distance-metrics-in-vector-search.ipynb

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    "# 🚀 Introduction\n",
    "In the world of Large Language Models (LLMs) like ChatGPT, vector similarity search plays a crutial role in efficiently finding and retrieving information that is semantically relevant to a given query. This process is fundamental in applications such as semantic search, recommendation systems and natural language processing tasks where understanding the meaning and context of text is essential. \n",
    "\n",
    "In this notebook I will talk about different distance metrics and their background. \n",
    "\n",
    "But before we start let´s define some key terms. \n",
    "\n",
    "## ❓What is a Vector?\n",
    "\n",
    "In machine learning, a vector is a fundamental data structure used to represent data in a multidimensional space. Vectors are used extensively in varous algorithms and porcesses, from data reprensation to model parameters. It is an array of numbers that can represent various types of information such as text, images or numerical values. \n",
    "\n",
    "## ❓ What are Embeddings?\n",
    "\n",
    "Embeddings are a type of vector representation used primarily in machine learning and natural language processing (NLP) to map discrete objects, such as words or items, into continous vector spaces. These vectors, often referred to as embeddings capture semantic relationships and similarities between the objects in a way that makes them useful for various tasks like classification, clustering and recommendation. \n",
    "\n",
    "<img src='https://miro.medium.com/v2/resize:fit:4800/format:webp/1*dT3594m7X1N0zdZhydw2FA.png'/>\n",
    "Source:<a href='https://towardsdatascience.com/exploring-the-power-of-embeddings-in-machine-learning-18a601238d6b'>The Power of Embeddings in Machine Learning by Rian Dolphin</a>\n",
    "\n",
    "The image above showing how embeddings can capture relationships between words when visualised in lower dimensional space. \n",
    "\n",
    "Here are some key characteristics of Embeddings:\n",
    "1. Dimensionality: Embeddings are represented as vectors in an contiuous space of fixed dimensions, such as 50, 100, 300 etc.\n",
    "2. Semantic Similarity: Objects that are semantically similar or related tend to have embedding vectors that are close to each other in the vector space. \n",
    "3. Learning: Embeddings are learned from data using machine learning models, which means they can capture complex pattterns and relationships inherent in the data. "
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    "# ⚙️ Distance Metrics\n",
    "\n",
    "Distance metrics in vector spaces are mathematical measures used to quantify the similarity od dissimilarity between vectors. The metrics are fundamental in various applications, including machine learning, pattern recognition and clustering. Of course they also play a crucial role in RAG applications combined with LLM´s. Depending on the machine learning model used, the vectors can have more than 1000 dimensions. The `text-embedding-3-large` from OpenAI for example has `3072` dimensions. \n",
    "\n",
    "As the dimension growths also the time to calculate the similarity between two vectors grows. Some simialrity measures are more compute-heave than others. That might be a challenge for calculating distances between vectors with thousands of dimensions. \n",
    "\n",
    "Fortunately, there is a wide selection of string similarity algorithms, each with its own advantages and disadvantages. \n",
    "\n",
    "The following distance metrics will be discussed now:\n",
    "- Cosine Similarity\n",
    "- Dot Product\n",
    "- Euclidean Distance\n",
    "- Manhattan Distance (L1 Norm)\n",
    "- Jaccard Similarity\n",
    "- Hamming Distance\n",
    "\n",
    "Before we start let´s quickly define some small fake vectors for whose we can later calculate the similarity. \n",
    "\n",
    "In this notebook we want to check the similarity between cities.\n",
    "Each of the cities contains three features `[population in mio, average yearly temperature in °C, area in square km]`. The vector representation for the cities are the following:\n",
    "\n",
    "- Toronto `[3, 9, 630]`\n",
    "- New York `[9, 13, 789]`\n",
    "- Moscow `[11, 0, 2510]`\n",
    "- Munich `[2 ,8, 310]`\n",
    "- Cape Town `[0.4, 17, 496]`\n",
    "- Amsterdam `[0.9 ,10, 219]`\n",
    "- Athens `[0.6, 18, 38]`"
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy.linalg import norm\n",
    "from scipy.spatial import distance\n",
    "from scipy.spatial.distance import cdist\n",
    "import plotly.graph_objects as go\n",
    "import spacy\n",
    "from sklearn.manifold import TSNE"
   ]
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    "toronto = [3, 9, 630]\n",
    "new_york = [9, 13, 789]\n",
    "moscow = [11, 0, 2510]\n",
    "munich = [2, 8, 310]\n",
    "cape_town = [0.4, 14, 496]\n",
    "amsterdam = [0.9, 10, 219]\n",
    "athens = [0.6, 18, 38]"
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   "source": [
    "Three dimensional data is still easy to visualize and interpret for the human eye. The following visualization shows the cities in a three dimensional space. "
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   "source": [
    "cities = [\"Toronto\", \"New York\", \"Moscow\", \"Munich\", \"Cape Town\", \"Amsterdam\", \"Athens\"]\n",
    "values = {\n",
    "    \"Toronto\": [3, 9, 630],\n",
    "    \"New York\": [9, 13, 789],\n",
    "    \"Moscow\": [11, 0, 2510],\n",
    "    \"Munich\": [2, 8, 310],\n",
    "    \"Cape Town\": [0.4, 14, 496],\n",
    "    \"Amsterdam\": [0.9, 10, 219],\n",
    "    \"Athens\": [0.6, 18, 38],\n",
    "}\n",
    "\n",
    "x = [values[city][0] for city in cities]\n",
    "y = [values[city][1] for city in cities]\n",
    "z = [values[city][2] for city in cities]\n",
    "fig = go.Figure(data=[go.Scatter3d(\n",
    "    x=x,\n",
    "    y=y,\n",
    "    z=z,\n",
    "    mode='markers+text',\n",
    "    text=cities,\n",
    "    marker=dict(\n",
    "        size=10,\n",
    "        color='blue',                \n",
    "        opacity=0.8\n",
    "    )\n",
    ")])\n",
    "\n",
    "fig.update_layout(\n",
    "    scene=dict(\n",
    "        xaxis_title='Population in million',\n",
    "        yaxis_title='Annual temperature in °C',\n",
    "        zaxis_title='Area in square km'\n",
    "    ),\n",
    "    title=\"3D Scatter Plot of City\",\n",
    "    width=1000,\n",
    "    height=1000\n",
    ")\n",
    "fig.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d471e9b2",
   "metadata": {
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     "start_time": "2024-07-22T10:08:40.090039",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "When selecting the cities, I tried to choose some that are very different from each other. The 3D scatter plot shows this quite well. Moscow can be distinguished very well from the other cities. Mainly because of its size and location. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec40d482",
   "metadata": {
    "papermill": {
     "duration": 0.012029,
     "end_time": "2024-07-22T10:08:40.126771",
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     "start_time": "2024-07-22T10:08:40.114742",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 1️⃣ Cosine Similarity\n",
    "In simple terms, the cosine similarity metric calculates the cosine of the angle between two vectors, which ranges from -1 to 1. A value of 1 indicates that the two vectors are identical, a value of 0 means the vectors are orthogonal (i.e. they are not similar), and a value of -1 idicates that the vectors are exact oppsites. \n",
    "\n",
    "Cosine similarity is advantageous because it is not affected by the magnitude of the vectors, only the direction. This makes it a robust measure of similarity for text data and other high-dimensional spaces.\n",
    "\n",
    "Mathematically, cosine similarity calulates the cosine of the angle between two vectors. The formula for cosine similarity is:\n",
    "$$cos(\\theta) = \\frac{A \\cdot B}{\\left \\| A \\right \\|\\left \\| B \\right \\|}$$\n",
    "where:\n",
    "- $A \\cdot B$ is the dot product of vectors $A$ and $B$\n",
    "- $\\left \\| A \\right \\|$ is the magnitude (or length) of vector $A$\n",
    "- $\\left \\| B \\right \\|$ is the magnitude (or length) of vector $B$\n",
    "- $\\theta$ is the angle between the two vectors\n",
    "\n",
    "The dot product of two vectors $A$ and $B$ is calculated as:\n",
    "$$A \\cdot B = \\sum_{i=1}^{n}A_{i}B{i}$$\n",
    "where $A_{i}$ and $B_{i}$ are the components of vectors $A$ and $B$ respectively, and $n$ is the number of dimensions. \n",
    "\n",
    "The magnitude of a vector $A$ is given by:\n",
    "$$\\left \\| A \\right \\| = \\sqrt{\\sum_{i=1}^{n}A^{2}_{i}}$$\n",
    "\n",
    "Let´s calculate the cosine similarity between Munich and Amsterdam real quick. \n",
    "\n",
    "```\n",
    "munich = [2, 8, 310]\n",
    "amsterdam = [0.9, 10, 219]\n",
    "```\n",
    "$$A \\cdot B = (2*0.9) + (8*10) + (310*219) = 67971.8$$\n",
    "\n",
    "$$\\left \\| A \\right \\| = \\sqrt{2^{2}+ 8^{2} + 310^{2}} = 310.07$$\n",
    "\n",
    "$$\\left \\| B \\right \\| = \\sqrt{0.9^{2}+ 10^{2} + 219^{2}} = 219.19$$\n",
    "\n",
    "$$cosine_similarity(A,B)= \\frac{67971.8}{\\sqrt{310.07} * \\sqrt{219.19}} = 1.00016$$\n",
    "\n",
    "Given that consine similarity values range from -1 to 1, a value of approximately 1.00016 suggests that there might be a slight calculation or rounding error. However in practical term the value is pretty close to 1, indicating that the vectors are alomost similar. That makes a lot of sense to me. Munich and Amsterdam are very similar. At least if you only consider size, climate and number of inhabitants and ignore all other characteristics. \n",
    "\n",
    "Nevertheless, we check the similarity score again by having it calculated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f363f1a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-22T10:08:40.153124Z",
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     "iopub.status.idle": "2024-07-22T10:08:40.159336Z",
     "shell.execute_reply": "2024-07-22T10:08:40.158281Z"
    },
    "papermill": {
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     "exception": false,
     "start_time": "2024-07-22T10:08:40.139175",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cosine Similarity between Munich and Amsterdam: 0.9998006567220649\n"
     ]
    }
   ],
   "source": [
    "A = np.array(munich)\n",
    "B = np.array(amsterdam)\n",
    "cosine = np.dot(A,B)/(norm(A)*norm(B))\n",
    "print(\"Cosine Similarity between Munich and Amsterdam:\", cosine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eb98a84",
   "metadata": {
    "papermill": {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "We get the same results as calculated manually. Let´s try the extrema and compare Moscow and Cape Town. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e88df6a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-22T10:08:40.213820Z",
     "iopub.status.busy": "2024-07-22T10:08:40.213417Z",
     "iopub.status.idle": "2024-07-22T10:08:40.220178Z",
     "shell.execute_reply": "2024-07-22T10:08:40.219199Z"
    },
    "papermill": {
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     "end_time": "2024-07-22T10:08:40.222633",
     "exception": false,
     "start_time": "2024-07-22T10:08:40.199624",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cosine Similarity between Moscow and Cape Town: 0.9995954987454997\n"
     ]
    }
   ],
   "source": [
    "A = np.array(moscow)\n",
    "B = np.array(cape_town)\n",
    "cosine = np.dot(A,B)/(norm(A)*norm(B))\n",
    "print(\"Cosine Similarity between Moscow and Cape Town:\", cosine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4653f89c",
   "metadata": {
    "papermill": {
     "duration": 0.012779,
     "end_time": "2024-07-22T10:08:40.248222",
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     "start_time": "2024-07-22T10:08:40.235443",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Huch, the score is again almost 1. Why?\n",
    "\n",
    "Cosine similarity measues the angle between two vectors, not their magnitude.  This means that it focuses on the direction of the vectors rather than their length. Even if two cities have vastly different features in terms of magnitude, if their features scale similarly, they can still have a high cosine similarity. Cosine similarity might not be the best metric when dealing with features that have vastly different scales or when the magnitude of the vectors plays a crucial role in their comparison. \n",
    "\n",
    "For comparing cities based on features like population, mean temperature, and area, other distance metrics like Euclidean distance might provide more intuitive results.\n",
    "\n",
    "Also note, if you work with normalized vectors, cosine similarity is equivalent to the inner product. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "260e4533",
   "metadata": {
    "papermill": {
     "duration": 0.012436,
     "end_time": "2024-07-22T10:08:40.273753",
     "exception": false,
     "start_time": "2024-07-22T10:08:40.261317",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 2️⃣ Dot Product\n",
    "The dot product, also known as the scalar product, is an algebraic operation that takes two equal-length sequences of numbers (usually vectors) and returns a single number. The dot product shows the alignment of two vectors. The dot product is negative if the vectors are oriented in different directions and positive if the vectors are orientied in the same direction. \n",
    "\n",
    "As already mentions in the cosine similarity section, the dot product is defined as:\n",
    "\n",
    "$$A \\cdot B = \\sum_{i=1}^{n}A_{i}B{i}$$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ae621926",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-22T10:08:40.301327Z",
     "iopub.status.busy": "2024-07-22T10:08:40.300878Z",
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     "shell.execute_reply": "2024-07-22T10:08:40.306205Z"
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    "tags": []
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dot Product between Munich and Amsterdam: 67971.8\n"
     ]
    }
   ],
   "source": [
    "A = np.array(munich)\n",
    "B = np.array(amsterdam)\n",
    "dot_prod = np.dot(A, B)\n",
    "print(\"Dot Product between Munich and Amsterdam:\", dot_prod)"
   ]
  },
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     "exception": false,
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     "status": "completed"
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   "source": [
    "Since the dot product is positive, it means that the vectors are directed in the same direction. With a three-dimensional array, it is quite difficult to get a dot product between two cities that is negative. \n",
    "\n",
    "For demonstration purposes, let's define a fictitious city that has only negative features and calculate the dot product. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "810d6a9f",
   "metadata": {
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dot Product between Munich and Gigacity: -3101000\n"
     ]
    }
   ],
   "source": [
    "gigacity = [-100, -100, -10000]\n",
    "A = np.array(munich)\n",
    "B = np.array(gigacity)\n",
    "dot_prod = np.dot(A, B)\n",
    "print(\"Dot Product between Munich and Gigacity:\", dot_prod)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e6cb94b",
   "metadata": {
    "papermill": {
     "duration": 0.012383,
     "end_time": "2024-07-22T10:08:40.397628",
     "exception": false,
     "start_time": "2024-07-22T10:08:40.385245",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "And now we have a negative dot product which indicates oppsosite vectors. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b7a4daa",
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   },
   "source": [
    "## 3️⃣ Euclidean Distance\n",
    "\n",
    "Euclidean distance is one of the most commonly used metrics to measure the \"straight-line\" distance between two points in a multi-dimensional space. It's derived from the Pythagorean theorem and provides a way to quantify the distance between two points with coordinates in any dimensional space.\n",
    "\n",
    "Euclidian distance is always non-negative, i.e. $distance(A,B) \\geq 0$. The distance is zero if and only if the points $A$ and $B$ are identical. Also the distance from $A$ to $B$ is the same as the distance from $B$ to $A$. \n",
    "\n",
    "The Euclidian distance between two points $A$ and $B$ in an n-dimensional space is given by:\n",
    "\n",
    "$$\\sqrt{\\sum_{i=1}^{n}(A_{i} - B_{i})^2}$$\n",
    "\n",
    "Where $A_{i}$ and $B_{i}$ are the coordinates of the points $A$ and $B$ in the i-th dimension, respectively. \n",
    "\n",
    "So consider Munich ($A$) and Amsterdam ($B$) array again:\n",
    "\n",
    "$$A=(x_{1},y_{1},z_{1}) = (2, 8, 310)$$ \n",
    "$$B=(x_{2},y_{2},z_{2}) = (0.9, 10, 219)$$\n",
    "\n",
    "Calculate the difference and square each diffeernce:\n",
    "\n",
    "$$ (x_{2} - x_{1}) = (0.9-2) = -1.1^{2} = -1.21$$\n",
    "$$(y_{2} - y_{1}) = (10-8) = 2^{2} = 4$$\n",
    "$$(z_{2} - z_{1}) = (219-310) = -91^{2} = -8281$$\n",
    "\n",
    "Sum the squared difference and take the square root of the sum (we can use the absolute value):\n",
    "\n",
    "$$1.21 + 4 + 8281 = \\sqrt{8286.21}= 91.0286218725$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f6f74c8",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "Check our manual calculation again. "
   ]
  },
  {
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   "execution_count": 9,
   "id": "8c329270",
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    "execution": {
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     "shell.execute_reply": "2024-07-22T10:08:40.484188Z"
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Euclidean Distance between Munich and Amsterdam: 91.02862187246382\n"
     ]
    }
   ],
   "source": [
    "A = np.array(munich)\n",
    "B = np.array(amsterdam)\n",
    "dist = np.linalg.norm(A - B)\n",
    "print(f\"Euclidean Distance between Munich and Amsterdam: {dist}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3e06a0c2",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Euclidean Distance between Moscow and Cape Town: 2014.0765526662585\n"
     ]
    }
   ],
   "source": [
    "A = np.array(moscow)\n",
    "B = np.array(cape_town)\n",
    "dist = np.linalg.norm(A - B)\n",
    "print(f\"Euclidean Distance between Moscow and Cape Town: {dist}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7d7ca8",
   "metadata": {
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   "source": [
    "This time we have a much higher distance between Moscow and Cape Town. Munich and Amsterdam is much closer compared to the other two cities. \n",
    "\n",
    "The Euclidean distance has some major limitations:\n",
    "- In high-dimensional spaces, Euclidean distance can become less meaningful due to the \"curse of dimensionality\". Distances between points tend to become more similar, reducing discriminative power. \n",
    "- If the features (dimensions) have different units or vastly different ranges, Euclidean distance can be misleading. It´s often necessary to normalize or standardize features before calculating the distance. \n",
    "- Since Euclidean distance squares the differences, it can be havily influences by outliers or large difference in certain dimensions. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b21790ff",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "## 4️⃣ Manhattan Distance (L1 Norm)\n",
    "\n",
    "Manhattan distance, also kown as L1 norm or taxicab distance, is a distance metrics used to measure the distance between two points in a grid-based system. Unlike Euclidean distance, which measures the shortest straight-line distance, Manhattan distance measures the distance by summing the absolute differences of their Cartesian coordinates. \n",
    "\n",
    "Mathematically the Manhattan distance between two points $A$ and $B$ in an n-dimensional space is defined as:\n",
    "\n",
    "$$\\sum_{i=1}^{n}\\left | A_{i} - B_{i} \\right |$$\n",
    "\n",
    "where:\n",
    "\n",
    "- $A = (A_{1}, A_{2}, ..., A_{n})$ and $B = (B_{1}, B_{2}, ..., B_{n})$ are the coordinates of the points $A$ and $B$ in the n-dimensional space\n",
    "- $\\left | A_{i} - B_{i} \\right |$ is the absolute difference between the i-th coordinates of points $A$ and $B$. \n",
    "\n",
    "We calculate the Manhattan distance between Munich and Amsterdam again. \n",
    "\n",
    "$$A=(x_{1},y_{1},z_{1}) = (2, 8, 310)$$ \n",
    "$$B=(x_{2},y_{2},z_{2}) = (0.9, 10, 219)$$\n",
    "\n",
    "Calculate the absolute differences:\n",
    "\n",
    "- $\\left | x_{2} - x_{1} \\right | = \\left | 0.9 - 2 \\right | = 1.1$\n",
    "- $\\left | y_{2} - y_{1} \\right | = \\left | 10 - 8 \\right | = 2$\n",
    "- $\\left | z_{2} - z_{1} \\right | = \\left | 219 - 310 \\right | = 91$\n",
    "\n",
    "And then sum those values $1.1 + 2 + 91 = 94.1$. Therefore the Manhattan distance between Munich and Amsterdam is 94.1"
   ]
  },
  {
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   "id": "7de7f345",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manhattan Distance between Munich and Amsterdam: 94.1\n"
     ]
    }
   ],
   "source": [
    "A = np.array(munich)\n",
    "B = np.array(amsterdam)\n",
    "print(f\"Manhattan Distance between Munich and Amsterdam: {np.sum(np.abs(A - B))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5b2e293d",
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    "execution": {
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     "shell.execute_reply": "2024-07-22T10:08:40.649520Z"
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manhattan Distance between Moscow and Cape Town: 2038.6\n"
     ]
    }
   ],
   "source": [
    "A = np.array(moscow)\n",
    "B = np.array(cape_town)\n",
    "print(f\"Manhattan Distance between Moscow and Cape Town: {np.sum(np.abs(A - B))}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78672835",
   "metadata": {
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     "end_time": "2024-07-22T10:08:40.679501",
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   "source": [
    "## 5️⃣ Jaccard Similarity\n",
    "\n",
    "Jaccard similarity, also known as the Jaccard index or Jaccard coefficient, is a measure of similarity between two sets. It is defined as the size of the intersection of the sets divided by the size of the union of the sets. The Jaccard similarity ranges from 0 to 1, where 0 means no similarity and 1 means identical sets.\n",
    "\n",
    "For two sets $A$ and $B$, the Jaccard similarity $J(A,B)$ is given by:\n",
    "\n",
    "$$J(A,B) = \\frac{\\left | A \\cap B \\right |}{\\left | A \\cup B \\right |}$$\n",
    "\n",
    "where:\n",
    "\n",
    "- $\\left | A \\cap B \\right |$ is the number of elements in the inersection of $A$ and $B$\n",
    "- $\\left | A \\cup B \\right |$ is the number of elements in the union of $A$ and $B$\n",
    "\n",
    "Jaccard similarity is primarily designed for comparing sets or binary vectors, where it measures the overlap between two sets. Applying Jaccard similarity to numerical vectors is less straightforward, as it doesn't directly account for the magnitude of the elements, only their presence or absence. There are some approaches that Jaccard can work with numerical vectors but we will loose a lot of information due to some transformations. \n",
    "\n",
    "For numerical vectors, it is generally better to use similarity measures like cosine similarity, Euclidean distance or Manhattan distance, which are designed to handle the magnitude and continous nature of numerical data more appropriately. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8651e860",
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   "source": [
    "## 6️⃣ Hamming Distance\n",
    "\n",
    "Hamming distance is a metric used to measure the difference between two strings of equal length. It is defined as the number of positions at which the corresponding symbols in the two strings are different. \n",
    "\n",
    "Mathematically, it can be represented as:\n",
    "\n",
    "$$d_{H}(A,B) = \\sum_{i=1}^{n}\\delta (A_{i},B_{i})$$\n",
    "\n",
    "where:\n",
    "- $A$ and $B$ are the strings of length n\n",
    "- $A_{i}$ and $B_{i}$ are the symbols at position $i$ in strings $A$ and $B$, respectively\n",
    "- $\\delta (A_{i},B_{i})$ is an indicator function that equals 1 if $A_{i} \\neq B_{i}$ and 0 if $A_{i} = B_{i}$\n",
    "\n",
    "Consider two strings, `A = 101101` and `B = 110110` the hamming distance is 4 because the strings differ in 4 positions. \n",
    "\n",
    "We can also apply the hamming distance to an numeric vector and count how many times the number at the current index differs from the other value. \n",
    "\n",
    "The distance between munich = `[2, 8, 310]` and amsterdam = `[0.9, 10, 219]` is 3. \n",
    "\n",
    "The distance between moscow = `[11, 0, 2510]` and cape_town = `[0.4, 14, 496]` is also 3. \n",
    "\n",
    "Also the Hamming distance has some serious downsides. It is only defined for vectors of equal length. If the vectors are of different length, they must be padded or truncated to the same length before computing the distance. Hamming distance only counts the number of differing positions and does not account for the magnitude of differences. For numeric data where the magnitude of difference is important, other metrics are more appropriate. "
   ]
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   "source": [
    "# ⏭️ One Step further\n",
    "\n",
    "In the upper sections, we have worked with very simple three-dimensional vectors for easy calculation of the values. \n",
    "\n",
    "As I said at the beginning of the notebook, embeddings have far more than these three dimensions. These can go well into the hundreds and can no longer be represented so easily. \n",
    "\n",
    "In this section we work with real vectors. Once again, I'll simply try to visualize the concept of embeddings. We use [SpaCy](https://spacy.io/) for this. \n",
    "\n",
    "SpaCy is an open-source software library for advanced natural language processing (NLP) in Python. It's designed specifically for production use and provides a variety of features and tools to handle different aspects of NLP tasks. Here are some key aspects of SpaCy:\n",
    "\n",
    "- Tokenization: Breaking down text into individual tokens (words, punctuation, etc.).\n",
    "- Part-of-Speech (POS) Tagging: Assigning grammatical categories (like noun, verb, adjective) to each token.\n",
    "- Named Entity Recognition (NER): Identifying and classifying named entities (like people, organizations, locations) in the text.\n",
    "- Dependency Parsing: Analyzing the grammatical structure of a sentence and establishing relationships between words.\n",
    "- Lemmatization: Reducing words to their base or root form.\n",
    "- Sentence Boundary Detection (SBD): Identifying sentence boundaries in the text.\n",
    "- Word Vectors and Similarity: Using pre-trained word vectors to compute similarities between words or phrases.\n",
    "- Text Classification: Categorizing text into predefined labels or categories.\n",
    "\n",
    "SpaCy is known for its high performance and efficiency, making it suitable for real-world applications that require processing large volumes of text quickly.\n",
    "\n",
    "In our case, only the pre-trained vectors are of interest. "
   ]
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   "source": [
    "# Some random words\n",
    "words = [\n",
    "    'cat', 'dog', 'fish', 'kitten', 'man', 'woman', 'king', 'queen', 'doctor', 'nurse',\n",
    "    'apple', 'banana', 'orange', 'grape', 'strawberry', 'car', 'bus', 'train', 'bicycle', 'airplane',\n",
    "    'computer', 'laptop', 'phone', 'tablet', 'mouse', 'keyboard', 'television', 'radio', 'book', 'magazine',\n",
    "    'student', 'teacher', 'professor', 'school', 'university', 'scientist', 'engineer', 'lawyer', 'judge', 'police',\n",
    "    'house', 'apartment', 'building', 'city', 'village', 'forest', 'river', 'mountain', 'beach', 'desert',\n",
    "    'love', 'hate', 'happy', 'sad', 'angry', 'excited', 'bored', 'tired', 'hungry', 'thirsty',\n",
    "    'run', 'jump', 'swim', 'walk', 'fly', 'drive', 'write', 'read', 'sing', 'dance',\n",
    "    'cat', 'dog', 'bird', 'fish', 'elephant', 'lion', 'tiger', 'bear', 'wolf', 'fox',\n",
    "    'flower', 'tree', 'grass', 'bush', 'leaf', 'root', 'stem', 'petal', 'seed', 'fruit',\n",
    "    'bread', 'butter', 'cheese', 'milk', 'egg', 'meat', 'chicken', 'fish', 'rice', 'pasta',\n",
    "    'morning', 'afternoon', 'evening', 'night', 'day', 'week', 'month', 'year', 'today', 'tomorrow',\n",
    "    'horse', 'cow', 'pig', 'sheep', 'goat', 'chicken', 'duck', 'goose', 'turkey', 'rabbit',\n",
    "    'lion', 'tiger', 'cheetah', 'leopard', 'jaguar', 'panther', 'hyena', 'wolf', 'fox', 'bear',\n",
    "    'whale', 'dolphin', 'shark', 'octopus', 'squid', 'lobster', 'crab', 'shrimp', 'jellyfish', 'starfish',\n",
    "    'eagle', 'hawk', 'falcon', 'owl', 'parrot', 'penguin', 'flamingo', 'sparrow', 'robin', 'canary',\n",
    "    'rose', 'lily', 'tulip', 'daisy', 'sunflower', 'orchid', 'poppy', 'violet', 'lavender', 'daffodil',\n",
    "    'oak', 'pine', 'birch', 'maple', 'willow', 'cedar', 'spruce', 'fir', 'cypress', 'redwood',\n",
    "    'carrot', 'potato', 'tomato', 'cucumber', 'lettuce', 'spinach', 'broccoli', 'cauliflower', 'pepper', 'onion',\n",
    "    'pizza', 'burger', 'sandwich', 'salad', 'soup', 'steak', 'pasta', 'sushi', 'taco', 'burrito',\n",
    "    'piano', 'guitar', 'violin', 'drums', 'flute', 'trumpet', 'saxophone', 'clarinet', 'cello', 'harp',\n",
    "    'winter', 'spring', 'summer', 'fall', 'autumn', 'snow', 'rain', 'sun', 'cloud', 'storm'\n",
    "]"
   ]
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  {
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   "source": [
    "# Load the biggest english model from SpaCy\n",
    "nlp = spacy.load(\"en_core_web_lg\")\n",
    "# Create embeddings using SpaCy\n",
    "embeddings = np.array([nlp(word).vector for word in words])"
   ]
  },
  {
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   "execution_count": 15,
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     "iopub.status.busy": "2024-07-22T10:08:47.929599Z",
     "iopub.status.idle": "2024-07-22T10:08:47.935692Z",
     "shell.execute_reply": "2024-07-22T10:08:47.934568Z"
    },
    "papermill": {
     "duration": 0.023615,
     "end_time": "2024-07-22T10:08:47.938255",
     "exception": false,
     "start_time": "2024-07-22T10:08:47.914640",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of dimensions per word vector: 300\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of dimensions per word vector: {len(embeddings[0])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57472f49",
   "metadata": {
    "papermill": {
