{"cells":[{"cell_type":"markdown","metadata":{"id":"v9VXT5unCaBd"},"source":["# Introduction to Computer Vision: Plant Seedlings Classification"]},{"cell_type":"markdown","metadata":{"id":"4ZgcS1MyVGZp"},"source":["## Problem Statement"]},{"cell_type":"markdown","metadata":{"id":"WCxSmokWEKUJ"},"source":["### Context"]},{"cell_type":"markdown","metadata":{"id":"-2mC12JhVNGp"},"source":["In recent times, the field of agriculture has been in urgent need of modernizing, since the amount of manual work people need to put in to check if plants are growing correctly is still highly extensive. Despite several advances in agricultural technology, people working in the agricultural industry still need to have the ability to sort and recognize different plants and weeds, which takes a lot of time and effort in the long term. The potential is ripe for this trillion-dollar industry to be greatly impacted by technological innovations that cut down on the requirement for manual labor, and this is where Artificial Intelligence can actually benefit the workers in this field, as **the time and energy required to identify plant seedlings will be greatly shortened by the use of AI and Deep Learning.** The ability to do so far more efficiently and even more effectively than experienced manual labor, could lead to better crop yields, the freeing up of human inolvement for higher-order agricultural decision making, and in the long term will result in more sustainable environmental practices in agriculture as well.\n"]},{"cell_type":"markdown","metadata":{"id":"q_I9gQJMVWL_"},"source":["### Objective"]},{"cell_type":"markdown","metadata":{"id":"ZkD5j4o4VYYQ"},"source":["The aim of this project is to Build a Convolutional Neural Netowrk to classify plant seedlings into their respective categories."]},{"cell_type":"markdown","metadata":{"id":"aZq8uFtOVfnm"},"source":["### Data Dictionary"]},{"cell_type":"markdown","metadata":{"id":"75fTG3prVjUU"},"source":["The Aarhus University Signal Processing group, in collaboration with the University of Southern Denmark, has recently released a dataset containing **images of unique plants belonging to 12 different species.**\n","\n","- The dataset can be download from Olympus.\n","- The data file names are:\n","    - images.npy\n","    - Labels.csv\n","- Due to the large volume of data, the images were converted to the images.npy file and the labels are also put into Labels.csv, so that you can work on the data/project seamlessly without having to worry about the high data volume.\n","\n","- The goal of the project is to create a classifier capable of determining a plant's species from an image.\n","\n","**List of Species**\n","\n","- Black-grass\n","- Charlock\n","- Cleavers\n","- Common Chickweed\n","- Common Wheat\n","- Fat Hen\n","- Loose Silky-bent\n","- Maize\n","- Scentless Mayweed\n","- Shepherds Purse\n","- Small-flowered Cranesbill\n","- Sugar beet"]},{"cell_type":"markdown","metadata":{"id":"d9B4COveVqnm"},"source":["### **Note: Please use GPU runtime on Google Colab to execute the code faster.**"]},{"cell_type":"markdown","metadata":{"id":"qqFzmTb0BKKW"},"source":["## Importing necessary libraries"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JAkYVkhPb0p7"},"outputs":[],"source":["# Installing the libraries with the specified version\n","# uncomment and run the following line if Google Colab is being used\n","#!pip install tensorflow==2.15.0 scikit-learn==1.2.2 seaborn==0.13.1 matplotlib==3.7.1 numpy==1.25.2 pandas==1.5.3 opencv-python==4.8.0.76 -q --user"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lw8IuZwV-PAL"},"outputs":[],"source":["# Installing the libraries with the specified version.\n","# uncomment and run the following lines if Jupyter Notebook is being used\n","#!pip install tensorflow==2.13.0 scikit-learn==1.2.2 seaborn==0.11.1 matplotlib==3.3.4 numpy==1.24.3 pandas==1.5.2 opencv-python==4.8.0.76 -q --user"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hbDlH15fpR9A"},"outputs":[],"source":["# Import Libraries to help with reading and manipulating data\n","import numpy as np\n","import pandas as pd\n","\n","# Import Libraries for data visualization\n","import matplotlib.pyplot as plt\n","from matplotlib.ticker import FuncFormatter  # Import FuncFormatter\n","import seaborn as sns\n","\n","# Removes the limit for the number of displayed columns\n","pd.set_option(\"display.max_columns\", None)\n","# Sets the limit for the number of displayed rows\n","pd.set_option(\"display.max_rows\", 200)\n","\n","# To supress scientific notations for a dataframe\n","pd.set_option(\"display.float_format\", lambda x: \"%.3f\" % x)\n","\n","# Import Library to provide informative messages to users without interrupting the flow of the program\n","# Warnings are typically used to alert users of potential problems,\n","# such as deprecated features or unexpected runtime conditions\n","import warnings\n","warnings.filterwarnings(\"ignore\")"]},{"cell_type":"markdown","metadata":{"id":"NbFXvdwks2iR"},"source":["**Note**: *After running the above cell, kindly restart the notebook kernel and run all cells sequentially from the start again.*"]},{"cell_type":"markdown","metadata":{"id":"oq-hAEOAV4aZ"},"source":["## Loading the dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23742,"status":"ok","timestamp":1723867336751,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"},"user_tz":300},"id":"1XztFCIActAb","outputId":"6f4b121f-12de-427e-c88c-419d75f9cc4e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["# Mount google drive to Colab\n","from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c2q2QUVZtpFb"},"outputs":[],"source":["# Read the Data from excel into a dataset\n","folder = '/content/drive/MyDrive/AI_ML/Projects/Computer Vision/' # Path of the data files\n","labels_file = 'Labels.csv'                                        # Name of the label file\n","image_file = 'images.npy'                                         # Name of the image file\n","\n","labels_ = pd.read_csv(folder + labels_file)                       # Reading the label file\n","images_ = np.load(folder + image_file)                            # Loading the image file"]},{"cell_type":"markdown","metadata":{"id":"uE84hQU7CSZa"},"source":["## Data Overview"]},{"cell_type":"markdown","metadata":{"id":"57vwo75fcXbU"},"source":["### Understand the shape of the dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1723867361061,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"},"user_tz":300},"id":"S2F57JGGcbzz","outputId":"d3b198b2-0285-4d3d-ad17-f17721424bef"},"outputs":[{"output_type":"stream","name":"stdout","text":["There are 4750 labels in the label dataset.\n","There are 4750 images in the image dataset and the shape of the images are (128, 128, 3)\n"]}],"source":["# Understand the shape of the dataset\n","print(\"There are\", labels.shape[0], \"labels in the label dataset.\")\n","print(\"There are\", images.shape[0], \"images in the image dataset and the shape of the images are\", images.shape[1:])"]},{"cell_type":"markdown","metadata":{"id":"EYv5uX-MC9KC"},"source":["## Exploratory Data Analysis"]},{"cell_type":"markdown","metadata":{"id":"Vf_sWYNOjDvK"},"source":["- EDA is an important part of any project involving data.\n","- It is important to investigate and understand the data better before building a model with it.\n","- A few questions have been mentioned below which will help you understand the data better.\n","- A thorough analysis of the data, in addition to the questions mentioned below, should be done."]},{"cell_type":"markdown","metadata":{"id":"F4dZsdgujrtK"},"source":["1. How are these different category plant images different from each other?\n","2. Is the dataset provided an imbalance? (Check with using bar plots)"]},{"cell_type":"markdown","metadata":{"id":"gFCXmN9Ien7n"},"source":["### Define Useful Functions for Improved Graphical Representations"]},{"cell_type":"markdown","metadata":{"id":"-IZ0P7gdeqIj"},"source":["#### Functions to Render a labeled Barplot"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JFG9_oeXesna"},"outputs":[],"source":["# function to create labeled barplots\n","def labeled_barplot(data,             # dataframe\n","                    feature,          # dataframe column name\n","                    hue = None,       # second dataframe column name\n","                    mapping = {},     # if column represent binary value then map to the actual value\n","                    perc = False,     # whether to display percentages instead of count (default is False)\n","                    n = None,         # displays the top n category levels (default is None, i.e., display all levels)\n","                    Title = None,\n","                    xlabel = None,\n","                    palette = \"Paired\",\n","                    fontsize = 12, degree = 0):\n","\n","    # Handling NaN values\n","    data[feature] = data[feature].fillna(\"Missing\")\n","\n","    # Apply the mapping to the feature column if a mapping dictionary is provided\n","    if mapping:\n","        data[feature] = data[feature].map(mapping)\n","\n","    # Get the order of the categories by count\n","    order = data[feature].value_counts().index[:n]\n","\n","    hue_count = 0 if hue == None else data[hue].nunique() # number of unique values in the hue column\n","    count = data[feature].nunique() + hue_count           # number of unique values\n","    total = len(data[feature])                            # length of the column\n","\n","    # Set the Plot size\n","    plt.figure(figsize = (count + 1, 5)) if n is None else plt.figure(figsize = (n + 1, 5))\n","\n","    # Generate the Plot\n","    ax = sns.countplot(data = data, x = feature, hue = hue, palette = palette,\n","        order = order.sort_values(),\n","    )\n","    # For each bar: Place a label\n","    ax = annotate_the_labels(ax, total, perc = perc)\n","\n","    # Render the barplot\n","    plot_styling(data, feature, mapping,\n","                 Title, \"Barplot of \",\n","                 xlabel, degree = degree,\n","                 )\n","\n","    sns.despine(left=True)    # Hide the plot border\n","    plt.show()                # show the plot"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iuO1AdNKeuzH"},"outputs":[],"source":["def annotate_the_labels(ax, col, fontsize = 12, perc = True):\n","  for p in ax.patches:\n","    # Set the Label\n","    label = \"{:.1f}%\".format(100 * p.get_height() / col) if perc else p.get_height()\n","\n","    # Get the x and y position of the label\n","    x = p.get_x() + p.get_width() / 2   # width of the plot\n","    y = p.get_height()                  # height of the plot\n","\n","    # Annotate the Labels\n","    ax.annotate(label, (x, y), ha = \"center\", va = \"center\",\n","      size = fontsize, xytext = (0, 5), textcoords=\"offset points\",\n","      )  # annotate the percentage\n","\n","  return ax"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"P-W194RIewwg"},"outputs":[],"source":["def plot_styling (  data,\n","                    feature,\n","                    mapping = {},\n","                    Title = None,\n","                    Title_prefix = \"\",\n","                    xlabel = None,\n","                    fontsize = 12, degree = 0,\n","                    ):\n","    # If Title is Not Specified, Set the Title of the Plot\n","    Title = (Title_prefix + \" \" + (feature.replace(\"_\", \" \"))) if Title is None else Title\n","\n","    # Add the Title to the Plot\n","    plt.title(Title, fontsize = fontsize, y=1.02)\n","\n","    # if x Axis label is not specified, then\n","    # Set the x axis label by replacing Underscores in the Column Name with Spaces\n","    plt.xlabel(((feature.replace(\"_\", \" \")) if xlabel is None else xlabel),\n","               fontsize = fontsize)\n","\n","    # map the xticks label\n","    labels = [mapping.get(x, x) for x in np.sort(data[feature].unique())]\n","\n","    # Set the xticks labels\n","    plt.xticks(tuple(range(0, len(labels))),\n","               labels = labels,\n","               rotation = degree, fontsize = fontsize # Rotate the x-Axis Label and set the front side\n","               )"]},{"cell_type":"markdown","metadata":{"id":"H94yp9JOj4cs"},"source":["#### Functions to Plot Images"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"164PweGRGeY3"},"outputs":[],"source":["# Function to plot an Image\n","def plot_image(fig, image, position, title = None, xlabel = None, ylabel = None):\n","    ax = fig.add_subplot(*position)          # Add a subplot to the figure\n","    ax.imshow(image)                         # Display the image\n","    ax.set_title(title)                      # Set the title to the category and index\n","    ax.set_xlabel(xlabel)                    # Set the x-axis label\n","    ax.set_ylabel(ylabel)                    # Set the y-axis label"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mKppI0Hvj0lJ"},"outputs":[],"source":["# Plot multiple images in subplot\n","def plot_images(images, labels, samples_per_class = 1, img_size = (64, 64), title = None):\n","    categories = np.unique(labels)                        # Get unique categories (classes)\n","\n","    fig_width = samples_per_class * img_size[1] / 100     # Convert pixel size to inches (approximation)\n","    fig_height = len(categories) * img_size[0] / 100      # Convert pixel size to inches (approximation)\n","    fig = plt.figure(figsize = (fig_width, fig_height))   # Create a figure with the specified size\n","\n","    # Create a dictionary mapping each category to its indices in the images array\n","    class_indices = {category: np.where(labels == category)[0] for category in categories}\n","\n","    for i, category in enumerate(categories):              # Loop through each category\n","        indices = class_indices[category]                  # Get the indices of images for the current category\n","\n","        # Randomly sample a specified number of indices for the current category\n","        sample_indices = np.random.choice(indices, samples_per_class, replace = False)\n","\n","        for j, index in enumerate(sample_indices):          # Loop through the sampled indices\n","            position = len(categories), samples_per_class, i * samples_per_class + j + 1  # Calculate the subplot position\n","            plot_image(fig, images[index, :], position, title = category)                         # plot individual Image\n","\n","    plt.suptitle(title, y = 1,fontsize = 14)                # Set the title of the plot\n","    plt.tight_layout()                                      # Adjust the spacing between subplots\n","    plt.show()                                              # Show the figure"]},{"cell_type":"markdown","metadata":{"id":"_UYZu4uelScF"},"source":["#### Function to Render a Histogram"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WnHI7nUJlWA2"},"outputs":[],"source":["# Plots a histogram using seaborn's histplot function.\n","def plot_histogram(data, ax, title, xlabel=None, ylabel=None, fontsize=12, bins=24, color='blue', palette=\"winter\"):\n","    sns.histplot(data, ax=ax, bins=bins, color=color, palette=palette)  # Plot the histogram\n","    ax.set_title(title, fontsize=fontsize)                              # Set the title\n","    ax.set_xlabel(xlabel, fontsize=fontsize)                            # Set the x-axis label\n","    ax.set_ylabel(ylabel, fontsize=fontsize)                            # Set the y-axis label"]},{"cell_type":"markdown","metadata":{"id":"anhuyUKeukYK"},"source":["### Plotting random images from each of the class"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QuFTD6gn3HBj","colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"149TkXdyDL1o66rNvEQNw3uCGbB-Clrnf"},"executionInfo":{"status":"ok","timestamp":1723865696987,"user_tz":300,"elapsed":31307,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"}},"outputId":"7611915f-e3f8-44db-be16-7ede08447e9b"},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["# Plot random images from each of the classes and print their corresponding labels.\n","# Assuming images is a numpy array of shape (num_samples, height, width, channels)\n","\n","plot_images(images, labels, samples_per_class = 6, img_size = (256, 256),\n","            title = \"Random Samples from Each Class\",\n","            )"]},{"cell_type":"markdown","metadata":{"id":"UPEjMXZlboz2"},"source":["### Checking the distribution of the Target Variable"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":696},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1723865696987,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"},"user_tz":300},"id":"pUJNZJ9CeAeP","outputId":"833ba854-32c1-4ac6-d7d2-d3c50352e13a"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1300x500 with 1 Axes>"],"image/png":"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AAAAAgE0h2QEAAAAAAGwKyQ4AAAAAAGBTSHYAAAAAAACbQrIDAAAAAADYFJIdAAAAAADAppDsAAAAAAAANoVkBwAAAAAAsCkkOwAAAAAAgE0h2QEAAAAAAGwKyQ4AAAAAAGBTSHYAAAAAAACbQrIDAAAAAADYFJIdAAAAAADAppDsAAAAAAAANsVmkh1ffPGF8uTJo/Tp06t8+fI6fPiwtUMCAAAAAABWYBPJju+++079+vXT6NGj9fvvv+vNN99U7dq1de3aNWuHBgAAAAAAkllaaweQFKZNm6aPPvpIHTp0kCTNnz9fGzdu1OLFizVkyJBY7SMjIxUZGWk+NgxDDx8+lIODQ7z3uH/3TtIHnkqFh4e/8jWiIiOSIBLbkBTP8/5dnmeMpHiejyMeJ0EktiEpnmf4/ftJEIltsEuC53k3KioJIrENSfH+vHPvQRJEYhvsk+J5RkYnQSS2ISnen/fu302CSGxDUjzPh4/4vMdIkp+fd/j9HsMwXv15Rjzk358xXub9mTFjRplMpue2MRmGYSRVUNbw8OFDZciQQT/88IMaNmxoLm/fvr1CQ0P1008/xTpnzJgxGjt2bDJGCQAAAAAAkkJYWJhcXFye2ybVj+y4ceOGoqKi5OHhYVHu4eGh06dPx3nO0KFD1a9fP/Pxy4zsSAnCw8OVK1cu/fvvvy/8H4sX43kmLZ5n0uJ5Jh2eZdLieSYtnmfS4nkmLZ5n0uJ5Ji2eZ9JKbc8zY8aML2yT6pMdieHg4JDiExvP4+LikiregKkFzzNp8TyTFs8z6fAskxbPM2nxPJMWzzNp8TyTFs8zafE8k5YtPc9Uv0Bp1qxZlSZNGl29etWi/OrVq/L09LRSVAAAAAAAwFpSfbLD3t5epUuX1o4dO8xl0dHR2rFjh3x9fa0YGQAAAAAAsAabmMbSr18/tW/fXmXKlFG5cuU0Y8YM3bt3z7w7i61wcHDQ6NGjU/UUnJSE55m0eJ5Ji+eZdHiWSYvnmbR4nkmL55m0eJ5Ji+eZtHieScsWn2eq340lxpw5czRlyhSFhISoZMmSmjVrlsqXL2/tsAAAAAAAQDKzmWQHAAAAAACAZANrdgAAAAAAADyNZAcAAAAAALApJDsAAAAAAIBNIdkBAACQDPbu3avr16/HW3/jxg3t3bs3GSMCAMB2kezAf8L9+/df2Ob8+fPJEAmA16ljx446dOhQvPWHDx9Wx44dkzEi4P9Vr15d27Zti7d+x44dql69ejJGBAD4L8qbN6/WrVsXb/2GDRuUN2/eZIzo9Uhr7QCA5FCvXj39/PPP8e4bfeLECb377rsKDg5O5shsh2EY2rVrlyIjI/X2228rY8aM1g4pxUrMH9smk0mLFi16DdHYlqVLl6pmzZrxbj1+4cIFLVu2TIsXL07myFKvU6dOacmSJTp//rxu376tZzdxM5lM2rFjh5WiS11etAFeZGSk0qRJk0zR4L/Ozs5OJpMpwedFRUW9hmhs08WLFzVx4kTt2rVL169f148//qgqVaroxo0b8vf3V4cOHVSqVClrh5ki+fj4JPj9aTKZ9M8//7ymiGxLYGCg7t69G2/93bt3FRQUlIwRvR4kO/Cf8Ndff6lhw4Zat26d0qVLZ1F35MgR1alTR9mzZ7dSdKnP8OHDtX//fu3atUvSk3/Av/vuu9q5c6cMw5C3t7d27NihfPnyWTnSlGnnzp2xfoHfv3/fPLw9c+bMkqTbt29LkrJlyyYnJ6fkDdJGXb58WY6OjtYOI9X45ptv1KFDB6VLl04FCxY0vzefxg72z3fx4kUFBgaaj0+fPh3nVJXQ0FAtWLBAuXPnTsbobM+VK1d07do1vfHGG/zcfIFRo0bF+l20du1anThxQrVr11bBggUlPXnPbt26VcWKFVPDhg2tEGnqdPLkSVWuXFnR0dEqX768zp07p8ePH0uSsmbNql9//VX37t3ji4x4VK1aNVHJOLy85z3f3377TZkyZUq+YF4XAynW9u3bjcmTJ1uULVq0yMiVK5fh7u5u9OnTx3j8+LGVoktdjh07Zri5uRn169c3Hj16ZC7fs2ePkTFjRqNMmTLGzZs3rRhh6lKwYEFj0KBB5uPvv//eMJlMxsSJE42NGzcaOXPmNNq2bWvFCFOXEydOGDly5DCGDx9uXL9+3Vx+/fp1Y9iwYUbOnDmNkydPWjHClO3HH380OnToYHTo0MEwmUxG1apVzcdPvxo1amRkzJjRqFatmrVDTjXy5s1rlClTxuJ9iYQZM2aMYTKZDDs7u+e+TCaTkTZtWmPRokXWDjlV+vHHH42CBQuan+eOHTsMw3jyc7RkyZLG2rVrrRtgKrBgwQIje/bsxunTp2PVnTx50vD09DQWLlxohchSJz8/PyNv3rzGtWvXjOvXrxsmk8n8vjQMwxgxYoSRP39+K0aI/5oZM2YYPj4+ho+Pj2FnZ2e4u7ubj59+ubm5GXZ2djbxb3lGdqRgY8aMsfiG5/jx4+ratatKlCihN954Q7NmzZKnp6cGDx5sxShTh5IlS2rz5s2qVauWWrVqpe+++04///yzmjVrprJly2rDhg1Mu0iA4OBgvfHGG+bjNWvWqEiRIho6dKgkqVu3bpo3b561wkt1evbsqbp162r8+PEW5VmzZtWECRN07do19ezZU9u3b7dShCnbyZMntWrVKklPvqU4dOiQjh49atHGZDLJyclJVapU0bRp06wRZqp0+fJlDRgwQFmzZrV2KKlW8+bNVaxYMRmGoebNm6tXr16qXLmyRZuY92fJkiXl4eFhpUhTr/Xr16tx48by9fVV69atNWbMGHNd1qxZlSNHDi1ZsoRRCS8wZcoU9ejRwzyi42mFCxdWjx49NHnyZH300UdWiC712bt3r0aNGqVs2bLp5s2bseq9vb2ZPo1k5e7urqJFi0p6Mo0lR44cypEjh0WbmN9HpUuX1ieffGKNMJMUyY4U7NSpU2rSpIn5+JtvvpGLi4t++eUXZciQQR9//LG+/vprkh0vqWzZstq4caPq1Kmj6tWr69ChQ6pRo4bWrFmj9OnTWzu8VCVt2rSKjIyU9GQI+44dO9SuXTtzvYeHh27cuGGt8FKdgwcPqmnTpvHWlypVSitWrEjGiFKXoUOHmhNtdnZ2WrRokVq3bm3lqGxDiRIldPnyZWuHkaoVLlxYhQsXliQtWbJEVapUkY+Pj5Wjsi3+/v6qUqWKdu3apZs3b1okOyTJ19dXCxYssE5wqcilS5diTfV9Wrp06XTp0qVkjCh1i46OVoYMGeKtv379erxryUGJ3pmqSpUqSRyJ7WjVqpVatWol6cmC2SNGjNA777xj5aheL5IdKdi9e/fk4uJiPt68ebPq1Klj/sFZtmxZffvtt9YKL1WqVKmS1q9fLz8/PzVo0EABAQFKm5aPQUIVK1ZM3377rdq0aaO1a9fq5s2b8vPzM9cHBQXxTXACuLm56eeff1a3bt3irN+0aZNtzJtMBtHR0dYOwaZMmzZNzZo1U926dVWxYkVrh5PqtW/f3toh2KS//vrruSO2PDw8dO3atWSMKHUqVqyY5s6dq9atW8f6tvfSpUuaO3euihcvbqXoUp+33npLGzdujPPb8cePH2vlypWqUKGCFSJLHapVq5agNTsMw5DJZGIB3ZcUs+6ereOvvBQsV65c+u2339SxY0edO3dOf/31l/r372+uv3XrFhnheGTMmPG5PyAfPnyoTZs2yc3NzVxmMpkUFhaWHOGleqNGjdL7779vTmhUqlTJYrvEjRs3qmzZstYKL9Xp2rWrRo0apQYNGqhnz57mKUJnz57V7Nmz9fPPP2vs2LFWjhL/RZ999plcXV1VuXJlFSlSRN7e3rF2CzGZTPrpp5+sFGHqw+42SS9Dhgy6d+9evPXnz59XlixZkjGi1Gn69OmqXbu2ChQooEaNGln8Lvrxxx9lGAZfsiXA0KFDVa9ePXXr1k0tW7aUJF29elXbt2/XxIkTderUKc2ZM8fKUaZc/5U/xq0pPDxcc+fO1a5du3Tt2jUtWLBA5cqV061bt7R06VLVr1/fYtp6akSyIwVr06aN/P39FRwcrBMnTihz5sxq0KCBuf7o0aMqUKCAFSNMuZo0acIKzq9RrVq19Pvvv2vbtm3KlCmTWrRoYa67ffu2qlSpYvFexfONGDFCkZGRmjJlijZs2GBRlzZtWg0ZMkQjRoywUnSpz88//6xp06bp999/V1hYWJy7hfDNz8v53//+J5PJJG9vb929e1cnT56M1YaftS+P3W1ej+rVq2vZsmXq06dPrLqQkBB9+eWXqlevXvIHlsq8/fbbOnTokEaOHKm1a9cqIiJCkuTo6KjatWtr7NixjOxIgLp162rp0qXq3bu3Fi5cKElq27atDMOQi4uLvv76a6ZcPEfVqlWtHYJNu3TpkqpWrap///1X+fPn1+nTp81b0bq5uWnBggUKCgrSzJkzrRzpqzEZ/FZNsR4/fqzRo0ebh7D7+/ubFzW7deuWihQpot69e5vnqgPJ4cGDB1q4cKFKlizJL+kkduPGDW3btk0XL16UJOXOnVs1a9ZkSlACrF69Ws2bN1fRokVVuXJlzZs3T61bt5ZhGPrpp5+UP39+NWzYUKNHj7Z2qPgPypcvn3naGp/rpHPmzBlVqFBBefLkUbNmzTRy5EgNGDBA6dKl04IFC2QYho4cOaI8efJYO9RUIzo62rwderZs2WRnZ2fliFKve/fuadu2bTp79qyio6OVL18+1a5dm4XxYVWtWrXSjh07tHv3brm7u8vd3V3bt29XjRo1JEmDBw/Whg0bdOLECStH+mpIdgBIMEdHR82aNYsV2ZHilClTRunSpdOvv/6q27dvW/zyDgwMVIUKFTR58mSLBXWB5OLo6Khp06bFuz4PEu/EiRPq3bu3du3aZTE6plq1avriiy/Mi8Ti5YWFhcnZ2TnW1DUgOXTs2FEmk0kLFy5UmjRp1LFjxxeeYzKZtGjRomSILvXLkiWL+vbtqxEjRujmzZvKli2bRbJjwYIFGjhwoMLDw60c6athGksqdP78eUVGRvKLOwFmzZqljRs3asuWLXHW161bV/Xr1+cfoC+pWLFiCgwMtHYYNiUqKkqrVq0yz5v09/dX8eLFFRYWph07dqhSpUpsSfkSTp48qUmTJilNmjTmxYcfPXokScqTJ48++eQTffbZZyQ7EoD3ZtJhd5vXp2jRotq+fbtu376tc+fOKTo6Wnnz5lW2bNmsHVqqcuTIEY0YMUJ79+7Vw4cPtXXrVtWoUUM3btxQp06d1LdvX1WrVs3aYaYKFy9e1MWLF/X222+by/788099/vnnioyMVKtWrdgO+Tl27twpOzs7RUdHK02aNNq5c+cLp00yrfLlRUREPPfn4507d5IxmteHMWkp2KxZs8wLGsXo0KGD8ufPr2LFiqlMmTKsLv6SFi1apCJFisRbX6RIEfN8SrzYhAkTtGDBAm3fvt3aodiE0NBQVapUSa1bt9aKFSu0bt068/BhZ2dn9erVK9XPmUwuGTJkkL29vSQpU6ZMcnBw0JUrV8z1Hh4eunDhgrXCS3V4byatadOmadGiRdq/f7+1Q7EpFy9e1K1btyRJmTNnVtmyZVW+fHnzP+QjIiLM0wMRv/379+vtt9/W2bNn1bZtW4vdrbJmzaqwsDC28E2AXr16WWyDfPXqVVWvXl1r1qzR3r171aRJE61Zs8Z6AaZwgYGBOn/+vHk75MDAQF24cOG5r/Pnz1s56tSjSJEiz93e98cff1SpUqWSMaLXg2RHCvbVV19ZfFu2ZcsWLVu2TF26dNHs2bN1/vx5dmh4Sf/8889zR8IUKlRI//zzTzJGlLrNmTNHbm5uql27tt544w3VqlVL9evXt3ixQOnLGzJkiE6cOKEtW7bo/PnzFkOw06RJo6ZNm2rTpk1WjDD1KFiwoMUimiVLltQ333yjx48f68GDB1q+fLm8vb2tGGHqwnszaT29u03x4sXl5+fHz84kkCdPHuXOnVvLly+Ps37NmjXy8fFJ5qhSn2HDhqlw4cI6efKkJk6cGKu+evXqOnTokBUiS50OHz6sWrVqmY+//vprRURE6M8//1RwcLDeeecdTZ061YoR4r+sT58+WrlypT777DPzbpTR0dE6d+6cPvjgAx04cEB9+/a1cpSvjmksKVhQUJDFH+jff/+9fHx8NG/ePElPVhj/5ptvrBVeqmJvb6+QkJB4669cucLiWwnw9A4NUVFROnfuXKw2DCV8eT/++KN69uypWrVq6ebNm7HqCxQooKVLlyZ/YKlQo0aNNGvWLE2dOlUODg4aPny4GjRooEyZMslkMunevXtavHixtcNMNXhvJi12t3l9smbNqg8++EC//fabPv/8c36nJ8Jvv/2mSZMmycHBwbwrw9Ny5Mjx3H9LwdKtW7fk7u5uPt6wYYOqVq2qfPnySZIaN26sYcOGWSu8VOuvv/7Spk2bzNOp8+TJo7p167JTUAK1bdtWQUFBGjFihIYPHy5JqlOnjgzDkJ2dnSZOnGgT06xIdqRgz64du3XrVotvfPLkycMvnZdUoUIFLV26VH379o21+nVYWJiWLFmiChUqWCm61If1OpJWWFjYc791fPTokR4/fpyMEaVeAwYM0IABA8zH9erV0+7du7VmzRqlSZNGfn5+ql69uhUjTF14byYtfna+PhMmTFBwcLCGDRumP//8U9999x3rdSRQunTpLKauPCs4OFjOzs7JGFHqli1bNgUFBUl6MiXw4MGD+vTTT831jx8/5udnAkRGRqpr16765ptvzH+QS09GIwwdOlRt2rTRV199ZZ7KihcbPny4PvjgA61evdq81lG+fPnUuHFj5c2b19rhJQmSHSlYgQIFtHbtWn388cfasmWLLl++rLp165rrL126pEyZMlkvwFRk9OjRqlq1qkqWLKk+ffqoaNGikp5kh2fMmKErV67EO/wVeN3y5cun33//Pd76rVu3PnfNGTxf5cqVzdt2I2F4byI1GThwoMqUKaOWLVuqbNmyWr16tUqXLm3tsFKNChUq6IcfflCfPn1i1d27d09LlixR1apVkz+wVKpmzZqaNWuWXFxctHv3bkVHR1t8U37y5EnlypXLegGmMoMHD9bXX3+tTz75RD179lS+fPlkMpl07tw5zZo1S/PmzZObm5tmzJhh7VBTFW9vb5uYrhIvAynWihUrDJPJZGTKlMlIly6dUbRoUePRo0fm+qpVqxp16tSxYoSpy9atWw0fHx/DZDIZdnZ2hp2dnWEymYy8efMaW7ZssXZ4qc7jx4+NFStWGF26dDEaNmxo/O9//zMMwzBCQ0ON1atXGyEhIVaOMPWYPn26YW9vb6xcudK4fv26YTKZjJ07dxoPHjwwhg0bZtjZ2RlfffWVtcNMVS5dumQsX77cmDFjhvHvv/8ahmEYUVFRxs2bN43Hjx9bObrUg/dm0uNnZ9IzmUxGQECA+fjff/81ypcvbzg6OhpLliwxvv32W8POzs6KEaYOBw8eNBwcHIz33nvP+OabbwyTyWRMmzbN+PLLL42CBQsaGTJkMP78809rh5lqhISEGBUrVjRMJpPh4OBgzJgxw1z34MEDI0uWLEbPnj2tGGHqkiVLFqNdu3bx1rdt29bIkiVLMkZkGw4cOGBMnDjR6NOnj/H3338bhmEY9+7dM44ePWrcuXPHytG9OpIdKdzWrVuNPn36GGPGjDGuXbtmLr9586bRqFEjY82aNVaMLvWJiooyjhw5Ynz33XfGd999Zxw5csT4v/buPKrm9I8D+Pt7W1S0oGKINtnKNhohhezE2EK27FnG0oxlyD52Y4zsDGEoW5EtJpSyxAwztoRC9ok21FTqfn9/OO5v7lQU1ffeer/OcY77fZ7ydk/L937u83weuVwudSy1k5SUJDo4OIiCIIj6+vqiTCYTT506JYriuxv5qlWritOnT5c4pfqQy+XiiBEjREEQxPLly4uCIIiVK1cWtbS0REEQxNGjR0sdUW3I5XLRy8tL8dz9+2szOTlZ1NfXF1euXCltSDXCr83CxZ+dReO/xQ5RFMXMzEzR09NTFARBtLOzY7Ejn06dOiXWrFlTFARB6U+NGjXEsLAwqeOppeTkZDEjI0PpWlpamvjXX3+JCQkJEqVSPwYGBuK6devyHF+3bp1oaGhYfIHUXEZGhtijRw/Fm7///n30zz//iBUrVhQXLFggccrPJ4jifxpDEBF9xOjRo7Fr1y4EBgaiUaNGMDU1xcmTJ+Hi4gLgXYfnsLAw/PXXX9IGVTNnz57Fvn37lPZN9unTB87OzlJHUxvLli3D9OnTMW3aNLRp0wbt2rVT+tocMmQIYmNjERERIXFS9XL27Fns378fd+/e5dfmZ+DPzqIhk8mwc+dO9O/fP8eYr68vxo4di8zMTGRnZ0uQTj39+eefSr+LGjduzOa5BZCWlgYnJyeMHDkSo0ePljpOidCnTx9kZmbi4MGDuY5//fXXKFOmDPbu3Vu8wdTUtGnTsHLlSqxZswatW7dGrVq1lH4fjRkzBpcvX8alS5ckTvp52LODSo3s7Gzs3LkTR48eVTSMMjc3h6urKwYMGAANDQ2JE6oPntBQNFq0aIEWLVpIHUOtbd68GYMHD8aiRYty/dqsX78+goODJUim3vi1WTj4s7NofKip5tChQ9GtW7dcTxehvDVq1AiNGjWSOoba0tPTw/3791kg+gyJiYlKj3/44Qf06dMHPXv2xLhx41CjRg0AwN27d7F27VrExcVhz549UkRVS/7+/hgzZgxGjRqV6++jOnXqYN++fRIkK1wsdqi4a9euYfXq1bhy5QpSUlJy/EIXBAGxsbESpVMfKSkp6NChA37//Xfo6+srOgyHhIQgICAA69evx4kTJ2BgYCBxUvXAExqKxpMnTxAeHo74+Hj06tULZmZmyM7ORkpKCgwNDVmQy4dHjx6hefPmeY6XLVsWr169KsZERP/Hn53SqFixIipWrCh1DLXw6tUrrFu3DqGhoYiPj8fGjRvRpEkTJCYmYtu2bejWrZviRSZ9WMeOHXHixAl4enpKHUUtGRsb5ygWiaKI69evIygoKMd1ALC1teXP0HyKj4//4HG9GhoaSEtLK8ZERYPFDhUWFhaGjh07onz58rC3t8eff/4JFxcXpKen48KFC7C1tWWX8Xzy9vbG5cuXsXr1aowcORJaWloA3t1Y/vLLL5gwYQK8vb2xevVqiZOqB57QULhEUcR3332HNWvWICsrC4IgoF69ejAzM8ObN29gYWGB+fPn59ohn5SZmpri0aNHeY5fvnwZ1atXL8ZE6qdbt24Fmi8IQo4bT8odf3YWjmHDhkEQBGzatAkaGhoYNmzYRz9GEARs2bKlGNKpr8ePH6Nly5Z49OgRbGxsEB0drVgRU6FCBWzcuBFxcXFYtWqVxEnVw6xZs+Dm5oZBgwbB09MTlpaW0NXVzTGvQoUKEqRTfbNnz+bKmCJUrVo1REdH5zl+7ty5ElHYZLFDhc2ePRtWVlaIjIxEZmYmTE1NMWPGDLi4uODixYvo1KkTli5dKnVMtXDgwAGMHTsWY8eOVbqupaWFMWPG4NatW9i/fz+LHfk0YsQITJs2Da1atUKbNm0AvLuRzMjIwPz583H8+HFs2rRJ4pTqY/ny5Vi1apVSn4n3DA0N0bNnTwQEBLDYkQ89e/bEhg0bMGTIEBgaGgKA4mbpt99+w7Zt2zB16lQpI6q8a9euKd1gyuVyPH78GKamptDR0ckxnzej+cefnYXj9OnTkMlkkMvl0NDQwOnTpz/6dciv04+bMmUKXr9+jb/++gumpqYwNTVVGu/evTuOHDkiUTr1Y2trC+DdEbN+fn55zmMvmdzNnTtX6gglWv/+/fHTTz+hV69eqFmzJoD//5zcvHkz9u7diyVLlkgZsVCw2KHCrly5gnnz5sHAwABJSUkA/v8D0cHBAZ6enpg1axY6deokZUy1kJCQgFq1auU5Xrt27Rx7AylvEydOxM2bN+Hu7g4jIyMA735oJiQkICsrC56enhg+fLi0IdUI+0wUnnnz5iE0NBQNGzaEk5MTBEHA0qVLMWvWLFy4cAGNGjXCjBkzpI6p0h48eKD0+OXLlzA1NcWuXbsUjcvo0/BnZ+H479fofx/Tp/ntt9/g5eWFunXr5vq7yMrK6oMr50gZVyYUj8zMTLx9+xZly5aVOopa8fb2RmRkJJydnVGnTh0IggAvLy8kJibi8ePH6Ny5M7y8vKSO+dlY7FBhmpqa0NfXBwAYGRlBS0sL8fHxinErKytERUVJFU+t1KhRA4cOHcqxsuO9Q4cOwdrauphTqS9BELB582Z4eHjwhIZCwD4ThcfQ0BCRkZFYsWIF9u/fDx0dHZw5cwbW1taYM2cOpkyZkusyYsobb9YLz79/dvLkJVI1//zzD0xMTPIcf/36dTGmUX9cmVC4du/ejYsXL2LlypWKa/PmzcPChQshiiJcXV3x66+/oly5chKmVB/a2to4fvw4du3ahf379yM7OxsZGRmoX78+FixYgEGDBpWI3/8sdqiwGjVq4O7duwDe3SDVrl0bBw4cwIABAwAAR48eReXKlaWMqDbGjh2Lb775Bp07d8akSZMUy7Vu374NHx8fhISEYM2aNRKnVD88oaFwsM9E4dLV1cXMmTMxc+ZMqaMQ5Yo/O0kV1a1bF+Hh4Xk21Dx48CBPaCHJrFixQunr7/z585g3bx66dOmCOnXqYPXq1Vi4cCEWL14sYUr1IggCBg4ciIEDB0odpciw2KHCOnfujK1bt2Lx4sXQ1NTEt99+i6FDh8LGxgYAEBsby2/ofBo7dizi4+OxZMkSnDhxQmlMS0sLs2fPxpgxYyRKp3769OkDd3d3dO7cGWXKlJE6jtpjnwmi0iUxMREnT55UbL+wtLSEi4sLTwz5DMHBwfjpp58Up9e9P53h39gb4cMmTZoEDw8P1K9fH25ubgDe9eyJiYnBvHnzcOHCBQQEBEicUn3Mnz//o3MEQcCsWbOKIY36i42NhYeHh+Kxn58fKleujAMHDkBTUxNyuRwBAQF8bVRA2dnZuHz5stLvoy+//LLEnAAoiLn9NiCV8PbtW7x69QoVKlRQvPDZuXMnAgICoKGhAVdXVwwZMkTakGrm5cuXOHnyJOLi4gAA5ubmaNu2LYyNjSVOpl6qVq2KZ8+eQV9fH926dUPfvn3RoUMHxSk3VDApKSlwdnbG/fv34eTkhOPHj6Ndu3Z48+aNos9EeHg49PT0pI6qFm7dugVfX1/cu3cPSUlJOV70CIKAU6dOSZRO/SQkJMDExAQnT55kz45CMHfuXCxduhQZGRlK17W1tTF16tR8vUAiZQEBAejTpw9sbW3h5OSE9evXo3///hBFEUFBQbCxsUH37t0xZ84cqaOqvIULF2Lu3LkQRRFyuRwymQyiKEImk2HBggWYNm2a1BHVhkwmy3NMEASIoghBEFiEy6eyZcti1apVGDFiBACgZs2acHJyUpyytHXrVnzzzTcl4rjU4rJt2zZMnz4d8fHxinslQRBgYmKCRYsW5eukK1XHYoeKevv2LW7duoUKFSrAzMxM6jhESkRRREREBPbs2YOAgADEx8fDyMgIPXr0QN++fdGmTZsSUxEuLv/884+iz8R/e6Cwz0T+/frrrxg6dCi0tLRQq1YtlC9fPtd5oaGhxZxMffy3WfP7Bs8HDhyAk5NTrh/DoxPz54cffsCcOXPQpUsXfPPNN0pbKtesWYPg4GDMnTuX7/QWkL29PbS0tHD27FkkJSXB1NRUUZx78OABmjZtimXLlmHw4MFSR1ULDx8+REBAgFJPmZ49e8LKykrqaGpPLpcjLi4Oa9euRXh4OIKDg7miK5/s7OxgZ2eH3bt3448//kCTJk2wZ88exSqkxYsXY+XKlUr9DSlvGzduxJgxY9CwYUN4enoq/T7auHEjrl27hrVr12L06NESJ/08LHaoqOzsbOjo6GDFihWYMGGC1HHUzsOHDz/p49gXoeDkcjlCQ0Oxd+9eHDhwAC9fvkTFihXRq1cvbNiwQep4VMpYW1ujQoUKCA4O5oqtTySTyXI0JXv/DmRe+M5k/lStWhX29vYICgrKdbxr1664fPkynj59WszJ1Juenh4WL16MiRMnIjk5WfEzoEOHDgDebSfYs2cPbt68KXFSov8bMGAARFH84LG09H+rV6/GxIkTYWdnh8ePH6NcuXK4ffu24s0gV1dXpKam8s2MfLKyskK1atVw8uTJHCuz3759CxcXFzx58gT37t2TKGHhYM8OFaWhoQFzc/Mcy1wpfywsLD6pgzBv2AtOJpOhTZs2aNOmDdatW4etW7di8uTJ2Lx5M4sd+XTixAk4Ojqyg3ghePr0KSZPnsxCx2fgcYlFJyUlBR07dsxzvHPnzggLCyu+QCWEnp4etLW1Abw7va5MmTJ49uyZYrxSpUq4f/++VPHUhru7O1q2bIkWLVrAzs5O6jglnrOzM7cFFcD48eOho6ODY8eOoXHjxpg2bZqi0JGYmIjnz5+r/SqE4vT8+XN89913uW5B19LSQr9+/UpEvzgWO1TY+PHjsWbNGgwfPpxLhAto69atvFkvRs+ePcO+ffuwZ88eREZGAsAHj1IlZZ06dYKGhgYaNGgAJycnxZ8PHQFIuatfvz7fFf9MPC6x6Dg6OuLixYt5NsS+ePEiHB0dizmV+qtVqxaioqIUjxs2bIhff/0VAwcORFZWFvz8/LhyMx/++usv7NmzB4IgwMjICI6OjnBycoKzszMaN24MTU2+bChMf/zxxwf7elBOI0eOxMiRI3Ncr1ChAv744w8JEqmvRo0a4c6dO3mO37lzBw0bNiy+QEWE21hU2E8//YStW7fiyZMn6N27NywsLHLs2xcEAV5eXhIlpNIsPj4e+/fvx549e3Du3DnI5XI0adIEffv2RZ8+fVC1alWpI6qNS5cuITw8HGfPnsXZs2eRmJgIQRAUzbfe/7GwsJA6qso7d+4c3NzcsH//fhbcSOXcv38fHTt2ROfOnTFu3DhFD4R79+5hzZo1OH78OI4fP87v9QL68ccf4ePjg7t376JMmTI4cuQIvv76a+jq6kIQBKSmpmLr1q1s6p4PL1++REREBCIiInD27Fn8+eefkMvl0NXVhYODA5ydndnoNZ927NiR6/Xk5GSEh4cjMDAQI0aMwKZNm4o5mXrLyMjAlStXEB8fD0dHR67k/ERXrlxBly5d8P3332PUqFGK15j//PMPNmzYgGXLluHYsWNqf9w0ix0qLD/VXnZx/ri0tDRUq1YN33//PaZMmSJ1nBKhTZs2CA8PR3Z2Nho2bIi+ffuib9++vEEvJFFRUYqbzfDwcDx58gSCICArK0vqaCqnW7duOa7dvXsXd+7cQd26dVG9evUczXIFQcizZwJRYdLX18+xyjArK0uxRfX973m5XA4AKFOmDLS0tJCSklK8QUugiIgIBAQEQFNTE126dEHr1q2ljqSWUlNTsW/fPixZsgR37tzhfWcBfOg+3tjYGCNGjMDs2bOho6NTjKnUm4+PD+bOnYvk5GQIgoCQkBC4uLjg5cuXqF27NpYtW1YiThApCvXr189xLTExEc+ePYOmpiaqVKkC4N124KysLHzxxReoWLEirl69WtxRCxXXo6kw7i8tHHp6etDU1ETZsmWljlJixMfHY86cOejbty9sbGykjlOipKenIz4+HvHx8fj7778VR6daW1tLHU0lXbt2Ldcta9WrV8ebN2+Ulra/xy1uVFx69erFrzeJvF8RRwV3584dRcE9IiICDx48QLly5dChQwc+pwWQ2328IAgoX7489PX1JUik3nx9fTFp0iT069cP7du3VypqGBsbw8XFBbt372axIw8VKlTI8fuoYsWKOe7jS9obl1zZQaXC2LFjER0djVOnTvHGk1TOkSNHFDeVly9fRnZ2Nuzs7ODs7KzYL12pUiWpYxIRqYzcVnV9CFd0fVzv3r1x9uxZvHjxAhUrVlQUjJydndGwYUP2lyiA7OxsvHjxAkZGRly5UUjs7OxgY2ODAwcOICEhASYmJoojpgFg6dKl8PHxwZMnTyROSqqEKzuoVOjXrx/Gjh2L1q1bY+TIkbn2PwGAL7/8UoJ0VNp169YNGhoa6NWrF2bOnAlHR0cYGhpKHYuIipgoinj06BEqV66sOE2E8ufIkSPQ0dFB5cqVkZ/37fhGx8cFBgZCJpPBzc0No0aNQrNmzXK9V6K8iaIIb29vrFmzBqmpqdDQ0ECXLl2wZcsWHjbwmWJiYjBhwoQ8xytUqICEhIRiTETqgMUOFWZpafnBX86CIEBHRwdmZmZo3bo1PD09Ub58+WJMqD5atWql+HtERESOcVEUuQ+1gIKDg/HTTz/hypUrSElJyfVmk89n/nTp0gXnz5/H3r17ceHCBcU7aU5OTqhTp47U8dRKs2bNFEcntmjRAkZGRlJHUmvh4eGoU6dOnicDvXz5ElFRUXB2di7mZCVDfHw8LC0tFfvOKf+qVq2KJ0+ewNjYGP3790e/fv1QuXJlqWOptf379ytWGXbo0AEymQxffvmlYoVHixYteJ/5Edu2bcOSJUtgZmaGjh07IjY2FkFBQZDL5VxZ9JmMjIzw8uXLPMejoqL4M4By4DYWFTZkyBBcuXIFN27cQL169VCjRg0A7yqb169fR7169VCzZk3ExMTg6tWrqFKlCiIiImBpaSlxctWzffv2fM3z8PAo4iQlQ0BAAPr06QNbW1s4OTlh/fr16N+/P0RRRFBQEGxsbNC9e3d2bC+gGzduKO2Tfvr0KSpUqKA4/u+7776TOqLK69SpEy5cuIBXr15BJpOhbt26SsWj9w24KH80NDTw66+/on///rmO79mzB/3792dh8xP9/fff+OKLL5SWYlP+nTlzBn5+fti/fz9evXqFli1bYsCAAejduzd7Inym169f4/z58zh79iwiIiLw+++/IyMjA3Xq1MH169eljqeymjRpguzsbJw9e1axKmbixIlYu3Ytnj9/zpNDPsOwYcNw+vRp/PXXX8jOzlbaxnLz5k04ODhg2LBh8PHxkToqqRKRVNaBAwfEChUqiOHh4TnGwsLCxIoVK4pHjhwRRVEUQ0NDRT09PbFv377FHZNKocaNG4tNmzYVs7KyxBcvXoiCIIinTp0SRVEU79+/L1aqVEncvn27xCnVW3p6uujr6yvWrl1bFARBlMlkUkdSG3K5XLxy5Yq4atUq0c3NTaxcubLiObSyshKHDBkidUS1IQiCuGvXrjzHt2/fLmppaRVjopLl+fPnokwmU/z8pE+TmZkpHjx4UOzTp4+op6cn6ujoiD169BD37dsnpqenSx1PbcXGxoq+vr7i0KFDRXNzc/4uyocKFSqIP//8s9K16OhoURAE8fz58xKlKhmePHkimpmZiVWrVhVHjx4tymQycfDgweKAAQNEHR0d0dLSUnzx4oXUMUnFcBuLCps9ezbGjx+fa+frli1bYty4cZgxYwa6dOmCVq1awdPTM88zvYkKU1RUFBYvXgwNDQ1oar77MfL27VsA77o4jx07FkuXLsXgwYOljKlW3rx5g3PnziE8PFzxLlpmZiY0NTXRtGlTdsAvAEEQ0KhRIzRq1AgTJkxAZmYmdu3ahaVLl+LOnTt48OABfH19pY6psh4+fIgHDx4oHkdHRyM8PDzHvOTkZGzcuBHm5ubFmK7kEbnA9rNpaWnh66+/xtdff403b94gMDAQGzZsQN++fTF37lzMmjVL6ohq4caNG4rfQREREXj27BlEUUT16tUVW1n4u+jDkpKScmz7e7+aIz09XYpIJUaVKlVw+fJlzJgxA3v27IEoivj111+hr68Pd3d3LFmyhCtnKAcWO1TY3bt3P7g3skKFCrh7967icZ06dZCamloc0dRSeno6AgICFD0m5HK50rggCNiyZYtE6dSLnp6eopmekZERypQpg2fPninGK1WqxKOTC6Bx48a4du0asrOzUa5cOTRr1gwzZsyAk5MTHBwc2CCugN68eYPz588rbtgvXbqEjIwM1K5dG56enrxZ/whfX1/MmzcPgiBAEAQsXLgQCxcuzDFPFEVoaGhg48aNEqQsGSpUqIDQ0FA0bNhQ6iglQkZGBk6cOIGgoCD8+eef0NHRKXHHKBal+vXrQxAE1K1bF926dVP06ahWrZrU0dQKm+EWHVNTU/zyyy/45Zdf8OLFC8jlcpiYmPCkoAJKT0/Hpk2b0LBhwxLfc4s9O1SYra0typQpg3PnzuV4sZOWlobmzZvj7du3uHnzJgBg3rx52LJlCx4+fChFXJUWFxeH1q1b48GDBzAyMkJKSgoqVKiA5ORkZGdnw9jYGOXKlcO9e/ekjqoWHB0d8eWXX2L16tUA3jWF1NPTw4kTJ5CVlYUOHTrg77//RnR0tMRJ1UOvXr0U75g1atSIv7Q/g729Pa5evQpBENCgQQNFrw4nJydUrFhR6nhq4datW4iKioIoiujTpw8mTJiQo0AkCALKli2Lhg0b8ljkAhgzZgwGDRqE5s2bSx2lxJDL5QgJCYG/vz8OHjyItLQ0tG3bFv3790ePHj1QtmxZqSOqjcOHD7MJ6WeSyWSoVq2a0olq2dnZuHXrFiwtLXN8PQqCgKtXrxZ3TCLo6urCx8cHI0eOlDpKkWKxQ4Xt27cP/fr1g5mZGTw8PGBtbQ3gXYPSHTt24MmTJ/D394ebmxuys7NRu3Zt2Nvbw9/fX+LkqqdPnz44deoUgoODYWVlBVNTU5w8eRKOjo7w8fHBmjVrcPLkSdjY2EgdVS38+OOP8PHxwd27d1GmTBkcOXIEX3/9NXR1dSEIAlJTU7F161YMGTJE6qhqITs7GxoaGh+ck5iYyGPr8kEmk0Emk6FHjx7o3LkznJycFM2dqeC2b9+Oli1b8t3xQmJoaIg3b97AwsICAwcOxMCBA/l75xOdP38efn5+2LdvHxISEtC0aVP0798fffr04VL2T3Tx4kU4ODhIHUOttWrVqsArO0JDQ4soTcmTlJQEf39/3Lt3D0lJSTm2AXKVdv599dVXaN++fa4rN0sSFjtU3NGjRzF9+nTcuHFD6bqdnR0WLVoEV1dXAEBWVhaePHmC8uXLw8DAQIqoKs3Y2BhjxozBDz/8gMTERBgbGyMkJARt2rQB8K7D899//42jR49KnFR9RUREIDAwUHGmfOvWraWOpDYGDBiAnTt35nmD9PTpU7Rv3z7HzwHK6cqVK4rtK2fPnsWLFy9gamqqtN+8QYMGXGZMksjIyMChQ4ewc+dOHD9+HFlZWbC3t8fgwYPRt29fvkgvAJlMBl1dXXTu3Bnu7u75Ksh9+eWXRR9MjclkMtSoUQODBg3CgAEDYGVlJXUkIoUTJ06gd+/eSE1NhYGBQa4rkARB4CrtfPrtt9/Qv39/7N69G23btpU6TpFhsUNNPHv2DHFxcQAAc3NzfPHFFxInUi96enpYvXo1hg8fjqysLJQpUwb79u1Dz549AQCbN2/G5MmTkZKSInFSKo3Kli2Lvn37YuvWrTnG7t+/jzZt2iAzMxOPHz+WIJ16u3PnjqL4ERYWhkePHsHAwABJSUlSR1Mb7HdUNJKSkrB3717s2rUL586dg6amJtq1a4fBgwejW7du0NHRkTqiSvv3dr+PFS9FUYQgCDwi+SP8/Pywa9cuhISEIDs7G02bNsWgQYPQp08friwkydnZ2SEjIwOBgYGoV6+e1HHUXrdu3RAdHY3Y2FhYWlrC0tIyR9sEQRAQFBQkUcLCwQalauKLL75ApUqV8PjxY+47/wTVq1dXvFDU1NRE1apVERkZqSh2REVF8cbyEzx58gTh4eGIj49Hr169YGZmBrlcjuTkZBgaGn50awa9c+DAAXz99dcoU6YM1q9fr7h+69YttG3bFmXKlMn1NAz6sH/++QePHz/Go0eP8PDhQ7x48QKiKOLNmzdSR1Mb+e13RAVXvnx5eHp6wtPTEw8fPsSUKVOwb98+BAcHQ19fH71798aECRNQv359qaOqJJ6oVPj69++P/v374+XLl9i9ezf8/PwwduxYTJo0CR07dsTAgQPRrVs3RYNyouIUExOD5cuXs9BRSK5duwZBEFC9enVkZ2cjJiYmx5wSsQq2+E+7pU/1/PlzUSaTiadOnZI6itoZM2aM2KhRI8XjWbNmiZqamuKIESPEYcOGiVpaWuLw4cMlTKhe5HK56OXlJWppaYmCICh9XSYnJ4v6+vriypUrpQ2pZg4fPixqa2uLEydOFEVRFP/44w/R2NhYrFu3rvjkyRNpw6mRw4cPi1OmTBGbNm0qamtri4IgiLq6umLLli3FmTNniidOnBBfv34tdUy14ebmJlaoUEG8ePGi+OLFC1EQBPHUqVNienq6uGzZMrF69erinTt3pI6pth4+fCguXrxYtLOzEwVBEI2NjcVx48aJXl5eoomJiaihoSGuW7dO6phUisXExIjz588Xa9WqJcpkMrF8+fLiyJEjxYiICKmjUSljZ2cnLly4UOoYpGZY7FAjz58/V9xoUsHExcWJ+/fvF9PT00VRFMV//vlHHD58uGhkZCRWrFhR9PDwEFNSUiROqT6WLl0qymQycfr06eLJkydzfF16eHiILVq0kDChegoICBC1tLREd3d30dDQUGzcuLH48uVLqWOpFUEQxPLly4uurq7i0qVLxfPnz4uZmZlSx1JbFStWFGfOnCmKoigmJCSIgiCIJ0+eVIwPHTpU7Ny5s1Tx1FJSUpK4ceNG0dnZWdTQ0BDLlCkj9uzZUzxw4IDS12p6errYo0cPsXLlyhKmpdLuyZMn4o8//ig2aNBAFARBrFChgmhqairKZDLR3t5evHnzptQRqZQ4ePCgWLVqVfH+/ftSRyE1wm0saqZELCeSQPXq1VG9enXFYx0dHcU53VRwmzdvxuDBg7Fo0SIkJCTkGK9fvz6Cg4MlSKbeevbsiR07dmDgwIFwdHTEkSNHoK+vL3UstXL16lXY2dnxZ2UhSUtLUzR+NDAwgCAISr2NmjVrhsmTJ0uUTv306NEDwcHByMzMhIODA1avXo1+/frl2mivTJky6N27Nw4ePFj8QalUe/36Nfbv349du3bhzJkzkMlk6NSpE2bPno2uXbtCJpPhwIED+O677zB06FBcvHhR6shUCpw6dQomJiaoU6cO2rVrh2rVquXYLi0IAlatWiVRQvX1+vXrXHtyAVB6/aSOWOxQMyL7yZIKePToEZo3b57neNmyZfHq1atiTKRePrYHX0dHB8+ePYOjo6PimiAIuHr1alFHU3vcy1u42O+ocP3111+YMmUKBg8enK8jZ9u1a8djKanYBAUFYdeuXThy5AjS09Px1Vdf4eeff0a/fv1y9Ivr3bs3kpKSMG7cOInSUmmzZs0axd+PHDmS6xwWOwpm/fr1+Omnnz54go26N3ZmsUONVKhQAaGhoWjYsKHUUdQSz+YuPKampnj06FGe45cvX1b7SnBRqlChwgdXHrAJMakKFxcXBAUFYc6cOQCAIUOGYPHixUhKSoJcLsevv/6KwYMHS5xSffj5+aFZs2YfnLN+/XqMGTMGAGBiYoKWLVsWRzQi9OjRA9WqVYOXlxcGDx6MWrVqfXB+gwYNMGDAgGJKVzKkpaVh9+7dyMjIQOfOnWFubi51JLWR26oD+nQbNmzAuHHj0KFDBwwbNgze3t7w8vKCjo4Otm3bhkqVKmHChAlSx/xsPHpWhUVGRqJp06YfnPPvmyLKG8/mLlyTJk2Cn58fIiMjYWhoCBMTE5w6dQqtW7fGb7/9BldXV0ydOhULFiyQOioRfYaHDx/i999/h6urK8qUKYP09HR88803CAgIgIaGBlxdXeHj4wMDAwOpo6qF8uXL49SpU/jyyy9zHV+8eDFmzpyp9u+kkXoKCwtDq1atpI5RYgwfPhwXL17EjRs3AACZmZmwt7dXPDY0NMTp06fRqFEjKWNSKWVra4vq1asjODgYCQkJMDExwcmTJ+Hi4oKUlBTY29tj9OjR+O6776SO+nmkbRlCH2JkZCRevnw5z/FFixaJMpmsGBOpL1tbW7FGjRritWvXpI5SIiQnJ4v169cX9fX1xc6dO4symUzs0KGD6OjoKMpkMrFx48Ziamqq1DGJ6BNMnz5dvHr1qtQxSqQuXbqIFStWzPV30ffffy8KgiBOmzZNgmREVNgsLS3FWbNmKR77+vqKgiCIfn5+4s2bN8XatWuLX3/9tXQBqVQrU6aMuHbtWlEURTElJUUUBEEMDg5WjC9ZskS0srKSKl6h4TYWFebo6Ij27dsjNDQ0xz706dOnY+nSpZg6dapE6dQLz+YuXIaGhoiMjMSKFSuwf/9+6Ojo4MyZM7C2tsacOXMwZcoU6OrqSh1Tbfj7++PEiRPYtm1bruNDhw5Fp06d0KdPn+INRqXSkiVLYGdnp+gtk5CQAFNTU4SEhMDFxUXidOotMDAQrq6uaNu2Lc6cOYPatWsDAMaNG4f169dj4cKFmD59usQpqbQ7d+4crly5kmvDQkEQMGvWLImSqZfnz58rGjwDwMGDB2Fvbw93d3cAwMiRI7F8+XKJ0qmn4OBg/PTTT4qvTzGXDQpcGZc/hoaGyMrKAvCuAbmenp7SFnV9fX08f/5cqniFhsUOFcabosJjY2OD169fSx2jRNHV1cXMmTMxc+ZMqaOovZUrV35wGauuri5WrlzJYgdJJrcbSio4bW1tHDp0CB07doSLiwtOnjyJxYsXw8/PD6tXr2azR5JUYmIiunTpgkuXLkEURQiCoPjef/93Fjvyr2zZskhOTgYAZGVlISwsDOPHj1eM6+vrK51uRR8WEBCAPn36wNbWFv369cP69evRv39/iKKIoKAg2NjYoHv37lLHVBt2dnZKje+bNm2K9evXo3PnzpDL5di4cSNq1qwpYcLCwWKHCuNNUeFZsGABxo0bh/79+ytV2YlUwe3btzFs2LA8xxs0aAB/f/9iTKT+oqKi8mxGDIBNNUkyOjo6OHr0KNq1a4dGjRpBFEVs374dAwcOlDoalXJTpkzBtWvX4OfnBwcHB1hZWeHEiROwtLTEypUrceHCBR4rXwBffvklNm/ejNatW+PQoUN4/fo1unbtqhiPjY1FpUqVJEyoXhYvXowmTZrg7NmzSEpKwvr16zFs2DC4uLjgwYMHaNq0KSwtLaWOqTYGDhyIDRs2ICMjA2XKlMG8efPQtm1bxQEDWlpaCAgIkDjl52OxQ8XxpujT5NY9mGdzf7oPvRDPC0+3yT9RFBXv/uQmKSkJb9++Lb5Aaiw2NhYDBw5UvDOZG0EQWOygYhEYGJjn2IgRI3Djxg10794denp6SnPfH+1LVJyOHTsGT09P9O3bFwkJCQAAmUyGGjVqYO3atejZsycmTZrE4ns+LVy4EB06dIC9vT1EUUTv3r3RpEkTxfiBAweUjpinD4uKisLixYuhoaEBTc13L2Hf3xtZWFhg7NixWLp0KX+/59PQoUMxdOhQxWNHR0fcvHkThw8fhoaGBtq3b8+VHVS4eFNUeP59Fvd/8Wzugjt9+vQHj0rNTUHnl2aNGjWCv78/vv32W2hrayuNZWRkwM/Pj93a88nT0xPXr1/Hzz//DCcnp1xPXqKPe/DgAa5cuQIAimXWd+/ehZGRUa7z8zpdpLTr3bu30laAf3t/fefOndi5c6fSde45JykkJyfD1tYWAFCuXDkAwJs3bxTj7du3x4wZMyTJpo7s7e0RHR2N8+fPw8jISOkY6eTkZIwdO5ZHSxeAnp6e4h7JyMgIZcqUwbNnzxTjlSpVwv3796WKVyJYWVlh4sSJUscoVDx6VoXIZLKP3hTldp03RUTqLTg4GK6urmjatCm+//57xc3mjRs3sHjxYly6dAmHDh1Cly5dJE6q+nR1dTFjxgzuKf8M738X/dv7vfr/9f46fw/l7syZM5/0cXwBRFKwtrbGsGHD4O3tDQCoXLkyxo4di9mzZwMAZs2ahXXr1ilWfRAVJ0dHR3z55ZdYvXo1AKBZs2bQ09PDiRMnkJWVhQ4dOuDvv/9GdHS0xElJlXBlhwoJDQ2VOkKJkpGRgYkTJ8LW1lapIdR/+fj4IDo6GqtWrYKWllYxJlQv6enpmDRpEp/PItCpUyds2bIFEydOVGquJYoi9PX1sXnzZhY68snY2BiGhoZSx1Brvr6+UkcoMVi0IHXi7OyMkJAQRbGjb9++WLZsGTQ0NCCXy/Hzzz+jQ4cOEqdUXQ8fPvykj3vfI4E+rEePHvDx8cGPP/6IMmXKwNvbG19//TWMjIwgCAJSU1OxdetWqWOqjdze2PgvHR0dmJmZoXXr1pgyZQqsra2LKV3h4coOKrF8fHwwdepUREVFwcrKKs95sbGxsLW1xcqVKzFmzJhiTKhe+HwWvVevXiEkJASxsbEA3r3L1r59e+jr60ucTH0sXLgQQUFBuHDhQo6+PERElLfr168jJCQE48aNQ5kyZZCUlAQ3NzecPn0awLtiiL+/P7744guJk6qm/Lx4zA1Xxn26iIgIBAYGQkNDA126dEHr1q2ljqQ25s6di6CgINy8eROdOnVCjRo1ALzbsnr8+HHUq1cPLi4uiImJwbFjx6Cjo4Pw8HA0aNBA4uQFw2KHCktMTMTjx49Rv379XMevX78OMzMz7knPQ4sWLWBubo5du3Z9dO6gQYMQFxeH8PDwYkimnvh8kjrYt28flixZgoyMDAwbNizXZsQAex1R8XBxcSnwxwiCgFOnThVBGqJPk5ycDA0NDRbeP2Lbtm1KxQ65XI5Vq1YhLi4OAwYMQK1atQAA0dHR8PPzg4WFBSZMmKDUJJJyl5GRgRMnTsDCwiLP10VUMJs2bcIPP/yAM2fO5HgTMyYmBq1atcIPP/yAoUOH4u7du2jWrBkcHBxw9OhRiRJ/Gm5jUWFeXl64ffs2IiMjcx339PREnTp1eOJFHq5fv44BAwbka27z5s1x+PDhIk6k3vh8Fp73S13fL13N79JXLnX9uL59+yr+Pnny5FznsMcEFRe5XF7gd3r5HhSpmrwaE5OyIUOGKD1euHAh0tPTERMTg4oVKyqNzZ07Fy1atMDz58+LMaH60tbWhpubG1atWsViRyFZvnw5xo0bl+tq7Ro1amDcuHFYvHgxhg4dChsbG4wePRpr166VIOnnYbFDhZ0+ffqD2wC6du2KDRs2FGMi9ZKZmZnjZIu8aGtrIyMjo4gTqTc+n4XHwsICgiDgn3/+gba2tuLxx/AF+sex9xGpkrCwMKkjEH3Q+1OXCoKnL+XPhg0b4OXllaPQAQAmJiYYOXIkfHx8MH36dAnSqRdBEGBjY4OXL19KHaXEePz4seII39xoamri0aNHiscWFhZqeW/PYocKe/HiBYyNjfMcr1ixIuLj44sxkXqpUqUKbty4ka+5N27cQJUqVYo4kXrj81l4tm7dCkEQFA1c3z+mz8eGkERE+Wdvb5/v3z88falgEhISkJaWlud4WloaT7YpgBkzZuDbb7+Fm5ubYksQfTpbW1usX78egwYNQqVKlZTGnj9/jvXr1ytOBwSAe/fuoXLlysUd87Ox2KHCvvjiC/z55595jl++fBkmJibFmEi9tG3bFjt27MD06dNhamqa57z4+Hjs2LEDbm5uxZhO/fD5LDz/Xer638dUOKKiohAXFwcAMDc3R926dSVORKUNt6yROtDR0UGXLl3QoUOHD77TSwXTtGlT/Pzzz+jUqRMaN26sNPbHH39g1apVcHBwkCid+omMjETFihVhZ2eHVq1awcLCArq6ukpzBEHAqlWrJEqoXn788UdFY9Lu3bsrGpTGxMTg4MGDePv2reJ0m/T0dGzbtg2dOnWSMvKnEUllTZo0SdTS0hKDgoJyjB08eFDU0tISJ0yYIEEy9RAbGyvq6emJtra2YmRkZK5zIiMjRTs7O1FPT0+MiYkp5oTqhc8nqYuDBw+KVlZWokwmU/pjbW2d689ToqIiCIIok8nEjIwMpccf+0NUXDZt2iS2atVK1NDQEE1MTMQxY8aIERERUscqEW7cuCGamJiIMplMbN68uejh4SF6eHiIzZs3F2UymWhsbCzeuHFD6phqQxCEj/7hz8+CuXLliti1a1dRT09P8Rzq6uqKXbt2FS9fvix1vELB01hUWEpKClq0aIGoqCg0aNAAdnZ2AN5tEbh69Srq1KmDs2fPsnHUBxw9ehTu7u5ITU2FlZUV6tWrB319fbx+/Ro3btxAbGws9PT04Ofnh65du0odV+Xx+Sw6SUlJ8Pf3x71795CUlJSjSaEgCGxGnA/Hjh1Dt27dYG5ujlGjRqFOnToAgFu3bmHTpk2Ii4vDkSNH0LFjR4mTUmnw/nSGwYMHQxCEHKc15MXDw6MY0hH935MnT+Dn5wd/f3/89ddfqF69Otzd3eHu7s6GkJ/h77//xpIlSxAcHKy00rBz586YOnWqWm4LoJJHLpcrWiOYmppCJpNJnKjwsNih4lJTU7Fs2TIEBgYiNjYWAGBtbY1evXphypQpKFu2rMQJVd+DBw+wdOlSHDlyBE+ePFFcr1KlClxdXTF16tRcOxFT7vh8Fr4TJ06gd+/eSE1NhYGBQa7HSQuCgHv37kmQTr00a9YMGRkZiIiIyPHzMTU1FS1atICOjg4uXLggUUIiItV269Yt7Nq1C/7+/njw4AHq1q2LH3/8ER06dJA6mtrgUalEqoHFDipVXr9+jVevXsHAwIDnxRcCPp+Fw87ODhkZGQgMDES9evWkjqPWypYti0WLFmHixIm5jq9atQozZsxAampqMScjyp1cLsfLly9hYmLCRsWkUq5du4ZJkyYhLCwMc+fOxezZs6WOpDZEUYSOjg5WrVqF0aNHSx1HbaWnp2PSpEmwtbXF+PHj85zn4+ODW7duwcfHR9H8nT5s/vz5H50jCAJmzZpVDGmKDrsQUamir6/PF+WFiM9n4YiJicHy5ctZ6CgEOjo6SExMzHM8MTEROjo6xZiISrs7d+4gMjISXbt2VVq1lZKSgvHjx2Pv3r14+/Ytypcvj7lz5+Kbb76RMC2Vdvfv34e/vz/8/f0RFRUFKysrzJw5k420C4hHpRaOTZs2Ydu2bYiKivrgvC5dumDq1KmoX78+xowZU0zp1NvcuXPzHBMEQXH6kroXO7iyQ8Wlp6cjICAAV65cQUpKCuRyudI49/ETqb969erB3d0dM2bMkDqK2nNzc0NISAiCg4PRrFkzpbGLFy+iY8eOaN++Pfbs2SNRQiptPD09cfz4cTx48EBp5Ua/fv2wd+9e2NjYoH79+jh//jyeP3+OgIAAdO/eXbrAVOrEx8djz5498PPzw8WLF1G5cmX06dMH/fv3R5MmTaSOp7b8/Pzw7bff4syZMzwq9RO1aNEC5ubm2LVr10fnDho0CHFxcQgPDy+GZCWTXC5HXFwc1q5di/DwcAQHB6NixYpSx/osLHaosLi4OLRu3RoPHjyAkZERUlJSUKFCBSQnJyM7OxvGxsYoV64c9/ETqbmgoCCMGzcOZ8+ehYWFhdRx1Nr9+/fRrFkzvHjxAk2aNFHcYN6+fRuXLl2CqakpLly4wOeZio2dnR1atmyJtWvXKq49evQI5ubmaNasGc6cOQNNTU0kJyfjq6++goWFBUJCQiRMTKVJ+/btERoainLlyqFnz55wd3eHi4tLiWpQKJUJEybg1KlTuHPnDo9K/USGhoZYsmRJvlZrrF+/HtOnT0dycnLRBysFBgwYAFEU4efnJ3WUz8JtLCpsypQpSElJQWRkJKysrGBqaoo9e/bA0dERPj4+WLNmDU6cOCF1TCIqoAkTJuS4ZmJigjp16qBdu3aoVq0aNDQ0lMZ5Q5Q/lpaWuHbtGhYvXozg4GDFCg5zc3NMnDgR33//PUxNTSVOSaXJkydPULt2baVrR44cgSAImDhxIjQ1392KGRkZYfDgwfw+p2J18uRJ6Orq4quvvsKLFy/g4+MDHx+fPOcLgoCgoKBiTKi+1qxZo/j7qVOncp3D3+0flpmZCW1t7XzN1dbWRkZGRhEnKj2cnZ0xbdo0qWN8NhY7VNjp06cxduxYNGnSRLEHXRRFlClTBlOmTMGtW7cwadIkHD16VOKkRFQQ/74B+q8jR47kep03RPlnamqKlStXYuXKlVJHIYJcLs/RMO/s2bMAgJYtWypdNzMzw+vXr4stG1H16tUhCALu3r2br/lsopt//916TgVXpUoV3LhxI19zb9y4gSpVqhRxotLjjz/+KBErvFjsUGFpaWmKpdYGBgYQBAEpKSmK8WbNmmHy5MkSpSOiT8UbIKLSw9raGpGRkYoTGbKzs3H69GnUrl0blSpVUpqbmJgIExMTKWJSKfXgwQOpIxDlqW3bttixYwemT5/+wVWZ8fHx2LFjB9zc3IoxnXrbsWNHrteTk5MRHh6OwMBAjBgxophTFT4WO1RY9erV8fjxYwCApqYmqlatisjISPTs2RMAEBUVxVMFiIj+Iy4uDtu3b8e9e/eQlJSE/7am4jJsKk4eHh6YMmUK6tSpg+bNm2PXrl2Ij4/PdTtbREQEatasKUFKIioqkZGRCA0NRXx8PMaOHQsbGxukpaUhOjoaNWvWRLly5aSOqLKmTZuGnTt3wsXFBVu2bIGDg0OOORcvXsSIESOQnp6OKVOmSJBSPX3ohCVjY2N8//33JeK4aRY7VJiLiwuCgoIwZ84cAO++KBcvXoykpCTI5XL8+uuvGDx4sMQpiehT8Oz4ouHv7w8PDw9kZWXByMgIhoaGOeZwGTYVp7Fjx+LkyZOYPn264ji/li1b5liZ+ejRIwQHB2PBggUSJSWiwpSZmYl+/fohKChIcYxn165dYWNjA5lMhvbt28PLywve3t5SR1VZVlZW2Lt3L9zd3dG8eXNYWVmhXr160NfXx+vXr3Hjxg3ExsZCT08Pu3fvhrW1tdSR1cb9+/dzXBMEAeXLl4e+vr4EiYoGT2NRYQ8fPsTvv/8OV1dXlClTBunp6fjmm28QEBAADQ0NuLq6wsfHBwYGBlJHJaIC8vHxwdSpUxEVFQUrK6s858XGxsLW1hYrV67k2fH5YGFhAQMDA+zfv5/vkJNK+eOPPxAbGwtzc3M0bdo0x3hMTAyuXr0KZ2dnbmUhKgGmTZuGlStXYs2aNWjdujVq1aqFkydPwsXFBQAwZswYXL58GZcuXZI4qep78OABli5diiNHjuDJkyeK61WqVIGrqyumTp36wXspKr1Y7CAikgDPji8a5cqVw7JlyzB27FipoxARUSlWvXp19OjRA6tWrUJCQgJMTEyUih0+Pj6YP38+Xr58KXFS9fL69Wu8evUKBgYGJWoFgtSio6Oxb98+PHv2DLVq1cLQoUNLxBvq3MZCRCSB69evY8CAAfma27x5cxw+fLiIE5UMDg4OePjwodQxiIiolIuPj0e9evXyHNfQ0EBaWloxJioZ9PX1WeT4RGvWrIGPjw/Onz8PY2NjxfXDhw/Dzc0NmZmZimurV69GZGSk0jx1xGKHCpk/f36BP0YQBMyaNasI0hBRUeLZ8UXj559/RqdOnWBvb4/evXtLHYeIiEqpatWqITo6Os/xc+fOoUaNGsWYiEq7Q4cOwdraWqmAkZWVhREjRkBDQwO+vr6wt7fH0aNH4e3tjYULF2LlypUSJv58LHaokLlz5xb4Y1jsIFJPPDu+aNSrVw8LFy5Ev379ULZsWZiZmUFDQ0NpjiAIuHr1qkQJiYjUhyiKCA0NRUZGBlq0aMF31Augf//++Omnn9CrVy9FD6n3DbI3b96MvXv3YsmSJVJGpFImKioKI0eOVLoWGhqKFy9eYMaMGfDw8AAA2Nra4urVqzh27BiLHVR45HK51BGIqJjw7PiisW7dOowfPx46OjqwtrbO9TQWIiLKydvbG+fPn0doaCiAd4WO9u3b4/Tp0xBFEdWrV8epU6d44kU+eXt7IzIyEs7OzqhTpw4EQYCXlxcSExPx+PFjdO7cGV5eXlLHpFIkISEB1apVU7p26tQpCIKAHj16KF13dHREYGBgccYrEjKpA1D+ZWVl4datW/j999/x5s0bqeMQ0WeYNm0a0tPT4eLigosXL+Y65+LFi2jTpg3Pji+ARYsWoXnz5nj69CmuXLmC0NDQXP8QEZGygIAANGnSRPF4//79OHXqFBYsWIAjR44gOzv7k1Yhl1ba