{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learn to simulate with the superconducting system module\n",
    "**Engineering and simulating gates in superconducting transmon-cavity systems**\n",
    "\n",
    "In this tutorial you will simulate a realistic superconducting transmon, visualize the system's dynamics, and implement a basic quantum gate.\n",
    "You will also carry out an optimization to achieve a leakage-free target state transfer process for a transmon coupled to an oscillator.\n",
    "\n",
    "Through this tutorial you will get introduced to the [Boulder Opal superconducting functionality](https://docs.q-ctrl.com/references/boulder-opal/boulderopal/superconducting) and visualize the results of the simulations and optimizations with the [Q-CTRL Visualizer package](https://docs.q-ctrl.com/boulder-opal/toolkit/discover/adopt/visualize-data-using-the-q-ctrl-visualizer)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating a transmon gate\n",
    "\n",
    "Your task will be to simulate and visualize dynamics of a superconducting transmon.\n",
    "\n",
    "In a frame rotating with the transmon frequency and assuming that the drive is resonant, the system can be described by the following Hamiltonian:\n",
    "\n",
    "$$\n",
    "H_I = \\frac{\\alpha}{2} (b^\\dagger)^2 b^2 + \\Big(\\gamma(t) b^\\dagger + H.c.\\Big)  ,\n",
    "$$\n",
    "\n",
    "where\n",
    "$b$ is the annihilation operator of the transmon system,\n",
    "$\\alpha$ is the transmon anharmonicity,\n",
    "and $\\gamma(t)$ is a complex time-dependent drive applied to the transmon.\n",
    "\n",
    "You will set up a drive $\\gamma(t)$ which aims to implement an X-gate for the transmon,\n",
    "\n",
    "$$\n",
    "U_\\mathrm{target} = |1\\rangle\\langle 0| + |0\\rangle\\langle 1|  ,\n",
    "$$\n",
    "\n",
    "simulate the system dynamics using the Boulder Opal superconducting module, and extract and visualize the resulting unitaries and system state dynamics to evaluate the gate performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import libraries and start a Boulder Opal session\n",
    "\n",
    "Before doing any calculation with Boulder Opal, you always need to import the necessary libraries.\n",
    "\n",
    "In this case, import the `numpy`, `matplotlib.pyplot`, `qctrlvisualizer`, and `boulderopal` packages.\n",
    "To learn more about installing Boulder Opal, see the [Get started](https://docs.q-ctrl.com/boulder-opal/toolkit/get-started-with-boulder-opal-toolkit) guide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages.\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import qctrlvisualizer\n",
    "import boulderopal as bo\n",
    "\n",
    "# Apply Q-CTRL style to plots created in pyplot.\n",
    "plt.style.use(qctrlvisualizer.get_qctrl_style())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Define the system\n",
    "\n",
    "In the superconducting module, the physical system is defined by objects relating to the different components and their interaction, such as [transmons, cavities, and transmon-cavity interactions](https://docs.q-ctrl.com/references/boulder-opal/boulderopal/superconducting).\n",
    "The attributes of these objects define the different terms in the Hamiltonian as constant terms (if a scalar is passed) or as piecewise-constant functions (if an array is passed)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the pulse\n",
    "\n",
    "Define an array with the values and the duration of the piecewise-constant $\\gamma(t)$, following a Gaussian profile implementing a $\\pi$-pulse.\n",
    "In an ideal qubit system, this pulse would swap the populations between the two computational states, and implement your target gate, $U_\\mathrm{target} = |1\\rangle\\langle 0| + |0\\rangle\\langle 1|$.\n",
    "\n",
    "The Gaussian pulse takes the form\n",
    "$$\n",
    "\\gamma(t) = \\gamma_0 \\exp\\left(-\\frac{(t-T/2)^2}{2\\sigma^2} \\right)\n",
    "$$\n",
    "where $T$ is the pulse duration,\n",
    "$\\sigma$ is the Gaussian standard deviation,\n",
    "and the amplitude $\\gamma_0$ is such that $\\int_0^T \\gamma(t) \\mathrm{d}t = \\pi$.\n",
    "You can also easily visualize this drive with the `plot_controls` function from the [Q-CTRL Visualizer](https://docs.q-ctrl.com/boulder-opal/toolkit/discover/adopt/visualize-data-using-the-q-ctrl-visualizer) package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x180 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total gate duration.\n",
    "gate_duration = 4e-6  # s\n",
    "sigma = gate_duration / 6\n",
    "\n",
    "times, dt = np.linspace(0, gate_duration, 64, retstep=True)\n",
    "drive_values = np.exp(-((times - gate_duration / 2) ** 2) / 2 / sigma**2)\n",
    "drive_values = np.pi * drive_values / np.sum(drive_values * dt)\n",
    "\n",
    "# Visualize drive pulse.\n",
    "qctrlvisualizer.plot_controls(\n",
    "    {\n",
    "        r\"$\\gamma$\": {\n",
    "            \"values\": drive_values,\n",
    "            \"durations\": np.repeat(gate_duration / 64, 64),\n",
    "        }\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the transmon\n",
    "\n",
    "Create an object defining the transmon with the `boulderopal.superconducting.Transmon` class, passing to its constructor the number of dimensions in the system, as well as the coefficients of the different terms in the Hamiltonian.\n",
    "Use the array of drive values to create a piecewise-constant `drive`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Physical properties of the transmon.\n",
    "transmon_dimension = 4\n",
    "alpha = 2 * np.pi * 0.45e6  # rad.Hz\n",
    "\n",
    "# Create transmon object.\n",
    "transmon = bo.superconducting.Transmon(\n",
    "    dimension=transmon_dimension, anharmonicity=alpha, drive=drive_values\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the initial state of the system\n",
    "\n",
    "Create an array with the initial state of the transmon: its ground state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial state.\n",
    "transmon_ground_state = np.zeros(transmon_dimension)\n",
    "transmon_ground_state[0] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Run the simulation\n",
    "\n",
    "You now have objects that define the system that you want to simulate.\n",
    "Use the `boulderopal.superconducting.simulate` function to obtain its dynamics, passing to it the system objects, as well as the duration of the simulation and the initial state of the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1828683\") is queued.\n",
      "Your task (action_id=\"1828683\") has started.\n",
      "Your task (action_id=\"1828683\") has completed.\n"
     ]
    }
   ],
   "source": [
    "simulation_result = bo.superconducting.simulate(\n",
    "    transmons=[transmon],\n",
    "    cavities=[],\n",
    "    interactions=[],\n",
    "    gate_duration=gate_duration,\n",
    "    initial_state=transmon_ground_state,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Extract the simulation outputs\n",
    "\n",
    "After the calculation is complete, the results are stored in the output of the [returned dictionary](https://docs.q-ctrl.com/references/boulder-opal/boulderopal/superconducting/optimize), `simulation_result`.\n",
    "It contains the `\"sample_times\"` at which the dynamics are sampled, the `\"unitaries\"` defining the system's evolution, and the `\"state_evolution\"` of the initial state you provided.\n",
    "\n",
    "#### Visualize the system dynamics\n",
    "\n",
    "You can easily plot the dynamics of the system under the pulse with the `plot_population_dynamics` function from the [Q-CTRL Visualizer](https://docs.q-ctrl.com/boulder-opal/toolkit/discover/adopt/visualize-data-using-the-q-ctrl-visualizer) package.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "states = np.reshape(\n",
    "    simulation_result[\"output\"][\"state_evolution\"][\"value\"], [-1, transmon_dimension]\n",
    ")\n",
    "populations = np.abs(states) ** 2\n",
    "\n",
    "qctrlvisualizer.plot_population_dynamics(\n",
    "    simulation_result[\"output\"][\"sample_times\"][\"value\"],\n",
    "    {rf\"$|{idx}\\rangle$\": populations[:, idx] for idx in range(transmon_dimension)},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the dynamics are far from what one would ideally expect (with the final state being $|1\\rangle$), and leakage to higher excited transmon levels prevents a full transfer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare implemented vs target gate\n",
    "\n",
    "You can also see the failure of the dynamics by comparing the final implemented unitary (the last element in the `\"unitaries\"`) with the target gate $U_\\mathrm{target}$ the pulse aims to implement (in the subspace of the two lowest transmon states)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the target gate and retrieve the implemented unitary.\n",
