{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantum kernel methods using linear optics\n",
    "\n",
    "This notebook follows closely the theory and implementation of Yin et al, \"Experimental quantum-enhanced kernels on a photonic processor,\" *arXiv*, 2024. https://arxiv.org/abs/2407.20364 [1].\n",
    "\n",
    "Kernel methods are a class of algorithm in machine learning used for classification, clustering and regression. These methods exploit feature maps which project a dataset of interest to a higher-dimensional space. For classification tasks, the data is then linearly separable by a hyperplane in the new space. \n",
    "\n",
    "![Feature map transformation](../_static/img/kernel_transformation.png)\n",
    "\n",
    "Behind kernel methods is the kernel trick where the mapping is not explicitly calculated. Instead, the inner product between feature vectors is evaluated for each pair of points.\n",
    "$$0 \\leq \\kappa(x_{i}, x_{j}) \\leq 1$$\n",
    "\n",
    "For pairwise datasets, $x$ and $x'$, these inner products can be contained in the kernel matrix:\n",
    "$$ K_{i, j} = \\kappa(x_{i}, x_{j}') = \\kappa(x_{j}', x_{i}) $$\n",
    "\n",
    "In quantum kernel methods, we often consider the fidelity-based quantum kernel, which for an input feature map $U(x)$, is expressed as follows in the computational basis:\n",
    "$$ \\kappa(x_{i}, x_{j}) = |\\langle \\mathbf{0} | U^{\\dagger}(x_i) U(x_j) | \\mathbf{0} \\rangle|^{2} $$\n",
    "\n",
    "For identical input datasets, the kernel matrix is symmetric, positive-definite with 1s along the diagonal. Generally, we consider two types of kernel matrices. The symmetric training matrix in which the kernel matrix is constructed using two copies of the training set. And the test matrix in which the kernel matrix is constructed using the test set and the training set. For classification tasks, we can utilise a classical support vector machine whose parameters are optimised in order to linearly separate the data of interest.\n",
    "\n",
    "In this notebook, we will simulate a photonic processor to estimate a fidelity-based kernel. We then use this kernel function to classify an ad-hoc binary dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perceval for linear optics simulation\n",
    "from perceval import Circuit, BasicState, NoiseModel, Processor, PostSelect, pdisplay, Detector\n",
    "from perceval.algorithm import Sampler\n",
    "from perceval import catalog\n",
    "\n",
    "# Sci-kit learn for ML functionality\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Maths packages\n",
    "import numpy as np\n",
    "from scipy.linalg import sqrtm, inv\n",
    "\n",
    "# Matplotlib for plotting graphs\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "# Pretty typing\n",
    "from typing import Callable, Tuple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "Firstly, we consider an $m$-mode linear interferometer described by the matrix, $U$. We note that the probability of obtaining an output state, $|\\mathbf{t} \\rangle = |t_1, t_2, \\cdots , t_m \\rangle$, for a given input state, $|\\mathbf{s} \\rangle = |s_1, s_2, \\cdots , s_m \\rangle$ is:\n",
    "$$ |\\langle \\mathbf{s} | U | \\mathbf{t} \\rangle|^{2} = \\frac{|\\text{Per} \\left( U_{s, t} \\right)|^2}{s_1! ... s_m! \\ t_1! ... t_m!} $$\n",
    "\n",
    "where $\\text{Per}(U_{s, t})$ is the permanent of the matrix, $U_{s, t}$ which is constructed by repeating the $j^{th}$ column $t_{j}$ times, followed by repeating the $i^{th}$ row $s_{i}$ times [2]. First, we will define a function which will estimate the kernel associated with a given feature map, $U(x)$ and initial state, $| s \\rangle.$ \n",
    "$$ \\kappa^{(\\mathbf{s})}(x, x') = |\\langle \\mathbf{s} | U^{\\dagger}(x') U(x) | \\mathbf{s} \\rangle| ^ {2} = |\\langle \\mathbf{s} | U (x, x') | \\mathbf{s} \\rangle| ^ {2}$$\n",
    "\n",
    "We consider two possible kernels with indistinguishable and distinguishable photons. In the case of indistinguishable photons, we have the \"quantum kernel\":\n",
    "$$ \\kappa^{(\\mathbf{s})}_Q(x, x') = \\frac{|\\text{Per} \\left( U_{s, s} (x, x') \\right)|^2}{\\mathbf{s}!^2} $$\n",
    "\n",
    "where $\\mathbf{s}! = s_1! s_2! \\cdots s_M!$. We will also consider the kernel constructed using distinguishable photons. In the absence of indistinguishability, the photons do not undergo quantum interference. In this case, we have the \"coherent kernel\":\n",
    "$$ \\kappa_C^{(\\mathbf{s})}(x, x') = \\frac{\\text{Per} \\left( |U_{s, s} (x, x')|^{2} \\right)}{\\mathbf{s}!^2} $$\n",
    "\n",
    "In experimental circumstances, the experimentalist may not have access to photon-number resolving (PNR) detectors. In this instance, we consider the \"unbunching kernel\" in which there is maximum 1 photon found in each mode. In this circumstance, we lose the commutative property of the kernel function and the kernel matrix is not symmetric, positive definite for identical input datasets. We will force the kernel matrix to be symmetric by projecting the upper triangular onto the lower triangular. We will also force this matrix to be positive definite by decomposing the matrix spectrally:\n",
    "$$K = Q \\Lambda Q^{\\dagger}$$\n",
    "and setting the negative eigenvalues in $\\Lambda$ to $0$. The kernel matrix is then reconstructed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kernel(\n",
    "    x1, x2: np.ndarray, \n",
    "    feature_map: Callable[[np.ndarray], Circuit], \n",
    "    input_state: BasicState, \n",
    "    indistinguishability: float = 1.0, \n",
    "    unbunching: bool = False, \n",
    "    nshots: int = None\n",
    ") -> float:\n",
    "    \"\"\"\n",
    "    Estimate the kernel associated with a given feature map.\n",
    "    Args:\n",
    "        x1, x2               : Data inputs\n",
    "        feature_map          : Quantum circuit to perform feature map\n",
    "        input_state          : Initial state quantum feature map is applied to.\n",
    "        indistinguishability : Photon indistinguishability (Hong Ou Mandel)\n",
    "        unbunching           : Specifies whether to not use PNR detectors\n",
    "        nshots               : Number of circuit runs to estimate kernel\n",
    "    Returns\n",
    "        Scalar kernel value\n",
    "    \"\"\"\n",
    "    # Compile circuit to estimate kernel\n",
    "    U = feature_map(x1)\n",
    "    U_dag = feature_map(x2)\n",
    "    U_dag.inverse(h=True)\n",
    "    processor = Processor(\"SLOS\", U.m)\n",
    "    processor.add(0, U)\n",
    "    processor.add(0, U_dag)\n",
    "    processor.min_detected_photons_filter(input_state.n)\n",
    "    processor.noise = NoiseModel(indistinguishability=indistinguishability)\n",
    "    \n",
    "    if unbunching:\n",
    "        for m in range(processor.m):\n",
    "            processor.add(m, Detector.threshold())\n",
    "\n",
    "    # Apply settings to processor\n",
    "    processor.with_input(input_state)\n",
    "    sampler = Sampler(processor)\n",
    "    \n",
    "    if nshots is None:\n",
    "        # Exact probability calculation\n",
    "        prob_distribution = sampler.probs()\n",
    "        results = prob_distribution['results']\n",
    "        overlap = results[input_state]\n",
    "        return overlap\n",
    "    else:\n",
    "        # Estimated probability of input_state\n",
    "        sample_count = sampler.sample_count(nshots)\n",
    "        results = sample_count['results']\n",
    "        overlap = results[input_state] / nshots\n",
    "        return overlap\n",
    "\n",
    "def kernel_matrix(X1, X2, feature_map, input_state, indistinguishability = 1.0,\n",
    "                unbunching = False, nshots = None):\n",
    "    \"\"\"Estimates the kernel matrix associated with specified feature map and pairwise \n",
    "    datasets, X1, X2\"\"\"    \n",
    "    matrix_symmetry = np.array_equal(X1, X2) \n",
    "    K = np.ones((len(X1), len(X2)))\n",
    "    \n",
    "    for i in range(len(X1)):\n",
    "        # For symmetric matrix, calculate upper triangular only\n",
    "        start_j = i + 1 if matrix_symmetry else 0 \n",
    "        for j in range(start_j, len(X1)):\n",
    "            if not np.array_equal(X1[i], X2[j]):\n",
    "                K[i][j] = kernel(X1[i], X2[j], feature_map, input_state, indistinguishability, \n",
    "                                unbunching, nshots)\n",
    "                if matrix_symmetry:\n",
    "                    K[j][i] = K[i][j]\n",
    "                \n",
    "    # Make unbunching training matrix positive\n",
    "    if unbunching and matrix_symmetry:\n",
    "        eigenval, eigenvec = np.linalg.eig(K)\n",
    "        K = (eigenvec @ np.diag(np.maximum(0, eigenval)) @ eigenvec.T).real\n",
    "        np.fill_diagonal(K, 1)  # Ensure diagonals are equal to 1\n",
    "    return K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will consider an ad-hoc dataset in which the geometric difference between quantum and coherent kernel matrices is maximised. For a known unitary as feature map, Ref. [1] presents the following algorithm:  \n",
    "\n",
    "***  \n",
    "**Input**  \n",
    "- Feature map, $U$ with $m$ modes,  \n",
    "- Dataset size, $N$,  \n",
    "- Feature dimension, $d$,  \n",
    "- Number of input photons, $n$,  \n",
    "- Initial state, $s \\in \\Phi_{m, n}$,  \n",
    "- Regularisation parameter $\\lambda \\neq 0$  \n",
    "\n",
    "**Output**  \n",
    "- Dataset, $\\{x_i, y_i\\}_{i=1}^{N}, \\ x_i \\in \\mathbb{R}^{d}, \\ y_i \\in \\{1, -1 \\}$  \n",
    "\n",
    "***  \n",
    "1   Generate $N$ random datapoints, $\\{x_i\\}_{i=1}^{N}, x_i \\in \\mathbb{R}^{d}$  \n",
    "2 &nbsp; **while** $0 < i \\leq N$ **do**  \n",
    "3 &nbsp; &nbsp; &nbsp; **while** $0 < j \\leq N$ **do**  \n",
    "4 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; $K_{Q}(x_i, x_j) \\leftarrow |\\text{per}\\left(U(x_i, x_j) \\right)|^{2}$  \n",
    "5 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; $K_{C}(x_i, x_j) \\leftarrow \\text{per}\\left(|U(x_i, x_j)|^{2}\\right)$  \n",
    "6 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; $j \\leftarrow  j + 1$  \n",
    "7 &nbsp; &nbsp; &nbsp; **end while**  \n",
    "8 &nbsp; &nbsp; &nbsp; $i \\leftarrow  i + 1$  \n",
    "9 &nbsp; **end while**  \n",
    "10 &nbsp; $S \\leftarrow \\sqrt{K_Q} (K_C + \\lambda \\mathcal{I})^{-1} \\sqrt{K_Q}$  \n",
    "11 &nbsp; $g \\leftarrow$ max eigenvalue of $S$, $\\mathbf{v}_{g} \\leftarrow$ max eigenvector of $S$  \n",
    "12 &nbsp; $y \\leftarrow \\text{sign}(\\sqrt{K_Q} \\mathbf{\\mathbf{v}_{g}})$  \n",
    "\n",
    "***  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_data(\n",
    "    feature_map: Callable[[np.ndarray], Circuit], \n",
    "    input_state: BasicState, \n",
    "    data_size: int, \n",
    "    data_dim: int, \n",
    "    reg: float\n",
    ") -> Tuple[np.ndarray, np.ndarray]:\n",
    "    \"\"\"\n",
    "    Generates ad hoc dataset for choice of feature map and initial state.\n",
    "    Args: \n",
    "        feature_map : Unitary to generate the dataset\n",
    "        input_state : State on which the feature map acts on\n",
    "        data_size   : Dataset size\n",
    "        data_dim    : Feature dimension\n",
    "        reg         : Regularization parameter\n",
    "    Returns:\n",
    "        Data features, Data labels\n",
    "    \"\"\"\n",
    "    # Generate features dataset\n",
    "    X = np.random.uniform(0, 2, size=(data_size, data_dim))\n",
    "\n",
    "    # Construct quantum and coherent kernel matrices\n",
    "    K_quantum = kernel_matrix(X, X, feature_map, input_state, indistinguishability=1)\n",
    "    K_coherent = kernel_matrix(X, X, feature_map, input_state, indistinguishability=0)\n",
    "    \n",
    "    S = sqrtm(K_quantum) @ inv(K_coherent + reg * np.eye(data_size)) @ sqrtm(K_quantum)    \n",
    "    eigenvals, eigenvecs = np.linalg.eig(S)\n",
    "    v_g = eigenvecs[:, np.argmax(eigenvals)]\n",
    "    vector = sqrtm(K_quantum) @ v_g\n",
    "    \n",
    "    # Assign labels\n",
    "    y = np.where(np.real(vector) >= 0, 1, -1)\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will generate a feature map which encodes each component of a feature vector into the phase shifters of our linear optical set-up. Specifically, we let each pair of components be encoded into the phase shifters of a Mach-Zender interferometer (MZI): $\\phi_i \\rightarrow 2 \\pi x_i$.\n",
    "\n",
    "Each MZI is arranged in a brickwork pattern as shown in the figure below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "     width=\"2383.75\" height=\"406.25\" viewBox=\"-28.5 0 1907.0 325.0\">\n",
       "<defs>\n",
       "</defs>\n",
       "<path d=\"M10,25.0 L25,25.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
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       "<path d=\"M27,2 L323,2 L323,98 L27,98 Z\" stroke=\"black\" fill=\"lightblue\" stroke-dasharray=\"1,2\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"29\" y=\"105\" font-size=\"8\" text-anchor=\"start\">MZI</text>\n",
       "<path d=\"M25,25 L75,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M30,40 L39,40 L53,10 L44,10 L30,40 L39,40 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"47\" y=\"38\" font-size=\"7\" text-anchor=\"start\">Φ=0</text>\n",
       "<path d=\"M25,75.0 L75,75.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M75,25 L103,25 L122,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
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       "<path d=\"M128,56 L147,75 L175,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M100,43 L150,43 L150,57 L100,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"125\" y=\"85\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"125\" y=\"26\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M100,43 L150,43 L150,47 L100,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M143,50 L153,50 L153,60 L143,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"148\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M175,25 L225,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M180,40 L189,40 L203,10 L194,10 L180,40 L189,40 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"197\" y=\"38\" font-size=\"7\" text-anchor=\"start\">Φ=2*pi/15</text>\n",
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       "<path d=\"M250,43 L300,43 L300,47 L250,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M293,50 L303,50 L303,60 L293,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"298\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M27,102 L323,102 L323,198 L27,198 Z\" stroke=\"black\" fill=\"lightblue\" stroke-dasharray=\"1,2\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"29\" y=\"205\" font-size=\"8\" text-anchor=\"start\">MZI</text>\n",
       "<path d=\"M25,125 L75,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M30,140 L39,140 L53,110 L44,110 L30,140 L39,140 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"47\" y=\"138\" font-size=\"7\" text-anchor=\"start\">Φ=4*pi/15</text>\n",
       "<path d=\"M25,175.0 L75,175.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M75,125 L103,125 L122,144\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M128,144 L147,125 L175,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M75,175 L103,175 L122,156\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M128,156 L147,175 L175,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M100,143 L150,143 L150,157 L100,157 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"125\" y=\"185\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"125\" y=\"126\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M100,143 L150,143 L150,147 L100,147 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M143,150 L153,150 L153,160 L143,160 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"148\" y=\"157\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M175,125 L225,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M180,140 L189,140 L203,110 L194,110 L180,140 L189,140 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"197\" y=\"138\" font-size=\"7\" text-anchor=\"start\">Φ=2*pi/5</text>\n",
       "<path d=\"M175,175.0 L225,175.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M225,125 L253,125 L272,144\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M278,144 L297,125 L325,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M225,175 L253,175 L272,156\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M278,156 L297,175 L325,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M250,143 L300,143 L300,157 L250,157 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"275\" y=\"185\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"275\" y=\"126\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M250,143 L300,143 L300,147 L250,147 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M293,150 L303,150 L303,160 L293,160 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"298\" y=\"157\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M27,202 L323,202 L323,298 L27,298 Z\" stroke=\"black\" fill=\"lightblue\" stroke-dasharray=\"1,2\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"29\" y=\"305\" font-size=\"8\" text-anchor=\"start\">MZI</text>\n",
       "<path d=\"M25,225 L75,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M30,240 L39,240 L53,210 L44,210 L30,240 L39,240 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"47\" y=\"238\" font-size=\"7\" text-anchor=\"start\">Φ=8*pi/15</text>\n",
       "<path d=\"M25,275.0 L75,275.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M75,225 L103,225 L122,244\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M128,244 L147,225 L175,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
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       "<path d=\"M100,243 L150,243 L150,257 L100,257 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"125\" y=\"285\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"125\" y=\"226\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M100,243 L150,243 L150,247 L100,247 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M143,250 L153,250 L153,260 L143,260 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"148\" y=\"257\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M175,225 L225,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M180,240 L189,240 L203,210 L194,210 L180,240 L189,240 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"197\" y=\"238\" font-size=\"7\" text-anchor=\"start\">Φ=2*pi/3</text>\n",
       "<path d=\"M175,275.0 L225,275.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M225,225 L253,225 L272,244\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M278,244 L297,225 L325,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M225,275 L253,275 L272,256\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M278,256 L297,275 L325,275\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M250,243 L300,243 L300,257 L250,257 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"275\" y=\"285\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"275\" y=\"226\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M250,243 L300,243 L300,247 L250,247 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M293,250 L303,250 L303,260 L293,260 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"298\" y=\"257\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M327,52 L623,52 L623,148 L327,148 Z\" stroke=\"black\" fill=\"lightblue\" stroke-dasharray=\"1,2\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"329\" y=\"155\" font-size=\"8\" text-anchor=\"start\">MZI</text>\n",
       "<path d=\"M325,75 L375,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M330,90 L339,90 L353,60 L344,60 L330,90 L339,90 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"347\" y=\"88\" font-size=\"7\" text-anchor=\"start\">Φ=4*pi/5</text>\n",
       "<path d=\"M325,125.0 L375,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M375,75 L403,75 L422,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M428,94 L447,75 L475,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M375,125 L403,125 L422,106\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M428,106 L447,125 L475,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M400,93 L450,93 L450,107 L400,107 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"425\" y=\"135\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"425\" y=\"76\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M400,93 L450,93 L450,97 L400,97 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M443,100 L453,100 L453,110 L443,110 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"448\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M475,75 L525,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M480,90 L489,90 L503,60 L494,60 L480,90 L489,90 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"497\" y=\"88\" font-size=\"7\" text-anchor=\"start\">Φ=14*pi/15</text>\n",
       "<path d=\"M475,125.0 L525,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M525,75 L553,75 L572,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M578,94 L597,75 L625,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M525,125 L553,125 L572,106\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M578,106 L597,125 L625,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M550,93 L600,93 L600,107 L550,107 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"575\" y=\"135\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"575\" y=\"76\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M550,93 L600,93 L600,97 L550,97 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M593,100 L603,100 L603,110 L593,110 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"598\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M325,25.0 L625,25.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M327,152 L623,152 L623,248 L327,248 Z\" stroke=\"black\" fill=\"lightblue\" stroke-dasharray=\"1,2\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"329\" y=\"255\" font-size=\"8\" text-anchor=\"start\">MZI</text>\n",
       "<path d=\"M325,175 L375,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M330,190 L339,190 L353,160 L344,160 L330,190 L339,190 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"347\" y=\"188\" font-size=\"7\" text-anchor=\"start\">Φ=16*pi/15</text>\n",
       "<path d=\"M325,225.0 L375,225.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M375,175 L403,175 L422,194\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M428,194 L447,175 L475,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M375,225 L403,225 L422,206\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M428,206 L447,225 L475,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M400,193 L450,193 L450,207 L400,207 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"425\" y=\"235\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"425\" y=\"176\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M400,193 L450,193 L450,197 L400,197 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M443,200 L453,200 L453,210 L443,210 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"448\" y=\"207\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M475,175 L525,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M480,190 L489,190 L503,160 L494,160 L480,190 L489,190 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"497\" y=\"188\" font-size=\"7\" text-anchor=\"start\">Φ=6*pi/5</text>\n",
       "<path d=\"M475,225.0 L525,225.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M525,175 L553,175 L572,194\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M578,194 L597,175 L625,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M525,225 L553,225 L572,206\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M578,206 L597,225 L625,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M550,193 L600,193 L600,207 L550,207 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"575\" y=\"235\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"575\" y=\"176\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M550,193 L600,193 L600,197 L550,197 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M593,200 L603,200 L603,210 L593,210 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"598\" y=\"207\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M627,2 L923,2 L923,98 L627,98 Z\" stroke=\"black\" fill=\"lightblue\" stroke-dasharray=\"1,2\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"629\" y=\"105\" font-size=\"8\" text-anchor=\"start\">MZI</text>\n",
       "<path d=\"M625,25 L675,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M630,40 L639,40 L653,10 L644,10 L630,40 L639,40 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"647\" y=\"38\" font-size=\"7\" text-anchor=\"start\">Φ=4*pi/3</text>\n",