2to4fvw4fH19YWVlhdq1ayMjIwP169fHtm3bcPjw4RwrD4mKUqVKlfD8+XOlaxEREdDT00ODBg2Urmtra+d7u7Uq48oOFXTs2DH4+/tDU1MTgwYNgouLCw4ePIhvvvkGz549A/DuC/C7777DggULJE5LRJ+CZ8cXjZSUFAwYMIArOkilZWRk4MqVK4iPj4ejo6PaN4CjkuHJkydKPSQCAwNRt25dTJ8+HcC7o1LXr18vVTy1JAgCBg4ciIEDB0odhQj29vbYvn07xo8fD319fdy8eROXLl3C119/DU1N5bJAdHQ0zMzMJEpaeFjsUDHHjx+Hq6srtLS0oKuri507d2Lr1q0YPnw46tatCzc3N2RlZeHEiRNYvHgxzM3Nc+y9IiL10KVLF1y7dk1xdvzBgwcVY1WqVMHIkSN5dnwBtWzZEtevX5c6BlGefHx8MHfuXKSkpAAAQkJC4OLigpcvX6J27dpYtmwZhg0bJnFKKo00NTUVzbBFUcSpU6cwePBgxXilSpV4TCoVm/Dw8E/6OGdn50JOUnLMmTMHX331FWxsbGBra4vLly9DEARFQfPfDhw4oDgmWZ0JoiiKUoeg/3t/wxMeHg4jIyOMHj0aO3bsQOvWrXHkyBFFY6OsrCw0bdoUoiji8uXLEqcmosLAs+M/36NHj9CpUycMHjwYw4cPR8WKFaWORKTg6+uL4cOHo1+/fmjfvj2GDRuGkydPKm4o+/Tpg+TkZPz2228SJ6XSyMnJCW/fvkVwcDAOHDiAkSNH4uTJk2jdujUAYPbs2fD19cWjR48kTqo+Tpw4gS1btuDevXtISkrCf192CYKA2NhYidKpNplMpnjdA7wrwP37cV6ys7OLMpbaO3/+PBYuXIh79+7B3NwckydPRtu2bZXmhIWFYfz48VixYgXat28vUdLCwWKHiqlUqRKmTZuGb7/9FsC7rrl2dnbYtWsX3N3dleauXLkSM2fORGpqqhRRiYhUjr6+PuRyOdLT0wEAOjo6uZ7G8v5ddaLiZGdnBxsbGxw4cAAJCQkwMTFRKnYsXboUPj4+ePLkicRJqTQKCQlB165d8fbtWwDvGhT++931xo0bw9zcvEQ0LSwOy5cvx/fff49KlSqhSZMmKF++fK7zfH19izmZejhz5ozS44yMDEydOhVpaWkYNWoUatWqBeDddovNmzejbNmyWLZsGdq1aydFXFJR3MaiYl68eIFKlSopHr8/peHf1/499v6GnoiIgF69euXrnR8iKcTExGDChAl5jleoUAEJCQnFmIjo/9q1a4crV64gJCQERkZG6Nu3r2IsKSkJzs7O+PrrryVMqF5WrVoFFxcXHDt2DFpaWlLHUTstW7ZUevztt99CW1sbkZGR0NHRUVzv2rUrxo0bh5YtW+L48eMsdpASFjtU0L9v1HnTTkSUf9u2bZM6AlGejIyMPtjzICoqCpUrVy7GRETK6tati7p16+a4Xr58eaxcuVKCROorKSkJvXv3ZqGjkOzatQszZ85UKnS8p6enh0GDBmHhwoVYsWKFBOlIVbHYoYIePHiAK1euAIBiqfXdu3dhZGSkNO/+/fvFHY2IiIg+UefOnbFp0yaMHTs2x9jNmzexefNmNiclybx+/RrJycmoVq2a4trTp0+xYcMGZGRkoHfv3vjqq68kTKhemjRpgtu3b0sdo8RITU1VnEqZm2fPniEtLa0YE5E6YM8OFfPfZjxA3g153l9nIx4iImXh4eEfbAjn5eUlUTIqzZ4+fQoHBweIooiuXbti06ZNGDhwILKzsxEQEIAvvvgCly5d4lG0JAl3d3fcv38fkZGRAIBXr17Bzs4Ojx8/hkwmg6amJo4fP45WrVpJG1RN3Lp1C506dcKiRYvQv39/qeOove7duyMkJAS//vorevbsqTQWEBCAQYMGoUOHDjhw4IBECUkVsdihYrZv317gj/Hw8CiCJERE6uevv/5C3759ERMTk6PI8R6LxCSl+Ph4zJgxA4GBgUhOTgbwrrFur169sGTJEkWvLqLiVq1aNXh6emLmzJkAgHXr1mHChAk4e/YsbG1t0aZNGxgYGODkyZMSJ1VN9evXz3EtMTERz549Q7ly5WBmZpZrw+yrV68WV0S19uTJE7i4uCAmJgZffPEFatSoAQCIjY3F06dPYW1tjdOnT8PMzEzipKRKWOwgIlIRUVFRea5GAIDBgwdLkEq92NvbIzY2FsuWLYODgwMMDQ1znWdubl7MyYhyevHiBeRyOUxMTCCTyaSOQ6Wcrq4u1q1bh6FDhwIAOnbsiPT0dISFhQEA1qxZg3nz5uHFixcSplRdrVq1+qRee6GhoUWQpmRKT0/Hxo0bERwcjLi4OADvfp937twZI0eOhK6ursQJSdWwZwcRkcRiY2MxcOBAXLp06YOrEVjs+LibN29i/vz5GDlypNRRiD7KxMQEAJCZmYm3b9+ibNmyEiei0szIyAjPnz8HAPzzzz+IiIiAt7e3YlxTU5M9ET7gfVGIio6Ojg4mTpyIiRMnSh2F1ASLHUREEvP09MT169fx888/w8nJCeXLl5c6ktqysbHhKVaksnbv3o2LFy8qnWoxb948LFy4EKIowtXVFb/++ivKlSsnYUoqrZo3b45169ahdu3aOH78ONLT05WOmr1z5w6qVq0qYUIiICMjA1euXEF8fDwcHR3Z44g+iNtYiIgkpqurixkzZmDWrFlSR1F7gYGB+O6773D27FnelJPK+eqrr9CoUSNs2rQJAHD+/Hm0aNECXbp0QZ06dbB69WpMmjQJixcvljgplUYxMTFo3749Hjx4AAD47rvvsHz5cgBAdnY2LCws0LFjR2zevFnClOolIyMDmzdvxrFjxxTPq4WFBTp37owRI0bkeowq5c3Hxwdz585FcnIyBEFASEgIXFxc8PLlS9SuXRvLli3jiVakhCs7iIgkZmxsnGdvCSqYnj17Ij09HbVq1UKbNm3ybAi3atUqiRJSaRYbG6vUVNzPzw+VK1fGgQMHoKmpCblcjoCAABY7SBI1atTA7du3ERUVBUNDQ1hYWCjG0tLSsGbNGjRo0EC6gGrm8ePHaNeuHW7fvq3UUPPq1as4fvw41qxZg5MnT7KhZj75+vpi0qRJ6NevH9q3b69U1DA2NoaLiwt2797NYgcpYbGDiEhio0ePxs6dOzFu3LgcL8ypYM6cOYMxY8YgLS0Nhw8fznUOix0klYyMDKV3cn/77Td06tQJmprvbsfq1q2LdevWSRWPCFpaWrkWNPT19ZW2tNDHjRs3DnFxcdi7dy969+6tNLZv3z54eHhg3LhxCAoKkiihelmxYgW+/vpr+Pn5ISEhIcd448aN4ePjI0EyUmUsdhARSaxmzZrIzs5GgwYNMGzYMFSrVi3Xosd/z5WnnMaPHw8DAwPs378fDg4OMDAwkDoSkYKlpSVOnjyJESNG4I8//kBMTAwWLlyoGP/777/Zr4Mk9erVK6xbtw6hoaGIj4/Hxo0b0aRJEyQmJmLbtm3o1q2bYoUCfdipU6fg5eWVo9ABAG5ubrhy5QpWr14tQTL1FBMTgwkTJuQ5XqFChVyLIFS6sdhBRCSxvn37Kv4+efLkXOcIgoDs7OziiqS2YmJisGTJErRr107qKEQ5eHp6YuLEiYiKisLjx49hZmYGV1dXxfi5c+dga2srYUIqzR4/foyWLVvi0aNHsLGxQXR0NN68eQPg3QvJjRs3Ii4ujivj8klfXx+mpqZ5jleuXBn6+vrFmEi9GRkZ4eXLl3mOR0VFoXLlysWYiNQBix1ERBILDQ2VOkKJYWtri5SUFKljEOVq/Pjx0NHRwbFjx9C4cWNMmzYNurq6AIDExEQ8f/4co0ePljgllVZTpkzB69ev8ddff8HU1DTHC/Xu3bvjyJEjEqVTP0OHDsW2bdswcuRI6OnpKY29efMGvr6+GD58uETp1E/nzp2xadMmjB07NsfYzZs3sXnzZvbroBx4GgsREZUYZ86cwYABAxAYGIgmTZpIHYeISG1UrFgRXl5emDlzJhISEmBiYoKTJ0/CxcUFALBx40ZMmTIFr169kjipeti7dy8WLlyIpKQkeHh4KLb/3L17Fzt27ECFChUwY8YMRc+e97hlNXdPnz6Fg4MDRFFE165dsWnTJgwcOBDZ2dkICAjAF198gUuXLvEoWlLClR1ERCokKioKcXFxAABzc3PUrVtX4kTqZcWKFdDX10ezZs1Qt25dVK9ePdfTWNgQjqTG73VSNf/88w9MTEzyHH/9+nUxplF//fr1U/z937153nv8+DHc3d3x7/eduWU1b1WqVMHly5cxY8YM7NmzB6Io4tdff4W+vj7c3d2xZMkSFjooB67sICJSAUFBQfj222/x4MEDpeuWlpb46aef0K1bN2mCqRkLCwsIgvDBOYIg4N69e8WUiEgZv9dJVdnb26NWrVrYtWtXris7WrRoAQ0NDZw5c0bipOrhU5+nli1bFnIS9ZeRkYETJ07AwsIC9evXBwC8ePECcrkcJiYmkMlkEickVcViBxGRxI4dO4Zu3brB3Nwco0aNQp06dQAAt27dwqZNmxAXF4cjR46gY8eOEiclos/B73VSZTt37oSHhwcWLVoENzc31KhRA7/99hssLCwwb948+Pn5ISAgAN27d5c6KpUyoihCR0cHq1atYl8jKhAWO4iIJNasWTNkZGQgIiICZcuWVRpLTU1FixYtoKOjgwsXLkiUkIgKA7/XSdUtXLgQc+fOhSiKkMvlkMlkEEURMpkMCxYswLRp06SOqHYyMjJw5coVxMfHw9HRkVstPpGdnR369euHmTNnSh2F1AiLHUREEitbtiwWLVqEiRMn5jq+atUqzJgxA6mpqcWcTH2dOXMGR48eVeqJ0KVLFy4PJknxe53UwcOHDxEQEICYmBjI5XJYW1ujZ8+esLKykjqa2vHx8cHcuXMVp4SFhITAxcUFL1++RO3atbFs2TKeIJJPfn5++Pbbb3HmzBnUqlVL6jikJtiglIhIYjo6OkhMTMxzPDExETo6OsWYSH1lZmbC3d0dBw8ehCiKMDIyAgAkJydjxYoV6NGjB/z9/aGlpSVtUCqV+L1O6qB69erw8vKSOoba8/X1xaRJk9CvXz+0b99eqahhbGwMFxcX7N69m8WOfIqMjETFihVhZ2eHVq1awcLCQnF093uCIGDVqlUSJSRVxJUdREQSc3NzQ0hICIKDg9GsWTOlsYsXL6Jjx45o37499uzZI1FC9eHt7Y3Fixdj8uTJ+O6771CpUiUAQHx8PFasWIHly5fD29sbP/zwg8RJqTTi9zpR6WFnZwcbGxscOHAg14avS5cuhY+PD548eSJxUvWQnyakPM2G/ovFDiIiid2/fx/NmjXDixcv0KRJE8XyzNu3b+PSpUswNTXFhQsXYGFhIW1QNWBpaYlWrVrB19c31/EhQ4YgLCwsx0kYRMWB3+ukSmQy2UdPr/ovQRCQlZVVRIlKFh0dHfj4+GDUqFG5Fjs2b96M8ePHIz09XeKkRCUXt7EQEUnM0tIS165dw+LFixEcHKx4V9fc3BwTJ07E999/D1NTU4lTqodnz57BwcEhz3EHBwfs3r27GBMR/R+/10mVzJ49u8DFDso/IyMjvHz5Ms/xqKgoVK5cuRgTEZU+XNlBREQlRo0aNWBvb59nQaNfv374448/EBMTU8zJiIioNBk2bBhOnz6Nv/76C9nZ2UorO27evAkHBwcMGzYMPj4+UkdVK5GRkQgNDUV8fDzGjh0LGxsbpKWlITo6GjVr1kS5cuWkjkgq5OObn4iIiNSEh4cH9u7di9GjR+P27dvIzs6GXC7H7du3MWbMGOzbtw9DhgyROiYR3rx5g1u3buHWrVt48+aN1HGIMH/+fNy4cSPP8Zs3b2L+/PnFmEi9LViwANnZ2bCzs8PMmTMhCAK2b9+OgQMHwt7eHqamppg9e7bUMdVGZmYmevbsCUdHR3h7e8PHxwePHj0C8G5LVvv27dmclHLgyg4iIhUQFxeH7du34969e0hKSsJ/fzQLgoCgoCCJ0qmP7OxsDB8+HDt27IAgCIqGZnK5HKIowsPDA1u2bMlXozOiovD7779j6tSpOHv2LORyOYB3N+pOTk5YtmwZ7O3tJU5IpZVMJsPOnTvRv3//XMf37NmD/v37swFkAcTHx2PGjBkIDAxEcnIyAEBfXx+9evXCkiVLuG2tAKZNm4aVK1dizZo1aN26NWrVqqXUA2XMmDG4fPkyLl26JHFSUiXs2UFEJDF/f394eHggKysLRkZGMDQ0zDGH+6rzR0NDA9u2bcO3336LY8eOIS4uDsC7ngidO3dG/fr1JU5IpdnFixfRqlUraGtrY8SIEahTpw4A4NatW/D394ezszPCwsLQpEkTiZMS5ZSYmAhtbW2pY6gVU1NT/PLLL/jll1/w4sULyOVymJiYsOD+Cfz9/TFmzBhFw9f/qlOnDvbt2ydBMlJlLHYQEUls+vTpqF27Nvbv34+aNWtKHadEqF+/PgsbpHK8vb1RtWpVnD17Nkdjwrlz5yqWZ4eEhEiUkEqb8PBwhIWFKR4HBgbm2tMoOTkZe/bsQb169YoxXcliYmIidQS1Fh8f/8GvPw0NDaSlpRVjIlIHLHYQEUns5cuXmDp1Kgsdnyg9PR2TJk2Cra0txo8fn+c8Hx8f3Lp1Cz4+PtDS0irGhETvXLx4EbNnz871BIZKlSph1KhR+OGHHyRIRqVVaGgo5s2bB+DdCsLAwEAEBgbmOrdu3bpYvXp1ccZTe0lJSfD39//gFtUtW7ZIlE69VKtWDdHR0XmOnzt3DjVq1CjGRKQOWOwgIpKYg4MDHj58KHUMtbVp0yZs27YNUVFRH5zXpUsXTJ06FfXr18eYMWOKKR3R/8lkMmRlZeU5np2dzeXtVKymTp2Kb775BqIowtTUFBs2bECvXr2U5giCAD09Pejo6EiUUj2dOHECvXv3RmpqKgwMDFC+fPkcc7hFNf/69++Pn376Cb169VK8OfT++du8eTP27t2LJUuWSBmRVBAblBIRSez69evo1KkTfv75Z/Tu3VvqOGqnRYsWMDc3x65duz46d9CgQYiLi0N4eHgxJCNS1qlTJ1y/fh3nzp2Dubm50tjDhw/h6OiIevXq4dixYxIlpNIsLi4OJiYm0NPTkzpKiWBnZ4eMjAwEBgZy+08hyMzMRNeuXXH69GnUqVMHN2/eRL169ZCYmIjHjx+jc+fOCAoKgoaGhtRRSYWw2EFEpAK2b9+O4cOHo2zZsjAzM8vxy1oQBFy9elWidKrN0NAQS5YsyddqjfXr12P69OmKrvhExenPP/+Es7MzsrKy0KNHD8W7k7dv30ZQUBA0NTURERGBBg0aSJyUiD6Xjo4Oli9f/sHtlVQwoihi165d2L9/P+7evQu5XA5ra2v06dMHgwYN4koZyoHbWIiIJLZu3TqMHz8eOjo6sLa2zvU0FspbZmZmvk8I0NbWRkZGRhEnIspdo0aNcPHiRXh7e+PQoUOKZnp6enro2LEjFixYgLp160qckkorURSxadMmbNmyRdFj4r8EQfjgViz6PxsbG7x+/VrqGCWKIAgYOHAgBg4cKHUUUhMsdhARSWzRokVo3rw5jhw5wkLHJ6hSpQpu3LiRr7k3btxAlSpVijgRUd7q1q2LAwcOQC6X48WLFwCgOIoyNTUVT58+5dcoSWLq1Kn46aef0LBhQwwcODDXHhOUfwsWLMC4cePQv39/WFhYSB1H7W3cuBHOzs6KI7uJ8oPbWIiIJKavr4/ly5dj9OjRUkdRS56enti/fz9u3boFU1PTPOfFx8ejTp06cHNzw4YNG4oxIVH+LFy4ELNnz0Z2drbUUagUMjU1RatWrbB3716po6ilCRMm5LgWERGB6OhotGvXDtWqVct1i+qqVauKK6Jak8lkEAQBFSpUQIsWLeDk5AQnJyc0btyYjZ0pTyx2EBFJzNXVFebm5li7dq3UUdTSvXv3UK9ePVhaWmLLli1wcHDIMefixYsYMWIE7t27h2vXrsHa2lqCpEQfxmIHSUlfXx8rVqzAqFGjpI6ilj7lBbcgCPx+z6f4+HiEh4fj7NmziIiIwNWrVyGKIsqWLYumTZsqih+tWrWSOiqpEBY7iIgk9ujRI3Tq1AmDBw/G8OHDUbFiRakjqZ2jR4/C3d0dqampsLKyQr169aCvr4/Xr1/jxo0biI2NhZ6eHvz8/NC1a1ep4xLlisUOklL37t1hYmKCzZs3Sx2F6KNev36N8+fPIyIiQtGwlD1l6L9Y7CAikpi+vj7kcjnS09MBvOvgnttS15SUFCniqY0HDx5g6dKlOHLkCJ48eaK4XqVKFbi6umLq1KmwsrKSMCHRh7HYQVJ6+vQpOnToAHd3d3h6erLwTiorNjYWERERiIiIQHh4OGJjY1G2bFk0a9YMv/32m9TxSIWw2EFEJLEhQ4bk67g0X1/fYkhTMrx+/RqvXr2CgYEB9PX1pY5DlC8sdpCUWHgvWtHR0di3bx+ePXuGWrVqYejQoTAwMJA6ltpYs2aNosDx/PlzRe8OZ2dnODk54csvv8zx9UrEYgcRERFREbly5Uq+527ZsgUbNmxgsYMkwcL751uzZg18fHxw/vx5GBsbK64fPnwYbm5uyMzMVFyzsrJCZGSk0jzKm0wmg4aGBnr16oUpU6agcePGUkciNcBiBxEREVEReX+CQH6IosiGhURqrH379tDQ0EBwcLDiWlZWFqpWrYo3b95g3bp1sLe3x9GjR+Ht7Y1vvvkGK1eulDCx+hg/fjzOnj2L69evQ1NTE/b29oqmpI6OjjA0NJQ6IqkgTakDEBHRO+Hh4bh37x6SkpLw3zq0IAjw8vKSKBkRfSq+C05UekRFRWHkyJFK10JDQ/HixQvMmDEDHh4eAABbW1tcvXoVx44dY7Ejn1avXg0AePXqFc6ePas4lWXlypXIysqCnZ0dnJycFPOIAK7sICKS3F9//YW+ffsiJiYmR5HjPb7bS0RERe3hw4dYtGgRQkNDER8fj6CgIDg7O+Ply5eYP38+hg4dikaNGkkdU2Xp6upi7dq1GDZsmOLa999/j+XLl+PSpUtKWy/WrVuH7777Dv/8848UUUuE+/fvIyQkBCtXrsTt27d5r0Q5cGUHEZHERowYgfj4eGzYsAEODg5ciklERMUuKioKTk5OkMvlcHBwQExMjOIYT2NjY5w9exapqanYsmWLxElVV6VKlfD8+XOlaxEREdDT00ODBg2Urmtra0NbW7s446m9qKgoxQksERERipPXqlSpgn79+sHJyUnihKRqWOwgIpLYzZs3MX/+/BxLX4mIiIrL1KlTYWRkhMjISAiCAFNTU6XxLl26YM+ePRKlUw/29vbYvn07xo8fD319fdy8eROXLl3C119/DU1N5Zdd0dHRMDMzkyip+jE2NlZs861duzY6deqEFi1awMnJCRYWFlLHIxXFYgcRkcRsbGzy3cCQiIioKISHh2P27NkwMTFBQkJCjvHq1asr3kmn3M2ZMwdfffUVbGxsYGtri8uXL0MQBEyfPj3H3AMHDsDFxUWClOrJw8MDTk5OaNGiBU+woXyTSR2AiKi0mzt3LtauXcubSCIikoxcLoeenl6e4y9evECZMmWKMZH6qVevHk6fPo3GjRvj6dOnaNq0KY4dO5bjmNSwsDDo6enBzc1NoqTqZ8WKFejevTsLHVQgbFBKRKQC/Pz8MGrUKLRp0wZmZmbQ0NBQGhcEAatWrZIoHRERlXTOzs7Q19fH0aNHkZCQABMTE5w8eRIuLi7IysrCl19+CTMzMxw7dkzqqFSKnTlzBkePHkVcXBwAwNzcHF26dEHLli0lTkaqiMUOIiKJnTlzBt26dcPr16/znMMO40REVJSCg4Ph6uqKUaNGoV+/fmjdujV27doFExMTLFq0CBERETh16hScnZ2ljkqlUGZmJtzd3XHw4EGIoggjIyMAQHJyMgRBQI8ePeDv7w8tLS1pg5JK4TYWIiKJjR8/HgYGBjhx4gSSk5Mhl8tz/GGhg4iIilKnTp2wbds27NmzR9FLYuDAgWjfvj2uXLmCHTt2sNBBkpk3bx4OHDiA7777Ds+ePUNiYiISExPx/PlzTJ48GYGBgZg/f77UMUnFcGUHEZHE9PT0sGTJEkyYMEHqKEREVMqlpqYiJCQEd+/ehVwuh7W1NTp06AB9fX2po1EpZmlpiVatWsHX1zfX8SFDhiAsLAwPHjwo3mCk0ngaCxGRxGxtbZGSkiJ1DCIiIpQtWxbdu3eXOgaRkmfPnsHBwSHPcQcHB+zevbsYE5E64DYWIiKJ/fjjj9i4cSMuXbokdRQiIiqlTp48iRkzZuQ57u3tjdOnTxdjIqL/MzMzQ1hYWJ7jZ86cgZmZWfEFIrXAlR1ERBJbsWIF9PX10axZM9StWxfVq1fP9TSWoKAgiRISEVFJ98MPP6B69ep5jj958gQLFixQ9PMgKk4eHh6YM2cOjIyM4OXlhRo1akAQBNy9exc///wz9u3bh3nz5kkdk1QMix1ERBK7du0aBEFA9erV8ebNG0RFReWYIwiCBMmIiKi0uH79Otzc3PIc/+qrr3DkyJFiTET0fzNmzEBsbCw2bdqEzZs3QyZ7t0FBLpdDFEV4eHh8cGUSlU4sdhARSYzNtIiISGoZGRnIzMz84HhaWloxJiL6Pw0NDWzbtg3ffvstjh07hri4OACAubk5OnfujPr160uckFQRT2MhIiIiIirlHBwcoK2tjYiIiBxjoijCyckJ6enp+OOPPyRIR0RUcFzZQUSkIs6cOYOjR48qvVvRpUsXtGzZUuJkRERU0o0fPx6DBw+Gm5sbZs+ejTp16gAAoqKiMH/+fFy4cAFbt26VOCURUf5xZQcRkcQyMzPh7u6OgwcPQhRFGBkZAQCSk5MhCAJ69OgBf39/aGlpSRuUiIhKtHnz5uGHH36AKIpKPREEQYC3tzcbQFKxkclkBe5XJggCsrKyiigRqSMWO4iIJObt7Y3Fixdj8uTJ+O6771CpUiUAQHx8PFasWIHly5fD29sbP/zwg8RJiYiopIuNjcWBAwdw7949AIC1tTW6d+8Oa2triZNRaTJ37txPas4+Z86cIkhD6orFDiIiiVlaWqJVq1bw9fXNdXzIkCEICwtjI1MiIiIqFa5duwZzc3MYGhpKHYXUmEzqAEREpd2zZ8/g4OCQ57iDgwOeP39ejImIiKi0i46Oxg8//ICxY8fCx8cHr169kjoSlSKNGjXC0aNHFY9dXFxw6tQpCROROmKxg4hIYmZmZggLC8tz/MyZMzAzMyu+QEREVCqsWbMGNWvWxMuXL5WuHz58GA0bNsScOXOwYcMGTJo0CV9++WWOeURFRVdXV+mo47CwMPz9998SJiJ1xNNYiIgk5uHhgTlz5sDIyAheXl6oUaMGBEHA3bt38fPPP2Pfvn1sCkdERIXu0KFDsLa2hrGxseJaVlYWRowYAQ0NDfj6+sLe3h5Hjx6Ft7c3Fi5ciJUrV0qYmEqLBg0a4KeffoKGhoZiK8vvv/8OHR2dD35cz549iyMeqQn27CAiklh2djaGDx+OHTt2QBAEpQ74oijCw8MDW7ZsUVwnIiIqDGZmZhg5cqRSU8eQkBB06NABM2bMwIIFCxTXBwwYgD/++AO3b9+WIiqVMn/88Qd69+6Nhw8fAnh30srHXrYKgoDs7OziiEdqgis7iIgkpqGhgW3btuHbb7/FsWPHEBcXBwAwNzdH586dUb9+fYkTEhFRSZSQkIBq1aopXTt16pTi2PN/c3R0RGBgYHHGo1LM3t4eMTExiI2Nxd9//41WrVrB29sbbdu2lToaqREWO4iIVET9+vVZ2CAiomJTqVKlHA2wIyIioKenhwYNGihd19bWhra2dnHGo1JOU1MTtWrVQq1ateDh4QFXV9cPNnQn+i+uiSYikkB6ejpGjx6N1atXf3Cej48PxowZg7dv3xZTMiIiKi3s7e2xfft2vH79GgBw8+ZNXLp0CR06dICmpvJ7otHR0WyWTZLx9fVVKnS8ffsW4eHhSElJkTAVqToWO4iIJLBp0yZs27YNXbp0+eC8Ll26wNfXF7/88ksxJSMiotJizpw5iIuLg42NDdq0aQNHR0cIgoDp06fnmHvgwAE0b95cgpREOSUmJqJ169a4fPmy1FFIhbHYQUQkgb1796JXr16wsrL64Dxra2u4ubnB39+/mJIREVFpUa9ePZw+fRqNGzfG06dP0bRpUxw7dgyNGzdWmhcWFgY9PT24ublJlJQoJ56zQR/Dnh1ERBK4fv06BgwYkK+5zZs3x+HDh4s4ERERlUbNmzfH0aNHPzinVatWuH79ejElIsofQRCkjkAqjis7iIgkkJmZme9Gb9ra2sjIyCjiRERERETqgys76GO4soOISAJVqlTBjRs38jX3xo0bqFKlShEnIiIiIlIPJiYmuH//PipXrix1FFJhXNlBRCSBtm3bYseOHYiPj//gvPj4eOzYsQPt2rUrpmREREREqk0mk8Hc3BxlypSROgqpMEHk+h8iomJ379491KtXD5aWltiyZUuu58ZfvHgRI0aMwL1793Dt2jVYW1tLkJSIiIioeA0bNqzAHyMIArZs2VIEaUhdsdhBRCSRo0ePwt3dHampqbCyskK9evWgr6+P169f48aNG4iNjYWenh78/PzQtWtXqeMSERERFQsLC4sCNyAVBAH37t0rokSkjljsICKS0IMHD7B06VIcOXIET548UVyvUqUKXF1dMXXq1I8eT0tERERERMpY7CAiUhGvX7/Gq1evYGBgAH19fanjEBERERGpLRY7iIiIiIiIiKhE4dGzREREREREpDJkMtkn9ezIysoqokSkjljsICIiIiIiIpUxe/bsAhc7iP6L21iIiIiIiIiIqESRSR2AiIiIiIiIiKgwcRsLERERERERqbzHjx/jzz//REpKCuRyeY7xwYMHS5CKVBW3sRAREREREZHKSk9Ph4eHBwICAiCXyyEIAt6/jP13b4/s7GypIpIK4jYWIiIiIiIiUlkzZsxAYGAgFi5ciLCwMIiiiO3bt+O3335Dp06d0KBBA1y9elXqmKRiuLKDiIiIiIiIVFb16tXRsWNHbNq0CQkJCTAxMcHJkyfh4uICAHBxcUGtWrWwfv16iZOSKuHKDiIiIiIiIlJZ8fHxaNKkCQBAV1cXAJCamqoY79WrFwIDAyXJRqqLxQ4iIiIiIiJSWZUqVUJCQgIAQE9PD+XLl8ft27cV469evUJ6erpU8UhF8TQWIiIiIiIiUlkODg44e/Yspk2bBgDo2rUrli9fji+++AJyuRwrV65E06ZNJU5JqoY9O4iIiIiIiEhlnT17Fvv27cOyZctQpkwZPHr0CG3btsXdu3cBANbW1jhy5Ahq1aolcVJSJSx2EBERERERkVqRy+W4fv06NDQ0ULt2bWhqctMCKWOxg4iIiIiIiIhKFJa/iIiIiIiISOU9fPgQ9+7dQ1JSEnJ7z75nz54SpCJVxWIHERERERERqayHDx9i2LBhCA0NBYBcCx2CICA7O7u4o5EKY7GDiIiIiIiIVJaHhwcuXLiA77//Hg4ODjA0NJQ6EqkB9uwgIiIiIiIilaWrq4upU6di3rx5UkchNSKTOgARERERERFRXszMzFC+fHmpY5CaYbGDiIiIiIiIVNbkyZOxZcsWpKWlSR2F1Ah7dhAREREREZHK8vT0RHZ2NmxsbNC7d2+YmZlBQ0NDaY4gCPDy8pIoIaki9uwgIiIiIiIilXXjxg24urri4cOHec7haSz0X1zZQURERERERCpr1KhRSElJwcaNG3kaC+UbV3YQERERERGRytLT08O8efMwZcoUqaOQGmGDUiIiIiIiIlJZlpaWUkcgNcRiBxEREREREamsefPmYe3atXj06JHUUUiNsGcHERERERERqazw8HAYGRmhVq1aaNu2LapVq5braSyrVq2SKCGpIvbsICIiIiIiIpUlk318QwJPY6H/YrGDiIiIiIiIiEoU9uwgIiIiIiIiohKFPTuIiIiIiIhIbURHR2Pfvn149uwZatWqhaFDh8LAwEDqWKRiuI2FiIiIiIiIVMqaNWvg4+OD8+fPw9jYWHH98OHDcHNzQ2ZmpuKalZUVIiMjleYRcRsLERERERERqZRDhw7B2tpaqYCRlZWFESNGQENDA76+vrh+/TqWLFmCuLg4LFy4UMK0pIpY7CAiIiIiIiKVEhUVhaZNmypdCw0NxYsXL+Dl5QUPDw/Y2tpi6tSp6NOnD44dOyZRUlJVLHYQERERERGRSklISEC1atWUrp06dQqCIKBHjx5K1x0dHfHw4cPijEdqgMUOIiIiIiIiUimVKlXC8+fPla5FRERAT08PDRo0ULqura0NbW3t4oxHaoDFDiIiIiIiIlIp9vb22L59O16/fg0AuHnzJi5duoQOHTpAU1P5UNHo6GiYmZlJEZNUGE9jISIiIiIiIpVy/fp1fPXVVzAyMoKtrS0uX76MtLQ0XLhwAY0bN1aaa21tDRcXF2zevFmitKSKuLKDiIiIiIiIVEq9evVw+vRpNG7cGE+fPkXTpk1x7NixHIWOsLAw6Onpwc3NTaKkpKq4soOIiIiIiIiIShSu7CAiIiIiIiKiEoXFDiIiIiIiIiIqUVjsICIiIiIiIqIShcUOIiIiIiIiIipRWOwgIiIiIiIiohKFxQ4iIiKiApo7dy4EQfikj7WwsICrq+tH54WFhUEQBISFhX3Sv0NERFSasdhBRERUwgmCkK8/qvai+vz585g7dy6Sk5M/OO/t27cwNjZGixYt8pwjiiKqVauGL7/8spBTEhERkSrSlDoAERERFa1ff/1V6fGOHTsQEhKS43qdOnWKM9ZHnT9/HvPmzcOQIUNgZGSU5zwtLS24ublh48aNiIuLg7m5eY454eHhePz4Mby8vAol28yZM/H9998XyuciIiKiwsdiBxERUQk3cOBApceRkZEICQnJcf1TiKKI9PR06Orqfvbn+hwDBgzAhg0b4O/vn2sRws/PDzKZDP369fusfyc1NRVly5aFpqYmNDV5G0VERKSquI2FiIiI4OvrCxcXF5iamqJMmTKoW7cu1q9fn2Pe+34TJ06cgL29PXR1dbFx40YAQFxcHLp164ayZcvC1NQUXl5eOHHiRK5bZC5evIiOHTvC0NAQenp6aNmyJc6dO6cYnzt3LqZMmQIAsLS0VGy1efDgQa75HR0dYWFhAT8/vxxjb9++xf79+9G6dWtUqVIF165dw5AhQ2BlZQUdHR1UrlwZw4YNQ0JCgtLHve/LERUVhf79+6N8+fKKrTK59ezI73P43m+//YaGDRtCR0cHdevWRWBgYJ5zC/LcEREREVd2EBEREYD169fD1tYW3bp1g6amJg4fPoyxY8dCLpdj3LhxSnNv374Nd3d3eHp6YuTIkahVqxZSU1Ph4uKCZ8+eYeLEiahcuTL8/PwQGhqa4986ffo0OnXqhMaNG2POnDmQyWSKQkFERASaNGmCnj174s6dO/D398fKlSthbGwMADAxMck1vyAI6N+/PxYtWoSbN2/C1tZWMXb8+HEkJiZiwIABAICQkBDcu3cPQ4cOReXKlXHz5k1s2rQJN2/eRGRkZI4ihpubG2xsbLBo0SKIolgoz+Hdu3fRt29fjB49Gh4eHvD19YWbmxuOHz+Odu3a5flv5Oe5IyIiIgAiERERlSrjxo0T/3sLkJaWlmNehw4dRCsrK6Vr5ubmIgDx+PHjStdXrFghAhAPHjyouPbPP/+ItWvXFgGIoaGhoiiKolwuF21sbMQOHTqIcrlc6d+3tLQU27Vrp7i2fPlyEYB4//79fP2/bt68KQIQp0+frnS9X79+oo6OjpiSkpLn/9Xf318EIIaHhyuuzZkzRwQguru755j/fuzfCvocBgQEKK6lpKSIX3zxhdioUSPFtdDQ0E9+7oiIiEo7bmMhIiIipZ4bKSkpePnyJVq2bIl79+4hJSVFaa6lpSU6dOigdO348eOoWrUqunXrprimo6ODkSNHKs3766+/cPfuXfTv3x8JCQl4+fIlXr58idTUVLRp0wbh4eGQy+Wf9H+oW7cuGjVqhN27dyuupaam4tChQ3B1dYWBgUGO/2t6ejpevnyJpk2bAgCuXLmS4/OOHj06X/9+QZ7DKlWqoEePHorHBgYGGDx4MP788088f/48189flM8dERFRScNtLERERIRz585hzpw5uHDhAtLS0pTGUlJSYGhoqHhsaWmZ4+Pj4uJgbW2dYwtIjRo1lB7fvXsXAODh4ZFnlpSUFJQvX77A/wfgXaPSyZMn4/z582jevDkOHjyItLQ0xRYWAEhMTMS8efOwe/duxMfH5/i3/yu3/29uCvIc1qhRI8dzVbNmTQDAgwcPULly5Ryfv6ifOyIiopKExQ4iIqJSLjY2Fm3atEHt2rXx008/oVq1atDW1saxY8ewcuXKHKsFPufklfefa/ny5WjYsGGuc8qVK/fJn9/d3R1Tp06Fn58fmjdvDj8/P5QvXx6dO3dWzOnTpw/Onz+PKVOmoGHDhihXrhzkcjk6duyY68qI/Px/C/ocfoqifu6IiIhKEhY7iIiISrnDhw8jIyMDhw4dQvXq1RXXc2sumhdzc3NERUVBFEWlFQsxMTFK86ytrQG827bRtm3bD37O/658yI8qVaqgdevW2LdvH2bNmoWQkBAMGTIE2traAICkpCScOnUK8+bNw+zZsxUf937VxKcq6HMYExOT47m6c+cOgHcn3uSmIM8dERFRaceeHURERKWchoYGACidNJKSkgJfX998f44OHTrgyZMnOHTokOJaeno6Nm/erDSvcePGsLa2xo8//og3b97k+DwvXrxQ/L1s2bIAgOTk5HznAN5tZYmPj4enpyfevn2rtIUlt/8rAPz8888F+jf+q6DP4dOnT3HgwAHF41evXmHHjh1o2LBhrltYgII9d0RERKUdV3YQERGVcu3bt4e2tja6du0KT09PvHnzBps3b4apqSmePXuWr8/h6emJNWvWwN3dHRMnTsQXX3yBXbt2QUdHB8D/V2nIZDL88ssv6NSpE2xtbTF06FBUrVoVT548QWhoKAwMDHD48GEA717cA4C3tzf69esHLS0tdO3aVVEEyUuvXr0wduxYBAUFoVq1anB2dlaMGRgYwNnZGcuWLcPbt29RtWpV/Pbbb7h//36Bn7d/K+hzWLNmTQwfPhy///47KlWqhK1bt+Lvv//+YIGpIM8dERFRaceVHURERKVcrVq1sH//fgiCgMmTJ2PDhg0YNWoUJk6cmO/PUa5cOZw+fRouLi5YtWoVFixYACcnJ8yaNQsAFEUPAGjVqhUuXLgAe3t7rFmzBuPHj8e2bdtQuXJleHl5KeZ99dVX+OGHH3D16lUMGTIE7u7u+Vq9YGBggK5duwJ418Pjv9th/Pz80KFDB6xduxbTp0+HlpYWgoOD8/1/zU1Bn0MbGxvs2bMHx44dw/fff4+3b99iz549OU65+a/8PndERESlnSD+dx0nERERUSH5+eef4eXlhcePH6Nq1apSxyEiIqJSgsUOIiIiKhT//POP0skl6enpaNSoEbKzsxXNN4mIiIiKA3t2EBERUaHo2bMnqlevjoYNGyIlJQU7d+5EdHQ0du3aJXU0IiIiKmVY7CAiIqJC0aFDB/zyyy/YtWsXsrOzUbduXezevRt9+/aVOhoRERGVMtzGQkREREREREQlCk9jISIiIiIiIqIShcUOIiIiIiIiIipRWOwgIiIiIiIiohKFxQ4iIiIiIiIiKlFY7CAiIiIiIiKiEoXFDiIiIiIiIiIqUVjsICIiIiIiIqIShcUOIiIiIiIiIipR/gcDw4tqoOSvIgAAAABJRU5ErkJggg==\n"},"metadata":{}}],"source":["# Count Plot for each category\n","labeled_barplot(labels,'Label', perc = True,\n","                Title = \"Distribution of the Target Variable\",\n","                xlabel = \"Target Variable\",\n","                degree = 90,\n","                )"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1723865696988,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"},"user_tz":300},"id":"wzF5WZuzkk9z","outputId":"51b7f204-bd79-4262-951a-d2d762ecdb27"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Extract image dimensions\n","# Check the Distribution of Width and Height of all images\n","img_widths, img_heights = [], []\n","\n","for img in images:\n","    height, weidth, _ = img.shape\n","    img_widths.append(weidth)\n","    img_heights.append(height)\n","\n","# Plot the histogram\n","fig, ax = plt.subplots(1, 2, figsize=(12, 6))  # Create a figure with two subplots\n","\n","plot_histogram(img_widths, ax[0], \"Distribution of Image Widths\", \"Pixel\", \"Width\", fontsize = 14, color = 'blue')\n","plot_histogram(img_heights, ax[1], \"Distribution of Image Heights\", \"Pixel\", \"Height\", fontsize = 14, color = 'green')\n","\n","plt.tight_layout()  # Adjust spacing between subplots\n","plt.show()          # Show the plot"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vVnTK40HvEsC"},"outputs":[],"source":["# Function to check if an image is grayscale\n","def is_grayscale(image):\n","  # Check if the red and green channels are equal, and the green and blue channels are equal\n","  return np.all(img[:, :, 0] == img[:, :, 1]) and np.all(img[:, :, 1] == img[:, :, 2])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1723865696988,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"},"user_tz":300},"id":"PFRggws0vPPf","outputId":"866f9fac-2537-4c0a-ff02-c2345da7d1db"},"outputs":[{"output_type":"stream","name":"stdout","text":["Total grayscale images: 0\n"]}],"source":["gray_images = [img for img in images if is_grayscale(img)]  # Filter images using the is_grayscale function\n","print(f\"Total grayscale images: {len(gray_images)}\")        # Print the result"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":545},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1723865696988,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"},"user_tz":300},"id":"9tzhrAiusEEA","outputId":"df9b8e1d-bd28-45a2-c295-be4601a7a73d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 3 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA+IAAAIQCAYAAAAFN9TtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABwnUlEQVR4nO3dd3gU1eL/8c9uSIFAEloSEqqgNFEQFSMJoCABsaNIUYELghJEQAH5XqWqKCrqVUS9XkHFqGBDUYHQW7AgSFG5gCAQTECRhJ5l5/z+4Je9LGmbkEwK79fz7PMkM2d3zmw5O5+dM+c4jDFGAAAAAADAFs6SrgAAAAAAABcSgjgAAAAAADYiiAMAAAAAYCOCOAAAAAAANiKIAwAAAABgI4I4AAAAAAA2IogDAAAAAGAjgjgAAAAAADYiiAMAAAAAYCOCeBkwa9YsORwO7d69u0ger0OHDurQoUORPFZOJkyYIIfDUWyPn5uifp6KW/369dWvX79i387u3bvlcDg0a9Ysz7J+/fqpcuXKxb7tLA6HQxMmTLBte0BxK+73dHG307np16+f6tevb/t2CyOntq245PT9Ur9+fd10003Fvm1JWr58uRwOh5YvX27L9lDySvv3ZnG3FSV1TFfWPmt2flec+57MOt7/888/bdm+XcfNdiKIF1JWA5F1q1ChgqKjo9WvXz+lpKSUSJ2yPhBZt0qVKqlZs2Z6/PHHlZGRUSJ1yonL5VKNGjUUGxubaxljjOrUqaMrrrjCxpoVXocOHTzPu9PpVEhIiBo3bqx7771XSUlJRbadr7/+utR+MZfmuqHo7dq1S0OHDtUll1yiSpUqedqbhIQEbdq0qaSrV2hnf5YdDoeqVaumq666Sm+//bYsyyrp6nn8+OOPcjgcevzxx3Mts337djkcDo0cOdLGmhXeud+p1apVU+vWrfXwww/r559/LrLtvPbaa7aE98IozXXD+Tn3uNHhcCg8PFzXXXedvvnmm5Kunse59QwKCtIll1yioUOHKi0traSr5+Wyyy5T3bp1ZYzJtUzbtm0VERGh06dP21izwunXr5/Xc1+5cmVddNFFuvPOO/XJJ58U2XfQ2rVrNWHCBB0+fLhIHq8olea6FYcKJV2Bsm7SpElq0KCBTp48qXXr1mnWrFlavXq1tmzZoqCgoBKp04wZM1S5cmUdPXpUixYt0lNPPaWlS5dqzZo1cjgcWrRoUYnUK4u/v7/uuusuvfHGG/r9999Vr169bGVWrlypffv2acSIESVQw8KpXbu2pkyZIkk6duyYduzYoU8//VSzZ89Wjx49NHv2bPn7+3vKb9u2TU5nwX4L+/rrrzV9+vQCBd569erpxIkTXtsuDnnV7cSJE6pQgeamvJg/f77uvvtuVahQQX369NHll18up9OpX3/9VZ9++qlmzJihXbt25fjZLgvO/iwfPHhQ7777rgYMGKD//ve/euaZZySV/Hv6iiuuUJMmTfTBBx/oySefzLFMYmKiJOmee+6xs2rn5YYbbtB9990nY4zS09P1008/6Z133tFrr72mZ5991utHhcK2ba+99ppq1KhRoDMr9957r3r27KnAwMACbaugcqtbu3btdOLECQUEBBTr9lH8so4bjTFKS0vTrFmzdOONN+rLL7+0rYeFL84+vl29erVmzJihr7/+Wlu2bFGlSpX073//u8R/nOzTp48ee+wxrVq1Su3atcu2fvfu3UpOTtbQoUPLzDFIYGCg3nrrLUlnvmd+//13ffnll7rzzjvVoUMHzZs3TyEhIZ7yhTmmX7t2rSZOnKh+/fopLCzM5/vZ8b2XV90Kc9xc2pWNd2Up1rVrV1155ZWSpIEDB6pGjRp69tln9cUXX6hHjx4lUqc777xTNWrUkCQ98MAD6t69uz799FOtW7dOMTExpeKLvE+fPnr99df1wQcf6LHHHsu2PjExUU6nUz179iyB2hVOaGhotgPeZ555RsOGDdNrr72m+vXr69lnn/WsK+4DutOnT8uyLAUEBJTYj0JZSnr7KDo7d+5Uz549Va9ePS1ZskS1atXyWv/ss8/qtddey/fL8tixYwoODi7OqhbauZ/lwYMHq3Hjxnr11Vc1efJk+fv7l4r3dJ8+ffTEE09o3bp1uuaaa7Kt/+CDD9SkSZMy07NIki655JIc29Gbb75ZjzzyiJo0aaIbb7xRkjxn64pT1vvUz89Pfn5+xbqtvDidzlLxnsP5O/u4UZIGDBigiIgIffDBB6UqiJ97fFu9enVNmzZN8+bNU69evYr9x31f9O7dW2PHjlViYmKOQfyDDz6QMUZ9+vQpgdoVToUKFbK1gU8++aSeeeYZjR07Vvfff78++ugjz7riPqa3LEuZmZkKCgoq8TaouI+bS0L5+lmhFIiLi5N05mD1bL/++qvuvPNOVatWTUFBQbryyiv1xRdfZLv/1q1bdf3116tixYqqXbu2nnzyyfP+xfH666+XdKYrqZT9epK+ffsqKChIv/zyi9f94uPjVbVqVe3fv9+z7JtvvlFcXJyCg4NVpUoVdevWTVu3bi1wndq2bav69et7zticzeVy6eOPP9Z1112nqKgobdq0Sf369dNFF12koKAgRUZG6h//+If++uuvfLeT2zVWOV1ncvjwYQ0fPlx16tRRYGCgGjVqpGefffa8nn8/Pz/961//UrNmzfTqq68qPT091zq4XC5NnDhRF198sYKCglS9enXFxsZ6urb369dP06dP9+xX1k3637WSzz//vF566SU1bNhQgYGB+vnnn/O8jvK3335TfHy8goODFRUVpUmTJnl18crtWqlzHzOvumUtO/d12LBhg7p27aqQkBBVrlxZHTt21Lp167zKZHWRW7NmjUaOHKmaNWsqODhYt99+uw4ePJj/C4AiN3XqVB07dkwzZ87MFsKlMwcRw4YNU506dTzLssYk2Llzp2688UZVqVLFc2BkWZZeeuklNW/eXEFBQYqIiNDgwYP1999/Z3tsX9qfrG2lpKTotttuU+XKlVWzZk09+uijcrvdhdrnSpUq6ZprrtGxY8c877uz39MnTpxQkyZN1KRJE504ccJzv0OHDqlWrVq69tprPdsuyP7mJ+s5zKkdXb9+vbZt2+YpM2/ePHXr1k1RUVEKDAxUw4YNNXny5HyfE1/bgCy+ftcVRPXq1fXhhx+qQoUKeuqpp/KsQ2pqqvr376/atWsrMDBQtWrV0q233uq5zrR+/fraunWrVqxY4Wmnsr4Ps9qbFStWaMiQIQoPD1ft2rW91uV0veqiRYvUsmVLBQUFqVmzZvr000+91uc2bsq5j5lX3XJ7HebOnavWrVurYsWKqlGjhu65555sl8cVx2cCRScsLEwVK1bM90xjbtdk5/b+mj17tue9Ua1aNfXs2VN79+4tdD3PPZY8tz7jx4+X0+nUkiVLvO43aNAgBQQE6KeffvIs+/bbb9WlSxeFhoaqUqVKat++vdasWVPgOtWpU0ft2rXTxx9/LJfLlW19YmKiGjZsqDZt2uj333/XkCFD1LhxY1WsWFHVq1fXXXfd5dM16Lldm5zT9dmnTp3S+PHj1ahRIwUGBqpOnToaPXq0Tp06VeD9O9tjjz2mzp07a+7cufrvf/+bZx1eeeUVNW/eXJUqVVLVqlV15ZVXer4nJkyYoFGjRkmSGjRo4Glrsp4Hh8OhoUOH6v3331fz5s0VGBioBQsWeNbldEz9559/qkePHgoJCVH16tX18MMP6+TJk571eR2Hnv2Y+dUtp9fht99+01133aVq1ap5vqu/+uorrzJZ7eecOXP01FNPqXbt2goKClLHjh21Y8eOXJ9zO3BGvIhlvVmqVq3qWbZ161a1bdtW0dHReuyxxxQcHKw5c+botttu0yeffKLbb79d0pkDiOuuu06nT5/2lHvzzTdVsWLF86pT1o8C1atXz3H9yy+/rKVLl6pv375KTk6Wn5+f3njjDS1atEjvvfeeoqKiJEnvvfee+vbtq/j4eD377LM6fvy4ZsyYodjYWG3YsKFAg3Y4HA717t1bTz/9tLZu3armzZt71i1YsECHDh3yHEAmJSXpt99+U//+/RUZGamtW7fqzTff1NatW7Vu3boiGRju+PHjat++vVJSUjR48GDVrVtXa9eu1dixY/XHH3/opZdeKvRj+/n5qVevXnriiSe0evVqdevWLcdyEyZM0JQpUzRw4EBdffXVysjI0A8//KAff/xRN9xwgwYPHqz9+/crKSlJ7733Xo6PMXPmTJ08eVKDBg1SYGCgqlWrlusPCW63W126dNE111yjqVOnasGCBRo/frxOnz6tSZMmFWgffanb2bZu3aq4uDiFhIRo9OjR8vf31xtvvKEOHTpoxYoVatOmjVf5hx56SFWrVtX48eO1e/duvfTSSxo6dKjXr8Kwx/z589WoUaNsr1F+Tp8+rfj4eMXGxur5559XpUqVJJ1578yaNUv9+/fXsGHDtGvXLr366qvasGGD1qxZ4znrUpD2x+12Kz4+Xm3atNHzzz+vxYsX64UXXlDDhg314IMPFmq/f/vtN/n5+eXYja9ixYp655131LZtW/3zn//UtGnTJEkJCQlKT0/XrFmzPGdTfd1fXzRo0EDXXnut5syZoxdffNHrjG3WQVfv3r0lnQl9lStX1siRI1W5cmUtXbpU48aNU0ZGhp577rlCPSfn8vW7rjDq1q2r9u3ba9myZcrIyPDqmnm27t27a+vWrXrooYdUv359HThwQElJSdqzZ4/q16+vl156SQ899JAqV66sf/7zn5KkiIgIr8cYMmSIatasqXHjxunYsWN51mv79u26++679cADD6hv376aOXOm7rrrLi1YsEA33HBDgfbRl7qdLet9dNVVV2nKlClKS0vTyy+/rDVr1mjDhg1e79Xi+EygcNLT0/Xnn3/KGKMDBw7olVde0dGjR4v0EpKnnnpKTzzxhHr06KGBAwfq4MGDeuWVV9SuXbts7w1f5Xcs+fjjj+vLL7/UgAEDtHnzZlWpUkULFy7Uv//9b02ePFmXX365JGnp0qXq2rWrWrdu7QnvM2fO1PXXX69Vq1bp6quvLlC9+vTpo0GDBmnhwoVePQo2b96sLVu2aNy4cZKk77//XmvXrlXPnj1Vu3Zt7d69WzNmzFCHDh30888/e76TzodlWbrlllu0evVqDRo0SE2bNtXmzZv14osv6r///a8+//zz83r8e++9V4sWLVJSUpIuueSSHMv8+9//1rBhw3TnnXd6AvGmTZv07bffqnfv3rrjjjv03//+Vx988IFefPFFTw/amjVreh5j6dKlmjNnjoYOHaoaNWrke3zfo0cP1a9fX1OmTNG6dev0r3/9S3///bfefffdAu2fL3U7W1pamq699lodP35cw4YNU/Xq1fXOO+/olltu0ccff5ztO+eZZ56R0+nUo48+qvT0dE2dOlV9+vTRt99+W6B6FimDQpk5c6aRZBYvXmwOHjxo9u7daz7++GNTs2ZNExgYaPbu3esp27FjR9OiRQtz8uRJzzLLssy1115rLr74Ys+y4cOHG0nm22+/9Sw7cOCACQ0NNZLMrl278qzT+PHjjSSzbds2c/DgQbNr1y7zxhtvmMDAQBMREWGOHTtmjDGmffv2pn379l73XbhwoZFknnzySfPbb7+ZypUrm9tuu82z/siRIyYsLMzcf//9XvdLTU01oaGhXsuz6pGfrVu3Gklm7NixXst79uxpgoKCTHp6ujHGmOPHj2e77wcffGAkmZUrV3qWZb0mZz9Pksz48eOz3b9evXqmb9++nv8nT55sgoODzX//+1+vco899pjx8/Mze/bsyXNf2rdvb5o3b57r+s8++8xIMi+//HKudbj88stNt27d8txOQkJCjs/trl27jCQTEhJiDhw4kOO6mTNnepb17dvXSDIPPfSQZ5llWaZbt24mICDAHDx40BhjzLJly4wks2zZsnwfM7e6GZP9dbjttttMQECA2blzp2fZ/v37TZUqVUy7du08y7Je006dOhnLsjzLR4wYYfz8/Mzhw4dz3B6KR3p6upHk1TZk+fvvv83Bgwc9t7M/t1nvt8cee8zrPqtWrTKSzPvvv++1fMGCBV7LC9L+ZG1r0qRJXmVbtWplWrdune8+tm/f3jRp0sSzH7/88osZNmyYkWRuvvlmT7mc2paxY8cap9NpVq5caebOnWskmZdeeqnA+5tVj3Pb6ZxMnz7dSDILFy70LHO73SY6OtrExMR4luXUjg4ePNhUqlTJ67upb9++pl69ep7/C9IG+PpdlxtJJiEhIdf1Dz/8sJFkfvrppxzr8PfffxtJ5rnnnstzO82bN8/xuc1qb2JjY83p06dzXHf290u9evWMJPPJJ594lqWnp5tatWqZVq1aeZbl9p2Y02PmVrdzX4fMzEwTHh5uLr30UnPixAlPufnz5xtJZty4cZ5l5/uZQNHIer3PvQUGBppZs2ZlK39uG3PuZzPLue+v3bt3Gz8/P/PUU095ldu8ebOpUKFCtuW51fPs49sPP/zQVK9e3VSsWNHs27cv1/ps3rzZBAQEmIEDB5q///7bREdHmyuvvNK4XC5jzJn24OKLLzbx8fFe3+nHjx83DRo0MDfccEO2euR37Hvo0CETGBhoevXq5bX8scce8xwTZ23jXMnJyUaSeffddz3Lcmrzzj1ey3JuO/3ee+8Zp9NpVq1a5VXu9ddfN5LMmjVr8tyXvn37muDg4FzXb9iwwUgyI0aMyLUOt956a57Ho8YY89xzz+X63EoyTqfTbN26Ncd1Z78ns957t9xyi1e5IUOG5NlW5/WYedXt3NchKzed/XwfOXLENGjQwNSvX9+43W5jzP9e06ZNm5pTp055yr788stGktm8eXO2bdmFrunnqVOnTqpZs6bq1KmjO++8U8HBwfriiy883dkOHTqkpUuXqkePHjpy5Ij+/PNP/fnnn/rrr78UHx+v7du3e7qRff3117rmmmu8fg2sWbNmga9tady4sWrWrKkGDRpo8ODBatSokb766qs8f+3r3LmzBg8erEmTJumOO+5QUFCQ3njjDc/6pKQkHT58WL169fLsw59//ik/Pz+1adNGy5YtK1AdJalZs2Zq1aqVPvzwQ8+yY8eO6YsvvtBNN93kOeNxdo+AkydP6s8///RcD/njjz8WeLs5mTt3ruLi4lS1alWv/evUqZPcbrdWrlx5Xo+fNVXYkSNHci0TFhamrVu3avv27YXeTvfu3XP95TAnQ4cO9fyd1R0pMzNTixcvLnQd8uN2u7Vo0SLddtttuuiiizzLa9Wqpd69e2v16tXZRvkfNGiQV8+HuLg4ud1u/f7778VWT2SX9brkNPVdhw4dVLNmTc8t61KFs5175m3u3LkKDQ3VDTfc4PW5a926tSpXruxpVwrT/jzwwANe/8fFxem3337zaT9//fVXz340bdpUr7zyirp166a33347z/tNmDBBzZs3V9++fTVkyBC1b99ew4YNK/D+FsTdd98tf39/r+7pK1asUEpKitd3x9ntaNZ3UVxcnI4fP65ff/21wNs9V0G+6worv3a0YsWKCggI0PLlywvV1T/L/fff7/P14FFRUV5nXUJCQnTfffdpw4YNSk1NLXQd8vPDDz/owIEDGjJkiNd1m926dVOTJk2ydc2Uzu8zgaIzffp0JSUlKSkpSbNnz9Z1112ngQMHZrukobA+/fRTWZalHj16eLUzkZGRuvjii31uZ84+vu3Zs6cqV66szz77TNHR0bne59JLL9XEiRP11ltvKT4+Xn/++afeeecdT7f7jRs3avv27erdu7f++usvT92OHTumjh07auXKlQW+HLBq1aq68cYb9cUXX3h6sBhj9OGHH+rKK6/0nDk+uw10uVz666+/1KhRI4WFhRXpsWTTpk3VpEkTr+c+q1t/Ydr4s/l6LLlv3z59//33hd5O+/bt1axZM5/LJyQkeP3/0EMPSTqTa4rT119/rauvvtprFqbKlStr0KBB2r17d7bZNvr37+91TX3W5cQl2Q7SNf08TZ8+XZdcconS09P19ttva+XKlV6DCezYsUPGGD3xxBN64okncnyMAwcOKDo6Wr///nuO3T0bN25coDp98sknCgkJkb+/v2rXrq2GDRv6dL/nn39e8+bN08aNG5WYmKjw8HDPuqxwmNWYnCu3boL56dOnjx599FGtXbtW1157rT7//HMdP37c6wDy0KFDmjhxoj788EMdOHDA6/5nX3N9PrZv365NmzblGmLP3W5BHT16VJJUpUqVXMtMmjRJt956qy655BJdeuml6tKli+69915ddtllPm+nQYMGPpd1Op1eQViS5wurOOftPHjwoI4fP57j+7pp06ayLEt79+71ulyhbt26XuWyLv04n4NtFFzW+zfr/Xy2N954Q0eOHFFaWlqOXSwrVKjg+YEyy/bt25Wenu7V1pwt63NX0PYnKCgo22e5atWqPr9f6tevr3//+9+ewcAuvvjiXOt4toCAAL399tu66qqrFBQUpJkzZ3r9gOTr/hZE9erVFR8fr88++0yvv/66goKClJiYqAoVKngNGLp161Y9/vjjWrp0abYfuoqiHS3Id11h5deOBgYG6tlnn9UjjzyiiIgIXXPNNbrpppt03333KTIy0uftFKQdbdSoUbbLo85uRwuy3YLI+hEyp3a0SZMmWr16tdey8/1MoOhcffXVXoO19erVS61atdLQoUN10003nffgW9u3b5cxRhdffHGO6329/CXr+LZChQqKiIhQ48aNfRqxetSoUfrwww/13Xff6emnn/YKdFlted++fXO9f3p6utflnb7o06ePPvvsM82bN0+9e/fW2rVrtXv3bj388MOeMidOnNCUKVM0c+ZMpaSkeI2HU5THkr/88kuJHkuOGTNGixcv1tVXX61GjRqpc+fO6t27t9q2bevzdgrSBkrK9l5r2LChnE5nsc8Bn1tuatq0qWf9pZde6lleGo8lCeLn6ewG9bbbblNsbKx69+6tbdu2qXLlyp5f9h599FHFx8fn+BiNGjUq0jq1a9fOc11FQWzYsMHTSGzevFm9evXyrMvaj/feey/HA4vCTmfQq1cvjR49WomJibr22muVmJjo+XUzS48ePbR27VqNGjVKLVu29DyvXbp0KfRAaucOUGNZlm644QaNHj06x/K5XYvjqy1btkjK+7Vu166ddu7cqXnz5mnRokV666239OKLL+r111/XwIEDfdrO+Y4ncK7crr+3e4Cf3M5Onf1FiuIXGhqqWrVqed7PZ8v6MsztizcwMDDbQZxlWQoPD9f777+f432yDmYK2v6c7+jWwcHB6tSpU6Huu3DhQklneu9s377d64DG1/0tqHvuuUfz58/X/Pnzdcstt+iTTz5R586dPY93+PBhtW/fXiEhIZo0aZIaNmyooKAg/fjjjxozZkye7aivbYAd33VbtmyRn59fngeJw4cP180336zPP/9cCxcu1BNPPKEpU6Zo6dKlatWqlU/bKY/taEmO+I68OZ1OXXfddXr55Ze1fft2rx+hz1aQz6LD4dA333yT4+ueU4+mnJz7g4GvfvvtN0/g3rx5c7a6SdJzzz2nli1b5nh/X+t3tptuukmhoaFKTExU7969lZiYKD8/P6+Zdx566CHNnDlTw4cPV0xMjEJDQ+VwONSzZ898jyXzeu7Pfo4ty1KLFi0844Sc6+xBTAvDl2PJpk2batu2bZo/f74WLFigTz75RK+99prGjRuniRMn+rSd820Dz32+SkMbKJXOY0mCeBHy8/PTlClTdN111+nVV1/VY4895jnj6O/vn++BXb169XLslrxt27Ziqe/Zjh07pv79+6tZs2a69tprNXXqVN1+++266qqrJMlzVj08PLzQB6g5iYqK0nXXXae5c+fqiSeeUFJSkvr16+f5Rfjvv//WkiVLNHHiRM+AG5J87r5dtWpVHT582GtZZmam/vjjD69lDRs21NGjR4t037K43W4lJiaqUqVKXt1nclKtWjX1799f/fv319GjR9WuXTtNmDDBE8SLYmC6LJZl6bfffvP6kSFrJM6sgTmyfi089znMqUu4r3WrWbOmKlWqlOP7+tdff5XT6TzvLysUn27duumtt97Sd999V+BBdc7VsGFDLV68WG3bts3zi7+42p+itmnTJk2aNEn9+/fXxo0bNXDgQG3evFmhoaGSfN/fgrrllltUpUoVJSYmyt/fX3///bdXr6Lly5frr7/+0qeffuo1xU/W6Md58bUNKMh3XWHs2bNHK1asUExMTJ5ng6Qzz/MjjzyiRx55RNu3b1fLli31wgsvaPbs2ZKKth3N6glw9mPm1Y6ePUjW+bSj9erVk3Tm+ODcniLbtm3zrEfZcPr0aUk59zbKktPxjJT9fdSwYUMZY9SgQYPzPolQUJZlqV+/fgoJCdHw4cP19NNP684779Qdd9zhqZt0phdTUbYTgYGBuvPOO/Xuu+8qLS1Nc+fO1fXXX+/1w+3HH3+svn376oUXXvAsO3nyZI7P6bnyeu7P7lnYsGFD/fTTT+rYsWORtjNZ3nvvPTkcjnwHggwODtbdd9+tu+++W5mZmbrjjjv01FNPaezYsQoKCiryup37o/OOHTtkWVaxHktKZ9rB3I4ls9aXdlwjXsQ6dOigq6++Wi+99JJOnjyp8PBwdejQQW+88Ua28CfJawqmG2+8UevWrdN3333ntT63sydFacyYMdqzZ4/eeecdTZs2TfXr11ffvn090y3Ex8crJCRETz/9dI5TRJzPVFJ9+vTRgQMHNHjwYLlcLq8DyKxfr879tcrXUcwbNmyY7fruN998M9uvcD169FBycrLnbNbZDh8+7PmSLCi3261hw4bpl19+0bBhw/Lswn/udGyVK1dWo0aNvKa8yJp32ZcvDl+8+uqrnr+NMXr11Vfl7++vjh07SjrTiPn5+WV7Dl977bVsj+Vr3fz8/NS5c2fNmzfP6+xpWlqaEhMTFRsbW+hLHVD8Ro8erUqVKukf//iH0tLSsq0vyC/LPXr0kNvt1uTJk7OtO336tOe9VJztT1FxuVzq16+foqKi9PLLL2vWrFlKS0vTiBEjPGV83d+Cqlixom6//XZ9/fXXmjFjhoKDg3Xrrbd61ufUjmZmZub4OT6Xr21AQb7rCurQoUPq1auX3G63ZzTxnBw/ftxryhzpzHdAlSpVsrWjRdWG7t+/X5999pnn/4yMDL377rtq2bKlJwRkhY+zn8Njx47pnXfeyfZ4vtbtyiuvVHh4uF5//XWvffvmm2/0yy+/5Do7B0ofl8ulRYsWKSAgwNOlNicNGzZUenq6Nm3a5Fn2xx9/eL3/pDMjT/v5+WnixInZ2mNjjE9TvxbWtGnTtHbtWr355puaPHmyrr32Wj344IP6888/JUmtW7dWw4YN9fzzz+f4o8P5Hku6XC4NHjxYBw8ezDa+kp+fX7bn45VXXvHprGzDhg21bt06ZWZmepbNnz8/23RwPXr0UEpKiv79739ne4wTJ07kOwtDXp555hktWrRId999d66XHUjZjyUDAgLUrFkzGWM8359FfSx57pgwr7zyiqQzc9FLZ354qVGjRpEeS0pnctN3332n5ORkz7Jjx47pzTffVP369Qt0nXtJ4Yx4MRg1apTuuusuzZo1Sw888ICmT5+u2NhYtWjRQvfff78uuugipaWlKTk5Wfv27fPMrTh69Gi999576tKlix5++GHP9GX16tXzaniL2tKlS/Xaa69p/PjxuuKKKySdmQarQ4cOeuKJJzR16lSFhIRoxowZuvfee3XFFVeoZ8+eqlmzpvbs2aOvvvpKbdu29Qp1BdG9e3cNGTJE8+bN88wJmSUkJETt2rXT1KlT5XK5FB0drUWLFvl0JkeSBg4cqAceeEDdu3fXDTfcoJ9++kkLFy7M1nV/1KhRnkHi+vXrp9atW+vYsWPavHmzPv74Y+3evTvf7v7p6emeMy7Hjx/Xjh079Omnn2rnzp3q2bNnjgffZ2vWrJk6dOig1q1bq1q1avrhhx/08ccfew2o1rp1a0nSsGHDFB8fn63rVUEEBQVpwYIF6tu3r9q0aaNvvvlGX331lf7v//7P06U1NDRUd911l1555RU5HA41bNhQ8+fPz/E6p4LU7cknn1RSUpJiY2M1ZMgQVahQQW+88YZOnTqlqVOnFmp/YI+LL75YiYmJ6tWrlxo3bqw+ffro8ssvlzFGu3btUmJiopxOZ7brwXPSvn17DR48WFOmTNHGjRvVuXNn+fv7a/v27Zo7d65efvll3XnnncXa/hSVJ598Uhs3btSSJUtUpUoVXXbZZRo3bpwef/xx3Xnnnbrxxht93t/CuOeee/Tuu+9q4cKF6tOnj+dgRpKuvfZaVa1aVX379tWwYcPkcDj03nvv+fSjSUHaAF+/6/Ly3//+V7Nnz5YxRhkZGfrpp580d+5cHT16VNOmTVOXLl3yvG/Hjh3Vo0cPNWvWTBUqVNBnn32mtLQ0r7aodevWmjFjhp588kk1atRI4eHhuY4/kJ9LLrlEAwYM0Pfff6+IiAi9/fbbSktL08yZMz1lOnfurLp162rAgAEaNWqU/Pz89Pbbb3vew2fztW7+/v569tln1b9/f7Vv3169evXyTF9Wv359rx+AULp88803njN2Bw4cUGJiorZv367HHnsszx+he/bsqTFjxuj222/XsGHDPFM4XnLJJV6DjTVs2FBPPvmkxo4dq927d+u2225TlSpVtGvXLn322WcaNGiQHn300SLfr19++UVPPPGE+vXrp5tvvlnSmSn2WrZsqSFDhmjOnDlyOp1666231LVrVzVv3lz9+/dXdHS0UlJStGzZMoWEhOjLL78s1Pbbt2+v2rVra968eapYsaLnLHyWm266Se+9955CQ0PVrFkzJScna/HixblOx3a2gQMH6uOPP1aXLl3Uo0cP7dy5U7Nnz842BtO9996rOXPm6IEHHtCyZcvUtm1bud1u/frrr5ozZ44WLlyYb3f/06dPe44lT548qd9//11ffPGFNm3apOuuu05vvvlmnvfv3LmzIiMj1bZtW0VEROiXX37Rq6++qm7dunl6E2Udr/3zn/9Uz5495e/vr5tvvtnre6Mgdu3apVtuuUVdunRRcnKyZs+erd69e3umrJPOPIfPPPOMBg4cqCuvvFIrV670mg89S0Hq9thjj+mDDz5Q165dNWzYMFWrVk3vvPOOdu3apU8++cSnMQ1KnL2DtJcfWdMqfP/999nWud1u07BhQ9OwYUPPFCg7d+409913n4mMjDT+/v4mOjra3HTTTebjjz/2uu+mTZtM+/btTVBQkImOjjaTJ082//nPfwo0fVnW1FO5OXuqg4yMDFOvXj1zxRVXeKaXyDJixAjjdDpNcnKyZ9myZctMfHy8CQ0NNUFBQaZhw4amX79+5ocffshWj4K46667jCQzevTobOv27dtnbr/9dhMWFmZCQ0PNXXfdZfbv359tyoOcprpwu91mzJgxpkaNGqZSpUomPj7e7NixI8epKI4cOWLGjh1rGjVqZAICAkyNGjXMtddea55//nmTmZmZZ/3bt2/vNR1J5cqVzcUXX2zuueces2jRohzvc24dnnzySXP11VebsLAwU7FiRdOkSRPz1FNPeW379OnT5qGHHjI1a9Y0DofD8zxnTQ2R07Q9uU1fFhwcbHbu3Gk6d+5sKlWqZCIiIsz48eM90z1kOXjwoOnevbupVKmSqVq1qhk8eLDZsmVLtsfMrW7G5DzV048//mji4+NN5cqVTaVKlcx1111n1q5d61Umt89ZblMqwT47duwwDz74oGnUqJEJCgryvGcfeOABs3HjRq+y+U3J8uabb5rWrVubihUrmipVqpgWLVqY0aNHm/3793uV86X9yW1bvrZL+U1FmOXs9/T69etNhQoVvKYDNObMZ+Kqq64yUVFR5u+//y7Q/vo6fdnZ26pVq5aRZL7++uts69esWWOuueYaU7FiRRMVFWVGjx7tmbby7M9RTlMS+doGGOP7d11Ozm5DnU6nCQsLM61atTIPP/xwjlPpnNu2/fnnnyYhIcE0adLEBAcHm9DQUNOmTRszZ84cr/ulpqaabt26mSpVqhhJnuc5r+/13KYv69atm1m4cKG57LLLTGBgoGnSpImZO3dutvuvX7/etGnTxgQEBJi6deuaadOm5fiYudUttzbvo48+Mq1atTKBgYGmWrVqpk+fPp7ppbKc72cCRSOn6cuCgoJMy5YtzYwZM7ym8zIm5+/NRYsWmUsvvdQEBASYxo0bm9mzZ+f6On7yyScmNjbWBAcHm+DgYNOkSROTkJDgmc4rv3rm9Dk429ltRVZbV7t27WzTimZNEfXRRx95lm3YsMHccccdpnr16iYwMNDUq1fP9OjRwyxZsiRbPfI79j3bqFGjjCTTo0ePbOv+/vtv079/f1OjRg1TuXJlEx8fb3799ddsx2K5fdZeeOEFEx0dbQIDA03btm3NDz/8kGM7nZmZaZ599lnTvHlzExgYaKpWrWpat25tJk6c6JmWNzdZUw1m3SpVqmTq169vunfvbj7++ONsx2fGZP+ueOONN0y7du08z23Dhg3NqFGjsm178uTJJjo62jidTq/nWXlMI3nuezLrvffzzz+bO++801SpUsVUrVrVDB061GtaRWPOTB83YMAAExoaaqpUqWJ69OhhDhw4kOP7PLe65XTsvnPnTnPnnXeasLAwExQUZK6++mozf/58rzJZr+m5bXNe06rZxWEMox0BAAAAAGCXMnDOHgAAAACA8oMgDgAAAACAjQjiAAAAAADYiCAOAAAAAICNChTEp0yZoquuukpVqlRReHi4brvttmwTqXfo0EEOh8Pr9sADD3iV2bNnj7p166ZKlSopPDxco0aNyjZP8/Lly3XFFVcoMDBQjRo10qxZswq3hwAAAAAAlCIFCuIrVqxQQkKC1q1bp6SkJLlcLnXu3DnbBPX333+//vjjD8/t7HmB3W63unXrpszMTK1du1bvvPOOZs2apXHjxnnK7Nq1S926ddN1112njRs3avjw4Ro4cKAWLlx4nrsLAAAAAEDJOq/pyw4ePKjw8HCtWLFC7dq1k3TmjHjLli310ksv5Xifb775RjfddJP279+viIgISdLrr7+uMWPG6ODBgwoICNCYMWP01VdfacuWLZ779ezZU4cPH9aCBQt8qptlWdq/f7+qVKkih8NR2F0EcIEyxujIkSOKioqS01m+ruKhfQRwvsprG0n7COB8+do+VjifjaSnp0uSqlWr5rX8/fff1+zZsxUZGambb75ZTzzxhCpVqiRJSk5OVosWLTwhXJLi4+P14IMPauvWrWrVqpWSk5PVqVMnr8eMj4/X8OHDfa7b/v37VadOnULuGQCcsXfvXtWuXbukq1GkaB8BFJXy1kbSPgIoKvm1j4UO4pZlafjw4Wrbtq0uvfRSz/LevXurXr16ioqK0qZNmzRmzBht27ZNn376qSQpNTXVK4RL8vyfmpqaZ5mMjAydOHFCFStWzFafU6dO6dSpU57/s0707927VyEhIYXdTQAXqIyMDNWpU0dVqlQp6aoUuax9on0EUFjltY2kfQRwvnxtHwsdxBMSErRlyxatXr3aa/mgQYM8f7do0UK1atVSx44dtXPnTjVs2LCwm8vXlClTNHHixGzLQ0JCaEgBFFp57JqYtU+0jwDOV3lrI2kfARSV/NrHQl3UM3ToUM2fP1/Lli3LtztSmzZtJEk7duyQJEVGRiotLc2rTNb/kZGReZYJCQnJ8Wy4JI0dO1bp6eme2969ewu+YwAAAChyzLwDAN4KFMSNMRo6dKg+++wzLV26VA0aNMj3Phs3bpQk1apVS5IUExOjzZs368CBA54ySUlJCgkJUbNmzTxllixZ4vU4SUlJiomJyXU7gYGBnl8v+RUTAACg9GDmHQDwVqBR04cMGaLExETNmzdPjRs39iwPDQ1VxYoVtXPnTiUmJurGG29U9erVtWnTJo0YMUK1a9fWihUrJJ1pRFu2bKmoqChNnTpVqampuvfeezVw4EA9/fTTks40opdeeqkSEhL0j3/8Q0uXLtWwYcP01VdfKT4+3qe6ZmRkKDQ0VOnp6YRyAAVWntuQ8rxvAOxxvu1IaZ15h/YRwPnytR0p0BnxGTNmKD09XR06dFCtWrU8t48++kiSFBAQoMWLF6tz585q0qSJHnnkEXXv3l1ffvml5zH8/Pw0f/58+fn5KSYmRvfcc4/uu+8+TZo0yVOmQYMG+uqrr5SUlKTLL79cL7zwgt566y2fQzgAAABKr7xm3qlRo4YuvfRSjR07VsePH/esy23mnYyMDG3dutVTJqeZd5KTk3Osx6lTp5SRkeF1AwA7FGiwtvxOntepU8dz5jsv9erV09dff51nmQ4dOmjDhg0FqR4AAABKudI0805ug/0CQHE7r3nEAQAAgIIoTTPvjB07ViNHjvT8nzXtEAAUt0KNmg4AAAAUVGmbeYfBfgGUFII4AAAAilVpnnkHAEoCQRwAAADFKiEhQbNnz1ZiYqKqVKmi1NRUpaam6sSJE5KknTt3avLkyVq/fr12796tL774Qvfdd5/atWunyy67TJLUuXNnNWvWTPfee69++uknLVy4UI8//rgSEhIUGBgoSXrggQf022+/afTo0fr111/12muvac6cORoxYkSJ7TsA5IQgDgAAgGLFzDsA4I3B2gAAAFCsmHkHALxxRhwAAAAAABsRxAEAAAAAsBFBHAAAAAAAGxHEAQAAAACwEUEcAAAAAAAbEcQBAAAAALARQRwAAAAAABsxjzjkdrtlWZZPZZ1Op/z8/Iq5RgAAyff2mbYZQGnGsSaQHUH8Aud2u1U/Olr70tJ8Kl87IkK7U1JoIAGgmLndbkXXjVba/vzb54ioCKXsoW0GUPq43W5FR9dXWto+n8pHRNRWSspu2jOUewTxC5xlWdqXlqZjcXHydzjyLOsyRsGrVsmyLBpHAChmlmUpbX+a4t6Ik8Mv9/bZuI1WDaZtBlA6WZaltLR9ios7JofDP8+yxri0alUw7RkuCARxSJL8HQ75O/MZMsDHLkUAgKLj8HPIWSH39tkSbTOA0s/h8JfTmXcQ51ATFxIGawMAAAAAwEYEcQAAAAAAbEQQBwAAAADARgRxAAAAAABsRBAHAAAAAMBGBHEAAAAAAGxEEAcAAAAAwEYEcQAAAAAAbEQQBwAAAADARgRxAAAAAABsRBAHAAAAAMBGBHEAAAAAAGxEEAcAAAAAwEYEcQAAAAAAbEQQBwAAAADARgRxAAAAAABsVKGkKwAAwIXE7XbLsqx8y7lcLhtqAwAASgJBHAAAm7jdbkXXjVba/jSf72OMKcYaAQCAkkAQBwDAJpZlKW1/muLeiJPDz5FnWXemW2uGrJHI4QAAlDsEcQAAbObwc8hZIe9hWqzT+XdfBwAAZRODtQEAAAAAYCOCOAAAAAAANiKIAwAAAABgI4I4AAAAAAA2IogDAAAAAGAjgjgAAAAAADYiiAMAAAAAYCOCOAAAAAAANiKIAwAAAABgI4I4AAAAAAA2IogDAAAAAGCjCiVdAQAAAADI4nK58i3jdDrl5+dnQ22A4kEQBwAAAFDijHFL8ldwcHC+ZSMiaislZTdhHGUWQRwAAABAiTPGkuRSbOwROZ2BeZRzadWqYFmWRRBHmUUQBwAAAFBqOBz+cjr9c11vWTZWBigmDNYGAAAAAICNCOIAAAAAANiIIA4AAAAAgI24RhwAgHKA6X4AACg7COIAAJRhxjKSU75N9xMVoZQ9KYRxAABKGEEcAIAyzFhGsqTYGbFy+ud+xZlxG60avIrpfgAAKAUI4gAAlAMOP4ecFXIP4paY7wcAgNKCwdoAAAAAALARQRwAAAAAABsRxAEAAAAAsBFBHAAAAAAAGzFYGwqMuWoBAAAAoPAI4vCZ2xj5y7e5amtHRGh3CnPVAgAAAMC5COLwmWWMXJKOxMYq0Jn7VQ0uYxS8irlqAQAAACAnBHEUmL/DIf88grgs5qoFAAAAgNwwWBsAAAAAADYiiAMAAAAAYCOCOAAAAAAANiKIAwAAAABgI4I4ABSBKVOm6KqrrlKVKlUUHh6u2267Tdu2bfMqc/LkSSUkJKh69eqqXLmyunfvrrS0NK8ye/bsUbdu3VSpUiWFh4dr1KhROn36tFeZ5cuX64orrlBgYKAaNWqkWbNmFffuAQAAoAgRxAGgCKxYsUIJCQlat26dkpKS5HK51LlzZx07dsxTZsSIEfryyy81d+5crVixQvv379cdd9zhWe92u9WtWzdlZmZq7dq1eueddzRr1iyNGzfOU2bXrl3q1q2brrvuOm3cuFHDhw/XwIEDtXDhQlv3FwAAAIXH9GUAUAQWLFjg9f+sWbMUHh6u9evXq127dkpPT9d//vMfJSYm6vrrr5ckzZw5U02bNtW6det0zTXXaNGiRfr555+1ePFiRUREqGXLlpo8ebLGjBmjCRMmKCAgQK+//roaNGigF154QZLUtGlTrV69Wi+++KLi4+Nt328AAAAUHGfEAaAYpKenS5KqVasmSVq/fr1cLpc6derkKdOkSRPVrVtXycnJkqTk5GS1aNFCERERnjLx8fHKyMjQ1q1bPWXOfoysMlmPkZNTp04pIyPD6wYAAICSQxAHgCJmWZaGDx+utm3b6tJLL5UkpaamKiAgQGFhYV5lIyIilJqa6ilzdgjPWp+1Lq8yGRkZOnHiRI71mTJlikJDQz23OnXqnPc+AgAAoPAI4gBQxBISErRlyxZ9+OGHJV0VSdLYsWOVnp7uue3du7ekqwQAAHBB4xpxAChCQ4cO1fz587Vy5UrVrl3bszwyMlKZmZk6fPiw11nxtLQ0RUZGesp89913Xo+XNar62WXOHWk9LS1NISEhqlixYo51CgwMVGBg4HnvGwAAAIoGZ8QBoAgYYzR06FB99tlnWrp0qRo0aOC1vnXr1vL399eSJUs8y7Zt26Y9e/YoJiZGkhQTE6PNmzfrwIEDnjJJSUkKCQlRs2bNPGXOfoysMlmPAQAAgNKPM+IAUAQSEhKUmJioefPmqUqVKp5rukNDQ1WxYkWFhoZqwIABGjlypKpVq6aQkBA99NBDiomJ0TXXXCNJ6ty5s5o1a6Z7771XU6dOVWpqqh5//HElJCR4zmg/8MADevXVVzV69Gj94x//0NKlSzVnzhx99dVXJbbvAAAAKBjOiANAEZgxY4bS09PVoUMH1apVy3P76KOPPGVefPFF3XTTTerevbvatWunyMhIffrpp571fn5+mj9/vvz8/BQTE6N77rlH9913nyZNmuQp06BBA3311VdKSkrS5ZdfrhdeeEFvvfUWU5cBAACUIQUK4lOmTNFVV12lKlWqKDw8XLfddpu2bdvmVebkyZNKSEhQ9erVVblyZXXv3j3b9Yx79uxRt27dVKlSJYWHh2vUqFE6ffq0V5nly5friiuuUGBgoBo1aqRZs2YVbg8BwAbGmBxv/fr185QJCgrS9OnTdejQIR07dkyffvqp59rvLPXq1dPXX3+t48eP6+DBg3r++edVoYJ356UOHTpow4YNOnXqlHbu3Om1DQAAAJR+BQriK1asUEJCgtatW6ekpCS5XC517txZx44d85QZMWKEvvzyS82dO1crVqzQ/v37dccdd3jWu91udevWTZmZmVq7dq3eeecdzZo1S+PGjfOU2bVrl7p166brrrtOGzdu1PDhwzVw4EAtXLiwCHYZAAAAAICSU6BrxBcsWOD1/6xZsxQeHq7169erXbt2Sk9P13/+8x8lJibq+uuvlyTNnDlTTZs21bp163TNNddo0aJF+vnnn7V48WJFRESoZcuWmjx5ssaMGaMJEyYoICBAr7/+uho0aKAXXnhBktS0aVOtXr1aL774It0vAQAAAABl2nldI56eni5JqlatmiRp/fr1crlc6tSpk6dMkyZNVLduXSUnJ0uSkpOT1aJFC0VERHjKxMfHKyMjQ1u3bvWUOfsxsspkPQbKBpfL5dPN7XaXdFUBAAAAwDaFHjXdsiwNHz5cbdu21aWXXipJSk1NVUBAgNccuZIUERHhGUE4NTXVK4Rnrc9al1eZjIwMnThxIse5ck+dOqVTp055/s/IyCjsruE8uY2Rv6Tg4GCfyteOiNDulBT5+fkVb8UAAAAAoBQo9BnxhIQEbdmyRR9++GFR1qfQpkyZotDQUM+tTp06JV2lC5ZljFySjsTGKrNduzxvx+LitC8tTZZllXS1AeCCQG8llAQG/AUAb4UK4kOHDtX8+fO1bNky1a5d27M8MjJSmZmZOnz4sFf5tLQ0z8jAkZGR2RrVrP/zKxMSEpLj2XBJGjt2rNLT0z23vXv3FmbXUIT8HQ75O5153xyOkq4mAFwQjGUk55neSgEBAfneoutGE8ZRZBjwFwC8FahrujFGDz30kD777DMtX75cDRo08FrfunVr+fv7a8mSJerevbskadu2bdqzZ49iYmIkSTExMXrqqad04MABhYeHS5KSkpIUEhKiZs2aecp8/fXXXo+dlJTkeYycBAYGKjAwsCC7AwDABcNYRrKk2Bmxcvrn/Tu8cRutGrxKlmVx2RCKBAP+AoC3Ap0RT0hI0OzZs5WYmKgqVaooNTVVqampOnHihCQpNDRUAwYM0MiRI7Vs2TKtX79e/fv3V0xMjK655hpJUufOndWsWTPde++9+umnn7Rw4UI9/vjjSkhI8ATpBx54QL/99ptGjx6tX3/9Va+99prmzJmjESNGFPHuAwBwYXH4OeSs4Mzz5vCjtxKKV2kZ8PfUqVPKyMjwugGAHQoUxGfMmKH09HR16NBBtWrV8tw++ugjT5kXX3xRN910k7p376527dopMjJSn376qWe9n5+f5s+fLz8/P8XExOiee+7Rfffdp0mTJnnKNGjQQF999ZWSkpJ0+eWX64UXXtBbb73FL5kAAABlXEkO+HsuxhgCUFIK3DU9P0FBQZo+fbqmT5+ea5l69epl63p+rg4dOmjDhg0FqR4AAABKuawBf1evXl3SVdHYsWM1cuRIz/8ZGRmE8TLE5XLlW8bpdHKJDUqlQk9fBgAAABRE1oC/K1euzHXA37PPip874O93333n9XjnO+AvYwyVTca4Jfn7NFVuRERtpaTsJoyj1CGIAwAAoFiV5gF/UfYYY0lyKTb2iJzO3H9IMcalVauCGXgSpRJBHAAAAMUqISFBiYmJmjdvnmfAX+nMQL8VK1b0GvC3WrVqCgkJ0UMPPZTrgL9Tp05VampqjgP+vvrqqxo9erT+8Y9/aOnSpZozZ46++uqrEtt3FB+Hw19Op3+u6y3LxsoABUQQBwAAQLGaMWOGpDNjAJ1t5syZ6tevn6QzA/46nU51795dp06dUnx8vF577TVP2awBfx988EHFxMQoODhYffv2zXHA3xEjRujll19W7dq1GfC3GLndbln5pF1fruMGLkQEcQAAABQrBvwtf9xut6Kj6ystbZ9P5X15DwAXEoI4AAAAgAKxLEtpafsUF3dMDkfu3cPd7uNasyZMEkEcOBtBHAAAAECh5H+ddu7rgAuZs6QrAAAAAADAhYQgDgAAAACAjQjiAAAAAADYiCAOAAAAAICNCOIAAAAAANiIIA4AAAAAgI0I4gAAAAAA2IggDgAAAACAjQjiAAAAAADYiCAOAAAAAICNCOIAAAAAANiIIA4AAAAAgI0I4gAAAAAA2IggDgAAAACAjQjiAAAAAADYiCAOAAAAAICNCOIAAAAAANiIIA4AAAAAgI0I4gAAAAAA2IggDgAAAACAjQjiAAAAAADYqEJJVwAAgPLA7XbLsqw8y7hcLptqAwAASjOCOAAA58ntdiu6brTS9qf5VN4YU8w1AgAApRlBHACA82RZltL2pynujTg5/By5lnNnurVmyBqJHA4AwAWNIA4AQBFx+DnkrJD78CvW6by7rgMAgAsDg7UBAAAAAGAjgjgAAAAAADYiiAMAAAAAYCOCOAAAAAAANiKIAwAAAABgI4I4AAAAAAA2IogDAAAAAGAjgjgAAAAAADYiiAMAAAAAYKMKJV0BAABKM7fbLcuy8izjcrlsqg0AACgPCOIAAOTC7XYrum600van+VTeGFPMNQIAAOUBQRwAgFxYlqW0/WmKeyNODj9HruXcmW6tGbJGIocDAAAfEMQBAMiHw88hZ4Xch1WxTufddR0AAOBsDNYGAAAAAICNCOIAAAAAANiIIA4AAAAAgI0I4gAAAAAA2IjB2lAq+DIHr9PplJ+fnw21AQAAAIDiQxBHiXIbI39JwcHB+ZatHRGh3SkphHEAAAAAZRpBHCXKMkYuSUdiYxXozP1KCZcxCl61SqdOnZK/v3+ej8mZcwAAAAClGUEcpYK/wyH/PIK42+3mzDkAAACAcoEgjjKhoGfOLcsiiAMAAAAolQjiKFPyO3Muy7KvMgAAAABQCExfBgAAAACAjQjiAAAAAADYiCAOAAAAAICNCOIAAAAAANiIIA4AAAAAgI0I4gAAAAAA2IggDgAAAACAjQjiAAAAAADYiCAOAAAAAICNCOIAAAAAANiIIA4AAAAAgI0I4gAAAAAA2IggDgAAAACAjSqUdAUAAAAAoLi4XC6fyjmdTvn5+RVzbYAzCOIAAAAAyh1j3JL8FRwc7FP5iIjaSknZTRiHLQjiAAAAAModYyxJLsXGHpHTGZhPWZdWrQqWZVkEcdiCIA4AAACg3HI4/OV0+udZxrJsqgzw/zFYGwAAAAAANiKIAwAAAABgI4I4ABSRlStX6uabb1ZUVJQcDoc+//xzr/X9+vWTw+HwunXp0sWrzKFDh9SnTx+FhIQoLCxMAwYM0NGjR73KbNq0SXFxcQoKClKdOnU0derU4t41AAAAFCGCOAAUkWPHjunyyy/X9OnTcy3TpUsX/fHHH57bBx984LW+T58+2rp1q5KSkjR//nytXLlSgwYN8qzPyMhQ586dVa9ePa1fv17PPfecJkyYoDfffLPY9gsAcGFxu91yuVz53gAUHoO1AUAR6dq1q7p27ZpnmcDAQEVGRua47pdfftGCBQv0/fff68orr5QkvfLKK7rxxhv1/PPPKyoqSu+//74yMzP19ttvKyAgQM2bN9fGjRs1bdo0r8AOAEBhuN1uRUfXV1raPp/KG2OKuUZA+UQQBwAbLV++XOHh4apataquv/56Pfnkk6pevbokKTk5WWFhYZ4QLkmdOnWS0+nUt99+q9tvv13Jyclq166dAgICPGXi4+P17LPP6u+//1bVqlWzbfPUqVM6deqU5/+MjIxi3EMAQFlmWZbS0vYpLu6YHI7cRxp3u49rzZowSQRxoDDomg4ANunSpYveffddLVmyRM8++6xWrFihrl27yu12S5JSU1MVHh7udZ8KFSqoWrVqSk1N9ZSJiIjwKpP1f1aZc02ZMkWhoaGeW506dYp61wAA5UzWlF+53fIK6QDyxxlxALBJz549PX+3aNFCl112mRo2bKjly5erY8eOxbbdsWPHauTIkZ7/MzIyCOMAAAAliDPiAFBCLrroItWoUUM7duyQJEVGRurAgQNeZU6fPq1Dhw55riuPjIxUWlqaV5ms/3O79jwwMFAhISFeNwAAAJQcgjgAlJB9+/bpr7/+Uq1atSRJMTExOnz4sNavX+8ps3TpUlmWpTZt2njKrFy50mu02qSkJDVu3DjH68MBAABQ+hQ4iDNPLgDk7OjRo9q4caM2btwoSdq1a5c2btyoPXv26OjRoxo1apTWrVun3bt3a8mSJbr11lvVqFEjxcfHS5KaNm2qLl266P7779d3332nNWvWaOjQoerZs6eioqIkSb1791ZAQIAGDBigrVu36qOPPtLLL7/s1fUcAAAApVuBgzjz5JYdzAEJ2OuHH35Qq1at1KpVK0nSyJEj1apVK40bN05+fn7atGmTbrnlFl1yySUaMGCAWrdurVWrVikwMNDzGO+//76aNGmijh076sYbb1RsbKxX2xcaGqpFixZp165dat26tR555BGNGzeOqcsAAADKkAIP1sY8uWWD2+1W/eho7TvnWtLcMAckcP46dOiQ52dp4cKF+T5GtWrVlJiYmGeZyy67TKtWrSpw/QAAAFA6FMs14lnz5DZu3FgPPvig/vrrL8+6/ObJzSqT0zy527Zt099//53jNk+dOqWMjAyv24XMsiztS0vTsbg4ZbZrl+vtcNu2kpgBEgAAFB8ubQQAb0UexJknt3Txdzjk73TmfnM4SrqKAACgnOPSRgDwVuTziDNPLgAAAM7GpY0A4K3Ypy9jnlwAAADkh0sbAVxIij2IM08uAAAA8sKljQAuNAUO4syTCwAAgKLUs2dP3XLLLWrRooVuu+02zZ8/X99//72WL19erNsdO3as0tPTPbe9e/cW6/YAIEuBgzjz5AIAAKA4cWkjgPKuwIO1MU8uAAAAilNelza2bt1aUs6XNv7zn/+Uy+WSv7+/JC5tBFB6Ffs14gAAALiwcWkjAHgjiAMAAKBYcWkjAHgr8nnEAQAAgLNxaSMAeOOMOAAAAAAANiKIAwAAAABgI4I4AAAAAAA2IogDAAAAAGAjgjgAAAAAADYiiAMAAAAAYCOCOAAAAAAANiKIAwAAAABgI4I4AAAAAAA2IogDAAAAAGAjgjgAAAAAADYiiAMAAAAAYCOCOAAAAAAANiKIAwAAAABgI4I4AAAAAAA2IogDAAAAAGAjgjgAAAAAADYiiAMAAAAAYCOCOAAAAAAANiKIAwAAAABgI4I4AAAAAAA2IogDAAAAAGAjgjgAAAAAADYiiAMAAAAAYCOCOAAAAAAANiKIAwAAAABgI4I4AAAAAAA2IogDAAAAAGAjgjgAAAAAADYiiAMAAAAAYCOCOAAAAAAANiKIAwAAAABgI4I4AAAAAAA2IogDAAAAAGAjgjgAAAAAADYiiAMAAAAAYCOCOAAAAAAANiKIAwAAAABgI4I4AAAAAAA2IogDAAAAAGCjCiVdAQAAUDq5XK58yzidTvn5+dlQGwAofrR7sAtBHAAAeDGWkZxScHBwvmUjoiKUsieFg1IAZZoxbkn+vrV7EbWVkrKbdg/nhSAOAAC8GMtIlhQ7I1ZO/9yvYjNuo1WDV8myLA5IAZRpxliSXIqNPSKnMzCPci6tWhVMu4fzRhAHAAA5cvg55KyQexC3ZNlYGwAofg6Hv5xO/1zXWzR7KCIM1gYAAAAAgI0I4gAAAAAA2IggDgAAAACAjQjiAAAAAADYiCAOAAAAAICNCOIAAAAAANiIIA4AAAAAgI0I4gAAAAAA2IggDgAAAACAjQjiAAAAAADYiCAOAAAAAICNCOIAAAAAANiIIA4AAAAAgI0I4gAAAAAA2IggDgAAAACAjQjiAAAAAADYiCAOAAAAAICNCOIAAAAAANiIIA4AAAAAgI0I4gAAAAAA2IggDgAAAACAjQjiAAAAAADYqEJJVwAoDi6Xy6dyTqdTfn5+xVwbAKWN2+2WZVn5lvO1LQEAACgIgjjKFbcx8pcUHBzsU/naERHanZJCGAcuIG63W9F1o5W2P83n+xhjirFGAADgQkMQR7liGSOXpCOxsQp05n3lhcsYBa9aJcuyCOLABcSyLKXtT1PcG3Fy+DnyLOvOdGvNkDUSORwAABQhgjjKJX+HQ/75BHH50C0VQPnl8HPIWSHvdsI6TTsBAACKHoO1AQAAAABgI4I4AAAAAAA2IogDQBFZuXKlbr75ZkVFRcnhcOjzzz/3Wm+M0bhx41SrVi1VrFhRnTp10vbt273KHDp0SH369FFISIjCwsI0YMAAHT161KvMpk2bFBcXp6CgINWpU0dTp04t7l0DAABAESKIA0AROXbsmC6//HJNnz49x/VTp07Vv/71L73++uv69ttvFRwcrPj4eJ08edJTpk+fPtq6dauSkpI0f/58rVy5UoMGDfKsz8jIUOfOnVWvXj2tX79ezz33nCZMmKA333yz2PcPAAAARYPB2gCgiHTt2lVdu3bNcZ0xRi+99JIef/xx3XrrrZKkd999VxEREfr888/Vs2dP/fLLL1qwYIG+//57XXnllZKkV155RTfeeKOef/55RUVF6f3331dmZqbefvttBQQEqHnz5tq4caOmTZvmFdgBAABQenFGHABssGvXLqWmpqpTp06eZaGhoWrTpo2Sk5MlScnJyQoLC/OEcEnq1KmTnE6nvv32W0+Zdu3aKSAgwFMmPj5e27Zt099//23T3gAAAOB8cEYcAGyQmpoqSYqIiPBaHhER4VmXmpqq8PBwr/UVKlRQtWrVvMo0aNAg22NkratatWq2bZ86dUqnTp3y/J+RkXGeewMAAIDzwRlxACjnpkyZotDQUM+tTp06JV0lAACAC1qBgzijAgNAwUVGRkqS0tLSvJanpaV51kVGRurAgQNe60+fPq1Dhw55lcnpMc7exrnGjh2r9PR0z23v3r3nv0MAAAAotAIHcUYFBoCCa9CggSIjI7VkyRLPsoyMDH377beKiYmRJMXExOjw4cNav369p8zSpUtlWZbatGnjKbNy5Uq5XC5PmaSkJDVu3DjHbumSFBgYqJCQEK8bAAAASk6Bg3jXrl315JNP6vbbb8+27txRgS+77DK9++672r9/v+fMedaowG+99ZbatGmj2NhYvfLKK/rwww+1f/9+SfIaFbh58+bq2bOnhg0bpmnTpp3f3gJAMTp69Kg2btyojRs3SjozQNvGjRu1Z88eORwODR8+XE8++aS++OILbd68Wffdd5+ioqJ02223SZKaNm2qLl266P7779d3332nNWvWaOjQoerZs6eioqIkSb1791ZAQIAGDBigrVu36qOPPtLLL7+skSNHltBeA0D+6FEJAN6K9BrxkhwV+NSpU8rIyPC6AYCdfvjhB7Vq1UqtWrWSJI0cOVKtWrXSuHHjJEmjR4/WQw89pEGDBumqq67S0aNHtWDBAgUFBXke4/3331eTJk3UsWNH3XjjjYqNjfXqDRQaGqpFixZp165dat26tR555BGNGzeOqcsAlGr0qAQAb0U6anpJjgo8ZcoUTZw4sWh2BAAKoUOHDjLG5Lre4XBo0qRJmjRpUq5lqlWrpsTExDy3c9lll2nVqlWFricA2K1r167q2rVrjuvO7VEpSe+++64iIiL0+eefq2fPnp4eld9//73nZM4rr7yiG2+8Uc8//7yioqK8elQGBASoefPm2rhxo6ZNm8aPlQBKnXIzajqDEQEAAJQ99KgEcCEq0iBekqMCMxgRAABA2VOUPSpzeoyzt3EupncEUFKKNIiX5KjAAAAAQEHQoxJASSlwEGdUYAAAABQVelQCuBAVOIgzKjAAAACKCj0qAVyICjxqOqMCAwAAoCCOHj2qHTt2eP7P6lFZrVo11a1b19Oj8uKLL1aDBg30xBNP5Nqj8vXXX5fL5cqxR+XEiRM1YMAAjRkzRlu2bNHLL7+sF198sSR2GQDyVKTTlwEAAADn+uGHH3Tdddd5/s+63LBv376aNWuWRo8erWPHjmnQoEE6fPiwYmNjc+xROXToUHXs2FFOp1Pdu3fXv/71L8/6rB6VCQkJat26tWrUqEGPSgClFkEcAAAAxYoelQDgrdzMIw4AAAAAQFlAEAcAAAAAwEYEcQAAAAAAbEQQBwAAAADARgRxAAAAAABsRBAHAAAAAMBGBHEAAAAAAGzEPOJlkNvtlmVZeZZxuVw21QYAAAAAUBAE8TLG7XarfnS09qWl+VTeGFPMNQIAAAAAFARBvIyxLEv70tJ0LC5O/g5HruWOu90KW7NGxHAAAAAAKF0I4mWUv8Mhf2ful/j759N1HQAAAABQMhisDQAAAAAAGxHEAQAAAACwEUEcAAAAAAAbEcQBAAAAALARQRwAAAAAABsRxAEAAAAAsBFBHAAAAAAAGxHEAQAAAACwEUEcAAAAAAAbEcQBAAAAALBRhZKuAAAAAIDi5Xa7ZVlWvuVcLpcNtQFAEAcAAADKMbfbrejo+kpL2+fzfYwxxVgjAARxAAAAoByzLEtpafsUF3dMDod/nmXd7uNasyZMEkEcKE4EcVzwfOmC5XQ65efnZ0NtAAAAiofD4S+nM+8gbll5rwdQNAjiuGC5jZG/pODg4HzL1o6I0O6UFMI4AAAAgPNGEMcFyzJGLklHYmMV6Mx9AgGXMQpetUqWZRHEAQAAAJw3gjgueP4Oh/zzCOLyYYRRAAAAAPAV84gDAAAAAGAjgjgAAAAAADYiiAMAAAAAYCOCOAAAAAAANiKIAwAAAABgI4I4AAAAAAA2YvoyAAAAACgAl8uVbxmn0yk/Pz8baoOyiCAOAAAAAD4wxi3JX8HBwfmWjYiorZSU3YRx5IggDgAAAAA+MMaS5FJs7BE5nYF5lHNp1apgWZZFEEeOCOIAAAAAUAAOh7+cTv9c11uWjZVBmcRgbQAAAAAA2IggDgAAAACAjQjiAAAAAADYiCAOAAAAAICNCOIAAAAAANiIIA4AAAAAgI0I4gAAAAAA2Ih5xAEfuVyufMs4nU75+fnZUBsAAAAAZRVBHMiH2xj5SwoODs63bO2ICO1OSSGMAwAAAMgVQRzIh2WMXJKOxMYq0Jn71RwuYxS8apUsyyKIAwAAAMgVQRzwkb/DIf88grgsy77KAAAAACizGKwNAAAAAAAbEcQBAAAAALARQRwAAAAAABsRxAEAAAAAsBFBHAAAAAAAGxHEAQAAAACwEUEcAAAAAAAbEcQBAAAAALARQRwAAAAAABsRxAEAAAAAsBFBHAAAAAAAGxHEAQAAAACwEUEcAAAAAAAbEcQBAAAAALARQRwAAAAAABsRxAEAAAAAsBFBHAAAAAAAGxHEAQAAAACwEUEcAAAAAAAbEcQBAAAAALARQRwAAAAAABsRxAEAAAAAsFGFkq4AAABFxe12y7KsPMu4XC6bagMAAJAzgjgAoFxwu92KrhuttP1pPpU3xhRzjQAAAHJGEAcAlAuWZSltf5ri3oiTw8+Razl3pltrhqyRyOEAAKCEEMQBAOWKw88hZ4Xch0CxTufddR0AAKC4MVgbAAAAAAA2IogDgE0mTJggh8PhdWvSpIln/cmTJ5WQkKDq1aurcuXK6t69u9LSvK933rNnj7p166ZKlSopPDxco0aN0unTp+3eFQAAAJwHuqYDgI2aN2+uxYsXe/6vUOF/zfCIESP01Vdfae7cuQoNDdXQoUN1xx13aM2aNZLODEbWrVs3RUZGau3atfrjjz903333yd/fX08//bTt+wIAAIDCIYgDgI0qVKigyMjIbMvT09P1n//8R4mJibr++uslSTNnzlTTpk21bt06XXPNNVq0aJF+/vlnLV68WBEREWrZsqUmT56sMWPGaMKECQoICLB7dwAAAFAIRd41na6XAJC77du3KyoqShdddJH69OmjPXv2SJLWr18vl8ulTp06eco2adJEdevWVXJysiQpOTlZLVq0UEREhKdMfHy8MjIytHXrVnt3BAAAAIVWLNeIN2/eXH/88Yfntnr1as+6ESNG6Msvv9TcuXO1YsUK7d+/X3fccYdnfVbXy8zMTK1du1bvvPOOZs2apXHjxhVHVQHANm3atNGsWbO0YMECzZgxQ7t27VJcXJyOHDmi1NRUBQQEKCwszOs+ERERSk1NlSSlpqZ6hfCs9VnrcnPq1CllZGR43QCgNOFEDoALTbF0TafrJQBk17VrV8/fl112mdq0aaN69eppzpw5qlixYrFtd8qUKZo4cWKxPT4AFAXG0ABwISmWM+Il0fWSMz4AypqwsDBdcskl2rFjhyIjI5WZmanDhw97lUlLS/P8sBkZGZntDFDW/zn9+Jll7NixSk9P99z27t1btDsCAEUg60RO1q1GjRqS/nciZ9q0abr++uvVunVrzZw5U2vXrtW6deskyXMiZ/bs2WrZsqW6du2qyZMna/r06crMzCzJ3QKAHBV5EC+prpdTpkxRaGio51anTp2i3TEAKGJHjx7Vzp07VatWLbVu3Vr+/v5asmSJZ/22bdu0Z88excTESJJiYmK0efNmHThwwFMmKSlJISEhatasWa7bCQwMVEhIiNcNAEobTuQAuJAUedf0kup6OXbsWI0cOdLzf0ZGBmEcQKny6KOP6uabb1a9evW0f/9+jR8/Xn5+furVq5dCQ0M1YMAAjRw5UtWqVVNISIgeeughxcTE6JprrpEkde7cWc2aNdO9996rqVOnKjU1VY8//rgSEhIUGBhYwnuHC5nL5fKpnNPplJ+fXzHXBmVR1omcxo0b648//tDEiRMVFxenLVu2FPuJHC7dAVASin36srO7Xt5www2erpdnN6bndr387rvvvB7Dl66XgYGBHIgCKNX27dunXr166a+//lLNmjUVGxurdevWqWbNmpKkF198UU6nU927d9epU6cUHx+v1157zXN/Pz8/zZ8/Xw8++KBiYmIUHBysvn37atKkSSW1S7jAGctITik4ONin8hFREUrZk0IYRzacyAFwoSn2IJ7V9fLee+/16nrZvXt3STl3vXzqqad04MABhYeHS/Kt6yUAlHYffvhhnuuDgoI0ffp0TZ8+Pdcy9erV09dff13UVQMKxVhGsqTYGbFy+ud9tZtxG60avEqWZRHEkS9O5AAo74r8GvFHH31UK1as0O7du7V27VrdfvvtOXa9XLZsmdavX6/+/fvn2vXyp59+0sKFC+l6CQBAKebwc8hZwZnnzeHnKOlqogyxawwNACgpRX5GnK6XAAAAKAjG0ABwoSnyIE7XSwAAABQEJ3IAXGiK/RpxAAAAIC+cyAFwoSnya8QBAAAAAEDuCOIAAAAAANiIIA4AAAAAgI0I4gAAAAAA2IggDgAAAACAjRg1vZRwu92yLCvfci6Xy4baAAAAAACKC0G8FHC73aofHa19aWk+38cYU4w1AgAAAAAUF4J4KWBZlvalpelYXJz8HY48yx53uxW2Zo2I4QAAAEDp5mtvVqfTKT8/v2KuDUoTgngp4u9wyN+Z92X7/j50XwcAAABQcoxxS/JXcHCwT+UjImorJWU3YfwCQhAHAAAAgCJkjCXJpdjYI3I6A/Mp69KqVcGyLIsgfgEhiAMAAABAMXA4/OV0+udZhg6vFyamLwMAAAAAwEYEcQAAAAAAbEQQBwAAAADARgRxAAAAAABsRBAHAAAAAMBGjJoOFDGXy+VTOafTyRQVAAAAwAWIIA4UEbcx8pcUHBzsU/naERHanZJCGAcAAAAuMARxoIhYxsgl6UhsrAKdeV/14TJGwatWybIsgjgAAABwgSGIA0XM3+GQfz5BXJZlT2UAAAAAlDoM1gYAAAAAgI0I4gAAAAAA2IggDgAAAACAjQjiAAAAAADYiMHaAAAAgDLK7XbLymcQWJfLZVNtAPiKIA4AAACUQW63W9HR9ZWWts+n8saYYq4RAF8RxAEAAIAyyLIspaXtU1zcMTkc/rmWc7uPa82aMEkEcaC0IIgDAAAAZZjD4S+nM/cgblm5rwNQMhisDQAAAAAAGxHEAQAAAACwEUEcAAAAAAAbcY04AAAAAJQwX6aZczqd8vPzs6E2KG4EcQAAAAAoIca4JfkrODg437IREbWVkrKbMF4OEMQBAAAAoIQYY0lyKTb2iJzOwDzKubRqVbAsyyKIlwMEcQAAAAAoYflPQ2djZVDsCOIAAMA2XAMJAABBHAAA2MBYRnLKt2sgoyKUsieFMA4AKLcI4gAAoNgZy0iWFDsjVk7/3GdPNW6jVYNXcQ0kAKBcI4gDAADbOPwcclbIPYhb4iJIAED5l/s3IQAAAAAAKHIEcQAAAAAAbEQQBwAAAADARgRxAAAAAABsRBAHAAAAAMBGjJoOACj13G63LCvv0bRdLpdNtQEAADg/BHEAQKnmdrsVXTdaafvTfCpvjCnmGgEAAJwfgjgAoFSzLEtp+9MU90acHH6OXMu5M91aM2SNRA4HAAClHEEcAFAmOPwcclbIfWgT63TeXdcBAABKCwZrAwAAAADARgRxAAAAAABsRBAHAAAAAMBGBHEAAAAAAGzEYG1ACfJl3mOn0yk/Pz8bagMAAADADgRxoAS4jZG/pODg4HzL1o6I0O6UFMI4AAAAUE4QxIESYBkjl6QjsbEKdOZ+hYjLGAWvWiXLsgjiAAAAQDlBEAdKkL/DIf88grgs5kUGAAAAyhsGawMAAAAAwEacEQcAAKUOg1kCAMozgjgAACg1jGUkp2+DWUZERShlD4NZAgDKHoI4AAAoNYxlJEuKnRErp3/uV9AZt9GqwQxmifLJ7XbL8mGcGF96jqD88fV1p9dQ6UYQBwAApY7DzyFnhdyDuCUGs0T55Ha7FR1dX2lp+3y+jzGmGGuE0sIYtyR/n3oMSVJERG2lpOwmjJdSBHEAAACglLAsS2lp+xQXd0wOh3+eZd3u41qzJkwSQfxCYIwlyaXY2CNyOgPzKevSqlXB9BoqxQjiAAAAQCnjcPjL6cw7iFtW3utRPvn23rCpMig0pi8DAAAAAMBGBHEAAAAAAGxEEAcAAAAAwEZcIw6UAb5MU8EUFQAAAEDZQBAvZr7MA8kckMiN2xj5Sz5NU1E7IkK7U1II4ygzmCcXAABcqAjixcjtdqt+dLT2paX5VJ45IHEuyxi5JB2JjVWgM/crSVzGKHjVKqaoQJnhdrsVXTdaaft9ax8l2kjkzNcfaug1BAAoTQjixciyLO1LS9OxuDj5Oxy5ljvuditszRpmgESu/B0O+ecRxJmjAmWNZVlK25+muDfi5PDLvX2UJHemW2uGrGGaXHgxlpGcvvUYkqSIqAil7KHXEACgdCCI2yC/EOVPiAJwgXL4OeSskPe4odZp2khkZywjWVLsjFg5/fN+Dxm30arB9BoCgLLK18vZpLLTA4ogDpQjJdVFszw2jgDKBp9+zFHZ+DHH17aUdhTAhcTtdis6ur7S0vb5VD4iorZSUnaX+naSIA6UAwUZ1E0q2oHdCjoWAoPKAUB2BRk3IbxWuH7/7Xef2lFCO3BhK6mZd4ryh0XLspSWtk9xccfkcPjnWdYYl1atCtapU6fk75932ZJuHwniQDng66BuUtEP7ObrWAjFsW0AKC98HTfBcllanbBaFStW9OlxuTa+dGE2HdjFGLckf59O0hT1GeSCnMEuyLYdDn85nXmHa7e75Pa7oAjiQDmS76BuUrEN7FaS2wYAX5TUmaGCyK+rvXXa4tr4Mqqg3WuZKQLnwxhLkkuxsUfkdAbmUe7MGeSibCd8PYPt69nrgvw4VZL7XVAEcQAAUK4VZIT1snIGuTxdG3+h8DWcuN3HtWZNmJgqAkUhv7PIxXmOJL9tF+TstVSwH6dKcr99RRAHkKvi6kJXFs5KASg/fB1hPesMsi/XFhpj5MjncpyClKU78oUj/4CQ93sPKA6+tEG+tnu+tme+nr0urz9OEcQLiWt8UN4VdBA2X36lLMigctHh4drxO4MRASg6+Z1Fdltun8+cO/2dslw+zhZRgLJ0RwZgp4JcS+50BsqyThXgsX1rzy7UH6cI4oVQHAEFsFt+PxS5XC6fBmE77nYrbM0an36j9HVQuZOWpeqrfR+MiJHYARQFX8+cuzPdWjNkjU/XaftaNqtcOTvhA6CUK+hZ6fzKnV2WBi1vBPFC8HWU6IIEFMAuBZ3qrIKU5yBs/oW4yCa/gd1cllVio8ADgE8DpvlQriBls8oBQEnw9ay0LyOXl9cz2EUtnyGOS9b06dNVv359BQUFqU2bNvruu++KdXtut1sul8unm/S/MJHrzcdrxwA7nX1WOrNdu1xvh9u2lVSyv2Xm+xm7gD9ndrePBeFrWwrAHr58Hs8MmlR+lOY2EgCkUhzEP/roI40cOVLjx4/Xjz/+qMsvv1zx8fE6cOBAsWwvq7t5QEBAvresM4l0OUdZVt5+SLqQDjTtbh8Lwu12K7pu/m0p7ShQ/M4eLT6/z2R03WjaSAAXnJI8fiy1XdOnTZum+++/X/3795ckvf766/rqq6/09ttv67HHHivy7fna3VyiyzlQmhSkq315uZbc7vYxi6+DVKbtT1PcG3Fy+OXelnI9LFD8CjpafHm5xKek2kgAZUdBBqmLiKitlJTdRd4+lsognpmZqfXr12vs2LGeZU6nU506dVJycnKO9zl16pROnfrfKH7p6emSpIyMDJ+2mdVNMuP0aZ+CuCT95XIpMI9fSIq6XHnbdnE8Jtu+sLadVdYlaXebNnleT+4yRnXXrdPhw4fznZZI+l/bUdrO2JZE+yidCeGNmzXWwdSDPpV3HXflPTp15pnX1XXUJbd/3q+xr2WLuhzbLt2PybZ93/bpE6flPJ13EJfOtAm+tI9ZZaWy30YWRfsoFWw2HZfrr3znEc8q53bnPyiWL2VL8jHZNq9j6d22S23a7M5z8DljXFq3rq7Px49SAdpHUwqlpKQYSWbt2rVey0eNGmWuvvrqHO8zfvx4ozPnVrhx48atyG579+61o9nzGe0jN27cStOtrLeRtI/cuHErrlt+7WOpPCNeGGPHjtXIkSM9/1uWpUOHDql69eo+TTyfkZGhOnXqaO/evQoJCSnOqhYb9qF0YB9Kh/PdB2OMjhw5oqioqGKonb3Ot31EduXhM1Ka8HwWLTuez/LSRpaX9pHPUPHhuS0+5fW59bV9LJVBvEaNGvLz81PaOfN0p6WlKTIyMsf7BAYGKjDQu1tBWFhYgbcdEhJS5t8I7EPpwD6UDuezD6GhoUVcm/NXku0jsisPn5HShOezaBX381ke2sjy1j7yGSo+PLfFpzw+t760j6Vy1PSAgAC1bt1aS5Ys8SyzLEtLlixRTExMCdYMAEoW7SMA5I42EkBZUSrPiEvSyJEj1bdvX1155ZW6+uqr9dJLL+nYsWOeETAB4EJF+wgAuaONBFAWlNogfvfdd+vgwYMaN26cUlNT1bJlSy1YsEARERHFsr3AwECNHz8+W/eksoR9KB3Yh9KhPOxDbuxuH5FdeX5/lQSez6J1oT+fF2IbeaG/5sWJ57b4XOjPrcOYUjbvBAAAAAAA5VipvEYcAAAAAIDyiiAOAAAAAICNCOIAAAAAANiIIA4AAAAAgI0I4v/f9OnTVb9+fQUFBalNmzb67rvvSrpKOZoyZYquuuoqValSReHh4brtttu0bds2rzIdOnSQw+Hwuj3wwAMlVOPsJkyYkK1+TZo08aw/efKkEhISVL16dVWuXFndu3dXWlpaCdY4Z/Xr18+2Hw6HQwkJCZJK3+uwcuVK3XzzzYqKipLD4dDnn3/utd4Yo3HjxqlWrVqqWLGiOnXqpO3bt3uVOXTokPr06aOQkBCFhYVpwIABOnr0aKnYB5fLpTFjxqhFixYKDg5WVFSU7rvvPu3fv9/rMXJ63Z555hnb9gFlS3lpr0pKeWh3SpP8ns9+/fple7926dLFqwzPZ9lFe1R0aJuKD+2U7wjikj766CONHDlS48eP148//qjLL79c8fHxOnDgQElXLZsVK1YoISFB69atU1JSklwulzp37qxjx455lbv//vv1xx9/eG5Tp04toRrnrHnz5l71W716tWfdiBEj9OWXX2ru3LlasWKF9u/frzvuuKMEa5uz77//3msfkpKSJEl33XWXp0xpeh2OHTumyy+/XNOnT89x/dSpU/Wvf/1Lr7/+ur799lsFBwcrPj5eJ0+e9JTp06ePtm7dqqSkJM2fP18rV67UoEGD7NqFPPfh+PHj+vHHH/XEE0/oxx9/1Keffqpt27bplltuyVZ20qRJXq/LQw89ZEf1UUaVh/aqpJSHdqc0ye/5lKQuXbp4vV8/+OADr/U8n2Ub7VHRoG0qPrRTBWBgrr76apOQkOD53+12m6ioKDNlypQSrJVvDhw4YCSZFStWeJa1b9/ePPzwwyVXqXyMHz/eXH755TmuO3z4sPH39zdz5871LPvll1+MJJOcnGxTDQvn4YcfNg0bNjSWZRljSvfrIMl89tlnnv8tyzKRkZHmueee8yw7fPiwCQwMNB988IExxpiff/7ZSDLff/+9p8w333xjHA6HSUlJsa3uWc7dh5x89913RpL5/fffPcvq1atnXnzxxeKtHMqN8tpelYTy0O6UJjm1gX379jW33nprrvfh+SzbaI+KB21T8aGdytsFf0Y8MzNT69evV6dOnTzLnE6nOnXqpOTk5BKsmW/S09MlSdWqVfNa/v7776tGjRq69NJLNXbsWB0/frwkqper7du3KyoqShdddJH69OmjPXv2SJLWr18vl8vl9Xo0adJEdevWLdWvR2ZmpmbPnq1//OMfcjgcnuWl/XXIsmvXLqWmpno976GhoWrTpo3neU9OTlZYWJiuvPJKT5lOnTrJ6XTq22+/tb3OvkhPT5fD4VBYWJjX8meeeUbVq1dXq1at9Nxzz+n06dMlU0GUCeWtvSotymu7U9KWL1+u8PBwNW7cWA8++KD++usvzzqez7KP9qj40TYVP9qpMyqUdAVK2p9//im3262IiAiv5REREfr1119LqFa+sSxLw4cPV9u2bXXppZd6lvfu3Vv16tVTVFSUNm3apDFjxmjbtm369NNPS7C2/9OmTRvNmjVLjRs31h9//KGJEycqLi5OW7ZsUWpqqgICArIFp4iICKWmppZMhX3w+eef6/Dhw+rXr59nWWl/Hc6W9dzm9DnIWpeamqrw8HCv9RUqVFC1atVK5Wtz8uRJjRkzRr169VJISIhn+bBhw3TFFVeoWrVqWrt2rcaOHas//vhD06ZNK8HaorQqj+1VaVEe252S1qVLF91xxx1q0KCBdu7cqf/7v/9T165dlZycLD8/P57PMo72yB60TcWLdup/LvggXpYlJCRoy5YtXtcHSfK6hqJFixaqVauWOnbsqJ07d6phw4Z2VzObrl27ev6+7LLL1KZNG9WrV09z5sxRxYoVS7Bmhfef//xHXbt2VVRUlGdZaX8dyjOXy6UePXrIGKMZM2Z4rRs5cqTn78suu0wBAQEaPHiwpkyZosDAQLurilKuPLZXKL969uzp+btFixa67LLL1LBhQy1fvlwdO3YswZqhKNAeoTygnfqfC75reo0aNeTn55dtVMm0tDRFRkaWUK3yN3ToUM2fP1/Lli1T7dq18yzbpk0bSdKOHTvsqFqBhYWF6ZJLLtGOHTsUGRmpzMxMHT582KtMaX49fv/9dy1evFgDBw7Ms1xpfh2yntu8PgeRkZHZBjA8ffq0Dh06VKpem6wQ/vvvvyspKcnrbHhO2rRpo9OnT2v37t32VBBlWllvr0qT8tTulFYXXXSRatSo4fne4fksX2iPigdtk70u5Hbqgg/iAQEBat26tZYsWeJZZlmWlixZopiYmBKsWc6MMRo6dKg+++wzLV26VA0aNMj3Phs3bpQk1apVq5hrVzhHjx7Vzp07VatWLbVu3Vr+/v5er8e2bdu0Z8+eUvl6SNLMmTMVHh6ubt265VmuNL8ODRo0UGRkpNfznpGRoW+//dbzvMfExOjw4cNav369p8zSpUtlWZbnR4aSlhXCt2/frsWLF6t69er53mfjxo1yOp3ZukEBOSnr7VVpUl7andJs3759+uuvvzzfOzyf5QvtUfGgbbLXBd1OlfRocaXBhx9+aAIDA82sWbPMzz//bAYNGmTCwsJMampqSVctmwcffNCEhoaa5cuXmz/++MNzO378uDHGmB07dphJkyaZH374wezatcvMmzfPXHTRRaZdu3YlXPP/eeSRR8zy5cvNrl27zJo1a0ynTp1MjRo1zIEDB4wxxjzwwAOmbt26ZunSpeaHH34wMTExJiYmpoRrnTO3223q1q1rxowZ47W8NL4OR44cMRs2bDAbNmwwksy0adPMhg0bPCOKP/PMMyYsLMzMmzfPbNq0ydx6662mQYMG5sSJE57H6NKli2nVqpX59ttvzerVq83FF19sevXqVSr2ITMz09xyyy2mdu3aZuPGjV6fj1OnThljjFm7dq158cUXzcaNG83OnTvN7NmzTc2aNc19991n2z6gbClP7VVJKA/tTmmS1/N55MgR8+ijj5rk5GSza9cus3jxYnPFFVeYiy++2Jw8edLzGDyfZRftUdGhbSo+tFO+I4j/f6+88oqpW7euCQgIMFdffbVZt25dSVcpR5JyvM2cOdMYY8yePXtMu3btTLVq1UxgYKBp1KiRGTVqlElPTy/Zip/l7rvvNrVq1TIBAQEmOjra3H333WbHjh2e9SdOnDBDhgwxVatWNZUqVTK33367+eOPP0qwxrlbuHChkWS2bdvmtbw0vg7Lli3L8b3Tt29fY8yZ6TqeeOIJExERYQIDA03Hjh2z7ddff/1levXqZSpXrmxCQkJM//79zZEjR0rFPuzatSvXz8eyZcuMMcasX7/etGnTxoSGhpqgoCDTtGlT8/TTT3s1/sDZylN7VRLKQ7tTmuT1fB4/ftx07tzZ1KxZ0/j7+5t69eqZ+++/P9tJBZ7Psov2qOjQNhUf2infOYwxpshPswMAAAAAgBxd8NeIAwAAAABgJ4I4AAAAAAA2IogDAAAAAGAjgjgAAAAAADYiiAMAAAAAYCOCOAAAAAAANiKIAwAAAABgI4I4AAAAAAA2IogDAAAAAGAjgjgAAAAAADYiiAMAAAAAYCOCOAAAAAAANvp/wi9NpIbk9sUAAAAASUVORK5CYII=\n"},"metadata":{}}],"source":["# Choose a random image and plot pixel value distribution\n","random_img = images[np.random.randint(0, len(images))]\n","\n","colors = ['Red', 'Green', 'Blue']                       # List of colors\n","fig, ax = plt.subplots(1, 3, figsize=(12, 6))           # Create a figure with three subplots\n","\n","random_img = images[np.random.randint(0, len(images))]  # Choose a random image\n","\n","# Plot pixel value distribution for each color channel\n","for i, color in enumerate(colors):\n","  plot_histogram(random_img[:, :, i].ravel(), ax[i], title = f\"{color} Pixel Value Distribution\", fontsize = 12, color = color)"]},{"cell_type":"markdown","metadata":{"id":"kNszUXeNuBrO"},"source":["**Observations**\n","\n","- The training dataset is imbalanced. Certain plant classes, including Common Wheat, Maize, and Shepherd's Purse, have a relatively low frequency of occurrence.\n","- All images in the dataset share a consistent resolution, with no deviations in width or height.\n","- The distribution is uniform and consistent, indicating that no additional pre-processing steps are required to normalize or balance the color channels.\n","- All Images are color Image."]},{"cell_type":"markdown","metadata":{"id":"vOvazq-OWpNB"},"source":["## Data Pre-Processing"]},{"cell_type":"markdown","metadata":{"id":"hzpcKHaDWsG7"},"source":["### Convert the BGR images to RGB images."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"U9u82V2TWsuQ"},"outputs":[],"source":["import cv2    # Import necessery Class\n","\n","# Converting the images from BGR to RGB using cvtColor function of OpenCV\n","for i in range(len(images)):\n","  images[i] = cv2.cvtColor(images[i], cv2.COLOR_BGR2RGB)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","output_embedded_package_id":"1VLiHnrlKYONRcstOdfNvWokAjdYHIgAw","height":1000},"executionInfo":{"elapsed":35482,"status":"ok","timestamp":1723867422025,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"},"user_tz":300},"id":"UbqwL2vVBDjS","outputId":"2028bf09-75c1-4b40-9bb1-e463217d1882"},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["# Plot random images from each of the classes and print their corresponding labels.\n","# Assuming images is a numpy array of shape (num_samples, height, width, channels)\n","\n","plot_images(images, labels, samples_per_class = 6, img_size = (256, 256),\n","            title = \"Random Samples from Each Class after converting image to RGB\",\n","            )"]},{"cell_type":"markdown","metadata":{"id":"STMonBqiWxM5"},"source":["### Resize the images"]},{"cell_type":"markdown","metadata":{"id":"pESDU0AEMOFk"},"source":["As the size of the images is large, it may be computationally expensive to train on these larger images; therefore, it is preferable to reduce the image size from 128 to 64."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kJYZ4IpGkwre"},"outputs":[],"source":["# Downscale the images\n","image_downscale = []      # List to store the downscaled images\n","height, weidth = 64, 64   # Set the height and width of the downscaled images\n","\n","for img in images:        # Loop through each image in the list\n","                          # Resize the image using the INTER_LINEAR interpolation method\n","  img_downscale = cv2.resize(img, (height, weidth), interpolation=cv2.INTER_LINEAR)\n","                          # Append the downscaled image to the list\n","  image_downscale.append(img_downscale)"]},{"cell_type":"markdown","metadata":{"id":"kQXBPzW3CgI3"},"source":["**Compare image before and after resizing**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1723867422025,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"},"user_tz":300},"id":"a5afziCoCS82","outputId":"c7e5af22-652c-44f1-c4da-e774085ccf64"},"outputs":[{"output_type":"stream","name":"stdout","text":["(1, 2, 1)\n","(1, 2, 2)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import random         # import necessery Library\n","\n","random_index = random.randint(0, len(images) - 1)               # Select a random image index\n","titles = [\"Original\", \"Resized\"]                                # Set the titles for the subplots\n","image = [images[random_index], image_downscale[random_index]]   # Create a list of images to display\n","\n","fig = plt.figure()                                                                      # Create a figure\n","plt.suptitle(f\"Class: {labels.iloc[random_index,0]}\", y = 0.9, fontsize = 14)           # Set the main title\n","\n","# Create the plot\n","for i in range(len(titles)):\n","    position = (1, len(titles), i + 1)                          # Set the subplot position\n","    print(position)\n","    plot_image(fig, image[i], position,\n","               f\"{titles[i]} ({image[i].shape[1]}x{image[i].shape[0]})\",\n","\n","               )\n","\n","plt.tight_layout()                                              # Adjust the spacing between subplots\n","plt.show()                                                      # Display the plot"]},{"cell_type":"markdown","metadata":{"id":"x-lbQu5G6-3X"},"source":["### Split the Data in Train, Test and Validation Set"]},{"cell_type":"markdown","metadata":{"id":"KljdsjFCmJIZ"},"source":["- Before you proceed to build a model, you need to split the data into train, test, and validation to be able to evaluate the model that you build on the train data\n","- You'll have to encode categorical features and scale the pixel values.\n","- You will build a model using the train data and then check its performance"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"USifnEb_m85i"},"outputs":[],"source":["# Import Library to tune different models\n","from sklearn.model_selection import train_test_split\n","\n","# Function to split data into training, validation, and test sets\n","def split_data(X, y, test_size = 0.2, val_size  = 0.2, random_state = 1):\n","  # Splitting data into training, validation, and test sets\n","  X_temp, X_test, y_temp, y_test = train_test_split(\n","     X, y, test_size = test_size, random_state = random_state, stratify = y\n","  )\n","\n","  X_train, X_val, y_train, y_val = train_test_split(\n","     X_temp, y_temp, test_size= val_size,  # Adjusting val_size to be a fraction of the training set\n","     random_state = random_state, stratify = y_temp\n","  )\n","\n","  return X_train, X_val, X_test, y_train, y_val, y_test"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"q1iMJFUDWRVq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1723867422025,"user_tz":300,"elapsed":4,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"}},"outputId":"0d1d15a0-340c-4360-dafe-4c66e74932cb"},"outputs":[{"output_type":"stream","name":"stdout","text":["Data split into:\n","Train set: (3847, (64, 64, 3)) (3847, 1)\n","Validation set: (428, (64, 64, 3)) (428, 1)\n","Test set: (475, (64, 64, 3)) (475, 1)\n"]}],"source":["random_state = 42  # Set a random state for reproducibility\n","\n","# Splitting data in train, validation and test sets\n","X_train, X_val, X_test, y_train, y_val, y_test = split_data(np.array(image_downscale), labels,\n","                                                            test_size = 0.1,\n","                                                            val_size = 0.1,\n","                                                            random_state = random_state\n","                                                            )\n","\n","# Print information about the split\n","print(\"Data split into:\")\n","print(\"Train set:\", f\"({X_train.shape[0]}, {(X_train.shape[1:])})\", y_train.shape)\n","print(\"Validation set:\", f\"({X_val.shape[0]}, {(X_val.shape[1:])})\", y_val.shape)\n","print(\"Test set:\", f\"({X_test.shape[0]}, {(X_test.shape[1:])})\", y_test.shape)"]},{"cell_type":"markdown","metadata":{"id":"FAJ9B0wKNiY3"},"source":["### Target Labels Encoding"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"88OIAwNoEPfx","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1723867422025,"user_tz":300,"elapsed":4,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"}},"outputId":"a6de8624-62f3-4418-b481-e7f079b33925"},"outputs":[{"output_type":"stream","name":"stdout","text":["Data after Target Lebel encoding:\n","Training Dataset Shape: (3847, 12)\n","Validation Dataset Shape: (428, 12)\n","Test Dataset Shape: (475, 12)\n"]}],"source":["from sklearn.preprocessing import LabelBinarizer  # Import LabelBinarizer\n","\n","enc = LabelBinarizer()      # Initialize Label Encoder class\n","\n","# Convert labels from names to one hot vectors\n","y_train_encoded = enc.fit_transform(y_train)\n","y_val_encoded = enc.transform(y_val)\n","y_test_encoded = enc.transform(y_test)\n","\n","# Print information after Target Lebel encoding\n","print(\"Data after Target Lebel encoding:\")\n","print(\"Training Dataset Shape:\", y_train_encoded.shape)\n","print(\"Validation Dataset Shape:\", y_val_encoded.shape)\n","print(\"Test Dataset Shape:\", y_test_encoded.shape)"]},{"cell_type":"markdown","metadata":{"id":"exJFCDSMNrEG"},"source":["### Data Normalization"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jVUuPJS9OB_U"},"outputs":[],"source":["# Normalize the image pixels of train, test and validation data\n","X_train_normalized = X_train.astype('float32') / 255.0\n","X_val_normalized = X_val.astype('float32') / 255.0\n","X_test_normalized = X_test.astype('float32') / 255.0"]},{"cell_type":"markdown","metadata":{"id":"d9_M19L-OLng"},"source":["## Model Building"]},{"cell_type":"markdown","metadata":{"id":"yo0NaFt8fERL"},"source":["### Define useful functions"]},{"cell_type":"markdown","metadata":{"id":"ScTeQapk-3nE"},"source":["#### Functions to Build Model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NutoC_zwfRDM"},"outputs":[],"source":["# Import Important Libraries\n","import tensorflow as tf\n","from tensorflow import keras\n","from keras import backend\n","from keras.models import Sequential\n","from keras.layers import Dense, Dropout\n","from keras.callbacks import EarlyStopping\n","\n","# importing different functions to build models\n","def create_model(models, params):\n","    if params[\"model\"] in models:\n","        del models[params[\"model\"]]             # Delete the existing model if it exists\n","\n","    clear_session(random_index)                 # Clear the Keras session to reset the state\n","\n","    model = Sequential(name = params[\"model\"])  # Initialize the Sequential model with a specific name\n","\n","    for layer in params[\"layers\"]:\n","      layer_type = next(iter(layer.keys()))     # Get the first key in the dictionary\n","\n","      # Dynamically get the layer class from tf.keras.layers\n","      layer_class = getattr(tf.keras.layers, layer_type)\n","      model.add(layer_class(**layer[layer_type]))  # Add the layer to the model\n","\n","    # Compile the model with the specified optimizer and loss function\n","    model.compile(**params[\"compile\"])\n","\n","    return model                                # Return the compiled model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"w0fnV8yNKmYr"},"outputs":[],"source":["import gc  # Import the garbage collection module\n","\n","# Function to clear the Keras session and reset the state\n","def clear_session(random_index = 1):\n","    backend.clear_session()\n","    #Fixing the seed for random number generators so that we can ensure we receive the same output everytime\n","    np.random.seed(random_index)\n","    random.seed(random_index)\n","    tf.random.set_seed(random_index)\n","    gc.collect()  # To ensure any lingering model instances are cleaned up"]},{"cell_type":"markdown","metadata":{"id":"6qFefyY3-92A"},"source":["#### Functions for Confusion Matrix"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-5huljbp_NYB"},"outputs":[],"source":["# function to plot the confusion_matrix\n","def confusion_matrix_sklearn(target, pred,\n","                             xlabel = \"Predicted Labels\",\n","                             ylabel = \"True Labels\",\n","                             ticklabels = False,\n","                             xtick_rotation = 0,\n","                             ytick_rotation = 0,\n","                             annot = True,\n","                             title = \"Confusion Matrix\",\n","                             title_fontsize = 14,\n","                             label_fontsize = 12,\n","                             cmap = None,\n","                             ):\n","    # predicting using the independent variables\n","    cm = tf.math.confusion_matrix(target, pred)   # build the Confusion Matrix\n","\n","    fig, ax = plt.subplots(figsize = (len(ticklabels), len(ticklabels)))\n","\n","    # Define the frame and return the confusion Matrix plot\n","    #sns.heatmap(cm, annot = annot, linewidths=.4, fmt=\"d\", square = True, ax = ax)     # plot the confusion matrix\n","    sns.heatmap(cm, annot = annot,\n","                linewidths=.4,\n","                fmt=\"d\",\n","                square = True,\n","                cmap = cmap,\n","                ax = ax\n","                )     # plot the confusion matrix\n","\n","    ax.set_ylabel(ylabel, fontsize = label_fontsize)                          # set the y-axis label\n","    ax.set_xlabel(xlabel, fontsize = label_fontsize)                       # set the x-axis label\n","    ax.set_title(title, y = 1.1, fontsize = title_fontsize)                                     # set the title of the plot\n","\n","    if ticklabels:\n","      ax.xaxis.set_ticklabels(ticklabels, rotation = xtick_rotation)\n","      ax.yaxis.set_ticklabels(ticklabels, rotation = ytick_rotation)\n","\n","    plt.tight_layout()\n","    plt.show()"]},{"cell_type":"markdown","metadata":{"id":"4c7jLe_Q3T6F"},"source":["#### Functions for display Performance"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vqgvIG7z3dfP"},"outputs":[],"source":["def plot_side_by_side(plots, plot_data, params,\n","                      title = None,\n","                      figsize = (15, 6),\n","                      fontsize = 14,\n","                      ):\n","    \"\"\"Plot two plots side by side.\"\"\"\n","    # Check if at least two plots are provided\n","    if len(plots) < 2:\n","        raise ValueError(\"At least two plots are required\")\n","\n","    # Create a figure and a set of subplots\n","    fig, axs = plt.subplots(1, len(plots), figsize = figsize)  # Create a figure and a set of subplots\n","\n","    for i, (ax, plot_func, data, param) in enumerate(zip(axs, plots, plot_data, params)):\n","        plt.sca(ax)  # Set the current axes to the subplot's axis\n","        plot_func(data, param)  # Call the plot function with the axis\n","\n","    fig.suptitle(title, fontsize = fontsize, y = 1.05)  # Assign a suitable title to the plot\n","    fig.subplots_adjust(wspace = 0.3)  # Add some space between the subplots\n","    plt.show()  # Show the plot"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8JU3PWT53sWz"},"outputs":[],"source":["def plot(data, param = {\n","                          'title':\"\",\n","                          'xlabel':\"x axis\",\n","                          'ylabel': \"y axis\",\n","                          'color': ['b', 'g'],\n","                          'linestyle': ['-', '-'],\n","                          'marker': ['o','s'],\n","                          'legend':[],\n","                          'legend_loc':'best'\n","                        },\n","          ):\n","    for i, d in enumerate(data):\n","      plt.plot(*d, color = param['color'][i], label = param['label'][i], linestyle = param['linestyle'][i], marker = param['marker'][i] )\n","\n","    plt.title(param['title'])    # Set the title of the plot\n","    plt.xlabel(param['xlabel'])  # Set the x-axis label to 'Epoch'\n","    plt.ylabel(param['ylabel'])  # Set the y-axis label to the metric being plotted\n","    plt.legend(loc = param['legend_loc'])  # Add a legend to the plot\n","\n","    # Hide the top and right spines (borders)\n","    plt.gca().spines['top'].set_visible(False)\n","    plt.gca().spines['right'].set_visible(False)\n","\n","    return plt  # Return the plot"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Y8xFEWK3oPJW"},"outputs":[],"source":["def print_model_summary(model, param):\n","\n","  model.summary()  # Print the model summary\n","\n","  print(f\"Epoch: {param['fit']['epochs']}\")               # Print the Loss Function\n","  print(f\"Batch Size: {param['fit']['batch_size']}\")       # Print the Optimizer\n","  print(f\"Optimizer: {param['compile']['optimizer'].__class__.__name__}\")   # Print the Optimizer\n","  print(f\"Loss function: {param['compile']['loss'].__name__}\")     # Print the Loss Function\n","  print(f\"Metrics: {param['compile']['metrics'][0].__class__.__name__}\\n\")  # Print the Metrics"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CwQ4AxT6AljB"},"outputs":[],"source":["def prediction(model, predictors, classification_type = None, verbose = 0):\n","    # predicting using the independent variables\n","    prediction = model.predict(predictors, verbose = verbose)\n","\n","    if classification_type is None:\n","        return prediction\n","    elif classification_type == 'binary':\n","        return prediction > 0.5\n","    elif classification_type == 'categorical':\n","        return np.argmax(prediction, axis = 1)\n","    else:\n","        raise ValueError(\"Invalid classification type. Use 'binary', 'categorical', or None.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"V9mvfuETbDsv"},"outputs":[],"source":["def print_classification_report(report, mapping):\n","    # Create a mapping dictionary for numeric class labels to class names\n","    modified_report_dict = {}\n","\n","    for key, value in report.items():           # Iterate through the original report dictionary\n","        if key.isdigit():                       # Check if the key is a numeric class label\n","            display_index = f\"{key} - {mapping[int(key)]}\"\n","            modified_report_dict[display_index] = value\n","        else:\n","            modified_report_dict[key] = value\n","\n","    # Return the modified report dictionary into a DataFrame\n","    return pd.DataFrame(modified_report_dict).transpose()"]},{"cell_type":"code","source":["def convert_seconds(seconds):\n","    hours = int(seconds // 3600)\n","    minutes = int((seconds % 3600) // 60)\n","    seconds = seconds % 60\n","\n","    return f\"{hours:02}:{minutes:02}:{seconds:006.3f}\""],"metadata":{"id":"zsO78075WVvh"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.metrics import(          # Import the necessary libraries\n","    accuracy_score,\n","    recall_score,\n","    precision_score,\n","    f1_score,\n",")\n","# Function to compute different metrics to check performance of a classification model built using sklearn\n","def model_performance_classification_sklearn(model, predictors, target, classification_type = None, average='binary', verbose = 0):\n","    # predicting using the independent variables\n","    # Use threshold for Binary Classification\n","    pred = prediction(model, predictors, classification_type)\n","    if classification_type == 'categorical':\n","        target = np.argmax(target, axis = 1)\n","\n","    # evaluating the model performance\n","    acc = accuracy_score(target, pred)                             # to compute Accuracy\n","    recall = recall_score(target, pred, average = average)         # to compute Recall\n","    precision = precision_score(target, pred, average = average)   # to compute Precision\n","    f1 = f1_score(target, pred, average = average)                 # to compute F1-score\n","\n","    # creating and return a dataframe of metrics\n","    return pd.DataFrame(\n","        {\"Accuracy\": acc, \"Recall\": recall, \"Precision\": precision, \"F1 Score\": f1},\n","        index=[0],\n","    )"],"metadata":{"id":"EqnbZO84uFSw"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"u_sZBqis3AjT"},"source":["#### Define Constants and Variables"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jaWyOQRL27ih"},"outputs":[],"source":["# Define metadata of the model performance matrics\n","Loss_Matrics = {\n","    # Loss Matric\n","        'type' : 'Loss',                               # Type of the Curve\n","        'title': 'Training v/s Validation Loss',       # Title of the Loss Curve\n","        'xlabel': 'Epoch',                             # Label for the x-axis\n","        'ylabel': 'Loss',                              # Label for the y-axis\n","        'color': ['b', 'g'],                           # Define the Color\n","        'linestyle': ['-', '-'],                       # Define the Linestyle\n","        'marker': [None, None],                        # Define the Marker\n","        'label': ['Train', 'Validation'],              # Define the Legend\n","        'legend_loc': 'best',                          # Define the Legend Location\n","}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"08dxc5rt297e"},"outputs":[],"source":["Accuracy_Matrics = {\n","    # Recall Matric\n","        'type' : 'Accuracy',                           # Type of the Curve\n","        'title': 'Training v/s Validation Accuracy',   # Title of the Recall Curve\n","        'xlabel': 'Epoch',                             # Label for the x-axis\n","        'ylabel': 'Accuracy',                          # Label for the y-axis\n","        'color': ['b', 'm'],                           # Define the Color\n","        'linestyle': ['-', '-'],                       # Define the Linestyle\n","        'marker': [None, None],                        # Define the Marker\n","        'label': ['Train', 'Validation'],              # Define the Legend\n","        'legend_loc': 'upper right',                   # Define the Legend Location\n","}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xsYQv-BshE6w"},"outputs":[],"source":["model_parameters = {}                               # Initialize empty Dictionary to hold model parameters\n","models = {}                                         # Initialize empty Dictionary to hold models\n","history = {}                                        # Initialize empty Dictionary to hold training history\n","y_test_pred = {}                                    # Initialize empty dictionary to store test predictions\n","train_loss,  val_loss = {}, {}                      # Initialize empty list to store training and validation loss\n","train_accuracy,  val_accuracy = {}, {}              # Initialize empty list to store training and validation recall\n","train_perf, val_perf, test_perf = {}, {}, {}        # Initialize empty dictionary to store training and validation performance metrics"]},{"cell_type":"markdown","metadata":{"id":"lt6HzKPyfIqu"},"source":["### Build the Model"]},{"cell_type":"markdown","metadata":{"id":"4eBCKSWC6mI5"},"source":["**Convolution Model 1**: Build a model with the following hyperparameters:\n","\n","- Convolutional Layers:\n"," - 3 with increasing filter sizes.\n"," - Each followed by a pooling layer, to extract and downsample features at multiple scales.\n","- Dense Layers: 1 Layer including one dropout layer\n","- Output Layer: 12 units\n","- Output Activation Function: Softmax (for multi-class classification)\n","- Activation Function for Hidden Layers: ReLU\n","- Loss Function: Categorical Crossentropy (suitable for multi-class classification)\n","- Optimizer: Adam (adaptive learning rate)\n","- Epochs: 10\n","- Batch Size: 32\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Hd6MI8ZHgm9L"},"outputs":[],"source":["# Import the necessary libraries\n","from keras.optimizers import Adam\n","from keras.metrics import Accuracy\n","from keras.losses import categorical_crossentropy\n","\n","# Define hyperparameters\n","model_name = \"Base Model\"                                                           # Define the model name\n","model = \"Model_1\"                                                                   # Model Sequence\n","\n","model_parameters[model] = (                                                         # Define a tuple with model hyperparameters\n","        {\n","            \"model_name\": model_name,                                               # Model Name\n","            \"model\": model,                                                         # Model Sequence\n","            \"layers\": (                                                             # Model Layers\n","                                                                                    # Initial Conv2D layer with 128 filters, ReLU activation, and same padding\n","                      {\"Conv2D\": {\"filters\": 128, \"kernel_size\": (3, 3), \"activation\": \"relu\", \"padding\": \"same\", \"input_shape\": (64, 64, 3)}},\n","                      {\"MaxPooling2D\": {\"pool_size\": (2, 2), \"padding\": \"same\"}},   # MaxPooling layer with pool size of 2x2 and same padding\n","                                                                                    # Conv2D layer with 64 filters, ReLU activation, and same padding\n","                      {\"Conv2D\": {\"filters\": 64, \"kernel_size\": (3, 3), \"activation\": \"relu\", \"padding\": \"same\"}},\n","                      {\"MaxPooling2D\": {\"pool_size\": (2, 2), \"padding\": \"same\"}},   # MaxPooling layer with pool size of 2x2 and same padding\n","                                                                                    # Conv2D layer with 32 filters, ReLU activation, and same padding\n","                      {\"Conv2D\": {\"filters\": 32, \"kernel_size\": (3, 3), \"activation\": \"relu\", \"padding\": \"same\"}},\n","                      {\"MaxPooling2D\": {\"pool_size\": (2, 2), \"padding\": \"same\"}},   # MaxPooling layer with pool size of 2x2 and same padding\n","                      {\"Flatten\": {}},                                              # Flatten layer\n","                      {\"Dense\": {\"units\": 16, \"activation\": \"relu\"}},               # Dense layer with 16 units and ReLU activation\n","                      {\"Dropout\": {\"rate\": 0.3}},                                   # Dropout layer with a rate of 0.3\n","                      {\"Dense\": {\"units\": 12, \"activation\": \"softmax\"}},            # Output Dense layer with 12 units for classification and softmax activation\n","            ),\n","            \"compile\": {\n","                \"optimizer\": Adam(),                                                # Optimizer configuration\n","                \"loss\": categorical_crossentropy,                                   # Loss function\n","                \"metrics\" : ['accuracy'],                                           # Metrics to monitor during training\n","            },\n","            \"fit\": {\n","                \"epochs\" : 30,                                                      # Number of training epochs\n","                \"batch_size\" : 32,                                                  # Batch size for training\n","            }\n","        }\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cBfZqZFvneDm"},"outputs":[],"source":["models[model] = create_model(models, model_parameters[model])  # Create the model"]},{"cell_type":"markdown","metadata":{"id":"DiukKue07SVH"},"source":["**Print the Model Summary**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"voJiYq9goD1T","colab":{"base_uri":"https://localhost:8080/","height":598},"executionInfo":{"status":"ok","timestamp":1723867422335,"user_tz":300,"elapsed":11,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"}},"outputId":"6f77e680-49c2-4a82-a81c-6d1880fc804c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Training initiated for Base Model (Model_1):\n"]},{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"Model_1\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"Model_1\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │           \u001b[38;5;34m3,584\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m73,792\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │          \u001b[38;5;34m18,464\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m32\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)                  │          \u001b[38;5;34m32,784\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)                  │             \u001b[38;5;34m204\u001b[0m │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">3,584</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,792</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,464</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">32,784</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">204</span> │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m128,828\u001b[0m (503.23 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">128,828</span> (503.23 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m128,828\u001b[0m (503.23 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">128,828</span> (503.23 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Epoch: 30\n","Batch Size: 32\n","Optimizer: Adam\n","Loss function: categorical_crossentropy\n","Metrics: str\n","\n"]}],"source":["# Print the model summary\n","print(f\"Training initiated for {model_name} ({model}):\")\n","print_model_summary(models[model], model_parameters[model])"]},{"cell_type":"markdown","metadata":{"id":"R8fgbKsB7VxU"},"source":["**Train the Model**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OeTfzMUcpAYJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1723867468030,"user_tz":300,"elapsed":45703,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"}},"outputId":"95509cd5-423a-441e-ef6b-df5ff2686591"},"outputs":[{"output_type":"stream","name":"stdout","text":["Training initiated for Base Model (Model_1)...