    "target_operation = np.zeros([transmon_dimension, transmon_dimension])\n",
    "target_operation[[0, 1], [1, 0]] = 1.0\n",
    "\n",
    "implemented_operation = simulation_result[\"output\"][\"unitaries\"][\"value\"][-1]\n",
    "\n",
    "\n",
    "# Plot operators.\n",
    "def plot_operator(ax, operator, title):\n",
    "    image = ax.imshow(np.abs(operator), vmin=0, vmax=1)\n",
    "    plt.colorbar(image, ax=ax, shrink=0.7)\n",
    "    tick_labels = [rf\"$|{t}_t\\rangle$\" for t in range(transmon_dimension)]\n",
    "    ax.set_xticks(range(transmon_dimension), tick_labels)\n",
    "    ax.set_yticks(range(transmon_dimension), tick_labels)\n",
    "    ax.set_title(title)\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(1, 2)\n",
    "plot_operator(axs[0], target_operation, \"Target gate\")\n",
    "plot_operator(axs[1], implemented_operation, \"Implemented gate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizing transmon-cavity dynamics\n",
    "\n",
    "Your next task will be to optimize the dynamics of a transmon-cavity system aiming to achieve a target state propagation.\n",
    "\n",
    "In a frame rotating with the transmon frequency, assuming that the transmon drive is resonant, and in the dispersive limit, the dynamics can be described by the following Hamiltonian:\n",
    "\n",
    "$$\n",
    "H_I = \\frac{\\alpha}{2} (b^\\dagger)^2 b^2 + \\Big(\\gamma(t) b^\\dagger + H.c.\\Big) + \\delta_C a^\\dagger a + \\Big( \\Omega(t) a b^\\dagger + H.c.\\Big)  ,\n",
    "$$\n",
    "\n",
    "where\n",
    "$b$ is the annihilation operator of the transmon system,\n",
    "$a$ is the annihilation operator of a cavity excitation,\n",
    "$\\alpha$ is the transmon anharmonicity,\n",
    "$\\gamma(t)$ is a complex time-dependent drive applied to the transmon,\n",
    "$\\delta_C$ is the detuning between the drive frequency and the cavity transition frequency,\n",
    "and $\\Omega(t)$ is the Rabi coupling between the transmon and the cavity.\n",
    "\n",
    "In this task, you will set up a drive $\\gamma(t)$ and a coupling $\\Omega(t)$ to drive the dynamics and achieve a target state propagation.\n",
    "In particular, you want to take both systems starting in the ground state,\n",
    "\n",
    "$$\n",
    "|\\psi_\\mathrm{initial}\\rangle = |0_t, 0_c\\rangle  ,\n",
    "$$\n",
    "\n",
    "to a particular entangled state between the cavity and the transmon,\n",
    "\n",
    "$$\n",
    "|\\psi_\\mathrm{target}\\rangle = \\frac{1}{\\sqrt{2}} \\Big(|0_t, 0_c\\rangle + i |1_t, 1_c\\rangle \\Big) .\n",
    "$$\n",
    "\n",
    "You will then simulate the system dynamics using the Boulder Opal superconducting module, and extract and visualize the resulting unitaries and system state dynamics to evaluate the gate performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Define the system\n",
    "\n",
    "#### Define a cavity and an optimizable transmon\n",
    "\n",
    "Similarly to what you did in the previous task, create objects representing the transmon and the cavity in the system with the `boulderopal.superconducting.Transmon` and `boulderopal.superconducting.Cavity` classes.\n",
    "\n",
    "You can label a Hamiltonian coefficient as optimizable by using the [optimizable constant or signal classes](https://docs.q-ctrl.com/references/boulder-opal/boulderopal/superconducting).\n",
    "In this case, assign an optimizable drive for the transmon by passing a `boulderopal.superconducting.ComplexOptimizableSignal` to its `drive` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gate duration and number of optimizable piecewise-constant segments.\n",
    "gate_duration = 5e-6  # s\n",
    "segment_count = 20\n",
    "\n",
    "# Physical properties of the transmon.\n",
    "transmon_dimension = 5\n",
    "alpha = 2 * np.pi * 0.3e6  # rad.Hz\n",
    "gamma_max = 2 * np.pi * 0.3e6  # rad.Hz\n",
    "\n",
    "# Create transmon object with optimizable drive.\n",
    "transmon = bo.superconducting.Transmon(\n",
    "    dimension=transmon_dimension,\n",
    "    anharmonicity=alpha,\n",
    "    drive=bo.superconducting.ComplexOptimizableSignal(segment_count, gamma_max),\n",
    ")\n",
    "\n",
    "# Physical properties of the cavity.\n",
    "cavity_dimension = 4\n",
    "K = 2 * np.pi * 4e5  # rad.Hz\n",
    "delta = -2 * np.pi * 0.3e6  # rad.Hz\n",
    "\n",
    "# Create cavity object.\n",
    "cavity = bo.superconducting.Cavity(\n",
    "    dimension=cavity_dimension, frequency=delta, kerr_coefficient=K\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the transmon-cavity interaction\n",
    "\n",
    "Define an optimizable Rabi coupling between the transmon and the cavity with a `boulderopal.superconducting.TransmonCavityInteraction` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Physical properties of the interaction.\n",
    "omega_max = 2 * np.pi * 0.2e6  # rad.Hz\n",
    "\n",
    "# Create interaction object.\n",
    "interaction = bo.superconducting.TransmonCavityInteraction(\n",
    "    rabi_coupling=bo.superconducting.ComplexOptimizableSignal(segment_count, omega_max)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the initial and target states of the evolution\n",
    "\n",
    "Define the initial state of the system (with both the transmon and the cavity in their ground state) and the target state $|\\psi_\\mathrm{target}\\rangle$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define initial state |00>.\n",
    "transmon_ground_state = np.zeros(transmon_dimension)\n",
    "transmon_ground_state[0] = 1\n",
    "cavity_ground_state = np.zeros(cavity_dimension)\n",
    "cavity_ground_state[0] = 1\n",
    "initial_state = np.kron([transmon_ground_state], [cavity_ground_state])[0]\n",
    "\n",
    "# Define target state (|00> + i |11>) / √2.\n",
    "transmon_excited_state = np.zeros(transmon_dimension)\n",
    "transmon_excited_state[1] = 1\n",
    "cavity_excited_state = np.zeros(cavity_dimension)\n",
    "cavity_excited_state[1] = 1\n",
    "excited_state = np.kron([transmon_excited_state], [cavity_excited_state])[0]\n",
    "\n",
    "target_state = (initial_state + 1j * excited_state) / np.sqrt(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Run the optimization\n",
    "\n",
    "Similarly to how you've run the simulation above, use `boulderopal.superconducting.optimize` to obtain the optimized pulse, infidelity and system dynamics.\n",
    "You also need to provide the target of the optimization, in this case, the `target_state`.\n",
    "You can use the `cost_history_scope` parameter to request the evolution of the cost value during the optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1828690\") is queued.\n",
      "Your task (action_id=\"1828690\") has started.\n",
      "Your task (action_id=\"1828690\") has completed.\n"
     ]
    }
   ],
   "source": [
    "optimization_result = bo.superconducting.optimize(\n",
    "    transmons=[transmon],\n",
    "    cavities=[cavity],\n",
    "    interactions=[interaction],\n",
    "    gate_duration=gate_duration,\n",
    "    initial_state=initial_state,\n",
    "    target_state=target_state,\n",
    "    cost_history_scope=\"ITERATION_VALUES\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Extract the optimization outputs\n",
    "\n",
    "Similarly to the simulation, the results of the optimization are stored in the output of the [returned dictionary](https://docs.q-ctrl.com/references/boulder-opal/boulderopal/superconducting/optimize), `optimization_result`.\n",
    "Besides the `\"sample_times\"`, `\"unitaries\"`, and `\"state_evolution\"`, it also contains the optimized `\"infidelity\"` and the optimized values of the optimizable drives.\n",
    "\n",
    "#### Retrieve the optimized infidelity\n",
    "\n",
    "You can retrieve the optimized infidelity to assess the quality of the optimized pulses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimized infidelity: 1.876e-06\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f\"Optimized infidelity: {optimization_result['output']['infidelity']['value']:.3e}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize the cost history\n",
    "\n",
    "You can retrieve the evolution of the cost during the different optimizations from the optimized values dictionary, and plot them with the `plot_cost_histories` function from the [Q-CTRL Visualizer](https://docs.q-ctrl.com/boulder-opal/toolkit/discover/adopt/visualize-data-using-the-q-ctrl-visualizer) package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qctrlvisualizer.plot_cost_histories(\n",
    "    optimization_result[\"cost_history\"][\"iteration_values\"], y_axis_log=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize the optimized pulse\n",
    "\n",
    "You can also retrieve the values of the optimized drive from the optimized values dictionary, and plot them with `plot_controls`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimized_drive = optimization_result[\"output\"][\"transmon.drive\"]\n",