       "<path d=\"M625,75.0 L675,75.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M675,25 L703,25 L722,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M728,44 L747,25 L775,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M675,75 L703,75 L722,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M728,56 L747,75 L775,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M700,43 L750,43 L750,57 L700,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"725\" y=\"85\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"725\" y=\"26\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M700,43 L750,43 L750,47 L700,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M743,50 L753,50 L753,60 L743,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"748\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M775,25 L825,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M780,40 L789,40 L803,10 L794,10 L780,40 L789,40 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"797\" y=\"38\" font-size=\"7\" text-anchor=\"start\">Φ=22*pi/15</text>\n",
       "<path d=\"M775,75.0 L825,75.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M825,25 L853,25 L872,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M878,44 L897,25 L925,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M825,75 L853,75 L872,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M878,56 L897,75 L925,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M850,43 L900,43 L900,57 L850,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"875\" y=\"85\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"875\" y=\"26\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M850,43 L900,43 L900,47 L850,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M893,50 L903,50 L903,60 L893,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"898\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M627,102 L923,102 L923,198 L627,198 Z\" stroke=\"black\" fill=\"lightblue\" stroke-dasharray=\"1,2\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"629\" y=\"205\" font-size=\"8\" text-anchor=\"start\">MZI</text>\n",
       "<path d=\"M625,125 L675,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M630,140 L639,140 L653,110 L644,110 L630,140 L639,140 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"647\" y=\"138\" font-size=\"7\" text-anchor=\"start\">Φ=8*pi/5</text>\n",
       "<path d=\"M625,175.0 L675,175.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M675,125 L703,125 L722,144\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M728,144 L747,125 L775,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M675,175 L703,175 L722,156\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M728,156 L747,175 L775,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M700,143 L750,143 L750,157 L700,157 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"725\" y=\"185\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"725\" y=\"126\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M700,143 L750,143 L750,147 L700,147 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M743,150 L753,150 L753,160 L743,160 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"748\" y=\"157\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M775,125 L825,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M780,140 L789,140 L803,110 L794,110 L780,140 L789,140 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"797\" y=\"138\" font-size=\"7\" text-anchor=\"start\">Φ=26*pi/15</text>\n",
       "<path d=\"M775,175.0 L825,175.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M825,125 L853,125 L872,144\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M878,144 L897,125 L925,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M825,175 L853,175 L872,156\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M878,156 L897,175 L925,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M850,143 L900,143 L900,157 L850,157 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"875\" y=\"185\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"875\" y=\"126\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M850,143 L900,143 L900,147 L850,147 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M893,150 L903,150 L903,160 L893,160 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"898\" y=\"157\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M627,202 L923,202 L923,298 L627,298 Z\" stroke=\"black\" fill=\"lightblue\" stroke-dasharray=\"1,2\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"629\" y=\"305\" font-size=\"8\" text-anchor=\"start\">MZI</text>\n",
       "<path d=\"M625,225 L675,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M630,240 L639,240 L653,210 L644,210 L630,240 L639,240 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"647\" y=\"238\" font-size=\"7\" text-anchor=\"start\">Φ=28*pi/15</text>\n",
       "<path d=\"M325,275.0 L675,275.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M675,225 L703,225 L722,244\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M728,244 L747,225 L775,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M675,275 L703,275 L722,256\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M728,256 L747,275 L775,275\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M700,243 L750,243 L750,257 L700,257 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"725\" y=\"285\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"725\" y=\"226\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M700,243 L750,243 L750,247 L700,247 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M743,250 L753,250 L753,260 L743,260 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"748\" y=\"257\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M775,225 L825,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M780,240 L789,240 L803,210 L794,210 L780,240 L789,240 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"797\" y=\"238\" font-size=\"7\" text-anchor=\"start\">Φ=2*pi</text>\n",
       "<path d=\"M775,275.0 L825,275.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M825,225 L853,225 L872,244\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M878,244 L897,225 L925,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M825,275 L853,275 L872,256\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M878,256 L897,275 L925,275\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M850,243 L900,243 L900,257 L850,257 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"875\" y=\"285\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"875\" y=\"226\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M850,243 L900,243 L900,247 L850,247 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M893,250 L903,250 L903,260 L893,260 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"898\" y=\"257\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M927,52 L1223,52 L1223,148 L927,148 Z\" stroke=\"black\" fill=\"lightblue\" stroke-dasharray=\"1,2\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"929\" y=\"155\" font-size=\"8\" text-anchor=\"start\">MZI</text>\n",
       "<path d=\"M925,75 L975,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M930,90 L939,90 L953,60 L944,60 L930,90 L939,90 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"947\" y=\"88\" font-size=\"7\" text-anchor=\"start\">Φ=2*pi/15</text>\n",
       "<path d=\"M925,125.0 L975,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M975,75 L1003,75 L1022,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1028,94 L1047,75 L1075,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M975,125 L1003,125 L1022,106\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1028,106 L1047,125 L1075,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1000,93 L1050,93 L1050,107 L1000,107 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1025\" y=\"135\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1025\" y=\"76\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M1000,93 L1050,93 L1050,97 L1000,97 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1043,100 L1053,100 L1053,110 L1043,110 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1048\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1075,75 L1125,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1080,90 L1089,90 L1103,60 L1094,60 L1080,90 L1089,90 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1097\" y=\"88\" font-size=\"7\" text-anchor=\"start\">Φ=4*pi/15</text>\n",
       "<path d=\"M1075,125.0 L1125,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1125,75 L1153,75 L1172,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1178,94 L1197,75 L1225,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1125,125 L1153,125 L1172,106\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1178,106 L1197,125 L1225,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1150,93 L1200,93 L1200,107 L1150,107 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1175\" y=\"135\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1175\" y=\"76\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M1150,93 L1200,93 L1200,97 L1150,97 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1193,100 L1203,100 L1203,110 L1193,110 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1198\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M925,25.0 L1225,25.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M927,152 L1223,152 L1223,248 L927,248 Z\" stroke=\"black\" fill=\"lightblue\" stroke-dasharray=\"1,2\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"929\" y=\"255\" font-size=\"8\" text-anchor=\"start\">MZI</text>\n",
       "<path d=\"M925,175 L975,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M930,190 L939,190 L953,160 L944,160 L930,190 L939,190 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"947\" y=\"188\" font-size=\"7\" text-anchor=\"start\">Φ=2*pi/5</text>\n",
       "<path d=\"M925,225.0 L975,225.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M975,175 L1003,175 L1022,194\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1028,194 L1047,175 L1075,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M975,225 L1003,225 L1022,206\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1028,206 L1047,225 L1075,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1000,193 L1050,193 L1050,207 L1000,207 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1025\" y=\"235\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1025\" y=\"176\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M1000,193 L1050,193 L1050,197 L1000,197 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1043,200 L1053,200 L1053,210 L1043,210 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1048\" y=\"207\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1075,175 L1125,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1080,190 L1089,190 L1103,160 L1094,160 L1080,190 L1089,190 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1097\" y=\"188\" font-size=\"7\" text-anchor=\"start\">Φ=8*pi/15</text>\n",
       "<path d=\"M1075,225.0 L1125,225.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1125,175 L1153,175 L1172,194\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1178,194 L1197,175 L1225,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1125,225 L1153,225 L1172,206\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1178,206 L1197,225 L1225,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1150,193 L1200,193 L1200,207 L1150,207 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1175\" y=\"235\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1175\" y=\"176\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M1150,193 L1200,193 L1200,197 L1150,197 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1193,200 L1203,200 L1203,210 L1193,210 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1198\" y=\"207\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1227,2 L1523,2 L1523,98 L1227,98 Z\" stroke=\"black\" fill=\"lightblue\" stroke-dasharray=\"1,2\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1229\" y=\"105\" font-size=\"8\" text-anchor=\"start\">MZI</text>\n",
       "<path d=\"M1225,25 L1275,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1230,40 L1239,40 L1253,10 L1244,10 L1230,40 L1239,40 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1247\" y=\"38\" font-size=\"7\" text-anchor=\"start\">Φ=2*pi/3</text>\n",
       "<path d=\"M1225,75.0 L1275,75.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1275,25 L1303,25 L1322,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1328,44 L1347,25 L1375,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1275,75 L1303,75 L1322,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1328,56 L1347,75 L1375,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1300,43 L1350,43 L1350,57 L1300,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1325\" y=\"85\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1325\" y=\"26\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M1300,43 L1350,43 L1350,47 L1300,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1343,50 L1353,50 L1353,60 L1343,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1348\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1375,25 L1425,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1380,40 L1389,40 L1403,10 L1394,10 L1380,40 L1389,40 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1397\" y=\"38\" font-size=\"7\" text-anchor=\"start\">Φ=4*pi/5</text>\n",
       "<path d=\"M1375,75.0 L1425,75.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1425,25 L1453,25 L1472,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1478,44 L1497,25 L1525,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1425,75 L1453,75 L1472,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1478,56 L1497,75 L1525,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1450,43 L1500,43 L1500,57 L1450,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1475\" y=\"85\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1475\" y=\"26\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M1450,43 L1500,43 L1500,47 L1450,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1493,50 L1503,50 L1503,60 L1493,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1498\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1227,102 L1523,102 L1523,198 L1227,198 Z\" stroke=\"black\" fill=\"lightblue\" stroke-dasharray=\"1,2\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1229\" y=\"205\" font-size=\"8\" text-anchor=\"start\">MZI</text>\n",
       "<path d=\"M1225,125 L1275,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1230,140 L1239,140 L1253,110 L1244,110 L1230,140 L1239,140 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1247\" y=\"138\" font-size=\"7\" text-anchor=\"start\">Φ=14*pi/15</text>\n",
       "<path d=\"M1225,175.0 L1275,175.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1275,125 L1303,125 L1322,144\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1328,144 L1347,125 L1375,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1275,175 L1303,175 L1322,156\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1328,156 L1347,175 L1375,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1300,143 L1350,143 L1350,157 L1300,157 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1325\" y=\"185\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1325\" y=\"126\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M1300,143 L1350,143 L1350,147 L1300,147 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1343,150 L1353,150 L1353,160 L1343,160 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1348\" y=\"157\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1375,125 L1425,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1380,140 L1389,140 L1403,110 L1394,110 L1380,140 L1389,140 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1397\" y=\"138\" font-size=\"7\" text-anchor=\"start\">Φ=16*pi/15</text>\n",
       "<path d=\"M1375,175.0 L1425,175.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1425,125 L1453,125 L1472,144\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1478,144 L1497,125 L1525,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1425,175 L1453,175 L1472,156\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1478,156 L1497,175 L1525,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1450,143 L1500,143 L1500,157 L1450,157 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1475\" y=\"185\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1475\" y=\"126\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M1450,143 L1500,143 L1500,147 L1450,147 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1493,150 L1503,150 L1503,160 L1493,160 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1498\" y=\"157\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1227,202 L1523,202 L1523,298 L1227,298 Z\" stroke=\"black\" fill=\"lightblue\" stroke-dasharray=\"1,2\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1229\" y=\"305\" font-size=\"8\" text-anchor=\"start\">MZI</text>\n",
       "<path d=\"M1225,225 L1275,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1230,240 L1239,240 L1253,210 L1244,210 L1230,240 L1239,240 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1247\" y=\"238\" font-size=\"7\" text-anchor=\"start\">Φ=6*pi/5</text>\n",
       "<path d=\"M925,275.0 L1275,275.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1275,225 L1303,225 L1322,244\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1328,244 L1347,225 L1375,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1275,275 L1303,275 L1322,256\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1328,256 L1347,275 L1375,275\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1300,243 L1350,243 L1350,257 L1300,257 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1325\" y=\"285\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1325\" y=\"226\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M1300,243 L1350,243 L1350,247 L1300,247 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1343,250 L1353,250 L1353,260 L1343,260 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1348\" y=\"257\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1375,225 L1425,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1380,240 L1389,240 L1403,210 L1394,210 L1380,240 L1389,240 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1397\" y=\"238\" font-size=\"7\" text-anchor=\"start\">Φ=4*pi/3</text>\n",
       "<path d=\"M1375,275.0 L1425,275.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1425,225 L1453,225 L1472,244\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1478,244 L1497,225 L1525,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1425,275 L1453,275 L1472,256\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1478,256 L1497,275 L1525,275\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1450,243 L1500,243 L1500,257 L1450,257 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1475\" y=\"285\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1475\" y=\"226\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M1450,243 L1500,243 L1500,247 L1450,247 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1493,250 L1503,250 L1503,260 L1493,260 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1498\" y=\"257\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1527,52 L1823,52 L1823,148 L1527,148 Z\" stroke=\"black\" fill=\"lightblue\" stroke-dasharray=\"1,2\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1529\" y=\"155\" font-size=\"8\" text-anchor=\"start\">MZI</text>\n",
       "<path d=\"M1525,75 L1575,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1530,90 L1539,90 L1553,60 L1544,60 L1530,90 L1539,90 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1547\" y=\"88\" font-size=\"7\" text-anchor=\"start\">Φ=22*pi/15</text>\n",
       "<path d=\"M1525,125.0 L1575,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1575,75 L1603,75 L1622,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1628,94 L1647,75 L1675,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1575,125 L1603,125 L1622,106\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1628,106 L1647,125 L1675,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1600,93 L1650,93 L1650,107 L1600,107 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1625\" y=\"135\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1625\" y=\"76\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M1600,93 L1650,93 L1650,97 L1600,97 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1643,100 L1653,100 L1653,110 L1643,110 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1648\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1675,75 L1725,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1680,90 L1689,90 L1703,60 L1694,60 L1680,90 L1689,90 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1697\" y=\"88\" font-size=\"7\" text-anchor=\"start\">Φ=8*pi/5</text>\n",
       "<path d=\"M1675,125.0 L1725,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1725,75 L1753,75 L1772,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1778,94 L1797,75 L1825,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1725,125 L1753,125 L1772,106\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1778,106 L1797,125 L1825,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1750,93 L1800,93 L1800,107 L1750,107 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1775\" y=\"135\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1775\" y=\"76\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M1750,93 L1800,93 L1800,97 L1750,97 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1793,100 L1803,100 L1803,110 L1793,110 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1798\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1525,25.0 L1825,25.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1527,152 L1823,152 L1823,248 L1527,248 Z\" stroke=\"black\" fill=\"lightblue\" stroke-dasharray=\"1,2\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1529\" y=\"255\" font-size=\"8\" text-anchor=\"start\">MZI</text>\n",
       "<path d=\"M1525,175 L1575,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1530,190 L1539,190 L1553,160 L1544,160 L1530,190 L1539,190 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1547\" y=\"188\" font-size=\"7\" text-anchor=\"start\">Φ=26*pi/15</text>\n",
       "<path d=\"M1525,225.0 L1575,225.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1575,175 L1603,175 L1622,194\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1628,194 L1647,175 L1675,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1575,225 L1603,225 L1622,206\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1628,206 L1647,225 L1675,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1600,193 L1650,193 L1650,207 L1600,207 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1625\" y=\"235\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1625\" y=\"176\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M1600,193 L1650,193 L1650,197 L1600,197 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1643,200 L1653,200 L1653,210 L1643,210 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1648\" y=\"207\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1675,175 L1725,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1680,190 L1689,190 L1703,160 L1694,160 L1680,190 L1689,190 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1697\" y=\"188\" font-size=\"7\" text-anchor=\"start\">Φ=28*pi/15</text>\n",
       "<path d=\"M1675,225.0 L1725,225.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1725,175 L1753,175 L1772,194\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1778,194 L1797,175 L1825,175\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1725,225 L1753,225 L1772,206\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1778,206 L1797,225 L1825,225\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1750,193 L1800,193 L1800,207 L1750,207 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1775\" y=\"235\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<text x=\"1775\" y=\"176\" font-size=\"7\" text-anchor=\"middle\"></text>\n",
       "<path d=\"M1750,193 L1800,193 L1800,197 L1750,197 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n",
       "<path d=\"M1793,200 L1803,200 L1803,210 L1793,210 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n",
       "<text x=\"1798\" y=\"207\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n",
       "<path d=\"M1525,275.0 L1825,275.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n",
       "<path d=\"M1825,25.0 L1840,25.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M1825,75.0 L1840,75.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M1825,125.0 L1840,125.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M1825,175.0 L1840,175.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
       "<path d=\"M1825,225.0 L1840,225.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n",
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       "</svg>"
      ],
      "text/plain": [
