docs/tutorials/Error-Suppression-and-Error-Mitigation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a367f910",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Error suppression and error mitigation with Qiskit Runtime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b65d69a-4816-473e-9ea0-de9e18c052df",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "To illustrate the need for error suppression and mitigation, we will start by taking a look at some results without error suppression or error mitigation. After setting up our environment, we will use a noiseless simulator to get an idea of what ideal results look like. Next, we are going to run the same experiment in a real backend without using error suppression or error mitigation.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7888bb4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams.update({\"text.usetex\": True})\n",
    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
    "mpl.rcParams[\"figure.dpi\"] = 200\n",
    "\n",
    "from qiskit_ibm_runtime import Estimator, Session, QiskitRuntimeService, Options\n",
    "from qiskit.quantum_info import SparsePauliOp\n",
    "from qiskit import QuantumCircuit\n",
    "\n",
    "service = QiskitRuntimeService()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2485bf5",
   "metadata": {},
   "source": [
    "## Create a Trotter circuit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19601a22",
   "metadata": {},
   "source": [
    "The following code cell defines a list of Trotter circuits for use in the experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7881bfe5",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trotter circuit with 1 Trotter steps\n"
     ]
    },
    {
     "data": {
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       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌─────────┐                                            \n",
       "q_0: ┤ Rx(0.1) ├──■────────────────■────────────────────────\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐                      \n",
       "q_1: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├──■────────────────■──\n",
       "     ├─────────┤└───┘└──────────┘└───┘┌─┴─┐┌──────────┐┌─┴─┐\n",
       "q_2: ┤ Rx(0.1) ├──■────────────────■──┤ X ├┤ Rz(-0.2) ├┤ X ├\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐└───┘└──────────┘└───┘\n",
       "q_3: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├──────────────────────\n",
       "     └─────────┘└───┘└──────────┘└───┘                      </pre>"
      ],
      "text/plain": [
       "     ┌─────────┐                                            \n",
       "q_0: ┤ Rx(0.1) ├──■────────────────■────────────────────────\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐                      \n",
       "q_1: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├──■────────────────■──\n",
       "     ├─────────┤└───┘└──────────┘└───┘┌─┴─┐┌──────────┐┌─┴─┐\n",
       "q_2: ┤ Rx(0.1) ├──■────────────────■──┤ X ├┤ Rz(-0.2) ├┤ X ├\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐└───┘└──────────┘└───┘\n",
       "q_3: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├──────────────────────\n",
       "     └─────────┘└───┘└──────────┘└───┘                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trotter circuit with 2 Trotter steps\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌─────────┐                      ┌─────────┐                                                                        \n",
       "q_0: ┤ Rx(0.1) ├──■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■────────────────────────\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐                      \n",
       "q_1: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├──■────────────────■──\n",
       "     ├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘┌─┴─┐┌──────────┐┌─┴─┐\n",
       "q_2: ┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■──┤ X ├┤ Rz(-0.2) ├┤ X ├\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐└───┘└──────────┘└───┘\n",
       "q_3: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├──────────────────────\n",
       "     └─────────┘└───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘                      </pre>"
      ],
      "text/plain": [
       "     ┌─────────┐                      ┌─────────┐                                                                        \n",
       "q_0: ┤ Rx(0.1) ├──■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■────────────────────────\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐                      \n",
       "q_1: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├──■────────────────■──\n",
       "     ├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘┌─┴─┐┌──────────┐┌─┴─┐\n",
       "q_2: ┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■──┤ X ├┤ Rz(-0.2) ├┤ X ├\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐└───┘└──────────┘└───┘\n",
       "q_3: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├──────────────────────\n",
       "     └─────────┘└───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trotter circuit with 3 Trotter steps\n"
     ]
    },
    {
     "data": {
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       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌─────────┐                      ┌─────────┐                                                  ┌─────────┐                                                                        \n",
       "q_0: ┤ Rx(0.1) ├──■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■────────────────────────\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐                      \n",
       "q_1: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├──■────────────────■──\n",
       "     ├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘┌─┴─┐┌──────────┐┌─┴─┐\n",
       "q_2: ┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■──┤ X ├┤ Rz(-0.2) ├┤ X ├\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐└───┘└──────────┘└───┘\n",
       "q_3: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├──────────────────────\n",
       "     └─────────┘└───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘                      </pre>"
      ],
      "text/plain": [
       "     ┌─────────┐                      ┌─────────┐                                                  ┌─────────┐                                                                        \n",
       "q_0: ┤ Rx(0.1) ├──■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■────────────────────────\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐                      \n",
       "q_1: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├──■────────────────■──\n",
       "     ├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘┌─┴─┐┌──────────┐┌─┴─┐\n",
       "q_2: ┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■──┤ X ├┤ Rz(-0.2) ├┤ X ├\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐└───┘└──────────┘└───┘\n",
       "q_3: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├──────────────────────\n",
       "     └─────────┘└───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trotter circuit with 4 Trotter steps\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌─────────┐                      ┌─────────┐                                                  ┌─────────┐                                                  ┌─────────┐                                                                        \n",
       "q_0: ┤ Rx(0.1) ├──■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■────────────────────────\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐                      \n",
       "q_1: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├──■────────────────■──\n",
       "     ├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘┌─┴─┐┌──────────┐┌─┴─┐\n",
       "q_2: ┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■──┤ X ├┤ Rz(-0.2) ├┤ X ├\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐└───┘└──────────┘└───┘\n",
       "q_3: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├──────────────────────\n",
       "     └─────────┘└───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘                      </pre>"
      ],
      "text/plain": [
       "     ┌─────────┐                      ┌─────────┐                                                  ┌─────────┐                                                  ┌─────────┐                                                                        \n",
       "q_0: ┤ Rx(0.1) ├──■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■────────────────────────\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐                      \n",
       "q_1: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├──■────────────────■──\n",
       "     ├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘┌─┴─┐┌──────────┐┌─┴─┐\n",
       "q_2: ┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■──┤ X ├┤ Rz(-0.2) ├┤ X ├\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐└───┘└──────────┘└───┘\n",
       "q_3: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├──────────────────────\n",
       "     └─────────┘└───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘                      "
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trotter circuit with 5 Trotter steps\n"
     ]
    },
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     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌─────────┐                      ┌─────────┐                                                  ┌─────────┐                                                  ┌─────────┐                                                  ┌─────────┐                                                                        \n",
       "q_0: ┤ Rx(0.1) ├──■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■────────────────────────\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐                      \n",
       "q_1: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├──■────────────────■──\n",
       "     ├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘┌─┴─┐┌──────────┐┌─┴─┐\n",
       "q_2: ┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■──┤ X ├┤ Rz(-0.2) ├┤ X ├\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐└───┘└──────────┘└───┘\n",
       "q_3: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├──────────────────────\n",
       "     └─────────┘└───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘                      </pre>"
      ],
      "text/plain": [
       "     ┌─────────┐                      ┌─────────┐                                                  ┌─────────┐                                                  ┌─────────┐                                                  ┌─────────┐                                                                        \n",
       "q_0: ┤ Rx(0.1) ├──■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■──┤ Rx(0.1) ├──────────────────────────────■────────────────■────────────────────────\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐└─────────┘                 ┌─────────┐┌─┴─┐┌──────────┐┌─┴─┐                      \n",
       "q_1: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├─────■───────────────────■──┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├──■────────────────■──\n",
       "     ├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘   ┌─┴─┐   ┌──────────┐┌─┴─┐├─────────┤└───┘└──────────┘└───┘┌─┴─┐┌──────────┐┌─┴─┐\n",
       "q_2: ┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■─────┤ X ├───┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├──■────────────────■──┤ X ├┤ Rz(-0.2) ├┤ X ├\n",
       "     ├─────────┤┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐┌──┴───┴──┐└──────────┘└───┘└─────────┘┌─┴─┐┌──────────┐┌─┴─┐└───┘└──────────┘└───┘\n",
       "q_3: ┤ Rx(0.1) ├┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├┤ Rx(0.1) ├────────────────────────────┤ X ├┤ Rz(-0.2) ├┤ X ├──────────────────────\n",
       "     └─────────┘└───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘└─────────┘                            └───┘└──────────┘└───┘                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "backend_simulator = \"ibmq_qasm_simulator\"\n",
    "backend = \"ibmq_montreal\"\n",
    "\n",
    "qubits = 4\n",
    "trotter_layer = QuantumCircuit(qubits)\n",
    "trotter_layer.rx(0.1, range(qubits))\n",
    "trotter_layer.cx(0, 1)\n",
    "trotter_layer.cx(2, 3)\n",
    "trotter_layer.rz(-0.2, [1, 3])\n",
    "trotter_layer.cx(0, 1)\n",
    "trotter_layer.cx(2, 3)\n",
    "trotter_layer.cx(1, 2)\n",
    "trotter_layer.rz(-0.2, 2)\n",
    "trotter_layer.cx(1, 2)\n",
    "\n",
    "num_steps = 6\n",
    "trotter_circuit_list = []\n",
    "for i in range(1, num_steps):\n",
    "    trotter_circuit = QuantumCircuit(qubits)\n",
    "    for _ in range(i):\n",
    "        trotter_circuit = trotter_circuit.compose(trotter_layer)\n",
    "    trotter_circuit_list.append(trotter_circuit)\n",
    "    print(f\"Trotter circuit with {i} Trotter steps\")\n",
    "    display(trotter_circuit.draw(fold=-1))\n",
    "\n",
    "obs = SparsePauliOp(\"Z\" * qubits)\n",
    "obs_list = [obs] * len(trotter_circuit_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fc5c3d7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Simulator run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c0d41f46",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job id: cdkrpbpg1234sbmtrt3g\n",
      "EstimatorResult(values=array([0.98 , 0.926, 0.872, 0.796, 0.68 ]), metadata=[{'variance': 0.03960000000000008, 'shots': 1000}, {'variance': 0.14252399999999987, 'shots': 1000}, {'variance': 0.23961600000000005, 'shots': 1000}, {'variance': 0.36638399999999993, 'shots': 1000}, {'variance': 0.5375999999999999, 'shots': 1000}])\n"
     ]
    }
   ],
   "source": [
    "options = Options()\n",
    "options.execution.shots = 1000\n",
    "options.optimization_level = 0  # No optimization\n",
    "options.resilience_level = 0  # No mitigation\n",
    "\n",
    "with Session(service=service, backend=backend_simulator) as session:\n",
    "    estimator_sim = Estimator(session=session, options=options)\n",
    "    job_sim = estimator_sim.run(circuits=trotter_circuit_list, observables=obs_list)\n",
    "    print(\"job id:\", job_sim.job_id)\n",
    "    print(job_sim.result())\n",
    "\n",
    "expvals_ideal = job_sim.result().values\n",
    "expvals_ideal_variance = [\n",
    "    metadata[\"variance\"] / metadata[\"shots\"] for metadata in job_sim.result().metadata\n",
    "]\n",
    "std_error_ideal = np.sqrt(expvals_ideal_variance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a738093",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Unmitigated, real backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "29f060f3",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job id: cdkrpeaan60ka16e8340\n",
      "EstimatorResult(values=array([0.786, 0.74 , 0.636, 0.562, 0.446]), metadata=[{'variance': 0.382204, 'shots': 1000}, {'variance': 0.4524, 'shots': 1000}, {'variance': 0.595504, 'shots': 1000}, {'variance': 0.684156, 'shots': 1000}, {'variance': 0.801084, 'shots': 1000}])\n"
     ]
    }
   ],
   "source": [
    "options = Options()\n",
    "options.execution.shots = 1000\n",
    "options.optimization_level = 0  # No optimization\n",
    "options.resilience_level = 0  # No error mitigation\n",
    "\n",
    "with Session(service=service, backend=backend) as session:\n",
    "    estimator = Estimator(session=session, options=options)\n",
    "    job = estimator.run(circuits=trotter_circuit_list, observables=obs_list)\n",
    "    print(\"job id:\", job.job_id)\n",
    "    print(job.result())\n",
    "\n",
    "expvals_unmit = job.result().values\n",
    "expvals_unmit_variance = [\n",
    "    metadata[\"variance\"] / metadata[\"shots\"] for metadata in job.result().metadata\n",
    "]\n",
    "std_error_unmit = np.sqrt(expvals_unmit_variance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3036f49b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "toc-hr-collapsed": true
   },
   "source": [
    "## Theory and background\n",
    "\n",
    "### IBM Quantum\n",
    "We want to build a tool which, beginning in 2024, is capable of estimating _noise-free observables_ of _100-qubit_ circuits, with _depth 100_, within _one day_. \n",
    "\n",
    "Although this is not a promise for quantum advantage _per-se_, the goal is to get to the point where we can solve interesting or relevant problems faster, more accurately, or more cheaply in a practical time frame as compared to efficient classical computations.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaea4b7c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The threshold for simulability seems to be at around 100 qubits. However, this is not enough qubits, as we also need a sufficiently complex quantum circuit to ensure that it cannot be reproduced classically.  For example, deep, non-clifford.\n",
    "\n",
    "<div>\n",
    "<img src=\"images/100x100.png\">\n",
    "</div>\n",
    "\n",
    "With errors per gate of order `1E-3`, we estimate a final fidelity of such a computation of about `1E-6`, which implies totally meaningless results.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "821d863b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Fault tolerant quantum computing\n",
    "Errors are a natural occurrence in any computer. In quantum computers, these errors are usually more complex and happen when the  quantum states are not prepared exactly how they should. For example, overshooting their target values or drifting away. Furthermore, the disturbances that generate these issues are typically unavoidable.\n",
    "\n",
    "There are several ways to handle these errors.  The terminology can get confusing, as there is disagreement even within the field. We will break it into three core pieces:\n",
    "\n",
    "1. Suppression\n",
    "2. Mitigation\n",
    "3. Correction\n",
    "\n",
    "\n",
    "Let's start with the error correction, since it was the first one to be formally discovered within the field."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9390b002",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Error correction\n",
    "Error correction in classical computers is the standard procedure of encoding information in redundant ways to make it resilient to unwanted noise —allowing us to detect and correct errors as they occur.\n",
    "\n",
    "It has being known for a long time that the coupling of quantum systems to their environment sets a limit for any quantum computation, both in time and size. Nonetheless, theoretical discoveries in the form of _Quantum error correction_ solved this issue early on.\n",
    "<div class=\"alert alert-success\">\n",
    "    <i class=\"fas fa-book\"></i>\n",
    "    <b>THRESHOLD THEOREM</b>\n",
    "    \n",
    "    If errors in quantum computers can be reduced below certain (finite) level, a fault-tolerant quantum computation could be carried out arbitrarily long even in noisy hardware.\n",
    "</div>\n",
    "\n",
    "__Reference__: M.A. Nielsen, and I.L. Chuang, _Quantum Computation and Quantum Information_."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "809d95bc",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "This is how we hope to achieve the ultimate goal of _fault-tolerant_ quantum computation. However, even if we build hardware that can achieve the prescribed threshold, the overhead for performing full error correction, such as the necessary qubit count, makes it infeasible in the short term; as we will try to encode single qubit logical values across multiple physical qubits:\n",
    "\n",
    "| $$n/d = 100/100 \\quad$$ | $$p = \\text{1E-3} \\quad$$ | $$p = \\text{1E-4} \\quad$$ | $$p = \\text{1E-5} \\quad$$ |\n",
    "|-------------------------|---------------------------|---------------------------|---------------------------|\n",
    "| Runtime [$s$]           | 2.3                       | 1.2                       | 0.8                       |\n",
    "| Physical qubits         | 140k                      | 40k                       | 16k                       |\n",
    "| Distance                | 21                        | 11                        | 7                         |\n",
    "\n",
    "These _surface code resources_* are well beyond our 2025 roadmap plans, although we are working hard in this area to make it feasible as soon as possible."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fc2531a-e7e0-421c-a45b-76d7e3195f9d",
   "metadata": {},
   "source": [
    "\\* The data in the previous table is based on simulations for surface codes with certain conditions. The code distance was chosen so that none of the T-gates, or 100 logical qubits at the end of runtime, had error greater than $1\\text{E}-2$. The number of T-gates is $n \\times d$ and each parity check cycle takes $1 \\mu s$.\n",
    "\n",
    "__Reference__: Daniel Litinski, _A Game of Surface Codes: Large-Scale Quantum Computing with Lattice Surgery_, [arXiv:1808.02892](https://arxiv.org/abs/1808.02892)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e203546d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "toc-hr-collapsed": true
   },
   "source": [
    "## Error suppression\n",
    "Error suppression is the most basic level of error handling. It often refers to a set of techniques performed close to hardware, such as altering control signals (that is, at Qiskit Pulse level). To make it simpler, we define these techniques as introducing _classical overhead_, usually within the transpilation/compilation process.\n",
    "\n",
    "These techniques are very old and were first developed outside of the field at the same time as some of the first controllable quantum systems such as magnetic resonance imaging devices.\n",
    "\n",
    "\n",
    "### Dynamical decoupling (DD)\n",
    "Quantum computers have adopted some of these techniques like spin echos (a sequence of pulses that can help refocus the state of a qubit). As a matter of fact, spin echos play a big role in a class of techniques known as _Dynamical Decoupling_ (DD), which sends pulses to idle qubits to reset their value to their original states, undoing the potentially harmful effects of nearby qubits that are being used for the calculation.\n",
    "\n",
    "__Reference__: Lorenza Viola, and Seth Lloyd , _Dynamical suppression of decoherence in two-state quantum systems_, [arXiv:quant-ph/9803057](https://arxiv.org/abs/quant-ph/9803057)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d0865e62",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job id: cdkrpk5f14jb6brgbmt0\n",
      "EstimatorResult(values=array([0.87 , 0.752, 0.598, 0.484, 0.396]), metadata=[{'variance': 0.24309999999999998, 'shots': 1000}, {'variance': 0.434496, 'shots': 1000}, {'variance': 0.642396, 'shots': 1000}, {'variance': 0.765744, 'shots': 1000}, {'variance': 0.8431839999999999, 'shots': 1000}])\n"
     ]
    }
   ],
   "source": [
    "options = Options()\n",
    "options.execution.shots = 1000\n",
    "options.optimization_level = 3  # Dynamical decoupling\n",
    "options.resilience_level = 0  # No error mitigation\n",
    "\n",
    "with Session(service=service, backend=backend) as session:\n",
    "    estimator = Estimator(session=session, options=options)\n",
    "    job_dd = estimator.run(circuits=trotter_circuit_list, observables=obs_list)\n",
    "    print(\"job id:\", job_dd.job_id)\n",
    "    print(job_dd.result())\n",
    "\n",
    "expvals_unmit_dd = job_dd.result().values\n",
    "expvals_unmit_dd_variance = [\n",
    "    metadata[\"variance\"] / metadata[\"shots\"] for metadata in job_dd.result().metadata\n",
    "]\n",
    "std_error_dd = np.sqrt(expvals_unmit_dd_variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0a2b303c",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Trotter circuits expectation value\")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_ideal,\n",
    "    std_error_ideal,\n",
    "    fmt=\"o\",\n",
    "    linestyle=\"--\",\n",
    "    capsize=4,\n",
    "    c=\"red\",\n",
    "    label=\"Ideal\",\n",
    ")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_unmit,\n",
    "    std_error_unmit,\n",
    "    fmt=\"o\",\n",
    "    linestyle=\"-\",\n",
    "    capsize=4,\n",
    "    c=\"green\",\n",
    "    label=\"No mitigation\",\n",
    ")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_unmit_dd,\n",
    "    std_error_dd,\n",
    "    fmt=\"o\",\n",
    "    linestyle=\"-\",\n",
    "    capsize=4,\n",
    "    c=\"blue\",\n",
    "    label=\"Dynamical decoupling\",\n",
    ")\n",
    "\n",
    "plt.ylabel(f\"$\\langle ZZZZ \\\\rangle$\")\n",
    "plt.xlabel(\"No. Trotter Steps\")\n",
    "plt.xticks([1, 2, 3, 4, 5])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "347acafc",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "toc-hr-collapsed": true
   },
   "source": [
    "## Error mitigation\n",
    "Error mitigation introduces _quantum overhead_ to our calculations.\n",
    "\n",
    "Typically, error correction uses the outputs of multiple ensembles of noisy calculations to reduce or eliminate the noise in estimating quantities like expectation values — commonly by using classical post-processing. In this regard, error mitigation is different from error correction. Rather than fixing errors in every circuit run, errors are allowed to occur, then better results are inferred from several of these noisy calculations. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "385a8abf",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Since there is no threshold that one must surpass before quantum error mitigation becomes practical, we see error mitigation as the key to realizing useful quantum computers in the near term.\n",
    "\n",
    "We are developing a portfolio of different error mitigation techniques, looking for three main things:\n",
    "\n",
    "- Small overhead (_speed_)\n",
    "- Improved accuracy (_quality_)\n",
    "- Few assumptions (_scale_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4e8ed6",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Each method come with their own associated overhead: a trade-off between the number of quantum computations needed (time) and the  accuracy of our results. The level of accuracy that can be achieved is also specific to each method, and is usually inversely proportional to the previously mentioned overhead. Defining $\\lambda$ as the base noise in our computation:\n",