     "duration": 0.012997,
     "end_time": "2024-07-22T10:08:47.964797",
     "exception": false,
     "start_time": "2024-07-22T10:08:47.951800",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "With the SpaCy large model we will get 300 dimensional word embeddings. Since we can not visualize data in such spaces, we can use t-distributed stochastic neighbor embedding (tSNE) to convert high-dimensional data into low-dimensional spaces. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4fdff704",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-22T10:08:47.993199Z",
     "iopub.status.busy": "2024-07-22T10:08:47.992758Z",
     "iopub.status.idle": "2024-07-22T10:08:49.239506Z",
     "shell.execute_reply": "2024-07-22T10:08:49.238251Z"
    },
    "papermill": {
     "duration": 1.264594,
     "end_time": "2024-07-22T10:08:49.242772",
     "exception": false,
     "start_time": "2024-07-22T10:08:47.978178",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "tsne = TSNE(n_components=2, random_state=42)\n",
    "embeddings_2d = tsne.fit_transform(embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "29037283",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2024-07-22T10:08:49.273254Z",
     "iopub.status.busy": "2024-07-22T10:08:49.272807Z",
     "iopub.status.idle": "2024-07-22T10:08:50.595308Z",
     "shell.execute_reply": "2024-07-22T10:08:50.594035Z"
    },
    "jupyter": {
     "source_hidden": true
    },
    "papermill": {
     "duration": 1.34133,
     "end_time": "2024-07-22T10:08:50.599443",
     "exception": false,
     "start_time": "2024-07-22T10:08:49.258113",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AgkUiInLHKSgI8s4771hlIvj4+FyyndKlS1O1alVzxbGChIeHExgYyOzZs6+8s4XQrVs35s6dy4oVK9i5cyeGYVxyClouGxsbWrRoQYsWLZg6dSpvvvkmo0aNIjo6mocfftjMXLhwNS3Im2GzdetWdu3axdy5c+nTp4+5/eeff77qMQ0dOpRNmzaxZs2aPNlVJ0+eZMWKFYwbN44xY8aY23fv3p2nncIUI8/l5+fHP//8k2f733//be6/VuXKlcPR0bHA69jY2OTJkimsgIAA3n77bX755RfKli1LzZo1sVgs1K5dm5iYGGJiYujQoYN5fO54CupL2bJlcXJyuuQ1c9vYvXu3VcbOsWPHLplhlatq1ar88ssvPPTQQ9c9EJWf7Oxs/v33X6usu127dgGY2TeF7dOVjN3Pzy/f+zO/1/5q5fZnz549VtNXMzMziY2NpV69elbHOzk50a1bN7p160Z6ejohISG88cYbvPrqq5QqVeq69UtERIonTUMTEZE7Tu4b4IuDIPfee6/VFLBatWoBsHnz5nxXKtu/fz87duy47FSS5s2bExgYyNtvv01aWtr1GcQFHn74YcqUKcOiRYtYtGgRjRo1yjNd6GKJiYl5ttWvXx/ArMFUtWpVAKtVtrKysvjwww+tzsvNeLgww8EwDKZPn37lgwE+/fRTZs+ezfvvv29V4+VS1wOYNm1anmML+l7np127dvzvf/9j/fr15rbTp0/z4Ycf4u/vb94P18LW1pZWrVrxzTffmNOeIGcltoULF9K0aVNcXV2vqu2AgADOnTvHtGnTaNq0qRkoCwgIYP78+Rw6dMiscwM5dZLq16/P3LlzrV6fbdu2sXz5ctq1a3fZaz788MPY29vz3nvvWX0/8vte5Kdr165kZWXlO5UzMzOzUN+3KzVjxgzzsWEYzJgxA3t7e1q0aHFFfbqSsbdr147ffvuN//3vf+a2Y8eOFZi9dDXuu+8+PDw8+Oijj8jMzDS3L1iwIE/w6sSJE1bPS5QoQa1atTAMg4yMjOvWJxERKb6UWSQiInece++9F4BRo0bx+OOPY29vT8eOHQvMovj5558ZO3YsjzzyCI0bN8bZ2Zl///2XTz75hHPnzhEeHn7Za44dO/aSxap37drFZ599lme7p6cnLVu2vGTb9vb2hISE8Pnnn3P69GmmTJly2f6MHz+eNWvW0L59e/z8/Dh69CgzZ87E19fXrGlTu3ZtGjduzKuvvkpiYiJlypTh888/t3ojClCzZk2qVq3Kiy++SHx8PK6urnz11VeFyiy52PHjxxkyZAi1atWiZMmSeV6TLl264OrqSrNmzZg0aRIZGRlUqFCB5cuXs2/fvjztXcn3+pVXXiEyMpK2bdsydOhQypQpw9y5c9m3bx9fffWV1RLs12LChAn8/PPPNG3alCFDhmBnZ8fs2bM5d+4ckyZNuup2H3zwQezs7Pjnn394+umnze3NmjVj1qxZAFbBIoDJkyfTtm1bHnzwQfr378/Zs2d57733cHNzK9R9Xa5cOV588UUmTpxIhw4daNeuHX/99RdLly6lbNmylz2/efPmDBw4kIkTJ7Jp0yZatWqFvb09u3fv5ssvv2T69OmEhoZe2QtxCaVKleKnn36ib9++PPDAAyxdupQffviBkSNHmtPLCtunKxn7iBEjmD9/Pm3atGHYsGE4OTnx4Ycf4ufnx5YtW67L2EqUKEF4eDjPPvsswcHBdO3aldjYWCIiIqhatapVll2rVq3w8vLioYcewtPTk507dzJjxgzat2+Pi4vLdemPiIgUc0WzCJuIiEjRev31140KFSoYNjY2BS6tnuvff/81xowZYzRu3NgoX768YWdnZ5QrV85o3769sXLlSqtjc5cYz29J+ubNmxuA0b59e6vtQIFfFy53fSk///yzARgWi8U4ePBgnv0XL1m/YsUKo1OnToaPj49RokQJw8fHx+jevbuxa9cuq/P27t1rPPzww0bJkiUNT09PY+TIkea1LlyWfMeOHcbDDz9sODs7G2XLljWeeuopY/PmzQZgfPrppwX2wzCsl7DPXS6+oK/c71NcXJzRpUsXw93d3XBzczMee+wx49ChQwZgjB071qr9gr7XF173wvGGhoYa7u7uRqlSpYxGjRoZ33//vdUxuUuZf/nll1bbc/t+4XgLsnHjRqN169aGs7Oz4ejoaAQFBVktCX9he5MnT75se7nuv//+PEu0x8XFGYBRsWLFfM/55ZdfjIceeshwcHAwXF1djY4dOxo7duywOuZS93VWVpYxbtw4w9vb23BwcDACAwONbdu25fv6FuTDDz807r33XsPBwcFwcXEx6tata4wYMcI4dOiQeYyfn1+enx3DyPn5eeaZZ6y25ffa9e3b13BycjL27t1rtGrVynB0dDQ8PT2NsWPHGllZWVfVpysZ+5YtW4zmzZsbpUqVMipUqGC8/vrrxscff5zn90/z5s2tfu6v9H579913DT8/P6NkyZJGo0aNjHXr1hn33nuv0aZNG/OY2bNnG82aNTM8PDyMkiVLGlWrVjVeeukl49SpU3leBxERuTNZDKMIqjCKiIiIiMgNl52dTbly5QgJCeGjjz4q6u6IiEgxoZpFIiIiIiK3gbS0tDy1vObNm0diYiKBgYFF0ykRESmWlFkkIiIiInIbWLVqFc899xyPPfYYHh4ebNy4kY8//pi7776bP//8kxIlShR1F0VEpJhQgWsRERERkduAv78/FStW5N133zWL0vfp04e33npLgSIREbkiyiwSERERERERERGTahaJiIiIiIiIiIhJwSIRERERERERETGpZtFFsrOzOXToEC4uLlgslqLujoiIiIiIiIjIdWEYBikpKfj4+GBjU3D+kIJFFzl06BAVK1Ys6m6IiIiIiIiIiNwQBw8exNfXt8D9ChZdxMXFBch54VxdXa+6nYyMDJYvX06rVq2wt7e/Xt2T25DuFSkM3SdSWLpXpDB0n0hh6V6RwtB9IoWle6XoJScnU7FiRTP2URAFiy6SO/XM1dX1moNFjo6OuLq66odALkn3ihSG7hMpLN0rUhi6T6SwdK9IYeg+kcLSvXLruFzZHRW4FhERERERERERk4JFIiIiIiIiIiJiUrBIRERERERERERMChaJiIiIiIiIiIhJwSIRERERERERETEpWCQiIiIiIiIiIiYFi0RERERERERExKRgkYiIiIiIiIiImBQsEhERERERERERk4JFIiIiIiIiIiJiUrBIRERERERERERMChaJiIiIiIiIiIhJwSIRERERERERETEpWCQiIiIiIiIiIiYFi0RERERERERExKRgkYiIiIiIiIiImBQsEhERERERERERk4JFIiIiIiIiIiJiUrBIRERERERERERMChaJiIiIiIiIiIhJwSIRERERERERETEpWCQiIiIiIiIiIiYFi0REROS6sbOz49VXXy3qboiIiIjINVCwSERERERERERETAoWiYiISLFz5syZou6CiIiIyG1LwSIRERG5rv744w8cHR2xWCyULVuWjRs3ArB9+3b8/f2xtbXF1taWBg0akJycDEBCQgLe3t7Y2NhgsVhwc3Pjiy++MNsMDAzE09OT2rVrY2NjQ7NmzYpkbCIiIiJ3AgWLRERE5LpatWoVixcv5tixY7i5udGiRQuys7N54IEHKFu2LEeOHOHvv/8mNjaW9u3bA5CZmUnXrl1JSEjg5MmTVK5cmV69epGdnW22e/ToUR588EHOnDnDTz/9VFTDExEREbnt2RV1B0RERKR4S09PZ+bMmezduxfDMHj44Ydp164dAN988w1169Zl5syZnDlzht9++w07OzvKli3Lyy+/zGuvvQaAr68v06dPN9v85ptv8Pf3Z+PGjdx3330AlCpVijlz5piPRUREROTGULBIRERErtqIESOYOvVdsrLOmduWLfuZESNGMGnSJOrUqQPADz/8gGEY2Nvb59tOYmIizZo14++//yYrK8vc/u+//5rBIjc3txs4EhERERHJpWloIiIiclVGjBjB5MmTycpqCawHUsj508KDyZMnM2LECLZv3w5Ax44dsbGxwTCMPF8AXbt2JTY2lvXr12MYBrGxsQDmfgCLxXJTxyciIiJyp1KwSERERK5Yeno6U6e+C3QAvgEaA86ABTgJNGLKlOl06tQJNzc3BgwYgIODA02bNuXQoUNkZ2ezbt06xo8fD0BycjJ2dnb4+fmRkJBg1jISERERkZtPwSIRERG5YjNnzjw/9WwUef+cCAQ2YxjpHD58mJ9//pkSJUqwYcMGjhw5QsWKFbG1taVFixZs2bIFgDlz5mCxWPD09MTX15emTZve3AGJiIiIiEk1i0REROSK7d279/yjOhftyTz/bwrgypNPPsn9998PQO3atdm9e3e+7dWrV4+TJ09abfvggw/Mx6tWrbrmPouIiIhI4SizSERERK5Y1apVzz/aVsAR2y46TkRERESKCwWLRERE5IoNGTIEW9uSwBtA9kV7s4E3sbUtxZAhQ25+50RERETkmihYJCIiIlesRIkSPP/8UOB7oBP/rYa2/vzz73n++WcpUaJEEfZSRERERK6GahaJiIjIVZk0aRIAU6e+S1bW9+Z2W9tSPP/8S+Z+ERERESleFCwSERGRqzZp0iQmTJjAzJkz2bt3L1WrVmXIkCHKKBIREREpxhQsEhERkWtSokQJhg8fXtTdEBEREZHrRDWLRERERERERETEpGCRiIiIiIiIiIiYFCwSERERERERERGTgkUiIiIiIiIiImJSsEhERK6bqVOnUqlSJVxcXPD392fOnDmEh4fTuXNnq+Pc3d1ZtWoVAOHh4XTs2JGwsDDc3d2pVKkSixYtuvmdFxERERERQKuhiYjINcjKyiImJobDhw+TmZnJ6NGj2bhxIzVr1uTIkSMcOXKEuLi4y7azbNkyPvvsM6ZPn86CBQsYMGAA7dq1w8XF5SaMQkRERERELqTMIhERuSpRUVH4+1cjKCiIHj160KdPH86eTePjjz/m7NmzeHp6Uq9evUK11bBhQ7p27YqtrS29e/cmPT2dXbt23eARiIiIiIhIfhQsEhGRKxYVFUVoaChxcXWB9UDK+X8bMmXKFDw8PGjVqhWbNm0qVHteXl7mY4vFgoODAykpKTeg5yIiIiIicjkKFomIyBXJyspi2LAXMIwOwBKgMeB8/t//YbF0pEyZ8tStW5fevXvj7OzMmTNnzPNPnz5NcnJykfRdREREREQuT8EiERG5IjExMcTFxQIjsf5v5B9gBYbxPPHx+0lMTMTOzo6GDRuyfv16/v77b9LS0hg5ciQWi6VI+i4iIiIiIpenYJGIyB3A2dmZrVu3Xpe2Dh8+fP5RnYv2pAOvAY8A8NtvvxEREUFwcDADBw6kSZMmVKtWjbp166pwtYiIiIjILUyroYmI3AFSU1OvW1ve3t7nH20jZ+pZrrrAb+TULmrCrFmzuOeeewCYMmUKU6ZMMY8cMGCA+Tg8PDzPNZKSkq5bf0VERERE5Moos0hEpJgzDIOsrKybdr2AgAB8ff2xWN4Esi/am43FMpGKFSsTEBBw0/okIiIiIiLXj4JFIiLFxDfffEPVqlVxcXHBzs6OkJAQqlatiq2tLa1atcLOzg4bGxtKly7Na6+9hmEYGIbBO++8g8ViwdXVlTZt2jBs2DA6duxI69atsbOzo1KlSixatIhHH330gqwheOGFF3j22Wfz9MPW1pbp098Bvsdi6cyFq6HlPP+eadOmYGtre1NeFxERERERub4ULBIRKQZ27drFggULeP311/nwww8pU6YMGzduZMCAAdjY2FCpUiU2b97MihUrSElJYdasWSxcuJD58+czdepUAH7++Wdq167NwoULWbZsGY8++igAo0aNYsCAAaxZs4ZSpUqxc+dOAFauXElwcHC+/QkJCWHx4sVUqLAVaAK4Ak3w9d3G4sWLCQkJuQmvioiIiIiI3AiqWSQicgvLysoiJiaGzz77DICnn36a9PR0ANzcShMfH0+5cuX49NNPAahduzaVK1emRo0arFq1itjYWIYOHcorr7xCyZIlefPNN5kxYwbVq1fn6aefZvr06fj6+nLu3DkqVapEcHAw0dHReHp6sm3bNgIDAwvsW0hICJ06dSImJobDhw/j7e1NQECAMopERERERIo5BYtERG5RUVFRDBv2AnFxsTg4ODB06FCmTl0B7AHOceqUN++//z7+/v4sW7aMcePGsWvXLk6ePElsbCwdOnQgLi4Of39/s82SJUvi4uKCo6MjAEFBQaxatQpbW1vq1atHixYtWLBgAZ6entSrV4/SpUtfso+2traXDCiJiIiIiEjxo2loIiK3oKioKEJDQ4mLqwP4AG1p2rQpJUtuAI4ApYBUoD5xcfGEhIQwcOBA4uPjqVu3LkFBQRiGga+vL7GxsWa76enppKSk4ODgAOQEi6Kjo8nMzKRBgwYEBgYSExPDihUrCAoKuunjFhERERGRolesgkVr1qyhY8eO+Pj4YLFYWLJkidX+fv36YbFYrL7atGlTNJ0VEblKWVlZDBv2AobRAXgOOER2diibNm3CMM4CJQALOb/C25OZmUFaWhoeHh6ULFmS06dP89tvvwHQq1cvZsyYAeQEikaPHo2Li4uZMdS8eXM2b95MVlYWdevWxd3dHV9fXxYsWFBgvSIREREREbm9Fatg0enTp7nnnnt4//33CzymTZs2HD582PyKjIy8iT0UEbl2MTExxMXFAiPJySICqMTChQtJS/MFPIA0YCjgC+QEy59++mlcXV05evQojRo1AqBPnz7mimYtWrRg8+bN9OjRA4vFAkDZsmWpVasWNjY2ZrZRixYtOHPmDM2aNbtJIxYRERERkVtJsapZ1LZtW9q2bXvJY0qWLImXl9dN6pGIyPV3+PDh84/qkBMUAhsbC5MmTaJ793acPWt/wdHrAejduzcff/xxvu2NGDGCESNGFHi9zZs3Wz1/++23efvtt6+2+yIiIiIiUswVq2BRYaxatYry5ctTunRpgoODmTBhAh4eHgUef+7cOc6dO2c+T05OBiAjI4OMjIyr7kfuudfShtwZdK/Ixby8vM5n+WwBGgM1cHB4F+iOg8OF90k2FssUKlSoSePGjXUPCaDfKVI4uk+ksHSvSGHoPpHC0r1S9Ar72lsMwzBucF9uCIvFwtdff03nzp3NbZ9//jmOjo5UrlyZvXv3MnLkSJydnVm/fn2BSzmHh4czbty4PNsXLlxorhYkIiIiIiIiIlLcnTlzhh49enDq1ClcXV0LPO62ChZd7N9//6Vq1ar88ssvtGjRIt9j8sssqlixIsePH7/kC3c5GRkZ/Pzzz7Rs2RJ7e/vLnyB3LN0rkp/vvvuO3r17A20wjOdxcIjlk0+cefLJJzl79iwAvr5+vPXWG3Ts2LFoOyu3FP1OkcLQfSKFpXtFCkP3iRSW7pWil5ycTNmyZS8bLLrtpqFdqEqVKpQtW5Y9e/YUGCwqWbIkJUuWzLPd3t7+uty816sduf3pXpELhYSEADBs2AvExTUFHIBInJxc6dOnD506dSIgIKDArEkR/U6RwtB9IoWle0UKQ/eJFJbulaJT2Nf9tg4WxcXFceLECby9vYu6KyIiVywkJIROnToRExNjFr3etWsnpUqVKuKeiYiIiIjI7axYBYtSU1PZs2eP+Xzfvn1s2rSJMmXKUKZMGcaNG8ejjz6Kl5cXe/fuZcSIEVSrVo3WrVsXYa9FRK6era0tgYGBZGRk8OOPPyqTSEREREREbrhiFSz6448/CAoKMp8///zzAPTt25dZs2axZcsW5s6dS1JSEj4+PrRq1YrXX38932lmIiIiIiIiIiKSV7EKFgUGBnKpetzLli27ib0REREREREREbn92BR1B0RERERERERE5NahYJGIiIiIXJK/vz9Lliy5qdcMDw+nc+fON/WaIiIikkPBIhERERERERERMSlYJCIiIiIiIiIiJgWLROSOFhERQf369Yu6GyIit7zt27fTsGFDXF1dad26NYcOHQLg6NGj9OzZE29vb3x8fBg+fDjnzp0DIDU1lU6dOlG+fHnc3Nxo1qwZmzdvtmo3MjKSe+65B1dXV/z8/IiIiDD3ZWVlERYWhru7O5UqVWLRokU3bbwiIiJ3MgWLRESugWEYZGVlFXU3RESuu6ysLFatWkVkZCRpaWnMmTOHhQsXkpCQgJeXF7169cIwDB555BG8vLzYu3cvW7duZfPmzUyYMAGA7OxsevTowb59+zhy5AgNGjSga9eu5uq23333HWFhYfzf//0fSUlJ/P7779xzzz1mH5YtW0azZs04ceIEEyZMYMCAAaSkpBTJ6yEiInInUbBIRO4YU6dOpVKlSri4uODv78/kyZMZNGgQW7duxdnZGWdnZw4cOIBhGLzzzjtUrVqVMmXK0KZNG/7991+zHX9/fyZOnEjjxo1xdHRkx44dWCwWZsyYQa1atXBycqJ3796cPHmSbt264erqSoMGDfj777+LcPQiIoUXFRWFv381goKC6NGjB0eOHOHkyVPs2LEDR0dHJk2aRHR0NGvXrmX37t1MnjwZR0dHPDw8GDlyJAsXLgTA1dWVbt264eTkRKlSpRg3bhy7du0ys5JmzpzJsGHDCA4OxsbGhvLly9OgQQOzHw0bNqRr167Y2trSu3dv0tPT2bVrV5G8JiIiIncSBYtE5I6wa9cuRo8ezfLly0lJSWHDhg20bt2aDz74gLp165KamkpqaiqVKlVi/vz5TJ06lSVLlnDo0CFq165Nx44dyczMNNuLiIhg7ty5pKamUqNGDQC++eYb843T8uXLad68Oc8++yyJiYnUr1+fESNGFNXwRUQKLSoqitDQUOLi6gLrgRTAi1OnKhMaGkpUVBSenp6ULFmSX3/9laSkJMqUKYO7uzvu7u6EhoZy5MgRAM6ePcuQIUPw9/fH1dUVf39/AI4fPw7A/v37qV69eoF98fLyMh9bLBYcHByUWSQiInITKFgkIre13GkUP/74I1lZWWzdupWzZ8/i6elJvXr18j1n/vz5DB06lLp161KqVCnefPNNDh48yP/+9z/zmMGDB1OjRg1sbW0pUaIEAC+++CJlypTBx8eH5s2bU7t2bZo2bYqdnR2PPfYYGzduvCljFhG5WllZWQwb9gKG0QFYAjQGnIGSQFegA8OHv8jhw4c5d+4cDz30EOXLlycpKcn8OnXqFKmpqQC88847/Pnnn6xdu5bk5GRiY2MBzGlofn5+7Nmz56aPU0RERC5NwSIRuW1dOI3iueeeIz09nd69++Dh4UGrVq3YtGlTvufFxcWZn34DlCxZEh8fH+Li4sxtlSpVynOep6en+djR0THP89w3TyIit6qYmBji4mKBkeT9M/FDDKMHBw/u48knn6RZs2Y8+OCDVKxYkdGjR5OSkoJhGOzfv5+lS5cCkJycTKlSpShdujSpqamMHDnSqsWBAwcyffp0Vq9eTXZ2NkePHuWvv/66GUMVERGRS1CwSERuS/lPo1hPenpLzp49i4ODA71798bGJu+vQV9fX/PTb4D09HQOHTqEr6+vuS2/80REirvDhw+ff1Qnn71PAm8BEB8fz4IFC7C1teX7778nPj6eu+++Gzc3N9q3b29mCz3//PPY2tri6elJnTp1ePDBB61a7Ny5M1OnTuWZZ57Bzc2N+++/n61bt964AYqIiEih2BV1B0RErre80yhsgH+AFAwjEnicVavWUKWKP56enhw+fNgMIAH06tWL0aNH07FjR6pWrcprr71GhQoVaNSoUZGNSUTkZvD29j7/aBs5U9ByxZ7/NxhowrvvvmsG0MuXL8+nn36ab3teXl6sXLnSalvv3r2tnvfp04c+ffrkOTc8PDzPtqSkpMsNQURERK4DBYtE5Lbz3zSKSP5LoEwHXgN2AAbJyak888wzBAcH07hxYypUqEB2djZbtmyhT58+HDlyhA4dOnDy5EkaNWrEd999h52dfmWKyO0tICAAX19/4uPfxDCWYJ2Eno3FMhFf38oEBAQUUQ9FRETkZtA7HxG57eQ/jaIu8Nv5xymAK05OTtjb2/PNN9/kaWPEiBEFrl524RS1XLnFWnNFRERYPQ8MDNQn4iJyy7O1tWX69HcIDQ3FYumMYbxKzu/SbVgsE4HvmTZtMba2tkXcUxEREbmRVHRDRG471tMo8rPtouNERCRXSEgIixcvpkKFrUATwBVogq/vNhYvXkxISEgR91BERERuNGUWichtR9MoRESuTUhICJ06dSImJobDhw/j7e1NQECAMopERETuEAoWichtR9MoRESuna2tLYGBgUXdDRERESkCmoYmIrclTaMQERERERG5OsosEpHblqZRiIiIiIiIXDkFi0TktqZpFCIiIiIiIldG09BERERERERERMSkYJGIiIiIiIiIiJgULBIREREREREREZOCRSIiIiIiIiIiYlKwSERERERERERETAoWiYiIiIiIiIiIScEiERERERERERExKVgkIiIiIiIiIiImBYtERERERERERMSkYJGIiIiIiIiIiJgULBIRERERkdtWeHg4nTt3LupuiIgUKwoWiYiIiIiIiIiIScEiEREREZFi4NixYwQHB+Pq6oq9vT1NmjQp6i6JiMhtSsEiEREREZFiYPbs2dja2pKUlERGRga//vprUXfppjpy5Ahdu3alXLlyVKpUiVGjRpGZmUlqaiqdOnWifPnyuLm50axZMzZv3lxgO6NGjeKee+7h8OHDN7H3IiLFi4JFIiIiIiLFwL59+6hduzY2Npf+E94wDLKysm5Sr26crKwsVq1aRWRkJKtWraJ79+7Y29uzb98+YmJiWLJkCZMmTSI7O5sePXqwb98+jhw5QoMGDejatSuGYVi1l5mZSf/+/Vm3bh1r1qzB29u7iEYmInLrU7BIREREROQW99hjjzFv3jxmzpyJs7MzH3/8MfXr1zf3+/v7M3HiRBo3boyjoyM7duwous5eB1FRUfj7VyMoKIgePXoQFBREdHQ0LVq0wNnZGT8/P0aNGkVERASurq5069YNJycnSpUqxbhx49i1axeHDh0y2ztz5gxdunTh1KlTLFu2DDc3tyIcnYjIrU/BIhERERGRW9yXX35Jz549GTJkCKmpqdja2uY5JiIigrlz55KamkqNGjWKoJfXR1RUFKGhocTF1QXWAynAHMCGAQMGEBUVBUCVKlWIi4vj7NmzDBkyBH9/f1xdXfH39wfg+PHjZpubNm3i559/Jjw8nJIlS97sIYmIFDt2Rd0BERERERHJKysri5iYGA4fPoy3t3eeaVUXGzx4sBkkyi+YVBxkZWUxbNgLGEYHYAn/fbbdBsjGMFoxfPiLdOrUidjYWHx9fXnnnXf4888/Wbt2Lb6+viQlJVG6dGmr16tJkyZ07NiRli1b8ssvv1C7du2bPzgRkWJEwSIRERGRy6hduzZvv/02HTp0KOquyB0iKiqKYcNeIC4u1tzm6OhEUFBggedUqlTpRnfrhouJiTk/5kisJ0FUAIIACwcP7mPx4sW88cYb9O3bl1OnTlGqVClKly5NamoqI0eOzLft/v37Y2trS4sWLVi+fDn16tW74eMRESmuNA1NRERE5DK2b99uBooiIiKsasWIXG/5T8Naz5kzpfnhhx/MaVgXu1zh6xvJ2dmZrVu3XnM7/61QViefvQuBUgAMGTKE9u3bM2LECJ5//nlsbW3x9PSkTp06PPjggwW2369fPyZPnkzLli3ZtGnTNfdXROR2pcwiEREREZFbRMHTsBoDwUAMw4e/SHj46KLqYr5SU1OvSzv/rVC2jZwxX8gLeBn4hq+++orAwMCcrV5erFy50urI3r17m4/Dw8Pz7Ltwv4iI5KXMIhEREZHzkpOTCQsLw8/PD1dXV+6//34OHjyIv78/S5Ys4a+//mLQoEFs3boVZ2dnnJ2d2b9/P6VKlWLfvn1mO2lpaZQuXZoNGzYU4WikOPpvGtZI8v6pbgHu5+DBfezateum9+1mCAgIwNfXH4vlTSD7or3ZWCwTqVixMgEBAUXRPRGRO4aCRSIiInLHysrKYtWqVURGRrJq1Sr69u3Lnj17WL9+PUlJSXz44Yc4ODiYxzdo0IAPPviAunXrkpqaSmpqKn5+fnTo0IG5c+eax3399df4+PjwwAMPFMWwpBi79DSsCHJWBYN77rnHahpVbGwsnTt3vqF98/f354033qBhw4a4urrSunVrc3l6i8Vi9ic8PJyOHTsSFhaGu7s7lSpVYtGiRWY7hmHw7rvvUrNmTdzd3QkMDGTnzp1ATmHu6dPfAb7HYunMhdPwcp5/z7RpU4ptAW8RkeJCwSIRERG5I0VFReHvX42goCB69OhBUFAQS5YsoUuXLvj4+GBjY0ODBg0oW7bsZdvq378/8+bNM1dfioiI4IknnrjRQ5DbkPU0rPxsu+i4m2vOnDksXLiQhIQEvLy86NWrV77HLVu2jGbNmnHixAkmTJjAgAEDSElJAWDWrFl8/PHHfPfddxw/fpyQkBA6duxIeno6ACEhISxevJgKFbYCTQBXoAm+vttYvHgxISEhN2ewIiJ3MAWLRERE5I6TfwHhOYANgwcPLrCAcEFat25Neno6q1evJj4+ntWrV6smilyVW20aVlZWFgCLFy8mLS2NgQMHUrNmTRwdHZk0aRLR0dHExcXlOa9hw4Z07doVW1tbevfuTXp6ujl17v3332f8+PFUr14dOzs7hg4dytmzZ62mbYaEhBAbu4fo6GgWLlxIdHQ0+/btVqBIROQmUbBIRERE7ih5Cwg3BpyBDkA2htGC4cNfNN8kXyy/FadsbGzo168fERERzJs3j9atW+Pp6XnjBiG3rVtpGlZUVBR169YHcrLnjhw5wuTJU81gqqenJyVLliQ+Pj7PuV5eXuZji8WCg4ODmVkUGxtLr169cHd3N79OnjyZJ+hka2tLYGAg3bt3JzAwUFPPRERuIq2GJiIiIneU/woIR2L9uZkn0Ak4ysGD+1i9ejWlS5emUqVKVud7enpy+PBhzp49a1XP6Mknn6R+/fqUL1+ed95558YPRG5budOwhg17gbi4JuZ2X9/KTJt2c6Zh5WbflSrV5fyWQ8DdJCaWJjQ0lMWLF9O0aVPOnTtHhQoVrqjtihUrMm3aNNq0aXPd+y0iIteHMotE5I4zaNAgXn755aLuhogUkUsXEJ4L3A1Ahw4dGDRoEGfPnrU6Ijg4mMaNG1OhQgXc3d05cOAAAFWqVOG+++4jJSWF9u3b37gByB2hKKdhWWffLTy/1QkoCZzDMAIZOvR5RowYQbNmzfD19b2i9p955hnGjBnDP//8A+SsQvjNN9+YmUciIlL0lFkkInecDz74oNDHrlq1is6dO5OUlHTjOiQiN5V1AeHGF+11AwYAn/Djjz8SGBgI5EybyWVvb88333yTb9v+/v40bNgQOzv9iSXXLnca1s1WcPYdwJPAPOLj97N9+3a+/vrrK24/LCwMW1tbQkJCOHjwIC4uLjRt2pTg4OBr77yIiFwX+ktGROQGy8jIwN7evqi7ISLn5RYQjo9/E8NYgvWb4ZwCwr6+V15AeO/evSxevJg///zzenZX5Ka7dPZdbeAPwJXnn3/ezCrKXQkQIDw8PM9ZF37oYrFYGDJkCEOGDLleXRYRketM09BE5LaxaNEiGjf+L0vg0UcftVpa+IUXXuDZZ5+lX79+DB8+HMjJFrBYLMyfP59q1arh7u5Ov379yMjI4MSJE7Rt25ZTp07h7OyMs7MzMTExAPzyyy80atQId3d3ateuzbfffmtep1+/fvTv35+uXbvi6up6RZlMInLj3YgCwgMHDqR+/fq8/PLLVK9e/UZ0W+Smsc6+y8+2i44TEZHbjYJFInLbCAwM5M8//yQlJQXDMFi7di2lSpVi586dAKxcubLAFPelS5fy119/sWPHDlasWMGCBQvw8PBg6dKluLm5kZqaSmpqKgEBAWzZsoXHHnuMt956i8TERGbPnk3v3r3N2gsAkZGR9O/fn6SkJPr3739Txi8ihZdbQLhCha1AE8AVaIKv7zYWL77yAsKzZ88mJSWFUaNG3Yju3nRt27Zl5syZRd0NKSK52XcWy5tA9kV7c7LvKla88uw7EREpPhQsEpFiLSsri1WrVhEZGcnOnTu56667iImJYdOmTfj5+dGhQweio6NJTExk27ZtBdZ+GDNmDC4uLvj4+NCmTZtLTiOZPXs2/fr1Izg4GBsbG5o2bUqHDh344osvzGNatWpF69atsbGxwdHR8XoPW0Sug6IsIHyrW7p0qaYI3cGss+96nN+aCkRisURwNdl3IiJSvKhmkYgUW1FRUeeXFY41tzk5uTB79myaNWtGUFAQDz74IAsWLMDT05N69epRunTpfNvy8vK6oA2nSxa0jo2NZeXKlXz66afmtszMTFxdXc3nFy+1LSK3pqIqIHwrMwyD7OzsGxIIUA234iM3++7ll0ee31IBOIuvb2WmTbvy7DsRESlelFkkIsVSVFQUoaGhxMXV5cJ6I6dP38W3337LwoULCQ4OJjAwkJiYGFasWEFQUNAVX8fGJu+vyYoVKzJs2DCSkpLMr9TUVGbNmnXJ80REblX+/v5MnDiRxo0b4+joSPny5Zk2bRoA99xzD/PmzbM6vm3btkycOBGA1NRUwsLCqFSpEuXLl6dPnz6cOnUK+K8u3Keffkq1atWueIl1KVohISFs3boJgI8//ljZdyIidxC9mxGRYicrK4thw17AMDoAS8hZ+tr5/L8/AhY2bdrEgw8+iLu7O76+vixYsOCqluT19PQkJSWFo0ePmtsGDhzIp59+SnR0NFlZWZw7d47169ebtZFERIqjiIgI5s6dS2pqKjVq1DC39+7dm/nz55vPExISWLFiBb169QLgySefJDExkS1btrBv3z4yMjIICwuzavvbb7/ljz/+YN++fTdnMHLd5GaYhYaGEhgYqKlnIiJ3CAWLRKTYiYmJOT/1bCR5f42VB6qSnZ3Nxo0bAWjRogVnzpyhWbNmV3ytGjVq0L9/f2rVqoW7uztr166lQYMGREZGMnr0aMqVK0eFChV47bXXOHfu3DWOTETk5lq7di2RkZGkpaUxcOBAatSoga2tLSVKlDCP6dmzJ6tXryY+Ph7IKeAfEBBAxYoVOXbsGF999RXvv/8+7u7uODk5MX78eBYtWkRWVpbZxtixY3F3d1cNNxERkWJCwSIRKXYOHz58/lGdAo7YaHXc22+/TUZGBs7OzkDOp+e50yv8/f0xDAN3d3fz7GnTphEREWE+//DDDzl+/DhJSUk0bdoUgODgYNatW0diYiLHjx/nl19+oX79+nnalztLuXLlqFSpEvfffz9OTk54eXnh5eXFkCFDcHd3p3Tp0oSGhpKRkUHHjh0pUaIEFosFR0dHnn/+eQzDAMBisWCxWPD29sbGxgZ7e3vatGlDSEgIrq6uNGjQgL///tu87tSpU6levTouLi7UrFmTH374wdyXOw1o/vz5VKtWDXd3d/r160dGRsZNf33E2ptvvkn37t3N5xZLTlYkQHh4OJ07d75h1/7uu+8AaN++PT169ODIkSO8+eZbREVF5TnW29ub4OBgFixYAMC8efPo06cPkHN/ZWdnU7lyZdzd3XF3d+f+++/HxsaGhIQEsw3VcRMRESleFCwSkWLH29v7/KNtBRyx7aLjRG6cC1fkAzh48CDz5s0jPj6e48ePc+LECWrWrMmJEycoVaoUv//+OxMnTmTr1q0sX76c5ORkAgICmDFjBgsXLrRqu1q1amzatInSpUuzevVq3N3dSUxMpH79+owYMcI8zs/Pj5UrV5KcnMwHH3zA3Llz+fXXX63aWrp0KX/99Rc7duxgxYoV5ht/KTojR44075ubKSoqit69e59/9gs5Nd+8OHHCj9DQ0HwDRrlT0bZt28auXbt49NFHgZwabjY2Nhw6dMiqjltaWhoVKlQwz1cdNxERkeJF/3OLSLETEBCAr68/FsubQPZFe7OxWCZSsWJlAgICiqJ7cgeJiorC378aQUFB9OjRg+PHj2Ox2DBv3jz27dtHuXLl8PDwwM7OjlOnTnH8+HEOHDjAvHnzePvttwkMDMTFxYVPP/2UjIwMq4wggFGjRlGvXj0CAwOpXr06x44dw87Ojscee8ycZgnw6KOPUrFiRSwWC4GBgdSvX5/Vq1dbtTVmzBhcXFzw8fGhTZs2/PnnnzflNZJby38139qc33I/OTXfSgKvAh0YPvxFM8stV5cuXdi/fz8vvvgiXbp0MTM1vby86Ny5M2FhYRw/fhzIqWn09ddf36whyU02fPhw+vXrV9TdEBGRG0zBIhEpdmxtbZk+/R3geyyWzly4GlrO8++ZNm3KLVGEU1N9bl//rchXFvAxtxtGNm+99TbPP/88VapUwc/Pj+joaKKjo6lZsyYA8fHxJCQk0KRJE8qWLcvdd98NwKFDh6yu4eXlBYCjoyOlS5cmJSXFfJ6ammoet2DBAho2bEiZMmUoV64cGzdu5MSJE/m2BeDk5GS2JTfW//3f/+Uprr9o0SJq1qx5RVPNRowYgZ+fHy4uLtSqVYsvv/zSav/ixYupVq0abm5uPPXUU3To0IHw8HBz/8aNGwkKCqJ06dLExcWSmVk9n6vYYBivcvDgPnM1s1yOjo48+uijLFu2zJyClisiIsKcfubq6kpAQICCkSIiIsWcgkUicsuaOnUqlSpVwsXFBX9/f+bMmWO+uQoJCWHx4sVUqLAVaAK4Ak1wcYmhYcOGLFu2DDc3NypXrsyqVatYsmQJ1apVo3Tp0owaNcq8RkREBPXr12fMmDGULVsWLy8vFi1axLp166hTpw5ubm7079+f7Oz/Mphy33SVKVOGatWq8dFHH5n7wsPD6dChA4MHD6ZMmTK88sorN+8Fk5vmv+yMe4E/gXvJCVpWAnoCrqxatQpbW1vKli1LTEwMK1asoHHjxkDOFMkRI0YwcOBA4uPj+eeffwAoWbLkFfflwIED9O3bl0mTJnH06FGOHTtGw4YN82SGyM2VOz3RwcGBmJgYYmNjzX3z58+/YBpY4dxzzz38/vvvJCUlMWbMGHr37m2uLLZr1y569+7NjBkzOHHiBI0aNWLZsmXmuQkJCbRs2ZLBgwcza9YsADIy5rJ58+YLrhALdCa3FtzLL7/M8OHDrfrw6aefYhgGrVq1stru4uLC1KlT2bdvH8nJyezevZsJEyYA+deFkzuXPkARESk+FCwSkVvGhbVf5s+fz+jRo1m+fDkpKSls2LCBRo0aWR0fEhJCbOwenJycGD16NNHR0Qwb9ixbtmyhdevWJCYm0rt3b3r16sU333zD5s2bWbduHe+8847VFJ5t27ZRtmxZEhISeOONN3j66aeZPn06q1evZufOnXz//fcsWbIEsH7TdezYMZYsWcLYsWNZsWKF2d5PP/3EAw88wNGjR3n99ddvymsnN9d/K/IdAjoAS4DGgAVoBZwG7Pnjj43Y2dnh6+vLggULaNKkCQCdO3cmPT2d7OxsMjIy6Nu3L/b29jg4OFxxX1JTUzEMg/Lly2NjY8PSpUvNIslSNC6cnjh48GAyMzO5554GREVFcfToUX7++ecrDhb17NmT8uXLY2try+OPP07NmjXNulSLFi2iRYsWtGnTBjs7O5566inuuusu89z58+fTrFkzunbtatYRsrNrxZo1a/K5kmq+3a6Sk5MJCwvDz88PV1dX7r//fg4ePEhqaiphYWFUqlSJ8uXL06dPH6vMspiYGIYOHUrp0qUJCQnJk5XYq1cvfHx8cHV15d577yU6Otrcl/uBzNixY/Hy8uLxxx+/aeMVEZFro2CRiNwSLq790qdPH86eTePjjz/m7NmzeHp6Uq9evTzn2draYmdnR4sWLQgMDMTGxoZ7772XkJAQ801VfHw8r7zyCk5OTtSqVYt69epZBYvKlSvH0KFDsbOzo3v37iQnJ9O/f388PDzw8fGhefPm5vEXvumytbWlTp06PPHEE1aFievUqUO/fv2ws7PTMtG3qf9W5DsEjMT6v1NXoBZwN2fPnubEiRO0aNGCM2fOmAHPV155hZYtWzJgwABcXV3ZunXrFQcPctWqVYtRo0YRHByMh4cHX375ZZ7Aqtw8/01PrMt/U2THk5ycSWhoKK+88gpNmjS54tXB/u///o/atWvj5uaGu7s727ZtM2sEHTp0iIoVK1odf2H7sbGx/Pjjj7i7u9O5c2csFguZmQs5efLkRVdRzbfbzYUfwnTo0IHdu3ezfv16kpKS+PDDD3FwcODJJ58kMTGRLVu2sG/fPjIyMggLCwPg5MmThISE0L59e44dO8YTTzzBZ599ZnWNFi1asHPnTk6cOMHjjz9OaGioVUBp27Zt2NnZceDAAebPn39Txy8iIlfPrqg7ICKS++bKMDoAkeRMg9gGhDFlyhTef/99mjZtyqRJkwrVnqenp/k4N1hz8bYL671cyfEXvunKyMjg3LlzODg4WL2x0hLRtz/rrIs6FzyOPf9vZ3KCBK4888wzdO/enbfffhvAnB62fPnyAtu/eApZRESE1fPAwECSkpLM5+PHj2f8+PFAzjSPH3/8kXbt2gH/TQO60LRp0wq8tly9/6Yn5mab5QYRXwSmYBhN+eyzBcyc+f4Vtbt27VrCw8NZuXIlDRo0wMbGhvr165vfVx8fHzZs2GB1zoEDB3jggQeAnBXLunTpwueffw78txramDFj6N79f0A9YBsWy0Ryar4tviVqvsm1iYqKYtiwF85nQebw8vLlt99+IyQkhAYNGnDs2DG++uorjh8/bk4VHD9+PLVr1yYiIoLvv/8eb29vWrdujZ2dHR07dsxTg+uJJ54wH7/00ku8+eabbNmyhYceeggANzc3Ro0ahY2NDSVKlLjh4xYRketDmUUiUqTyvrlqTM7KPI2B/2GxdKRMmfLUrVuX3r174+zszJkzZ8zzT58+TXJy8k3rb+6brqSkJE6fPk1mZiYpKSn8+OOP5jFaIvr2FxAQQNmyuQHFbQUcpek8d5r/pidenG3mAIQC6WRkpOPj45Pf6QVKTk7G1taWcuXKkZ2dzSeffMK2bf/dd127duWXX35h+fLlZGZm8sknn7Br1y5zf+/evVm5ciVfffUVGRkZdOzYkfHjx7N7926gJbk133x9t7F48WJCQkKu9iWQW0TeDLdooARHjjQgNDSUqKgoIOcDkOzsbCpXroy7u7tZqNzGxoaEhAQOHTqEn5+fVdsXPs/OzmbUqFFUr14dV1dX3N3dzZUfc1WoUEH/L4qIFEP6zS0iRargN1f/ACswjOeJj99PYmIidnZ2NGzYkPXr1/P333+TlpbGyJEjsVgsN62/uW+6Fi1aREZGBhkZGWzatInff//9pvVBip6trS0zZ75HToLuG0D2RUdkA29qOs8d5r/piXXy2dsHyMn+uXilsctp06YNoaGh1K1bFx8fH7Zv325mbQDUqFGDuXPnMnjwYDw8PFi/fj3BwcFmwfQKFSqwbNkyZs+ejbe3N56ennz11VecOXOGH374gYULFxIdHc2+fbsVKLoN5P8hzN1AOobxHtCB4cNfJCsri4oVK2JjY8OhQ4dISkoyv9LS0qhQoQI+Pj7s37/fqv0DBw6YjxcuXMjChQv54YcfOHXqFElJSbi5uVllMypQJCJSPOm3t4gUqYLfXKUDrwGPAPDbb78RERFBcHAwAwcOpEmTJlSrVo26devi4uJyRdeMi4tj3LhxuLi48OKLL3LixAmzCOfIkSOBnDdnM2fONM/57bff6NChAxMmTCAtLY2XX36ZMmXK4ODgwDPPPENycjL+/v6sW7eONWvW4OLiQvPmzTl48KDZxvbt22ncuDEuLi4EBQUxYsQIAgMDr+wFk1vGY489xksvPQd8T859mlufZj3QCYvlB6ZNm6LpPHeQ/7LI8ss2aw78anVceHi4WTwfcqYf1q9fP88+GxsbPvzwQ06dOsXRo0d55513WL16tdVqZd26dWPv3r2cOnWKjz76iNjYWKspsQ0aNGD58uUcP36cxMREVq9ezT333EPTpk3p3r07gYGBuldvE/l/COMJdAKGYBhPcfDgPj755BPs7e3p3LkzYWFhZjZQQkICX3/9NQDt27fn0KFDZtbaDz/8wMqVK81rJScnU6JECcqWLUt6ejrjx4/PUwBbRESKJwWLRKRIFfzmqi7wG5Cz/POsWbO45557AJgyZQqJiYnExcUxYMAAkpKSzKDLxW++Ll62edeuXRw6dIj169eTkpLC9u3b+eGHH3J6sG0bFouFc+fO8fXXX/PKK6+wZs0aIiIiaNOmjbnKWWJiIjt27OC9996jTp06rFu3jhYtWgBw5MgR/vzzT44dO4aTkxOvvfYakFNH5pFHHqFt27acOHGCt956i08++eQ6v5pys02aNIkvv/yScuU2Ak3Inc5TseJ2Tee5AwUEBODr64/F8ib5ZZvdyOLR3333HSkpKZw7d4533nmHw4cP06ZNm+t+Hbn1FfwhzFygIjAQgLfffpuzZ88SERFhTj9zdXUlICCAP//8E4AyZcrw1Vdf8f3331OuXDnmzJlDz549zRb79u1L7dq18fPzo0qVKjg4OODr63vDxygiIjeeClyLSJHKfXMVH/8mhrEE6xh2zpsrX99re3OVlZVFTEwMhw8fJjs7G8Mw2L59O35+fnh6euLp6cnGjRtxcnIiPDwce3t7HnzwQXr27Mm8efNo1qwZ8N8qZwB2dvn/+hwyZAiVK1cGcpa6fuutt4CczKQTJ04watQo7OzseOCBB+jWrRvbt2+/6nHJrSE0NJQuXbqY95i3tzcBAQHK0rgD2draMn36O4SGhmKxdMYwXiW3YP+NLh69bNky+vbtS0ZGBjVq1ODbb7/Fw8Pjul9Hbn3WH8I0vmCPG/AB0Bdowpw5c8zAztSpU5k6dWq+7TVv3px3332Xdu3aYW9vb7XPycnJrH+U66WXXjIf9+vXz/x/U0REihdlFolIkcp9cwXfY7F05sKpPDnPv7+mqTxRUVH4+1cjKCiIHj160KtXLxwdXRg7diyenp60atWKTZs2ATkrCl34h7Cfnx/x8fHm88Kscubl5WU+dnJyMtPxDx06hLe3t1WQSaum3T5sbW0JDAzUdB4hJCSExYsXU6HCVi7MNrvRxaNnzJhBYmIiKSkp/PHHH5riegcrygw3ERG5fShYJCJF7ka9ucq7GkxOEOrkycbs2LGD2bNnc88999C7d28gJ6CTkZFhnn/gwAEqVKhgPr+WIp0+Pj4kJCSQmZlp1b6I3H5CQkKIjd1DdHS0ikfLTXejP4QREZE7g4JFInJLuJY3V6tWrTJrEuXKfzUYZ6A0hvEMhtGWESNG4ujoaGb7nD59mtdff5309HQ2bNjAggULrGozXIvGjRvj7u7OxIkTycjI4Pfff+eLL764Lm2LyK1H2WZSlIoqw01ERG4fqlkkIreM3DdX18N/q8FEYh0XTwfGAluJizvDkiVLmDdvHn/99Rd16tQhMzMTb29vHB0deeONNwgKCrou/bG3t+ebb77hqaee4u233+b++++nV69e7Nix47q0LyIicqGQkBA6deqkemoiInJVlFkkIreMqVOnUr16dVxcXKhatSozZswA8s8c6ty5M+Hh4Zw4cYK2bdty6tQpnJ2dcXZ2JiYmhvfee+/8kReuBuMOnCBnlbWhAJQtW5ZmzZrx0ksvkZqaSqNGjShdujSpqakXrCiTs6pabGwsr7/+OuXLl8fT05OkpCSz3hFAbGwsnTt3tupjbGys+bxevXps2LCB1NRUoqOjOXPmjOoWiYjIDXOnZrjFxsZisVhISkoq6q6IiBRbChaJyC3Dz8+PlStXkpyczJw5c3jppZdYt27dJc/x8PBg6dKluLm5kZqaSmpqKgEBAbi4uJw/YlsBZx4FclZ5SUxMpEmTJhw4cIBvvvmGzZs3s27dOt555x02btxonrF9+3YcHR2Jj49n0aJFvPTSS+zdu7fQ44uJieHgwYNkZ2ezYsUKFixYwGOPPVbo80VERERERG4GBYtEpMhkZWWxatUqIiMjWbVqFZ07d6ZixYpYLBaCgoJo3bo1q1atuqq2K1WqRKlSDgWuBgNrKVGiJKNGjcLW1pZGjRqRkZHBK6+8gpOTE7Vq1aJevXpWwaKyZcvywgsvYG9vT2BgIP7+/laZRZfz77//0rhxY5ydnRk8eDBvvfUWrVq1uqrxiYjcKdq2bcvMmTOLuhsiIiJ3FAWLRO5wx44dIzg4GFdX18tmufTr14/hw4cD157iffGS9kFBQZQv70WVKlUoU6YM7u7u/Pjjjxw/fvyq2rexsaFu3TpYrwYDsP38813cc089MyU/t5C1p6en2YajoyOpqanm8wv3ATg5OZGSklLoPvXt25f4+HjOnDnDrl27CAsLu/KBiYgUYxEREdSvX/+Kzlm6dClDhgy5MR2Sm8rf35+JEydy//334+TkRNu2bUlMTGTIkCG4u7tTvXp1fv31VwA+++wz6tSpg4uLC5UqVeK1117DMAyzLYvFwvTp06lRowbu7u5069aNU6dO5XtdwzB49913qVmzJu7u7gQGBrJz586bMma5fvz9/VmyZAlwdb9LROTKKFgkcoebPXs2tra2JCUl8eWXX96Ua+a/pP3XJCaeYN++fcyePZukpCTatWuHYRg4Oztz9uxZqz8SL6wnlN+S9s7Ozri7u1+0GswpIAxf32107doVHx+fGzxSERGRO9uFWcRpaWksWrSIqKgoDh06xMGDB2ncuDEPP/wwJ06coEePHgwaNAjImWYeFRVFcnIy3377LR9++CELFy60anv+/PlER0cTGxvLyZMnzQ+0LjZr1iw+/vhjvvvuO44fP05ISAgdO3YkPT39Rg9fRKTYUrBI5A63b98+ateunW/A5UYoeEn7uwAL0Jznnx/Bd999x/LlywG46667sLe3Z+HChWRlZREZGclff/1ltunp6UlKSgpHjx41tzVs2JD169dTq1Yt/v57GyEhIVgsFv7v//6Pfft2c/fdd9+U8YqI3Aouzujo2LEjKSkpPPvss3kyOlJSUnj66afx9vbG29ubQYMGcfr0aeDSCw5cuH/OnDlUrFgRDw8PRowYAcBff/3FoEGD2Lp1q7kgwYEDB/jrr79o2rQpZcqUoVy5cnTv3p0TJ06Y7QcGBjJt2rTLti+Fc2F2xsWcnZ3ZunXrVbV7YfZxrouziI8cOcL+/XH8/vvvuLm50a5dOzw8PAgJCcHW1pZu3bqxbds20tPTadu2LXfddRcWi4X69evTvXv3PFPTR4wYgY+PD+7u7rz++ussXLiQ7OyLp57D+++/z/jx46levTp2dnYMHTqUs2fPsmHDhqsaq4jInUDBIpE72GOPPca8efOYOXMmzs7OjBs3jpYtW1KuXDlKly5N+/btrVbzuh7+W9J+JNa/gmoBo4BNxMXFMmPGDB555BEAXF1d+eijj3jllVfw8PBg3bp1tG7d2jyzRo0a9O/fn1q1auHu7s7atWsJDg5m4MCBNGnShBo1atC2bVtcXV2pX7/+HbMajIjc2S6X0fHyyy8THBycJ6Nj2LBh7Nmzh23btrF161b+/vtvnnvuuUJfNyUlhR07drB7927Wrl3L+++/z6pVq2jQoAEffPABdevWNRckqFSpEjY2Nrz11lscOXKEbdu2ER8fzyuvvHLF7cu1S01NpW7dutelrfyziL1ISqpMaGgoUVFRODo65pn+bRgGZ86cYdmyZTRp0oSyZcvi5ubGBx98kGdqup+fn9Xj9PR0jh07lqcvsbGx9OrVC3d3d/Pr5MmTxMXFXZexyqUtWrSIxo0bm88fffRRvL29zecvvPACzz77LMuXL+e+++7Dzc0Nb29vhgwZwtmzZwt1jQ8++IAqVarw999/X/f+i9ypFCwSuQPlvoEICQmhRYsWDB48mNTUVPr27cvzzz/PwYMH2b9/P46Ojjz11FPX9dr/TR+rk8/e8cBBIOcTys8//9z8NLl79+4cPHiQpKQkZsyYwXfffWd+kg3w4Ycfcvz4cZKSkmjatCkAU6ZMITExkbi4OAYMGEBSUhKBgYEAhIeHW32y6u/vj2EYVp+Yr1q1yvyUtF+/fnmKWW/atIl+/fpdzcsgInJDXS6jo02bNri4uNClS5c8GR0LFixg4sSJeHh4ULZsWd58803mzZuXb8ZGfgzDYMKECZQqVYq7776bJk2a8OeffxZ4/D333EPTpk2xt7fH09OT559//pLBnyttX/6TkZFxU84tOIu4JPAq0IHhw18s8J5KT08nJCSEgQMHEh8fz6lTpxg0aJDVdHSA/fv3m48PHDhAiRIlKFeuXJ72KlasyJdffklSUpL5debMGbp3717oMcmVy/178+TJk/zxxx8kJSVhGAZr166lVKlSZt2olStXEhwcjIODAx999BGJiYmsW7eO6Ohopk6detnrjB07lvfff5+YmBhq1qx5o4clcsdQsEjkDnPxG4hly5bxySefnt/uT9u2bSlVqhSurq6MGjWKmJiYQr9BKIz/PkkqaEn7bRcdJyIiV6KwGR1ubm7mObkZHSdPniQ9PR1/f39zX5UqVTh37lyhFxxwdXXF0dHRfO7k5ERCQgLBwcEMHjw4T8bqnj176NSpEz4+Pri6utKrV69LXiu/9q9ksYHiaurUqVSqVAkXFxf8/f2ZM2eOWeR35MiReHh4UKlSJauV48LDw+nQoQODBw+mTJkyvPLKKyQlJTFy5EgaNmyIq6srrVu3xs3NjVWrVmGxWNi0aRORkZF4eXlhb29PmTJl6NatG6NGjSI7O5t+/fpRsmRJLBYLpUqVsnozf/r0aR5//HFcXFzOZxG3Ie/bDRsM41UOHtzHgQMH8h3ruXPnSEtLw8PDg5IlS7Jhw4Y89YoAJk+ezKFDh0hKSmLMmDE8/vjj+U6rf+aZZxgzZgz//PMPAMnJyXzzzTd3xH1TVC78e3Pw4MFkZWVRrVoN3nnnHfz8/OjQoQPR0dEkJiaybds2AgMDCQgIoEGDBtja2lKlShUGDhx4ycBxVlYWTz/9NCtXrmTNmjVUqFDh5g1Q5A6gYJHIHST/NxDtSE0tS2hoKJ9++ik9evSgYsWKuLq60qxZM86dO3dd/5gKCAjA19e/wCXtLZaJVKxYmYCAgOt2TRGRO0VhMzouztDIZW9vT4kSJawCOrGxsZQsWZKyZctedsGBgvz555/Y2toya9Ysq0AUwKBBg6hQoQI7duwgOTmZzz77rMD+Xan86ugUR7t27WL06NEsX76clJQUNmzYQKNGjQDYtm0bFouFw4cPs2jRIl555RXWrFljnvvTTz9x//338+WXX1K3bl0yMzOJjY1l4cKFJCQk4OXlxZkzZ8zjV69eTVhYGK1btyY7O5vx48czYcIEwsPDGTx4MJ999hmffPIJmZmZTJ8+nfHjx5s1phYtWsSgQYP46KOPzrf2dgEjyskuLujvCxcXF95//32efvppXF1deeONN+jWrVue43r16kVQUBB+fn64uLgwffr0fNsLCwujX79+hISE4Orqyt13351v8Emuj/z/3nyUEydceOmll6hQoQItWrQgOjqa6Oho6tWrR+nSpfn99995+OGH8fT0xNXVlZEjR14ycHzw4EHmzZvHa6+9RunSpW/W8ETuGAoWidwhCn4DUQ7oCHTg2WeHcvr0aTZu3EhycrL5x+b1+qMdwNbWlunT38F6SfsUYP35598zbdoU1RUSEbkKBdeFgwszOg4ePJjv+TY2NvTo0YNRo0aRmJjIiRMnGDlyJL1798bGxuayCw4U5OTJk9SuXRtvb28OHz5sVYckOTkZFxcXXF1dOXjwIJMnT7Y693r+H3QrurBA+MVyp/H8+OOPZGVlsXXrVs6ePYunpyf16tUDcjKrwsPDKVGiBA8++CA9e/Zk3rx5ZhsVK1Zk7NjXefjhh3niiSc4ffo0GRmZ7NixA0dHRyZNmkRmZiZBQUEAfPHFFwwbNozKlStTt25dwsLCqF69Oo6OjixYsIDHH3+cnj17Ymtry8CBA6lduzY//vgjAO3atSMwMPCCDI8DwIkLRhQLdCY3i/jZZ58tcEr4oEGDOHTokLka2nvvvZenMHfz5s35559/OHXqFF9++aU5lfziqeUWi4UhQ4awfft2kpOTiY+PZ9GiRbi4uFzJt0oKoeC/N7sD7kB51q5dT0BAADExMaxYscK897p3705QUBD//vsvycnJvPnmm5f8+ff39+frr7+mR48eqlsmcgMoWCRyh7j0GwgLhvEqp0/nFBx1d3fnxIkTjBs37ob0JSQk5KIl7V2BJvj6bmPx4sWEhITckOuKiNzuLl0X7r/tqampBbYxffp0/P39qVWrFrVr16ZatWrmVKPLLTiQn99//53Nmzczc+ZMQkJC8PHxoVy5ctja2uLq6kpaWhqLFy/G1dWVTp06kZCQQFpaGq1atcLJyYnExETOnTtHWFgYXbt2JTk5mT59+nDq1CkAsrOz+eabb8xCyHXq1OH333/n3XffZcGCBeYiDrVr176i17KoXTiN57nnniM9PZ3evfvg4eFBq1atzDp6Pj4+2Nvbm+f5+fkRHx8PwM6dO4mNjb0ow8OJzExHc0rihQWmIeceql69OgCVKlWy2nfmzBm++uorq0LRmzZtMq/n5eUF5GQR+/hUPH/WqYtGpizi29n69esL+HuzObAZOMvx40fYunUrvr6+LFiwgODgYCAncOzu7o6TkxM7d+5k1qxZl71e27ZtWbBgAaGhoaxYseL6D0jkDqZgkcgdorBvIGJjYyldujQPPfQQbdu2vWH9CQkJITZ2D9HR0SxcuJDo6Gj27dutQJGIyDUouC5cLBdmdISFhTFy5Ehz74WZGK6ursyZM4eEhAQSEhL48MMPrTIwLrXgQGBgIElJSVZXjo+Pp0+fPgwZMoTTp08zffp0LBYL0dHRnDhxgieffJKUlBTi4+PZuHGjORV6woQJpKam8r///Y8///yTxMREdu3aRUpKChkZGYSFhQHQoUMHbGxs2LNnD0lJSURFReHl5cXQoUPp2bMnQ4YMITU1le3bt1+vl7lAhmGQlZV1ze3kP41nPenpLTl79iwODg707t0bgEOHDlkVnz5w4AAVKlQgKyuLpUuXAZ5YZ3jYA+XJnZK4d+9eq2t7e3uzZ88egDz1fxwdHXn88cetCkWfPn06z+p1tra2vPXWG+efPY2yiO8cCQkJ5x9d/PdmWXJWvs0J2h4+fJgWLVpw5swZmjVrBsDs2bOZMmUKzs7ODBo0iMcff7xQ12zdujWff/453bp1Y/ny5ddlHCKiYJHIHaPgNxARwDRz+8cff0xqaip///03Tz/9tFUad0REhLk6WX6rh10pW1tbAgMD6d69O4GBgfqjUUTkGhW2LtyDDz54w/uSO4UqMjKShIQEczrJ/Pnz6dWrF82aNcPe3p7hw4dTunRpfvjhB/PcHj160KhRIywWC6mpqXz11Ve8//77ZtbB+PHjWbRoEVlZWdjb25OSksLOnTsxDIO77rqLihUrFtSt687f35+JEyfSuHFjHB0dWbt2LT179sTb2xsfHx+GDx/OuXPnzOO/+uorqlWrhpubG0899RSZmZnmvrZt2zJz5szz03haAz+SE+iJB5IxjF+BpqxatYasrCymTJlCcnIynp6eTJkyhQ0bNrBgwQJ69uzJmDFjSEk5BRwHmgEbz1+lBLAbw9jPwYP78qweFRoayvTp04mNjcUwDI4ePcq///4LwOOPP85nn33GwoULyc7O5p9//uHjjz/Odwn6jh07AuDl9Tc3IovYMAzq169/TW3I9ZebXZb/QiabgZwsRW9vb95++20yMjJwdnYGoEuXLuzbt4/U1FRWr17NuHHjrFaijY2NpXPnzkDeVWoffvhhjh8/TqtWra77mETuVHZF3QERuTly30DEx7+JYSzBOlac8wbC11cp4SIixVluXbjQ0FAsls4YxqvkfMK/DYtlIjkZHYtveHA+KiqKYcNeOD8dJce6db/SvHlz4uLiCAwMtDq+cuXKVgGHC6c/xcbGkp2dTeXKla3OsbGxISEhgd69e3P48GEGDRrEwYMHeeSRR5gyZQply5a9IWODnEBYTEwMhw8fJi0tjYiICL799luqVavG/fffT1BQEHv37uXs2bOEhoYyYcIEXn/9dXbt2kWPHj1YvHgxbdu2Zc6cOYSFhXHfffcBEBQUxJdffnn+dRsM7AaigZ7AC+TU//mL5OTTODqWokGDBtjY2NCoUSNGjBhB6dKleeONN7C1tb1glbJW57/anG/PgZx6hVsAKFu2LEePHjVXPg0KCsLDw4MXX3yRxMREmjRpQpcuXQD48MMPSU9P58knnzTrFt133320bNmywNdq7drVHDx4kMOHD+Pt7U1AQIA+HLqNPfjgg/p7U+Q2ocwikTuECkuLiNwZirou3OVW3sxdjetCsbGx+Pr6ms8vnP5UsWJFbGxszCXSc7/S0tKoUKECdnZ2jBw5ks2bN7Nz504OHDhg1tzLbxn16zG+3FpCPXr04MiRIxw5cozt27ezceNG9u/fz+TJk3F0dMTDw4ORI0eaK28tWrSIFi1a0LFjR+zs7Bg0aJBZHwhyAjUbN+ZmAMUCw4C9gB/wJDlTCXOmlZ86dYqQkBBsbGyYM2cOEyZM4N577yUsLIz58+dfkGExBhgOlAZ+ON/eKWAOAJGRkWb/crN1+vTpw9GjR8nMzGTPnj1mAWIbGxvmzZtHWloahmGQmZnJb7/9RqVKlayyjwHc3d0xDIOqVasqi/gOor83RW4fChaJ3EGK+g2EiIjcHEVVF64wK29u3ryNBQsWsG7dOjIzM3nvvfc4ceIE7dq1y7dNLy8vOnfuTFhYmLmMdkJCAl9//TUAK1euZNOmTWRmZuLk5ESpUqWws8tJnvf09OTff/+9biuq5R8I8+LUqaqEhoayaNEikpKSKFOmjFkAOjQ0lCNHjgA59YX8/Pys2rzwecOGDS+oebQMeBhoCsQAK4Fgcqf3lCtXzhwnQJUqVczsrLi4OBo3bnzRlMTKQBw509LSgS/MItMX90nkWujvTZHbg4JFIncYFZYWEbkzFEVduMKsvHnsWAJDhgyhf//+eHh48Pnnn7N06dJL1sCLiIjA3d2d+++/H1dXVwICAvjzzz8BOHLkCN27d8fd3Z3KlSvj5ubG2LFjARgwYADx8fGUKVPGXGr+ahUcCCsJvAp0YP78hZQvX94qA+rUqVPm6nM+Pj7s37/fqt0DBw6Yj3O/Zy4ubsAhoAY5AaKfgTVAcyyWiZQv701SUpJVvaMLs7N8fX05cODARRkeOwEPcqaiWYDlZobHhX0QuR6K69+b16tIvcjtQMEikTuQCkuLiMiNUPDKmxHkLKaQs/2ee+7h77//5tSpU6xbt86s2QOwatUqhg8fbnW2i4sLU6dOZd++fSQnJ7N7924mTJgA5KzOtnPnTlJTUzl27BiRkZFm4Klq1ar8+eefnDx5ki1btlzT2C4dCLPBMF7l6NHDuLu7M3r0aFJSUjAMg/3797N06VIAunbtyooVK/jhhx/IzMzko48+YteuXVYtBQcHk5WVAaSdD/KUAT4FbLFYRgPf89570/D09GTv3r1s2LCBbdu28d5779G3b18AevXqxYIFC/D09GTRokW4ua0DDgADgAAcHR2pX78+wcHBHDp0iMmTJ1/TayOSn+Ly9+bFRerLly/PkiVLzP1LlizB39/f6vhJkybRuHFjXFxcaN68OQcPHrz5HRe5wRQsEhEREZHrouCVN7Ha/t9xxUfBgTCstg8bNoz4+Hjuvvtu3NzcaN++vbkUfY0aNZg/fz5Dhw7Fw8ODDRs20KZNG6tWgoKCOHPmDIMHDz4/jac3kAycNKfxdO3ale+//54///wTLy8vHnnkEZ5//nl69OgBQPPmzXnvvffo378/AwYM4O67a/DBBx+YGR7798dStWpV/Pz8CA4Opnfv3tf99RIpTiIiIpg7dy6pqamULFnyssd/9tlnREZGcuzYMZycnHjttdduQi9Fbi6thiYiIiIi18XtvPKmdSCs8QV7Ys//ux6AmjVrMmjQoALb6dq1K127di1wf4MGDcwaS++999+qaxevJHbXXXexfPnyAtvp27evmWmUn8WLF1s9Hzp0aIHHitxuLl7RcOjQodSoUQOAEiVKXPb8IUOGmCs09uzZk7feeuuG9lekKBSrzKI1a9bQsWNHfHx8sFgsVumBkDPHdMyYMXh7e+Pg4MDDDz/M7t27i6azIiIiIneY23klpNxA2H8Foy+UEwjLLRh9vRSXaTwixUl+Kxq++eZbREVFFboNLy8v87GTkxMpKSk3oqsiRapYBYtOnz7NPffcw/vvv5/v/kmTJvHuu+/ywQcfsGHDBpycnGjdujVpaWk3uaciIiIid6bbdSWk2zkQJnKnKGhFwxMn/AgNDSUqKgpnZ2fOnDljnvPfFFSRO0uxmobWtm1b2rZtm+8+wzCYNm0ao0ePplOnTgDMmzcPT09PlixZwuOPP34zuyoiIiJyxwoJCaFTp04FTqEqrnIDYcOGvUBcXBNzu69vZaZNK76BMJE7Qd4VDXPzJnJXNIxg+PAXad78ISIjIwkJCeHQoUMFJiqI3O6KVbDoUvbt20dCQgIPP/ywuc3NzY0HHniA9evXFxgsOnfuHOfOnTOfJycnA5CRkUFGRsZV9yf33GtpQ+4MulekMHSfSGHpXpHCuFn3yUMPPWQ+zs7OJjv74ulbxU/Hjh1p164d69evJyEhAS8vLx588EFsbW1vy587/U6RwigO98natWs5ceIIDg4LgKzzX5CWBvb22djavszx4y1p3749M2bMoFy5ctSqVYuePXsye/Zsq7FlZmaazzMzM4Fbe+y3kuJwr9zuCvvaW4zcCnrFjMVi4euvv6Zz584A/Prrrzz00EMcOnTIaoWNrl27YrFYWLRoUb7thIeHM27cuDzbFy5ciKOj4w3pu4iIiIiIiIjIzXbmzBl69OjBqVOncHV1LfC42yaz6Gq9+uqrPP/88+bz5ORkKlasSKtWrS75wl1ORkYGP//8My1btsTe3v56dFVuU7pXpDB0n0hh6V6RwtB9IoWle0UKozjcJ2vXrqV9+/bAL8D9+RzxP6AlP/zwA02bNr25nbuDFId75XaXO5vqcm6bYFFuRfojR45YZRYdOXKE+vXrF3heyZIlKVmyZJ7t9vb21+XmvV7tyO1P94oUhu4TKSzdK1IYuk+ksHSvSGHcyvdJs2bN8PDwJD7+LQxjCdZrPWVjsbyNr68XzZo1K/b11YqDW/leud0V9nUvVquhXUrlypXx8vJixYoV5rbk5GQ2bNjAgw8+WIQ9ExERERERkaKkFQ1FrkyxChalpqayadMmNm3aBOQUtd60aRMHDhzAYrEwfPhwJkyYwLfffsvWrVvp06cPPj4+Zl0jERERkeJmyZIl+Pv7F3U3RESKvdwVDStU2Ao0AVyBJvj6bmPxYq1oKHKhYjUN7Y8//iAoKMh8nltrqG/fvkRERDBixAhOnz7N008/TVJSEk2bNuWnn36iVKlSRdVlERERkWIpMDCQzp07M3z48KLuiojIdRMSEkKnTp2IiYnh8OHDeHt7ExAQoIwikYsUq2BRYGAgl1q8zWKxMH78eMaPH38TeyUiIiJy+zAM47ZY4l5Ebj9Llixh+PDhxMbG5ru/bdu2dOzYkSFDhlyyHVtbWwIDA69/B0VuI8VqGpqIiIjI7S4uLs5clfXee+9lx44d5r7U1FTCwsKoVKkS5cuXp0+fPpw6dQqAc+fO8eSTT1K2bFnc3NyoU6cOv//+O5ATAHr33XepWbMm7u7uPPzwwxw8eNBs19/fn4kTJ9K4cWMcHR3p2rUrMTExvPzyyzg7O9O2bdub+yKIiFyFpUuXXjZQJCKFo2CRiIiIXBf+/v4sWbIkz/ZBgwbx8ssv3/wOFVM9evTA29ubhIQEFixYwEcffWTue/LJJ0lMTGTLli3s27ePjIwMwsLCAJg7dy6bN29mz549JCUlERUVZa4WO2vWLD7++GO+++47jh8/TufOnXnjjTdIT083246IiGDu3LmkpqYSGRlJQEAAb7/9NqmpqSxduvTmvghSoIyMjKLugsgtxzAMsrKyirobIrcVBYtERETkhvrggw94++23i7obt6ysrCxWrVpFZGQkX3zxBTExMUyePBlHR0dq1qzJoEGDADh27BhfffUV77//Pu7u7jg5OTF+/HgWLVpEVlYW9vb2pKSksHPnTgzD4K677qJixYoAvP/++4wfP57q1atjZ2dHWFgY6enp/O9//zP7MXjwYGrUqIGtrS0lSpQokteiuEtOTiYsLAw/Pz9cXV25//77GTduXJ7pLp9//jm1atUCIDw8nA4dOtC/f39cXV2pXr06X3/9tXlsv3796N+/P127dsXV1ZUPPviAjIwMxowZQ9WqVfHw8OCRRx7h0KFDQM6b5pdffhkvLy9cXV256667+P777wHYuHEjjRs3xtXVlbJly9KxY8eb88KIXKVLZVpenBG5Y8cOAgMDmTZtGgD33HMP8+bNs2qvbdu2TJw4Ebh0pqaIFLOaRSIiIiK3k6ioKIYNe4G4uNgLtlpYu3atuSqPn58fALGxsWRnZ1O5cmWrNmxsbEhISKB3794cPnyYQYMGcfDgQR555BGmTJlC2bJliY2NpVevXlYFXM+ePUtcXJz5vFKlSjdsnLerrKwsqyK506dP5+zZs6xfvx4vLy82b96Mj48Pb731Fvv27TO/d59++ilPPPGE2c5PP/3E+++/z+zZs1m6dCmPPfYY27dvp2rVqgBERkby9ddf8/nnn5OWlsaoUaP4888/Wbt2LR4eHowcOZLHH3+cNWvW8PPPP7Nw4UI2btyIj48PBw4cIC0tDYCwsDA6duzIr7/+SkZGBhs2bLj5L5rIJVz8M/Xaa69RpUoVEhISOHDgQJ4psREREXz77bdUq1YtT2ZR7969mT9/Pn369AEgISGBFStW8OGHHwI5mZp2dnZs2bIFe3t7BgwYQFhYGPPnz785gxW5xSmzSERERK67I0eO0LBhQ0aMGEHfvn3NFbViY2OxWCzMnz+fatWq4e7uTr9+/aym1ixevJhq1arh5ubGU089RYcOHQgPDy+agdxAUVFRhIaGEhdXF1gPpABLAINHH32UqKgoAA4cOABAxYoVsbGx4dChQyQlJZlfaWlpVKhQATs7O0aOHMnmzZvZuXMnBw4cYNy4cea5X375pXnOsWPH+OKLL3j88cfN/tjYWP9ZePFzsRYVFYW/fzWCgoLo0aMHQUFBLFmyhC5duuDj44ONjQ0NGjTA09OTRx55hLlz5wIQHx/P6tWr6d27t9nWXXfdxcCBA7Gzs6Njx44EBQURGRlp7m/VqhWtW7fGxsYGBwcHZs6cydSpU/H29qZEiRJMmDCBdevWcfDgQezt7UlLS2P79u1kZGRQqVIl7rrrLgDs7e3Zv38/hw4domTJkjRr1uzmvmgil5Dfz9TatWsJDAzMk2mZ61IZkT179mT16tXEx8cDmNNrK1aseNlMTRFRsEhERESuwYVTqNLS0sjOzmbPnj00bdqU3r17M2nSJCwWS57zli5dyl9//cWOHTtYsWIFCxYsAGDXrl307t2bGTNmcOLECRo1asSyZctu9rBuuKysLIYNewHD6EBOgKgx4Ax0Ah4CKjJ06PPs2LGD2bNnA+Dl5UXnzp0JCwvj+PHjQM4n5blTllauXMmmTZvIzMzEycmJUqVKYWeXk0T+zDPPMGbMGP755x8gZ7rUhg0bSElJKbCPnp6e7N2794aMv7jLP9A3B7Bh8ODBZqAv15NPPsm8efMwDIN58+bRqlUrs54U/Jc9duHz3De4YJ31dfz4cU6fPk2zZs1wd3fH3d0dLy8vSpQowcGDBwkKCmLcuHG89tprlC1blkcffZR9+/YB8Mknn5CWlsa9995LzZo1mTFjxvV+aUSuyqV+pvr372/+TF38s3KpjEhvb2+Cg4PN/1/mzZtnZhldmKmZ+3N0//33m5maIqJgkYiIiFyliz8FPnLkCP36PckDDzzAuHHjeO655wo8d8yYMbi4uODj40ObNm34888/AVi0aBEtWrSgTZs22NnZ8dRTT5lZEbeTmJiY81PPRpL3z7GFgBfx8fvp3LkzTz75pLknIiLCfFPj6upKQECA+dodOXKE7t274+7uTuXKlXFzc2Ps2LFAzvSjfv36ERISgqurK/Xq1WPNmjWX7OPw4cP55ZdfcHd3p0OHDtdt7MVdwYG+DkA2htGC4cNftMpOaNmyJZmZmaxevZq5c+daTUED2L9/v9XzAwcOUKFCBfP5hVleHh4eODo6smHDBqsMs7Nnz9KkSRMAhgwZwm+//caBAwcoWbIkQ4cOBaBq1arMmzePhIQE5syZw4svvmjeP7e6ggroS/FX8M9UK3J+plqaP1O5mZa5LpcBmTsVbdu2bezatYtHH30UuHympogoWCQiIiJXIf9Pgb1ISUkjMTERe3v7S55/YVaFk5OTmeFy6NAhsyhzrtuxls7hw4fPP6qTz95KwAoAxo0bx6hRo4iNjQXAxcWFqVOnsm/fPpKTk9m9ezcTJkwAoHv37uzcuZPU1FSOHTtGZGQk7u7uAFgsFoYMGcL27dtJTk4mNjaWl156CRcXFyDnU/bOnTtb9eKBBx5g586dJCUlmQWS5VKBPk9yMsNOc/DgPlavXs1ff/3FiRMnsLGx4YknnmD48OEkJibmCb7t2rWLjz76iMzMTH744QdWrlxJt27d8r2+jY0NgwYN4oUXXuDgwYMAnDhxgkWLFgHw+++/8+uvv5Keno6DgwNOTk5mhllwcDBhYWFYLBZOnz7NuXPnOHPmDJBTSDt3umhxk5mZiWEYRd0NuUoF/0xVJCfT0p6DB/exYMECM9OysLp06cL+/ft58cUX6dKlC87OzsDlMzVFRMEiERERuUIFfwpcEvgEKEffvv3MorpXwsfHx3wDnOviT5JvB97e3ucfbSvgiG0XHSe3iksH+uYCdwPQoUMHBg0axNmzZwF44okn2LJlC7169coTTG3Tpg2//fYbZcqUYdiwYXz22WdUr169wD5MnDiRBx98kODgYFxcXLj33ntZvnw5kDPFcMiQIXh4eODl5cWhQ4eYPn06AL6+vixevBhnZ2eefvppAOrWrXv1L8ZN8thjj3HgwAG6d++Os7MzgwYNwmKxMGPGDOrUqYOTkxOpqans3buXjh07Uq5cOfz8/JgwYQLZ2dlmO7/88guNGjXC3d2d2rVr8+233xbhqCTXpX+mFgI5Ac2xY8daZVoWhqOjI48++ijLli0zp6DlulSmpohoNTQRERG5Qv99ChxJ3s+dHIEvOHs2iBYtWhAdHX1FbXft2pU33niD5cuXExwczLx589i1a9f16fgtJCAgAF9ff+Lj38QwlmD9OmZjsUzE17cyAQEBRdRDKYh1oK/xRXvdgAHAJ/z4448EBgaae8qXL4+jo2O+b3bt7Oz4+OOP+fjjj/Psi4iIyLOtRIkSjB49mtGjR+fZ16JFCzZt2pRv3y9cRjw2NjbPynq3qi+//BJ/f3+mTZtmZsDNnj2bhQsXsnz5cjw8PMjKyqJFixYMHz6cr776ioSEBNq1a4e3tzf9+/dny5YtPPbYY3z11VcEBgby66+/0r59e/73v/9Ro0aNoh3gHe7SP1OVgDeAJnz66acEBgYyatQoADPj8kKrVq3Ks+3TTz/l008/zbM9N1Nz6tSp19B7kduXMotERETkilz6U2CAe4Gcpdm7dOnCuXPnCt12jRo1mDt3LoMHD8bDw4P169cTHBxMyZIlr63TtxhbW1umT38H+B6LpTP/TeVbf/7590ybNsVqqXu5NeQG+iyWN4Hsi/bmBPoqVrQO9BmGwXvvvUeDBg2oU6egn5urt2jRIho3/u9N9qOPPmqVlfbCCy/w7LPPXtFUsz/++IOHHnqIcuXKERYWxueffw7kTPlycXHh77//BuC7777DYrHw008/AbB161bc3d2vy4pS+RXQv9CIESPw8fGhZMmS/PDDD5QuXZrhw4dTokQJKlWqxLBhw1i4cCGQE1zq168fwcHB2NjY0LRpUzp06MAXX3xxzf2Ua3M1P1MicuMps0hERESuSMGfAsee/3c9AFOnTrXKrICcIrUX1xaZNm2a1fNu3bpZ1WupUaPGbVm3KCQkhMWLFzNs2AvExTUxt/v6VmbatMWEhIQUYe+kILmBvtDQUCyWzhjGq+QETrdhsUwkJ9C32Az0ZWVl4e7uTtmyZfnqq6+ua1+ysrKIiYnh5MmT/PHHHyQlJeHm5sbatWtxdHRk586d3H333axcuZIxY8bwzTffFKrdpKQk2rRpw9ixY+nfvz//93//x+DBg6lSpQoPPfQQAQEBREdHU7NmTVauXEnVqlWJjo6mTZs2rFy5kubNm19zoDMqKur8z0asue3pp3OWTc/92bjw90JsbCzbtm0z63QBZGdnmzXQYmNjWblypVWGSWZmJq6urtfUT7l2V/ozJSI3hzKLRERE5Irc6E+Bv/vuO1JSUjh37hzvvPMOhw8fpk2bNtfc71tRSEgIsbF7iI6OZuHChURHR7Nv324Fim5xuYG+ChW2Ak0AV6AJvr7bWLzYOtBna2tLSkoK+/bto2HDhnnaCg8Pv6pVvi5cjXDw4MFkZWVRrVoN3nnnHfz8/OjQoQPR0dEkJiaybdu2PIHbS/nhhx8oV64czz77LPb29tSpU4fHH3+cuXPnAhAUFGROMV25ciVjx461eh4cHHzF47l4bHkL6Ptw4oQfoaGh5jLqF66EVbFiRe69916rla2Sk5PZvn27uX/YsGFW+1NTU5k1a9Y19VWujyv5mRKRm0PBIhEREbkiN3oK1bJly/Dz86Ns2bJERkby7bff4uHhcb26f8uxtbUlMDCQ7t27ExgYqE/Pi4miDPTlH0x5lBMnXHjppZeoUKGCWTMsOjqaevXqUbp06UK3HxcXh7+/v9W2ypUrExcXB+QEi1atWsWxY8c4duwYPXr0IDY2lpMnT7JmzZprChYVXEC/EtAN6MDw4S/mOa9Dhw4cOXKEmTNnkpaWRlZWFv/8849Zw2bgwIF8+umnREdHk5WVxblz51i/fj07d+686r7K9aXgucitRdPQRERE5IrdyClUM2bMYMaMGdejmyI3VG6g72bKG0zJ/ey3OzlTQcuzdu165syZw6BBgyhXrhxBQUFXdA1fX988xYP379+Pr68vAA0aNCA9PZ0ZM2aYU86aNm3KtGnTzEykq1VwAf2RwFAM4zgHD6bmOc/Z2ZlffvmFESNGMH78eNLS0qhatSovvfSS2efIyEhGjx7Nzp07sbGxoX79+kyZMuWq+yrXX1H8TIlI/hQsEhERkasSEhJCp06diImJ4fDhw3h7exMQEKDMGJEbqOBgSnPgccCB48dT2Lp1K76+vixYsIDIyMgruka7du0YOnQoM2fO5IknnmD79u1ERkaydOlSIOcNfbNmzZg2bRqTJ08GIDg4mFGjRtG6dWssFstVj6/gAvodz3+lAK4sXLiQ+vXrWx1RtWrVS9aFCg4OvuYpciIidwpNQxMREZGrpilUIjdXwcGUskAtoLZ5XIsWLThz5gzNmjW7omuULl2apUuX8tlnn+Hl5cWsWbN47733aNq0qXlMUFAQycnJZvClRYsWVs+vlnUB/fxsu+g4ERG5EZRZJCIiIiJSTBS8GiHAZnJqGDXB29ub7t278/bbb5t7IyIizMcXr0x44T6ARo0a8euvv5KRkcGPP/5Iu3btrPY/99xzPPfcc+bzu+++O89Kh1cjt4B+fPybGMYSrD/bzimg7+urZdRFRG40ZRaJiIiIiBQTN3o1wqJ2owvoi4hI4ShYJCIiIiJSTNwJwRQtoy4iUvQULBIREZFrFhcXR6tWrXB1deXee+/lzTffNJfetlgsbNq0yTx22rRpVqvdHD16lJ49e+Lt7Y2Pjw/Dhw/n3Llz5v6NGzcSFBREmTJlqFatGh999JG5Lzw8nI4dOxIWFoa7uzuVKlVi0aJFN3q4IkXqTgimaBl1EZGipZpFIiIics169OhB5cqVSUhI4MCBA7Rt27ZQ5xmGwSOPPMJDDz3E3r17OXv2LKGhoUyYMIHXX3+dhIQEWrZsyaxZs3j00UfZuXMnrVq1okqVKrRo0QKAZcuW8dlnnzF9+nQWLFjAgAEDaNeuHS4uLjdyyCJF6k5YjVDLqIuIFB1lFomIiMgVy8rKYtWqVURGRvLFF18QExPD5MmTcXR0pGbNmgwaNKhQ7fzxxx/s3r3bPNfDw4ORI0eycOFCAObPn0+zZs3o2rUrtra21KlThyeeeMLcD9CwYUNzf+/evUlPT2fXrl03ZNwitxKtRigiIjeKgkUiInJLSU5OJiwsDD8/P1xdXbn//vs5ePAgR44coWvXrpQrV45KlSoxatQoMjMzAfh/9u47rqryD+D453LZ8yqyp5obZ7lTASvTnIgDnGmWKyVXKWpaKoa5MmeamIpailRmv8yAwpm7HJkayEYEmbI5vz+uHLkMw1RAfd6vFy/vvc855z7neO443/t9vk/Pnj3ZsGEDAGlpaWhra/PBBx8A6swVCwsLzpw5U2379KwJCgrC2fkF3Nzc8Pb2ZsiQIYCCI0eOyMs4OTlValuRkZGkpqZSu3ZtVCoVKpUKT09PEhMT5faDBw/KbSqVis8++6zE9OFgbW0t31YoFBgYGJCRkfF4dlYQBEEQBOE5JIJFgiAIQo0yevRorl+/zvHjx0lNTWXTpk0YGBjg7e2Njo4OERERhIeHExwcjL+/PwBubm6EhoYCEBYWhrOzs3z/jz/+oLCwkNatW1fbPj1LgoKC8PT0JCamOfcL6wYDEgMHDiQoKAiAqKgoeR0jIyPu3r0r3y8Z6HFwcMDS0pLU1FT5Ly0tjczMTLl9wIABGu0ZGRkcPHjwye+sIAiCIAjCc0oEiwRBEIRqVXI4U1BQEPv372fTpk3Y2tqipaVF69atyc3NJSQkhBUrVmBsbIyTkxO+vr4EBAQA6mBRWFgYACEhIUydOpUbN26Qnp5OSEgI3bp1Q0tLfOQ9qsLCQqZOnY4k9UYdIOoAGAP9gM6AA1OmTOPy5cts3LhRXq9NmzZs376dgoICzp8/z/bt2+W2tm3b4uDgwNy5c8nIyECSJG7evMmPP/4IwIgRIwgJCWHfvn3k5+eTn5/P+fPnOXXqVNXtuCAIgiAIwnNGfHMWBEEQqk3p4UwDBw4E1HVsSoqJiUFfXx8rKyv5sXr16hETEwOogxE5OTlcunSJkJAQXnnlFV5++WXCw8MJCQnB3d296nbqGRYeHk5MTCQwh7JfIQIBa2Jjb9K/f3/GjBkjt6xZs4bjx4+jUql4//33GTVqlNymVCo5cOAAsbGxNGnSBDMzM9544w2uX78OgJ2dHT/99BMbN27ExsYGKysrJk2aRHp6+pPeXUEQBEEQhOeWmA1NEARBqDRjY2OOHz9O8+bNH3lbxcOZ1FkquwAX4DfgDQYOHMi+ffvkKZLt7e3JyckhMTFRDhhFRkZib28PqAMOXbt2Zc+ePaSkpNCkSRPc3d35+eef+e233/Dz83vk/golh4+5lNPqCPwCmLJw4UIMDAzklpYtW3L+/PkKt2tpacnWrVsrbG/dujWHDh0qt23BggVlHktNTa1wW4IgCIIgCMK/E5lFgiAIQqVlZmY+lkBRxcOZegF9AUvefdeH/Px8zp07h76+Pm5ubsyYMYOsrCyioqJYvHixRoaKm5sbq1evplu3bgC4u7uzdetW9PX1cXEpL7ghPCwbG5t7ty5WsMTFUssJgiAIgiAITyMRLBIEQRCq3IOHM30FdCIuLhqVSsX48ePJzs4mMDCQ7OxsnJyc6Ny5M2+88QazZs2S13JzcyM9PV0ecubi4oKBgQFubm5VtFfPvi5dumBv74xCsQQoKtVahELhh4NDXbp06VId3ROecs7OzgQHBz/WbY4ePRofH5/Huk1BEARBeB6IYJEgCIKgwdnZmcWLF9OmTRtMTU3p0aMHcXFxgHpa8uLhREVFRcydOxcrKytsbW1Zu3YtKpVKLjT9IA8ezmSGOmAEmzdv5uTJk9jb22Ntbc3evXu5ffs20dHRLF26FB0dHXmt1q1bI0kSb731lvxYQkICu3fv/g9HQSiPUqlk9erlwAEUiv7cnw3t+L37B1i16lOUSiX9+/cnMjKy+jorCIIgCIIg/GciWCQIgiCUsXnzZgIDA0lISMDa2prhw4eXWWbr1q3s3LmT8PBwbty4wdmzZ8nIyKjU9sVwpqeXh4cHe/fuxc7uT6ATYAp0wt7+Inv37pXrTAmCIAiCIAhPLxEsEgRBEDSmr8/JyeGdd96hcePGGBoa4u/vT2hoqDzzWLHAwEAmTZpEw4YNMTAwYOnSpRQVlR6aVD4xnOnp5uHhQWTkdUJDQwkMDCQ0NJSIiGsiUCQ8skuXLpXJaoyMjEShUGgULvfx8WH06NEA5ObmMmbMGOrUqYOZmRkuLi6cOnVKXjYrK4uhQ4diYmJCo0aNKpX9KAiCIAjPOxEsEgRBeM6Vnr4+MTGRZctWEBQUBICVlRV6enrExsZqrBcXF4eDg4N838LCAn19/Uo958MMZxJqJqVSiaurK15eXri6uor/K+GhlQxSFwdwKpPVWNq2bdu4cOEC169fJzU1laCgIKytreX2PXv2MH78eFJTUxkxYoQcZHreSJJEYWFhdXdDEARBeEqIYJEgCMJzrHj6+piY5twP2FiTklIHT09PgoKCuHXrFrm5udjZ2Wmsa2trS3R0tHw/KSmJnJycSj+3GM4kCM+v0kFqNzc3YmJi6NKly79mNZamo6NDRkYGV65cQZIkGjZsqBHI7tWrlxzQfPPNN7l58ybJyclPeherTExMDK+++iqmpqa8+OKLLFmyBGdnZ0Bdg87Pz48OHTpgaGjI5cuX2bFjBy4uLpiYmODo6Mi8efOQJAlQB5Tef/99rK2tMTU1pWHDhvzwww8AnDt3jg4dOmBqakqdOnXo06dPde2yIAiCUAVEsEgQBOE5VfH09XpALpLkypQp05g1axZdu3bF3t5eY30vLy/WrVvH9evXyc7OZs6cOWhpPdzHSk0azrRgwQL69+9f5c8rCM+b8oPUxyks1GX79u1lshpLFrIvT3G20Pjx46lTpw6jR4/m9u3bcnvJLCMjIyOAStdXexp4e3vj5OREYmIiu3btYsuWLRrtAQEBbNu2jczMTBo1aoS5uTlBQUGkp6fz3XffsWnTJgIDAwH4+eefCQwM5OzZs6Snp3P48GEaNGgAwNSpU+nTpw+pqanExsYyc+bMKt9XQRAEoeqIYJEgCMJz6sHT148BYomNvcmlS5fYuXNnmfXHjBnD0KFD6dSpE/Xr16dVq1bo6+ujp6f3UP0Qw5kE4flRcZC6A2AJNMHHZwaFhYVlshrv3r0rb+f+jIqgra3NnDlzuHDhAleuXCEqKoqFCxdW3U5VsZLD977++mvCw8NZunQpBgYGNGzYkPHjx2ssP2HCBBo1aoRSqURXV5eePXvSsGFDFAoFrVq1wsvLSx4GqKOjQ05ODpcuXSI/Px9HR0caNmwot928eZO4uDj09PTo2rVrVe+6IAiCUIVEsEgQBOE59eDp65sBpwGYNm2anFUkSRKtWrUCQEtLiyVLlnDr1i3i4uLw8PDg7t27ODo6PtF+P2jIRWJiIoMHD8bCwgJHR0d8fX0pKCgAIDMzk379+mFpaYmZmRldu3blwoULAAQHB7NkyRIOHDiAsbExxsbGT3QfBOF59eAgNUAa0dERHD58mPfff1/OanR0dGTbtm0UFRURGhrKwYMH5TVCQkI4f/48BQUFGBkZoa+vj7a2dhXtUdUqPXxvyJAhgILffvtNXqb0e3Dp+z/99BOdOnWSC4Jv2LBBzsRyc3Nj4cKFzJs3jzp16jBw4EAiIiIA2LRpEzk5Obz44os0btyYzz///MnurCAIglCtRLBIEAThOfWo09cXFBQQHBxMfn4+d+7cwcfHh06dOpWpbfS4PWjIhbe3Nzo6OkRERBAeHk5wcDD+/v4AFBUV4e3tTUREBImJibRu3ZrBgwcjSRL9+/dnzpw59O7dm8zMTDIzM5/oPgjC8+rBQWqAsQAMGDCA2NhYOavxyy+/ZOvWrZiZmbFx40aGDh0qr5GYmIiXlxcqlYq6detiZmbGhx9++AT3onqUP3wvGJAYOHCgPHwvKipKY72Sw4Pz8vLw8PDgnXfeITY2lrS0NMaPHy/XLAKYOHEiJ06cICoqCj09Pd577z0A6tevz1dffUVCQgKbN29mxowZnDlz5snutCAIglBtns2fXQRBEIR/VTx9fWzsEiQpGM3fD9TT19vbVzx9vSRJLF26lJEjR6KtrU2nTp3kuhePU2FhIeHh4cTHx6NUKgkPDycoKEhjyMXatWuJjY0lJCSEhIQEOTvI19eXBQsWMGfOHExNTe/9Cq+2cOFCPvvsM+Li4p54gEsQBDXNIHWHUq2RqIMgH3Pw4EFcXV3llu7du/P333+Xu00vLy+8vLzKbQsICNC4r1KpNAIjT4uyw/eK36/7AZ2BaKZMmUbTpk3ZtGlThdvJzc0lJycHc3Nz9PT0OHnyJIGBgXTq1AmAU6dOkZ+fz0svvYSBgQFGRkZkZWUBsH37dt544w2srKxQqVRoaWmJYcOCIAjPMJFZJAiC8JyqePr6XSgUAfzb9PU6OjqcOHGC9PR0UlJSOHDgAE5OTo+1jw8z5CImJgZ9fX2srKzktnr16skzKWVnZzNx4kScnZ0xNTWVh66VLIQrCMKTVRykViiWAEWlWtVBageHioPUz6sHD98LBCyJjb1J3759GT58eIW140xMTFi7di1vv/02pqamLF68WCOInp6ezsSJEzE3N8fa2pq4uDhWrFgBqIf7tWzZEmNjY/r168eyZcvkYcmCIAjCs0dkFgmCIDzHiqevnzp1OjExneTH7e3rsmpV9U5fXzzkQv1L+i7Uw1Z+AfozcOBA9u3bh4eHhzzkwt7enpycHBITE+WAUWRkpFxvafny5Zw5c4YjR45gb2/PunXrmDRpkpxl8LAzuQmC8PCKg9Senp4oFP2RpNmoX9sXUSj8UAep94qMlVIePHzPEQgBTFm4cCGRkZFyED0yMrLM0uPHjy9TBLtY9+7dOX/+vMZj+fn5XLx4ka1bt/7rzHSCIAjCs0N8MxYEQXjO1aTp64tVPGNS8ZALR6ZMmcZff/0lD7mws7PDzc2NGTNmkJWVRVRUFIsXL2bUqFGA+hdzfX19atWqRWZmJnv37tV4TisrK27evCkXxBYE4ckoDlLb2f0JdAJMgU7Y219k797qDVLXVA+uMXcWOACo3+fWrFnDoEGDqqprgiAIwjNKBIsEQRCEGjd9/X8dchEYGEh2djZOTk507tyZN954g1mzZgHqWd2USiVWVla4uLjwwgsvaGx10KBBmJqaYmFhgUqlesJ7KAjPt5oYpK7JHjx8LxEYi0KhwM/Pj3HjxjF27Nhq6KUgCILwLBHBIkEQBKHGqdyQC3WRal1dXXnIhbW1NXv37uX27dtER0ezdOlSdHR0WLFiBe3atePUqVPUqVOHuXPn0qlTJ1q2bMn333+PpaUlTZo0YcCAAdy5c4fU1FQAdu/eTYsWLVCpVLRt25Zjx47JvcjPz2f+/PnUr18fc3Nz+vbtS1xcnNyuUChYvXo1jRo1QqVSMWTIENLS0p7A0RKEp1NNC1LXZBXXmDuOQrEehSKHvXv3EhkZycKFC8WxFARBEB6ZCBYJgiAINc6jDrkoLCwkLCyMXbt2sX37dubOncuhQ4fIyMjg5MmTtGvXDoBLly5haGhIbGwse/bsYebMmdy4cQOAgwcPMmPGDAICAkhJSWH27Nn06dOH5ORkAHx9fTl69ChHjhwhPj6ehg0bakznDerZg0JDQ4mMjOTOnTv4+Pg8rkMkCMJzRgzfEwRBEKqSCBYJgiAINc6jDLkoPYPayJEjyc7OYcuWLWRnZ2NlZUWLFi0AqFOnDtOnT0dHRwdXV1ecnZ3l4q5r165l5syZtGnTBi0tLTw8PGjcuDEHDx5EkiTWrVvHihUrsLGxQVdXl0WLFnH06FGio6PlvsyaNQtbW1tUKhUff/wxgYGBFBWV3h9BEITKEcP3BEEQhKoigkWCIAhCjfNfh1wUz6AWE9NcYx1ow6effoq5uTmvvfaaHBAqnjWtmJGRERkZGYB6FqE5c+agUqnkv/PnzxMbG8vt27fJysqia9eucpu1tTW6uroawSInJyeN23l5eSQlJT2ZgyYIwnNBDN8TBEEQqoIIFgmCIAg10sMOuah4BrUOwO8oFH2oXduS5s2bM2LEiH99fgcHB5YvX05qaqr8l5WVxQcffIC5uTmGhoacPHlSoz07O5tOnTrJ27h586Z8OyoqCl1dXSwsLB752AiCIAj3OTs7Exwc/MjbSUpKwt3dHVNTUzGjnCAIzz0RLBIEQRBqrIcZclHxDGpXgV+QpGnExt4kJSUFbW3tf33uSZMmsWzZMs6cOYMkSdy9e5fDhw8TExODlpYW48ePZ/r06XImUXJyMnv27NHYxrJly4iLiyM1NZX58+czdOhQtLTER68gCEJNtHHjRpRKJampqXzzzTfV3R1BEIRq9e/flgVBEAShGhUPufg3Fc+glgfMAy4DcOLECXbv3s25c+ceuL0+ffqQk5PDuHHj+Oeff9DT06Ndu3asXbsWAD8/P/z9/XF3dychIQFzc3O6d+/OkCFD5G0MHz4cNzc3EhISeO2111i9enXldloQBEGochERETRr1kwE9QVBEBCZRYIgCMIzouIZ1JoDJ4CfAFi/fj0tW7Zk9OjRcu2iYufPn2f06NHy/UGDBnH27FlSU1NJTEzk+++/x9HREQBdXV3mzp3LtWvXyMjIIDIyki1btmhsr1u3bly9epW0tDS++eYbVCrV49pdQRAEoRyHDx+mXbt2qFQqmjVrxnfffSe3HTp0iJdeegkzMzNsbGyYOHEi2dnZgPr9/quvvmLdunUYGxuXeT8XBEF43ohgkSAIgvBMePAMakUoFH44ONSlS5cu1dE9QRAE4Qn7448/GDRoEEuXLiUlJYWNGzcyYsQIrl69CoCBgQFffPEFKSkpHD16lNDQUFasWAHAN998w7Bhw5g4cSKZmZkas2wKgiA8j0SwSBAEQXgmPHgGtf7AAVat+lTMHCQIgvAMKCwsJCwsjF27dpGTk0NRUREbN25k9OjRuLu7o6Wlxcsvv0zv3r35+uuvAfWPCq1bt0apVFKvXj3eeecdwsLCqndHBEEQaihRs0gQBEF4ZhTPoDZ16nRiYu7PSmZvX5dVq8rOoPYkSZJUZc8lCILwPAkKCrr3Ph8pP/b22+NxcnLg8uXLbN26VX68oKAAU1NTAE6dOsXs2bP5888/yc7OpqCggEaNGlV19wVBEJ4KIlgkCIIgPFM8PDzo168f4eHhxMfHY2NjQ5cuXURGkSAIwjMgKCgIT09PJKk3sAv1pAYNSE62Jzn5NP3792f//v3lruvl5cWbb77Jt99+i5GREatWrSIgIKAKey8IgvD0EMEiQRAE4ZlT2RnUBEEQhKdHYWEhU6dOvxcoCuZ+RQ09YDawmu+++47Dhw/j5uZGQUEBZ8+eRaVS0aRJE9LT01GpVBgZGXHlyhXWr1+PgYFBde2OIAhCjSZqFgmCIAiCIAiCUOOFh4ffG3o2h7KXMVrAUoqKivDx8cHCwgI7OzvmzZtHbm4uABs3buTTTz/F2NiY8ePHM3To0KrdAUEQhKeIyCwSBEEQhGrg7OzMqlWr6N+/f3V3RRAE4akQHx9/75ZLqZbIe/9mAODr64uXl1eZ9QcMGMCAAQM0Hlu4cKF8WwxJEwRBuE9kFgmCIAiCIAiCUOPZ2Njcu3WxgiUullpOEARB+K9EsEgQBEEQnmL5+fnV3YUnxtnZGT8/P9q2bYuRkRE9e/YkJSWFiRMnolKpaNCgAceOHQMgIyODt99+GxsbG2xsbBg/fjxZWVkAhIWFoVKpNLbdv39/FixYAEBKSgoDBgygVq1aqFQqXnzxRW7evAmoj+/8+fOpX78+5ubm9O3bl7i4uCo7BoIg3NelSxfs7Z1RKJYARaVai1Ao/HBwqEuXLl2qo3uCIAjPFBEsEgRBEIRq8vfff9OhQwdMTEzo1q0b0dHRAFy/fp0ePXpQu3Zt6tevz6pVq+R1AgICaNWqFR9++CHW1tYMHTpUI9hhYWHBtGnTnplgx549ewgKCiIuLo7o6Gg6dOjAK6+8QnJyMt7e3owfPx6AqVOncv36dS5evMiff/7JX3/9xXvvvVep5/j0008pKCggNjaW5ORktmzZgomJCaAeznL06FGOHDlCfHw8DRs2FHVOBKGaKJVKVq9eDhxAoegPHEc99Oz4vfsHWLXqUzH7pSAIwmMggkWCIAiCUAUKCwsJCwtj165dhIWFAbBjxw527dpFUlISRkZGzJs3j4KCAnr37k3Lli2Ji4tj//79+Pv7ExgYKG/r4sWLaGtrExUVxfbt2zWCHQkJCUyePPmpDXaUPE45OTm88847ODg4YGZmRq9evTA3N8fDwwOlUsmQIUO4ePEieXl57Ny5Ez8/P8zNzalTpw5Llizhq6++oqiodPZBWTo6OiQnJ3Pt2jWUSiWtWrWidu3aSJLEunXrWLFiBTY2Nujq6rJo0SKOHj0qB/YEQahaHh4e7N27Fzu7P4FOgCnQCXv7i+zduxcPD49q7qEgCMKzQRS4FgRBEIQnLCgoiKlTp9+bxUdNqVQyduxY6tatC8CwYcNYunQpJ0+eJD4+nkWLFqGrq0uLFi2YPHkyAQEBeHt7A2BmZoavry9aWlro6upqBDuaNm1KvXr1NIIdR48elWt4LFq0CCMjI6Kjo3FwcKjyY/Eg5R2nefM+xMrKCg8PDwwNDbGyspLbDA0NkSSJO3fukJeXh7Ozs9xWr149cnNzuX379r8+78yZM8nJyWHw4MGkpaUxZMgQli5dSmZmJllZWXTt2hWFQiEvr6urWyOPnyA8Lzw8POjXrx/h4eHEx8djY2NDly5dREaRIAjCYyQyiwRBEAThCQoKCsLT05OYmOaUHDJRWKjLpk2bCAoKAsDIyIiMjAxiYmKwtbVFV1dX3ka9evWIiYmR79vZ2aGldf8jfObMmXTp0oXBgwfj4ODA5s2byc7O5vbt23KwQ6VSoVKpsLa2loMdNUn5x8ma5GQnPD095eNUHh0dHXR1dYmMjJQfi4yMRE9Pjzp16mBsbEx2djaSJMnt92dVAmNjYz755BOuXr3K8ePH+eWXX1i3bh3m5uYYGhpy8uRJUlNT5b/s7Gw6der0+A+CIAiVplQqcXV1xcvLC1dXVxEoEgRBeMxEsEgQBEEQnpDCwkKmTp2OJPUGgoEOgPG9fy2Bl/DxmUFhYaG8jr29PXFxcRqFqyMjI7G3t5fvlwwUgWaw47fffuOPP/5gw4YNT02wo+LjpAfMBnrj4zOjwiFlWlpaeHt74+vrS0pKCsnJycyZM4cRI0agpaVFw4YN0dHRITAwkMLCQnbt2sW5c+fk9Q8cOMDff/9NUVERpqam6OjooK2tjZaWFuPHj2f69OlycC05OZk9e/Y82QMiVIni+l8lOTs7ExwczM6dO3F0dJSnXy9+vKRVq1bh6upaNZ0VBEEQhComgkWCIAiC8ISEh4ffG1I1h/I/cgcSHR1BeHi4/Ei7du2wsrJi/vz55ObmcvHiRdasWcOoUaMqfJ7SwQ6lUvlUBTsefJy0kKTZREdHEBUVVeE2Vq9ejbOzM02bNqVZs2a88MILrFixAgBTU1O++OILPvjgA8zNzTl69Cg9evSQ171+/Tqvv/46JiYmNG3alI4dOzJhwgQA/Pz86NixI+7u7piYmPDiiy9y6NChx3sAhBpn2LBhREVFsWvXruruiiAIgiBUC1GzSBAEQRCekPtDnVwqWMJJXs7AwABQD6k6cOAAkydPxtramlq1ajFt2jS5XlF5rl+/zpQpU0hMTMTY2Jg2bdrwzjvvAOpgh7+/P+7u7iQkJGBubk737t0ZMmTI49rNR1bxcYq8928GAD169JAzPUCd7VFyaNnmzZsrfA4vLy+NdUvy8fHBx8en3DZdXV3mzp3L3LlzH7AHQk2Un5+Pjo5OdXdDEARBEJ5KIrNIEARBEJ6Q4qLScLGc1kjAWV6uf//+cs2dhg0bcujQIe7cucM///zD9OnT5QLLo0eP5vz58xpb8vHx4Z9//iErK4uYmBjGjx8v1zwqDnZcu3aNjIwMIiMj2bJly+Pe1Ufy4ON0//H7ywnPs8TERAYPHoyFhQWOjo74+vpSUFBAWFgYKpWK9evX4+joKA+1/Pnnn2nfvj0qlQobGxv8/Pw0tvfxxx9jaWmJlZUV6enpgHqImrW1Nf3795eXi4qKon379mhpadG6dWvi4uLktiZNmtS4jD1BeFSurq6sWrWqurshCEI1EcEiQRAEQXhCunTpgr29MwrFEqB0vZ0iFAo/HBzq0qVLl+roXo0hjpPwbwoLCwkLC2PXrl307NkTbW1tIiLUQziDg4Px9/cHICMjgwsXLvDXX3/x66+/cu7cOfr168esWbNISkrir7/+ws3NTd7upUuXMDQ0JDY2lj179nDnzh2N4ufFJEliyZIlvPLKK0ydOhUbGxs5k+348eMkJiZqBJYEoSaQJEmjJp4gCMLDEMEiQRAEQXhClEolq1cvBw6gUPSn5Gxo6vsHWLXq0+d+Fh9xnIQHCQoKwtn5Bdzc3PD29ubcuXOEhR3h0KFDODk54evrS0BAAABFRUUsXboUQ0NDDA0N2bRpE0OHDmXgwIHo6OhgZmZGhw4d5G3XqVMHHx8fjh49Snx8PEqlkn/++adMH3Jzc8nIyGDBggW8/fbbhIeH4+HhAaizkLy9vdHT06uS4yEID+Ls7Iyfnx8dOnTA0NCQy5cv06NHD2rXrk39+vXLZArt2LGDJk2aoFKpePnllzl79iwA06dPJzw8nPfffx9jY2N69uxZDXsjCEJ1EsEiQRAEQXiCPDw82Lt3L3Z2fwKdAFOgE/b2F9m7d698wfm8E8dJKE9QUBCenp7ExDRHHUQMAXRJSGiFp6cnQUFB1KtXj5iYGABMTExQqVTy+jdv3qRBgwYVbl9fX18jEFVYWMj69Rs5ffq0xnKFhYXUqlULHR0dmjRpgouLC2lpaRQVFbFnzx7GjBnz+HdeECqpZOZdTk4OAQEBbNu2jdTUVPr160fLli2Ji4tj//79+Pv7ExgYCMBvv/3GhAkT2LhxI0lJSXh6evL666+TlpbG8uXL6dKlC5988gmZmZn8+OOP1byXgiBUNREsEgRBEIQnzMPDg8jI64SGhhIYGEhoaCgREddEAKQUcZyEkgoLC5k6dTqS1BsIBjoADYE8JGkj0Bsfnxn8888/2NvbA6ClpfnV1snJievXr5e7/dOnTxMZGVkiEJUBaJOVZc7atWvJycmRl1Uqldy5c4f8/HwAxo4dy7Fjx7h9+zZOTk60adPm8e68IFRS6cy7xMREEhOTuHTpEqdPnyY5OZlFixahr69PixYtmDx5spyJt337doYPH07Xrl3R0dHBx8eHWrVq8cMPP1TvTgmCUCOIYJEgCIIgVAGlUomrqyteXl64urqKIVUVEMdJKBYeHk5MTCQwh/tfWe0AN2AWkuRDdHQEc+fOZdSoUeVuY9y4cezatYv9+/dTUFBAWloaJ06coLCwkMDA3agz2IJRB6KMAQXwBtCKjIwMebY9PT09jI2N+fjjj8nLy6NevXrExcURFRUlsoqEalM28y4DsCYtrb6ceWdraytPeABoZOLFxMTg7Oyssc26devK7YIgPN9EsEgQ/qPZs2ejra1d3d0QBEEQhGfS/ULTLqVaAoFsYJC61cWFWbNmlbuNNm3asG/fPhYvXkzt2rVp0qQJv/76K+Hh4dy5kwxYUfbrsBbwBkVFRSQnJwOgUCiYM2cOP/30E7Vr1+bjjz/GxcWFu3fvMmzYsMeyv8+KpKQk3N3dMTU1xcLCglatWlV3l55J5WfeGQN6wGygNzt27CIuLk7OiAOIjIyUM/Hs7e3lWTjLay+dqScIwvNFvAMIgiAIgiAINY6Njc29WxdLtVgDe4EDAEybNg0dHR1cXV1JTU0ts52ePXty+vRp0tPTiYuL4/333y8RiDpbauk84DPgfQAmTZoEqC+gp06dysmTJ8nMzCQ0NJRBgwbh4eFBnTp1HnlfnyUbN25EqVSSmprKsmXLqrs7z6zyM++KaSFJs7l1Kx5TU1Pmz59Pbm4uFy9eZM2aNXIm3vDhw9m5cydHjx6loKCANWvWkJycTK9evQCwsrLixo0bVblbgiDUICJYJAiCIAiCINQ4Xbp0wd7eGYViCVBUqrUIhcIPB4e6dOnS5aG3XXEgCo3H7y+nKSkpiS+++IIJEyY89HM/6yIiImjWrFmNyUopmVXzLKk48w6Nx9977z3OnDmDtbU1ffv2Zdq0aXh7ewPQrVs31qxZw9ixYzE3N2f37t38+OOPcpF4Hx8fDh8+jEqlonfv3k92hwRBqHFqxru4IDwFTp06hbm5OQqFAkNDQ86cOSO3Xbx4EQcHB7S0tNDW1qZz584ahTGnT5+Ojo4OCoWCxo0bY2VlhaurazXsxcM5cuQICoWCmzdvVndXBEGowRQKBefPny+3LTIyEoVCUW7GB6h/HS8e8iAIJSmVSlavXg4cQKHoz/2aLMfv3T/AqlWf/qe6Vo8SiFq8eDHOzs688cYbdO/e/aGf+1k2aNAgvvrqK9atW4exsTGFhYUa7YmJiQwePBgLCwscHR3x9fWloKAAUGeAbdiwAYC0tDS0tbX54IMPAJAkCQsLC/m7161btxg2bBg2NjbY2tri4+NDbm4uAGFhYahUKtavX4+joyOdOnWqqt2vUhUHPCOB/vLjbdq04dChQ9y5c4d//vmH6dOno1Ao5KVHjRrFX3/9RVpaGkePHuWll16S29q3b8+VK1dITU3lwIEDT3BvBEGoiUSwSBAq6ZVXXkGlUpGUlMTevXsJDQ2V27p27YpSqSQuLo5ff/2VM2fO0LdvXwB++uknVqxYwYcffkh2djbt2rXj1q1b1bUbgiAINUqXLl00iqm6urqyatWq6uuQUKN4eHiwd+9e7Oz+BDqhLkjdCXv7i+zdu/c/z5T3KIEoX19fsrKy5MCGcH/qdg8PD7p3786ECRPIzMwsc/y8vb3R0dEhIiKC8PBwgoOD8ff3B8DNzU3+bhUWFoazs7N8/48//qCwsJDWrVsjSRJ9+/bF2tqaGzdu8Oeff3LhwgUWLVokP09GRgYXLlzgr7/+4tdff62io1C1nmTmnSAIAohgkSBUKC8vj1WrVvHuu+/i4+NDeno63333HXXq1KFXr1688sorgHrq3Tt37nDgwAGsra3p3LkzY8eOJSwsDAB/f38sLS2ZO3cu+vr6fPXVV+jp6VX5/sTExNCiRQu0tbVRKBQYGRlx8uRJ+vXrh66uLgqFAh0dHQYNGiSv07VrVwCcnZ1RKBRMnDixyvstCEL1kiSpTHaA8OQEBAQ8sCDwkiVL8PLyqroO1QAeHh5ERl4nNDSUwMBAQkNDiYi49p8DRSW3+yQCUc+b0lO3//TTT3z55VaCgoI0louNjSUkJIQVK1ZgbGyMk5MTvr6+8jTubm5u8nenkJAQpk6dyo0bN0hPTyckJIRu3bqhpaXF6dOnuXbtGsuWLcPQ0BBzc3PmzJlDYGCg/FxFRUUsXboUQ0NDDA0Nq+pQVKknmXknCIIAIlgkCOWaNWsWhoamvPfee3z++eesXr0agG3btsnLNGjQAIALFy4A6tlYirVu3VoeI5+QkFCm+KWJickT7T9oBrtWrVpFu3btiIuL4/fffyc/P5/NmzejUqlo2LAh4eHhFBYW8sknn7B3717Wr18PwG+//Qaoh5FIksS6deueeL8FQah+zs7O+Pn50aFDBwwNDVmyZAkuLi6YmJjg6OjIvHnz5CnFi/366680atQIlUrFkCFDSEtL02j/5ptvcHZ2xtzcnIkTJ5KXlwfcHzIC6iG74eHhvP/++xgbG9OzZ88q2d+nyZw5c9i1a1d1d6PKKZVKXF1d8fLywtXV9bFdAD+pQNTzovyp23uRmVkHT09PTp8+LS8bExODvr4+VlZW8mMlp3Fv06YNOTk5XLp0iZCQEF555RVefvllwsPDCQkJwd3dHVB/J0lNTaV27dqoVCpUKhWenp4kJibK2zUxMZHfV55lIuApCMKTJOb9FoRSZs2adW/2jt6AL+oCgbuBcfKsHv7+/ly/fh2Ali1bAuq6RcUBoz/++AMdHR0ArK2tuXhRczx5RkbGE9+HFSs+o7AwV+Nxb29v2rRpAyD/Ml1yppJp06axbNkygoKCRNFOQXjOFBYWEh4eTnx8PDk5OQQEBPDdd9/xwgsvcODAAYYMGUKDBg24cOECPXr0oHHjxhpThm/fvp3Q0FAMDQ0ZPHgwPj4+bN26VW7fv38/58+f5+7du/Tq1Qs/Pz8+/PBDjT4sX76cM2fO0L9/f3x8fKpq16uUJEkUFRWJX/trkOJAlPBwyk7dXvwbtAXwAhDBrl17cHCwA9TTtOfk5JCYmCgHjEpO065UKunatSt79uwhJSWFJk2a4O7uzs8//8xvv/2Gn58fAA4ODlhaWpYo8FxWTSmuXRU8PDzo16+f/P5tY2NDly5dxHuMIAiP7Pl5JxWESsjLy2PFis9QB4q+BToAxsBbgAlgxKefrua7777j559/BuCll15CpVLRt29fbt26xfHjx9m0aRPdunUDYMaMGdy6dQs/Pz9ycnJ488035SKMT0JxsKuw8FXu/8o3B4DAwEBmzZqlsfzEiRMxNDRES0sLhUJBQkICycnJT6x/giDUPKWHkSQmJpKYmMSlS5dQKpX069ePhg0bolAoaNWqFV5eXvJwkWKzZs3C1tYWlUrFxx9/TGBgIEVF9+toLFiwAJVKha2tLbNnz2b79u1VvJfVp3Sm1uXLl+nRowe1a9emfv365dZomjNnDubm5jg6OmpkdS5YsID+/fvL9xUKBRs2bMDFxQVTU1P69u1bJqtLEJ6EB0/drkCSZpOScpvMzEwA7OzscHNzY8aMGWRlZREVFcXixYvladxBPRRt9erV8ncod3d3tm7dir6+vvyDXNu2bXFwcGDu3LlkZGQgSRI3b97kxx9/fPI7XUM9qcw7QRCebyJYJAglrFu37l42ji9lXx4/AUokKQ9PT0+NXyF//fVX8vLysLa2pkuXLrRq1Ypvv/0WUM/uMWXKFObPn4+BgQHHjx/H3Nz8idQtqjjYVVzfojMrVqyRh38cP36c9evXs2DBAu7evYskSVhbW8vDS8SXDUF49pU/jMSatLT6eHp6EhQUxE8//USnTp2oU6cOZmZmbNiwgdu3b2tsx8nJSeN2Xl4eSUlJFbbHxsY+4T2rXsUFf3ft2iVnam3bto3U1FT69etHy5YtiYuLY//+/fj7+2vUW7l48SIKhYL4+Hj27NnDBx98IA8LLs/XX39NSEgIUVFRxMTEsHLlyqrYReE5V9mp24tnOwP1j1bZ2dk4OTnRuXNn3njjDY0fsdzc3EhPT5eHnLm4uGBgYICbm5u8jFKp5MCBA8TGxtKkSRPMzMx444035IxvQRAE4fEQw9AEoYQbN27cu1XeF5+OQAxgyjvvvMOaNWvklhYtWmjM5lPa6tWr5bpHALq6utSvX/+x9LmkioNdLoA1cInCwhw+++wz7Ozs5Kms69Wrh7a2NgsXLiQhIQFra2sAGjVqBKh/PSx5oScIwrOh4mEkesBsIICpU6eTknKLdevWMXToUPT09PDx8SEyMlJjWzdv3qR9+/YAREVFoauri4WFBVFRUXJ78dCTqKgo7Ozsyu3TszB8JCgoiKlTp9/LulDLycnj0qVL3L59m+TkZBYtWoSuri4tWrRg8uTJBAQE4O3tDYCRkRELFixAR0eHjh07MmzYML766it50oHSZs2ahaWlJQADBw7kxIkTT3wfhZqhOoc1ak7d3qFES8C9f4+r7wUEyC3W1tbs3bu3wm0Wz3ZWUkJCQpnlLC0tNYa5luTq6ip/vxEEQRD+u6f/G5kgPEb3AzgXK1jiYqnlKmfevHnExcWRnp5Onz59yM/P57333vvvHa3Ag4NdJwH1hdrMmTMZN24cL730El26dGHw4MHo6ury1Vdf4eDgIK9Ru3Zt3NzcGDlyJAqFgkmTJj32PguCUH0ePIxEC0maTUxMJDk5OXJG5MmTJzWyYIotW7aMuLg4UlNTmT9/PkOHDtUI/Hz00UekpqYSFxeHn5+fRr2jkqysrEq8lz19KpOpZWtri66urrxOySK/ALa2tnLdO/j3TKziAD+oA01Pui6eUL0eZlhjREQEr7zyCmZmZtSuXZvOnTtz9+5dADIzM5k8eTKOjo5YWloycuTIhxrCKKZuFwRBeLaJYJEglDBx4kSUSj1gMeV98YElKJX6Dz2F/Lfffou9vT1mZmaEhISwcuVKeTa1x+nBwS5HQP0r3MqVK8nMzKRt27b89ttvFBUVUVRUxI0bN4iKiuLcuXPyWiEhIRQVFSFJEmvXrn3sfRYEofpUdhjJ6NGjefvttzE1NWXx4sUMGTKkzJLDhw/Hzc0NJycnTExMNLIpAfr160erVq1wcXGhffv2zJkzp9xn9PHx4fDhw6hUKnr37v1fd61alM3UKh4KXJyp1ZsdO3YRFxcnz5gJmkV+gTLtD8rEEp4P/3VYo6+vLy+88AK3b98mMTGRZcuWoa2tHlgwZswYUlJS+OOPP4iIiCA/P5/JkydXuk9i6nZBEIRnmxiGJggl6OrqMm3alHszhPVD/Wu7C+rgyxLgANOmzdT4Rbgy/vjjj8fe1/JMnDiRGTM+oLBwMeqaRSXjwf892CUIwrOp4mEkkff+VQ8jGTFiBFu2bKlwO8XDRqZOnVqmzdnZWW4fN25cmfbSQ0bat2/PlStXKrkHNcv9TK1dVJSpdetWJ+zt7Zk/fz4LFizg2rVrrFmzBn9/f3nJrKwsPv74Y+bOncu5c+fYuXMnwcHBVbcjQo3yKMMadXR0iI+PJzIykgYNGtCpUycAkpKS2LdvH7dv35anmP/oo49o1qwZAQEBlQ7wFE/dru5fJ/lxe/u6rFolpm4XBEF4monMIkEoxd/fn5kzZ6JU/gx0AkyBTiiVh5k5c6bGF/qapjjYBQdQB7vu/8qnvn+AadPefehglyAIzyYxjOTxqmym1nvvvceZM2ewtramb9++TJs2Ta5XBOqivgUFBdjY2ODp6cnixYs1CvwKz49HHda4bNky7OzseOWVV3B2dmbBggUUFRURGRlJUVERdevWRaVSoVKpaNu2LVpaWuXWCHoQDw8PIiOvExoaSmBgIKGhoUREXBOBIkEQhKecyCwShHL4+/uzaNEi1q1bx40bN6hfvz4TJ058KoIsxcGsFSs+o7DwgPy4UqnPtGk1O9glCELVKh5G4unpiULRH0maTXE2pULhh3oYyV4xjKSSKpup1aZNG6ZNm1buNkaPHs3o0aMBWLJkSZn2BQsWaNwvXQzYx8cHHx+fh+m2UENVpgD9jh27yMnJIj8/X65zVXJYo6WlJevWrQPgzz//5NVXX6V58+Z07twZLS0t4uLiMDQ0fOS+Fk/dLtwXEBDAqlWrOH/+fHV3RRAE4T8RmUWCUAFdXV18fHxYs2YNPj4+T0WgqJi/vz9376azcuVKJk+ezMqVK7l7N00EigRBKKN4GImd3Z+UzKa0t7/I3r1iGMnDEJlajyY4OBhnZ+d/XS4qKgpjY+OHKsb8NKpMAfpbt+IxNTVl/vz55ObmcvHiRdasWcOoUaMA+Prrr4mKikKSJFQqFUqlEm1tbaytrenfvz+TJ0/m9u3bgHrWsf3791flLtYoo0ePFoFWoco4Ozs/seHFlX0vFYR/I4JFgvCMepqDXYIgVK3qGEZS0Rfl8ePH8/777z+x532SRMHfquHo6EhmZiZmZmbV3ZUn6nEMazxz5gydOnXC2NiYjh07MnbsWPr27QuoM1+Kh5+ZmprSpUsXzpw584T3qmqEhYXJtZiqwooVK3B0dMTExARnZ2eWLVvG+PHj+fPPPzE2NsbY2JioqCgAdu/eTYsWLeRjf+zYMXk7rq6uzJo1i+7du2NkZESHDh2IjY1lwYIFWFhYYG9v/1wH9ARBqFpiGJogCIIgCDVmGMmGDRvk25GRkdStW5c7d+7IF35hYWH0799foyh2TSIK/gqPy+MY1vjJJ5/wySeflNtmYmLCihUrWLFixePo7nOlsLCQ8PBw4uPjKSgoYO7cuZw9e5bGjRuTmJhIYmIiFhYWZYahHTx4kBkzZvDdd9/RqlUrgoOD6dOnD3///Tfm5uYA7Nq1i//973+88MIL9O7dm27dujFlyhTi4+PZtm0b48aNo3fv3vKwQ0EQhCdFZBYJgiBUgStXrlCrVi0UCoXGFNmCIDx7RMHfyomJieG1117D1NSUF198kcuXL8ttK1asoEGDBpiYmFC/fn0+//xzuS0yMhKFQiEHDH/++WdatGiBiYkJVlZWTJgwAYABAwaUqfE0fvx4ub2mE8Ma1RmIixcvpk2bNpiamtKjRw/i4uIAuHXrFsOGDcPGxgZbW1t8fHzIzc0lOTmZnj17kpaWJmf1hIeHExUVxauvvoqFhQW1atXijTfeIDIy8qH7FBQUhLPzC7i5ueHt7c3IkSPJzs5hy5YtZGdnY2VlRYsWLcpdd+3atcycOZM2bdqgpaWFh4cHjRs35uDBg/Iyw4cPp1mzZujp6TFgwACysrKYMmUK2traeHl5kZyczM2bN//T8RSqXnp6OpMnT8bJyQlTU1Patm1LdHR0meV27NhBkyZNUKlUvPzyy5w9e1ZuK52JW3qY2YPeSwXhUYhgkSAIQhV499130dLSIj8/X56lRhAEtcTERNq0acOsWbMYNWqUXDekXbt2ANjb22NsbMzOnTvLvQgEOHz4MO3atUOlUtGsWTO+++47efujR49m3LhxDB06FBMTExo1akRYWNgT3afiTC0vLy9cXV3F0DPU2RhhYWHs2rWLsLAwvLy8sLGxISEhgZ07d/LFF1/Iyzo5ORESEkJ6ejqbN29m5syZHD16tNztjho1ipkzZ5KRkcE///zDiBEjABg7dixfffWVXAQ8JyeH3bt3M2bMmIfqd+ngVFUNlXxehzWWPE9ycnLYvHkzgYGBJCQkYG1tzfDhw5Ekib59+2Jtbc2NGzf4888/uXDhAosWLcLc3Jwff/wRMzMzMjMzyczMpEuXLhQVFTFt2jSio6O5efMmhoaGjBs37qH6Vv7sdMeBNnz66aeYm5vz2muvVVjUOjIykjlz5sgz0KlUKs6fP09sbKy8jJWVlXzb0NCwzH2AzMzMh+q3UH1Gjx7N9evXOX78OKmpqWzatAkDAwONZX777TcmTJjAxo0bSUpKwtPTk9dff73Sddm8vb0rfC8VhEfxTAWLFixYgEKh0Phr3LhxdXdLEASBqKgoHB0d0dYWo38FoaTr16/z8ssvM2LECPz9/VEoFHLb77//Dqh/Nc3MzGTYsGHlXgT+8ccfDBo0iKVLl5KSksLGjRsZMWIEV69elbe1Z88exo8fT2pqKiNGjJBnHBOqRulsDDc3N44cOYKrqyuGhoY0btyY8ePHy8sPHDgQBwcHFAoFbm5u9OjRo8IAn46ODtevXycpKQkjIyM6dVIP/+vZsye5ubn8+uuvAOzfvx97e3vatm37SMVlN2zYUOHQrsfteStAX/o8SUxM5M6dNC5fvoyhoSH+/v6EhoZy5MgRrl27xrJlyzA0NMTc3Jw5c+YQGBhY4badnZ3p2bMn+vr6mJqa4uvrS3h4OEVFpbO2yld2droOgPG9f39HoehD7dqWNG/enBEjRqClVfYyy8HBgeXLl5Oamir/ZWVl8cEHH/yHoyXURCWDnUFBQezfv59NmzZha2uLlpYWrVu3pk6dOhrrbN++neHDh9O1a1d0dHTw8fGhVq1a/PDDD//6fNHR0YSHh8uvhdLvpYLwKJ6pYBFAs2bNiI+Pl/+OHDlS3V0SBOE5Z29vz7Vr1zh//jwKhYLRo0czYcIE9PT0UCgUmJqasnPnTgDmz5+PlpaW/KtkWFgYWlparFq1qvp2QBAek9IZA7///juurq4sXLiQ99577z9vd+PGjYwePRp3d3e0tLR4+eWX6d27N19//bW8TK9eveQMnzfffJObN2+SnJz8OHZL+BflZ2NsBrQYO3YsQUFBgDqbqNjOnTtp06YNtWvXRqVScfDgQXnWrtL279/PxYsXadSoEa1bt5b/35VKJSNHjiQgIABQF3R+2KyimuB5GdZY/nliTVpaXTw9PQkKCsLKygo9PT2OHTtGamqqfH6oVCo8PT1JTEyscPtJSUl4e3vj4OCAqakpXbt2JTc3l4yMjEr1r+LZ6a4CvyBJ04iNvUlKSgra2tpYWVkRHx9Pdna2vOSkSZNYtmwZZ86cQZIk7t69y+HDh0XG8TOidLBz4MCBAJw+ffqB68XExJSZvaxu3bqVOi/i4uLQ19fH0tJSfqzke6kgPIpn7ifu4ulAKys3N5fc3Fz5fnp6OgD5+fnk5+f/534Ur/so2xCeD+JcefZFRETQuHFjTExMOHXqFGvXruW9995j5cqVvPnmm4wcOZIRI0bQsWNH5s2bR3BwMN27d+fvv/+md+/edOjQgUmTJvHzzz+L80T4VzX1PeX777/n/ffnEBurnhEoJycHf39/GjVqxIABA+T+FhUVUVRUpPE5XPJ2QUGB/FixiIgIQkND2bp1q/xYQUEBxsbG5OfnU1RUhKWlpbxO8eyQKSkpmJqaPuE9r5mq6jwpLCzk/ffnoK8/AAik+CK7qMiN3Nwi9PV78cEHvvTq1YuIiAgAbty4wahRozhw4ADdunVDW1ubgQMHUlhYWO550bx5c3bv3k1RURHffvst3t7edOrUCSsrK0aMGEH79u2ZMWMGv/76K19++SUDBw4kKioKLy8vlEolnTt35tChQ9y6dUsupD59+nRSU1PZsmVLmecbO3YsKpWK5cuXExkZScOGDVm/fj1Lly4lIyODQYMGsXz58sc+C2nnzp3l28Wvk6pQFedKRedJTo4eSuVAdHWd+eADX1566SVyc3Np3749lpaW8gxjpftbfGxK9vn9998nMzOTkydPYmFhwfnz52nXrh15eXnyOsXvPeWJj4+/N3yoMXB/maKiLPLy5iJJ6joxJ06cYPv27TRt2pR27dphZ2dHUVERZ86c4fXXX2fRokW89dZbREREoKenx0svvcRnn31Gfn4+kiTJ53nxcZEkqUyfHvU65UmoqZ89VeX7779nxIgRSNLrGBjsBJoiSUfIyenDsGHD2LlzJ3369NFYp6CggPz8fGxtbfnnn3/KfK5ZW1uTn5+PkZER6enpcntxECk/Px8LCwtycnKIjY2VA0bF76U19f/ieT9XaoLKHvtnLlh07do1bG1t0dfXp2PHjvj5+eHo6Fjh8n5+fixcuLDM44cOHZLHBT+Kn3/++ZG3ITwfxLnybLt79y6SJHHw4EE+/fRTHBwcqFu3LmFhYYwZM4YffviByZMn88477zB79mxGjRqFjY0NSqWS9957Tz4/noXzpH///kyfPl0uyjp37lwuXbqEJEmsXbsWOzu7au7hs6GmnStKpZJPP70/dGfcuHGMHDmSX375BTc3N2bOnIm2tjYxMTHcuXOHgwcPkpSUBKg/k42NjQG4dOkS+fn5GgVhi4qK6NWrFyNHjizzvAcPHtTYJtyv9xEaGqpRD+R5VBXnyf3/9/9pPD57dhNsbXN5++2FfPnll6xevZqioiJ+/PFHJEni6tWrZGdnc/bsWX766Sdee+01Dh48KGePHDp0CD09PY4cOULbtm0xNjbm6tWrSJJEaGioHAh0cnKiT58+tGrVitOnTzNy5EjCw8MZO3YsHTp0IDExkUOHDmmcZxEREWRlZZV5PmNjY43zqbjtiy++YPHixeTl5fHRRx+RlpbG0KFDn/ixrUpP+lwp7zwZN+4uWlqfMX/+fOrUGcKbb75Js2bNSElJwcTEhGHDhuHh4YG+vj5JSUlER0fz4osvEhsbS0ZGBoGBgXIA8OrVqyiVSo4fP87du3dZu3YtUP7/a3kMDQ3ZtWsX8Gs5rXM07sXGxhIbG8tbb73FW2+9BcDFixe5ePEihoaGZQqvF7cVz2pX3Ic6derw0UcfafQpODiYuLg4udB3TVPTPnuqilKpLDEMMoni82TJknYUFhaSlpbGgQMHiIyMpE6dOty9e5czZ86go6NDvXr1WLx4MXXr1qVhw4b8+OOPJCQkoFQqOXjwIJaWlqxZswYDAwNSUlJYtmwZ2dnZ8nnRpEkTRo0axdtvv83t27fl99KKzuWa4nk9V2qCu3fvVmo5hVRc9e8Z8OOPP5KZmUmjRo2Ij49n4cKFxMbGcvHiRUxMTMpdp7zMIgcHB27fvv1Ivzbm5+fz888/8+qrr4qpLYUHEufKs2f+/Pl8/vkGCgvvv7fk5ORgYWFBTEwMtra2vPjii3z//fdyu42NDW3btpWL8g4aNIhvv/2W9957j08++eSZOk90dXXZsWMHgwcP5uTJk3Tp0oWwsDC5zojwaP7tXCnOhCiZRfEodHV1+f3332nVqlW57YWFhTRv3orY2GZoZgw0QEdnGUrlThSKw7i5ubJnzx4mTJggZ21kZ2dTq1Ytjh07Rps2bQD1BV/Lli2JioqSf0U9d+4cffr0YceOHXTp0oWCggLOnTuHmZkZTZo00cgEAUhNTcXS0pK///67TOr/86Kq3lP27t3L2LFjgTjASKOtqCiK/PyxFBX9irOzM2+++SZffvkl165dY8GCBWzcuJHCwkJ69+5Nbm4u1tbWGtk8t27dwtDQkIEDB3Lq1Cny8vJwcHBg7ty5DBo0SH6e7du3M3bsWD7++GPq16+PtbU1o0ePZvny5fTr16/c10TJzKLS7eVlFh09epS2bdsC8PXXX/Phhx9y5cqVJ3Zcq1JVnCsVnSc5OQ1QKsdQWLgPSfqD5s2b8+2332Jvb8+tW7eYM2cOv/zyi/z9fdy4cUyaNAmACRMmEBwcTEFBAcHBwdSuXZuxY8dy5coV7Ozs8PHxYeLEieX+v5an+L0sLq4ZknT/vUytCIXCGzu7y/zxx7lnruh4ZTxL31Me1pEjR3jjjTeAw0BbjTZJSiM//y0KC7/F0NCQZs2asWfPHtzc3Pj000/p168fAF999RX+/v4kJCTQrFkzVqxYwYsvvgio616OGjWKCxcu0LRpU/r378/GjRu5du2a3P7OO+9w8uRJGjRoQP/+/eX30proeT5Xaor09HTq1KlDWlrag2Me0jPszp07kqmpqbR58+ZKr5OWliYBUlpa2iM9d15enhQcHCzl5eU90naEZ584V54tM2fOlAAJektwXIKMe/8aSoA0c+ZMqVGjRlLTpk011tPV1ZUmTpwoSZIkhYaGSgqFQmrcuLGkra0tRUdHP1PnCSDt3r1bkiRJWrNmjfSMfxRVuX87VyIiIiRAunPnzmN5PkA6d+5che2hoaH3XhPHJZBK/DlJsF+CYxIgdezYUerVq5c0dOhQaerUqZIkSdKHH34oNW7cWLKwsJDMzMyknTt3SpIkSePGjZPMzc0lMzMzKTw8XJIkSfrll1+kTp06SbVq1ZLMzc2l7t27y/0aNWqUvE1JUn8/AKSIiIjHcgyeRlX1nlLx/3/xn/r/PzQ09In14aOPPpK0tLTu9UP9p1QqpVmzZkmSVP5rYurUqdKoUaPKbS95PhW3JSQkyOseP35c0tfXf2L7U9Wq4lyp7PvEkzxPKmPfvn2SQqGQFIo+9/qULsExSaHoIykUCmnfvn3V2r/q9Cx9T3lYgYGB987fjAre59IlQAoMDKzurtYIz/O5UlNUNubxzBW4LkmlUtGwYUOuX79e3V0RBOE5kJeXx4oVnwG9gW/RnCnFBjBlxYo1jBs3jsuXL7N+/XpycnLw9PQkPz+fWbNmkZOTQ+/evenUqRNXrlzBysqKl19+uRr36r6pU6fKQzQA7OzsNH49femll2jRogV3796lY8eOaGtro6WlhaOjY7m/sM+ePZt3330XAIVCQa1atZ78TghVLj4+/t4tl1ItkUB/+fF3332XH374gV27dmkUdG/UqBG3bt0iNTUVb29vADZt2sTt27dJTU2VXx/u7u4cPXqUlJQUbt++zeHDh+Vsp4CAAI1tqlQqJEl6brOKqlKXLl2wt3dGoVgClK6xU4RC4YeDQ115WOrjtmfPHubPn09RUX1KTnVeWKiHv78/QUFB8vtaybT8++dt5dy8eVO+HRUVJYbTPqTqPk8q63mbnU6oHBsbm3u3LlawxMVSywnC0+GZDhZlZmZy48YN8cIUBKFKrFu37t7QM1/Kf3u1orAwB6VSyVtvvYWPjw8GBgYcOnSIgIAAnJyccHNzQ6FQcPjwYQCOHj1KTEwM77zzTlXuSrnGjRtHVlYWcXFxFBUVER8fj5aWFgcOHADg8uXL9OzZk969e3Pu3DnCw8PlmgrlBbz8/PxYuXIlAJIkcefOnarbmedAZmYmkydPxtHREUtLS0aOHElaWlqZ5SRJ4rPPPqNx48aoVCpcXV01gnvOzs4sXryYNm3aYGpqSo8ePcrUyjhx4gQuLi6YmprSt29fjef58ssv792yAl4EQkusGXDvMZg4cSJWVlZyUCc4OJglS5Zw4MABjI2NNQKVwtNDqVSyevVy4AAKRX9KBmzU9w+watWnT2TYTkhICF5eXkAt4Hc0A/gtgKb4+MygVq1aODo6sm3bNoqKiggNDX3oWh8fffQRqampxMXF4efnx7Bhwx737jzTKj5PcgE/nuR58rCel9nphMp7WoKdgvCwnqlgUfFMF5GRkRw7dowBAwagVCrvfVEQBEF4sm7cuHHvVukMCoDrwBl5uS+++ILc3FwkSSI9PV0uzHv8+HEyMjLQ19cH1IVZCwoK2Lhx45PfgXLk5eWxatUq3n33XQ4fPoyuri5ffPEFe/bswdDQkCZNmrBjxw5u3LhBdnY248eP58iRI7zzzjt07NgRa2trgoODSUlJ4ezZs9WyD8+L4mnp9+7dC8Bbb71FSkoKf/zxBxEREeTn5zN58uQy661fv54tW7bw/fffc/v2bTw8POjTpw95eXnyMps3byYwMJCEhASsra0ZPny4xja+/vprQkJCiIqKIiYmRg4CAgwdOhRbW0fADRgCeKK+CAT1l+rrmJnVJj4+nj179jBz5kxu3LhB//79mTNnDr179yYzM1MuSi08faorG0NLSwtJkoCDgKpU6xzgDtHREXh4ePDll1+ydetWzMzM2Lhx40MXp+7Xrx+tWrXCxcWF9u3bM2fOnH9fSdBQ/nmSgINDco3L2lEqlbi6uuLl5YWrq2uNCGIJ1ac6g+KC8CQ9U7OhxcTE4OXlRXJyMhYWFrz88sucOHECCwuL6u6aIAjPgfr169+7dRH1L9elXSy1XM02a9YsVqz4TKNQN8CGDRt46aWXaNasGe7u7mzfvp0NGzZgYGBA3bp1yc/Pp0WLFvLyxYWJL1y4IN8WHq+goCCmTp1OTEwkBgYGbNiwgaCgILZt2yYX7P3oo49o1qxZmVl41q5dy5IlS2jQoAEAU6ZM4ZNPPpGLj4O6UGzjxo0B8Pf3x9rampiYGOzt7QH1uVJcbHrgwIGcOHFC3v7YsWOpVasWnp6egBaSlA+cQJ3hsQaQ+PLLL9DX18fV1RVnZ2fOnz//1LxOhMrx8PCgX79+hIeHEx8fj42NDV26dHmiF08VD4EE6AO4AqYMHTqU7t278/fff5e7HWdn53tBJ7WAgIAyywwaNIhx48Y9Yo+F6jhPBOFxKA52qj+L70/YYW9fl1WralawUxAq65kKFu3evbu6uyAIwnNs4sSJzJjxAYWFi1HXLNKcKQWWoFTqM3HixOrp4EOYNWsWy5YtQ11/yRf1xdZFYBgJCf/wyy+/4Ovry/Dhw/H39+fHH3+kadOmAOjo6PDHH3/I2yq+3bJly6rejedCUFAQnp6eSFJvYBfQmFu3NgMwatQoJkyYIM82oqWlRUJCgsb6kZGRDB8+XONiLC8vj5iYGPm+k5OTfNvKygo9PT1iY2PlYJG1tbXcbmRkREaGOnOoqKiIefPm8fXXX6Ovr09OzgHU9YVfA6B27TqYmjprfIkuub7wbCnOxqgqmnVEKg7gi3IFNUtVnyeC8LiIYKfwrHmmhqEJgiBUJ11dXaZNmwIcAPpRMg1Zff8A06a9i66ubjX28t89uFC3uj5RdnY2o0aNwsnJCX19fS5dukSfPn0A6Ny5M5s2beLkyZMkJCTQt29fateuLbKKnoDCwkKmTp1+L1AUjPr/yIg6deqg/ojvibm5FcnJyaSmppKTk1Om8K6DgwPffPMNqamp8t/du3c1hnCXLN5769YtcnNzK1XANzAwkMDAQH744QeysrLIz8/H2NiY9957j9DQUJYt+wQzM7MK19fSEl9ThP+uS5cumJtbAO5Upo7IiRMnaNq0KSYmJnz22WcAvP3229SuXVsjIPowVCoVYWFhACxZskTjdaVQKDh//vx/2q4gCDWTGKIoPEvEtzBBEITHyN/fn5kzZ6JU/kzJ2hxK5WFmzpyJv79/Nffw3z24UHcTQB3sKq6N4+KiHuJRXIT7+++/p3nz5nTq1AlbW1sKCgr49ddfq6bzT5CxsTGGhob8+eef1d0VWXh4ODExkajrr9z/v6pVqxZaWn0BLaKjIwgPDychIYH9+/eX2cakSZOYP38+V69eBSA9PZ1vv/1WI7tn48aNXL16lezsbN5//326du0qZxU9SHp6Orq6utSpU4e8vDwWL15MdnY2Xbt2xdXV9V+DQVZWVty8eZOCgoLKHA5B0KBUKhk79k0gu1J1RObOnYuXlxcZGRlMmTKFI0eOsHfvXiIiIspk5BUrHqJWPNzzQebMmcOuXbsey74JgiAIwpMmgkWCIAiPmb+/P3fvprNy5UomT57MypUruXs37akIFMG/FeoGuK2x3MmTJ5EkSf7l3djYmN9//53CwkKKioqIiYmRA0qgnn1ryJAhAPj4+GjUAqlJnJ2dsbS0JDg4GFDPLnb37l2aN29evR0r4UE1WXR1twDOAPTq1YsuXbpw5syZMstNnjyZ0aNH4+HhgampKU2aNCEwMFBjmTFjxuDl5YWVlRWxsbHs3LmzUv0bNWoUzZo1w8nJiXr16mFgYFCpIFOxQYMGYWpqioWFRaUuxgWhtI4dO2JhYVGp4toREREar++IiAgcHR0fmP0mCIIgCM+qZ6pmkSAIQk2hq6uLj49PdXfjP3nWCnU/DYozZ7S1H+5j+UE1WRQKE2AYsJaDBw9q1AApGaBTKBRMnDjxgbW0mjVrhq+vb7ltpYN9Pj4+8rlvZGREUFCQRvvMmTPl26NHj2b06NEa7SWH5dSuXfuZyEoTqk5MTAxjxozhxIkTNGjQgIEDB2JoaMiNG9d59913CQoKIj09HR0dBXFxcfJ61tbW3Lp1Cy8vL5RKJUuWLGHWrFny0ElPT08CAgI4ffo0U6dO5dKlS9ja2jJv3jx5aFlRUREffvghmzZtQqlUlnnNLFiwgPPnz8sBaEEQBEGoyURmkSAIgqBh4sSJKJV6wGLKq/PxNBXq/q8GDRrEzZs3SUpKYsCAAejo6KBQKOQaI0VFRfj6+mJkZIRSqcTMzAwDAwOMjY3l+iS7d+/G0dERpVKJtrY2TZo04dixYwC4urri6OiIs7MzSqUSY2Njfvzxx4fuZ5cuXbC3d0ahWEJlarIIwrPO29sbGxsbEhIS2LlzJ1988QWgHpLWvXt3Tp06RVZWFps3b2bmzJkcPXoUgISEBBwdHdm1axeZmZlMmTKFDRs20Lx5czIzMwkICCA1NZXXX3+doUOHkpSUxPr16xk3bpy8jYCAAAICAvj111+5fv06p0+fFsXaBUEQhKeWCBYJgiAIGp6VQt3/RV5eHqtWrcLa2ppatWphYWHB/v37yc/P11hu69atbNy4kVq1anHu3Dn69+9PTk4O2dnZABw8eJDJkyeTm5vLyZMn2b17NzExMfTu3Zvk5GRAfXGanp7OsWPHSElJ4ZVXXnno/iqVSlavXg4cKFGTJRMAhcKb0jVZBOFZU1hYSFhYGLt27eLrr78mPDycZcuWYWhoSOPGjRk/fry87MCBA3FwcEChUODm5kaPHj3k4G5l/PDDD1hYWPDuu++io6NDt27d8Pb2Ztu2bQDs3LmTd999l8aNG2NoaMjSpUspKiodxBUEQRCEp4MIFgmCIAhlPAuFuh/WrFmzMDQ05b333uPzzz/nzp07JCUlyReCJQUGBlKrVi18fHxo0aIFn376KYB8Ybh27VqsrKyYM2cOL730Ep6enrRo0YI6depw8OBBACwtLRk5ciTt27fH0NAQAwOD/9RvDw8P9u7dW6Imi3qWMju7y2VqsvwXkZGR9O/f/5G2IQhPQlBQEM7OL+Dm5oa3t/e9WmgKjhw5Ii/j5OQk3965cydt2rShdu3aqFQqDh48yO3btyv9fDExMTg7O2s8Vq9ePWJiYgCIi4vTeD4rKyv09PT+28495UrOIhcZGYlCoSA1NbW6uyUIgiA8BBEsEgRBEMr1tBfqfhizZs1i2bJlFBa+yv1MKmtAl+DgYGbNmqWxfFxcHHfv3sXBwQEACwsL9PX15WyryMhIrly5go+Pjzx87dixY0RGRhIbGwuAvr4+jo6Oj6X/Hh4eREZeJzQ0lC1btgDwxx/nHjlQ9DxbsGCBCJLVYEFBQXh6ehIT05z7r9lgQGLgwIFyrayoqCj531GjRuHv78+tW7dITU2lV69eD1Vg397ensjISI3HIiMj5aLttra23Lx5U267desWubm5/30nn1KVmUVOEARBqPlEsEgQBEGoUHGh7jVr1uDj4/PMDj1bseIzoDfwLepC0caAAaACXmLFijUa69ja2mJoaEh0dDQASUlJ5OTkkJeXB4CDgwNNmjRh/fr1SJIk/+Xl5fHBBx/I2/m3qeMfhlKpxNXVFU9PT/m+UDkBAQG0atWqurshVFJhYSFTp05HknqjDhAVv2b7AZ0BB6ZMmcbly5fZuHEjoJ7NUJIkLC0t0dLS4uDBgxw6dOihnrdXr17cunWLdevWUVBQQHh4ODt37mTkyJEAeHl5sXbtWq5evUp2djazZ88GYPv27bRp0wY/Pz+OHTumUVh7woQJ1K5dm/r167Nq1Sr58eJzcs6cOZibm+Po6Mi6devk9gULFtC7d2/Gjh2LqakpDRo0YP/+/QBcuHABExMTMjMz5eVjY2PR09PTeO4npSbOIldQUFBjZ94UBEGoqUSwSBAEQXiurVu3jsLCXMAXzY9FK9QBow4UFuZorOPl5UVqaiqrV6/m4sWLcuZRcfBn0qRJJCcns2jRIk6fPk1WVhYHDhxg165d8pCVynB2dn5qZk5SqVQPVf/leSJJEoWFhdXdjWdGeHg4MTGRwBzKfpUNBKyJjb1J//79GTNmDABNmzbF19cXd3d3zM3N2bNnD3379n2o561VqxY//vgjO3bswNzcnLfffpv169fz8ssvAzBmzBiGDx9+r/C8PYWFhSgUCn744QcCAwOZMWMG+vr6DB8+XJ4BsWHDhsTFxbF//378/f0JDAyUn+/ixYsoFAri4+PZs2cPH3zwAb/99pvc/r///Y927dqRkpLCihUr8PLy4saNG7Rs2ZJGjRqxd+9eedmvvvqKV155BVtbW419WrFiBY6OjpiYmODs7MzmzZvLzarz9vaWZyZcsGABffr0YfLkyahUKhwdHdmzZw8An332GePGjePPP//E2Ni4zGyHAPn5+cyePRtHR0csLCwYMmQISUlJAHzyyScMHTpUXvbFF1+kQ4f7Mz0OHDiQ5cuXy9uZP38+9evXx9zcnL59+2oEwxQKBZ9//jkuLi4YGRlpBM8EQRCEfyeCRYIgCMJz7caNG/duuZRqmQNkA2vLrDNmzBjeeustkpOTadmyJUFBQejo6KCrq4uenh59+vRhzZo16Ojo0L59e0xMTBg4cCBbtmwRBW8fgbOzM4sXL6ZNmzaYmprSo0cP+eJw1qxZODk5YWJiQtOmTfnmm2/k9cLCwlCpVGzevBkHBwfMzc3lAN+5c+cYP368fHFrbGwsD10qLCws94IY1AGgzz77jMaNG6NSqXB1deXKlSsaffXz86NDhw4YGhpy+fLlqjhEz4X4+Ph7t0q/ZgEcgV8AWLhwIb6+vvLQsY8++ojbt29z584dtm3bxu7duzWyeUrX5xo9ejTnz5/X2Hq7du04duwYaWlpXLlyheHDh8ttWlpavPjii+jpGZGSksK2bduQJImcnDwuX77M4sWLOXXqFKGhoRw9ehRTU1O++OIL9PX1adGiBZMnTyYgIEDenpGREQsWLEBXV5eOHTsybNgwvvrqK7m9YcOGvPPOO2hra9OnTx/c3NzYtWsXAGPHjtXY1rZt23jzzTc1CoJv376duXPncujQITIyMjh58iTt2rWr1P/BTz/9RNeuXeWg+FtvvUVGRka5s8iV5ufnx4EDBzhy5AgREREoFAqGDRsGgJubmxx0vnPnDrGxsVy7do2MjAwkSSIsLAx3d3cAfH19OXr0KEeOHCE+Pp6GDRtqBJpAXV/u0KFDpKenY2RkVKl9EwRBENREsEgQBEF4rtWvX//erYulWvoAiYB6WuyVK1ciSRKtWrVCS0sLPz8/7t69S2FhIX/99Rf5+fnk5OTIdYgGDRpEREQEhYWFFBUVkZuby+HDh3F0dCQsLIzr16/j4+NTRXv5+JSeGa6koUOHPvZMqJIXtzk5OWzevJnAwEASEhKwtraWL9ZbtmzJqVOnSE1NZf78+YwYMQI7Ozu5PxkZGVy+fJlr165x5MgR1q5dS1hYGK1bt9a4uM3MzJT/Dyu6IAZYv349W7Zs4fvvv+f27dt4eHjQp08feSgiqIcSbdu2jczMTBo1avRYj8vzzMbG5t6t0q9ZNB6/v1zVKL+OkjVpaXXx9PQkKChILnp94sQJbG1tNYb2liyWDerhrjo6OvJ9JycnueZZ8f2SSrZ7eXlx6tQpIiIiOH78OLdv36awsFCjIPjIkSPJzs5hy5YtZGdnY2VlRYsWLSq1r23atGHw4MEolUpGjBhBXl4ef//9d6XWLQ5SOTo6YmxszIoVK/j555+Ji4vjxRdfJDs7m8uXLxMWFkbXrl3p1KkT4eHhcuCuVatWSJLEunXrWLFiBTY2Nujq6rJo0SKOHj0qDw8GdRDZ1tYWPT29xzrsVxAE4Xkg3jUFQRCE59rEiRNRKvWAxUDprJ8iYAlKpT4TJ06UHy0oKGDfvn3s3buXW7duMXHiRMzNzenUqRN2dnZPpJ+HDx+mXbt2qFQqmjVrxnfffSe3lRzWYWtry7Jly+RhHaAejrF69WoaNWqESqViyJAhpKWlye3Dhw/H1tYWU1NTXnzxRUJDQ+W24topH374IdbW1gwdOpSioiLmzZuHlZUVtra2rF1bNvvqcSg921ViYiJ37qRx+fJlDA0N8ff3JzQ0lJiYGIYNG4alpSVKpZKhQ4fSuHFjjeLCkiSxaNEi9PX1adKkCZ06deLMmTMPfP4HXRCvXbuWjz76iAYNGqCtrc2UKVPIzs7m5MmT8voTJkygUaNGKJXKZ7LeV3VRD/NyRqFYQnmvWYXCDweHunTp0qXK+lRxHSU9YDDQGx+fGcTHx5Obm0uHDh2Ii4vTCL6WLJYNlGmPiorSeH8pWUy7dLtKpWLAgAFs27aNgIAA2rdvj5eXV6lA1nGgDZ9++inm5ua89tprZTKpKmJtbS3fVigUGBgYyIHUf1N6VrniYE5MTAxKpZIuXboQGhpKSEgIbm5udO/eXb7v6uqKQqHg9u3bZGVl0bVrV1QqFSqVCmtra3R1dTWCRY9rEgFBEITnkQgWCYIgCM81XV1dpk2bAhxAXSC35IVUP+AA06a9q3GxL0kSn3zyCUOGDMHKyooffviBli1batQb+a9KZ9IUFRXxxx9/MGjQIJYuXUpKSgobN25kxIgRXL16FdAc1vH333+jUCgYNWqUxna3b99OaGgokZGR3LlzRyOrqXv37ly5coXk5GSGDh2Kp6enxoXfxYsX0dbWJioqiu3btxMQEEBAQAC//vor169f5/Tp05W+UKysslkaswG9crM0YmNjWblyJc2aNcPMzAyVSsXFixc1hvyZmppiaGgo3zcyMvrXPj/ogjgyMpLhw4fLF6oqlYo7d+5oZIbU9AvV4ro5TxulUsnq1cuBAygU/Sn5mlXfP8CqVZ9WaZH3B9dR2oQkeRMdHcGYMWPkbBkrKyvmz59Pbm4uFy9eZM2aNRqv26ysLD7++GPy8vI4efIkO3fulIdrAfz999988cUXFBQU8MMPPxASEsKQIUPk9uKhaHv27OH06fPlBLI6AL+jUPShdm1LmjdvzogRIzA2Nubu3bsa/cjOzn5sx6r0rHIJCQnk5ubKgTI3Nzc5OOTu7o67u7tG8AjA3NwcQ0NDTp48SWpqqvyXnZ1Np06d5G2LbCJBEIT/TryDCoIgCM89f39/Zs6ciVL5M9AJMAU6oVQeZubMmfj7+2ssr6Ojw++//05hYaE8y9kvv/xSZljIwyovk+btt8fz/vvvM3r0aNzd3dHS0uLll1+md+/efP3110DZYR1jxozh8OHDGsVei4djqFQqPv74YwIDA+VgyptvvomZmRk6OjrMnDlTDlAVMzMzw9fXF11dXQwNDdm2bRsODg706NEDa2trzp07J2/r77//pkOHDpiYmNCtWzeNX/lv3brFsGHDsLGxwdbWFh8fH43sn7Nnz+Lm5kbt2rUZPHgIktQc9cVtAvApkAv8iSRp4eMzg127dpGbm4u7uzvTp0+nadOmxMXFkZqaiouLi9yfiRMnkpaWVqY/xf7LBaWDgwPffPONxoXq3bt38fLyqtR209PTmTx5Mk5OTpiamtK2bVuio6NZsWIFDRo0wMTEhPr16/P555/L60RGRqJQKNi+fTsvvPACKpWK0aNHy9knmZmZ9OvXD0tLS8zMzOjatSsXLlyQ1y+eQWvChAlYWVnx1VdfYW9vX6YweZMmTTTqM9VEHh4e7N27Fzu7Pyn5mrW3v8jevXvx8PCo0v48uI7SGGApoJ6VbOfOnejo6HDgwAHOnDmDtbU1ffv2Zdq0aXh7e8trubi4UFBQgI2NDZ6enixevFgOlgC8/vrrnDhxgtq1azN16lR27NhBgwYN5HZXV1eUSiWWlpbcuhVH2UDWVeAXJGkasbE3SUlJQVtbmzZt2nD8+HH++usvcnJymDdvHgqF4jEdKXUm45IlS4iOjiYzM5Np06ZpFN92c3Pj0KFDpKWl0ahRI5o3b05MTAy//vqrXK9IS0uL8ePHM336dPk1nZycXOPPW0EQhKeJCBYJgiAIAuqA0d276axcuZLJkyezcuVK7t5NKxMoelIqqneSnOzE//73P9atW6eRxfLtt9/KwaDSwzpq164tD+soVjKQ5eTkRF5eHklJSRQVFeHr60uDBg0wNTVFpVKRlpbG7du35eXt7Ow0Ah9nz54lKyuL48ePk5qaytatW9HT0wNgx44d7Nq1i6SkJIyMjJg3bx6gzsbq27cv1tbW3Lhxgz///JMLFy6waNEiQJ1d8OqrrzJhwgS++eYbCgsLgDggFOiP+kLXAHAAdhAdHcGqVato06YNu3fvxtTUlHPnzrF8+XK+/PJLLl68KPdn3rx5mJmZafSnJCsrK+Lj4x8qe2LSpEnMnz9fzu5KT0/n22+/fWC2Usmssd69e3Pt2jX5GG7atAkDAwOcnJwICQkhPT2dzZs3M3PmTI4ePaqxnR9//JFz585x+fJlfvnlF3bu3AlAUVER3t7eREREkJiYSOvWrRk8eLDGlOH/+9//aN++PbGxsQwbNoxhw4ZpFCE+fvw4iYmJZWbDqok8PDyIjLxOaGgogYGBhIaGEhFxrcoDRfBvdZSaAesB9WxhxRk0DRs25NChQ9y5c4d//vmH6dOnlwnKLFmyhOTkZKKjo5k8ebJGm7a2Nlu2bCE9PZ3r16/j6emp0a5QKHByciqRaVM6kJUHzAPUs8KdOHGCgIAA3N3deeedd+jUqRMvvPACLi4u6OvrP9TxeJDZs2fTo0cPOnbsiLOzM/n5+ezYsUNub9WqFdra2ri6usr70a1bN7l4fTE/Pz86duyIu7s7JiYmvPjiixw6dOix9VOoOsHBwRqfYYIg1BCSoCEtLU0CpLS0tEfaTl5enhQcHCzl5eU9pp4JzypxrgiVIc6TZ1tBQYFkb+8sQR8JCiWQ7v05SbBPAifJxMRMKigoKHf9F154Qdq9e7ckSepzZevWrRIgxcbGSpIkSYC0Z88eefmTJ09Kurq6UmFhobR9+3bJ2dlZunr1qlRUVCRJkiSpVCpp//79kiRJ0tatW6WWLVtKBQUFUmhoqLR+/XoJkN5//315e4mJiRIgWVlZSevXr5cf37Fjh+Ti4iJJkiT9/vvvUu3ataXCwkK5/dChQ1K9evUkSZIkf39/qX///pIkSVJgYKAESDBDgjH3jsWHEhhIsEiClhIgNW/eXIqOjpYKCwulcePGSfr6+pKOjo40bdo0qWvXrlKtWrWk9evXS6GhoZKZmZlGf/r16yd9+OGH8jHr27evVKtWLcnMzEy6efOm9OGHH0r9+vXTOM5mZmZSaGioJEmSVFRUJK1du1Zq2rSpZGJiItna2kqDBw+W0tPTJUmSJCcnJ/kYSpIk7du3797/MfKftbW9tG/fvgecGep+Llq0SJIkSYqIiJAA6cqVK3L7W2+9JU2ePLncde/cuSMBUkxMjCRJkvThhx9KLVu2lPc5ODhYunDhgmRsbCxlZGRIkiRJb7/9tjRp0qQH9kkoq/g1rFCU/xpWKPpIDg51K3wNl1b8uqtIeednaceOHZNMTEyk77///t45d7xEv0r+HZMA+dwuTXz+CJXxKOfJ/v37JScnp8ffKaFGEu8p1a+yMQ/taohPCYIgCIJQwv16J7som/SrBSwgI+NNPvvsM6ZMmUJBQQFnz55FpVLRpEkTeVhHp06dMDY25ssvv6R79+7ysA6AZcuW8fLLL2NoaMj8+fMZOnQoWlpapKeno6urS506dcjLy+OTTz4pkx2TlpaGs/ML9/qo9umnn2JnZ8dbb73F7Nmz5cyjknV+StYFioyMJDU1ldq1a8vtkiRRWFgotx88eBCVSlWils46oFup49EMcAc6MXHiREaPHs2ff/5JdnY2CoWCZs2asXz5ckA9fb21tTWurq6kpqYSHBws96fkrG06Ojp8++23Gs+yYMGCMv9Pqamp8m2FQsHEiRM1Cp+XVLImS3HWmLpmzC4gB+hBYmJrPD09NYZN7dy5k+XLlxMZGUlRURF3796lbt26GtsufYyL+5Wdnc306dM5ePAgKSkp8v/J7du35cLHpesoNWnSBBcXF/bu3cvQoUPZs2cPISEh5e6TULHiOkqenp4oFP2RpNmoM3lyAT/gDKtW7a2yOkrFQ9RWr15Nz549sbd3JjZ2CZIUjOZ7jLoguL191RYEFwRBEGo+MQxNEARBEKrZg+udAAwEYMOGDVhYWGBnZ8e8efPkej8lh3U0aNCAwsJCjaFFoK4T4ubmhpOTEyYmJqxevRqAUaNG0axZM5ycnKhXrx4GBgYaMzKdPn2ayMjIEsPjbgBQWOjAlClTsLW1pXXr1piYmDxwHx0cHLC0tNSo8ZOWlkZmZqbcPmDAAPlx9WxX3VEXHof7X1nuz3b16aef4ubmxj///EN6ejpLlizRGHJVE5Q/S1YTIA9JWkPxLFmFhYVERUUxatQo/P39uXXrFqmpqfTq1avS+7R8+XLOnDnDkSNHSE9PlwNWJdcvr45ScSHk/fv34+TkRJs2bR5xr59P5ddRSsDBIfmh6yiNHj36gTOTLViwQCPgWdr//vc/UlNTefPNN2tkQXDh6bFixQocHR0xMTHB2dmZzZs3A2Vn6Pz+++/ldSRJ4rPPPqNx48aoVCpcXV25cuWK3B4TE8Nrr70mz8B5+fLlKt8vQRD+nQgWCYIgCEI1q7jeSSTqej3qxzdu3EhKSgq3b9/m8OHDtGrVClDP6Obv709MTAzx8fHMmjULKysrjS1169aNq1evkpaWxjfffINKpQLUmSlBQUFkZGQQGxvLzJkziYyMpH///hQWFvLttz8Afbgf6KiHepa4JsCrGBur6NChAzdu3HhgXZO2bdvi4ODA3LlzycjIQJIkbt68yY8//gjAiBEjCAkJYd++fRQVFbF8+SdI0veAK+qLWzMgH1hC8cVteno6KpUKIyMjrly5wvr16x/quFeF8mfJskJ9DCciSeOIjo7gyy+/JDo6GkmSsLS0REtLi4MHDz5UDZb09HT09fWpVasWmZmZzJkzp1LrDRkyhDNnzrB06VLGjBnzcDsoaKhJdZRK96smFQQXaq6StdWKJ084dOgQGRkZnDx5knbt2pU7Q+ebb75JbGwsAOvXr2fLli18//333L59Gw8PD/r06UNeXh4A3t7e2NjYkJCQwM6dO/niiy+qc5cFQaiACBYJgiAIQjXr0qXLvUyaJUBRqdb7mTRVPUyk4unAtwGOwDliYiIZMWLEvxaHViqVHDhwgNjYWJo0aYKZmRlvvPEG169fB9RFtH/66Sc2btyIjY0N48ePp1GjRtSp8zfqi1sfoACF4hwGBgZ4eHiwceNGPv30U4yNjRk/fjxDhw59/AfhEVWcNbYNdbHudwD45JNPcHJywtfXF3d3d8zNzdmzZw99+/at9HNNmzYNpVKJlZUVLi4udOzYsVLrmZiYMGjQIP766y+NqdmF/0apVOLq6oqXl5c8I1lNUFMDWULNUXpGzpEjR5KdncOWLVvIzs7GysqKFi1asHHjxjIzdPbq1YsjR44AsHbtWj766CMaNGiAtrY2U6ZMITs7m5MnTxIdHU14eDjLli3D0NCQxo0bM378+Grec0EQyvWkiyc9bUSBa6GqiXNFqAxxnjz79u3bJykUinsFco9JkC7BMUmh6CMpFIp/LYRcrLxzBZDOnTv30H26X2g6o4LCuOkSIAUGBj70tiuruLB2YGCgFBoaWukCwTVFaGjoIxUXflJKnycLFy6UBg4cWKV9EJ4O4vPn+VD8GaSeaOH4vff94xK8KAGSgYGB9Oqrr0rnzp2TevXqJenr60tmZmbyn5GRkfT6669LeXl5kqGhoWRsbKzRbmBgIAUGBkonTpyQ9PX1NZ57165dosD1c0S8p1S/ysY8RGaRIAiCINQAT3KYiCRJ8pC1h/Hg6cDvP35/ucevpmZpVFZNzRorKSkpiS+++IIJEyZUWx+Ex8fZ2fmB9YyqQ2pqKgqFQqPwu1BzlF9bzfjev7+jUPShdm1LmjdvzogRI3BwcGDq1KkaNeju3LkjZwg5ODjwzTffaLTfvXsXLy8vbG1tycnJ4datW/LzR0VFVf1OC4Lwr0SwSBAEQRBqiEcdJuLs7FxmVq9H8TQEOmq6ml5c2M/PD2dnZ9544w26d+9eLX0QBKF6VTzk+CrwC5I0jdjYm6SkpKCtrc0777zD1q1bCQ0NpbCwkNzcXE6cOEF0dDQAkyZNYv78+Vy9ehVQ11P79ttvycjIwMHBgc6dO/PBBx+QnZ3N1atX2bhxYxXvsSAIlSGCRYIgCIJQg9SkTJriQIe60HRHalqg42lRk4sLz549m6ysLDZs2FBtfRCEB5EkicLCwuruxjOt4tpqecA8QF077cSJEwQEBNC6dWt27drF3Llz5Rk6FyxYQH5+PgCTJ09m9OjReHh4YGpqSpMmTQgMDJS3GhgYSHR0NJaWlnh7e4vC+oJQQ4lgkSAIgiAIFfLw8GDfvn3Y29+ipgU6noSkpCTc3d0xNTXFwsLiPw3fK095WWMpKbdo0KDBY9m+IJQWFRXFq6++ioWFBbVq1eKNN96Qh4GdO3cOExMT7t69Ky8fHx+Prq6uPKPV2bNncXNzw8rKivHjx7NlyxZ52QULFtCnTx8mT56MSqXC0dGRPXv2yO25ublMmDCB2rVrU7duXfbu3avRN+lfplZ3dnbGz8+PDh06YGhoKKZWf8IqHnLcHDgB/ASoZzlr2bIlAO7u7hw9elSeofN///sf9erVA0ChUDBx4kQuXbpEeno6sbGx7NmzBxMTEwAcHR35+eefycjI4MyZM/j6+oohioJQA4lgkSAIgiA8Q65du8asWbOoXbs23bp1Izo6mvfee4/Ro0drLLd06VJ69uwJ/PuF27Rp05g06R2aNGmCvr4+LVq04LffQuRA0axZs3BycsLExISmTZvyzTffPPH9LCgoQJKkx77djRs3olQqSU1NZdmyZY9126WzxjIzM2nevPljfQ7h+VRyuvOcnByKioooKipi2rRpREdHc/PmTQwNDRk3bhwArVu3xsnJif3798vb2LlzJ926dcPOzo6EhAReffVVJkyYQFxcHLNnz+ajjz7il19+kZf/6aef6Nq1K8nJySxatIi33nqLjIwMABYvXszx48e5ePEi586dIygoSKO//za1OkBAQADbtm0jMzOTRo0aPcnD99wTQ46F6hIQEPDYfpQRHj8RLBIEQRCEZ0hgYCDTp08nLi4OIyMj5s2bx9ixY9m3bx+ZmZnycgEBAXLq/79duMXExLBhwwY2bdpEx44duXLlCg0bNpQzFVq2bMmpU6dITEzE1taWwYMHY29vz5YtWzSK2rq6urJq1Sq5D+fPn0ehUMj3d+zYgYuLCyYmJjg6OjJv3jyNgJBCoeDzzz/HxcUFIyMjjf15XCIiImjWrBlaWuIrkvB0KD3deWJiIm+/PZ6zZ8/Ss2dP9PX1MTU1xdfXl/DwcIqK1MGAkSNHsn37dnk727dvZ+TIkfLtrl27MnjwYJRKJU5OTowcOVJjKFGbNm3k9hEjRpCXl8fff/8NqANPc+bMwdbWFpVKxYcffqjR5wdNrV5swoQJNGrUCKVSia6u7hM7fkLNr60mCA9SUFBQ3V14ZolvQoIgCILwFCudUfD2229jZWWFvr4+w4YN48yZM7i4uNC0aVN5KMjx48dJSkqib191HYrSF26TJk0iLS2NxYsXExYWBsDrr7+Ovb09M2fOZOPGjTRo0EDOVBg2bBiWlpb4+fmRmppK06ZN+fDDDzWyFirD3NycoKAg0tPT+e6779i0aZPGxSmog2GHDh0iPT0dIyOjRz+AJQwaNIivvvqKdevWYWxsXKZOyooVK2jQoAEmJibUr1+fzz//XG6LjIxEoVDw5ZdfUq9ePYyNjZk1axbx8fG8+uqrmJqa0q1bNxISEuR1FAoF58+fByo3rGf8+PHysJ7Sgbj8/Hxmz56No6MjFhYWDBkyhKSkpMd6fISaJygoCE9PT2JimnP/At+a5GQnBg4cSJcuXXBwcMDU1JSuXbuSm5srZ/8MGzaMkJAQ4uPjuXDhAjdu3JCzBSMjIzl48CAqlQoLCwu8vb1Zu3Ztido2YG1tLd9WKBQYGBjI246Li8PJyUluL3m7ePvDhw9HpVLJf3fu3CEmJkZextHR8TEfLeFBanJtNeG/SU9PZ/LkyTg5OWFqakrbtm2Jjo4mMTGRwYMHY2FhgaOjI76+vnLAJSwsDJVKxbp167Czs6NWrVqsWrWKv/76i/bt22Nqakr//v3JysoC7n/2ffHFFzg7O2Nubs7EiRPlH5vKyxx66aWX+OWXXzh37hzjx4/nzz//xNjYGGNjY3lmvN27d9OiRQtUKhVt27bl2LFj8vqurq7MmjWL1157DSMjI3788ccqOJrPJxEsEgRBEISnVHkZBUuWLJXbjYyM5Iu3MWPGEBAQAKi/vA0bNgw9PT1A88LNyMgIHR0dbt++zUcffYSbmxuFhYXcunULZ2dnevbsSa1atcjKypIzFVasWEGzZs1YvHgxV65c4erVq9y9e7dMNsG/6dmzJw0bNkShUNCqVSu8vLzkYFWxWbNmYWtri56e3mPP/vnmm28YNmwYEydOJDMzs8yv6E5OToSEhJCens7mzZuZOXMmR48e1VgmNDSUP//8k99//53Vq1czePBgVq1aRVJSErq6uixZsqTC53/QsJ5FixZx+vRpLl26xPnz58sE4vz8/Dhw4ABHjhwhIiIChULBsGHDHtOREWqiiqc71wNmA46cOXOWU6dOkZ6ezm+//QYgZ+vZ2dnRrVs3AgMD2b59Ox4eHnIA1sHBgQEDBpCamkpSUhKBgYGkpKRw8ODBSvXN1taWmzdvyvdLT43+oKnVixVnQAlV51Fn5BSqV8kfj8LCwhg1ahTXr1/n+PHjpKamsmnTJgwMDPD29kZHR4eIiAjCw8MJDg7G399f3k5GRgaRkZFERETwzTffMGPGDGbMmMHevXuJjo7m+vXrZWaw279/P+fPn+fPP//k2LFj+Pn5/Wt/W7duzYYNG2jevDmZmZlkZmbi6OjIwYMHmTFjBgEBAaSkpDB79mz69OlDcnKyvG5AQACLFi0iMzOTV155pdztl/xBRvhvRLBIEARBEJ5CFWUUpKSof43//vvvNZb38vLi9OnTXL58mT179vDmm2/KbcUXbl9++SXZ2dn3Lj7vD0MABfv372fr1q14e3vz1ltvERUVJWcqLFy4kK+++gpdXV1CQkJwcXFBkqSHzgz46aef6NSpE3Xq1MHMzIwNGzZw+/ZtjWWeRLZByS/YCQkJFdZCGjhwIA4ODigUCtzc3OjRo0eZYNbcuXMxMjKiadOmtGzZkpdffplmzZqhp6fHgAEDOHv2bIX9eNCwnsDAQD744ANsbGwwMzMrE4jbvn07c+fOxdHREWNjY1asWMHPP/9MXFzcox0cocaqeLpz7t1/gezsu1y8eJHk5GQWLlxYZhsjR45k27ZtBAYGykPQAEaMGEFISAj79u0jPz+fgoICzp8/z6lTpyrVNy8vL5YuXUpcXBypqal89NFHcltiYiIGBgb06dMHGxsbfH19SUlJYdGiRZiZmbF+/XpiYmL44IMPABg+fDi2traYmpry4osvEhoaKm+rOGvh448/xtLSEisrK42hrkVFRcydOxcrKytsbW1Zu3YtKpVK43X7oAyG51FNmpFTqLzSPx65ubkRHBzMgAEDsLW1RUtLi9atW5Obm0tISAgrVqzA2NgYJycnfH195R+Tii1cuBBdXV1eeeUVateuTZ8+fXBwcMDMzIxevXqV+SxbsGABKpUKW1tbZs+erTHE9WGtXbuWmTNn0qZNG7S0tPDw8KBx48YawWpvb2/atWsnZzUKT4YIFgmCIAjCU+bBGQXTAPjgA1+NX+ZNTU0ZOHAg3t7e1K1bl9atW8ttkyZNYt68eUycOOXeNr8CEgHp3ra1gPq8++4UsrKyWL58OY6OjnKmgpaWFhYWFtja2rJt2zYuXlTPqFM6m8DY2LjM7EvF8vLy8PDw4J133iE2Npa0tDTGjx9fJnDzuLOJSn/B/umnn/jyy61lCvKCug5LmzZtqF27NiqVioMHD5YJZllZWcm3DQ0Ny9x/UJ2lfxvW4+DgILeXDprFxMTg7Ows3y/Ovio5rEd4tlQ83XmxTwDo3bs3nTt3lgval+Th4UFERARaWlq4u7vLj9vZ2fHTTz+xceNGHB0dGT16NFOnTiU9Pb1SfZs7dy4vvfQSLi4utGrViv79+8tt3t7eNGnSBH9/f0xMTFi6dCnOzs788ssvZGRkcOHCBezs7Fi8eDEA3bt358qVKyQnJzN06FA8PT3l1wXApUuXMDQ0lGfcmjlzJjdu3ABg69at7Ny5k/DwcG7cuMHZs2c11q1MBoMg1HTl/3i0GdBiwoQJGp9nMTEx6Ovra3w21atXT+OzwsTERCMAU5nPstLDTotnVfwvIiMjmTNnjsYw1fPnz2tsUwxTrRoiWCQIgiAIT5kHZxSoC0bHxNwsM9302LFjuXDhgkZWEcDkyZPp3LkziYmxwC9AM0CzVhB0JCtLnSZuZGREUVGRnKnQr18/mjdvTmJiIkFBQbRt25bs7Gw+/vhjjS20adOGoKAg0tLSuHXrlkbae25uLjk5OZibm6Onp8fJkyfL1Ct63Mr/gt2LzMw6eHp6cvr0aXnZqKgoRo0ahb+/P7du3SI1NZVevXo9kRnZymNra0t0dLRGf0qyt7fXmHo6ISGB3Nxc7O3tq6R/QtWreLrzSKA/kA/A//73P/766y/efvttJElCpVLJSxYPVY2JiSkTiG3dujWHDh0iPj6eHTt28Ouvv9K9e3dAnUUQHByssXxqaiqurq4A6Ovrs2nTJlJSUrhx4wYvvPACgYGBnDp1ipCQEFauXMl7773H33//zfbt27G2tubDDz9EkiSWLl3KzZs3GTJkCABvvvkmZmZm6OjoMHPmTIqKivjjjz/k561Tpw7Tp09HR0cHV1dXnJ2d5aEngYGBTJo0iYYNG2JgYMDSpUs1guiVyWAQhJqs4h+PegNFSFJ3fHxmyDX47O3tycnJITExUd5GZGTkI39WlB52amdnB5T9kQjQeO7yfgBycHBg+fLlGsNUs7Ky5GzDkus5OzuzePFi2rRpg6mpKT169NDIqD1x4gQuLi6YmprSt29f0tLS5LbTp0/TuXNnVCoVTZs2ZdeuXXLbv9UR/LdZZJ8VIlgkCIIgCE+ZijMKIlEq+8n36tevrxFAcHZ2RldXt0wtG4VCQfv27e/dSwRigT2Ayb3H7IFe6meIjGT06NEYGhrKmQqrVq0iLS2N5ORk+vTpw9WrV9m0aRO9eqnXKa6N9N5772FjY4ODgwPu7u7yxSCof8lcu3Ytb7/9NqampixevFij/XGr+Au2BdAH6M2uXfe/GGZmZiJJEpaWlmhpaXHw4EEOHTr0xPpXmpeXF/7+/iQkJJCWllYmEDd8+HCWLFlCdHQ0mZmZTJs2jVdeeQVbW9sq66NQtZ6G6c5LZ+4NHjwYUGjU+iqZ1WBiYqIRzCoqKsLX15cGDRpgamqKSqUiLS1NI6OvZMYDaNZqK52RZ2Fhgb6+vny/MhkMDyspKQl3d3dMTU0ZNGjQQ6+/c+dOOnXq9J+fX3i+VPzjkRXQD8giOjqCX3/9lXPnzqGvr4+bmxszZswgKyuLqKgoFi9ezKhRox6pHx999BGpqanExcXh5+cnf89o1aoV//zzD+Hh4RQUFODv76+RuWdlZUV8fDzZ2dnyY5MmTWLZsmWcOXMGSZK4e/cuhw8flt8nJEni+vXr8sQemzdvJjAwkISEBKytrRk+fLi8ra+//pqQkBCioqKIiYlh5cqVgDq4/frrrzN06FCSkpJYv34948aN03hvelAdwX+bRfZZIYJFgiAIgvCUqTijoKLl1MGRTz75hMGDB2Nubv6Q2ywE1L/UbdmyhczMzHIzFfT19fniiy9ISUkhIiKCli1boqurKw+vqlWrFt9//z3p6elcvHixzDCz8ePHExcXJ8+GtmbNGo3sBUmSysyq8l/9W3aWJM0mJeW2nGrftGlTfH19cXd3x9zcnD179sizyVWFuXPn0rJlS5o2bUqrVq3KBOJmz55Njx496NixI87OzuTn57Njx44q659Q9Wr6dOflZ+59C0gMHDhQHhpTMquhdJZBYGAggYGB/PDDD6SlpZGamoqZmVmlM/pKZ+QlJSWRk5Mj369MBsPD2rhxI0qlktTUVL755puHXn/YsGEadZOcnZ3LZHEJQrEHD0fdBjQB1MNRx48fT3Z2NoGBgWRnZ+Pk5ETnzp154403mDVr1iP1o1+/frRq1QoXFxfat2/PnDlzAHjhhRfw9/fH09MTGxsbcnNzadq0qbyeu7s7HTp0wM7ODpVKRVRUFH369GHp0qWMGzeOWrVqUbduXVavXk1RURFBQUGcOHGStWvXyhN73LmTxuXLlzE0NMTf35/Q0FA5sDRr1iwsLS1RqVQMHDiQM2fOAPDDDz9gYWHBu+++i46ODt26dcPb25tt27bJfXtQHcHSs8hOmTKF7OxsTp48+UjHscaRBA1paWkSIKWlpT3SdvLy8qTg4GApLy/vMfVMeFaJc0WoDHGeCCUVFBRI9vbOkkLRR4JCCST5z8AgRwoODpYaNGgsFRQUSJIkSf/8849kaGgoNWvWTLp58+ZDbjNOAl0J3CQHh7ryNsuTmJgohYSESAUFBVJsbKzUrVs3ycvL64kcg0cVGBgoARJkaBy/+3/pEiAFBgZWd1fLdezYMUlXV1cqKir6T+uL95SqsXjxYmno0KGVWnbr1q1Sy5YtH/o59u3bJ9nbO987n9V/Dg51pX379j30tsrzX86V4vcTKPseBW4S2El2dk7SP//8I7m4uEiLFi2SQkNDJTMzM43trF27VmrYsKGUnJws5eTkSAsXLpSUSqW0+9hPiAABAABJREFUf/9+SZLKP2YtW7aUtm7dKkmSJH3xxRdS3bp1pWvXrkl3796V3nrrLUlLS0sKDQ2VJEmSvvvuO6levXrS6dOnpaKiIikrK0v6+eefpejo6P94tCRpzJgx0tSpU8tt+y+vNycnJ3l/azLxnlI9QkND773uj1fwWXZMAuRz/nGLiIiQAOnOnTuVXue/niv79u2TFArFvfeV4/c+v60laCMpFAr5PU9PT086ceKEBEjnzp2T11+5cqXUrVs36f/s3XdcVeUfwPHPZcu8gAzZjpyoaI7coOZeucVFWjnSJE1z75WmoqaZWVIqaj8HlqsywdzmSnFkKihDQFFkyOb5/YEcuQIKCoL2vF+v+4J75nMuh3vv+Z7n+X6FEGLhwoWiXbt2GttfsGCBaN++vRBCiBkzZoiuXbtqzDczM1NeR0NDQ2FsbCzMzMyUR5kyZUrtd4anFTTmIXsWSZIkSdJr5tk9CjwBWLhwntKjoHz58iQmJhIUFJRvUsi8t/kzUAVwQKUKfG4vhYyMDD799FPMzMxwc3PD3t6elStXFtFRF63n984Kemq5khUdHU1AQAAZGRlEREQwadIkevTogUqlKummSc8wefJkjTwYRSUkJASVSkVsbGypLHf+7J57fsBbhIffomHDhs/s1TB48GBq1KiBs7MzFSpUoEyZMoXKrTJkyBD69u1L48aNqVixIm5ubhgYGCg98p7Vg+FF9OrVix9//JHVq1djbGzMd999h5ubGzNmzMDW1pa+ffsqFdxycnNzU6pR5Zzfq1cvbt++Tb9+/TA2Nmb48OEv1C7pzfU6DEctCs8u7NEb6IS392fcuXOHlJQUJWdSfp7O8weFy92UXUU2Z6/ER48e0a9fv0IfW2lWqGDR6tWrad26Nb179+aPP/7QmHfv3j0qVKhQpI2TJEmSJClv3bt3Z9u2bdjbXwQaA6ZAY+zts5Jad+7cuQi22RWIx9FRsG3btudefJYrV47z58+TkJBAdHQ0mzZtynPIW2nwun3Bfp0CcdKrV9rKnT97aIwtWYFoWL58OQsXLlSSU8fGxmosaWRkxI4dO4iPjyc8PJzx48cTEhKiVFfz8vJSkllnO3/+PF5eXkDWsLb58+cTHR1NREQE3bt359GjRxpB8169enH27FliY2OJioril19+eeFKS//73//o378/I0eOJCEhAW1tbYKCgtDR0eH27duFLif+v//9DycnJzZv3kxCQgJr1qx5oXZJb66SGI5aEkMjnx2AXosQnoSGBjNkyBCaN2/+3KBPhw4diI6OZvXq1aSnp3P48GE2bdrEoEGDCtSejz/+mOnTp/PPP/8AEBcXx65duzSqLb4JChwsWrFiBePHj6dq1aro6+vToUMHFixYoMzPyMjQyIIuSZIkSVLxyqtHwYUL54p8myXdS6E4lPZ8L097nQJx/zXLli3TKDsPsHXrVqpWrcrMmTM1ysZfv36dtm3bYmFhQcWKFfHx8cl3uwkJCYwaNQonJyesra0ZNGiQUsmnQYMGQNbdcWNjYzZt2lTkx/UySkvPvfT0dPz9/UlLS+PBgwd4e3vTuHHj5/Y6KIyMjAwCAwPZvHkzgYGBufIpmZmZMWXKFPT09DA0NCyy/UpStvxuHjk4BBXoRs/LcHFxyVVlsaBat279zPfAnJ4dgB4CLAQgPDy8QO+H5ubm7Nu3j40bN2JpaclHH33E119/TdOmTQvUnlGjRuHl5UX37t0xNTWlWrVqxV7BtSToFHTBb775hm+//RZPz6zu7SNGjKBbt24kJSUxe/bsYmugJEmSJEn5y+5RkC0tLa3It/mmyv6CPWbMOMLCnlQfcnAoj49P8X7Blt4cnp6efP7554SGhiqVtzZs2MDAgQM1/h/T09Pp1KkTXbp0YdeuXVy7do127dphbW2tfL/OaciQIejo6HDhwgV0dXX54IMPGDVqFBs2bODUqVOUL1+esLCwF7pIK27ZPffCw+cjhD+a96ezeu45OBR/zz0hBAsXLmTQoEHo6OjQuHHjIr2g27Fjx+P3jxBlmqGhER4e7spze3v7PMuDS1JR6t69O127duXw4cPcuXOHcuXK0axZs1Jzw+NlaQag33lqbg2gJdCYFStWKL2Kng7cent74+3trTxv0KCBRjL5nGbOnJlrWs6ejyqVipEjRzJy5MjCHMZrp8DvXMHBwRplHBs3bszBgwdZu3YtkyZNKpbGSZIkSZIkFaf/Sk+qF5WzDLiVlVWRVaN7E2T3KDl48CB169ZVhhhFR0fz+++/M3DgQI3lT548yZ07d5g7dy4GBgbUqlWLUaNGKblqcrp79y7bt29n1apVqNVqjIyMmD17Nlu3biUjI+NVHN5LKS0993R1dTlx4gRxcXHcv3+f3bt34+zsXCTbzrva23EePTJnz549SrW3pwNFxsbGPHr0SGNaZGRkvvuRgSapoF71cNTbt2/z7rvvYmVlhbm5OR07dtTIA+Tl5cWQIUPo1q0bxsbG1KpViyNHjgDw/fffc+TIET7//HOMjY1p3749AFFRUfTu3RsrKyucnJyYMmUK6enpNGvWjLJlbYBmwAqgHFlDWmMpjUPH3xQFfvcpW7asRulJAFdXVw4ePMj69etfutyeJEmSJElSSSht+V5Kk5xlwBcvXlzSzSk1duzYgYtLJTw8PPD09OTkyZPMmDGTHTt2sHnzZho3bpwr701YWBh2dnbo6ekp0ypUqKCUeM4pJCSEzMxMypcvj1qtRq1WU79+fbS0tJ4ZWChNSnJoTHHLP9nuO2T1cCiPt/dneSbKdnNz4+bNmxw+fJj09HQWLVpETExMvvuysbHhxo0bxXIckvQyMjMzGTt2LKGhody6dQtDQ0M+/PBDjWX8/PwYOnQosbGxjBw5ki5duhAbG8uQIUNo2rQpX3zxBQkJCezbtw/I6qmpq6tLcHAwhw8fxt/fn0WLFqGtrc3o0SOBdOBLshLl7yErSDuO0jZ0/E1R4GBR06ZNlQh5TtWrV+ePP/5Q/sCSJEmSJEnSmyE4OJgaNWrI3g055N2jJID0dEGPHj1YuXJlrl5FkJVfKCIiQmNoWn7VdxwdHdHS0iIiIkKj2k5ycvJrNazpTe259+xkuyqgPqGhwVy7di3XupUqVWLRokX07NmTcuXKkZKSQo0aNfLd1+TJk/nqq69Qq9Vv/JAXqfTKmZsrOTmZzMxMXFxcaN++PQYGBpiamjJlyhQOHz6sESRt2bIlnTt3RkdHh+HDh2NjY8OePXvy3Ed4eDgHDx5k6dKlGBsb4+zszJQpU5Tel82bNwcga0RaS6AekIm+fuRrH4AurQr8STNx4kRq1aqV57waNWpw8OBBpk+fXmQNkyRJKo10dHSYNGkSI0eOxMTEpKSbIxWDvMoaS9J/0dNlwHMOf1q+fHmuvFZbtmyhevXqr7iVr1b+PUrcgYGAFTdv3szzoqVBgwbY2Ngwffp0UlJSCAoKYuXKlQwePDjXsra2tnTr1o1Ro0Zx7949IGuo0s6dOwGwsrJCS0vrtehx8ib23Ht2sl1fYB0AtWvXzlWtDWDs2LFERUVx9+5dpk2bplHB7ekKb507dyY4OJjY2FhWr15dhEchSQXzdE/KqKgoPvpoOOvXr8fT0xNHR0dMTU1p3rw5KSkpGhXBnh726ezsTERERJ77CQsLw8DAABsbG2Xa070vDQwMCA0NVgLQ06ZNo3x5FxkoKiYFDhbVqlWL999/P9/5rq6uzJgxo0gaJUmSVNqtXr36jSuPKUlS6VKjRg12795dYvvPqwx4tgEDBnDy5EmCg4OVaevXr3/md8U3wbN7lAwG7iKEyDNAoKury+7duzlz5gy2trZ06dKFsWPH5pncGrIC19nDz0xNTWnWrBlnzpwBoEyZMsyYMYP27dujVqvfyCo8pVlpqfYmScUt756UtsTEODNkyBCuX7/O2bNniYuL488//wQ0E0vfunWLwMBAJRH/7du3sbOzA57k48q+Sefg4EBycjJRUVHK+k/3vkxOTiYmJkYJQBsbGxdpdUNJU4GroUmSJEmSJEmvzqVLl175PjMyMjSq6TxdTSabpaUlXbp04YcffmDmzJmEh4dz6NAhfvjhh1fc4lfr2T1KWgBxgKmy3NMVdSpXrsxvv/2W57a9vLyU3iUAJiYmLF26lKVLl+a5/PTp02Wv/hJSWqq9SVJxyt2TMvs81wduAeZcunQZExMTYmJimDVrVq5tHDx4UPk/+Pbbb7lz5w4dOnTg+PHjWFtbc+PGDd5++20gq3Kgh4cHn332GWvWrCEmJoZ58+Zp9L5UqVTY2NgQERFBXFwcq1atyrNymVQ0Xo8Bz5IkSaXMBx98QJkyZZTnQUFBSo4JHR0dmjRpQnJyMgA+Pj6oVCoGDx6Mjo4OWlpaNGjQoKSa/sZISEhg1KhRODk5YW1tzaBBg3j48CGQ1evBzs4OU1NT3n77bQICAjTWXblyJY6OjlhaWjJ16lTc3NzyrEj0vP0URM5qUr169Xrmsl5eXhplXSXpVXp6qIGHhwfbtm3n+vXreS4/ZMgQfvzxR4QQ/Pjjj7Rp0wZbW9tX3OpXS/YokaD0VHuTpOL07J6UAF48epSIubk5TZo0USqa5eTp6cmePXt4+PAhK1asYNeuXZibmwPwySefcODAAUaOHKn0UvXz8yMpKQlnZ2eaNGlCx44dNQppGRsbA1mJ4ps3b86gQYPyHMorFQ0ZLJIkSXqO1NRUfHx8GD16NEII0tPTcy3TvHlztLW1iYiI4NChQ5w5c4YuXbpoLBMUFMS9e/f4+eef+euvv/Dx8XlFR/BmyJlcMTAwkPfff5/79+9z4cIFgoODSUtLY9SoUQC0atWKK1euEBMTQ9++fenZs6cybPCPP/5g+vTpbN++nTt37qClpfXMHhxDhgzJdz8FkbOa1P/+97+XexGkYjN//nz69etX0s3Q4OLigr+/PzNnzqRbt24a89RqNYGBgUBW75VOnToxbNgwzMzMKF++PIGBgfj7+1OpUiXMzc2ZMmWKsq6vry/16tVjw4YN2Nra4uTkxEcffVSgMuA5vfvuu6Snpys9it70IWjwpEeJSjUfeLrSlSzf/F/yJld7kyR4Vk/K+sA94GsAmjRpQpcuXZg3bx7GxsY0btxY+b5jamrK3LlzMTMz46OPPqJPnz44OjqyefNm6tevz5UrV1i9ejXly5cHsvK1+fr60rdvX1QqFd9//z1Dhw5VbtIlJCQAkJiYSGJiIlWrVn1tEv6/juQrK0mS9AwTJkzA0NCUTz/9lK+++orMzEyWLFnKqVOnlGVOnz7NgwcP2L17N7a2tjRp0oShQ4cqF3LZfv31V9RqNZ06dcLCwoI//vjjFR/N6yvvHg/baNeuHWq1GiMjI2bPns3WrVvJyMjg/fffx8zMDF1dXcaPH09mZiYXLlwAsu5a9e/fnwYNGqCnp8e0adMwMjLKc793795l+/btrFq1Ks/9FISsJvV6mDx5Mps3by7pZuRZcaYgfvvtN9q2bcv9+/cZOHAgAwYMYNeuXfz9998cPXqUJUuWcPbsWWX5S5cuoVKpuH37Nn5+fqxbtw4hGlGYMuBaWlq8//77eHt7c//+fTp16lQ0L0IpJnuUSDm9qdXeJAny6kmZAQQC3QFrYBoAU6dOpXbt2vz111/ExsYyffp0Bg4cqJHbMz4+nrNnz3Ljxg1+//13Dhw4wIYNG/Lc77Nu0mX3LAoLCyMhIYH+/fsX9WFLORT6m2vLli2JjY3NNT0uLo6WLVsWRZskSZJKhQkTJrB48WIyMt7lyQWBFkKU5+LFi0r547///hvISvSfrU6dOhrlkQHKli2r/K6npycTZBdQ3skVvwNg8ODBGBkZKUlgtbS0iIyMZMqUKbz11luYmpqiVqt5+PChUlEoIiICR0dHZfu6urr5DhkJCQkhMzOT8uXLo1arc+3neZ6uJjVr1izeffddrKysMDc3p2PHjoSEhOS7/r///kuXLl2wsrLCwsJC4wLk9OnTNGnSBLVaTfXq1TUCHZs3b1YqKanVapycnNi6detz2yuVrPwqzly5cuW567799tt0794dbW1t+vbtS3h4OBMnTsTIyIjq1atTq1YtjWCRkZERffv2RU9Pj/T09Me5iSwpbBnw999/nwsXLjBgwAB0dXVf6vhfF7JHiZTTm1jtTZLg6Z6U24BKgAfgCUQBc7C0tKZZs2b0798fa2tr5TOoatWq3L17V9lWZmYmX3zxBYaGhlStWpWOHTvmmZi/KG7SSUWn0MGiwMBAUlNTc01PTk7m8OHDRdIoSZKkkpaamsrSpSuATsAuntxpVwE9AEcyMjJITU2ldu3aQNYws2wXLlz4z1w4Faf8y1R3IOsjrD2WljbExMQQGxtLcnKycoc3e4x8bGwsZmZmSqJeOzs7QkNDlX2kp6fn6GqtKTsPVUREBLGxscojOTm5QNU3nq4mNXjwYMaOHUtoaCi3bt3C0NCQDz/8MM91ExMTad26Na6uroSEhBAZGcno0aMBiI2NpV27dvTt25e7d+/y9ddf8+GHH3L06FFl/d9//53mzZsTExPD3Llz+eCDD0pFgNLFxYV58+ZRt25dTE1Nadu2rVJGNzo6mv79+1OuXDns7Ozw9vYmJSUFQKmmsm7dOiXfVM48BvDsXFQFGcqVc75KpWLNmjW4urpiampKly5dCpWrqrCeVXHmp59+yrfUcLacpYYNDQ3znJbdfR+y7hjr6GTVOXly/ifnsWVfnlUG3NraGkNDQ4YMGfL8g3yDyB4lkiS96bJ7UgrxC9Ab0Px8ghrExNxl165dLFu2jBo1amBmZoZarSYoKIgePXooKRcMDAywtrZWtm1lZUV4eHiufT7rJl3VqlWVXuLSq1HgYNGFCxeUP87ly5eV5xcuXODcuXN89913smydJElvjNWrV5ORkQJMIfdbpRZQW1muXr16qNVqunTpQnR0NMePH2ft2rW0aNHiFbf6zZN/ckVboBugRWhoMIcPHyYyMpKdO3cSFxeHnp4eZcuWJTU1ldmzZ2sESfr164efnx+nT58mLS2NuXPnkpiYmOf+bW1tlR462T2TsvfzLDmHEkVGRiqBKhcXF9q3b4+BgQGmpqZMmTKFw4cP5znUaPfu3ejq6jJv3jyMjIzQ09PDw8MDgD179mBlZcXo0aPR1dWlRYsWeHp6alSiqlOnDr1790ZbW5uBAweSmpqaZ8+QkrBu3Tr8/PyIjIzE1taWAQMGIISgS5cu2NracuPGDS5evMjff//N3LlzlfXi4+O5fPky//77L0eOHGHVqlVKsKewuagK4qeffuLgwYPcvn2bsLAwli1b9lLby0/+QVF9YBJQnQsXLip3VRMTE4mLi3upfd65c0fJv/akZ51BPkvnnbRZCMHKlSupU6eORs/K/wrZo+T1lV2qW5KkZ+vatSuWljZARzQ/n8oAk1GpOjFixChmzpzJjz/+yIMHD4iNjcXV1VWjmmZycjLR0dHK87t37+YZO3jeTTo5pP/VKvCr7ebmRp06dVCpVLRs2RI3Nzfl8fbbbzN37lxZvlOSpDfGjRs3Hv+W3wWQhcZyhw4dIjU1FVtbW5o1a4abmxu7du0q/oa+4Z5dptoXcAGgQ4cONGvWjDNnzjB48GBq1KiBs7MzFSpUoEyZMjg4OChrtW7dmhkzZtCtWzdsbW1JT0+ncuXK6Ovr59kGX19f5c6Wqampsp/8PD2U6Ndff+X779ezY8cO7t69i6enJ46OjpiamtK8eXNSUlLy7PFz69YtKlasiEqlyjUvLCwMFxcXjWkVKlQgLCxMeZ6zV4lKpaJMmTIl1rPo6Tw8w4YNo2rVqhgaGrJo0SICAgI4cuQI//77L4sXL8bQ0BBLS0smT56s0U1dCMHcuXMxMDCgWrVqNG7cWPlbFCYXVUFNmDABa2tr1Go1PXr0eObf/WU8u+KMFjCclJRkNm3aRHJyMpMnT87zvCiMxMREfvrpJ1JTU9HT03u8vXsUNGlzRkYGpqamrFmzhuXLl79UWyRJBm+KlhBCDtmRisThw4eJiYki981TGyAYISYRHX0HIQRWVlZkZmby/fffa/S2h6wcd5MmTSIpKYl//vmHffv25VlU4nk36aysrNDS0srxPV0qTgUOFgUHB3Pjxg2EEJw6dYrg4GDlER4eTlxc3H+uC7IkSW+uihUrPv7t6fLI6cACYLjGcrVq1SIsLIzMzEzS09M5ceKEMhTE29tb4+4KZAVBnk6ALeX27DLVJkBWYsO9e/fy77//MnfuXIyMjNixYwfx8fGEh4czfvx4QkJCNIYYjRkzhrCwMGJiYpg+fTq3b9/GyckJyCpfn3OojYmJCUuXLiU4OJi4uDhlP3nJeyhRBxISytKzZ0/69evHo0ePOHv2LHFxcfz5558Auc4PAGdnZ+Vz92kODg65ch2FhIRoBMVKi7zy8EyePJnPP/8cyApq6evrc+zYMWJjY7GwsFC6nvfs2ZOoqChlW6ampsr/FWTl3ckOgBUmF1VB5SwDn3NfRe3ZQVEALwBGjhxJpUqVqFmzJiYmJi+1zxo1apCRkYGTkxN9+vRhyJAhqFQnCpy0WVtbm/j4eIKDg6lbt+5LtUWSCuLpPIClydNVUosrWBMWFsa7776Lqakpb7/9NvPnz1duHLi4uLBgwQLeeecdDA0NuXz5Mhs3bsTV1RUTExOcnJyYNm2axmeKSqVi+fLlVKlSBbVaTZ8+fTSG2964cYPOnTtjZWWFs7Mzc+fOLXDSfenNkP/n02TgK6A9APXq1aNmzZrY2dlx6dIlmjRporG0iYkJbm5uVKhQgVatWuHu7s7AgQPz3OezbtKVKVOGGTNm0L59e9RqdZ55j6QiJCQNDx8+FIB4+PDhS20nNTVV+Pv7i9TU1CJqmfSmkudK6ZSSkiK0tfUFdBKQIUDkeGQI6CS0tQ1ESkrKK2nPf/U8SU9PFw4OLkKl6pzn30Gl6iwcHcuL9PT0Qm13+/bt4tGjRyIhIUF4e3sLZ2fnl/5bZrcVnm7rYAGfCJWqsyhTxkj07dtXpKaminv37olu3boJQDx48EAIIcTgwYPFmDFjhBBCxMfHCwcHBzF16lSRkJAgUlJSxMGDB4UQQty/f19YWFiIVatWibS0NPHnn38KY2NjcfjwYZGamir69OkjOnfurNE+MzMzERAQ8FLH+LS8zsec07Zv3y5UKtXj1+S4gHgBtgJ0BCC2b98uoqKiBCAOHz4sbG1t891XQECAMDMz05jWtWtXMWPGDCGEEEOGDBEff/yxMi8tLU2YmZmJ9evXCyGEWLx4sXj33XeV+QkJCUKlUimvyYwZM0TXrl2V+YA4d+6c8nzZsmWiRYsWz35BXlBAQIAAHr9GOc9xBwF7BBwTQJH9/davXy9q1aqV6z1l+/btj89hlIejY3mxffv2Itmv9Hoq6s+fJUuWCEdHR2FsbCycnZ3FokWLhL6+vtDS0hJGRkbCyMhI3Lp1S8yYMUN07NhRDB8+XJibm4uxY8eKW7duidatW4uyZcsKtVotOnToIIKDg4UQQty5c0fo6uqK+Ph4IYQQK1asEIC4cuWKEEKIn3/+Wbi6ugohsv4HateuLSZNmiQsLCyEo6OjWLVqlUY7N2/eLGrWrCnMzMxEvXr1xNGjR5V5LVq0EOPHjxfvvvuuMDQ0FD///LNwdnYW8+fPFw0bNhQGBgbCx8dHlC9fXmRmZirrHT9+XJibm4ukpKQCvVbp6ekiICBA+Pn5iYCAANG0aVMxdOhQ8ejRI/HPP/+IChUqCGdnZyGEEM7OzqJy5cri6tWrIj09XaSkpIi9e/eKf/75R2RmZopz584Ja2trsXHjRmX7gHj77bdFeHi4ePDggXj33XeFl5eXEEKIxMRE4ezsLJYtWyZSUlLErVu3RI0aNcS6devybOt/9XvKmy7/z6fsR+E/n+S5UvIKGvN4oUF///77L2vXrmXu3LnMnj1b4yFJkvQm0NPTY+zYT4DdQFdy3mnPer6bsWNHo6enV4KtfPMVV5nqDRs2KEmUz549y88///zSf8tnDyVSIcQkkpISOX/+PObm5jRp0oT27dvnuz1jY2MOHDjAmTNncHJyoly5cqxatQoAc3Nz9u3bx8aNG7G0tOSjjz7i66+/pmnTpgVq67OSTE+YMAFnZ2dMTEyoXr06//vf/5T1spNMf/311zg5OdG4cWNl+MiMGTOwtbWlb9++CCFYvHgxvXv3QQgdIIWsMrvGQBxZPfRU9OjRg2bNmtG8eXMaNWqEo6MjU6dOJT4+HiEEt27dYt++fQU6pufloqpbty7Hjx/n6tWrRTaUq6hoVpzJvmt/B4gGyuc5DKw4yKTNUnG7du0aU6dO5bfffiM+Pp6TJ0/Stm1b1qxZQ82aNUlISCAhIUHp6bl