    "optimized_rabi_coupling = optimization_result[\"output\"][\n",
    "    \"transmon_cavity_interaction.rabi_coupling\"\n",
    "]\n",
    "qctrlvisualizer.plot_controls(\n",
    "    {r\"$\\gamma$\": optimized_drive, r\"$\\Omega$\": optimized_rabi_coupling}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize the system dynamics\n",
    "\n",
    "You can plot the optimized dynamics similarly to how you plotted the simulation results earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnMAAAFUCAYAAABP8bodAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAAsTAAALEwEAmpwYAACUd0lEQVR4nOzddXhTZxvH8e+Jtk3dDXd3dxgMGBOYu7s7Y8IYGxtzN7Z3vjFXGD6GuxWXllKl7vFz3j8CBYZD27Tp/bmuXmmTI3fbNPn1OY8omqZpCCGEEEKIOknn7QKEEEIIIcSZkzAnhBBCCFGHSZgTQgghhKjDJMwJIYQQQtRhEuaEEEIIIeowCXNCCCGEEHWYwdsFeMuutFyMBr23yxBCCCGEOCmny02LBlHHfKzehjmjQU/juHBvlyGEEEIIcVJ7swqO+5hcZhVCCCGEqMMkzAkhhBBC1GES5oQQQggh6jAJc0IIIYQQdZiEOSGEEEKIOqzejmYVQpw9TdMoKrNSVmFHVTU0bxckag0F0OkUAgPMhAb6oyiKt0sSwmdJmBNCnLGcwjIUIDYiGINeJ2/YopKmabjcKoUlFeQUlhETHuTtkoTwWbU2zP07dzErF68mKy2Lrr27cu3tVx532wV//8u8GQtw2h107tmJy264BKOx1n5rQvgMm91Jg9gwdBLixH8oioLRoCcyLJC07EJvlyOET6u1feZCQkM494Lh9B7Y64Tbbdu0nXl/zefe8Xcy6c2nycvJZ+Yvs2qoSiHqNw0kyIkT0imKXH4XPs/t8u6zvNY2X3Xu0RGAtJQ0HAXFx91u5ZLV9B7Ui7jEWABGXjScLz74hgsvH1MjdR5PyfwFFPzwE4pOAZ0OFB2KXgeKgqLTg04BvR7FYEBnMqGYTChG4xG3OpMJxd8fnb8fuoAAdP7+no+AAHQBnlt9aKhc2hJCCCFqmNOmsf53O0s+txLeQM8NHwd7rZZaG+ZOVVZ6Nh26tq/8OqFhPKXFpZSXlmMJshyx7dIFy1m6cDkAo6++BKpxOS9XQSH2Xbuq7fgH6QICMDVogKlhA0yNGhHQpRN+LVui6GXdWSGEEKKqZe9wseh/VlZ+Z6Oi0NMiZwl34bRrGM3eaVyp82HOYXfgH+BX+bW/vz8ANpv9qDDXb2gf+g3tA5x4jbOqEDxsKAGdOqC5VdBUNFUFVQNVRVPdoGpobjeay4XmcKA5nZ5bh7Pya9VhR7Va0aw21IoKVKv1sFsr7tJS1LIybDt2YNuxo/Lc+pAQLD17YOnVk8A+vdEd+JkIIYQQ4sxtX+jg3XFFuJ2erxt0NtD/Bn96XGL2WpADHwhzJrMJm9Ve+bXNagPAz8/srZIAMISHYQgPq/bzuIqLcaTuw7EvDfvu3ZSvWo0zO5uSufMomTsPxd+f4CGDCBk5Er92beWSrKj37r32oRM+3rN/jxMOuKpOEx+czMBz+jPsvCFnfIyZv8xiw6pNTHjpsdPab8WiVfz45S+89slLZ3xuIXxZ1nYXH19TjNsJ3caZGfFgAA07G71dFuADYS4uMZaMfZl07dUZgPR9mQSFBB3VKuerDCEhGDp2IKBjB8AzHYBj3z7KV66idPESbFu2UjxzFsUzZ2Fq0IDQcWMJGXUuOpPJy5UL4R0vvPNs5eebN2zlu09/OOI+o+n0XpxdLhcGQ51/KRVCnEBJjsp7lxRhLdbofIGZmz4LRqerPY0jtfYVyO12o7pVVFVF01ScDic6vQ79f/qC9ezfna8/nk73vl0JCQ1h9u9z6TWgh5eq9j5FUTA3aoS5USPCL7sUx740imfNpnjOXBxpaeS89Tb5X31N2CXjCD1/DHpL/Qi9QhwUHHqok7J/gP8R9+Xuz2P6Zz+yd88+7FY70XFRnHfxSNp3aVe5z8QHJ9NrQA8K84vYuGYTrdq14ub7rmf5vyv5+9fZlJWW06ptC9p0asOPX/zMO1+9Xrlv0rot/P3rbLIysgkOCaZ7366MGjsCg8HAWy+8R0FeIb9N/5Pfpv8JcMS+h9uwehN//zqb3OxcjCYj8Ylx3Hjv9WzduI2/f50DHGqBvPrWK+g9sCcL/l7IysWrydufj7/Fj7Yd23DRlRcQYPFn17bdfDNt+hH7jRo7gtHjRuJyuZjx09+sWbaO8vIK4hJiGXPJKNp0bF0lvw8hajuHVeODy4vIT1Vp1M3AjdNqV5CDWhzmZv8+t/JFCWD10rWMGjuC3gN78cL4qTz50uOER4bRtmMbzjlvCO9MeR+nw0mnHh0ZPW6kFyuvXUwNGxB12y1E3nwjpYuXUPDtd9h37yHv408o+HY64VdcTtgl46SlTlSZO4NyvHLeD0qjz/oYdpudth3bMOaSURhNRtat2MAnb33O+CmPEBsfU7ndP3//y7kXDufR5x5E0yBl116++/QHzr9sNJ26dWDX9j389ePMI469bdN2vvzgay6+dizNWjWlML+Q7z/7CZfTxdirLuCW+2/gpSdfo/fAngwY1ve4NZYUlfD5e19x/mXn0blHR+w2O3v3pALQtXdnstKz2bxhK/dPuAsAvwN9ihVFYdzVFxEZHUFBXgE/ffUrP331C9fdcTVNWjTm4msu4s8fZjLxtQkAmA90Vfnm4+nk5eRx/V3XEBoeypaNW/no9U95ZNIDJDZKOOufuRC1maZpfHlHCXvXuAhvoOPO70MwBdSuIAe1OMyNHjfyuKHsv306ho4azNBRg2ugqrpL0esJHjyIoEEDqVi9hvxvv8O6KYm8Tz6l6M+/iLr1ZoKGDJY+daJeS2yUcERAOffC4Wxev4UNqzYx8qLhlfc3b92Mc8YMrfz6s59n0bp9S4aPGQZAdFw0+5LTWLZwReU2s/+Yx7DzhtB7YE8AomIiueDyMXz54TdcdOX5WAIt6HQKfn7mI1oP/6u4qAS3202Xnh0Jj/SMyI9vEFf5uNnPhF6nO+oYQ0YOqvw8IiqcC68Yw7Q3/sc1t12JwWDAz98PlCNbLnP357F2xXqeff0pwiM9fYAHDR/Ajs27WPrPci6/4ZJT+KkKUXdt/MvB2l/s+AUr3PVTKCExtXOmiFob5kT1UBTFM9K1Zw/K16wl58OPcCSnkPX8FAp//oWY++/Dr2ULb5cp6rCqaCHzFrvNzt+/zmHLhq2VocnldBHfIP6I7Ro0aXDE1zlZObTv0vaI+xo3a3hEmEtLSSc1eR9z/1pQeZ+maTgdTkqKSwk5QYA7XELDeFq1a8mUJ16hdfuWtGrXks49OxEUHHjC/XZs2cXcP+ezP3M/VqsNVVVxudyUFpcSEhZyzH3S96ajaRovjJ96xP0ul4uWbeV1Qvg21a3xx+QyAC58xkJC29obmWpvZaLaWbp3o/FHH1A8azZ5//sc27btpN51D+GXXULE9dehM3t3RLAQNe237/5k66btjL3qfKJiojCZjXz14Xe4XO4jtjObT79bgqapjLpoBF16dTrqscDTGLCl0+m4+/Hb2bs7lW1JO1i+aCV//jiD+ybcfdzLngV5BXz02jT6Du7NeRePxBIYQNreDD5//6ujvrfDqZqGoig8OulB9IYjFwwyGmvHKD4hqsvqH2xkbXMT0UhH/xtr9xRfEubqOUWvJ/S80QQPGUze519S+MuvFEz/gdLFS4h9+CECOh/9xiOEr9qzM5me/bvTuYfnee90OMnLySMqNuqE+0XHRZOanHbEfanJ+474OrFxIvuzcoiKOf6x9AYDqqaetE5FUWjSojFNWjRm1NgRTBn/MutWbiCxUcIxj7EvJQ2Xy824ay5Cp/OEss0bth6xjcGg98yHeZgGjRLQNI2S4hJpiRP1isuh8eeUcgDOe8KCwVS7uyDV2rVZRc3SBQQQfdcdNHznTUyNG+PMyCTtoUfImDgJ25493i5PiBoRHRvFprVJpO1NJzMtky8//Aan03XS/QaPGMD2pB3Mm7GAnOxcli9cwcY1m4/YZtRFI1izfB0zfv6bzLQssjP3s37VRn777s/KbSIiw9izI4WigiLKSsuOea6U3XuZ9dtcUpP3UZBXSNK6LRQWFBGXEFN5jIK8QtL2plNWWobT6SIqJgpN0/hn1iLycvJZs3wdC2cvOuK44ZHhOJ0utiftoKy0DIfdQXRcNN37duXrj6ezftVG8nLy2ZecxvwZ/7Bh9abT/fEKUWcs/dJK/l6V2FZ6el3hd/IdvExa5sQR/Nu0ofFH75P/3XQKvvmOssVLKFu8hMD+/Yi45iqMMbFoaKAd/AA0FTQNTQM4/H4N0NBUrfLzwx/T0DyrYhx4TDH7YYyLlUEYwmvGXX0h33zyPW9OfpcAiz+Dzx14SmGuSYvGXHnzZcz8ZTYzf55Fq3YtOWfMUGb8dGhEa5uOrbnj4VuZ9dsc5s9ciF6nIyou6oiplEZfPJLp//uRSY9MweV0HXNqEn9/f5J3pbBo7mKsFVZCw0MZeeFwevTrDkCnHp3YuCaJd178AGuFtXJqkouvuYh5MxYw46eZNGnRhIuuvIDP3v2y8rhNWzah/9C+fP7+15SXlVdOTXLNrVcy+4+5/D79T4oKigkIDKBR04a0aNv8bH7UQtRajgqNv6dWAHD+0xZ0+tr/nqRomuctuL7Zm1VA42pcm9UXuPLyKJj+A0V//oXmdNbIOS29exF9792Y4uJOvrHwOvk7Or6fv/6NHVt2MuHF01uJwRfJ80TUJXPeLOfXp8tp2MXA+H/Dak0Dw4n+jqRlThyXITKS6HvuIvyKyyiY/gMl//yD5nSBooACiqKr/PzAHZ4bFNDpjrhfOXw73YFtOex+RcGZk0v5ipXsXb+BiGuvJvzSS1Ckk7WoI+bNWEDr9q0w+5k8U3csWMaYS8/zdllCiNNgK1WZ87qnVe7CZyy1JsidjIQ5cVIHQ130PXdV63lc+fnkvP8hpf8sJO+T/1Eydx5xTzyOX8uW1XpeIarCvpR0FsxciNVqIyIqnPMvO4/B5w70dllCiNOw5mc75YUaTXsaaDOs7kymL2FO1BqGiAjin36S8lEj2f/W2zhS97Hv3geIvu8eQkaPqjP/IYn66aZ7rvN2CUKIs7TsSysA/W/yr1PvOTKaVdQ6lu7daPzpNELOPw/N6WT/a2+Q/cprqHa7t0sTQgjho7K2u0hZ7cIvSKHrRbV/BOvhJMyJWklnMhH74APEjn8MxWymZNZs9t17P87s/d4uTQghhA9a+oWnVa77JWbMlrrTKgcS5kQtFzJiOA3ffRtjQjz23XtIvfc+mfdOCCFElXI5NFZ+ZwOg33W1e7WHY5EwJ2o9v2ZNafTB+wR07ow7v4C0Bx6ifN16b5clhBDCR2z6205ZvkZ8Wz2NutW94QQS5kSdoA+0kPDSCwQNHoRaXkH6+AmULPjH22UJIYTwAcu+9LTK9b2ubg18OEjCnKgzdCYTcU9NIOziceBykfX8FIr+/MvbZQkhhKjDCtLdbJ3nQG+EXpfXrYEPB0mYE3WKotMRddcdRN12KwD733iLor9mnmQvIYQQ4thWfGNDU6HTGDOBkXUzFtXNqkW9pigK4VdcRtRddwKw//U3KP57lperEkIIUdeoqsayrzyjWPtdVzdb5UDCnKjDwi8ZR9QdtwGQ/errFM+e4+WKRF2ya9tuJj442dtlHNeKRavIysj2dhlC+LSUVS7yU1XCEnS0HlJ3Vnz4r7o3ZEOIw4RfdimaqpL38Sdkv/wqitFI8NAh3i5L1FGL5i1l/ox/KCkuIS4hlnHXXETzVk29csx9KWnsS0njsusvPqvzCyGOb8Ofnsnou1xkRqevewMfDpKWOVHnRVxxOZE33wiaRtZLL8u0JeKMrF2xnp+//pURFwzj8ckP06RFYz545WMK8gq9csyOXduTtG7LGZ9bCHFimqax8S9PmOs0xuzlas6OhDnhE8KvupKwcWPB5SLzmWdlYmFx2v75+196DehBvyF9iE2I4dLrxhESGsyS+Uu9cswWbZpjt9nYl5J2xucXQhxf1nY3ucluAiMUmvU2erucsyKXWYVPUBSFqLvuwFVQQOnCf0kfP4FG77yFMTbW26XVOzuGDvfKeVstmHvG+7pcLtL2pjNs9OAj7m/dvhUpu/Z65Zh6g542Hduwae1mGjZpcEY1CCGO7+Al1g6jzOgNdfcSK0jLnPAhik5H7PjH8O/cCXd+AemPT8BdXOLtskQdUF5ajqqqBIUEHXF/UEggJcWllV/n5eSTtG5zlR7zRDp2bc+mtUmntK0Q4vQcvMTa+fy6fYkVpGVO+BidyUTCc5PY98CDOJJTyJw0mcSXX0QxyFO9ppxNC1ltt3XjNmw2Ox26tq+R87Xt1JovPviagrxCwiPDauScQtQHBelu9q13YbYodXoU60HSMid8jj7QQuKLL6APC6NiwwZy3vvA2yWJWs4SZEGn01H6nxaz0uIygg+0rO3atpsZP//Nin9XMvWp17Db7Gd9TICCvAI+eu0Tpj71GlPGv3zE4IiCvEKMJiNBwYFn+y0KIQ5zsFWu7TkmTP51+xIrSMuc8FHGqCgSJj9L2oOPUPT7H5ibNiH0/DHeLkvUUgaDgQaNE9m+eSddenWuvH/7lp107t4B8AxIiG8QzzW3XUlEVHiVHNPlcvHBq9O47PqLadGmORXlFZjNhy75JK3bTJsOrTCa6nbnbCFqG18ZxXqQtMwJn+Xfti0xDz0AwP6336Vi4ybvFiRqtSGjBrFy8WqWLVxBdsZ+fvrqV4oLi+k/rG/lNoX5hacU5E71mBvXJNG4WSNatGkOQIAlAL1BX7n/pnWb6dCtZi7pClFflOWr7FriRGeA9ufW/UusIC1zwseFnDsCe3IKhT/+ROazz9Hw/XcwxcV5uyxRC3Xr3YXysgpm/z6XkqIS4hLjuPORWwmP9IS3woIiQsJCjtpvxaJVfDNtOs++/tRRQe9kx8xKy6JRs4bHrKewoIiMfZm079y2ir9TIeq3zbPtqG5oPcSIJcw32rQkzAmfF3XbLThSUylftZqM8U/S8J030QcHe7ssUQsNPKcfA8/pd8zHCvMKCQ45+nmTn1tAbEIMoeFHB72THTMoJIisdM+SXaqqYq2wYgm0AJC0bgvNWjap/FoIUTU2/uUAfOcSK8hlVlEPKHo9cU89ialpExxpaWQ8PRHV4fB2WaKOiUuMJT83nylPvHzEmqlbN27jsusvRq/Xn2DvY+s1oAd5+/OYMv5lXnnmDXKycysfS1q3mY7dOlRJ7UIID4dVY8u8A/3lzvOdMKdomqZ5uwhv2JtVQOO4U+/7Iuo+Z24u++6+D1deHkGDBxH31AQUnfw/czbq8t/Rrm27+frj75j0xtPeLsXn1eXnifAtW+baeXdcMQ27GHhiUd16Tp7o70jeyUS9YYyKIvGlKegsAZQu/Jfcj6Z5uyThReGR4Qw+d6C3yxBC1KBtCzxXZdoO842BDwdJmBP1irlpE+InTQSDgcIff6Lgx5+9XZLwkoiocIaMHOTtMoQQNehgmGszVMKcEHWapWtXYh99GIDcDz6keI7vrlgghBDCozjbTeZWN6YAaNLTt+ZulDAn6qWQ4ecQdeftAGS//CplK1Z6uSIhhBDV6WCrXMv+Jozmur/qw+EkzIl6K/zSSwi/8gpQVTInTaZi86ktoC6EEKLu2faPJ8y19rFLrCBhTtRzkbfcRMjokWh2OxkTnsaekuLtkoQQQlQxTdPYvsAJ+N7gB5AwJ+o5RVGIefABAvv1Qy0rI/2xJ3BmZ598RyGEEHVGxhY3JTkqIXE6Ylud/pyQtZ2EOVHvKXo9cU9PwL9TR1z5+aQ9+jiuwkJvlyWEEKKKbJt/YEqSoSYUxbf6y4GEOSEA0JlMJEx+DnPzZjgzMkkf/yTu8nJvlyWEEKIK+HJ/OajFa7OWl5Xz7Sffsz1pJ5YgCxdcNprufbsdtZ3T6eLnr39l05ok3G43TVs04fIbLyE0PLTmixZ1mj7QQuJLL7Lvvgew79pF5tPPkvDSC+hMvvnHL4QQ9YHTprF76YEwN8Q3X89rbcvcD1/8gt5gYMp7k7j+zqv5/vOfKxekPty/sxeRsmsv46c8yvNvP4u/JYAfv/q15gsWPsEQHkbiyy+hDw+nYsMGsqe+gqaq3i5LCCHEGdq93InTBokdDQRH1drYc1Zq5Xdlt9nZuHoTYy4eidnPTLNWTenQtR2rlq45atv83ALadGhNcEgQRpORrr07k32M0CfEqTLFx5E4dQqKvz+l/ywk77PPvV2SqAa7tu1m4oOTvV3Gca1YtIqsDHktE+JsbV9wqL+cr6qVl1lzsnPR6XVEx0VX3pfQIJ7d2/cctW2fQb346etfKS4sxj/AnzXL1tK2U+uaLFf4IL9mzUiY+DTpE56i4JvvMMbFETp6lLfLEtVo9/Y9zJ+5kLS9aRQXlnD1rVfQe2BPrx1zX0oa+1LSuOz6i8+qBiHqu60LfLu/HNTWljm7Az9/vyPu8wvww2azH7VtVGwkYeGhPHXfJB69bQL7M3MYedGIYx536YLlvPzM67