkqaDJcHYYuSGGYmnghOfKZQ1J8pCfElR//iKxkA1qMuNF8YC6LD8hW/x7uEE8EPXZImW3hd/nZ9QlwNXC6WFKTujMnReLccEx42tKSbhTQ7QXbYJIZOtXo8bEg+9z/J8K6RSZh8ViM1icAqfOnZUaY/GkuL5nT1+P7goxe6nYCBtTBu2KhD6Wp2VCgMuJgABNekHFcEJdQU8K24bpWcHclJUySrGXUhJyeTdLRrxknfp69VisdJy9+1qEb1DOAU16h4ajp2e/ZFBwLBc8ulZlqJT6JkJmGSSJp2JiUWhhFF94Ad2/uINl/+iIZeUQq/vDLBgPcvrRKLrPqXM3Z8GtME2RSATfqpXUtQomwCKJMT1NICrAxmtWtPKhuLhHTRdfgAQM7N2F6hPjH5gxcMkKk33HuOPjH+LQs0+hKlEAbB+oa1ZjrRanb29O/H8ls2auY/CVK4LEvE7WlG8pWlMHZTNCOedFlmyW1w/wb1eJsOGAU3us29n0KqBJVuoJ1XlxeRXkGYmy6uKxFWcy2NpD79ZNmEaN1u9P5Gl1QFNW7ULTxLzqVcKMZsp0r10PwMyRvVw+USv+arj8xL/0bQJnn0Xg3HMByG/axEXN4toOF0ts7pvB9Mmc/pqahqxlYYQzr+mu/tyxQgDLkeMCbKsNQke3KiXuUUXX9PjhSbabixkqC0BsA29oqasCTI9XbH7l8iTRmPjMx3YfJ13QefzxxxkcHMQtGVzHo7gz4vo9TgjsiuVN7G4WesFS0oV6WGiq8oEALReIOeNVCrxt5R1s7Z8hmUyycccOfG98gxib2757kmP0yMERzps8j9XHVjNw+CCebAoA/6ITzDKBgqagecQ8XJw/AmO7cblcdATFc6t5kkz2Z6qvL1sWF287zMu2H52nAZIkCW0gT3EsT8kvDjhhr1hPtu/djPttNyFHIrg6Oog5fYc5ZVhmk5xhCNCUiK/nyaMibHvx0sbKF8CaN2ABaa2v+v43LruLyXThBd3An0xmSOom4VyW1z/0iBi35NPzXtPudXPHaT18brEY8z9Mp5/Xibsw5+ADwv36xbZ37hZ1/4pacB4jNpUpMZUtI0mwvGU+aErf+3uUvbtYmhrm+r0PVL2lQDiIz+Q1XIrEylbxvlWt4vBw13iSV4xfyceUW8lbsHffu9G0GWF/MfR9ADra34osi3VGkiRiLf+CZUuc3bKDcPm/AejqfFfVe+nchWJ8N76I8jETdhSARiZwuxuIRE7nuoYIF8dC6LbNd0f+907qf23tJdD0Z25Vlsm2kWwbJLGZ+2xX1RFc8Vi0usXi7NLEhLXyYuEtOw+1Xu8Ak3gA2SzXmCZNA8lLYcrJjHN00EdKMmM5ESYyrVBVDD4aibMiei6N/gXkvRJet5sxSSwikUQeJR8FQE042WCSEKlHZ92YtkHaFj+XG5aj1IvFzh8SD7JZ0Cn3pgA4lD2G5FgjTKUTrB88gmzZ9HUt4kh7M7mlIp21a9rA54Qr2gsC8E27GzCR0fMqkuMDFHdc06dTYnHXZQ1vZqA6zh0TYXJOdmDE1FkVEoB0Mi4AWD4nxtXVVESSTQxFYkdZo+ARUMmn105EuVCQVc6JraSIxaaVMXxOCKO+YpAnu4mXxee2JrzkbXE/K0yTbdtVpikcFmNUt0qAMEPNMf6JT+CxxTiP3vZTGp8Q9HrovHOYGuijmEnjDor+hRM6V9V3svQcEUZ94sffpTQ6A86B9K4fHcBYJgCvMiDGvRqeK/RXje3KJbEZaQU3piWuzQhGyIyIeXnzuZnqojkXNNm2XQ3PSUoDPesaeN1Hz8QT83OwuYvepk42briMUj7H4L7d1bEcn53E73HmUjGEJIlNJOoRBpfda0TWmj16jDWz81mdxLCYa4GzzwJJQjt+HFdSMHUJxUKVJW5983ouvqybDdctoLknwhVvX4ms1Ja5rpUC4EwWQthIxJyQUd3IcVTLqDqD/2b7CLvMpfSyiq9eH+W35wZRpkbxqmVMS+bIrGBwnjhwkF/sFaf+HUeHeO0X7+TZZ58F4GrlWersFDHHA83j2E3EAm4Wr1yAGZYBidJOJ/nC56Mjnqr2dVX9YfTMb3n88cd54okn2FhXBz4fpQMHmH36GXSHfc7n8xhHxf30WB7uuPO3zMTjTPpiNDbV9JCVduxQH7ZsIpsy8dQMjImEhHZHNqB5kkwNZKr+SYfzJQaKGkfyJY7PYQoM3WTTXb0YriyGmkdVVV5+0fWoWhjTMnh42zZ6HvgDC353F7K35mSuBANVsBrN5dFshYdSbfRN51FkifMXzwFBp72OXFDFlC0UOYCJj57IIFLxoRfMnLtzSIC4y7duxH8IsCGXO0S5PH/TliSJN7XUIQFpw2RGP3XYrfIMR6NCAvBiQ3RmOo0nK0LswYEihx7dWP3dAUfPtLAhOM9k09Z1ErfeWv35Vb1Ps+nRmtHqrmEx31a2Rqri8aWtIfTlEe5VNTKmxbgV5WH1RkqlUfbtfz8zM0+Syx9BUfy0tb1xXh83Ddbz7Ki4LtsqEYudQ0/PP1d/f05PHbIkTGnn2j2cqlUy55qYpLnpeiRJRpIkbu4Se8OvxpPM6s9vqvm30F4CTX/mVgNNYsGTHdDksVzV2nOK26LZMf+rGFuaeR0tk8F09DdRh3afibqc8Jy4lZpWRpoDmirtcElhNCcyjgwjUC3ca9ctYXXsAprW/AOEmiiiYkgu/HaeUNbEZXuxtTzKiFigCmaCoqnickz7Rt2in3a4E7lJ0LC+uGBxfFYUgKIskTeS1dCaZVlESnnOnhAP/x/OXsgu51nsmCqjFsWJs6Fs4zZtLElhhgaMgoqsiRdGbPFwTjtO34puIls6FQVTJO+h4BZjENJKrHbSuhOhCEq5SDSyCF13IykW5eAoZb/KmF1jmry6RswQm0ba52fxnKKfiq3TKCVESRYgoChITrkViXosRUU1ZWYTAugWHKapWCxiOEakFdDkdzLeNI9G7qmnMbeITKH0sRGsXA4lGiV44YUM7BGhhUizAMtKVsI1neCqmz9EvK2DYibNc7+5AynvhF71JMWoACf2ljFs28brbUWWfdi2RqkkQhH5vNBaFHK19GszFKPg6I3ch3/F9Ne/cZKeKT+bpJQT+jBJiVPfHiTa5Gf6in2MRcUmNhsV43PkWXHCz5cNFMPxOCp5sSwXfr/QbUQ8aYaTBRo6u/FHosiWQdYnmMJKYlJiRIAPJRLB69SbnBlywp3Nfr76+rVcuERsxmdes4Ab/nU9ofj8siNNPRFcHgVN9pENthO+9jqUaBRJ01iYGqV3KsfhiQxPHp1mWDuNvgY/OZ/MoXYXM7ro+1Sxnm3DAmAOJ4aY0sSz0KYWWI9g8fppYK2xE7Mk49PKIMtVLQ/AlSubSTWJe+nK6VWD2nJR3I9gSBQOvrj1V0xMik33aF8fszcIcfSzn/wyF/73ExQ1kyeeeALZlEm5UyQ8CTTD5MlLLuZQ5wKkQxsxjfmbVMV1PZBTMHJyFTQ1Dwv2V3elKWtlZsfFweRgpcQLIpmi0vY8Nkx2poQREc/rypUrWXhaC+HMYrAljh49yrGJCZRgzWer0ipjoWVU9toL+e5z4vla3xUj7J1joBjrJtUtwH7MbqDseQsAi3w/qwKZE0FTamSEh6bFvDhzz27sgoprSEyiZHJ+uBjAp8i0Od/Z9zyi8Mp3tbW+AVUNv3CIbg5bVT7eh94qfvaO2pgffB/Zx0Vyx77n0TOl7/09+vAwSl0d5oZzUG2L037zHYplsZ5U9UydUafPZb5vFzA7xThfXifWlvvsl5OVW0iltrBv//sBaG153UllUB49NMldvddiEMHn7WTVyluQ5RqIi/hd1T7+sWLFAw5oOrvnLfT01HzazosGWRHwUrQsbh97YW3UX3t7CTT9mVvVDbyykDk2/16r5ggO0BR1skJswYxYBZ1sJRxiWVyTu5pb+j/CeEBBmSMENzQNJA9GUUXLOouPrjCk1ZgmQw9WmSY7Ksp9SIqbNc1vI6GLDaDVHMUnCaChj+1EGSsBMqZVZNCIICGRCmr0BcRi4ne5ycaETiNQ75wi3aLvKcsGq4Csz49nv7o/iWxZHGuPcrQkHraGVBKvk20kA21FAS4naSKUkYmkxeksZMiAzWgiBUAw54jdveeCDYqtUHKJRSRQKrDK0UslglGChTwtbe1VMXgp3E9eNdAlSDlY06eXWZAWoC7lcbHg9NdV+93MBH5PY3VhMSwDyRQLYMEXI79kLXq0gcxQhVERY1QJzfn9flyOK7hi+MCWsBQRnvE54U/5zAvwfP/N6N87B8KuKmjyRhxDzAzoU5Moqsqlb3sXAPt3bcMsic1c9c1iKoKtlPfNovX3I0kygYAI11R0TfmC+LtQqoUNTK+fzJRYeLVWg9TjfzhJz1RhmWQ1hiSp1LUFyWpZ7h/7JROObqzo9VFye+nd/hyGptGfyLNCPeyMSRSAuOM6HvVkGJktIskyLfWOxYRbzJfWxeK1ieGatUDg3HMxFA/5oljCvnPzOVy3ppU/1hRFpqVFvCfVth5XWyu+dSIEd5EuQOHHfrcf07JpdbczEXa+U5IYaXYOPGoXqbLYRHriJT7ySgFwvHYJj2SSVcLMlMV8TKSd8HZHB7KnZvHx8pXNDDWKbEsJCJTzzriIcV2+9GMMZFfgUXTa2h5AFEyEZ10uij4/i8aPEu07zNaDfezYITbvPfE9bGzeSMwoYbhcTJ7Ww7HH7uHg0/ONMUcnxb2MJYtoWRVSg1BI4jl0iFAmAxLo7tmqn9Vc0LQ3K5i9fLrMjgcGsSSDoluApvXr1+MNuGjrbMWfF2HwBx54oLbmzWkV0FTOqmyzllbL6Fy89GSTytlGMZ+iE1NE69/MZKEev5piePhH4rPmgCZtcJDb/+cWSm43rTPT3NZ1NYPtS/EcFKBpJvk06CXITc37joU+sQ4fL55ac1N18w4soqFe+I+dMkT31P/A5ztgQDBKWt9xjBYxF4ZzrSi6xsj730/qt7+t6pkqITZwWCanuHzd29/Oks9/hpLqZnmin+3f+RkAO+fomTbOZrli+xH6NB00E9eOBF/uamFdyE/Bgidi/wOAZRUBmY6Ot83rbr5ssOn4DBktTPeyP3DWWX84ZSZiRdf0QqDJtm2GHL+/0xpPR1Fq812SJN7ZIe7tD0cT6H+kRMxfc3sJNP0Zm2Vac0qoVHQePhRAQUayFWRZAKdAVGywHkksrFbeIOtsXAHJw5LjzSwpdVGWY8imVmWadF1Dctiriq7JPduIhVRlmsqavwqaCkHxgNimRp17ATMp8f0t2iRBWYCO1NROrEyhWjV8UBUb7Fh9iRGHaQrKEtOqWAi9jQ3IlkXcKZiZKJnY9smgqasgcUZfzfemPm0g2RmiDotlL/DS6oCpSZqJDGdZPCw2NllSCVp5JmYcnZZmIbt9KN7T8ThhMV0Vi5G3kGNZwItsWZRdbrxeP42NjWSzYiEoRfrIFcVikHFSmr26Rkc6CTaUJYvFwdrC1sooXm8tlfvQzCFkS/QjGxfsihGKYhwXAKhYHD5laA6glDVQK1YTP/wBHf8kjDCtth6GzF8zMX0Pu3e/i/FeYZRYyIlNXM5KGFPT2JZF1+q1LDn7fGzbpqgLMOZvOAaShVxWkdOQ3ygW8bm2A+JvMf75ch3YdjVEXJQa0dMqKJB3DVaduk/UMyHVI8sS8eYAtx+6nVmi6HMMRRONLWjFIv17dtCfyLPY0+eMSZhQXYisU1Mx4skwPFvAzOUJbt8txkYSB4eFp4sxTQzn0C2bV+48xsfXX0A+IOajN6zy876fMpl/cdk5jbKYs8nmtUiSVAVN63OCfdsxkKSzMETT8C+YCKWq7+uLiWd2ncfiPxtEHxfES7z89FohUp/Px8dv/nu+sVzcr7wpwIN70fxipY1hL5nFK5Eq5YRkHUXRsCzxfX5/DxPSP1PUvYTDMyxecpyGhgbyxSKPnC/MLF939DG2PvM4tm0zHBgm4UtgyiZrDu6gaXwcFIVix2L2HThY1eqUSiXSBfHsN04l0Irifpu9m9DHxmgZF4yP5knSe0isFwdzNdCzzznQHHluAr1s4m7LYFoGDQ0NdHSIa+1cGcef68CjBMhmszz22GMn3YOKrknLqmy1am7Rl1T0TE6zbYsU4pmPjo3RbQzy6yOvFH02BYCrgCYjmWTwzW/h4cXi8LbQE2EiUE/ytA14Dop5lkw8if31NfC102Bke/V7GlSxNp1KqGwY2Wqmns/XWTVxPClEt/fX8MRnQcvC/t+K8T5+HN1JyLi99TU83HkGmCbjH/s43Q/8Cmx7nt1A+t57qyxT7A2vx93WxrGrhB4s8ONbyU8lOOiE9dZ1RPnv/gnypsXZkQDLj+VREmUOjGf4xELxbPx21oerXWTINTdfj88334LimWMJNMOiq87PkpYmFGV+hKLSqqDpeOJ5dV+TmkHJspERerET26uaYjS4VcbLOvdNp075GX8L7SXQ9GdshzaN88APhOmeZJqARNnbjKuiOJapUqeeiOOq6hKT38rrZB0TwrbgYiRLvMmyQvOZJkMHyYONTeJAHGVMoj4vXHzH8mNYtoVW9lVB07RPoVScprT7dgCGTbGINhRn8OAib2SY0Eaxcnn8vm4Akk5m1FhdiQlVLCYBRWIqIx44tb4et2FR54CmWd3GtgpI2vwFyWe7uP5gP5Jz6uieMiiqBdrK4kRyZOUkTZoIyUwYrdSnSrSmslVd05ryXmadOkeSaVC//GwkyYXXrsMGym4xlu5sGqtcIlYQgMNsW0BjY2NVDF4KD1CYEAxIWVw+XkMnmEgRsgXwjEzUKGUBmmqsxqbRLcgO05QPRsV3+AIwoQIyllVE06bnicArLZ/WUB2gMplM4g+LxaaQm8Y0xbWlM1tov2gYf6QexSMYCbvoA8OgdGiUmTsOce7Zr0ORJJzoJf5GcT1eqxUJiVwFNPkd92eHaSo4oKlYiKCUJLwlMfZGKEJmQvSr3KMz0CtAVgU0Tc8RgUeb/eTtHD878DN079J593iiQQjdj2x6huNjSZodIXWhGGKbuY3bj/5ejIkTnkt885vUj06AbWNbCWwrR8860adsssSu6SzPpfPcayrMNAjWbNQ4wNd2fo2PPPMRXkyLTYlncFaqRysa+NetBaB15BhNpQleO34Xr5i8H0sbZ7qupfq+vrAAPjueGiHnmEdq2iSqqtLW1oYkSdxwww3EXBoLhkS2lz8kwoieE0ATQMeyVbSclaJhdQY17MPvlADyuJtQ1SBrupfy1BHh5N7UtJWXv1yAgWJdgNHmZtpcWfIz48iKzP5YzQohNDzFyp3bUbIpkGWOzqT4xS9+QTabdXRpNorhIzY7jpYR5XPKO0RYuN1xstc8sxw+OMRjg4/PY5r25QrYts3o0VlsbAoewV6vX7++avDYtaoOCYVg2hmvHTsoFmufAeB2BPxaVmWHJbIKm8NeljXPd+nO53sxjDSyLRPKGbRu+k/6pxdwYKY2zyolVBK3foepksb25SLN35UV25ty3oW4+yWkIuhmlqydAKMIv3s36EUOzhzkoaOCteotzO8n1FgmlyuOqgaJx887OUQ3sh3uubn2puFtAMwWtmJFQbZdLOpcz1fXvZ6Dl7wKgFftuo9/3H0nyxvFQVVomWosk+xYWSx73zvoD7fgK+Y4+pnPY1g2DSEPTRFvlfn78rIO1jWIw9j+0QznxoJcFg9j2HC7fgUbNtzPsqX/ddK1PeoUVL58edNJBp39hTKv2nWMZ5JZ1nfFcKsyk5lytbzRiW3AYenavG5csnTS7z2yzE2tAnx9Z3jqecHXX3t7CTT9GVsmUcQ0He2LZYLkRfPFq6BJ9qlV0FSZv16PA5oKOvkppxTKnIKPDXYrijkHNFkmkiShq5Af9xP4Hz/tp78RRVIwLIPpwjTFYq1o76TXpji+A2P4OY5Yu+hzNDZ1+QxuW2Uwd4DZgAe7WMTnFaE8228AFhN1JRKK+JygDJOTJpZpodbVEZGDeJUAtgQpwwK7gGQamNT0FV7c9JRcnHFoGMmGFcMakqQStPzYks0meweKLTb1abuNSLGMBMiOWHqxNo6cE2Ni2TpL14uikG6phbLbi+EwHnJmlv07dlCXc8pndCygoaGBbE6ApnJwBNmpaK85EdKQAeboCBEH52kDY1SCpy2M4p3jY7NpbCuyJQBR0eO8SlGxZA+SJe5nsTh0SqapkNFQjCDDsQbeZwTY5XIsBUzBeggXb5nYwiyt586geByfrpDYGNIPDFPcm6B4zyhXNr0RRRObjjcmFnrVuxqA2c0buXXrLfOYJtMsUXS0TYVCGFfJi7fgiPj9YbJjgmnMXWJhOunaXV0CBCUGBfMkKfXUtwf52cGfkdWzeIIiVCU76em5sNjc+nZsZXrvFnzOgBaLYUb9oyQcUWjUkyE+OUzyZz/DY1oUnUxAl3uUQMRDqE6A1x1j6erY7VssngNvcZRXzVxK/0gvOyd38seacmAbvsIkNhIjR2bxrl4Nqoo6O8OrSr9nSdNxbFlFD55FOlwLWw67uingJ5f0sXFYsKqGkcU0C/zd3/0d73//+4VJ5HO3glGCtjMoOyVxPKfIYjv9tNOIdBWpX5mjrr4en0/MIVVqppBJs2FBnJnpLmZm2pAkC5sdDDu2IVvOPofdDthrXNlIwSU2z1DBxp/VmPV78I304pkcBtvm6NGjfPvb32ajA55d5Sj+4hRWycTUZMoHBJDsaGjAlEwsRcNnuvm3R7/KrGGiAG5JImNY9OdLjPemMdQc6XwSRVE47bTTqtfV0BHCG3Qh5yLEInVYlsXRo7VMSACPKtg+LeeiLAtwftGShurGPVnWyRkmqZQAH9HgamR3BGV0K3d5Ps0TRy6GSgkqdz3ayAizv/wlT5xxNpYssy7kZ2JcHDo6Vy7E21KH57ATolu4BILNMHMMHvsMvzryKyRdgL+ds/PDdlDLnPP5xNyXZXe1LNHU1B8gPQq/fBOYZeg6T7xp6gB2KcvMQmF30OR6GdeuWQSSxOfbLiXzzg9gIXHV4BbSH/oAVj4vWKaRkSrLVGmru+r49QVCvO195A+snOlnXUeUw4USJcsmoios8HlY6TBWlbDfxxaKItr3TqXotTrnhcsATMvmcad48eVOKZa57baRaTan8tw2Mo3XpXBmt3gWni9EVxGBV0yJT9VubKvDI0vsyRbZ+jfqEv4SaPoztg3X9rD6MnFylUwTbyBIw/QumpKCype9Kqo6P/XU66sxTYXkDBIycW939fcL9E4kauE50xGYW7KF6fGx58wz0ds7aQ6IMN9YfgxdtwjpjrutV0YeEoDhjsB9pJUgkm0STpdwozKYO0gy4MUGvIrouyesEffnMFQbn5bCtgwUSULVbaaHc6h1ddQ5btllr4FlFxGFDCDvqhkK+mw3fjXMpdv28MWdBl3TBkFbbNRJX5Zdyd0oulh0pqVWJEdD5PGLa3UrKg26iO9Px0rUORk5sq+Dold8jkfXMHJ5jhzYT30uBcDRcIxgMIhixdE0L8gWdmAQ1TIoOz5N/qKoyRfOif5OjU9xUSiIxyqygv1VMGtYBgdmdiObYsMrzClNY/r8lBy/m2Jx8JRMUyFTRtUDHGvsIKm6+WJqBksCSxKsYji8huntInwR7DxYDeekA2uQYwswEiYoEqgyQV8H3S4BWiSntptuLEGPh3DrNpsf/BE49yWfPcbId/4TsNE1N7ruxa3FaR07iGx4QZZJTLSjD0fADctXPM3SZccJBoOYhsHMqBANS0o9gWaV2w/ejg1oHgGSFiTEJpQJ1SOF/ejlEqG9D+COiBBXRveRcqfIOI7fMW+WawY2gWkysPxMJhx7BCxHS9Uu7ue+2dpC21cv5vTFUhfvnHoN/zD1ar6373u8UDMzGbT+fuqSYl4NHUwie714V6xAD8Da6w7Sc9UIbedcxmyLKDPRmEzQnpzElmSOsAyv/xosTcbUHYf64jh+v1/U4yulYZtI7bbP/yCaw9B5Fi1iZmSYzb/9BUUnxNrSUWOflvT04POL+TG4c5A7vvDvNIa8NKlFUilxncfHD/BUrok8HnSvm0IggLtUxuoUz/yi6CLanf1sIhxCAtzJSYKDh2lubqZYLDIyIu6bz4oTiAtmWMsqlI8PAOBetIApr9hINXeKoCnmk89OstgvQr/PDsyil030sDiwrFy5smrwiW0jyRKdjrVDzGFkDx8+PO8+uEqHQbaxDTjLKfdUKaB79+QsZ2w+yFnPHWLLtDg0RRovgb9/EMJtLJRGuU3/FgHPW2hueiXR6Aamv/510HWeuFQcnF7REGVwpoCCyekH/otgeLAaopvpaIfrvwFAfsutPHD8flRDhACnDIXx3MS8vlaYJr+vs/p/jY1XiddPPYD9yzdAbhIaV8KbfgXhNrAtkkd+Srm9DDp0L34f5y+uJ+Z3kciV+Xp4DZ/d8FYM1UX+qafpe+tN9P/wh8B8lgmEHmjZ5RfwQJfIcHv90cdY1xljp+Pxti7kR5YkVjnaqEpW3oqgj9c0C6Dz2eNjJzE7u4ZmSeY1Ij4XZ3TPNwe1bZvHZ8TnHMsL+rqSRft8oKnCNHX7PKf8PUCD28UNTeK7/lbtB14CTX/GprhkUJ0YuGUSa6lj1cEfsGBCnP4k33zQZOk+VCfzxCwYFNJpGrztqLIH2a9iSTYRM4QVCYDDNNmOYFSRVIqdSxhp7+C5bdtoC4oT6mhuFE3TCJTFg5BXFYolwRYNOkVYm5nALHjxBHzkSaO5VApuFa8tQIknorHMP8sHBpt474iBlXd0TYrE6JFZlPr66mKZlfLYtni4Fb+XvGMVIFk2blSCahTJylMaEf8ftsT1H1eG6E31UlcWi+aUEkdpFo7m0XhUjI/bi6SI6062+3B7BUCxfSFk51QVdIw2hyenqHeYpkOq+F1UDldNLos9Nt2ZCYpO0ogvJ2EoHiIlcVqdSSf5ztoFfLrwYeqYIeVYQRyaOUTZKiIZ4hqLrtqCYXkD5CbEhlAoDlVBU4Vpsm2bQkZDNQLM+gVDNKTp7OtyozphLIUGxnbajG2uhYnMcoAZbw9up7q9f00Dje9bSXpmAFWfH96YHWlidLm4bzc+WGbs2r93PFN1Jo/9zulbFEwTnxZiSXgTnrLjYRWI0/9oN/l94hTa2LiJAwc/yMzIcSzTQJLdSHKY7fqz5PQcHXXnkrNk3LrO2uOO/07EJhsT98vvyaKoFpYlcYxZkKiCpqAry/njuwF4fOn5aG7HgT5zHMsyqe8Q13WgkKteWzoiNtl6B2ivyy9j48hGDs7UDP1ObKX9IozV4Oiahg4IDxv/urXM3mDhcsxDPcEdTEbF3Fo4MsiqYXGwOWCcTiG9lnUvux6jICZLYtN3al+w7ftQzkDDcsy6DZjpNMgyE4Usd3z8Q2z69c954sffFa91eaHtDPBEqF+wEr/DNJXTHtLHBxkb6Mdja+TzAmSnMscwUGg97fzq163fsYOZ8V6ihSYu3vlWlk+ehSFL5KqZhzJSMc9rr7uWiy66CEmSkCyF+mgrHoc11PJ+ygkxTzPtURKOQ7zuztAdEXNMLxxmelbU4ds2nhYCcI+Yo6efLmwiePIL8D+LYOdPq6DJmhFzvbe3F31O3TxpZAvuoPjOj50W5EuvXcMVK5r48WiC9xwcRLdtZnSDD6Vezl7WEIueCU0r4O2PMORaQKOUYv0Tt7LSew3asT4yv7+P0YYmDtQ3o0iwVnVhWjbv8DxGYM+PCLaV8RwS210mswd9wdlw+o38IeijaJXp8fmRbBNbcvOZHXPuJzWfNd8c0FQN0ekJUoWD4K+DN/4CPKGqm3zfmJA8BLZ58TUvwaXIXOW4fD/bO8Pm1lXs/dDnUWIxtisy95x5JpNLlszztKq0a09r4f4Fwp9s+cwA69rD7KqAprAAWCsc0DSaKpLMC9bnIwta8MgSz6ZyPJnMzvvMR5zQ3MVLG3Ap86FAf1FjyNGTDpU0CqZV1TVt7pvBME92pR90Xt95Cj3T3PYOp8boA9NpBp9HeP/X3F4CTX/mNrfunC8UohR0IbnEpJd9Ki61xkLoWRdyQCx+Vl6nmM/R6pzAvcvrKEbFomPUNVWZJlsyMSQNvXU5tireOz4+TmtQgJix3BiapiGXFfyOx1IiKpB/Li4W0U4GqCt0cNqZa2heKDQwswEfbk28zh3R6AjMstrIsLA8heWEyAKyxOjRFGpdHWHH2HFWn8G2xMPtDYfJuwQ48moaEhJ+VwTbLqA79d8anIy9AfcoLl2iW59Gsi2KspvcEnHyb2sVi5fp9mA7teXCDU24fAotLomIJ4jpcsTVJfF9ZcuugqYxC5K6QUT3VXVNZqfFktlB8rIYx2BRpeBvIiaJhzqpZXBZ0OQTAPPApLhn2yZF+MAuOA7sbg8uR1xbDngpp8U9OFV4rlwwsAwbyXaRDtTAzsZVfpSg2LjyzqFOKZ7HzGFhNqflGtGlCGqrMIIMnteGnU9wcGaco5kAc9vE4Qg7F4gTZlsS3NMZ1EnHluBS0Y9CIYyaz9HQEmDxa9fj1sSGZwYj5DWN3v4NHDt6NqAwOfl7DvffjOw2keR6JEnid8lfALC2S6SDr+w7ygVbhFdRKuAhh2BKPFGxqJZKIUYD45xdWMPC7BIsp6ZgyFtAC0d5xN2OS2kB3Jh6gam+41WmaXiOUXUq4EW1LVwhsRFFzRBd5Ra+v+/7DO7bzbO/vn1emRmA4j4BmtoWBpEViexMifRUEeP0EMbZtdBxsGUn03WiXwtHh1iZFqDpiCzCnbHOlyMb4p5v2fQMRnYGtAJsFnXWOP+DlPuE6H28p4PffflzaEXxHBx+9inSTqFj3vYH+OA+ok0d+BxNUyklNp1tz4ist8m8AK0xzyQxv8w/XH0211xzLc3HhugYHsa3cYhrDr0HdTJMg3Y5yYAPJJBCceJtQvibmZ7kkksu4fLTbyA6s466pgjubvG8l9wXU3KA2VizmxmvmOO6K804jiWKMU4hJ0riHCyW0bwJLNukrq6uGrJl98+hkIB730/H0Y8jAUuSLoIuP7qu0+eMB8UUTO7H7TBMDakJbji9jVsGJ/m3oyPYwI2tdZwXUSnh4UvSv/No0bFriLTx3YXfYpO5ApeRhztez9QXPwe2zcY3iVIzF0RDJJNinb3UIxhF3w0fwmPHUSfAxmR2dhO87HP8Nirm+k0DBh2OTuKx8cNsm9hWmzPV8FwNNMmymwaP0JhNNPrg9bdDzBmH9jNJhVUynikwoL5/aTXseP0JGZ5d559F9y9/wbgzhjMXXYTsO1mMvbI1jL2gh5LiImiUWG7MsjMj1rYKaAp5XSyoF8//ASeM3e51c1ObADu3Dc9ndh479PyhuceTNXNTGzheKLG6LULIq5ItGbzvjp3VLL5KG3wRTBPA8qCPi2MhLOAHI3/cMPOvrb0Emv7MrQqaLANfKEzxBNCkuuboXWbBdjkV6/M6Ja1EmwOafMvjqK1O5kukuappsmSDTOwAptcPpliUJiYmaAmIjWUsN4au65S1OWLwqMO2hET2S4+a5K3vfgedl62gdekKAJIBL5nhHLYFisumLpJnqTRMSCtgO+m7QQXGe1PYiptAQICmRH4cbPFwB6KxKtNUqSHnlj245uicmhzmaNg9QXPSi99XrvZzvGs5kjfCsiVrATC8HixV7KJddR2o0wU2BFTW+HyU/GITCBZr4RxPMU/rtAB4+1J54mVfVdfkbSqzItVLwQkBhgpu8v5m6qLi3hSkMrP9x3HJYuPfOiReV1lcvWmxqRddHmIlMY62N0g5IxaQU4XnChnxWVrchaaoSLZFGJtkQGZnnfiMGceZOda6jOm9N5A98s8UB29mgUdGkhWwZ3C3BdGGh0mHe8gUa6BJzscpZmyerpf5xYUyP79Y5j//TsG/8mIAykGx6BUKUdSiSXzpYrzXf4bmulYkS0Fze5lt7CAViTAxsZhlS7+DqkbQ7T7ql88iyfW4AjIJaYKwO8ysLDbo1RMHqH/XflRLx5QVrFATciyKo9elUAyhqyU+MfQOPjv0j+RNJ7QcsTm07CxSmk3UdiG7xBgM7NlJfUeQvEei4KmhpmRQpt2ti3Fw2prCUrYcfJK7v/Kf7Hz4p/TtrGVIART3iY0/uHoFLYvEfTi2Y4ShoGDdZg5HMEs+FE+eRFTMnUWJKZZbgr0akFspq9C/Z4bmHlHUt6hKPPmVf4VdtwvQEO2EVTdQOtZLb2OUXQEVyzRZeu6FdK46Dduy2PZ78X2oHvBG8Pu9+HyCCcgWxZzrPSLYupFSIyXDjSqbvPNcDyGvizPPPINZtQVD9RGafiUh50AjSQ1Mh8VcDC5YTqxFbNKz4yJcqmddqKafaJMPd1c3astajNg7cK+6CWToi2rMumexJRtb0RnxiPVnoRdUbQCAfrdF2e0YLK5cKQBBIQkpxzVbduHvu5Ol/hk63QqdRQFMDh0SAIbhLYCNu0H0s9Q/wCd7x/hCvwCSH+xq4otL2vlK82HOsZ/BROX9R8a5dUisM7F4PTfpH2HUt4TCOOQ3bcNWVR5dLQ4Rr2yKcmxSHAiXWAKoST0XErzoolqILvk0hwtjHFAlrt5msvK7A3TsFXPDdDXzX1v+C92pPnAq0ATQlBRAaKzFwyBzahK2n0l/p7iH/udk/I77O8CG7jjN4Zp32Kq2MFJrKxmnxMx0oBaWm9tsy+bahjp2rH4Xxxdcy+Bzh+l1Mv0qoAlq9gX7R8W6Yds2KzTRz6eTWZ4bnMWybAbGp7Gmj6LKEhedwubhiZn5rNTRfAlFlnj3RUKb99CBSV797U28+tvP8od94ximVfVoeiFNU6W9KSCzwtY4K+D6o6/9a2svgaY/c5vLNHmDIcY7AkgucbIQmqYa06RlXWRyAolbBR1VjhB0xbAlG8/iGPFucYJ3B9qq2XSGO4PpziIbBv6ho8iShK7r1NvitDGaFeE5bY7twLRTBqLgFaedJZ4y7tYgkiTR5oCm2YCX4SNHq95PJb9MRCpgluQq0xR2yehlk/EdUyiKG90qk8xOVJmmSKyeWbdTSys1SxbHt0ipbYTNjjnlkGeC1mkfqt+o1jEa9Su4Os+la9EKbGwUVCQnZX1V0yK0JxzxtCSRbBTXEpoTzvFl0yxJiM/aM54mZgWqTJMnqlHnSWJVmKaCh3LXalrf+lb8ilN9/rgQy6ZKYbYMZDEsoyo87nDYgZLLw9W+ZiRbQpZcbAjdCEAu2YdpCtYjFBKsUgU0ZRoFsIoU81xeTgFwX+AyTGTG9okUcLe/B5A4mgtTCnnpcjtAI70bgPTxccreOGa5ZiToy3XjNvqIZNp48KIAw684g0OdEoMnuB4XChHUkpvtLSpv3tvH187286PzruQHF1zHD179Lp5adjqhTIbWxgvo6fmguNedeSSlHupEweEVdSt5LiXGenHoAJLXolkSfZ+NSIwv6sZsENc5rUus1hYj2zIqCpLTZysMv4qIDabOlpFd3QAM7N2JFSgzGnc8rBAbeTKk0BISn2k7G9xl9rlcP+VlyasPs+JNx5kc3jTvWksO0+Q7bTUL14nw3vFjt1EoD2AVFMaeayI73CPGPiyexQ0vv4xYdJoGexJLkhmudzF+PI3bIw4GroDBnoOT7Pvd95ksBjne9Dr2PvEojz/9EEdbxPw68xWv4Zr3/wtnvUqEXg488Qj5VO2knk4eRZYtLEtmR5sIfRRK4poMT4wJh226alkt/X923bnsXfVuDHcrBTWNJ6iCJDPtzK/WFauJNgvQlJocc/4WQDDa5Mfd1YXSKNgStWk17oiLgdIolmzhjrkxJJmZkHg210fjqPowkm2T98ikQqIfPT1ObbsJYcBJbAH8w6NQv4Ru50DTbTnZsEeOiGdgUNwTd89ibOCzDV3c5uhbPr2olY/0tCBJEvn0dt7LLbw+JMJjnzo+xo9GEzSFvWi42Oc6jak9AiRMvfVt9OoWHlni6oYovdM5YmSIGg670rSK4CWX1EDTzNPceeQ3rOu1uPExMZ/aR8X64VM66U318qvDv8KyNEqOsW1FCA6iIPORvTnyQ2IO9B7/Ir29/y2sRYISybgbLJvgQwqenloSgCxLXLdGHGDbYz6ifjeTk5NVvdHk5OS8MGYxp7HjwQF+9vHNeLbOUo4uZ7DrKn7aF8UGGk0JJmpzomJfsGtolt/uGOGqW57hIz/dhZTTsSR47e92cebnHmXsRzfyuOdfuLFtfL6ZKKLO4KaUAE3LDDFeRx2A9r5LFvHgBy7gtevbcSsyO4dSvPfnO7nhe5uZcZI6upKH4N5/hIn5xa3nttDmx7jmtk9j/eqHz/uav9b2Emj6Mzbd1Mk5m7hkmQwbE+xaAJITShLhuRrTpGVVkjNisbM1i4aIWOCkFhXZo+DviAIQdXeizDULs6Hr4F6UUoGAY8/vzTsbf1Z8njaPaYpTdLnIqWKBXR6onRRal4pNLO9103vkAOWM+F3WOU0YJQXL8ccJu8WJf/cvRQHZZHkcvZyvgqZgNE5TscTV993P6l3bmXROqwHZjW3bqBKEnVDNiHuS1oQXl9+gyfFpGfHLuJdeg5XQMD21mLohGazqb8aarcXHx5oFIxcs1E5MsUSC5XkB1Pal8sTsILruo1TyI0kwvsoprWIauE0Z+5yX4Vu9ijrHRmDwmNgY9EIdozNFfn/scQpGAcnys9Ipc1Nyu0FSiDpeUemSl0ma2KsuQFE0gsEgqrOZFDKiv7Nx8XO0kGXFSB8h02ZKamKjfSH5GRt/JErRCZ/sMjczObsTjyxRNAyMIcGkTA06ANSuAVBvrp1WtUBzupvFscVcv/B6ADYnBpjbSikvhhznFiXPozMZBl02JXdtIT3W2E5Z1ygNDVEXv1Dcs6YCqjdEJihO/62x05nQDFTDoKtD2Bm04GzUvhBSTiEQEYv7kK1xuXJB7b7kxSaSbPSzy9+GxwKPBbLaLe7l0cN8Z9s3GagXG/4ap8h13ivj8grQpPc9gaWUCNU/whkXTuIOOBqdbM21WZ+cwpicBFnGu2IFqy5sY/XlMnXL7wdgYusCzLJC8vg6JmhBl934bItlp6/BaLJZjgjRJRYHwIbstLjHzQ0afjXM5pFl3D6wjrvv3sIj3/smE/YksmJy7pnnc+GbbkKSZTpWrqZ50RIMXWPnA/dW+3Zw052AKC2TDitMRctYXvH5Pd2djDugCUPcO8uyaUvHSEcXoRoFEp7v0dIZwLaKFJzoyJK1p1eZptR4BTQJhjfa6Mfd3Y0SF8+JpLjwtC1kaEY8u/Ut9cwGQtiyRMiCi62lLC40VzV+U5EAqqrS3u74/oyLAwUtp0HrWspX/QGPLJ6nZiuKB4liscjw8DAMCW2UZ/UGfnXFtdy1ZBUycMuyTt7ZUfNpSqW2ImPziQURPuhYcXxlzxF6fvM9Xnf0Mcpb0xRn3EiqxNNXirl9WTxMWFU4NpllhSzAFvEe8IYJnHcengE36FAuj7Mq/VPeZWsYnRahzgKds2KMOmdbcek239r9LcZT+wELRfFTKKg88cQT3HrrrXz961/n4eIadg5cyvCQ0DANDt3G4cP/Tv+w0KwFd0moMxLuhT3MbW8+u4vOuJ83bhDMVcVpHwQzNO54ZW1/YICf/Nsmnru7j9xsGW/QxfLFFg3TuxiLi3WyfrTEr/9rG8MHhfVLpQbdwwcn+dBv9nB4IovfrbDKySSWmn0o+UnOKot78GrfLk5sT43NUrRsQkWLzr1i/TyarwGzZc1h/ue1a9j4b5fwj5cuIuRR2TUj5kUcjfCPr4CdP4E7Xgf5k92/TUPnkGO4uuLCS0/6/V97ewk0/RnbbXtvo39GpGpjmtwzcj+PN8+AwzTZRmmeEFzLupkZHxTZUUBHWKT1elc4m3tLEBuboBzFa9ZAUzgZp25anLB8TizdygiQUUyJjWeeV1MszpGuNmxJIWBn6fTX6lX5giHCithAk7MzlNPi4Uv6nNBgucY0eUyLzmVRoo5Hx0x5HF0vgiMED0RinDHiIZTLcbjDJhcSQCOghgGNkPM+OezmvLoziRRUXH6DRgc0DUsZJMXNzE8P4g/W4v6mbCJvcmLwLaJfSccBPJSrxeZbxydYbovvPFDW8OLCZbnIOSaXuW5xQg846fJJp4xEvZOVZ3lEPzy5el6fc3PHtt8AsHD4VUTJilqCwKRbolgS/XjUfJYv2R/j89J/kom7CIfDHNu6ifu+9kWObhbV6hNOxeBYIUd6coJXGCkA7uZ1mJZK12nrmHGE8gn/KGcXxMm1T5fIObXXppwDdd2ckhWeXAed/g5akyGWx5dzRfcVuGU3u9O1tGrDUDGTBhNNnZhAq8fF9xe0844HE7xt4/3Ec2ksWeFgaydP3vEjZLuOUsqNpECgJcGgW6SSm17BSC4d60WpLyNJLlpssQkVIwo+JEJ+cV3HpRwrirXTt78sGNOB9kaQJKIOaxqMNxBracO2LLZsvJ/xmBjfnpxNSBP/HleFAWLW+D0D53ycTLvwG8rlBHgsm4PV7yntF6DXs2iRyE6SILjwh8iqTm5iOVNHxMGiNLuK/pI4LCzyG3gWdGA0wDJEiG64WTwPqRGBTnzhdq7reA9Xtr2dzmgPTT2LWHTuUpa/4Thrru9l1ZUXV/tgY7PhlaI49e6H7qdcyGMaOiO9otRMsRAhZIYYbbYcTaLNh64/k7OWCLF1xYx00529qOMlbNtk9b7bOKtvmpi3iGUItiTpV1jQ2Uy0yQnPTYxRyuvkUwKoR5v8qHVNyqfqygAAeKFJREFUKJGadYbccBpDWRGK6unsYSYgNuDWrMXS+0N8ZeDDtCYFO5oIRujq6qoeABjfw6bpTm57MM/sxBi5rWJtSVsWMjKdpniGDj/8YxgV7OzjK1/Od18pUuk/uaCJ17fU1p1yecoRYMtEI+t5zfe+Tn0qybQvwJaZad528AGW7BYgNraswL1ZcV2vbIphWjZ9iTwrpQHxYc1i3VSCAYLrziJ0nwKGQshtUbrUIvERg8EPqixcIzIdx/1RPvZQkHw5y0PHhAu3x9PO97//fZ566ikmJyeRJOhmiICsMzCwjHDoZkBmbPzXzMw8ATb473eyfRfOt5voqgvw9L9ewvsuEYB1LmgCGBkZIZ8us+WePkzDoqEzxGVvXc5bP38uF7z5NFYf+D5lxHO31BLfMXhQjPeqtjBuR9TdGPLwr1cuZfO/XcbnzxXf5WkN8PNzR1Gc7NoV5T3V79VKBlvv6+c7D4pxWDiu05ARrPRc0FRpjSEv//yypdz65vU0+MX61JXpBdsEdxAyo3D3u8GaLxo/vn0LxWyGYCzOAqdA999Sewk0/RlbzBvDZTlZLZZBa0M3Zy+7AtMJj+kjffPqAuk5F4mRQWQn1Tfg/C7k0LuyR6EQFiCgyfThLbQQTC+icVrF5YSC3I7zeGYmgyqpnJ4Rm4FeDlDn1J+bjtZxpFOcejoZxO+fH7tv8NU2YskQfc0HnOKuJRm7lAbJAhuufNMyFrSKGHuyPI5tF7Et8UD5I1GWDokH6ECnhFLndq4rjG0V8DnSFFejn5uC16O4LWTVrobnhqQUtpHGTJXpztXi8DEzBKaNZ0mMhlctwgYSPjG1AwUhiJR0jdbpGVY5mR39ikVRAbcRqOqajKh4TwX0ZaaLmLpFwQm9mX6xMGnFOtpNhbOeu5Zl02dz/vjpWG4br2O6ORB1oxpizCaiMcZkRxTf5GWm7xj3fvm/OLL5GY5u+jW2lWXSJ76vrpRH13VexgFCdpopuYmDi9fQtuw0cg6L1ib56dZaMbAZ1CxmPB2YuRxJx5WzqStWrYnnznQS97RQV5xikXsZYXeYizouImFIWM6jXyxGUPJZRlsFcDkvFuTa7noWyT6CBT+LpkSK+pGORRzcv5uHv/N1ssPiu4KtRzloi5PqpCnGcEVeAItY9CyWlMUcKUQVZNnA6xWn0Sw+fJPiZqcMG7UcBWCmUYxZzHJcwht8LDtfiP8XDwYoOs9JbKJMzFnMh/wyueZNJG7OovsTqMU67G2v4u6s2EwUXxLdyRQt7hWgyXuaEHNPTz/M7OyzyLKb9oZ/RFQ7VpDkGEOzwuah0+6jVB4BBVaUxSbdK5ssDsp0jIp5oUkpAGRJ5vyev+NNn/oy665chiSD3WhxtPQ1LEvjd8d+x9qfrmWksUS8rQOtWGDPIw9wZNMzSG7xGYVimEXeRWSjYuzkUhEfOmsWrAUgX+hFKxrsdULRWwI7iKV7WXysQCg7iKUL0DNRZ+FS5CrTlJ6aZPSICPVHm/x4Ay60cTEutiOWt4LrSBqCiVqzZA0zjgt+Z6KEZIILlQtTtXJEC+bU0rPH9rBntoVcvszBBx+luE9810xnhJJl02WK+XVoLI1t6extOIf3zyrYsswrnnqYt5Rrhxug6s8UCi7HOD6G9uRTvPqJhwC487Vv4eGOM9jVsJhQl07/hjZGyjoBReaKujAjswU0w2K1MsiwqnB3wMdQRoxL8JJLCD2i0PIhmdhtKq4DXmTZR8kDLBDP90R9I4sP5njL4xbDSTG/0yk3+XyeeDzOK1/5Sj7ctZ+b+C3rusQYHT3ayOpV30ByWNAGexnqhIykgKv15PI+2ngeqyzGfWxszLnX4gDU39vL2DExH+o7grz2o2ew7JwWSvkRvrj5H7EaohzuEuzVpY4eacIpeRP1u/nx35/Jt950Ohs/cinvvXgREb+L08N+GtwqWdNibLbmmSVP7oNCkpmxHL/41Ba23dfPsQbxbN6wqpmGtOhjf7FM+QTwIy6kwPmpe3hrRDBHrcUphi++RdhDKB449jBs/sa8t+x7/GEAVl58ObKinPSRf+3tJdD0Z2yvXfJafIhFRzJN/uWCf+Orl3wVf70QvJaPHTqBaXIxMzKEEqiFXGbL43gaaplWUouTPq8ohDKL8RVbcekl3E6dK0UTi+DkxCRN/ibOzAmHYn0u09TSwtNrugHoYgCfr2Nev5siNQ+PWIMIEZYdoscsiUkv+8UDpY/lsGfFYpwsj4NdxhsQG74/HKGjT9C9hzokQg3ic30BlUjnZnyK+AxXo5/BvbtR/SLE0qoI0DXe1ExgrYHkUajL1QTPMTMIqkTsFQtxtwrBcFEVQCTqROc8+Qxe06KlIU6TW8WWJI4FZSTDX9U16Q7jU+9x4fGr2DY885tjTBwVp2vbK8KJ4RVLmfbM4DNCXNz7RhS1ALJEQBOvS3YFq6n/E7HaxpIIh3GVTPyRKJGmZmzbwijtZMwR+3dmxfUqUh/XcA8Am8+4BG9cmDhmPAmuyYh08yO+GXQbZmNLKY+Ok3YJoNS6uoXTVt/Kaau/g7degN8Oj5/4uLjG63quw0JiRndChPkwSiHLeJu4F2dFBHBpWRTFXa5jsQOaRlq6yQZCHNu6icywIzRu3k/CO0LEE2G3UztsaVQsyHX1F3NOz3UATKn1dLUfRJJsNFPmlel3INugWTaHSmYVNAWj4v7HHKYp0uhndqkPU7JpnPWR9Ypnw3U0WwNNQZvpVXeDDMZgN92bPsuC3sVcdFCMhzdeZs8hkclXckTgvlUOaEo8AkB721uI1wtAJskRJEmmTxEbXWPpOTIZAbZaJqZpKuYxgEyTG68jvjb9aZo+cDpKxIM5q5F5aIDZ/qeq9z2V2cbhI//BfX2/x8bm18d+zYZXvAaAHfffzfb7flfNLCwWw9RTT0QR7KdSzNO7bXPVyT2fP85EfwrbhkDcw9a2IcZioBoW0v0/xzIEOEjUiWcuGIujuj3YlsXA3n42Lvey6fQgtm2jDQigYoxuxzbKoNTRU26nwddAY6yRjJMgsqBQS6ZYlxP3KBGM1EBTOcfs+AgF0wnZ78uCZePuDtN0ZjOTuk27FUeVZNJE2B5cwo3LPk7RsjlrqI/3//on6AM1RhBqoCkaPZOk41/0RrdNQJE57g9yy1Vv49/Pexf1r1nMva3CFuGq+gg+ReboRBbFN8B9zcNc297KJxLPcs3vruE9j76Hg8vFgU7SJbRjMkuuvIMLL9iKarsJutL4bQ1Llhmvb+LabTaNEwLQTE6K6371q1/N2hWL8Q8LZnDNOSK81Nvbi99/PuvW/ZT2trfQVhSu3+6QXi3mXWmFfQmmbtnJ9K17MAoak5PiUOiaFXTx8PAQY0dm6XRLrAi60Mey2Ft/wC9+djm/Sh9gU6fCTDSGYttcsFDco+mhLIYmnolzF9ZzzWktuNXa9i5LEi9zCvk+qLSB4oZIJ2Az8dxmfvelneRmyxitPhIRFRl49WlNRPRjeDQLizklZixL1Na7533Y/7OEQ3d8nsPOwfDZ3DLe8Fwns6GlcNUXxOsf/RQ45sGZxBQDe3dR72ljkbEGbU49yb+V9hJo+nM2EywHrUuWEIIDyH4xmUsH9+BWxEPgcjVgGTLpqUnw1tD4dLF/3kdGu4TWIeyqASvVLBFaIkwG7VxGiCrzec4rrmdJsRsArSgTd5imqcYGeqtM0ylAU30tJbW1+xwALI+GjWCaABSnkGxhb0LUFlUNioZ4IMqOj5Mrm8Ob1ym5oL8Zmpx0aGPdw7Sd9zt83cKvSq73MLR/Dy5Hl9LpZPAkAyE8b7yO+BuXEbZrGSNeXIQv6USt8yGpMkmn2negbBEuLcM3dJTOIbGZq83N1fj+3rCCqfvJZuuwLJWSW4RnGn0eYs3i8w88PYpi+JEkE29IhLWa2lv43ZovcqjhOQCKrpS4F86ipTd6UfUAtg0TsVr4Y0jtZNnyVt757R9x2d+/R4yXfYSkQ5UvyjpifnuYK3gIv54jFY7zVFKwhZp/mrOzYsP/dehBAHYtOY3X9CWZjHpw6TnqV3YRiayloeEK5HViU+8MLCOxRQCG89vOJ+qJknCEL8V0ABsXg2Fx/zpUcWJtWRjBW2ym225gZSmHLcsklggqPTfuxzJlXIEkDW6DBfEzGZZUZMuiu0mwMfV1l7BqoZNEINUR7xLjP6O56D7iAACXzJRhYxfF/Wr1OKdlx7sp2ujjJwO/YLC5QCoco4SEYtrEsxbxnBjr4w2T6EoCLedi7KkOFNOHO7yADb+bxNIkZNXmyC0f5ei/f4jiHgc0OUxTJiNCE7HYOcw4ImBDnqBu5GscjYsTfId9lKFhYVbpmpBY2ys0PztjCgdSjoBdKiLV28RucErUbBojmxQi2HjmNEBmfPw3xIoCCGwZ30LHhjMI1TVQSKeYHuzHWwFNhTB20abVFKBNLuU5tmUTPl8nkuTCsoqMD4jwSevCKB5/hq1LHQuJ6WFsK4Uoz+RDN3UkWSbaLJjp7aPjPHGan9+GDQ7nS5QHxXgb04cxk+L+bMitotPXgG3bJJx1qaGYxMZiv6+/6qqf8/px1zls7+R+RhwneUVSaZcdPeF5bXStrGPKsFBRaJcbCDeM8P7TP8qEHGSJ38t/H92JYlloAwPMbamU2GRD9mLS94vCuN1vfQtvbnVqZS4Ua2ciupJ7Gy4GRGjuyeEn+cLe9+Lv/g6bAhKWJLEo3A3AxtGNvGf/f7C/W6bghsfeeybNi9egKH7qPMuQgHZT6IlSbxb2GZ1OYm+xFOLss88WGq7+Z4T7d6SThsVn0NbWhm3b7Nu3j2jkDJYs+U8cRQGesAHje6vXZWkm6ftEiFWfyHP8Z9uFON408RfTYNuUdAPr4Cjr/Cqx8RxT39jD5O/8qPkbWFhq54kVIty3MDVDU6MfX9iNZdpMDb0wAHl5vaN3qjsPe8nLYcnLGCyv457fuigXDJoWhAm/RTBY68MBeh/6PYXEvdQlxZgc2f8I3PfP8PU18ONrsHfezmNDTfxhbBk7ZDH3vXKQ0VSRD/xqN+a6m2Dlq0W47s6/h0KS/U88CrbN6o5LMPZnyG+bOGVf/5rbS6Dpz9iqxXptGyyrCppAbFhmKgHHUqxc8VVOW/1N/JEoAIZUy6aYtUbmfWa4SyxcIXdNvK2YZSJrRRFSPZ+jvl6cWs/qX4UuVcw1LZocnJWzFAy3ODUukCdwueZXuQ7F64nmS3gVlQVrLkeSFCTZouyW0csC0Kn1gnoqHRFATI2C2xAA0TTEyiP1CcB3tE1Ccrlob+vGUkqUG8VGpDSIDXd6dpBSLkugXlxTnSdEVBXfM1TU8C2L03bB4mr/JFUhdFGtEOWMEx5syhdRXQsIqH9HQ0qMpau1ldOcFNxdEZmi5cc03UxPLSGDWPjjsk2spcZkrTizk7aWITyeIrIR5fGZXiylzGMtWzm60seU491UX3Y2r7ALCQUsP6PRWgX2cdpQo8MoqovuNaejehqZiYp+NZQsWg3Rb9McxUuJ1aMiNHB/3gEybpkjyhhbA4fZGd2GZJs8eGYTO70eNi3zESmPI3tqHinHmsbQMQi76ygPHcM0dFyKi5d3v5ze4eUMDa4mcbSOycYl6DJIZoZ/ffRNzJZmaVkURbZVPJMLeV1E9GtP5zJWXnQ5tqlSmBLzZbnXJKQI09GFiX58UgG/fwF+fzcRl0rcCVlOSmLjds8sJ+aEPxtXi/GezosxCKopMY5OeQwjWOBQ8hBHu/Mk4gK4NxdMZBvaHNA0GHRCHFsa8Hc1I3kVJJcP3+oLsSYc+4iQiXnXH7DyeSSPB8/ixeh6hkJBpKOHw6dVQdO+7gLfuQlMl2CeOhkklxOp/+qkxFonLX5HvcpQwYNtinlfLk/hXRLDf0YTNgZag2DeFp3/XyxZ/HEAXh4qsNpnoFs6W6a2csZ1go2QXWb1gFAoRMhmsngLAsArpQJD+/egFUr4/d0AJCZF6n5zTxi3Z5YtS8UynnB0fpLSTLzcwUTBKXTbJMZ+U7S23P9hfAxtWIRLzWQvSOJgsyG3ii5LZkLTycsKkm0hFyaZNTKMpWYZMxNEnOSK/RWdy/geRotiQ+4MrMCj+DHcBr4VdaKcSkcIy7bpLNZxrKuVAamHMFl+trqbunZxqND6a4dBXU+RywsQZ/3+MBgG/rPOwrd6Ne9ob0CRoBh2YYVcPBg4k4Q7TtzMEy738sNf/5CFw/WcPr2eGyYW8YXxTr6w4kvce/293LjiRkLuEJ95vcS7b1a44Mp/qH5nXcPlADTZgqmbPEPU61TiQhqh/D/tnXecXFX5/9/33ukzOzuzvbfsZpNsyqYnQEJIQkIniDRRQAVEUeGLooIiKiqIDVHEimCh9y4BUiAJ6b0n27K9z+70cs/vjzM7myUBoz8gRO/79corydwzd849t5zPfZ7nPI+ay2mnnSYbH5AWyt6qczhr5RZ+MWoqfznpLC4KWihcvpWKldt5K7mS1OKOIQ6tTa2OG1x+iIQviuq2oJhV2hpl5n8tEmSy62DKM2AR8p43KbuBKHFRxOn9Z/Ob+ltJFMqCwWP2y+sgf5Qc+yEXHYEemQLiXcxJd2BPhGmx5fJG3+epX30OK33fJp4wUVKTwfk3TubtgHQRzstIY8frcoFEZp8UTZu3vwUb/gz9TSQsbl4On8HW/nxQFPrd8n45r/cdPEqEFfu6+NWbB+DcX8lAfN8h9Ge+yI7lSzErFrKFfClwTDsyR9THHUM0fYR0LZVviCahoAD25NJgPSQf/CIWJPD2W+TlnYfHM42sYmn9CceleTwYHyBhHRmQZymQb7t2kyVVw84UD+OZLnPIhAMB8vJkPEEkEmNAkTezqqpk2DzYknXcdNWBKhJU2tQjCjdqaS5mHmzlrMwSnO4MbDYpUIJ2jUREXkLm5KoNkgHp5jwblsTIpe1ihxRFu4sVqjxV2DJd+DO3gyonjFjmHgQ62zdJ/3hRjVzia7FmU5pcrZcqCrloDEoyPUHW+EKUw0zR3VlSOOSFdbwdq1BUO+15ZyT7lUdNr/y9XR6