//34aNmxIdHQ0c+bMITMzM98iAba2tlSqVEkpuHPw4EEqVqxIQECA8jxn5eagoCBUKhV37txh69atTJw4UentuXfvXj777DN8fX25f/8+kyZNonPnzsTExCjr+/r6MnfuXBISEmjdurUy7YcffiAhIYERI0bw6NEjDh06pKyzfv16+vXrh4FBfvnBnni6V6aHhwdHjhyhadOmlClThsqVKzN8+HCNdUaMGEGVKlXQ1tZGT0+P9u3bU7lyZVQqFW5ubvTr1y9Xr+YJEyZgZ2eHWq1mzpw5+Pn5kZmZyZ49ezA3N8fb2xs9PT2cnJwYM2aM7MnxH5P351O2vIcpS2+Qwkah1q5dK7S1tYWNjY2oXbu2cHNzUx516tR5wdhW6SF7FkmvmjxXSrfx48c/7mH05E67traBGD9+/Cttx3/9PHkdejz4+fk9blt8Pnff4gQg/Pz8irUdeZ0rT9+ddnZ2Fi4uLuLKlSsiMTFRDBo0SHh4eAghhNi4caOIiooS6enpYvPmzUJfX1/cvHlTCJF1h1FLS0sMGzZMJCYmisTERLF+/Xqhra0tZs+eLVJSUkRiYqL44YcfRNmyZR+/HoECxgqoLiBNgLMAtQA7AYh69eqJ0NBQIYQQUVFRwsvLS9jb2wsTExNRo0YNsWLFCmXfz+pZJIQQPj4+wt7eXlhYWIgpU6aI6tWra7ze48aNE+bm5sLe3l58++23Gr2tSrJnkRBPemFl9aJb/vg16itUqs5CpVIV6bmeX88iScpLUXz+ZL8HLVu2TOjp6YmffvpJPHr0SJmf3dMnpxkzZuSa9rRz584JfX19kZGRIYQQYuTIkWL8+PEiIyNDWFlZie+//1706tVLCCGUcz57f6amphrHNHz4cDF06FAhhBAdOnQQPj4+Gvtq3Lix+PHHH4UQWT2LsnuBZsvuhZPThAkTxODBg4UQQiQlJQkzMzPx119/PfOYhMivV+Y6AVoa7wdbtmzR6Fn09PvE/v37RaNGjYSlpaUwNTUV+vr6olu3bsp8QJw4cUJ5fufOHQGIyMhIsWjRIqGjoyPMzMyUh4mJiahevXqebf6vf095k2l+Ph17/H3m2At/PslzpeQVNOZR6GCRk5OTWLhw4Qs3rLSTwSLpVZPnSumXkpIili1bJkaNGqV0x37V5HmSO+BR2KFnxa04umq/iKfPlbwCbdra2mLgwIHKOpGRkQJQgjY51a5dWxm2kH2M2cPmhMi66LKwsFAu1oQQonXr1qJv3745gmfJAkwEHH0cLLIS4FeswbOUlBRhbGwsjhw5UizbLw6vMigq31OkgnrZcyWv81pf30CUKVNGvPvuu+LcuXP5BoueHk4bHR0t+vXrJxwcHISJiYkwMTERgIiNjRVCCPG///1P1KtXT5w+fVo0aNBAxMTECBsbGxEdHS10dHSU967169eLqlWramx7wYIFol27dkIIIapXry4MDQ01AiWGhoZiwYIFQoisYNGSJUs01s8rWHP16lVhbGws4uPjxbBhw4SBgcFzX6/8hzTffvz6tVGGXy9atEgjWLRz505lOykpKcLQ0FD4+vqK5ORkIYQQY8aMyRUU37p1q/L85MmTQk9PT2RkZIjNmzeLhg0bPre92eR7yputKD+f5LlS8optGNqDBw/o1atXYVeTJEl6benp6eHt7c3KlSuV7tjSq1fcZapdXFzw9/d/4fUL0lUbeOnExIWRd8Lt42Rk6LFhwwZ27NgBPEkyHR4ezrJly6hRowZmZmao1WqCgoKUiiTZ7Ver1Rr7ebqcbVhYGG+99dbjZ0FklYC3A8JyrHULyF2O/WWPNykpicTERD7//HMsLS2pX79+kW2/uMlhYNKbJr/3oNTUd0lKSqJMmTIMHDgw33LYT0+fNGnSM4sEuLu7c/78eXbu3EnLli2xsLDAzs6Or776itq1a2u8d0VERGgkzb59+7ZSytvR0ZElS5ZolO5OTExk4sSJ+bYtr2lVqlShdu3abNu2jSNHjmBhYfHc1yz/Ic2OQBPAgNDQYDZv3szatWvz3U5KSgrJyclYWlqir6/PyZMn8xxCtnjxYqVM+fTp0+nbty9aWlp06tSJqKgoVq9eTXJyMhkZGfzzzz+yOMd/lPx8+m8qdLCoV69e/Pbbb8XRFkmSJEl6bRUkv1L2cq9CRkYGY8aMQ4hOgD/wDlk5g94hK39QNby9PyMjI4Po6GhSUlJIS0tj5syZ/Pjjjzx48IDY2FhcXV01qucU5ALJwcGBMmXK5AieJQMRQHa1NhWwvcjzHBRHLqpXrbiDopL0quT9HhQOxCPEZqATgYF/oqOjg42NDXfu3CEpKemZ24yLi8PQ0BC1Wk1MTAyzZs3SmF+2bFmqVavGypUr8fDwAKBly5b4+Pho5CsCSExMZM6cOaSmpnLy5Ek2bdpE//5ZFTY//vhjFi9ezJkzZxBC8OjRIw4cOEBYWBiFNXToUJYsWcK1a9cwNzd/7vLPro7oR1beN5g2bRoDBgxAX18/z+2YmJiwatUqPvroI0xNTZk3bx59+vTJtdyAAQPw8PBQctUtX74ceJI3748//sDFxQVLS0s8PT2JjIwsyGFLbyD5+fTfo1PYFSpVqsS0adM4ceIENWvWRFdXV2P+J598UmSNkyRJkt5cKpWKc+fO4ebmVtJNKTLdu3dn27ZtjBkzjrCwxsp0B4fy+Phso0ePHq+sLcePH398d3ozed8bekhoaAQHDhxgy5YtNG/enLi4OLS1tbGysiIzMxNfX1+CgoIKve8BAwYwdepUPvvsM7y9vcm66LEAqj1uT0fgND4+24v0y+bOnTuLbFuSJL2cJz1kcr4HpQLTgMuAIC4ugY8//piWLVvyzjvvYG9vT2ZmJhcuXMhzm7NmzWLw4MGYm5vj4ODA2LFjc/UI9fDwYM2aNUrC/1atWrFkyZJcwSJXV1fS09MpV64choaGzJs3Dw8PD5YuXcrXX39NREQEjRs3RltbGxMTE1xdXTl48CBr167lxIkT/PXXX1y7dg0fHx/09PRITk6mf//+LFy4kPnz5yOEYNiwYYwfP54xY8ZQs2ZNMjIylP0nJCQwceJEfv75Z5KTk2nXrh0rV67M0dsyiKwAW05OwHygMevXr+f48eNKMvCQkJBcr9fw4cNzJcF+WosWLRgzZkye8ypWrMj27dufub4kSW+uQvcsWrt2LcbGxhw6dIivvvqKZcuWKQ8fH59iaKIkSZIkvRrXrl3jnXfewcTEhBYtWhAaGgo8u0JY+/btWbNmDQAPHz6kd+/e9OvXi4CAADZt2oSZmRnbtm3J1VX72rVrVKxYka+++qpYjuXJ3d+87k4DDAXgvffeIzw8nE2bNtGuXTt69uxJzZo1sbOz49KlSzRp0qTQ+x40aBCjR49m+fLlGBkZoa8fTtawMwugMWXL6mFtbc2QIUMYOXLkCxxd6XX79m2MjY15+PBhSTdFkkpU3j1kagInyOodk1WB0cjICF1dXXbt2sX9+/eJjY3FycmJmTNn5goEVatWjVOnTpGQkMDVq1f56KOPEEJoDC9bvnw5KSkpGBoaAlnv0UII2rVrpyzj5eXF+fPnmT9/PtHR0WzYsAFLS0sCAwNxdHTk4MGDPHr0iP379yOEYMeOHXz33XdAVlA6MjKSf//9l2PHjrFgQdYQ4y1btpCcnMzZs2e5ceMGgYGBfP/992zfvh0rK6tcvSiHDBnC/fv3uXDhAsHBwaSlpTFq1KjnDGk+DUzGwcEFQ0NDVq5cKdODSJJUfIo7edLrRia4ll41ea5IBfEmnic8VW2qpDk7O4uaNWuKmzdviqSkJNG+fXulis2zKoR98cUXonfv3kIIIfz9/UXFihVFgwYNhBBCnD9/XpibmyvJn7OP+eTJk8Le3l4jsWhRyT5XDh48+IyE284CFryShNtClP7k5P9FRf2ecuvWLWFkZKQkGn4RgwcPzlVhSip5L3qulJak/8+SV9JeBwcXjaS9Xbt2FXPnzhXBwcECECdPnlTmbdmyRVSsWFEI8eR4o6KilPkLFy4UNWrUEM7OzuK7775TknhHR0cLLS0tcf/+fWXZa9euCV1dXZGenp5v9SloKAChp6cnnJ2dxfTp01/q/bQoP4ffxO8pUvGQ50rJK7YE19lSU1P5559/SE9Pf6lglSRJkvT6iYqKonfv3lhZWeHk5MSUKVNIT0/P1ctGR0dHSQgqhMDKyoozZ86UZNM1ZGRkEBgYyObNm0lOTmb48OGUL18eAwMD+vfvr7S1f//+WFtbo62tTd++falatSrHjh0DsoY8ZCf8PHjwIGPGjOHGjRvExcVx8OBBWrRooZHTZ//+/XTr1o0ff/yR3r17F9uxNWrU6Bl3p6E4cgblR+Y5ePM5OTmRkJCAmZlZSTdFKiUKkvT/Vb0H5SW/5NthYeb06NFDSea/d+9ejST/zs7OGr+Hh4crzw0MDLC2tlaer1ixgqtXr7JmzRqNz4GQkBAyMzMpX748arUatVpN/fr10dLSIjIyUhnSbG9/EWgMmAKNcXSMZvv27aSkpBASEsKsWbNe6v1UCPFGDQWXJKloFTpY9OjRI4YOHYqhoSE1atTg9u3bAIwePZqFCxcWeQMlSZKkkpczqBIYGEi/fv3Q1dUlODiYw4cP4+/vz6JFi/Dw8CAgIACAwMBAXFxclOcXLlwgIyODOnXqlOShKHbs2IGLSyU8PDzw9PQkKiqK6dNnKhXCjIyMiI+PB3hmhbC6deuSnJzMpUuXOHjwIK1bt6Zp06YcPnyYgwcP5sqT4ePjg4eHR67pRe1ZCbchmqycQV/KwE0eli5dipOTEyYmJri4uLBu3TqqVavG/v37Abh48SIqlUojMKqrq8u9e/cICQlBpVIRGxsLZA13GTp0KD179sTY2JgaNWoQFBTEN998g4ODA1ZWVqxevbqkDvWVyVl1SvpvKEjS/5J6D8q/AIAdcAF4B7W6LDExMXTo0EEjyf+tW7eU33NWUANITk4mOjpaeT5mzBjc3d01hsBBVrU1LS0tpQpZ9iM5OVnZnqw+JUlSSSt0sGjSpEn8/fffBAYGYmBgoExv3bo1W7duLdLGSZIkSSXv6aBKdkCoVatWGBsb4+zszJQpU/D19S10L5uSkvcdZVtiYpzp2bOnEjACOHLkyDMrhGlra9O8eXO2bt3K/fv3qVatGi1btuT333/nzz//VCryZPPz8+PKlSuMHj262I8z/7vTtmzfvl1edOSQHRBdunQpkydPZt++fcTHx3Py5EkaNGigEQg9ePAgFStW1AiMVq9enbJly+a57f/97398+umnxMbGUr9+fbp27cqNGze4efMmW7Zs4dNPPyUqKuqVHevLWLp0KW+99RYmJiYaObfyC5L17t0bU1NT1qxZw8yZM+nUqRNDhw7F1NSUt95665lJyQcMGICdnR2mpqa8/fbbyusN4Ovri5ubG3PmzMHa2hobG5tcuTO3bNlCrVq1lF4b2b0BpVcnv/cgB4cgtm3bVmLvQfmXp08gazTaSMLCQli8eHGuKtCzZ88mNjaWiIgIFixYoFRQg6zKkJMmTSIpKYl//vmHVatWaczPZmtrS7du3Rg1apRy4yEyMjLX/4PslSk9i4uLS668Xi9LpVJx/vz5It1mYfj7++Pi4lJi+5c0Ffobu7+/P1999RVNmzZFpVIp02vUqMGNGzeKtHGSJElSyco7qLIO0OKDDz5QgioVKlQgLCys0L1sSkL+d5T1gUlAJ7y9PyMzM2vYxNMVwr7//vtcFcI8PDxYvnw5LVq0ALJKNa9fvx4DAwNcXTUTTFtYWPDHH39w/PhxRowYoXHHujjIu9PPlzMgOm7cOFJSUmje3IPNmzdjY2NDrVq1cgWLpk2bxqFDh5Tnzzq3O3bsSJMmTdDR0aF3797K8BE9PT1atWqFmZkZFy9efCXH+rKcnZ05ePAgcXFxrFu3jvHjx3P06NE8l928eTNDhw4lNjaWoUOzEqrv37+fBg0acP/+fZYuXUq/fv3y/f7YqlUrrly5QkxMDH379qVnz55Kbz+AS5cuYWhoSHh4OFu3bmX8+PHKtvbu3ctnn32Gr68v9+/fZ9KkSXTu3JmYmJgifkWk5ymN70H5l6evDkwBvIGs86hLly4aS3Tt2hU3NzdcXV1p2LAhkydPVuaZmJjg5uZGhQoVaN68OYMGDWLw4MF5tsHX11cJZJqamtKsWbNSNUxbkiSp0MGiu3fvaozFzZaYmKgRPJIkSZJeb/kHVdoBmQjxLt7en5GRkUFISAgODg6F7mVTEvK/owyghRCTCA0N5vLlywAFqhDm4eFBXFycEjBwdXWlTJky+R6vubk5Bw4c4OzZs0o1n+JUkLvTT+cgFEJolHl+U+UdEJ3D/fuZeHp64ubmxvnz53F3d+fcuXM8ePCAY8eO0b17d2xtbZXA6LOCRTY2NsrvhoaGmJiYUKZMGY1pCQkJxXeQL+HpIajdunXD0dERlUqFh4cHbdu2VXoTPq1Nmza0bdsWLS0tpTJV5cqVGTZsGDo6OnTu3BkPj6ygXF7ef/99zMzM0NXVZfz48blKqpctW5Zx48ahq6uLu7s7Li4uyh3xVatWMX78eOrWrYuWlhbdu3enatWq7N27t0hfH6lgSlsPGc3y9E+bDezO+m32bLZs2aLRa61Xr16EhIRw//591qxZg76+vsbao0eP5s6dO0RFRTFnzhylN212BbZsJiYmLF26lODgYOLi4vj333+ZO3du0R2kJJVyMvdx6VfoYFG9evXYs2eP8jw7QLRu3ToaNWpUdC2TJEmSSlT+QRV7wANQERoazLZt25g3b55y97QwvWxKQv53lEOAbsr0ihUrEhISgpaWFmvXruXhw4dER0ezZMkSDh06hLe3t7JmnTp1EELwwQcfKNMiIyPZsmWLxh5yJhNVq9WcPHmSb7/99rk3W/LKoZM9DGfy5MlYWlri5OSkkfvm3LlzNG3aFAsLC6ysrOjXr59Grwp3d3cmTJhAmzZtMDIyYt++fbi4uLBgwQLeeecdDA0NuXz5MtevX6dt27ZYWFhQsWJFjYumgubxKa3yD4hOJSuvUwdu3gxh4MCBWFlZUbVqVXx8fKhUqRImJia0bNmSrVu3cvXqVZo3b16CR1I88hqCam1tS4UKFbCwsMgz+W9OTk5OuablTA6c/TxnguBsmZmZTJkyhbfeegtTU1PUajUPHz7U2FfOIBxo5hkLCQlh8uTJSvJgtVrN+fPn89yX9N9T2pNvS1JhRUVFUbduXSZMmMCNGzfo3LkzVlZWODs7M3fuXDIzM0lLS8PGxiZXgL9atWoa6WQOHTpElSpVUKvV9OnTh4cPHyrzTp8+TZMmTVCr1VSvXl0j2F/Q7x0dOnSgT58+7N+/n7CwMNq0aaMMN86+USeVDoUOFs2fP5/JkyczYsQI0tPTWb58OW3atGH9+vXMmzevONooSZIklYD8gyoAfkBW3rqRI0fSsWNHJkyYABS+l82r9uw7yk+mP1nu1cvZm2PDhg1MnTqV3377TSOHDkBQUBAqlYo7d+6wdetWJk6cyOHDh4Gs3BkLFy4kKiqKoKAgwsPDlcp02Xx9fZk7dy4JCQm0bt1amfbDDz+QkJBAlSpV6NSpE7Vr1yYiIoKdO3eyaNEi/Pz8AF4qj09pkHdA9B/gdyAFmER8/EOSk5OBrOPNTlAOWYHQ5cuXU6dOnTeuCljePa52cv9+DMHBwXzzzTfExsbmSv6bU175yXImB4bcCYKz+fn54efnx549e3j48CGxsbGYmZkVuBeeo6MjS5Ys0UgenJiYmOt/QPpvKs3JtyWpsK5fv07Tpk0ZOHAgM2fOpFWrVrRq1Yrw8HAOHz7Mli1bWL9+Pbq6ugwcOBBfX19l3ePHjxMVFUW3bt2UaRs2bCAgIICQkBAePHig3ByLjY2lXbt29O3bl7t37/L111/z4YcfKkORC/q9Y9asWWzZsoVWrVrh6elJuXLliIyMZNOmTXz77bfF/XJJhVDoYFHTpk05f/486enp1KxZk99++w1ra2uOHz/O22+/XRxtlCRJkkrAs4MqtsDnAGzfvp2FCxeiq6sLvFgvm1eptN9Rfro3x6BBg0hKSua7774jKSlJyaEDWT0pZs6ciZ6eHo0aNaJ///5s3LgRgNq1a9O0aVN0dXWxsbFh7Nixue4menp60qBBA1QqlTIsasSIEVSpUgVtbW3++usv7ty5w9y5czEwMKBWrVqMGjVK+aL5Mnl8SoO8A6KpwDTABugEoOTbeToQ2qJFCx49elTqj7Ow8u9xVRlQAS0YO3YCv/zyS67kv89z7do1vv32W9LT09mzZw8HDx6kT58+uZaLi4tDT0+PsmXLkpqayuzZszXyFT3Pxx9/zOLFizlz5gxCCB49esSBAwcICwsrVHulN1dhk2+7uLgghECtVue5PXd3dyXBuyQVl5w3k5KTkzl16hTu7u7MmjWLTz/9lD179mBubo63tzd6eno4OTkxZswY5SbP0KFD2b59uzL02dfXF09PT43hlBMmTMDOzg61Ws2cOXPw8/MjMzOTPXv2YGVlxejRo9HV1aVFixZ4enryww8/AAX/3lG/fn1UKhX37t3j8OHDLF68GENDQ6pWrcrw4cNfzQspFYjOi6xUsWJFGfWTJEl6w2UHVcLD5yOEP5r3F7KCKg4Or183/ew7yj179kSl6oYQk8gKFgShUi0g647ythK5o5zdmyPrIn2z0i4YxZdffsmqVato2rQpixYtAsDOzk4J0kHWkJ7sL2bXr19n4sSJ/PXXXyQkJJCZmamxLOQ9TCjntLCwMOzs7NDT01OmVahQQQlIubu74+npqeTx2bhxI8uWLVPy+MyfP79IXpfiohkQfefx7zWBE49/Pw405p13sua99957Gj1bTExMcpWEz76gzJbzDi7kfUEZEhLyEkdR9J70uNqM5v99dvLfFYSFPeSrr77Klfz3edq1a8eJEycYN24c1tbWbNy4kbfeeivXcoMHD+bAgQM4OztjamqKt7c3Dg4OBd5P586dSU5O5sMPP+TmzZvo6+vToEEDVq1aVaj2SiXHxcUFHx8fjR4PRa179+507dqVw4cPc+fOHcqVK0ezZs1kjyKpVNqxYwdjxox7/P6c5YsvvqBq1ar07t0byPo8CQoK0ghqZmZm4ujoCGQNOXN1dWXbtm307duXrVu3cvDgQY395Bwu7OzsTGpqKnfv3iUsLCxXpbIKFSrw559/AlnfO8aNG1fg7x137tzBwMBAIx/y00OVpZL1QsGizMxMrl+/TnR0tFItJtubOGZfkiTpv6g0B1VeVvYd5awvXY2V6Q4O5fHxKZlyzrl7c2RfpL8DnEKl6oaFxQVq1qzJwIEDGTduHBEREaSlpSlfxm7fvo2dnR0Ao0aNokqVKvzwww+o1Wr8/f3x8vLS2Gdew4RyTnNwcMi1j+xk5sBrn8fnTQ2IvqxnD0GdDYwHTPHy8qJfv37KnGcFybLp6Ojw3Xff8d133ynTsoMCOdcxMjJix44dGBsbs3//fmrWrMn48eOV+V5eXrnO56fLPffq1YtevXrlf6Bknc/ly5fnwYMH+fYYkd5s2cm3Jak0y/tm0ltkZtpx+fJZmjRpwpEjR3B0dOTtt9/mxIkT+W5r6NCh+Pr6oq+vj7OzM3Xr1tWYf+vWLRo2bAhkfa/Q09PDysoKBweHXDc3cn4nGD58OJUrVy7w945y5cqRnJxMdHS0EjC6ffv2C70+UvEo9DC0EydOUKlSJapVq0bz5s1xd3dXHqUlH4UkSZJUNArbTf91UtrKOeefUPwf4A+EGEt4+C3u37+Pjk7WvZ7ExETmzJlDamoqJ0+eZNOmTcrFe1xcHCYmJpiamhIaGsrixYsL3aYGDRpgY2PD9OnTSUlJISgoiJUrV2qUgi6OPD7+/v657l7mJTAwsFAX+C4uLvj7+yvPc+ctMSSrV9F/O29JacrrlZCQQM2aNYt9P5IkSaVV/kOD9cnq7dmBCxcu0r17dzp06EBUVBSrV68mOTmZjIwM/vnnH43hYH369OHMmTMsXLiQIUOG5Nrf4sWLiYiIIDY2lunTp9O3b1+0tLTo0KED0dHRrF69mvT0dA4fPsymTZsYNGgQUPjvHY6OjjRp0oSJEyeSlJTEP//8wzfffFM0L5pUJAodLBo+fDj16tUjKCiI+/fv8+DBA+Vx//794mijJEmSVIJKW1ClKJWmcs759+bIzqGTNdznxIkTSg8MV1dX0tPTKVeuHD179mTevHnKHfLFixeze/duTE1N6dq1Kz169Ch0m3R1ddm9ezdnzpzB1taWLl26MHbsWDw9PZVlXvc8PpoB0SSgEW9KQPRFlfa8XtJ/x6VLl6hbty6mpqa0bduWiIgIICunirOzMyYmJlSvXp3//e9/yjrZQeR169bh6OiIpaWlUoABsnouvPvuu1hZWWFubk7Hjh01ekt4eXnx4Ycf0rdvX0xMTKhSpYrGhfbGjRtxdXXFxMQEJycnpk2bVuDE65L0IvK/mcTj51NJTk4iJiaGfv36sX//fv744w9cXFywtLTE09OTyMhIZQ0TExN69erF1atX6d+/f679DRgwAA8PD+V/bPny5QCYm5uzb98+Nm7ciKWlJR999BFff/01TZs2BbIqtxb2e4efnx+hoaFYW1vj6emZZ/BKKkGikAwNDcW///5b2NVeGw8fPhSAePjw4UttJzU1Vfj7+4vU1NQiapn0ppLnilQQ8jx58wUEBAhAwHEBIo/HMQGIgIAAIYQQ69evF7Vr1861nTfhXNm5c6dwdnZ+7nIBAQHCzMyswNt1dnYWO3fuzHNeenq6CAgIEH5+fiIgIECkp6cXeLuvo+edJ9u3bxcqlUqoVJ0fn3txAo4JlaqzUKlUYvv27UXWFmdnZzF37lxRp04dYWJiItq0aSPCw8OFEEIA4ty5c8qyfn5+olatWsLExEQ4OTmJ9evXi+joaKGvry9u3rypLJeUlCTUarU4ceKEEEKIa9euic6dO4uyZcsKc3Nz8d577wkhhAgODhaAePDggRBCiMzMTLF8+XJRpUoVYWZmJlq0aCEuX75cZMf6Oiqp9xRnZ2fh4uIirly5IhITE8WgQYOEh4eHEEKIjRs3iqioKJGeni42b96s8fcPCAgQWlpa4tNPPxVJSUni8uXLwtDQUHnvDA4OFnv37hVJSUni4cOHomfPnqJ169bKfgcPHixMTEyU94E5c+ZovB/t3btX/PPPPyIzM1OcO3dOWFtbi40bN76y16W0ehM+e0orPz+/x98P4vP5fhAnAOHn51fgbc6aNUv06NGjGFudP3mulLyCxjwK3bOoYcOGXL9+vSjjVZIkSZL0n/df7s0RFhZGmzZtMDU15e233+by5cvKvKioKHr37o2VlRVOTk5MmTKF9PT0PLfj7u7O+PHjcXd3x8TEhEaNGnHlyhWNZa5du8Y777yDiYkJLVq0IDQ0FMjqZebh4UG1atVwd3dnzpw5dO7cmVGjRqFWq3FycmLr1q3F9yKUMsU9BPXpij7r1q3Dz8+PyMhIbG1tGTBgQK51fvnlF0aNGsWyZcuIjY3lr7/+onbt2lhZWdGpUyelIg/Azp07sbOzo2HDhiQmJtK6dWtcXV0JCQkhMjKS0aNH59mur7/+mu+++45ffvmFe/fu0b17dzp37kxqaupLHa9UME+fF8OGDaNq1aoYGhqyaNEiAgICCAsLo3///lhbW6OtrU3fvn2pWrUqx44dU7YjhFCqOFarVo3GjRtz5swZIGs4avv27TEwMMDU1JQpU6Zw+PBhjTysHTp0UHqbvv/++9y6dYuYmBgA2rdvT+XKlVGpVLi5udGvX79cFZ8kqSgV9dDgu3fv8u233zJixIiXb5z0Rit0sGj06NGMGzcOX19fzpw5w4ULFzQekiRJkiQVXu78OceBeN7E/Dk5LwgDAwPp168f5cqVIzIykk2bNmlUXPX09ERXV5fg4GAOHz6Mv7+/Ug0uL9999x0LFiwgJiaGli1b0rVrV43g0saNG9m8eTN3797FyMiIadOm5butX3/9lebNmxMTE8PcuXP54IMPClW+/XVXXENQd+zYgYtLJTw8PPD09CQqKooHDx5y+fLlXEGBnFavXs2YMWNo2bIlWlpaWFtbU6dOHSArYeuPP/6oDAfy9fXl/fffB2D37t3o6uoyb948jIyM0NPTyzfP5qpVq5g9ezZvvfUWOjo6fPLJJyQlJXHy5MmXOmbp+fI6LxYvXsqOHTsAsLGxQV9fn/DwcJYtW0aNGjUwMzNDrVYTFBTEvXv3lG2ZmppiaGioPDcyMlL+d+/evYunpyeOjo6YmprSvHlzUlJSNP63bW1tNdYFlPm//vorjRs3pmzZspiZmbFmzRqNfUtSUSvKm0nz5s3DxcWFjh070qpVq2Jpr/TmKHSwqEePHly5coUhQ4ZQv3593NzcqFOnjvJTkiRJkqQXU5jeHF5eXrmqP70Onr4g9PDw4MiRI7i7u2NoaEjVqlUZPnw4AOHh4Rw8eJClS5dibGyMs7MzU6ZMybfSFkDfvn1p1KgRenp6zJw5k6ioKI2qMCNHjqR8+fIYGBjQv39/pbdBXurWrUvv3r3R1tZm4MCBpKamcu3atSJ7LV4HRZ3XK7uiT1hYTZ4ERG15+LA8PXv2ZMeOHRpBgZxu3brFW2+9led227ZtS2pqKocOHSI8PJxDhw4xcOBAZb2KFSuiUqme276QkBAGDBiAWq1WHg8ePMgVuJKKVn7nxf37ZZXzIjo6mpSUFNLS0pg5cyY//vgjDx48IDY2FldX1wLnDZo0aRKPHj3i7NmzxMXFKWW/C7J+amoq3bt3Z9iwYYSHh/Pw4UOGDx8ucxZJxaoobyZNmTKFxMRE1qxZU5xNlt4QOoVdITg4uDjaIUmSJEkSWQGjrl27cvjwYe7cuUO5cuVo1qzZG9GjKO/Sv1uBjxg6dChmZmZ0794dZ2dnIGt4moGBATY2Nso2KlSo8MwL9+x1IStBd7ly5TSCDk/3GHhWT6Gcy6pUKsqUKfOf6llU1HJX9Mm+Z6kP9Abs8fb+jEaNGpGSkoK9vb3G+s7OzvmmQtDS0sLLywtfX1+qVKlC27ZtlfPG2dmZGzduIIR4bsDI0dERHx8f2rVr9zKHKhXCs8+LFIRw55NPxtK6tTvNmzcnLi4ObW1trKysyMzMxNfXl6Cg/Ibn5BYXF4ehoSFqtZqYmBhmzZpV4HVTUlJITk7G0tISfX19Tp48iZ+fH40bNy7EEUtS4WXfTBozZhxhYU/ONweH8vj4/DeLMUjFr9A9i5ydnZ/5kCRJkiTp5ZSmKm1FJf/Sv22ATIR4F2/vz8jIyOD27dsAODg4kJycTFRUlLKdkJAQHBwc8t3PrVu3lN/T0tK4c+dOrqCDVDKeXdFnLUJ4EhoazJAhQ2jevHmuv/OwYcNYvnw5hw4dIjMzk+joaM6dO6fMHzJkCDt27OC7777TqKjTsWNHUlJSmD59OomJiaSmphIQEJBnGz/++GOmT5/OP//8A2QFFnbt2iWDhMUo7/NCRVYlyCFAOOHht7h06RKbNm2iXbt29OzZk5o1a2JnZ8elS5do0qRJgfc3a9Ysrl+/jrm5OU2aNKF9+/YFXtfExIRVq1bx0UcfYWpqyrx58+jTp0+B15ekl/EmV6eVSqdC9ywCuHHjBj4+PkrSyOrVqzNmzBgqVqxYpI2TJEmSJOnN8OSCcDOagQJHoAmgS2hoMJs2beKbb74BwN7eHg8PDz777DPWrFlDTEwM8+bNY/DgwfnuZ+vWrQwePJg6deowZ84crKyseOedd4rvwKQCu3PnzuPfXPOYOwRYCGQNP9y7d2+uJbp160ZcXBwff/wxt27dwsLCgjlz5ihpECpUqEC9evW4dOkSHTt2VNYzNjbmwIEDfPrppzg5OQHg4eGRZ96iUaNGoa2tTffu3QkNDcXExISmTZvSsmXLlzp2KX/5nxd7ATfgE8CUsWPHKgHEtWvXsnbt2jy35+7uTmxsrMY0f39/5fdq1apx6tQpjfkfffSR8vvTw1zVarXGMLPhw4crQ2Ul6VXLvpkkSa9CoXsW/frrr1SvXp1Tp05Rq1YtatWqxcmTJ6lRowa///57cbRRkiRJkqTX3LMDBX7AIwBmzJih0SvEz8+PpKQknJ2dadKkCR07dmTChAn57mfIkCF8/vnnWFhY8Pvvv+Pv74+OzgvdGyvVPvroIywsLLC1teX27dsYGxvz8OHDZ64TEhKCSqXKdSH9quRf0ScEmAJ8DcCKFSuUoIAQAjc3N2XJQYMGERQURHx8PLdu3WLQoEEaW3JxcWHgwIG5/uZVqlRh7969xMTEEBMTw7Zt25TlhRCo1Woga7jhyJEjuXTpEnFxcYSHh7N161ZMTExe/gWQ8lTUlZ6elpaW9kLrSZIk/dcVOlg0ceJEPv30U06ePMnSpUtZunQpJ0+exNvbm88//7w42ihJkiRJ0mvu2ReETsA8ANavX8+UKVMICQkBsvIGbdu2jXv37hEaGsrChQvR1dUF8u5BYG9vT2BgIAkJCZw4cQJX1yfBqZCQELp166Y879atm7If0AxMzJw5U6M3AkBsbOxL3dH19fXVCHy8qCNHjrBt2zaCg4OJjIzEycmJhIQEzMzMXnrbxakoK/rk5caNG2zbto1hw4a9dFvfZHFxcYwaNQpnZ2dMTU2pX78+oaGhqFQqjaT5Pj4+Gud7ZGQkAwYMoFy5cqjVapo3b05SUlKeQUhvb2+8vLyAJ0HK77//ngoVKmBsbMyECRO4c+cO7777Ll26dEFPTx+YjuZ5cQioAjSnTBkjatWqpcy5ceMGnTt3xsrKCmdnZ+bOnUtmZta62f9nM2bMwNbWlr59+xbxKyhJkvTfUOhg0ZUrVxg6dGiu6UOGDOHy5ctF0ihJkiRJkt4sxR0o+K9IT08nODgYJyenUh8celpRVvR52rBhw3Bzc+Pzzz/Pt2Laf1VGRgaBgYFs3ryZwMBABg8ezPXr1zl+/DixsbGsXbuWMmXKPHMbmZmZvPfee+jo6HD58mXu3bvH/Pnz0dIq+KVEQEAAFy9e5NSpUyxfvpzevXvj4+PD3bt3qVq1CvB7jvMCYA3gDKRTuXIlxo0bB8CjR49o1aoVrVq1Ijw8nMOHD7NlyxbWr1+v7CsoKAgdHR1u377Nhg0bCvNySZIkSY8VOlhkZWWVZ6ne8+fPY21tXRRtkiRJkiTpDVOcgYLSaOnSpTg5OWFiYoKLiwuLFy9m+PDhXLx4EWNjY4yNjbl9+zbnzp2jadOmWFhYYGVlRb9+/YiJiVG24+7uzoQJE2jTpg1GRkasWrWKDz/8UNmOl5dXrp4dv//+O7Vq1cLExAQbGxtGjBih0bZffvmFatWq4enpydChQ1/pMJ3sij729heBxoAp0BgHhyC2bXvxij7ffPMN8fHxTJkypSib+9rbsWMHLi6V8PDwwNPTEw8PD/z9/Xnvvfews7NDS0uLOnXqULZs2Wdu5/r161y9epWvv/4ac3NzdHR0aNq0Kfr6+gVuy9SpUzEyMqJ69erUrl2bpk2bUqNGDfT19Rk2bBhVq1bNcV4AXMXR8Trbt2/nm2++wc/Pj8zMTPbs2YO5uTne3t7o6enh5OREWloaX3zxhbIvMzMzpkyZgp6eHoaGhi/wykmSJEmFHsT/4Ycf8tFHH3Hz5k2lTOTRo0f54osvGDt2bJE3UJIkSZKkN0Nxl/4NDAx8yRa+uIyMDA4fPsydO3dIT09n6tSpnD17lqpVqxIVFUVUVBRWVlb4+Pho3HR78OABCxcupGHDhty/f59evXoxceJEvv32W2UZX19fdu/eTf369UlOTsbMzExjOzmH0gEMHjyYL774goEDB5KYmMjff/+tMX/fvn2cOnWKXbt2MX36dDZt2qQMGXoVunfvTteuXZXXq1y5cjRr1uyNCRSWFjt27KBnz56PKxBuJitf2FbgI0aMGIGVlVWB/+eio6Oxs7N7bg+kZ7GxsVF+NzQ0zPVcX1+fc+fOceLECTw8PFi1ahXDhg0jMzOTmJgYUlNTuXv3LiEhIQQFBSl5pgASEhI0Al729vaF6vUkSZIk5VboYNG0adMwMTFhyZIlTJo0CQA7OztmzpzJJ598UuQNlCRJkiTpzfEmBgp27NjxOAAWkmOqiu+++47Zs2djY2ODjY0NZ8+ezbVu7dq1ld9tbGwYO3Ys48eP11jG09OTBg0aABToYl1XV5fr169z9+5drKyslJt72aZPn46JiQkWFha0adOGM2fOvNJgEciKPsUtIyODMWPGPQ4U+fNkMEEnIBMhWuPt/Rldu3ZV/veMjIx49OiRso0nSenB2tqaiIgIkpOTMTAw0NiXsbExAFFRUUydOpVffvmFiIgIzMzMCA0NZd26dQA4ODhgY2PDp59+qqwbEhJC+fLlGTJkCFeuXMHZ2ZmtW7cCcPbsWcqXL4+NjQ2rVq1CR0eHFi1acPv2bQwMDDh48CB169Zl3Lhx+Pj48ODBA4yNjSlfvvxr/X4iSZJUWhQ65K5Sqfj0008JCwvj4cOHPHz4kLCwMMaMGYNKpSqONkqSJEmS9AbJDhT069cPd3f31/rCLrv3RlhYTXIOrYO6fPnll1haWtKmTZs8h/BD1vCerl27Ymdnh6mpKQMGDODevXsay2SXey+onTt3EhQURJUqVahTpw4//fSTxnxbW1vldyMjI+Lj4wu1fan0O3z48OPg5WQ0v+7bAF2BREJDgzl06BDnzp0jJiaGunXrsmHDBtLT0zl//rxGrp9KlSpRuXJlRo4cSWxsLOnp6Rw5coSUlBTKli2Lk5MT3bt3599//+XLL7/EwMCAxo0bU6ZMGezt7QGUwNH48eNzVe47d+4clStXJjg4WJn2888/ExAQwK5duxg9ejQAa9euJTw8HD09PVq0aEFUVBSLFi3i7bff5sMPPyQhIUHJbSRJkiS9nBfunxkdHc358+c5f/48d+/eLco2SZIkSdIr5+Likqv6VTZjY2MuXrxY4G3FxsaiUqlyDQ+S3iy5e2+8Axg//nkKlaozFhbW1KxZk4EDB+Y5LGb48OHY29tz+fJl4uLi2LhxI0IIjWUKO5ymbt26bN++nXv37jFt2jQ8PT2Jiop6waOUXkdPegW55jH3B6AaAJ06dWL48OEkJSWxcuVKjh8/jlqt5vPPP2fw4MHKGlpaWuzcuZNHjx5RpUoVypYty9SpU0lLSyMwMJD33nuPy5cvc/ToUbZv307fvn2xsLCgbNmytG/fHsi64ezh4UHbtm1zVTHs1q0b2traGvmFxo4dS4cOHahWrRp3795l4MCBNG/eHHNzc06dOgVAlSpVsLS05MqVK8TFxRXVyydJkiTxAsPQ4uPjGTlyJJs3b1ZKVGpra9OnTx9WrVr12lXmkCRJkqTnSUhIKOkmSKXQk94bm9G8//YPcBshxhIe7sH9+/fR0dHBxsaGO3fukJSUpAwni4uLw8TEBFNTU0JDQ1m8ePFLtSk1NZWtW7fSqVMnzM3NlbwuOjqF/sr3xktLS0NXV7ekm1EsypUr9/i3ILKClzmZAR8A37N3715lOKCDg0OePeCyE6Db2dmxZcsWZfqOHTuoVq2mxvBLc3MrevfurZEL6ejRo9SpU4cKFSqQmZnJo0eP+Pjjj/H29lYC6qNGjWL69OnKOiYmJkycOJGJEycC0L59e6pUqaLMr1ixIk2aNKFly5ZMmDABd3d36tatC4CXl9crH1YpSZL0Jip0z6IPPviAkydPsmfPHmJjY4mNjWX37t2cPn2aYcOGFUcbJUmSJKnUepWVpKTSJf/eG6nANKALAAcOHMDX15eWLVvyzjvvYG9vj1qt5vbt2yxdupTdu3ejr6/P22+/TY8ePcjMzNSobvY8Bw4cALKS+u7atYuxY8fi6OiIiYkJo0ePxs/PD0tLy1zrTZkyJd/hcaVdXFwco0aNwtnZGVNTU+rXr09oaChLly7lrbfewsTEhIoVK/LVV18p62RXjVu/fj2VKlXCwcGhBI+geDVr1gwHBxdUqvlA5lNzM1GpFuDoWJ5mzZq90PZzD7+8AUBYWCV69uzJjh07ALh9+zaDBw9m0aJFREdHExsbS4cOHZ7be+7p5w4ODrl6aoaEhCh/Q5nMWpIkqegV+p119+7dfP/997Rt2xZTU1NMTU1p27Yt3377Lb/88ktxtFGSJEmSXolLly5Rt25d5bMtIiICyBo+kX1RPXPmTDp16sSIESOwsLBg4sSJpKSkKM/Lly/Ptm3bSvAopFdFs/dGTjWBE8CvAGzYsIHatWujq6vLrl27uH//PrGxsTg5OdG0aVMuXbpE//798fT0ZOzYsVy4cEHZUmBgIN7e3hpb9/Ly0gjyLFy4kB9//JHExER69erF3bt3SUhIID4+nkuXLtG7d28ga6ilEEKjilTXrl3x9fUtipejWGVkZBAYGMjmzZsJDAxk8ODBXL9+nePHjxMbG8vatWspU6YMzs7OHDx4kLi4OCU/ztGjRzW29fPPP3P69GmN/DhvGm1tbZYvXwLsRqXqRs58WlnPd+Pj8+UL5QvLe/hlBbJyIekiRGvGjBnH6dOnCQ0NRQiBtbU1Wlpa7N27l99++63Q+xwwYACbNm3i6NGjpKens3LlSmJiYujQoQOQlRz+xo0bhd6uJEmSlL9CB4ssLS3zHGpmZmaGubl5kTRKkiRJkl6FnBegycnJrFu3Dj8/PyIjI7G1tWXAgAF5rrd//34aNmxIdHQ0c+bMYd68eRw/fpygoCDOnTun3FWX3mzF3XujoIKDg6lZs2ax7qMk7dixAxeXSnh4eODp6YmHhwf+/v6899572NnZoaWlRZ06dShbtiw9evTA0dFRIz9OYGCgxvZmzJiBWq3WyI/zJurevTvbtm3D3v4i0BgwBRrj4BDEtm3bNIaKFUb+ybN/AJyAc4SFhTBw4ECcnZ2ZMmUKLVu2xNLSkq1bt9KlS5dC77NFixasXLmSoUOHYmlpyZYtW9i3b58S+PT29ubAgQOo1Wo6der0QsclSZIkaSp0sGjq1KmMHTuWyMhIZVpkZCTjx49n2rRpRdq4F7Vq1SpcXFwwMDCgYcOGShI8SZIkScr29AVoVFQUDx485PLlyxgaGrJo0SICAgIICwvLta6rqyteXl7o6OhgaGjIpk2bmDx5MnZ2dqjVambMmFECRyS9agXpvZGcnKD0vD5w4AANGjRArVZTo0YNfv755+fu4++//8bExEQjb1Z4eDj6+vqEh4djbGxMZmYmjRs3xtjYmJSUFNzd3fHx8QHg/v37vPfee0r+orfffptbt24p24qKiqJt27aYmJhQt27dQiVyfxXyrja3DtBixIgRuQKzmzZtom7dulhYWKBWq9m7d+9LV5d7nXXv3p2QkOsEBATg5+dHQEAAwcH/vnCgCJ41/NIMWAPcBGD69Ok4ODgwe/Zs7t27x4MHD/jhhx/YsmWLcn7m1dvN3d09zyGYgwcP5urVqzx8+JCjR49Sr149ZV7Dhg25cuWKkh5DkiRJenmFDhZ9/fXXnDhxAicnJypVqkSlSpVwcnLi2LFjfPPNN9StW1d5lIStW7cyduxYZsyYwdmzZ6lduzZt27YlOjq6RNojSZIklT55X4Da8vBheSXfho2NjXJB/rSnLzYjIiJwdnZWnuf8XXqzPa/3RnbvlQsXLtCrVy8WLlzI/fv3+eabbxg4cCD//PPPM7dfu3ZtqlSpojG08ccff6R169bY29srQaRjx46RkJCAvr6+xvpffvkl6enphIeHExMTw3fffYeJiYky38/Pj0WLFvHgwQPq1aunlCgvDfKvNtcJyESIVnh7f0ZGRgbw4vlx3nTa2tq4u7vTr18/3N3dX2joWU75D79EY/qT5SQpt/T09JJugiRJz1HoT8tu3brx2WefMWXKFAYOHMjAgQOZMmUKn332GV27dtV4lISlS5fy4Ycf8v7771O9enXWrFmDoaEh33//fYm0R5IkSSpd8r8A1Qd6A53w9v6MO3fukJKSgr29fa5tPH2xaWdnp9Fb4/bt28V3AFKpk7P3xsaNG1m2bBkLFszBwsJCWeabb77By8uLli1boqWlRdOmTenUqRM//fTTc7c/dOhQjbxCP/zwA++//36B2qarq0tMTAz//vsv2trauLm5abSrX79+1K5dGx0dHQYPHsyZM2cKfuDFLP/hTjZk5cdJJDQ0mEOHDnHu3Lkiy48jPVtpGX4p5c3FxYV58+blmX8vOjqa/v37U65cOezs7PD29iYlJQXIyo+mVqtZuXIl5cqVw9bWlhkzZijBVl9fX9zc3Jg8eTKWlpY4OTmxevVqjX0fPnyYunXrolarqV+/PseOHVPmubu7M2HCBNq0aYORkRH79u17Ra+IJEkvqtB1VEtz1/rU1FTOnDnDpEmTlGlaWlq0bt2a48eP57lOSkqK8iYJWdU1IKu6zctUuMleV1bJkZ5HnitSQcjzpOgcOXKEmJgoypTZBGQ8fkByMsBa9PTmcvfuEN5//32aNWuGjY0N8ORzISMjg8zMTI2/Re/evVmwYAENGzbE0NCQWbNmaazzKslzpeTcv3+fmTPnEB7+JFiYlpbG8ePHCQ4OJiAggPXr1yvz0tPTMTY2Ji0tjczMTOW8yvk3TEtLo2fPnnz22Wdcu3aNqKgo7t27R/v27TX+xjnXE0KQkZFBWloa3t7eSuLruLg4evXqxbx589DRyfoKaGVlpaynp6dHQkJCqTl37ty5Q5kyZYCqgGabhFhHWtp4MjKO06lTJ1xdXdm6dSsTJ06kZcuWZGRk0KlTJzp16pTv6yoVTF7vKcuXL2HgwIFAL4QYC1QHLqNSLQX+wMdng3JOl0ZvvfUWX375ZYnc3J49ezZ///0327dvL7JtZmRkcPz4cSIjI0lOTlYKDzk5OTFq1Cj69+/Pr7/+SufOnWncuDFXr14lKSmJvn37MmvWLGbNmkV6ejrx8fGcPn2aq1evcvv2bTp06ICTkxODBg0iIyODoKAg2rVrx+3btzl79iwdO3akatWqNGvWjF9++QVfX1927dpFvXr12LVrF507d+bSpUtYWloihMDX1xd/f3/q1atHcnKy/D/8j5LfU0peQV/7QgeLckpISMj1IWBqavoym3wp9+7dIyMjQ/lin83GxoarV6/muc6CBQuUL/U5/fbbb0WS+PD3339/6W1I/w3yXJEKQp4nRWPz5s3AXWCvMu3DDx/Rpk0bjh+fyp07KiIjIxk9ejR792Ytc+TIESIiIvj333+JiopSpgPUrVuXM2fO4OrqSpkyZZTqUwEBAbk+k14Vea68etra2nz55Rca0z788EO0tLTIzMykQ4cODBo0KNd6e/fuJSwsjAcPHrB3716ioqKArO8ixsbGANSvX5/p06fz4MEDGjVqxIEDBzS2kX1+AsTExHD58mXlHG3WrBnNmjUjKiqKefPmkZCQQLdu3QD4559/lOVu3ryptKc0MDQ0fPy/eiifJbo9fmS5cOECDRo0oEGDBrmWzD4mf39/jd4OUsHlfE/R1tbGz8/v8bN7wJ+Pfx/0+FF6zqO8PHr0iDNnzqCrq/vK953XZ0hRMTQ0JDMzkxYtWnDz5k1u3rxJ69at8fLy4ssvv+TKlStMnDhRSfru4eHB119/TcOGDbl48SKZmZm0bNlSmd+yZUtWrFhB2bJl+fvvv9HX16d+/frK+0+TJk1YsGAB8fHxzJ8/n27dunHv3j3279+Pvr4+1tbWLFy4EA8PD2JiYmjYsCF3796VvYokQH5PKUmPHj0q0HIq8fRA7ucIDg5m1KhRBAYGkpx1GxbIuoulUqmUceMlISIiAnt7e44dO0ajRo2U6RMmTODQoUOcPHky1zp59SxydHTk3r17LxX4SktL4/fff+fdd98tkQ8i6fUhzxWpIOR5UnSOHDlCx44dgQNA/TyWOAW8y549e2jatOmrbVwRkOfKq5eRkUHNmm6Eh9cA/Mg5ZCo5uRK6unbY2ESSkvKIjRs30qxZM9LT0zl37hxmZmZUq1aNoUOHolarWbJkCSEhIVSuXJno6Ggl8W9gYCAffvghsbGxHDhwgNq1ayv70NPT49SpU7i5uQHQunVrunTpwieffMKePXt46623qFSpEg8ePKBdu3YMGjSI4cOH06BBAwYNGsSnn34KwPnz52nQoAGpqamv6JV7tuzXNSKiBkJovq5Zw508sbe/zIUL5146D4+Uv2e9p+Ts0WJra0ujRo1K/d+ib9++7Ny5E319fbS1tfH09OTTTz9lzJgxnD59GnNzc0aOHMknn3wCZOUIW7FiBZ06deKbb75BR0eHJUuW4ODgwMiRIwkLC6N79+6sWbMGLS0tEhISGDRoECdPniQlJYVatWqxbNkyateuza5du/D09CQzMxMDAwMAHjx4QFpaGjNnzmTLli0kJSXh7u7O8uXLsbKyeuax/PLLLwwcOBAh2gHjgOokJ1dDV9cJHZ0gNmzYQOfOnTExMWH69OlMmzZNI2dZdi/EBw8ecOjQITp37qyMsoCsXLBz587l4sWL/PjjjyxevFgjCf6iRYs4fPgwv/zyC7Vr1+bmzZvKcUHWuTN58mQmTJhA69at6dSpE97e3i//R5Rea/J7SsmLi4ujbNmyPHz48Jkxj0L3LBowYABCCL7//ntsbGxQqVQv1dCiVLZsWbS1tZU7ctmioqKwtbXNcx19ff1cySAha4x/UZy8RbUd6c0nzxWpIOR58vKaN2+OpaUN4eELEcKf3BegX+DgYEvz5s1L/UXPs8hz5dU5evQo16//A/iSlfsqJxWpqV0JDZ3EkiVLmDlzJleuXEFLSws3Nze+/PJLdHV10dLSQktLS+PvlvP31q1bo62tTYUKFTSqQGXLuaxKpUJbWxtdXV1CQkIYO3YsUVFRGBsb06NHD0aNGqV8f8teLnsbOX+WNF1dXb74Yj49e/Yka7jTJLIqcAWhUi0AdrNw4TaNi1Op+OT1nqKrq4uHh0cJtahwMjIyOHz4MD179uTo0aOsXr2a7t27k56ejqurK126dOHnn3/m2rVrtGvXjnLlyuHp6Ym2tjaXLl1i6NChREZG8sMPPzBy5Ejatm3Ln3/+SUpKCnXq1GHPnj10794dbW1tBgwYwJYtW9DW1ubzzz+nf//+XL16lZ49exIUFMT58+fx9/dX2rZgwQL27dvHkSNHsLCw4IMPPsDLy+uZObey8+89etQK+B9PPsv0SU3tTlqaDd7en9GoUSNSUlJo3rw51tbWOarZadLR0SE5OZkHDx5gbW0NZN2Id3BwQFdXF21tbWXd7PMgPDxcme/o6EiLFi1YsWJFnu8hKpVKfi5JGuT5UHIK/LqLQjIyMhJXr14t7GqvTIMGDcSoUaOU5xkZGcLe3l4sWLCgQOs/fPhQAOLhw4cv1Y7U1FTh7+8vUlNTX2o70ptPnitSQcjzpGht375dqFQqoVJ1FnBMQJyAY0Kl6ixUKpXYvn17STfxhZXUuTJ48GAxZsyYF14fEOfOnSuy9rxKfn5+AhAQL0A89XAQ8D8BCD8/v5faj4eHh1i5cmWRtPl1ek/Zvn27cHBwefwaZz0cHcu/1v+nr5PX6VzJT17nkKWlldi+fbs4cuSIMDU1FSkpKcry8+bNE++++64QQoj169cLW1tbZV5iYqIAxP79+5VpvXr1ElOmTMlz3w8ePBCACAsLE0IIMWPGDNG1a1eNZSpVqiS2bNmiPA8PDxeACA8Pz/eYAgICHh/L8afec5wFVBCwWQCiXbt2onnz5iI9PV3Ur19fTJkyRcTFxYnMzEwREhIi9u7dq2xPS0tLDBkyRDx69EhcvXpVODk5ie+//155HbS1tcW0adNESkqKOHHihDA1NRUHDx4UQgixY8cOYWNjI06cOCEyMzNFYmKi+P3330VoaKgQQogWLVqIZcuWPfPvJP03vAnvKa+7gsY8Cl0NrX79+oSGhhZ2tVdm7NixfPvtt/zwww9cuXKFESNGkJiYWOCqIZIkSdKb73nlzrt3717CLZReJ/mXEr8DRAPpTy1XeMePH+f06dOPkwr/t+SsNufn50dAQADBwf/K/1OpQHbs2EHPnj0JC6sJHAfiAVtiYpzp2bMnO3bswM7ODj09PWWdChUqEBYWpjzPmXsuO6fp09MSEhIASEpKYuTIkbi4uGBqaoqLiwuQlVs1P2FhYcpykFVhU19fX6MNT3vSQ8g1j7lDgIVAVu+fTZs2oa2tze7duwkPD6datWqYmZnRsWNHrl+/rqxlYmKCm5sbFSpUoHnz5gwaNIjBgwcr811dXUlPT6dcuXL07NmTefPmKT3LOnXqpAxxNTc3p3z58ixfvrzUJjmXJOn5Cj0Mbd26dQwfPpzw8HBcXV1zdWGqVatWkTXuRfTp04e7d+8yffp0IiMjcXNzY//+/SWWYFSSJEkqnbp3707Xrl05fPgwd+7coVy5cjRr1uy1HnomlYzsUuLh4fNzDG08APQCPgZWoFJpvXAp8Xbt2nHixAmWL1+OmZlZkbX7daKtrY27u3tJN0MqIYGBgXTr1o3Y2FgA2rdvT+fOnRk5ciSQlbtn9OjR3Lt3j40bN+Lj40O3bt0YPXo0Y8aMQ4hOgD9PhmqVASYBvmzcuJnk5ETS0tKU65qQkBAcHBxeqK1LlizhzJkzHDlyBAcHB2JjYzE3N1dK0Gtp5b5X7+DgQEhICA0bNgQgMjKSlJSUZ7ZBM0j9zlNzawAtgcasWLFC2Y61tbVGRca8jB49mtGjR+c7f/78+cyfPz/PeU2aNGHevHl5DnHJTpotSdLro9A9i+7evcuNGzd4//33qV+/Pm5ubtSpU0f5WRqMGjWKW7dukZKSwsmTJ5U3XkmSJEnKKfsCtF+/fri7u8tAUR62bt3KO+88uRDp0aOHRg+ZcePGKRcWiYmJ9O3bFxMTE6pUqaJxcbBx40ZcXV0xMTHBycmJadOmKRdPedmyZQu1atVCrVZTv379Ul3BSltbm+XLlwC7Uam6kdV7oSGwF5XqOnCcMmUMXvj82r9/P7GxsbKXtCQ9tm/fPiVQBPDpp58yZ84cjUp/AIcPHyYsLASYjOZljw0QjBCTiI6+g6mpKdOnTyclJYWgoCBWrlyp0aOmMOLi4jAwMMDc3JyEhAQmT56sMd/GxoZbt26Rnp6uTBswYADz588nNDSUhIQExo4dS+vWrbGzs8t3P9lBapVqPvB0751MVKoFODqWf+EgtSRJUqGDRUOGDKFOnTocP36cmzdvEhwcrPFTkiRJkqTXX0ZGBoGBgTx48IDTp08TGxuLEIIjR45gYGDAlStXADh48CAtW7YEsgJLw4cPJzY2loEDB+Ll5aVsz9LSkh07dhAXF8fPP//M2rVrc5Te1rR3714+++wzfH19uX//PpMmTaJz587ExMQU+3E/T1RUFL1798bKygonJyemTJlCeno63bt354svvkBP73dyDm0sV+5v9PT0ePToEcbGxhgbG3P48GEgK4BWrVo11Go1TZs25ezZs8p+3N3dGT9+PO7u7piYmNCoUSPlNYesZLHnz59Xnvv4+Cg9b4QQfP7559ja2mJqakrlypXZvXt38b84klRCgoODqVmzZq7p+Q/Vmgx8BbQHsoJNZ86cwdbWli5dujB27Fg8PT1fqC1jx45FW1sba2trXF1dNSo0A/Tq1QtTU1OsrKyUaoeTJk2ibdu2NGrUCBcXF9LS0ti4ceMz95N3kDoeSAGyEsD7+Hwpb4JIkvTiCpsMydDQUPz7778vlEjpdSATXEuvmjxXpIKQ54lUUEVxruSdDNZaLF68WNSvX1+MGjVKrFq1SsTExAgdHR1x//59MXjwYNGnTx9lG2FhYQIQ9+7dy3MfY8aMER988IHynBwJrjt06CB8fHw0lm/cuLH48ccfX/iYXkZ6eroICAgQfn5+ok6dOqJfv34iPj5ehISEiOrVq4t58+aJsLAwYWpqKlauXCl+/fVXsX79erF8+XJlXTMzM41tHjp0SBgbG4tDhw6J1NRUsWzZMmFlZSViY2OFEFnJYM3NzcWxY8dESkqKmDx5snjrrbdEWlqaECJ3QvBly5aJFi1aCCGE+PXXX4WDg4OSHPfWrVvin3/+0di/fE+RCqqw58rDhw/Fxx9/LJycnISJiYmoV6+euH37tliyZImoVKmSMDY2FhUqVNBI1p7X/0jXrl3FjBkz8pyfnSz53r17wsjISACiTJkywsjISCQnJyvznySB9hHgJsBUQB0Bvz9OBv2LAJQkzytWrBCAuHLlihBCiJ9//lm4uroq+/39999F/fr1hZmZmahevbrYtWuXMm/w4MFiyJAholevXsLExESsWLGiMC/zCystCeDle4pUUPJcKXkFjXkUOmdRy5Yt+fvvv6lUqVKRBKskSZIkSSo9spPBZuX42EzWHXkvYmLOM378eLp160arVq3YtGkTNjY21KpVC3NzcwBsbW2V7RgZGQEQHx+PpaUlv/76K7NmzeLatWukpaWRkpJC+/bt82xDSEgIkydPZsaMGcq0tLQ0wsPDi+uw87Vjxw7GjBn3eChLlsjIe/z22290796dKVOmMHPmTLS1tXn77bcZNWpUgba7YcMGBgwYQPPmzQHw9vbm66+/Zs+ePUqPhr59+yq9EmbOnMlXX33FiRMnaNq06TO3raurS3JyMpcuXVJ6QElScckuSZ+d+2358uUkJSVx/PhxbG1t+fvvvylTpgzOzs4cPHgQBwcHAgMD6dChA3Xq1KFJkyYvvG9LS0sSEhJQqVQcO3YMNzc3jfnNmjXD1taeyMixwFagG1m5i7oAF1Gp1qKtrUtGRgaQ1VOyYsWKBAQEULVqVY2ekxcuXKBXr15s374dd3d3jh07RseOHTl16hRVqlQBYPPmzezcuZMtW7aQnJz8wsdVGDL/niRJxaXQwaLOnTvz6aefcvHiRWrWrJkrgVmXLl2KrHGSJEmSJL06GRkZ+SSD7QeEANYcOXJcKXZhZWWlVMJ5ltTUVLp3787q1avp27cv+vr6eHt7ExISkmtZX19fQkNDWbJkCcOHDy+S4/Ly8kKtVuPj41Oo9XIHzpKAdkRGutGzZ0+2bdumVE26desWb731VoG3HRYWlithc/ny5TWqHzk7Oyu/6+rqUq5cuQIFzDw8PJg1axbTpk3jypUrtG7dmi+//JLy5csXuH2SVBB5BVMB1qxZo+Tbyc5p2qNHD2W+h4cHbdu2JTAw8KWCRc+jra2Nh0dzNm/ejEr1I0LYA23JCoJ3Av6hdeu2/Pnnn3To0IGjR4/yxRdfsG/fPkaMGMHBgweZPXs2AN988w1eXl5K8Khp06Z06tSJn376iWnTpgHQpk0b2rZtCzypmvYqyATwkiQVh0LnLBo+fDhhYWHMnj2bXr160a1bN+Xx3nvvFUcbJUmSJEl6BfJPBtsC+BtI4t69KC5evIiDgwObNm1SLpyeJSUlheTkZCwtLdHX1+fkyZP55isCKFu2LIsXL+bMmTMIIXj06BEHDhx4ZhnpopY7cPYOUBlIRYhvgE54e3/GzZs3cXBwwNnZWaMEdU7Pqn6U09MVmG7duqX8npaWxp07d7C3tweyem49evRImf8kN0uWkSNHcuLECW7fvo2+vj6ffPJJIY5ekp4v75L06wAtRowYwY4dOzSW37RpE3Xr1sXCwgK1Ws3evXufWU6+qJiZmdGmTRvs7S/yJJ/YXxgZhbNt2zaGDh1KQEAA586do3z58nTt2pU///yTu3fvcvnyZVq0aAFk/X+uWbMGtVqtPHbt2kVERISyL9mLT5KkN0mhg0WZmZn5PrK7cEqSJEmS9PrJPxlsWaA6WeWYs5Zr1aoVjx49UoZRPYuJiQmrVq3io48+wtTUlHnz5tGnT598lzc1NWXhwoV8+OGHmJubU758eZYvX05m5tMVf54vZ8Whwsg7cGYPeAATEMKb0NBgpk6dyuDBg+nfvz+nTp1izZo1pKSk8OjRIyWRtY2NDfHx8URHRyvbHzBgAJs2beLo0aOkp6ezcuVKYmJi6NChg7LM1q1bOXnyJKmpqcyePRsrKyulMl3dunXZsGED6enpnD9/ng0bNijr/fXXXxw7dozU1FTKlCmDkZEROjqF7kwuSfnKO5hqTFZvnUyEaIW392fKtcHt27cZPHgwixYtIjo6mtjYWDp06KBURDQ2NiYpKUmjQuLTAdAX5eDggJaWFiEh1wkICMDPz4/69eszceIEunfvjru7O+fPn2fnzp20bNkSCwsL7Ozs+Oqrr6hdu7aShNrR0ZExY8YQGxurPBISEvj666+VfeUVGC4qmzZtonHjxsW2fUmSpKcV3zuaJEmSJEmvlXLlyj3+LSiPuX8DS5XlvvjiC9LS0jA2Ngayho/lHOalVqsRQuDi4gJk9UyOiIhQqqGtXLmS5s2b4+TkhImJCc7Ozpw+fVpZ/+rVq4SFhaGvr8+kSZP45ZdfcHJy4ty5czRt2hQLCwusrKzo16+fRpU0d3d3JkyYQJs2bTAyMmLfvn0aR5Geno6XlxetW7cmPj4+39ci/8CZH1nD0XplzXV1ZcKECTg4OPDHH3/g5+eHjY0NLi4ubNu2DYAqVaowdOhQqlevjlqt5siRI7Ro0YKVK1cydOhQLC0t2bJlC/v27VMuTCGrAu3nn3+OhYUFv//+O/7+/krQZ+XKlRw/fhy1Ws3nn3+uUeY7Li6OkSNHYmlpia2tLRERESxfvjzfY5Wkwnp2SfquQCKhocEcOnSIc+fOERoaihACa2trtLS02Lt3L7/99puyVuXKldHV1cXPz4+MjAw2b97MuXPniqStffr0ITAwkN27d9O0aVP09fUJCgpScoOVLVuWatWqsXLlSmVYbcuWLfHx8dHoOTls2DDWr19PQEAAGRkZpKSkcPz4cY0qhcWpf//+HDt27JXsS5IkCV4wWHTo0CE6d+5MpUqVqFSpEl26dFHunkmSJEmS9Hpq1qwZDg4uqFTzgad78WSiUi3A0bE8zZo1e+l9Xbt2jalTp/Lbb78RHx/PyZMnadCgAQCXLl3C0NCQ8PBwtm7dyvjx47lx4waQded+4cKFREVFERQURHh4OBMnTtTYtq+vL3PnziUhIYHWrVsr0xMTE+nSpQtJSUns3bsXExOTfNuXf+DMFtgGZJWiHzt2rJK/sUGDBvz555/ExsYSHR2tEaBZu3Yt9+7dIzY2VklQPXjwYK5evcrDhw85evQo9erV09iTvb09gYGBJCQkcOLECVxdnwSuateuzfnz50lISODXX39lwYIFBAYGAtCqVSvOnz9PfHw89+/fZ8+ePXJ4jFSk8g+mAvwAVAOgU6dODB8+HGdnZ6ZMmULLli2xtLRk69atGnlOTU1N+fbbb5k4cSKWlpYcPXpUyf3zsipVqsSOHTuYMWMGFhYWzJ49m507d1KhQgVlGQ8PD5KTk5X/zVatWhEXF6cRLKpTpw6bN29m6tSpWFlZYW9vz7Rp00hJSSmSdkqSJJU6hS2ztmHDBqGjoyN69+4tli9fLpYvXy569+4tdHV1xaZNm16kclupUtAycs8jSwJKBSXPFakg5HkiFdTLnivbt28XKpVKqFSdBRwTECfgmFCpOguVSvXS5ZizS8kvW7ZM6OnpiZ9++kk8evRImb9+/Xpha2ursU6lSpXEtm3b8tzezp07RaVKlZTnLVq0EGPGjNFYZvDgwaJ///6iQYMGYvTo0SIjI6NA7XRwcHn8OmQ8LrOd/cgQKlVn4ehYXqSnpxfi6Asuu/R3cZHvKVJB5XWuPClJf/yp/43sxzEBiICAgJJreCkVGRkpevXqJcqWLSscHR3F5MmTRVpamggICBBmZmbi22+/FQ4ODsLCwkKMHz9eWW/9+vWidu3az92OEOK52yoO8j1FKih5rpS8gsY8Cj2Afd68eSxatIhPP/1UmfbJJ5+wdOlS5syZo3TplCRJkiTp9dO9e3e2bdv2uMLRk/wYDg7l8fHZRvfu3V9423lVTho4cBBaWiqaNm3KokWLgKwcPzkZGRkpQ8auX7/OuHHj+Ouvv0hISCAzMzNXZda8etEcOHCA9PR0/P39C5RXRFtbm+XLl9CzZ09Uqm4IMYmsXhRBqFQLgN34+GyT5aml/6TsXojh4fMRwh/NwQpZvRAdHIqmF+KbICMjQyltv3jxYqpWrUpwcLCSp8zIyIjGjRsTHx/P5cuX+ffffwkODqZevXp06NAhz0pnnp6e2Nra5trO5MmTAQq1LUmSpLwUehjazZs36dy5c67pXbp0ITg4uEgaJUmSJElSyenevbtGMtiAgACCg/996UBR7spJx0lNfZekpCTKlCnDwIEDn7ud4cOHY29vz+XLl4mLi2Pjxo0aSXEh7ySzffv25eOPP8bd3b1A5efhSeBMs4pSYxwcgti27eUCZ88TGBiIt7d3sW1fkl5GdjAVdqNSdSPn/3TW8934+Hwpg6lkvfe5uFTCw8MDT09Pzp07R2DgEX777TdleJ6vry8AQgjmzp2LgYEB1apVo3Hjxpw5cybXNsPDwzl48CBLly7F2Ng413YKsy1JkqT8FDpY5OjoyB9//JFr+oEDB3B0dCySRkmSJEmSVLK0tbVxd3enX79+uLu7v9RFX96Vk8KBeITYDHQiMPDPAlXsiouLw8TEBFNTU0JDQ1m8eHGB2zFr1iz69++Pu7s7oaGhBVqnOAJnkvQmGDt2LOPHjy+RYGpBubi44O/vD5RMNbHcQfKDgB6RkW707NmTHTt2UKFCBcLCwoCs3E2GhobK+jl7VeYUFhaGgYGBRi/MnNspzLYkSZLyU+hhaOPGjeOTTz7h/Pnzyhvu0aNH8fX1lZU2JEmSJEnK5UnlpM08uU+VCkwDLgOCuLgEPv744+dua+nSpQwbNoxVq1ZRuXJlBgwYwKVLlwrclunTp6Ojo4O7uzsHDx7E2dn5uetkB85Kq/nz53Px4kU2b96c53wfHx/8/f2VBNjSf4uLiws+Pj5069atyLfdqFEj5s+frwyxKleuHM2aNSuVPYr69+9P//79X9n+cgfJtcgKkqcixDeoVMPw9v6MhQvn4uDgUKhtOzg4kJycTFRUlBIwCgkJKfR2JEmSnqXQwaIRI0Zga2vLkiVL+OmnnwCoVq0aW7dupWvXrkXeQEmSJEmSXm95V06qCZx4/Hs8YIqRkRH9+vXDy8tLY/3z588rvzdt2jRXcGjs2LHK73kFRHIOzQCYPHmyktfjTfAmHYtUcry8vFCr1fj4+CjT3N3dlQrI+XleMDU9PR1tbW1UKlURtrb0yztIbg94ABMQwpvQ0FZMnTqVoUOHFmrb9vb2eHh48Nlnn7FmzRpiYmKYN28egwcPLtJjkCTpv63Qw9AA3nvvPY4cOUJMTAwxMTEcOXJEBookSZIkScpT/mXo0Zj+ZDlJkl4Hly5dom7dupiamtK2bVsiIiIAUKlUfPXVV7i6umJkZERCQgKnT5+mSZMmqNVqqlevnqsn3ObNm6lduzampqY4OztrBHnzmyeEYMmSJVSsWBELCwvatWvHzZs382yrr68vbm5uxfEy5CnvIDmAH5AE9Mqa6+rKhAkTCr19Pz8/kpKScHZ2pkmTJnTs2PGFtiNJkpSfQgeL/vrrL06ePJlr+smTJzl9+nSRNEqSpPwZGxtz8eLFAi07c+bMYul2LkmSVBjZlZNUqvlA5lNzsyonOTr+NyonLV26FCcnJ0xMTHBxcWHdunUAfPXVVzg6OmJpacmUKVNwc3NTLojzei9Xq9VKL6qn51+6dIl33nkHExMTPDw8lAt4STpw4AANGjRArVZTo0YNfv75ZwBWrFjBpk2bWL16NcbGxtSoUYNx48Zx+PBhJk+eTN++fencuTMZGRmoVCoWLFhAcnIy69ato23bttSuXRtbW1sGDBhAZGQkkNXj7/bt29SvX5+4uDjatWtHx44dad++PVFRUfTv35/evXuTkpLCL7/8wqhRo1i2bBmxsbH89ddf1K5dG+CZ8zZs2MDSpUvx9/cnIiKCGjVq0LlzZ9LT00vmBc4h/yC5LbAN2A1kvU66urq4u7sTGxursaS/vz8zZ84Esnp+5exlaWtry7Zt27h37x6hoaEsXLhQqQz5vG1JkiQVRKGHoX388cdMmDCBhg0bakwPDw/niy++yDOQJElS0UlISCiybeXV5VySJKmo/ZfL0OcsmZ2ens7UqVM5e/YsVatWJSoqiqioKA4ePMiUKVPYv38/b7/9NrNmzSIoKL9eWM+Wnp5Oly5d6Nu3L3/++SdnzpyhY8eO1KpVq4iPTCrNcp53ycnJZGZmcuHCBXr16sX27dtxd3fn2LFjdOzYkVOnTvHJJ59w9uzZXN8Jzpw5owxDy8jIwMUlazha9tBHMzNzoqKi0NbWZtGiRdja2tK2bVsAvv/+e/r27cuJEyf4/fffsbKy4ueff6ZJkyaEhoYyfPhwDhw4wNy5czl9+jRjxoyhZcuWAFhbW2NtbQ3A6tWr8523YcMGPvnkE2rWrAlk5e/69ttvOXXqVJEms/b19cXHx0cjWPM8M2bMwMzMnLi4+Qjhj+Y9+qwguYPDfyNILknS66nQPYsuX75M3bp1c02vU6cOly9fLpJGSZL05rt9+zbGxsY8fPiwpJsiSdIrUJJl6EvK0yWzBw0aRFJSMt999x1JSUnY2NhQq1YtNm3aRP/+/WnUqBF6enrMnDkTIyOjF9rn8ePHuXfvHjNnzkRPT49GjRrRp0+fIj4yqTR7+ryLiorio4+G8/nnn+Pl5UXLli3R0tKiadOmdOrUSclB+jwDBw58XNUL4Chgy8OH5Vm/fj337t3DxsYGPT09rl27BkD16tXR0dGhadOmREVFYW5uzr///svixYsxNDSkevXqODg44Ofnx61bt3jrrbfy3O+z5oWFheHi4qI819fXx87OTqMqWElRqVR0794N2I1K1Y2samjxwPHHz3fj4/PlGxkklyTpzVDoYJG+vj5RUVG5pt+5c6dAJW8lSXo+FxcXFi1apAwjaNGihVLmWaVSKXe2MjMzmTp1KjY2NtjZ2bFq1SqNoQmQdXdx1KhRqNVqnJyc2Lp1K5B3l/NXycnJiYSEBMzMzF7pfiVJKjn/pTL0uUtmxz/+WZcvv/wSS0tL2rRpw/nz54mIiNCoyqarq/vC+ZsiIiKws7NThqMABar4Jr0Z8j7vbImJcWb//v2sXr0atVqtPHbt2vXcYYqZmVlDR4VoR1ZVLwBDQB/oDdTg+vUb3Llzh9TUVOXc1dJ6cpnh4ODA7du3iY2NxcLCArVazYwZMzh//jxRUVE4Oztz/fr1PPf/rHkODg6EhIQoz1NTU4mIiMi3Ktjly5eZN29ennmWrl+/Ttu2bbGwsKBixYpKD6tz584xfPhwLl68iLGxMcbGxty+fZtz587RtGlTLCwssLKyol+/fsTExAAoQ/g2bdqEvr4+enoHyBkkV6n2YGlpyZkzZ0rFkDlJkqS8FDpY1KZNGyZNmqTRGyA2NpbJkyfz7rvvFmnjJOm/bOPGjWzevJm7d+9iZGTEtGnTci2zfv16Nm3axOHDh7lx4wZnz54lPj5eY5lff/2V5s2bExMTw9y5c/nggw+Ij4/nk08+oX///owcOZKEhIRClZ6WJEl6UdmVk/r164e7u/sbeVc9d8nsdwDjxz9PoVJ1xsLCmpo1azJw4EDs7Oy4deuWsn5aWlqO5LhZueoePXqkPE9MTCQuLi7PfdvZ2REREUFaWpoy7fbt20V6fFLplP95pw9MApzR1y9DTEwMsbGxxMbGkpCQwNdffw1oBneyaWlp5QjGjCPr0sEIyD4f1wIVSElJZsiQIdSpU4fo6Ohc2+nQoQPx8fEYGxtz7949fvnlF/T09Dh48CAJCQkMGzaM5cuXc+jQITIzM4mOjubcuXMAGvPmzp3Le++9p8wbMGAAX331FZcvXyYlJYWpU6dib29PgwYN8n2d1q1bh5+fH5GRkUqepfT0dDp16kTt2rWJiIjAy8uL8ePH4+fnR506dVizZg01a9YkISGBhIQEnJyc0NLSYuHChURFRREUFER4eDgTJ04EYMmSJTRr1owvvviCpKQkEhPjCQgIoEaNGrRq1Yr79+9z+vRp/P39WbRoUSH+ypIkSa9OoYNFX375JaGhoTg7O+Ph4YGHhwfly5cnMjKSJUuWFEcbJek/ISMjg8DAQDZv3kxycjLDhw+nfPnyGBgY0L9/f86cOZNrHT8/Pz7++GMqV65MmTJlWLhwoXIHMFvdunXp3bs32traDBw4kNTUVKWLeLaEhARGjRqFk5MT1tbWDBo0iIcPH9K1a1dmz56tseyIESMYNmwYkHVBM336dCpWrIilpSVdunTRuEOpUqlYs2YNrq6umJqa0qVLFyXQHBISgkqlUhIwenl58eGHH9K3b19MTEyoUqWKRg+p2NhYevXqhVqtpmrVqqxcufI/V4ZXkqTXw5OS2ZPR/Kr1D/AHQowlPPwW9+/fR0dHh379+rFp0yZOnjxJamoqs2fPJjExUVmrbt26HD9+nKtXr5KcnMzkyZPzff975513sLCwYM6cOaSmpnLy5EmlR6n0Zsv/vOPx85nExz9kxYoVZGRkkJKSwvHjx7ly5QoANjY23Lx5EyGEspaNjU2OXj3VH/+sC2x4/Ht7YB+Qlb/U39+fKlWqABAfH096ejpHjhzB0NCQ/fv3k5GRgbGxMR988AGrV6/G0dGRffv20a1bN5YuXcrHH3+MmZkZ9evXV4p55Jz3xRdfcPbsWWXeoEGDGD16NJ06dcLW1pa///6bX375RWO0Q2ZmJoGBgZw4cQIhBMOGDaNq1aoYGhqyaNEiAgICOHr0KHfu3GHu3LkYGBjg6OhI2bJlNSqyPa127do0bdoUXV1dbGxsGDt2rMb3lpy0tbV56623uHTpEps2bcLMzAxnZ2emTJnyzH1IkiSVpEIHi+zt7blw4QKLFi2ievXqvP322yxfvpyLFy/i6OhYHG2UpP+EmjXdNPILTJ8+kx07dgBgZGSUq8cQZA03yPl/Z2VlhYGBgcYytra2yu8qlYoyZcrk2taQIUO4f/8+Fy5cIDg4mLS0NEaNGsXAgQPZuHGjslxqaio//fQTgwYNAmDKlCkcPXqUI0eOcOfOHSpXrkzfvn01tv3TTz9x8OBBbt++TVhYGMuWLcv3Ndi6dSvDhw8nNjaWgQMH4uXlpcwbPXo0iYmJ3Lp1i4CAADZs2JDvdiRJkkpS/iWzU4FpQBcATpw4ga+vL61bt2bOnDn06NGDcuXKkZmZiavrk3VbtmzJsGHDaNy4MZUqVaJmzZqYmJjkuW9dXV1+/vlnfv31VywsLJg4cSJDhgwp8mOUSp/8z7tsPQBYs2YNVlZW2NvbM23aNFJSUgD44IMPCA8Px8LCQkmI7u3tzdWrV/H09CQlpePj7awka4jbPeBfoN//2TvvuKyqP46/H7bMR5Apy5V7YZpbwNwzIg0UNc2Zvxwl5bbcWWpuy5QcqKWIWZoTFQtXjnKkaSJLHChTNuf3xyM3HoaigqKe9+v1vHjuPeeee859Dnd87/f7+QKa8HZnZ2d27NhB79698fb2pkKFCkyaNImcnByaNWvGv//+i4+PDykpKXzwwQd06dJFMUb169ePc+fOkZSUxPXr15V7jYeVqVQq/P39+ffff7l37x67d++matWqynbz589n1KiP8PDwYOXKlWRlZTFv3nzl/srW1hZDQ0OOHj2Kg4MDBgYGyraGhoYP1T66cuUKPXr0wMHBAXNzc/r27cudO3eKrB8VFYWRkRG2trbKusqVK5cJfSWJRCIpFCHRIiEhQQAiISHhqdrJyMgQwcHBIiMjo4R6JnlZ2bp1qwgODhblynkJCBOQJMBOwOtCpVKJrVu3im3btgkXFxchhBCAOH36tBBCCE9PTzFv3jylrVu3bglAhISECCGEmDp1qujRo4fW/iwsLERISIjIysoSHTt2FJ6enkJHR0fcvn1bqXP58mWhr68v0tLSRPny5UVYWJgQQoigoCBRpUoVIYQQOTk5wsTERJw5c0bZLjU1Vejo6IiIiAilr7t27VLKZ8yYIbp27SqEEOLatWsCEPfu3RNCCNG/f3/Ru3dvpW5UVJQAxJ07d0RWVpbQ19cXJ06cUMp/+OEH8SqdwuQ5RVJc5Fx5/oSEhAjgwTldFPL5XetcXRj169cXa9asKbU+ynny8lES864wUlNTRXBwsDA29hKQna/NbKFSdRNOTpVEVlbWY7X71VdfCScnJ2FqaipcXFzEt99+K9asWSPq168vxo8fLywtLYWTk5NYunSpsk3++xpALF++XNSuXVuYmZmJbt26ifj4eCGE5v5KczwMBRgLaCCgvICayv1V7r2EiYmJUKlUYvr06UIIIdasWSPs7e1FlSpVhLW1tTA3NxcODg5a/W/btq0YPny4ch+zbds2YWFhoZR7eHiIBQsWKMu59zWxsbHKuo0bN4pq1ao91nErq8hziqS4yLny/CmuzeOxPYskEknJkZ2dzSefTHiwFEhBfYGujB79cYHQslx8fHxYtmwZV65cITU1lQkTJhSqOZCfw4cP4+palV9//ZUDBw6Qk5ODjY0NJiYmqNVqGjdujI6ODnfu3KFXr16sXbsWgLVr1+Ln5wfAnTt3SElJoXXr1opQpp2dHQYGBooYN2h7NhXlIVVUXdC4sd+5c4fMzEwtLypnZ+dHjlMikUieB61atcLR0RWVahaQ//ytSZnt5CRTZktKltKad//piv36VFm98obbr1u3jkmTJrFnzx6SkpI4duyYojN07tw5VCoVN27cYPPmzXz66accPny4yHYL82DO1W+C+sANIB7wBRKAVIRwZ9iwkfj4+FCzZk1u3bpFpUqVuHz5Munp6URFRXHjxg3eeOMNoqOjmThxIjExMVr6jomJiZiZmWFubk5kZCTz5s3T6petrS1Xr15VlitWrIiHhwcff/wxKSkpREREMHPmTPr37//oH0EikUieA9JYJJE8R0JDQ4mOzhUeLagvIMR4IiOvceHChUK3HzhwIO+++y7NmzenSpUqNGjQACMjIwwNDYvcZ2ZmJlOnTn2QKeVHQJMFTQhTUlNTWb16NfHx8aSlpVGxYkX8/PzYvHkzsbGx7Nq1SzEWWVlZYWxszLFjxxShzPj4eFJTU2nevPnTHZh8VKhQAX19fS0jlBRslUgkZRVdXV2+/vorZMpsybOktOfdunXrqFjxL/Jm9XJ0PMeWLVsemdEwKCgIV9eqSrh9v379SE1N47vvviM1NRVbW1sl9M3ExIRp06ZhYGBAs2bN6NOnj/LSqjD8/f2xsbFBrVbz9ttv88cff+TRb1oBlAf0gXEPtmgHRHP79g3UajV79uzB2NiYXbt2KaLXX3/9NWZmZqxfvx59fX3GjBmDiYkJb7zxBmq1moiICObPn8/PP/+Mubk5PXr04O2339bq1+jRo9m3bx9qtZquXbsCGq3J1NRUXFxcaNGiBV26dMHf3/+xfwuJRCJ5FkhjkUTyHMmb7UabcKAnuboDVapUUbKRCCFo0KABoMlSMmvWLG7dukVMTAxeXl7cv39f8bqZNm0awcHBSqvZ2dlYWtoA3dBkSvEGzgFewNsI0Y7Roz8mOjqabdu2AdCiRQvKly/PgAEDeP3116lcubKy72HDhvHRRx8pRpy4uLhSEVLV1dWlV69eTJs2jYSEBCmoL5FIyjxeXl5s2bLliR+uz5w5o6XbJpEUh6eddw+jW7duhIdfISQkhMDAQEJCQrh27Z9iGYq8vb0fvKT6z4AFbnz55ZdYWVnRvn17zpw5A2gy+unr6yvbu7i4EB0dXWT7hXkw/3d/tRWohuY4qNF4XFkBJwF48803cXR0BOC1115jz5493Lt3j3nz5lG5cmVFSF5fX5+qVauyZMkS4uPjcXZ2pmXLlpw/f57k5GROnTrF2LFjlaQdAG+88QYXL14kPj6en3/+Wenrli1buHPnDpGRkcyZM0drrBKJRFKWkMYiieQ5Ym9v/4ga5x5aLysri+DgYDIzM7l37x6jR4+mefPmVKxYsdD6RWdKCUDz5u0vIiOv8cYbb2hlX/Pz82P37t1aYpMAs2fPplmzZnh6emJmZkajRo3Ys2fPI8b0ZCxevBhDQ0OcnZ1xd3enV69eWkKUEolEUtbw8vJ6oodrieRpKM15p6uri7u7Oz4+Pri7uxcr9GzUqI8Qoiual1S54fZNgeOoVN2wtLShbt26iudyTEwMmZmZShsRERFF3tcUxX/3TRuAX9CEn8Xz372P5v4qMTHxsdqVSCSSV4liG4uOHz9OdnZ2keXp6en88MMPJdIpieRVoVWrVlSsmKu98/j6AkII5syZg5WVFVWqVCElJYXAwMAi91d0phQzYD6atM4wb948ZsyYoZROnToVIQRDhgzR2srAwIBJkybxzz//kJSURHh4ON999x2gyfJTs2ZNWrVqxaJFiwCNS3ZuWllXV1eEEKjVagACAgJo3LixEsKmVqsRQuDq6gpA+fLl2bp1KwkJCfz999/Y29tL3SKJRFLmedyHa4mkJCgr867ol1SXgP0IMZbo6OvcvXtXSXefkpLC9OnTycjI4NixY2zYsIE+ffo81n5btWqFWm0JJAKWaDIRfo7mXktzf2Vr68DBgwfZtm0bWVlZJCQkcPTo0aces0QikbwsFNtY1KxZM+Li4pRlc3Nz/v33X2U5Pj4eHx+fku2dRPKSo6ury9y5swBQqXx5XH0BfX19jh49SmJiInfv3uXnn3/GxcWlyP3996btXBE1Hu7J9DhMmjQJHx8fkpKS+PDDD3F1ddUKiSuMPn368Pvvvxda9s8//3Dy5EmEEPzzzz/MmDGDd95556n7KZFIJBKJpHQo+iVVBjAZ6A5oXjAFBARoatapQ1ZWFvb29nh7ezNz5kw8PDwea7+6urosXboYSAHsAEc0jz12wAHgZ5YtW8zWrVuZOXMmlpaW1KxZk0OHDj3ROCUSieRlRK+4FYUQD10uap1EInk43bp1Y+fOnTg4nOfKlf+EoR0dK7Fw4dPpC+QnN1NKdPQshAhG216sedPm6FgyGXquXbvGyJEjn7qdXOLj4/Hz8yMyMhILCwu8vLyYNGlSibUvkUgkEomkZNF+SdU0T0ld4Cial2TNWb58OfXr1+f06dMAzJo1i1mzZhVob9q0aVrL+Z89Ro8ezejRowHw9fXFyMiIUaM+euDdNBkAJ6dyWvdXnTp1KrCfAQMGFNAMy9VUkkgkkleFEtUsyhWBk0jKGsHBwUo4U1nlr7/OlLquRXEypaSlJfPFF1/QuHFjTExM6NSpE3fv3mXEiBGo1WqqVaumeP/Mnj0bCwsLdHR00NPTo2XLlqSkpGBnZ8e1a9d46623MDIywtTUlOvXr9O7d29UKhVt2rTBycmJ8uXLU6tWLezt7XFwcKB9+/ZKNpSDBw+io6PDW2+9haGhIU2bNlXC0oYPH84PP/yAk5MTLi4umJmZ4eHhwbhx46hSpQpVqlTB0tKSjh07anlA3rx5k169emFtbY2zszMTJ04kKytL2Z9arWbVqlU4OTlhZWUlM5RIJBLJE1Icb1IAd3d3Fi5cWOr9kTwfcl9SqVSzeJJw+6dF6oZJJBLJkyMFriVlnoCAACX718vMs9IXeFSmFGNjYzZv3kxQUBAxMTFERkbStGlT3nzzTeLi4vD19WXYsGEA7N27l9q1a3Pr1i22bNlCWFgYPj4+xMbG4uDgAGje2EVFReHs7MzkyZPR0dGhUaNGXL58GWdnZ/755x/WrFnDX3/9RWRkJDdv3lT6KoTg8OHDnD59mtu3b2NoaMjFixcxNjYmPDwcQ0NDoqKiOH78OHPmzGHFihVERUURHBxMTEwMtWvXplu3bopByNfXF319fa5du0ZoaCjBwcF88cUXyv6SkpK4cOEC//zzD0eOHGHp0qWKxpJEIpFIyg6vyr3Bi05xXlI9LNy+pPpQFvSbJGWLTp06sWzZMuC/F4a5SCO25FEU94XI41LW5t5jGYsuXLjAn3/+yZ9//okQgr///ltZPn/+fGn1UfIKkf9knZ/w8HBUKpVWalLJ45P/Tdu+fftYs+Zb0tPTSUtLY+jQoTg5OWFhYUHnzp2xsrLCy8sLXV1dvL29OXfuHOvWrePIkSN8+eWXVKhQgZ49e9KiRQt27txJTs5/bw+nTp2KWq1WPA+FEMyYMYNz584RFRVFmzZtOH/+PFZWVnTt2rXAbztx4kRq1aqFpaUlzZo1Q1dXl48++og//viD+/fvU7lyZS5cuMAbb7yBWq2mYsWK1K1bFyMjI2bNmkVkZCTHjx8nOjqaAwcOMH/+fExNTXFxcWHixImKRkLevhkZGVGzZk2aN2+ulRVOIpFIJC8PuS8SJKXLo15S5fXyGTBggAz3kjwTdu3axYgRI553NySSMs1jGYvatm1LgwYNaNCgAffv36dr1640aNCAhg0b8uabb5ZWHyVlmKysrDKpVRUVFUX79u0xNzenUaNGXLhwQSmbP38+1apVw8zMjCpVqrBkyRKl7K233ioQDz9s2DCGDx/+rLr+zMh902ZoaMiAAe/z5ptv4uvry82bN5k8eSpBQUEAGBsbY2trC0BQUBDt23dCCEG/fv3IzMykTRt3zMzMUKvVHD16lOzsbO7cuaPsJ3/GMnNzc8UzKD4+nkOHDjFx4kTUajVLly7VunlXqVRUrlxZWTY0NFSMTjExMdjb22NiYkJSUhKgycpoZGSkVd/BwYGoqCiioqIwMjJSxgJQuXJloqKiCvQtl7xtSyQSieTJWL9+PTVr1kStVtOyZUtOnTqlVR4dHY27u+Za0qxZMy5evKiUzZ8/H2dnZ8zMzHB1dWXVqlWcPn2aYcOG8ddff2FqaoqpqSkREREAbNq0iXr16qFWq2ncuLFW0gR3d3f8/f1p3749JiYm7Nq169kcAIkMB5NIJJIXkGIbi65du8a///7LtWvXCnxy1+fVBpE8X77++mvc3d211m3atIlatWop34u6mVq/fj116tTBzMxMCR1ycXFh9uzZNG7cGJVKRa1atahZsyaGhoYFdGySkpIYMmQI9vb22NvbM2zYMFJSUoD/PIPWrVtH1apVUavVDBgwgMzMTOLi4ujUqRMJCQnKzV9oaKjSr+nTp/P6668DsHz58oeO39fXF3t7e2JjY9mwYQPffvutUubi4sKBAwdITExk1apVjBs3jt9++w2AQYMGsXbtWsUAlpaWxqZNmxg4cOCT/AxlnqCgILy9vYmKqst/ruF2xMW54O3trRiM8taNja3xYI3m987Kqk5ycjKrV69WPH8qVKigbKejo6P1NxcnJydsbGzo0qULn376KfHx8Sxbtoy6desWq+8ODg7ExsZqGSsNDQ1JS0tTljMyMoiJicHR0RFHR0fS0tK0wtzCw8NxdHQs1v4kEolE8vgcPnyY4cOHs3LlSm7fvo23tzcdO3YkISFBqfPdd98xe/Zs4uLi8PT0pEePHmRlZXH58mUmTZrEnj17SEpK4tixYzRp0oSGDRuyYsUK6tatS3JyMsnJyTg7O7Nz504+/vhjAgICuHv3LuPHj6dbt25a2XwDAgKYMWMGycnJ8kXnM0aGg0lKG1dXV+V55VG6m8UN90lOTqZDhw706dOHzMzMUh6B5EXi/PnzuLm5YW5uTocOHYiJiQHgypUrdOjQAUtLS6pUqVJgnj3qBUouZWHuFdtY5OLiUqyP5PmSnZ3NwYMHKVeuHGFhYVy5ckUpW7NmDe+9994jb6asrKwICgoiMTGRn376iW+++YaUlBRFxwY0D9np6ekEBgZy584dfH198fHxoWnTpowaNYorV67QqFEjcnJy+PvvvxkzZgwfffQRU6dOBWDhwoXo6OiQk5PDpk2bmDdvHlZWVuzatQsLCwsGDBiApaUlb7/9Nt9++y3nz5/H2NiYsLAwAKZMmcLVq1c5evQoFStWZMuWLRw8eJCNGzfyww8/EBoayrx58zA2NqZGjRqKxg7A22+/jZOTEyqVCg8PDzp06KDo0nTq1In09HQldeq2bdtwdHSkcePGpf7bPWuys7MZNeojhOgKBKPJUmIKGALjga6MHv0xOTk5CCHy1P3mQQuqBx9joD2DBw/jyJEj1KhRo4BhCMDW1lY5iQI0btwYJycnLl68SHp6OkII7ty5Q2JiYrH637RpU9RqNbdu3SI7O5sTJ04QHx9PTEwMFy5cID09nUmTJlGxYkWaNGlCxYoV8fDw4OOPPyYlJYWIiAhmzpxJ//79n/gYSiQSieQ/cu9BNm7cSFpaGjk5Oaxbt46+ffvSunVr9PX1GT16NOXLl+eXX35Rtnv33Xdp1qwZBgYGTJs2jZs3b3L06FF0dXURQnD+/HlSU1OxtbVVkiAUxtKlSxk3bhxubm7o6Ojg5eVFjRo12Llzp1LH19eXJk2aoFKpKFeuXKkeD4lEUvrkP+8UV3ezONy+fRsPDw9q167N+vXr0dfXL8WRSMo6+efaqlWrCAwMJDY2Fjs7O/r27UtWVhZdu3alfv36xMTEsG3bNr744gsCAwOB4r1AgbIz94ptLIqIiCjWR/L8CAoKwtW1Kh4eHgwdOpSMjAzc3F4nKCiI6OhoDh06hJ+f3yNvpjp16kSVKlU4dOgQFy9epFWrVqSnpzN8+HCcnJwA6NChA7a2tnh7e/Pbb79hampKZGQkJ0+eZMOGDcyaNYsTJ05gbGzMkCFDWLt2Lfv376d5c01q+OTkZH755Rfu3r2Lm5sbc+fOJSMjA4D79+9z9+5d/vzzT65du0ZWVhZ6enp89NFHyj+Ks7Mz3333HV5eXgwbNowxY8bh4eGBr68vvXv3BlQcOXJEOTZ5DZkbNmzAzc0NS0tL1Go1O3fuVMKmdHV16devn6JjExAQ8NJ6FYWGhj5IJTuBgqcCHYQYT2TkNSIiIoiLiyukbg1gHHAW2Mvdu7extbWldevWhe5vwoQJbNu2jYSEBEaMGIGuri4///wzqampLF26FAsLCxYuXEh6enqx+q+vr8/27dtJTExkxIgR+Pv7M2jQIJydnenatSt2dnacPXuWHTt2oKenB0BgYCCpqam4uLjQokULunTpIjOeSSQSSQmQ9x4kN6R5yJBh/PHHHwWykVaqVEkrBDjvNVpfXx97e3uio6OpUqUK33//PUuWLMHW1pb27ds/VM8mPDycCRMmoFarlc+ZM2eIjo5W6uQPjZZIJC8uhZ13rl+P4sSJE4Xqbvbu3Ztz584pzxwP499//6VFixa88847zJ8/X2b9fsUpbK7du5fAhQsXMDY25osvviAkJITffvuNGzduKBqo9erVY+TIkcqzZXFeoJSluadX3Iqurq6FdlQIoaxXqVRSLPA5kRsipPH82AjUAVaRlDSRt99+mz59+tC+fXvs7OyUm6lcLx+AzMxM5WZq8uTJfPHFPDIytB/a84YZ2tjYEB0djatr1QdGhFxUZGRkkJycjIuLC2+88QbXr18nPT2dc+fO0bRpU0DjGVStWjUAmjRpwpkzZzh27Bjx8fFkZmaydOlSRejay8uL48ePk52drewlLS2NlStXMnHiRD7++ON8494P9OTtt99m69ateHl5KYbMiIgI+vfvz6+//oq7uzt6enr07NlTK5Rp4MCBuLm5MX78eA4dOsS6deue/Icpw9y4cePBtzr5SsIf/NVo9XTo0IEOHTo8CNWrg8b7KPd4zX3wSQLM+eqrr/Dx8QHQehAA6NatW559arCxsSnSyOzu7q4llA2waNEiFi1apCzXq1dPCXEEGDp0KG3atNEKO8yLnZ0dW7ZsKXJ/+cW1SyPLgUQikbxsFH4PUo24OEfi4k5ibW3NJ598otTPHwJ8/fp15XtmZiY3btygYsWKAPTq1YtevXqRmprKlClT8PPz46+//irUg9XJyYn//e9/D/UcKGw7iUTyeLi6urJw4UJ69uz53PpQ1HknPt4Rb29vJcNvXq1KY2NjhBDcv3//ke3/8MMPqNXql1K3VPJ4FDXXEhIclLnm5eWFoaEhR48excHBAQMDA2X7ypUrs379ekDzfJRfKib/C5SyNPeKfcU8ffo0p06dKvQzbtw4DA0NsbS0LM2+Soqg6HCiDwEroBmbN/+ghNs4OTnx1VdfER8fr3xSUlL49NNP2bx5MzNmzCAjoyZwCI0RoBegwxdffKGEoV27do0TJ07k0bo596Av9gAsWLAADw8P2rZty969e9HX16devXpYWFgAmof63Ld+y5cvJyMjg6ioKEVPplKlSkr5Z599hkqlIjY2VhnzrVu3aNq0KQsWLC5k3D2AFoATH344lgsXLrBy5UpA49EkhMDGxgYdHR127tzJnj17tI5ntWrVcHNzo3fv3nTq1AkbG5sS+qXKFvb29g++nSuixjml3uPUfZaEhoYSGRlJTk4O+/fvZ8OGDbzzzjvPtA8SiUTyKvPokOZm7N27l8OHD5OVlcXixYuJi4ujc+fOShubN2/m2LFjZGRk8Pnnn2NtbU3Tpk25dOkSe/fuJTU1FQMDA0xNTRVPUVtbW27cuEFqaqrSzgcffMC8efP4448/lAfCffv2FXh5IZFIng+FZTV+VCbkwngcKYUnxd/fn2bNmtGhQ4diSyRIXj4ePtd6kTvXbty4QXp6Ok2bNiUmJkZLYyjvCxJHR0fCw8O19pH/BUpZmnvFNhbVr1+/wOf27du8//77LFu2DH9/f65evVqafZUUQdHhRDrAe8AdsrIyFUNNUTdT169fZ+zY3JCcz4DWwHkgBM0/xOuMHv0xAL/9FgbY8t8/jcmD7T4H9Pn1119xc3OjXr16HDlyhKpVq+Lp6an0LCAgQDFUDR8+nL59++Lj40Pt2rUBOHPmjFK+bNky6tWrp7xlBI2F9siRIw8JowoE7IiOvk7Pnj2VULJatWoxceJEPD09sbKyYvPmzXTv3r3AMR00aBBnz57lvffeK9Zv8CLSqlUrHB1dUalmAfkvpjmoVLNxcqpEq1atHqvus+Tff/+ladOmmJqaMnz4cObMmUP79u2faR8kEonkVeZRIc3wFUII+vbti5WVFZs2bWLXrl1aD4cDBw7kk08+wdLSkr179xIcHIyenh4ZGRlMnjwZW1tbrKysOHDggOLK7+npSdOmTalYsSJqtZqIiAi6devGnDlzGDx4MOXLl6dSpUp8/fXXT/XAKJFIyh4Fzzt5xX+1pRSeFB0dHb777jtq1apF+/btC2jKSF4NHn6N+wYhfImMvMbAgQNp3bo1zZs3x9bWlilTpiiRNYsXL1acNvr27cuGDRv47bffinyBUqbmnngC/vjjD/Hmm28KQ0ND8cEHH4ibN28+STNlkoSEBAGIhISEp2onIyNDBAcHi4yMjBLqWdEEBgYKQECSAJHvc02ASgAiMDBQ2eaHH34QDRs2FBYWFsLGxkZ07dpVbNq06UE74wTYCzAT0E3ASAHlBMx+UJ77aZ1vPwi4IkBHAKJ8+fLC1tZWVKhQQZiZmYlffvlFXLumqdewYUPx999/CyGEGD58uPDw8BCJiYlCCCFcXV2FoaGhMDc3F6GhoWLhwoXC1dVVCCGU7evUqSN8fX0f7HNqIeMWAhILjLu4HDp0SNja2orMzMwS+IUezrOcK/nZunWrUKlUQqXqJuD3B8fsd6FSdRMqlUps3br1iepKSp7nOU8kLxZyrkiKQ0nNk4ffgzzdtVgIIdasWSPq16//WNusX79eNGvW7In2JymIPKdI8uPi4iJmzJghGjZsKMzMzET79u1FeHi4WLlypQDEvXv3lLqjRo0S/fv3F0IIYW1tLQBhYmIiTExMxPr164WRkZHWusOHDwshhNi7d69o3LixsLCwELVq1RLbt29X2mzduvWD885bD55XFj0437gI2Kacd7y8vESPHj2U7XKfI+7duyfatGkjFixYIIQQIiQkRFhYWCj18pbl5OSIoUOHitdff13cvXu3NA7nK8eLdE4p+hrnImCGgPoCEHXr1hWRkZFCCCEuXbok2rVrJ9RqtahUqZL48ssvRU5OjtJmQECAqF69ujA3NxfNmzcXJ06cUMqe1dwrrs3jsYxFV65cEb169RK6urrCx8dHXL169ak6WRZ5EY1FISEhDyZxWCE3aSkPDD2IkJCQh7ZT3Bu+kSNHPqJeHa0bQ39/f6GnpyeSkpKEEJqJv3TpUlGrVi1hZmYmHBwcRK9evRRjUWJiohgzZoxwdXUVZmZmomrVqmLixIlCCO2T/H/jrijg80L68Xuxxp2f9PR00b17dzFp0qTH+yGekOd9wty6datwdHTVMgQ6OVUq1PjzOHUlJcvznieSFwc5VyTFoaTmycPvQZ78WpzLkxiLJCWLPKdIhBAiKytLhISEiMDAQGFraytcXV3FxYsXRUpKiujXr59wd3d/pLEo7318LvkNNUIIcfbsWaFWq8X+/ftFdna2CA0NFebm5sqL5g4dOjw47ywQkP3geafkzjuS0uVFOqeU9jXueVFcm0exw9BGjBhBrVq1SEhI4OTJkwQGBlK5cuXH8GGSlBZFhwgJYBFQrlghQsXVpalSpcoj6n2j1d7cuXPJzMzE1NQU0AihjxgxgvPnz5OYmEh0dDSbN2/GzMwMADMzM+bPn8+1a9dITEzkn3/+YcaMGYBGUE8IgVqtzjNuN2Bivj48WWjUoUOHKF++PHfu3GHcuHHF3u5FxsvLi/DwK4SEhBAYGEhISAjXrv2Dl5fXU9WVSCQSyctPWQtTzqsTIZG8zLi6uj6zRBzFyQR18OBB4uLiSmR/K1euZMCAAXh6eqKjo0PLli3p2rUrP/zwA6DRLDMyKodKdeDBFsZ5tn5+8giSl4+ydo171hTbWLRixQp0dXW5deuWki2qsI/k2aOrq8vXX38F/IxK1RON4HQ8GvGtz4G7LFz4Jbq6ug9tp7j/DCNGjCgT/zSFjzsJCHuw/HOxxp2XNm3akJKSwm+//Ya5uXlpdLtMoquri7u7Oz4+Pri7uz/0mD1OXYlEIpG83JTktXj+/Pk4OztjZmaGq6srq1atUsqmT5+OjY0Ntra2LFy4UFk/bdo0unbtyvDhw7G0tOTTTz8lICCABg0aKHVcXV2ZPXs2jRs3xsTEhE6dOnH37l1GjBiBWq2mWrVq/P7770p9d3d3xo0bh7u7O2ZmZjRr1oyLFy8+3YGSSF5QcjNB/ZfUJgmwIyGhEt7e3gQFBWFra4uhoaEiQP+0hIeHs2LFCiXZjVqtZvv27cTExACaF8+enh6U5DOARFIYpfG8+SJRbGPR1KlT+eSTT+jRo8dDP5Lng5eXF1u2bKFixb+A5kB54D5OTnZK+vhHUdx/BgMDgzLzT1Nw3OZAcxwdzylpDCUSiUQikZQeJXEtvnz5MpMmTWLPnj0kJSVx7NgxmjRpAsD58+cxNjZWPJHHjRunlVTl119/5Y033uDWrVtMnz690PY3b95MUFAQMTExREZG0rRpU958803i4uLw9fVl2LBhWvW/++47Zs+eTVxcHJ6envTo0YOsrKwnPUQSyQvJ42aCys2MnTc1/Y0bN5TvOjoFHz0LW+fk5MSoUaO0MjcnJyezfPlypU61atXkM4DkmfBKP28+m6i4F4cXUbMoL3njiUNCQkRWVtZjt1FcXZqypF9TEuN+XrxIcbuS54ecJ5LiIueKpDiUxjx53Gtx3vq5QrdbtmwR9+/fV+qsWbNG2NnZaW1XtWpVsWXLFiGEEFOnTi2gaZRf58jFxUWsWLFCWR43bpxo2rSpsnz+/HmhUqlEenq6EEIjMDp8+HClPCMjQ0m68SoizynPl4SEBPHBBx8IZ2dnYWZmJl5//XUREREhXFxcxNy5c8Ubb7whTE1NRevWrUVERISy3c2bN4Wvr6+ws7MT9vb2YtSoUSItLU0p/+OPP4S7u7soX768qFKlivjmm2+0yt544w1hbGz84B6/RR6NlpsCjAXoClALQLRv3160atVKBAcHC2dnZzFr1iyRnZ0tDhw4IExNTRXNovv37wsdHR1x8uRJZV9///230NHR0UqYdOrUKWFjYyMOHDggsrKyRFpamvj999/FhQsXhBBC9O/fX4waNUoI8WI/A7yqvKjnlJdprpW4ZlFRHDp0iJ07d3Lv3r2nbUpSApREiFBxdWnKkn6NDI2SSCSSF4eoqCjatWuHubk5jRo1YtasWbi6ugJw8+ZNevXqhbW1Nc7OzkycOFHLo2PPnj00bNgQCwsL3Nzc2Ldvn1K2d+9e6tWrh5mZGba2tgwfPlwpu3r1Kt26dcPa2hoXFxdmzJghU6qXMI9zLc6vgdK3b1+Mjc2YOnUqtra2tG/fnjNnzgAafZK8mJiYkJSUpCw7Ozs/sm952zA2Ni6wLITQ8oZwcXFRvuvr62Nvb090dPQj9yORPC3Z2dkcPHiQjRs3cvDgQfr378+VK1cICwsjPj6eb775hnLlygGwfv16Nm7cyO3btzExMWHy5MkACCHo3r07dnZ2XL16lb/++ouzZ88qGqCxsbG0a9eO4cOHc/v2bYKDg5k6dSr79+8HYOTIkXTr1o1vvvnmQa8mPfgrgO6ALhq90IoAnD59mu+//x6Ab775hjVr1mBhYcHKlSt59913lbGVK1eOqVOn0qlTJ9RqNYGBgVSvXp1BgwZRq1Yt1Go1R44coWHDhmzcuJFJkyZhbW1NxYoVmTx5Munp6QWOl3wGkDwrSnOuderUiWXLlpVYeyVFsQNL586dS3JysuLeK4SgU6dO7NmzBwAbGxv2799P7dq1S6enkmdK7j9DSdWTSCQSyatLdnY2oaGh3LhxA3t7eyZPnkz16tX56aefiIyMpFOnTkpdX19f7OzsuHbtGnFxcXTu3BkTExMmTJjAlStX6NGjBxs2bKB79+4EBwfTvXt3zp8/T6VKlejfvz9z587Fz8+PlJQUzp49C2hCItq2bcvo0aPZunUrsbGxdO7cGXt7ewYNGvS8DssrS64Giia0ZSNQBzjHvXuzuHfvZzZs2MCpU6fw8/Pjo48+emR7hYWxPC3Xr19XvmdmZnLjxg0qVqxY4vuRSPISFBTEqFEfERUVrrV+xYoVODg4ANCwYUNl/YgRI6hUqRIAffr0Yc6cOQCcPHmSf/75h99//x0dHR2MjY2ZMGECw4YNY/r06axbt47WrVvTq1cvAOrUqcN7771HYGAgbdu2RV9fn+vXr/Paa6892JP6wd+TwD9otFF1gI5Ac/T19XF0dOTPP//E09OTy5cvFznGKVOmMGXKFK1133zzTR7DlAZPT088PT0LbSMgIKDI9iWSF5Fdu3Y97y4USrGvrps3b6ZOnTrK8pYtWzh8+DChoaHcuXOH119/nc8++6xUOimRSCQSieTFJL8HiYeHB0eOHKFly5aUK1eO1157TdGLiY6O5sCBA8yfPx9TU1NcXFyYOHGi8mCwefNm3N3d8fLyQk9PD29vb1q2bMnGjRsBjQfIlStXlLfszZs3B+CXX36hfPnyjB49GgMDA5ydnRk1ahSBgYHP5Zi8yhStgVIeIT5AiE74+0/A2Ni4xMRyn4TNmzdz7NgxMjIy+Pzzz7G2tqZp06bPrT+Sl5/ChaRXAToMHz6coKCgAtvY2dkp3/N63IWHhxMfH4+lpaUiEO3t7c3NmzeV8p07d2oJSC9atEjRF1q9ejVpaWmMHDnywf/hYDRJbcLRGIos0RiQWqNSqUhISCiVYyKRSJ4vxTYWXbt2jXr16inLO3fuxNvbmxYtWmBpacmkSZMICwsrlU5KJBKJRCJ58XjYw8/AgQOVh5/cMKKoqCiMjIy0QoQqV65MVFSUUp4brlZY+bZt2zh37hzVq1enYcOGSprl8PBwzp07p/Vg9NFHHxEbG1uaw38lUalUSvhYYYSGhj7wmpiA9m1oBjAVOEhUVDjBwcHP1Xtg4MCBfPLJJ1haWrJ3716Cg4Ofq/FK8nJTtBG1K5CDEG0ZPfpjsrOzi9Wek5MTNjY2WgLRCQkJJCcnK+VvvfWWVnlSUhI7d+4EoEqVKqxdu5bY2FimTZsGnAPc0RiKrIFdqFStUamy2bJli9KuRFIWeZyMmOvXr6dOnTqYmZnh7OzM5MmTEUIobZ0/f56mTZtiZmaGh4cH/v7+WlE2/v7+uLi4YGZmRq1atfjxxx+VsrfeegtTU1Plo6+vz4ABAwBNFs7cTJ8HDx5ErVazatUqnJycsLKywt/fX2tM+/bto0mTJqjVamrXrs1PP/1UKseu2MairKwsDA0NleWwsDDljR2Ag4MDd+7cKdneSSQSiUQieSEp+uGnPZqHn3bKw09ERAQAjo6OpKWlKW+/QWPocXR0VMrDw8O19pO33M3Nja1bt3Lnzh0mT56Mr68vN2/exMnJiUaNGmk9GCUmJnL+/PnSPgySfPyXGalOvpK6wFFAY8D79NNPqV+/PgMGDChgfDpz5oxygz1t2jSCg4O1yvNvEx4eTs+ePZXl/Nu4uroihECtVivrKlasyMGDB0lOTubo0aNa3vUSSUlTtBHVFugBpBAZeY1Dhw5x+vRp4uLiHtpe48aNcXJyYtKkSSQlJSGE4Pr160qoi5+fHwcOHGDr1q1kZmaSmZnJmTNnOHHiBABr167l5s2bqFQqevTogYGBATY2V4AhwE2gOQ4Of/Ljjz/SqFGjMhtCI3k1ya/7BcXPiGllZUVQUBCJiYn89NNPfPPNN4oXcmZmJt27d6dTp07ExcUxZ84cVq9erbXv+vXrc+LECeLj45kyZQp+fn5cu3YN0LzQSk5OJjk5mePHj2NhYYGvr2+hY0hKSuLChQv8888/HDlyhKVLlypj+fPPP3nnnXeYM2cOd+/eZeXKlfj5+XHp0qUSP5bFNhZVqVKFw4cPAxAREcHly5dp3bq1Uh4VFYWVlVWJd1AikUgkEsmLR9EPP05AC8CIyMhrbNy4UdGqqFixIh4eHnz88cekpKQQERHBzJkz6d+/PwC9e/fm4MGDbN++naysLIKCgjh8+DDvvvsuGRkZrFu3jnv37qGjo6M8+Ovp6dG1a1du3rzJsmXLSEtLIzs7m0uXLik3XpJHI4QotlfDw7C3t3/w7VwRNc7lq/di4urqWsCIVZJERERgamoqw3+ekAEDBjB69Ogn3r5BgwYl6vlWtBEV4HugJgBdu3Zl2LBhpKamPrQ9XV1dfv75Z6Kjo6lZsyYWFhZ06dKFK1euAJpz7e7du1m5ciX29vbY2trywQcfkJiYCGi8FurXr4+pqSk9evRg/vz5xMREEhISwvLly+nYsSOQxXvvvafVrkTyvCks9D0qKoqmTZvi5OSEhYUFnTt3xsrKCi8vL3R1denduzfnzp0jIyODTp068dprr6FSqWjQoAE+Pj7KvcLRo0eJi4tj4sSJGBgY8MYbb9C7d2+t/ffp0wcbGxt0dXV59913qVGjhuK1lMutW7fo0qUL06dPp3379oWOQwjBjBkzMDIyombNmjRv3pw//vgDgJUrVzJgwAA8PT3R0dGhZcuWdO3aVfGmLkmKbSz64IMPGDlyJIMGDaJTp040a9aMWrVqKeUHDhzQElyTSCQSiUTy6vLwh59AQPNQMnnyZPr27at4LwcGBpKamoqLiwstWrSgS5cuivt11apVCQoKYurUqVhaWvL555+zbds2KleurGxbtWpVzMzM+N///kdgYCBWVlaYmpqyb98+9u/fj6urK1ZWVvj6+sowtAckJiYycuRIXFxcMDc3p3HjxkRGRiqu+02bNsXY2JgLFy480kUfNJlyq1evjlqtpnfv3loGjVatWuHo6IpKNQuNBkpeclCpZuPkVIlWrVqV/sBfYJydnUlOTsbCwgJ4euOH5PnycCOqBfA+oJEBOXbsmOJlmddjrmfPnlqelzY2NqxZs4aoqCgSExM5d+4c//vf/5Tyhg0bsmfPHu7cucPdu3f57bffaNu2LYASgpacnMzVq1f54IMPlKQ2w4YNY9euXUW2K5E8LwoPfQ8jO9uAlStXKqHvD8uIuXv3bpo3b06FChWwsLBgxYoVSvRUTEwM9vb2WiHJ+bNxLliwgNq1a2NhYYFarebcuXNa0VdpaWn06NGDnj17amVszY+5uTnGxsbKcn5NshUrVmiF1m/fvp2YmJgnPXRFUuzg68GDB6Orq8uOHTto3bo1U6dO1SqPiYlh4MCBJd5BiUQikUgkLx7aDz/5hYGdgVlAc9asWUNYWJhyw2VnZ8eWLVuKbLdTp05a2dNyMTAweGgoRJUqVdi6detjjeFlJTs7myNHjgBw5MgRFi1aRGpqKmFhYdjZ2XH27FklNXdAQAA//fQTVatWJTs7m6ioKIKCgqhWrRpnz56lQ4cO1KhRgz59+ijtr1u3jpCQEIyNjenVqxejR49mzZo1gMbj4euvv8Lb2xuVqidCjCc3G5pKNRv4mYULtzzX9NdlyeMsMzMTfX39R66TvNjkGlGjo2chRDDa7/M1RlRHR2lElUiKomDoe+7/UFPABrBm9OiP6dGjR5FtZGRk4OXlxbJly3j33XcxNDRk9OjRihHWwcGB2NhYsrKyFINRbhg9aK6n06ZNU5xodHR0aNCggfJCRQjBgAEDsLa25quvvnrisTo5OTFq1Cgl+2Fp8li5RgcOHMi2bdtYvny5lvo+wLJly3jrrbdKtHMSiUQikUheTB7uQXISmICjoyvGxsYsXryYd9555zn08tUj10W/S5cuAHTu3Jng4GDeeustHBwc0NHRoWHDhlSoUAGA4cOHU716dXR1dTEwMHioi34u/v7+ODg4oFarmT59OoGBgeTk/DcHvLy82LJlCxUr/gU0B8yB5jg6nmPLli14eXk9m4NRypw/fx43NzfMzc3p0KEDMTExhIeHo1KpiI+PV+qNHj1a0WDKLV+zZg1Vq1bF0dFRETtdvnw5zs7ONG/eXKudRYsWsWHDBpYtW4apqSm1a9cGNJoXQ4YMwd7eHnt7e4YNG0ZKSspzOBKly/z583F2dsbMzAxXV1dWrVpFREQE7dq1w9ramvLly9OlS5cCemd56du3Lw4ODpibm9OoUSNCQkK0ypcsWaIIzU6cOLHEx5BrRIWfUal6ktcrQrP8MwsXfvlcjagSSVmm6ND3XN4mMvIaoaGhRbaRnp5OWloaVlZWGBoacuzYMa2sqU2bNkWtVjN79mwyMzM5ceKEVuhXYmIiurq6WFtbk5OTw+rVqzl37j9vwSlTpnDp0iU2btyIjs5jmWG0GDp0KGvWrCEkJITs7GzS09MJCwvj4sWLT9xmUTx5L4EuXbrkcTOXSCQSiUQi0fCwhx8YCRzk1q0YevXqxeDBgxk0aNBz7O2rgbaL/j4AhFjMw1Jz53exf5iLfi4uLi5a3zMyMrh9+7ZWHS8vL8LDrxASEkJgYCAhISFcu/bPC2soKkxQddWqVQQGBhIbG4udnR19+/Ytdns//fQTJ0+eVIRRk5KSOHv2LH///TeHDh3Sqvvhhx/Sp08fRowYQXJysiLcPmrUKK5cucK5c+f466+/+PvvvxkzZkzJDLiMcPnyZSZNmsSePXtISkri2LFjNGnShJycHMaOHUtkZCTXr1/H2NiYwYMHF9lO27ZtuXjxInFxcbz77rt4e3srIR8HDhxg4sSJ/PDDD8pzT94HwJLiVTGiSiSlwcND3wFc8tUriJmZGUuXLmXIkCGYm5szc+ZMLU0ifX19tm/fzs8//0z58uXx9/fXCqPv2LEj3t7e1K1bFwcHB86fP0+LFi2U7detW8fFixextbVVMqLlCms/Dg0bNmTjxo1MmjQJa2trKlasyOTJk0lPT3/sth6JeApMTU3F1atXn6aJMkdCQoIAREJCwlO1k5GRIYKDg0VGRkYJ9UzysiLniqQ4yHkiKS5lba5s3bpVODq6CkD5ODlVElu3bn3eXXulyMrKevA7dBOQLcqV08wTI6PIB7/Lm8LJqZLIyspStnFxcRHbtm1TltPT04WxsbEICAgQaWlpQgghRo0aJXr06KHUAcTmzZuV5WPHjgkDAwORnZ1d6mN8XhQ2x3V1dYWfn59SJzY2VgAiNDRUAOLevXtK2ahRo0T//v2FEEJcu3ZNAOL06dNKeUhISIFtcuvlruvfv78YNWqUUp6dnS0MDAzE0aNHlXW//fabMDQ0fOzfoqydU7KyskRISIgIDAwU69evF0ZGRmLLli3i/v37RW5z+vRprbHnP175UavV4siRI0IIIQYOHCiGDx+ulGVkZAhzc3OxZs2aEhlPfvKOLyQkROt/sixT1uaJpOxSGnMl9zwJYQJEIZ/fBSBCQkJKbJ9CCDFkyBDx/vvvl2ibz4Li2jyeyrNIIpFIJBKJBpVKVSDFdy6vcuaisupBEhwcjKur61O387DfvSxRlIu+SlX81NyPctHPZd68ecTExCipg999992ncrkvyzxMUHXdunWKt5atrS2GhobF1hrK79FlZmamZPgrDrdv3yYjI0NrjleuXJn09PQCnmAvEvkzHfXt2xdjYzOmTp2Kra0t7du358yZM9y+fRtfX1+cnJwwNzendevWpKenK95CecnJyWHixIlUq1YNc3Nz1Go1CQkJWqK2eb3l9PX1SzVbX66QtI+PD+7u7jL0TCIpBs8qeUJoaCiRkZHk5OSwf/9+NmzY8FKH0T/VldvFxUUK7EkkEonkleRxUmPnz1z0uLwoBomikA8/z5+SSM39KBf9XPr27YuHhwcuLi6YmZnx9ddfl9xAyhAFBVWbAqb8J6hak9GjPyY7O5tbt26Rnp5OxYoVAbh//77STmFhEfmNa48ytuUvt7a2xsDAQEunJzw8HENDQ0WP6kWjKMPcvXtNuXDhAitXrqR+/fr4+fkxfvx47t+/z6lTp0hMTOTw4cMABTL3gSaLYmBgIL/88gsJCQnEx8djYWGh1HVwcOD69etK/czMTCnDIZGUMZ6V7te///5L06ZNMTU1Zfjw4cyZM4f27ds/bffLLMXOhpZLREQETk5OqFQqrXhdIQSRkZEF3oRIJBKJRCJ5ODK7kaS0eXh2utzU3KvZuXMn7u7uAIUKAg8bNuyhGgu5D9ijRo16yh6Xff7z1tpI4e9fE4iMjGHfvn1s2rSJ1q1b4+joiLOzM99//z2ffPIJhw4dYufOnbz99ttP1RdbW1vOnz+PEAKVSoWOjg6+vr6K1o4QggkTJuDn5/dCenkVnemoPEJ8AGTj7z+BgQP7oaenR2JiIsbGxqjVauLi4vjss8+KbDsxMREDAwMqVKhARkYGc+fO1fJA8vHx4e2336Z///40bNiQ6dOnv5RC4RLJi06u7teoUR8RFdVcWe/oWImFC0tG96t///7079//qdt5UXjsq0WlSpUKiBQC3L17l0qVKpVIpyQSiUQiKcu88847RERE4OPjoyVQePToUerUqYO5uTndu3dXws7yZ0AaMGAAgwYNolevXpibm7NixQpOnTpF06ZNMTc3p0KFCnTr1g2AJk2aANC8eXNMTU2ZNWsWb731FtOmTdPq07Bhwxg+fPizOQAvIFFRUbRv317JdnThwgWlLL/n1sKFCxWDCUBsbCx9+/bF3t4etVpN69atC/W8uXz5MlWqVGHJkiWlOZQn4lm56L9KPFpQVSPa/tZbbxEdHc2GDRsAWL16NWvWrMHCwoKVK1fy7rvvPnVf3n//faKjo7G0tKRevXoAfP3117i6ulKrVi1q165N1apVmT9/frHaO3PmDCqV6rH7MWvWLHx8fIpV93E8JovOdJQBTAUOEhUVTnBwMAEBAXz22WdcuXKF8uXL06JFCzp16lRk2/3796d27dq4uLhQuXJlypUrh6Ojo1L+5ptvMn36dN5++23s7e3JycmhTp2ifnOJRPI8Kauh7y8sjyuGpFKpxK1btwqsDw8PF8bGxo/bXJlDClxLnjVyrjw9+UVYX0bkPCkb5BUetbW11RJpBoSHh4e4efOmuHfvnmjYsKGYOnWqEKJwMdpy5cqJX3/9VWRnZ4uUlBTRrFkzMWPGDJGdnS3S0tLEoUOHtNrOK3i7Y8cOUalSJZGTkyOEECI1NVVYWFiI48ePy7lSBK1atRL9+vUTKSkp4uLFi8LV1VW4uLgIIQoe3wULFog2bdoIITRCwa+//rro37+/uHv3rsjMzBShoaGKwHPutseOHRMVK1bUEncua2zdulWoVCqhUnUT5codEcHBwaJcuSNCpeomVCqVFB1/TJ6XoOqz4PTp0yL3MaG0zin5/+8eRmBg4INjnVTEsU4UgAgMDCzRPkqKj7z2SIqLnCvPn+LaPIodhjZ27FhA8xZg8uTJGBsbK2XZ2dkcO3aMBg0alIT9SiKRSF5YCgsnysrKQk/vsaN+JWWMoKCgB67N4cq6IUM0HkW5b6z8/f2xsbEB4O233+bo0aNFtte+fXs6dOgAgLGxMfr6+ly/fp2YmBgcHR1p3bp1kdt26tSJ9PR0Dh06hLu7O9u2bcPR0ZHGjRuTmZn5tEN9KcjOziY0NJQbN26gq6tLaGgoW7ZswdjYmBo1ajBs2DCWL1/+yHZOnDjBxYsXOXz4MOXKlQOgZcuWWnV+/fVXFi1axPr16/H09CyV8ZQEeV304+LaoQmfaoejo12Juei/SuR6a0VHz0KIYLQ9XjTeWo6O0lurJHh4GGXuekpVeFoikUheNYodhnb69GlOnz6NEIK//vpLWT59+jR///039evXJyAgoBS7KpFIJAUpLBzoypUrdOjQAUtLS6pUqcLChQuV+gEBATRo0IApU6ZQoUIF7Ozs2Lx5M7/99ht16tTBwsKCQYMGkZPzX5jGnj17aNiwIRYWFri5ubFv3z6lrLBwInd3d/z9/Wnfvj0mJibs2rWLmzdv0qtXL6ytrXF2dmbixIlkZWUBmgf/FStWAJCQkICenh6ffvopoNH/sLa25tSpU8/gaEoKw9XVlU8++aQQYVU74uJc8Pb2VjIe2dnZKduZmJgUmnknl/waf6tXryYtLY1GjRpRo0aNh4Yy6erq0q9fP+W6GxAQwMCBA590iC8d+TMmaUSYVRw5ckSpkze70cO4fv06FStWVAxFhbFw4UI8PDzKtKEol1wX/V9++QWAX375RbroPyHPSlA1P/Pnz8fZ2RkzMzNcXV1ZtWoVERERtGvXDmtra8qXL0+XLl20NKcGDBjA4MGDeffddzEzM6N69eocPHhQKY+Pj6dXr16o1Wpq1KihCEIDHD9+nPfee09Z/uijj9DX1yc5ORmAxYsXK2Gz06ZNo2fPnkrdR4VwFhW6mx8ZRimRSCTPgcd1WRowYMBTh2iVZWQYmuRZI+fKk1FUOFBmZqaoXr26GDdunEhNTRVnz54V9vb2YsOGDUIIIdasWSN0dXXF119/LTIzM8WqVauEubm5eOedd8SdO3dEdHS0sLGxUdr7559/hJGRkdi6davIzMwUP/74oyhXrpz4999/hRCFhxO1adNGWFtbi2PHjomcnBxx//594enpKXx9fUVSUpIIDw8XtWrVEjNnzhRCCDF37lzRq1cvIYQQwcHBokqVKqJJkyZCCCHOnDkjypcvL9LS0uQ8eU64uLgIKytrAd0EZOcJe6gkYKtQqboJJ6dKDw1lKiwMbdSoUYXuLycnR4SGhgpDQ0Nx8uRJIYQmBDx/uMbly5eFqamp+Pvvv4WhoaG4efOmEEKeU3JDrTS/V9iDsJXgByEsKP/bc+fOVcLQTExMxG+//aa04e/vr/x2R48eFSYmJiI1NbXQ/QFi//79omHDhmLkyJGlOraS5FWfJyXJ1q1bhaOjqzLHAOHkVKnEwvryXu/Wrl0rypUrJy5evCiEECI2NlacPXtWXLt2TezcuVOkpqaKhIQE4e3tLd58802ljf79+wszMzMREhIisrKyxPTp05X5L4QQfn5+ol27duLevXsiOjpaNGrUSAlDu3//vihXrpw4e/asEEKIBg0aiCpVqoidO3cKIYTo2bOnmD9/vhBCiKlTp4oePXoIIYoXwllU6G5Rxzk3jFIT4pco4HcZRllGkOcUSXGRc+X5U1ybx2MLXK9ZswZzc/OSslVJJBLJY5Pfa+DmzZsMGTKMoKAgjh07xo0bN5gxYwZGRkbUq1ePkSNHank+Wltb8+GHH6Knp4ePjw+JiYkMGjQIKysrHBwcaNOmjeLJs3nzZtzd3fHy8kJPTw9vb29atmzJxo0blfZyw4l0dHSUEF1fX1+aNGmCSqXi7t27HDhwgPnz52NqaoqLiwsTJ05U+uTh4aG84T1w4ACjRo3i6tWrJCYmcuDAAdq0afNCZq95FI+Tev55kpaWRlzcbQoKq9oC1xBiPJGR1556P2vXruXmzZuoVCrUajU6OjqKR4KtrS2XLl3Sql+tWjXc3Nzo3bs3nTp1UsLfXmWKTmXeA2gBOPHhh2OVNNu5uLm5sW7dOrKysjhz5gzr1q1Tyho3bkz16tUZMWIE8fHxZGVlceTIEdLT05U6lpaW7N+/n7CwMIYPH15oem7Ji83DzlelKaia/3rXr18/UlPT+O6770hNTcXW1pZ69erh6upKp06dMDIywtzcnIkTJxIaGqrlJdu5c2fc3d3R1dXlvffe4/r168TFxZGdnc3mzZuZMWMGarUaBwcHxo0bp2ynp6dHrVq1OHToEHfv3iU2Npbhw4cTEhJCTk4Ohw4dKtSrLjeEc/ny5ZQvXx49PT1atmyJoaGhUic3dFetVvP222/zxx9/FHkscsMoK1b8C2gOmAPNcXQ8x5YtMozyefE8r+Xx8fGoVKpCMzdKJJKn5+V7+pBIJC81QUFBjwwHcnBwwMDAQNmmcuXKREVFKcu2trbK9+bNmxdYZ2xsrLjXR0VF4erqqtWH/O3lDyfKvy4qKgojIyOtfeRtw83NjbS0NM6fP8+BAwd48803admyJaGhoRw4cKDEQlteFOPMozh48CBqtfqZ7S87O/vBt/8BZkAbIBJNpqOPgY5K3Xnz5jFgwABAkyX00KFDrFu3jjZt2gAwYsQILU2hLVu2ULVqVSwsLJg+fTouLi4YGBjQo0cPBg8ejLu7O8uXLyc9PR0/Pz8MDAx48803le0HDRrE2bNnSUtLK8Uj8OJQdMYkgEDAjujo6/Ts2VMrbG/x4sWEhYWhVqv55JNPtNLi6ujosGPHDu7fv0/16tWpUKECkyZN0noIByhfvjz79u3j1KlTDBkyRBqMXjF0dXVxd3fHx8dHMcg8LYVf78IAN7788kusrKxo3749Z86c4fbt2/j6+uLk5IS5uTmtW7cmPT1dKxQ2f5gsQFJSEnfu3CEjI0MrNDN/mGbdunU5ePAgISEhtG7dmrZt2xISEsLp06fR0dFRMrDlpTghnI8Tugsy05FEIpE8S6SxSCKRvDAU7jVQB43X/3igK+vXbyQmJkbrgTw8PFwrDe7j4OjoWOCNVf72CvP6ybvO0dGRtLQ0bt68WWgburq6tG7dms2bN3P37l1q1qyJp6cne/fu5fDhw3h4eDxR3yVPTnZ2NgcPHmTjxo15jEXjgduACTAZyDXa/AhASEgI1tbWShu5xohdu3Zx7tw5oqOjCQ0NZcOGDQQEBDBixAj8/PxYsmQJcXFx+Pv7k52dzYQJE7h69SpvvfUWSUlJnD17lqioKOLj4wkKCuLff/9VDBEODg6oVCqmTJnyLA5Lmefhqcydgf0AfPbZZ0ycOFH5365fvz5nzpwhOTmZ3bt3M3v2bC09FwcHBzZt2sTNmzeJj4/n4MGDygOwEEJJ8KFWqzl27BjffvvtE6Udl5RNCtPG8/f3x8XFBTMzM2rVqsWPP/6otc0ff/yBp6cnlpaWWFtb87///U8pe5gOXi5Fe8k1BY6jUnXD0tKGunXr4ufnx/jx47l//z6nTp0iMTFR0RwqjtGyQoUKisB+LhEREVp16tSpw+HDh9m/fz+enp7Ur1+fiIgItm3bhru7e6Hz3cXFhejo6BI3ZpeGYU5S+mRlZUkjukTygiGNRRKJ5IWhaK8BC3LDgW7duoG5uTlTpkwhPT2dc+fOsXjxYi1Pgcehd+/eHDx4kO3bt5OVlUVQUBCHDx/m3XffLXYbFStWxMPDg48//piUlBQiIiKYOXOmVp88PDz4+uuvFQ8UT09P1qxZg5GREXXqFPbg+3Jw+fJlmjZtipmZGW3atCEyMhKgyAexuLg4OnXqREJCAqamppiamhIaGgrAvn37aNKkCWq1mtq1a/PTTz89UZ/yh33cu3cPlUoPWAMYAH2AvKESCx4qrDplyhTMzMxwcHCgY8eOSpjF5s2badu2LR07dkRPT4/Bgwfz2muvaW2bk5PDnDlzMDY2xtjYWCsLWkZGBp9++ilWVlY0a9bsicb6sqGdMakwZMYkyePz448/4uzszMaNG0lOTmbFihXUr1+fEydOEB8fz5QpU/Dz8+PaNU04anR0NJ6ennh7exMTE8P169fp1asXAFeuXKFHjx5MnjyZuLg4JkyYQPfu3ZVtcyn6encJ2I8QY4mOvs7du3fR09MjMTERY2Nj1Go1cXFxfPbZZ8Uen66uLr169WLKlCnEx8cTExPDvHnztOpUrlyZrKwsNmzYgIeHByqVilatWrF48eIivV+LE8IpeTk4f/48TZo0wcfHhy5duhATEwNosmgvWbKEOnXqYGJiQnJyMlevXqVbt25YW1vj4uLCjBkzFE/NRwm1p6enM3z4cCwtLalUqRJbtmx5HsOVSF4ZpLFIIpG8MBT0GngHiACuAeMAd0ATRvbll19ibGxMmzZtGDt2LL6+voDGoHDhwgWsrKyYOHGiVvu5NymbNm1i+fLldOnSBT09PYKCgvD390dfX59p06axbds2KleuTHR0NOvWrVNC1h5GYGAgqampuLi40KJFC7p06YK/v79S7uHhQWJionLTXadOHcqVK1dqXkXr16+nZs2aqNVqWrZsqWg0bdu2jSpVqmjVPXbsGGq1Wnk7/DRGmbweO2lpaaxfv56NGzdy+/ZtTExMmDx5MkCRD2JWVlbs2rULCwsLkpOTSU5OplWrVvz555+88847zJkzh7t377Jy5Ur8/PwK6Pw8iqLCHIVwAX5Go30TCSQCuRnqfn1oxqOiwixiYmJwcnLSqps/pNHMzEwr5C43C9qcOXMoX748V69eZcyYMY81xpcZmTFJUpLkP1/lDT3s06cPNjY26Orq8u6771KjRg1+//13QHN+bdSoESNGjMDIyAhjY2NlzhVHBw8e5iWXgcazsTugySYWEBDAZ599xpUrVyhfvjwtWrSgU6dOjzXWxYsXK5p6np6e+Pn5aZXr6OjQsmVLzMzMFKN227Ztta5b+SluCKfkxWfVqlWsXbuWgIAAbG1t6du3r1IWGBjInj17SExMRFdXl7Zt29K2bVvF23bTpk2sWbMG0LwgGTt2LJGRkVy/fh1jY2MGDx6stDVz5kzCwsI4d+4cp0+fVjKRSiSSUqLUpbZfMGQ2NMmzRs6V4hMSEvIg00xYnoxULgK2Pfh+WACid+/ehWZC279/vzA3Nxe///67SE9PFxMmTBC6urpizZo1QgjxyGwyjRo1UuoKIcSsWbNE586dn8nYn3SeFJY17tChQ8LU1FQcOnRIZGRkiAULFghra2sRHx8v0tPThaWlpThy5IjSxgcffCDef/99IYQQZ8+eFWq1Wuzfv19kZ2eL0NBQYW5uLv7+++9H9qWwjEFqtaWSwWb9+vWiTp06hW5bv359sX79eiGEZh5YWFholY8YMUKMHj1aa52vr6/4/PPPH+tYafqXP+uZi4CtAl4XurqGWv0HxLfffqu00atXL9G/f38hRMEMaEIIMWrUKKX8888/F126dNHqQ61atZRsQIWNU4iis6Dl8qqfU2TGpOLxqs+TR1HY+crKylqZP/Pnzxe1atUS5ubmwsLCQujq6oqFCxcKIYQYPny4GDJkSKHtDhs2TAwbNkxr3dChQ8Xw4cO11hV+vcv7+V0AIiQkpOQHnw85VyT5yX9vMXv2bGWeREZGCkD5u23bNmW7H374QTRo0ECrrW+++UZ4enoWup/Tp08LQ0NDkZ2dLYQQonLlymLz5s1K+dGjRwUgrl27VuJjlJQe8pzy/Cm1bGgSiUTyvHiU1wCMR6VSKeFb+TOhbdiwgT59+tCsWTMMDAyYNm2aIvIJPDKbzKBBg7Syqn3//fe89957pTrmp6GorHHTp0+nb9++tG7dGn19fUaPHk358uX55ZdfMDAwoHfv3ko2qMzMTDZv3ky/fv0AWLlyJQMGDMDT01N509y1a1d++OGHR/alMI+d+PjKijB5Xq+bBQsWULt2bSwsLFCr1Zw7d447d+4U2X54eDgrVqxArVYrn+3btyuu8MXh4eLIOsAisrPTGThwIBUqVCAkJARnZ2du375NTk4OISEh7Ny5s9j769WrF/v27WPPnj1kZWWxevVqLl++/MjtZBa0hyMzJkmelsLPVw5KIoWZM2cybdo01q5dy71794iPj6dOnTqKHouLiwtXrlwptO3i6OCB9JKTlF0Ku7eYN28+O3bsADQJQwwNDYmOjga0PWbDw8M5d+6c1rX6o48+IjY2FuCRQu0xMTEPFWKXSCQlizQWSSSSFwZdXV18fN5BiB1oXPDD0Lzw/RtNeNBvODg4aGVeyZt1LP9Nhr6+vpZ2yaNuUnx8fDhx4gTXrl0jLCyMO3fu0L1791If95PwsKxx+/btKxA6V6lSJeU49evXjx9++IH09HR27tyJmZkZLVu2BJ7MKFO0UKshucLko0d/rBjljhw58tAHscIExZ2cnBg1ahTx8fHKJzk5meXLlxf7mD1cHPm/9RYWFpiYmODu7s7q1atZs2YNFhYWrFy58rG0rKpXr87333/P8OHDsbKyIiwsDE9PT6200kWRmwWtLBsrnycyY5LkSSn6fOUM9Aa6snDhYnR1dbG2tiYnJ4fVq1dz7tx/Oll9+vTh+PHjrFixgvT0dO7fv69oqxVXB09XV5evv/4K+BmVqid5s6Fpln9+aPirRFIaFHVvcfduBSV08datW6Snp1OxYkVA+5rt5OREo0aNtK7ViYmJnD9/HuCRQu0ODg4PFWKXSCQlizQWSSSSF4agoCC+/PJL4HXgNBqvgQg0Bodf8fHxISkpqchMaPlvMjIzM/MYCB59k6JWq3nrrbf4/vvvCQgIoE+fPhgYGJTuoJ+ARxtnnNm2bXueLF/ax6lp06