\n","\n","Epoch 1/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.1285 - loss: 2.4652 - val_accuracy: 0.2313 - val_loss: 2.3407\n","Epoch 2/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.2534 - loss: 2.2798 - val_accuracy: 0.3201 - val_loss: 2.0513\n","Epoch 3/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.3196 - loss: 2.0922 - val_accuracy: 0.3341 - val_loss: 1.9905\n","Epoch 4/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.3165 - loss: 2.0133 - val_accuracy: 0.3738 - val_loss: 1.8646\n","Epoch 5/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.3361 - loss: 1.9167 - val_accuracy: 0.4019 - val_loss: 1.7806\n","Epoch 6/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.3601 - loss: 1.8556 - val_accuracy: 0.4229 - val_loss: 1.7103\n","Epoch 7/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.3647 - loss: 1.7991 - val_accuracy: 0.4416 - val_loss: 1.7019\n","Epoch 8/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.3985 - loss: 1.7500 - val_accuracy: 0.4626 - val_loss: 1.6036\n","Epoch 9/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.3983 - loss: 1.7578 - val_accuracy: 0.4953 - val_loss: 1.6459\n","Epoch 10/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.4083 - loss: 1.6850 - val_accuracy: 0.5000 - val_loss: 1.5498\n","Epoch 11/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.4033 - loss: 1.6680 - val_accuracy: 0.5374 - val_loss: 1.5565\n","Epoch 12/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.4198 - loss: 1.6519 - val_accuracy: 0.5374 - val_loss: 1.5772\n","Epoch 13/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4393 - loss: 1.5946 - val_accuracy: 0.5631 - val_loss: 1.4735\n","Epoch 14/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4376 - loss: 1.5749 - val_accuracy: 0.5678 - val_loss: 1.4970\n","Epoch 15/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.4552 - loss: 1.5403 - val_accuracy: 0.5678 - val_loss: 1.5066\n","Epoch 16/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.4512 - loss: 1.5566 - val_accuracy: 0.5491 - val_loss: 1.5276\n","Epoch 17/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.4560 - loss: 1.5526 - val_accuracy: 0.5631 - val_loss: 1.4650\n","Epoch 18/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.4624 - loss: 1.5292 - val_accuracy: 0.5841 - val_loss: 1.3283\n","Epoch 19/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.4594 - loss: 1.5343 - val_accuracy: 0.5794 - val_loss: 1.3272\n","Epoch 20/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.4839 - loss: 1.4546 - val_accuracy: 0.6075 - val_loss: 1.3090\n","Epoch 21/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.4646 - loss: 1.4820 - val_accuracy: 0.6168 - val_loss: 1.3089\n","Epoch 22/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.4665 - loss: 1.4969 - val_accuracy: 0.6028 - val_loss: 1.3091\n","Epoch 23/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4722 - loss: 1.4530 - val_accuracy: 0.6098 - val_loss: 1.2946\n","Epoch 24/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4825 - loss: 1.4545 - val_accuracy: 0.6075 - val_loss: 1.3132\n","Epoch 25/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4773 - loss: 1.4659 - val_accuracy: 0.6262 - val_loss: 1.2501\n","Epoch 26/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.4973 - loss: 1.4167 - val_accuracy: 0.6121 - val_loss: 1.2906\n","Epoch 27/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.4861 - loss: 1.4235 - val_accuracy: 0.6075 - val_loss: 1.2696\n","Epoch 28/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.4859 - loss: 1.4158 - val_accuracy: 0.6168 - val_loss: 1.2576\n","Epoch 29/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.4900 - loss: 1.4180 - val_accuracy: 0.6168 - val_loss: 1.2576\n","Epoch 30/30\n","\u001b[1m121/121\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.5021 - loss: 1.3805 - val_accuracy: 0.6379 - val_loss: 1.2148\n","\n","\n","Training Completed for Base Model (Model_1). Total Time taken: 00:00:49.782 \n","\n"]}],"source":["import time  # Import the time module\n","\n","print(f\"Training initiated for {model_name} ({model})...\\n\")  # Print a message to indicate the start of tuning\n","start = time.time()  # Start the timer\n","\n","# Fitting the model on train and validation Dataset\n","history[model] = models[model].fit(X_train_normalized, y_train_encoded,\n","                                   validation_data = (X_val_normalized, y_val_encoded),\n","                                   batch_size = model_parameters[model]['fit']['batch_size'],\n","                                   epochs = model_parameters[model]['fit']['epochs'],\n","                                   verbose = 1,\n","                                   )\n","\n","duration = convert_seconds(time.time() - start)  # Calculate the duration of training\n","print(f\"\\n\\nTraining Completed for {model_name} ({model}). Total Time taken: {(duration)} \\n\")"]},{"cell_type":"markdown","metadata":{"id":"DqdW7AMY7skd"},"source":["**Model Evaluation**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hCvtVjde2OeZ","colab":{"base_uri":"https://localhost:8080/","height":644},"executionInfo":{"status":"ok","timestamp":1723867468661,"user_tz":300,"elapsed":634,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"}},"outputId":"35f9529b-b961-4250-e97a-5d2768a8a2e8"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x600 with 2 Axes>"],"image/png":"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Extract the Loss Data of Training and Validation Set\n","train_loss[model], val_loss[model] = [history[model].history['loss']], [history[model].history['val_loss']]\n","\n","# Extract the Recall Data of Training and Validation Set\n","train_accuracy[model], val_accuracy[model] = [history[model].history['accuracy']], [history[model].history['val_accuracy']]\n","\n","# Plot the Loss and Recall Curve side by side\n","plot_side_by_side([plot, plot],\n","                   [\n","                       [train_loss[model],val_loss[model]],             # Data for First Plot\n","                       [train_accuracy[model],val_accuracy[model]],     # Data for Second Plot\n","                   ],\n","                   (Loss_Matrics, Accuracy_Matrics),\n","                   title = f\"Model: {model_name} ({model})\\nModel Performance Evaluation\".replace('_', ' '),\n","                   figsize = (15, 6),\n","                   fontsize = 14,\n","                   )"]},{"cell_type":"markdown","metadata":{"id":"bllBUK_kB6JK"},"source":["**Observation**\n","\n","**Loss**:\n","- Both the training and validation loss decrease consistently over the epochs, which is a positive sign indicating that the model is learning.\n","- The gap between the training and validation loss is small and remains consistent, suggesting that the model is not overfitting significantly.\n","- The final training loss is slightly lower than the validation loss, which is typical, but the difference is not alarming. This implies that the model has generalized well to the validation data.\n","\n","**Accuracy**:\n","- The training accuracy steadily increases over time, as does the validation accuracy. This is a good sign that the model is improving its predictions.\n","- The validation accuracy increases more rapidly than the training accuracy initially, which could indicate that the model is finding patterns in the data that generalize well.\n","- As the epochs progress, the growing disparity between validation and training accuracy suggests that the model is becoming increasingly overfit."]},{"cell_type":"markdown","source":["**Compare Performance of Train, Validation and Test Datasets**"],"metadata":{"id":"eMprI2Bf2SU7"}},{"cell_type":"code","source":["train_perf[model] = model_performance_classification_sklearn(models[model], X_train_normalized, y_train_encoded, classification_type = 'categorical', average='weighted')\n","val_perf[model] = model_performance_classification_sklearn(models[model], X_val_normalized, y_val_encoded, classification_type = 'categorical', average='weighted')\n","test_perf[model] = model_performance_classification_sklearn(models[model], X_test_normalized, y_test_encoded, classification_type = 'categorical', average='weighted')\n","\n","# Add the 'Dataset' column to each DataFrame\n","train_perf[model]['Dataset'] = 'Train'\n","val_perf[model]['Dataset'] = 'Validation'\n","test_perf[model]['Dataset'] = 'Test'\n","\n","# Concatenate the DataFrames\n","combined_perf = pd.concat([train_perf[model], val_perf[model], test_perf[model]], ignore_index=True)\n","\n","# Reorder columns to have 'Dataset' as the first column\n","combined_perf = combined_perf[['Dataset', 'Accuracy', 'Recall', 'Precision', 'F1 Score']]\n","\n","# Display the combined DataFrame\n","combined_perf"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"Df0fr-dIurHP","executionInfo":{"status":"ok","timestamp":1723867471369,"user_tz":300,"elapsed":2712,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"}},"outputId":"b52bff45-d717-4dea-8d55-20b0cec5d363"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Dataset  Accuracy  Recall  Precision  F1 Score\n","0       Train     0.694   0.694      0.666     0.644\n","1  Validation     0.638   0.638      0.589     0.579\n","2        Test     0.642   0.642      0.588     0.592"],"text/html":["\n","  <div id=\"df-6e01129e-a64c-4729-8ecc-e380b11b4954\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Dataset</th>\n","      <th>Accuracy</th>\n","      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This indicates that the model has learned the patterns in the training data well.\n","- **Validation Data**: There's a noticeable drop in performance from the training data to the validation data. This suggests that the model may be overfitting to the training data, meaning it's capturing details specific to the training set that don't generalize well to unseen data.\n","- **Test Data**: The performance on the test data is slightly lower than on the validation data. This further supports the possibility of overfitting, as the model's ability to generalize is not as strong as its ability to learn from the training data."],"metadata":{"id":"7RJ6otYa24j5"}},{"cell_type":"markdown","metadata":{"id":"m1TB--Zk_kVj"},"source":["**Confusion Matrix**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cjRXZIoqLxXA","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1723867473508,"user_tz":300,"elapsed":2142,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"}},"outputId":"96f8df1f-ab04-4cfa-9413-124c8c91422e"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x1200 with 2 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Determine the predictions for the test data\n","y_test_pred[model] = prediction(models[model], X_test_normalized, classification_type = 'categorical')\n","\n","# Plot the confusion matrix for the test Data for the current Model\n","confusion_matrix_sklearn(np.argmax(y_test_encoded, axis = 1),\n","                         y_test_pred[model],\n","                         ticklabels = list(enc.classes_),\n","                         annot = True,\n","                         title = f\"Model: {model_name} ({model})\\nConfusion Matrix for Test Data\".replace('_', ' '),\n","                         xtick_rotation = 90,\n","                         cmap = plt.cm.Greens,\n","                         )"]},{"cell_type":"markdown","metadata":{"id":"EvoYV5U-Djuf"},"source":["**Observation**\n","\n","The confusion matrix reveals valuable insights into the model's performance:\n","\n","- **Correct Predictions**: Diagonal elements indicate correct predictions, with 'Loose Silky-bent', 'Common Chickweed', and ' Small-flowered Cranesbill' achieving the highest accuracy.\n","- **Misclassifications**: Off-diagonal elements highlight misclassifications, with 'Black-grass' and 'Common Wheat' showing zero correct predictions.\n","- **Class-specific Performance**:\n"," - 'Sugar Beet', 'Shepherd's Purse', 'Maize', 'Cleaver', 'Charlock' and 'Shepherds Purse' have no accurate predictions.\n"," - Notable misclassifications include 'Black-grass' and 'Common Wheat' being frequently predicted as 'Loose Silky-bent', 'Shepherds Purse' as 'SCommon Chickweed', and 'Sugar Beet' as 'Fat Hen'.\n","\n","**Recommendations**\n","\n","To improve the model's performance:\n","1. **Address Class Imbalance**: Implement data augmentation techniques to overcome the imbalance in training data, ensuring underrepresented classes receive adequate attention.\n","2. **Adjust Learning Rate**: Reduce the learning rate to potentially enhance overall prediction accuracy.\n","3. **Increase Epochs**: Boost training accuracy by increasing the number of epochs.\n","\n","By addressing these recommendations, the model's performance can be significantly improved.\n"]},{"cell_type":"markdown","metadata":{"id":"o_mCoxw7oEZG"},"source":["**Classification Report**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oMBiyMmpQba1","colab":{"base_uri":"https://localhost:8080/","height":554},"executionInfo":{"status":"ok","timestamp":1723867473508,"user_tz":300,"elapsed":8,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"}},"outputId":"f244842e-12da-4a5f-a165-1938633077c7"},"outputs":[{"output_type":"stream","name":"stdout","text":["Classification Report of Model: Base Model\n","\n"]},{"output_type":"execute_result","data":{"text/plain":["                                precision  recall  f1-score  support\n","0 - Black-grass                     0.000   0.000     0.000   26.000\n","1 - Charlock                        0.636   0.538     0.583   39.000\n","2 - Cleavers                        0.512   0.724     0.600   29.000\n","3 - Common Chickweed                0.696   0.902     0.786   61.000\n","4 - Common wheat                    0.000   0.000     0.000   22.000\n","5 - Fat Hen                         0.540   0.708     0.613   48.000\n","6 - Loose Silky-bent                0.558   0.969     0.708   65.000\n","7 - Maize                           1.000   0.455     0.625   22.000\n","8 - Scentless Mayweed               0.733   0.635     0.680   52.000\n","9 - Shepherds Purse                 0.333   0.043     0.077   23.000\n","10 - Small-flowered Cranesbill      0.789   0.900     0.841   50.000\n","11 - Sugar beet                     0.733   0.579     0.647   38.000\n","accuracy                            0.642   0.642     0.642    0.642\n","macro avg                           0.544   0.538     0.513  475.000\n","weighted avg                        0.588   0.642     0.592  475.000"],"text/html":["\n","  <div id=\"df-9a7118c8-df26-4571-9db5-ff456633a275\" 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 <td>0.724</td>\n","      <td>0.600</td>\n","      <td>29.000</td>\n","    </tr>\n","    <tr>\n","      <th>3 - Common Chickweed</th>\n","      <td>0.696</td>\n","      <td>0.902</td>\n","      <td>0.786</td>\n","      <td>61.000</td>\n","    </tr>\n","    <tr>\n","      <th>4 - Common wheat</th>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>22.000</td>\n","    </tr>\n","    <tr>\n","      <th>5 - Fat Hen</th>\n","      <td>0.540</td>\n","      <td>0.708</td>\n","      <td>0.613</td>\n","      <td>48.000</td>\n","    </tr>\n","    <tr>\n","      <th>6 - Loose Silky-bent</th>\n","      <td>0.558</td>\n","      <td>0.969</td>\n","      <td>0.708</td>\n","      <td>65.000</td>\n","    </tr>\n","    <tr>\n","      <th>7 - Maize</th>\n","      <td>1.000</td>\n","      <td>0.455</td>\n","      <td>0.625</td>\n","      <td>22.000</td>\n","    </tr>\n","    <tr>\n","      <th>8 - Scentless Mayweed</th>\n","      <td>0.733</td>\n","      <td>0.635</td>\n","      <td>0.680</td>\n","      <td>52.000</td>\n","    </tr>\n","    <tr>\n","      <th>9 - Shepherds Purse</th>\n","      <td>0.333</td>\n","      <td>0.043</td>\n","      <td>0.077</td>\n","      <td>23.000</td>\n","    </tr>\n","    <tr>\n","      <th>10 - Small-flowered Cranesbill</th>\n","      <td>0.789</td>\n","      <td>0.900</td>\n","      <td>0.841</td>\n","      <td>50.000</td>\n","    </tr>\n","    <tr>\n","      <th>11 - Sugar beet</th>\n","      <td>0.733</td>\n","      <td>0.579</td>\n","      <td>0.647</td>\n","      <td>38.000</td>\n","    </tr>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.642</td>\n","      <td>0.642</td>\n","      <td>0.642</td>\n","      <td>0.642</td>\n","    </tr>\n","    <tr>\n","      <th>macro avg</th>\n","      <td>0.544</td>\n","      <td>0.538</td>\n","      <td>0.513</td>\n","      <td>475.000</td>\n","    </tr>\n","    <tr>\n","      <th>weighted avg</th>\n","      <td>0.588</td>\n","      <td>0.642</td>\n"," 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The macro and weighted averages provide more insight into how the model performs across all classes, considering their support.\n","\n","- **Focus Areas**: Consider focusing on improving the classification of underperforming classes, especially those with low recall and precision, to enhance overall model performance."]},{"cell_type":"markdown","metadata":{"id":"kNKUalx8Jcoi"},"source":["## Model Performance Improvement"]},{"cell_type":"markdown","metadata":{"id":"gmIvuK3egNqM"},"source":["### **Reducing the Learning Rate**"]},{"cell_type":"markdown","metadata":{"id":"s_oS4D_AXFqX"},"source":["**Hint**: Use **ReduceLRonPlateau()** function that will be used to decrease the learning rate by some factor, if the loss is not decreasing for some time. This may start decreasing the loss at a smaller learning rate. There is a possibility that the loss may still not decrease. This may lead to executing the learning rate reduction again in an attempt to achieve a lower loss."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fvd64POne9nA"},"outputs":[],"source":["from keras.callbacks import ReduceLROnPlateau\n","\n","# Define the ReduceLROnPlateau callback to adjust the learning rate during training and validation\n","# This technique helps improve the training process by reducing the learning rate when the model’s performance plateaus\n","\n","learning_rate_reduction = ReduceLROnPlateau(monitor = 'val_accuracy',\n","                                                patience = 3,\n","                                                verbose = 1,\n","                                                factor = 0.5,\n","                                                min_lr = 0.00001)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"zU6vqL67bd5a"},"source":["### **Data Augmentation**\n","\n","Remember, **data augmentation should not be used in the validation/test data set**."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wtNSSoZjgIYy"},"outputs":[],"source":["from tensorflow.keras.preprocessing.image import ImageDataGenerator    # Importing the ImageDataGenerator for data augmentation\n","\n","clear_session(random_index)                 # Clear the Keras session to reset the state\n","\n","train_datagen = ImageDataGenerator(         # Initialize the ImageDataGenerator\n","                              rotation_range = 30,\n","                              shear_range = 0.20,\n","                              zoom_range = 0.20,\n","                              width_shift_range = 0.20,\n","                              height_shift_range = 0.20,\n","                              vertical_flip = True,\n","                              horizontal_flip = True,\n","                              fill_mode = 'nearest'\n","                              )"]},{"cell_type":"markdown","source":["### **Early Stopping**"],"metadata":{"id":"84K1UoljYPJ2"}},{"cell_type":"code","source":["# Callback for Early Stopping to Prevent Overfitting\n","callback_es = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy',\n","                                               patience = 20,\n","                                               min_delta = 0.0001,\n","                                               restore_best_weights = True)"],"metadata":{"id":"_axEeuSsYSbF"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"x3w9RarYhu8B"},"source":["### Build the Model"]},{"cell_type":"markdown","metadata":{"id":"qBld4IX1h1rg"},"source":["**Convolution Model 2**: Build a model with the following hyperparameters to improve the performance:\n","\n","- Convolutional Layers:\n"," - 3 with increasing filter sizes and 'He Normal' karnel Initilization\n"," - Each followed by a pooling layer, and Batch Normalization Layer to extract and downsample features at multiple scales.\n"," - Dropout Layer after 2nd and 3rd convolution layer\n","- Dense Layers: 1 layer including dropout layer\n","- Output Layer: 12 units\n","- Output Activation Function: Softmax (for multi-class classification)\n","- Activation Function for All Layers: ReLU\n","- Loss Function: Categorical Crossentropy (suitable for multi-class classification)\n","- Optimizer: Adam with learning rate 0.001\n","- Epochs: 120\n","- Batch Size: 32\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5GDn4xIMhwyS"},"outputs":[],"source":["# Define hyperparameters\n","model_name = \"Augmented Model\"                                                      # Define the model name\n","model = \"Model_2\"                                                                   # Model Sequence\n","\n","model_parameters[model] = (                                                         # Define a tuple with model hyperparameters\n","        {\n","            \"model_name\": model_name,                                               # Model Name\n","            \"model\": model,                                                         # Model Sequence\n","            \"layers\": (                                                             # Model Layers\n","                                                                                    # Initial Conv2D layer with 128 filters, ReLU activation, and same padding\n","                      {\"Conv2D\": {\"filters\": 32, \"kernel_size\": (3, 3), \"activation\": \"relu\", \"padding\": \"same\", \"kernel_initializer\": \"he_normal\", \"input_shape\": (64, 64, 3)}},\n","                      {\"BatchNormalization\": {}},                                   # BatchNormalization layer\n","                      {\"MaxPooling2D\": {\"pool_size\": (2, 2), \"padding\": \"same\"}},   # MaxPooling layer with pool size of 2x2 and same padding\n","                                                                                    # Conv2D layer with 64 filters, ReLU activation, and same padding\n","                      {\"Conv2D\": {\"filters\": 64, \"kernel_size\": (3, 3), \"activation\": \"relu\", \"padding\": \"same\", \"kernel_initializer\": \"he_normal\"}},\n","                      {\"BatchNormalization\": {}},                                   # BatchNormalization layer\n","                      {\"MaxPooling2D\": {\"pool_size\": (2, 2), \"padding\": \"same\"}},   # MaxPooling layer with pool size of 2x2 and same padding\n","                                                                                    # Conv2D layer with 32 filters, ReLU activation, and same padding\n","                      {\"Conv2D\": {\"filters\": 64, \"kernel_size\": (3, 3), \"activation\": \"relu\", \"padding\": \"same\", \"kernel_initializer\": \"he_normal\"}},\n","                      {\"BatchNormalization\": {}},                                   # BatchNormalization layer\n","                      {\"MaxPooling2D\": {\"pool_size\": (2, 2), \"padding\": \"same\"}},   # MaxPooling layer with pool size of 2x2 and same padding\n","\n","                      {\"Flatten\": {}},                                              # Flatten layer\n","\n","                      {\"BatchNormalization\": {}},                                   # BatchNormalization layer\n","                                                                                    # Dense layer with 16 units and ReLU activation\n","                      {\"Dense\": {\"units\": 512, \"activation\": \"relu\"}, \"kernel_regularizer\": \"l2(0.01)\"},\n","                      {\"Dropout\": {\"rate\": 0.3}},                                   # Dropout layer with a rate of 0.3\n","\n","                      {\"BatchNormalization\": {}},                                   # BatchNormalization layer\n","                                                                                    # Dense layer with 16 units and ReLU activation\n","                      {\"Dense\": {\"units\": 128, \"activation\": \"relu\"}, \"kernel_regularizer\": \"l2(0.01)\"},\n","                      {\"Dropout\": {\"rate\": 0.3}},                                   # Dropout layer with a rate of 0.3\n","\n","                      {\"Dense\": {\"units\": 12, \"activation\": \"softmax\"}},            # Output Dense layer with 12 units for classification and softmax activation\n","            ),\n","            \"compile\": {\n","                \"optimizer\": Adam(learning_rate = 0.001),                           # Optimizer configuration with learning rate as 0.001\n","                \"loss\": categorical_crossentropy,                                   # Loss function\n","                \"metrics\" : ['accuracy'],                                           # Metrics to monitor during training\n","            },\n","            \"fit\": {\n","                \"epochs\" : 120,                                                     # Number of training epochs\n","                \"batch_size\" : 32,                                                  # Batch size for training\n","            }\n","        }\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lGaLAM-CnK43"},"outputs":[],"source":["models[model] = create_model(models, model_parameters[model])  # Create the model"]},{"cell_type":"markdown","metadata":{"id":"u_YlKo-InOqd"},"source":["**Print the Model Summary**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":921},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1723865770191,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"},"user_tz":300},"id":"QZ1auvHnnSdK","outputId":"00db14f5-621c-46fe-e6ab-d5f24d748ff4"},"outputs":[{"output_type":"stream","name":"stdout","text":["Training initiated for Augmented Model (Model_2):\n"]},{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"Model_2\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"Model_2\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m896\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ batch_normalization                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m128\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m18,496\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ batch_normalization_1                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m256\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m36,928\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ batch_normalization_2                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m256\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ batch_normalization_3                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)                │          \u001b[38;5;34m16,384\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │       \u001b[38;5;34m2,097,664\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ batch_normalization_4                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │           \u001b[38;5;34m2,048\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │          \u001b[38;5;34m65,664\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)                  │           \u001b[38;5;34m1,548\u001b[0m │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ batch_normalization                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ batch_normalization_1                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ batch_normalization_2                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ batch_normalization_3                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)                │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,384</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,097,664</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ batch_normalization_4                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">65,664</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,548</span> │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,240,268\u001b[0m (8.55 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,240,268</span> (8.55 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,230,732\u001b[0m (8.51 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,230,732</span> (8.51 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m9,536\u001b[0m (37.25 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,536</span> (37.25 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Epoch: 120\n","Batch Size: 32\n","Optimizer: Adam\n","Loss function: categorical_crossentropy\n","Metrics: str\n","\n"]}],"source":["# Print the model summary\n","print(f\"Training initiated for {model_name} ({model}):\")\n","print_model_summary(models[model], model_parameters[model])"]},{"cell_type":"markdown","metadata":{"id":"h4mqTImgnWnY"},"source":["**Train the Model**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":240267,"status":"ok","timestamp":1723866010456,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"},"user_tz":300},"id":"drkKZjx4ndgS","outputId":"26d32a8c-0795-4e56-ef10-02d085a5d396"},"outputs":[{"output_type":"stream","name":"stdout","text":["Training initiated for Augmented Model (Model_2)...\n","\n","Epoch 1/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 101ms/step - accuracy: 0.2794 - loss: 2.4106 - val_accuracy: 0.1379 - val_loss: 6.1255 - learning_rate: 0.0010\n","Epoch 2/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 576us/step - accuracy: 0.3750 - loss: 1.9985 - val_accuracy: 0.1379 - val_loss: 6.4592 - learning_rate: 0.0010\n","Epoch 3/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 46ms/step - accuracy: 0.4759 - loss: 1.5161 - val_accuracy: 0.1472 - val_loss: 5.2601 - learning_rate: 0.0010\n","Epoch 4/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 838us/step - accuracy: 0.5625 - loss: 1.5353 - val_accuracy: 0.1472 - val_loss: 5.1471 - learning_rate: 0.0010\n","Epoch 5/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 46ms/step - accuracy: 0.5736 - loss: 1.2706 - val_accuracy: 0.1519 - val_loss: 8.5461 - learning_rate: 0.0010\n","Epoch 6/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 549us/step - accuracy: 0.5938 - loss: 1.1524 - val_accuracy: 0.1519 - val_loss: 8.5591 - learning_rate: 0.0010\n","Epoch 7/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 40ms/step - accuracy: 0.6188 - loss: 1.1173 - val_accuracy: 0.3248 - val_loss: 2.5536 - learning_rate: 0.0010\n","Epoch 8/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 545us/step - accuracy: 0.7188 - loss: 0.9705 - val_accuracy: 0.3037 - val_loss: 2.7575 - learning_rate: 0.0010\n","Epoch 9/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 40ms/step - accuracy: 0.6490 - loss: 0.9865 - val_accuracy: 0.5140 - val_loss: 1.6706 - learning_rate: 0.0010\n","Epoch 10/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 718us/step - accuracy: 0.6562 - loss: 1.0160 - val_accuracy: 0.5140 - val_loss: 1.6640 - learning_rate: 0.0010\n","Epoch 11/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 53ms/step - accuracy: 0.6848 - loss: 0.8955 - val_accuracy: 0.7056 - val_loss: 0.8646 - learning_rate: 0.0010\n","Epoch 12/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 818us/step - accuracy: 0.6562 - loss: 1.0083 - val_accuracy: 0.6986 - val_loss: 0.9104 - learning_rate: 0.0010\n","Epoch 13/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 40ms/step - accuracy: 0.6892 - loss: 0.8620 - val_accuracy: 0.5958 - val_loss: 1.2726 - learning_rate: 0.0010\n","Epoch 14/120\n","\u001b[1m  1/120\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.6562 - loss: 0.9404\n","Epoch 14: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 598us/step - accuracy: 0.6562 - loss: 0.9404 - val_accuracy: 0.6121 - val_loss: 1.2363 - learning_rate: 0.0010\n","Epoch 15/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 41ms/step - accuracy: 0.7200 - loss: 0.7629 - val_accuracy: 0.8224 - val_loss: 0.5697 - learning_rate: 5.0000e-04\n","Epoch 16/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 967us/step - accuracy: 0.7812 - loss: 0.6034 - val_accuracy: 0.8271 - val_loss: 0.5684 - learning_rate: 5.0000e-04\n","Epoch 17/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 41ms/step - accuracy: 0.7565 - loss: 0.6681 - val_accuracy: 0.7967 - val_loss: 0.6328 - learning_rate: 5.0000e-04\n","Epoch 18/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 612us/step - accuracy: 0.7188 - loss: 0.6761 - val_accuracy: 0.7921 - val_loss: 0.6243 - learning_rate: 5.0000e-04\n","Epoch 19/120\n","\u001b[1m115/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.7707 - loss: 0.6244\n","Epoch 19: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 41ms/step - accuracy: 0.7705 - loss: 0.6255 - val_accuracy: 0.7757 - val_loss: 0.6746 - learning_rate: 5.0000e-04\n","Epoch 20/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 540us/step - accuracy: 0.8125 - loss: 0.4931 - val_accuracy: 0.7710 - val_loss: 0.6731 - learning_rate: 2.5000e-04\n","Epoch 21/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 47ms/step - accuracy: 0.7703 - loss: 0.6065 - val_accuracy: 0.8575 - val_loss: 0.4578 - learning_rate: 2.5000e-04\n","Epoch 22/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 596us/step - accuracy: 0.7188 - loss: 0.6428 - val_accuracy: 0.8528 - val_loss: 0.4618 - learning_rate: 2.5000e-04\n","Epoch 23/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 56ms/step - accuracy: 0.7848 - loss: 0.5814 - val_accuracy: 0.8621 - val_loss: 0.5197 - learning_rate: 2.5000e-04\n","Epoch 24/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 552us/step - accuracy: 0.7143 - loss: 0.9376 - val_accuracy: 0.8528 - val_loss: 0.5237 - learning_rate: 2.5000e-04\n","Epoch 25/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 41ms/step - accuracy: 0.8057 - loss: 0.5532 - val_accuracy: 0.8598 - val_loss: 0.4904 - learning_rate: 2.5000e-04\n","Epoch 26/120\n","\u001b[1m  1/120\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.8438 - loss: 0.3406\n","Epoch 26: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 614us/step - accuracy: 0.8438 - loss: 0.3406 - val_accuracy: 0.8575 - val_loss: 0.4885 - learning_rate: 2.5000e-04\n","Epoch 27/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 52ms/step - accuracy: 0.8141 - loss: 0.4932 - val_accuracy: 0.8341 - val_loss: 0.4774 - learning_rate: 1.2500e-04\n","Epoch 28/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 707us/step - accuracy: 0.8125 - loss: 0.5082 - val_accuracy: 0.8294 - val_loss: 0.4821 - learning_rate: 1.2500e-04\n","Epoch 29/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 40ms/step - accuracy: 0.8129 - loss: 0.5202 - val_accuracy: 0.8832 - val_loss: 0.4086 - learning_rate: 1.2500e-04\n","Epoch 30/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 558us/step - accuracy: 0.8125 - loss: 0.3986 - val_accuracy: 0.8808 - val_loss: 0.4090 - learning_rate: 1.2500e-04\n","Epoch 31/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 40ms/step - accuracy: 0.8180 - loss: 0.4828 - val_accuracy: 0.8505 - val_loss: 0.4601 - learning_rate: 1.2500e-04\n","Epoch 32/120\n","\u001b[1m  1/120\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.8438 - loss: 0.4109\n","Epoch 32: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n","\u001b[1m120/120\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 40ms/step - accuracy: 0.8132 - loss: 0.5069 - val_accuracy: 0.8832 - val_loss: 0.3930 - learning_rate: 6.2500e-05\n","Epoch 36/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 721us/step - accuracy: 0.9062 - loss: 0.4139 - val_accuracy: 0.8832 - val_loss: 0.3930 - learning_rate: 3.1250e-05\n","Epoch 37/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 55ms/step - accuracy: 0.8333 - loss: 0.4585 - val_accuracy: 0.8879 - val_loss: 0.3885 - learning_rate: 3.1250e-05\n","Epoch 38/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 555us/step - accuracy: 0.7188 - loss: 0.6984 - val_accuracy: 0.8879 - val_loss: 0.3878 - learning_rate: 3.1250e-05\n","Epoch 39/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 41ms/step - accuracy: 0.8318 - loss: 0.4580 - val_accuracy: 0.8832 - val_loss: 0.3962 - learning_rate: 3.1250e-05\n","Epoch 40/120\n","\u001b[1m  1/120\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.7500 - loss: 0.7850\n","Epoch 40: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 824us/step - accuracy: 0.7500 - loss: 0.7850 - val_accuracy: 0.8832 - val_loss: 0.3954 - learning_rate: 3.1250e-05\n","Epoch 41/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 53ms/step - accuracy: 0.8433 - loss: 0.4680 - val_accuracy: 0.8879 - val_loss: 0.3919 - learning_rate: 1.5625e-05\n","Epoch 42/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 740us/step - accuracy: 0.8438 - loss: 0.3737 - val_accuracy: 0.8879 - val_loss: 0.3912 - learning_rate: 1.5625e-05\n","Epoch 43/120\n","\u001b[1m115/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - accuracy: 0.8315 - loss: 0.4615\n","Epoch 43: ReduceLROnPlateau reducing learning rate to 1e-05.\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 40ms/step - accuracy: 0.8315 - loss: 0.4618 - val_accuracy: 0.8785 - val_loss: 0.3939 - learning_rate: 1.5625e-05\n","Epoch 44/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 555us/step - accuracy: 0.8438 - loss: 0.4233 - val_accuracy: 0.8785 - val_loss: 0.3935 - learning_rate: 1.0000e-05\n","Epoch 45/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 57ms/step - accuracy: 0.8345 - loss: 0.4560 - val_accuracy: 0.8762 - val_loss: 0.3992 - learning_rate: 1.0000e-05\n","Epoch 46/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 560us/step - accuracy: 0.9062 - loss: 0.3451 - val_accuracy: 0.8762 - val_loss: 0.3997 - learning_rate: 1.0000e-05\n","Epoch 47/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 40ms/step - accuracy: 0.8226 - loss: 0.4666 - val_accuracy: 0.8808 - val_loss: 0.3948 - learning_rate: 1.0000e-05\n","Epoch 48/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 571us/step - accuracy: 0.8125 - loss: 0.4485 - val_accuracy: 0.8808 - val_loss: 0.3944 - learning_rate: 1.0000e-05\n","Epoch 49/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 55ms/step - accuracy: 0.8272 - loss: 0.4673 - val_accuracy: 0.8855 - val_loss: 0.3883 - learning_rate: 1.0000e-05\n","Epoch 50/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 565us/step - accuracy: 0.8438 - loss: 0.5019 - val_accuracy: 0.8855 - val_loss: 0.3880 - learning_rate: 1.0000e-05\n","Epoch 51/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 40ms/step - accuracy: 0.8237 - loss: 0.4748 - val_accuracy: 0.8785 - val_loss: 0.3873 - learning_rate: 1.0000e-05\n","Epoch 52/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 590us/step - accuracy: 0.8750 - loss: 0.3300 - val_accuracy: 0.8785 - val_loss: 0.3875 - learning_rate: 1.0000e-05\n","Epoch 53/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 40ms/step - accuracy: 0.8395 - loss: 0.4414 - val_accuracy: 0.8832 - val_loss: 0.3863 - learning_rate: 1.0000e-05\n","Epoch 54/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 573us/step - accuracy: 0.8438 - loss: 0.4703 - val_accuracy: 0.8832 - val_loss: 0.3864 - learning_rate: 1.0000e-05\n","Epoch 55/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 41ms/step - accuracy: 0.8278 - loss: 0.4861 - val_accuracy: 0.8762 - val_loss: 0.3915 - learning_rate: 1.0000e-05\n","Epoch 56/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 729us/step - accuracy: 0.8750 - loss: 0.3688 - val_accuracy: 0.8762 - val_loss: 0.3912 - learning_rate: 1.0000e-05\n","Epoch 57/120\n","\u001b[1m120/120\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 55ms/step - accuracy: 0.8342 - loss: 0.4554 - val_accuracy: 0.8785 - val_loss: 0.3882 - learning_rate: 1.0000e-05\n","\n","\n","Training Completed for Augmented Model (Model_2). Total Time taken: 00:04:00.252 \n","\n"]}],"source":["print(f\"Training initiated for {model_name} ({model})...\\n\")  # Print a message to indicate the start of tuning\n","start = time.time()  # Start the timer\n","\n","# Fitting the model on train and validation Dataset\n","history[model] = models[model].fit(train_datagen.flow(X_train_normalized, y_train_encoded,\n","                                                      batch_size = model_parameters[model]['fit']['batch_size'],\n","                                                      shuffle = False),\n","                                                      validation_data = (X_val_normalized, y_val_encoded),\n","                                                      epochs = model_parameters[model]['fit']['epochs'],\n","                                                      steps_per_epoch = X_train_normalized.shape[0] // model_parameters[model]['fit']['batch_size'],\n","                                                      callbacks = [learning_rate_reduction, callback_es],\n","                                                      verbose = 1,\n","                                                      )\n","\n","duration = convert_seconds(time.time() - start)  # Calculate the duration of training\n","print(f\"\\n\\nTraining Completed for {model_name} ({model}). Total Time taken: {(duration)} \\n\")"]},{"cell_type":"markdown","metadata":{"id":"chiVCFtznh4l"},"source":["**Model Evaluation**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":644},"executionInfo":{"elapsed":610,"status":"ok","timestamp":1723866011056,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"},"user_tz":300},"id":"dgksNY5dnkmW","outputId":"3f976bdd-4df8-41e7-8b41-33df0837003f"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x600 with 2 Axes>"],"image/png":"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Extract the Loss Data of Training and Validation Set\n","train_loss[model], val_loss[model] = [history[model].history['loss']], [history[model].history['val_loss']]\n","\n","# Extract the Recall Data of Training and Validation Set\n","train_accuracy[model], val_accuracy[model] = [history[model].history['accuracy']], [history[model].history['val_accuracy']]\n","\n","# Plot the Loss and Recall Curve side by side\n","plot_side_by_side([plot, plot],\n","                   [\n","                       [train_loss[model],val_loss[model]],\n","                       [train_accuracy[model],val_accuracy[model]],\n","                   ],\n","                   (Loss_Matrics, Accuracy_Matrics),\n","                   title = f\"Model: {model_name} ({model})\\nModel Performance Evaluation\".replace('_', ' '),\n","                   figsize = (15, 6),\n","                   fontsize = 14,\n","                   )"]},{"cell_type":"markdown","metadata":{"id":"syGA5jQ4norj"},"source":["**Observation**"]},{"cell_type":"markdown","metadata":{"id":"fOLZlJvLVQ8g"},"source":["**Observation**\n","\n","**Loss**:\n","- The training and validation loss both show a significant decrease over the epochs, particularly in the early stages, indicating that the model is learning effectively.\n","- The training loss reaches a stable point with minimal fluctuations towards the end, suggesting that the model has effectively minimized the error on the training data.\n","- There is a noticeable gap between training and validation loss, especially in the initial epochs, but this gap narrows as training progresses. This might suggest some initial overfitting that reduces over time.\n","- The final validation loss is slightly higher than the training loss, which is typical but may indicate slight overfitting. However, the difference is not too large, suggesting the model has generalized fairly well.\n","\n","**Accuracy**:\n","- Both training and validation accuracy show a consistent upward trend, with validation accuracy increasing more rapidly in the initial epochs.\n","- The training accuracy shows more fluctuations compared to validation accuracy. This might indicate that the model is still adjusting to the training data, while the validation accuracy suggests more stable performance.\n","- Validation accuracy plateaus towards the end, with both training and validation accuracy converging closely. This is a positive sign indicating that the model is not significantly overfitting and is able to generalize well to unseen data.\n","- The final accuracy values for training and validation are very close, which is a strong indication of good generalization by the model.\n","\n","**Overall Conclusion**:\n","The model shows good learning behavior, with both loss and accuracy metrics suggesting effective training and generalization. There is some minor fluctuation in training accuracy, but the validation accuracy remains stable and converges well with the training accuracy, indicating that the model is well-tuned and not overfitting excessively."]},{"cell_type":"markdown","source":["**Compare Performance of Train, Validation and Test Datasets**"],"metadata":{"id":"TJiIpTZ83xbE"}},{"cell_type":"code","source":["train_perf[model] = model_performance_classification_sklearn(models[model], X_train_normalized, y_train_encoded, classification_type = 'categorical', average='weighted')\n","val_perf[model] = model_performance_classification_sklearn(models[model], X_val_normalized, y_val_encoded, classification_type = 'categorical', average='weighted')\n","test_perf[model] = model_performance_classification_sklearn(models[model], X_test_normalized, y_test_encoded, classification_type = 'categorical', average='weighted')\n","\n","# Add the 'Dataset' column to each DataFrame\n","train_perf[model]['Dataset'] = 'Train'\n","val_perf[model]['Dataset'] = 'Validation'\n","test_perf[model]['Dataset'] = 'Test'\n","\n","# Concatenate the DataFrames\n","combined_perf = pd.concat([train_perf[model], val_perf[model], test_perf[model]], ignore_index=True)\n","\n","# Reorder columns to have 'Dataset' as the first column\n","combined_perf = combined_perf[['Dataset', 'Accuracy', 'Recall', 'Precision', 'F1 Score']]\n","\n","# Display the combined DataFrame\n","combined_perf"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"4vtY_EtV33Fh","executionInfo":{"status":"ok","timestamp":1723866015014,"user_tz":300,"elapsed":3961,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"}},"outputId":"68a5599b-d6ef-4e4b-b7bc-8aee8fe78a23"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Dataset  Accuracy  Recall  Precision  F1 Score\n","0       Train     0.885   0.885      0.893     0.887\n","1  Validation     0.888   0.888      0.891     0.888\n","2        Test     0.848   0.848      0.860     0.851"],"text/html":["\n","  <div id=\"df-776d26c6-e48e-4d93-8abc-ed692ec105bb\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Dataset</th>\n","      <th>Accuracy</th>\n","      <th>Recall</th>\n","      <th>Precision</th>\n","      <th>F1 Score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Train</td>\n","      <td>0.885</td>\n","      <td>0.885</td>\n","      <td>0.893</td>\n","      <td>0.887</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      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This suggests that the model's learning is not overly specific to the training data.