z8zOuUlZRVS+3Cd1h69iDmgfsA2P/6m5SvWevlikR1stvsxCXGcvE1YzGaqmapn7M5Zseu7Ulat6VK6hCivipId5O+yYXRD5r38a0lvA5XK8Oc2WzCZrUdcZ/NasfPz3zUtj988TMul4uXPpjMq5+8RKfuHfjglWnHPG6/oX147LmHeOy5hwgMDqyW2oVvCR1zHuFXXH5glYjnsCfLpMK+ql3ntlxw2Xl06dmpyqYuOJtjtmjTHLvNxr6UtCqpRYj6aN47FQB0GmPG6Od7U5IcVCsvs0bHRqG6VXKyc4mOjQIgY18msYmxR22bkZrJmEtHYwm0ADBw+ABm/DyLstIyAoMksImzF3nLTTizsyld+C/pE56k0btvY4iM9HZZtVa3b773ynnXXn25V85bXfQGPW06tmHT2s00bNLA2+UIUeeU5Kos+cwKwLkPW7xcTfWqnS1zfmY6de/AjJ9nYbfZSd6ZQtK6zfTs1/2obRs2bcCqJauxVlhxu9wsnr+UkLBgCXKiyig6HbGPP4pf27a4cnJJf/IZVKvV22UJL8jLySdpXc2t4duxa3s2rU2qsfMJ4UsWvFeB0wodRppIbF8r266qTK397i674WK+mfY9E+6eiCUogMtvuJi4xFh270jmg1c+5rVPXgJg7JUX8NNXv/LcIy/idruIS4zjlvtv9HL1wtfozGYSnp/Evnvu98xBN3kKCZOfRdH73rIwZ8vXWsgOt3XjNmw2Ox26tq+R87Xt1JovPviagrxCwiPDauScQviCiiKVf6d5/uke+ahvt8pBLQ5zlkALtz1401H3N2/VtDLIAViCLFx/1zU1WZqopwyhoSS++AKp995H+YoV5Lz3AdH33u2TS8OIo+3atpsZP/+NJdDC+pUbeOCpezAfox/v6SrIK+DHL36hqLAYt8vNHY/cWhncCvIKMZqMBEkfXyFOy7/TrNhKNFoNMtK0p+8OfDio1oY5IWojU4NEEp6bRPqjj1P02++YEhMIGzfW22WJGtCiTXPiG8RzzW1XEhEVXiXHdLlcfPDqNC67/mJatGlORXkFZvOhgJi0bjNtOrSqstG1QtQH9nKN+e96Bj6MfMT3W+WglvaZE6I2C+jYgdjHHgEg5/0PKV+12ssViapgt9lJT80gPTUDTdMozC8kPTWDgrzCym0K8wtPK8id7Jgb1yTRuFkjWrRpDkCAJQC94dCl+03rNtOhW81c0hXCVyz53Ep5gUbj7gZaDaof/whJmBPiDAQPG0rEtVd7piyZ/Dz2vaneLkmcpX0paUx96jWmPvUaToeTmb/MZupTrzHj51kAFBYUERIWctR+Kxat4t5rHyI/t+C0j5mVlkWjZg2PWU9hQREZ+zJp37ltFX6XQvg2e7nGvLc9rXKjHrXUm24wcplViDMUcf112FP3UbZoMRlPPk3D99/BEHL0m72oG1q0ac47X71+3McL8woJDgk+6v783AJiE2IIDT/6d3+yYwaFBFWuOa2qKtYKa+U0S0nrttCsZZPKr4UQJ/fDY6UUZao06GSgwyjfXfHhv6RlTogzpOh0xI1/DHPLFjizssicOAnV4fB2WaKaxCXGkp+bz5QnXj5izdStG7dx2fUXoz+Dkc29BvQgb38eU8a/zCvPvEFOdm7lY0nrNtOxW4cqqV2I+mDNzzaWfWnD6AfXfxxcb1rlABRN0zRvF+ENe7MKaBxXNZ2YRf3mzM0j9a67cecXENCtKwmTJqILCPB2WTWiLv8d7dq2m68//o5Jbzzt7VJ8Xl1+noi6IW+vmxf6FWAr0bjyzSAG3uzv7ZKq3In+jqRlToizZIyKJPHFF9CHhVKxdh37HnoEV2HhyXcUXhUeGc7gcwd6uwwhxFlyOzU+vbEYW4lG5/PNDLjJ7+Q7+RhpmROiijgyMkh/7AmcWVkYE+JJfPklTHFxJ9xHU1U0lwvN6URzueDAreZ0obmc/7k9sN2BbfSBgQR064qi897/ZPJ3JE6FPE9Edfrt2TJmv1ZBWKKOJ5eGYwn3zXaqE/0dyQAIIaqIKSGBhu+8Sfr4Cdh37yH19rswREfBsQLZgVtU9azOGTRkMHHjH0Mx1o/h90IIcbjUdU7mvF6BooMbPwn22SB3MhLmhKhChvBwGrzxGpnPPEvF+g04yspOuo9iNHo+DAYwGlAMRpSDtwbDoc+NhgNfm1AMespXr6H0n4W4S0s9/fT8fa+PiBBCHI+mafzwaCmaBufcG0CLfvVn9Op/SZgToorpLRYSX5mKY28qmqZ6AtjhAc1oqAxw6HRnPOLKtnMX6eMnULFmLWkPP0bCi8/L1ChCiHpj1fd2kle5CI7WMfrx+jHo7HjqZ3ukENVM0ekwN22CX7NmmBs1wpQQjzE6GkN4GPqgIHR+fih6/VkNnfdr2YKGb7+BMTYW2/btpN33IM7cvCr8LoQQonaylar8+rTnysdFz1nwD67fcaZ+f/dC1HGmxEQavvMm5qZNcaSlkf7oYzKSVgjh82a9WkFxtkrj7gZ6XVn/Rq/+l4Q5Ieo4Q0QEDV5/BVPTJjj2pZH+2HjcJSXeLksIIapFzh4X89/1LNl12ctB6HT1Z3Lg45EwJ4QP0AcH0+DlqZgaNMC+J5n08U+iVlR4uywhhKhyPz1RhssBva/yo0kPGckPEuaE8BmG8DASX51a2YcufcJTqDabt8vySbu27ebeax+irPTko5WFEFVn9zIHSX87MAcqXDRJ1i0+SMKcED7EGBVF4qsvY4iMxLopiQxZL/a4igqK+O7TH3j6vkk8cMOjPHXfs3z76fcUFhQdsd1bL7zHD1/87J0iT0F+bgH3XvsQ+5LTTnvfe699iPWrNlZDVUJUjxkvlQMw7B5/QmJPfz1kXyVhTggfY4qPI/HVqehDQ6lYvYasyVM8q0uISnk5+bzyzBtkpWdzze1X8syrE7jujqvJTt/Pq8+8QX5ugVfqcsnvSYjj2rPCyfZ/nPgFKQy9s35PRfJfspyXED7KtieZtIceQS0tJWjoEOKeeBxFX7X/ydbVv6MPXvmYjLRMnnllAibzoYlGHXYHzz06hYSGCdz5yK189dF3rFqy+oh9n339KQryCnh7yvvcM/4O/vxhJpnpWcQmxHDlTZfRoHFi5bbJO1P484cZpKakEWDxp0OX9lxwxRj8/T2j79564T1i42MwmU2sWrKa8MhwHn3uwaPqLcwv5Mcvf2HPjmScThdhEWGMHnsu3fp04d5rHzpi2+atm3H/k3eTmryPv36cSdreDNwuF/EN47noivNp0qIxABMfnExB3qGRz+GRYUx642kAktZt4e9fZ5OVkU1wSDDd+3Zl1NgRGAyeqUk3rN7E37/OJjc7F6PJSHxiHDfeez3BIUHH/HnX1eeJqF3eGVvE1nkORj4awIXPBHq7nBony3kJUQ/5NWtK4ktTSH/0cUoX/IPOz0zMQw9W+1quX/3QvVqPfzzXXrbmlLYrLytnW9IOzrtk1BFBDsBkNjFgWD9m/DyLivIKLrn2InKzc4mJj+b8S0cDEBgcSEGep+Xujx9mcOHlYwgODebnr3/jiw++5smXHkdRFDLTMnnv5Y8YPW4kV95yORVlFfz8zW98O206N993Q+U5Vy9bS98hvbn/qXvgOP9a//D5zzidLu594i78/P3Iyc6pfOyRSQ/w6sQ3uevR20hoGI/e4AnsNqudHv26c/E1Y0GBRXOX8MGr05j46gQsQRYemfQgE+5+hitvvoz2ndtWPi+2bdrOlx98zcXXjqVZq6YU5hfy/Wc/4XK6GHvVBZQUlfD5e19x/mXn0blHR+w2O3v3pJ7Sz16IM5Wy2snWeZ6+csPulla5/5LLrEL4MP82rUl4YTKK2UzxzFnkfvgx9bQxvlJudh6aphEbH33Mx2MTYtA0jZzsXPwD/NEb9BhNRoJDgwkODUZ3WBgec/EoWrZtQWx8DKMuGsH+zByKCosBmDdjIV17dWbY6MFEx0bRuHkjLr/hEjas3kRpcWnlMSKiwhl31YXExscQmxBzzJoK8gtp2rIJiY0SiIyOoG3HNrTt2AaAwCBPC4Ul0EJwaDCWQE+n8FbtWtCzf3diE2KIjY/h0uvGYTQa2LppGwBBwZ79/AP8CQ4Nrvx69h/zGHbeEHoP7ElUTCQt27bggsvHsGTBMjRNo7ioBLfbTZeeHYmICie+QRx9B/c+bqucEFVh5oG+coNv8ycwQqLLf0nLnBA+LqBTRxKee5b0J5+m8Kef0QcFEXHt1dV2vlNtIfMF8Q3jKz8PDg0GoKykjLDwUNL2ppG3P491Kzcc2uFAjs7LySfoQPg5/LLs8QweMYDpn//EtqTttGrbgo7dO9CwSYMT7lNaXMqMn/9m57Y9lBaXoqoqToeTgvyiE+6XlpJOavI+5v614FDZmobT4aSkuJSEhvG0ateSKU+8Quv2LWnVriWde3aqDINCVLXUdU42z3FgCoBh90ir3LFImBOiHrD06E78k0+QOfkF8j77HF2ghbCxF3m7LK+IiolEURSyM/bT6RhXhLMz9qMoClExkSc9ll5/qIXg4NJsmupJbJqm0WdQb4aMGnjUfiFhh9bQ/e+l3mPpM7g3rTu2ZuuGbezYspM3nnub4ecPY/S4kcfd56uPv6O0uJRxV19IRGQ4BqOed178EPdJBllomsqoi0bQpVenox4LDLKg0+m4+/Hb2bs7lW1JO1i+aCV//jiD+ybcTWKjhJN+L0KcroMjWAfdGkBQlLTKHYuEOSHqiaBBA4kpL2f/q6+T88576AIDCRl+jrfLqnGWIAutO7Ri8fylDBk56KgBEIvmLaVNx9aVlysNBn1lQDsdDRolkpWRTVRMVJXUHRYeSr+hfeg3tA9z/5rPwtmLGT1uJIYDfeRUVT1i++SdKVxy7Vjad24LQElxKSVFR64Motfrj9ovsXEi+7NyTli3oig0adGYJi0aM2rsCKaMf5l1KzdImBNVLmOri6S/HRj94Zz7pFXueCTiClGPhI4eRdTttwGQPfUVypYt93JF3nHpdeNQ3SrvvvQhO7bsojC/kF3bdvPu1A9B07j0unGV24ZHhpOavI/83ALKSsuOCj/Hc86YoaQm72P6Zz+Stjed3P25bF6/hen/++G06/3pq1/ZumkbeTn5pKdmsG3T9sr+dYHBgRhNRrYlbaekuBRrhRWA6NgoVi9dS1ZGNqnJ+/j8vS8rB0dUfm9R4ezcsouSohIqyj0rhoy6aARrlq9jxs9/k5mWRXbmftav2shv3/0JQMruvcz6bS6pyfsoyCskad0WCguKiDtOfz8hzsbCDzzPyz7X+BMcLZHleKRlToh6JvzyS3GXlVLwzXdkTppM4tQXCeh89CU1XxYVE8mjzz3I37/N4auPvqG0pIzAoEDadWrDjfdcR1h4aOW2w0YP5quPv+OF8VNxOpw8+/pTp3SOhIbxPPDkPfz100zefuE9VFUjIjqcTt07nHa9mqbx05e/UlhQhJ+fmZZtWzD2qgsAT+vaJdeOZdZvc/j71zk0a9WU+5+8m6tuuZzp//uRV55+nZCwEEaNPZeykvIjjjv2ygv49dvfefqB5wgNC2HSG0/TpmNr7nj4Vmb9Nof5Mxei1+mIioui14AeAPj7+5O8K4VFcxdjrbASGh7KyAuH06Ofd0YxC99Vlq+ycrpnFZshd/h7uZraTeaZE6Ie0jSNnLffoej3P1H8/Wn4+iv4tWp12seRvyNxKuR5Is7ErFfL+X1SOW2Hm7j3l1Bvl+N1J/o7kjZLIeohRVGIvvcegoYNRbNaSX98AvZUmStMCFE7uJ0a/07zdBkYeqe0yp2MhDkh6ilFpyPu8Uex9O6Fu6SE9EfH48zO9nZZQgjB+j/sFGWqxLbU02bYyUd813cS5oSoxxSDgfiJT+PfsQOuvDzSHn0cV4F31iUVQoiD/nnfM/Bh8J3+6HSKl6up/STMCVHP6cxmEl6YjLlFC5wZmaQ/9gTusjJvlyWEqKf2rnWSvMqFf6hC7yvlEuupkDAnhEBvsZA4dQqmBg2wJyeT/sSTqFbrSfdToN4vDyZOTNM0pF1FnI5/DkxH0u86f8wWefacCglzQggADKGhJL7yEoboaGxbtpLx7HOoFRUn3kevw+5011CFoi6yO90Y9PJWI05NSY7Kmp/tKDoYfLu0yp0q+QsTQlQyRkfT4JWp6MNCqVi9hpSbbjnhxMKhQf7kFpZhc7ikhU4cQdM0bA4XuYVlhAbJm7I4Ncu/tqK6oOMoExEN9SffQQAyabAQ4j9MDRJp8NqrZL00FfvOXWQ89QyBAwcQc89dGCKPXK/U4m8GIL+oDJdbReKcOEjB03IbHuxf+TwR4kQ0TWPpl55JgvvdIP8AnA6ZNFgIcUya203Rb7+T++lnaDYbip8flm5dCejeHUuP7pji47xdohDCh+xY5ODN84oIS9Dx/JYIdHrpL3e4E+WW02qZe+nJV+kzuBc9+nYjwCIL3grhyxS9nrCLxxHYvz8577xL2bLllC1dRtnSZQAYE+Kx9OiBpUd3Ajp3Qucv/0kLIc7cks89g676XOsnQe40nVbL3J8/zmT10jWUlZTRoVsH+g7uRat2LauzvmojLXNCnB7n/hzKV6+hfM0aKtauQy0/bJ1Pg4GA9u0J6NENS4/umJs1Q1HkxVgIcWrK8lWeaJmH2wmTN0dIf7ljOFFuOe3LrJqmsXXTdlYuWkXS+i0EhwTTe2APeg3oSXhkWJUUXBMkzAlx5jS3G9u27ZSvWUP56jXYtu+Aw15K9OHhWLp3w9KrJ5Ye3dEHBnqxWiFEbTf/vQp+Gl8m67CeQJWGucOVl5WzdMFy/v5tDqpbpWW7FgwZOZC2HduccbE1RcKcEFXHXVJC+br1VKz2hDtXXt6hB3U6/Nu1w9K7J4G9e2Fq3Fha7YQQlTRNY3LPArK2u7nt62C6XOjn7ZJqpWoJcym797Li31WsW7kBS2AAvQf2pLiohFVL1tB3cC8uvmbsWRVd3STMCVE9NE3DsTeV8lWrKVu5EmvSZnAfmovOEB2NpVdPAvv0JqBrF3QmWXdRiPpszwonrw4vJChK4cUdkeiN8s/esVRZmCstLmXVkjWsWLyKvJx8OnRpR9/BvWndoVXlNim79vLu1A957ZOXzqro8rJyvv3ke7Yn7cQSZOGCy0bTvW+3Y26btjedn7/+jbS96ZjNJkZccA6Dzx14wuNLmBOiZrjLyqlYu5ayFSspX7UKd2FR5WM6SwCBffsSNGggAd27SbAToh768s4Sln9tY8QDAYydLF0yjqfKRrM+ff9zRMVE0ntQL3oN6E5g0NE/9NjEWBo1bXBmlR7mhy9+QW8wMOW9SaSnZvDha5+Q0DCBuMTYI7YrKy3j/Zc/ZtzVF9K5ZyfcLhdFBcVnfX4hRNXQB1oIGjSQoEED0VQV+65dlK1YRdnSpdh376Fk7jxK5s5DFxhI6IXnEzZuLIawutP/Vghx5iqKVNb+cmBuuevl8uqZOq2Wud07kmneqml11gOA3Wbn8TueYsKLjxIdFw3Alx9+Q0hYCBdePuaIbf/4YQZFBUVcd8fVp3UOaZkTwvsc6emU/ruI0oX/Yt+TDIBiNhMyehThl12KMSbayxUKIarTnDfL+fXpcloNNvLAn/JP3ImcKLec1nJeM3+eRUX50YtvW6023p7y/plVdww52bno9LrKIAeQ0CCe7PTso7bduzuVAEsAr096myfueoaPXvuEgrzCYx536YLlvPzM67z8zOuUlZRVWb1CiDNjSkwk4uqraDztIxq+/SaW3r3R7HaKfv2N5GuuI2vqK9j37fN2mUKIauB2aiz8yJMpzrlH5q49G6d1mXX39j24Xa6j7nc5nOzZmVxlRdntDvz8j2xu9Qvww2azH7VtUWEx6anp3P34HcQnxvH79D/5/P2veOiZ+47att/QPvQb2gfwJFwhRO3h374diVMmY9uTTMF30yld+C8ls+dQMmcugQP6E3HVFfi1rJvzWgohjrbudzuF6SqxLfW0HS79Zc/GKYW5tL3plZ9npGVhKTzUJ01VVbZt2kFoWEiVFWU2m7BZbUfcZ7Pa8fM7en0/o9FAx24daNS0IQCjxp7L+LuexlphxT9AZqQXoq7xa9aU+Kcm4Ljxegq+/5GS2XMoW7SYskWLCejWlYirr8K/U0eZ3kSIOkzTNOa/WwHA0LsD0Onk7/lsnFKYe+WZNyo/f//lj4563Gg0csl1VTcVSXRsFKpbJSc7l+jYKAAy9mUS+5/BDwAJDeOPfFGX54MQPsGUkEDsQw8Qed01FPz0C0V//EnF2nVUrF2HX9s2RFx5BZY+vVF0p9VbRAhRCySvcJK61oUlXKHXFTLw4WydUph79vUn0TSY9PALPPLsAwQGWyof0xsMBAUHoqvCF1Szn5lO3Tsw4+dZXHXzZWTsyyRp3eZjXjrtNaAnn779OYNGDCAuIZZZv82lacsm0ionhI8wREYSfcdtRFx1BYW//U7hL79i27qNjKcnYmrSmIgrryBoyGAUvSz/I0RdMe9dT1+5gbf4YwqQVpizdVYrQFSn8rJyvpn2PTs278QSFMAFl51H977d2L0jmQ9e+fiIeewWz1vK7D/m4rA7adayCZfdcDFhESceFSOjWYWom1SrlaK/ZlL440+VK00Y42IJv/wygkeeK3PVCVHL5aa4mdgpH50BXtgaQUis/CN2Ks5q0uANqzfRoUs79AY9G1ZvOuGJOvfoeOZV1jAJc0LUbarDQcm8+RR8Nx1nRibgWRM2/JKLCb1gDLoAGR0nRG30w+Ol/PO+ld5X+XH9R8HeLqfOOKswd991D/PCO88SFBLEfdc9fMITvf3la2deZQ2TMCeEb9DcbkoXL6Hg2++w794DgC4wkLCLLiT04rEYQqpucJY4mqq6sTuKsNuLcTrLcDhLcTjLPJ87SnE6y3E4y3C5ynG57bjdDtxuO35+4TRrPIa4mJ4oivR7rC+Kstw827UAe5nGk8vCSOxg9HZJdUa1rM