       "<drawsvg.drawing.Drawing at 0x1dd4e8d8ed0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mach_zender(x1, x2):\n",
    "    \"\"\"Mach Zender interferometer with phase shifter values, 2pi x1 and 2pi x2\"\"\"\n",
    "    return catalog[\"mzi phase first\"].build_circuit(phi_a=2 * np.pi * x1, phi_b=2 * np.pi * x2)\n",
    "\n",
    "def brickwork(\n",
    "    x: np.ndarray, \n",
    "    num_modes: int, \n",
    ") -> Circuit:\n",
    "    \"\"\"\n",
    "    Generates brickwork Mach-Zender feature map for input number of modes\n",
    "    and input data.\n",
    "    Args:\n",
    "        x         : Data feature vector\n",
    "        num_modes : Width of circuit\n",
    "    Returns:\n",
    "        Circuit\n",
    "    \"\"\"\n",
    "    circ = Circuit(num_modes)\n",
    "    even_modes = np.arange(0, num_modes - 1, 2)\n",
    "    odd_modes = np.arange(1, num_modes - 1, 2)\n",
    "\n",
    "    # sub_index determines which mode MZI is applied to\n",
    "    sub_index = 0\n",
    "    for i in range(0, len(x) - 1, 2):\n",
    "        # Select modes based on the cycle position\n",
    "        cycle_position = i // 2 % (num_modes - 1)\n",
    "        modes = even_modes if cycle_position < num_modes // 2 else odd_modes\n",
    "        \n",
    "        # Add MZI to the appropriate mode\n",
    "        circ.add(int(modes[sub_index]), mach_zender(x[i], x[i + 1]))\n",
    "        \n",
    "        # Reset index which MZI is applied to\n",
    "        sub_index = (sub_index + 1) % len(modes)\n",
    "    return circ\n",
    "\n",
    "# Generate a sample brickwork circuit for sample input data x\n",
    "x = np.linspace(0, 2, 31)\n",
    "num_modes = 6\n",
    "pdisplay(brickwork(x, num_modes), recursive=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training with Support Vector Classifiers\n",
    "\n",
    "Let us generate our dataset of interest using this brickwork feature map. As in Ref. [1], the dataset we will use is 30-dimensional, for which we generate 100 total datapoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input parameters for data generation algorithm\n",
    "N = 100\n",
    "input_state = BasicState([1, 1, 0, 0])\n",
    "reg = 0.02\n",
    "feature_map = lambda x: brickwork(x, input_state.m)\n",
    "\n",
    "X, y = generate_data(feature_map,\n",
    "                    input_state,\n",
    "                    data_size=N,\n",
    "                    data_dim=30,\n",
    "                    reg=reg,\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now pre-calculate the quantum kernel matrix for the total dataset. Here, we consider 1.0 indistinguishability and PNR detectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "K_Q = kernel_matrix(X, X, \n",
    "                    feature_map,\n",
    "                    input_state, \n",
    "                    nshots=100_000,\n",
    "                    indistinguishability=1.0,\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pre-calculate the coherent kernel matrix with 0.0 indistinguishability and PNR detectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "K_C = kernel_matrix(X, X,\n",
    "                    feature_map,\n",
    "                    input_state,\n",
    "                    nshots=100_000,\n",
    "                    indistinguishability=0.0,\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's construct the unbunching kernel matrix with 1.0 indistinguishability and unbunching."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "K_U = kernel_matrix(X, X,\n",
    "                    feature_map,\n",
    "                    input_state,\n",
    "                    nshots=100_000,\n",
    "                    indistinguishability=1.0,\n",
    "                    unbunching=True,\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then split the X, y data into training and test sets. We allocate 2/3 of the datapoints to the training set, and 1/3 to the test set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate training and test indices to split matrices\n",
    "indices = np.arange(N)\n",
    "np.random.shuffle(indices)\n",
    "split_point = int(N * 0.66)\n",
    "train_indices = indices[:split_point]\n",
    "test_indices = indices[split_point:]\n",
    "\n",
    "# Train test split \n",
    "K_Q_train = np.array([[K_Q[i, j] for j in train_indices] for i in train_indices])\n",
    "K_Q_test =  np.array([[K_Q[i, j] for j in train_indices] for i in test_indices])\n",
    "K_C_train = np.array([[K_C[i, j] for j in train_indices] for i in train_indices])\n",
    "K_C_test =  np.array([[K_C[i, j] for j in train_indices] for i in test_indices])\n",
    "K_U_train = np.array([[K_U[i, j] for j in train_indices] for i in train_indices])\n",
    "K_U_test =  np.array([[K_U[i, j] for j in train_indices] for i in test_indices])\n",
    "\n",
    "X_train, X_test = X[train_indices], X[test_indices]\n",
    "y_train, y_test = y[train_indices], y[test_indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualise the three different training and test matrices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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elJeXY/r06Tj11FMRHh6OBQsW4NFHH/X6XQe6du6KNWvWYOTIkViyZAmmTJmCyZMno6SkBL///e9x/vnnO96/d+9enHnmmbjxxhuxdetWTJkyBdOmTcOqVaswbtw4rFy58oDbcDC89NJLOPHEE7Fjxw6cdNJJmDFjRnsp94Gux7vLgfZVV7S0tOCUU07BXXfdhdTUVJx66qkICgrCq6++iokTJ3p9qMLzzz+PKVOmYNGiRUhNTcXpp5+OwMBAXHLJJfvVDBwI+/vDy4cffogdO3Zg3rx5nZZQPvvss7jllluwc+dO5OTkYN68eRg5ciS+/PJLXHnllTjnnHPM+Tdy5EjjUeuYoy666CIEBweb7VdWVmLs2LF45plnMHToUMydOxfJycmd7tMNN9yA008/HVu3bsUVV1xhXlu7di2uueYa9O3bFy+88MIP/xfInl7MhRde6AHgmTp1arfe//HHH3sAeAB4cnNz2//9oosu8gDwPPnkk14/9+STT3oAeC666CLz7y6Xy/Paa695mpubzb/v3r3bc+utt3oAeE455RTH9qZMmdLejn/84x/mtffff9/j4+Pj8fX19RQUFJjX7rjjDg8Azx133LHffWzb7v5o++4PP/zQ67/7+Ph4nn32WfPa9ddf7wHgGThwoCc4ONizePHi9tdaW1s9Z511lgeA5/LLL9/v93bFhx9+uN+2t+03AM+Pf/xjT1NTk9dtPP/88576+nrzb3v37vU8/PDDHgCeY445xrN3716v2+Y+bTvmADyzZs3y1NXVtb9WXV3tGTlypAeA5w9/+IP5XG5urgeAJy0tzdG+tu2Fh4d7li1b5rUdOTk5js+NHj3aA8Bz/vnnm30vLCz0DBw4sH27fEw7o+37LrvsMo/H4/HMnz/fA8DzySeftL/nscce8wDw/OY3v/F4PN+OER4fBQUFnsWLF3v27Nlj/r2hocHzk5/8xAPA8/Of/9zRhv2NxTba+hKAJzk52bN582av7+tsO2eccUZ737Vx/vnnewB4TjvtNMd4EMIbbblm2rRpB/X5v//97x4AnujoaM/q1avb/33v3r3t52J4eLinvLzcfC4tLc0DwOPn5+d5/fXXzWttc1RYWJinsbHRvHbbbbd5AHgmTJjg2bFjh3ntpZde8vj6+noiIiI8NTU17f/enfNt3bp1Hn9/f0///v09r7zyinlt586dnmHDhnkAeJ5++mnz2uHMeQfC/ubKjvln+PDhnpKSEq+ff+uttzzFxcWOf1+6dKknNDTU4+fn5yksLPS67SlTpph/79jfaWlppr9bW1s9l156qQeAZ+bMmY7vaxsXHdcxHs/B9/N1113nAeAZMmSI2b9du3Z5zj777PZtHshxaNvvrKwsj8fj8TzyyCMeAJ677rqr/T1btmzxAPBMnjzZ4/E481JHDmV+70hbX/r6+npee+01r+/pbDt//vOfPQA8AwYM8LhcLo/H8+35npqa6qmqqtrvd4veRcdzms9Npm3c8XXHwZ7DbdcxbeutlpaW9tfWr1/viYmJ8brNg73+Odg1cn5+vicsLMwDwHPLLbc4rp/Kyso8S5YsMf92sGvn/a0NO/bV7bff7mltbW1/7auvvvIEBQV5AHiWLl1qPvfAAw94AHgSEhI8mzZtav/31tZWz7XXXtu+Te6rA6FtfzhfdNwfAJ6HH37Y6+cP9Xr8YPtqf+OmY64dNWqUybW7du3yzJo1ywPAc8UVV5jPFRUVeYKDgz0APA888IB57eOPP25vx4He9uh4XbNnzx5PcnKyJyQkxNPQ0ND+nh/96EceAJ4PPvjA4/F8Ox75HFy+fLnnq6++cnxHUVGRZ8SIER4AnhdffNHxelft7niuTZ8+3Zxr3dlOTU2NJz093QPA8/e//93j8ey7fzFgwAAPAM8999yz3+/+IdGrb2jNnj3bcbHaGZs2bWo/4MuXL2//94Od0LsiMTHR06dPn/ZFThttJ9C8efO8fq5tv5555hnz79/HDa1zzjnH8Zmqqqr27d50002O11euXOkB4MnIyNjv93ZFd25oRUZGempraw9q+8cdd5wHgOfrr7/2uu393dAKCgryegHz/PPPe73A7c4NrQcffNDxWlNTU3uSz8/Pb//3Tz75xAPAExwc7HVh/MYbbxySG1rvvfeeB4Dn4osvbn/Pscce6/Hx8WlfmO3vhlZnNDQ0ePr27euJiYlxvHYgN7T4XOjudmpqajwZGRkeAJ6//e1vnr/97W+60BAHzIHmGiYrK2u/5/7evXs9w4cP9wDw/P73vzevtV30XH/99V63O2jQIA9gb0RXVVV5AgICPP3793fcXGnj5z//uQeA56GHHmr/t+6cb+edd54HgOfee+/1+vry5cs9ADxjxowx/344c96B0J0bWh378kBo+yMWX2h054bWwoULHdsrKSnxAPD4+/t7du/ebV7r6obWgfRzY2Nj+4XCokWLHJ8pLy/3BAYGfucbWnV1dZ7AwEBPZmZm+42nW265xQPA89RTT3k8ns5vaHXGgeb3jrT15aWXXrrf93S1nXnz5nkAeM477zzPqlWrPP7+/h4/Pz/P559/fkD7IXo2h/KG1oHOlW3XMQkJCZ5du3Y5PvfQQw95gH03Xr197mBvaB3oGvkXv/iFB4Bnzpw5Xr/PGwezdvZ4ur5JM2bMGK9/1Lzyyis9gL357vF4PJmZmR4AnkceecTxmebmZk9SUtL3ckPrYP+wdjDr8YPtq65uaPn4+HjWrFnj2N6yZcs8ADyZmZnm3++66y4PAM9xxx3ndd9uvPHG73xDy+PxeG6//XaTk2praz0BAQEmb+3vhlZnLFq0aL/X2N29oeXn5+fZvn37ft/X2XaWL1/u6devn8ff39+zevVqz7nnntt+HvaUP+z3+qccHgieDj/181ZLfrCsXbsW77//PnJzc9HQ0NBeJ9va2oq9e/di27ZtGDVqlONzc+bM8bq9wYMH45133um0/vxwccoppzj+LTIyElFRUaiqqvL6eltJxv6cYYeKk046qcsnZmzbtg3vvPMOtm3bBrfb3X6cy8rKAACbN2/GkCFDuv2dY8eO9So1bBM+H8wx8nbc/f39kZmZidWrV6OoqAgpKSkA0O6Zmj17tldvT9vPtPdXF99dpk+fjrS0NLz00kt46KGHUFBQgGXLlmHatGlefWDeWLp0KZYsWYL8/Hw0Nja2n2/9+vVDRUUFampqDlosv78nMHZFeHg4XnzxRRx//PHtP9P38/PDiy++2KUHSYhDQWFhIbZv3w4AXh/T7ePjg0suuQTXXXcdPvzwQ9x2222O93SWKzZt2mTmoQ8//BC7du3C9OnT9/sUvBNPPBF/+9vfsHTpUlx11VWO172db3v37sXbb78NADjvvPO8bnfs2LEIDg7G6tWr0dTUhP79+3d7P45UzutIbGysV9l/R6qqqvDmm29i/fr1qKmpaS/53rp1KwDs19e4P/r27ev18ezx8fGIiIhATU0NqqqqEB8f3+1tHkg/r1y5EvX19YiOjsbMmTMdn4mJicGMGTPw2muvdfv7vREaGoqzzjoLzz77LD766CNMnjwZzzzzDEJCQtrLiLriUOf3jpx99tkH9TlgX4nKmjVr8MILL+Cdd95Bc3Mz7rvvPhx77LEHvU1xdHOwc+W5557rmHeBfbnn6quvxtatW1FcXIzExMRD0s4DXSO3lbpz+VN3OJC1c3c47bTTvDqXvLW9sLAQO3bsALBP08H069cPZ5999kGXnR8I3ZmrDvV6/ED6qjukpqa2l4R3Z3tt10I/+tGPvG7vRz/6Ee69994DaoM3LrnkEvzhD3/AE088gYsuuggLFizArl272l1iXdHc3Ix3330XX375JcrLy9Hc3AyPx9OuPTnQ9UFHRo0ahczMzIP67Lhx43DvvffimmuuwYknnoi6ujqkpaXh6aefPmjv2PdNr76h1Vbv2baY6Yry8vL2/4+JifnO39/Q0IALL7ywy9ptl8vl9d/399Sj0NBQAPt8Ht83+2tTcHAwqqqqvL4eEhICAPuVKB4qOruxsmfPHlx11VV45JFHOnUE7O9Y7I/DcYwOZJuFhYUAOt/3tLS073xDq004eOedd+KFF15ofxJod57oUV5ejrPOOguffvppp+9zuVwHdUMrNjb2Oz15ZezYsbjjjjtw++23AwDuvvtuTJgw4aC3J44+2vJFxxzSXdoWZlFRUe3nONP2VNT9LQoPZM5oW3S///77XS5UKioqHP+2v/Otqqqqff7szkVDVVWV44baDzHndaSrm/ePPfYYrrvuOjQ0NOz3PQeaYxISEvb7tMLQ0FDU1NQccL8c6hzT3T9qdMWll16KZ599Fk888QQaGxtRXFyMyy+/vMv5/XDl9458l30MCwvDs88+2+4MOuWUU3D99dcf9PZEz6TjfNvZOO34+v7m6IOdKzMyMrz+e0hISPsfpgsLCw/ZDa0DbWebj+9gnsZ2qPPHwcyT0dHRDrdRG4dqnuyKzr7ncK3Hv+++5+vJrvLUoer7rKwsTJ48GZ988gm2b9+OJ554An369OmW6H/ZsmU477zzkJ+fv9/3HKkcBex7Uvcbb7yBd999Fz4+Pnj++ed/sE+v90avvqE1ZswY/Otf/8KqVavQ2tqKvn07393ly5cD2Lf42N+k7w1+MkEbt956K1599VUMGjQIf/zjHzFu3DhER0ejX79+AICJEyfi888/329i69Pn+3f2729f2uiqTUeizW0EBATs97UHHngA//jHPxAfH4/7778fEydORFxcXPtfqi644AL8+9//PmAh5uHY34PZZmcXpofq7voll1yCu+66C48++ijy8vIQFhaGefPmdfm5yy+/HJ9++imOO+443HnnnRgxYgQiIiLaL9ISExNRUlJy0DLSzo57d2hqasJLL73UHh+MZFkc3YwZMwbPPvssVq1ahT179nQqiz4cHMic0TbHZ2dnd/nAA28XFfs73zrmDm+/NGPaJLUdOZL5ozt0NtesXLkSP/3pT+Hr64u7774bc+bMQWpqKgIDA+Hj44NHH30UP/3pT5VjOmHKlCnIysrCK6+8gtLSUgDd+6PJ4crvHfmueebZZ59t//+NGzeirq6uy1+Ui95FUFBQ+/93dtMb2PcUTAD7vTlyOOfKAzlPvus1w6HkUH/XD3We7IrO5qrDtR7/IfQ9sP8+PpR9f+mll+Ljjz/GddddhxUrVmDmzJld/hGvsbERZ5xxBsrKynDJJZfgZz/7GbKzsxEaGgpfX19s2bIFAwcOPKI5auvWre1PcPR4PO0PkOgp9OobWnPmzMENN9yAuro6vPbaa52WJXk8nvYFx9y5c83J1HYDytsjLQHs9wk/L774IgDghRde8PrUtbYShO8TPz8/tLS0wO12t/9yqiPf9WlFP1TajsUjjzzi9SlkR+JYHArafuHg7YkfbRyqY5qWloZp06bh/fffBwBceeWVXU6gDQ0NeOutt9CnTx+89dZbCA8Pd7zeduFypPjFL36BNWvWYMqUKSgsLMR//vMfPPjgg7jmmmuOaLtEz+G0007D9ddfj9raWixcuBBnnnlmtz/bdg63/cLJ26+02n5Vtb8SwQOhbeE1cODA/T6K/WCIjo5GQEAAdu3ahXvvvfeH/0ScQ8xLL70Ej8eDq6++2utT63pzjunstQOh7ZfAv/71r7F48WIMHjwYxx13XJef+6Hn9+effx7/+Mc/EBcXh7Fjx+LNN9/EpZdeildeeeWItkt8v0RGRiI4OBj19fXYtm0bhg4d6vV91dXVqK6uBrD/X6ocLLm5uV7/3e12tz/druOT0Q72+udgSU1NxebNm7Fp0ybHE61/yLTNkxUVFWhoaDA3L9s4VPPkwdIT1uMHS1JSEjZv3rzfPj6UfX/22Wfj6quvxuuvvw6ge390+eSTT1BWVobRo0c7npIIHPkc1fGJ7z/60Y/w8ssv46abbsLEiRMxduzYI9q27vLD/nPodyQrKwvnnnsuAOCmm27qtOzqb3/7G9atW4d+/fo5FqNtE9XGjRsdn/N4PO3eEKYtIaWlpTleW7RoESorK7u1H92lLfG0trbu9z2d7cu6descj8zuLXR2LL7++mvHI7V7CpMnTwawzztQU1PjeP3tt9/2+u8HyxVXXIGoqChERUXhsssu6/L9dXV12LNnD0JDQx3JEwD+9a9/7fcvEt0Zz9+Vf//733jkkUcQFxeH559/Hi+++CL8/f1x0003YcWKFYfte0XvIisrC/Pnzwew7zHIbfPN/igvL293JSQnJ7eXFHq7weTxeNr/ferUqd+5rdOnT0e/fv3w0UcfHVSJ5P7w9fXFjBkzAHx7g+Fw833MEd2lsxzT1NTUY29ejBkzBoGBgaioqMDixYsdr1dWVuK99947ZN938cUXIyYmBlFRUfjpT3/arc8cbH7/PsbPli1bcMUVV6BPnz547rnnsGDBAmRlZbX/4UQcPfTp0wdTpkwBgE7ng5dffhkAEBERgZEjRx7SNrz00kte9R9tf9DPzs42fzg52Oufg6XNF/jYY48d0u0eblJSUtpLvv797387Xt+9e/cRzwHfZT3+Q6ftWshb3wPAggULDtl3BQYG4uKLL0ZUVBQyMjJwxhlndPmZrm5Q/+tf/9rvZ9t+PXc489S1116LNWvWYOrUqXjmmWdw3333Yffu3Tj33HO/s7Lm+6JX39ACgIcffhjp6enIzc3FtGnT8PXXX5vXW1tbcf/99+Paa68FADz66KM45phjzHtOOukkAPsm/A0bNrT/e0tLC375y1/iyy+/9PrdbfK6hx56yPz75s2bceWVV363HfNC219VeB870rYvd955p0lqO3fuxEUXXdRjJ7OuaDsWDz/8sPmJdElJCX7yk5/8IC6IDobJkydjxIgRcLvduPrqq7F79+7214qLi3HDDTcc0u8799xzUVlZicrKym7dtY+Li0NERARqa2tNyQWwr5781ltv3e9nuzOevwubN282Fxrx8fEYPXp0j5zIxZHnoYceQnZ2NnJzczFp0iSvjordu3fjiSeewKhRo8wFwo033ggA+N///V+sXbu2/d89Hg9+97vfYc2aNQgPD8f/+3//7zu3My4uDldffTUaGhowZ84cfPXVV473NDc3Y+HChe2uvO5yxx13oF+/frjpppvw9NNPey1HWb9+Pf7zn/8cdPs7crjniAOhLcc8/fTT5tcMTU1N+PnPf77fX0b80AkMDMTll18OALjuuuuMk7S5uRlXXXVVl+VTB0JycjLKy8tRWVnZvi7rioPN74d7/DQ1NeGcc86B2+3Gr3/9a0yfPh2hoaHmDyf7Wz+K3snNN98MHx8fPPfcc3j88ccdr3/++eftD/644YYb9uvPO1iKi4tx4403mgdfbdy4EXfddRcAtD8cp42Dvf45WK6//nqEhIRg4cKF+NWvftX+UI02ysvLu/Q/HSnaftV/xx13YMuWLe3/vnfvXtx6661H/AcD32U9/kPnsssuQ2BgID799FM8/PDD5rXPPvsMf/vb3w7p9z3wwAOorKzEjh07vOoTmLYc9f7775vzCNh33+GFF17Y72cPd55asGABHn30UcTFxWHBggXo06cP/ud//gdnn302cnNzu/ULtB8Cvf6GVmRkJJYsWYIxY8Zg9erVGDZsGMaPH4/58+dj7ty5SExMxA033ICgoCA88sgjXt0fxx9/PObOnYv6+nqMHTsWM2fOxNy5c5GZmYlHHnlkv4uuO+64Az4+Pvj1r3+N4cOHY/78+Zg+fTqGDRuGzMxMTJw48ZDu66xZsxAUFIT//ve/mDRpEi655BJcfvnlePLJJ9vfc9tttyE8PBxvvfUWcnJycPbZZ2PKlCkYMmQIoqOjD3mbfijcdttt6NevHx577DEMHDgQ5513Hk4++WRkZWWhubn5gEqEfkj4+PjgX//6FyIjI/Hcc88hMzMT5513HubMmYOcnBxERka2l2y0/TX6+8TX1xe/+c1vAAA/+clPcOyxx+KCCy7ApEmTMHHiRJx22mle/6oOfPsktZtvvhlz5szBZZddhssvvxxLly79zu3atWsXzjnnHNTX17dfaLTREydyceSJiIjAZ599hhNPPBEbN27ECSecgMzMTJxxxhm44IILMH369PZfNtbX1xvp7k9/+lNceOGF7TeKTzrpJFxwwQUYPHgwfvOb3yAgIAALFiw4JA8rAYA//vGPuOCCC7B8+XKMHDkSo0ePxtlnn43zzz8fkyZNQlRUFObOnXvAP9MfPXp0+18aL774YqSlpWHWrFn48Y9/jFNOOQUpKSkYNmzYIfsFV3dy3vfFJZdcgrS0NKxevRoZGRk488wzcfbZZyMtLQ0vv/xyt2/O/BD5/e9/jzFjxmD9+vXIzs7G3Llzcd555yEzMxPvv/9++7rpSOQY4ODz++EeP1dffTXWrVuHadOmtedBYN95cu+992L37t0477zz9IeTo4jJkyfjL3/5C/r06YPLL78cWVlZOOecczB//nyMGzcOxx9/PKqqqnD++efjlltuOeTff+WVV+Kf//wnBgwYgPnz52P27NkYOXIkysrKcOaZZ+JnP/uZef/BXv8cLKmpqXj55ZcREhKC3//+90hJScGZZ56Jc889FxMmTEBycjL++c9/HtLvPFRcc801OPnkk1FcXIzhw4fj5JNPxvz585GTk4O///3v+PnPfw7gyM2T32U9/kMnOTkZjzzyCPr06YOrrroKI0aMwAUXXIATTzwRkydPbv8RyaG+QdxdRo0ahblz58LtdmPUqFGYNWsW5s+fj8GDB+PKK6/0+vTqNtquhU466SScd955uPzyy3H55Ze3lwh/FzZv3oyf/vSn6NOnDxYsWGCelvzPf/4TmZmZePXVV7+Xp3N+V3r9DS1g30Bfvnw5FixYgNNPPx1FRUV45ZVXsHDhQlRUVCAwMBCrVq3q9DGxL7zwAn71q18hISEBH330EZYtW4YTTjgBq1at2u9PgufNm4ePP/4Y06dPR0lJCRYuXIjy8nL89re/xdtvv33IT6y4uDi8/fbbOOmkk7BhwwY888wzePzxx9sfZwrse8LJ0qVLMW/ePLjdbrzxxhsoKyvD7bffjrfeeuuIneyHmwkTJmDFihU4/fTT0dDQgIULF2L79u24+uqr8fnnn+/36WI9gaFDh2LlypW48MIL0dLSgv/+97/YuHEjrr32Wrz33nvtf1E/Uk6bX/ziF/jvf/+LiRMnYvPmzXj99dfR3NyMhx9+GE8//fR+P3fqqafisccew9ChQ/HBBx/giSeewOOPP27+8nWwXH311fjqq68cFxpt9LSJXPwwiI2NxYcffoi3334bP/nJT+Dr64v3338fL7/8MjZs2IDjjjsOf/nLX5Cbm4vx48e3f87HxwfPPPMMFixYgEmTJmHlypV4+eWX0djYiIsvvhirV6/GySeffMja2bdvXzz33HN46623cMYZZ6C8vBwLFy7EokWLUF1djTlz5mDBggXtP+M/EM455xx8/fXXuO666xAeHo7PPvsMr7zyCjZs2IDs7Gz88Y9/xO9///tDsh/dyXnfF+Hh4VixYgV+/vOfIzw8HG+//TY+//xzzJw5s9N1Qk8gODgYH330EW677TbExsbinXfewSeffILp06dj5cqV7Q9BOFI55mDz++EcP8899xz++c9/Ii4uDs8995xDcnzVVVfpDydHKddccw1WrFiByy67DL6+vnjrrbfwn//8ByUlJZg7dy5ee+01/Pvf/z4sDxeZMGECli5diqFDh+K9997DRx99hAEDBuD+++/Hiy++6FWefTDXP9+FmTNnYv369bj22msRHh6Od955B2+//TZqa2tx4YUXHpYKl0OBr68vXnvtNfzpT39CVlYWPvzwQyxevBjDhw/H8uXL228WHEm/5MGux3sCP/7xj/HBBx9gxowZ2LlzJ1577TW43W489thj7b+eO5J9/9JLL+Gee+7BwIED8emnn+Ldd99FamoqFi1a1P4raG/87//+L26++WaEh4fjv//9Lx5//HE8/vjj+/XadRf+w/60adPM62FhYe2/Jr755pt/8L8m9vH01hqzblBXV4epU6di9erVmDlzJhYuXNitnw4K0VPIzc1FdnY2QkJCUF1d/YN/ipgQQoieQ0tLC4YOHYotW7Zg5cqVGD169JFukhBC/OCYNm0aPvzwQ7zyyivdekK4OHQ888wzuOiiizBnzhwsXLjwSDdHHAaO6qvbsLAwLFq0CIMHD8a7776L8847r8e6lMTRS0NDg9fa6ry8PPzoRz/C3r17cdFFF+lmlhBCiINi5cqVDidafX09rrrqKmzZsgXDhw/XzSwhxFHNmjVrjMsW2OfO/O1vf4sPP/wQsbGxOOWUU45Q63o3+fn5Xp/S+Nlnn7V7Si+55JLvu1nie+Ko/oVWG8XFxXjsscfg8Xhw8sknY8KECUe6SUJ0m507dyIjIwNZWVnIyclBaGgo8vPzsWrVKjQ3N2PEiBH45JNPenRZpRBCiCNHeno6GhsbMWzYMMTGxqK8vBxr1qxBdXU1IiMjsXjxYowaNepIN1MIIY4YJ554ItasWYMRI0YgISEBNTU1+Oqrr1BSUoL+/fvj1VdfbX+Sozi0PPXUU7jsssswYsQIpKamwtfXF9u3b29/0M4ll1yCJ5544gi3UhwudENLiB5OfX097rzzTnzwwQfIz89HbW0tAgMDMXDgQJx11lm4+uqrERgYeKSbKYQQoofy4IMP4tVXX8WmTZtQU1ODPn36IC0tDTNnzsSNN96IlJSUI91EIYQ4ojz33HN47rnnsG7dOlRVVcHj8SAxMRFTp07FDTfcgCFDhhzpJvZaNm3ahHvvvRdLlixBWVkZGhoaEB4ejpEjR+LSSy/F/Pnzj3QTxWFEN7SEEEIIIYQQQgghRI9CUh0hhBBCCCGEEEII0aPQDS0hhBBCCCGEEEII0aPoe6QbcLjZu3cviouLERISAh8fnyPdHCFED8Dj8cDtdiMxMVFPhxRdojwjhDhQlGfEgaJcI4Q4EI6WPNPrb2gVFxdLViqEOCgKCgqQnJx8pJshfuAozwghDhblGdFdlGuEEAdDb88zvf6GVkhICACg35CL4OPbDwDw7KM3m/cMTww3cYC/r4kbmlpN7O1vIv372c+AVfv0ob177Rv69LFvyC1vMPHgpFAv3/otjc22jUH97aEtqGrs9PMA4Otr79wmhvc3cese2+Yd5fUmDg7wM3FkUD8T9+trt9/SutfEfX1tHzS32NcBZz81t+xxvMe8Tt8RE+JvYteuFhPzcaxp2G3ipt32+3ypPQ3NzvaEBVK/BNt+4bHAY+eLvGoTn5AdbeLPd1SZOCXcPtGQ+yglyr7e19d5x76V+m1npR2PQ5LDTFxSu8vEYf3tPrfyeKfzYRcd6+gQ20eldU0m3u1lbKTFBJl4D30nP/+C/7rZuufbbbrdLhwzIL19/hCiM7zlmfsfuNa8Z0pGrInDaV7g+dDb01p4DDfQfMTbLHc1mzgm1M5/eRX2vE6MCDDxbmpTPeXC2DCbI7aXuh1t9qN5P4Dm2ORIOx+5aU5eUVBj4jEpEbaNe2wbQ2ju8dA8UOG2fRLo71wGldF8lhpt5xaevwqr7fvjqF/qKT9zbiyqsZ/nXMl5aWeVPW4AEBdsjy3P89zGQFrnrC6qNfG4ZNvPRfT5pEg7VjbTeqA/5ZVhKTZnAM55PJ/WKek0p/N47EsHoole70/9WFlvj31sqD1OdTT28qud66ZRqeGOf+vI14V1Jh6aYt9f12FNUe92Y9ywLOUZ0W285Zob7vqZec/ZwxJNHEbXA5wXeK0EANG0VmbcTfZcCaJ51I/O/8/z7Dp1SlaMiXmO4zkxltqTV+mcA92Un8LomoTnE75uWrrTtjEu0H4n5yre5wbaHs/jPH8BzvzVp5N1KQC00HVYWrRtU3GtXSv703Eod9nXG+j6oB/Nqdtr7bwOADmR9no0mtYVgX72WLbQPry+qcTE542wN2ifWZln4sZmu89Z0XbePi41ysS8BgCcY2Ndca2Jpw+KM/H6fPt6NOV07ic+hZzrEjtWKmkdwteiADAiLcLxbx2pp8/0pfHWdu+i3u3G+KMgz/T6G1ptF60+vv3aJ//AYHtQQ0LtyckLvT79vvsNLf5lMCcQvlETvMtuLzS08xtavjSRBtPJE7y760PNNzZCQzu/oRW8y74/hJJHaPB3u6HV5OWmBd9AaurihlY/2kYoTbwePzsh8MVWax97Q6tvFze0fPo52xNCF5mhdLPGsZigMDDYtpHHQmCwbWNwiE3aftRHIaE2AfLCA3Aem+Dmzsdj/R67jzwWurqh1ZePE/VRw14be7vZGRp66G5o7e89QnjDW54J6CLPhNK8wIvd7tzQ6kM30EOD7DYbPXbRxPNfcJM9r0NCO7+hBT+bZzhHBDU4zxee93mODaX5CDQnBwbb7+R+5DaG0tzDfzDYBdsn/McfwMt8Ftr5Da3gFn6/7RfQYjqEcmNwq/18V30W1Oycs4ND7Hdyv3Ibg2idExjMecL2c5BjH+1YCWq0ndLft+s1DM/jvE7hOZ3/QMU3tPy6uKHV5MPng+2zvX3pIn03revQ9VosKNiON37/Xl+brwHlGdF9vOWa/kGUa0LsmAsJsOcV5wVvN7RCQju/ocVrZ77m4HUlr1P5vOBrKBfNiY7c1ew8N/f2pesgXnvTfMLXTdzGoEA7P/DaOZhuaPU5mBtarZ3f0OKbQXxDi+d59x6bW/ypDY0e+zp2U5upJC2g1Tk3BfP4opuNgf06v6EVEGRvkjnGAo3nPX3t5wODbe7h8c5rAAAAXccHumjtxPkuhG5IUX7t59vFDS06rnwONtE6ZI+v84ZWV7nGh85B/uOh495FL88zvf6GVhvPPnpz+42ssy/8X/NaxbIHTVxCd7j55Gzc7bxpwQvOfn701wn6Fc2EDHtHme/sD06yJzT/hZfv9vJfXzNi7cRdSnflASCV/trAE19+ld0m/3U+I8Z+vqtfK/GkVtdoT8YNZS4TD451nsxxYfwLK9smvtDgXxxw2v6qyP41NYr+IpPOf/2osf3Iv7ZKibLfBzhvgHLC2tVq+2kXjS/+RRZfBLy91Y6tG0+w/cbHydviheEbrDw5e/trQkd4ccLHhbe3scT+soP7gC/kvX0/34RbvtP+si2BLsD4F4gdb47yL/GE6A73P3Bt+42sn1/xJ/Na1RcPmZh/dciLDT9f5+KD532+6c9jPis62MQumnOz4+zrPEdvqbC/UKlqohsCdMHg2u08LwdF2PmIcyX3wztby0w8Jc3OfzQVOP7Qwms2/kv/oysKTTwp3ZlnhsXaXxNx7uMbKzkJth85F64vtnkmgNYHfDG4ocy+Pyfargcy6bgCzhtUPM/X0q+NG5psG0Yn2b8G8y/XnlpTZOLfzx5o4rH01+TNNKfzugpw/mKwrMG+J6nVmU87wnmKf03fhy4ieM1RTn8lj6ZfuUUE0MWfF97cYH9tkB1hjxX/CqRjv7Z6+XWgEN3hhrt+1n7h/4eb/2Jeu/KTP5t4e5n9NRP/0d7bjRZeJfL1wZdFNtdMzbK/QOY12vQc+3pNg309kubtLwvtupa3n1fr/PVkLK3f91Ky+Gx7pYnLGu35PzPH/kqHM3AdXW9UuGy/cm7767J8E5+WY3MZAEzIiDTxxiJ7HeSiG07HZtr383J+8XabP0tdtp8vHZdq4tx828+pIfZ6YTL9shxwjgXOuXzseT1/wSj7iywef59vrzXxr08aYOIErh6iTuDracB5DdKPbrhyflpSaMf3lcnpjm12hG80vrGh2MQDwm1e4Judq8prHdsclGg/894We2w51yTTr6bbrsG93bPojfReO5gQQgghhBBCCCGE6JXohpYQQgghhBBCCCGE6FHohpYQQgghhBBCCCGE6FH4eFhO08twuVwICwvDpryKdslpKHkVYo69xsSvPXeHiQfG2TrViCCnV4Hl5AH0lAd2YLGXy5/ev2ZnrYnZicX1t1wz7KZa782Vti4bAMbTkyF4H2JZoE4jpZZ8FPyELa5JZikeO7YcDq4mZx00b8PxNCjqR35KYRQ5r1bTkyyOSbQ+FXabcN0/O2eW7LQ1+gBwYqZ9mgs/PYafHsW+FvazsL+FnVzsU5vxu3dN/PFvZ5s4JsQ5nvkpOFybvbmEpI50XNhdxk9arKq3x4WfCsZPAOEnRXINPwDkku+HpzY+b9l/1vHJovVuF8YPSkRdXV2XYkYh2vLM+tzy9jwTTeMrasLVJn7yiVtNPCTajrMilz2PASA9wuYBHsM7+Om45GDgOb6szp5nqeQMZPeFP7mfqtz2PN7h5el7If3s3JBBT5riuaOopnOHJecAni85j3Au5M/zPgLOp1dxHuG5hHOdc760PqksyufsuOD5Lo/mtjXl9smPAHBMlPV+JdNTDh3ewlK7Jpg60OaprvyUFXTsp93xtomX/d9pJuY5HwDiyYnJMl9+IiVL4PnpuOwNY2fWiFTbRwWUK9kz5u3JjGvzrd8sMcLmLhZF7/Gwy+zbNtW7XRiTk6A8I7pNW65Zta20XYYdRnNa6uTrTPzg328y8YQk62Hy9kRDvjDk85/Xwux35Hn7w23lJubrjyB+kmulnfP4+7dUO5+oO5hyaHWjPZ8nkH+Kcwdf0/DTaJ9eaZ1Y80cmm5hdZZzbcmuc+dGPJOwDYux6n9fG20rt2juNHLkr82xuiCeZeSrlBZ5jeQ5lDxgA/Gy8dWDxkxbz6NhtrbLHahq5ynje5fz32Xbr+Trvf22ueeOuOSbOpDUGACzNs9dmJ1EbFpOfKiHI5vDRaeEm5nXDWnq67bAkmzvyqU/iyAPG19sA8OlW2+aaZttPx5Drk53PH22pAAA01rtxznHZvT7P6BdaQgghhBBCCCGEEKJHoRtaQgghhBBCCCGEEKJHoRtaQgghhBBCCCGEEKJH0bfrt/QOAvx9Eei/r0abfRfszJr7oztNvOKNP5qYXVFA104rdj80kfuB/RJDU2yda6W78xpjhl1ReTWNjvewf4W9SVRKDSphRwzV/LKjw7WL6tHp+xLD7T7sJh9VP1/+Rmd9N9cxc8x1ydzGlAhbc8zw+9krwM6uqeTL6g7hQbYf2MvFY4X7gJrgcHq8fetJJmafVSsfaAAx5FTYSnX7DPcTO7DYecV+lsQIe6zZG1BeZ89Zb84HPvZ+NH54PLKLzLh1Wo6aqVEcQsID/RD6zdxcSmOWnVmXXPp/Jl779p9MPCIp3LH9z/OsS2JikPWQ5MRb/wb7e/hUZ/9GYZV1CmXFWRcFz0WcA9aW1jraPCUn2sTsXmLYnch+juU7qk2cTL4+9l85/HvkAWuhecAbPuQpZO9hJjmx3LQPPJ/x3MPzGc9l6XScUqLsPgPOY1tcY9vIPsg0yn3cZnYx8thi/9Ubt80wMbvMWvc4+zmQjtWGIuv14mPXl97fRP0UQbk0z4uzpiPs/WGXC+dab/9WQesmH1KUfJRbYeLp2d+6W/buVp4RB0dY/77taxZ2N7Ez65qf3WPij17+nYm9rQH30r+x5y/Y3y48ef7ga6ATaG28bKfNZbOGxJs4juYX9hYuL7F5AAByEqwz8q2vS0zMTj2e11PIL8XvD6F95rmAr9s4t7DvFnB6vPLpWq1lj/2OQeTF5H5nJxfn6F20/uecznPoleOsLwsAAsh3xvmM5/XYAJv/eGzw8ON4YKzd5//eeaqjTR3haygAmJYda2J2ZmWF2/zWSP2UX9V5PuV+53sCfNw413EuAoCGFttP/r52m8V1tk0l5F1NCd83nut9nT7q3oh+oSWEEEIIIYQQQgghehS6oSWEEEIIIYQQQgghehS6oSWEEEIIIYQQQgghehQ+Ho/HWTzdi3C5XAgLC8OGneUICd1X38xVzFw/zjXNY0+7xcRcfw44PR+hAbYO+akVeSa+YJStS2ZnBtduc5u4hvlvX+Sb+J7TBpu4iHwaALA0v9LEw2PDTbyytMbEUzNsDXJCuK2L9uab6Oz1vr7s2LCvu5qcdb/R5OHib2QHBvvShiTZGnf2hPA+cS02O7PYfeYNdjcx7Pni78ivsjX17IjhGnl2obG/jd1Sfr7O+9r8nfHUL/wZrqFnhxt7RrhevA+1kZ1cQeQu4OMGOI8db4NdBHxc4jt4blwuF5LjIlBXV4fQUJKiCEG05Zncoqr2PMPzl5vGG5/nI06+2cTsQQGAecOSTNyPzsPHl+808dwhiSbuKt1zHmLHyOMrCkx845RME3OeAoAVRdZ1MiohwsQf7bSOoYQgex7PGBRnYs4j7ONg1wu7nDh3skMEACK6cFSyh6Sszs5v7E4JovkuLKDz+S+33DpxsslfxX4bb21ghwf7qPqTSyyv0s753M9ZcbYNpZRbuV95rHEuBoCqXTZ/JodZNxi7WNiryV7OOnIzbiq1npLRqXbsNdFxDKBzsrbRmbvpUKEvnYOcXxua7Xfs7dAvbrcLI7PilWdEt2nLNZ9vKEJwyL4xs/cA5/UTz/6ViR/6hzPXTEm3ziteZ/71s1wTXzI21cR7aO3M51JerZ1vYgLs9u/+aJuJn7xglIm/zHM6tFbR+Z5Ga8IdtG6cmmb9jkOT7TnI14Z8bvN8w/M6n/u8DgaAZPIh8tzO1xicT2fkWPcYz8u8dud5mVNJNX0f51MA2FJt+zk91OYGdjxyblidX2vi8P42N7ELbWdF5y5EzhN8nAAgt9JuIyvGtpn1Zo107FLpGp89mryPjOO6i/Lz8lzneOZ8xTmZvZTs+RrwzbrB7XIhMymq1+cZ/UJLCCGEEEIIIYQQQvQodENLCCGEEEIIIYQQQvQodENLCCGEEEIIIYQQQvQo+nb9lt6BD751Z3FNcFyYrTEOJ9cEO7O4/hwA/vOv35h4fHqkibdU2NpW9qewC4UrgNlN4k+fv31atonZH9TsxRNy6mDrVwmkGuCkSFsHzW1g7wf7KNiZkRRht8d1/V35MgCgnnwpXO+dSN/BPil2DSRG2GPPzhh3g/0+bjN7mNgdBQDry+tM3Ie+IzrEugq2ltabOJD8UezwYNjVk03uEz5uDV68N6lRtl7cn3wrPL54bPA2o0Nsv5TRcUmkOn/2u+wgpwz7YQDnsWN4m00tNu7oS2B3ghDdwYNvvX7sTChy2RwwIincxOzMuuZn9zi2P5xyEXsdXl1ZYuLLxqebeMk2600cHG99Cjw38Xl/K+UZ9lPxfAwAwX42n/K5O2dQgonZ5cTzFZ+bbppr2Gm0vsTOv2NTbW5ubHbOf+xCKSA3RQTN8+z42+3wQ9r5kv0Z7PFi3xU7tVJofgaA7dU2b1Q12Tl2WqZ1kZXV2fwcHkQ+Ki/+qI5soLw2IMp6T3j94M03GUjrmBByj/IagLfBY4HHVkq47Sc+J+ubyKvYyp47Z55hL06QPx/LztcIHR2YvdtgKw4ne/Z62p06vB7jc4/PE3ZmXX2lM9f84vdXm/j6E6wv8dXPrU/xF/Q6e1X5uqu+xZ4XPBfcf8ZQE1eTH5dzFQDMHWR9Uuyn5TbwvM75jHPLujI7x2bHBpmYrzfY8dffi2eJc2Y5uRDZn1hDbeI5jZ1cTHmd/T6er7ZSHhmdZD1OALDyK/ue3BqbS84OtteWqwqtj5nnvdQIO0+zK6qwzh6niVlRJuaxwMcBAJLpO9h5xdd2fOw3FllvGOfgAvIOs3OSr9s+2FJuv7/V6SpjuI3sIuZ81daPXfmtewv6hZYQQgghhBBCCCGE6FHohpYQQgghhBBCCCGE6FHohpYQQgghhBBCCCGE6FHohpYQQgghhBBCCCGE6FEcNVL4/v1824V8LINjKR6LRdNIvMsCeACY9+O7TLzstf8z8SWjkk3MItp6Eo1GkBSWhekscA8liS3LDVnuCwCVbivOY7FkToKVvLp32Tbnk7w3Iqhz+S8LEXOLrOh2AMkPvcGi2k0VVtTH/cTUNNjP9yWhIssKWTYYFWIlkyzSbW5xiv1Sgu34KW20AsWV+VaYmBNr+523ubyg2sTjU6zkOJrauJuExK277U6yVBIA2CHIomWHPJqONQsVeWyxBL6CxiJLDFl+yMcBADx08Kr5WNM58ezqQhPfMCWr/f95f4ToDh6Pp30c8phNj7AC2c/zqkw8b1iSiVkADzgfSLLkP7838TM/GWNilgEnh1lhbCTN2bU0FTjmcMoj/CAGb/LRcalWKstzAwvQI2hOrSDBa3GNFcTyg0A4D4XX2O3xuc3fBzjl4+srrAA9udHO6fE0n4WS3JznP5a8szyYxw7nJW9zdkR/uw13i+3nlUU2z4xKDLfvJ3n5BtpnXmNkhNt8zbmXxwLvIwAkUL/VNvJDVjp/yIuL1iT8kBc+1vx5biO/35tLl8cXb5PPmX8st/Ls60/IaP//o0PVKw4H0SH+CPlGes7jiOO9NJCnpNsHEbEAHgD+cvtDJp757ztNfO3J9gEhfB4U0jw9LDnMxK48e+6OS7b5MZgetMEP0uC8AAClLru23lBurw9mDLQPxtjc6rZxiY2DaR72pXwXRHPelrLaTl/39gASlrBX77K5h6/lQmmbhdW2nzn38AOb+AEmsWF2Lc2ycxbpA8CkNHss8+gBIxup32MD7by8vc7u8yd5FSY+LtlK3xNC7Of5IWcs1udcBTivYfg+AOdUXgOUuu0++tH2+AE9nMt4vCaH2Pfz5wGAn3vA178smudcc//cIQCAvbuPjls9umoTQgghhBBCCCGEED0K3dASQgghhBBCCCGEED0K3dASQgghhBBCCCGEED2Ko6OwEthXVP5NGXk/8oD4UcF5A9U5c03y+HTrLAKczqxj595q4sovbD16QZX1T7HLISbE+ibYDcE1xFznvIti3gfAWUv95092mPiKMPud60utU2NkUninbWBc5OjoynfV38/X8W/byqx7ZEisreXmunv/vtbBwW1orLdtZhcKH5faBltX/XWJrRUfTb4YwOloGZwUamIeC1zbnRxp9yGcnDPs8eI66xbyt9y/xB7nGydngamnc4D7gWu3uf48K9b6ENghU1Jr69GDyFXAbeZi8jzytwFAJn1nPLkBfvvuFhNfMtp67Tq6xtg7JkR3aNi9B32+8SE20RzNDqGJQdYT0a8LJwPgdGadMO92E1cse9DEfJ6wr4q9ErXknQsnX1ADuR63lloXBvuFAKeL4slV1l33/8almriwzrY5x986BbPirLuJvScte+wcnkLzJ89t7EkBgIIq2+apWbEm5v3mOZvnx7XFtSZOC7dzFXtO3LQmKXTZPuGcAADhlOMHJ1hfDPsj2dPFXs4TQ+w+s89ja7ntg7QYO15/t3iriW+fPsDR5hrKp5wn2DnD7kT2Jn6ea71049PsWo3XQewR9ac1x+qCWkebTxgQbWLO779ZZPPMTVMy6f3ftqHvHuf5IsSBwu63lj2dXx/w9cT1J9gxCjidWafMv8PE+Z/82cS8pqtusucWrxFPG5hg4mY69yvcdm4ocVlX1JAEu44GnPPwz15aZ+IJqXY+WFtm1+9T0+25XUVtYA+rw/lHcwG/31tO/ziv0sTHkz+K1w0RFOdV22uiYuqn9Eiba7hf68hD+EWRnUOnZdo8AAAJQTb/HEf9tqrA+hqj6Xp2VLpde68vIEcl5bfSOjuW2Nf2BK0prjs+3dHmQuon/g6+BmFf6LGZduxwPi2ssv0eSNeinF85n26jNQUAjEwPN3EGXePc8uYmE587zOb8tnzKebW3ol9oCSGEEEIIIYQQQogehW5oCSGEEEIIIYQQQogehW5oCSGEEEIIIYQQQogehY+HJQS9DJfLhbCwMOQWVyE0dF/N9cp8W987NNF6mLju+aWviky8pcLWygLAJaNsTXB2vPV8RE+42sTbPrzfxEXVdpt5LlvvOy3b1jGz54vdD0wp1bcDwIZKW7c8IyfexFxDXEhtTI2y9eCt5FnaS0OL/Svs+eKhGE2+DG/vKaPa6gRyA1RSvXhcqN3mFqpbZg