    "\n",
    "| Methods           | T-REx | ZNE                        | PEC                         |\n",
    "|-------------------|-------|----------------------------|-----------------------------|\n",
    "| Assumptions       | None  | Ability to scale the noise | Full knowledge of the noise |\n",
    "| Qubit overhead    | $1$   | $1$                        | $1$                         |\n",
    "| Sampling overhead | $2$   | $N_\\text{noise-factors}$   | $\\mathcal{O}(e^{\\lambda N_\\text{layers}})$    |\n",
    "| Bias              | $0$   | $\\mathcal{O}(\\lambda^{N_\\text{noise-factors}})$ | $0$      |\n",
    "\n",
    "More detailed information is available at:\n",
    "\n",
    "- T-REx: [arXiv:2012.09738](https://arxiv.org/abs/2012.09738)\n",
    "- ZNE: [arXiv:1612.02058](https://arxiv.org/abs/1612.02058)\n",
    "- PEC: [arXiv:1612.02058](https://arxiv.org/abs/1612.02058) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe804335",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "By having a portfolio of methods, users can choose which technique makes the most sense for their problem of interest, based on accuracy demands and how much overhead they are willing to accept. We are excited about this because, in the best scenarios, we can achieve unbiased results (that is, noise-free); and we want to make this capability accessible all across the stack: from kernel to algorithm and model developers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae33a249-8226-4ce2-99fc-85956c8f7400",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Why is error mitigation important?\n",
    "We need improvements in _scale_, _quality_, and _speed_.\n",
    "\n",
    "The promise of error mitigation is to translate the continuous progress in quantum hardware into continuous improvements in quantum computations and quantum information processing. We expect that, while insufficient to fully overcome imperfections, every advancement should incrementally push the frontier of what can be achieved practically; as opposed to a scenario where little to no progress is made until we suddenly overcome the fault-tolerant threshold.\n",
    "\n",
    "<div>\n",
    "<img src=\"images/path_to_quantum_advantage.png\" width=\"600\">\n",
    "</div>\n",
    "\n",
    "Beyond the 100x100 circuit, we envision a future where error correction and error mitigation can work in a hybrid way. In that regard, this approach has proven to be both practically effective and intellectually challenging."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a06883d3",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Concepts\n",
    "\n",
    "The following terms are used in this section.\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "    <i class=\"fas fa-exclamation-triangle\"></i>\n",
    "    <b>TERMINOLOGY</b>\n",
    "    \n",
    "    As with every nascent field, terminology is sometimes incosistent: people use the same terms to refer to (slightly) different things.\n",
    "</div>\n",
    "\n",
    "<ul>\n",
    "    <li> <b>Estimators</b>: Strategies for approximating certain physical values that are fundamentally unknown.\n",
    "    <li> <b>Sampling</b>: The process of taking several measurements of one or several things.\n",
    "    <li> <b>Overhead</b>: Extra costs introduced by new techniques, relative to a base implementation.\n",
    "    <li> <b>Bias</b>: A systematic drift in the measured quantities, usually caused by errors.\n",
    "    <li> <b>Shot/sample noise</b>: Uncertainty in the obtained results due to a lack of infinite precision (that is, a finite number of samples).\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd42bd98",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "### Implications\n",
    "When implementing any error mitigation technique, we expect the _bias_ in our results to be reduced with respect to the previous, unmitigated, bias; in some cases even disappearing. However, this comes at a cost. As we reduce the bias in our estimated quantities, the statistical variability will increase (that is, _variance_),  which we can account for by further increasing the number of shots per circuit in our sampling process. This will introduce overhead beyond that needed to reduce the bias, therefore it is not done by default. The user can easily opt in to this behavior by adjusting the amount of `shots` per circuit in `run_options`.\n",
    "\n",
    "In mathematical terms, we expect our error mitigated estimator to be such that:\n",
    "\n",
    "$$\n",
    "0 \\leq\n",
    "| \\text{Bias}[{\\langle}O{\\rangle}_\\text{em}] | \\leq \n",
    "| \\text{Bias}[{\\langle}O{\\rangle}_\\text{noisy}] | \\\\\n",
    "| \\text{Var}[{\\langle}O{\\rangle}_\\text{em}] | \\geq \n",
    "| \\text{Var}[{\\langle}O{\\rangle}_\\text{noisy}] | > 0\n",
    "$$\n",
    "\n",
    "<div>\n",
    "<img src=\"images/bias_variance_trade_off.png\" width=\"700\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbd5604e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Twirled readout error extinction\n",
    "Twirled readout error extinction (T-REx) uses a technique known as Pauli twirling to reduce the noise introduced during the process of quantum measurement. This technique assumes no specific form of noise, which makes it very general and effective.\n",
    "\n",
    "#### Overall workflow\n",
    "1. Acquire data for the zero state with randomized bit flips (Pauli X before measurement)\n",
    "2. Acquire data for the desired (noisy) state with randomized bit flips (Pauli X before measurement)\n",
    "3. Compute the special function for each data set, and divide.\n",
    "\n",
    "<div>\n",
    "<img src=\"images/trex_data_collection.png\" width=\"600\">\n",
    "</div>\n",
    "\n",
    "__Reference__: E. van den Berg, Z. Minev, and K. Temme, _Model-free readout-error mitigation for quantum expectation values_ [arXiv:2012.09738](https://arxiv.org/abs/2012.09738)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cab568f6",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job id: cdkrpq7tlcfkm5f0demg\n",
      "EstimatorResult(values=array([0.95941791, 0.81652588, 0.68894371, 0.55625825, 0.45674416]), metadata=[{'variance': 0.733385959939609, 'shots': 1008, 'readout_mitigation_num_twirled_circuits': 16, 'readout_mitigation_shots_calibration': 8192}, {'variance': 0.9871541699414397, 'shots': 1008, 'readout_mitigation_num_twirled_circuits': 16, 'readout_mitigation_shots_calibration': 8192}, {'variance': 1.1792252443219302, 'shots': 1008, 'readout_mitigation_num_twirled_circuits': 16, 'readout_mitigation_shots_calibration': 8192}, {'variance': 1.3444454335924159, 'shots': 1008, 'readout_mitigation_num_twirled_circuits': 16, 'readout_mitigation_shots_calibration': 8192}, {'variance': 1.4452534486647257, 'shots': 1008, 'readout_mitigation_num_twirled_circuits': 16, 'readout_mitigation_shots_calibration': 8192}])\n"
     ]
    }
   ],
   "source": [
    "options = Options()\n",
    "options.resilience_level = 1  # T-REx\n",
    "options.optimization_level = 0  # No optimization\n",
    "options.execution.shots = 1000\n",
    "\n",
    "with Session(service=service, backend=backend) as session:\n",
    "    estimator = Estimator(session=session, options=options)\n",
    "    job_trex = estimator.run(circuits=trotter_circuit_list, observables=obs_list)\n",
    "    print(\"job id:\", job_trex.job_id)\n",
    "    print(job_trex.result())\n",
    "\n",
    "expvals_unmit_trex = job_trex.result().values\n",
    "expvals_unmit_trex_variance = [\n",
    "    metadata[\"variance\"] / metadata[\"shots\"] for metadata in job_trex.result().metadata\n",
    "]\n",
    "std_error_trex = np.sqrt(expvals_unmit_trex_variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bc5fe3a7",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Trotter circuits expectation value\")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_ideal,\n",
    "    std_error_ideal,\n",
    "    fmt=\"o\",\n",
    "    linestyle=\"--\",\n",
    "    capsize=4,\n",
    "    c=\"red\",\n",
    "    label=\"Ideal\",\n",
    ")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_unmit,\n",
    "    std_error_unmit,\n",
    "    fmt=\"o\",\n",
    "    linestyle=\"-\",\n",
    "    capsize=4,\n",
    "    c=\"green\",\n",
    "    label=\"No mitigation\",\n",
    ")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_unmit_trex,\n",
    "    std_error_trex,\n",
    "    fmt=\"o\",\n",
    "    linestyle=\"-\",\n",
    "    capsize=4,\n",
    "    c=\"violet\",\n",
    "    label=\"T-REx\",\n",
    ")\n",
    "\n",
    "plt.ylabel(f\"$\\langle ZZZZ \\\\rangle$\")\n",
    "plt.xlabel(\"No. Trotter Steps\")\n",
    "plt.xticks([1, 2, 3, 4, 5])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad387be0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Zero noise extrapolation\n",
    "Zero noise extrapolation (ZNE) works by first amplifying the noise in the circuit that is preparing the desired quantum state, obtaining measurements for several different levels of noise, and using those measurements to infer the noiseless result. \n",
    "\n",
    "#### Overall workflow\n",
    "1. Amplify circuit noise for several noise factors\n",
    "2. Run every noise amplified circuit\n",
    "3. Extrapolate back to the zero noise limit\n",
    "\n",
    "<div>\n",
    "<img src=\"images/zne_stages.png\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ee72d3b6",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job id: cdkrq4i2h6kvivdb2an0\n",
      "EstimatorResult(values=array([0.896     , 0.86833333, 0.82716667, 0.63116667, 0.58366667]), metadata=[{'zne': {'noise_amplification': {'noise_amplifier': \"<TwoQubitAmplifier:{'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\", 'noise_factors': [1, 3, 5], 'values': [0.84, 0.788, 0.664], 'variance': [0.2944000000000001, 0.37905599999999995, 0.5591039999999999], 'shots': [1000, 1000, 1000]}, 'extrapolation': {'extrapolator': 'LinearExtrapolator'}}}, {'zne': {'noise_amplification': {'noise_amplifier': \"<TwoQubitAmplifier:{'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\", 'noise_factors': [1, 3, 5], 'values': [0.778, 0.614, 0.43], 'variance': [0.39471599999999996, 0.623004, 0.8151], 'shots': [1000, 1000, 1000]}, 'extrapolation': {'extrapolator': 'LinearExtrapolator'}}}, {'zne': {'noise_amplification': {'noise_amplifier': \"<TwoQubitAmplifier:{'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\", 'noise_factors': [1, 3, 5], 'values': [0.718, 0.498, 0.28], 'variance': [0.484476, 0.751996, 0.9216], 'shots': [1000, 1000, 1000]}, 'extrapolation': {'extrapolator': 'LinearExtrapolator'}}}, {'zne': {'noise_amplification': {'noise_amplifier': \"<TwoQubitAmplifier:{'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\", 'noise_factors': [1, 3, 5], 'values': [0.54, 0.376, 0.19], 'variance': [0.7083999999999999, 0.858624, 0.9639], 'shots': [1000, 1000, 1000]}, 'extrapolation': {'extrapolator': 'LinearExtrapolator'}}}, {'zne': {'noise_amplification': {'noise_amplifier': \"<TwoQubitAmplifier:{'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\", 'noise_factors': [1, 3, 5], 'values': [0.498, 0.31, 0.142], 'variance': [0.751996, 0.9039, 0.979836], 'shots': [1000, 1000, 1000]}, 'extrapolation': {'extrapolator': 'LinearExtrapolator'}}}])\n"
     ]
    }
   ],
   "source": [
    "options = Options()\n",
    "options.execution.shots = 1000\n",
    "options.optimization_level = 0  # No optimization\n",
    "options.resilience_level = 2  # ZNE\n",
    "\n",
    "with Session(service=service, backend=backend) as session:\n",
    "    estimator = Estimator(session=session, options=options)\n",
    "    job_zne = estimator.run(circuits=trotter_circuit_list, observables=obs_list)\n",
    "    print(\"job id:\", job_zne.job_id)\n",
    "    print(job_zne.result())\n",
    "\n",
    "expvals_unmit_zne = job_zne.result().values\n",
    "# Standard error: coming soon!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a728c6ae",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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REUkoJSdEREREREREJKGUnBARERERERGRhFJyQkREREREREQSSskJEREREREREUkoLSUqIiIiIiIiIgmlkRMiIiIiIiIiklBKToiIiIiIiIhIQik5ISIiIiIiIiIJpeSEiIiIiIiIiCSUkhMiIiIiIiIiklBKToiIiIiIiIhIQmUkOgDpmi+//JK3334bgGHDhpGRoadUREREREREYufQoUPs3LkTgEmTJtG3b98u96kr2RT39ttvM3Xq1ESHISIiIiIiImlo5cqVTJkypcv9aFqHiIiIiIiIiCSURk6kuGHDhvn+vXLlSkaMGJHAaERERERERKS72759u28Ev/81aVcoOZHi/GtMjBgxglGjRiUwGhEREREREUkn0ap7qGkdIiIiIiIiIpJQSk6IiIiIiIiISEIpOSEiIiIiIiIiCZXWyQmn00l2djZutzsq/ZWXl5OXl0dhYSEOhwOHw4HT6YxK3yIiIiIiIiLdVVoWxHQ6nZSVlVFeXh61Ph0OBy6Xi2XLlmG1WgFwuVzk5ORQUlJCQUFB1G5LREREREREpDtJq5ETpaWl5OTkUFZWhsPh8CURuqq8vJzKyspWiQkAm83GokWLKCws1AgKERERERERkSAsHo/Hk+ggEmXw4MG43W4aGhq6lKgYPHgwkydPpqqqKuBxi8VCbm5u0ONdsXXrVkaPHg3Ali1btJSoiIiIiIiIxFQsrkPTauRELDidTtxuN3a7PWgbu91OdXV11GpbiIiIiIiIiHQnSk500eLFiwEYMmRI0DZZWVkAVFdXxyUmERERERERkVSi5EQXeRMOoaaFeI+tWrUqDhGJiIiIiIiIpJa0XK0jmrxTNbyjIwLxHuvMtI6tW7eGPL59+/aI+xQRERER8WpubmbPnj00NjZy4MABmpqaEh2SiMRQz5496d27N5mZmQwYMIAePZJjzIKSE11UX18fk7Ze3iIjIiIiIiLRtnv3brZt20Ya18gXSTuHDh1i//797N69G4vFwsiRIxk4cGCiw1JyQkREREQkHQVKTFgsFnr27JnAqEQk1pqamnx/9x6Ph23btiVFgkLJiSS3ZcuWkMe3b9/O1KlT4xSNiIiIiHQHzc3NrRITAwYMICsri379+mGxWBIcnYjEksfjYd++fdTX17Nnzx5fgmLChAkJneKh5EQXZWVl4Xa7w5qyEaouRTDRWC9WRERERMSf94IETGJi1KhRSkqIpAmLxUL//v3p168fW7du9b0f7Nmzh8zMzITFlRyVL1JYqFU6vLyJi3DaioiIiIjEWmNjo+/fWVlZSkyIpCGLxdLqC3T/94VEUHKiiyZPngxAXV1d0DYulwuAKVOmxCUmEREREZFQDhw4AJiLk379+iU4GhFJFP+pXN73hUTRtI4uysvLo7y8POQyod5j+fn58QlKRERERCQE73KhPXv2jN+oiYULzRZt8+ebTUQi5i2Ce+jQoYQvI6zkRBd5Ew7V1dUBj7vdblwuF3a7PZ5hiYiIiIgkl8ZG2LYtNv2KSMpTciIMLpcLh8PB5MmTKSsra3e8pKSE4uJiXC4XNput1bElS5YAsGjRorjEKiIiIiKSlDIzYeTI4Mebm2H79tb7RoyAjlYPSGABPxGJnrRNTrhcLt90i9WrV5Obmxu0bVlZGU6nE6fTSWFhYbtREEVFRaxatYq8vDxqa2t9hS+dTifFxcWUlJRo5ISIiIiIpLeOpl/s3AnDh7fet3YtDBsW27hEJCmkVXKisrKSBQsW+Jb+9CYRHA4HWVlZWK1W5syZQ1FRUavz5syZQ2VlJTabLWiSoaKigvLychwOBzabjfr6etxuNxUVFSETH2mjsRG2boU9e2DAABg1SlluERERERERAcDi8S5wLClp69atjB49GoAtW7YwatSoBEfkx+OB5cvhzjvhiSfAv8BKz54wezbMmwczZoCWrxIRERGJm/Xr13Po0CEyMjIYP358osMxAo2cqKuDNtOmRSS6OvN+EIvrUC0lKrHhdMKkSTBzJjz2WOvEBJjfKyvN8UmTTHsRERERST8eD9TUwFVXtT82YQI4HOZ4Cn6nWl5ejsVi6dSKKG63u9PnRkNOTg4WiyVo4X+RaFNyQrpu4UIzTcO7DR0KkyfDunXhnb9unWk/dGjrfmKx1JSIiIiIJA//L7Seeab9cX2hJZI20qrmhMRINJaF8nhg1672/YqIiIhI91RVZab57t0bXvt162D6dFi6FPLyYhubiMSdRk5I13mXhTr6aMjoYr4rI8P0M3KkCmaKiIiIdFdOZ2SJCa+9e815GkEh0u0oOSFdN3++WYnj3/+GQ4e61tehQ/DQQ6a/UEtNiYiIiEhq8njg8ssjT0x47d0LV1yRkjUoRCQ4JSckeu66K7n6EREREZHks3x5+LXJgnnnHXjppaiEIyLJQTUnJDoaG838v2h4/HHTn6Z1iIiIiCSHnTs7f+6AAXDEEYd/j9YXUX/7m1mSPpD6+varxYUybFhUQhKRztPICYmOrVsj+w8glKamrhfYFBEREZHoGT6889v99x/uJ5pfaD3xRPAC6mefHVmMCeRyuXA4HAwePJjBgweTl5dHZWVlWOcWFxf7lvzMycmhuLg4aNvy8nLy8vLIzs72tS8tLY3W3RDpMiUnJDr27Iluf598Et3+RERERCTxovmFlseT8l9oVVdXk52dTWVlJW63G5vNRn19PQ6Hg1mzZgU9z+l0kp2dTWlpKS6Xi9zcXNxuN6WlpWRnZ+N2u1u1z8nJobCwkNWrV2O1WsnPz8flcvmSGyLJQMkJiY4BA6LbX24unHYa3HQTPP989JMfIiIiIhJ/0f5Mt3t3dPuLM4fDAUBubi4NDQ3U1tZSW1tLQ0NDh+e5XC7KyspoaGigqqqKuro6KioqcLlczJ07t1X73NxcKioqfLfh/bfdbsfpdGoEhSQFJSckOkaNgp49o9dfczO8+SbceitccAEMHqwlo0RERERSXbS/0Bo4MLr9xVFxcbFvtERVVRVWq9V3zGq1UlFREfQ8l8tFSUkJBQUFrY7l5+dTVFTkG4nhVVJSQn5+fru+brrpJgDKysq6fodEukgFMSU6MjPNmtNhzo+LWEYGnHhi4GP79pmf/frF5rZFRERE0t2OHZ0/1z8h4f1CKxpTOzIyYOTIwMdefjl600dipLq6GiBknYhQ59XV1QU81+Vy+doFSkj4s9vtrc4RSSQlJyR65s2LTnIi0H9YZ5wBffoEbv/II+a2p00zFZtnzIDTT29dFVpEREREOi9aq1lE8wut2bODr+6WldX1/mPMmxCYPHlyROc5W0YTl5eXh2xXX18fso/6+npfXyLJQMkJiZ4ZM8zohq6sWz1xIrz+utmWL4eaGli1KvgyUWDaHDhgMuQvvwx/+AP07m1qVpx7rjn3tNOgb9/OxyUiIiIi0RGtL7Tmzet6HynIarXidrvxeDwRnVdaWsrixYuVkJCkpZoT0nULF5oheqNHmyF/Fkvn+rFY4NNP4StfgSuvhCFD4LXXoKEBfvKTwOd4PCaJ0daBA7BiBdx8s0lQWK3m5803m/3793cuRhERERHpGu8XWl0xcSKcc05UwkkUm80GwOrVqyM6zzvSItwkg3dlj+LiYrKysigrK/MV3ayrq4ssaJEYUnJCuq6x0SzjtG0b7NxpEgad4fGY8719edetHjDAFMQMZOvW8JYd3b/fJDF+9zvzH5nVCrNmwb/+1blYRURERKRzLBZ48EHo379z5/fvDw880PkvxJJEbm4uELwYZbA6EN4VPsKtVeEtoFlRUUFVVRUFBQXY7fZWBThFkoGSE9J1mZmmGJH/NmyYKVIUjowM075tH8HmEPobPRrq6+E//4Ebb4QpU6BHGC/rL7+EF1+EDRvCi1FEREREosduh6VLI09Q9O9vzmsp5JjKvCtlOJ1OCgsLWx1zuVzt9nkVFBSQm5tLdXW1L1Hhz+l0kpeX5xtZ4U1CrFq1ql3bkpKSrtwFkahSckK6bv58M4LBf9uxw0ytqKmB/Pz2y4xmZIDDcbhexI4d7fuYPz+828/MhAsvhNJSWLnSJCueeQZuuAFyckInK0LVsvje9+BPfzJTSw4eDC8WEREREQlPXp6ZbhvuFI+JE037vLzYxhUn/suFlpeXM3jwYPLy8sjJySE7O9u3kkYgFRUV5ObmUllZicViIScnh7y8PAYPHkxOTo5vRQ/Al+QoLS3FYrGQl5dHXl4eFouFJUuWxPZOikRABTEldiyWw6tneKd+7N5t1qMOd2REZwwaBF/9qtkA3G545RWTCFm+HNasMVNI+vQxq3oEsmkTPPTQ4d/794czzzxcYDMnB3r1ik38IiIiIunCboe334aXXjJ1zJ5+uvXxjAyzKse8eWZqbopP5WgrPz+f2tpaFixYQHV1NS6XC7vdzqJFi7DZbLjd7oDnWa1WqqqqKC8vp6KigtWrV+NyuZg8eTJ5eXkUFBT4Rkzk5uZSUVHBggULcDqdrF69msmTJ1NSUkJBQUHA0RciiWDxRFrmVZLK1q1bGT16NABbtmxh1KhRCY4oBTQ0mFU9Nm+Ga68N3Oaf/4Srrgrex4ABcNZZJlFx7rnmP9Zwp7GIiIiIJNj69es5dOgQGRkZjB8/PtHhGDt3wvDhrffV1UFL4UgRiY3OvB/E4jpUV1OSfgYPhosvDt0m0Aog/vbsgeeeMxuY0SBnn314pIjd3n4qi4iIiIhEZuDAREcgInGi5IRIIJddZqaH1NTAO+903H73blOU8z//MdM9Gho6X4FaREREREQkzSg5IRLIeeeZDcwQw5deMqMpli+HdetCnzt1avDExHvvmZVCTjpJIytEREQkvSxcaLZgmpvb7zv55I5XYps/P/xC6iKStJScEOnIsGFmxZH8fPP7jh0mWeEtsPnee63bn3tu8L5uuw3uv99MLZk+/XDNikmTwlsCVURERCRVeQukR2L79vD6FZGUp+SESKSGDzfLoHorG3/yyeGRFTU1oZcn9dayaGiAJ580G0BWlqlA7a1ZMXGikhUiIiLSvWRmmhXbYtGviKQ8rdaR4rRaRxLyeAIvc/XRR3DsseH1MWSISVZ4ly494QQlK0RERCRqknK1DhFJiGRZrUNXOyLRFmz97W3bYNy48PrYtQsef9wsdTppEhx5JPz739GLUUREREREJIkoOSESL6efDuvXw5YtJtHwwx9CdnZ45372GQwdGvy4BkCJiIiIiEgKU80JkXgbNcosVXrZZeb3LVsO16tYvhw2bmx/Ts+ecNZZgfv7+GOw2w/Xqzj3XJgwIfgIDhERERERkSSj5IRIoo0eDd//vtkANm8+vGxpTY35fcoUGDAg8PnLl8Onn8LixWYDOOqow4mKGTNg/HglK0REREREJGkpOSGSbI49Fq64wmwAmzaZGhTBeFcA8ffJJ/Doo2YDOPro1smK7GwlK0REREREJGkoOSGS7MaMMVswgZITbX38MTz8sNnATC2ZMQNmzYIf/KDLIYqIiIiIiHSFCmKKpLr//AcWLYJLL4URI8I7Z+tWU5TzzjtjG5uIiIiIiEgYNHJCJNWNG2e2q682q3Zs2HC4uGZNjZniEcy55wY/9q9/QXOzaXPMMVEPW0RERNLLwtcXsvD1hVHvd/7p85l/+vyo9ysi8aXkhEh3YrGY4pfjx0NBgUlWfPjh4WSFt3im14wZwfv605/g/ffNv8eObV2zYvTomN0FERER6Z4a9zeybfe2mPQrIqlPyQmR7sxigeOOM9s115hkxfvvH05UBFue9JNPDicmwCxvunEj/POf5vfs7NZLl44cGdv7ISIiIikvs08mIwcG/8zQ7Glm+57trfaNGDCCHpbQM9Ez+2RGJT4RSSwlJ0TSicUCxx9vth/9KHi7l14K3U9dndnuu8/8Pm5c65EVRx8drYhFRESkm+ho+sXOvTsZftvwVvvWXrOWYf2HxTo0EUkCKogpIu0deSR8+9swZEh47TdsgHvvhcsuM6Monn8+tvGJiIiIiEi3ouSEiLQ3YwZUVsKOHfDWW3DHHTB7NmRldXyuxQJTpwY+tm9f6AKdIiIiIn5279+d6BC6pLKyEovF4tsqKytDtq+ursZisZCdnR2nCGOrtLSU0tJSnE5nQvuIp1SLN5koOSEiwfXoAZMmwXXXweOPw86d8L//we23wze+AVZr+3NOOQUGDw7c33//a5Y7PeEEmDcPliwxCRARERFJWx6Ph5qNNVz15FXtjk34xwQcFQ5qNtbg8XgSEF10FRcXJzqEuCouLqa4uJjq6uqE9hFPqRZvMlHNCREJX48ecPLJZrv+emhqgrffPrwayEsvhV4BZPly8/O998x2993m9xNOOFyv4pxzYJjmloqIiKQD53Ynly+9nHU71wU83uRpovLdSirfreTEYSfy4OwHsY+wxznKrrPZbNTX1+NyuaisrCQ/Pz/RIYkkHSUnRKTzevY0IyVOOQV+9jOTrNi3L3h7b3KirXffNdudd5rfJ048XGBz+nQYOjS6cYuIiEjCVdVVMXvxbPYe3BtW+3U71zH9n9NZOmcpedl5MY4uuqxWK8XFxRQWFlJcXJw2yYmORrsUFxczZMgQioqKOt1HPKVavKlG0zpEJHp69oSBAwMf27MHPvoovH7eeQf+8Q9TlHPYMDNS47bboheniIiIJJRzuzOixITX3oN7mb14Ns7tqTefv6CgAKvV6hs9ke7cbjelpaWUlZUlOpSwpFq8qUjJCRGJjwEDYNcuePNNKCmBCy6A/v3DO/ett2DLltjGJyIiInHh8Xi4fOnlEScmvPYe3MsVT1yRkt9Ql5SUAOlXe0IkHEpOiEj8ZGSYlTyKikxxzIYGeP11WLAAzj8/dLIiVC2Lyy6Dn/8cnn4aPv886mGLiIhI9CzftDxojYlwvbPjHV7a/FKUIoof/9ET5eXliQ5HJKkoOSEiidOrF5x2GvziF/DccyZZ8dpr8Mc/Ql4eHHGEaWexmNoTgdTXwyOPwMKFcPHFZrnTyZPhhhvg2WeVrBAREemkZk8zO/fujPr21zf+GpX4/vr6X6MeW7OnOSqxheIdPeH92Rnl5eXk5OT4lh11OBxdWh3C4XCQnZ2N0+nE5XLhcDgYPHiwr3//aSiVlZW+2x48eDB5eXm4XK6A/Xrb+cdWWlrqOxfA5XK1Wm41Ly+vwz78ud1uX7zeeKqrqykvL8fhcJCTk8PgwYPbxVheXk5eXh7Z2dlYLBZycnIoLS1t13+04/XediTPX6Dnxxt32+cnlakgpogkj1694PTTzfbLX8KBA7BqlVkRZMiQwOesWAH+wzqbm6G21mx/+YtZYSQn53CBzbPOCl4XQ0RERHx27dvF8NuGJzqMoJ768Kmox7fjhh0M6x/bVcMKCgooLi72jZ4oKCgI+1y3282sWbNwOk3NDbvdrFxSWVnpWwWkoqIi4phcLhcul4vVq1f7ppzYbDbffofDQUVFBatWraK0tBS73e47Xl1dTU5ODhs3bsQaaJn5Nux2OwUFBdTX11NZWYnVauWSSy7xHc/Ozo4obm/73NxcrFYrlZWVVFdXY7VamTx5MjabjaysLNxut++8nJwcnE4nVqsVm81Gfn4+1dXVFBcXs3jxYmpra2MSb2efP+/z4I3RG7f3mPf5SfVCq0pOiEjy6t0bzjzTbMHU1ITuo7nZJDhWrYI//9kU7czJObx06YwZ0LdvNKMWERERCamkpMS3ckckyYmcnBxcLhdFRUWtRl54Rw9UVlZSWFjY6aKNhYWFlJSUtFqNwjsSweFwYLVaqaura3VhnJOTg9vtpry8POQqFl65ubnk5ub6CoNmZWV1Ol7vqIXa2lrfhb7b7Wbs2LG43W4qKioCJkxyc3O56aab2l3Me5MWpaWlvvsSzXi7+vwVFxcHfX4WLFiQ8skJTesQkdR2+eVw880mydCnT8ftm5pg5UpTlPPCC6GxMeYhioiIiPgrKCjAZrP5LurDUVpaisvlIj8/v92UEKvVSlVVFTabjfLy8k5P8bDb7e0SDP63ddNNN/kSE2BGV3hHEdTV1XXqNjvLO5rAbrf7EhNgHgtvwifY41BSUhLwQv6mm24CiMmKHNF4/kI9P97RGKlMyQkRSW05OfCb35gRFG63+fnb35oaFb17hz73hBNgeJDhoO+9B1VVsLdzlcQ7ZeFCGDUq+tvChfG7DyIiIhKWSFfu8F4wey+gAyksLATo1NQOMKME2vJPRgS6oPdOa6ivr+/UbXaWd5pGVlZWu2NDWqYDB6uFEYw3yRHpeeGIxvMX6PkJZypNqtC0DhHpPvr2PTxV43e/gy++gDfeMAmL5cvNvw8ePNz+3HOD93XffaZmRa9eZoURb82K00+Hfv1iE39jI2zbFpt+RUREIjSk3xB23LAjqn3u3r+bCf+YQJOnqct9ZfTI4IMff8DAPtGrJTWkX5AaVzGQn5/vq9sQTu0J7wWz/yiBtrzHOjtyoqP6CYESAYniTZqsXr263bFVq1YBoR8rf06nk/r6+piOPojG8xdJfYtUpOSEiHRfRxxhEgreJMS+fWbp0uXLTcIiQPbZx1vL4uBBePVVs/3xjyZZMW3a4ZoVp59+eFWRrsrMhJEjgx9vbobt21vvGzHCFP3sqF8REZEI9bD0iHpxyGH9hzH7+NlUvtv11QVmf2U2tixbxw2TWElJCQ6Ho8PaE/7FHN1ud8K+LU+mb+m90zfKy8t9tTLArIRRWVmJ3W4PONLAq7S0lMWLF8dlOkSyPH/JTskJEUkf/frBrFlmC8XthjVrAh87eBBeecVsf/iDmTpy2mmHkxWnndb5Apvz55stmJ07209DeeUVsKX2BzMREUkv8ybPi0pyYt6UeVGIJrH8R094V8EIxGq1YrVacbvdrF69OuhFd9tVILo776iJ8vLyVrU77HY7y5YtC3iO0+nE4XDgcrnIzc2lrKzMt6pHfX19TEYn6PkLj2pOiIi09cknZipHz54dtz1wwCxnevPNJkFhtZrpINHk8ZiRHFdd1f7YhAngcJjj/kuqioiIJKkZY2Zw4rATu9THxOETOefYc6IUUWJ5v/FfsGBByHbewpPBalS43W5fH3PmzIlihLHV2VoVbrcbp9NJRUUFtbW1lJSUUFJSQlVVFbW1tUFHJ3iXca2oqKCqqoqCggLsdnvYoxk6G293ff6iSckJEZG2vvIVU5+ioQH++18oKjLJio6mTwDs3x96asahQ5HF4nTCpEkwcyY880z7401NUFlpjk+aZNqLiIgkMYvFwoOzH6R/r/6dOr9/r/488M0HsFgsUY4sMbyjJ9xud7tVHPyVlZVhs9l83/z7TxVwuVzMmjULt9tNbm5uSiwp6a1f4Xa7WxWgrKyMbFRNVVWVbxWLoqKikFM54PDUFG9dCn+hHv+uxtvdnr9YUHJCRCSYgQPhggvMsqNvvmmSFc8+CzfcAJMnB09WnBPkm5zGRsjKgrw8U7/i1VfNyItgqqrMqiPr1oUX77p1pn1VVXjtRUREEsQ+ws7SOUsjTlD079WfpXOWYh/RvYa9ey+KOypk6V1usrKyksGDB5Odne376XQ6yc3N7fRKHfFmtVp9RS2zs7PJzs7GYrHgcDjCPt9ut1NeXo7FYmm1DR48mLy8PEpLS9ud510Ro7S0FIvFQl5eHnl5eVgsFpYsWRKzeKF7PX+xoOSEiEi4MjPhoovgz3+GVaugvh6efhp+/nOzpKnFYkZdjBgR+PxXX4Xdu6G6Gn79azjrLBg8GM47DxYsMMU6vauJOJ0we3bkS5nu3WvO0wgKERFJcnnZeay4ckXYUzwmDp/IiitXkJedF+PI4i8/Pz+sOgM2m426ujpKSkrIzc31TTHIz8/3TVNIpWKLFRUVvgt+b4HLqjC/ZHG5XK1GH/hzu91UV1dTXFxMTk5Oq3beBID38fbWrSgpKWHjxo3k5uYGHX3RlXih+z1/0WbxeDRJOZVt3bqV0aNHA7BlyxZGjRqV4IhE0pjbDVu2mOkVgRQVmcRGKP37w5lnwtq18OmnnY9l4kR46y2TMBEREWlj/fr1HDp0iIyMDMaPH5/QWDweDy9tfomFry/k6Q+fbnUso0cGs78ym3lT5nHOsed0m6kc0nU5OTk4nU5KSkooKChodVHvn5xwuVy+i38JrDPvB7G4DlVyIsUpOSGSQqZNg5Ur43d7NTVmBREREZE2kik54bVz706G39Z6Vaq6a+tSfrlQiT63283gwYN9IxGCcblcZGdnY7VaaWhoiGOEqSVZkhOa1iEiEi///S8sXQrXXQcnnRT727vrrtjfhoiISAwN7DMw0SFICvMWsfT+lOSm5ISISLxkZcE3vwl33GGmbXz2GTz2GFx7rZmGEW2PP26KcIqIiIh0I95imC6Xy1fgsi23282sWbMA0nb1i1STkegARETS1pAh8K1vmQ1g5074979h/vzo9N/UBB99FJvEh4iISIQWvr6Qha8vDHq82dPcbt/J95xMD0vo71Pnnz6f+adH6f9OSRnLli1j1qxZlJeXU15eTm5urq9Y5erVq3G2FAcvKCgIuUSoJA8lJ0REksWwYaYYZjSdcQbMnGlqT5x7rinWGWwJVBERkRhq3N/Itt3bIjpn+57tYfUr6cdqtVJbW0t1dTVlZWU4nU6qq6t9S34WFBRQWFgY1iookhyUnBARSSYDBkS3v9274cknzQZm6dJzzjGJihkzzKgKJStERCQOMvtkMnLgyJj0K+kr1NKfklqUnBARSSajRkHPnmZKRiw0NMATT5gN4MEH4fvfj81tiYiI+NH0CxEJRV+XiYgkk8xMmD07frc3fXrg/V9+Ce++C1ptWkRERETiQMkJEZFkM29edPpZvBj+9S+46iqwBVgjfuxYOPbYwOe+9hqceCIcdRTMmQN33w3vv69khYiIiIjEhKZ1iIgkmxkzTGJg3brO9zFxIjgcYLHA975n9n30ESxfbraaGnM7wdTUmJ87dsCSJWYDk6yYMeNwgc3x481tiIiIiIh0gZITIiLJxmIxtSCmT4e9eyM/v39/eOCB9kmDY46Byy83G8D+/cH7WL488P5PPoFHHzUbwIgRhxMVM2bAuHFKVoiIiIhIxDStQ0QkGdntsHSpSTREon9/c144y2b16RN4f1OTSUKEY/t2eOQRKCiACRNg9Gi44Ybw4xURERERQckJEZHklZcHK1aYKR7hmDjRtM/L69rt9uwJH34IdXVw331mWsjIMJd+27bNTAUREREREYmApnWIiCQzux3efhteegkWLoSnn259PCPDrO4xbx6cc070plRYLKaIps1mCmp6PCZZ4a1XUVNjRk0Ecu65wfv93vdM8sM7FWTMmOjEKyIiIiIpTckJEZFksXCh2YJpbm6/b8gQs7LGa68FP2/+fLN1hcVi6kmMGwdXX22SFRs2mCSFN2HhnQoSrNDml19CZaWpdfHgg2bfsce2rlkRbPUQERFJeQtbtmib37KJSGpTckJEJFk0NpppEZH49NPw+o02i8Ws1DF+vKk34fGYqSCvvx58NMQbb7Qvwrl5syne+cAD5vexY1uvBjJ6dPRjFxGRhGgEIvxfLux+RST1KTkhIpIsMjPDr+0Qab+xZrHAcceZLZhgK4D427jRbP/8p/ndZjs8quLrX4dBg6IRrYiIJEAmEOp/uWag7YTBEXRcJC8O/8uJSBwoOSEikiyiMf0imX3/+zBs2OGpILt2dXyOy2W2++6DDz5QckJEJIV1NP1iJzC8zb61wLCYRSQiyUSrdYiISHxkZ8OPf2zqTuzYYQp9/u1v8K1vQVZW6HOPPtpMIQnkww/h0UfDX/5URERSxu5EByAicaPkhIiIxF+PHmbp02uvhcceg507Ye1auOMO+OY3YfDg1u1nzAi+EsmSJfDd78KIEXD88fCjH5l94dTjEBGRhPMANcBVAY5NABwtxz3xDEpE4i5tkxPl5eXk5eVRWFiIw+HA4XDgdDqj1q93i1a/IiLdWo8ecNJJcN11sHQpfPYZrFkDf/0rfOMbcNFFwc/1r2Xx/vtwzz0wZw4cdRSceKIZrVFRYRIgIiKSVJzAJGAm8EyA401AZcvxSS3tU4nb7cZisUS8VVdXt+qnsrKy1fHKysqQt1tdXY3FYiE7Ozvg8crKSgYPHhz2FqwfkWhKy5oTDocDl8vFsmXLsFqtALhcLnJycigpKaGgoKBT/ebl5eFyuaiqqsJmswHmDz8nJ4eKigry8/OjdRdERLq3Hj3glFPM9tOfBm+3f3/oZVTffddsd91lfj/xxMMFNs85B4YOjV7MIiISkSpgNrA3zPbrgOnAUiAvVkHFQG5ubtBj3msRt9vdKiExefLkkH0WFxd36dqivr4et9sN4Ltu8T8mkghpl5woLy+nsrKShoYG35sBmD/KRYsW4XA4mDx5Mna7PaJ+CwsLqa6ubpWYAMjPzyc/Px+Hw9HuNkVEpIvq62HWLFixIrwlU9etM9s//mF+//nP4bbbYhujiIi04ySyxITX3pbzVgCRfVpPDKvVSlVVVYftKisrfcmJsrKyoNcMNpuN+vp6XC4XlZWVUfnys66urst9iERD2k3rKC4uJjc3N+AfvPePu7i4OKI+nU4n5eXlWK3WgJnRwsLCTvXbnTQC7wIrW35qPWoRiYoRI+Dpp83KH6tWQWmpmQIycGB4548bF/zY3kg/MouISDg8wOVEnpjw2gtcQfepQeF2u5k7dy5gRlmEGsVttVopKSkB0vvaQrqntEpOOJ1O3G53yFERdrud6upq3zCncJSVlQHBh1959y9ZsiT8YLsBb3GjfCALOBGY1vIzCxU3EpEoysiAyZPhxhvh2WfNiIo334SSErjgAujfP/B5M2YE3n/woEl8nHoq/Oxn8OST0NAQs/BFRNLJcswUja54B3ip66Ekhblz5/quPbzXFaEUFBRgtVp9oydEuou0mtaxePFiAIYMGRK0TVbLcnbV1dVhD5NavXo1QNDhV/5zyZxOZ8RTRlKRE5MRD/Yfj7e4USUmWfEgqTE0T0RSREYGTJ1qtqIik2yorTXFM5cvh1deMaMrjjsu8PmrV8Pu3fC//5nt9tvNaiGnnHK4ZsXZZ4Om6olIN9YM7IpBv3+NYj8nRqkvryHE99vbyspKX4KhrKysXf2HYEpKSigsLOxy7QmRZJJWyQnvPK5QdR+8x1atWhWTP/TVq1d3++REuhQ3EpEU0qsXnHaa2X7xC5Os2LQp+PKk/iuAeHk8ZgWRNWtg4UJTtPPUU02i4txz4ayzYNCgGN4JEZH42gUMT3QQITxF9OPbAQyLcp/BRDKdo62CggKKi4txuVyUl5d3uqC/SDJJq2kd3uFS3tERgXiPRTKtI9wMJ0RecGbr1q0ht+3bt0fUX6x1tbhRqi0PJSIpqlcvGD8++PFXXum4j+ZmMxrjL3+Br30NsrIOj9TYty96sYqISLcU6XSOtry1J7w/RVJdWo2ciGRZnEjaTpkyhcrKyqAJDf/9kSQ9AEaPHh1R+0SKVnGjt4Ag32WKiMTH44/DypVQU2NGUbz2mlm2NJTmZlOUc+NGuPXWuIQpIiKpqbPTOfxFa/SEw+HosM2iRYu06qDEXFolJ2LF+8bgvzaxv3QphLmc6BU3mtHVYEREuqJPH1NT4uyz4Te/gS+/NAU2ly83CYvXX4cDBwKfe845ZspHII88Am+/baaCnHlm8EKdIiLSbflP57Db7V2akuFfe6Kz/YRTVLOkpETJCYk5JSeiwGq1UlZWRmFhYcD1hmtra7HZbLhcroj/qLds2RLy+Pbt25k6dWqkIcfEXVHsZ0aU+hIRiYq+fU3S4Zxz4Le/hS++MMkK78iKN944nKw499zg/fz73/Cf/8CCBYeLdnprVpxxBvTrF497IyISliGYGgzRtBuYgCmO3lUZwAdAmItHhyV42fzo8Z/OUVFR0aW+CgoKKCkp6dLoCY9Ha+dJckir5ERWVhZutzusKRuh6lIE4n0j8GZB8/PzcTqdLFiwgJKSEt/oiVArhQQyatSoiNonSiOmoGU0PN7SX2aU+hMRibojjjBJBe9SpPv2mQTF8uVw/vmBzzl0CF5+ufXvr71mtj/9ydTBmDbtcLLi9NPN7YiIJEgPol8cchimzlg0FsCcDUQ+GSKxqqurfSMVSkpKOjWdo62SkhIcDkeXRk+IJIO0KogZzqgFb+KiM8OWCgoK2LhxI/X19RQWFlJdXc2iRYuw2Wy+7Ghubm7E/Sa7bwBWopMBp6WfQcAk4GnMVI89UepbRCQm+vWDmTPh97+HceMCt3E6zfKkwRw8aApx3nILzJpllimdPt2M1KipMVNLRES6gXlJ1k88ees72O12ioqKotJnfn6+73qjvLw8Kn2KJEJaJScmT54MhF4xw+VyAabIZWdYrVYKCgooKyujqKgIq9Xq69NqtXbLZUQ/xxTDjLZ3gIsxSYqBmKWipgHfAW4CyjHLlm4Agsz8FhFJHkOHwi9/aUZEZIQxcPHAATPS4ve/N4mPe+6JfYwiInEwAzixi31MBM7peihxVVhYGLXpHG15V+woLi6Oar8i8ZRW0zry8vIoLy8PuWKG91jbuhEdqa6uxuVyBRxK5S2U2V2X+RmEWV0j1rPVdrZsKwMcswCjgLFBtqNJs0yciCQfmw3++Efz7z17zHQOb82KVaugqYPxZ8FqWTQ1mb6mTjWFPEVEkpwFeBCYTudWeesPPEBqre5WXV3tG9UQrekc/ryjJ1wuF6Wlpd3yC1Hp/tIqOeFNOARbVcPtduNyuSL+Y3a73eTl5QFgs9naTd0oKyvzjajojp7E1IjIInpTOyLlAba0bCsCHO8NHEvw5MUQUus/OBFJcQMGwHnnmQ3MdI9XXz28GkhtbetkRVYWTJoUuK+1a830j759TVFNb82KqVOhd+9Y3xMRkU6xY+qVzSayBEX/lvNS7dI7FtM52vLWnliwYEHUR2aIxENaJSfA/NF61wNum7H0Fq1ctGhRu/NcLhcOh4PJkydTVlbW6pi3PoXVavVNHfGqrKzE6XR2+zeITKJX3Oh44FRgY8v2SRT6PACsb9kCGUDwxMXYluMiIjEzcCBccIHZABobTf0Jb7JizJjgy5MuX25+fvklvPii2cAU0zzzzMOFO6dMUbJCRJJKHuZLpcsJbzn6iZgRE6mWmIjldA5//qMnuuuIbene0i45UVRUxKpVq8jLy6O2ttaXWHA6nRQXF1NSUhJw5ERZWRlOpxOn00lhYWG7NgUFBWRnZ7cqpFleXk5hYSEVFRURTxNJRfOITnKi7VKiXwCbOJysaLu5o3Cbe4C3W7ZAhhI8cXEMoIHUIhJVmZlw0UVmA2huDt62pibw/i++gOpqs4Ep2nnmmWZUxYwZMHmyWSFERCSB7JjPXy8BCzHF0P1lYL4Am4epMZFqI12dTqdvOofVavWNoOhIYWFhp0Zde0dPBBspHsjgwYMD7m87Fb6qqqpbFveX5JF2yQkwGcvy8nIcDgc2m436+nrcbjcVFRVB/+DmzJlDZWUlNpstaPKitLSUnJwcwPwx5+bmUldXF/U5ZclqBqa4UTiZ72ACFTc6AjOa4vgg57gJnrjYhEludNVnLduqAMcswEhC17voGYUYRCSNBRs1AaZ+hcUCHa1Tv28fVFWZDaB/f/jqV2Hx4ujFKSLSCRYOf44c3ubYB6TecqH+/JMEbrcbp9MZ1nmhauSFkp+fj91uD/t2unJbItFm8Xg6+jQjyWzr1q2MHj0agC1btjBq1KiExuOka8WNVhDdoXoe4FOCJy8+IvZ1MnoRut7FUFLvWwARSTL19WZlD2+BzbVrwzvva1+Dp9t+Tyki6WD9+vUcOnSIjIwMxo8fn+hwAFP4vG1yYgcwLAGxiKSTzrwfxOI6NC1HTkjsJFtxIwtwVMt2eoDjh4CtBE9ebI9CDAcxy51uCHK8P6HrXQyMQgwi0s1lZcE3vmE2gF27YMWKwzUr3g4yaS3YCiAAV14JO3cerllx6qnQU+PAREREJDaUnJCoS6XiRhnAmJYt0Ef0L4DNBE9eNEQhhr3AOy1bIEMInrg4FtW7EJEAhgyB2bPNBvDZZ/DSS4eTFeta3p1nzAh8fnMzPPWUGZHx7LNmX2amWRXEW7Pi5JOTO1mxcKHZom3+fLOJSMQWtmzBBKquczIdLwc/v2UTkdSm5ITEhH9xozsxoyL8p0+kSnGjI4CvtGyBfE7wxMVGolPvYlfLtjrAMQumpkWw5MVIVO9CRIChQ+Hb3zYbwI4dZmTFyScHbv/22yYx4a+xEZ55xmwAVqtJVniXLj3ppNC1MeKtsRG2bYtNvyLSKY1ApH+V4Yxi1V+lSPeg5ITEjLe40QwO/2e0GzNNYSRm+dFUNwg4pWVry4OZJxmq3sWhLt6+B/O4bgNeCXC8F2Y1kWDJi2Ekb2JIRGJo+HAItYpUsBVA/LndZnTFU0+Z3wcPhnPOMcmKSy6BESOiEWnnZWbCyJHBjzc3w/Y2lz0jRnScYMnsDv97iSRGJuYzYCz6FZHUp4KYKS7ZCmJK+A5hkgrBkhcfxyGG/pgpLcGSF/rPXiRN7dwJL754uMDmBx9Edv6bb8LUqTEJLWp27jRJGn87dsAwld6T9JCMBTFFJDFUEFMkzWVgakYcixld0taXhK53UR/gnEjtxdQFCVYbJIvQ9S76RiEGEUlCw4bBnDlmAzPCYPnywzUr1q8Pfu7AgRBgyW0ANm0y00JmzIATTzRLoIqIiIig5IRI0uoLHNeyBfI5sIngyYt9UYihvmWrDXI8VL2LUajehUi3MWIEfPe7ZgNTy+Gllw6PrNjgtx7R2WdDRpCPF88+C9dea/49bJiZBuItsHn88UpWiIiIpDElJ0RS1CBMBetA5ew8mLXCgyUuNtP1ehdgpp58DLwa4FgGoetdDEf1LkRS1siRcOmlZgPYsuXwaiBnnBH8PP9aFjt3QmWl2cBMsfAuW3ruuXDccfFPVuzerWkdIiIiCaLkhEg3ZMFc/A8HpgU43kTgehcuolfv4lBLf64gx/sRut7FoCjEICJxMno0fO97ZgumudkkMILZsQOWLDEbwJFHHk5UzJgBEyZEJ1nh8ZgkSqBlRidMMMuvzptnblMjOUREROJGyQmRNNQTM6rhGMxSrm3Fo97FPuDdli2QwQRPXIxB9S5EUs6ePfDVr5rREx991HH7Tz+FxYvNBmYFEO+/O8vphMsvh3VBKu00NR0ezXHiifDgg8HrZ4iIiEhUKTkhIu10VO+ikeCJi2jVu2ho2ZxBjo8gdL2L7vrm1ghsBfYAAzD3VauqSErIzIT/+z8zcmHTpsP1KmpqYOvWjs8/OdAkthY7d8LQoaFHOlRVmVERe/eGF++6dTB9OixdCnl54Z0jkkJ69uzJoUOHaGpqwuPxYNFIIZG05PF4aGpqAsz7QiJ118/vIhJDmXS+3sVHwMEoxLC9ZXstwLEMYDTBkxdHklr1LjzAcuBO4AnMtByvnsBsYB5m1ZdUul+SpiwWGDvWbFddZZIVLtfhREVNDXwcYHLZuecG7s/jgZNOgl69WtesGDPmcLLC6YwsMeG1d685b8UKjaCQbqd3797s378fj8fDvn376N+/f6JDEpEE2LdvHx6PBzDvC4lk8XgjkZQUi/VlRWIpWL0L7/Yx5mI8lo4gdL0La4xvPxJO4HKCL/fq70TgQUCXUJLSPB6oq2s9sqKxERoaTAKirfffNyt9tHXMMYeTFbfeCh9+2PmYJk6Et95SDQrpVhobG9m2bRsAAwYMYNSoURo9IZJmPB4PW7duZc+ePQCMHDmSzMzwxuTG4jpUyYkUp+SEdDf7CV3vYlccYrASut7FEXGIAaAKMyoiku96+wNLAQ1Cl27D4zEjKUaODHz87rtNActYq6kxiQ6RbqK5uZkPP/zQ943pgAEDyMrKol+/fkpSiHRz3hFT9fX1vsSExWJhwoQJ9OjRI6w+YnEdqmkdIpJU+gATWrZAdhO63kWEg7YDcgNrWrZAjiJ48mI00XljdRJ5YoKW9rOBFWgEhXQTFkvwxATA6tXxieOuu5SckG6lR48ejBw5km3btuHxeNizZw979uzBYrEkfN65iMSWt9aMl8ViYeTIkWEnJmJFIydSnEZOiBzmAT4jeOJiM9GpdxFKT0LXuziKjutCeIBJhDeVI5iJwFth3JZIyvN4zNQO7xSQ5ctNgcxo69kT6utNYU+RbmT37t2+BIWIpB9vYmLgwIERnadpHdKOkhMi4WvC1LQIlrzYRuzrXfQldL2LwUANMDMKt1WDKZIpklY8Hnj3XZOkeOopeOGF6PX97ruB61uIpLjm5mb27NlDY2MjBw4c8FXuF5HuqWfPnvTu3ZvMzEwGDBjQqRETmtYhItIF3lENo4HpAY7vx6wmEix58VkUYvgSeL9lC2RQFG7D6y6UnJA0ZLHAiSeabcqU6CYndu+OXl8iSaRHjx5kZmaGXQhPRCQWlJwQEWnRBxjfsgWyh9D1LvZEIYbPo9CH1+NAI2bpV5G0NGBAdPsrK4M+fczSpSoYKCIiElVKToiIhGkAphbEpADHPJiVRELVuzgQnzB9mjArj3wdeDLOty2SFEaNMrUiojVE/f77zXbCCXDZZXDppTBmTHT6FhERSXNKToiIRIEFGNqyTQlwvJnQ9S62Ept6Fx7Myh+PAKdiRoWoBrukjcxMmD0bKiuj2++778KvfmW2M84wiYpLLoGhQ6N7OyIiImlEBTFTnApiinQPB4ALgRdjfDv9gJMxiQrvNhEzpUWkW6qpgZnRKDHbgYwMOP98M5riW9+Cvn1jf5siIiIJEovr0MQuZCoiIgD0BpZhak7EcmTDPuB1TLHMucBkzHSVk4Ergb8BL2NqVYh0CzNmmOKYXTFuHBQVQcuHsIAOHYJnn4Uf/AD27eva7YmIiKQhJSdERJJIJjA7zrd5CHgL+D/gesxKJoMwU0AuARYAzwGfxjkukaiwWODBB6F//86d378/LF4MJSWwaROsWAGFhZCVFbj9RRcFPyYiIiJBKTkhIpJk5kWpn98BvwIuAkZ04vwNQAXwS8yUk6OAkcDXgP+HWQ1kI7GplSESVXY7LF0aeYKif39znt1ufu/RA84+G+65B7Zvh6eegjlz4IgjDp9z6aXB+/v97+HXvzY1K0RERKQV1ZxIcao5IdL9eDArgqzrQh8TMaMh/Bc7/BRY02bb0IXb8LICp9C6jsVXUMVlSUJOJ1x+OawL469r4kR44IHDiYlQdu+GJ56Axx6DRx5pnazwOnAARoyA+nrz+ymnmEKa3/mOWVVEREQkhcTiOlTJiRSn5IRI9+TETK/Y24lz+wMrgDAuqfgcWEvrhMW7mKkeXdEXOInWCYtJQIBLNpH48njgpZdg4UJ4+unWxzIyzOoe8+bBOeeYKSHR8vTTcPHF7fdbLOa2LrsMvv1tGDw4ercpIiISI0pOSDtKToh0X1WY+hORJCj6A0uBvC7c7peYURv+CYu1mGKaXdETM6LCm6ywY0ZcWLvYr0in7NwJw4e33ldXBzZbbG7vO98xtStC6d3b1Ky47DL46lcDj8AQERFJArG4DtWoWxGRJJWHGQFxOeFN8ZgIPEB4IyZC6QvktGxeTcCHtJ8WUh9Bv02Y+7EO+Lff/rG0HmFxKqZGRhS/sxYJz8CBseu7uBhGjjTTPrZvD9zmwAEzPeSJJyAz0yxJetllcO650DOW6/iIiIgknkZOpDiNnBDp/jzAS8CdmFERTX7HMjCjK+YB5xDfC3oPsIXDiQpny8+tUeh7OIdHV3gTFjZUxVmiKNDIiR07YNiw2N5uU5OZVvLQQ1BZCY1hLNw7fjy8/74pyCkiIpIENHJCRCQNWYAZLVsjsA3YDQzErJ6RmcC4jmnZvuG3/zPaj7D4kMhW9dgBPN+yeQ2kfeHNE4BenYpeJEF69oSZM812553w7LPw8MPwzDNm5EQg06YpMSEiIt2ekhMiIikkk8QlI8I1FDMlxb/uxR7M6iHe0RVrgHeAgxH0uxt4uWXz6o0ptOmfsDgJU3tD0tzChWYLprm5/b6TT+44CTB/vtmioW9fUwTz298Gt9us9vHww1BTYwp3eoVanvS//4VTT4WjjopOTCIiIgmi5ISIiMTcAOCMls3rAGZlEP8RFv/DJDLCdQCobdm8egATaF/HYkjnQpdU1dgI27ZFdk6wWhBt+40FqxV++EOzbdsGjz5qpn5s3Qp5QUrc7tkD+fnw5Zcwa5apTzF7tqlXISIikmKUnBARkYTojZmmcQpwZcu+ZmAD7aeF7Iyg32bg/ZbtEb/9x9A+YTEKFd7stjIzTQHKWPQbayNHws9/brbPPjNLnAby5JOwr2Udnaoqs11zDXz96yZRccEF0KdP7OMVERGJAhXETHEqiCki3Z0H+JjWyQonsDkKfQ+h9dKmpwLjUeFNSRFf/Sr85z/Bjw8ebEZWXHYZnH226laIiEjUqCCmiIikHQum8OdI4Gt+++sx00D8kxbvY0ZOhGsXUN2yefUHTqb1CIsTAX3/LEnF4zF1JgYMMNM7AmlogEWLzDZqFHz3u6Z+xckng0VjhkREJLlo5ESK08gJEZHD9gFv0zph8Rawv4v99sKsDOK/tOnJmBVERBJq3z54+mlTn+K//4VDhzo+54QTYN48+PGPYx+fiIh0Sxo5ISIiEkI/YFrL5nUQM6KibeHNzyPo9yCwtmX7Z8s+CzCO9nUshnc6epFO6NcP5swx265dUFlpEhUvvxz8nHffhXXr4hejiIhIGDRyIsVp5ISISOQ8wEZa17BYA3wShb5H0j5hcSwqvClxtnkzPPKIWZr07bfbH3/lFTjzzMDnHjgAvXvHNj4REUlpsbgOVXIixSk5ISISPZ/QfqWQuij0OxizKol/8c3jgJ5R6FukQ2+/bUZTPPwwbNkCxx4LLlfgApm7dsG4cXDRRaaQZl4e9OoV/5hFRCSpKTkh7Sg5ISISW59jpnN4R1esAd4FmrrY7xHASbQeYTEJ6NvFfkWCam6GV1+F+nr4xjcCt7nnHvjRjw7/PnQoXHKJKaR5xhkqpCkiIoCSExKAkhMiIvH3JfAOrUdYrAW+6GK/PYHjab206SnAoC72KxK2s882Uz4CGTPGJCkuvRROPDGuYYmISHJRckLaUXJCRCQ5NAEf0H5aSEMU+rbRvo7FiCj0K9LKzp1w9NHhrfhx8slm2sd3vgMtn0NERCR9KDkh7Sg5ISKSvDzAR7ROVjiBbVHo+0haL216KiaJoUH30iU7dsCSJaZGxRtvdNzeYoHp002i4tvfhqys2McoIiIJp+SEtKPkhIhI6tlJ+xEW6zHJjK7IpHXhzVMx00RUzlA6pa7OrPjx0EPw/vsdty8qgpKS2MclIiIJp+SEtKPkhIhI97AbeIvWIyzWAQe72G8fTKFN/4TFSUC/LvYracTjgTVrzGofjzwCH38cuN3atXDSSfGNTUREEiIW16EZXe5BREREumwgcGbL5nUAk6DwH2HxP2BvBP3uB1a3bF49MEuZtq1joQH5EpDFAna72UpK4KWXzGiKxx6Dzz83bSZODJ6Y2LIF/vIXM/Vj8mSt+CEiIgEpOSEiIpKkenM4ceDVDGyg9QiLNcBnEfTbDLzXsj3st/9Y2icsRqI6FuKnZ0+YOdNsd94J//mPSVSceWbwcx59FO64w2zjx5vVPi67zPxbRESkhaZ1pDhN6xAREQ+myGbbOhabo9D3UA4nKrwFOMdhRl+IhOWUU8yUj7amTDGJiu98B446Ku5hiYhI56nmhLSj5ISIiARTj5kG4h1dsQaz3GlzF/sdAJxM6xEWJ2JGeoi0sm6dmfIRSo8eMGuWSVR861uQmRmf2EREpNOUnJB2lJwQEZFI7KN14c01wNuY2hRd0QuToPBf3vRkTCJD0tjHH8M995ipHy5Xx+379oWvf91M+7jgAujTJ/YxiohIxJSckHaUnBARka46iKk/0bbwZmMX+7UA42lfx2JYF/uNlkZgK7AHk0QZhVmOVWLA44GVK02SYvFi2LGj43MGD4Z//Qu++tXYxyciIhFRckLaUXJCRERioRnYSOuEhRP4NAp9j6J9wuIY4lN40wMsB+4EngCa/I71BGYD84AZcYonLR06BMuWmUTF0qWwZ0/wtnV1YLPFLzYREQmLkhPSjpITIiIST9tpX3gzjMH6HcoCTqF18c0JmIRBtDiByzHLs3bkRODBljgkhvbtg6efNomK//7XJC68Tj8dXnst8HmffAJffAFjx8YnThERaSUW16FaSlRERETCNqJlu8hv3+eYaSD+S5u+R+tRCR2pB15s2bz6ASfReoTFRKBvJ+KuwoyK2Btm+3XAdGApkNeJ25Mw9esHc+aYbdcuqKw0iYqXXzZ1J4K580645RaTwLjsMrjkEhiWLBOGRESkMzRyIsVp5ISIiCSjL4B3aD3C4q2W/V2RARxP66VNTyF0rQgnJtEQbmLCX39gBRpBEXcffQSDBpmtLY8Hxo1rXWCzZ084/3yz4sc3vgEDVIpVRCSWNK1D2lFyQkREUsUh4ENaj7BYA7ij0Hc27etYHIWpMTGJ8KZyBDMRk1hRDYok8cYbZsREMP36wTe/aRIV550HvXrFLTQRkXShaR0iIiKSsjKAE1o274B9D7CZ9nUstkXYd13LVum37yhMoc2uJCbAjAB5CVMkU5LA5s2QlQX19YGP79sHDz9stqFDzZSPSy+FM84Ai1JMIiLJSiMnUpxGToiISHe0g/YJi/UJjMcBLEng7UsbBw7ACy+Y+hRPPmmKY3ZkzBiTpLj+ehg+POYhioh0Z5rWIe0oOSEiIuliN7CW1gmLdzDTRWKtJ6ZoZ6jaFpIgu3ebBMVDD0FVFTSFKMXaowd8/DEceWT84hMR6YY0rUNERETS1kDgrJbNaz9m2oZ/wmItnSt+GUoTYAW+DjwZ5b6liwYOhO99z2w7dsCSJSZR8cYb7dvm5ioxISKSpHokOgARERGRzuqDWUnjh8A/gFcxS5tOicFteVr6liQ2fDj85Cfw+utQVwd/+AN85SuHj4danrS42BTSrKgIb5qIiIhElZITIiIi0q30BEYQ/dU1LECAhS0lWdls8Otfw7vvgtMJN9wAs2cHbnvoEDzwgJkecsklZnTFD35gpokcisfEIRER0bQOERER6XaeBBqBLMyUjK7KAHahmhMpyWKBU081WzA1NfDpp4d/373bJCseeMAkKr7zHTPqYvJkrfghIhIjGjkhIiIi3VImEOR78ojNRomJbu2RR4If+/RTuOMOmDoVjjsOfvc7WJ/ItWNERLonJSdERESk25qXZP1Iklq4EO67D2bNCj0yYv16uPlmmDABpkyB22+HTz6JW5giIt2ZkhMiIiLSbc0ATuxiHwOJTYFNSSJWK1x1FVRXw9atJlmRkxP6nNWr4Wc/g3HjVEBTRCQKlJwQERGRbssCPAj070Ifu4HzgIaoRCRJ7+ijTdJh9Wp4/334zW8gOzt4+wsvhCOOiF98IiLdlJITIiIi0q3ZgaV0LUHxGjAd2BaViCRlHHecmcaxfj28+SZcd51ZrtTfpZcGP//BB2H5cmhujmmYIiLdgZITIiIi0u3lASsIf4qHDRjQZt87wJnAh1GMS1KExWIKYt5xB2zbBs8/D5dfDqNHw0UXBT7nyy/h2mvh3HPhmGPgxhvhf/8DjyeuoYuIpAolJ0RERCQt2IG3gRogH+jZ5ngG4Gg5vgF4HTi6TZvNmATF6phGKkktIwPOO88sM7pxI/TpE7jds89CY6P597ZtcNttZjnTE0+EP/7RnCsiIj5KToiIiEjasGCKZFYA9cC7wJstP3cBS1qOW4CJmOkcE9r08RlwLlAdj4AlufVsm+Ly8/DDgfe/9x78+tdgs8EZZ8Cdd8LOnbGJT0QkhWQkOgARERGRRMhs2UI5FngFuIjWoyX2tOz7N3BJTKKTlDd9OmzYAG+9FbzN66+b7frrzWiMyy6Db3wDBrSdVJRACxeaLdrmzzebiEgLJSdEREREQhgGvAh8i9ajJQ4C38GMpJiXgLgkyV1/vdneeQceesiMpPjoo8Btm5rgv/81W79+pp7F3XfHN95gGhvNtJRY9Csi4kfJCREREZEODASeAa4AFvvt9wA/BnYAv8VMBxFpZeJEWLDA1Jl47TWTqFiyBOrrA7fftw/2749vjKFkZsLIkcGPNzfD9u2t940YAT06mD2e2dG4JRFJNxaPRyWDU9nWrVsZPXo0AFu2bGHUqFEJjkhERKT7agKuB+4McGwe8DfaF9oUaefAAXjhBZOoePJJ+OKL1serq2HWrMDnbtsWOlkQbzt3tl9edccOGDYsMfGISFzE4jpUBTFFREREwtQT+Dtwc4BjdwHfBZLoO29JVr17w9e+Bo88Ap9+Cv/6F1xwgSmwOWIEzJgR+LzGRsjOhlNOgdJS2LIlnlGLiMSUkhMiIiIiEbAAv8EkI9pO46gAvgrsjndQkroGDoTvfc/Um/j4YzPlI9gqII8/bqZ8rF0LxcVw7LEmkVFeHnyaiIhIilByQkRERKQTfoSpP9G7zf5lmKVGtTikRGz4cDjrrODHH3qo9e8eD7z0EhQWwlFHmZU+liwxdStERFJM2hbELC8vp6KiApvNRn1Lpvmmm27Cbrd3qd/KykoWLzalstxuNwCFhYXk5+d3qV8RERFJPg4gC/gmZnlRr1rgTOAFYEzco5Ju6Ysv4MMPgx8/eBCeespsAwbAt75lliadORMy0vYjv4ikkLR8p3I4HLhcLpYtW4bVagXA5XKRk5NDSUkJBQUFne43Ly+PiooK3z63243D4WDx4sWt9ouIiEj3MAtYDlxI69ES64EzgOeBSfEPS7qbI44AlwtWrDAjKCor4fPPA7fdswcefNBsRx4Jc+bAFVdAF7+EExGJpbRbraO8vJzCwkIaGhp8iQmvyspKHA4HtbW1EY+gKC0tBaCoqCjg8ezsbIqLizud+AgmGVbrWPj6Qha+vjDq/c4/fT7zT58f9X5FRERi4UPgPGBzm/1WzDKkZ8Y7IOne9u+H//zHJCqeeabj5UevvRb+9rfox6HVOkTSUiyuQ9Nu5ERxcTG5ubntEhOAb+pFcXExVVVVEfVbVlYW8pzCwkIqKiqinpxIBo37G9m2e1tM+hUREUkVE4DXgPOBd/z2u4FcTLHMr8U/LOmu+vSB2bPN9vnnpljmQw/Biy+aWhRtXXZZ/GLbvVvJCRGJWFoVxHQ6nbjd7pCjIux2O9XV1b56EeFyuVw4nc6gx61Wq6+2RXeT2SeTkQNHBt1GDBjR7pwRA0aEPGfkwJFk9slMwL0RERHpvKOBFbQfJfElpi7FA/EOSNLDoEFw5ZVQXQ1bt8LChZCTc/h4djZMnRr43B074Lzz4J//DD5NJBCPB2pq4Kqr2h+bMAEcDnM8vQZpi0gXpNXICW+hyiFDhgRtk5WVBUB1dXXERSznzp2LzWYLmPyoqqoiNzc3ov5SRUfTL3bu3cnw21oP91t7zVqG9VdGXUREup/BmEKYczDTObyagB9g6lLcEP+wJF0cfTT87Gdm++ADePhhU3fC0nbh2xaLF0NVldl+9CP4+tfh0kvhoovM6IxAnE64/HJYty7w8aYmUxOjshJOPNHUvlC9CxHpQFqNnKiurgYIOKXDy3ts1apVEfWdn5+P2+0mJyeH4uLidrdbXV3NTTfdFFGfIiIikpr6AUsxyYi2bgSKAH2fLDF33HFw880wb17wNv7Lk+7fbxIK3/qWWZp07lwz+qG5+XCbqiqYPj14YqKtdetM+winTItI+kmrkRPeqRre0RGBeI9FOq1j0aJFvukgpaWlVFZWUlZWhsvloqysjI0bN4ZMigSzdevWkMe3b98ecZ8iIiISexnA/cAw4M9tjv0ZM4JiEWn2YUySS10dvPlm4GNuN9x7r9lGjoTvfhdOPRUKCmDv3shuZ+9eUxtjxQqNoBCRoNLq/8NIaj5EWh/CarWyceNGHA4H1dXVuFwu8vLysNls1NbWdioxAfgqoIqIiEjqsQClmARF2/W8/g/4DFiMGWkhEndHHw2PPmqmfvz3v3DwYOB227bBbbd17bb27jXLmb71VvApJiKS1tJqWkesWa1W8vLyWtWccLlcjB071jelRERERNLPjZhkRM82+5/BrO7REO+ARACOOALmzIEnn4Tt2+Gee8wUjFh55x146aXY9S8iKU3JiSjx1psAqK2tpaGhwVdQ0+12k5eXR2VlZcT9btmyJeS2cuXKqN4PERERiY0rgCeAvm32vwJMBz6Od0Ai/oYMgcJCkzzYvBlKSuCkk6J/O3fdFf0+RaRbSKvkhLeeRDhTNkLVpQhk1qxZ5ObmUlRkBm1arVYqKiqoqqryTemYO3duZAEDo0aNCrmNGNF+mU4RERFJTl8DqgBrm/3vAGcAH8Y7IJFAjjkGiopg7Vp4+22YH3xVtog9/jg0NkavPxHpNtIqORFO3Qdv4iKSGhHl5eW4XC5KSkraHcvNzWXjxo3Y7Xbcbjfl5eVh9ysiIiLdz1nACuDoNvs3txyrjXtEIiFMnAg//GH0+mtqMjUsRETaSKvkxOTJkwGoq6sL2sblcgEwZcqUsPutqqoiNzc36HGr1cqyZcsAM+VDYPf+3YkOQUREJGEmAa8C49vs3wnMAF6Md0AioezZE93+dutzoIi0l1bJiby8PCD0MqHeY956EeFwuVwdTgOxWq3YbDays7PD7jfVeTweajbWcNWTV7U7NuEfE3BUOKjZWIPHo5XeRUQk/YzB1JvIabN/D3AhEHmlKpEYGTAguv0NHBjd/kSkW0ir5IQ34RBs5Qy3243L5Wq12kY4cnNzw1qNw+VyhRxh0Z04tzuZdPckZj44k2fWP9PueJOnicp3K5n54Ewm3T0J53ZnAqIUERFJrOFADTCrzf4DwCXA3XGPSCSAUaOgZ9u1ZjqpRw+zSoiISBtplZwAKCkpweVy+aZv+FuyZAkAixYtanfM5XKRk5NDYWFhu2OFhYW4XK6Q9STKy8vJz8+POPGRiqrqqpj+z+ms27kurPbrdq5j+j+nU1VXFePIREREks9A4FnA0Wa/B5gH/L7l3yIJk5kJs2dHp6/mZjjlFLj1Vti3Lzp9iki3kHbJiaKiIvLz88nLy2s1vcPpdFJcXExJSUnABEJZWRlOp5Py8nKcztbf8ttsNqqqqiguLqa0tLTVMbfbTXFxMRUVFVRUVMTkPiUT53YnsxfPZu/BvRGdt/fgXmYvnq0RFCIikpb6AI8APwpw7LfAtUBTXCMSaWPevOj19fnncNNNMGEC3HcfHDoUvb5FJGVZPGk64b+8vJyKigpsNhv19fW+JEKwaRdOpxOHw+FLRATidrtZsGCBb4qHtw5FYWFhRDUsIrF161ZGjx4NwJYtWxg1alRMbiccHo+HSXdPCnvERCATh0/krWvewmKxRDEyERGR1ODBjJT4XYBjlwAPYhIZInHn8cCkSbCu85/zgjr+eFiwAC6+GPQZUCQlxOI6NG2TE91FMiUnajbWMPPBmV3v54oaZoyZ0fWAREREUtRdwE9oP50jF3gcMxVEJO6cTpg+HfZGNkIWgH794Oyz4fnng7e5/3648srOxycicROL69C0m9YhsXPX6rui08+q6PQjIiKSquYBjwK92uyvBmZilhwViTu7HZYuhf79Izuvf3944gl47jlYvRpmtS0BC4wYAXPmRCVMEUlNSk5IVDTub2Tpe0uj0tfj7z1O4/7GqPQlIiKSqi4B/gO0vQxcDZwFbI57RCJAXh6sWAEnnhhe+4kTTfu8PPN7Tg5UV5sRFKeccrjdzTeb0RUikraUnJCo2Nq4lSZPdEp1NXmauLf2Xja5N6FZRyIiks5ygeXA0Db7PwTOAGIw+1+kY3Y7vP021NTA17/e/nhGBjgc5vhbb5n2bZ13HtTWwkMPwYUXhp7O8cwzpoimiHRrqjmR4pKl5sTKbSuZdu+0qPc7vP9wpo6cyrSR05g2chpTRk7B2tca9dsRERFJZh8C59F+tMRg4BlMokIkIXbuhOHDW++rqwObLTr919WZgpkDB8Kvf21WDemjsrAiiRaL69CMLvcgAgzoPSAm/e7Yu4NnPnyGZz58xrfvK0O/wrSR03xJi5OOPIlePdvOyhUREek+JgCvAufTerREA2Z0RQXw1QTEJRLQwCiWbP1//w8OHoT6epg/H+64A265BS69FHpoELhId6KREykuWUZONO5vJKskK2pTOyLRN6Mv9hF23+iKaaOmceygY7UcqYiIdDv1wNeB19rs7wncD1we94gk7QUaObFjBwwb1vW+a2th8uTAx04+GUpKzPQQfeYTiTuNnJCkldknk9nHz6by3cou93XUgKM42HSQXV/sCqv9l4e+5LUtr/HalsMf1TQdREREuqMsoApTLPNZv/1NwBXAZ8D8BMQlEhNWK3z72/DYY+2PrV0LF1wAM2eaJEWwJIaIpAyNnEhxyTJyAqBmYw0zH5zZ9X6uqOGcY8/B1eDizW1v8ubWN3lz25us+WQNB5oOdLpf73QQ7+iKScMnaTqIiIikpIPA1cCDAY4VAbcC+i5Z4iKWIye83ngDiorg5ZeDt5kzB/74R8jOjt7tikhQsbgOVXIixSVTcsLj8TDp7kms29n52uETh0/krWveCjglY/+h/az9dC0rt630JS3W16/v9G1pOoiIiKSyZqAYuC3AsSuBcjREVuIgHskJAI8Hnn0WfvELWBfks2ZGBlxzjalT0TYmEYkqJSeknWRKTgA4tzuZ/s/p7D24N+Jz+/fqz4orV2AfEWC5qSDqv6g3yYqW0RUrt60MezpIIMP7D29VbFPTQUREJNn9GTNaoq2LgUeBI+IbjqSbeCUnvJqa4MEH4Te/ga1bA7cZMACKi+FXv1I9CpEYUXJC2km25ARAVV0VsxfPjihB0b9Xf5bOWUpedl6Xbtvj8Wg6iIiIpJ1/AnMxtSf8nQ08BVjjHZCkj3gnJ7y++AL+8Q/405/A7W5//PvfN0kMEYkJJSeknWRMToAZQXH50svDmuIxcfhEHvjmAxGNmIiEdzqI/+gKTQcREZHu5ilgDvBlm/2TgOeBEXGPSLqFhQvNFkxzM2zf3nrfiBEdL/M5f77Zuqq+Hm69Ff72N9i/3+zr3Rs++ADGjOl6/yISkFbrkJRhH2Hn7R+9zUubX2Lh6wt5+sOnWx3P6JHB7K/MZt6UeZxz7DkxvbDvk9GHqSOnMnXkVK7lWqD9dJA3t71J/Rf1YfUXbHUQ/2TFlKOnMKjvoJjcHxERkUAuBl7ALDX6ud/+t4EzWo6NT0BckuIaG2HbtsjOaZusCNZvNGRlQWkp/OQn8NvfwgMPwI9/HDwx4fFoqodIktLIiRSXrCMn/O3cu5Pht7Ue7ld3bR22LFuCImrP4/FQ11DHm1vf9BXc1HQQERFJRW8BFwBtLw+HAc8BsRmnKN1WRyMnOitaIyfaevttOPpoGDIk8PG77oJnnjGjLU46Kfq3L5ImNK1D2knV5MSOG3YwrH+M5yJ2UdvpIG9ue5MN9Rs63V/fjL7kjMjxFdvUdBAREYmVjcB5QNv/tQYCTwBdX/hbJAXt2WOWGt2xw4ye+P734fe/h2OPTXRkIilH0zpE4ijQdJBd+3ax6uNVnZ4O8uqWV3l1y6u+fZoOIiIisTAWeBW4EHD67d/dsu9h4NsJiEskoRYuNIkJMNM7HnwQHn0Urr0Wbrop+GgLEYkLjZxIcRo5kVj+00G8xTY1HURERJJFIzAbeLHNfgtwN1AY94hEEqSpCY4/HtYHKYo+aJBJUFx3HRyhBXhFOqJpHdKOkhPJJ1bTQaaNnGamhGg6iIiIRGA/8D2gMsCx3wO/xiQrRLq9PXvgr381BTT37AncZuRIuPlmuOIKyNAgc5FglJyQdpScSA279u3yFdr0/gx3Okggmg4iIiKRaAJ+AtwT4NhPgDuADhZ+FOk+duyAP/4R7r4bDh4M3OaEE2DBAvj617W6h0gASk5IO0pOpKa200He3PYm//vkf52eDmLBwleGfqVVsU1NBxEREX8e4OaWra3vAA8AveMakUiC1dXB//t/8MgjwducdRaUlMAZZ8QvLpEUoOSEtKPkRPcR7ekgR2QcgX2E3ZesmDpyqqaDiIgI/wCuwyQr/J0HPAYMiHtEIgnmdEJxMVRXB2/z8ssmUSEigJITEkAyJCcWvr6Qha8HX/+62dPM9j2tV1sfMWAEPSyhB5DOP30+80+PwfrXKSTa00GO7H9kq9EVmg4iIpKeFgPfB9oOaJ8C/AcYGveIRJJAVZVJUqxZ03r/1Knwxhua3iHiR0uJSlJq3N/Itt3bIjqnbbIiWL/pbki/IVw4/kIuHH8h0PXpIJ/u/ZSnP3yapz98Gjg8HWTaqGlMPXqqpoOIiKSJOUAWZiWPvX77VwFnAS8AxyQgLpGEysuDWbPM8qK/+hVs2mT2l5QoMSESB0pOSJdl9slk5MCRMelXWrNYLIzLGse4rHFcdtJlgJkO8r9P/tdqdEW400E8eHjvs/d477P3+L///R/QfjrItJHTOGbQMZoOIiLSzeQBNcCFwC6//R8AZwDPAycmIC6RhOrRAy69FL79bSgrg9pamDEjePvKSlM0s0+fuIUo0l1pWkeKS4ZpHZJ8/KeDeJMWmg4iIiKBvA+cD3zUZv9g4Fng9LhHJJIiXnzRjLQ49lj4wx/gsstMckMkDajmhLSj5ISEI1arg3hHVkwdOVXTQUREUthW4AJgXZv9R2CKZF4Y94hEklxzM0ybBqtXH9538slw661w/vmaBiLdnpIT0o6SE9JZ/tNBvKMrork6iKaDRK6j4rKdpeKyIhKOeuBrwOtt9mcA/wS+F/eIRJLYkiUwZ07gY+eeC6WlMHlyfGMSiSMVxBSRqOmT0cckEUZN8+3rynSQLw59watbXuXVLa/69h3Z/8hWxTY1HSS0zhSXDbdfEZGOZAHVgAOzYofXIczKHjuBnyUgLpGkZLOZpUVfeaX9sZoamDIFLrkE/vhHGDcu/vGJpCAlJ0TEJ9DqIBvqN7RKWKzZvoaDzW0Xnwvs072f8tQHT/HUB08B7aeDTBs5jYnDJ2o6SIuOist2dlleFZcVkXD1A54Afgj8q82x+cAO4E+AxsRJ2ps8GVasgGefhV/8Ata1nRSFGV3x+ONQWAi/+Q0MHx7/OEVSiKZ1pDhN65B4azsd5M2tb1LXUNfp/jQdJHw79+5k+G2tP9jsuGEHw/oPS1BEItJdNQM3AoEmml0FlKFvuER8mprgwQdNAmLr1sBtBgyAG26A+fNh4MD4xicSA6o5Ie0oOSHJ4LN9n7Fq26qorg6i6SDtKTkhIvHkAf4MFAc4djHwKKZgpoi0+OIL+PvfYcECcLsDtxk+3By/6qq4hiYSbao5ISJJaWi/oQGng3hHVqz8eGXUp4NMOnISGT30FiYiEisWoAgYCszFjKbwegqz/OhTgDXukYkkqSOOgKIiuPpqs2rH3/4G+/e3brNjB2yLfn0pke5AIydSnEZOSKqIxXSQnKNzfKMr0mE6iEZOiEiiPAXMAb5ss/8k4DlgRNwjEkkBH30Ev/0tPPAAeC+5jjwSNmww0zxEUphGTohIygq0Oshn+z5j5baVhwtubn2Thi8bwurvi0Nf8MpHr/DKR4erZHung3hHV0w+erKmg4iIRMHFwPMtPz/32/8WcCbwAqD1CETaOOYY+Oc/TZ2JX/4SnnkGfve74IkJjwe68ZcsIh3RyIkUp5ET0p20nQ7y5rY3+d8n/wt7Okhb3W06iEZOiEiirQUuAD5ps384ZgTFqXGPSCSFvPoqTJ0KvYKsUnbDDbBzJ/z+93DssfGNTSRCKogp7Sg5Id3dl4e+ZO0nazUdBCUnRCQ5uDD1Jja02T8QM/1jRrwDEukONm2C446DAwegd2/4yU/MaIshQxIdmUhAmtYhImmnb0bfoNNBvKMrVm5bGdXpIFNGTiGzT2bU74uISHdgA14BLgTW+O3fjUlaPAJ8KwFxiaS0//f/TGICzM+FC+G+++AXv4DrrzfFNkW6OY2cSHEaOSGSPtNBNHJCRJJJI/BNoKbN/h7A3UBBvAMSSVW7dkF2Nnz+eeDjI0fCzTfDFVdAhr5bluSgaR3SjpITIoF9eehL/vfJ/1oV24zGdBBvsmLqyKlxnw4SKDlRd20dtixb3GIQEfH3JXAZ8HiAY7cAv8QsSSoiHdi5E265Be6+Gw4G+XLlhBNgwQL4+tdVOFMSrlsmJxobG1myZAm1tbVcc801nHzyyYkMJ+UoOSESvq5MBwkkXtNBPB4PyzctZ+HrC3lm/TOtjvW09GT28bOZN3keM8bMSInaGSLSvTQBPwbKAhy7FrgdM5pCRMLgcsGvfw2PPBK8zZlnQkmJ+SmSIN0qOfHiiy9yzz338Nhjj5Gbm8vYsWNZsmQJQ4YM4ZprrmHu3LlkZmrOd0eUnBDpvFhMBzl+2PFMHTk1atNBnNudXL70ctbtXNdh2xOHnciDsx/EPsLe6dsTEekMD/Bb4A8Bjn0X+D+gdzwDEkl1TicUF0N1dfA23/iGGUlx/PHxi0ukRconJzZt2kRZWRllZWVYLBZuuukm5s6dy6BBg3xtKisrKS8vZ9myZeTm5vKLX/yCc889N14hphwlJ0SiyzsdxH90RTSng0wbNY3RmaPDGuFQVVfF7MWz2Xtwb9i3179Xf5bOWUpedl6nYxYR6ay/A9djkhX+zgMeAwbEPSKRFFdVZZIUa9YEPp6RARs3gq4BJM5SNjlx7733cs8997BmzRry8/MpKChg1qxZIc9xu92Ul5dTXl5OQ0MDBQUFXHPNNRyrNX9bUXJCJPYSMR3Eud3J9H9Ojygx4dW/V39WXLlCIyhEJCEeBS4H2o5Bmwo8CwyNe0QiKa65GR59FH71K7PkqL9LL4WHHkpIWJLeUio58b///Y977rmHRYsWYbfbKSgo4JJLLmk1SiJcTqeTW2+9lcrKSnJycrjmmmv44Q9/GIOoU4+SEyLx5/F4WF+/nje3vukruBmN6SDeQptTj57K5U+EN5UjmInDJ/LWNW+pBoWIJMQLmOVE26ZXj2s5dkzcIxLpBvbvh3vugT/8wazw0asXvP8+2FQYW+IvJZITy5Yto7Cw0DfaoaCggLFjx0atf+9oijVr1lBQUMDdd98dtb5TkZITIsmh7XSQN7e9iavBldCYaq6oYcaYGQmNQUTS10rgImBXm/2jgOeBE+IekUg38fnncNttcOiQqTkRyKFD8MQT8K1vQQ+VpJXoS4nkxMaNG3E6nXz729+OZrftuFwu1qxZE/PbSXZKTogkr2hPB4mU4wQHSxxL4nZ7IiJtvY+pN7Glzf4szBSP0+IekUiaKC+HwkI4+WSzssd552n5UYmqlEhOSHwpOSGSOvyng3hHV6z9ZG2np4N0pKelJ/XF9TFZ3lREJFxbMQmK99rs7wdUAhfGPSKRbm7vXhg3Dj755PC+mTNNkmLy5MTFJd1KLK5DO7++nYiIRMRisTBhyAQmDJnA90/+PtB+OsjLH73M1satUbm9Jk8T2xq3kTlMyQkRSZxRwMvA14A3/PbvAy7GLDN6WfzDEum+br+9dWIC4MUXYcoUmDMHbrnFJC9EkkzMJiCNHz+etWvXxqp7EZFuoW9GX04bdRrXn3Y9D3/7YR675LGo9r/7wO6o9ici0hlDgGraj5I4BHwPuCPuEYl0Yzk5cOKJgY8tXgzHHw8/+Qns2BHfuEQ6ELPkRF1dHTk5OdTU1MTqJkREup0BvQdEtb+BvQdGtT8Rkc7qDzyJSUa09VPgl4DmGotEwQUXwNq1cP/9EGio/aFDcOedkJ0NN98Mu/VFhiSHmJZubW5uJjc3l/vuu6/Dto2NjbEMRUQkJYzKHEVPS8+o9Xev81527t0Ztf5ERLqiF/AAJhnR1gJgLmY0hYh0Uc+ecOWV8OGHUFoKVmv7Nnv2wO9+Z6Z43HUXHIxNDSyRcMUsOWGz2SgrK8Pj8VBQUMBf/vKXoG03btzI1Vdfzfnnn8+9996rRIWIpK3MPpnMPn521Ppb+MZCxtwxhvnPz+fj3R9HrV8Rkc7qASzEJCPaug9wAF/GNSKRbuyII+DGG6Guzvzs06d9mx074Mc/hhNOgP/+N/4xirSIWXLCYrEwd+5cXnjhBTweD0VFRcybNy9g27Fjx7JkyRKWLFlCQ0MDVquVqVOnctttt7Fp06ZYhSgikpTmTQ78XtlZ+w7u469v/BXbHTZ+/OyP2ezeHNX+RUQiZQF+AdxL+w+jTwAXAJ/HOSaRbi0ry4yg+PBD+MEPAi8rumED6EtiSaCYLSU6fvx41q9fD4DT6WTWrFk0NjaSl5fHc889F/Jcl8tFXl4emzZtwmKxcOiQBvgFo6VERbofj8fDpLsnsW7nupj0n9Ejg8tPupxfnPULxg8ZH5PbEBEJ1xPAd4D9bfafDDwHHBXvgETSwdtvwy9/Cc88c3hfTg6sXAk9YjrzX7qJWFyHxuWVZ7fbqa2tZcyYMVRVVTF16tSQUzdsNhu1tbV4PB5ilDsREUlaFouFB2c/SP9e/Tt1fv9e/Xnskse44uQrAtavONR8iPv/dz9fufMrXPb4ZazbEZskiIhIOL4JPA+0XfR4LXAmUBfvgETSwaRJ8PTTsHw5TJtm9pWUBE9M6JpM4iBmyYn6+vpWv9tsNpxOJ6eccgqrV69m8uTJbN4cfGix1WrlxhtvjFV4IiJJzT7CztI5SyNOUPTv1Z+lc5byreO/xf998/9Yf+16CnMK6dWjV7u2zZ5mHn77YSbePZFvL/k2zu3OaIUvIhKRc4AVwJFt9rswCYo1cY9IJE2ccw68/jrU1MCsWcHbORzw85/Drl3xi03STsySE263u92+QYMGUVtby6xZs9iwYQM5OTmsXbs2aB+FhYWxCk9EJOnlZeex4soVnDgsyFrlbUwcPpEVV64gLzvPt2/s4LHc87V7cF3v4vpp19M3o2/Acx9/73FyynP46sNf5fUtr0clfhGRSJwMvAZkt9n/KTADWB7neETShsUCM2YEP/7SS/DYY7BwoVl+9NZbYd++uIUn6SNmyQmPxxO0mGVVVRVz586lvr6enJwcampqAraz2WyxCk9EJCXYR9h5+0dvU3NFDV+f8PV2xzN6ZOA4wUHNFTW8dc1b2EfYA/YzKnMUt19wO5uu30TxmcUM6D0gYLv/rP8PZ9x/BrMenEXNxhpNrRORuLIBrwCntNnfiCmSuTTeAYmkO48HiosP//7553DTTTBhAtx3H6g2oERRTGtOLFu2LOixsrIybrzxRpqbm8nNzeW+++4L2E4fjEUk3VksFmaMmcF9F7d/n/zgxx+wxLGEGWNmYAlUebuNIwccya25t7L5p5v5zfTfYO1rDdjuxY0vMvPBmZz1z7P47/r/6r1YROLmKMwoiRlt9u8H8jErfIhInLz1FjgDTPvctg2uvhpOOgmefFI1KSQqYpqcKPbPsgVQUlLCPffcg8fjoaCggL/85S/t2lit1hhFJyKS+gb2Gdip87KOyOLmc29m0/Wb+NPMPzG039CA7V7b8hoXPXwRkxdNZul7S2n2NHclXBGRsAwC/gvMbrO/GZgL/AnQpZBIHJx8Mrz/Plx6aeDj770H3/wmnH02vPpqXEOT7iemyYn6+nqmTp3K7t27g7YpKCjghRdewOPxUFRUxLx581odD+ebQBER6ZxBfQdx09k3sen6TSw8byEjBowI2M653cm3lnyLk+4+iUfefoSm5qY4Ryoi6aYvUIFJRrT1K+BnmGSFiMSYzQYPPQS1tZCbG7jNq6/CWWeZRMV778U1POk+YpacqKur48Ybb6S+vp4xY8Zw2223BV0+NDc3l9WrV5OZmUlZWRkXXHBBrMISEZEA+vfuz89O/xmu613cddFdHDPomIDt1u1cx6WPX8rxdx7PP9f8k4NNB+McqYikk55AGSYZ0dYdwPeBA3GNSCSN2e1QVQUvvACnnhq4zZNPwsSJMHeumfohEoGYJSfGjh1LSUkJGzZswOVyMWjQIBwOR9AEhd1up7a2ljFjxlBVVcXUqVODthURkdjom9GXH035ERuu3cD9F9/PuKxxAdutr1/PVU9dxfi/j+ee1ffw5aEv4xypiKQLC3ALJhnR1sPAxcDeuEYkkuby8mD1ajOaYsyY9sebm+Hee00C40t9PpDwxXRah9egQYOYO3cuzz//PJmZmUHb2Ww2nE4np5xyCrW1teTk5ARcklRERGKrV89eXHnqlbz34/d4+FsPB13OdPPnm/nRsz8i+2/Z3P7G7ew7qKXFRCQ2rgMeAjLa7H8emAXsintEImmsRw9Th+L99+GOO2BogNpV8+ZB38BLmIsEEpfkRCQGDRpEbW0tM2fOpK6uLtHhiIiktYweGXx30nd560dv8fgljwddqvTj3R/zs+d/xpjbx3DrK7fSuF8j30Qk+i4Fngb6tdn/JnA2sCXuEYmkuT594LrroK4Ofv1r6Nfy1zl8OPz854mNTVJO0iUnvKqqqpg7N1AJJBERibcelh7MPn42q+eu5j+X/ofTR50esN3OfTu5adlNHHv7sfxu+e+o/6I+zpGKSHd3AfAikNVm/3vAGS0/RSTOMjPhD3+ADRugsBBuvhkGBllRrL4enntOy49KO0mbnAAoKyvjhRdeSHQYIiLSwmKxcOH4C3n1qld58fIXmTl2ZsB27i/d3PzSzRx7+7H8ovoX7Ni7I86Rikh3Ng14BRjVZv9W4CzMSAoRSYARI+Cee+Caa4K3WbAALrwQZs0ytStEWiR1cgJg1qxZiQ5BRETasFgsnDv2XJZdvoxXr3qVi8ZfFLDdngN7KHm1hDG3j+Gnz/2UbY2q3C0i0XE88FrLT3/1wExMLQoRSTIffQR//7v5d00NTJkCc+aYEReS9iwej8bTpLKtW7cyevRoALZs2cKoUW2/QxCRVLHw9YUsfH1h0OPNnma279neat+IASPoYQmdZ55/+nzmnz4/KjGGUvtxLX98+Y8sfX9p0Da9e/bmylOupPjMYsYOHhvzmESk+9sFfJX2oyUygAcwdSpEJEn84AfwwAPt92dkmOkgv/mNqVchSS8W16ExGzkxfvx41q5dG6vuRUS6ncb9jWzbvS3o1jYxAbB9z/aQ52zbvS1uxSlzjs7h8TmP8/aP3ua7E78bMGlyoOkAZbVljP/7eH7wxA/4cNeHcYlNRLqvIcAy4Pw2+w8BlwF/i3tEIhLUzJkQ6CL20CG4807Izjb1Knbvjn9sknAxGznRo0cPevToQVVVFeeee24sbkLQyAmR7qSjkROdFa+RE219uOtDbn3lVv711r841HwoYBsLFuZMnMMvz/olk46cFOcIRaQ7OQBcCTwc4NivgD8AlrhGJCIBffEF/OMf8Kc/gdsduM3w4fDb38LcudCrV1zDk/DE4jo0pskJMPOSy8vL+eEPfxiyfWNjI5mZmbEIpVtTckJEkt0m9yZKXy3lvjX3caDpQNB23/zKN/nV2b9i8tGT4xidiHQnzcB84I4Ax+YCdwM94xqRiATV0AC33gp33AH79wduM26cSWLk54NF6cVkklLTOmw2G2VlZXg8HgoKCvjLX/4StO3GjRu5+uqrOf/887n33ntpbIzPEGQREYm9MdYx3PXVu9h4/UZ+dtrPOCLjiIDtnnj/CaYsmsKFD13Iqx+9GucoRaQ76AH8FfhTgGOLAAfwZVwjEpGgBg+GkhJYvx6uvDJw8mHDBrjkEpg2TSt7pIGYJScsFgtz587lhRdewOPxUFRUxLx58wK2HTt2LEuWLGHJkiU0NDRgtVqZOnUqt912G5s2bYpViCIiEkdHDzyahecvZNNPN3HTWTcxsHfg9c+f2/AcZ/3zLM594FyWuZahus0iEgkLcBMmGdH2g+5S4ALg83gHJSLBjR4N998Pb70FX/ta4DarVsU3JkmImE3rGD9+POvXrwfA6XQya9YsGhsbycvL47nnngt5rsvlIi8vj02bNmGxWDh0KPBcZdG0DhFJXQ1fNPD3lX/n9jdup+HLhqDtTht1Gr8++9dcNP4iLBrSKSIRWAp8F2g7YPwU4DngyHgHJCIdW7ECiovhjTcO75szBx59NHExSTspNa3Dn91up7a2ljFjxlBVVcXUqVNDTt2w2WzU1tbi8Xj0jZmISDc1+IjB/Oac37D5p5spyS1heP/AS4e9sfUNvvbI18gpz+Gxdx+j2dMc50hFJFXNBp4H2lY1+x9wJuCKd0Ai0rHp0+G11+Cxx2DCBLPM6C23BG+v68VuI2bJifr6+la/22w2nE4np5xyCqtXr2by5Mls3rw56PlWq5Ubb7wxVuGJiEiSGNhnIEVnFrHx+o3cfv7tHD3w6IDt1nyyhvyKfCbdPYmH3noo6AogIiL+zgFeov0oiTrgDEyiQkSSjMUC3/oWvPMO1NSYwpiBHDoEZ54JCxbAvn3xjVGiLmbJCXeAZWEGDRpEbW0ts2bNYsOGDeTk5LB27dqgfRQWFsYqPBERSTL9evXj+tOux3Wdi3u+eg9jrGMCtnt357t8b+n3OP7O47l/zf0hVwAREQEzjeNVwNZm/6ccTl6ISBLq1QvOOiv48QcegNdfh1/+EsaPh3vvNQkLSUkxXUrU5XIxZsyYgMcLCwtZtGgRPXr0oKqqinPPPTdgu549e9LU1BT1+MrLy6moqMBms/lGedx0003Y7fZO9edwOLDZbMyZMwer1QpAVlZWwLbe49GgmhMi0l0dbDrIw28/zJ9e+RMf7vowaLtjBh1D8ZnFXHXqVfTN6BvHCEUk1XyCKYjZ9quxPsCjwDfjHZCIdN6+fSYh8fHHrfcff7wZSXHxxVp+NIZicR0a0+TEokWL+OEPfxi0TXFxMX/+85+xWCyUl5cHbNujRw+am6M7v9jhcOByuVi2bJkvUeByucjJyaGkpISCgoKI+8zJycHpdIbVtqqqitzc3IhvIxAlJ0Sku2tqbqLy3UpuefkW3tnxTtB2Rw04ihvPuJHCnEL69+4fxwhFJJV8DlwMrGizvwdQDgT/5CoiSeXJJ+Gb3wx+/MwzzVKlZ54Zt5DSScolJ7Kysvjss89CtisvL+eaa67BYrFQWlrKz3/+81bHs7Ky2tWv6Iry8nIKCwt9S5b6q6ysxOFwUFtbG/EIinAryOfn51NRURFR36EoOSEi6aLZ08zTHzzNLS/fwuqPg691PuSIIcw/fT4/nvJjBvUdFMcIRSRVfIlZxeOJAMcWAMWYJUlFJMk5nWZlj+rq4G2+8Q0zkuL44+MXVxpIudU66uvrmTp1Krt37w7apqCggBdeeAGPx0NRURHz5s1rdTzay8YVFxeTm5sbcGpFfn6+r00k3G43drudhoYG3wojbbeGhgbsdjuLFi2Kxt0QEUk7PSw9+MZXvsHKq1fy3GXPcebowN+E7PpiF7968Vcce/ux/KbmN+zatyvOkYpIsusLVBB4lMRNwHxA6wKJpAC7Haqq4IUX4NRTA7d58kmYOBHmzoVt2+Ibn0QkZsmJuro6brzxRurr6xkzZgy33XZb0OVDc3NzWb16NZmZmZSVlXHBBRfEJCan0+lLJARjt9uprq4OWNAzmNWrV7eqNRGIw+Fg0aJFUa03ISKSjiwWC+ePO5+Xr3yZ5VcsJ9cWeJrc5/s/5w8r/sCxtx9LUVURn+z5JM6RikgyywAWAb8McOx24HLgYDwDEpHOy8uD1avh4Ydh7Nj2x5ubTbHM8eNN8cwIrvUkfmKWnBg7diwlJSVs2LABl8vFoEGDcDgcQRMUdrud2tpaxowZQ1VVFVOnTg3atrMWL14MwJAhQ4K28RaxrA41NCgA76iLQEpLS7Hb7Z0utikiIu1ZLBbOGXMOVd+v4vUfvs7XJnwtYLu9B/fy59f+zNg7xnLdf69jy+db4hypiCQrC/BH4K8Bjj0EfAPYG9eIRKTTevSA734X3nsP7rgDhg5t3+aLL8wUj9xciE11g+AWLoRRo6K/LVwY3/sRQxnxuJFBgwYxd+5c5s6dG7KdzWbD6XQyc+ZMamtrycnJiWgEQ0e8CYdQoxe8x1atWhUy4eAvVHFLp9PJ4sWLqa2tDTtOERGJzGmjTuPp7z7Nmu1r+NMrf+Kxdx/DQ+sPHV8e+pK/r/w796y+hx+c8gN+cdYvsA1uu7CgiKSjnwLDgB8A/osQ/hfIBZ4FAq/BJiJJp08fuO46+MEP4M9/Nhfv+/a1bvOzn8V/JY/GxthMK4nyF/qJFJfkRCQGDRpEbW0teXl5LFu2LKo1J7yJjmBLfPofi1ZSxOFwUFVV1enzt27dGvL49u3bO923iEh3c+qIU6lwVPDuzndZ8MoCHn77YZo9rWeOH2w+yCLnIu5fcz+XTrqUX579S74y9CsJilhEksVlwBDg24D/ZcwbwNnA84DKjoukkMxM+MMfYN48+P3vYdEiaGqCU04xIywSEc/IkcGPNzdD22u7ESPMiJCO+u0mYrZaRzQUFhZy77330tTUFJX+Bg8ejNvtpqKiIuioiMLCQsrLy6OyqkZpaSl1dXWUlZV1uo9IkjNarUNEpLUN9Ru49ZVbeWDtAxxqPhSwjQULjhMd/PKsX3LyUSfHOUIRSTZvAF8F2q4VNxp4AVAqUyRFffAB/OpXUFAA550XuM369aYexZQpcQ0NgJ07Yfjw1vt27IBhw+IfSxhSbrWOriorK+OFF15IdBid4na7WbBgAYWFhYkORUQkbY3LGse9F99L3XV1/HjKj+nTs0+7Nh48LFm3hFPKTuHiRy5m5baVCYhURJLFacDLtB8lsQU4C9A7hEiKOu44qKwMnpgAuOEGmDoVLrkENmyIX2wCJHlyAmDWrFmJDqFTFixY0OHKIOHYsmVLyG3lSv0XKSLSkWMGHcM/LvoHG6/fyM9P/zn9evUL2O7pD59m2r3TOP/f5/Py5pfjHKWIJIsTgFdpP0piFzATM4JCRLqZV16Bp54y/66ogOOPhx//GD79NLFxpZGkT05Ek7eeRH1924F6wdt2Vnl5echCmeEaNWpUyG3EiBFdvg0RkXQxYuAIbjvvNjb/dDO/OvtXZPYJPE/zhboXmP5/0znn/86hqq6KJJ4BKSIxcgxmBMXUNvv3Al8DHol7RCISMx4PFBW13nfoENx1F2Rnw+9+B7t3JyS0dBKz5MT48eNZu3ZtrLrvlFCrdHh5ExfhtA2muroat9vdpT5ERCR2hvYbyi0zb2HzTzfzh3P/QNYRgRPSKzav4Lx/n8dp953G0x88rSSFSJoZCiwDzm+z/