NcLKiXGPTaAaToomufeSVy+PJr0zn1MurKCpJLp89NJcnD0jXWbRnLi+39+LV5Bh6I9Ja4LcpKKpCt6uAuGbCqUdJj/sRisrBtF4SkTiKomBxzaDXK0VveUAnQ7gAgWaWD7xxjf0AbHYoxFUo0Ny8bd7DbqWduBoj4OqhOUs+1PcXmPFaR75p7vLvZqNDTvhFtjL2rJJuqsWFi0nrq6SxsRalN0BLvnxzNUf2EYwF2NS5icwCJxa7iVgkwRxHJloizv78Isov+xzj5n2PQLssrzHGnsDfLQXmOJMUIZmZp6X6MCoZQ9aKfEgKXzFZyXQJdGwnbaCBlkE57oppkAwEGckVlDtiyXp3YycQHCVj8fIDcmyqwvI6a1dyCXTY8Ddmsei6r2Ct8Mg+fPHbuPLkqqbBSoWnT1JYN9GG9q0vo5jNDAwmrU62EiyWTA41SOEeSde4ZNItAFgTndgZvt/sSj5TI3Jy2OnWiGkQC8rrJBKVLpb0xSVELAcQWhw1bsOVWU1R0RWEnbNQFfhMRpR8s87yQ8uZMH8RTo8Xu1feJ4MJiCfM0hodgwQJIrYoeiLBwY1rcTjkeQr45XMkryIdXeuhLh8CHg89ycUPqrmEbH8xrX7pWvLmF5BQVXZWDE9QL7Y1QlxHsWvYx5bgOVeW6KgOlVHpi7LLL4/bE/TTh48DEQvR8GjqlD6yk9amTUPlNNq20pwchwkFMgatLrQdZWgBy6RseuKCItVBo0Nasc8RT5OpN2JJuvgOtzR1d78JCJz2SvwPy4LOmVd/HpDJGpfkSAtqvMzFC5Yyub+u5Ty+6mEqBisoCBZQ7i+D0CTWh6fzyCOPsOedPdw8/WZe/+TrfP+UO7j51O8yp3B4BWdmySdBCLJMUiA02Z101VSjJQunzz75PKxWq3zh3S9jch5LTGaTrtCnqETMFuKqhg6EdJ3fF8jjsqTFefQfb/LnG64h1NrP4Er50us9bxQZl1TTo8p7NkNPIz8tTqEmn0dd6gD1kR7yrDdTMPUZts9sYK1Luon70+S+q/fsItbSQl6FHPu2gz4ZZP+riXDvZOjenzq+3t5eTHtXcEqPvE5XaA7MCROjrBaqbCs56zP5hPbsYGW3PLcTBrro7+pGUwRZPimkVqiTaC7/LLEL/sLz6ufY0xBA1TQWf+Vm/MkQDnXXJq7qeoacSCf3vrGf1+tCcNGDoFlo2vwOg91dlGdMQtEVYh4rluKRFQxOBAzR9BGie5OrKbBgMlswW22IhEAkAwKJBfG/9XaqfWaRFA2DMTlRHArsxeH2jNinajcRSMYMpScfUgktgmuMDGSNRyNkp8kHfY86yFpNLlG3Wq3YbLmpuCaAAlpwO/J5N5rLhSYEWlDePENvvD9XlpD0LGEtzRrxHUupB0t8WDRZHU4im6TlJGvWHG6cciOKSaU/Z7jAqm4OEPQ2cXDHWlAUMsvkPq2WnFSW2YZQlPZIjKe7fKyaOItXamaiFo1MztblkpaKvLiFsfX/RCfBgLsMv7cExZXG6H1ypVuXU8Ne4CYtLY1IOI2eqJxUtPA7jJmdx3k31nLeV2vp7nkRs6WfaNTKYEMNo/oLybaUkAiMJkMPo6txNKGShhyf3kQCT46dQ14ZV1M82EVmMhv4QVsmffvWoyd0EvooejzyTbs0oJPl8GA2RzCZYggBhe3jyNM0oiaV+gIzkag8z7F4AlfMxfry4RWTIatK56jhRQQAe3r38LpX5sQpdY1lzVOPkYjHUToUNKERSwRQwwGa82QfxtmlZXBzx2YUVUm9wcZ6VGbslYL3yYOH6G2LEmiXsW2jrDq7k7nFRqcPuebmpfpQlS2vvUNB6apLDJQxtO4x8ObTZHdvYyDkBqGAqnO+JYIl6RJZ4ZOrC08vXUS4Sn7f07oHIXTGWKRFwK+kcWBDObM++Sm8+YVYkxNIpM5HRr4UTa7MBBvPr+ZnZ8f5iuVJookoA8n8Vu50aSUbWpI/urIWrFKciEgjcdPwteXKnkyZs4rssE5cga5iW0o0RSPSUjP4xqsM9j0qvzAwikjjIIqisCZawp6wikWFM90xVjavRDGb+NQPf878L1wMQGdMxewYTvXgs/qoz5HXzb61q3A6Zb80Wwsms4o1RxAjgFAU9lVNJjYlhiUtimoqJiswLJo8eQU0FFXhtzlSVQB2JzLocAawlnso/dtf8Sw5gxZHByoq1a3p7BqU13K2f5C4kuCQCBMXZsJmH1mD/QBsaeoj2hagb38rggJKXGOxxxzE9Rg7mlfQ0yytd6UTMumMC6JZu9mHDBuoZjfd3W9iKSuT43eYpamzSwZ8u5pz0YNBrKNH4zxluHTMdcXJ1ax5djap8rlwUtfrdCaTX3YoNjIT7UxjK6OT4Zjr16+nu7sbh9nBBVUXcHH1xSNeDi32XNxhSypNxoFgiO3Tq1FVHaErhLzJcIDu/TKJp2bhjYD87UuWvsA36rfyqXde445YDxqwuaCUg4Ul9NjttPbE8XW00/XkTogLrJUebDWZ2Mdn0Z+MPRtjGYM77w6KRbLAdbyRSLkGX1yNetFv+JvlFX5Q9Dv6zQH2ueU1MrbhAKG1bx9maepF/OMSiPoh3A+PXAZhH/v27ePee+/luccPcHKHfCa9niOfkQVWwQL3n9C3vMSr372DQQGeaJjo0mcBGJXmJy8s42C7PNk8tbSJxx5dSd3mjZjMFs6/+TukTZlJAgWLAkXuNBKDfVzc/hzT+jfy9UfWU2+uhNrL2Z6soTgmR1rx/tDv489vjww3OREwRNNHiJ4n37ptWMlPl75jPTycDVvEQoR37CDeJwOOM4ukpak+uJ39zU+xrW8FzsxM3o2eK28iT1I0RTxxbA4nJB8Kzv3yN7rNfroj8qFgcziwWEeKpmIaj8h6C6Aml7HrfvnAtdvLALBapYVFS0/HnOsa8R3bmPykaBKAwO50yhw5ZjNfuvweTio8CV2PE86Sb/xKWL5x+DK2AMilqJp8C7NYh0XT3fXt1K7eyVd2N7HNm0tjVj4rLCOLHLcpcvLPiYDTlUVUkRaQluLZRA/6cPREKEsGtJoq3WQnUwr0xuTfHmcjA759FI/JQDNDQ6MsjdHSPI5+ovyk6UZ+fegmzsbCGWoy703CRka6HO+uaJyMAieN2ckl2+1tZA3Iya+eSjpaXyTkj6Gg4c+WQd6l/jg5Z1Thsck3/FjIgYKXSUkXyL5SM+1qf+oYc8K5bE7GsznD8li2Vw3HT4EUTetdO4krAofJjblfsPaZx9i6VcbyaME+QKE5Vwqbs/KkSN/cKcXtUMbsPe+0Mb9FXjfP+sL0dwaJDubRG1N5VLmKQxk5mBJxKtXNaJoLj2d6qg+VTimR2lrG0LzyK8yoORsAPdRJcP8evP59IDRERJ7/y7PlsVhdGpt9G1FQmF8yn1a7PMee1jpErA7TmEfwCnntBjNmMO0c6eqyjvIAEK33kZsv4++sngD3Tv8ZOfYcWvwtPLHviVQ8k9s9iaCvHyUkXeBzJ5zJroA8p6ZoE+v98pwqioYj8zRUzcKUHrn9wFgn8bD8vUi0AxGP033//UTcTdQxims8t3LJ9joius7W7h080yfv//H2BFq8ly2dW3Bn52ByJHMfxRXSPcPCd8A2wP5ced00bt2ExSStNBZ3G9mlabSFpFVEjztpmKJRuKiDknltqKYCvKEcmvuGLU07qqW7/lSWUSGkpWrp6INYy4brIK5JWjIyfGNZuVP+u9SfzFpv9hGz+EARuINSIO52KHT+ahOBnq9xVtE1zM6WuZJ6bO3E9Aj7k6V0MvKd+J1m9hY04lfcWESEMurp7nkTS7J8SMLnI97XR2+wjc7u5QAE/ipfqDKv/vwIgTMhzcEoRQNVIaZAftzHNhrIDcr7YXukmLnKOs7hTS5bMIlRxeXous7rr7/O+5GllpKXFE17+/1ErTJQXu2F1Q0y8HvINefPOpn1mVIELFz7NrP278UdCdK/eSPzB+U1/My8xRyIJ3NV2ctRW3VQFTznjWJPIExHOErnoHShZgo3sY5ssnV5/gfMMWLmPsitodXfyvbu7QgFGmvchEwKtlickvYWQq/+jeziNDSTQjig4xvQZOHgtALo2Q9Pf4Ety2Us3UGll7ndh1CETr3bxB6LwIRGQJ/PwD9f5OnTFgEwdcsGDqyVeZxqywW5ISmw+jzZRBJxOuoOYLHb+cSt36di8vRUJvBSu5XP/PgXVE05mTGuyZw2sJMLDv6N7//sL/QULODAYCZp5gzsYQdxBP8kRvQoNew+7hii6SMkmlz+bxEmSpzyzVmEkvE+FhXr6EoQgsAqaR3IKpYPlK7metp6t6OLBK7cvCP2m1YihdSQpYksFUVVsTmcFNhHkX5IXvR+PYQ7nEzv70rDas0ZIZpKacBuK+LdqM5k0eCAfLg7kplxcx3yzU7LykJ1mIllddFW80eaZ/+MjXWfIuf6FiZds4fqi+qwadLqZJ8wAdUm4zV8A5sRlgBq1Im3QdZV87vkZDbp9DNTb+9WSw5jnPI7IV1HASal2VmcKSfaZRExooJ3WzKTcl5YR8uoILNbCrOO9EmpwMPCpIuuJ8tCTo58+wqocmzS1QH27PiZ/E7nywSD9ahqGm1t1bRo3USVGOm9Fm7BTrUi38jV4CBFyQmvJxpHLXTQkVw2XtzTSfagFMJ1jKJHX5bKl9Of/I6zu5ENW18k0yo/16PyPOfvlOb0nfkW2rRg6hjTrDMYdGVgieks2Jo0n2cMW0UGogOyOLMaI5onhcvo9Gm88+ILHDp0CEVRyBqI0uPNJmw1Y1dVziuU1+Su3l2E4iFGTc7BZFHpPuQH3xSskSiHVI3mDBPYBX8Qn+VVRa6Qu779WVz4ycg4GVUdjq+rdMgx6LcLpr+zDNs7ciL19R7gMz/8FTf96IeQYyKajImJRzpwaxBPk8KkNqeWNGsm9clioNm9HRSUriWcuYd8Ic9l/rkXoSXzBZlzHagOEyKmY/OXIASY7QlC7fV8YdIXAPjjtj/QP7BFnmv3JDbultbdgD3BnPJT2eWXv+3UO1jaOwCqHY9nJiTPydlr5QKAFW7BDjW5mjTSie+FF4k1NhGosPNbbsCv2VjrUvjm9oM0DDTQEVdxe6Sbbk5anOWHlsvrZSCZNDNuIj9r2NJr99rpd8WwZHlIxOP0Nch+Wd1t5Fa4aR6Urh4RyyA3S7qPnXlBwvYBFFR8DSHCe3uJubM4UCYtz3NZxvykLnsry4ZaJJ8Z/ZF+3nZIkaIlatmd9EqOTWYCiLCRMNJKnYhLV2azQ8Vvh4QeJJIIkVATmLLt2GbK63DfOvkcUxSFrAnpbE5affIDXZiI4/NtJq6FMBXIY15/+fk8/MuFqMTx+xXS9scRuVm4zzwTPZJg8O0WEskksuenDbt1FtPKMmsezoQTVIVO3ckYVVq5fHtKqT2QgwLs2bOHhncFnA+hR+K4ogvJI5nFXlFRHHIQLJ3QsUKKpSHX3JvhWiIWK1kD/YxqaSL9lVew22wEQkHm7ZHX5euz5tFhWoCKxuQMucLPdVIBL4kIp63fy6Q1u3hk0qlsKq6k0RFCAIUnjZYLaTQTTQfWIYTg9UYp9qbmTqVzTFHyvCTQUAnt2ovWf4Acm7wW2rXZ8Okn4dK/g2YltncDDS1yZWJIieL3/pb0QflMeTJPXk99+lkc2N/Djgr5Alc22E1CVXGGo+hbHJSnWbDEBLqq4lx4LtqocSy55QcUj5MLKoZqzpXarVgdTuaOv4TazPnMK7wMtx5mQv2r/PmXD6GjMt4jLWlrSXDLJyfwpXmVRz0fH2cM0fQRMhQIbsFErlqCSOgpS5NqN+GaI03QgbfeAiAzGdM02NNNIJmwzJV/ZM6PzHL52ZClyVqcjHNypTM5cyE2zKRZZNzJUEoDi9uJ1TLS0iRXzr2fpSmZbynpnst1SyFgysggHvdzaMLdDBSuIpC2g0BgL6pLR1HBnhElrVA+xBzTpqX2290lH0Su7kmk9Uo3iZ55iLSsTMonTyMSlaLMYs3hvBwPP6su5s/jy9h1ynj+Oa2a+8aVYVMV6kIRticnOoCWZGxRTligZVQwbv9aTLEgiimd4E55vO5u+fDdqyVSoilskRN8GoP4oyuJRLpoaLhPjnHGxSQSZiJ6nDcmvEP6mWX0WxQ6ktafwEADxVlSvHbH4hzIkpN41qAPeyxKdtKl0aHk02/109eyCV2FDkfyFmzfwsbXnsNmlWMaSy7Dzz9oIWMwQURTacjKxZTMa9KSJmN8ag92MaY5ipbQaVDNHAjKa2xvr5zYCpwFZM2VIrfYWU1RwTwAystKiQ300pwvt011Oyh1F5FjzyGux9nRvQNProOLb51OTpkbNWFmdJsUpttLLbw01cx2qwxIvXbgt8zLkcIj67B4JoBRSdHUnJOPaNxPvFuK55fmjqPNm0UdKstne4mFk6LJ2k+ZRaXDJN1vC0sWsj8YJiHkS0FRUTPpU+Sbc54vKXzTPKnfU1QlZW2K10cQyXFsb32Hdusc7Bnnose6icd6URQzLlcN63ctB0DLSkNTrewLyEngzPxqehMqr4lTGV/+G2Kt8rqauOpZPm2T5+0feacRwEEo0Eb3/fcjFMGDpZ+mTSnEnUigCMGjvQHCzlMoc5dRXnoNADOdcd4+9Losu+FPruy0FZLpHbYk5xfkgwLRKnkM+1btRggFzRogp0ynxS+DiM0xN8W5h5LHD6JAWokW7hxD91928symXnTNRJmoo0zp4MIsmdV9uzKepsTTADQONLLP1ohf8aObbPQmFzCM7ZfXU8LhIGaX94zP0oxV7wegd3E7y9ru4tmme4kusZD3tWmUz5mBoqp0NdTR3yEFRE51PXs16V4s6I/i93sBQVf3mxyYIS2knrouJpuT9+4aBQWFHQvKZAzaaw30v3iQvqdknM6CLDdKfxR0gVPsISMsn4E2bwFlSjtWosS0avybZOb/6rj8jX++/GpqBTOAiOsMLDtE24/WEl4zA3fEhFvIYwsnA+vN0WKKNjXT3lcHjasRAl5PFrWea1awjRmDGg5Tqcr2psEGqnwxIiaVQxMv4NziL+C2ZBKK++mvdXDr/uHFPJ3uDNZVjOcLp4zi4vMy+VuuCSUuBWHf4CDtB/extFE+JxeWLmRncp3HRB9oWaOp1z088a1r8MTky0hb0bXgLoDCqYhzfkVd7DqCynDKg+cdo6hqked0fYmVNmuYR4vLuPby79GdvI/SNHmei/sGCG5rxLpzNVnJFB9bhZV+i4PNe4fjpQ6vOSeEILRNxrqla5mcOvkLhFUrWiyMgkKRUyYaLT+tmIumjVylfaJgiKaPkCHRpOlgxkp4fz/6kKXJZsJ5igxM9K9ahdB1bE4XrmRCv4FkILQjO+eI/VoKk3lzNAUTkJGXgYglqHXMw2X2IOxgEnIiSDhkWx8DR1iajpZuAGQmXTjSPed2DyIUgZaZyf4DPyZm7sIcyqLS9T1qJz2I/ngVLatlf+1jmhEmgWOaXLYuhKCrW75BubomY/UXokXTUExxak4fi6KoRKNDlqZsNEXh0wWZnJ3twZtMr+AyaZyeKSfbZzv6U/1ti8ibPieso3orSAsHyO3cSLFZRdEFe0mQ25UMBo9HycjKIqEoRE3JlTKdERQ1wbZt1xII7EfTXGxd1i/zk6gqXTsH+euO53nG9jY+VVp5lOAgFfnyjbk7GmezOVmHqUu+qtviMTIT8uFSTwW+npfR0zUSqoIzLogMSpeJZpUPnFBQWr1MwsS0Zvm9AzlFjB8/AbPDycEs+cZ51luvkNnXxMQD8iH2z275e3t6ZQD4mIwxZEzOobPCQ0BP0JAsXZOzX+AyeWnOl27imR4niqIwOVe6cYZcdN48JxfePIVJo6NMSFo6NlRa2VSYDkLnmvh9nJr2BkGTnLQzM2UgsB6MIYSgxGbFpEDYaqXbm4WWUU5UgYfHlKXO11u2BGtV6dJLWHyUWFR69KRoKl3I5m7pXi1M7KbstFYUZwBTKJOsNnkd1A/lj0kyHNfUj0WVovDVuMqP6ztocl1M2PtpdBTszipU1UJjvVyFVFBSxYFghJgQuE0qV4+RVrR/HlpF98ZWECBinYhgNzfW7aDcbsFncfBXPs9Ay15iTU28s+AklqrSzfGTgI1rDspzN+j9LEVZc8nMmIvNXopdhQLRQJ3vICIqhUV2+sRUMWez2czYEimM64rkNda4dQexgDTXuHK7UpamudYYmmnY0pqev49is0JRRO7rBSGfO3NYhkWrpKojQU40TFSx8krrJuLxwWQmcEF7qJ56p4quKqRHBadkdKAoCsJsTZV16bR3Qlgum9/Y1UNPVH5eOEZa3RzudIrHyTiYIRedsK1hH9LaNbE/QW+PvH5f2Xk3363Yxrcvd/LKhQWE5bsTjcFxvLp4IRu0PH7605/y643/4AHrMrbt3U6sI0B+uh3Lhm4cq1t5s/dN8kLSCqi02FngTycuzPj4IuhgKXcz3TUGs9Bo62xn85vSghPa3UP7Lzcy8M8GRFTH5LXh6fGm4pr8NnkP2myzmN01ilVbHoRElFC4mDUlUgAuHl+N99JLAChrlGKsSe3inFceBuCJYjNmkxRB2/pWcOuBJnpjCcZZ4d537mLuvi2UdhzCjKA+EuXuQD9/P+UUduWXEbWnsX7p82zp2gJAjVrDa3XSalTjS0DpdHbnZdHkT6Mjmcm9vW14Sg/q86lXRsaodvdbmFYn+9PqTGfJ3Ax+OcZGh8eLKRFn1sEdRKNxQDD7wlFYKipwdu5Liaa2ZEHzTZs2sXOntJA2huX9V2a3Ej00SMIXgWSevJy+NKacfxvrPFOxZ0xBVTwoygDT552YggkM0fSRkp+fT0mGB3Oy/lxoS2dKNKl2E44pk1EcDhLd3UT2yEkvM+miG4pPGkpDcDiay0JQkQ9Gt6aQY8+l64/byaGIhEiweWAZgdZkBfJkcdzGQCPWw2Ka3MKHh/7UyrjDSVmaAgEp5myFKIoJocRJeCA0KkRr62MAjKn+CSXTP01m5hwckRy6d3qJBkyorgTBmQL7ZDkpB4N1skyCMOHsHo+CiqM3Wax1tEIi4SeRkJOF1XqkUBxiSa4cj+c6+9CFYDCewJ/0k+dGdVRrGoozm7yOtZRY5eX+uh6lYEDHEhOEhGDA7cWRdHuqQHhnMq4gucLKZT6dztW7Uhl7o2YrXV1dJKJJEez34bDZKPTKCS0qBMuTVrmK9uG3vFEks+syiqBpJbFkzblRaIyfJ833tgz5G30BWUID4PQ2KQKbMnLILisjUD2eqMmMNepj5rbXmLHxJ8zdIa0vryVXvxwumgBqPj2GN5V+gkoEizAxNl7BGUWfoy25cm4oqeXkHHl+NnUOB+irmsrUhQVc9MpdOENxeS0Kwckb/siiA8tS7dLSarBac/Cva6P1h2vpfWwvJgXKk/Foge/ciaKaeanMSmc8QYHVzFdK5Ll9JPt0eshg0NKHpiickiijJrOGUMcjrKh/CoAiUYdJySZz/wWUrr2dqRPlsdWF3iWakpamSOMgaa5xhLHxpOvk1PYmx5ncz1dpiFrZ17cPNZmMdXz1DHYkLZbjnHZGZ4xmau5UEiJB7wZ5/5iz5blQNm/m3rGlKELwtjKPt/VifM40fr5ELmM/izWEXuvk6oNRZndFQbWwUpyKL65TWvw5AOa64rzd8BwqcRICqr2zyU/mRCsrK2NclnSXbovvp7hmIghBpD+ZloImmv1SNE1zSNE16Euu+Mw6wLhkRvwDGSb2uDU0keBk3oJwAdHGQU5tl8+T9YlxNDf/g8aBRsraHHT4DrI/TX63ajCB99yzU6tvURTUSBhPJIQWlQG8D5hG05JbTEaWB4d7OB6raqa0mu9fuxohEhzoWU+Hko8iBCd1mejtlZafPKWHRZtzOXlHHh6hglkHPZ093lp83kwcMSeBQIAEOkIRbDTV0b+8iew0K5ouUE0b6A/5yQpLC5190IsrlsvqgTsIBypBU/BeOJqy62cyxSXdT8veWk7b7zbT89AuEj1hFKeZujQrLeOzUUKhlIuuM5m3zRLKJXPitTSvXgvAzoFxHMorRNN15uVn4T7nHBR3Btn28Xh0JwlFgFPFFgnR5tBYlb2evsgrvJ4Z401hQUXwvT1/Ixq2Mq6tgSXLn+GdMbn8sKoQd1QwaLeycnQtf118GX8LqMQtUxmduZCXX11JV7J8lfDXY8qrJZKsEdkaUNHj7fS2BogEYyQGo/S/WEeLKl+SakjWFAxlkelXyBvUESgkVI3KgRhz9m3hitWvMKXlALrVTr43Rv28RXz1k4OYPOFUOZU+RxpOp+zDCy+8QH9/P41JS1OJzZKyMtlrskhLCqOJu4PMP+sSSvNlWgen+ibKobc4UTFE00dIZWUlZW474R5pVQjt6iHhkxecajehWCw4Z8mVBUOr6IbSDgxx+IPpcIIWKcSKLAquHSVEmwaJK3FWtD/G/uZ1qJHgiPaHgocICxtj2EW12MXZPIfQ0tE02xH7HhJNCIEeDKGqppRFKlYqaB0tgwaLi64ip3JeKmjT7vEgdJWurcnSK2erKMnA4O6klUk1T0VN5rqxdcuHWiC0mUgynknTXGjacCLLdzM/w41LU2mJxNjgC6Rccx6TRlq6dGVpGaPwKAnSNYWEEPh1FVVAUX/S5ByMcN6nPi2/Z9bI8p5KOBm0q6p21v1DThAt2g6qS/OwtRwkvesQn73yCiZPmIL90H68+YU4TBqupBt1MKFjSQhKO4YDWMdYpUiqE1UkrF30ZUurUJXHwbwrrubsG76JOU2eJ3/YRbpFHkttOI43MICuaux1Z7IzQ05io3ta8LmksDq1V8YprPcF6I7G2d0rrSdDosnqMGMpSwbWB+J0hZrospvpdznQkO45GBZN2zq3kdCHVz9aSkvxDDRy6WtPYUWQ1vsHrnz97VTVeJCpBkJ7eul/5gDogtCWLoIbOlIuusa0XOIKPFQux/a64my+UZ5PbZqDkGbl93yFvmSuplnhKi7wnEJj0x85hLwHZhfNZ2zd78mqPx/d5mDmFCkq6kORETFtpmw7apoZ4joZpkm8yBJ8WjplNgv3jClGI8FqZS53Bs/jH3ueIt0vJ5784lHsHBJNSdfMpdWXUhTJxdPnAFXBMUO+VIS2bGF6upML++S4P+C8kru+8g16NAeF4hCL6rfQlxDEdcEPt0dwhQP0JCxcv6uJzJwl6IqNbLMgb48Uu/ZgHtP+mkvit3VcFJnNAq2WSm8lmqLRH+mn+CRpiQslY/ECgQO0+Fsw6YK8LHmvvLDrdGIJEw5rL4qrnYGEznOnSKvClMRW0hgk3J1OtHGAuZ3y3G5mGg1ND9DSd5BJ+z10hOrZlyav2epoENOoKoqLh60CWnCAaY0ObIGVeNQobbYMHjn/GtbMXUL0MLdX1YzZoCi0HdhL26EV7IhJcVw2kKAq6mFgMJNo1IZdhRKbFKKuYvkC19boBRRMtigr8lYwwzaKiyKzcWg2AkqEndt3oAxGyXRZsGS8RW4oFwUVS9yElrCjANkmafVyzS7AnGVHS7Ow4PrzcZns+JUwm5t3gqbgOLmA1QK2H/Kz/uVGNvXPJk9I0dTtkLFZCb8V1e5hbtfFhKMabySzX08xK6SbTWguF2kLr0Q12ZgeK0BLJAi50xjTIa2vf6yoZND+BkvnyED5ixreYPu6s6nTpZXXpggK8vK5IsvLl1/o5/TNPhyREH67k7emLqAv70ZWOa/k3olzEYqKMxKiXt9Pg3WAbFvx8CQeX40Qgvb6AfqfP0giFKVdk6KpdlIVUTWKCY24eZAbw3b+rzSX8YO/59vL11DT1kBRLIo5mRPOlJfPL5pfo0nr43uL95E5VFzc6eb8UaMoLCwkHA7z1NNPpyy9pVZLqnyOY2IW7tNLsZS6EZEE527rYGJQXh9ObSnsfIYTFUM0fcSEBwfpjbQRtyUQUZ3gJvnQVe1Jl1MyrqnnD3/gwIKFxP/+yIjv29NGrhQbQkvGG5RbNURQRcuw0Vh0gK6wvHHnnn/hiPZRJcqK1vU4VZ3vchvn8ByqOfuI/QIoViuYhhJwyptwyEXnuyxO3OzH4Shn1Kivj/ieI0O+/fXs9kJAIe6J0tn1CgBd3W/I/XhOQxfJJJgdUgz4BrYki3W+v5UJwK6pnJHMdPtsZ38qCDzfasZaIcWa5q3AUirHtS0mGJMs/TIqGQa10RekL2mdyjSbqJg8g7b1cizCLeMJdUfod8bIO3M6l1xxDblpLvTuDhrXrCSXIArgLZAP0mzLcGb2mqiGNSaLKgNMdElB2iCSFpJMKXSrs12YLBYqpo0jkZDCJhx2YbUEcKrgN/tSNeAe7wuxMS4ntXHNrXR65IRf7HEz3mVHB/7Z1UNdv3Rpjc0cS0s4yhVbDvCqRX5PC41jbf8brEvWLRwnNJzJ5KGjvaNxmBwMxgY50H8gdSxaejqa18unX32We0IryO18i8pDcWw7h0VhemImvQ/vBiGFC0D/CwcpTyZv3T8Q4vVcE81myDBrXF6QiVlV+M24EiwiwU5lIq97JtAWk0VeR3c1IUSM5mSG6ZrufKL7B8CkUnzNTMpsVhRgIK7TExsWeIqiYB8nr73+Q8W8hJysvuLWuTjXzdf5KVYRptM8kZWdC+mY9gVen3Me341aeboj+WaeFE0LShdwTnAeAL6iKM4ZtaAoxJqbabvtNj7zkzsoEQ0MKum8U1qJiQRf4h7iXQXkVaQzkKaSHoNb39Ex6/BG7wDj1tRxt/keHuNTbMsSBHFgCQy7UdKFg/jWXjSfoMIjJ9ZgmRNVsxLuleMdCByg1d/KrKgJkyVBNGhi2cBJtPbLsQpm7WRTSOelpBVgjvZP0FX89Rbi3SGm9iZwqgr9ipd9cQ85bRvwBMy05+XyYrEUuZNLZJ9KSoZf3LTAIJktHkyxPs6JPsKUg+sRqsrzOZWctXE/u4eC6D1eCquT7sU9D7MP+e/y7gQWTGQJd8ra5C71UzFtGtlj5Tns8sljsOzaT0ZAY3x/CenCwYwZMvfcdrWRwRXNnDS+B83WQV5AxjNpkVwgwWnu1aRpChFdsMMXTQlqi8PGwnPkgpOt1ia4vIi36wfpbE1akJVBGgNu3MmyMe2K3O879avQ42GybaNpClzH2tHS9bgwOT5CCNR0+VnujnWc9+xzZDQeZELDbhQh2O0p4YZpd+B3pePxdZO7O4GuxNA12a/8/AIURaGjbgAtLljYbeLqHas5Zf9Wqut2ktXXjjk+nOB4WjIh31umPTi9FdQORNBMJqKhBvR4E32rmwlt76ZTGyRGArvdztuV5XTaZIxo1NLHqWOy+WZFPp+MeWiNyTmiUC9E7ZWip95ayrYuaWnvtflx6LJNv91F9LbvcmZJCRaLhT3tnQSSYtnT4Cfhi6BYNGyjvSiaQsZlY1BsKnrABKgk+hvp2dxHYsuLkBg+phMJQzR9xIT9clKMF8gbJtYmb1DVlhRN8+ahWCzogQCxlhYc3cOZXW1W23sWOLSnD59KU56ZnC9NIm/KGEwWK3M+dRWzz1mC3W5PtUkoCV5t/CcWy3DCO81y5Mo8kJOQtUpm4O78yd0IIVLB4HoaIBTGjf0pmmYf8T1njty3HlcxvS0n98aG+4lEu/H5pPsnI2s+/qQbrt9vwWotQIgYnZ1SXFksRxdyh3NBrnSLPd/Zz6HkKqsCqwXr6GT9rqzRmIvlA7cpqkNcjn21kGO+cSBAT0y+wWeaTZRPnoav3s22B0az6/lBdEXw9qQerpr0OVRN49RPS/fKppefo3HHFnkcBdICkWUezrMz22pDFSb0ZCXyk/KlAOzUPAySRkMyJ0/V0MrApFBMxNPRdROKJUieWaVN7WdUlwz63TIYJA7khQbxBv105MoxNhcVsihLCupH6xsp6y8jV8nFZs7iU9vqeK3Pz1sV49ldNQlzIhPdfCbLXPINcWJzmMSgHDeTamJStgwsGYprGmJoeXjPvm1M2y9QBLi848nLXUK25wwij4OI6lirPOTeMAVLeToiqpO3TV7DB2MxHqyQVqari7JxJq/lSoeNG/Pk7z/lPItX7GHiagSf/TX8uOgV0lqSt1QKx/QzyjBnO7BpKgVWOYb173LROafJa/lXMY2oYmWM2ElN11r8gb1MFOu5VbkLVyzOnnQLz9SUs7lmBi/3BeiKxlGAGenJhRMJlYV+mbrgBfubqE4n1ko5qfc/8STugV6ujf0BTcgJ4JP6U5TRgB4uZ9HVNfROkSJsRkDnvDWDOGOCkK6zPZbN88qF/Fy7hRu4nxe8Lgq+N5vCO07GWuUBAf61bYzNkGJjz+ABNOsYwsni0YO9e7j+0MWcapFW4PqWEhTFRFm3XNHUmbWF9dkmBlQVVyzCJDZjGyylqE+uXnLkODgtWYtsE9MZ5+1g0JvG0+d8lkFNYZrJwoWzpGAbsjQpikKaJiBmpqTDQUPrBhYufZbzXnsEj6aywx/ijI372JHM8VQ142RAEIiuZ28ynknzdRDUBeXx3FRck7vEz6iT8tF1P4rixjeQi11TUCMhrmw9HRWV3rwQM+bMwqyZ6FH97N+wmxCvg4DiSDIVQySDfNNG3Kq0lu6J6Gx9q5V1LwznApo4cSJFRUVE9RgPPvkP6vbXY7KojJtTQMAmi0072qW7tp0C9JgDX6ifrW3Py/47z2TzGBnMvCA5frFmP4m+BEKPE2t4GyF0YoFecnet56RkXcmhnG2LVz6PvUslbpYvTEo0TLMYRAhB64F+AAqrvJQV5DO+tZ6F297mk5tX8blVL/EHdZDXpo3mL6dOw2XVEIpgV3oQS1+A0XlyDJzxHbj3yfutuzQZa1Rext/3/B2/KZnOxNpPUbV8Zk47qNLultdUsV5EsZ6FqscJRVS8ES+nFZ+Gw+RA0xsxJeIkNI32jCwCt32XM2bPZkOpPK+5fREan5RxlbZxGSjmZKUIjxVV3ZEa/1jjKvr2uzj4lJW+3/wQ8a4EyCcChmj6iAklRZMyaqQbTElamsz5+VS88DwlDz5I2aOPMP6BB1NtHFnvLSDULOiO6zREEmRfNQrNZaF69hy+8qAsDqooynBsAhBX4qxrX4eWLBcBYLEeuTJviPwf/ADMZgaXLqXvkUewJ9MOABTYlpCePvmI7zjzhvfnWKGhqQ78gb3s3ftdQJCWVkOmu4geXd7MLQ4nGV45QQ0luPtXliaAud40vCaN7licp5KWggKbGWtJsh6WuwDF7KDd1MtWx3CF7wnJkiP7gxHu3f4kAC41gdPjJbeiCj2mAQpbK31MnjSX8vRkqZnJ0ygZP5FELEbzLvlAGKomn3WYpWl+jrSAWQfL8M7xUpGXy6ihJJ2RybQq8nwMiaZgsAGAOEl3puan1KLSpvbhCQWoOkwvn5YsbtxSUsiAHXqmVaRqS20Oq4zvm8Ls+rmcsXQ1ewNhrMmH04qCcg7Oz0I1ZbMnR4rK2u44A68NF0wdctEdIZqSiQhDDQeZvldOBu7TT2ds+V3kLP8sYjCBOc9B5uVjUUwqGZeMRrFpFB6Sk+iGNIUDaRpOVeVzhSOToV5XVkyt2EhcMfP7edn8ZmYLUUuAtoQUcAUxcIR0LGVuXCcNX1cVSddf3buCwc1FLvaUO3gl34widD7Ng/T1bWbAJ1NaTLM6efCdMBc0hVnSGOTqgxFuPhjlHk8mb0yvpjws6H+5jrY71+H0WwipYZ7kZbZ2bU3F5Sk2G4X3/JJyJcSN/JQLu/dxtvIEQtc4+bwFpGXYWG2TY+gxqczsFfzf03185U0/31BcLAhuIVt0EFRc/MNyHbvjMRSzimuWPL7g+nZq3NIFWdfQjKLVEOmTxxtXepgfmEhennzpivTO59NYKOmRE3oiYx/rR8vJcLp/FyYS2PtGk2uSViNrmZtFyetlE7MJ2xw8df4V9GsWqp02/j5rNPak9TE9PZ0LL7yQiy66iEmnSUtNdZMLf2cIgcL0zn2smDmWWelOIrrg7mSpmaqZs7F6Y8QdcRoVee/EEhvZFUpQpGfQ35+PrqvYPFFiNpkHaWCgAlCZNnMWJrOFakW62V5IW47D4aB2StJ9TAOj9meRl8hDiysoQkP19+BJWBC4UbR2SpfIeL0NLzfw9M82svWNQwT6o3zqU5/C68ohrsfoz9jOmLOclE2GqFNep6Ma2lGETlBxItJrsdpt7IvX03noDTZ7NcIWMzmKkrJG+tdKd545KwaxIK0eFygKBVXV/N/44XJP41rrydZ1iDUTTZeuSC0cJNFSyO3Lv0/LPvnsyqt0Y82U5znhzQVNI01TOGfOyUxMc+AwmXD7O8hLpBNXdN4+bR7e5avJt5YwP/dMLJqJRH8TdR0yUPsl30t0BjvxxqWwilsGEclyWuHdrcQsZkw6ZAs3Y9KmkeeQ11RJoISbp93MF0d9EbOawJOMxW2dfQr6wAD1jz3L7oIyAGY1rKchvocwUezjh+/teE8Pvqd/R9+uJ9lnrcN508VY81wkohrt9z9Oy9dHeidOBAzR9BETHpSiyVroxlw4nBByyD0H8o3eOWsm9tpa3LW1pCXFkiP96PFMACa3k1X+BDvDQTT38LLlwy1Th4um/PR8dKHTGx+OQzjayrnUtgnjyfnaTQB03vUT7L0e+bvNCuVF1x31O9bcHEq7feT1+/FmllFYdDkAXUlBlJV1Om67madMZjr0CFuKy/BmnARAIiFvUKvlX4sms6pwTo7sz1qfvOELrGa0dCuR+HCF9qXOg+zNXp/6f4HbRllyVeLemBzbDa1vsrZtLZXTZWxZd3qUbaN8fH7851PfUxSFuZ/+fCo4H8CbtDQNueeKbRZqi+U+PaF8epNFfTMV+Ra4NXg+EcWGJuLka8m6Y0lLU2vS6tWr+LFqCboVec0sSVrUNAUuKU5amJzFXH2DxjU99/Lstp+RFosQ10zUZ6bz5pgpNDjcWOIxztu8gmmNMjj8kWzB6nF2upJFlif1JwhsaCfaIsd8cu5kEGDflaD9lxvp+uM2AhvaMZfIiS+9rpsJjQLF4sJUOJuuP2wn3hlCc1vI/Oz4lNXU5LHhXVJJWSDpfk3WnLuyMAvPYQWmAWy2bL7Ab6gR20hoKn93T+I2fsK6wfMBGNUXRzGrZFw0GkUdHvehIPN3B4MD/LJabpsfPEA5dYTCBxhI5meydpVSFhR8om4bk5b+hiub+rjkQIRTHmsg62/7aP/ZBvwrW9CDcTSvlRVTdxNRY/xj9z/IvPrzeC69hLKH/4H7jDOw2XKZwkZODz2Bio6aKGXUZOl6Wjuwnn02eV7POreMvHI3nq4o459p5vbtce7iJqrEHkLYuHjLQfYFwtjGZqB5rOjBOJO65MTva4qgaLmoWj5KWD43DhW9TMI6gBqz88mBWVyDDau/CFVksFWdSF2eFTUhWKTKUiT0DItNS6mbBRluVKBJKeLHfJ8uWz4FFpVHJlYccX4mTJjAuHHjmLhwMYoCBT12qpqlBbCorIhcq5mfjSlGAV7rGWDHYBB3Vg6VczI4QBU6Gmq8m+aMlbTpAiXmxBx34PPJ67jfJ3NfNTZkoCgK02fN5uRTLsZpSieih3haeYU9vXuYPVu+VDVrPcztm8GnM2QsoiVsZ4I1RKVbrs5d2/0WJeOtnPSJSlCg7YCPt5/Yz19vXc0Lv9yBdqAKSzgDFJ3la1/msUf/DoqKFoyQHa4lW8g4sT7bBE6ZII9zZXQ9yxwyDnFWS5RYix89HCe0VbZNP3cSqtNJq1een7GnzONkj4uzs9MZb9WYVbeTuCeLiGMQT7LwpxoO4hCVDLzupKVOPhv+b98X+G39b4cHPxGHvdvwJ11ng73dDB5sZHSfGa/uJGS1sH/xEubkX4JZtdIZbqF5++N0JZ8je5W9OM1OpicWoCasCHS2rXuHZX+8j7ft0lJr8g8ghE6OvYTCmHxRqQhXUOgqJLdfnqPMpDW658rPInJzuPM06fauaWsjf6CbXeYmHrWu5rm1bxEMBmlra+O13/+el09bzFOTvaxU6vnT8g08N+t61s/7KjvGf57eyecdcd9+3DFE00dMyC9vOrvLjWPSsOXocNH0brKSmcHfXULlcMzJBJQWNQzq0U9rfv5w3MSU/CkA1PmH3X/OpMvtvci48kpc8+YholH6v/4bsn9kIuvnJsyZR5ZeATBlZlLT0s2Uxg6c06ZRUvy5EYkPs7MWYNZUXrOZuFCNEPZYUpamISzHYGkCOD8pmobIT7ptBhUpmoQQdJWbqMvYgp4UKS6vjQlO2S5ukS6XaLSLa167hk0lXUTnl/H6tA5mFs6iJqtmxP5zy0cx7pR58j+KkqokX5GcxBdnuXGmW1Htukw02B6iK9jFwXbpdlzmkuc0lzZW7LwdgFDS0rQrKB+OA0qQFrUXoQi8Xi9XlBcw3mXnuuIcJo8qR1VVtLBGmaWcweggWzZuprhTuvGWjZnMwZwiTAiu7GkiKzjI1V471ydXq70xQb4ll5nMFI7NAiHjj4QQ1Jiq+eGhL3N1w/nEO4JEDvroe3I/kUNjsE2/ltmJ+bhn3ojzzJ/hXzVArD2AYtXIvKoGU/pwwWAAR20O+eOySI8m40oEfKH4SIupqlpxKFFu4fucGv4bLjFIo1LBy14pGioHE6SfVY4pc6QLeGi8321peqHLx0YRx5YQXNsgLR+61o4vmQncdCB5zbrCBOMD+KYGcc6Wn0UbB0CAdbSXzCvGkXfzdOYkLSxLG5fSm2kh/3vfwzZOWoHS0pPFR3Plm31ukby3fBEfDQMNbHDJz5W2AJ/4+hRmnltOuVXF3j8atXUcnwv9inEOEz2xOBdvOUhjJIpzluxLxk4TCLD1SDFRM+p07EH5cjNQLF8+LMEaOpKrYjeTwJ45l39wFQCzDg5SnCZjU9p8w3X0rGXpZFpMjIlJ60qTUoZTDPJj94sU2Ibv0XfjzsqhYrw87rL2ZKqBCVKoVDpsqfvwnoY2/P59pJX2pFxzTv8BPjn5fMadUsj2YIxCPZPensOz2Nvp78+joqICt9tNqUPec03+XZS0WnnuwHOsG1hHi0Ne4/VKF6E6KfSnqvmMTp+Cqqg0BzZQ31/H4z+4hcqpDq788UmccnGVzHCvQPchPwoap0xZTFVpCUIIwsml9I7wGA6E51Aakb9xSBnNpNCrZFkiJFSF5QXSKnxSZ5zuB3cy8GYTIqZjynFgG5OD9okL8DlsKIpC9ew5KIrCn8eX88caF+1WuQIznFdIr0+mNbAkEihaOlU9U1F1jYDZR7vWRMQZScVCFppAiUXY+trLAOx+azkIwUF1H6fGakBAnSVBh+qjKbiPFW2PsLEqB6GqmGMhbpl1M8+c+gSBg/vRAvJ5+M/HHmbT668QTc4pjY5GHs35C0ILMT5QjlloqFGV+vp6tm+Teb9y+uW8smKwnVfv/CXNOZk4wjrfOOTn3FHzyNTTiCsJ9h7ayt13383vf/971kWjDKTbQAEEJMwBfLYG6vI62Dta5cBA+3teax9XDNH0EaLrCSLJi9aeliZFU/Kleejt/Ghkl8o3fFfGkSVUhsiomYDT5KOkIPCebQ63NM0qkpaU/YOdqc/SXaPet/+KopB/548x5eYSa2rC3KKiYUN1Hn11m3ZYyRfHtGlYrTnk538SAKs1H5dLxmu4bVK4uO1mrNbcVGFSODZLE8Bsj4vcw1xjBVb54O8qk29J+2MDXHjyScRMEXYVv0Vapo3isRmE/EkXlCJvhUkZZQgE9+/8PQ/bVhC26lw94WqOxsmXXoHTm0HphFrMycSYVxZmcf+4Um6tkMGdjuQbZaRT4QdrfkAiJFe1hZNFawtoIdb7PP5Ib8rS1K4nQBMIBXZrMo6nrKyMbIuZ16dXc9uoAqxWK0VF0rr13arv8qtZv2Ji/0TKeoYEgnRN3juujDsuvZBvf/vbnH/eeXynIp/PH+YaOyk7jfQzy1HMKtGGAfqe2MfAr3cxNTCOqBKjfVoE9+IyTDl2EArmwmnYJ1yMKWcciqJiLnSRfmYZeTdNTRWPfjfe80dRFpETwCctDnKt5qO2s1vzUIBr7K9zN19lYXKSARjvtOOceaQ4H3LPHR7T1ByO8u1kAsHPB0wU9cprwOrxEwzK/EK2/gqso9Jp7ZTu2oySYrznV5L5mXGkzS8m92tTyf7ceOzjMlFUheqMaqblTiMhEjy+9/ERfXA45X2lmWUf3Olyst/aJQXaoRzpignv60dBoaY0DbuqEEOh4e0vEnzhR1yy1E+p0GiPxvjklgP0T8gAk0KiNcRcdSa5g2UAVKplmP35rOYUVttPlVmkJ1zIIxPSuAI/31bDrDSfTbtSQJo+wKKurSiKwOEop8U6wJaeZbxkeoVdiX1se+NVsjeskH3XI3ydOzF3/QW/fx/vJhisJxSSwcC151wyYtsvAi9z/9b72dzwMGeEZTLYl7p8PLXuGsLRulQQ+LjGdq6deC1TFpfSFzuILRCkt3c4xUl/XwlCaIytruGlezYT3t0PQN3gNibv8/Dinuf49lvfZl+67N8BrZ32AWnlKRW5DMQ7Sdduo8b9YzyZXnydHTxxx61AkEnzi/nE16dy1V0nc+qnqpn9iVLigRW0vfo0lm7pXvO43WS5KokmbFS3ylVkv+wt5UdFn0F4z8eX5qXH40HRdcYFw+j+GP6VUlw5Z8hr4GCmvAdKJ01JpYcRQnDHO3dgCxSiBeOgakSSVqD8/FymnVmeGgNvuZUnz3uSFZev4JKLL2HJkiXMO1Mmkd325mvEohF2rZQFzTvH6dgVE2MSUrSvSNvDk9oqdBIMupPPQv8gB+94kIdv+iLRwZfQkvmcdFc6uU4PejIX1bqKBh7O3soDc95ij72O8oR87r7w2LMEg0GcVjs1AflM3eET/CpZqH3RJh8Ff7uTvIMaS6LTmeSYjhaTYhpdxRLOIK2/jLDbxu7xe9ji3YaWPvSiOoAW233EtfZx571naoMPnHAyOSSA1elCM5lw1OYQ2tmNpejoEw7AlLPOR1FVJi444z3bWL0errh3Cephrot3k5WVhclkIh6Pk5+ez8TsifgCMiA7qoP7fdxzQ5i8Xgp/9lMar7wKdB1TZuaIulCHo3k8qC4XejCIY4ZcMl1e9hXC4Tbycs9LfS/dbqZ9IExaUjh6vbNTk5vF+q8DwQE0ReG8HA9/bJZWmoKkECtZMpHf/3EjE8+s4fyCQsyqmbfzn+XWL11LQOtnW9NjkHNbaj+fHn0upgIPP3znh4QTYcZnjmdG3oyj/qY7K5ur7/0T2mHB33ZNTQWmA2QVuvA3DJDoMfF283K8iTx8QiCSx54pWnGpOi9t/ga5kQYAuuIqs3Iy6G3rozW5ZLgsGU90OKNGjaKpqYm6ujrC4TCKrjAzzcYyJUFIaHynIp9PJPtiMg3f6ndUFaIDf2vtZkmOF5PHStqpRQy83kRwkxTRnZkD3Jr+C+YUzWfarIWkzSsieKCDjlvuQ3UXkejaQ+43r8B1ypGxbO9GdZj52oRiHm3q5ltTjzyOIdLshfSGG1B0P+ko/KF2Im9vVdjY2s8Fi0e65YY43D0nhCCY0Llyex1d0Tg1Lhtfzs/Gv6YXJWFGNclgbXMoG1PMjfOkfPpWyEkvo0Be+/aaTOw1R385uXzs5Wzo2MAT+57g2onXYjNJq4PJMrJ9WpoUTUOrj9LLs1EbTOjBONFDA/hXJ2vCzS2kNirYvryZWHOQTzwf4q8L3DQDi3cc5JvTvCx4p5cLeheyN5RDkVmhL5LgBxkXsC65smufPoa/lZ1DxaFWnkKnIMvJH3tktu1Llb+TnS+tS20JF6tCm5k4kM4ezyDL/76e6Wtt1JrM9JTnckh5mZJiFSI6Bw/+lEmT/ghAIhGmru4XNB16AFW1UDvpQUonTsPl0vD7E4QsCTbGdtO/ewcV2RHSFJjOKNYrs3iBS7jB/ip7AtWgwJi9hxhoaiFvVBV2Rx0dXS2E3VX4/V5crj5aWvKxWCw0vh3H0RBEdWhE7Cb0dHB2mSjer7C9MsrEqokUaUU0N0th7EnY2d6zktM/XUbaMvkSdNG3f8Bjd95BX1srT/7wO9ScugBFVVFVFRSFra+9TG9rMwowfdIEKuefQVZOLrtXdLP+xXqm79II5G3lGeckflP6KbI9cUo75LVb2N7EhoP7Obv8ZLRAAkwq9tpMXvv9r9mxTGbvbq7UeWjnQ6Rb02n1t7K5ZQtXdH8SInsYsPQgTGaUWJTC6kqmn1POoV29dDYOcvLMWqoz5Lkdl7Rk6okEaZnZDPZ08dbDD9LT3IRmNlN90im8NrCaRb0nc8DSwWA0SE+eSlFnIQlHMp4zMJi6LhU1k7ycGhoIkrDacQaSws3rpTCjkHpfPU92PMvTpSr3KD9kX30b/VH5/YpANnkWld8B/uTig8rOGOd3NmJx5yPCoCg6Z31hPqF7Munq7EFLWMnt3MTacS9w8xf+zp7ePXz5zS/Toh3iidN/QOuWzVTNfO857eOKIZo+QoZWzlnsjlStLO9Fo/F+sgpFe2+jn9Pj5ZRLr/iX+38/wSS3qyxYsIDW1lZyc3NZXLqYx7fJh0xnXMVmsr/v94dwTJ9O1vVfovvXv8F82HLkd6OoKsW/ux89FMKcXOVlteZQO+lPI9q5k67JIYtThvckWlr+LtsftrrvX7Ekx5sSTfnJpG/VhW7u+N5waY9xmePY2rWVLV1b2NixET18EFXE0RXZhwyzidMqz6cms4an9j91RDX0d2OyvLcrA6CwNIuGVQNkBPPxBHO5tO7r/C4jkYonKnUXwyA4/ctJqNIa47CXUJUxmrVta1P7KS0tPWLfFRUVLFu2jN275duayWTik+efz1STjdZIjHOzjx4DpyoKd44u4vuVBViSrlzX3CKC27pJ9EdIP7OMXXk7aFnZmQoG14XOD9p+yXkNz5PhB5GVgfOkn7/vsR/OovJsFpW/vwA+XCBnZS3A4Shj0WxYxJHHPkSp3YIKBBM67dEYt+5rYac/TJbZxEMTKvBYzITT7Vj9xYTTZRoGW38FWoaNsDdKIhbDZLbgzv7X4vy04tMocBbQGmjl5fqX+UTVJ9jZs5MHtz/MGclbRxfwwN7XuLq2KmVpmpA9EWuVl9DWLgZXthCp84ECaScVMttjZeoZpex9p53tK1q4/M1BHp3rotNj4lvpOrOn2vnG7iJsSpTOUjPfnOCg3+LCLKLE0VipzudLezq4rEBOkvHKNAYTggq1lbmJZVAmr6mXWvcy4JDXQ1lPGuZmWa9x0slzWO94AUv/AVz53yHReBvdPW/S17cOVTWxa/c3CAbl6jNdj7B12zVMmfx3pi86i2VPv0DNqBJ+OOM8LO2/wgTsCGmEQ89D5izeFrNo6Woi7rJhiYXJ6utkzZMPs/iLNzLQuRehJ3DGHezeNZcM1yC9vQWUmXLxNgUpTJap2dsToWLaeWx55U9MqEsnw5PDEnUCLT3Dbp1QXyPZ8z9Fem0ZvO2E/Em4C8u56Ds/4rHvf4ue5iZW/uMvR5xPp8fL4utuoHzycGmnMbNNrH+xnu7geH7yzrWckjWf71ZfR1e6ia50aRUrP7SPUGAlrx0yc/bYOTgmZPLyH37B/rWr0RGsmdDL/uCzJEv1AVDVOw1LwoYjo5po6z8IFVVi8veTWz4KTVM574ZaWvb2UzYpi3ejahqTFp3F2488xOZXZHzaqGmzmFg2m3NyfsQ/sl8hbzCf2t5aKuLFOAtmMGBbK1cWDk6i4uBr7D31GwQCKvMuncjLbz1OZ2cnu0bJ1ZHVEyfic0Wo98nzPCl3EnMXncW6u3cRisgUEjV5VQifGVNCENcUTLrgrn1RymsnoyfnrljTelo/fx+LvvV9Xn9kKyWrXsJvOsDFv36AXGcuOY4cJmZPZFvXNh4JbOGWT9/6L++5jyMfC9F033338dOf/pT29nYmTZrEr3/961ROjqPxxBNPcNttt9HQ0EBVVRU/+clPOOuss1LbhRDcfvvt/PGPf6S/v5+TTz6Z+++/n6qqqvfc50dBKBkEbj+s2KR8e35/sfNBMhRICbCobBE/3XA3D/da6NWdXPk+4uDdZH3xi1irqrCNHfu+7Q6vNfdeZDgtI/72emeiKBaZ6sB67KJpitvBV0tySDMN5x16N7XZtWzt2spzB59jQ/sGFBKMcWrsSub+zEy6+Cq9lXxzxjeP+bffi7wiaenJ8Zdy4a6bIGamNAhdST2zpOpimjb8kjQ1mTwurvDJMZ8iLzLsSvV6vXg8niP2XVAg3XSRiPzu/PnzyczM5L2duCOxHBb7plo0cr8yGRRQTCqTg1J07uvbhy/i4651d/Fy/cvMyFDI8AsyFi1GeY/Yuf+Uw12xxcVXHfMxFNksNIWj3LC7iZV9fiyKwoMTyilKxuY4puZgbSsZFk2+Cpyz8uk4JN2h3vwCVPXo18vhaKrGZWMu4+cbf87fd/+dpoEmHtz5IMXmaEo0dcYV/rL7YZ6ue4lIXJ6XSTmTsI2Woim8S7rq7OMyMSULOltsJibMK2L8qYW07uun9q1m/t7iZ8UYG2uyTFx6kpPaHitrc+Q5ccXb+I72E9rJ5z5u5qUuHwOeBF+/ZAI/7pcxil/Lakbt0FOPloaomatmfJrO7a9iCwAoNOUG6RzbxqGkxaYsaxah2CW0tDzMjp1fJRrtAXQslhyqR3+PQ80P0d+/li1bP8uUs/5BeuVkssqz2LH7SsLESHPXMrnyS/haVlHv24nfWkODQ5aimeDU0BSFuk3rWfn3BxB6Akd6EYPhTMKmIK1hKfrGBbMosMlzIRRojukk1qfjLSijr7WB8nUxtvA8AlAratAtNmzxCUw7azqkW+ErG8EiXU6evHwu+d5P2PTyc0SCspqBnkggdJ20rCxmXnDJEXnv3Jl2isekc2iPjx2hM2H/DK6r8/H26RmsTSaSnV+cSXAzDPhe56l9EbJ9/bTs3k5CFayo7SJ/8kQuS6/AF/Hhi/rwhX1MqzsbgPGnjmPrq3b6920BoZNTIWMprQ4zFZPfW7hPmL+INU8+TCImraU1c+dTklZChj2D3nAvde46psSmoA/qBPP3wgCY4i76vVPYNG0KBEAzqeRmJCgymegEYskXvqrRo7EoVv62628AXD3hajRNo2ZCDRs2bKCwsJAx18xh9+pWstpbafeaWLI/QllAR+wdQLFI92Kibzexuh0Er7qYaiWBLQadN57DokIZCqIoCl+d/FWufu1qntj3BFfVXEW+6+jxsB9njrtoeuyxx7jpppv43e9+x8yZM7nnnntYvHgxe/fuTRVSPZzVq1dz2WWXceedd3LOOefw8MMPs2TJEjZt2sT48TLB2N133829997LQw89RHl5ObfddhuLFy9m165d2GxHZrz+qBiyNNlcR09Q+VGT58xjSs5U1nVuIsf+3ivzjoaiqrgXLfpA+vHFeZVkp1k5c7wUCmazh9raB0DomEzOY++TonDrqPdOmwBQm1PLQ7seSllx5hXNIzMzh11BGRuRYf5gb4mMAtl/a8IOCcgtd3POzCI2NMk35dEuF9HizxNokatlehMal1aej7972JV7NNccgKZpVFRUsHv3bgoKCpg5c+b/V18V87AIynZkU+QqotnfzDWvXcPu3t1oikbepy7H+uxavJ/+zP/Xbx0Nm00GBbtcY/F6Zh3z9yrsVprCUVb2yTH7xZhipqUPXzfOaXlYHxl2PVt8Jfzjz98gGJaLMjIKj70O1gVVF/Dbrb9lf99+9vfJvDST8+eBLoOyCzJmMioU4KBPupftJjuVnkqU0fqI/ThPOvI6VRSFwmovhdVeTo8meGtLBz9q72BnmsLaHDOKEFxjdnLLnNNZt/bnlCUOMHNsIVfv7uCtfj+rAB04L8fDaQUT2Czz5jKYUPnBqfdT6xrH7/78KgDuihJWjV5DpFEuTNAUjQJXAXrZV2lvfzZV9zEv7wJGV92G2ZxORsbJbN78GQYGt7Fl62eprf0Lu3bdSDjSit1eRu3EP2KxZDC3ZAFn+fycs+kAQpXnYVFhCdVzTmPnijfYuUImtp10+kLWr9IIImOlrLqVwaAbf4WL3GIX1iovRW+1Ur+1m4SYR27FGhzpaXjy8nFl5NK0L8qh+gSVNWNxDi1AcI+chD25ecz/7BeO+fwCjD25iEN7fGwOXABAtlPlsfnjWB0O0ReL84l5k7i3dTvxDQeI9r5FSy/oisrWsRpnn/5Zrpv8BVRl+F7ydYX4+4trQIHqWXn0tdTie+OfmK22VKqSf4XDnc6Yk09l5/LXcaR7KJ04Wa7iLZrLswee5bMTPst5nvP429/+hm+gX47v9BqsbR4ObOlBKBqevj00LPgizoJ8mCuLNtvNZvLz88kROczMm4nb6mZOoayBOnfuXEKhUOpFO6sojbNeDNCYY6ZqX5g7Rz3DJwdnURUuAZvGqpvGY/31RibtjWID+nIdnHv1j0ccx8z8mczIm8G69nX8btvv+P5J3/+3zs3HgeMumn7xi19wzTXX8NnPfhaA3/3ud7z00ks88MADfOtb3zqi/a9+9SvOOOMMbr75ZgDuuOMOli5dym9+8xt+97vfIYTgnnvu4Tvf+Q7nny+XK//1r38lNzeXZ599lksvvfSjO7h3MSSaDrc0HW8Wly1mU+cmHOb3LlXyYVNb7KG22DPis3evovugGErcCKCg8NUpX2VPzAl8OKLJYjPhzXPQ1x6kfFIWp3++hj2RCDS1U2G34jJpzKi6gRdbHsJJAKejArfFjT3bLgulCvGeoglg3rx52Gw25syZg/YeiU//U6bkTqHZ35wSTHfPvZuTyxaRXJj1gZOXdz6hUBP5+Re+r0v03ZQ7rCzvk/fWV0py+GRexojtpgwbDocs0YOu0doymBJM9jQ34+bOP+bfSremc0HlBTy852Gy7Fl8Z9Z3OK3oFJYtl/EnFbmn8eTUq3h6/9M8vPthFpQuwKSaIA3MhS5iLX5MuY5UUeH3wmzRmD+jgHnRXH73l40s9yh8plfhnGsrUTSVU2a9hBAxTCYXT1rTuHzbQXpjCWyqwm2jCvBY8kGxgoiQm3kys4tORgjB6NlzCA8OcO7/3YK7+SV+uPaHABS6ZLwf1myqq++gpeVhSku/QHbWguFxNLmorX2AjZsuIxDYz9p15wA6ZnMGtZMewGIZHvdp6S5O9aaxInlepqc7GfeJS9n11jKErqMoKpNOX0A40spb+3Yg1ARaMIfYuCyqrxmfOv8LKz08dfdGelvBk3spBWMyaN7bx561AwgBFmDMrA/WWlFem4XVFCUSl5aY+VeNx+Y0M985HLv4xf+7m+/c9ily68Kg2LC5LuCk1nzsj1l4e98B0rPt2NPM2F0W6pIpCYrHeHFn2qmYMoPtb/yTguqxx2ThHGLWBZfQ1VjPpNPPTIV3fHvmt7mq5ipGeeTimbFjx6bc9TUTq6lYUkHVL//Ivpe2kN21BRSFwjQ3SjKusmrsWFRVRUXlT4vfFTbhdnPRRRel/p+R76S4X6ewN0xGgZOxF57H/y3/OtP8NfRYfRxoaYILBOccyGDJnjRqbr4dzXTkoo+vTP4Kn3nlMzx34Dk+N/5zlLrf2/3+ceS4iqZoNMrGjRu55ZZbUp+pqsrChQtZs2bNUb+zZs0abrrpphGfLV68mGeffRaA+vp62tvbWbhwYWp7eno6M2fOZM2aNUcVTZFIJOXiABgYGPj/Oaz3JDSYrHjv+viIpvNGncc7be9wSuEpx7srHwnZjmwKXYW0+Fs4d9S5VHmrcEdiWBSFDLMJx/vElv2nnP75Gnqa/YyemYeqKkywOHhgfBkltqH6diam1vycHXtvZ8G4bwOyyn1VVRUtLS1UJjNQH43c3NzUy8EHzeScyTx/8Hk0ReOuuXexqOyDsSy+FyZTGlVV/36cw8x0J39p6easrHRuqTj6BJpXexp92+ZhCebhvXwBk8suxenNwGI7tji+w/n6tK8zI38G03KnkW6V4sdsziAW6yXNNQ6TauLi6ou5uPriEd9zTs+jv+UA7gUlxywKVYvGZ8YVct4r9WR+Zmwq9lHTrECy3InbwbOTq7jjYCsX5HopTl5X2Zlz6epeSnGuPG+KonDujcMu50vGXMK+vn08vu/xVI1CgPy8JeTnLTlqf8xmL5NrH2LjxksJhZtQVRuTJv0Jh+PIie/GslxW9A1iUxVq3Q4cmouaUxewY9lSSidNxunxMu1MGxu3FhK2dpHvrmT+FWNHjI3FZuKsL07gibs20Nk4SGfjcGCzJ9dB+cQsRk05tsUix4rJrDFmuoeta4JMmJVGybgjHd4Wk4XLbvw23334qxTZSznbWk379iBBX5Tty5uPut+xSeviqKkzOP/m28gtf//Vyu/Gk5fPZ+761YjPbCZbSjABLFq0iAMHDmA2m1OZ3Mu++jkyRr+KKePT2CZMQHO5KH3wQRoaGhgzZgzHimZWySxy0dU0yKT5xYwrK6B31i38aO2PAFnn8qqaq1h0xSIpwN+D2pxa5hbNZWXzSu7bch93z7373xmG4484jrS0tAhArF69esTnN998s5gxY8ZRv2M2m8XDDz884rP77rtP5OTkCCGEWLVqlQBEa2vriDYXXXSRuPjii4+6z9tvv10gS8qP+OPz+f7TQzsqa599Qtxz+QXi9T/f/4Hu1+Df4+l9T4ur/3m1aPe3pz7b7AuI/YHQcezVkSQSCRGPx4/b7wdjQfHDNT8UKw6tOG59OBZ0XRf7AyGR0PX3bhNLiJ5Hdov+1xo+lD4cav6H2LnrGyKRiL13H3Rd6NH/7Hzqifc+tvciHG4XLS2PvW+fYomYWNqwVHQGOv+tfQeDh8Tu3d8Wvb3vvG+7p9p7xRvdw8/RkH9QrHr876KvvS312aZ/NoqnfrpB+LqC77mfln194vEfrxOvPbBD7F7dKgZ7P9x7NR5NiOY9vSLxL8Y9EA0IPXndxWMJUbe1S6x4dK949Q/bxTO/2Cge/v474s9fXyme/tlGEfsPz/2/S29vr+jr63vfNv39/WLnzp2pvh8rnY0DYtuyQyPGZVXzKrGubd2/ta/dPbvFDW/eIPb17vu3fv9Y8Pl8H8r8PYQixGHlwT9iWltbKSwsZPXq1SMClL/xjW+wYsUK1q5de8R3LBYLDz30EJdddlnqs9/+9rd8//vfp6Ojg9WrV3PyySfT2to6IpnjxRfLVVCPPfbYEfs8mqWpuLgYn8+H2/3Bxx/peuLfMssaGBgYGBgY/GsGBgZIT0//0Obv45rcMisrC03T6OjoGPF5R0fHiESMh5OXl/e+7Yf+/nf2abVacbvdI/58mBiCycDAwMDA4MTjuIomi8XC1KlTeeONN1Kf6brOG2+8McLydDizZ88e0R5g6dKlqfbl5eXk5eWNaDMwMMDatWvfc58GBgYGBgYGBv+K47567qabbuLKK69k2rRpzJgxg3vuuYdAIJBaTXfFFVdQWFjInXfeCcANN9zAqaeeys9//nPOPvtsHn30UTZs2MAf/vAHQAY73njjjfzwhz+kqqoqlXKgoKCAJUuWHK/DNDAwMDAwMDjBOe6i6ZJLLqGrq4vvfve7tLe3U1tby6uvvkpuMoN0U1OTTH2f5KSTTuLhhx/mO9/5DrfeeitVVVU8++yzqRxNIGOiAoEA1157Lf39/Zxyyim8+uqrxzVHk4GBgYGBgcGJzXENBP+48mEHkhkYGBgYGBh88PxXB4IbGBgYGBgYGJwoGKLJwMDAwMDAwOAYMESTgYGBgYGBgcExYIgmAwMDAwMDA4NjwBBNBgYGBgYGBgbHgCGaDAwMDAwMDAyOAUM0GRgYGBgYGBgcA4ZoMjAwMDAwMDA4BgzRZGBgYGBgYGBwDBz3MiofR4aSpA8MDBznnhgYGBgYGBgcK0Pz9odV7MQQTUdhcHAQgOLi4uPcEwMDAwMDA4N/l8HBQdLT0z/w/Rq1546Cruu0traSlpaGoij/8X4GBgYoLi7m0KFDRg27jwhjzD96jDH/6DHG/KPHGPOPnv9kzIUQDA4OUlBQgKp+8BFIhqXpKKiqSlFR0Qe2P7fbbdxkHzHGmH/0GGP+0WOM+UePMeYfPf/umH8YFqYhjEBwAwMDAwMDA4NjwBBNBgYGBgYGBgbHgCGaPkSsViu33347Vqv1eHflfwZjzD96jDH/6DHG/KPHGPOPno/jmBuB4AYGBgYGBgYGx4BhaTIwMDAwMDAwOAYM0WRgYGBgYGBgcAwYosnAwMDAwMDA4BgwRJOBgYGBgYGBwTFgiKYPifvuu4+ysjJsNhszZ85k3bp1x7tLHwtWrlzJueeeS0FBAYqi8Oyzz47YLoTgu9/9Lvn5+djtdhYuXMj+/ftHtOnt7eXyyy/H7Xbj8Xj4/Oc/j9/vH9Fm27ZtzJkzB5vNRnFxMXffffcRfXniiScYM2YMNpuNCRMm8PLLL//bfTkRuPPOO5k+fTppaWnk5OSwZMkS9u7dO6JNOBzm+uuvJzMzE5fLxYUXXkhHR8eINk1NTZx99tk4HA5ycnK4+eabicfjI9osX76cKVOmYLVaqays5MEHHzyiP//q3jiWvnzcuf/++5k4cWIqKd/s2bN55ZVXUtuN8f7wueuuu1AUhRtvvDH1mTHuHyzf+973UBRlxJ8xY8aktv9Xjrcw+MB59NFHhcViEQ888IDYuXOnuOaaa4TH4xEdHR3Hu2vHnZdffll8+9vfFk8//bQAxDPPPDNi+1133SXS09PFs88+K7Zu3SrOO+88UV5eLkKhUKrNGWecISZNmiTeeecd8dZbb4nKykpx2WWXpbb7fD6Rm5srLr/8crFjxw7xyCOPCLvdLn7/+9+n2qxatUpomibuvvtusWvXLvGd73xHmM1msX379n+rLycCixcvFn/5y1/Ejh07xJYtW8RZZ50lSkpKhN/vT7W57rrrRHFxsXjjjTfEhg0bxKxZs8RJJ52U2h6Px8X48ePFwoULxebNm8XLL78ssrKyxC233JJqU1dXJxwOh7jpppvErl27xK9//WuhaZp49dVXU22O5d74V305EXj++efFSy+9JPbt2yf27t0rbr31VmE2m8WOHTuEEMZ4f9isW7dOlJWViYkTJ4obbrgh9bkx7h8st99+u6ipqRFtbW2pP11dXant/43jbYimD4EZM2aI66+/PvX/RCIhCgoKxJ133nkce/Xx492iSdd1kZeXJ37605+mPuvv7xdWq1U88sgjQgghdu3aJQCxfv36VJtXXnlFKIoiWlpahBBC/Pa3vxVer1dEIpFUm29+85uiuro69f+LL75YnH322SP6M3PmTPGFL3zhmPtyotLZ2SkAsWLFCiGEPC6z2SyeeOKJVJvdu3cLQKxZs0YIIcWuqqqivb091eb+++8Xbrc7Nc7f+MY3RE1NzYjfuuSSS8TixYtT//9X98ax9OVExev1ij/96U/GeH/IDA4OiqqqKrF06VJx6qmnpkSTMe4fPLfffruYNGnSUbf9t4634Z77gIlGo2zcuJGFCxemPlNVlYULF7JmzZrj2LOPP/X19bS3t48Yu/T0dGbOnJkauzVr1uDxeJg2bVqqzcKFC1FVlbVr16bazJ07F4vFkmqzePFi9u7dS19fX6rN4b8z1Gbod46lLycqPp8PgIyMDAA2btxILBYbcaxjxoyhpKRkxLhPmDCB3NzcVJvFixczMDDAzp07U23eb0yP5d44lr6caCQSCR599FECgQCzZ882xvtD5vrrr+fss88+YmyMcf9w2L9/PwUFBVRUVHD55ZfT1NQE/PeOtyGaPmC6u7tJJBIjLgKA3Nxc2tvbj1OvTgyGxuf9xq69vZ2cnJwR200mExkZGSPaHG0fh//Ge7U5fPu/6suJiK7r3HjjjZx88smMHz8ekMdqsVjweDwj2r57PP7TMR0YGCAUCh3TvXEsfTlR2L59Oy6XC6vVynXXXcczzzzDuHHjjPH+EHn00UfZtGkTd9555xHbjHH/4Jk5cyYPPvggr776Kvfffz/19fXMmTOHwcHB/9rxNv1brQ0MDE5orr/+enbs2MHbb799vLvyX091dTVbtmzB5/Px5JNPcuWVV7JixYrj3a3/Wg4dOsQNN9zA0qVLsdlsx7s7/xOceeaZqX9PnDiRmTNnUlpayuOPP47dbj+OPfvwMCxNHzBZWVlomnZEVH5HRwd5eXnHqVcnBkPj835jl5eXR2dn54jt8Xic3t7eEW2Oto/Df+O92hy+/V/15UTjy1/+Mi+++CLLli2jqKgo9XleXh7RaJT+/v4R7d89Hv/pmLrdbux2+zHdG8fSlxMFi8VCZWUlU6dO5c4772TSpEn86le/Msb7Q2Ljxo10dnYyZcoUTCYTJpOJFStWcO+992IymcjNzTXG/UPG4/EwevRoDhw48F97nRui6QPGYrEwdepU3njjjdRnuq7zxhtvMHv27OPYs48/5eXl5OXljRi7gYEB1q5dmxq72bNn09/fz8aNG1Nt3nzzTXRdZ+bMmak2K1euJBaLpdosXbqU6upqvF5vqs3hvzPUZuh3jqUvJwpCCL785S/zzDPP8Oabb1JeXj5i+9SpUzGbzSOOde/evTQ1NY0Y9+3bt48QrEuXLsXtdjNu3LhUm/cb02O5N46lLycquq4TiUSM8f6QWLBgAdu3b2fLli2pP9OmTePyyy9P/dsY9w8Xv9/PwYMHyc/P/++9zv+tsHGDY+LRRx8VVqtVPPjgg2LXrl3i2muvFR6PZ8QKgf9VBgcHxebNm8XmzZsFIH7xi1+IzZs3i8bGRiGEXObv8XjEc889J7Zt2ybOP//8o6YcmDx5sli7dq14++23RVVV1YiUA/39/SI3N1d85jOfETt27BCPPvqocDgcR6QcMJlM4mc/+5nYvXu3uP3224+acuBf9eVE4Itf/KJIT08Xy5cvH7E0OBgMptpcd911oqSkRLz55ptiw4YNYvbs2WL27Nmp7UNLgxctWiS2bNkiXn31VZGdnX3UpcE333yz2L17t7jvvvuOujT4X90b/6ovJwLf+ta3xIoVK0R9fb3Ytm2b+Na3viUURRGvvfaaEMIY74+Kw1fPCWGM+wfN1772NbF8+XJRX18vVq1aJRYuXCiysrJEZ2enEOK/c7wN0fQh8etf/1qUlJQIi8UiZsyYId55553j3aWPBcuWLRPAEX+uvPJKIYRc6n/bbbeJ3NxcYbVaxYIFC8TevXtH7KOnp0dcdtllwuVyCbfbLT772c+KwcHBEW22bt0qTjnlFGG1WkVhYaG46667jujL448/LkaPHi0sFouoqakRL7300ojtx9KXE4GjjTcg/vKXv6TahEIh8aUvfUl4vV7hcDjEBRdcINra2kbsp6GhQZx55pnCbreLrKws8bWvfU3EYrERbZYtWyZqa2uFxWIRFRUVI35jiH91bxxLXz7ufO5znxOlpaXCYrGI7OxssWDBgpRgEsIY74+Kd4smY9w/WC655BKRn58vLBaLKCwsFJdccok4cOBAavt/43grQgjx79mmDAwMDAwMDAz+9zBimgwMDAwMDAwMjgFDNBkYGBgYGBgYHAOGaDIwMDAwMDAwOAYM0WRgYGBgYGBgcAwYosnAwMDAwMDA4BgwRJOBgYGBgYGBwTFgiCYDAwMDAwMDg2PAEE0GBgYGBgYGBseAIZoMDAwMDAwMDI4BQzQZGBj813PVVVehKAp33XXXiM+fffZZFEU5Tr0yMDA40TBEk4GBwf8ENpuNn/zkJ/T19R3vrhgYGJygGKLJwMDgf4KFCxeSl5fHnXfeeby7YmBgcIJiiCYDA4P/CTRN48c//jG//vWvaW5uPt7dMTAwOAExRJOBgcH/DBdccAG1tbXcfvvtx7srBgYGJyCGaDIwMPif4ic/+QkPPfQQu3fvPt5dMTAwOMEwRJOBgcH/FHPnzmXx4sXccsstx7srBgYGJxim490BAwMDg4+au+66i9raWqqrq493VwwMDE4gDEuTgYHB/xwTJkzg8ssv59577z3eXTEwMDiBMESTgYHB/yQ/+MEP0HX9eHfDwMDgBEIRQojj3QkDAwMDAwMDg487hqXJwMDAwMDAwOAYMESTgYGBgYGBgcExYIgmAwMDAwMDA4NjwBBNBgYGBgYGBgbHgCGaDAwMDAwMDAyOAUM0GRgYGBgYGBgcA4ZoMjAwMDAwMDA4BgzRZGBgYGBgYGBwDBiiycDAwMDAwMDgGDBEk4GBgYGBgYHBMWCIJgMDAwMDAwODY+D/AeUpjU4bBgtXAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bz = 1000\n",
    "\n",
    "exact, res = [], []\n",
    "exact_expec = c.expectation_ps(ps=ps[0])\n",
    "for ob in ps:\n",
    "    exact.append(BK.real(c.expectation_ps(ps=ob)))\n",