\n","\n","**Test Data: The model's performance on the test data is slightly lower than on both the training and validation data. This drop in performance suggests that while the model generalizes well, there might still be some challenges in achieving the same level of accuracy and effectiveness on completely unseen data. However this variation is in acceptable ramge."],"metadata":{"id":"ytrFXqYWkvpl"}},{"cell_type":"markdown","metadata":{"id":"GiA77Vfdnn0e"},"source":["**Confusion Matrix**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":853,"status":"ok","timestamp":1723866015865,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"},"user_tz":300},"id":"ZEWTPFPKns9K","outputId":"a82aaa8a-2be8-404f-a199-733269c3a3aa"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x1200 with 2 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/SejRk1gx6L9q166tNGnSaPLkyWrSpIkmT56sNGnSqHbt2o98z+7du+Xl5aUSJUrYbEu4BSnhOEoP/i54e3tb3RaVoHz58jYF0Ge9fp9VfHy82rRpo40bN6p9+/aJFokSBAQESNIjn94FwAG9gFuc8WgUZAAAr7yEFQFz585Vo0aNdOjQIX333XePnJ/wm+dH/YY9oQiQMC/hvw+vbnhYYvu5fPmyJGncuHFJxn7r1q0kCzLPYvHixYqOjlbbtm3l6elpGXdyclLz5s01dOhQzZ49Wx07dnzmz7h+/bqcnJwSjT2plQuJNWFNWC2T1Lb79+9bxhKO7aMeaZzg1q1bj/3shM94mkazT3tur127Jinx6+dJVnkk5a233lLatGk1efJkHT16VPHx8Umu/njaaz+p2B+1n8uXLys2Nlb9+vV7ZBz/PTdPI+GafpKGyK6urmrRooXGjBmj33//XWvWrFGXLl2SfNz19evXlTlz5kS3/ff4SA+O0aPmJ/W94Wmv32eRcD3Mnj1bLVq00I8//pjk/Dt37kiSTWEUAJA8uGUJAPBaaNeuna5fv642bdrIw8PD5vaKhyX8YH7+/PlEt587d85qXsJ///skpwSJ7SfhPfv27ZPZbH7k67+3vLwICb+RnzJlitVTghJuGXl4ToKE23hiY2Nt9pfwQ/nDfH19FR8fn+hv0h91XF+UhGO7dOnSJI9tYk+aeZGf/6Tn1s/PT1Li18/zHisXFxe1atVK69at07hx41SiRAlLc+KkYn/Saz+p2B+1H19fXwUGBiZ5bI4dO/bkSf5H6tSpJf2vsPE47dq1U3x8vBo3bqz4+Hi1a9cuyfm+vr6PzPe/x0d6cIwuXryY6Pykvjck9/UbHx+vtm3batq0aWrWrJmmTp1qdbteYhKOacIxBgAkLwoyAIDXQmhoqDJmzKjTp0+rXr16Sd6O4+vrq2zZsunw4cM6ffq0zfaEWzASbnny9fVVcHCwDh8+bPmB7GGbNm2yGStZsqQkJfnEoeRw4sQJrV27VmnTplW7du0SfQUHB2v37t3avXu35X0Jxyux4/HwvAQJT9dJ7BHhv//++4tKJ1FGHNuEHhuJrZx52s9POFaJXSeJjT2t9957T/Hx8Tp79mySq2Ok/13TGzdulNlsttpmNpu1ceNGq3kJsW/evNlmXzdv3rS6dSdByZIlFR0d/cjHlT+vAgUKSHpwy8+TyJs3r0qWLKnTp0+rVKlSypMnT5LzixQpotu3b2vHjh022/77vUF6cIxu3bpl83htyX7fGxKKMdOnT1eTJk00Y8aMR/aNedihQ4ee6TZJAMCzoSADAHgtODs7a/HixVq0aJGGDBny2PmtW7fW/fv31atXL6sfTPfu3aupU6fKz8/P6pHZLVu21L179/T1119b7WfVqlWJNtxt27atfHx89OWXX2r//v0222/fvm3pJZGU+/fv6+DBg4n2KknMlClTFB8frw4dOmjixImJvj7//HNJ1qtkihcvLkmaOnWq1f4WLFigiIgIm89JWIHUv39/y20O0oMVBGPHjn2iWJ9V3bp1lSVLFo0aNcpSQHjY/fv3Ey0gPI2EXhoJzW0f9rTn9t1335Wzs7NGjRpltfLi+vXrGjhw4HPFKUm5c+fWihUrtGjRoiRXhkkPGtNWrlxZ+/fvt+nx8vPPPysqKkpVqlSx3IKTJUsWVahQQXv37rW5xWbw4MGJ9t755JNPJD0oFCX0e3nYuXPnnutx3wkrRxKaeD+JyZMna9GiRU/U0Lp169aSHjy6++Fb5U6dOqVRo0bJxcXF6jgn9GT58ssvrQp4+/bt04wZM2z2n9zXb8JtStOnT1ejRo00c+bMJyrG3Lt3T7t379Ybb7zBLUsA/sf0Gr1eQvSQAQC8Nt54440nboL52Wefafny5ZoxY4aioqJUtWpVXbhwQfPmzVNsbKwmTJggHx8fq/kLFy7UhAkTtH//flWoUEGnTp3SL7/8orfeekvLly+32n/q1Kk1Z84cNWrUSIUKFVKNGjWUO3duxcTE6Pjx44qIiFCZMmUUHh6eZJynT59Wnjx5FBQUpOPHjyc5Nz4+3nKbUps2bR45r0mTJvr00081a9YsjRgxQh4eHqpbt66yZ8+uqVOn6tSpUypSpIiioqK0bt061apVS7/99pvVPqpVq6Z3331Xs2fPVoECBVSvXj3FxMTol19+UcmSJbV06dLH3h7xrNzd3bVgwQLVrFlTFStWVJUqVVSgQAGZTCadOHFCmzZtUmBg4HM1jq1SpYoWLFighg0bqmbNmvLw8FChQoVUp06dpz63ISEh+vrrr9WnTx8VLFhQjRs3louLi/7v//5PBQsWfOKVHkmpUaPGE88dP368ypUrp/bt22vp0qXKmzev9u/fr19//VWpU6fW+PHjreaPGzdOZcuWVatWrbR48WLlyJFDO3bs0M6dO1W+fHmbVSA1atTQV199pQEDBigkJEQ1atRQUFCQoqOjdfjwYW3atEkDBw587EqVRylYsKCyZcum1atXP/F78ubNq7x58z7R3JYtW2rhwoVasmSJChYsqNq1a+vWrVuaN2+eLl++rJEjRypbtmyW+a1bt9bs2bMVHh6uIkWKqGbNmrp8+bLmzJmjN998U8uWLbPaf3Jfv/3799e0adOUIkUK5cyZM9GiX7169Wyanm/atEkxMTFWhWgAQPKiIAMAcEgeHh5at26dhg0bpnnz5mn06NHy8vJSxYoV9cUXX9g8Vcbb21sRERHq1auXFi1apF27dilfvnyaN2+erl27ZlOQkR40XN29e7e++eYbrVmzRqtXr5a3t7cyZcqktm3bqkWLFi80pzVr1ujkyZOqWLGigoODHznPz89PDRo00KxZs7Rw4UK9++678vT0tDQ8Xbt2rbZt26ZSpUpp48aNWrZsmU1BRnrweOA8efJo8uTJ+u6775QpUyZ9+umnqlq1qpYuXfrIJrovQvHixbVnzx598803+u2337Rlyxa5u7srY8aMqlevnuVx2c+qffv2On78uObOnathw4YpNjZWrVu3tjxO+mnP7ddff60MGTJo9OjR+umnn5QmTRo1bdpU/fv3N3w1Qq5cufTHH3+oX79+Cg8P1/Lly5U6dWq1bdtWffr0selrlD9/fm3ZskU9e/ZUeHi4Vq5cqXLlymnLli0aMWJEorfl9O/fXxUqVNC3336rtWvX6urVqwoMDFRwcLD69u372JU8STGZTOrQoYN69uypHTt2JPo0pOdhMpm0YMECjR07VtOmTdN3330nNzc3FS1aVF27dtXbb79tNd/JyUlLlixRv379NGvWLI0dO1bZs2fX6NGjlSNHDpuCjJS8129C4fbmzZsaNGhQonOyZs1qU5CZOXOm3Nzc1LZt22f+bADA0zGZ/3sDMQAAwHOYOHGi2rdvrx9++EEfffSRvcPBa+jy5cvKli2bGjVqpAkTJtg7nFfelStXFBQUpHfeeeeRjysH4JhMtbLYO4QXxvzbSXuHYIMeMgAA4JmcO3fOpjHs6dOnNXDgQDk7O6t27dp2igyvu4CAAPXq1UvTpk3TiRMn7B3OK2/UqFGKi4vTgAED7B0KgJeNyfT6vF5C3LIEAACeydChQ7V8+XKVL19eadKk0cmTJ7Vs2TLduHFDffv2tTSGBZJD586dFRMTo5MnTybL4+MdSUBAgKZPn66MGTPaOxQAcCjcsgQAAJ5JeHi4Ro0apT179ujKlSvy8PBQwYIFFRYWpnfffdfe4QEAgOdkeuv1KXibl798KyopyAAAAAAAABsUZJIXtywBAAAAAABbdJ1NVhxeAAAAAAAAg1GQAQDgFXX9+nV17txZwcHBcnV1lclkUmRkZLJ+ZtasWZU1a9Zk/YzXWd++fWUymbRhw4Zk2b89rgkAAPBsKMgAAPCE/vzzT7Vr1045cuSQt7e3PD09lT17drVs2VKrV682PJ7PPvtM3377rfLnz6/PP/9cffr0Ubp06QyPw56yZs0qk8kkk8mkv/76K9E5cXFxypgxo2Xe8ePHn/nzpk6dKpPJpKlTpz7zPpKTPa+JSpUqWY7xk7xedFFqw4YNMplM6tu37zO97+FXihQplDlzZtWsWVNDhw7VmTNnXkiMbdq0ee5rEADw+qCHDAAAjxEfH6/u3btr9OjRcnFxUZUqVfT222/L1dVVR48e1fLlyzVz5kz1799fX331lWFxLVu2TDlz5tTSpUsN+8y1a9ca9llPysnpwe+XJk+erFGjRtlsX7Fihc6cOSMXFxfFxsYaHZ6Vjz/+WE2bNlWWLFmSZf/2uCYStGnTRpUqVbIaW7x4sfbs2aPWrVvbrKx62VZaFStWTLVr15Yk3b59W+fOndPvv/+u8PBw9evXT8OHD1enTp3sHCUAGMxksncErzUKMgAAPEbv3r01evRoFS5cWAsWLFD27Nmttt+5c0fff/+9oqOjDY3rzJkzqlChgqGf+d/cXwaurq6qUKGCZs6cqWHDhsnV1dVq++TJk+Xn56dChQpp48aNdorygVSpUilVqlTJtn97XBMJ2rRpYzN2/Phx7dmzJ9FizcvmjTfeSHR1zZIlS9SuXTt98skn8vb21nvvvWd8cACA1xK3LAEAkITDhw9r+PDhCgwMVHh4eKIFCU9PT/Xo0UP9+vWzGr906ZI+/fRTBQcHy93dXWnSpFHjxo0TvbUm4VaGY8eO6dtvv1Xu3Lnl7u6uoKAg9evXT/Hx8TZzzWazIiIiLLdZJPzAm1SfkkfdcrN+/XrVrFlTGTJkkLu7u9KmTavy5cvr559/tpr3qB4yt27dUp8+fZQ7d255eHgoICBAb731lrZs2WIz9+H4Zs+ercKFC8vT01Pp06dX586ddefOHZv3PM57772nixcv2qwMuXjxopYtW6ZmzZrJ09PT5n337t3Td999p9DQUGXOnNlynho0aKDdu3dbzW3Tpo3atm0rSWrbtq3VLS4JEm7buXv3rnr37q3s2bPL1dXV8oN+Yufmww8/lMlk0tChQ23iS9g2bNiwJPN/3DUhSbGxsRo1apQKFSokT09P+fn5qXLlyomupnn4Olm6dKnKli0rHx+fF7qqZcmSJapatapSpkwpDw8P5c+fXyNGjFBcXJzVvPj4eE2cOFElSpRQQECAPD09lSlTJtWpU8dyHPv27avKlStLkvr162d1bp739qC6detqwYIFkqSePXvq1q1blm1nzpxRnz59VKpUKaVJk0bu7u7KmjWrwsLCdOHCBav9ZM2aVdOmTZMkBQcHJ3qOFi1apGbNmikkJEReXl7y8/NT+fLl9X//93/PlQMA4OXEChkAAJIwdepUxcXFqUOHDkqbNm2Sc93d3S1/vnjxokqXLq0jR46oUqVKatq0qY4dO6YFCxZo+fLlWrlypcqVK2ezjx49eigiIkK1a9dWaGioFi9erL59++revXsaNGiQJKlevXrKmjWr+vXrp6CgIMvKhGf9YXn58uWqU6eO/P39VbduXaVPn14XL17Unj17NGPGDH3wwQdJvv/u3buqUqWKduzYoaJFi+rTTz/V+fPnNW/ePK1cuVJz5sxRo0aNbN73/fffKzw8XHXr1lWVKlUUHh6ub7/9VpcuXdKsWbOeKof69esrZcqUmjJliho0aGAZnzFjhu7fv6/33nsv0dvJLl++rE8//VTly5dXrVq1lDJlSh09elS//vqrVqxYoY0bN6p48eKSHhz3q1evasmSJapbt64KFy78yHgaNmyoPXv2qEaNGvL391dwcPAj544ePVobN27U119/rapVq1o+b9GiRfrpp59UpUoV9ejRI8n8H3dNmM1mvfPOO1qyZIly5sypjh076tatW5o3b57efvttjRo1Sl26dLHZ7/z587Vq1SrVrl1bYWFhun79epJxPKlevXpp6NChypgxoxo0aCA/Pz9t2rRJPXr00Pbt2zV//nyrucOHD1f27Nn17rvvysfHR6dPn9bmzZu1Zs0aVapUSZUqVdLx48c1bdo0VaxY0arI4e/v/9zxVqpUSeXLl9emTZu0bt061alTR5K0ceNGjRw5UlWrVlXJkiXl6uqq3bt3a/z48Vq5cqV27dolPz8/SdKnn36qqVOnas+ePercubMlrof/3vbq1Utubm4qV66c5e/hr7/+qnfeeUfffvstt0wBwOvGDAAAHqlSpUpmSeY1a9Y81fvatm1rlmTu1auX1fjy5cvNkswhISHmuLg4y3jr1q3NkszBwcHmM2fOWMYvXrxo9vf3N/v4+JhjYmKs9iXJXLFiRZvP7tOnj1mSef369TbbpkyZYpZknjJlimWsQYMGZknmyMhIm/mXLl2y+jooKMgcFBRkNdavXz+zJHPz5s3N8fHxlvFdu3aZ3dzczP7+/ubr16/bxOfn52c+ePCgZfz27dvmnDlzmp2cnMynT5+2iSUxQUFBZnd3d7PZbDZ//PHHZhcXF/PZs2ct2/Ply2cuUKCA2Ww2m0NDQ82SzMeOHbNsv3v3rvnff/+12e9ff/1lTpEihblatWpW44kdv4dVrFjRLMlcuHBhc3R0tM32R52byMhIs7u7uzl79uzmGzdumE+dOmUOCAgwBwYGPvGxMJsffU1MmzbNsu3h6+jEiRPmVKlSmV1cXMxHjhyxydPJycm8evXqJ/78/0q4rh/Od9WqVWZJ5tDQUPPNmzct4/Hx8eYPP/zQLMm8YMECy3hAQIA5Q4YM5lu3btns/+FjvH79erMkc58+fZ4qxoT3dejQIcl5X331lVmS+auvvrKMnT9/3nzjxg2buQnHe+DAgVbjCcfj4WvwYQ+fgwQ3btwwFyhQwOzn55foMQCA5KS3g16b18uIW5YAAEjCuXPnJEmZMmV64vfcu3dPc+bMUWBgoHr37m21rVatWqpevboOHz6c6O08X331ldKnT2/5OlWqVKpbt65u3LihQ4cOPWMWTyaxW3oCAwMf+75p06bJ1dVVQ4cOtbp9p0iRImrdurWuXr2qxYsX27yvc+fOypUrl9XnN2vWTPHx8frzzz+fOv733ntPsbGxlttCtm/frv379yfZ88Pd3V0ZM2a0Gc+XL58qV66sjRs36v79+08dS79+/RQQEPDE8wsVKqRhw4bpyJEj+uijj9SyZUtdvnxZkydPVoYMGZ768/8r4ZgMHz5cbm5ulvEsWbKoS5cuio2NTXRVUt26dVWtWrXn/vyHff/995Kkn3/+Wd7e3pbxhNu2TCaT5syZY/UeNzc3OTs72+zraY7x80o4D5cuXbKMpUmTRilSpLCZ27JlS/n6+mrNmjVP9RnZsmWzGUuRIoXatGmja9euaefOnU8ZNQDgZUZBBgCAF+zgwYO6e/euSpQoIS8vL5vtCb0uIiMjbbYVK1bMZiyhGHT16tUXGmeCpk2bSpJKlSqljz/+WIsWLbL6oTMp169f19GjRxUSEpJo0crIXIsUKaLChQtrypQpkh4083Vzc1OLFi2SfF9kZKTeffddZcmSRW5ubpbeHkuXLtW9e/ee+Fg8rESJEk/9nk8++UQ1a9bUzJkztWHDBn300Ud6++23n3o/idm9e7e8vLwSjSupc/QseTzOtm3b5O3trcmTJ6tv375Wr9GjR8vT01MHDx60zG/atKmOHz+u/Pnz66uvvtK6deueqc9Qclm4cKFCQ0OVOnVqubi4yGQyycnJSdevX3/qx2VfuHBBXbt2VZ48eeTl5WW5Frt16yZJL+zx2wCAlwM9ZAAASEK6dOl08OBBnT592mo1R1IS+mw8qudMwgqYxPpx+Pr62oy5uDz43/V/m52+KI0aNdLixYs1atQo/fjjjxo3bpxMJpMqV66skSNHJtkr5WXL9b333tMnn3yiNWvWaO7cuapTp06STzX6/fffVaVKFUnSm2++qRw5cihFihQymUyWRzbHxMQ8dRyP6zeUGJPJpHr16mnFihWS9EL7hVy/fl2ZM2dOdFtS5+hZ8nicy5cvKzY21qYJ9sMebpw7duxYBQcHa8qUKRo4cKAGDhwoDw8PNW7cWCNHjkzWp1Y9LKEYkjp1asvYyJEj1b17d6VOnVpvvvmmMmXKZFlpNmbMmKe6di5fvqzixYvr5MmTKlu2rKpVqyZ/f385OzsrMjJSS5YseaZrEQDw8qIgAwBAEsqWLasNGzZo7dq1lh/cHyeh0HD+/PlEtyfcBpVYQeJFcHJ6sAA2NjbWZtu1a9cSfU/dunUtt0Zt2bJFCxcu1KRJk1SjRg0dPHjwkY1R7Z3rfzVv3lw9evRQmzZtdP36dbVr1y7J+YMGDVJMTIw2bdpk02R527Zt2rNnzzPF8fCtW0/q2LFj6tGjhwICAnTlyhW9//772rhxY6K36jwtX19fm6f+JEjqHD1LHk8Si8lkeuKVRy4uLurevbu6d++uM2fOKCIiQlOmTNH06dN17tw5rVy58oXHmJiEJzolNF2OjY3VgAEDlD59ekVGRipNmjSWuWazWcOHD3+q/U+aNEknT57UgAEDbG51HDp0qJYsWfJ8CQDAs3B68f8fwP9wyxIAAElo06aNnJ2d9fPPP+vixYtJzk347XXCo5937typ27dv28xL+MEuqZUnzyNlypSSpNOnT9ts+++jnP/Lx8dHNWrU0M8//6w2bdro/Pnz2r59+yPn+/r6Klu2bDp8+HCin5fcuf5XQECA6tWrp9OnTytjxowKDQ1Ncv6RI0cUEBBgU4y5ffu2du3aZTM/oTjyolcrxcbGqnnz5rpx44bmzZunrl276vfff09yFcnTKFKkiG7fvq0dO3bYbDP6HJUsWVLR0dH6559/nvq9GTJkULNmzRQeHq6QkBCtWbPGcvtScp0bSYqIiNCmTZuUJk0aS2H20qVLunbtmkqXLm1VjJGkP/74I9HbqpKK8ciRI5IeFEf/a9OmTc+dAwDg5UNBBgCAJISEhOizzz7TpUuXVLNmTR07dsxmzt27dzVq1Cj17dtX0oMGpM2aNdOlS5c0ZMgQq7nh4eFauXKlQkJCVLZs2WSJOeE3+NOnT1d8fLxlfOvWrYk2bt24cWOiPyAmrKjw8PBI8vNat26t+/fvq1evXjKbzZbxvXv3aurUqfLz81O9evWeJZVnMnToUC1atEiLFy+2rBZ6lKCgIF25ckX79++3jMXFxal79+6JFuASmsieOnXqhcbcr18/bd26Vd26dVO1atU0ePBgFS1aVIMHD34hP4y3bt1a0oPHKj/cpPjUqVMaNWqUXFxc1Lx58+f+nCfxySefSHpwe1l0dLTN9nPnzikqKkrSgyLn77//bjPn1q1bunnzplxdXS3nOLnOzdKlS9WwYUNJ0rBhwyx9odKkSSNPT0/t2rXLqvB65cqVR95ullSMQUFBkqTNmzdbjc+ePVu//fbb8ycCAHjpcMsSAACPMXDgQN29e1ejR49Wrly5VKVKFeXPn1+urq46duyY1qxZo+joaA0cONDynmHDhikiIkIDBw7U77//rpIlS+r48eOaP3++vLy8NGXKlMcWC55VqVKlVLZsWa1bt06lS5dWhQoVdOLECS1ZskR16tTRokWLrOZ/8sknOnPmjMqVK6esWbPKZDJp8+bN2rFjh0qVKmWzeuS/PvvsMy1fvlwzZsxQVFSUqlatqgsXLmjevHmKjY3VhAkT5OPjkyy5JiZr1qzKmjXrE83t1KmTVq1apXLlyqlx48by8PDQhg0bdPr0aVWqVMmyeiRB6dKl5enpqTFjxujKlSuWfiL/vcXkaWzcuNFSgBk0aJCkB0W92bNnq1ixYmrRooX27NnzyNvGnkTLli21cOFCLVmyRAULFlTt2rV169YtzZs3T5cvX9bIkSMTfcJPcqhRo4a++uorDRgwQCEhIapRo4aCgoIUHR2tw4cPa9OmTRo4cKDy5MmjO3fuqGzZssqZM6eKFSumLFmy6ObNm1q2bJnOnTun7t27y93dXdKDlWkZMmTQ3Llz5e7urkyZMslkMqlTp07y8/N7bFx//PGHpah69+5dnT17Vr///rsOHz4sT09PjRs3Tm3atLH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\n"},"metadata":{}}],"source":["# Determine the predictions for the test data\n","y_test_pred[model] = prediction(models[model], X_test_normalized, classification_type = 'categorical')\n","\n","# Plot the confusion matrix for the test Data for the current Model\n","confusion_matrix_sklearn(np.argmax(y_test_encoded, axis = 1),\n","                         y_test_pred[model],\n","                         ticklabels = list(enc.classes_),\n","                         annot = True,\n","                         title = f\"Model: {model_name} ({model})\\nConfusion Matrix for Test Data\".replace('_', ' '),\n","                         xtick_rotation = 90,\n","                         cmap = plt.cm.Greens,\n","                         )"]},{"cell_type":"markdown","metadata":{"id":"qlEsfIcwnxtN"},"source":["**Observation**\n","\n","The confusion matrix reveals valuable insights into the model's performance:\n","\n","- **Correct Predictions**: The diagonal elements reveal correct predictions, showcasing an overall increase in accuracy across all classes.\n","- **Misclassifications**: Off-diagonal elements pinpoint misclassifications, allowing for targeted improvement.\n","- **Class-specific Performance**:\n"," - Most classes exhibit exceptionally high prediction accuracy.\n"," - Notably, 'Black-grass' is often misclassified as 'Loose Silky-bent', indicating room for refinement in distinguishing between these two classes.\n"]},{"cell_type":"markdown","metadata":{"id":"DRux-P7foNSH"},"source":["**Classification Report**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":554},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1723866015865,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"},"user_tz":300},"id":"xhhJ0JyyoLrA","outputId":"0714cfb5-ecb9-49ba-ca6c-6337aad1062c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Classification Report of Model: Augmented Model\n","\n"]},{"output_type":"execute_result","data":{"text/plain":["                                precision  recall  f1-score  support\n","0 - Black-grass                     0.432   0.615     0.508   26.000\n","1 - Charlock                        0.783   0.923     0.847   39.000\n","2 - Cleavers                        0.857   0.828     0.842   29.000\n","3 - Common Chickweed                0.918   0.918     0.918   61.000\n","4 - Common wheat                    0.909   0.909     0.909   22.000\n","5 - Fat Hen                         0.957   0.938     0.947   48.000\n","6 - Loose Silky-bent                0.818   0.692     0.750   65.000\n","7 - Maize                           0.880   1.000     0.936   22.000\n","8 - Scentless Mayweed               0.836   0.885     0.860   52.000\n","9 - Shepherds Purse                 0.833   0.652     0.732   23.000\n","10 - Small-flowered Cranesbill      0.957   0.880     0.917   50.000\n","11 - Sugar beet                     0.971   0.895     0.932   38.000\n","accuracy                            0.848   0.848     0.848    0.848\n","macro avg                           0.846   0.845     0.841  475.000\n","weighted avg                        0.860   0.848     0.851  475.000"],"text/html":["\n","  <div id=\"df-9edd0554-109d-4f54-ac74-17faf4c5e41c\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1-score</th>\n","      <th>support</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0 - Black-grass</th>\n","      <td>0.432</td>\n","      <td>0.615</td>\n","      <td>0.508</td>\n","      <td>26.000</td>\n","    </tr>\n","    <tr>\n","      <th>1 - Charlock</th>\n","      <td>0.783</td>\n","      <td>0.923</td>\n","      <td>0.847</td>\n","      <td>39.000</td>\n","    </tr>\n","    <tr>\n","      <th>2 - Cleavers</th>\n","      <td>0.857</td>\n","      <td>0.828</td>\n","      <td>0.842</td>\n","      <td>29.000</td>\n","    </tr>\n","    <tr>\n","      <th>3 - Common Chickweed</th>\n","      <td>0.918</td>\n","      <td>0.918</td>\n","      <td>0.918</td>\n","      <td>61.000</td>\n","    </tr>\n","    <tr>\n","      <th>4 - Common wheat</th>\n","      <td>0.909</td>\n","      <td>0.909</td>\n","      <td>0.909</td>\n","      <td>22.000</td>\n","    </tr>\n","    <tr>\n","      <th>5 - Fat Hen</th>\n","      <td>0.957</td>\n","      <td>0.938</td>\n","      <td>0.947</td>\n","      <td>48.000</td>\n","    </tr>\n","    <tr>\n","      <th>6 - Loose Silky-bent</th>\n","      <td>0.818</td>\n","      <td>0.692</td>\n","      <td>0.750</td>\n","      <td>65.000</td>\n","    </tr>\n","    <tr>\n","      <th>7 - Maize</th>\n","      <td>0.880</td>\n","      <td>1.000</td>\n","      <td>0.936</td>\n","      <td>22.000</td>\n","    </tr>\n","    <tr>\n","      <th>8 - Scentless Mayweed</th>\n","      <td>0.836</td>\n","      <td>0.885</td>\n","      <td>0.860</td>\n","      <td>52.000</td>\n","    </tr>\n","    <tr>\n","      <th>9 - Shepherds Purse</th>\n","      <td>0.833</td>\n","      <td>0.652</td>\n","      <td>0.732</td>\n","      <td>23.000</td>\n","    </tr>\n","    <tr>\n","      <th>10 - Small-flowered Cranesbill</th>\n","      <td>0.957</td>\n","      <td>0.880</td>\n","      <td>0.917</td>\n","      <td>50.000</td>\n","    </tr>\n","    <tr>\n","      <th>11 - Sugar beet</th>\n","      <td>0.971</td>\n","      <td>0.895</td>\n","      <td>0.932</td>\n","      <td>38.000</td>\n","    </tr>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.848</td>\n","      <td>0.848</td>\n","      <td>0.848</td>\n","      <td>0.848</td>\n","    </tr>\n","    <tr>\n","      <th>macro avg</th>\n","      <td>0.846</td>\n","      <td>0.845</td>\n","      <td>0.841</td>\n","      <td>475.000</td>\n","    </tr>\n","    <tr>\n","      <th>weighted avg</th>\n","      <td>0.860</td>\n","      <td>0.848</td>\n","      <td>0.851</td>\n","      <td>475.000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9edd0554-109d-4f54-ac74-17faf4c5e41c')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 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The overall accuracy and macro-averaged metrics reflect strong and consistent performance across various classes."]},{"cell_type":"markdown","metadata":{"id":"A5cgkAz_YKcc"},"source":["## Final Model"]},{"cell_type":"markdown","metadata":{"id":"F3MjkGfHYOPn"},"source":["Comment on the final model you have selected and use the same in the below code to visualize the image."]},{"cell_type":"markdown","metadata":{"id":"vrtIuaAHpgr-"},"source":["The tuned CNN model (Model 2), which incorporates a reduced Learning Rate, Data Augmentation and Early Stopping techniques, has demonstrated superior performance compared to the base CNN model (Models 1). This tuned model achieves more accurate predictions, highlighting the effectiveness of these adjustments in enhancing the model's performance."]},{"cell_type":"markdown","metadata":{"id":"dvDkLMO7YIdY"},"source":["### Visualizing the prediction"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":391},"executionInfo":{"elapsed":2328,"status":"ok","timestamp":1723868899598,"user":{"displayName":"Joyjit Roy","userId":"03421354862574022261"},"user_tz":300},"id":"LgLGTZgxf9DZ","outputId":"81ea40c9-1fec-426a-b2fa-d01be10e8c00"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1800x500 with 6 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAABv4AAAF2CAYAAACmt+8TAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9eZhkZXn/j9/nnNq3rt6mZ1/Yd3BBUHaCEqJ+3XH7KBA/JhrEuMXrQ2IAjcbliooa9yhE/YFGRQSNRkUBUcAFQfZlmH3p6a2qu/Y6y++P6Wnm/T5D9wwM0DN5v3Jxxaer6tQ5z3I/z3PO1P1yoiiKTAghhBBCCCGEEEIIIYQQQgixT+M+0ycghBBCCCGEEEIIIYQQQgghhHjy6MGfEEIIIYQQQgghhBBCCCGEEPsBevAnhBBCCCGEEEIIIYQQQgghxH6AHvwJIYQQQgghhBBCCCGEEEIIsR+gB39CCCGEEEIIIYQQQgghhBBC7AfowZ8QQgghhBBCCCGEEEIIIYQQ+wF68CeEEEIIIYQQQgghhBBCCCHEfoAe/AkhhBBCCCGEEEIIIYQQQgixH6AHf0IIIYQQQgghhBBCCCGEEELsB+jBnxBCCCGE2O9Zu3atOY5j559//jN9KrvkyiuvNMdx7Morr4S/r1y50lauXPmEj+s4jp1++ulP6tyeLuZ7Gz1TPF7feCrbdj62Rbfbtcsuu8wOPvhgS6fT5jiOXXvttc/0ac179qUYsDOP1++FEEIIIYQQc6MHf0IIIYQQYp/hDW94gzmOY1dfffWs75ucnLRcLmflctmazebTdHZiLk4//XRzHOeZPg1hT/6h8tPNJz/5SfvgBz9oixcvtve973126aWX2mGHHfaMnMvExIR9+MMftuc///nW399vyWTSBgcH7ayzzrLPfe5zVqvVnpHzEkIIIYQQQggzs8QzfQJCCCGEEELsLm95y1vs6quvtq9//ev2+te//nHfd/XVV1uz2bTzzjvPstmsLVmyxO6//37r6el5Gs/2yXPDDTc8qc/ff//9lsvl9tLZiPnEU9m283G8/OhHP7JCoWA///nPLZVKPWPnccMNN9i5555r4+Pjdvjhh9trXvMa6+/vt7GxMbv55pvtne98p11++eW2evXqZ+wchRBCCCGEEP+70YM/IYQQQgixz3DmmWfaqlWr7Je//KWtX7/eli9fvsv3ff3rXzez7Q8KzcySyeQz9uugJ8OBBx74pD6/L16z2D2eyradj+Nl8+bN1t/f/4w+9LvrrrvspS99qZmZfetb37I3vvGNsffceOONdvHFFz/dpyaEEEIIIYQQMyjVpxBCCCGE2GdwHMcuuOACC8PQrrjiil2+595777Xf/e53dswxx9hzn/tcM3t8Z9mWLVvs7//+7+3ggw+2bDZr5XLZDj/8cHvb295m1Wp15n2zpag8//zzzXEcW7t27czfqtWqffzjH7fTTjvNFi9ebKlUyhYvXmxvfvOb9+iXQJyOccd1zPbfjTfeCPXFfq8d57tmzRr77Gc/a4cddpil02lbsWKFffCDH7QwDGPn0Wg07P3vf78tW7bMMpmMHXXUUfbVr37VbrzxRnMcxy677LI5r8VxHLvppptm/veO/3blkXvkkUfsFa94hfX29lo+n7ezzjrL7rrrrl0ed9u2bfbud7/bDjroIEun0zYwMGCvetWr7J577pnznLhOHn30UfvEJz5hBx98sGUyGVu1apV96EMfsm63C+/f+bp/+9vf2ote9CIrl8vQR6Iosq9//et20kknWalUslwuZ8997nNnHkoz4+Pj9ra3vc2GhoYsl8vZ8ccfbz/4wQ8e95wfz93W6XTs05/+tB1//PFWLBatUCjYEUccYe95z3tsYmJipg+tW7fO1q1bB22xox1nc/ytW7fO3vKWt9iSJUsslUrZ0qVL7S1veYutX78+9t4d42aHn2/lypWWTqftkEMOsS984QuPe207c9lll830153Pl9OUXnHFFXbCCSdYoVCwQqFgJ5xwwi79cLvTdo/HO9/5Tms2m/a5z31ulw/9dlzzzmOw0+nY5z73OTv77LNt2bJllk6nbcGCBfbKV77S/vSnP8U+v7Pb7mc/+5m94AUvsFwuZ/39/XbeeefZ2NjYLr/3P/7jP+yoo46yTCZjy5Yts/e///3WarUe91qe7nbcmZtvvtle/vKX29DQkKXTaVu2bJm98pWvtFtuuWXOz/7gBz+w17/+9XbQQQdZLpeznp4eO+WUU+z73//+Lt//q1/9ys455xxbvHixpdNpGxoaslNOOcW+8pWvwPvuuOMOe/WrX23Lly+3dDptg4ODdvzxx9tHPvKRPb4+IYQQQgghnmn0iz8hhBBCCLFPcf7559tll11mV155pV1yySWxG/Y7Hgju+LXf49FoNOykk06ytWvX2ote9CJ7xSteYZ1Ox9asWWPf/OY37X3ve98TTnV4//332yWXXGJnnHGGveIVr7B8Pm8PPPCAXXXVVfbjH//Y7rjjDluxYsUeH7dcLtull14a+3sQBPapT33KGo3Gbqd//Id/+Ae76aab7CUveYmdffbZdu2119pll11mnU4HbnYHQWAveclL7Fe/+pUdffTR9oY3vMHGx8ftve997y4fPD0el156qV155ZW2bt06uIbjjjsO3rd27Vo78cQT7cgjj7S//uu/ttWrV9sPf/hDO+OMM+z++++3oaGhmfeuXr3aTj/9dNu4caO96EUvspe//OW2bds2+/73v2//8z//YzfccIOdcMIJu32O73rXu+w3v/mNnXvuuVYoFOz666+3Sy+91P785z/b9773vdj7f/vb39q//uu/2hlnnGF/8zd/M/PQJIoie+Mb32hXX321HXzwwfaGN7zBUqmU/fznP7e3vOUtdt9999m//du/zRyn0WjY6aefbnfffbc9//nPt9NOO802bNhgr33ta+1FL3rRbp9/s9m0F77whfab3/zGDj74YLvgggssnU7bww8/bF/+8pftzW9+s61cudIuvfRSu/zyy2eueQdztedDDz1kJ598so2MjNhLX/pSO/LII+2ee+6xr3/963b99dfbLbfcYoccckjsc69//evtd7/7nZ1zzjnmeZ7913/9l1144YWWTCbtrW9966zfueOc+HzL5fLMe975znfa5z73OVuyZMnMuP/+979vF1xwgf3pT3+yz3zmM7HjPl7bPR6PPPKI3XzzzbZs2TK74IILZn1vOp2e+d/j4+P2rne9y0455RT7q7/6K+vt7bVHH33UrrvuOvvJT35iN998sx1//PGxY1x33XX24x//2F760pfaC17wArv55pvtG9/4hq1evTr2gOxf/uVf7JJLLrGhoSF761vfaslk0r7zne/Y/fffv8vzeybacQef+cxn7N3vfrdls1l7xSteYcuXL7dNmzbZLbfcYt/73vfs5JNPnvXzF198saVSKTv55JNt0aJFNjIyYtddd529+tWvts9+9rN20UUXzbx3R/2Vy2V72cteNvP+u+66y775zW/a3/zN35iZ2Z133mkveMELzPM8e9nLXmYrVqywSqVi9913n33lK1+xf/qnf9qtaxNCCCGEEGLeEAkhhBBCCLGP8Zd/+ZeRmUW/+MUv4O/dbjcaGhqK0ul0NDY2NvP3NWvWRGYWnXfeeTN/u+666yIzi971rnfFjj81NRW1Wq2Z8mmnnRY93tL5vPPOi8wsWrNmzczfKpUKfP8OfvnLX0au60b/9//+X/j7FVdcEZlZdMUVV8DfV6xYEa1YsWKX37szb3/72yMziy666CL4u5lFp5122i7Pd9WqVdHmzZtn/j4yMhKVy+WoWCxG7XZ75u//8R//EZlZdM4550S+78/8/d57740ymUxkZtGll1465zlG0ez1uKONzCz62Mc+Bq994AMfiMws+uhHPwp/f8ELXhB5nhf99Kc/hb8/+OCDUbFYjI4++ujdOq8ddTI4OBht2LBh5u/tdjs69dRTIzOLvve97838/Ve/+tXMuX7961+PHe8rX/lKZGbRBRdcEHU6HTjeS1/60sjMoj/84Q8zf7/00ksjM4ve+ta3wnF++tOfznwP941dte173/veyMyiN73pTdBWUbS9T05NTc2UZ+tbuxovURRFZ5xxRmRm0Ze//GX4++c///nIzKIzzzwT/r6jvU844YSoWq3O/P2BBx6IEolEdOihh+7y+3fF453vTTfdFJlZdPjhh0eVSmXm7+Pj49EhhxwSmVl08803z/x9rrZ7PK688srIzKL/83/+z25/JoqiqNVqRRs3boz9/Z577okKhUJ01llnwd93xIJEIhHdcsstM3/3fT86/fTTIzOLbr311pm/P/zww1EikYiWLFkSDQ8Pz/y9Wq1Ghx566C77yTPVjnfeeWfkum60ePFiiJdRFEVhGEabNm2K1QP3+9WrV8eOOzU1FR199NFRT09PVK/XZ/7+yle+MjKz6M4774x9ZnR0dOZ/v+c974nMLLr22mtnfZ8QQgghhBD7Ckr1KYQQQggh9jl2/KqH0yb+6Ec/suHhYXvZy15mfX19u3WsbDYb+1uhUIBf7ewpPT09u/z+M844w4488kj7xS9+8YSPzXz605+2L37xi/ZXf/VX9ulPf3q3P/fP//zPtmjRopnywMCAvexlL7OpqSl78MEHZ/7+rW99y8zMPvKRj5jneTN/P+KII+zNb37zXrgCZNWqVfYP//AP8Lcd7f373/9+5m9/+tOf7Le//a2dd955dvbZZ8P7DznkEHvrW99qd9999x6l/Pz7v/97W7p06Uw5lUrN/PpxV2kjn/3sZ+/y11///u//bvl83j7/+c9bMpnc5fGuvvrqmb9/4xvfsFQqZR/60IfgOGeffbb9xV/8xW6du+/79pWvfMV6enrsM5/5DLSV2fY+WSgUdutYu2L9+vX2q1/9yo444ojYr7ve9ra32WGHHWa//OUvbcOGDbHPfvSjH7VSqTRTPvTQQ+2kk06yBx980Kampp7wOZmZ/ed//qeZbU8JuvMvdHt7e2d+Wbonbfd4bN261cwM+sfukE6nbcmSJbG/H3nkkXbGGWfYzTffHEsla2b2hje8wU466aSZsud5dt5555kZjoOrrrrKfN+397znPbZgwYKZv5dKJfvABz4QO+4z2Y5f/vKXLQxD+/CHPxxL1eo4ji1evHjOYxxwwAGxvxUKBTv//POtWq1C3exgVzG+v7//Cb9PCCGEEEKI+Y5SfQohhBBCiH2Ol73sZTY4OGg/+MEPrFqtztzw3/EgcK40n2Zmp556qi1atMg+9rGP2V133WUveclL7LTTTrPDDz98t3xfc3HjjTfa5ZdfbrfffruNjo6a7/szr6VSqSd9fDOz66+/3t73vvfZMcccY9/+9rdjD3tm4znPeU7sbzsealQqlZm/3XXXXZbP5+1Zz3pW7P0nnXRSzJX1ZDnuuOPMdfHfJ+7qvG677TYzMxseHt6lY/CBBx6Y+f9HHXXUbn33KaecEvvb85//fEskErv0se0qRWOj0bC7777bFi9ebB//+Mdjr+94yLPj/CYnJ23NmjV2xBFH2MKFC3d5TjfccMOc5/7AAw/Y1NSUnXXWWdbb2zvn+/eUO++808zMTjvttNj4cF3XTj31VHvggQfszjvvtGXLlsHrc/W1YrH4hM9rR7vsKk3pGWecAee+M7tqu6eKO++80z7xiU/YLbfcYlu3bo096BsdHYWH8GZ7Nj7Ndt13d/W3Z7Idf/e735mZ7VH6Wmbbtm32sY99zH7yk5/YunXrrNlswuubN2+e+d+ve93r7JprrrETTzzR3vCGN9hf/MVf2CmnnGIDAwPwmXPPPdcuv/xye8UrXmGvfe1r7YUvfKGdeuqpu3xgK4QQQgghxL6AHvwJIYQQQoh9jmQyaW9605vsU5/6lF111VX29re/3bZu3Wo/+clPbPny5XbWWWfNeYyenh677bbb7JJLLrHrr7/e/vu//9vMzJYtW2b/7//9P/u7v/u7J3x+3/3ud+21r32tFQoFO/vss23lypWWy+XMcZwZz92T5c4777TXv/71tmDBArv++uv3+OHJzr/c2UEisX17EATBzN8mJydjDwB2sLNvb2+xu+c1Pj5uZts9Xj/+8Y8f93j1en23v3tX1+N5nvX391u1Wt2t909MTFgURbZp0yb74Ac/OOd5TU5OmpnBr7Xm+o5dseP8nqqHFTvO8/HOZ8eDqx3v25ndbdMnel6u69rg4GDstaGhIXMcZ5fntKd9d8dD2U2bNu3R537729/amWeeaWbbH3gdfPDBVigUzHEcu/baa+2uu+6ydrsd+9zu1tmOdt9V/9nVNT6T7VitVs1xnNhDzt1lfHzcjj/+eFu/fr2ddNJJdtZZZ1m5XDbP8+zOO++0H/7wh1CXr3nNa+zaa6+1T33qU/alL33JPv/5z5vjOHbGGWfYJz/5yRm/6AknnGA33nij/eu//qtdddVVM57Y448/3j7+8Y/PPEAWQgghhBBiX0EP/oQQQgghxD7JW97yFvvUpz5lX/va1+ztb3+7ffOb3zTf9+2CCy6I/WLs8Vi+fLldeeWVFoah/fnPf7af/exn9tnPftYuvPBC6+3ttde//vVmZjPH831/5kb3Dnb1QOiyyy6zTCZjf/zjH+3ggw+G17797W8/kcsFNm/ebC95yUssDEO77rrrbPny5U/6mI9HqVSykZGRXb42PDz8lH3vXOx4CPG5z33O3vGOd+yVYw4PD9uhhx4KfwuCwMbGxnb5oGRXvwzdcV7Pec5z7A9/+MOc37nj/du2bXvcc9odyuWyme35g6ndZcd5Pt757EiFuauHQ08lpVLJwjC0kZGR2MOvbdu2WRRFuzynPf1V7460mzfeeKOFYbjbMeYjH/mItdtt+/Wvf20nn3wyvHbbbbfN/GLvibLj187btm2zFStWwGu7aqtnsh3L5bJFUWRbtmx5Qg+ov/a1r9n69evtX/7lX2JpTD/2sY/ZD3/4w9hnXvayl82kMP7Nb35j11xzjX3ta1+zv/zLv7QHHnhgZtyccsop9pOf/MSazabdfvvtdv3119sXvvAFe/GLX2z33HPPLlOMCiGEEEIIMV+R408IIYQQQuyTHHHEEXbiiSfaH//4R/vzn/9sV1xxhTmOs0ferh24rmvHHXecvf/9759xr1133XUzr+9IncgPVcIw3OWN+9WrV9vhhx8ee+i3ZcsWe/TRR/f4/HamXq/bS1/6Utu8ebN94xvfeMpTFh577LFWr9d3mS7xt7/97R4da0cq0if7Ky+z7b/SMTO79dZbn/SxdvDrX/869rdbb73VfN/fZarTXVEsFu3www+3+++/H1IyPh6lUslWrVpljzzyyMxDl7nOaVcceuihViqV7Pe//71NTEzM+X7P8/aoHXb8Ourmm2+2KIrgtSiK7Oabb4b3PV3saJcbb7wx9tqOv+2NczrooIPs1FNPtQ0bNsx4BR+PnX91tnr1auvr64s99Gs0GnbHHXc86fM69thjzWzX/WRXf3sm2/F5z3uemZn97Gc/e0KfX716tZltf5jHzDVOisWi/eVf/qV95StfsfPPP9+Gh4ft9ttvj70vm83a6aefbp/85CftH//xH63ZbNrPf/7zJ3S+QgghhBBCPFPowZ8QQgghhNhn2eHy+7u/+zu7//777ayzzor96uXxuPfee3f5q5cdf8tkMjN/2/Fw7corr4T3fupTn7I1a9bEjrFixQp75JFH4PitVsve/va3x/xee0IYhvbGN77R7rjjDvvIRz5ir371q5/wsXaXN77xjWZm9oEPfMDCMJz5+wMPPDDnAxCmr6/PzMw2bNjwpM/rec97np1wwgl29dVX23e+853Y62EY2k033bRHx/zMZz5jGzdunCl3Oh37p3/6JzMzO//883f7OO985zut0WjYW9/61l2mGl2zZo2tXbt2pvymN73JOp2OXXLJJfC+n/3sZ7vl9zPbnnLxb//2b61ardrf//3fxx7qVatVq9VqM+W+vj4bHR21Vqu1W8dfvny5nXHGGXbvvffOuDR38JWvfMXuv/9+O/PMMx83LexTxXnnnWdmZh/84AchPWW1Wp1JtbrjPU+Wz3zmM5bNZu0d73jHLvuc2fYHUDtSe5ptjwUTExN27733zvwtCAJ73/ve97i/pN0T3vCGN5jnefapT30KfjU6OTlpH/7wh2Pvfybb8W1ve5t5nmcf+MAHYumOoygCP9+u2BHbb7nlFvj7VVddNZOqeWduvvnmXT7c3lFPO2L8rbfeustxsKu5QAghhBBCiH0BpfoUQgghhBD7LK997WvtXe96l/3mN78xs8ceBO4OP//5z+0f/uEf7KSTTrJDDjnE+vv77dFHH7XrrrvOMpmMXXjhhTPvveCCC+wTn/iEXXbZZXbnnXfagQceaH/4wx/snnvusdNOOy32gOmiiy6yiy66yJ71rGfZq1/9avN9337+859bFEV27LHHPuH0ft/73vfshz/8oQ0ODlq73bbLLrss9p7zzz/fVq5c+YSOvysuuOAC++Y3v2k//vGP7VnPepadc845Nj4+bt/+9rfthS98oV1//fW7nfbwzDPPtO9973v2qle9ys455xzLZDJ27LHH2ktf+tIndG5XX321nXHGGfa6173OLr/8cnv2s59t2WzW1q9fb7feequNjIzs9oMtM7MTTzzRjj32WHvta19r+Xzerr/+envwwQftla98pb3qVa/a7eP87d/+rd122232n//5n/ab3/zGzjrrLFu8eLENDw/bAw88YLfffrtdddVVM+30/ve/36655hr76le/avfee+/ML8v+67/+y1784hfP6jDcmQ996EN222232Te/+U277bbb7JxzzrF0Om2PPvqo/fSnP7Vbbrll5pdcZ555pv3hD3+wc845x0455RRLpVJ26qmn2qmnnvq4x//iF79oJ598sr31rW+166+/3o444gi799577brrrrPBwUH74he/uNt1tLc49dRT7aKLLrLPfe5zdtRRR9mrXvUqi6LIvv/979vGjRvtne9856zXtCccd9xxdv3119u5555rr3vd6+xDH/qQnXrqqdbX12fj4+P2m9/8xu6++2476KCDZj5z0UUX2c9+9jM7+eST7dxzz7VMJmM33nijbdq0yU4//fRd/lJxTzjooIPskksusUsvvdSOOeYYO/fccy2RSNj3v/99O+aYY+zBBx+MfeaZasejjz7aLr/8cnvnO99pRx55pL385S+3FStW2NatW+3mm2+2F7/4xXb55Zc/7uff9KY32cc//nG76KKL7Fe/+pWtWLHC7rrrLrvhhhvsla98pV1zzTXw/ne+8522efNmO/nkk23lypXmOI7dcsst9rvf/c5OPPHEmV9hfvzjH7df/epXduqpp9qqVassk8nYHXfcYTfccIMdcMAB9opXvOIpqQ8hhBBCCCGeKvTgTwghhBBC7LMUi0U799xz7YorrrC+vj57+ctfvtufPfvss23t2rV288032zXXXGO1Ws2WLFlir33ta+3973+/HXHEETPvHRoasl/96lf23ve+1372s59ZIpGwM844w2677Tb78Ic/HHvwd+GFF1oymbTPfe5z9tWvftXK5bK9+MUvto9+9KP2mte85glfb6PRMDOzkZGRmV8zMaeffvpeffDneZ7993//t1166aV29dVX2+WXX24HHnigffKTn7S+vj67/vrrd9sH9ta3vtXWrl1r3/72t+3jH/+4+b5v55133hN+8Ldq1Sr705/+ZJ/61Kfs2muvtSuuuMI8z7NFixbZqaeeuse/iLz88svtu9/9rv3Hf/yHrV+/3hYtWmSXXXaZXXzxxXt0HMdx7Morr7S/+qu/sq9+9av2ox/9yGq1mi1YsMAOPvhg+7d/+zc766yzZt6fz+ftpptusosvvth+8IMf2B133GFHHnmkfec737FqtbrbD/4ymYz9/Oc/t3//93+3b33rW/bVr37VPM+z5cuX29ve9jboF//8z/9sExMT9qMf/ch+/etfWxAEdumll876kOzQQw+1P/zhD/bBD37QfvrTn9qPf/xjGxwctAsuuMAuvfTS3f617d7ms5/9rD3rWc+yL37xi/aVr3zFzMyOPPJI+9CHPvSEUv/Oxl/8xV/Yww8/bF/4whfsxz/+sX3nO9+xqakp6+npsaOPPto++9nP2l//9V/PvP8lL3mJfe9737N//dd/tW9961uWy+XszDPPtB/84Af2oQ99aK+c0yWXXGKLFy+2T3/60/blL3/ZFixYMPNgMpfLxd7/TLbjO97xDjvqqKPsk5/8pP3kJz+ZGRcnnHCCnXvuubN+dunSpXbTTTfZ+9//fvvFL35hvu/bs5/9bPvZz35mGzZsiD34u/jii+2aa66xP/7xj/Y///M/lkwmbeXKlfbxj3/c/u7v/m4m9fDb3/526+npsdtvv91uuukmi6LIli9fbv/4j/9o7373u592b6UQQgghhBBPFifixP5CCCGEEEIIsRt84AMfsI985CP23//933bOOec806fzhDn//PPtP//zP23NmjV79aGpEEIIIYQQQgjxdCPHnxBCCCGEEGJWtmzZEvvbfffdZ5/97GetXC7b6aef/vSflBBCCCGEEEIIIWIo1acQQgghhBBiVt7+9rfb2rVr7XnPe5719vba6tWr7frrr7dut2tf+9rXLJvNPtOnKIQQQgghhBBCCNODPyGEEEIIIcQcvOY1r7EvfelLds0111i1WrVCoWCnnXaavfe977Wzzz77mT49IYQQQgghhBBCTCPHnxBCCCGEEEIIIYQQQgghhBD7AXL8CSGEEEIIIYQQQgghhBBCCLEfoAd/QgghhBBCCCGEEEIIIYQQQuwH6MGfEEIIIYQQQgghhBBCCCGEEPsBevAnhBBCCCGEEEIIIYQQQgghxH6AHvwJIYQQQgghhBBCCCGEEEIIsR+gB39CCCGEEEIIIYQQQgghhBBC7AfowZ8QQgghhBBCCCGEEEIIIYQQ+wF68CeEEEIIIYQQQgghhBBCCCHEfoAe/AkhhBBCCCGEEEIIIYQQQgixH6AHf0IIIYQQQgghhBBCCCGEEELsB+jBnxBCCCGEEEIIIYQQQgghhBD7AXrwJ4QQQgghhBBCCCGEEEIIIcR+gB78CSGEEEIIIYQQQgghhBBCCLEfoAd/QgghhBBCCCGEEEIIIYQQQuwH6MGfEEIIIYQQQgghhBBCCCGEEPsBevAnhBBCCCGEEEIIIYQQQgghxH6AHvwJIYQQQgghhBBCCCGEEEIIsR/wlD34+/znP28rV660TCZjJ5xwgv3ud797qr5KCCH2GMUoIcR8R3FKCDGfUYwSQsx3FKeEEPMZxSghxFOJE0VRtLcP+p3vfMfe/OY325e+9CU74YQT7PLLL7fvfve79uCDD9qCBQtm/WwYhrZ582YrFovmOM7ePjUhxDwliiKbmpqyxYsXm+s+tT9GfjIxykxxSoj/rewrcUoxSoj/vTxdcUprKSHEE0FrKSHEfEdrKSHEfGaPYlT0FPC85z0vuvDCC2fKQRBEixcvjj760Y/O+dkNGzZEZqb/9J/++1/634YNG56KsAQ8mRgVRYpT+k///W//b77HKcUo/af/9N9THae0ltJ/+k//PZn/tJbSf/pP/833/7SW0n/6T//N5/92J0YlbC/T6XTsj3/8o1188cUzf3Nd18466yy79dZbY+9vt9vWbrdnytH0DxCXDg3OPLXsNDvwGc+NoBx5+HQz8vH1hIVQTibwdfPx9ZaP/1LC8Twou/QPKXqLWI195RyUC1l83Ung+bqJPJRHxqagvHHDBJRDw/Mv5jJ4/AivJ8Ciuak0lHMF/P5MNgvlZDIF5XK5F19P4/fnc3i8dAa/L5PF92fp9cDD8uTYNihXJytQdhJYv6VCEcqHH/fsWc9neO06KI9MjOL55gpQXjA4COXlBx6A50P/0mb96jVQHh/D4zsevj+fxfpbftAhUE6lklCe2DYC5dUPPYTHp/dHfgDlThc7SL2B/a/VaEO5UZvE15t1KB9/2ulQXrp0OZTbnRaUb/3VjWZm1u127Lvf/44Vi9h+e5s9jVFmjx+n3n7uGZZObe9/zWYDP1NrQpmGpUWUaTnkzMvUr9tdbAfHwfen0zhO+d97efyH0MdigGU+HYfiLMcxv4ufj0KMU91OF8ttvJ5uF/ulOfyvVvAC+Lfqrotx2qH3u3S8Dn1/u4XtFVJ7GR0/mvNf1OEJ8uFi1xfR9VH9+V2sP25g/lc+HIe4fSOuwNiP//nzdD00bzr0/X5A7RlhOU3zYqGE80KxgPNoJo1x23Xw+106f4fqn+dFvv7Ax/oNaOIMpt/f9UO75rZ18y5OPV6MOuV5x1tiOpa0mtjHCzms4y7VQcLhtRDOJUmKAQ51oVwa1xKNDsXINq7teK0V66MUs6odnHt8w/N3HDzfTEQx0sE+6NLizkvSEtmhtSDHIOpjXR5SNAZcL0WvYzEM6Xro+nkOWbFqFZRf/vo34/lQEHI45lCU8mLtQWMiwPbr+jSHGH7epxgU0pwRUHvH1ypYH502riU6FMNbNOe0Wtj/QjrfLh0/5MUzxeSArsen9uIY2aU5vNHC8/fbeLzJahXK7RauzXI0fls7jX/f9+03v//9Uxqn9uZa6g1/eaKlpsdbrUFxKI3jeHxkHMor+7EfNzo4Lo45aiUer4795I71FSinsjguS2XcA9Rr2C/b1G48LjI5nLs4rITUL+rbNkPZy/bg8WncZ4plKE+O4x5jeBz7/cIhfH+a5tYkxRmXzneyjtc/Pl6B8oIFfXj8DO2BadzUqD3CBF5vbwnPL5XB9qlXhqFcLOEeldfeHBc4zjWq2L/ahv1vcqIC5RLtmT2qz6CK51fowT1eso9+zRHi+eTK/fhyh+IczeuVEew/LRc/n8fTs04X49DICM6rhQKuzTJJOr8CHrBWw/6WzpbwfOrbj9/1A/vxrX/eZ9ZSF7z6hZZKbu8L3OcCmku2rMX7NqkUjoFFRxwOZR4zf/79nVAu91EMoLnOobVLSNuLiWGMCdkS1rljeH5BhAcY6scx16X9buBgH/C7GAOXLMTra7Tw9QfWYH2efPpJUC4VMAbf8+e7oDxM7eHR2rSYwDEwOVWBcj/9oiqiudnp4vsTAc7F9QBjRIL6/IIBvG8UhRRDR/D4+V78fMD76TbtV1ys/zbdlynmaO1LMS+Vx7WET2urZAI/v21kjM4PitaYwrVLtoAxmYaDeYb10e1gfw4cfD2b4vsDdN+X9sdRB88ng81llTr2x0T+sf7Q9QP7wY137TtrqZeePrOW2krjPqT7DiXaZ/u0xuQ9VyaJn8/mcVxu3Ib9bny8BuWXveR4KLcmMU5OrF8N5TUT2O9a1O8XLcK4WOrDcR7RfYltW3Fu3zaM90+TdH0R7VFKfRjHRodxHAwOYT8PKRDX61i/Ht1X6tB9Fl47LVq+Eo9Xq0C5Mo712WriuFmxCPtw/wCeb6uJ5xeGWP95Wgvce99aKB/6rGOhbB1c227ejPVVKGL/C5vYXzZtxLju0z2JHK19jeIqP++o0f33DK31E7TY9TwMbNk8rvXWr90K5YDWignqTwM9ePz+Hjw/h+4RVOv4/SHtiXcsO/wgsF/c+ehuxai9/uBvdHTUgiCwoaEh+PvQ0JA98MADsfd/9KMftQ9+8IOxv7uuO3Mj06WG5l8x8sCOXLohSDcwPZ4vXL5BTDe75vh+j/6QoC9IJuiGdOzBH77On+fzYfj7+eYbwzeIEx5/P5XpAUQymZy1nEqlZi3zJpPL/OAvzcej7+MHf/x9WdqUpTO0iaHyXOeXyeDx+OYLP5CJfT89KHVphcLnw8fn62tlMVDy+Tr0/sjDicyh8dL1cQEU0oP0LtV/4NPNVTr/LJ2/S/2br+epTlGwpzHK7PHjVDqVsPT0g9XQx34Y0eY4/uAPX5/rwV/IN41pHKfobtLcD/6oGLuLjUXeKMTiGC28+cEVP3ixAI/HN72f6gd/EX1/SP0y4PrieWavP/jj48caiD7PpzfHgz9q0D1+8Eevx04nNjFTf6BybJ6cY95M0XiKrQvmfPA3+4PVgFqI258fSsy3OPV4MSqRSMzM4Ty3JynGRLHNM9WxR2uBOR78xdYKId1corsF7hwP/jiEJGgM84Mrhx7UJSJv1td5bvIST+7BX2xIRbPXZ/zBH/W5Of7xCP+joHwebzB39vDBH/eXkB/80dzPD455i+EH9GBsjgd/vMnx+IY9r93p22P/WIOuL6QHfRxD537wh8f3AppDXDx/hwaIT8d3QrweXnsHvBZPcP3SP7awpzZO7c21VCqZmFnD8D/OTCb5unluwGv0aXLK0oPDBLU7r524nKZx1U3yXAJFC2hNHT8+vZ9velJc9Wju4wd/fHyun9hcm+T6o8/zPxalMMUPLvn4c9UnL334fMM9bg+PXqd5LTaMeS1C/0CB1+40byRj8yjvmam9qD1Tsf7L58vXg9cbGv0DCoq7fPzAnb3/8fXF1wmzl+fqf/x6wPdE9pG1VCqZnJlju3xNzlzrW64TbAT+h5tz1aHv85ii9Tptr+a8L2W8tpljf+lTH6N/RMU3MHlM8tzHfS5D9ZGhf7A9V5/jB39zzyE0BgO+Hhoj/A9JndnrNxaTaIMxV0zgB38Or0VojIfdOWIw90cKCi498Imt5Wnt4fLafI69Bs8h1F3NQt6/48upBG9A53jwF3L94uu8l+EYbravraW2X2BsbuV+Ose45jUv1/vca4/ZxzU3RDo5+1zj+7y2mr0f8/MB7occF/l8I/oX4HOurbi+w9mPz//oiH/YE49TeH0dqn/ut9yNub44LvF9zDCcfe3F18Ov850vrn/uP/z9XL/m8B6J+ysWeQ8fa29e27jcPvh9c7U/70Ln6h/cHrF7KAm+j4dwRNqdGPXUJlXfDS6++GKrVqsz/23YsOGZPiUhhAAUp4QQ8xnFKCHEfEdxSggxn1GMEkLMdxSnhBB7yl7/xd/AwIB5nmfDw/jzzOHhYVu4cGHs/el0OvYLJTOzsN2Z+Sce9I+sLUt5Klz+l0Vl/BcFQ4P4U9Z+ShuS9PAR68QYpjCoVukn72k8oWwBv8/jXwbQE96IHum2fEzZkMryL1HoX97QT/IzlFq0yT9ZpyfyOfqpqk8/naZ/UGH1Gv6ibMP6tVDu0L/C5vZ0+V/S4OEtGfulBb6/G0ujQ99HqU75lyP33H4zng+lK+t26Bdt/C/H6F8AZJJ4fQP9A1DmnwJXxvCn11Mt/Cl8gv4FB6f63LoFf0rMqVlrE/jT9c0btkA5SvC/6sfrdelf/fO/GAjoX7zwvzhI0k+pPUpf1qV/hR50KD1WZ/vrPqd6fIrY0xhl9vhxqtvpmDtdI1xvSfqXTZyag3/NwD9F5uPNlXYtpHbif/fBv57hX0TxryP4X9bwz1E4tU3A7TxHGjV+f+yHynS+8V+0UXmOX7S1OxgXWw2Mu7FUo/x9fIYUx/lf3PIVxX4ByfVL8xj/i8f4r4FiP//B43N/ovblfyXO58fHi/36Cl+O/TqmG8z+65yQ/kUp/7N8LzYe8O38i854/ya4v1B3j/0i1OHUtdvPj9cbTxV7ay1VrVRnYkexB9OicB9KxNK1UpvwT87oXy27CZzL6i1M88G/6ErRr8NjYzDiPoQv52muaVIniShmLllyIJSPPOJQKK86EF/3KKbEzm+ObAncCflfbYccA2ku5rUOjwluv8lJTNPS5vTLsX/rR+cT8i/6MGaObsO06Hfd9wsoJ6j9n/fc1+DrtPbi1LH8q/SQfvnF2RL8IqYZ8qj+/Nhax/YIjumzR/j4v9bkXxbx2j/2SyRKU9OhVLjfv/obUB6dwLQ5//fvLpr5381mw256nBRRe4u9uZayyJ+p0MVLMC1a0fD469ZgvfiLlkI5k8J6vuthrKfaFO7xEinsVzaF6ezblLUi6eAaPqCekSLdQKmfUjlGGBdHH3wQysUs7ilaAa5VSj14vhtGcY/Rm8a4sbBMaeNbGBeaU1g/DsWBnkE8/2QCPz9AaQo5u0OxB9PWJSLa04aU9p32OAnaA3fb2H5bxvDzayhd2JFHHgzlWByOsD/VaA/dpjjcojTxSYoTvXn6VUQvzrvloUVQnqzOHrdzFOemqH92JrEcUE7nQi+2fy7LWXvw/Mdz+PkmpXFMu3hPJEdpI/MFbO8xig876p/3lk8Ve2st5TgJc6d/2RZRnfClrFiBY2LLZhyjWx/GPjq6Fff5Pb2cgg3rnNOEd9uYyjCVopiVovU/HS9K4X2LFClORqs4JqZGMUUeK26K9H0Prcf7FB1KAVcmZc5t0wqQHXgU82oepmwL0rwfw3KzgfexEl283vEJSs3q0i+TmjgmE9l+KuP1R7RP2LAFz7fdwBiWT2EHGtuKr+f6sZ8mKZVom9LrRh7W1wSlY25RKs+eQTx+nVN1pjGmTdboviGlRi1SDAxIIeOUMSZWJqg/9OPrKZ4jKJWpVTEG1inFnkfZF8zB+uFf5jR3irFdvlfzFLBX11IW2Y47dQccgqqgLevXQ3mS0lqnSZfAc6VRKspGA9t9jOZi/iXz5gfvhDJnbK12aE9A90MLGRyH2TTGjSxnA6A9QZd0Ew79UrZcwu9bwGne23j93gKc+/i+HKcVdyjLS+RR6kYHz69BzfvQKK7pXXp+YSWMu4uKK/B1ut7Nm7C/8S/8Eg6lNK7yfR5s79/+8vdQzhUpbT6tDVwHy8lSGd/fh2vf0RFUfw30YP33DeLnm7RW6+SxvuuTWN8eK5P4F3t0v7unD7+vQqltU3RPoslrb+pPaXq+EHF/oedFzo77z/GcYo/LXv/FXyqVsuc85zl2ww03zPwtDEO74YYb7PnPf/7e/johhNgjFKOEEPMdxSkhxHxGMUoIMd9RnBJCzGcUo4QQTwd7/Rd/Zmbvec977LzzzrPnPve59rznPc8uv/xyq9frdsEFFzwVXyeEEHuEYpQQYr6jOCWEmM8oRgkh5juKU0KI+YxilBDiqeYpefD32te+1kZGRuySSy6xrVu32nHHHWc//elPY9JSIYR4JlCMEkLMdxSnhBDzGcUoIcR8R3FKCDGfUYwSQjzVPCUP/szM3vGOd9g73vGOJ/z5ROiYG23PRNpDTr4VB2IQTJJWpVTAXNtZcvKlPM7XTPn0c/h9UyOP4rkl6PhJcvyR5yQin0GXyrHUrC7+YcFSzHWdLWI5EWAzdpuYmzvm7GtgrnaXcsiaT/4D8lcEIX6+2Is5dh3KOey5s+fMzZE/o13D3N3VGuagHlqIufPTydm9N+s3Ye58SqlrDqumXD4e9hdKrW+PPIr9K+2xpwc/36bjmU/9M4Htcfddf4RyknJWJ9khiUc3l3LdR5SjO0HnmyCHppfBcoqdftQ/bv/FdVDOFzGHcy6H/aVe3e4k7JLX4qnmycYoMzO/2zbPtrenQ/0mIIcZ5/5m1yL7obwUdjR+f6uNuZ475KpMUj9i555LXhKP+gG/zr4inxxxXO6Sn8in9g0pMLHvKObYIwcbfyAkp0LAzjl2mfLrlMff4fogP1NM6cfeGB7nNE68BE+/FDci7h/UnhRX2LHHjgn2VYWcu3+OMrdPxG83dmxQbnKHctn7szsfY+WQXLE075rR9fL50Lv5Dw4LuuiC3ekPuHs9QfrsPNk45UThjMfVpZiU4BhDbeySH5Z9t0FAaxt27NEBfe5jXcy/n6C5LIyJHXnuw/PvG0AXxvEvOAnKy5athHKKfBCxMUJ9MJ2JiSHxfFK78m3sfHzyBdCY7lKM7nTIw0PHi0Ie8xSjaa53KCaMj6Gnp9lET8q6LXdB+e4Hf47HczHGp6i/9BePgvKqgw6HsscxwhAeaxxj2MHIR6DWink+efEdUVCYy+kXDyJUjB2A3zC799WjCmg0cc53abw4bnKX//upZm+spRw3mvEyJbvoQ6oHWA+HH43OtvoY7hGcNjrrlg5iP3PJxzRFvquxOq0VyCeVori5iDzy2Ty2y+at6MbsRHg+wxPoUVm1GPc4m7fi+RyzGJ172Tp6RSyB/qqI/MpTY7hH7B8oQ3nRAUdAOUUena2P/BnKOdojJNlHTe1hhnGjTmvB5hi+33WwvrMF/L5yD9b/I2NYn9vIgZigeau8AJ2KQSxyYNxMkVcpQU7HgCdSXhvT9WbIK5OitdLmRx6AciPAsd2p45651sLjr8DLs2wR+9e6ezDOB10cb6UyHqCYx3sQVepPvWXc8w0OobOz0dh+vp19bM83XqlaMrl9LKSSNMZouZ/J4Jg56GjydtK+n+f+NrmWHFq7efyF5MuttWntRXNPbx/GwDr5Yh3yahZz+PmBpTjmwlQZyhMV7BO5HNZX1qPzozHZM4RjanNnC5QDvnFDa1Per/l52m9U8ONcnU12oNN9sc4YuqGWHogusID2k90Wzt1JiiG8lnIphrc75Iqq45zXpbWqS/fdxivk2KPVTI4ciCmK4Z02udWyeH4hueFSuTKUowDn2MYUxuRSGd1qk+O4NmXPaZLcb8US9mf2b09VKlDukJ+82qD2bT0WU/lewVPJ3lhLbd28cea+4KlHHwuvjQyjIy2sYD13yOMehrSn4/sWtAdaOFSGcoKOlyS/8foN2A8qAbZjOof9uN7AcTRJtwFSPeR0IzenYxgnyz3ktef7am28vlKJ1l55HAebN+DzAhzVZt00Ht+ydH+ZqjeguM17IDdJex7aQyRoH+BQv/fIKZek949sxfY55fkHQTm7HveMf34A+xN/v5vg+6TYXg/ftwHKJXIEptJY3+yKNfI1h01ynwb4erkPXZ/tAOujTXF7qopxLJ3G6+vrxfPj+0p8n26qjq+3M9TeHfz+XInWYpnt3799LYXe4Mfjab6FJYQQQgghhBBCCCGEEEIIIYR4KtCDPyGEEEIIIYQQQgghhBBCCCH2A/TgTwghhBBCCCGEEEIIIYQQQoj9gKfM8fdkyWfdGb/FsmWYT76Ux5yq6RRehsfuqpiGg3LkUrnbxpywyTzmmM0UMbd0FGEuac4HzVoRVj9hZmyzOvkBEkXMGeuyZ6eKOW4d8uj4lBuevTIheWCS5E7q+JgTd9EK9Oj4bf48nR+5xwJ2KZHHJEG5wyMHa6iPcvKmKLd9l3wWuYMxJ/PaDRUoN+rYIKVezCXO/SdBHhv2W7Azj70vPnmNJsbQzxCSy6rVwutpsruLPDUu1UfIbrmYTI366yTmeo9ijkD8NPs8PAdzNEfUH9kDlc1v749B+PTlUd9bBJ2O+dMVkiZXJbd7xP0+Jh3D6/fIhemRz4eTh7NTzmd9Bn09u0iDgJ1rFJkojvE4Y98XX6DrkPeFlX0xZx05EimuchxkP5PfIV8WnS/rxmLJ1dnZyFI741zq5CSk97OPy+9iAzoOOT1onARdzr0/u9+Lv4/h/sI+KnaBRpRrPqAK7NDxWGflkifHo/HQodz67TbOQ8kUOwPpfNnBiC/H6pP7S3y8skPF2fUXz3NSyeSMPzSiMdXxsZZStHZiTwn7h5mcg/nvi1mMMdsoP35ErqsOjQke4y616tKVK6B82tlnQzlBfYZjEOfbZ8+qS4u3TAbXJhwTeK7zkjQXkzemNUeQZgce91H2RafTuDblybrL3k6aUtZtQXfXfat/COWQxlgqhWsZ9lGk8+wxokmLL4/GqB/OPkZ5MR2LYTzlcsyk/uxQ/+IYEXf8EQ7HTPI7UHmO4WQhrd075PhLpnAt7O3UX72Yz3B+407/n5nZ2Dh5NArkT2qid6Q8QHvELnpHcgnsJ4k+XLP3JnGPMD6Ox2932BNCPqUE+YxbWB7fNozfR1704561HMo33vgIlI981lIo18nfVSph/TRqeL68WEwF6HsaGya3ZQ59Wlu3ov8r45LPKYn9tJ/2aMkA+23vAMZRHghtdxGeD60dirQnvvfuh6GcT2JgqQ5jf8j0o+OulMS42Ts0BOWRYbz+bpPWkvUKlH0X+2OmF9svIM9OvYbHy+axfzgO7smcNu4ZF5ZwrTRVwvrxKNCPbUMPT98C7P+VKh7f8/H7wy4ef9s29Lm1yae9ZAF5eaa9O50ue7DnN51WzaJpN183wjZOpLmMcyM707tTWKcj41jnhR70JCbpvoJDHspanfZHNBkO9ZehHDQxBpTy5B+mud2M9rc57DMTwxgzpmq0tvGwrbM0V6d5rRhWoOzb7I762Pqd7ls4LsWQxf1QDkfJscgxnvzN7HaqTaBzr93CGNzbg2PaaK3bcPD4kY8xq8j7pwy2R2UU23NgAOeEcj86Gac2rYey38UYmMliDE/w/Qj6/o5L9xnJg1sdx/oYoBjrdPB6XbrP66ToPhPtT9tU5vo16j9eAs8/W8T+kCk9drzt/s3Ntq9QmazNtNePvvc9eC1Pc4tL7ZSi+yDsec8UqB9VsN34/rmTwn5TrVGc6sG1Ty/taVJ0P7rbwXbduo3WJuRWbXRpj0lruVwO+31I/WLjFpzbFkVUX7TWSPRjv/VD9EPH7oORq9RN4rjr0v3iNN1nCvn5At1HrLVwXmlXae1C42ZoiBxy/ThOb/0TxflJ8o7T+XWoveoT+P50hGv9RYPYP6cmsf4TdB+0S/cp+4rkRR/F9m41sD5Dcrc2yTvskt+6kMf24bVcQHv+iJ7wdNq85zR6HeuH9xp+C+N8M9x+Pp3YvYTHZ9/aGQohhBBCCCGEEEIIIYQQQgghdoke/AkhhBBCCCGEEEIIIYQQQgixH6AHf0IIIYQQQgghhBBCCCGEEELsB8xbx1//QHomR3GxSE6/DDvF8LMJj3Kocipw8mwElJPYKOdtlvwJ7EZqNihnLj1O9VLkOKOcrTXyzHCm1piriEwjThrf0a2z9wTrq9XGnLsFylXeamLu+S55RtwGvj9PbjO/S7noKWd0TxFzmdfIR+FRTudsBnPq+j77JfD6OMczu6nYfRJSTul6DXPouuQhWHX4MignY6nF8XjsDUp76DtIDFL9d/D7Gtswh7ZHDr8O5ZLn/m3U3xLkJOQcyQ6936P6LfaieyDNOcIpV//wuq1Qrncwx3Krur192H+zL9Bp182CaX8WOQfYIRdE7GjD17vUjm2WgZLvih1v7KsKKeezQ/2enYEBfb7rk+PBwdcDigsxZxy70JzZHRXsbHDoeAFdT0gONtYlRfx9nAudGojrx+Fc6uTLCklSyHE19q9q2AdGL7NDgc/PZ4ciXTCPH5/GNbs92QHCfqvYCbK/KuaknL292VcVsZOSnYwtjFOdNMYZntfZ7Rtr8FjcZwcI596n8nR7hPuY48+1aMZjzN5HvpJmG8e8R3MXeywz5GYqZ8l3QG2WnMJ8/rUO9tEErRV4LutdgL6BU170Iiin0uQ8Y98zeU0dep3HhEdeEe5DCfbK0Bji43FMctmDSjGAp8RWC+uP24f9zHPNCZs2PQDlex/6MZSdJJ1PiN/PY4Gddtsq90O5rx9dV16I589rRdbiMhwD2GfM19+l/l2rkhdnEfqr495YjPEjW9D7smARusnmcgS6PCfE1hC0V6HrGeorQ9nZqcKcuSpvnpHOpCw97UupUT2nPLyWnkH0sjgpcgBO4uv1FnpWMotxDc9LrSL5mLY9WoFygbwf69ah36ng4Jp9xSDtkVJ4fhVyny455nA83wLtocplKI9t3gTlgHxQyQR+fvlCdoHiOKgn0QtTLNBcTHF8IblWFy/BOL1+zYNQfvCRbVB2m3i++Ty29wtOPBbfT+NqqB/rl+8RJLN4vc0G+bsobqfSuEfzyVlXzpIniaYJnzwug0tWQXliFL1EK44+BMoP3/kHKLe7+AW+g/PYJO2hi4vQv5YvsS+O/N20LugjvxZpa6zt4/n0kLNy4xps32SA/afYt/38vO7ue2nmAytWLLJ0antljIyie6gT4Nyy8IADoHzvH/4E5UMOxjHT14N9ot7ANu1G2CcjqrtOg/yv5CNOpDFGOklsE4u5iKhPldBhOFXB6w+pTxZ6yRmXw+8b3Yoe1ROfg2Ng3RqMaTEhbmx+4/U67S9pf9JJ432ufBdjWi5DY4yubwF5NKeqeD0rV+AcE3SxfaYmsP4myfno0mohRYvNvt4ylD1y+rnkVEyXyGXWxbVOPkceTnJXRXTfrdlCT2i3Tfu7OtanT/fRtmzGObmnQPeRaG1X7GBM8Xqxfh0P1/5b12P/8ch/nS2i5zWYxDkvsdP46Oxjv5HJFcszLrRWE8cxr5GTvEdwcO0VdLDft0fId0vDkkclO/oaCWynTg37PW2BYvcxWpPYDwb6ce5pTuLxKnR/vt2i+/UUR4s9GOd6F+JaZrKOa6WQ7ou1enGce9Hsa3rXwfpxKc4l6H5vRE5Bl+6r8GLESeK4W7BoMZRL5M8Ou3h+9REcp5Vxuj9O80g6h+fbrJFfmRx5qQTtmWmxsYXWhscduQTKE5M0b0V0/AL2jwzt4cuD6DSc2FaBMluIk2mKi3X8/oDm0WyujAdwyTFYwzjqk3vXIXdwkx4wbdmwfTz4Ad8BfXz2rWgmhBBCCCGEEEIIIYQQQgghhNglevAnhBBCCCGEEEIIIYQQQgghxH6AHvwJIYQQQgghhBBCCCGEEEIIsR8wbx1/5XLGktM5ij3Koet3MGex62GO3JjsKUk5dl1yIVHO1ybl5w841zVlMfZDcimF5AWh06lTjuUOZ5GlJMnsymLH31Qdc1NnKVf3JPkYjI7nUs7gDnlg2DPTIRdTZNgeUYTH8zvkzaEcxqUezL3NucWLPb1Q3rABc+L292EO3Cz5CbqU47lRxxzWvsNJqvH72WtUr2F9lslZyGopn+qzSx2UPUMO+Uxc8it02uxeI+8QPc+PKGeyQ2KcJOVYTufxejxKut1bRp9GnnwYHTp/I0dgMsT+UShtH79hGNpoFfNBz3eCoGvBtGvO6VK/omrgHPcBuUQ5R3PYpdzRNBDZ8edSP4h8dn3i+XS72I8idrg5s/dTdvpx4PKoH/LnI4fjzOzX45FLMmBJonH9cCC1Wcsc17nM44x9XaFx/bBPbfbjB+SYiLUnvT/uROT6g2KsvtixyHGL/WTs1OD2NHJGcq7/MKBc7tx/yJnok/+tSfMc978k9Q/Ojc/9Ma4w5Pah8wm3j+8g1u/nOZHNXCzHEI5ZRl5D7tMcRNoU0yo1jCmpFh6/S37XBHlT2HmWIk/NX7zkxVD2yAnHHlLHIR8CvZ/ndvZWhuS75THHMZOdewHFYHa+xeZ+I+bwsnrsfeXVJnlJm02cX++871ood43m3w7WVxBh+7lUv9R9rN1CH4dPPoq0gz4G9qpGFMNdilk8iuuTeP7rHn0Iyhs3rIPyylWHQnlwMfovOAROjOHa86YbfgHlLtV3RF5W0tJagtxrLzn3XCjzHJAmP3Opp4wHdHc6njNvt3e7JGGRJabHU88AOspYwlcnsUy7gu3SIi9IMjkI5eDhR6CczuMeY9u2YShnc+hI87Lk24pwrqrTHsnI0Vbow++rjq2H8qIePF4mhT4m9iuvJR9Wl8bRkkXoM5oid+lIgHu4VkDOwl70wAz1YH0OkNPvoW14PZs76C3pFnFgpUvoYVnoYly45bd34/spDqZcXBv05PF4vKfNkQe+RWuL2iR6jIKA/E8p3ANFtElr0vX29eL7U4Yeos2btkC5OolrwWwW6yNJa/fyEI6X3iH01kzR9ZTJDRrSvBe0Z7+nEdAetNvG800mae9CTkAnsT2OORHH8/lNZapuqen7JZUJbONjV2Abb3voXiiPjOH7p2rovTx4FcaERYM45tauwxg3kMa6qxgeP0F9pFLDuSOTJSdfBs+/6+IYmZzCubVJrqyI5vKJOn7/YroPl6KYunUYXVIhT74xHzLf12BnPTnEY2stPL/sEjx/t4UxPkUb+lKW+jSvVbp4Pd0O7VepvtN0XyTr4vl7hmvpegVjdLm/H8rsW65M4fUW8/h9qTS53Og+VyaP/dEjD2/Sw/fnezEGhbQWDnzsrx7dju5QDEoXsT2yCbovTPWZovuAzSYeb3Irts9AH31+p/p3Yqav+U3g+zP72Sa3exrjQJ7urzp0v7BeRRdlokj9lvZwfpfuB1O/nZjAuW9wCfmWaxUoWx3nxqV9fN+FHIDUVK2APPMFvN4U3f/2aZhyP4/oeUPsPodbgTI7/BxytIXct+h+a4LiMDsDjc6fT6jjt2ctd2ktxT09EeH1J+j5SS6H19dD43QTOeyWlbC/DJbwfO5Zje8for3AxAS6VPsG0avebODxJqboHgTVfz85J126Hx6xOy/2fInm4a24d1i0EtfeBVrrtmqPQtkjR6JXwrjeaeP1ZPPb698Pdj9G6Rd/QgghhBBCCCGEEEIIIYQQQuwH6MGfEEIIIYQQQgghhBBCCCGEEPsBevAnhBBCCCGEEEIIIYQQQgghxH7AvJVA5LIJS03nTo3Y50A5XJuUg9bh3NhpzOEaRpjvP0E5e+stctKlMGft6BjmhuYUsEYupQTlwO2ylibm1iJPCKX0Ddl5SG8okeMvm0WHXmW8AmXOhd2dxPoMyVPSIq9Pmxx+HXZrkcssl8Ucy0EVj1dr4vdlKOdug3J1J0YxZ3SzQbnXqX56erA/FMhJl3CxvWpVPP5EBXOTJ8mXEZDfwycfQsDV08X3N6h/B+wOo/aPKHd9wiMfA/dHytE8uAD9IRwUEkms/0KBnH7NJpRb5AJIprF9U5Qbv69/ew5kPwjs0XXoBJrvRNP/Z2YWBDhuWpSjvktxjFMyR+TkiXk3yHMT80+x424OP1VIcSeRplzkHgceOt+IfVrkcCNHA/uaQnbe0T9DIa1IzGfl0gdijkAOzP6e5elnh1zEzr2AXaez45J3JwzYo8LJ5dkfRvMS5YL3qMJccqV6PvtUWHJI/YHiLvvLYhJB7l8R93d2/FHuero+7r+tBjn+qP6DLLoHPA+/3yMnhOtyfVB7U3/d8XVdlvbOcyInmnHRubxW4bkvJpnDNuO5JKQ+40a0liKfbkBOn9iYp+MdeewxUM6Qn8LjPsoxghxqMUcfx0x2DCZp7UhBsE5O2lSO/bjkg/BxzMecijzGyBkXdlkSxzGavDY0hu64+3+gXO9sxq9j7yi5y4wce+yM9Oj1iRqulS3itQu+nIj5jqm/8GKYjlfoQW/QYUc/B8oNWqvkCrg2ZqceOyMntqF/4pVvfAO9n86X2mNyogLlLRvQhZbP4/k06+hLKZfQZ10sl6Gc2GlO4PlhvjNR61gqub2+e8s4blIZ9At1ptA7k6R2qtIeotWmcdOmcV5HX1KJur1Pe0rSYJvr4R/aCfRyZMrYLzPktzr82cdBeXLLGiiPb0HPTZjCz6eo31RpT7RpHPut9WE/bQfsucFxVo/weB0P9ygbH/ktvs6eexZeU9/s0vGGaU9YpOvNZLF/uOwKpXmtt8R7fFoLkBu1j/ZE1QmM89vIJTq4EPe0GXL+3frr26DMXvXJSRznMR83xfGBIl4fa34mRrC/FAvolQloXtq8Hh2AlXEcD/0D+PkmaebztCcs0/Hz5BPbunWjmZl193BN/kwzNlyx5LQTqZgn326hDOUSeTwT67FOA5JJrVmPMa2HY0wL179eCsc8L0yjDM4VLXIS+pPYR0fJSzkwiGMuV8JyTx+e4Ib1W/H7yLM67mJQLdDaZeskzZ0p3lHNPre6Kb4PQv5odpjT0SdpDinTGM3kyUk4gq6pBUPoB27QnOIlcIwkyS3Vm8T69Lu4VknS/YGDn30SlNfd92col3qx/Z1JjOEBrXXaHexfFbrP1euWoRySQzIZc5PRZoPuLzS6+P4WOd1LZXR7rVn/AJTzeeofKbw+Xv802zg+Jsn9lUji+fWXH2uPILYvmt8cesQRlkpt7//btmyE16Y24zgda6GbMaQ9FPtlPdrDdGkctmmN7STp