2ising_model-2d.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d8586e3b-1bd1-497d-b595-56846607413c",
   "metadata": {},
   "source": [
    "In this project the following paper served as guidance:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86bee0ce-1ceb-4cdf-9ab2-e24cf6f615e2",
   "metadata": {},
   "source": [
    "Evidence for the utility of quantum computing before fault tolerance. Nature, 615(7950), 596-600"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caadf9f9-9baa-4b03-88cc-5f6a95a43b75",
   "metadata": {},
   "source": [
    "The base 2D-Ising model is derived from the 1D version displayed in the classiq-library on github:\n",
    "    \n",
    "    https://github.com/Classiq/classiq-library/blob/main/applications/physical_systems/ising_model/ising_model.ipynb \n",
    "authored by Amir Naveh."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fc9a0f9-2c30-4eae-b877-3934fc330e4c",
   "metadata": {},
   "source": [
    "In this project a noiseless 2D Ising model is presented. It is then trotterized, noise is added to it and zero noise extrapolation applied. It is also run with depth and width constraints."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f19f49-8eda-4505-ad82-9db5b6f317c1",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 0. Prerequirements\n",
    "\n",
    "The model is using several Classiq's libraries in addition to basic python tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "060aa402-e010-4292-bfee-12806b193a09",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -q --upgrade pip "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aaa83687-0dc3-4fee-995e-019c1ec01075",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "qiskit-ibm-runtime 0.25.0 requires pydantic>=2.5.0, but you have pydantic 1.10.17 which is incompatible.\n",
      "qc-grader 0.19.7 requires networkx==3.2.1, but you have networkx 2.8.8 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -q classiq\n",
    "%pip install -q pyomo\n",
    "%pip install -q qiskit\n",
    "%pip install -q matplotlib\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da013f9d-dbd4-49fb-8177-592ed5101a9c",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 0. Pre-requisites\n",
    "We need to ensure we have the required libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a7b2836-c08b-472c-afdc-fb704fdb4e05",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pyomo\n",
    "from pyomo import * \n",
    "import pyomo.core as pyo\n",
    "import classiq \n",
    "from classiq import * \n",
    "from classiq import construct_combinatorial_optimization_model\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "from classiq import set_execution_preferences, write_qmod, show, synthesize, execute\n",
    "from classiq.execution import ClassiqBackendPreferences, ExecutionPreferences\n",
    "import pandas as pd\n",
    "from classiq.applications.combinatorial_optimization import get_optimization_solution_from_pyo\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0163a2bf-7829-4a5c-bad9-d4cc663b8d51",
   "metadata": {
    "tags": []
   },
   "source": [
    "# ZNE for 2D Ising Model with Trotterization\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec26fb41-2368-4658-ba64-a10ae1decd89",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Zero Noise Extrapolation (ZNE)\n",
    "\n",
    "**ZNE** is a technique to mitigate errors by extrapolating results from noisy quantum computations to an ideal, noiseless scenario:\n",
    "\n",
    "1. **Procedure**:\n",
    "   - **Run Experiments at Various Noise Levels**: Increase the noise level intentionally in your quantum system and run the quantum algorithm.\n",
    "   - **Collect Data**: Measure the results for different noise levels.\n",
    "   - **Extrapolate to Zero Noise**: Use statistical methods to fit the data and extrapolate to estimate the result in a noiseless scenario.\n",
    "\n",
    "2. **Concept**:\n",
    "   - ZNE assumes that the relationship between the noise level and the error in the results is known or can be modeled. By systematically increasing the noise and observing how the results change, you can estimate the results that would be obtained with zero noise.\n",
    "\n",
    "3. **Use Cases**:\n",
    "   - ZNE is particularly useful when you can adjust the noise level and want to infer the ideal performance of your quantum algorithm.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac5002bc-5eb7-44bc-b516-ded3e7af8946",
   "metadata": {
    "tags": []
   },
   "source": [
    "\n",
    "##  Noise Handling:\n",
    "Adding noise directly to the cost function is a simple approximation. In real implementations, you may need more sophisticated methods to account for errors and noise.\n",
    "\n",
    "##  Linear Fit:\n",
    "The ZNE extrapolation uses a linear fit of noise scales to estimate the noiseless energy.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da686ab6-d665-402d-b048-54b848446eaf",
   "metadata": {},
   "source": [
    "We'll use the `suzuki_trotter` function from Classiq to add Hamiltonian evolution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6afb0aa0-b79b-4588-b268-d1972bf62f72",
   "metadata": {
    "tags": []
   },
   "source": [
    "To integrate the 2D Ising model with Suzuki-Trotter decomposition, add random noise, and then apply Zero Noise Extrapolation (ZNE) to mitigate the noise and show improvement, we follow these steps:\n",
    "\n",
    "## 1. Define Function for 2D Ising Model with Suzuki-Trotter Decomposition\n",
    "First, we need to define a function to create the 2D Ising model with Suzuki-Trotter decomposition. This function will also generate Pauli terms for the Hamiltonian."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4d98f2d2-6223-46a3-aa36-ef0f48632ede",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pyomo.environ as pyo\n",
    "from classiq import CReal, Pauli, PauliTerm\n",
    "\n",
    "def ising_model_2d(J: int, h: int, rows: int, cols: int, periodic: str):\n",
    "    model = pyo.ConcreteModel(\"ising\")\n",
    "\n",
    "    # Define the variables:\n",
    "    model.z = pyo.Var(range(rows * cols), domain=pyo.Binary)\n",
    "    z_array = np.array(list(model.z.values()))\n",
    "\n",
    "    def index(i, j):\n",
    "        return i * cols + j\n",
    "\n",
    "    E = lambda i, j, k, l: -J * (2 * z_array[index(i, j)] - 1) * (2 * z_array[index(k, l)] - 1) - (h / 2) * (\n",
    "        (2 * z_array[index(i, j)] - 1) + (2 * z_array[index(k, l)] - 1)\n",
    "    )\n",
    "\n",
    "    # Create the Ising Hamiltonian\n",
    "    model.H = 0\n",
    "    for i in range(rows):\n",
    "        for j in range(cols):\n",
    "            if j < cols - 1:\n",
    "                model.H += E(i, j, i, j + 1)\n",
    "            if i < rows - 1:\n",
    "                model.H += E(i, j, i + 1, j)\n",
    "            if periodic == \"True\":\n",
    "                if j == cols - 1:\n",
    "                    model.H += E(i, j, i, 0)\n",
    "                if i == rows - 1:\n",
    "                    model.H += E(i, j, 0, j)\n",
    "\n",
    "    # Setting the objective:\n",
    "    model.cost = pyo.Objective(expr=model.H, sense=pyo.minimize)\n",
    "\n",
    "    # Create Pauli terms for the Hamiltonian\n",
    "    pauli_terms = []\n",
    "    for i in range(rows):\n",
    "        for j in range(cols):\n",
    "            if j < cols - 1 or (periodic == \"True\" and j == cols - 1):\n",
    "                pauli_terms.append(PauliTerm(\n",
    "                    pauli=[Pauli.Z if k == index(i, j) or k == index(i, (j + 1) % cols) else Pauli.I for k in range(rows * cols)], \n",
    "                    coefficient=-J\n",
    "                ))\n",
    "            if i < rows - 1 or (periodic == \"True\" and i == rows - 1):\n",
    "                pauli_terms.append(PauliTerm(\n",
    "                    pauli=[Pauli.Z if k == index(i, j) or k == index((i + 1) % rows, j) else Pauli.I for k in range(rows * cols)], \n",
    "                    coefficient=-J\n",
    "                ))\n",
    "            pauli_terms.append(PauliTerm(\n",
    "                pauli=[Pauli.X if k == index(i, j) else Pauli.I for k in range(rows * cols)], \n",
    "                coefficient=-h / 2\n",
    "            ))\n",
    "\n",
    "    return model, pauli_terms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc086a67-51f0-407a-a133-4d28e1898682",
   "metadata": {},
   "source": [
    "### 2. Create Your 2D Ising Model\n",
    "Instantiate the 2D Ising model with the chosen parameters:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0abcbead-03b0-45c9-9dda-56ebfe925230",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "ising_model, pauli_terms = ising_model_2d(J=10, h=-20, rows=4, cols=4, periodic=\"True\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1487abed-c88c-4d34-9932-d38a6ba4eb3e",
   "metadata": {},
   "source": [
    "\n",
    "### 3. Optimize Using Quantum Optimization Algorithm\n",
    "Set up and run the optimization using QAOA:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "97bf40c5-adcf-4f1a-942e-c0481ad3e10a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The circuit width is 16 and the circuit_depth is 91 when optimized for depth\n",
      "The circuit composition is:\n",
      "{'cx': 320, 'rz': 240, 'rx': 80, 'h': 16}\n",
      "The circuit width is 16 and the circuit_depth is 91 when optimized for width\n",
      "The circuit composition is:\n",
      "{'cx': 320, 'rz': 240, 'rx': 80, 'h': 16}\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classiq import construct_combinatorial_optimization_model\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "from classiq import set_execution_preferences, write_qmod, synthesize, execute, show\n",
    "from classiq.execution import ClassiqBackendPreferences, ExecutionPreferences\n",
    "\n",
    "qaoa_config = QAOAConfig(num_layers=5)\n",
    "optimizer_config = OptimizerConfig(max_iteration=100, alpha_cvar=0.7)\n",
    "\n",
    "qmod = construct_combinatorial_optimization_model(\n",
    "    pyo_model=ising_model,\n",
    "    qaoa_config=qaoa_config,\n",
    "    optimizer_config=optimizer_config,\n",
    ")\n",
    "\n",
    "backend_preferences = ExecutionPreferences(\n",
    "    backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\")\n",
    ")\n",
    "\n",
    "qmod = set_execution_preferences(qmod, backend_preferences)\n",
    "write_qmod(qmod, \"ising_model_2d_suzuki\")\n",
    "\n",
    "@qfunc\n",
    "def main(a: CReal, x: CReal, qba: Output[QArray[QBit]]):\n",
    "    allocate(16, qba)  # Adjust the number of qubits as needed\n",
    "    suzuki_trotter(\n",
    "        pauli_terms,\n",
    "        evolution_coefficient=x,\n",
    "        order=1,\n",
    "        repetitions=1,\n",
    "        qbv=qba,\n",
    "    )\n",
    "\n",
    "constraints = Constraints(max_width = 100, optimization_parameter = \"depth\")\n",
    "qmod = set_constraints(qmod,constraints=constraints)\n",
    "qprog = synthesize(qmod)\n",
    "circuit_width = QuantumProgram.from_qprog(qprog).data.width\n",
    "circuit_depth = QuantumProgram.from_qprog(qprog).transpiled_circuit.depth\n",
    "print(f\"The circuit width is {circuit_width} and the circuit_depth is {circuit_depth} when optimized for depth\")\n",
    "print(f\"The circuit composition is:\")\n",
    "print(QuantumProgram.from_qprog(qprog).transpiled_circuit.count_ops)\n",
    "\n",
    "constraints = Constraints(max_depth = 100, optimization_parameter = \"width\")\n",
    "qmod = set_constraints(qmod,constraints=constraints)\n",
    "qprog = synthesize(qmod)\n",
    "#show(qprog)\n",
    "circuit_width = QuantumProgram.from_qprog(qprog).data.width\n",
    "circuit_depth = QuantumProgram.from_qprog(qprog).transpiled_circuit.depth\n",
    "print(f\"The circuit width is {circuit_width} and the circuit_depth is {circuit_depth} when optimized for width\")\n",
    "print(f\"The circuit composition is:\")\n",
    "print(QuantumProgram.from_qprog(qprog).transpiled_circuit.count_ops)\n",
    "\n",
    "res = execute(qprog).result()\n",
    "vqe_result = res[0].value\n",
    "vqe_result.convergence_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "260bbae3-8d96-4ee7-862b-d7b8c9752933",
   "metadata": {},
   "source": [
    "We observe convergence after about 80 iterations with the built-in trotterization function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55f47253-0f05-4fcc-b780-c57888f5fff7",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 4. Present Quantum Results\n",
    "Retrieve and present the optimization results: \n",
    "Since this is a quantum solution with probabilistic results, there is a defined probability for each result to be obtained by a measurement (presented by an histogram), where the solution is chosen to be the most probable one.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "15df8406-af67-48bf-822e-088ec4f472d7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probability</th>\n",
       "      <th>cost</th>\n",
       "      <th>solution</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.708496</td>\n",
       "      <td>-960.0</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>1451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0.000488</td>\n",
       "      <td>-800.0</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.006348</td>\n",
       "      <td>-680.0</td>\n",
       "      <td>[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.008789</td>\n",
       "      <td>-680.0</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0]</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005859</td>\n",
       "      <td>-680.0</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0]</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     probability   cost                                          solution  \\\n",
       "0       0.708496 -960.0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
       "146     0.000488 -800.0  [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]   \n",
       "2       0.006348 -680.0  [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]   \n",
       "1       0.008789 -680.0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0]   \n",
       "4       0.005859 -680.0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0]   \n",
       "\n",
       "     count  \n",
       "0     1451  \n",
       "146      1  \n",
       "2       13  \n",
       "1       18  \n",
       "4       12  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vqe_result = res[0].value\n",
    "\n",
    "solution = get_optimization_solution_from_pyo(\n",
    "    ising_model, vqe_result=vqe_result, penalty_energy=qaoa_config.penalty_energy\n",
    ")\n",
    "optimization_result = pd.DataFrame.from_records(solution)\n",
    "optimization_result.sort_values(by=\"cost\", ascending=True).head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b9cdc739-985a-4d4c-978b-7ce975696bdd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'cost'}>]], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+xrZQ1tmf3gxn9CU4ehMYfQmMvgRHb3q/9jBjTK9/Gu/fv1+JiYnatGmTMjMz/eOLFy/Wxo0btXnz5m7H1NbW6v/+7/80ffp0tba2aunSpXrttde0Y8cOnXbaaQGfp7i4WCUlJd3GV65cKafT2dvpAgAAi9rb2zV37ly1trYqJiYmaF1IZ0b6IjMzs0twmTlzpqZMmaLHH39c9957b8BjCgsL5XK5/Ntut1tJSUnKzs4OuBiv16uqqirNmjVLkZGRIc1vWvGGkOoHg/rinF7X9qc3wxl9CY7eBEZfAqMvwdGbr65s9CSkMBIbG6uIiAg1Nzd3GW9ublZCQkKvHiMyMlIXXHCB3n///aA1DodDDocj4LHH+4b2tD8QT2dYSPWDQV9e1H3pzUhAX4KjN4HRl8DoS3AjuTe9XXdIN7BGRUUpNTVV1dXV/jGfz6fq6uouZz+Op7OzU++++65OOeWUUJ4aAAAMUyFfpnG5XJo/f77S0tKUnp6u8vJytbW1KS8vT5I0b948JSYmqrS0VJL085//XBdddJHOOussHT58WA899JD27t2rf/u3fxvYlQAAgCEp5DCSm5urlpYWLVmyRE1NTUpJSVFlZaX/47qNjY0KD//qhMtnn32m/Px8NTU1afz48UpNTdWmTZs0derUgVsFAAAYsvp0A2tBQYEKCgoC7qupqemy/fDDD+vhhx/uy9MAAIARgL9NAwAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAqj6FkWXLlik5OVnR0dHKyMhQXV1dr45btWqVwsLCNGfOnL48LQAAGIZCDiOrV6+Wy+VSUVGRtm7dqhkzZignJ0cHDx487nF79uzRT3/6U1166aV9niwAABh+Qg4jZWVlys/PV15enqZOnaqKigo5nU4tX7486DGdnZ264YYbVFJSokmTJvVrwgAAYHgZFUpxR0eHtmzZosLCQv9YeHi4srKyVFtbG/S4n//854qLi9NNN92k119/vcfn8Xg88ng8/m232y1J8nq98nq93eqPjQXa1xNHhAn5GNtCWWd/ejOc0Zfg6E1g9CUw+hIcven92kMKI5988ok6OzsVHx/fZTw+Pl67du0KeMwbb7yhp556Stu3b+/185SWlqqkpKTb+EsvvSSn0xn0uKqqql4/xzEPpod8iHXr168P+Zi+9GYkoC/B0ZvA6Etg9CW4kdyb9vb2XtWFFEZCdeTIEf34xz/Wk08+qdjY2F4fV1hYKJfL5d92u91KSkpSdna2YmJiutV7vV5VVVVp1qxZioyMDGmO04o3hFQ/GNQX5/S6tj+9Gc7oS3D0JjD6Ehh9CY7efHVloychhZHY2FhFRESoubm5y3hzc7MSEhK61X/wwQfas2ePrrzySv+Yz+f78olHjVJDQ4POPPPMbsc5HA45HI5u45GRkcf9hva0PxBPZ1hI9YNBX17UfenNSEBfgqM3gdGXwOhLcCO5N71dd0g3sEZFRSk1NVXV1dX+MZ/Pp+rqamVmZnarnzx5st59911t377d//XDH/5Q3/ve97R9+3YlJSWF8vQAAGAYCvkyjcvl0vz585WWlqb09HSVl5erra1NeXl5kqR58+YpMTFRpaWlio6O1rRp07ocP27cOEnqNg4AAEamkMNIbm6uWlpatGTJEjU1NSklJUWVlZX+m1obGxsVHs4vdgUAAL3TpxtYCwoKVFBQEHBfTU3NcY99+umn+/KUAABgmOIUBgAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArOpTGFm2bJmSk5MVHR2tjIwM1dXVBa394x//qLS0NI0bN07f+ta3lJKSomeffbbPEwYAAMNLyGFk9erVcrlcKioq0tatWzVjxgzl5OTo4MGDAesnTJign/3sZ6qtrdU777yjvLw85eXlacOGDf2ePAAAGPpCDiNlZWXKz89XXl6epk6dqoqKCjmdTi1fvjxg/WWXXaarrrpKU6ZM0ZlnnqlFixZp+vTpeuONN/o9eQAAMPSNCqW4o6NDW7ZsUWFhoX8sPDxcWVlZqq2t7fF4Y4xeeeUVNTQ06Je//GXQOo/HI4/H4992u92SJK/XK6/X263+2FigfT1xRJiQj7EtlHX2pzfDGX0Jjt4ERl8Coy/B0Zverz3MGNPrn8b79+9XYmKiNm3apMzMTP/44sWLtXHjRm3evDngca2trUpMTJTH41FERIQeffRR/eu//mvQ5ykuLlZJSUm38ZUrV8rpdPZ2ugAAwKL29nbNnTtXra2tiomJCVoX0pmRvho7dqy2b9+uzz//XNXV1XK5XJo0aZIuu+yygPWFhYVyuVz+bbfbraSkJGVnZwdcjNfrVVVVlWbNmqXIyMiQ5jateOjdu1JfnNPr2v70ZjijL8HRm8DoS2D0JTh689WVjZ6EFEZiY2MVERGh5ubmLuPNzc1KSEgIelx4eLjOOussSVJKSop27typ0tLSoGHE4XDI4XB0G4+MjDzuN7Sn/YF4OsNCqh8M+vKi7ktvRgL6Ehy9CYy+BEZfghvJventukO6gTUqKkqpqamqrq72j/l8PlVXV3e5bNMTn8/X5Z4QAAAwcoV8mcblcmn+/PlKS0tTenq6ysvL1dbWpry8PEnSvHnzlJiYqNLSUklSaWmp0tLSdOaZZ8rj8Wj9+vV69tln9dhjjw3sSgAAwJAUchjJzc1VS0uLlixZoqamJqWkpKiyslLx8fGSpMbGRoWHf3XCpa2tTQsWLNDHH3+s0aNHa/Lkyfrtb3+r3NzcgVsFAAAYsvp0A2tBQYEKCgoC7qupqemyfd999+m+++7ry9MAAIARgL9NAwAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAqj6FkWXLlik5OVnR0dHKyMhQXV1d0Nonn3xSl156qcaPH6/x48crKyvruPUAAGBkCTmMrF69Wi6XS0VFRdq6datmzJihnJwcHTx4MGB9TU2Nrr/+er366quqra1VUlKSsrOztW/fvn5PHgAADH0hh5GysjLl5+crLy9PU6dOVUVFhZxOp5YvXx6w/ne/+50WLFiglJQUTZ48Wb/5zW/k8/lUXV3d78kDAIChb1QoxR0dHdqyZYsKCwv9Y+Hh4crKylJtbW2vHqO9vV1er1cTJkwIWuPxeOTxePzbbrdbkuT1euX1ervVHxsLtK8njggT8jG2hbLO/vRmOKMvwdGbwOhLYPQlOHrT+7WHGWN6/dN4//79SkxM1KZNm5SZmekfX7x4sTZu3KjNmzf3+BgLFizQhg0btGPHDkVHRwesKS4uVklJSbfxlStXyul09na6AADAovb2ds2dO1etra2KiYkJWhfSmZH+euCBB7Rq1SrV1NQEDSKSVFhYKJfL5d92u93+e00CLcbr9aqqqkqzZs1SZGRkSHOaVrwhpPrBoL44p9e1/enNcEZfgqM3gdGXwOhLcPTmqysbPQkpjMTGxioiIkLNzc1dxpubm5WQkHDcY5cuXaoHHnhAL7/8sqZPn37cWofDIYfD0W08MjLyuN/QnvYH4ukMC6l+MOjLi7ovvRkJ6Etw9CYw+hIYfQluJPemt+sO6QbWqKgopaamdrn59NjNqF+/bPP3HnzwQd17772qrKxUWlpaKE8JAACGuZAv07hcLs2fP19paWlKT09XeXm52tralJeXJ0maN2+eEhMTVVpaKkn65S9/qSVLlmjlypVKTk5WU1OTJGnMmDEaM2bMAC4FAAAMRSGHkdzcXLW0tGjJkiVqampSSkqKKisrFR8fL0lqbGxUePhXJ1wee+wxdXR06Ec/+lGXxykqKlJxcXH/Zg8AAIa8Pt3AWlBQoIKCgoD7ampqumzv2bOnL08BAABGCP42DQAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsGmV7AhgZku9eZ3sKXTgijB5Ml6YVb5CnMyxo3Z4HZp/AWQHAyMSZEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVfQojy5YtU3JysqKjo5WRkaG6urqgtTt27NA111yj5ORkhYWFqby8vK9zBQAAw1DIYWT16tVyuVwqKirS1q1bNWPGDOXk5OjgwYMB69vb2zVp0iQ98MADSkhI6PeEAQDA8BJyGCkrK1N+fr7y8vI0depUVVRUyOl0avny5QHrv/Od7+ihhx7SddddJ4fD0e8JAwCA4WVUKMUdHR3asmWLCgsL/WPh4eHKyspSbW3tgE3K4/HI4/H4t91utyTJ6/XK6/V2qz82FmhfTxwRpo+ztCeUdfanNwNpsPXZEW66/DMY232zYbC8ZgYb+hIYfQmO3vR+7WHGmF7/lNi/f78SExO1adMmZWZm+scXL16sjRs3avPmzcc9Pjk5Wbfffrtuv/3249YVFxerpKSk2/jKlSvldDp7O10AAGBRe3u75s6dq9bWVsXExAStC+nMyIlSWFgol8vl33a73UpKSlJ2dnbAxXi9XlVVVWnWrFmKjIwM6bmmFW/o93xPtPrinF7X9qc3A2mw9dkRbnRvmk//+Va4PL6woHWh9Hq4GCyvmcGGvgRGX4KjN19d2ehJSGEkNjZWERERam5u7jLe3Nw8oDenOhyOgPeXREZGHvcb2tP+QDydwX8QDVZ9eVH3pTcDabD22eMLO+7cRuobiGT/NTNY0ZfA6EtwI7k3vV13SDewRkVFKTU1VdXV1f4xn8+n6urqLpdtAAAAeivkyzQul0vz589XWlqa0tPTVV5erra2NuXl5UmS5s2bp8TERJWWlkr68qbX9957z//v+/bt0/bt2zVmzBidddZZA7gUAAAwFIUcRnJzc9XS0qIlS5aoqalJKSkpqqysVHx8vCSpsbFR4eFfnXDZv3+/LrjgAv/20qVLtXTpUn33u99VTU1N/1cAAACGtD7dwFpQUKCCgoKA+/4+YCQnJyuED+wAAIARhr9NAwAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsGmV7AgCQfPc621PokSPC6MF0aVrxBnk6w7Tngdm2pwQMG5wZAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABW8WkaYJjp7ydT/v5TIwDwTePMCAAAsIowAgAArCKMAAAAqwgjAADAKm5gBY5jKPyacgAY6jgzAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACr+GgvAPTBUPzY954HZtueAhAQZ0YAAIBVhBEAAGBVn8LIsmXLlJycrOjoaGVkZKiuru649WvWrNHkyZMVHR2t888/X+vXr+/TZAEAwPAT8j0jq1evlsvlUkVFhTIyMlReXq6cnBw1NDQoLi6uW/2mTZt0/fXXq7S0VD/4wQ+0cuVKzZkzR1u3btW0adMGZBEAgJ59E/e5OCKMHkyXphVvkKczbMAfn/tcRoaQz4yUlZUpPz9feXl5mjp1qioqKuR0OrV8+fKA9f/1X/+l73//+7rzzjs1ZcoU3Xvvvbrwwgv161//ut+TBwAAQ19IZ0Y6Ojq0ZcsWFRYW+sfCw8OVlZWl2tragMfU1tbK5XJ1GcvJydFzzz0X9Hk8Ho88Ho9/u7W1VZJ06NAheb3ebvVer1ft7e369NNPFRkZGcqSNOqLtpDqB4NPP/2017X96c1AGmx9HuUzam/3aZQ3XJ2+gf+/uaGM3gRGXwL7pvsSyvvdYDNY3n9tOnLkiCTJGHPcupDCyCeffKLOzk7Fx8d3GY+Pj9euXbsCHtPU1BSwvqmpKejzlJaWqqSkpNv4GWecEcp0h63YX9mewfAw1/YEBjF6Exh9Ceyb7Avvd8PDkSNHdNJJJwXdPyh/z0hhYWGXsyk+n0+HDh3SxIkTFRbWPXm73W4lJSXpo48+UkxMzImc6qBHbwKjL8HRm8DoS2D0JTh68+UZkSNHjujUU089bl1IYSQ2NlYRERFqbm7uMt7c3KyEhISAxyQkJIRUL0kOh0MOh6PL2Lhx43qcX0xMzIj9hveE3gRGX4KjN4HRl8DoS3AjvTfHOyNyTEg3sEZFRSk1NVXV1dX+MZ/Pp+rqamVmZgY8JjMzs0u9JFVVVQWtBwAAI0vIl2lcLpfmz5+vtLQ0paenq7y8XG1tbcrLy5MkzZs3T4mJiSotLZUkLVq0SN/97nf1q1/9SrNnz9aqVav01ltv6YknnhjYlQAAgCEp5DCSm5urlpYWLVmyRE1NTUpJSVFlZaX/JtXGxkaFh391wmXmzJlauXKl7rnnHv3Hf/yHzj77bD333HMD+jtGHA6HioqKul3aAb0Jhr4ER28Coy+B0Zfg6E3vhZmePm8DAADwDeJv0wAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwZ1GLn//vs1c+ZMOZ3OoL+BtbGxUbNnz5bT6VRcXJzuvPNOffHFF11qampqdOGFF8rhcOiss87S008/3e1xli1bpuTkZEVHRysjI0N1dXXfwIq+Obt379Y//dM/KTY2VjExMbrkkkv06quvdqkZqF4NRevWrVNGRoZGjx6t8ePHa86cOV32j+TeeDwepaSkKCwsTNu3b++y75133tGll16q6OhoJSUl6cEHH+x2/Jo1azR58mRFR0fr/PPP1/r160/QzL8Ze/bs0U033aQzzjhDo0eP1plnnqmioiJ1dHR0qRuJvQlkqL93hqq0tFTf+c53NHbsWMXFxWnOnDlqaGjoUnP06FEtXLhQEydO1JgxY3TNNdd0+03kvXnPGVHMILZkyRJTVlZmXC6XOemkk7rt/+KLL8y0adNMVlaW2bZtm1m/fr2JjY01hYWF/poPP/zQOJ1O43K5zHvvvWceeeQRExERYSorK/01q1atMlFRUWb58uVmx44dJj8/34wbN840NzefiGUOiLPPPttcccUV5u233za7d+82CxYsME6n0xw4cMAYM3C9GorWrl1rxo8fbx577DHT0NBgduzYYVavXu3fP5J7Y4wxt912m7n88suNJLNt2zb/eGtrq4mPjzc33HCDqa+vN7///e/N6NGjzeOPP+6v+fOf/2wiIiLMgw8+aN577z1zzz33mMjISPPuu+9aWMnA+NOf/mRuvPFGs2HDBvPBBx+Y559/3sTFxZk77rjDXzNSe/P3hsN7Z6hycnLMihUrTH19vdm+fbu54oorzOmnn24+//xzf80tt9xikpKSTHV1tXnrrbfMRRddZGbOnOnf35v3nJFmUIeRY1asWBEwjKxfv96Eh4ebpqYm/9hjjz1mYmJijMfjMcYYs3jxYnPeeed1OS43N9fk5OT4t9PT083ChQv9252dnebUU081paWlA7ySb0ZLS4uRZF577TX/mNvtNpJMVVWVMWbgejXUeL1ek5iYaH7zm98ErRmpvTHmy7VPnjzZ7Nixo1sYefTRR8348eP9PTDGmLvuusuce+65/u1//ud/NrNnz+7ymBkZGeYnP/nJNz73E+nBBx80Z5xxhn+b3nxpqL93DoSDBw8aSWbjxo3GGGMOHz5sIiMjzZo1a/w1O3fuNJJMbW2tMaZ37zkjzaC+TNOT2tpanX/++f7f/ipJOTk5crvd2rFjh78mKyury3E5OTmqra2VJHV0dGjLli1dasLDw5WVleWvGewmTpyoc889V//93/+ttrY2ffHFF3r88ccVFxen1NRUSQPTq6Fo69at2rdvn8LDw3XBBRfolFNO0eWXX676+np/zUjtTXNzs/Lz8/Xss8/K6XR2219bW6t/+Id/UFRUlH8sJydHDQ0N+uyzz/w1w60vgbS2tmrChAn+bXozPN47B0Jra6sk+V8fW7Zskdfr7dKXyZMn6/TTT/f3pTfvOSPNkA4jTU1NXb6ZkvzbTU1Nx61xu93629/+pk8++USdnZ0Ba449xmAXFhaml19+Wdu2bdPYsWMVHR2tsrIyVVZWavz48ZIGpldD0YcffihJKi4u1j333KMXX3xR48eP12WXXaZDhw5JGpm9Mcboxhtv1C233KK0tLSANf3py1D5b6c33n//fT3yyCP6yU9+4h+jNxoW75395fP5dPvtt+viiy/2/4mTpqYmRUVFdbvP8et96c3rZ6Q54WHk7rvvVlhY2HG/du3adaKnNSj1tlfGGC1cuFBxcXF6/fXXVVdXpzlz5ujKK6/UgQMHbC/jG9Hb3vh8PknSz372M11zzTVKTU3VihUrFBYWpjVr1lhexcDrbV8eeeQRHTlyRIWFhbanfML05b1n3759+v73v69rr71W+fn5lmaOwWrhwoWqr6/XqlWrbE9lyAv5D+X11x133KEbb7zxuDWTJk3q1WMlJCR0u3P72B3LCQkJ/n/+/V3Mzc3NiomJ0ejRoxUREaGIiIiANccew5be9uqVV17Riy++qM8++0wxMTGSpEcffVRVVVV65plndPfddw9IrwaT3vbmWBibOnWqf9zhcGjSpElqbGyUNDCvo8EilNdMbW1ttz/glZaWphtuuEHPPPNM0DVLPffF9n87gYT63rN//35973vf08yZM7v9lfHh1pu+iI2NHbTvnSdCQUGBXnzxRb322ms67bTT/OMJCQnq6OjQ4cOHu5wd+XpfevOeM+LYvmmlN3q6gfXrd24//vjjJiYmxhw9etQY8+WNh9OmTety3PXXX9/tBtaCggL/dmdnp0lMTBwyN2G98MILJjw83Bw5cqTL+DnnnGPuv/9+Y8zA9WqoaW1tNQ6Ho8sNrB0dHSYuLs7/yYeR2Ju9e/ead9991/+1YcMGI8msXbvWfPTRR8aYr27S7Ojo8B9XWFjY7SbNH/zgB10eOzMzc8jfpPnxxx+bs88+21x33XXmiy++6LZ/JPfm64b6e2df+Hw+s3DhQnPqqaea3bt3d9t/7AbWtWvX+sd27doV8AbW473njDSDOozs3bvXbNu2zZSUlJgxY8aYbdu2mW3btvl/6B77eFR2drbZvn27qaysNCeffHLAj2TeeeedZufOnWbZsmUBP9rrcDjM008/bd577z1z8803m3HjxnW503kwa2lpMRMnTjRXX3212b59u2loaDA//elPTWRkpNm+fbsxZuB6NRQtWrTIJCYmmg0bNphdu3aZm266ycTFxZlDhw4ZY0Z2b47561//2u3TNIcPHzbx8fHmxz/+samvrzerVq0yTqez28dXR40aZZYuXWp27txpioqKhvzHVz/++GNz1llnmX/8x380H3/8sTlw4ID/65iR2pu/N9TfO/vi1ltvNSeddJKpqanp8tpob2/319xyyy3m9NNPN6+88op56623TGZmpsnMzPTv7817zkgzqMPI/PnzjaRuX6+++qq/Zs+ePebyyy83o0ePNrGxseaOO+4wXq+3y+O8+uqrJiUlxURFRZlJkyaZFStWdHuuRx55xJx++ukmKirKpKenm7/85S/f8OoG1ptvvmmys7PNhAkTzNixY81FF11k1q9f36VmoHo11HR0dJg77rjDxMXFmbFjx5qsrCxTX1/fpWak9uaYQGHEGGPefvttc8kllxiHw2ESExPNAw880O3Y//mf/zHnnHOOiYqKMuedd55Zt27dCZr1N2PFihUB33f+/kTySOxNIEP9vTNUwV4bX38/+Nvf/mYWLFhgxo8fb5xOp7nqqqu6hFljeveeM5KEGWPMCb0uBAAA8DVD+qO9AABg6COMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwKr/B6yk3bJnr6opAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16ec3c90-ac56-42b2-9ee4-e59b13350a55",
   "metadata": {},
   "source": [
    "## 5. Add Random Noise to the Models\n",
    "To simulate noise, we will add random perturbations to the coefficients of the Pauli terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6f48b173-306c-44ab-8a83-f78117312930",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def add_random_noise(pauli_terms, noise_level=0.1):\n",
    "    noisy_pauli_terms = []\n",
    "    for term in pauli_terms:\n",
    "        noisy_term = PauliTerm(\n",
    "            pauli=term.pauli,\n",
    "            coefficient=term.coefficient + random.uniform(-noise_level, noise_level)\n",
    "        )\n",
    "        noisy_pauli_terms.append(noisy_term)\n",
    "    return noisy_pauli_terms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f87579f-1c55-492f-be2f-4c0c6466146b",
   "metadata": {
    "tags": []
   },
   "source": [
    "\n",
    "## 6. Implement Zero Noise Extrapolation\n",
    "Zero-noise extrapolation is a technique used to mitigate noise by running the quantum circuit at different noise levels and extrapolating the results to zero noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bf6d688f-2c4a-49ab-873a-01780ace1e6f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def zero_noise_extrapolation(results, noise_factors):\n",
    "    \"\"\"\n",
    "    Perform zero-noise extrapolation based on the results at different noise levels.\n",
    "    Args:\n",
    "        results (list): List of energy values obtained from noisy simulations.\n",
    "        noise_factors (list): List of noise factors corresponding to the results.\n",
    "    Returns:\n",
    "        float: Extrapolated energy value at zero noise.\n",
    "    \"\"\"\n",
    "    # Fit a linear model to the results\n",
    "    p = np.polyfit(noise_factors, results, 1)\n",
    "    # Extrapolate to zero noise (noise factor = 0)\n",
    "    zero_noise_value = np.polyval(p, 0)\n",
    "    return zero_noise_value\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a00e42e-b6e0-42f1-a92a-0230fb9a2127",
   "metadata": {},
   "source": [
    "## 7. Run Optimization and Extrapolation\n",
    "Set up and run the optimization using QAOA for both noisy and noise-free cases. Then perform zero-noise extrapolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "01aa0bc6-8e77-433f-920b-68ecfba2bf23",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def run_optimization(pauli_terms, model_name):\n",
    "    # QAOA configuration\n",
    "    qaoa_config = QAOAConfig(num_layers=5)\n",
    "    optimizer_config = OptimizerConfig(max_iteration=100, alpha_cvar=0.7)\n",
    "\n",
    "    # Construct the optimization model\n",
    "    qmod = construct_combinatorial_optimization_model(\n",
    "        pyo_model=ising_model_2d,\n",
    "        qaoa_config=qaoa_config,\n",
    "        optimizer_config=optimizer_config,\n",
    "    )\n",
    "\n",
    "    # Set execution preferences\n",
    "    backend_preferences = ExecutionPreferences(\n",
    "        backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\")\n",
    "    )\n",
    "    qmod = set_execution_preferences(qmod, backend_preferences)\n",
    "    write_qmod(qmod, \"ising_model_2d_zne\")\n",
    "\n",
    "    constraints = Constraints(max_width = 100, optimization_parameter = \"depth\")\n",
    "    qmod = set_constraints(qmod,constraints=constraints)\n",
    "    qprog = synthesize(qmod)\n",
    "    circuit_width = QuantumProgram.from_qprog(qprog).data.width\n",
    "    circuit_depth = QuantumProgram.from_qprog(qprog).transpiled_circuit.depth\n",
    "    print(f\"The circuit width is {circuit_width} and the circuit_depth is {circuit_depth} when optimized for depth\")\n",
    "    print(f\"The circuit composition is:\")\n",
    "    print(QuantumProgram.from_qprog(qprog).transpiled_circuit.count_ops)\n",
    "\n",
    "    constraints = Constraints(max_depth = 100, optimization_parameter = \"width\")\n",
    "    qmod = set_constraints(qmod,constraints=constraints)\n",
    "    qprog = synthesize(qmod)\n",
    "    #show(qprog)\n",
    "    circuit_width = QuantumProgram.from_qprog(qprog).data.width\n",
    "    circuit_depth = QuantumProgram.from_qprog(qprog).transpiled_circuit.depth\n",
    "    print(f\"The circuit width is {circuit_width} and the circuit_depth is {circuit_depth} when optimized for width\")\n",
    "    print(f\"The circuit composition is:\")\n",
    "    print(QuantumProgram.from_qprog(qprog).transpiled_circuit.count_ops)\n",
    "\n",
    "    #Synthesize and execute\n",
    "    res = execute(qprog).result()\n",
    "\n",
    "    # Analyze results\n",
    "    vqe_result = res[0].value\n",
    "    return vqe_result.energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "39fe046f-0d01-4223-a212-dfb26bd0eacd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ising_model_2d_noiseless\n",
      "The circuit width is 16 and the circuit_depth is 91 when optimized for depth\n",
      "The circuit composition is:\n",
      "{'cx': 320, 'rz': 240, 'rx': 80, 'h': 16}\n",
      "The circuit width is 16 and the circuit_depth is 91 when optimized for width\n",
      "The circuit composition is:\n",
      "{'cx': 320, 'rz': 240, 'rx': 80, 'h': 16}\n",
      "ising_model_2d_noisy_0.1\n",
      "The circuit width is 16 and the circuit_depth is 91 when optimized for depth\n",
      "The circuit composition is:\n",
      "{'cx': 320, 'rz': 240, 'rx': 80, 'h': 16}\n",
      "The circuit width is 16 and the circuit_depth is 91 when optimized for width\n",
      "The circuit composition is:\n",
      "{'cx': 320, 'rz': 240, 'rx': 80, 'h': 16}\n",
      "ising_model_2d_noisy_0.2\n",
      "The circuit width is 16 and the circuit_depth is 91 when optimized for depth\n",
      "The circuit composition is:\n",
      "{'cx': 320, 'rz': 240, 'rx': 80, 'h': 16}\n",
      "The circuit width is 16 and the circuit_depth is 91 when optimized for width\n",
      "The circuit composition is:\n",
      "{'cx': 320, 'rz': 240, 'rx': 80, 'h': 16}\n",
      "ising_model_2d_noisy_0.3\n",
      "The circuit width is 16 and the circuit_depth is 91 when optimized for depth\n",
      "The circuit composition is:\n",
      "{'cx': 320, 'rz': 240, 'rx': 80, 'h': 16}\n",
      "The circuit width is 16 and the circuit_depth is 91 when optimized for width\n",
      "The circuit composition is:\n",
      "{'cx': 320, 'rz': 240, 'rx': 80, 'h': 16}\n",
      "2D Ising Model: Noiseless Energy: -699.7377232142858, Extrapolated Energy: -670.6063988095241\n"
     ]
    }
   ],
   "source": [
    "noise_levels = [0.1, 0.2, 0.3]\n",
    "\n",
    "ising_model_2d, pauli_terms_2d = ising_model_2d(J=10, h=-20, rows=4, cols=4, periodic=\"True\")\n",
    "\n",
    "# Optimization for the 2D Ising model\n",
    "energy_noiseless_2d = run_optimization(pauli_terms_2d, print(\"ising_model_2d_noiseless\"))\n",
    "energies_noisy_2d = [run_optimization(add_random_noise(pauli_terms_2d, noise), print(f\"ising_model_2d_noisy_{noise}\")) for noise in noise_levels]\n",
    "\n",
    "energies_noisy_2d_values = [energy for energy in energies_noisy_2d]  # Extract energy values\n",
    "\n",
    "# Perform zero-noise extrapolation\n",
    "extrapolated_energy_2d = zero_noise_extrapolation(energies_noisy_2d, noise_levels)\n",
    "\n",
    "print(f\"2D Ising Model: Noiseless Energy: {energy_noiseless_2d}, Extrapolated Energy: {extrapolated_energy_2d}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b602606c-f6dc-4747-aa85-d62754f2fcdc",
   "metadata": {
    "tags": []
   },
   "outputs": [
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PfvIT4BP6KYW+Gg0/RaiWepCw0WyTTvEbb7zx2GOPZepDbcolXgcgzaNf/vKXwDwaAjTSvBTmMbxKGHXNNdfQTTJTmxaxV8uB9uRAaArRnsRZqiwH9pYDoB0IxPhOQYQkblECoz1mvNYUnq5bt45ExmsESmRcsikE6hDPFcAAKsCGv/mbv6EUwAAYEAGuuII3XClOnazgkl+FbCRpUB/4oVqQj0RqwKoImCE/gZzg3Ac+8AGUt9wiAlIhqE88ggraAlyhB1AByJlAUAmBPFRLNmqGftKpWQuSDq6TGdsr4AfBlD7SL5oDh8hPBlBNyaAqsJnus46L1poaEOXRtCOI0zSKeoCWwGQFrIWNMBBiKMstgi/FiRMBBUnnqrp9MJKqCDCT1lnohQyo5cpMhUCjZKa/kEQ6NZOodlukgKOUpeNwHs0EAAyu60tE3H/+85/PU7hNj+Cw2nWTwoI9uEsHqQGGgMpKMHxgwhFVSMQGy4H25IAF4PZ8L5aqfeSAjto6yitqInIxdoMEDMoM+iAE8EPtinmgjrYEtChIM46TCMpS8C1veQtG1ChmGd+pUyHkKU95CshBZiokJxFQinYV4agfbMZuS+uncurhSv3gx1vf+lYWjDFZYmEYAEbsQzzlaUSGEvPkr/QUwghaFeTpvEHnEIjdH/vYx4A6wBKF8Kte9SoIUMiEctZ9kePpCKCuAMxWZlZeqQpLabKx8UmnLOA9swHS6R3X3fSCzoKd4DS2bzAT5oCjFNfJAXXSEOhLJTCEpgnEKQLTomrpjjZEKfJTA7I4S8vI6EyYoJZJD7MH5Gl93Voh9dhgOdCeHLAA3J7vxVK1jxxggGZwV+2lgrFWpFDE0K+3CqXEGaMBIWQvRnwVrcBRBneggjzgDcADDPCUdJAAeYunIAc4AdCCylRCQ0TUWJoFSBCaRMUALHhBBeiBKnCLgAIW8OMWGVdFYSWJK8QTIAltLVIgCmpmAwpXPKVp0AjsYQJBOkpabJfITzryIogLtSpTQi1opLTpFZppkUq48hTExeiMRWImB+jPWeXlEf1FkuYp/aXXPKJOek39PIVFXJWNpNMjsjEFIQDGSP88JcBMSEJ9TReI8BRKCCyNwwc4hihMNuihL9BGd4ggMdMQdUKM9kLfBSmgKeZyMJNS1KYvC5Igj5wIwTpXIJv2FPYKHSbQF/I37uxfy4G244BdA267V2IJejIcUJhhWKcSRnkGbuKKUgAGKaQDAwzf4CVxoIunYJ6O+KSAMQAtxlmXX345W1xYBwUbwBiGcoU6BaQNGzaQGT0w4z4DPZIlymeauOKKK7R10AITZVJ4Sk6whCsBA2aWXb/yla9Q9tJLL1XY0EdQQtMUp7m//Mu/hFqMfrmSCLKShwgNITdDMBbFpGh/sT2mHuRaZgY0x4SA1hHcyQzZ9Ej7q5SQiCE0EIsREwuxrJtieAyjwDNqe8UrXoG0+sgjj1A5wElnoQomUAldIELTSgkwTITiaLBZ6EXxyy0ozlo4uEhmigCur3nNawDvj3zkIzzVIvSFp/SRq4rmZOMRvYMGstGE9pdesPoLALNiTZz8EMProFNEIJXXRy8oTimdWxBR7Cdi0Rcm2NDOHLAScDu/HUvbXnMAjFGooCRoxPX3v/89alLGbm4RHBmUL774YqAFtSojPiKpYhgjPnFGeRY7deBmU+ndd9993HHHYW+MrEn+m2++GQTCeIrayEN+gArpjTjtAhvsk8FumWVdLKjBP7bbIq0CeDQEQIKs4BmPaAUDItAX+AE8yECFXKEWwAZggEMkObYhUSGSLgTTKYpgdP33f//3IDeKcUyu0Crj1euee+4BzlnQRROrhIHZ4BnkUSGEQR4VQgCRqCFss1mNZu8s6As8kxNIo9R//Md/YPfEU7TliJWoqekjJsrMWsiDOA6aUgm3iMvkp0df/OIX0ahDGzTQEDRDCUhMnJ5CPOkf+tCHYCacgfMwkAqxL4OxkAp+kxPaqJ9b6IRdNAQrSHz3u99NZrTiGJrpC2XRmtpY12cqg3E1lWAuh4TNe4QVTClQqrO7DN5SoQ2WA23NAX51NlgOHEwcYASnOwzf7J1ViYpBnx8hUKGjPHbIZEBKY0AnD0gZdZ9swFJ0C2ZgCI3ulHREPWAA2yWeIu19+9vfpnJshjWzSrrEUe2yr4YiOPfgKUiGDEc6QKuyODkR3aiQ1VBN1HSV28gJCGmdCNkf/ehHqQdoAcnYRoxSGvDjKbMBpF7U1PQIjGfPMVgI1vIIgGSXLaSSwi3cAAsRmnXvECkQwBU5lbKgFAvSSkDU7gMPPPAP//APulLO9fzzz//MZz5DETIgiaI5AO+5BTW5EugFYig9BThBPgRoNigzcdGGlCr4TD3AKoE9Uf/6r//KrEjL0gssmanEVCbqa8ywMbxitqQp2HmxOYpSzFFoHfNvpkH6iHkAO7DZKExHmEYAw4jaWGbp06hOvbVXy4F244A4VWcssMFy4ODgAFhCYKRGZNQe8Q0HwIA3ABgYA0UYrMnDLRBCNiKgFHFGcECLOBkoq2itv1ighRTAj+IUIXALhChKMdCTonijZVnLVNgGMFhhRUYkPxmQbqnwggsuoKGrr74aIZKaI2Igj2zckkeb4LY5QJsSoLSpJIryXMVccpJChVHrUbo2HVVFPSpwK3O0FHlglNYc5WwuCIugkKVrIBNQh12aQvcpSFvoALQv2GCjw0fTziOq4soj1MVcCdoc6VSu3VT2Ro2SgVkRrKMgdPJUyeDKLcTDRvLQKEG7QFnSaZ1HWg+3PJ1Wc9SEjVgOtAMH7BpwO7wFS8OccQA0BQIZ1hEEdfRnFAYnWHEkEZBQgCTCaA6ckJ84wzpwguyFrIzmFhBSHGKUJxtDvNLHLfjBKK+3oK/GKY64xtBPW8AMTxV9kZJJRxglGwFI4BFmWVjqYjqEAE1+UrR+KCFONq7ABonEgVuCFqR10qEQ2jSd4qqVVYiiID0l0DqZNT+903VunlJKNcnUTDZtjqmJohT0UzOP6DJXCpI5wjNlJkVAX9JhF3ElDMZSs84eKIvcDyfZYE0GHhFgGnFegUZojoIKnMT1FkbxykinaVJAX4oQp480rWTAIgJ5COTRR0wFyAm1ZOZFECdonDx6a6+WA+3JASsBt+d7sVTtIwdAAkZhBn0tz0itgzLpJHIFwHToJ4NGQCCwgVtGbRW8gC7iCtXkUTlM6wF7dG2VPNRGHp6SRzPjAgK7LXXuwUrk97//fVxSoOOlfipkyRYfGj/60Y+QfVHzoqQlEUTRq+JlRFtEnnaEK30BchRUwDm6o3FIAtioJ8pJhdCmGBklqswa3RIBtLjqVIMKFeQijmlOqAIXeaQsZaUcyKcgamHWbuk1DbFVF5X1U5/6VFTf6NVRwvOIhWTkYCpBXa8LAXSNglzhJFcqpDtkQJ0O3DbTr0xQ9pIBDkMGGYgon0mEYK7R/IB4c6AXZKDUNCY057Fxy4HWc4Dvug2WAwcTBxh5dfmTThEnTOsdUIT8F+XhKbcM90SALq5A2rQi3FIP8Kxw21xW1261FWRNdqayQwmcQDPMsQEqfYIEQALrx/zgUTtjOkQKdWqjPNq5OR6BfNBDThpVwpqzAWZkIAVc13SQTMlQnKMXBCVYM2hzLKBOaxeVtWagFfJTCUEbVTr1KdVyO40SbiEAr1vMJ+gdgMcKrq7RQo8WnPEKDdQGoutT4rwXJUxTYCwdhNVKg3YEfk4jgMxIz1yhmfxcNb9WYq+WA+3MASsBt34OZCmYcw7wk1PpqrlmcA5AwgiIoRzxi3EcCZLxWnWnoAuJjN1ENIUMPKUGxKxm+YwU8vBUZUfkuUiSQ/KjFVIULagNJAa2AWNIohT1qPynRUihEqVWr1AIGSSSrZn4KA7GQHYkCIJPZKZIlCGKaOXcgq/0WsXl6Cm0UZau0RABgklpFigpTpFpHae4CsF0n0dAb6Qr5jZqkWygL9wAUOGh6slJJAPdbG5F6aFp0FdVC6RQKuogj5RyOIP2AlI1g/KNCvVlaT1RbZTamfLmPDZuOdAOHLAA3A5vwdIwZxwAVBimtTqwMIo0j/sKdTzSgV6zMbJHEAgSRHm0BgZ6IIo4wBPBCXlIAUoVjaIiQAWjv653kgGhDalXyyLVgYXEo6BluVXk1nSq4pYrAfghgE/gFqjTPLFAcFf4AeTICWgBPFRIJQKqBquiPNRABlNlQD0RWFI2QkQa1frJpsWpigyKczwikRkAXdOrUqs1KP0KydBADdG0oBlQtQh0kh+WUhUTFBhOH0kkwDrtuyKosp107TvVUgOt61umU7qmAA0REpNHmUCdUS+0XXu1HGgrDlgAbqvXYYmZGw4oGFAXAzejsFaqwzojsgzzZt8tj8AnRnYSFQj1qjIr+Qk8IjSTRSKIwrivGKZ5IhiLAA+QAEJoCNgjEVSIIDlqlGqpBKAC20AaIhBAEQoq2cR5RGgmgESC4hPpNE1mytJEhEyk69o2iVo8QkFAS1dGlX6FK2qY1k1qoBXogT/aOpUTp10lVa/aX5hAHq2B+Qf1UyGTIdIjuVYr4QrBUKsdjBKJUCGVN3dWX0dzFygFDRG3p2WgOeVMcyXNTdi45UBbcaBlAMyPil8LPycCHCHOb4a4/qpJ0Z8WEX5RpJMhGiCinx8jCL9YqooeRcWVy5QlREOV/pJ1AKJ+xg61viGPkqGl7NVywHLAcsBy4IDjwLSZbvvT3zIAVtYoDIN/Co3gKIE4T8FjQJcAfEboCL6SwiMSyUk2zUwigMq0Wosz9dY5vsCvAVdqICfIzXoYlTArR5FIZp4iKCjAt//bshRaDlgOWA4cghxg9FY5iqGeuCICcezv8OaGTIV6BpsD5YyO+QcEl1oGwJhygJcsPimORoyDubBSBdOIgzBUmd6sWQJNeQ1UEmXTCDnJH8EzcUoREJfRwmEOgyc/nAmQLSqIbpCn0a2NWA5YDlgOWA60Dwd0SGcYR9CCKhWoAGC2vHPUNz5TlVRdEGkfsvdIydTewT1mndsMkSULyAdzo9kNkKzoCyvhtcaRWQFUuE+ERLW20GUkygKrFNeVKo1zC74K6jYCxCswIxnjWhZngVRCoBXqYc1Pp1Rz20dbm+WA5YDlgOXAk+cA47wO0USojTgR4GDp0qVqh69NqDwWrVE++Xbnu4aWScBMXppl36if0RRGEZT0KBLloazOgHgHitzR6+GRxkHeaZjK20K2RgImQimgmgxk453tipioRRuxHLAcsBywHGgrDiC84fGUXQaM+YztkbnfAQTALXPVBssAQpCPN8oVlhGIIJWSwiIuSEwEtqLf1xQSiRAoq/gKjiK8kgKUUhtFAHUAdUZoJ4+mkxMxmls16aQ4tXG1wXLAcsBywHKgDTmAGKZUMfhrBLBgJGcNmJ1vAIGir47t0ba6NuzINJJapoJWuRNuAooaIspAR93kh5CqOzdAVlJAYpWGKQujuZKOREtBinBLIA6QU60CObfNAjRAy54QspGomUnRqshpg+WA5YDlgOVAG3IgkpGiiBIJ7upQDwQAw4zqB5Y1T8skYNgHTCoKcgLrJz7xCY4SY+aCywIOa7vrrrvIoPIxdsscXsbaLbx+y1vegiN70rUgOw6f+9znAtKU+rd/+zdcwFOKSiL05bZ56kSLgDoIzauK0sljw37jAPNY/ZgWmczqR+52fETC1COT2V4sBywHLAemOMAYDgSgJSWJIR0MJqLXqUztHWslAKtKGfaxkH7FFVcArhwUwxFmGEhzGihXpFW4x/GfmE1x5vZtt93GSZ8vf/nLkZhJR/nA+d7Mdzjf+1vf+hYHjn7uc58jXWGbCHDLFVUzVwKYraBLOkHTiSiWax57nVcOALEsOeBQihcThPhKggAtjxRv5Z1J8J3AfCTZBssBy4FDnQM6sCMBM2jDC1BAI8CEPgKDSYnc3RwQ/GqZEVYzd0Bi1TmDqTjqY1Gd41NAYk7wBl9h6He+8x1OmCECAK9atYqzxM8666xbb731kksuYUPRsmXLeCsf/OAHP/WpT3HITOTqD7glnSuvh3dDiwjTyMpMmqhKUZwXFiF0M0k2Ph8cAGVBX65MoGJOzZUovyXuvMDxIqQ1+7tB37rD0rwrT+eDGFun5YDlwAHEAYbxaYM2Izm7aQ7o8byVQxtAC0/RGIC+OpcBO1nWZa0X8yg2eOHP/Y9//CN5nvnMZ5IHNGX70AknnHDDDTeQgePezjjjDFKAbb5Gb37zmzkiTQ9goRL0zOQnnSVehGnkbJTPB9bywAH025g9qXzhBFUdfw9mb5LJZJx91Tan5YDlgOXAAcWBVgIwkxcwUmVTxFDFUZZvX//615977rnHH388ePzggw+STa3aAGkiBHxXEQdT1QKLfWAgLvUQ1q1bB+JSCdl4ERjIfehDH8JVCvhNIpED6u0chMQCqnznmr52YTRKb3p0EHbfdslywHLAciDiQMuGO+RaFl/BXSK6NAvcIrZyhConeH/+85+HRAD1sMMOQ35FYxxRPMsI8Ey1YPM///M/b9iwAcBGsL799ttnWdxmmw8OGJT1Y+FqLy1EMi7qZ5GJo3vBaNd8msF6PmiydVoOWA5YDrSIAy0DYLT56O7pteqKgWFuActvfvObN954I94ieYR8TCLOsMBgbkFrMhNIIQ4q6yZgxFykW1TZhBUrVoDrCMToqFUFzRX05YoQfOyxx1KPDa3jACgbYi3Lv6z76qchEssj81EC+XK27PvZOhbZli0HLAcOFQ60bIADR9lWhJyq0i14/L73ve/rX//6/fffj09t8JVHvATsnFEp//rXv8ZQC4kWWRYzqwsvvJDVXCyl77jjDlLU6uorX/kKXlEoC9aCx0jP4C41gMe6R5s4svWh8mLbt58BtlfG+Aqrq/ADDBt6d7B8Ngjdvt2wlFkOWA5YDjxJDrQMgFmjVQtnpFX68IY3vOG///u/r7zySuIbN24EfRU+gedXv/rV//AP/3DPPfegQGar0mmnnXbeeech4J566qknn3zyG9/4RkyjMcj6yEc+8ta3vhUwRphWpmgNxJGMNUXXjDVury3kANAL6PKe+BgwNrQQk7tALg1Z2ETNU3uxHLAcsBw4uDjQMgCGjWpTjrQK3KJ5BjixvWLR96STTgJHv/zlL2uej3/84y95yUsuvfRSPHUcccQR3//+9wFUpGHyXHXVVSAxMPy6173u7W9/+9ve9raoWiKqgiaCPM2VgDCtEXttFQfkWCsnVne8fM1nWsSnFAgMi7bZBW130EJb9G3Va7LtWg5YDuwHDrRsHzAmUTi3QhGtplis5qp4qumIxQAz/cfgWQ9EIs4jBGLQlDEcKAV6QW7VYAPJ6JyVX9SJ7EtxEP3g2ze2H74T89oE/jdA37FS5fpb/pDJ9Zx/zkns88WDaFIssKpOACIzW4o5bpwYgadYZtlgOWA5cIhz4KAcz1smAYO+fJ8ASAAVNAV9sbQCdzUd+ARl0VETAYOJgL5ItCrUKvpSHPRVOyzQl0TVZqPcVvA+xL+vbdh9I+F6IOuW4Ymrfn3TVdffwjo/4i8pslxPIAcOsIwOWmVhTbZXywHLAcuBg48DLTuMAVaqsEsEIZgrYAxwIssi12I2BQAj45KuErDGuSWoEEwGJF2Vm4lQCYFZEnEyqOyr+e21fThQCcQD1mQ5eGLj1mxedM+sCoC14nRUggKxXgWHiSEE22A5YDlgOXDwcaBlEjDwqcIu7iGJs91Ib5Fl1WgZEAVKkWvBVIIaUqmYSx4VhYmQB8DWp7weMvDIom97flMFV10HDJ7k5MlERzWWRvblI3JvSDEWWI1oe/bBUmU5YDlgOTBHHGgZAIOadAG8xOUkkKmgq8u3pOsWYTAYoRY0JbMiLreRehnQJZAHRXQkH+OYm/wA9hzxx1YzlxxQoK27zuhk0U1mAi+N+KuvakrmdfUMhrls19ZlOWA5YDnQhhxoGQDjFgN2oIVG/CXCKi+B5VsAlVvwWEVbFoC5RQutQi2IC0iTk6eALhIwxQmk4+o5wl1dDKagDe3GAeAWo6rRyUIQS9QCN2+UzKCvFXvb7U1ZeiwHLAfmmwMtWwMGL8FU3bar4KpdBVnBV8AVkVctnFULTQrYDOISyKA4zSPKAsCaTg2kICWrNnu+eWfr31sOAL04R8HFZL4W1GMZ/JNVak4i7sTYmcQzXHR4rjwOmIT5njkfqWUzxL3tm81vOWA5YDmwlxxoGQDrWb9Qq+gb6ZBB1uiR9gVAJTT3K8o8LZ08O6c0F7Tx1nIAc6pO1B5YQY8V8/FMwqlpSgcHD4K0rhe4KRTQHlpoB5fRHsBsTrRqLdW2dcsBywHLgXnhgBUw5oWtttKZOWBcPwO6Zd+pxOJ1ELcum4BdfHKIGtqrOvGquOkgBLjlEM8cNlgOWA5YDhykHLAAfJC+2LbtljmNoVyust0XlXMZG2iCyrnW40bbvjVLmOWA5cA8cMAC8Dww1Va5Kw4AsQaAMWtn5Z6bcnnnrGoQrVdrnbUzf2yK5YDlwEHCAQvAB8mLPGC6gSMO38kXUUKLy6tS2RjBu6ilZwpWJp6JKzbNcsBy4ODggAXgg+M9Hji9cJ16AAAX6yigHTng+cAh3VJqOWA5YDkwlxywADyX3LR1zYYDSMDFcpXtSEBwpQoczxCs6DsDU2yS5YDlwMHFAQvAB9f7bPPehEf9OrhMCXwXs2f+7kzyFPrO8HDn7DbFcsBywHLggOSABeAD8rUd0ESXyk65Uounkuz+rclCsFhmyccGywHLAcuBQ4kDFoAPpbfd6r6icxbDqwoSMEbQro9BFqpownRJ1yS2mlrbvuWA5YDlwLxywALwvLLXVj6dA0ArFtAooBVzSxXMoXcGYJIsBk9nnb23HLAcOMg40DJXlAcZH213ZskBdh0VCiVZ+ZVTN2IVnEHbYDlgOWA5cEhywErAh+Rrb12n0ULnCyU/cD03jt/vYrksorD9GrbujdiWLQcsB1rFATvytYrzh267HBbJmVccuQAGVyqcCGwtsA7dL4PtueXAocwBC8CH8ttvQd85jjBfKgPA2nboiGO6EZZ5OGNiC0i2TVoOWA5YDswLB+wa8Lyw9RCsFLjcYSeR3EeGVB533LDeW3SdYrXq1auJeq3OGnDdk0w8NlPBxoW/nlRHug2WA5YDlgMHKQcsAB+kL3Z/dSsIzZUFR1WdEp4hKKcd4e3K0OF6nDDIp8RBhJ6zYWisO1Hzg8pQPTtRjVMw8GquK19F/nNiocsRhYGpjOI7oPr+6pVtx3LAcsByYP45oGPm/LdjWzioOSBSbPPOISTX8DMlwxIDgysIwfV60q93phOem6wFScr6nAeMuMxHzwBW9KVGi76GsfZiOWA5cFBywALwQflaW90plVyn4BO3kwKmnHkEDGOE5bpud3c3VtC6GGyOJmw1zbZ9ywHLAcuB/csBC8D7l98HZ2s7fosi3JVIeEMO0JcbIgAwiNvT0wP6VqtiBR2L7eI4woOTXbZXlgOWA5YDwoEdh07LE8uBveZAuPrLN4mP4K1qnV1N0CvpPhirGKzbkJCAOZKhUkEn7bjilMMGywHLAcuBQ4sDduA7tN733PYWqNXVX602lHan2sCSOQRg0sBgzVAoFFQC5ooErHg9VcjGLAcsBywHDg0OWCvoQ+M9z38vm2RfaSzCZhDYFdlYjKJjxhy6VMIa2kECZiUYLXQzhM8/mbYFywHLAcuBduGABeB2eRMHPh2NTUeObDoCgLkSAGajfEYarhMHbpF6gd6Ojg6WfhGCLQAf+K/e9sBywHJgXzhgVdD7wjVbZhoHVLcsG3+NThr0zdfE7UaJAwcNEhc5gzCQHcLlssO6byKR6OkR8XdsbCwxrS57azlgOWA5cGhwwALwofGe90MvBX0D809kXy/ubB6p//SqW274/YPcJtNJ0lA3o35G6k0mk4kE5yF5xPcDabYJywHLAcuBNuSABeA2fCkHGEnyHQphFC204DCIi+C7esPWX113069+fRM7jbhVAM7nJWsmk0mnHV0DtggsvLHBcsBy4NDjgF0DPvTe+Zz2uDGDM+KvqRlABYAB3S2jE4MTpZozDvrWKk4yKWLxZKGI4MsCMBIwKuhazRphzen7sJVZDlgOHDgcsAB84LyrdqdUnT/LtmAwuOw4Q2MlN91VcRMCwHWnynPPyefzEQDXJLR7ryx9lgOWA5YD88SBhgAzT9Xbag9qDqjtldhW7ahHRgLm0bot2ypBrFANBotOMiPoS67JfEEBOOmpBFwDsG2wHLAcsBw4BDlgAfgQfOnz0GVzjoLWq3AMBj+xYVPVjZV8Z+16cXdFOuIuEjBxWQMGpHUfsEXgeXghtkrLAcuB9ueABeD2f0dtTWF4+CA0IvOK3ys5UpAoa8AbR0peqjOWSG0f2kY6OIvXq4kyJx95qaSTwjQ6JlprUNmXrcIayGUAWYXrHQXrRh7713LAcsBy4GDggAXgg+EttrIPipRCQcxx2dPrxoIiW442VJ1ievFIxa36/uTYEA8ETF1nqBLUvPSi7g5wOuuVY251NA9au3X1liXAXRcAl8wGjC0GG2bYi+WA5cDBxwELwAffO93PPTLn+NJm5PY5EI9Xm4edopOKpzLomSfHh3kOkvKZrPjVwMskYxnHSSdczw2wz2qyxAolYIFddeshMGyD5YDlgOXAQcgBC8AH4UttaZf4RolaevNGP/DrnR0YX/lbtw8pinIt5QtB3WcbEiCNPyzg2RxIOCPJ9ss5I1tsouWA5cBBwgE7xh0kL7KNuuHGAdpt27bFglp/T5dfqw4Oj2CTBSqTzlmE6JZzuRwEx+Oy9MtW4Cbid/pCYjxtg+WA5YDlwMHIATu6HYxvdf/3yZhfmWY5dCEG1o4ODyVcf/mSBTE3GJ0sYAYtWmUk4GLeC5zObA7UVVeUNZx07BB4bgyxplaXd3hsbywHLAcsBw4ODlgAPjjeYzv0QoDTfOLIuyOjQzGnfuyRKzKJWKkajJRDAOYwYGjN5VgCdhIx8QPD4UhcDTzv+G1E9gXXLQzDHRssBywHDkYO7DjkHYw9tH3aLxwQ9CUg14K+fIa3b3f9yglHdnbl0nU3sXVEHgGmhcIkO49yHbLxiCMZ+P4BwM06aDJxK7VZ6DUstRfLAcuBg5UDFoAP1je7P/sVoq82CfqCoPmJiVi9tHyB05lJ1r3E0FgFTAVSq6WyOQ1Jdh0l4x5GWHWCovf+JNm2ZTlgOWA50GoOHEgArNpLOKYehFV1qQzkWFkiZGAwJ6JXsmlE1xrJbw+/U3bN7TXUIRsQ5cKeokc2VGuV8pL+PkytFvb1IAGv3bodVB6dkHXfTDKRMdJtPB7ndmx0NDkl7OKvMrwx9bXj9xNnXtFXUTmpXzPiHC+hcXjCscdzy2dbm+WA5cBBxoED6TCGNCfYmcDAzd8ITcfHx7u7u7llc0u5XGbgw9MhGVIpvC1JIJEibHpR41tNtNe54gBSLFUx8dEvE5GxfMkN6t0ZTjxyujpSdSc+UfIB1BKnAbsBL0KbjsVivDXCXFGyf+rJZrM0BNnM8Ljy1aIjWHdrhDhPtY/0N/rS7h/abCuWA5YDBxAH2lHCmJF9DHYqyIKmUQYGuI9+9KNnnXUWY+L5559/4403Aro69iGIqKBMZtYadVgcGhqKytrIXHEAAEa6VbmV5VxMqrZuH6HyhQt6gOQFfb1VJz44USAPKMUe4Uw6RWY+yXgCnI7ER0OPi+EVgExmPm2IzNHXD+jlmybL2J78iABabjUe6Wb0dq74bOuxHLAcOMg4cMAAsI5lKmzpOyD+i1/84r3vfe/HPvaxa6+99rjjjnvZy162evVqBJHJyUkQN8qsEYZFNIQH2ftrh+40Xo2hxRNReMvQEKrkZYv6EXUX9nVXfHdwNI9CVtEr0kOk0gmVI9uhF7OkgW8XOZk0QLkWYZ437XvFN017CjzPslqbzXLAcuAQ5MCBBMAq0SLj6ugGrH7+85+//PLLL7roovPOO+8zn/nMwMDA97//fbIxxDNERoKICFn1OgLK4sWLD8F3PN9d1vmNwhFyLXOcwe2jmFctWdgPWPUv6EUs3j4ySbp5cX5np6hwCUiNQFf0mjSRq8q+bSj+QhsTO13oVZ0KxPN9YwpCIr1TVGYpBJzWb13UKRuxHLAcsByYxgFdtpuW2Ka3DHYqf6jAwQD361//+oYbbkD/TJx136OPPvrWW2/VPIyPOkTSGSKMjOir1VarTbt3gJLF2i4n+zZU0HQCCXjb4HbPc5cM9LEcOtDXG7gx8cXhO2gmyKBrqESYS+1eAqZaWVBtswDiqmiLtQGTD13lNV+3GI/4KtIvbvm6WhV0m706S47lQHtx4ICRgGFbBKgMcIx0amUKGIOs+mjlypXDw8PkZBCMbFAxWGXpl/xIwJHasL1ewgFNjbHAQh0rYmsgJtB8tm0fBpn6+8Q/1oK+ZCyRzJfK+NsYHR3Fs0ZHOkM6snIKV5S+LAIfQAzgKxQRTIQvFcRv2rTpscceI8LkT3xtNkL0jW0k2L+WA5YDlgNTHDiQABixAxyFdgQLQBeIZbxD3Qcek8jAx3jH+EiIBj5G/A984AOrVq2iLGHJkiVTXZ+fGIpT/ey++llm230l0562SGcrXyGgV/6wMlp3SrjByudjgd+ZFlakPCfNwnClXKg7I4Va4HqphIi15I/HOAypXudQ4EiANkcq8UittKTOdgrQ6TOziEEX38NqUKWvztBk7Tu/uOGD//3jBzeLk6/OtFkkJoe/m4mF6XM7dc3SYjlgObD/OdBuQ9wuOYCkC7KCuCrassqLJhM83rBhA3DLU1TQwG1PT48uSQK3PO3s7Py3f/u39evXo/wcGRlZt27dLhuYiwcyQDc+xHfzac62dy03V2pKRgkig+7wQTtM2rwHGgU3g8BHFZ2KOQ9vdGLZ3NK+nlRDIF7Rl8569XseLw07Obb59uVy4CshSf5EfGSyApqZLyIOOTxQC3xGriRFs817B2bdANzElMxNphy/5PjFjmwCjnu5+DUPjNxZXH7LhoDbWKJWZ4MSvUjF4b++kdD4j+d1dgpjJx69//AFRS9u1rTYjJYDlgMHPAcOGABG6o1UfyAx4Mrt05/+dNaAeQmksBuYbUgvfOELgWG10qII2IySEGwGrbu6utguvN/e2P6AvqbOaHM6rjclz3tUWwQ/ZCHYyMFj+XItcAa6uzI4gzYTgr5sKu06Y2V/rBTgZiNhDkGCsqQXA2I5DIlsgrVmvqBx/FG2G/oaAgU56axcAkDWpzvDqNadjtHkwidGke9J4yrHUZjAtBHEDe8oQ5AJoiTAruilSd6Ik6agvVgOWA4c/Bw4kIywgFUkYDBVZVww9W1ve9urXvWqc8455/jjj//617++cePGCy64ACGY98ZIB0KTU4O+SYrP6ytl+G2e0cwIIToYR9lmzLMPREYVNsruJwBDtuPQBFTJpl0PNJXN1kGweNFAwpykQAcH+vq8xzePjU6MT06QMZsJHaToqnw0r2pQ3r5/6Yt+5D0DthxmHDjbhlFG+44XrFm/ruqcnGx8BTwHxUwF/I3Hw81IAr5o3amDjdOKvlxF626D5YDlwKHIgfkFpLnlKJhKhXpVKL3sssu++MUvvv3tb0cgPuWUU66//vqTTjqJ5WHWgyMYpghPyU/ByDJrbglrro0BGiq57iroo93n2VXZGdO1xR0fqWi1Y9q83ckCsOt6AAlaCrxwbN3qBv7i/gURsCxa2B9UK9u3by/kZdGU5QMeQSJOU1g74O3MG2lzXzHqcTPZ0JOaYnXX2TI85nuxulPfuGWTAHCc6YVYptFH4YkR8lnv5usXkG5s1lgrj7GQPCUBC6vm8Csx9922NVoOWA7MAwcOJABWYyvGa4YyNbNCCP6Lv/iL173udSiZ2fWBRMVTNM+9vb3wSi1UiZCZRMZ6Hf7mgY07VCnH+chRejskTruZTZ5pRcLb5mobY3YjbQfc5SGh8SgsPed/ABT5CJ5KYwDwlm1bOWShv69bARgC2I9ULZcGh4ZKVcFazkcikads4HEbANzoSjOB2h2tpjm9ZXFIEfsx6JJFb8A1wXLulpExNxn3ndrIRH6s5mQFgNFDMxuhlzHHhSVALd46PC8uN6RidyYArExrek8abVn3bMOWA5YD+5cDbTS67b7jKsWSR9EUQCUAqAi7oC+ibSRIYX4FGJMTxNU6FXfJr7C9+4ae9FOFIq7A8IyVmUc8EQeOJvOMuWaZONXEVFWkmdVYXu78vl9pXMyvuISt43Nj27ZtgMtAXzeSogLtkoVpx68ODQ8XimXWP/HDQTqfVDKUgBudCBXZjdtZsmC/ZpPVXfoqEyxsrGJMKDaz8y0OIqNKr64dLFQcwNjx1ACOzIi5xhzai4t6gA9FvIRZJBblMzziM/Xu9mtnbGOWA5YDLeXA/A7Qc9s1xVHk2ghHSQF9aQVwVQDmpBpuNScjoqj9mpBYb+eWqh1rM2t7jLmiXWR5z1x3yGGGWh2Xo2w7ZJjFjfSpOTS3giNljwVJHet5MD1vc7k5iotqVdqpsyBa9p3hodGYEyzuT8TBWEPAogVOwnMnJjhFqOi6QTYt8wIeppNxzIJ3uQYsPGyvIPMGUFd4KjJ/1fB5cHikWq8nY+ih/UfXbpbvHxpnxF/kXIL5JrgeaC3QO1ZyJioSmepb4w2RMpUoJW2wHLAcOMg5cMAAsC76qnkzoq26O2C5F+NnXhFP2XEEBnPVW71Gsq++xvkH4LAd+RMOrMLhxhhLVBmuI21TshSYXdhdofAYA0Vfvc6u0ieVy3BVjuEDgPkwBwKderOirQWxoJcl32TMKVbKxUqV/qcSkk5gIkXZSFExExG70iLMlHd/pZllXERcmeUIpubztXol6fmA7rrtw5N16bIcicTExKjcg5psRiLneMXZsH18y1A+bwy/G/Ra5G1wwv61HDjEOKB4cMB0WpeBkXdV8GURkc1FEfXAMI+i292O7FGueYjg2gntt4tQ7pcrbMkRyYYrcbaIgsHFotgi+ZwXawT0faGAavgQprSXXqXGicei5Jws14cmyoz4HICgUL8vTcy6TEpEXT8ei9PVBx4dz3V3LV7UX6+JpKgvAzBaurifu3gCJxwenipI5wP6YpCFcRZdaaZT4Ln9xF/hh/LcXL1kCg7DbdxgsZL9nGc8bWx0aO2mYfZYyb5m8hCTbwL9jrF7mEWRex7Z+P6PfvqL3/huzayZC3t4ffIGsahmF7UNlgOWA4cWBw4kI6wD482AqTHDVSzF4gaaGKbZAeo6cTSuZmQWpStbUjwsdFQU3JueaQlFgib0reMHI55gLAcSfn7VdROThcue96L+HpGJ5zuICtpQ4iWckQm8QXkLerqT0GnSYwaFc5mU65bYIJtK4qtDJGA+iIxMkrAfDil0UZ7vGKBeam4vbArY/RsXxoKzzKRY6MC117GHL0p7wfZxOfRJNrthheAmWOV2vKrqnwHgOx5cvT1fDYYneO30mY/5QtBBYBh+tFc3d3wT9s5ywHJg7jlgf/NzzFPOYJIaEX+BRIVbRuO6MzZZYcAlQmKus1uwaR/Ql5oZ+CNQ1QgGQTKEC3rxARJ+c9Nt1938+7VbhzVF6Jm30KAlqJRLdHDjtiEwdfniRQkB4BrGWcAtn4HeHtAXWTDXkVYAhiL8KAsAG1/QjXqmOtfUz3mjfp8qFi2DQVBKj447tUq1M5k86XCnKxUfHJ4Yxv6ZF1EtG+kWA3F2KMVB5ZGac+dDa6rxjm3jRb4igDdvJwxGCG7c2L+WA5YDhwoHLADP7Zv23DgrnHCVNUKx6WWQBXgRiTtySW4LbMQxy5pIRaSikp4DucdYQIHmtFU0I/vwRHmiHIwWxedhBGxz28+otlCJLiu5Yuy7fvMggIoXDlUCgMIKwIv7e5H5UceycwyShEGOg0MOtNCEqLYDICKSKgSLhRV/RobyzKoGujoXOM6i7q5iqbZ5m3QC2V4AmE66YinN58HHJjYOT6R6Fta89FBFxN9Gt1GPTGHxAcABS6LlgOXAHHGAEcKGueQAW520ulgixrDKthwgSAdrxtxMNqFCsIh9LmAtZ+nsa5gatYlRebEOwDvbx526l4ylu7ZNlrDIbYzy+9rInsrhcEsleSyqIGPrtu1AzoK+XgGpUAUgVeCLA/RlkRgABsL0gwE7hzHsoyZgT4TNy3Oh23dZWDB95m5ocDjm1w/r62fz78qFA7FYfO3GcWmaCYgXVGvVcl3mJUjAv7vzvtFCrewmvY7usWIzAFMnzBLPZfbXOC9vzVZqOdCuHLA/+bl/M9heCfqYitkRy8Ba9J3rb7rrT3evJq6Ya+yTngzzG+hr9M8yfous5cTTzpr128q+V4unN24bBgUa+ea+m1ojPTISrA8Ao4MeHp/EMq4fG2h5gE2YeEcGiBcuHEDSRSuQ7cjAGmVOOhlKwA0i98eK9ZNjBJT64oTDvF+6Nrx9O/OO5YsWgqhHLl8aj6WfWLtOmojzcv2azC/iiL846LjnwUfrXnq87FfdBEcjg8qNXquld+PuydFnS1sOWA4cQBx4MhhwAHVzf5IqHhlknGY5tipXPnfe9ciPfvKzn13xSyAqHLtjYn/U8BSyV+RplVNFFMwY0MEDXufqNevrOB+OJzduGSZRn07lnoeY9IIJhecNjTh4RGEnmHFERmLAhmBtsL8PcyRZ9kYCRoAEaaE2EZeTmwliKT5z2OWDmbPvj1TmG1BFn+X/6PAIxt6L+hZ0eM7hS5dikLVu/WZDtGSIxzH6Fqy97+HhwbGJZUes6upfVPFj4wWpoqlvZNbP/uiAbcNywHKgTThgAXhPL8IMt4yhyDEoEvUTApuOw42hlL+k88EjAxeUy0FBtr2OBs6ND65eX03cvX5k26jPHpXJyTrpHGCAeFhWS1qhAkmopmfshDRJzZpC46LJ5CMLzGAXHwJbV8hjglcNkHfJt2ZwWz3V5WR6hkdGGxmNjGV2KEE/VlpcDQLgpEkKcwqR2S5jKtqbi4ColK9RT9VNbs1LzZ3pWK8ksgKKXZg8B247Mxw+WEkHpc5EwNEEqrUWAV18NAYo6qVrjQVj6ZOso2s3jVgsrJAELpBKZk1QnuhtmNSMZCH3widSgWjFQ/2E1qBXqbpRreYmXSsn3QmKTsCL4oknUrxkFqpKgYNWueokMtmOlOv0d2f8Sn5kdEieM/eoV10nRrHBsvPgww9zf9LRhx+zYgmagLFxUTibtQrepvnsq/q5uRfEbbAcsBw4gDhgAXi3L8ug7+BwhWGU7TWT5rMxP8EtA3lpsih/AD0ASDDIybPxlztObHIKbmmoG5cTBQcY/P79j6zuWLk+fvj1v38oFQeUkH8Zo9mp4o0BmSWBYarMb98oB81yqH04lAKQVScgRRrkv2JneMovYzgQYdplNO+Il9xqHlru3bRuPN4zVEpVSlWGePOC/Toily/F6cKo42wnH/SyDl13OFQeQb3m1EtVY0YllMw++G69GI/V6/EuOnL7mi2cV3X84YvEGSMdceJ1xyvVHHbp9CadJalqV3VoIFmWwwoEzIRjmXQKLmwdk67Qr7ixmjbdMvMMw11AHZQTeqWzcIS/sq2WDxoFmuFWrMspJkmwi4qd/OS48EzY5oxOsudarOHYfO1UCjwumbkH28Mowc4piCmUhHvcyko1T81hxtxOFiacyjbHH0Xy5dZlD68YQXt8CYDiB9YPjrmpBYd10qPjVuZSfqlWKAxO0JmEUyarM87+75Rz0003ddQnX37RMYmJjfFYMJ6fZBaCDbhhNHMUeVGgu96bxFldlIfSrcaHFP1o+eg2isyqXpvJcsByYL9wwALwHtgMSPX2Jhnj8gz8jnPTXfd+4OOf+dVNv0f6SSDWMcQy+HEOkNnumpaVP9SwgAKyHwpWvC0i/m4upHOxrn43233T7+8sVpyODjPSy8ZYZ8xxvvLD337qGz/Ch3C2fzGyYXEy30STDq0yfvJfB2iuRZxsiq9/EWZNogBTgmOfAJJ6UPFStSCGaAnAFAR6kEUF9KCf5h7YWsEEW9DD+EpkTZpphoAfXkxMlU2t7ykK1DFdMEcPAHRbxieKtcrSBd1ZmiPd1MaEg78Q34VjkrHNvR1MPtA6szwssCMqWqYruKQMm+J4IezEtVNSnjh/8GlRLjuVuPP1n1/7vd/csqlUB0EJHDVEJUIB7wCUZl5j1thxOp3N5YSGAthbyebAO9MEonciJc4gPQ5RKNHYTb9fe+Vvbntiy1As3Uk9rBpUxUGKUwbHTevsm5KJRCmPgVsZXsZEpucZan7uJqq+l0pnsjKZyXjOssX9+fzkxs3DQSzppHLUAFv+9KCPf/LDFvas6nVy8VoqHisUCpCNTGw08TBLPqZDUvdeBeVbg3vKt72qwGa2HLAcaBkH+OXbsGsOYO4a1NjSmkFWCxxEt8JI6b57H/vRFb++8Z51o4y/MWdb1a8zmopsFCQDn5GeEbYuedNsUEHXfMutv/fKlWecctSxi3JPbFy/mjPrCECgm0JEuua2zTf/6b5b7lr96GCp7KYqnKaTY8uswgV5yEftAkXEqNyti2RZi8UmXQcJt94h8BMgFgKybgptZ7XipOOcOouuu5ovMwMQ7HGTkm0i8H921TUf/8/P3vnIcA2wRU1qAoIwKmTZuUuIxnJzt+cL9YQabmfzxk21SmXl4SvkWyXW4H69HFKPtH/8sUcetnTRooF+Vot19ZtsekykTC52EcgjVAK0Gedntz7+zd8++Nmf3/HT3z3MoQfs6+I0JUfUE4ZqFM5InZOjvLOkHDlsZOiOTKVUxiMkxudmZcAbHS+hBC/SdHf6R1f98Ts/+dW3f3bdb+94GLYKvHpOIsnuKNkiVWNyI5MBqo076c4UWnTAFy6ZpXtWssdLTqkw3pVN9qdJFHO4JcuXkHvN5m1UhR+WPEx3nFtuvqFUKJx68kkUXdDbZ1a9zQOpi0Lhldg+BArrh+L60VutKnoaRfahCVvEcsByYJ44sG+/+nkipg2r9WOJeq08jiv9zqScLHfOaWe//KWvG56offn7P7/unk3jjHo5j7FbZE+gD/Or4ngmhjoxVazGEdxwvr/28Q0djnP5BUedffSiVC7zu3vuEy+RrjPmi37yl9f8puwk65kFP/vtnyawqqUOFoZRQodDJt6yECEBX9C3xkf9KNPiDfcNXfOnrXlP9KLABuO956W3bZmsVd2+XC6bileCKq0TjB6dvUlI194d9z74xPotf7rnQUEthFMCDQl8AAloePdeDAN32OCL7Ihme9uWuOcvX9otVcrWGpZBETVFREUZ/6yLzn/+s56xfOliZjXqMZRsADAevJFuZw5IiOAV2mXPWTca/M+VNw5V08VY73d/dv2D6wZLhkWBX+HtSE+whnL9OIIvK6++M5ov1QtFepfryrLMig4Agnid6e7OrUVZUPjkl376rZ/+aqgUPDE4fvfa4VG6UHPETRcUV1ibltedlXoR5Xn3aTTTo+gmkNDrBpd5WcNI3aUFXRnmOEkzfxkY6E+mU5uGx0eqgvHMzCZ8Z/2ax1GGnH/eU6EKa/CxsTGUE9S761kHD2cVYADE6mdWBWwmywHLgXbigAyPNuyaA8BSGcMaVmHlyPVxZ3Gv84qXX/TUC575yMbB7/zymt8+PMyYDHxMoJ0Einwn6Yo8LHCciNWyzg23P1gulE9Yvvy0HufcY5fVXf/We+8dKToVUNZzfnvPpq1DI30Ll24aq/3mzkdHQIi0yGHiTFiDoC9ilwqBVRdB0uiLt1SdL/zo6i/8+JePDTllowSWcdjFLzG7gJPLBxYO9HRhvjU0XmaU5+AKSKMnjPobBscXLjn8ngcfG/cdjgYUWyBK4j0T6KWeGqixVxhMo17goaJ1xiac/PhIb3euB9lRsSXgfAIjZRtbrBNW5C586indWWRTpgKmYeYAKbmtyPLsjMFHlgc7N1Wcj33rR9vy5dNOO/2lz39hvlD+2i+uv/HRbROOl0wzNarGanlUEDITiSVKHFLE32w6lusxExk/CQAbdyWwHfZuHHFe8ZcfeWzriJ/pvuT5L+k7/Ng1I9VtdQdXoYYsYQXrzOie0TIgxpdYXnezmFRlcvLinERa+bZ9aAhLusU9WQBY3pDjML2g72u3DLIeAMbT1oMPTEyObV+18vDDFyahLpvOoOKemJgw2QU6zYY0c7dPF8iZ9pmpGt5p9JnpuU2zHLAcaAUHLADvietBvaOH1UQOXA9yGYBKlvpe9MJzX/CiF69eu+GL3/zm7Y8htbLex5pfQhb1kkmwbAJPC0g/rnP17/6QcL1nnHkactnJR/XH4sGmoZEtYw7SG2Pwlb+9BcOnC849Z9HS5YPj5evvKCDOsjBsUEkIA5fwbIgvQxlmqRf7X+SvwFm9yXlw29jD2yce214AAiEOCCB948Yh10mtXLJoyYJuQGLbKJiO8Cfri+QYrDrbx4rxVPb+hx57cL2cCIEZNVUm8A5BvobNsDQ8yyB4BUKK0dXGLWN+rXr44oVUJc6tjFo5neTAI7F55o4+dCNJ0ik/dL4BTHOoBudLRmc579SsQBSTgit+d9+1t/9p2dKB17/knGeeveDyy5796z/c9bPb7nl0QiRvQVnxvYyPjNgogr+ZEm3Lk5xiqZznk4UC7QKN0Ils+rmvfNtPZroWLPrLt//t0y9e3L/ksEe3DK/eKI2Dtk6pyKtED02RSjGfL1edjkUF19s04Ty8TvQNdVaejTw9OLQdUF3c30m1COIUX7mijyXnxzZu4cVBGD45rvrVlfVi/oyTjydPRpgl50/oEV6k8HnSgemCmsrPUX1PmiBbgeWA5cAsOSDDkw2744A4M8ZOSaAGL5OomkGsRTnnFS8+/dILnjY+NPKjH/7ktns3ARLlmJMns5csleuY4CAW37nVeWjr8EA297QTVoBRnXHntNNPZPi+6Y4HkJtvvL+4ev3WZf3dzz5/4EXPfx4nBV39mxsYxVlcJBjPkgrAgp2sYIpSl3mA74Ipf3xoXaxvSWJg+d1r1gFwNTGGEjF0y+AkFk3LBhYs6utCCtw2OsqonIglIZ+n28aCroFl8Viqf2DpbXc/BM2ouw1QgguCm3Jq4N6CgoumVipfv2ETitbDly6EfE7/FdSFU9xgrQZNglmyJ4krC8AqAVMKCZj5gZ4yOcNbcBNMU+5YU/zJb25aceThzzj7+NN6naNSzssvOfH408/4w+Nbv/ebO9dMwiy0/dl6PJOXuUQC0fM3927/zNd+8K2fXzlZQ67HO5mLZ25oAPLvf2j9ZLGaSKb/7u2XnbTK7Yw5y5YtKVSDex96AgJEU4BQChMMNuJ0I5Hpo4XbHpt43ye/9//9vw88sR3ltDzks2Xb9ljcW9rXnaCPnnxFlvQ5uVzH8ERhQ9750+P1n131u7VrHgN9Tz/5hEpB+t6RSQHunNhoqieBeuSqYSrWSNnTX947NUGw+W5IblPxjMUkpw2WA5YDbcSBvf/JtxHx808Kgyr7SfiAFSzyGWtnQAq1KTjzlldc/LwLzn/onvu+/4Mf33rvlu3IWNlY3udg2BQWSADwH1cPVRLZRZ25lTnMhWTPzEXnnwvRV970OwD4iutuct3YeScfuyzunH2Sy6F+g1s3P7pemjLmyQyXMmLynxSjqGSEj7HpFAPi3/0RYTmYdOK/u+cB8MBlkZJtviwqTxYxV1qQi/d0JBFDxwtQgc9pkVDJtm7zoO8m1jy+PpZI3ffYE+AKAEzgS2BWbAX29y4IbAteUfng0DA+N/GBxa1UwyPZcFuvmwVeSamyKi6qWkVfnvGcOHispzvv3DQdHwucr/74ysGhodNOOupFFx6fc5z+mLMk57zp1S+plPNXc+7E/VuGq6yNg7tx1NB06sZ7t3/26z/89R/ue2Dt1pKbBlEBPVyCaOd6enryxUlcY0IM7xHt8fGrVi5cuHDDRhGBY6iJMbkuID4L271UtuzEBx3nuj/cf//jG3MDy9dsGRo19vCYYY+OsQTv9ubSGWOMXamVM2DwwAIQ/+HHi9f85rdXXX310oULLn/+c45cku3toEqnK5ujs5NyZpTc8jFBSFPywoS9+COMlOwyPwuL6d/GHYn6M7c/9pA/9o/lQJtwwP4md/8iWBVkUM2w7McghtEyyMqQ3cHHd1YmnLe88NJXPvs569du+MxXvvGzW9aOshjsx1i0FRAFCe5+YOt4/o0ve7HPmTmMgjHnsMVdfd0dWycLP7ml+Mia9WD6Gy8/N1l1lnrO005ZFasVr/vVrwSlzGup1Y2tjiEQ0K0UsAsSoN0y7IyOjuLxEVst/E2OiAoyIVOFmPPIo2uwxTr2CGegJwcAbx8clu0+gaigh7A8qvmThcpFF16I94snNg9uHHVQr9IjxPpEosNIq02D9u4Zo08FNORceurfsGlzUCsdffgK+CO1YPjEsXysLovpsIRsAkGVhVQ5ObhaJSJiMftzJN5YEtac0RWmff+ax1dvGn7Kyce84fkXdQOZyJowP3AuXOH9xQvP70k7n/nyV//jKzfevFZ2WJH/ylse/exXvztSS/jZ/rF6Kp6Kj0wWmDjFZYohGDwyMuTWaz25dKeRyDkq4YIzOlnmv+/eu1AvS8CkLM3WI3ndY9U4muTf/mnrzXc9lOpZtG108ubb72WjE13m+7Bp2/aObK43m0nTdLncwcQLO+eujlQifsUVV/z2t9evOuqId/7TW085upd3yivlaVdnBolfph0UkVpMUC2EzFYMlDaSZ/d3WhGV32Vyo01UsP+WmjmmS5TkNlgOWA60DwcsAO/pXRhIQpxjcDdDnTiLAJN7XCddcpZ3OG9+2bNf96pXsx30B1de/dWr7paB2+R8ZJ3DpqOObHpRJon0MzQqtlIDSeeEVSuxTfrxr34V+LVLzj2no+r3J5xqwXnRM5+WjftbNq4bmZSGEHkBKn09FESni78lx0tUEs7dj25ii8yq5Ys49S+Zzq1es6lmDrzbNow7aK+jI5WJO725jF8LisWKGOcadXY844xgKBW4i3p7jz36mGrg3vXAJpTq6Inl/FojmoqtMPn3Khjl9mTgcAJxOu72d2fDCvBMLd6shGeh7Cmw1eBiowk1hwYeGgk7/IX3k5Wam0gO9HX3pZFiEbflfeSwbXacF5xzwltf+cJjDl9x05/u/Px3rvjar+7+2W2PfeO7P0jnuk89+2kVr2PTkPjW6Mx1QJLBPynLJmTmKilPxF+2lnW6Tk/SWdCRyCS9rYOjFTk9Q0y22LmEij6WdDeUnf/58c9LlfrZ5zwl2ZF7aM069npRLaFYc0fG8py0CP/iqUSlXGDyce7pJ9fLhUcfvO/Uk455x9veiFdsvi1kYN7ARwzP6/74+KS8YjHak8lK6FyLF7WrmYhpbpcX0S2ji0btL1nCOo2+HTo5FIQmYrEEWnfDhl1WYx9YDlgO7GcOyNBrw+44wKBmMMaMawhFdUFYhjQcLiOgxjIL0s5zLjp1vB787LbbvvODH9/566tXLuhfvuzwLQzPtcmnn33aygE5Dqmr10XtCNA854Jzf/vH1ZXq5MLOjmede0ZvkuXG4pKOTHeHs6QnMzo6fM1vrn/tCy8uA8AyRMvYjfWVYJkXL9RlpfnWO+92q6Pnn3L06k35+0Y23nnv6vOOWAZWr9s44caDBQtybJ5ZxMBfrY+PTggGssnXY1eys3nLVnp6xJLFfG577IFb//iHN5/3YqyXkFFZicRtRxzt694HjJvGJ5Esx3Lp5KIF8o0SoBPIE9qJga78kaqBihCfJQdRjnAAdHZlhEWG0tjWhFNZPLCUuqgHqMYEih1C3TE/7aaef8bK5X39195838133XfXTx9JJL0u33v6+RcsPvWIu+66ZzQ/SBGIwXAN4ZQlBOLlSo0/mYQnAAwoMuVwnaOW9T36xPB9Dz++8pyTAFEWlUVaN8ZcX/rmFeOTE8cdd9orXnzMEw/cs3lw3eObKguPSI5XnXzFr9SDJYtSMTMtqJVLyVTHws7kgs7MEcuOf8ULLj28R/QlCr0strP0zVEU7LxiDZjvjnhH2THsG/421SEd5AO3mUDIvMpDySHKEZQNqPqZ86E4acpvo5YDlgOt5MBOY0AriWnHtsFAXSiFOKPlNUDMgFsuqK8rdu70ZpyXPP+0117+glNPOLZUrD306GN/uP2Wu/54Q6czfuZRixh/GRR1Z07ad45fhl+mSjW/9YRlC05YxGBZLwyCMYJXT3/aU0ql0h/uvA/ZC9mlKocKs5/Fx7EU6AWWTHLcEDtb1q5P1wtPO6nz1CMX14vV1WvYlCT65yc2rnXj9YWLROHZ3+3EfS8/kget0EUbgHA2bdns1v1l/Z0nHduJ9dOatU9wnrxaXIMHdU/30+7dV4LhHr4MjTkT+XxfV7YrQnCmDKJY5hrqqOXtyjqllNBAS5hAgzq7MsKifH5s0K1XursWarF65JvT971KETA5fWXuba976mtfctnJJx7f19n99j//s+ddesQRi8T2Ox5Huy+AZOTsUDdbKNUBwkw6DaUJtBI+Kg3nzBNW1SrlP9z7EG44VBrHhwnhR9c/fP/qJ3qyqb//i2cMpJ2zTz0ecfz+R9agzN28rQoA9/QvAWI5dZBmOIUCUD9i8YJXvfCyv/9frz7v+AEso3mz1I/zL6Zu9BfPaEAg+uFiWeYf0BZpnWFNxLyQQbP/I1M1CXw7zRdUjsaict5ALIE0z7v14klUKjK7sMFywHKgTTjA4GDDLjmA7KDDGTkYHA2kmA0ojG2InCKLietD7voc5/lnLrvgpNdwBu/6NRtL+S1bBzfEcpkLjh7QKpJs/PWdnoQzOFF73Usu+f2f7nrtsy7MUr4edPQvwnViPJN69kWnfP9nVw4V6r/+45pnnnUEq7J+Be9YDKUey7x+IoWfrXs2sjBZW9IdP7bDKS1BDVoaHU8M+Q6n8WzYupGV4mVL+iCL3bYdiVSlUC7hGCvmGkqd0ZFhZLWBTmeg11m6eGDdE8N33XH3YeefSgeYH8RiaTa9JkTbvddhy+B2dmAtXiQm0ASYxnTBeOckGips5QFHGTQJwWRWAGbrjjydKZRKY369ls0u0IcJINwrG9TqTsST7B/mPXS7zuXnLT37rKVD253Tl8ru21GE/smxZCerveaURlEgCFV88iWMsFNp4ydSEjC2KjinnrDqWz917n18U563jDMPVnldZ8OQc91Nv0ft/6wLTl+O3qPsXHruiVfdfOOd9z30ymcfOzSWL9e8hUtWUHNc6gdA2QXuHNbjDDzlMBI74IDBu0CkXeED/eVbBACz7F3kfAcEcCgQUZhXHKAbx1xOkvY2KNPZkG0QnSvMp7EnNg4HtTquP7KZBCmQCAzzVLPvbSM2v+WA5cCcc2CffvBzTkUbV4gtC2Mn46bokWWEEziUQYyB36yYksY9oy3DaXdKPO8v7FvW4SxLOGemnLzo+7CXDUTdyAZi9M0Dufhzzjrx3NOOWhlP++PGRUUaKTAcFp/+jEu/97Mrr7zhD2eeegSa4QwDZh1AiaHrxIkxld12/wY/mTr96BVdjnNYt9Of6y5Wi09sdJYe5mwf3lYDlJf20xYqx97O7vHh7ePD1d6F6FllW2q5WEolE30doo4+4YTjBreuu/OOP73gaadipoPAZMZoAQo6O8tA3wlcn1i7Hs3q4SuWU5Z6hE88ECFYdM6CBzsFLbt7ACbPeIVTdWN9OZHd+KAZloBDZg5/Mi40EWDBL5h/VNI5Zqms7BZYoReVe82lY7rCLeAoi6RQMllG5kwkM2KtrcGvV5YvSPf09a7ZsGVjxTk67jGZQDHw35/54chk+YgjV7z2uef4hcrijmRyQHYIP7pm/dCog99rHHz19i3EuXc31JWwSY9Xa5VkR5ZW2GRdq4+mxetIigMrFOmZfqBx5xVMMA/IF4M+rKdFP2FUBQEq4tlzvkG7/uX1Cqry7vhIT81buOIXvxoaGnrpS1588nGHs7Rdq/i5NDltsBywHGgXDtgf5O7fBMJLmeE0hj6YgRIlIkvA7Epy0e96OEUqAmyBjJupwE/X6u5oWUxozQiYcCbTzpBTGjSHBDgTw+MCxjVBkcDfvjReT3FUg9gCJScLfiIdRz6mtmdedubAiqNWb9z+q+vvYdiWswpqAp1JV3w6kvLHBx7i2ITzzzwp6/gLk87xR64qFusPPb4et8Mj4yO1enHxEk7Gk/F4QRcwFBsBK8y4PDSax79iZzrdlROIPf20VezN2bhubUnOdpJJBThP/REskTjLQKnH16yNJZJHrDiMIgIFpkXzV75gCglmomLwxjzQhnQNGInQpE2/QNKmkldwM4szTp+PUh3FvOwqcryMaAU46wh9hNnxVasVckNrOiY3xPKbmQz18IG31UJVnFGyAUqspmiRP4Uqbq1Sbiorm6TMZCORku3Ph688Mkj13nLndrTGvPW77htcv349Ry689GWXoahY3hGkglpvwjn+mGNr9eCuu1dv3jrkJlK57n5xek1VmDjFYskURzRUecU9aSfXwRQB1XSdIwxl8ZslYMMcdf5VLJYhgHJTq+KRLdZ0Nuz+PvoJh6xWGKatjZu3PvrYmjHW542+JpkU8dcGywHLgfbhQPTrbR+S2osSGBTyaMfRi9G0xJFERv0o7qQKk0612JlLYXHLsh9LlSJuyeO4eLfwnAV9XSK8GQmlz0unBOzEryQhnUUwFokacak35rzmJS90a6Xrrr16MA/i43yLM5fSTqoT8GANeOvGTZwqdDxmzEWxuT3q8IWFcmXttrFtVWe06nrV8sKOUITNZdOuG4zgyskYxI6N4qMD/4wsukpDJ/U7Xq3CwUiPjvlFo0WvVeqyYAlJBhhoDvwDnPkwBUA3K0Ato7v4XUIxQAZgkw/C+dZtmxMxv3+gBzDSD3mROEUINodYULP0FUHbiHs8IXDjxRBwmXzITmVJlDImXZ57NDFaCDjWj8VVs40IR2DAVgoXV7CYC0bFYq5cdTLxtNsJBLKhiilLBc4k0RrUqhNFOZKZ1c/ABQOlCZ/dPy6HWUjlRkZnCoQI7SxfsjiT67zjwcfBq/G486XvfB9aLn3aU47plxmVOAEtl3mDTzn9lFyu6/b7H1o7uDWZqHfnEoLeQrVXm5wkEk+67PwhTdyi8XGxRuddA8C44pbdRx3peJLZWzkQFyHy7ZI8fALDK8N+4VXzh8p2HWhbWjPvRgjhhmplRpPtdbILxioBcztJN4snJqe9WA5YDrQFB0JwaQta2o8I7GbxfGEcb7A2Cn6xtzUc4RjmsqgTQSwZfr1YNuekBPoQPxGY+jw2DmcdF58MCwRayaSBAozRThrtslQIUCCWlWTQlCdFp4t9LKuyzzjliHRt8lvf+lbe8fLxrryXHXaT4N/tt67pKEwc2dOT7cjVEjle3iknLoKqPzy09v5hZ7ufPWbJ0h48IgOWrDf3ZXy3MlgqcR4AUDo4mOcs2xOOOYrTg2iru+68+OKnr48v+fa9W8dFjnf6k0GynkdanNi4leF7qO6PsGvWnGi7hRN+zFGGMm3gtEROvXVqBTQDBpuHJkDBPBrXJQvQsjvJakD94IdgAAM/KgGcObIoK4ge92K4imZaYvTpNNqbY94wHoi1FEKtzzkVdTcW1DzcWjoeUxB4lIwn0llgHqeccJKjIWVF00HAZbWWKUvcWcDL4V+s30kMOF4HecA5PFKV4umCmxDpWtBXrJ3IX58ci9cn+7qTYhgsAFkp5Uch+GlnHOfnCw8+tnab43zi5w89MVnt6ul+1SXHL4MuphigcIAfUuf8M7oqk+O3P7R64/h4Kb/upFWZIkctCZh78Vw3VDA94WAGuu+4WSfWKdZZfIMoyRlWxhP1wIIcDBwbqfC94KznDoykzAnHyVQCcshIY/ohrh9q0w+1mBA9wf8WW9X164U6QCZ/KTlWWd74uvFg0Osb9rOwKsleJ+YAjfL2r+WA5UA7cEB/uu1ASXvSgEUSw1ojACeNoKJG446/AgEEwI+PCfCW9cnwJvxjasAzo9xqbYib9UqME/LY22qOOlqUdp570bkP3vvH+x94+KZ7Npx9ynLJbDYib9oymHODp51+OgMw3jOQrlcsYxNRbDxfvevRcqEeW9a3QNc22eDb1dURePXRwiSnFtLeyFgRkaszi8hGU/mFsezSnq4JJ/nbex990bNWHoeUx/jM8UFVp3Pxogn8VubLg0PF0cn8E+senqzHa+nDLzn5iIU5+sSIjy5epH9wguvgMFKtv7C/m47T3RRmzaQasVsoJ0Au7jLFzaMxS6LjmFJhoAtzTWLZl72qBFyDSUksnB1x6QzOE1LmiF+fAi4LpoKCZI4jNWJeHaBpiLFnSIp7oA/FxfUIZCRiAJ7HYRRiiw2wuSJc8xhjKhYUEnHAzsjb1Uo6i7csWUXO4JAznvjjeueaW/+IV43nPOeZS9KSLiczMGEwh0eh4TjhmFWPj2xdu2ldOo7OIMCFmSzyCgVyCZ1Yyp1agAO7kCy35indkU6z4Zhb+R400QbfyKq5pYCUgh2SMu17ZJ7IRTNLv6Q35i6oMjOEIryAFXHzKaBMQD3CNMMGywHLgTbigEGCNqLn0COFxWNwpcpBeZ5KSgy4x69a8vQLn3HNr2+44uprlx7xJuyWGay3jXN6/O14NDrnrCPNgIsW3MP4eclA7+T2wt133OZXSisOWwX0EnivC/r70I0PDw8zdjMcbx4cKteDvv4BYA+7XODp7FOOOOXoJx5as+nKX/zp6JedWcXZZqo7lk0UPWytR//jo5/MZfrWrVu3aHFPPohNxtb0x5MXnL0M9CBQs351KoGzfv1GUlYsP4wnAnKGOCIS9I+5ciGDmBzpozCz7APeaQ1YdAqASaEggnEHxxJRLehkllEbpXf5l/r5IFCSA/tq8dPZCLCxXK0gDyuoy75bBOO6TzvLe5yTjj3qiutv+e5X/Vi1cOpxR15yjvSoUq7nMqlaqRxPpRTtzj7jlMeu/HlQKWU6Ut05MYDms6tgTPXoMy0rRmt3/EKpIGvClHSxISAqsxYSyBfVxi1BbzUuj0WNbQIiraRG2cNk/pCDj7o3Ec2BCTPkmyphY5YDlgMt4ID9VbaA6U1NylDpcnaBGVWBRpSZKvY989JLjz/h5DvuvPeXV9969a0bbr5n/I/3bhmZyHdnM0v7RJJLJnCdIZGTj1uVjTlb1q9h5Xj50iUq7zBQd3fiMCoYHBwkDwC8dXAMI9yevn4dvwv5sZ6E86oXXNybdm/73S1/fHAQlXI5mdhcd9aXna/96JejxaBWi7/o+S89/7ynH33kscOjE0jDLEabAb9Wq5cAYD5A2NoN61nnXHGYwJUEWhURthEUOgSnTEwgRB4Bp/xNxWNsn6lUSga1qU1zCwwB1eMTkyA9rqyksD4KM1BeW0BUbjQlRWUxlT8kZVJpGihwlCDpgm2yBixyYalEBvYBSxvkY+EAP5X1GgScdcpxxx62pDK6pStee/4zL4CThULNrNvKAi75CdRyxonLiqODi3tyncnYshxHLQkTJHAak5iFSf8kwSTi/8N0DZAVwviPbhzj7LHCODud5JmcE2V2ABvOaGfCGpTZUnUjROjbSJjpr9QB6tb4fuDSSw3c2Hs1U1abZjlgOdBCDqgY00ICDvmmceghR98BJHiR9lBzMg7z6e9yLr744rvuf+SGG28dL1zP8Xi5rj5G+vPOPgPNK1ImTqsYaPOBc+ZJx1z121vitWrS9Qd6u3SclYG+g00nwcjICKjDcLx9nNMKkl1ycg/jfizbkS4GzjkrnRNXLlq3euiX11x3zKpXd+B0KuF88Ts3PbZleMVRx7/r7a/qTYtV07eveuCmh0eLNR+3IEmWaUX2Qq5EiSpj/aatW0DcZQuX0rTih7jLiLHyaEIjlVvpGlhoSESbjO42csQhafqMGFgFYLL8PD5OcndnJ1f6Yqrb6WJqm5ZKWx0pObW3WC4FbpdImAYPqSJf5CBmeSrEkCr7ytyeLAc5OMv7uvyJbdl0+lkXPP2MIzoQnKtBNSl7c9mDHa/AU+zXPOfIfnyWdZbzE9294nkjw/+weqHCwC33NCLpvCVDoDGEliSnsyvLpt/R/JgAMrn1zCjivCeU1oY/prBkNmUlYmiVEhJkLjFDMK8DLyOSF0U8/jdYj9Y91lJilxycoSqbZDlgObAfOBD90vdDW7aJmTiQZJxndMRISex1GJC5Z9glctYJAy+9/AVPP++cp51z1mmnnnLMkYdfeN5TL7v0KQCCjPJBjQhgfPSKmFce72LRN+n29XZrGwzB3VkEPH9ikg1KsjV2tFDuyOS6ACNyIHkF9Xg1zwLnn73iEo4PePixtdfd/DDZ7tjs3HrPfaP50ksvf1kmJv498AGysHcAv1IYVItGGOSPxdGXy4qjMa3aPjyKJ4pFff1ClQlIq3RhCjzMjenU1PcNI2BSkgnxBckZ9ZSTHbGCVlAt4iaLtGPI3I7fZZw5S5UsYjckUVM7mZsCZRqBZlQCzpdRQUvVmhVWAMnUnmVPTpjfLYyO8RRmrlredczy/tOPPew1L3iKrJfW6n1ZjOrq+cIEeVX3jljM52mnn1wd3b68r4cKsYJmPd7QIzVSlbYlfwQSBeAN+oVPkYCxNhsvTFBWkuiUXPkKhLAK8VpJWI88Jil8qnfNV+FpKJ8T09JCUq0GGMNbqZ9NxlwbXSZqg+WA5UDrOcCwY0NLOWDwwch8LjpfrIAYrpGHGXwZ6F9w6alirGsgmiEahEMHDGqyaYUxlydpz1vgOUcM9A6NjXen4wOolU3gT28P9tF+sVIdDxz0y+P58oL+Rd1pY5zMMF0uJVLZ+mTlqJ7k2aef+ptbb7/u5t+nFi3/1k9+uGnrhr/9y7eevCo+gCA16nAGRC7Bft3YeAGDX46jp26jaBUSnO24pCjm8e7U3yMm0JAt7prF4xJUi9mR6J7lj5DFRTDAxAVugb04YnCoJiXFBeWkv4IndLGQF7DszIj/CEFekhrFTS93uphypAqjkthCe0U9Akgh0LCxzKowO5mY5mhVbqyju49oMe8vznoffddfThblkAYMmVNxBGfzabwCIdi0eck5Z9QLI8yKvAoHQGuPDECaOqGWG+YXYjdGTJTkCKM8E3jsyLDo7+dL7CkzQi/qevkOUMZcpTLEZfhgKjTN7eZChc2BMsjctMRWZj5BnD1rvDQBYCgXw7OwkeZCNm45YDnQGg5M+/22hohDt1VGQxeQNBuCExjzclKdk/SMRS5MqTldrsTxOMEg32HQl2FZ3hl2t2zErVVjdeynneNXLkEIziXZGSWDLKMwecBicAhj24mSMzzplMo1ToMHv8XXMYCE5B1UexI4lHJe86Jz+vr6hscnv/yVr4wOD55/zmmXnnN4N7nGnAUZOXooqBQ60uCZWCazGQgVeAnsMEIVXqCLlUo6me5iu7IZ3lXYUuiCUoEdGfSBVvkIRBoMUAA20IMCG1NhEMo8k92zIZBW5MS+gF28WkK0tdK5pmAecN/4K4+QoOk4G57oJduGhQABbvlL+aqPCbcwmUBdRXZVGap68bhdkw1aqaCEqXMqYAMxynauQUcG3rM2T2lhKd7CTlrZ95dvePmFZxzbjQusss+5GYKXApkwSYihetpropUtYEIAIY13Zgzf67jpkjwCimQPYkj3U93YPfrqBE0aaQ5TZWiXautGU63fB80nfkBtsBywHGgbDkz7DbcNXYcIISLoxDz2EMu2WN6FADA7chjPQcqeuDl42HEWppxu1nTZeQwMeyIZyyogyBLzMgkXFyDPPP+crFc/+egjxPci+2cN94CKgf6eWCqzcZuzadBJd2SXD/TzCFtrEeRoulJOJXxMezlX+M9e/5qVK1fiurI/4/6vV7+w3+B9N+AP3rKL16tXipOsJgIWhSqSFWrodLnio8les3F9LfDxD8XwL+AEEgnS6EeGewED8xGBEJUsmXgIGfEE65Q9Pd1IzIVCgWyhJ2TZVxRHb8qm66HhYY4uXrJ4kRSgy+bsYuBNa5dEQjP2CsBIo5hQd3VkIXN4fEwmHKyuO3FmDxQsVMu1YrknKxjMkwwLzKYG2J6LC28X4HKS46PUf6RiqWknbeYBJCCPk63L7OLmTXXoYvcUGRBAOzI9MR0lykSgGk+LhzJcY7FdK+XFx8bHlS2yIzqI5UcmeOU4LZVg5gxcVTNPAm6i9evBFTWBUajLt8V8TJEGFwxnRAIu4AuT2rx4Hq/TcFtWOvh6UcQGywHLgXbhAAOIDa3jgIzaTWNiY8BWKxskT8HKSI4ymat19sqyExdpSe7534dTyoGev3zjaxYv7u/A2NbUJ88A7I50afvkcKE+NimH3ndl2AMsMClBUMF3knWKdDnsZXKWLexb29Pxquc987huOSEJsA+BLgZMIt7WEW0h0PMQhSlMMwJ3FaQqD4LwNSU1027YCf0junIkZtMoKeGzEDW1E7IPSSqeCkiu3FN5qVTmbyohamyTgHTZxC7iQsL0AA18VAKuNFZYKUYilFAzj4iHFfGnUQdSu3GZQS6SNHV6RvpICeUhOZROA48YSsuiuCSZRQR9ZG6ZU4TybzrupDgfwg9YV2ZCYJrhqIxYtqe3QK/Fv7VwWpCS96w7uqTKkFgmK7j3irPjGaZhP6aqgZABgrtUCMVcBX2lHVcVEvptmc4pe285YDnQUg5YAG4l+8Mx2pDAECuQShIf+Y+Mq1pNzSW6TTmnFoBFNpSABMdRA7EUBlBd7sKzjiGJClDbushq4m/L6R/oLT2xfetIfttoETFqcQ8uMBsAHMPHBEO+Xy9NBukcXo7f8vKnvvnlT0XZyifmVAQLmACI/6lkUoQ3ZFFRFGOCZdpB/BUhkcP9EOJZI8YXBk+FBgECPgShkzx8COIsA1QIoYQcsjCrEANIaFkgHnA02CGlJgp5pGamEaYCczHTDmLkURQ0y6VTz80TPx54aQ4WxNMUa8C0KDVKQBiEj4i4QoxZ2ZW/Id1k4kOzGmkwWZZU4Xh0TIUwQaDSTDhQZ2tXKTb1WzJJmi6PRR0vAExBDkHktOY4rj08VxbUpY+Vno40visHS042LZWI6pzVcNbDzYsWzbzr4YAauPVwN40XTUOi0Yjry2h0z/RRqGNHWYX2YrC1Wp+a3TSY18hn/1oOWA60lAON4bClRBzKjTNWMgrvFIwMSCqP+YQAIsAQF8gigh0PNlt1OcPOGE6jFPXl6HXyBxgok8inv7fHj6W3jE5u2joYC/wl3XI6D+lSJfKXUWbHWHvG0ZNRcfdiO22037XKpNCFiRHYF6tXpWqfHaXUj9BFDUREBAPSOGQn5uItkkZJlO+TDvM8M6vR/KWDU33kvhHILJupmEjU5KgJgi4Mi6IU+RxwmixgQ9yFz095plMQiTYFLdeUIFSIGl+soCGvguivHZYH+QqUBOxBaoClkaB5IIUIEXFUy8egoXRXWjFJTH/IQ8dCYVcehB0U/brcRHWYdMFGXqZotCVKjQBwkpxBAERKXTHZGAZnr7vtgRvu2chrwTBN6jEB8AR6yc50h/L5Ym0iX2bJgLRILdDIK38NcRIpoztgFZzNSFVxDSZmbUZzHXZUsthgOWA50GIO6ADSYiJs8ztygNFZRlwZrjHvYdUUn9NoHXEl6HI1rwzTLUTFRNLDDzB2RrU6kINtD2U6kvgYFkAhuasz6yY7to0Xtw4OsZ1mCSfLGwBmKMfzlhzrJJrKgGN7UE3HfX9y+3ZZKQV2E4jBIC2rnS7WvNWkV3c5P0AAOC0ALJtnQVxCqYINkmznpVH9Mhn8BXBDoyRpywDDFDhIOclOBWANXcW9l6SR5OHcOIQwALho1i+7Ok0VAvcYKgma7BxM0w0zKCNbZ9JJqioUi0ImFBieTGI85vs8EvtkU4up1MQ0k0SpDAkZqyg+nBIhdZOZlrWI5tbuCK41YE8aiW4wLDN3In7CeplP8FIFgNnclUt01EvVQl6yp9K04mwZc35y7fVf/Pb38MklJTwReYnIQoFposyWZGZamXgHrp3NzibpHZ42m4LpZcjtQom1CnHdyVvTTDq5acpuo5YDlgMt5oAZuFpMw6He/AzvQJYAFXpZ7QN3U0h0dfkQQRJyRSjCdyUgauQ7ka8CX84ubATGXMZ6bJvZEjQ8mh8eHcOGaHFfNkl5Mx6L9TP5K1W2i1IMAO7yqksXdIqJM4276bITqyJle16Ru44MAFyt1tHDUtzDpRZYKEpZTiIqgaQAMNulGPsFqMgCdtCKNmTgT7FBU5pxDHkenGBpU0XkSFqlZpKQX0FKzk8MaxNIagqCaBKmuq33pnV2RnFXKJckrdHrfBHrLpGAoyIKk0318kR4APTSQ6iaeiQieNiPMJVnRsonW+OBqbhRhkSZRxn0NZuSxDqMJfROnFjGExMTNYRgygLPG7dNDOZLm0ZHt2FWbgKmbWGdTLYMa01r0vIT67eNTfIVUH5r9vCqLZNfHH6xpQ0TOiRgk2oBeAdO2RvLgTbgQDQQtQEthyQJBrEaPdfhkyQTWAc0WmD86YOCHgMqEYBBEE4eNYKUQkplMDcOLMyYzrCfdN2+ni7knxGWUvN5xL5FrPRy6rsBx4mikRMxwGYnLkcc4XPKzzuVkgzwqD1JqQWVeKrgJEaCYizXVeMEPcRlngryok8V0KEgNkGM8vjl4JtkFm+FKsV4yWxC4294GyYaWFAAZg1YDhRqlAc/uAMqRentuthih6EBwDNWqJQ3siLEJ6EP8gTG5L+ESqWGIjaZhDeaEF55Tp36AX3pkWIqKWFRfTZVqLH2a1KislPPQ2iW2YGpBHlVmpTpS4CPlFwikSoUShVOUjJW6Ju3j2T6BjoHFq3duKkM9tJug2YlNZmQGQn5f3vTHz/3X1+4+rrreC5OuHYR6DhPAF1W7hts20VWm2w5YDnQIg5YAG4R402zDMm8AP1IQhMqMGAzws44smtG8jKw1gFF8WUYalRJkTfKRlpTWcxLlKq1SbYAV8rpZCzZWFsEnnMZo94sAKcCwJ1ZdM5mgZcmDWbg3IqjGLnBz3M3dsjsflE5deorIyiFVIy9F0cnRJDGbiFfzshDjhZisCumubBQUwelNSOmkwbYlFjX9MV8i1sEN7AYZBJ7skAO9zWKXHTmcvQeVeknBOwGbJsW6SGeTKRhDqWIUaHsnJpiLEcL++zDwuw4Ikkea6BWGg9JjGIh5WQJnzRijRxkoFUj4CpdECkVahMK5HAUozaAXaYuGHSlM2Mlv1D2Uf6LMB5zNo/iZswdnqhuGJrMmwOEwU7zRN6tfA3Y9V0JMDevxnMPb594bPsYX49woiOtkUXs3yMamS2xSCF6i8CcfCz6iobGQPLbYDlgOdB6DvCbbU1A6NHxPNwmYahA4kFWI1oWGxIRgNSTrZI4OjqqEdRrqk9TLz9jY2OazrW5tiixnSM6jOtV6Gzc81fHXwZ3YBBRRz8M64z1PJIPy7FInkANCbLRU8xmydARkxNiKXXUss54MjM5OZnLeH1dHcjIcgie7CQVzJDWOnM1N8FQnpStTSmqw/EH43Ta8btjrDn7Sae+KNHdQ9WlCiu1JbSpICrgiv8nL4aQVSzEalW3uyeFw6UYC6aOz5pwTRatUS5DIGcD++iCIUb7I3gofeQMohqJaJdTcc7OS+Aws0iyfB9rXr0ALG0bAbXShy/opDhL4SU/FSQ7Wen2q3VYIfMTIyhSQAOTDp4aG7IYPjN7+mOxolsbNSugSSRqAfHxSTyQdKVzCW6gQk9KUKRUBDW8hXSpCBbxMQxXmplSCMulJ1x4Zl4DOc2r4R1gUYaRuPm4smjPIzgyiQsUsvsFpzLKu2JR2utfUEp058uedJ9vu+Os3gqgZpPpgbse34p7MxiLktqt1NwKxwbjc7SWcqo9KTGcfnR7Pb/k2NUVmaywoAAhEkQ9XsccwBAnCRP5yVgyybsYmpiUOQtkxcQmywbLAcuB9uGA/DRbEjC30W2O6BixtQEkgFWWErPZLG4ZMAQFnsmjELt9+3aI7OnpAY9JYXmPUqQQp1R3dzfpoDUpCsBaqiX9msNGBacMADCq6oDPVbqtcBC1JEkGCswzLnw0JxF4BTKkYk5nTk69RSriaF6AWEsT4xMGI7ayHimaZRTNcvYf+3lkeVgwBjPcQI55J78O40aZipDqsXMIn5fIWkb5LevAoI4AFPVQiQH78HtGNpHajCo76pTsW/Kw7G3UjCxcoWi+jCTvdSSM60u0x54QTc2menMgREiItCPIbz7aWsA+pDiqZCkjxLD0KnumUGvHMRKXExuNyoCCEMaVjwnCRr3lqmxvPNJMhs8U0kymV02vRhBaJkMyjxAU5yOtyzyELrP/V7YAQ4ebStW89GS+bAAel2bOcKFcKQdo3AfHS0xEpBQBQV0MwFnuNf01681FJz0ey3KQs2SAl1OYKgwwb09K4wKMV8JShWwFlqwI4VNdkQQbLAcsB1rNATOEtIgIUFNcIGGVmsnkcjmggjgBKJXpugkp8Qnh9Pf3g8dgLat3ZFApGdimCEHJJ51I8xaOFnWrjZrl7fbkOmJBjQMDBuR4YCM7R8piQ2nTqCzPp1GvEx0SmRuZBUV5HiVSVuc96tJ5Wtnd32ol1KCvzBzHMFWCdzkxyXFNXhbPySZZgNGEqPXwfsc/ZoEbcQ+7MKGT03BlhiFdlv+qUJE14B1LzevdFMENzmcyKSaXQ6MjdBN05cqhVbUKy/L+0MjwmJzKIRp4MxmQc5GjANlohfB9omyP0neMyEskA+8LbbzYuQPTEt0xl72zHLAcaDUHpg+4+40elMyMgx0dsvQIDKvOWSVXJFoEYk1h4CADx9IBwGAtGSiFlMz4AmwrtVu3biURqNZRiauFYeUMQ24v2ud62QtqSxYNkNgYhWUw5t3rmLz7LwGyItngMO9Cz5YFPLR+rkAat7INqZHYaCLKsmMEzxqIYqLqDSuhLPUDwI0EoZKUcY5Cct3O8CxCkSY1mIKmrFLfSNe/+oVBbjSKebEBNggcZmIOR0xndTuWm687CBU/mCqAis9oYXtnNsd5UqOjY3y52eaEZD8+MYphOcdSFYvlwRGRkuUQIzoonjRw/aEvQYiE4fwWKhifTwsNbqhITDYDujEyi2rB/I6sCnoaz+yt5UBrOWAGslaQwLBLswwTCLrAMFduAU5GTIYMBGIdJRVTu7q6AAAgmRE5HGFjMZWDuV20aBFltSARalZpmPghHni7fZ0pr5qP+5WlC/sbQ3QDeGfgzlSWaQ95HbAaANbxPXqqL8WAKL5BRNraZRWNMuHbMbuPSEuzgwlRtcLisga0uGi+nZGxCS8e6+nqokJu6QsRuUYILzrV6V9gY0Am9STYxuV59Vog9tWUN+ADANMLpm60Nb1k2Prc/2lMLFBNy7cXors6Wab3JvOc5iy9Gss71VK5K51cvngReLx5cAjwlXw8NqviQhN6esFhrN1LOCXjh9NEKJ02H+lT2C3ei7SFe0xOuhAlgA2WA5YDbceB/TYKTe854wJAyxjBg/vuu+9Nb3rT0UcfDdAec8wx73//+9HIaQFG9gcffPC0005jKDnuuOM+9KEP6fiLKIwcfO211x5//PGMs6tWrSKOiKwFtdrpTR5694zXi7pzKb+Mx41F/bmQAdGYbkZr8vCRIEO+gSpzN+2iEjBDeaQRVWUvSMAbCQHYvE1qkZe66xBNj/gCkIuyXKknqlmAChX0RB6Lp85OTikKzX2pno98AUxD0kJIegN2SJCnct4RwibzOb4J0My5QHo0UKFUppfsA5ayZo22UYEmzMvVTCygSD70nT+ccMxxyGAk/eczuF26PtDTdcwRh+HTc/2GLXQfqzjZ6kswc1OJmFsphQRcKVPPVNixG2QUfgr+yh5reWvm5U7ltzHLAcuBNuDADr/i/UkPgyNSL+MjNsxr1qxZunTpBz7wgQceeOBLX/rSe9/73s9//vNDQ0M8ZRw588wzzz333HvvvfeDH/wgeT75yU9CJ6LM3Xff/axnPev1r3/9Y4899qpXveplL3vZQw891Nvbu2XLFpWn92d32rMthmW8X2W9atqtL+puolFwLMQ7/QbIAC7/G9+HCOEahRpgNjW30ZiqKJTh0TMFjkbR6X+jbKARbSY4hDHwedFN+YS4CTkokPMkRFrFhomcOxHVVMJElXrqx8iZuFKFs2rkYEySqFNbYTJB9t0TOb3qfboXeqBEGSvthRJwRyZR99koJMphPpsHt8c9t7+7Y9WK5QDlmvWbRALmY16Rcb5temaYUOc0KmMnEVIklc8QqmwZNtMRseDCAmyPvJuhDptkOWA5ML8ckEWplgREWMZ0mmbF9wUveMGFF16I+Mv4uHz58v/4j/8AZf/lX/6Fp1/4whcYRj/1qU8xaCLs3nXXXV/72tfe+MY3ArT/9m//9prXvOad73wnK8Tve9/7rrzyyg9/+MOf+cxnFi9e3JIetWGjQNeCbLLTq3V4bAcyA7oZyZEkddzWq0mDfP6GO3F37ouCGUN/gBespkFfQNTFc6LUsedRHk0pdrrSOPmpSkBJa+b7QBLAE5eNUiIB58ssg+JlWuyAuZcC0oRMFChIm405hNDcFMQSWPIpVbLkLCbIkp/6jcyN0bbU2VRm/qK0ahpCfSwEsHObayqBL5Mq/jYASTo1Oj7JbLQnnVjUlwzq/pbBYSYj0tmwJHZYVRGjTT+BbXpUx6MmKWQw71IywhaTRdLRVEs2YRuZFXxJhzUNekwme7EcsBxoKQdkUJjvoMtRtIK0RFybA32BW5TGLOUSAX1JZyxmKJ+YmFiyZImKVg8//PCpp54K+jJ08hRR+J577sEwBwkYmfjEE08kHV00ZZ/97GejhWY5maFcm9CRnbgOu1q/PjpErozay/tzS3syx65cClc4uLbI0iBjtcFLRm1e/9Q3AHQ0Q7oZzeXSHHgvLAOj4WfjsYzobBIziIItOhjc09NRrYvtOnDAQD9VZ3MVJo6L41RKdM41Y0YHHOlif42lTRBCCnuKLuMTeQgdWNBbKlRSZk+TLqYK5BMTAnXh09RrGpU02e6LN03ZddTV04vDqbVr16XwoSzHNTljE+MQyRqwMEE+tDfPQX1JSyNwxY3HE3w7+3pxxRVs2bYVIqFk9Zq1eKY88rAlK5eIjJ4vltQjtBFiBWGlSwTMtNgJVuZ8qjiuVsbYrkTx8EXxh/qnGM/PyjM/Maaqw8OiSyBEluR6a6+WA5YDreXAvEvAIK6OsIzautpHhxlDGSDUwBUkVpWgQuwjjzzy0Y9+9GMf+5hmRuS96KKLKALikoI6mnpYFT755JNRXD/1qU9VlEWPjXzMwMr4pfVTm9YAkAMSlOLRhg0beHroBEbl4w/v/LNXvnhhT45DeAjKE/aIGvhUiWhW/FAZlxfXnBtgEDBmZBcDIwlgDAnydxfB5JFnGiEjNROfqhlcN2BZrrF4WePLE1eBVXW5YK6QANIYSqY3hJY2BCye6HdjyqkyUzFONjBrw9KuUDG/QWgIKTToKFpwccKNX0mln0kHy9ETuKiulBd1Z7Mxp7+vl2nq8IizaCHeUYQ8KDbTDaGcIPKvnDwcNz42