yCmgObf4x6RiMREUxPMng2Brt/27oWbb4Zx4+DOO+HAgbiHly5ilpyoq6sjJyeHmpqaWN1ExCZPngyY2IJxuVwATOlCERTv6hxdHX0hIiKxZe1r5dfTf83mn27mz3l/5sj+RwZst3LbSi5+9GJOLTuVinUVNDVHp1CziCS/AcBTQNva/h7gOuD/tfxbRFJYRgbceCPU1ZmffdrXqGLHDvjJT+CEE2DJEjPaQqIqptM6mpubyc3N5b777uuwbWMc1mfNy8sDQi8T6j0WbDWPcFRXVwNdG30hIiLxM6D3AG444wY2Xr+Rv1/4d0ZlBq44vfbTtVxSeQkT757Iv9b+K+gKICLSvfQG/g1cG+DYLcA1gFKWIt1AVhaUlppVO668EgKtnFhXB3PmmMKZSfRFfHcQs+SEzWajrKwMj8dDQUEBf/nLX4K23bhxI1dffTXnn38+9957b8wSFd6Egzd50Jbb7cblcnW5iKXT6QQgOzu7S/2IiEh8HdHrCH4y9SfUXVfHoq8vwjbYFrDd+5+9z+VPXM5x/ziORbWL2H9of5wjFZF46wHcgUlGtFUOXAJ8GdeIRCRmRo+G+++Ht96Cr30tcJvVq2HmTLjwQmgZfS9dE7PkhMViYe7cubzwwgt4PB6KioqYN29ewLZjx45lyZIlLFmyhIaGBqxWK1OnTuW2225j06ZNUY2rpKQEl8vlm77hb8mSJQAsWrSo3TGXy0VOTo6WBhURSQO9e/bmavvVfPCTD3jwmw/ylaFta/YbrgYXBc8UMO7v4/j7m3/ni4NfxDlSEYknC/AroIz2H6IfBy4EYj8WWETiZuJEePppeOklmDYtcJsVK+CII+IbVzcV89U6cnNzWb16NZmZmZSVlXHBBRcEbTto0CBuvPFGNmzYwK5duyguLmbcuHFRjaeoqIj8/Hzy8vJaTe9wOp0UFxdTUlIScOREWVkZTqeT8vJy38iIQPz7VM0JEZHUltEjg++f/H3e+dE7LMlfwklHnhSw3dbGrVz33HWMvWMst712G3sO7IlzpCISTwVABWa6h7/lwAxACw+KdDPTp8Prr8Njj8GECa2PzZ8PWkExKiyeGJUeHz9+POvXr/f97nK5yMvLY9OmTeTk5FBdXU1mZuAl3MBc5GdlZWGxWGhqiv4svvLycioqKrDZbNTX1+N2uykuLg66/KfT6cThcGCz2XwFL4PJycnB5XJRW1uLzRZ4SHC0bN26ldGjRwOwZcsWRo0KPE9aRES6zuPx8MyHz3DLy7ewctvKoO2yjsjiZ6f9jJ9M/QnWvtb4BSgicVUDfANou8BgNvACENtPgSKSEAcPmikfv/udWW60rg6CXdd6PIHrVgSycycMH956344dMGxYl8KNlVhch8YsOTFkyBB27drVat/nn3/OzJkzWbNmDePGjaOqqopjjz02aB/FxcXcdtttMUlOdBdKToiIxJ/H46HaVc0tL9/Cis0rgrbL7JPJtVOv5aen/ZSh/YbGMUIRiZc1wAXAjjb7jwKeBwKPtxKRlLd3L6xbZwpjBtLQAOecA9dfD1dcYVYECSVQcqKuDmL8ZXdnxeI6NGbTOgKtiDFo0CBqa2uZNWsWGzZsICcnh7Vr1wbtQ/UdREQkGVksFvKy83jpBy/x0g9e4rzs8wK2a9zfyB9f/iPH3n4sN7xwA9t3b49zpCISa6cCrwJj2+z/BJgOBE9fikhK698/eGICoKQE3n4brr4aTjoJnnwy8PKjHo9Z9eOqq9ofmzABHA5zPA2WLo1ZcsLj8QQtZllVVcXcuXOpr68nJyeHmiBLsMR6SoSIiEhXTT92Os9/73nevPpNLj7u4oBt9h3cx19e/wv/v707j4+qvvc//p4kbAbCEATLJjABRBIUJ4GKCy4kaHHFZiR4rYqVhGLVFiVpr/bX1ro0KaJSFRPqWr0EkgIu1QtJRIGKV5KpQIKomVH2CpoMm8iSzO+PYSLJTPbZknk9H495SM75nu98DhDJec93Gf70cP3ynV9qx4EdAa4SgD+NkCugaDhK4oCkKZLeCHhFAIJq1y7p6ad/+PrTT6Ubb5QuvVT6179+OG61SmPHunb9ePttz35qaqTCQtf5sWNd7Tsxvy6IWVJS0ui53NxczZs3T7W1tUpOTtYLL7zgtZ2fZp0AAOBTEwZN0Btpb+iTjE90c/zNMshzjumxmmN6duOzilsYp7vevEuVVZVBqBSAPwyQ9IFcoyVOd0zSTZJeDHhFAIJm6VLpey+bC//rX9Ill7iCihdecC20WVHRsj4rKlztm1n/sCPz25oTERERio2N1TfffNNku7y8PM2ePVsGg0E5OTm6//77652PjY1VVVWVP0rsFFhzAgBC07ZvtumxdY/pf7b8j2qc3tdOijBEaEbCDP33pf+tMf3GBLhCAP5wVNIMeR8t8WdJmZKX6BJAp1NUJGVlSf/+t2/7jY52bV/qZYfJQOpQa05IUlVVlSZMmKBDhxquYfyD9PR0rV69Wk6nU5mZmZozZ06984aWrm4KAEAIGX3maL067VV9fs/nSjenq0tEF482tc5avb7ldSU8l6DUZan6914f/wADIOB6SCqU5GX2uH4j6QFJtQGtCEBQpKRIpaXS//yPNLzhqjTtcOSIa4HNTjjDwG/hhM1m07x581RVVaVhw4Zp/vz5OnjwoNe2ycnJKi0tVUxMjHJzc3X11Vf7qywAAALK1Mek3OtyZbvXpnsm3KPuUd092jjl1D8+/YfMeWZdt+Q6fbTroyBUCsBXoiT9Ta4woqEFku6QdCKQBQEIjogIacYM15oTTz8tnemjnbvKy6UPPvBNXyHEb+HE8OHDlZ2drcrKStntdvXu3VsWi6XRgMJsNqusrEzDhg1TUVGRJkyY0GhbAAA6miG9h2jhTxbqy/u+1LyL5im6S7TXdm9//rYmvjBRKX9P0QdffcDaS0AHZZD0uFxhREN/l3SjpCOBLAhA8HTrJt17r2tr0HPP9U2fzz3nm35CiN/WnGirAwcO6Morr9Qnn3wik8kku92umhrvc3XBmhMA0FF9+923evr/ntbC/1uoA8cONNrukrMv0UOXPqQpcVOY6gh0UK9JminpZIPjEyW9LSk24BUBCIqDB6XYWNcuHO0VGSlVVUkxMe3vqw063JoTbdG7d2+VlZXpyiuvlM1mC3Y5AAD4Rd8z+urhKx7W9l9t16NXPqq+Pfp6bbd+x3pd/frVmvC3CXpj2xuqdTJbHehobpVrgcweDY5vkHSppF0BrwhAUOza5ZtgQnL1s3u3b/oKESEXTrgVFRVp1qxZwS4DAAC/6t29t/770v/W9l9t1xNTntCPev7Ia7vSPaW6cemNGvf8OC0tX6qaWkYVAh3JVEklkvo0OL5V0sWSPgt4RQAC7vBh3/bXxMYTHVHIhhOSlJubq9WrVwe7DAAA/C66a7TmTpyrL+/7Us9OfVZDYoZ4bbdl3xal/SNNY54bo1c+eUUnalhWD+goJkpaJ2lQg+M7JF0iaWPAKwIQUD17+ra/Xr1821+QhXQ4IUmTJ08OdgkAAARM96jumjN+jirvrdQL17+guD5xXtt9/u3nuuONOzTqmVHKLc3VsZPHAlwpgLaIl/ShpHMaHP9G0hWSigJeEYCAGTzYtVaEL0RFSYMaRp0dm9/CiZEjR2rTpk3+6h4AgE6ta2RX3XnBndr2y216/abXNabfGK/tvnJ8pdn/nK24hXF6+qOn9d2J7wJcKYDWOlvSeknjGxw/IukaSUsDXhGAgIiJkaZN801f06YFbTFMf/FbOGGz2ZSYmKg1a9b46y0AAOj0oiKidMvYW7TlF1v0j5v/oQt+dIHXdrsP7davVv1Kw54apuz12Tp0rHPNQwU6mzMlvScppcHxE5JmSHom4BUBCIg5c0KrnxDi12kdtbW1Sk5O1gsvvNBs24MHD/qzFAAAOrQIQ4RuOvcmlaWX6e0Zb+vCwRd6bbf/u/36TclvNPSpofrj+39U9dHqAFcKoKV6yrWV6PQGx52S7pH0+1O/BtCJXH65FB/fvj4SEqTLLvNJOaHEb+GEyWRSbm6unE6n0tPT9cQTTzTa9ssvv9Rdd92lq666Sn/7298IKgAAaITBYNA1o67Rh3d+qJLbSnT5sMu9tqv+vlp/+OAPGvrUUP13yX9r/5H9gS0UQIt0lfQ/kn7p5dzDkuZIYm8eoBMxGKRXX5Wio9t2fXS09Morrn46Gb+FEwaDQbNmzdLq1avldDqVmZmpOY0MPRk+fLiWLVumZcuWqbq6WkajURMmTND8+fP11Vdf+atEAAA6LIPBoCuHX6k1t6/RupnrdPWIq722O3T8kB5f/7iGPjVUc1fN1Z5DewJcKYDmREhaKOlPXs49LylNEkveAp2I2SytWNH6gCI62nWd2eyfuoLM4HQ6/TJabOTIkfriiy8kSVarVZMnT9bBgweVkpKi//3f/23yWrvdrpSUFH311VcyGAw6efKkP0rsFHbt2qUhQ1zbze3cuVODBw8OckUAgGAp3VOqR9c9qpXbVjbapmtkV/38gp8r8+JMDTMOC1htAFomV9Iv5Dmd40pJKyR1ruXvgDBntUq33SZVVDTfNiHBNWIiRIIJfzyHBmQrUbPZrLKyMg0bNkxFRUWaMGFCk1M3TCaTysrK5HQ65afsBACATidpYJJWTF+hzbM3Ky0hTQZ5Dvk8XnNci0oXaeRfR+rON+7UF99+EYRKATQmQ9IyuaZ7nO49ubYa3RfwigD4jdksbdkirVkjXXed5/moKMlicZ3fvDlkggl/8Vs4UVVVVe9rk8kkq9WqcePGqbS0VElJSdq+fXuj1xuNRs2bN89f5QEA0GmNPWuslvx0iT69+1PdMe4ORRo891Q/WXtSL33ykkY/O1q3/OMWle8rD0KlALxJlfSupF4NjlslXSzpy4BXBMBvDAbXIpneNpH47DNp2TLX+U64xkRDfgsnHA6Hx7HevXurrKxMkydPVmVlpRITE7Vp06ZG+8jIyPBXeQAAdHrnnHmOXrrhJX1xzxeanThbXSMbfhYr1TprtaR8icYuGqublt6ksj1lQagUQENXSnpfUr8GxyslXSRpc6ALAhB4vRpGlJ2b38IJp9PZ6GKWRUVFmjVrlqqqqpSYmKg1a9Z4bWcymfxVHgAAYWN4n+FadO0i2e+161c//pV6RPXw2m7FthVKWpykqa9P1Yc7PwxwlQAaMkv6l6RhDY7/R9IkSesCXRAA+JFf15woKSlp9Fxubq7mzZun2tpaJScn6wVvw1gk1pwAAMBHBsUM0pNXP6mvfvWVfnPxb9Sza0+v7d6tfFcXv3ixrnzlSr335Xv8WwwE0Ui5AoqxDY4fkDRF0lsBrwgA/MOv4URWVlaT57Ozs/X888/L6XQqPT1dTzzxhEcbo9Hop+oAAAhP/aP76/Hkx7X9V9v1+8t+L2N3o9d2a75ao8mvTtYlL12id794l5ACCJKBktZKurTB8e8lTZP0UsArAgDf82s4UVVVpQkTJujQoUONtklPT9fq1avldDqVmZmpOXPm1DtvCIOFPwAACIbYHrH6w+V/0PZfbdfjkx/XmWec6bXdhzs/1NT/maqkxUla8ekK1TprA1wpAKOkVZIarudfI+lOSTmBLggAfMxv4YTNZtO8efNUVVWlYcOGaf78+Y1uH5qcnKzS0lLFxMQoNzdXV199tb/KAgAADcR0i9FvLvmNvrrvKz151ZMa2Gug13bWvVbdtOwmnbfoPC3ZskQ1tTUBrhQIbz0kLZc008u5LEnzJBEdAuio/BZODB8+XNnZ2aqsrJTdblfv3r1lsVgaDSjMZrPKyso0bNgwFRUVacKECY22BQAAvhfdNVq/uvBXst1r06JrFmlo76Fe21Xsr9Aty2/Ruc+eq5f+/ZJO1JwIcKVA+IqS9IJcYURD8+UKLviOBNARGZwhNoH0wIEDuvLKK/XJJ5/IZDLJbrerpoZPZhqza9cuDRkyRJK0c+dODR48OMgVAQA6ixM1J/T6ltf12LrH9EXVF422G9p7qLIuztLMC2aqe1T3AFbYuAUbFmjBhgU+73fuxLmaO3Guz/sF2uIJSQ94OX6NpGWSzghsOQDaY/9+qX//+sf27ZP6NdxQODT44zk0qt09+Fjv3r1VVlamlJQUlZSUsOYEAABB0iWyi+4Yd4d+dt7PVLC1QI+ue1Tl+8o92m0/sF1z3pmjP639k+ZdNE/piemK7hodhIp/cPDYQe0+tNsv/QKh4n5J/eRac+L0j/L+KSlF0tuS+gShLgBoC78uiNkeRUVFmjVrVrDLAAAg7EVGRCotIU2bZm/SiukrlDgg0Wu7vYf3au7quRr29DA9vu7xoD7Ix3SL0aBegxp9Deg5wOOaAT0HNHnNoF6DFNMtJgh3AzTuNklvyLUexek+lGt3D99HdADgHyE3raOhkpISTZ48OdhlhCymdQAAAs3pdGqVbZX+tPZP+nDnh422M3Y36t4J9+q+C+9TbI/YAFbYvP1H9qv//PrDZ/c9sE/9okNz+CzQnA/lms7haHD8bEmrJZ0T6IIAtA7TOkJ35IQbwQQAAKHFYDDo6hFXa/3M9Xrvtvd05fArvbZzfO/Qw2sf1tCnhuo3xb/RviP7AlwpED4ukrROUsO9dnZIukRSacArAlDPggXS4MGNv84/3/Oa889v+prBg139dhIht+YEAADoGAwGg64YfoWuGH6FPtz5oR5d96je+eIdj3aHjx9W9r+ytfD/Fio9MV0PXPSABscw0g/wtQS5RlBMkfT5ace/kXSFpBWSkoNQFwBJBw9Ku1s50Wrv3pb120n4JZw4ePCgYmL8PyczUO8DAACadtGQi/TPW/4p616rHl33qJZ/utyjzdGTR/X0/z2tRaWLNHPcTGVdnKXhfYYHoVqg8xoqab2kqao/WuLwqWOvSbo5CHUBYS8mRho0yD/9dhI+X3Ni8eLFysjIkMVi0ezZs3XFFVf4snsdPHhQeXl5ev7552UwGPTFF41vbRYOWHMCABCKKvZV6LH1jym/PF+1zlqvbSINkbr1vFv120t+q3PODOyMeNacQGd3SNJNkoobHDdIekbSnIBXBKAz6RBrTsyaNUuVlZUaPny4fvrTn6pv37767W9/q+3bt7er3+XLl2vKlCnq06ePioqKlJ2dHfbBBAAAoSq+f7xev+l1bbt7m+4cd6eiIjwHa9Y4a/TKpld07rPnKq0wTZu/3hyESoHOqZdcW4lOb3DcKeluSX849WsACBV+WRDTZDLpz3/+s6qqqrR06VLZbDYNHz5c48eP14oVK1rcz1dffaXZs2erb9++ysrKUkpKiqqqqrRq1Sr99Kc/9UfpAADAh0b2HakXbnhBlfdUak7SHHWL7ObRximnllYs1fnPn68b82/Uxt0bg1Ap0Pl0k/S6XGFEQ388dbwmoBUBQOP8vltHcnKyli1bpqqqKlksFs2bN0+RkZH6xS9+oU2bNnm0P3jwoP72t78pKSlJcXFxMhgMKi4u1hdffKF58+apd+/e/i4ZAAD42FDjUD17zbOy32fX3Avn6owuZ3ht98Znb2jC3ybo6teu1vod6wNcJdD5REr6q1xhREOLJM2QdCygFQGAdwHbStRoNCozM1OVlZXauHGjnE6nLrjgAo0cOVLz58/Xe++9p5tvvllGo1G5ubnKyMhQTU2NFi1apAsuuCBQZQIAAD8a2GugnrjqCX1131f670v+W7269vLabpVtlS596VJd/vLlKrGXyMdLZAFhxSDp/8kVRhganCuQdI1ca1QAQDAFLJw4ndls1vPPP6/a2lo9/vjjWr16tVJTU2UymWSz2bRx40bNmjUrGKUBAIAA6BfdT49OflTbf7VdD1/+sGJ7xHpt98H2D5T892Rd9OJFevvztwkpgHaYLWmZpK4NjpfItdXovoBXBAA/8PluHQgsdusAAHQGh44d0vOlz2v+hvnad6TxR6RxPxqnhy59SNPOnaYIQ9s/Y2G3DoSzEkk3yrW96OlGSlotaViA6wHQ8XSI3ToAAABaq1e3Xpp38Tx9ed+XevrqpzWol/e94D/5zydKLUhVwnMJen3z6zpZezLAlQId32RJ70tqGMV9IekiSVsCXRAAiHACAACEkDO6nKF7f3yvbPfalHttroYZh3lt9+k3n+rWFbdq9DOj9YL1BR2vOd7u9z50jFn3CB+JktZLGtrg+F5Jk06dA4BAIpwAAAAhp1tUN6UnpuvzX36uV258Ref0PcdrO1u1TXe9dZdGLByhZz9+VkdPHG2yX6fTqTVfrtGdb9zpcW7UM6NkKbBozZdrWNsCYWGUpA8lJTQ47pCUIumtQBcEIKyx5kQHx5oTAIBwUFNbo398+g89svYRbdnX+KDzH/X8kR6Y+IAykjLUs2vPeuese626bcVtqthf0ez7xfeL16vTXpV5gLndtQOhrlrSdZL+1eB4pKQXJN0e8IoAhDrWnAAAAGEpMiJSN8ffrE9mf6I30t5Q0sAkr+3+c/g/eqDoAQ17apgeXfuoDnx/QJJUZCvSpJcmtSiYkKSK/RWa9NIkFdmKfHYPQKjqI9dCmNc2OF4j6Q5Jfwl0QQDCEiMnOjhGTgAAwpHT6VSRvUh/Wvsnrd/R+Oz43t16K3VMqpaUL9F3J75r9ftEd4nW2plrGUGBsHBS0ixJL3s594CkHEmGQBYEIGQxcgIAAECSwWDQlLgpWjdznT644wOlmFK8tjtw7IBe+PcLbQomJOnIiSO6feXtrEGBsBAl6UVJ87ycmy9pplwBRkMHJW2V9PGp/x70V4EAOjXCCQAA0KFNGjpJq3+2Wh/9/CNdN+o6n/dfvq9cH2z/wOf9AqHIINcICW9TOV6RNE3Sd5KcktZISpUUKyle0o9P/TdWkuXUeWI9AC1FOAEAADqFHw/+sd6c8ab+nfFvpY5JlcGHA9Cf2/icz/oCOoIH5JreEdng+NuSLpI0RtKVkv4h19oUp6uRVHjq/FhJVn8WCqDTIJwAAACdyrgfjVOBpUAf3fWRzwKK5Z8u18FjDFZHeLld0kpJ3Rsc3yRpWwv7qJA0SRJLywJoDuEEAADolHp27SmnjwaV1zhrtPvgbp/0BXQk18oVLBjb0ccRuaaDMIICQFMIJwAAQKd0+Phhn/Z36Pghn/YHdBSXSPpArgUz2+qIXCMxWIMCQGMIJwAAQKfUs2tPn/a33bGdXTsQtr6V9506WqNcrpADALwhnAAAAJ3S4JjBijQ0XM6v7W4uvFmjnhml3733O1Xsq/BZv0BH4KslYVlaFkBjCCcAAECnFNMtRtPOnebTPiurKvXIukeUsChB5y06T4+te0y2KptP3wMINQclrfBRX8tP9QcADRFOAACATmtO0hy/9b1l3xY9+N6DGvHXEZqweIKe3PAki2ai07lBrsUwG24X2lY1p/q7wUf9Aeg8CCcAAECndfmwyxXfL75dffQ7o5+GG4c32Wbjno2au3quhjw5RJe9fJkWbVyk/Uf2t+t9gVBwQL5fxNIpqVTS65JsfugfQMdEOAEAADotg8GgV6e9qugu0W26PrpLtP731v+V7V6bPr7rY829cK4G9RrUaHunnFq7fa3mvDNHA54YoKtfu1ovf/KyHN872ngHQHD1lmTwQ797JN0qaYSkH8k1kuJxSe/LtbMHgPBjcLLsdIe2a9cuDRkyRJK0c+dODR48OMgVAQAQeopsRZq2dJqOnGj5Y090l2itmL5CKXEp9Y7XOmu1fsd65Zfnq2Brgb757ptm++oa2VVTR05VWnyarh11raK7ti0sAYLhoKRY+W5qR3MiJY2VNPG0V5z8E5IAaBt/PIcSTnRwhBMAALSMda9Vt624TRX7m99pI6F/gl658RWZB5ibbHey9qRK7CXKr8jX8k+X6+Cx5pf6i+4SrevPuV5pCWm6Ku4qdYvq1uJ7AILFIqkwiO9/pqQLT70mSpogybebBQNoDcIJeCCcAACg5ZxOpz7Y/oEWbFigtz5/q965qIgoTRs9TXPGz9FlQy+TwdC6z2m/P/m9VlWu0pLyJXrzszd19OTRZq/p3a23bjr3Js1ImKErhl+hqIioVr0nEChrJF3pg36elHRU0oZTr+bHHXkXISlBP4ysuFDSKDG6AggUwgl4IJwAAKD19h/Zr/7z+9c7ZrvHJlOsySf9Hz5+WG9//rbyy/P1buW7Ol5zvNlr+p3RT5YxFqUlpOnisy9WhIGlwRA6nHJNtWh+3FHjEiRt1g8BglOSXa6Q4qNT/92ktk8fiZXn6IqYdtQLoHGEE/BAOAEAQOt5Cyf2PbBP/aL7+fy9HN87tOLTFcqvyFeJvUQ1zuYfvQbHDNb0+OlKS0hT4oDEVo/iAPzBKmmS2rZgZbSktZKanijl6rtMP4ys2CBpXxveT3KFIPGqP7riHLEjAOALhBPwQDgBAEDrBTKcqPceR/apcGuh8svztW7HuhZdMyJ2hNLi05SWkKb4/u3bFhVoryJJ09S6gCJa0gpJKc019MIp6SvVH13xiaSTbehLkoyqP7rix3LtSAKgdQgn4IFwAgCA1gtWOHG6nQd2alnFMuVX5Kt0T2mLrhnbf6zSEtI0PX664mLj/Fwh4J1V0m1q2RSPBEmvqPkRE63x3akaTh9d8Z829mWQdK7qj644V4yuAJpDOAEPhBMAALReKIQTp6usqlR+eb6WlC/R1v1bW3TN+IHjNSNhhm6Ov1mDYgb5uUKgPqekDyQ9K9eoiNMnK0XJNbpijqTL5P9FKp2SduiHkRUbJP1b0ok29tdbrhEVp4+u6NP+MoFOhXACHggnAABovVALJ05Xvq+8LqiwV9ubbW+QQZcOvVRp8WlKHZMaEveA8HJQ0m5JhyT1kjRIwV+I8nv9MLrCHVrsbkd/o1V/dMUYSZHtrBHoyAgn4IFwAgCA1gvlcMLN6XSqdE+p8svztbRiqXYfav7RKtIQqWRTstIS0nTj6Btl7G70f6FAB7FT9UdXWCU1v4+Od71Uf3TFhXLtFgKEC8IJeCCcAACg9TpCOHG6Wmet1u9Yr/zyfBVsLdA3333T7DVdI7tq6sipSotP07WjrlV01+gAVAp0HMfkmv5x+uiKne3ob5Tqj65IEKMr0HkRTsAD4QQAAK3X0cKJ052sPakSe4nyK/K1/NPlOnjsYLPXRHeJ1vXnXK+0hDRdFXeVukV1C0ClQMezW/VHV5TJFWK0RU9JE1R/dMWZPqgRCAWEE/BAOAEAQOt15HDidN+f/F6rKldpSfkSvfnZmzp68miz1/Tu1ls3nXuTZiTM0BXDr1BURFQAKgU6puNybV16+uiK7e3ob4Tqj64YK9cCokBHQzgBD4QTAAC0XmcJJ053+Phhvf3528ovz9e7le/qeE3zs+n7ndFPljEWpSWk6eKzL1aEgQ0UgebsVf3RFaVyLcDZFtGSxqv+6Ir+TV4BhAbCCXggnAAAoPU6YzhxOsf3Dq34dIXyK/JVYi9RjbOm2WsGxwzW9PjpSktIU+KARBkM/t4AEugcTkjapPqjK75sR38m1R9dcZ6kLu2sEfA1wgl4IJwAAKD1Ons4cbp9R/apcGuh8svztW7HuhZdMyJ2hNLi05SWkKb4/vF+rhDofP4j6f/0w+iKjZKan3TlXQ/VH10xUdJZPqgRaA/CCXggnAAAwNOCDQu0YMOCRs/XOmu19/DeescG9BzQ7LSGuRPnau7EuT6pMRh2HtipZRXLlF+Rr9I9pS26Zmz/sUpLSNP0+OmKi43zc4VA53RC0hbVnw5ia0d/w1R/dMX5krq2r0SgVfzxHMr6KwAAoNM5eOygdh/a3aprGoYVjfXbkQ3pPUT3X3S/7r/oflVWVSq/PF9Lypdo6/6tjV6zZd8WbXlvix5870GNHzheMxJm6Ob4mzUoZlAAKwc6ti6SzKdec04d26/6YcVGSUda2N9Xp15LTn3dXVKifggsJkoa0P6ygYAinAAAAJ1OTLcYDerl+4fnmG4xPu8zWEbEjtBDkx7SQ5MeUvm+8rqgwl5tb/SajXs2auOejbp/9f26dOilSotPU+qY1E45HQbwt36Srjv1kqSTkspVP7D4ooV9fS/pX6debmer/uiKC8ToCoQ2pnV0cEzrAAAAvuJ0OlW6p1T55flaWrG0RaNPIg2RSjYlKy0hTTeOvlHG7kb/FwqEiW/0w9oVH5369eE29tVNrtEVp69dwfgntBVrTvhQXl6eCgoKZDKZVFVVJUn67W9/K7PZ3O6+7Xa7srOzVVpaqtjYWEmSxWJRenp6u/tuiHACAAD4Q62zVut3rFd+eb4Kthbom+++afaarpFdNXXkVKXFp+naUdcqumt0ACoFwkeNpArVH13xWTv6G6z6oyvMcoUYQHMIJ3zEYrHIbrerpKRERqNRkitQSExMVHZ2drtChLy8PGVlZWnx4sVKTU2tO56VlaW4uDifBxSEEwAAwN9O1p5Uib1E+RX5Wv7p8hatvRHdJVrXn3O90hLSdFXcVeoWxSMP4A9V8hxd0dbVcbrKNf3j9MBiiCQ2FkZDhBM+kJeXp4yMDFVXV9cFE26FhYWyWCwqKytr0wgKd99FRUVKTk6uO15cXCyLxSKTyaSysrL23kI9hBMAACCQvj/5vVZVrtKS8iV687M3dfRk8xsk9u7WWzede5NmJMzQFcOvUFQEy54B/lIj6VPVH13xaTv6G6gfgoqJck0N6d7OGtHxEU74QJ8+fZSUlKSioiKv5w0Gg5KTkxs93xi73a64uDilpqaqoKCg3rmcnBxlZWXJZDLJZmvPpkGeCCcAAECwHD5+WG9//rbyy/P1buW7Ol5zvNlr+p3RT5YxFqUlpOnisy9udvtWAO3nUP3RFR9JOtDGvrpIGqf6oyuGitEV4YZwop2sVqsSExOVmZmp7Oxsr20SExNltVq9jqxoSkpKioqLixsddZGXl6fk5GSZTKa2lu8V4QQAAAgFju8dWvHpCuVX5KvEXqIaZ02z1wyOGazp8dOVlpCmxAGJMhh4vAECoVbSNtUfXbFVUlsfDH+k+qMrkiT1aH+ZCGH+eA4Nq6h66dKlkqS+ffs22sa9gGVxcXGL+7Xb7XXtG5sOkp6e7vNgAgAAIFQYuxs184KZWnXrKu25f4+enfqsLj370iav2XVwl57Y8ITGLx6vUc+M0u/e+50q9lUEqGIgfEVIGiPpTkmL5drCtFrSakkPS/qJpD6t6O8/klZIypI0SVKMpPGS7pH0P5LsanvwgfARVhP+3AFCUyMi3Oc2btxYb0HLphQWFkr6IZiw2+3Kzc2V5BpRcfr6EwAAAJ1d/+j+mjN+juaMn6OdB3ZqWcUy5Vfkq3RPaaPXVFZV6pF1j+iRdY9obP+xSktI0/T46YqLjQtg5UD46i0p5dRLco2u+EI/jKzYIFeI0ZKQ4aSk0lOvZ04d6y/P0RXs54PThVU44XA4JP0wOsIb9zl325bYuHFj3bXFxcUqKipSRkaGYmNjlZeXJ4vFopKSkjYtsrlr164mz+/du7fVfQIAAATKkN5DdP9F9+v+i+5XZVWl8svztaR8ibbu39roNVv2bdGW97bowfce1PiB4zUjYYZujr9Zg2IGBbByILxFSDrn1OuOU8cOSfpY9deu+LaF/e2T9MaplyRFSjpfP4QVF0qKE2tXhLOwCieqqqr80tZut9f9uqioqN56FpmZmfr222+VmJjYpl1A3PN4AAAAOroRsSP00KSH9NCkh1S+r7wuqLBX2xu9ZuOejdq4Z6PuX32/Lh16qdLi05Q6JlX9ovsFsHIAktRL0uRTL8k1iqJSP4ys+EjSZrlGXTSnRpL11Ou5U8f6yRVSuAOL8ZJ6+qh2hL6wWnPCX9yjLIqLi5WRkeFx3n1s1qxZgSwLAAAgZCX0T9AjVz6iynsq9fFdH2vuhXM1qFfjIyOccmrt9rWa884cDXhigK5+7Wq9/MnLcnzvCFzRAOoxSBop6TZJiyT9W65dQN6T9Jik6ySd2Yr+9kt6S9KDkq6Ua6rJOEm/kPSqpM/F2hWdWViNnPAX9zoVJpPJ66KX7mNWq1VWq7VVoyd27tzZ5Pm9e/dqwoQJLS8WAAAghBgMBo0fNF7jB43XX6b8Ret3rFd+eb4Kthbom+++8XpNjbNGq2yrtMq2ShlvZ2jqyKlKi0/TtaOuVXRXZrEDwdRT0hWnXpIrTLCr/uiKTXKNnGhO7am2myQ9f+pYX9UfXTFBrhEd6PjCKpyIjY2Vw+Fo0ZSNptalaKxtU7txGI1GORwOlZaWtiqcYGtQAAAQLiIMEZo0dJImDZ2khT9ZqBJ7ifIr8rX80+U6eOyg12uO1xzXym0rtXLbSkV3idb151yvtIQ0XRV3lbpFdQvwHQBoyCDXWhJxkm49deyIXItlnr6V6b4W9vetpH+eern7T5ArqHCvXTFKTBHoiMIqnGhqlw43d3DRkrZuLdki1B2MlJWVtbhfAACAcBUVEaWrRlylq0ZcpUXXLNKqylVaUr5Eb372po6ePOr1miMnjmhJ+RItKV+i3t1666Zzb9KMhBm6YvgViooIqx97gZAWLemyUy/JNbriK9UfXfGJXLt+NMcpacupV96pY33kObqit08qhz+F1f+lk5KSZLVaZbPZGm3jXtxy/PjxLe43MTGx2Tbu0CMuju2wAAAAWqN7VHfdMPoG3TD6Bh0+flhvf/628svz9W7luzpec9zrNQeOHdBLn7yklz55Sf3O6CfLGIvSEtJ08dkXK8LAZ6pAKDFIGn7qdcupY99JKlP90RX/aWF/1ZLePfVy9z9G9UdXjBajK0JNWP15pKS4du1taptQ97nU1NQW95ucnCxJKi1tfO9ut7ZsJwoAAACXnl17Ki0hTSvTVurrB77Wi9e/qClxUxRpiGz0mv3f7ddzpc9p0suTNPSpoXpg9QMq3VMqp5Ol9YBQdYakSyXNk7Rc0h65RlcskXSvXKMhurSwL6ekCkl/k/RzSfFyrV1xtaQ/SFolyeGrwtFmBmeY/V/ZYDDIZDJ5HT3hcDjUp08fmc3mVk+/SExMlNVqVXV1tceUELvdrri4OBmNRlVXV7enfA+7du2q2250586drFEBAADC0r4j+1S4tVD55flat2Ndi64ZETtCafFpSktIU3z/eD9XCMDXjsq1Fenpoyv2tKO/c1V/dMUYBfbT/IOSdkk6LNfCooMlxQTw/VvDH8+hYTVyQpKys7Nlt9vrpm+cbtmyZZKkxYsXe5yz2+1KTEz0ulWou9/T+zhdYWFho/0CAACg/fpH99ec8XO0duZa7fjVDs1Pma+kgUlNXlNZValH1j2ihEUJOm/ReXps3WOyVTU+/RdAaOkh6WJJ90sqlOvBfoekpZJ+JVfA0LUV/X0q6UVJsySNlWvtiimS/p9cU0Sa31ah9ZyS1khKlRQr16iOH5/6b6wky6nz4TCiIOxGTkiSxWKR1WpVWVlZ3SgHq9WqyZMn67e//a0yMzM9rsnKylJOTo4kqayszOv0jJycHGVlZamoqKhuqkdxcbFSUlKUmZlZF2D4EiMnAAAAGldZVan88nwtKV+irfu3tuia8QPHa0bCDN0cf7MGxQzyc4UA/OmYpH+r/mKbO9vR3zmqP7oiXlLjk8qaZpV0m1xTTpoTL+lVSaGySIA/nkPDMpyQpLy8PBUUFMhkMqmqqkoOh0NZWVl1oUJDVqtVFotFJpNJRUVFjfZbXFys7OzsugUwTSaTMjIyGu23vQgnAAAAWqZ8X3ldUGGv9hxF25BBBl069FKlxacpdUyq+kX3C0CVAPxtt34IKjbItfDmsTb21Uuu9S/cO4NcKNd6Fs0pkjRNrm1VWypa0gpJKa2s0R8IJ+CBcAIAAKB1nE6nSveUKr88X0srlmr3od3NXhNpiFSyKVlpCWm6cfSNMnY3+r/QELBgwwIt2LDA5/3OnThXcyfO9Xm/QFsck7RJ9UdXbG9HfyNVf3RFgupvk2mVNEmtCybcoiWtVfBHUPjjOTSsthIFAAAADAaDxg8ar/GDxusvU/6i9TvWK788XwVbC/TNd994vabGWaNVtlVaZVuljLczNHXkVKXFp+naUdcqumt0gO8gcA4eO9ii8KYt/QKhoptcox8mSLrv1LE9coUU7tEVpZK+b2F/X5x6vXrq62j9MLriQklZalswoVPX3S5ps1xbpHYmhBMAAAAIWxGGCE0aOkmThk7Swp8s1Htfvqcl5Uu0/NPljT5AH685rpXbVmrltpWK7hKt68+5XmkJaboq7ip1i+oW4Dvwr5huMRrUq/F1N2qdtdp7eG+9YwN6DlCEoel192O6heoeBIDLQEk3nXpJ0nG5AoHTR1d82cK+jsi1qOUaH9VWLukDSZf7qL9QwbSODo5pHQAAAL73/cnvtapylZaUL9Gbn72poyePNntN7269ddO5N2lGwgxdMfwKRUV0/s8B9x/Zr/7z+9c7tu+BfazPgbDwH9UfXbFRru1NA8EiyXOfyMBhzQl4IJwAAADwr8PHD+vtz99Wfnm+3q18V8drjjd7Tb8z+skyxqK0hDRdfPbFzY4k6KgIJ4AfnJC0RfVHV/hrc+JIubY2DdYYJMIJeCCcAAAACBzH9w6t+HSF8ivyVWIvUY2zptlrBscM1vT46UpLSFPigEQZDJ1npjjhBNC0fXKFFA/ItQ6FLxkkXSfpDR/32xL+eA7tnBEuAAAA4AfG7kbNvGCmVt26Snvu36Nnpz6rS8++tMlrdh3cpSc2PKHxi8dr1DOj9Lv3fqeKfRUBqhhAMPWXdL1ca1j4mlPSAT/0GyyEEwAAAEAb9I/urznj52jtzLXa8asdmp8yX0kDk5q8prKqUo+se0QJixJ03qLz9Ni6x2Sr8tfAbwChord8v7uG4VS/nQXTOjo4pnUAAACElsqqSuWX52tJ+RJt3b+1RdeMHzheMxJm6Ob4mzUopvHdMUIN0zqAljsoKVZS85PBmhcl6Vt1rjUnGDkBAAAA+NCI2BF6aNJDqphToS2/2KIHL31Qpj6mJq/ZuGej5q6eqyFPDtFlL1+mRRsXaf+R/QGqGEAgxEia5qO+pil4wYS/EE4AAAAAfpLQP0GPXPmIKu+p1Md3fay5F87VoF6Nj4xwyqm129dqzjtzNOCJAbr6tav18icvy/G9I3BFA/CbOSHWTyghnAAAAAD8zGAwaPyg8Xriqie049c79MEdH+gXSb/QmWec2eg1Nc4arbKt0sw3Zuqs+Wdp2tJpWlq+VEeOHwlg5QB86XJJ8e3sI0HSZe0vJeQQTgAAAAABFGGI0KShk/TcNc9p7/17terWVbpj3B2K6db4IO3jNce1cttKpf0jTWfNP0u3/OMWvfnZmzp28lgAKwfQXgZJr0qKbuP10ZJeke8X1wwFhBMAAABAkERFRGlK3BS9dMNL+vqBr7Vy+kpNj5+uHlE9Gr3myIkjWlK+RDfk36Cz5p+lO9+4U0W2Ip2sPRnAygG0lVnSCrU+oIg+dZ3Z5xWFBsIJAAAAIAR0j+quG0bfoPzUfO2bt09LfrpEN5xzg7pGdm30mgPHDuilT17SlNemaOATA3X3P+/Wuu3rVOusDWDlAForRdJatXyKR8Kp9il+qyj4CCcAAACAENOza0+lJaRpZdpKff3A13rx+hc1JW6KIg2RjV6z/7v9eq70OU16eZKGPjVUD6x+QKV7SuV0OgNYOYCWMkvaImmNpFRJDb+7oyRZTp3frM47YsLN4OT/Vh2aP/aXBQAAQGjad2SfCrcWKr88X+t2rGvRNSNiRygtPk1pCWmK79/epfjq239kv/rP71+/xgf2qV90P5++DxAODkraLemQpF6SBil0twv1x3Mo4UQHRzgBAAAQnnYe2KllFcuUX5Gv0j2lLbpmbP+xSktI0/T46YqLjWt3Dd7CCds9NpliTe3uG0DoIpyAB8IJAAAAVFZVKr88X0vKl2jr/q0tumb8wPGakTBDN8ffrEExg1r1fk6nU+9/9b4WbFigt794u965SEOkpp07TXOS5ujyYZfLYOiM+woA4Y1wAh4IJwAAAHC68n3ldUGFvdrebHuDDLp06KVKi09T6pjUZqdkWPdadduK21Sxv6LZvuP7xevVaa/KPKCzz5YHwgvhBDwQTgAAAMAbp9Op0j2lyi/P19KKpdp9aHez10QaIpVsSlZaQppuHH2jjN2N9c4X2Yo0bek0HTlxpMV1RHeJ1orpK5QS15n3GQDCC+EEPBBOAAAAoDm1zlqt37Fe+eX5KthaoG+++6bZa7pGdtXUkVOVFp+ma0ddq8++/UyTXprUqmDCLbpLtNbOXMsICqCTIJyAB8IJAAAAtMbJ2pN678v3tKR8iZZ/ulwHjx1s9pozos5Ql8guOnDsQJvfN6F/gjbP3swaFEAn4I/n0Ih29wAAAACgw4iKiNKUuCl66YaX9PUDX2vl9JWaHj9dPaJ6NHrNdye/a1cwIbnWwvhg+wft6gNA50U4AQAAAISp7lHddcPoG5Sfmq998/ZpyU+X6IZzblDXyK5+eb/nNj7nl34BdHyEEwAAAADUs2tPpSWkaWXaSn39wNd68foXNSVuiiJ8+MjQ0mkkAMIP4QQAAACAeozdjZp5wUytunWV3r/jfZ/1W+Os0e6Dze8aAiD8EE4AAAAAaFS3qG4+7e/Q8UM+7Q9A50A4AQAAAKBRPbv29Gl/vbr28ml/ADoHwgkAAAAAjRocM1iRhkif9BUVEaVBMYN80heAzoVwAgAAAECjYrrFaNq503zSV62zVn/64E+qOlrlk/4AdB6EEwAAAACaNCdpjk/6qXXWav6G+YpbGKecf+Xo6ImjPukXQMdHOAEAAACgSZcPu1zx/eJ91p/je4eyirM08q8j9eK/X1RNbY3P+gbQMRFOAAAAAGiSwWDQq9NeVXSX6DZd3yWii9d1K3Yf2q2fv/lznff8eXrrs7fkdDrbWyqADopwAgAAAECzzAPMWjF9RasDiugu0frnLf/UZ7/8TGkJaV7bbN2/VdfnX6/LXr5MG3Zu8EW5ADoYwgkAAAAALZISl6K1M9e2eIpHQv8ErZ25VilxKYqLjdOSny7RxlkbdeXwK722X7djnS568SLdtPQmbftmmy9LBxDiCCcAAAAAtJh5gFlbfrFFa25fo+tGXedxPioiSpYxFq25fY02z94s8wBzvfNJA5NU/LNirbp1lcb9aJzX91ixbYUSnktQxlsZ2nNojz9uA0CIMTiZ2NWh7dq1S0OGDJEk7dy5U4MHDw5yRQAAAAgX+4/sV//5/esds91jkynW1KLra521WrJliR5a85C+cnzltU2PqB6aO3Gu5l00T727925vyQB8wB/PoYycAAAAAOAzvbr1anHbCEOE/uu8/9K2u7fpyaueVN8efT3aHD15VI+ue1RxC+P01EdP6djJY74sF0CIIJwAAAAAEFTdorrpVxf+SrZ7bXrw0gfVI6qHR5tvj36rX6/6tUY/O1qvb35dtc7aIFQKwF8IJwAAAACEhN7de+uRKx9R5b2VSjene91+9CvHV7p1xa1KzEvUattqth8FOgnCCQAAAAAhZWCvgcq9Llflc8o1bfQ0r20++c8nuuq1q5Ty9xSV7SkLcIUAfI1wAgAAAEBIGn3maC2fvlwf3vmhLjn7Eq9tSr4sUdLiJM34xwzZqmwBrhCArxBOAAAAAAhpE4dM1No71urNtDc1pt8Yr23yy/N17rPn6t5379W+I/sCXCGA9iKcAAAAABDyDAaDrjvnOm2avUkvXP+CBvUa5NHmRO0J/fXjvypuYZwe/uBhHT5+OAiVAmgLwgkAAAAAHUZURJTuvOBOfXHPF/rz5D+rd7feHm0OHz+s37//e41YOEKLNi7SiZoTQagUQGsQTgAAAADocHp06aGsS7Jkv8+u+yfer66RXT3afH3ka815Z47in4tX4dZCdvYAQhjhBAAAAIAOK7ZHrOZPma/Pf/m5bj//dhlk8GjzRdUXshRYdOELF+r9r94PfJEAmkU4AQAAAKDDG2ocqpdvfFmfzP5EU0dO9drm490f64pXrtA1/3ONNn+9OcAVAmgK4QQAAACATuO8s87TP2/5p9bcvkYTBk3w2uadL97RuOfH6Y6Vd2jHgR0BrhCAN4QTAAAAADqdy4ddro9+/pEKLAUaGTvS47xTTr2y6RWN+usozVs9T1VHq4JQJQA3wgkAAAAAnZLBYFDqmFRVzKnQc1Of01nRZ3m0OVZzTPM3zJfpaZOy12fr6ImjQagUgMHJkrUd2q5duzRkyBBJ0s6dOzV48OAgVwQAAIDOYsGGBVqwYUGj52udtdp7eG+9YwN6DlCEoenPQOdOnKu5E+f6pMbWOHz8sJ7c8KRyPszR4eOHvbYZ1GuQHr7iYd1+/u2KjIgMcIVAx+CP59CodvcAAAAAoFM6eOygdh/a3aprGoYVjfUbDD279tTvLvudMpIy9MjaR7SodJFO1p6s12b3od36+Zs/1xMbntCfJ/9Z1466VgaD5w4gAHyLcAIAAACAVzHdYjSo1yC/9BtM/aP7a+FPFuq+H9+nh9Y8pPzyfI82W/dv1fX51+uSsy9RdnK2LhpyURAqBcIH0zo6OKZ1AAAAAO1TtqdMWcVZKvmypNE2N46+UY9PflyjzxwdwMqA0OSP51AWxAQAAAAQ1hIHJqroZ0VadesqjfvROK9tVm5bqYTnEpTxVob2HNoT2AKBMEA4AQAAACDsGQwGTYmborL0Mr027TUNMw7zaFPjrFGeNU8jFo7QgyUP6sD3BwJfKNBJEU4AAAAAwCkRhgj913n/pW13b9NTVz2lvj36erQ5evKoHlv/mOIWxumpj57SsZPHglAp0LkQTgAAAABAA92iuum+C++T7V6bHrz0QfWI6uHR5tuj3+rXq36t0c+O1uubX1etszYIlQKdA+EEAAAAADSid/feeuTKR1R5b6XSzemKNER6tPnK8ZVuXXGrzLlmrapcJfYcAFqPcAIAAAAAmjGw10DlXper8jnlmjZ6mtc2m77epKtfv1opf09R2Z6yAFcIdGyEEwAAAADQQqPPHK3l05frwzs/1CVnX+K1TcmXJUpanKS0wjTZqmwBrhDomAgnAAAAAKCVJg6ZqLV3rNWbaW9qTL8xXtssrViq0c+O1j3v3KN9R/YFuEKgYyGcAAAAAIA2MBgMuu6c67R59ma9cP0LGtRrkEebk7Un9czGZxS3ME4Pf/CwDh8/HIRKgdBHOAEAAAAA7RAZEak7L7hTX9zzhbKTs9W7W2+PNoePH9bv3/+9RiwcoUUbF+lEzYkgVAqELsIJAAAAAPCBHl16KPPiTNnvs+uBiQ+oW2Q3jzZfH/lac96Zo/jn4lW4tZCdPYBTCCcAAAAAwIdie8TqL1P+os9++ZluP/92GWTwaPNF1ReyFFh04QsX6v2v3g98kUCIIZwAAAAAAD8Yahyql298WZtmb9LUkVO9tvl498e64pUrNPX1qdr89eYAVwiEDsIJAAAAAPCjsWeN1T9v+afW3L5GEwZN8Nrm3cp3Ne75cbpj5R3acWBHgCsEgo9wAgAAAAAC4PJhl+ujn3+kAkuBRsaO9DjvlFOvbHpFo/46Sg+sfkDffvdtEKoEgoNwAgAAAAACxGAwKHVMqirmVGjRNYt0VvRZHm2O1RzTExueUNzCOGWvz9bRE0eDUCkQWIQTAAAAABBgXSK7aHbSbFXeW6mHL39YPbv29Ghz4NgB/abkNxr515F68d8v6mTtySBUCgQG4QQAAAAABEnPrj31u8t+J9u9Nt0z4R51ieji0Wb3od36+Zs/1/nPn683P3uT7UfRKRFOAAAAAECQ9Y/ur4U/WahP7/5UaQlpXtts3b9VN+TfoEkvT9KHOz8McIWAfxFOAAAAAECIiIuN05KfLlHprFJNHj7Za5v1O9br4hcv1rSl07Ttm20BrhDwD8IJAAAAAAgxiQMTVfSzIq26dZXG/Wic1zYrt61U/HPxSn8rXXsO7QlsgYCPEU4AAAAAQAgyGAyaEjdFZellem3aaxpmHObRptZZq8XWxRqxcIQeLHlQB74/EPhCAR8gnAAAAACAEBZhiNB/nfdf2nb3Nj111VPq26OvR5ujJ4/qsfWPKW5hnJ766CkdO3ksCJUCbRe24UReXp5SUlKUkZEhi8Uii8Uiq9Xa5v7sdrtSUlKUl5cnu91e73hhYaEsFku94wAAAADQGt2iuum+C++T7V6bHrz0QfWI6uHR5tuj3+rXq36tc545R69tfk21ztogVAq0nsEZhvvQuIOCkpISGY1GSa4QITExUdnZ2UpPT291n1arVYmJiY2ez87OVmZmZltLbtSuXbs0ZMgQSdLOnTs1ePBgn78HAAAAgNCz59Ae/fH9P+qFf7+gGmeN1zbnn3W+spOzNSVuigwGQ4ArRGflj+fQsBs5kZeXp8LCwnrBhCSZTCYtXrxYGRkZ7RpB0ZDZbFZZWZlfggkAAAAA4Wtgr4HKvS5X5XPKNW30NK9tNn29SVe/frWS/56ssj1lAa4QaLmwCyeysrKUnJxcL5hwS01NrWvTFqmpqbLZbCoqKlJZWZmcTqfKyspkNpvbUzIAAAAANGr0maO1fPpyfXjnh7rk7Eu8tnnvy/eUtDhJaYVpslXZAlwh0LywCiesVqscDkeTYYHZbFZxcbEcDkeb3sNkMik5OZlAAgAAAEBATRwyUWvvWKs3097UmH5jvLZZWrFUo58drXveuUf7juwLcIVA48IqnFi6dKkkqW9fz9Vt3WJjYyVJxcXFAakJAAAAAHzFYDDounOu0+bZm/Xi9S9qcIznWgAna0/qmY3PKG5hnB7+4GEdPn44CJUC9YVVOOEOHLxN6XBzn9u4cWMAKgIAAAAA34uMiNTMC2bq819+ruzkbPXu1tujzeHjh/X793+vEQtHaNHGRTpRcyIIlQIuUcEuIJDcUzXcoyO8cZ9r67QOq9Wq3Nzcuq+rqqo0ffr0uvUsWmvXrl1Nnt+7d2+b+gUAAADQ+fXo0kOZF2fqLvNdenzd4/rrx3/VsZpj9dp8feRrzXlnjhZ8tECPXfmYUseksrMHAi6swomqqiq/tHUrLi7W+PHj64UTdrtdcXFxSk9Pr3e8pdzbswAAAABAW8X2iNVfpvxF9/z4Hv2/Nf9Pr256VU4567WprKrUzYU3a/zA8cpJydHlwy4PTrEIS2E1rcOf3FuRNtwy1GQyKTMzU3l5ecrLywtSdQAAAAAgnd37bL1848vaNHuTpo6c6rXNxj0bdcUrV2jq61O1+evNAa4Q4YpwwkeMRmOjUzemT58uqW1blO7cubPJ18cff9yuugEAAACEn7FnjdU/b/mn1ty+RhMGTfDa5t3KdzXu+XG6feXt2nFgR4ArRLgJq3DCvZ5ES6ZsNLUuRWuZTCZJrnUsrFZrq64dPHhwk68BAwb4rE4AAAAA4eXyYZfro59/pAJLgUbGjvQ475RTr256VaP+OkoPrH5A3373bRCqRDgIq3CiqV063NzBRUvautntdmVkZKiwsLBFbQEAAAAgVBgMBqWOSVXFnAotumaRzoo+y6PNsZpjemLDE4pbGKfs9dk6euJoECpFZxZW4URSUpIkyWazNdrGHR6MHz++xf3m5uYqLy9PFoul2bbuURQAAAAAEEq6RHbR7KTZqry3Ug9f/rB6du3p0ebAsQP6TclvNPKvI/WC9QWdrD0ZhErRGYVVOJGSkiKp6W1C3edas/Vn3759JUnp6elez58+WsJsNre4XwAAAAAItJ5de+p3l/1OtnttumfCPeoS0cWjze5Du3XXW3fp/OfP15ufvSmn0+mlJ6DlwiqccAcOxcXFXs87HA7Z7fZWBwhms1mZmZmNbhXqfr/WBB4AAAAAEEz9o/tr4U8W6tO7P1VaQprXNlv3b9UN+Tdo0suT9OH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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Trotter circuits expectation value\")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_ideal,\n",
    "    std_error_ideal,\n",
    "    fmt=\"o\",\n",
    "    linestyle=\"--\",\n",
    "    capsize=4,\n",
    "    c=\"red\",\n",
    "    label=\"Ideal\",\n",
    ")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_unmit,\n",
    "    std_error_unmit,\n",
    "    fmt=\"o\",\n",
    "    linestyle=\"-\",\n",
    "    capsize=4,\n",
    "    c=\"green\",\n",
    "    label=\"No mitigation\",\n",
    ")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_unmit_zne,\n",
    "    [0] * (num_steps - 1),\n",
    "    fmt=\"o\",\n",
    "    linestyle=\"-\",\n",
    "    capsize=4,\n",
    "    c=\"cyan\",\n",
    "    label=\"ZNE\",\n",
    ")\n",
    "\n",
    "plt.xlabel(\"No. Trotter Steps\")\n",
    "plt.ylabel(f\"$\\langle ZZZZ \\\\rangle$\")\n",
    "plt.xticks([1, 2, 3, 4, 5])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "775527ac",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Probabilistic error cancellation\n",
    "Probabilistic error cancellation (PEC) samples for a collection of circuits that, on average mimics a noise inverting channel to cancel out the noise in the desired computation. This process is a bit like how noise-cancelling headphones work, and produces great results; however, it is not as general as other methods, and the sampling overhead is exponential.\n",
    "\n",
    "__Reference__: E. van den Berg, Z. Minev, A. Kandala, and K. Temme, _Probabilistic error cancellation with sparse Pauli-Lindblad models on noisy quantum processors_ [arXiv:2201.09866](https://arxiv.org/abs/2201.09866)\n",
    "\n",
    "#### Overall workflow\n",
    "\n",
    "<div>\n",
    "<img src=\"images/pec_layers.png\">\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af666478",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Step 1: Pauli twirling\n",
    "<img src=\"images/pec_pauli_twirling.png\" align=\"left\" />  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68306da2",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Step 2: Repeat layer and learn the noise\n",
    "<img src=\"images/pec_learn_layer.png\" align=\"left\" width=\"600\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90fd95cd",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Step 3: Derive a fidelity (error for each noise channel)\n",
    "<img src=\"images/pec_curve_fitting.png\" align=\"left\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ae02f68",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job id: cdkqv522h6kvivdat0vg\n",
      "2022-11-07 20:23:43.013816 *** Callback *** {'unique_layers_detected': 2, 'total_layers': 60} \n",
      "\n",
      "2022-11-07 20:30:41.251325 *** Callback *** {'msg': 'Sampling overhead for layer 1/2: 1.0263820029324646'} \n",