ZUqFCBn3/+mXXr1tG3b18l082TGGUe5bEjxHgiI69x4cIFAEXXoKgHMVtbW5KSkrh165aybujQoaxZs4aQkBCys7NJT08nLCyMixcvFvu4FS2OHA70VNZ3795d8Qxo27Ytly9fJikpiU2bNvHtt98q3meurq4IIbQ0hxYuXKjlnda7d2+uXr1KQkIC3377LeHh4cpbWHd3d+Lj4wvtq6urK7a2tnTu3LnY43vVkBmTJE9C0eerCcBShAjhzp2bNG/enLp16+Lg4MD58+dp0aKFUtPR0ZH9+/cTGBiIra0trq6uihBv1apVCQoKYurUqVhaWvL5558rOnj5kV5yz4758+fj7OyMmZkZrq6urFq1Ciha3w805+Hg4GBlOTg4GFdXV63ymTNn4ubmhrm5OR06dHgsb9eyxsPvLdLJyWlBeno648ePp3Xr1oVmoe3atSs3b95k2bJlpKWlkZ2dzaVLl5Tsk48Savfx8WHOnDnExMQQHx/P559/XrqDlkhedUo/Iu7FQmoWSZ41cq4Uj4J6MlkCQgRUFTBUQFfh6OgqqlWrJj799FORlpYm/vrrL+Hg4KBo3ezdu1eYm5uLo0ePivT0dDFp0iQtzaJ33nlH+Pj4iIyMDHHnzh3Rs2fPApozBw4cEC4uLsLCwkKcOXPmmY3/ceZJ0VoXufpOSwUgFi1aJDIzM8WiRYtEhQoVtMb5+eefizZt2ggjIyPxzz//KOtPnTolbGxsxIEDB0RWVpZIS0sTv//+u7hw4UKR/QkMDHzQn6Qi+pMoADFmzBjh4uIisrOzxeDBg4W5ubmwtrYWY8eOFa1btxYLFixQ2hw8eLCwsrISFhYWIjQ0VAih0aRq3ry5KF++vLCyshJt27YVp0+fLvYxzp1jGq2b7Hx9zRYqVTfh5FRJZGVlFbvNR/HTTz+JxMREkZaWJr788kthZmYm7ty589Bt0tPTRffu3cWkSZMKLZfnFElxkPOkcIo+X+V+NOerwMDAZ9anvPowISEhJXoOKg4v61zJPa5fffWVMDQ0FOfOnRNCCBEbGyvOnj37UH0/IYRwcXHR0uPZtm2bcHFxUZZdXFyEq6uruHjxokhJSRH9+vUTHh4ez3KIJcrD7y1mCJXqNVGuXDnRqFEjERkZKYQQAihwHb5y5Yrw8vIStra2wsLCQri5uYmNGzcKIYS4cOGCaNy4sTAxMRHVq1cXK1eu1LoPS01NFYMHDxbly5cXLi4u4ttvv5WaRS8gL+s55UWiuDYPaSzKhzQWSZ41cq4Uj6JvUn4S4CrAVABi7dq1ol27dkKtVotKlSqJL7/8UuTk5CjtfP3116JixYrC0tJSTJgwQdSvX18xFj3qJkUIIXJyckTlypVFw4YNn+n4H2eeFNc4Y29vL8zNzUXz5s3FiRMntNq4du2aUKlUolmzZgXaf1yjTFkSan0Uz1oc+YMPPhDly5cXpqamolGjRo88BgcPHhTGxsaiefPmRV6n5DlFUhzkPCmcF+l89awoyblSmPHgeVCYgLmlpbWWEfD9998vIEb+2muvKUkzimMsmjt3rrIcGxurCD+/iDzKkFqu3F0RHBz8TA2pkhcTef15/hTX5qH3LLyXJBKJ5GkpWk+m24NPEmCOnp4ee/bsKbKdDz/8kA8//FBZnjlzpvK9Zs2aHD9+XKv+kCFDtJZVKhUuLi5l2v1fO5yqaZ6S8Ad/wwBNOlt3d/dC23B1dS0yvbGnp2eRqZILI1eoNTp6FkIEox3aoRFqdXQsG0KtuWEfo0Z9RFRUc2W9o2MlFi4s+bCPJUuWsGTJkmLXb9OmDSkpKSXaB4lE8h8v0vnqWVOtWjUWLlxIz549n3dXnopc3R1NONVGNPcVC7l7dyG+vr7MnTuXgIAAoqKiClwj8+r7FYe8Ool5hZ8LC9Eq6xR9b5GLJpTczs7uWXVJIpGUMlKzSCKRvBAUrSeD1vq8gtWlQVhYGCdPnlSEHMsiZS2Lzosm1CrFkSWSV5cX7Xz1opBXS/B5UrTuziTgFtCZf/8Nx8/P75GZ60xNTbl//75SllcDMZe8Oon5hZ9fNB59bzEfgGbNmj3zvkkkktJBGoskEskLQVkwgHTs2JFOnTrx9ddfY2FhUWr7eVrK4sPOiybUKsWRJZJXlxftfPUs+OKLL4iIiMDHxwdTU1M6duyISqXSEuEfPXo0AwYMADRGFZVKxZo1a6hatWqhnjSXL1+mSpUqinflqVOn8PDwwNLSkqpVq/Ltt98CmkylRkZGXLt2Tdk2LS2N8uXLc+zYsccaR+EC5peAvUA6MJ6kpATS0tLo27cvGzZs4LfffiMrK4vFixcTFxenJBZwc3Nj48aNpKWl8e+//7J06dIC+1u5ciWXLl0iNTWVTz75pEjh5xeBR99b/KrUk0gkLwfSWCSRSF4IyoIB5NdffyU+Pv6FSFdeFh92pMeORCJ5UZDnKw3Z2dkcOXIEf39/bGxs2LBhA8nJyaxYsaJY2//000+cPHlSy9ADcPz4cTw9PZk9ezYjR44kNjaWdu3aMXz4cG7fvk1wcDBTp05l//79WFtb07VrV77//ntl+23btuHg4MAbb7zxWOMpPKQ9A5gM2AJdARg0aBBt2rRh8eLFDBo0CCsrKzZt2sSuXbuUDJczZswgPj4ea2trfH196devX4H9DRw4EB8fH2xtbYmOjmbDhg2P1d+yxsPuLdatW/eceyeRSEoaqVkkkUheGJ61nsyLjpeXFz169CA0NJQbN25gb29Pq1atnutbv1yPHYlEIinrPMvzlaura5nTAwoKCmLUqI+Ii7vJxo0buXXrFkOGDAM0XjXFYerUqYpxJZdff/2VRYsWsX79ekX/bt26dbRu3ZpevXoBUKdOHd577z0CAwNp27YtgwYN4oMPPmDq1KmoVCoCAgKe6MVN4bo7dYGjD76HAc1p2lRT1r9/f/r3719oW87OzoSGhmqt8/f311quXbs2EydOfOx+lmWKurfIyclh586dz7t7EomkBJHGIolE8kJRFg0gZRlpnJFIJBLJ45JXBLpcuQ3AbcCWuDhHvL29WbZsWbHacXZ2LrBu4cKFtG3bVitRQnh4ODt37tQyLGVnZyuh5R06dCAjI4NDhw5RrVo1Dh06xNq1ax97XK+agLlarcbDw4Nt27aVaLuF3VsUlRRDIilJDh48SM+ePbVCYJ+UYcOGYWFhwdy5c0uk3suINBZJJJIXDmkAkUgkEomkIJmZmejr6z9VG/lFoIVIQ6PpUw4YDwQwffpsAO7fv68YeG7cuEG5cuW02tLRKah4ERgYyMcff8z//vc/Fi9eDICTkxNvvfUWmzZtKrRPOjo6DBgwgICAAKpXr06HDh2wtbV97LHlhrR7e3ujUvVEiPFoQtLOoVLNRhPSvkW+gJJIniHPy7OyuOG0xa33MiI1iyQSiUQiKWXKSiYgiURSdjl//jxubm6Ym5vToUMHYmJiFKHoxxWS3rJlC1WrVsXCwoLBgwfTtWtXpk2bprRRlJg0aPR6NCLQekAFsrJyw6hsgGsIMZ6YmAhsbW35/vvvycnJISQkpNghSJaWluzfv5+wsDCGDx+OEAI/Pz8OHDjA1q1byczMJDMzkzNnznDixAllu4EDBxIUFMR3333HwIEDH/v45vKsNP3Cw8Mf6+FXT0+Pdu3aYWxsjEqlokKFCpw6dQqAJk2aoKenh0qlwtDQkLFjxyrbXb16FQcHB3R0dFCpVBgbG/Pbb7/x+uuvk5CQQHBwMCqVCmtrawB69OiBgYEBKpUKfX193nnnnRIZr0QiefmQxiKJRCKRvLLMnz+fatWqYWZmppWVJ/cBbN26dVStWhW1Ws2AAQO0jD4Pexg7ePAgarWa5cuX4+zsTPPmzXnrrbe0HtZA49o8fPjwZzVciURSAqhUKs6cOVNoWUREBKampiQkJDyynezsbA4ePKhk1Fq1ahWBgYHExsZiZ2dH3759i92nvELSly9fxs/PjyVLlhAXF0eTJk3YvXu3UvdhYtIASUlJD2q2B26hpzcNAH39T4AlQCdAY1Ras2YNFhYWrFy5knfffbfY/S1fvjz79u3j1KlTDBkyBAcHB3bv3s3KlSuxt7fH1taWDz74gMTERGWbypUr8/rrr5OUlESXLl2Kva/CKKsC5gcPHmTLli3cvn0bCwsL2rZtC8Drr7/O2bNnSU9PZ+jQoSxYsIDDhw8D4OfnR3Z2Nrdu3SI9PZ01a9ZgZ2fHyZMnsbCwoGfPngghuH37NgCvvfYaoaGhZGdnM3fuXLZs2cLy5cuf25glrzbvvPOOVqbFYcOGcevWLfr06YO9vT0ODg6MHj2a9PT0QrfPzMxkypQpVKlSBSsrK7p3705MTAwAo0aNKmBYnjt3Lh06dABgwIABjB49GoD09HQGDhxIhQoVsLCwoE6dOoqxOm89gJMnT9KiRQvUajW1atVi48aNStm0adPo1q0bI0eORK1W4+zszObNm0vqcD17hESLhIQEAYiEhISnaicjI0MEBweLjIyMEuqZ5GVFzhVJcZDzpHTYsmWLiIiIEDk5OeLAgQPCyMhIHDlyRFy7dk0AwsfHRyQmJoro6Gjh6Ogo1qxZI4QQ4tKlS8LIyEjs2rVLZGZmim+++Ubo6emJqVOnCiGECAkJETo6OmLo0KEiJSVFpKSkiB07dohKlSqJnJwcIYQQqampwsLCQhw/frxExyTniqQ4yHny5ADi9OnTT9XG1q1bhaOjqwCUj4VFebF161YhhBCxsbECEKGhoQIQ9+7dU7YdNWqU6N+/vxBCKOeqvP35/PPPRZcuXbT2V6tWLeX89MUXX4iePXtqlU+YMEEMHDhQCCFE//79H/QpTIAQ5cpp5kq5chkChIDfBSBCQkKe6hg8Ce+995746KOPnvl+S4v09HSxYMECMXLkSKGjoyM6dOiglP31118CKPQaYWRkJIYPHy6EEKJ169bC1NRUbN68uUA9CwuLAr91fuzs7MSbb775lCOR5xRJ8ck/V1xcXMS2bduEEELk5OSIN954Q4wdO1akpKSIO3fuCHd3dzFp0iQhhOb+ysLCQmlr3LhxwtPTU8TExIj09HTx0UcfiVatWgkhhDhx4oQwNzcX9+/fV+rXrl1brF+/XgihOdeNGjVKCCHEypUrhZubm7h3757IyckRly5dEhEREQXq3bt3T1hZWYlFixaJjIwMcfDgQWFiYiKOHDkihBBi6tSpQl9fX2zevFlkZWWJ77//XpiamorExMRSOZZPSnFtHtKzSCKRSCSvDHnf5OeKJDo5OaFSqfDw8KBDhw4cPHhQqT9lyhTMzMxwcHCgY8eO/PHHHwBs3ryZtm3b0rFjR/T09Bg8eDCvvfaa1r5ycnKYM2cOxsbGGBsb06lTJ9LT0zl06BCgSf3s6OhI48aNn9n4JRLJ8ydXPDoqqi6a7FtJgB0JCZXw9vYmKCgIW1tbDA0Ni60/lFdIOiYmBicnpyLL84pJ534WLVqkpJV3dnbGyKgcKtUsIL9osUYE2snp2YtAX716lS1btjB06NBnut/Swt/fH2Njc8aMGcOSJUvIyclh9+69Ska1OnXqAPDnn3/Ss2dPjIyMUKlUqFQq0tLSiI2NBTTXo1q1atG3b190dXVp0KABd+/eLXK/I0aMwNjYWAlbi42NJS4urvQHLJHk48iRI4pnZa5A+smTJ/nnn3+YN28exsbGWFlZMWHCBAIDAwtsL4Rg2bJlzJ8/H3t7ewwMDJgxYwa//fYbkZGRvP766zg6OrJ9+3YATp8+TUREBG+99VaBtvT19UlKSuLixYsIIXjttdcKnEcBfvnlF6ytrfnf//6Hvr4+bdq0wdfXl++//16p4+bmRq9evdDV1cXPz4+MjAwuX75cUoftmSKNRRKJRCJ5JQgKCsLVtSoeHh74+vri4eGBjY0dlStXxtLSErVazc6dO7lz546yjZ2dnfLdxMRECc941MMYgJmZmVZmH11dXfr160dAQAAAAQEBT6W7IZFIisf8+fNxdnbGzMwMV1dXVq1axbRp0wroyajVasVYfOrUKZo2bYq5uTkVKlSgW7duWnWPHj1KnTp1MDc3p3v37krYWWEaQ3nJLx6tSd9uChgCvYCujB79MTdu3CA9PZ2KFSsCGiHpXHKNOnnJKyTt4OBAZGSkVnlERITyPVdMOj4+XvkkJSUpmkM6OjrUrVsH+BmVqidw/MGWxx8s/8zChV8+UxHooUOH0qBBAz755BOqVav2zPZbWvj7+zNv3jyys9vxn8FQB7Bi3rx5+Pv7c/78eUATHrN9+3ZWrVpFZmYmQgiMjIwQQgCa69SxY8fIyMggJCSEv//+m/79+wOakMm8hIWFsXz5cqZNm8b9+/cRQmBnZ6e0JZE8C3bs2AFAly5d8PX15ebNmwwZMoygoCDCw8OJj49X7svUajXe3t7cvHmzQDt37twhJSWF1q1bK3Xt7OwwMDBQzoF+fn5K5sS1a9fy9ttvY2xsXKAtPz8/BgwYwLBhw6hQoQIDBgzQuh/MJSoqCldXV611lStXJioqSlnOe++oUqkoV65cnvDeFwtpLJJIJBLJS0/hb/K3cfduHNeuXWPlypXEx8fTuXPnYt00P+phDArPAjRw4EC2bt3KpUuXOHTo0GNpkkgkkuKR14Nw3bp1TJo0iT179pCUlMSxY8do0qTJI9sYOXIk3bp1Iz4+nujoaMaNG6dV/sMPP3DgwAEiIiKIiopiwYIFxepbaGjoA/HoCRS8Df8GIXyJjLzGwIEDad26NY6Ojjg7Oz+WkHSvXr3Yt28fe/bsISsri9WrV2u91S6OmLSDg0MeEeh2D9a2K3ER6OKycuVKkpKSmDhx4qMrl3EyMjKYP38R0BXYzn8GQxVwD2jCl19+TY8ePbCwsOD27duoVCqqVKlCVlYW7733HmlpaUp7U6ZMYffu3WRlZWFvb4+Ojo7ikWZubs7Vq1eVurm6RZUrV0ZPT4/PPvtM8VCSSJ4FQUFB+Pn5PVjah+Z+zIG4OBe8vb25evUqNjY2WsbshIQEkpOTtdpZuHAh77zzDsbGxhw7doysrCxCQ0OJj48nNTWV5s2bA9CnTx/2799PdHQ0GzduzLNvbfT09JgwYQJnz57l4sWLRERE8NlnnxWo5+joSHh4uNa68PBwJbnAy4Y0FkkkEonkpaboN/mvobk5b8PYsf7s2LGDPXv2FKvNRz2MFUW1atVwc3Ojd+/edOrUCRsbmycel0QiKUh+D8J+/fqRmprGd999R2pqKra2ttSrV++R7ejr63P9+nViYmIwNDSkdevWWuX+/v7Y2NigVqt5++23lRDVR/GfV1CdQkoHAnMAiI6OZsOGDQCsXr36sYSkq1evzvfff8/w4cOxsrIiLCwMT09PDA0NAahYseIjxaThPxHoX375BdCEX5QFEegXnWXLlpGdnQ5MpOCjmDtwFiEyuHHjBnv37mXixIlUr16d5s2bY2xszF9//YWFhYWyxV9//UXXrl3R19enevXqVK5cWfGkmDJlCpcuXUKlUmFra0v37t1p1aoVvXr1wsDAgLVr1xYaaiORlAb/3Y91fLCmMZr7MWegN9CVpUtX4ujoyKRJk0hKSkIIwfXr19m1a1ehbQ4bNoyPPvqIixcvUrduXeLi4rQEpZ2cnGjZsiWDBg3CwMAADw+PQts5cOAAZ86cISsrCxMTE4yMjNDT0ytQr3Pnzty6dYtly5YpBqoNGzbQr1+/pzo2ZRVpLJJIJBLJS03Rb/JroblZP0NUVDhLliyhe/fuxWrzUQ9jD2PQoEGcPXuW99577/EHI5FIiqRwD8IwwI0vv/wSKysr2rdvX2Qms7ysXr2atLQ0GjVqRI0aNZRMibkUFaL6KOzt7R98O5evJBzN+UiTlWrRokXKm+q2bdty+fJlkpKS2LRpE99++60Szurq6ooQQivkFaB3795cvXqVhIQEvv32W8LDw7VCZRs2bMiePXu4c+cOd+/e5bffflMyb02bNo3g4GBAEz7bsmVLAFq2bPlMQ89eVv7z9CnMYPg6oPH+GThwII0bN0ZPT0/RUcnJyeHkyZPEx8ezbds2QKN/lxuelpOTw7lz5zA1NQU015v09HSEEEoYz+HDh8nJySEnJ4erV68SERHB6dOnS3nUEkne+7GP8pWMB5YgRAhRUeFMmDCB6OhoatasiYWFBV26dOHKlSuFtjl79myaNWuGp6cnZmZmNGrUqMCLv379+rF792769u1bIDQzl5s3b+Lj44NaraZSpUpYWFgwderUAvXKly/Prl27WL9+PVZWVgwZMoTly5cr58mXjYLmMolEIpFIXiIe/ib/c2AcYM6AAQPw8fFRSvKHoy1cuFBruXfv3vTu3VtZrl69uvIw5u7uXqRmiaurK7a2tnTu3PnxBiKRSIqkoAdhrmG4KblaO5aWf1K3bl38/Pzo37+/lg5QSkqKlmdNlSpVWLt2LUIIfvvtN958802aNWtGo0aNnqqfrVq1wtHRlejoWQiRt5+QKx7t6Pj04tE7duzA3d0dAwMDlixZwo0bN+jYseOjN5SUOlWqVHnw7Rya+Zmfc/nqSSQvB//dj9Vi8ODXyMz8EPgZOIvmhd5G4CKDBw9m+PDhREZGKsad8+fP07RpU86fP8/rr79OTEwMAAYGBkyaNInJkydz+vRpGjRogBCCr776imXLlnHv3j2aNGnC1atXqVy5slZ/co3uAD4+Plr3gEXVA2jSpAm///57oXWnTZtWYF1R94MvAtKzSCKRSCQvNUW/yUdr/X/1iseOHTtISkoiPT2dr776qlgPYxkZGXz11VcMHjy4UPdmiUTyZBTtQXgJ2I8QY4mOvs7du3fR09PDzc2NsLAw/v77b9LS0pgwYYLWG+e1a9dy8+ZNVCoVarUaHR2dEvGq0dXV5euvv+I/8ej/PKBKUjx69+7duLi4UKFCBTZu3MhPP/2ElZXV03ZfUgKMGDECXV1DYCaFZZuDWejqGjFixIhn3zmJpBT5L/Re46mZnb0W+B5IBuoBGk+eOXPm8M033ygZ0LKysujevTtt27YlLi6OWbNmsWrVqiL3s27dOubPn09wcDAxMTHUrl2bbt26kZWVVWpje1mRxiKJRCKRvNTkvskv6TTQj/swdujQIcqXL8+dO3cKiOVKJJKno2gPwgxgMqAJMT169CgBAQF4enoydOhQmjdvTtWqValbty5mZmbKVvv27aN+/fqYmprSo0cP5s2bR4MGDUqkr15eXnnEo5sD5kDzEhWPXrJkCXfv3iUpKYmTJ0/i7u7+1G1KSgYDAwPGjs31qOjBfwbDUOAC8DNjx/4PAwOD59hLiaRkCQoKYsCA9x8szQJATy8eOA/oAt1QqTbg5FSJ9957Dx8fHyU7ZVhYGHfu3GHatGkYGBjQrFkzLc/u/Kxbt44PP/yQunXrYmRkxKxZs4iMjOT48eNFbiMpHPlaUyKRSCQvNblv8r29vVGpeiLEeDQPlOdQqWajeZO/5bHf5C9ZsqSAjsnDaNOmDSkpKY+1D4lEUjy0PQjzhvbUBY6ieSBvzvLly6lfvz4AX375JV9++aVS8/3331e+5woEF0b+ENXRo0czevRo4D8NoUfh5eVFjx49CA0N5caNG9jb29OqVSupCfSK8MUXXwAwf/4isrN/Vtbr6hoxduw4pVwieRnI1ZPThAlvBGoAr6FSVQK80XjZbUeIY8THm2FpaUl6ejqdOnUCICYmBgcHByXLH4CLiwsXL14sdH/509sbGhri4OCgld5eUjxeKs8iV1dXVCqV1mfOnDnPu1sSiUQiec48izf5Eonk+VFaHoSlia6uLu7u7vj4+ODu7i4NRa8YX3zxBffvJ7JgwQJGjhzJggULuH8/QRqKJC8VhWekNXlQ+jHQGZiMSnWckSNHcvv2bRISEhg2bJhieHdwcCAmJobMzEyl3YiIiCL3mT+9fUZGBjExMS9tevvS5KXzLPr8888ZPHiwspzXpVgikUgkry7yTb5E8vJSWh6EEklpYmBgoHilSSQvI//pyW2koJ+KCk0WyF9QqXTo0KEDhoaGHDt2jMDAQJo3bw5A06ZNsbS0ZPr06UyaNInTp0+zefNm6tatW+g++/bty6RJk+jWrRtVqlRh8uTJVKxYkSZNmpTeQF9SXirPItAYh+zs7JSPiYnJozeSSCSSFwhXV1clrbHk8ZBv8iWSlxfpQfjyoKenx/jx44ssV6lUbN269ZHtuLu7P3byAolEUnI8PCPtf+sHDBjAkCFDMDc3Z+bMmVqaRPr6+vz000/s3r0bS0tLPv30UwYOHFjkPvv168f//vc/unbtip2dHWfPnmXHjh0yscgT8NIdsTlz5jB9+nScnZ3x9fVlzJgxD50Y6enppKenK8u5aVMzMzO1XN0el9xtn6YNyauBnCuS4pB/nmRlZck5IylAfHw8NjY2rFy5Us4PyUN5Wa893bp1o3PnzoSFhREbG4udnR3NmjVDV1f3pRvrs+J5zZXs7Owi95mRkQE8uk85OTkIIeRv/wx4Wc8pkqfDzs6OcuXKAX8CjQEoVy6Tb7/9loED25GaehooR9++fVmxYkWB7XPnU40aNThy5EiR5fnPCWPGjGHMmDGF1pUU/1i8VMaiDz/8EDc3NywtLfn9998ZP348N27cYP78+UVuM3v2bD777LMC6/fs2YOxsfFT92nv3r1P3Ybk1UDOFUlx2Lt3L/fv3+ePP/7QEvqTSACSk5OV7/KcIikOL/M8MTY2JjExkd27dz/vrrwUPMu5IoTg6tWr7Ny586nauXv3Lunp6U/djqT4vMznFMmTsXHjRuA2oP1/uHp17lzZSGJiovw/fYbcv3+/WPVUojgpG54jn376KXPnzn1onYsXL1KjRo0C61evXs3QoUNJTk7G0NCw0G0L8yxycnLizp07mJubP3G/MzMz2bt3L+3atZMPdJKHIueKpDjknSe1atVi6NChBAcHc+HCBRo2bEhAQABOTk58+umnbNmyhbt37+Lo6MiUKVPw9vYGNKnbvb29mTZtGnPnzkUIwfvvv8+UKVNQqVSsXbuWRYsW0bFjR1atWoWJiQnjxo1j2LBh3L59m8qVK/Pnn39SqVIlANLS0nB2dubnn3+WceBlhLyeRX379pXnFEmRyGuPpLiU5lyJjo6mW7duXLx4kezsbIyNjdm9ezfu7u60a9eO0NBQUlJSMDc355dffuGNN94ANFo/69evp1evXgCMGjWKNWvWkJaWhq6uLv369WPlypW0bduWy5cvExkZCWiyUp4+fZrDhw/z2muv0aVLF44dO0Z2djbW1tbs2LGDhg0bKvvo3bs3wcHBpKenY2NjQ1hYGE5OTiV6DF4W5DlFUhQ7duzAz88P6IgQYylX7jVWr/6dQYM2kpq6g3Xr1tGtW7fn3c1XisTERCpUqEBCQsJDbR5l3rPoo48+YsCAAQ+tU7ly5ULXv/HGG2RlZREeHk716tULrWNoaFioIUlfX79ETnQl1Y7k5UfOFcnDyM7OBmD79u2kpaWxceNGtm/fjr29PV5eXnz++ecEBATg5ubGJ598gpWVFT/++CMDBgygadOmVKpUCT09PZKSkjh79ixXr14lIiKCdu3aUa1aNfr374+uri7nz5+na9eu3Lhxgz/++IMOHTpQv359WrduTdeuXQkMDGTatGkAbNmyBQcHB1q0aPEcj8zLw/z581m4cCH37t3DysqKSZMm0b59ewYNGsSZM2fIysqiefPmLF26VEkJm56ezujRo9m8eTMWFhZMnDhRaU+eUyTFQc4TSXEpibmSkZHBsmXLuHr1KlWqVOGLL74gIyOD48ePU69ePX788Uesra0BOHjwILt376ZevXq89tprDBgwgCtXriht6enpoa+vz+TJk1mxYgXz5s1j1KhRXLp0iT///BN9fX10dHRQqVRkZ2dTr149bt26xaVLl3B2dqZJkyZcuXKFEydOULVqVcVAlZCQoOxj9+7d/PHHH5iamlKzZk0GDhzIwYMHn+oYvOzIc4okP7l6caNGfURUVEugHLARS8uzzJmzTurJPQeK/T8qXmLWr18vdHR0xN27d4u9TUJCggBEQkLCU+07IyNDBAcHi4yMjKdqR/LyI+eK5FFs3bpVVK1aXQQHB4ty5coJQKjVlmLr1q1CCM25rk6dOoVuW79+fbF+/XohhBAhISECEDdv3lTK58yZI9q2bSuEEGLNmjXC3Nxcay4OGzZMDBo0SAghxM6dO0WlSpVETk6OEEKI9u3bi3nz5pX8gF8RsrKyREhIiAgMDBRr164V5cqVExcvXhRCCBEbGyvOnj0rrl27Jnbu3ClSU1NFQkKC8Pb2Fm+++abSxuTJk0X9+vVFdHS0uHfvnujUqZMAxMqVK+U5RfJQ5LVHUlxKaq6MGzdO6OoaCkDr4+vrW6Curq6u8PHxUZaHDx8uDA0NlWVAbNq0SQghRIUKFYSHh0eh+2zTpo2wtLQUNjY2omLFisr9fXZ2tgDE5s2blbr37t0TgDh69Kiyj+nTpyvlb775prCxsXmKI/ByI88pkkeR974nODhYpKamPu8uvbIU1+bx0mRDCwsLY+HChZw9e5Z///2XDRs2MGbMGPr27Uv58uWfd/ckEonkiQgKCsLb25vo6NoP1sQAdsTHV8bb25ugoCBMTExISkoCYMGCBdSuXRsLCwvUajXnzp3jzp07SntGRkbY2NgQGhqKo6MjLi4uREdHK+UODg5abxvylnfo0IGMjAwOHTpEdHQ0hw4deuBWrGH06NGP9ASVaAgKCsLVtSoeHh74+vrSr18/UlPT+O6770hNTcXW1pZ69erh6upKp06dMDIywtzcnIkTJxIaGkpOTg4AGzZsYMKECTg4OKBWq5k6depzHplEIpEUxN/fn3nz5pGd3Q4IA5KACQAEBgbi7+9fYBsXFxflu4WFBVlZWYW2nZiYSO3atQstA7h37x63bt1i8eLFSrjFpUuXAOjduzcqlQqVSqU8L5w5c0bZtlq1asp3U1NTLekKiUTyeORmpM2VR5AZacs+L42xyNDQkE2bNtGmTRtq167NzJkzGTNmDN98883z7ppEIpE8EdnZ2Ywa9RFCdAUCH6w1AQyB8UBXRo/+WDEcHDlyhGnTprF27Vru3btHfHw8derUQeSRpktLS+PWrVu0atWKqKgoIiIiqFixIu7u7uzZs4eYmBitDAm55QA6OjoMGDCAgIAA1q5dS4cOHbC1tX0mx+JlItcAGBVVl/8emsIAN7788kusrKxo3749Z86c4fbt2/j6+uLk5IS5uTmtW7cmPT1dMQ7GxMRoPVDl/S6RSCRlgYyMDObPXwR0BbYDTQFTwOdBjRbMn79YyWb0uJibm3PhwoUiy21tbenfvz/e3t5s374d+M8I9MsvvyCE0PoMHTr0ifohkUgkLxsvjbHIzc2No0ePEh8fT2pqKhcuXGD8+PFFCltLJBJJWSc0NJSoqHA0b1/zn651EGI8kZHXlJvkxMREdHV1sba2Jicnh9WrV3Pu3DntrXR0GD9+PKmpqVy6dImlS5fSp08fpTwlJYXp06eTkZHBsWPH2LBhg1b5wIEDCQoK4rvvvmPgwIGlM/CXGG0DYDD/PTQ1BY6jUnXD0tKGunXr4ufnx/jx47l//z6nTp0iMTGRw4cPAygGQAcHB65fv660HxER8YxHJJFIJBpUKpWWV04uy5YtIzs7HZiI9rWsDmAHnCc7O41FixaxceNG/vnnn8fa7/vvv09ISAhff/01WVlZnD9//kH2pf8ICAjgvffew8vLiy1btqCnp0ejRo0YMGAAx44dA+Cff/5h1KhRj7VviUQieZl5aYxFEolE8rJx48aNB9/qIEQy33zzDWlpVYBI4GtAk5FlxYoVCCHo2LEjbm5uuLq6Ymdnx/nz56lXrx5Lly7F0tKSnj17oqenR4MGDahYsSI1a9akX79+/O2LOl0AAJw9SURBVPXXX4SGhvLjjz8ihGDDhg2KcHb9+vXp378/NjY29OvXDysrK15//XWSkpIwNzenbt26mJqa4uXlpXi7SIqmaAPgJWA/QowlOvo6d+/eRU9Pj8TERIyNjVGr1cTFxfHZZ59ptefj48OcOXOIiYkhPj6ezz///NkNRiKRvLAEBATQoEGDZ7Kvq1evPvhWp5DSY4DGQ3XcuHEMHjyY+Pj4x2p/9uzZvP/++3zyySfo6+tTv359QkJCCtRbtWoVgwcPplevXmzevJmDBw9Sr149WrVqhUqlombNmuzateux9i2RSCQvM9JYJJFIJGWQ7Oxsbt68+WBpLZmZ75OUlISh4R9AIpq3se8DYGJiwqBBg4iMjOTUqVP8+uuv3Llzh7Fjx3L16lXGjBlDTEwMmzZtQl9fn//9738EBQVhbm7O9OnTmT9/Pq1ateKdd96hbt26XL16lbi4OFq0aIGjoyN//vkn165dIzMzk5EjR+Lq6so777zDW2+9xciRI4mPj+e9995j/fr1z+lovTjkNQBqkwFMBroDcPToUQICAvjss8+4cuUK5cuXp0WLFnTq1Elrq0mTJvH6669Tp04dGjRoQM+ePUt5BBKJRPJ4VKlS5cG3c4WUOgNrAI3mXnJyMo0bNyYrK4vZs2crtWbPnq2lWSSEoHfv3sqy5mVKGkIIsrKyFBmKgwcP5jnval6u5OTk0Lt3b0xNTdm3bx8ZGRnKdpcvXy5yH9u2bXtsQ5ZEIpG8yEhjkUQikZQxcsWPx4wZ82DNB2RnBzF06FBUKjUa3aJpwB4cHV3Zvn07CxYsoHPnzgwaNIj27dsDsH79eho1asSIESMwMjLCyMgIPT29YvXh9u3bbN26laVLl6JWqzExMeHzzz9n8+bN/Pjjj7i4uODg4MDQoUPR09OjW7dueHp6lvzBeEJcXV0JDg5+Lvs+c+YMKpWq0DJ7e/sH3/I/NNUFjgK7AVi+fDn169enZs2aHD9+nOTkZP7++2+GDBmCEAK1Wg1oBMu/+eYb7t69S3h4OO+//z4ZGRlSS0oikSjMnz8fZ2dnzMzMcHV1Zd68eQwbNoy//voLU1NTTE1NlRDWTZs2Ua9ePdRqNY0bN+b3339X2tmwYQN16tTBzMwMZ2dnJk+erKWJl5fLly9TpUoVlixZwogRI9DVNQRmAjn5auYAs9DVNWLEiBGlMn5J6dKpUyeWLVv2vLshkUhKgeI9NUgkEonkmZArfqzRtNmIxgPlB2AQQ4YMITV1CBo7fxogmDJlAjVq1MDd3Z2ff/6ZAwcOKG1dv35dK5PL4xAeHk5OTg6VKlVS1qWmppKZmcmYMWPIyckpIKbs4uJCWlraE+3vVaFVq1Y4OroSHT0LIYLRfmeTg0o1G0fHSrRq1eo59VAikZQkpqamhIWFUbdu3We2z+zsbEJDQ7lx4wZZWVlMmjSJU6dOUaNGDW7evMnNmzextrZm4cKFWhpDO3fu5OOPP+ann36iQYMGBAcH061bN86fPw+AlZUVQUFBVKtWjbNnz9KhQwdq1KihpWsHcPz4cby8vJg/fz69evUCYOzYD5k3bx7QA00Ybh00RvNZwM+MHTsOAwODZ3B0JCVNbuhe3uQYEonk5UB6FkkkEkkZoWjx486ADmvWrKFcOV0gGScnJ7Zu3crgwYPZsmULR48epUuXLnzwwQdKey4uLly5ckVZdnd3L9KFXkdHhyZNmigPDk5OTujo6ChaOPHx8aSnpyOEYO7cuQWEleHlFFcu6ZtfXV1dvv76K+BnVKqe5M2Gpln+mYULv5TpZCWSl4Tk5ORnaijK9Uz18PDA19eXfv36kZqaxnfffUdqaiq2trbUq1ev0G2XLl3KuHHjcHNzQ0dHBy8vL2rUqKEYAzp27Mhrr72GSqWiQYMG+Pj4cPDgQa02fv31V3r27MnatWsVQxHAF198wbhx49DV3Qs0B8yB5ujq7mPcuHF88cUXpXNAJKWKEILs7Ozn3Q2JRFJKSGORRCKRlBGKFj+2Q0enO9988w1CZLJgwQLCwkJRqVREREQwdOhQvv/+e9auXcvp06cVrYY+ffpw/PhxVqxYQXp6Ovfv3yc0NLTQfdva2uYRIQU7Ozt69uzJyJEjuXPnDgCxsbFs27YNgC5duhAdHc23335LVlYWv/zyi5ZXU1ng/PnzuLm5oVKpaN68OTExMQBcuXKFDh06YGlpSZUqVVi4cKGyTa7o69SpU7Gzs+Pdd98FtEMzqlatmiecDOLj4+nVqxdqtZoaNWooGcuKIjcbT8WKf5H3ocnR8RxbtmzBy8urhI+E5EUjIiICU1NTEhISnndXJC8QuZ6pUVF1yWuIBje+/PJLrKysaN++faEZy0DjUTphwgTUarXyOXPmjHLu3LNnD82bN6dChQpYWFiwYsUK5fqQy8KFC/Hw8Cg0LPmLL77g/v1EFixYwMiRI1mwYAH37ydIQ9ELhqurK7Nnz6Zp06YYGxtjY2OjdR09deoUnp6eWFpaYm1tzf/+9z+tMg8PDywtLalatSrffvvtcxiBRCIpLtJYJJFIJGWEosWPwcDgO0xMTEhPT2fChAm4u7tz/Phx+vTpw3vvvUf79u0xNzdn48aN+Pv7c/HiRRwdHdm/fz+BgYHY2tri6urKli1bCt336NGj2bdvH2q1mq5duwIaw0muboW5uTmtWrXijz/+AMDS0pLt27fz9ddfo1arWbVqVYFQhGdNdnY2Bw8exNramvj4eFatWkVgYCApKSlUq1aNvn37kpWVRdeuXalfvz4xMTFs27aNL774gsDAQKWdc+fOoaenR0REBOvWrVNCMwICArh79y5vv/02t27dIi4uDoAPP/yQ+Ph4wsPDOXDgAGvXrn1kX728vAgPv0JISAiBgYGEhIRw7do/0lD0kvC0mlnOzs4kJydjYWFRcp2SlBqurq7MnDkTNzc3zM3N6dChg2JgyZtOvkKFCopGkKmpKTo6OgQEBDB//nyt9UZGRri6ugJw+vRpWrZsqTx4+/j4KOeevBTtmdoUOI5K1Q1LSxvq1q2Ln58fOjoFHwGcnJz46quvFG/S+Ph4UlJS8Pf3JzMzk169ejF06FCio6NJSEhg2LBhBTSLAgMDuXjxopaBIC8GBgaMHj2axYsXM3r0aBl69oKQe33duHEjaWlpBAQE8P3335OcnEz16tWVenFxcbRv3x5vb29iYmK4fv264mEWGxtLu3btGD58OLdv3yY4OJipU6eyf//+5zUsiUTyKIREi4SEBAGIhISEp2onIyNDBAcHi4yMjBLqmeRlRc4VSS4hISECEBAmQGh9ypXTzJNy5cqJkJCQ593VMsfWrVuFo6Prg+On+VhYlBdbt24VQggRGxsrAHHw4EFhbm4u0tPThRCa/7+ZM2eKdu3aCSGEWLNmjbC0tBTZ2dlK2507dxYLFy5Ulrdt2yYMDQ3F2rVrRVZWljAwMBDHjh1Tyjdt2iSe5+VVnlOePy4uLmLbtm2FlmVmZoqcnJxn26FCkPPk6cjKyhIhISEiMDBQ2NraCldXV3Hx4kWRkpIi+vXrJzw8PIQQQgDi9OnTBbZft26dcHBwEJGRkVrrk5KSRIMGDcTkyZOFEEKcOXNGhIaGioyMDBEbGytatWol3n///QLtFX39+FvAHgGa8gEDBogGDRqIX3/9VdjY2Ij79+8rbfz000+icuXK4uTJkyInJ0ekpKSIvXv3in///Vds3LhR6OjoiB07dgghhDh69KiwtrYWPXr0ULbPHevdu3dFo0aNxLBhw8rEXJc8HY+6vrZp00YsWLBAZGRkiH79+gl3d/dC2/niiy9Ez549tdZNmDBBDBw4sNTHIClbyOvP86e4Ng/pWSSR/J+9e4/Puf7/OP64dm3GjhfDzgcih4j0Jadpo3I+fGeEyCIlKb4UOZRDoZCfksK3spLh+51FOXxTbDWlAzlTKDObOduJbbZr1++P2YcxLGEOz/vttpvr+rw/n/f1/nx8zHW9rtf7/RK5RRQufmwyTab4ijHg5xeoxY8vUnTqRShQUIksLS2Nrl27YjKZSElJwdHRkddff/3cN/BDqFChAq+88gpVqlRh27Zt1KxZk0GDBpGTk8Pvv/9u9L93716GDRuGyWTCbDbTo0cPzp49S3JyMsePH+fs2bNFFvu+eOFvubt069aNxMREevbsiYuLCwMHDsRkMvHee+9Rp04dnJ2dyczMZMaMGVSvXh1XV1ejalShhIQETCaTscZYREQEAwYMoEePHri6ulKjRo1L1oqRm+fidYGOHDnCqVNp7Nq1CycnJ6ZOnUpsbCxJSUnFHr9+/XpefPFFvvjiC/z8/Izt+fn59OzZk5o1azJhwgQA6tWrR/PmzXFwcMDT05Nhw4YV+3d/+czUs8CrQCcAfvzxRyIjI2nZsiWNGzfG19cXi8VCYmIiHTt25M0332TAgAGUL1+eKlWq8M4775Cfn0+5cuV49913eeaZZ3Bzc2PSpElFyspfqHz58nzzzTf8+uuvRgVHuT0VP7XRi7S0ewgPDycmJqbI/kePHqVatWrF9pWQkMCqVauKTHN89913L7h3ReSWc3NiV7cPZRbJzaZ7RS60dOlSm8lksplMHW3wgw3SbfCDzckpzLZs2TLjmzwpkJeXd+4bzw42WGuDKBt42qCSDd48dx2xrVmzxgbY+vbtawNsH374oS03N9d2+vRpW6dOnWwuLi62PXv22D788EObj4+P7Z577jGyj8qXL2976KGHbKdPn7bt3r3bFhQUZAsMDDRe38HBoUhm0ZIlS5RZdJe5MMskNjb2kswiwNakSRNbcnKyLTs722a1Wm3R0dG2xMREW35+vm3dunW2smXL2tavX2+z2Wy2/fv32wDbqVOnbDabzda3b1+bq6urLTY21paXl2d7/fXXjXvwWuk+uTaFv6Oh47ksngwbeNmggc1kMhm/ox0dHW0//vjjJZlF+/bts1WuXNkWExNzSd9DhgyxNWnSxJaVlWVs27t3r61Tp042b29vm6urq83Z2dlmsVguOfZKmakFPz/YgGvKTNW9cnc6//9rRxtYL7iXAm2w1GYydbT5+1extWjRokhmUWFW3cWmTJlie/zxx2/yWcitSL9TSp8yi0REbkOXW/zY13cXAB07dizN4d1yzi8K/ivQCugFHAFOAP+HzdYLgPHjx9OiRQv8/f0pU6YM+/btw2q18ueff7Jq1SoGDBhA9erVMZvNVKpUiaysLH766ScOHjzIqVOnOHz4MLt376ZGjRr069eP7OxskpKSMJvNdO/enddee43U1FQOHTp0rjy03C0uzjIJDQ0lKSmJDRs2FNlvxIgR+Pj44OjoiJ2dHV27dsXf3x+TyURoaCitW7e+YrZQu3btCAkJwWw289RTT3HgwIFi166RG+fy6wI5At2BDgwd+hIpKSnk5OTg6+tb5PhTp07Rvn17XnrpJf75z38Wafvggw9Yvnw5y5Yto2zZssb2gQMH4uvry65du0hPT+ezzz4rNlPnapmpJtMU/P2rKDNVSuzyRTcA7LDZRnHw4P4iC/G3aNGCX375pdjCGn369GHdunUsXbqU3NxccnNz2bJlC7/88svNOiUR+YsULBIRucUUt/jxtm2bS3tYt6Tly5efe/QgF6bIgz0FQaNXgILU+IULF2I2m2nWrBmbNm3Cy8uLTp06YTKZ+Pe//43FYmHQoEFs376dU6dOkZSUxKFDhyhbtizTpk0zpmZMnz6d9PR08vMLPpDNmjULFxcXAgMDadmyJX369Lnp10FKx+WqT1mtZZg6dWqRKRoBAQFFjl24cCENGjSgQoUKWCwWVq1adUllqQt5eXkZj52dnQHIyMi4nqcjV3HlD8/zsNl6cfDgfvr160eLFi2KTDHLy8uja9euBAcH8/LLLxc5cs2aNbz66qusXLmSypUrF2lLT0/H1dUVNzc3Dh48eNlgtNls5p133gZWYDJ14cL7seD5CmbOnI7ZbP4bV0DuJlcqunHh9rNnzxpbKlasyFdffVVsYQ1fX1+++uor5s6di7e3N56enjz//POkp6ffwLMQkb/DvrQHICIilzKbzYSEhBjPc3NzS28wtyir1crChUuAwm/5Cz+8lQMWAZEUZBzByJEjjQ9ubm5uRSpV1axZk5kzZ9KmTZtLXuPgwYNkZ2fz8MMP061bN6Cg/PP7779vfPgvX778JVXmXnzxxet0lnKrujTLpPD+a0xBwNKDoUNfonPnzgBFqk8lJibSt29f/ve//xESEoK9vT1dunTR2i63uCt/eO4HvAlAcnIyq1atKtKalJREbGwsP/30E4sWLTK2z507l6+//pq0tDQaNWpkbA8MDGTnzp3MmDGDZ599ltmzZ3PvvffSu3dvdu7cWez4CjNThwwZTlJSU2O7n18VZs6MVrVF+Uu8vb3PPdpBwe+1Qgnn/izInnz//fcJCQkx3qc0bNiQ7777rtg+H3jgAdasWXMjhisiN4CCRSIicluKj4/n2LEUYAxFv+X3BPYDoyiYylfwBvVynn/+eV577TWqVKlCjRo1SE9PJzY2lpYtW+Lv70+zZs145ZVXmD17NomJicydO/fGnZTcNs5nmSzi0iwTT+AfHDz4njEF40KZmZnYbDYqV66MnZ0dq1atYs2aNTzzzDM3fuByzS7/4RngPqAl0JR3333XCE5fGAC8XDDwiSeeIDIysti25s2bXxIcGjZs2GXHGBYWRufOnYmPjyclJQVvb2+Cg4OVUSR/WeHUxuTkydhsyyj6e65gaqOfn6Y2itzJNA1NRERuS5f/ln808B7Q1thypQ9KgwcPJiIigrCwMNzc3KhVqxZRUVFGe1RUFAcPHqRy5cr06tWLfv36XbdzkNvXlbNMRgNfAjBp0qRLWmvXrs2YMWNo2bIlHh4eLFmyhE6dOt2wscr1cbusC1SYmdqzZ09jnSuRv0pTG0VEmUUiInJbuvy3/B3P/WwAmhIbG0v9+vWBgoWuL2YymRg0aBCDBg0q9nUCAgL4+uuvi2wbM2bM3xu83PaunGXSEagINGXMmDGX3D8AEydOZOLEicX2HRQUVCQL5eKsE4vFoilrpaDww3N4eDgmUxdstlEUBAtzgCnAJmbOjNaHZ7ljaGqjyN1NmUUiInJbul2+5Zc7k+6/u1PxFSsP4+9/guhofXiWO09xRTf279+re13kLqDMIhERuS1d/lv+HZhMUyhIkde3/HJj6P67e2ldILnbXFx0Q0TuDsosEhGR21bx3/I3xc9vh77llxtO99/dS+sCiYjInU6ZRSIiclvTt/xSmnT/iYiIyJ1IwSIREbntKUVeSlNx919QUBAzZ86kS5cupTImERERkb9D09BERERERERERMSgYJGIiIiIiIiIiBgULBIRERG5AXbu3EmDBg1wc3OjdevWHDp0iISEBEwmE6mpqcZ+Q4cOJSIiAoCcnBz69etHxYoVcXd3p06dOvzyyy+lcwIiIiJy11KwSEREROQ6sFqtxMXFsWjRIrKzs/nwww+Jiori8OHDeHl50bt376v28cknn7B161b27dtHamoqMTExeHl53YTRi4iIiJynYJGIiIjI3xQTE0NQUDVCQ0Pp1asXR44c4dSpNHbt2oWTkxNTp04lNjaWpKSkK/bj4OBARkYGu3fvxmazce+99+Lv73+TzkJEriQzM5PBgwcTEBBA5cqVefLJJ0lLSwPgu+++o27duri6uhIWFkb//v2NjMGStIuI3GoULBIRERH5G2JiYggPDycpqS6wAcgAvEhLq0J4eDgxMTF4enri6OiIg4PDFfvq06cPERERDBw4kIoVKxIREcHx48dvxmmIyEUuzBaMi4vjqaee4uTJk2zbto39+/eTm5vL4MGDOXXqFJ06deJf//oXp06d4umnn2bhwoVGP1drFxG5FdmX9gBEREREbldWq5UhQ4Zjs3UAlnH+ezhHoDvgy9ChL9GkSRNycnLw9fUF4MyZM1gsFgBSUlIoV64cAPb29owePZrRo0dz5MgRevbsyYQJE5g1a9bNPTGRu1xMTAxDhgwnKSmhyPZPPvnE+Lc7ceJE7rvvPh555BH8/Pzo168fAO3ataNVq1bGMStWrLhiu4jIrUiZRSIiIiLXKD4+/tyHydFc+rZqHjZbLw4e3E+/fv1o0aIFfn5+BAQE8Mknn5Cfn09sbCyrVq0yjli3bh1btmwhLy8PZ2dnypYti729vtsTuZK4uDgjgHM9FJ8t+BEAffv2xdnZGYvFQsOGDbGzsyMhIeGS6aIBAQHG40OHDl2xXUTkVqRgkYiIiMg1SklJOfeoTjGt/YA3AUhOTjamnXz88cfMnz8fd3d35s6dS48ePYwjCrOJLBYLVapUwd3dnXHjxt3YkxARw6XZgo0BF6AdBR+d2uLh4cmJEydITU0lOzubqlWrcvDgwSL9JCYmGo99fHyu2C4icitSsEhERETkGnl7e597tOOilgRgDPABAO+++y5+fn4AtGrVij179pCRkcHixYv597//TWRkJAA9e/Zk9+7dZGZmcuzYMRYtWnRdMyZEbqagoCCmTJlCw4YNcXZ2pm3btpw8eZJBgwZhsVioXr06P/zwAwC5ubm89tpr3HPPPXh4eNCpUycOHTpk9DVixAgCAwNxdXWldu3a/Pe//wXgxIkTtG3blrS0NFxcXHBxcSE+Pv6ax3z5bEEvoAtgx8GD+4mPj+fw4cN8/vnntG/fnoMHDxIZGUleXh7/+9//WLdunXHk1dpFRG5FChaJiIiIXKPg4GD8/IIwmSYD+Re15mMyTcHfvwrBwcGlMTyRUrdkyRJiYmI4dOgQBw8epHHjxjzyyCOcOHGCXr16MXDgQADGjBnD999/z/r160lJSeHee+8tknVXr149fvnlF1JTU3nttdfo06cP+/fvx8PDg9WrV+Pu7k5mZiaZmZl/69/blbMFI4EgoGDdoeDgYDZt2kSFChVYtmwZ06dPx2KxMG/ePLp164ajoyPAVdtFRG5FChaJiIiIXCOz2cw777wNrMBk6sL59U02nHu+gpkzp2M2m0txlCI3z4UVxLKzs3n22Wfx9/fH3d2ddu3a4eHhQVhYGGazmccff5wdO3aQk5PD+++/z4wZM/D29qZMmTK88cYbfP/998b0rSeeeILKlStjNpvp0aMHNWvWNLKSrqfLZwsCuAJPALBq1Sr27t3LG2+8AUBoaCg7duwgMzOTmJgYjhw5UmRdoqu1i4jcahQsEhEREfkbwsLCiI6Oxtd3O9AUcAOa4ue3g+joaMLCwkp5hCI3R0xMDEFB1QgNDaVXr14cOXKEV18dR0xMDABOTk54enoa+zs5OWGz2UhPT+f06dO0aNECi8WCxWLBy8uLMmXKGMGi//u//+O+++7D3d0di8XCjh07OH78+HU/h2vNFlyzZg3Hjx8nLy+PxYsXs27duiL/9q/WLiJyq1F5DREREZG/KSwsjM6dOxMfH09KSgre3t4EBwcro0juGoUVxAoWhl5EwTSu6pw44Ud4eDjR0dGXPdZsNuPk5MRPP/1EzZo1L2lfv34948ePZ926dTzwwAPY2dlRv359bDYbAHZ21+/778JswfDwcEymLthso86dyw5MpikUZAtGX/Jve9OmTTzxxBOcOXOGKlWqsGjRImrVqlXidhGRW40yi0RERESuA7PZTEhICD179iQkJESBIrlrXL6CmCMwCujA0KEvkZ9/caZOATs7OwYOHMjw4cONTKITJ06wZMkSANLT0zGbzVSqVIn8/Hw+/vhjduw4P03M09OTjIwMjh49el3O51qyBUeNGsWxY8c4ffo0O3bsIDw8/C+1i4jcahQsEhERERGRa3b5CmIAdthsozh4cP8Vy8VPmTKFJk2a0LJlS1xdXXnwwQdZs2YNAG3atCE8PJy6devi4+PDzp07adasmXFsjRo16N+/P7Vr18ZisbB+/fq/fU5hYWEkJOwjNjaWqKgoYmNj2b9/r6aOichdQ9PQRERERETkml2+gljCuT8zAGjdujU9e/Y0WoOCgoypZABjx45l7Nixl/RvZ2fHvHnzmDdv3mXHcLX2a1GYLSgicjdSZpGIiIiIiFyzK1cQO7/9/H4iInKrU7BIRERERESu2bVWEBMRkVuXgkUiIiIiInLNCiuIwQpMpi7ABgqmnm0493wFM2dO16LvIiK3EQWLRERERETkb7mWCmIiInLr0gLXIiIiIiLyt4WFhdG5c2fi4+NJSUnB29ub4OBgZRSJiNyGFCwSEREREZHrQhXERETuDJqGJiIiIiIiIiIiBgWLRERERERERETEoGCRiIiIiIiIiIgYFCwSERERERERERGDgkUiIiIiIiIiImJQsEhERERERERERAwKFomIiIiIiIiIiEHBIhERERERERERMShYJCIiIiIiIiIiBgWLRERERERERETEoGCRiIiIiIiIiIgYFCwSERERERERERGDgkUiIiIiIiIiImJQsEhERERERERERAwKFomIiIiIiIiIiEHBIhERERERERERMShYJCIiIiIiIiIiBgWLRERERERERETEoGCRiIiIiIiIiIgYFCwSERERERERERGDgkUiIiIiIiIiImJQsEhERERERERERAwKFomIiIiIiIiIiEHBIhERERERERERMShYJCIiIiIiIiIiBgWLRERERERERETEoGCRiIiIiIiIiIgYFCwSERERERERERGDgkUiIiIiIiIiImJQsEhE7jiTJ0+mZ8+eV93PYrEQFxd34wckIiIiIiJyG7Ev7QGIiFxvo0ePLvLcZDKxefNm6tevXzoDEhERERERuY0os0hEbgs2mw2r1XrV/XJzc2/CaERERERERO5cChaJyC1pyZIlODo6MmXKFBo3boy9vT2VK1fmiSeewNvbGxcXF+rXr0+fPn3o378/YWFhODg4UKFCBcqWLUvFihXZunUrjRo1AqBJkyaUKVMGFxcXfHx8GD9+PKdPn6Zz584EBgbyxhtvkJ+fX8pnLSIiIiIiUvoULBKRW4rVaiUuLo5Tp05x9uxZPv74YyIjI/Hw8CAzMxMHBwf++OMPqlSpgtVqZdu2bSxatIjevXvz6aef8ueff/LSSy/h7u5O9+7d+emnnwAYOXIknp6ebNy4kW3btjF9+nSsVivR0dHEx8ezePFi5s+fX8pnLyIiIiIiUvoULBKRW0ZMTAxBQdUIDQ3lueeeA+DQocOsWLGCihUrYjKZ+Mc//kF2dja//fYbEyZM4M8//+Sxxx4jLCyMnj17UqlSJezt7alZsyZ79uzh0KFDAKxevZoXXniBmjVrEhsbS1BQEDabDQcHBwICAhgyZAhRUVGlefoiIiIiIiK3BC1wLSK3hJiYGLp2DQfaA4uAOoAnZ8448/LLL9OwYUNyc3MZNmwYI0eOxGaz8dRTT5GdnU1AQABZWVkMHz6cVatWkZKSYkwpO378OADHjh0jMDAQgISEBH7//XcAOnTogL29Pfn5+fj7+9/8ExcREREREbnFKLNIREqd1WrlmWcGURAoWg40BlwA53N/VmLnzt+oWLEiFSpUoG/fvgwdOpS0tDSeeOIJ7OzsePvtt9m0aRPr169n1KhRPPbYY0DBwtgmk4lKlSpx4MABAPz9/bn//vsBWLFiBampqaSnp7Nz586bf/IiIiIiIiK3GAWLRKTUxcXFceLEEWAMRX8tlQX2A9mcOZNB+fLlMZlMfPbZZ4SGhnLgwAGSkpIASE9Pp2zZspQvX56zZ8+ya9cuoxdPT08aNGjA7Nmz+f3332nVqhV79uzBZDJx9uxZrFYrv//+O3FxcTfvpEVERERERG5RChaJSKk7H6Spc1GLHeAP3AdAu3btqFixIhkZGfTs2ZP27duTkZEBwLBhwzCbzXh6evL+++9ToUIFo5fXX3+d5cuXc+TIER588EHuv/9+hg0bhr29PT169MDDw4NevXpx+PDhG32qIiIiIiIitzytWSQit5AdFExBK5Rw7s8NQFOcnZ3Zvn37ZY9et25dsdvr16/P008/fcn2CRMmXOtARURERERE7ljKLBKRUhcSEkJB7HoSkH9Raz4wGbA/t5+IiIiIiIjcSAoWiUipCwkJwcOjPLAS6EJBJlHGuT+7ACvx8KigYJGIiIiIFBEUFMSyZcv+8nGJiYm4uLiQlpYGQEREBEOHDr3s/kOHDiUiIqLYY0XuRAoWiUipM5vNzJs3B7ABsUBTwO3cn3GAjXnzPsBsNpfeIEVERETkjhEQEEBmZibu7u439ViR24WCRSJySwgLC2Pp0qX4+noU2e7n58HSpUsJCwsrpZGJiIiIiIjcXRQsEpFbRlhYGAcO/EFsbCxRUVHExsaSkLBPgSIRERERuaydO3fSoEED3NzcaN26NYcOHSIhIQGTyURqaqqx34VTyYprv9B3331H3bp1cXFxISwszKjAW9yxERERDBgwgB49euDq6kqNGjUuqPYLqampdOvWDYvFQs2aNZk1axYmk+k6XwWR60vBIhG5pZjNZkJCQujZsychISGaeiYiIiIiRVitVuLi4li0aBHZ2dl8+OGHREVFcfjwYby8vOjdu/ff6v/UqVN06tSJwYMHk5qaylNPPcVnn312xWOWLFnCwIEDSU1NpU+fPkZQCuCFF17g9OnTHDhwgNjYWBYsWPC3xidyMyhYJCIiIiIiIreFmJgYgoKqERoaSq9evThy5AinTqWxa9cunJycmDp1KrGxsSQlJV3za6xYsQIfHx+effZZ7O3t6dixIy1btrziMe3atTO+6Hzqqac4cOAAJ06cwGq1smTJEiZOnIi7uzve3t68/PLL1zw2kZtFwSIRERERERG55cXExBAeHk5SUl3OV8/1Ii2tCuHh4cTExODp6YmjoyMODg7X/DqHDh0iMDCwyLaLn1/My8vLeOzs7AxARkYGx48fJzc3F39/f6M9ICDgmscmcrMoWCQiIiIiIiK3NKvVypAhw7HZOgDLgMaAC+AIdAc6MHToS6SkpJCTk4Ovry8AZ86cMfpISUkp0Wv5+Phw4MCBItsSExOvadwVK1bEwcGBgwcP/u2+RG4mBYtERERERETklhYfH09SUgIwmks/xs7DZuvFwYP76devHy1atMDPz4+AgAA++eQT8vPziY2NZdWqVSV6rfbt25OcnMy///1v8vLyWLlyJevWrbumcZvNZrp378748eNJS0vj8OHDvP3229fUl8jNdNsEiyZNmkTTpk1xcnLCYrEUu09iYiLt27fHycmJypUr8/LLL5OXl3dzByoiIiIiIiLX1fmsoDrFtPYD3gQgOTmZhQsXAvDxxx8zf/583N3dmTt3Lj169CjRa1WoUIHly5fzzjvvYLFY+PDDD3niiSeueeyzZs3C0dGRgIAAQkJC6N69O2XKlLnm/kRuBvvSHkBJnT17lm7dutGkSRM++uijS9qtVivt27fHy8uLH374gZSUFJ588kkcHByYPHlyKYxYRERERERErgdvb+9zj3ZQMAWtUMK5P1sCTXn33Xfx8/MDoFWrVuzZs6fY/oKCgrDZbMbzyMjIIu0hISHs2LHjmo61WCxF2suXL8/SpUuN54sWLdK6RXLLu20yiyZMmMC//vUv6tatW2z7mjVr2LVrF5999hn169enbdu2vP7668yePZuzZ8/e5NGKiIjcOSIjI6lfv/4N6TsuLu6yGcMiIiKFgoOD8fMLwmSaDORf1JqPyTQFf/8qBAcHl8bwrmjv3r1s3LgRm83G3r17eeONN+jWrVtpD0vkim6bzKKr2bBhA3Xr1sXT09PY1rp1a5577jl27tzJAw88UOxxOTk55OTkGM/T09MByM3NJTc395rHU3js3+lD7g66V6QkdJ9ISd2Ie8VqtWKz2W7I/Vc4XVz39s2l3ylSUrpXpCRu1n3yzjtv06dPH6AbNtswoDawC5NpBrCWmTMXkJ+fT37+xcGk0pWamkrv3r1JSkrC3d2dLl26MHLkyLvy35V+p5S+kl57k+3C/LjbQGRkJEOHDiU1NbXI9meeeYYDBw7w1VdfGdvOnDmDs7Mzq1atom3btsX2N378eCZMmHDJ9qioKJycnK7r2EVERG5Ha9eu5csvv2TmzJnXve/t27czZcoUoqKirnvfIiIiIlLUmTNn6NWrF2lpabi5uV12v1LNLHrllVd46623rrjP7t27qVmz5g0bw6hRoxg2bJjxPD09HX9/fx577LErXriryc3N5euvv+bRRx/FwcHhegxV7lC6V6QkdJ9ISZXkXpk5cyazZs3i1KlTeHh4MGrUKPr168c333zDuHHj2LNnD+XKleP5559n5MiRHD9+nG+//ZbNmzfz/vvvYzKZGDFiBC+++CIANpuNmTNnMnfuXE6dOkXDhg159913qVq1KgBHjhxh6NChfPvtt5QrV45evXoxbtw47O3tcXZ2xsHBgXbt2t20ayT6nSIlp3tFSuJm3ydWq5UNGzZw+PBhvLy8aNKkCWaz+Ya/7oWqV6/O9OnT6dy5c4mP+fTTT3n33XfZuHHjDRzZlT3yyCN06tTJ+D/8ZtPvlNJXOJvqako1WDR8+HAiIiKuuE/hG82r8fLy4ueffy6y7ciRI0bb5Tg6OuLo6HjJdgcHh+ty816vfuTOp3tFSkL3iZTU5e6VPXv2MG7cOH799Vdq1qzJkSNHOHLkCDt27KBr164sWLCATp06cebMGXbv3o2DgwNms5ldu3bx5JNPkpyczPfff8+jjz5Kly5duOeee/j000955513+N///kf16tUZM2YMYWFhbN26FXt7e/r27YuXlxf79+/nxIkTtGvXDjc3N0aPHo29vb0xXrn59DtFSkr3ipTEzbpPHBwcCA0NveGvczX29vYlPt/c3FzMZjMmk6lU/y2ZTCbMZnOp/3vW75TSU9LrXqoLXFeqVImaNWte8aekJQWbNGnC9u3bOXr0qLHt66+/xs3Njdq1a9+oUxAREbnlWa1W4uLiWLRoEb/88gs2m42dO3eSlZWFp6cn999/P/PmzaNHjx507doVBwcH3N3dadz4fLWZihUrMnz4cBwcHAgJCSEoKIgtW7YAsGDBAl588UXq1q1L2bJlmTx5MgcPHuTnn38mOTmZdevWMWPGDFxcXAgMDGTMmDGXVI4REREpDTNmzKB69eq4urpyzz338N577wGQkJCAyWTi3//+N0FBQXh4eDBo0CCjeFJ+fj6TJ0+mcuXKuLu706JFC7Zu3Wr0O378eDp06MBzzz1HhQoVaNOmDQMHDmT79u24uLjg4uJCYmKisd+zzz6Lu7s7VapUIS4ujmXLllGtWjXKly/PmDFjioz5m2++oVGjRlgsFu677z6++OILoy0iIoIBAwbQo0cPXF1dqVGjBnFxcUBBskZ8fDwjR47ExcXlsku1iMBtVA0tMTGRLVu2kJiYiNVqZcuWLWzZsoXMzEwAHnvsMWrXrk2fPn3YunUrX331FWPHjuX5558vNnNIRETkbhATE0NQUDVCQ0Pp1asXvXv3xsnJlXHjxuHp6cljjz3Gli1bOHDgANWrV79sPxcWkABwdnYmIyMDgKSkJIKCgow2R0dHfHx8SEpKIikpibJlyxY5vmrVqiQlJV3fExWRu8L333+Pg4MDJpPJyJC4mhYtWmBnd9t87JGbLDAwkHXr1pGens6HH37Iyy+/zPfff2+0f/7552zZsoXt27fzww8/MGXKFKOtevXqBAYGYrVa+eOPPwgLC8Nms7Fv3z4WLFjAypUrWbp0KWPGjOHLL79kzpw5VKpUiUceeYTMzEwCAgIAWLlyJb6+vpw8eZJHHnmExx57jG7dunHq1Cnq1avH22+/za+//srRo0dp164djz32GH/88Qd9+/Zl1qxZ9OnTh99//90Y15IlSxg4cCCpqan06dPHmM3z9ttvExwczFtvvUVmZiarV6++ORdZbku3zW/N1157jQceeIBx48aRmZnJAw88wAMPPGDM9zSbzaxYsQKz2UyTJk3o3bs3Tz75JBMnTizlkYuIiJSOmJgYwsPDSUqqC2wAMoANnDrVmF27djF37lzq1atHnz59CAwMZN++fdf0On5+fiQkJBjPz549y6FDh/Dz88PPz4/s7GxjajgUfFvr5+f3t85NRO5OTz75JFBQ0dhms2G1Wkt5RHI7ujDj1sPDAx8fH0wmE6GhobRu3drIxIGCDCGLxYKPjw8jR45k3rx5LFq0iLNnz7J9+3b+85//cPz4cWrXrs2ff/5JYmIiHTp0wMvLi/vvv59vvvmGt99+m2XLll12PGazmRYtWmA2m/nll1/Izc1l27ZtHDp0iIkTJ3L//fezadMmOnXqxKFDhxg0aBB79uxh27ZtxMbG0qFDB/7zn/8Y/bVr146QkBDMZjNPPfUUBw4c4MSJEzfwisqdqFTXLPorIiMjr5qyHhgYyKpVq27OgERERG5hVquVIUOGY7N1AJZx/vuh8thszwNWRowYTb9+T2Jvb8+AAQNo3rw5HTp0oGPHjpw+fZrdu3cXmYp2Ob1792bs2LF07NiRe+65h1dffRVfX18aNWqEvb09oaGhvPTSS8yZM4cTJ04wadIk+vbte+NOXkTuWMePH8disZR4qQqRi8XExDBkyHCSkhKMbQ4OZXB0LIPZbObMmTNUqVLFaAsMDLzguGEcO3aYXr16AXDqVCoPPfQQ2dnZRuAyNjaWlJQUunXrxtatW7n//vsZPHgwkZGRxnEXM5lMxuPCWTH5+fk4OjrSokULnJyc2L17N3v37uWhhx7io48+4rPPPiMvL4/vvvuOcuXKFSnOdOGavc7OzgBkZGTg4eHxdy6d3GVum8wiERERKbkNGzaceyM8mqL/3Z8FxgFxJCUlsGzZMiIjI2nQoAFLly5l0qRJVKhQgVq1avHtt9+W6LWefPJJXnjhBeOb1K1bt/Lll18ai1dHRUWRlZVFYGAgzZo1o3379owYMeL6nrCI3PHKlStHeno6x48fx2Qy0axZsyLTy+rUqWNMTzOZTDRt2rTI8VWrVjXa6tate7OHL7eASzNudwFmcnMbkJmZyccff0y7du2w2WzGMQcOHDCOO3bMF/ClIFPXhbNn7Tl27BiRkZHMmzcPKCiy5OPjg9lsNu7PwunXJZkOOXXqVABCQkKoWbOmsYbSyZMnSU1NZe3atcb4zGYz5cqVIzMzkw8++KBE10BTMqWkbpvMIhERESm5w4cPn3tU56KWusCPFLzRdeOVV16hXr16ALRt27bYxS4jIiIuqV5auLg1FHwjOmLEiMsGgLy8vIiOji62LSQkhNTU1KudjogIWVlZuLm54ejoyLFjx2jRooXR9tFHH7Fz507mzJnDs88+S1xcHJs2bTLabTYbZcuWJTU1lTFjxjB79mw+++wzevfuXRqnIqWg+IzbXYAN+AB4lWefHcTp0+k888wzxnETJkxgy5bt2GyPAMeA/oDLueP9gEBefHEYderUBArW+Dt06FCRKZKF0689PT05ffq0seYfFEzdzsvLM54XZjLt2bOHnTt38sgjj3DfffdhsVioXLkyq1atok2bNixevJgWLVqQl5fHhg0bsFgs1KpV66rXwdPTkz/++ONaLqHcZRRWFBERuQOdT0HfcZk9CrZ7e3vflPGIiFyrrKwsBg8eTOvWrcnOzi52n8KpO6