1a10mYE8K3aJpG+arVFHw7HWtSEgCKnx+ho0cRdtklGKOjj9peLS3Fmb0fZ3Y2rtxcXEVFuIuKcRcV4S4pRbVa0ex2VJsNzek8tLMCik6P4u+Pzt8fnb8fhuhoQkYMJ6BrF58ZlOF2Oygp3UdJ6V7KyjOx2vKx2Qqw2j23NlsBdkcRmqae8TkCLQk0b3IhzZtcgL9/ZBVWL2obTdN4d1wxW+c56DDKxF0/hHq7pDpFwtwxSJgTwndVbN5MwbfTKV+x0nOHwYClezc0txu1pBR3WSnuoiLU8ooqP7cxPp7QMecRPHIEhtDQKj9+dSso3MGOPT+xP2cNZeUZpxTUTKYQ/MwhmIzBGI2BmIyBGE1BmA5+bgzEaLSg15s9HzoTeQWb2Z38O+UVWQAYDAEM7PMiCXH9qvtbFF6y6FMr3z1QiiVM4elV4dJX7jSddZ+5UyV95oQQtYltzx4Kvp1O6b+LQD06lCj+/hjjYjHGxGCMjkYfHoYhNBR9SAj6kGB0fn4ofn7o/PxRjEbP1EcHXjE1twvNakO1VqBWWLFu2ULRXzNx5eR4ju3nR/Q9dxEyamStnxPP7XayL2M+O3b/SG7exsr7FUVHoCWRkODGBAUm4u8XgZ9fBH7mcPz9wvEzh+PnF45Od1rzz1dSVTfZOavYtvNbMrOXoyh6enZ9nJbNxlXVtyZqiZw9Ll7oW4CjAm75Iphu42Q6ktN11n3mTpX0mRNC1EaOjExs27ejs1jQBwV5PkJC0AUHVWnQ0txuyletpvC336lYvQaAoEEDiXnoAfRBQVV2nqridtvZlfwbW7Z/SYV1PwBGo4Vmjc+naeMxhAY3Ra+v/tHBmqaxYfMHbN72PwDatb6BLh3ukr50PkJ1a7w2opDkVS66X2rm5v9JP9YzIZdZj0HCnBCiOhXPnUfOW++gVlRgiI4ibsITBHTs4O2yAHC5bOxK/oUt27/EavNM7xIS3ITWzS+nSaPRGI3eGQm8K/k3Vq59EU1z06jBcPr2mIjBIC04dd3s18r57dlyQuJ0PL0yHEuYhPQzIWHuGCTMCSGqmyMjk6wXXsS2fTvodERccxUR117jtQmOVdVN8t6/2LD5g8oQFxbako5tb6FBwuBa0RKWmb2CRcsex+kqJzysDYP7vYIl4OjVf0TdkLPHxXM9CnA74d7fQmg77OhlOcWpkXnmjkHCnBCiJmguF3lffEnBt9NB0/Br25b4p57AGFuzASUzewXrNr5FYfEuAMLD2tCp3W0kxPWvdX36Cot3s3DJw5SVZ+DnF8GgvlOJjuzs7bLEGfj4mmLW/26XOeWqgMwzdwwS5oQQNaliw0aypryEKy8PnSWAmAcfIHjokGo/b1l5JqvXv0J65mIALAFxdOl4N40bjKgVLXHHY7cXsWj5BLJzVqHTGejZ5XFaNKu6NcBF9duz3MGrI4ow+sNzGyIIjZfRq2dDLrMeg4Q5IURNcxeXkP3q65QtXQpA8LnDibn3nmpZrUJVXWzd8TWbtk7D7bZjNFpo3/pG2rS8Er2+blzqUlUXaze+xfZd3wHQtPEYenZ9HKNB1t6u7TRN4+Whhexd42L04wGc/1Sgt0uq8yTMHYOEOSGEN2iaRvGMmeS89wGa3Y4xIZ64Jyfg37pVlZ0jJ28jK9a8QHFJMgCNG4yge+eH6uykvHv2/sXKtS/idtsJCW7KoL5TCQlu4u2yxAms/cXGJ9eXEBytY9KGcPyCam8rcF1RpWEubW86/8xaRHZmNgCx8TEMGTmIBo0Tz77SGiRhTgjhTfa9qWQ9PwV7cjLo9UTedAPhl116VoMj3G47GzZ/yNYdXwMaQYGJ9Ow6nvjY3lVXuJcUFu9m0bLHKSlNxWDwp1e3J2jaaLS3yxLH4LRrTOqeT/5elaveCmLATdKSWhWqLMytXrqWrz76lpZtW9CkRSPPwXensnPrbq657Qp69OteNRXXAAlzQghvUx0O8j7+hMJffgXAv0MHYsc/iiku7rSPlVewhWWrnqW4JAVF0dG21XV0bHuLT03t4XRWsGLtFPbumwV4Whx7dhuP2SQd62uTee9U8POEMuJa63lyeTh6Q+0aYFNXVVmYm/jgZPoO6cO5F5xzxP1z/pjH0n+WM+mNp8+u0hokYU4IUVuUrVxF9iuv4S4oQPH3J+aeuwgeee4pjTJVVRdJWz8ladv/0DQ3wUGN6NtzElER7Wug8pqnaRq7U35nzYbXcLmsBPhH06fHMz7R+ugLrMUqT7bPx1qkcfdPIbQ/t270z6wLTpRbTusidllJOV17dTrq/i69OlFaUnZm1QkhRD0X2KsnjT/9mMCBA9CsVrJfeY3MZ57FVVh4wv3KyrOY889tbNo6DU1TadvyGs4b/o3PBjkARVFo0fQizhv+LVERHamw5jB/0T2sWjcVp7Pc2+XVe/9Os2It0mjRz0i7EdW/eojwOK0w16Jtc3Zt23PU/bu27aF562ZVVpQQQtQ3hpAQ4ic+TewTj6OzWChbuoy9N99G6dJlx9w+NW0eM+ZcRW7+JgL8oxk++AO6dX7Apy6rnkhwUANGDPmYzu3vQlH07Nj9I3/Muoy0jIXeLq3eclRozH+vAoCRjwbUuvkLfdkpTRp8UElRCX//OptOPTrSuNmBPnN7Utm4OolR485l4Dn9qrfaKiSXWYUQtZUzJ4fsqa9QsX4DAMEjzyX67jvRWyw4nRWs2fgGu5M9/ewS4wfSt8czmM2h3ivYywoKd7BizQvkF24FoEHCYHp0eRRLQIyXK6tfFn5UwfePlNGwi4Hx/4bVqzCnaVq1f79nPWnwqZJJgw9Z9YONeW9XoNODogNFAUWnHPr6wIdOr3hulYNfe7Y7+LjeCHqD4rk1Hrg1HPjcBJYwHYGROoIidQRF64huqscUUH/+gITwVZqqUvTrb+RO+xTN4cAQE4Ph7vNZV/wtZeUZ6HQmund6gJbNL61Xb5rHo6puduz+kQ2b38flqkCvN9Oq+WU0U3phm7scR2oqmsuN5nIeuHWB23XoczTQ6VB0es+IYp2u8ha9DkVvQDEaUYwGFIMBxWjyfH7wPqPpwP0H7tPpOfT2euD24NeVdx/5uKYdvs1/9/3PPgduNTTPY6qKpqpw4ENT3WhuFdzuIx7TDn59+P1u94Hbg9u5D50P4ODTq/J5pvzna4+MrS5cDoWoJjosYfqjtznucQ7bRPnvsQ8/x3++d007rM7DHjvuz/f4j52JHLMfqyMjWB0RSbhez+t33nbGxzoVMs/cMVR3mJv/bgU/PVHz/QgVBcIb6YhvbSCujYEmPYw0620kKErm+BGiLrKnppLx0oskhyaR1dkOCoQFN6dfn+cJC2nu7fJqnfKK/axePZW0/YsA0DkhJsmPqG0m9E4FRQVFVdC5QdGOH4I1NDQdlR+qXkM1ariNGqqRA7eer91GDYNdwZJjwK9Yh4KEa1/lVhRmdOjAsqbN2BcRUXl/oNPJ/OuuwqCrvvfaE+UWQ7WdtZ7rcbkfzfsZ0VQqP1S35vlc8/zzpLlBUzXP5wf/GVI992ma52u3E9xOzXPr0o742uXQKC/QKMtTKctXKc5WyU12k79XJX+vg6RZjsp6YlroadbH0yG17TATfoES7oSoCypCnGy5qIiiYjuoELfej0YpYAzJgz4S5g7nLi/H+tMcEn7cQ5AliIzuNoobOcnqaiOrq+2o7RV06HQGdIoBFAVNc6OqLjRNRUM9oxoMmplQLYZwdyLx7tb4K8EHznVYK9TxWp8Oe0z5b+uXcqztlUM3yoEWRJ3uiFbFyludDvT6wx73XAo68vGD++s9l4vgpK2J4Hlv++T6Ygr2uRk93kKHc01HbXO842hHbPPfbQ8/139/Hod+Lqf/szpxC+Px2NwqE/cks6SoGIAAnY7eoSH0Dw2hb1RktQa5kzntlrmK8gq2btxGQX4RbpfriMdGjT23SourTr7aZ87t1MjZ4yZru4v0JBd7VjhJWe3EaT20jcEErQab6DjaTNeLzARGSLATorZRVTfbdn7Dhs0foKpOggIT6dHwTlwfzsC2dRsAwecMI/ruu9CH1O951lSbjaLf/qBg+ve4S0oACOjSmdALzsfWOpiknZ9TULQDVXUe+HDhdjs42SU2nc6AohjQ6fTodWaMxgAMhgAMBn+MB24NhgAMen+stjzyCjZjteZW7q8oOuJietO8yQUkxg9Er/fN0Z3rf7fx8TUlhDfQ8dzGCPRG32uZLLbbefDfJWzMzSPYZOKZ3j3oFx+H6Swm+T5dVXaZNWX3Xj587RMMBgNlpWWEhoVQUlSCwWAgPCqcJ6Y8WmVFVzdfDXPH4nZqpG10seNfB5tm2klZ7ar8h0dvhE7nmelzjR9thplkckchaoHSsgyWrXqWnLz1ALRsdjFdO96P0RiA5nZT+Mtv5P3vMzS7HX1YKDH33UvQoIFerrrmaZpG2ZKl5Lz3Pq4cT4jy79CeyJtuJKBTx5Pue7AlTlWdaJrqaaU7EOAURXdGfRErKnLIzd9Eato80jIXoqqeRg8/czitml9Gq+aX+NRgFU3TmDq4kNR1Li5/NZDBt1f9OsPetr+ignsW/EtycQkxAf68O3QQTUNCaryOKgtzb0x+hwaNErj42rE8etsExr/wCCazic/f+4o+g3rRo1+3Kiu6utWnMPdfJTkqm2fZWfubnW3zHWgHriaExOkYdKs/A27yl9Y6IbxA0zRSUmeyat3LOF3l+PtF0KfHMyTEHT1TgCMjg+xXX8e60TPjQOCA/sTcezeGyLq5/urpcmRmkfPOu5SvXAWAuXkzom65mYAe3WvNgBC7vYiUfbPYnfw7hcW7ANDrzTRvcgFtWl5NUGDdWgbzWHYtdfD6yCICIxVe2BLpcwPwCm02rvl7LtkVFTQNCeadIYOItXgnsFZZmHv0tgk8OukBouOieez2CTz0zP3EJsSQmryPL97/mmdenVBlRVe3+hzmDleY4WbFtzaWf2Mjd48bAFMA9L3Wn6F3BxDVpOaakIWozxyOUlaufZG9aXMAaJg4lN7dJpywFUdTVYr+nEHux9PQrFZ0AQFE3nQjoReef1ZrvNZmmttNwfTvyf/qGzSHA53FQuQtNxE65rxa+z1rmsb+3LVs2f4lmdmeeQMVRU+zxmPo0PYWAi2nv3xbbTHtumLW/Wpn1GMBXPB0oLfLqVKapvHo4qX8k5ZBu4hw3hkykBCz91a0qLIBEAbDoT+UoOAgCvILiE2IwWw2U1xYcnZVCq8IS9Az6lELIx8JYPtCJ/PeqWDrXAcLP7Ly7zQr7c4x0e96fzqMMvlkPwghaoP9uetZuvJpyiuyMRj86dHlUZo1Pv+kLUyKTkfYhecT2LsXOe+8S9my5eS8+x7Fc+YQ++D9+LVqVUPfQc1wZGSQ9eLUQ30Gh59D1O23YQgP83JlJ6YoCrHR3YmN7k5h8W62bv/K02KX8jvJqTNo3uQiOrS5iYCAaG+XeloKM9xs+MOOTg8Db/b3djlVbmZKKv+kZWAxGJjav69Xg9zJnFbL3Hsvf0TPft3p0a8b0//3A/v2pjNo+ABWL12L3W7n4Yn3V2etVUpa5o4vY4uL+e9UsOoHG26n576gKIWel/sR19pAUNSBue2idARFKpgDlVpzWUOIukRVXWzc8jFbtn+OpqlEhLelf6/nCQ5qeEbHK126jJx33vX0H9PpCL3wAiJvvAF9oKWKK69ZmqZRPGMmOe9/iGazYYiKIvaxR7B06+rt0s5YSek+Nm2ZRsq+WYCGXm+mTcurad/6eozGuvH7+mNyGX+/XEHXsWZu/bLm+5BVp+zyCi6bMYtyp5OJvXtwQbOm3i6p6i6z7ktOw2az0bJtC0pLyvjqo29J2ZlCVFwU19x6BfEN4qus6OomYe7kSnNVVn1vY+kXVrK2u4+7ndGPIwJeYMShoOcfoiMgVME/RME/WOe5DVEICNFh9JMAKOqvktI0lqx8ivyCLYBC+zY30qndbeh0ZzdjlGq1kvfFVxT+9DOoKvqIcGLuvovAQQPr5D9d7tJSsqe+Qtmy5QAEDRtKzH33oA8K8nJlVaOoOJmNWz5iX/p8wDNQolP722ne5MKzfi5UJ6dd48k2eZTmajw0K5QW/XxnpK6qady94F9WZe9nUGI8rw3sXyv+dmTS4GOQMHfqNE0jZZWLTTPtlOxXKc1VKc078JGrHjHtyekwmME/2BP4/EMULOE6GnT0THTcpKeRYJnoWPggTdPYs/dPVq9/BZfLSkBADP17TSYmqmpbmWx79rD/jbcqL0laevYg+q47MTVsUKXnqU623bvJnPgczqwsdIGBxDxwH8FDh3i7rGqRm7eJtRvfJDffM6AlJLgp3TrdR3xsv1oRJP5r5XdWPr+tlMQOBiYs9a2lu77fsYuX16wj1Gzmh/NGEuFfO9Y7rvIwl7s/j/2Z+wGITYglMjriJHvUPhLmqo69XPMEvFyVsrxDYa8sT6WiWMNWolFRrGIt1rAWH/r84CXc44lsovMEuwMfiR0MGEy+84Ih6h+7o4SVa6aQmj4PgEYNhtOr2xOYTdUzT5ymqhTPmEnutE9Ry8pAryds3EVEXHsN+sDa3Vm9eNZs9r/5NprDgblFCxImPYMxNtbbZVUrTdPYlz6fdZveoaw8A4DYmJ5063g/4WG1q//jS4MLSF3r4pp3g+h3ve/0l8sur2DcnzOxu928MqAfQxvWnhHHVRbmykvL+eaT79m8fktlCtc0jfZd2nL1LVdgCaob1/lBwpy3aZqG0wbWA8GuolijOMtN6joXKaud7F3rxFFx5D4GMzTsbKRJDwNNenoCXnhi7Ry9JsR/ZeesYenKiVRY92MwBNCz62M0bXRejbRouAoLyfvf5xTP/Bs0DX1oKJE33UjIqHNr3QhQzeVi/zvvUfznXwCEjB5F9H33oDP5zmW8k3G7HezY/QNJWz/F4SwFFFo0vYjunR/GYPB+K9HeNU6mDikkIEzhxe2+NR3J8ytX8+vuZIY1TOTlAUdPCeRNVRbmpr35P3Kz87jipktp1MzTQTd1zz6+//wnImMiufX+G6um4hogYa52c7s0sra5SFntImWVZxWL7J1H99uLaKyjZX8TLQcYaTnARHiD2vXGJITb7WTjlg/Zsv1LQCMyogP9e032yhxjtl27yXn3faxJSYBnbrbou+866QS7NcVdVkbmpMlUrF2HYjQSff+9hI4e5e2yvMZuL2bT1k/YuedHVNVFWEgLBvZ9meAg714q/+zWYlZNtzP8/gDGPV+7W3hPR1ppKRf/+Tca8OOYkTQOrl0rq1RZmHvo5se5d/ydNGnR+Ij7U3bt5d2XPuS1T186q0JrkoS5uqe8QGXvWk+wS1ntacGzFh/59JVwJ2qT4pK9LFn5NAWF21AUHR3a3EyHtjd7tWO7pmmU/ruI3A8/qlw1IWjwIKJuuxVjbIzX6nJkZZEx4Wkcqanow0JJeP45/Nu08Vo9tUlh0W7+XfYYpWX7MBot9Os5iQYJg71SS2muyoTWebidMDkpgohGvvMa+8yyFcxISeX8po15tk8vb5dzlCqbZy4wKBCT+eimbqPJiCXI95bwELWLJVxHu+Fm2g33zPWjujXSk1zsXOxk52IHu5c5yd+rsnyvjeVfexbVjm2pp91wE+3ONdO8rxGj2XcuB4jaS9M0dif/xuoNr+F227AExNG/92SiIzt7uzQURSF48CACe/ei4IcfKfjue0oX/kvZsuWEjRtL+FVX1Gh/Ok3TsG7YSObkF3AXFWFq1IjEF5/3+f5xpyMstDmjh3/J8lWT2JfxDwuXPkKHtrfQuf0dNV7Lim9tuBzQ/lyTTwW55OJiZqakolcUbu3QztvlnLbTaplbvnAFq5et47o7riI0PBSAooIivvr4O7r17kLfwb2rq84qJy1zvue/4W7XUie2kkNPb7NFoeVAI+1GmGg/3OxTL0Ti+Morstm87TNy8jZiMgZiMgVjNoXgZw4jMDCRoMAGBAc2ICAgGkU5uxHULpeNfRkL2LXnF3LyNgDQuOFIenUdj8lUOy9HOXNyyP34E0oX/AOALjiIiKuvJvTC86u1n5pqs1Gy4B+K/vgT+07PUlcB3boSP/GZOj8vXnXRNI1tO79h3aZ30DQ3vbqOp2XzS2r0/JO6FbB/l5s7vguh05jaO4nu6Xp88TLm7Uvj4hbNmNCzu7fLOaazusw65YmXj+igm59bgNPpJDTMM0FgUWExRqORiKhwnpjyaBWWXb0kzPk+t1Njz0onW+Y42DLXQcZm1xGPx7Y60Go3QlrtfJHVmsfm7Z+zc8/PqOpJhk7jWV7J3z+SAL8o/P2j8PcLx2gMOhAAgzAYAjwLsSuGA5dJFVTViVt1oKpO8gu2kpL694EO62A0WujZdTxNG9WNPl/W7TvI/Xga1g0bAdAFBGCIisIQGYEhPBxdYCBoGmgqmqp5PldV0DS0A7eoKtrB+w///L/bqCq27Ts8I2zxBMiwCy4g4rprUAy1d2612iJ57wyWrpqIougZPvhDYqK61Mh5D67DGhKr44VtEegNvvGauaOgkKv+noNJp+O3C88jJqB2Xmk8qzA385fZp3yi0ePOPb3KvEjCXP1TmOFmy1xPsNv+jwNb6ZGtdq0GGWk3wky74SYiGtauVju3UyM3xU3Obs/H/l0uHFaN/jf6+9RknVXB4Shl8/Yv2L5rOm6353J74wYjaNXiMjRNxW4vxu4oxmrNo7Q8nbKydErL0rDa8qvk/BHhbWne5CIaNxyByVg7W+OOR9M0yleuInfaJzhS9lb7+fzatCb0wgsIGjQQXS1eKqk2WrPhDbbt/AazOYzzzvkKi6X6L0t/flsJK7+zMfKRAC6cWLee2yfy4MLFLMrI5KrWLXm4W80E4zMhkwYfg4S5+q2y1W62gy1z7WRsOXKkbGwrPe1HeFrtmvWpuVY71a2xf5ebfeudpG1yHQhubvL2ulGPswhH+xEmLphooUFHY43UWFu53Xa27/qBzds/w+