      "\n",
      "2022-11-07 20:37:38.920151 *** Callback *** {'msg': 'Sampling overhead for layer 2/2: 1.0692954781746'} \n",
      "\n",
      "2022-11-07 20:37:39.334815 *** Callback *** {'sampling_overhead_by_layer': [1.0692954781746, 1.0263820029324646], 'unique_layers': 2, 'total_sampling_overhead': 1.4508651011392564} \n",
      "\n",
      "2022-11-07 20:37:39.336883 *** Callback *** {'sampling_overhead_by_layer': [1.0692954781746, 1.0263820029324646], 'unique_layers': 2, 'total_sampling_overhead': 1.2045186180127132} \n",
      "\n",
      "2022-11-07 20:37:39.336983 *** Callback *** {'sampling_overhead_by_layer': [1.0692954781746, 1.0263820029324646], 'unique_layers': 2, 'total_sampling_overhead': 1.7475940265471324} \n",
      "\n",
      "2022-11-07 20:37:39.337099 *** Callback *** {'sampling_overhead_by_layer': [1.0692954781746, 1.0263820029324646], 'unique_layers': 2, 'total_sampling_overhead': 2.105009541703825} \n",
      "\n",
      "2022-11-07 20:37:39.338120 *** Callback *** {'sampling_overhead_by_layer': [1.0692954781746, 1.0263820029324646], 'unique_layers': 2, 'total_sampling_overhead': 2.5355231840766654} \n",
      "\n",
      "2022-11-07 20:44:17.157534 *** Callback *** {'values': array([0.96681631, 0.96346463, 0.81458917, 0.83279075, 0.71165489]), 'metadata': [{'standard_error': 0.04741526161355738, 'confidence_interval': [0.6946850904749993, 1.2389475260753755], 'confidence_level': 0.95, 'shots': 15360, 'samples': 120, 'sampling_overhead': 1.2045186180127132, 'total_mitigated_layers': 4}, {'standard_error': 0.06003453067758544, 'confidence_interval': [0.6917633123010369, 1.235165948939192], 'confidence_level': 0.95, 'shots': 18560, 'samples': 145, 'sampling_overhead': 1.4508651011392564, 'total_mitigated_layers': 8}, {'standard_error': 0.07893102265063534, 'confidence_interval': [0.5423767896024972, 1.086801560321982], 'confidence_level': 0.95, 'shots': 22272, 'samples': 174, 'sampling_overhead': 1.7475940265471324, 'total_mitigated_layers': 12}, {'standard_error': 0.08198342212333354, 'confidence_interval': [0.5608466609152284, 1.104734830411759], 'confidence_level': 0.95, 'shots': 26880, 'samples': 210, 'sampling_overhead': 2.105009541703825, 'total_mitigated_layers': 16}, {'standard_error': 0.08955468433998504, 'confidence_interval': [0.43973826826406204, 0.9835715192472465], 'confidence_level': 0.95, 'shots': 32384, 'samples': 253, 'sampling_overhead': 2.5355231840766654, 'total_mitigated_layers': 20}]} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "def interim_results_callback(job_id, result):\n",
    "    now = datetime.datetime.now()\n",
    "    print(now, \"*** Callback ***\", result, \"\\n\")\n",
    "\n",
    "\n",
    "options = Options()\n",
    "options.optimization_level = 0  # No optimization\n",
    "options.execution.shots = 100\n",
    "options.resilience_level = 3  # PEC\n",
    "options.environment.callback = interim_results_callback\n",
    "\n",
    "with Session(service=service, backend=backend) as session:\n",
    "    estimator_pec = Estimator(session=session, options=options)\n",
    "    job_pec = estimator_pec.run(circuits=trotter_circuit_list, observables=obs_list)\n",
    "    print(\"job id:\", job_pec.job_id)\n",
    "\n",
    "expvals_pec = job_pec.result().values\n",
    "std_error_pec = [metadata[\"standard_error\"] for metadata in job_pec.result().metadata]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b431f474",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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aFZHmy2S1Wq2IiIiISKNU3+qxsLAwIJYPiIiIBAvVnBARERERERERv1JyQkRERERERET8SskJEREREREREfErJSdERERERERExK+UnBARERERERERv1JyQkRERERERET8SluJioiIiIiIiIhfaeaEiIiIiIiIiPiVkhMiIiIiIiIi4ldKToiIiIiIiIiIXyk5ISIiIiIiIiJ+peSEiIiIiIiIiPiVkhMiIiIiIiIi4ldh/g5AGqe0tJQ1a9YA0KlTJ8LC9CMVERERERER7ykvL2f//v0ADB8+nIiIiEaPqSvZILdmzRpGjx7t7zBERERERESkGVq6dCmnn356o8fRsg4RERERERER8SvNnAhynTp1qvr70qVL6datmx+jERERERERkaZu9+7dVTP4q1+TNoaSE0Gueo2Jbt260bNnTz9GIyIiIiIiIs2Jp+oealmHiIiIiIiIiPiVkhMiIiIiIiIi4ldKToiIiIiIiIiIXyk5ISIiIiIiIiJ+peSEiIiIiIiIiPiVkhMiIiIiIiIi4ldKToiIiIiIiIiIXyk5ISIiIiIiIiJ+peSEiIiIiIiIiPiVkhMiIiIiIiIi4ldKToiIiIiIiIiIXyk5ISIiIiIiIiJ+peSEiIiIiIiIiPiVkhMiIiIiIiIi4ldKToiIiIiIiIiIXyk5ISIiIiIiIiJ+peSEiIiIiIiIiPiVkhMiIiIiIiIi4ldKToiIiIiIiIiIXyk5ISIiIiIiIiJ+peSEiIiIiIiIiPiVkhMiIiIiIiIi4ldKToiIiIiIiIiIXyk5ISIiIiIiIiJ+FebvAERE5Dcb5sDPczw/7qAZMHiG58cVEREREfEQJSdERAJFWTEc2+mdcUVEREREApiSEyIigSI8Elr1cN5urYTS3fbHIrqBqY4VeuGRjY9NRERERMSLlJwQEQkUg+tYflG6H97rbH9s6iqI6OTduEREREREvEwFMUVERERERETEr5ScEBERERERERG/UnJCRERERERERPxKyQkRERERERER8SslJ0RERERERETEr7RbhzTehjnw8xzPjzuojp0LREREREREpElQckIar6wYju30zrgiIiIiIiLS5Ck5IY0XHgmtejhvt1ZC6W77YxHdwFTHqqLwyMbHJiIiIiIiIgFPyQlpvMF1LL8o3Q/vdbY/NnUVRHTyblwiIiIiIiISFFQQU0RERERERET8qlknJ8xmM7GxsRQVFXlkvMzMTJKSkkhLSyMlJYWUlBTMZrNHxhYRERERERFpqprlsg6z2UxGRgaZmZkeGzMlJQWLxcKiRYuIiooCwGKxEB8fT3p6OqmpqR67LxEREREREZGmpFnNnJg9ezbx8fFkZGSQkpJSlURorMzMTLKzs+0SEwAxMTHMnTuXtLQ0zaAQERERERERcaJZzZyYOXMmM2fO9Pi4s2bNIjEx0WGyIzk5uapPTk6Ox+9bREREREREJNg1q5kT3mA2mykqKiIuLs5pn7i4OHJzcz1W20JERERERESkKVFyopHmz58PQMeOHZ32iY6OBiA3N9cnMYmIiIiIiIgEEyUnGsmWcHBVv8LWtmzZMh9EJCIiIiIiIhJcmlXNCW+wLdWwzY5wxNbWkGUdO3bscNm+e/fueo8pIiIiIiIiEkiUnGikgoICr/S16dWrV73PEREREREREQkmWtYhIiIiIiIiIn6lmRMBbvv27S7bd+/ezejRo30UjYiIiIiIiIjnKTnRSNHR0RQVFbm1ZMNVXQpnevbs2ZCwRERERERERIKGlnU0kqtdOmxsiQt3+oqIiIiIiIg0N0pONFJCQgIA+fn5TvtYLBYATj/9dJ/EJCIiIiIiIhJMlJxopKSkJMD1NqG2tuTkZB9EJCIiIiIiIhJclJxoJFvCITc312F7UVERFouFuLg4X4YlIiIiIiIiEjSUnHCDxWIhPj6etLQ0h+3p6elYLJaq5RvVLViwAIC5c+d6NUYRERERERGRYNVskxMWi6VqucXy5ctd9s3IyMBsNpOZmYnZbK7VPnPmTJKTk0lKSrJb3mE2m5k1axbp6emaOXEU2AHk//Zn8WH/xiMiIiIiIiIBo1ltJZqdnc3TTz9dtfWnbfeMlJQUoqOjiYqK4sorr2TmzJl251155ZVkZ2cTExPjNMmQlZVFZmYmKSkpxMTEUFBQQFFREVlZWSQmJnr7oQUmqxW++gpenAMfApXV2v44EC67DG69FSZMAJPJPzGKiIiIiIiI35msVqvV30FIw+3YsYNevXoBsH37dnr27OnniH5jNsN118G6dXX3HToU3n4bmvvsEpG6lO6H9zrbH7t8H0R08k88IiIiItIseeM6tNku6xAvysmBs892LzEBRr+zzzbOExERERERkWZHyQlpvDlzoGdP49a5M0yZAiUl9RujpMQ4r3Pnk2PNmeOdeEVERERERCSgNKuaE+IlxcWwc2fjx7FaYf9++3FFRERERESkyVNyQhovMhJ69IDjx+HAgcaPd8op0LKlMa6IiIiIiIg0eVrWIY03Ywbs2GHsuuEJ555rjDdjhmfGExERERERkYCm5IR4RnExvP9+3f1auDHWe+9pSYeIiIiIiEgzouSEeMaOHVBR4bpPBPAw0LKOsSoqPFPDQkRERERERIKCkhPiGUeO1N3nEiD2tz/rsmIFVFY2MigREREREREJBkpOiGe0beu6vQtw/m9/n/rbv1255hro2BEuvBDS0+H7742CmyIiIiIiItLkKDkhntGzJ4SGOm+/Bgj/7e/hv/27LkVF8Mkn8MADMG4cmM2NDlNEREREREQCj5IT4hmRkXDZZY7bRgDxNY7FA8PrMX5EBMTXHOQ3O3bAf/+rOhUiIiIiIiJBSskJ8Zxbb619LBS41kn/a39rr6mLgzUfY8ZACydbfXzyCVx1lTF7IyYGrrsOMjNhwwawWt2LXSRYHbH4OwIRERERkUYL83cA0gTMmWPcrFYIC4Py8pNtk4HuTs7rASQBC6sdCwszlod07QqJicaMjO++g7PPdn7/33578u9bthi3d94x/t2xo7EkZNw4GD8eTjvNeZJDJBiteQzO/czfUYiIiIiINIqSE9J4xcWOl1REApfXce4VwA9A8W//Li+HXbuMv8fGwmOPGX93NQOienKipoMH4YMPjBtAq1bGLIzx442Exdix0K5dHUGKBIg9X9Y+tnsh7Pocuk/xfTwiIiIiIh6i5IQ0XmQk9Ohx8t+lpVBQAFdaoXUd57YGpgGvmyA62qgtUX1cG5PJ8flHj8KwYXDokHGry7Fj8NVXxg2M2Rk5OXWfJ+JvFSdg9cOO28x3Q9fVEBLuuF1EREREJMApOSGNN2OGcavu+3/CVmfFJmqYANzwDpzpzhYeNbRubdScqKyEtWuNJSDffWfMptixo+7zx41z3vbVV0bSpX9/58kREV/Z+DIc2ey4rfhno33QPb6NSURERETEQ5ScEM+zVkLJK+Du9bwJOPJ3sF7d8CRASAiMGGHcbr3VWAby669GksKWrFi/vvZ548c7eQxWuOYaY4lJly4na1aMGwcjRxq1MUR85dheWPu46z5rHoO+10BEZ5+EJCIiIiLiSSarVdsZBLMdO3bQq1cvALZv307Pnj39HBGw5R348br6nzfwDuhyLpjCICTM+NMUWu3vtuOhdfQJddA/xKg/8f33J5MVq1fDvn3Qpo2Dx7DF2PnDkbZtjVoVtoTFmDHGDA4Rb1lyE1jeqLtf7E0wZp734xERERGRZs0b16FKTgS5gEtOlB2Gj0+FY7v9G0ctJvtkRs3kRUiNfxcdhq3boQKo/O1W8++2f1tNEBUNnbpCl27QtTu0aut+MsXR/dfs7+z8ECePp677M4VoqUqwOLgcPh8NuPOr2gTnLYPoeG9HJSIiIiLNmDeuQzU3XTxr3VMBmJgAsEJlGVDmXncT0K8eY3PQuJWvAzdKXQQEp8kMDyVAGnO+w9kv3kzYBGiyxloJeXfiXmICo9/yOyDp+8B8PCIiIiIiTig5IZ5TcQI2/t3fUYi7rOVQUe7vKAKHXQKjHskQjyRTnPQpXAkHfqzf4zjwI2z9F/T7nVeeJhERERERb1ByQjwntAUMvA3W/7XhY7ToAOFRYK0wLp6t5VD525/WCvu/i3iSteK319hxf0fSeCtnQc9LIbytvyMREREREXGLkhPiWUP/CFveatjSjlbd4cJf3LugslqNKe92CYwKF8mMGv921t/V+ZU1EyYVcPwobN8GO7bCju2wdxecNhLGjnZ8f8uWGP1DgZDfbqFAmAnatYX2baFtK2gTYbTZnV9HjCI2x3YZS6xGPeXvSERERERE3KLkhHhWeDsYld6w3TpGpbv/Ta/JZEx7JxRoWf/78qTqtQfLyuDYMYiMdNz3hgGw2VGDFTj82+03w4bBuIkntzDt3dt5DFXJGgcJFG8lbKr391mCyN37cLO2SFO28WUY/pgxo0lEREREJMApOSGe1/ca2PRq/dbKnzLWOC/YhYcbN0eOHIHKSvfHWrvWuL32mvHvlBRYsMBxX7tkjS5GASNZ05AEiqMZMg1JuDT2/KJ1UGJp+OMfeLsSEyIiIiISNJScEM8zhUD8i/Xb/jDhpaa/u0DbtpCfD7t2wfffw7ffwnffwapV7iUtBg503rZ5M/TqBS39PIskkJhCgvvivFHb8pqgbYwxo6apv69EREREpEkI8XcA0kR1TICYG9zrG3sjRMfX3a+p6N7dmAXx4otgNkNBASxcCA89BOecAxERjs8bP975mImJEBVlnP/QQ8Z4hw55JXzxEdsSqQaxwtLp8PWFULLdo2GJiIiIiHiDyWq1uvPVtgSoHTt20KtXLwC2b99Oz549/RxRNcf2wscDoazYeZ/wSLhoE0R09l1cge74cSNpYZtZ8d13RqKhsNBxLYvt2x3XowgJgREjjHoVtroV3bt7P37xHGsl5Iyr/3ai1YW1g9NmQ/9UYzaJiIiIiEgjeeM6VJ9UxXtadYFhj7ruM/wxJSZqatkSxo6FmTPhww/hwAH45RfnRTa/+87x8cpKWLkSXn4ZrrwSevSA2Fi4/nqYNw9+/tmY9i+By7ZEikYszSg/DMv+AIsmQvEmj4UmIiIiIuJJSk6Idw28Hdr2d9wWOchoF9dCQqC/k+cQjHoT7tYVsFjg7bdh+nQYPBg6d4bVqz0Tp3hHvZZI3QTj34OIrrXb9n0Nn42ADc8ahTdFRERERAKIkhPiXaEtYMSTjtvi/wYhTna2EPf96U9w8CB8/DE88ACcdRa0cLMQZGEhxMQ4bjt61NhhRPxv5FPGEihXwiONfr0ugwvXO05oVJTCivvhizOhaI13YhURERERaQAlJ8T7uk6sfazbedBtsu9jaao6dIALLoCnnz5Zo+Kbb+Cpp+D8850vCYmLM3YRceTdd40im6efDvfcA++9B3v3eu0hiAv1XSLVogOc8Qac+wW06VO7b8EyWBgPqx+DihMeDlZEREREpP6UnBD/GP64vyNo2iIijCKYDz4In35q7AiyciW89JJRf8JWGNPVDiDffQcVFbB8ObzwAlxxBXTtCqeeCjfdBG++aSwpUd0K32jIEqluSTB1LQy8g1p1KyrLYO3jRpLi4DKPhysiIiIiUh/arSPIBfRuHTal++G9GkUvL98HEZ38E48YCYWtWyE01PFOHwBDhsCGDXWP1bWrsROIbVeQESMgLMyj4cpvts6HH/6v9vFzP697JtL+7+Gnm6D4l9ptphA49R4Y8QSEtfZMrCIiIiLSZGm3DhHxDJMJ+vVznpgoLjZmRbhjzx7Izoa774b4eIiOhhkzPBaqVNOYJVKdzoLzV8KQB8EUat9mrYSfn4NPR8LerzwRqYiIiIhIvSg5ISK1RUZCURF8+SU88QQkJTmvTVHT4cMQ7qLQaWmpR0KU39RniVRoBIx6CqYsgw6jarcf2QyLzoWlt0BZscdCFBERERGpi+Zei4hjrVvDuecaN4Dycli1yqhF8e23xp/OCmS6qmUxcqSxnMS2DGTcOOjb1/3tUMVe2371Pyf6NJiyFDY8A2seh8oaRTE3Z8CuT+D0DOgx1TNxioiIiIi4oOSEiLgnLMxYthEfD3fdZdSt2LzZSFLYEhabNhl9zzzT8Rh79sDGjcbfN2yAuXONv/foYZ+sGDbMSGCI94SEw9A/Qs/LjVoUB36wbz+6A76+APpeA3EvQMQpfglTRERERJoHJSdEpGFMJhgwwLjdcINxbO9eY1eQ6GjH53z3nePjO3fC/PnGDaB9eyPBYUtWnH66sQOJeF77QZD4DWz6O6x8ECqO2rdv/Rfs/gISXobeKZrhIiIiIiJeoZoTIuI5XbrAlCnO23/4wXlbdYcOwWefwR//CGefbSQrxo07OetCPCskFE69Ey5YC10Ta7cf3w/fXwnfXg5Hd/k+PhERERFp8pScEBHfSU+Hn36CZ5+FSy+FU9xcKnDihJHY6NLFcbt2RPaMtv3g3C9gzOsQ3r52+47/wSdDIP91PeciIiIi4lFKToiI74SHw+jRcO+98P77sG+fUXsiMxOuuw5iYpyfO3KkMYPCkfnzjaKa114LGRmwfj1UVnrlITR5JhPE3ggXrIeel9ZuLzsEP90MiyfDkS0+D09EREREmibVnBAR/zGZYNAg4zZ9unFs5074/vuTO4KsWmV8S+9qB5DvvoNt24zbP/9pHIuONpaC2AptxsVBixbef0xNRevuMP49+DULlt9uLO2obk8ufDIMRj0NA24zloaIiIiIiDSQkhMiElh69IBp04wbGPUnfvwRund3fs6339Y+VlAAH35o3MAoqDlmzMkim2PHQmSk5+NvSkwm6DMNukwE8z2w9Z/27RVHIe8u2DYfxsyD9oP9E6eIiIiIBD0t6xCRwNa+PZx3HowY4bi9qAjWrKl7nNJS+PprePJJY7wOHYzZFM8/79Fwm6SIU+DMd+Ccj6F1z9rtB36Az0bBuqegsszn4YmIiIhI8NPMCREJbpGRsHr1yWUg334L27fXfV5lJaxYARMmeD1Et82ZY9ycaVMBj9Y4NnIElNSxpGLGDOPWWD0ugM7rYMUs2PyafVvlCVj1kLEMZMwbEH1a4+9PRERERJoNJSdEJLiFhMCwYcbtD38wjv36q32yYt065+ePG+e8bcIEo3aFbSnIaadBmBd/bRYXGzU3nGnn4NjuPXDYjXE9JTwSRr8Kfa40CmMeybdvL1wJn58Og2fC8EcgNMJz9y0iIiIiTZaSEyLS9PTuDddcY9zAqD/xww8nExbLlkHZb8sPnCUnDhwwloGAsbMIQJs2cMYZJ4tsjhkDbdt6Lu7ISKPmhjOtyuDoPigAjgMtgU6nQGTLusf1tC4TYOpqWPMo/DwHrNV2R7FWwPqnYcd7xraknc7y/P2LiIiISJNislq1WX0w27FjB7169QJg+/bt9OzpYD24v5Xuh/c62x+7fB9EdPJPPCLHjhkJijVr4LbbHPf54AO49FLX44SGGnUrbMmKs86Czp1dn9MQVit89RU8+1dY+AVU3yU1NBQuuwxuvdWY6WEyef7+63JgKfx0Exxa66DRBANvh5FPQbgHEzkiIiIi4jfeuA5VQUwRaX5atYKzz3aemABjhkVdKiqMJMfzz8Pll0OXLsa2qDffDDt2eCZWsxmGD4eJE+HTGokJWwzZ2Ub78OFGf187ZTSclwfDHoWQ8BqNVtj4Enw6DHbn+D42EREREQkKSk6IiDhyxx3w1ltGomHQIPfP++UXeP11aN268THk5BhJFFc1M6pbt87on+OHJEBoCxjxmJGkiD69dnvJNlg8GZbcCCcKfR6eiIiIiAQ2JSdERBzp3Ruuuw7mzoUNG2DfPqP2xL33wujRrgtjDh1qFNJ05OOPYcoUY0vTr74ylpg4YjYbyzVKSuoXd0mJcZ4/ZlAARA2HyT/Aac86LoZp+Qd8PAS2v+/72EREREQkYKkgpoiIOzp1MmpQ2OpQlJTATz+d3BHkxx9PJhLGj3c+Tm4ufPGFcQMID4f4+JM7gpx1lpHYuO66+icmbEpK4PrrjS1W/VGDIiQMBt8LPS8xdvTY97V9e+ke+PZy6J0C8S9Bqy6+j1FEREREAooKYgY5FcQUCRDl5bBypZGsiIszllc4kpAAeXmux+rTB7Zta3xMixcbRTL9yVoJm+fCivuh3MGepy2iIf4F6Ps7/yRSRERERKTeVBBTRCRQhYUZiYe773aemDh8GFasqHssTyQmAF55xTPjNIYpBAakwQXroPvU2u0nCuDH6+CrC6Bku+/jExEREZGAoOSEiIivhIXBggVw113GUo4QL/8Kfu89KC727n24q00vOOdjGPuOMVuipt2fwSdDYdOrxmwLEREREWlWlJwQEfGVVq3giivghRdg+XIoKjJqT/zpT8byi1atPHt/FRWwc6dnx2wMkwn6/Q4u3AC9p9VuLz8My26FRedC8SbfxyciIiIifqPkhIiIv7RrB0lJ8MQTRn2IoiKYN8+z9/Hee4GVoACI6Azj5sP496FVt9rt+76Bz0bA+megstz38YmIiIiIzyk5ISISKFq0gLFjPTvmww9Dz54QE2PsAJKZaWyNGgi1kHtdatSiiLmxdltFKaycCV+MhaI1Pg9NRERERHxLyQkRkUDSsyeEhnp+3C1b4J13IC0Nhgw5uTXqs8/CwYOevz93tegAZ7wO534BbfrWbi9YDp/FwepHoeKEz8MTEREREd9QckJEJJBERsJll3n/fg4ehA8+gPvvN2pT+Fu3JJi6BgbeCdTYUtRaDmufgIVxcGCpX8ITEREREe9SckJEJNDceqtnxjnjDIiKct1n4EDo3Nlx2zffGDuLZGfDnj2eicmV8LaQ8DdI+hYiB9VuP7QOcsaC+T4oP+r9eERERETEZ8L8HYA0ARvmwM9znLc72hbw05FgqiM3NmgGDJ7RuNhEgtGECTB0KKxb1/Axhg2DH34wakusWwfffgvffWf8uWPHyX7jxzsf46OP4MUXjRtA//4wbpxxzrhxMGCAsQOHp3U6C85fAWv/DOvTwVptZoe1En5+Dnb8D8bMgy4TPH//IiIiIuJzSk5I45UVw7F67gZQutu9cUWaI5MJ3n4bzj4bSkrqf36bNvDWW8Y4JhMMH27cbr3VSFb8+uvJRMXUqc7H+e47+39v3mzc3nzT+HfnzvbJilGjIMxD/62ERsDIv0CvZPjpRihcad9+JN/YcrR/KoyaDS3ae+Z+RURERMQvlJyQxguPhFY9vDOuSHMVFwfvv2/Un6hPgqJNG+O8uDjH7SYT9Olj3K65xvk4R49CXp7r+9q3z9iq9L33Tt732LFGouK882DMGPfjdib6NJiyFDY8C2seh8rj9u2bM2HnJzA6A3pc0Pj7ExERERG/MFmtgbCfnDTUjh076NWrFwDbt2+nZ8+efo5IRDzKbDa2AHVnicewYcaMCWeJifo4dAheftmYXfHDD3D4cP3OT0uD115rfBx2Mf0MP90EB35w3N73Goh7ASJO8ez9ioiIiIgdb1yHqiCmiEggi4uDNWtg8WK4YHLt39phYZCSYrSvXu2ZxARA+/bw0EOwcCEUFhpJkhdfNO6ra9e6z3dVy+LJJ41lK1u2GMtM3I5pECR+A/EvQlib2u1b/wWfDIFt8+s3roiIiIj4nWZOBDnNnBBpQubMMW7OtCqDWfugECgFIoDXToHjLV2PO2OGcfMUq9VILFQvsvnLL/Z9tm41lo7UdOSIsYOIbfvS7t1P1qwYP96Y/REaWncMR7bC0lTYk+O4vcfFcPqr0Lp7PR6YiIjUUlfh84ZS4XORoOaN61DVnBARCRTFxbDTRXHZdkDr3242+w9AXSsuij1cXNZkgpgY43b99b/Fsd9IVHz3HWzc6DgxAbBkycnEBMCuXTB/vnEDiIyEM888mbAYPRoiImqP07YvnPs5WN4E8wwoK7Jv3/kh7Psa4p6DmBu9s6uIiEhz0JDC5+6OKyJSjZITIiKBIjISergoLtumAthjf6xbV4isY6ZBpA+Ky3bqZBTvvOwy1/2+/dZ1e3GxsZRk4ULj3y1aQELCyWTFxInQ+rfsjMkEsTdA9/Ng2a3G9qLVlR2Cn26Grf+BMXOhbb8GPTQRkWatrsLn1srau7BFdKt7y3gVPheRGrSsI8hpWYdIM1K6H97rbH/s8n0Q0ck/8TTEu+/Cv/5lzLDYv7/+5+/Y4TiBY7XC9mxYfjuU7qvdHtoaRj4FA2+HEDeWjYiIiHuawv9NIlJvKogpIiLB7YorjK1H9+6Fn3+GefOMpSGxsXWf26+f85klq1bBJwcg5l3o42CL1IqjYL4bcsfDoQ2NeggiIiIi4nnNdllHZmYmWVlZxMTEUFBQAMCDDz5IXCMr3dvGtYmKivLIuCIiTYrJBKeeatxuusk4tmsXfP/9yUKbq1ZBZeXJc8aNcz5eVhY89ZTx9w4dIPl0GLcRwg7Z9zvwI3w2CoY9AkNmQki4Rx+WiIiIiDRMs0xOpKSkYLFYWLRoEVFRUQBYLBbi4+NJT08nNTW1QeMmJSVhsVjIyckhJiYGgOzsbOLj48nKyiI5OdlTD0FEpOnp3t3YqjQlxfh3cTH8+OPJHUESE52fW72WRWEhzF0G/wSuBJJq9K08Aasfhl+z4Iw3IFrJYxERERF/a3bJiczMTLKzsyksLKxKTADExMQwd+5cUlJSSEhIqPdMh7S0NHJzc+0SEwDJyckkJyeTkpJS6z5FRMSFyEiYMsW4uXL8OCxdWvv4MeBNYAkwHehao71oFSw8HXrfCmOfgVAHu4KIiIiIiE80u5oTs2bNIjEx0WGSwDazYdasWfUa02w2k5mZSVRUFIkOvtlLS0tr0LgiIuKGQ4eM2RbOti/9GXgQ+BiorNlYCb++DJ+OhH3feTVMEREREXGuWSUnzGYzRUVFLmdFxMXFkZubS1FRkdvjZmRkAJCQkOCw3XZ8wYIF7gcrIiLu6dwZ3nkHtm6FX3+Ff/8bbr0VRowwalsAnAD+AzwKbHcwxuGNkHs2LL8Dyo6cPP7Pf8JPP0FZmdcfhoiIiEhz1qyWdcyfPx+Ajh07Ou0THR0NQG5urts1IpYvXw7gdMmG7XhRURFms1nFMUXEsQ1z4Oc5ztuttb72N77xr2sv+UEzYPCMxsUWLHr1gquuMm5g1J/48ceTRTaXLoWHTsAlGDe7/wWtsPFl2PkRjM6EqPFw441GYqJVKzjjDBg/3ijMecYZ0K6d7x+fiIiISBPVrJITubm5gPMkQvW2ZcuWeaWA5fLly5WcEBHHyorh2M76nVO6271xm6sOHWDqVOMGUFoKy5cbyYqFn8Fpy6BHqf05Jdtg8RRofR6El0EZcOwYLF5s3ABCQ2HUKCNRYUtYdOniy0cmIiIi0qQ0q+SEbamGbXaEI7a2+izriImJwWw2u9U3Pz/f7XEBduzY4bJ99243LkxEJDiER0KrHt4ZVwwREUYiYdw44EGoKIeNf4PVf4KKY/Z9jy6E2RhFNZfXGKeiAvLyjNvf/mYcGzDg5Njjx0P//ieXlYiIiIiIS80qOVFQUOCVvqeffjrZ2dlOExrVj9cn6QHQq1evevUXkSA2uBktvwgUoWEw+F7oeQn8NB32fWXf3gG4B/gJI0nhahLKpk3G7R//MP69davzIp0iIiIiYqdZFcT0ltTUVODkspGaVAhTRCTAtesPkxbB6a9BmINaEmOAlyLg+lgIC617vJ49oXdvx235+cbykKNHGxWyiIiISFOi5IQHREVFVe3YkZ2dXas9Ly+PmJiYqr71sX37dpe3pUuXNjp+ERHBKCw6IA0uWAfdp9ZuDyuFyfmwcBLk/BeeeAImT4a2bWv3HTfO+ZKOd96BiROhfXujsOb998MHH8CBA559PCIiIiJBpFkt64iOjqaoqMitJRuu6lI4Yps9MX36dACSk5Mxm808/fTTpKenV82ecLVTiCM9e/asV38REWmkNr3gnI9h67/BfBccP2jfvvcLCPsBrkyHhz+DikpYvfrkjiDffmvUnHDm22+NP8vLjW1Kf/oJnn3WODZ4sH2Rzb59VbdCREREmoVmlZxwZ9aCLXFR3xkOYCQopk2bxoIFC0hLSyM2Npa5c+cSFRVVVWsiMTGx3uOKiIiPmUzQ7xrolgTL74Rf59u3lx+B5bcZx0fPg7g443bXXWC1GokHR8rKYMkS5/e7YYNxmzvX+HePHvbJimHDjJ1CRERERJqYZpWcSEhIwGw2u9wxw2KxAEaRy4aIioqqmkVRc8yoqChtIyoiEkwiOsO4/8KOq2DZH+BYjR2S9n0Dn42A4U/AoHsgJMxIbISHOx5v714YMgRWrDB2/KjLzp0wf75xA7jpJpg3r3GPSURERCQANavkRFJSEpmZmS53zLC1JScn12vs3NxcLBZLrcSErQ0gPT29XmOKiEiA6HkJdD4HVtwH+a/bt1WUwsqZ8OsCGPM6dBjhYpyesGwZHD5sLOewLQVZssS9ApmjRztv++EHY1lIhw7uPaambsMc+HmO58cdpF11REREvKFZJSdsCQdnu2oUFRVhsVjqPbuhqKiIpKQkAGJiYmot3cjIyHA4o0JERIJIiygYMw/6/J+x7WjJVvv2guWwMB6G/tG4hbZ0Pla7dpCYaNzAWO6xYsXJmhXffee4QKazWhbHjxtFNk+cMJZ+jBt3cjlIc92SuqwYju30zrgiIiLicc0qOQHG7IVZs2ZhsViqdtCwsRWtnGtb61uNxWIhJSWFhISEqp05bGz1KaKiokhISLBry87Oxmw2k5WV5cFHISIiftM1EaaugdUPwy8vAtaTbdZyWPsEbH/XmEVxyhj3xgwPN2ZFjB4NM2YYdSt++cU+WXHoEAwa5Pj8vDwjQQGwZo1xe/VV49+9e5+sWTF+vDG7IqQZbNYVHgmtejhvt1ZCaY1lOhHdjF1b6hpXREREPM5ktVqtdXdrWlJSUjCbzeTl5VUlFsxmM5MmTeLBBx9k5syZtc6ZNWsWs2fPBoytQWvOrrAVwKx+bmZmJmlpaWRlZdV7mYi7duzYQa/fvhXbvn27dvcQEfGl/T/ATzdB8c8OGk1w6t0w8kkIa934+yoqAmfFmmfPhlmz3BsnOhrOOutksiI+Hlq0aHx8waZ0P7zX2f7Y5fsgopN/4hEJVnoviTRL3rgObXYzJwCysrLIzMwkJSWFmJgYCgoKKCoqIisry+luGldeeSXZ2dnExMQ4XPaRkZHB7NmziY+PB4ylHomJieTn59eaoSEiIk1EpzPh/BWw9klY/1ewVi9yaYVfnoedHxjLQbqc27j7crWLVFERRERAaWnd4xQUwEcfGTcwztu82dgZRERERMRPmuXMiaZEMydERAJE4UpYciMUrnDc3j8VRs2GFu29c//Hj4PZfHIZyHffQWFh3ed16QK7dxu7jNS0b5+xLWr37p6P19/0ba+IZ+i9JNIseeM6tBksOhUREfGBDqNgyk8w8mkIcVAMc3MmfDIUdn7snftv2RLGjoWZM+HDD42CmmvXGrUnrrnGqD3hyLhxjhMTAHPnGjMqYmPh+uuNbUx//tmoiRHo5swxdkdxdhvpYFeVkSNcn9OzpzGuiIiIeFyzXNYhIiLiFSHhMPQB6HkpLL0Z9n9v335sJ3x9EfS5GuJf8O43iyEhMHSocbvlFuPYr7/C99+fnF2xdq3zHUDA6ANgsRi3t982/t2pk/2OIKNGGUU9A0lxMex0sVtHOwfHdu+Bw26MKyIiIh6n5ISIiIintR8Eid/Axldg1QNQXmLfvu3fsOcLiH8J+lzpfOaCp/Xubdyuusr4d2Gh8/uuqIAffnDctn8/vP++cQNo3dqYtWFLVowZA23bej7++oiMdF1Ho1UZsM/+WJfOEFlHkiVSu3WIiIh4g2pOBDnVnBARCXBHtsLSVNiT47i9x8Vw+ivQOsAKUloscNppDZspEBpqLC956inPx+UpOzfA10Psj52zHnoM9k88IsFKNSdEmiXVnBAREQk2bfvCuZ/DGf+A8Kja7Ts/NGpRbJ4XWLUcYmKMnT1WrICXXoJp06BbN/fOrahwPWth9+7AeqwiIiLid1rWISIi4m0mE8T8HrpNgWW3wY737dvLDsHS6bDtvzAmE9oGyBbUoaFGPYlRo+D2242EwpYtJ3cD+fZbo0CmI85qWZSVwYAB0K7dyWUg48bByJHG/fnSUaAAOA60BA4f8e39i4iISBUt6whyWtYhIhJkrFbYng3Lb4fSfbXbQ1vDyL/AwDsgxMcX6w2xf79RZNOWrDCboU0bOHjQcbJh2TIYPbr28XbtjLoVtmTFmDHQqpXn47Va4auv4Nm/wsIvoLJaW2goXHYZ3HorTJjgu1ogIsFMyzpEmiVvXIcqORHklJwQEQlSxw+CeQZsedtx+yljYcw8aD/EcXugKimBzZuNmRCOPP88zJhR9zjh4RAffzJZcdZZ0LFj42Izm+G662Ddurr7Dh1q7E4SF9e4+xRp6pScEGmWVHNCRESkqWjZEca+BRM+hda9arcf+BE+Ow3WPgmVZb6Pr6HatHGemABYtcq9ccrKYMkSeOYZuOQSOOUUI2Gwd2/D4srJgbPPdi8xAUa/s882zhMRERGvU3JCRETEn7qfDxeshQF/qN1WeQJW/wkWng4FZt/H5g3/+Ads3AhvvAE33GDUn3DX3r3QubPjtmPHjEKcjpjNxnKNkhLH7c6UlBjnmZvIcy8iIhLAlJwQERHxt/BIYzvRxK+hbf/a7UWr4PPRsPIBKD/m+/g8yWQyEhI33GAkKDZuNHbvyM6Gu+4ylnKEOPl4ctZZzutAvPSSsezjggvg6aeNGhilpUaNieuuq39iwqakBK6/XruLiIiIeJl26xAREQkUnc+GqathzaPw83NgrVat0VoB69Nh+/sw5nXoPM5/cXpa165wxRXGDeDwYWNJh63I5pIlxsyIcS4e83ffwaFD8Omnxg2gRQsjEeLuUg5n1q6Fr782imSKiIiIV2jmhIiISCAJawWnzYbJSyBqeO32wxshdzwsux3KDvs+Pl9o1w6SkuDxx+HLL42kw5IlcNVVjvtXVhrJiZpOnGh8YsLmlVc8M46IiIg4pOSEiIhIIOp4OkxZDsMfh5Dw2u2b/g6fDINdn/s+Nl8LDze2FnVWCXzjRigs9G4M770HxcXevQ8REZFmTMkJERGRQBXaAoY/AuetgI6ja7cf/RW+Og9+/D0cL/B5eAFj0CDYsQP++1+4/XZjtxBntSkaqqICdu707JgiIiJSRckJERGRQBc1FJJ+gNOeg9BWtdu3vAWfDIHt7/k+tkDRowdceaVRGHPlSmMmxQsvePY+DjfRZTQiIiIBQMkJERGRYBASCoNnGAUzO0+o3V66F769Ar5NhmN7fB5ewGnf3qhb4UkrV0JZmWfHFBEREUDJCRERkeDSrj9MWgSjMyCsXe327e8asygsb2v7y549ITTUc+OlpUGXLnDjjbBwoRIVIiIiHqTkhIiISLAxhUD/VLhwPXS/oHb7iUJYcj18NRVKfvV9fIEiMhIuu8yzYxYWwj/+AeeffzJR8dlnxs4gIiIi0mBKToiIiASr1j3hnI/gzH9By46123cvhE+GwsZXwFrp+/gCwa23em9sW6Ji6lQjUXHDDfDpp0pUiIiINICSEyIiIsHMZIK+V8MF66HP/9VuLz8Cy2+D3AlQvNHn4fndhAkwdGjjxujaFUY72C2luqIiePNNuOACI1Hx5z837j5FRESaGSUnREREmoKIznDWf+DsD6BVt9rt+7+Fz0bC+tlQWe77+PzFZIK334Y2bRp2fps28Mkn8NNPsG0bPPccnHGG63OKijxb60JERKQZUHJCRESkKel5sTGLIvam2m0VpbByFnxxBhSu9n1s/hIXB++/X/8ERZs2xnlxcca/e/eGGTPgxx/rTlSkpDgfNzcXjh+vXywiIiJNnJITIiIiTU2LKBgzDybmQpt+tdsL8mBhPKx+BCqayUVyUhJ88437SzyGDTP6O9uOtHqi4tdfYc4cGDvWaBs5EgYMcHzeunXGmF26wHXXwUcfKVEhIiKCkhMiIiJNV9dJcMEaOPVuwGTfZi2HtX+GhXFwYIk/ovO9uDhYswYWL4YLJtf+FBQWZsx4WLwYVq8+OWOiLr16wT33wA8/GImKzEznfbOyjD8PHYJ33oGLL4bOneHaa5WoEBGRZs1ktTb3TdCD244dO+jVqxcA27dvp2fPnn6OSEREAtL+H+Gnm6B4g4NGk5HAGPlnCGtgbYZgs3MDfDYECoFSIAK4aCkMOt279zt0KKxf77w9MtJIWKSkwOTJEBHh3XhEGqt0P7zX2f7Y5fsgopN/4hERn/DGdahmToiIiDQHncbC+Stg6MNgCqvRaIVfnodPR8CeL/0Snl+0BnoAsb/92a6td+/v8GGIjnbdp7gY/vlPuOQSY0bF734HH3wApaXejU1ERMTPlJwQERFpLkJbGrMjzlsGHRwsWThigS8nwU+pcOKQ7+Nr6tq1g2+/hR074G9/g3HjjN1EnDl8GP71L7j0UiNR8fTTPgtVRETE15ScEBERaW46jIIpP8HIpyGkZe32/LnwyRDY8ZHPQ2sWevSAO+88mah48UUYP77uREW7dr6LUURExMeUnBAREWmOQsJg6AMwdRV0Gle7/dgu+OZi+P5qY015sJkzB3r2dH6bMKH2ORMmuD6nZ09jXE/q3h3uuMPYGWTHDnjpJTj77NqJCpMJrrjC8RiVlUYxzWPHPBubiIiID9VcdCoiIiLNSeSpkPg1bHoVVs6C8hL79m3/gT05EP8i9Pk/19/uB5LiYti503m7o0kIe/fBYTfG9Zbu3eH2243b7t3w3nvG7h7ffGMsAenWzfF5P/1kFNFs2xYuvNAopnn++dCqlfdiFRER8TAlJ0RERJo7UwgMvA16XGjUm9jzhX378QPww9VGouL0V6F1D//EWR+RkcbyCWfaVAB77I916wqRoXWP6wvdusFttxm33bthv4vZK7btSY8cgf/+17i1aWOfqGjd2jdxi4iINJC2Eg1y2kpUREQ8ymqFLW9B3j1QVlS7PTwSTnsWYm8OnlkUjjSV7Q8rK6FvX9i+3XmfNm3ggguMRMXUqUpUiGc1lfeSiNSLthIVERER7zKZIOb3cOEG6HV57fayYliaCl8mGrt7iH8VFED//hDi4iNdSQksWGAkJzp1gmnTIDsbjh71XZwiIiJ1UHJCREREamvVFca/C+OyIaJL7fa9X8Inw+HnF6CywufhyW9OOQW+/BJ27YJXX4WJE10nKo4eNZaB2BIVzzzju1hFRERcUHJCREREnOt9BVywHvpdV7ut4iiY74GccXBove9jk5O6dIFbboFFi4waFe4mKro4SDyJiIj4gZITIiIi4lrLaBj7Fkz4FFr3qt1+cAl8dhqs+TNUlvk+PrHXubN9ouK112DSpNqJivBwY5cPR8rKjKUgR454P14RERGUnBARERF3dT8fLlgHA26t3VZ5AtY8AgsToCDP97GJY507Q1oa5ObCnj2QkQGJiRAaCpMnQ1SU4/O+/BKuvNI4/4orjB1AlKgQEREvUnJCRERE3BfeDk7/OyR+De0G1G4vWg2fj4GVD0D5Md/HJ8516gSpqZCTY8yomDPHeV/b9qTHjsF778FVVxnnK1EhIiJeouSEiIiI1F/ns+H8VTB4JphqfJywVsD6dPhsFOz71i/hSR06dYKBAx23lZXB++/XPl5aap+ouPxy+M9/4PBh78YqIiLNgpITIiIi0jBhreC0dJj8E0QNr91+eCPkng3LboMyXcAGjf37ISHBWPrhTGmpkcC4+mpj6cdll8G//61EhRi0zbCINICSEyIiItI4HRNgynIY/gSEhNdu3/QKfDIMdn3u+9ik/rp3h88/h717Yd48mDIFwsKc9y8thf/9D665xphR8fzzPgtVAtSax/wdgYgEISUnREREpPFCW8DwP8F5K6DjmNrtR3+Fr86DH38Pxwt8Hp40QMeOcNNNsHChUUzz9dfhvPNcJyqOH4eYGN/FKP6358vax3YvVDJSROpNyQkRERHxnKihkPQ9xM2B0Fa127e8BZ8MgV/f9X1s0nAdO8KNN8JnnxkzKpwlKtq1M2ZaOFJSAv/8JxQXez9e8Y2KE7D6Ycdt5ru1tbCI1IuSEyIiIuJZIaEw6B6Yuga6nFu7vXQvfJcM3ybDsT2+j08aJzraPlHxxhtw/vlGouKiiyAiwvF5n3wC115rLP24+GJ45x04dMi3sYtnbXwZjmx23Fb8s9EuIuImJSdERETEO9rFwsRFMDoTwiNrt29/15hFYXkLrFbfxyeNFx0NN9wAn34K+/bB008775udbfx54gR89BFcd51RTFOJiuB0bC+sfdx1nzWPQek+n4QjIsFPyQkRERHxHpMJ+k+HC9ZB9wtrt58ohCW/h6/Oh5JtPg9PPKhDB+jd23Hb0aPGzImaaiYqLroI3n4bioq8Gqp4wKo/QlkdS3TKio1+IiJuUHJCREREvK91TzjnQzjz39DylNrtuz83dvTY+HewVvo+PvGuAwfg3HMh3MFuLjYnTsDHH8P11xuJigsvhLfeUqIiEB1cDpZ/uNc3/w0oyPNuPCLSJCg5ISIiIr5hMkHfq+CC9dDn/2q3lx+B5bdD7jlQvNH38Yn39O5tJB727YM334QLLnCdqCgrM2Za/P73RqLi73/3VaRSF2sl5N0JuLsUywrL79DSLRGpk5ITIiIi4lsRneCs/8DZH0Cr7rXb938Hn46A9elQWe77+MR7oqKMmRG2RMVbbxkzJFq0cH5OWRkMGeKzEKUO+W/AgR/rd86BH2Hrv7wTj4g0GSarVWnMYLZjxw569eoFwPbt2+nZs6efIxIREamHE0WwYibkz3Xc3iEOzngDOoz07P2W7of3Otsfu3yfkTgR3zt0CD78ELKy4PPPjSUeNp07w65dEBpa+7yDB43zLrnEKM4p9VNZDicK4PgB4z1x/AAct/1Z/dhvx0v3Q2Vpw+6rVXe48BcIb+vZxyAifuGN69CwuruIiIiIeEmLKBiTaSzz+OlmKNli315ohoUJMOQBGPYwhLb0S5jiZe3bG9uMXnutkaj46CMjUbFwIVx+uePEBMD778P06cY2pomJkJICl17aPBMVVquxNOr4fig9UDvRYPt79YTDiULcX57RSMd2wbqnYNRTvrk/EQk6mjkR5DRzQkREmozyElj1MPzyNxxeMEUONmZRnHJG4+9LMyeCQ3GxsdNH166O2887z5hpUV1YGEyadDJR0bGj18P0isqyaomFumY2/PZn5Ym6x/WnsHZwxQEIdbGMR0SCgmZOiIiISNMV1gbin4c+V8KSG6F4g3178Qb44kw49S4Y+aTRX5q2yEjj5khBASxaVPt4ebmRsPj8c7jllsBIVFitUHbI8VIJZ4mGskP+idWbup0HIS4KoYpIs6bkhIiIiASWU86A81fA2idh/V/BWr0ophV+eQF2fgij50LXif6KUvytsNAopvnZZ3D8uOM+1RMVaWknExWXXda4REXF8RpLJaonGpzUbLB7HQeg8Chjm9+ITsafLU+Blp3sj4W2gu+vhuP7GnYf27MgdxeMfBo6j/do+CIS/LSsI8hpWYeIiDRphauMWRSFZsftsTfDac8YtSvqQ8s6mo7Dh43dP7Ky4NNPnScqqgsNhddeg5tvNrbGPFHovAiko5kN5Ue8/7gaI6SFkVioSjRUSzg4Sj607Oj+jIYt78CP1zU+xu5TYeRTni92KyI+4Y3rUCUngpySEyIi0uRVlsPPz8HqR6HSwYVnq+5w+qvQ82L3x3SUnJi8BE4Z07hYxb8OH4aP3oVP/gvLF0PLE9AOiMT4s/ptZAyYDsOJg0aCIpC1iHacaKg5s8F2LKwNmEzeicVaCTnj6r+dqDN9roYRT0C7WM+MJyI+oeSE1KLkhIiINBvFG+Gnm2D/d47b+/wfxL/o3uwHR8mJbufBuZ81Pk7xnMqK37a6dLG9Zc1jFUf9HbVroa1dz2KoeaxFBwgJsJXYB5fD56Nxb6cPEwx/HCxvQMlWJ13CoP90GPYnaNXNg4GKiLcoOSG1KDkhIiLNirUSNr0KKx9wPLW+5SlGgqLP/7n+5njrfPjh/2ofn7AQuk/xXLxyktVq7Mjiqghkrb8X4LOtLhvCFAItOtZIKjhKOFT7e1hrf0ftGUtuMhIOdYm9CcbMg4oTkD8X1v4ZSvc67hvayih4O2SmkZQRkYCl5ITUouSEiIg0SyXbYGka7P7ccXv3C2H0a9C6R+22ihPwyVA4srl2W+QgmLpaOwq4o7IMjh90XQSy5swGR8tyAklYWzhYBgeOw2GgGDgCHDFB91MhfgKMvwC6DjASDS06GAmK5ujYXvh4IJQVO+8THgkXbYKIarOUyo4Y2wVvmO383PAoGDILTr2z6SRzRJoYJSekFiUnRESk2bJaYcvbYL7HKGhYU3gknPasUTSz+iyKDXNgxb3Ox42bA4Pu8Xy8gcxqNS4U69resvqxsiJ/R+2aKazaDAZ3llCcArv2Q+/erscNCYEJE4xdPy6/HDp3dt2/KWvMe+n4QVifDhtfgopSx31adTOWesTerIShSIBRckJqUXJCRESavWN7YPntsP1dx+1dzjW2HW0X2/Bve4NNxfHfZjU4qMngbAlFZZm/o3YtvL19ciHCyRIKW9IhvH39i0Ju3AgPPwyffAJH3ahdERIC55xzMlHRpUvDHluw8sQspKM7Ye0TkP86WCsc92kbCyP+DH2ubL4zVUQCjJITUouSEyIiIr/59V1Yfpvj9eyhrWDEk1C0Frb8o+6xbOvkA4G1Ek4U1b29ZfVj5Yf9HbVrIeHVkglOCkFWTzS06AihLXwXX0kJfPYZLFhQv0TFm2/Ctdd6PbyA4qx+y7mfQ7fJ7o9TvBFW/wl+XeC8T9RIY/vR7ud7bzcSEXGLkhNSi5ITIiIi1RwvAPMM2PJWIwcywXnLIDreI2HZKT/mereJWjMbDjr/RjlQtOhQO6FQK9Fgm+1wCoS1C56Ly6NH4dNPISsLPv7YdaJi40YYMMB3sQUCT+98U2CGVX90Xk8GoNN4GPU0dDqrYfchIo2m5ITUouSEiIiIA7sWGgUzj/7a8DFOGQtJ37u+iK6sMOpdOFoqUVo90VCtvbyk4TH5QmiE6+UStXak6Bh4W116y9GjxowKW6KipNrPcuRIWLnS8XmbN8Pnn8MVV0DXrj4J1WccJScm/wSnjG7cuHu/gpUPwsElzvt0vxBG/gU6jGjcfYlIvXnjOrSZ/E8iIiIizUr38+CCtcbFzaa/N2yMAz/Csj9A5KkuZjYcJKC3usRkJA9cFoKskXwIbR08sxp8rXVrI8FwxRVGomLhQiNR8dFHRt0JZ/71L3jsMbjjDhg/3uh7xRXQrZvPQveptv0aP0aXCTD5B9j5Iax6CA6tq91n18ew6xPoezWMeALaxjT+fkXEbzRzIshp5oSIiEgddn0OX18Q+Esj3BHWxsHMhWrLJWrWcWjRAUJC/R1103fsGJSVQWSk4/ahQ2H9evtjJlPTSFQ4mjlx+T4j0eUplRWw9V+w5lEo2eq4jykM+qcau3u0amKzU0QCkGZOiIiIiNTXvq8CMzFhCv1tVkNdW1xW+3tYK39HLY60amXcHFm/vnZiAoztW7/5xrjdeSeMG3cyUdG9u3fjrY85c4ybM20q4NEax0aOgJI6kmIzZhg3d4SEQsx1xm4dmzNh7Z+NGUzVWcth0ytgeRMG3Q2D74cWUe6NLyIBQckJERERaboqTsDGBi7rqK+wdo6TCs4SDS2itC1ic2AywdVXw4cfwpEjjvtYrfDtt8btrrvgrLNOJip69PBtvDUVF8POnc7b2zk4tnsP1LVhTLGL7XydCW0Jp94BMTfALy/AhmdqbwtccRTWPQWbXoUhD8DA2yGsdf3vS0R8TskJERERabpCW8DA22D9Xxs+Rus+cMqYOopDdjQunERqGjzYqDlRWmoUxczKMhIVh51cvVut8N13xu3uu+G//4Vp03wasp3ISNcJklZlwD77Y106Q2R43eM2VHhbGPYw9L/FeG9vfBkqj9v3OVEIK2cZSYxhj0LsjcYWtiISsFRzIsip5oSIiEgdyg7Dx6fCsd31P7dVd7jwF+NiSMRTSkvhiy9gwQLXiQqTyZi1EMj1KHZugK+H2B87Zz30GOy7GEq2w9onwPIGWCsd92nbH0b8GfpM04wlEQ/wxnWo3pkiIiLStIW3g1HpDTt3VLoSE+J5ERFw8cXwz3/Cvn3wwQfwu9/Vnk0wfrzzxMTq1fDCC7Bjh9fDDXhtesGYuXDBeujtZNeUI5vhh6tgYbyx1bC+nxUJOEpOiIiISNPX9xo4ZWz9zjllrHGeiDfZEhXvvGMkKj78EK691khUuNqe9M034Z57oFcvOPNMeP552L7dZ2EHpMhTYdwCOG85dJ3suE/hSvjqfFg0Afb/4MvoRKQOzXZZR2ZmJllZWcTExFBQUADAgw8+SFxcXKPGzc7OZv78+QAUFRUBkJaWRnJycqPGdUbLOkRERNx0cDl8Phpw56OPCc5bBtHx3o5KxLHjx6GiAlo7KOZYWQl9+zpORpxxhpHUSE6G3r29HmZALOtwZs+XsOpBOLjUeZ8eF8HIv0DUcN/FJdIEaCtRD0lJScFisbBo0SKioqIAsFgsxMfHk56eTmpqaoPHTUpKIisrq+pYUVERKSkpzJ8/3+64iIiI+FjHBKPKv+WNuvvG3qjEhPhXSxcFVpcudT5LYskS43bvvb5PVASarhOhyxLY8QGs+iMUb6jdZ+dHsPNj6Ps7GPE4tO3n+zhFBGiGyzoyMzPJzs62S0wAxMTEMHfuXNLS0jCbzfUed/bs2Zx++um1EhtRUVHk5ORgNpvJzMxsbPgiIiLSGCOfgvA6dgkIjzT6iQSqdu3g97+Hap9lHbIlKfr0MRIVzz0H27b5IsLAYTJBr0th6ho44x/Q2lGSxgpb3zEK5y6/A47t9XWUIkIzXNbRoUMHEhISyMnJcdhuMplITEx02u5MbGwsOTk5xMTEOGyfPXs2OTk59R63LoGwrGPOj3OY8+Mcj487Y+wMZoyd4fFxRUSkmdswB1bc67w9bg4Musd38Yg01IkTkJtrbE/6v//Bb0uK6/Thh3DRRZ6JYecG+GwIFADHgZbAxUth0OmeGd/TKo7Dptdg3V/g+H7HfcLawKl3w+D7oUV