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    "# Lab 8 — Generative Modeling: Probabilistic Models, Autoencoders and Variational Autoencoders\n",
    "\n",
    "**Course:** Mathematical Foundations of Machine and Deep Learning  \n",
    "**Level:** beginner+ / intermediate  \n",
    "**Format:** guided lab in Jupyter/Colab\n",
    "\n",
    "## Content description\n",
    "\n",
    "In this lab, we move from simple probabilistic generative mechanisms to neural generative models.  \n",
    "The lab starts with intuitive probabilistic generation and a Naive Bayes-style toy example. Then it introduces:\n",
    "- autoencoders as compression-and-reconstruction models,\n",
    "- latent representations on Fashion-MNIST,\n",
    "- variational autoencoders as probabilistic latent variable models,\n",
    "- sampling and latent-space exploration,\n",
    "- attribute-based manipulation in the latent space of a face VAE.\n",
    "\n",
    "The original LAB12 cells are preserved. Additional markdown cells propose where the material can be turned into student tasks.\n",
    "\n",
    "---\n"
   ]
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    "## Learning objectives\n",
    "\n",
    "By the end of this lab, students should be able to:\n",
    "\n",
    "1. Explain what it means to model data as samples from an unknown probability distribution.\n",
    "2. Distinguish between feature-based probabilistic generation and neural generative modeling.\n",
    "3. Describe the encoder–latent space–decoder structure of an autoencoder.\n",
    "4. Use a trained decoder to generate new samples from latent vectors.\n",
    "5. Explain the additional probabilistic assumptions introduced by a VAE.\n",
    "6. Interpret latent-space plots and relate them to class structure or semantic attributes.\n",
    "7. Experimentally compare deterministic AE reconstruction with VAE sampling and generation.\n",
    "\n",
    "## Suggested minute-by-minute plan\n",
    "\n",
    "- **10–15 min** — Probabilistic generative models and toy character generator.\n",
    "- **20–25 min** — Autoencoder on Fashion-MNIST: reconstruction and latent embeddings.\n",
    "- **25–30 min** — VAE on Fashion-MNIST: sampling, KL term, generation.\n",
    "- **20–25 min** — VAE on CelebA: faces, latent distribution, image manipulation.\n",
    "- **10 min** — Wrap-up: conceptual comparison AE vs VAE, task discussion, possible mini-projects.\n",
    "\n",
    "---\n"
   ]
  },
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    "## Intro — from probability distributions to generative models\n",
    "\n",
    "A generative model tries to learn or approximate a mechanism that could have produced the observed data.\n",
    "In simple cases, this mechanism can be written explicitly using probabilities over features.\n",
    "In neural generative models, the mechanism is usually represented by learned transformations between data space and latent space.\n",
    "\n",
    "In this first part, keep the focus on intuition:\n",
    "- What is the sample space?\n",
    "- What is being sampled?\n",
    "- Which assumptions make generation possible?\n",
    "- Which dependencies between variables are ignored or simplified?\n"
   ]
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    "### **Probabilistic Generative Models – Summary**\n",
    "\n",
    "**Probabilistic generative models** are a class of statistical models that aim to learn the probability distribution of observed data $p(x)$, or the joint distribution $p(x, z)$, where $z$ represents latent (hidden) variables. These models simulate the process that could have generated the data and offer several key capabilities:\n",
    "\n",
    "* **Generating new samples** that resemble the training data,\n",
    "* **Inferring latent structures or causes**,\n",
    "* **Imputing missing data**,\n",
    "* **Detecting anomalies** based on low-probability observations.\n",
    "\n",
    "Common types of probabilistic generative models include:\n",
    "\n",
    "* **Mixture models** (e.g., Gaussian Mixture Models) – assume data comes from a combination of distributions,\n",
    "* **Naive Bayes** – a simple but effective classifier based on conditional independence,\n",
    "* **Latent Dirichlet Allocation (LDA)** – used for topic modeling in text corpora,\n",
    "* **Hidden Markov Models (HMMs)** – for modeling sequential data with hidden states,\n",
    "* **Variational Autoencoders (VAEs)** – deep learning models that learn a probabilistic encoding-decoding process.\n",
    "\n",
    "These models differ from discriminative models (like logistic regression or SVMs) by learning the **underlying data distribution** rather than just the decision boundary."
   ]
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    "The **sample space** is the set of all possible outcomes that can be observed in a given probabilistic experiment or generative process. In the context of machine learning, it defines the domain from which data points (samples) are drawn and on which the model operates.\n",
    "\n",
    "**Probability density** refers to the value of a **probability density function (PDF)** at a given point, which indicates how likely it is to observe a value in an infinitesimally small region around that point. For continuous random variables, the probability of the variable falling within a specific interval is given by the **area under the PDF curve** over that interval, not by the density value itself.\n",
    "\n",
    "**Parametric modeling** involves assuming that the data follows a specific distribution or functional form characterized by a finite set of parameters. The learning process focuses on estimating these parameters (e.g., mean and variance for a Gaussian) to best fit the observed data, often enabling efficient inference and generalization when the assumptions are appropriate.\n",
    "\n",
    "**Likelihood** is a function that measures how probable the observed data is, given a specific set of model parameters. Unlike probability, which evaluates the chance of data under a known distribution, **likelihood** is used to estimate the parameters of a statistical model based on observed data — the higher the likelihood, the better the parameters explain the data.\n",
    "\n",
    "For independent and identically distributed data points $x_1, x_2, \\dots, x_n$, and parameter vector $\\theta$, the likelihood function is:\n",
    "\n",
    "$$\n",
    "\\mathcal{L}(\\theta; x_1, \\dots, x_n) = \\prod_{i=1}^{n} p(x_i \\mid \\theta)\n",
    "$$\n",
    "\n",
    "Often, we use the **log-likelihood** for convenience:\n",
    "\n",
    "$$\n",
    "\\log \\mathcal{L}(\\theta) = \\sum_{i=1}^{n} \\log p(x_i \\mid \\theta)\n",
    "$$\n",
    "\n",
    "This is commonly maximized in **maximum likelihood estimation (MLE)**.\n",
    "\n",
    "\n",
    "**Maximum Likelihood Estimation (MLE)** is a statistical method used to find the parameter values of a model that maximize the likelihood function — in other words, the parameters that make the observed data most probable under the assumed model.\n",
    "\n",
    "Formally, given data $x_1, x_2, \\dots, x_n$ and a probability model parameterized by $\\theta$, the MLE estimator is:\n",
    "\n",
    "$$\n",
    "\\hat{\\theta}_{\\text{MLE}} = \\underset{\\theta}{\\arg\\max} \\; \\mathcal{L}(\\theta; x_1, \\dots, x_n) = \\underset{\\theta}{\\arg\\max} \\; \\prod_{i=1}^n p(x_i \\mid \\theta)\n",
    "$$\n",
    "\n",
    "In practice, the **log-likelihood** is usually maximized for computational convenience:\n",
    "\n",
    "$$\n",
    "\\hat{\\theta}_{\\text{MLE}} = \\underset{\\theta}{\\arg\\max} \\; \\sum_{i=1}^n \\log p(x_i \\mid \\theta)\n",
    "$$\n",
    "\n",
    "MLE assumes that the data comes from a known distribution and uses the observed sample to infer the most plausible parameter values.\n"
   ]
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    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import matplotlib.lines as mlines\n",
    "\n",
    "# Function drawing one item\n",
    "def draw_character(ax, character, x_offset=0, y_offset=0):\n",
    "    # Head\n",
    "    head = patches.Circle((x_offset, y_offset + 4), 0.5, facecolor='yellow', edgecolor='black')\n",
    "    ax.add_patch(head)\n",
    "\n",
    "    # Hair\n",
    "    if character[\"hairStyle\"] == \"long\":\n",
    "        hair = patches.Circle((x_offset, y_offset + 4), 0.65, facecolor=character[\"hairColor\"], alpha=0.6)\n",
    "        ax.add_patch(hair)\n",
    "    else:\n",
    "        hair = patches.Arc((x_offset, y_offset + 4.25), 1.0, 0.5, theta1=0, theta2=180,\n",
    "                           color=character[\"hairColor\"], linewidth=5)\n",
    "        ax.add_patch(hair)\n",
    "\n",
    "    # Eyes\n",
    "    eye_y = y_offset + 4.15\n",
    "    left_eye = (x_offset - 0.2, eye_y)\n",
    "    right_eye = (x_offset + 0.2, eye_y)\n",
    "    ax.plot([left_eye[0], right_eye[0]], [eye_y, eye_y], 'o',\n",
    "            color=character[\"eyeColor\"], markersize=6)\n",
    "\n",
    "    # Glasses\n",
    "    if character[\"glasses\"] == \"correction\":\n",
    "        ax.plot([left_eye[0], right_eye[0]], [eye_y, eye_y], 'o',\n",
    "                color='black', markersize=10, fillstyle='none')\n",
    "        ax.plot([left_eye[0], right_eye[0]], [eye_y, eye_y], '-', color='black')\n",
    "    elif character[\"glasses\"] == \"sunglasses\":\n",
    "        ax.plot([left_eye[0], right_eye[0]], [eye_y, eye_y], 'o',\n",
    "                color='black', markersize=10)\n",
    "        ax.plot([left_eye[0], right_eye[0]], [eye_y, eye_y], '-', color='black')\n",
    "\n",
    "    # Mouth\n",
    "    mouth = patches.Arc((x_offset, y_offset + 3.8), 0.3, 0.2, theta1=200, theta2=340,\n",
    "                        color='black', linewidth=2)\n",
    "    ax.add_patch(mouth)\n",
    "\n",
    "    # Short sleeves (arms as part of shirt)\n",
    "    sleeve_height = 0.4\n",
    "    sleeve_width = 0.3\n",
    "    left_sleeve = patches.Rectangle((x_offset - 0.8, y_offset + 3.3 - sleeve_height / 2),\n",
    "                                    sleeve_width, sleeve_height,\n",
    "                                    facecolor=character[\"clothesColor\"], edgecolor='black')\n",
    "    right_sleeve = patches.Rectangle((x_offset + 0.5, y_offset + 3.3 - sleeve_height / 2),\n",
    "                                     sleeve_width, sleeve_height,\n",
    "                                     facecolor=character[\"clothesColor\"], edgecolor='black')\n",
    "    ax.add_patch(left_sleeve)\n",
    "    ax.add_patch(right_sleeve)\n",
    "\n",
    "    # Body\n",
    "    body = patches.Rectangle((x_offset - 0.5, y_offset + 2.3), 1, 1.2,\n",
    "                             facecolor=character[\"clothesColor\"], edgecolor='black')\n",
    "    ax.add_patch(body)\n",
    "\n",
    "\n",
    "# A function that draws n figures in a grid\n",
    "def plot_n_characters(characters):\n",
    "    cols = min(len(characters), 12)\n",
    "    rows = (len(characters) + cols - 1) // cols\n",
    "    spacing_x = 3\n",
    "    spacing_y = 5.5\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(cols * 2.5, rows * 3))\n",
    "    ax.set_aspect('equal')\n",
    "    ax.set_xlim(-1, cols * spacing_x)\n",
    "    ax.set_ylim(-1, rows * spacing_y)\n",
    "    ax.axis('off')\n",
    "\n",
    "    for i, character in enumerate(characters):\n",
    "        col = i % cols\n",
    "        row = i // cols\n",
    "        x = col * spacing_x + 1\n",
    "        y = (rows - 1 - row) * spacing_y  # rysuj od góry\n",
    "        draw_character(ax, character, x_offset=x, y_offset=y)\n",
    "\n",
    "    plt.suptitle(f\"{len(characters)} Generated Characters\", fontsize=16)\n",
    "    plt.show()"
   ]
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   "source": [
    "import random\n",
    "\n",
    "def generate_random_character_with_probabilities():\n",
    "    options = {\n",
    "        \"glasses\": {\n",
    "            \"values\": [\"sunglasses\", \"correction\", \"no\"],\n",
    "            \"probs\": [0.1, 0.3, 0.6]\n",
    "        },\n",
    "        \"clothesColor\": {\n",
    "            \"values\": [\"red\", \"blue\", \"green\", \"black\", \"white\"],\n",
    "            \"probs\": [0.1, 0.2, 0.2, 0.3, 0.2]\n",
    "        },\n",
    "        \"eyeColor\": {\n",
    "            \"values\": [\"brown\", \"blue\", \"green\"],\n",
    "            \"probs\": [0.5, 0.3, 0.2]\n",
    "        },\n",
    "        \"hairColor\": {\n",
    "            \"values\": [\"orange\", \"brown\", \"black\", \"red\"],\n",
    "            \"probs\": [0.2, 0.4, 0.3, 0.1]\n",
    "        },\n",
    "        \"hairStyle\": {\n",
    "            \"values\": [\"short\", \"long\"],\n",
    "            \"probs\": [0.7, 0.3]\n",
    "        }\n",
    "    }\n",
    "\n",
    "    # Choose options with probability density\n",
    "    random_character = {\n",
    "        key: random.choices(info[\"values\"], weights=info[\"probs\"], k=1)[0]\n",
    "        for key, info in options.items()\n",
    "    }\n",
    "\n",
    "    return random_character"
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'glasses': 'correction', 'clothesColor': 'black', 'eyeColor': 'brown', 'hairColor': 'brown', 'hairStyle': 'short'}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 180x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# example of usage\n",
    "character = generate_random_character_with_probabilities()\n",
    "print(character)\n",
    "plot_n_characters([character])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T19:57:00.560449Z",
     "start_time": "2026-05-05T19:56:58.556728Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Gxth1z6KP9D6",
    "outputId": "f9c9d148-c0ff-4da3-c0dc-72329ace32a6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run for 120 examples\n",
    "my_characters = [generate_random_character_with_probabilities() for i in range(120)]\n",
    "plot_n_characters(my_characters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODULE 1 — Naive probabilistic generation and feature assumptions\n",
    "\n",
    "This module introduces a simple feature-based generative model.  \n",
    "The didactic goal is not to build a production-quality generator, but to make the assumptions visible:\n",
    "- features are represented explicitly,\n",
    "- probabilities can be estimated from observed examples,\n",
    "- new objects can be sampled from these estimated distributions,\n",
    "- independence assumptions simplify the model but may produce unrealistic combinations.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "24yG29GNTov8"
   },
   "source": [
    "### **Naive Bayes Model – Description**\n",
    "\n",
    "**Naive Bayes** is a family of simple yet effective **probabilistic classifiers** based on applying **Bayes’ Theorem** with the strong (naive) assumption of **feature independence** given the class label.\n",
    "\n",