9TK7kIdu+x38+Oj3jyjgHAMvJ6DIqztf99yS0QRp4P9iH4UPE1+1r4OMWEOvuxI1zbDTh9COwe4c+4yQHDHi/2APD22RnHrh/28LDTC3COlw82l5v4hCxb58/b6OiZcLtcOCYjFnV1de3zhhD7oy3P5JdWt4+X5TurzXuC/eyYzqEc8eyqfBO/urLE8T3P/GSMiaPovIo59hoTc55ZutP6Opppbjl5oM0BxXSe8vdxjmAXFAC8u63MxHMGJ5qYc9m2Cjsns7OoZU/nSxb2mqwvtnkuKoCcg832/QAwIcP6MopqbD+kRln/Bs937CbbRJ6v0WnhJt5DeY3zNe9zWIDT+5VLvrRYmg95TuXvSCcHFns3+lCiKaE+4TwW20WuBZz+Kf4MrxF4zmYPSSv1Ezu62EFSTXmFj5svJ1cATdSmdaW1Jp41yJ5D7C/quIZxu1zISY1RnhHdpi3XfLm5GMEh+8bMl0U214xJtPMXu2WfWW2dQ69+XuD4nmtPzjYx54bUydeZmD3CeXX2GmZXqz0Pjk21rig+F/uRF6yg1q5ryxqd12Eby+2/XUduMD6f82nOHJJsz0Geb9jBy/N6CHmB3ZTbsmLs/AMAfrSffC2YEGFzDXt8U+g6bGOR9YLxtSh7Mjmbcg5n3xoAPLnCrlXOGWpzOvcbrxvYl8buM74rwS7QSHJ1sgfTz9f5W51tZTb/8PUr5zdel/Bx4Wsa9nrxtSD3QW6FPT9qdznXIeMyrJP5wU+t//jy8Wkm5vzXtg/1bhdGD0jo9XlGv9ASQgghhBBCCCGEED0K3dASQgghhBBCCCGEED0K3dASQgghhBBCCCGEED2Ko8ahtb2wEiHf1I6yb4q7oKnF1vf6kyekvxdfFbuUuJ42hBxE2VOvN3HRp38xMdcMsweJa5SDqXZ7V4utV/dWU8w1wlx/yzXClW67T1y7zazNq+10e+xh4n73Vrv9xgbrlZmUbv0qfUiqEUK11ez1YvdTV26oLeW2Zn58unUVsOsJcNaks8uE4xaqL2dPWGWDjQfEhZjYn2ryeaz8ZtEWG88Y4Ggzu0u4xt1N9d58TrF7jGmmY80egQiqke9H4/ftTU6/0JRM65krq+vc/8NtNg4ttwsjMuN6fc25ODS05ZkVW0ravSZ+5BSKJF8Qe5Ncu+w8EU3jFXB6CotrrNchnL6D88y2D+4zcX6V/XwouZnYccTepWjKS0FefHz1lGfYzdSVb4/nInYINlOuC6RcyPtU5WYHiXWUAMDH2ypMPDTeejbZxcKOQXY38ZzMbkee7/i4DEywHhReXwBOR2V8uM2fnNvYY7KywHp4wvrZ48DON15T8Drq+tc2mPh3Jw90tJlzUxAdO3bWcD/zPnBe4u2/tdV6FX96bHqn7391vfWnAsCJGTGdfobPGV5zdPShuV0uDJWrURwAbblmw87y9msahq/qeB3rWIN6cfLxHMPrWP6OY+feauIt79tcsyzfumQHRNp1K+dLvs5ixxHP+4DTwxXnxWnbkZ3V1mM0Ns06i1jV+saGYhMnB9troHCanxh2BALA/UtyTXzt8en2DZTvuJ+4jVV0ncZ+Kc49nxbY43Lu8CQTe/NiltDaOpk8XzyPMxtLredraUGtiecNsb429qlx/r3+ta9N/PBZwxzfyW5N9iXy2OF1A69TunIZb6mx14oDI+25yuuOd7aUOto8Oc3mmg92kBc4zV7/8jnT0aF17OCkXp9n9AstIYQQQgghhBBCCNGj0A0tIYQQQgghhBBCCNGj0A0tIYQQQgghhBBCCNGjOGocWrnFVe315uzwYOcBe0nYHxTmpU66kupv2eXAfovsuCATJ036hd3eFw+ZmL1K7NjielzeR66jBoAPtth63LEp1gfFdcrsEmOHBvcbO7nK3LbuekVpnYmTqb58MrkqvPHOFutSGhVna+AHkPeDHVuN5GdhF1os1eCzV6Av9RG7U4CuHRv5VfZYsuuJ/WkBVCfN22c3jy/tc1+qwefPA0BhtT2WXMOeQH4WrrPnz3O9elq0dQ/w+/n1Cup3ds4AzmObW2H9CFxfzvvd0cdS73bhhKHJvb7mXBwa2vJMXml1+3hxkXOBzyE+7ZrICbKpzDoYACA5zM7z7EJZTh6kiWlRJs6edoOJC8nd2EDnMc+PSeTKYMcRtwcAlu2wbYoKtPNbIm2T51TutwpyhPB5vLak1sRvbqw08byhcSbOibEuFwDoT7nu2dWFJp6RYd0VxyTbOYLzPfs1imvIQUL5u4Re53we7mX+4zmyrNFuo67ZHqtjoq0XLIvWJNUNNH4pb6wqrDXxBPJJsrOL+wQAlu20/pbsKJuvOQ+8vdF6RsbTmoXdjOzh3Fpab2Je27F/LdJLP/OxYDcLv15QS/k94Nt8Xu92YcrwFOUZ0W3acs3Wgm+9wDwPs6+R50w+N4O8XB98XWQ9R9VNdhu83hqVZNfeOdNtrqlY9qDdHs0vX1Luyomy8zLnFvbjAsBfP7M+qnOHJZo4yN/utze3cEfYifvednvNlBhi1+bPrbCOraxYO59dPjbF8R3sxXyacs3AGJsbzh9pt8FusQK6nuDjxHNiKTl62ffI7jMASCF3GF9zbK6xY+eYKJtr+HqU53m+K8HXB+zZ5GtTvl4GgC009/N1GLehiL7z1Y32WjM22I7HaeTw3VZhv29kSriJc8vt9UlypNPlyWufz3PtsUiPsDm7ot4ey93fuIwb6t2YNyGr1+cZ/UJLCCGEEEIIIYQQQvQodENLCCGEEEIIIYQQQvQodENLCCGEEEIIIYQQQvQojhqH1gdr8hEcsq92dHCSrc1eX2DrfYem2BpTrt8N9+IJCSL3AnuK3vja1t9mR9ja6pwE26boCVebOO+TP5s4mL6vntxP7K/imnkAaCA/SliA3Sa7Sbiel7+jrV63/f1Un86esThyQ7GvZddup3ODXU0JVA/O/pWmls59F9Ehto6a6825Hp3dUdwnZVSPDlg3EwBkxNhabXaLcD+wj41rveton9gLsr3M1mqzkys0wOkiYOeVcz+tn6Ur4shFxm4CrnlnL0Atucn4fAGAVhp/G4usg4g9NzzxdRxvbpcLmUlRvb7mXBwa2vLMJ18VtOeZ7Dg7x+8gZ0IazQPVdE740FwEOJ0+fJ6++lWRibPCOM/YOJncjUXk1OL5sJrOQ8577CgBgIIqmz9jaf5hh2BmrPVCOH2Rtt/8KNeW1Nq5iedsnm95fgYAXhaxk8YxP9EcvGcPO6/s53muqqc2cb7m9QQ7tgCng43dZOxGqaO8ws5MdoVyP+2lPqohJw57UppbnPk8hhw0ftTG7WXWQ8JrL16jhFKbt9HnebXL5yCvL1y7bAw4XT7byZVybKb1erEjp2PicblcSEuIVJ4R3aYt1zzz6WYEBu9bB03Psf6exZvLTHxCpvXR8vqN13eAc83G8+ybG+w1DeeKkwfHmzjm2GtMvO6dP5mYHUJFNMfVUH5kjxIAtNC8ys5HvgYJpTmKr2E4J/PamtfenLs4L3i7pmHH3phU6yLjeddx3dWFX5Eduh9stR6wSZnWB8m5h48DABTX2pw+PNk6shroepSa7Jgzj8uyrk/2S/Gcu6qwxsTB/exxHJEc7mgzr6Y415TWdn5Nw/N4Knkv+T4BH2vOVRur7H2H04+xvjdv37kyz+73OPJWco4v+sZrXe92YfSAhF6fZ/QLLSGEEEIIIYQQQgjRo9ANLSGEEEIIIYQQQgjRo9ANLSGEEEIIIYQQQgjRozhqHFolFbXttaPsm+Ie4NrXSrfTi8QEkkskkdxOe6iIeBf5JNiJxbXcaZOvM3HFsgdNzK4oR/u8OLTYY8QODnZgcO02w/XiXxRVmfjMY5JMzB6S/n72/mpjs7PefDvVVrP/iev4XeQJGZJk64e51ps9IuyxYZ9LTrx10ribnM4N9qX4+9ljwXX/XBO/pqjWxMdTzTtvv8Jtj2s6Obe+LuzcGQc46/aLqD48iXwHn26vNPHoZOsBYB0Qu07YlcL+hs2l1oc1jGr2AacHwnEs6f2bS+w2O473o6XmXBwa2vLMtsJKhHwzXtjnx3MJn1N8jvBcBgCB5KiqJW8Re0hKyZUSQnM6z/FJ5NTiPMP+Kd5HdowAzvNsXXmtiU8bYt0RnH/ZC1Husvl4bZnd3oi4cBP7kWcslebDBi9zNvvOOJ/z/NhM+ZedV+x+2k25lPuR58OBidYZ6K3NnJ6DvfjMOsL9vHi79e6clG0dOOxHe/mrQhOfNzzFxLkVtg/ZKQc4HVfshGPf2ofbrf9lYqp1r/D4ZF8kO7I497IzJ8tLm3mdxLmMnZe7Wvaf3+vdLkwYlKg8I7pNW67JK6luHzM1jZ378FaTc4hzS32L/TwAuHbbc+W0gQkmZr8PO/TYccUOveGzbzZxwZK/2O+n84jnBvZJAsDOCrs+L3HbHJseaR1XPG/zFQ47sl7cUGriGydnmpg9Y3xdx/sEAC+R9zKafIvpIbbNK0rtsZw/0s67PD85VxGWUvL+ZpEHjL2ZgDOn8ljg2wqFNfY4bKu18/6sHJtr+JpmZZHd51mD7PsXfl1s4tMG27EKOK9PeZvjUqyP6q3N1hF3UlaciXk8s6vzP+vtcT0+xV63vUIOukvG2OMIONdTfI3Da8qX1xaYeEzCvn2qd7tw3JCkXp9n9AstIYQQQgghhBBCCNGj0A0tIYQQQgghhBBCCNGj0A0tIYQQQgghhBBCCNGjOCodWqwmYf8U162yz4IdSIDTUcU17Fxvy53OLpQB5GbaS4cp5thrTPzxK783MbtSUqKsWwUAqrnmnWrS39lo68WPz7A1wOVUe83+Fl+qJ+edXp1fa+KiBltnfWJmjKPN3M9Uuo2CKltDz26zFGqj04Vij5Mf1SyzJ4Drpr2dTuysYpfI0lzrGpsywLnfHWHvB4819ohwXT97AfjzgPOc4Pew24Q9c+yMYSdNSpT12HTlBooLs9+3qdh6eQCn14s9EbwP3OaO573b5cKQ9NheX3MuDg1teWbDzvJ2hxb7PDLJTRFB820uvZ+dgoAzN7Fvz7XLek+iyGPCDo94ctWx+4nzzJ//dqOJx8RZVx67ngCnq4n5LNf690Yl2W26aL6KpbkgiOZ49iKW1FiP2MLN1hV1/nDr8AKc/cLOy5bWzp2V7G6qIO8Xz6/sIOG56uuyOhMPiXXOSS0k0eKcv2KndYYMTrDbYOcW+zne3midH8en2/UAH2c+LuxFAZzrIN4G9wPnc15iNNLnOR2zB2wP9Xt/Or9Ka+3YAZxjg90sfM7xGqPjPrldLmQmRSnPiG7TlmvKqr4dM7xuZd9tEF0PrNlZa+KGVqeTL418U7xM5HUk+302Vdo12ilDrPeI16kpJ/zCxI8/fouJo/vbOXV0qs0TAFBGzshUWme+tM46hqZmWi/SZ3k2F80YYF/nfeS1OHuT3LSPp5IrCnBeq3FO/zSvwsSxgXb+GUP9wHMeHydeQ2wqs9cnI5LDTezNnfz+NptDj02xLsOX1lun1U+PTTcx5z92G3bl/fKntdFGcuxG9Hf61ZrpWIXRe+LpWo6vJcPJbfaHD7aZ+JcnZpk4jtYA7BI9JtnO90+tyHO0mb11fA6G0nXWBrrWjP0mV7ndLozOju/1eUa/0BJCCCGEEEIIIYQQPQrd0BJCCCGEEEIIIYQQPQrd0BJCCCGEEEIIIYQQPYq+Xb+ld9DY3Arfb9wahdXW1cS+C+ZvX+Sb+PZp2Y73hAbYrmSXA3u3+lIxLNdR15MHhH1A7MyactbtJl755t0mZr8GANTU7zZxGfkiNpLTZUSCbVM09Rs7s3gfm6lueky6rf3O2WXdUl6a7KhhZ6cVu5m4fryGYv++nd/TzaM+yCa3Gdd+76ywrijA6dlihsTZmmaHD4E8H+wN20v/0NTSubOGx1pogLPePJ/qx9mvxrXc0SF2LHCdPsd8vripH9lNwA4a3h7gHG/cT/mVdp94/Hb0tzR14cgRwhv1Ta2A376xXNVkx+xgP+uXYo/E4yus3+NWL3mGvVpOX559nR1A7GasII8ce4/YmXXdz+818bLX/s/EPHcBTscQzx1L8qwfKjbAzpc5CbbN7FHkOZ6/L4PcZSfvsY7CsEDn/LeHRCScf9nlxG3YWmq9mxHk3wih+a+YPF/sjhqeEG7idSW1jjZPTLceE25zZIBtA3tK2PnG/TI2KbLTNvLYY19bTIhznZVL+ZLn7Ow4e+wYh1OOFg3VtMapabAx5609zZ27iACgD+03t6GSvpN9kh0dbw27ne4iIbpDUc0uuFr3ja0vC62HNSfSrinZQXr3R9b/c/8ZQx3bZ6dehduOa16L96drnJwom++KaI7jPMDOrMsu+6OJF7/4vyZm/x0ArC2rNfHb28pNvKPStmFYtM1/E1PtHMquJr6GYT/emcckmZjzAM9HgNO1xLlkYmp0p6+zb5bno67csaNS7HVYFbVxY7nNzwAwKt5+hr9jdlasidmNyLHDTUb9yv3GRz4jyuYJb9dc6wvsfrC3l68fODfwfYLTh9jjwvM85wG+Bi+osvch2EcKOHMNj/nVebUmjqU2V35z3dRAx7y3ol9oCSGEEEIIIYQQQogehW5oCSGEEEIIIYQQQogehW5oCSGEEEIIIYQQQogexVHj0Arq3xfB37iD2KlRQrXdUcHWNXHPaYNNvNuLX4driLlGmOvLd5FrpCv/T7++nTu32Jk15tRfmnjFG7YeHQCSIwPoO+2XzvTYGuFYqiEuJ69RGHlBuN6cvUhcf86eEfaQAUBf8nRwLfWGYpeJB8RZ/0ojOTIqaR8yeWzU2zrn+CZbm83HnR013uAad67lZscb14Pz2NheZuv0o6iOmscK1+AH+nuRlRHspYkLs22qphpt0rc4XCR8bPl8Yb8Q16dvLnc72phK/jQez0k03tnF09jRW+fpuk+EYGLD+iM0dN+5EUpjtoocJOxkuHFKpol5bgEAHzqx2NExJMl6S9gTx14jdmZxHmKvAzuzjp17q4k5DwHOvNCfzrth8fa8HJEaZmKeK3j+Y28Sz/HsOeT519nLQAN9hj2GeeQYHJFi2+xHThCeP3mNkVtrj+PAaOvA8SM/5YQ063oBuvZ6sf+R8zfnPnYxsueQvSc8djjfs48GcK6LokNsv/A0zF4SN43njVU2/0/LjjOxi/pkW4Xt97RIm0OKXDYXA84cz+OT8y33Y8f1I7tihOgusSH+CP1mfp9KzqIScify2vrJC0aZuLrB6XbiOayEzgV2Ag2Ms7mHHXzs6I2jdW10f5ur2Jl10rm/NvE7z9/laPOEZDsvBtO5uGhLqYkHkJ9xS4mdD9j715XftpHcxzxXtO5xnu99aW7ndcP728tMfBzt4/Yq2+aEEJtP2Z/2wQ7rFWP3IedHb7mGvV08vvj6YButU/jak5Pwwg3FJh6faP2N4fR9nAd47Q8AzdT3ZXU2/0V2cf25o9z6Hmua7XfyWOA21e2251hqf5trVpbUONo8mNZzfJ3Eziy+xmnzBu/d7XQO90b0Cy0hhBBCCCGEEEII0aPQDS0hhBBCCCGEEEII0aPQDS0hhBBCCCGEEEII0aPw8Xh6tyzG5XIhLCwMX24uRnDIPi9FqcvWctc229rW/r627jor2rol2A0FOP0QoQG2DrqU6sfjqX48iDxK7BxiXwXXCHM9e3GNrXcfe9otjjaXf/6giffQNtmJ0UBuEq795lpsdmiw02MP1RwnRtj6X94HwOmd8ad+qSRPDXuSuD58U7F1MbFnpIbcAtXk8BicZF0nRdXONnMNO9dmcxtLqbab3WW5VMsdST4W9rMUUT9Gh3TeHgDYS8emivY7McKOXz6W7LkJon3kscOw34UdWuwZA5znVD8aj8tyq008Pt36gTqeYy6XC0mxEairq0NoqD3GQjBteeb91XkI+ibPuHbzfGc/426xr49MCDexN1cjn2fs2WJ/DzuyvHmMOtKHHF3shWCHSAXNt+xuBID1i+4xMc/BoeTYWpdfZ+JBidYjwe6L8CA7NzA8l3Efsm8GcOYZdqmwc4bzcwXNX+X1Ns6KsXmG28RupdRo69toIFcL4DzW7O3ifF5J3kPOC0t2VJh4bLL1mHDe4e+rpT5i/wcAJJP3kPNnapRdE9AyB1W0D+zkYo8Pr7PW5NmxxrmT/WsAkEieEt7PJfmVJj51YAK18dux4na5kJUcrTwjuk1brvns68L2a5q8Wuv0a2y147aWfD+ZYfaahud9AAj2s/MJz0Hvko+K/VUOzyqtIXldzN5UnhM3ldi1+uzzf+No846P7jcxX5PwPM1epEjKJZyrOCfzWp1fH5Ziz+mvC63jDwDSY+yx4DlqZ4U9thE0R/WjtfRG6qfBCTZ/cptf32yP4/9MzDDxF7RuBoAxqXbtzNcQvA/ri+x+c5s20HVYEl0LRtBxYdcZ+x/ZzwwAO8iXWN1kx+P4VJvfeLzyWou9Yby24jNqdUGtiQeRc47XUvu+w+43b/Pl9dY1Nu+YRBO3HYejJc/oF1pCCCGEEEIIIYQQokehG1pCCCGEEEIIIYQQokehG1pCCCGEEEIIIYQQokehG1pCCCGEEEIIIYQQokfRt+u39D5SI63cMMfPytlYdvrC6nwTnzrYitcAp2CVpXgbKq18NIREuF/kVZl4TIoV1LlJTsom/xoS2CWTuJQF8AAQe9w1JmZ5L4u4W8lq3J+kkSxcZLHtp3lWljo+ye7jtlIr7UsmISzgFPKzPJ/FsyzyY/dlLIkp3busPJOF7ByzuNIbLDhnWSY/l4GPHYvruQ0sI/xsu+3noYlhJuY+CQtwTgNLt9vxOCk72sS7SA7Nx56Pwz+/zDPxBSOSbRtorLGMNyyAx6Kz3/nxFizDZwl8PT3koF+H8drgRQYsRFf49e3Tfi4MirDyzfwqK3edkmPPqf9+VWTiYD+n7HwcyVj5YQ3vbisz8YzsOBOvLrBzbFKYnWua6LzmB3Ww+JvnDs4hADB01k0m/s+/rMx3YqaVCbPIl+MUygt1u+y5+sSXNl+fP9zma56rMkgoCwAu2mYd5QWG80AjzS0JoXbOb6L5kR/ywnJyF32/t7TDD57hdQxL4fkhGpspz4xOsmONt/fuJisTHpm4/4dsAEBwf2eeKai050QKiacZfpYI59K7Fm+18awcE3Pe6E+5k/vE24Np9tA6iB8ANGtAvIm5HzqKo709+EGI7uBuasXevvvmqdhAuyYM6EfXOCThfmDJdhPPHWTHLOB8eBYL0jeW23NjQLjNRS+tLzHxOUPtwxFYds5r87VltSZm6TwL4AEg88TrTfzBS78zcRKtrXnt7U8PleC1Ms/zC9ZZKffZQ2w/vrSu0MTTs2w+BpwP+OA28YNZ+LpqNy1VY+nhHixcn5Blr7uui8ui9tjcxA8HAICtdK02JNmudTifjc+037l6Z62JM0mMz59/c6MdS2MSbK7h48RyfwD4dKcdr9NpbcQPquKHpPADwm59a5OJbzjByvR57cT75EMDvtTtfDgNS+EL6KEpPxmTamJ+GE1bt/D1Wm9Fv9ASQgghhBBCCCGEED0K3dASQgghhBBCCCGEED0K3dASQgghhBBCCCGEED0KHw8X7PYyXC4XwsLCsGpbKUJC9tX5BpA3oZlqkvuSs6OK/FgDqR4dALaV2Zri1zZZl8nPjks3cTW5T9gvEUpukhLyYySQ62Fjka2TTo+xNfR+fZ33Lqvctg3sOln91t0mZhdEkL9tI3s+2MEVSO9n/0so9QG7oQDgqwKXidl5UUt1znws68gBMzzV+qW+yq/r9HV2qby92dZ2j0u0df4AkBhh28h+FvZ4cI0813JvKLJ9MCjRjsevC+3r7D5hvxV7Bby1oazOngNZcbbmnd06geRb4GPPLgJ29/hSfXk1HVeuJQeA4Slhjn/rCNews+emo8fL5XIhJS4CdXV1CA21fgAhmLY88+XmYgR/k2f4vGVfDrsxGsmlwfMt4DxvHllufVE3TbE+DJ6T2U2XGmXzxBsbrRNk9kDrPeHt8dzCjkMAWFtUa+J5P77LxFvev8/xmY5wHuDc2UJeJO7nGHIOBvt3PjcBwNq8WhOnk+ODnZl8rHi+5Dl2MbnOZudY9wrPh18UVpt4ckaMo83s/evKh8beER4bhTTHDqY88/J664eZS27RdUU2l45Ns94TACiqsesaHt9Z5DeroH5n1xj71Ni5FUK+K84JvD7YVmHXdQAwIjncfgf145YSuxZLIy9YRyec2+VCRlKU8ozoNm255vMNRe25hsdxdaOdI6ubbLx7r50jZ+Y43U4fbasw8UtrrTPvL2cMNTHPH5zfEmmtvizXelqPzbBr50e+2GniC0elmJjPO8Dpdpp2zq9M/MIzNo4NsG0KpWsWnm/iKJfEk8OvnnI4+/WavbiMXqecOzHFujVrGuycVNZo52Xu5xMybW54n3LN6cfYeZrz6Qvk8hwV51xXZ8cEm5hzDdOH1vNRIXbeXrTZjq1ZA20+fHalXeckk5MyPtDmNr7+BQA35QZ2YnGOZ38iu43ZKce5q6t1CbvTdlZbpxwADE20OYHv1vA2eV0yIH7fcXK5XMhI7P15Rr/QEkIIIYQQQgghhBA9Ct3QEkIIIYQQQgghhBA9Ct3QEkIIIYQQQgghhBA9iqPGoVVaWdteO5pfZWtjk8hxRKWweHFtgYlPH2JrkAFnrTS7GILI29FErhGuv2WvUiN5jz7LrTTxxgpbfzsz09ZhH5PsrJtl3wnXi4865ZcmrvriIROX1ln/BTuzuIaYvWD8+nbykA1Ocra5dY9tM+8Du5bY5cSOjaYufFL+5Ebhk4WPSxB9HwC4yC3gorHBji3uFx4LeZX2WLPHhr0ifIbvonr3fr7O+9otezofn1wTz54b9tLx+cDHpYJ8bo4+6cLpBTj7wY8EKmE0Pn1oHzqOBZfLhbSEyF5fcy4ODW15pqTi2zzD82Ms+TeYf62ynog5gxIc7+ExzA4iHvPsx0ghZxZ7IlLJ9/MxeVSW5Fkv0rB4O1+eO8J6TgDn3MEOwZzpN5i48NO/mLiWHCLsm+xLLpUGmpPZmfXFTuujOi7T6T1keA5lZyDnd24zuy6CaB/28qKDoKnK4e8AnP3A3k12hPBx4aUg+2iy4603hefwVspbnBvZ9QI4nTIRQXYO522wZ7OY9pGPNR+n/FqbOyekRzra1BEeKwAwNtW6wHhNEU1+GD4uHXG5XEiKlatRdB9vueaz7fZ6YFK2Xf/z+uuJFTbX/GS0c94Oo2sa9if6+9HaupFfJzcszQ+8ll+SZ3PNioIGEx+bZh1+ZwxNcrSZz/c1RTUmPu8nvzNx+ecPmpj9tMmUL/lc5jmQ56uPtpeb+OTBzpzOcz+7lXhtzPP0x3n22E9ItHMaX3fxHMvHqYjciXHkCQOAMlrbVNF1V068PVbsV2MXGa8z4oPsd/K1IE+pbtp+d65p+Bqc107sW+Txv7qw1sQDom1+ZEd0Oq2tuM1fFFqnHABMSLZrE/ZW83qQ97Ft3eF2uTAwNabX5xn9QksIIYQQQgghhBBC9Ch0Q0sIIYQQQgghhBBC9Ch0Q0sIIYQQQgghhBBC9CiOGodWYVlNe+3oFnJD5JAbgil3WV8P+ywAp2Njfal1jQyMsTXFTHy4rRlmd1Ml1VH3pXpfrlFmXwvXCwNefFTk+Yin2umoCVebuIzqz7eUuE0cSPW+zIZy6qNoW9v7yJfWXQYAd84cYOL+VMfPrhKuKeZ+2lJux0IgbS82xPbjLqqRZ48Au58AoJCcbVyzzm3KINdJEXlu+tHY4DawQ6uJPCUvrC008YiYMEebR6Taf+Pxxz4UHq+B7K3JtS4SrlefkGFrxX+3eKuJr5yQamL2XwFAP9omO2J4H3ypEL+jI8blciExJrzX15yLQ0NbntmSX4GQb8bLf74uNu+Znhlr4gY67wtc1u8TE+B0brH/orDOfobzUHaMzW3sReE8wl4H9p6wX4Pnia8K7JwOAH7ks+Bcx3NF8qRfmLjkswdMzA7BvbTTMZT7VhTYuScl1M6v/1pnjxMA3Do1y8TB/p27m3jOZZfKG5vsdxyfbB037NT6PN96UaZkxNj29Hfm1lLyCrLXq4a8Xjxnc+7kKZbzOc+v3Acvr7N5Zmul7TMAuHRMsomXF9ljdXyq7SduEztFFm0pNXFoP/v61AH2HLzrPZtn/t846xLy5irjcyy/2o7Hfn1sv0aRU6vj+HS5XEiLl6tRdJ+2XLO9sLI91yzaUmbeMysnzsSsr9tRbv1U3mhstblgbZn1S52YZs/Nmia7vhqaaHNDV44/ngMr6bprQILNZTvJGww4/VLs2GWXcOxx15h4x0f3m/jzndZrlBZmc8fjK+0cNzzRepLGxFnf3oNLdzra/MfTBpu4Ky9XDbmZOGd/SE6tRuqD41PC7ff1tf1e32LzxKhkuw8AsK7I5vmI/nZdwXNkAl0XrSi0brPo/jZnR9I6JyPG9mtDs+2TJ8kJx2sCAPjZcRkm5utVnus55mvN5bSu6EvJaUyK7bfb3tpk4muPt+3x5lr0oxzNOZed0bXNNp70jUvb7XIhIymq1+cZ/UJLCCGEEEIIIYQQQvQodENLCCGEEEIIIYQQQvQodENLCCGEEEIIIYQQQvQojhqH1idfFSA4ZF/taFp05/W47ODYS3WrfbzUuuaT14M9STHkYuqq0/k7axttXXM11VFHU5sbm21ddST5LQBnzS57thpoG9G0D3FUf75+0T0mZncEfx/XA7Ofip1e3trYn/qZXQHN5I9KjbLHnv1S9TQWIoNsbTj7rtgPs6rA1oYDwPj0yE4/U7fLHlv21AxKtP41Hq889tKp3vzrYus+GEk19OzDApx1/KV11nfAfrVd1I/sYyklXwL7WPicZG8dnw9xYU6/EH8nu8bYu1VL9edhgd8ea5fLhfhoObRE92jLMws+24LA4H3na3aUdX6Ekr/qYPJMBZ8XlL7ZnciuFJ7/civs6wnktyqusectuzB49cBuvH3/Zs9Dnivcu+ycyi6mhOOvNfHX79o8wy6ojucx4HSQ+Pp07g0DABfNyeys4n3iOZSPw07qZ/Zz8Ps5z3Ab62g9ADhzPHtI6imfc78nRwaYmPPvh9vKTTyDPD0bimye4ePAOQNw9gP7ItnhxuOTfWoRlK95jcExn5PczwF+zr/5srOS1zWcz9lb1/GcdblcyEjs/W4TcehoyzXPfrq5Pde0uXLa4Pkhheb9Fjq32ScKAJvJMcQMTrLjldfavFbnXMVO07VltSaemGq9qvz+eC9rQD43q9g3RedmKq07M0+83sSfv/Z/JuZ1JzstHb5lal+luxlMVaNtY1KYnYfL3DYH72q1/XxCNrnMyJXIl/ixNA9zruM58u6PtjvafNu0bBP3o3zIbqd3tlrH2/yR1lXI43VdSa2JB8fasbay2F5nHU8+Ny/D2XE9wA43dhfz9S+PpRXF1qE1PsmOV86n68gvuoeOy/AUp8u4q/VVH1rL8HVa2xrA5XIhLaH3uxr1Cy0hhBBCCCGEEEII0aPQDS0hhBBCCCGEEEII0aPQDS0hhBBCCCGEEEII0aM4ahxaq7aVIuQbh1YMuZ24DpU7hMtxW/Y43U5cd8zeDq615pr1YnJHsA+DXSaJ5JLwpdpa9jp40SQ5aooLqqyPgl0m7GPhfRo66yYTb1p8r4nL62z9eCL5WLgPP86tcLR5Wlasibmf2JPUSsfqv+uLTXxiZoyJ2R3FNfnsuWF3Cvc74PRysE8qiPwsXHudS/3O2zsm2dZEs0uKPSVc559XZbcPAEOTbD03+83YXca+H3a8sSOGPWB8HLm+nZ1wPHYBoC/V8XM/hAd27kPr6GdxuVxIiJFDS3SPtjyzOb8CId+MF86sgf72PGSnwqjUcBN7S8wbyVOUFWc9XXxeFJKPj89bnsO7ykMhNFdxXmH/HwCkRNn5jNvEbeD5MSTAfucxM22eyf/kzybOrbB5LCvWujG4Xz/d4cwzkygvsB8GlGe4XxZtKjXxZNre9rJ6E49ICzcxrzE473ib/3g+K6iy/RxOfil2YnK/v7qxxMSnkTOL/TPsFeP2LPHSz9Npm9yGUDr2/SmXNVFeYtcZu1nYRRpOY4/XC617nGch53z2EfF45VzY8Zx1uVxIPwrcJuLQ0ZZr3l21E0HB+8bMgHibB/iahscszx+8vgO8OITIYTUs1a4Reb3Fazi+1GTXE+8De4jZfcj+W8B5XfVVoc2X2ZQvv8irMnFmhH39uLm3mvjL1/9o4twau3ZOjyAPE82Bf/siz9HmW6daH1Usebocx5LmqOdWF5h4VradU/3oWPM1Dq8J+DrM27Ujj6cv820/xgXanJ8dZ/vls9xKE5c22GuS2QPiTczHOjHCbp/XRVtqnP632QPtNnmdwH7Grpxv0XQfgZ1xSXR9+/kO20cBvjZP+HjxfnFO5XUAu7MdueabtY/b5UJGUu93NeoXWkIIIYQQQgghhBCiR6EbWkIIIYQQQgghhBCiR6EbWkIIIYQQQgghhBCiR9G367f0DiKD+iH0Gx8I+3vY9eDaZWu7ByaEmJhrwwHvTouOcD3u+jzrT2GPSD+qnf2iyNbfnh2VbGJ2NzVTPa9DBAan+4EdQy3kjwj0t8MlimqI2Zk16KQbO32dXVDsqzplcIKjzfd+vN3EY5NsPfBJOdaxVU/H+sxhSSbOq+zcG8bOjdAA20cBdNxX5tY42syuEvajsA/tPfKvDIm1roI4qrFnbwCPX+7nJTuty+Ts4XYsAc7xyh4uPmdaWjr3dlVRbTefg+xLiKPa8JoGu4/sPgGc/dqfxhO7B9iL82WHY9dQb2vyhegOu/fsbXeJsH+Hz6lk8kCws4TPW8DpjthcYl0Rw1LsXMFj3I9cixyvLas18WlRiSZmJ9Fu2kf2NAFA3S6aK/Z07h3kfvKnOZadWamTrzNxHr1e3WDnniDKY1MH2JwBANcv3GDiOYOjTezabfdpaqbdxsmDra/j7Y12Tj91iM1tnIe4H3kue/1r64IEgFEJESbmObWWnDU7q63/pXKX9ZicP9zmSvaEbS62Yy85yjq1nl1l3S7XTMp0tPnTbdalwm6WMal2n9hjwmNjJ3lR+tL4TiPvF3sX2cUyKNGu/QCnK4Vz3dLtdp8SQ+05+8HW8vb/b6x3ul6E6A7JkYEICd03nnmOXUj+uxDyN84flWLideT0A5zOKp6neZ25eGuZiaupTXMG2TnxkS/t/HBnco6J+RqGcxmvewGn/4nXkbwPaWHW7cRra3ZmjZtzi4lXv3W3iZfk2XM/g7Z/56yBjjbf9tZGEw8il9iFo+z6nF1ll09IN/GHW+z6/oQsm7vyyZXMa4oQusb571dFjjbnRNh5keftJPIA/2OZdYdNTAk3cXKInZf5OmxVXq2JuQ8Wbik38e3TBzjazOM1ldyevA/Nu/l6wuYK9lXl1tp8yuNzfHqkiTdR/hyc5HRb8RqQ0pljvLOnbsGafefYroajI8/oF1pCCCGEEEIIIYQQokehG1pCCCGEEEIIIYQQokehG1pCCCGEEEIIIYQQokfh4+Giy16Gy+VCWFgYiitqERq6r0aVXSVch+pHXgWuu/ZGf/IocL0ue0Pc9Hqpy3q50qJs7XUo+Su4Ppf3gX1Cjbtt/TkAfEr13meRS4l3O5f8FFyDvLHI1unGUj06O7W+WnRPp9/n4+Ps92iqreZ+4DpodpsVUP04OziaqTY7gfxWtHns2t11Xb/D80G+lBhykXGb2dmWHmPHRlmdfT2MXGjsQuBu9ebqCaTxzM6rYBqPvI88Ht8ip8OwuHAT76X3s/uE28MuM8A5xnlqc75uP9/Rn+ByuZAaH4m6urr2eUOI/dGWZ3aWVLePFz7PeK7oQ2/gvORt/uO5otxlvUfsBOLv/Kq01sQjEsJNzG6KSJpv2RvRSPNCMuUEAPjn8nwTn0LOqhjynPAUym7HDZRn0mOsfyONnFrrKc+EBNi5i+cqAAgmz9Y2csxkx1nPCR+qheS4mp4dZ+JlO60T8/hMcnTR+oDXILyeAJz9WFyzy8Scm3jO7uNwmdntcxuayGnDji2GxzsA1JCTjY89u0VryIfGuW7RZusqG0F5hmHPHbvL2Je1r032PbxffM5xfs7p4GR1uVxIiYtQnhHdpi3X5HXINbkV1t8TT+tWnuMcucXLZSC7Btknm1tpv5OpbLK5KTvCzplR5JZlRxGfe+xArSEnIAAsWGfn3V+fZF1KPAf+8s1NJr5/7hATv7fResEGRNt9GHXKL028nJxbVQ22D5panfPJxKwoE3MO5nUsO/vW5VsfM7N7r93eyNRwE/Pam68HvKRHxzXKplLrHsyg61e+Xvh4u/V8zcix+ZGdkgkRdjzzNU0d5UuHQxpOpySvXXheZ08vO9weW269YMclWd9jXbNtU0g/2wfRQXb8s78NcF535VfbfuF+4HsXQ5L25RqXy4Wk2N6fZ/QLLSGEEEIIIYQQQgjRo9ANLSGEEEIIIYQQQgjRo9ANLSGEEEIIIYQQQgjRo+hcetCLaGndi5ZvapPrqBac68kTw20t7V6qYWZHAgDkFtl68kD/zp1X7ARaUWrroMdnRJqY3U6r82tNPCbd1u/y+715QsYn2e+oIj+Kw11SbtuYRHXNiRRzLTY7s4bNusnES/7zexNX77LtAYA9e20NewN9Bx+bAfH2/exe4n5Kigjo9PXnVlsfzPyRKSbOp9pvAKhrsuMtM9rWl3flzGJfCrtR2J3Cnhv+PLtT+DgDTicM17QPTLD9ynX/7ANKC7H7nBxp+5k//8r6QhNfMDLVxOxeAQDSbjmcMHxs2anVsd/r3fYYCNEdPHs97U6KIjpP+/vZOd/huqPxur7E6cYIr7FehxQ6j9YX28+MSgk38ZsbrTdx9uB4ExfX2HFfQnFGrD2P69m34SXPnD880cT+1A/B/jb+hPwaEzOsXyqL2lBNXiV2Zg2lPLPohbtMvKHK+j8AYGqG9Xzx/MROLfYazsqx/cptnJRl94ldGQvWFpl43pAEE7OTBACW7bBeLvaYdJVn2LvDbih23Ly31fqqTkiPMTEfZ7eXfL6l0vrQovvbXMbernUltSYeTd6S0Qk2Zgema5cdr499aT0o1xyfaWL/vk4XC7t9unK+8dhZsu3b8d1Yb/dfiO7S0NyKPt+ss9irtL3MXo8MTbHeHD6v2MUIAFvKak0cRU6hkC6ucR74NNfET/9olIl5/fWf9XbOO/OYJPt+WlPyeQUAZw+x8249fYYdQ8MTrX+R+yE9ws6hS8g7zM6s8XNuMfHbz99p4gK38/ogv9Lm8GKXXTe4W+w+jEu2c1w0ucjYbZhJ+ZL38RXyPZ4z1Pb74m3WIwYALZTnJ6VYDxjnmsVb7TYaqY3rCu26ZXhKmInXkids9177+ehA2wfsZwOc14ZraHyPTbT92pWfNJDy4ZBEe47x+PzlGxtN/PDZw0zMfmdv28ikdUYknZPFlNO/Lty3tmlwO9c4vRH9QksIIYQQQgghhBBC9Ch0Q0sIIYQQQgghhBBC9Ch0Q0sIIYQQQgghhBBC9CiOGodWX18f9P1GsrOhzNaTDk8MN/HuPVSbTVoQ9mMBTlcTw76Kpha70WTyIHF9eRB5RooabJ11zi77/RFUW8u+FgDYVmo9IPFhdr+2kydkYLStEeaaZPZ0sUPDQy4ydmadMO92E3+x8P8cbY4Ps/3E1d3sEuBabtsrTucVuwVcu6yraXKqdZ+wF2dgQgg3GTXkT9lNTqtludUmnpJjXSTspWEvQCP5V2JpLHEtONfYl9Y5nXBBNMaHJNn9YlNOX/KtBPTr3IXCvjZ2yl00Nt3EbjoOTV48duxwu/i51Sb+y5lDbRvp2HX0AbEbSIjuUOFuxi7sO98eXWE9cLdPyzKxPzkY2Fs3NtU6DgGn/47ngqgAO8NVue15Nm9onInZ3ZQabZ0if3x/q4lP3mPnJp5vvfn4eA5mZ9YXO+38lxJqPRG+1DE89/Bc1ZdkeuzMmnXeb0z8+OPWewI4vUjs/KugfqW3O44t+zdaKQd8vKPcxLOzbD+zvyrEi0NreJL1jvCaY3uNzefHpdtcxvvMx439amcOSzZxBeWZMvp+3h4AjKcxzu/hNcakTNsvjve32O/keZzXVecPs74YprB6l+PfBiXaXPiTf9k889ezrBuF25gY8u1YqofTBSlEd+jXt0/72PrrMut2vWKsPTdbyMXTTJNoZDCvjL3Mq+yzI+9ROPlps2LtNUkteU/Z/+OmXLSVrk/4GovnZAB4aZ3Nucck22uWtzeWmHhMnPUm8TzOjt6MMJubqhrsnMfOrJPPv8PEb/z7t442p0TZ/WD/LLsM+dqO57TF620u4dxTTn7Y2dl2TRASYI/7OSPsWAKc103soX5+nfWhXT4+zcR8rchuw1LKHaPSwk2cW24dcXxNw9cLgDOXTKb851inUI7la71x8XbsrC+y50NKhF1LPXLucBOzB7ii3umxG0kO1LmPLDPxPy+wXjpej1Xu2rfNxianv7I3ol9oCSGEEEIIIYQQQogehW5oCSGEEEIIIYQQQogehW5oCSGEEEIIIYQQQogehY+HxUa9DJfLhbCwMOwoqkJI6L566mry9+ym+vKIIFsLTroL9PPlSmsnXG/Lnezny64mW0PMNcbsGNpFNcN72fNF72ePE+Csm+fPcF30ja9vNPFNkzNM/EluhYlPGZxgYt7HreVuE8eHWAfShNNvdbQ596P7Tcz7za6wVvKhsVMjlOr+q6mfuK6a3Sdcb15W56yDTqFa/6Ia6+XwJTcB+1Hc1OamFrtP7KQJos9zPTof9+1lth4dAFKprp/79bPtVSbmOv8c8id01e/sDbj3kx0mvm6SHWvx4XasAMCmEjueYkOsx4vdA9yvHf1sLpcLSbERqKurQ2io9TAIwbTlmfW55e15ZmlepXnP+GTrC2InCUu0GpudHjcew+xqWk4+vgkZ9jtLaX5i50IQxRVu+36eBzgT5pFbAwAyYq13hM999l/c+Z71dl07Md3Ea4prTDx1QKyJ2Sny2oZi+/3khrnssj862vz1u/eYmH2QEdQPe2kZxW3g+Y9zLXsVq6jf08htxj4awDmvc75lF+P2SnJkxlk3VE2D/Q4+1nHks2QfG/fJ4ysKHG3+n+PSTcy5cGW+PdYBfW2/5cTZPMO+SO4TP/LJXPZv67/6wymDTZzgJc+sL7YO1p0umz9PHWTXPUzH/O12uzAiM055RnSbtlyzYee3uYbHZPMeey5mRXbu+O1P8xHgzD/sV3TRHMTnLs8/7IjkdW4dvZ+v0zJi7Pd789slRNh1awDlR57H/+flr0x8/Ql2nfnnT3NNfOesgSbeXGbXnAVum/8yw23uO23+bx1t3vjevSZupuvRaJrD+HX2/LL7jF9nBzT7aAPpemJNUa2jzScOsC5D9p3xtSNfR/E1d1m9vUZJoGvBGPICbyeHVkKYff+ibWxXdjqvEum67K3N1q/G15bHJ0eZmJ1cfE2yicbG/75lr5/vPdM6tfi+AwBsqrDn9a5W+51jk+z6Lr/Gjr+RyeEAALfLhYykqF6fZ/QLLSGEEEIIIYQQQgjRo9ANLSGEEEIIIYQQQgjRo9ANLSGEEEIIIYQQQgjRo9ANLSGEEEIIIYQQQgjRozhqpPA7S6rbZWjs4mXBKstPo0kuXe9F1ltH22A5+JAkK1x9d4uV1k3OsJI93h4LrLPjrGyQpXt9SX7K4l0AaCa5OAt9uc0sI6x0W2kjy8n/+vlOE982bYCJy0jgGk+SWRbpAkDGideb+O3n7zTxsKQwE7NsnIXpLMplEWB/kkpyH7Dct4QE7IBTLMtnHIuXK1ydC4H5OwYm2OO0Nr/OxCwrXLS11MTnj0xxtJmFnCyDZqE1CzpZNh1FYkvug9I6u0/8UAXudxYSe/tMMcn3+XU+J2JDJYUXB0dbnnl/dR6CQvaNl2CaaziPMDyXsFQbAKrovOTzdEC8lf9+vM0+qGNEYninbSinvMXb20NtaqA5upUnUDjnVBaeD0y08xcLY/mhFokk/v3lm1a2+pe5x5i4mD7P+Z9lxgBwzMybTPyPR2828fDYcBOnRNk5upnmFhYe89gop/kvm/qdZebby6yEFwCySZDOx2JjkRXMssCYj+3oRCvRZZl/pbtzAfstb24y8f2nD3G02UXjx5/WMdxPm4qtaJcPXQi1MSbEtim3wu5zOOU1FuVnRjtl2tG0TT4HeK3Gx65j3ql3u3Ds4CTlGdFt2nLNmu2lCPkm1/DDQj7fYR/aE9nfvj6I1vacRwBgdXGtiYvddo46e1iSie/92D7I5+LRySYuddv12NcV9lw+a6jdHufDvvQgLX44EuBc73+UW27ieUNtmxpI7L2xxM6RSWE219y1eIuJHzl3hInz6RoqhR6uxA/aAIDBM2408X0P23j2gDgTV9F1V2KEXd/zA0n4AWM8T/N1ViSNpU+22zUEAJyQZa9X+XpgI83T22ptPDTGXqdtrLT9PpfG1h/ftw+KmUXff8/H20386Hn2uADAZnpoFF838QPEVhfWmjg2qHNRPeeudSTTTwixY4H7fUCCM9ew4H9lrn1ISjzJ8POr6Ro+cd/84Ha5MCAlutfnGf1CSwghhBBCCCGEEEL0KHRDSwghhBBCCCGEEEL0KHRDSwghhBBCCCGEEEL0KPp2/ZbeQXPLnnZ3gYt8FqEBtht2UQ0yW0G81ZtvqrA1wENibY0wO7UmpUeb+J0tJSaeN8zWevuQB4S9JFybzW1mlwQAtO6x72LXEju2+lNNO/uluDZ7bJKt1WVXCbsnrFXM6WECnM6sk8+/w8RfLPw/E7P3Y1updWSwd4R0F2ihPgqmmmbu5493OuvNzzjG1oOze+TZNUUmHhxrfSzcxjiqm+5D/cp1+zyep2fGmtjbeOa6e64P30r9yK6Thmb7nf5+Ng6jc66EHDITMiNNzH3WtNvphGP/Dztk2DN3+9vW8XLnzIHt/1/vdrrQhOiK1OgghITu8xuyI6GmwZ5nPj72vGVnSEGVdY4AwPoK68ebmmXP5aIaO26Hxts89OzqQhNfOynTxOzC2EUuqD7UZj7HYrx4wup22dyUHmP9j6wK4zmW5xb2U80ZbHPpNvJLsZMrOdLOj/BxOrTYmXXlFX8y8aev/sG2ib6jmvJxLPk2isg5mBFr+4RbxGPn47xKR5vZLca+tb9+nmfiydnhJj4uOcrEIY45nddNNrcW0ni9eYodW+zLApzHkpWu/J2uZtuvcSE2F/L49SdvIju3/MjLw86cIH/nGiS3vMG2gfIxe7yuenmdif8051uXGOckIbpLHx+f9vmY/XgDYuyaMb/GunXYHctrJwCo3mXnj+NpfuA57Nrj00384NKdJr5tWraJyxrtd/I8zR7hUPLdRQbZGHDOHxNTbG7g+SSQcg2f/7Hkah1EbkNuc7HL9gl/X3iQ8zqMnVk3/M+9Jh77yu9NzHPSF/nV9v0pdu3M12ncZnZ78thY58XXyN+xrdy+5w+LrfPq9JHWA8bz8rhkuz1u45S0zveJx1ZBlR3vgHM/2cm2usCurQL62jYWuuw2+b4BO6S319o8cWyGPX/Y6cXX4wCwjnzIja12PPHS5Vevf23iRy8YDQCor3ee370RZVMhhBBCCCGEEEII0aPQDS0hhBBCCCGEEEII0aPQDS0hhBBCCCGEEEII0aM4ahxaHWGPQn+q52XnUC35K9hVAThrsbmelj1HjeQYGhUXYWJ2QbC7hGuEU6Ksd2lDsa2pd1pCnPvZRD6L5ha7D9F9qNaaSn7ZCXNSjvW7sG+Fv5+dWWGBzhr5YUnWCcPOrAmn32rimi//amL2ePlTLTa3cS/VNe8gfwa7U84fmeJocxO5yCKojv7SMfYz7PV4b6u1i52YGWPiVbk1Jq5vsXXW4zNs/Tn7rbzBfoLzn1ph4j+fMdTEXP/NDq6lO63zZcZAW1OfEmnHbz6N73AaC+zVAZxOlxpyMLCj6KIx1lO3trS2/f8b6219uxDdoY/Pt/mF54Yocgyyg4R9VRHBTt9GcqM9T9hlNzLNzo/sUZqRYZ0inJe4zZyHOM/l0XnK86s36sidwtvsyt/H4gjXbjvfsXOQnVoVbtsnOeRFAYDhseEmZmfWpDNvM3HZ5w+amB0f7KgZQN+5Or/WxMm0xmCnyAXe8gy9p4C8OWcNt/l4HHlQlpGLJTTArkk4X2+qsXPkpGw7trZTv8eHOefsUnLM3Pz6BhN39E0BQGqEHf97yZmzosjuw6R+NldyjvCjscVrEM4ZAJBJvjN2shbX2PP6+AG2n9/cXNr+/00NyjPi4Gjdsxcte/ad8zwHDg2zeYBdsJwXsr3MgTyXsyOXzx2etwfG2DmMc0t6iD2PPs2z/tmJqXY+eX+7XQePS7DnFdD1NQavxWMol5SRO5W9wheOsmtGdna5ae3N122cmwBg9gC7FmZn1pSzbjdx8WcPmJhzA7sPG2mO5T7ZWGqvFZkrj013/Bu7wWLJT/WbGTkm5rH03Frr8rxkTKqJ73pvi4njQuy8PX+EPQ7sK+V9BJzrr9kPfGriv88fZeLlJTaXjIgON/F/Nljv9SVj7T5MSLTj8z9f2X3m8c1jFwCyKNfUNtp94Hw1dVi8iZfk77vu2nWU5Bn9QksIIYQQQgghhBBC9Ch0Q0sIIYQQQgghhBBC9Ch0Q0sIIYQQQgghhBBC9Ch8PFwE3MtwuVwICwvDuh1lCAkJBQDEhNp637I6WzcdS69vKrb1p93xhLBny001vnH0HVRq7aj13llh3U0h5IJwuk5sbW0/X+e9S65pryXnUFiArcXmmvlqqsNPjrT7XEduCa5hbqA+6UfbZz8GANRRG7nsmGveI8ZdZeKiT/9iYj6W3O9B1I+8T1y7zfsIOMcXHzvuZ25TXqV1oaRFW48ItyGInDSl9P3s8PI2BfDYCKM2cxu5Zp2PHe8zw/sYF2bPDx7ffD4BXvwIIXY/uc28jY5+FZfLhbioMNTV1SE0NLTTtgvRlmc+XleA4G/yTE4Cu5zsHJ5O53EBObV47gGc8x07FEDnHfup2M/H85mL5lduA7tYIsi1x/MG4HSr8FzBrkQ/ylWcl3iO5ZzAbeI2c/bu6+vM5zyX8H6xpyTuuGtM/PW795iY93Ev9UkZObbYV1NSa+dwPo4AsIKcHycPTDAx+6DYeemYD2kf23w938bkryIH1/FZ1hHS5MVrwv3KuYk9XJz7OHPxPjbSd+ZTnskgRwnj3uXMM2uKak08baB1k1W57bHkfNyx210uFzISo5RnRLdpyzVfbCpuzzWJEXbdu4NyzaDEEBO/tr7IxDVe1lOhNG6PTYkycTM5H3nOCqbP8zz+CrVhYJTNlzGBdg3I3lRva8oWmk/WFFu/bFqYPd851xTU2flhbKr1CLJ7jK95+JqI5zNv61Z2afK1G19LJh5/rYm3fXi/idk5yf3ucGzRHLmtwl7vHpNgfWwAsGhrqYknpti5nr+D9+mpL/NMfNHYNBPzNQ2v3TeX2jYmhts+Yocl4MxXvI7gnFrusvM4e4L5WpOvUdjdGUWeMe4jzk2A8zqK858/+b8rqM1t++hyuZAQE97r84x+oSWEEEIIIYQQQgghehS6oSWEEEIIIYQQQgghehS6oSWEEEIIIYQQQgghehRHjUOrpKK2vXaUd7iV3BDs/OhPdare3E41DbZOuYnqy+PDbL0tb4NruYvJmZFI9bqO76c6afZVJHj5PLdxc4mt+WXvB9cl8z795ytbE3/msCQTF9fYWvFQqoFn/4Vrl90nAOhP79lWatvM9ebsgEma9AsTV33xkIn5uLA/jV0ETF8vrjJ2vHAdfWOzjflYtdJ43FDiMvGEjEgTs5eE68/ZXca14oCz3punCfadsSeEX99Rbp0OvD3e50LyCvBx5bEEOH0FPH65vvyTvAoTD4n8tra8we3C9FFpvb7mXBwa2vLMprwKhHwzXtjl1NBs51t2SfH4ZO8E4JwzWefIXqSG5s79euxB4bmDHSFbab4Np3OutsE6RACgkfab5zN2VgaRWyKczuO3NpSY+OTB8SZe+HWxiWfl2Nf9KY+xQwRwuliqKb+zs4bn+GNm3mTiimUPmrjSbfuphvotk9xOnFe8LdvYEcLb5I/w+GQ/zJqSWhPPHBhn4t3UR31oMLq8+KcYHn88vhk+J9hbwvvQTC40dpWyg64rf9u+Ntrx2ZXf8etSm6+TQ789p+rdLkwelqI8I7pNW64prfz2mobdiry2Z/8Prwm9Ofl4DVbqttck49LtupPHPc/bpXRNw04sPjdrac7dXmVzD/utAKCecs3KQuv1K3TZNkxItPswLMWeg//8YqeJL5+QbuJ1+XUmjqZ1M69BeU4GnC6lL8hFmBFuc0EsXXdlT73exOwJ5nz7dZGdj8Zn2j7gdYm3scHXDDzP7qixx2pYQriJ2TO4ssy6zs4fkWK3T7mGxzNfK360067tAWASeb4c5wBds0TQseN1yapc22bO0XxNs7rIvn9wrB1rPN4BoLyRxmu69dh9vN3u5+c0Hmdm7Xt/Q70b8yZk9fo8o19oCSGEEEIIIYQQQogehW5oCSGEEEIIIYQQQogehW5oCSGEEEIIIYQQQogeRd+u39I7cO1qgcdvX43qV0W2zrQf1b6mRASaeM9eW/fM/gwA6Et1xo31tg55C7lH4sJsrTV7uqJDrJuEXShcr+tP9b+VVOPMLigAiKU2DE8NM/Fm+kx+TaOJuUb+xMwYE+dV2vcHk/+C66Lz6f3efFXsBsiOCzYx+1HYh8bOrKgJV5v4i4X/Z2Kuied69CLyDKRE2bHjDXaRBfjZY8213AFU613XbGutuX69hZxw+eQuKCEXwuQBtrYcAHIr7LGIp7HSh4QHXGXPx55dP336dO6x4X7k7adGO/uZHUSwTcDfv8gz8e3TB5i4Y7+5+zu9OkJ0RX1zK/CNS2R9sc0zQX423caRC4MdDex2BJzeorXFtSYO97dzSTa5mPg8ZAcWO434nGJPHTuGyuudPr6EULufgeTxYqffh5usA4vdF5Mpz7y9sdTE07Ot66mavCXd8VE10ZzKnq/yOrufnEvZmRVz7DUmXvv2n0zMjkD2ra0sqDTxgGib9wCnV4T9UOyfYq8hH1uGHV3sFePxuniH9XucMSTBsU12j7G/hc8R9lV9vNV+h6+PPbZZ0Xb8R/J4p/OJvUFpXvIMrymYx77MN/EvT8w2cUdPp6tf154xIbxRXNsE955943nx9jLzWmaYHfd+tN6KCbbzVXKUnX8Ap8+u2GXPjY3kYuL5xk1eoxiaQ+tp3udrIN5eQoht48YSL9c0tF4/gXIFz3kf5tl5lZ2RsyiXfLjFzjdR/e18wtcni9eXm3jOIOccyGvfsSnWacWOLZ5/2JnFnuA//+1GEw+Jsg4ldvhyH+UkOK/D2DXI15/D+4ebmK95MmLs+Fy4xY5fzgvsu1pHa6tCtx2b84ZafzMA7KRrmqhguu6ia3jODdvyak3MPtLcWnuNz8dpQpr1X/FajNdFgNPpzO7gf6+0a6UnLhhl4janqTuwV6vS29EvtIQQQgghhBBCCCFEj0I3tIQQQgghhBBCCCFEj0I3tIQQQgghhBBCCCFEj8LH400g0YtwuVwICwtDXmk1QkP31Q5vL7M1wqle6sc7wrXdfbj4FV3X/Pbr27m3g+vJ2W3Cfgp2qTBc3+vtIHONe0GVrc/NIP/K5lJbs55Ofgp2ZHENPPspksgbwj4WdkEBzmPBdc98bNgvFdSFR2TC6beauObLv5qYHR815Gdh55a3NpaRfyWYauS5bjqQ+rGG3CXcr1xzz34E1y471mppHwCnM4bh8cTjtysHTVmd9XhxTX4Y+dnYjbbXi8dkF30ntykh3PpYHGOngxfM5XIhISYcdXV17fOGEPujLc/sKKpCyDfjhd0UnAPY0cAeCnbpAUAuzVfsg8qiOZvnNyaF5gY+h9hvFRJAeYVOQ94nAGiiOfPTfOstOYk8Jfyd7H4qJSfgyLRwE7+3yfo4JmVZBxe7prwtgXbTe9hJOSDeOqzYncIOLM7XI06+2cRln1vnVkGV9X1wbhxBvksAKKW8wt4SnjI5X3MvcD+7KW/w+MyKs2NvG3lDeawCzrzA/cTzfnW9M1fZ99t94rHHa4ooytdhNL5f31Di+I5pWbEm5n6KozzDJ0lkB3eLy+VCfLTyjOg+bbnm69zy9lzzOHnb5g2JNzH7q3i9xQ5gACipaer0PZHkIOJrIJ5n+Vx30fUHrxn5GorzAPv0AGADeX+LGuw8OiIu3MT//so6iK4Yl2pizgPRtM9rCmpNPDY9wsT5dE3lLdfwfnK/ttCcyDk6lOas/6wvMvF1P7/XxNXLrUe4nHIbXx/wOhkA0smBtWZnrYl5vPF1UwJ5qHldU9Zgx15kf7s9dugWUr7cUuP0q03OsD41zod8Pcxt7uo4sW+0vsX2Y1KYXWuxs5KvJQHnOVFCrrAxqXa88XndduxcLheSYiN6fZ7RL7SEEEIIIYQQQgghRI9CN7SEEEIIIYQQQgghRI9CN7SEEEIIIYQQQgghRI+ib9dv6R3UNOxGa599NarpVH/LtdnscWDvgrvB1sYC1r8DAHuoQJfLvRupPjcuzNYUby+z/olE8k2xvyKvwtYQl9TbWtuhCU7nBte0c00v90Ms+SYig6znyIf6gL0f7DBi3wrX1LNHCXB6QVqoxn0vxezM2kQ19uy8YmdWxLirTLz9w/s7/bw3Xllna9pHxYebOC7MboPHDrsJIqjf2bHFY6PSbWu72cGVRucD4HS2VZG7JCnSjkf2djF8nDLJ9ZNXaccv+9f4HCwibxgADEwIMTH3I/dDMu1DR5eAu96+V4juUFSzC8Gt+85PduO17rVzPs8d7GFifwLg9DawJ24POTp4rgiiNtWT64k9Xq10DhWTVyW31uapkYnW6QA4nYCzc6zfhfMMtzEmxO4z50qeO47PtM4s9ll9vKPcxFMyrRMJAMrJ8cc+ydX5tSaODCTvCc1XnL/ZmRV33DUmLv7sAUebOuJFIYiNZS4TZ0baNnM+Zacb9yPnBR6PnK8Lq5xzckeSyJsCOPeDPT3sMWHvJjsz82kf2LWypcTmf/bD8Fgc62U883oxPMi5TrHvt+P5g83fjr/GeqfrRYjuUO5qQqNn37xzKbmfQmkO3UW5hV1O5XVOfw+vnSvc9j18zcNrxCFJdj32LrkNxybbc4vXfLxe+4Dm7WOToxxtnpAVaeLWPeEmZj/U8Sn29VjKLezsyydX08hU+3n2UZW7bR5Ji7JzMuDsZ56DeA4MDbDzzddFdt4fEmUdSezMihx/tYmfePwWE8+g/OxFVYYbFm4w8dXHpZmYr2l4H9iZlR1nnZTRjfbzvJZiB++SwmoT/3hksqPNfA2ycKP1p50/wn6GnVk83vn6NoeuP9iDyceNr39zq52+0xNzyPtFCZPz3ZAke+zX5tcBABrcdoz0VvQLLSGEEEIIIYQQQgjRo9ANLSGEEEIIIYQQQgjRo9ANLSGEEEIIIYQQQgjRozhqHFpNu/eg7zd1vOwBYZ8P6ykiyD3B9cAAEEU+lNoGW3u9m+p3t5RbdwL7p9g3xX4LrinOjrc1yPFN9vNccww4HVp15MTo72dru3m/2d8SRK4Irhlmp9Fzq/NNPDnVuk9a9zhFIVx3HEzfuYNqsyPIOTMo0dY5syOG95GdWVlTrzfx8tf/aGJvLqnJGbYOOjTAvof3iWu12XVSSP4o0iFgc6Wtlz4h034/b59dBYBzfH2QW2Hiuf0TTPzJzkoTT82yXhruFx4L7BmpIBcBu4DqmuxYBZyuHhfVuA+kY99Ex7rjeG/pa8e+EN2hX98+7efrhrI68xq7nXiu2Vlh5y5vDkE+j9x07pfW2nm+krwn4bTNeMoz7BjivMZeiYHR1tnQ0ur0fkXRHOymvMHz0ef5di6ZRnNJ/36dO4zY7bRgrXUYzs6y82GVl/mP8ylrRJLJ5cS5kr0o7PFivwY7sxKPv9bEm9+/z8TsVQSA+CB7LNk1xr4onoN5PVBWZ/uFc+XywioTnzYk0cSOfaxxrkHqm+1YeGubzTPXn5Bp4ie+zDPxj0almJj9a5zbeDx/vMN+38mDrD9mVUmNo81zo5LoO8gvSa4wdtDkxHybh+r7e5GhCdENGlr2ALv3nT+5+fZcHJkQbmL23/G5zv5GAIgN4+sDe67yyP20wLaBfY+TKP/tJO9vXbM9j0alWMeWn69dBy/cXOpo83VxWSZmBxbPD4F9bcy5g5297EJkp9ErX1sv0+zsOBPzmhNw+szYrbmx1K7ns2PsOnZ8pvWGNdKcyl4vdmZdepm9hrnnrzeYeBbtAwD8ZISd6/laj68NOT/y+zfTuqOJ1hF5brs2OiHd5vCZdI3D14GAMx9+XWrH344E+5l3ttvcwF4uvkbiscDru63kxc6hNUZJg9NByXmerxX5eraBjn1q1L7x6vZzrhd6I/qFlhBCCCGEEEIIIYToUeiGlhBCCCGEEEIIIYToUeiGlhBCCCGEEEIIIYToUfh4uIC3l+FyuRAWFoYvNxcjOGSf74NKXR2+C6733U11q1xnDQDpMdbDtWyHrSdPjQg0Mdews2ODPUvsGeGDxjXLjDcfCzs0XlhjnVazcqwniet542gf+pAsIoBcJ/z5vuT04H5v2eP0sfSl72D32KZiW4vNdc7st2A3GbeZ69n5/ePn2Hr0yi8ecrS5mfwEXNPOXhw+VjUNnfdbI42drRW2Vnt8unUR8D4WeXGbRIdY/wH7qFYX1Zr4uIwoE7O7IDXKjv8KcpuwL409OQyPA8DpgYgIsvvAHjmu4+84FbpdLuSkxqCurg6hodYTJATTlmfW7ihDyDd5hs97Pk/TY+w5wXN6rhcPBHsKd1Tbcz013OahePKgFFRZT0NmnH0/54SPyWk0nNws7GXi+RJw5sv3t5WbeGKqnTv8yRnYSC4KdnJxDnDT99U3syvPbp+9YgDgT7mohpyYnG85f7Pb4oPNdp8j+9t94DaE0Nw0cLr1mqx9+0+ONq8nZ9vQuDATc37uR8eO95mXhiXkZ1tXXmviUfE2zyRFWt+MNycme7n607H/utj6YzKi7XhlH1sYrZt4n3gNwv41Pkf9/Zx/8+VzhPOpm1xDgbS+7OiZU54RB0pbrnl31U4EBe8bM+w25DUkr4N5jG6osOcZAEwbYN2Fr3xVaOKxCdbdxGtGvrJkD2ozeb320Acc694+nGvs9wFAC80xT66wzr1Z5GMsqLPr+dHk7eLv5HmZ15S8zyE0H3m7TgsLcOafjnyZV93p6+wa206uJvbTsp/5+bUFJr7pKutrLPr0L47v5FxQUW/X80nhdu7ntXgReYDZbVZYY49LrsuuhaZn2uPIvrYvcp19NjTR5kNeX/3tCztWbpxs/Y21tK4YmRpu4uIau0/s0GIXMq8ZdtTZ4+aNmTnWZ/ZVoT1v2ffdduzdbhdGZ8f3+jyjX2gJIYQQQgghhBBCiB6FbmgJIYQQQgghhBBCiB6FbmgJIYQQQgghhBBCiB7FUePQ+mhdQbtDKyXK1ve+u6XMxFMzY0zMzi32hgDO+lj2enAtNjuEuN6WfVJcA8/er6+plnZAfLCJuR7dG+wqSSEHBmi3v9xp65SPJY/SugLr9BiYYL0iBVWNnb9OddYAwOqkj3dax8v5I1NM3ER1+tyP7EfrCnZDxZBHLHrC1Y7PfLXoHhNznXMR1V7ftPBrE7902XgTszeE/SvsrLnyxbUm/utZw03sbTyvLqw1MTuyeKywm4T9Kdxv7B3pqt6cJyn26ABAY7PtF3bGsDeCp76OdfxulwsZiVG9vuZcHBra8sxrX+5AUPC+eSwz2s7BH+6weWbO4MROt8l5B3Ceq36+9jxh1xPPFXzesceE8wqf5xvKbZ6ZkGbnBW9zCXtN2EHEnwmkNm4ttW4J9lO9/nWxiccnU5toLmFnIfcZ4HSdfJxXaeILKM/wHJ4Za11PhZTLkml+5GPN8ynPjyNOvtnR5o9e/p2JuZ9W5NWY+O73t5n4z2cMNTH3ky+NNT5O859aYeL7aXvs0AGcHhPOp9yGalrHBNN45TmfjyNvj9dpX+bbNc3UHOtqAZzrlhTyQ7JLhb13HdcgLpcL6QmRyjOi27Tlmr9/sB4B3+SayRl2nN63JNfEV46z81UzrXvZQQQ412DshOS1NK/xviKn3/D4cBPzWp7Pk+X51kPMuearIrt9AAj2s9tobLVtHpZkPUqci/708XYT3zo128QfbrcuxAkptk2Lt9kcf86IZBN/vNVerwAATUFYRw6sK49NNzFfs3DO5lzCni/2N1fRceO1ddKkX3CTsfS/fzAx+2gryBP8m0WbTPzUj0abmH3J7IB7dqX1Oz//ufV+/fNHY0zszcH78voiE19Ax6aWci57wQYn2PmZXch8Hca+ZW+uzo5k0zU7AFTRfQLGRdc0vNZp+063y4XMpN5/PaNfaAkhhBBCCCGEEEKIHoVuaAkhhBBCCCGEEEKIHoVuaAkhhBBCCCGEEEKIHoVuaAkhhBBCCCGEEEKIHsVRI4Vfva0UId9I4UMDrDiQBYmstWVZ3Ppyp4wwJdiKQSOCrFivsM4K4uKC+3fa7r4kYA2jNrNkNiHcbo+FjSx4BJxSOxbxNTRboaKL+oHl+vx+FhInUhtXkqR2QKyV4nEfAsBeGq4sfWdYiBgd4r+fd+6Dx8Yr66xIcHKGfWAAS2lZWgsAw2bdZOLNi+81McswmUqSNqZF27HGAmF/P3usN9ADA1gamRVnBcbe3tOftskyam4Djz+WS7tJXM/HhWWdLL78MteOHQBIjbT9Eh1ixw8LgFvovN/dQUzpdrkwMDWm10sUxaGhLc8s21jU/vARFlyzADs9xp53LFLdXm3lsAAQ0d+O6XCSsdbQeRhADxfhdM95IZzmXJZqx9A5FUhzly+bfgGU1to5mKW2fJ7yHNqVNJ7zc2KEzUvLdli58HASA3sTyPaj+Y3nL14zLNpSYuIzh1rhbBHla/7OjWV2jo4PsrmywG3XD0nB9MAWACee/SsTr3vnTyZm2S/nZ35wDa9BeKxsLLJtzq+3bcwKs/k8OtSZe3kO5jUIjxXudxYcs5iajxuPTx57/PSRnST2BZzjkaXwLDjmfqtyf9tGt9uFUdnxyjOi27TlmvfX5LfnGn7IBD/ogh8G8kWBnRNXFjlzzaQ0O08mBNnv+Iik7eMTwu139qUH/5CgPSPa5r8l9ICnUfERJuZzl899wPkAkXi65uAHJhW7bG4anRpuYn6gSRnlsjja/msb7ANKJpE0ntfugHOtzQ8A4fnkgx1WTD8pNdrEvDbnPrhh4QYT/2SEfThNdx6cNfGM20z8xcL/MzHnYJ6XN9LDZfjhG3zdxtdIH5Ccvz/t8+gkO3YAbw+jsZ9xPkTKjhUebxVuyjX0UKrYMNvv/FAFfhDDn5fscLT5rMHxJo6i6yReC7XSWGkT/te7XTh2cFKvzzP6hZYQQgghhBBCCCGE6FHohpYQQgghhBBCCCGE6FHohpYQQgghhBBCCCGE6FEcNQ6tvNLq9trRPuTS8eaX6sj2MluXvaHC5XiPL21z6gBbE8wuEnZBcZvY7cB+KqepxBJIziH2ZQDOumR2ajXQZ9jNlER10puK3Sbm2m/2rbAXjMrVHfXAABBPdclFNdZNwt4tdm5wPTi7oVqpEQ1UF51ErgL2w8R4cXTxNgaedKOJC5b8xcSVbnsc+AxdtK3MxPOOsTXw7BoIIc8Ojz33LufYYP8PTxNcE9+fXD28z9ymOvrOFOpXrmdn/1BChNNBx+OVnVm8Ty+uKTDxiR38aG63C0Mz4np9zbk4NLTlmaLymvbxws6GeprDy7vwRHyab50iANDP155ns3LiTFxJXocg8mGsKbLuuQlp1vHBCiw/yo2cp9idx+ct4PQ87Kyw5zLPDeyWCKZ+YU8K5yXO57x9zsXspQCAcHI5ldA8X1Bj3UrHJNo5gj0oPIdzm7kf4yjP8XwbF+ZsM+fv4bNvNvGXr//RxLzGYF/MEyvyTfzz49JNzGsKdpKwD8vbGoRzPPtkGB5etQ3sT2Nnjv1OzvehlBvzq8hVFul0lZXU8Hlrxze7xx78LNfEF45Mav//o8VtIg4dbblm7Y6ydi8wj+uQ/p17greU2Dn0w7xKx/cE0bx9/gjrBeQ1Huc7no8GJYSYmH2PvMbkeZvX9jyPA845p5iuD0oo5wbSunUgtfGjbdbV1Er7yE7LmCA7L/MalD1K3r5zdX6tiWNDOt8mewjZA1xM81V4kO0jngO3lNnruJQIp/eLj/2E02818fpF95h4Z7XN+RW77Nh4e6P1sZ073K5rYgPtvD4g3voZ+VqzrM5uHwAyYq2zrYlydAVdd/E6oryuc+d0Hq0JxqU7PV4d2UrnoLdcw843zrGc725+Y5OJrxiXAmBfnjlhaHKvzzP6hZYQQgghhBBCCCGE6FHohpYQQgghhBBCCCGE6FHohpYQQgghhBBCCCGE6FH07fotvYO9ez3tNd67Wql21mVjrjGOJfdOdEgMmJX51k1SQC4G9vcE9LNd/3Whi163td1cI7yCvm9InK2LZYcWe0wAZ8061+GHBdj63LxKu091VN/LvpbEcPt5rrvmGuVludUmHhRna8sBZ53zs2uKTHzpmBQTsyeE9yHAj3wttA/sKmEXAbsK2OkFOF1j7MxKOeEXJt76wX0m5hr3+SPtPvb1tTX1/lz7TS4Ddg/wWAOAz7fZmvYJmZEmZh8ae21So+w+sx+IaaR6dh7vvI/eHHK8H9XkV1m+w46vf39ZbOKJqdHt/7+bhW5CdIPC6l0Ibtl3vtY2WO9RAs0DPNfweT4t03okAGAlObBqaIyz14Hn2Lpm+36eKxx5Zqf9vsgAO19mk8uC3VEA0JdcKInUD3ze8hzNcH5mjxJvj/tge42dq9jNAgAbi2w+/uvneSY+a7h1ZHJ+bSRfGudj9lXxcfOj+Y7dZCvy7HEBgFEp4SZmZ9a4ObfYbbxhX+c2XTUxw8Q76bjEUG6tofHO/o+oYGc/55PfhRmabNc1vE7KSbDjj2dtXtOw32r3HnZq2uPkLc9E0Pjjc7Cg1jpoVtM5dPaQ+Pb/93a+CNEdAv182+cdPtfYucfrtRRan50dbD2sALCx3J5rqwrsOM6Otucer/E211gHJHtS+frjpfV2PTY7y86x7BVkdx3g9MMyOfH2mqKA5rRqmsPiAm2b2XPEDkDONc+vs9cnU9OiwWwk9/AfFm818W9m5Jg4Pca6oFYU2HXt8P7hJuZrT76m4euBpHC7jxUup4+K8z47s4bOusnEq9+628RjUq1f6njqF3aVBdA8Xlhtr7Pya+1x3L3XOa9+lGfH449Hp5qYx+/XJXb8p5FLjF2f7MXkvMDjvXyXzX1JcDq0eC2zpdyuXVaV2HNyS0kdfWey1+/uregXWkIIIYQQQgghhBCiR6EbWkIIIYQQQgghhBCiR6EbWkIIIYQQQgghhBCiR+Hj6eXFlS6XC2FhYcgtqkJI6D4fA7uf3LtsDTHXwrIDiX1BABBKDgyufY2g19knVdVFzTsfJvZ+NLfYmuHaRruPPj5OG0Qy1YOX19maXn/ySbBvZVuZ7YfBSdZ38d6mUhNPzrLusZJa+31cG95IfQQAO8obTLy11rZhcrqtxeY6Z3ZW+NE+7SZHFrtRqLQbpDbBWf9c7mjzgovGmpg9YOzSGTDtBhNz/Tn7VrhNobS9T7bZ2vGsKFv/XupyekyGJ4d1+h176B/YJeBLHcN+IHb1sL+Fh2tzi30/O2YA53hlrwTD+9TxHHO7XRiWEYe6ujqEhoZCiM5oyzNf5ZYhJGTfeCmvs+d5RaON2cnA87E3lxTPFezwK6iybok48hiW0ZwbRR4kPqt8yTnEr7P7jvMQAMRTG3jFwXMFOzt4ruHtrcizDpGBsdaTwnkmOcr2+6ZS68oAgJqm3RTbueSUgfEmDqH5jedDzluZsTbXsXuF97mO8vnlz69xtPmx80eamPM1rzHGnmadWivftHmGcx/nOnaOvLe1zMQJ5J+panK6WKblWE8Ojw124mwvs/3I/cTnA38+hY79dlrD/Hez3YcbJ2c52sx5pYF8aaEBtg18bDvmOrfLhUFpMcozotu05ZoNO8vbr2nYmbWDPIGx5MMdmGjnyM92VDq+J4Y+wz5aduI10RqNPcKx5MDaWWnP5eEpds3Ja/Uqt93Huibn+m48uV7zKYd25dh7c7O9ZjlnWJKJ/7HMuhR/emyaiRfTHHjK4AQTc34GgK/Ka01cS06rMXHhJuZjx64m9k1VUr+xQ8uPcjz7qy54eoWjzX89a7iJC8hhlRRm5/5Rp/zSxEv/+wcTc/5soeswnlMXbbXHKSfczp01zXafAWBiZpSJ39xYYuLjUuzrPJ45t/BarKtrGr6eXV5k1y2n0Vjxtg32IfN6rD/l7NyqffNAQ70bZ47P6vV5Rr/QEkIIIYQQQgghhBA9Ct3QEkIIIYQQQgghhBA9Ct3QEkIIIYQQQgghhBA9iqPGofX80q0IDN5Xe3xCtvUssWuCa2eLa2zdMzs/AKfLISvOeorYLcK11AkRtr68lLwf26psTfxUck+wk8vpknA02eH1yIm3beaa4MJq2+a0aOuj2Flh66id7ojOa4yzyCvCrinA6fXg+m/2eAyLtXX57NTifmLYXcL7xGOF/VaA0+Gylw5GErlzeLxx/fm2D+4zMbtM1hdaJ8zINNsHX+Ta2m2udwecrpH1BXWdvs6nBLvJluVWmXhCuq1XLyN/GzvfEsib482hxfXlsaH+jvd09v6+HXbC7XZhqBxaopu05ZlnPt3cnmdGJ0WY98STu8JNrgye88PJkwgAbvL3sMeE56dPyY0yJdt6DLkN75Kb4qxhySbmOZ5dUOzUApyeokRyUrIfo7jG9gPnGc5Da0prTHxCht1Hzs1d+aoA4LOdtt+OibFz6OYqt4mPTbPzGfsy+tB8Fh5k8wQfe567uImVXnJjeBduRXZocT+MOdXmGXY38hz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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5))\n",
    "im1 = ax1.imshow(K_Q_train, cmap='Blues', vmin=0, vmax=1)\n",
    "ax1.set_title('Quantum Training Matrix', fontsize=16)\n",
    "ax1.set_xticks([])\n",
    "ax1.set_yticks([])\n",
    "\n",
    "# Plot Coherent training matrix\n",
    "im2 = ax2.imshow(K_C_train, cmap='Blues', vmin=0, vmax=1)\n",
    "ax2.set_title('Coherent Training Matrix', fontsize=16)\n",
    "ax2.set_xticks([])\n",
    "ax2.set_yticks([])\n",
    "\n",
    "im3 = ax3.imshow(K_U_train, cmap='Blues', vmin=0, vmax=1)\n",
    "ax3.set_title('Unbunching Training Matrix', fontsize=16)\n",
    "ax3.set_xticks([])\n",
    "ax3.set_yticks([])\n",
    "\n",
    "cbar = fig.colorbar(im1, ax=[ax1, ax2, ax3], orientation='horizontal', location='bottom', aspect=50)\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5))\n",
    "im1 = ax1.imshow(K_Q_test, cmap='Reds', vmin=0, vmax=1)\n",
    "ax1.set_title('Quantum Test Matrix', fontsize=16)\n",
    "ax1.set_xticks([])\n",
    "ax1.set_yticks([])\n",
    "\n",
    "# Plot Coherent training matrix\n",
    "im2 = ax2.imshow(K_C_test, cmap='Reds', vmin=0, vmax=1)\n",
    "ax2.set_title('Coherent Test Matrix', fontsize=16)\n",
    "ax2.set_xticks([])\n",
    "ax2.set_yticks([])\n",
    "\n",
    "im3 = ax3.imshow(K_U_test, cmap='Reds', vmin=0, vmax=1)\n",
    "ax3.set_title('Unbunching Test Matrix', fontsize=16)\n",
    "ax3.set_xticks([])\n",
    "ax3.set_yticks([])\n",
    "\n",
    "cbar = fig.colorbar(im1, ax=[ax1, ax2, ax3], orientation='horizontal', location='bottom', aspect=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fit SVC models with our training and test data for the three different kernels. We will also consider the completely classical Gaussian kernel for which, we will perform cross-validation to maximise its performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The SVC training accuracy using the quantum kernel 0.9696969696969697\n",
      "The SVC test accuracy using the quantum kernel 0.9696969696969697 \n",
      "\n",
      "The SVC training accuracy using the coherent kernel 0.9545454545454546\n",
      "The SVC test accuracy using the coherent kernel 0.6764705882352942 \n",
      "\n",
      "The SVC training accuracy using the unbunching kernel 0.9545454545454546\n",
      "The SVC test accuracy using the unbunching kernel 0.7941176470588235 \n",
      "\n",
      "The SVC training accuracy using the Gaussian kernel 0.9545454545454546\n",
      "The SVC test accuracy using the Gaussian kernel 0.47058823529411764\n"
     ]
    }
   ],
   "source": [
    "# Train SVC with quantum matrix\n",
    "model_Q = SVC(kernel='precomputed')\n",
    "model_Q.fit(K_Q_train, y_train)\n",
    "print('The SVC training accuracy using the quantum kernel', model_Q.score(K_Q_train, y_train))\n",
    "print('The SVC test accuracy using the quantum kernel', model_Q.score(K_Q_train, y_train), '\\n')\n",
    "\n",
    "# Train SVC with coherent matrix\n",
    "model_C = SVC(kernel='precomputed')\n",
    "model_C.fit(K_C_train, y_train)\n",
    "accuracy_C = model_C.score(K_C_test, y_test)\n",
    "print('The SVC training accuracy using the coherent kernel', model_C.score(K_C_train, y_train))\n",
    "print('The SVC test accuracy using the coherent kernel', accuracy_C, '\\n')\n",
    "\n",
    "# Train SVC with unbunching matrix\n",
    "model_U = SVC(kernel='precomputed')\n",
    "model_U.fit(K_U_train, y_train)\n",
    "accuracy_U = model_C.score(K_U_test, y_test)\n",
    "print('The SVC training accuracy using the unbunching kernel', model_U.score(K_U_train, y_train))\n",
    "print('The SVC test accuracy using the unbunching kernel', accuracy_U, '\\n')\n",
    "\n",
    "# Gaussian kernel\n",
    "param_grid = {\n",
    "    'C': [0.1, 1, 10, 100],\n",
    "    'gamma': [1, 0.1, 0.01, 0.001]\n",
    "}\n",
    "grid_search = GridSearchCV(SVC(kernel='rbf'), param_grid, cv=5, scoring='accuracy')\n",
    "grid_search.fit(X_train, y_train)\n",
    "best_model = grid_search.best_estimator_\n",
    "accuracy_G = grid_search.best_estimator_.score(X_test, y_test)\n",
    "print('The SVC training accuracy using the Gaussian kernel', best_model.score(X_train, y_train))\n",
    "print('The SVC test accuracy using the Gaussian kernel', best_model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Training Curves\n",
    "\n",
    "We will investigate the performance of the different kernels by plotting the test set accuracy versus the training set size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Different training set sizes\n",
    "data_sizes = np.arange(20, N, 10)\n",
    "\n",
    "# Lists to contain accuracy for each kernel\n",
    "accuracies_Q = []\n",
    "accuracies_C = []\n",
    "accuracies_U = []\n",
    "accuracies_G = []\n",
    "\n",
    "for size in data_sizes:\n",
    "    accuracy_sublist_Q = []\n",
    "    accuracy_sublist_C = []\n",
    "    accuracy_sublist_U = []\n",
    "    accuracy_sublist_G = []\n",
    "    \n",
    "    for rep in range(10): \n",
    "        # Generate indices for Train test split\n",
    "        np.random.shuffle(indices)\n",
    "        train_indices = indices[:size]\n",
    "        test_indices = indices[-10:] # Constant test set of size 10\n",
    "        \n",
    "        # Train test split \n",
    "        K_Q_train = np.array([[K_Q[i, j] for j in train_indices] for i in train_indices])\n",
    "        K_Q_test =  np.array([[K_Q[i, j] for j in train_indices] for i in test_indices])\n",
    "        K_C_train = np.array([[K_C[i, j] for j in train_indices] for i in train_indices])\n",
    "        K_C_test =  np.array([[K_C[i, j] for j in train_indices] for i in test_indices])\n",
    "        K_U_train = np.array([[K_U[i, j] for j in train_indices] for i in train_indices])\n",
    "        K_U_test =  np.array([[K_U[i, j] for j in train_indices] for i in test_indices])\n",
    "        \n",
    "        X_train, X_test = X[train_indices], X[test_indices]\n",
    "        y_train, y_test = y[train_indices], y[test_indices]\n",
    "        \n",
    "        # Fit model with quantum kernel\n",
    "        model_Q = SVC(kernel='precomputed')\n",
    "        model_Q.fit(K_Q_train, y_train)\n",
    "        accuracy_sublist_Q.append(model_Q.score(K_Q_test, y_test))\n",
    "        \n",
    "        # Fit model with coherent kernel\n",
    "        model_C = SVC(kernel='precomputed')\n",
    "        model_C.fit(K_C_train, y_train)\n",
    "        accuracy_sublist_C.append(model_C.score(K_C_test, y_test))\n",
    "        \n",
    "        # Fit model with unbunching kernel\n",
    "        model_U = SVC(kernel='precomputed')\n",
    "        model_U.fit(K_U_train, y_train)\n",
    "        accuracy_sublist_U.append(model_U.score(K_U_test, y_test))\n",
    "        \n",
    "        # Cross-validation grid search to obtain optimal gaussian kernel\n",
    "        grid_search = GridSearchCV(SVC(kernel='rbf'), param_grid, cv=4, scoring='accuracy')\n",
    "        grid_search.fit(X_train, y_train)\n",
    "        best_model = grid_search.best_estimator_\n",
    "        accuracy_sublist_G.append(best_model.score(X[test_indices], y_test))\n",
    "    \n",
    "    accuracies_Q.append(accuracy_sublist_Q)\n",
    "    accuracies_C.append(accuracy_sublist_C)\n",
    "    accuracies_U.append(accuracy_sublist_U)\n",
    "    accuracies_G.append(accuracy_sublist_G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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He8e31D5fPqgBRyb4Gz04AKhUqRLjx48nKiqKzp07ExISovO6Wq1m2rRprFq1CgcHB8aNG6d9rUGDBtrMQWlSUlIYP3683nG8vLwATdakl+cdPHr0iC+//DJP3oujo2YeR9pQqFdlp70lWaHtQRAEoXiwtTDB3U63IIuEpL2L5mprrhljbGA7QRCE4uTlYUQNvR2NOqzoVT/88AMxMTEsWbIEX19f3nrrLXx8fIiNjWX37t0EBQVhYWHB2rVrqVAhvXd3/Pjx7N69m06dOvH+++9jZWXFnj17cHBwwMNDN/jx8PCgR48eBAQEUL9+fdq0aUNYWBj//vsvbdq04c6dO7l+H61bt0Ymk/HVV19x9epV7O3tcXBw0GZJyk57SzLxF1gQhHw12K+C3lChhJRUbW7xPeOaYW9taYymCYIgCC+YmJiwePFievfuzaJFizhy5AgbN27Ujtdv3Lgxq1at0iv81b59e/755x+mTp3KX3/9haOjI++99x4zZsygRo0aesdZvnw55cuXJyAggHnz5lGuXDnGjx/PhAkT2LBhQ67fR7Vq1Vi2bBk///wz8+bNIzk5GS8vL22AkN32llQiQBAEQRAEQRAA8Pf3x9/fX/v81q1bNG7cmHv37ulkjHzZu+++y7vvvqu33NA8URsbG3766Sd++uknvdcM7T8wMDDDti5fvpzly5frLR8wYAADBgzIcLvstDejY2TWNkP7KkpEgCAIgiAIgpDHDCVokF7KYdT6p8AikaChUqVKBAQE0KFDB9q1a8eRI0dynOJUKDpEgCAIgiAIgpDH0hI0ZOTlbEavblfYtG7dmoCAAM6ePcvhw4fp3bu3sZsk5DMRIAiCIAiCIOQxQwkasrpdYdS5c2c6d+5s7GYIBaRwfgoFQRAEQRCKMEMJGgShqBB1EARBEARBEARB0BIBgiAIgiAIgiAIWmKIkSAIglDgVlxdwcprK3WWSRJYV9RM6uy2fTYG6ufRv1p/BlTPOH2hIAiCkHsiQBAEQRAKXLwynvCEcL3lclPNvxGJsRluJwiCIOQvESAIgiAUEEN50UGTGz0pScGMqweLRF70vGBtao2rlavOMpUawqJskFJtcbCSY2X3BJlM0tuuuBmx7ReORAQYfE2SJL79638GX2vu0oOFnT/Jz6YJglBCiQBBEAShgLw+L7qMmJSikxc9twZUH6AzVGjXlRC+23qFxNgUAEIBD3sLJnWuRscaHkZqZcGIS3mOpIjJ8HXDtWs12wmF2LH5cHxB9rdrMhKajsr79ghCNogAQRAEoYAYyosuIWkLJrnamiM3MPC+sOZFzyu7roQwfNU5vQvh0Jgkhq86x2996xbrIMHWzAaZyl5nmYQEihfDrFR2BnuWbM1sCqJ5Qk4lx0Hck5xtJwhGVrz/6giCIBQihvKiJ6SkUu27/wDYM64Z9taWxmia0ajUElO2XTN4l1wCZMCUbddoV80dhdzArOViQDNMSHeoUFRCHK3WNwVgT49tuNs7GqFlQq6Y24Jt6VcWShAXonlo6wEGAj/MbfO7ZYKQKREgCIIgCEZzKvgpITEZDbvSBAkhMUmcCn5KEx+ngmuYIORW01H6Q4VS4mHGi6Bh9FkwK35zajIycOBAVqxYQXBwMOXLlzd2c4RMiDoIgiAIgtGEx2UcHORkPUEQcufs2bN89NFH+Pr6Ym1tjaWlJT4+PvTr1489e/YYu3mF1vLly5HJZCxfvtzYTckTIkAQBEEQjCIhJZXTwU+ztG5YbBKSlNF0XUEQckutVjN+/Hjq16/PypUrqVChAsOGDWPs2LHUq1eP7du30759e6ZNm2bspgoFQAwxEgRBEArU8+RUVh6/x9LDwTyNT8nSNjN23GDnlVDG+PvSqrILMkNV1IRiSSWptI8vRl3Cxak0CrnCiC3KBXX6e+H+MfDxh0LyXr755ht++eUXateuzYYNG/Dx8dF5PTExkfnz5xMVFWWkFgoFSfQgCIIgCAUiJlHJ3H1BNJ+1nx933eRpfApeTlb0a+JlaKqmdlnrKi6Ym8g5/yCaQctP02X+UXZfDRU9CiXA3vt76b27r/b5Zycn0iGgA3vv7zViq3Lo2lZY0DD9+d/vwpwamuVGdvv2bX788UecnJzYtWuXXnAAYGlpyeeff86UKVO0yyIjIxk3bhze3t6Ym5vj6upKz549uXLlSobHkiSJuXPnUqVKFczNzfHy8mLKlCmo1WqD62/ZsoU2bdpQqlQpLCwsqFGjBj/99BMqlUpnvZeH+Gzbto1mzZpha2urM98hJSWF2bNnU7duXaytrbG1tcXPz4+tW/X/DwYOHIhMJiM4ODjT9g4cOJBBgwYBMGjQIGQymfanqBI9CIIgCEK+ik5I4c8jwSw7dk9b06GCizWj/SvSuWZpTBRymvk48d3WK4THpvcouL9UByE8Lomlh4P56/h9Lj+OYehfZ6nqYcdo/4p0rO6OvJhmOCrJ9t7fy/jA8ZqUry8JTwhnfOB4ZreaTVuvtkZqXTZd2wr/9EevqkVsiGZ5z5VQrYtRmgaai2uVSsXHH3+Mm5vba9c1NzcHICIigiZNmnDnzh1atWpF7969CQ4OZsOGDWzfvp3//vuP5s2b623/+eefc/DgQd5++206dOjA5s2bmTx5MikpKUyfPl1n3S+//JKZM2dSpkwZunfvjr29PYcPH+bzzz/n5MmTrF+/Xm//69evZ/fu3bz99tuMGDGC2FhNuuDk5GQ6duxIYGAgtWvX5qOPPkKpVLJ9+3a6du3KvHnzGDVKv/5EVtr7zjvvEB0dzZYtW+jatSu1a9fO0nkvzESAIAhCvlpxdQUrr63UWSZJYF1RM+m02/bZGLrJ0r9af51CWkLRE/U8maVHgll57B7xKZq7fZXcbBjl78tbb3jopC3tWMODuuWtabmyH1KqLT+3mkbHGuW167jaWvBVp6p83KICfxwJZsWxe1wPiWXE3+eo5GbDyNYVebtm6WKbCrWkUalVzDw1Uy84AE2NCBkyZp2aReuyrfN3uJEkgTIhd/tQq2DnFxguefcime+uCVChVc6HG5laYfCLNIuOHj0KgL+/f5a3mTBhAnfu3OHLL79kxowZ2uU7duzgrbfeYtCgQdy8eRO5XHewyrlz57h06RIeHpraJt9++y2+vr7MmzePSZMmYWZmBsCePXuYOXMmHTp0ICAgAGtrTcYnSZIYMWIEv//+OwEBAfTo0UNn/7t27eK///6jbVvd4HHq1KkEBgby7bffMmXKFO3d/bi4OPz9/fn000/p3r07pUvrpqbNSntfDhDeeecdBg4cmOXzWFiJAEEQ8sHSw3dZejhYb7mERFKSghlXDxosfDTYz1svT35RF6+MJzwhXG+53FTzb0RibIbbCUVTeFwSSw7dZdWJByQqNYFBVQ87xvhXpMNr7vYr5DJMrO8CUM/L3uDFvpONOV90rMLQFhX48+g9lh0N5lbYc8auvcCv+4IY2aoiXWtreiWEoutc+DnCEsIyfF1CIjQhlHPh52jg3iD/GqJMSE9Lmm8kiH0CM8vmfBdfPclVytTQ0FAAPD09s7R+SkoKa9aswcnJiW+++UbntU6dOtGuXTv27NnD0aNH8fPz03n922+/1V5sAzg7O9O1a1dWrFjBzZs3eeONNwCYP38+AIsXL9YGBwAymYyZM2eyaNEi1qxZoxcgdO3aVS84UKvV/Pbbb/j4+OgEBwC2trZ89913dOnShY0bN+r1ImS1vcWNCBAEIR/EJaUSGptRWkYZMSnJGW5X3FibWuNq5aqzTC1JRCZGAOBs4WLwgtHatOTkBy8uQmOS+P3gHdacekByqmZ8bk1Pe0b7+9K2qmuejsd1sDJjfLtKfNTcm5XH7rH0SDB3I+L5dP1FTaDQ2odudTwxMxGBQlGjUqs4/OhwltaNSIjI59YIhty4cYOkpCRat26NlZWV3uutW7dmz549XLhwQS9AqFevnt76aYFJdHS0dtmJEyewtrbmzz//NNgGS0tLbty4obe8YcOGestu3rzJs2fPKF26tM4cijQRERHa9/WqrLa3uBEBgiDkA1sLE9ztLHSWSUiExWoCA1dbc+QGLpZsLYrfr+SA6gP0hgq9XCV2TccNokpsEfc4OpHfAm/zz+lHpKg0gUGdcg6MaeNLq0r5m3HI3tKU0W18GdTcm7+O32fJ4bs8eJrAhIDLzN13m+GtfHivvifmJoUjU4yQsSfPn7Dp9iY2BW16be/By1ysXPK3UaZWmrvzuXH/mGZCcmY+2ABeTXN2DFP9i/TscHd358aNGzx+/JjKlStnun7auP6M5iuk3XFPW+9ldnZ2estMTDR/+16eePz06VNSU1MNXtCniY/X72k21KanTzXplK9evcrVq1eztb+stre4KX5XI4JQCAz2q6A3VCghJZVq3/0HwJ5xzbC3tjRG0wQhzzyISmBh4G0Czj1CqdKMr25Y3pExbXxpVtGpQDN42JibMLyVDwOaerH65AN+P3iXx9GJfLP5CvP332ZYywr0blgOC1MRKBQmSpWSwEeBBNwK4NiTY9o5B3ZmdqSqU0lIzXj8v7uVO3Vd6+ZvA2Wy3Fc79vEHu9KaCckG5yHINK8bMeVps2bNCAwMZN++fVmah5B20RwWZjiQSxuyZOjiOqvs7OyQyWRERkZmaztD3ztp7ejRowcbNmzIcZtKEtH3KgiCIGTL3YjnfPrPRVr/HMja0w9RqiSa+jixdmhj/hnWhOa+zkZL72dlZsJgvwocmdCaSZ2r4WZnTmhsEpO3XcPvxwMsPXyXhJTiN5SvqAmOCebnMz/TdkNbxgeO5+iTo0hINHJvxCy/WezvuZ+PbNoikyRkr6SzTVv2nlnDolEPQa6AjrNePHn19+LF844zjVoPYeDAgSgUChYvXqwdbpOR5ORkqlSpgoWFBadPnyYhQT+ICwwMBMhVNp9GjRoRFRVFUFBQjveRpmrVqtjZ2XHmzBmUSmWu92eIQqH5/ysuvQoiQBAEQRCyJCgsjrFrz9N29kECzj1CpZZoUcmFDcOasHpIYxpXcDJ2E7UsTBUMaubNwc9bM+2dGpRxsCQiLpnvt1/Hb9YBfj94h+fJIlAoSImpiWy7s40BOwfQZXMXll9dztOkpzhbOjP4jcHs6LaDpR2W0qlCJ0wkBV3Pr+GnsEhcX7ngclOp+Ckski7n16BKLSL/h9W6aFKZ2rrrLrcrbfQUpwAVK1bkiy++IDIykjfffJPgYP0kG0lJScyePZvJkydjZmbG+++/T2RkJD/88IPOemlZhCpWrEizZs1y3KYxY8YA8OGHHxoszhYaGsr169eztC8TExOGDx/O/fv3+eyzzwwGCVeuXCE8XD+hRlY5OmqGyj58+DDH+yhMxBCjXBLZagRBKO6uh8Qyf/9tdlwJIe1mbpsqroxu40vtsg5GbVtmLEwV9GvsRa/6Zdl0/hELDtzhwdMEZu68we8H7zC4uTf9m5bHzsLU2E0ttm48vUHArQC2391OnDIOALlMjl8ZP7r7dqeFZwtM5LqXIzdO/kd1omifCG0eJnLOwpwIhQIXlYq6Sclo7tUmcvXkf1Rv9laBv6ccqdZFk8o0LVvRBxsKVSXl77//nqSkJH755RcqV66Mv78/NWrUwNTUlODgYPbu3UtUVBTff/89ALNmzeLgwYN8//33HDt2jEaNGnHv3j3Wr1+PlZUVy5Yt00txmh0dO3bk22+/Zdq0aVSsWJGOHTvi5eVFVFQUt2/f5vDhw3z//fdUrVo1S/ubMmUK586dY+7cuWzfvp0WLVrg6urK48ePuXz5MhcvXuT48eO4urpmvjMDmjRpgqWlJXPmzOHZs2e4uGjmx7ya5amoEAFCLolsNYIgZFVRqwlx5XEMc/cFsfta+jjjDtXdGO3vS40y9gXentwwM5HTq0E5etT1ZMuFJ8w/cJvgyHh+2n2LxYfuMrCZNx82K4+DlZmxm1osPE95zo7gHQQEBXAt6pp2eRmbMnSr2I2uFbvibu1ucFtJkgh9fI/qL54rgAZJhv+WJj57nMctz2cvBwNeTQtNcAAgl8uZPXs2ffr04bfffuPQoUMcOnQItVqNh4cHHTp0YNCgQdoUoi4uLpw8eZJp06axZcsWDh8+jL29Pe+88w6TJk2iRo0auW7T1KlTadGiBXPnzmXfvn1ER0fj5OSEt7c3kydP5oMPPsjyvszNzdm5cyd//PEHK1euJCAggOTkZNzc3KhWrRrDhg3LVcpSR0dHNmzYwOTJk1myZAmJiYlA0Q0QZJKoVZ+p2NhY7O3tiYmJ0ZtwY6gHISvZakpSD4JSqWTHjh106tQJU9OSe5fu5UnKF7/1L9GTlF/OYrTnnYMlJovRwgsL+e3ib9nebnit4YyoPSIfWmTY+QfPmLf/NvtvaLrbZTLo9IYHo/0rUsU955MOdRybD8cX6Cx6jkQTZ813xPEIJTaGoqUmI6GpfrXT7FKpJf699IR5+29zO/w5oJno3L+JF4P9KuBobdxAoSj+jkiSxMWIi2y4tYHd93eTmKq5QDKRm9CmXBu6+3ansUdj5DL9u8pJShUng59y4EY4+26EUSb6LGvNvs/0mFfbrX5tD8Lr/n7rHD8pieDgYLy9vbGwsMhwvVxLiU+vq5DL2gWCkF3Z+ZyLHoRcEtlqBEHIqsJeE+LMvaf8ui+Iw0GarCFyGXSpVZpR/hWp6GqbtwdLjoM43fSRcpkMnDXDL+TxoWDo/lVyXJ4cXiGX0bV2GTrXLM3OK6HM2x/EjdA4FgbeYfmxe/Rt7MUQvwq42JrnyfGKs2dJz9h2ZxsbgzZyJ+aOdnkF+wp09+1OZ5/OOFroBzihMUkcuBnO/hvhHAmK1BbVAwiXVyVMKoUrzwz2qqklCJc5UaVRh3x5T3nCQBCsk8VoXj30Jy2TZ0GwIOSGCBAEQRAKSGGsCSFJEifuPmXuviCO39VMBFTIZXSrU4aRrSvi7ZxPwYm5LdjqVqhVS+r0x9ZuYOBOM+Z5G6jI5TLequnBmzXc2XM9jHn7g7jyOJbFh+6y8vg9+jT04uOWFXCzy8e7ykWQWlJzMuQkG4M2su/BPpRqzaRPC4UFHcp3oEelHtR2qa2TzUqllrj4KJoDNzRBwdUnujny3ezMaV3ZFf8qrjQraw6L7JDFP0OS0AkS1C+usUOaTMLdpBBfxhgIgnXEhWS8nSAYWSH+zRIEQRDyiyRJHLkdybx9tzl1T1NEyFQh4916ngxvWZFyTrkrvJSppqP07pImR4fClnYAPB+wFxuXsvnbhpfI5TI6VHenfTU3DtwMZ+6+21x4GM2fR4NZdfI+vRuUZVhLH0o7lOwe4bD4MLbc2cLGoI08fp4+/r+aUzV6+PbgTe83sTVLD+JiEpUcDopg/41wAm9G8DQ+RfuaTAa1PB1oU8WV1lVcqV5ak/ee1GT4+z2Iv4/SxIqYVFOcidFuFy5zIqTJJOp0KPh5OdliIAjO8naCYGQiQBAEQShBJEki8GYEv+4L4sLDaADMFHJ6NSjLsFY+lCnhF8AymQz/Km60ruzK4aBI5u4L4sz9Z6w8fp81px7wbr2yjGjlQ1nHfA6gCpFUdSqHHx0mICiAw48Pa3t6bE1t6VShEz18e1DVSZNJRpIkbofHsf9FL8GZe89IVacPq7E1N6FFJRf8q7jSsrILzjavDOFSpULARxB8EMxsMB2wFbW5G/1X9cFFqeDdWh/R0L934