7n4YnEiy0rKORzGYzRUVFLutKxMXFkZubS1FRkd3MirpYLBbMZrPT5ERUVFRVbYumpvh4MTsP7/TKuCIiIh438HbY9KpRvb+myEFGu0gwaNECpk41bhkZsGjRyURFYaHjc8LDjV1AGstqha++gmf/CguB6jt4/nEsXHYZ3HorTJhgzF4IFKEtYdBdxtKtn5+HDc9CeY2tXMtLjOTFpldhyAPG74SwVv6JV6QZaVbLOmyFKjt27Oi0T3R0NAC5ubn1Hn/69OlOl4Tk5OSQmJhY7zGDQWTLSHq06+H01q1t7S2wurXt5vKcHu16ENmyjmm3IiIiDRHaAkY86bgt/m8QEu7beEQ8oUULOP98eOMN2LMHPvsMbrgBOnSw7zdlivPlIEuWwDPPwJYtru/LbIbhw2HiRPj0C/vEBBiFPLOzjfbhw43+gSa8HQx/BC62wKAZEOKgxseJAlg5Ez4aAJvnQmW57+MUaUaa1cwJW8LB1YwIW9uyZcvqtcNGcnIy2dnZxMfHM3PmTNLTT+6nnpubS25uLlvq+kUfpOpafrG/ZD+dn+1sd2zVLavo1KaTt0MTERFxrOvE2se6nQfdnGw/KBJMWrSA884zbtVnVLz/vuvtSefNg9dfh5kzISHB6JuSAv2qFYnMyTFmRZSUuBfLunVw9tnGfSclNe5xeUPEKRD3HJx6F6x5HLa8CdYa2ZZjO2FpKmx4xkhs9k4GU7P6jlfEJ5pVcsK2tadtdoQjtrYid9fs/Wbu3LlVy0Fmz55NdnY2GRkZWCwWMjIy2LJlS72Widjs2LHDZfvu3bvrPaaIiIg4MPxxf0cg4nnh4ScTFa+9ZmxD6khZmZFAsFm+3LjNmgXx8UaSYvBguPpq9xMTNiUlRkLjm2/AxfJqv2rTG854HQbfB6v/BNvfrd3n8Cb4/kpYH2cUze02ObCWrEhg2zAHfvZ8nT4GzYDBTaNOX7NKTtSn5kN960NERUWxZcsWUlJSyM3NxWKxkJSURExMDHl5eQ1KTABVRUZERETEy7SFoDR14S6WLC1eDM4+/+blGbfGKCmB66+H1asD+4K+/WAYnw0Hlxnbj+5xsNS70AxfnQedJ8Cop+GUM3wepgShsmJjFo43xm0iNB/Jg6KiokhKSrIruGmxWOjXr1+DaliIiIiIiPhE9+4wfTq4qM3WaGvXwtdfe298T+p4OkzMgYm5EO1k55F9X8EXY+GbS6FonS+jk2AUHgmteji/RdSu00dEN9fntOpR9/bYQaRZzZzwpqKiIiZNmsSVV15JXl4eRUVFTJ8+nezsbIqKikhKSiIrK6tedSzA2JbFld27dzN69OjGhC4iIiIizd2wYZCZCa+8YuzCsWCBsczjwAHP3s8rrxg7eASLrpNgyk+w431Y9RAU/1y7z44PYMeH0O9aY3lY274+D1OCwOA6ll+U7of37Ov0MXUVRDSfOn3NauaErZ6EO0s2XNWlcGTSpEkkJiYyc+ZMwJhFkZWVRU5OTtWSjunTp9cvYKBnz54ub926OciwiYiIiIg0RFgYJCYaiYrdu40CmL//vefGf+89KA6yaegmE/S6HKaugTFvQGtHy66tsOVt+HggLL8LSvf5PEyRYNeskhPu1H2wJS7qUyMiMzMTi8Vit0OHTWJiIlu2bCEuLo6ioiIyMzPdHrcpO3z8cN2dRERERMR/bImK++/33JgVFbDTC+vufSEkDGJvgIs2Qtzz0PKU2n0qy2Dji/BhDKx+BE4c8n2cIkGqWSUnEhISAMjPz3fax2KxAHD66U7WljmQk5NDYmKi0/aoqCgWLVoEQF5jiwkFEavVyuIti7nxgxtrtQ18eSApWSks3rIYq9Xqh+hERERExC1Hjnh2vMNB/iVVaAQMuhsuzofhj0FY29p9yktg7Z/ho1jY8BxUlPo6SpGg06ySE0m/7a3saptQW1t9akNYLJY6l4FERUURExNDbGys2+MGM/NuM8NfHc7Etyfy8aaPa7VXWCvIXp/NxLcnMvzV4Zh3m/0QpYiIiIjUqa2Di+/GaNfOs+P5S3gkDH8ULrbAqXdDSIvafY4fhBX3wUcDYPM8qCz3eZgiwaJZJSdsCQdnO2cUFRVhsVjsdttwR2Jiolu7cVgsFpczLJqKnPwczv7H2azb717V4nX713H2P84mJz/Hy5GJiIiISL317AmhoZ4b73//g2PHPDeev0V0gvjnjeUeMTeAycEl1tEdsHQ6fDoMfs0GzRwWqaVZJScA0tPTsVgsVcs3qluwYAEAc+fOrdVmsViIj48nLS2tVltaWhoWi8VlPYnMzEySk5PrnfgINubdZi6bfxklZSX1Oq+krITL5l+mGRQiIiIigSYyEi67zHPj/fGPEBMDL70EpU1ouUObPnDGG0bhzF6XO+5T/At8lwKfnw67c5SkEKmm2SUnZs6cSXJyMklJSXbLO8xmM7NmzSI9Pd1hAiEjIwOz2UxmZiZms/0FdExMDDk5OcyaNYvZs2fbtRUVFTFr1iyysrLIysryymMKFFarlevev67eiQmbkrISrv/f9apBISIiIhJobr3Vs+Pt2QN33gn9+xvbix4/7tnx/an9EBj/Lkz+CbpMdNynIA8WT4YvE+HAUt/GJxKgTNZmeiWYmZlJVlYWMTExFBQUVCURnC27MJvNpKSkVCUiHCkqKuLpp5+uWuJhq0ORlpZWrxoW9bFjxw569TK2M9q+fTs9e/b0yv24Y/GWxUx828kv4PqMc/1iJvSd0PiAREREnHG0n/zl+5rVfvIi9WK1wvDhsM69Zbv11qsXPPywsW1pCwe1G4LZnlxY+YCRkHCm52Uw8kkjsSHNU5D9v+SN69Bmm5xoKgIpOZGSlUL2+uzGjzMkhQUpCzwQkYiIiBNB9iFQJCCYzXD22VDSgFmybdrA3/8O//43fPGF8359+8Kf/gTXXgvh4Q0ONeBYrbD9PVj9kLG0wxFTCPS7ztgBpE0fn4YnASDI/l/yxnVos1vWId5RfLyY9ze875Gx3tvwHsXHiz0yloiIiIh4SFwcvP++kWiojzZtjPOuvx4+/xy+/RYmOpltu3Ur3HST0acpMZmg9xUwdS2MmQetHVzIWSvB8iZ8NBDy7obSfb6OUsSvlJwQj9hRvIMKa4VHxqqwVvDF5i84UXHCI+OJiIiIiIckJcE338DQoe71HzbM6J+UdPLYuHGwaBF89ZUxE6OmcePg3HM9Em7ACQmD2Jvgok1w2nPQsmPtPpUn4Je/wYexsPoxKNOXdtI8KDkhHnHkxBGPjpeSnUK7p9uRkJnALR/fwty8uazYvUIJCxERERF/i4uDNWtg8WK4YHLtK4qwMEhJMdpXrzb6O3LOOUaCYtEiOPPMk8efeMKYadCUhUbA4BlwsQWGPQJhDmajlB+BtY/DhzHw8/NQ0YR2NhFxIMzfAUjT0LZFW4+PeaLiBHm788jbfbJ4UIvQFozsMpL4bvEkdE8gvns8QzsNJTy0Ca1JFBEREQl0JhNMmAADusBnQ6AQKAUigIt+gEGnuz/OxInGTImcHPj4Y+ezJqxWuPFGuPBCY2vTkCbwPWt4JIx4HAbeBuuegk2vGjMnqjt+EMwzjATF8MeMuhQhuoyTpkevavGInpE9CTWFemxphzMnKk6wbNcylu1aBr/lLFqGtmRk15EkdDOSFQndExjSaQhh+qUtIiIi4n2tf7vZtGvAl1YmE0yebNycWbgQ3nzTuI0cCY89Bpdc0jRmWUR0hvgX4NS7Yc1jsPUdowZFdUe3w083wYZnYMST0OvypvHYRX6jqzfxiMiWkVw2+DKP7NbRtkXbei0TOV5xnKU7l7J058k9oiPCIhjVddTJGRbd4hncabASFiIiIiLByGqFRx45+e9Vq4zZE3Fx8PjjcMEFTeNCvW1fGPsmDL4fVj8MO/5Xu0/xz/BdMkSfDqOehq6TfBykiHfoSk085taEWz2SnPjoqo8Y1XUU5t1m8nYZyzqW71pOfmG+22OUlpeyZMcSluxYUnWsVVgrRnUdVZWsSOiewKBTBhEaEtromEVERETEixYtguXLax83m+Gii+D0041aFVOmNI0kRdRQOPt9OLAEVj4I+76q3adgGXyZCF0mGUmKjm4upREJUEpOiMdM6DuBoZ2Gsm7/ugaPMazzMM7pcw4mk4mJ/SYysd/JbaYKjxUaCYvfkhV5u/OwFFrcHvtY+TF+3PEjP+74sepY6/DWnNb1NLsaFqd2PFUJCxEREZFAMnEizJ9vLOXYsKF2+7JlcP75MHaskaSYNKlpJClOOQMmfQl7cowkRaG5dp+9i+Dz0dDrCmO5R/tBvo9TxANMVqvV6u8gpOF27NhBr169ANi+fTs9ezrYM9mHzLvNnP2PsykpK6n3uW3C2/DNDd8Q181JRWcHCo4VYN5trkpWLN+1nK1FW+t93zXjOK3baXY1LAZ2HEiIqQkUXRIREUPpfnivs/2xy/dBRCf/xCMSrHZugK+H2B87Zz30GOyd+6uogAULjCTFxo3O+40fbyQpJkzwThz+YK2E7e/CqofhsJPHbgqBfr+H4Y9Cm94+DU8aKcj+X/LGdaiSE0Eu0JITADn5OVw2/7J6JSjahLfh/SvfJyk2qe7OdTh49KCxy8euPJbvXk7erjy2HdrWqDHbtmhLXLc4uxoWAzoOUMJCRCRYBdmHQBG/mTPHuDnTqgwe32d/7NHOcKyOndRmzDBuDVVeDv/5j1FvIt/F0t9zzzX6jB/f8PsKNJXlYHnTKJx5bKfjPiEtYMBtMPRB/V4LFkH2/5I3rkO1rEM8Lik2iW9u+Ibr3r/OrSUewzoP461L36rXjAlXOrbuyOTYyUyOPVnteX/J/lozLLYXb3d7zCMnjvDNtm/4Zts3VcfatWhHXLc4uxoWsdGxSliIiIhI01FcDDudXAADtHNwbO8+OOzGuI0RFgbXXgtXXQX//KcxS2LLltr9Fi+GNWvg11+hVavG3WegCAmD/jdD32tg099h3dNwosC+T+UJ+OV5yJ8Hg++FQTMg3NEPSyRwKDkhXhHXLY41f1jD19u+Zs6Pc/ho40d27WEhYVw26DJuPf3WqhoT3tSpTSem9J/ClP5Tqo7tK9lnV3Azb3ceO4p3uD3m4ROH+Xrb13y97euqY5EtI4nvFm9XwyK2Q6zXH5+IiIiIV0RGQo8eztvbVAB77I916wqRddTvioxsdGiAkaT4/e/hmmvgrbfgz382EhHVzZrVdBIT1YW1gsH3Qex02PCskYworzFzufywMcNi48sw9GEYcAuEtvRLuCJ10bKOIBeIyzpq2l+yn87P2k9Ryr8jn5joGD9F5NzeI3vtkhXLdy1n1+FdjRozKiLKmGFRrYZFv6h+SliIiPhTkE2fFQlYgfZeOnEC3ngD/vIX2LEDunQBiwVat/ZPPL50bC+s+wtsfg0qyxz3ad0bRjwOfa8FFYAPLIH2XqqDlnVIk9GuZWBOK+vStgtTB0xl6oCpVcd2H95tV8Ni+a7l7Dmyx8Uo9opKi/hyy5d8ueXLqmMdIjoQ3z3eroZF36i+SliIiIiINEaLFnDLLcZsinnzoE0b54mJX3816l48/DCMGuXLKL2jVRdIeBEG3WPMltjyDlDje+ijv8KSG2D9bBj5F+h5adPY1USaBCUnROrQrV03Lmx3IRcOvLDq2K7Du4xkRbUZFntL9ro9ZmFpIbmWXHItuVXHoltF2yUrEron0Lt9byUsREREROorIgJuv911n6eegnffNW5XXGHsADJsmE/C86q2/WDsWzD4flj1EOz8sHaf4g3w7eXQcTSMfBq6TvR9nCI1KDkh0gDd23Wn+6nduejUiwCwWq3sOrzLLlmxfNdy9h/d7/aYBccKyLHkkGPJqTrWsVVHu2RFfPd4ekX2UsJCREREpDG2boXXXz/573ffhffeg2nT4NFHYbCXtkL1pahhcM4HsP9HWPUg7Pu6dp+DS+HLSdA1CUY+BR0TfB+nyG/8npwoLi5mwYIF5OXlccsttzBy5Eh/hyRSbyaTiR6RPegR2YNLBl0CGAmLHcU7atWwOHD0gNvjHjx2kM/zP+fz/M+rjnVq3cmoXVGthkWPdj2UsBARERFx13PPGduRVme1wvz5sGABXH01PPIIDBzon/g8qdNYmLQYdn9hJCkKV9TusyfHuPVKhpFPQuSpvo9Tmj2/JSe+/PJLXnvtNd59910SExPp168f5557Lh07duSWW25h+vTpRHqqiq+IH5hMJnq170Wv9r24dNClgJGw2F683UhW/FbDIm9XHgePHXR73P1H97Nw80IWbl5Ydaxzm852MywSuifQvV13Tz8kERERkabhqaegWzd49lkoLLRvs1rhX/+C//wHfvc7I0kRG+ufOD3FZILuU6BbEvyaBav/BIc31e63PRt2vA8xN8DwR6F14BXbl6bLp7t1bN26lYyMDDIyMjCZTDz44INMnz6d9u3bV/XJzs4mMzOTRYsWkZiYyAMPPMC5557rqxCDTrDu1rHvvn10ahOYlWd9zWq1su3Qtlo1LApLC+s+2YWubbvWqmHRrV03D0UtIhLkgqwqukjACvb30qFD8Le/wZw5xt8dCQ2F6683Cmf26+fb+Lylsgws/4A1j8MxJzvThbSEgbfDkAcg4hTfxtccBdl7yRvXoT5JTsybN4/XXnuNFStWkJycTGpqKpMmTXJ5TlFREZmZmWRmZlJYWEhqaiq33HILffr08Xa4QSUQkhNzfpzDnB/nOG2vtFay+8huu2Pd2nYjxBTictwZY2cwY+wMj8QYbKxWK1uLttolK/J251FUWtSocbu17VarhkXXtl09E7SISDAJsg+BIgGrqbyXCgvh+efhhRfg8GHHfcLC4MYb4aGHoHdvn4bnNeXHYOPLsP5pOOHki7GwdkZxzUH3QHhb38bXnATZeymothJduXIlr732GnPnziUuLo60tDSmTZtmN0vClaioKGbOnMnMmTMxm8389a9/pV+/fsTHx3PLLbdw0003eSt0qafi48XsPLyzXufUTFY4G7e5MplM9OvQj34d+pEyNAUwEhaWQotdsiJvVx6HjjvJ8juw+8huPtr4ER9t/KjqWI92PWrVsOjcprOLUURERESamA4d4Ikn4K67jHoUL74IJSX2fcrLITMTjhwxln00BWGtYMj90H86bHgGfn4BKo7a9yk/DGsegY0vwbCHoX8ahLb0S7jStHk8ObFo0SLS0tKqZjts3ryZfo2c/hQXF8eCBQsAyMzM5NVXXyU1NZXU1FReffVVT4QtjRDZMpIe7Xp4ZVw5yWQyERsdS2x0LNOGTgOMWSmWQotdDQvzbnO9Ejs7D+9k5y87+fCXk9tM9YzsaT/Dolu8luGIiIhI09exo1GP4p574Jln4OWX4dixk+0hIfCnP/kvPm9pEQUj/wID74C1T0J+prH0o7rj+yHvLvh5Dgx/AvpeAyGhfglXmiaPL+vYsmULZrOZK664wpPD1mKxWFixYoXX7yfQBcKyDgksldZKNhdstqthkbc7jyMnjjRq3N7te9dKWHRs3dFDUYuI+FiQTZ8VCVhN/b20dy+kp8Orr0JpqVEg8513/B2V9x2xwOpHYeu/ACeXi+2HwognoeclRsFNaZwgey8Fbc0J8R4lJ8QdldZKNh3cZFfDYsWeFY1OWPSN6muXrIjvHk90q2gPRS0i4kVB9iFQJGA1l/fS7t3w9NNwxx0wYIDjPm+/DWvXwv33Q6cm8viL1sCqh2DnR877dBwDo/4KXSb4LKwmKcjeS0pOSC1KTkhDVVRWsPHgRrsaFubdZo6WHa37ZBf6RfWzm2ER1y2ODq06eCjqpq2u4rIN1ZyLy4o4FWQfAkUClt5LhuPHYeBA+PVXaNMG7rwT7r3XWCbSFOz/HlY+CPu/dd6n62QY9RREx/surqYkyN5LQVUQc8CAAWRnZzNy5Ehv3YWINEJoSCiDOw1mcKfB/G7E7wAjYfHLwV/salis2L2CY+XH6hjtpC1FW9hStIWs9VlVx2I7xNoV3YzrFkdURJSnH1LQa0hxWXfHFRERES964w0jMQFGIc2nnzbqVdx1F8yYYRTcDGadzoLEr2H3Qlj1RyhcWbvPni9g4RfQexqM+DNEDvR5mBLcvJacyM/PJz4+npycHM4991xv3Y2IeFBoSChDOg1hSKchXDfyOgDKK8v5+cDPdjUsVuxZQWl5qdvj5hfmk1+Yz4J1C6qO9Y/uX2uGRXMvglpXcdmGbsvb3J9XERERr6qoMOpS1HT4MDz5JLz0klFg8+67wc2dCwOSyQTdz4duU2DbAlj9MBzJr93v1wWw/V2IuRGGPwKtNbNb3OO1ZR0hIcaHZZPJRGZmZp1bfxYXFxMZqQ/Q9aVlHeIP5ZXlbNi/wa6Gxaq9q+qVsHBkYMeBdjUsTut2mi6sq9lfsp/Oz9pP99t33z7tpCLSEEE2fVYkYOm9ZNi82UhEvPMOVFY67hMVBffdZyz5aNfOp+F5RWUZ5L8Oa5+AY7sd9wmNgIG3w5AHoGUTWeLiLUH2XgqqmhP9+/dn1qxZpKWlYTKZmD17Nvfee6/Dvlu2bGHWrFkcOnSIlJQUpk2bpkSFm5SckEBRVlHG+v3r7WpYrNqziuMVxxs8pgkTAzsOtJthcVq302jboq0HIw8eSk6IeFCQfQgUCVh6L9nbuBGeeAL+/W9wdpkVHQ0zZ8Jtt0HbJvCZpvwobHwJ1v0Vyooc9wmPhMH3w6l3Q3gTeMzeEGTvpaBKTgwYMIBNmzaRm5vL5MmTMZlMpKWl8corrzg959ChQ2RmZjJr1iwSEhKYNm0aycnJ9O3b1xshNglKTkggK6soY93+dXY1LFbvXc2JihMNHtOEiUGnDLKrYTGq66hmkbBQckLEg4LsQ6BIwNJ7ybH1640kxYIFzpMUnToZSYpbb4XWrX0bnzecKIT1z8AvL0CFk3plEZ1h6J+gfyqEtvBpeAEvyN5LQZmcADCbzUyaNIni4mKSkpJYuHChy3MtFgtJSUls3boVk8lEeXm5N0JsEpSckGBzouIEa/ettathsXrvasoqyxo8ZogphEGnDLKbYTGq6yhahzeB/+irUXJCxIOC7EOgSMDSe8m1NWvg8cfh3Xed91mwAFJSfBeTtx3bDWufhM2ZYHVyHdemL4x4AvpcDSGhPg0vYAXZeylokxNgn3CIj48nNzfX5dKNoqIioqOjMZlMVFRUeCPEJkHJCWkKjpcfZ+2+tXY1LNbsW0N5ZcMTkyGmEIZ0GmJXw2Jk15FBnbBQckLEg4LsQ6BIwNJ7yT0rV8Jjj8EHH9gfHzoUVq+GENfFrYPS4XxY8yhs/Tfg5JKz/TAY+RfocZFRcLM5C7L3UlBtJVpQUGD375iYGMxmMxMnTmT58uUkJCSQk5NDnz59HJ4fFRXF/fffz7PPPuutEEUkQLQMa0l893jiu5/cF7u0vJQ1e9fY1bBYu2+t2wmLSmsla/etZe2+tby16i0AQk3GbiTVZ1iM6DKCVuGtvPK4RERERAAYNQr+9z/IyzOSFB9/bBx//PGmmZgAaBcLZ/7TqDWx6iHY9UntPofWwjeXwCljYeTT0OUc38cpAcNrMydCQ0OdznhISkpi0aJFREdHs2jRIkaOHOmwn8ViYcCAAZo54YJmTkhzUlpeyuq9q+1qWKzbt44Ka8N/R4SaQhnWedjJGRbd4xnRZQQRYREejNwzNHNCxIOC7BsqEb/ZMAd+nuO83VoJpTV2aojoBnVsc82gGTB4RuPjC1ZLl8Lbb8OLLzpPTqSlQUIC/P73EB7u0/C8Yt93sOpB2P+d8z7dzoORT0H0ab6LK1AE2f9LQbWsIyQkBIvF4rSYZVpaGnPnziUkJIScnBzOPfdch/1cJTlEyQmRY2XHWLV3VVWyIm9XHuv2r6PS6mQbLzeEhYQxrPOwqoKbCd0TGN55OC3DWnow8vpzlJzIvyOfmOgYP0UkEsSC7EOgiN+sfgzWPu75cYc9CiMe8/y4TcWSJTB2rPH3fv3gT3+Ca6+FMK9NfPcNqxV2fWYkKYpWO+/X+0oY8WeIHOC72PwtyP5fCqplHQCLFi3ipptuctiWkZFBVFQUzzzzDImJiWRmZjrs66XciYg0Ea3CW3FGzzM4o+cZVceOlh1l1Z5VdjUsNhzY4HbCoryynJV7VrJyz0rmrZgHQHhIOMO7DLerYTG8y3Ba+KDStNVq5autXzHnx9rfXA18eSCXDb6MWxNuZULfCZia+3pNERHxrPBIaNXDO+OKc48+evLvW7bAjTfCU0/BI4/AVVcFb5LCZIIeU6H7ebDtv7D6T3DEUrvfr/NhezbE3gzDHoHW3X0fq/icV2dOREdHc+DAAZf9MjMzueWWWzCZTMyePZt7773Xrj06OrpW/Qo5STMnRNxTcqKElXtW2tWw2LB/A1ZnBZrc0CK0BcM7D7erYTG081CPJizMu81c9/51rNu/rs6+QzsN5e3L3iauW5zH7l+kyQqyb6hEpBlZsQLiXPxffuqpRvJi2jQIDfKdLipOgOV1WPMElO5x3Cc0AgbeCUNmQcto38bnS0H2/1LQLesASEhIYNGiRbRr185p39zcXCZPnozJZCItLY1XXnmlqq1jx44cPHjQGyE2CUpOiDTckRNHWLlnpd0Mi18O/NLohMXILiPtalgM7TSU8ND6rxXNyc/hsvmXUVJW4vY5bcLb8P6V75MUm1Tv+xNpVoLsQ6CINCNWKyxaZMyS+PFH5/0GDzaKayYnB39RzfIS+OVFWJ8OZYcc9wlvD0Nmwql3QVgb38bnC0H2/1JQJSe2bNnCa6+9xrvvvkthYSEPPvggqampTrcPNZvNTJo0ieLiYpKSkli4cCGg5ERdlJwQ8azDxw+zYs8KuxoWvxz8pVFjtgxtyciuI+1qWAzpNISwEOdTMs27zZz9j7PrlZiwaRPehm9u+EYzKERcCbIPgSLSDFmt8PnnxiyJpUud9xs2zNj149JLgz9JcbwANsw2EhUVxxz3iegCw/4EsdPBB8trfSbI/l8KquREdYcOHWLBggVkZ2eTlZXlNEFhsVhISkpi69atxMfHk5ubS79+/ZSccEHJCRHvKz5ezIrdK+xmWGwq2NSoMSPCIhjVdZRdDYvBnQYTFhKG1Wpl+KvD3VrK4cywzsNYfctq1aAQcSbIPgSKSDNmtcKnnxozKcxm5/1GjTKSFBddZNR2CGZHd8HaP0P+PLA62Ua+TT+jaGbfq+reHSYYBNn/S0GbnKiPQ4cOMXHiRFauXElMTAwWi0W7dbig5ISIfxwqPYR5t9muhsXmgs2NGrNVWCtGdR1F17Zdef/n9xsd4+LrFzOh74RGjyPSJAXZh0AREaxW+PBDYybFqlWO+7RpA9u2QceOvo3NWw5vhtWPwLb/OO8TNdzYfrT7BcGdlAmy/5eaRXLCJikpiUWLFmEymZSccEHJCZHAUVRahHm32W6GhaXQQQVqH0kZksKClAV+u3+RgBZkHwJFRKpUVsL77xtJinU1Zln+8Y/wl7/4Jy5vKlwJqx6CXZ8679PpLBj5NHQe77OwPCrI/l9qVskJgLS0NObNm6fkhAtKTogEtoJjBcYMi2o1LLYUbfHJfYeaQimYVUBkS23XJlJLkH0IFBGppbISsrONopgbNkC7drB1K0Q34R0t9n0DKx+EAz8479N9Koz8C3QY5bOwPCLI/l/yxnVoQG+Qm5GRwbRp0/wdhohIg0W3iiYxJpHEmMSqYwePHqw1w2LboW0ev+8KawU7i3cS2UnJCRERkSYnJMTYTvSKK2D+fDh0yHli4uBBuPZamDULzjnHt3F6UuezIek72PUJrPojFK2p3WfXp8atz1Uw4glo19/3cUqDBHRyAmDSpEn+DkFExKM6tu5IUmyS3XafB44eIG9XHv/75X+8tvw1j93X4ROHPTaWiIiIBKDQULj6atd9nn0WPvvMuE2caBTOHDfON/F5mskEPS6EbufDtv/C6j9BiYNZqdv+A79mQezNxu4erbv7PlaplyZQ1lREJPid0voUpvSfwh2j7/DouO1atPPoeCIiIhJk9u2Dl146+e8vv4Tx42HyZPjxR//F1VghodDvGrjwZ0h42dhitCZrOWx+DT7qbywHOVHo+zjFbV5LTgwYMIBVzqrIioiIQz0jexJqCvXIWCZMrNyzkkprpUfGExERkSD06qtQUlL7eE4OnHkmTJ0Ky5b5Pi5PCW0BA2+Di/ONWhPhDpazVhyD9X+FD2Jg3V+h/Kjv45Q6ea0gZkhICCEhIeTk5HDuued64y4EFcQUaYpSslLIXp/tsfEGdhzIvWPv5bqR1xERFuGxcUUC2oY58PMc5+3WSijdbX8sohuY6vjeZtAMGDyj8fGJiPjK8ePwxhvGLh47dzrvd9FFxnKP007zXWzecLwA1qfDxhehotRxn4iuMPwRY8lHSLhv43NGBTG9u6yjsrKSxMREXn/99Tr7FhcXezMUEZGgcWvCrR4db+PBjaR9nEafF/rw56//zMGjBz06vkhAKiuGYzud32omJsA45uqcYzuNcUVEgknLlvCHP8DmzfDii9C1q+N+H30EcXFw+eWwerVvY/SkltFwWjpctBn6p4GjGamle2DZrfDxYNj6byNhLX7nteRETEwMGRkZWK1WUlNTee6555z23bJlCzfffDNTpkxh3rx5SlSISLM2oe8EhnYa6vFx95Xs45GvHqHX8724/dPbyS/I9/h9iASM8Eho1cPzN0fThUVEgkFEBNxxB1gsMGcOdO7suN/778PIkcZOIOvW+TZGT2rdA0a/BhdsgN5XOu5zJB9+uAY+i4Odn4J3FhWIm7y2rGPAgAFs2rSJ3NxcJk+ejMlkIi0tjVdeecXpOYcOHSIzM5NZs2aRkJDAtGnTSE5Opm/fvt4IsUnQsg6Rpsm828zZ/zibkjIHa0Tr0Ca8DQ+Me4Cs9Vms3uv8m48QUwiXD76c+8bex5ieYxoTroiIiASbkhKjHkV6Ohw44LjPeecZO3w0BQUrjO1Hdy903qfTOBj5NHT2w04mWtbh/eQEgNlsZtKkSRQXF5OUlMTChS5eEIDFYiEpKYmtW7diMpkoLy/3RohNgpITIk1XTn4Ol82/rF4JijbhbXj/yvdJik3CarWSa8nlmR+eIceS4/K88b3Hc9+Z93HhwAsJqWvNvYiIiDQdR47Ayy/DM89AQYF925IlMKaJfYGx92tY9SAccLFTSfcLYORT0GGE7+JScsI3W4nGxcWRl5dH3759ycnJYfTo0S6XbsTExJCXl4fVasVLuRMRkYCXFJvENzd84/YSj2Gdh/HNDd+QFJsEgMlkIik2iS+u/YKVaSu5dsS1hIWEOTz321+/5ZL/XsKQvw9hbt5cSsudFJASERGRpqVtW3jgAdiyBf78Z4iKMo5fcEHTS0wAdDkHkr6Hsz+A9sMc99n1CXw2Cn74HRyx+DS85sxryYmCGlm3mJgYzGYzo0aNYvny5SQkJLBt2zan50dFRXH//fd7KzwRkaAQ1y2ONX9Yw+LrF3PRwItqtYeFhJEyJIXF1y9m9S2riesW53CckV1H8vZlb2O508J9Y++jXYt2Dvv9cvAXUj9Opc8LfXjymydVPFNERKS5iIyEhx82khSPPmrs3OHM++/DzTfD1q0+C8+jTCboeTGcvxLGvg1t+jroZIWt/4KPToVlt8ExB4WUxaO8tqwjNDSUiooKh21JSUksWrSI6OhoFi1axMiRIx32s1gsDBgwwOk4omUdIs3J/pL9dH7Wfrpf/h35xETH1HusQ6WHmGueywtLXmDnYefbirUOb82No27knrH3ENOh/vcjIiIiTUxFBYwYAevXQ1gY3HQTPPQQ/HZNEpQqjsPmubDuz1C6z3Gf0NZw6l0wZCa0iPJ8DFrW4b2ZE1arla1OMmk5OTlMnz6dgoIC4uPjWbx4scN+MTH6ICwi4kq7lo5nQNSlfUR77jvzPix3WXj70rcZ3nm4w35Hy47y8rKXGfDSAKZlTWPpzqWNCVdERESCXVaWkZgAKC+HjAzo3x9uvx12Ov/CI6CFtoRTb4eL8mHEnx3vzFRxFNY/DR/GwPrZUH7U93E2cV6tObFo0SKnbRkZGdx///1UVlaSmJjI66+/7rCfak6IiHhPi9AWXDvyWlbdsorPf/c5iTGJDvtVWivJWp/FmHljOOfNc/jol4+o1J7gIiIizc8zz9Q+duIE/P3vEBsLd98Ne/b4PCyPCG8Lwx6Giy0w+D4IaVm7z4lCWDkLPuoPmzKgssz3cTZRXk1OzJo1y2V7eno6r732GlarldTUVJ577rlafaJsBVlERMRrTCYTk2Mnk3NtDivSVvC7Eb9zWjzzm23fcPF/L2boK0OZZ56n4pkiIiLNySefGAmIiIjabcePw9/+BjExcN99sM/JEolA17IjnPYMXLwZYqeDKbR2n2O7Ydkt8PEQ2Ppf0Jc2jebV5ERBQQGjR4/m8OHDTvukpqbyxRdfYLVamTlzJrfeeqtdu8lk8maIIiJSw6iuo3jnsnew3Gnh3rH3Oi2e+fOBn5n+0XT6vtCXv3zzFwqOFTjsJyIiIk1I167w/POQnw933AEtWtTuc+wYPPcc9Otn7ARy4IDv4/SE1j1hTCZcsB56T3Pc58hm+OEqWBgPuz4DzfxvMK8lJ/Lz87n//vspKCigb9++PPvss063D01MTGT58uVERkaSkZHBeeed562wRETETb3a9+LZyc+y/Z7tzE6cTY92PRz221uyl4cXP0yv53tx52d3sqVwi48jFREREZ/r3h1efBE2b4Y//AHCw2v3OXoU0tONJMVDD0FBkH6RETkQxs2H85ZD18mO+xSuhK+mwqIJsP8Hz9xvM9vG1GvJiX79+pGens7mzZuxWCy0b9+elJQUpwmKuLg48vLy6Nu3Lzk5OYwePdppXxER8Z32Ee25/6z7sdxl4a1L33JZPPOlpS/R/6X+XJl9Jct2LvNxpCIiIuJzvXrBK6/Apk0wfbqxg0dNR47AU0/BggW+j8+TouNh4ucw6UvoOMZxn33fQM5Z8PXFULSmcfe35rHGnR9kvLaVaEMdOnSIiRMnsnLlSmJiYrBYLNpK1AVtJSrSfDjaSnTfffvo1Ma3W0xZrVa+yP+CZ398llxLrsu+Z/c5m/vPvJ+pA6YSYvLqSkIREREJBBYLPPkkvP22se2oTe/eRgLD0TKQYGS1wo4PYPVDcGi9k04m6HsNjHgc2taxE+XW+fDD/9U+PmEhdJ/S6HA9Lai2Em2o9u3bk5eXx8SJE8nPz/d3OCIiUoPJZGJK/ynkXJuDOdXMNcOvIdRRoSiM4pkX/ecihr0yjNfNr6t4poiISFMXEwNvvAEbNsC110LIb5ecDz/cdBITACYT9LoUzl8NZ7wJbfo46GSFrf+EjwfB8jvg2F7HY1WcgNUPO24z391sdgQJuJkT1aWlpTFv3jzNnHBBMydEmo45P85hzo9znLZXWivZfWS33bFubbvVOSNhxtgZzBg7wyMxOvProV/525K/kWnO5MiJI077dWnThTvH3MktCbcQ3SraqzGJiIhIAPjlF2Ob0eeec1yXAuCuu6BHD7jtNmjTxrfxeUrFcdicAWufhOP7HfcJbQ2D7oHB90OL9iePb5gDK+51PnbcHOO8AOKN69CATk4ALFq0iEmTJvk7jICl5IRI0/HYV4/x+NePe3zcR895lMcmPObxcR0pKi0iMy+Tv/30N3Yd3uW0X5vwNtx02k3cfcbd9OvQzyexiYiISADasAGGDjWWSXTuDLNmwS23QOvW/o6sYcoOw88vwIZnoNzJrpUtomHIAzDwdigrho8HGn86Ex4JF22CiM7O+/hYs0xOiGtKTog0HXXNnGgoX8ycqOlExQn+s+Y/PPvjs6zdt9ZpvxBTCClDUrjvzPtI6J7gwwhFREQkIFx1Ffz3v/bHunY1tiBNS4OICP/E1VilB2D907Dx71B53HGfVj2gTV848H3d48XeBGPmeTTExlByQmpRckJEApnVauXz/M959odnWbRlkcu+5/Q5h/vPvJ/zB5yv4pkiIiLNwa+/GtuMVlY6bu/eHf74R7j5ZmjZ0rexeUrJdlj7OFj+AVYnj9MtJjhvmbFjSAAIqoKYAwYMYNWqVd4aXkREgoDJZOK8/ueRe10ueal5XD38aqfFM7/e9jUX/udChr0yjDdWvMHxciffMoiIiEjT0Ls3/PQTXHCB4/Zdu+D222HAAMjIgBMnfBufJ7TpZcx4mLoOeiU3YiCrUVSzCc8t8FpyIj8/n/j4eBYvXuytuxARkSAS1y2Of13+L/LvzOeeM+6hbYu2DvttOLCBmz68ib5/68tT3z5F4bFCH0cqIiIiPpOQAB9/DEuWwBQnW2Zu327UoRg4EF5/HcqCcPeK9oNgfBZMWQZdkxo2xoEfYeu/PBtXAPHqvNnKykoSExN5/fXX6+xbXOyiAIiIiDQZfaL6MGfKHLbfs52/Tvor3dp2c9hvz5E9PPTlQ/R6vhd3fXYXW4u2+jZQERER8Z0xY2DhQvjuO3C2IcK2bcYSj0GD4M03obzcpyF6RMcEGP8utOjYsPNXzoIy5zujBTOvJSdiYmLIyMjAarWSmprKc88957Tvli1buPnmm5kyZQrz5s1TokJEpBmIiohi1rhZbL17K/+45B8M7TTUYb+SshJeXPoisS/G8n/Z/0ferjwfRyoiIiI+c9ZZkJsLX30FZ5/tuI/FAqmpsHOnT0PzmHVPwYmDDTv32C7j/CbIa8kJk8nE9OnT+eKLL7BarcycOZNbb73VYd9+/fqxYMECFixYQGFhIVFRUYwePZpnn32WrVu3eitEEREJAC1CW/D7Ub9nzR/W8OnVnzKx30SH/SqtlcxfN5+EuQmc+9a5fLrpUyobVVhKREREAtY55xgJikWLjIRFTTfdBH36+DysRqs4Yezg0RgbXzbGaWK8tlvHgAED2LRpEwBms5lJkyZRXFxMUlISCxcudHmuxWIhKSmJrVu3YjKZKPfCdJ3MzEyysrKIiYmhoKAAgAcffJC4uLgGjZeSkkJMTAxXXnklUVFRAERHRzvsa2v3BO3WISJNUd6uPJ778TkWrFtAhbXCab8hnYZw79h7uWb4NbQMC9Iq3iIiIuKa1Qo5OfDII0YBzRYtYPNm+O06KOisfBDW/7Xh5w95EEb5d/ZEUG0lWj05AfYJh/j4eHJzc4mMjHR6flFREdHR0ZhMJioqnH8wbYiUlBQsFguLFi2qShRYLBbi4+NJT08nNTW13mPGx8djNpvd6puTk0NiYmK978MRJSdEpCnbWrSVvy35G3PNcykpK3Har2vbrtw5+k5uSbiFDq06+DBCERER8RmrFT77DDZuhLvvdtynpAQuuQRuuw0uvRRMJl9G6J6yw/DxqXBsd/3PbdUdLvwFwh0XFveVoNpK1DYbwSYmJgaz2cyoUaNYvnw5CQkJbNu2zen5UVFR3H///R6PKzMzk+zsbLvEhC2+uXPnkpaW5naSoTp3z0lOTvZYYkJEpKnrG9WX5897nu33bOfpSU+7LJ75xy//SK/ne3H3wrvZVuT8/xcREREJUiYTTJ3qPDEB8MorxlKQyy+H+Hj46KPA234zvB2MSm/YuaPS/Z6Y8BavzZwIDQ11OuMhKSmJRYsWER0dzaJFixg5cqTDfhaLhQEDBnh05kSHDh1ISEggJyfHYbvJZCIxMdFpuyNFRUVMmjSpVsKjvn0aQjMnRKQ5OV5+nH+v+TfP/vgs6/evd9ov1BRKytAU7j/zfuK6NWy5noiIiASZw4ehXz84WKPYZEICPP44nH9+4MyksFZCzjhje1B3nTIWkr4PiMcQVDMnrFar02KWOTk5TJ8+nYKCAuLj41m8eLHDfjExMR6NyWw2U1RU5LKuRFxcHLm5uRQVFbk97vLly+1qTTiSkpLC3LlzPZqYEBFpblqGteSG025g7R/W8unVn3Ju33Md9quwVvDftf8lPjOeiW9N5LNNn+GlXLyIiIgEinfeqZ2YAFi+HC64AMaOhS++CIyZFKYQiH8RcDfRYIKElwIiMeEtXktOACxatMhpW0ZGBvfffz+VlZUkJiby+uuvO+znyQ+T8+fPB6BjR+d7ytqKWObm5tZr7OTkZKdts2fPJi4ursHFNkVExJ7JZOL8Aefz5fVfsnz6cv5v2P8Ragp12Hfx1sVM/fdUhr86nH+s+AfHy4/7OFoRERHxibQ0+M9/4NRTHbf/9BNMmQLjx8OXX/o2STFnDvTsaX8beSksbeXe+UtbwYhLao8xZ45Xw/YlryYnZs2a5bI9PT2d1157DavVSmpqKs8991ytPp6caWBLOLga09a2bNkyt8dNTEx0Osvj/9u79/Cmy/v/46+05VgoaREUAYWUUznoTACHm4iQeJonaCO6OU8breI8MW3GDip+t2E7xcM8tWw6cZvDtiBO5yFhIB7YpI2iYBVoQChWQdpQzoc2vz/6S0Zt0mMOPTwf19VLzOf+3Hmn5dC8et/v2+12a+nSpcrJaeWeIgBAoyynWvRi+ovacvsW3XH2HUrslhh03MbdG3XTKzdp+GPD9eC7D8p72BvdQgEAQGTFx0tXXy1t3Fi3imLEiODj3ntPmjFDOv98ac2a6NRWXS3t3Nnw47mD0sEm7j2ounHB7q+ujkb1URHRcKKyslKTJ0/Wvn37Qo7JzMzUW2+9JZ/Pp+zsbM2dO7fedUMYl634t2qEOuLzxGst2dbRGLvdroKCglbfX15e3uhHRUUrOrwCQCc0zDhMj170qHbctUO/n/57ndLnlKDjKvZXaP7K+Rr6yFDd9cZdNM8EAKCziY+Xrr1WKi2Vnnuurg9FMG+/LZ13nmS11gUWkZSUJA0e3PCj72BpZehTLCVJK/vVjQt2fyMnYHY0EQsnysrKdM8996iyslLDhg3TQw89pOoQqY7ValVxcbGSkpKUl5eniy66KCI1ffsEkXCNDSU3N7fRVRXNMXTo0EY/Jk+e3OY6AaAzSe6VrPnnzte2O7bp2cuf1dgBY4OO2390vx7976NKfTxVPyz6odwVLT+pCQAAtGMJCdINN0iffy796U/S6acHH7dypXTXXZHd5jFvnlReHvzjr7ulXiEClKQx0t92h7533rzI1RxlEQsnhg8frpycHG3ZskUej0f9+vWT3W4PGVCYzWaVlJRo2LBhcjqdmjx5csixHYHX69XChQuVlZUV61IAoEvyN8/85JZP9NoPX9O0YdOCjqvx1ejFDS/Kkm/RjCUzaJ4JAEBn062b9JOfSJs2Sc88U9er4dseeCB2zSbju0sjfhH8muUxKa5bdOuJkYhu6/Dr16+f5syZozfffFNJjSw7MZlMcrvd+s53vqOSkhJZLJawba+ItoULFzZ5Mkhz7Nixo9GPDz74IEwVA0DnFGeI0yUjL9Gq61dp3Zx1mj1utuIMwf/5+/fWf+uSv1+iM545Q3/56C86WnM0ytUCAICI6d69rmnmli3SE09Ip55a9/iUKXWNMmMp5dyGj/U/Xxp0QfRriZGohBMt0a9fP5WUlGj69OkqKysL69z+fhLN2bLRWF+K5sjPz5fVam3THJI0ZMiQRj8GDRrU5ucAgK5i4qkT9Y+Mf2jLbVt0++TbQzbP3LBrg25ccaOGPzZcOe/m0DwTAIDOpEcP6dZbpbIy6bHHpIULQ6+a+Pe/pfR06ZNPolujJKU2fsBEZ9Puwgk/p9OpOXPmhHXO5pz84Q8u2nJKiMvlktfrDetJIwCA8BmePFyPXfyYtt+1Xb+b/judnHhy0HFf7vtSv1j5Cw19ZKjmvTmP5pkAAHQmPXtKt99e1xQzGJ9Puvdeadky6YwzpNmzpU8/jV59vU6L3nO1A+02nJCkvLw8vfXWW2Gbb+LEiZLU6IoMj8cjSZo0aVKrn8fpdEpq++oLAEBkpfRK0S/P/aW+uPML/fnyPyvtpLSg4/Yf3a9H/vOIUh9P1Y+W/UgfVnwY5UoBAEDUOZ31T/F46SVp/Hjphz+sa7KJsGrX4YQkzZgxI2xz2Ww2SY0fE+q/lpGR0erncblcktq2+gIAED09EnroprNu0oa5G/TqNa/qvNOD/wSlxlejv3/yd5nzzbIuseqNLW/QPBMAgM5q0aKGj/l80osvSmPHStddV9e/AmERsXBi5MiRWr9+faSmbxV/4OAPD77N6/XK4/G0uYml2113HF1qamqb5gEARFecIU4/GPUDrb5htT746QeNNs9cuXWlLv7bxTrjmTP0/EfP0zwTAIDO5qWX6k7x6Nev4bXaWumFF6QxY6SbbpL+/wp8tF7EwomysjJZLBatWrUqUk/RKjk5OfJ4PIHtGyd66aWXJEmLFy9ucM3j8chisXA0KAB0EZMGTwo0z7xt8m3q3a130HEbdm3QDStu0PDHhiv3vVyaZwIA0FkkJUm/+Y20bVtd74m+fRuOqamRnntOGj1aysyUvqA/VWtFdFtHbW2trFar/vznPzc5trq6OpKlBGRnZysjI0M2m63e9g632y2Hw6GcnJygKyfy8vLkdruVn58fWBkRzIlz0nMCADq+4cnD9fjFj2vHXTuabJ7pcDl02iOn6edv/lzb926PcqUAACAijEZpwYK6kOKXv5QSg5z2dfy4tHixNHKkNHeuVF4e7So7vIiFEyaTSXl5efL5fMrMzNTDDz8ccuzWrVv105/+VBdeeKH+9Kc/RTyoKCgokMPhkN1uV1ZWlux2uxwOhwoKCpSdnR30ntmzZ8tkMslqtTa67cNoNMpsNgf+CwDoHPzNM7fduU1/uuxPGnPSmKDj9h3dp0X/WSTTYyZdu+xaffTVR9EtFAAAREZKivS739WFFA6H1DvIqspjx6Snn5aWLGn5/AcllUsq+///3be/TeV2NAZfhDp5jRw5Ups3b5bL5dIFF1wgg8GgrKwsPfXUUyHv2bt3r/Lz8+VwODRx4kRdddVVysjI0LBhwyJRYqdQXl6uoUOHSpJ27NihIUOGxLgiAOgaan21+tfmf+mh9x/S21+83ehYq8mqu6fcrQtS6/49BAAAncCuXVJurvTkk9Lhw/97vH9/aevW4NtAvs3nk1avlh56UHrjLan2hGvx8dLMmXUrMaZNk9rR9xCReB8a8XBCqtsyMWPGDFVXV8tms+mNN95o9F6PxyObzaZt27bJYDDo+PHjkSixUyCcAIDY+2DnB3p47cMq/LRQtb7akOMmDJygu8+5W1ePv1rd47tHsUIAABAxFRVSTo70zDPSkSN1vw6xIr8et7vuxI+NG5seO25c3WqMdrI6v8OGE1L9wMFiscjlcikpKSnk/V6vVykpKTIYDKqpqYlEiZ0C4QQAtB+eKo8e/c+j+vOHf9bBYwdDjhvcd7DuOPsOZVoy1a9nkA7gAACg49m5U3r0Uen++4P3pZCk+fPr/jtxonT99dKBA82fPzFRWr5cstnaWmmbdahwon///tqzZ0+9x/bu3avp06frww8/1IgRI+R0OnX66aeHnMPhcOihhx4inGgE4QQAtD97Du7RM8XP6PEPHteuA7tCjuvbva8yLZm64+w7NLTf0ChWCAAAoq68XEpNlY624fjxxERpzZqYr6CIxPvQiDXEPPHUCr9+/fqppKREM2bM0JYtW2SxWLR+/fqQc3BsJwCgI+rfu79+NfVX+uLOL7T4ssUa3X900HH7ju7Tw2sflulxk368/Mc0zwQAoDP7/e/bFkxIdSstrr++rldFJxOxcMLn82nbtm1BrzmdTs2ZM0eVlZWyWCxatWpV0HEmkylS5QEAEHE9E3rqp+af6tNbP9UrV7+iqadPDTrueO1x/fXjv+qsvLNke8Gmt8reUoQWNgIAgFjweqW//CU8c23YIL3deDPujihi4YQkrVy5MuS1vLw83XPPPaqtrZXVatWf//znoOP45gwA0NHFGeJ02ejL9PYNb+s/P/mP7GPtijME/yfY5XHpwr9eqO/kfUcvrH9BR2va+BMWAAAQe0ajtH69dNpp4ZmvkVMwO6qI9ZyIi4tTSkqKvvnmm0bH5efn6+abb5bBYFBubq5+/vOf17uekpKiysrKSJTYKdBzAgA6prLKMj36n0f17EfPNtk8887v3qk55jk0zwQAoCOrrpZSUqRw9FSMj5cqK6VGDpmIpA7Vc0KSKisrNXnyZO3bty/kmMzMTL31Vt3y1ezsbM2dO7fedc6DBwB0RqkpqfrjJX/U9ju36//O/z8NTBwYdNzOfTt1j/MeDX1kqO556x7t2LsjypUCAICwKC8PTzAh1c2zc2d45monIhZOlJWV6Z577lFlZaWGDRumhx56SNXV1UHHWq1WFRcXKykpSXl5ebrooosiVRYAAO1K/9799eupv9YXd36h/EvzG22e+dDahwLNM9d/FbqhNAAAaIf27w/vfI0sAuiIIhZODB8+XDk5OdqyZYs8Ho/69esnu90eMqAwm80qKSnRsGHD5HQ6NXny5JBjAQDobHom9NQcyxx9euunWnH1Cp172rlBx/mbZ34n7zu64IUL5Cxz0p8JAICOoE+f8M7Xt29454uxiPWcaK29e/dq+vTp+uijj2QymeTxeFQTrqUvnRA9JwCg8/pv+X/10NqHtKx0mWp9tSHHnXnymbr7nLs1e9xsdYvvFsUKAQBAs4Wz50RCgrRnDz0nIqlfv34qKSnR9OnTVVZWFutyAACImbOHnK0Ce4E2/WyTbp10q3ol9Ao6bv3X6/Xj5T+W6XGTHn7/YVUfYeUhAADtTlKSNHNmeOaaOTNmwUSktLtwws/pdGrOnDmxLgMAgJhLTUnVE5c8oR137dAD0x7QgN4Dgo4rry7X3c67NfSRocp2Zqu8ujzKlQIAgEZ96wCImM/TjrS7bR3ftnLlSs2YMSPWZbRbbOsAgK7n0LFDeuHjF/Tw2oe1ac+mkOMS4hJ0zfhrdPc5d+uMk8+IYoUAACAon0+aMEHauLH1c4wfL338sRTDky27xLaObyOYAACgvl7deinTkqnSW0v18uyX9f3Tvh903PHa43rh4xd05jNn6sK/XiiXx0XzTAAAYslgkJYskRITW3d/YqL0/PMxDSYipd2HEwAAILg4Q5yuGHOF3rnxHa39yVqlp6XLoODfrLxV9pZsL9hkzjfrbx//TcdqjkW5WgAAIEkym6Xly1seUCQm1t1nNkemrhiLSDgRrSNAOWoUAIA63x3yXRVeVahNt23S3IlzQzbP/Oirj3Tt8muV+niqFq1dRPNMAABiwWaT1qyRxo1r3vjx4+vG22yRrSuGwh5OLF68WEajUbNnz9aqVavCPb2qq6v10EMPacSIEbJYLGGfHwCAjmxEygg9+YMntf2u7VowbUHI5pk7qnfo52/9PNA8c2f1zihXCgBAF2c2S598Iq1aJf3ggobvzhMSJLu97vrHH3faFRN+EWmI6fF4lJ+fr/z8fBkMBmVmZurmm2/W6aef3uo5ly1bpmeeeUYrV66U1WpVZmam0tPTw1h1x0RDTABAY1rSPPOHE36ou6fcrQknT4hihQAAQDtLpdfHSlWSDkvqKemyD6Qxk2JcWHAdpiGmyWTSgw8+qMrKSi1dulRlZWUaPny4Jk2apOXLlzd7nm3btunmm29W//795XA4ZLPZVFlZqTfffJNgAgCAZjixeeby2cv1vaHfCzrueO1xLVm/RGc8c4Yu+utFNM8EACDaeksaLCn1//+3b5/Y1hNlEW+IabVa9dJLL6myslJ2u1333HOP4uPjdcstt2j9+vUNxldXV+tPf/qTJk6cqNTUVBkMBrlcLm3evFn33HOP+vXrF+mSAQDodOIMcbpyzJV696Z39f5N72tW2qyQzTPfLHuT5pkAACCqonZah9FoVHZ2trZs2aJ169bJ5/PprLPO0siRI/XQQw/p3//+t6666ioZjUbl5eUpKytLNTU1evrpp3XWWWdFq0wAADq9KUOnqOiqIm26bZNumXiLeib0DDrO3zxzxB9H6JG1j2jfkX1RrhQAAHQVEek50RKFhYXKz89XcXGxMjMzlZWVpeHDh8eypA6FnhMAgLbafWC3nlr3lJ5Y94S+OfhNyHH9evRTliVLt599uwYnDY5ihQAAdHI7S6W3x9Z/7LxPpcFpsamnCZF4HxrzcAJtQzgBAAiXQ8cOacn6JXp47cPaXLk55Lhucd3qmmeec7fGDxwfxQoBAOikCCeit60DAAC0b7269VLWxKxA88xzhp4TdNyx2mN6fv3zmvD0BF38t4u10rOS5pkAAKBNEmJdAAAAaF/i4+J15ZgrdeWYK/X+jvf10PsP6eXPXpZPDQOIN7a8oTe2vKGzTjlLd59zt+xj7eoW3y0GVde3aO0iLVq7KOzzzpsyT/OmzAv7vAAAdHWEEwAAIKRzhp6jZbOXafOezVq0dpH+sv4vOnz8cINxH371oX607Eeav3K+7jz7Tv3U/FP17dE3BhXXqT5SrZ37dkZkXgAAEH6EEwAAoEkj+4/U05c+rQfOf6DR5pnb927XvLfmacHbC3TzxJt1+9m369S+p0a93qQeSRrcN3TTzlpfrSr2V9R7bFCfQYozNL7jNalHUljqAwAA9dEQs4OjISYAIBYOHjsYaJ65pXJLyHHd4rrpR2f8SD+f8vN21Txz94HdGvjQwHqP7bp7lwYkDohRRQCATm3RorqPUHodkxbsqv/YfQOlQ01slZw3r+4jyiLxPpSVEwAAoMV6d+utmyferDnmOXrl81f0h/f/oLXlaxuMO1Z7TH/56C/6y0d/0cUjLtbd59yt84edL4PBEIOqAQCIkepqaWcj2w2D7YT8epe0rxnzdhKEEwAAoNXi4+I1M22mZqbN1Ps73tcf3v+DVny2ImjzzNe3vK7Xt7wu8yCz7p5yt+zj7EqI41sRAEAXkJQkDQ693VCJNZK+qv/YoFOkpPim5+0k2NbRwbGtAwDQ3mzas0mL1i7S8+ufD9o80++0fqfpru/epZ+c9ZOoN89kWwcAoF05vFtaVv/fJc3aJfVsn/8uReJ9aONdnwAAAFpoVP9ReubSZ/TFnV/ovvPuU/9e/YOO2753u+568y6d9uhpmu+ar4p9FUHHAQCAzo9wAgAARMTAxIG6f9r92n7Xdj11yVNKTU4NOs572KsH33tQpz96um5acZM27toY5UoBAECsEU4AAICI6t2tt26ZdIs+/9nnKrqqSN8d8t2g447VHtNzHz2n8U+P1w/+/gOt2rpK7D4FAKBrIJwAAABRER8Xr1lps7T2J2v17o3v6soxV8qg4Kd2/GvzvzR9yXRNWjxJ/9jwDx2vPR7lagEAQDQRTgAAgKj73mnf0/LZy1V6a6myLFnqEd8j6LiSihJdU3SNRjw+Qo/95zHtP7o/ypUCAIBoIJwAAAAxM/qk0Xrm0me0/a7tunfqvSGbZ36x9wvd+eadGvrIUP1y5S9pngkAQCdDOAEAAGJuYOJALTh/gbbftV1PXvJko80zF767UMMeG6afrPiJPt39adhq2HdkX9jmAgAALUM4AQAA2o3e3Xpr7qS5+vxnn6vQXqizB58ddNzRmqN69qNnNe6pcbr075dq9bbVzWqe6fP5tGrrKt204qYG10Y9MUr2AjuNOAEAiAHCCQAA0O7Ex8UrfWy61v5krd658R1dMfqKkM0zX9v8ms5//nxN/tNkLd2wNGTzTHeFWxOenqDpS6br1c2vNrhe46tR4aeFmr5kuiY8PUHuCndYXxMAAAiNcAIAALRbBoNB3z/t+3r56pdVemupMs2ZIZtnFn9ZrKuLrtbIP47U4/99vF7zTGeZU1Ofm6qNuzc263k37t6oqc9NlbPMGZbXAQAAGkc4AQAAOoTRJ41W3mV5+uLOL/Sbqb9RSq+UoOO2ebfpjjfu0GmPnKZfrfyV3ip7SzOXztSBYwda9HwHjh3QzKUzWUEBAEAUEE4AAIAO5eQ+J+uB8x/Q9ju364mLn5Ap2RR0XNXhKv3+3d/ror9e1OJgwu/AsQO6/uXr6UEBAECEEU4AAIAOKbF7om6dfKs2/WyTCuwFmjx4ctBxPrUtWNiwa4Pe/uLtNs0BAAAaRzgBAAA6tPi4eGWMzdB/fvIfrblhjS4ffXnYn+OpdU+FfU4AAPA/hBMAAKBTMBgMOvf0c7Xi6hUqvbVU1595fdjmXla6TNVHqsM2HwAAqI9wAgAAdDpjThqj7O9lh22+Gl+NdlbvDNt8AACgPsIJAADQKZ14lGg47Du6L6zzAQCA/yGcAAAAnVKf7n3COt/Wqq2c2gEAQIQQTgAAgE5pSNIQxRviwzbf1UVX67RHT9Mdr9+hNV+sUU1tTdjmBgCgqyOcAAAAnVJSjyTNTJsZ1jnLq8v1+AeP67y/nKfBiwbr5ldvlrPMqWM1x8L6PAAAdDWEEwAAoNOaO3FuxOb++sDXyivJ0wV/vUAnP3Sybnj5Bv3z83/q8PHDEXtOAAA6K8IJAADQaU0bNk3jBoxr0xwDeg/QqJRRjY6pOlyl59c/r8v/cbkG/mGgrim6RoWfFurA0QNtem4AALoKwgkAANBpGQwGLZm5RIndElt1f2K3RL1x7Rv67GefacMtG7Rg2gKdcfIZjd6z7+g+/WPDP2QvsGvAHwZo1tJZ+uvHf9Xew3tbVQMAAF2BwUfb6Q6tvLxcQ4cOlSTt2LFDQ4YMiXFFAAC0P84yp2YunakDx5q/kiGxW6KWz14uW6qtwbUtlVu0rHSZikqL9MHOD5o1X7e4brKarEpPS9cVY67QSb1PanYtAIBO7vBuadnA+o/N2iX1HBCbepoQifehhBMdHOEEAADN465w67rl12nj7o1Njh0/cLyev/J