    "It estimates the **posterior probability** of a class $y$ given features $x_1, x_2, ..., x_n$ using:\n",
    "\n",
    "$$\n",
    "P(y \\mid x_1, x_2, ..., x_n) \\propto P(y) \\prod_{i=1}^{n} P(x_i \\mid y)\n",
    "$$\n",
    "\n",
    "This means the model:\n",
    "\n",
    "* Computes the prior probability of each class $P(y)$,\n",
    "* Computes the likelihood of each feature given the class $P(x_i \\mid y)$,\n",
    "* Assumes **all features are conditionally independent** given the class.\n",
    "\n",
    "###  Types of Naive Bayes:\n",
    "\n",
    "* **Gaussian Naive Bayes** – assumes continuous features follow a normal distribution,\n",
    "* **Multinomial Naive Bayes** – for discrete count data (e.g. word frequencies in text),\n",
    "* **Bernoulli Naive Bayes** – for binary/boolean features (e.g. word present or not).\n",
    "\n",
    "###  Advantages:\n",
    "\n",
    "* Very **fast** to train and predict,\n",
    "* Performs surprisingly well on **high-dimensional data** (e.g. text classification),\n",
    "* Robust to **irrelevant features**.\n",
    "\n",
    "###  Limitations:\n",
    "\n",
    "* Assumes **feature independence**, which is often not true in real-world data,\n",
    "* Performance can degrade when features are strongly correlated.\n",
    "\n",
    "Despite its simplicity, Naive Bayes is widely used in spam detection, document classification, and recommendation systems due to its **efficiency and effectiveness**.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aHfQMQudTxnz"
   },
   "source": [
    "\n",
    "###  **Chain Rule of Probability (Feature-Only Version)**\n",
    "\n",
    "For a set of features $x_1, x_2, \\dots, x_n$, the **joint probability** can be expressed using the **chain rule** as:\n",
    "\n",
    "$$\n",
    "P(x_1, x_2, \\dots, x_n) = P(x_1) \\cdot P(x_2 \\mid x_1) \\cdot P(x_3 \\mid x_1, x_2) \\cdots P(x_n \\mid x_1, \\dots, x_{n-1})\n",
    "$$\n",
    "\n",
    "This form captures **all conditional dependencies** between features — but is **computationally complex** and requires a large amount of data to estimate accurately.\n",
    "\n",
    "---\n",
    "\n",
    "###  **Naive Independence Assumption (Without $y$)**\n",
    "\n",
    "The **naive assumption** simplifies this by assuming that **all features are mutually independent**:\n",
    "\n",
    "$$\n",
    "\\forall i, j \\quad x_i \\perp x_j \\quad \\text{(i.e., features are independent)}\n",
    "$$\n",
    "\n",
    "So, the joint distribution becomes:\n",
    "\n",
    "$$\n",
    "P(x_1, x_2, \\dots, x_n) \\approx \\prod_{i=1}^{n} P(x_i)\n",
    "$$\n",
    "\n",
    "This eliminates the need to model any conditional relationships between the features — it treats each feature as contributing independently to the overall probability.\n",
    "\n",
    "---\n",
    "\n",
    "### ️ **Comparison**\n",
    "\n",
    "| Approach         | Joint Distribution   ........................................................................................                                                        |\n",
    "| ---------------- | -------------------------------------------------------------------------------------------------- |\n",
    "| Full chain rule  | $P(x_1, x_2, x_3) = P(x_1) \\cdot P(x_2 \\mid x_1) \\cdot P(x_3 \\mid x_1, x_2)$ |\n",
    "| Naive assumption | $P(x_1, x_2, x_3) \\approx P(x_1) \\cdot P(x_2) \\cdot P(x_3)$                  |\n",
    "\n",
    "---\n",
    "\n",
    "### ✅ Notes\n",
    "\n",
    "* This independence assumption is rarely true in real-world data — features are usually **correlated**.\n",
    "* However, in practice (e.g. in text classification), Naive Bayes often performs well because **relative differences in feature likelihoods still help separate classes effectively**.\n",
    "* The assumption makes modeling **tractable, interpretable, and fast**.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task candidate 2 — Build and inspect a small tabular dataset\n",
    "\n",
    "The next cells define or inspect a small collection of generated characters.  \n",
    "Possible student task:\n",
    "\n",
    "1. Convert the list of characters into a DataFrame.\n",
    "2. Count feature values.\n",
    "3. Estimate marginal distributions for selected features.\n",
    "4. Decide which features should be treated as categorical and which as numerical.\n",
    "\n",
    "**Expected takeaway:** Generative modeling starts with assumptions about the representation of data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T19:57:08.529512Z",
     "start_time": "2026-05-05T19:57:06.995163Z"
    },
    "id": "P_RooBHZVrPC"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T19:57:09.870428Z",
     "start_time": "2026-05-05T19:57:09.741714Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "3WeeivkFVtBV",
    "outputId": "cafe11b8-3b63-47cc-b59b-fc909f70c7a8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>glasses</th>\n",
       "      <th>clothesColor</th>\n",
       "      <th>eyeColor</th>\n",
       "      <th>hairColor</th>\n",
       "      <th>hairStyle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no</td>\n",
       "      <td>blue</td>\n",
       "      <td>brown</td>\n",
       "      <td>red</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>no</td>\n",
       "      <td>white</td>\n",
       "      <td>brown</td>\n",
       "      <td>black</td>\n",
       "      <td>long</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>no</td>\n",
       "      <td>green</td>\n",
       "      <td>brown</td>\n",
       "      <td>black</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sunglasses</td>\n",
       "      <td>white</td>\n",
       "      <td>brown</td>\n",
       "      <td>brown</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>correction</td>\n",
       "      <td>green</td>\n",
       "      <td>brown</td>\n",
       "      <td>brown</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>sunglasses</td>\n",
       "      <td>black</td>\n",
       "      <td>green</td>\n",
       "      <td>brown</td>\n",
       "      <td>long</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>no</td>\n",
       "      <td>black</td>\n",
       "      <td>brown</td>\n",
       "      <td>black</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>no</td>\n",
       "      <td>white</td>\n",
       "      <td>brown</td>\n",
       "      <td>red</td>\n",
       "      <td>long</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>no</td>\n",
       "      <td>white</td>\n",
       "      <td>brown</td>\n",
       "      <td>orange</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>correction</td>\n",
       "      <td>white</td>\n",
       "      <td>green</td>\n",
       "      <td>red</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        glasses clothesColor eyeColor hairColor hairStyle\n",
       "0            no         blue    brown       red     short\n",
       "1            no        white    brown     black      long\n",
       "2            no        green    brown     black     short\n",
       "3    sunglasses        white    brown     brown     short\n",
       "4    correction        green    brown     brown     short\n",
       "..          ...          ...      ...       ...       ...\n",
       "115  sunglasses        black    green     brown      long\n",
       "116          no        black    brown     black     short\n",
       "117          no        white    brown       red      long\n",
       "118          no        white    brown    orange     short\n",
       "119  correction        white    green       red     short\n",
       "\n",
       "[120 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(my_characters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T19:57:16.823583Z",
     "start_time": "2026-05-05T19:57:16.774473Z"
    },
    "id": "dmdbuqJPVvMw"
   },
   "outputs": [],
   "source": [
    "def get_feature_distributions(df, drop_columns=[]):\n",
    "    stats = []\n",
    "\n",
    "    for col in df.columns:\n",
    "        if col in drop_columns:\n",
    "            continue\n",
    "        value_counts = df[col].value_counts(normalize=False)\n",
    "        total = len(df)\n",
    "\n",
    "        for value, count in value_counts.items():\n",
    "            prob = count / total\n",
    "            stats.append({\n",
    "                \"Feature\": col,\n",
    "                \"Value\": value,\n",
    "                \"Count\": count,\n",
    "                \"Probability\": round(prob, 4)\n",
    "            })\n",
    "\n",
    "    return pd.DataFrame(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T19:57:16.896587Z",
     "start_time": "2026-05-05T19:57:16.824469Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 582
    },
    "id": "otg48xQZWyr6",
    "outputId": "45f90866-3187-4483-a7a8-34f7fbdb9666"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Feature</th>\n",
       "      <th>Value</th>\n",
       "      <th>Count</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>glasses</td>\n",
       "      <td>no</td>\n",
       "      <td>60</td>\n",
       "      <td>0.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>glasses</td>\n",
       "      <td>correction</td>\n",
       "      <td>43</td>\n",
       "      <td>0.3583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>glasses</td>\n",
       "      <td>sunglasses</td>\n",
       "      <td>17</td>\n",
       "      <td>0.1417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>clothesColor</td>\n",
       "      <td>green</td>\n",
       "      <td>29</td>\n",
       "      <td>0.2417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>clothesColor</td>\n",
       "      <td>white</td>\n",
       "      <td>27</td>\n",
       "      <td>0.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>clothesColor</td>\n",
       "      <td>black</td>\n",
       "      <td>24</td>\n",
       "      <td>0.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>clothesColor</td>\n",
       "      <td>blue</td>\n",
       "      <td>21</td>\n",
       "      <td>0.1750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>clothesColor</td>\n",
       "      <td>red</td>\n",
       "      <td>19</td>\n",
       "      <td>0.1583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>eyeColor</td>\n",
       "      <td>brown</td>\n",
       "      <td>59</td>\n",
       "      <td>0.4917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>eyeColor</td>\n",
       "      <td>blue</td>\n",
       "      <td>34</td>\n",
       "      <td>0.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>eyeColor</td>\n",
       "      <td>green</td>\n",
       "      <td>27</td>\n",
       "      <td>0.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>hairColor</td>\n",
       "      <td>brown</td>\n",
       "      <td>54</td>\n",
       "      <td>0.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>hairColor</td>\n",
       "      <td>black</td>\n",
       "      <td>28</td>\n",
       "      <td>0.2333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>hairColor</td>\n",
       "      <td>orange</td>\n",
       "      <td>24</td>\n",
       "      <td>0.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>hairColor</td>\n",
       "      <td>red</td>\n",
       "      <td>14</td>\n",
       "      <td>0.1167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>hairStyle</td>\n",
       "      <td>short</td>\n",
       "      <td>82</td>\n",
       "      <td>0.6833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>hairStyle</td>\n",
       "      <td>long</td>\n",
       "      <td>38</td>\n",
       "      <td>0.3167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Feature       Value  Count  Probability\n",
       "0        glasses          no     60       0.5000\n",
       "1        glasses  correction     43       0.3583\n",
       "2        glasses  sunglasses     17       0.1417\n",
       "3   clothesColor       green     29       0.2417\n",
       "4   clothesColor       white     27       0.2250\n",
       "5   clothesColor       black     24       0.2000\n",
       "6   clothesColor        blue     21       0.1750\n",
       "7   clothesColor         red     19       0.1583\n",
       "8       eyeColor       brown     59       0.4917\n",
       "9       eyeColor        blue     34       0.2833\n",
       "10      eyeColor       green     27       0.2250\n",
       "11     hairColor       brown     54       0.4500\n",
       "12     hairColor       black     28       0.2333\n",
       "13     hairColor      orange     24       0.2000\n",
       "14     hairColor         red     14       0.1167\n",
       "15     hairStyle       short     82       0.6833\n",
       "16     hairStyle        long     38       0.3167"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_feature_distributions(pd.DataFrame(my_characters))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3 — Generate new objects from estimated feature statistics\n",
    "\n",
    "Generate function `generate_character_from_stats` wchich generate new character from given distribution `df_stats`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T19:57:31.013635Z",
     "start_time": "2026-05-05T19:57:30.989238Z"
    },
    "id": "BcP4PoHIW2q9"
   },
   "outputs": [],
   "source": [
    "def generate_character_from_stats(df_stats):\n",
    "    character = {}\n",
    "\n",
    "    df_stats = df_stats.copy()\n",
    "    df_stats['Probability'] = df_stats['Probability'].astype(float)\n",
    "\n",
    "    for feature in df_stats['Feature'].unique():\n",
    "        subset = df_stats[df_stats['Feature'] == feature]\n",
    "\n",
    "        values = subset['Value'].tolist()\n",
    "        probs = subset['Probability'].tolist()\n",
    "\n",
    "        if not values or not probs or sum(probs) == 0:\n",
    "            continue  # pomiń błędne dane\n",
    "\n",
    "        selected = random.choices(values, weights=probs, k=1)[0]\n",
    "        character[feature] = selected\n",
    "\n",
    "    return character"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T19:57:31.988353Z",
     "start_time": "2026-05-05T19:57:31.688073Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "id": "wZh3swugXkVN",
    "outputId": "1064e87a-5d78-45a3-db89-b9b85a5df834"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run for 12 new characters\n",
    "my_characters2 = [generate_character_from_stats(get_feature_distributions(pd.DataFrame(my_characters))) for i in range(12)]\n",
    "plot_n_characters(my_characters2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define a function wchich help us in next steps of generation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T19:57:33.434970Z",
     "start_time": "2026-05-05T19:57:33.404482Z"
    },
    "id": "F9wH8hvNXsC2"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\n",
    "import numpy as np\n",
    "\n",
    "def draw_character_to_array(character, figsize=(2, 3), dpi=100):\n",