NW36lWZQvJuMzGbkIGemJJQPaZme3d5bDlgOtIYD8z4KMYDiwepf//VfkU0ZZzUg+K5YsUJH3ieeeEJRk+GS+DOe8Qw0zH/1V3/FTiRYoluS2KeEmAtONzu9opJNmzbpTmKqAmXXr19PROFcB/Th4WG008uWLYMMTiZA0d0aNreoVQZwtmo95djFRyzKMeZzNCz8YXSWE+5C4c/AmJJndJi7olSRW3UJvETFWIQ5UrgV70/i4mPPIzxidlOTAgmsAYvdlFEOG5BhbsBBCM5kqYLfrlxHJolaBBzy8RFixNawFQNphtwwXUlnsdcVWZ6+QzO0qSqFh7RrvmnhN2RXPZ3bdCgRfbrAPWRyFeuwNG4yOSIpcPHYxYRxdCxfKZcPW9yPJmfZsiXlap0vNpK8sU6jGPMkZhVSlsQyG6vkyGCvhHOOKRGeiQn1TwV6LSyNedV6wGqzyr47Tp+mMtuY5YDlQEs4IL/qeQ2MAniwesc73nHTTTc9+uijWEitXr0a7fGvfvUrRNiNGzdi/KyQiW758ssvR5P8X//1X6ApO5EgDNU0ak/AmzgRABjcBVC5XbBgARWqzhlhGm9ZJ510EiguyjejLyUPoAv20yhkDA4O0iiJh07g7WJuhOxTk72jQcocl4RBUiwuZ9AaSAKVkJUMS/S6C+7oO0Ikbc6FWhsxC24DwGaPjBQGH5shdhf1ieArmRtCeQSTJPIIdCkUy6wW53IcWCzwoaL2rmprpJvVX3NDEWhGIi5zAIMIjpKKHoUrs7EGWJnum/zzdaEl05j47zQAzJ8MQj7qele8faFIHp2cpHdL+sUmYtmSxbBi8+bNkbrYnDxsqDNQXMYIC+dWXqJQEjylWyG3Dds1hdxMNVivTySSvLJm3s5XN229lgOWA3vPAX7y8xtkoAkCXFkRkEoZW9m2y+hw7LHHcgUWdCAGSt/61reCoB/5yEfQWqNMZuDg6Ytf/OK3ve1t4s04l2M168c//jEFifP04osvRmKGesZZlo1/+MMfoqBG4tElYRqlOK2j4iZOKwjTZJjf3rZZ7Yz8+AfmsAVcOMvxhcZsySgkjXIzBF5jXGuwIRzKZ+oFTIaNDOWN4V5QRbYFNwB4pkIzpBkwknQRowOk5xjHFkVW0CrcAioAJcCZTKfMgT8CqxGs06KU3V0QNTQtALTkMlIvFQgQKgBzGKCUDrDx2l0tc/LMiL/StExMTHNc0EakU4liTQCYeQiifkcm04UXbtbsFy9im+/gtm28IaAafbuPzsIst/MYLnCCkpy14cbyQLH4GJ058CPynRTV1idDC3Mqp5wNlgOWA+3DgXkHYLrKCBgNhYyegCujOehIII7Aynz/RS960RFHHIFhMzIrm5EAaQRchksEYpAbjTQyNNk+/elPf/zjH6c41b72ta997nOfyw7gV7/61T/60Y8Qpr///e+TjsYbmZsxWrXQADAwTJwhCXvp9mH9fqHEr5cKsVSCo5AAMcVOzJGM4NRo3yBTiMWNtJ3/Ks+Z94jYBTKIOhSHiBJjctMMAyShQN5NQI9qLJtxi4yONMZRwmTm7WgRyKGJUgVH335fV48c1RQFQypfoShhpojQx+vG6zXfOkEsOahRSKJYsSxxlChhQVPhTJXMURr1K7EgprjBwucVKCwUZrOZyeHJYtkp5dmNFAwMLDBrBM7yZQvhJ+ocFdnJH2r2ma4Y8Z55CXZd6JSLnDTppKhLmN/MJdMmK+jUg35owvwAoYJi5lSGOeqarcZywHLgSXPADKVPupbdVIAymaeKhQx8BI0DiqAviIjAiqUVdlXoh7FqBji5qnMrJF3Cddddh8oaeytE4fe85z1cUSlTJ5uAf/nLX37rW99Cif3d736XDcTYTjNygb5RE6Cv0sZgRHM6D9gNtQfbIzbE4rOQYwACHFHUylVMoMVGSkHBdFatlvbcb+Ukc6ZwHdHMb1Br877gbfg1akDjjnAwvXJjpyyJ+pq0ZlWTqmRLtaAmx0Hw9qUqQR50J8AMcBNuXlJo0+sODQgNYZ7ICk/Q1/SZuSD4jStoQEuAsJkTO9QydzdCTgOG5S+EiCDa2ZGFmcVCecvgdkjq7++DJNTOhy3luERveHBbqWBEdA67cjlkiipkAZg/nEYikrsbY5eWpO4iMFVSAGZ2pGcD8+J2/152UZNNthywHJgvDoT4NF/Vm/U2HZ+xllI7LNTFrOwi8QCHRPDkjA8NxiCGY81JnGEdQRalMYANGIPQUKhFGLbY6ctISp7LLrsMIZgIQK4bmYBz1okpSAYV2qiWOPVQAznnr6ftWDMukbOdcpAsh9Emk5yLBPoKH0Ja+SuwFlHOTWNMR+4SycpkkEwJF1eRdWQuREhjjywgJ68s8OPGQEhqEaUueVVClsJRCNtwfU4oAgmMRRHHCQsqxmL4OkbcFSFVCqN/duQgvphb7cw07/E1D2m1AfMm74wX2Z4E9QmPI3jZbisH4gLidKheF2+mrADz1PTCwPKMdcxZIr30ULVTn7jUCGqAPpOJVDLGXGa87m4bZ07g9nTm4DyLBAsQ0N36YL40VHF6kg77pbApM6oCth4Z/mD1xhHObpylYOqEKcq06T2p12KJIJf0RjiD2Pgv2zPb5qzLtiLLAcuBWXFAf7yzyrpvmSLpSA2pqESRMhJGVdglPcqpMKlqZNKjgloE0ZlEwFXlG82sdZJOAH25kgGoJkK1isTEDzkAZnB2E04i4yaZfzDsCyzxIWJcMvD2zZ1kEyDmEnNQK9c4GYGPDPhG6oJ1vSkqKo0WCpw6K4pUeeSVJvOpWCzNBjMpyJHxImqGIrLk2TEgvwKDbA8ja+AUMFdOiLVzT455QalcFZ9QflmEUs6/3Tg80RGv9qSNAwpA3cN3MpNF6ERlTWtKb3htasacjuzG2LlE4mF9Sbc+sXn7BJMGCo0WqIYGK90pkaVrdW7YHDzPPwE4HQ8PgBTkRfx2YixB92STsXT8sdHyvZtGEPSXL+yDMkjpcpxTjlg+mcjevkk3+sJY4Rj4ymG/k2NONhakOWiiGjCjFdI54ZAuGdZjWK3a/xF2UGM7Xc0v7Iyn/WqhKB4/5rujTW/BRi0HLAdmxYF5Hn1mRYPNNH8cALH4GOgybhgMzsqQbcZj3r75mHtDBAO1iIsy3HNvpCouPE8iTWJ0hWvlhpSM/RRTnBgSsDnux2RGvcosx9SkFyPqTt2L/hV9qihD2UhjqmdhUmTrkpyZgPdmaRSBfaxU8fxqLqWooXROVbPbmGQWKROaXfC7hNmYzAtkxxQ98TmpUNQ+ZmJBwvwG0ZlLozQ01RYL3xhCJ2IclTxZd8fLtWq5sqCnWykBm5f295RjyU2jRTHWgm7xHIZfEXEtIpObwJfTjcUVilRJN7UvzG5I1G3HOgdCAZDyQO26z5nNUCHvxdCjLdmr5YDlQKs5IL9fGywHdsMBxdNopZZbQQGjsGChFoVwpGAQGXuPweAAleiHAgkxCguNsHgI2lA/awrUrK5a9ljlzhkAeupXWZkjgYEfAmpuVCAkhlMEcrQuqF4HC0SWWrCTWLKkD1qUoiNWrnT9gE3t4LR4dZMlYwn8keMgG7eynt3ogkx8NI9J0VMj6SmvBjayHqxP7dVywHKgrTgwixGzrei1xOxPDjTGd/6mEmJ5LkvvDQJAW1blGd91fV2kMwPAIbw1ss3w11Shum+ibEMij25wUkShBjaeAZboZmcoPoskI2M7iTTypAfNWoII0A4mKYV7pnMWDe11FhFFpZB0zfdHh0fGRkawj1u8UGRZfbiCIxnq/rq1a2WiQ2bhKiIs8q1TqGCNRmk8PMuWKlPTDiRop7THLNkoADcbuO2Q295YDlgOtJQDFoBbyv4DoXHVFKfw3uTLCXoM+iABQiXwUChz5l0IwOHarzngaHfdorzRkCp48P1TOVUVqlWziIkeVbypsKU707G7qmZ8hiFY2AIONzIQWa6i0hZqFYmBJUE0oypv1befvnfKXvbK0PAgS7m5TAeOQoUhst0ZDzNpVnmHtg2Wqg6rvSwiIOCqjJuvlPSMBdC4UCrRqRmnEcjGVEZPmRsxQ7IAPOM3xSZaDrScA60aglrecUvALjlgdMCCByJzEYy9Vrrp1F6SxdUw5spNEjADvWTWq8RmClIpWQAOqd/HEQc4gUjKum8NC2vxV0XNJGIqT56ufZOA2XxlaDG6cY/TC7jjAxIjOwoAS9uyELs/gi4Dm5aEn/oBgDuzfq0+OjyE243Fiwb4HcqsQM4ndLJpp6e7s1apbtpSEQZJQOSViorVGjbQ9I7MSMBSmcmgCK02hjzVqQbdp7MUDgHYVGQvlgOWA+3DAQvA7fMu2pISVX025FQdyiGUDUjgAf6GwQaDc8AqcAAgmOtuuiKmumEet46J0NQaMKnYR2G2RYQ1YOS+rlxnCEC7qXDaI63bQJRZ7nUxchLzpQYOqcBtkFCQviWBdrtyHfhqK0yOJxOxw5YtgTwS1S6NJfEl/QuzqfSaDetIJ5htU6KJKMk6tvnN+m5FPHJMhWgZmBfDoi+vg54SiOgaMPVHeaaK2ZjlgOVA6zhgAbh1vG/vlkNwElgQQmPGJZWBXbkF0sADTndgfNeNYSEAy8M9BJVP5ZtnTgaOu2K0zBkDojs2zYEr4r/FD7JpzpLYq4D4TGloERyGMAREAElBGVtkbsPdbtI0mfTJXjWxD5kFKEN+mtL0nTMZOZaiWilx4HF/Xx9mUmRgtxEE8XSgty+TSg9u305Jo7DH9lkq4HQKAJjDJkBjDgZuJqW5L6rPp/sE8vDW9k8/m+mxccsBy4E9csAC8B5ZdGhnYOQ2FsVsNUJoE+NkA71ypLwodWteQlxtw6M42k5gJB7fnX8mAZEA21zUzewZxkoXEMmakzaoBF+UiutkGhseiXteb/feM98ox1Npt1ByFi5KILJDs9YyPDqG2Tb7y4XCuJzTBwbvfQN7WcK0sPPPrJNV38BPxePpZLynO8uyN3n0xCeoW9y/ID8xxiGFxgiaJ25c9lnjO7pUxsNGLFavinsZsyFb3kfc4blgbbHIU9EfEMf1G6vdmK+zoA7jqdbs9N5L+m12ywHLgXnjwM4jw7w1ZSs+IDnAuC3YwPZZMDKSgIEVHgiSBRwyL/uIRHgzFkEzmgVFXReRFCRoQEGMQ/c88ZrBGrB+FxVUBNQ5zCcuTe9dAHPMsit/jfjncaaHwmwkF6o0KoLkjoLp3jW0l7mnOmK0+qmEk4wnwMV6tdrb3QkLiIt1mCEJb5RoBaridVKDPIBvGJ2zlSoVT5G9aiRgIxiHmfijy8D6msBprVAlYJn82GA5YDnQThyYGhbaiSpLS9twwGAXYzemUvW6OWJWJK7wg9oYpNQ14AiA90Q68AgKG3lNV2oNTmJfzXcRvEE4LpkqkLn11KI9Vbjjc4BLJfK4eHYkgL8iRwrGi4k2y6JihNXCYFpPJkQLzTJwtVxeNJADiiHW+O8SyjqSKaYmhWpZVAIs+hp/JnShjFMNV7yaI++WisbWeceOKADroi89xe0WL8gsCe+Yz95ZDlgOtAEHLAC3wUtoZxIELhG9DFogSXEUngEzkvhwZALju5rayjYkI3DtXgKmlFmKNXmN56akkZ9lS7GpGal6PC+wkYzFo2N7Z8uhkDjZIwUt7C/mpAjWSlle5onaBifwwizVIUmSpv2YbfVzkI82jUYB2jrwTy5r6PUBswnYqAZAWtnom0un8QBdKBahr3m2gBV04Lmd2S5mJ4VSkaqE26JJN/WKDC2/6CYr6CkAbq5HiLDBcsByoNUcsADc6jfQlu0zWMs3A+gNB3anIxVHhauiFWmaXK7g51HOl5S8Ru4ksvuBnoIYEkk28gG2rpNOGv/PvuizBW9iztiYnLQhR3eQb2+DSMCiuNUuIAUiPhZLUjPU0q1U0hh2iTC8+6nC3jY8u/yNLjEJSCcTblDPpJPd4tyaEM51iOXSHZhZsUUYshtvwHShWsEIC8/n8Ac7tUZlUlgxWLvEbIYUJkasy/NewGO15JJ8NlgOWA60DQcsALfNq2hTQlixBAUYzcX5cATASiwjO+O7esIKl4cbMLyb3nAyIk9F/sSbccBSqKxcEtA8kwaojIyMcNvV2cm3sxljdlPnDo+QAhvmVajHqQqTamqO5ELJ3GpEomvQBqX9fb2QY1BWleeyqpvrkOXhfEkk4OZQqlSRgPGihYXaNHiOAJg3oD2NABhLNH0tmqe5Qhu3HLAcaCEHLAC3kPlt2/SO3wpjBY0kCcSqHZNBCwNpxnHEzvuANcOM3RORLgJpg4JqwoXFEAuePEU0lE3AnrePjqABdcH2EGHNGYyxUsmsLlergLHIxARoaIUAbJqWCyGFF+x4fOHChWCtbPIVtoSAa/xnGE+fmrWBw1VhuJdJZ1kyVjFXn3NVcAXRqSZaA9a+8taU5RaAI3bZiOVAO3Bgx6G2HSiyNLSSA3wf5CsRfi3kHMCEnKckVtACwH4g66cABh8CI3vMr6TkvF2cWPGIrTVSlqcGg42Wl6N5TSJ5SBSAME6djI8nCvqYSsUCTo9HbpPjfyg+Xq7XvWRHOmxLWpploGFRLRu/joGDFyjO1vW9WL4uRygiPCb9Stbh+GHtA2cRsv4c9nWWLexdNpZ0hXti9SWNEhox1oA73WqPU1qWk9MJjV2YUM5WYLrN0Y9VJ8YmLy1hSpoLZygGfjqBYVYNL6DCPpMDgd+Tvb5yh7PQauDGgnraCdK8QL9SAq2NSsAsMzfAfKpSG7McsBxoDQfmc/RpTY9sq0+OA2b8FwxAZ+vKR/0zsfEl5iSLFZdlSQDDr8pgXyhMdHj5zoR4kUxlMn4RpE0EdQqxRQZAAG1D11YIpSzMGuG0lo6n0TaLBZEIcpMp38nG/XK5CAaL52PH2V7yxgJvQa+44djLLygbYjv9IFX3HOA75zjLs4my7w3VpblNW4e765XDs5xzKCr1si+obCYKT45juykdci88hjk0/o7LJCHrOGct6u4ZXnNil9+JOzAoAXU5QRGWB06mzxnnpMK8zwShDpWeUyyLbqA8PJ6qVlYu7yyWh51Ux1CjC3HfS3Fecr1erPpV19meLzvV0tLOxMIEk5vStlK5xlK4tCIk2GA5YDnQJhwwFqhtQosloy04AGpiIQy8iqsNgkKUyHFenMN/sSgGKVHfgguIxBwSzI4Z8kgBIwG7IosZ8ypzvK+U5yNHylOh+J7kTkRmCRhhiSSMGwogk7OKFG6Ldafi4aFCcGtvAQPSMdQW4EP8BWdxGOXE8z5H1gvBKb/S4dZ0G5IhWMimlfkLyhnthfJTu8Qk5tnnnb1yQddppx0H8BaKlXgmCTLHPJkBiTG0eDgRuoymHNMruC46h0RQS8bYIc3xwB5zCHM4UoNLqJg9F96WeU901q2nsDP32GEd051d5i3NX19tzZYDlgN7xwELwHvHr0M2NxDCmmW54spZs6iljUqTJUkUyqwBh1AhGBGizK4YBbzzqIGsAusIZW6io16vcPhPPSPlJwsl1kSxgjYAtKuaZk5HfATOKQjB1G5WfOt6PB+2SFQex/ZY8XDmCuY+VYBzp8Cy7Ipli5cuWhiPm+d11M1JUFYzQiGnNNaCEtbMmsYV4jmLAbu1ZNxNJuP4sZR+Nf+CWeI2WKwuu+k7pQBuVoB5afu30zt12CZYDlgO7MSBGQeHnXLZhEOPA83fDMZuPljVMsIrmLEkDMip/2bSFYBVgBUU2GUAbZHZZBXW1A8QJ5D4vETa9+L5qkjGfDgKiWxd3ahm9zqw5KrtK0kCwJ7LuUFQG1lBa4bdkbnXze51ASUGX1VQxgkLasgmtQTCP/iSTiWxUcOhptDJsVGGqRzohBPrVJqniM1Yae3YrhsiOJVTi1pZY93GJGkGnx07FrV3lgOWA/ufA83D7P5v3bbY1hxgzNePUim7Ss2gz2oueCaqzkoFgSyTCZWlghtmf+2uemW+bWBwoGZZdSdecxIIdl4qh9XVZFkUxVQuroz9endnbl++neifIQ4KzRUraKrPG48WZfGsLNMIlSlbC8CZjDCzLqdZoKn3EnjdlFtWyYXnoGsmmeBhoagAzPELwvNylS1cHrrqbEa8UbK9alpQ7ToG0gCw9NQVX9AA8HSonlbM3loOWA60ggP7MsS1gk7bZms4YOSucGkWiYphnaEcJOAjAGyOvcOhk6KdkChHymuhXREsRdEwk4lVTGx9Qdy4SMCxQjmUgCfHR2MOALwvErC0atpXB9A4Y0SkLJVEAlShU6cRzBSUhhb+ABR6dTYA1Sil2ZaEBMw0xkjACZav4QkTFNhKCozCExa3qSS+KjGjdsolMX9rDnSdFN2hxOuSqoynT9VbNOe0ccsBy4GWc6CF40/L+24J2AMHGkAqriH4oM4FgMWjshnlsbPicCEkMrS8YEmIwVNQvJvK/Zixt2K5FolPJOBEEjkQR1VgDPhRzOfZmNTZgUnwPgUDSopMelCEaG6Nwtx1Y3RCKmWisI+17x1JNDLjp1at1qrGxtxkQFSV4xcMYVzEHjoRYw7BurhJhDHCHNaARQUdd9Kye1qOBDaPd7hQC/uAeVO8F5pWFbSs3BNm9XZ2qM3eWA5YDswfBywAzx9vD4aa5fthHFfxN+6BWeKLg2EcPAMeVMqMDkOabYcBXoM6xi5aCmEoBPpIzcaIK6gU40EVmAE/9u4LqqhrSrnmDFwst2mqLnIjltlyCHAocUq0lYHZjK770muMySEFyDQEiRaZDwZUNSfAOYnpk2SA7WxlZurCzulEDJnYFZX1ToH8mo7oK1WZak0LO2W1CZYDlgMt5YCMb3j+0yFgdHQU+0mmz6iw1JCSpzzSW1aSWkqqbXw/csCc/x6BHxFwDBUu4pR4WTJIgc+IYqUMjoGmmASF3w6DxrvzuCQg6wGGlKoZ9SpAmUl49UqhOxe7+fahj37sh/mRwaUDvdmk1Lz3wUe4pBTGzuWKc9hhh3EYw2ShOOkDSz4CsTqZIgMEq8y5903MZQlYqjuMGgAsnYbB2Y50IpUeZxFY7mUSxNEUW4eG3Vi8p8vpzmZ5EZN5dl6b5wrepuOU1/OAWRqgHs4/LpVKvDQbLAcsB9qNA+Jhv7e3V/zK+j6/VaVPFXdqS8lUXZfN+MEzUuhg0W7dsPTMEwcM1IYTL11NVOmKJFAOEZZg8uzQ/m6WgQN2qgIonKonsqhsNQZXejNJr5L/3je+M/LQQz0dqVOPW3XGqcd2GAl4h3pndxPNG0AlkYADD5qRgUXtDbVKrnFwrTOJ2dW6v3IZ+gDRZBzmuPgEMw0HQDDur0Rv7sm+JSRg0plShGSFvZoiEjhXWV9eEHujZxCVpzLbmOWA5UBLOCC2lyArey6JME7l83munG8TqciYPoPE3OqhNy2h0jbaWg4IZMrpPfJtUdUIw7+cLQQaxPdipRZEIb/gtisn3AIKVFspO7mk05eJj2xdv6i387mXXPz0y87qTcg6KJhp/GDuTe+RaxuCLTZIYtbENqRKrYSBtSeOoCOjJ/H+QTcUj/emhb3M28DI2RQTeoQgAJizklgXL3ECsClIpxBiMVVjURfOpOIxeqlmZVMVm7Iq64K7xvpK1oABYPPWhKM2WA5YDrQPB+L8PtEtA7pEkIM56UyJQyAmEOesGz3uhjhgHMXbpw+WkvnhALZXilYhhOirly0uZqVWDGvN2QaRxDkLMuTQAYp7cXGWwVHxxNiOs2pZf+y2sWc87fxXXPLMFQvFbSTnAMXrld4sm4j2JohcK86gZeEU3Ti+LYy1MN9bzmMgoM1RWIoWtvem9v2UF37y6cikmahMcJKi0206FRRQ2bsJLy47lrIpnJ+wuQi/k0aXLqhNMXkVTDVAXH7OrBEQzJxDT26wACwMscFyoH04EFdMRQjmR8v5MywD84vNZrPMoAHgaN0XbOYnbdG3fd7c/qUE7xleJhz0xfsDQCcypSPeHnTwF+kL2NutoQDPS/UggwgHUgYcwwAO4/LJOf/040445z0cSrAQAQ/s4aCCjJfBlaRUaBB79r0lvzi3EJjnA3ns6ymUymyZ5RueSAm1AlPtsQA8Q7cCmaOgps91pPE/mdc1YElj+ssDcFUUEZk08xYcRDd54mC6bABYNwfz+1W8hQPEzcaknIrXMzRqkywHLAdawYG4YipjEwECgF4AGLMsRGEQlxSUVywJq456cnKSs0hbQadts3UcUDRD7Wnsl4o4gzC0lDnlHj8PeuJdE3XGAku+SzsHRLog5lHeYAMgjiJavmP9XfEkhlmIa6BmgLsJwKTKkT8JwRqBy70LfJXVDMwV+Y+yDQnYA42MWBjWR9UzE7p37c1xbqYPAHAHR1G4bl6cUcInH6V9SXb9Itom6EJjMqS7iyQL5ukeIr/itNl9JAsGDQl4urLaPLIXywHLgdZywFPdnMgxZqGIAWvbtm0rVqz4m7/5m2uuuQbiSGFhGO+AxC36tvZttah1kVYBXb4kEKBrwIAnTrDQ8qq9nkKykCc6k6m7nQkGIsLHgInP2kdVHCr6DlXTRmFSzK07k14uyfcuVhgb37mGWaaoKM5mH9SxWDGUq+LkEWpbgLiYMM/yY/rG7AEiU8bZZKg6N+mAKBZsiLPc4ZebPAqr2tNIWVUucyBFmI2cdJkiUtZUYi+WA5YD7cMBDwmYBWD9nSvWckL4//zP/2zYsOGVr3zl4sWLP/CBD6C/QjvNFU11+5BuKZlHDphTbBnpBa7keF2RptCIIJvhCkLSxGSPPz4qXZW0hBhSWYAlk9yYi0lpROXkHhI4yZbNbcQwiEqiZRGXVLVapcThhN2oV+ocwFfhTEPkv47OvVe3SItiIgwZSIr4WqZyDo3gFMWaF8MJB9SK6A1N5Nw/QRrS5vZ0FXrMwb5iBc0OL35wBjgDzmxM1Kp+gmOO3Ir0K4YSP865v43ZDCwVwZnfJzuT6i57tuvI/jCcLnMic80Xg/PwpUjEBssBy4HWc0AWelXVrLQAxkQuueSSn//85/fcc89f//Vf/+AHPzj88MOf+9zn3nbbbawQ8zSCYWBb82tZez1IOMB2F8eT74EJaIJRg8pQztjvuJzuziNgbDw/AgoMZHFKbDA4BgTUAT2ENDeQExYozWYjcaJI1GXjr590aki64sZYjJMR5eSDZM3fbJK1YMkYiyPeJeMxMvJw77YiCdmufFhOQdSN1ZwlvamYUx4rjA7XnckgtqgnhxMu6Pc9fG9BMD0zwBT2dT7+0Ke9+CAqM9uBZ8l4Pebmq2VMr6AzB821cj3tFBZ3SXWZDpaFerYNcyCh2JpxPnMslSrV45OcbBjz8nVnQXeWVWKY2JlJ1eruZNF0Uw8vpOc7f5q7rqiu1+Z0G7ccsByYUw4wCnpomJGDUWEVi0W1gma1D53V0qVL3/Wud91xxx3//u//fu2111500UUnn3wyqmmUWioxUwTwNvYdc0qUrazFHBBpieEXyY0rQz8fAu8abKsZH1KM+cy9kL8SXgAYCwSQWaRPyQkAkkJoLm7uyCKOLU02syFY4siFEgd1TGkpKFVy03SvqXu84l9aqaVtyBMYxibL9Sc4asmN40szbvrGzEA2BjcEyD1W++Qy7EnwlW6G/YVjfAjxhB8L6r5ROAjXYHiNfVXVpCeWzzE5DzhWMbw0BSgke6tFAmaG4XqoqHkFsJT9SrwYX7Z9ETjSQSPmzl4sBywHWsqBOECL4RU0MJ6y0xeBGDGXtV5GWyJf+9rX/vM//xObrLe85S3Pf/7zSfnc5z6HfHz11VdTJNoZTCmRZ2w4qDnAGjBTLtaAFRZxBA3SNq+qGjs+hY/2YAQiNEKx+Ll0JyfzCPYco2tsDUPyDMHtQWqDCnjLrAAimQRDXvMaMJyH/6wTkZen/OjQRQm7pYy8E36C/KlVuTM7nk2dvCBKkRMI12wmeafLtPemt1ylYhssBywH5oUD8uOkYnYZ8aNVe42+vj70z1/60pduvPFGln7/9V//9Y1vfCMgrSh74oknnnnmmeTELEt3K6lx7LxQZyttDQcYq8PpVPPwqwbzHGxAIjlK1QriVCIuKuh2DHyvjaqbqaRbd8dHR5EJjbGhARWAWWTN5v61RSei+YGZ3XqFkjibFOxsmFypKZzOlRtGWEjI8hLoDB+MpTms2fjxkKWBdCKJnFwBljWH/NxtsBywHGgLDogjDmRf3WWkc2rE3Fe/+tWvf/3rr7rqqnPPPRcy2X3EFUkCnD7jjDM+/vGP88sHm5lWq+BrJeC2eJnzQoSAsUJsmnNoHa9UZN1UAl4gGNkRyBTEZPRvoIexxNJc+/sqFkiieQ01uayPiozuu2hxII85RLOmxjjs2N8U7rE90YuzDck4p+MXF+UXz9usxBtbdAA48GsKwNhYi0k0KwIsrKOCLha1pwrbCP2qt5hCXn1hDRaF9atvzqlMJlly6ssPc9k/lgOWA3PIAUanOKotYBhlF8MTt1hBb968mcVgZFyWh/mp6+4jxgIGBTK/7W1vYzhbtGgRmYFeAhBu5eA5fCvtUJUZpWUMJ8KHkVlfMYM+qaSUKxWWelUjqgSHAIxOpYHE+78jbIIy8GtaZg0YxPLEayPTSiaLKj6il6Y7Bqr3P4G7bdGQxQX2ZlkPcv1CQTx4IeGysM3hg6xmNyRgUUGHEjDzIA9DLV0QRgLG54ifTuHJUoLmZ71AlsZZAKbqvQpKzV4VsZktBywHZscBD+lWx1AwmCLDw8PMlwHawcFB8BUlMykMXlhaMbMmA5kZiEFfnZuTSDaLvrPj9oGYSw1nBbF0KC/XxBEHnworkgjH4hhRbqOgixrR7X6OYICtLTb+ivyOPdLk5DjuOdhbHNEDnXsLRlHZeYxgJmV6kDKqfXxdcad0VsxhR+xsRiZNo4zw637VrOya58ZvrNBVr1bpMS9L6+EHS09ltTiqKKKeeqOPJka32mSU00YsBywH5oEDnkq3TKUVgPv7+5Fj+NGyAxglM+LvwMDAggULSCfgoIMlYSVDlc9kML/tcNSbBwptla3jAG/VLJPKsCwGz6Abyg551wjBuKsS54ccD9BmQbADsg3xRFUur5TK0gXWgyNqI4iOUtojohTqKcv8uCAK2ZbemLOPxMkzKVyAVcywiBurb0y95b1Qtl6tAeKJGG9LbvV3irHWnqYbwPpOn32QmCHCBssBy4HZcSD83fKr5oeqGi2u3/3ud4844oi3v/3t3/zmN3/5y1++853vRCb+zne+gy30l7/85Xe/+91Urj9sRjckYP3xz65Fm+sA4oBu1BFFNPt5eOMElirkHsO9YhknU93d3Rwar5ih35/wGDyTZ/9fYm6Mo48giJkCrUNY/4IFfD91s5wa/CNJIhbHPPx1CGi1VzC0QZJ4KPFEBTU4Jhp94DVfKgK/iLbgJGc48rsDnscLbDSSHqgeiwi6Kr1FhIYF5Ccnii40FdN6y23zp1ypTUwWNAWvHky09oTZ0q4NlgOWA/vMAXZlyODJCMWApTpGhtFPf/rTgO6b3vQm0vmRX3bZZccdd9wnP/nJ3/3ud0jD+Mn6p3/6J7WJJTMFNb7PRNiCbcgBRnkJjMfGxFZlKb4tIveaZLSfDNBAcusWfA2FO1x0biBJsXBWYHwx0gsTgCL+Sl+kX5DfeBA+b4M/DVdiEMm0OKhiYGFg0rjFgr5QAgZxcYOC1zDzVLpkXgN/2TrM4cG4wpJuNvLXkZZ33Tke5QvlbEcqhvsPzLhKtUSaCpzJQqWrYy+Om9x1C/aJ5YDlwAwc8FTHpVIsUEpAKb169Wo2+65bt44Bl5k1JlfnnXfe7bffzmj7qle96tZbbwVxiVMfq78Ejc9QvU06sDkAPjE4h0N33Pj65wujqXjkQE7CGTQjveKYTuZa22ODRFxERYMtEn+RAEWJK0ck+epguZ1mDDNxS8k3witzhLI6oxSrt1rQsHojC+vZ+OfAx7UE8jV6Va2VmTdHArH5aYs3zqaW9J2Gr5U/vL5EJkUOKrvyNzd/6nNfuv7mP6LdTmSSYaamwjZqOWA5MFcc4EB1sYLGoooIk2uCmmXhcIMVXxV3gOT//u//PuWUU3gEKrM2rIiLcRZ0UBxj6bkiyNbTUg6Ecu8ONOhSooz44gJCAVgj6J6xyRMvVia0AwCDtSLd8kExa/w/ZdMZCEQgxh0URwzxNISqcA24zSCGN9BgOFNbfoAoz3V+o045SITZ9CidFE8dxaIsAxvf2uFrID/A3CFmWpJN1VrRWyOr+UghgqKvsMB40aKuJzZtu+vBh57YuJV4rTG1MnntxXLAcmCOOWA0cq6rv2p+ugyywO0Xv/jF5z3veTfddJPaXqF5Bne//e1v8+i+++577WtfC+KyKqylrP55jt9Jy6ubMr0xg3XoVhKXDrLYyHxLAa5aq6ukxSivCBESDrA1pLGWdEUOIySAZMZeDDtBJQNq+c4Sbyl1Ssuur3DTOJ6E1/y+2FvEYcagLQVK5gRm/dFxS2Rigl0IuIOOo2BmeVjfQpVtSOapAjIATMdlqcjArVkvJiNvNpxsKQaThPgrgJ3OualsMfDkuOfASWstxG2wHLAcmGsOeOicVfalZmBVRd6LL76YbUinn346u4GHhoZe9KIXPfTQQ6wEkwcfHZ/97GexDWH2zdBG8bGxMT2scK5ps/W1AQcaWAYpCMC8dDbDGFjmOEKU0KLqjKRmlYAZ7ltLt5IhkwJDiQKwJioWA8DACmZGraVz5tYN89DtQ2E6lYGZaj5G5mKpxO0UABsZV8VifoZkUL6b/H4mJZhNSCVlQ7BOmzSl+SpMMnbUXIFoTnHYPDwyXiiPjOeRgCPdRnMRG7ccsByYKw6I2hn1FBNkJFqcUFIv0IsbLEyuPvShDzF4Reu7IDG4q3pIddDBcEBxQDoc8uaKKFtPqzkApk5JPnJAjwhMnFNEYKwPR20RgMUGnkRBukYBSW0p/fJthN6QLCclxyzJ1IEla9S3kNr+uAIPoVh/a9XG8i2OOOhILCndIZinoS8OLLHMyxGpVrDWTIx4J8i7TJu4xZar8Y6UNVpHeCWnecEO+oFEsiPb3efGZUkYJyDZHTLaG8sBy4G55IDHfJkxFPQlgj6L4ZWNv3fddRcpgKui7/j4OG1i/4w6i0TQGiTmkc7NuZ1LimxdbcABhmMGZdFSiv4ZwBLMwuYHZFNzWjLUHA7kCcTXowFfwexQtxvir/7hyiMTiIaPwoS5/kPtQI60oU25cqxT3E1wBCE+2/C5ytmEpl9itNTwHDXXRDzJ+oSPQhtczbCjy69Xgzo6aPn4tVjgpxvnGcXY/YcCyzeqYoOudJaPmSAxW5KXwSfOOyQZAA4J09fFjXjOMhppyUazPAB0B0cL24u1CQ7aMIpvHklB/kcf7KkDOXYJkkBo1VQ7QS3M8CS7b4tbDhxKHPDQaCm+MqFG3kUFDQyzAekLX/gCi7uq4NIzCplHKx6rWQdcAqS5RjqxXfFNZCYZIKZCdEv9qvRWFN+2bRv7ndha+qc//SnCdayvWZCGvOXLl3M0E2WpUAn73ve+d9pppzHuH3/88RzQRBFNJ0JgrkCT1E9c247anSLFxnbggBnDsVYyQzkn25shWtjIcNyT5mz7WjoRH56QwXZwfIKT3ntzaXSXbEtlwdUAMJ45cL0koz9FKMmVuPlLVD87NDnXNwEHErOKXQZxzKaadDyV9t10LUj7dVY0MY4u14zAR+8EgyCpbQLbl/0Kk4MM5yDXnGUdXR2+u218fBJ/7HipK0ym6+XlGZHoYWy2J1NzS9WqrPhiCodgrwA8yWEMXoIzlclG9zo7QFZ/fHQEn90NDJW9RvKDNKoNcJfa6rU6EcIwzqf7lm0plJGG+XnDHaoNS2oDZufTWMUZd5ythrBKMY/7D8mnDZh67MVywHJgjxyIA2aKryiiyQ24AnXbt2//wQ9+8I1vfIPVX1AWG2mEXSywPvGJT+yxxp0zKBCSDhAqeDNSY1BNhWA82mxdUWazE0cwAaUPP/wwSKk5t2zZgvcPKHzkkUeuvPLK9773vdT2V3/1VxTENIwF6Y9+9KNf+cpXbrvtthe/+MXA9gknnMB8AjttmqBrZGbSEJmJWQDe+e3sKkUG6HD4DSGK0dy4VxIZmKc1nxHfTbh+3KnHnFjdgFoDz6S0wd3m6vcL1MnyKYcvYAzNGUhCZyIWRwL2Al+PbYKqUFAPO9hMYcvjGFOJ70/MyOIBErCLyIvNm0rANTeI1X3mENg3I3qKUy8mRHUzuaTLRksBstZ9HJa5eMCWeoxciy/squwZliBoqugLbxrvmGwJl91lMtOq+F7RSTAL4AYM1lJhGZNfSsHjpHP9ndsf3zj4succ35fOOLW8KPdx17LTW5eyNlgOWA7MxIG4CrURNJIHo6q1a9euWrXqmGOOufvuu8mAjAuGPfDAAzPVsIc0RFIQXTGYCDAPNBJAX5ASZFWghYB77733q1/96k9/+tPrr79epWpAGvdbzAbuueceVrbe8IY3PP7445///OeBZMy+PvOZzwC6nAyB4H7ssceyb0rBmDppBb26St66SIkczByCsAdy7ePdcoA3yHGEZicwY7/M2GAvb1MLaaRNZjlmm7rQxRfMrAlPmS+FjyB7d94ptE/7+xq5NYE6KIeZfPOVCOKEJB6yzDQnZYzSVeUD5yNw1DVg/VlRkM7yE+aEKByWMVFSMXeGXsVk9xahUpYjH1h4VnGXq8ybIlglE3FzOMQ11/1m7ZbBC848ftVicrDLWo5lkipssBywHJgdB2QTML9V/bmCaoynaIBR5/KTBvmIo5EG0kBl4rOrc4dcIDejhiKiDBMmMI4jVVMtd4wOICWWX+94xzsAUfLTFgBMu4A0LkHYf0yNZOP28ssv/9jHPkYeAqclfuADHwB9GYOQoV/zmtdwjDGl6AL1a80AP5XTR/KTk7ADcfZmbzjAwMv3pFhG7YkhdFIBGFbzaqZVo0g8LXE/3yIAKlhkMwhyEogojugjTWzPK8Ik6yf8Cvga66xRRFs0Oj6aoZBknoK6YhpNgvHIDViSTRdc0ukUt8At2MsvwqvWZR1m119/dBjkpyomWBi3cwjEDJwJYVjE3FrdGRzaXqhUObHYcamX+UIMshuTsRlK2yTLAcuBaRyQA9oYTwk80NMX0PrqLQIooifQtXXrVtAXK+hphWd5y3CMAKqnJzFe6+is68e48gAjCW9961tZ6H3Ws54FlNIiNOjaMOZgS5cupSEdhjAEIwNyOU8hTyvR+ilFCgMWTUA/iSqcgROMXOQH19FRz5Jmm21GDigkMMQzUiuTdwZgXsGMZfd/osq3GTS5stjrM/2KKGsbGqdzRQEMykUCzqR5zPwSstE28B2Gt6mkiKAE3WjEPmCeAnzaNQBYf7xMNc3WJFEr8/vi5wCkzthrxV1TpSioDX67cvShwWOuJEqTTR9WqZkfVPx6IpNm7VdWJPTp9JmY1mqvlgOWAzNzQFxRMoYSQC+VaTgHCVTGuOnEE08866yzNmzYwOGD//iP//jP//zPM9exp9RNmzYhquJXi1GAwCCOzAR2ApBsfEL25Sno/p73vAcCGG4Ivb29AC3i7MqVKyGMFsgPgjIAsWbMgEINMq83shcRMuAzBAW1ppBHAZuzFN///vfTI4pg3a1bmfdEr32+Sw7wxeAVMMQzKOtAr5uDKcCb1WItBmADMopGijcdgmLiHqsjLaKwPlJsVoLb7yr+nwk4zkRtXDaeTyIAhuEKcxmgmM3BZXkXCpbcUpD3wq+ATcI6heWt6Ey0XDarxVLxjMGjTeqR7d1GDjYkhDmFaWZBXYGWDHzGC6B/BQeZzBakKBcLwDOy1iZaDuyCAwJjKiASIZANqfSlL30pcufGjRsVmEk8//zzb7755l1Usodkqvrbv/3bX/ziF0iuYCS+tFjuvf/++2+44QY0zBTGzplERnBA9Mgjj0QQP/PMM1/5yldCGARwVTRFCtdBn5OaSAGhgW3U5oAr6VSFARdyMPT39PQoEgPw73rXu+gI+QFjWtwDrfbxbjnA6wBfmScxIvNeyGu+MjuUaTEAgwPG7zM0YXgFIoBTaKPZBYxLCm6Nf2jZGis7rARY2itwiCDCuk4doukOJGLTjRkVZOPZGmkXsEsl2WHks1ZLJ4BDXgkR9gyzq595Kb9kBWDK8lvgx8XZwWTbVaAsNQiy0pIXw8sZb1cW+ZsCagSIg2884oOquu56OOOSamMMI1K8/Tja1AEbtRxoMw4I4hL4fQJd/GKRPkEvEPGKK64ACElE/AXeTj75ZJaEGXkVpLXUbK6UZdRGfn3KU56ioAhkUjPp4KjW8KlPfQrzZuKov2gFRfSPfvQjIJ9F35NOOomjmYBPUJam2YaEXA70Mvo/+9nPvvbaa//mb/6GqpCqWRI+9dRTqQT6GadoVCvXCFeGs8MOO0wT7XXfOMAbhLeiC9XxXQx89q2m/VFKUNZMEUBfYFm/uiYRGGnPIAgIZQrAfOdBT53oRNt4ZUe2AeAkP9oAxBVYpZ9iNMW+KtTBsglMOsjCj/aTW4LWQzpvzJixm0sTJ6SimHHf6WFhJ3cmQRnFG5c3LSlGFB4tO5lsNp7tLZXFNSlLzXwTeGqD5YDlwOw5IOOp/j6BMfAPpOSHimQJyPGIMYvlWNALPTDbhPYWfaFD8U/VzkoW6Bulawq4CKwScH551FFHMcoffvjhoC9PWRuGPEyd0YTjApPDiUkhnakARltI1bjreuyxx1BfM2n4l3/5Fx4xh9BqoxHHDGSSFqVoBnudPQcYqEs1sRLgdcDhibwYZMHYdMPuVWdXVLgPX5LZkzHLnAAYiJGU84KwvXKCehWxEolRFkQ9p1pzEnFA2ZeVzLYLQd2vsa8H+vv6hNubtmyBRtZh4HBnNse80od+HOP09TETQgvM+cwGAuWKyxzWhtPGUos/2jl+1JRl04GwYxeBsthXbxl0UplsFaeUgZsvmG1Jkl+AlaLSigSxl+acBqTk8UmcV+rys+xCbmTQbPZqOWA5sAcOiG6KpVZ+ySAl8i7ZgeFLL730y1/+MiMpuLts2TJS3v3ud7P7Vpdj91Dlk3uMsAsx1MGoQRzDGWRxFNeIy7jdgIY///M/1xYQyn/84x9/7WtfA7nZuUTk6KOPRrwGGOjUk6PClp6BAzBVUZYXpIuU0cxmhtytToq+AghuBnn9BJYDgITMOFtN3G7bb2wpkukB32T9OUxjOD1gzy3dqbIcYERPlT7NT0esrmiBPCK0cjVqit3MPgFO/eCihJODwVSWddFma51Uz/TF1BRCLDd1nJmg6kdAV/r0sb1aDlgO7A0HRAUN9OpPlLGJXymYhyjJhh+Waflx/d3f/R2aXjKwBqxS6d7Uv9d5WcdFumUE0V3ClGcbEicQQxiCF3I5KTwCZZkfsA+YwOIuXVDBWq973aotMAsOMIzDcyCBF2T8DYuCZBblWplF0NeV5VImc+ZgYAFgg0etpGq3bePJS9ZfmSWIgVssPLHbbFNw+dpTVqYRIrBKP8os7Tbgk0ilItsZZA5qaiBORLVQvLUZ21WxVQGYrUpo62EQbj2KZcfvFCFYra+MHCyAHubEE6kXx8UdympTrZy3LI3ZYDlgOTBrDsgKk/6qieipwJR96lOfitCJZPmKV7wCmfLv//7vsZ/CNQdq6lnXvI8ZkbahhMLsEtbmIIBbIDmSa3mEsE42JHJMo7G0AncpqHNxVpGR6fexeVts1xxAgakADKvBMzKysrDr7K16IiABSpg/gg1EUqkkwlqWDUnyqK6rmGylEcBAfmyvIDimWl3Qlu+8fLEFWUkXhkMyH4hGz8xTZqXEyaBiPXgcuHjOwe9kiIZE+IEj0WKuvJuOSg1y4BJwapxke27JWE1TecQgMkShXKJFrNg8fqRCGYzUP1EOG7EcsBzYEwdCN0aKeSpfUoRb5GB2B+nvn2GXUQDRQafSe6rzST1nsKAtqnjiiSfYg0SjOj8gEg33/OYhCUhWiZxH0EkKZCOpY0r9pCiwhXfBAQZivgD6lQCAo1ezi+wtSt5RvwwoIKRzOn1+LAi3IfFtUdJ2zNkicqc1C71AoVhBQx3TG77PYCq/B7Vs0EUiXgT5AFnOWcgjqJoypPBpzibpcpKV7PUnLj9h0NTUPK1VbqmTaS/bf4FVD7nbRWfAwrk4ETP659CtSYTBOHEXtVldilAtQW2zTdReLAcsB2bFgRCAddGIEuqZGSQjcs011/CjVfRl5CUFgXi+tdBI4az+QgnoyxUfIOzihQwGEYjkKZQQeERguAEPoJYiiL9RL9h5rO47NJu9PnkO6CCrnGcCxBuhzmhK9OTrn5cawDFcSnkIi0jqfkdGTpeX6ZrBKmmxDaU2wV5oFOoUgEvmGMIdVA6s/gZOiv1IxoCDnCq/EtH3ojgtVWDKFVMJONy3LYkmCJSaUybDe1OJseiSaqXmStmNTiM09NBo2JCLYxzgGddX4mZOvxtCeFSXjVgOWA7MggMcaRZuWiAzGMbPiQiLvm984xuxhWYPEmpeRUSUvW9+85tnUeeTyqJjDzt3Mf4C8kFfqgNZ0SqDwUqJNsCQFA00kM18nKADkEXfJ/UOZioskNBQQZs14PDQ2Znytk0a8p+x5FURUKcLeiqBIlzbEDqdECVP1oDNijt7kCrmh0lHIsrjCXYEBeWKOOII+xkCsBhUKhbKoozZv0cD+tPWlnbeMaQgys+HIvwGaYXfF/DdAFU006wBhXTyl9qgjftatW4U5pGdVpjH/rEcsBzYIwcEtMjEzwm0I84mWtZccXr12te+Fo8W7EECgDmJiIMC+UE2/4b3WPW+ZVA1OOhLcxpHwKUqVOJqRQISa4reqs5NRhxxtoehjZzmpDC8bwQc6qV0+dGM6TIoy3nAZpuLWVXl4CM2qBSdVJGzhQI/ib1Oe/FLDr9FnypUS8CeSY706XQKHfWJDrcinTHGw5LL5GwAjMne+gs0YfYU2jCj/+VIp7rvsXhbM3t6E+JuWfTFHPTESja9q2IHRVc4iMgQb3YFy4RVsiHhi9tmB/8j5lQlzm+WgibQxA6vjhs+JczrjJKAhWCWjCtif6VPaAOUD8tQubTr4qgrWeVQYtMY+8PJGlavjdir5YDlwG45EAfAkCMBLdCLnGz2xQ3kgw8+yOYfPE8Bxtg3gcpAL3kIu61tLh9G6s1IsawpILE2o/AcCcHMHkIRxw2NReeSmkOnLoZZTvMzCMZ4zdE7bPgEthJefLjkLOrp4JuwtZTOOt1JDgOWZcg2C25MFiNc8VeBDTFHEBZLwf9+w/MefnjVMYf1COwkUghtcXOUEM4r2mwnMBOITLFUZD8gQMjUIZsOxmtd20adgjHCwpqKjhVcB/fQC7NOUCkmOnq2TDon5ASAdamYH4IIymB23edcEiI9GZhSmizXimZFHCagy5AVYzQEHqcqyHtO+fG654xhw5VJMh8H4vNlthrJu4/LhICvBbuTaFxeODfFSm2sUPMyXUPjBebwQbWWTMkmRhssBywHZs8BOSsXDEbA1cVd0BcM5pxddTwJ+iJoMqIBvXqawuyrtjkPTA6IoENgfCYmNzJSy7opIhen/6JpKATJgo8nC7ltOwAGXVCNGktecAayO+P1/pR/7klHrRjo4pYzdumWwHM7BtjJtilxtiEMx4A5Dse9si+nDxHiRgIGFHkf8hRg5ehAphGSmUVZMZ+kpBvnXTHXCHuZ8jiHENFWNPKmmsbbJQ9vUUBWNBlEy3W/4vismrt+QFy/AKFFefh1CL8UWEv7XqzuxjgWWr4qUk/4SAi1wXLAcmAWHIgDrmAwgc21CMHs8CFceOGFH/zgB/HYzK5cVmGRgwHghQsXsiXXbrSdBVcPziwIOqqNMGvADLjhBtN26y0AzLeaoMsrWBESIiKBKE0nJVKuRE/bIQJ5IKeCJ7JsHem3IotE0BapoMBLoDSZipfrUx4zKNKczWxmknVa7b4u2eymg7ruS7ZUKhNUOZAQI6xGQNMhL3wqVGp11CQC//gVk2nNVN6pTDZmOWA5sFsOyIFCDEksrIK7UU51APnAAw+gfGYNmHGKXyMGUKwTR3ls5FDjANpaAIChllGe7wOGOtEyQbuxYkY8AFqgGTTSp2Jo1JbIodMCAA36ZMElKLGtvVQMzc5J5wMA8zSbzoxO+lW8Z8itYCDvhU5lklNLRSSC4iTOxjACZRijAdPx0uQoGi/K7ipQG2xklqOQzzACP2dk+65qsOmWA5YDHtDLDx79cwSu7OHht8TPjytGWPyk+VkS59xAy69DmQNIwArADL4KwDqytyFPwANCRBjfXuJAr9JPHORQPI7ytEkEUiO5HJVuqkMsHorFgN8gEZ3x0BmgkStICeqVRDYWAOZaQGz1w4mRGKQFNbgQAjB+JvcUeK1wRq0rZlxyEj6aQE7gFmr5MkiiiUdPw0z2j+WA5cBuOeAxxeaXhsEzvzquz3ve83SrDz/ad7/73fzgQWhqAJ5RR++2KvvwYOYA4zILjREAqzjVthJw85uAcqS6nVPA42aQbs7QwrjOFbgqxGYwd3PcQgm8E9EWhgNyYKo89XEwksFCvVwRM2RKANiK0+nQYVboR5rfsiLlHvulr5XpOJzRHz5FqHZaoDly4rGjqdrG5uVpWe2t5YDlwK45IFICPzZWdlE1o3C++uqrmdjyMyaRg4b0iHtuERfWrVu363rsk4OcAwoMKpzxZUDxSIouLrZzzyGS7zNkQzAwTNBIpIhuO+LFDzQ7cUWcJSSTIK7HBkDjjcNjAqToK79bHx/XCcyxjDfoECZVIUw2nstmXxZpge14AkOphtNmqXZXgTfLD58pOFeF/FCopRoxeJeP2mRxYhJ8xdZNW6dC8w0RumywHLAcmCUHPHAXRRYjlOqp+BVhEU0KQxWrwvwIEZG5jRTUs6zXZjvIOGCG1+bTkESmjJSl7dlZRV8AWDEYagmAE4EUaFaMaSvilc/htWE/xY+xxhlFsqM33OCLNoIDE+SW4xDYgWskYDIE2EZhne6JFTQfrQc05RGV7LGnaAvgDEMBV8B4Kr9wa4dQNfQgf1OtAWkPH9Q75LA3lgOWA3vigMx29Veq0gy/QLb/6sIwpxqoOpppNYn8JmfzG95Ti/b5AckB/XpwxCxfAwzm+Z7wtdFJW9v2B1Ij2hSEoluNtKEKPRaPY3PFRAEvVJhud3X2ILnmC6UKLqFrNZTDAr0gKw6uk3LKkxdPDA2P4w0S02kWY/mp8mqy2Q567terHABFPcyhYYWql6dxoPkWmGbyDU/wpk5b1KPK51AIlkbl0F8Be6OCZvrCt0KMRUwt8diUqXlztTZuOWA5sCsOMC55Opiq8op9wAys/GLZDczxR7ifpCQSA/pncjIu7Koim34ocID3z9eDoOKR/T7M/UsXa+bGAYBgbTzGvl64zdyXrb8EkJUPGMxHZkWcR8QRvuoI0g3FXA4qJE8U+OUSb+BplLxjxKCoDgWRCjqC3h2zyp0Ivk1taE4F7J0z2xTLAcuBGTkQ57ctv2rXZebLfl9w9/Wvfz0jLM4g8UD5D//wD8i+zLuZGlvxd0YOHlKJMuCboMpbZmaHVPf3T2ebpzVsyQViWQBmCZvW8bMK1AF8TIS5TyfjuKeq1lQJLOnk5Io/VjIbK2jZnKSvSWfYu+qC6JtjAquEnp4sAwKireB2GIjuAK+VKucmoQB30Tw3qaob2e1fywHLgVlwgDm0nMdATia/mGKxA5ifq+oYX/e615Giq7+443jDG95AHp1Nz6Jmm+Ug5IDZUCrDtJrItqEK98BmuhEk457RMxkxGPMLeoQVpM54uAUJ+ajwiaaK5V/FZrKRrtlSOi8yGngSU+J3k4N7Q5zeDYuYjuNYo7tbpllqUL0D6jaVFCto9gEzdHAyEluUk6KdJuj8oCmjjVoOWA7skgOybMOPjfGUXxRW0B/+8IexveKnBQY3u+Ygm0XfXXLxkHkAAPNt4Zug0zILwPPy5tnegzFUTFw8ZjKyDYlDf1kFdryYMhwzK1TMTduQ5ERePmCtoiaoLRgOSks9CMTy1na/BqyLS0jJQGouJ/l1lYFqIgwmQiuaAtIzSpCN2xI7pMTVR/hUYjZYDlgOzIIDHr9Yfkis+jCbJj8KZ66MsIq+w8PD3OqvGtk32ho4i5ptloOQAxEANyRga3cz12/ZQFq0DSmFjGvmx6FomxKfyxrYr5TJpMhZMs4gNVGzqaJCUkxFKYOO1ZLg9K4CJsy4+uCHj0qM87hplPiuMpMOnLPBSzNUKpxqYYPlgOXAXnPA4/emU2MsWnWVCDyO9Mx9fX1aJVpHIgrSe92ILXCwcADVpg7NKgDZJeC5f7EGgBX8wMtkErGWNWD2AYvKdwpZ5UWgWxYPGyqqKriSDcE5zUHCUBYgJcvPVj1hRxLtrmguFOQJv/EMbZpQauDqzsjNqrRKwByopOPGznnCWuwfywHLgV1wQACYXyY/Y35OkfUHP+NwCDDDAXjMI5WDd1GPTT5YOBBKNTKCy9hPtxCOjLKRFARedJ8VhzMAZLzl4B4b5pID8FN+cXBd/I7B7Qxm56ig6k7Z9zh6SE72pT1zyhP633iCAvxSZSmWAFxW/SrW0oEqJjhIyTio5HQjj2Orapw3aIorxRSVnbuoqNFnuxhSTVTEADubSHSLRpmbWNE4uZTvgPkuSG5TltaQpnXjL+4+qmH78kwzmFz2YjlgObAHDsiPCyeU/PCIRIIv0rDGNV23e6o9yB7qs48PAg4wtJteMNbztai7DP1xxmakKU6ozXWkS256uMQwX+/O2AF3jt93EGPnrh9Pit1Uohb0Z51YIrlhaLLgZOtuGusq1Mk+RwvKPmA3k3K9pLt56wYBYscZrznxjtREvZQ0p/069SouOfiFd6WYNAXUFpQFgEMtNkUEURGPvbgTx+J5PO5wHHC6Wh5wnAVeebLiTxqvVxQB2mXdGc/SfgWoRqwuVoB6QXWOSSpXHbYgU1/oJWSOWWKrsxw4aDkgAGyD5cDOHJDBWQZeUWLyIY5YxYeNqPVADoJl5EUgs1+gnVn3ZFLYXIt7KdjO36RTSzLP4czdIF7zkkEsCRYaBJX3ApDGY6LB8sUJNAGnVPKH6bJCpsFNSSHofJoMU2vIvEc+PGNnA6Iz/t556sQ64jGMQXJ4Bwi8QlVg1YC7fBWQzvWWUpxPjNos4TFz9zkSGHLlC6MTN2nQBssBy4E9c8COn3vm0SGeI/qKqM2NbipV1YguLh7i/Jnb7oNqUYVxz00gn6LkDWqOi3mU+NcA8JT5ZMNTNLuE6+Y4IkTQSsF360E6hgBsgtnOBFjyCVKxSsIFYgHdGQOtYoQFTqudB+bWUCJmWQK7TSVM2ySwSkUGtcpmDUtr3SFnUyEbtRywHJiRA9HoOuNTm2g5IBzQb4nxp2Q2lQb1GKJP4FsjrLn9fijSRdbFwCFTnERMHD7HHDfNwpABOkCQnGBxMpXCR1YedbCho1KosZbbERejZ/PKKOQh2goAp2OlhDvB6Qy7oJiK2WzMQ11p4kor5gSmsEAzClMhhldkAK2hTeImF/FdVG+TLQcsB2bggAXgGZhySCfpUDoTC/SJykaq0sTa1oa55QCqXCNkaq1+iuVZz+XYP6A3GXpbnpKA2SXMOmyh5LMuC/ThBxpjq85kGglYftjYVxk1NHjppkUCnqjvDoDZWgaCosGmKgXgqjHCUs1HhK08xQKLbREYZqonebWClgY1q9Jur5YDlgN74oAF4D1xyD43HGBoBoD5uiTRiiIBo/p0fIu/c/7tAMLkH6f+GcTDzpxVXsFkc0iR/lwjnEuyDzgWL/rYpTtgcLFc9QKvK51BBDbLsTJlAi/5E4+79Zibx8hq1xTr3m6gF8BuSMDUGgYhx5AkAGySIQz7TehEHU0mtg1b/G1wy/61HJgVBywAz4pNh1ImvhJi1TNNVym2QSYw7DLmIgEHgWxrsWGeOKD7AOFwPIYkG+AcKwEQ01gkivIo4Qis1mrojkvsI8oXatVy2gjKAsAYKbNVyBe7rRRrBmzY5YijXZOrPnnQKtOKLu6SsvMrpn0A2KjH4+A0X4ZwHzAtCn02WA5YDsyWA6G5xmyz23yHKgcEdI1oZjSiYqOLfe6hyox57LcCZMB0B09XZtcuUIcOWg490EV4jj5iQ5CZJLFCXHPrhVqQB20DZyw/WamUvKDKr9pYRoOedc41ZOlYZGLyYK+8a9rFlqohZ2NqF4m2UyUE+2WPGtbUQow5XJmn6qVnKpuNWQ5YDsyOA7v5Pc6uApvrEOAAYzeDPvtTiWRS7D1x2ITUleuU9EOg+/u5i3Ww1JgZc4W9fT3d2Lux+yshUizKaY8Dg/lLtkzCSWWSo6Vx7r/34+u/+vUvs4v4JS96Lm6i5dhCwNzHJkvQdyCb80p+tTClUqaGaQEARpZV2Vc9AahjSxV5eeMa4uwYHhf/tawBqwsBzk3ikT2qdBo/7a3lwB45YCXgPbLoEM+gOzyFCcAAshgjO6u//3975wFgWVGl//ty6Nwz05Mjw5CjDFGCIKJIUNEVEUUxhzXvmhNi+huWdXVdFVF0BUVEYEkiCKKg5DTAMEzOsadzv3jv/3fqdN/p6Uk9obtf95yaN7fr1q2qW/XVrfrqnEqiEeUQy/0cm8HJvhvidVOhBXCZhIW+AcxF9yBvZBKW/NGR4LbWjRPGN/73Nbc/8efbjj34kPNe9eojDnJ7P4e65rLHpCx2UImzT0bZUbiE7m/gfNgXuRbqJXodaNDBXbz2lLRIwGJ61OO9B4TrrT6yqyFgCAwcASPggWO1f/hkrpW28/2yi1TlmmFdB0yby34PRsD9QNr7W3hTSkAE3R6RU1QOaPuDMqf/SvwBlCw8qt6mjKvr8IuPP/LgEYcddMkbX3fcwdNkFw4E3V6BlRAEy7DdVRAv50Na7p9SHuj+3luvA2ZkuZd9+4RAVuaOLwGDpprbHmbmj30TfYAyqyGwcwSMgHeOjz3tRUDGgKVR53BZxwd+JpWyxrYXnX35V1Fl+S+RYs+kkgz5MiosFvce5Wbs+MgG+cULF584Z/K/vuttc8bXMi+dQoIHmaQeYWdIWNoXpmZrS8YN+p6b1C/FhApnQRO8dxa0rAzG9EjAeuNkZXgXWVnXpCE69xBwrwf7awgYAgNBwAh4ICiZnx4EaPRFRcm+0H4pnWZs0cy+R0BZliFWt7bIY62t7IUVBJCiexnbgEq1xRt/Xn38cQeMH3fJeWceOr660N3N8YU8YpUYM7hk0jQUHHBuRpzNpUuxWHehV1TdJtXEpuuA4VTEWyVgnd7czy+ysm6D5QhYJGAhYCf7ctXE9wtit4aAIbBdBIyAtwuLOW6DAI2rG0BMJZmEFUAIOlvHGtxtkNorB+EwxZQ/zMaKeJk0A7jwaJBm1S82VhL16v6h6EvOPqo1d1RTWg5pqOVwBs5T4Hk0wZYbnKIRiwSFskynjiYT5WicdcA71EHLTliicE6lEjB2KAGraNuTJKFZWQblGFclYElYSMA8NWMIGAIDR8AIeOBY7b8+RfUs61qk/WV6DlfkqgQ7cpgZHARgNeE8CDgqS3Lp8PDj9F+hZhxZFyxErYO7DAp4cajTz0eQfks5VM6JJIcriF8KrsShwJRXIlaKRvMld4rkDtLcuw5YegAqbSsl9/POm/uMAUtKZAxYibqfV7s1BAyBnSIgTasZQ2BrBGS1KD93GJ3MBUIWg3lp+LGLYlMIOBKPhStTtg5td3uBgKz0AnD4TGgTzL0YlwiDrAUsuMlJwXISkljxG815tfjxRT5GOM4V88lkolBiAw3nj34Ti4jxGaXcOOBIJk8xJszhCWxehTsFCt9KXG5k14/F4imPgeRUHMdIviBnYWHc1HfxydQrOLwsE7HLySinHeJYLgZI26L0duc4uQB2MQQMgQEgIBXQjCGwBQFp+znQXYiAJpUdF2iuaVqDaConW3F49bXZfKGUyGRT7My0JZjZ9gECECEII2GK1Npd9KJpXFJZtsLq4oeFsuHIXy/PWYDYOKGBZb7Ce6l0BAosxFKRbB3MipYiKQPBQam7kGW7LM9rGjOu2L45yLVjx8OqLm/pZq+tKy/bfUSiQsue19ra6mVqCcZQc1WknCuUOD1J3kOq6AOQlHiazaF5a2tHMREt1aX9VMzLpmPrW1pgcmZYx3p14y6QXQwBQ2AXCFgTuguA9tfHIhTx3wlAQslYtC3mDFo2ZUKMYh8G/NgHNChfiMDvplAJmzKhmaOHilgc7eojekhaIO79TgBlg8gSi32lTNxGWj7bd7Bxt5QjU6ATSK2+rB9i14xf3vzXL3z7P19YuMyPxFo7cxoRS8vKsseoaLZZ8cRUr3zZCdoSAT83FdtNDCsQjFnQMT9BfyFSKrpDo3ltnwQNCioWqSEwyhCw9nOUFeggZocGnPY2GYuj//QZA7Z1wIMDtsAM0Ex1dhybTspyL/YhYwxYGI4bOeiXmss2z+4WF2dwksOUWEIsZCi/aEzEX/TXkXgikkh0l4V9//rMhsdeXPHCio3zlq1nmVG6KutUHW57Db/Irh0iWKekdPN5mZbVQ6t9hnmLxTxDv8jZnEepY8B4602FS4pdDAFDYAAIGAEPACTz0gcBXYaETjrjiKHPE7PuAwQc26E8hoBFwYDJptIxn0lYYunhQjcu32MXcpZhXQwXwvCTzcrkB5HLiAKjtlHGC+Kx9mJxU+Dd+eBTa9rL9VMP6o5k4WPYFyJHt4wEHPXLbG9JSLbjQMPBJKyet0j0W0zRHRRMhyDFBG36YgXRYfckd4svsxkChsAuELCJrLsAaL97LNs2bKfVpXHXRj2VZDoPInAp3Bdiv4NoCDIsg67Ci5iaTFpmX/l+dVqWIcGmMvnN6YZDCRgLbu6KdOz+yhImkYRRVZd9liGlgni0ub3rb092Pbd8w/rOcqyz0FJKwrut3V51xsujR/Y5X7JUnRIiZ+o1ymj2xiIuQsd65mnJNCzS4NYBl1PpBASMBLzd5cJDAJK9whAY6QhQbc0YAtsisMMPg90H2YiDM3HYCWvbYOay9wjAeSrSumXACL6xhB/E/XJVWnTROIoJeiiRuVcq/uJGmeFB/soUOkZ/pRCJDQ9sxAF3bmrZfPtd97R1l4498eXpmvp5Ly5hQlZVRuLkSF/ZXjQaVOGfwOy6FYsXi4zvqomy8ltjl05AsUQYJGCWKDsCLmiievzaH0PAEBgYAjtsZwcW3HztJwjIwiSn2/TY1FAOi5d1wLYMabBKv4wEHNUZcF426cV9P+7OPhLy451R5kkLTQu59l65hXVFD0xZCfVK1YY0xR/rhRLMmiq3t2xeuXzpQQfMeP35cyY0jV2zdj1PCcKvWEDxUU5FfTb0gI+Ro5lqV2Tdkky90rc4cic20lBmmbHPpDDGgJkYxnGERsAOGLsYAruHgBHw7uG1f/juaWppVbf9PnoOCUAmM/4dtK9BxU0VbVPQJ8phv4QFgzIapg0JDwZl0yth2R7KdWzpZF884Y5PEYdjAecEF7vaGqvj577i5Al13oqFL9bWVTNHi7MF2eCjxB5ZfjkRYfWw0La8IprADfU1kWj8mh5SwbpfeJfdqpmgTV8M0XkLObuU2MUQMAQGgsC2DexAQu0DP1RaYmEPnc2bN2t0Ila5mZYMKYXnoPGovb099MAjAoYb9OBf49m0aRN+NLi6cNtDFRrYrnuDgIhBTLWRuTapONNjfchXj6e1lndvcO0XltrIL1coM70Y9izJhhne5FrP72irjUezMVkgFCDKIqM63Auyt3MPP+IgY/f603hVFS0Cq1dXnSl3dmaD4smHzjpudnxGwmtM+e3N6ynRsbUsIi5xpi+zmtMxWVvMHC92OWM/rUgivakVCmZ/y2LU9bdyeRGXOzramAdQlclkGTQuFEsF2eGyRzfeL0t2awgYAjtGYNgImPkd8CV1vqGhQQm1ra2NdEKxjDIy1Rbe7ejoUBeuqLkYeeQRlt5d6cWRR+vWrRszZox60E3ylL/dDnk0SDK4xRVDDGqx6w4R2AFCTM2KRWT2LZstsMMwrTCTsXYYiT3YUwTANBmTY4yIALgpjc4OLw7FdbR1t3RwG3M7kfFt4zObjGtxQbX9CiPf3Q2Zawxp9tYI/PpUrC5evuCM4yenvSy8m41G/aLUuXKJaXWySSWLltwaJF5NhYlG4tC8Mrys/HampwKxwpgpXqxykkTKiISxr+JjV0NgtxAYtlnQeuxoZ2dnVVWViqp1dXU0K1AsV5iypqaGnEDDjY2NWFgUAY9CybW1tRzbwi0kDYsTXD0QCkcYHSbmkQZRxg1FYSPg3fo4+nqmoVf0kgwnRqLliM+u/f0a/b7+zb5nCIBznLOMykicEeZBIaHWZrxXvfxk+pTjUR/L3Cq6k2Vl1hB/iFCZWF7qbKlsBotIpoiznjdtQuL8V5w6tq7q2FmNcDtzr8ZUJdrbyxs3d8dr0syp6yqUOLwhw1FXEkKMnGAY+IU8FByDjdEyy1vcEuVSiQiYhCUrpZxCBFWWpKxH3+2C28UQMAR2iUBYhXfpcx97QNKFKaFP4oVHoc8//vGPJ5xwQnV1dX19/Vvf+lZanObmZjysXbv2lFNOwTJr1qxPf/rT+KfHrRT79NNPT5gwAao+9NBDf//738PQaNKgYU1ryLuhalol5n2ck9EWnX4SfT4MVry4KbdOo+kL/6LrZBKs6+WMttwPa34gv2JeRmHZZ9tJoaJpro55b73ovItfd0FTjVBjWdjTB3zsKKPxoL8tymfueeZOJCwGJWRTCLgu6r3mtBNee9rhzFyPFAtBwRs7ppFR3pa2bvZ1LgbR9lzJjySzLP91EXJNx9nuyi8UZS8OFha70SERqTHFPGngYEp5D11e7EyiNu2SQGPGENgdBIZNAkbtDHFCjSrsXn/99ZDrV77yleOOO66pqenGG2+EYlW0Pfvss2fPnn3PPfcQ5Nxzz50yZcrnP//59evXM3j8ile84nOf+9zb3va2u+++mysBTz/9dM0+MatMTPyqncYFszvg7O9+acl7DLAxK0fQiyRRPuLKOmAj4F549uHfeFRUzGwHLWcO+UW/XErFM+NqEYRlU8lCOVcqd8dT7NbM8DALwqQknODZc5GUuIVJBb/AAQwosx1jSpE1xLyMfP4lNvHOooIe15RfsHZdZ5GFRt1FvyXHxKokZw+HBFyThLlL+Xy3D327NBFYCRhWZh4ApzBB7ULABfrQKMTl5WYMAUNg4AgMGwGrhhlORU5F7fzNb34Tqffyyy/XpH/4wx/GsnHjxvvuu2/JkiX33nsv5Irnq6666pOf/OTHP/5xbgly1FFHffnLX2YG1rvf/e677rrrBz/4wRlnnEFARGHVl3LF0EaoNKxys77CrgNBQFr4HiMn4mGNi+JZZumyVwPtubbvvX7s794i4ORJrzvfnUqBdIzK0d3Bkms6kazlRSqNl+JJn0lRZSZjJeJuu0p5pTBrj0FqppxYH8yOHJQOemgmb7FrJf2nSJ4VwWVmXuAhnq3L+cm1rTknzEZbIeBYgoEhShwO5peR8WCoNi/dLt7gokXM5Rb1FS+jAyaeORQriBbYHlp2CHcp6fPR9CbK/hoChsB2EBjO9hMplgpPOw6/Pvvss6eeeuoRRxxBH/yNb3zjY489xkDv2LFjly5diuZZB4yp6ueffz7DxgsXLiQrDz/88Mknn4yFp1Ds61//+jvuuAOKRaHNyDG8i3+ueAgFX2Ti7WBgTgNDQMFkm/64zMZiFGBgwczX7iIQMCFKl/vKKX/JmCiitafMrdtfI8q3HTA9WQmP+BF0sfcarIzTu5lcDN2i0y6xcQonJsGogTthkB0oY+lqP1W7ob0MAeO/A2E7mmRvSQkbcCSSEHA8YPqzbIaF0UpESviVC0WiTSL/OgLmkdYsUzAJImYMgQEjMGwEzBCvCrXoh3W2M1LvFVdcAa1Coq985Su1Sj/zzDN4U3EZAmB4eOLEiQsWLGCsF8FXddQq3TI5CyYmKiJkPhcIIAdjp7cOl0PbzLKWA9fM7AwB/R56voqePzIDVn5KwGg/a7z2Wr+d0cJh05/sLAsj/1kkkkpmmMKcE0ET5N1IL5OvinAuO2NAtkw/TiRSOAlPi3CqxNgn6yU5ohdnukr8lanO7upFMhnCQJ0NtZnaqjQ1oyPwGOYtyDxmv+8Jk6w2Ew5HF+5Il6t+DwRnzJhbRqH5ABLs1xGJFxDI1Zt7m7zLjCFgCOwKAa1Tu/K1d88hRRTFiLa04Mi7XKFMJlthgVlffPFF1WihWEaKPeyww6677jo8//jHP+a1qjTWLrYGhFNDQRaKxY/2zbmGBkcIHrEYUfiKK66YOnUqr2O907Rp0/YuK/tDaD4J+Sr4jxjkvg9GfRO5XCEapb2NFjq6r3zPhV97zwXJ7lburb3dx9+EABqNRxBX05kE/cgYi3OlJJiJzKzoKFtKVvGIAWApHOZq0T3SNUIE5Of4Fu/ZKPzMWUis3nXFyJ5VlFZKZnDJC/LewU3pSPvKzk0rWMkEOee6O/1SN7FT4kyHhmUbx41nEnVnexdvIuJkJFIqyISsrgBxOcLwdENGTg6uTWcKXrrdj3F+sc3DcujaxRAYKAJUykE3kN+HPvShV7/61civECrCK6Iq8uj48eNbWlqYwIyemUQcf/zxECokih2pF00ylkmTJj3yyCNQrxpEWBTXBx98MAw9btw4guNHg8C1SLowOkPFuOOBR7zxs5/97Mc+9jHkY1h/w4YNCNC4m9klAkIEPUZYuHftdbSuKn3kjHFgTv+mj59ev/Z33yDQp2JuhTJl4TpF+hZ55G638tPTK4Kyt6TFeYBWpXZRmjG/JunHC12lXAd9WH6sLGKWe5wlUOJHZr1H5CjDKLtMhrGgwIaqOdywHGENeDEeCKcnWUMcSaCnFvYlHN63Tox7oV0MAUNgOwj0qefbeboPnKBD2m4IFaPRIcIygqV2HsHHp512GrLpAw88wDIk3KFh9tZQBmVg+Morr1y9ejVMjKB82223QecHHngg3vD897//HQuvQLC++eabzzvvPARf4lQ5W19BPBgIA0aHxdXRrnuAAAVHKMpO1RLY+xblHkRoQYYeASoApZiOR2uqqqlo1D44ldqYZ21SJJJOZ0gSfiDdlDt1gwoFGWt11bEe5lFQm2SChWNmZlAShB3qZBKWGUPAENgdBAa9zkCHYXqUBbUaU/khVAZxYUc49YwzzvjhD38IvyINX3zxxbTsOhF67ty5J5100mWXXfbQQw89+OCDH/nIR5BoWROMHPy+972PEeIrrrgCsfjqq6+GgHmq74JoiQFi1lvsvI6UIHOHiTHLbiEAhqBK2YEkdoy2wrsViXkeXgS0tutMxOpq9tGIQcA5NzqM9ojC1dmO2kulm4WCO5eXk474QcOow7kwMM3YsKwscP60gtO71RVKJv4ObxHb20cWAoNOwPSgGQPWUV7GbqnVyo468Qo7NMkMqWuvvfaSSy55xzvewURoFvgyLxopFgsDt+ywQUtx1llnQcNvf/vbWfiLf+TpOXPm/OUvf2GoGCU2C5D+93//l6lbEIMWAEG0b84tdgxpYE+PkVU8lZNaAKSBVg6GejHYKyd5lpIBIkCFd+uE0UJ79H3ZTZqt1uHX7i70QzG6tmE8aTfLmeop1KvGlbjMDIvIfiwqFrMhGg+ls2ufQy9O9tcQGCACg66ChnExmpqQEeFCBmW5KsXyFCn261//Okt7cVRvy5cvRy8NfzNqyxpf/MDfIW3Tc6f5OProo9esWaNBVMNMFx4Ohi1URNP3KlsQrQrf6mjX3UUA2ZcgYIj0o2FxMUh3F8Zh95+I9iiLqYPNXRtaW7vHN2RgUHpUVB8dJCaRVFtcuguyDlh/FD5fAARMhQpnACABcysSMGGYwGE0POwFbAkYOQgMugSsUMCdUClX6iouVFpGeWFEety440Jt16YcR9oCfMK+uFPPkXexKK0yqYpGH+rFEBVSMrfsWIkHSJcrAWk1aA64DXmCV6hkzJkN+DGzBwiAsxotQY0Blz2IyoIMIwLwIxOjGUAgDdQmKh3TIbnJsbQ3EhM2dYnDWzIRl6okBy65nT2cO3YqMQ69Q8AshxIJmCrGpyD7s5gxBAyBASMwRAQMHUKxMCJECDuSPIZjIUscMYw/6eCTkjHu+MSRpoEGgonNNPrEwBVhFxep7W6vK+w4EhXTmwmCI1dcIGZewYuUd7nFJ2FVgB4wOOZxCwK0xWCLUWBFM+nQ3uLDbCMEAciVYXxqPjs/o87IdQt3as81Hpctpnt+blAXquZpyKswrPa6OKiB7OITJkYjrRWNqjdCMLBkGgIVgQA1aCgMzbe+hhZc2ZFbbc2xKPtigYy5MidLHVGRYcFocEhUu9t4w66PVPBlSRK36qgu3BK/2pWtCRsmQ8PadeAIhNBhoQQRlcIiGHgk5nN4EaASSrXxqWiyGyX1iwqybuMG3EvFMv1dah4ect3duDTU1WBp62DXLE/U0BJO/pTKhVyOASDZtBJDteJLyJfyTL4Iq557YhdDwBDYBQJDRMC7SIU9NgQMgSFBQDiY/26bSggY7uzs7II7EWHZqVI29nAdWa5x2Yk6Wiq73bbcFtBwNh1pfLLUF6/adsREUR2oBDwkObCXGAKjBwEj4NFTlpYTQ2CXCKAkhlxRGcPBLCVAacwUCgg4VyxxeJIbz5V9pIknnRCVUrFc0nUFwr6e6J0LhTz7TIebVqpSivEdgpgCepf4mwdDoC8CRsB90TC7ITDaEYBIoVLOewi8xsa6YqHEnuq4MQbMtEU56cppkvmbSXPqBpOzeg4wgVz5Qd5MrdBhCPxwm0rJsJFMopagZgwBQ2A3EBj0ZUi7kRbzaggYAoOMQBCIqhkNMsrkmhqZvdzeJkJu4EcSHBTs3s5+K9BzJiNzL/AQSsA8hGXdgiUZReYWSs4kZXd3YeVBTrlFbwiMPgRMAh59ZWo5MgR2gEDgJmExm4pNzSIeu24od7a1ibY5meCMhx41MmcjZVKy3pctKtn8WUTk3ijhWvHs5ktCwMzIQ2PNAcY8d9J1rz/7awgYArtCwAh4VwjZc0NgNCGA5Ou0yQiw1UlZkc86o3Ub8ixA0tFct0JYmBQRFwIulXTZEauPelBguBfaxjf3uCXYsFJ255AxYDOGgCGwWwgYAe8WXObZEBjJCCDhMoFKDiwSvTKr/WpTsVIkta4t78UTumhPnkQcs+IjKHNOMHwN2SbKon/mYZcX5fijWFT01Qz/sq8WNFyUSV38rD0ZyZ+HpX3IEbAKM+SQ2wsNgWFCAI5kCVGJA4AjQpWJgje5OlVMN764KdeRLzfW18CpmRja5mi3O2qwOp30EqnmzR47RNf6pVjgIeduLMTamaHFrlls5eF5NYko2+K0F8oiIfMCM4aAITBgBIyABwyVeTQERj4CwsEQsFClXxv1xlRV5f3oms1dfjQmUq3jXcTcwC0IZq2RH4kWfU+27ZD1wKKHzgWJUoSTkuSshiR7eiBR48ctLBZ4jINH/kdiORgyBIyAhwxqe5EhUCkIMA4sEnDMGzdmTFD2165Zw7wqmU7VO5VZTzrKJGHkaEGHd3sf6aaVDP2yKBj/up8djpWSN0uHITByEDACHjllZSk1BPYaAXbgYP5z1A3WIs9yHgObWHEoGQ1BuEcsL+EWwsWFeVgF5kH3si+PCkXZy108szFHRLaPZWmTEnDI33udTIvAENgvEDAC3i+K2TJpCPQg4CZKuWov20dWV2eZccXmVqiX04mkLiQqB4wRizWVyrBiqVtFYJzcBGpWBrP3ZDot550gBKcI5U5DMoQNAUNgdxEwAt5dxMy/ITCCEeg9xETEV7JRW13DmiJRJnt+is0nMTJK7Gu7kEolvCCSyzMKjHvU6a1l7w7u0klOAhVKZh+OIPCLRT2vAe8ShxlDwBAYCAKynY0ZQ8AQ2E8QUAKOinwbhV7raqpiUG7ER7hlIyw5pwGqDZhvJQY9M8uLciW3NthtEI0jR3uzlCmZirs4OA1JfCJN94SROzOGgCEwIARMAh4QTObJEBgFCECvUba1Cryyo1gE32w2iwCMyBuPsLBX9qeEjqMRkWNpGphgxR0HIsHPYtwVqiUgpyHpfGdldHbzsOnPo+ALsSwMMQJGwEMMuL3OEBhWBNgJWpgUSvVLRW/6tGipWGBn6FK5WFNTAz1jknGRavHGydysNtrU0irOMC4Ljjyvs7MdETkZj7EFFv6RhNlCC710yYfHJbgZQ8AQGCACRsADBMq8GQKjAQE33KsZ6WFLpjFDq7jH2SCLJ72ztGgaOOvXD+Bm2QyLZzq+K8rmSABni28JIN64QsBmDAFDYLcQMALeLbjMsyEw4hFg0hTzqciGUCxbWWUz0YBZV4wBy3xmjJOPxYcsMYp4BXTQzp0rP11xpBtHO2fRVLOMOJ+XVUlmDAFDYOAIGAEPHCvzaQiMCgRk3jMV31e+rK2tJVc4MefZESijuZCvcK47qiHSnS8IXYuR5zoLOskMLmdwgowh4O7uwAhYMbGrITBABIyABwiUeTMERgUCwqWQq/yBO6n/9XU1sr434qd1GRKPA9nWCk8QMNrn7lxB2NiRM385jpBrxu2EhQUjSuxIpKurS7yZMQQMgQEjYAQ8YKjMoyEwChDoFWZ7Bns9r66ujmzBoBwALDtQIv+WZYoW/zKptO8F3bJNB2uTejJfKOZgZzbCCg0EzHnCuRzuZgwBQ2A3EDAC3g2wzKshMOIREB5FUmXKcs/C3WyWY418Dl+ARp3hAELxxEXGgP1IoSjrgPWHO0f/Mvk57nYQUBm6dwzYjgRWAO1qCAwUAduIY6BImT9DYMQjIMSqvBtjBJghW26yiUjKz7P2N+FOKoxH4xw8yAojDgBORaFq1gFLvuFafhJBKcohwPjgnlt8MHzMmmE26LDuvCBlxhAYMAJWZQYMlXk0BEY6AhFflu4Ko2b8aLbd87o976Dp42Ltq5Od62pSXqfndUW9WLI6GkRrYl42UojGMy0dTgVd7oZzN7cGVdnGcimaSXjFUgD78iyeGVsoJdFcK0mPdJAs/YbAkCFgBDxkUNuLDIHKQCAW4/DAXNlj+2Y4dWxNsj5RbkxH6qpEou0oeUx6LhVLkUipNiMq6KKTgKOQN/IuY8Ic/xsVzRlbSItAjIwcZUZ0jPXB1po4POxiCAwUAasyA0XK/BkCIx4BFgDLeG40Gomy7UY6JgQcKeb9fGeQ7yx1yW0iLptyxIRixRf7ZZXYMQsTE9KFZRkhZh9K7LJnpeih2QyLR0GpKKczKCWLfzOGgCGwKwSkUpkxBAyB/QSBsg99ynHAkCcsCl/WZZPHHHIgC3lTKJ/d1hw8lxW9QTSRQrHs++iaQSciA8Kwse/7Mu1ZJGDBjCCpZBzFdr4YLhcWdzOGgCGwSwSMgHcJkXkwBEYLApCu485CwY8xw6rsJWLerPHZd731jdDqmLSwKQPE0RhEy47QXl1DJuKXS0VZ+etHOJhBduEIyqV0sgpEIGAUaPwyCdjcz7NjluPj0QKW5cMQGHQEjIAHHWJ7gSFQOQjofs4ooSHOZNQr5MvJVGx6U52msFQMknGRbCMx0VVz1CCzo/1SoVjySm7z53ypyPbQ6UyPBMyaYdYvsSkH5xvminJsMD9Y3IwhYAgMBAEbAx4ISubHEBgNCCDC5pBgZfNIyQ47T8a8YhLSLOT8XJcsP/KKuh0l/CzbQyPjRspBKc9WWGyO5VTQvuyZlUhiJzakYVFBy7FIpXxJFjXhaMYQMAQGiIBJwAMEyrwZAqMBgSgHHEGlZRF/EXUznCbolRNeKcJEqmiQikbRRTPOG8TiKKI5cFDOIJStsYSA6a2X3PYd7pzgHjRwZAExZI3E7KZLjwaULA+GwNAgYBLw0OBsbzEEKgIBOYWBlbsojmUKFYuKEFoZ3kWGZVsNuUX2TSbjOGDwO23KBMh10dJN+GcydGd3rjqbgcAJx9QsDiUkmmycCIKOTtkLmp8ZQ8AQGCACRsADBMq8GQKjAQGt8G7+cm92VGssTNzrwhhwr7WhthptdGsby4Pluaw16p0FrcPJMHU8Uo75fsEX5XafOHqjsL+GgCGwAwSMgHcAjDkbAqMUgW04GGU0bvqTPCv7cg+5Th4/1isX12xoRvyFXPOop8ulbIZDGhzX+sLCaZm3FRTKsg7YjCFgCAwcARsDHjhW5tMQGPEIhOQqOWFfjh7jVhTpLVd3B5viecrEpkhQXrN2HdItjQUnMbAUOOXOQpKognLMS+gs6ELRJOBeOO2vITAwBIyAB4aT+TIERj4CUCb6ZJcPds5ww8CObp342yv5uiMW8INPiHjC2AbGgNdt2EQwfrBvUC6mWD7svDMLmrlaqUScxUo5mSttxhAwBHYDASPg3QDLvBoCIxwBOei3d5hWOHjLYG//jAnhMrt5XEM8Fo9s3tzKGDBnHgkBB0FClzHhw8ePn2bPjoiPcKwk3T8muzcEDIEdIGAEvANgzNkQGJ0I9GzFoTS8rcyqWmmnqRaf9XVs9Zxo72xnmS/bRBdLHGMYpFIJgsuPkxlk++hINAjy7tjg0YmZ5coQGBwEjIAHB1eL1RCoUAQct6oCeQcp7PEh22F5daxKikU2l2JtnGTIVhtl2akjHe85hgECZuFSEE+XIzFkYG1NkJX5YbiV7StZ3cTaY3f2cMCMaeZ24ejeEYhWW6LiKAcJAI977ghieY4L/kiCk9RxkcMP+SFti18zhsAoQCCchTHUecnn81J5e41b/s8qf2ZSSqdcNV1YuMWn+hIdl3uKN3XRW64aFe7sVauO6sGuhoAh0AcB6nv4EyILb0JLL7uJg4z0FrxDZ09rjdU8ucrz47IXdMorVkVLkCu67EQ6TVWMVVUX/FTQ2ZZ2b2LC9GbPWxXIYcNCmiXfK1OFcyWv2OmVmvNytENXe4EI8uW81GTu/SJ7WeJbvLMzCI5+wfO7vaBYLpa6Sm7gmt1DijkeuJfYxRAYDQhQzYbHMJEyEol0d3d3dHRQq9mblu11OObMbVLLAn9X99n5PUDflYKPSSUjTzzdlnRxJCp8EopzWrglzuHJlb3VEKh0BJRqe1IJ3fb79Um+yKaZmFdfm/aTVatbhRepsJBlOsHmz3JqIQY+9Nk4Ggm4LBIwbpwOfNODz37npzesafdaO51TmfpbynnM0oonU/EiO3BVJ1nPlIi5LTGJpUe6dWpt5Vhip4NeLscSNAziLkaaBSNgxcKuowGBYSNg5dFMJlNdXQ1rqvCKsIt7LpeDUDG4KxMj+0LSijf8qjwN46pd3fEfCtDEORoKx/JgCAw3AuxGOXbsWE5bWLlyI3zc0Z1D3yS9ZwiY/25AOcmZDV6kUHanFroE3/Hn+/760OOPz1vqcWoDi4xl4leUCgwPr8t5jzy7urvsFUpM4OpVJ+NHFN49vQEsQYQ2Ie5Hk0LdbITJHwTpLuIYtiZruIvC3j8KERi2rxnu7AunCq9UbNzTaVFlwcSQrqqdkX31CFIc6YPjCEkrJUO6nZ2d0jGX/nEMd1z6xmx2Q8AQ2GMEINnx48YiC69asQJ7QbTBoqwiQjd4KxGznzRHJXFkkmNJjx0pN7V0j5k08/6Hn4pSleVgBxg4lvSq4c/f3nzvL264dd6i5kgiVfZZRuxMBFE4Cf0qJUPXRS9RTlYVo3GJ0/divDCeiKYzRsAKmF1HBwJbseAQZwkRFpFX1cu8GkJtaWnREV+VbiFdqBd3KjzMCu8i5iI04whJKyVDulVVVThq4rPZLBGaCnqIi9JeNyoRUDocW18T9YubNqx3A7TRSEyGiiBj13bIPI4U1BkJCoUSu2XBl8tWd0aS1aVY1cI1m9FAc5CDl8yUZHZVjNv7Hpv32Pxljzz3EgJ0mS2lVfkc4SgIOBhOR58thsHe+Ws7l2zIEyernDBuvrWQtBlDYNQgMGwErGyKyKu9aQCFUOvr63HBztOuri4VgiFdXEKxWJ8i8kK0+Am5Fp5WOZgYTAUNSmYMgb1HgAZiTGNNMhp05zq7ZCYVAz+cnCSzmzHCnkrATJoqupFez1u+em05kl60bL2XqZ+32GvpYu5zitFfxN+F671CLJudMOW5JSuFWRMIyEThl92kZ4lLFh+Lwnpj3vvVH++6+rc3LV1POC/fXWrL2XGHDiG7jCIEho2AkWJDGMMBYBV/YV/IGFkWdywQ6saNG/Hc1tamamfU1Ii86ke5FomZWxxh5b5SdfgKsxgChsAeIRCMqZJTC6OBv77Na+0qsFJIe8O0Hb5sIu0lUR0jAZf9IgKt5y1ZuabkxRuaJuXL8ceeXxStjpRlfVECpn3o8efKyep048RlGzbLoBHGLYVg0IgfMasb9pdWdz02f+kzC1du7MgTZzoTT6bFj2N89WVXQ2DEIzBsBAxyCKzKuGi05s2b96EPfWjcuHE1NTWnn376nXfeiQdolevzzz//wQ9+kDp/xBFHfP/734eM8Y87fHzPPfccc8wxPJozZ87VV18NYdM3V4MHM4aAIbA3CDg9s09POctsyGikuc1r64Zko3R2VRNMDaWKUhv5yZnBEQ8pGQk48GMnn3za6vWbFi5fHcS95k6WGYl55vmXmtu7NnV2b2jr6i6IvloNkUCuocG+YNm6lmKsmKpp7S6JCOzmZxX6egp9m8UQGLEIDAUBo0mmnnJFtFWgsGNBCEa6RaiFON/xjncsXrz4sccee+mll2bPnv2mN70JokXSRcl86aWXomf+29/+9pvf/OanP/3p1772NcIS4apVq173utede+65jz766Oc+97n3ve99t912G4+gZ32R6q5x6TfhCxczhoAhMAAEOCEY9bB3wLRJbPu8eEVXOSZ7blBzYVzcI1EZnqU2V6Xj2drati6WDXtLV66JRaKvPH0ytfulxUtWtnlJFg6jf15bbG1rT2Wr0tXV7Ga5odmJs64pYJ6zxOM02i2d3Xh+6ImnM3XjNncW+bFYmAnWzP9iSrYZQ2A0ITAUBAz/wYVcVe0Md2KHd2FK7PSmmXv17LPP/vCHP2TBQ0NDwzXXXMPE5hdffBFZ9o477njuued+/vOfz5079+STT/7EJz5x6623Njc3Uwbw8bRp0770pS8hGV944YVwNvSMO+yrL1IC5hXhPK+wBzCaitDyYggMHgLRiMyKGltfyxkMza1dpUiCHScTqR5lMZWLV8ej/CIlP2jr9ta2eQVGgGPRCQ3eAVMnRhPRhStb0DbT9V66bFVHW/sB06dMmtTEntRr1jfn4d2YzPmgGeJHv7zMXKxs1YYub11z65rmlkS2dm3zZhYoMQ8kDdczA3vwsmoxGwJDjsCgf8/KeVx1+JY6phbIFYKEGrkdM2ZMXV3dV7/61cbGRtyvuuqqKVOmHHTQQSiZ4drp06fjAWToUCPyLl26dAUrIiKRBx544FWvehWOPCLgBRdc8OCDD2JHntaRYCVgXt3a2ookHa5cGnKQ7YWGwAhFAIYMIOCpE5vKpcKqdRuLXrxY9rNZyQ5th47astY3mYiXg0hb3luypqMY+BPqaybGvGMPOzAo5x99Zp7sg+V58557iWmTc48+/JCDZkS98qJlK5hwJfJ1BJpWAmYedRRufmzeopaOrmQqE0+lFy5ZQSLQPZOMSCGvqu8RiqYl2xDoh8CgE7AKo1yViZF9YUdoGAu8iDs0ST96/vz5S5YswQ4Bf/e730XMrXXmH//4x+TJk5GSoVWSPn78eDygfMa+bNmyqVOnqjvkygwsNNXt7e0MCSNbKzHD39/+9rcPOOAAXaqEpV/+7dYQMAR2igD0502bPD7il1etWSva4DJd4Z7VQNRinvI/gdI5lqCKLl21rlguHzR9CusCX3bY5GKu65n5L3V63qact3DxsqpE5Lgj6ufMnOCXuxGIEZ/RNrNPNOSacAppbvk99dwC2oSTTjw+XygtW7ka6dkRsJ+UiVySHjOGwOhAYNAJGJiWL1/++c9/HvKDXzHMW8bAiCw6QvCFd+HmT33qU01NTffff/8zzzyDmHvRRRdBpTA0bKoi7O7CjchLHUYy/vSnP71y5UooH3pmtHh34zH/hsB+jQAk6fvjG2VHyI2b2/LlAEmX8xgwcmHNkLPEZXu6GCO1i1esLhbLRx44gwVGB0/xYNz1La2ruryFy8rrN26eMXni9Kw3qSESLedXr12vdEuEEY/tKYusLiY2xOUlK9cS96vOOpxlhq2dXUzsEtYNeFJkTBqrGUNgdCDglrgPclYYqf3oRz963nnnwaYwLjIrnIoczC0si4CL6pgB3Q0bNjAGDOn+z//8z6GHHvqjH/2I8V1IGkU0oTSN69atIyBBuEU1jS6aao+dqJhTDa8ziRppGClZJmq6BcRcYV+uUD5q7UHOq0VvCIwiBGSElyOLytXZaHU2u6mrHInEsknhX3nSa6iBqXgMYuzIeUvXrGXHjAMn10XzXkPKO3DG5EcWrXhhYW7tiwvZqvKoQw+EmOuzXjYVbaWHnfPiaU+iY5Y0g83RNES7YE1pQ2snFXnOeC8tO8aXN3Z4TdVosbu8OGPEMuRkxhAYHQgMOgFDt3Ak0i2mH2RQJnpjViJheKRjw3AtkivEDI/iePbZZ3/rW99CSp45cybebr755hkzZqB5xs9pp51200034Yi2GVUzWutTTjmFILwOAsaiim603BhuMXC8WuxqCBgCA0SAQ5EgPRgxWuDUwEgqlYF9VXVWdKphCDSVThQL5Q0bOze3dlZnqpsavGpOaPC8ucce8fDSNf98/Km2ZWuy2erjjjyCqtgY85oaa1rXeavWeTXTvCzhywU3xiunEj49f0F3oXjMUXOow1OnTt60dvXi5V0HH5qVMw15zkxp/JsxBEYFAoOugoYO0QbDhcAFa6IHRoRV6CBO7FyZxswKYJYSPf300y+88MLHP/5x2PcNT/XOvQAATX1JREFUb3gD3k499dSjjjrqwx/+MNrjhx56iHXATLZCscyjt771rSi3r7jiCmZQ33LLLb///e/f+9734g7d6nizvhSX8I3I09yaMQQMgYEiEETikSjTrOqYESWLjzwWIXF1DYcfFAs4yS+R6PCZAt3NVI3amppsSpYMMVo854AZUb88f/6Lq9euYcfYOTPTcDnS7LiGBlYirWvhCEIJ7vkwK8QaYz3SolUtOT869+BpyMqzJk2MsQHIioXyumS1F632Ignov9+Ph2YMgZGIgKtHg5xw9rTSyRo6AKziKe9UCRXLpEmTbrjhBliThUbHHXfcP//5z2uvvRYLMjFsev311+vuHG9+85svv/zyr3zlKwQhqokTJ95+++2s/cXnN77xjZ/85CdouTVaFXnxwy1G345Fidm52cUQMAR2iQDTlKPRIMZGVgdPn1QsdBVK+VRGluNK1fLLGSZfOYm0uq6qkG24/8n5ES81Z8aMXJlNsrx0zDtwQjwbT5a7c6mqxAEHTkkHHhOoI6XyEXPmdMcT85evgI/jXr4Uj8l6JD9azHv/mLcymqk966jJ4wLvgHGN7NdT2LQQaTgfzRSitaVInL58v1/Ix7vMj3kwBCoKgUFXQe8ktzAxsik0jJR8xhlnoECGIGFrrn0Z9JBDDmGJ8G9/+1sUyDxlKFeZG3GWPbMQmnlFyOVovDHEHJLuThJgjwwBQ2BXCDjhM+I1NdTEgnIsKicxwH+u5w73RpJRWeNLdct5Mb8YYdPnyWPHQr2YLo4uTEePP/Lwx+YvLna3nzT3GLTNSc+vj8caaqoZ9V26bnUyeiAqqngs2ZH3M0nvxUWcuRKb2NTUGCtWRbyGumzAusW2zbRTbPHBsiXEYqV/SDdURfemZ1dZseeGQIUhMBQS8HazrEO8MCVGRVVIF37FMxaImRFiqjresDA5C3f26GDMGAsMzdAvftivA502LtR/xGUNwvQrY18wMWMI7AME3G7PcN6kieO8oIgSmjlYkJ8cmCAsHNVJydQ4Or6FXDci8Yypk5Ujk7GA6nr0YQe2b1hdHY8cfdhYAhQQcllP2DSRir9y9WpoVWRgL5nrCjoj3j/nzfMKHXPnzMrGUHTHxkysL0aC9RvaoVgMwSFdfmqR1/f+3HO7GAIjDAE+/eExDP0yJAz1ckV+pfZyxQ7pwqCOlyVtOr0ZC0QbqrIJq4nGW5h6QqkdboahmREdPjKLIWAI7CECjOO4ejp+bBYJOFIuZhJZJ9/ChEKFQsZYY+ylEfilfLScmz2jmn2hY1GvOhFjIuURs5uOmD115tTJ9bClF3CoA3+amhqo6c0tLZsLXlUynghiqWyijRlYi5YkSp0nHz4b+uUEw4YmL0jG1m9q47zvVMYNNjsJmBg0DSoHS2fAjCEwAhHYQmBDnHilXqRY+s5URUy/BMDHPMUbHIw9JFSYGN5V/8jKPELwJRIVjpnwBROHDN0vTrs1BAyB3UZAts2J1bF2KOF1FnKZeBV1lZN7IVO4FP0z/Ed9ZDdKPyjF/fKEao/TCiNMxwqK5WJ0Sm3ykx+4hI0k6TVzoFEkmexmSleNLDFqay2vb+0cP66KcV2GgBe2eyuaW8akgyOnJjjbgWVJvCldXdWypnVjmzc5I/KuayZUHpZ8kAqS0TvZY6ucKSuLBzOGQKUiIJ3SYTFKrlCpqouRfXFBjcwVEiVJuEOlUCwcrDpnTSdMDCXjruIv3nSXK54iRkO96j4smbKXGgKjCwHaB0RWUUPDoE21VTE/j0WmQYuJlgM5BAmqS8biqViU3aymjB/D03I+CIo5NtZIRcuM2k6t8cZnvO52dMxorP2yH+BnwrixVHC3vSV7cMj+Gw8909xdDg6bOXEsYSBg2R7LGzu2MedlOZVQTjQUYdutREKMlp9Lheze0Wt3DnYxBEYKAsNGwCFNwrgM9HLFhQrJFVWz6qXhYzgVKOlfq1CLuz7Cv2qtkYAVa9zxhsHCMuKRUgCWTkOgohFwY8A0E6moN2kcW2sUMtFyj7YKOo3IxChMIh5Nxnzo+ciDZsKakXIBLRXLdlPJqJ/Le8Uyg8Fp2ceqFBTzHM5CoJnTJrOuadnKdXS30VQv2+T949Fn0snUmccfmYRZnaqaUaXJTU2lbN2Klk5ZABwQA8/cz41EOxp2BKw0XNFQWuIMgf4IDBsBI9TClCQHxlWJFkKFcTWB0CqCL3zMLRpmZWsGd/GDO1IvBke1EA8Gd57ih7AsW+qfUbs3BAyBPUBAZjrrXs1eU10mHSklPSHgHh7s1fAm45GEV0yUczMmNNKm1FWlvBibSxZgXBTXdYlIvqOlrjpDMCo+wQnXxAlLxSJnBncGcorwS6u7Fy1aVptIzj1iNrprt/RJ5jyPq64pp7Pru0vyRnrbXOWljEz3vnsPMmVBDIHKQGDYCFh1y31BQHhVxsURO9VeLTq4i72vehnqDcPiM/SMH8KGj8xiCBgCe44AVZDKFI/kcwVGcKeMqy+0bmzIxuFCxntlGyxmNZdErTxxbH2psyXt5Q47cKqfdxTJs1gcypSaGpSrMnHZxyrHGUcpYu0qekceMr6Y49TRZfGI1+x5P7/+OlYJX3L++Q1VhMzTAe/Ml1gUMW1cQ2cQfXLREplwKT12L5/Ll3lrJCqUjCwdiRbySMa8ZkubgDfeIi2IGUOgghHY6pOt4HRa0gwBQ2AYEECjxFuDcgn6mz11/CtPnXvMoQdCofFYJGD4V3aBltlVTH5O+bnqWLnRzdVyTMm0aBhQW5iSCK8ojWFj13XOJLzajFeTTHR2FdE/3/PwmnzZnz2x6dgDqyMMLLN7FuPKyTgxN9XWdvulDZ2dju6jfqEcjaViKWTjKO9nxQOWOAcUG9kOw9dhr9xbBIZtFvTeJtzCGwKGwOAjwORmXpKpSrN/7DGzG5oaz53VGGeUl8lQZZ8hYIaGRUgOutuq4wUItTHDVhtOVJWVwjGPjaSVG1Fj4wCloquGttEtp73Gmuza1tyzK7y7H/hbsdR99onHzBrD+DGvYnmxn4zIwQtTm+p8r7yptaUz72VRjDHaHPMKvtfZ2VVfk2Xb986uXDaL8CxmaxaW/kFvD8BZ7WIIVBgCRsAVViCWHEOgYhCA1QplxoO8UndXPMNgbnRqYxx+DUpd7P7MIC/CKiNFcDCTn1/+ssOrM2wCLYIuJmB7jSh77PDQESG0yUmCMULLdClcmZbF7lrrOoM/P/DUytVrZjbUnXPyhIyPhBzjuLQgIdTOr6nRq8nIWWetHcG4hjhzvhCFOfcwmkg7mdhLZeUMJQye+xh16+NgVkOg8hAwAq68MrEUGQIVg0BC5FmRLINyMR5Lwa1J6I/TA9khI5UpMNGK1UERjwOAaydMyqTctMmgyMgvvK3TpFA6w5kRWTjMj20sY2ij2aYDM3n82CdXd/3z8aezkeRpc49Bei52enGmaoliW6TZQpHd4JNTGutXLO/a0NI+c0wtszTbuoKabIRVT+25gAFlhpA7O3K11SilQ+PYl7RKHGYMgcpFYOteY+Wm01JmCBgCw4AAAi7HKsRTKSRYtMkiwHrFKDtSulVAXEoy5cpPeKWJNcn6pCxA4vQxxFHYFwmVHwwtC4kjCMQJrsVyIAPBbo3vrOmTOD6puaPrgKnTzz7lKHTTmQQMy5huOsHZv+V8OUpU3szxTckgWLWhudtJ09lsZPHK1qeeXxZPC6135b2qalFBw7pO9g5RwsHWB4domKUSETACrsRSsTQZAhWCAEpmOa27WPTKMCmbW5XK+W5PJGDoLmCVPkcjBaV8uZRD2hR9mkq6rAl0jJiHofEYlb2jURvjWIKAeeRk1JnTp3FaeF3D2JfPPWFyrdvfI677bESh9nKpEI0l8Dh1zJhUOVi1bj0JIbb2vPfbG37/459ds2jpJiLszhW25t0KQc6SYQjsGgEj4F1jZD4Mgf0WAQZ6ZVlfkvFfzgGOMTEqlkwLo7pVgrAtTyKsyJdjfXs36JAF+j0jsgz76hCwLBlSE7gFQzLTOZg0pmpsrDijLnbaUWM47SgCO7N0OBLxoX3EZp+9N+TkpYaqZKxUWNfS1eF56zzv13c9+siCVWta83f8+a/rN/sNdUm6B2rwjLzd+yKmYZNOJwSHb3fPuNuDX0+0W/7wot53bXHc5zZ9S7/rPn+LRTg8CNgY8PDgbm81BCofARmz5SeNhPzlv0x1iiTdXCqU0eiU1bALZTatnMtsZzfyimf+JlA8Oy8yGUsCe1mGd3viirA/5c8/+55YPFKXkI2mEaFjqWqkY1Y4cThLNlOLyEuQA6dPCLzC04tXN3vevDbvxmdW+9kpNenuR59dfNRhCyedPAeFNdruXKErnUzCVKVCKZVgJDkKobOIuWfWF5RLghDE0Zjj3ZmteVmcXNr1Yf+rZqTXjzKi+gmf9A+y1/f0HsLE9omsfyoHLwF9XmrWQUDASm4QQLUoDYHRggBNvWvtaSh6plGJppkBXX5bXPo+FTu5J5SQtrOEfKGxhVdGlKdWBVPSXm1SiNIZ9utgV7uA9UXFUpE52HDr2Aa4vlSOxbs87ze/fawzXz7kmONef/Hbmgul39x8q+qlkZXTyYxLEqHRirNTB3S7LcPKSyR9zoQpCS29T0RExvQNvz0mDL0PqoVU9P5k6J274UvLoGZ0/4s8/BT3v6xbjg0BQ2C4ERB1tZOsSQh0yZ6yotyWw4CRuFn2K+kbUyV7TTdvWH/33Ss3PPnwtEj3288/6uUn1o2bPmVpW/uNDz7fxtToEouD414pEfXZT68qwvLlaFGWG4vo7Vo5FxWsrFwbQ2e+5ccu1u4X+DG3uTUB8BZeNQoXi6RnyI0m2V11bxPVJ2xJh4rjet3iarbKR2D4PqrKx8ZSaAgYAkOCAHOy5PRCke5gSCHkfC7HkQ3QKKxCIzV5fFM8EvzjnrsmRXNvOuXoQ+q9Ws975WvOqp806br/u2tZpxdAvoz5wp8EkEleQWtHS5HjH6AtNT0ypFPqsimmnOuQl5/PnDK9OotM3Gbet5wx0cPBTuZUybM3Lv2LF/1t7bwv71D48yPG8KqWffkOi2sYEeADMmMIGAKGwPAgUC5y7Levcq+Qr/KlzPwSGuaOCVbYpk5saqzORHKtR0yqfvOZh7H1BttEn370xJq6+tYu/+4H5m8seyxS2lzyNue8Eic1xBKZ6irXuslCJjdbCguTtYqObpV6He8G8G6XF+ivG+W1GyMWDnZMLIF7EyUWfj1U7izyeHBNSPPbI3t00X1/g5sSi33fI2AEvO8xtRgNAUNggAjIsWbyky2iYV/HbUHg+/FEoljIQS7VzL32vJp0vKV5XbTY+ZbXv6o67mXKQsAIweedcebExvG33XH3pg6vlelXTMmulqOFIduol+S4CGZhobUNRI5k3JqY2B1TB7CZLMYPO4uXCeb2yJRbEu5k3p6pWk6gVgcl3p6MDWXLGYq/vNtN7d4huG7K9w6f2oOKQ8BmQVdckViCDIH9B4G+o5lwnaM1PdyMAyBKbmMtAeOQOTPnL1wyY2LDIYfOLHS2VmfrOnJefdp77VHjVs0/5P/uvfeDH/3auAnjpkwc21SXOmji2Jcfc8SspupIJA4ZK3tCrLA8W2MKw7rtM0OQxcV5w8K2m277EJcQmesE4XGMxHboVjbNDKMYNIsmvm/0uv7LyfRkJExnXy9mHzEIGAGPmKKyhBoCow0BkSmdiUQZuxVRlS0shQ8RVX058Js/7MhR8E49dkZN3UVHHlC3qW3z+NosY7yZgD2jZRfM8089rnn9qmeWLc2Xul9auuCp1s0vNNQxI3p849EcgYhRfg3JUimNkV4Mdifh9liY+hWV6djOi0x3wmyHg3mgkWiPQf05z4Ny0bfsLGpoWPoKGBsh3hlOFfgsItMOR6lhLaGOJPlyKKlUwLa2trq6Oq7ZLAerUMmpyD2PRikGli1DoIIRkLanh1/gu14OUcrkTh6VZTYzu3SIZArVoXlOMkkKooZCCc7mHTGvOyIDwMu7vE1thYWLF/322usaqmq++Kl/P2iSV0UUxRxbhQgKkLxrB0qypgnNc6QjV0qm40TT3FGuqXY7jsgqK15F5I6IVS0u98rkPQxHkK1Tqz72/VUQ2tpsl+/ZNQxfrN0SDh6lZlS256O2tEbpR2jZMgRGMwIhsUkmhXxkNjI/iEV/LvOu1dIHuAdysBJHMUxIekdOSJ5z8iFnveKV65rbfv+n+9qgbc6BQFKW2c45+cnM5yI8le9qhfHTblcQqBT25W05ji0WBXQ8X/TZ4dKNGUeL+ZxM0d7aKPtu7TYod0q325JuKDjliwVmsdGf4NfdjcbdzEhCwH3KIynBllZDwBAYRQjAez3q0x69LnkT5u0xyKGIuKKShoBRESdZJ+zYDxk4YJ9K9o6OFpFZawNvguc1cnyh573+nNNnzZp9198fuv1vL7EwSRhc9uWQqdXlrq7O5mac0lXV7IbJi1o7i7AWQnZLTvb86vblFySSyUyGDTFRgCfSWU9PQJYk8e4hI195H0ZUA+TaSeBcBTD5ycIofvEE24PKJtv8Inp2lQaz60hAgK/ajCFgCBgCw4GAcCnsskUM4H7LjexoJROg3MQo2SmLRyH7wje+yMfluDKiTHeOx7q9fNmb1uBd+NpzF//iV3+4/Y5XHvfRiZlMkMy0tnaOqavyqqvi1V6O0ajuYNmq1ZtbO1dyyFIpaOvKQV/nnHXKzEavvb0wtiZJ/G3tnbXZbDIe8YscAAX7k5ihRwl8lPUVGJlEBu/SnegulFNJEdxJajFfTqbQQMvtMKRx6FEZLW80Ah4tJWn5MARGIgIwhjDoFtrVexFc5RHXLRy8lSf3sOcChXNGU7JG9sfMyZmJJx/T9NRzR939t3989/r7L3/nGbURr1BX1eqifOLxDY8++ujipcs3t7Yl0lWc8RRPZaDtrlxx0abcle8/q6omiUBMAqpqquTc4sDLFUpZIWBuSOpQGmVfB4TqCYRde2BgXgvrrLR7wi7a0iNhby+j36Esn71+lxHwXkNoERgChsBgISDaVz1NQSgZPbRjH1iGW67u1CU3MUoOGxanbLXMoaqPeq84+oili5YtWLr+2luWVadTy5cs3LBmVVfbZr+Yy6ZT+Xy+rnHc5KnTstW1NfX17Nvx7HPPP/jggzfNbLz4nGOg2VzRq0mIcNnW2tmA6Ly1GUIqJv/K+ryTHDqFAWcyo32PR9gru1CUA6nwgT+kYywOqK2Ta3eVioARcKWWjKXLENgvEHDincup8sxWmZaHysFYGLIt+W7lq7Av0p7M0BJ3iKmcSeTK0FKQTqCdlTlZJ86YsPH4Y//ntgfuv/Pm6nRiXH1tqtgVFFumTW469sjDznj5CbV1XsbNdFb2enRK6ieL/nnXbbfOmjTu+COmwGqwL++Pc/zicBoVgh35Su+DzG0xzNtGAib9L7y0urW19ajDDqlnQpqZkYOAEfDIKStLqSEw+hAQuuwxSqfciEUGhnt3wBAvPQ/7H/fLE55CwPAxVBRzY8RlL1by6rLeeaceu76zuyOaqM2mxtTXTmisnTp5TFONyIgqJqLCTcY4xcFLRL0T54ybf8Lhf37oiT8//ETTzCnjq72uglfPMU3Z2M0PPPWq044W1nfLmHghqdEYIGnR/bppXNqB4CmPUFjzYwaZkDgzySQL2tjiix8RyBQwjIZy1p5Mil+Mdj6wSJ9DX8hVDJ2PJFl2YQm+ZEP+B7++YX1L64ff+55XHD4ppOCeeDQMsbn7fi/tiVH9DODau1hsAF6dF3Tk+sZ+AcK09UkA239i+jj0CzMab20dsK0DHo3fteVpxCCgBCTNbthS97bO+miLh9CPeOgzdQtqIaz64wlxOQ9CfOEC4h5H90jb+N63bHlvp+dd/PkfdCVq586de+FrD6vxvL880nzfn+/euHr1zEnjP/7+t84c4+U7vHHVMhm70JlLZtOFqLem04tXeWsK3rf/+57Va9e94dxzXnPq2AkRr1rmbLPRtFvOFInlitFUMlXsbktmoGbIOFYMot1FmUgFlWqSfDoEbAVCHjAy6FsuBaVYLAWNqZvI/b1Z9ctBhxcpp7zP/dfvnlnb3OynZo0f88uPXZgueIkYg8KeXywnsXk+e4rF5FRniaTsmJs49Mfjnqj1BTu9gliMCeM6Gr1Tnz0PI7G8zFPfjiEq3su110Jm9QAMnLefolG5DtgI2Ah4O9XDnAyB/RABCPi+5YVPXvm9pvGTzn7Vq+9/4KHnnntu+tSppUI+XiqMq8t85PK3HzMrgUqaeV5BQXaV3sQ17a3Mef919T0LVm1asWrNuPqq2RMb3vfm1x47K1MrTMuMLqG7IJJo6c5nMqm8723e3M4g9NRJY0tMAXPicT7vpZOysZfQknAj/3kg865hX2EnFjS7p3LDemVOTkxHuyLetXc8eO1t9yenzFrRXqr2i1944xkXzJ3FbCxEZMg1wkZDOnGcTUgIH8TKQRQOdquzJF3iR/4OyDjKdDK9BHIp3MU1yioxuhdqCBCaPtRLlvXoC43QCDgEaYRbRmWPaYSXiSXfEKhcBBCX2z3vJ9c/9OjjT+TLfmehNHXmrLPPedVBh2av+dmfHn3kH01jaj7+4fdPn1wFvdWKMOh1MUury/vut3+6ZPGi4447bu7xJ/zl/gdWrl5VX984Z/YBl192JnIwVN2Q9Eq5fCSdWul585Z71//il2mv+IZzzzxr7gH5jvIY2YTLjfXCuzRb+XI0EY8kOB/CYaWbFaquPgggYoT/HB4j3otr8v/21a9nGiaeed7rOgr+/XfeevT47Nc+eRnh6CIg8+Y6OpLRIImGXbb5guF66Q3lAN0HjNLzQMsEQt2iadhlIFHC+9JZ6GH5Hl16H84XTnZj/BKrOwpaDuUgkdsxo7I9d2WwncyakyFgCBgC+xcCcBR8eenZJ3csW/jCggXveMc7TjltdpcbJ/7g5edcXx2d99xz3//hD84++6xXnHn8ypbCmPrkhmbvZ//z67XLFx49a/y/v+PCxrrkyYdd+n/3/v3mex9e8+SCBZtyl1967sFNsiHXuHQKdl+wwfvk139UE4sU2zZsvO7mpcuPe9dFp0P85c58VYb5Y8it0VhVXBTs6M+78slYSiY3I/ZG2EuEY4yDcoxVUnF239zse5//1vfT1WMOnXPQRadMbO32FvwltnTpsideWnf4geNhSpHUqzlNClMKCjk5h0KoDapzk8fZ3guxOK40zwPnvourcKfS6UCu8mY5d9nJwCJ/kykYx1nkWa8JOwEqF/c67w9/TQVtKuj94Tu3PBoCA0IATS88BbuwPhgRs8ikIzeTCo7E8Ye/uu2hRx9OZavaOzuraqr9cswvJoqtzaceMePTH3pntKu5rr6WUc82z3t6nffdn/5x+fq2MY31//Ka0y84UaYnP/n82qt+9+fmYvTQ2QcedvCsO/94Y2fLxoNnTP7a598J8UNNotlGK13Kp5FZSUcZ7SxnJsJZ8GnBQzSORwuxbKeXQFv+nV/c+uzzS8bUj/mPz1/a0e5NrfF+9JNb7nvyqcOOOebD77sgUvbqEKvLXg2Seq6YTcWCUp4jL6KsFBZp2BkZR3fLtwZCp+pHyJLfAA3Uyz5jJJ4X6Y8hbHoTzMwiIlzUSITckFGX7dC997n7OyolYCNgI+CtvnK7MQT2awQCr2VzK+Qal+lLMtOYX1dJdqns8EXn/Kf7n3js6WfbuvLLlq/MZqsixfIrTnzZO99wTkMyyMRKqHwTNQ2wYxuHQ+S8X9zw5GOPP1mfTrzipGPmTGm87bZbF63ecOyJJ//r5WdBZ0++kPv1r3+V62ivySTffsnFU8Y3zpgQyzomhoJSEBKe9EdSip3wcyka7/JSzZ536wNP/Op3t86cdfDlb7n4sElePduGlLxN7d6HvnXVqta2b3zlSwdOkGnYGIgfC+wOHcscZh1Mlngx5NEpQbmD/XZ5JYQI4hrWRbCLC8wKZlyFXpV63bxooVpuXWhhX51c7RZV0e/YvjEC3j4uFes6KgusYtG2hBkCIx0BiIVDGyAAfu3drZGSn8lk0vG0k9iERhDlVKbjNOKqtNfe5W1ev/ngGQ3wGGSCqhXZsrWjUIol8xGP9cOQzw13zb/73vvyRVqjoCEbPfqAMR9498UMdBIbUa1q937+y5uffGoeyudZM6efcsJxJx138OR6ATIoiUCcisl0KtKTLweJWARBfEVb1yPPzv/NTbczUvz2t1x66sum1wbeWOTYdj9eHf3eDX/+w5/uPeesMy5/66uhXse7Ek9Xd1cmEc3IcUmOPnVOFhErmXLlHbu8Qo6R7S8r2m7RE2WvuCxc6whYnPSd7qncCjkLHmqUlXvv+vwdle25ScAmAff5xs1qCOzHCDg9L6reIsJi0m1x4aTGoFzg+MIUQ5hBJN4pm0Z7Mbf5FFyBcFnIFTgFIRITXukueWm3W5fSmUb4t2fWXXP9zeta80cdMvPKD55fz8pjx7487XKk/tDjK++4+56Vq9d2duXGjx9/4OxZ06dPnTVjxrQJ2fFVQp8YNLmQJ/7v/PMTN91ySyyZev15F5x75uFVTsZNOZUux0gsLHpf/t61y5cu/MJnPxMNSh0tmxbPf4HNtKdOnjRlwoSZ06fWVwnVQncknn4D6RdJf2CGDGrKCasx7PzKi4hcoej3BhyJpOeqhMxV9Nsy+L1dYwS8XVgq13FUFljlwm0pMwRGOAIwIucKK6kkvFJcTnrADZHNbaahI6aqs3Wio2MKpkeVy5EEwi4/SASaRHscY/JRqVuIOpraXIouWFds8RJj67w51aITJqCSDu/Cwjvg10efW3bv3x975sXFLXnOYMqUmX5Vzk2q9ebMnDpp6qzj5h41oc77453zb7/l5jHV6Xe95c2HzJo4qUFeF5T8RFw4cbMXXel519y95s9/urtcyNfVVvulIscpJhOJdCLOwicWBFdla6bNmH7EEUcddljtjDqvxnHwAMuNpCK18+NlLUVv9Yr8iwsXLH5p0doN6w4/5LBDDj/08EMa026CF/nCkC/iJ7NKqYTCYMfCr6vTozcAQxe6S6l4NEYWmBeGDkF9O899L6OyPTcJ2CTgvh+52Q2B/RcBqAUihGZgBRgiIXOwWEWjLMlqGdx4EhcROCQJiJbJUr0EjA8hYKIotgoBs6FlJOXFqtcXvK64BznVOnrus/KVyGVGEhOb4e/mord8U3HBig3PL1m5eMXazS0bmtcuqK7OFv1UVdWYqmzjhjXrmrLpC898+QVnzGlMsxknhVUMysVIMsE4NQS82fPue8G/++67V69eXVddM3ny5EmTJjU2Nj76xJO5HMPNXV25XLFQDmRKNCdNFI4/ajaTtBield26+lxZO6QuZDt052UdHd1dXV1se9nW1gaj45JMJuOi2fYKhQIae143bty4RCKBYyaZam3ZHAl0y1A/FvjY6bqgAoj6pWSkdMIxRx9/9AES2G1GRkdH5oHvwBgB7wCYSnUelQVWqWBbugyB0YBAz5pbsgKJQguyhIZpR46DoQahB/gyzmJcVvDgAxUuTnjCu3CpI2D2oPLK3TKKK6taUUmn2IUDssIDYZTdCRsOfDI42s1mVXHZNEqlYWE2Z5a2B4tWrX740eeffmZh88bOSQ1j33LuOW985cRaZEXHvl6pU45ASqZzXoJNQQikKdHE6G170csmxL2521u1uoOToF5atHjpkmVrm1uCqppiVI565GnfazQC7YoLWQ7dY76fKJei7AESi1VVVY0dO1YJvr6+/qWXXlq0aNGKFStKpRLsS9sLH7Nxd5BOMplckiUafF/CMpMsKCdjfl0mcc4rTj33zJNZLsW7nDJcNP/y0u2ZUdmemwRsEvD2PnZzMwT2QwTgAYiL6xYjGmZhW/mJK2QJ3YZTeVG4wi480WvP2b3QdrhfI7tUs7MEmtUgUioHcWRiZ/q+Sph069cq5zHiy6/TPYKYN3d4fifrib2JWa8OgTXPsuAumaOF8jYah4CJJC0yPAmMFHL0AOLJDGPEUGm8QM/CCZf6Xt7Ynfcg5nsff77AQVLbGJVEw6taYgHzyNLZZKK6urq2NlNT42V6FcZEiAS/fn2wcuXKjo4OGNr3/fZc0U9nSxEIXrII+2KJsZKZtVblAnuEHTJr2qEzJtIpKXlsqcnOIAjH+xcBSyfOjCFgCBgChoAgAEEpAasg1rMrEzwjFKKSbijvwin44gEMJgIjIq/wHA4wIm4i+JX9UqzHV5H9mZ1CG7GYCIXI8arXIqpuEWoLVYkgGWexEHRWSnHkYLSWWJTdx1Z7CX5uubC8LgNhZeXFOiCNWElq8uxMTWqiSTaVJlyQ94olxqhTqQwvQ/MsryaJ7FUZDbKJ8mWnH6rZlbz3MZJbZ3pg6L3d1jM+iY0s8r7xTZEjmqZySCJHXGB4RL+hbxBi46fZkX4BKgGSLz7ooLglUnQm9JW9bxzdf42AR3f5Wu4MAUNgdxCAHJQxtjCPUAIUA53oE6FRz6fpVJZSOnF3vc9RvUbEvxhWEBOVCMTOIeBWOAjWgbQIIHwIheMch5BgriKzkkTsdvGGy4LxU2CyVTTKyG3J98ui3MaLa8CDwC/7KRTRSN7sVM2wNBzIuCwvgtgTSbemGZHZicDyzkB244hH0feKZ9lgi9jcIHd4pV+gdjTuW56SB4JEA6epJ76oyP8uA1B/Po8LQ8LpmNfdzWw2L5upUpW++OmFS+09oJEWXiRx8HbWWRfkEAl3r95G/dUIeNQXsWXQEDAEBoYAlCPCIbSADVrYas0rFMg2TlzFyIQsZ7jnRwj8i3wpt3CKODiDm5BvmTFVp65mXjRG3iDCX1xYWYi5o2VzKpOOpTPyIFlbDpj6xdgsmz77GUkVNJlPcpUXwMFJ5NzAY4GyGEZZMxwLXOIOXk1DwkE84iZGRYUre4kXn9yINC42ubAvFh0F/NMbkBT1uYrK3L1Mrk6DLOGE8mWelMah0ZQhf8aG44lEKs0wLW/APS2qb4wf8fO91M4DXsdLEPBRgOdJIUSeQFvNgDPyP50KPczZhdxPLkbA+0lBWzYNAUNgFwhANghieJL/SozO0ku27qbvM4iIn7rw0Em0Soq4QaFIq2LgZdkDmRsYSPjWKWgJiNVpoAO/uq5aoiqzaXSMYGW33wUHFafYkxJvZQjYDUUTG17iiNXsCC0hiIobSQNycHcuWks8Qtyi+xW6ZZaPU267V/I+UXTLdhoSBPG1RBRYt7mSrNBo/rjSH4ltId+e5+xdza87151JZ0hXochwc8DBizwulwoxYVuCEp+EdIpqNtiMwLXEVw7omNC/8PLFPCxehZ58PzP7noBBn/lvUgaplHaVQFY1Fe3t7Yze40hHCZx12rrObdNr6LNvKbS0tDDLDs8oN5hip1Pe1QMT65lxp2/RV6AG4b24p9PMraMO+DxVD9ySqr4xm90QMAQMgRAB6KEP1yJU9jfKIUIoPOPqeEuuvXasvZzba1Nf4kfk4x4P/BHjJGYmHgkHOX9QmRO7iQQnSY20WMzhQhWNzXkTlTYjzEK7aiSdRBiPRWuqsW7Jgr6lz97PiL8yBKzGPeUudOh9sIO/zr9Gua2PrMjuYlIJ3ThE7LE4dpdmuVMjqSMSl8hoVDoCYjIJCe4i35J892SUX/ZZblkcBtsJiBEG/FPwHxa4ExdYs7m5GUtNTQ2OUCl2FA9QKUMFWPQWelYSVQ+Ewp2AsC8W2Bf61CBYoFgc9S2EKhaLuCjrr1+/HncSI7oNijgS0Uf4JwFczRgChoAhsF0E4Lben/BBv19PEHWl7VQLrlh6Tb8gW5702nr/aiiRBmX4V0RkftIgaww0i+JTbtSDW1LMqmLZ54ONquU0Qf31RKjBeoPr3bbX3mT2/N3Ww85d+gXf1S3ZCZOpFlzk50Duyal7o7j08PKuIh1Nz8nzvjHZbFbpEy5EDFUyhju5hTtZCc7c9M997nOHHXbYzJkza2trN2/ejPDKwm31OX/+/MsuuwyyZG3ZF7/4ReVaHhHw9ttvP+aYYyB1wv7gBz8gCN54tGnTpp/85Cc48t4zzzyThWjq3tTUtGbNGhw1ZrKnQWD0pUuX7pvcWiyGgCFgCBgChsDeIbDPCBixFXEWkoNWIUsZjXf6XiVFEonyecqUKW9729sgWvw0NDTgSBAYGsuFF15I2CVLllx33XW/+MUvPvKRj+COCzz9pje9CX59+umn3/Wud11xxRW/+93vCAXf33nnnZ925rHHHjvkkENOP/10loHzUszEiRMJqx0CblVcRnqePn363sFloQ0BQ8AQMAQMgX2EgCOsQbkggGI0amRiLOiTscCgpJ3bdevW6dMbb7wRmZi90/AA71577bUTJkzgEWrt97znPSeddBLaafV59tlnX3TRRapSPv744+FydSdaSPdLX/oSt8TANXx16IJl7dq1vJpN1KBnbjHaUVC7XQ0BQ8AQMAQqE4FtG212xBzp7fk+k4A3bNhAsQEHXIi2GTuyL7dIq1zRQmNB6sXCwDCSKFd0xdAkT9nDjC3NYFA8IKcedNBBMOUDDzxADBs3bjz44IORnmFKzJvf/Ob7778fP8T/xBNPXHrppcTQ2dlJtBdccAEuxEY5cdWBZLXwLvyQME0SjmYMAUPAEDAEDIHhRWCfETAbcENvcCT6Z53qTMZwYfwV8gsziVCLgaF1PpTSM+pl2Ff9IL+ecMIJMDG9G4IvWLBg9uzZiLzMqCLUgQceyNAvBr6HaHEkcnYlJRQvZRc0ImEGFoprkoEdqfr//b//d/jhh/MUdwTrMCVmMQQMAUPAEDAEhhGBfUbASKIMyEJ7sKZOTsbCKCxX2BSKhYkRWxm7nTNnDiIp3IliWWkSdwiSK5zK8DDuMCgMzVPUxcRMhLBvXV0d1AtYY8aM0YAorvGGH0LhB0cYnU4Ab1TZmqM5PvnJTz7//PNQOMS/ePHiYcTaXm0IGAKGgCFgCIQI7DMCRgb91Kc+xWTmefPmPfXUU0iuL774InYszz77LFplXgkZQ5ZMUUb5zC30jDIZCzIu85OJQW+ZS8Wp1DAoZu7cucuWLcMPDA0Ho1iGdHGHcTFEDuPiggfsM2bMgOZ5BbfQOVeMzsGGm3kEc+MCZxMPkWBXTXXoWQJsY3ivuqk3ndIVBtF4+nrYJoLKdVB1PekL81i5ad0mZWGaR1+5hFkLC2ib3FeoQ1gvSF9YNbZbQGEeKzQn20tWmGYrl+3BM7hu8AXCFd+Szq6lCOALOIW36pcGg4Sf3OAmZd/Fvs8IGIZDCw0FoiVWg6QL7zLzmVvEU9ABO5VZ4WCygOpYsZs1a9YzzzwDrMptjPKif2bmM7cojWFxYMXgnwlcJ598MtRLhK961aseeughaJXBXURqBoBZkrR8+XLEX2hVC4n4eSkGC4WHwZ1bQqkH4uGRcjaWbQ1B4H7cmSBGGrBoELWrf40fe2hR90q+ht0ItZBHQFN7JSc7TNsoLhcKQj85ikO/qBFULmEVCC0UWb+KQ1XCkTxSiGGBVriFIrByGd4y4otS0zcZuGhLDr/AC1AyxYTp66eS7SKA7itDNaM/AgQh+aFnhurAiFfgCDtCvc899xwS8D//+U/8s8AXPnvHO97B+iLWEbGuFyXzl7/85c9+9rOItoRFgQytsoCY6VePPPIIob75zW8SkNje+MY3vvOd7yQGpORrrrmGeVvvf//76QTApioTa75IEv5JCbVdS4t46BzwXmge+ZiuA/YdcTBjzKxFJixv5MqkMEpX1zdr/4vIeRHxc8WD9sj01ZV8BVsGyMk73RoaRLLJ6ABt4o5wqLS8jNZyoUQYgmG4hHKhRCgO+pf6VVdaEWw3PVSxsC7ggVqDC8IKjSO9ZKob/WMcySDe+PC0F77dqCrK0cpleItDm3G+JSx8OfpR8V1xS+uNi5IuLTD1RScYDW+CB/p2crJPDFiQeY0KTsKuBneVfSHID3/4w9RAUkatgwCwIMIy3AvXMn/q3e9+Ny5w50c/+lFqJlWU2Aj797///cgjj6TeIl5/5zvf0VeAMpYf//jHbOtBKGiY1cB4Vnf1o1dNBnZKSNkFoZwgajQ9tAi9Dv3/hloOHmjLwlYhoaeQbrGE9vDpiLDQMpJOzRSZHRFpJpGjtVy0CLQ4tGhGSomE6exbF/pWCs2UViI882hkfW+k2colLOVhtPT9qKgjtPxKFtrg03/t2/5XuJ2tunvGSvctoEQbwgTzUdNUglThFX0OwMGX2v+FF+nFaG1Ub1pL8aDyJd1PkkcPGtETz5A08fOIa986rJGHPvvlCM/0Bhhs5qXEg3hBsREEWUo1fv38660mlSDkguAk7IUXXmAJMouYIW/erskg2eQXe5jr7cZWOY6aYCBl/rlMK9+0iZECrhRE5SRyJykZreVCvpipwLZuXDFojPhW9WvfCRqV84ik8mlpgqkL2Kk11B1mdaB5YsMcOsTUGuoO9Y4KPoK+N4rDymUYvzQ+KgwfFUbtXKEDFagQvfi0aNWHMYV78Op9RsBgoa9XaLAriYbsiwtVsS9f4qKcigUuBLvQA7BihxdVQsUD8agdoJWPqcDK31io5zhyVZ+8RcuJ220N5UTNJ378a4ERZ/iifv6JBwGdyMkXQbAQllAI7mGnQXlXP4t+wSv2lkyRC/BHwQ6AqpDAooBUbLLDhI3WcqEi6FdNAWFBFQQB46gFFGa/ki1aEagvWh+pNfQq0GCRC7rd1DWtnljIVNhuVHKOSJuVS2UWEK0x1MB3pU00354W1khpx/aZylFzTuZDixYYxKZKYPiYigde0BV2qiVXGheeYqE2clV6A81wEBeGgOeIE0NYBZrKAL40T9ReHLXB4nVIpdCJUmkomGoywqsK0LwLF9oFjZNoiT/009eiDYQ2JYRCv0HLSOT4CYNI4lxwvfYNXrF2ANSV1rq4i74OZREiWbHJDhM2WstFi4DPngm3FA1fFMWEJcz4iLD0rQhqp8rQZ4Vx+47PUYh9fVZy1qxcKrN0aO21wSd5kAgNNSwzUtiXNO9w7HPP4NZmUcNCsSCCC7xFNYPqsCuDghEGO50X9UwHmVuCcFWGIyyPYAUlYFDmES6hlAYNE62iD5XyCDUXdI4FluWK0fRwVT7GBWonZl6BnZiVSsXrDgxJUuGD1+GF5kP11cSJ0UB97TuIphKdadZJOXhiAWdyuks0Kicbo7VcKAKyRnFQKBQNBYSlcmAfYEr61ojQTsVR9tWqRLUipwOMcNi9WbkMexGECQi/KFxgEP2K+Khoz6ED5YLQc4Vb9jEB9+3PQrFItOpCxwT67PcUaMBLhVHs2nnBQs3ETtgQO2VZvQ2ltL7dHKDXp2qBZYkBo2/kGtrxRszKqWrnylMNvu2VZNN1wPA6mFvp/wtf+AJNCRnkUyAIrM8rwjWC20YyjC5hFyds7LTzQZLIFLlj9TZZ46slOyEO2o8Jk93vNnQfGou216RBU66J0XIhAdppIC98SF/72tcoXPVAfikdnqK0GJp07u5btIupWdCkYifxFAQWMvLVr34VFTT2fj5x0Q8PS6UZCoIkIbWH3xJVg8rCugatQeQFC+mngCjECky/fj8kTPOChTTzIWm56GfGLVoKHunnRxC1kK8wFE8rylARtCWkTdD6To74xrR1JeXaKpJZvFVUyvslhuSFKSTxWi58VOottPQLVZm3+2wMuDKzt/epglapcnya1DHtK9Bw0LgwX4kpkfod8GXTlMD9MERI7Xv/6n0SA5Ut7L7QNPCxkmaUgVQz3ZgTR20H6VvQudGOpLae5JSnw54jmrawNQcTzYU6kgtyx1NFnsySQfXMo7APV4HlQnvHF0VSyQjfVdigaKFTCrQj4K+Ni+YaP/jHqB9i6BdK3Yf3CuzkSL8ZVZ5TUyBg/QjDr1FLpwLLBfT6VnntJSjm1BoUdVR2sqMgU2X4xsIyCpEP24rQpXIsfT8qSoraRBbCYQ6tVpWT2lGfEiPgXRexUpTyE77Xr1+vO3lhpzZCWtRP/PDthi3+riMdQh+0eiSetoNE0mqHfEwSdERcm/u+KepbDwlCcxnqGPp6GwK7NuikmSRpR0HTplftyJNCfaSNC1d1Ib9kDTLgttKKhiRBn+QCi3bsAJNb0qxJJWtgTlNO2YVTIhTwbctxCApi4K+AwKgXzK7XMgoDhtSlWcM9bPdDP8Nu2S539ks5/QZNp/YzqF+ECu2UZligw56dMAGKOV8OXx09BuoIuaAnofnlI8QDjtq3qMyOUZiX0WQxAh5QaWrLzucLH6hoQrPIp8xYHeG1X6+d+gFFN4SeqFokjyqnjQJtInnBaEa0rQ/rG44kDZ/aqccblu02SUOYg55XaTK4klo6PYAfAk650HxQLmHDpyWiJDf0SR3IG5WcwhSSL0Jp+tWRrgPdDtzDrgOZwtDbC5VsislAXjdkflDMaqXgjZQLCaawlGg1y9rbw50+E4U4ZAkb+Iv47PmcSB7Ih33T8GPTSqGxhcUX3uJCAfVzH/irB88nZaGd1H6v0L5F3wTTf9JpLv182u1gILDDsc/BeNlIjJMPl2QrM/EF82lSwWj4ECipnzrbi08WP9zSrFRaHmnjSBgtO4nU1gEXWnBNP6nFXaVb2kd8Ysgg7mRcaRj/NDrDlS9lJt6u5MSVIiBJQM0Vd23K6bmrB1xgaLpHWPBApvCgBYRL5RjKgsSEHwwtfph+zRdlxFOKA2+AAIfBx+Rd2ZfgGkPl5EhTAvtqlaFqUF9gXzS3zCODycLvCp/khdzpCHdFZYEvh88enDUXWgVIvBYKSSXZfFQKPuyFPkx94sIjzWM4CbRyshayL5hTIpow0knFoXHj8yNTOFJ3+Ngq89OqHDD3YUqMgHcBJpUKH7Qj7OSFhW+XkxBRN7EnJVdlJtY+8eHyHdOs7CK64XhM2mgjlLdoXxiTIxWklppGdnDXuqftCI/0lisB9VaboeFIuywVA2SlYb2SDMQskqdoayJJPE0hj2hTKBe6F4TCkQYRn+RxWBK/k5cq3WpLx1WJVltGbqFbwmpBUFJYdC40CJBfbuEDzDB2jHaSNW3rVW1OXjSn9Bu0+DQjUBdZpuLsJJ5heRQSlVr4/EgGidePjQLCzkcFjdEykEc91JyCoDjIqXb1NI/Dkv4dvZQUYigOMKd2YNH04588UmXIFB6wkHjysqN4zH3fImAEvAs8oV7acT5WVaz993//9znnnENtPPvss//0pz/xNWsV5cOtTPYle7R0ylL33Xcf226TfioYdtoLqiIecMSFLHDFXHLJJatWrSIULjwd3u48+EP/2oiTCwx9iL/+9a+nnnqqpvYtb3nL73//e5pLmkJ4i23DmRxH4v/lX/6FvceHPf0kYCcm7NlgIWu/+MUvyNQnPvEJ7TGcf/753CKjkDUs7OTK9t0UCm0lhcKHRzZ3EvlwPXr44Ycvu+wyqgZpPuKII/Q0MxJDT4Ks8cmRu/e+971/+9vfhiuFO3kvtRhg6eGBLdfwMyMvGDL1xS9+Efai+msGcUSB9K//+q94phDJWthN3Mlbhv4RqSJ57APIqgf2I6NCcZjNgw8+SOMG49JvwJ29fsnOeeedx/l1Q5/C/fSNfExmdoKAdhvxwJd6ww03wAc//elPObvp4x//OBWPrT94RI1FKMGi153ENvSPaKl5KR0IrrfeeuuVV15J+mllfv3rX+MiveJS6V3vetcrX/lKJEiEY3KEiM8j1G4aloxzO1wGSGEmTT8tuCbjN7/5ze23304pcHLlZz7zGVoWtjnE5wc+8AFO1rrzzjvZLvTwww+niSEsQcKAw5WLbd9Lk4cjzWKYvMcff5zEw1icKaL+TzzxRIqGQlm0aBGbpVMi6q6hsFN26lI5V/bOPPbYY+n9QMN04+jnURYkmK/oe9/7HhPvr7vuOkrtjDPO4JAVOkyVk3JNCTyqFvp5WDg5Bl0XVYO8PPDAA3R97rnnHtzpxdJPJbPIxEuWLEGgx5F2QCtLBX5vJI8cXX755ZxQRwUhRx/72McmT55M4nnEHvvYb7rpJg5u55AbdqXF0cwQICALCs3sHAFtxGnfzzrrrLe//e3qmco2bdq0//zP/9RGBJUU7sPLVTvPBU9Js/qhs4nUqM0EVfHSSy9FItGMQAzarGtnAj+a/V1GPqgeFFiSpLylaaNZx8DNCIhoJkg/TEwnSVPCcdRkk43aK7M11CyQVLIG4LT706dPv/fee9lmnFXmPMUgZiFa0W5i14zjnyxriVTmx4YGgn6DslffFGKnvvzXf/1X2CNE2KLFH9TPZo8jB2TCKuaUjn5ClAXdI+0G0YH40Ic+1Df+sHLh2Nfe188w2skCRUAHgoqvyaBjxzlylBe3FM23vvUtzTX9b7xpB71vggmuHvo6mn0vETAV9K41HzpHCdGQ9hHdGg29hnn1q1/9hz/8gTEhqigKaj5xCGDX0Q25Dz4RbUFUY0nzzdgVu4ZpO85Rd+igEIs5ovHggw/+yle+Qk3DP+0j3IasTPbJ4JCnuueFpAGbAkuS0EBwiwVOopmgpaMRJ7WoCum844goT77wc8ABB6AP5PxKsqBjqz0xVsYfsoBQS1rIGvYvf/nLb3jDG0444QQ0tGjOcYGoyDsSJAXEGdh09SgXHMm1osGV/FZGbrak4vrrrz/ppJMgJ5KHKHz11VdDYzymWSdffGDhQpfTTjvtL3/5y5aQlWcDahJF9eET4qNCdn/ta1+r4zL64VHr+cw+/elPk0cqlxYHH1sFDg1oFkghnxO9PfJFvSadaKE5YYKiueiii+hb4I5qne8N0Z88UvHJPo4EpEAVEG7N7CsEKpEw9lXe9kk8tHqMl9D1o02k7rG6kRaET5PPl0/5+eef57ukoeSKNzyHkzj2ydv3PhJqDtWGWkSHVw9Tg65oxxlNpDtPpngFY0I333wzzeWjjz7KWZA0i3Q1qJxkClFGR2H3PiV7FoOO/hJWC4K80BSStvnz55N+VJqUxY9+9KNDDz2Upp/MQmAUFnnED0pO2hHC6lD3niVgkELxzegpLvTnAJ/5BIyJAjXJplknU/SKLr74Ytp3enhQNQdj33LLLZQL6aFcNFV44+kgpXDPooVoGSBAQztv3jzGCNCsUFNQseh4PNzMp8hXRxeQEUdUuHv2lsELpclT1gFnLMo6VArQpnbwar4upk1Qa+AqRgcYPaXzd9ddd+GTb09nnw1eCvcsZhLPp/Wa17zmS1/60u9+9zs+P3RFDNxMmjSJOk6c1BE+P20uGAqhvKh64ZdG7SP4nr3aQu0EASPgnYAjj5RQaTKw0PTTrFDxaFP4aulIUt/4lOld4pOPldtdRDfkj7WNJsG8WRsX2neyQ8ONResb8goWmpULL7yQcVN6GMzCQD1FKK2ThNVmaMiT37P6iPeSQloEskCvnJ4QzffChQsXLFjw5JNPfuQjHznuuOMAnwQzFkCziB2fFJBSL0HCpmTos7DdN2p6GF8kbVddddWNN96oDRxcS/NHRigOBoPJAg064MPWL3/5y8kXhk8ONCiaSmNfcooseNRRR/3qV7/CTprf+ta3otuEgKkv9P8WL15Mt4lHrCmggCqwTdc6wpVEUkZY9MsnF4xA6Zxnkv2+972PHi01iOFSSoFa849//IPuhfYXaShUUCaSCjE0X6QN/RazQJAi+MBQt1xwwQV0IFSlR3Y4bxEhmO+K3jn2bWUJvj3NYIVkahQkw1TQuy5EmkIYS1tMqhwBcOEzpWZSFZV9mYVBs1iBXyetA6klzVQ52nr4mHRyS7Jxp33hiiEvNBk8wkLWmJqh/vFJa6JtEPZhMfS+qflATTIoCNVhki84GEmLSVjMK0FDyxgqtARdkU1yh0+Gt+EAgmMflpTv5KUKKZzEjO6nnnqKKWOkn3TSn2A8G/DJL/Ss7Eu5oLzl6R133ME3RklRRlCdFuVO3jL0j8gFY8C8V+cl0Y3j++EzY44PfSOtILT1qC7QfGptGvpE7uSN+qmEVy0m5hP8/e9/ZxY6t9QgPjBigKLIDsV0+umn4063j+KgUPgIK419Sa12KeguMBOTEgF8RHYolgrCx8bnRJrpJGnKqW50Nag4tAzYCa5krMW3E/Ts0e4iYAS8a8S0meMbZXyRz1fHe6iHLBp505veRHi+UWUF/cp3HePQ+qDyaC2i/lDZICfSSWtI24ELT7GQIvTSVD+GunGhcSGb2vrgSJsytEne6m2kR5u80BX9GOQUpoocYUcLTUn99re/1a4So7/cHnnkkWS2MhsO+gfkiAnDDz30EJpbyBWBnoEABoMRp3gEPVMEPCKDqHMhY7QvuJMv8qtFGWJSIZZTTjkFjTqJoUZgoC6+LviM3hLZYe46KefromUnjwwDV0iyw2SQVFKoH4xWZ3jo//7v/+gAoWXhO+Rr5AOjdhBE5xZoYTFNGhemHUBvYWyVYyHN9HtUIcSXA7+SOziY3gOiMJ/Wj3/8Y5o4MksLwHxvioacgoP2RfgO8Y+pnByNkpQorHbdEQIQbfjoZz/7GV8w8wNpVpg9yBeApEhTop19vFE/Q88VYqGxJiVcSRutBqM+NPdIHoiMqNPRPkED5IUOPqzG+gRm3qKhQhNFM0RAOsWQt9qHJUe0CLxXpShyQWIoke9///sMV8NMJJthOaSr//3f/8Xbe97zHhpKBkohMxSGNC6aZhS5aqmcKxkhMRSKwstoKLd8SzR8FAfuaGsZrmMSFms3KRda/5kzZ+Knb3FU4PfG4AXKCVKOjHXNNdfQ1sPHpBlDqdHEc4uUj84WTTUERo4qzWiVIVVqgVnpxjHMofOfcado0OWit6DDxBzA2bNnU2qaCz5UCrHSckR6+FRIG+oWSJelAVyPPvpoens8oi+O0oXWjCmlfIdolVAp0SyEudDPLGwTQnez7D0Copows3MEaPHDSvWNb3xDlTbIIlQ/mhUNqzyx83iG5ammUBmUmT70ZGkEqWzasUWCp15BukyKpo9ME49ABk+TVL1qxsMmaeizQBeBlyoBK8g0ByzBZKCUXNBXYLcKXVkhbXy5/O53vxs+pofx+te/noaSsH27UEOf/t19I50GVJ2Eoj/EoC9zZJBOUNjSzyB3uGtZ9LXv7isG1T+fDfITM+qhYXQSP/nJTzTBev33f/93vjQeIeUz6WxQU7LHkYcfjH516B740ugGUYnUha1FkBopFDQTqNw50Y8h7b6doWGsLzvKtX4wv/zlL6nj6LRoxFgHjNJLWwZq1je/+U00E+T0zDPPRMAgnrBN01yHYsaOXmHue4BAhDCAbsYQMAQMAUPAEDAEhhIBGwMeSrTtXYaAIWAIGAKGQA8CRsD2KRgChoAhYAgYAsOAgBHwMIBurzQEDAFDwBAwBIyA7RswBAwBQ8AQMASGAQEj4GEA3V5pCBgChoAhYAgYAds3YAgYAoaAIWAIDAMCRsDDALq90hAwBAwBQ8AQMAK2b8AQGAYE2O6AMzD0xWyGgIUdUbiyCwTXvqvzdb9DtiTDnW0uuKrRPfTVHoYiILtAqCN7L7AFmNrZd+W2225TO1f8sLtCeKvBuWWDT64E1K0ksIcpYVsGfRS6cGvGEDAE9gYBI+C9Qc/CGgJ7iAB7VrM1IxuNceAgGwHCvuwPxW697N1IjNCt8igsy+6n7LKk+5fprt3sSYQfzitUn1CphmJjI4iW7Zm++MUvTp06lY182fMIzxobm1niX7kWIucRt+y3xZXgGifbNWtvgEiICu7XbgExs1Ea1EucIcET0IwhYAjsDQLxvQlsYQ0BQ2DPENDd8AmLZAmrYbCzFyDSJ1yoxwRBe8qykB9PYT4sOELV0CS7iurZizyCJqFMPGPBcAoCm3TiDmErZ2PnuB6ucC2sr3ZeNG3aNBxhaIIQP5EQLfTMK0iY7t0fvgWfJI8IEaw1hbiYMQQMgT1GwCTgPYbOAhoCe44A+29/4AMfuOiii1asWPHBD34QvkTihDthYnbiPfnkk7k95phjOF6C02l4DczHkYWf/exnOecA1nzLW96CPPqDH/yArX1hzeOPP/4LX/gC4jLEyeE8n//85zmsghi4/fnPfw6DwrucRKTaY85L4FwvXgSJvu1tb4N3IV0Y/V3vetfFF1/Mds3ss80BA//2b/8GEyNew9bI6IjUeMP+5je/2dh3zwveQhoCfRAwAu4DhlkNgaFCAMKDUyE2GPR73/seBwQhVkKZnL3z6le/GnrmOBpGbXH/+te/joQKX+Lhj3/8IxSI+3e/+11EYYRRVNnz58/ncB5OGWI/fZLPiffvf//7uXJCALFBsQRB6lUlNmetX3bZZePGjeO4Rs6Sgow5kAN1NOTKK373u9/B2Zwlxfnzv/nNb371q1/xCD8MV3N8FocQPP7446iyiW2ocLL3GAKjGQFTQY/m0rW8VSwCcCrH6aA0RiqFjJEvkTVhu8985jOvfe1rOWBHpWGI9nWvex23cDPnCCGYci5eKIAiEJNBNMlvfOMbOfoJeuaWk3fVA/4ZPNbJU7xITzzkrEYk7IcffpiTa/GMrMz5V7Ayxy5xiyT9yU9+EnU0LHv99ddD0u985zshcgRoTpciDaQKyZiuA57NGAKGwF4iYAS8lwBacENgTxBgeBXdMoyLBhiORABFQkXly6wopF6oDnqGkjmLEMUyBlpFAua0Y8iVW+gTgfXGG29kJtdzzz0HIzK7ikhICnYkYwRZOJ44iRz/iMsExAOH66HfJjjeiOGkk06Cm59//nnSQ1cAPwTUR5zqyJG3pBBW5oBYxOgLLrjg1FNPRQUNQ+9Jni2MIWAIbI2AqaC3xsPuDIEhQQBy1alVCKmwLEIwBl5E3EScRSTl7HSk1fvvv1/FU50hhaCMDAplEopj1dEec7I9ciqaYTTV0C3uGKLCGzSMHQbFv3IzjvAxY8Z40Fyii4aeIVS84YKkCytjeB0uWHCEkmHiG264gTliSOScWIwuekhAspcYAqMcAZOAR3kBW/YqEwHYFzGXtEFySJwwJbwIO77sZS9DmcycLIy6w6PIpnhGAoZHlRR5xDQrxGJGf6FVOJLJXIi8Gi3ciUCMHR7lFXjGEAnemMn1s5/9jEFc/HC9++67sfAu7HjQyNFpQ8kkiQgJnkgkeMqwNObKK6+E5ufNm8dB9JUJrKXKEBhBCJgEPIIKy5I6ehBA2wyzkh+mHDNvGYZjLhVUxxJeVNAf+9jHGKZFR33rrbd+4hOfgD7xiX9oVUNBpSxkWrp06S233ELA66677pe//CWSLt6QrVFfI0kjFusyX6VVAsLHKJAZG0aZ/MILLyA6MwaMnWnVPCVOJGNoGDmY9ED5aKG5ZXrXT3/606effprZWFiQg/E/ekrCcmIIDB8CRsDDh729eT9GgDFXVQt/5zvfQaBEC828ZdjukEMOuemmm5gnhbgJ//F0ypQp8CJkjFSKjhr7hg0bQI4lTHAzc6MuueSSq6++mnnL0DPu+EGJfdZZZ/GIMeP/+I//IIiOKEPwyK9/+MMfeN2hhx56/vnnz50796qrriIUe2Ahf0PVbL6BN/iYUCtXroSG4WNo/pRTTjnxxBMJy4wwkkoQM4aAIbCXCESo83sZhQU3BAyB3UUAEROG0+FYCI/gOmeK+qiqaVwgVMiP2cuoiLlFaIZc9UVabfGJcKwCrj7lSjzIuLhD8KEHBGI4WMOiW0Y7Dd3qe1XhzCNcVINN2oiTqJiThV1lbg2rSdIY1MWuhoAhsMcIGAHvMXQW0BDYWwTgPKVhLJAx0UF7kB/UiDsGAsZRZ2zpADA+8QAvMnYbvl6HgXmknKoztpQsNTiemWwFHyP7MiMaIic2POATF67cwr7QtjKukjpEiwtx6pWoCAIfQ//4D99uFkPAENgzBKwW7RluFsoQ2AcIwG3wGTIr7AvzESPUCFnCfLhgmJCFIxaukCKUrGSJH0gXXmS/Dh4xfMuVGFQy5koMkDd8ibvyKCPEcC2PuCp9Eht2PBAn7IuFV5AeZV9uceQWz6ST12GIExY39gUcM4bA3iNgEvDeY2gxGAK7jQCch1yr06Y0MPSmPKp7L8Od8BzUi1wL58GFzIJGJ4xn1Tb3fSWxheKyysG4EJZIVIbW21BExoILj+BaVUGr4BvGqcytYZGYGX4mDTzlLXQXuA19msUQMAT2GAGTgPcYOgtoCOw5ArAj7AsRQmnEAsnBvtAet2x5AclBfvjhFj8qnkJ7KuAi/uIOYWtYLPjEP5IuUSGtEkSFVByx44gHnV2lMRCE2ViQLlxOtNwSCm/qGT8ExCBh4wj1YlRA5xb/Ss/YzRgChsDeIGAS8N6gZ2ENAUPAEDAEDIE9RMAk4D0EzoIZAoaAIWAIGAJ7g4AR8N6gZ2ENAUPAEDAEDIE9RMAIeA+Bs2CGgCFgCBgChsDeIGAEvDfoWVhDwBAwBAwBQ2APETAC3kPgLJghYAgYAoaAIbA3CBgB7w16FtYQMAQMAUPAENhDBIyA9xA4C2YIGAKGgCFgCOwNAv8fHXV7tSYMc/gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classiq.execution import VQESolverResult\n",
    "\n",
    "vqe_result = res[0].value\n",
    "vqe_result.convergence_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23a40cc2-7b4d-48e0-90c5-927078fed2d9",
   "metadata": {
    "tags": []
   },
   "source": [
    "Convergence happens after about 80 iterations in the most noisy run."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c203a6e-0f83-4f10-86c5-7f222052f30b",
   "metadata": {},
   "source": [
    "## 8. Analyze the Results\n",
    "Finally, we compare the energy values obtained from the noiseless simulation with those projected from zero-noise extrapolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b75e59b7-a278-4411-937e-856d97d60da7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_results(noise_levels, energies_noisy, energy_noiseless, extrapolated_energy, model_name):\n",
    "    plt.figure()\n",
    "    plt.plot(noise_levels, energies_noisy, 'o-', label='Noisy Energy')\n",
    "    plt.axhline(y=energy_noiseless, color='r', linestyle='-', label='Noiseless Energy')\n",
    "    plt.axhline(y=extrapolated_energy, color='g', linestyle='--', label='Extrapolated Energy')\n",
    "    plt.xlabel('Noise Level')\n",
    "    plt.ylabel('Energy')\n",
    "    plt.title(f'{model_name} - Energy vs Noise Level')\n",
    "    plt.legend()\n",
    "    plt.xlim(left=0) \n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# Plot results for 2D Ising model\n",
    "plot_results(noise_levels, energies_noisy_2d_values, energy_noiseless_2d, extrapolated_energy_2d, \"2D Ising Model\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ac9aaa8-d203-4019-92eb-7b1bdf0b3eec",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Summary\n",
    "This code extends the original Ising model implementation to include zero-noise extrapolation. It adds random noise to the Pauli terms, runs the quantum optimization for different noise levels, and performs zero-noise extrapolation to estimate the noiseless energy values. Finally, it visualizes the results to compare the noiseless, noisy, and extrapolated energy values. The results show that the extrapolated energy behaves as a random variable. Many more runs at more noise levels could narrow down the target energy sought. The linear regression and zero noise extrapolation leads to variable differences between the noiseless value and the extrapolated value.\n",
    "The number of qubits needed seems to depend solely on the system size, namely the number of spins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e531ef44-1f87-4dc4-8da5-5c1ce4e998be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebd65368-f1f0-4d3c-84c1-423dab875a79",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 [Default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "a07aacdcc8a415e7643a2bc993226848ff70704ebef014f87460de9126b773d0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}