5mQeZmxy7Y++OQFDx7vZ35VPT31rFG+J13rDzlJ6WrivHXKlT+57arNcAAOikCCcIJzo6wgkAAJrP5/Pp7S/e1qK1i/TPTf+sdy0hLkEzx8zU3Elzdd7p58lgMLR4/q/2f6UVn61QUWmR/r3136rxNX3cqEEGTRk6Relp6ZqVNkvDjMNa/LwAgA6OcIJwoqMjnAAAoOV2H9itgQ/V/yaw7LYymVJMYXuOykOVeuXzV1RUWqS3yt7S0ZqjzbrPMsii9LR0pY9N16j+jTfiBAB0EoQTSmjzDAAAAJ1A3x59wzpfSq8U3fCdG3TDd25Q9ZFqvbbpNS37bJn+tflfOnjsYMj7SipKVFJRol/++5caN2BcIKiYMHBCq1ZzAADQERBOAAAARFhSjyRdM+EaXTPhGh08dlBvbnlTRaVF+uemf6r6SHXI+zbu3qiNuzfqgTUPaETKiLqgIi1dE0+dSFABAB1J6SLps0Whr/tqGz72rzMlQxMHbI6ZJ6XNa1tt7QThBAAAQBT17tZbM9NmambaTB05fkQrt65U0adFWvH5Cu05tCfkfVsqtyjnvRzlvJejoUlDNSttltLT0nXO0HMUHxcfxVcAAGixY9XSoZ0tu+dwRfPm7SQIJwAAAGKkR0IPXTLyEl0y8hLl1eZpzRdrVPRpkZZ/tlwV+0N/U7qjeoce++9jeuy/j+nkxJM1c8xMpY9N13mnn6du8d2i+AoAAM3SLUnqNTgy83YSNMTs4GiICQBAywVriLnr7l0akNg+Go/V+mq1dsdaFZUWaVnpMn2x94tm3ZfSK0WXj75c6Wnpspls6pHQI8KVAgC6Ik7rQAOEEwAAtFx7DydO5PP55K5wq6i0SEWlRdq0Z1Oz7uvbva8uHXWp0tPSddGIi5TYPTHClQIAugpO6wAAAOhiDAaDLKdaZDnVot9N/5027t6ook/rgopPdn0S8r59R/fpxQ0v6sUNL6pXQi9dNOIipael69JRl6pfz35RfAUAADSNcAIAAKCDMBgMGj9wvMYPHK/7pt2nzXs2a1npMhWVFmndl+tC3nfo+CEt/2y5ln+2XN3ju8tqsio9LV1XjL5C/Xv3j+IrAAAgOLZ1dHBs6wAAoOU60raO5tq+d3sgqHhv+3vyqelv8eIN8Tpv2HlKT0vXzDEzNajvoChUCgDo6Og5gQYIJwAAaLnOGE6c6Kv9X+nlz15WUWmRVm1dpRpfTZP3GGTQOUPPUXpaumalzdLpxtOjUCkAoCMinEADhBMAALRcZw8nTrTn4B698vkrKiotktPj1NGao826zzLIovS0dKWPTdeo/qMiXCUAoCMhnEADhBMAADS0aO0iLVq7KOT1Wl+tKvZX1HtsUJ9BijPENTrvvCnzNG/KvLDUGAvVR6r16qZXtax0mf61+V86dPxQs+4bP3B8XVCRlq7xA8fLYDBEuFIAQHvGaR0AAADNUH2kWjv37WzRPd8OK0LN25El9UjSDyf8UD+c8EMdPHZQb2x5Q0WlRfrn5//UvqP7Qt63YdcGbdi1QQveXqCRKSM1K22W0tPSNfHUiQQVAICwIJwAAACdTlKPJA3uOzgi83YWvbv11qy0WZqVNktHjh+Ry+NSUWmRVny+QpWHKkPet7lys3Ley1HOezk6rd9pmjVmltLHpuucoec0ufIEAIBQuuy2jvz8fBUUFMhkMqmysu4f4Pnz58tsNrd5bo/Ho5ycHBUXFyslJUWSZLfblZmZ2ea5v41tHQAAIJyO1x7X29veVlFpkZZ/tlxf7f+qWfed0ucUzRwzU7PSZmnasGlKiONnYADQWdFzIkzsdrs8Ho9Wrlwpo9EoqS5QsFgsysnJaVOIkJ+fL4fDocWLFysjIyPwuMPhUGpqatgDCsIJAAAQKTW1NVpbvlZFnxZp2WfLtH3v9mbdl9IrRVeMvkLpaemymqzqkdAjwpUCAKKJcCIM8vPzlZWVpaqqqkAw4VdYWCi73a6SkpJWraDwz+10OmW1WgOPu1wu2e12mUwmlZSUtPUl1EM4AQAAosHn86mkokRFnxapqLRImys3N+u+pB5JunTUpUpPS9dFIy5S7269I1wpACDSCCfCIDk5WRMnTpTT6Qx63WAwyGq1hrweisfjUWpqqjIyMlRQUFDvWm5urhwOh0wmk8rKylpdezCEEwAAINp8Pp827NqgotK6oGLDrg3Nuq9XQi9dPPJipael69JRl3aqHh4A0JVwWkcbud1ueb3eRldFmM1muVwueb3eBisrGpOVlSWprm/Ft2VnZ8toNNZbTQEAANBRGQwGTTh5giacPEH3T7tfm/Zs0rLSZSoqLVLxl8Uh7zt0/JCWlS7TstJl6h7fXTaTTelp6bp89OXq37t/FF8BAKC96VItlZcuXSpJ6t8/9D9+/gaWLper2fN6PJ7A+FDBR2ZmpkwmU7PnBAAA6ChG9R+lX3z/F1o3Z5223bFNiy5YpO8N/Z4MCn3M6NGao3pt82u66ZWbdPJDJ8u6xKqn1z3d7AacAIDOpUuFE/4AobEVEf5r69ata/a8hYWFkv4XTHg8HjkcDjkcjhaFHAAAAB3d6cbTddeUu/TuTe9q57ydeuqSpzRj+AzFG+JD3lPjq9HKrSs1919zderDp+rc587Vo/95tNkNOAEAHV+X2tbh9Xol/W91RDD+a/6xzeEPMlJSUuRyueR0OpWVlaWUlBTl5+fLbrdr5cqVrWqyWV5e3uj1ioqKFs8JAAAQDYP6DtItk27RLZNu0TcHv9Ern7+iotIiOcucOlZ7LOg9Pvn07vZ39e72d3XXm3dp4qkTlZ6WrvS0dI3sPzLKrwAAEC1dKpyorKyMyFiPxxP4tdPpVE5OTuD/s7OztWfPHlkslladAuJvMgIAANCRndT7JN101k266aybtPfwXr266VUt+2yZXt/8ug4dPxTyvuIvi1X8ZbHmr5yvCQMn1AUVY9M1bsA4GQyht40AADqWLrWtI1L8qyxcLlegMeaJ/I/NmTMnmmUBAAC0S/169tOPzviRiq4q0u57dqvQXqhrxl+jvt37NnrfJ7s+0f1v368JT0/QmCfHaL5rvoq/LFYXO3wOADqlLrVyIlL8fSpMJlPQppf+x9xut9xud4tWT+zYsaPR6xUVFZo8eXLziwUAAGhHErsnKn1s3WqIw8cPy+Vxqai0SCs+W6Gqw1Uh79u0Z5MefO9BPfjegzq93+malTZL6WnpmjJ0iuIM/PwNADqaLhVOpKSkyOv1NmvLRmN9KUKNbew0DqPRKK/Xq+Li4haFE+E4LxYAAKAj6JnQU5eOulSXjrpUxy49pre/eFtFnxZp+WfL9fWBr0Pe98XeL/TIfx7RI/95RIP6DNLMMTM1K22Wzht2nhLiutS3uwDQYXWpWLmxUzr8/MFFc8b6NeeIUH+AUVJS0ux5AQAAuqpu8d1kNVn19KVPa+e8nVpzwxrdcfYdGprUeD+uiv0Veqr4KVlfsOqUh07RT1b8RP/a/C8dOX4kSpUDAFqjS4UTEydOlCSVlZWFHONvbjlp0qRmz2uxWJoc4w89UlNTmz0vAAAApPi4eJ17+rl69KJH9cWdX+iDn34gx/ccGpEyotH79hzao2c/elY/+PsPNPChgbp22bVaVrpMB48djFLlAIDm6lLhhM1mk9T4MaH+axkZGc2e12q1SpKKi4ubHNua40QBAABQx2AwaNLgSXrQ+qA2/WyT1t+8XvdOvVfjBoxr9L7qI9X62yd/U/pL6RrwhwHKeClDL37yoqqPVEepcgBAYwy+Ltbe2GAwyGQyBV094fV6lZycLLPZ3OLtFxaLRW63W1VVVQ22hHg8HqWmpspoNKqqKnRjp9YoLy8PHDe6Y8cOelQAAIAu6/NvPtey0mUqKi1SSUXzvpfrHt9dF6ReoPS0dF0++nKl9Gp+3zEA6Koi8T60S62ckKScnBx5PJ7A9o0TvfTSS5KkxYsXN7jm8XhksViCHhXqn/fEOU5UWFgYcl4AAACEx+iTRmv+ufNVnFmsrXds1cMXPKxzhp7T6D1Ha47q1U2v6sYVN2rgHwbK9oJNzxQ/o6/2fxWlqgEAUhdcOSFJdrtdbrdbJSUlgVUObrdbM2bM0Pz585Wdnd3gHofDodzcXEl1TS2Dbc/Izc2Vw+GQ0+kMbPVwuVyy2WzKzs4OBBjhxMoJAACAxn2570u9/NnLKiot0uptq1Xrq23yHoMM+v5p39estFmalTZLp/U7LQqVAkDHEIn3oV0ynJCk/Px8FRQUyGQyqbKyUl6vVw6HIxAqfJvb7ZbdbpfJZJLT6Qw5r8vlUk5OTqABpslkUlZWVsh524pwAgAAoPm+OfiNVny2QkWlRXJ5XDpWe6xZ9006dZLS09KVPja9yUacANDZEU6gAcIJAACA1vEe9urVTa9qWekyvb7ldR0+frhZ951x8hlKT0vXrLRZGjdgnAwGQ4QrjZ1Faxdp0dpFYZ933pR5mjdlXtjnBRAdkXgfmtDmGQAAAIAOyNjTqGvPuFbXnnGtDhw9oNe3vK6i0iK9uulV7T+6P+R9H3/9sT7++mPdt/o+jeo/qm5FRVq6zIPMnS6oqD5SrZ37dkZkXgA4EeEEAAAAurzE7onKGJuhjLEZOnz8sJxlTi37bJlWfLZCVYdDn7a2ac8mLXx3oRa+u1DDjMM0a0xdj4opQ6coztDxe88n9UjS4L6DQ16v9dWqYn9FvccG9RnU5GtP6pEUlvoAdB5s6+jg2NYBAAAQOcdqjmn1ttUqKi3S8s+Wa9eBXc26b1CfQZo5ZqbSx6Zr6ulTlRDXOX8muPvAbg18aGC9x3bdvUsDEgfEqCIA0UDPCTRAOAEAABAdNbU1em/Heyr6tEjLPlum8uryZt13Uu+TdMXoK5Selq4ZphnqHt89wpVGD+EE0DURTqABwgkAAIDoq/XVat3OdVpWukxFpUUqqypr1n1JPZJ02ajLlJ6WrgtHXKje3XpHuNLIIpwAuiYaYgIAAADtQJwhTmcPOVtnDzlbD1of1Mdff6yi0iIVlRbp092fhryv+ki1/vbJ3/S3T/6m3t1665KRlyg9LV0/GPkD9e3RN4qvAADaF8IJAAAAoA0MBoPOPOVMnXnKmXrg/Af02TefBbZ+uCvcIe87eOygCj8tVOGnheoR30O2VJvS09J1+ejLldIrJYqvAABij3ACAAAACKMxJ43Rr6b+Sr+a+ittrdoa2PqxtnxtyHuO1BzRq5te1aubXlVCXILOH3a+0tPSdeWYK3Vyn5OjWD0AxAY9Jzo4ek4AAAB0DDurd2r5Z8tVVFqkNV+sUa2vtsl7DDLo3NPPDRxROrTf0ChU2nz0nAC6JhpiogHCCQAAgI5n94HdWvH5ChWVFmmlZ6WO1R5r1n2TB09Welq60tPSlZqSGuEqm0Y4AXRNhBNogHACAACgY/Me9uqfn/9TRaVFerPsTR0+frhZ95158pmalTZL6WnpGjtgrAwGQ4QrbYhwAuiaOK0DAAAA6GSMPY368Zk/1o/P/LH2H92v1ze/rqLSIr22+TXtP7o/5H3rv16v9V+v132r79Po/qPrVlSMTddZp5wVk6ACANqClRMdHCsnAAAAOqfDxw/rrbK3VFRapFc+f0Xew95m3TfMOEzpaemalTZL3x3yXcUZ4iJWIysngK6JlRMAAABAF9EzoacuH325Lh99uY7WHNXqbatV9GmRln+2XLsP7g553zbvNj289mE9vPZhndr3VM0cM1Ppaek69/RzlRDHt/8A2qfIxagAAAAAwqJ7fHddkHqB8i7LU8XPK7T6+tW6bfJtGtx3cKP3fbnvSz257klNXzJdgx4epDmvzNEbW97Q0ZqjEat135F9EZsbQOfFto4Ojm0dAAAAXVetr1Yf7PxAy0qXqai0SJ4qT7Pu69ejny4bfZnS09J1YeqF6tWtV4ue1+fzafW21Vq0dpFe3fxqvWvxhnjNTJupuRPnatqwafS/ADohTutAA4QTAAAAkOoCg/Vfr1fRp0UqKi1S6TelzbovsVuiLhl5idLT0nXJyEvUt0ffRse7K9y6bvl12rh7Y5NzjxswTktmLpF5kLlZtQDoGAgn0ADhBAAAAIIp3V2qotIiLStdpg+/+rBZ9/SI76ELUi9Qelq6Lh99uZJ7Jde77ixzaubSmTpw7ECz60jslqjls5fLlmprUf0A2i/CCTRAOAEAAICmeKo8ga0f/yn/T7PuSYhL0PTh05Welq4rx1yp8upyTX1uaouCCb/Ebolac+MaVlAAnQThBBognAAAAEBLlFeXa3npchWVFumd7e+o1lfb5D0GGdSrWy8dPHaw1c87fuB4fXzzx/SgADqBSLwP5bQOAAAAoAsZkjREt519m1bfsFoVP69Q/qX5ujD1wkaPGfXJ16ZgQpI27Nqgt794u01zAOi8CCcAAACALmpg4kDNsczRG9e+oV1379LzVz6vy0dfrh7xPSLyfE+teyoi8wLo+AgnAAAAACi5V7KuO/M6rbh6hb7J/kZLM5bqqnFXqXdC77A9x7LSZao+Uh22+QB0HoQTAAAAAOrp072Prhp3lZZmLNU7N74TtnlrfDXaWb0zbPMB6DwIJwAAAACEdNx3PKzz7Tu6L6zzAegcCCcAAAAAhNSne5+wzte3e9+wzgegcyCcAAAAABDSkKQhijfEh2Uugww6dPxQWOYC0LkQTgAAAAAIKalHkmamzQzLXD75NGnxJN3w8g0qqywLy5wAOgfCCQAAAACNmjtxbtjmqvXV6vn1z2v0E6P1kxU/0daqrWGbG0DHRTgBAAAAoFHThk3TuAHjwjpnja9Gz370rEY9MUpZ/8zS9r3bwzo/gI6FcAIAAABAowwGg5bMXKLEbomtuj+xW6Ju/M6NQe8/Xntc+e58jXh8hG597VaVV5e3tVwAHRDhBAAAAIAmmQeZtXz28hYHFIndErV89nI9e8Wz2nrHVt1zzj3qldCrwbhjtcf0VPFTGvH4CN3++u2q2FcRrtIBdACEEwAAAACaxZZq05ob1zR7i8f4geO15sY1sqXaJEkDEgco15arrXds1V3fvUs9E3o2uOdIzRH98YM/yvS4SfPenKev938d1tcAoH0inAAAAADQbOZBZn1yyydadf0qXTbqsgbXE+ISZB9r16rrV+njmz+WeZC5wZiT+5ysRRcuUtntZbpt8m3qHt+9wZjDxw/rkf88ouGPDVe2M1u7D+yOyOsB0D4YfD6fL9ZFoPXKy8s1dOhQSdKOHTs0ZMiQGFcEAACArmL3gd0a+NDAeo+V3VYmU4qpRfOUV5dr4TsLtdi9WMdqjwUdk9gtUbeffbt+PuXn6t+7f6trBtB2kXgfysoJAAAAAGHTt0ffFt8zJGmInvzBk9p822ZlmjOVEJfQYMyBYwe08N2FGv7YcN276l5VHaoKR7kA2gnCCQAAAADtwunG05V3WZ42/WyTbvrOTYo3xDcYs+/oPv3fmv/T8MeGa8HqBdp7eG8MKgUQboQTAAAAANqV4cnD9ecr/qzPfvaZrjvzOsUZGr5t2Xtkr+5/+34Ne2yYfrfmd9p3ZF8MKgUQLoQTAAAAANqlESkj9PyVz+vTuZ/qRxN+JIMMDcZ4D3v161W/1vDHhivn3RztP7o/BpUCaCvCCQAAAADt2uiTRuuvs/6qDXM3aPa42UFDij2H9ugXK38h02MmPfz+wzp47GAMKgXQWoQTAAAAADqEsQPG6h8Z/9D6m9crPS096JjdB3frbufdMj1m0qP/eVSHjh2KcpUAWoNwAgAAAECHMuHkCSq8qlAfZn2oK0ZfEXTM1we+1l1v3qXUx1P1xAdP6PDxw1GuEkBLEE4AAAAA6JC+c8p39PLVL6t4TrEuHXVp0DEV+yt02+u3aeQfR+qZ4md0tOZolKsE0ByEEwAAAAA6NMupFv3zmn/qvz/9ry4acVHQMeXV5brltVs08o8j9Sf3n3Ss5liUqwTQGMIJAAAAAJ3C5MGT9fqPXtd7N70nq8kadMz2vds1559zNPqJ0Xruw+d0vPZ4lKsEEAzhBAAAAIBO5Zyh58j5Y6fW3LBG04ZNCzpmq3erbnrlJqU9maYX1r+gmtqa6BYJoJ6EWBeA2Dt69Kj279+vAwcO6OjRo6qtrY11SQAiLD4+Xj179lRSUpISExNlMDQ8kg0AgI7u3NPP1arrV2nV1lW6d/W9enf7uw3GbKncoutevk6/e+d3uu+8+3TVuKsUHxcfg2qBrs3g8/l8sS4CrVdeXq6hQ4dKknbs2KEhQ4Y0+16fz6dvvvlG33zzTaTKA9AB9OrVS6eddpri4lhMBwCob9HaRVq0dlHI67W+WlXsr6j32KA+gxRnaPzflHlT5mnelHlhqbG5fD6fVm5dqXtX3au15WtDjhs7YKzuP+9+pY9Nb/J1AF1VW96HhsLKiS6soqJCe/furfeYwWBQfDxJMdDZ1dTUyJ9NHzp0SNu3b9fpp5/OCgoAQD3VR6q1c9/OFt3z7bAi1LzRZjAYZDVZNWP4DL1Z9qbuXXWv1n25rsG4T3d/qqsKr9KEgRN0/7T7deWYKwkpgCggnOiiDh8+XC+Y6N+/v5KSktSjRw/enABdQG1trfbv36+vvvpKNTU1OnTokA4cOKA+ffrEujQAQDuS1CNJg/sOjsi8sWIwGHTRiIt0YeqFem3za7pv9X1yV7gbjPtk1ydKfyld3znlO1owbYEuG3UZ3ycDEcS2jg6utctpvvrqK1VVVUmSBg4cqP79+0esRgDtV3V1tXburPuJmNFo1KBBg2JcEQAA0eXz+fTK56/o3tX36uOvPw45buKpE7Vg2gJdPOJiQgp0eZHY1sH6pC7q4MGDgV8bjcbYFQIgpvr06RP4BuvQoUMxrgYAgOgzGAy6YswV+jDrQxXaCzVuwLig44q/LNYP/v4DTfnzFL255U3xM14gvAgnuqiamrqjkhISEugxAXRhcXFxgb8D/H8vAADQFcUZ4pQ+Nl0f3/Kx/pH+D405aUzQcf/d+V9d9LeL9P3nvq+VnpWEFECY0HMC0bNoUd1HuM2bV/cBAAAAtFGcIU6zx89WxtgMLd24VPevvl+bKzc3GPf+jvdlfcGqqadP1QPTHtB5w86LQbVA50E4geiprpZ2tqzbc7PnBQAAAMIoPi5eP5zwQ1017ir9/ZO/a8HbC+Sp8jQYt+aLNZr2/DRNHz5dD0x7QN877XvRLxboBAgnED1JSdLgRro919ZKFd86emrQICmuid1HSbHr9gwAAIDOLSEuQdedeZ2uGX+NXvj4Bf3fmv/TNu+2BuP+vfXf+vfWf+uC1Au0YNoCfXfId6NfLNCBcVpHB9faLqmbN2/W8ePHlZCQoJEjR0ayxObbvVsaOLD+Y7t2SQMGxKYeoItol38fAADQTh2tOaq/fPQX/XbNb7WjekfIcRePuFgLpi3QpMGTolgdEB2c1gEAAAAAMdQ9vrsyLZnafNtmPXnJkxrcN/jK4Ne3vK7Jf5qsy1+8XB9WfBjlKoGOh3AC7du+fbGuAAAAAGigR0IPzZ00V1tu36LHL3pcp/Q5Jei4f276p8z5Zs1aOksff/1xlKsEOg7CCcSezyetWiXddFPDa6NGSXZ73fUOuAMpPz9fBoNBBoOhxfd6vd5W3xsOFotFBoNBLpcrJs8PAADQEfRM6Knbzr5Nnts9WnTBIg1MHBh03PLPluvMZ87UVQVXaeOujVGuEmj/CCcQW263NGGCNH269OqrDa/X1EiFhXXXJ0yoGw8AAAC0M7269dJdU+6S53aPcq25Oqn3SUHHFXxaoAlPT9APi36oz775LMpVAu0X4QRix+mUpk6VNjYzOd64sW680xnZugAAAIBWSuyeqHu+d4+23rFVC2csVEqvlAZjfPLpxQ0vatxT4/Tj5T/W5j2bY1Ap0L4QTiA23G5p5kzpwIGW3XfgQN19rKAAAABAO9anex/94vu/0NY7tur/zv8/GXsaG4yp9dXqrx//VWlPpunGFTfKU+WJfqFAO0E4gejz+aTrrmt5MOF34IB0/fUdsgcFAAAAupakHkn69dRfa+sdW3XfefcpqUdSgzE1vhr95aO/aPQTozXnlTna5t0W/UKBGCOcQPStXt38rRyhbNggvf12WMoBAAAAIs3Y06j7p92vrXds1a/O/ZX6dO/TYMzx2uP604d/0qg/jtItr96iHXt3xKBSIDYSYl0AOpDdu1t/b58+Uq9edb9+6qnw1PPII9KkSVJiYvDrlZV1DTWbY8CA8NQEAAAANCKlV4p+O/23uvO7d+rh9x/W4x88roPHDtYbc6z2mJ4peUbPfvSs5pjnaP7352tw0uAYVQxEBysn0HwDB7b+49ln6+aorpaWLw9PPa+8Iv32t6Gvn3tu8+uLIY/HI7vdruTkZCUnJ8tms6mwsLBZ9zocjsCRnxaLRQ6HI+TY/Px82Ww2paamBsbn5uaG62UAAACgBU7qfZIWWhdq6x1bdfeUu9UroVeDMUdrjurJdU8q9fFU3fnGnfpq/1cxqBSIDsIJRFd5efNXMzRHdXX45ooBl8ul1NRUFRYWyuv1ymQyqbKyUna7XTNmzAh5n9vtVmpqqnJzc+XxeGS1WuX1epWbm6vU1FR5vd564y0Wi7KyslRcXCyj0aiMjAx5PJ5AuAEAAIDYGJg4UH+44A/y3OHRnWffqR7xPRqMOVJzRI/99zGZHjPp7rfu1q4Du2JQKRBZhBOIrv37wzvf0aPhnS/K7Ha7JMlqtaqqqkolJSUqKSlRVVVVk/d5PB7l5eWpqqpKTqdTZWVlKigokMfj0Zw5c+qNt1qtKigoCDyH/9dms1lut5sVFAAAADF2Sp9T9MhFj8hzh0c/m/QzdY/v3mDMoeOH9PDahzX8seH6hesX+ubgNzGoFIgMwglEV5+GjX/apHvDv7Q7CofDEVgt4XQ6ZTQaA9eMRqMKCgpC3ufxeJSTk6PMzMx61zIyMpSdnR1YieGXk5OjjIyMBnPNnz9fkpSXl9f2FwQAAIA2O7XvqfrjJX/Ultu26GbLzeoW163BmIPHDirnvRwNf2y4frXyV6o8VBmDSoHwoiEmmm9XG5aP+UOJIUOk+PjwbO1ISJB+/evQ1995J7xbSMLM5XJJUqN9Ihq7r6ysLOi9Ho8nMC5YIHEis9lc7x4AAAC0D0P7DdXTlz6tX3z/F/rdO7/Tcx89p+O1x+uN2X90v37/7u/1xLondOfZd+quKXfJ2NMYm4KBNiKcQPOF40SLpCRp5kypmQ0fGzVzpjRoUOjrKSltf44I8gcCEydObNF9brdbUl2Dy8ZUVoZO0N1utyorKwNzAQAAoH063Xi68i/L1y++/wv9ds1vtWT9EtX46v8ArvpItR5Y84Ae++9j+vmUn+uO796hpB5JMaoYaJ0uu63Df3JBVlaW7Ha77HZ7m96oeTwe2Ww25efn1/sptMfjUWFhYaBHACTNndu+5ulg/Ns/fD5fox/f3vKRm5tb72QPm83W4lUbAAAAiA1TsknPXvGsSm8t1Y/P+LHiDA3fyu09slf3rr5Xwx4dpoXvLNS+I/tiUCnQOl0ynLDb7crLy1NBQUHgvzk5OZoxY0aTP40Oxev1yuVyKSsrK3BUo8FgUGpqqux2uyZNmiSTyRTmV9JBTZsmjRvXtjnGj5fOOy8s5cSK//dDcXFxi+7zr7RobpjmP9nD4XAoJSVFeXl5gaabZWVlLSsaAAAAMTWy/0gtmblEn879VD+c8EMZZGgwpupwlX7571/K9LhJue/l6sDRAzGoFGiZLhdO5Ofnq7CwUCtXrqzXgNBkMmnx4sXKysoK61J3s9mskpISZWdnh23ODs9gkJYskRITW3d/YqL0/PN183RgVqtVUuhmlKFW2vhP+Gjuqgd/A82CggI5nU5lZmbKbDbX+/0PAACAjmX0SaP1t1l/04a5G3TVuKuCjvnm4DdyuBwyPW7SorWLdPDYwShXCTRflwsnHA6HrFZr0Ddm/uaBrV3qnpGRobKyMjmdTpWUlMjn86mkpCTQdBAnMJul5ctbHlAkJtbd1wk+p/6TMtxut7Kysupd83g8DR7zy8zMlNVqlcvlCgQVJ3K73bLZbIGQzf97fd26dQ3G5uTktOUlAAAAIMbGDhirpRlLtf7m9ZqVNivomF0Hdunnb/1cqY+n6vH/Pq7Dxw9HuUqgaV0qnHC73fJ6vY2GBWazWS6Xq94xjC1hMplktVoJJJrDZpPWrGn+Fo/x4+vG22yRrStKTjwuND8/X8nJybLZbLJYLEpNTW3091BBQYGsVqsKCwvr9ZBITk6WxWIJnOghKRBy5ObmymAwyGazyWazyWAw6KWXXorsiwQAAEBUnHHyGSq6qkjuTLcuH3150DFf7f9Kd7xxh1IfT9WTHzypI8ePRLlKILQuFU4sXbpUktS/f/+QY1L+/wkPJ765QwSZzdInn0irVkmXXdbwekKCZLfXXf/4406xYuJEGRkZKikpCaza8Xg8MplMKikp0eLFi2W1WgPbP05kNBrldDqVl5cnq9Uqj8ej4uJiTZw4UTk5OaqqqgqEG1arVQUFBYH/9/e4yMnJ0datW0M+BwAAADqeswadpRVXr9C6Oet0ychLgo75ct+X+tnrP9PIP45UXnGejtYcjXKVQEMGn8/ni3UR0WKxWOR2u5WXl9fgJAM/u92uwsJCZWdnt2jJu9vt1sKFCwM/CY+W8vJyDR06VJK0Y8cODRkypFn3bd68WcePH1dCQoJGjhwZyRKbb/duaeDA+o+VlUk0EgUiql3+fQAAAMLiv+X/1X2r79ObZW+GHHN6v9P1m6m/0XVnXqdu8d2iWB06qta+D21MQptn6ED8WzX8qyOC8V9r7bYOf/jhV1lZqdmzZwd+Mt5S5eXljV6vqKho1bwdRt++sa4AAAAA6LDOHnK23rj2Db23/T3dt/o+rdy6ssGYL/Z+oZ/+86f6/bu/171T79WPzviREuK61FtFtANd6ndcZWVlRMb6uVwuTZo0qV444fF4lJqaqszMzJCnMjTGn0YBAAAAQGt977TvyXWdS29ve1v3rr5Xa75Y02CMp8qjG1bcoN+98zvdd959unr81YqPi49BteiKulTPiUjyH0X67SNDTSaTsrOzlZ+fr/z8/BhV104sWiQNGRL648wzG95z5pmN3zNkSN28AAAAAJp03rDztPr61Vp53Up9b+j3go7ZXLlZ1y6/VuOfHq9/bPiHan21Ua4SXVGXWjkRSUajMeTWjdmzZys3N1cOhyNkr4tQduzY0ej1iooKTZ48uUVzxkx1tbRzZ8vuac62lerq1tUDAAAAdEEGg0HTh0/X+cPOl8vj0m9W/Ub/3fnfBuM+++YzXVN0jX675re6f9r9mpU2S3EGfr6NyOhS4URKSoq8Xm+ztmw01peipUz/v6Gj1+uV2+1u0TGj4Wgs0m4kJUmDB0dmXgAAAAAtYjAYZEu1yWqy6o0tb+je1feq+MviBuM27t4oe4FdZ5x8hhZMW6ArRl8hg8EQg4rRmXWpcMJoNDY5xh9cNGesn8fjUU5Ojmw2W5ONLz0eT4vCiU5l3ry6DwAAAADthsFg0MUjL9ZFIy7Sq5te1b2r79VHX33UYNzHX3+smUtn6qxTztKCaQt06ahLCSkQNl1qTc7EiRMlSWVlZSHHeDweSdKkSZOaPW9eXp7y8/Nlt9ubHGviWEwAAAAA7ZDBYNBloy+TO9OtZVct0xknnxF03IdffajL/3G5Jv9psl7f/Lp8Pl+UK0Vn1KXCCZvNJqnxY0L911py9Gf//v0lKWQ/CX/gIanrrpoAAAAA0CEYDAbNTJupD7M+VIG9QGMHjA06rvjLYl3y90t0zrPnyFnmJKRAm3SpcMIfOLhcrqDXvV5vq7ZdmM1mZWdnhzwq1P98LQk8AAAAACCW4gxxyhiboY9v/lgvpr+o0f1HBx33n/L/6IK/XqCpf5mqVVtXRblKdBZdKpyQpJycHHk8nnqrGfxeeuklSdLixYsbXPN4PLJYLMrKympwzWq1hpxTqtv2YTQag84LAAAAAO1ZfFy8rh5/tTbO3agXZr6gESkjgo57d/u7mr5kus5//nyt+WJNlKtER9flwons7GxlZGTIZrPV297hdrvlcDiUk5MTdOVEXl6e3G638vPz5Xa7G1zPyclRVlZWvVUZXq9XFotFlZWVWrlyZYuabAIAAABAexIfF69rz7hWpbeW6rkrntNw4/Cg41ZvW63z/nKerEusen/H+1GuEh2VwddFNwbl5+eroKBAJpNJlZWV8nq9cjgcslqtQce73W7Z7XaZTCY5nc6Q8zocDrndblVWViolJUVms1k5OTmRehkqLy/X0KFDJUk7duxo9tGjmzdv1vHjx5WQkKCRI0dGrD4A7R9/HwAAgNY4VnNMS9Yv0f+t+T99sfeLkOMuTL1QC6Yt0NlDzo5idYik1r4PbUyXDSc6C8IJAG3F3wcAAKAtjtYc1XMfPqffvvNblVeXhxz3g5E/0IJpC2Q51RLF6hAJkQgnEto8A9BMi9Yu0qK1i8I+77wp8zRvyrywzwsAAACgad3juytrYpZu+M4N+pP7T/r9u7/Xl/u+bDDutc2v6bXNr+mK0Vfo/mn36zunfCf6xaLdIpxA1FQfqdbOfTsjMi8AAACA2OqR0EO3Tr5VN511k/JL8rXw3YX6+sDXDcat+HyFVny+Qulp6brvvPs04eQJMagW7Q3hBKImqUeSBvcdHPJ6ra9WFfsr6j02qM8gxRka79ua1CMpLPUBAAAAaLte3Xrpju/eoTmWOXqm+Bk9+O6D2n1wd4NxRaVFKiot0lXjrtJ9592nsQPGxqBatBf0nOjgOlPPid0HdmvgQwPrPbbr7l0akDggRhUBXUN7/PsAAAB0HgeOHtCT655U7nu52nNoT9AxBhl0zYRrdO/UezX6pNFRrhAtFYmeE13uKFF0LPuO7It1CQAAAADaILF7orK/l62td2zV76b/Tsk9kxuM8cmnv3/yd419aqyuf/l6bancEoNKEUuEE4g5n8+nVVtX6aYVNzW4NuqJUbIX2LVq6yp1xEU+hYWFMhgMgY/CwsJGx7tcLhkMBqWmpkapwsjKzc1Vbm6u3G53TOeIpo5WLwAAQLT07dFXvzz3l9p6x1Y9MO0B9evRr8GYWl+tlqxfojFPjNFPVvxEW6u2xqBSxALhBGLKXeHWhKcnaPqS6Xp186sNrtf4alT4aaGmL5muCU9PkLuiY7/hczgcsS4hqhwOhxwOh1wuV0zniKaOVi8AAEC09evZT7857zfaduc23Tv1XvXt3rfBmBpfjZ796FmNemKUMv+ZqS+8X8SgUkQT4QRixlnm1NTnpmrj7o3NGr9x90ZNfW6qnGXOCFcWfiaTSUajUR6Pp8nVEwAAAEBXYOxp1ILzF2jbndv0y+//UondEhuMOV57XIvdizXyjyM197W5Kq8uj0GliAbCCcSEu8KtmUtn6sCxAy2678CxA5q5dGaHW0FhNBqVk5MjqWutnvD5fPL5fMrOzg563eFwKDc3t01zRFNHqxcAAKAjSOmVot/N+J223blN2edkq3e33g3GHKs9pqeLn1bq46m6/fXb9eW+L2NQKSKJcAJR5/P5dN3y61ocTPgdOHZA1798fYfrQZGZmcnqiRN4vV7l5uYqLy8v1qU0S0erFwAAoKM5qfdJyrHlyHO7R/O+O089E3o2GHO05qj++MEflfp4qu564y59tf+rGFSKSCCcQNSt3ra62Vs5Qtmwa4Pe/uLtMFUUPV1x9QQAAADQEif3OVkPX/iwPLd7dMfZd6hHfI8GYw4fP6xH//uoTI+ZdM9b92j3gd0xqBThlBDrAtC+1fpqtedg8LOIW+uR/zwSnnnWPqJxA8aFZS6//r37K84QucwuMzNTDodDHo9H+fn5yszMjNhzAQAAAB3ZoL6D9OhFj+qec+7RwncXarF7sY7WHK035tDxQ3po7UN6uvhp3Tb5Nt19zt3q37t/jCpGWxBOoFF7Du7RwIcGxrqMoF7Z9ErYa9t19y4NSBwQ1jm/LScnR1lZWcrJyWl1OJGfn6+8vDy53W6ZTCaZzWZlZWXJarW2aj673S63262CggIZjcbAaRNer1cmk0k5OTnKyMiQVHc86sKFC+V2u2U0GjVx4kTl5eXJZDI1mNdiscjtdsvpdAZqy83NrbdyxOPxyGAwBP7farXK6XQ2OseJvF6v5syZEzgdY+LEiYEAyOl0yuPxyOPxqKSkpF6N+fn5KigoCFw3m82aPXt2g14R4a7X/9wt+foF+/q43W55PJ4GXx8AAIDOZnDSYD1xyRPK/l62fv/O7/Xsh8/qWO2xemMOHDugB997UE+se0J3nn2n5k2Zp+ReyTGquKFFaxdp0dpFYZ933pR5mjdlXtjnjQXCCSDK2rJ6wuv1asaMGXK76xqCms1mSXWBQWFhoTIyMlRQUNDimvxv0IuLiwNvxE0mU+Bxu92ugoICrVu3Trm5uTKbzYHrLpdLFotFW7duldFobPK5zGazMjMzVVlZqcLCQhmNRl111VWB66mpqS2q2z/earXKaDSqsLBQLpcrEJyYTCalpKTI6/UG7vMHCEajUSaTSRkZGXK5XHI4HFq6dKlKSkoiUm9rv37+r4O/Rn/d/mv+rw8BBQAA6MxO63eanrn0Gf3i+7/Q79b8Ts999JxqfDX1xuw/ul+/fee3evyDxzXvu/N053fvVL+e/WJU8f9UH6nWzn07IzJvZ0E4AcSAf/WEw+FoUThhsVjk8XiUnZ0d6F8h1b3ptdvtKiwsVFZWVqubNvpXdJy4esBms8nlcslut8toNKqsrKzeG2OLxSKv16v8/PxmnVBhtVpltVoDjUFTUlJaXa/NZpMklZSUBN7oe71eDR8+XF6vN7DSIFgN8+fPb/Bm3h9a5ObmBl5LOOtt69fP4XCE/PosXLiQcAIAAHQJw4zDtPjyxfrF93+h377zWy1Zv0S1vtp6Y6qPVOv+t+/Xo/99VD+f8nPdfvbtSuqRFKOKpaQeSRrcd3DI67W+WlXsr6j32KA+g5rcch7L1xRuNMQEYiAzM1Mmkynwpr45cnNz5fF4lJGRUe+NrVR3VKnT6ZTJZFJ+fn5gi0NLmc3mBgHDic81f/78elsjTCZTYBVBWVlZq56ztU7cjuEPJqS6z4U/8An1eQi1DWL+/PmSFJETOcLx9Wvs6+NfjQEAANBVpKak6rkrnlPpraW69oxrg76R9x726jerfqPhjw3Xg+8+qP1H98eg0rrtF+XzykN+rL95fYN71t+8vtF7yueVd5otHRIrJ9CE/r37a9fdu8I2374j+zTqiVENll+1RkJcgj6/9XP17dE3DJXViWbznJycHNnt9mavnvC/Yfa/gQ7GvxqjoKCgVf0ngt1zYhgR7A29f1tDZWVli5+vLfzbNFJSUhpc69+/7uvo8XhaNKc/5Gjpfc0Rjq9fsMeas5UGAACgMxvVf5RemPmCfvn9X+qBNQ9o6Yal8slXb0zloUrNXzlfD699WI7vOTR30lz17tY7RhUjGMIJNCrOEBfWBpEDEgdoZtpMFX5a2Oa5Zo6ZKVNKwyaMHUVGRkagb0Nzek/43zCfuErg2/zXWrtyoqn+CcGCgFjxhybFxcUNrq1bt05S45+rE7ndblVWVkZ09UE4vn4t6W8BAADQ1aQNSNOL6S/q1+f+WgveXqCCTxv28vrm4De6x3mPHnr/ITm+59DNE29Wr269YlAtvo1tHYi6uRPnhmeeSeGZJ5b8S/JPPA0imBObOZ7462hrTz+l92/f8Hq9ysrKktfrldfrVW5urgoLC2U2mxtdPZKbmyuLxSKDwSCLxSKbzdbk16G12svXDwAAoCsYN3CcXrK/pPU3r9fMMTODjvn6wNea99Y8pT6eqj/+9486fPxwlKvEtxFOIOqmDZumcQPGtWmO8QPH67zTzwtTRbHjXz3hf1MditFoDAQDwVYK+H37FIjOzv+5yM/PV3JyspKTk+VwOGQ2m7Vy5cqg97jdbqWmpsrhcASaW5aUlKiqqipifTP4+gEAAETfGSefoWWzl6kks0SXjbos6JiK/RW6/Y3bNeLxEXp63dM6cvxIlKuEH+EEos5gMGjJzCVK7JbYqvsTuyXq+Sufl8FgCHNlseFfPbFw4cJGx/kbT4b66b7X6w3MMXv27DBWGFmt7VXh9XrldrtVUFCgkpIS5eTkKCcnR06nUyUlJSFXefiPcS0oKJDT6VRmZqbMZnOzV4W0tt7O+vUDAABo78yDzHrlmlf0wU8/0MUjLg46Zue+nZr7r7ka9cQoLS5ZrGM1x6JcJQgnEBPmQWYtn728xQFFYrdELZ+9XOZBnecnyyeunvj2KQ4nysvLk8lkktvtlt1ur7c9wOPxaMaMGfJ6vbJarR3iSEl//wqv11uvAWVhYcv6kTidzsApFtnZ2U02AvWHEP6+FCdq7PPf1no729cPAACgo5k0eJL+9aN/ae1P1uqC1AuCjtm+d7syX83U6CdG67kPn9Px2uNRrrLrIpxAzNhSbVpz45pmb/EYP3C81ty4RrZUW4Qriz7/m+KmGln6j5ssLCxUcnKyUlNTA/91u92yWq0qKGjY+Kc9MhqNgaaWqampSk1NlcFgkN1ub/b9ZrNZ+fn5MhgM9T6Sk5Nls9mCbpXJysqSVNdzwmAwyGazyWazyWAw6KWXXopYvVLn+voBAAB0VN8d8l29ee2beufGdzR9+PSgY7Z6t+qmV27SmCfGaMn6JYQUUUA4gZgyDzLrk1s+0arrVwXdB5YQlyD7WLtWXb9KH9/8cadaMXGijIyMZvUZMJlMKisrU05OjqxWa2CLQUZGRmCbQntqWtmUgoKCwBt+f4NLp9PZrHs9Hk/I5pJer1cul0sOh0MWi6XeOH8A4P98+3tA5OTkaOvWrbJarSFXX7SlXqnzff0AAAA6su+f9n2tvG6lVl2/SlNPnxp0TFlVma5/+XqNe2qc/v7J31VTWxPlKrsOg8/n8zU9DO1VeXm5hg4dKknasWOHhgwZ0qz7Nm/erOPHjyshIUEjR46MZInNtvvAbg18aGC9x8puK+vQx4UiciwWi9xut3JycpSZmVnvTf2J4YTH4wm8+Udw7fHvAwAAgGjy+Xz699Z/697V9+r9He+HHJd2Uprun3a/MsZmKM4Qvp/1B3svtOvuXRqQOCBszxFOrX0f2hhWTqBd69ujb6xLQDvkb4ZpMpmUnZ3dYLWB0WhURkZGYFVDU9tlAAAA0LUZDAbNMM3Quze+qzd+9IbOHnx20HGl35RqduFsnfnMmVpWuky1vtooV9p5EU4A6LT8TSz9/wUAAAAaYzAYdOGIC7X2J2v12g9fk2WQJei4Dbs2KP2ldFnyLVrx2QqxIaHtEmJdALqORWsXadHaRSGvB0sdz3zmzCaXS82bMk/zpsxrc33oOPzNMN1ut7KyspSXl9dgjNfr1YwZMySJ0y8AAADQIgaDQZeMvEQXj7hY/9z0T9276l6t/3p9g3EfffWRrlx6pSyDLFowbYEuGXmJDAZDDCru+AgnEDXVR6q1c9/OFt1Tsb+iWfOi61m5cqVmzJih/Px85efny2q1BppVFhcXy+12S5IyMzMbPSIUAAAACMVgMOjy0Zfr0lGX6uXPXtZ9q+/Thl0bGowrqSjRpS9eqsmDJ+uBaQ/ogtQLCClaiHACUZPUI0mD+w6OyLzoeoxGo0pKSuRyuZSXlye32y2XyxU48jMzM1NZWVnNOgUFAAAAaEycIU6z0mbpyjFXqujTIt23+j6VflPaYNwHOz/QRX+7SOcMPUcLpi3QjOEzWh1S7Duyr902xIwETuvo4DrTaR0AYoO/DwAAAFqmprZGL218Sfe/fb827dkUcty5p52rB85/QNOGTQt63efzafW21Vq0dpFe3fxqvWvxhnjNTJupuRPnatqwae1qJQandQAAAAAAEGPxcfG6ZsI12jh3o5ZcuUSpyalBx72z/R2d//z5mv78dL27/d1619wVbk14eoKmL5neIJiQpBpfjQo/LdT0JdM14ekJcle4I/Ja2gvCCQAAAAAAWiEhLkE/PvPH+uxnn+nZy5/VMOOwoONWbVulc587Vxe8cIHW7lgrZ5lTU5+bqo27NzbreTbu3qipz02Vs8wZxurbF8IJAAAAAADaICEuQTeedaM2/WyTFl+2WKf1Oy3oOKfHqXOePUcX/+1iHTh2oEXPceDYAc1cOrPTrqAgnAAAAAAAIAy6xXfTT80/1ebbNuvpHzwd8kCAGl9Nq+Y/cOyArn/5enXG1pGEEwAAAAAAhFH3+O66eeLN2nL7Fv3x4j9qUJ9BYZt7w64NevuLt8M2X3tBOAEAAAAAQAT0TOipn03+mcpuL9MjFz6iHvE9wjLvU+ueCss87QnhBAAAAAAAEdSrWy/ddNZNOl57PCzzLStdpuoj1WGZq71IiHUB6EJKF0mfLWreWF+tZGhmdjZmnpQ2r/V1AQAAAECElVeXt7rXxLfV+Gq0s3qnkgYkhWW+9oBwAtFzrFo6tDMy8wIAAABAO7b/6P6wzrfv6L6wzhdrhBOInm5JUq/g3Wol1a2WOFxR/7Geg5peQdGt86SFAAAAADqnPt37hHW+vt37hnW+WCOcQPSkNbH94vBuadnA+o9NXS6ddHZk6wIAAACACBuSNETxhviwbO1IiEvQ4KRGfvDbAdEQE+3bJ/fHugIAAAAAaLOkHkmamTYzLHPNHDNTST061wpywgm0H1/9u+FjFW9IX74Z/VoAAAAAIMzmTpwbnnkmhWee9oRwAu1DzVHp418Hv+a+U6o9FtVywqWwsFAGgyHkR2pqqux2uwoLC5ucJzk5udkfqampza7PbrcrNTW1Xk02m00Oh0NutzscnwYAAAAAkqYNm6ZxA8a1aY7xA8frvNPPC1NF7Qc9J9A+bHpC2r8l+LXqz+quj7krujWFWWZmpoxGoyTJ6/WqsrJSHo9HhYWFKiwslNlsVkFBgUwmU4N7Kysr5fV6JanB9crKyhbX4na7NWfOnHrhg39ej8cjj8cjl8ulwsJClZWVtXh+AAAAAA0ZDAYtmblEU5+bqgPHDrT4/sRuiXr+yudlMBgiUF1sEU4g9g59LW1Y0PiYT+6Xhv1I6jmw8XHtWF5eXtDHPR6PsrKy5HK5ZLPZmgwD2hoW+J9Hqgsk8vLyZLVa641xu91auHBhm54HAAAAQEPmQWYtn71cM5fObFFAkdgtUctnL5d5kDmC1cUO2zoQe+t/KR2rbnzMseq6cZ2QyWSS0+mU2WyWx+ORw+GI2HN5PJ5AMGE2m1VSUtIgmPBfKygoUEFBQcRqAQAAALoqW6pNa25c0+wtHuMHjteaG9fIlmqLcGWxQziB2NpTLHmea97YsmelypLI1hND8+fPl6Qm+0+0hd1ul1QXiJSUlAS2mQAAAACILvMgsz655ROtun6VLht1WYPrCXEJso+1a9X1q/TxzR932hUTfmzrQOz4aqWS2yX5mnuDVHybZHtP6oR7rE7s+RAJLpcr0GMiJycnIs8BAAAAoPkMBkOgSebAh+pvYf/81s9lSmnYj66zIpxA43y10pE9kZl7+0vSN2tbds83a6XNT0un2SNTU4/+kiE2C4r8oUSkVjP4e16YTCZlZGRE5DkAAAAAhEffHn1jXUJUEU6gcUf2SMvaWRPK4lvrPiJh1i6p54DIzN0Ef6+Jq666KiLzu1wuSQraYwIAAAAAYolwAogx/2kdHo8ncHpGY/x9IxqzePHieiswvF5v4CjS1NTUtpQLAAAAAGFHOAFESVZWVr3/r6yslNvtDmzn8J+Q0ZTmNMzMycmpF05UVlYGfu3vbQEAAAAA7QXhBBAl+fn5DR7z93+YPXt2s/tA+HzNbSD6PykpKYFfR6rhJgAAAAC0FuEEGtejf10fhnA7tl9yTpEOf93ye3ueIl3wvpTQJ/x19egf/jn/v6qqqpgd3Xni85aVlcWkBgAAAAAIhXACjTPERaZBZM8B0ll/kNZe1/J7z/qD1Gd4+Gvq5KxWq1wuV6AxJgAAAAC0F7E5MxGQpGE/kk6a0rJ7TppSdx9azN/zwuPxBN1iAgAAAACxQjiB2DHESZbHJRmae4M08Y+SobnjcaKMjAyZzWZJ/zu2FAAAAADaA8IJxFb/iZLpxuaNTb1JSrFEtp5Ozn8aiNfrlcViCRwvGkpT1wEAAAAgHAgnEHtn/l7qltT4mG5JdePQJiaTSU6nU5LkdrtlsViC9qDwer1yOBxKTk7mdA8AAAAAEUdDTMRer5Ol8fdJH/489JgJ90s9B0atpPYsOTk56OPfXuXgdDpltVobjLNarSopKdGcOXPkdrtls9kk1QUXklRZWRmYy2w21zuGFAAAAAAigZUTaB9G/UzqMyL4taQxddchqS6ECPbREmazWSUlJXI6ncrIyJDJZJLH4wmskrBarXI6nSopKYnZ8acAAAAAug5WTqB9iO8unfFb6f2rG16zPCbFdYt+TWGQkZEhn8/X5nkyMzOVmZkZhorqs1qtQVdXAAAAAEA0EU6g/ThlesPHBl0kDbog+rUAAAAAQJgsWrtIi9YuCnm91lfb4LEznzlTcYbGNzvMmzJP86bMa3N97QHhBNq3CQtiXQEAAAAAtEn1kWrt3LezRfdU7K9o1rydBeEEoqd0kfRZ6LRQQdJCrblSaiIt1Jh5UlrnSAsBAAAAdD5JPZI0uO/giMzbWRBOIHqOVUuHWpYW6nDTaaGOdZ60EAAAAEDn05m2X0QK4QSip1uS1KuZaaGvtukVEyfOCwAAAADosAgnED1pbL8AAAAAADTUzB9NAwAAAAAARAbhBAAAAAAAiCnCCQAAAAAAEFNdNpzIz8+XzWZTVlaW7Ha77Ha73G53RJ7LYrFEbG4AAAAAADq6LtkQ0263y+PxaOXKlTIajZIkj8cji8WinJwcZWZmhu25HA4HwQQAAAAAAI3ocuFEfn6+CgsLVVVVFQgmJMlkMmnx4sWy2+2aOHGizGZzm5/L7XYrNze3zfNEQnx8vI4fP67jx4+rpqZG8fHxsS4JQAzU1taqpqZGkvh7AAAAADHT5bZ1OBwOWa3WesGEX0ZGRmBMuJ4rHCFHJPTu3Tvwa6/XG7tCAMTU/v375fP5JEm9evWKcTUAAADoqrpUOOF2u+X1ehsNDMxms1wuV5vfsDscDjkcDqWkpLRpnkg5MZzZtWuXdu3apcOHDwfepADo3Gpra1VdXa2vvvoq8Fjfvn1jWBEAAAC6si61rWPp0qWSpP79+4cc4w8TXC5XYCVFS3k8Hnm9XlmtVuXk5LRqjkjr2bOn+vXrp71790qS9uzZoz179shgMLC0G+gCampq6oWRvXr1UmJiYgwrAgAAQFfWpcIJl8slSUG3dPj5r61bt67V4URWVpacTmer7o2mQYMGqXv37tq9e3fgMZ/Pp+PHj8ewKgDR1qtXL5122mkyGAyxLgUAAABdVJcKJ/xbNRrbauG/1tptHbm5uWHrWSFJ5eXljV6vqKho9dwGg0EnnXSSkpKStH//fh04cEBHjx5VbW1tq+cE0DHEx8erV69e6tu3rxITEwkmAAAAEFNdKpyorKyMyFg/j8ejsrIyZWdnt/jeUIYOHRq2uULp3r27UlJS2m1/DAAAAABA59alwolIy8rKUkFBQazLAAAAAACgQyGcCBP/do7G+lm0xo4dOxq9XlFRocmTJ4f1OQEAAAAAiKYuFU6kpKTI6/U2a8tGS7Y4RGI7h9+QIUPCPicAAAAAAO1JlwonmrOqwR9ctGQFBNs5AAAAAABovS4VTkycOFFut1tlZWUhx3g8HknSpEmTmjWn2+1WcXGxLBZLo/PNmDEjsBqjsecHAAAAAKCr6VLhhM1mU35+fqPHhPqvZWRkNGtOs9msqqqqkNeTk5Pl9Xq1cuVKmc3mlpQLAAAAAECXEBfrAqLJHzi4XK6g171erzweDyECAAAAAABR1KXCCUnKycmRx+MJbLc40UsvvSRJWrx4cYNrHo9HFotFWVlZrXre5jThBAAAAACgK+py4UR2drYyMjJks9nqbe9wu91yOBzKyckJunIiLy9Pbrdb+fn5crvdzXour9cbeI5gYQgAAAAAAJAMPp/PF+siYiE/P18FBQUymUyqrKyU1+uVw+GQ1WoNOt7tdstut8tkMsnpdDY6d25urvLy8uqtlvB6vTKZTDIajSopKQnb69i2bZuGDx8uSfrggw80aNCgsM0NAAAAAMC3VVRUaPLkyZKkrVu3atiwYW2es8uGE53FunXrAr8pAAAAAACIpg8++KDZp102pstt6wAAAAAAAO0LKyc6uMOHD+uTTz6RJA0YMEAJCe3zdNgTl/2w/QRoPf4sAeHDnycgPPizBIRHR/qzdPz4ce3evVuSNGHCBPXs2bPNc7bPd7Jotp49e4ZlCU00DRo0SEOGDIl1GUCHx58lIHz48wSEB3+WgPDoCH+WwtFn4kRs6wAAAAAAADFFOAEAAAAAAGKKcAIAAAAAAMQU4QQAAAAAAIgpwgkAAAAAABBThBMAAAAAACCmCCcAAAAAAEBMGXw+ny/WRQAAAAAAgK6LlRMAAAAAACCmCCcAAAAAAEBMEU4AAAAAAICYIpwAAAAAAAAxRTgBAAAAAABiinACAAAAAADEFOEEAAAAAACIKcIJAAAAAAAQU4QTAAAAAAAgpggnEHFut1upqanyer2xLgXosHJzc2Wz2ZScnKzU1FTZ7Xa53e5YlwV0OF6vVw6HQzabLfBhsVhUWFgY69KATsNisfBvFNAMHo9HNptN+fn58ng89R4vLCyU3W6v93hnRziBiHG73crKypLFYulSf6iAcPJ6vbLZbDKZTHI6naqqqlJBQYHcbrcsFoscDkesSwQ6DI/Hozlz5igrK0tOpzPwMXv2bNntdlkslliXCHR4DoeDYAJoJq/XK5fLpaysLKWmpspgMMhgMAR+EDVp0iSZTKZYlxk1hBMIu9zcXFksFuXl5clut8toNMa6JKDDstvtcjgcysjICDxmNptVUlIio9Go3NxcfuILNJPdbpfX622wki87O1smk0lut5vAD2gDt9ut3NzcWJcBdHj+7/Wys7NjXUpUEU4g7LKzs1VSUqK8vDxZrdZYlwN0WP6fPAX7c2Q0GpWZmSlJmjNnTlTrAjoqt9stl8ulhQsXNrjm/8mUy+WKdllAp+FwOGQ2m2NdBtChZGRkqKysTE6nUyUlJfL5fCopKemSf5YIJwCgnXK5XHK5XLLZbEGv+x/3er1snQKaITMzU0ajUVlZWSHHpKSkRLEioPNwOBxyOBz8GQJawWQyyWq1dslA4kSEEwDQTpWVlUmqCymC7d89cQ8i+3uBpuXl5amqqiroaiT/igm73R7tsoAOz+PxyOv1smIWQJskxLoAAEBwWVlZcrlcMpvNQZP0E/fN09sFaD1/3xaTyRTYLgWg+fxNZgGgLQgnAKCdMpvNgdUTwRQXFwd+PXHixGiUBHQ6/hM8zGazVq5cGetygA4nNzeXRrJAG7ndbuXl5QX+v7KyUrNnz67XEL0rIJwAgA7K/4+Yfx89gObxeDxyOBzyeDxyu93Kzs5WTk5OrMsCOhyPx6OysrIud6IAEE4ul0uTJk2qF054PB6lpqYqMzOz3uOdHT0nAKADKiwslNvtlslk4k0V0EImk0kFBQUqKSlRSUmJXC6XUlNTOakDaKGsrCz+DQLawGQyafHixQ0CPpPJpOzsbOXn5ys/Pz9G1UWfwefz+WJdBDq35ORkeb1eVVVV8dNdIAy8Xq+GDx8uSSopKanXGBNA6/j/rSooKOhyy2iB1sjNzZXZbG7QBNNms8nlcnXZoxCBcHG73bJYLDIajaqqqop1OVHBygkA6GD8pwkQTADh42+EOWfOnHrNZgE05N/OwekcQOT4v8fzer1d5lQ2wgkA6EByc3NVXFxMMAGE2aRJkyTVfRP40ksvxbgaoH1jOwfQdh6PR1lZWYETo5oa2xXQEBMAOoj8/Hzl5eVp69atbJECWig/P18Oh0NWq1UFBQUNrp8Y9pWUlESzNKBDcbvdKi4ulsViCXrd/yZqxowZSklJkaRGT54Cuqq8vLxAT4mmOi10lR9IEU4AQAfgcrmUl5enkpKSBsFEYWGhjEYjy2uBRuTl5cnr9aqwsFAej6fBN3qVlZWBX4d60wWg7pjrxva/+/u3rFy5kp4TQCP69+8v6X/bCr/txNUSXeXPEts6AKCdc7vdcjgcWrlyZdAVE+vWresyiTrQWhMnTpRU99OnYH9eTtzPS9AHAIg0s9ms7OzskEeF+k+Q6kpNmgknAKAd83q9mjNnjhYvXhz4f/+Hx+OR2+1WYWEh4QTQBLvdLrPZHHJ5uf+bw8zMTP48AWFw4mokAA1ZrVZ5PJ6Q/STy8vJkNBoD3wN2BYQTiCiPxxPoel5cXBzbYoAOxuv1ymKxBI6SSk5OrveRmprK8nOgmaxWq7KysgJ/pk6UlZUlj8ejjIyMkD/BAtA0f3gudZ0GfkBb5OTkKCsrK7BKQvrf93+VlZUhV812VgZfU903gBYqLCzUwoUL5fV6G6TmKSkpMhqNmj17trKzs2NUIdAxOBwO5ebmNjnOarXK6XRGoSKg4/N4PMrJyZHH4wn8G2UymZSVlcV2DqCVcnNzlZeXV+/7Pq/XK5PJJKPRSJNZoAkOh0Nut1uVlZVKSUmR2WzukifiEE4AAAAAAICYYlsHAAAAAACIKcIJAAAAAAAQU4QTAAAAAAAgpggnAAAAAABATBFOAAAAAACAmCKcAAAAAAAAMUU4AQAAAAAAYopwAgAAAAAAxBThBAAAAAAAiCnCCQAAAAAAEFOEEwAAAAAAIKYIJwAAAAAAQEwRTgAAAAAAgJginAAAAAAAADFFOAEAAAAAAGIqIdYFAADQ2RUWFsputwf+v6CgQBkZGSHHu1wu2Ww2mUwmlZWVRaPEFklNTW3T/WazWQUFBWGqpuvweDzKyclRcXGx3G63jEajTCaTJk6cqKysLJnN5liXCABAqxFOAAAQZQ6Ho9Fwor3zeDwNHjMajZIkr9fb6GMnPo7mczgcys3NDfy/yWRSZWWl3G633G638vPzlZGRQegDAOiw2NYBAECUmEwmGY1GeTweFRYWxrqcVvP5fA0+qqqqVFVVJZPJJEnKzMwMPPbtsSUlJTF+BR1Lfn6+cnNzZTQalZeXJ5/Pp7KyssDntqCgQCaTSSkpKbEuFQCAViOcAAAgSoxGo3JyciTV/SQckfPtlQZtHRcrXq9XWVlZkqSVK1cqMzOzwZiMjAyVlZWF/D3V3l8jAAAS4QQAAFGVmZnZKVZPtGder1e5ubnKy8sLy7hYKi4ullTXp6OpnhL+VSsn6givEQAAiXACAICoY/UEmsvf3yNYnw8AADoTwgkAAKLsxNUT+fn5sS4H7ZjVapVUtwKC3ysAgM6McAIAgBjwr57w/7c18vPzZbFYZDAYlJqaKrvdLpfLFa4SI8putys1NTWwIsD/WpKTkxuMbe7rzM3NlcFgCMzh8XhkMBgCHzabrUXjvs3hcATqsFgsIVe+tOS1NcVkMgVOdsnKygq89m+fgNLaz0VrX6PD4ZDdbpfb7ZbX6w3cl5ycrOTk5JD3+Y/JTU5ODtRnsViUm5vb5GsCAHRuHCUKAEAMZGZmyuFwBFZPBGt0GIrX69WMGTPkdrslKdCLoLCwUIWFhR3iSEmv1yuPxyOPxxN4kyvVP2a0pa/TbDYrMzNTlZWVKiwslNFo1FVXXRW4npqa2qJxfm63W3a7XR6PR0ajUVarVR6PR7m5uSosLFRJSUmDupt6bS1RUFAgm80ml8sVeO3S/4KL2bNnN+hHEenX6Ha7AwGRPywxmUyBgCE3N1cej6fe1yc/Pz/Q3NNqtQZWD/mPQz0xiAEAdEE+AAAQUQUFBT5JPrPZXO/xvLw8nySf0Wis97jT6fRJ8plMpqDzmUwmnyRfdnZ2vcerqqp8VqvVJ8mXmZkZ3hfRTP7amnp+f50njq+qqgo6V0tfZ1lZWaOfv5aO89eRl5dX73H/1zUjI6PFr601CgoKAnN/+yPU5zvSr1GSr6CgIPB4VVWVLyMjI+g1/2NlZWVBX1s4PkcAgI6LcAIAgAgLFU74fMHfFDYWTuTk5AR9sxhsTqfTGZ4X0AItDSeCvSH2+dr2OsMZTmRnZ/sk+XJychq9fuIb66ZeWzg4nU5fdnZ24HMQ6nMV6dcY6h7/9RN/z/vrBAAgGHpOAAAQQy09ucN/JOT8+fNDjvEvnW/vWzuk/20/+Lb28jr9WxfKysrkcDgafPj7SgTrgRHqtYWD1WpVTk6OysrKlJ2dLaluu0treo605TWG2qri/33t39Lir1mSLBaL8vPzOYEEAFAPPScAAIihjIwMmUymZvee8L+h+3aPgRP5r3WE5pizZ88O+nh7eZ3+N9dNnZRRWVnZ4LFQry3ccnJy5HK55Ha7lZeXFwgBmqstrzGUE79uHo9HJpNJeXl5stlscrvdgWDJ399i8eLFre7JAQDoHFg5AQBAjDV39cSJpxl0lpMNTCZTg8fa0+v0v2H21W2FDfkRLFQK9toixR+EtObz1ZbX2BImk0llZWUqKCgIhHJer1eFhYVKTk6ut8oCAND1EE4AABBjJ75Ry83NDTnOaDQG3kgWFxeHHPft0y06mvb0OidOnFjvudqrsrIySa37XETiNZ4417dDGv8pK2VlZaqqqgqc0NHcrU0AgM6JcAIAgHbAv3pi4cKFjY7zHwcZ6o2c1+sNzBGtbQWREI7X2dxtCI2Ns9vtjdYRafn5+U1ut/BvCZIkm80WdEykXmOolRr+uZraYmI0GrV48WJJjQdRAIDOj3ACAIB24MTVE/6gIpi8vDyZTCa53W7Z7fZ6bw49Ho9mzJghr9crq9Ua+Im0n9frlcvl6hC9KNryOlNSUiTVvd4Tmy4WFha2eFxmZqasVqtcLlfgTfyJ3G53oI9CJDidTmVlZSk1NVW5ubn16vRvibBYLJLqfg99OwyI9GtcuHBhg8ftdnvg95i/salUF5wE+73nD1Za2isDANDJRP+AEAAAupbGjhINNs7/Eer4x7KysnpHSJpMJp/RaAz8v9VqrXfsY7D5I6WlR4kWFBSEHNPa13liHf77Qr3u5oyrqqqqdzyo2Wz2Wa3WerWUlJS06LU1V0lJic9sNtf7fSGp3nOrmUeuRuI1+mszm80N6jzxGFX/kaaNzV9WVtbmzxcAoONi5QQAAO1ERkZGs3oG+BsL5uTkyGq1Bpbs+/fyO53OTnHyQVteZ0FBQaDXgdFoVGZmppxOZ6vGGY1GOZ3OwEkYHo9HxcXFmjhxonJyclRVVRWxvhdms1klJSUqKSlRdna2rFZrYIWNyWRSRkaGnE5no8epRvI1ZmVlqaSkJFCT0WhURkaGSkpK6jXQNJlMqqqqUnZ2tsxmszweT2AVRUZGhsrKyqLaQBQA0P4YfD6fL9ZFAAAAoOPwb9HIy8tr8ykeAABI9JwAAAAAAAAxRjgBAAAAAABiinACAAAAAADEFOEEAAAAAACIKcIJAAAAAAAQU5zWAQAAAAAAYoqVEwAAAAAAIKYIJwAAAAAAQEwRTgAAAAAAgJginAAAAAAAADFFOAEAAAAAAGKKcAIAAAAAAMQU4QQAAAAAAIgpwgkAAAAAABBThBMAAAAAACCmCCcAAAAAAEBMEU4AAAAAAICYIpwAAAAAAAAxRTgBAAAAAABiinACAAAAAADEFOEEAAAAAACIKcIJAAAAAAAQU4QTAAAAAAAgpggnAAAAAABATBFOAAAAAACAmPp/jl1bqHsZ/skAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Trotter circuits expectation value\")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_ideal,\n",