/m8J42KH7ue6KZxrJrdtgPKO69oB3x91aY1cpn6YTFE7D+JabfMm9CVnaO7sIb9zoQfLj0zingd7mVlIkYfjlEd7IN5jxT3ntCenvpoh517RwXmjTfenJ8YwrgUdfH3JEH4+lcbzy+fwBBYegu3d14dx9O6Hcd6bwKWQ9S7E43daOI7Z+TcyjnEglcH2K9Ae3ae4EPLal9Z6PsWpRBaPF3S5vfB4SdrTNlxsH/68T/7pTB7fn+jdXp97spbat3aGQgghhBBCCCGEEEIIIYQQQohdogd/QgghhBBCCCGEEEIIIYQQQuwH6MGfEEIIIYQQQgghhBBCCCGEEPsB89bxF1nCoulctm4Sc6ImXcw5HFDu6xblQq+SLyCVwxy1uQTm/PUylJWXch5P1cnJNoVlh3Le9hbxfM2jnLKUw5ddVWaYUzYkp2G+gOcfkDtqaOEQlJt1yvVNLjLXxfpsh5izuUkOOovw/aT5MbZfbRrFnLzdFl4Pe2JSlLs9TzluSyV0O+WpvrMenR+5zYxyq3fofCrkpBsex5zEG4fRj5DlHNQt7H+cY5pFN+xBctiVxS4z8kl0yQuUL5H/gnLZZ1J4/ZkM5cYPKQc45YLv7cP6LZdwfPWSd6dBOZWDHQnU2Q25D+B6rrnTzoeAnGPdDtZbq0O5uNlxxqIOEn2wAs6nXOsBlT2SGiTZaRZz6nGOaPItxZR/2F6cBj8iP1REca7TxfpJ0DhNUb9kf5LDfilKxB+Q4y92flShEVUId8eIHHAOO/ao/YIutU/Micg+K8pVT+3DDrwkOTIcipuhT/0nweOLnRh4/hwmHfZvkSs05qKlb/PJBeuSUyER83lx+2C5S/NWhxwlSXLzejTPOzHHH/nf6PrDaU8R+3bmO55F5k2PVYeddtTJO+Q/TlFIimhuMvITsLspnaGY18S5NZvANhzowzY74NBDoMxjhmOaS3M5x0COQTyGU7SW5BgZkZcnnaO5NcFjiPL90xjjssM+X+rzHq2FQ4px7PTrdHGufmT9rVBuk3c2Qe2RIHdZl2K2m8D2dwIsRyE7C7GYoPpnx57D7cf1Q/2XHY9RSHMy+SzYqRfR2ihBxzv06GPx+yhm+bxW62J/37wa3W1HnXA8fh/1nw55dJw0u7Pw/Hc+n5iTdZ6TdBxLTo/v8QruOSan0JfkkJOth/YEy1ctgvK6LbhmH5vC42+jeh5ciGvWcg86Bv0Gej/S5A2hbmf1SZyb2FGXobiW6cE9mz+C59dq4Pkv7MXzWzGAe6L7Wnj9dR73LntJ2PVJbk8fL5C7Whjw2jCc9fUOx2Va+6VpbdVu47jKsfeEfL5RiHHNYT8Y1X8qhfXp0Z4tSc7HRhPFNIuXoO+r1cT2Z79wL/ngJsYrUGbPDkv9Ip/W0nSPY8WRx0F5wZLlUK5W0P06VcX+VeoZxK9v4j0EjsuVFtZfu47t2zMwYGZmna5vZg/avkLUbc6sgfi+zyMPYhuUh7CPlMvYxkkH63h0C3o4p4roJjp0KXonJ8axT3VKS6HcbWOf5O1lq0ZOv0UYMyIa1PkecsbRmMrSfYNsHcdgm/a/7ApzvQyVaUPBznaH3Vi0VmDZFd2nCmk/V6e1T7ZLFUZz+eBijNGJJJ7PxFb8/hWHroCyM0luK3Ywkovr4GOPg3JPH7rJyovwfDY9jE68LjkgQxqzLR/Pnx19QRvn4HIvOvhia19a206Q39gxug9G+9PRLeg/LvZh/6+TS84lV10uh3NC3yCOvyXLylD2qT/DFMg++3lOFAUWTd9XTKZwXE1MYVzo7cNx7VM/6VCsbzVwT5HMYdzgXxNlc9jOHYpLE+RQS9O+vemSE9DD1/k+TpbGvUf9MOHhuPcoDhQKeD1T1QqUF/Tj2mTbJnQmuj0Ux1xaKxntkSiusZuStiQzz0UeK+PagJ1yAd3X82kPNFmpQLlFrxeztEej5w9GfuWQ9qBuGePSnWvx/n/HxTg3tHQAyhPjw1AeXIxr+8oIzptBA/tXrh/jRrOJ/XcLOS/T5Ozj+uZYENJ9x1QB1z7joxjHynQ/PNuL59eq4XiboL1D18f6L/VuHx+RQ5uOWdAv/oQQQgghhBBCCCGEEEIIIYTYD9CDPyGEEEIIIYQQQgghhBBCCCH2A/TgTwghhBBCCCGEEEIIIYQQQoj9gHnr+Gv5jgXTuWo5d3abfABVyofvUq7vNvkEIvIjuCQzSqTIZ0A5j3N5yoXuYM7YRJ4dhOQbYPdULJU5u5PIt0C5tzOUQ3nzenTO9S7og3JpEHPHb1y3BcqNSaqfNNZnIks5fX125nEOXHzdJz8Epcg1h663Te60YhbrP0nfl+D2JC8Ou7bYA8M5fptbsT6DLnuS8HgtloNxbvo2vl7uxxzHbgaHJaX0tUSAf+i0Mbd5RP4IzoXODsKInJDsUMym8PzqFczR3Krh9/vssqN/XxBxDvH29pzQ7GTbF8jkeyw13b84d3TMSefM7pDjfhLFfFyUc58O4FM/diluOmnKDU65v2OOPw5MfLo2uxPQc9mXRcfLkK+KjubT+bjknHAol7tDkjn+Vy38Ovu0HPqES+fL7cHNx7nmY+9g3xcd36X+QmHLEuS8SMSql3LD0zzW7VJ7+5w8ntyhXN+xOG30B7o+eoNLfir+vMfjhXOpU3/nXPIxbxD55AJ2ODjsD2PHIL59x/D097E4FUWPXYtPfTSRoD5PY6JLc3Wa+hS3MddNihxw+Sy+f0ke56ZlhxyG7y+gfyLmLWNPJjcae0i5T7JHlT/vk9eT+myCXFTsxQxC9oJSH+ezpQOwUzB2vvT+DrmgNm4jz0uAc3ea5gSfHH3GTkeek9gDSu/OZcgLREE54PpNYTnhsu9i9pgaaz+KaTXyJyTTNEfipy3k/sVuLXaTUf/f8NBDUD7ouGPw+9lJSX4PdjiSis2SNMfu7KllZ+18p9OaMvO397cqLiltYAg9L9k07kF8cqy1prCfH33kMij/z42PQDlNPqBcmnxQbVzb9SxCL0eGJuP6uo1QjgKc+ya2bYNy08O5apS89NtGK3h+tEbPO+Sgoz1am9ytvPZhPy+7W8OQxyW9ncZpl/YUHPdC2tN5NK55bZMkf5h18fuadfSgLFzFXhes31YL93SZDNYfT/HlPvS2TFB71OvYPyrb0BvTkybPO/ne+vpwj05h2erklOy2qb+Rdylo4Pn96ef/DeWxGnl/KDCXyfsUsks3gf3Lo3scJbonEdC80Z12NLJ3bL7TCYKZsdKcxLkkVcC1TM0nN4+VoZw0bLNCDvs496nf3Y3lKIn3QfJlbDPeLwR03+CYEw6A8sQ4jonxCfKgjmEf3LoeY9yKVeiwO6SX1hbU1j6tbSp1rE/2LyfJ0R1z+PH+zaP9cQf7fIr3w1Rfjkvf18bjj1XR0ZfKlaGczuD766MYEygkWZPqZ3gC68Puw7XEYfVRKPst/Hyb7vvE/MxZdE1lstj/XJdcVORAnKhg/y4vwJjb349e0E4Hz290GPtbfz/OaS7NyZkC33fF9q3Tfd1khtbmFIOak1h/gY+fTziPXW/X53sd85tuq2ZOsH2OyNCerXcQ55pWi+5fk+c8maL7rQXsF1Eb40JAe8xCDu83t+i+UYncjRVyl/LaJFvC72/WaO4jd+iRR66CciaPc/mahx6GcpccccUC9sMWuTnzdL+4G2HZ500j3b/ntZOXoLUZ3W+O3XYh12mH7yPSpoEdi406xoViEe/3Jmkt7IT4+Wwe551GBeNWO6A4nsH+EHTIK+9g2SPXrd/BwMm+ZI/uM7XbOK596h952ksUyJs+OY5uU75TOUXXm8ph/3SM7jvRnq3dIp85HY/ri9269bHt57cnMWrf2hkKIYQQQgghhBBCCCGEEEIIIXaJHvwJIYQQQgghhBBCCCGEEEIIsR+gB39CCCGEEEIIIYQQQgghhBBC7AfMW8ffxi0TlpjO8R25mMN2lBx03RbmfGUNTB/lPk8W8Hg+e0YoN3hPmRxslKN3fAKz7jbC2fPnG+UmD2MeGvKaRLN7aHw63yQ5Bh+4bzWUI/Yr0On1kwMwTX4FdotVJzEns0M5osMkORfJu5IlmVVEjjiPvTyUYzdfxBzM7GRkv0SKciCHlGO5SbnnGyQcCak92KnXaZPDka43SGL/GKtiTt8ANT3Gz+d9w+tj/wU79fwu5gQOaLxw9zSHnIEh5aBOYv8aHR7B40fk8jLKnU659HPZ7e/3g30rj7qZWTKdtVRqe3/nftH1sd6CEBuWHWUxBwLFES9BuazJN+Rl8vQ6+YI86ofUL419Suxjotzs7DeKaFwGDvk72LGX4LhGb494HFF2c47b1H24fjnHv+eyv4oumH1a7LeiODqXXypmpGBfWMybw8682Y/PDcbDieuTc6PHRT90eI7D/Hk6v5D6QyLmN2Mf2+zfz34zdoBEEY2nNrlEyXHYpdz55nIc5fbfXmaHwXwnsMdqlvP9x9qAvYdUpy65nxyKSX4H545Ol/3GeLwWzQX5gSX4fdTmySTOLa5H/gHqQuzdDNizaQR/PhZzcO5MpMjnHPM389qPvar8heTlifmIae3CXldeyzSH8fgxMSf5B2hI+12OGbx2pjGexuOXCgvo+2KGCizFQlw0a5lXDOwB7XbJ80l+B5cclHM5Izlmh9Q+LfKDTJF77EDykzjUXq0WuerI0+O5HANpTeDu+n/vC0zUfEsmpuubfUs0rjLkZ5qcwnoapzX15lFs94OX0Z6Q9nRZ8m151O89ips18j3lyEfkt7Gnjk9UoNxTQu9Mkpxzq1bg8SZr2E9yvQNQHiPHYeTySMHjs/s1RWuz2JzH/uNYnKJxS45Bl/bArIPOJ3BPx+M6Qx6XBHl2skUcZxvX3Y/HJ0cirwUbU+h18XipQnE6RXFjsA/bi/vr2Bj6perkSW918HjlMvYPN8QK612Ie/YG9a8S7cm3bsPry+Vw77B0Edbf/Q+ugfKixf1QTibJ8bcA+2NjbAuUW+3te6FgH9vzHXbU0ZZObY8FGx5E55rfrUC5lcA2S5DDukWeRnZgT9F9FUtyH8A+kzV0bdWoD/X0otuLtyulHoyJ69dXoDw2sgHKuR7s46UCua7a5I4qY58a2UqOtz5cK9TaeF8htp+l+ow5xRO01o14g0VFGiOdEOeUqEv3xbJYXynyGZeS+P2VzeugPNLG+qo2yItK7q4tGzfj9/vkIithTBtavBiPR3Pq8CjOkUWKmWGE9yv4PlijQc75SgXKdJvKRqm92+RcHB8njy71p2H6fEBe2xLdR6xP4fnGPK0tutFG/ufB8mPH92Lu8PlNLpueiVNOGq+7S061qQrGDfYHd9v4/mIPtkuxeCCUA/KMNxsYx9I5jBuLVuCeL5vDtctgAv3MHi0WKrTWi1p4PSGN+w7JNReuOATKPdUKlCfJ6dek42XJeZgKcFzUDK8nSWsNvk/CcSjwqJ/S/dguzaHsRec9ULYH2zc7hccv0lqjMoHt17t4KZRTGeovEe6JW3UMBKkkxbWtGNcGyQ2aofp1fDpf8lk7tFZv0/3uYh7HcilP9wnpvuX4BMal3gQeL0eeeI+87a0mPT8YwThm5N51aXw4dGOTnYIDO5ydcvwJIYQQQgghhBBCCCGEEEII8b+LPX7wd/PNN9tLX/pSW7x4sTmOY9deey28HkWRXXLJJbZo0SLLZrN21lln2cMPP7y3zlcIIWZFMUoIMd9RnBJCzGcUo4QQ8x3FKSHEfEYxSggxH9jjB3/1et2OPfZY+/znP7/L1z/xiU/YZz/7WfvSl75kt99+u+XzeTv77LOt1Wrt8v1CCLE3UYwSQsx3FKeEEPMZxSghxHxHcUoIMZ9RjBJCzAf22PF3zjnn2DnnnLPL16Iosssvv9w+8IEP2Mte9jIzM/vGN75hQ0NDdu2119rrXve63f6ekdGaudMegRb5ANgrk2DvBvkg/A7mwm7WKRdqF3NFp3oxVzd7W5IpfP+CoUV47lXM31/tUuB2uEguIfK2RJTTmH0HYYL8FFlyIm7BHMO9A3h9hRz5Eki04lEFe+TVSZBpJaBulcqiP8D3sT4yaTz/XJq8J5T7vNWg66dc3UnKlR+Qi4uTuzfJv7Fh4zYoT3a4PShHM7nZ0uTniMjzk8hjDuCIcvNybvpUFo/XbuHx6mOYg5hT2bPXKE39bXQYfQ9BB3Mmp+n8m+QXyfZiDuapKTyfFOUszmWw/nK57fXR3YMcxbPxdMUoMzMvkTIvsb2/p7OUS5y6XafDcYm8KRRnAnIzhuRJ8XI0zmgcBw5+vkO5pNlxENNNUe7xkJP2sxKPcouzEy+mdGPnX8wxR87EiPxMdH4xPxR9Ycwox05DHjizno1ZIkG52Km92LdFRXMpbpKCIebCdNhoReM6JN8W+8k8cmI45GuLVRB7eDxeLtD3k78s1twxxyBdX2xepM/HFID0B5onI4f6L50QXR4PP+uyr2067vP7nihPV5xyPcfcHX2J/bSsbqI+59Ig5zb1qFMnEjT3kV+A585xQ59Apnchvp89oTHPJL7MfZ7P16W5zOUxTL4EL8ExjXzHsT7C3z+7VzSiIBwEFEPoeiNyukWxOYKcdB67rMgjQz6ENI2ZgGMwrU3THq4VsskylIuplfj97K+moM1OxJhHlspxbyt5Z8gJ2SFfSYocfzwncH/hOZGDyOqH74PyklUH4Ptn15haJkVuOJ/nXPIa0V4ITm8vxKmncy21fGHR0tPe4okmzy3kUa/gnibDnvPFy+no+PlJ8meVejEORTQOfdrTuLRWSKVxHJiDca9dwzXx0BB6TNwUjtNY3CLHXkSuSi+F/ThF3m9rUj839sKwz5a8M3OsFdn/y3tE9lWH5I7NBRSn0lh2qP6HN6G7dOly3IPne7B+250HoNxsorcnT36rJnnazSfXJo27/oEylJPkm2o0cM9ba+DxR8jJmGB/Mq1tOuSt6foY55p0/C7NY4NLcJ4dH94E5dEN6N0J6xUoO04vlCNq/y65SdlZ2DO9x++4e2cx9XTFqaDdnvHuZnvIq0hKvvFxdE3ly+R0G8N9tM8eyyyv1bBOHbpP0ib3U+8A9ukuPUB49L6tUE73lqGcoBgyPoVuoUyA19OoY4xLUEwcWDCE50tTGzu18z46CRtOBcpJWmvy2svzeD/EayOaS2nMNcmh6ER4H6s1ie27lNxY/RSTt9yHY7ZB+/EszyEuvu5RzG9OYgW22rh26C9ifyiQwzGXoe/jObKC/W3rNnLgLcIYkqA5Y2qMYgjVbzpDMa2D39do4BwxNo73+QoJPJ82eWx9uu/UbpGzkdaCvUPomGzu5Lfuxhy5e87TuZaanGpaKtmdPjaOyzZ52PvJfZjO4Lge34b3s4t034mdaB71q3YVvz9Jc1GtioHTobUB+2rTRXIWTuE4PP7Eo/H1Ks6t7S4ev11B51qWnHVbOK7x3E73X2u01nH6aO1Da9nYHoem/hR9X7tNcYH27CGtXfm+SeRR3GiSMy+F7ZNfgHGYN928Ben6eH4O7bETNOcv7ce41Arx8xznegv0vIbmzUYbr8f18P091H9Cej7QbWJcWb4C15JBFftLl/Zo7SbeT29TnAmTWB+FAo6XGjkVkznsj6U+XHu1u9H0eey+h3SvOv7WrFljW7dutbPOOmvmbz09PXbCCSfYrbfeusvPtNttm5ychP+EEOKp4InEKDPFKSHE04fWUkKI+YzWUkKI+Y7WUkKI+YzWUkKIp4u9+uBv69bt/4JoaAj/Zc/Q0NDMa8xHP/pR6+npmflv2bJle/OUhBBihicSo8wUp4QQTx9aSwkh5jNaSwkh5jtaSwkh5jNaSwkhni726oO/J8LFF19s1Wp15r8NGzY806ckhBCA4pQQYj6jGCWEmO8oTgkh5jOKUUKI+Y7ilBBiT9ljx99sLFy4Pefz8PCwLVr0WM794eFhO+6443b5mXQ6bel0Ovb3wd6yJbztzyWHJ/Dny23yHbCzLqRc5oU0JrldMog5U+NOOs5hTF4Zh3OLx0Qz+P6IXVmc157dR3Q4Mj50IsxhmwgoJ28Oc8b29GI5RQ65XHJ2rwlntw4oOXu2QLm0ycmXIo9LknJAZ8g7U6Dc8sUCttfIZsoJTTl8k5QrvTKBOZ4nJ/H7pqbIv8A5eam/hewBSrIAA683YHcY5bDmXPScaz5FOafHRtBvEZKXJ00dKJ1k7xL5S3zsT80m+Rq4B1BufM7hnCvieC6W0TXAvhB/OiezH+wdx99sPJEYZfb4cSoKQ4tm+gf7hbBe2A3abpBzj+JERPURkcfGpe/rhNiOPnlmSAERcyI41I+4HzsOxUU63ly2jrjzjaVr7HOa3WnnUn04AX2e4nYsjtEJ8/vZvcp6Jo6TCU52zk67+Bng8WP1QW+PSe+4vvjt1F58PVzfLMGjfxfEcZCvj1/neYv7U9y5wf6v2X1t7JBklyk7Q9jpx+3ts2OT+pNNlwOuh6eAvbmWstCfqfsUjWG+xIgcbhzb2UGXjOj1mMsJj5/P4ly25AB0oOUK6M1hx10sRoY0F1EMi41Zuh6HjseOOJ8chewR5THiJdgvTGOe5uZYfce8pLOPGe6JEV3xgcufB+U1G2+H8nh9I50e+ajJAZgIycmY7odyKTgGyj09A/h5qh+PPKPs/nJYeMENyr4Mqi+OqbwW57UWz6n8LyNDEmawU3Hdo2ugfOiRWB9xxyHHKGpf8kfHYzwS7dR/I5df3bvs7bVUsxPO+LM86ne8xE6H5NEw9JCwFzyMcBwX8rhHaDXR01LuR99Pt0UeF/LG8JTQ7rBnhNyc1JAB7VmTWTy/Dnl5utTP+fyz7Ays89qOvOzsA+a1Ic/NeLTY9QUO+pQC8g+nyAFY6JCjME9x1cX6XDCEnhNeOzx495+h3FvA6235+P2jfI9hHPsXaV6s1LcEyoOLMM6NjKJDcPMw+ty8JO6ZFwxg/Y0No1fJb2P7dzq0h8Xmt3y5DOUowBbLlXBP5oyshXKrVoFybx95eBrYHmGCPUMUt+ieSa22vX909pLXfTb25lqq3mhYt7u977g0d3WpTXIl/PVO4GMfSNF9msqGESj39WIbJXiM8XKc1vv1Fr4hSQ61JN0H4vtYi5djH3fJSbdgMQ6KyQr2+cDB+zQD9Gum2jjG0GH6ZdPAKnKuudjJHXJbsSOd5xAnjdfbDXh/R/epaP8+0dgG5ZCyKh50IDrvlh95FJQ3bsL2Xehhf9iwBdsnU8A5LZvA/pXFIWXjbbyeZJrWVtQ/mm28gALdNxscwu/3u1j/OYrxpB6zahWP3zOAa8WJcbx+18NY0E/e3XqH7mu1yIuax5iaLWP/7Lawf+boPm99HO+rpXceb/5Tu+fb22up0dEpSyZ2fXvfI490D91/zJDjrUiu0fo4jtPBQZwb6rSFcChOOilymjVxrujvIcfgKM6FYYDt6FFceuAPv4fyyoNWQXmK0qF2aa02Tm7Kdg2db32Lca43Hy+4SnN9coC83eHsDj52xqWpPTzaM7So/ix2GwrjWpNcoaUSuksDmsf8Lq09GlgfkYdxeqqGn+/L0T2DKsZB3tSMTWIcWLAUx7GXpLUt7QGzWXLlFspQ3rgB+++qQ1ZAuUv9Y/FSjOtbyUHoOTT+augAHBjCuNdskKuX+kOOAnsmj8fPFbE/dafX/p1nyvG3atUqW7hwod1www0zf5ucnLTbb7/dnv/85+/NrxJCiD1GMUoIMd9RnBJCzGcUo4QQ8x3FKSHEfEYxSgjxdLHHv/ir1Wr2yCOPzJTXrFljd955p/X19dny5cvtXe96l334wx+2gw8+2FatWmX//M//bIsXL7aXv/zle/O8hRBilyhGCSHmO4pTQoj5jGKUEGK+ozglhJjPKEYJIeYDe/zg7w9/+IOdccYZM+X3vOc9ZmZ23nnn2ZVXXmnvf//7rV6v29/8zd9YpVKxk08+2X76059ahtI7CiHEU4FilBBivqM4JYSYzyhGCSHmO4pTQoj5jGKUEGI+4EQx0dIzy+TkpPX09Nipzz1ixpk0WcVc4U0fc7r2D2Iu6i7lw08mMGds3sMcrdks5pLmXNiuS+Vkkl7H8xmexByvU13MiRs67K5irwjnXse3s9so5WHO2ww579p1zAHcbWGO2XQCJ5Z8mpx9lIPWpRzBrovXk8PqsUoDL8Bn91TI14Ovl/sxxy1rfxp1zNHcperl9h8dxfbYMkw+CpKheYb11zdUhnIuT7n+ycvSbeD3Dy7DnMHNGuZO77Swf1br+HqD/ArcfzLkW3Cpfql6bdlyzNFMKaLNI+9Mg5K5B5RbuFjEnNFuCuunOoX1HXa25xj3g8B+f+9qq1arViIHxXxjR5y68HWnWTq1vULZKdZqYzux02+KcuBPVbFfNmmccippt0C50zPktHPJD0VxyiWHQRRRrvCYD4lcoNSRYg6/2F9m9yPx97HEwqN+7tDpWpecc23yJdGwodTl5pH/LJEgvxXLKcmw5bCTkB1+VB8uOxTZ2BXzlc3uOwvo+l12MtLIZl9VxMnmaeKZc6nApxebx6j9qH/GPEIsu6Uyn3+H4lK3g/XR6WB7+LSO4BzpXSo70/URhKE9tG3bvI9TM2upY46Y6bvc57rk6EvQmHbo/VnahIbUx3zyHXAIyOZwbXH4s9CBduSxz4ZyvkBzCTvZeDFAnTDm0GM/LXs9Yz5n8h3TGE+yv4L6KDv+IvY5O3g+vs8xGCuQPbhtmmOatHaI+Y4n0Yvym7uuhHJ1ci2UY9pSH9tjae+JUC7klkJ5xUEHQ7m3B91cXmwOmb09efHnxqYQrN827QX++4fXQPklr3wNlNPUv2NzHLVHYwrn7B9f810o/3/nvh7KySRP4lhkh+Q9d/2JyndC+djnPAfKh+3kFWo0Gnb+/3nNPhOn/uq5h1lyusMNrUC/lNPEPdXCLK6JH92E42a4hfVcKpAnI0vOPXKTrjwIvR+b126Bcq2G425wxTIoT1UqUF5Ano9ECr+/USUvDPm/xraiX6pNTr+A4niphJ9/qE3OOFo88VQbW+tQx0xTYOC4VqM9mdPBcbvQoz3mBI5Tri/SOVuhjC7YVgvf0CEXbc7B+g1o4PEezOjzZRq2S47EcTewEPdQd/zxPij3FMg7k8L6+tOfsX1z9P2F5Suh3Grg+Y6O414hQXvABPmeeC8SdPAeS5fqu9yD9xjK5Mhs86Y7pHUF+bd27HW6fmDfv/GOfSZGvfTEQ2diFDv0eK7OFckFFdF9KRqDB5CbaGMd27RL+/hcGcdIKotz8/g4zk1GLqxWA89ncNkiKEcRjpH6KLqZ8jwG29gnQ1pvdwJyh5XRDRaROyldLEN5k78Wj0/3mRIuDlIvQQ52l/YD5G3j/Q/7osNxLA+mcY6qjGD9BLTB7CFX2sIFeN9y8wi2VyaJ9dcYwznQZYddgPf9chn8fGwt45PzMUn3Dbm9yC3FAu+xsQoej3zOWXJX5cm5uHkYr2/BIK4lJ0hkWqOY3ZPDOSmVwfu6CRoffLshpPHh7OTk7HQDu/In+06cesUpx87EqbaLsbtRx7nQb2Hs781hP52sYT9ZugjHfW8vHn+8iv1u01Zqp37stwMLMe5M0R4l7OL3R7THXFDC8+1SYPDJIzs5jF7zgO5LdCJyqPk4DgYHsP0zCYzjbcPvr2exX9U8dpXSHpLmkWQSv79L1++Tr5g96UZr23SI19fXwfZc3Et7Uopj4xVc222dxLi7dCW2b1BBX3JA7etQ/ywvXgzl+hTeJy0vQ2djq4n94+H7Hoby8oMPwO/n5xeG11MoYNzgTebW9bgXcLJ4/rVxXMv19eM8HdHaju9RdMj3XaJ5MpmkPf3Edudl1w/su7+8Z7di1F51/AkhhBBCCCGEEEIIIYQQQgghnhn04E8IIYQQQgghhBBCCCGEEEKI/QA9+BNCCCGEEEIIIYQQQgghhBBiPyAx91ueGRwvOeNjSaYwh2xICf87HcyhW6Lc50l6f+hjTmOH5aldTv7MIhHMyRqFlBucXU7kLgpj72e3E7uF2AVFriwjtxEdLpvHHLRJup4wZM8M5gT2KAdyMoXdpi+J399LueozdLyagzmFK+QgLFHO5/IQOvG2bHwQyp0Ac+KGXcrJvAhz5IZU38NjmPM6QR6ZPnJIFgqYI5ndVaUS5myOinh9GXLeWQ5zj2fSWC6UMV/v6Aj6OjodPP7iRZhjeXwr5p7vtDDHcEgutSS73ygHcYKuN6Bk6VXKydwi11aVcoAX+8rbj8OKtH2AMIwsDLb3d4ecAh55NRKU0z6dwXHZpnbxKVd3mCDnm8txgJ1+LG5hhxo76wh2/LH/i31a7HSL+bfINTmHjysuN53dMcffzw5B9kcl2VnIbk/6vEuuT3ZAeB4e33N5HJHzj515fL08LzAhC6Ko/ri+uDlih49bGneGHY98vCDkARyT/mGRyjFfm8t+L55HqT6pP3vsOuXuResCds3y5UYz42GOdplnhEEwc8YZmru75HFhzWNEMSiIdSnuczSX0NqN+5DfxZjHXYbHMPtqQ1ob8fdFNEYSNPdyvv+IYh77omOeVDxd89zZfcQO9eFuh45PHk5eG/o+rrXYkZdOU3vS60Xy5px49P+B8q13/ieU292tUO4pLofyQYc+D8qFPK61cuR05PYPYjGW6tfl/jn7nDIximuj4W3oYxhahN6Yh+5HF9cycmkN0NqT/Rkt8i+7NOfH/dw0Z1AP4gjMPg/eWyTJQ4sDiOtqftPTN2ip6bVnbRu242AZx2kjxD3bwCLyO7XId5TF17OlMpQ3rXkEysObsd9XJ3FNyw1Vr6LHhN2TPJnw2sIojk6Sz6lL/quI3KpN8oFlHNzjpmiui7B6Yi5T7udJcv6lqT6bDfy+gDznvYZxIGzRHqCIa+HhYfTA9JG/KaI9dTqHe6R8Hs9vzT0boFwiPxm7Zzt19G2FSfz+e+5Gj8ziCvbXTevXQ3npcUNQTjnolSmSg7I1jnGlOoxxrENOxwztLVJUP5WpKpTbtOd1szieGnRnqDuKn1/Shx0oXcI9e6OJx69O4Pjp793+fR3qJ/MdN5kwd9qd5XQwJrWa5PBrV6Ccoz6ZorVCuoyxfOUgeiPXbMM6ZJ8wey5TaTx+sRf7YJfXXl0cwy1ypPf04dxeTuL5rJ7AMT1I5++QF7RGfTKbwz61+REcY84i7KOpzOz7BY5hIa8taC4OaS7m7YdbxPpoV/D62d/bauIYb4R4fVtGcIwX+tiNtRnK+T5sv3bE+x0ctGNj2J45GtOlErXHJJ5Pl8amw/s7D9sjncYY3iLf9MIFWD/Lesk9lkT/8xTdxyvlKebTfdRyEes3crB/j/AcSf7r0MHPLz7wkJn/3W53zOwO21fozdQsNT1nNUKKSz1lKLdo7ij20f3OBdiOGcN6LNBa4pFRXAs12nTfhcbFxDiudfg+CbsnN23G+5kLFuKexNo8F+L3RbTWiVLY76Iunm//Qlxb9PXgQKqRzzmkPWoqJEFwgs6HvO68x+n4dJ+C9qg+uSn59kREca1NzwvKi7D+giQ9n2hhfZd9bN9UD8algMblRAXjkBvw/WO6/jFc+zXppkOyjv05laa1UBHHddAlty25ZwtJrLBEEu+/b9qIa8cunU9Invd6A18v5XEtlKL7sMk0xuF8rgzlRx9FZ+DAIM7D3en+0fV5T/H46Bd/QgghhBBCCCGEEEIIIYQQQuwH6MGfEEIIIYQQQgghhBBCCCGEEPsBevAnhBBCCCGEEEIIIYQQQgghxH7AvHX8+UEwo6gIyX+QTpEHhR5feuT6SbA3hrwnYYDHZ6cdezk4K33QJf9ARI63EHO8kpYm5jZiVxebk9jMxa4tn51s5H5iN5hLOYYd8hl45AsIyHtDSjjLUa7vRUN4PSMtzKHbDDDn8eQk5szNZMlr4+Lxszlsn0nKbb5mNebIHR/F47sOXm+5jOdXKmE5mcCc0wmqH65/j/wYKfYMke+j2cD65afz5VIBylXKiRy1MAd3bxHPf4r8EvUavj9LOYStTddDWpkM1V+ryW44rK9WB3Pjd8e3n38Y7lvuLLPtnrEdOa1d8mW55HijbmBeEisyQQMpSSKWIEmeFmqHwKFc3yya4ThIcY2dfA77phwu0/sp93hMcMXOQM61TvXFvjA+f4fGnc1R32GCnA9tcplSYHfp+10apzGXK9cHlWPKOiceyXcmikV++n6+/CT9gT4eksAtoutjByJPVOzM4PPl8cs+sxjcvuSt4bgcUO78iMqx+uZ1AI0X7v/sjzNyn+5woe5rcSoydybPf92nNqZ88xH1cXaI+fw6t3HMEYh1xU69Ugn9CYlYI1Gbe9RmLvcR7tPUVuSDjnlFaXJjjw6fn0NBhv0OMWlbbMyQ99Tj12cfsxxT2NuTpDHh0fWUyPl3yIpTofzIxp9BuVxER14+j64sXtvwmIx5YFmnbeyXpjmVq5euv28IfRPlwQE8Ptd3gmPO7HMWt1/PAPrE/+plL4dygo/PqzmuH/bosHeU9i5Z8tTsfP7cN+Y7qc64paZ94+UJXKN3fNqDFNGbQppw61I/atBc5mZxnGQK2I95XA/04pq7TZ6SFHlojPYI4+TsO/SIg6Hcv2QxlFfffT+UR7eiQ25g6TIoZ0t4PbVx9LKkyRXZZReqQ+OO1uy8FuEpkMMez/xp8uaUcthvjeaFLnmARiI836NWkQ+K/FRuCueVF/7l6VAeo/ezp/zOP6D7c7yB19/bi/1jeBi9N4Uc1sCGzbjnGezDcVyfwuuvR+gcbI7i51MZbJ9e8trwWs8N8fi9C3GPN1XH+jafHIQlciDSWq1VIyciu1nZ2+ROr6XcfWstlfabltzRuwvYx9IZrJNcBudCnsvTKWyzdVVcm3QjrFO/jW3SpSabqlegfNTxJ0J56UqMMa0GjoHbbvo1lBctQW9jt4r3UQKau4pFjKEV6lMO3edJUwxwXOxj7OnskCusQ66lhMv7A1oLurxfwD7pUwzyeK1Ba7PA6P18XxE/boFP55PF+1jsvG+lMcZlyVm3cPmBUH7gHpwz+HqqPq1FsnhfKPDRuZgj72enhq+nyLuaKeD7e2lttHnjRjxeC9t7zUaco0jDax3yyK5ahf7l0RFyg5HrbYxcYytX4HgolzAmjm97zPPbYR/mPCeIopm9kBthXGHfcamMa5uQPOIJw/d3m9gP/EYFyocuxnqvRLhniLJYz1Vy/CVoXPXkcZxkVmC7+x1ytJFLNOlhP2un0aWZI0edX8fr82itNNBD/Z78xau34P3UqEWL036MOwGN6/h9E9pz08sJimMtWpvG9kw0N1erOC4G+nDPlHRobUFnN7YN54VCuYxlOl6pF+fNLRvw82NNvJ5MGq/n/nsewPPL4PklaK2zpYG+5cE0zntZctHW1v8Jj5/E11s08SYiils0b+VLGAeNHIs9ZTz/8QauJRMF7D/dgOont318uF2+P/f46Bd/QgghhBBCCCGEEEIIIYQQQuwH6MGfEEIIIYQQQgghhBBCCCGEEPsBevAnhBBCCCGEEEIIIYQQQgghxH7AvHX8RUHTomnHVMrF3KUu5dp2KE98GJLHxcV8+N0Ofr7SwFzqHuXYzVEu8k6bcvhG7HmBoiXIF+FTjlfOmsvmp4jdRSRUCMjR55JXJHLJkch+BVY7cX7+Luaw5fpo+OgfGJ7AHLijk1gensT3JylXfquO35fKYg7lNLnRkuRzcAbx/fffvwbK7S45+dqYE5hVVoUC5kJnL0zQwvbsdMl1RR6i5hRePzsa06QLSZEzkU7f2pzEmfKRs/OyVKSc19S+1RH0ifSV8fuNXHZd+noviTmOq+SnsBR5h6ark30/+wJhFFk47b2KfOo4VO/sUAujmAAKIS9NRB2TP+2Rr8vYScD6InacscNvjvNlhRC7L4OQvTAUxxOzu1ZjX8B6Mo9dquSkc9jdSo4DkiA5lCuewqZF5HzjeYJ9Y2EH455L80RsHot1Bz4+lmNOvZjTkdqfXub2ZP8VH99nvxg5Ftkdy80Xd07x9VN/Y8cexwf22LAzkgIKtzeX2WXLDpCZ093H3FlBFJkz3dZJ6kM+x2721ZL7KE+OO+5DHsWQBPdZGuPZHM4tDsUIx2VnHDsJoWihz74E6lPkf4jIu5MggULgch+nMUnH9yn/fuiz75diNNVvq4lrUY7BsX+pR0EjSWKa6gh6VZK0lmo20T9d8HAtlSHvbD7bB+VOrYKnk0VfiEPtHZLnhrWuPrVHi66/06S1YRrXGkb9j33dLs0ZLnl02U3F/mWedbk9suSbYCFGRHsVbl/2xvLaLUVr32wB23Pn4TGXYnW+UWuFlpwWsnco2DeT6I9K+NhuHEeS7Dmnepsc2QrlJPXbyakKHp/mgjQ5/XhPYOTVSeVwD7H+kQ1QDikOVDduhvIRJfTgDDewPjI9ZSjX2UXawc6QnsLr6fZQXGWvDB2v22FPOq1N6fozefT8lHqwvtgftXAR7QkDrG+H1pr5EvmdyXfdIT/Zpoe24PHI8RiQQDuVpj0UOe/SCexvHYpLxR7sn1sqeA8hkcX67i+jB8lx8PyqFdpD0/f1L1qC5zeK19/Ar7cempc81OBYYwrP754a+rQOXIrnV29Qf0pg/680t8/TnT3w0swHnEKfudNzYoLm+lSa5haae9p1XJuwK6hF/uF8jjymVFfsw00lsI0euufPePwp7AM01ZlDXs/VD63G95M7qVjEPj1BnsdyCfuEl8C5sd3EMdNjeH4ZijER9ck29fmgh9ZysRtpLCKdw68c4flFtN9Il7G+FhbRiVjuR3dWfQrbO0PusPEqxsCeXnT8VWs4aKsP3Avldh3rq+vhnFat4PHLvXSjKYOus/EauqZ4/5pI49oxpPuqAd33TJfwerp0p/NZJxwA5TX3ocurTK6vTgvrM5kvQ3mC7rO94IyToDw1hh7cFnlxu85jc0q3SzcD5jmtIGHhjrVwBtfs7Rb2k6TPcx15oylQVNpYnqjhXN0mP+/Sw9DxFtGms5TFfpD1yM9Mzj0/Vcbjkf+3PYFz0yQ5BPNlPB83JGcb7e/rU1hfd4xiIOrrwTi3aCnGgYDuY3TruPao0/MNJ8H3PWgPxTdSaO3lRrOv5Xy6z1MZx7VoJsBxvSSPcWA8gWuTUg/GkclRdOp1Q3zdTWG5RouRTA7XVuV+dOSFDrZv3wKMW33kEOT2726+B8pRB/eUHtVncxLnpZ4UtQdvtOh5UrNN99cD7M/3T+H1uHwfr4f2zEb+5ennH9EeSN33sa2hEEIIIYQQQgghhBBCCCGEEGJX6MGfEEIIIYQQQgghhBBCCCGEEPsBevAnhBBCCCGEEEIIIYQQQgghxH7AvHX8ZZMJS0wn6Wb1U4dyBLNrqD6JubWrHcxNniBvSaNGubcpx2xrkrwilDs8naL8+PQ8NelhDuKIcocHLA2KuZ7IvUQeF3ZDOSQW4cOF5LFJBeSmonzWPnlZkpQD1yEfx/AY5ggebuHxO5Sb3jXy/mQw53RlDH0cZfJZ5CmnL7uesjnMKVwoo28gRd6X+gjm6K1PYQ7iQoE8LuRtYY2Pg4e3fBG/P+qSj4Kcge0G5gTuI19ENoU5jrsk8/LpeFXyS/QOYc7mdqUy6/HY/RU5WL+jo5izuEb1F5IfJZdN7fK4+wKO55kzncs+5vALOVc3e1DwWHHjHzsJ+Lvp3TEnGTnlKFCwXcNx2FExu8OO/UScS5wdguxLcsjHFdHr/K9S2AkYl63R8Tn3NqdGp0/HpXTUH0lmyQ5Dhzw97O3xKM54HrlCfZYKsuORHYuzO/3Yb+aEsZkAj8/9ld/N8lN6A18Pfz9fj0sOiPj72QE4hx8rJrWaw89Fb09ncJ6mac+CqDv9PbZPEbqehdNj1w9oLURDKqA6p6WGBdRHAvIPGJV9Wnulkzh35chRZuymisUY9jLiBfjk1UyQRyXocgzGC2y3cK6Ku63IH819KuYlJb8zeYEa5CHhOcGhxUQ+Sy4qqo8EeV5zRXx/m86/08a1Wr06Rq9jfTTIn7GO1noJ8hEnaUyGtJZgL22C6rtD7eGTLyOdobVcivzWMUcg+jnSOfIQkQvOpf6e5r1BHc/PSc5+/ux0LJTK+HkakB75PgYGca1Wn0Ifc2qnr2826bvnOU6+OHP9Q4cfAq+Nj6JnJJPFeqpVyZNB/qAM7QEyKfQfTdE4NPaoZyhOFcifNIp+oKV9tAdtr4Ryi/acdXJX8p5upIHXU0nROPLx+wKjTQet7epj2G/aW2mPm6O4sxDHiZUoLobk9Etiv21P4h5mMznjCjm8vp4i7rHCOh7v4XsexPOj9uDF3IYOjoUWze0+eY9c8hzlac/XIA99eRF6g5Ip9n3z2ob8z+xPprVWqYTz2Og4vv7wWnRCJtLYXikqVyewv1LYtP4B9EKNkx/MoQocH6P6y2F9WAb36NXqWjMz67IXfZ7jJZIzPs8EtWmjgWMgQQ66kJx+Bx+JHsbVj+J9joEli6G8YCWOufUP4hiIImzEtIdrr6lt5LWk85+awDZOkoB3ZALHUK2DYziVw7mp1sK1YCaJ5YKDY8ilBXeOfMQtum+VHsXzaadpP5Xm/R87/Xj/g+3j+9iejoPtWeviWmKKvJvsAe2l+1b33Hk3lDsNrI8srV35PlHWw/dbBq93kuaYww7E/pZI4VqtTW6qfA+25/gYrg1L5O3MF/H9Ie3gCy62/+Bi7N/syz5yJcasJXRf8OZHsX/nFmDMGVyM9bduLfb/oRLWZ4PGi7PT2tRx9q17U1G636Lp+9xNWqPmenAuG9tC91cHBqHcpXapN7CeXHKLFhbg3FEbr0A5Qe24dQuu3ZwA1zKH01pwiuae2hSOwybF4XqH7v/Snoz90JN0vHwex/FYBcfJ0NIylFNpur9LcSdfxdfrLn6fTzdWErQnCGmtlaA9It3ON9/nTSXOueynThieD/t5N66p4OddrN+hZejqHKvg8dg1ynuepVSfYcDOPewfuVUroNyJ+Tip/pcfC2V2wTam8PhLBvENE+sfhrJPFZ4uL4By1ce9y6SD84TD923pPiHfY5hysP8lpvc63dgd3cdHv/gTQgghhBBCCCGEEEIIIYQQYj9AD/6EEEIIIYQQQgghhBBCCCGE2A/Qgz8hhBBCCCGEEEIIIYQQQggh9gPmreMvDLf/Z2bmkxckRbnTg5irCHO8NiuYQ7hETrhuG3Om9pQ5VzXiUU7dbhdzHgfkh4jo/Q7lTnfYFeWyi4gcazGXFeZgZncRu7Z8o1zW7BqjnLquhzl8ow7WVy6JOW6TZfIttClnbQZzTCcz5MsYxxy7ETkAJ+volcmXMEezT76MhX2YczqTx+vpKeLnmwvLUN6ytYLHp1z0RfJRBAHmYPbIjRa0KSdwCs+nQbnjPcrdG7Qot3tInhyPXHPkKot8/HxjitqPHJgZyt3ebWN7NJoBvc5eJjxeknwj2Wl/ShDsW74Hs+0OoRmPEI3DmAuRP0wDL+I88jSOXfIhOQn8vm6Iudw947iA7cx+q5gjLyasIijQzOUwjH0/O/fIBxXTZcXiHB7Po3HEPi6HxkVEuekjF+Mi50KPyM/EcYan0wQ7Edl3RcfjXPAOOxbJiRdQHOI4HvIfYv2Jzo8dm+ydcXlcz+50nMsJGW/v2V+P+Pt5ng1mP39+PzssE+T4a5MPrtPxp89r35L8RUEw01IRtTm3UUiDlvP/uyQDCps4xpIp8lgmaW4kj2WKnH/scQzISZiiMcBekCTFgJDWguyDbjVwLeGwOJW6FF+fzwJqcg7GPJXUpz3y3bITjh1/k1PoC3DJiTdO7q4Nq9FHsG0Lek4iiokBXY9fxArYOrEWvz/AtYHH1dfB43nk6Qmo/RI8ZvmfJsaWCBRDojnWyrwWD2efUz1aqydorcvHN2pfl2IOx7gujQd2HnYppiVpvK6+B11dO3t0O+TXnO8kXMeS0/1xYgL3GIUe9Iy06ugJYX9uTy+u+V3DuZrXKoctxfLaLTQ35LGfJzx8/2byKY3RHqtnMcZBj8ZtdQr3qG0aB+NT5FMqkC+L1ugV8jF12A+VI0/5FHnWk+Sh2YKfX5DC75tKobfEI99YTx77fTaF/XjLNozDKXKFZjN4vL6BpVD2Kc48fP+jUC714B6z0yX3p497rgVD6IvKkfPQb2F5irxCvDaO2jRPUtz3Q2z/qUl8f2WK9uxJ8tbksb0qNA8cfDD6tNoNcmQ2sf5KtEfvK5Oz0cf2m5ykPWuEx8v52L7V2vbxyM7W+U7S8S053XU8cta1aLEwMrwNyitWoftnURn7wHCa6pR8utzHO9QGKfKQLu7DPrNlFGPgyDjdR8ljmw/SGFi4dBmUx8dwLdLXTzGInHfNBq5t+ko4RtoR1keCPJP+FoxpEb0/U8U+3Sjj9XvkRQ0oJsYc4hTDuuSkn+pW8Hjb8P1TY9j+A30Y03oS5EVdsRDKCbw8a07i99XbOOcMLFsOZbeG18MOx26I7ZNIcH/C61mYw+vPBDjmO1iM7a/6+zBGmY/nb7S2qnpYH24Jx4+bQ8dlbwHn1ExjDZRHwiEoV2pY/y6tHdM73Tdz3H0rTrVqExZMt6dH94+DNq0pyWdbGae4Q3NXSGviPPmSO+T2bAbYzp1x8qrTHilfQsdgk/y7QYuuJySvN63pl64gV+qKA6Gcinm1ceC1K+ugfMBC7GfNKYxLPm1afLqP4NPaI2rQnmGAny/QHoH3MLSHc2lPwveZeE9Kt8msTnuM8QzG9cWL8PoDOl6thvNKmu4n9/Uvwi+kPVI5j9fXaOLzgHwR+9sk+bXTaTo/itvcHh3yDOeyGKc8Ov/cUnQETlUx8DU6GFcrLpb5Pl8izQ5HKJrr4X0pj+Lq5HQc9/mDs6Bf/AkhhBBCCCGEEEIIIYQQQgixH6AHf0IIIYQQQgghhBBCCCGEEELsB+jBnxBCCCGEEEIIIYQQQgghhBD7AfPW8WeOM+PMSlAO3gR7XsjX4FM+5qFlmBs6RTlbC72YG71OvobAOB8/uYVI85GiXNXtFh4vylD+fxbJxBx9c3h3jHMek9eGnURU9rKYQzZXwNzuzSl2yuH1hOx1cfD8iv3oVPQymOu8Nok5nNnDk3Sx/uvk1qqMksMxh8cv92AO6x7yheRy5HYiB2CL2q8VYg7heh1fz2Tx+1Mp8tqwSizF/op+PJ8G1o+FlJuc3HAueXTy5FDMUA7jag37C2t0ppp4/NYk5uqPKKe1RzmLhwax/QPKqdyc3J6jmJ14+wSOPTZe2ZXJviFyGPqUezrmI6J2NHY1ssONc3tTHIiFAXbuURziOML+K4fiIPuNjI7P58fOPDancf11ud97s/uW2OfEcTrm3DM+H4q7hnHCyE/i+HQFlGs+jGZ3BsbgBuN5j+uT+lfMRUfjlONEgiooov7J7cN+s9j4TWKcZL+Vxa6f2ov7F12vS8cLY4en/kCHS9D5xZSX/O3Tb4i7Mec3jrnm7KhbjknUZglyTyXJORZRfvyQPJMRubbCkNcuNFfE/Me0NqG1Dscs9oiw9zIi/0CCHHzJFM7V7EPYunUjlBcOLcHzYUceO9s4BlKIZCddktYOPnlcJsjdtW4NuqwmJtC7E5vN2SFHx+92cG5PlXDtNFFBT0zQprUhXU9tEn0PNOSsQK6xYo59w1i/jRY58shP4fHam/pDp4X1USA/NPsm+Hp47+DwnEExyCWHpd9CH0Qqh3sPh7Zk7Ldma1+Kxk9np+vtkBtkvjNZrVpy2kszSGviehX72WQNy50mrpHZQ+1FWHNuSN6PviKU211s9xWL0TdVLmJDT5KXfKyCr+da5PkgL3y3jeOkPIh+oxp5RMoF8mNNoIeGveEF8ta32rQWpTjALtWmT/NAB/ttMUHOvwJ6V4ydcjQOaUtgm7dhnFm5EuMuD7TxzVuhvKQf48pIA+s7m8X6Wd7fC+WQPOh+gPVRIM9MvoRlduw1yaFXGcH68fIYGN0MBTajuOjj9Tnkm1u4BD1JnRr2T3Yo9mewPoounn/XI3843TlqT+IfxoexPcIAP79g2km4b1lIzZqNhvnTMarWwE471FuGcpEcbmEd71P86hacq0enyAdM3sl8ltbnKbxPw26urQnsI72L0QHXtwDbZJTarDaBMatQwBiZTeLc1+1gDMuRx3OgjGOsyscnj+oUOQgbHfy+wUG8r5fO4/mNrvsznu9y2r9Fs++XQrrPxTe6ItqPhwHGjGYdy14O56ieXoohC9BBN7oO13aZAsbcCvmgQ15rtHGtyPdlQor5+SLWH8umirTWqDWwfxb6ylCuk1PQ6cP6b4ythXKlg9+fovt4qTT2F4d91DXsvwcehP19+Dasz/6DD4BylRyRUeKx74+ifStSZdKepXY4/mgPZQ65MTPYr4sFcvj14f3IymgFyi1yd/KekaZaG6X7qdzOxTKutcYmce7K0Bq/t0j3w0tY9ul+dER7tJDuAxX78PuzGYwLW7egu3Pto+uhnMthP83lcdwGFHcSEcWlOlZYkMG4zvfV2Fft0X06j++r0J44ovvr2ybZB43ty/5idoP2Uxxmn/FkndymWfbUD0N5lNaCTbr/XmzjPFpKU5xJl6G8YRMer9yH8062iO01OYX9r9CDa/OA1maP/Hk1lJO0dI3d56L7cknaJPN9yYjmKXf6nosrx58QQgghhBBCCCGEEEIIIYQQ/7vQgz8hhBBCCCGEEEIIIYQQQggh9gP04E8IIYQQQgghhBBCCCGEEEKI/YB56/hzvKQ5OzwDLnlpSAjA7qQEuYAylDM1laYcuuSyCtP4+fXrMOdsgpxpScqRPNHA3OStGubIzQ9hDtkMppiNOdvYZ8BuKJdy7LqUgN8luVGCvDQueUw8yvnbO4CONifCEw58vL52HXN/c47fJGlUiiXMCRx0MKduSDmFU+SviMhjE1CO4yR5UjwP83WzlycivUK7jtcXpMj7ksL+VatgjudyP9UXmQ3aHayvbBb7B7f/FDkXiz2YI9uh8RAG5MmpY//JUE5kh9prfIS8Qdy/KPd7rhfbs1TG/pOgnNrRtE/D9wN7lFwd850oimZcYnO5v9in5XO/Y2dbzJnHfi5yFLizh/P42c3uUAsicvzR9XGciTnc6PzZh8TfniS/WEg5/D3O1U5f16X3zyVtcyIWblGcTfD5Ix7NI+yM4G/n9uf6csir41DcY4dhOo0Oj4CEV/x9XhLf7wds8yRo3mD/mUu55gN2HkZ8fHYeUn/iCuYa5ONT/3KpQ/DhuMzu4A7XB53fDgdIzJ04z3GdYKZuXfIVcx9zqI1ZE5Ygr0sixU4/8gvT3JMt4lyVzGCfdNkjOocX0ucYyPnv2eM5h1dyooJuo5t/810ov+L/+zs8HXLIsSOx2yUXGXlZ2LXENOrohXn4vnuh3GzhWrPbJm+sjzFhZASvL6IY3yWHY28Prk2qk1T/LZL2dfDzKVpb9PSi58cnF1uni9fj0pCcGqe1ZoTfn01jeWoKfSIbt1agvGI5+hrYsdiawvenU9SfM3T9NEd1u+gDWbMWfR3lPqyPNAlROnWsDy/E/rRkAa6txquPrT39YPedD/OBgf7CjEOxQv6nBNV76NMaPItrbPaWJwNyABqusbs0jqkb2dhm9AM1ac+yeBm2I21RrD2GHhjHyH/rkXengNdTIoecX6tAeZIcdDXytKQa2O88WmssGCpDudmkuJrC+uE98eIS9ktnAI9f8TCO5LrYb8uDOA80Gni84a3oq6IwZaRxt6WL0X82fDfuK/r7sP57ymUob1iLbtcEeeCTvehJX//gA1DOlGhTT/cgEr00z3l4Qe0pLHdq+P4VK9FDlErQPQ/qgPWxCn5/Hv1ovBeZGMH+0teP17t5K8alhUsPwvfTWn/svjvwfJ3pOM73OuY5Uapo0XSMWtyPMYR9q1NVbBO/VYFyo4lzdTJFfYx8svkc9qGOR52e6rLtYB/kud0lz2iG1iIRzWUBraVqk+ipXLngSCi36PhG+7Nime770Psdup4k3efqW7AIysMbN0A5P4rf18zjHJIs4+lFEbnHaH8Q0hjh+4bJMr6/J7kMyqO0Vlu2EmPUxs24VqjWMIYPLcX29B28T/bAXfdB2aP9W55iUorWGilyp6XzOOYnt2BMCDzs/z7NoS1ai979AF7f4CJsv1IvzgnsEvNpLcv3/dZuxLXhtjF8PV3Gtd7ICM4x3N+9nfqrzxPOPKeYSc60ZzfAepucxH4epjCODAyga5L34UlaiwUhxi0vwrl+cIAcgdsews8bxoHaGPaTI487Bsp+A9cCjRZeX6OGDrd1azdDOTdcgXKa5t7Dn/UcKBcL1E+HlkI5NL4PhvU1OYlrzyY9DyiVsf49oz0y7VnrHbwfGyVYLM73LfBlPyQveBPPx6GwPYqnbzna8yQS2P75Aj1vyeD1ZGjtXq/RHha7j5343KOh/OCjGOdzHra3F+DxmlXs72VyStbGcK8xsXELlH2ah4tF7G9bN+HaPkVrec8wznRpbRTQPZJEgu6zUfsFIe2FpuNkGLvD9fjoF39CCCGEEEIIIYQQQgghhBBC7Afs0YO/j370o3b88cdbsVi0BQsW2Mtf/nJ78MEH4T2tVssuvPBC6+/vt0KhYK961atseHj4cY4ohBB7F8UpIcR8RjFKCDHfUZwSQsxnFKOEEPMdxSkhxHxgjx783XTTTXbhhRfabbfdZj//+c+t2+3ai170IqvvlNrx3e9+t11//fX23e9+12666SbbvHmzvfKVr9zrJy6EELtCcUoIMZ9RjBJCzHcUp4QQ8xnFKCHEfEdxSggxH9gjx99Pf/pTKF955ZW2YMEC++Mf/2innnqqVatV+9rXvmZXXXWVnXnmmWZmdsUVV9jhhx9ut912m5144om7/V2Fcnkmx3l9sgKvuQ7m5OXc5SHlCk9Rfv1OG3O+GruV6Hlo7wDm06838Psmxskf0cacuT7lurZxcqIVMbd1yiMXlkPeHHLM8dPb0Mhxx9dHOYGjgNxeAdVfAnN5hwHWp5vG41uXzp9cUAlyU1XJOxOSa6nRwvbqcL5tcpm0prD+B8hP0KWc0CGd3+gI5k6fJGdfz0ISeFCu8XwPtmeLPDbZFHmHyFPEucwbDWyPWg3LJfJTcC76ah2P1+mSC4sdgyXqnznK5R7h90/W8POuw24CbG+H2itT3O5H6bJQ6gnydMYpx6KZnN4xo1yIf+l2OlTGeuySu9S4mxEROdQc6sccGGJGv5gDb3Ynm0MOC46T7Ofib+Sv8+j97N9i56HHzgU6Hjvf+II5rrAzj68v5pyj8/Mo93fgcxyl/kwn7JHDwHPY+UfnE3P+8fWwL4WdkDSP0PES5NCIKJe4wxXOjkLjz3N/Ykckz7tzQB2Ijx/x+TicO536Gzs7Yk6sXddfGKuHPefpjFFu+JjjL4xwboza1Gbkd3Vpidis4VycJE+IsceS+lg2g3MxxwCOaRH1KfZO+uS15BgTa3NqY5/WOnf9+ddQDmiFzN5M9uqwK2lsHH0Bv73tGih36HxOOv4lUN7w8Dood2kM1+u4Vqk3sH03rMV/LZzJUX3TmOmQk7BMMSCiGNOltU2KYnCaPai01qTuYY0WVvij5FtYshT9I0k6/zatDTvUIQoF9FG02+iDyJL/IU2urKkpXAtmyIvj0/l4DvkwPDyfTpN8zSVcO/ppvJ5+WktF5K9Opx+rUHcvLKeezjiVyeVnHH+D5JuqtXCc1kfRN5Qjf1NEe65FK9DBNzKK9Z7I4BrX5TjkYr/fNopr3t5+PL4X4Os1Wsx5efQNWbuC57cVx22aXKqNKvbDWpPmdta+tPF6kgX06hQKtEfy8fge9etsGr+vHdD1TdIeqIzjuDmFx+ulPVM+T95y2vMHdSzXang+49vw/AsUtnMtfP/D9zwC5baDcSKcwDg8NYF+s3wBr7/SRA+PS45EN4HjuI2naz0pdDq28jTOqYEnx9BzM9LeBOWhxdjfxshly96nTGYQyk1yuybK5Gol9+jkKHqaQnJWun3by243MLMn94uWpzNGLVi4cGYsss83m8G5oERuq9Wr10I5Q2uHLnk7LUm+WfLZ+uTBNLqv053EPlHh+za09iGleWwt4E9Sn6a5LfTIm0lz1cY1OMZS5DQs9+F9tgU0Fz66Gl1dGzagt3OY7tvUHIxp9iiujfoPw++3DPmGyfmXoAoKyGcckWczJOd4gu7bBT7Wf4vuSzk0B46O4X3G4W1YzpOYtkD3mVK0dnZcXGslk3g9Xh3b20+Ql5XEqqsfRS/qwuUHQnkoTfv3kH3UOB5qNfz+Ft0vCWiOHx0n7+gQ1t9CWjuuvf9hKFfJubmw77E1QWcv3Jt6OuPU1tHajBMzQXuuFt0HSdMauUZr3N4+jN0Dg+iHbZKHemwEx1mF/LJDQzS3UBwjvbCtf2Q1lFstfD/fX5yo4vfT7WAr9dMehO4rjQ3jWmXBEnR1ui6Os2Ie11JRkvZAORw3YYBxZet69EcXE7iWyHaw/kNqr5aLcZ6lfuxzjuj+RSrLzjm6L0X1k6b7wez4W7MR10a8ls7kcW3jkNPRo7VrtY4NyPNUk/zWrov9IUN7qMlJXJu3pvCA5QKe31gd6zfMYNztJTfpgiXYv0ea2J/GA1x7uXTfNogoLtM9CL5vuMPZ6MTu3z4+T8rxV61ur5C+6Qn7j3/8o3W7XTvrrLNm3nPYYYfZ8uXL7dZbb93lMdrttk1OTsJ/Qgixt1CcEkLMZxSjhBDzHcUpIcR8RjFKCDHfUZwSQjwTPOEHf2EY2rve9S476aST7KijjjIzs61bt1oqlbIy/QJpaGjItm7duoujbM973NPTM/PfsmXLdvk+IYTYUxSnhBDzGcUoIcR8R3FKCDGfUYwSQsx3FKeEEM8UT/jB34UXXmj33HOPffvb335SJ3DxxRdbtVqd+W/Dhg1P6nhCCLEDxSkhxHxGMUoIMd9RnBJCzGcUo4QQ8x3FKSHEM8UeOf528I53vMN+9KMf2c0332xLly6d+fvChQut0+lYpVKBf7UwPDxsCxcu3MWRzNLptKXT6djfgyBh7nQO6sFFS+C19tQYlEe3Yk7ZegtznebIe5KmJLKs7AnZFeRhzt1MEV9P5TGnbDaH1bplA+awrdUxx+zoRswhO7QCcwa7KUp6S56ZiPwH6Qyej8uuLspR7IXs4sKczUGXfAchu7/w9OqT+Pm8g9fT8jHHcYv8FNUK1k+qiDmqM1k8n0aVHHOU07hSwfafID8Iv58dkKwOa3Xw+jJpzAkcUa5ydn81yeXWJD+C6+L3syqs0IvugKka5WannMFJ8peMj2DO4eWHHwLlVgOvj11aLo2PDI3fVgNz1TcpR3Mug7nXm83t7+/6e0FKsxNPR5xybCc3GfmLOuSmbDfJVUl+JJ98T+woC8nnE7LHhR0E1G4OeUHYyce5prnjO/R+Pr+I4pLrcpwlxwCdv0P9KiInATv/2MjGB+Trcel6wohzYvP58fXPXn8RtR87K7k+uH7Zucjjjp2EMd9YxDVC9elzXOPpn/ufPakyO/Qicl6we5YdjwE7L6PZ6y/WfnQ9rPALyDERxaSYeLz0tCs44Hp/EjwdMcpzQ/Om+55DdZokL0y7i33Eoz5bJf/DYAq9LA6PWar0IrmBHPIHsKOP+1RsTNEbuG18n2IQvf7Qw3+G8sNr7oRyJs8xjMcceyvx5dvu/BGU14+hV4S79PU3fh7Ki7PHQjntkq+DPSe0Fg4M23N4FMtHrkRfR8fHtUK3jWtTz5lDPEuOyPXrce27fAV+X4a8L5OT5K0poD+B1wgdWjtu3obnm8riWiOTxbXxFH0f96eIPEjsEyeNjmU8mtPp/UO9WL8dEpqMb0PfVbaI75+o4tpsUR9eT7322FqS1xNPhqcjTo1NVGe8NA6Ns2Qe40zfAiz75HUPaY9XbeCeJ4jYb0X+LPKc1Cu4Z2BPSJe85vkc7gn6C9gPq+SoS5HPN4itbsj7QV6bBUV8ndfkhV6sr1oNfVhpclt2J3BPUiri+S0ZxD3ItmEc560OzZ1t9J4EC3FPkAywvhIuedjJS9Tbg3vKdC9ebz6N7T+8Bce5kT8s7WLcatcxTvrsYSFnXzeL/cmjwB40MW4nPNrTJrE9+nvx+1JJvL6pKWyfZJK86eQHG96CXpk09c/BxUuh3KCUcY0p7P9hBvtDYxzbczBLa2/yINWmfWzdYPe9NHPxdMSozes3WHJ6zZIgv+vCFfhrm4nN6Fks5TGWDxyC77/nT7gWqU7gXOamaXFEMe9Zxz8bX+5gn6+MYxvy4iqgtRVt462Uorm3g31sYhTvyzU9bNsB8mB6FPMStB+bGMMYOziEMWfTJow5jRrOja5DHlKai7sbaT83RG4rVGtZyPdBaO3WidhFhe/vofs2bb6PRs3bQ+6oBDkUQyN/8CSO8ZD2e6XBAXx/GueQVYcdDeVCD84Z99+AfupmBZ2LMZ9zBzuQT3NyZPh6EGBMe2Q99ld/CsdDMokVtnDhYijne3CvsXEdPhRLdshFVsXxVE09Nmd1eS/6JHg64lSUKFo0vZaibh/znvvk8pyaqEA5T/db83Q/doLikJfG17cO468VF/VjHLSQJHyGnx/ehv26OoX9utyL7dxq41yYTtNcRC60oIPX/9A990I5IidfZBj33BSuvdpNvJ6Oj8fv0p4tTZ/nG+ouecYzDXL+TVFczmMcTNLzg7CLcadreD7lXtyjFXvpeYTPe148362jOE67Po7zY46mPSdOG5bqxTjlFspQrm7ED3Rp7eglsT7qNC+M0do7R/fHM+RqTZOLtknt1z+A9cP3eTNdciIa3ffiiSSguOji9ycx7M/ctXL34Gd8e/SLvyiK7B3veIf94Ac/sF/+8pe2atUqeP05z3mOJZNJu+GGG2b+9uCDD9r69evt+c9//p58lRBCPCEUp4QQ8xnFKCHEfEdxSggxn1GMEkLMdxSnhBDzgT36xd+FF15oV111lf3whz+0YrE4k3e4p6fHstms9fT02Fve8hZ7z3veY319fVYqleyiiy6y5z//+XbiiSc+JRcghBA7ozglhJjPKEYJIeY7ilNCiPmMYpQQYr6jOCWEmA/s0YO/L37xi2Zmdvrpp8Pfr7jiCjv//PPNzOzTn/60ua5rr3rVq6zdbtvZZ59tX/jCF/bKyQohxFwoTgkh5jOKUUKI+Y7ilBBiPqMYJYSY7yhOCSHmA3v04I8dRLsik8nY5z//efv85z8/53tnw41Cc6d9LH4Tc7QWejDH7cbVmLvZYzdTAnP+hh67lci51sbrbLUxB2uacrXz+XXa7IrCz5MOwtrkoMuXMKdraQHm4o4CPF4ihUlf2enHzrconN21RanrzQvw/FLktOPrS1NO6GYd66fJTkWqD5fEKckUnT859Ap5zIGcSmN9dNuYYzkiv4JLx+ccvQlyRwV18k+ksP8kHHJlkfstXcQc0e0W5iSeqmFu+yTlTm/WavQ61neCksUnyTeRJZ/H6BbMwZ0vYP0E5LmJeHyRS4zdcm3KAZ7iXPDTPpMwbm17Qjy9cco1L9peH12SMXZb5Adqk7PNZ8cCnfccPimWDEQ0bl2Pc4dzXMDXPX5/rBrJfUrj3mdfB+Uq5zjEjjWHcl/HHHURO+6oPqmfpykOuOwYCLi+Z4/bDl0fzxsOxZFEQP28xZK52b+f5bNhhP2LwnbsLxzXI+pP7PzjBo85GkkqyU7HiF/nM5zjfAKK6yGVvQS2J18/OxD9gB2a1L7e7MdLsiMyuf0de8Px93TGqMh28mNSgviQEsMnHEogTzGjl1xVnI+ea8alMZhIze6IS9Lr7O5KUB/w+PjUB5stXLsEIfaJP951I5RrLfLbJsifERtUsU4NxS0jOLf67J3Ey7MwxLl9zL0fytn2AVBes3otvl7E88241F7Uy5tNXBs55Hxskoc2gaouK/WQL6KLrqqojtfbaqA7qtnGMdZTwrVHfxHXmk1am63Zhr7iNrlYMh6t3SiGBxQzJmtYH4UMnt/ShegTyafYO4tzKLveUuQ/qZBrLRmQ/4HmRId8ExPkYRpvPHY+AcvLnwBPZ5xqBa4F0/Em6tAaPMJ66hnAelxXx3GWYMffFvSCOLQJWbQUPfJBk9bg5AHJkQOwMonn22ji9y8cxH7BHvjKODZkgubebBbfPzpCc3eIcWvlQeg7isiz0qJxX6uglyUw3HPWqlj/By0tQ3lwAH1QUQrbx2jtsr7+EJTbCfx+v4bvbzYxUC5eiYGoXsX3j5MLs+9ArI8arQ3cHM2LbfRLZQfIsUjDIuhgfbk+zmNJckimKS6NbMXrH+jBuL1tAuMmLx0L7E8jZ6F1sVwjgVswifdQChTXyh7Oo9uo++XJKZjpQZ/Z5gdX4/Gn53GOx0+EpzNGLSymLJ3c3hc2TmAlrHt4LX4nrU0KFEP+dMP/QLnhYB8q9g3h6yHOBfUA1wqbNmAbZulGTruBYz6Rwe/zXSxnScVVmSK/7IpFUK5N4Pl4FLOWHXQQlDc/ugbKW6k+S33oTqpO4JjMOPj+HH1fkudOWluwA3xiLfbx4ip8v5fDOcFz8fONDp5fs4ZzdYpcWz7dp/Np7cD3gfhm7RQ5Gwshvr9QKkO51IPXMzqG7ZUtYH37tD8fpzkgS/fpevvx+wKX97O0NiP3Gu8H83Tf1uml+3oBlkuD6A6bGEEHpEcxd9URh0G5SP23MvZY/XrRvhWnCn39lpre6zXG0TXaWyZHXQL7RW0C+9Gah9FLnuvDuTeZwkDB910cus802SB/bpfvY+Hreb4fXMLjN1v4/kIR49jAIPpr0wm+D4b9rNSH5Y2P3ANlS+Hzh3wW1/hlGgfs8U7T/foW7Sk9D+fqNN+nSNN9jSmsf6+F7dsy8pjTuHTSeL0uSSHHNqHLs9yP7b/hQXIi0n2boIPlGjkaN47j2qeX1gRdui3o0x4vIO/75s24VnLpfvzKFbgWDNj92sG4N9hbhnJrCo+/7pH1UOb74QuW4d6i4OLaqBqhwzLitRvt41w6vjfts+b7lbOxR44/IYQQQgghhBBCCCGEEEIIIcT8RA/+hBBCCCGEEEIIIYQQQgghhNgP0IM/IYQQQgghhBBCCCGEEEIIIfYD9sjx93SSTDiWnHbqpBKYwzVNCfaXLcecu5GHSWG7HXy/R96WRpPy3ePXxRxzgU/OM/rA1CS+3u6yjwHLC/oxJ3GXcsi2PMxBm+vDnMysArMIn+e6ITZzm1xjkWHZN6pvyk1ufP0Jyq2ewRy1fHopctK5U5Q7PF+CMueIjuj6WuTxScScgPj9fhf/wL4Gj3ICp/OYM3liGL+vG2J75XJ4/Zkc5k7vUM7eDsnZAi43uX3w/DrkLcol2NOE9Zfuw5zUuRz2J5/8H+wWazXwfDqU/pzHA/tFAvJNONP500P/yedRf9qJnBnPU0Dyzm6Hc0dju3M/5n4exuqRvpucgKTUM4c8MR79Ow+XXY3sLaHvZycgD+yI+mHkcNxN0uvsWmXHHjnWuEz/bIVza7uU89p1+XrZiUe53+fImc1OAqO4w27PsOPP+jr7wgLym9HpxZyMXF+x8yNijsVYe5CTgnKp++zkm8PxxznI44pDymVOLl4+P1ZYsXsvDHm80ffxCVDc5/Z3p9cV3M/mO61uYInpukiTV8PnfPHkF4ioThLk8nGM+lAC1zIx7yR7Scmd0Wrj2oi9oLx2CKlROWYlyafwp9//Bsrbqhvx8xTDwhCvp01zYzqL9cGeycFe9AlMbaF8/oZ0aA6pOON4/A6uJfr68PsnyRcdOrzExzE0TB6ZnhXkI86TX4JiepjE9s8k8XyW9+LrQR3Pz63h9fSWsOxQDPB8bP9VS9Ez1E/9h1cUU1PUfnlae5M/u9HG8+3tQb8Fz0HsOQ1DXPskk+xZxc8vHsD6W78F/RftLh4vHWJ9FIqPrZ39IDQz7G/zmbSXsOT0nNZysN/UDPcIm0fWQblBzrIooj0Jea2L5Nib3PAIlAsUN9okvOp28PsySdyzJLPovNs8huOsXUU35aIcXm86j+061iX/VAnPJ5WktSOtHboUJ3Pkx/KS2G+nAjzfKZoLH16P/WrVcvQrjY5hv82wh7yKe/ZKCR2NA6kBKKeNXJ9T5CQkf5RPa+tKBa+n42J/Csmbk8TmM5ukPbSPcaO3H92fzSY6JVtt8mmRE7FA7lyHAotLnp9Oh9d2WO7twfoukh9r21acV1ptbN92gxyIFKgGyAuU7sf29GgdcOgAnk93ur06/pP3kD6dTAWRtacdSYk09gmf5o52E514o8M4Jo855QVQ/tPv0OfL9zUG+spQTjq4b+f9c6JIXknD97t0nyxVwJhSG8Mx3tePbqJwCvuQT/vRfBEdhZbEMWx5PB5pLWPr+bSLMWuKYn6C1+vk8uL9SyaH11vK0+sTeLyu4fd10+Qzpjkn0YtzQpdeHxvFmLR2E9Z3tYnnU/Lw+wfKWJ9hHmPQ4iOOhHKjhvepSnTj69fX/f+g3FPCBkn34NosncVys4Yxuevj9bl5jHm1KsbEWouut4QxIwpoLUtrW7+NaztL8H1DrL+pKvmmp3C87Xz7pruP3ZpKpzMzjr92Gvs57/ubFRxXTbrYFDnwrD4CRb+DcSWdw9i/eBH2y3FyKfrkv+W1Wb1OrkvWxNOau0VrgxG675LM0v3/Op5PL42r/sXoJm20sB9l0xhnsnT9nRatpWhuTaVpz+xjP/ZDvOCpKTzfNLlaoxa2b94pQ7mZwn7Pe9Z2hOMgncD+Mzk2DOV+chr2GtbvtlHsL50axrl8geapLLk8aY2Q7cHvq9I8VaT77wXyN+eS2L+dFPZvP4PnnyXPe4s6YCGN81h54UIoD49gf3T9MpS7tBZvG9Y/P5/p+jRv77gPGO3+fSn94k8IIYQQQgghhBBCCCGEEEKI/QA9+BNCCCGEEEIIIYQQQgghhBBiP0AP/oQQQgghhBBCCCGEEEIIIYTYD5i3jj/Xicyd9omQ5sOSCcz52t9PucMph/HoJB6gG2A5Sf6GcBJzsibIbVVvYQ7eDuVW59zi7I3p68XjHXco5kKPKLf51BTmim/UyauTphzLURnK7ALLkWfHzeL5JsgNxfn+nQTlsq9QrnPywATkFGxTTmGfvq9AHqEseVgqY5ijmHM8+5Sj2iE/Qj6LxytSjuIune/mLRUo1+l138X26JD/I6DzDXys7w55kLo1bO/eAfQn5AvY3i3y0HSoPtwkXj+73rh9Og3MOewm8f0par+JrXh9CeofRjnC2RcRTOdwDv3ZnWTzkTAMZ1wEQUDuRcot3qV+GZGTgJ13AVcHOSRYcuaFs78eywBN444daGygCimOuRQXE+SM4H9WEtHxXPJ38eux91P9JGOfRzw6H77+VIp8YeSJ4TjMEkR20EX0eeNxQPNW2KE4zjm6yX/msD/KuH1ZtklFauCIJXkeOfioA3Ku9Q6df0DOPJecjuytYafkXI7BIMDriw0POl+frs/neTrm8OMeQv1txvG3b8WpYtqbGZsRtUFg3Eb4WR6j7Nlkp1ncO4mvZ8j/m/TYR0trEfJ0tGjtlUqRT5hiWquBc+ldD9wG5S452JgWubW2jayF8tCC5VD26HpWLT0Gyo9uvgfKTsgxGK+n2yFnnzsB5XwavSnDdbzegQW4dhidwONll5PXMk9jkMZcQEGFY77DcxQ5IBN5aq90BcpjTTxecopcZg65rsjbE7EPmnzUBXKneQncO3DMadbwfFevRa/RsiXktqL6cCkmFskJWTqAvt/w830N7O9bR7D9HIp5/b3lmf/d3cecyY4/ac709fvkFd/W3AblgOcu8pg7FIc65EasUTdNkk9ojNbAOYr5Pns30jju/BC9LuxdSZaxHzxEHvdBWjtmyC+1ZAnuGTlOJCnustf+8BNOh/J1V10H5SCN/b42Scdv4Z4vfcQBUE5U0EMTkis279EcOonfN1pAL8xAZgmUc7Rn9UMcNyH5wCZd2tOzrpq2lN1hrL9iH8ZZ0u5YZaIC5aGF6AJN0LjeNoyOx8UrsP5qk3hCvoP9pUAOv8iwv/FaiHXPvNaJyC07OITXmyksg3JlFOuTv6A+VYFyiVyxnaq/y/Oa7yxacYClp8fG8BZ0HWXJ+Zd06L4H+Vi3bq1AOZXBPl3qRYdaDznW8nmMKRHNhTVyQbXoPlCJHG5hA9cWPJPH+nQdv6/exO+rTuDc5aXRxdRq09zcg3NhhTyh7NA76rnPhfJkhb2a+P1jIzTI6T5YIY3f323jmGptxTHc6cX6S2RxDpjoYv8otcpQZu/rVJfus6Rw7cP7DlrqWG8f3hfy23g+FpATMU1zBO+/aD/bbeAcsGWE3Gg95DSk+gsdrH/ef+cKWP8dcrV5Saz/oIPtG+VwTsqmMeaEPrbXpnF8f4JiUS73WH129jHJX7dZNcff3kGmyKVYpfsAvGfyOzh39pWxHgtFdJq1Wvj+Dq15+X6hl8TJs0ne9AQeznoH0ePd6WLcqW7GcZ/Pl6HcIK/4RIXuV1K/inrovgH1s4UL0D8c0lybpuO1G9jPerI4Tut03zPfg/enE+RfbtVwnLLjrtEkXzQ56hLkvLMi3f/16P5vA+tjYJAEyLTnrdWwvvqL+P0rlqEPetNGuk9KcYjXXpVx2gPTXoFvw/ESg+9z+lT/Feov1UlaS9ERk+7ssaFYxLgzNYxx0do4Hrw+Ol7MI7/r+8z83GM29Is/IYQQQgghhBBCCCGEEEIIIfYD9OBPCCGEEEIIIYQQQgghhBBCiP0APfgTQgghhBBCCCGEEEIIIYQQYj9g3jr+Wt2WBdOnV6Tc5jHvjEc+ggbmsm51MCeq8/9n709jLcuu+05wn+nO97775hdz5MAcKA4iKRWZZdlVoCkLasNlQ6luQ/AEQ19sEGpbghtufrIFGKAAFyzYAGUYhiB/aKvYVjXcsKpKlm12iy1ZpEylxSHJZDKnmOPN7873nrk/xMvI/P8O+YLJ8UZ4/YEEueKee84+e6+99tr7RqyfrzVpWfM1qqEePTgndTDiSk9r4qbgSZCT0m5ot8N0tYbW9m6hFvdopDV09w5QC311Vdsz1/bUu9p+ssFaXa3VHXS1FncGht10jFrlc7WThXZwtAD3B9yVAmyrBe6XgcvT7GoHFgsw+MCU88CJOdnTGtGHBwOx22vaHz44Or2O/n6+2gczsiRrSv3xGOOZNdTfm23UekeN32ZI3gX6D5yjAtwbV1e7AJugRE1tuLOL8XmCzzsTnR/tBpiNp4xIcikeBpWef5/NV2GQEUPjaIOFSaYericDzssRONDvwKo8kFlXvGN2Ge9HKgQ6AH/NhP1FxpsHnpgHqEEEUEuB9/PxQLJHyafKWPvecd1A/+P+BWp9l0Aeeqj97iLwqcBZKXP9nEwK8p3YnrJkf5K9SUYi3geUj4LsVrJawfPyEOeCUJ+X4nMy9wK2D+ObM65iwpGZyeeXLInO+UXkYqn/+7DI85zzTseuwmgDfzUEt7Ld0txqityKAGbGPDL6ojrYROjkOnIvH/yACsMPzD/mDiejgdjTmbKjfLx/gdwkARvqlZtfEnt9XdlTBK9cvvS02O89/DNif+XVz4pNZqIr9P2PBrqWbwaaK2yua//WAx2vrW1waprgpIB/nTvwBzAe8f7Zc7q1AY4mAR5gfeVd5WEAi+Qmu/oHKXgkEbmilZiuKsAtzcjrAOvsZKT9+dJryvwrseg+vtMXu9VWvofvYQ1DDFpD7p0nanvY+6y8jZNDFsqyK6rVXO00Ro/mOu9ijBNjc4g9HUe6AP/KcY8HjkuA+3HPkOU6D8MaOB5z9YvODjj0ofoBtpBugDi3uVB7Bv5RBq6O1+uL/a4feUrsbl+5KRvnd8SeIK52mupnyXAg9pf+y1fF9pHrbIGLQy75xoYyCwe57vmOkl2xQ1/bPy3BOa9jPLFO9XLlBs0XykFq1cCdGSpXJgc/Ks80LhRTddAZksFmW+M2U+UZ+GwDMBO3+joeTXB7TsbqL3Pw1wZj7Mk9JkPa/nZXWaY+/H0fe+h2V/39xqH2z/Hw3vWMt8uuwzu3XC26F7N7azpnSszZ619Xnm8X5wK9jvrEAfLV9Q2dMwOwnKK6jkEOhttGV33i8FjnFHnOJWJcD+dE588pm6nmqz0NbordXNXc6Norr6C94JjWdC0cgMUVRfp5t6c+dvfGDX1+T89ttrf1+xPkhoMjnSONmo7nxgpYVlOco4ETO4yV47nqa8yPsEYFyB3KQnMhr66fX7ik/tHv6v1vvKH9sbqu/bV/V9eQjY76T9vXGLG2ov03xLnOYKwxd2tDmZBFou/DqT/GmtJpkMmu1/s4p5zF6k9IGdx0oM9f39AYvIX+8d6Wm8XJQ5ZLNWr350t/FezEWMc5R6xvtTSujcF2fOW1a3o9zquZnNWbOi8mc3DDcQ5UA+uT5zBBTePmk49dFPvgSNfqINN5XcM5xfr6ebFbTX0fH3E9QH/VwCycTeBn65qr+OBPxxiPTk/9cgS/5Z4m8DWQpziArdW0fRnQnxG/X9d1oo/z7xXM62+8fE1s8pNz7CmnC51LcYZ1EGxXL9A4imMod+uO5toNbAUmDe3fHnLtxUI/n021/x6/rP5w4ZzG3Ru7+vzbt9T/ZuCFL3iuRz71Hs6xtvR6P+TvMW8y/r79XMr+xZ/JZDKZTCaTyWQymUwmk8lkMplMJpPJ9AjIfvgzmUwmk8lkMplMJpPJZDKZTCaTyWQymR4B2Q9/JpPJZDKZTCaTyWQymUwmk8lkMplMJtMjoKVl/OVF5vxTbkGSgl8AZls81d8vJxOygtRezMmF0WdPJloTeDjU60PU9y9Y3x8/p6aJFtXtdpVnsJjr5wFqAJPt5YN91QCHJEUR3xB8hiwG/6BO1pXWLvcjfX6n0Rc7CvX67KbWFp8e74vtOXBhIu2wJMT4gWGXLvT9GjV9fqejdoEauvsHWpO3cKhtv648gxqYjv2e9mejqeNVgjtTw/vWwK2prYFZGYGjk6JG9vFA7CLXGtweHhChdn4UoFh6rjWv+bcByHWagdnYRf9kibZnAV7FTTAhp5N79ycL8WFQXuQuP+UykVHIt/F88rDIBASPCLXSPTLbyHUButEH1wT4qQqDji2uMPdQ25yMBhbhL8mkA6Mtw/uzVnno0xPP9g+/Aq3A+/BuGC9+n+NVkiFHqCIhBJhnZUQwEe6XgrVZqk3+lg8mh8/+R3eRp+Zh+S8Rp/g+PmrZ1wLw4sjcSzUOkuFHpmDVBmNQW1dhMmbwrwDtJTOyBAQiQH+W38rf6EhLrnsc0nvvTp9OyQWFTw6GGrsDcGWw9FW5jZyD5I6C70CfLQqyh1R+wDHVB9y69brYOca8LHTtIo9iY1vr+R+P9X6f+aNfF/vJKz8m9s7aM2JfXn9c7BFykd3J18UejzQGhJnyImpgdRUVfoDOwayLtR7cHAdmnIc5UGLNqW2AEVkZrxpszSUY4wMwCf1oIPasoeMdZX2xM3Dtag0dT64oAWJ+AzyMstBcMwErro4Y0wDgIgBPJMDepQA7Jmzo/HLgi3SR206n3Ku8FcPS7Oy5s2wKo8CFp2y4wclAPmPuAfyQywr0M/2KezJwwgOHOFBXOwVzsNkA826u8yzAWheDa5KDdUkGXeHUDw46yrvaSMGLAt8oADj+i3/8NbHPXdgTu4G452HPdXKoe7hOT9tH3lPoaf9xz91aVY5QgD1La67zfABO/WszbU8bnPVWoFyWTqHPOznQedPEwjTGPE0RBwPEmdCBL4U9FRe+O9e1/w9u3ha7jT1mp6O5CeNIGSoXqCh0/Ecjfd+8BC+trxyfErnrKy+/pu1rk0Oke75WS/1zAU5Us3nvfdKHjPGXO9/lp6tIMtN9bA6O4nobax/OOdZWtI9u7YLlg2RoBhaRQ8xJca5T6+uc2t9XRmAd319p6/WdTWX4ffGLXxF7vYe1COcEh8fKOmp31McKT2PIrdvKifSRiyRYz37///OfxX7fR35c7MmRxgi+f6Ouc2C1q88LwSklD/uxSzoHZphTr440dzhMcQ6G/ciVx3SNCSLkbjOw13S43Xyh/rg31PGYxtqeqK4xcjDR/u0jFTmZq39uX7kk9ksv6blfgEV6a03ZbeMp/BfjO8X7RYih/a42cDTWGLO3r7n1lSuaK8/mGqNnmc7P+clbMTrhRmnJlSXJW2w67IvJfu6Ahenh/Nl33NfjvB182IbHPYh+3mvr+Wp/XZ8fYO05PtZ50V/D+Sy42Ws8KUjVL0q0B8ejLpnr9TXk5Dn64/Enror96ot/rN9vavsGJxrnikDj7tGh5nonx2oHOB+PcP7daKhfr62vij0Z6/3Sgc7Dpg6HO0puiT2faBwpYu2PPbBDHbjra6s6Dy89/pjYW1f0/cYnOo9rde2vdfw+NDnRdSfEnmyB62epPu/yeb3/pQt9vX6h/jFPwGkHt30VLNEJGJDkus8Qt6MY5/+ePj845aKX7+D83P7Fn8lkMplMJpPJZDKZTCaTyWQymUwmk8n0CMh++DOZTCaTyWQymUwmk8lkMplMJpPJZDKZHgHZD38mk8lkMplMJpPJZDKZTCaTyWQymUwm0yOgpWX8FZ7vitMa2CPwA9rgK6SsH9/W10pSrZmaAoY1R63pEjWBPdQMHh9pjdteV2tlt9paczd0WtN2MdPnx6itvjjQGrl5prXAC9Q+r3X0+TEYav2e1vhNY9T7z7V/8onWyC1WwNEBg251Q4sCt3tPi719TmvFH9y8JvbRRNtbkotS4XVoewLUSl/E+v06GIjndtbFnk61JnXik5kIzo6ntXRzMP08sMtYM7so9Hkeak6Hnj4/xv3I40jAiSnA4RkfaG379qrWyI4wn2Yj9Yf5RIutr6K2/9ZKX+xmqP0boMb43WP1v6PTGtB5Ubjbg4F7mLRYxK445VTFifZbBh5QhnEvUCucdonaz46MNNTEz+GnHphlQU1t+l2BOOeTBUm2Ge0Kk09VglcUkmVKRh9U4Pus1V5h/FUaoCaZc3yhAvcrOe9R276CfiPzL0QcxXiUGWvjY7xRuj4vH+QvhDiifwB99AP4C+Ig21O5O/qLfDaOf2W0yLgs+MLgk4EPR8agj1rrFYYj/DsKNa6SYXm/PQRMLbnKvLjPSyxL9jreBRwN8nWzWNfqtASwFsy9DH3YaCnng4Qf9iy5lQU4mx7mJKdAnOvaFUXa3u31vtjvf+ajYnea+nkda+UC/Oabd78o9u9941+I3QQfeZZrvf8M7KYWmG6rq/r8dkjWGGyAXJNA2+thDfEwhxo15VsUYKl54CeXYKeV5G4ScIzx8jm34K7t83q/4iZizly/f+eO5j6dpl7fAyemmCn/wosHYtcQUxq+9t96S3PhFDnBK9d2xd7c7Is9Q27kZWBsIvdOR7pXidO3xjd7yPhZ0yxzyel89hyYcwFyHa4NjBNArGHLxFTK5WRh5pg3ZDsijnlOv5+Ax1srwYXPwcQDhyRHrsjAeOIpJyZo6B5wNnlV7MYxOCdj5U1twA9DMAfnmDcRuC1dxKEIuV2jpt8fY0++wDrR7Ov7hIV+P58gDp/o88JI52F0TjkrzoFTA0596en4tHranjxRf5yAe7OJOF5r6P17q3q/kyNdVyMf3CXkLkP0Xz7Q56G5bm1V59PJnnJzJkN9nyjQPeIC79vp6v26HeTmYGL9yFPbal99wjnn3GyRuv/9hZvuYdH+nT0XnebxIXy+11Mf3Div7xwgdwk6+nkt0nOXyV1lK7XgU0mkMaiG/dc+GHtN5BJ39wZiF5s65rlTHxkca8zpNJU5l8JHxjPyonXt27qojLg7h3ru9cwz+nkH5zjTib7PZO+u2HWwy8g4H83I+gIzHVzW6UJ9fBTr2vvGrYHYORadzgWNuevPbIqdnuicmh0oX7pR1/HZPqf9szjR/t1Y0/ef4BynhVxsAq7sV4fYL2UDsR+/oOeavVX93HPav/Wutr9wOt6NpvrzEfw3BfM+2ztQ26n6iFExoIGNpr7vIWJimL+Vez1svOST5MhFb+b+mIfjkfZ7Z1X9sHJuhbUmBKt0p6f26qqeN8eFjkOK1G0w1D3QaKhr6Tpyk0YL55fIiY/g59ORrq1dwCuTBXjL4NfWsSc+PhyI/eXJl8RuN3jOhbW1qXErOVa/jpB78fyacbbW0vfZbqvdwHi1232xRwPt/waYhHPsUWdT8KE9nP/jfLoItT0nYDbGOD+Pwd7cuaC/H9SxJ1/b1HV0PtX2Xv/ql8UOmshVsY67QOfL3T1d927vavsaaxqno1Lny/BA1/Fap682OPH1up6fR2DLzo/ARl25194i/fbPpexf/JlMJpPJZDKZTCaTyWQymUwmk8lkMplMj4Dshz+TyWQymUwmk8lkMplMJpPJZDKZTCaT6RGQ/fBnMplMJpPJZDKZTCaTyWQymUwmk8lkMj0CWlrGXytsuCi617zcaQ3fWEuouu6K1uRNZlqr2QcvoR5pzdQIzLMWuB8e+AvHB1rbezbVmsR11PNvdLRmLhlxr9/S9s5QS/3C5fPa3jpYSLHWfM0BuJhOBmJvnLug16daAzlO9PkZOCdhR/vH+fq8GkrmbpzXGtIluDtHX/u6fh88jC6eN0Tt7zgGPwO19Ot1HQ9giJyPGrpk8JFb06ij9nmhL5wDyJEu1D+iCIzCSGvPz8faP+QoeagBXYDLM1vo9xfqrq7w9Q/a+H4ANteVdZ1fW5gfPdSULhJ9fulr/3eu6P0uZPf6P8ky9+Xrd9zDpNl05tJT5kMSk7WIuvBk1OFeZfmAGs1grHn4exvkd6VgFLgAjDVOY3BYHBl/xIFVmH5oH75ORl8IBqEHTg8ZawEYDBWmHkUoHtsDTlCl//n6lY/5/j4vUBPcG2CMKtwiMgyLBDwzxLkSD/RRuzwAw670GQjB6wLFL6yByejT/8DYo3+wAx9gc3zoDyHmV16Zb2p6AMD5eN+QNqfj6bqdZg/X35cKgtAFp4se56RPJwWbh2uhDx9OkHuQfcWeWt/SXICsqMqcTLA2e2f7TJpoLpIsNLcJwLj7yAf+qtjtxtlspXpD59B4qgy2MtFcr9V4Uuy7B6+JXatr/2fgtNbBZ8jqykfYn6udtnStXWT6/h74vz53AJyDGfjFZCpyvCoxSOdUJcaB6ecX4CGDrVKLdNGat8HGynT8Ong/zmkPfPBeXb/QbCssazLRG0Tgojow/1LMp+vgg9880dwyQn/1wSs/V9PnbwTg1HhvvY+fP3CFXCoNxyMXnjJFq0sD+h3s0Rhcby/E2g/OTB25TpbRMcC1gePM4Tf9VeV8uIXOS+YijJvE+7ZamCee+uE8U45JVMM8xdpdO6/v25lrnJ5N9H7cU6Sr2p7Fob5/CfbppCSrUtvT6+m8WZS65zwCp74Gnll/HRzx23p9Y02v3z6nPLIYnCPSMNMC/ZvrPOysKccojPT6vYH2z2ak3++uKh+rgTODDP6S4qgmx54qTdWBjge6B2uW+r597OEyrMPtFX2/EnHu2mvXxK4jTs5vKddmgD1uPL03fnH6cLGz0tnYlaf776KjPgh0kMuQiyxKHcObf/Ki2KOpxpzC1zGNsLY7rI2l0+ddvaQxaXVFx+izf6gcUPJ9mQ+vt/R5h9dviJ3XdM5FgT4vgo8vRjpnnnrqij5vQ9lOsxNluoVYAza2dO2Pcx2QnYsXxb57S5mA3F94EeYkYsLNgZ7bBRf0XKqYg9GO5DsGT/hC5wmx+2AyXjvR8UmGygzcPqfjvX3xstivvnxN7MkQDD2n41WQEY/zhJNDjdmzOVhmSI3oT644+7yk39LxyyYa45HKuoan31/dwBqBg78ZGI8LnJuVb4PRpcwPllzj8siFp+dBQV3fu9EBZ53nDDynwrzY2OiLneH8eO9Yc9zti3q+PjvQtegYa+XOjq495y/onnHvtnK7h0PwasH0S2Id13bJOKXzMhlr+1Y6ev4ep5pr7R9oex6/rPOwxCYrRe5XIk+PwMgLa1jrsa6MD5W512NuBGbdbKbPX8y0f7p9zU1y5HKFD3sMNmpTx++xxzSucZ4VOCWIpxpXFlONkxH26E3sIR87j/4b98V+9ba2v4Xff6YYj1sH2p6d7XNiP/uhHxP7jRf/q9hz+ueJ3q+3jtwUZxZeS9+nCf7yyunvJWmZO+euuW9HD9cJlslkMplMJpPJZDKZTCaTyWQymUwmk8lk+qayH/5MJpPJZDKZTCaTyWQymUwmk8lkMplMpkdA9sOfyWQymUwmk8lkMplMJpPJZDKZTCaTyfQIaGkZf4vp3OWnbL3euta4LXOtcdtoaG3qeK41YR24GR7r54OdFIIJ54Hxto5a4uGJ/n46x/39AryFFmppr+kwZOCqNFHb3QfjLU5QG97T58/HWoN4Dubg2obWrK2DqTiLtSbt4OS22OubWju93gA/YK7tIxtqZ12vX13B+8JLj8EILLy+XoBS4jHYax5YafWavu9kqDWnG6i57IOV5lCLv4Va9tPy7FrmWaz+N57Av8Fj8ENtf7ujzLwQHCYPnJ8aalqfa+j17TZ4KKG+TwjmZRFrf1Q4QhWWGdgHp3yWNHu4eA/O3auPnZ/Gl5xMHZSFD1Djn0wBH3HDg+OTf/UgO0Ut8rLUiRFhogQYqMLpeJDB56NmfoUoRMYdmXsl7g8GBhlxJWvPoz8r/Kl3yJAjU48MOA/MA36f8nF9yf7APCrryjRge/Ic44m/t+MDqhA1tZZ9pVY8wEIFGkiqQUEEJP0BPDSPvC+MVxDx7x1x/nB8zmYcZmSeYHzYngD9HwacDxywe/9Dns6yq/A8V5z2fVGZlGqWmJM56ufXkFt4kcaMDGtbiFwljMDDhY8HHn3i7DlMXgCZfEcn18Ve6YN701WbHFH64O//4f8h9qs3vyx2hvdJUl3rmpiDI7CsyGVrISbUfF3rXai+OIuV95CDP0wGXQFeZYQ5WeT6/lmqc8SvaW7nM/nymSshpuP6FIxDpGYuRS4TrWiMG95VPkcQaMxMM+2PZlttv1A+yd4MnFFf+QoD7DX6PeVr9BCDL5T6vH2wuDpgDHaQAxyB/9wH/6Hde6t99/Ip5SItsxIvus8VKp3m4AUYOzFSxXpdHQWplcsRNxKwQyOsnT5y+izD2oG4OJkox6O90hc7nSkfipxuHzl5nKJ9gfqZD+adQ07tkLsNc+U5jbJdscup+mWrq341LbX9YR9xutC4NJ/pPErRfzlyge6qfn864Pvq+7Xr4Iq3tT0Hd/X9FiONix3sqY9OdF7tXNA9bZaAyYjcurOhe+rpSOftl17W5z/zGHhc2xo3CjD8bt/SPXeGZGxrBePXJou2L/bgRPvHA8OyDq5Op6n9/YHn3if2rWvKZp+Da+/39czj1uTePE/Th4ud9fgzl13tlNd447rGVjLl4onOwRE4mjOu/Tin8le2xY4aiDlH+vzRWMes3VAfXsS6Nv74h5Q/PARjcAoWVNhVdlOE/HkwGIjNLLmFGO3n4NuCo+k5/fzwQGNYHczx3QNlN+3f1Tl3/jF93xrOqUosKt3NC2IvMqylJ9qfyaG2Z+txjVmjqc6JDHzLeaLjv32+L3Zjqv4zHOJcp6UxdG1HWV2rG/r9Jx5XdtkxYu4b1zRG+DgnuntH53y20PFqBrpG5dhfLsAq4/6xONb+XmvrmhhuK8MwAzOw3tJcbTHX/ient8R4pG87B03dw3U25Qel80/ZbmkN+/yG+s3MG4gdD3VcMvCTkwR7hlDnUY7z6zzluGgcCEdg/J3TuFfCb8hFzxPs+5Fb8DwzQE7d6Oh5folcrfA0rtdbOk/3DzUuff0NPW+vlZpbYIvsGi1w5X19XoDc0IcvFshlIvYPzrvHY50HOB53g6OB3r+vcW6BNbvRRn9c1zjSW9PcuNvVeZnhHIqc+GvXNc786IrmWo9fea+2ByzWwuf44Qwi0f7w6n2xm01w3pFqv/z1b4g9Hw7EZm6fzTROxgn2gH3NBWdT8Kvx+83o+N74vJPzc/sXfyaTyWQymUwmk8lkMplMJpPJZDKZTCbTI6B39MPfP//n/9y9733vc71ez/V6Pffcc8+53/md37n/+WKxcB//+Mfd+vq663Q67vnnn3d7e3tn3NFkMpm+t7I4ZTKZllkWo0wm07LL4pTJZFpmWYwymUzLLotTJpNpGfSOfvi7ePGi+5Vf+RX3wgsvuD/+4z92H/3oR91f/It/0X31q191zjn3i7/4i+63f/u33W/91m+5z372s+7OnTvuZ37mZ74vDTeZTKZvJotTJpNpmWUxymQyLbssTplMpmWWxSiTybTssjhlMpmWQV5ZASG9M62trbl//I//sfvZn/1Zt7m56X7zN3/T/ezP/qxzzrmvf/3r7tlnn3Wf+9zn3Ec+8pFv636j0citrKy4/8v/9JP3uQtk79TBWMtjrYE6j+dn2mGgNV4T8BRKR5YQanGD+5GA5bRAEd+iYM1hrdHaaGqN3tlQawRvr4L9lGlN2MGx1jafowZvhPr9rbrWFO70tKby+kXlD7gS/TnXmr9kXbXaWsN3jprRo0Ot+euBq7K+qjV1ye4ao0buItOazSkAHx0yFRN9Hw+19YdjsLTApemBf1HD52QYvvoN5QxNR+ofK+Az9Ne1Vnu7p7X1CQur17Q9eU42GOxDrd2+3lJ/dL7W4C5Ry9+hFnoN/R1gvngNvV+Mz69P4tPb5u4/fulVNxwOXa+nPvrd6vsVp/7cBx53UfAm40+vqSDmEFdyd3ZtawfGWIXhl2Sw1W+TVGtdB3XU5u6o3zRWdJ5EDf28RK1qR5tMtAojDexTxHWfME/yvmAHZAyitrsDA44fM65XlkIiCSuMQPDMwKsqwccqcrI+NS6WcCCyMlMyCcDNiUJlKtQaOof8ELXN2T5mAhUoYQUIpx/j62Tq1QDsqmPdI9OxwnjkeLqzP+f4BD79jTaYkXC/N9uTpKn7V//mP37P49T3K0b9D+994n4OVSJX8RDrOcYBp6Aj403HsMI1xdr6k3/pL4ndbqvP1usac8IIMaGkz1a97u363/79vxT76mM/IvbFrafFbna0PQF4uv+Pf/M/iz1LNRfyOIcQIzkn+g2t51+OwEGZaowIGpobTKbIpcDryHqYEyGYfJhj5F8wtyD/w2uBhbYAo7AHxiP4w1wTA6wROfgYUURgsPpvsqu56+xA+6cAE7Ln65pZlNreDLmyDy5QnijfY7Wta2gYgoEJNtcUXKOVrn7fBx/l1g3N3XYuboodBG/1Z5rl7v/9+195aOLU//inn3Xhabw4XOi8ygodtwQ8nxDzjDxlcl8CuFFZ6DisrSkfiXysxYI8DXDcwb3pruieKF2o3xTggPuh5uSYpi7L4Nc5WZ06L9rgwHS72IPWwLnB2p5ijz0FTywdY97E2t+TVHOXPNA9awNclSRWP+8hV3j/pT8l9i7mhd8Bp/6Q3CF9fhxp3Gn00Z5A4+bMIbcO1R96pcahP/q9W2Kf7+v7/Oj7n9D7YcB37x6JfXBnX+wPfPAxsTOn/jw80f5PwMycgCPTAxepv9YXmwzL4z3t//GJ+vfGzo7Y8/m98UjSzH36P/zRQxOj/tqfe4+rRffmerPTl2t8rGUHh+pj7VWNKZOTgdgJ+L41sIXq4NW24eOLWH14Ah5sb1X7N5/jHAT5b4x9fqer36+D13wy1Rg5ONYY0QA/eWVFc5kQa3utrZ/fuq7nKBFyiUYbjHikhu2+rpW3b+s5G5mCvW5f7BHYWDPwfY8wx65cwNrc05g9mL8hdmP2HrEvXlLG4Fe+9KLY58EhnQ51zl15TD8/3NMYcvUJjRke9kOvv67tG8OfykL7q9tUfyDHdufKVbFP7twVOwUbrhvp/RuerpE5GJHTQs/JTsY6Hm3wnJ96/3N6v0zvd/3Vl+7//yTN3Kf//ecfmjj1P/5Pay485XVnqc6LHOcK2BK6yMeeLFc73tV5UPN4bqNmDWzIFDntKtaWnQvKnlwsdBwHR+rn/NdLd3cHYrfhl3XYAdo/nWMtRJw6PNQ4Rx50HyzW0UT7a6imayDuFehAvt8TTyvbssB4TsBarffAQd/XODSZgRWKPWYa67yflzfFjnywX2sa9+Zz7Z926+w9bhNszvlkIHYL55Y/8uxVsW/exL+SxV6hgd9fBgPN5SYTjUM8t8wLnTBz+PPWCli1+L2mCHRdb+D3id6a5o45+v/GHWWfttr39hpplrvf+ewL31aM+o4Zf3meu09/+tNuOp265557zr3wwgsuTVP3sY997P41zzzzjLt8+bL73Oc+9y3vE8exG41G8p/JZDJ9L2RxymQyLbMsRplMpmWXxSmTybTMshhlMpmWXRanTCbTD0vv+Ie/r3zlK67T6bh6ve7+1t/6W+7f/tt/69797ne73d1dV6vVXB9/U257e9vt7u5+85s55z75yU+6lZWV+/9dunTpHb+EyWQyvV0Wp0wm0zLLYpTJZFp2WZwymUzLLItRJpNp2WVxymQy/bD1jn/4e/rpp90Xv/hF90d/9Efub//tv+3+xt/4G+5rX/vad9yAT3ziE244HN7/7+bNmw/+kslkMp0hi1Mmk2mZZTHKZDItuyxOmUymZZbFKJPJtOyyOGUymX7YCh98iapWq7knn3zSOefchz70IfeFL3zB/dN/+k/dX/7Lf9klSeIGg4H8rYW9vT23g/rub1e9Xq9wXZxzrt0IXa12r3k+akmPUUucrCsHTkmG2ulJQq6H1jKPM63pGs/0/p6nNYKjnvIcyqkW8Y1Qszdb6PNjsJ+aLa2xu8i0Bm6/o59PUZM2QftrNa1Jm4PfcOvmNb1fqjVl1za1hm8d3JPFXN9397bW7l6MtX8XI72eXBcMj1sBDyEKycTT2vlHqJ2foRa4BzbYcKQ1fjPwOtrkDoVgUeH6ItP3vXRF/xbOADWYtze1/XWwsOAeFZZWXqh/kJ0WptqhzTr8FzwQsr58NCAvON/AOMT9QnCeFqm+wfT0jTJHbsp3rh9UnCqK0hWn/vsgWmoUgXeEv3dRYYqBYZCRnViCtwRmW+hpe3E7F+B6b65+4hHYBb8vIvDCMO4VRpvezXkVCCJqz5NxCOheivZzZvjg+kRgGHjscPQvGXFsv8+4DkZAker45IjjZJdyYgfgmwXg9OQJ5gvep/AIneS8BX8M3UmmZI64RqYiWbw+2sP3DcBlYpwIwBbNUEudvDTfA68OIjeHfLEKLQ7v9+bzuF59p/pBxSjP+fd5iGS45Yy5fDUwz0qMcZGoz5f0KfTV6qZyS8idJC+XMYTN88n8Q0z48If/z2L3OsoP4Bwjf6IEP+HP/7m/JfbJGJyYstKBev3dO2JPh5rLHoz0b/cGGJ9bb+jGvkTu2u0ovyDsaEz3wQkqcf8I8DPG6AgcHeZSUQe5CPgZGZl9PhiRiBn1ELxnMCnnx4jBY/Cmm/o+flPvX3PKvxjN0L6GPv/xc9of/bbySArsNW7cVp7G+avKlGyAGfjmnudNDQeaK09j9c9ZrOPZeFvMTLOHK07NpwsXnnKBCnKpuXZ4zLmR+2CcGSdKMM58MNpOTpSHtL6pHPQUDLoSHPW8UG7KaKR+1WrquK+DW+JhXjSaOq+bdfW7CP2ZLNRvYvKfM+WP3T3+ulPpvEjwPt6htr/l6R4xHmrJsTXEjbGne9h5rHuwuq/rwgJ7uGu3lHOyuwDXxdf2dgrlptRaev9hU/esoxIMvxxcIDXdIlb/GR9r+7xM5+2Vpvb/jZdfF3vn6lWxQ+wJt3eUo7O6rvat2/p8H7ytEfa8x3P9PMG6/64f1bj10he/IPYuxuPZdyuHaHVb40HzlL8VJ7pefKf6QcWofiN09Tf3PdhPxWBun7v0uNhXn7wi9pde+GOxBzfUB5NEY8bKY1fFvnFNGWzNts7J1Q3lB7ca2r459vWLmPs/vZ5MwbXNLbHHX9Fc6NIFzfWmI/08xvvNU3A2wW/u4F9DNevYLyCB7+J92129/nhXfa+7ru+T4pxudVVj8s4aGIuL22I3WjoeE5yrxQs9R6sh5s8RU86fV2bfYqSsM+auOc5Fm8iF0lRjagvnikfgI6/3tf/qOM84GoBHjc9vf+NlsdfW+2JneN8U/Owez/mONMYDce86He3/AAcgjz31pNjc3958+av3/79fOWv4zvSDilPOc/c3tFEdEyPGngrvljr1U1fDPv0K9gCJ+sXktn5/Ai74xobmCq7APF3VtfrWl78odrOOfT642Wur6see0+eXyC0XWJtXwK8NcK7VrIFPHPF8VNvT6+v7FIH2T60GNiZy26im92MuyHOlEXLlwYHmstOR2hFYrXfvKj+4t6rPc/g9oRb1xc4W2p7VNTAjwY0vMuQiyJ3q8L8sUfsLn/uvYkfgQffATS/Apy4zZUj62FMHTW3/Vq8v9mim3x/h/gHWMRx5uHedV//gseArdzXOBziXnI3vxcEs//bPz79jxt+bKorCxXHsPvShD7koitxnPvOZ+5+9/PLL7saNG+6555474w4mk8n0/ZXFKZPJtMyyGGUymZZdFqdMJtMyy2KUyWRadlmcMplMP2i9o3/x94lPfML99E//tLt8+bIbj8fuN3/zN93v/d7vud/93d91Kysr7ud//ufdL/3SL7m1tTXX6/XcL/zCL7jnnnvOfeQjH/l+td9kMplEFqdMJtMyy2KUyWRadlmcMplMyyyLUSaTadllccpkMi2D3tEPf/v7++6v//W/7u7evetWVlbc+973Pve7v/u77id/8iedc8796q/+qvN93z3//PMujmP3Uz/1U+7Xfu3Xvi8NN5lMpm8mi1Mmk2mZZTHKZDItuyxOmUymZZbFKJPJtOyyOGUymZZBXlk+iEz1g9VoNHIrKyvur/6Fn3C1U/7KZKS1pmNwZVixNDkb/eR8sJ7iXGvSJqjtXYLbMT4BN6WrtaTJNak3UHN2RWt7p3ifEGyjEKymyYFyYoChcUEIXoRHNpa+z507WvM3RG3ufl9r3K6jZm+J4up5AFYWuC3pSN83mKE2LdoXgBsEzIvb2Nba34SZTSZaC3zvrvIgmuBrBBFrx6u9uq6188n6KjKt4Rz6ZFexVjqK/vIFMR7pXGsKk0XVAr8imuj1xbHWVPZr6p9prO33UaO4rEAHyfgjTA7cGzAfj07ZXFmeu8//yRfdcDh0vZ7OkWXTm3Hqo++9eJ9L43Gc8Z0I/J4gALeG7Ejwk3LUcGbcSMFoKMhiJNPM17gQoJZ4iWGkXWH8sdY83scFfD6YcJi3Hothk+tDKBvmIflUAT7n+ORg9GWo/V6SE4Ta+DmYCSXul2dYNxAHyMr0sa7VItQqB6cIyEcXgPHoM65wXleQg9+ccXffJgMPcYDMhBrYoq2WriOVuAt/IMeJOLUQ7xdg/jAPIEeK78fnv5kqJUnqfv3/+TtLH6fejFH/w/vedZ+/6Aca6z0y/jDHAjhFgs9D+EBZmZOqv/Z//b/r/UOOGbmnZ1ejJ4e0wqvAHOb9yQFN6BN4/zjWtTQHVyXF2jhH7vrGq18VezpWjsnRnrLDBsOBXg9W2AIc0YicULhn/6rGhDLEJEKu6PF+zHXIEUVQ9gEs8H3tHz/g/fT6ELl0PNIYcvtF7Y8mmICdNvgcofZ3o6G5XDxTPkMGbuiHntUOLZBbkjd+MNP5cuXdH9T2gAOEJcS9cfOW2Lvgb8zHynLbXHvr/dMsd//bH371oYlT73v6/P35mTUwr+E3HnKJDLlOowl2aA3zBDkvYz35sowbfXDdvQp9lLxjJE++tn8+Vz+agisfwzHCQG3yxRp1nQdpppwQIA3dTtIXu0j0fU/IG0bcazvtzzH8slvT66cF1nZFKLoScTsudV6mMeJyAH4y+MQR2s+13mEdcj74UD6TB+R2BeOW3u/CRMcjBN8s85VL1ASz7+iO8sN+5IMaR86d0z3wfKbt+5MvKsNxOtf+HcHfdnaUO/OBH3+P2H/8u/9O7EWi78/croMzkvB0/iVp5v7Vb//BQxOj/m9/42OufspbPB5qLhDj4GllTcckwdowAxsoQQzwcv08w9q6d6DX93Eus3n+ktiLmbKpxgNdCzstXasG+8rke88HlPPYBhOO50hk+IVg3BVILhYzjRkJ2FEbYE1luH8yBTMQrKlGX/nOc4wXGeXzY11rN8HV3NjS+33lqzpHzz/xmNiv7v2u2MVU36fjXxXbHek532oHuVBD/au1ouMxGes5z+BIx+fyY8qgnMXaX9eu6/Obgfrv6po+L0dMj8DGylNddPyZ+lcCNpoPlli/q/ff2NB4ESLGf/Vrmkt7WIO3zp0Tu5XrGtPuvRWz4yRz//Nv/v8emjj1p/5Pq/fPcZnrkCGWIHZj6XQR92g4x+L5X+g01mcHesPttubcz7z//WIfgkl3441X9f7Y5zfA+iwd94Bg/PH3A+zZchwsBJG+zwhxZgWsymaAPRTPo8cah9OYDEIdj8lA95DnLqrf8nx1ONRzpvlcn3e4r35e4Byqj98nag29XxppLtn03yV2WJ4dV+NE40CrpXvSjMzCgba/xDq2ivuPkCsvcC4Z4ExhfZV7MP2+h1x+dUuTVZ6D8dxwfGcgdlTT8f3Yx94rdpJr+z77+y+KPUWu/+aeOs1y9x//6OvfVoz6rhl/JpPJZDKZTCaTyWQymUwmk8lkMplMJpPphy/74c9kMplMJpPJZDKZTCaTyWQymUwmk8lkegRkP/yZTCaTyWQymUwmk8lkMplMJpPJZDKZTI+Awgdf8sPRfDJy2Wnt2RI8AjLVWF/fA6dlkWjN2Brr+S+0trhDjdf1da13H4EOldeUJwH8gQvId0At61Zba2WjZG+F0ddY1xq/JdlHqH0+W2it+KOjgdjkHzRaWhM6BN9iMNXv11eUX1CiveS4BH216+BxZEPtf+AUKnyH3Ztac7jV1vaXqAG8sbml7Yv0fikeOANXZ4ba6Y2m1hBut/V9Qvy+7mHadVBTmSyrxVz9MyTzEDySeKL+Hu9q/zTAXitnygoIULPbgdUVYL4FdW1PDM7SHtozzHX+Rc173/ceBIhaQnl+6bxTXksAZl6I2tkR/KwyjmT+wSYDrgb+Ue4zLuq4leBhcZ6S71SgVnqGWujkNzkftcpL+FEJph+AVPR758DnqiNu4/KCkLoKvZY8MLBI8X4luEF8/wK1vDPwvxwYf+QSFfnZ/VWQb4Z1MAKvzQd/rbJwVhh94LVxnUJ/sj/I9GNtfTa/9LmQY92qjBcYkpW/psT30U8LwAr4efWB4NUxrzi9QYUPtOQKvPA+p7bAHCB7Kgc30gN3sY61psJoA4cmCHQtJO/XxxjkWDsCjiHW0hyDmoOPEKG9VWYgrufQ4n45YrpfWSvRvgZ4y/SxyvuDVQaeRtHAWr8AzwD9VZzonFt5AuPnAQyK3Ji8CR/8ipxAEOauPr+vn/sekmXkxulQ23v0msbY2QicpBrZVfrAHU21XeiDq0TOqg+m4K72VzvS5y3m4G3MtP1NstcS5WcMJ2rH4Dl3u8rNGY+1PYP5W++bMfwvuaIwus/SK3xw1pmrQJ2O+i3nUQFOx3xB3i6unyNXC5XHlIO/NUWOnoPD0QFzI54rZ4VxuARQOQLHJknVT6Ma10ow7hi3j9SPThZ6faeN3OhQ40ybDEXMgw1wU+iKDXDgA/CbghPNbe6WyoOq8J+xbuV0F8Z1rBu1QP2jAGeGjysyxMkSvG3kwhMmq12NA33wxxp17GFPdLxvnyinfhW8qwjrzgZ4c1f6q2KPhsoRqjf0+S//0efE7jV0/OpNDazEvLtC51t0uhcoS3LIl1tBY80Fp6zqcx0dk71dZYotpsoi6p+7LPYK8veXvvglsYsUuRh8bGdLfej8FWX67d4daNsjxAzk48lUY9rmzo7Y86GulccDvT5qaHuO7x6IvdXX5zdqGoMKTNI4Vt/ormgMPjhAVOnoJF3d7ot9BGYh9+cpzkG4Ozg80vfJEDM4Z2axzqmk1HOk831lJjYDjQFJpmvEDPu1Zlv722OMx/VdMgAPboq9q49z62vanrUeedzgY5fanqiu41VH7jrQEOa6hc4XH+dUd/f13M2v6/vs3lbGYreBGI41NsGafTxVe6f1FosuwXqw7ArDjgtP+48cdB7L1BvYkyAkZyX3eDinwjhlyI38DcyzEOzNFV2b/uTf/XuxO+vqR3ECrvYC54nYM8VzXXvqEff5zH00Lg1GYI9ONA4fZgOxM8SxCHF7HazSFnK7EfYA589rHCajjixPH3zh1abanR2NU0mBed3RXGLhK+vUwx5/Ac45+XKTsX7eauL3hgYYe7n2RwCWbtnR/jvE+HR6GhdqDR1Pave2ctQvX1R/zOH/k8FA7BbiMI5dXRfz7fJVzb3iobJ2xzMwFXHDjFvm03PELP/2N3z2L/5MJpPJZDKZTCaTyWQymUwmk8lkMplMpkdA9sOfyWQymUwmk8lkMplMJpPJZDKZTCaTyfQIyH74M5lMJpPJZDKZTCaTyWQymUwmk8lkMpkeAS0t4y/N8/t1+XPAKrKcnBGHz/X3zMUU1zutiYsSu64Waa1w1sruNVFjN9Giq/WG1qBNUXs1jNCeXGsA1yKtz0/0Fbk5Yag1aFNwWsZHynhbgCvSX9Gas50WaoejFnyAWt9lTT/3KzwO8CLAxUnghX5X7++D6RfkeF9wbmLUHC4wfnUw+eqoAeyHWnP47onWhq9j/Or1Pj7X+wWosR1GYHOhxnSaaE3jwCMnR/tvPEPN6evKGrgYns3u8gBkyFFbn2wAlPh2Y/j/ACCfEWpOhw3UnD4d7zR7uNhZzjkX1Wsuul9LHYw/8I1Csi7x1y7IzPPBaCPblNcHiAN5oeNQcB7ifgVqWVdHA36AwFskaG8BvwMvqkBcLyssVG0Ba8c7XE/mnFdh/p3NrHMlmHMF4zRrbS9ga9zxUZs7CjXucDxIlchzcgXA0KO/+BpXAl/nGRl3/oN4XFVIH1oLxgPYuGTkUfNYmQ71mrY/irAOgNVKpmOJ8SN+zPfOru3P+VvBAr3ZH+XZvKllk3g5uYXsY/AIcrxrVD6ABwG2Vr2l9fbJ1SwxZ29dewOfa3tHB7oWR03NtUKsVav9vthdsI12b90Ruw0OSg9MtQXq77f7yjMIIsZAssrIjTzbroGf0QA7azrTGFQmiOkATk9ua0zpX9WY5Dzwlf2z5wRzkwL+49cQ87Kz52h8rDEg29Mndpv6efuS9sd8DD5zrA+4i8/JjGz1lGuzc+6ifo7cZTrVmL870tyrjvbeAveoSNU/xuAs1cAEjAqu4RjP3lv98bDlU+1G4MLTGL8ouZZw7dF+iBdYm8HpZg6Opc4VCXN6HbcGOSwj8Iiw1tbAWMsyHVcvQO4CBmGzpd9fxAOxowb2NDXEcaffj0/UTzywLzPsGfdONK5EYHHWKrkDOD5noztd7by+fw3jVxTaXzEWmtW+zvvkiLkB+MlM9YhDxvUFGhzHZCiSD40bInfbD/XMoRbpujUYIy5M1PY62l+juXJ3xq+oP67MwbVBbs7M8wRnBFPw6Th/zl24qrdXd3F5rn8QYg8anTpQQU74kuvFL33ZRadsuA9++L+Tz86BsXf71l2xY3BFc8SoEkzwxQLcUeyfxjOwp9LXxU5zcE8zfX4AL8gwxpsXN8Seov0l9rOtms7JVkft/ZOB2D4Y7nlD195mqP3x6ss3xA5wTucaYEtNj8Q+PtTn10KN+efWdE4G7b7Yw7m29+RQ5wxz5ZPBy2J7iMn1ic7h0UJjXh3ncj2wyy5c3BT70lPvFjtA7vDK535f7BuvvCQ2g3QLa9jj735W7DxWNtX+HYX2xYiJh2BONtC+MlF/TXA+0ECuv38AKGGhMccrtf3czy4SnT9RHdzbwVvvl3JBW3JlM9+5071D1AM3HftgzwNfF+fb5RxxintA7Pl4LlPiHOsk1T3cf/rPn9a2Yym9/sp1sR9b0wsWWNxvjRDHLmgOn+G8shHq9ZOZzsOVdZ2HyfgVsc+Bn7s30vYNxtr/Cc7fPfTn6qYy/daw9o8HGndqKxoHDq5rf233dZ51wIOeO42jc8SxNNR5Bsx7Zc/MH5XCkLk7/UNzpXiquVLp1P+2N7W/i3WN+z5y68ODgdhI9d3Omo7vYgx/xu8F3FWdnGj/TJDLBlN9v/kt/fyL39AziMOBPt+LwDycKju2tXqv//0K2Ppby/7Fn8lkMplMJpPJZDKZTCaTyWQymUwmk8n0CMh++DOZTCaTyWQymUwmk8lkMplMJpPJZDKZHgHZD38mk8lkMplMJpPJZDKZTCaTyWQymUwm0yOgpWX8FWV5n+ETZ6h5itriZE2RiQc0kZsuwC9oaW3ngwOtMeuBydZtaLe1a/qADPCkOWoGJ1PwElDr+s5d5YR0++ti+6j1zf7JwD5qdbV2+mKu7Qlxv7RkDWTwJOp6/Qw8jgDjQ/4Da0w7MAFzsJyIVCpTcGSa2t4malYvpnp9NtEau0OMj4+auht9rS3e76u/eOAmTUfKfWmC84ISxC6e6/UxamYvwM4aD7W9kxFqm4PZB5xHhVuUo365VwcrDuN3kmjN4lduKo+itt0Xu93WGsm9Nhh/7Xs1ppP04ft7CEHg3fd38oJCMvwqDDX93IOjs/Y3VWGQwebzWJs9APuSaM4ccYSMQdZ298Hk88gAxANKPhCcGZeBH0Wmn8/a8j5MtQN2OJgWBfhJ7M8QEzdLMV6E5uH+GViuAdaVMAK3JgWrtNB5RwZCDXwz1jp3XDdZ65/QyQDjAR4bGXklOEAlxp8MvgQcH9aOL0qtPU+OUY7+JceH/U/bx3h59G+0Nzvtb3Lsll1eWbwVSzCnczDGIi7N6MIcvAYPczLAHCaHkTGxAFeRc3R4opyWrfPntT3wwWYXPIMVzZ1ef+mrYrc7uha2WjoHozoYePAZxgSEQFfWyd3UmNsAY3G9p9fPwZVxHrk9YFPBZ8OazqEgBrss1ftNPPAckDtk4KJyzfMLtG+g/hXm5DcrH8JNtQMXMbg34M40wRCs98FKA5PRlfp5gPFxgY7/7r7mNufPKU+jgb3DucvKEzk6Uh7DrRs3xa4h9+719f5T5H4euLKXNxAj38ajeNi4NINJ4oLTNShrgCuDtYpLFVOlAEwxj5A5csXhl/Wm9utkrDwj5iLkx3Jez2KMI3IvP9TnZanyhxzWVn+qfj+/A1ZoTeNCHf3RX9c4iZTeFeBDzqdYu5v6vJOh+mV3VflgZQdxAGF1pvguN47x/is6L48HGhc2N/pi7x0oo465CfeoMcJECAcjm5WsUvKRmQvm4NJz3SpzMgTJYUJuBV7YyVxfoN/bEjtFHLp9+5bYHayD6+AWbSOu5YD6LXI9M0m5B5/r5/XTdTBLSRtcbtVq4X3G396ta/JZva1r2RDcxDgGZxGwoTb25fOBsrDIc/Vx7pDH2ucNnFO1kHytrGpu9PUbypzb3dPnb+8oe2ow0pg4GOr3HZiF/VX1qRhBp2zqOdUM759zPwhW1gJcyjYQgCvrfb0e7T8BgzAAczxErlDi3C2ogclYAy8bzMbIAxMRc65EbsWpwpgb3NH+P7ylrK/ZSP0x6V0Wu13X8ZiMBmK/8dKLYm+sYhEF4z5DMsx90+Gxju/OtrK2ihlzAPC1EbPOPfOY3h8xLgYTswX+93io/hB5bzlQmT9ccerojdH9XOr8B1fxKc+FeN6KPVUb54U4xyBLusC5ROnIHgMDjXsidUs3n2sulZXqZy3uAWr6vAUW961NzU0K7Gl5zhHG3ANof2y29fst+Okt7BE3tpRJ99o3lI25/ZjG5R/9Mx8VO54MxP76l74i9smxrvXzBXMz8H9zbd+AjL06cg/uQRHHc6xLzZaen9MfuEfluWLAc6PJ2XvU9raeETRw/v7FLymjsd7huSD2yGAgJuA3Hx5pe6apNiiq6/eHR/o+s5F+/8mnntTnN9VfpoeaLMeT07hujD+TyWQymUwmk8lkMplMJpPJZDKZTCaT6b8t2Q9/JpPJZDKZTCaTyWQymUwmk8lkMplMJtMjIPvhz2QymUwmk8lkMplMJpPJZDKZTCaTyWR6BLS0jD9X7zh3yiUoS62pururtax91IDt9rR2dnMF3I++1r8nSysEV2U41+c3wOWYz7SGriMWBvX6kxnYG6iZXENN2BhMOw+sJ4/MPLK5wOlZ6Wgt+oSAg6b2H1lNDjWg+XySlcj5IbSPXJ+SHKE2ai6jdniA+ts5WE3HC63tvb6pNYDLGXgQqE0fgscxPUStdox3AvbXHHyPaKA1mDsNjDfG4/b+QOyjAXgf5Iv0tIZzAT5Ft4v+R83lKbg6c9QAvw2GYdpWf5keau329Q0t2l0DI/K+PxXffo3ipZHnnDvt/xDFpkNwRPxKbXPUWkdt7Mq8wucVAFcFCXj2/cjX8sE1yVGr3fH5ORmA5KKweXo9usflqca1bIE4Bw6Oh9rpHot9wyzAKkVpeecK1nrH5w/ozwBMPHpzBdmIWvIe4mAU6fvmuc7LHLwt8sXI8OP4lmTaPeD7ZACyv8nE8wjtw+c+ujvHOj+PNc6QURl62j8F3ofrLtdFisso/yA/hWwUD1mc8krfeaeg4wJ9UuHt+urDZQFOCV7dhw8X8MkATLbSI5xL7W5P69lvnVOuzCsvf03s3qryK8gtDRAjQ3AwK5xSxmzyHjDHyWIKwD8oCCNDjKnMYTDc2H4/YMxGDAR3hjF+7ivH5PVbmot0O9qfXYxfsNDcYnas7c0W4A45zQ38QD/fWNPxagbwH/26c3X4G/q3XiO3Vvs7AycoAKstq+SC2p7dTHPJzXPKCyGPYbWtvIgZ2GBkie3dfV3s1ooy/8JU+Rs5uDXe2xmVXM+WXI2odj+HmmW61pWevosfwc8Q2vk3Wj3MoyBTP6m1dR6nYMwViFOhr/Oi21Nm3mIBbngJDk0THJBI/WI00j1AE3zb9A2Ny3OiLJHrtMC+9Ne0P+ZD5YxMJtqeCHEzccptqa/qRJ3Ntf96l7R/Z7vgV8VY2zFvKrxgxPnxVJ935aLu8e/c1T1JnJKhR3YtcuHKmq/PL8joY+7ALT9zdyRDIdaVEkc1/HqK3OnW+JrY5yKN2+0m4s6ucowuXDgndg0cntGJ8sKO9nUdaTn1zwt9be+bewvyoJZd2+e2Xf30/Gc01Fh8dxd9ALZ9I9BJeudE51wDLK33fPjDYh/cUrbPCIy6AucWW5u6Fi2GYKyBifeuyxrDhlP1qdlMY9r+gfrAyoqeKxWezuGorvfvgWf8xs27YldYUYhBbTxvb0/7owRvuIk50NjQtTVLtD/iGDxj8JTJPhsUOj4eoOMe+mMeaa4VYUO6v6/nnB2wsoJIY8JX/+Pvi31+W/vnsSeuir0G/vGFJx4X+8uf+7zYZKG1+pfEvrP3DbHbHb0+AlNvMlD/8bGfbGENCEP4QwfnvMi9mDs12mezyBoNvd/tW7v3/3/2DvhZy6Bmbd0Fp/2ZaZhy9e7Z+2DuAVP4PZllBfaIvgMDDn6DMOWcj7Ud52QrT+m4FHfB5wUHu7+uOXmzq35BXvQxGG3r68rYGw80LnQ7+n63D3UekSW5s6l+2G73xb58WdszPdF5PxpoXOv0tX29Tc11ere1vxbgMy/AZCyRaxQtPXfxK78SgeGI3zPiqX5/tQ5GX6Bx08ee973PKk+41cIBO84B//CzL4g9u6W5zNY2+quj94uwx5xN9f2KmU6gdl37a2dD4/I4Vf9o4Hy8BtZu1tX+64JhmeJ+ta6uG5PT3DfzjPFnMplMJpPJZDKZTCaTyWQymUwmk8lkMv03Jfvhz2QymUwmk8lkMplMJpPJZDKZTCaTyWR6BGQ//JlMJpPJZDKZTCaTyWQymUwmk8lkMplMj4CWlvE3HKcuDO/VLK1FWuN0FTV8J0OtwZsUZMLpvVMw9VwGTsiK1gQeD7QG8KKF2tJdrVl7MBjo/RqoaQs+QA4WRw+8iNEEDEFPa9TOUFM4QK1Xso7qddSgDfR+J0OtKXz+Ca25u8jBvwBwwANsqqxwc8guY3udfq6m8xvgP8zIutLPV7e01nlaaPvXNvR6D/4zG2sNZyAW3Ry184MIvArUOPba+kaTVMeXNYCvXlXO0dpE2zOZ6/MW4CYMUSP61cMjsRvgzJDJSPZX1FD/AT7E+W5FbTIhwR3yTjlK5LA8DArDmgtPeSqcZ1WGHAIREWR4/4xxit/nRKH9AEYg511ZYaCR0aZm6JEHRnYn+EngWWWoHU8GIO1srvOEHJYwRG158KQ8rHYeODgO32dcq8Q5Tgy8L+Oc58iVASOP48FAQ4aeRwfD5bBTrDNk9FUZkCoyHMk3q/DjfL4fuTXoX3AU0lzHO8p1nfIxXhn6L8R4PADZWOkvXu+fMof9B7ACl02Bf+8/56qYwxLsqgJMM86xkhhMTqEa6tmDDVRpAObUbKq5VlrqWrd5Tvm884nWw89SMNlu3Rb7wqWr+ngy1nB9HbnR1tULYpOT6aGDYvCFU/i4j6B0NNDv1wO1a4GuCZe2NFecJfo+wzFytQ64KuA9AHXl1jzNtff3lZWVxfq8Bfo/QxRhTIkCcnS0vS3wRBohgAzwLw9rXL2vnycLrCHgdZcpOLBkNmJNHg/21D7eFZucoQX4J15JjhH2BmPt73ZX36cO/kW+eCvXK7OHi0vjVhPnTnPf1hxc7TlyF7IaI7LCdFwjrAVhTccxWyDXAkfER64QYm3LEo1Tno/7Ye3OM7AvsZb5WI1qqe5Rzl8FUw/zfBd7krQkE0/b09vZFnuno3Hl5S+/JrbDHsBLNQ43L4Jx58BSnWn7+6vYo7mzufZEwy1i/YOjE+WtbW+tiX1nTz8vkNuQBcpch5si36d/kEdMpp++jw/GX57pvCYvPAVnx0dkTdG+oM77qd1b0fdfXcOZxmvK7/IPlWcWFTqeJeJmVlfOThHf4+YU7uFi/LXaTVc/5W2+8tod+Yy5xHZP+zAH3CpOMYaxrk0NMM5qTe3jwV1lQXGtunOoPtfFPr2O+y1Kbd/quj4/hguv9/T7rYa+/+XLV8SezzUGkOneBsf0+i19v+Y5ZRvNxpor1jBHyljPBZFquNTXGH+0p2t5p6Xvf+mJy2KPFvr8O7HmVtz/Bsilax4YiuCs+kiuZzgHJOc0wJoxWej9v/Lll8XeuXxVbMbgD/6pj4j96kv6/Zs3tL/CGnjGCfZ3Cx0PD2vibKRrWA3cWx+59uFdMiG1f+c4F710Ttc0xuz9A+2v1a23ct80y5xzyl9eZnm16P7+PtkHm7KJfXvIPczZ985inteqn+c4fy4Y58Kz95TkkvNcMFnX57UXura0K3FX75dhnqxvaG5Qx7y/O9D7ZbnGqbCu1691dB7XC2XEBZ7O4xHimBeqn77w2f9dn1/o+49OBvgczMMVZdCV6PAcuZWH9jv0X4F1Zj7VwNrxlQXb9TQ3Pp5rHMgjPS/+6ou6rqZO43SAPeD6OY3LRarvP8H597ktXUemYx0fpOIuQO5fA5+7VtfxmN3UdSDHuWMB7v36lvKUg3Sg7Ztprhrj96rsNHfF8nam7F/8mUwmk8lkMplMJpPJZDKZTCaTyWQymUyPgOyHP5PJZDKZTCaTyWQymUwmk8lkMplMJpPpEZD98GcymUwmk8lkMplMJpPJZDKZTCaTyWQyPQJaWsZfvV5z4ZtMHdTGZi31WqI1a9NUazsnsVY/LVF/vwR3JQQvotVT5t8M6CXf1++36lrDlUyzLmru4vH33/v+5wutmZvjeeTItBpav7/R0vslZOKBjVRDzeYFarMHTdTcLbR9QDlVuCv8tdkD56bKWtI/SVGTOgWnqIOavOfPK7/COR3A6a7WFHah1mxurWvtczIMJyfa/9kC/jYjH0LN2hZ4HxH6A7XL+yvgl8BfT4ZaUzmGPzVWtSb2YqQ1jpNUx7MWaoOjCFyZBCwu1OofHmuN5VoNvInTmtHFw8akcc75fnS/VrxXYVOCqYYLKnwLMvnIlKv8NQ3OlAexx9Ae3I+MtRI1+Mmp8cFIyMndgd+VeL+8BPfFI3NOH08ekstho1Z5pX99MAcQR12k71MGBJiB2QC7Etj4PLwv/SPP0f4SHJhA51UNtc4DcIm8gFBDvA8cgP5JVfwBcdCvTAC8PxmOZFKC4ZAl4BSBr+XB/9g/vB8Zi/RnMi7Jb3vz8yoLc7mVe/7bOKpgUz3gu7UAcwKczoIxAXaIManwY5EbLebq88ORrh1hQ3OnCPzn22D0bW0qH/eVF18U++n3v1/sc5eUF5Am6nN3b2qucPGKcmxKBIEcuU+I3DVjzENu43LECLCvQuaeWHuLlo5flvb1/m3lLbQL5I6I6a228iemU/0+UkW32tP3Y+57NNBco15yzqndbGsulpOLipgax9pfzE1yMAl9X9+/ZAxHjuIB5bYYKutsDp50vac8i0osgb+0a4ypOj/ISHw7BzhnvF5yJcXsPqcnaIKJN2bugH6ogbHGtSggpwRxjQw67Ck82mBtzsCYa4BNGYADEwZnMwPJ260f6fuGLW3vSlfnZdiGnyGO+GCxsv/2bypfy0NuMRnpHqO1htwiUr/3JtgzjDRuDCO9X1qgfzb0+yn50Mh1yLfKc+WaX72sXJWbt5WlmSC3IXszxp6nhs/JM07JbHQ6XgHaX5YaF9NU/bdRB3co4zqhz7+20PFsgCPU2gQLNdbnH4N/RjZsDpTvDO0ZXTsQu9u/F8dT3GfZNT3addnp/rwHpl2B/cPePve96iPtrs7BIte++JMXviz28BDcykuXxK6TWY790OV3vVfsEuc8L39Fc6P2qrKvzm8q33jtHNhNt2+J3e33xT4EF3KO/H4805habyB3AX/63HnN7RgDjm8pk+3oQH2w2VXWU7OjMfP8ObVzrBkHuY5HDeNfIFd2OHfM5tp/Vx9TfvWdG5pr5mSWIzfY2OmLDYSiC8BlPdrXXPngpsaU1U09J8rBsU1jtReIeRG4pcz1uT8cHWsMvnRZz+3u7uuaQX/gmhkCWH1ygjUhA1uN58xvy58qZw9LrpPDo/vn5mt9XTuLI407+Rr25TUdV4fzx5x7OIy7B4ZfUNnScA8IIdcqwcLMQx3XWVs/r3EtRM4+ecCeIJ3xPFxfgHvUKNe4e2uqb/TUut7/2mvKpiwj3RNxTxch9yoWGJ9U7RK/B5zMdC3vkXveRK7GPahDbpqAvz3V59cb2v7xArlcU993ra+/r0wWGufuXtc4uLFBHjaE8ZogrlR+/0Eu7oN/nONc1AMnfo72Nj2es6q9GGh7GqubYmfYgzbQ/xOcp++cuxen0yx37iVdY7+V7F/8mUwmk8lkMplMJpPJZDKZTCaTyWQymUyPgOyHP5PJZDKZTCaTyWQymUwmk8lkMplMJpPpEZD98GcymUwmk8lkMplMJpPJZDKZTCaTyWQyPQJaWsZfPE9dHt6rrdpBDdh4rjVWExQJZq3vGWrQRjWt8VpDTdsIHJYItb3JOyjrqBF8ciL2+obWtO3ifdIUXBIw4hod5UOQ3NFsa83mWgRmHFhhCWsmJ1oDeHVd2zefag1gB2air6XBXYCayjn6l6wnv8JC0/uB1OV81LKvd7Umbgv8g0ZN+88vdbzKTl/bB+bgHMXSiwK16Ff0fl6k75ePwaqa6AuOM/Xn1gp4IOB35At9vg92mFfo/RpN/f7BTa0x3eyov2Rk7aE/yCfxA/1+jNrri7m2N0Zt/+L0+jTjSC+/grDmglO+h0+mGbkqZPjhXj44LxHsANyaLNN+zXMw5MALYuVuLn9ScQAAzr5JREFUMt3KyrwEP6nC3EPta9w/w7wh4y8Ao835bI+KbMiy0P4twLTISzAG0B8Z+isBs8CBb1Zpbo2fYzkNwT9DsfuigncCI9HXeRWGGmfCiLwzMP8ijSMx5hf9zfM5r89mAlYYCxVeFfyjYG1/XE/GAuwM8ylA7fQA/cP5QiYk/ZG8LbKFi9P+e8hwDy4v3mL8JZilEX0YY1ZJNvB5BuYcYwJnsV/hJurVd29pjfrzjytzbw4OTIhcgLlbCRdexBojDg+V++IwRza2lDOCKUKcQIUfkIA/TdZWCg5MuwF+L7g/GZhIc4aspnJpKiEpIndGB6Dr9H1jBL3umsaU/oZyYF744qtir4Mn0W5rbraYIfcFQDsDa2oxVlZVp6+cnwWZk9gLkBMagEeSYw2hfwXgj0zGOgDci/RWlJNUwD/n2Mvk8I8V9FeJ+ZbieWGz/9az0ocsn4oWzp3uFfxcWY4O4xaBoUcWJhB2Lk04rsipAWv0sbbU6mC2gblWgx95gX6f8zpGe2JOZMSF1RXtDzzOzQudlxnWuskQ3O4GcvCJvs8IuVPYJ3dHueB5T9+vhj1BWIJp1wWHJSbHB3tI5HrMhbDFreSOC0zMO/u7Yl84r3Hs5ER5UoMxc1nkorh/xPaBIZmR9wx/CuDAntM4keVqk2HJ8S8RB/n9OfxtjnWiu6O5ZjZSf4xn4Mpj3Soy/bx/uk4WXECXXMPJzNVO14QueK3HYO9sX7gqdq2rfXi4p7kH82Ev1rUnWVEfvHiuL3ajqWN885r6+PFA+bOTkc7hXkdj6gwsouMjvb7tw4dSbe/n/vMXxa4jBjZbes7E/Rz3Y6tXNDdpd3RtHB1rbpBjfzxPNIYtBsp4O3f1SbFv3LwpdtRXTuikred83N97YPIFCFIh1pgCLKkEi3uO70c4Z8rAo16ABTadqP+srmr/f+nLykTc3lL/vPaGMgGzifr75hNPaXvBYR2c6LnTGs4tn31Kc/12R2NMY13HJ6yTfaXjMx6pv08Dtbn3WAfT0HsbA9B7yM6mHlsP7vPEv/Sa+qmHXKlbgvm3eTZ3vMC5SeUcg6lMidyNPFzskSqMP3K/wf+dlLpW13ONsyniksPanOAFDm9r3Ox3NbeqNTSXGWHPEtX0+V+4oe936aIy3VysfpnMNK4elxqXOzivdqXG6W6Ec5cIcYl74qb2n0emX4hNZKz388EkTANdF5OW7oHmE2WjruOE/xyYk2utLbGPpjp+x1OcQSQa1ztYF3kOeAR/LT3karh+5Yqybj3E5euTa2IjdXbnn3pM7CTT3DyI8PsE4niA3zNa9Xvvl7yDf8b3Xf2Lv1/5lV9xnue5v/t3/+79P1ssFu7jH/+4W19fd51Oxz3//PNuD2Bok8lk+kHIYpTJZFp2WZwymUzLLItRJpNp2WVxymQyLbMsRplMph+WvuMf/r7whS+4f/Ev/oV73/veJ3/+i7/4i+63f/u33W/91m+5z372s+7OnTvuZ37mZ77rhppMJtM7kcUok8m07LI4ZTKZllkWo0wm07LL4pTJZFpmWYwymUw/TH1HP/xNJhP3V/7KX3H/8l/+S7e6unr/z4fDofv1X/9190/+yT9xH/3oR92HPvQh9xu/8RvuD//wD93nP//571mjTSaT6SxZjDKZTMsui1Mmk2mZZTHKZDItuyxOmUymZZbFKJPJ9MPWd8T4+/jHP+7+/J//8+5jH/uY+0f/6B/d//MXXnjBpWnqPvaxj93/s2eeecZdvnzZfe5zn3Mf+chHvu1nLNKFC09r+WYnWgM2Ry3sACCaeK41U1st1KwldwbcjXoDNY9j1MxFXXoPNVlZQ3jvUGvaRi2tEXzhktaMHQ215u7enTtir2xv6P0iHUbWRie7iLXPw7ryChZjreUetbXGcIGaz3kBdhfYUKwJHZDAAD5BWdJGjWkw5zgeGWrF8/0d/KfR0prE0xlrgaOmMbhIyVwZiAk5M3Vtb5s1keFfs2PU9AVisdkH0xHjt7mtnx+faM3o3Ttaa53jGaCmsR+BX4L+jeD/IYpIN3taq31RkDN0r3+0UvZ3px9EjHLOudAPXXTaftZ6JmPO0S9QS5usyxI8rbxCu1SRc8LnVcAnZPbBJuOM7XHwA5/MwAcw3SrzEvevMO9Qa9zjvERc8FnMHO0LMD5eCb4U3LQkt4QMJXBV/Dr+Xg0ZgQE4MAHjGvhSYA6Gka4jUV3tAnGZ6x4ZjxXaygN4bVXGY8UBeUe0D4wLNCAEI6NE/5KJATSAK8Evq/rz2ROC/vmm/34vEX8/iDhVlJ4rTucKWVScs9UgglwrOpsJmMOOcD05oZyjP/6n/4zYYQN85QLcRcfckNxP1OdfU95AE2un7zO30ffZ3jmv92cuCCeOp5rLBZjT5KDeOdTFfrUOthX68wQ8iHigHJYcc2brGX2fVqq5ZIUbCo5K6M7OJa9cVg4PuUN15nq5vv9//yMaE/td5fhkOXLdDLkX+vOrN9VutPT96hH8B+M9Hg/ErkXqL6urGI+J9leO+RUPlT9RjcHqP4uJjm+9j5hY19xq+jY++PeKmfyDyqUiv+7C0xhdniDOgHFWYC0mhyZJyGTTZ5VOc2wPawX5v6HDHg/zoEBOX8PaNfqqxoE25sUlMObmtVWxp2NsAvBCUU95TZN95R01N3SPM5jr57EP0Im6uas14NfIlchtxxbK1edgaU40bpwc4wttrFNd7f+ohjiLPVSAtT7w1J+yTD/fx/M74Pg0yOEBk88rdTwL7HH4N6w98sDIGwbT0nn6/uSwB1yHcL+i4N5DW5QCzDQCn2uM+62Cb1fHfMQRiJsgjnmn65j33dFmRD+IOFVvdl3t9Lyl09NcokS8HYAfXB7rORD5vms758QuwOB78qqurTVsb2YzjRGNNY0phJptbOvn2Rz5fQBW1u4tsfs9HbsQ3Mt6k1xScEdnyojzsbaurev3k0TP9Y52NddxNY2BfgcsppHyo/MY43Wi4+PhHCtuagwIfO7X9H4Z2GVZrvcbxhqTNlo6/mtX9P1bkb7/DLnESanj1a4hhtV0DgY8N1zo56MBznFW4A8NsM/A3W2vaozYCtSf54jBCVhmWaIxZf2izrdOF/6xpey0W9fVX3urfbEb4Mj2VtR/1tbfel6cpM79/tfcd6sfVC6Vpvn9/W0De6gMa9V8qOPQQC7jrehaVNlnV46FsAdDrpLjC0GA89ucezq9P9cMnhefxMgVZ+CiT3UePfPsFbEvX3iPtgd7yAHiTr2pcWs80FyvhnOrGnItIAFdgbVzONc4eXig49Hb1HWh2dLnHV6/JnbQxzkX4laIczTylVM0sMSZwgpYmf11nbfpXPvnzl2NY234a4Tz9jr6Z4H2xHN9v1YNZxp13VPyHCueapxa3VTGIHnRXIfTmsalrKE2zzUj8MbHC/CocV5f7+j1i9M4mWbfPi/5Hf/w9+lPf9r91//6X90XvvCFyme7u7uuVqu5fr8vf769ve12d3cr1zvnXBzHLn7bD1Wj0eibXmcymUzfjr7XMco5i1Mmk+l7K8ulTCbTMstyKZPJtOyyXMpkMi2zLJcymUzLoHf0161u3rzp/s7f+TvuX//rf+0a+Nse36k++clPupWVlfv/Xbp06XtyX5PJ9N+evh8xyjmLUyaT6Xsny6VMJtMyy3Ipk8m07LJcymQyLbMslzKZTMuid/TD3wsvvOD29/fdBz/4QReGoQvD0H32s591/+yf/TMXhqHb3t52SZK4wWAg39vb23M7Ozvf9J6f+MQn3HA4vP/fzZs3v+OXMZlM/23r+xGjnLM4ZTKZvneyXMpkMi2zLJcymUzLLsulTCbTMstyKZPJtCx6R6U+/+yf/bPuK1/5ivzZ3/ybf9M988wz7u///b/vLl265KIocp/5zGfc888/75xz7uWXX3Y3btxwzz333De9Z71ed3VwVpxzrtOtuTC8Vzz16OBEPuuuKv+gjoLyLXBRai39fHB3T+zRCPX4Cy3a2u5o7Wcv1L+xEcf6/RS1wZ3TIr6He8pX6K/q/Weo7e6BV1DDqJWl1vytoejsHHyIGGyq2VCf11/TGrJ+TZlxJdhYPnkPKKlbgluSe2RFsWazft/5On5kQ5F3MUu1hvIsQU3kur6Py7Xmcwq+QQmWVDLXGtLzud4/g//0OuRTqL+0fPWnyUDvl6Nm8XSBGs09fZ8Wxm+lr/373vc8KfZ8oc9b4P4pmJEReB6LRP2LLDffkcWl49V406G/B7iH70eMcu5bxynf8+9zsnz6NXhORXY2h4O8K9ZSZ+30CrgGIq6LV5P7wYkXhmB7Voqtk4tDphCZfvppAMadQ3/5JeMEHk9uD16HnBfen1C5BtaRHM9PAf2rhKEFGIXwe49MiArTD7wn1Ib3wfhzYESmiOuMu6yNT85LiPdPwe8qSqyTZCbCf8nLqoT1CodG71+Cq1TBz1WYg1x4yOg7uz10KNZif2uef/u11L+VfpC5lPO9e/8550L4XFporI/wOaeQB1aS8/T77Bv+7Vb6HPm8jmNODmrAMdH2krtSGUM8rcJGYsgAD4NMtgrV0gPfgkw8xjDwLbrgKU/ner/pSNfiyUz7f5GCG3Re7bHiFdx8op9fOo81yEHgYWcL8H7BphoeaW5wJ9U7xmBDTRfa392efn5hS7kxi8lA7ANUOmq2NTcKQ7DbwKWpMean4EFE6s+tut5vCpZVAgYjY5QPvoaPNTdGfx3s6QA2+zpf3o4Vgiu8Y/2gc6miTF1+mtP4mxqr1zf1+vkYfoScteBaGoBTkmrOXwOPt8gYR7QzPV/tRoD32UNcitSP2x31qzzWeTM4ABs01HnZAdNvdVs5JEeHumfeH++LPcmUVxViXjNV4h6T/ZmFTFZhFrrH7HWUx3SlrxyY1orOszcW30D7EIc8Hc8KB6iyjqmdAql3QM5LX9sTAYk4mmIPHmkHVtc5MP3Iw0YuxNyokpuTd43+ybGndcjVmIvz+Vw4R55yh7inbmPdvHT1qt7/lMGZpA9XLuW32s4/ZaPNAbJsgR+7KHQOj3FOEOMcYV0Rby5d6P3bGzpnyBcucx2kBVhWI5yDpHdwboHnPa7oK1fWdY66vp7D7b2iDL32OnjPYM4lR+pDIXK7lRW9/8nxQB+/rv3RXVFecaOjkzSMdTxiBKkTMPNqfZ0ji0JzGVJfOaczJHse90fIJW+DxbW1pf603tP+S8F0bCImJ2Cd+ch1Uqw5bP+Na8oy64OP3WiAN8xzs0Jjwjzs6+c1nNuu6v3jhY7fl/7zH4i9tg0mJtbosK39l+B+Zaoxfhfzs7fyFpsr+S6TqR90LuU1N+8zKi9dwHnvieYCCTjbeaG5STnU73cvYW1FCPc88mRxToHz1BDnMA4sUJ/nw8XZ88rH+XuAc5T25tk5dj4CTxn3Hy6wx61/i/PM++3V/p0M9fz/6nZf7BF8bQF+8SLBnu2xi2L7mMfdx5QBeOSuix2CH8w9co7z7FqAtb6D8/QTLU370l193+1zmrs2wUisN3AGkPH3GY1ri7nG7cORzuPokv5wvrmm68TadCD2SYQ9IXjPE7Bb7+D9NsEaTXBQOJggFwsxfz3NNTv4fQg4c9fu3Ytz7yRGvaMf/rrdrnvPexR82W633fr6+v0///mf/3n3S7/0S25tbc31ej33C7/wC+655557x3BSk8lkeqeyGGUymZZdFqdMJtMyy2KUyWRadlmcMplMyyyLUSaTaVn0jn74+3b0q7/6q873fff888+7OI7dT/3UT7lf+7Vf+14/xmQymb4jWYwymUzLLotTJpNpmWUxymQyLbssTplMpmWWxSiTyfSD0Hf9w9/v/d7vid1oNNynPvUp96lPfeq7vbXJZDJ917IYZTKZll0Wp0wm0zLLYpTJZFp2WZwymUzLLItRJpPph6Hv+b/4+15puLfrgtM69U1wYoa7B2K3WlqDtruutaIzMMrWz10Qu5OAVQWuSoYawCUYcg5cFy8/m7W0AAvq7q1DsX1wanprfbFXVrUGcwYWUzIHy6iuNXPPn9OatzezO/p88BqyFBwfsric1qjNwHlhzeaiwnZC0Vpwb3L0J7k+ZGORzXU80v6N1pSHUYJFNZ5qjWdyhrKEcC99Xq8HrlGF+wMeQ13fZ+Oy1rZfjLSm8eRIa9XPUZM6Ra3y5obWCGbN4gg1jVtNHe+jQzAF4W/kJKWZ9uferjI1Ny9ozenaKTOBzLuHQV5Z3uf20I/IGSEfKmAt8wdB+SoIM8xzlHiu9Gfl9mcz0YqcDMCzxycgVwS13mESUefCCIy9DPOMzDgCs9gf4EeR/1Vhj6I/yDtjHCwr7UNzOL6Iw2T+OXBWgpDXgyoBf2Jc8R5gM66SQ1OA5VqSmQd3yFDLnPOhzMkIPNufclxfI8+tMr/OnjAVXllF5MHx/qd/ThDckivP8/sjn4LVVAT0ITDbHsDnraE+PdfeCssI3w/IacQQ5vAp5h6VIIn7h2T4VXyEjD/EDFzN1+PtiEZqNnWtrcRscHlKTKoQXM+wpv0Rj9Re31HuSXNduTtHN3QOLU6Uj9CIdDxD8Cq8WLlBrVJzkXYd74tc0CuVB9Fp6/O+cku5K3tD7a8bTeVHBIgJJ3ONkc2O9m+E68m5nYObMx5r7tUBc3A00/aG8I9hot8/t6W5WODr+wfgHC0Sbd/oAP2PUFR7W8z0MJeXXXESuOB0LxDVECdC3XPVwW1JfHJgMM88HYdWQ/k/5EulDnsY3K8W6vejEx3XPNXrm03kHvj87hBsyAV5yzqWvZ5+f7AHbg94RkWk15N/XM1VkQuAsxIgMmKpdhm5LAicbeRmvqfX51Plf9XA6lyAR+Z53GOCX4zcqeQWzpHhqPNyNNP7dzGe24h7x2PskcDGDdC+3K8kj2gPchmyQUEcIxMwqnNPr9/PwYcpkKwDU+Qi7NHHgCRORmAJp+rfq6e8upQ5/JIrHs9debpfJbOPa92lKwrJCye6tmSJdupoX/fJxVxj/RS82LCG3KChc8Qf6feb8NHZia5dj58Ds+/2sdi1Xl/s9uZ5sS8UOubHh3ruUsBHel2dYys457qBc7EuuKZFcXbuGu9rrkCmegNsqWAMJqB2l/OQK5fMXZFLBh73n2BHNcEPRgwkyyvBnB7s6bldHRvQHLnDpNRzKcbIx1f1/Rvr2j+3DjUmFwHWPLDijo6UM+vDX5uhtifOtD9bHdwfufPh3bt6f+Tui5nmpj2w2B7fBk8b54Sv3Xrr+yn3+kuuj/y5v+Tqp+fm3Md+4ytfEvvp92kJ0i/9lz8Ue5FoHHE4X45WkKMudM9BXmyEtTiD33CP5WOxzjHvQ+Qim3093y7bOu4zsFnriIt1+Oki1vbPZzpP4hly8oa2p93ris3z6ht39PeMEJ+/+ob6cYlcYW1D4/Qq9hjTUD/nuRXPxchUrJxfR2AiZroutZvgWde1f4KOrhvNJpiLC70+RXI5Q66SpNjj1TWuzHFe//Krb4h9847mzk+/S5mJaaL+wnOwAnEpxh6y3dL2HBwMxA7a6h/tGvwLLGBXw7p3eo5X5N9+jHq4TrBMJpPJZDKZTCaTyWQymUwmk8lkMplMJtM3lf3wZzKZTCaTyWQymUwmk8lkMplMJpPJZDI9ArIf/kwmk8lkMplMJpPJZDKZTCaTyWQymUymR0BLy/jrt1suPK2pnaKmax2MOw81eccTrSUehSC1lFpTOEft8bIESwickwWYgQVqfadk/IFLU4/UnqF+frOhv8f2t9b1ejDdKgzBWGvC1mtgRaH2+M4FZSKyFntY0xrIrE1P7M6UpWZRs9iB20OeADk35E+UgJlluH+I95snOl5HQ615HICzU0T6vnmm70smXm9Na+OzhvL+Xa0h3FsFHwTcngb4CZ019fdmV78/QO31FP4x3lWOT62j/lDvqn/nYLsNp2ALYD6t9VbEjodaEzkBLqUB7pF7k/eRk7+0/ArCyAXhm4xC1H7OyInRfuM89FC7uyzIvzr772kEYMRVmH2Ydx6YALQrTL0Kv0vlM86C3VmpjY3nkQ1ajQNnMxU8T5/P903piA9gzJWOHBV8zvEllwWsVFdhEuJzjG/5Du0HMSX5tjlq58fgUXFdqyAj+TpkQsL22QLW8sf4eaidToYDWXtkBtLf/AdwoCq17tnfp/d72Bh/fuk5/3QuZVhrQ8zRHGtrXOqYBKzvD5/LyfctmAyAHXXmp1UmXlHhD5Crwjl1dgxh7lQRYnBlElS+Dl40YkSGOcE5PBjq2h0wVwVvIgJ/Ok801/BKXRM6nuYSrRWMN3LbKThBeaq8ifXzuvZnC/3cC8mj5niBKaim2wWHaHyk7VnHHK43lXPkIszpjj5gglzp+ATvC/+egdO00tL3S8C7qIWaK9bBGeKanYKH0kKud7inXJt6rt9vtd+yk4eMSxNEngvCe/7BPVMYIfaP9L3nYzD4drjWgB98Tcd1dasvdoI9kL+KtXCufjYfgJ1aqN0IdBxzRD6ufe2utrfbIhtT/W401px7a11z7OuH2DMyVQzB6QYDzseeJMD4ZCnjnsathq/tHw11Xncbev86WLRlU+NSJbfFPI3QviAAk7ACxAb7E2E/AJcoTnVe+029/8aqxpkTMBxLsFtdBq48xiMo9HryXMoKrxl86gXWEXCRyHD0grNz/Ywfd8BjTtX/BnMdn8bpOsaznWVXWnrOO82l2i0d497qltiTka6FYak+PMLntbrOkUZDx3wyAnOPPh0pKygEU25lXc952k5jxpNX9Jyp3lAf2h1pzHrtxS+LPZ2oj2+sqg+0wOi7/XVl1Pm59s8Ya/PmjrKhYnA+T/b0+fmxMuZaXY2ZKXKrJvo7bWl7ArLFQu43MtjIjZHL3rmja/kKcp/5GDF5pudMXgTuKbioKdrrI9eIkBuVyFUquTtzaewfZ1PE9P6G2HPEoN2jI7E3dzbFTmZkIur7hg847zgZ6vyKnfrHy1Ntz5hc3+Zb45Vl3Kkst7KscMEpRzjA2u03NRdpdjUujOc6roMRzgXgxz/5Yz8tdl7qvP3q1/6D2LNU56VXcJ+NeYWfKcgnLhY6cRob+j6rO7pH2T6nHPQ7N6+LHeH3hHWkkuuIo3mK3ATngCUYdcydXK7t4R7xxzfU3j/QeRPi94Tj+KbYia97mijkOQfiQCU3UrNEHLg7AHMP0+jcijLubr6u/X3xir4/jiTcGzfUX97z7sfE3kLu8icvahyi/5TYZF64oOvKIlb/j3CONwfbt1HX+1++qnHPwzp9566yZ2+8okzM9zyzLXYPueV0ou9XpN3T//32c6mH6wTLZDKZTCaTyWQymUwmk8lkMplMJpPJZDJ9U9kPfyaTyWQymUwmk8lkMplMJpPJZDKZTCbTIyD74c9kMplMJpPJZDKZTCaTyWQymUwmk8lkegS0tIy/0PdcdFrLlzWFy2TOi8VkrfQSnBrWr1/ra+3x4dFA7MUcNVXJTiJnpqbPSxNwWVpa+5wlf+NMawbv3lZG2wqZamDYkXNSzLS/Gm2tuZugBnGBIr019O9KD7XGUQs81BLP7gT8AvIFSnCHCrLNnLavwoqqsK/IO9DrJ+ivKNX2+OA5kCHZaWqN4Gyh/ZugRvB4Qiah9u98pjW0H2vq+JZgp4VgNq6e19r8ix54GnsDsSeofT4Zswa43t8P1V+mC4znUN9/Ptb+LQNwcvC+K6v3art73sPFe3DOOc/3nXcap6IIvB7ynlLUSgeDpxbx72GgRr9PpgCuJ8oU86jCJsu1xn1WYaLhhh7r3JOXpe+TFRxP8rdwN17v8YU471kLHn5MtCj6gxwXqsKAw/OiUMc7A/PAw/c94sIq73M286/KFzubacfxCVDbvQC3oMKc5LqL2vNkALJ/AzIPK8w8cGUwfhXmXsU+++8tVfyd84Hs2Ap/jf152r4HsC6XTp57a66hgH6GdyQ/wQOTjBEgBcMu5P3Aw+Wc88mJrCD0HsCFRHuqqQF9+szLK3OA7al8Ax3CmJuSl+zzfcA8XIC5B55yf0vr/zdbyN3Wr4mdDHT8Vjqae1ZYXWhPva09fOeO5qLX9pWzU6vj/nXwhANtb7Opz0vA526uKreoHYN9tiCDEIzC3T2xX02wJnPRBPdmva08lBDjMZrp3qCJ+bOzprlTHXzjLNb2Fx4Yhbm27/w5HX/f01zu7Zhgv3i4uDTO8+6vaT72aCHW2t6K9kOQKAdkraOcjclYc69FW/1sdVWvX4zUr+OBjlMIbsfqju4hvRxMN1eHrWOTLrQ9nab6YS3EnmKkfhfWdZ404WfhXe0fhzhU+NxzYY+GPR5ZnT457fBbD/PqA8+9T+wT8K5K9F+AedbHnnTm654k83S8mFsWyH3J3COuupqrqZlnYHlij93vav/H2AvMcEZAblEQgLeF3Iy5Z4U/TS5Squ8fYI9WAOLnwT9y5BE8o1ndBuPwdY3LJ6f8tfQh45DOZ7HLTudir6djPh7q2njnprJ8treVYUb+69a5HbHrYPAt2FelzqmDI10L2l2NAbOZts8V6gPzhY7R4HggdrOha+HGOT0HGhypDzdqag+OlVG4vaI+NOP+oabtJ8ez3dA5dunqJbH/BOccEeb8yS44ris4t8N5hF8jGwtzUG9f3YCSSY5zpqanjL4y0+fFsT4gKjXGeci1bp9o//ZX9H69VT03GoLxPoK/1HGuSf7wwYHGuEWs98ti8Jgxfgd3NVfrwX8rMc3T9w3AXOTmYNTUP8gKnV8BOMJF+NbzsvThyqVGo5Gr1e7NZ+6D01j7cTIYiE3ON5lwPLdqtsAMbGic++8//FfF/vf/x/8idruP3KggbxZrs6/Pu3VD42x5Hufbhd7/4rs099g70Djxnh/7MW1fW3OrEpvQL37uP4vdxPl8jrX25rXXxK41yPvV8XnqaWXare/q+/oN9c2X95QZR34093yeA/+Xe1o1Xaup7f3wn9L+5DzNsWe6uI04zziKuLG5rnFqra/9e/fugdj1mo5/iXXOR+49nuE8G3H2lVc1Dm5t6bq3BZZtutD5cefWbbRHn7+1rnuHvUON6zvb6u8ZAltyynZ9J7mU/Ys/k8lkMplMJpPJZDKZTCaTyWQymUwmk+kRkP3wZzKZTCaTyWQymUwmk8lkMplMJpPJZDI9ArIf/kwmk8lkMplMJpPJZDKZTCaTyWQymUymR0BLy/jLk8x5wb1aqAEK7ufgDeQpa4vrvaK2cjMi1N6OULu619fvZwOtvT0DM48Mv7zCOtL7pahB64d6wWI20Pb0tAZsmZOrA05LrO1hLe05uCis71/v98Vu1bT/MjDyEnBsmnWtSTvPtD1T1LgtiE8gm8nX75OjQ15DhZsDXkXAmtfgF0S4XR2Mvzw7e7zJvqqDb+EFWnO61UMte/RPSBASa897ev9GW+/vn9OayMWBfn8OBuFspB3AGts11Er3I/Wv9pr6S2cd3CMUkR4M7tVQzh4y3oNzyvgjQw2l1J0DL4vDWrC2eYV5htrcRPaV5FNpfxacaPDTkMy+kkwJfL0C1AIDr8L3IhgFXJjyAc9n+yu8Lr4feGWOHBsyCvltfJ98LnBgXKW7+L5n88EqzDn0RwXxh3WMtcNzcl9YvP3s4asA2fhxQAZjQc4M1mn2P96vQP9G4CiFZK+SgUmGBj4vHsDmY+3+soJ1KE///OHiPZS+78pTXwnJ8GMMwpgW+dm83Ig3wBhFWDs55x4kspXoo+QwVnyS9yN3hTHpAXOOKjCnGCLIJmOukIOv7GMN6IFnkICvG7XA6WzqeMV39fmlU35CCyyqKGKM0uu3+rq255UYSU6Ptm9zQ/kIfN/a2gWxO+BJZ6OB2PNvvKrPR/sXc7C8wHvodTtiHxxorj/EIt5ELtTrKf/DgecxGIJttVBeRIQg01jRXD8DayuM1B8ytO9k8Ba3hnnWssvzvPu5PxlrQYp5BH7sGNzoNczrNliNkxNlxu29/LrYrb6Oa5aCx9wGBxuLfwgG3cFA/aDX1Bfsrmj7+i0w9sjrDdg+9YPj/YHeD7ykeAzOOPcU4CWR18ultGBug1woxh75zuEd/T644A7rRrPoiz3aU55UCSZiqZe7rJzC1usjh0OD8uw9UFTJ1fTrCfbIcap7snZTG1gLlDM0BJ+MiCkv1/sXeJ+SfGbyjXG/nPxmrmPM3cBbLtDAo8WR2Dtbyn3y0nv3q7ALl1z1VtNFp/v5ta0t/awG5hhyk6MjjTk7O/r9COcEB4fap7Opntucu3BR7NLT3CCdgxmHGJZ56tPzurZn+4qu9Ud7+2Jff12ZbJtba2L3EdPqqb7fcKy5B9lNHUDz9m8ry+mZD75L7DvXtD0RmHc5OKkrm/r8k7r2LzdoHoJahEUqyzEH0X4yHTuIydOx5gb9zb7YrY7au3c0RgRob6fHXEJj8KXHrop9/aWXxS4bOh7kVR/sH2r7VrR9tZ6yub5+W+/fBhsuBd+63tT502hqrpZOB3p9Q3PFDeSae6Wytgpfx9N36i+h/7bxYsBccvme5/zTzctsqn7SaqvfjUcaJ7IcawX2VB7Oi1dWtJ/JSc+QE89H+v2nnwJzDwy7ZqRxqN7QtfS4pWv7PuLU0b6uRc/+6AfEvv665n4/8r4Pil3CDwPsIcdTfX4bjL8pmHG3bmmcSsHE63U0t3v6/cqzfvH/+3mxO9s6fi7guQhzSZ1nAXKbHHtEv3I/7Y+rz/yo2FGFZarN83COWWBPtpjqOra/d0vsFMlJf+2c2D+6qqzcHGcYtMnGiyL1rxjsU3zdeYXmYhn4zc0u4qiv94/muu7EeL+DocaeZKGfp6e5YPYO9nv2L/5MJpPJZDKZTCaTyWQymUwmk8lkMplMpkdA9sOfyWQymUwmk8lkMplMJpPJZDKZTCaTyfQIyH74M5lMJpPJZDKZTCaTyWQymUwmk8lkMpkeAS0t469wxf2K1fx1kvXoS9Tu7q1qjdwQrKkctaRJnZnNtUZrghrFAe7nh6jZitriyVxr1haokTvB5wFAMbdvKg9hdV1rZ2coOusDOEBeRqOjtbJnA60BvbWpNZvZnhI1nD30R9jQ928stH8WZJl5ZFMRQEAuD97XYy121jQma0rHswj0+0Gu3/fx/glqNpNlli4GYq9tgXmY6PNGqLHdAf8jqJ3NuCQLjQxGMhBr4IfMY70+KMC/AGtreqLtjdZZI5rACzwfNbsX8b3rWVv8YZDn+W8xKTEvyP0owZ1hTWYfNfoj1NYucvKnzq49/sDeRO3vkPMQz3sQI80jH4wsNMSpgJwQ3K/KUjubOUfGH+NUgSeQ1ckbhmCxViF7vL8q5f0x733WCqe/kIVa8Av6fB/z1CejDw+ocGAQJysMRNwvQ236dKHrZoE4SeZewPYiLjjEvZIANdjkrVXHy0H0p6pHfdPrH8B9WzqV5f1gRBQTcyvyG0L0YRCBbUW+MHy0wuslQo5rPYeQY14Be57NwvI5R9CAvDJrVcx98kpM4hxSVWIY2lMHC6yxonMg8MhP1uvj3jGeD551qTyPCPyIFIzBBLzsVqBzuNHU+xfwoMVcc8lz4H9kuJ4xtgF7PtPc2DHXAgsshr8lHR2RWqi5zsGRtrfw9PPzTR2v4Zy5FXjd8K/pQufDHCysZl3bX2KvUUN/e0jmw5ZynubXX7n//9OHC5/l7s2ee/0T+eq3ranyoxpg7K1v6+c7O7pHuvGG8n3IIQmQI2fYk9V7yijbvaMcmnOb2t6UvC/yaBu6ByN3fntd90x7h8oHSzC4IzAL5+CBtduaa611lDtyHOP+YBqVwdm5YIA9V4Fcgnumg7Fyd4IF+Ndgcx6CJ9UFgzE+Bru0pXwr19TxbNb0+36ge7QiV25Pnut4ZswV0R9ZSvasXl/k2DPX1H8vXOyLPRwq3yzJ9PsB2LppJfcD7ztDLlwwEyBXRtch8ps95HI5eGDj+kDsndq9dSFJH649n1fm97nDB/s6Jv2WjsH2ms7hOvLbFLnByYHerwS3sYa1fzLWtX+1q2vR9qbGrDkYcntDHaMQa2np6RyZLPQcqr+tbKUQfN0p+LO7d/T7jz3ztNjHh/r+W2Ag9lc0Zr7whZfEfs9lvX7e1f6exbrWj8EdLVbAvsKUCALuV8mHBifVY+6I/W+Ic8Bc1xB/Ss6oxri9Q81FNrY39P44F/rwc8o2W9s5L/b4RP3jjes3xJ5PdI0gZ7e7peNz54ayuQLwrjsh2FkYr/FQnzfz9Nyph3PM+Uxj9hh23sC5IXIpH0H67ccfD9vRVFivu6h+L/5M9u7KZyurmhvFGZlviOXIjSIw2mp1nH9jnDOsFYwzvb7uEd74xitib2yAD1s559LBmYGZ1miAaThQPxrhPJN7Qp7/85zhxhvXxI7qiPOIg1l29v3Y/yfHmitNFso3btY0tymZ9+NcL3gQgy/F7xs4qGJu0YI/9bs6XtwzB5h3gYffC7Anejf2yMw9yd/Ocu3vAufh0/FAbDIMs0S/T2blYqFxZQruPPfAGc7Xo4b6R5bp5/ORfj8BY5Dndm9uLZIkdZ9/8Zr7dmT/4s9kMplMJpPJZDKZTCaTyWQymUwmk8lkegRkP/yZTCaTyWQymUwmk8lkMplMJpPJZDKZTI+A7Ic/k8lkMplMJpPJZDKZTCaTyWQymUwmk+kR0NIy/i48+ZirRfeax5qmr774dbE3wHeIQn2tFDVWk1hrGs/H+nnI7ydaQ3Z/T2ur18AiuvTEZbEd6ulPwNSbzNRudPT506m27+jgutjNFa3FHkUBbL1ft9CazGGkteFjsKiyGooGg80V4edj34NbEYtDvoDHmrVns8o81PgtPdRqB7eGsDWyvjxPawpnodZgnib6/SZq9ftgZ7XrrB2u/dGs6/MX8EfWgh8NtNa5D35GAW4MGXtkmU0XWgP7eIxa65m2lzWh2x19/7AAl6ah/TkFU3Mw0prVbzIyq/yk5VeRpfeZnXFBPzz7fQrUyqafZrhf4JHbARu1qok4Yy3rCmMPbEc/O7s2eQVx8ID3icBCLfH3ThgHKjQtFC/n81iLm/f3UJu+BPeEPDHWjg98sjYZR/A8MB2LhMw/vA8+98FKrTLoVKwNX4DT48hf4w0Qd8loLBlHWat+oetUPNFa6IxTEdadsIva8ljHQh+fs7/5RvTPkv7C+fIARueb/1vhzi23St935enYVurD450j9kEAXgNgFxEYdJVOr+B59fkHu8rKWj+n3Bh2dSVmoL6/DwahwxxMwKs42d8XewPPJ0+aIE76UImY3WwrXyLkHAPvoNtEjK7p9+O2rt1kaXk5GIHIZblmZBWuq9pTMJj8SPuv0wDvoa+srAxbjOlE23/hkubKKdhQHO8UMabYVK4NY2wIPkevBo7MQGOUD94EeR1N8L+Pj5W3UXb0/U/Q3hCcpjna1+npeBfwpxzvN5vo3mFj/a29UJLlzjn172VW4Hv3OcUtpwy6ZK5+3m2CWbemvJ9kpryi6Uj9LgcbNCbEZwYOeao5a72t40y+rsMexAODrdEEkw0c7G/cANdlpn7kBeTYaHviBXN6rKWYB034VR1rf+KDewLmn/MIlmGupZ8Ox2SBbmt7PB3PBeIC+2t1BYxFhO1mXe9Xeno98FwVHlXhyEHSXIS5HrnrBfZgiUPcwTK6iPV+bP9irlyiaazjzVzWq8Bz1Szh/wHW/W5H7QXOVFLsFXhGULI/4zef6x4qhbXw/nnK/r6eAwUbypVcYK1jn5We5r+9NY15nLMBxjxPcX9PY8BoDr5vU9fKTqlsqFkMVhbOncqa+iBjXHdTGXuvvaQMvqOBPq8FbmcOthL3dw3sh2rYHxyDvztFEDiZanvLLnIFnNOQfeWQ65F95iFXKX3kigiZ47GOXz3USZkhxjU8jZntDnjIYJv1WhrjV7d2xJ6CY3v7juYKXg3c05CTVd93ONH2loiJ3UjHowUm5CH2iw2cIzWaYPqNdI0PcH0Q8dxS2xc4Xq+Xh+6tP/AfwAJfNnVabVdv3POP4YnGqZU1jQN5DkZaSrakjlMLuQ/Zlz7WHjLOQux5NrbVL8l9bzQ1jk2m6ic8IE5Sfd46GIGjoebslXM2vE+O9qQ495gMNS47XH9yrHEuzXmOhj1UT8/zxyfa3k4P7XsAb5d7zgjzrsD5LddunteXaO8xfo9IxtqfPA+ezzRXuXD1qraHqWXlBwDskRE3OVN9nMf36uq/JX8PwPdL/EmP7aF/YF38nd/6X8W+eLUv9vv+uz+tz8f9Xn9Z19HbN6+J/fSz91i5i8XCOffb7tuR/Ys/k8lkMplMJpPJZDKZTCaTyWQymUwmk+kRkP3wZzKZTCaTyWQymUwmk8lkMplMJpPJZDI9ArIf/kwmk8lkMplMJpPJZDKZTCaTyWQymUymR0BLy/hzLndvVlslu+rJ975H7AU4GuSETFFTljyGsA6OTaw1c49PlLE2j1Eruo569ig6HKLmcQAOzQpqu9dRkzZFzdwCfIUs0fdvNLVGcGdFGYgxmGsO7KrhGMw21GRuNuk2YBgOB/op2rcC3sUChf5nYFGV4NBUWVzk7JC9Rd4ErgfgIScqKkJ/g+8QhXo/P6tWCX670J2ujRrIrLnt+2RWai33BHwHfj9J9IGHI62RfXCiteP9EjWh0eBLj53X54EZuAC/g6wsD/01Oq3VztreD4OyJHb+qf9y3leYaDmZaXqvSm1rRxscDrIcyRwgexOcjaimzD2yICskMzLrKvP0bJamj1rjOe7HuMxS2o7MOfLDKn+PBZwTdEgYgTlIhmDJ/j37czLxqBK10jMwDRgXHJmN4HWxg1LUtmd/VtB04Ln5BeIqxssDs4KMPR9x1isxnnj/ynxH4CUrNmAc53zhukAWLDqMTD8yOr6JB55e93Ax/nzfvz/32GcVVBM/hw9xDgUPmOPsQvJ5b9y4Jna9ozwHspQaday9ZPo9ICYxhkzmulatIoYGHnM76uwOJC+iJGcT1/f6fbGTQNf2k1J5ET64l1mhazFQWS4Znc2JjcBLWOno/S+eVz5CdU3QNWT3QLkxg5Hm5peuPiZ2AM5svNAY+erLr4t97oLySrxM+6sd6njPEYNqLeXkeFhU7oL15rBmuIZev7Gh9+sX2l+Dob7P/kD7o93S/u6v9MWeTZQnUo/0/sn4rb0KkbJLLy+4D1bKa+rH4xOdN/OR7sm2L6yLPVvouDS7OhHSofKNUqfj5mOPQvbpyorerwaeEnP0mg5rZe3ptuGHLfB9ffUbD7xkcnO6K5qjx+CIHx4rHymCXzN3bGf6B6td3bMOY23fXqlcmgJrZgD7cKpMwwY4PGTotZAr9er6fmlbO3wBnnAJlidznQS5MLlFjHt+hf+sl3sh+VdYp7DOZuD2jNVdXWdlVf8ArM8YfLAa4gTX4Rz8Lu6hZ1Os81gnglD9P080Dqeh5qZHh/ful+UPF+Sv3++7eu2e7zAGRU5jeeG439C1bTzXPu35mNMR9/GaG7lArx8ONeY4X8ek09M5EAPL2MC5VjzVMUTz3SLR9t+8eVvsHPl/Aj7uaKL22rrG8Nu7GgN2r+na39tUHvNLr9/U9mJORnXs99rqw6lHTiaY6ZizIbmuyB0CzLEQQXUBLqaPNSGeYI3COaG30Ji5hjUpAHf1pa98Xexrr7wh9mSm7en31V+adbUDX+12U2NugRhQ5jjnwn5ybVPPKV1KxiTmD3LXBnjXBffDTYwXYnqO3LPZ4Ib84VG903aNxr35H081LgR47zkXl8q+GLnKal9ssjF5LjMaac7Kc7BaU+MU2Z6tlvr18ZHugbiv98EX7q/qWjkfI5fi+/JcxHGPAyYi1rB2V1mv41e+IXYORiBzhzfZjG8qxvl50IBfk6fr8ZyRuRcYgWQOYg/nYw+e4/2TAvPcB/MOucTGtvKcC/RfjD1skJ19BkHxFK7CCOQ5EH8/wBd4jsQ4zWS5LLS98zl4zlhIQ+w1eM47HGpuN8LeZZFGp//77edS9i/+TCaTyWQymUwmk8lkMplMJpPJZDKZTKZHQPbDn8lkMplMJpPJZDKZTCaTyWQymUwmk8n0CMh++DOZTCaTyWQymUwmk8lkMplMJpPJZDKZHgEtLeNvMJ7eZ4utdLW2uY/a1TUw8WZg2JFfkKRaQ3VwpLWyjwbKP1jkqAnbAIcENXsHRwOxWaOYNYfrNa0ZGwVapfb8uta+vnt7V+wSLKj5DDVlSzIAtT8jvs9Ma8gOJqgd78CDKPT+M/AZ2uBXNOr6/EWm30/AjMsDcoNQY5fsL7LGwPDzyO0BayonwwlFq+NSaxq7WD/vRGBrgc1VA5en8r45mIP6NFegJnOSgy0AVldaYXdpexo1HZ8ItdTJ5ItRk7nW0PEswU3KS21vjvnkv1l7/wGMtGVUWWbuvvs8oJa0qzAMUVsc41I49QPePkct7sAjv4m12sH1KHh/PgHfr7wOa2Hr52S+FbieTDy46X3ez5sK6+CIpGh/5Qacx+wfXM3a4eSgVPqL31e7wjtjezLcYQFGYw21v8FoLFl73mMtckIez65Vz/fl98nQ8xEXSuDWmm2kF02MJ2qb1xo6vkGgnxcPaB+Xhao/433cO9Obtd6r911ueUVxPxZwDDkHQ7CPHDgcQciYQvCL5g4cowxjuL+/J/bdO8pp8cCPaIMH0cDaQ14AfYaclgwgtFZb+QxbW1vaHnoNzBq5qSnmtF7uCqzu45HmntnqQGyGFL4vuZrhNthSM809yILa6Cofut3W+5F5GJOFVej9QrwxQpobniivIwC36GQwEHtlTds3w/fXu/qANnLvg7nyRtb6gK9hjT4ZIjdv6v18R39iTNVccaur/dlq6P2eeOIq7qf92+/o9Rly25vDl+7/f7I1ll/Bfd4G49J7fkx5Tk34yasvH4g9cZpTk5ty7tKO2HdvaRxaXQE7EWvzYq72yclA7H5f40iJeXI41D1cHYzAGH64eUWZfdxjLvZ1Hly+rByV7rryoQ7v6vt6NTx/pP23uqHzrtXUeZYVev+VWOfBNNR1IQOJJcGep9HTuF6b6zzsdHU8F5jXNd2COA+cmqKO3BO5necY97CnSsG10cdVUrso0O9neF8ftofch+voYgaGZKj9X6/pnnoRa4eEgY4nOUZpovePIuQN8E8f8zUn8xBh8c005OGiJTt3643r98+lEjDM+iubYifgNM4y9dkM5zLDIdcmvb67dVFsH7zac1f6Yt+5fVfs0UDPZaYTZRSeHCmP18McbXfUJ48PNOa2e/r8ANzRx5++op9j7bp7/brYTz77tNhlojEwxSRb62oMq+NccA6G26yu4+djA0FmPI51Krksz5kqTHIy5bAm1HDu10WMixo6x5v+HbEbOEer19T/br78JbG9SHOJzW3NdbPFQNuD+8VgibWdXp8W6l/zGtheYFh2WzhHwrnYCDEvRq61vqrvs+Lr/WaJrpFxhHM8nE/E6VvjlWUPV6Rq1pqucXpeQiZbC+e/h/saJxjbi1TtVlu/z3OmHMF+MtQ9DZl2PlbPDGtxvaO52Jj3Q5whv9bD53Gi56XcY3LPWDnnyrhWIzcBp30+1nkQkMmH/q5HYOoxrjTBvsQeJHAap/F6FQZj6fG8F3t8HkAjTp4c6rzaXn9W7NlMc4+y1DhGfyngTzxXdGQIxtofd67dELvVU//ZvqDraJqqP0zBSx4fH4t9/splsUPklh5yoTgGKxfAb54bZsiKEpxz1sErn5+u6zH64Sy9o3/x9w//4T90nufJf88888z9zxeLhfv4xz/u1tfXXafTcc8//7zb29s7444mk8n0vZXFKZPJtMyyGGUymZZdFqdMJtMyy2KUyWRadlmcMplMy6B3XOrzR37kR9zdu3fv//cHf/AH9z/7xV/8Rffbv/3b7rd+67fcZz/7WXfnzh33Mz/zM9/TBptMJtODZHHKZDItsyxGmUymZZfFKZPJtMyyGGUymZZdFqdMJtMPW++41GcYhm5nZ6fy58Ph0P36r/+6+83f/E330Y9+1Dnn3G/8xm+4Z5991n3+8593H/nIR7771ppMJtO3IYtTJpNpmWUxymQyLbssTplMpmWWxSiTybTssjhlMpl+2HrHP/y98sor7vz5867RaLjnnnvOffKTn3SXL192L7zwgkvT1H3sYx+7f+0zzzzjLl++7D73uc+948C1t3/iwlO2Wz3SZrbAeipRi7zZVP7DwRv6z6Xv7GpN2nmqNWZL1NMvUKu9QD3+zqYy+zbPay3yeKx8A9YgDkOt+Rp4WnPWL7TmbK8LjgyK+PbIOQHD73igNYfTGcArqLm8QC3tvUP9fheMwg74D2S3kVFHfkILNaAXqFEdo+YvmX0paiZ7qEUObFGFOedwfe6B7UV2WqTvO4G/tFHrPUdNZjIFY9Qczuda2zyZg9+AGtQh+iND/+VggTVQG70WaHsTcIoc+iv3WGMatfXBwHSoud08nef595BJ84OKU2VeuPLN96/UoiZT7Ow68Vnl/VGrnIw+xKGQPC78g+7cqd+QK8La4x7mUUnGGu5fOF5PhiHZl6zlTQagU5FZBw5KBdGXMw6gPZUvsP2o5Y55xvdh+1kbnnGHtd1L8JyKKWu5o7/q4MDUWDse6xoYE1XmpIq1+Pl+OfsX/lBvKkcmINMP614Ntc/5fNYn8MF+rfQ/+qvKXDybHcva62+OJ5/zneoHFaPyonTeacwHjsAF4AmUWCvSBfixIdZSrGUevl+gHr+PTn3sySfFftcz79b2AQoXIdcJyVEh3wFjzrWw0h/kHZD78gDUB2N0Hcw/5o4x1vK4UB7AHIy4AEEkCjUXXqTKdy4yff/mFc0limtoHxh9SYL+cyryqYcDrEGYYo2ajtft66+K3QHnM4yUc3NwpFyhx7c1d6kjBs7Rv5NY/bnV09y9yDRXDwICLsiVRQz1tb3k/Fzc1JhY622InWfgjyCXYm6bgtFY77/VH37Ktn9n+kHFKd/3nH+6RpGjejPRcS/ugtsyBasTrMoM/RQqpsbVEGcGQ91zMdchl6ZZ1zjabmNPMNbnx4XOwyl5SA19n+MD3cOSaxNsaPtGhfLCVmOwNpELdHvIHbp9sXPMm+lY+ycAX7lRgI+V67yYRfp+c/DO4PZuNtPrux29YBJr/69EOk8a2INPa8p9KQmywTqSMpfDmUSOPRtzi4ycdcfcTuNeAW6788hU1PYksyOxA6w7IXhXXqD3zxKwfkP9fgWXHYNLD+6RC5HLI094Mxfz/YcrRoVBfn/7Sr7qaKRzbjTWd9u+qIy72QK5BhbL48OB2K0WGHBtXfvaWPtSMNIi8G7nR5pbLObwOXIsU/1+r43cAT4xxfrTDDUGZp7GhB/90Hv1c8Rs7oeTVJ93eQdr5VRzoVeOsL/qgw2Gcw+ec5Rk1uNcw8P+medc5Gd313XO18gsx9QIwHiPkCsdj3QNeWJLc5sdnIu+sqf908DnFy4rC2uKc6hsrv44Add1gZhGFtrrN3RNf8+7NUYtsP/1EZO6Le2P2Uz7+3iqa9T+ga4BnSd1PGs9rvFv6XtFdf9BxSkv8O73d461rLOijLXRQJl53MOUiEsR1hbuq2uwySHn59xOk63ZX+mLPRlrewuuzR5zHeQ2yNnXV9fxfbQfE3F4pMw3+kYNcZgcdR4sVM518P61ts6DAGhKnmuR0VjD/QOcmzmcbztw4rNS2889uMu1vTdf0T1dE+tUsY49InMLB5sOwriM648PNa5EDfVX3m7/trJSa/Dv9W1lk/7hf/qM2M/92Y+JncHfMzD61tbU35hb8ZxxPte41UTcn52uu++E8feOfvj78Ic/7P7Vv/pX7umnn3Z37951v/zLv+z+9J/+0+7FF190u7u7rlaruT7Altvb2253d/eb3/C0sW9v8Gg0+pbXmkwm04NkccpkMi2zLEaZTKZll8Upk8m0zLIYZTKZll0Wp0wm0zLoHf3w99M//dP3///73vc+9+EPf9hduXLF/Zt/829cs9k845vfWp/85CfdL//yL39H3zWZTCbK4pTJZFpmWYwymUzLLotTJpNpmWUxymQyLbssTplMpmUQa1+9I/X7fffUU0+5V1991e3s7LgkSdxgMJBr9vb2vmlN4zf1iU98wg2Hw/v/3bx587tpkslkMoksTplMpmWWxSiTybTssjhlMpmWWRajTCbTssvilMlk+mHoHTP+3q7JZOJee+0199f+2l9zH/rQh1wURe4zn/mMe/75551zzr388svuxo0b7rnnnvuW96jX665er1f+fHQ8c8FpLeDhOhhvidY8baPWeI7a5CtrPbFPpvp5NtPaqKOx1spuo0YsOTBporWvB4dab7/d0FrmYaS/twaooRyUev92R2tfF6XWYk9SspbAqEv0fVd7ykNInLavKFmjGcxBspRiHQ/n6f1T1FTOUCucvIw6ahQDmedS9F/Bn6/zs2sa56jNz5rIrsIB0vaRE1SSU0A+Argt5CixRrUHHsR8DE7NAlylSGsyd9dQix81medgXM7AwcnBK6mzhndd7RBsrnmK2ux4X5Sqd0Fxbzxy971j/L1d3884leepy06ZkGS6VWpV408KMu1KsiTJnqR9NnPAYRwisCh98LfICPQrYI8HAK4qnz/ofc+6uirWqmet7gqjEKzOErW3Kww3NoD4JjAgKu3l63O8HGuxoz9iHT+fHQSOitfUeeewTjnEBS9EoGStecTBSlxEXM0X2t4C61AL63KjqetYhelX8WcSxVTsPzIlq7X0KawjFRYt2KincSzPvvdx6vsZo5xfOPcmOwudQAacB24kltrKWk0GHjmmPngBZO5duvqE2DUwB0OyrLB4kL1FcY6HSBYYYwNyVipOwxgPhiHWWs6hagzDA8DMY+5FLmmGXJBYTPIZUozPxmN9sVdK5RmU4BPsH2opocVE7XpL/+ZyhAat9zXXrONzsqL2h5qLk00WNfV+aaqf39k90e93wT8GmytELt5r6/2Jg45TrplcNPR++yfK1ekV6u+9hsbIHGs62Wq3b90Se/VtbLsy+16Rad7S9zNOFbnv8lP/zkKs5dip1i7DDy6DNzUHU+82WJQ3leuRgHFG5l9UJ9NN+3a6AG95onuuuAZ2Zxt7AMSVEBDCCt6YcQpx7NBTv58OdM9Va2l/dbrKvDsZ6J4uQ241x/txj9lva/u64PT4YGve2Lsh9uRY77e5rten8O0OcqGar98fj7BHa4LP1UKugTjjYeIvYvK1EbeRu5GHHIGBl4OtSiA0uUQB+Gh1NV2acg+NXCbBOh5yzwwGYK7jT4ZgQYA1nldinYrejKvc3H8P9P2MUb2652rRvb5KwJWcjXDu1FHO6HSisTte6FrgY470N5QJeOFdykN+6U/+RGzvROd8Cab22saW2DsXL4v92ktfFXsORlqvo/1x5fFzYr/6yl2xY7zv/ggxFvvPk8N9sUcHWuKwiRi1u6v3z3o6CZIM+X9EX8NBBMRctYKER27F/RFZYUjNnFdoDJlMdY4EYKAPRgOxm+vKBy7Q3tevK4ssK3X8Eqf3zxLtn4TMKCzCTXBoh4d6/QxrahZrjJhhzczRf7NMx6fAeYYH/46xJviR5qKNBnLx67omeo8jZrXfGrEKy/l7oO9rLpVlrjjN3ZkFdjp6PpjMNQ7xC3z3Os6RmLvwGCRFDuuTw404U4KDHjbUTwuuGThf9HGuECGXIzOtu6bMQ7+Sa4EnPNE9SYg9TGWPzXMFdBBzAzICxwvlOyc5mXt6vwi5c4g9fYG4wz1oDiZhEPDcSr9fw79WXYym+FxzWZ+b1LOPyaoXIK5GON/3A72+u9rH7bS/D3Z1L3DxscfFrjd13am19X3ovzwDyHEIQf545dgR/jwdDsTeunBBn386XklKJu631jv64e/v/b2/5/7CX/gL7sqVK+7OnTvuH/yDf+CCIHA/93M/51ZWVtzP//zPu1/6pV9ya2trrtfruV/4hV9wzz333DsGk5pMJtN3KotTJpNpmWUxymQyLbssTplMpmWWxSiTybTssjhlMpmWQe/oh79bt265n/u5n3NHR0duc3PT/cRP/IT7/Oc/7zY3N51zzv3qr/6q833fPf/88y6OY/dTP/VT7td+7de+Lw03mUymbyaLUyaTaZllMcpkMi27LE6ZTKZllsUok8m07LI4ZTKZlkHv6Ie/T3/602d+3mg03Kc+9Sn3qU996rtqlMlkMn2nsjhlMpmWWRajTCbTssvilMlkWmZZjDKZTMsui1Mmk2kZ9F0x/r6fysPgfrHd117T+v8OTL0LlxR+eu681jKPIq2xOjjQ2tcoRe2aTa0ZG6K2NfkP/XNrYnfaWhM2nYOHgJq/CTgqOWrQhjPUPAZHJKpwYVBrPGANaP08nWmNWnJSCrCtEtRubzXATQF3J2RNaDDvnAfexRy1zhMyEHE/lGr3A9SEzslO0+sfUEK4Ita0TslAxPUJmI0hqr2voVY7mzccvyF2BObj+rrWDG5jPDzUor9yoS/2BDyU0VD9dYbxSEL6GzhHqFG8Dt7HRpc9dM9Os8L9yTX3UCnLM+ed8lVYK7tS67viaOhH3JtxoijBngRXpCTCAOOSY96FqAVekvOBiVBhe6J9fP/iAYy1vGCDwVRArWyPHeSd3d8earP7eD/H9pMBCK4NWays3c5a7TnjDjsAxeRzrANkRjrwqFys8zQE78oHWzbH+1fjIPwRLDsy/PIY7E6sM0WkcSXzwSvD80Jwb4hIrPDS0D4PteMrPLRE+zcAl4bsgBSMizd5YOkD2IPLpjBP7+cMIUEj5JiAZRSGWr+/wu2ED/nwYQ/Xk3vSa6PePdvDXAKfc46Qy1LB9yJIkmPKEEOGn8eYBmYgY/xsBv4xecfo79EEfAWs5QwiIeZ05rT/W2AxzefgytSUq9PJ+mIzpnV66/q8hc4RTLFKzGR/5xiP8Uz5GfOZ5vp1MCCzGDEBW5oIzEEfnFPG6FZL739lW3P58UTbM8m0f3f3dW/RwPMn4EC11jR5nY7188VcGYpprLn3BpiJ07f5W/p94Gd9P5WX+X3/Zk5dxhq4YvBlmXvUmvADcEiihX4+Q27UbmAPhbiS5Or3JTg2OeYtcGAu8NEebjoYVwmegUrMowKbokld58l6hnk0US5Ls60cIOYK5OS0632xuz0wGMGubCDuN9GesAmOS6T96YOjvsCedFFq+9c1bDnP1/YcLoZiB4DbJp7OS3LfczLtcuSeldQT60au92d/c0/Lddwv4WCljk/O3Jp8a/C9UsyvqAH/w7obIldmXlGCK9Q6Hf80fbhyqbJ8awnePK+Mu+HhoV6L/HI8VAZfF8y6x59QttBspGvz7qvK4GuDnXQ0UB9qrui51PBI16bVbeX5OrCSuh1t3+om2nte59gLX9D7Ox+MuhNdOy9dVm7n6FBZVtsXNsWez8AwQnI3miGXOh6IHaC/c4bUyv4MH4Ntxf2Fq+xXsf6CSVfHhha7O7e+ruyxEOdwA5zT9Df1HGkxAfjToT1DzS1agb7f4VDH69n3vkfso5uv6fdx7np7X+8/Amf2XZd0fJM5GH6Rxngsua7Z7YldR0xuB/oFpLLu6O5A7OE39P2bT74VJLPvA9f9+6npeOTy0/0qz4kiMAEreSLmFY8JfNwvxXl8iDiSIrdaBct0Aj/kuRM3ZZU9GSZuBtZZA+cihwcHYnf7Os/IqU+xts+mWFu5B60w67j2cu3U5zURd29c198/fOR6JRbzDGuti8Ez9nR82H4OOOcNcxn+vrB2WX+PCZG7kF9cyXUdPvaYi+vnPIOIcT7D3x/4wPf/uJbSpfvFyFHGR0di17EODwb6OdeVGvbkOVnsSP5GI81Nz4PNuzgNjNyrn6UH/MRhMplMJpPJZDKZTCaTyWQymUwmk8lkMpkeBtkPfyaTyWQymUwmk8lkMplMJpPJZDKZTCbTIyD74c9kMplMJpPJZDKZTCaTyWQymUwmk8lkegS0tIy/MGjc54MUYJTVm1oDd7bQGqx3b94Vu9PWWtDvetdVsXf3B3g4eAJgsrWbWqO1gW48uHZb7O6aPr9WV75DCc5Iip9j906Ue9JEjdyVFXB4CgAAChbpBTMPtbT5eQ01clnb/ORIa9HX61rT1w/1+5UaxPj9OcP94wQ1lcFeKuqooQw+RImixAXZX6idzpq8RaWmtIq8hCRnLXj9fAp/ilAzutvQ2vmtto5vt9/F51rDOuLzHZiVqAVcx/g2YO8eqP8lqBkdBHq/lba+cK+h8zVCsXb/dH4HFYjb8isvcuefNruEZ7D2dpW5451psrY4a5tnZO6xxnNOR9Zxq5GpRjYkmXiOXBM8r8Lcw/1R+70K0yRUQZ9f4ZKwNDYnLpmDqF1f/Vsv5OyAT4Y4wfuxFjmfn6TKZGBt9Mr7PKB2PLlCZQl+Fbgt5dnd64ikIEMvBww3AMOjrIFrBM4MGYFlE+9XB6MQfLAS/pzi/bnMVeJ4zv4EbyzR+yULHa8kvscyyPKHi501n89deNqXK231OZaXJ+MvRh+QD8G+SPH9AOwfzhkOkl8SQgh+AZ4XIaaU5EDi+tdfeVXs6Ui5NJeuKGdnFdwUj7kC1qwMa2+SKv/C4fsB+QUVNhRjDrie+H491DmUkZUGzosfaG4xi5VTlE91jsfgis5SsMUyfd8AvIoYuWmB+zlfr1/rgBeC/j04Uj7Iys4FsTcuKL9jgVyr5uv9zm2BbQZ/Xutq/26ABZamynsYjsmt0fkwnur7z8FNbUTavh74zpwf0/Hb+CXvgPmwbCoQmBYZc1r166imfgUck0vAh8qBi/KweJDlWEfuNp3p81stjYt+X+dV26nfFJiHHvZ05CdzD5Im4DUjDlZyHcSdgwRMP+Qix/u6pxsuELewVq7v6LyZTzSukp8btfT6CNC6NAGfKdb33drUPVDR03n+4levid2q6x48Ry5WzzXO9df7Yo8TjTODhfrHoolcJND2ZuDUe06v92vkKyNXRi64wLriYd2sYT54vvb3ItU46Hvc42p7Fgv1l6im/uwxV/QwXi3leW327tkx/GjZtbax6eqnee4hebxguOUIMj65kEi4D24oM6270Re7jjl9/Zqyn17b1X16p67tuXpVmX7tvt6/5sMHcvWRwOmce/XmQOwCucT6ljLfijt3xM5jnOuEiHng2e5s6v0bzSfFns3URw/2lKnocMz1oP0b50DlnCrF9WRXYU76+Bzu4xptzX0yrIElcqsmzr2a+H4dbLNeXWPqzobmEljyXIhzwZNDPVcNsBc43NPcsV7XXGd1XWNQgA1vPD+bnbZ1XnNxsuAGJ+ovxzOdX3minzd6fbF7a3r/yeHb2FoP255vfOKK5N74fOCD75fPhod6Pv3Us0+JHdV0LczBOFsDMJe5U4J5m8zVbvU0jszHGrcCcLizDLkAz9EIvMU0bIMFebyvLNFeX1mjnPbkyic4d2g2wNkGLxmv4wK0d55wD6zvmxVnr5FE9OUZ1x3uwXVtZq5dOd/20b/wh1ZDc6853r/Xw89MPDZE/3qECEIlz234+wm488wtyaikcuw1vv6FPxb7vR/6oNi1mq6zY7B+Q7xwELA9+nwyBYuYex2dn7ODXeecc2lKSuy3lv2LP5PJZDKZTCaTyWQymUwmk8lkMplMJpPpEZD98GcymUwmk8lkMplMJpPJZDKZTCaTyWQyPQKyH/5MJpPJZDKZTCaTyWQymUwmk8lkMplMpkdAS8v4a0Th/VqoC3BEVte1Jm+nBy5HrMWqfTD7QjDGNja1BjDQSG4KDoeLwFvA/ZJU7b3bWtv8qaeeEHux0BrIo5HWuh6idvX2hr5vLdSasBnYHqxlHoIxGKPGMAl8If8E/ROjlrbzwK5CTd+orrXFyX5yNdQQ7msN5RxViOdT1IhGTeMYDSb/I6iwr1CLn+yySo1isN0cBH5CXKh/jHKwrBK9X7OlNX17HfAbwDthjWwHpmCjoTWJfdQ0LtCe9VXt/+GR9nevodf3wRwkt6kA9yk4Nb0sd85p/e1lV1a+xV4js4HzMCNEjQw7R6YduB8FaltXxg3cGP69DrI/8XzfwzzB84i3qlYDx+0fdDX5Yix2/QBV5ll5di1tvk+F4BDqPCsKMuTOZhjyhn6gfh45sF05/j7ntSoHr4tQuxIMviJnXAKHhUhI1M5PwXfzPI0bQRMsADIFEec8QAYrcQF8KzIIOeAZ/J/tJyORrIISXJos4XhjPqf3Pue8Xna1GnUXns6FFDHBJ2cTTLkA3MYyAL8XY+zwuYc5QEbfi3/ygtj9Vc3troK512hr7uBzDmEx9xFTLj/+mNif/Y8vi/3Ue98ndiU5cGfzEAhcqIFvSyYfeQfNSNfOEtydBPyHCkcIa2sUai7mgT1VEl0SKZ+hRWYfGrw/xpwDK6wAC4186F4H7KmJsrTWwf1JEXMb4B832nq/MAJPuYbcE4zByVzfp9vS8Yqaev8E/IXHLyr/5M7+UO1j7A0OjsRe6+lepNPX/ozQ/zFifPttDMAqU3i5lS0KV566b4lx9zDvQuSceQq/j7Rf9u/qHorszGZL512dOTSmbYfXb2CeIZdoNfTzNFG/S8lsw56DuV+zTtajinsY7omKHV0LX7+jfplOkTth3