    "    fig, ax = plt.subplots(figsize=figsize, dpi=dpi)\n",
    "    canvas = FigureCanvas(fig)\n",
    "    ax.set_aspect('equal')\n",
    "    ax.set_xlim(-2, 2)\n",
    "    ax.set_ylim(-1, 5)\n",
    "    ax.axis('off')\n",
    "\n",
    "    # HEAD\n",
    "    ax.add_patch(patches.Circle((0, 4), 0.5, facecolor='yellow', edgecolor='black'))\n",
    "\n",
    "    # HAIR\n",
    "    if character[\"hairStyle\"] == \"long\":\n",
    "        ax.add_patch(patches.Circle((0, 4), 0.65, facecolor=character[\"hairColor\"], alpha=0.6))\n",
    "    else:\n",
    "        ax.add_patch(patches.Arc((0, 4.25), 1.0, 0.5, theta1=0, theta2=180,\n",
    "                                 color=character[\"hairColor\"], linewidth=5))\n",
    "\n",
    "    # EYES\n",
    "    eye_y = 4.15\n",
    "    ax.plot([-0.2, 0.2], [eye_y, eye_y], 'o', color=character[\"eyeColor\"], markersize=6)\n",
    "\n",
    "    # GLASSES\n",
    "    if character[\"glasses\"] == \"correction\":\n",
    "        ax.plot([-0.2, 0.2], [eye_y, eye_y], 'o', color='black', markersize=10, fillstyle='none')\n",
    "        ax.plot([-.2, .2], [eye_y, eye_y], '-', color='black')\n",
    "    elif character[\"glasses\"] == \"sunglasses\":\n",
    "        ax.plot([-0.2, 0.2], [eye_y, eye_y], 'o', color='black', markersize=10)\n",
    "        ax.plot([-.2, .2], [eye_y, eye_y], '-', color='black')\n",
    "\n",
    "    # MOUTH\n",
    "    ax.add_patch(patches.Arc((0, 3.8), 0.3, 0.2, theta1=200, theta2=340, color='black', linewidth=2))\n",
    "\n",
    "    # BODY\n",
    "    ax.add_patch(patches.Rectangle((-0.5, 2.3), 1, 1.2, facecolor=character[\"clothesColor\"], edgecolor='black'))\n",
    "\n",
    "    # SHORT SLEEVES\n",
    "    ax.add_patch(patches.Rectangle((-0.8, 3.1), 0.3, 0.4, facecolor=character[\"clothesColor\"], edgecolor='black'))\n",
    "    ax.add_patch(patches.Rectangle((0.5, 3.1), 0.3, 0.4, facecolor=character[\"clothesColor\"], edgecolor='black'))\n",
    "\n",
    "    # ARMS\n",
    "    ax.plot([-1, -0.8], [3.3, 3.3], '-', color='black', linewidth=3)\n",
    "    ax.plot([0.8, 1], [3.3, 3.3], '-', color='black', linewidth=3)\n",
    "\n",
    "    # RENDER to pixel array\n",
    "    canvas.draw()\n",
    "    img = np.frombuffer(canvas.buffer_rgba(), dtype=np.uint8)\n",
    "    width, height = canvas.get_width_height()\n",
    "    img = img.reshape((height, width, 4))[:, :, :3]  # Remove alpha\n",
    "    plt.close(fig)\n",
    "\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T19:57:35.891308Z",
     "start_time": "2026-05-05T19:57:35.784629Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0HX2IaascJcw",
    "outputId": "ab0db6f6-3db0-4a2b-e579-884eb1415296"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(300, 200, 3)\n"
     ]
    }
   ],
   "source": [
    "pixel_image = draw_character_to_array(character)\n",
    "print(pixel_image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T19:57:36.668681Z",
     "start_time": "2026-05-05T19:57:36.574411Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "bDv0lUV8cL3w",
    "outputId": "075b3df8-8260-4d92-f231-4880c19ce4f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        ...,\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255]],\n",
       "\n",
       "       [[255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        ...,\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255]],\n",
       "\n",
       "       [[255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        ...,\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        ...,\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255]],\n",
       "\n",
       "       [[255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        ...,\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255]],\n",
       "\n",
       "       [[255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        ...,\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255]]], dtype=uint8)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pixel_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T19:57:50.534704Z",
     "start_time": "2026-05-05T19:57:50.491170Z"
    },
    "id": "7HoFMmMYcdzg"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from collections import defaultdict, Counter\n",
    "\n",
    "def get_rgb_pixel_distributions(images):\n",
    "    h, w, _ = images[0].shape\n",
    "    pixel_stats = []\n",
    "\n",
    "    channel_map = {0: \"R\", 1: \"G\", 2: \"B\"}\n",
    "\n",
    "    for i in range(h):\n",
    "        for j in range(w):\n",
    "            for c in range(3):\n",
    "                values = [img[i, j, c] for img in images]\n",
    "                counts = Counter(values)\n",
    "                total = sum(counts.values())\n",
    "\n",
    "                for val, count in counts.items():\n",
    "                    pixel_stats.append({\n",
    "                        \"PixelPosition\": f\"{i},{j}\",\n",
    "                        \"Channel\": channel_map[c],\n",
    "                        \"Value\": int(val),\n",
    "                        \"Count\": count,\n",
    "                        \"Probability\": round(count / total, 5)\n",
    "                    })\n",
    "\n",
    "    return pd.DataFrame(pixel_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T19:57:51.378742Z",
     "start_time": "2026-05-05T19:57:51.358377Z"
    },
    "id": "-yOAZADNc7mY"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "\n",
    "def generate_rgb_image_from_distribution(df_stats, shape):\n",
    "    h, w = shape\n",
    "    img = np.zeros((h, w, 3), dtype=np.uint8)\n",
    "\n",
    "    channel_map = {\"R\": 0, \"G\": 1, \"B\": 2}\n",
    "\n",
    "    for i in range(h):\n",
    "        for j in range(w):\n",
    "            for ch_name, ch_idx in channel_map.items():\n",
    "                subset = df_stats[\n",
    "                    (df_stats[\"PixelPosition\"] == f\"{i},{j}\") &\n",
    "                    (df_stats[\"Channel\"] == ch_name)\n",
    "                ]\n",
    "\n",
    "                if subset.empty:\n",
    "                    img[i, j, ch_idx] = 255  # default white\n",
    "                    continue\n",
    "\n",
    "                values = subset[\"Value\"].astype(int).tolist()\n",
    "                probs = subset[\"Probability\"].astype(float).tolist()\n",
    "\n",
    "                img[i, j, ch_idx] = random.choices(values, weights=probs, k=1)[0]\n",
    "\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T19:58:16.952422Z",
     "start_time": "2026-05-05T19:58:14.031720Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XCDxRRusc5WU",
    "outputId": "532c3d6f-1735-40fd-c900-c563855141a4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  PixelPosition Channel  Value  Count  Probability\n",
      "0           0,0       R    255    100          1.0\n",
      "1           0,0       G    255    100          1.0\n",
      "2           0,0       B    255    100          1.0\n",
      "3           0,1       R    255    100          1.0\n",
      "4           0,1       G    255    100          1.0\n"
     ]
    }
   ],
   "source": [
    "images_rgb = []\n",
    "for _ in range(100):\n",
    "    char = generate_random_character_with_probabilities()\n",
    "    img = draw_character_to_array(char, dpi=28)  # wynik: (H, W, 3)\n",
    "    images_rgb.append(img)\n",
    "\n",
    "rgb_pixel_stats = get_rgb_pixel_distributions(images_rgb)\n",
    "\n",
    "print(rgb_pixel_stats.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T19:58:17.380770Z",
     "start_time": "2026-05-05T19:58:16.996084Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vAhi623WgvGM",
    "outputId": "b36c5ab1-77dc-43fa-e414-604773e69b4d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      PixelPosition Channel  Value  Count  Probability\n",
      "0               0,0       R    255    100          1.0\n",
      "1               0,0       G    255    100          1.0\n",
      "2               0,0       B    255    100          1.0\n",
      "3               0,1       R    255    100          1.0\n",
      "4               0,1       G    255    100          1.0\n",
      "...             ...     ...    ...    ...          ...\n",
      "16768         83,54       G    255    100          1.0\n",
      "16769         83,54       B    255    100          1.0\n",
      "16770         83,55       R    255    100          1.0\n",
      "16771         83,55       G    255    100          1.0\n",
      "16772         83,55       B    255    100          1.0\n",
      "\n",
      "[16773 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "print(rgb_pixel_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T20:04:56.468287Z",
     "start_time": "2026-05-05T20:04:14.706536Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "id": "2pGC0AzUdS9y",
    "outputId": "c3e86ed2-4872-45c6-ebbb-5d901f775af2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "rgb_img = generate_rgb_image_from_distribution(rgb_pixel_stats, shape=(83,51))\n",
    "\n",
    "# 3. Wyświetl\n",
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(rgb_img)\n",
    "plt.axis('off')\n",
    "plt.title(\"RGB image generated from pixel distributions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODULE 2 — Autoencoders on Fashion-MNIST\n",
    "\n",
    "This module introduces autoencoders as deterministic neural models that learn to compress and reconstruct images.  \n",
    "The core structure is:\n",
    "\n",
    "1. **Encoder:** image → latent vector.\n",
    "2. **Latent space:** low-dimensional representation.\n",
    "3. **Decoder:** latent vector → reconstructed image.\n",
    "\n",
    "Compared with the previous probabilistic toy model, the representation is now learned from data rather than manually specified.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VTWumuzXA-X8"
   },
   "source": [
    "# 👖 Autoencoders on Fashion MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T20:05:00.076693Z",
     "start_time": "2026-05-05T20:04:56.500780Z"
    },
    "id": "7JXcpTrIAgJn"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:50:23.369337: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2026-05-05 22:50:23.571607: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2026-05-05 22:50:23.572826: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2026-05-05 22:50:24.636717: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tensorflow.keras import layers, models, datasets, callbacks\n",
    "import tensorflow.keras.backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T20:05:06.537291Z",
     "start_time": "2026-05-05T20:05:06.500468Z"
    },
    "id": "zS6Ko3PVBuqH"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def sample_batch(dataset):\n",
    "    batch = dataset.take(1).get_single_element()\n",
    "    if isinstance(batch, tuple):\n",
    "        batch = batch[0]\n",
    "    return batch.numpy()\n",
    "\n",
    "\n",
    "def display(\n",
    "    images, n=10, size=(20, 3), cmap=\"gray_r\", as_type=\"float32\", save_to=None\n",
    "):\n",
    "    \"\"\"\n",
    "    Displays n random images from each one of the supplied arrays.\n",
    "    \"\"\"\n",
    "    if images.max() > 1.0:\n",
    "        images = images / 255.0\n",
    "    elif images.min() < 0.0:\n",
    "        images = (images + 1.0) / 2.0\n",
    "\n",
    "    plt.figure(figsize=size)\n",
    "    for i in range(n):\n",
    "        _ = plt.subplot(1, n, i + 1)\n",
    "        plt.imshow(images[i].astype(as_type), cmap=cmap)\n",
    "        plt.axis(\"off\")\n",
    "\n",
    "    if save_to:\n",
    "        plt.savefig(save_to)\n",
    "        print(f\"\\nSaved to {save_to}\")\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fh93wgINBDYh"
   },
   "source": [
    "## 0. Parameters <a name=\"parameters\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T20:05:07.137097Z",
     "start_time": "2026-05-05T20:05:07.111363Z"
    },
    "id": "8cGc316edTRB"
   },
   "outputs": [],
   "source": [
    "IMAGE_SIZE = 32\n",
    "CHANNELS = 1\n",
    "BATCH_SIZE = 100\n",
    "BUFFER_SIZE = 1000\n",
    "VALIDATION_SPLIT = 0.2\n",
    "EMBEDDING_DIM = 2\n",
    "EPOCHS = 30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WbUN0su0BHRT"
   },
   "source": [
    "## 1. Prepare the data <a name=\"prepare\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "Wx1qYR_UBFk0"
   },
   "outputs": [],
   "source": [
    "# Load the data\n",
    "(x_train, y_train), (x_test, y_test) = datasets.fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "CSM68zDgKaA3"
   },
   "outputs": [],
   "source": [
    "# # Load the data clasic MNIST\n",
    "# (x_train, y_train), (x_test, y_test) = datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "VAg9Zt1nBJ-a"
   },
   "outputs": [],
   "source": [
    "# Preprocess the data\n",
    "\n",
    "\n",
    "def preprocess(imgs):\n",
    "    \"\"\"\n",
    "    Normalize and reshape the images\n",
    "    \"\"\"\n",
    "    imgs = imgs.astype(\"float32\") / 255.0\n",
    "    imgs = np.pad(imgs, ((0, 0), (2, 2), (2, 2)), constant_values=0.0)\n",
    "    imgs = np.expand_dims(imgs, -1)\n",
    "    return imgs\n",
    "\n",
    "\n",
    "x_train = preprocess(x_train)\n",
    "x_test = preprocess(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 149
    },
    "id": "t81hL9nkBQiT",
    "outputId": "932de9d2-8a3f-426a-d49a-c06386b32c2e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show some items of clothing from the training set\n",
    "display(x_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RlVPRCMBB2OO"
   },
   "source": [
    "## 2. Build the autoencoder <a name=\"build\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BDgkEli8K3tH"
   },
   "source": [