    "    std_error_ideal,\n",
    "    fmt=\"o\",\n",
    "    linestyle=\"--\",\n",
    "    capsize=4,\n",
    "    c=\"red\",\n",
    "    label=\"Ideal\",\n",
    ")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_unmit,\n",
    "    std_error_unmit,\n",
    "    fmt=\"o\",\n",
    "    linestyle=\"-\",\n",
    "    capsize=4,\n",
    "    c=\"green\",\n",
    "    label=\"No mitigation\",\n",
    ")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_pec,\n",
    "    std_error_pec,\n",
    "    fmt=\"d\",\n",
    "    linestyle=\"-\",\n",
    "    capsize=4,\n",
    "    c=\"orange\",\n",
    "    label=\"PEC\",\n",
    ")\n",
    "\n",
    "plt.ylabel(f\"$\\langle ZZZZ \\\\rangle$\")\n",
    "plt.xlabel(\"No. Trotter Steps\")\n",
    "plt.xticks([1, 2, 3, 4, 5])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acdbc900",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### PEC callback messages explained\n",
    "\n",
    "__Layers detected:__\n",
    "\n",
    "`2022-11-07 20:23:43.013816 *** Callback *** {'unique_layers_detected': 2, 'total_layers': 60}`\n",
    "\n",
    "In the list of circuits passed to the Estimator, a total of __60__ layers were detected and __two__ of them were unique. Qiskit Runtime will learn the noise model for the two unique layers.\n",
    "\n",
    "__Sampling overhead for layers:__\n",
    "\n",
    "`2022-11-07 20:30:41.251325 *** Callback *** {'msg': 'Sampling overhead for layer 1/2: 1.0263820029324646'}`\n",
    "\n",
    "`2022-11-07 20:37:38.920151 *** Callback *** {'msg': 'Sampling overhead for layer 2/2: 1.0692954781746'}`\n",
    "\n",
    "These messages show the sampling overhead for each of the unique layers. Notice that it took roughly 7 minutes to learn the noise model of each layer.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81537fbc",
   "metadata": {},
   "source": [
    "### PEC callback messages explained\n",
    "\n",
    " **Layers detected:**\n",
    "\n",
    "`2022-11-07 20:23:43.013816 *** Callback *** {'unique_layers_detected': '2, total_layers: 60'}`\n",
    "\n",
    "In the list of circuits passed to Estimator, a total of **60** layers were detected and **two** of them were unique.  Qiskit Runtime will learn the noise model for the two unique layers.\n",
    "\n",
    "**Sampling overhead for layers:**\n",
    "\n",
    "`2022-11-07 20:30:41.251325 *** Callback *** {'msg': 'Sampling overhead for layer 1/2: 1.0263820029324646'}`\n",
    "`2022-11-07 20:37:38.920151 *** Callback *** {'msg': 'Sampling overhead for layer 2/2: 1.0692954781746'}`\n",
    "\n",
    "These messages show the sampling overhead for each of the unique layers. Notice that it took roughly 7 minutes to learn the noise model of each layer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "690f9377",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "__Circuit total sampling overhead__\n",
    "\n",
    "- Circuit 1 total sampling overhead: `1.2045186180127132`\n",
    "    \n",
    "    `2022-11-07 20:37:39.336883 *** Callback *** {sampling_overhead_by_layer: [1.0692954781746, 1.0263820029324646], unique_layers: 2, total_sampling_overhead: 1.2045186180127132}`\n",
    "\n",
    "  \n",
    "- Circuit 2 total sampling overhead: `1.4508651011392564`\n",
    "\n",
    "    `2022-11-07 20:37:39.334815 *** Callback *** {sampling_overhead_by_layer: [1.0692954781746, 1.0263820029324646], unique_layers: 2, total_sampling_overhead: 1.4508651011392564}`\n",
    "\n",
    "\n",
    "- Circuit 3 total sampling overhead: `1.7475940265471324`\n",
    "\n",
    "    `2022-11-07 20:37:39.336983 *** Callback *** {sampling_overhead_by_layer: [1.0692954781746, 1.0263820029324646], unique_layers: 2, total_sampling_overhead: 1.7475940265471324}`\n",
    "\n",
    "\n",
    "- Circuit 4 total sampling overhead: `2.105009541703825`\n",
    "\n",
    "    `2022-11-07 20:37:39.337099 *** Callback *** {sampling_overhead_by_layer: [1.0692954781746, 1.0263820029324646], unique_layers: 2, total_sampling_overhead: 2.105009541703825}`\n",
    "\n",
    "\n",
    "- Circuit 5 total sampling overhead: `2.5355231840766654`\n",
    "\n",
    "    `2022-11-07 20:37:39.338120 *** Callback *** {sampling_overhead_by_layer: [1.0692954781746, 1.0263820029324646], unique_layers: 2, total_sampling_overhead: 2.5355231840766654}`\n",
    "\n",
    "The sampling overhead increases when there are more layers to mitigate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "286bdbf5",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EstimatorResult(values=array([0.96681631, 0.96346463, 0.81458917, 0.83279075, 0.71165489]), metadata=[{'standard_error': 0.04741526161355738, 'confidence_interval': [0.6946850904749993, 1.2389475260753755], 'confidence_level': 0.95, 'shots': 15360, 'samples': 120, 'sampling_overhead': 1.2045186180127132, 'total_mitigated_layers': 4}, {'standard_error': 0.06003453067758544, 'confidence_interval': [0.6917633123010369, 1.235165948939192], 'confidence_level': 0.95, 'shots': 18560, 'samples': 145, 'sampling_overhead': 1.4508651011392564, 'total_mitigated_layers': 8}, {'standard_error': 0.07893102265063534, 'confidence_interval': [0.5423767896024972, 1.086801560321982], 'confidence_level': 0.95, 'shots': 22272, 'samples': 174, 'sampling_overhead': 1.7475940265471324, 'total_mitigated_layers': 12}, {'standard_error': 0.08198342212333354, 'confidence_interval': [0.5608466609152284, 1.104734830411759], 'confidence_level': 0.95, 'shots': 26880, 'samples': 210, 'sampling_overhead': 2.105009541703825, 'total_mitigated_layers': 16}, {'standard_error': 0.08955468433998504, 'confidence_interval': [0.43973826826406204, 0.9835715192472465], 'confidence_level': 0.95, 'shots': 32384, 'samples': 253, 'sampling_overhead': 2.5355231840766654, 'total_mitigated_layers': 20}])\n"
     ]
    }
   ],
   "source": [
    "print(job_pec.result())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a3ea12",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### PEC metadata\n",
    "\n",
    "- Circuit 1:\n",
    "```json\n",
    "    {\n",
    "      \"standard_error\": 0.04741526161355738,\n",
    "      \"confidence_interval\": [\n",
    "        0.6946850904749993,\n",
    "        1.2389475260753755\n",
    "      ],\n",
    "      \"confidence_level\": 0.95,\n",
    "      \"shots\": 15360,\n",
    "      \"samples\": 120,\n",
    "      \"sampling_overhead\": 1.2045186180127132,\n",
    "      \"total_mitigated_layers\": 4\n",
    "    }\n",
    "```\n",
    "- Circuit 2:\n",
    "```json\n",
    "    {\n",
    "      \"standard_error\": 0.06003453067758544,\n",
    "      \"confidence_interval\": [\n",
    "        0.6917633123010369,\n",
    "        1.235165948939192\n",
    "      ],\n",
    "      \"confidence_level\": 0.95,\n",
    "      \"shots\": 18560,\n",
    "      \"samples\": 145,\n",
    "      \"sampling_overhead\": 1.4508651011392564,\n",
    "      \"total_mitigated_layers\": 8\n",
    "    }\n",
    "```\n",
    "- Circuit 3:\n",
    "```json\n",
    "    {\n",
    "      \"standard_error\": 0.07893102265063534,\n",
    "      \"confidence_interval\": [\n",
    "        0.5423767896024972,\n",
    "        1.086801560321982\n",
    "      ],\n",
    "      \"confidence_level\": 0.95,\n",
    "      \"shots\": 22272,\n",
    "      \"samples\": 174,\n",
    "      \"sampling_overhead\": 1.7475940265471324,\n",
    "      \"total_mitigated_layers\": 12\n",
    "    }\n",
    "```\n",
    "- Circuit 4:\n",
    "```json\n",
    "    {\n",
    "      \"standard_error\": 0.08198342212333354,\n",
    "      \"confidence_interval\": [\n",
    "        0.5608466609152284,\n",
    "        1.104734830411759\n",
    "      ],\n",
    "      \"confidence_level\": 0.95,\n",
    "      \"shots\": 26880,\n",
    "      \"samples\": 210,\n",
    "      \"sampling_overhead\": 2.105009541703825,\n",
    "      \"total_mitigated_layers\": 16\n",
    "    }\n",
    "````\n",
    "\n",
    "- Circuit 5\n",
    "```json\n",
    "    {\n",
    "      \"standard_error\": 0.08955468433998504,\n",
    "      \"confidence_interval\": [\n",
    "        0.43973826826406204,\n",
    "        0.9835715192472465\n",
    "      ],\n",
    "      \"confidence_level\": 0.95,\n",
    "      \"shots\": 32384,\n",
    "      \"samples\": 253,\n",
    "      \"sampling_overhead\": 2.5355231840766654,\n",
    "      \"total_mitigated_layers\": 20\n",
    "    }\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f8e8adf",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pec_metadata = job_pec.result().metadata\n",
    "fig, ax = plt.subplots()\n",
    "fig.subplots_adjust(right=0.75)\n",
    "\n",
    "twin1 = ax.twinx()\n",
    "twin2 = ax.twinx()\n",
    "twin3 = ax.twinx()\n",
    "\n",
    "twin2.spines.right.set_position((\"axes\", 1.2))\n",
    "twin3.spines.right.set_position((\"axes\", 1.4))\n",
    "\n",
    "(p1,) = ax.plot(\n",
    "    range(1, num_steps),\n",
    "    [m[\"total_mitigated_layers\"] for m in pec_metadata],\n",
    "    \"b-\",\n",
    "    label=\"Total mitigated layers\",\n",
    ")\n",
    "(p2,) = twin1.plot(\n",
    "    range(1, num_steps),\n",
    "    [m[\"sampling_overhead\"] for m in pec_metadata],\n",
    "    \"r-\",\n",
    "    label=\"Sampling overhead\",\n",
    ")\n",
    "(p3,) = twin2.plot(range(1, num_steps), [m[\"samples\"] for m in pec_metadata], \"g-\", label=\"Samples\")\n",
    "(p4,) = twin3.plot(range(1, num_steps), [m[\"shots\"] for m in pec_metadata], \"c-\", label=\"Shots\")\n",
    "\n",
    "ax.set_ylim(0, 20)\n",
    "twin1.set_ylim(0, 2.8)\n",
    "twin2.set_ylim(0, 300)\n",
    "twin3.set_ylim(0, 35000)\n",
    "\n",
    "ax.set_xlabel(\"No. Trotter Steps\")\n",
    "ax.set_ylabel(\"Total mitigated layers\")\n",
    "twin1.set_ylabel(\"Sampling overhead\")\n",
    "twin2.set_ylabel(\"Samples\")\n",
    "twin3.set_ylabel(\"Shots\")\n",
    "\n",
    "ax.yaxis.label.set_color(p1.get_color())\n",
    "twin1.yaxis.label.set_color(p2.get_color())\n",
    "twin2.yaxis.label.set_color(p3.get_color())\n",
    "twin3.yaxis.label.set_color(p4.get_color())\n",
    "\n",
    "tkw = dict(size=4, width=1.5)\n",
    "ax.tick_params(axis=\"y\", colors=p1.get_color(), **tkw)\n",
    "twin1.tick_params(axis=\"y\", colors=p2.get_color(), **tkw)\n",
    "twin2.tick_params(axis=\"y\", colors=p3.get_color(), **tkw)\n",
    "twin3.tick_params(axis=\"y\", colors=p4.get_color(), **tkw)\n",
    "plt.xticks([1, 2, 3, 4, 5])\n",
    "\n",
    "ax.legend(handles=[p1, p2, p3, p4])\n",
    "\n",
    "plt.title(\"PEC metadata\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "261d964a",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Combined plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed989258",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_ideal,\n",
    "    std_error_ideal,\n",
    "    fmt=\"o\",\n",
    "    linestyle=\"--\",\n",
    "    capsize=4,\n",
    "    c=\"red\",\n",
    "    label=\"Ideal\",\n",
    ")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_unmit,\n",
    "    std_error_unmit,\n",
    "    fmt=\"o\",\n",
    "    linestyle=\"-\",\n",
    "    capsize=4,\n",
    "    c=\"green\",\n",
    "    label=\"No mitigation\",\n",
    ")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_unmit_trex,\n",
    "    std_error_trex,\n",
    "    fmt=\"o\",\n",
    "    linestyle=\"-\",\n",
    "    capsize=4,\n",
    "    c=\"violet\",\n",
    "    label=\"T-REx\",\n",
    ")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_unmit_zne,\n",
    "    [0] * (num_steps - 1),\n",
    "    fmt=\"o\",\n",
    "    linestyle=\"-\",\n",
    "    capsize=4,\n",
    "    c=\"cyan\",\n",
    "    label=\"ZNE\",\n",
    ")\n",
    "plt.errorbar(\n",
    "    range(1, num_steps),\n",
    "    expvals_pec,\n",
    "    std_error_pec,\n",
    "    fmt=\"d\",\n",
    "    linestyle=\"-\",\n",
    "    capsize=4,\n",
    "    c=\"orange\",\n",
    "    label=\"PEC\",\n",
    ")\n",
    "\n",
    "plt.title(\"Trotter circuits expectation value\")\n",
    "plt.ylabel(f\"$\\langle ZZZZ \\\\rangle$\")\n",
    "plt.xlabel(\"No. Trotter Steps\")\n",
    "plt.xticks([1, 2, 3, 4, 5])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38462f9b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "toc-hr-collapsed": true
   },
   "source": [
    "## Advanced ZNE options\n",
    "\n",
    "\n",
    "### Noise amplification\n",
    "As a first approach, we provide a noise amplification technique known as _unitary folding_. \n",
    "\n",
    "__Reference__: Tudor Giurgica-Tiron, Yousef Hindy, Ryan LaRose, Andrea Mari, and William J. Zeng, _Digital zero noise extrapolation for quantum error mitigation_ [arXiv:2005.10921](https://arxiv.org/abs/2005.10921).\n",
    "\n",
    "#### Digital vs analog\n",
    "<div>\n",
    "<img src=\"images/analog_vs_digital.png\">\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd545012",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Local vs global\n",
    "<div>\n",
    "<img src=\"images/global_vs_local.png\">\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a173644",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "### Extrapolation\n",
    "\n",
    "#### Useful noise profiles\n",
    "<div>\n",
    "<img src=\"images/useful_extrapolation.png\">\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d05a5da2",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Useless noise profiles\n",
    "<div>\n",
    "<img src=\"images/unuseful_extrapolation.png\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f17aca9",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job id: cdkrqbdf14jb6brgbrl0\n",
      "EstimatorResult(values=array([0.9285, 0.8165, 0.835 , 0.6815, 0.4945]), metadata=[{'zne': {'noise_amplification': {'noise_amplifier': \"<LocalFoldingAmplifier:{'gates_to_fold': None, 'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\", 'noise_factors': [1, 2, 3, 4], 'values': [0.838, 0.798, 0.738, 0.752], 'variance': [0.297756, 0.36319599999999996, 0.455356, 0.434496], 'shots': [1000, 1000, 1000, 1000]}, 'extrapolation': {'extrapolator': 'QuadraticExtrapolator'}}}, {'zne': {'noise_amplification': {'noise_amplifier': \"<LocalFoldingAmplifier:{'gates_to_fold': None, 'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\", 'noise_factors': [1, 2, 3, 4], 'values': [0.732, 0.676, 0.572, 0.522], 'variance': [0.46417600000000003, 0.543024, 0.6728160000000001, 0.727516], 'shots': [1000, 1000, 1000, 1000]}, 'extrapolation': {'extrapolator': 'QuadraticExtrapolator'}}}, {'zne': {'noise_amplification': {'noise_amplifier': \"<LocalFoldingAmplifier:{'gates_to_fold': None, 'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\", 'noise_factors': [1, 2, 3, 4], 'values': [0.674, 0.51, 0.436, 0.328], 'variance': [0.5457239999999999, 0.7399, 0.809904, 0.892416], 'shots': [1000, 1000, 1000, 1000]}, 'extrapolation': {'extrapolator': 'QuadraticExtrapolator'}}}, {'zne': {'noise_amplification': {'noise_amplifier': \"<LocalFoldingAmplifier:{'gates_to_fold': None, 'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\", 'noise_factors': [1, 2, 3, 4], 'values': [0.512, 0.372, 0.266, 0.188], 'variance': [0.7378560000000001, 0.8616159999999999, 0.929244, 0.964656], 'shots': [1000, 1000, 1000, 1000]}, 'extrapolation': {'extrapolator': 'QuadraticExtrapolator'}}}, {'zne': {'noise_amplification': {'noise_amplifier': \"<LocalFoldingAmplifier:{'gates_to_fold': None, 'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\", 'noise_factors': [1, 2, 3, 4], 'values': [0.442, 0.35, 0.268, 0.13], 'variance': [0.804636, 0.8775000000000001, 0.928176, 0.9831], 'shots': [1000, 1000, 1000, 1000]}, 'extrapolation': {'extrapolator': 'QuadraticExtrapolator'}}}])\n"
     ]
    }
   ],
   "source": [
    "options = Options()\n",
    "options.execution.shots = 1000\n",
    "options.optimization_level = 0  # no optimization\n",
    "options.resilience_level = 2  # ZNE\n",
    "options.resilience.noise_factors = [1, 2, 3, 4]\n",
    "options.resilience.noise_amplifier = \"LocalFoldingAmplifier\"\n",
    "options.resilience.extrapolator = \"QuadraticExtrapolator\"\n",
    "\n",
    "with Session(service=service, backend=\"ibmq_montreal\") as session:\n",
    "    estimator = Estimator(session=session, options=options)\n",
    "    job_zne_options = estimator.run(circuits=trotter_circuit_list, observables=obs_list)\n",
    "    print(\"job id:\", job_zne_options.job_id)\n",
    "    print(job_zne_options.result())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "659c606f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "```json\n",
    "{\n",
    "  \"values\": [\n",
    "    0.928499999999999,\n",
    "    0.8165,\n",
    "    0.835,\n",
    "    0.6815000000000001,\n",
    "    0.49449999999999983\n",
    "  ],\n",
    "  \"metadata\": [\n",
    "    {\n",
    "      \"zne\": {\n",
    "        \"noise_amplification\": {\n",
    "          \"noise_amplifier\": \"<LocalFoldingAmplifier:{'gates_to_fold': None, 'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\",\n",
    "          \"noise_factors\": [\n",
    "            1,\n",
    "            2,\n",
    "            3,\n",
    "            4\n",
    "          ],\n",
    "          \"values\": [\n",
    "            0.838,\n",
    "            0.798,\n",
    "            0.738,\n",
    "            0.752\n",
    "          ],\n",
    "          \"variance\": [\n",
    "            0.297756,\n",
    "            0.36319599999999996,\n",
    "            0.455356,\n",
    "            0.434496\n",
    "          ],\n",
    "          \"shots\": [\n",
    "            1000,\n",
    "            1000,\n",
    "            1000,\n",
    "            1000\n",
    "          ]\n",
    "        },\n",
    "        \"extrapolation\": {\n",
    "          \"extrapolator\": \"QuadraticExtrapolator\"\n",
    "        }\n",
    "      }\n",
    "    },\n",
    "    {\n",
    "      \"zne\": {\n",
    "        \"noise_amplification\": {\n",
    "          \"noise_amplifier\": \"<LocalFoldingAmplifier:{'gates_to_fold': None, 'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\",\n",
    "          \"noise_factors\": [\n",
    "            1,\n",
    "            2,\n",
    "            3,\n",
    "            4\n",
    "          ],\n",
    "          \"values\": [\n",
    "            0.732,\n",
    "            0.676,\n",
    "            0.572,\n",
    "            0.522\n",
    "          ],\n",
    "          \"variance\": [\n",
    "            0.46417600000000003,\n",
    "            0.543024,\n",
    "            0.6728160000000001,\n",
    "            0.727516\n",
    "          ],\n",
    "          \"shots\": [\n",
    "            1000,\n",
    "            1000,\n",
    "            1000,\n",
    "            1000\n",
    "          ]\n",
    "        },\n",
    "        \"extrapolation\": {\n",
    "          \"extrapolator\": \"QuadraticExtrapolator\"\n",
    "        }\n",
    "      }\n",
    "    },\n",
    "    {\n",
    "      \"zne\": {\n",
    "        \"noise_amplification\": {\n",
    "          \"noise_amplifier\": \"<LocalFoldingAmplifier:{'gates_to_fold': None, 'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\",\n",
    "          \"noise_factors\": [\n",
    "            1,\n",
    "            2,\n",
    "            3,\n",
    "            4\n",
    "          ],\n",
    "          \"values\": [\n",
    "            0.674,\n",
    "            0.51,\n",
    "            0.436,\n",
    "            0.328\n",
    "          ],\n",
    "          \"variance\": [\n",
    "            0.5457239999999999,\n",
    "            0.7399,\n",
    "            0.809904,\n",
    "            0.892416\n",
    "          ],\n",
    "          \"shots\": [\n",
    "            1000,\n",
    "            1000,\n",
    "            1000,\n",
    "            1000\n",
    "          ]\n",
    "        },\n",
    "        \"extrapolation\": {\n",
    "          \"extrapolator\": \"QuadraticExtrapolator\"\n",
    "        }\n",
    "      }\n",
    "    },\n",
    "    {\n",
    "      \"zne\": {\n",
    "        \"noise_amplification\": {\n",
    "          \"noise_amplifier\": \"<LocalFoldingAmplifier:{'gates_to_fold': None, 'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\",\n",
    "          \"noise_factors\": [\n",
    "            1,\n",
    "            2,\n",
    "            3,\n",
    "            4\n",
    "          ],\n",
    "          \"values\": [\n",
    "            0.512,\n",
    "            0.372,\n",
    "            0.266,\n",
    "            0.188\n",
    "          ],\n",
    "          \"variance\": [\n",
    "            0.7378560000000001,\n",
    "            0.8616159999999999,\n",
    "            0.929244,\n",
    "            0.964656\n",
    "          ],\n",
    "          \"shots\": [\n",
    "            1000,\n",
    "            1000,\n",
    "            1000,\n",
    "            1000\n",
    "          ]\n",
    "        },\n",
    "        \"extrapolation\": {\n",
    "          \"extrapolator\": \"QuadraticExtrapolator\"\n",
    "        }\n",
    "      }\n",
    "    },\n",
    "    {\n",
    "      \"zne\": {\n",
    "        \"noise_amplification\": {\n",
    "          \"noise_amplifier\": \"<LocalFoldingAmplifier:{'gates_to_fold': None, 'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\",\n",
    "          \"noise_factors\": [\n",
    "            1,\n",
    "            2,\n",
    "            3,\n",
    "            4\n",
    "          ],\n",
    "          \"values\": [\n",
    "            0.442,\n",
    "            0.35,\n",
    "            0.268,\n",
    "            0.13\n",
    "          ],\n",
    "          \"variance\": [\n",
    "            0.804636,\n",
    "            0.8775000000000001,\n",
    "            0.928176,\n",
    "            0.9831\n",
    "          ],\n",
    "          \"shots\": [\n",
    "            1000,\n",
    "            1000,\n",
    "            1000,\n",
    "            1000\n",
    "          ]\n",
    "        },\n",
    "        \"extrapolation\": {\n",
    "          \"extrapolator\": \"QuadraticExtrapolator\"\n",
    "        }\n",
    "      }\n",
    "    }\n",
    "  ]\n",
    "}\n",
    "```"
   ]
  },
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     "data": {
      "text/plain": [
       "'0.11.1'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
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   ],
   "source": [
    "import qiskit_ibm_runtime\n",
    "\n",
    "qiskit_ibm_runtime.version.get_version_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a773669",
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       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.22.2</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.0</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.19.2</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.10.4</td></tr><tr><td>Python compiler</td><td>Clang 12.0.0 </td></tr><tr><td>Python build</td><td>main, Mar 31 2022 03:38:35</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Tue Nov 08 12:15:46 2022 EST</td></tr></table>"
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       "<IPython.core.display.HTML object>"
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     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2022.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "import qiskit\n",
    "\n",
    "qiskit.version.get_version_info()"
   ]
  }
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