    "exact = np.asarray(exact)[:, None]\n",
    "\n",
    "bzs, res = [], []\n",
    "for i in range(0, nps, bz):\n",
    "    res.append([])\n",
    "    ss_states_batch = ss_states[: i + bz]\n",
    "    bzs.append(ss_states_batch.shape[0])\n",
    "    t0 = time.time()\n",
    "    for j, ob in enumerate(ps):\n",
    "        expcs = sejit(ss_states_batch, ob)\n",
    "        res[-1].append(np.median(expcs))\n",
    "        t = time.time()\n",
    "        if i % (bz * 10) == 0:\n",
    "            print(\n",
    "                f\"observalbe: No.{j}\\tnumber of Pauli strings: {bzs[-1]}\\ttime: {t - t0}\"\n",
    "            )\n",
    "        t0 = t\n",
    "res = np.asarray(res).T\n",
    "bzs = np.asarray(bzs) * r\n",
    "\n",
    "error = np.abs(res - exact)\n",
    "plt.figure()\n",
    "plt.xlabel(\"N\")\n",
    "plt.ylabel(\"Error\")\n",
    "for i, y in enumerate(error):\n",
    "    plt.plot(bzs, y, label=f\"No.{i}\")\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T12:10:35.293046600Z",
     "start_time": "2023-08-08T12:08:03.154791700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Estimate the Entanglement Entropy"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can also use classical shadows to calculate entanglement entropy. `entropy_shadow` first reconstructs the reduced density matrix, then solves the eigenvalues and finally calculates the entanglement entropy from non-negative eigenvalues. Since the time and space complexity of reconstructing the density matrix is exponential with respect to the system size, this method is only efficient when the reduced system size is constant. `entropy_shadow` is jitable, but since we only calculate the entanglement entropy once here, it is not wrapped."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact: 1.326616283813521\n",
      "shadow entropy: 1.328468470127564\n"
     ]
    }
   ],
   "source": [
    "sub = [1, 4]\n",
    "\n",
    "exact_rdm = tc.quantum.reduced_density_matrix(\n",
    "    psi, cut=[i for i in range(n) if i not in sub]\n",
    ")\n",
    "exact_ent = tc.quantum.renyi_entropy(exact_rdm, k=2)\n",
    "\n",
    "ent = shadows.entropy_shadow(ss_states, sub=sub, alpha=2)\n",
    "\n",
    "print(\"exact:\", exact_ent)\n",
    "print(\"shadow entropy:\", ent)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T12:10:35.850421Z",
     "start_time": "2023-08-08T12:10:35.292528800Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "On the other hand, for the second order Renyi entropy, we have another method to calculate it in polynomial time by random measurement:\n",
    "$$\n",
    "\\begin{eqnarray}\n",
    "    S_2&=&-\\log\\left(\\text{Tr}(\\rho_A^2)\\right),\\\\\n",
    "    \\text{Tr}(\\rho_A^2)&=&2^k\\sum_{b,b'\\in\\{0,1\\}^k}(-2)^{-H(b,b')}\\overline{P(b)P(b')},\n",
    "\\end{eqnarray}\n",
    "$$\n",
    "where $A$ is the $k$-d reduced system, $H(b,b')$ is the Hamming distance between $b$ and $b'$, $P(b)$ is the probability for measuring $\\rho_A$ and obtaining the outcomes $b$ thus we need a larger $r$ to obtain a good enough priori probability, and the overline means the average on all random selected Pauli strings. Please refer to [Brydges, et al. (2019)](https://www.science.org/doi/full/10.1126/science.aau4963) for more details. We can use `renyi_entropy_2` to implement this method, but it is not jitable because we need to build the dictionary based on the bit strings obtained by measurement. Compared with `entropy_shadow`, it cannot filter out non-negative eigenvalues, so the accuracy is slightly worse."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shadow entropy 2: 1.3039560994783876\n"
     ]
    }
   ],
   "source": [
    "nps, r = 1000, 500\n",
    "\n",
    "pauli_strings = BK.convert_to_tensor(np.random.randint(1, 4, size=(nps, n)))\n",
    "status = BK.convert_to_tensor(np.random.rand(nps, r))\n",
    "\n",
    "snapshots = shadows.shadow_snapshots(psi, pauli_strings, status, measurement_only=True)\n",
    "ent2 = shadows.renyi_entropy_2(snapshots, sub)\n",
    "\n",
    "print(\"shadow entropy 2:\", ent2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T12:10:41.738126600Z",
     "start_time": "2023-08-08T12:10:35.851060800Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Reconstruct the Density Matrix"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can `global_shadow_state`, `global_shadow_state1` or `global_shadow_state2` to reconstruct the density matrix. These three functions use different methods, but the results are exactly the same and all of them are jitable. In specific, `global_shadow_state` uses `kron` and is recommended, the other two use `einsum`."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact:\n",
      " [[0.5+0.j 0. +0.j 0. +0.j 0.5+0.j]\n",
      " [0. +0.j 0. +0.j 0. +0.j 0. +0.j]\n",
      " [0. +0.j 0. +0.j 0. +0.j 0. +0.j]\n",
      " [0.5+0.j 0. +0.j 0. +0.j 0.5+0.j]]\n",
      "\n",
      "shadow state:\n",
      " [[ 0.49579 +0.j        0.003945-0.003675j  0.00729 -0.00345j\n",
      "   0.498825+0.001125j]\n",
      " [ 0.003945+0.003675j  0.00457 +0.j        0.007875+0.003465j\n",
      "  -0.0081  +0.00204j ]\n",
      " [ 0.00729 +0.00345j   0.007875-0.003465j  0.00898 +0.j\n",
      "  -0.000645+0.002085j]\n",
      " [ 0.498825-0.001125j -0.0081  -0.00204j  -0.000645-0.002085j\n",
      "   0.49066 +0.j      ]]\n",
      "\n",
      "shadow state 1:\n",
      " [[ 0.49579 +0.j        0.003945-0.003675j  0.00729 -0.00345j\n",
      "   0.498825+0.001125j]\n",
      " [ 0.003945+0.003675j  0.00457 +0.j        0.007875+0.003465j\n",
      "  -0.0081  +0.00204j ]\n",
      " [ 0.00729 +0.00345j   0.007875-0.003465j  0.00898 +0.j\n",
      "  -0.000645+0.002085j]\n",
      " [ 0.498825-0.001125j -0.0081  -0.00204j  -0.000645-0.002085j\n",
      "   0.49066 +0.j      ]]\n",
      "\n",
      "shadow state 2:\n",
      " [[ 0.49579 +0.j        0.003945-0.003675j  0.00729 -0.00345j\n",
      "   0.498825+0.001125j]\n",
      " [ 0.003945+0.003675j  0.00457 +0.j        0.007875+0.003465j\n",
      "  -0.0081  +0.00204j ]\n",
      " [ 0.00729 +0.00345j   0.007875-0.003465j  0.00898 +0.j\n",
      "  -0.000645+0.002085j]\n",
      " [ 0.498825-0.001125j -0.0081  -0.00204j  -0.000645-0.002085j\n",
      "   0.49066 +0.j      ]]\n"
     ]
    }
   ],
   "source": [
    "n, nps, r = 2, 10000, 5\n",
    "\n",
    "c = tc.Circuit(n)\n",
    "c.H(0)\n",
    "c.cnot(0, 1)\n",
    "\n",
    "psi = c.state()\n",
    "bell_state = psi[:, None] @ psi[None, :]\n",
    "\n",
    "pauli_strings = BK.convert_to_tensor(np.random.randint(1, 4, size=(nps, n)))\n",
    "status = BK.convert_to_tensor(np.random.rand(nps, r))\n",
    "lss_states = shadows.shadow_snapshots(psi, pauli_strings, status)\n",
    "sdw_state = shadows.global_shadow_state(lss_states)\n",
    "sdw_state1 = shadows.global_shadow_state1(lss_states)\n",
    "sdw_state2 = shadows.global_shadow_state2(lss_states)\n",
    "\n",
    "print(\"exact:\\n\", bell_state)\n",
    "print(\"\\nshadow state:\\n\", sdw_state)\n",
    "print(\"\\nshadow state 1:\\n\", sdw_state1)\n",
    "print(\"\\nshadow state 2:\\n\", sdw_state2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T12:10:43.360675500Z",
     "start_time": "2023-08-08T12:10:41.746265700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OS info: Linux-5.4.119-1-tlinux4-0010.2-x86_64-with-glibc2.28\n",
      "Python version: 3.10.11\n",
      "Numpy version: 1.23.5\n",
      "Scipy version: 1.11.0\n",
      "Pandas version: 2.0.2\n",
      "TensorNetwork version: 0.4.6\n",
      "Cotengra version: 0.2.1.dev15+g120379e\n",
      "TensorFlow version: 2.12.0\n",
      "TensorFlow GPU: []\n",
      "TensorFlow CUDA infos: {'cpu_compiler': '/dt9/usr/bin/gcc', 'cuda_compute_capabilities': ['sm_35', 'sm_50', 'sm_60', 'sm_70', 'sm_75', 'compute_80'], 'cuda_version': '11.8', 'cudnn_version': '8', 'is_cuda_build': True, 'is_rocm_build': False, 'is_tensorrt_build': True}\n",
      "Jax version: 0.4.13\n",
      "Jax installation doesn't support GPU\n",
      "JaxLib version: 0.4.13\n",
      "PyTorch version: 2.0.1\n",
      "PyTorch GPU support: False\n",
      "PyTorch GPUs: []\n",
      "Cupy is not installed\n",
      "Qiskit version: 0.24.1\n",
      "Cirq version: 1.1.0\n",
      "TensorCircuit version 0.10.0\n"
     ]
    }
   ],
   "source": [
    "tc.about()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-08T12:10:43.409851700Z",
     "start_time": "2023-08-08T12:10:43.360099800Z"
    }
   }
  }
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