e45SGMgCBaEoqII/IYJgiAIuSVJEnuuhTFv/20uP9bcjTU3kdOnUTk+buGDu70YQvMymUxGi0ou+Pk6c/xuFHP3BXHi7lPWnHrAP2ce0v3FEKzy+TUEqxB4GPuQTbc3sfn2ZiIS04tn1XWtS49KPWjn1Q5LE0uSlCoO3orQDh168FS3cJaPizX+VVzxr+JG/fKlMFVkkPpSkuDfsXB9GyjMoPdqKFMPRXQo50s9A+DzOi1RFIXgQBCKOPFbJgiCUIyp1RK7roYyd59mEi6ApamCfk28GOznjautCAxeRyaT0dTHmaY+zpy+p5mrcTgokvVnHxFw7lH+TeI2kmRVMvsf7CcgKICTISe1yx0tHOni04Vuvt2oYF+BsNgkNp/TBARHb0eSkJI+wdhMIadRBccXQYErXk5ZCKIkCfZ8C+dXaeaevLsMKrTMj7coCEIWiABBEAShGEpL4zl//22CXqTxtDZTMKBpeT5q7o3Tq0M7hEw1KO/IXx810kkDu/nCE7ZcfMJbb3gw2t+Xyu5FM1C4/ew2AUEBbLu7jZhkTQ+TDBlNSzelu293WpZpxbWQeDadDGf/jcN6E4xdbc3xfzGXoHlFZ6zNs3l5ceQXODZP87jLPKj6dl68LUEQckgECIIgCMVIqkrNlgtPWHDgNncj4wGwtTBhUBEoBKaS0u9CX4y6hItTaRSFqJBUmjrlSvHnwAZcfhTDvP2aQnL/Xgrh30shdKzuzij/ikWikFyCMoH/7v1HQFAAFyMuape7WbnRzbcbbcu+TdBjU3adCufLmweJMjDBOK2XQDvBOCfOLIN9UzSP20+HOn1z87YEQcgDIkAQBEEoBlJS1Ww6/4gFB+5ox4DbW5oyuLk3/ZuWx96ycBcp3Ht/LzNOTNc+/+zkRNwu/8LEhhNp69XWiC3L2Bue9izuX5/rIbHM33+bHVdC2HU1lF1XQ2lb1ZXR/r7UKutg7GbqkCSJq1FXCQgKYGfwTuKVmiBSIVPQqmwrmrh04llUeQJPRfHLP1cNTjBuXcWVVoYmGOfElY3w7yeax36fikm9glBIiABBEAShCEtOVbH+zCN+C7zD42hN5VpHazOG+FWgXxMvbLI71MMI9t7fy/jA8UjoFkYLTwhnfOB4ZreaXWiDBICqHnYs+KAuQWFxzD9wm20Xn7D3ejh7r4fTspILY9pUpJ6XcSshxyTHsP3udjYGbeTms5va5WVty1K3VEdSY+px7HQKm58mAEHa19MmGLeu4kqD8o4ZTzDOidt7YeNQQIL6H4L/t3m3b0EQcqXw/+UQBEEQ9CQpVaw99YDfD94lNDYJAGcbc4a1rECfRuWwMisaX+8qtYqZp2bqBQeAdtnMUzNpXbZ1oRxu9DJfN1t+7V2HMW18WXDgNlsuPOHgrQgO3oqgWUUnRvv70riCU4G1R5IkzoSdYWPQRvbc30OyKhkAU7kZlW2aooptxJULTlxLUQPRQA4nGOfEw1Owrh+olVC9O3T6CYMlk4uwFVdXsPLaymxv179af72CioJQ0IrGXxBBEAQBgISUVFaffMCiQ3eJiNNc8LnZmTOspQ/vNyyHhWnhvohOE5UYxfWn19lzbw9hCWGvXTcsIYyW61pS2qY0LlYuuFi64GzprPnXSvNv2jJThfGHUvm42DC7Z23GtvFl4YE7BJx7xNHbURy9HUVDb0fG+PvSrKJTzsfsZyIyMZKtd7ayMWgj92Pva5eXMikHcY148KAqx9VpdRzUuZ9gnF1hV+Hvd0GZABXbQrdFUMiDv5yIV8YTnhCeo+0EwdhEgCAIglAEPE9O5a/j91l6+K52smgZB0uGtfLhvXqehTYwkCSJ8IRwrj+9zvWo61x7eo3rUdczDQpeFZMSQ8zTGK4/vf7a9UqZl9IGDWlBhIvVS49fBBWWJvlfEM7LyZpZ79ZkdJuK/BZ4h/VnHnEq+Cl9/zhJ3XIOjG7jS6tKLnkSKKjUKo49OcbGoI0EPgwkVUoFQIEF0vPaxEXUIy7JE5Ahk0HNspoKxrmeYJxdT4Phr26QFANlG0HPlWBSeCfO54a1qTWuVq46yyRJ0taUcLE0/H9vbVp8a2sIRYcIEARBKHAqtURqfAWkVFvO3o+hY41SKOTFa3hBVmV2LmKTlKw4eo8/jgYTnaAEoJyjFSNa+dC9ridmJnk4JjyXJEkiJD6Ea1HXuBZ1TRsURCVF6a0rQ4aXnReuVq6cCj2V6b6/bfwtblZuRCRGEJEYQWRCpOZxguZ5VGIUqVIqz5Kf8Sz5GUHPgl67P1tT29cGEmmv2Zja5Pri2bOUFdO7vcEo/4osOniXNacecO5BNIOWnaampz2j/X1pW9VV5zgpqlRUiR5Ikin/XDnCsEadMDNQICzkeQibbm9iU9AmQhNCtcvViWVJedYQZVxNUJtrJhi/kccTjLMrLhT+egeeh4FrdeizDsyK78XwgOoD9IYKJSgTaLS6EQD/dvsXK9OSU5EbYODAgaxYsYLg4GDKly9v7OZkm0wmo2XLlgQGBmZp/aL8fkWAIAhCgdp1JYTvtl4hMXYoACNXX8PD/i6TOlejYw0PI7euYL3uXDSu4MSfR++x7GgwcUmau8HeztaMal2RrrVLY5KXk0VzQC2peRT3SBMMvOgVuP70ujaH/svkMjkV7CtQzakaVR2rUs2pGidumLHyaBjhqJG53UJSRIOh63AJZCoHfg4ohYxUBvvVZbhfBYPtiU6OJiIhgshETfAQmRipDSDS/o1MjCRZlUycMo64mDiCY4Jf+z4tTSzTgwZLZ4O9ES6WLjiYO2QaSHjYWzK5S3VGtPJh8aG7/H3yAZcexTBk5Rmqedgx2r8iHaq78/PRDay89SsKS825XHL7a5be/B/9fMfwud97KFVKAh8FsuFmAMdDjmnnakgqS5QxdVFGN0Cd7E4FF2vaNMunCcbZlfhM03Pw7B6UKg/9NoJlKeO1R9ARGBhI69atGTBgAMuXL8/xOkLxIQIEQRAKzK4rIQxfdU5vOmpoTBLDV53jt751S0yQ8LpzMWzVOSxM5CSlqgGo6GrDaP+KvF2ztFF6WlRqFfdj73M16qq2V+DG0xs8Vz7XW9dEboKvgy9VnapS1bEqVZ2qUqlUJb0hPQcv3dJOrjaR3saizCpNMPDS25NenJzE0LeJjdMMq0oLll4ll8lxtHDE0cKRylTO8L1IkkScMi69B8JAb0RagBGvjCcxNZGHcQ95GPfwtefIVG6acSDx0mNHC0dc7Sz45u1qDGvlw9LDwfx1/B7XQmIZ/vc5XNxuklhqGSh04yW1PJoVd6ZyInw3jxNvEK+K1r6WGl8BZXRD5Alv0MjbDf+G+TzBOLtS4uHvnhB+DWzcod9msHU3dquMQqVOr/VxNuwsTUs3LfST74Wc++GHH5g4cSJlypQxdlOyTQQIgiAUCJVaYsq2awZy1YCE5mJoyrZrtKvmXuyHG2V2LgCSUtVUdrNhTJtKvFnDHXkBnROlWsnd6Ls6Q4RuPrtJYmqi3rpmcjMqO1bWBgJVnari6+CLmSLzMeW2Fia421m8eFaf1KempNgHIJmkV+iVqxwwi+mGjawW2KVvlxsymQw7MzvszOyo4KDfE/GyBGWCNlh4XSARkxyDUq0kJD6EkPiQ1+5TLpPjZOGkDSJc7F0Y9HYpbj6WcfxmEgl2G5Ghn9An7fmt5ycAUKfaooyuh52yKW/5Vqd1ywKaYJxdqSmabEWPToGFA/TbBI7exm6VUey9v5cfTv2gfT5i3wjcrNwKda0PIXc8PDzw8CiaN70K2TeJIAjF1angp4TEJGX4ugSExCTR8n/7i0yKzpxKSEl97blIM6lzdZpWdM63dqSoUgiKDtIEA1GaYODWs1ukqFP01rU0saSKY5X0YMCxKhUcKmAqz1nWoMF+FRisM1SoDXHxH9B0g+ZCaV6z/+FXoZ1R765amVpRzrQc5ezKvXa9FFVK+rCmF0FEeEK43lCnp0lPUUtqbcDx6oRrmYfhUVavso5oSu8EE9rWf4NqHXsXWPCYbWoVbBoKd/aBqRV8sB7cqhm7VUZR1Gt9ZCRtXP2VK1f4+uuvWb9+PVFRUVSuXJnvvvuOd9991+B2arWaH3/8kSVLlvDw4UM8PDwYNGgQX375Jaam6d8py5cvZ9CgQSxbtoyBAwfq7CNtyNOkSZOYPHmydnnaPIF169bxxRdfsH37duLj46lVqxYzZ86kVatWeu2Ji4tj9uzZBAQEcPv2bUxNTalQoQJvv/023333nU6bAMLCwrK0b0NzEF5u99tvv82XX37JiRMnkMvl+Pv788svvxicr7Bx40ZmzJjB1atXsbOzo0uXLvz444/UqVMHgHv37hk81zlVvP8KC4JgdJIkcSM0jr9P3s98ZeDRs8wvnEuKiOfJebavxNREbj27lR4MPL3O7We3tdluXmZjaqMzRKiaYzW87Lzy/WJdIUvff12XWkVm6IWZwozSNqUpbVP6teulqlN5mvRUG0iEJ4ZrAoo7u4kIu8RFU1uemaleuw+ATqrrjFNfBEtPKKzBgSTB9k/h6iaQm0KvVVC2obFblW2SJBnsPcsOlVrFD6d+yLTWRyP3Rjn+zFuaWBZcJqpXKJVK2rdvz7Nnz+jRowcJCQmsXbuWnj17smvXLtq3b6+3zbhx4zh69Cg9e/bExsaGbdu2MWnSJC5dusSGDRty3abo6GiaN2+Ovb09/fr1Izw8nHXr1tGhQwfOnj1LjRo1tOuGh4fTsmVLbty4Qe3atRk+fDhqtZobN24wa9YsPv30UxwcHHK079c5ffo0P/74I61bt+bjjz/m/PnzbN68mcuXL3PlyhUsLCy06/7555989NFH2NnZ0b9/f+zt7dmxYwft2rVDqVTqBTB5QQQIgiDkucQUFcfuRLL/RjgHboTzJAt3y9N806kq1Urb5WPrjO/ak1i+3/H6dJ0ArrYWma5jSLwynhtPb+gEA3dj7qKW1Hrr2pvbU82xmnaIUDXHanjaeiKXFZ7sSMWFidwEVytXTerLl+ulJajg3g2WJzrzs3Pm6V/LmZiBbWkwt82/xubW/mlwdhkggx5LoGIbY7coRxJTE7VZh/JTWEIYTdc2zfH2J/ucNFpGpCdPntCgQQMCAwMxM9MML+zTpw9t27Zl9uzZBgOEEydOcPHiRTw9PQGYPn067dq1IyAggICAAHr06JGrNl28eJERI0Ywb9485HLNd5m/vz+DBw9m/vz5/P7779p1R4wYwY0bN/jqq6+YPn26zn7CwsKwsbHJ8b5fZ8eOHaxdu5ZevXppl/Xv35+//vqLzZs307t3b0ATkIwdOxZra2vOnDmDr68vADNmzNAGJV5eXtk8Q5krtAGCWq3m119/ZdGiRdy7dw8XFxd69uzJ1KlTsbbOfOLV8+fPmTt3LmvWrOHevXuYm5tTqVIlhg4dyoABA4wWaQtCcfXoWQIHboSz70Y4x+9EkZyafjFqbiKnqY8TZ+8/IzaDSaYywN3egkHNvYv9HIRGFZz442gwoTFJSKhRWAUjM4lDSrVFleCNDDnu9hY09HbMdF8xyTF6wcD92PsG71Y6WThpMgm9CASqOlXFw9pDfB8aW9NR0HQUfVJTmb2yNWp5tMGiwpKkmZfRZ+wuMJDytNA4Ng8O/6x53HkOVO9m1OYI+e+XX37RBgcAbdq0wcvLi9OnTxtcf+zYsdrgAMDMzIzp06fj5+fH8uXLcx0gWFtbM2vWLO0FPMCAAQMYNmyYTptCQ0PZuHEjPj4+OsOU0ri5ueV435lp0aKFTnAA8OGHH/LXX39x+vRpbYCwZcsWnj9/zpgxY7TBAYCJiQnff/89TZvmPLB8nUL7DfPJJ58wd+5cunXrxqeffsr169eZO3cu58+fZ+/evTr/Ma9Sq9W8+eabHDt2jAEDBjB69GgSEhJYs2YNgwYN4vr168yaNasA341Q0qy4uoKV11bqLJMksK6ouZPebftsgxcA/av118ubXVilqtScexDN/hvh7L8Rxq0w3Yw2ZRwsaV3FhTZV3Gji44SFqSLDzD1pp2JS52rFPjgAUMhlTOpcjVFbVmLutg25aXpqULXSnuSwzkzq3F/vXDxNeqopNvZiAvG1qGs8fv7Y4DHcrd11hghVdaqqV7RJKFzMTEzo5zuGFXemImWQ0alfpTEG6yEUGudXwe5vNI/bToZ6A43ZmlyzNLHkZJ+TudrH2bCzjNg3ItP1FrZZSD23ejk6RkEU/suIg4MD3t76E889PT05fvy4wW38/Pz0ljVp0gQTExPOnz+f6zZVqlRJ786/iYkJbm5uREdHa5edOXMGSZJo3bp1lofpZHXfmalXT///Oi1oenk/Fy9eBKB58+Z66zdq1AiTfPo+KJTfMlevXmXevHl0796dgIAA7XJvb2/GjBnD2rVr6dOnT4bbnzx5kiNHjjBu3Dh++eUX7fIRI0ZQpUoVFi1aJAIEIV/FK+MJTwjXW542nzMiMVbvtbTtCrNn8SkcvBXB/hvhHLwVQUyiUvuaXAb1vErRuoorbaq4UclNv8BUxxoe/Na3Lt9tvUJ4bPpEWHd7ixJXB8HE9iqWnqt4NVqSm8Rg6bmKeLOKHHzorFNnIKPqw542nppA4EWdgSqOVXCydDK4rlC4fe73HgArb/0KJumBo1zlQL9KY7SvF0rXt8HW0ZrHTcdA80+M2548IJPJcj10p2npprhZuRGeEG6wZ0+GDDcrN6OmPE276apW6w9DTJP22qs3aO3t7Q2ub2JikuH+DN2ZVygUODk5EROjX0slu+zsDA9TNTExQaVKn+eTdqzspCHN6r5zsp+0i/2X9xMbq7lecHXVv8Ejl8txds6fRBaFMkBYs2YNkiQxbtw4neVDhgxh4sSJrFq16rUBQtrJLF1ad8KYmZkZzs7OJCfn3cQ/QTDE2tRa726tWpKITIwAwNnCxWDmEWvTQpK3/IW0CcaaXoJwzj94hvqlv28OVqa0rOSCfxVXWlZywcEq8/SWHWt4ULe8NS1X9kNKteXnVtPoWKN8ieg5SKNSq5h5aqbmyatv+8Xzyccn622XVn345V6BKo5VsDc3/AdaKJo+93uPvtWa0Gb9h0iSKcOrD2JY03cKd8/B3UDY8CFIaqjTD9pNNXaLCg2FXMHEhhMZHzhe7zXZi1/4CQ0nGHVSftpFflSUftXzNJGRkTrr5kZYWBiVK+vWK1GpVERFRekED2nBSGqq/tDUvAgk0iYfP35suCe2MEgLJMLD9W86qtVqIiMj86XOQqH8tjl9+jRyuZyGDXUzHlhYWFC7du1Mx3g1bNgQBwcHfvzxR8qXL0+jRo1ISEhgxYoVnD17NtMJJMnJyTpBRFrAoVQqUSqVGW2mpVSm6jzOyjbFWdr7L0nnoU+lPvSppBvERiXE0W5zSwBWtlmLu73hKqLGPk+JKSqOBz8l8GYEgbci9dJxVnazoVUlF1pVdqa2p71ORd+stl2tSsXE+i4ANUtbo1alos76jZciS35yIfKTv3HGVEaYfeZfv6VVEvWUElXKNKVyncFULlXZYBBp7M9MXkl96X2U9O9OmVpCYampqdDNtz4ySSq050P25ByKtX2QqVJQV+mMquNPYOCCLqdeft/KVFW2zkNhOWdtvdoyu9Vsfjj1g07vspuVGxMaTjB6itPKlStjZmbG6dOnSU1NNThsJW24UM2aNXN9vMOHD9OiRQu9/aempmrTdgKUKqX5O2noAj4vhiLVr18fuVzOgQMH8i0bUG7VqlULgKNHj/Lee7o9iKdOnTIYPOWFQhkgPHnyBGdnZ8zNzfVeK1OmDMeOHSMlJUVnQszLSpUqxdatWxk8eDA9e/bULre1tSUgIIB33nnntcf/4YcfmDJlit7y3bt3Y2WVeVdjsgrSTu3+/fsxLxqZ+vLdnj17jN0Eo4pLTR9Sc/DgQWxNMr/bXlCeJsPVZzKuPZMRFCNDKaXf1jaVSfjaS1QvJVGtlISjeTSkRhN+NYjdV3N2vMJ8LvJLipRCUvh+7polsds6a0MWxkZF0Sk+gRtSCjdVoYQSms+tNC5Vapz28cGDB1GYFOIsPfksISX9XBw5cgQrs8J5LmwTH9MsaDomqnjCbatz0vwd1Lv+y9Nj5OZcJCQk5GlbcqOtV1sauTfSZita2GZhoamkbGFhQc+ePVm1ahXff/+93oTdy5cvs3TpUmxtbenWLfeTzn/99VcGDBigHXOfkpLC119/DaBT76BevXrIZDLWrl3LhAkTtKk/g4KC+PXXX3PdDjc3N3r06MH69euZMmUK33//vc7r4eHhODo65ts4/6zo2rUrNjY2/PHHH4wePRofHx9A06vy7bff5ttxC2WAkJCQYDA4ALQfjoSEhAwDBAAbGxtq1KhBly5daNq0KU+fPmXBggX06dOHLVu20K5duwy3/fLLLxk/Pr0rMDY2lrJly9K+ffsMx57ptD8llS9O7Qc0qa/srXOWqrC4UCqV7Nmzh3bt2hXK6LygRCXEMWuzptu9ZcuWGfYgFIRUlZrzD2MIvBVB4M1IboXrTjAubW9Bq8rOtKrkQmNvRyzN8vYPWGE6F/np8fPHHH58mCNPjnAm7Awp5inZSk3pbO6AJLfHt0ZdfBp1yseWFg6JCZFM2qyZH9ayZUvs7N2N3CLjiYoOZcYOzblo3rw57s55P4Qg12IeYrJiAjLVc9Sl61Lqg410NLPJfLtsys25SBsBUFi8HAzUc6tXKIKDND///DMnT55kypQp/Pvvv7Rs2RILCwtu3brF1q1bkSSJv//+W6cmQE41btyYWrVq0atXL6ytrdm2bRs3b96ke/fuOhmMSpcuzfvvv8/q1aupV68eHTt2JDw8nE2bNtGxY0edeao5tXDhQq5cucL06dPZsWMH/v7+SJLErVu32L17N2FhYXnynnPKwcGB2bNnM3ToUOrVq0fv3r21dRDMzc0pXbr0axP35FSOAgS1Wp0vjUljZWVlcKwVQFJSknadjFy+fJmmTZvyyy+/MGzYMO3y999/nxo1ajBkyBDu3LmDQmH4F9Pc3NxggGJqapqlC1zTl+++mpqU6Ivil2X1/BVXL793Y3wusjrB2L+KK5XdbPM19aWxz0V+UaqVXAi/wOFHhzn06BB3Yu7ovF7Gpgx+Zfzw8/RjyvEpRCREvHbSYr1+u5DJFSiAwnMZkX+UxfRzkRM6vyMmisJ3Lp6Hw+p3IS4EXKog7xuA3Cp/Av3cnItCd94KMVdXV06fPs0vv/zC5s2bWbRoESkpKbi7u/Puu+/y2Wef6Qz/yY05c+awfv16li5dyoMHD/Dw8GDy5Ml8+eWXeusuXboUZ2dn1q1bx4IFC6hcuTKLFy+mdOnSeRIgODs7c+LECX766SfWr1/P/PnzsbCwwNvbm4kTJ2YptX5+GzJkCKVKlWLGjBksX74ce3t7unTpwqxZs/Dy8tL2KuQlmSRJ+n+dMlG2bFmGDRvGkCFDDM6qzq0OHTqwd+9egz0JzZo149atW0RERGS4/YcffsiyZcuIjIzEyUk3k8fo0aOZP38+t2/fzvIJjY2Nxd7enpiYmCz3IFT7TtPFevFbf+ytjZd+rDBQKpXs2LGDTp06legv66iEOFqt13Qt73nnIO72mee4z42XJxgfuBHOuddMMG7h60Ip64Ib5lPQ5yI/PUt6xpHHRzj06BBHnxwl7qXhEAqZgtqutWnp2ZIWni2oYF9BG3jtvb+X8YHj9QKEtEmLs1vNNvq45IKWkBBJo/WtATj6zm7s7EtOVqtXRUWH0mqLpqd7T6cduLuUNXKLXpIUA8vfgtDL4FAOPvwP7F5fRTqrDKWIVqvVRCZpJsg6WzghN3DXPaMU0Vn9+52UlERwcDDe3t46FWzzWoIyQVt4zZjFzYTi4fbt2/j6+tKzZ0/WrVuX6frZ+ZznqAfh8ePHfPfdd0ybNo333nuPkSNH0rhx45zsyqAGDRqwe/duTp06pZMrNykpiQsXLuhNbDHUPsBguqm0yRz5NalDEIwpswrGVdxttb0Edco66EwwFrJGkiRuPbvFwUcHOfToEJciLulc5DuYO9C8THNaerakSekmGWYYSpu0OOPEdCJeXPxA4Zm0KAgGpSTA6t6a4MDaBfptzrPgADJOEZ0mMslwlp3CmCLacD2c9O+Ktze9bbCntijVwxEKxrNnz7CystK5aZ6YmMgnn2hSCWc2tzYnchQgnDx5kvnz5/PPP//w999/s3r1aurWrcuoUaPo3bt3hvMHsqpXr17MmDGDOXPm6AQIS5YsISEhgQ8++EC77M6dOyiVSqpUqaJdVq1aNXbv3s3y5cv54osvtMujo6PZsmULpUqVomLFirlqoyAUFmkVjPffCOeYgQrGzSo6a4OCMg4luzcrpxKUCZwMOcmhx4c4/OiwXj2CyqUq08KzBS08W/CG8xtZHlfc1qstNe2q0mZrRwB+ajSTtpU6FqpxyYKgpVLC+oHw4BiY20PfjeCUt0MbDKWIliQJWZwmo5Nk447MwBDnwpYiGjIPdiISDY+EKIzBjmBcBw8e5KOPPqJ9+/aUK1eOyMhI9u/fz7179/D399eryJwXchQgNGjQgBUrVjB79myWLFnC77//ztmzZ/nwww/57LPPGDx4MMOHD6dcuXI5atQbb7zByJEjmT9/Pt27d6dTp07aSsotW7bUqYHQpk0b7t+/rxOVjxs3jpUrVzJx4kQuX75Ms2bNePr0KUuWLCEkJIQFCxZkOP8gu0pCxVyhcNFMMI5m33VNL8HNsDid10vbW+BfVRMQNKngnOcTjEuKR3GPOPToEIceH+J0yGlS1OmZlywUFjT2aIyfpx8tPFvgbp3zybQKWfr/Ty2nmiI4EAontRo2j4Cg/8DEEvqsA4/cp7t81YCYOAY8eKJ7aEmN/Lkmg5faRo1cZqDns0yc/jIjMxTsZHU7QXhZ9erVadeuHUePHmXz5s0AVKxYkWnTpvHZZ58VnknKaZycnJg4cSITJkxg27ZtLFiwgL179zJr1ix++ukn3n77bUaNGkWbNm2yve85c+ZQvnx5Fi9ezPbt23F2dmb06NFMnTo10xPh5eXFqVOnmDp1Kvv27WPt2rVYWlpSu3Ztfv75Z7p3757Tt6ynuFbMFQqXzCYY1y1XShsU5PcE4+IqVZ3KhfALmqDgNROMW3i2oIF7AyxMSnZ2MqEEkSTYNQEu/wNyE+i5Erya5M+xkuMgTjdAePkvflqgYHC7QmZA9QHiRqCQJ3x9fVm7dm2BHjNP0pzKZDK6dOlCly5dCAoK4ueff2bx4sVs3bqVrVu3UqVKFcaPH8+gQYOyHOUoFAo+/fRTPv3009eud+/ePYPLfXx8WLFiRXbfSrYVl4q5Qv5TqSVS4ysgpdpy9n4MHWuUyrB6sCRJ3AyL0/YSvDrB2N7SlFaVjTPBuDjJygTjFp4taOnZUmeCsZBP1CrqJybholKheHgSbDuD6FExvsCZcGoxIINui6BS+/w7lrkt2OrOaVAjERarKV7qZmuO3NDvYTbSBwuCkLk8rYNw//59li5dqk07JUkS7u7uXL9+naFDhzJ//ny2bdumLYxRHBi6Q/ByhpY1HTcU6QwtQt7YdSWE77ZeITF2KAAjV1/Dw/4ukzpXo2MNTaaWxBQVx+9GaoOCVycYV3az1fYSiAnGOZPVCcYtPFvQtHTTDCcYC/ng2lYsdn7OsrgXPbIbBmgmv3acBdW6GLdtJdmJ3+HgTM3jTv+DN97N3+M1HaX5eUlSSipN0jIDfiEyAwpCQciTAGH37t3Mnz+fnTt3olKpsLCw4MMPP2TMmDHUrFmTvXv3MmnSJI4fP84nn3zC+vXr8+KwglAk7LoSwvBV5/Sy3YfGJDFs1Tl6NyxLWEySwQnGTX2c8K/qRuvKLniWEunwciJBmcCp0FMcfHQwTycYC3no2lb4pz+yV39LYkPgn/6aIS0iSCh4F9dqhhYBtP4aGg4xbnsEQSgwOQ4QYmNjWbZsGb/99htBQUFIkkSZMmUYPnw4H3/8sU79gbZt2+Lv70/t2rXZv39/njRcEIoClVpiyrZrBkphoV229tRD7TIxwThvPH7+mEOPDnHw0cF8nWAs5AG16sVFqIT+wBEJkMGuiVDlLTHcqCDd3KmZlAzQeAS0+Ny47REEoUDlKEAYPnw4f//9N/Hx8UiSRJMmTRgzZgzvvvtuhtmB5HI59evX5+rVq7lqsCAUJaeCnxLyylAhQ3o3KMvAZuXFBOMc0k4wfnyIQw/FBOMi5f4xiH3ymhUkiH2sWc/b7zXrCXnm3hFNOlNJBbX6QPvpGEzHJwhCsZWjAGHRokWYmZnRp08fxo4dS/369bO0XYsWLchB4WZBKLLC4zIPDgCa+DhRxT3zKt1CurQJxocfHebIkyMZTjBuUaYFPg4+IvAqrJ6HZb5OdtYTcufJBU0htNQkqNwJusyDfEihWBJELVvO0+XLs72d48CBOA0amOftEYTsyFGA8N133zF8+HDc3Nyytd3AgQMZOHBgTg4pCEWSq23W7lRndb2SLG2CcdrQocuRl1FL6XM2xATjIkil1PQMZMWpxeBYAcrUzd82lWSRt2FVD0iJg/J+8O4yUORpLpMSRf38Oalh2Q9s1c+f50NrBCF7cvSbP3ny5DxuhiAUT76uNpjIZaSqDfecyQB3ewsaepesTFcqSaV9fDHiPC62rQxODk5MTdRUMH5Rm0BMMC5G7uyHnRMg8lbW1n94Epa0hortoOUXULZh/ravpIl5BH+9AwmR4FEbeq8GU3HjIjfkNjaYvHojVZJIDddk6jJxdTU4dEtuY1MQzROE18pRgPDs2TMuX76Mj48PZcqUMbjO48ePuXPnDjVr1sTBwSE3bRSEIinqeTJ9/zj52uAAYFLnahnWQyiO9t7fy4yTP2iff3Z0HG7n3ZjYcCJtvdpqJxgfenSIUyGnxATj4ubZPfjva7jxr+a5lTNUewfO/PFiSvLLvy8vfi86zoQn5+Hyeri9R/NToRW0nABeTQu0+cVSfBT81Q1iHoKTL/QNAAsx5DG3nAbpDxVSJyRws249AHx27URuJbLTCYVTjgKEX3/9lWnTpnHy5MkMA4SQkBBat27N1KlT+frrr3PVSEEoaiLikvlg6QluhT3H2cacEa18+P3QbcJj0y923e0tdOoglAR77+9lfOB4ndoDAGEJYXwS+AnuVu6EJuhWShUTjIuJlAQ4OgeO/qoZ3y5TQKOPNRf5lg5QoSXSzs+Rxb30/29XWhMcpKU4bfkFHJmtSb95N1DzU95Pk2HHu4WYSJsTyXHwdw9NT46dJ/TbBNbORmvO0sN3WXo4WGfZy98X7eYcNVgobbCfN4P9KuR7+4Sib+DAgaxYsYLg4GDKly9v7OYUWjkKEHbs2EGFChVeOzm5fv36eHt78++//4oAQShRwmOTeH/JCe5ExONmZ87qIY3xcbHh7dpOtFzZDynVlp9bTaNjjfIlqudApVYx89RMveDgZaEJociRU8etjphgXFxIElzbAru/0dyhBvBuwRbfZsx9vAf+7aFdVebpQdlnalxUKpSWjlyxsUN99Ve4+qt2nf7V+jOgxRdw5Bc4vwruHdb8lG0MLT8HnzYiUMgqZRKseV/TO2PlBP03g0NZozYpLimV0NiMkzuExyVnuJ2QNy5cuMDvv//OoUOHePToEUlJSTg6OvLGG2/QsWNH+vfvj4uLi7GbKeSzHAUI9+7do2HDzMd/VqlShTNnzuTkEIJQJIXGJNFnyQnuRsbjYW/BmiGNKe9sDYBCLsPE+i4A9bzsS1RwAHAu/JzeHAJD5rSeQ+tyrQugRUK+C78OO7+A4EOa5/ZlocN0qNqFxxd/IzwhXG+TMMu0HqJkSIzQez1eGQ+lvKDzHGjxmaZH4uwKeHhCM8G2TD1o8QVU6iAChddRpcKGDzXBlZmtZliRs6+xW4WthQnudvq9hBISSUlJWFhYIDNQMcPWomhMppZU6fOvEs6cwbpZM2QZpIcvaGq1mi+++IKff/4ZhUJBixYtaN++PdbW1oSHh3P8+HE+++wzJk2axM2bNzMcQVLY/fDDD0ycOLHItr+g5Og3KjY2Fnv7zDOE2NnZER0dnZNDCEKR8zg6kT5LTnA/KoEyDpasGdKYck5ifGlSahJ7H+xl8cXFWVo/MTUxn1sk5LvEaAicqck8JKlAYQ7Nx0GzcWCm+Z2wNrXG1cpVdztJgrgQzWNbD4MX+Nam1ulP7D2h0//A71M4OhfO/AmPz8KaXuBeUzMkqfJbIk3nq9Rq2DYGbm7X/N+8vwZK1zF2qwAY7FfB4FAhpVLJjh076NSpJaampkZoWe7F7t5N2PfTtc8fDv0YE3d33L76Erv27Y3YMo2vv/6an3/+mbp167Ju3ToqVqyot865c+eYMGECiYlF93vaw8MDD4+SM7Q3p3L0reni4sKNGzcyXe/mzZs4Opas7CygqZ6bGl8BZUwtzt6PQZXBJFWh+Hj4NIFei45zPyqBso6WrPtYBAc3n97kh5M/4L/eny8Pf0lwbHDmGwEuVqLrushSq+HcSphXD07+pgkOqrwNo05B66+0wQHAgOoD2PfePt2fd7ax7+ET9j18wq63Nuq//t4+BlQfoH9cW3foOAPGXYZmY8HUGkIvwbq+8HszuLJRU7FZ0ARhu7+BC39r5oG8t1wUoCsAsbt383jsOG0GozSpYWE8HjuO2N27jdQyjVu3bvG///0PFxcXdu3aZTA4AKhbty579uzRGbv/559/0rVrV8qXL4+FhQWOjo506NCBAwcO6G2/fPlyZDIZyw3UhwgMDEQmk+llyjx37hzvvvsu5cqVw9zcHBcXFxo0aMD06dN11gsKCmLQoEF4e3tjbm6Oo6MjtWrVYty4cTo1uAYOHIhMJuPevXvaZSkpKcybN48OHTpQtmxZzM3NcXV1pXv37pw/f/6172P37t00bdoUKysrnJycGDBgAFFRUQbPX1GSox6Exo0bs3HjRg4dOkSLFi0MrnP48GHOnz/PO++8k5v2FTm7roTw3dYrJMYOBWDk6mt42N8tcZNRS5L7UfH0WXKSx9GJlHeyYvWQxpR2sDR2s4wiXhnPzuCdbAzayOXIy9rlHtYedK3YlQ23NhCVGGVwHoIMGW5WbtR1FXnui6RHZ2DHZ5rx7ADOleDNWeDjX3BtsHGBdlOh6Vg4sRBOLoLwa7BhkKY9LT6H6t1Ldm7/wz/BiQWax10XQJVOxm1PISdJElIu75ZLKpWm58BQoVhJAhmETZ+BdZMmOR5uJLO0zNVcrRUrVqBSqfj444+zNL/AxCT9d2jkyJHUqlWLtm3b4uLiwuPHj9m8eTNt27Zl48aNdO3aNcftunDhAk2bNkWhUNC1a1e8vLyIjo7m2rVrLF68WDvH9cmTJzRs2JD4+HjeeustevXqRXx8PEFBQSxcuJCffvpJp82vevr0KePGjcPPz49OnTpRqlQp7t69y9atW9m5cyeHDh2iQYMGettt3bqV7du307lzZ5o2bcqhQ4dYuXIld+7c4ciRIzl+34VBjr4lhw8fTkBAAO+++y5LlizR+8/fsmULQ4cORSaTMWzYsDxpaFGw60oIw1ed07v0CY1JYviqc/zWt64IEoqZ4Mh43l98gtDYJCo4W7N6SGPc7UtWlh1JkrgUeYmNQRvZGbxTO0TIRG5C67Kt6eHbg8YejVHIFVQuVZnxgeP19pE2pnhCwwmilkFR8zwc9k7W3JEGzXj2VhM1GYoURhoKYu0Ebb6FpqM0QcKJhZosPRuHaIY++X0KNXsar33Gcnop7P9e87jjTKj9vnHbUwRIiYnatKT5dxBNT8KtBjmv7VH53FlkuUiZevz4cQBat87+/K9r167h7e2tsywkJIT69evz+eef5ypA+Ouvv0hOTmbz5s16+3n5Ln1AQADR0dHMmTOHsWPH6qz39OnT1wYHAKVKleLBgwd68xKuXr1K48aN+eqrr9izZ4/edtu2bSMwMJBmzZoBoFKpaNu2LYGBgZw4cYLGjRtn6/0WJjkKEPz9/Rk1ahTz58+ne/fuODs7U7lyZUDTTRUREYEkSQwfPpz2hWBcXUFQqSWmbLtmMD+LJrc3TNl2jXbV3Evc5NTi6nb4c/osOUF4XDIVXW1YPaRRiaqIHJ0Uzb93/yUgKIDb0be1y8vblaeHbw86+3TGydJJZ5u2Xm2Z3Wo2M07+QERiele7m5UbExpOoK1X2wJrvzGsuLqClddW6ixTq9OrQb+/vx9yAwFS/2r9DQ+tMSaVUnPxfXAWJMdqltX+ANpMAlu3129bUCxLaYKVxiM08yGOL4Cnd2DLCE27/cZDrT5gYmbslua/yxtg+2eaxy2+gMbDjdseoVAJDdWkFy5durTea4GBgQQGBuosa9WqFa1atQLQCw5AM86/R48ezJs3j/v37+Pl5ZWr9lla6vfKOzk5ZWm9rAx1Nzc3NzhpuXr16rRu3Zr//vsPpVKpN/+lT58+2uAAQKFQMGDAAAIDAzl9+nTJCxAA5s6di6+vL9OmTSMiIoKIiPRsE87Oznz99dd6UVxxdir4KSExGadmk4CQmCROBT+liY/+h1ooWm6FxdFnyUkinydT2c2Wv4c0wtnG3NjNyndqSc3p0NMEBAWw9/5elGoloClg1r58e3r49qCOa53XdnW39WpLTef6tNmgGff8U7M5tK1guJJycROvjDeYuSdNZJLhcavxyvj8alLO3NkPOydC5E3N89J14M3/QVn9LvhCwcJOk/Go0TA48wccmwfR92HbWDj4P80E6jr9im/l4KA9sOljQIIGQzTzQYQskVlaUvnc2VztI+HMGR4O/TjT9couXoTVa9LHv47MwIVxXgkMDGTKlCl6y9MChLt37/LDDz+wf/9+Hj9+THKybiraJ0+e5DhA6NmzJ3PmzKFbt2706tWLdu3a0aJFC72L+c6dO/Pll18ycuRI9u3bR8eOHWnZsiUVKmS9NsaFCxf48ccfOXLkCKGhoSiVSp3XIyMj9SY316un37vk6ekJUOST9ORqIObo0aMZMWIEZ8+e5f79+wCUK1eO+vXroygkabsKSnhcxsFBTtYTCq/rIbF8sPQkT+NTqOphx9+DG+FoXbzvQEYkRLDlzhY2Bm3kYdxD7fIqjlXo4duDThU6YWeW9cqrCln690MtlzolIjgAw5l7JElC9iJzj2TjjsxAxh2dzD3G9Ow+/PeVbhXktpOgdt+ikSnI3EYzibnBEDi7XJMiNfaRZu7E4Z81r9UbCKbFaA7RgxOwrh+oU+GN9+DNH0X612yQyWS5GroDYN2sGSbu7qSGhRmehyCTYeLmZtSUp25ubly/fp0nT55QpUoVndcmT56snTi8du1a3n8/fWja7du3adiwIbGxsbRu3ZrOnTtjZ2eHXC4nMDCQgwcP6gUM2dGoUSMCAwOZMWMGq1evZtmyZQA0aNCAWbNmaYdElS9fnhMnTjB58mR27NjBP//8A2jS7U+dOpX33nvvtcc5duwY/v6a+VLt27fH19cXGxsbZDIZmzdv5uLFiwbfh52d/t+9tOFMKlXRToyQ65laCoWChg0bZqkuQnGW1aElJWkISnF05XEMff84SXSCkhpl7Fj1USMcrIpncJCqTuXo46MEBAVw6NEhVJLmy87a1Jq3vN+ie6XuVHeqbuRWFi0Dqg/QGyqU8DwGq5/KARAz9jD2pYxXxTZDhqogNxyqGb5j6WDs1mWfmRU0GQH1P4Tzf2mKrsU+hl0T4fBsaDZG85pZIQnMcir0MvzdE1ITwbc9vPNb0QjkihmZQoHbV1/yeOw4zXjjl2OEF8Ga21dfGrUeQtOmTQkMDOTAgQPaC+Ws+OWXX3j27Bl//fUXffv21Xlt2LBhHDx4UGeZ/MXnLzVVv7BdTEyMwWP4+fmxc+dOEhMTOXnyJNu2bWPhwoW89dZbXLlyRdtLUKNGDTZs2IBSqeTs2bPs3LmTuXPn0qtXL0qXLq0zFOhV06dPJzk5mcOHD9O8eXOd106cOMHFixczPxnFjPimyCMNvR3xsLcwUL4lnYlchott8R+GUlxdehRNnyUniE5QUsvTnr8/alwsg4NHcY+Yd34eHQI6MGr/KA48PIBKUlHHtQ7Tmk1j/3v7+bbJtyI4KAnSqiAvaKgZs5+aBOX9YNgReHNm0QwOXmZqAQ2HwJjz8PYcsC8H8eGaNKBz3tD0KiTFGruVORN1B/7qDskxUK4JvLei5E3KLkTs2renzK9zMHHR7UE0cXOjzK9zjF4HYcCAAcjlchYvXkxkZGSWt7tz5w6A3gRiSZI4evSo3vqlSpUC4PHjx3qvGUon+jJLS0tatWrFzz//zFdffUViYqLBicOmpqY0btyYKVOmMHfuXCRJ4t9//830fTg6OuoFBwkJCZw7d+612xZXue5BuHHjBjdv3iQ2NlYnz+zL+vfvn9vDFHoKuYxJnasxfFXGH6RUtUS3hUf5pWdt2lYrJJP4hCw5/+AZ/f88RVxSKnXLObD8w4bYWRSfP7YpqhT2P9zPxlsbORFyQpuG1MHcgS4+Xejh24MKDlkfyykUA4aqILf/Hqp1LX5DVEzMof4gqNMXLq2DQz/Bs2DYN1VTgK3xCE1WpqISEMWGwF/vaIIdtzfg/bU6NSgE47Br3x7rJk202YrKLl5UaCopV6pUiS+++IKZM2fy5ptvsmbNGoO1EF4dV582t+DIkSO8+eab2uUzZ87kypUretvXq1cPmUzG2rVrmTBhAhYWmlEVQUFB/Prrr3rrHz9+nDp16mjXSxMWFgagXX727Fl8fX31hvy8ul5GvLy8uHXrFlevXqV6dc3NL5VKxWeffaYzx7YkyXGAcOLECYYOHcrVq1czXEeSJGQyWYkIEAA61vDgt751+W7rFcJjU7TLPewtGNvGl3/OPOTcg2gGrzzDaP+KjGtbSWQ0KgLO3HvKwGWneZ6cSsPyjvw5qAE25sUjj/rd6LsEBAWw7c42niU/0y5v4tGE7pW641/WHzNF8eslMZalh++y9LBuwThzKZG0TvjOC06QItcf/z7Yz9tgddl8kYUqyMWWwlQTJNTsDVcC4ND/ICoIAmfA8fmaIKHxCLDK2wKgI7b9wpGIgFeWSvDiurH91t4Gg7LmLj1Y2PkT3YUJT+GvbhD9ABwrQL+NRSewKQFeDgas6tcvFMFBmunTp5OSksLs2bOpUqUKLVq0oFatWlhZWREeHs6lS5c4deoUNjY21K5dG9AMI1q2bBk9evSgZ8+eODk5ceLECc6dO8dbb73F9u3bdY5RunRp3n//fVavXk29evXo2LEj4eHhbNq0iY4dOxIQoPt7MGvWLA4cOECLFi3w9vbGwsKCc+fOsW/fPipUqEC3bt0ATTrURYsW0aJFC3x8fLCzs+PatWvs2LEDR0dHBg0a9Nr3Pnr0aHbv3k3z5s3p2bMnFhYWBAYG8vjxY1q1aqWXxakkyNFVzq1bt2jXrh3x8fE0adKEsLAwgoOD6d27N0FBQVy4cAGVSkW3bt0MTuAozjrW8KBueWtaruyHlGrLz62m0bFGeRRyGd3rejJ9+zVWHL/PvP23ufgohrm9axfLYSrFxcm7UQxafpqEFBWNKzjy58AGWJkV7eAgQZnA7vu72Ri0kfPh6V26rpauvOP7Dt0qdsPT1tOILSy+4pJSCY3VTVRgSTK8uLkV8TyZRAMDFeOS9Mfr5jm1Gi6sgr1TIOHFEIMqb0OH6VCqfP4fvzBRmECtXvDGu3B1k6ZHIeK6JmA48Rs0GAxNR4N13swXiUt5jqQwPP4aQDIxPMwpLuW57oLk5/D3e5q22npAv81g42pwW0F4lVwu5+eff6Zv3778/vvvHDp0iNOnT5OcnIyjoyPVq1fnf//7H/3798fVVfO5qlOnDrt37+abb75h48aNKBQKmjZtytGjR7VFxF61dOlSnJ2dWbduHQsWLKBy5cosXryY0qVL6wUIw4cPx97enpMnT3Lw4EEkSaJcuXJ89dVXfPLJJ9przPfff5+kpCSOHj3KqVOnSE5OxtPTk+HDh/P5559Trly51773t99+mw0bNjBjxgxWrVqFlZUV/v7+bNq0ialTp+bRGS5aZFJG44Je46OPPmLZsmUsXLiQYcOGMWjQIFauXKmdsX316lX69++PUqnk+PHjWFsX7YlesbGx2NvbExMTk6WAJyohjlbrmwKw552DuNvr3m3adP4RX268TJJSjWcpS37vW48aZezzpe2FgVKpZMeOHXTq1Ekvh3Bhdux2JB+tOEOiUkXzis4s6V8fS7Oc3+3J7HOR365GXWXjrY3sCN7Bc6XmwkIhU9DCswU9fHvQrEwzTOQFE/wY+1wYi6EeBAspiUBlHwBamv5NsswIPQiPzsCOz+HJiyGSzpU0hbQqtsm/YxqSEg8zNHnYlZ/fx9TaoWCPnxG1Gm5s06REDXtRIdzUSjORuemYXNd9MNSDIJMknHkKQASOmfcgpCbD6l5w94Cm/sOgneBaNVftKkxy+nckq3+/k5KSCA4O1t6lzi/qhARt4bXK584iz2WGJEHIjux8znN0NXDgwAF8fHwyrJJcvXp1/v33XypWrMj06dOZMWNGTg5TbHWr40llNzuGrTrLg6cJ9PjtGNO7vcG79cRd28LicFAEg1ecITlVTctKLizqVw8L08LTFZxVsSmx7Li7g41BG7n+9Lp2uaeNJz0q9aCrT1dcrFyM2MKSZbBfBf0L/ZR4ePEVuXdc84K9KDZYBXkCNPy4ZBQPyyq5XDP3omoXuLVLM2H7yXnNsKPTSzWpUZuNBTv9IlNZobnIf2WokE6wdPz1nwu1SlMl+u4BMLWGDwKKVXBQVEUtW87T5ct1F750T/ZOxzcNBn6OAwfiNGhg/jZOEDKRowAhJCSEjh07ap+n1TxISUnBzEzzR8XDw4OWLVuyceNGESAYUK20HdtGNWfcuvMcuBnBZ+svcv7BM77rXA1zk6J3IVqcHLgZzsd/nSUlVY1/FVcWflC3SAUHkiRxLvwcG4M2svvebpJUmiEtpnJT2nq1pYdvDxq4N0AuE0nMSiyVUjPHIHBmehXkWn2g7eTCUwW5MJLJoPKbUKkj3N6nCRQenYKTv8OZPzXF1pqPA4fXD2fIU5IE/47TZJtSmEHvv8FTv3iTUPDUz59rah9kIDXccNFE9fPnBpcLQkHKUYBgaWmpLQQBYGtrC2hmi5ctW1a73M7OjocPH+ptL2jYW5nyx4AGzN0fxK/7gvj75AOuPonlt7518bAvRoV6ipC918IY8fc5UlRq2lVzY0GfupiZFI0L6ajEKLbd2UZAUAD3Yu9pl1d0qEgP3x68XeFtHCwcjNY+oZC4cwB2TkivguxRGzr9D8qW7Fo22SKTgW9bzRCs4INw8Ee4f1RTpfncCqjdB5qPB0fv/G/L3slwbiXI5NDjD/Bpnf/HFLJEbmODiVv2A265jU0+tEYQsidHAUKZMmV48OCB9nlaKqzjx49rAwRJkjh37pw2561gmFwuY1zbStTydGDs2vNceBhN53lHmPd+XZr4OBm7eSXKriuhjF5zDqVK4s0a7sx9vw6misIdHKglNcefHCcgKIADDw+QqtZMZrU0seRN7zfp7tudms41kRW3tJRC9j27D7u/huvbNM+tnKDNJM1db1E8K2dkMqjQSvNz74gmUAg+qLlgP/831OwFfp+Cs366yDxxZI6mgB1A51+hWpf8OY6QI06DxFAhoejKUYDQqFEj1q1bR2JiIpaWltrhRp988gnW1taUK1eOBQsWcOfOHbp0EV9YWdG6iivbRjdn2KpzXA+Jpe8fJ5nQsTJD/CoUmYs7QxMwASQkkpIUzLh6EJmBDC0FmsIxAzsuhzBmzXlS1RJv1/Tgl161C3VwEBofyqbbm9gctJkn8U+0y2s41aBHpR686f0m1qZFOzmAkEcMVkEe8qIKsriBk2fKN9f8PDgJh36E23vh4mq4tBZq9AC/z8C1St4d7+wK2DtJ87jdVKhbMtKJC4JQMHIUIHTq1IkVK1bw77//8t577+Hj48PQoUNZtGiRNiCQJAlzc3O+//77PG1wceblZM3G4U35atNlNp1/zIwdN7j4MIZZ79YsEnn3DaVwTCcjJiU5w+2MaevFJ3yy7gIqtcQ7tUvz03u1MCmEwYFSreTQw0MEBAVw9MlR1JIaAFszWzpX6Ex33+5Udqxs5FYKhYYkwfWt8N/XEPNiqGd5P3jzR3CrZty2FWflGkHfAHh0VpMW9dZOuLweLm/QTHRu8Tm418jdMa5u1sw7AGj+iWaCtCAIQh7K0VVn9+7dUSqVOssWLFiAr68v69ev5+nTp1StWpWvvvpKW5FOyBpLMwWze9aidlkHpv17je2XQ7gZFseifvXwcSnc4xJtLUxwt9NNmyUhERarCQxcbc2RG+gNsbUwXvCz8dwjPlt/EbUE79bzZFaPmoWueN392PtsDNrIlttbiEqK0i5v4N6A7r7daVuuLRYm+ZeWTyiCwq9r5hkEvyjBZucJHb6Hau8UvyrIhZVnPeizFkIuagKF69vg2mbNT5W3NYFC6drZ3++d/RAwGCS1JntSm0l52+4SLgeZ3wWhyMjO5zvPrszkcjnjx49n/PjxebXLEksmkzGgaXlqlLFj+Kpz3A5/Ttf5R/npvVp0rOFu7OZlyFAKx4SUVKp99x8Ae8Y1w9668Ey+/uf0QyZsvIQkQe8GZZnR7Q3khSQ4SFYls+f+HjYGbeR06GntcicLJ7pW7Ep33+542XkZsYVCoZQYrcmsc3JRehXkZmM1d5mLexXkwsqjFvRaBWFXNQXXrm6CG/9qfnw7QMsvwLN+1vb16Ays7QtqpSbYe2u2CPjySFrildRU4/ZoC0J+Sru5r8hCBe8cBQgffvghzs7O/PjjjznZvNhTSSrt44sR53GxbYVCnv00mfW8HPl3THNG/X2eU/eeMmzVWYa38uGz9pUL3V3uomb1yQd8tUlT8Khv43JM7VIj34ODrHwubj27RcCtAP69+y+xKZr0kzJkNCvTjHd936VF2RaYyotOsTmhgKjVmloG+6ZAfIRmWZW3of33BZNJR8icW3V4b5lm7sfhnzXDjoL+0/z4+EOLL8CrSfr66vTvC9mD4+DgCat6gDJes373xZCDvyuCYQqFAoVCQWxsrDYzoyAUJ5IkERMTg7m5eZaKDeYoQFi1ahVdu3bNyabF3t77e5lx8gft88+OjsPtvBsTG06krVfbbO/P1daCv4c0YubOG/xxJJjfAu9w+VEMc9+vg6O1KGSUEyuP3+O7LVcBGNi0PJM6V8v3ieCv+1w0Kd2EncE72Ri0kcuRl7XreFh70M23G90qdsPduvD2HGXG0OR1tSwJXtSU6rzgOAr0h0gVhsnrRcKjs7Djs/QqyE6+8Oasgq+CLGSNS2XNxX3LCXB4Nlxcoxk2dGe/Zo5IywmQ+FQzROwFk3W9NWlMJTV4NtD0SJiYG/FNFD8ymQxXV1dCQkIwNzfH2tq6yCQIEYTXkSQJpVJJTEwMz58/p0yZMlnaLkcBgru7u/jFMWDv/b2MDxyPhO4Yr/CEcMYHjmd2q9k5ChJMFXK+fbsatco6MGHDJY7cjqTzvCMs/KAutco65FHrS4Y/jwQz9d9rAAzx8+arTlULJDgw9LkISwjjk8BPMJObkaJOAcBEZkLrcq3p4duDxh6Nc9TzVNgYnLwuS8H2RYAQEZcCBoZFGnvyeqH3PBz2ToELqzTPRRXkosXJB95ZAC0/hyO/aNKi3jus+THkRVIC6n8IZiJDWX6wt7cnMTGRyMhIIiIijN0cQchT5ubmlClTBjs7uyytn6MAoV27duzatQulUpmlboqSQKVWMfPUTL2LQNBM1JUhY9apWbQu2zrHF31dapWmspstw1adJTgynvd+P87UrtXp3bAAq3YWYYsP3WHGjhsADG/lwxcdKud7cPC6z0WaFHUKXrZevFvpXTr7dMbJsnjVvzA4eV0mI+HFY1c7M+SSfg+CMSevF2oZVkGeBLZFt6epxCpVXlPDwO8zTaBw5o/XrCyD/d9r6isUg5sHhY1MJsPDwwNXV1e9RCyCUJQpFIpsX6/n6C/w5MmT2bJlC0OGDGHevHlivB5wLvwcYQkZl1SXkAhNCOVc+DkauDfI8XEqu9uyZVQzxq+7yN7rYUzceJkLD6OZ3KU6FqbiD0ZGFhy4zf/+01SOHeNfkU/aVSqQXrDMPhdpvmvyHQ09imclW4OT15UJNFqtebx3nB92llm7o1HiiSrIxZdDWajeLZMAQYLYx3D/GHj7FVjTSpq0+QiCUJLlKEBYtmwZHTt2ZOXKlWzfvp22bdtSvnx5LC31M9TIZDK+/fbbXDe0sItIyFp3ZFbXex07C1MW96vHbwfv8NPum6w9/ZBrIbH81rceZRwKT5agwuLXvUH8svcWAJ+0rcTYtr4Fduys/n9HJkbmc0uEIi3DKsh9xZ3k4uR55jcTsrWeIAhCDuW4ByHt7mtUVBTr1q3TW0cmkyFJUokJEFysXPJ0vczI5TJGtq5IjTL2jF17nkuPYnh77mHmvV+X5r7OeXKMok6SJH7Zc4u5+28D8HmHyoxsXbFA21DQnwuhCHo1W03l9ukX/cpEODJHUwlZVEEu/mzc8nY9QRCEHMpRgPDdd9+JScqvqOtaFzcrN8ITwjMcb+5k4URd17p5etyWlVzYNqo5w/8+y5XHsfT/8ySfdajM8JY+Jfr/SJIkfvzvJr8F3gHgq05VGNrCp8DbUdO5JuYKc5JVhqtIy5DhZuWW558LoYi4thV2fqF9arKuN9iVho4zNQv++wZiHmgel/fTZCdyE8Uniy2vppr//9gQDM7cR6Z53atpQbdMEIQSJsc9CIIuhVzBxIYTGR+YcaG4ZFUyj58/ppxd3k4qLutoxYZhTfl28xXWn33Ej7tucvFhND+9Vwtbi5I3iVySJGbsuM6SF6k1v327Gh81L/hc8GpJzbQT014bHABMaDihWGQrErLp2lb4pz96F4KxT14sf0FUQS455AroOOvF/78M3c/Gi//7jjPFsDJBEPKdSBOSh9p6tWV2q9nMOPkDEYnh2uWuVq4oZApC4kMYumcoK99ciauVa54e28JUwY/v1qR2OQcmb73Kf1fDCAo/yqK+9fB1KzmTyCVJYuq/11h29B4AU7tWp3+T8kZpx89nfmbLnS0oZAoGVB/Atjv/6nwu3KzcmNBwQo5S3wpFnFoFuyZg+C7xS/w+