tWrSI5OZmQkBCGDx9eZJ9du3bh7u5uZGr897//vbEDl1tKfHx8MRm3tYExwCPAtxw/fuSS6de1atXi0KGDFHzR8tC54wHczvUTR3LyAVxcXAC477778PT0JDY2lvz8fHbs2MGsWbPo27cvLVu25P7772fdunW4ubmxZ88e1q5dW+T1li5dChR8EWOxWLCzs8NkMhEQEIC/vz9Lly7lo48+YuzYsXh4eFC5cmVeffVVcnJySnQdhg4dyjfffIPFYqFDhw5/7SLKXUXBIhERkTtQkyZN8PMLwmSaDORf1JqPyTQFf/8qBAcHl8bwRERKpEuXLjg5uTN79mzWrFlDbm4ux48fp0uXLkX26927N+3atWPVqlX4+fnh4ODAW2+9ZbRfuCZM4fNTp07djFOQW0RKSsq5Rxdn3E4EjgPJAAwYMICZM2carTVr1jz36BAwByistG0GXgcKCjgEBAQQGBhIw4YNWbFiBU5OTsTHx9OpUyeGDRtGr169cHBw4Pvvv2f48OHY29vTsmVLBgwYgLu7u/F6W7duxdPTEz8/Pzp37sy0adPYuHEjw4YNY8WKFSQnJzNw4EC2b9+On58fb7zxBt988w3169cHCtb6vXD8FosFm81mVC196KGH2L17N6mpqaxYseJvXFG502kamoiIyB3IbDbzzjtvEx4ejsnUBZttFAVvkHdgMk0BVjBzZnSJyk6LiJSGLl26sHz5cqADBdkfdQBPII/ly5dToUKFIvuvXLkSgOTkZB588EFGjRrFyJEjb/Ko5VZ1PpN2B1Bc8YbiM26LZupeeFzCuT83ANCpUydmzJgBwL333suaNWsuO5bp06czffp04/nTTz9tPP70008ve1zlypWZP3/+ZdtFridlFomIiNyhwsLCiI6Oxtd3O9CUgpT5pvj57SA6OpqwsLBSHqGISPGysrJYvnwVBYGi5RR8SHehIJvDDejAyZMnjf3//e9/079/f5KTk6lQoQLlypUrjWHLLSw4OPiaMm6VqSt3KwWLRERE7mBhYWEkJOwjNjaWqKgoYmNj2b9/rwJFInJLe/nll4FcCjKKivvIUrBuTGFVqIyMDCIjI/Hz88PJyYmDBw/y5ptv3qzhym2gMOMWVmAydaEgIygD2HDu+QpmzpxuZNwGBQVhs9nw8PD4S8eJ3Ck0DU1EROQOZzabCQkJKe1hiIiU2N69e889unh9mfRzfxZUk3rssccAGDZsGMOGDSu2r+++++6Sbfn5F2eIyN2gMON2yJDhJCU1Nbb7+VVh5szLZ9xe63EitzMFi0RERERE5JZSvXr1c2u+XHl9merVq9/MYckdICwsjM6dOxMfH09KSgre3t4EBwdfNTPoWo8TuV0pWCQiIiIiIreUadOmMXv2PGASBWsWXTgVLR+YDDgwbdq00hie3OauNeNWmbpyN9GaRSIiIiIickspV64cnTu3A1YAnblwnZiC5yvo3LmdFrIWEblBFCwSEREREZFbzrJly+jcuTPwFRdWdISv6Ny5M8uWLSvN4YmI3NE0DU1ERERERG5Jy5YtIysri5dffpm9e/dSvXp1pk2bpowiEZEbTMEiERERERG5ZZUrV4733nuvtIchInJX0TQ0ERERuWaJiYm4uLiQlpZ201/7vvvuY8WKFTf9dUVERETudAoWiYiISImlpqZiMplISEgAICAggMzMTNzd3W/6WHbu3EmHDh0AiIyMpH79+jd9DCIiIiJ3IgWLRERE7hK3YkDFZDKxZcuWYttyc3Nv7mBEREREBFCwSERE5K53ccBm5syZhISEFGmfM2cOderUISAgAID09HQAEhISMJlMpKamsnnzZlxdXTlz5oxxbEpKCmXKlCE5ORmAX3/9ldDQUCpUqEC1atWKjGP8+PF06NCB5557jgoVKvDKK6+wf/9+HnnkEdzd3alQoQLNmjUz+g8KCmLZsmVs3ryZgQMHsn37dlxcXHBxceHAgQOULVuW/fv3G/1nZ2dTvnx5fvrpp+t6/URERETuNAoWiYiI3IFmzpxJQEAArq6uBAUFMW3atEsCKomJiURERBQ5LjU1lX/9619kZ2cDkJOTA8ALL7xAeno6b7zxBgAff/wxADabDYCGDRsSGhpKfn5+kYVoa9euTWBgIF27dsXZ2ZmHHnqIbt26cezYMcqWLQvAQw89hIuLC/Hx8fzvf//joYce4ujRo7z++uuMGTOGatWqcfz4cY4cOcK0adOwty9an+OBBx5gzpw51K1bl8zMTDIzMwkMDKRDhw588sknxn6ff/45Pj4+PPTQQ9fxSouIiIjceRQsEhERuQNYrVbi4uKIjo4mOTmZcePGsWbNGjIyMvjpp59o3bp1kYBKWloaf/75p5F5Y7Vai+130qRJQMEUtm3btvG///0PgO3btwPw2WefAbB48WKOHz/OI488wvjx4zl79iwAp0+fJjs7m0WLFjFmzBg8PDz4+eefMZvN7NixA4C2bduSmZlJcHAwderUISIiAnt7e5ycnHBwcCAlJYWEhAQcHBxo2rQpZcqUKdE16d+/P59++qkR0IqMjOSpp566lssrIiIicldRsEhEROQ2FxMTQ1BQNUJDQ+nfvz92dnZkZ2fz0UcfkZWVhaenJ/fff3+x+3/33XcAtGvXkZiYmEv6XrhwIVBQecxisTBu3DigIAgE8OmnnwJwzz33YG9vz/vvv09WVhYrV65k69at5OXl8dJLL1GlShWSk5M5ceIECxYswGKxYLFYADh+/LjxeoXT3ApNmzYNX19fHnnkEYKCghg/fjz5+fklui6tW7fm7NmzfPvttyQnJ/Ptt9/Sp0+fEh0rIiIicjezv/ouIiIicquKiYkhPDwcm60DsAioibf3tzg4NGT69OnMnj2b5s2bM3XqVADS0tIu2n8W8B+OHq1KeHh4kWlbAIcOHSryPDAwsMjzpKQkY7vJZALAzs6OpUuX4uXlhZOTk3GMv78/jRs35uDBg0Y1NZPJVGTamp1d0e+xKleuzPvvvw8UZDM9+uij1K1bl65duxbZ7+LjCrdFREQQGRlJjRo1aN26NZ6enle8niIiIiKizCIREZHbltVqZciQ4ecCP8uAxoAzAGbzd5hMHalQoTJ169Y1MmqSk5Mv2n8vYAbqYLO141//Gl7kNXx8fIo8T0xMLLb9wIEDpKamkpqaakxZi4qKwtnZ2di3T58+bNu2jdOnT5Obm2tUO9u5c+dlz/E///kPiYmJ2Gw2LBYLZrP5kjWLADw9PUlJSSErK6vI9n79+hETE8NHH31Ev379Lvs6IiIiInKegkUiIiK3oMJKX1cSHx9PUlICMJoL/0tPTk7Gal2HzTaM5OQDnDx5Ent7e44ePXouQDPsgv29C3sD1nLixLEir9GzZ08Ajh49SmpqKhMnTizSXhiE2rt3L1BQJa1MmTLs378fOzs7YxFrAF9fX8aNG0dmZibe3t54enri4ODAnj17LnuOmzZtomnTpri4uNCkSRP69+9Pp06dLtmvZcuWNG7cGF9fXywWixHUqlq1Kv/4xz/IyMigffv2V7qcIiIiInKOpqGJiIjcplJSUs49qlNke0HWznhgNwA//vgjixcvZtu2bef2CAPygW3A/cCvQBLgBSQABQtWA4wdO5ajR4/So0cP3NzcGDt2LCtXrjTax40bR+XKlYmIiCAxMRFXV1eCg4M5dOiQUYntQlWrVsXT09OYhvbhhx8ybtw43n33XUaOHHlJgOytt97irbfeKvb8C/sAcHBwYPny5cXuFxQURIMGDYrNSBIRERGRSymzSEREbjuF05fuVN26dSMxMZGePXvi4uLCwIEDOXr0KE888QTe3t74+PgwdOhQPDw8zh3xM9AZqExWVkXmzZtHmTKzga8A+OCDD9i1axfjx48/t78jMBMIoGAKWn2gD1Cw0PSrr75qBHkcHR2pU6cOlStXJjU1lc8++4xdu3YZ7VWqVCEtLQ1XV1fy8vL46quvWLJkCa6urkBBQKdLly7GuXXp0qVIkOfpp58mOTmZU6dO8corr1zHq1jgjz/+IDo6mmefffa69y0iIiJyp1KwSEREbpqgoCCmTp1K48aNcXV15eGHHzYWOzaZTKSmphr7Dh06lIiICACjff78+VSrVg0/Pz9sNhsjR47Ey8sLNzc37r33XlasWGEcv3jxYu6//34sFgsNGzbkhx9+uMln+9dZrVbi4uIICwujcuXKLFy4kMzMTD744AM6deqEl5cXf/zxB9u3b2fr1q18++23+PkFAdOAHsB+ypZNomrVqpw92wuYjL9/FVJTUxk8eDBz5szB1zeQgmyiuhe88ldAcyCEChUq8n//939kZGQABYGmjz76iC+//JLjx48TFhZGx44dOXv2rHF0ZGQkn3zyCZmZmdSoUePmXKwSePbZZ6lfvz4jR46kevXqpT0cERERkduGgkUiInJDFQZAFi1aRHZ2Np999hmLFi3i2LFjODs78+qrr5a4ry+++IKNGzeyf/9+vv76a6Kiovj1119JT0/nm2++4d577wVg1apVvPTSS0RGRnLy5ElGjRpFx44dOXHixI06zb/twnL2vXr14siRIzzzzEBiYmLYuHEje/fuZdq0aTg5OeHh4cHo0aNZvHgx77zzNibTV5hMi4BtmEx59OjRA5ttL7CCmTOnM3fuXIYMGcKjjz7Ku+/OwGT6GpNpArAByAHuxWSKwmRaybx5czh79qyxjtDs2bOZOHEi1atXx97enhdffJGsrCx++uknY+zPPfccNWrUwGw2U6ZMmVK4esWbO3cuGRkZjBkzprSHIiIiInJbUbBIRERumOICIAcPJrN582bKli3LE088waZNm0rc37hx47BYLDg5OeHg4EB2djY7d+4kNzeXgIAAI1g0e/ZsXn75ZRo0aICdnR1hYWHUrFmTVatW3ahT/VtiYmIIDw8nKakuBQGcDMCLEycCCQ8PZ8mSJaSmplKhQgUsFgsWi4Xw8HCOHDlCWFgYCxcuxMkpDmhKVlYFnnnmGQDefvttwsLCOHDggJFZExYWRnR0NL6+24GmwJvATvz8dhAdHU3Xrl0pV66ckVmUkJBA7969jde1WCycOnWKpKQkY/wBAQE372LJXakkC76LiIjI9aOVHkVE5IYoDIAUlGlfRMEizNVJTfUjPDyc6OhoduzYwe7du0vc54VBidDQUCZMmMCrr77K7t27eeSRR5g+fTpVqlQhISGB0aNHM27cOGP/3NxckpOTr98JXidWq5UhQ4ZfUM6+8HuccsAoIJIFC6KoXLnyBQtaF/XHH39Qu3ZNXn75ZfLy8sjPz6d37960bNkSgMDAQPbt22fsHxYWRufOnYmPj2fWrFkcOXKEb7/9FrPZfEnf/v7+zJw5kzZt2lz2HOzs9N2TiIiIyJ1E7+5EROS6uzQA0hhwoWBh5VFAB4YOfYn8/HwAXFxcADhz5ozRR3GBkYuDEoMGDeLNN98EChZifvHFF4GCAMfbb79Namqq8XP69OkbsoDy3xUfH09SUgIwmqL/LXsC+7HZRnH0aAoWi4WxY8eSkZGBzWbjwIEDrF69GigoV1+uXDnatWtHmzZt+Oyzz4q8xrPPPss777zDt99+S35+PkePHmXbtm2EhIRQt25dKlasWGygCOD555/ntdde4/fffzdea/ny5Ubmkcjt5E5fHF9EROR6UbBIRESuu8sHQADssNlGcfDgfg4ePAhAxYoVCQgI4JNPPiE/P5/Y2NirThn75Zdf+OGHH4wPf87OzkZp9Oeff55p06axadMmbDYbZ86c4ZtvvikydepCeXl5f+Ns/57zQbE6F7WMBt4D2gIwZMgQkpOTqVWrFu7u7rRv397IFho2bBhmsxlPT08eeOCBSxaZ7tKlCzNmzOD555/H3d2dhg0bsn379hKNb/DgwURERBAWFoabmxu1atUiKirq2k9Y5Brt2bPnksXxAUaMGEFgYCCurq7Url2b//73v8YxcXFxWCwWPvjgAwICAmjatKmxYP6///1vgoKC8PDwYNCgQUUWbRcREbnbaRqaiIhcd5cPgFiB8cCfACQmJhotb7/9Nk899RRjxoyhbNmy1KhRw8g8KtSxY0d27tyJ2WymWbNm/PHHH+zYsQOA+fPnU6ZMGeLj4+nYsSNr1qyhWbNm5OTk4ODgQOPGjY2Mm5CQEBo1asSWLVv4/vvvWbx4MR07drz+F6IEvL29zz3aQUEGVqGO5342AE2pWbMmAwcOLLYPLy8v1q1bBxRkTqxatYpp06bh4OBg7PPkk0/y5JNPXnLs+PHjL9l2YVU6k8nEoEGDGDRoULGvnZCQcLlTE/lbrFYr8fHxpKSkGIvjL1++HG9vb8LCwnj11VeJjIykXr16vPTSS3h4ePDf//6XPn368I9//IMqVaoAkJGRwdatW/ntt98AOHr0KACff/45W7Zs4cyZM7Rr144pU6YUmboqIiJyN1NmkYiIXHdFAyAXqgLUA5YDBWvt+Pn5ARjl4dPT09m9ezdnz56lZs2aADg4OODm5kbPnj05dOgQBw4cYPjw4Wzfvp3Y2Fjc3d3Jy8vjzJkzBAcH89133xEZGcmaNWs4e/YsU6dO5bfffsPd3d0YSWRkJG+88QaZmZk88sgjN/aCXEFwcDB+fkGYTJOB/Ita8zGZpuDvX4Xg4ODSGJ5Iqfgri+M/8cQTVK5cGbPZTI8ePahZsyY//PCD0Vd+fj5vvvkmTk5OODk5GdvHjx+PxWLBx8eHUaNGsWDBgpt+niIiIrcqBYtEROS6KwyAwCTOB0AOAvHAW8AM/P2rGGsMJScns27dOmbMmIGLiwuBgYGMGTOGyMhIAD777DMefPBBBg0aRNmyZXFycrpi8GTBggX07t2bFi1a4ODgwNChQylfvjwrV6409unVqxeNGjXCZDJRrly5638RSshsNvPOO28DKzCZunC+GtqGc89XMHPm9MuuKSRyp7lcdcDU1KqEh4cTExODs7OzsW7W//3f/3Hffffh7u6OxWJhx44dHD9+3OjP1dUVi8VyyesEBgYWeXwrLoAvIiJSWhQsEhGR685sNtOzZzdgBdCZgg98e4EywABgBT16hBvTRJKSkihbtiyenp5GH1WrVjXWGLqw9HtJJCUlERQUVGRblSpVbtly75eWs3cDmhrl7MPCwkp5hCI3x19dHH/9+vWMHz+eTz/9lFOnTpGamkqdOnWw2WxGn5er1nfgwAHjcWJiIr6+vjforESkNEVGRlK/fn3j+X333ceKFStKb0AitwmtWSQiIted1Wpl0aL/Av+gYCpa0wtatwH/YPHiaCpUqACAn58f2dnZHDlyxAgYJSQkGFPUAgMDWbNmTbGvVdwHQT8/v0vW0rmwv8sdV5ouLGefkpKCt7c3wcHByiiSu8r5xfEXcfnF8Zuya9cuoKA6n9lsplKlSuTn5xMZGWmsY3Y1EydO5LPPPuPMmTNMmTKFJ5544nqeiojconbu3FnaQxC5Ldxa75RFROSOcP4D3yxgHxALRFGw4HVLYBoHD+5n1qxZAPj6+hIaGspLL73E6dOnSUxMZNKkSfTt2xcoWJPk559/Zs6cOeTk5HDmzBni4+MB8PT0JCMjw1i0FqB3794sXLiQ77//nry8PGbNmsWJEydo167dzbsI18BsNhMSEkLPnj0JCQlRoEjuOpdfHJ8i20+ePAlAmzZtCA8Pp27duvj4+LBz506aNWtWotfq3Lkz9evXp06dOjz00EOMHj36b45eRETkzqFgkYiIXHdFP/CZgRCgJ7ASSKKgyhc0b97cOCYqKoqsrCwCAwNp1qwZ7du3Z8SIEUBBptDatWuJiorC09OToKAgoqOjAahRowb9+/endu3aWCwW1q9fz8MPP8ysWbPo378/Hh4eLF68mNWrVxe7bomI3Douvzh+AtDF2N6pUycSEhKws7Nj3rx5pKWlcfToUd5++22+/fZbhg4dChRUPrywut+FunXrRkJCAidPnmTOnDk4Ojpe9/MRkesnKCiISZMm0aBBA9zc3GjdujWHDh0CYN++fbRu3ZoKFSpwzz33MHPmzCv2s2zZMuP5119/zUMPPYTFYsHb25spU6YYbd988w2NGjXCYrFw33338cUXX9yo0xO55WgamoiIXHeXLwcfAHxNYTn45557jiVLlgAF5d8LA0DFadSoEd99912xbfPmzWPevHlFtvXt29fITLpYXFxcSU5DRG6AY8eO8fjjj7Nx40YcHR3x9fVly5YtwPnF8ZOTJ2OzLaPo95oF1QH9/FQdUORu9eGHH7J69WoCAgJ47rnn6N27N2vWrKFDhw506tSJ5cuXs2fPHtq0aUPlypXp1avXFfvbvHkznTt3ZsGCBXTq1IkzZ86we/duALZt20a3bt1YunQpISEh/PDDD7Rv356ff/6ZGjVq3IzTFSlVyiwSEZHrTuXgRe4sF38T/1dNnjyZnj17AjB37lzMZjOpqalMmzatyH5Xqw5os33JkCHPa4qmyF3CarUSFxfHokWLyM7O5tlnn6VmzZo4OTkxdepUYmNj+f7770lJSeGNN96gbNmy3H///QwePNioqHol8+bNo0ePHnTt2hUHBwfc3d1p3LjgS665c+cSERFBy5YtsbOzo3nz5nTo0IH//Oc/N/isRW4NChaJiMh1p3LwInKh0aNHs2jRIgD279/Pfffdd9lF5q9UHRDg9OnTRSob/VVBQUHYbDZNSxW5xcXExBAUVI3Q0FB69erFkSNHmDZtBjExMUDBmoWOjo78+OOP+Pj4UKZMGePYCyuqXsmVqq0mJCQwZ84cLBaL8bN8+XJj6pvInU7BIhERuSFUDl5EAHJzc43H3bp149NPP+X999/HxcUFq9VaZF+TycSWLVsICwsjIWEfzz//PLVq1SI2NhZPTw8A3njjDbZv387kyZMB+OOPP+jYsSOVKlUiMDCQN954g/z8izMaReR2EhMTQ3h4OElJdTn/hZMXJ09WJDw8nJiYGI4ePUpOTg6NGzfm0KFDRX7XXFwB9XICAwPZt29fsW3+/v4MGTKE1NRU4yczM5MPPvjg+pykyC1OwSIREblhCj/wxcbGEhUVRWxsLPv371WgSOQWlZ6ezuDBgwkMDMTNzY2GDRty8ODBIvskJiby6KOPUqlSJcqXL0/79u1JSEgw2iMiIujfvz/du3fHzc2NOXPm8Nprr9G8eXPCwsJo1aoVubm5TJ8+nXHjxrF9+3Y6depEWlqa0ccff/xBly5diIyM5M8//2T9+vX89NNPQEFACQqmtrm4uPDwww/TqlUrkpOTiY+PZ/HixcyfP//GXywRuSGsVitDhgzHZusALKNg7UMXwBHIwWYL4cUXhzFixAhatGhB06ZN8fT05LXXXiMnJ4cdO3Ywa9asy65beKEBAwawaNEiPv/8c/Ly8khLS+PHH38E4Nlnn2X+/PnExsZitVrJyclhw4YNxppGInc6BYtEROSGUjl4kdtHREQE+/btY8OGDaSmpjJv3jzKlStXZJ/8/HyGDRvGwYMHOXDgAE5OTgwYMKDIPosWLaJ///6kpqZSsWJFZs58l++//55evXrx1VdfATB79mxGjBhB7dq1SUpK4v/+7/8AyMrKolWrVrRq1Yrx48dTv379IgGgMWPGULduXTIzM5k/fz6VKlVi6NChlClThoCAAIYMGUJUVNRNuFoiciPEx8eTlJQAjObSj6v9gGSSkw+wc+dOFi5ciIODAytWrGDTpk14eXnRqVMnhg0bdtXFrQEaNGjA0qVLmTRpEhUqVKBWrVp8++23ADzwwAMsWrSIsWPHUqlSJXx9fXn11VfJycm5zmcscmtSNTQRERGRu5TVajUWh3V0dOTzzz/nwIED+Pj4AAUfli4WFBREUFAQAGXLlmXMmDE0btyY/Px8Yx2ixx57jNatWxMTE8MTTzyBzVYdqAesBB4HVrFjxw727NmD2Wyma9euxrf569evp3z58gwdOpSZM2dStmxZ+vfvX2wAKCEhgR07dhRZfyg/Px9/f//reJVE5GZKSUk596hOMa33ARsBN4YNG2ZMNbv33ntZs2ZNsf1FREQQERFhPL8wExKgbdu2tG3btthjW7ZsScuWLf/S+EXuFAoWiYiIiNyl6tatz759vxfZtnHjRgICAi57zLFjxxgyZAjx8fHG1LGcnBwyMjJwd3cHICAg4KKpJA8AWymYSlLpXE8tWLRoCf7+vjg7O5ORkYGzs3ORAFB2djZ5eXn8+uuv+Pv7G1PQCvn7+/Pggw8agSYRuf15e3ufe7SDgiloF9tx0X4iciNoGpqIiIjIXebLL78EIDn5Ps4vHrsSgK5duxrVhoozatQozpw5w6+//kp6ejrfffcdADabzdjHzs7uoqkkpmJ66sfJk8fJzMw0tjRo0IA9e/bQoEED4uLiqFChAs2bNyc9PZ2dO3fi6enJsWPHjP07dOjAkSNHeP/998nOzsZqtfL7778TFxd3TddFREpfcHAwfn5BmEyTgYsXq8/HZJqCv38VgoODS2N4IncNBYtERERE7iJWq5WRI0efexbF+cVj2wGdgMq88MJQcnNz2bx5MydOnChyfHp6Ok5OTlgsFk6cOMGECROKfZ0rTyUBqAZAXl6esWXWrFkcPnyYX375hZ49e56bwmYzAkCvv/46UVFRbNu2jddffx0XFxe++eYb1q5dS1BQEB4eHvTq1YvDhw9f28URkVJnNpt55523gRWYTF04H9BehMkUCaxg5szpWgNR5AZTsEhERETkLhIfH09ycuK5Zxe/FfwUaMqhQwexWCwMHDiQrKysIntMmDCBffv2Ub58eZo1a3bZtT6KTiW5UOS5PwvKVUdGRhot9erVY/v27ezdu5fatWuzYMECtm7dagSAnn76aQ4fPkzHjh35v//7PywWCw4ODixdupTDhw+TmprKpk2b6NGjx1+7KCJySwkLCyM6Ohpf3+1AU8ANaIqf3w6io6NVVVXkJtCaRSIiIiJ3kfMZP8VxpyBg5MaHH35Iz549gaILwtaqVYuff/65yFHPPPOM8bgw+GO1WvHzCyI5eTI22zKKBqasmExd8PMrmEoSEhLC0KFDjdZ77rmHpUuXFjtCBwcHli9ffrXTFJHbXFhYGJ07dyY+Pp6UlBS8vb0JDg5WRpHITaLMIhEREZE7VFBQEMuWLSuy7eqLwl6fxWMvP5Vkw7nnl04liYuLK1LZTETubmazmZCQEHr27ElISIgCRSI3kYJFIiIiIneR4OBgfH0LqpXZbNaLWq/v4rGaSiIiInJ7UrBIRERE5A7UrVs3EhMT6dmzJy4uLgwcOBCTycQHH3xATs4ZHn/8cUym7hRUKvuewowfm+1L3NycjG/wTSYT7733HrVr18bZ2Zk+ffpw6tQpHn/8cdzc3HjggQf47bffjNcNCgpi0qRJNGjQADc3N+bOncsPP3xHbGwsUVFRxMTE0Lx5Y55//nl8fHwYOnQoOTk5nDhxgrZt25KWloaLiwsuLi7Ex8eXyrUTERG52ylYJCIiInIH+u9//0tAQACLFi0iMzOTOXPmABAVFUV8fDyLFi3Cx+f3c3s3pzDj56mnnqJixYpF+lq+fDnr169n7969rFmzhocffpgXXniBkydPUr9+fUaMGFFk/w8//JCoqCgOHz6Ml5cXffv2JSQkhB49evDWW2/h7e3NH3/8wfbt29m6dStvvPEGHh4erF69Gnd3dzIzM8nMzFRpbBERkVKiYJGIiIjIHcRqtRIXF8eiRYvIzs4mPz+/SPuIESPw8fHBwcGBHTu2AjB58mRiY2PZv38v999//yV9vvTSS1SoUAEfHx8efvhh7rvvPpo3b469vT3dunXj119/LbL/c889R82aNXFycmLq1KnExsaSlJTExo0b2bt3L9OmTcPJyQkPDw9Gjx5NVFTUjbsgIiIi8pepGpqIiIjIHSImJoYhQ4aTlJRgbHvmmYEAxvpAAQEBRlvhVLO2bdtSv379y/br6elpPHZyciqyCLWTkxOZmZlF9g8MDCxyrKOjI8nJySQmJpKamkqFChWMdpvNhtV68dpJIiIiUpqUWSQiIiJyB4iJiSE8PJykpLqcrzzmw4kTgYSHhxMTEwOAnV3Rt3/Ozs6cOXPGeJ6SkvK3x3LgwAHj8dGjR8nJycHX1xd/f38qV65Mamqq8ZOWlmYEmy4e282QkJCAyWQiNTUVgIiICIYOHXrTxyEiInIrUbBIRETkLjdhwgQqVaqEi4sLJ06cKO3hyDWwWq0MGTIcm60DsAxoDLgAAcDjQAeGDn2p2GMbNGjAggULyMvLY8uWLSxYsOBvj2fu3Ln8/vvvZGVlMXLkSFq0aIGfnx8NGzbE39+fsWPHkpGRgc1m48CBA6xevRooyELKyMjg6NGjf3sMIiIicu0ULBIREbmLHTx4kNdff52NGzeSmZmJh4dHaQ9JrkF8fPy5qWejKfr2bjQwG5stloMH9xd77KxZs9iwYQMWi4WRI0fSt2/fvz2efv360bNnTzw9PUlOTmbhwoVAwbS3FStWkJycTK1atXB3d6d9+/bs27cPgBo1atC/f39q166NxWJh/fr1f3ssIiIi8tdpzSIREZE7VF5eHmazGZPJdNl9EhIScHFxKbLGTGnLzc3FwcGhtIdxWzk/dazORS0dz/1kAG5ERUVRv359cnNzjT3q1avHli1bLtu3zWYr8jwyMrLI85CQEGMKV6H77ruPMWPGFNtf5cqVmT9/fpFthVPBTp06xbx585g3b95lx3Ml6enpjB49mi+//JJTp05Ro0YNYmJiKF++PK+88gpffPEF2dnZtGnThlmzZuHu7n7VPjdu3MiQIUPYuXMnPj4+vPrqq/Ts2fOaxiciInK7UGaRiIjIbSYoKIhJkybRoEED3NzcaN26NYcOHQKgTJkyrFy5kvr16+Ps7ExmZiYbN26kWbNmWCwWateuzaJFiwBYtmwZjz32GGlpabi4uNCyZUugYI2ZJ554Am9vb3x8fBg6dCg5OTkAnDx5kn/+85+UL18ei8XCgw8+aKxPs3DhQqpXr46rqyu+vr68/vrrxpjXrFnDAw88gLu7Ow0aNOCbb74x2iIiIujfvz/du3fHzc3NKPEuJeft7X3u0Y7L7LHjov1ubZMnTy5RQMZqtfLKK68QFBREXFwcffv2JTIykokTJ5Kamsq8efMoV64c/fr14+TJk2zbto39+/eTm5vL4MGDr9p/amoqbdq0oUePHhw7dowPPviAAQMG8P3331+P0xQREbllKbNIRETkFme1WomPjyclJcX4sP/hhx+yevVqAgICeO655+jduzfr1q0D4LvvvuOrr77Cy8uL06dP06ZNG8aNG8fAgQP54YcfaN++PQEBAXTp0oXVq1fTpUsXIzPEZrPRqVMnmjVrxh9//EFWVhbh4eG88cYbvP7660yfPp28vDySk5NxdHRk+/btuLq6cvr0aSIiIli7di0tWrQgNTWVvXv3ArBv3z46d+7MwoUL6dSpE8uWLaNTp07s3LmTKlWqALBo0SI+//xzFi9eTHZ29s2/yLe54OBg/PyCSE6ejM22jKLfB+ZjMk3Bz68KwcHBpTTCv2b06NFX3efiym+hoaEAzJkzx5hK98ADD3Ds2DGWLl3K8ePHjSpuEydO5L777rskS+piK1eupFKlSrzwwgsAPPzww/Tq1YtPPvmEZs2aXdvJiYiI3AaUWSQiInILi4mJISioGqGhofTq1YvQ0FCSkpIIDg6mZs2aODk5MXXqVGJjY0lKSgLgn//8Jz4+Pjg6OrJ69Wrjw66Dg0ORD7vF2bhxI3v37mXatGk4OTnh4eHB6NGjiYqKAsDBwYETJ06wd+9ezGYz9evXN8qgOzg4sHv3btLT07FYLDRs2BCAJUuWEBISQlhYGPb29oSHh9O8eXMjwwngscceo3Xr1tjZ2eHk5HQjL+kdyWw28847bwMrMJm6cL4a2oZzz1cwc+Z0zGazcczy5cu55557cHV1JSgoiA8//JDExEQeffRRKlWqRPny5Wnfvj0JCQlAwbTGJk2aMHbsWKOP559/nlatWpGfn09ubi6jRo0iPz+fAQMG8Pjjj3Ps2DFjX5PJxDvvvEONGjWwWCw8/vjjpKWlFTmPL7/8kmrVqmGxWIiIiCgyXe7C7LSqVavStWvXc5XfxgIWoCAQNnDgQHx9fYmLiwMKprjl5+fj7e2N2WzGzs6O2rVrY2dnx+HDh694XZOSkggKCiqyrWrVqsa/NRERkTuVgkUiIvK3BAUFsWzZstIexh2p+FLoG7Bay7BgwQKjFLqnpyeOjo4kJycDUKlSJaOPv/phNyEhgdTUVCpUqIDFYsFisRAeHs6RI0cAePnllwkODqZ79+54eXkxZMgQsrKycHZ25ssvv2T58uX4+/vTvHlzYmNjSzyGgICAa79QAkBYWBjR0dH4+m4HmgJuQFP8/HYQHR1NWFiYse+ePXtYuHAhK1euJCMjg59++olGjRqRn5/PsGHDOHjwIAcOHMDJyYkBAwYAYG9vT1RUFO+//z5xcXEsX76c//73v3z22WfY2dkxZcoUVqxYwfr169m/fz8mk4knnniiyBgXLFhAbGwsCQkJbNmyhZo1axZpnzNnDnZ2dvTv358lS5YYC2OvXbuWtm3bsnfvXjw8PDhy5CgFb2P/D7gHMAG/nuvFkVOn0oiIiCAyMpKIiAigIEOvfPnyTJ06lSFDhtCoUSN8fX2veE39/PyMYFmhhIQE/Pz8Svi3IiIicntSsEhEROQWdPlS6I2BykAthg59CavVytGjR8nJyTE++F64oPVf/bDr7+9P5cqVSU1NNX7S0tLIzMwEwMXFhbfeeovff/+dDRs2sHbtWt5//30AWrVqxapVqzh+/DjdunWjS5cu5Ofnl2gMdnZ6S3I9hIWFkZCwj9jYWKKiooiNjWX//r2EhYVhtVqJi4tj0aJFbN68GYBdu3aRlZWFp6cn999/P0FBQbRt25ayZcvi5ubGmDFjiI+PJz8/H4AqVaowe/ZsevfuTf/+/fn444+NqZELFixg7NixBAQE4OLiwowZM/j666+N9bQARowYgaenJ1u2bKFLly4cPnyYP//802h3cHCgb9++uLq64unpyaZNm8jLy6NHjx4EBgZy/PhxxowZw5kzp4EgYMkFZ98B6AxAVtZpDhw4wO7du/ntt98wmUy0bduWjz76iFGjRtGxY0fWr1/PwYMHr3g927Vrx9GjR3n//ffJy8sjPj6ehQsX8uSTT/7dvyoREZFbmt6ZiYjIHevCKSy3m8uXQi+UxsGD+/nmm28YOXIkLVq0KDYA9Fc/7DZs2BB/f3/Gjh1LRkYGNpuNAwcOsHr1agBWrFjBnj17yM/Px83NDQcHB+zt7Tly5Aiff/45GRkZ2Nvb4+bmhr19wdKIjz/+uJGJkpeXR0xMDN999x09evS4PhdLijCbzYSEhNCzZ09CQkIwm82XTGd85plnePHFF3n99dfx9PTkscceY8uWLRw7doxevXrh7++Pm5sbLVq0ICcnh4yMDKP/f/7zn1itVnx8fOjQoYOx/eIMssKpkBdmkP3555/GOKZOnQpAvXoP8L///Q+AH374gT59+hjnUZj1lJaWxiOPPELZsmUvmKaYDVyYIecFfAKcr6S3aNEiypcvj81m46uvvuKf//wneXl5hIaGYmdnd9VgUfny5Vm9ejWfffYZHh4ePPPMM3zwwQc0b968xH8fIiIityMFi0RE5Lq40lonhw8fpkyZMkZ2yqxZszCZTPz2229AwToldevWJTc3F09PT2OtkUK1atViyZKCDIIrVeqKi4vDYrHwwQcfEBAQQNOmTW/Oyd8Aly+FXqg/UPDBPTk52Ziuc7G/+mHXbDazYsUKkpOTqVWrFu7u7rRv3559+/YBBYtVt2nTBldXV2rXrk2TJk147rnnyM/P55133sHf3x93d3dmz55NdHQ0dnZ2VKtWjZiYGMaNG0eFChWYOHEin3/+OVWrVv07l0hKqPjpjN/QvHlz9u/fz9y5c6lXrx59+vRh1KhRnDlzhl9//ZX09HS+++47oGDh80IvvfQStWvXJicnh1mzZhnbL84gO3z4MDk5OUWCmKNGjbpgHLGAmcxMK8899xwAjRo1umRKYlJSEu7u7kZg53xFt5PAxQFSdwoy8Aq8+OKL+Pr64uTkxNatW8nPz6devXp8/PHH5OXl0bRpU4KCgrDZbMbi15GRkcycOdPoo1GjRvzwww+kpaWxe/duevfuXZLLLiIicltTNTQREflLiqvMBRhrnYSGhnL27Fn69+/PgAED+Prrr/Hy8qJatWrEx8fTtm1b1q1bxz333ENsbCw1a9Zk3bp1tGzZEgcHB/r06UNkZCQhISEAbNiwgSNHjtClS5erVuoCyMjIYOvWrUYg6nZVtBR642L2KMiuWLVqlXGtAM6ePcuqVauK7Fn4Ybc4ISEhRiW0QpUrV2b+/PnF7j906FCGDh1a7HgvDvJdqG3btrRt27bYtqtVpJJrd+l0xoLvCfPz3diy5WtstkcZMWI0/fo9ib29Penp6Tg5OWGxWDhx4gQTJkwo0t8XX3zBf/7zH7Zu3UpKSgqhoaE8/PDD3H///fTu3ZvJkyfTtGlTypcvz7Bhw3jkkUfw8fHBarWe68EdeJ+CgE6Pcz9fAg2BX+jevfsl5+Dn50d2draRndamTRvKlXMiK+sM0B1Yf8He+UAa5ctX5NSp40DBtMyBAwcyfPhw5syZA0BmZiZLlizh8ccfvx6XWURE5I6jzCIRESmxy1Xm2rBhw1XXOgkNDSU2Npb8/Hy+//57xowZYyyAXBgsAujfvz9Lly41spAiIyPp1asXjo6OV63UBQVBqzfffBMnJ6fbuqpWYSl0k2kyBR+AL7YUf//bpxS6lI7LT2c8S1RUFFlZX5OUlMCyZcuIjIxkwoQJ7Nu3j/Lly9OsWbMiAb5Dhw4VWaeoQYMGjB8/np49e5KVlcWoUaNo3bo1TZo0ISgoiNzcXD777DNjHAWeAloBgYAr8B4QDhRMGfXy8rrkHBo1aoS3tzcdO3bktddew8PDg7NnC7IJTaZhwD7ABqScq/yWRZ8+vYr0MWXKFJo0aULLli3Zvn0748ePZ82aNX/n0oqIiNzRFCwSEZESuVJlrqlTpzJ//vwrrnVSGCzavHkzVapUoXPnznz33XccO3aMXbt28fDDDwMFU87q1KlDdHQ02dnZLFmyhH79+gFXr9QF4OrqakwnuZ1dqRQ6HAU2XlIK/VosXLjwhk7Xu9H9y5VdbjqjnV1dpk6dSrlyBWv+vPLKK9SrV49atWrx888/k5mZyW+//cYzzzxjTNHy8fHh2LFjRdYp+te//sXOnTspV64cZcoU/C5ISkri+PHj/Pe//8XT0/OicXQDfgfSgP9SUPL+SWALwAUZSFC3bl0iIyNxcHBgxYoVpKamkpiYSOXKlXnrrbcuqPw2CUgF/oOf3w4qVapEaGgoNpuNihUrAlCmTBnGjh3L3r17qVu3LtOnT+ejjz66TldZRETkzqNpaCIiclWXm8pyvjJXJV544UVatWrJr7/+SqVKldiyZQsPPPCAsdZJ4YK7n3/+OS1btqRChQr4+Pjw3nvvUa9evSIBnv79+xMZGYmjoyOBgYE0aNAAOF+p6/wHz0vdSVW1CkuhDxkynKSk8wEXf/8qzJw5vUgp9Gv1xBNPXFLe/FrFxcXRpUuXItPaStp/4ToxW7ZsuS5jkQJXn86466L9bvQ49gEXBw8fBn4Amhr7jR8/vsge9957b7GZQF26dCkyLTY4OLhIADUiIoKIiIgix+geExERubo75x21iIjcMPfcc89VKnN15fTpTDIzMy9Z6yQwMJC4uDgqVqxIrVq1mDVrFqGhoQC0bNmSmTNnGlPQCj3++ONs2rSJN99808gqgqtX6roTXakUusjVXG06o8k046ZMZzzf/0fFjCMfk2nKNY2juMpvIiIi8vcpWCQiIlc1ZcqUc48uV5krECiYJlbcWieFQkNDyc7ONipxtWrVivT09EuCRa6urnTr1o3ffvutSFbK1Sp13an+ygfimTNn8vTTT1OhQgWCgoL48MMPAfj666956KGHsFgseHt7G3+nkZGR1K9f3zg+MzOTwYMHExAQQOXKlXnyySdJS0sDCv5+TSYTCxYsoFq1algsFiIiIsjNzeXEiRO0bduWtLQ0XFxccHFxIT4+/pL+Z8yYQUBAAK6ursb4Nm/ezMCBA9m+fbtxbGJi4vW/kHehy09n/PncHv+7LtMZSzKOpUuXYjLFXzKtsuD5ipsyDhERESkZBYtEROSqik5luVgCEATARx99dMlaJyaTydjznXfeIScnx1h4um3btthsNtq0aXNJr0FBQXTs2NFYc6RQYaWupKQk0tPT2bFjBy+88AJQfGWvu8mePXsYN24c48eP5+TJk/z00080atSIzZs307lzZ0aMGMGxY8f47bffjOyui/Xr14+TJ0+ybds29u/fT25uLoMHDy6yz+rVq9m8eTO7du1i7dq1LFy4EA8PD1avXo27uzuZmQVZZhdniezZs4exY8eyZs0aMjIyjPE98MADzJkzh7p16xrHXlw+Xa5d4XTGgvV9mgJuwKMALFiw4KZlqRU/jqb4+e0gOjpa2XIiIiK3EAWLRETkqiIiIvDwqAT0BTpf1GoBhuPvX4VmzZrx6quv4unpiY+PD7Nnzy6yZ35+PmPHji3SbrFYipRcX7x4MbVr12b8+PFs3779siXf5Tyr1UpcXByrVq3CarWSmJhIVlYWnp6e3H///cybN48ePXrQtWtXHBwccHd3p3HjS9evOXbsGEuXLjX+XpydnZk4cSJLliwpsvDwa6+9hqurKz4+PrRp04ZNmzaVaJxmsxmbzcbOnTuLjE9uvIunM65cuRKAjh07luo4NK1SRETk1qRgkYiIXKIw+LBo0SIjkNO//1PAHuAnilbmOg1sYObM6SxYsIDIyEi+/fZb9u3bx8aNG41qaADz589n4cKFxMfH88cff/Drr78WaV+1ahXPPvss+/fvZ8CAAUyZMoWOHTty4sSJm3but5uYmBiCgqoRGhrKv/71L2w2G6tWrcLLy4vHHnuMLVu2cODAAapXr37VvhISEsjPz6dKlSpGtbmGDRtiZ2fH4cOHjf0uLG/u7Oxc5O/wSu655x4++eQT3nvvPTw9PY3xyc1x4XTGwqmgpT0OrTMkIiJya1KwSEREirgw+NCrVy9CQ0NJSioor929e3fKlk3nwikkJlM+EyZMICwsjIULF/LCCy9Qs2ZNnJycePPNN8nPP7+YbVRUFM8//zz33nsv5cqVu6R99uzZTJw4kaysLObOnUtYWBg1a9Zk1apVN/ci3CZiYmIIDw8nKakuhQE8e/s43njjDQDKlStHnz59CAwMLNG6Tv7+/tjZ2XHo0CFSU1ONn+zsbHx9fa96fEkq0XXv3p3Y2FiOHDlCvXr16NOnT4mPFREREZGbQ+/MRETEUFzwATZgtZZh6tSpADz22KNFppC4ubnSokULAA4dOkRgYKDRn6enJ46OjsbzQ4cO4e/vbzyvVKkSZcuWNZ4nJCQwevRoI6vFYrGwZcsWkpOTb+h5346sVitDhgzHZusALKOgLHoyVmsaOTk5QBvi4r7D3t6eAQMGsGjRIj7//HPy8vJIS0vjxx9/vKRPLy8vunTpwuDBgzl+/DgAhw8f5vPPPy/RmDw9PcnIyODo0aPFtv/+++98/fXXZGVlUaZMGVxcXLC3tzeOTUlJISsr669fDBERERG5rhQsEhER4HLBB5dzf1YG/sFXX33DmTNnjCkkDRs2JD093ejDx8eHAwcOGM+PHj16LnBxvv3gwYPG82PHjpGdnW089/f35+233y6S1XL69GleeeWVG3Xat634+HiSkhKA0Zz/7/wsubnjiYiIICvra9LTU3n++edp0KABS5cuZdKkSVSoUIFatWrx7bffFttvZGSkMf3Mzc2N4ODgEq9JVKNGDfr370/t2rWxWCysX7++SPvZs2eNNa08PDxYt24dkZGRALRs2ZLGjRvj6+uLxWJRNTQRERGRUmRf2gMQEZFbw/ngwyKK/y6hK2lpo/j+++/57bffCAoKYvTo0UWqnfXs2ZPXX3+dzp07ExAQwKhRo4pML+rZsyeTJ0+mS5cu+Pr6Mnr06CLtzz//PEOHDqVhw4Y0aNCArKwsfvjhB2rWrImfn98NO/fbUUpKyrlHdS7YWpeyZdezaNEqevZsQVZWBZydnYGCynNt27a9pJ+IiAgiIiKM566ursyYMYMZM2Zcsm9QUBA2m63ItpkzZxZ5Pm/ePObNm2c8b968udF/3bp1i81oAnBwcGD58uXFn6yIiIiI3FTKLBIREeBywYcLFUwva9myJU2bNqVatWrUrVsXV1dXY49+/frRu3dvgoODqVq1Kg888MAl7T169KBp06bcc8891K9fn7JlyxpT1Tp27Mibb77JgAEDKF++PFWqVOGdd94psq6RFPD29j73aMdl9th10X4idy4XFxe2b99e2sMQERG5YyizSEREgIuDDxeXVbcChwB46aWXCAkJMVqefvpp47GdnR2TJk1i0qRJxrbBgwcXaZ88eTKTJ08GCgJUgwcPJiAgwNinW7dudOvW7Xqc0h0tODgYP78gkpMnY7Mt4+Lvf0ymGfj7VyE4OLhUxidyM2VmZpb2EERERO4oyiwSERHgfPDBZJoMXJjJkwIcBVb+7eBDXl4ey5YtIzc3l1OnTjF06FCaNm1aokpbUpTZbOadd94GVmAydeH8guQ/n9vjf8ycOV1lyUVERETkL1OwSEREgMsFH74AagB+mExxfzv4YLPZePPNN/Hw8OCee+7h9OnTREVFXZfx343CwsKIjo7G13c70BRwAx4FYMGCBYSFhZXm8IiPj9daU3LdBAUFMWnSJBo0aICbmxutW7fm0KGCjEeTycSWLVsA2Lx5M82bN6dChQpUqlSJnj17cuLECaOfkJAQRo0aRevWrXF1daVBgwZFprAdOXKE7t27U6lSJQICAhgzZgx5eXk39VxFRERKm4JFIiJiuDT40BnIwN/fRnR09N8OPjg4OPDjjz+Snp7OyZMnWbFiBYGBgddj6HetsLAwEhL2ERsbS1RUFCtXrgQK1n8qbcHBwSQlJZX2MOQ2ZrVaiYuLY9GiRWRnZ/Phhx8SFRXF4cOH8fLyonfv3pccY2dnx5tvvsmRI0fYsWMHycnJl1RUXLBgAVOnTuXUqVP84x//4IUXXjDaevXqhYODA/v37yc+Pp5ly5YxderUG36uIiIitxKtWSQiIkWEhYXRuXNn4uPjSUlJwdvbm+DgYE1nuoWZzWZjHanc3FxWrVpVugO6BeTm5uLg4FDaw5C/ISYmhiFDhp+r0ljA3b08u3btombNmkydOhUvL69LApL16tUzHnt6ejJs2DBefvnlIvv07t3b2K9v3760adMGgOTkZNatW8fhw4dxcXHBxcWFMWPGMH78eEaPHn2DzlREROTWo8wiERG5RGHwoWfPnoSEhChQdIfLzMw0FhqvXLkyTz75JGlpaSQkJGAymViwYAHVqlXDYrEQERFBbm6ucWx0dDTVqlXD3d2dAQMG0KFDB8aPHw9AXFwcFovF2Pdq038uN45Cf/zxBx07dqRSpUoEBgbyxhtvGJXyIiMjqV+/PuPGjcPLy4sePXrc2IsmN1RMTAzh4eEkJdXl/HpcXqSlVSE8PJyYmBg8PT1xdHQkOTm5yLH79u2jc+fO+Pj44ObmRu/evTl+/HiRfby8vIzHzs7OxgLZSUlJlC1bFk9PT6O9atWqypATEZG7joJFIiIid5kLp/bExcXx1FNPcfLkSbZt28b+/fvJzc0tUsVu9erVbN68mV27drF27VoWLlwIwJ49e+jTpw/vvfceJ06coFGjRnz11VdXfO0rTf/p16/fZcdx5swZWrVqRatWrUhOTiY+Pp7Fixczf/584/gdO3Zgb29PYmIiCxYsuJ6XTG4iq9XKkCHDsdk6AMsoqM7oAjgC3YEODB36EikpKeTk5FyyQP7AgQPx9fVl165dpKen89lnn2Gz2Ur02n5+fmRnZ3PkyBFjW0JCgtbeEhGRu46CRSIiIneRmJgYgoKqERoaSq9evQgNDSU6Opo2bdpgsVhwdnZm4sSJLFmyBKvVCsBrr72Gq6srPj4+tGnThk2bNgGwZMkSWrVqRZs2bbC3t2fAgAHce++9V3z9wuk/9vb29O3b1+jr2LFjLF26lNmzZxc7jpUrV1K+fHmGDh1KmTJlCAgIYMiQIUUWSHd3d2fMmDGUKVMGJyenG3QF5UaLj48/N/VsNJe+VZ2HzdaLgwf3069fP1q0aHFJICc9PR1XV1fc3Nw4ePAg06ZNK/Fr+/r6EhoayksvvcTp06dJTExk0qRJ9O3b9++eloiIyG1FwSIREZG7RPFTez4CCtZtcXZ2xmKx0LBhQ+zs7Dh8+DBw6ZSdjIwMAA4dOoS/v3+R1wgICLjiGC43/SchIYH8/HyqVKmCxWK5ZBwJCQns2LHDaLNYLAwfPtwYIxR80Lez01ub211KSsq5R3WKae0HvAkUrC9UmOV2oRkzZrBixQrc3Nzo3LkzXbt2/UuvHxUVRVZWFoGBgTRr1oz27dszYsSIv3YSIiIitzktcC0iInIXuHRqT2FQpd25x63x8PiN/fv3GmtUJSQkXLFPHx8ffvrppyLbEhMTeeihh/7y+Pz9/bGzs+PQoUPFZgX5+/vz4IMP8uOPP162DwWK7gze3t7nHu2gYArahe4DWgJNeffdd42sogunmTVv3pydO3cWOWrYsGHG47i4uCJt9evXL3K8l5cX0dHRf+8kREREbnN6VyUiInIXuPzUHi+gC2DHwYMFpcIPHz7M559/ftU+u3fvzjfffMOaNWvIy8vj448/Zs+ePdc0Pi8vL7p06cLgwYONxYgvHEeHDh04cuQI77//PtnZ2VitVn7//fdLPvhLgWXLlhEUFFTaw7gmwcHB+PkFYTJNBvIvas3HZJqCv38VgoODS2N4IiIidwUFi0RERO4CV57aEwkEAdCuXTuCg4ONtYSupEaNGnzyySc899xzeHh4sGHDBlq2bImjo+M1jTEyMtKYfubm5lZkHC4uLnzzzTesXbuWoKAgPDw86NWrV5FpaHJnMJvNvPPO28AKTKYunJ8ymQNMAVYwc+Z0VWkUERG5gTQNTURE5C5w5ak9rsATwGxWrVpFSEiI0XJxFamZM2cWef7444/z+OOPG89r1KhhrFsUEhJCamqq0Xa16T+urq7MmDGDGTNmFHsO99xzD0uXLi22LSIigoiIiGLb5DybzUZ+/sXZOtefi4sLGzZsoG7dutd0fFhYGNHR0QwZMpykpKbGdn//csycGU1YWNj1GqqIiIgU47bJLJo0aRJNmzbFyckJi8VS7D4mk+mSn8WLF9/cgYqIiNyCbtTUni+//JKMjAxycnJ4++23SUlJoU2bNtdt3FIySUlJPPbYY7i5ufHggw+ya9cuoy0oKIgpU6bQuHFjnJyc2LVrF9WrVy+y/tPF09YKj2nYsCHOzs60bduWkydPMmjQICwWC9WrV+eHH34w9l+4cCHVq1fH1dUVX19fRo4caQSKxo8fT5cuXf7yOYWFhZGQsI/Y2FiioqKIjY1l//69ChSJiIjcBLdNZtHZs2fp1q0bTZo04aOPPrrsfvPnzy/yJvVygSUREZG7SeHUnvDwcEymLthsoyiYkrYDk6lwak/0X57a89VXX9G3b19yc3OpUaMGX3zxBR4eHjfiFOQCVquV+Ph4UlJS8Pb25tVXX6Vq1aocPnyYxMRE2rZtW2T/yMhIvvjiC6pVq4bVai3RayxZsoQvv/wSNzc3mjVrRuPGjXnzzTeZNWsWEydOZODAgWzbto3Tp08TERHB2rVradGiBampqezdu/e6nKfZbObhhx8mPz9f085ERERuotsms2jChAn861//umo6s8ViwcvLy/gpW7bsTRqhiIjIra1wao+v73agKeAGNMXPbwfR0dc2tee9997j5MmTZGRksHHjxiJT2OTGiImJISioGqGhofTq1YvQ0FDWr19PSEgITk5O1KxZk4EDBxY55rnnnqNGjRqYzWbKlClTotfp0aMH/fr1w9/fn2PHjpGdnc2wYcMwm81MnDiRHTt2cPbsWaAgu/vpp58mPT0di8VCo0aN2LJlC8uWLWPy5MmsWLECFxcXXFxcAFizZg3/+Mc/cHd3x9vbm0GDBpGVlWW8dnHZUCIiInLz3DaZRSX1/PPP8/TTT1O1alUGDhzIU089hclkuuz+OTk55OTkGM/T09MByM3NJTc395rHUXjs3+lD7g66V6QkdJ9ISV3tXunYsSPt2rVjw4YNHD58GC8vL5o0aYLZbNb9dRv48ssv6dOnDzZbG8qVWwjUJj//v+TkDGTw4MG4u7vTsWNHfH19gfP3gY+PT5G/38K1ogq35eXlYbPZiI2N5fDhw2RnZ/PJJ5/QpEkTli5dyiuvvMInn3xCpUqVjGNsNhtpaWlYLBb69evHf/7zH/z9/alTp47Rd/v27Rk5ciRbt2411pvKzc3FwcGB999/n/vvv58DBw7QuXNnpk2bxqhRo4wxzp8/n5iYGCMbSvdn6dD/P1ISuk+kpHSvlL6SXvs7Klg0ceJEWrZsiZOTE2vWrGHQoEFkZmby4osvXvaYKVOmMGHChEu2r1mzBicnp789pq+//vpv9yF3B90rUhK6T6SkSnKvODk5kZ6ezldffXUTRiTXg9lsJioq6tyzY8C3HDtmYsCAfN5//33MZjOrVq3i66+/5syZM6xatYozZ86wefPmS6rU5eTkGPfJN998Q1ZWFunp6Tg5OZGXl8dvv/3G6NGjiY2N5eTJk3h4eBh9FlqzZg0uLi7k5ubi4+PDhAkTWL16NT/88APfffcdhw4dYu/evRw5cqTIcVDwBV1hlb5mzZoRExNDvXr1ADhz5gytWrXijz/+4I8//rgxF1P+Ev3/IyWh+0RKSvdK6Tlz5kyJ9jPZLi5zchO98sorvPXWW1fcZ/fu3dSsWdN4HhkZydChQ4tUV7mc1157jfnz53Pw4MHL7lNcZpG/vz/Hjx/Hzc3t6idxGbm5uXz99dc8+uijODg4XHM/cufTvSIloftESkr3yp1r/fr1tG/fHvgGaFikLScnBJPJgoPDOubMmcPEiROxWq3s3buX6tWrM336dDp37mzs37dvX/bu3ctXX33F4sWLGTx4MFCOsmW/BmqTlVUNOImTUzkWLFjA5s2bWb16NcePH2fv3r3GVLajR4+Sk5PD2LFj2bt3L7GxsXz66acMGDCAH3/8kQYNGjBx4sQimUUAGzduZOzYsezYsYOsrCzy8vK49957+eWXXwCoXr06U6dO5Z///OeNvahyVfqdIiWh+0RKSvdK6UtPT6dixYqkpaVdMeZRqplFw4cPv2qZ26pVq15z/w899BCvv/46OTk5l3ybVsjR0bHYNgcHh+ty816vfuTOp3tFSkL3iZSU7pU7z+HDh8+t63M/cPHf7SIgAqs1m/Hjx/P000/z73//27gH7O3ti9wPr7/+Oh07diQwMJCzZ89is9UEzpCVVVimvhxg48yZ5gwd+hIREb3Jzs4GCu6tcuXKkZWVhYODA3l5eaxbt45Dhw5RsWJF7r33XqDgPVbhfWhnZ1fk9fv06cNTTz3FF198gbOzMzNnziQyMrLIPmXKlNE9fAvR7xQpCd0nUlK6V0pPSa97qQaLKlWqRKVKlW5Y/1u2bKF8+fKXDRSJiIiI3C68vb3PPdoBNL6oNQCYBDRl/vz5hISEMGbMGAASEhIu6SsgIIApU6bg5uZGy5YtgY8v6vMg0Bwoy8GD+7n33nuLZGL/4x//4L777sPFxYWEhATy8vJo3rw5cXFxAEXWi/T09OTAgQPk5eVhb1/w1rNwIWxnZ2d2797NBx98QLly5a710oiIiMh1dttUQ0tMTGTLli0kJiZitVrZsmULW7ZsITMzEyhY8PHDDz9kx44d7Nu3jw8++IDJkyfzwgsvlPLIRURERP6+4OBg/PyCMJkmA/kXteZjMk3B378KwcHBJe7z8OHD5x7VKaY1Cigo/PHqq6/Su3dv4wu4WbNmsWHDBiwWCyNHjqRv376XfY1u3brh5uZGpUqVsFgsAMydO5fp06fj4uLCwIED6dGjR4nHLCIiIjfebbPA9WuvvcYnn3xiPH/ggQcAiI2NJSQkBAcHB2bPns2//vUvbDYb1apVY8aMGQwYMKC0hiwiIiJy3ZjNZt55523Cw8Mxmbpgs42iIMizA5NpCrCCmTOjMZvNJe7Ty8vr3KPLZStNpjBbacOGDQQEBABQr149tmzZctl+L1wSs0KFCnz77bdF2v/5z39esh7RhQVHisuGEhERkZvntsksioyMxGazXfITEhICQJs2bdi8eTMZGRlkZmayZcsWnn32WezsbptTFBEREbmisLAwoqOj8fXdDjQF3ICm+PntIDo6mrCwsL/UX5MmTa6QrbQRGI2fXxBOTk7MmjWLbt26XZfzEBERkVvbbZNZJCIiIiIFAaPOnTsTHx9PSkoK3t7eBAcH/6WMokJXylaCfwE/cfRoGbp3786AAQPo37//dT4bERERuRUpWCQiIiJymzGbzUZ29d9VmK00ZMhwkpKaGtv9/aswc+bSv5ytJCIiIrc/BYtERERE7nLXM1tJREREbn8KFomIiIjIdc1WEhERkdubVn8WERERERERERGDgkUiIiIiIiIiImJQsEhERERERERERAwKFomIiIiIiIiIiEHBIhERERERERERMShYJCIiIiIiIiIiBgWLRERERERERETEoGCRiIiIiIiIiIgYFCwSERERERERERGDgkUiIiIiIiIiImJQsEhERERERERERAwKFomIiIiIiIiIiEHBIhERERERERERMShYJCIiIiIiIiIiBgWLRERERERERETEoGCRiIiIiIiIiIgYFCwSEREREbkLLVu2jEqVKtG0adPSHoqIiNxiFCwSEREREblLOTs788MPP5T2MERE5BajYJGIiIiIiIiIiBgULBIRERERuQu89tprlC1bFpPJhKOjI5988gmZmZnUr1+fGTNmUL16dezs7PDw8CAoKAhXV1datWrF7t27KVOmDAsWLCAgIAB7e3scHBx4+OGHGTFiBCEhIWzatImWLVtSoUIFKlWqxAsvvGC87q+//kpoaCgVKlSgWrVq/Pvf/y7FqyAiIiWhYJGIiIiIyB1uz549TJo0iTZt2nD69GnWrl3Lzz//bLQHBgaybt06/P39cXd35/DhwyxfvhxnZ2eWL18OwMqVKzGbzQwZMgQvLy9atGjBxx9/TE5ODi1btiQ8PJxDhw5x4MABunfvDsDhw4d59NFHee655zh27BjLli1j3LhxrF27tlSug4iIlIyCRSIiIiIidyCr1cratWt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      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 10))\n",
    "plt.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1], c='blue', edgecolor='k')\n",
    "\n",
    "for i, word in enumerate(words):\n",
    "    plt.annotate(word, (embeddings_2d[i, 0], embeddings_2d[i, 1]), fontsize=9)\n",
    "\n",
    "plt.title('t-SNE visualization of word embeddings')\n",
    "plt.xlabel('t-SNE component 1')\n",
    "plt.ylabel('t-SNE component 2')\n",
    "plt.grid(True)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ae0091",
   "metadata": {
    "papermill": {
     "duration": 0.016322,
     "end_time": "2024-07-22T10:08:50.632727",
     "exception": false,
     "start_time": "2024-07-22T10:08:50.616405",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The labeling in the plot makes it a bit confusing. Let's take a look at the point cloud at approx. (14,2.5) :\n",
    "- Computer\n",
    "- Phone\n",
    "- Laptop\n",
    "- tablet\n",
    "- Keyboard\n",
    "\n",
    "All terms have a certain similarity. Just take a little time to look at the diagram, I think it's self-explanatory. \n",
    "\n",
    "In comparison to the 'en_core_web_lg', there are far more powerful embedding models. \n",
    "\n",
    "The [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) from Huggingface provides a good overview of the various models. But always keep in mind, while higher-dimensional embeddings can potentially capture more complex relationships and patterns in the data, it does not always mean that the model is better. The choice of the embeddings depends on the specific task and dataset. \n",
    "\n",
    "Increasing the dimensionality can lead to higher computational complexity and memory requirements. It can also make the model more prone to overfitting, especially if the data is not large enough to support a high-dimensional representation.\n",
    "\n",
    "Moreover, in some cases, lower-dimensional embeddings may be sufficient for capturing the relevant information in the data and may even improve the interpretability of the model. It is important to find a balance between the complexity of the model and the performance on the task at hand when deciding on the dimensionality of embeddings."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3507e70",
   "metadata": {
    "papermill": {
     "duration": 0.016578,
     "end_time": "2024-07-22T10:08:50.666013",
     "exception": false,
     "start_time": "2024-07-22T10:08:50.649435",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# ❓How to choose a Distance Metric?\n",
    "We discusses six different distance metrics, so which one is the best to use? As always the appropriate distance metrics depends on the nature of your data and the embedding space you are working with. \n",
    "\n",
    "Given that RAG systems often deal with high-dimensional text embeddings, cosine similarity is generally a strong choice because it measures the similarity in direction, which is very important for text embeddings. However if you are working with other types of data consider the following:\n",
    "\n",
    "- **Text Data:** Use cosine similarity or dot product similarity\n",
    "- **Numerical Data:** Use Euclidean distance or Manhattan distance, depending on wheather the data is dense or sparse. \n",
    "- **Binary Data:** Use Jaccard or Hamming similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77e3f2ce",
   "metadata": {
    "papermill": {
     "duration": 0.01659,
     "end_time": "2024-07-22T10:08:50.699608",
     "exception": false,
     "start_time": "2024-07-22T10:08:50.683018",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 🔚 The End\n",
    "\n",
    "**One last advise: Experiment with different metrics to see which yields the best retrieval performance for your specific use case**\n",
    "\n",
    "I hope you enjoyed my short introduction to Distance Metrics in Vector Searches. If it was helpful, I would appreciate an upvote and a comment. ❤️"
   ]
  }
 ],
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