HwrBWdGD+Izu3vICy0xSntX2HNw2rLxWrNw2rLx+ksw3Hgw+WqQFVdqKoLTXOhaRp6vQmdzoheZ8TPL4ImjUYRHtqyur/VaqdpGu7iYlz5Bbjz83Hl5+Mur0DRKaAooNN5rtjodEd+rigoB27/e/9/vzZGR2Fu0sTb32qdpaouFiy+n6z9KwkPbcW5Qz+t1mlLygtVnmiZh9MGz22KIKpJ7fqH90wdbJXz0+v548IxtWaC4GOp8jC3Y8susjOzUVCIS4ylRZvmZ11kTZMwJw5XmOGuvBy7feHxW+1aDzIS1UxfJXODqapG7h43qetd7FvnJHW9i7RNLuxlR/9JKgqEN9QR3dxATHM9MS30lOSoLHjfWrl990vNXPpSEMHR9WvlDFV1k5w6g42bP6LC6pnMPCaqG1063ktURHsvVydE9bHbi/l7/vWUlqXTuOFI+veaXG3zFi78qILvHymj9RAj9/8RVi3n8IZJy1fxR3IKl7dqwWPda/dav1UW5ooKipj21mekpaQTcqDPXHFhMQ2bNODWB26svK8ukDAnjsfl0Ehe6WTzgVa7zK1HNomFxutoNcgz/Umrgac+osvt0kjb4GLnYgc7lzhJXnn01CoA4Q10NOxipGFnAzEt9cS0MBDVRI/J/+gX6dJcldmvlfPvNCsuBwTH6LhhWjBthvj+pVdN00jP/Jf1Se9TXJIMQFhoS7p0uIf42D4+tbyQEMdTVLyHv+ffiMtVQe/uT9Gi6UVVfg5N03ihbyEZm13c8kUw3cbV3tar01FoszH61z9xqiq/nD+ahsG1e73fKgtzn7z1GcVFJVx/5zWVS3jl5eTz5QffEBIWzM333VAlBdcECXPiVBWku9k618HW+Q52LnZQXvCfue0a6Wg10ETzfkaimugJS9QTGqcDhSPC2+5lzqNa3ULidDTqYqBRVyMNuxho2OXM1qTN3+fmyztK2LnYiaLAuQ8HMOZJi890UP6v/bnrWb/pncp1LAMtCXRqfwdNGp571iNShahrklNnsnTlM+j1fpw3/CtCgqv28vXBFR8CIxSm7Ij0mcFi/9u8lfc2JtE/Po63hgz0djknVWXzzO3YvJP7nrz7iLVYI6MjuOTasbzz0gdnV+V/lJeV8+0n37M9aSeWIAsXXDaa7n27HXd7l8vFS0++it1qZ/LbE6u0FlG/hSfq6X+jP/1v9EdVNTK3utm5yMGOfz3Tn+Snqiz7ysayr2yV+yg6MJjAaTvyWNHN9LQcYKTFABMt+hkJS6iaficRDfXc/2cos16t4K8p5cx6tYKdi53c+lUwoXG+0bcFoLB4N+s3vUdG1mIAzOYwOra9mRZNL0avr999BkX91bTRaLKyV5CcOpPFK55k1LDP0OurbkDJ0i+sAPS+ys9ngpxTVflx524Armhd9/u5Vs0Y8Gr43f7wxS/oDQamvDeJ9NQMPnztExIaJhCXeOwRO/Nn/ENgUCB2q73qixHiAJ1OIbG9gcT2BobeFYDq1kjb5GLnIs96sgVpbgozVEqyVZy2I8Nby/5GQuOrL1jp9AqjH7fQop+R/91cQvJKJy8PLeTun0NJaFu3p3soK89i45aPSN47A9AwGPxp2/Ia2ra6BqNR5iQTomfXx8nNT6KwaCfrNr1Djy6PVMlxbWUqq3/yvK/2u96/So5ZG/yTlk6O1Urj4CB6e3Hlk6pyWq/wLdu14KevfuWGu64hLMLTAbIgr5Cfv/6Nlu1OPlrsVNltdjau3sSEFx/F7GemWaumdOjajlVL13Dh5WOO2j4vJ5/Vy9Yy9qoLmf7pD1VWhxAno9MrNOpipFGXI1uFXA4Ne7mGJazmL/m16G9iwpJwPryiiORVLl4dXsjt34TQenDd60dntxeRtO0zduz+AVV1otMZaNF0HB3a3oy/X8TJDyBEPWE0WhjQ+wX+nn8j23dNJzamJw3iz/7S4frf7NjLNJr1NhLbqm7/U3i46dt3AnB5qxY+0b/2tH4zl1w7lo/f+B/PPvwCIaEHBkAUFROfGMcl146tsqJysnPR6XVEx0VX3pfQIJ7d2/ccc/ufvvqV8y8djcl04sssSxcsZ+nC5QCMvvoSkD5zopoYTAoGk/deIIKidNz/Vxhf3FbCut/svDO2iGveDaLP1XXjP2uny8r2nd+yZceXOJ3lADRueC6d29/plQXqhagLIsLb0qXDPazb9BbLVz9H5Ijp+PtHntUxl3/j6SvS5xrfGPQAsDW/gI15+ViMRsY0aeztcqrEaYU5S6CFR559gF3bdrM/KweAmPgYWrev2uvNdrsDv//M9eIX4IfNdvQl1I1rNqGqKp26d2TXtt0nPG6/oX3oN7QP4OlIKIQvM/kr3PxFMBHPlDP3rQq+vKOUikKNYffUztnNwTN31u7k39i0dVrlJL7xsX3o0uFuwsNae7k6IWq/tq2uJnP/crL3r2LZ6ucYOuCtM255yk1xs2uJE1MAdBvnO5M6T9/hWT7uomZNCDD6Rl/bUw5zqqry6O0TGP/CI7Tu0IrWHVpVW1Fmswmb9cie4zarHT+/I59Mdpud36f/xR2P3FpttQhRl+l0CuOeDyS8gY7vHynjpyfKUN0w/P7aFeg0TSM1fR4bkj6gtGwfcLCV4V7iYnp4uToh6g5F0dGvx7P8OecKMrOXsWvPz2e8fuuKbz0DH7pc6IdfkG+MEi+02ZiTug8FuKxl1XUP87ZTDnM6nY7wiDDcruNMQ1+FomOjUN0qOdm5RMdGAZCxL5PY/wx+yN2fR35eAW8+/y4AbpcLa4WNCfdM5OGJ9xMRJZdRhQAYfHsABrPCN/eW8stTZahujXMf8u7AAU1TycvfTGr6fPalL6C8IguA4KCGdG5/Fw0Th/lEXxYhalpAQDS9uj3B4uVPsGbjm8TG9CA4qNFpHUNVNVZ863uXWGekpOJUVfrFx5EY5DtLkp3WZdaRF43gj+//4ro7ryawGn8IZj8znbp3YMbPs7jq5svI2JdJ0rrNPPTMfUdsF5cYy+Q3n6n8OnnXXn788hcen/wQgcG+80sSoir0v8EfnQ6+vqeU3yaWo6kw8pGaDXQOZxnZ+1eTmb2cjKwlVFhzKh8L8I+hQ9ubaN7kwgML2QshzlTjBsNJz1xESurfLFn5NCOH/u+0/q52LnZSsE8lopGOFv1941Kkpmn8vsczwfhFzZt6uZqqdVqvmPNn/kN+bgFP3zeJ0PBQTOYjR8c9MeXRKivsshsu5ptp3zPh7olYggK4/IaLiUuMZfeOZD545WNe++Ql9Ho9waHBlftYAgPQKcoR9wkhDul7nT8o8PXdpfw+qRyjv8Kwu6vvkqumqRQU7SQzaxmZ2cvJzd+Eph1q3Q8IiKFR4jAaJg4lKqKjTPgrRBXq2eVxcnLXk1+wlaStn9Kp/e2nvO/yrw/NLafT+UYL+eb8ApKLSwgzmxkQH+ftcqrUaa0AMfOX2SgKHG+P0ePOraq6qp2sACHqs2VfWfnqrlIAbvwkmJ6XV91lFLu9iMzsFWRkLyUzewV2e2HlY4qiJzKiPfGxfUiI7Ut4WBu5lCpENcrOWcPchXeiKDrOHfoJUREdTrqPtVjl8RZ5OK0wOSmCyMa+MfH48ytX8+vuZK5p04oHu3b2djmn7axXgHDYHfz23Z9sWpeE26XSsl0LLr1ubLVeahVCVJ++1/pTUajx85NlfHFHCQFhCu1HnNloNU3TKCzaSUbWUjKylpBXsBlNUysfDwiIIT62L/GxvYmL7onJVLvXPxTCl8RGd6dtq2vYuuMrlq58mvOGf4vReOLW+LW/2nFaoeUAo88EOavLxZy9nsFVFzar2uXOaoNTCnMzf5nFysWr6d63K0aTkbXL1/H9Zz9z833XV3d9Qohqcs59AZTkqMx9q4Jp1xbzwF9hNOlxan1jnM4KsnJWkZG1lMyspUf0fdPpDMREdSchri8Jcf0IDmosrW9CeFHn9neSlb2CwuJdrNn4Bn26P3nC7Zd/7XsDH+btS6Pc5aJDZARNQ0K8XU6VO6Uwt3FNElfdcjnd+nQBoHvfrrwx+R1UVUWnkz4uQtRVYydbKMtTWf6NjfcuLuKReWHEtjz2y0JJ6T4yspaQnrWEnNz1qKqz8jF/v0gS4vqRENefuJiessSWELWIXm+iX6/JzJx3LbuTfyUxrj8NEgYdc9ukWXaSVzoxByp0udB3wtzvu1MA32yVg1MMc4X5RTRrdegH0LhZI/Q6HcWFxZXLegkh6h5FUbj63SDK8lWSZjl4/7JiHpsfRmDEoX/SioqTWb3hVbL3rzp8T6IiOh4IcP0IC20lrW9C1GJhoc3p0uFu1m58k+Vrnicyov1RS+KVF6p8c6+nL+2YCRbMFt/4m04tKWV9bi5+ej3DGzX0djnV4pTCnKqq6A1HbqrT63G71ePsIYSoK/QGhZs+C+a1c4tI3+Ri2rXF3PtbKCrlbNzyETt2/4CmuTEaLSTE9Schrj/xsX3wM4d6u3QhxGlo0/Iq0rOWsD9nDctXP8+Q/q8f8U/YT+PLKM5WadbbyNC76sbSf6fijz2eVrnhjRoQ6CMrPvzXKU9N8uWH32A4LNA5nU6++98PmEyHpie5/aGbq7Y6IUSN8AvUcdcPIUwdXMjOxU6+e342ft1ex2YvQFF0tGx2CZ3b34FZApwQdZai6OjX81n+mn0lGVmL2brja9q1vhbwXF5d8a0Nox9c+34QOr1vtMpZXS7+Sjl4idW35pY73CmFuZ79ux91X4++3aq8GCGE94Ql6LljegifP/ULWpvXsNlVoiI70bPLo7IuqhA+whIQS9+ez7Jw6cOsT3qXyIj2BBo68c19nsurFzwTSEwL35i02+pycf/CxeRZbTQNCaZzVKS3S6o2p/Qbu+a2K6u7DiFELeAKm0mzG18FNNJnXE6r8+4hPMx3LrcIIaBBwiDatbqOLTu+ZPHyJyif+yHFWQE+dXnV6nLxwMLFrN2fQ6S/H68O7O/T/XplKKoQAoAdu39k+ZrJgIal6Bb2/XI9n99aytpfbN4uTQhRxTp3uIvoqK5YbXmURE7G6O/2mcurB4PcmgNB7uNzhtIo2Lfnt5QwJ4Rg285vWbVuKgDdOj3IuNvuYPT4ADQV/ndTCet+l0AnhC/R6Qz07vQCrrJQQttsZPCUH+r05VVN00gtKeXHnbu5fd4/lUHuo3OG+HyQg9Ncm1UI4Xu27fyWNRteB6Bn18dp1fxSwDM1geqCWa9W8OkNJei/Uug05sxWiRBC1D4L3vRn+8zHaffIk5QHf8Wevc1o1niMt8s6LXtLSvh2+06WZGSyv8JaeX+EnyfINQ6uH2u1S5gToh7btnN6ZZDr1W0CLZuNq3xMURQueMYT6Oa8WcG064q5+u0g+lzjG31qhKjP0pOczHu7Ak3tRPOoB9iT/zrLV0/G3y+S+Nje3i7vpLbmF/D5lm0sSEvn4HLxoWYzPWKi6RkXw5DERML86s8/nxLmhKintu/6njUbXgWgV9fxRwS5gxRF4aLnLGgazH2rgi/vLCV9s4txzweiN9T9vjVC1EeqW+Ob+0pR3TD4Dn/6DrsKv415bNnxJf8ue4wRQz4mopaOYE8rLWXq6nUsz8oGwKjTcX7Txlzcojktw0LR+fAghxNRNE3TTr6Z79mbVUDjuHBvlyGEV+zY9QOr1r8MQM+u42nV/JKT7rPkMyvTHy7F7YQ2Q43c/FkIlnDpditEXfPPhxX88GgZYQk6nlkdjl+QDk1TWbpyIin7/sbfL4Jzh/6PoMAEb5daya2qTN+xi/c2JmF3uwkwGLikRXOuat2SqID6cbXgRLlFwpwQ9cyO3T9WDnbo2eUxWrW47JT33b3MwUdXF1OWpxHVVM+d34cQ11oa+IWoK4qy3EzqVoCtVOOO70KO6AfrdjtZsPg+snNWExLchFHDvsBoDPBitR4pxSVMWrGKpLx8AEY2bsgj3boQ5uc7a8eeihPlFvm3Woh6ZOfunyqDXI8uj5xWkANo3tfEE4vCadDJQG6ym5eHFrJppr06ShVCVINfnirDVqrR6TzTUQOa9Hojg/q+QkhwU4pLUli17iW83d4zM2UvV/89h6S8fKL8/XljUH9e6Nen3gW5k5EwJ0Q9sXPPL6xc9xIA3Ts/TOsWV5zRccIb6HlkThjdLjZjK9X48Ipi/n6l3Osv+kKIE9u1xMHqH+wY/eDSqceersNkCmRgn5fQ6/1ITp3Jnr1/1nCVHi5V5Y11G3h62UrsbjdjmjbmxzEjGZhYey791iYS5oSoB3bt+ZWVa6cA0L3zQ7RpeXarupgCFG7+LJgLJ1oA+OO5cj69sQR7uQQ6IWojt0vj+0c8S3ad+7CFiEb6424bGtKUXl0fB2DVuqkUFe+pkRoPKrbbue+fRXy9bQd6RWF8j24827snQYetBS+OJGFOCB+3K/k3Vqx9AfBMCNym5VVVclxFURj5iIU7pofgF6Sw9mc7U4cUkL3DVSXHF0JUnX8/tpKxxU1kEx0jHjh5P7hmTc6naeMxuN12Fi1/AqfLetJ9qkJ6aRnXz57Hyuz9hJnNfDhsMJe2bO7TS3FVBQlzQviw3cm/s2LNwSD3AG1bXV3l5+g42sxjC8KIbaUna5ublwYXyhJgQtQiJTkqf75QDsClLwVh9Du1YNSz6+OEBDehuCSZVeumVntXil2FRdw0Zz5ppWW0Cgvlq1HD6RoTXa3n9BUS5oTwUXtS/mT5mucBja4d76Ntq2uq7VxxrQ08vjCM7peYsZdpfHJ9CT88VorLIZddhfC2X58uw1ai0f5cEx1Hn/pEukaDPwP6vIhebyZ571/s2vNztdW4MTePW+ctIN9mo0dMNNOGDyXOYqm28/kaCXNC+KA9e/9i2ernAI0uHe6hXevrqv2cfoE6bvpfMJe/GojeCP98YOX1kYUUpLur/dxCiGPbs8LJim9tGExw6dTA094/LKQ5vbs/BcDqDa+Sk7exqktkWWYWd85fSKnDyeDEBN4aMhCL0Vjl5/FlEuaE8DHJe2ewbNUkQKNzh7tp3+aGGju3oigMvj2Ah2eHEZaoI2W1iyn9Ctg6X6YvEaKmqW6N6Q97Bj0MfyCA6GZnNidk00ajaN3iSlTVxaJlj1Fhza2S+lyqysebNvPAwsXY3W4uaNqEqQP6YtYff3CGODYJc0L4kOTUmSxd9Syg0bn9XXRoc6NX6mjSw8iEJeG0HW6ivEDj3bHF/PViOapbLrvWBZnbXGxb4MBRIb+vumzx/6ykb3IR3kDHyIfP7pJlt073ExPVDastn0XLHsftdp7V8TLLyrlt3j98lLQFVdO4uV1bnundA4NOYsmZkBUghPARKamzWLrqGTRNpVP7O+jY9hZvl4Sqavz9cgUzppSjadCiv5EbpgUTnij/eddGu5Y6mP16BVvmOAAw+kObIZ5+Vh1GmwmOkjfauqI0V2Vi13ysRRq3fR1MlwvPfpJdq62AmXOvpcK6nxZNx9Kr24TTHmVaYncwb18ab67fSLnTSZS/P8/17UXP2Jizrs/XyXJexyBhTviS5NSZLFv1rCfItbudju1u9XZJR9i2wMHnt5ZQkqMSEKZwzTtBVfLmIs6epmlsm+9g5ssV7FnuaW0x+kNMCwPpmw5NM2Mww4Cb/Rn5sIXgaAl1td3X95Sw9AsbbYaZuPfXkCqb2iO/YCuzFtyCqjro3P5OOrS9+aT7pBSXsCAtnaWZWSTl5aMeiB2DExN4uncPQs2nPiijPpMwdwwS5oSv2Lrja9ZufBOAjm1vpVP7271b0HGU5Kp8dWcJm2d7Wn36Xe/HxVMC8Q+WYOAtOxc7+OO5cvas8IQ4/1CFIbf7M+SOAAIjdRRlukma5WDDX3a2zvX83kwBMOTOAIbfH4AlTH53tdHeNU5eHlqIzgBPrQgntmXVrp+cmj6fRcvGAxq9uz1Ji2Zjj7ldsd3OexuT+GXXHg4GDb2i0Dk6ivObNmZMk8Yyf9xpkDB3DBLmRHXSNI3Ssn2ADoPBH4PBD4PeD52u6l5UNU1l7cY32bbzW+DgPHLVN/1IVdA0jX8/tvLzk2W47BAar+PyV4LofIH8Z16Tklc5+fP5Mrb/4wlxlnCFEQ8EMPAWf/yCjh3Q0pOc/DG5nKS/PaHOP0ThnPsCGHrn8fcRNU91a7w8tJDUdS5GPBjA2OdOfwTrqdi5+ydWrnsJRdExsO/LNEwYfKgGTeOv5BTeWr+JIrsdvaJwXpPGDEiMp2dsDIEyUvWMSJg7Bglzoro4HGUsWv44WftXHvWYTmf0hDv9gYB3+Of6A19X3nfwfn/0/93W4M+2nd+yd99sdDoDfXpMpGmjUV74bs9MxlYXX99dwt41nst4nc4zcflrQYQlSF+66pS20cmfz5eTNOvMA1nKaid/TD4UBAMjFM592MLAW/wx+Usri7f9+0kF0x8sIzRex8S14fgFVl/Q3rjlYzZt+RidzsQ5g94lJqorhTYbjy1exrocz4jXbtFRPN6jG81CQ6qtjvpCwtwxSJgT1aG8Yj8LFt9PUfFujEYLfuYwXC4rLpcNl9uKpqlVej6DIYDB/V4hLqZXlR63JqhujUWfWPl9Ujm2Ug1TAHS/2I++1/nTtJdBLr9UoaztLv56oZx1v3mmiDFbFIbc5c859575pdKdix38Pqmc5JWeUBcSp2PUYwH0u84fg0l+d95QkqPybNd8rMUat34dTNdq7peqaRqr1r3Ezj0/YzD406bTM0zc7CC9rIwIPz8e6NqJUY0byd9yFZEwdwwS5kRVKyjayYLF92O15hIc1JihA94iKDCh8nFN01BVxxHhzuWy4nLbDtz3n8+P99iBW6MhgG6dHyQirLUXv+uzV5jh5ofHytjwx6G56GJb6el7rT/dxpkJbyCtdWcqZ4+LGS+Vs/p7O5oGRj8YdKs/Ix60EFQFI1M1TWPLHAd/TC4nbaOnlTWisY7zxlvodYUfOr28idekz24pZtX3dtqNMHH3T1U36OFEVNXN8jWTSd77FyoKC1zDcYYO4u