A7/xxTOt5bH5cHzBKwvTz4fJb400uf9f1WQkNB2Vv20zpmpdoOdKTc9SXEj68rSepWpdjNc2QRBKDAPfvkJutPVqy9q3ArTPf2o2h909drP6rdWUtS3L4+eP+XjPx8Qkx+T5sWUyGR808uKfj5vgYW/B3Yh4ui44yvZLIZlvXAyo1RLfbbmqDQ5mdHvDKMEBwNLLS1l5bSUAU5pO4ZN6n+h9Lnb12CWCg5Lq/jFNT0FmKrQqnsEBQHIcxD155Sf9u0r2PNTA60802xV31brAyFPap6m91sK4yyI4EAShwOSoB2Hq1KlZXrekTFJ+mUKW3v1by6UOCrkCZ0tnFrdbzICdA7gdfZsR+0awpN0SrEyt8vz4dcqVYtvo5oxefZ7jd6MYufocFx9V4IsOlTFRFM+YUK2W+HrzZdaceohMBrO616Rng7JGacs/N/9h7vm5AHzR4Au6VtRUHTf0uRBKKJGtBsxt0VbLe4mERFJSEhYWFtpheHrblQQvfT9I5ZqIYUWCIBSoXGUxkiT97vGXJ8aWpCxGWeFp68nv7X5n4K6BXIq4xLgD45jfZj5miryveupsY85fHzXkf//dZNGhuyw+dJdLj6KZ36cuzjbmeX48Y1KpJSYGXGL92UfIZfC/d2vRo56nUdqyM3gn35/4HoChNYfSr1o/o7RDKOREthrNMCEDQ4VSlUp279hBp06dRCFOQRAEI8lRgDBp0iSDy9VqNffv3+fAgQM8fPiQjz76CE9P41yoFVa+pXxZ2HYhQ3YP4XjIcb48/CU/tvgxX+4mmyjkfNmpKrXKOvD5+oucuPuUt+ceYWHfutQtVzxSJKaq1Hy+4RKbzj9GIZcxu2ctutYuY5S2HHl8hK8Of4WERK/KvRhVuxiPkxZyx6sp2HrojjHXIbLVCIIgCMaTpwFCmsTERIYMGcJ///3HuXPnctSw4qyWSy3mtJ7DyH0j2X1/N7YnbJnUZFK+pSXt9IYHldxsGPrXWe5GxNNr0XEmda7OB43KFelUqKkqNZ/8c5FtF59gIpfxa+86vFXTwyhtOR9+nk8OfEKqlMqb3m/yVaOvivS5FfKZpNYUOzMYIIhsNYIgCIJx5cuAdEtLSxYvXkxycjLfffddfhyiyGtauimz/GYhl8kJCApgzrk5+Xq8iq62bBnZjI7V3VGqJL7ZfIXP1l8iSanKfONCSKlSM3rNebZdfIKpQsb8PnWNFhzcfHqTkftGkqRKonmZ5kxvPh25oewrggAgSbDjcwi7AgozsHqlsKFdaU0WGzEhVRAEQTCSfLuKsbKyon79+vz777/5dYgir3359nzXWBNA/XnlT5ZdWZavx7O1MOW3vnWZ+GYV5DIIOPeIHr8d4+HThHw9bl5LSVUz8u9z7LwSiplCzm8f1KNjDXejtOVh7EM+3vMxcSlx1HGtw+xWszGVi3HTwmucWAhnlwEyeG8FjDmvfUlkqxEEQRAKg3ytgyCXywkPD898xRKsR6UexKTE8MvZX5h9djZ2Znb0qNQj344nk8kY1tKHN8rYM3rNea4+iaXz/CP82rsOLSu55Ntx80pyqooRq86x70Y4ZiZyFvWrR+vKeVtTIqvCE8IZsmcIUUlRVC5Vmflt5mNpYgnA0sN3WfqiUFsatSwJXiRt6bzgOAos9PY52M+bwX4V8r3tgpHc2AH/fa153P57qNIJUuK1L4tsNYIgCEJhkG89CE+ePOHIkSO4uRXjLBx55MMaHzKoxiAApp6Yyp77e/L9mM0qOrNtdHNqedoTnaBk4LJTzNsXhFqdSeEmI0pSqhi68iz7boRjbiJnaf/6RgsOYpJj+HjPxzx+/piytmX5vd3v2JnZaV+PS0olNDZJ5yc8NkX7ekRcit7robFJxCWlGuPtCAUh5CIEfARIUG+gpuCXIAiCIBRCOepBOHToUIavxcXFcf36dRYsWEBsbCz9+/fPUcPUajW//vorixYt4t69e7i4uNCzZ0+mTp2KtfXrCwdNnjyZKVOmZPi6iYkJSqUyR+3KL5/U/YTY5FgCggKYcGgCNm1saFK6Sb4es4yDJes+bsKUbddYc+oBP++5xcVH0fzcszb2loVrmExiioohK89w5HYklqYK/hhQn6YVnTPfMB8kKBMYsW8Et6Nv42rpyuJ2i3G21G2LrYUJ7na6PQSSTEbaYC5XOzPkkn4Pgq2FKG5eLMU+gdW9QZmgKX7W6ScQk9gFQRCEQipHVyOtWrXKNEOLJEnUr1+fadOm5ahhn3zyCXPnzqVbt258+umnXL9+nblz53L+/Hn27t2LXJ5x50f37t2pWLGi3vJLly7xv//9j86dO+eoTflJJpPxbeNviU2JZc/9PYw9MJal7ZdS06Vmvh7XwlTBD93foE5ZB77ZcoW918PpOv8Ii/rVp7J74ShIlJCSykfLz3D8bhRWZgqWDWxAowpORmlLiiqFcQfGcSniEvbm9ixqtwhPW/1UvoP9KugNFUpQJtBotebx3nF+2Fna6W0nFEMp8bC6l6YKsHNlzbwDReEKwAVBEAThZTkKEFq0aJFhgGBmZkaZMmVo27YtPXv2xMQk+4e4evUq8+bNo3v37gQEBGiXe3t7M2bMGNauXUufPn0y3L5mzZrUrKl/Yf3xxx8D8NFHH2W7TQVBIVcw028mz1OeczzkOMP3DmdFxxVULKUf7OS1ng3KUsXDluGrznEvKoF3FhxlZo83jFZTIM3z5FQ+XHaaU/eeYmNuwvJBDahf3tEobVGpVXx5+EuOhxzH0sSShW0WZvx/c2w+HF+gv9xZ8/ugWNgQDFWJbTLSYPEooYhSqyBgCIRe0qQ17bMOLB2M3SpBEARBeK0cBQiBgYF53Axda9asQZIkxo0bp7N8yJAhTJw4kVWrVr02QDAkPj6etWvX4unpSceOHfOwtXnLTGHGnNZzGLJ7CJciL/Hxno9Z2WklZWzy/0K9pqcD20Y3Z+za8xwOimTs2gtceBjNV52qYqoo+LSdcUlKBi47zdn7z7A1N2HFRw2NVuBNkiSmnZjG7vu7MZGbMKf1nNf37iTHae4Yv0wmA+eymofPQzXpLg1tJxQfeyfBze2adKa9V4Ojt7FbJAiCIAiZKpTJ2k+fPo1cLqdhw4Y6yy0sLKhduzanT5/O9j7Xr19PbGwsAwcORKEo3FlCrEytWNh2IRUdKhKeGM7Q3UOJTIwskGM7WpuxfFBDRrb2AWDZ0Xt8sOQk4XFJBXL8NDGJSvr+cYqz959hZ2HC30MaGbX686/nfiUgKAC5TM4sv1k0LZ1JhVtzW7Atrftjk56KVbJ203/dtrRmO6F4OLscjs3TPO66EMo1NmpzBEEQBCGrCuWMyCdPnuDs7Iy5ubnea2XKlOHYsWOkpKRgZmaW5X3+8ccfyGQyPvzww0zXTU5OJjk5Wfs8NjYWAKVSmaXJzS+vo1Sm5mhCtJXcigWtFjBozyAexD3g490fs6TtEmzNCuYCcpy/D9Xdbfl842VO3XvK23OPMLdXTep5ZXyRvur6KlbdWKWzTAKsK2qCi3f+/Rm5gaFpfav0pW/Vvtrn0QlKBq04y5UnsThYmrJ8YD2qulkbbWL5imsr+OPKHwB83eBrWpdpnXlbGnys+XlJakIkbG4PQNLAvZjZZ1C7oZBNoM8Pqanp2ZqUSiVKk+L1nmXBh1Bs/xQZoPL7AnXVdzL+f1UqMdU+TC0R//+vk/a7VdgSSRQ48bnQkdPPRYn/HAlCDuUoQJg/fz5jx45l8+bNGU743bZtG++88w4LFy7Ujv3PqoSEBIPBAWh6EdLWyWqAcPPmTY4cOUKbNm3w9s68i/+HH34wmAVp9+7dWFlZZbp9XGp6OsuDBw9ia5L1QOZVveS9WCJbwq3oW/Tb1I+BNgMxk+V8f9k1rir8cVNBaFwyff44RTcvNX7uksEELOcTzxOerF/3Iq1uWGQGnRDnr53HMVgzr+C5EhZeU/A4QYa1icRQ30TuXzjC/Qt59Iay6UzyGTYnbgagg0UHzG+Zs+PWjhztS5WaPnzo4MGDKExKbm9BipT+O7J///4C/UznN5ukx7S4NQ2ZOpWHpZpwLq467Mj4M6NQJfP2i8f79+9HpTD83VfS7NmT/+meCzPxuTAsu5+LhISiVQhUEAqLHAUIW7ZswcXFhbfeeivDdTp16oSzszObNm3KdoBgZWWVYYG1pKQk7TpZ9ccfmru/gwcPztL6X375JePHj9c+j42NpWzZsrRv3x47u8wzz0QlxDFr81QAWrZsibt97obGNHrWiMF7B/NA+YB9VvuY3WI2pgWYBeW95FS+2nyVHVfCCLinINXeg2ldqmFppjtU6+n1p1y9cVVnmVqSiEyKAMDJwhmFTH9UW50qdehUtRNRz5MZsPwsjxOe42xjxsqB9fF1s8m/N5aJfQ/2sfXoVgAGVhvImNpjcrW/xIRIJm2eBWg+F3YZ9SCUAImpiUz9R/M74u/vX3wyOsVHYrK8IzJVAmrPhrh/sJ5OJvrpbHWkxMMlzUN/f39Mre3zv52FmFKpZM+ePbRr1w5T0xKc7Ul8LnTk9HORNgJAEITsyVGAcOPGDWrUqPHaVKMKhYI33niD69evZ3v/pUuX5tq1ayQnJ+v1JDx+/BhnZ+cs9x6kpqaycuVKnJyc6NatW5a2MTc3N9iDYWpqmqUvppfXMTU1yfUfuequ1VnYdiFDdw/laMhRJp+azEy/mcgNXGznBwdTUxZ8UI8/jgTzw84bbLkYws2w5yzqVw8vp/SaFINqDmJQzUE620YlxNFqvWa8/tqOAbjbG85AFB6XRL9lZwkKf46rrTmrhzSmoqvxgoPjT47z1bGvUEtqevj2YHz98Zmm9s2MMo8/F0WZkvRu/6z+XhV6qckQMBCi74GDF/L31yC3zEIvkSQ+F4YUm89FTonPhUHZ/VyI8yYIOZOjK8yIiAjc3TO/++nu7p5hT8DrNGjQALVazalTp3SWJyUlceHCBerXr5/lfW3bto2wsDD69u2b4bCloqCOax1+af0LJjITdgbvZMbJGUiGsuDkE5lMxmC/Cvw9uBHONmbcCI2j87wj7L8Rlut9h8Um0XvxCYLCn+NuZ8G6j5sYNTi4FHGJsQfGolQrae/Vnm8bf5vr4EAo5iQJtoyChyfA3B76/APWxinkJwiCIAi5laMAwdbWlidPnmS63pMnT7I1FChNr169kMlkzJkzR2f5kiVLSEhI4IMPPtAuu3PnDjdu3MhwX2nDiwpr7YPsaF6mOTP8ZiBDxrqb61hwwUCe/XzWuIIT/472o045B2KTUvlw+Rl+2XMLtTpnwcqT6ER6LTrO3Yj4F5WdG+Pt/PpK2fnp9rPbjNg3gsTURJp4NOEHvx9QyAt31iuhEDj4I1z+B2QK6LkCXKsYu0WCIAiCkGM5ChBq1arFsWPHePjwYYbrPHz4kGPHjvHGG29ke/9vvPEGI0eOZOPGjXTv3p2lS5fy6aefMn78eFq2bKlTA6FNmzZUrVrV4H6ePHnCrl27aNiwYY7aURi96f0mXzf6GoBFlxbx17W/CrwN7vYWrBvahP5NvAD4dV8QH604TXRCSiZb6nr0LIFei49zLyoBz1KWrB3aWGfIUkF7/PwxH+/5mJjkGGq61GRO6zmYKYrP5Fkhn1zeAIEzNI/f+hl8Whu3PYIgCIKQSzmag9CnTx/2799P9+7d2bZtm95wo9DQUHr06IFSqcx2QbM0c+bMoXz58ixevJjt27fj7OzM6NGjmTp16mvnPrxs+fLlqFSqLE9OLip6VelFTEoM887P48fTP2Jvbk8Xny4F2gYzEzlTu9aglqcDX226zIGbEXSef4Tf+9ajeunMJ9M9iErg/SUneBydiJeTFauHNKaMg2UBtNywyMRIhu4eSnhiOBUdKrKwzUKsTLPf+yXoW3F1BSuvrdRZ9vLwuHe2vWNwCFf/av0ZUH1AvrcvVx6chM0jNI+bjIL6g16/viAIgiAUATkKEAYMGMCyZcs4evQoPj4+vPXWW1SpoulSv3HjBjt27CAhIYEmTZpkqe6AIQqFgk8//ZRPP/30tevdu3cvw9e++uorvvrqqxwdv7Ab8sYQopOj+evaX3x39DtsTG3wL+df4O3oUc+TKh62DFt1lodPE+m+8Bg/dH+D7nU9M9zmXmQ87y85QUhMEt7O1qwZ0hh3+0wyveSj2JRYhu0ZxoO4B5SxKcOidouwNy/ZGUPyUrwynvCEjOciRSRGZLhdofbsHqztA6pkqNwJ2k01dosEQRAEIU/kKEBQKBRs376dQYMGsWnTJjZs2KC9A5h2Z7Br164sW7YME5NCWYst7xybD8d15wKYI4GzJnOCzZKmGCwa0GQkNB2V48PKZDI+q/8ZscmxbLmzhc8Pfs7v7X6ngXuDHO8zp6qXtmfbqOaMW3eBwJsRjP/nIhceRvPNW9VQqSVS4ysgpdpy9n4Mvm6m9PvjJGGxyfi4aIIDVzvjBQeJqYmM2jeKm89u4mThxOJ2i3G1cjVae4oja1Nrw+dU0iQesLCwAAO/ItamxhtulqmkGFjdCxIiwb0mdF8CYq6KIAiCUEzk+Ordzs6OgIAALl26xK5du7h//z4A5cqVo2PHjtSqVSvPGlmoJcdBnO6EbblMBs5lNY/jQzUZTgxtl0tymZzJTScTlxLH/of7Gb1/NH90+IPqTtVzve/scrAy488BDZizL4i5+4JYefw+h4MiiEtKJfH5UABGrr6GXAZqCSq52fD34Ma42Bovs5RSpWR84HjOh5/H1syWRe0WUc6unNHaU1wNqD7A4FAhpVLJjh076NSpU9FKRahSwj8DIOIG2HpAn3VgbrysW4IgCIKQ13J9e79mzZrUrFkzL9pSJEUF3uHpv6V1lkmSxG+KVAAeqdwNjq92lO7glAdzGU3kJvzY8kdG7B3BqdBTDN8znOVvLqeCfYXc7zyb5HIZ49tVopanPSNXnyM4Ur+CZVqyoyF+FYwaHKglNV8f/Zojj49gobBgQZsFVHasbLT2CEWEJMGOz+HuATC1gvfXgl3pzLcTBEEQhCKkYCptFWNq59qkxqPzo0qQ4RQHTnGax6++nhqv2S6vmCvMmes/l+pO1XmW/Iyhu4cS8jwkz/afXa0qu2JrnvEdYRkwe88tVDlMjZpbkiQx4+QMdgbvxERmwi+tf6GOax2jtEUoYk4shLPLABn0WAqlaxu7RYIgCIKQ53IUIOzatQt/f3/279+f4Tr79u3D39+fPXv25LhxRcHp8GSeWjno/Dx7qXrqM0tbvdefWjlwOjw5T9thbWrNb21/w9vem7CEMIbuGcrTpKd5eoysOhX8lIjnGb8/CQiJSeJUsHHat+DCAtbdXIcMGTP8ZtC8THOjtEMoYm7uhP80KYZpPw2qvGXc9giCIAhCPsnREKNly5Zx6tQpGjTIeEJsw4YNOXnyJMuXL6ddu3Y5bmBhd7NFZ35V6hZFclBFsmbbTABGtB1JtEK/ourYFr50yuO2lLIoxeJ2i+m3sx/3Yu8xbM8w/uzwJzZmBTs+OjwuKU/Xy0t/XfuLRZcWAfBN42940/vNAm+DUASFXIINHwES1BuoSWkqCIIgCMVUjnoQzpw5Q+3atbG1tc1wHVtbW+rUqcOpU6dy3LiiwNbCBHc7C50fF5v0rDzONuZ6r7vbWWBrkT/Zndyt3VncbjGOFo5cf3qd0ftHk5RasBfirrZZy0qU1fXyytY7W/nx9I8AjK4zmp6Vexbo8YUiKjZEk7FIGQ8VWkGnnwxnJhMEQRCEYiJHV6khISE0atQo0/XKli3LhQsXcnKIImOwXwUG++lOCH4e9ZCHazSPN3zcEAf3gp0w7G3vzW9tf+PD/z7kTNgZPj/0Ob+0+gUTecGknG3o7YiHvQWhMUkYmmUgQ1ONuaG3Y4G0B2D/g/18d/Q7QFOAa8gbQwrs2EIRlhIPa3ppMpU5V4L3VoCiCGVcEgRBEIQcyFEPgpmZGXFxmafpfP78eZarHgt5q5pTNeb5z8NcYU7gw0AmHZuEWlIXyLEVchmTOlcz+FrafddJnauhkBfMXdjToaf5/ODnqCQVXX268ln9zwxmlhIEHWo1bBwKIRfBygn6/AOWDsZulSAIgiDkuxxdvfv6+nL06FESEvTTWKZJSEjg6NGjVKhQ8Ok2BY0G7g34qeVPKGQKtt7Zyv9O/09byC6/dazhwW996+JqZ6az3N3egt/61qVjDY8CacfVqKuM3j+aFHUK/mX9mdx0sggOhKzZOwlu/AsKM+i9Ghy9jd0iQRAEQSgQORpz0rlzZyZPnsyoUaP4448/9C64JEli9OjRxMTE0LVr1zxpaGG14uoKVl5bqbPMNDmVX1487r1nEEoD8w36V+tvsHhUXmtVthXTmk3jqyNfser6KuzN7RlWa1i+Hxc0QULd8ta0XNkPKdWWn1tNo2ON8gXWc3A35i7D9wwnXhlPQ/eG/NjyxwIbZiUUcWdXwLG5msddF0C5xsZtjyAIgiAUoBxdLY0ZM4bFixezYsUKLl26xIcffkiVKppMPjdu3ODPP//k/PnzuLu7M3bs2DxtcGETr4wnPCFcZ5l5Svpd+sikKJLV+hfE8cr4fG9bms4+nYlNiWXmqZksuLAAe3N73q/yfoEcWyGXYWJ9F4B6XvYFFhyEPA9h6O6hPEt+RnWn6sz1n4u5wniF2YQi5G4gbB+vedxyItQUk9kFQRCEkiVHAYKDgwPbt2+nc+fOnDt3jvPnz+u8LkkSnp6ebN26FUfHgpuIagzWpta4WrnqLDNTqIAwAFwsnUkxVxjcriB9UPUDYpJj+O3ib8w4OQM7MzveqlA887g/TXrK0D1DCUsI007YLujzLbzk2Hw4vkBvsQkS7ZOSMLk9gfTZKS9pMhKaFnA60YhbsK4/qFOhxrvQamLBHl8QBEEQCoEcj7eoVasWN27cYMmSJfz333/cv38fgHLlytGxY0cGDx6MtXXxvygbUH2A3lAhdXQkN7/3A2BzxzWYOxfMePvMDK81nOjkaNbcWMM3R77B1syWFp4t8u4ABi4EzZHAWZP1xWZJU8PpIfPwQvB5ynOG7RnGvdh7eFh7sLjdYkpZlMqTfQs5lBynyQL0ChlgCaB8zXYFKT4KVr8HyTFQtpFmaJGYryIIgiCUQLkakG1lZcXYsWOL/TCi4kImkzGx4URiU2LZfnc74wPHs6jdIuq51cubAxi4EJTLZOBcVvM4PhQMTZLOowvBpNQkRu8fzfWn13G0cGRxu8W4W7vnyb6FXDC3BdvSryyUIC5E88jGHZnMQL4E84zrrOS51GRY9wE8uwcOXppJyaYFW6dDEARBEAoLMWOzhJHL5ExrNo24lDgOPTrEqH2jWNZxGVUcq2S+cWYMXAi+nFpV/f/27ju8ybIL4PAv6d4tdEPZU1myd9kiCAoKKCiUrQxFQGUJyBJElA2CSkGUJYooKPDJ3sgQZMkso6W0tHTSNuP9/ggNDemmu+e+rlw0zzty8pB13mc5eEEu/RDU6rV8uP9D/g79G0crR5a1W0Y5l3LPfF6RA5qOMG8hSoqDWYbXivbdY1g5uOZ9XMkUBbaOhFtHwMbZMJ2pg/nq50IIIURxIQlCMWSltmKe/zyG7hrKqfunGLprKGteWkNZ57LPduJUfggmPrwHv7YHILbf/3D08Hu2x0iFXtEz5fAU9t7ei42FDYvaLOK5kqmvwyCEmf1z4ewGUFlAz9XgmQPJshBCCFGIPVOCsHnzZjZt2sTly5eJjo5OdY59lUrFtWvXnuVhRC6wtbRlcdvFDNgxgEsRlxiycwhrXlqDl4NXts/5YFUgEYGBJmV6vY5lCVoAHn7Tmxi1+YDtEgEBlOwfkK3HVBSFuSfmsvXaVixUFnzh/wX1vetn61yiGDr3E+yZafi78xdQsU3+xiOEEEIUANlKEBRFoWfPnvz8889pLrylUqlQFEUWpSrAnKydWNZuGQF/BhAUHcTQXUMJ7BiIq61rts6nj41FGxpqVl7y8b9KTDjaNI7Lrq/Pfs3ai2sBmN5sOq38WmX7XKKYuX0ctgwz/N1kBNQfkL/xiGIrtYsrKArEGC7YKLu7pjpO51kurgghRHqytZLyypUr2bx5M7Vq1WLHjh10794dlUrF5cuX+f333+nVqxcAkyZN4vr16zkasMhZ7nburGi/Ak97T65FXWPYX8OyvUaD2tERSy8vk5vavaRxu8q9pNl2Sy8v1I6O2Xq8dZfWseSMYdakcQ3H0aVil2ydRxRDkTdh3ZugS4SqnaD9tPyOSBRjyRdXTG7376N9ZIH2kQW6+2Hm20NDn+niihBCpCdbLQjff/89NjY2/PHHH3h7e/Pjjz8CULlyZSpXrkynTp1o3bo1w4YNo1WrVpQt+4x920Wu8nX0ZUX7FfT7sx/nws/x/p73Wdp2KdYW1lk6T8n+5lezwkNuENa6EwAuP36HT5kqORLztuvbmHVsFmCYvrVP9T45ct6cktoK20qKAduv/tkbldo8P8+rFbaLtYQo+LEXxIeDd03ovhJS6fomRF5JvrhiQq9HGxYGgIWHO6pUXqPZvbgihBAZyVaC8O+//9KkSRO8vQ1TSCZ3I0rZpWjIkCHMnz+fuXPn0qaN9Ost6Cq6VmRZ22UM2jmIYyHH+Hj/x8z1n4uluuCNY99/Zz+TDk4CoHe13rxb+918jshcaitspxSWEJ7mcSIX6bSwKQDCLoGTD7y5AWzkR5bIX6ldXNE/DOdyY8N6OmV/2Vhg1tMRQhQP2epi9OjRI3x8nnxY2djYABAdHW2yX506dfj777+fITyRl2p61GRBmwVYqa34363/Mf3o9DTHmOSXk6EnGb13NFpFy8sVXubjhh8XyHEuyStsm9zsPPDUag03Ow/z7faesuJzblIU+ONDuLYbrOzhzfXgUiq/oxJCCCEKnGxdHvby8iLscdMngKenJwBXr16lXr0ni25FRESQkJDwjCGKvNTYpzFzW85l9L7R/HzlZ5ytnRldb3SB+BF+KeISI/4aQaIuEf/S/kxrNg11ausqFACprbCdcu5/zYdH8nfu/+Lo6DL4+ztAZehW5FsnvyMSQgghCqRsJQiVKlUyGXzcoEEDFEVh+fLlrFy5EoCLFy+yd+9eqlWTOcULm7Zl2zK1yVQmH55M4PlAXGxcGFRzUL7GlDzLUqwmlnpe9fjC/wus1Fb5GpMoRC7/ATsmGP5uPw2qv5y/8QiR0uHFcGSJaZlGAQwXZixXtgLrVC6GNBluvgihEELkgGxdfu3QoQM3btzgwoULxvt+fn589913NGjQgNdee40mTZqg0Wjo27dvjgYs8ka3yt0YW38sAAtOLWDTf5vyLZbQuFCG7BxCREIE1UtUZ1GbRdha2uZbPKKQCTkLPw0EFKjbD5qOzO+IhDCVGAMxwaa32HvGzaq4UPPtMcGG44QQIhdkqwXhzTffRKvV8ujRIwCsra3ZsGEDr776KidPnuTkyZMAvPLKK7z//vs5F63IU/2e70dUYhQrz61k+pHpOFk70bFcxzyN4WHCQ4buGkpwXDBlncuyrN0ynKyd8jQGUYhFh8C6N0ATB+X9ofM8KADd5YQwYeMETr6mZZonfyqO3mCVyuvWRj4LhRC5I1sJQpkyZZg4caJJWePGjblx4wb79+8nIiKC6tWrU6dOnZyIUeSjkS+M5GHiQzb9t4nxB8bjZOVEs1LN8uSx4zRxDPtrGNeiruFp78mK9isoaVcy4wOFAMOYj3VvQPRdcK8CPVeDhXRLEwVQ0xHmXYXi4yHQMKZP++4xLFxc8iEwIURxlaNzWNrZ2fHiiy/m5ClFPlOpVExsNJGYpBj+vPknH+z9gBXtV1DHs06uPm6SLon397zPufBzuNq4srL9SnwdfTM+UAgAvR5+HgIhZ8C+JPTeAHZu+R2VEEIIUSgUzClgRIFiobZgVvNZNCvVjEfaRwz7axj/Rf6Xa4+n1Wv5eP/HHAs5hr2lPcvaLaOCa4VcezxRBP01FS79DhbW8MaPUEJeP0IIIURmFbxVsESBZGVhxZf+XzJ011DOhJ1h6K6hrHlpDX5Ofjn6OIqiMP3odP53639Yqa1Y2GYhNdxr5OhjiCLu5Go4tMDw9ytLoEzj/I0npdRmq+HJWiOWyxpBalP3ymw1Qggh8pC0IIhMs7eyZ3HbxVR2q0z4o3CG7BxCWHxYxgdmkqIofHnyS36+8jNqlZq5/nNp5NMox84vioHr+2DbaMPf/h9DrZ75G8/TUputJibEuFkVe09mqxFCCJHvJEEQWeJi48LX7b6mtGNp7sTeYciuIUQlRuXIub/991sCzwcCMLXJVNqWaZsj5xXFRNh/sPFt0GuhxuvQanx+R2Quebaap26Kkw+PrNxQnHxS3S6z1QghhMhL0sUoN+h12HsmYmmrQ3XnGJToAmqL/I4qx3jYe7Ciwwr6/dGPqw+vMvyv4axovwJ7K/tsn3PTf5tYcMrQLWRs/bF0q9wtp8IVxUHcA/ixJyREQemGhq5FBXE609RmqwG0Gg07t2+nU6dOWFnJTEtCCCHylyQIOe3CVlTbP6RsmweG+1v6wW5f6DgHnuuav7HlID8nP5a3X07AnwH8E/YPo/eOZlGbRVhlYxrJP2/+yfQj0wEYXHMw/Z7vl9PhiqJMmwgb+kDkDXAtYxiUbCUL6RV0D1YFEhEYaFauKArlExK4Me9LVKkkeSUCAijZPyD3AxRCiGJMuhjlpAtbYWNfkxUwAcNiTRv7GrYXIVXcqrC07VLsLO04FHyI8QfHo9PrsnSOQ3cPMf7AeBQUelbpycgXZJVbkQWKAlvfg1tHwMYZem8CR4/8jkpkgj42Fm1oqNlNd/8+VtHR6O7fT3W7PjY2v0MXQogiL1sJwoABA/juu+8y3C8wMJABAwZk5yEKH70O/vwYUDC/5vV4lpI/xxn2K0LqeNZhfqv5WKot2XFzBzOPzURRlIwPBM7cP8MHez9Aq9fSsVxHJjSakOoVQyHStP8LOLseVBaGhdA8q+V3RCKT1I6OWHp5md48PY3bLTw8zLd7eaF2dMzHqIUQonjIVhejwMfNwhn9+D906BCrV6/OVDJR6AUdhujgdHZQDCu6Bh2G8i3yLKy80LRUUz5r8Rkf7fuITf9twtXGlffqvpfuMf9F/sfwv4bzSPuIZqWaMav5LCyK0DgNkQf+3Qx7Zhj+7vwFVGyTv/GILCnZ37yrkD4+nst1DasHl/39N2xk9WAhhMgXudrFSKfToVYXk15MsaGZ2+/oMoi4kbux5IOO5TrySZNPAFh5biWrz69Oc9/bMbcZumso0UnR1PGow5f+X2Zr7IIoxm4fh1/eNfzdeDjULyYtlUIIIUQeyNVByleuXMGluFwBcvQy/qnoIT7MGm2CBZa2Ouw9kp6sfXR5m+FW3h/q9YNqL4OlTf7EnMN6VOlBVGIUC04t4Iu/v8DZ2pmmDjU5X0ZFpCP4Rf6LqqQzQ3YOIfxROJXdKrO47eJnmv1IFEORQbDuTdAlQpWXoMP0/I5IiByn6J50R3108iTW/v6oLKSVVQiRNzKdIEybNs3k/pkzZ8zKkmm1Ws6fP8/hw4dp167ds0VYWJRtCs6+RJ+PJPSUM9pHTz7ILe10eNWNxrmKHfjUget74MY+w82uBNR+E+r2LRL9pwfWGEhUYhSB5wOZcngKTlaORPd5XBdnpmD5jyVaRUtpx9J83e5rXGyKSQIpckZCFPzYC+LDwbsmvPZNkZpCWAiA6J07CZ0x03g/ZNhwwry98ZowHucOHfIxMiFEcZHpBGHq1KmoVCrjANQzZ85w5syZdI9xcHBg8uTJzxRgoaG2INr1be4eWmO2SftIzd1DrtCsL859JxiugJ75AU6vNYxLOLrEcPNrZEgUnu8G1g55/xxygEqlYnS90VyMuMixkGNEa0xXgNUqWgD6Pd8PD3uZbUZkgU4LmwIg7CI4esObG8BGBqyKoiV6507uvj/KMENXCtrQUEP5gvmSJAghcl2mE4TJkycbE4Rp06ZRp04dXnnllVT3tba2pnTp0rz44ot4ppiVoihTdDpC1+yCVOYwSi4LXbMLp34fo3IrC60ngP/HcPUvOLUaLv8Bt48Zbn+Mg5qvG5IF3xcK5oJP6dArem5G3Ux3n2/PfUuPKj1kYLLIHEWBPz6Ca7vByh56rweXUvkdlRA5StHpCJ31mVlyYNiogEpF6KzPcGrbVrobCSFyVZZaEJIlJwhTpkzJjZgKpfi/T6K9dy/dfbT37hH/90kcGjU0FKgtoEoHwy0m1NCqcGqNYcGnk6sMN6+ahrEKNV8HO7c8eCbP7tT9U4TGpz9o+178PU7dP0UD7wZ5FJUo1I4th7+/BVTQfaUhcRaiiMnwe0RRzL9HhBAiF2RrkLJer8/pOAo9bVjYs+3n5AUtRkOzURB0yNCqcGErhJ6D7WNh5yR47hWo288w3qEAtyqExWeuLjK7nyjmLv8JOyYY/m4/Daq/nL/xCJFLnvl7RAghckiOzGJ09epVwsLCKFmyJFWqVMmJUxY6lh6Z608fd/wYjq1aYeGYxhgDtdqwTkL5FvBSBJzbBCdXw/3zcHaD4VaykqH7Ue03wbHgdOH65sB1vjlwA531LchEdUz5+RbTkv5iUIvyDGpRIfcDFIXPvXPw0wDD1GB1+0JTWWlbFF0WrpmbtCGz3zdCCJFd2V6kQKfTMWPGDLy9valatSrNmzdn9uzZxu0//PADTZs25fz58zkSaEFnX78elt7eGV7Zj9q4iWtt2xK+bBm6mJh098W+BDQaCu8egkG7DT+QrBzgwVXYNRm+rA4b3oYr/ysQKzTHJGi5F51AWHgp9BqXVLvRgqErrV7jQlh4Ke5FJxCToM3bQPNLiv8j1a0jBeL/rECLuWeYsUgTB+VbQucvC3TLmRDPIuHiRUJnz8lwPwtXV+zr18uDiIQQxVm2EgSdTsfLL7/MlClTiIyMpHr16sbZjZI1a9aMo0eP8vPPP+dIoAWdysICrwnj09ioApUK17f6YF2uHLqoKMIWLORqm7aELVyE7uHDDE6ugtL1oOsiGHsZuiyEUvVBr4WLW+GH12BBbdg7Gx7ezvHnlllOtpZ4O9vi7WyPXXR3w9Dsp5MExTBk2y66O97O9ng72+Jkm6vLcRQMF7bCkid9hi03vAHzaxjKhbmkOENyEH0XSlaGnmtAFtMTRZCi1RK+fDk3evQk6epV1I6PZ+ZKIxfWRUcTs+t/eRegEKJYylaCsHz5cnbs2EHr1q25ceMG//77r9k+5cqVo2LFiuzcufOZgywsnDt0oNSC+Vh6mjb/Wnp5UWrBfHwmTaLCtt/x/eILrCtVRB8TQ/jSpVxt2477X36FNjIy4wexcTIMWh78F7x7GBq9A7auEHUb9n4G82vC2tcNPzx1mtx5omkY1KICRye05eiEtvw9ejRftf4KTzt3k328Hbz5qvVX/D16tHHfIt+96MJW2NgXYkJMy6NDDOWSJJjS6+GXoRByxrBOSJ+NhWaAvhBZkXjjBjf79CFs/gLQanFq356KO3dQauECLD1Mu49aentjV78+6PXcHTOGqG3b8ilqIURxoFKevvSfCQ0bNuT69etcuXIFNzfDF7darSYgIIDvvvvOuF/Xrl35559/CAoKyrmI80F0dDQuLi5ERUXh7Oyc4f66B6H816wVAD4Lv8SlbQezKekUvZ6YnbsIX7aMxMuXAVDZ2eH25puU7B+QtT6mmgS4+JthYPPNA0/KHTygTm94oS+4V8r8+XJQVPhNdvXuRKQjVJrxGS2rvVy8pjbV6wwtBdHBaeygAmdfGHWueC34lRQHs3wB0HwYhJWD65Ntu6bAoflgYQ19t0LZJvkSYl7SaDRs376dTp06YWVVfFtK9PHxXK5r6D5T4dhRbFyK5kKKil5P5I/ruP/FFygJCaidnPD+ZBLOXbqgetyNThcTw38NDK2OPkuX4OLvD0DIpE+I+uUXUKvxnf0ZLl275tvzyA0PVgUSERhoVq4oCgkJCdja2hrrKKUSAQGU7B9gVp7V728hhEG2+nZcunSJ5s2bG5ODtLi4uHD//v1sBVaYpUwG7OrWSXW+apVajXPHF3Hq0J7YPXsIX7qMhPPnifjuOyJ/+AHXXj0pOXAgVl5eGT+glS3U6mG4PbgGp7+H0z9A3H04tMBwK9vcMIbhua5gZZeTTzddFioLnr9lyEF9StQsPslBXLjhCviFX9NJDgAUQzeaedXArZxhNitH7xT/eoOjl+Ffe3fDIPai7NQaQ3IA8MqSYpEciOJFExJCyMSJxB0+AoB9k8b4zpqFlY+PyX4m3yP16hnv+8ycgcrSkoebNhH88TgUjRbX17rn3RPIZfrYWLShqU+TbYWhi1Vaxwkhck62EgSdToeNjU2G+4WEhGRqv+JMpVbj1LYtjm3aELd/P2FLl5Lwz1ki13zPw3Xrce3xOiUHDcLK1zdzJyxZEdpNhdYT4b8dhh9cV3dB0EHD7Y8PoVYvQ7LgXTNXn1uxER0CIf+Y3qLvZO0ccfcNt/SoLcHBM5Uk4ql/HT0LZ3/9G/vh9w8Mf/t/DLV65m88QuQgRVGI3rqVezNmoo+JQWVri+fYsbj1fhNVFhJ/lVqN96dTwdKCh+vWEzJxIopOi1vPovF+UTs6Yvn0hTFFQfv4YqOFh0eq9WUcuyGEyBHZShDKli3L2bNn091Ho9Hw77//Urly5WwFptfrWbBgAV9//TU3b97Ew8ODnj17Mm3aNBwc0pgi9CkRERHMmjWLLVu2cOfOHZycnKhRowbTpk2jRYsW2Yort6hUKhz9/XFo2ZK4w4cJX7qMRydPEvnjOiI3/YTrq69ScugQrEuXztwJLawM88VXfxmi7j5ehO17iLoFx1cYbr51DYlCzdcNYxtE+hTFMNbj6WQgNo1F4UpWAidfuLk/43O/NNfwAz8mFGLvmf4bEwLx4YZB6THBhlu6VODgnn4SkdwyYWWb5WrIEU/P6FSyPGx4y/Aca7wGrdIY8C9EIaSNiODelKnE7NoFgG3tWvjOno1N+fLZOp9KrcZ78mRUllZEfv899yZPQdFqKdG7d06GnS9K9jfvKpSy61nZ338rsl3PhChIspUgdOzYkQULFrBixQqGDBmS6j6LFi0iLCyMd955J1uBffDBByxcuJBu3boxZswYLl68yMKFCzl9+jT/+9//UGdwxSUoKIhWrVoRGxvLwIEDqVKlClFRUZw9e5a7d+9mK6a8oFKpcGzWDIemTYk/foLwpUuJP3aMh5s28fDnn3Hp2hX3oUOwLlcu8yd1KQX+H0GLsXB9j6FV4dI2CD5luO2YCDW6GRZhK91AppIEQzIQcd08GXgUYb6vSg3uVcGn9pObd02wdU4xBiEE8ymdwDgGocHA9Mcg6DQQe988eUiZRMSGGvZRdBAXZriFnkv/edq6mnZjevpfJx/D3zY5eHXuwlb44yPjXcsNb4DKwhB36YbwylJ5DYoiI2b3bkI+mYzuwQOwtMRjxHBKDhqEyvLZZm9TqVR4TRiPytKSiFWrCJ02HbRaSvTtm0ORCyGKs2x9Qn344YcEBgYybNgwLly4QM/HTZtxcXGcOnWKjRs38uWXX+Lu7s6IESOyfP7z58+zaNEiunfvzubNm43l5cuX57333mP9+vX0zuBKyVtvvYVWq+Xs2bP4PNW3szBQqVQ4NGqIQ6OGxJ88SfjSZcQdOkTUL78Q9euvOHfujPs7Q7GpWDHzJ1WroVJbwy02DM6uNyzC9uAKnF5ruHlUf7wI2xuGdRiKA73OMHYj5EyKZOAsJEaZ76u2BM/qjxOBOoab1/NgbZ/6udUW0HGOYbYiVJgmCY9/BHecnfEAZQsrQ6LnUirj5xL/wLCGQGzo439TJhMp/tYlQcJDwy3sUvrntXZMO4lw9DIkEk5ehoQjvR/3yTM6PZ0sKY9bFOr2y79WDSFykC42ltBZnxH1eKpvm8qV8J0zB9vnnsuxx1CpVHh+9CEqS0serFxJ6KzPUDRaSg4ckGOPIYQonrI1ixHA/v376d69OxEREWYzCiiKgqurK1u3bqV58+ZZPvekSZOYOXMm+/fvN+kKlJCQQMmSJfH392f79u3pxubv78/ChQsZOXIkGo0GjUaDvX0aP+IykNVZEPQPw7nc2BB3hYO7sXHPmQTl0T//EL50GbH79hkKVCqcOr6I+zvvYls1mytYKwrcOmpoVTj/C2gfGcotrKF6F0OyUK5ltgfHxj64ze1mHQDw2bsNV+98ntJUp4XwyxB85kkycO+cYTGup1nYGH78J7cK+NYBz+fAMhvjapKvmqec6tS5lCE5eC4fZiFRFHgUmSKJSPlviGnrRGp1kxYLm7S7NTl4wtbhhpaNtDiXKn4zOiGzGCUrKrMYxR07Tsj48WiCg0GlosSA/ni89x7qLIzJy0pdKIpC+KJFhC9dBoDHBx/gPjT11v3C6FleFzKLkRDZk+02zpYtW3L+/Hm++uortm/fzvXr19Hr9fj5+fHSSy/x4YcfUqpUBlc703DixAnUajUNGzY0Kbe1taVOnTqcOHEi3eOTk4cyZcrQpUsX/vjjD3Q6HZUrV2by5Mm89dZb6R6fmJhIYmKi8X7041kTkhONjOi1T/bRaLSoM3FMZlg+9xzeixeRcP4CkStWELd7NzF//EnMH3/i0LYtJYYOwaZ69ayf2Le+4dZuOurzm1Gf/h5V6Dn4dzP8uxnFtRz6On3Q13rDcKU4C7QpnrtWo81U/eUYbSKEXUJ17x9U984abvcvoNImmO2qWNmjeNVA8a71+FYb3KuYD/ZVgOw8h8ovgV8zrOYZEqSE137Aoko7ww/hvKyTlKycwM0J3DKYAjcxBmJDUcWGpvj33lP3Q1ElRIEuER7eMtyyI/ou2uv7Ucpm/cJCYRC5eg0P16xJdVv5hARuzPsy1W2uffvi1q/odx3Rp3gvaLQ599mZV/QJCTxYsJCotWsBsCxVCq+ZM7CrVw8doMvC88lqXbi++y56VEQsXUrYV1+hS0ykxLvZ6+Jb0DzL6yJPv3OEKEKeqROkl5cXs2fPZvbs2TkVDwDBwcG4u7unOgNSqVKlOHz4MElJSVhbW6d6/OXH6woMHjyYypUrs3r1apKSkpg3bx5vv/02Go2G/v37p/n4n332GZ9++qlZ+c6dOzPVCmH5KIbk6+T79u1Da5cLA4Bf7IB1rZqU/Gs3jv/+S9xffxH311/EVq9GRNu2JPj5ZfPE3uD7IS6uNyn7YC+lI45g9fAmFntnotr7GaEutQkq2Yr7zrVQVBlf5dXHP6Da478PHDyI2v5iNuNKn1qfhMuj27jE38D1URAu8TdxTriDWtGZ7atR2xFlX5aHdmWJsi/HQ/tyxNr4GMYS6IC7wN0gIGfX77DQJfLy47//dyUe3fUdOXr+vGELlDXcrAC3xzcM/we2mihsNA+x1T7EVvPkZqN5iFPCXew1qYzheMqZAzu4ez71qQwLu5JnTlMyjamf05vC8b8zp3ng4Z7qtqJElZRE8rQWu3fvRknjM74gsrlzB+/1G7AJM7SQPWzYkLCXO3MhNBTSafFOS7bqomwZ3Dq+iMefO4hYupQrly/xoH37Qj+m51leF/Hx8bkTlBBFXLa7GOWmihUrotFouHXL/Cpk3759+f7774mMjMTV1TXV49u1a8dff/1FhQoVuHjxojGRiIyMpEKFCtja2nL37t00Bzqn1oLg5+dHeHh45roYRYVzvXkbAPz27MTG3TvDY55F0rVrRKxYSeyffxpWoQXsmzXFbehQ7F544RlPHofq0m+oT3+P+s4xY7Hi6I2+dm/0dfqAa9k0D497cJuQVp0B8Nj1Ky7e2Zu1w0RiDKr751GFPGkZIPw/VKkkA4qdW4pWgcctA27lDMlAXkuKw2quoa7iR13DyqFwdp/ILlXQQSzXvprhftq3thSvFgRFQff4R6WFh0eqP+aKTQtCfDzXGzUGwO/QQWwKQZcQRaMhYuVKIlesBJ0OC3d3PD/9FIeWzzZT3rPURWRgIA8et0a5DhxIyfffS3VxscLiWeoiOjoad3d36WIkRBY92zQKT1EUhTVr1nDmzBnKli3L4MGDMz0laUr29vZpLrCWkJBg3CctdnaGhcDefPNNk1YGNzc3unbtypo1a7h8+TLV0+iOY2Njk2rrhZWVVab6B+stn+xjZWWZ632KrapVw+HLeSSOHMGDr1cQ9dtvxB86TPyhw9g3boz7sHdxeKq7VuZP7gr13jbcwi4bxir8sw5V7D0sDn2JxaEvoUIrw1iFai+b9c+3tFBj75mIpa0O2/tnsCpVMWv9yx89hHtnTccMPLhKqjMCOXg8Hjj8ZMyAysWv4HwxKnn7uihwKrQ0zNiUwYxOlhVaFtkxCJ6DBuI5aKBJmUzh+IQ+xXvCyrLgv0cSr10j+KOPSTh/HgCnlzriPXkylhksIpoZz1IXnoMHY2ltTehns3n47beo9XrDYOaC8lmYRc9SFwX9NSREQZWtBGHevHnMnDmTzZs307p1a2N5t27d+O2334z3AwMDOXLkiPEHe2b5+vpy4cIFEhMTzX6o3717F3d39zS7FwGUfrxWgLe3+ZX75BmNIiMjsxRTYWBTvjy+sz/DffgwHqxYwcNfthB/9Ci3jh7Frn49PIYNw75Jk+x/SXhUhRdnQtvJcHm7YQak63vg+l7Dza4E1H7TkCx4VoMLW7HfPhbHNg8Mx/82GPZNMczqk9rA3Ljwx0nAmSfJQOTN1GNxLmU6rahPHcOMOoX0C7BYyKkZnYTIZ4peT+T333P/y69QEhNRu7jgPfkTXDp3zu/QjEr06weWloROn0HEqlUoOi1e48cX2iRBCJG3spUg/PHHH1hYWNCyZUtj2Z49e9i6dSuenp707t2bPXv2cPbsWQIDA3n33XezdP4GDRqwc+dOjh8/bjaL0ZkzZ0weNzUNGzZk+fLl3Lljvpptcpmnp2eWYipMrP388Jk+Hfd33iH8m2+I+mkzj/4+ya0BA7GrXRv34cNwaNEi+18UljbwfDfDLfLmkylSY0Lg6BLDrWRleHAFs0eIDjH8QOy60DA9ZsqWgbRWH3Yta5oI+NQGR4/sxS7y13NdoeeaVGZ08s2/GZ2EyALN3bsEj59A/PHjADg0b47PzBlYPb36bwFQok8fVJZW3Jsyhcg134NWi9ekSVlauVkIUTxl61Piv//+4/nnn8fC4smVvp9++gmVSsW6dev48ssv2b9/P87Ozvzwww9ZPn+vXr1QqVTMnz/fpHzlypXEx8fTp08fY9m1a9e4dMl0DvdXX30VJycn1q5dS2xsrLE8JCSELVu2UKVKFSpVymDmliLAqlQpfKZMoeL/duH29tuobGx49M8/3B4ylJs9ehKzezfPPATFrRy0mQSj/oXeG6FqZ0BtWFsBzBMEFMNt60j4sSfsnQWXtz1JDkpWMqyk234a9N0KH92AUWeh1/fQcixUbifJQWH3XFcYftx4V9trvWFqU0kORAGmKAoPN//M9a6vEH/8OCo7O7ynTsVv5YoCmRwkc+vVE5+ZM0GlIvLHddybMhXl8Vg1IYRIS7ZaEB48eGC2vsHBgwdxd3c3djlycnKiWbNmnD59Osvnr1mzJsOHD2fx4sV0796dTp06GVdS9vf3N1kkrW3btgQFBZn80HVzc+OLL75g6NChNG7cmAEDBpCUlMSyZctISkpi0aJF2XnahZaVlxfeEyfgPmQwD75bReT69ST8+y93hg3Hplo13N95B6cO7Z/tqpKFJVR50XC78OvjbiQZcCkDZZs+WWPAq4Zh9WFR9KXoRqSUaSLdikSBpg0PJ2TyFGJ37wbA7oUX8J39GdZl056goSBxfa07KksLgsdP4OGmTSg6HT7Tp6GykPedECJ12UoQ9Hq9cbAwGFZQvnDhAl26dDHZz83NjYiIjKc1TM38+fMpV64cK1asYNu2bbi7uzNy5EimTZuW5uxDKQ0ZMgR3d3c+//xzPvnkE9RqNU2aNOHHH3+kWbNm2YqpsLP08MDr448oOXgQEasCifzhBxIvXeLuqFHYVK5EyaHv4PxSx2f/0tBlct7pdlOg5uvP9lhCCJGLonfu5N6UqegiI1FZWeHx/nuU6N+/0P24dnnlFbCwJPjjjw2rO+u0+MyaVeiehxAib2QrQShTpoxJy8DOnTvR6XRmP7wjIyMpUaJEtgKzsLBgzJgxjBkzJt39bt68mea27t27071792w9flFmWaIEnmNGU2JAfyK//56INd+TeOUqwWPHEr5kCe7vDMW5c2dUltmc5Moxk83tmd1PCCHymC46mtCZM4n6dSsANlWr4vv5HGyrVs3nyLLP5eXOqCwtuDv2Q6J+3Yqi0eL7+Zzsf9YLIYqsbPUp6dixI7du3WLYsGH8+uuvjH88M0Lnp2ZwOHPmDGXKlMmRQEXOs3Rzw+O996i0+y/c3xuJ2sWFpBs3CP54HNc6debh5s0o2VmFsmxTw6DTVEYgGKgMsxCVbfos4QshRK6IO3yY611fMSQHajUlhw6l/KaNhTo5SObcsSOlvvoSrKyI3r6du2PGZu9zXghRpGXrssH48eP56aefWL58OV9//TWKovDWW29RrVo14z6nTp0iODiYXr165ViwIndYODvjMWwYJfr2JfLHdUSsWoXm1i1CJk4ifMlSSg4Zgkv3bqgzu3pliuksFVSoZDrLYu3BqkAiAgNNCxUFYgwtSMrurqhSWbiuREAAJfsH5H6AQjymf/SI+1/MI/Lx5BpWZcvgO3s29s+64GQB49y+PaoFC7j7/vvE7NjBHZ2W0l9+iaoQrVwthMhd2UoQvL29OXXqFCtWrCA0NJSGDRvy9ttvm+xz/vx5XnnlFeniU4hYODriPmQwJd7qQ+T6DTz47js0wcHcmzqV8OXLKTloEK49XkedyiJyZh5PZ6lsH4sqNvRJuUxnWezoY2PRhoamsuVxgvgoLM3jhMgrj/75h+CPx5H0uNuqW+838Rw7FnU6i3LmlDST6MeCXu6S6iQSz5JEO7VpTekli7kzYiSx//uLO++9T6mFCzJ/IUgIUaRlu+Ohl5cXn3zySZrb3377bbOkQRQOant7Sg7oj1vvN3m4cRMPvvkG7b17hM6YQfjXyyk5cCBuvXqhzmgBvOe6Eu9RiwevtcHSVofLZ3NxfOE1aTkoZtSOjlg+PQ2kXo82zJAYWHi4o0rlNaF2dMyL8EQxpyQlEbZ0KQ9WrAS9HksvL3xmzsSxed5NZpF2Em2gC8udJNqxZUtKL13KneHDid27lzvDR1B60ULUtrbPdF4hROEnI5NEmtS2tpTo+zauvXoS9fPPhK9YiTYkhPuz5/BgxUpKDuiP6xtvYuHokM5JLIi/b2hxcChVX5KDYqhkf/OrnPqH4VxubFgEsewvG7Fx98mHyERxl/DffwR/PI7EixcBcO7SBe9JE7FwccnTOFJNojGsvZCQkICtrW2qC1vmRBLt2LwZfl8v5/a7w4g7cIA7w4ZResmSjC8ACSGKNEkQRIbUNja4vfkmrq+9xsNff+XB1yvQ3LnD/S/m8WDlN5ToH4Bbnz5YODnld6hCCJEhRacjIjCQsPkLUDQaLFxd8Z46FeeOL+ZLPKkl0QAajYbt27fTqVMnrKyscu3xHRo3psyKr7k19B3iDh/h9jvv4rdsaZ50rxJCFEyy3rrINJW1NW49elDxj+34fGZYJEgXFUXY/AVcbduOsEWL0UVFmRyj6HTGvxNPnzW5L4QQeS3p9m2C+vbj/twvUDQaHFu1osJvW/MtOSgo7Bs0oMw3K1E7OBB/7Bi3hgxBFxuX32EJIfKJtCA8q8OL4cgS0zKNQvJsPZYrW4F1KnlYk+HQdESuh5cbVFZWuHZ7FZcuLxP9x5+EL19O0rVrhC9ZQkRgIG5vvUWJgH7EnzjBvenTjcdFfDCeaO8FeE0Yj3OHDvn4DIQQxY2iKDzcuInQOXNQ4uNR29vjNWE8Lq+9lmr3neLIvm5dynz7DbcGDebR3ye5PXgwfitXYCHjgYQodqQF4VklxkBMsOkt9p5xsyou1Hx7TLDhuEJOZWmJS5eXqbD1V0p99SU2Vaqgj4vjwddfc8W/FXffex9dWLjJMdrQUO6+P4ronTvzKWohRHGjuX+f2++8w70pU1Di47GvX5/yW3/F9fXXJTl4il2dOpRZtQq1szOPTp/m1oCB6KKj8zssIUQekwThWdk4gZOv6c3xyYBLxdHbfLuTr+G4IkJlYYHzSy9RfssvlF68COvq1SApKfWdH0/dFzrrM+luJITIddF//MGNLl2J27cflbU1nh9/TJk1q7EuXTq/Qyuw7GrWoGzgKixcXEg4e5Zb/Qege/gwv8MSQuQh6WL0rJqOMO8qFB8PgfUA0L57LM9nxMgvKrUap3btUDk5cbtfQNo7Kgrae/eI//skDo0a5ll8QojiQ/fwIfemzyB62zYAbJ97Dt85s7GpXDmfIyscbJ97jjJrVnMroD8J588T1H8AZb77Fks3t/wOTQiRB7LVgjBt2jS2bt2a4X6//fYb06ZNy85DiELs6W5FadGmMbe3EEI8i9gDB7ne9RVDcmBhgfuwYZTbsF6SgyyyrVqVsmtWY+HuTuLFi9zqF4D2wYP8DksIkQeylSBMnTqVLVu2ZLjf1q1b+fTTT7PzEKIQs/TwyNR+Fh7uuRyJEKI40cfFETJ1KrcHD0Z7/z7W5ctTbt2PeLw3ElUuThNalNlUrkzZNaux9PAg8b//COrXTy7uCFEM5OoYBL1eLwPAiiH7+vWw9PaGDP7vI7//Hm1ERB5FJYQoyuJPneJ6t+48XL8BALe+b1P+583Y1aqVz5EVfjYVKlD2+zVYenmRdPUaQX37oQm9n99hCSFyUa4mCLdv38ZRpkcrdlQWFnhNGJ/GxsdJg4UFsf/7i+svdyHmr7/yLjghRJGiT0ri/rx5BL31Nppbt7D08aFM4Cq8J0yQ1YBzkHW5coYkwdeHpBs3uNW3L5p79zI+UAhRKGV6kPKaNWtM7l+9etWsLJlWq+X8+fPs2bOHJk2aPFuEolBy7tABFszn3vTpJmMSLL288JowHms/P4I/+pjEK1e4M3wELt264TVhvKzGLITItIRLlwyfI//9B4DLq6/iNXGCfI7kEusyZSi75ntu9etHUlAQQW/3pWzgKqxKlcrv0IQQOSzTCUJAQIBJd6FDhw5x6NChNPdXFAW1Ws3YsWOfLUJRaDl36ICqThXutHwJgBJffYZnhy6oLCwAKLf5J8IXLuTBt98R9csvxB07iu+sz3Bo3Cg/wxZCFHCKVsuDb78jbPFi0GiwKFECn2mf4tSuXX6HVuRZly5F2e/XEBTQH82tWwS93ZcyqwOx9vPL79CEEDko0wlC3759jQnC6tWrqVixIs2aNUt1X2tra0qXLs2rr75KzZo1cyZSUSglJwMANi/UMrmvtrbGc+xYHFu3JnjceDS3b3MrIAC3vm/jOXo0alvb/AhZCFGAJd28SfC48Tw6cwYAx3Zt8fn0UyxLlszfwIoRK19fyn6/hlt9U7QkrA7EumzZ/A5NCJFDMp0gBAYGGv9evXo1zZs357vvvsuNmAqVB6sCiUhRN4BxMTCAoJe7oFKbD/UoERBAyf4BuRtcIWFfrx4VtvxC6OdzebhhA5Frvifu4CF858zGThJMIQSGVunIdeu4P/cLlEePUDs64jVpIi6vvCKTYeQDKy8vyny/hlsB/Um6ft3QkhAYiE2F8vkdmhAiB2RrobQbN27I4OPH9LGxaEND09yuS2M6OH1sbG6FVCipHRzw+XQqTm3bEDJxEknXr3PzjTdxHzoU93ffkSkKhSgGUq6u/ujkSaz9/VFZWKC5d4+QiZOIe9yt1b5xY3xnzcTK1ze/QhWAlacnZdes5lb//iReuUpQv76UXbUKm0qV8js0IcQzylaCUDaDZsSoqCicnZ2LxVUdtaMjll5eZuWKopCQkICtrW2q9aCWBCtVji1bUuG3rdybNp3o7dsJX7qU2L178f18TqH60kmzZSnG8FpRdndFpZKWJSGSRe/cSeiMmcb7IcOGE+bthVOHDkRt+RV9dDQqGxs8x47FrU/vVFtmRd6zdHenzOrV3Oo/gMTLlwnq248ygauwrVIlv0MTQjyDbCUI//77L7t376Zjx45USfEhsGfPHgYMGMCtW7coUaIEc+fOJSAgIKdiLZBK9k/9B51Go2H79u106tQJK7n6nSUWrq6U+nIejm3bcG/adBIuXOBG99fw+OADSvTrWyh+GKTdsvR4DMYjaVkSIln0zp3cfX+USfdMAO29UCLXfA+Abc2a+M6ZjU2FCvkQoUiPZYkSlAlcxa2BA0m8cJFbyUlCtWr5HZoQIpuy9Utr4cKFjB49GrsUc0w/ePCAV199laCgIBRF4cGDBwwaNIjTp0/nWLCieHHp3JkKW7fi0LIFSlIS9+fM4Va/AJLu3M3v0DKU3LJkckuxwrSFh7v5di8vaVkSxY6i0xE66zOz5CAltaMjZdd+L8lBAWbp5kbZVauwrVED3cOH3OoXwKPz5/M7LCFENmUrQTh06BDPP/88fimmNfv++++JiYlh6NChPHz4kDVr1qDX61m0aFGOBSuKHysvT/y+/hrvTz9FZW9P/IkT3OjalYc//YSSzg+K/FayfwCV9+01uVX87Wfj9rK/bDTbXnnfXuleJIqd+L9Pos1gwS19bCyPzvyTRxGJ7LJwcaHMqu+wq10bXVQUt/oP4NG5c/kdlhAiG7KVIISGhlKmTBmTsl27dmFhYcGMGTNwdnbmrbfe4oUXXuDIkSM5EqgovlQqFW69elJhyy/Y1auHPj6ekEmfcOfdYWjTGAQuhCjY9ImJxB44wIMVKzK1v7zXCwcLJyf8vv0Gu7p10UdHc6v/AOKlJ4EQhU62EoTo6GhcXFxMyo4dO0adOnUomWIu6sqVK3P3bsHvDiIKB8Mqnqvx/HAsKisrYvfu5XqXrkT/uSO/QxNCZIImNJTIDRu5PWw4/zVuwu3BQ4wzE2UkZRc9UbBZODpSZuUK7Bs0QB8by+2Bg4g/eTK/wxJCZEG2EgRnZ2eTH/4XL14kIiKCpk2bmu1bHGYyEnlHZWFByYEDKffTT9hUr47u4UPujhrF3Q8/QhcVld/hCSFSUHQ64k+f5v78+Vzv1p2r/q24N2UKsbt3ozx6hKWnJy6vv46Fqyuk9VWhUmHp7Y19/Xp5Gbp4RmoHB/y+Xo5948bo4+O5NXgIcceP53dYQohMytYsRnXq1OHAgQNcvXqVSpUq8e2336JSqfD39zfZ78aNG/j4+ORIoEKkZFu1CuU3rCds6VIerFhJ9G+/EX/8OD4zZ+LYPPUVvoUQuU8XHU3cwYPE7ttH7P4D6CIjn2xUqbCrVQvHVv44tmqFTbVqqFQqolu2MMxipFIg5dCixxeYvCaMN1mFXRQOant7/JYv487wEcQdOsTtIUPxW7YUhyZN8js0IUQGspUgDB06lN27d1OvXj0qVKjA2bNn8fT0pHPnzsZ9YmJiOHPmDF26dMmxYIVISWVtjeeoUTi1akXwx+NICgri9qBBuPV+E8+xY1Hb2+d3iEIUeYqikHT9OrF79xK7dx/xp05BigXP1E5OODRvhlOrVji0aIFliRJm53Du0AEWzCd0xky09+8byy29vPCaMN6wXRRKaltbSi9dwp333iNu335uv/MupRcvxrFF8/wOTQiRjmwlCD169ODixYvMmTOHf/75h3LlyrFmzRpsbGyM+2zcuBGNRmPWqiBETrOrU4fyv/zM/XlfEvnDD0T+uI7YQ4fwnT0b+xdeyO/whChy9ImJxB8/YUgK9u1Dc+eOyXbrihVx9PfHsZU/9i+8kKmV0J07dMChSRP+a9AQAJ+lS3B5vJKyKNzUNjaUXrSIu6M+IHb3bu4MG0apRQtxatUqv0MTQqQhWwkCwOTJkxk3bhzR0dG4u7ubbW/fvj2nT5+mYsWKzxSgKDxWn1/NmgtrTMqsErV89fjvN3b1R2Nr/pLr+1xf+j3f75keW21vj/cnk3Bs05qQCRPRBN0iqM9blBw0CI8Rw1FZWz/T+YUo7jShocTu3Ufsvn3EHTmC8uiRcZvKygr7Ro2MSYF1iimwsyJlMmBXr54kB0WI2tqa0vO/4u6YscTs2sWdke9Rev5XOLVtm9+hCSFSke0EAcDa2jrV5ACgTJkyZlOhiqItThPH/fj7JmU2SU86FIcnPCBRbz4SMU4Tl2MxODZrRoXfthI6cyZRv27lwYoVxO7fj++c2dhWrZpjjyNEUafodCScO0fM3r3E7ttP4sWLJtstPT0NCUHrVjg0bixd+kSGVNbWlPpyHnc//IiYP//kzvujKDVvHs4vShcyIQqaZ0oQAC5cuMDhw4cJCwvj+eefp2vXrgDo9Xq0Wi3WcuW22HCwcsDT3tOkzNpCB4QC4GHnTpKN+RVBByuHHI3DwtkZ3zlzcGzTlntTppB46RI3X++Bx/vvUaJ/f7kqKUQadNHRxB06ZOg6lMkBxkJkhcrKilJfzCXY0pLo33/n7ujRMPdznDt1yu/QhBApZDtBuH37Nv3792fPnj3Gsn79+hkThJUrVzJs2DB27txJW2lCLBb6Pd/PrKuQ/mE4l2e0AGBLx3XYuOfdrFbOL3bAvl5dQj6ZTOyePdz/Yh4xu/fgO/szrKV1S4gUA4z3Ebt3b5oDjB39/XFs2TLVAcZCZJXK0hLfObNRWVgQ9euv3B37IYpOh4tMaiJEgZGtBCEiIgJ/f39u3rxJjRo1aNmyJUuXLjXZp2fPnowYMYKtW7dKgiDyjaW7O6WXLiHq518InTWLR6dOcf3Vbnh99BGuvXrKFdC8cHgxHFliWqZRSJ743nJlK7BOZUmWJsOh6YhcD6+4MQ4w3mdICnJigLEQWaWysMBn1kywtCBq888Ef/QxilaHa7dX8zs0IQTZTBDmzJnDzZs3GTt2LHPmzEGlUpklCG5ubtSsWZODBw/mSKBCZJdKpcL1te7YN2pEyPjxxJ84wb2pU4n56y98ZszAyssz45OI7EuMgZhg0zKtCjC0JqniQiFRSf04kSM0oaGPE4LcG2AsRFapLCzwmT4dlaUVDzdsIGTCBNBpcX399fwOTYhiL1sJwq+//kq5cuWYPXt2uldgK1SowKFDh7IdnBA5ybp0KcqsDiTy+++5P+9L4g4c4HrXrnhP/gSXFGt4iBxm4wROvqZlmid/Ko7eYJXK54iNU+7GVYQZBxg/TgrSHGDcyt8wwNghZ8cBCZFZKrUa76lTUFlYEPnjj4RM+gRFq8XtjTfyOzQhirVsJQhBQUF07twZtTqVbgEpWFtbExERka3AhMgNKrWaEv364dCsGcEfjyPh/HmCx4wl9q+/8PrkEyzd3PI7xKKn6QjzrkLx8RBYDwDtu8ewcHHJh8CKlicDjPcRu3+/DDAWhYZKpcLrk0morCyJWL2Ge1M/RdHqKPFWn/wOTYhiK1sJgq2tLTExGTf/37p1Cxf54hcFkE2lSpRbv47wr1cQvmwZ0dv/IP7E3/jMnIFjy5b5HZ4QGZIBxqIoUalUeI4bh8rKigfffEvojBkoWg0lAwLyOzQhiqVsJQjVqlXj1KlTxMXF4ZBG03R4eDj//PMPjRo1eqYAhcgtKisrPEYMx9Hfn+CPPybp+nVuDxmKa8+eeH38kXS7EHlCSfGj/tHJk1ins3qwDDAWRZlKpcJjzBiwtOTB8q+5P3sOaLW49upl3Cej94gQImek30coDa+//joPHjxg9OjR6PX6VPf58MMPiY+Pp1eKN7YQBZFdzRqU/3kzJfoZpmh9uHEj11/tRvzJk/kcmSjqonfu5Hrnl433Q4YN52rbdkTv3Gks04SGErlxI7eHDee/xk24PXgwkWvXorlzB5WVFQ7Nm+M1cSIVd+2k4rbf8froQxwaNpTkQBRKKpUKj/ffx32EoVvi/S/mcbVVa+P21N4jQoicl6kWhDZt2tCxY0c++ugjAIYPH87q1av55ptvOHnyJN27dwfg2rVrfPnll2zatInjx49Tp04dAqR5UBQCaltbvMaPw7F1a4InjEdz+zZBb71NiQH98XjvPdQ2Nvkdoihionfu5O77o0AxncFJGxrK3ffeJ+rFDmhu3ZYBxqLYUalUeIwYTtLNG0T/vg19XJzJdm1oqOG9s2A+zh1kFWYhckOmEoS9e/dSrlw5431bW1t27NhBjx49OHz4MKdPnwbg4MGDHDx4EEVRaNCgAVu2bMFKrmKJQsShcSMqbN1K6KzPiPr5ZyK+/Y64/Qfw/XwOttWr53d4oohQdDpCZ31mlhwYNhrKYnc8vkIqA4xFMaTodMT/nUYrrqKASkXorM9wattWuhsJkQuyvZKyj48PBw8eZMeOHWzbto3r16+j1+vx8/PjpZde4pVXXpEvMVEoWTg64jtrJk7t2hLyyWQSr1zhRo+eeIwYTslBg1BZZvttIwQAcX//jfbevQz3KzF4MCX7B8gAY1HsxP99Mv33iKKgvXeP+L9P4tCoYd4FJkQx8cy/dF588UVefPHFnIhFiALFqU0b7OrU4d6UqcTs2kXY/AXE7NmD7+zZ2JQvn9/hiUIm5QDj6O3bM3WMbdWqkhyIYkkbFpaj+wkhskYuhQqRDssSJSi1cAHRv/3GvekzSPjnLDe6dcdz7Fjcer+JKq21QA4vhiNLTMs0CmBoVbNc2QqsUzm2yXDzNQNEoWVcwXjffuIOHzZZwTgzLD08cikyIQq2zL725T0iRO6QBEGIDKhUKly6dsW+QQNCJk4k7vARQmfMIHb3X/jMnImVj4/5QYkxEBNsWqZVAYZ9VXGhkJhK//PEjNcXEQWXyQrG+/aReCH1AcYOLVsQOn2G4epnauMQVCosvbywr18vjyIXomCxr18PS29vtKGh8h4RIh9kOkFYvXo1q1evzvIDqFQqtFptlo8ThVARv2pu5eOD3zffELluHffnfkHc4SNc7/oK3pMm4ty1q+mYGxsncPI1PYHmyZ+KozdYpTJGx8Ypd4IXuUYXE0PcwYOGBcsOHECXcvX4lAOM/f2xqV79yetEUQwzsaiAlL9/Hm/3mjBeBl+KYktlYYHXhPHyHhEin2Q6QVBSy+CFSKkYXDVXqdWU6NMHh6ZNCR43joR/zhL88Thi/vcX3p9OfdJfvOkI86QnPh4CDVe7tO8ew0JWGS+UTFYw3rfPsIJxiosgakdHHFo0z3AFY+cOHWDBfEJnzER7/76x3NLLC68J42X6RlHsyXtEiPyT6QShY8eOfPzxx7kZiyjsitFVc5vy5Sn3ww88+OYbwhYvIWbXLuJPncJn+jSc2rTJ7/BEDjNZwXjfPjS3b5tsN65g7O+Pfd3Mr2Ds3KEDDk2a8F8DwywsPkuX4CKrxAphJO8RIfJHphMEb29v/P39czMWUdgVs6vmKktL3N95B8eWLQn+eByJV65wZ9hwXLp3x2vCeCwcHfM7RPEMNKH3id231zDA+MgRlPh44zaVlRX2jRoZFyyz9vPL9uOk/KFjV6+e/PAR4inyHhEi78kgZSGeke1zz1Hup02ELVxIxHeriPr5Z+KPHsXns89kfu5CRNHrDQOM9+5Nd4CxrGAshBCiqCuwCYJer2fBggV8/fXX3Lx5Ew8PD3r27Mm0adNwyMQXc1qLtDk4OBAbG5vT4YpiTm1jg9eHH+LUujXB48ajuXOHW/36UaJfXzw++AC1rS2KTmfc/9HJk1hLM3m+y2iAsW2tmji1amU+wFgIIYQowgpsgvDBBx+wcOFCunXrxpgxY7h48SILFy7k9OnT/O9//0Od1vzzKbRo0YIhQ4aYlFllsm+wENlhX78+5bds4f7nn/Nw40YiVq8h9sBBXLp3J3LNGuN+IcOGE+btLQPt8piiKCTduEHsnr1pDzBu3tww61CLFliWLJmP0QohhBD5o0AmCOfPn2fRokV0796dzZs3G8vLly/Pe++9x/r16+ndu3eG56lQoQJvvfVWboYqhBkLRwd8pn2KU9s2hEz6hKTr1wn74guz/bShoYYp/BbMlyQhF+mTkgwDjB93HTIbYFyhAo6PWwmyMsBYCCGEKKoylSDo9frcjsPEunXrUBSFUaNGmZQPHjyYcePGsXbt2kwlCABJSUkkJSXhKANGRR5z9Pen3C8/c61de5SEBPMdFAVUKkJnfYZT27bS3SgHaULvE7t/H7F796U+wLhhQ0NS8IwDjIUQQoiiqEC2IJw4cQK1Wk3DhqYDPG1tbalTpw4nTpzI1Hl++ukn1q5di06nw8PDg169ejFjxgxcMphJJzExkcTEROP96OhoAKpVq5Zu16ZZs2bx5ptvAqDRaLh79y7lypXLVL/lw4cP45NiRd5vvvmGmTNnZnhc5cqV2blzp0lZ3759OXDgQIbHDhgwgE8++cSkrHz58hkeBxAYGGgyq9W+ffsICAgw31FRjPNXW1Svjkqt5saNGya7TJ8+ne+++y7Dx2zRogVrUnTTAejQoQNXrlzJ8NiJEycyaNAg4/2QkBCaNm2a4XEAf/75J1WrVjXeX7duHRMmTMjwOC8vL3YvWpR6cpBMUdDeu8c3Varyn15PhKLngaLQoEMHxsyYgdrVFdXj11yNGjWIi4vL8HEXL15M586djfdPnTrFa6+9luFxAGfPnsXJ6cm0s/Pnz2fBggUZHlenTh1++eUXk7Ju3bpx5syZ1A9I8bp4f+FCxo4bZ9wUExNDrVq1MhXv5s2bqVu3LopeT+K//3Lm2++I3LWLyk8lW+F6Pcd1Wo5qdZyOjcHi4AH+Xb4MMLxXAcaNG8eGDRsyfMyXXnqJpUuXmpQ1btyY0NDQDI9N+RkBcPnyZTq++KLZeyQ1RfYzIqXHr4s9FSuh0WpRP/6/KcqfEUePHjUpGzZsGH/88Qdg6BKXmJiIjY2N2fdIr169mD17tklZkfqMSOG9d9+ly+O/NVotcRERmf6MePr1IITInAKZIAQHB+Pu7o6NjY3ZtlKlSnH48GGSkpKwtrZO8xwNGzakR48eVKpUiejoaLZv387ixYvZt28fhw8fTrdF4bPPPuPTTz81Kw8JCUk37uPHj5skHzqdjuDg4HSOeGLXrl24u7sb7584cYK7d+9meJxarWb79u0mZZcuXcrUsf/884/ZsZk5DuDgwYMmX0SnT5/O+NjH9ff0Y/7zzz+ZetxLly6ZHXvt2rVMHXvixAl8fZ+s0RAeHp7p57pnzx6uXbtmvH/8+PFMHZuQkMDJXbvwyXBPaG5lRfOUBQcOcsO/FYpajdbJCZ2TE6MeJRCq1RJmvOkI02q5r9USodOSPAT68OHDJj8mMvt6ANixYwf29vbG+ydPnszUsY6Ojmb/N1evXs3UsWfPnjU5Nj4+PnOPqVZzeVUgyoKFOFy+jGVcHO6Au4UFekXhXEIC++Ji2Rcby8UUCT+AnZ2dWbxnz57N1ONeuHDB7NigoCAePHiQ4bFPf0bcunWLuyk/I9L5jCkWnxEp7N69G+XxZ3xR/ox4Ot4LFy5k630DcPv2bR49epThsYXtM+L0mTPGBGH37t3EabWZjjczibAQwlyBTBDi4+NTTQ7A0IqQvE96CcKxY8dM7vft25datWoxceJEFixYwMSJE9M8dvz48YwePdp4Pzo6Gj8/P3x8fNJtQWjYsCGdOnUCDFclAwMD8fX1zVQLQvv27U2uDgYHB5td9UtNxYoVjY+ZbP369YSFhWV4bO3atc2OLVWqVIbHATRv3tzk6qCDg0Pqx6ZsQfDwQKVWmz3myZMnOXnyZIaPWa1aNbNj58+fn6kucA0aNDA5NiQkJNPPtXXr1iZXB6OiotiyZUuGx3l5eVGvfXuC163PcN+/NEloUVFCZbj52tlhl5SESq/HKioKq6goWtnbQ4ov5pT0isJDRSFCUfC+fBkfC0ss3d2x8PCgtKMj7fz8eKAoRCpKyrXrzLz44osmVwf/++8/Dh48mGH8lSpVMvu/WblyZdozhqV4XdSqVcvk2JiYmDT/b/xUahpZWtDIwpIaFhZY7t1r3KZ2dORh+fIsP3GcEzodDxUFbG3B1panz+bg4GAW7/79+7l48SIZee6558yOLVu2rPGzKT0pPyPA0IJQytfX7D2SmiL7GZFSitdFmzZtsHF2Bor2Z8TT8f7+++8EBQUB6bcgPP2+AfDz88tUC0LTpk1NjvX29s70c82zz4gUXqhTB879CxheF0kqVabjbdGiRab2E0KYUimKouR3EE+rWbMm9+/fT7XJvmfPnmzatInExMR0E4TUaDQaHB0dqVevHocPH870cdHR0bi4uBAVFYXz4y+szDzW9u3b6dSpU7GeOUkfH8/luoaF0iocO4pNEVooLTMUnY6rbduhDQ01jDl4mkqFpZcXlf76n9kYBEWjQfvgAdqwMMPtfhja+/ef3E++PXgAKaZQzYiFiwuWnh5YeqRy8/Q0/q1OIxnJCbqYmEytjGocYLxvH7F796Y+wDh5BeN6dQvlAOPi/h5JSerClHyPGDzL6yI7399CiALaguDr68uFCxeMV05Sunv3Lu7u7llODsAwxamvry/h4eE5FaoQ6VJZWOA1YbxhtiIVkDJHeHxF0GvC+FR/HKusrLDy9sbK2zvdx1B0OnSRkeaJQ3Iycf9JmaLRoIuKQhcVReKVq+meV+3gkEoCYZpEWHp4oHZyytL6ANE7dxI640nf+aenfDUOMN63j7jDaQwwTl7BuEyZTD+uEEIIITKnQCYIDRo0YOfOnRw/ftykeTAhIYEzZ87QsmXLbJ03ISGBO3fu0Lhx45wKVYgMOXfoAAvmEzpjprH7BICll1eOrIOgsrDA0t0dS3d3qF49zf0URUEfFWWWSGjMWiXCUeLj0cfFkRQXR9LNm+k/vo1Nui0Rya0VFq6uxPzvf4Zk6anWFO29e9x9731CS5dCe8e0b7Glh4dhXQJ/fxyaNJEVjIUQQohcViAThF69ejFr1izmz59vkiCsXLmS+Ph4+vTpYyy7du0aGo2GatWqGcsePHhAyVQWOPrkk0/QarV06dLFbJsQucm5QwccmjTJVLea3KJSqbBwdcXC1RWbypXT3VcXG4c2LJXuTPdN7+ujo1ESE9HcuYPmzp30A7CwMCQG6fRqTE4ObGvXMnYdsn3uOVnBWAghhMhDBTJBqFmzJsOHD2fx4sV0796dTp06GVdS9vf3N1kDoW3btgQFBZFyKMWMGTM4evQorVu3pkyZMsTGxrJ9+3b27NlDo0aNGDlyZH48LVHMpUwG7OrVK9DrHlg4OmDhWB6bDKa01CckoA0PN0scTLo4hYWhi4jI9DiJUosW4ty+fU48DSGEEEJkQ4FMEMAw80S5cuVYsWIF27Ztw93dnZEjRzJt2rR0ZxICaNWqFRcuXGD16tU8ePAACwsLKleuzMyZMxk9enSmZhsRQmRMbWuLdenSWJcune5+ikZD5MaNhE6fkeE5lcSknApPCCGEENlQYBMECwsLxowZw5gxY9Ld72Yq/aNfeeUVXnnllVyKTAiRVSorK2wqpd+tKZmlh0cuRyOEEEKI9KR/KV4IIXKIff16WHp7G2dvMqNSYentjX39enkbmBBCCCFMSIIghMgTyVO+Gu48vTH9KV+FEEIIkXckQRBC5BnnDh0otWA+lh6eJuWWXl6UWjD/mad8FUIIIcSzK7BjEETh82BVIBGBgaaFKWaXCnq5C6pUBpiXCAigZP+A3A1OFBgFYcpXIYQQQqRNEgSRY/SxsWhDQ9PcrgsLS/M4UbwUpilfhRBCiOJGEgSRY9SOjlh6eZmVK4pCQkICtra2qS54pXZ0zIvwhBBCCCFEJkiCIHJMyf6pdxXSaDRs376dTp06YWVllfeBCSGEEEKITJNBykIIIYQQQggjSRCEEEIIIYQQRpIgCCGEEEIIIYwkQRBCCCGEEEIYSYIghBBCCCGEMJIEQQghhBBCCGEk05wKIYTIc7LyuhBCFFySIAghhMhzsvK6EEIUXJIgCCGEyHOy8roQQhRckiAIIYTIc7LyuhBCFFwySFkIIYQQQghhJAmCEEIIIYQQwkgSBCGEEEIIIYSRJAhCCCGEEEIII0kQhBBCCCGEEEaSIAghhBBCCCGMJEEQQgghhBBCGEmCIIQQQgghhDCSBEEIIYQQQghhJCspC5ELHqwKJCIw0LRQUYx/Br3cBZXaPD8vEZD66rJCCCGEEHlFEgQhcoE+NhZtaGia23VhYWkeJ4QQQgiRnyRBECIXqB0dsfTyMitXFIWEhARsbW1RqVSpHieEEEIIkZ8kQRAiF5Tsn3pXIY1Gw/bt2+nUqRNWVlZ5H5jIV9L1TAghRGEgCYIQQuQR6XomhBCiMJAEQQgh8oh0PRNCCFEYSIIghBB5RLqeCSGEKAxkHQQhhBBCCCGEkSQIQgghhBBCCCNJEIQQQgghhBBGkiAIIYQQQgghjGSQshBCCCEKBFkrRIiCQRIEIYQQQhQIslaIEAWDJAhCCCGEKBBkrRAhCgZJEIQQQghRIMhaIUIUDDJIWQghhBBCCGEkCYIQQgghhBDCSBIEIYQQQgghhJEkCEIIIYQQQggjSRCEEEIIIYQQRpIgCCGEEEIIIYwkQRBCCCGEEEIYSYIghBBCCCGEMJIEQQghhBBCCGEkCYIQQgghhBDCSBIEIYQQQgghhFGBTRD0ej1fffUV1apVw9bWFj8/P8aMGUNcXFyWzxUfH0+FChVQqVSMGDEiF6IVQgghhBCiaCiwCcIHH3zA6NGjee6551i0aBE9evRg4cKFdOnSBb1en6VzTZ48mbCwsFyKVAghhBBCiKLDMr8DSM358+dZtGgR3bt3Z/Pmzcby8uXL895777F+/Xp69+6dqXOdOnWK+fPn8/nnnzNmzJjcClkIIYQQQogioUC2IKxbtw5FURg1apRJ+eDBg7G3t2ft2rWZOo9Op2Pw4MF07NiR7t2750KkQgghhBBCFC0FsgXhxIkTqNVqGjZsaFJua2tLnTp1OHHiRKbO89VXX3Hp0iWTVojMSExMJDEx0Xg/OjoaAI1Gg0ajydQ5kvfL7P5FmdTFE1IXBvoUz1+j1aIu5vUhr4snpC6ekLp4Irt1IXUnRPYUyAQhODgYd3d3bGxszLaVKlWKw4cPk5SUhLW1dZrnuHHjBlOmTGHy5MmUK1eOmzdvZvrxP/vsMz799FOz8p07d2Jvb5/p8wDs2rUrS/sXZVIXTxT3ulAlJVH58d+7d+9GSee9XJwU99dFSlIXT0hdPJHVuoiPj8+lSIQo2gpkghAfH59qcgCGVoTkfdJLEN555x0qVKjA6NGjs/z448ePNzkuOjoaPz8/OnTogLOzc6bOodFo2LVrF+3bt8fKyirLMRQlUhdPFMe6iFy9hodr1pgWKgq6x39WW7gIVCqz41z79sWtX9/cD7AAKI6vi7RIXTwhdfFEdusiuQeAECJrCmSCYG9vz/3791PdlpCQYNwnLWvXrmXXrl3s378/Wx+qNjY2qSYoVlZWWT5fdo4pqqQunihOdaF69AhdGu9nAF0aM4ypHj0qNnWUrDi9LjIidfGE1MUTWa0LqTchsqdAJgi+vr5cuHCBxMREsx/qd+/exd3dPc3Wg8TEREaPHk2nTp3w9vbm6tWrxuMAoqKiuHr1Ku7u7ri6uubq8xBCgNrREUsvL7NyRVFISEjA1tYWVSotCGpHx7wITwghhBBPKZAJQoMGDdi5cyfHjx+nRYsWxvKEhATOnDlDy5Yt0zz20aNHhIWFsW3bNrZt22a2fe3ataxdu5a5c+cyduzYXIlfCPFEyf4BlOwfYFau0WjYvn07nTp1kqt8QgghRAFSIBOEXr16MWvWLObPn2+SIKxcuZL4+Hj69OljLLt27RoajYZq1aoB4ODgwKZNm8zOGRYWxrBhw+jYsSMDBw6kVq1auf9EhBBCCCGEKGQKZIJQs2ZNhg8fzuLFi+nevTudOnXi4sWLLFy4EH9/f5NF0tq2bUtQUBCKogCG/oavv/662TmTZzGqWLFiqtuFEEIIIYQQBTRBAJg/fz7lypVjxYoVbNu2DXd3d0aOHMm0adNQqwvk+m5CCCGEEEIUegU2QbCwsGDMmDGMGTMm3f0yu75BuXLljK0MQgghhBBCiNTJpXghhBBCCCGEkSQIQgghhBBCCCNJEIQQQgghhBBGkiAIIYQQQgghjCRBEEIIIYQQQhhJgiCEEEIIIYQwkgRBCCGEEEIIYSQJghBCCCGEEMJIEgQhhBBCCCGEUYFdSbkgSV6BOTo6OtPHaDQa4uPjiY6OxsrKKrdCKxSkLp6QunhC6uIJqYsnpC6ekLp4Irt1kfy9nfw9LoTIHEkQMiEmJgYAPz+/fI5ECCGEEFkVExODi4tLfochRKGhUiStzpBeryc4OBgnJydUKlWmjomOjsbPz4/bt2/j7OycyxEWbFIXT0hdPCF18YTUxRNSF09IXTyR3bpQFIWYmBh8fX1Rq6VXtRCZJS0ImaBWqyldunS2jnV2di72H+zJpC6ekLp4QuriCamLJ6QunpC6eCI7dSEtB0JknaTTQgghhBBCCCNJEIQQQgghhBBGkiDkEhsbG6ZMmYKNjU1+h5LvpC6ekLp4QuriCamLJ6QunpC6eELqQoi8JYOUhRBCCCGEEEbSgiCEEEIIIYQwkgRBCCGEEEIIYSQJghBCCCGEEMJIEgQhhBBCCCGEkSQI2fDff/8xefJkGjdujIeHB05OTtSpU4eZM2cSFxdntv/ly5d59dVXcXNzw8HBgRYtWrB79+58iDznXb58mT59+lC9enVcXFywt7enWrVqjB49mpCQkFT3L6p1kZr4+HgqVKiASqVixIgRZtuLcn2oVKpUb46Ojmb7FuV6SBYREcHYsWOpVKkStra2eHh40Lp1aw4cOGCy37Fjx2jXrh1OTk44OzvTsWNHzpw5kz9B57CpU6em+bpQqVRYWVmZ7F/UXxexsbHMmjWLmjVr4uTkhLu7O02bNiUwMJCn5w8pyq8LgNDQUN555x38/PywtramTJkyvP/++zx8+NBs36L+uhCiIJCVlLPhu+++Y8mSJXTt2pU+ffpgZWXFnj17mDRpEhs3buTo0aPY2dkBcO3aNZo2bYqlpSUfffQRLi4urFy5khdffJE//viDdu3a5fOzeTZ37twhJCSEbt26Ubp0aSwtLTl37hwrVqxg/fr1nDlzBk9PT6Do10VqJk+eTFhYWKrbikN9tGjRgiFDhpiUPf0jsDjUQ1BQEK1atSI2NpaBAwdSpUoVoqKiOHv2LHfv3jXud/ToUVq1akWpUqWYNm0aAIsXL6ZFixYcPnyYmjVr5tdTyBHdu3enUqVKZuVnz55l7ty5dOnSxVhW1F8Xer2el156icOHD9OvXz9GjhxJfHw869ato3///ly8eJE5c+YARf91cf/+fRo1akRwcDBDhw6lRo0a/Pvvvyxbtoz9+/dz6NAh7O3tgaL/uhCiwFBElp04cUJ5+PChWfnEiRMVQFm0aJGxrEePHoparVZOnz5tLIuJiVHKlCmjVKlSRdHr9XkRcp7buHGjAihz5swxlhW3ujh58qRiYWGhzJs3TwGU4cOHm2wv6vUBKP369ctwv6JeD4qiKM2bN1dKly6tBAcHp7tfgwYNFCcnJ+XOnTvGsjt37ihOTk5K+/btczvMfDNkyBAFUH7//XdjWVF/XRw+fFgBlFGjRpmUJyYmKuXLl1dcXFyMZUX9dfH+++8rgPLjjz+alP/4448KoEyfPt1YVtRfF0IUFJIg5KCzZ88qgDJ06FBFURQlNjZWsbGxUdq0aWO277Rp0xRAOXbsWF6HmSeOHTumAMq4ceMURSl+daHVapW6desqnTt3Vm7cuGGWIBSH+khOEBITE5WYmJhU9ykO9bBv3z4FUBYuXKgoiqIkJSUpcXFxZvtduXJFAZQBAwaYbRswYICiUqmUkJCQXI83r8XGxirOzs5K6dKlFa1Waywr6q+LP//8UwGUzz//3GxbgwYNFF9fX0VRisfrolatWoqdnZ3Zj3udTqfY2toqFSpUUBSleLwuhCgoZAxCDrpz5w4AXl5egKHZPDExkSZNmpjt27hxYwBOnDiRdwHmooSEBMLDw7lz5w47d+5k6NChAHTq1AkoXnUB8NVXX3Hp0iUWL16c6vbiUh8//fQT9vb2ODk54enpyciRI4mKijJuLw71sH37dgDKlClDly5dsLOzw8HBgSpVqrB27VrjfsnPM626UBSFkydP5k3QeWjTpk1ER0cTEBCAhYUFUDxeFw0bNsTV1ZXPP/+cTZs2cevWLS5dusT48eM5efIkU6dOBYrH6yIxMRFbW1tUKpVJuVqtxs7OjuvXrxMeHl4sXhdCFBQyBiGH6HQ6pk+fjqWlJb179wYgODgYgFKlSpntn1yWsv9xYfbNN98wcuRI4/1y5cqxdu1aWrRoARSvurhx4wZTpkxh8uTJlCtXjps3b5rtUxzqo2HDhvTo0YNKlSoRHR3N9u3bWbx4Mfv27ePw4cM4OjoWi3q4fPkyAIMHD6Zy5cqsXr2apKQk5s2bx9tvv41Go6F///7Foi5S8+2336JSqRgwYICxrDjUhZubG1u3bmXQoEH07NnTWO7k5MTmzZt59dVXgeJRF88//zyXL1/mzJkz1KlTx1h+5swZIiMjAbh161axqAshCgpJEHLIqFGjOHLkCLNmzaJq1aqAYQYbABsbG7P9bW1tTfYp7F599VWqVatGbGwsp0+fZuvWrYSHhxu3F6e6eOedd6hQoQKjR49Oc5/iUB/Hjh0zud+3b19q1arFxIkTWbBgARMnTiwW9RATEwMYfvjt2bMHa2trwPCeqVChAhMmTKBfv37Foi6edvnyZQ4ePEjbtm0pX768sby41IWjoyM1atSga9euNG3alIiICJYsWULv3r359ddfad++fbGoi1GjRrFlyxZ69uzJ/PnzqVGjBufPn2fUqFFYWVmh0WiIj48vFnUhREEhXYxywCeffMLixYsZMmQI48ePN5Ynz7qQmJhodkxCQoLJPoVd6dKladeuHa+++iqffvopq1ev5qOPPuKzzz4Dik9drF27ll27drFs2TKz2XpSKi718bQPP/wQa2trtm3bBhSPekie0ezNN980JgdguILctWtX7t27x+XLl4tFXTzt22+/BWDQoEEm5cWhLs6dO0fTpk1p3749c+fOpVu3bgwcOJCDBw/i7e3N4MGD0el0xaIuWrRowfr164mJiaFz586ULVuWLl260Lp1a15++WUAnJ2di0VdCFFQSILwjKZOncqMGTPo378/y5cvN9nm6+sLpN7kmVyWWlNpUVCrVi1eeOEFli5dChSPukhMTGT06NF06tQJb29vrl69ytWrVwkKCgIgKiqKq1ev8vDhw2JRH6mxsrLC19fX2LpUHOqhdOnSAHh7e5tt8/HxASAyMrJY1EVKWq2WNWvWULJkSbp162ayrTjUxVdffUVCQgI9evQwKbe3t6dz584EBQVx8+bNYlEXAD169ODOnTucPn2a/fv3ExwczPLly7lz5w6WlpZUqlSp2NSFEAWBJAjPYOrUqXz66af069ePb775xmyAVc2aNbGxseHIkSNmxx49ehSA+vXr50ms+eHRo0dEREQAxaMuHj16RFhYGNu2baNy5crGW6tWrQBD60LlypX55ptvikV9pCYhIYE7d+4YB/IXh3po2LAh8GQSg5SSyzw9PWnQoAFAmnWhUqmoV69eLkaat3777TdCQ0N56623zLqMFIfXRfIPWp1OZ7ZNq9Ua/y1OrwsLCwvq1KlDixYt8PT05N69e5w+fRp/f3/s7e2LxetCiAIjv6dRKqw+/fRTBVDefvttRafTpbnf66+/rqjVauXMmTPGsuQ5mytXrlzo52xOa3q93bt3K2q12mQ6uqJeF0lJScqmTZvMbkuXLlUApWPHjsqmTZuUy5cvK4pStOsjPDw81fKxY8earY9RlOtBURQlIiJCcXJyUkqVKmUy3WtwcLDi4OCgVKlSxVhWv359xcnJSbl7966x7O7du4qTk5PStm3bPI07t3Xu3FkBlLNnz6a6vai/LkaNGmX2XlAURYmMjFR8fHwUNzc347Svxel1kUyn0yk9evRQVCqVsnv3bmN5UX9dCFFQqBTlqfXcRYaWLFnCiBEjKFOmDNOnT0etNm2I8fLyon379gBcvXqVhg0bYmVlxQcffICzszMrV67k3LlzbNu2jRdffDE/nkKO6datGyEhIbRp04ayZcuSkJDAyZMnWb9+Pfb29uzdu9c4K0VRr4u03Lx5k/LlyzN8+HCTaU+Lcn188MEHHD16lNatW1OmTBliY2PZvn07e/bsoVGjRuzZs8fYN78o10OyFStWMHToUJ5//nkGDBhAUlISy5YtIyQkhN9//50OHToAcPjwYVq3bk3p0qWNs4ItWrSI0NBQDh06RO3atfPzaeSY4OBgypQpQ7169cwGsycr6q+LoKAg6tatS2RkJH369KFZs2ZERESwcuVKbt68yZIlSxg2bBhQ9F8XsbGxNGzYkG7dulG+fHmioqJYt24dJ0+eZObMmUyYMMG4b1F/XQhRYOR3hlIY9evXTwHSvPn7+5vsf+HCBaVr166Ki4uLYmdnpzRr1kzZtWtX/gSfwzZs2KB07txZKV26tGJjY6PY2toqVatWVUaMGKEEBQWZ7V+U6yItqS2Ulqyo1seWLVuUDh06KL6+voqNjY1ib2+v1K5dW5k5c6by6NEjs/2Laj2ktHnzZqVRo0aKvb294ujoqLRv3145ePCg2X6HDx9W2rRpozg4OCiOjo5Khw4dlJMnT+ZDxLln5syZCqCsWLEi3f2K+uvi6tWrSt++fZVSpUoplpaWipOTk9KiRQtl8+bNZvsW5ddFYmKi8sYbbyjlypVTbGxsFDc3N6VDhw7Kn3/+mer+Rf11IURBIC0IQgghhBBCCCMZpCyEEEIIIYQwkgRBCCGEEEIIYSQJghBCCCGEEMJIEgQhhBBCCCGEkSQIQgghhBBCCCNJEIQQQgghhBBGkiAIIYQQQgghjCRBEEIIIYQQQhhJgiCEEEIIIYQwkgRBiAJMpVJl+daqVatciWXq1KmoVCqmTp2aI+e7efMmKpWKcuXK5cj5ROa1atUKlUrF3r178zsUIYQQBZBlfgcghEhbv379zMru3bvHjh070txerVq1XI9L5Ky9e/fSunVr/P395Ue7EEKIfKdSFEXJ7yCEEJmX/GMSIC/fvuHh4YSHh+Pu7o67u/szn0+j0XDt2jWsrKyoWLFiDkRYeOV1gnDr1i3i4+MpU6YM9vb2uf54QgghChdpQRBCZEpOJQbJrKyspLUjn5QpUya/QxBCCFGAyRgEIYqQlOMEbt26xcCBA/Hz88PKyoqAgADjfj///DODBg2iRo0auLm5YWtrS/ny5RkwYACXL1/O8NwpBQYGolKpCAgIIC4ujvHjx1OpUiVsbGzw9vamX79+3L171+x86Y1BSB5PAbB582aaN2+Os7MzDg4ONGvWjO3bt6dZB0FBQQQEBODt7Y2trS2VK1dmypQpJCQkZKvvfWJiInPnzqVevXo4OTlhbW2Nt7c3DRo04KOPPiIiIsLsmEePHjFv3jwaN26Mq6srtra2VK1alY8++ogHDx6Y7NuqVStji9C+fftMxpNkdnyGXq9nxYoVNGvWDFdXV6ysrPD09KR27dqMHDmSmzdvmj3m0/WQ/P+Y0e3pc2m1Wr755htatWpFiRIlsLGxoXz58rz77rvcvn07U/ELIYQoWKQFQYgi6MqVK7zwwgtYW1vTrFkzFEUxufrfs2dPbGxseO6552jTpg1arZZ///2XVatWsXHjRnbu3EnTpk2z9JhRUVE0bdqUW7du0aJFC2rUqMGRI0dYs2YN+/bt459//sHFxSVL55wyZQrTp0+nadOmdOrUiUuXLnH48GFefvllNm/eTLdu3Uz2v3DhAv7+/oSHh+Pr68srr7xCXFwc8+bNY/fu3ej1+iw9vl6vp3Pnzvz11184OzvTokULXF1dCQsL48qVK8ydO5fevXtTokQJ4zHBwcF07NiRc+fOUaJECRo0aICTkxOnTp1i7ty5bNq0ib1791K2bFkAOnbsiK2tLTt27MDLy4uOHTsaz5XZFptBgwaxatUqbG1tad68OR4eHkRERHD9+nUWL15M27ZtM0w2KlWqlOqYFoC7d+/yv//9DwALCwtjeUxMDF27dmXv3r04OjpSr149PDw8OHfuHMuXL2fTpk3s2rWLF154IVPPQwghRAGhCCEKlT179iiAktrbd8qUKcZtb731lpKQkJDqOdavX6/ExsaalOn1emXJkiUKoDz//POKXq9P9dxTpkwxKV+1apXxMV988UUlKirKuC0iIkKpU6eOAiizZs0yOe7GjRsKoJQtW9YsvuTzubq6KkePHk01jipVqpgdV7duXQVQ3njjDZPnfufOHaVq1arG8+7ZsyfVennavn37FEB54YUXlOjoaLPtJ06cUMLDw4339Xq90qxZMwVQBg4caHKMRqNRxowZowBK69atTc6T/H/q7++fqbhSCgoKUgCldOnSSkhIiNn2CxcuKEFBQSZl/v7+ma6HyMhI5fnnn1cAZezYsSbbevfurQDKyy+/rISGhpps++qrrxRAqVy5sqLVarP8vIQQQuQf6WIkRBFUokQJFi9ejI2NTarbe/XqhYODg0mZSqVi2LBhNGnShPPnz3Px4sUsPaaDgwOrVq3C2dnZWObm5sa4ceMAjFegs2LatGk0atTIpGz8+PG4uLjw33//mXRhOXDgAKdOncLR0ZElS5aYPPdSpUoxb968LD9+aGgoAC1atMDJyclse/369SlZsqTx/o4dOzh06BB16tRh+fLlJsdYWlry+eefU6NGDfbs2cO///6b5XjSi7Fu3bp4e3ubba9evXq2xxwkJSXRrVs3zp8/T69evfj888+N2y5evMi6devw9fXlxx9/xNPT0+TYUaNG0alTJ65cucIff/yRrccXQgiRPyRBEKIIateuXYbdea5evcrixYsZNWoUAwcOJCAggICAAOMPzrTGIqSlfv36+Pj4mJVXr14dINVxCBnp0qWLWZmNjQ0VKlQwO+e+ffsAQ5edlF1+knXu3BlXV9csPX7dunWxsLDgu+++Y8mSJYSEhKS7/7Zt2wB47bXXsLQ078GpVqtp2bIlAIcPH85SLGmpVq0aTk5ObN++nZkzZ3Ljxo0cOa+iKAQEBLB3715atmzJ6tWrjeNCALZv346iKLz00kupJk+AcU2OnHquQggh8oaMQRCiCEqvv7lOp2PEiBF8/fXX6U6TGh0dnaXHTOsqdXKLQkJCQpbOl9Vz3rlzB0j/uZctW5aHDx9m+vErVqzIV199xYcffsiIESMYMWIEZcuWpUmTJrz88sv06NEDa2tr4/7Xr18H4JNPPuGTTz5J99xhYWGZjiM9Tk5OrFq1iv79+zNp0iQmTZqEj48PjRs3pmPHjvTu3RtHR8csn3f8+PGsW7eO5557ji1btpi1RiU/12+//ZZvv/023XPl1HMVQgiRNyRBEKIIsrOzS3PbggULWL58Od7e3nz55Zc0bdoULy8vbG1tAejduzfr1q3L8hoLanXON0hm55wpr3JnZVtaRo4cSc+ePdm6dSsHDx7k4MGDrF+/nvXr1zNlyhQOHDhgbDlJHgTdvHnzDNd2eP7557McS1pee+012rVrx9atWzlw4ACHDh3il19+4ZdffmHy5Mns2rWLmjVrZvp8y5YtY86cOfj4+LB9+3bc3NzM9kl+rnXq1KF27drpnu/pbmJCCCEKNkkQhChmNm7cCMDXX39N165dzbZfuXIlr0PKEaVKlQIwm4YzpaCgoGyd28vLi8GDBzN48GAALl26xIABAzhy5Ajjxo1j9erVAPj5+QHwyiuvMHbs2Gw9Vna5uLjw9ttv8/bbbwNw+/ZtRo4cya+//sqIESOMXbAy8ttvvzFy5EicnJzYtm2bcbalpyU/12bNmrF48eKceRJCCCEKBBmDIEQxkzxvf2o//M6fP8+ZM2fyOKKckdy3/88//yQyMtJs+x9//JFqeXZUq1aNjz/+GMCkvl566SUANm3alKUWmORuSlqtNkfiA8MP+E8//dQsxvScOHGCN954A5VKxaZNm9KdnjT5uW7dujVb3ceEEEIUXJIgCFHMJA8aXrJkicm6ACEhIfTt2zdHf6TmpZYtW1K7dm1iYmIYOXIkSUlJxm3BwcGMGTMmy+fcvXs327dvR6PRmJQrisLvv/8OmCZar7zyCg0aNOD48eP0798/1b73kZGRLF++3KSeS5cuDRhab55+rIycPn2aDRs28OjRI7Ntv/32m1mMabl+/Tovv/wy8fHxrFixghdffDHd/V944QVee+01bt++Tffu3VNtuYmLi+OHH34wDnwXQghROEgXIyGKmQkTJvDnn3+ycuVK9uzZQ926dYmOjmbfvn1UqFCBbt268csvv+R3mFmmUqlYu3Yt/v7+/PDDD+zdu5dmzZoRHx/Pnj17qFOnDk2aNOHIkSMmA4vTc/bsWT744AOcnZ2pW7cuvr6+PHr0iFOnThEUFISLiwvTpk0z7q9Wq9myZQudO3dm9erV/PTTT9SuXZsyZcqQlJTE9evXOXfuHDqdjoCAAONMR2XKlKF+/fr8/fff1KxZk/r162Nra4u7uzuzZ89ON8agoCDeeOMN7OzsqFu3Ln5+fmi1Ws6dO8fly5extrY2mZ40LTNnzuT+/ft4eHiwb9++NLskffHFF8YF3FatWsXDhw/5448/qFq1KrVr16Z8+fIoisLNmzf5559/SEpK4uLFi3h5eWWqzoUQQuQ/SRCEKGYaNWrE33//zaRJkzhx4gRbt27Fz8+PkSNHMmnSJEaOHJnfIWZbjRo1OHnyJJMnT2bHjh1s2bIFPz8/3n//fSZNmkSNGjWAzK9Q3KVLF6Kiojhw4ABXrlzh6NGj2NnZ4efnx7hx4xg+fLjx6n8yX19fjh49SmBgIBs2bODs2bMcP36cEiVK4OvryzvvvEPXrl2Ng8KTbd68mfHjx7Nnzx42bNiAVqulbNmyGSYIjRs3Zvbs2ezfv5+LFy9y+vRpLC0tKV26NMOHD2fkyJFUrVo1w+eq0+kAw4xDyWMqUjN16lRj/Tk5ObFz5042bNjA2rVrOXnyJGfOnMHZ2RkfHx/69OlD165dMxywLYQQomBRKVmdqkQIIQqhGzduUKlSJZycnIiIiMiVWZeEEEKIokC+IYUQRUZcXBznz583Kw8KCqJPnz7o9Xr69esnyYEQQgiRDmlBEEIUGTdv3qR8+fJUrFiRKlWq4OzszK1btzh16hSJiYnUrl2b/fv3GxdaE0IIIYQ5SRCEEEVGbGwsn376Kbt37+bWrVs8fPgQe3t7qlatymuvvcbIkSOxt7fP7zCFEEKIAk0SBCGEEEIIIYSRdMQVQgghhBBCGEmCIIQQQgghhDCSBEEIIYQQQghhJAmCEEIIIYQQwkgSBCGEEEIIIYSRJAhCCCGEEEIII0kQhBBCCCGEEEaSIAghhBBCCCGM/g+G/4PYeP/8PQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate means and standard deviations\n",
    "mean_accuracies_Q = [np.mean(sublist) for sublist in accuracies_Q]\n",
    "std_Q = np.array([np.std(sublist) for sublist in accuracies_Q])\n",
    "\n",
    "mean_accuracies_C = [np.mean(sublist) for sublist in accuracies_C]\n",
    "std_C = np.array([np.std(sublist) for sublist in accuracies_C])\n",
    "\n",
    "mean_accuracies_U = [np.mean(sublist) for sublist in accuracies_U]\n",
    "std_U = np.array([np.std(sublist) for sublist in accuracies_U])\n",
    "\n",
    "mean_accuracies_G = [np.mean(sublist) for sublist in accuracies_G]\n",
    "std_G = np.array([np.std(sublist) for sublist in accuracies_G])\n",
    "\n",
    "# Plotting \n",
    "plt.errorbar(data_sizes, mean_accuracies_Q, \n",
    "            yerr=(std_Q/2, std_Q/2), \n",
    "            fmt='-o', \n",
    "            capsize=5, \n",
    "            capthick=2, \n",
    "            ecolor='tab:blue', \n",
    "            label='Quantum',\n",
    "            )\n",
    "plt.errorbar(data_sizes, mean_accuracies_C, \n",
    "            yerr=(std_C/2, std_C/2), \n",
    "            fmt='-o', \n",
    "            capsize=5, \n",
    "            capthick=2, \n",
    "            ecolor='tab:orange', \n",
    "            label='Coherent',\n",
    "            )\n",
    "plt.errorbar(data_sizes, mean_accuracies_U, \n",
    "            yerr=(std_U/2, std_U/2),  \n",
    "            fmt='-o', \n",
    "            capsize=5, \n",
    "            capthick=2, \n",
    "            ecolor='tab:green', \n",
    "            label='Unbunching',\n",
    "            )\n",
    "plt.errorbar(data_sizes, mean_accuracies_G, \n",
    "            yerr=(std_G/2, std_G/2),  \n",
    "            fmt='-o', \n",
    "            capsize=5, \n",
    "            capthick=2, \n",
    "            ecolor='tab:red', \n",
    "            label='Gaussian',\n",
    "            )\n",
    "plt.plot([0, N], [0.5, 0.5], c='black', linestyle='dashed', linewidth=2)\n",
    "plt.xlabel('Training set size', fontsize=16)\n",
    "plt.ylabel('Test set accuracy', fontsize=16)\n",
    "plt.xticks(fontsize=13)\n",
    "plt.yticks(fontsize=13)\n",
    "plt.xlim(18, N - 8)\n",
    "plt.grid(True)\n",
    "plt.legend(bbox_to_anchor=[1, 1], fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "[1] Z. Yin, I. Agresti, G. de Felice, D. Brown, A. Toumi, C. Pentangelo, S. Piacentini, A. Crespi, F. Ceccarelli, R. Osellame, B. Coecke, and P. Walther, \"Experimental quantum-enhanced kernels on a photonic processor,\" *arXiv*, 2024. [Online]. Available: https://arxiv.org/abs/2407.20364\n",
    "\n",
    "[2] S. Aaronson and A. Arkhipov, ‘The Computational Complexity of Linear Optics’, Theory of Computing. vol. 9: pp. 143–252, 2013."
   ]
  }
 ],
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