WhjHowGB2L7YLx1u8pPOrmurFXG9SzW9yVX52BP51ULrM017gFj9afti7rO5Jm2d/eWcpDqLY3jxcCpwK3P6+BLkRPPPSP24KWDf4D96vD90Ff/yjPcv9QGeyX8gfML62YT3PmswqvW61sBWKiBcoD29u7FvTTjacNy64VXDlx4ygy6fPGcfFZh1IHT2O3qmFy8pNzD44n2xe2b4GQmei5EnrFPprnjPr4vtocYl2AOd1b0+gWWk9uv7+ofYL8Ujwdi75zbUfu8vv/u9W+I3cQ5Ra+lPrt3R9vb7WmM2bqiud5urnOa+0WuCURBkSXFNamSTDHVA6prZVVzBbKikukEn4PHjFypKHv4XP3laF/t1Tr2O03153Sm5zxeX+cwzxVnY13D2l2N6fVU/dkhV2z1lAnZ6mrM+fpLr4t98aIyK6dj9Eem77vRRk5wojF/vK8Mw+nb1tyHbc/3b//X/9f9vU8dXOnKOVFIBpt+XqtrLvP1r31F7C5yeDIAQ+wBS5yXj4c6jy+c03n8yleUsfbE07onrNfBm8XEI++221O/7ILPzH0/zxGYuzXBLBwdq58njBs8f8aeMPfA+CNfMsBazYMSzMs047kGcoOCuYTerkBu1KzreOfITaJI41g8xzkQm4v28lzMYc/oMW7znBPvW2HqIW5zfN548WtiX35S/a2zonGQ/jFB3C1SbQ+PDSvvj/Hm+PVWNM6//uq9PXuafvu8ZPsXfyaTyWQymUwmk8lkMplMJpPJZDKZTCbTIyD74c9kMplMJpPJZDKZTCaTyWQymUwmk8lkegRkP/yZTCaTyWQymUwmk8lkMplMJpPJZDKZTI+Alpbx126G97k05CNEvhZJPbqltca9QGvENrpaG3s60NrVLTACk6lyTljTdwDUEhBuLgJXpIba3+lEawDHM+UVRKi/3wlQO5611BPwJcB+Il+g3tD2DY+VMxK0lWfgN7TG8ww1e6eJ1sjdBwPu/I7yFLIFa/5qTeCU9bTBgiK3ZQXjx/5foEbxBDWUkxI1eFm7nWwwwsXArioI7sH4kw3G0vCsAVxDzem8IP9DP49QnD6q6fgR1VYHj8QLtT9Y49uBqbjWVv/LyBLD+IYhakq37tXcLtKHi/fgnHPz2cKlp3yLDAPH2tysE0/mWcBa1pXS3WCWVZgDKvKUeAUZgmWs456kOm/eZFvctyPywsC/AveDcalEewJCFRz7A+0l8w4ig8CrMPnQ/3i/MuW8JcPhAbXIwewLEMfYHN6PDInIxzzE9T4hjBVGJPojx3ghjpD7UrC9iIv8a0QBeGMOz0sXOn5kn1LsLzIuc9ayJ2Mzgb9UYaz6MVmvp/2TEfK65Ar90oWnOVNJjiTGKCHfIKMPwmdCje1ci0LU+2d9/sGJ5kKzmdbH37ujfOQIz7t4+bLYjz31pNi+j+eTu4IYSh4F52CVw6r3y5AL+YxZiBEp5xx8uFUgN/UG+vwADL5AczdyNgMAJsI6eLtz/TxifzEXC8GbQO61tqK598qKtu9gd19sH+OxKHTONhrK80jB2BsfKy9kdV25MdwbzJFzHB4qJ2atq/7aQ65Zh3+H4Im0O8pjaAzV38mbvn3rutjD0ZrY59Z0vFbXNLdO35bdffvEh+VQ+bYVJy/AGGuSS4K1NsQea6b9NI21ozc2wEPCnq8GdmQNTL/pGMzADLkEcqciYK6BuAlOd4G10mdOTU4PuC+Bp7lCCt5RiHWg/7i+X7HQz6e72BMU2r+9FZ0X3UDb1+rqvJ8gzi/GGqdq2IOWU/2ce6w6eFAXntL32bt5KPZsqOPXXe+LvbKtcWM+AdcH/LHkUPdE3QvqP4sIuTXiHHlsZO9W1tVS91zcRBbMPcHc8xBXa4Fyj7iuxQudHw55QI5oUxxq+2d9Xad892Z7H649X1G8hcAKMKcTrP3c53pgyKVOx2R0dEvslVWNUVOwsa5cUSbbT/zEBbH/w3/4A7EHRydiD0fgSHpYWwP14cFdZaAlOMcJEGNaDV2bfJyz3L6ua91kpP0XZ+qThwfqs/229k+eq4/VwS5LUpw7Zfp5jhial4jRWWXDoPJp6vWtmvZvH/u5a9/Q/lhZ05hWq2tMDHCSs3tHx6fA+QLXDIf9U3P4on6/qf506/qe2IOh5kqb68o6W4BR2Oro536u/lfHecLkWP11tab9eff11/R+GG8/0v69c6T+wfOOIRImWaIfMsZf4PsuOGX88RykwB6GuUCCfXIKzvdiruN2UGoOf/2Na9oY7DErDL1Ke7j24dyCTDzmQlibvvC5PxS72dS4dnykucEbryrjjZx08mxrnj5/9841sXe2lVnIc5AmcqPZQvuzcs6H90/A8vYr6w4en/GcSccnczxv1v5fizQuXbx8RewG4pyH8QyDs8/5uGeuoFTxg8u1l3W8RgONG9e+9pLYjz37rNif+0+fEbuxoutOua/t2b11Q+yn3v8BsVP83lADH5riuRa5x9MF2KlgDE4G9+Jw9g54yfYv/kwmk8lkMplMJpPJZDKZTCaTyWQymUymR0D2w5/JZDKZTCaTyWQymUwmk8lkMplMJpPJ9AjIfvgzmUwmk8lkMplMJpPJZDKZTCaTyWQymR4BLS3jL/QTF57W0l1FzdUMzL3rh1rbOmr3xR5MtUaqh1rd7S7hQahxu2A9ftbIVTXBVPMCLR69iLXWeB01cbMYzLaO1hhugn+QgkW1trYpNtFPJWr2rvS1Zuxspv01AP9idKS1uzvgnjRRe/1kDD4BapNnmfbH0Z5yfTbPnxe7Dr5DE/1TQ03rWoQazaixPE20ffr2zvlob4XrA05BifaRE1TiBhk4PDMyAAOtNb+AP9bJJgMrraQ/w91D1LCOwDkqPZ1vnQ6v1/v5YBmQScj3T+J792et6odB0/H8fk3yAu+VkuNRsna12vUauB2Yp6zF7j2AGUfGHtmVORhtGfq/QA3+wNf2tMC9icA3QqnxSg1+Rk72DxGFGfhX7M4K0RD9QW5KpT+ys2ubVxiDRHliXXkgw5FMQ5gBWbGoFR6AYUBVUKSsjY91I0uwTkXK7MgyfWEftds5HuQY8QXTWP0rZ2DieDEuVvhoxZmfs7/Z3goLgVDD+7XpueIvt9Isc+Xpu0Wexpgk1THnHPWwttTANMsLMO3Qx2Qv0Wc/9lN/Xj9nsgIfY8/PZspWevWll8VeB+OtB5YTxfZXYgbmNBtEzmsMHkaG+v/oTre2qv2VgYNZ62r/RRWM5gM4qUj5PV/HH0uWO4nPXpMbLYwvODmPX9kWO43BKttUTtF4VzkyHnIrH2tegEVmNtGYlcS3xT5/YV3sXlP7Z6WrPI4WmIKLud7/+GAgtvP1+jr42L2mtr+B9m+sXxR7MtLccG+A3Lan92t13srlk4eMmbzR791nRB6c6LzOEvKB1c86De3H6V0w8jCPI8S5sq5xMUXOXGJPFJAfhdwhyHTeO/h9WMPEryCEwOwjI5DsVEduDm5YgLuCeZqAgejAAu1eBj+J/OMZ5jXYqmmifhxjbSXfOR4jNyAPGq83xx51NtP2Daca5zbmau/dUs59f0vjRB17yO0d5QTt3dL3qXngGmEdvVMqizQGIy/gJhPKy4R/AFvNArl7jtzIj7X/IvhnVFe7RFzuFdo/U5y53Brqnv6pd92LcxWez5JrvVe/HzuY/w6GyhaKMMc2+rpWNkId86ee1nOO23eHYnuRnoPt7Q3EHoM7Gnk653zEuJVeX+ysi3O2Bc55wKsN2jpHJ0fIdVJMUuzfRhMw15B8PP2BD+rnJ8pAfPGlN8Qm4y4Eyyuv6/NDnlNwzoBtlmN/wdyP/GueE5Y4Z4rRvxsr6k/ddT2XG490TWwgJvVxjrl/rO2PIuaOuiYEbeUJ3zlUf/YbyCXB6poiBpOD6kIwAEsdnxZy1xjzqcx4booBwJrc7StjcoL23t7V+++s6f56tf3W/ZIsdy+8pjF7mVV6b+1vywcclHDe8dzJCzhPyJTTcQ5CsCUxbXzuoUKea2EeVXjGaA+uD3L4ZapxKV6ofTLQOHvnpu4ZeG5SOf9lf7D9enkll3OIQ6u3NE51d8h3rhxc4HY6z5kLlgX22Dgooz+UOCfMM73/3rVXtb19jSPtnq4bPOcMcL5NHjbZuB7e9+rT7xb78rue1utxbsrx/MCf+ojYswl+70AcWuno+3nkMeMcqbelv8dUjv34ewD8kzlSgee9uSc1xp/JZDKZTCaTyWQymUwmk8lkMplMJpPJ9N+Y7Ic/k8lkMplMJpPJZDKZTCaTyWQymUwmk+kRkP3wZzKZTCaTyWQymUwmk8lkMplMJpPJZDI9Alpaxl9Z5K48rVufLbS2cxNcjscfU07IK6jFXEZaI3UFNWcT1O6ez8GkAwONCuvajc2a2pOh1hD2e1pLPUT7mjWtfZ2CvZSNlTNy/omnxGZN4ZCsLPAAIl9rgTfB6qo1tfY1cASut6E8BB81eytMt1Sfl+bKK2iuaC18cn4C1Or2Sq1tm4GP4aNH+uCutCO9/3Gs7ZmiSLWH9kSouZuiFnlAFhZ/bkcNbpbkTmGfjEZir3a0dnoHfAbWFPZR85g8EXKOPNSqzwg3Q01mv2RNYlyOGs/Z/F7tejLWHgblmXefHcZxrfCgWPMf/U6kmIe/l1HpHfoNvl/iCxWmGe/nyAxEjX4yC1krnO0tWFucHBl8H5+Tkcfa2fTLnLXqK0w9xCX2F+II6SMlWZXg3BRkKpVnz7MStdXJq6qDUdHu9/V68tZQ4zt7AGOy2QaHJwYDI1IGRAw2bQY+HMeH48Fa6FXG49n+UsCfWPu/ZFyq1FLH87GOkE1agTierpsPG5fG+aFzpzE/ZT36BzD0fPh8nMNHsJbk8Ik6GGm+r7lRCL5DSHYV1yI0sFbXevtdcGsO95QZd/OacmHIjaz0AIMm1zK0j9zMNnjMFYZhqrlGiPsFiMEx127ENC/X/s0xXowZUaBrMdeE0UK/3+np+7RqatfBDASuwE1irBGl5mIhcs0SudgAMTZrKQenDoZeu6n9UQN/m2tEVEd70N9FoZ/vXLgg9u0byhlqdLA32NK9x3hxNne3Vld/SlPN/QO/hs/f6v/sIWP8Nduhi04Zf91Y/XKy0D1Us6lxZXoNa8FI+zHE2uGDN+UVsJmjFhwH3A/jFvbJhT+bwed5WPuwNoVOx7mOPWYMBmKak5NCPnFll6jXI5fLUgKT1c4i3ZOsNJRXdniIPTnW1gDs0x7i8tDpeNS5TpBfXGqusnVe96jXD7Q9/dW+2AXibobcZzI4FLvVVZ5Ujj3j3devi91ogPW5o8+fe+C1IQ7NcSbCTRb3XEWpgTgK1Z88D4xK7nERSnqFft6u6funLX3/NexR32QMkjW47Irj2BWnee3d6zfks3ZTfXQx0zGcgffL/QR9Zj4eiF1r6No4mmpMbDSVZ7y6dVnsm68q/9h77AmxowZygZSMd5xzpGoPU43JT5y7KnYO7mkSY06sKV/3P/y73xV7e1P3Q1/7unIjA6wJK03EBDLd8X4+2FgOzPay5H6D+/ez9zezka7dTcRQMhhT8IQrkxprSIRzrfM7GsPu3taYdYz2X95UFtX5FfW3IfaHnc2+2EdHA7EnM+Sevs6HOrihwwNlOHbauibN4P+tjvr7fKxr0AjnpEfHyu66CI5rF7lj8rbklWemy64yz11xOr4e+cDYM1T23TzHwr15fsg9SyW14LkX/D7AEzLsWSpMQszDkFsqnOeHYD+Snxsn4OWSMccDVDILyWhDblk5D8eeMgP/uCgwPjxHq4QBnC9XmIh6eYk1t0DuyfNbMgDvXNN5+qXfv4kGYQ9bOb/nHlbNCJ/313SPH2EP12hpXOi0NdeoIa4G9F+wT1sd/f1hc1vjTG+lL3aOPTzPxXpt/b2HhxjMhYeDgdjkk9cizt9741NUZuq3lv2LP5PJZDKZTCaTyWQymUwmk8lkMplMJpPpEZD98GcymUwmk8lkMplMJpPJZDKZTCaTyWQyPQKyH/5MJpPJZDKZTCaTyWQymUwmk8lkMplMpkdAS8v4a4SRi06L98ax1kBtgGEWola6H2tB+vaK1nzttsAzANupEbKWuT4/IxcHtbIj8Bc6q8oh2TmvXBCHmsZt1CqPwD2ZT7RWNuvks4Zv0NIatlkGxt5Ca2O3mtpfITg1rRYZifr+HtlTifYv2V8+uDizmV7fbGoN3QB8AsLPiHJqNfV6n1yhTMdrG0zHk4U+fxRrbfg8wPuTeUdeBmrxVpBRqGGN0v9uhiLT+YHWbg/9bbEL+KPzWKRaTbIGRlPUSh/BX3vqL4F3Nsssnav/JbN7n6fZw1VH3bl7TKrwdLzJ7PPx3gWZdGTGsbQ2+VGYNzlqebN2tk+2pEdbn8da5WTouVL9qEBt8QIMgkpp9Eqt8rOZBZWJQfdA3GUcJoOhJEeHUEWaD2DKFaglzzjEOOwzLmC8yGiogbnBdSUIyZ9CbXe0J8TzwwjjiVrjNXB16C+sxU9/KVDrnrXtPfDeWHudbNeUXCa92pWVP8HnFYAd+XHkKiFPOOUueQzIS67Sj1x5v2+1j8m9pE/5FQ4ifRj1+53erwlOJXkQlTnBz1kPnzwIcGF98By2zu2IXRtqvf1zQ819GHMZMyoxyQMHFTE/jHQOheQBw5emE+U9tNrafz1PeQejQllVvkduDXi/Tu/voT9DsK06PX1+vaO5H/kGCdaIkxm5pwiyYAySczMo9HlkpXW7mlv7AJTEifbHEVhjmzvKteEanSLGNRvMpXS+rIHh14b/Dwbqb3lOf0Huj/lJwLZXfus1iuvVsms0P3Zhdm/8NreU95Tc0pyRucVkX+12Tftle0u5HfSjelPjAlONAnvEdgt8XPR1OFc/SZ223wPnhixJslB9QNZi7EFS8pi5RHlkDJKDfjbLtNIfXNvBJ54VumdYjJRH1q5j7W3pHmIKsLn/gFxqBC5K4Wn/ddd0/BeJjocDi3aAONFr6/Mm2IPXcm3vaKjvWydaFLny6lzHPw3U/8MW2hce6/Xon9wHQxJnGszV6vCfjVLjVjrRuDUdKy8ruKBxeLWj7d3e0jOEeHKvfwImqUuupp+56HTutvrKNfzgRz4s9rUbygD8wu//F7G//o3bYjdauvZdAINvijk0GuucYQyinnn3s2IzNylTzQ3SQOdQgnOQOVBccazP/8ZXvyL2Y48rc7DZUp+Pp+pja1gDUqx1V57Uc7TjY52T7SbOodA+h/0XecjMRUunMdfD/ojfJ6M+LbBfQ4yfjPT9e6saNNY2dI4NB2OxD3b1HGhlRXOR5ormjsMjvX7v8Eif32IuIqaL5xoDmmBr8RzpCPfH9s79/9s78/iYrv//v2YmmcmekJAEkWhriVpqLz6EotEV7QcftGi19Wn5okVbv2p1U7po0YX20xb18VFd6EJRVVL7HoqIfWvtEpF9MnN+f2hunfdEMkmQkbyej0cenLln7j333HNe95xz575fN0fomuMj1h1Ndr3+zqekaGmrj/59ZdMPEFJJ76/ZuXr9eZnFWPSyy+8yd/R0TCZjrmIRcw65TiLnNBaL9M8VvtzSI66IwYIco8rBiVPOp0XDcIr1dzl2khfHu4g5pclU+PqrXDeT/o4u6w5yHUnOCaWHovi+PL68XrIfyXU8sWoChxiLiCkaHE6xni4WgJUYOzmUrmsOh65L3qKfOYQftEO0N7td6qhe3lyhCxeO6boOMefPk88TXBc29eMVtc4qrrdoTjDJdVVxfItc5xLPK3Zu3aKlQ6voHoJWb/2KKod+X05cv0pL5/uiy3ZdGHzjjxBCCCGEEEIIIYQQQgghhJByAB/8EUIIIYQQQgghhBBCCCGEEFIO4IM/QgghhBBCCCGEEEIIIYQQQsoBHuvxFxzsZ3j8Xbwg/AQseixpYUmH2++ooaWzsvRYziaz7pdw6g89drpZxFi1yvj4IlS4t/CV8fHR/SICg3V/AYtTj3EbYNFj7ioR0zhH+j2IWNa5mSL2+kX9fHyseqxvs/CtCauix/7Oy9NjXUsvJuUtfXb0GLQybr/ZqcforRSgH99u0dPZ6bo/hCNHnK8IamwRMYKtVum/oeeHiMkrvZ1sIrZ9mL++Px9vPWZvarbevjKcwtfGJSZ14TGrlYw5LPwuvEV5c0VM4mPHTmhpi/B7sAqfGpOIEp2Rpte/XRZf+PikXhAxo816e5BeAnbhDWAyX4olbzffWH4PAGDyMhneX0peV5lXpC0yeLRLIHnphyU9x0RHcAl9LmN3i3YmjiZDv5tl9HAXjzcRC1s0FG/pkSBjoYt2Lz3mZGx5GQtcxo43ifqT18Mpz1+WR3oQFuHp57Dr/dyl/qRfmbjc3jb9evoKTwMf4bHgJe5DTod+fFkfFnFAb+GfJWOve4n2pWTweOlZKb6fl6v3e2eeXj7pHeAt/ces0nNQxNaXsiruuy6eki7eAmJ/Ln5zwmdJNvi/2ktRXoKehkMpw9dBtlGr8JaSYeKFVENYFbn4ETjNMj6+fo3ypP+wi09o4f4R8pdq0hdSyXuv+H5wgN7HGjZtKnLL8oi9SX8IqXFCM/Ls0ptM32GgTfh8BglvMHE+pw+namnbLfrxfIRHXI5F/75ZeMZJf+vU8/r55GXoY1Uf4c1lll5fFv34UhO8bHLsoW9XJj0dFKy3OJvw4pJjzdR0MfaUYy+Hfv7HDus+S8GB+v5rVNd9hJRF+qWIe5LQvOwLqXpaeJGZfPT2aHcIDRL3HNn+nem6F1lm9t/57Xk31njK4hUEy1/3uLMpf2rbwsN1H4xTp3SPs8o19DlOnm6XhFzhqeYr/HSzhS+89DLNztXbeU6u7Ff68bxS9bTK1vPbw4QXp014o/oI/6ssfU4HU4ieFropjUyk3xSEblq8hNDJe7vQNTlncoh+kHpBnxP5+Yo5pbh3e9n0OZ4zV5+DhIXr/kyOHDFnDgzR0rmi32Rn6vuzijl6dqbw/BOy4V9Z14WLYuUkPU0/X+lnHRKq+5XZhHeuv/DJOZMi/NacYuyUqaeVnCM6hZer8KGXY9PKocIDU3ij5gr/9Ryl1589V3hmirGjvxjL2nDpeuTYb6yxVOXgyrD+5Y195E9do87+eUxLR9eK0dLJuw5o6VzRhn0DxUKW8I9NTdOvSYjw301N0z3XpIZ5h+mal5Ota5rVqm/PzdSPl5Gpa5CXt94GqkXr6255wgMuI13vIwEBep+G8DTPEfevC+d1TzezVW+zDjFfzczS61eusxTlySesslw8400muX8xlhZjk2Dh92v10uvPKjwWHWJ5NlOYKp45o9eHxUuvvwupev0HCs+/wBBxzxRD52xxPpkZ+vUzi4WcSlVDtLTK1Y9fXXgM/vmnPnb5M1Vvb6FitJMjfEcvnNc9AyuF6RqbnSPG3ll6fYVV1tvfxSzpm/v38e1yIuDheHt7G/N/2S7hlHMYfbP0TZdzHOlx5i3S2cJUTq4ryHm29PiT6zYWse4hx1pymi+XxfLkGNpc+FgwN6fwOYRScl1HL5CX8DuWnnFyzuB0WWYQ6zpOMdiQ66TStE7UR55cF3Pq5+s0Sx92ef31zRbhhekU11uuK8rrBYe4nqL8rmsUen7pASg9He12OZYV7clljl/4uqi8j8pZmEPM0ZX0TMzT6+fMOV1nzp7VvVZdPARFic6vXKGl89e55JizMIr9xt8ff/yBhx56CKGhofD19UXDhg2xefPmvwupFF566SVERkbC19cXnTt3xr59+4p7GEIIKTHUKUKIJ0ONIoR4OtQpQognQ40ihHg61ClCSFlTrAd/KSkpaNu2Lby9vbF48WLs3r0bkyZNQqVKf/9y4q233sLUqVMxffp0bNiwAf7+/oiPj0d2dnYheyaEkKsDdYoQ4slQowghng51ihDiyVCjCCGeDnWKEOIJFCvU55tvvomoqCjMmDHD+KxWrVrG/5VSmDx5MsaOHYtu3boBAL744guEh4fju+++w7/+9a+rVGxCCCkY6hQhxJOhRhFCPB3qFCHEk6FGEUI8HeoUIcQTKNaDvx9++AHx8fHo2bMnEhISUL16dTz11FN4/PHHAQCHDh3CyZMn0blzZ+M7wcHBaNWqFdatW1cs4cpxOuH8K/itRRjL+IvY1Er4G8gYvPYsEbXVImP86jFrw2oIj76gEC19MOm4ls4UMYEzM3Q/gVBhQmhT+q83lIg1nmUWfgcihq5dePrlZeuxrH2sIoawU/iwOGW8fz0osvR+gggp7JT+EdIbK0c//8qBegxcb+GTYxb+D0GVIrX0mbN6rPCsTH1/wcL/wUd4ZTlE+zCL9mHx0s8/V3h3mUXM5iBf4a/g1P0L0k7r5UWA8NMQMaadDhljWMSkzhP+DSL4vJ+IHS/sSJAtYrGnZ+qx5L1Ff7CK9moRMbK9rfrxHCKGd2aOiKks/VNE/fsGXUpLD7eScj11ymS+LAa2jOUtg1vLfiL9gcT5u1jiiX7qJTzRZKxvh0Pqop5dBrN28VRzaad6/jwRm90iTRGE559JWgaKWOUu7V56LOhJmBzSgEt4LIj80v9Lbs+zy+0ylrvwX/Iu3FNQCeE0CR3xFr4+PkIHfUVa1oBd+Gcp0f5kbHkv6eMjddzF00K2V327Q8Quz8jQjZW8pIefSMv2KfuHQ3YA0T7k+cr+5iW9CaSHn0X6HAn/MemR+Vd5TFfBO+t6ahRMFqNvmC1ybCDi+3vp18hLxsMX1yxLeLyZRBvduGqlvj9x7wgRYzmb8JmR11ReM+lJ6HJ+sg9LPwH96y5+t9LDUPo/+wfoPjNyLGoX3lyySVtMev35+epjiWN/6D4oNuHX7GsXvjgWfezhbdb351T6WNHmpd/ra4TrPilp5pNaWmWnamnpd5wn2sOFNP2EAwPE2NpPiI5I+tj0+oUSY1nhT2HK0c/fKjTUKTRZ+g1D1IfZptcfXPwchF+JXXi3OXWNrFolVEtnZOjlzZaabNPHsumnD2tpq1PX3FDT3/03V5p9lIDrqVOZx3IMH1tzsK4TGRbd0y+scrCWPiPavU9lvV1KPyWnWb8ufuLe6PWnft39hR/VsT90f6Hq1XT/IuTqvuZw6seXvh+w6e0mT3jcOZReHpPoxyaT8MeVwujij6y341xRHDk2kvd6F/9icZ8I8NfL6yvu/bnC58UvUK+f1FS9/jKF77jDrqctwlvUW/iaK+FRJ8sbGqL3ex8/vbw2L/38I6tV0dIXLujtzynuW75+enuWnoNnUvXreeGCvP76dl8f4fnnLbx0hQelv9L3J4aOOPOHnt9XXI9gcZ9zZOjt/6S4T4WE6Tq3/4Duh2f5y+v2aviQXk+Nyrh4Hva/1gtCKukatH/fIS194IDuH1spVG9jyb/r+TMv6mOF3It6HUdU0u/9oaKNnjupe7D5iHWHvIu6l5DNX79GYdVjtHT6+W16OkW/15itepsKFJrgyNb7TI5Yp0oRnn2Vq+p+utniXp0hPdgu6Gm5zhUYoWu2KU96iItO4OLlJOYXYrnUIcYiFjkdFyqaKcZOyNI17pTwpc0THoJhgbqG5InyBPjpfdbsrbePbOHZ6Cc06lSOvv2iWMZy2IWPp7gnpEivKlE/8BbzfemtZRG+q8KLzE9oaPWqelpBb1+p6Xr7FEN3eFv1D4JMYu5j/fuekmu/sXTKbDIZ3nNyHusQ6y5mcR3keqjsB3LZJVeaQyq5DiHm9Q6xHig82sxiHccmPP68xL0uPV2/7nJI7xDrOl5WeW8X83wxdMqT3mliXcRLPKCw5+glkGMlJcb43qLdSf9k6blnhlh/lusgFukRpyPXm2XgRxPkerm4PiK/VdSnXawROPKKmIcIHXJI33hZ/yIp/TdlfpfyifZqkXMucQCnaI9m2UCkt6uocadYE5DrUvJyOJVcFxX+ykKL8tcorpnH38GDBzFt2jTUrl0bS5cuxZNPPolhw4Zh1qxZAICTJy8tEoSHh2vfCw8PN7ZJcnJykJaWpv0RQkhJoU4RQjwZahQhxNOhThFCPBlqFCHE06FOEUI8gWK98ed0OtG8eXO88cYbAIAmTZpg586dmD59OgYMGFCiAkyYMAGvvPJKib5LCCES6hQhxJOhRhFCPB3qFCHEk6FGEUI8HeoUIcQTKNYbf5GRkahfv772WWxsLI4ePQoAiIiIAACcOnVKy3Pq1Cljm2TMmDG4cOGC8Xfs2LHiFIkQQjSoU4QQT4YaRQjxdKhThBBPhhpFCPF0qFOEEE+gWG/8tW3bFsnJydpne/fuRXR0NIBLRqURERFYvnw5brvtNgBAWloaNmzYgCeffLLAfdpsNths0ssI2LPnBCx/xX6tfbPu+WbP1WNRW3302NC5wgPP5qv7ZOTm6DGBAyvp8ev9A/SYsBdSdH+J7BzhdyBi/GZn6TFqncLrKlM8b7V56/4E6Rn6/k0+ekzYHOFXkJGSqqWrRYh4/CKmsnzeaxcecrkiJq3JJRa39D7Sr0eVYD3WrLDEgzNP+AZJzzcRjDs0SC/PRV99e0Cwfn0h/CPMNhGbXfgKyBDVTiVi+jr18nmZRczdDBFbN0s/nhJGMiYfvb1YpD+DqH/p6SdjEMNLxkjOEZtFzGiz3t+k1ZZvoO4lkCNiw+eKmM32HN1bQPpG+Qfq7dHPWy+v5a+o3NITraRcT50ymf72RpOxvGV0bRcPvaI8/ES7kB5kZhGL3SFijzvF8WUsd1eDK+kDU3jscBkMPU8GqxbZLdJnRnqNCv8up4gN7+KxZ9aFxSn9QpyyfLquWm26R4DFpt9HZKx1izBxkGnZ72TaW3gySv8piwytL8qr5PUQ/czsFLHwRWx/6c8GcT3zXHwMCvdHk+3HatXvY17e+vmZzEIXRQeQnoKy/k1iu1mkZfuSHoNeov/K/iVj70vPj3yvhKvhS3M9NUrlOQ0/UZPS25TTJuP3izoV19BF4kQjkPe6k3+e0NIm4QmXekG/d8t4/DL+vpdLJ9G358k2LpC/dJN+xkq6W8k27qXXR2CQfm8Lr1ZVSwcF6b4+SpQgJ0sfi2ZcTNXSZ9Kkr6V+frHhUVraZDqipZ3C68okxjLe2bpPEYR/hM1X18S8dOn/oGuQ0ynuScLj7tSfeigiFa7Xj/Sqsjv0sYfNpu8/9azuk+MfonuteQvPSiXKY/UP0dI5mfpYJk/42EhPQ6fw5/YS9xRvL30sdfqs7uPkE6y3F5P0chMaWUnMBRy5wif2srGu9GYuCddTp2y5Vnj9NXbPPCm8Oqvpec2++nUKsOrt9EK63m6ybfq9OET4tnsJ2Qiy6XV3XLSzyKp6v/eGvn+Lv37d5Rg/3UsP3WUSzjSZGWJsIfq9xUscT86xxNgpz8WvStxbpR914RaBcEDv98JWHsHeugeew67rnFP47uRm635MvsIPqorw68q9oJf3ojCkyhNjQ385hxa+43ZRnpwzev2eFv5kIeF6g/T1Fx6F51P1/MLL1OKjX9+0c/rxa0Tpax5/ntR1w9dX+Kpn6uXLydLr09+m16fVqt93TQ59f4GB+n2herTuv2YRvkR5p/U1klq3xOjlu6hvT0u/1MCUXa5NFJ/rqVF53t4w/TXvktttNjG2OaOPfewX9TZbM1L4G/vp18jPT78mGWJdSI7XgwP1e0eIWBZRYiyQnao/JDir5PxE30FYZA0tHRqphyTMTBM+l/768U6dOK6lc9L1/Fn++vECQ0K09EWxLhMQIvx7xe3ujPAJzRGa6BWk15fL/EqInlyfMCsxFhDrNmaT8Dbz0befPqffQ7KET6uU5Jw86V2l5w8T/sHZ0tvMSy/vrTeHaOlTCbv144l1S7Hsheq1amnpzAz9nnzunK5JDqVrRmBVXeOkH7cS9Qenvj+L6H9y3TUoQN8eEKprtlPMz83C9zfr4t/tx36DjaW8TCZj7uQt1lPzhGeb9BAziY4kxxLeYt3AS6zL5Ip6letESnrGiYbuJda15LKTHIv4+ertOkvc2y0+QqfF+qNVHE+OBaSnnxwryXUVYQkHi6gfu2hK3mJOm3ZWPD8Q91rfILEOJupDrjOazVLX5DqkSMsdijnIxRQxlhP92uziKanvTuqIQ65Ligq0iAbgEF6ucn1bPB5x+X620DWL8GmX67LSO08peV9wcVEUaZdFE7E/mVv6Z4s5n/Qg/GsH8roXRrEe/D399NNo06YN3njjDfTq1QsbN27EJ598gk8++eRSgU0mjBgxAq+//jpq166NWrVq4cUXX0S1atXQvXv34hyKEEJKBHWKEOLJUKMIIZ4OdYoQ4slQowghng51ihDiCRTrwV+LFi2wYMECjBkzBq+++ipq1aqFyZMno1+/fkaeZ599FhkZGXjiiSeQmpqKf/zjH1iyZAl8fHwK2TMhhFwdqFOEEE+GGkUI8XSoU4QQT4YaRQjxdKhThBBPoFgP/gDg3nvvxb333nvF7SaTCa+++ipeffXVUhWMEEJKCnWKEOLJUKMIIZ4OdYoQ4slQowghng51ihBS1hT7wd/1IjPPYsT+3XNANzsNOqbHi7859iYtLb17TFY9hqu3l+7DkSc8//7847SWdoiYxBZvPcaqjMVuz9Jjrdr85a819Ji46cIzKEt45jmy9HRGuh6//8I5Pf6/CJWO4GDhjyCOlyFiY9sd0jtJeNIJrzEfH+EL4yV8hISvjIyA6xSx06VNj034/lj99ZjN3sLjzl5EDGkZk1d6FtrE+Uj/DZNJL2B6uog9LkP6Co8+ZIsYzHZRXhG7P1t4feWJWL45Jv36e/vp7c3XT/fRyRL+DznClykrQ4+978jVr1iuaB/yhGXM6uy0c1o6OEL34TH/df2d6up4/F1PzGar0b5MSq9Hh4z5X4R/lIyt7hDXxcVDQHi6maT/lQxWbZKxvwuPbS39pEwyeLmMhS16tovdlvDrco1NLvJL3ZCeCSK7Eu3SIcw7XXxyrHr9+UhfHmGcIz0cXXTFqd9XXLw2RfV5i+ObpF+WuP4QscXNwvNC6qr0K5Ox+pWocBmr3iHy5+YID0OnjH2v3xdswntXtl8ZO11a7OXZpaeJnvYWnhXyvu8l7hve3t6FbndKL1u9OFBGPy+938P1RJmU4aco24RDXFMvcRGk74kyFX5vdQrvp389PFAvjBg7yOOZzNK3VPRyF78IsVn4jObYxVgqV29DEG1W3htd+ow4oH+gfm+1iPLm2XWfGafw+Pvzgn7+J/ShJzKy9f3lCn/r6qm6r0p0pVu09LG0vVq6sldNLa1y9cGixVv4PYuxiEXc48KibtbS0v4yJTVVS5+XPrRF+I0oh/Da+kM/3yxhmFGrge55aJP+GedT9HSa8Bbz18fKJ/88q6XDq+r3CLO4RwSG6r410tMxIjBMP3663h59hf9yyh97tHSQuEeEVtGPB/zdPrxcPFs9G3//AHjn+2c59eueatfHuOdz9TFqlVC93vzF2EgJXVHCCCTvtN7us8XdtFoN3c8qR4yR5Rg6VfhLRdbUr5PZq3CPOSl7Tjl2EnMy6eMu7+XyXinbpRLtSvq0OEW/9fHW7+0+wnPvTJbez/KE746X8EOzC53OzNTr00d45jmE96Zd+JOFVNY96kzifM1p+ppCVo7w/xI+QDYxtjh/TvcqjYzUPQ2lt6td+DEH+uv+XtV1WUZWnvB9zxPtK03UjxhLegn/6ZOZevt38fzz0ceuPsKzMCtbr+8AUb/hYmybeUHXTekP7R90aQ6YK8e4Hk6tynmweV/SjsPCr/Vsit6mQ8y6RpgC9IvsFG1eGnWaxDrVRbu+v2Onzmhp/8ohWvr8SX3/AUFibJSha2w16J5/J06maumbGjTW0r7CB9Jq08cSh/bu0PP76PUTWkUvb66w1vrz4GEtHRaha7C/8Fe+mKprjlmMBSs5xVgnT/SJ0AgtffL0US2tRJ8SQxeXeYHL/EF8kinW3eQ6lMks24O4Z2XrY6Fcu349RdJlHWjvYb39yuuRc0KvT5tDjKUu6N+X86lgX73P2+X8W9SnSdxzcjNTtbSPVW8/sOjrXE6xnB0cprePnCy9QuQ9EMJf2XnZkMJpubHGUmYvC8x/jX3lnMUi1jHk2MNikg1bjD3kep9cR7IIz0GxP7lOY7LIdSG5TqV/Py9XXAvhjRnoL/xn5bqQWHewCxM6m02sGwhdkusE0j9ZerpZ5bqc0CXZL8QUGmkn9Pypfwjfcx99/z5ifdxk0c/X5q+Xz8tX6I643l7ieuZk6QU0een7l+uKYmjqUh8W6fknxqKZYuxhcsrrX7gXq1MWQK4hiP0p0d6kR6NTXG/pEWgWPvfS1FCue5nlyp3sT/pWmOQaR3ZOgfstDGmbSQghhBBCCCGEEEIIIYQQQgi5AeGDP0IIIYQQQgghhBBCCCGEEELKAXzwRwghhBBCCCGEEEIIIYQQQkg5wGM9/mw+Cpa/YqMG2UTMVOFxl3pUj8UddlOMlpbeUWbxvNPbKryZ0vRqSU/VY1lHVK+qpYMr6Z5lcIrY3CK+f062Hts7/aIes1d6uHn5Ci8i4fWULWIUnzit+0tk5grfFuF95Ruoxz63+er1nXVB35+Xlx4L20dcH+mHIPPL4+fYZextEeNZpB3C5yZHBFv38tL9CWRMabOMpW6SXlPSm0s/P+k/kSnao0mYeVmEB6E8H2eO8B2SHlIiaRcxfp1W/fr6CL+G9Ay9fHnC+8UsfJdkh3GKmMdeIka4sOtA1RA9JrTTqe9fxkg2ma1//XtjxVEHAJvV12gfdtEulfBDUiJ4t0ma6kmU7Ac6zjwZu1o0FGmaJncvY0JLS8DCSwdIz78i/Lkcwo/J20vGspb+XTIGv0iK+nHmCf8o0U+sNuEN6qd7AFhsuk5ZhI7J+wZEbHAZGt/iLWJ/i+tvEcHNzVKHnNJfTN+/ScS2d8rY5VI3XWLryxZVuCeg3L9L7H7RnvNk+V3OR7Y/uV3olNRtWX6hU1KHXWPBS+8C6Tcm+Gt/N5bDH5DncBjnYhZ9RFahsFF06dNKnL1F+hcIv2Srr97nLMIvwEvce6SPpkS2QelHINOyjck+YSrCd9Upva8s+s3Oz0fXjNPH9bHo6QsntHSuqOCLmXp95QjNkvWTl6ePBaU/c/VA3Wsq0Cm8veT18tfrJytX399p4WUWWFUf+2YJ/2Kr8PEJj9B9c3x89LF0ujh/i7ccm+npkDDdIy/MS/iwintiWpruxWURPp852fo9OyhUr79jx3QfnYNHD2vpJs1itbRNeG1luoy99PpOE3OLIHE9KlfWx+YqS9fETDHmyLjs8knPJE8nuHJleP91z/0zW+9H0j8p0Ka3w5xcfY7iI+692aLeMw/r/Sr1vN4OQyvp9R4RKMYqwjMvNUV8P0T4yEP3pTeZ9TmGKUWfs6SnCU+9CHFvl74hwidGzjFcvVqFf7T0BBSDel/ofl55f+rnmyX9oKU/sKj/0EBdN5UYS1qEp53DS79vhNeI0dIXz+r+ZAF6deLMOb2+c5SuA+FV9PNNuyC8Xv10fzFv4TOUlpKqpavXrCbKd1I/fpauS6dO6Toj21eVML3+7Vl6fpuXXh67WfgfW/Tzl3O6EKHrTlH/uTl6/zKLNQibaC9nz+rlCw3TvVF9/8p+o/3yfMveLHj9NWgKrqJf41zhpXTkvN4Io4P1NpeWrtdR+rlULV2nnu7XGxAsxyq6wJ+/oN+rfYRXlTRdt8h1JjH/Camkt7nUs/pYJi9Hb8O5dr18fgH6/MpPeMBBrNOkHDqupQOCdU+6QFGezAy9TZ5P0dM5ubpGVRGa7u+r7//0Ef38rHYxGBSedN7iHmNS4l4v/KPPnNSvt/Q3lmPLXOFj6m0Svq5iLLd3z2G9PGLdsVpUDS198WKqlpbzr5Agff8ZGfr29Iu6pgSJ/DZx/VP+PKelw6roGpOVpbdfF79xh/B8F4bSFzL0/VnEWBTC+8vLKubPYt1W85E131hKZfbyMjz+xLICLGJOkSPq0VuMkR25+nWWnn5yjiiNvi3yKYOcZ4t1Ay/Rb+TYxSzTYn9yfVz6y/r46TrgEOs4DtEPbGJOmiM8Ea1incEp+p1c5rAKHbSKDLk54l7tMucV9Zcn5hypej+RHom5wlfe20uui8h+IX149f05HHKdUk/birjeFrmOKK6fReR3ivO3ivbqYhss9uclrpf0JJTlkWseSuzPJLab5BpGrvi+WY7FhaegqF+5TuhwyPo0af+6w42lZoQQQgghhBBCCCGEEEIIIYSQAuGDP0IIIYQQQgghhBBCCCGEEELKAR4X6jP/tV7nZWHBHCKckAxFmCfCitjFq7h28XzTjMLDQclXyF32L7bniuPBKV4FFa/+yvK5HE+GVHMpj552ihBqsr5kfnk+8vhmGbJNfF+ev3y1NtdLvJoqQlK41p8M8SheHXY5PxnSTtSHOJ58BdYJGR5MhAKVzU20N3m9HaJ8JlH/8vgu7U+cvlO0R5l2CZknr3cR7UVef3m9Zf3nOeUrxOJVbVF+e56ob/EqvQyVl/9qdH67kK/2eyL5Zby8bouqZxlq0SV0YlHHdGnHhQcdlPldIjUWmS68fLJVuDajwkM5uoTpKyI8VZGhPmX9in5iLkLHvWS/1g9XZKhP2ZFlKFez3KMsv0mG69LTsjqcYnuu7Hcy1KfUPacIkSF0zkXni7gvOpxSF0T5ixnqU/YXJWN2y9Co4njyvilDxzqLChMJwV8XwP5XOEFP16n88jkuqxcZBkWeglmGbBOhE13DXogdiO9nZurhjyziZuEadsPTQn3KcLMifLM436ws/XyzRSjJXBFKU94z5FjCpTxiu/x+tth/joj3qCzynqGnZRgbexGaaZFjX5dwuSLMvNy/iwZLTZTXWz+cbM85Lucvwj/JsOmifeeKwWxRmif3D1gK3e7Ik/Upjy/uYfKeKMO0y7HpZbu7UcZT+eW7fF4kx7Ay/FKeWYbXEWnRLkTkQpd7i+x3rnO+wtut3J/cbrLL8osxdl7h9z5ZfhcdlmGrZbsRYxUl7p1y7GQRYfdNYqzhEOcnhzYy1GdR9SnDPxU1x5b9ynW7pdDtRc1B5XZnEXN2aVPgWj5Z/1rStb1A7r/w8suxoj2v8PqXc0q5JmEW20X0NZdQny5rLkWtkfxVP/l6e6No1OX1KNuw7MMu6yZF1JG8RrLOimrDLvNPEQLOJX8R18ilTZoLL48M9Sm3y9CNUIXfa02yfEX0+aLWPYpqk3K7HNu5dFqZdLlHFX6PkPc0eTy57Olyzymi/UiriKLuYS7zdTEWl/uXou+yP1Px2rdreUTIP7ssT/Gur0uoW5d1vivv/0YbS10+55OhPuW6kEO2O7MM5SjbpQjHrwrP77IwJJNFbJeTVLl/2S3l+q5s167r54WPNeU6j+v5iexyXcjFgUda2BQ+FpVzXjknkKFMXcK6QyDn/C5TYHE8uf4uraDE9+XbZC7LeIXLqKtFkItlDArd7lJeaWEjd+Ai5HrSxTLHpbmK8sn+Jcvnss4pi+NyY9G/f4XzzT8vdzTKpDxMyY4fP46oqKiyLgYhpIw4duwYatSoUXTGMoQ6RUjFxtN1ihpFCKFOEUI8GWoUIcTToU4RQjwZdzTK4x78OZ1O/Pnnn1BKoWbNmjh27BiCgoKK/iLRSEtLQ1RUFOuvhLD+SkdJ6k8phYsXL6JatWouhrSeBnXq6sB+VnJYd6WjpPV3o+gUNerqwH5WOlh/pYM6RdyB/azksO5KBzWKuAP7Welg/ZUO6hRxB/azksO6Kx3XQ6M8LtSn2WxGjRo1kJaWBgAICgpi4ykFrL/SwforHcWtv+Dg4GtYmqsHderqwvorOay70lGS+rsRdIoadXVh/ZUO1l/poE4Rd2D9lRzWXemgRhF3YP2VDtZf6aBOEXdg/ZUc1l3puJYa5bk/XSCEEEIIIYQQQgghhBBCCCGEuA0f/BFCCCGEEEIIIYQQQgghhBBSDvDYB382mw3jxo2DzWYr66LckLD+Sgfrr3RUlPqrKOd5rWD9lRzWXemoKPVXUc7zWsH6Kx2sv9JRUeqvopzntYL1V3JYd6WjotRfRTnPawXrr3Sw/kpHRam/inKe1wrWX8lh3ZWO61F/JqWUumZ7J4QQQgghhBBCCCGEEEIIIYRcFzz2jT9CCCGEEEIIIYQQQgghhBBCiPvwwR8hhBBCCCGEEEIIIYQQQggh5QA++COEEEIIIYQQQgghhBBCCCGkHOCxD/4+/PBDxMTEwMfHB61atcLGjRvLukgex4QJE9CiRQsEBgaiatWq6N69O5KTk7U82dnZGDJkCEJDQxEQEIAHH3wQp06dKqMSezYTJ06EyWTCiBEjjM9Yf4Xzxx9/4KGHHkJoaCh8fX3RsGFDbN682diulMJLL72EyMhI+Pr6onPnzti3b18ZlvjqQY1yD+rU1YMaVXwqskYB1Cl3oEZdXahTxaci6xQ1yj2oU1cPalTxqcgaBVCn3IEadXWhThWfiqxT1Cj3oE5dPahRxacsNcojH/zNmzcPzzzzDMaNG4etW7eicePGiI+Px+nTp8u6aB5FQkIChgwZgvXr12PZsmWw2+248847kZGRYeR5+umn8eOPP+Lrr79GQkIC/vzzTzzwwANlWGrPZNOmTfj444/RqFEj7XPW35VJSUlB27Zt4e3tjcWLF2P37t2YNGkSKlWqZOR56623MHXqVEyfPh0bNmyAv78/4uPjkZ2dXYYlLz3UKPehTl0dqFHFpyJrFECdchdq1NWDOlV8KrJOUaPchzp1daBGFZ+KrFEAdcpdqFFXD+pU8anIOkWNch/q1NWBGlV8ylyjlAfSsmVLNWTIECPtcDhUtWrV1IQJE8qwVJ7P6dOnFQCVkJCglFIqNTVVeXt7q6+//trIk5SUpACodevWlVUxPY6LFy+q2rVrq2XLlqm4uDg1fPhwpRTrryiee+459Y9//OOK251Op4qIiFBvv/228Vlqaqqy2Wxq7ty516OI1wxqVMmhThUfalTJqMgapRR1qqRQo0oGdapkVGSdokaVHOpU8aFGlYyKrFFKUadKCjWqZFCnSkZF1ilqVMmhThUfalTJKGuN8rg3/nJzc7FlyxZ07tzZ+MxsNqNz585Yt25dGZbM87lw4QIAoHLlygCALVu2wG63a3VZr1491KxZk3V5GUOGDME999yj1RPA+iuKH374Ac2bN0fPnj1RtWpVNGnSBP/5z3+M7YcOHcLJkye1+gsODkarVq1u6PqjRpUO6lTxoUaVjIqqUQB1qjRQo0oGdapkVFSdokaVDupU8aFGlYyKqlEAdao0UKNKBnWqZFRUnaJGlQ7qVPGhRpWMstYoj3vwd/bsWTgcDoSHh2ufh4eH4+TJk2VUKs/H6XRixIgRaNu2LRo0aAAAOHnyJKxWK0JCQrS8rMu/+fLLL7F161ZMmDDBZRvrr3AOHjyIadOmoXbt2li6dCmefPJJDBs2DLNmzQIAo47KW1+mRpUc6lTxoUaVnIqqUQB1qqRQo0oGdarkVFSdokaVHOpU8aFGlZyKqlEAdaqkUKNKBnWq5FRUnaJGlRzqVPGhRpWcstYor1LvgXgEQ4YMwc6dO7F69eqyLsoNw7FjxzB8+HAsW7YMPj4+ZV2cGw6n04nmzZvjjTfeAAA0adIEO3fuxPTp0zFgwIAyLh3xRKhTxYMaVTqoUaS4UKOKD3WqdFCnSHGhThUPalTpoEaR4kKNKj7UqdJBnSLFhTpVPKhRpaOsNcrj3vgLCwuDxWLBqVOntM9PnTqFiIiIMiqVZzN06FAsXLgQK1asQI0aNYzPIyIikJubi9TUVC0/6/ISW7ZswenTp9G0aVN4eXnBy8sLCQkJmDp1Kry8vBAeHs76K4TIyEjUr19f+yw2NhZHjx4FAKOOyltfpkaVDOpU8aFGlY6KqlEAdaokUKNKBnWqdFRUnaJGlQzqVPGhRpWOiqpRAHWqJFCjSgZ1qnRUVJ2iRpUM6lTxoUaVjrLWKI978Ge1WtGsWTMsX77c+MzpdGL58uVo3bp1GZbM81BKYejQoViwYAF+/fVX1KpVS9verFkzeHt7a3WZnJyMo0ePsi4BdOrUCb///jsSExONv+bNm6Nfv37G/1l/V6Zt27ZITk7WPtu7dy+io6MBALVq1UJERIRWf2lpadiwYcMNXX/UqOJBnSo51KjSUVE1CqBOFQdqVOmgTpWOiqpT1KjiQZ0qOdSo0lFRNQqgThUHalTpoE6VjoqqU9So4kGdKjnUqNJR5hqlPJAvv/xS2Ww2NXPmTLV79271xBNPqJCQEHXy5MmyLppH8eSTT6rg4GC1cuVKdeLECeMvMzPTyPPvf/9b1axZU/36669q8+bNqnXr1qp169ZlWGrPJi4uTg0fPtxIs/6uzMaNG5WXl5caP3682rdvn5ozZ47y8/NT//3vf408EydOVCEhIer7779XO3bsUN26dVO1atVSWVlZZVjy0kONch/q1NWFGuU+FVmjlKJOuQs16upDnXKfiqxT1Cj3oU5dXahR7lORNUop6pS7UKOuPtQp96nIOkWNch/q1NWFGuU+Za1RHvngTyml3n//fVWzZk1ltVpVy5Yt1fr168u6SB4HgAL/ZsyYYeTJyspSTz31lKpUqZLy8/NTPXr0UCdOnCi7Qns4UrxYf4Xz448/qgYNGiibzabq1aunPvnkE2270+lUL774ogoPD1c2m0116tRJJScnl1Fpry7UKPegTl1dqFHFoyJrlFLUKXegRl19qFPFoyLrFDXKPahTVxdqVPGoyBqlFHXKHahRVx/qVPGoyDpFjXIP6tTVhRpVPMpSo0xKKVX69wYJIYQQQgghhBBCCCGEEEIIIWWJx3n8EUIIIYQQQgghhBBCCCGEEEKKDx/8EUIIIYQQQgghhBBCCCGEEFIO4IM/QgghhBBCCCGEEEIIIYQQQsoBfPBHCCGEEEIIIYQQQgghhBBCSDmAD/4IIYQQQgghhBBCCCGEEEIIKQfwwR8hhBBCCCGEEEIIIYQQQggh5QA++COEEEIIIYQQQgghhBBCCCGkHMAHf4QQQgghhBBCCCGEEEIIIYSUA/jgjxBCCPmLw4cPw2QyITExsayLUqGIiYnB5MmTy7oYhJBiMnDgQHTv3r3QPMXp3zNnzkRISEipy3WtoWYRUjpefvll3Hbbbddk31dbR24UXSKE3Jhw/kkI8XSoUzcufPBXjjGZTIX+vfzyy2VWtnzRkH8PPfSQW993d8HlSvmu5WSTEE/Ak/s/ACxYsAC33347goODERgYiFtvvRUjRoy4rmVwZ8H6epN/fdavX699npOTg9DQUJhMJqxcubJsCkfIVcDTtQkAvv32W3To0AHBwcEICAhAo0aN8Oqrr+L8+fNlXbTrilIKn3zyCVq1aoWAgACEhISgefPmmDx5MjIzM93ez6ZNm/DEE09cw5ISUrGgjhJCyhJP1yBPmGd6IitXroTJZEJqampZF4WQaw516saEOnX18SrrApBrx4kTJ4z/z5s3Dy+99BKSk5ONzwICAoz/K6XgcDjg5XV9m8Qvv/yCW2+91Uj7+vpe1+MTUl7x5P6/fPly9O7dG+PHj8f9998Pk8mE3bt3Y9myZdfl+J5OVFQUZsyYgdtvv934bMGCBQgICOCCGbnh8WRtAoAXXngBb775Jp5++mm88cYbqFatGvbt24fp06dj9uzZGD58+HUrS1nz8MMPY/78+Rg7diw++OADVKlSBdu3b8fkyZMRExPj9g8nqlSpcm0LSkgFgzp6Zex2+zXbNyHkEp6sQZ48zyyrNTdCKiLUqZJBnSp/8I2/ckxERITxFxwcDJPJZKT37NmDwMBALF68GM2aNYPNZsPq1asLfANmxIgR6NChg5F2Op2YMGECatWqBV9fXzRu3BjffPNNicoYGhrqUs4DBw6gW7duCA8PR0BAAFq0aIFffvnF+E6HDh1w5MgRPP3008avNa4Gn376KWJjY+Hj44N69erho48+Mrblv6E4f/58dOzYEX5+fmjcuDHWrVt3VY5NyNXGk/v/jz/+iLZt22L06NGoW7cu6tSpg+7du+PDDz90ydeiRQv4+PggLCwMPXr0MLbl5ORg1KhRqF69Ovz9/dGqVSvtTbj8sExLly5FbGwsAgIC0LVrV2MA+PLLL2PWrFn4/vvvDR250pt0O3fuxF133YWAgACEh4fj4YcfxtmzZ43t33zzDRo2bAhfX1+Ehoaic+fOyMjIAHDpF0stW7aEv78/QkJC0LZtWxw5cqTQ+hkwYAC+/PJLZGVlGZ99/vnnGDBggEve5557DnXq1IGfnx9uuukmvPjii8ai1+HDh2E2m7F582btO5MnT0Z0dDScTieaN2+Od955x9jWvXt3eHt7Iz09HQBw/PhxmEwm7N+/3616B4DVq1ejXbt28PX1RVRUFIYNG2bUBwCcPn0a9913H3x9fVGrVi3MmTOn0Pog5QtP1qaNGzfijTfewKRJk/D222+jTZs2iImJQZcuXfDtt99qfXDatGm4+eabYbVaUbduXcyePVvbl8lkwscff4x7770Xfn5+iI2Nxbp167B//3506NAB/v7+aNOmDQ4cOGB8Jz8aweeff46aNWsiICAATz31FBwOB9566y1ERESgatWqGD9+vHaso0ePolu3bggICEBQUBB69eqFU6dOuex39uzZiImJQXBwMP71r3/h4sWLV6yLr776CnPmzMHcuXPx//7f/0OLFi0QExODbt264ddff0XHjh21/O+88w4iIyMRGhqKIUOGaIvvMvpCamoqBg8ejPDwcPj4+KBBgwZYuHBhgeU4c+YMmjdvjh49eiAnJ4eaRQjKj44CKFSXlixZgn/84x8ICQlBaGgo7r33Xk0z8+dn8+bNQ1xcHHx8fK7YP4vS7NLoEiEVDU/WIHfmme6U5eLFi+jXrx/8/f0RGRmJ9957Dx06dNDeyJk9ezaaN2+OwMBAREREoG/fvjh9+rSxPf/NFVkXV2LPnj1o06aNoUEJCQna9qLmpIXV3+HDh42xW6VKlWAymTBw4EB3q5WQGw7q1CWoU4QP/io4zz//PCZOnIikpCQ0atTIre9MmDABX3zxBaZPn45du3bh6aefxkMPPaR1+JiYmBK/Op2eno67774by5cvx7Zt29C1a1fcd999OHr0KABg/vz5qFGjBl599VWcOHFC+yVHSZkzZw5eeukljB8/HklJSXjjjTfw4osvYtasWVq+F154AaNGjUJiYiLq1KmDPn36IC8vr9THJ6QsKKv+HxERgV27dmHnzp1XzLNo0SL06NEDd999N7Zt24bly5ejZcuWxvahQ4di3bp1+PLLL7Fjxw707NkTXbt2xb59+4w8mZmZeOeddzB79mz89ttvOHr0KEaNGgUAGDVqFHr16mU8DDxx4gTatGnjUo7U1FTccccdaNKkCTZv3owlS5bg1KlT6NWrF4BLvyTr06cPHn30USQlJWHlypV44IEHoJRCXl4eunfvjri4OOzYsQPr1q3DE088UeSPFZo1a4aYmBh8++23AC4t6v/22294+OGHXfIGBgZi5syZ2L17N6ZMmYL//Oc/eO+99wBcug6dO3fGjBkztO/MmDEDAwcOhNlsRlxcnLEIrpTCqlWrEBISYgz2EhISUL16ddxyyy1u1fuBAwfQtWtXPPjgg9ixYwfmzZuH1atXY+jQocbxBw4ciGPHjmHFihX45ptv8NFHH2mDT0LKSpvmzJljPGwriHyPpwULFmD48OEYOXIkdu7cicGDB+ORRx7BihUrtPyvvfYa+vfvj8TERNSrVw99+/bF4MGDMWbMGGzevBlKKa1vAJf60OLFi7FkyRLMnTsXn332Ge655x4cP34cCQkJePPNNzF27Fhs2LABwKXJU7du3XD+/HkkJCRg2bJlOHjwIHr37u2y3++++w4LFy7EwoULkZCQgIkTJxZaF3Xr1kW3bt1ctplMJgQHBxvpFStW4MCBA1ixYgVmzZqFmTNnYubMmQXu1+l04q677sKaNWvw3//+F7t378bEiRNhsVhc8h47dgzt2rVDgwYN8M0338Bms1GzCHETT9dRoGhdysjIwDPPPIPNmzdj+fLlMJvN6NGjB5xOp8u5Dh8+HElJSYiPj3c5ZlGaXVpdIoS44snzTHd45plnsGbNGvzwww9YtmwZVq1aha1bt2p57HY7XnvtNWzfvh3fffcdDh8+XOAitbt1MXr0aIwcORLbtm1D69atcd999+HcuXMAip6TAoXXX1RUlDG3TE5OxokTJzBlypRS1REhNzrUqb+hTpVjFKkQzJgxQwUHBxvpFStWKADqu+++0/INGDBAdevWTfts+PDhKi4uTimlVHZ2tvLz81Nr167V8gwaNEj16dPHSN9xxx3q/fffv2J5Dh06pAAoX19f5e/vb/xt3bq1wPy33nqrtr/o6Gj13nvvFXLGf+ezWq3aMfz9/ZW3t7dq3Lixke/mm29W//vf/7Tvvvbaa6p169ZaeT/99FNj+65duxQAlZSUVGQ5CClLPK3/p6enq7vvvlsBUNHR0ap3797qs88+U9nZ2Uae1q1bq379+hX4/SNHjiiLxaL++OMP7fNOnTqpMWPGGOcMQO3fv9/Y/uGHH6rw8PBCzze/r2/btk0pdUkH7rzzTi3PsWPHFACVnJystmzZogCow4cPu5Tz3LlzCoBauXLlFetCAkAtWLBATZ48WXXs2FEppdQrr7yievTooVJSUhQAtWLFiit+/+2331bNmjUz0vPmzVOVKlUy6nbLli3KZDKpQ4cOKaWU+uGHH1RwcLDKy8tTiYmJKiIiQg0fPlw999xzSimlHnvsMdW3b1+llHv1PmjQIPXEE09o21etWqXMZrPKyspSycnJCoDauHGjsT0pKUkBcEvTSfnC07TprrvuUo0aNSqy3G3atFGPP/649lnPnj3V3XffbaQBqLFjxxrpdevWKQDqs88+Mz6bO3eu8vHxMdLjxo1Tfn5+Ki0tzfgsPj5excTEKIfDYXxWt25dNWHCBKWUUj///LOyWCzq6NGjxvb88Ul+Pytov6NHj1atWrW64jnGxsaq+++/v8i6GDBggIqOjlZ5eXlaXfTu3dtIXz5mW7p0qTKbzSo5ObnA/eW3iT179qioqCg1bNgw5XQ6je3ULEJ0blQdLYkunTlzRgFQv//+u1Lq7zHb5MmTtXyyTorS7NLqEiEVGU/TIHfmmUWVJS0tTXl7e6uvv/7a2J6amqr8/PzU8OHDr3jsTZs2KQDq4sWLhdaFJF/LJk6caHxmt9tVjRo11JtvvqmUKnpO6k795ZcnJSWl0PIQUt6gTv0NdariwaCtFZzmzZsXK//+/fuRmZmJLl26aJ/n5uaiSZMmRnr58uVu7W/evHmIjY010lFRUUhPT8fLL7+MRYsW4cSJE8jLy0NWVpbxxl9xGT16tMsvGqZOnYrffvsNwKVfkx44cACDBg3C448/buTJy8vTftEOQPvlQ2RkJIBLIaDq1atXorIRUpaUVf/39/fHokWLjDdE1q9fj5EjR2LKlClYt24d/Pz8kJiYqPXHy/n999/hcDhQp04d7fOcnByEhoYaaT8/P9x8881GOjIysthvaWzfvh0rVqzQYsDnc+DAAdx5553o1KkTGjZsiPj4eNx555345z//iUqVKqFy5coYOHAg4uPj0aVLF3Tu3Bm9evUytKMwHnroITz//PM4ePAgZs6cialTpxaYb968eZg6dSoOHDiA9PR05OXlISgoyNjevXt3DBkyBAsWLMC//vUvzJw5Ex07dkRMTAwAoF27drh48SK2bduGtWvXIi4uDh06dDB+cZ+QkIDRo0cDcK/et2/fjh07dmihtpRScDqdOHToEPbu3QsvLy80a9bM2F6vXj3tDQBCykqblFJuHS8pKQlPPPGE9lnbtm1dfpF4+ZghPDwcANCwYUPts+zsbKSlpRn9NiYmBoGBgVoei8UCs9msfZavZUlJSYiKikJUVJSxvX79+ggJCUFSUhJatGhR4H6L0kN36wIAbr31Vu3NmMjISPz+++8F5k1MTESNGjVcdORysrKy0K5dO/Tt21cLEQpQswhxF0/XUaBoXdq3bx9eeuklbNiwAWfPnjXe9Dt69CgaNGhg5CvqXIvS7NLqEiHEFU+eZxbFwYMHYbfbtWgzwcHBqFu3rpZvy5YtePnll7F9+3akpKRoGlW/fn0jn7t10bp1a+P/Xl5eaN68OZKSkgAUPSe12+1u1R8h5G+oU9SpigAf/FVw/P39tbTZbHaZsF3u05LvobJo0SJUr15dy1eSUCdRUVFGOKZ8hg8fjmXLluGdd97BLbfcAl9fX/zzn/9Ebm5usfcPAGFhYS7HqFy5svH//HP6z3/+g1atWmn5ZIgXb29v4//54fpkuBlCbhTKuv/ffPPNuPnmm/HYY4/hhRdeQJ06dTBv3jw88sgj8PX1veL30tPTYbFYsGXLFpc+evkg4/L+Clzqs8VZkMo/1n333Yc333zTZVtkZCQsFguWLVuGtWvX4ueff8b777+PF154ARs2bECtWrUwY8YMDBs2DEuWLMG8efMwduxYLFu2DLfffnuhx833shk0aBCys7Nx1113ufhxrVu3Dv369cMrr7yC+Ph4BAcH48svv8SkSZOMPFarFf3798eMGTPwwAMP4H//+5/2cCIkJASNGzfGypUrsW7dOnTp0gXt27dH7969sXfvXuzbtw9xcXFGXRRV7+np6Rg8eDCGDRvmck41a9bE3r17i6hxQspOm+rUqYPVq1fDbre76EdJKGjMUNQ4oiDdKuiz4o49iruPOnXqYM+ePVd934Vpez42mw2dO3fGwoULMXr0aO2aUrMIcY8bQUeL0o777rsP0dHR+M9//oNq1arB6XSiQYMGLnNCea7FpbS6RAhxxZPnmUWVxR0yMjIQHx+P+Ph4zJkzB1WqVMHRo0cRHx9/1TUKKHpOmh8y8GrVHyEVAerU31Cnyi/0+CMaVapUcfHMS0xMNP5fv3592Gw2HD16FLfccov2d/mvzUvDmjVrMHDgQPTo0QMNGzZEREQEDh8+rOWxWq1wOBxX5Xjh4eGoVq0aDh486HJOtWrVuirHIORGoCz7f0xMDPz8/JCRkQHg0psyV/qlVJMmTeBwOHD69GmXckRERLh9THd0pGnTpti1axdiYmJcjpU/ODKZTGjbti1eeeUVbNu2DVarFQsWLNDKO2bMGKxduxYNGjTA//73P7fK9+ijj2LlypXo379/gT4za9euRXR0NF544QU0b94ctWvXxpEjR1zyPfbYY/jll1/w0UcfIS8vDw888IC2PS4uDitWrMBvv/2GDh06oHLlyoiNjcX48eMRGRlp/ALenXpv2rQpdu/e7bL9lltugdVqRb169ZCXl4ctW7YYx09OTkZqaqpbdUIqJtdLm/r27Yv09HR89NFHBW7Pb6exsbFYs2aNtm3NmjXaryavF7GxsTh27BiOHTtmfLZ7926kpqaWqjx9+/bF3r178f3337tsU0rhwoULJdpvo0aNcPz48UIfqJnNZsyePRvNmjVDx44d8eeff2rbqVmEFB9P09GiOHfuHJKTkzF27Fh06tQJsbGxSElJcbscl1OUZl8NXSKEFI4nzTOLKstNN90Eb29vbNq0yfjswoULmkbs2bMH586dw8SJE9GuXTvUq1ev1P6/69evN/6fP/bIj45V1JzUnfqzWq0AcNXW0Qgpb1CnioY6dePBB39E44477sDmzZvxxRdfYN++fRg3bpxmOBoYGIhRo0bh6aefxqxZs3DgwAFs3boV77//PmbNmmXk69SpEz744IMSlaF27dqYP38+EhMTsX37dvTt29fll+MxMTH47bff8Mcff+Ds2bMlO9nLeOWVVzBhwgRMnToVe/fuxe+//44ZM2bg3XffLfW+CblRuF79/+WXX8azzz6LlStX4tChQ9i2bRseffRR2O1247X/cePGYe7cuRg3bhySkpLw+++/G78cqlOnDvr164f+/ftj/vz5OHToEDZu3IgJEyZg0aJFbp9vTEwMduzYgeTkZJw9e7bAX1ANGTIE58+fR58+fbBp0yYcOHAAS5cuxSOPPAKHw4ENGzbgjTfewObNm3H06FHMnz8fZ86cQWxsLA4dOoQxY8Zg3bp1OHLkCH7++Wfs27dPC29cGF27dsWZM2fw6quvFri9du3aOHr0KL788kscOHAAU6dO1R445hMbG4vbb78dzz33HPr06ePyy/YOHTpg6dKl8PLyMsIWd+jQAXPmzDHenAHcq/fnnnsOa9euxdChQ5GYmIh9+/bh+++/x9ChQwEAdevWRdeuXTF48GBs2LABW7ZswWOPPebWr+1JxeV6aVOrVq3w7LPPYuTIkXj22WeNvrt8+XL07NnT2Nfo0aMxc+ZMTJs2Dfv27cO7776L+fPnY9SoUdeuEq5A586d0bBhQ/Tr1w9bt27Fxo0b0b9/f8TFxRU7fM3l9OrVC71790afPn0MjTty5AgWLlyIzp07Y8WKFSXab1xcHNq3b48HH3wQy5Ytw6FDh7B48WIsWbJEy2exWDBnzhw0btwYd9xxB06ePGlso2YRUnw8TUeLolKlSggNDcUnn3yC/fv349dff8UzzzxTonMvSrOvhi4RQgrHk+aZ7pRlwIABGD16NFasWIFdu3Zh0KBBMJvNRrSGmjVrwmq14v3338fBgwfxww8/4LXXXitVHX344YdYsGAB9uzZgyFDhiAlJQWPPvoogKLnpO7UX3R0NEwmExYuXIgzZ84Yby8RQi5BnSoa6tQNSBn4CpIy4EpmpgUZZr700ksqPDxcBQcHq6effloNHTrUMBBVSimn06kmT56s6tatq7y9vVWVKlVUfHy8SkhIMPJER0ercePGXbE8+cag27ZtK3Bbx44dla+vr4qKilIffPCBiouL0wxK161bpxo1aqRsNpsqrBlHR0er9957z+XzcePGqcaNG2ufzZkzR912223KarWqSpUqqfbt26v58+dfsbwpKSkKgFqxYsUVj0+IJ+Bp/f/XX39VDz74oIqKilJWq1WFh4errl27qlWrVmn5vv32W6NPhoWFqQceeMDYlpubq1566SUVExOjvL29VWRkpOrRo4fasWNHgeeslFILFizQ9OL06dOqS5cuKiAgwOjLBfX1vXv3qh49eqiQkBDl6+ur6tWrp0aMGKGcTqfavXu3io+PV1WqVFE2m03VqVPHMHI+efKk6t69u4qMjFRWq1VFR0erl156STkcjivWDQC1YMGCArcVpDmjR49WoaGhKiAgQPXu3Vu99957LuetlFKfffaZAqA2btzosu3cuXPKZDKp3r17u9TV9OnTtbxF1btSSm3cuNGoV39/f9WoUSM1fvx4Y/uJEyfUPffco2w2m6pZs6b64osvrqjVpHzjadqUz7x581T79u1VYGCg0YZfffVVrVwfffSRuummm5S3t7eqU6eO+uKLL7R9yL5ckLbI8y1obFKQsbscEx05ckTdf//9yt/fXwUGBqqePXuqkydPGtsL2u97772noqOjC60Hh8Ohpk2bplq0aKH8/PxUUFCQatasmZoyZYrKzMy8YvkuN55XynUsdu7cOfXII4+o0NBQ5ePjoxo0aKAWLlyolHJtE3a7XT3wwAMqNjZWnTp1yvg+NYuQS9yoOuqOLi1btkzFxsYqm82mGjVqpFauXKlp65XmkwWNAYvS7NLqEiEVFU/TIHfnmUWVJS0tTfXt21f5+fmpiIgI9e6776qWLVuq559/3sjzv//9T8XExCibzaZat26tfvjhB02TCquLy8nXsv/973+qZcuWymq1qvr166tff/1Vy1fYnNTd+nv11VdVRESEMplMasCAAYWWi5DyAnWKOlWRMSlVTMMjQgghhNxQvPbaa/j666+xY8eOsi4KIYQQQgghhNwwZGRkoHr16pg0aRIGDRpU1sUhhBAXqFOkILzKugCEEEIIuTakp6fj8OHD+OCDD/D666+XdXEIIYQQQgghxKPZtm0b9uzZg5YtW+LChQuG9UK3bt3KuGSEEHIJ6hRxBz74I4QQQsopQ4cOxdy5c9G9e3cj9johhBBCCCGEkCvzzjvvIDk5GVarFc2aNcOqVasQFhZW1sUihBAD6hQpCob6JIQQQgghhBBCCCGEEEIIIaQcYC7rAhBCCCGEEEIIIYQQQgghhBBCSg8f/JGrwsCBA9G9e/eyLgYhpAwob/2/vJ3PjcDMmTMREhJS1sUg5Qz25WvP4cOHYTKZkJiYeMU8xe3fMTExmDx5cqnLdi2hZpGKwrXUUZPJhO++++6a7JsQUn6oKOO5inKehJQ3KkrfrSjnWd7gg79yzMCBA2EymWAymWC1WnHLLbfg1VdfRV5eXlkXDYBevsv/9u/fX+R33V1wKSwfJ5ukPOPp/f/MmTN48sknUbNmTdhsNkRERCA+Ph5r1qy5bmVwZ8H6evPyyy/DZDKha9euLtvefvttmEwmdOjQ4foXjJCrhKdrEwCcPHkS//d//4ebbroJNpsNUVFRuO+++7B8+fKyLtp1Z//+/XjkkUdQo0YN2Gw21KpVC3369MHmzZvd3kfv3r2xd+/ea1hKQioW1FFCSFnj6TrkCXNNT6VDhw4YMWJEWReDkGsKNerGhRp1dfEq6wKQa0vXrl0xY8YM5OTk4KeffsKQIUPg7e2NMWPGuOTNzc2F1Wotk/JdTpUqVa5rGQgpr3hy/3/wwQeRm5uLWbNm4aabbsKpU6ewfPlynDt37rqVwVOJjIzEihUrcPz4cdSoUcP4/PPPP0fNmjXLsGSEXB08WZsOHz6Mtm3bIiQkBG+//TYaNmwIu92OpUuXYsiQIdizZ891K0tZs3nzZnTq1AkNGjTAxx9/jHr16uHixYv4/vvvMXLkSCQkJLi1H19fX/j6+l7j0hJSsaCOXh+UUnA4HPDy4rIJIRJP1iFPnmuWxbobIRURalTJoEaVL/jGXzkn/5cD0dHRePLJJ9G5c2f88MMPAP5+TXf8+PGoVq0a6tatCwA4duwYevXqhZCQEFSuXBndunXD4cOHjX06HA4888wzCAkJQWhoKJ599lkopUpVvsv/LBYL3n33XTRs2BD+/v6IiorCU089hfT0dADAypUr8cgjj+DChQvGLzhefvnlUtWTO+edX1/vvPMOIiMjERoaiiFDhsBut5f62IRcCzy1/6empmLVqlV488030bFjR0RHR6Nly5YYM2YM7r//fi3f4MGDER4eDh8fHzRo0AALFy40tq9evRrt2rWDr68voqKiMGzYMGRkZBjbY2Ji8MYbb+DRRx9FYGAgatasiU8++cTYXqtWLQBAkyZNCn2Tzul0YsKECahVqxZ8fX3RuHFjfPPNN8b2lJQU9OvXD1WqVIGvry9q165t/KAhNzcXQ4cORWRkJHx8fBAdHY0JEyYUWj9Vq1bFnXfeiVmzZhmfrV27FmfPnsU999yj5d20aRO6dOmCsLAwBAcHIy4uDlu3bjW2P/roo7j33nu179jtdlStWhWfffYZFi5ciJCQEDgcDgBAYmIiTCYTnn/+eSP/Y489hoceesjtes/JycGoUaNQvXp1+Pv7o1WrVli5cqVWhpkzZ6JmzZrw8/NDjx49PGKAS64fnqpNAPDUU0/BZDJh48aNePDBB1GnTh3ceuuteOaZZ7B+/Xoj39GjR9GtWzcEBAQgKCgIvXr1wqlTp4ztL7/8Mm677TbjgX1AQACeeuopOBwOvPXWW4iIiEDVqlUxfvx47fgmkwkff/wx7r33Xvj5+SE2Nhbr1q3D/v370aFDB/j7+6NNmzY4cOCA9r1p06bh5ptvhtVqRd26dTF79myX/X766afo0aMH/Pz8ULt2baPOC0IphYEDB6J27dpYtWoV7rnnHtx888247bbbMG7cOHz//fda/oMHD6Jjx47w8/ND48aNsW7dOmNbQdEXfvzxR7Ro0QI+Pj4ICwtDjx49rliWTz/9FCEhIVi+fDk1i5C/KA86KimqfEWNefr27YvevXtr+7Tb7QgLC8MXX3wBoOgx3cqVK2EymbB48WI0a9YMNpsNq1evxvbt29GxY0cEBgYiKCgIzZo1K9abz4SURzxVh9yZaxYU+SU1NRUmk0kbA/zwww+oXbs2fHx80LFjR8yaNQsmkwmpqakAgHPnzqFPnz6oXr06/Pz80LBhQ8ydO1crT4cOHTB06FCMGDECYWFhiI+PL7T8r7zyCqpUqYKgoCD8+9//Rm5urrGtKA0DgJ07d+Kuu+5CQEAAwsPD8fDDD+Ps2bMALl2XhIQETJkyxVhLu7z+CSlPUKOoUYQP/iocvr6+Wqdcvnw5kpOTsWzZMixcuBB2ux3x8fEIDAzEqlWrsGbNGgQEBKBr167G9yZNmoSZM2fi888/x+rVq3H+/HksWLBAO87MmTNhMplKXE6z2YypU6di165dmDVrFn799Vc8++yzAIA2bdpg8uTJCAoKwokTJ3DixAmMGjWqxMcC4NZ5A8CKFStw4MABrFixArNmzcLMmTMxc+bMUh2bkOuFp/T/gIAABAQE4LvvvkNOTk6BeZxOJ+666y6sWbMG//3vf7F7925MnDgRFosFAHDgwAF07doVDz74IHbs2IF58+Zh9erVGDp0qLafSZMmoXnz5ti2bRueeuopPPnkk0hOTgYAbNy4EQDwyy+/4MSJE5g/f36BZZkwYQK++OILTJ8+Hbt27cLTTz+Nhx56yHjb5cUXX8Tu3buxePFiJCUlYdq0aQgLCwMATJ06FT/88AO++uorJCcnY86cOYiJibli3eTz6KOPatry+eefo1+/fi6/vLp48SIGDBiA1atXY/369ahduzbuvvtuXLx4EcClBfAlS5bgxIkTxncWLlyIzMxM9O7dG+3atcPFixexbds2AEBCQgLCwsK0wWRCQoLxUNSdeh86dCjWrVuHL7/8Ejt27EDPnj3RtWtX7Nu3DwCwYcMGDBo0CEOHDkViYiI6duyI119/vcg6IeUXT9Gm8+fPY8mSJRgyZAj8/f1dtuc/vHI6nejWrRvOnz+PhIQELFu2DAcPHnRZcD5w4AAWL16MJUuWYO7cufjss89wzz334Pjx40hISMCbb76JsWPHYsOGDdr3XnvtNfTv3x+JiYmoV68e+vbti8GDB2PMmDHYvHkzlFJan1uwYAGGDx+OkSNHYufOnRg8eDAeeeQRrFixQtvvK6+8gl69emHHjh24++670a9fP5w/f77AukhMTMSuXbswcuRImM2u0wX5IO+FF17AqFGjkJiYiDp16qBPnz5XDKWzaNEi9OjRA3fffTe2bduG5cuXo2XLlgXmfeutt/D888/j559/RqdOnahZhFyBG01HJe6Ur6gxT79+/fDjjz8aPxYFgKVLlyIzM9P4cUFRY7p8nn/+eUycOBFJSUlo1KgR+vXrhxo1amDTpk3YsmULnn/+eXh7e1+xHgipiHiKDrkz13SHQ4cO4Z///Ce6d++O7du3Y/DgwXjhhRe0PNnZ2WjWrBkWLVqEnTt34oknnsDDDz9szDPzmTVrFqxWK9asWYPp06df8ZjLly9HUlISVq5ciblz52L+/Pl45ZVXjO1FaVhqairuuOMONGnSBJs3b8aSJUtw6tQp9OrVCwAwZcoUtG7dGo8//rixlhYVFVXiOiLkRoIaRY2qkChSbhkwYIDq1q2bUkopp9Opli1bpmw2mxo1apSxPTw8XOXk5BjfmT17tqpbt65yOp3GZzk5OcrX11ctXbpUKaVUZGSkeuutt4ztdrtd1ahRwziWUkrNnz9f1a1bt8jyWSwW5e/vb/z985//LDDv119/rUJDQ430jBkzVHBwcJF1MGPGDAVAO0b+HwC1YMECt897wIABKjo6WuXl5Rl5evbsqXr37l1kOQi53nh6///mm29UpUqVlI+Pj2rTpo0aM2aM2r59u7F96dKlymw2q+Tk5AK/P2jQIPXEE09on61atUqZzWaVlZWllFIqOjpaPfTQQ8Z2p9OpqlatqqZNm6aUUurQoUMKgNq2bdsV6y47O1v5+fmptWvXuhy/T58+Siml7rvvPvXII48UWM7/+7//U3fccYdWp4Uxbtw41bhxY5Wbm6uqVq2qEhISVHp6ugoMDFTbt29Xw4cPV3FxcVf8vsPhUIGBgerHH380Pqtfv7568803jfR9992nBg4caKSbNm2q3n77baWUUt27d1fjx49XVqtVXbx4UR0/flwBUHv37jXOu7B6P3LkiLJYLOqPP/7Q8nTq1EmNGTNGKaVUnz591N13361t7927t1uaTm58PFmbNmzYoACo+fPnF3oOP//8s7JYLOro0aPGZ7t27VIA1MaNG5VSl/qyn5+fSktLM/LEx8ermJgY5XA4jM/q1q2rJkyYYKQBqLFjxxrpdevWKQDqs88+Mz6bO3eu8vHxMdJt2rRRjz/+uFbGnj17av1M7jc9PV0BUIsXLy7wHOfNm6cAqK1btxZaF/k6+umnn7rURVJSklLKdczWunVr1a9fvyvuMzo6Wr333nvq2WefVZGRkWrnzp3admoWqeiUBx1VShV7LiaRYx673a7CwsLUF198YeTp06ePMVdzZ0y3YsUKBUB99913Wp7AwEA1c+bMIs+JkIqCJ+uQUkXPNQuaB6akpCgAasWKFUoppZ577jnVoEEDbb8vvPCCAqBSUlKueOx77rlHjRw50kjHxcWpJk2aFFpepS7VWeXKlVVGRobx2bRp01RAQIByOBxuadhrr72m7rzzTm37sWPHFABjXh0XF6eGDx9eZHkIuZGhRqVc8djUqIoFg9WXcxYuXIiAgADY7XY4nU707dtXC4vZsGFD7Q2S7du3Y//+/QgMDNT2k52djQMHDuDChQs4ceIEWrVqZWzz8vJC8+bNtdebe/ToUWjYpnw6duyIadOmGen8X4b+8ssvmDBhAvbs2YO0tDTk5eUhOzsbmZmZ8PPzK1YdBAYGamFg8qldu7bx/6LOO59bb73VeOMIuOTF9fvvvxerPIRcLzy5/z/44IO45557sGrVKqxfvx6LFy/GW2+9hU8//RQDBw5EYmIiatSogTp16hT4/e3bt2PHjh2YM2eO8ZlSCk6nE4cOHUJsbCwAoFGjRsZ2k8mEiIgInD59utCyXc7+/fuRmZmJLl26aJ/n5uaiSZMmAIAnn3wSDz74ILZu3Yo777wT3bt3R5s2bQBcClXQpUsX1K1bF127dsW9996LO++8s8jjent746GHHsKMGTNw8OBB1KlTRzuXfE6dOoWxY8di5cqVOH36NBwOBzIzM3H06FEjz2OPPYZPPvkEzz77LE6dOoXFixfj119/NbbHxcVh5cqVGDlyJFatWoUJEybgq6++Mn7BVq1aNUMvi6r3gwcPwuFwuFy3nJwchIaGAgCSkpJc2kfr1q2xZMmSIuuFlA88VZuUm2FakpKSEBUVpf36sH79+ggJCUFSUhJatGgB4FK44cvLHB4eDovFor1BFx4e7qJJl/f18PBwAJfq5PLPsrOzkZaWhqCgICQlJeGJJ57Q9tG2bVtMmTLlivv19/dHUFDQFfXQ3booaN+RkZEAgNOnT6NevXoueRMTE/H4448Xur9JkyYhIyMDmzdvxk033aRto2YRcuPrqMSduVhRYx4vLy/06tULc+bMwcMPP4yMjAx8//33+PLLLwG4N6bLp3nz5lr6mWeewWOPPYbZs2ejc+fO6NmzJ26++eYSnSsh5QVP1SGg6LmmOyQnJxtjunxkhAKHw4E33ngDX331Ff744w/k5uYiJyfHZc2qWbNmbh2zcePG2ndbt26N9PR0HDt2DOnp6UVq2Pbt27FixQoEBAS47PvAgQNXnFsTUh6hRlGjCMAHf+Wc/AdrVqsV1apVczEmlyFY0tPT0axZM22BJJ8qVapc9fL5+/vjlltu0T47fPgw7r33Xjz55JMYP348KleujNWrV2PQoEHIzc0t9oM/s9nscgyJu+ctQ7qYTCY4nc5ilYeQ64Wn938fHx906dIFXbp0wYsvvojHHnsM48aNw8CBA+Hr61vod9PT0zF48GAMGzbMZVvNmjWN/5e2z+aHi1q0aBGqV6+ubbPZbACAu+66C0eOHMFPP/2EZcuWoVOnThgyZAjeeecdNG3aFIcOHcLixYvxyy+/oFevXujcubNLnPOCePTRR9GqVSvs3LkTjz76aIF5BgwYgHPnzmHKlCmIjo6GzWZD69attRAW/fv3x/PPP49169Zh7dq1qFWrFtq1a2ds79ChAz7//HNs374d3t7eqFevHjp06ICVK1ciJSUFcXFxWn0UVu87duyAxWLBli1btB9JAChwcEcqJp6qTbVr14bJZMKePXuuyv4K0h93NOnyPPlhYgr6rLjjj+LoYf6ka8+ePS4L4kXtu6jyFaXvANCuXTssWrQIX331lebfB1CzCAHKn466Uz53xjz9+vVDXFwcTp8+jWXLlsHX1xddu3Y1jgEUPqbLR9bfyy+/jL59+2LRokVYvHgxxo0bhy+//NKtH7oSUl7xVB3Kp7C5Zv6PsC5frLfb7cU+xttvv40pU6Zg8uTJaNiwIfz9/TFixAhNlwDXuigJ7mhYeno67rvvPrz55psu38//YRYhFQVqFDWK8MFfuaegB2uF0bRpU8ybNw9Vq1ZFUFBQgXkiIyOxYcMGtG/fHgCQl5eHLVu2oGnTplelzFu2bIHT6cSkSZMMsfvqq6+0PFarFQ6H46ocD3DvvAm50bjR+n/9+vXx3XffAbj09sjx48exd+/eAn/107RpU+zevbtY5yfJ/3VXYVpSv3592Gw2HD16VFtMllSpUgUDBgzAgAED0K5dO4wePRrvvPMOACAoKAi9e/dG79698c9//hNdu3bF+fPnUbly5ULLd+utt+LWW2/Fjh070Ldv3wLzrFmzBh999BHuvvtuAJfMqPONkfMJDQ1F9+7dMWPGDKxbtw6PPPKItj3fM+u9994zzrFDhw6YOHEiUlJSMHLkSCNvUfXepEkTOBwOnD59Wnu4eDmxsbEunmbr168vpCZIecNTtaly5cqIj4/Hhx9+iGHDhrlMgFJTUxESEoLY2FgcO3YMx44dM9762717N1JTU1G/fn23j3e1iI2NxZo1azBgwADjszVr1pSqLLfddhvq16+PSZMmoXfv3i4+f/l1URIaNWqE5cuXu2jR5bRs2RJDhw5F165d4eXlpXk5U7MIufF1tCTlc2fM06ZNG0RFRWHevHlYvHgxevbsafwwwd0x3ZWoU6cO6tSpg6effhp9+vTBjBkz+OCPVGg8VYeuxOVzzfxF/BMnThg/cEpMTNTy161bFz/99JP22aZNm7T0mjVr0K1bNzz00EMALv3oae/evSUeg23fvh1ZWVnGj6TWr1+PgIAAREVFoXLlykVqWNOmTfHtt98iJibG5SFHPld7LY0QT4UaRY0igLnoLKQi0a9fP4SFhaFbt25YtWoVDh06hJUrV2LYsGE4fvw4AGD48OGYOHEivvvuO+zZswdPPfUUUlNTtf0sWLCgwPBO7nDLLbfAbrfj/fffx8GDBzF79mwXc9GYmBikp6dj+fLlOHv2LDIzM0t0rHzcOW9CyjvXq/+fO3cOd9xxB/773/9ix44dOHToEL7++mu89dZb6NatG4BLodzat2+PBx98EMuWLTPemssPrfbcc89h7dq1GDp0KBITE7Fv3z58//33GDp0qNvnW7VqVfj6+hqGwhcuXHDJExgYiFGjRuHpp5/GrFmzcODAAWzduhXvv/8+Zs2aBQB46aWX8P3332P//v3YtWsXFi5caIQafffddzF37lzs2bMHe/fuxddff42IiAi3F8x//fVXnDhx4or5a9eujdmzZyMpKQkbNmxAv379Cnyb5rHHHsOsWbOQlJSkPRwAgEqVKqFRo0aYM2cOOnToAABo3749tm7dir1792qDtqLqvU6dOujXrx/69++P+fPn49ChQ9i4cSMmTJiARYsWAQCGDRuGJUuW4J133sG+ffvwwQcfMGQeKZTrOTb58MMP4XA40LJlS3z77bfYt28fkpKSMHXqVLRu3RoA0LlzZzRs2BD9+vXD1q1bsXHjRvTv3x9xcXEu4eGuB6NHj8bMmTMxbdo07Nu3D++++y7mz5+vPSwrLiaTCTNmzMDevXvRrl07/PTTTzh48CB27NiB8ePHG1pdEsaNG4e5c+di3LhxSEpKwu+//17grz7btGmDn376Ca+88gomT55sfE7NIqT4eJqOlqR87o55+vbti+nTp2PZsmXo16+f8bk7Y7qCyMrKwtChQ7Fy5UocOXIEa9aswaZNm4yxHiHEPTxprunr64vbb78dEydORFJSEhISEjB27FhtP4MHD8aePXvw3HPPYe/evfjqq68wc+ZMAH9HN6hduzaWLVuGtWvXIikpCYMHD8apU6dKXEe5ubkYNGgQdu/ejZ9++gnjxo3D0KFDYTab3dKwIUOG4Pz58+jTpw82bdqEAwcOYOnSpXjkkUeMhfSYmBhs2LABhw8fxtmzZxnBipC/oEYVDTXqxoMP/oiGn58ffvvtN9SsWRMPPPAAYmNjMWjQIGRnZxu/eBg5ciQefvhhDBgwAK1bt0ZgYKDLrx0vXLiA5OTkEpWhcePGePfdd/Hmm2+iQYMGmDNnDiZMmKDladOmDf7973+jd+/eqFKlCt56662SnfBfuHPehJR3rlf/DwgIQKtWrfDee++hffv2aNCgAV588UU8/vjj+OCDD4x83377LVq0aIE+ffqgfv36ePbZZ43BQKNGjZCQkGAsSjdp0gQvvfQSqlWr5vb5enl5YerUqfj4449RrVq1Ky5kv/baa3jxxRcxYcIExMbGomvXrli0aBFq1aoF4NIvksaMGYNGjRqhffv2sFgshp9MYGAg3nrrLTRv3hwtWrTA4cOH8dNPP7m8PXMl/P39C31I+NlnnyElJQVNmzbFww8/jGHDhqFq1aou+Tp37ozIyEjEx8cXWEdxcXFwOBzGInrlypVRv359REREoG7dukY+d+p9xowZ6N+/P0aOHIm6deuie/fu2LRpkxGC9fbbb8d//vMfTJkyBY0bN8bPP//sMogl5HKu59jkpptuwtatW9GxY0eMHDkSDRo0QJcuXbB8+XLDk9hkMuH7779HpUqV0L59e3Tu3Bk33XQT5s2bd20qoAi6d++OKVOm4J133sGtt96Kjz/+GDNmzDD6c0lp2bIlNm/ejFtuuQWPP/44YmNjcf/992PXrl3ag7ji0qFDB3z99df44YcfcNttt+GOO+7Axo0bC8z7j3/8A4sWLcLYsWPx/vvvG59TswgpHp6moyUpn7tjnn79+mH37t2oXr062rZtq20rakxXEBaLBefOnUP//v1Rp04d9OrVC3fddRdeeeWVQuuBEKLjaXPNzz//HHl5eWjWrBlGjBiB119/XdtPrVq18M0332D+/Plo1KgRpk2bhhdeeAHA32Hrxo4di6ZNmyI+Ph4dOnRAREQEunfvXuI66tSpE2rXro327dujd+/euP/++zVPsqI0rFq1alizZg0cDgfuvPNONGzYECNGjEBISIgx/xw1ahQsFgvq16+PKlWqaN7whFRkqFFFQ4268TCpkjpwE0IIIeSGID09HdWrV8eMGTPwwAMPlHVxCCGEEEIIIeSGYvz48Zg+fTqOHTtW1kUhhBAXqFFEQo8/QgghpJzidDpx9uxZTJo0CSEhIbj//vvLukiEEEIIIYQQ4vF89NFHaNGiBUJDQ7FmzRq8/fbbxbKWIISQawk1ihQFH/wRQggh5ZSjR4+iVq1aqFGjBmbOnHlFA2VCCCGEEEIIIX+zb98+vP766zh//jxq1qyJkSNHYsyYMWVdLEIIAUCNIkXDUJ+EEEIIIYQQQgghhBBCCCGElAPMZV0AQgghhBBCCCGEEEIIIYQQQkjp4YM/QgghhBBCCCGEEEIIIYQQQsoBfPBHCCGEEEIIIYQQQgghhBBCSDmAD/4IIYQQQgghhBBCCCGEEEIIKQfwwR8hhBBCCCGEEEIIIYQQQggh5QA++COEEEIIIYQQQgghhBBCCCGkHMAHf4QQQgghhBBCCCGEEEIIIYSUA/jgjxBCCCGEEEIIIYQQQgghhJByAB/8EUIIIYQQQgghhBBCCCGEEFIO+P9/+pr9SbIgeQAAAABJRU5ErkJggg==\n"},"metadata":{}}],"source":["num_images = 6                                # Number of images to display\n","fig = plt.figure(figsize=(3*num_images, 5))   # Set the figure size\n","\n","indices = random.sample(range(len(X_test)), num_images)  # Randomly choose indices\n","\n","for i, index in enumerate(indices):                               # Iterate over the chosen indices\n","    true_label = enc.inverse_transform(y_test_encoded)[index]     # Get the true label\n","                                                                  # Get the predicted label\n","    predicted_label = enc.inverse_transform(models[model].predict(X_test_normalized[index].reshape(1, 64, 64, 3), verbose = 0))[0]\n","    # PLot the Image\n","    plot_image(fig, X_test[index], (1, num_images, i + 1),\n","                xlabel = f\"True: {true_label}\\nPred: {predicted_label}\",\n","                )\n","\n","plt.suptitle(\"Visualizing the prediction for Candon class\", y = 0.8,fontsize = 14)\n","                                                      # Set the title of the plot\n","plt.tight_layout()                                    # Adjust the spacing between subplots\n","plt.show()                                            # Show the figure"]},{"cell_type":"markdown","metadata":{"id":"Eg2x8AyJ4oPR"},"source":["## Actionable Insights and Business Recommendations"]},{"cell_type":"markdown","metadata":{"id":"jvq4_6CZYcrv"},"source":["1. **Enhanced Efficiency**: With about 93% accuracy, the model greatly reduces plant identification time and effort, freeing up human resources for other tasks.\n","2. **Room for Improvement**: Additional data could further refine the model, potentially improving accuracy even more.\n","3. **Eco-Friendly Integration**: Integrate the model with automatic weeding systems to target weeds over crops, reducing pesticide use and promoting sustainable farming.\n","4. **Mobile App for Farmers**: Develop a mobile app to help farmers quickly identify plants from photos, enabling faster decision-making.\n","5. **Precision Agriculture**: Use the model in precision agriculture to optimize resources like fertilization and irrigation, enhancing crop yield and reducing waste.\n","6. **Farm Management Systems**: Integrate with farm management software for better insights into plant health and growth, aiding in effective crop management."]},{"cell_type":"markdown","metadata":{"id":"geC4LwwIYfS_"},"source":["_____"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["4ZgcS1MyVGZp","WCxSmokWEKUJ","q_I9gQJMVWL_","aZq8uFtOVfnm","qqFzmTb0BKKW","oq-hAEOAV4aZ","uE84hQU7CSZa","57vwo75fcXbU","EYv5uX-MC9KC","gFCXmN9Ien7n","-IZ0P7gdeqIj","H94yp9JOj4cs","_UYZu4uelScF","UPEjMXZlboz2","vOvazq-OWpNB","hzpcKHaDWsG7","STMonBqiWxM5","x-lbQu5G6-3X","FAJ9B0wKNiY3","exJFCDSMNrEG","d9_M19L-OLng","yo0NaFt8fERL","ScTeQapk-3nE","6qFefyY3-92A","4c7jLe_Q3T6F","u_sZBqis3AjT","lt6HzKPyfIqu","gmIvuK3egNqM","zU6vqL67bd5a","84K1UoljYPJ2"],"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.12"}},"nbformat":4,"nbformat_minor":0}