0hA4n096/289cnEuaOQcKcqErZOWtYuORhnK5yoqO6MrjvK5jNclnhdGTvdLH8KxsrvrVRknOoBbNJDwPdxvnR5UIJdqeqIM3NzKnlLP/ahuoGvRH63+jPyEcCCI2r+p+hqmps+MPOn8+Xk73Ds+JHbEs95z9jocsFZmmZqQHb/3Xw1pgijH7w9KoIoprU3N/Kv2kZ/LJsKt2VZQC0anUDPTreLb/3KiZh7hgkzImqUlS8h1kLbsLpLKdxw3Pp22Mier3J22XVWW6nRtIsB6t/sJE0247TeuixuNZ62g4z0fYcE837mbzSR6uiSCVnj5vsHS4ytrjJ3OoiY4uL4iwVRQEUUHTgF6QQ38ZAYgcDCe0MJLQ3EN/WgNlSfTUXZ7uZ9WoFSz6z4nKATg+9r/Zj9OMWIhpW/5u76tZY9b2Nv14sJ3+vJ5A36mrgwmcDaTPEd/4mNE2jKFMle4ebrB0u9u904xeo0HWsmYZdar6LgNOu8UKfAvbvcnP+0xZGP1Zza5ouy8zigYWLcWsaV0dnEl00HU1z0zBxKD26PEaAf2SN1eLrJMwdg4Q5URWs1jz+nn8j5RVZNEo8hwF9pqAoMrKvqtjLNTbPtrPuVztb5jmwlx16udIboUEnA427G2nS3UijrgYim+jRG87ujVTTNCoKNQrS3eTscZOz+8DHHjc5u12U5Z/5S6aiQFRTPQntPeEu8cBteEMdOt2Z112aqzLnzQr+/bgCp81znh6XmTlvvIXo5lU7LcWpcDk0ln5hZebUCkr2e0Jdq0FGLpwYSJMedWcko6ZpFGepZG5zkbXNfeDWRdYON7aSYz8Poprp6X6xmV5X+BHTomZ+9jNfLufPyeXEtNDz5PJwjOaaCZOb8/K5fd4/2Nxurm7dkge7diYzexmLlo8/0BXEQsd2t9K6xRVVOpK/vpIwdwwS5sTZcrlszFl4G/kFW4mM6MDwQR9gMMhEuNXF5dBIXuVk6zwH2xY4SNvg4r+vXnqjJyzFtNAT1dRASIyOoGgdwdE6/IIUHFYNR4Xnw1qsUbxfpThbpWS/+8Ct58PlOH4dRn+IbmYgprme+HYGEtoaiG+nJ7KxHhTQVECDsnyVjC0u0pM8LXcZSZ4QoLqOPqZfkEJ8WwPxbfTEtTUQ38bTihcUpZywlaeiSGXeOxUseN9aGXS7XGhmzJMW4tt4/83TXq6x8MMKZr9ZgbXIU1+nMSYueCawVtR3kKZplOxXydzm9oS1bS7P59tdWIuP/RZpCVeIa20gtpWe2FYG8lPdrP3ZXtlFQFGg0xgz5z4UQOPu1Rdgs3d61j922uD+v0JpPahmWkBTS0q5ac58iux2xjRtzLO9e1Y+V0vLMliz4TXSMxcBEBLclE7tbycuphcmY/VMlVIfSJg7Bglz4mxomsq/yx4nLeMfLJZ4Rg37HH8/eT7VJGuJSuo6FymrnaSsdpKe5KIwvWpGC/sFK4TG6ohurie6mZ7o5obKz0PizrwVzeXQyN7pJiPJSfpmd2XIO7yP4OEs4Z6QF9fGQHQzPZYwBUuYjoBQhZ1LnMx9+1BIan+uifOfstCwc+1r+SovVJn7ZgULPqjAafVchu5ygZk2Q00072ckpoX+iNCqaRpuJzitGk47OG0aqttz2VhvAEUPer3iuTWATq9gMHPSAReaplGaqx1qYTsstFUUHvutMCDME9ri2+iJa+P5XcS3OXbQVt0aOxc7WfW9jdU/2nAdGNPTapCREQ8E0Hqo6axaYP9LdWu8dm4RySud9L7Kj+s/Cq6yY59IboWVG+fMI6u8gn7xcbw2qD/GY6ypmpG1hNXrX6W0LB0ARdETFdGBuJjeNG44guCghjVSr6+QMHcMEubE2Viz4Q227fwGozGQUcM+IyS4ibdLEnhagnL2uMje6SZ/r5uSXJXSHJWSHBV7mYbJX8FkUTAHKJgDFUJidQTH6AiJ1RES4/k8OEZfrf3ajqUkx9OKd3jAyNzmOu6lvMO1GmzkgqcCadqr9oW4/yrOdjNzagVLPrce0UIZHK0jsrGOimLPJe6KohO3jh6P0c8z7YrJomA++BGo4GdRqCjyhLjygmP/TP1DjhXa9ATH6M6oD1xxtpsF71tZ9IkVW6nnnNHN9Ay81Z8+V/sREHr23THmv1vBT0+UERKr4+lV4TWyIkeF08nNcxews7CI9hHhfHjOEPwNx29ldbvt7Nj9I2kZC8nNT0LTPANkdDoT3TrdT6vml8lAiVMkYe4YJMyJM7Vt53es2fAaOp2BoQPeIS6mh7dLEj7oYCf7zK2e/lr5+9xUFKlUFGqUF6oEhOkYfn8ArQbWvYEF+alukmbZ2bXUya4lDkpzj34b0hvB6K9gNIPRT0HRgeo+8OHSDn1+4GuXnaMuux+LX5BCXGt9ZQtbXBs98W0MhMSdWWg7mYoilcWfWvn3E2tly7EpALpf4kefq/1o1sd4RufN2e3i+b4FOK1w5/chdBxd/auoaJrGE0uWM3dfGo2Cgvh0xDDC/E79vA5HGdm5a0jdN4e9aXMASIgbQN8ez+DnF1ZdZfsMCXPHIGFOnInD1yTs1+s5mjYa7e2ShKjTNE1j/y43pbkqlnAdljCFgFAdRr/TCziapuG0gq1cw1GuYS/XsJcdujX6Q1wbA2EJ1RPaTsbt0kia6WDhtAp2LHRW3h/VVE+vK/3odYWfp9/lKVBVjddHFrFnuZOeV5i5cVrNTIP05dbtvLV+IxaDgS9GDqdJyJlf1k1Nn8+KNS/gcJTg7xdBv16TiYvpWYXV+h4Jc8cgYU6crpy8DcxdeBeq6qBzh7vp0OZGb5ckhKiDsne4WP6NjZXTbRRnHeov2bCLga4Xmel6kZmopse/dHnw8mpwtI5nVodjCa/+y6srs7K5559FqJrGqwP7MaRB4lkfs7wimyUrnyEndx2Koqdfr0k0aTiyCqr1TXUyzJWXlfPtJ9+zPWknliALF1w2mu59ux213bwZC1i1eA0F+YVYAi0MOKcv55w39KTHlzAnTkdxyV5mLbgZh6OYls0upmfX8dLPQwhxVlS3xvaFDlZ8a2PTDAf28kNvx4kdDLQfaaLDuWYadzdgL9NY/ZOdJZ9bSdvg6XB4x3chdBpT/ZdXM8vKuWbWHIrtDm5u15a7OneosmOrqpv1Se+ydcdXgEKvbuNp2eziKju+LzlRbqk9Y8P/44cvfkFvMDDlvUmkp2bw4WufkNAwgbjE2CM31ODaO64ivkEceTn5vDf1I8LCw+jWp4t3Chc+x2rNY/7i+3A4ikmMH0CPLo9KkBNCnDWdXqHtMDNth5lxWDW2znWw7ncbm2Y6SE/yTGsz65UKAiMOTqvj2S8gTGHE/QE1EuTsbjePLl5Ksd1B37hYbu/YrkqPr9Pp6dbpfsymENYnvcvKtS/idJbRrvX1VXoeX1crw5zdZmfj6k1MePFRzH5mmrVqSoeu7Vi1dA0XXj7miG3PGXOoFS4mLpqOXduRvCtFwpyoEk5nBQuWPEh5eSYR4e3o33uKTH4phKhyJn+FzheY6XyBGadNY9cSB0mzHSTNsleuptFyoJF+1/vT5QLzafcpPFNvrtvA9oJCEgItPN+vN/pjTEFSFdq3uQGjMZBV66aybtM72OxFdO14r0zCfopq5btSTnYuOr2O6LjoyvsSGsSze/ueE+6naRp7dqbQb0if6i5R1AOq6mLR8vEUFG4j0JLAkP5vYDTIwtFCiOpl9FNoe46ZtueYuezlQHJ2uzGYlRpZku1w8/al8cPO3Rh0Ol7q35cQc/W2BLZqfglGo4Vlq55l646vKCndS79ek2Wi4VNQK8Oc3e7Az//ImfT9Avyw2ewn3G/mL7NRVZVeA489ImbpguUsXbgcgNFXXwLSZ04ch6ZprFz7EpnZyzCbQhg28B2ZFFgIUeMURamxZcEOl15axnMrVgPwQJdOtI2omde/po1G4e8XzqLlT5CeuZhZ829icL/XCA5qUCPnr6tqZZgzm03YrLYj7rNZ7fidYD6bf+cuZtWSNTzw9D0Yjcf+tvoN7UO/oZ5Wu71ZBVVXsPA5m7ZOY3fKb+j1ZoYMeFNmKhdC1BtOt5sJS5dT7nQypEECV7RqUaPnj4vpxahhX7Bw6cMUlyTz9/zrGdD7BeJj5arb8dTKi9HRsVGobpWc7NzK+zL2ZRL738EPByz/dyXz/lzAvU/cSVh4aA1VKXzV9l3T2bTlYxRFR//eLxAVUXUjt4QQorZ7e8MmtuQXEGcJ4JlePb0y4Cs4qAEjh/2PxPgBOBwlzF90LyvWTMHhLKvxWuqCWhnmzH5mOnXvwIyfZ2G32UnemULSus307Nf9qG1XL13Lnz/O5O7H7yAyOsIL1Qpfkpw6k9XrXwWgd7cnaZgw2LsFCSFEDfpl1x6+3b4TvaLwYv++BJu9t8KIyRjI4H6v0bn9neh0BnYl/8Kfsy8nM3uF12qqrWr1PHPfTPueHZt3YgkK4ILLzqN7327s3pHMB698zGufvATAxAefp6iwCMNha8P16NeNK2689ITHl3nmxH+lZy5m4dJH0DQ3XTveT7vW13q7JCGEqDEL0zJ4dPFSVE3jyZ7dGdeimbdLqlRYtJtlqydRULgNgMYNz6VDm5sJDWnq5cpqTp2cNLi6SZgTh9ufu475i+7F7bbTrvUNdO14j7dLEkKIGrMxN4875y/E7nZzW4d23N6xvbdLOoqquti642s2bvkIVfUsidYgYTDt29xIZHjVzn9XG0mYOwYJc+KggsLtzFl4O05nOS2ajqVXtwkyKbAQot5IKS7hpjnzKXE4GNu8KU/27F6rXwPLyjPZsv0rdqf8jqo6AAgPa0N8TG9iY3oSHdkRvb76J1SuaRLmjkHCnAAoKU1l1oJbsNsLadRgOP17PY9OV7NzOQkhhLcU2uxcO2sOWeUVDEyI55WB/TBU08TAVc1qzWPbru/YufsnnK7yyvv1ejPNm1xA986P+NTruYS5Y5AwJ8orspm94BbKK7KJi+nNkP5voNcbvV2WEELUCLeqct/CxazIyqZdRDgfnTMEf0OtnLHshFwuGzl568nav4qs/SspLNoJQJOGo+jb81mfCXR1cm1WIaqTzV7EvH/vobwim6iIjgzq94oEOSFEvfLJ5q2syMom1Gzm5QH96mSQAzAY/IiP7VM5D11O7gbmL76XlH1/gwJ9e/hOoDueutGWKkQVcrls/LP4AUpK9xIa0kyW6RJC1DvLMrOYlrQFBXihX29iLQHeLqnKREd1ZtiAdzAY/ElJ/Zvlqyehqm5vl1WtJMyJekVV3SxeMYG8gs1YAmIZNvBdzOYQb5clhBA1JqusnKeWrkAD7ujYnt5xx56Qvy6LjurM0AFvYzD4k5w6kzUbXvV2SdVKwpyoNzRNY/X6V0jPXITJGMSwge8Q4B/l7bKEEKLGON1uHl+yjGKHg37xcdzUvq23S6o2MVFdGDrgLRRFz849v1BhzfN2SdVGwpyoN7Zs/4Kde35CpzMyuP/rhAQ38XZJQghRoz5K2lK5VNfkvr3Q1eIpSKpCTFRXEuMHoGlukvf+5e1yqo2EOVEvpOybzfqkdwHo32syMVFdvFyREELUrPU5uXy+ZRs6RWFy396EmH1vLrZjadbkAgD27P0DX53AQ8Kc8Hl5BVtYvvo5ALp1eoBGDc7xckVCCFGzSh0Onl7m6Sd3Y9s2dImuP11MEmL74u8XSUnpPnLzNnq7nGohYU74tAprHguXPoLbbad507G0aXm1t0sSQoga9/LqdWSVV9A2PIxbO/r+0leH0+kMNG08BoDdKb97uZrqIWFO+Cy3286/Sx/Bas0lOrIzPbs8VquXqBFCiOowe+8+Zu5NxU+v5/l+fTDWkRUeqlLzJucDkJo+D6ez/CRb1z317zcq6gVN01ix5oXKKUgG9n1ZJgUWQtQ7WeXlTFm1BoCHunWmUXCQlyvyjuCgRkRHdsHlsrI3ba63y6lyEuaET9qy/QuSU2ei1/sxuP/r+PvJ0m1CiPpF1TQmLl9FmdPJwIR4xjVv5u2SvKr5wYEQKX94uZKqJ2FO+JzkvTMqR6726zWJ8NCWXq5ICCFq3tfbdrB2fw7hfmae7t2j3nczadjgHIwGC7n5myguSfF2OVVKwpzwKZnZK1h2YORq984P0ShxmJcrEkKImrejoJD3NiYBMLF3T8L9/LxckfcZDf40ajgCgN3JvjUQQsKc8Bn5hdv5d9ljaJqbtq2upU3Lq7xdkhBC1Diby8WTS1fgUlUubdGc/gnx3i6p1qi81Jr6F2633cvVVB0Jc8InlJals2Dx/bhcFTRuOJKuHe/1dklCCOEVb2/YREpJCY2Dg7i/aydvl1OrRIa3Jzy0FXZ7EXv3zfF2OVVGwpyo88rKM5m78E5stnxio3vSt8dEFEWe2kKI+mdRegbf79iFXlF4vl9v/A0Gb5dUqyiKQusWVwCwbdd3PrMihLzjiTqtvDybOQvvoLwii8iIDgzqJ1OQCCHqp/0VFTy7YhUA93TuSJtwGcV/LI0bjsBsDqOwaCc5eRu8XU6VkDAn6qzyimzmLLyd8vJMIsLbMWzAO5iMgd4uSwghapxbVXlq6QqK7Q76xsVyTZtW3i6p1tLrzbRsOg6A7bume7maqiFhTtRJ5RX7mbvwDsrKM4gIa8s5A9/FZJIgJ4Sonz7dvJV1OblE+PkxqW8vdPV8GpKTadnsYhRFT1rGQsrLs71dzlmTMCfqnKLiZGYtuInSsnTCw1ozbNC7mEz1c1ZzIYRYtz+HaZu3ogDP9+st05CcgoCAaBolDkPT3OzY85O3yzlrEuZEnZKTt4HZ/9xCRcV+oiI6cs7A9zCbgr1dlhBCeEWe1cqTS1egaho3tWtLz9gYb5dUZxwcCLEr+VdcLpuXqzk7EuZEnbEvYyHz/r0bh6OExPiBnDPofczmEG+XJYQQXmF3u3lk0VJyrFY6R0VyW8d23i6pTomM6EBEeFscjmJS9s3ydjlnRcKcqPU0TWPrjm9YtOwx3G47LZqOZVDflzEY5FKCEKJ+0jSNKavWkJSXT2xAAK8M7IdBJ2/pp+OIaUp2fouqur1c0ZmT37yo1RzOMhYtH8/ajW+gaSqd2t1Or24T0Olk7iQhRP319fYd/JW8Fz+9ntcH9Zd+cmeoUeJwAgJiKC5JJjV9rrfLOWMS5kStVVS8h7/nXc++9PkYDRYG9X2Zju1urfeLRQsh6relGVm8vX4TAM/17UWr8DAvV1R36fVGOra9FYCNmz9GVV1erujMSJgTtY6qutmx+0dmzruektJUQkOaM3r4lzRMHOrt0oQQwqtmpuzlscVLUTWN2zu0Y1jDBt4uqc5r1ngMQYGJlJbtIzl1prfLOSMS5kStkl+wjVkLbmTVuqm43TaaNhrNqGGfExzUyNulCSGE19jdbqasWsPTy1Zic7u5sFkTbukgAx6qgk5noGO72wDYtGUabrfTyxWdPul4JE5JcUkKqWnzcKtOFEWHggKKgoIOFFDQVa6H6rlVUBTlyM/RHdhHgcOPceDz/MKt7NzzM5qmEuAfTfcuD9MwYahcVhVC1GuZZeU8tngp2woKMep0PNa9K2ObN5XXxirUuMG5bN72OcUlyexO+Z1WzS/xdkmnRcKcOKGi4mSStn7C3rS5QPUvSKwoetq0vJpO7W7DaLRU+/mEEKK2srpcfLNtB19s3U6Fy0VCoIWp/fvSJkLWXK1qOp2eTu1vZ9Gyx0na9inNGo+pUzMmSJgTx1RalsH6pHdJTZsHaOh0Bpo2Og+LJQ40DQ3Nc6uphz5HrXxM01Q0TQU4cKuiaRy4VT2x8D/b6PVmWja/hPDQlt75poUQohZQNY0ZKXt5f0MSOVYrAMMaJPJUrx4Em01ers53NUwYQnhoKwqKdrBzz8+0bXW1t0s6ZRLmxBFU1c32XdPZsPkD3G4bOp2R5k0uon2b67EExHq7PCGE8Gmrs/fzxroN7CgsAqBVWCgPdu1MD1nZodopio5O7e/knyUPsGnrNBo1OAdLQN34uUuYE5UKi3azfM1z5BdsBaBxgxF07XR/nXkyCyFEXZVSXMLb6zeyKCMTgGh/f+7u3IHRTRqjk75xNSYhrh8Jcf3JyFrC8tWTGTbwnTrRN1HCnMDuKGHz1v+xbdd3aJqbAP8YenUbT2L8AG+XJoQQPi2nooJPN2/l193JuDUNf4OBG9q25uo2rfA3yFt0TVMUhd7dn+LP2ZeTtX8FO/f8XCcGQ8gzpR5TVRc79/zExi3TcDiKAYWWzS6lS8e7MRkDvV2eEEL4rAKbjc+3bOPHnbtxqCo6RWFc86bc3rE9kf7+3i6vXgvwj6R3tycOrD70JnExvQgOqt3z+UmYq4dU1cXetLkkbZ1GSek+AGKiu9O90wOEh7X2cnVCCOG7ciusfLdjJ9/v2IXN7VkLdFiDRG7v2J5moSFerk4c1KjBOTTOGMnefbNYtmoiI4ZMQ6fTe7us45IwV4+43XZ2p/zJ1h1fUVaeAUBwUEO6dryfxPiBdaJfgBBC1EV7ior5att2/t67D5fqGcU/MCGe2zu2p7Usx1Ur9ez6GPtz15Kbv4nN2z6jY7tbvF3ScUmYqwdKStPYs/dP9qT8jtWWD0BQYEPatb6OZo3HyKL1QghRDRxuN/+mZ/L7nmSWZ2UDoFMUhjVM5Pq2rWkXEeHlCsWJmE3B9O0xkfmL7mHjlg8BjQ5tb6mVDR/yLu6j7I4S0jMXsSflD/bnrqu8Pyy0Je3b3EjDhKG1uslYeI/T7SbHaqXAZqPC6cLmdmN1uXC4VQw6BYOiw6DTYdAp6BXPrUGnQ68oR00rrWla5X3qgc/d/2/vzsPbqO88jr9npJHk+4jt2HGck5ADSCDkIglngULC0eUI0FJa2vJQlna35SiUdruk5SjLQp+yUKBNu0C5whFK2kBKSwllQ8IVSAgJuSCXk/iI40u2JY1m9g/Jih07J7ElxZ/X8+jRaDTHVx5Z85nfXK6L47ix5/gj6rqYBuT4fOT4LHJ9PnJ9PrIsS2fySVoJR6OsrtvFa5s28+rnm2gIhwHwezxcMGwoXxs9koocHZOcLgaUTmHyibfx7rJfsvyTR2ltq2XiCT9KufVnyoa5YHOQp+fM5dOP15KVk8UFs2YwYeqJXYZzXZf5c//C22++A8DUUydzwWXnpWRy7kmOY1PfsJ7KHUvYtn0xNTtXJC7I6/EEGFxxJkcNOZ+S4vF97m/T17iui+04RByHoG3TGrEJ2hFaIjYtth1/jtAcibCrLRR7hELsbG2lqqWVnW1tyf4ICaZhkG1Z5PgscuIBr2PYi/WzyLF8ZPssMr1eMrxeMi0vAa+XTK+XgMeDx9z3bajbA6Xrdg6ZttMeNh2ijovdodtxXex4d9R1iToOdns4dWLD2R2626eXGD8ec+M3uMMwiHcRv0Ve/L32bsPAMs3EI+D1UuD3kx/wk+/3Y+3nM+5Nq21THwpR29rG9uYg24KxR0vEJtuyyPZZZFsWeX4fRRkZFGUEKMrIoNDv3+/fNVlabZuallZ2tLRQ3dJCdUsrzZHu77eZ+Jt37NfNT6SJgcc0MA0DjxHbgGnv9hgG24MtLK+tZfXOOsLx3agAI/LzuHD4MM4dOph8v/+wfUbpPUcPv4hAoJC3ltzG2g0v0tZWx/Qpd+DxpM7yTNkw99zj8/B4vdz10Gy2bqrkkfvmUD6onLKBnS9cu/iNJaz4YCW33nkTBvDQPY/Sr7gf0780NTmFfwGu6+I4YaLRCFEnRDQaxrZbiNgtRCJBbLuViB3EjsT6hSNNNDZtoqFxI03NW3DdaGJahuGhf8kEhg46hyEVZ+nWWAehfYUejYei2Eo8/hxfYduOkwhMEcchHI3Gnx3CTpRI+7PjxLtjw9lRB9t1EuPb8Wknup32990Ow3SeZ7fjdBgm6n6x266ZhkFxfIWdEQ9HGV4PlumJBRRn93w61hR1ndhqMb4mbF8fdgwkpmF0eng6PEddl6ZIhKZwmMZQmKZwmKBt0xgO0xgOA8FD/kx+jwefx8Rx6dQa2L6sjwRZXi+ZlkWmFQuxfo8HjxG7L7LHMHBcl3A0Sij+fQ1GYiEuFI3uf+LdMA2DAr8/Ee76BQLk+Czy/P5OgTvX5yPPH2tltUwTn8eDzzQPaKMy6sT+d2zHocW2aQiFaAjFvg8NoTAN4TANoRCN4TA7W9uoige39tawZBmam8uk0hLOHz6UUQUF2oA+AgwqP40zT32QN/7vBjZXvsHLr15MRflpDBxwCv2Lxyf9cCXDdVPvlyzUFuKW7/6U2+6+mZKyEgCeeOQp8gryuPCy8zoNe//sB5h88kSmnXESAEsWLeXtRUu58fYf7HMeG7fXMaSs5+5v99L7f6Ry4/MYOICLgYvhuhg4xHZGxbpNbEzXxiSKB/uQ5+di0GLkU2sOo9ozglrPcCJG1/vKdbe03W7uudqlz6GOdxDzPNTx9hyu+yl3P153Ie1IWLl74q04sRW7RZYVC2VZlhV/jnW3t+wU+gMUBvyUZGZSlBHAmyItLrbj0Nwe8MJhGsMRmkLhRMBrCkdoisS6m8MRWm2bVjvWAhnrju0iPhAdg0972PQasdYYjxHbjRzrjr82d7fQtPczzfg4hoHH3Mc4holhxL+Vbux73f6tc93d3/OO3e0te+0bEC22TX1bG7tCIepDYZxD/N5apkmB309hRoABWVmUZWUyIDuLbMsiGLETf/+GUJja1lZqWlupbY3N94uwTBNfe7jzmICB7cQ2gsJR5wv9L3pNk/6ZGfTPzKQk/pxtWV1C1cGs/tpbbBMtsa6T2OiLOg75fj9ji4o4tqiQPLXAHbF21a9n0eIbEycRAlhWNuVl05k26fYeDXX7yi0p2TJXvaMG02MmghxAecUA1n+6ocuw2yt3UD5owO7hBpWzvbKq2+ku/scSFi9aAsCMr10CPRjmgq07yXO2HfR4tushiocoXmw8RPARcS3C+IjgI4xFxPURxkcYPw1uPnVuIbsoxMbqMKVw/CGHYveKvPOxYYljxOLPlseTWClZ8RYHy2PiM2P9Y92xFZY3vnus/XizjseeeU2z62vTxGt06O40zp7jdR7nSGkJ8Jom+X7/F9o95bguoWiUUDTaKajt2TqYzhzXJRiJdNqNHrKjsdZHYkHEINZC2d5Kmem1yPf7yPB6D+n7EnEc6traqG0Pd22hRMhujLeudnzdErEJd2jFbn8E9xO22/+3Mrwe8nx+8vy7W/sSr/0+Cvx+SjMz6Z+VSb7fn/bLVFJTQf5RfGXGS9TuXMmWbf9k67Y3aWj8nKbmzUltnUvJMBcKhQlkdG5VCmQGaGvruiUYagsRyAx0Gi7UFsJ13S4/UNPOOCnRgrdxe10PVL7bl46bRWXFFAxMMOK7FAwPBiaGYUL82TQtTNOHafowTC9GfIt9T90d19GdPcc90ONBDmT6Bzped1Pq/nfV2MerLzjPAxyv48H77UHIEz8WRo4MpmEkdhcfqUzDiJ880ns3YbdMk/6ZmfTPzDzocR3X7XR4QigaBRcsj5nYFXukbZjIkcMwTIqLxlJcNJbxY79HY9NmQuGGpNaUkr9ufr+PttbOB2G3tYYIBLpunfsD/k7DtrW24Q/4k/4DUJY/gLL8AfsfUESkjzENI9FKKJLucnMGJbsEUuPAmD2UlBbjRB2qd9Qk+lVu3kbpHic/AJSVl1K5eVun4crKdWN4ERER6RtSMsz5A37GTTiOBS8uJNQW4rO1n/PxspVMmjahy7CTpk/gjYVvUl9XT8OuBv7x6iImnzwpCVWLiIiI9L6UPJsVYteZe+p3c1mzci1ZOZlcMGsmE6aeyPo1n/Hwvb/lvjm/BGJnI7387F9Y8uZSAE46dQoXXr7/68z19NmsIiIiIofLvnJLyoa5nqYwJyIiIuliX7klJXezioiIiMiBUZgTERERSWMKcyIiIiJpTGFOREREJI0pzImIiIikMYU5ERERkTSWkrfz6g0RO9rj92cFaG5sJjs3u8fnIwdOyyT1aJmkJi2X1KNlkpp6Y7lE7Ohe3+uzYW5ERXGvzOe/Hn6MH/38hl6ZlxwYLZPUo2WSmrRcUo+WSWpK9nLRblYRERGRNKYwJyIiIpLGFOZ62LTTTkp2CbIHLZPUo2WSmrRcUo+WSWpK9nLps/dmFRERETkSqGVOREREJI0pzImIiIiksT57aZKeFmwO8vScuXz68VqycrK4YNYMJkw9Mdll9Wlv/u0t3nnrPbZv2c74KeP5+rVXJLukPi8SsXnusRdY88k6WoItFJX04/xZMzlm3Ohkl9anPf7wk6z9ZB3hUJic/FzOnHk6U0+bkuyyBKjeUcPdt93L8RPH8o3rrkx2OX3er+98iI0bNmGasbax/II8/uPeH/d6HQpzPeS5x+fh8Xq566HZbN1UySP3zaF8UDllA0uTXVqflZefx5cvOItPP15DOBxJdjkCONEoBf3y+fefXE9Bv3xWLV/N/z74BD++62b6FRcmu7w+6+zzz+Sr37kcy/KyY1sVD9z1GwYOLmfQ0Ipkl9bnPf/4i1oOKebSqy5K+saOdrP2gFBbiOXvreC8i8/BH/AzfOQwjht/DO8ufj/ZpfVpx08cy7gJx5GVnZnsUiTOH/Az46Jz6FdciGmaHHvCMfQrLmTLxi3JLq1PKxtYimXFtvUNw8AAaqt3Jrco4YMlH5KRmcHIY0YkuxRJMWqZ6wHVO2owPSYlZSWJfuUVA1j/6YYkViWS+hobmqjeUUNpuVqwk23uYy/wzlvvEQlHGDi4XLu+k6y1tY0F8xby/R9fx5JFS5NdjnTw5+cWMH/uAkrKijn/0hmMGH1Ur9egMNcDQqEwgYxAp36BzABtbaEkVSSS+qJ2lMcffpLJ0ydQOqB/ssvp8y775iVcetVFfL5uI+tWb8Dr1eoimRa88ConnTqJgsL8ZJciHVx42XmUlvfH4/WybOmHPHr/77nljhsp7l/Uq3VoN2sP8Pt9tLW2derX1hoiEPAnqSKR1OY4Dk888hRej4dLr7o42eVInGmaDB85jPpd9bz1+uJkl9Nnbd1UyZpP1nL6OacmuxTZw5CjBhPICGBZXiafPJFhI4awavnqXq9Dm1o9oKS0GCfqUL2jhpLSYgAqN2+jVCc/iHThui5Pz5lLU2MT373pGjxeT7JLkj04UUfHzCXRutXrqavZxc9+8Asgdly26zjcU3kft9xxY5Krk04Mg2Tci0Fhrgf4A37GTTiOBS8u5KvfnkXl5m18vGwlN/zs35JdWp8WjUZxog6O4+C6DpFwBNNj4vEoPCTT3MdeoGpbFd+79Tp8Pl+yy+nzmhqaWLtqPceeMAbLZ7Fm5Vo+WPIh37xel8FIlmmnn8SJU05IvH79lUXU1dYx65uXJLEqaQm2smnDJo4aNRzTY7Js6Uds+PQzLrnyK71ei27n1UOCzUGe+t1c1qxcS1ZOJhfMmqnrzCXZK/MW8upLr3Xqd+6/nM2Mi85JUkVSV1vHf/7wDryWN3GdJoDLr76UidP0/5IMTY3N/OGBx6jcsg3XcSkoKuDUs09m2um6J2iqeGXeQmqqanWduSRramzmkf/+HVXbqzFNg/5lJcy8+FxGHTey12tRmBMRERFJYzoBQkRERCSNKcyJiIiIpDGFOREREZE0pjAnIiIiksYU5kRERETSmMKciIiISBpTmBMRERFJYwpzIiIiImlMYU5EREQkjSnMiYiIiKQxhTkRERGRNKYwJyIiIpLGFOZERERE0pjCnIiIiEgaU5gTERERSWMKcyIiIiJpTGFOREREJI0pzIlIn7L0n+9y43duTdr8W4It3Hb9z6ipqj1s07z3P3/FR+8tP2zTE5H04k12ASIih8v3v37DPt+fNH0il119MceMG91LFXX12vy/M2bcaIr7Fx22aZ5z4dm89PTLjD3xOExT2+gifY3CnIgcMe78n9sT3Ss/WsUzv3+uUz/LZ+Hz+fD5fL1fHBAOhXl70Ttce8O3D+t0jzl+NM/84TlWrfiUY48fc1inLSKpT2FORI4Yufm5ie6MzIwu/SC2m/X5J+Zx35xfAvDKvIV89O4KvjTzdF6Zt5DmxiAnTB7H5d+6lLcXvcPf/vw64XCYydMn8pUrzk+0fNm2zYIXXuX9t5cRDLZQVl7KeZecy+ixo/Za3yfLV2MYMOzooYl+61av54G7fsPdv/k52TnZAOysqeP2G+7g5tk/ZNCwCqJ2lJeefpkP31tBS3OQ7NwcJkwdz4WXnQeAaZocM240HyxZpjAn0gcpzIlIn7ezto4Vy1Zy7Q3foWFXA3MeeIzG+kZy83O5/kfXUrW9ij88+ATDjh7C8RPHAfDUb5+ltrqWb/zrleQX5vPJ8lU8ev/vuWn2Dxg4uLzb+WxY8xkVQyowDOOg6lv02lss/2AlV1//dQqLCqmvq6d6R02nYQYPG8Rf5//90P4AIpLWdHCFiPR5ruNw5TWXM6CijNFjRzFm7Ci2bNzK5d+6lNLy/oybMJZhI4aydtV6AGqqavlg6Ydc/b1vcNSo4RSV9OPUs05mzLjRLH5jyV7nU1e7i7yC3L2+vze7ausoKS1m+MhhFBYVMOzooUw5ZVKnYfIKcmnY1UA0Gj3o6YtIelPLnIj0eQX9ChK7ZQFycnMoLi3G6939E5mTl0NzYzMAWzduxXVd7rz1nk7TsW2bo8eM2Ot8IpEIuVb2Qdc3+ZRJPHjPI/zi5rsZdexIxhw/mjFjR3U62cGyLFzXxY7YeDyeg56HiKQvhTkR6fO6hB+jaz8DcFwX4s+GYXDz7B/i8XbewWFZ1l7nk52dRUuwdb/1OI7T6XXFkIHMvv+nrP54DWs/WceTjz5D+aABXH/LtYlAFwy2YFle/AH/fqcvIkcWhTkRkYNUMbgc13VpbGjcZ0vcngYOLuedt97r9r2mhubdJ0BU7+zyfiAjwAmTxnHCpHFMPnki983+NbVVtZSUlQCwfesOBg4ZeAifRkTSnY6ZExE5SCVlJUyYOp4nf/ssH767nNrqnWz+bAuvL3iDj95bsdfxRo8dyY5tVQSbgl3emz/3L+yorGLTZ5uZ//wCACo3VxJqC/GPVxfx/pJl7KisoqaqhveXLCOQESC/MD8x/oY1nzFmH2fSisiRSy1zIiKH4MprruCv8//Gy8/+mfq6BjKzMxk8bBAjxhy113EGVAxg8PBBfLD0Q045a3qn9wYOGcivfvEAhmEy8+JzCAT8zH/uFUYeezT+gJ/XF7xBTVUtBrEWvutuugafP3a9vPq6ej5ft5GrrvtaT35kEUlRhuvGDwIREZEet2rFal7845/4yT23YJpmt9eZO1h/emY+rS1tXPHtWYe5WhFJB2qZExHpRWPGjqb6zBrq6+opLCo8LNPMzs3mjBmnH5ZpiUj6UZgTEellp335lMM6vTNnnnFYpyci6UW7WUVERETSmM5mFREREUljCnMiIiIiaUxhTkRERCSNKcyJiIiIpDGFOREREZE0pjAnIiIiksb+Hzym0yGJxrVeAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "state_evolution = optimization_result[\"output\"][\"state_evolution\"][\"value\"]\n",
    "states = np.reshape(state_evolution, [-1, transmon_dimension, cavity_dimension])\n",
    "\n",
    "population_target = np.abs(state_evolution @ np.conj(target_state)) ** 2\n",
    "\n",
    "population_00 = np.abs(states[:, 0, 0]) ** 2\n",
    "population_11 = np.abs(states[:, 1, 1]) ** 2\n",
    "population_others = 1 - population_00 - population_11 - population_target\n",
    "\n",
    "qctrlvisualizer.plot_population_dynamics(\n",
    "    optimization_result[\"output\"][\"sample_times\"][\"value\"],\n",
    "    {\n",
    "        \"Target state\": population_target,\n",
    "        r\"$|0_t, 0_c\\rangle$\": population_00,\n",
    "        r\"$|1_t, 1_c\\rangle$\": population_11,\n",
    "        \"Other states\": 1 - population_00 - population_11,\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that, although the dynamics populate other states during the evolution, the final state corresponds to the target entangled state, with an equal superposition of states $|0_t, 0_c\\rangle$ and $|1_t, 1_c\\rangle$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This concludes this tutorial.\n",
    "Congratulations on making it this far!\n",
    "\n",
    "You can extend the calculations you have done here by adding more transmons or cavities to the system, or with different terms/interactions in the system's Hamiltonian.\n",
    "You can find information on the terms available for the different subsystems and their interactions in the [reference of the superconducting classes](https://docs.q-ctrl.com/references/boulder-opal/boulderopal/superconducting).\n",
    "\n",
    "The functions you've just used to [simulate](https://docs.q-ctrl.com/references/boulder-opal/boulderopal/superconducting/simulate) and [optimize](https://docs.q-ctrl.com/references/boulder-opal/boulderopal/superconducting/optimize) have more options, such as creating filtered (smooth) pulses through a `cutoff_frequency` parameter, or optimizing target operations instead of state-to-state dynamics.\n",
    "Take a look at [their reference](https://docs.q-ctrl.com/references/boulder-opal/boulderopal/superconducting) to learn more.\n",
    "You can visit our [topics page](https://docs.q-ctrl.com/boulder-opal/toolkit) to learn more about Boulder Opal and its capabilities."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