    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VnKNDCUItVGEDBSxzTiJU5ZEFArEsQChCR4OItxClxKJuAxR777RIe+Gka4gAiIgAiIgAiIgAiIgAiIgAiLQGQkgD7IMgSMBYXPs2LE2CkuxY65xBa+6xfieIIkk/+c//5mqKIVOAdnTBElOESE5JSBXUhZhk0iuykknA+IwOU03YQIsE+omn1pBBG3THThvg5Gw9YGjKS8QrvFzafqL++67jw6YD0S6gRAdbNrGQn+on6MFWvTFXgaI5E7T1gQ10z1qIL85qiRnRgjWH4y3StEQrCifuA2Y4aG5IT9d9EtZd/1TizDC0hI3sDPPPJMM3Mgvv/ySCLwMGbocaoMpkdq62mBxMgPOslEEeweOZCB/xtE/teJks5x2qqMIiIAIiIAIiIAIiIAIiIAIiEDnJYAsjc8CJEQCo8C6ARHaZOk8B2UiOvIpRgRWxFYlkF5elnRciEjKJZpgxt1EVJM982yiBdlMbsWTAO4RTTFhPgpN6WCd4RgUgRu24om/SUm5gB1uc0VDUGg3ew+GjXKl4Qgbprjb76lhlu63NBoUrBi85SE5rRiCNdCKaV9I5MHCSMG0G6R7D5h7wixOBIOHYFnFRUAEREAEREAEREAEREAEREAEFg4CTD9vuummjAUJEYGcZf5EEDabOzp8MbIGn1JM+x911FFImbgzQGJF1DSJk0sWc6ee7UNzm8g/P034mRkOLh4QvfEHibLA1xdYJHj0iwQjvszuFwxebVk83bmWlW9WKSwuyG9qpHwK2oBNN8MCCrQMaInQOPhMiZCHW+isPqL1LFJgRJq1Qh4XIk5PE7zf9MRUDHYpny4pjwiIgAiIgAiIgAiIgAiIgAiIQOcigOTIYgdmmpEosWhA9WDSImKiWSIwnKxidjDx448/ZhWDDRy3AFgumIRLJSSaUOkEz1RoU0QmVjMQjCzo5Prrr4+6gUG5pRzeAgJaN8GZ7rjuBdYTtGnHrPI2VzQwHoR5jlOnTmVnClq1W0VKw2CKA0u3/gGOCO4Z0S9QkA1IAIqywOkIEgnyc7THhaUTxP0AZbLhRZLiZCaQjTiVE7G4cbdEv2BGhKsKIiACIiACIiACIiACIiACIiACnZSA7RuAS0XEQERxdpTEqQFWCTg+QPpjUCYDBiM2UqRIi2D+gGT6n//8x2bB2aaAIuYH0M9jOXMdTfbkiJTqLetP6yYogpTKJdN9+EqQXFUF0xkaA0FXQsdQMbAIgKromOWx0ZHCqTl08MuSmRZtIt/aZeY+Y/LeNrCgiE3Pc0qcgobLh+bXGYy0uaIh2Fiz4gyGewAv7hxaAzx2oGs48MADwUEAFgEixDmSjRCsn9vDViI8OqQTJwDRWHh6Bqd0QP+ECgNFFFXlAytYv+IiIAIiIAIiIAIiIAIiIAIiIALFT4ClDXRys80222abbRAMiV9zzTWIgXg3YMNBBEYkRJMWbSy+CI2wSQrSYkmsZNq0aa+++irCdu/evffaay/SEVf9/ERMGs8lV9pKDVMKOBnWs8cnQjoqAPqAjgDJ1y9uNTdypFdcRWeBL0nUDWO/HYtLRBZQUBtjoSoGaIPNmGW3OukJV8lGcfKTyMy9P3lvKf60PVfJ6esj6KRV0sixXRUNBnfAgAGNdMi/5Na6RCKMx+4uKgPie+65J6MiwsgBlyTongsX98sS4VaZHQv4iBPgws2gNp4SPkTmV84nMU+HEcHKFRcBERABERABERABERABERABEegUBEwmRxp//PHH2SSRPv/0008HH3IwUiTCIEekQkLWsZiYSbZTTjmFfTFRLvzf//0fUnrWzI0kop6gG3gDQBFAizW1nuV+rXMsiHiLpIxQyyXycDQRuJHauGRyMfnRL9C9p55+CpXBcccdh8WB1UAiEQRhUzQQyaiwb9++FLfZfS45mbmkhBQCEVLQqtATAuoMKqGrVO6bY2TUlnGaXdFgehSyMuaMAq05NRedw4YN87elbKR++gBuRkWLF110EUO9+uqrbeQkgszAMVqLEwn2Df3CbbfdBrWMdPL4liTfff8dpEgxTQ8RTq3FYFWKi4AIiIAIiIAIiIAIiIAIiIAIdEYCJtsi289fMB/Z8Ouvv0ZIRGy+9957r7v+OtM1zJkzh2xZR2fiJKLoM888Q/y5555beumls+ZsPPH5559HmKWVoKyO0I4oOnDgQMqi+0BeRkDmSK8ar82uohGg58TffPNNRoR/SjwbIs/6Ii0dRlGC9G2SeEadLzz/go2aDLSIWoGADE4wgf3TTz+dNWsWpXCBaZI7rfiVZ9SWcZpT0YANCVkx/6BPaHdo2PQi0DF3jHZ76DekWKRA5m+++YahktmxC4fnzptLbxzKanf6448/TpkyhWyofzhlDO5qPEHNVEIKnd5vv/1+//13OyXFnFiwpeWoUaP22Wefgw8+mDHbAOgGkU022YT6KXLuuecSoQiByPfff3/ssce+/fbblq1fv37gsLjds+UGLUfrX3zxxaOPPmomD2RgV1Jf42Ct6CgCIiACIiACIiACIiACIiACItCpCSAh0n9bm9CzZ098OiL6ITky/3/2OWcjGNoaAYR2pGCkUY58khJrInHPPfeQk6tjxozZbNPNyE9tnuiZPJAZqRkZkyawxDczfARMAgIvUjrWEA899BDCLEJ0r169SEe4NqmZlJVXXhlZFVH0vPPOc2UiERb4b7311u+++65ht2aQo88888zPPvvM8nBkXCQi4W6xxRYbbrjhddddR34bLBEqHD58OKfIy4cddhindICuUhvyOyN64sknyIYYvuyyyxLBMsIJ/p7lAinUzHBuv/12a4WFAHvsscdLL73ESEkhUIQQjFtK8mgXMo6wI9TWpN0rPvbYY7CgDCCWWGIJ2zbDslnZcePGYYXCVfI8+eSTJDIMv1qqOuSQQ6xJVAB+56yVBx54wAqSAXeP55xzDjoLuKM+OfHEE0k88sgj0alYbVYtaDh9//33Gb9Vu/vuu990002XX345a2822GCDF15Iqme4+qc//WnNNdc87bTTKGLKKpQXVgqlCYqfhx58aMkllxw6dCj9tEH5R38IioiACIiACIiACIiACIiACIiACHQuAibZIT9boPMmjZ5//vmI3EiF/fv3R9q1QfkiMKVIwcoAlQR5tt9+e3w0cJVKrLgvMBKxFDKT0ywg2APiiiuuQDilOCqA++67j2xcJSDDr7baakiv1iJH3BGSjkRMQCPAXPjmm2/ep08fJF8CLX7yySeoFciDLoDIEUcc8dFHH1Eh/bnzzjtJ33bbba02UtCPoNpAmiYFIR2BlwyEjTfe+LLLLkOXsdOOO9Glp59+2joMhEGDBq277rrI6RQxAuPHj6eI8TnooINGjx6NTI06hs4gpBvJIAFrPXhMaiCCSX7c2rj11lu9jiUPNGaD5IiVAfI/vYcCA+CSYeUSWolddtmFqhjhX/7yFwqbniIYQeNCBtixkSnpFB85cqRFrIkePXqgMsBGhZHQGYZEfsbj95A7avoL1A3cFfRAhAsvvJANTskzYcIEaw7DBzQxKBesrB3tlpCBQNOrr766VUt/Gkfmt66ICIiACIiACIiACIiACIiACIhAMRMICnd+nA4jYzJ9fsEFF5gMi1SI2IjpwbXXXssENrsQsEQCMXPnnXfGAgLBE4GU4kSQcIlQg18bIiTpVMhuFCZdOiHTE4HPOusszO25ip6ChhBaR4wYcfbZZ2MaYJVYT1555RXy+4I2O3H6E+1MsWOqT1mE1n333Rddg99hiqAguOOOO0ytYPJ7xr3An4DrSkoDQnHm8jHtJ9sPP/xAnXRpnXXWufLKKxkmA6ESa/q///2vFbQj+pGff/7ZKvcH7kcyGuXULTQIlrc4BbCsYJzI7biUYB0LQ6UHpMMIa5Pp06dDCi+d0KcGdDB4XiQdWwtOKctVFB6YCVAEsmgZTCVD/fQeENhvsICkS6lztfDwww/vvffe3Bgg0goDYyNMymKqgXqJIlRCZ+wY7C1V0RwKhU8+/uSnCT+hZ8LshAxYj1At3eCGbbrppozCSlEDtiushLFbSB8++OADbBwYnRUkA6yDTWScBi8pLgIiIAIiIAIiIAIiIAIiIAIiUMwEggKvCXcmFSIw+t1G5sXoAEH622+/RchlEh1rd8Rv3Asy+U02RFTfNIBTauDoi4o0YQImiSgv8ONABjwvYFaPKGpXkTox/EdTQB4CGfwitI78z/Gpp54i8+DBg2kauZs4mg6WcuB+kgl4+vDOO++sscYakyZNQpKlBoR/esuihnmV8yrKK/BJ4M/uWyt2xIcl9guI22uttRZGAL6eYurUqa+99hqKD3+VAC26vrG8whOZEbdffPFFhHT2c2AsiNIGzbIFm/BR+InZFQ2UJGtw8H6BFkesN3ZkkNQDdPAxqgcffBDiNIcCAlWF34R1t2GnqYTg3xg/fz4RdBDsbtKwTitb2CHn0x/lEQEREAEREAEREAEREAEREAERaFMCyI/Ub2Ig8VzyYJ59KGxtDRu1+u3IVZwymqLhrbfeQgdh+f08/lhQSSBN+6cNq80nhWqbrMGabjxbWqoPtmplWibJB+sJxq1OvzeoQ7hKFwmoS5ol4VOJX0+wiXzijWgZKF7YIefTH+URAREQAREQAREQAREQAREQARFoawK+COlHWtxisIZgvMUVNl7QpObG83C19VoGKinUcLIrGpocQztkKNQIM7raRtVmtKJTERABERABERABERABERABERCBIiGwEIiBqBs6Eczs21t2yAAwauiQdtWoCIiACIiACIiACIiACIiACIiACBQJAdQiGZqRoIcB62TDPEXSeetGhykacC9BD1iqgE+L0i6l1hvcOrKwhLgdDW6ehiJWg44iIAIiIAIiIAIiIAIiIAIiIAIi0NkJIA6zVyUiM84NTUBm30qzawhO0gfl5WC8Y4ffYYoGGzZ+LPHeOWXKFDtlgw18YJIIygwVTsdiUusiIAIiIAIiIAIiIAIiIAIiIAIi0A4E0CMQkIjZPAHpGP0C0/O0y6YYpmhgg8Xi0SlkBZJ914msWQubCBfooFZgG4i11l7ro48+QlXDVpRsvJGxu4a1K71DYfmrNhEQAREQAREQAREQAREQAREQgSIkYEoE9mpkAh6Nw5Zbbvnyyy9Ho9F+/fp99913vrxMz7lq/Q/Ky8F4R42uwxQNDHjy5MlsyIm6wQYPRFCiekBtw+6g7777bnAhSjHA6qibpHZFQAREQAREQAREQAREQAREQAQWKQJTp07t27evm5uvq2PgHE0oRg0xdOhQ9rwsLy/3gQTl5WDcz9DOkY7cdaKiogI7kFwDxtKhGADl6p7SRUAEREAEREAEREAEREAEREAERKCNCOBkAJ1Crsqrqqq4VLQic0daNORCpnQREAEREAEREAEREAEREAEREAEREIFGCORSQxSD9qGDnUE2Qk2XREAEREAEREAEREAEREAEREAEREAEOh0BKRo63S1Th0VABERABERABERABERABERABESgeAlo6UTx3hv1TAREQAREQAREQAREQAREQAREQAQ6HQFZNHS6W6YOi4AIiIAIiIAIiIAIiIAIiIAIiEDxEpCioXjvjXomAiIgAiIgAiIgAiIgAiIgAiIgAp2OgBQNne6WqcMiIAIiIAIiIAIiIAIiIAIiIAIiULwEpGgo3nujnomACIiACIiACIiACIiACIiACIhApyMgRUOnu2XqsAiIgAiIgAiIgAiIgAiIgAiIgAgULwEpGor33qhnIiACIiACIiACIiACIiACIiACItDpCEjR0OlumTosAiIgAiIgAiIgAiIgAiIgAiIgAsVLQIqG4r036pkIiIAIiIAIiIAIiIAIiIAIiIAIdDoCUjR0ulumDouACIiACIiACIiACIiACIiACIhA8RKQoqF47416JgIiIAIiIAIiIAIiIAIiIAIiIAKdjoAUDZ3ulqnDIiACIiACIiACIiACIiACIiACIlC8BKRoKN57o56JgAiIgAiIgAiIgAiIgAiIgAiIQKcjIEVDp7tl6rAIiIAIiIAIiIAIiIAIiIAIiIAIFC8BKRqK996oZyIgAiIgAiIgAiIgAiIgAiIgAiLQ6QhI0dDpbpk6LAIiIAIiIAIiIAIiIAIiIAIiIALFS0CKhuK9N+qZCIiACIiACIiACIiACIiACIiACHQ6AlI0dLpbpg6LgAiIgAiIgAiIgAiIgAiIgAiIQPESkKKheO+NeiYCIiACIiACIiACIiACIiACIiACnY6AFA2d7papwyIgAiIgAiIgAiIgAiIgAiIgAiJQvASkaCjee6OeiYAIiIAIiIAIiIAIiIAIiIAIiECnIyBFQ6e7ZeqwCIiACIiACIiACIiACIiACIiACBQvASkaivfeqGciIAIiIAIiIAIiIAIiIAIiIAIi0OkILFqKhoQXOt1NUodFQAREQAREQAREQAREQAREQAQ6kIBkyWbBjzUrd+fKzKMQDoetzwsWLOjatavFg+mda0TqrQiIgAiIgAiIgAiIgAiIgAiIQAcSQJysqakpLS2lD/F4PBKpN3nP1Tbtmy/htmkrra+8HpTWV1dUNVRXV9OfiRMnHnHEEauvvvr48eO5KxaKqp/qjAiIgAiIgAiIgAiIgAiIgAiIQDETQJA0JQKRcePGrbLKanvvte8bb7yRoVkIJ0Jt+gmhx2j4CcVRegQ+HQ+yIBYNDKmNQj56kFyth7t06VJXU/XRB+/ddNNN9O/qK6689vrrQ0kThzbqsKoVAREQAREQAREQAREQAREQARFYCAmY8UJVVdXs2bO/+OLzL774IhSO//3vf+/WrVtytM6aIZd8Wigg2QTahNdotiv1W81Hvq5foqVnrWqprraOTyjkVeLGFvy4HqUYB9OzxnN1P1VBrut+C5kZkgVvveP2HXfetaJrWZeS0tGjR2fmqn/u66L8SP3rOhMBERABERABERABERABERABEVh0CaBlYD77xhtvBEGPxbo/+OCDm222mScUpwwN2o0NagX7mFAcTgra9k+qF8kcqdP2+zdp/tGCBk0ar62tLSkpwXAgHq+NlZRU1VR37VIWCkUZXmBtSnKwYVNJtKCxJoo0VJfEI6FIbaiuzxJ90TaxdIbedomVrLHGGtdff+Oqq6/qbkk8EYrwjwssrbFIIp6IROvVhmGMXdJRBERABERABERABERABERABERgoSeA8IiciyMGRF2CjZdEZMM77rhj2rTfzzhtVCwU7lbR7fd5swYOGjL2m29KY1En6UcQgpFEPRGy/eTIpDBLPxNOynVLN4hHk3oIW2hhg6gn6lpSGx1brmjwOwTxUWecdu111/Xu1Wvm7DmRWGkkVpIeq+Vz1D3sfrHMSHrMgPFCZh2ZJeDoAKYL+hlcybBTLsyZNCWEpiBW0rUkuqByvmXgzg/fcourr7lm6ArL86yYUopLPEloHFBQEaesZZaiwTjoKAIiIAIiIAIiIAIiIAIiIAILNwHEQEJdHWb7IV/FQPy55557/PHHX3jhhUmTJqFwQJ8QC0drE3UhVuvH42UV5SWIwZ4IWReJII1GUlPaaVwJT2715OJQ/XjcSbUm/Jpsy6w5Emn2/GEm9clv9bja4065kTqtc42HunYpr5q/YNMNN3r4gYeiURQOVjlXrH5XrK1DaxUNWIlEY9H1N97krffe2Xm3PWbMml0XjnmKhsAYwgw+CC7roNL5W6FosFbikZIYQOO1NdFwYpl+/d56/bXvPvsM5YHT6XgKBLQJ6Bfoxz777HPJJZfwJC277LKcMpxwBN2Cu9UWgvFUmv4VAREQAREQAREQAREQAREQARFYOAnMmTOnvKwcOfeTTz656KKLxo4d69wxpMJiiy2G/FhZOX/xPktuvfNOVfHEvLmVyLARREksCsJuOrzOpsRNWZAqmPXfeFoLUO96JEfZhNMrJIOVjXk2DKaqSHi1VZSVPfnoo7HaeOXc2eG0nJ0q1i7/tlrR4Invm2+59Zzq6ldee3lOdSgaC1XHHV8/eCoWZ8DhQhqLf73lEe/2NSgeDtXGQwAti4bmzKtapqLL3Q88dNR+e9N2rKR0v733+fDDD3lQ8OSB/QJ6BLa9ZPPLY445hs0pBg4cSHoslvaRKUVDA75KEAEREAEREAEREAEREAEREIGFjQC2DCb9vf7662wngZbh2Wef9UwcEiY84o5h+PDhJ554orc4Inz0aaeN+vd5CMJdunTFIsGJvd7EN+YQLorqIQ/hNyg4B4HmLBtQHFCWMwRtPBdYstXWrTQ06uTTH7n33umTfwnW2Z7xtETd4lbr4om58+f9Nm3WH/NDv0yZgTnBgup4WgUQjoc9c4YUqQCYek3mSq+XKePENwFx6SlVEE1j7cLmIyXRSNW8P0oGDZwybQqGL6Gq6ni8ZqONNmJdDU47pk6devHFF0+ZMgUtA0/P9ddff//99+MydPPNNx85cqT0CxmodSoCIiACIiACIiACIiACIiACCysBm4T++OOPR40aNWbMGHw0MFKkQsJaa63FhDRahgMPPPDHH390iga0CKHE9BkzK6trvv95YmnXxVhOEY7HMENIeIsmuOxm3gNWCSlxOJNf3FvskJnKIgdv/UTD9FA0qL1wAnE4ESmhpxhT1MVr66qx6x8ysN/kqTMqF1RnKd5eSa1SNJh2xzFMgMcRioc5xsIlmIs4xYFbMeEUDURNJ5BDmxC4Ac0auFWXVmp4hZ25CO3iiYFFELGukVi0akFNj169Zk2dEq9JXHnllfvuu+9ee+2FfmG77bb77LPPPv/888svv3z+/PmzZs16/vnnMXBA0dCsbiizCIiACIiACIiACIiACIiACIhA5yXAyvqysrIZM2a8+OKLjAIHDUOHDu3Zsyey4a677uqPiziqh67lZbWhcC07MEZjiXCMuXbntMF5Z3Dr8D351KRgyplcnJSH/XpSEcRkz+dC6tz+RSsRjxJtKD5jkp9MdK24xphlZxsEt6NBHaoGJwnHKWumDdTesIr6TbXVWasUDelO4YUBLxR2Ho6XRkttYN7CEqdi4Go6c2asVWPPqJd2udNu84hwIhaNdImWljLEeLisS/mq6633xhtjvvj8sxf+998tt9qaJ2nIkCG4Zth+++2PPPJIHimMGp588slLL700s4M6FwEREAEREAEREAEREAEREAERWHgJoGVgcMsss8yIESN++eWXU089FZ2CbTzBFDWz0RZncprp9gEDB86aO4+tKUoj+FpEJYBI6z4oCJD/PYt+k4K9RRQetOwWDWEW82fTBZDobCYa4GYW3zOV4IJrwM9APRhT4BsiEsJDJZnqwjV1oZoG5dsvoVVCPt10OD3nEwbOjihW0OTg3dIFYk6t00hwK2FaHqz+1JF/XbsR/ovy4QzTlS4lpXPnzN12223Lu8bqEqHvvvuOp2TxxRfnEcFCBmVV3759hw0bdsMNN2DdMGDAgCbxU7DJPMogAiIgAiIgAiIgAiIgAiIgAiLQKQiYbIgwyCr7d99916wYEBWrq6uRVdE14MjvzjvvRAfBcO65++6uJaVYNGBIgKIhFiqNhWN8cPeHNOo+CKIZIm5KYk0KyHaakSfjFNVB+pMpVifFbYog8Xq1ucn2iNtckx64hAhqhw4LrWra58AwvMASCjZsiDqDDW6UC3WoHjztg9NI2Mctr8j8OKVLw08uKsGcwTy+9J9arIGWxyl6YqXRBQvmHX3syH79+pd1KbnwwgvcJh+epQmqKSLWV5QOvXr14mFiLMFqs8bJ7KdTHPsIjugvSJw9e7Z/SREREAEREAEREAEREAEREAEREIF2IGAyWmVlJW3N9QK7CvrtchV9gX+aEUEGxONjt27dysvLS0tL/asmHiLokYEdLtE7lJRE2XhiQQ0eAOvckv2It0rAeQNw4ifZnJYh5Cwdgh/E5CyfcL089fInlRWmskiVpeKUWE2EBQTeGgIEWnMcQOPoGpwwizwex7bB65I/lvaMtErRkLWjGGw4uZ01KbmCN2inimDoqCJc5hyfPGoIlvWr8av0Is6yAt0Edg0bb7YJXj0mTZnGI5K185ZIqUaucokKeQpRaL388suWk/0yeWrZ5oTHl2exyRoar19XRUAEREAEREAEREAEREAEREAEmkUAGY38tocgygK0BmxRiWh21VVX2f6UNs3crDoR/ciPZoEj+1xS7SabbLLSSsOYaXbz38j4OE2IRJ2dghfqOQ1o0hchGYKfXD3z6/Ejfk4r7p96kcC8eROCbf1yhTwrnKLBuWlIhdT4PcuCVGJ7/es0HdYWXiG9rSjQNtXNnVdZM//IIw83FYBpuTJ6lHw0vH8yLjU8ve222/71r3/h34Hn7P333//22295agnouljeg6KLgNJBGoeG6JQiAiIgAiIgAiIgAiIgAiIgAm1BAB//vqLhkUceWXfdddE+nHDCCfvttx9yXgtaRKGAcgHJ7vbbbx8/fjxTy0cdfZQT9FKVBautp2VoQWN5FDErhqQtQ0ruppwveptU7ubjm5o+z6O1lmcpjDNIRuUPrOV9abOSmBvgMuP3339fddVVrZEzzjhj9913d+ktDdhE8HjxHLPD6gYbbMADh96BLU8GDRqEuwcebq5iPcFjxzINT3eRehJb2qLKiYAIiIAIiIAIiIAIiIAIiMAiSwAJy2wWchEgA3IZVufsKvjrr79+/fXX5EToQ1kwc+ZMjpwyN5yreNZ0c9OA9fo333yDZMe8cu9evZHhPUGeozPn7zhJz2kV2OrRVCjmtQCvkERcrwKL/bMOrU0TC6JoSBoOuI46nYpzd+nFvWMzD2mziGYW9LOnVR71zUhqa7BtKdlxx51GP/b4tGnTHnvsMTa5tFKmhXI3w7tFfsSvs2Hk2Weevfueuz/55JO3334bF5JkuPXWW++//35UDwcddBBboeB7cuWVVyaddUHVtdVm7MCjT0pwzY+12LB+pYiACIiACIiACIiACIiACIiACPgEnFifEhcR2TAhj7E0IhZF4Ppt0m8TJ0487rjj8MyAsTlXTfLCueNKK6100kknsVElygL8L/i15RlBXqM2MmMfgZZhpx13WnvtdWvq8M5AG25/Cf6N8L+zqndHHDHkWXMLswWsGAwGDhC99Ruee0Jv5wtq9vwktrCFghQriKKhXk+Q5ostuIcsgTNQd8s33HDDjz76cL8DDtpxxx1b009q23fffQ8++GCe76uvvnry5MnPP/88Wi7qxKiGZxGjiRNPPJH9LNA49O/f3551HlO0DPaktqZ1lRUBERABERABERABERABERCBRYoAIhjCVzTi/kOwMvt07BfOPvtsNhZkLtmngenBVltthd7h73//e48ePShlPh1bIIjREMqMLt27nHnmmU888YS3G0WiBKP1pFMGt78D7fK/k+07VBZG78Cke9KNgM+igyKFVjQ4nwh8kroVXCTEAxqdlPqpsbG6Gf9UyCd/Km9T/7LfqGfqsM8++xxwwP4lXcqCZgU8cDxABKvFjzRSKcoLVkZQCeGE40/goT/nnHOwaGDDVZ5jdF0895deeik1n3baaagkLrvsMnKa3Q6RRmrWJREQAREQAREQAREQAREQAREQAZ8AwhcWBHXxOiQp4shr33//PW4X2Gxy0qRJ7mrKH8Gf/vSnK664YsiQIdiYW3Gkszlz5vTu3ZvTfAQ9v1E/4vaPDIX++c9/7r///tRm6gRn0eCCZ9GQyuokzubqGjyvgqkK8vg3a34nyRZSes6jH01kaaPemLqhibbb7bK7/8mQWLxnr4rFFrcHNJCeup73vzyjZnjDo8YTT7nu3bsf+q9Df/vtN5YDHX/88fPmzWOBEA2xmALPkcsuu2y/fv1QqpniLe92lFEEREAEREAEREAEREAEREAEFiECDcU0XDMws4tshVP/U045ZZVVVll++eU/+uijqVOnInORH7OFCy64gK0lPvjggy233BItA9oHBDGO2DIgqRk+chJagJKJZARARDlv+wlXA+4gqJx5df5tQYVtWsTWTaRdCrRpYzkqL6CiAb4Qb0zF0PAaCBpZPWL5Gx7ptP8JjitYW2pPUdarhFg5E3LbkGK+wQ4kdXx4vHhQvMeshY8a7VpxHlyCxbHhqaio6NWrF7YMPPHTp09Hzca3gquzZ8+eNWvWDTfcwHdg8cUXv+mmm+gAgXo8rVhwHIqLgAiIgAiIgAiIgAiIgAiIwCJKANHdxCsbPy4YiPz73//GFx5yPo4e2WYyeamubuTIkTjLw9cjEXzk4RrPyiJqEUcWI4K85qM0Ecw/bTJCbZbHj3hirzOO8ELjVhImtgYawR4hq0lCso36u12az8GAU4ZARemoayOgPDGLiubaVaSrK1CMXrVBMHyNEGyDNhup0vnmSAY/4s55MlLpbfIv9jm33HILpg0zZsxgkwvzB4nGgRRMG2h9qaWWevTRR3lkpWtokxugSkVABERABERABERABERABDobAXPBYMLahRdeyHaSxM8///y77rrLmRB4M76nn346rvGqFlRdddVVrGhAGcHGf85bZKOhxQKgbZnp1+0mspNOGkK2sMK/pIgRaAMfDU6/UE9/4buHDCpact0APzMZ8smfq57MdO9ByExs43PTpWFgg4rhvvvuQ5GGMc/hhx8+btw4rBtofMqUKXvvvTd2ONj23HzzzZtsskkb90jVi4AIiIAIiIAIiIAIiIAIiEDxEkCGQh3w4osvolZ4/PHH/RlZHOTR6aOOOmr99dffbbfdiKN04KotqSBCQTQUbTGwFqsncnamVVPyTtXSIDCD3iZjb9BQvgmF7k1Tdh359muhyMcTyQIK9mtFwYbjU3yQrDxs5Tdef4OlRC+88MLaa6/NKiO0DHwl8JLKLq9EFopxaxAiIAIiIAIiIAIiIAIiIAIikJNAI4KPuTzYdJNNx4wZ4/QI0Wi3bt122WWXK6+8kvUR11xzzc477YyERdUsiyBQFae2mD1ne4vUhUTEeWdIRAot6jcPYgEtGhiItxIBXUMrViQUzoqB/nhLXADi1B/2CbP9CAnsQdE8Tg1y56PW4qHHxsbMbMjvOQ5xFZG+8cYbsx3mq6++ypcHhw4ssjj8sMPzrBPdBBqKBj1SggiIgAiIgAiIgAiIgAiIgAgUEQEEH1/GIY5VAhOxmHvTRU79jvp5SGElAvLO3Llz0Smwo+Q666zDqnP2kiA/geKoHkqjpcQpZWKRudunWhJdDQVdIN+i2pA92yrEg5YLKcsIRhz1GsRToe1v6cTqpBSc5txWfcpWbwEVDfWrT44wq11H/ZyL3hkPK98B9A477LADo2cv1okTJza+tse+MxxZd9SiZ33Ro6wRi4AIiIAIiIAIiIAIiIAIdCgBk1wQYlAxoCAgRMLOBoFAvzLkGksknWx40N/ZC9Z9M2HAbIFJXBOmOnRYuRtHCg63oZYhd8NFd6XNFA1FN9Li6pB9qTBnIPBFYvPLxvtn+d0xgrIqap5Xk/5HCqqxa7wbuioCIiACIiACIiACIiACIiACRsBXDXBqAktDMuwx6Rl5xywzLhWI2JKHjMzUgNsFJlbJ419C8KmucRITlVDK0skZbNrPnKsPfgZF2o1A2yganJFGJwv2pLbzo4mpD8H/khBpsgPsXoFpEB5WKdjJEKu7IiACIiACIiACIiACIiACixgBVjQg46AvmPDzhP/+978si2ApBEdbYJ4Bw0lD2P4HV1VEwmVlZcGUjCLtcJqPmJbshlvLwCL99heHG7bYMKUdUKWbaLaiIeMec4p0nIjzSHhPRTgaDkdRZ9FCOI7VSDS4dsLfUcIcMfhDD+ZJdw0/oilHCs3NTyV16Mm8zvGU8i//Bza5dI24fnv95CnHgMf2UAm23vq41d9kPX42P9KwCN9M7BfQ4X366ad4jhw9evQaa6yxwQYbsMuLrVPiaB5QGA71mE1Rw3qUIgIiIAIiIAIiIAIiIAIiIAKtJ+CErICrBZNlsNdGKvHlmgULFqBoYAuJK6644rXXXsNgAbGFbfhKYiUNO2ACml/WMthpRiKXGqY0rLD1KSaFNastT8itJ+AGT+p5WPD6l4+PQl8upkQkFKjPn+B3grPT0MTDdQFvhL7A3XoSza6hzdp2Y26s8sauZRtFc/NnqyMzzb4blZWVXGjW05NZURuf83xjPsSXdsKECT/99BO2DHxj33333UsuuYSvMdYNzz33HN9YesGIUDFgWYSzVkrZANu4d6peBERABERABERABERABERgUSeAhAICm++0ffc4PfTQQ5Gzttxyy5dfftm3YmADvuDiCB9c8UhkJka5Ve411fTKOoZ4haLE720RR5L+IL0ehj2XkB3Q2baQ3ztgGC1rkifmuuuuw9HIsJWHtayG9illC5lQ+/Vfpv+PP/643377DRs2jM7zoBOOPvroHXfcsW/fvvfee+/48ePxs4LxhnUMdQNB6ob2uU1qRQREQAREQAREQAREQAQWKQIIGs5u3PtguYDogboBhcLZZ589aPAgBBYkFICY5MIU6Wmnnfb111+PGDGCPMUMig4zsrXXXru8vLxXr16IYPQWE3jfSUQxd75I+tbspRN59ds34cgrd8dk4tH5448/TjnlFJrHTKBjOpF3q/SWvKyeWHLJJW+77Tbi33777VVXXfXLL7+88cYbfJ9xsnLAAQfwlRg8ePCRRx75j3/8Y81/rEk2p3TwljlxiZB3g8ooAiIgAiIgAiIgAiIgAiIgAo4AwkiTogSyFRtSIpt8+OGHzH3aPhEI6hRH5lp//fU32mgj4lYPSgerlmNxBvr51Vdf0X/2CuzXr5/ZmBdnV3P0qoNNCgqoaEju3FlvxQQah9TenjnG32HJrDXAq+Jll112/PHHYxTz9NNPDx8+vDg9LNoXG/cXwOI76YwU4omVVlrplltu4dLrr7+OVxX0Dqx9YiAYNeBehZxbbLHF5ptvPmjQIPaFoYh9pdFH+BvMSiHXYQ+fGhYBERABERABERABERCBzkMAUQK5g0CEgD2C8xAXK5lXOW/y5MlPPvnkPffcM3Xq1BkzZiB3IG4gsyCcb7XVVgceeODAgQMrKip8h45kYNxUUuSjZ306shVD3mGHHRgRcfb+S/XZ7WCZ2gEwFPGYuDG5nS07YFx4cKBnYHWuHOpN+fsdTnW8Hf8toKKhHXtdiKZ4aHjccaloT/kJJ5ywzdbbIIeTWIjq27AO9yRHneNW/uNLvrEXGA7qhmeffXbqlKmPP/E4zaN3IBDhu8GXfJlllhm+5XCUC+Q0FQMRG3sb9lVVi4AIiIAIiIAIiIAIiIAIdH4CCA7YKZhjOARv5CbsrC+//HIsF9Av2FII0hdffPHtttvujDPOQKrq06ePjRu5hQ9LD3wMxS+JvP/++3SSDjMbTeeLXG6iozk1HDkv+HejTSIFVDQ4Z4SmSUkbNXSgOQNNw9Q64I7+px7HNddcs3fv3r/99hsLb3D1wfeBxyirb5J6xYrghE7yQVnIt5qvNw4gt/ECp7haOeuss37++WfUiqSjYnzqqacw1mA9BeZMQ4cOLS8rtzEW/ze8CEirCyIgAiIgAiIgAiIgAiIgAs7RIxTuuOMO0y9MnDjRoDCLiVRyyCGHYCqO0DFgwADSsVxASOFoV22+s7NApNtjx4613m6//fZEkKo6RecTnhScCNvWFCahd0zH01qlQrbvhmeCfSFrLWxdKKXmzJmDWH7eeefxxUA59/jjzhAAG5jCNtSmtbGGgq8Ba4f42s+aNQuTHjbBxH4BDRyOJ9AvLLXUUnzbTaHw0UcfmWJlx512/OKLL0wtR/E27aEqFwEREAEREAEREAEREAER6KQEzFSBzrNeG1XCiiuuyOaUP/zwA1oGZG8EChZr33XXXazjvvHGG5nRRMuAfIGcxdZ+KBeYx7XF6SQikhCKnIP18Kabbnrrrbfo/worrICNBn1mpF73i73/Ht7kjLu3KSYd7hhxryCKhqBOwVQM/vPTJqOi0/7Hb4kIe5D625BGEnE+DqvTG3i9Mt0HzhG9hxyBnAumcsMKCF0DSw+CtRVPnMe6YbDuoRZhCFxF0cB3gFFgksR3gBQslzBb4ieA7zl2TWRDp8gvBYN95plnVlllFdQT//rXv6in+L/wxXMv1BMREAEREAEREAEREAERWBQIICO4JQ91dbfeeiuTlyzXxhGD7b+AiuEvf/nL6aefPm7cONZusykeDul9JsjnOFDs1q0bsgmJCCb+paBQ4ycWVYRt+xg4M7gcUY7gqYGIPxUdHEt7dtvk3KC0m2zdm+CPeM4ZgjP9iMEYNXSUisH65oTtNggm4beJlqFQveVx4QtAbUzyo3tDAn/ppZcQxfku8TAVqpU2rSfPfqJi2H///XEDyxgvvPBCGzUdY6+KRx55hB8C4nlW1abDUeUiIAIiIAIiIAIiIAIiIAJFQgDZgaXluM//4IMPpkyZgtSAoNSjRw/WTeAJDlvpc889FzEqGnFrCjCsztVtEzRMxZArT/Gk1zgneDUPPPCAee5bd911rf8cO0rLkB+chqJ33NM15Fe6DXIVUNHgj82PtEF/m1VlevkGXUrpDlyiC/6DguDNaiL0bYjiDz74IM8QwfIU/9EfRdau2lW+KowOhRx58Hk5adIkfiPYApNhstMM6ZatE40662CVKAIiIAIiIAIiIAIiIAIiUCgCzFaWljjTaba0W3rppa+++ur//e9/M2fOHHnsyE022YSLTFti8oAyoskWG5dZmizePhmQhmyHPuZiMdxgBhotA03TeQvt043WthJGuqPTZuSAAJgUfltbbfPLF9AZZI7GnWBfQHVGjlZakcxzw1aRSOPUwdKjvffeGzm8Mz1MTY3dNAhmuWAaBzSOV3qBHwgrzXjJRiDi12cpVtBPVEQEREAEREAEREAEREAERGChIZAhAgTHZf7j2TwCFwxsUYmkwCQlPuJso0dWaqNoMG/6vlgRLN7p4iYKISuhQ2FEN9xwgy2xZyBBKanox+XpGnDHmRbsOqDLBVQB+FX5keR4WDSS/Hg+FHw3Cm0+XLeJqHWGYwozgrRtdRqQqDfaaCMWUNAfFA0289/mfStEAzzujQdrxNzDmtaAcE0IRQAAQABJREFUI98ZAhoHAqd+R6wq/9Qi6Fz8FNPFzPUCiXz9/EuKiIAIiIAIiIAIiIAIiIAIFCEBpBv/vZ2Xf3ul9/tp4gBHC356MLLkkktiA87sI/ICR4QI/6ppIuzoJ2ZEGhdYssogGTW0zyk9wfcE2xHie4II6oaVV14Zual9Ws+/laC/hixeG/KvqI1zZioFCtdcJOTk/E4TttxyS54nXCeyesL8RHaarjfV0cZNEvhNyVUBXzZ+R+zH6Kijjrr77ruxJsKtS1nXMkqxXitXQaWLgAiIgAiIgAiIgAiIgAgUAwFe5ktiyWXU9no/e/bsTz75xLzCF0MPi6QPaGSQfVhmjidIZlt33XXXoEqlSDrZibrRmXQBbYp1l112oX6+ex9//HGbNtSxlTNAC3l2w/0weTpLtnjhx2jttdfeeuutn/vvc1SCHVGelSibCIiACIiACIiACIiACIhAOxNAciYwO4jFgU09PvXUU6yfZgsJnCywLR3xRpw4tnNvO7w5Q/Thhx9iAEL82GOPpUvEmyU9dfgoiqcDbaRoKHy1be3Fgg1adtppJ3RXbP1ot4evZSOz/cVzC9u0Jyy7gAM/Q3zBaOjzzz/HBwyg2EqT9SaoQtu0dVUuAiIgAiIgAiIgAiIgAiLQMgJIywQkmocffviaa65B47DHHnvgG575QrzgY83dvXv3Ilwa0LLBtr4U/iYmT56MfqG8vBwsK6ywAnViDNL6mhfNGprtDNIEziAsz+dBlOUGiKIsbImEoras3zmfcKsnAiqC1I4PrniuhRWBPGl1hVdVoKL0lWD9QXcX8XCEj/PR4FYGeOs40i4bgt1Px3v37s3Jr7/+yoz99ttvzx6qdYm6hcZgxm6crzppeB/TIFIxMlu2f/7zn8stt1xlZSXbc4wfPx51DAZX77zzDgYO+Lbgq8gGs/3790clYb9lFEzEE6hOLSVVn/4VAREQAREQAREQAREQAREoMAF7w+eIBsEUB5aCa7VXXnnlsMMOmzp1Km/1vJnTMOoGHC5ss8025513Xr9+/fIRCgrc3WKtDuFlxowZoEPqQR2DZwoj1vz+mtgalFibX0czS+A9g1vpDk6A41/bLMMTh/3tF5tZZyuzt9n4m5LqW9nvFhU30NmLYjV02WWXcY3n6b333iPSuFOT7LUsXKk8owzIjptvvvmIESO+/vprPDVsvPHGw4YNIx2Nw5gxY/Cgueyyy+63334vvfTSd999R7q7FK9j2QV6B3gSFi4wGo0IiIAIiIAIiIAIiIAIFAUB3rRtRTMv3r6WganBIUOGYLCw2267TZkyxYyUmR0cPHjw2LFjp02bdtdddzFNyJQqmzgWxTCKoxPYbiPCoIvZYIMNWDQxZ84cttgojq51vl60maKhs6Hg+4mbw6222gojoieeeIIdYhmB7dfQ2YbSWH9NC8CxsUy5r/GtQ6Hw6quvonG48cYbBw4cyBoK+3m67777oLfKKqscc8wx/H5Nnz7djEEwbchdn66IgAiIgAiIgAiIgAiIgAg0mwD6BV7CCbxsI7OYCcP333+/ww47sOvk8ssv/+OPP1IpMg4KCGyTn332WTQO7FLJCzzpiAOkM3XPsdltL6QFQIoKBiBoZAAIRiQdZMOFdLhtPqy2ebByLYto8+G0vIEupW4PBSbqecJ+/vlnFA2s0jHtYMsrXYhK8uOFVo9dJ/wx4Rvyhx9+wC/rueeeu9lmmw0YMMDy3HzzzWDk5+zCCy988803p0ydkvX3y34N/doUEQEREAEREAEREAEREAERaEgAqQQJJSOdF2ymAAlz5s5h6ffOO+/Moma2Y3z66aex1OZNG4MF3s8fffTRF1544csvv9xiiy2YVcUe2eqxV3Ek6oxqF81To8FMKt4xIbD33nuvtNJKRCDPEfjZJZfik3nROllgqteL8NhkPjntdoub7aMhd89MZ8GxnvKCvT0Dw6t3KXdVzbgS9MtAsYQ3Vc+OjfWm7PNYx+EWsoRCp5xyCo8Xuj2EZPwO1FUnv4rN6NBCmhWtJ1q94OBMfVBWVnbmmWdyFRXpPffcw/fzkUcesWxnnXUWv2V9+/ZlYRi/dPz89ezZ0y7x8yfHM0GYiouACIiACIiACIiACIhAQwJY8iMxRkPReCjOKzdxewnHYIEpwEsuuQRn7QQryCXeunnN5j18jTXWYFo+w9+c/z5PVVYkI0PDDizcKfBkgBwBgiBjhIcOHWp8WAwO+QjeCFO4UjR8WbPw4m2qiWb/a2MJFOvgvhVG0YB4j8Bvx8DY2jtqWoaWtcrXknuD9RFfTmbpsTvC3yGOUlpW2yJVCq+ZfP1QKJx88skM/NRTT7322mtRJdx///0oGlBAYPLAlxPdzdlnn80PInoHaRkWqSdEgxUBERABERABERABEWgZgeRi5ESCt2vsF3ip/uqrr5gZxXLh999/D8qW2BSPHDkSL4+9evUiJ82ZgNOydhf6UqAjmIkHkFHoPPfcc+DFDGTPPffEioFLpJPSiVDUmR0DahMXi4fdGnY+HTOEAuo5zCrDmWegdPANDSye7Ui25CfEThOpT7acrrY4XgXt4yoPlHVWDHH7WNPOgMJtXeF/mvFs4NSQHU14qt544w1E4s71YDVjnAXKypeTL6F5zeS3j0DFK664Iu4bsAfhR/Dwww+3dU3kRO16wAEHkALkK664gi9zgXqhakRABERABERABERABERgoSXAizRvzgi9F198Mf4dN9xwQ1w54hDNBszLNlvR45Edv+wsbV5iiSXQMrB7pb2ZL7RQWj0wBD0UMVDFwwVLJFgSziagLJxnT70+ffogD5LeuDDoWe67foSxMnGGEcngpFV36kujaVHXl3mDkbScG2kiZ7KUaydQuYu7KX9/0p3W+dAlrxsud4eEAioanDrAjBo8sT8Zb9aoqKHpENj/ksy05RfxiqdPk+nUGcjjZ86IIDDzbcQNwUYbbcQlvrFnnHFGRh6dZhCw76c5s8CoiKtoZ2BoC5kGDRqEtpVNYiZMmLDddtvxNbbiEydOfOaZZ/zTjDrtlN/TjHRTanAMhow8OhUBERABERABERABERCBzkiAV9xc3ba3X6RidqNkHwQMGYiT+bjjjmOXBFzLHXLIIVj7L7PMMryWo2VASGbBsiyIc/HMSMc6m3lTz7FmFVLMqquuSoYmtQwZlXAaScQjnvzPjWwgojbMnj0lD3HYzbgHC1MkaykW20RzPlPBCtoqXpClEwyVAbsu2jht6E6PwrBdcjZ1RjbnGR6KNLig2oHKXE0W6sENpHutgZ7VSy6jK+/XlopYu8GqrU6ye7vL4sWQ5UwoBX01Yeq6/s1OAPcWkXiE1RN8guoDePJLVxIrQfOKcReF0RQeeOCBb7311vXXX5+9rlQqv6emQfQj/AS4i5HkdpuW0X6RLWeqqP4VAREQAREQAREQAREQgU5DgFk6W+lgb7bW7+D7LfFY1Elt+++//9FHH735Zpv/7e9/Y7VyMI+VMkNjeyE3ZUTDPJ2GS9t3FOAERBiawlUfrFg3MWrUKE7z4RYPUzDiliakBVuzrEcE5VJS/PS1AEGrh0YG5+dvJE/wkuV3CyWQyly/oxyi7h/mgZkJ5tMx+oaCKBrQL7jb41gmKbvTZPBSfEOOVKppAQLZkheoI5LVxgPNQMJrxWsno6CdxiOJSOrG1NW3Ykje5nTrDWJmOcO0fLhbeN9998WBypNPPom1/6BBgxrkVUKaAF9O90DzLOcIfHUtD9f56r7yyitYi9gPX44S6WSMxNziqFgJlfDFcW5UWWgUuPnuq6QgAiIgAiIgAiIgAiIgAp2WAFoG3ml5Yc41AveuHUtusogXOcwWOOXoZvu0OWUuanmkA5ZAxi+++IK9OYiw8ISjLVRpvAITL+2I/YITQpkL5x560klK+AzILQixTQkuOZ8AulJvot0UGY13MKXnaCJXG16uN/iWtuMtSkmrGCJufhvLEScRJj8sXMnyiUfCGR8vG/qChh+ngLDMDasKpFtB164tlfFvSR4SKc+Zm5OPlSy33HKg4BRXAi1lonL1CPB1xRIJ9SqBH0Ti9S43OLEfTUoR8X9Y+TFtkNElUJf/yZqhwxOtex3eDXVABERABERABERABESgeAigXOCt2GRdjhZppHvkJ/BuzIrveMK9TnPaSH5dyocAMNmMY+mllybz8OHD0eAQmirosDt3AZF4HeslIkRq61zcCTmYM3geEmLe0eJ5HZm8z/kJxeKBT8OaaRS7fufHgD6EQ3VuPUUEXZTTfKQNLpoaVkGvF8aigS45jYIbMWYFSeMMIsiFHHMH9/WoFzxm9VLqnXj5s1QYSE94uiLqcbInnUoFz5QkdZLzX77e6BrWXXfd0aNHI+LuuOOOObPqQt4EoIp+wc+OIpbFFPar2vjvKatXrrnmGgxMVlttNUp1q+jmV9JJI1n/GDQOoZOOVN0WAREQAREQAREQgUWWgBkdNDl8XgLHjBnz+OOPsyBi0KBBTb4TkoGXSabiiATfrptsSBkaIYAzCxbOs+dgZWUlbNHjoMTJ5w5694u1EtSN4OkJpDbJzbw4aVmE1qRonMu0ISC7NtJfdynpnyCQy60vd4IwKhIadr1C6VBreo/ksoBA7naJFkDRgEjPqEpjkRJGjEKoW3l1rVMC4fHAdA3NGkguvt6ty6smd09jLLVJxGurYyXR6hpPTRgJsw8q30xbl2H3PeuXmcTBXsirsUU+U1aGuaiYIQObhmaVtxuW+r//+z/cSV599dUsSOvWrdsJJ5wwcOBAy+abSKAP4m5WVVeVlnZhF5pINGlX1rC29kyx3ybGawpR7GTqamuj7DHk1oCxAoT1Jul+Bmk0i2d7jkhtiYAIiIAIiIAIiEDREKg3ociMNG9QxSN4uynxeJ23PD4NzF4LcTForgffeeedX375hanNxx57jJ5/+umnr732Wjp3ozEqafS6LraEQFlZGXYNVhLCwffzrNXxTh8NJ6LMssdD8dqqBfNrycaku7cDBRedfX29aW+vlqQomt1K25svz9pYZqKTstMhacLvRAw2teS7wFVi8+bNwx6cB89MxdP52ytWmMcUXUNdTTV1VVXOrK6rCUeIOkeQHuvmDYUbkivkqWtw/iCQNjEdqa3mrlfPX1BRVo5mp7aG/UpqSzxPKrmaUHqbEnBKgZR9F98BQtbm+D7gPrd3794XXXQRv7zoFNjCh5z3338/Nibsjnn88cdjE0EKOTnecsvNt9x66+ZbbDHq9FE9u7n0dAh+CdOpbRxjWM6YzbmimDZtGi4wL7zk4h132GH3XXerqKiAgA08iKKNO6TqRUAEREAEREAERGDhITBn7pzFui0+bty4Qw89dPXVV2deqiVjK9RbYv33WbQM/IfbhXPPPfeNN97YdtttR44cad1DfP3mm2/Yo+3f//43agXMdUk3N5D4g3RySsAEuMkR+a+UTeZUhjwJ+LKJk1JyyCmBqpxtyeLl5YvFQoOX7V/neaxjsQSqBKcFsE8gt0XtYcn16OWydMisJpxt0QAmDPEQs/10nA6UhkNDBw76tEvXjtIy0OeCKBqwzahdrFtJ11holZUHz2d83mQtI0RrYGqbIB2Hvjkhf8UPNXN7qB4Pm8h58bpQvCY0oGtoicW7h8LRkiiazphZtTSnfeVtbwJ8H7B6YI8ZNAtjx4798MMPzz77bDqB9uGBBx7ga3/zzTevtOLKG228gaWPHTfu808+/eqbr/fYc481VlnNPQF+CMb9xDaOJKrroqUlPGk/T/gFO5/tdhgxY/r0Dz/4YOONNhhUMYi1eJFIKV3wFQ1Zu2NX8/iNy1paiSIgAiIgAiIgAiLQWQk0KUIzmcPYLr/8ciT5N19/ffVVV9t3//3MnjT7mHkhbNN3woCuAcN7Pgzh2Wef/eijj8aPH3/EEUcwbfbqq68+9NBD7EZp+gX6yRtvnz59hg0btscee+y00062VXz2/iu1OAnEa1598YUrr7zup18ns0MmfXQGDm660XknrOcZIeU30AThXAqFRh/S9HS8t27CzbZ6wdLdqXOb7748uOKPDFxm6cce/k/t/Cpsqd3ijsAjmizX9v805uA0n9bdYBJ162+03jvvvL/rfvvOXlBXE4mxdMIBdhYNro4MXUMaUj4NpIpn2bfCK+4z5oyavdNI2NnPR+I1tfHamv5L9fno7THff/nZ5MmT+yzR20zWrTZJcfndgULm4nHxq2ucPzktA2YLbBc8a9Ys/pzceuutFDczh9KSrn2X6t21rOvsOfOnTJ9+5JFHXn75JV2jJfX+kKRb85tt+4iNMRqeMW36hptt8uU3X3sWTLUfv//hX1dbvaa2piTWxWwxrCtBDsS5xB8eAxW81Pb9VgsiIAIiIAIiIAIi0JEE/BfFJl+BJk78edllB9JX3v9LSiN4cD/yqKThQPYBBGWG7DlamhqQ4jwJKFnPCSedeN0112Kn0LdvX95jsVmwQfEey1qPgw46aJVVVkEHwam9+1mxJgn4GcjfJKWWDknlmiTgZIy+S/WZOn1mKF4S6loeipQ4Yd58IiQFkICuIaVoqFevCf9k9vx6pnQBnqzcUC8QdOjo8vsPtMnW3ikyCLWFzZIgEaqau0TPbtNmTPUS67XcPicFsWjwuhoOjX7gAae5gTImDagaUs4w3OUk7sCgkld9RsFLfn4DZ5eC8UCpereB9NQdZQ08jbITTNU89iixKlDzoOIhjkVJoD1F249A/j+Ifk6k7iW9cOMNNx511FGTJk1iqcRTTz/LoqNff/uVXUKqqri/0aefeGKrLTbfZqutvG9p6oGr93i00zBZsMMfjFi0/M777/7yyy/4HUg4O6bQeuuvc9P1N6y99npDBg2KlnQJfufRc5uxHH88+IOETxry6w9JO90wNSMCIiACIiACIlAcBOz1j/co3oJ4A6RTvCMRt1cjThHXiZ944vHP/fc5TjFYxkycKc4/Zk+tra6MlZYHx+EEAyd3eWlZ3/1TL4zBUvXeJOtd4KTpN0ve+eoSoV8nTlzA5FIN76hhJjutGgYyaNCgQw45ZNSoUcGKbaSW4r8ABzME401mCGZWvC0JOLlyk+FbHjbylJ8nT09Eu7DLg/kNwHcD7SYyXTAGBFivW2bXwCMaxg4irTnIfFK9CfKkhEsR8mM3QZHgNLzN6yNzuFSk4HC8X8+ed9x03f+9/3pbEmii7sJYNKy70QZzFlRed9Otv06ZGcdYvBQhCs+Xjqa3fsJ1IsOuIamGSCtjvB8CEKe+v541RBK0SwxqcbLqhFwjdv/cncBFg/nnKC+NvvHKC1dccsmknyf07buky+VC5i20VB2LmQC/zvbbyqqKH3+ccMzRxyRCC15++Q3uZklZt1C4GpMllrbFYm5HTP6zsaQeqPTIvEfLndrD5v+JaZjTyjQ3P34fKdiv/zKff/5tqIq/gV41fOtZMsXfyniovGvpbbfe0at37+Fbb+WrGNBt4zmSMbJ+j7+gRKx1O+qPSpCG4iIgAiIgAiIgAgsxAXNzwBwMYhOvdIyUFNwrXn/99TfccAM+FHGvz2ldrRMPErFQgjevcOjPfxk8Y9bchLcwnPcop6vwnHkhdGQst/fFAP8drz5Mdgd0GwRaCObBIp3E1CWcAPo1ubwm+JV3q5jw/Y8huhTDX1epcw2Ir8DaWkwYsMzFEyQ5bVCujEJnJcCLerz3Un03Gb7d1TffOXXO/GgX5xOQwDPB84bkG1QEBEfptlFIhrBTC7BfqVk0JBORZFMPn5eSPvO8BFB/NFNlkSyJ9IAzSNZt0ELvbqHjjxz5v0fvnzltinMr0BGhABYNbqfQcKQ6Hhu80ir9hrBpZ2mt0yIACLmQMfHlJprUI7gxJtF5iHOqDDzFREq5YPXk4OPfKtdUMg8/LXU1peFw7YJ5A/r1/uGH77jXldXMM7MaKkc1Si56AvxGo8bGfQNec1ZcYYUXX3q+LlRTUd6tan51zfw5a26y5rLLLTV12sT0Y9ARIzKdNFoDOrneUn/rUlrRvXuPJx56KjQ/lCjh54H9WaKVC6r3239ftN2symM3o+WWW27DDTfEzy1r8+wPD8V9zX1HDEJtioAIiIAIiIAIiEDHELC5Fl6EmGWZPXf2Z5999vTTT+OL4fPPP/cMBJz3RAwZcJ5IBl75Nx++xhJL9Jj42xTcQ/apqTYjBqdlcMGJBgmnaMguAKQlPvL5coQr4mxRGwY2GUgnJpCA0md+bN6Cyj4D/oLzhdqq0AuPvxKJR9hicOrUqbzB0mHe9KxnmkPyiXXeyJx5c2fPnVMVT0yd/ntpeY0TNE3ATWq2IimdFEP0pNT0M2ZCqz2W9beQCGqvUrKwhyj9DKO6ygotqVyLxHDiUFG22Ox5c+fNn89Tmu05zVpBgRNbq2gAHwMF67yqqvKKrr/8+PuC2srS8gpMR/hWp6EEMSXT7WompnogAqUaGJ/4III1JOPsbJmoY4OJRPWcOUv16/3b1Kks3oqVlHKZpR3JO+1XoEgnIcBfFza5pLNYNJiH3p9//rmqqjpSVhGvnnfBZef99a/DKmtmxsPokNNfxfYcnP9njD8edLK8rCIUj5ZGKj7+4OOfx05kBU/v3j3X+sffZsyc+e57H2Hrh+dh1ubxpzQajf71r3+95pprMGpYddVVMcfwx9ie/VdbIiACIiACIiACItCxBHjfY9Mu3Ci+//77t99+O64TeSniTcm2DGdXMvYg++Lzz9gPkrf7+Quqrrzu8v7LLVU5b0EkxoJaFAHOeNzpGEyg9wbDelv+DcoM/hjrvzK6LAHh0M+VjphRQ/q8fswzs0Cv4WzGY/GydT/d9LefppHlkksuwdcYEVv6IS1DfWztfcbdKcQtiJSUdo2VdKkoj9TUYljDo2RPE+YwLuLb9bvh1RNxPZE0PeiAPiIg/Hqlgo9nIB6YZ09X41p00/y0y74IrNqJsQ9CF6ewC+Zpz3hrFQ1eX6ETqUuEq+LOF4LTMoTY2sNpH5JKHUyG6uGwVRXAMsr1vvVphPVAB25AJh6rx6XyzTdLp2ikSxh/e7ULwl27VteFupR3w9DCsyOx6tONZFaW45yfPAzaO/A+5ejXIpTMLwLb29qfDPOagzeDC8+/MPlHIxaaMHFsv8Fdp8yc6Gm4mn2LC4IScwYeEv6Y8T/+kMOzIjXzant061NTU+m2YqnBdUnds//9XyhU+9joJ994fcw348a+/PLL2C/Q+gcffLD22mvzpPHXlBrqeHAVREAEREAEREAERGARI8ArN/tBPvbYY7xW2Ysf71XLL7/8gAEDzjnnnJVWWKFn796lsUhNXaIk5mZ2f/z1x1j3uknTpld0K6+pXuBsqT25w8wNkloDT9EAyHpShwc28MqYvNgCRUPQMqK2phazhYryitnTqyZP+S0ejzI9NmbMGFybM7eEXQMeKBjRInZXi2i4BeTvvfczuRiq6FYRwSG922yCpyjKv+5hq2dH493x5H0P2MXYQxiUlE0Eton5+uKwDxFZ2o8HI6wvwn7fKiv33JWkvkHBXO0XL4iiwXU3+AWzL7ZTLvBVx4O+p1BI6xoAF6RZH1S6nmAeNrDIOUed/sWgrLutrl2adOqPlGeHCJ5kuNuZdzUPztwe5EB+6Th6C/9bUEcezShLMwlwR9Ad3nXXXXhhceuQqkKRWF3XinDX6mich8B9LdN/OOyBbLyF9IPXeL5sVzPqx4ERD2Dc23WltBwjuaqyitIRI7b/Y9qct8e8eeABe91190NbbbXVzrvujk4Ba0B2geaIooG/OocddpgtmtBfoGyklSYCIiACIiACIrCQE+Adz+aWMGFYYw2WRSyBLQDyOYoGezvaduutQVBaGlt2wDI/TfqppEssXBYJl4UifHgNDOBBSEieev+gQfCv2stbvde/lEji5wnUlIoGBL+0aONdTJVGzEyw/V2sa6Kka6RH7x6JrqWoGHjNI1dFRQVHBpiqTv92AIEM/k40924Ks30RZgo9nyB5dgvBHlExz8ztkI0vCKNzA+JBdeYNeCnpyJnLgjzobr9QtAn4peDIJxyqRdByR5Q6zo4dpw21boGSfZx7FT617khK8tQlovphOjr5ieDgJe5/XA3ZP1ab2+QiEaEtF3GPCx9cQrpPKEan6iIlxrk5DwNPHnfIrQQrKeHYrCevHR6mRbgJd4unz5iOEyB8Af3lz39ebJnF5s7/I8ydjuJ3JxSLRNBx28f91hMPs1wp2ycjW+o0e2Zq8DOkIhn1o2KMetpK788PG9dWov74/fdpsVh0yJAheDJ6+NHRn/3fB+Xdus+fV8kodtlll5NPPvldL5x66qkoIHB3xFOHy4ZF+P5q6CIgAiIgAiIgAosugfPPP3/NNdd87733Xnnlleeff/7Pf/5z/2X6mxtFZv7efHMM5gwbrLcem5E52SNax6e2pIYPQj6eE/wPp/ZhSTcO99zC7tTHhAT/1EVc4NjIBzEuXQMVBj/OHZ33ofV0NxKJlVdeGV0J95LXPBog8scffyy6t7aYRo5mge6Y3oHJP8S9gsl6Zo/g5tr9TytGnndtZlod9fZYbEV7BSvaWkVD6jvlvll8dTxzEadrMIHf9A6e5O9TTkVAVo+aG5LpGgK3JJWZ+WHLn/sYKOssKVKE0qs2nM6yOVoGauDJY4EXC+kPPvjgjTfe2Oo0vVeqfv3bMQRqE9Unn3wSNit//dsap40aNWfmnBg7jdTW1NXVIMxzdN6JvU9tLbtA+GeZEefCOPXhD1j6w23OGoJ5vHiyeKoohUy1aXZ3Fd0rampr/5hZN3To0E033QzNW3V1AofJPORo62GHh0vy80v3j3/844ILLhg+fHjcc1xkf4paD5fKLbS+KtUgAiIgAiIgAiIgAlkJ8LLBOxbHrFeDiZan8ZeT/v37s9YA91UYMlCWlyskQBMCx44dO39+ZZdImJfz9dddr25uqJp9xXmTivMGyIugvfa5I3E/8IKY+QqYelGsn84oEu5FMntIv17WL+Uqd++g9om719Aa562hbuqUP/70pz+tuOKKCLETJkywt7vFF188CETx9idgKoazzz67V69ep5xyyrx585hRzufpbdDV3KJ0WhptUKgFCXnXltI1hJCSeN5sRXcLGixIkdx0mll9PdMjryyqh5TKADsHjBfqf1ja4FY3oFxwaxxc3LNC8FKcOYT3SVormBKBo32CXXPGFKlPOj1l1+T1wUt29g5eC+lMecX4UcNe64EHHnj99dd/+OEHnkt+eVr0IObVnDLlImA/zd4Xxh3Idv99DyGSV5SV9+7Rkz0dunYtLy/pxnPI8+AqSX0hTRdmj2Lq+UkrsHwDGiL1A3c566d+LlyueCY4lrkOJwy0y9oN74M9zdy5VZFwaWlJqHJO1Z67796zZ8+6mtA9d9+Pl6MF1VUsxuFx4m+PuVam16xLxGrL9T93sB78+uuvloU/Y8G8aPoJ/Ekm0bQewR4HcyouAiIgAiIgAiIgAgUhwFQNwr0ZHfBmYu8h1IzvQ468ivBycvXVVxMnj8vg7fLov6IE+0Aib3q8ERGI2ClHq/PII48kM1aie+y62/jvf2A3PxcSbs0E+zuwKwRLpe3jlspj5xrmzSqGmXMzPsyZOsvzLB97q0wd03WaLXXy6MSQqCvO62AkxBzSQQcdhATx0ksvzZw5k86alOv1W4cOI8CL9EUXXTRr1izeyW3ZMs+k/0ASyaNnTh7xbnSOvAgFKXnERfjYQ2XZsz1g6drsasMagik5mvWkHr507ntnTlFzZmzjC02INHm27mF2Rg3eFyxN3Esh0bcvSNXnvnte8BF7Z6ZrSH1Xk1nsH/QO+QXvRyEzK7fWzCWc+4bmhh49eqAQpRSb3/LTgNvb5tag/AUn8O3Yb1klgYnTHnvsMWjgwLKepdiezJ4/O9VQg0cudSHjX6e0KFzAXQSfQBOR8m6LscNlbQ2KBrdQ4rjjjuNYUxtCe8riCIwXcv2QeT8N7hCoLR3Fvmvbbbel4/hGevGlF/1sPJ/8VhKomL/HZpJD3C9p1fqnioiACIiACIiACIhA6wk4J9jOf1ZNZWUlcV5R7JWDiWJm7Fg10L17d96CXnzxRV6VnOkB70bNDNTJVhRvv/02E0FdysuxGJ07Zw4v9m538CjLY1FLlNCLjI+95bnONSPEMmvJqNROc1RIZwjeIZKoDTGl9Le//Y2xYsp60kkn8XrGDFMzh67sBSbALfjll194bnhK+/bt696ccTJf58S9lganVzLtUktrKHC5pKLBU54UuOq8q0sJ/HkXyJ0RuHbR2SmkggfdmSp4oZ5awb8UjHhKCsvmGTiklA7BPKnakpVmvZTMk7KMcPPbda5XaYkrWbqpf/g5uOKKK/i94Hfq22+/5YlE6UC8qXK63rYEDv3XvzA24UYcfvjhLHVDTc5fssXL6puimc6vcc1fME8wnqv7wTzBeK78lu6eO/yJ1vIHZsgKy0VjYbNH4GeORyujKH+AM1IyThk1SxbZVpqn8fvvv2eTJypZZ511HnzwQf76WmbgkMjTO3/B/Bb8Lc9oUaciIAIiIAIiIAIi0AgBXk54q2ESpby8nOkQ3s0e/s/Dq6++Om8jvPxcddVVJFJ86tSp5HSZY80Wtil1zDHH8G7DK99ee+2JO3AcdYVKsQ/nhdC9E7rZnlyBS8365KonmB6o0Hpgx0gCd4LOYxin7Dg2edrkYcNWXH6FIfPnz8crpE1eNkJSl9qBAIIDjth5Vvv06XPIIYdwV02UIFK41s2A2jOyRmRoVUhVla7EUhqmp3MUQyxTyGl9n1JahqyWBU1WH+iPb/XQZKF6GaghUIl3KdmlFt1jmxnebtvtePJ4IpHr0JvK5Kke8g44iXzzzTi8qFZULBYOhRdUzq+bj23e/AVxZ56XCq38Sqeqad2/8+bOY5EgriUXK+8e9ZZKHXroEbhxePHFl+eghg+F+GPZgha22Wabp556CgX5qquuar+JeJPce++9R4wYseeee5533nmcUi1/3bt164aatgVNqIgIiIAIiIAIiIAIGAHme5tEgS3D+PHj8d2IXzPWiu69z96ff/65leJtZ4cddjj99NOHDRtGVby6mMOFJusMZjDP2aRga3ziiSfFSlFrdMWigQo9C9HU0Z03CMzrNuvToIIsCbkq9HrDgu8EEkgixNwSPso33HBDxFpe3hAlgoNSvEMIoPa69NJLUS5wX7BooA8sZ0aHxV0udH9yyCMtlHML3bs2rq9g21u2cT+bVz0L9dlWk09q1UbL9T2mHF1xpRXjTzjvfY8++uhpp52GriH4IBZU+9W8kS5SueFsK/2Yz585fSZ/Wv51yD+zEvA8hiT1TcG9J1NrebIWqrdFa/YcpDb4/QnWn8VTSSTGnxmelsr58zGjY4XieuuuW1HRbe4fc3EFxC8aSqtcFnSNPFdUuJ0XyPPII4+8+eabH3/8Mc6ZZ8+e/Z///Af9wrnnnoveAS0Df9f5AUUfYSPiJ5WyLdNu5GSiCyIgAiIgAiIgAgsvASeAxROmGiCOkMabhr1L2GvM//73P3yZYVbJJo5cIg8wOKJWGDx48IknnrjMMssMHjTYavB9S9m7dMO3nYYphpbVshY544wzmEvB2YOrocGLWUHuA+4eWlwPJdMvoqzsiOJHom7V1Ve//bY7ESXw+4bz7xZXroItJsDTwqPFkZdh/DKYu082NLF1E00aFOdol7ud61HJlZ50MoDTkBx1WnJQQ5GZMzWv7+UMfAUy8zXaQDtcXDgVDTnABW9YjizZknko+UW47bbb8ODy9ddfk4UfSvtxzJZdaW1IAOxor7HEO+TQg2+/5Q5+HZwrAgzn4rhhdMHdF+dMsYi+aKbvBko4FuoS7rL2mutgO8cpWzdxzKVlyAei/SXGimG33XZj4Pzp+vHHH++77z7mEzglgu78jjvu4PcUlxDsPr3TTjthIWY1z507lzzs58wLQT5tKY8IiIAIiIAIiMAiS8A5b6xxL1roF0pLSk1lgGaBNRG//fab2VGyopOXClQPbOi41FJL4WkPuwa8NgKNgqSjriCOg0Q+vITkUijkgkyFWASMGzfO8weZequvc/aiVoQ6c5Vt9/TUu2giFE/U4d3twAMPfPThR9mBQlqGdr8XyQZ5Tuyp4xm+8MILeVx5LNdYYw1//th/kFrSw+TC/5YUXYjLLFKKhpbcR8zAmBZm1xN+Gpg3Hj16NHPF/GjiCLcl1alM6wjwt41fgaWXXvrGG2885phjB/RbllMC1g3JCD5JnaVa8q9Oh//JcX3zAr3iXywyyirKX37ppcHLDUHyJ6UFf2itQivLkT/qTA7wBrDPPvvwVxynFV999dWXX36Jvh9XumRAl4F5GEemFAho0/HJVNa1zJ+akK7BR6qICIiACIiACIhAQwK895oSgS3YMP5n+g3jBdzpkdN/i1hppZX69et35plnrrbaarx18L7BVWY7ODJpjHTHW4rTMniuIkls7ksai0MxasDNAWVd8DwyWNSfjua9q7nVWg0FPzrzaoTPslCvJXozR8k8GULEkksu6XbcSMRNKVPwRlVhLgL2VNhrOc8h83A8ohjasLs8D2Rr3sZztah0CCwS0nLjBvONPwdM+fKLgK6Bn7Y33niDzC+//DK/pEXyK9Z45xe+qyYbV9dVlUa7rLTSiiWhEm6EuxfeDi51IXakZM1MkYzbdO3O+IU/hdYnNrqcXz1/ow02rk3Uokb1/za3uMf8YvJDSVXVC6rRYrDdNF6dWW9GQO8wffp0FlaMGjWKv2f8qtIT9OhkZk6AqYZDDz30hBNOwACSP34BhYibDfBPW9wxFRQBERABERABEejUBHhtsPcBXlfQF7A2kzfhu+++mxcJxsUlAnkwW2AHx6233nqvvfbyi5DBlrs6NwqpwBtLMEMqOd9/MapgXwAcNLhKePezFz7k+dRblk3q5FtdW+XDSx1vpWlfdTNn/V4TZwVtVe/ePXnpordyntVW7HPXC3aeYchzNAUZL8b4FkX1Y89z7qK60nICbWg1naza+6ax0sl9+E1If+IRZMIGn3pDsW9psAavnuS310tnR0z/g27Tirtl896OANZcvTqbecJzaYqu7bfffoUVVuCn1tQNzaxG2QtAgL8r1MIyiVi0hGNJJJb8K+dZNJhi2/IQ5xngWQvhhTjkPjhiNF+M3FCL2zH4LKWfTW+rVhrL9gk8wt7jbDXUf9L4AxxP2J4U9JgHNRV4mspKu7BUj24knP40uVosdd0fENcb05e4y16wgjyWaMTQMlAhcw78YhJB48DCSIwXWISGI6IDDjjAdA0UwULnu+++w7qBOlg/iYrd6qEgzpAp6/dHEREQAREQAREQgUWTAC8JvBJcc801TEtgj7D//vvfeeedvCqQbgYLJ5988j333DNp0v+zdxaAdhRXA37+YkCEEDS8lAYPTnELUjQFwg9J8UDRQIHikAR3KE5whwQvbqF4Ke5SJFiRUJIQf3bf/82c3blz98q7stdnc7Nvdnb07Jkz55w5c+bHm2++mTOwhH/gLgFUDLaWQWBIXgNMwuYykSkCrDbRDKoml1ShEvuKBmLgfogwZdqBFMWG+4pmwOxFy+yooVk80nLag6BL+6NvXaiAEAD+1MYn2HvvveGc+RAGP+VVhm3RtspWHjnWRB1FolVgxo2CJwXHycJKpkn100WLLKyDRuAlYMsvVGTqUt5UlBCgLiUecVmGP1ZjCxS0RkIeasRqKL8VpN9mLUSmn9xOKciHMRhyGvGcAGy/deGCQSAxFdBDSLZOMJQYVwVrT6qKPC2DUvnroa6nVfbpdXYGckXfBl7k9kixcqkGRLpwPAlzgMaBjZRbb731pptuaor/5ptv9thjj/79+4PeJMD2ITGcTQYXcBBwEHAQcBBwEHAQqA4IYBSJNuHSSy+lu3AISMsEjjnmGFxQs2hx2mmn7bPPPqxwwHKwUaKQ/EPUeEEpGjxpg2aUzGehSbpVnL/ZzK6TqHqFFpZSO0sGYPlvCJoFPJRRDy5FwNWWlhYcZ2BjEmbNanHR1wKEWW65luWNzHJtfqHaLaQTakuFEFbIa6FqdvVkCgF8HassfDKzYS/TIkJML5hDgaLPDrHkQFFSkeq1vsxbJjPgwGnVLEGgR2DHxBOPP/HPf/6TIyowcdxxxx1JiVr3yiuvZNuk0bKTSy5Tjgs4CDgIOAg4CDgIOAhUFQRYgRgzZgym5rAQsAq77bYbzAPswUUXXTRs2DDemgVhVol55CosfBTHIzUaXUMp8H7GwtoK0EzNnhYWQK42GwKgMYzuc889BzLDEo8cOVLsGgwW2YldOBQIVJqPBgYx1iP5UJ+AhRwLvOiii86ePfu4447jhMVQPoArJDwIKAqu5PlYtXF45WdWkjnqEqU7RlQqczH07GKkgLWhKA5Q3MpyxDprr8MpFTRq0t2THnjwAZQOctyUo7aZfWaX2kHAQcBBwEHAQaBUIcDU3+20ThqanzAZUtnQoUPZg7nNNtvAA5MSVYLxZchbHolEy0BkgS0ipdkK8PB9PuOnImNMB4r2YTi50BxeGKmFN3VahqJ9C1MxMgI88F//+ldQF34YmQ5NGTjsHPwbEIUeqChFQ8wgtva0hAU1DptgJ/y0adNYEMYhDcIbujEKT0idw6rUlZMNBPj6vpLbzo4nhbxedvlmu1Rea7QLBw/NvGtw0kz8xMAKkEDSiOcRsu8xag9+RKa/CCDGEZSMnydO0DRtkJLNo2mDiXEBBwEHAQcBBwEHAQeBgkFATfl4pNZHThJg7YGp2Zgh0Az9vhtlBA6kpcHCSBA2IjQCm/dKHSaRj2U+Kb4871FJJEZAKc/OVE6rOZoNdcNaa63FMW2iIMtT33DNkLpk+3U3KKJ2ZCBwpi6vFN/afcy1fUhWygujdfHYDeCsxKUfhByff/750FmI6RtvviEk2xHW0v9wxWqhr8+Oeh4ufEsykvZB6dQt5PDqM84447fffoO3APPZkKlWNrS6DZUw2jdhWVIX4t46CDgIOAg4CDgIOAgUAALM1LAB8+fPZ9ZmsQH5Ci0DG9QnTpyIl2gawLYIlnkzYhUSNtsxwwYsyj+dzUzFSkYmmQsUHgKsEN9xxx1wqqyTsXiM3o025I78XkfwzobUq+8l5KOw8FCOrTEvFg2iblA27LGVlfsTuMghKBBTUPP0009//PHHMbZRyrCG+nLvWmW1v3o/hyGXtsrARKb+yukke/nll/H8xJ4L1ArstsDjw0EHHdTUoHZmMha419fUYyuRuiL31kHAQcBBwEHAQcBBoDAQYHKXVYFPPv2E1TK0DL/++iuCFu7Grr/+ejF1LExLqqkWWWFSx+2pVWhPBLXVD9UEjBLoK1wxAwG1mjHPweFIr569OKKBY+rSYYCTd0LrFwKv1Rfv/korUffFlHSKcBQNDB11mqA+XSORxXqJgCBX+QdXpZtvvvnSSy/9/fffI3Gxfss/5KoS6V6VNAOigIFeJXVWyF9Z9Gjy5Mko2rBrgDX5+9//ziIJqyIbbLDBaqutdsQRR3TLr2D74BY9yuJDu0Y6CDgIOAg4CFQGBD755JNrrrnmtttuQ5qaOXMmd7gODBzuueee6667jkekL3HeVCL9NSslAfGvjJglIBm0a0gJ3PLqWsqulOJLwCsb3lkSYwjQxA033PD//u//COSsZbD762sc0DI4nZIPmLwIbOyhUMC2TvX0qyvYXyP8Swe5h9BT5CjI8bhx47BoQOnw4EMPEoOuoWC9qs6KWCen44pMRCKE+dvFk95X6P1VETpBd5b/JQtA7GLCbRvTs7nskk1kmgE7L+GHH374ww8/5DQgBgJaBj4KSgcMHI488sj111+fUzMfe+wx4imcxLzlwt0OKyekVzF6a4V5xVvC7nIQcBBwEHAQcBBwEMgOArBGsBDMs5pBUrMqj4SvvvrqU045hel45ZVXvuKKK9jzOGvWLB6RtVgYkF2QMlk31Ge86EhGyZtdm7vNJewBHbH5BMJ0ttu8LoGDQDwEOkCmjs7LL7/8p59+4q34FgkNjRPYL8QgarwUmp2MjIGM+dl9lEg7pnTCGROXFE1XPhpSvC7QK22tlJ8NUagV6AQn+nDHE961116L4Q0ku0A9q9ZqmC8BMspIbBkItEfamGzyOsMVEtLSEbrW0dohjg8KWXumddHCFVdc8cYbb7zppps+/fRTNA6vv/56R0cH5fz73//m/tJLLzE01llnHRZPll9+eb4U6yR8QZgD2CDCxHCRsmK+YKYwdOkdBBwEHAQcBBwEcoSATKZq5YWFva4IDKoYDN5+++3vvfceR1aj5Renj0zczMIkw/VdS0sLCwPCzZoGlNr+XyP+mYbBQtBTNmbySlgI0/jyDdApPhmsVL9+/QYNGlS+HSn9loM2rBDDgrISBthpMKo37mHgklpY9yCAusGzZfBjSh80+W9hCOv80kgbqLKNQnZS5L8LhasBjEQf9oc//IHdE6DsCy+8QIwzBc/3B+BcA/YTNveARDTvt99+VIf6Pt+VFqx8Qapnn32WLZRrrLFGwerNriKwHRotOgI0Dq+88sq8efO4r7feen369BENMUPj3XffXX311dE47DZytzfffHP69OlkgePhTglcUkJ2bXC5HAQcBBwEHAQcBKocAjKfIorzY/KFIx09evQqq6yyzz77XHzxxSJBod8HSiNGjMAXw4wZMy655BIO9mMSFxvDEgcg/DY7PvDYR9foLN1kwUn6VeItT7N5mJzAF8H4wSylmcUlywgCoDoqNkYB+gVECYx6WCcD2iuttJKsGWdUWsrEqBiyWWoXu4bsrBtStqeEXoamaCihPuWtKaiHW9taKZ7zhKF6rK7fe++9eavNFexBADKhpPFOZUHHbMqk2t6ultDL5NLKTlFzJrH4aWtvO+qooxC/RVAvzX6Z2V0WRmgknAoXCyP4aHjttddmz54NT4ChJowOxB3Kzhh54MEHNttsM5ZQjjnmGHiFzz//XDZcmD5SLInNows4CDgIOAg4CDgIOAgEIGCm4EA8RoU77rgj+xb/+Mc/Tpo0CXcMJECUWmqppZh8X331VXin++6778ADD+zbty+TsrwNFFKaj2gWjj766K+++grnaNhIwipUmKtptAwwUQgWeJeXT4BuxXFEIWIjfDWSGnAWw9snHn/i22+/ZUQsu+yyIdbiikoNgdAUDXZBqHXkp7ytVtCFTAX5RtA68cQTIfogLmexVlD/SrQriN/HHnss5gye6remrrOjo1aZ3qub+lvHXf7mZ89MiIDx9nHFiNbQwZtvvplKPvjgA3wciCTPfBNitbkXJQwK5fAhpDS+i4QZC3KNGTPmrLPOev/992+44QaUDhj+kBKNAxMnHqe22GILNlOgieBsIdypMnwokztEP/fmuRIcBBwEHAQcBBwEKhICzLDSLxFEf/75Z0wF2bqLLSEXbINsWkSsQtGP/cJDDz305Zdf/vOf/8R3kpm7KYFZG36Di4ApszQhRvNgJGCH6BRre5hp0E76Yl+l2fL0WzVy5Ei6SQc5/gP3GWTE6ZtxZZV+OS5lCgiAMLwF4bn/9LPyzsB11VVXod+RVxKT851t+9kIIMbhQoXJyzY8w/QvAJiyAbPdnNIOQw5AVghfS0vLMsss88svv+B4/+STTzaiV2k3v4xb99RTT0EU0NDvtNNOdINJstQ701WXfDDEaBnoCEjF9jyOisRFDROP2DrOmz9voYUWKvVuxrUPNqi9ox2eAOUCbBB7KFhRgeOZMmWKHNnNkGG8LLbYYltttRVdHj9+PPojcqEtkiuuSBfhIOAg4CDgIOAgUL0QEIkIcfTjjz9GfYBagYVZeFHEVPgH9PWY3+PfcYUVVsDvI5YLFbAqTpdZt5COnHnmmQIB+iuBykAFXDMccMABGPNz4KjIESJi0DusOSqjj6XQC7AIPpMhI5YjgBoJLrwxwjq7v9QO58/Pewyy+qUAiqK0IUxFQ1E6UOBKja4BKzW0reyrxwn/2muvXeBmVFV1rH6zsYousyS+5JJLcmhMr+ZeEeN8pSJgwS5ELkRxTGbYj8OhO5zuW3Y9U0xAnfLFwP6Ipsam/v37b7LJJlhv0hHOE2K84GsDD1VwRd99990tt9wCx4A7STpOLkZWFv2lRjtXJbEgdr9c2EHAQcBBwEGgMiCQQlpO8Yq+Dx48mDP5MLY3cMBIEAPbjTbaCMEJ8Ym5lQPXUdzPnTcXC1yRXU3isgt88803tBkuYt1114WpgN9GOARE2XELpdl9DFJwrU2n8NNJgEbyBZ2WIdyPJZwhSrq5c+einkMlB8DLfXSEC6K8lpYNc5+6QcU2atA+P1M3Mbe3omvApw64i3MdLHByK8/l7gYCyKIs8qPoHTp0KPfOSEdHV8n7aFBbJORH70yYQIILvgHad9JJJ+GrhrA4i8bDcoKkpR3FiOCijTAEzJSEoeaoTjDUHDhw4PHHHX/hhReysYLjhQ4//HBeiZqZAQV7VNo9c61zEHAQcBBwEHAQyBUCTHzJikjxSrIwjSKXMmPuvffesKCYeWLjAIOEukG0DCTDiwFGhfhmRuOfrKJSjkdLIs07++yzJQDDAGRELKTvwmOUchcyatvw4cOxQEEr9OOPP9J3vF2y7pJRCS5xOhCAx4a7hucEfzC5BeDhWTSkU39Vp8mLRYNsNVE7T6KHfhQYypAq0aGItCaSXmhtgNhBx5F72f+25pprhlauKygOAtCCH374gWjWvceNG8d8Uxepr9Foy0dNoSeDmkCy48orZAQ6L6oLtME8RpuPHM6uvG233Xa11VbDq+KLL76IYhv1Np0t5Tm127aRwOZ1REkPS8QFXNCncOfQCu4KAsk7yyvScAVqlHnCjpSUdoxkdHcHAQcBBwEHAQeBokMgMEmxiILMA0spDQu8jW8tmwg4+AxVvrwirwQowYTNtJtwKkwYGV9RsWI6OjtgFZjcL7jgAhaWCHCCFWCRZgt85J6shaRkESPZ24LF0wy9HxRzzRjFge5INAZXVphsPPnkk88888zsObMrY9tLwYCcZkWCPPguYU3rkUcegQVFpyPfRV6lWU6qZGrHROCKiYnHyOycMnA+hbnsEiTejjHJih6IAUTWraHj5eD9Mf5DZ91jJfOwaeLWW2/FCCf7UlzO7iDAbgJ8DjODDhgwQNKisK9LMKS7K6jw7z2/j/EVg4cxqCinQ5PuuOOOA6+wa2CjgdHrx+evpBg6S3dSL1PITCDaaOk7ymkWc5wXyUrCBNcXBwEHAQeBiocA0xkzPvO7SMsoBYyWgVfqra9YTwgKtiIaLUPCBBUQCQTOP//8OXPmAA0O1OBOp4hE6ZAaOGXXd7gaLDXYFEyAxqNrUD2NJLV5KbsOlk6DUVrRGFCIJT3cn2EyLHgVdguRqfWvLISUsDufrLxwFA3JSq/UeCF2grj0scJoX0l9NczJkCq5dt5555JqWFiNwewCkicotOuuu2K4wSL/aaedFtCCh1VdmZaDEhprNw7uwssDLBpDj+mZlQINOYDnXWXaO9dsBwEHAQcBB4HKhgAzF/YLrKnKGY0i57D3AQ8L7MBFBCIBkUbvYEODV8iizHMm0g6byAoIsIQAU/Twww8DELrMeVV0CrCYqwL6aLpApwj/6U9/GjJkCAFOH1M4EGsBYRK7QCgQUBYmtbXwkAL8UMp0haSGQPiKhtK03PChEFxM9uMz/itUXkhhpVL8jIESRgaACVRNSQcddBAL15gzHH3U0SaykgLwHHgxoEdMMEyxxx9/PI/wIuZc5UrqbNZ9wUEmuoZVV10Vl1fjJ4zH7TYndGRdmsvoIFAZEAhQS9Mp4uUyMfkO2ERb6qIBBArcjHx305XvIJA1BNgRgP0CEg4e6b759ptRo0ZhnrDxxrYbrX8AAEAASURBVBv/+9//xpiRBXxKljOn4qsgL3adrLjwSsZURYpJdHDhhRd+/Y3X2UNKB1l85oCqeGhUTAwsH1+TLot55rPPPot/jYrpXQl2hKWpgkGYwy6jkkwJwqKATQpf0UDjvW0UBexGgqowXDe263Y4QdKMo4TEQyAIOMVYxuBLIwNSN6mYej/44AMCTDbs3JP51bsz29bwT/2UpZn3N6rvT6OSPCfJ0HQKuwZwCc6DKQe8Em+jeW5ieRTPF0bzwuIGnBaBc845Z7vttvvd73434bQJkyZPwuaFJSC56I+MTekYwg+XQZvy6K1rpYNA2hBg6U9RP32J8W0gq0F+AoFXuT8GymSsQbeJJBBYfc29LleCg0CJQwC0D7Rwlr4kknExderU//znPxwpxSyP6eLkyZPxx8RIYWkBNwS4xGepNTCmAgWyKhGIqbBHVpvpEWDhzpzOSVVCUpjWE17l3n28U9EFzuc69thjYW/g/R599FF6Wu79Ktn2s7BnPJiE2kjwNk6a1iIArhPkl6w6yZkof7Ic5RcfpjNIbBlQMdgXjxgBBamvnSLfYaVi8Oswegc/Iqy/ji6EBUnKkelkzrw5kGAOi5bJGwdImMrHT+Qh1puXohShiaM+SWoSu4bf//73G2ywAUscLHpwtDJhkrPKIS6XJWu14Ru8F4Ye8Gccg0wAILAOwF28UqNx2GabbTg//MADD8RFqxyCxR38YdoGqjKXC+jc3UGgkiCAZILqrbOtEwkEPpWuiaBixJW80gq7cKQCRALWXamaMIOOO4SrsaFRDMUrCeyuLw4C8RBAZYAMw5o8yC9TNs4UWDzgjMbp06dfccUVzz//PFv/UD5IXuYm1A2cIrHVVltxXDqrKQwoNW3pcRRfPjGMr4TxFRMJrUAdwzET9AjPBUsttRQBpnub/6mYztIRiLYQTNCAR1jcc889l50UoE0ldbN0+mLPWaXTqopvSZiKhooHlutgwSDQs4c6H3Hs2LFSIw4amL9FFC9YG4pSEeuTjz32GBpuascRMcea/Pbbb1jWFaUxJVIpEgtnu3AxJe+yyy5gwm233Ybuiebx6quvvpo4cSIBtrmidNhpp53YbgNfggAmqmsjdJVId1wzHARyh4BgNWgv+gUKhB0vInsq9hT1NfXIVJxiCwVDJ5iftaPcgedKcBAIHwKiZWAMoilgAsIe8/XXX7///vuxXEDxbe+JWHvttTFh2GGHHThuYNFFF2WqMiMXRUO3slC3CcLvW6FKpGuTJk2Sg8aQvaFyXAY4hWpF4eqBgMv6GSeJgDPgCTa8qKKM7/PCNcXVlBMEWE+PW1LP29p2Ti0tRuZ0l1uL0TZXZ/VCAC0DnOsvv/wCCJiVIcfMN5UCjlRmDixR9uvXb88992TWueaaa2bPVscdIVpXSt+z7AeTMXMwcFh22WXVponxE3CZiRstzgHdd9994U5I8OWXX+K74ZhjjsGPA5Ygr7zyyvfff58CbRQLo68s2+SyOQgUEALCj5oKwXku8HfGjBmffvrppLsnXXbZZW+++SbH9Jg0+Q4wHpGpqIXAF198ccIJJ6y00krYZKFomDJlSr5rd+U7CBQeAqmnDBGJL7/icpZGBg0atPXWW994442MUJnBBw8efNFFF6GJw7Th+uuvx/fzwIED0TLQCxJQsnTHBArfu8LXKPA0Xf7uu+/YGkkzIG4HHHAATCBLL4QL37CC1QhzK3WBG6ylYYbJClPBancVhQ0B0ThYO/fDrqAcy3MWDVl+tcqmfVkCJaRssNTMLmzPQ8ymSNarQyq4wMUk1OKZSBOIcSsgeMUqB5wHsyxzD0K1TMbVjHJMxsY8ATjU1tf2qO+BjcMaa6yx/fbbY8LAThMYFLacgDwwdpiBDB8+nISrrbbaLbfcQl7UE7L2C2ADKySGywE/qhnIBR4errpMISAmxKjYWPZ86qmnOAGO3VUgPAbGKOAww+a4ZZQOoDduU0eOHLn//vsvvvjiIDzMK4pLqjOoHgqeU5EUyM7qV1999cEHH8RKnFqIp17pHQlCqStTWLn0DgL5hgBDj5HFnYoYX3gsRn3wwgsvgPAMOolHj7DKKquwOeLSSy8NtEfGDmPZxEtMtY0Xes38LhtGcFQhTjH/+te/trS0AD2WXgx8KjvAAgnoBPGk7yw1SWcFi4wyorIhUN69S2C/AGWI8vlh9S7ZeQvJ4sOqN5dywodCLq1xeR0EgABUFYsGFA2E2eWIRQPTtrEQLkMQMcrML63m4wkSXQPS8rXXXvvL/35BTmYGSitn5SaC/ZKLLsKacPEIqrAoBIawdoQ5A06kOIkaiUucOJDmvffeQx/BQisuHlhH+vzzz5nImby5dBnqVrkwcz2rEAhgzgOqI8yAt1hiY+U0YsQIjAiQ7VFHsnvo5ZdfZj/RRx99BKFgFLBz4eSTT15yySUJQDk5NU25jcwDqjMGRULAQ8rXX39NFSzkQq5pFfG0lnCFfAPXDQcBXxMt8wtDEq0fVnW77bYbA2H33Xdn9gFISjyurWVKwrQHq8z33n2PgA08NevkYTDaVZRRGDDSWjkLgM0m0Cvm6L333ptIWwtTRj3KrqmbbroprAsEnJUSNtqAIR4hrXreLzt4FjCXWDHoCpVbQN+ooYAtKPGqnKKhxD9Q9TYPxTazNfv0WKwW4ZBHc8XAJYEqMeZ9GT3AoMPEMPXiEIgZd9q0aSyS0H7NmTiRWH1JcAD4cAlMmIyJ4c6FxgGvFp988gmvOH8b/QI6Gt6SC2fOLCsRgyD05z//WRXkLgeBkocA4jo/9K20FM0jwsxf/vIXmFHQmxNe8UiCtS3kEYRH5oF0INuz3Rd7h3fffRcCMnPmTDL27t07fwuDKIVpoQHkyiuvTJjhSY1ymVcu4CBQARAAq0Fv5uV11lkHRd5hhx3G5CIjlBGHrMh2CQRmDOBZmmazPQOEESqqN5mz5F4BoMi9CzI7Q7WYqSFWEyZMAFCHHHLIsGHDhHooSlLRWycMDIEAGz8BBRcoRK9haYgEeUwaF0gfAoyy9BOHkRIVQ4FrDKPVBSnDKRrCB7PNdYVfenWUeN5557E+BpHF/zCTt9Lv+qe4JQVA2agbxLohQT+YWugsF+/GjRvHjNurVy+8SeEcyEw2jkcRwJlZBMgAMcQaZmUiBVAgDPLYO++8w1Lw6aefjs8tOELEMy64GU6rTgD92CiKYr6PjXNPDgKFhoAS4yNKjEfLwIaIhx56CHMGbBmwrQXJWTtFuSCMOJiPyYMo4LBfgFMnAVl4CxkhPh9NZ5gw39FIUzhEmzCVysXANK9cwEGgAiDAnIJ2j/MI8dvHHEGYIcn2JWZqHDnjJwW3QWyXYMQxJJnE6TJzk0zreTIsKneoQiugJNh9ADQIyNChQ2UeL/d+Zdp+tLSyx+20004DzaDbgCXTQlx6gQBjzQYFkKxCYFq2FjYwCh12TEBoEGdB6R//+AfFCV8o5QpyVyF+5wJWwIVPIOZmSC379Di5TTHQ9T6udqkF6lpYWe+mJim2JvAH1Xcu9RYir3fgJX2RX0ydgid0hFg27LEeAlLh0RBQIFEwB8urmDzV+hAABaKOLU2JtCN8Hj7q0CzgJA/dzahRo+D8WBY2YAuUY+IVR2jpttwQNpBxgQJDQEQUDsDDvglNGZvA2VdFG6CKmBaD6iAnaEyYABevoJbEEH7kkUe4s7FC4kNvObUYWwlRzNFCalHxmo7xNk9Vh94XV2B1QgB9HB1HX8bUwFQrQBCk5S5XADLgNhYNvOLwCHwA4XiYTXk4d8RoDtMG4hmzkpE5iICMTTUq1bj0rkCZVfsIfOj7//73P7wsCazYRJARNDRLmFGOEk284YYbomShcfB74qvCIGSJtriEmwUDx9A++OCDUcq//fbbedPxJeDkbajk1XWCESQCAbsBpRCmeSFfeQVr+m1VoicXch1ETN31L/38Gaa84IIL2BCLq2GM6IQvhEAIAc2wJJe85scff2S/PbMxbod79ewlTDOP3YOmbIwaknYFnJGLFCeddBJ3NuypLdb+6jrSL1fS/NX0oluUQDXDYITV44LVY9f63XffjcNIo2hIUYIotpiouAB+ipTVBHLX1yJAABFon332AW8RhM4++2wWS5sam8DtdJqy9FJLs70Cn5H5Y1jTbEk6rXVpHAQKDwFUdcYKFVIvYSQ9ExloEqOP6YBJBP0dWyRGjxqNcCiTMndeyRXIJY+8ShhfzZECE2ZnKAnwx7UzC/sZMjlpEcPSBzKLarKvE7tLfO7QYPCw9Jtdmi1E5Y25Hw6S77rrrjFjxtgLwPlpMDJmzOjWC6D5qSrDUiO6XXLPMGs4yUMZn1EZHi2DAq7+mQZSR8F+fqXKkpNWANnCABcSiWkrFBPDOb8N7m8GELDnFZz2/ec//yGGMwVEyyAFyfyNKa6emQSnglXwweu6OvVdPn7MXblpif1pBIlJY8cEEvNov40PR9OD86INULoPfh01tUwYGZx5Q/dhgJhxmXpZk2+oV76pkTRYunecvfnqwqOYx/iA4BV3WWhF3GIHOwsmgkvx6U0MCQA1n4CLsJRj3oYfUPrQRL9kNSVMLCgXzSK4J/dorAuVFwQ+/+LzO+64gzYvtNBCWGULKjY2qJVSCafuDroJdg+J+jt1yhDfMmSkNBMIsXBXVHlAIBmNKmR8GpBSWyEa6vmhjGZuJUAmwsZUJ1CGJCCSqYRh1dbexjAUO22H7QFYpfmIOM1qCmDs06fP3/72NxYG0oYkfKAv3SlzUT2L+isx8RxawWJ0x/XMm8nqF6iFogGjGPAK8Rh+T3ZPABm50oRn1Sbz4aT+wrmxlUm2FqLGAqlAM0CaH+D4UrBGwvxUkU2pSCxa/K6NqJWJTHAxm9qS5lEjM9wL+aoeHr1G5EHIRWEv3zatC4tNzsVRcqrXP/2X/obfZXCaDXucnwcqc14AkiEX4XABW/GlsYAA3Ojmhx9+iFjIlL/xxhuDPfEdV4niLk+w94R52/WrCFrJ7nEFxUTE54p5nerBDGoCqhN2k1LlM+8WWWSRzTbbTOWMRE4+5WT4IaYf6Cb4ZtK4QDIIqKlGX5IAxpGJh1EJGNNcJSD33//+d5x7sSCcEA+TVV3MeJuJL2Y7XN25QkCIIaWwXxdUJLDffvuxFRwERgSCJYVCpkZLcmE+irOGrbfamtL0aMiSdEgD0u+SaRgBE04/u0tZ7hBQ2JslrhW66+LHEQ8L5557Lr6E5YjWZHhrDwT6iPskEQuN9qHQra+I+sRsENhyOBQn49pALtP+wY5m6hlQCP7gwYPZJYc8/MMPP/z8889l2v0SafZxxx1HS5gosT8CqRjUeZLL1Inr6sciaIMfFhKithYyT6upWv7E3/2E6m/829Qxdl4rDGtQo0VgcgOBiFp0z4vwm+aHjh7hm2aGRMkiXb5MJcKeDDBgphJ3KakQ6AcyxtqYqJeSS+LTHKKSWOls/EsH67rqFEqhZeB8XrVnAi5NtbGLkFJ/Wun9fDn9ZZkUkyeO2cOzwOzZs7F1X3jhhSlRtcFdaUMA06bG+kaOg7r44ovJdPTRRw9bdVh8boVP+vI0Rj7uEeejjU4S6WLc8+klvY1vfjKvHKN7EuyN6AINSqtiGadyKW2lH/az+eV7zVEJSVarVSbcVWmSRAes1nplpvyDAovTs5h1Hn744fPPP5+0DqlSAiz6UgDFXSYYAzdhKyUymjpRiOGMLQk6r4022ohc+BjntFHOrSCtZGfgo7Ngq/xVV1213Xbb/f73vzfFdGHCYmGExHfVKroUsVBI4uvQfiYnFd2nDzL0gm9qcaejva2hqfmXn39epH9f1sCZAjs721i8M+30sdiKcMHSgIDaJKURhvMspUXDhw+HFDQ3NaffQDzVwb/CvpDFDAHCoC78D2oIGC84WlZ+uBNebLHFOFhOHJKB5FxwaQmrQ+PJGvCv03/F+wnnvAwZMgQhjZQswSVLT7FoSRhKODN66aWXZJbcZZddxGOl5KJh6ARNCXabTaQLlBwEIjW33Xr7PvvvDcLwybxNB4kRp9BtpzFPPPkEZ1ftscceUjeDCJU94wLcfuKJJ/DpyGEHPBLPeMEvEsnoiGloMiQku+CwSWkHkuWy07gwEADsY8eOBVwMfLiddGGiv4/yo9RV06k/VgRTVtgu5VkJilenXuVhWTHd5ql0DIAMxgC8BGgJHFpaWghwqBAbc5ZeeulMaqzetPaAlTCQhHnjvu666wIXjQ8xk2CYwFKiAaImhszs/0GqxypKMfy0REmfmrvzl0J1tZYgED2xiTeRDBBGdSpJHzprVKm6ct8lpmph0a5c6xZTctUlDUoBKCS7I9LRHmlr56/SNHR1drWz941fJybM+qe2wsX+THwgmWQ0d1MCASmEUvlFS+tq78ATtvIZTxxpADefnOdaQvmANFoG9NnsqIdJglfDBoxa0NHmo65KLRNyABi547UFgssUziABqmqYdnvpQYsFCxI+eChIKDt40OQx/ru0Mo/JR7R6PEZ/ijp4P6X2k/T6Hk3vbQZShUtRMoVE01N+l8rr/ahIVSqDwh9iNNIiLt32iQRMwMsOXhZHFQDh008/PfXUU4lkBkoLJulUUB1pQCquLPqKKAURISODGgEJ0UgkIszX2Z3L7h64UqwkwFX2AbJzvrmhx4K5rVKR0qwq1Iv56WaAz2H/6Fz0x0qKeEpV5A4q1DJ4mcUWX2L99dZnhNG2WC1DFlBxWQoBARAPazwoIfvIqA8cQwWw4oorylwDIkEEuqUDKdAelEbL8O23366wwgpYSUyZMgVsf+ONN4488khc6KMy4GALO7sdRhKjeZygOWjxQay/Yeu71157bbHFFowFzH+wR6PBtJw0tJA5UdqJlgFsnDRpEmdt4px1t912g76xrWO11VbDBP37778XsKL+kIC7lwsE+L6PP/b4/mP2W3755WUHAe4NlI8bvmSULhUpXFOz2eab4TwLLyeo0gSkDCJ8N44YMQLJdvvtt0ebDGaKegukff/998FMCL4ktjGfmMBjuXyjUm4nJiRsDQPy2DJk1k7FG0bJRV1NZy2svuKy9LStJlqEu+L84B49mchvIY1VLdO/+G6iDhPUgrqy0w36SRrYDIOH8VlcTGoIMLlgEgJ1Yh0I7CIxDFvqLFm/VWItMmcHPmUjHQTaOyLtne2RzrbOds6nlotHfVY1dxao9F291T8e5afT+8lM+qSBNiRfkxd3tqwjqZ8qk/qRwXmtmNAuVt0VMY6Olqy7mlXG6NJBVtlVJmwzlBKnNtJZp2Q8hKxWNUaQstSYEssNlUyrakQI1G9VGnmM0fRAyhX58K7AK2JNCYRlJEtSiVcxymZJO29D16F+tJDqmfb4ky88Y2aCSiIEEjj++OPZZuYYJu8TpvdHVrpI++WXX0IUgCRnLhCA+AqfyqsEc7zGMaUpkElFSfgNrA53Iudp2ZK8BmEErfDfoFQS0UsZGXmXFy+zlNKHg25x6e2x6pWjaJiaz8ylhoM8MM/VdOlRJuoMpWikPt7abTAZgwFULeDS1VdfTd9//fVXeZ0ADsF87rkbCKQDQ1x8ISYhAuHhFTUiKMr0D2bipQl5ifAZZ5yB6oGd8/CmpNxxxx2bejZi0NTQHENnDDKoNtloYrUxFiejL2zLmmisIpJR/FFWW4muPj0Xunvyvd9891+OcX/nnfc+/OTDVVdaVSOeh5yJMrm4koAAyy9YYtKU5557jjuUEFKGiC4W2oTTQeAUPUGyuvLKK8ePH7/11luDvZSPoIX2gSwjdx35wIMPoIDAkpy5jFdEoukwpRGz+eabo3d79dVX11prLUYEY+Gzzz5D4/Dxxx/DzBFD8+RiHhT9O9nPPPNM9iLh1ZLdHDyS96yzzmJ/HFuTkPdwb0QW2mAIvqnRBUoZAnyvQ8ce2qNXD6T3Pff986Q7J7OeNq91Hl+zWM0Wkgjh7dXca6999+JUiEht50UXXYROAWtqdkmI2YJpHp68sVc/5JBD0HmBruAweFjE9puGVUOAaVSIzOjRozPrr+CX0qvzH44KGQOxqoPd20oOQbGeZGbMrJasUkOgETlYcIJ2qrtqJFwBjxaf6Jes6DlsaKcyNBOLMGgpVmaDBg0CFXOn9n49lf9X5g4xXsDzN9BDyGeiQVKn8/YsFh4swLq6xrp6fpiKNrKjsUGJsHxwtbVRmTMoNJWv7hNEH3GjjfDfqBg7HE2RLBTDUXpZET+kVpqgxkBDTVeTUn3pMjIrPlm1mcXnpGiAOqiWA02R1VQHYEqwJOhA4NfsdURtWyEJ1k1+w0TwE1bX0/lJ/3UCxc4IdcB7hZXLz63+GtFRhVWEykTQK622DjJTT50dbTUdCxi7Al8UTjpZ+De639TQtN5668F+PfPMM+A6M65tRx1+lRVXoqx3cWcBnyU1jvZhrQzqINx26u6i0WMkKTSQrTLQE4VCMp6iH50xLxhiTz+2kCbCGxnIDF6p9IJZVvXeWNUxXnpmOKgJw8AqX5cgvDkqjwb07Br9tOmEyhtXrlWFCarx1dWF7w+4H85lZOERgIikYdK4QF4hwAId54zuv//+hx56KBqE8847D1Nb3L5yMAr1io2JTGaoG9hCz7Jwv379ZLYz+gWFHf6lDD0TXXr5L8GL+H0TkshKD/FNjE6RzppJk+6Bv4EIQ35Hjx41edJk1pybG5via5I2O946HjJFiRFzBoR/tpLRAL7LgAEDQEXCfKncP9M111yDxIW33XvuuQeqy44/hcaavNx3/30c1PfQQw8hlVEdugbBDQMHrHmFNHGHdUO3TgCLHhQWuFlFqDMpJYDegcDtt9/O8GHzEVoGyDvV4b2f+Msuu4xD3TBfZ92J7XKMIEkfKMQ9liwE9txr9LRffpLmvfDP58EWxHsIFyYzxWqzUTS0ti9AF9x/YH+INiiNnss0CQxkTOFUC1sh1G0mXvRiguH5kUxMVS6gIABN4BPce++9aDYzgIjm7zCZrgfVCCPYabcI3NWiZlQxmnhyzKCi7JIqXpOq/dr1tM/sH6ClUrbQc6i9BKDGl156KWaSudP57NpevrmAmBhV0QVWhtAybLvtth6QoyuKYfevq6ZP7+aF+vRYpLlm4d6N9c0NfHzNlcGCJZAivNUjfzEy2poYecPHnOjrYAhUT3hRPqIzxBdkq+uMsHrQ2NXWXN+pRKIkWRKWE2JkTooGpTdSHw+VTVe/hRfqXV/TstSgNuLq1cgXoU6vyngNFrhIvK1usCGqBDmBhSYcCbsqJcgrVY6f3p7WGrpqGmtrFsxZ0LdP44B+iyhWTZWs7YoTFppDpCEHmOdxLiMTFaQTRwMJaUoO9VRyVmZ0wAjEWElAqKarhNHgCAwNhGNBwAdX3xyegCGE9UMrS86tbfWduErTGmIK0aowQTYrr4dxoiCQeI0eOp5RClKp8tTdyiUKBS9CvfTpgk6P3lpdOhflKBO+zq46TJiaGoljia+R9Lo7GNyQVmx9BHd1zrgbnBBxZHnttdfkpaGhcWldRF4gIGodJDG+Ar454YS4kP0Y4B999NGTTz7J1l8mM4zGYQ6wBl+038ATTji+b99+8+bNNYqGmJZhwZboQq2aKBqUicFAk8ZKL2o18yYaAFu+/vIbtvkjCi7Ua+EP3/to2CqrHXHEEYccetCKK6wMZpFUZEt6J0PMBKKluFAxIMCHkG/BVjLqJ8y0wjKseBhJv0U25SQsSgFQ95hjjoG8IHqB25SGmkzKFHx48MEHwXZwG10D7tmGDBmiaKy+2FtBMyZOnEiTRAwzegF0BNj+oMlCj8CgAP0g4OpPe4RXTI4oTA888ECKwbc8dxqDlefaa6+Nmwb8UDBjcuA5IgclS13uXmoQAA2wXmHrgdoZUVPDlhlw6b333tMTXk1TQ+PcWfNOG3f6kCEtWDQYnEnRCxs/UyRL/xWVcqnjodRcHMFYBlUaYbB96tSpBLA8ZRBhL8nKEJeUbFCO9siSMgEueevueYUAAGfsc6VZiz0jYjsT8bYp1rCw+Ou0GZH2mtZIm7Kxhp8s3j4stU7a1dDe3lZPgOV0bBpkjVmhWgL6ZiMb1NhGSMBiHtMEURUmEwAyfzHjQJ4uvezSr776CjhgDsydmciGcNjw6ajvanv1hSeOHPvXb36a1thTeSmq7+LDewvtsHFRpYAvX+g2CC7bvJ8V9uWLFK2NFmsSqfJZoWRcqLPu8FswsO8in338ZlNTOxv6WfVU9g0Fv3KqEo211h0qQ6WPP/rg9jvv/m0+m0NQ54jJkJK17K8rq8F6CvDUEIQlUjquoRYFNN8pIUCkBHmlTNP9i9ZIsKGWky+68O3T1da6/O+GPPPkU4zU3gv3UQ3Ow+4J08fVV18ddpA2wDaxeiO7/vzWub9JISBkVAx3kyaKeSEfmju/OrzcMYIGLrrEor0Xa1impr6htrEOwd6T0slna6ZUMfawjxaLwKYeNHZpXYOlcYwdz1K7lV5TBKkF5lqKlLXo2khDz7q+PXti9axYeWmVJOj2Ho8/sHduyukWbqEnkAEud7hYZCFszqkFgYohD/+KITq+YAf0HfDVV1NPPvmU9kh7QxJqnpDJkAZbRgqmB754ZyL8gEYyD9OSWTTA27B7YvUVhn342Qe0sHePngva21hAvvrqK0eNHoWoucbqa6FJgWSBVCIuGlLm1+P+FgcCyPBgGnWzswDXCXwg1Fu4BQHfcmkQX5lir7jiCgphR338pmhjMIU9M/YFJMOWRxRqsL/Tpk2Db6MxY8aMYb2IgO36EZ0F11ZbbcXGIqU41rwdGAVhx5yBotgxywIyniNxoYeWQVbwQD98nvEWp5IgJ1ZCqO14dFepQcB8cb47uw/YQYZ1N+gE4cJoa+nFlmxsalqwoO3iCy9e0OY5qNL7gi0WLa5LauUvpEsmXCGwhszSsKG/G/rNN9+2dbaCilwYo3FUhGjWeKQvXBIwd1pEOKR2uWLyCAH99RDc1d6EoUNX6rdI77mROfV1nlyjGTo1Syb/lt4cGlIT9bqWXp1iUq7rasDWtk/jQgtalVBQL6JHhOWmVA2Sljj0y+6LcNYshnLMYswyUsKQIUPyDtLamnnzZs+c/stdd93R1tZRo7dpKOeEChMQ9TFkDvRGrYxqZyKx8TGahfQwM1iyFEi92n4BuQW9FjsiWxf0X6ShJtJW4w+N2Irz/pSTokGxEtocRbn3nz137L771XQ11jT1USYNmOoqOU05xdOjPA7W9tCPKhQ0cHllg88OC0CiEqCiL7EXlZKdPBG2y9T8NgO5s75Hc0Nzc3ubGu3q4mUwl7zI/i5zFUZ66PjvvPNOZjJ8xjqHsdkDVH1D9eETEVyNV1bRamGtoeb6a2947c0Vpv3yfWNdQ2Mdu+QVbuOiwVZL6Uyyw4IgyAaXpBFGXuhVZSvGvNI6BahAzCkSoLahBejPVRF+BvwAqVUVVA9K6dHRPPPnGXgLYUNPba32XyJpYWXicVu3JPUtEUxS53BvQ4MAyIbWfO68uYhJLMyidBDFOWdSXHzBxYQbm3s21zUtVLdQfcRnd6L0qqaLHZfWo90s2+2CFS/2NVYEg8JDG4V+0FfFuCS6GutrZ8ycufigQZdOvOKq266ep0+Qbm5ubG1th0ZhqvqnP/3p1FPGt7S0IAoqgSFqbpqoOBdXQAigGEJoRxrHulsoIWLe9OnTc2wCXxnXa2z/YYGX6YkqxIQhvlgsF4466iji0W6QSwQznCzQmA022EDl0r4YAhnRGqBNgECJk1TeCmqhngC7ttxyS9aQsZWgNFKi9ZDTATGaIJIuE0+xgTLdY4lAAIoHw4fO6Prrr39VG9mhCuvdoze6+da2+ev/YSMOq581Y2bPnr2aG5u18R1LRWpeTMFtiV1fKB1UZgyyaKwJLJSWYtkxMaD/gEsuv2TSvZNa21sbahtgzAYuOpCUYDJorJqnDSclr9xDaY8rJHQI+PxVlG/io4NiaoN2bc24U8/qN7DP/HnzZT1GrFmZIZUIonDBcGteWM2bURYuhMbCOnbB4PnzO4tMbJjt07PPgnmiaGABlF82l0PLNKEmFn9oGTimjTBeVzCmY5rLMwDrapp6rrXGOlfceOt/vv6uplEZCXLaEwjHGrxuuY17gsVaMBFUiUFCK2VMfBQAVopopBWifAhzJ9itlA2RzpYllhh37FGvP/90TV3P2FFgZcpzMCdFg7QNnQmMCO6nTzxp3NT//q+hR+/5rYp8e2IXFiSSTg94L8i76IBT723Y8Rx9jF989ocxuYIr1bp0sterxRTa1dk2f+7gJQe98a9XH75ncmtHe10D7mFME8IMGDxmmYgJDDtqeDLsq8Osw5WVBALKTKC95rEH//nY/f/0koCA8qETfu5ApEFFiTePpjpTFK+SvSWx/UpSclfoWFPXoxkv7CQQmcEU7ALlCAFUl4hPKM5vvPFGs6tl+i8zcN00f878etjxjvoeneyJ86hrDK1LtFHQ0m0peIjfXBWyaJ1HTlUsV5RAitLB1jVEq+uMLNK738D+iy/Sp2//hfruPHqXG264ES0DNjfsHYTJnjzp3ocefBh1w9lnn237lBEsNTRNqnT3QkIAKy2+AqIdFg2I6CJ+YwfO4Q40g0+TBSVBqkf+xwwHwwSEfIqFCaMob6nN7x6zOTx6a1srTmFY/sUHLQ4UOPCCwyzZGEgujHcYAmyIiFdS0E5sHGgbKhIpj9LIgnaDSJz/c1Isl19V9C96B8YUz6gborEuVEoQwPsGR59ieEKj+JoQQYjS3AVzV1xuJfTpc2bO+d2yy30T+Rr9u8JPReggU4q7C1yaWAkFYyEqSsoCycyjrYH1Wb4ExZIed2fUqy1pVICYpkWaUagtsdgSqBg6a9H2tzOgcCbSXKOEAWXi6lsgmupcoEwg4OGAZ/gcqbn7mkdqBrI1XzeflyKFSGcCLJ/9aIdz7DkYJz/KoVh+qB161XTN9Wbs7nE9xwZUfXa29XH883vvvocBIPoF5ZFK7w3MKzMDFVvQ2bncSqsuM2TZPosNxpxaKb80enquA2wcU2TJln+TfzM7l5XKZvasaD+oy9ertHoERGoW61Wz5ODl6hvRMqgZtihXTooGMWdgDMGXLLH4kvsfeOCPM+bXNDR3+rQbiDAhoXTUfUswyqI8sd/7uO0SCXL5aeWvXoa2opRbPm2hjtVc64I5gxdf+NaePR++7z5aolLpz2AlDznIFujTTjsN7gqeTE23ulK55xXXQ+5GYYsTQGUFH9BDHQIHSzPunKOWbhnw66/4TkOHXcceOby+2v3whq1WgZt4xWTHKw6TCnjks3ZbmVJ0wOOBpDRVi3KJ2tYaWWyRZf528ITOznY99+gmqT1UihZRWmwZ7qk8ICCK82tv8DSJmDM0NTXO+20+TmHQYbNzBy2nzR8bHNOkIPjRfQpp+u6xUPLsGc7E4CRMTLCQaGafxEEHW9va57C3v7111pw5111zLZoRRgGq3x122Patt9/+6cdpiJrsYeZiezwW9Qii0jVK8wimFmtN4S5QGAggyQN/SCLuFZlNqJQwlginnHIKMrzZJN/tNyIX6WVfjGxVEDKLDwUumDAkfOmRKUoewQTc0KJogLrOnDkTLQD6Ag4IJEAWyqQ0XllWXVr9geimWxuA0v/+9z9iuINvKCxIRTl2jZQmj8IXBrK7x8JDQFYCpV68MGyzzTZ8Pvm4/QcuesH551/MLpgPPwIxoRunnnhqe0cb5gPz5s4FJer1tnkt8mOg4tMjytJETAuAnhTIOXBW1+Jpms062mErU6wuw5uFfWo5v7W1ftn6H7//qWWZlo++/Kipvoljrd55/x0c0fds6tnR2tHQ5PHAMi7scl241CBg8EOjgnriSEvu+lTLmqfffHDZ5Zf89X+/4vhfvVIMn143jsUQYgUPUyutVLIMLzXjdzX4ciCb3xoiHV29ei7cOq91rZU21ZViXKE8fGvLmwxLd8nTgADzCJPLK6++gsqba8stt0wjUzhJoH5tnTU/KB8NfdhAjSSrjmFUX9qzmjbYS31Cy6RiEy+RPloaw9Vg80z6QDkmHQk853RgWlfnIksvMmv23ASWsSZD/gO5KRq0IE2X6MOceQtmzWudNW9+VyN8rPa6qTZOKYFKgcOaa+xOGZiaSEUyYi4bqjEveBBSIrHGnJgyu2obMKbCGV/rgtY582vmzJtX09GOhMg0CbcULCXUZ05MgGXHMplZ+dNPP+WQMMiKApDtFTPUGl1hsETYBK288op/2GRYW7taEIPEc4tjHQwq6kEHL4I7s1RKPoOKosziUU0k6QEcVFOKhprOxsbOhWtqxqnG8M9f0FaHH7mrPCEgq6+33XnbW2+92dijccP1NuQA79Z5gnjKqSn+nzTBM/ijNt1IX709Nl7HFQ6gZYA1t3QNJpdKZGOJz8FIZoW9Nl21w0JXieGwL04dVp5RtXnwzTfcuO/+B5B/8cWX+PbbbyeMn3DeeRfIMIFkcWHEjvP/RRddVOpw9yJCwJAvfCXghJyWvPXWW7gzwKUiFnMoAmCkum2e7T+SLEx/sGLkQsvAHgcCYkdgl6Pq1WjHNgcWsfEug0sFUS5AabkIo7mQu2gH7OwSNo2XANorTBWwyGBr4fQZ0/v36x+fxcWUAgQUu8Kegs5OUU698847uOrkiF/FwSO11daefuaZu++x+0cff4yWoa6haffd/m+rbbY87NDDsORqVBipFAvaTEARwpgeeTQwhr4pbjqqM+WVTfBicvsPJk1sOf5rjw/UZUIGKa6ro6axrnHWrNkTr5h45NFjm5oavv566kbrbzR73qwejT3pEf31c7u/5QEBG0uQMtT0isPtXg1NPVi4rWfPYEw3vCdROsgbCghf8gL31BytWUTmdLZORNprm3t5KlSm+Cz3ysZ0xj2kggBU66uvvuLADojVYYcdhiEeqeMt71IVkdW7hHKlWvP21KwKYRMTrNh44dykCXaYGF/CTVyOnRhPIT6mqaV+KQ1RXBmy4tagGFeaUlPipnnMhO4JbEd7Z6QtoqYo8crA7hCyeZqV2IGfuDhifXY8miDRFBBluBWPHk2r5ixVKzNcJ21Dl9PaGcGmpZWdeF1sV4HQIDHaNCqaN6wQ+D106FDu8IIcKs6iEL671TphA4Yr7koFgUym/JiPqHggXA23YjMXmTHrVz1yG7A/N1K9X6tCSJHHzLDUZgX+e+uvriBKGcwgT4MTUqUoHw11HbBQ9TWN/Xr2bG9fIHybVUMIQTgkbwyGUJgrIl0IIJshbt1xxx0R5UWx/brrrjvogIM7IxHcgsBba21S0i1aGv0Er0AxD8FEy+D5brBoIFhnUBB2SlA32kqfqJo0vDKITRixQJfJ+wgiQNv8+XvsMWrixGvffvcdGr/vfvueeeYZ55xz3oknnsipb9BtsnAMAVbuCIQOr6JwLnYIz6N8IKgcsvqECRPwwIcvAzQI6SgamHrA0SuvvBLbhHPOOYeuoEXCoyRr1GyIQNfAfp9k/Xv11VeZyMjOthrIF7YGQ4YMYfvrlClTwH8ME5JlDMRj/rDkkkvC/33xxReMHXZeUCxpMiH4gSLdY74gwEdR30V7tbvvvvt23313eGi+F18cbRfIs8OInXg8+JCDaQH+F2+46UY4Pk6Y7te3b88ePVE3EK+4LCXqK+YMhajX1kSKBlg2jxWXRH5aL4v3x6ZwROlHi07GJg4+dXZ2cMD9zOnTDx570Olnjv915q+XXXY5/erdq0/bgjbGEf5Bgnncc5lAQPh/uc+Z/9us1p5z5s+ob9BOtehCLJLEoFHsq7C6qyy8PMWZtmjAjLU2smB+m0ZypWhgbIVVlysnHgKQJiY1JkqmLdFuK408W+bzeynjBWXCoBfX1SK3PubC1CmIZ8uq5pUdAD9SYEfq7DZuq5Qay0wWn1W0aytoOEZgy6VmdNtNTXrBBKViU2N9YzM/NprWNzXwwz4trV8jByTxQzcuv6aEuaRMdce/qC7fu/Po/ZppDz4BeaQtDWwmLJQiB2vScePGAUkYdw4JAyj9+vWDR8wFthWcF3Jg947HQIz9NnG4q64R5yudKBpaOXurrX1ea8cC7gs65rUSjvnh/Re/++reBouhf60kS/RbQAntC+SHlQRl+r/E6QOF0ABVfvs8fO9xwiWbrGke7RcGLnFH0osFPsKmq9L0lV4+lyo0CAB1POS9/PJLPXo3r7rqsMGDBxMzY/oMKoCyx+yYUHUmo7He1GBrGQjbE4anNYAlUkrS+PaTVn7xr6IxkhFvbZDB5p69cOTGmjS/zTbb/IMPP6TlnI+Dbfzxxx8PyYJ8XXjhhQnV89ESXaiwEEDCxwMfmnwYGIR8vhf128c9pGgOnxKTTjw74h+Bbw0VQlYcMWKEZGHLTIq8aCJ4u+aaa9IAPDUQRisB8YF1QyHFI/4pU2Q3r5gE8QNCLrYTvvjii5SAFp63tMekcYGSggCKA5CN83o5XgSycNFFFz399NMcCTl69Gi+5qGHHALFoMFXXnVV757NrOzwTTHhhBxFlNaBD8un7dR//E/syXU2eTM9tuiYpnUB+dCkU4GYBCZjwoBKzoTLQW8InrM7FcphHIRb+DdeewNXuGhDQGynZVBgKuBluJew6tSymcKrRixqmvjV1jfV1TfXqB8B/9fQXN/k/5p74B0ezWn4P1M1Mgc+yRFBuNOwGG1aJj2PGUSZZKzOtMx3LKJAsnAwNHbsWAY4SofQUS4hbFnjRskkrxQfyEqnT/wSpo+PNHqBjF6ZGk2AeilKSlMtybAZ8bXnHpOMCc6mZLYrMCrq9QHL5FcbBtiIzrJyt1biogW0dIGaO0+rbdgvyA89olnNQ9FO1bSBvazI+JiLFvJUD4DADE3tjzzyCEYNBAL7UYlxl0BA+6ZWQaUE9C8AKDFE2pf/3vqrlcdqZQ9/vg21aC4beGD9orGxoQk1Fy771EJc8OrR2Oz/VMpEP7KaS88WlKl/vg7MKMMSB/zM8+bPZwDogRDtoNWBjIN6VHmUA3oKfDIuwmXIDQLAHB+Q8+csWDC/9ewzz0SCmoE1eN++9jyhTBu8H18IMwf/ZyM0BgeoJTSJlGjJE00MARMCaqWJKUA/qJME/Z/9lrxccqfMdg5eqqlZaaUVb7jhOgIQSPZKkABBFN0/y91ff/31LbfcglMA3uqs3i03aLncuUKAr8OOA9gmvgdl4aZBbBMIi+IS7sr+XoTRSvBZJf2wYcMQDtlwwSNkCTtSVOGip8CYhUfKmTFjhpQAW0ZeimUru6gDMKbAJAESClnmzEsSo19Qke1tNIxILqz2uPMKf+/fffcdAXTr5ILDo1juNJ5H4hFWWUPGIgNJlYvqKE2q5swCTooljbuKDgE+t7QB/wsolTgKd+utt+ZjoYh884037pk0uWN+68prrn74QQfBZmHaQvr+A/rxHb2WMy9F8NSgKGBsX2DqYn4+kVRpmcX5ZXJJDcE72zf0T5emt4yp4aHbseqwVaU9V15xZTuHvbmrUBAQ+kBtQrLCqlaEqHREKUWn4MOwPOxUXmzz9AurX/HloMiTxar4Vy7GQIBZ76abbmLG2WSTTVCp88V5pXhm6zKJww6oTaqUqSkYfkJgvSBNRbgUOyA/r3KapOLUTo4iXWkJ8+m0je9IR7iTmCVXkfMJq2HNj+fwfhbORIPsjjCXqotaqVnaw10H0ulIjmlg6fi4OIejHJCehSBakmOZFZmdjwWtZzzQOzgYuE/ppsxDwDCzXmeYPLPCc05NZ2HBcy4mpgAYdBQNMVHuoVAQuPnmm6lq880322nHEWixG2rZ+q48+oouuVCt6L4eEI85JpCOLdbEsMxy0YWXoGtgTY9kjEGO6txnn30Cid1j0SEgPPHll1+OggAtAO3hSKPTTz+d4Q9V4YIBlXjmGr4jYaYhvAVxQADmNtw564tcqhwmRe1p/7LLLoPGknHPvfYkHrM7KYFkaAQodtSoUYQvueSSVVZZhWNcUQ2QfsMNN8RxA5q1f//73wcffDAZqZ30JCCA/4iDDjqIBqDOYJcEbUB1xWmCvGppaUHXgAr43Xff5eCMjz76CL8PIB4xJEbNseOOOyLEckBAxpSfVrorbxBAl4Q1CsWDKnx3dE9bbrVVYw+lhhCkWrCgDZeKbfPntfHh9YkhisHWjG3eGpVZwXojSKdslsalZd9+uEyq+derr7315ltwptLmzEp0qTOHACgBdQKLEPMhNYSZdDIvJlUOCtRChv83kciRKn8Y74yco7pnXVmXDdy4gBtis1H/ZV1axWdk3pERPWTIEOmsksjyLmCL8lRXGF0vr3hgZ9DBIA+aQVaXNAkEIKOcQI53Bt7fcMMNwoolSVu90VBPzA8ADlMOHIyssPGYI1GAtgNTTZzVrfLgq4BWr0QLWEDgVnkdLP0ePfDAAwhatHO//fefM3c2+xA4qoGzAxXCKUWy4rNLoRcc9qb2VtRqG2at1qdVDJCF+iwyZcozjDUeDzzwQIQHZDzajNSH9orxWAqNd20wEGDIt3eofRNsb3nqqadgoeClMC7A0xUKL+R8GFAxVeCbQhMIP//883xZUmJfgDyPdoAsomLgK4OlvP3HP/6B3mHy5Ml/Oegv0BNsEJi5yI7WAAMKnDice+65mJobFTDtoQ3PPPOMkB0ybrTxRuzp+OyzzyZOnLjyyiujB9ly+JY4dKAcvEhSkRxVoOSKzgg7/FE3UMibb77Jdoztt98eg3wsI7D+o51rrLEGe3ZgCoWAm767QHEhgC4JNRBtECEH/2odrW18o/Fnno6vjfntHQ3KhlDsCNVfqF9xGxyoXUixRqqutrnzUWs9+sijyo69q+aCCy9E6HVzaABieXrkE/AtwCLGOARKcXqlMUvmqb/hFgvcZNNZuMVWRmn2lIEPIxBspZVWIlCs3ikm0L+K1YZSq7e0ZoXcoQOPnGgzc+4Fp1sCE7NwbOwRIg+bYOXQ8nTzV1M6YAUPypSz2WabsYAmNrci82QHBj4958p0k9fbLOqlss3dk2TMRu5SEl5sRUkKzyYaqYCJ522OJ/zpp2zyuzy5QQB5j3mkoblhyLLL9u7Vm/U97L/NljFddndImFsD0s+t2Tsao+iinWv48OHIDIwVRM1JkybB+XExQ0O7RAFhJ7bDFMiwtWPssD3l2/EunCMERBxiTzGk8v3330fIRzL/6quvDjnkEPQC6667LvTzyL8eyYFHyPAk5qSAZ599dsstt+QYEXbXY93AxxUVg7QEwrvTTjux/jNy5Ei0FRwtgeR/3HHHbb755lg3vPHGG2jTUJfzQUUFLB8dPRTlYI+AaQN4gjICExhawoaIfffd9/HHHh812iPjqDbYBv/444+vuuqqoBaVohD57NPPSEkYrKN5J5xwAuwgZzPdfffdZ511FvFQNkaWtNDd8w2BdEYrSm2QR9afwQHcQ9KqPr17jx17xMLNyvUjhWirVZKov7yFz0aKDDZeb3IMRhbqmUZi0cAWSfyub7TRJuv8QRmcsq2VLT+FakK11yPjGrIDFkFYDDjSQUKTuGoD7GeE8osNUdUCIWHHBX8MFuEpmYmG6Sxh4tKPTEMe6b4TMaxe98kLkSI0J4XqjEt8pHOXEGe8qI/vuRT2wJfOZJNOGhsyljinVAxqPVtdhIvC7Is2SxaFXnrpJcgrigZskh3/JN/FvuNxCoH5ggsuwOYWCQeul8UuSWAIR/pw8xRMuOpQH14pFcEF7vECvz2YkZnsR117cJz6OKVfxnFQARFO2q/dWUk5wdK8BLRNG16YxzQDQAZ2n6VCsAsHbzgcTTOjSxYWBNi+TlFbbLbZppts2tHW1tBUD+3jBEv1459aL4MQ1RkcxqbAVB1D3uRBUzAhVhHBXZNa4ZZHxvhjfNBY7yVo4VgMfnqlxqavmzdvNkoFVpI5XeLSSy9HTCWB6BdEJpSWJxx6diQsI3IIvCM2Nlq6qEPSYFUztjr3FBoEgC0AR9RHfcAFtN98601sHDj2ku/CNj2sCXCnv9pqq/Xv3z/g5U6+r8FJ6C2CIWZ3d911FyYtuGmE/JJlo402Wn755VddZdWE3xFyTWc4QgKDBUYB9fL1OcIZbQLaDfABTQF7OlB2YKQg3aZG2gZ64JmZGOwaTj31VA5NRF0ydOjQtddeG408rKEklpMsbByTeHfPEQJKQ4SfbH9s8qVg08CBFMUaVJE08lHw6L77qD12330PUKVVr+konYJH3nwqpGihzmRTM6icf4hv3IRrWqHolf3WL9EkUHTVPKhzDVNdXkpaHuGYQXH4r5yCR0buuuvbb73z2cef4dMXjViqMty78CAArYB2weyxC+y2225DC7n++utTvI1pbuzHwxud78svvwwB5+QgRq5iMfz5Oj5xFcaIfhNpguGMMh1A7brrrkxVNl7lGyxqhsMPF8wfTmHUkT1a+FW1Rs8g9yQUn8RFCZnVOCiaTQCtN4mDpkzzWkm+MRKLeVPMQKqZJvd2qR3LWubPvaiyKAHWTSGZxiBWihCh2aSKjyuniUz4+YStfO6555iBsOxlPzCkgakoVwJhGJ0EtQprknCMJ0jtub+2mJtEiay4qNorNQ9kZckwyEwMJcUzECSVVcEffvgBvl9sHDIsySXPEgKsx7LF/Q96WUyKaIB/1QfCaT44bezKpH41A2WSXtKiARG+xM6qnKQ2NG244cYoGkQMhH0xQggpU3N7pGTAKpP7v/wFHuiPf/yjCLGIi6KnsOty4XAhYH8aPgRiEpcQTPgtvrV8i4SVBuiqFIXqou8ifXfZeRemLnILGpASlECpIWl4JMzsJsXySPxyyy2HnsJuD7Wj1sWYQtpgqpOASclmYzwLYm1hCgdtJI2NhAm74CKzgAADk312fFDyoh5ikwsC3gsvvJC6KPN1SCZhvs6AAQPYcdOzZy+i8Iolr3grF9+fU6U9LUPq0vP+1j9lUM/dCmNpVmNTTQ0OcevGHjF2hRVXxipn0KID1StnRJP3z6EqEMUWjkWBOWvOuKclEuDL2C9IE8qyEihqS0vL1KlT0eFiwsZwBmII0g5vBX+YubgACMABx5hfjj322KJ9aUVw9A/qGBUHgs3JgpcLFtHdM+oGvY+8u3QFeV+A/hakH8WuhJEPriOBsOuP1QOaA8Zjg0rElClTZI4vdhtLrn4YFzYAQ0CBEvuNxS96jq2USUv4nkolxBgzs3caoDHlMG0DMVlpzBF0LnuaEECOwgJ8gw026OxU5ziU7KVtepTb/8CE19TYA9cSaIBpP9JpRtRpzpw50l+0DCgHsbdn8RxdIeMOcZF9T5QGJZRhWLKQqbCGCaFDF0C/oAmogQLw5zEQYyCgtAl6iQzFpZHzKZBwPP0khouiqIIPLWECPPLReURNzJ0wl6mCgKQkkpRoRcnCICJSGkxALjuLC4cFAfn0n3z6yVprrYXNC4OU9Q99GmXGNfDtFurdR2+vEqOFjEsocAZfzco6H3y3ItewZ0yXfxrxpwH9B/AI4hW4SVVbHUMeyyksGoAALmZ5hFhxVS1A0uw4KCpKGcYsFqw8Yj7GSEwze8Ung/HggjPBcxbETTatV3yvy6uDTtEQzvdi8EM3QXf0C9gowk5hW3jAAQdQOrL0628o2uqueAiwPxx7J+IRV5j+YUCNJBOfOFWM4hZiWNtUiQvxLo8jCxzbYYcdlllmGbBu+vTpyHtuwi7EJ9V1iJQFzOvqlGRm6uVbEE7MtKZvEWOKCyOArIfyU06MC5TXq2cvuqAkS/y46R/94goki38UlRYYOHr0aML0GsadBWo8wz366KOEjYCKVGkKDIid8cW6mEwhAGyhlgx8gS0fQi5Efb4OpanP6V8pCldJIl18NeYviqJM+8PFZ1Saqc5O0UqQlUrJS40EeKQ9BCiKi0AQYpCeAABAAElEQVQgO5FqiqxvYA3KvBKMMo8uEDoEcEbAHm821Hz44YeIKHxfzsNi/SOjisjFpebomq52NJScXB5LLbS9rvqsgfiMagk9MeiqEZyjLtWM3ArRa6gnkgts5FXoNboCAxCApBg44xgIFOLiKBM+AdQAi6pAevcYD4FDDz0UWEF4v/32WwLIFwzk+GTVGQM0GMv4PwY+XJhYAiKnwCopZMijOOT3U9uwwWqn4rZphvn5+dL7i6Y68CNf8s3M6RWaQyrFZumVJWgrNu2wfRSGb+0ciqzkrHJUOz3keA5ABwMqEJM+x7Oq3cGiNBgdD9XzNbgAC6i19957o9WGX/z8889lIdFM591Byb3PCQJMY+Ihz1YsIFTJ9+bus9oWTRMCGKCBWNbFGtdJBkUq/Z8pgkA6Fx5K5EcJsNjisATPlap1Vu11dQ09evSMaT9YFScZxtcoQiwpOa36+++/xy+m+IpjII8YMQJ3D3fccYcuSUmb4CcXhehdi/GFuZicIMBqLQMfOJtS5AsK/CVSwqnvMmGRnqKUgYNWGSAGxBer0iAZ6H2whEkpaUz5AbHBxEtAElMCI8hklIC75wMC999/P0pADBlw6AOqwJGjbmCHPAdJ7LHHHunUaH9B0vPIXRAGRMHohR+7kutr2aLVRYwus66WXTj88NAVEOSFrEWZPZu8JUrfTRNjsisSp3+1NMSvWs+JaoDU1jTAF6o2K21XD/F5K93pphL3OjcIKMt2bTfHbh08v7KSxJm7WLkLagku5VZD5efGooHzgOnnSSedBDChwI7ZM1+dqQpoiJthINPS0sIr0X3b5MuETcYwA4q5UnQm5rI4rpj46nuIA00OIFDWaR7j4ZXCY5RbyaHkjLIG2pBR3rASCzvO2eAikHAIeVglV1I57BflRDQBDgfSQCwgE4BOaGjlMAFKmJRfmF/vz3/+s4AO6wbUt4Z3D7MOV1YcBMoFLaHs6O2SDCXY/4AEENfPJBF299lne9555+HY75RTTll88cWRUrFOQv+1zjrr4MOCAmTxSq1ya+P8JEW66GwgwIewVQzxRZCAKz4+EJMwWcJIycirZPXyiuXiQPnxjySLj3QxuUNAdENYKlEUJ5LgHArPoGxr4pHZAYedV199NdbFRxxxBBoHLrFeSbNeQQmhJ2TxdQoxufmuJfVplZrDt6tSWKctcRT+xrTaPeQLAoIt4B70H8rAAY2yjHT22WdLlQad8tWCiigXKOFjhV2K9IbwlOemQIEd6My3ZWhDzfCId9111+23335jxoyBsiWbpEwuFygkBPJCchH1M1QxGEmMQOVc4Dp70nDQfeKJJ1ZOr8LrCS6sWfBkVxWGYZQKvTDUQbEF2V3eKnFwrdguzDaB8VUA6SsCcsNVbwU7VzyHkqKaWWKJJWAuORYUp8R2B1248BBQPK2ulbuv6zSoooR+75dOy0xiD1u6yROLz4HEauc8S9TaHbIaUwyyQIosHuFyZC5HqsG4Zvz48RyICPvIBgpK4zACDkpEsLn44ot5hLnEogFWwF4kz6JSl6XoEMieLBe96VXQAGZPRhl74K+44gp8HuFcWRT37IfHgyMnOjHPMnLZ38SQ5BWLyWbCLRB4hLJ5anchj4WoGbUIBhcQLSordJcL0b8SrUPIBWRfYI5/AQI4kWXfRIm2uCSbpebvri4EaXXUS2srukKayY7skmxsERqFJosL7/v4jcIyGlgpI6sC74pSrBq/2Cs9/i02T2U+hcB0AhhPs+DvYqhMUGXYKyGyq6++Ok7jOMcrw9yVnNyQAEzp0DJAOiEQdocFdHZMmYcRMo0cSleyXEw2QEDGkzBrVgRgGTnZPv6tiXGBfEPA1yxACRNpuIo73ySoHQw0v2xgwwhFSkFcYacoprAEUDGgTsWiAfEGQwYYSnaGn3DCCZzD+sUXX5AYViAZiw9BMFc2rXF5HASqCQIMloTd5Qz522+/fauttmJDE2ONiZVztVnqwDXDdttthw6CC7trVkeRUkiQbJ6V8mVIJqyoXCIhyyhh7ctZM9jQKExYEO/OO+/kRFsU05zLy3xRmKorqRZsBsWC9aqrrsIrZJ8+fSqpd1n3BTIFHeOCD4GsgWyEhbglo5NZ15VWRsVu6V8CviutAioyUTiKhgBo1AobwI6j8rKDLiYxH6OyvofGeXWTbspjTJer/oHxj6g8bdq0CRMmEEbhzaFZAhUeQ4QY52wHjtqOcKpt/E6q6BcRGhF99rbKWxGxwdTpeRt/mUgTiE/TTYzaaYrKNtLF7gkEPPzfsKSMfy9Ap2Ab8dQQ3ZTiXocBAcPIEjDqBlWwULZ4+ia7lO17TDPiMMp6azYemzhJbfwyiEcG8zbrAFjEhC3Zk5khyDglJRsouJOYO0suRx111E8//QRC8kheXDShZsXs85tvviENjKYUyH4K0ZchDmXdTpcxdAjIZ40nwvExoVftCkwTAnwLGZ6yS4Kh9NRTT7EtgjW9Aw88kFfEI5O8+uqreFHhEEdGIpde9muUvS2ytTNhdZLYvJLHQKR5S0Cp0OMuWXmSu2XJFcFhZIoz3r30Vmmwpyl+VsKYoKk9JlY/QJL4K2RThdyVZwiAOTh/BffOOeccqWqnnXbKc50VVTwApD8Mee7bbrstARaWZs6cWVGdzKEzAhkKQPPCRnUwzRRmXpmY/AYUsxeowdEYDyBhKhoUfdf6hQCw3aODgA0B1I2wO3PnziUS3TbL8iy8ELZJqp2+jMO2nEnYs63KVbkm+lpgCL947rnnAh8mHoxjAaCYxZYxxMqz6WY+8dUNJqLI/cEvGifbKzdt7Kr2XROBJ+ZK2D7ecuYhigD148Reyy+gnZ5k9qMJ4x6SQY38w7nfqCHwYvXkk0/+7ne/w2L2v//9L9iLcoGBP3+BOgYY8wdpjGS3w6ZAF3AQcBCwIYCwwUjBnghF3mmnncbO5LFjxzKsiGS4sVvzhx9+wJSSR6MxtLOnWFImC0OeO+nh1FMw6zDVKbQGdnU6XFiSqGid/vlET9ojFg3qgPlSOmQ+DlaVE4GTYBgVDNo/+eQT8Ir9dGi+Kqd7+e8JNr/MpNTDLCxbERnaEsh/5a6GNCFgeHv1ofTauR2TZiGVnCw0RYNZ2QNaRqNsR6aCoshjtlSWKrV7VwkQYMsZPBAKyE022QSGyXA2YfSt0x/qiQsTGuBRgkxxL9P0gSaQPYxL+D/u6Gi4A0aEOhaygKSsaCXkL8Oo2ZURAwEjakP0YsldDJZF86SFP0nyRktJGso+p1UkugA2O4jhDKjFk/Wym6CRTMh4ysmnYLg0bNgwtAnwSU888QSORfAW+euvv/Ioxp9ZHmfbTSvcaweBioUA2mSGJDuSxowZg5aBM62wIWLcDRo06KGHHsK0TWQ5pgMzGNOHBSUzcpk+uJBwxOwo/ezdpRT6ZKcKhWLZBSYNK4Lmm5omTeRehAoB0AlS//bbbwN5ZgG8h4BXYSNVqC0uscIYjKgFBWKYKWH8C0iffvppJtYSa6lrDqTMXYkhkAEHmbgAF+sgkAkExNoTZujWW29l7sHWGtlDVLYVyATELqdkAqd007IHng0UTN5Yyd57771MQqjAGxsaUxjHplu0SxcLARgmOyLCoY3J3W2gd/CvsqGxdFD0fbR8//33x+kU6CS9MPF+p5L+tQvhYDPSTZkyhY3ia6+9NpjJotZdd92FdQMy0nvvvcfA9w8KTVqge+Eg4CBgQwAnC2yLYISy751BCvFfdtllWTRG3SBu9hBLkE9QOpMLfaGdN50wBV500UWsRStVRZmeF6MU+vpna/Y1KNIkZSRLM2U6IK3aNMwg8HVgKfMCS0qbb745mCn7d6oWJll0XPbDAsm99toLSKKpF+QknEVpLouDQIEhkPEklKx9thGd7CXm7kdSi/yS5Xbx1QIBuB+kix9//JFlGWglnoGWWWYZpOJQtQz1yrdCcl8MBh0JeF4YSJ+mUkBSdpNeStP1BIoNPOb82dF2H3DAAUAPqLIxHh6RdQNKDRWeObeyzAuAcQewOMWgHyCtPbsbfYJF7qS3depkefVDGwEh9H6ZQsLQUgLpXDG4nU4GKw2KAJ7o4KmnnooecOedd+aQPDoryynEc0kCE7ZyxwQlAeMaG284y/XWWw9n+JMnT8ZTHZiJjfcdd9yx7rrr4gWWeJhR4Ud5JVdMWe7BQaDqIWBozv3337/pppsis7322mvQfGzROdQNd4/oHQyQbEGOcKZjaosttuDYac6qEP8pMpZl7Jsqsg0Ifco2d475tDmDdISgbdwQ6B2PeM0MROZYeXVmR4987bXXYtFA90866STuIG11giLrXoOHnBBDdgJ4XGY4s1Ane2aZN7Mu1mUMGwLuWySFaJigUTxxoCIlVoVZRaB491iOEIB9+de//kXLoZjCzQhDU459SdnmWMzPz1gAhqwScGQU4tyMGTNw+8/c09HZ4ZiklJ8mrZdi1X/fffedfsbpGIywm4Bs8Emyh1lv8vWkf/OlUSekqRFIqwWFTWS4mVmzZoFOLJsg0oBOtqzSLV6BkHareTQXWkW81j3yyCO77rIrMESvcdttt7GTHE0ZK7TkEo2Gnd2FHQQcBIAAwxB3j4zH3Xff/aWXXkLvsPHGG99yyy0cK8siZ88ePY0mImtwQe4oBLM4XDygrYYC4Gwl69JKMyPkS5GjOEaVJXcuDuXlyG1aLqYcTorL/SMyXV555ZVSzgorrCCBbieR3OutsBKE96BTKG6wBwSFOa1WrIOZNAFy7sO/wiBW2O7AAEZ5QH+Z08QUti0lWZuDRZE/SxXSXPZmv/zyy8B95ZVX3mWXXTAE5SryZyjn6pl18ATJ0jHGrqwP05UqRKp8fEAQFWtkxGM8ZsODClS1C86GfFRX3DLhVNjpgPAPe3355ZcPGTIEXQDHaLHBwW5YLqgljBHrpfc/cP8zzzwzYsQI2Tdx00034U+bLRWy1cKuzoUdBBwEgADnthx++OGoAKDza621Fjvmpjw7Za8992I8Qv+xrMbeKpexibgCuUO3eMwxx1Ad9oZ470PXQOHmquAPgTC8/vrrc3AgR24jtgFkp2UI5XPDmbD4ATzZ4LnUUktJmYKo4FUoVVRJIQIu3DRgBghyQgpkIYRVJcxaHbpWCRqUaTfDVzT42yU0QLydcmUKnLw3uwrVkMwxH3/8MYbTAHfNNdfEtw0x2KXnHdZFqCDZ9rm0t2mk0WagB4+IyobFLqYiVr1kT0oaWV2SbiDAscwsuStXTJ2daMRkLjdrC2SOoXUxhYVPV2OKT/uhqzYSwRtXLbs+IoRT5KN3stMBjMLC6Oijj0YLwMBk4RTmWzLyKkUJ3b4ym3oolj0UOK5DL7bjjjuSkZjVVlsNfrTbQlwCB4EqhEC/fv0YPpttthkqBk6aePTRR7EzQsaA5jNq1F1vkcgFMozu3XbbjV2NlMYxmVSX2sIIWhDr/lZ5rElOIIpIErWzBg0ausYVD6Xzzz8fAgjdu+SSSzjCCZofn8bFpA8BM1OcffbZ0HYm0COPPFLpsiMRYzdn0qRfrEsJBCACwBM0Zo7mETA6SAIHUMuhR8lCIC/UX21Htrhw2Fz5ee55bGBgT84BbNbPflkxYaEFjARZ0zP9SjjnmbcVGQAULJvQcdijddZZhz6yiBqigKEXdvTiTqSrs6uWn4rxf7VdnfyQzc0vP0BOccKNGXEmkFMT2EoKi0kRLBeAYBjUsYkXnsnNPTmBVWeePn36CSecIAtcW2+9tRRoxqxoGYyugUDMpglN2SI1deqnD6SQe0yrkPyDwr8inPbPnOBjAnYJktQQWAJRzBYjYZrLxdGWuK70PCxEJE7udmmEieQOn33wwQfjEA6MYtnkkEMOkWQMVRIINcsOwchF4Wg0KJkLR7AseX300Ue40TaGtYEmuUcHAQeBG2+8ES3A9ddfj4oB0wNGkAwlIAO1x/QgFxCxVQpjIiYONmJQDqOSkyzwIikziynZoxvKuUH0h7dJZJ1OLe8IhTHpvYCmhDpsprwYEmeRu2DWUJ9x3ctxVIk1IYsPWnz06NFQJOw4UDcgG3MZQpcdrQu18WVWGBADgBxmLAZxI0eOxE0PfQCFRFMvE42HUdafMutnQZprgUfNzi0tLYxQIIxGDFUgw5/IKkRRDHgZsPIF6D78TUG+RvJKFKELvDUULxBfdY/hAALqzU/pF2IBHXisOuj6HVajoJPj7OsgvhInAaEgfqqq+AsQ8AnEXA65POKIIxRkUEPBt1TyBQGig+GMtQCcWNTChIHIY489Vl6xOFPp8AzAIF+PHFhN0WjBMFbkGFHCaj5LtKofZV+jXHW+WpV+uYH1xvQzSkpWNUEkuj916lSOjSASRUPC7mdaMumF9HGHIGCPs+EGGyIsCc+URWkui4NAZUMA+4Lbb7996aWXFgE4lM4y88pwXnjhhfFBgz6RlVJK5gxCdBloJwPrIskqFTqTI7VJVniW8dBh0diqgH8lYTMAKSoVtshB65Be8FkLLYLW+dnc38wgAFJByYHhs88+S078fx900EGwfEQask8gs0Jdag0Blj0WX3xxXMCir0fnOPXrqQTCmpTLCMbQLlyS/e1vf2PA0mwZwkbvUMyOJKQ8xWxQSdRtUeGSaE9lNgIK297RzjD4+uuv0eziDg3SAMmozN6m7BWnf7PqzjQDz4QOkvVSWCg366SEWdKXYBTTOR4uQCekNTnbjC2RxtY9aU73Ig0IsNjOyOUaO3YsyattOge1ODMPHOMaPnw4XKMcmJcG5DJIwpoM5cPoc682CGcAJpe0uiEgEpoIFWGt3SkbJ30B2qOOOgpLQ/h1TNwZ7EYsFCMmqb3sv0CSozohdFAhZk/2cwGPiy++GBnG+Y3K+nMbdg5cohA2+2w5fEt8GwUKNMkC8e4xBQTAT3CV3ROS5qyzzmLehEVJkaUiX82dOxcVAypRHNaCZmLhVYVwKJePGz6ClqAVQ3S9sXifpbGh8cEHH2SzwLvvvnvLzbfIzA3JKF6LilPz559/Tt+Z2jEExYgO3zYoXEJcpSlOrzKrNTRdPoQVYDL3iFHDySefTENYlYJVkhbxioDcM2tjtaa2YcUJUjyy0Lf88svb8SlgE9qnTVFHQV6BVxhU77nnnqyjMjypE+NtIkOvHCIg/AF3KoIkJgM1HFXC2omHhiTLlTCLi3QQKDAEwM9kKJoMsU0LSWBYBVhqbA3Mq1wCbJeQzd4sjT788MNo/AcPHoyTAsokzJ0hGeKuxlyamnFezztY/N60BCXxXVB0Qnzw50+XcQCMAGMAniCDi0oDAuy+YRkJeHKymNIjd0Wpdz7mkTRaVAlJ0NfwD8+a7J6gP2xOQSfYLQGphJ7H9gHBAfZMLLBwlAtGMX65YlO5p1KBQPiKBnrm7aTw+yhbKgqvgJDdHLaWofBt8GFQA3XAnxzjgVnt3ffexVKRKVw5i05kiW1yVVgAQiBGdKghWSWW+YZ7qNyMuFoEsZPidm0Xe9a9Xz4hnKgBniVnolfZNkXxSfX13HGnx+FnEF855FKmH8cwZQRXMx7ZioI6jEe8IUqAsFwJC4T3l4/K3ac5BOVn5QABzM+KzlMwI3tmRqK5aA8G1cgb6BqkbQ888MDEiRNhHLlYCzUpcw8YqFKU1GVibLCYhVzewl1BTERHCcJzEUliyShhO68LOwgUFwIyTESPJngLDtMk7gaxk7VQxoVgtZSTLGVG8VgrMHGwMQqlP8UyoC677DJmDbkoSik1/CGZUcnJE8fRw+RJC/yGzsoKPF1+/PHHZ89RJtnuygICICoohHsjQSQxtMyiHJclHgKiZMQ17MCBAxm8mBkGjoWKz1KRMb/88ss999xD11AOsnzLznTCuJUpWme9HROx9XvcfmxkVT6F+WEQ430O24OlEvWLAVZVr2I7rSuubda7AgVh0CG+VIaWQXYAMrcVqO7iVSOzDnf4GO70nbYgwxgBGCkOyITN0BSvwzE1a77Ko0H5GgqADpNaqsWyHVaJR3hHNpoCVSLFM3lMo9xDcggI0OD+sQoBXZnG0JeDuslzlPob5R4kt4sZnQUEoMHRdxx4SWH5QCrKhx7ah3oEWo2ulpgTTzxxt5G7vfPOO9AN0sNswV6InTN8bSCLe3QQKCkIzF+A8dl8FA0g7XXXXce5uRAcQewU7YSkpxgXKTKmfsWoofbjjz8ecsdQYmQhE9I2ycV4DARSl1a+b4X34I4/WraQ0BEOxsrHNrHyBVFGLWe6xHoXagxx5gzjddddlxjCGRXiEieEAFgKYBmbABb1OmmefPLJbjWVCYsq68jffvtNuDKUpIsssgj2MvAD3RLSsu5yWTc+X8JPRkCBPTS/jDKWUWKZtjfccEPIBCMEgVB2P5ZRF7JrKpQROojGEUJw9913UwhTOFJcW3ueXFSkOPHB6wEMlPll16nSzAVq4bNwmWWWoXmjRo3iDgcpOojSbHBptgowyiYUxumWW26JXzRium0qGCWJuPuyfSKqJidNJDhvotsaipYAN2nnnXce0MA53DXXXENAWPNCNkjoJ9vIsWp+8KEHN9hgA0wtcJKvZLZ6DvD0jtIoZJNcXQ4CGUEA3TrEBPGeQ3OHbzkcHuCUU07BDUpGhYSYmIGMyyQ80SAEcs40qyCMMtG0hlhLbFGGJJpA7PuiPkHWMLREbsHmn51iRW1LeVfOThzEYLRXMpMqDXL1+RHIxycERYEk4xTb1d///veM3KuuuqoKBWzxVA2E2ZjDHQQrPE8S8329vVoxcXFnisW+raanklA05BvgQeuGfNeXqHwZBvDHygmiNv2Fa2dxQxjoRDkqJ87QwcMOO4xeIf2ijnXOlkL/wEztzD3Dhg2T1YNLL70ULQPcZDXgWIjAZCJnfykFYqB42mmnpaVl8K0GuspKg5Am0BCQOJ9MEuN96qWXXhKndGlmDzEZZqLz5s0Dn8FwtJb4xEKhxol0VBHPZzi0DxHyrqjcIYBGDBXDWmutdfjhh7/44osUCBpjm5N7ydmVwNQwfPhwyB0TB7uiMDNkEMGyZ1qa8FelwGVl2vL49JhuQdy4xAGwJHCUJB5QqWPuuOMOcJszoc2plsDQgTE10NJ/yyLloEGDGL8wJziQSodFSb/wkk1pd1MmfXjd7bbbDkyLn/2L1gtP45CWd5iiNbLgFRdU0UBlgfrYQpzRLuJ04GNv32Dys+c/+1U6RYWehgUNTH0YGBwXh317c5NyuVQNl2wtw8oLe1EOC8yly3U17MTyBbuUBWnkYuUkekVq6vhFn8s/BIU1FxM5cw+nh8I70rNevXoR4G3597KgPWD7H5BkkLKSv+SSSwJScY0WaIQQLkO+CPiGDDqh1jjU1UQCv0Ah4Twapw/52ROIQVL//v3feustoIEFASw4qocQ8cogcCAQDxzkIj4NVhW8IjEqS07wWXPNNeFo8UtC24iECcN+yuzMii/ExTgI5BsCYClsseySoC6w8aGHHkJxue++++INWmpnW9YHH3xw7bXXptOYwNAwj+nktdPQMGkbkRwOh4kQg4gtUaj+5VWgZHm0SwiG9QYLm8sKJojqXmPm4mCyvD8b502palp00UWx7AAmdBw3PdwltQBH7qnyu3c1NWjTQH5gteuuuwIP4UZ4dLAJCwL1dWofCrYM+FZ/8YUXwyq2LMphbL7yyivTpk0Do/DOAG9WKjtHFKELgLC4FC/QmGI+VpTQlQqQsRqHVCnz9o6BAc1lFU72Vj311FMs9ZfKIMlbr6VgmWzwq4fJE6wVIkGeK6zG4lmHgTFixYzlqe+++w6/m0CBHSuGW6pGoGTYZ0SCo48+mkwAkx3UBIBeMiYpqmXwa/HVDaUzwfi+J+PdUvpt7vav2v1YXw9eCeOIm5XjTzi+21z5S3DAAQegTTjzzDP5RnC0hNHbcrwr1qTffvut7NWEtPLheMsHTfb58tdCV3KVQwDtJFMeVnug6L/+9a8Biw7AJggbRsBCDBvcPvzwwy+//BLX8QUmzqKMo9JvvvmGaQL7IHYyXnTRRVl8L5jqAPuIuiHIaUfLLSxJNH6RTCDaklQhKAZOu9HRQzSAD9QDWAn3kiqbe2dBYMyYMc8999y4ceP2339/iRbWt8CobrWo0oJiI7zDDjugrMROiqsaYCvDEM2CeCLjo7IVXe1stQ40KdKXhrgZ+tb93u0iNbKY1QZmiuI0Be7c/IrTgvzXygQmnlE33XRTDH6oEHd91cYB01+kAvqOMAA8coF6SqMGPdSVCVPiyyAbgcq4AKzCLy1Twd0y07NDx3St2tDMdDyLwNSvp955551kxJCYfctAFcGAQ5uTFWWWaTBnMOikAzL3xN+TlVS68QBB5vjjjjsO7GJJAe/0BW6uzUiJdTe7f2kJziP4QMSgvWVjMHak2KFw6JeN84TtxwK33FVXhRAAJxk1GCzgHB5uGNdlPCLko2J4+umnUYfJ1mIgQzxXwUAkDcMHweqrr06lDCtsgoQzKUgbhB7aVdkU0o4vThhl0JAhQ7DvQJ7BIOWMM8+gHaiNitOaMqxVkHmLLbZg16E0H9prU+8y7FPpNpkZsNoW7egyAxP16IorrsiyJd0v5mETMahRODIeU205PJSEoqEcAJV9GzWXiyDoiSRMYMIoszmTZQ1TriQzjxUZ0KrXOvFMIaJLyN2MWmmGXHCJF8dEDjzRcwsfCbcEOS7xNpdm85ClhSvCHgQwShiteWm2Np1W5bhfzLCJLH4OHjz41ltvZWqHgpljL9NpQ4hpaI8oKymTMHbO0BOUDohwfCxEgvvuuw8h6tFHH+XzyaDgLt9RmoE+LsT2uKIcBOIhgGXvRhtthGUvIj3WQCRg7LB7AmsgTBp5NBICyMkVX0KeYqgXldyhhx4qEwSHLLDyIQukeaqxOMWqZQb9S77ekLBh7GaFvuHeCIEZ8oLdB8nkCyZM7yIDEACZhQ5DloGkmT4CydxjLhAAqlxMZGBmQ33GTlVyqbqIeZncGZX333//119/DXYtt9xysrRWxCa5qtOEQOFmOBoktF8FwltJFi8M9t30vKB9M7XGBYTHxUmB+ClA0XDBBRcgEEIpnn/++cCxVZqAqFtcMRUSAV1kUdTm+zPtWKQGSyn1U1DSPxWQ50iXOtkU/OrmNFNQw/wyrb/U0zPTo+4FzUxDc4G2KaSyA7JmhVTAkASdgOGE8RMAowEdAXPZoDCUTCGeP2p1wCBYIGDnDidcR3v9n2A+d7lMk3KpiY6THXNi7riYWX/99Znv77rrLuPbNZfCs8iLmkMNfk0kJTBhwoSffvoJC3DUDXw7dp6zJsmKB94cWEymCpLNmjVL6sKBQxaVuiwOAgEIgFTC+wr14O2MGTMIo0oA/fDFgKCFtgt/eBhJIbLuuOOOCAaMHWZ/Lsz6BIcDxeb1EZr2ww8/wKzTMNogBkE0yRC3QCDzxvjifYYSfuYV5SUH/AmKGDztidk/elWAY1aJ8lJlxRUKCoHbsm+o4jpXEh2SGRnZAXrCvfBkpChQkJHIcTCyhLDXnnspLUt9gwhWRWlSbKVweu5KDIEigCZELUPiPiFEdiNnJsuXr3iZvE3p2Ob17duXRzQOvII1gVIE0pjEFRkQQlmRXfM65W0NreQuVlLfZHMEnkpnzpyptAwTJrR3ZGAua8ho6VAeW/ER4peCWL380ssiWQEuSmYss7wQYhVZF4VPO4zSUTew1kEhuClhwRZXkXhzgDVhLwyRYo7BSgg/xbhobUXWNbqM1QwBQR4ZC6ix2LDDnrWhQ4dyosTrr7+OycC2226LigFvbZyNgkEQsIK2cNnTX4ExEL0bg0LMGVi3pyW0X5xGVfOntPsOTPgo+JflSxGv1EP40nKEwoZR2mEb1dPO5BJ2DwEBbFWBV7wvMcUzGFlRYI8kk7hawfU9tnYPtXylgFBEecCaDP3C5KtRpVSugU4pNapC28J4kOkKPhgnLigjMaTkmBbGjwwVN5lV6Jd33SppCMB2s/zCpPXYY4/RUJbux48fz9JfSTe6UI2Ln8WZ2rfaaisxmeFwKaiW7AUrTIvi2yP10gwUB9BSfHnifu/CCy+EF0GtMHXqVL7mqquuyvYKHlnFJT2SA7+AyFeY9rtaKgYCbOEB3xgInLp6+umnc7QwmyPE2H6bbbZ54oknHn/8cVQMWNCg55IzgOg7iMpVLCDcfPPNOGWgATvvvPMhhxxC+526zf4WQAYKw5fdfvvtJZ7vCJ8mSgdiktEfuxAXdhAoJARsnFTEpXjkJa+9ZlSyDvTFF18wGFtaWjDlaG1rxdrI7n5eG+AKzxoC+VU0KAdpWTft/9k7DwBJiqrxT9rdu92LZAnKgfApf7KAghKEj6ACEkRAycinniIgWaKEDxEEQRCJnyQBSUoQBUSQKIgISIYDJBxR7m4v7s7O/H+vXndNTdqdme3p6Z6dvr7emurqqlevXr169erVq0Y/VGvhoqPmyMpx1dZoxkF+x5KpLiPgGkr5AmbbQRYwRvNqLj3XgVRvj2gdX3SSthADzBb+9ujf8JUNDFttuVWNkFTfmBAZOoQBQ4oeNQbAjJmZMMzfddddbO2Gg3FgLY4hmavoum6NeGtSMuBhRofCCA98HE33/PPPc8ANYWDmyFLMx5Zbbjl8cJSUbgSzthXOSirb+RkgBlBacXTU2Wefvf7663PUHLRH5mws+tvf/oaKYeutt4aqsCAgmZ5ioJQWIAA1ZkW59FBNrC76AOmSSy5BQAdmnlw1ZuUmQ1lSYCgs4uVTGKuWyl3uBy0LA2YB0hGhgL+BMZoMV3Mk5ueVV14JVyGyMUSNWGInQQcDo8eA7eOjzyqaOcC1rrnmGt19psbgeFSJwJETjWCrDn7USPbyDdw4hN0DNULXRIFYuDvjlxFztc5uzd1wjbDaZABtbxtJALnfrY8CIDD4VwtHQTOaew/AwTUU4i89BztGBjBiwlwV9PER778y6pvbBBLphBxohyFV3hhT8eBuaQ2FAOsScVoK7dgtXBfnmZQKISUS55wrRyqoZEnf1KscO6plsLoGAtYzgiRWyTuRyhXxpKJsknmciVS4fbIR4slxRKO9nReav/dUVYJ5Ypdtb6rDZSKkn2h3IIIaFcFRzw8y1ORoSBny+ckUCwSqomE0OdcOhdSq0gULhaNST12kZa/EAQcc8O6775533nnMFoj84IMPCGO9wvozxdHERJITXve9ZjZ/aoekk3IMYgCy0VpffPHFrHsfeuihkBBWUfgEwbszJ62uvfba6LyUtKZOnaqJKxGsF9dsHFKM+k/FwQqKD2BjCq1H6tAvZJO34Qm2C9QHD/xAVAyORCZqTXMpX6ovu+altvrWykXY6oMNMEbz6YiAadvpp5/+yoxXVAHBxzYlgcp5dWI7GGg+BixLoShI0Q7B7UqfdMmjjz4a9kXFN954Y2otlonG1W7zkV1DCSLylSST+agrIhK2P5s0PbD5l4DS2p/uxLy1kDSxdFfX0MRias4aSQUzyw022ABTXvoJxr3wiI6pds34q5oQl3hV33VedDBQHQP333//FVdcwQg9efJk/KcwbajLZWBBxWD0C9XLCfsNswBgk1uMugK+vrr9V8EYdsVsUmAOz+qozmcCLqah7FxhiwNEMLlkMwVTBYCE67Kui7By6WWXEkP2ZmYROHoagrvzUeQxgHTLFgno58ADD0T2hYTwHYiKgV2QHFrJgI7Ci4sE0akK2gR8l+ADEmg5k4WZs8IGnKMHEl0dmYhGNTrjr0Bi7jpBotXgBnb2wq4rOAbaGXzNIrZFxBnN6Jusk0M7YUAHO8twbKCd6khd4F3wWxZo7bYmjYxWNRvlPAHWIoKzoAiCFCDCI5oVIxkrb8cddxzwwSNk+E+m7DpJRIHugNXBQJtigNFLd1ZTPzYCcJglS/SBiOBtijCvWjfedONOO+4E9tiYwCwLVak6mYtUrVWVAJC4imQx5NRTT7Wn3nDIHy7fOImw41U+Uk0WZWAeeugh5Pidd94ZIKH2ZZdd9rHHHps5cyYqBoZyIpGDXUFf5wAtrxFqU3YSARsd4be//S0sjgBwcmHQwN1yCKMJAJ4stEF/8YtfoGCCgUQTzg5UYxwDDGHMKdChtzEejjnmmP7+fhZlEdLauJptWbXgB5hWqfFHsBiJ0u5BRneIaYUVVuCJIIILOjYa1eXlPo60iGQTR7A7MLc9BpiLstca5cK0adNw3kZ90foRqf20rupX5n51rqrVVWL1xLkUh8A286JHH3b4YZ/4xCcIsCOdopjANLPARvKmKRG/ZDFkSLCBS8j+Of0oeTFdgfcyS5QTMTsTrUZQOxa/YceQrfZ1113HSRMYCOjQBrtACFbNAk+brOUBwGMSglOGHXfcEc0g1pSA1ABza3lF6gBAtm+Y2+7jqPljFzNrrL4Gzu1pzZtuugmrt4kTJ2LgUHNO7ZwQnCjZl1SyRZTvTWRGmAWUwNpGP3UI020FELA1LWxRcwSJWVsF7MhYzMDaCG/6QRbQyav5GAhe0QDMJb1ddy7gH8G9g6oaZelNhgUDZj93ii74ZfBsWvx3rfurI9lqq63GGMbCKadhwbKJVFG4dXAFWbIOQtgc/vKXv8SsmqzH3mpAUzpXkI3UyctggLW+q666ivXJgw46iPUr4rA5qhc3fKDtbVvd7luGC2FXrHe92Y6Q3uyCTuZwS5DytkjoRglx/TDCp6N/zex9ww03fO2111zRfPTZNpaDwsCz5CI32hRhBVGMpwbYOvHRRx8dddRRrO42Vlznq7GJgeOOPQ7Nwmmnnfbss8/qFFTpTbHhcQ8TpTHu21ZhDBi0aDQjWCHRbflpI104G4bW3Z1qJC6d57eqxsOXWxk2t+42jBUDtiqipszlzj33XMWbshEtww0PX2qbvaXiSHQ8qRf6NZX39Ge4OEnLhMMZrhn4SiYgbYb5itVhUw87BFGa/+Y3vyGBTihoiJJuXvHbKEcqLVEdrCbR8XF985vfjCLAFWeXrVlhiiJ6rFTcLOB8LQPMPZyrwijisqFwgBi+FGXHpPnKV74Cj0ZFh0EvC6rYN9pXw+cQ/bdqRP3MM89873vfW2+99QioENZMyJtOzPUDD0ith4oDTViIY63+pZdeqr8K7f8FPtIQJRmSWZzXgZmfOsI1WPmKo45qYP2xxygCRqaNgszkfwhIBV8kOD7N+Zk4CZTqcItqlbANViT+n1WzVqB9TznlFM7pRHahlnEXyOLfUPGoATNPLBCPOOKIVVdZteLCQAQJKTSQVNDCwW2ltlQ25TOrohRE6l0UO6ofDjMUPa8zEa0IXLWyEM84LlenbTfccAPapWopx1o8Q6RKdLgY22abbTANE3XDkLcfJzSSiyza6QtQHc9wZh94SsI3LZLetddey9BmNZ5xn1Aoj0Wl9YMf/GDGjBlYZnHeRDS3mWuLKyuTVSXxj1vTZTmSDdT0WZVEwu58r+FKfkqBIaw8VYFIomvFxTBZjPRKam06na2+CahHYn06WeTQHfu3dzCbm7IsvfNpIah+2vGFbBBeiI9OSBXA22+/PVoGehEHv8MR4M5tcyosG6GpI0wBnL/xxhuc+EUg0C3cNC7zA/OUEFMF5oriBt/8KntYKpI3Shg+edhXbqAsAy/CTeOGq6UXiSeEXlateInHEx5uxlnO+tWvfjVcurH6btq0adDqEkss8fnPf17pp4HhmZUdpUVLkSlfBYBewNyCX9i9GiDgOM2PLwR0lJJzUzyyEeLxkhHyPzH5SBodPMoTGP1CEdWlZCotMVU7iEA3ti6YLSamWGMizURTdhlb7RHt2sITuGAU+GVAOSUuAzOspsb4ghXYazTV0CHXnVAJl7N3wd5Kra70aRMYayxNE+BTSi9igOZEMi+mRmMvmhuDU+Y2HFUDfi644IL/WvW/+DkaXLXZt9APRmGPPPIIp7pef/314sbCP5MlnJrqVEoUSZyrZEiuuFxavOQufh/0L2+qUyyECnjNvGiFVVddldkE2p/bbrsNf0m6UqK9u5klNz1vKIq6MEVCiKUwlFlYbUSQ8YpaAYaTN3NdHyva7rb1LW1UCPjSo/9pHX+tNiHP4UHmBhi2/nIbK1r253Mb1ieBOnIOMKnHeQPJMV3kwFgPE3IzthK4G2nCTNj0sgHz07aQ99b/46f2f/t/3aHOn0zKO06awpaIJ/Tqp23ZXyOueK3N2Vc4JAOUBx544O2330YNmR3KkgAG0TL4AipY3dJwnDiWGjDB3XffnYwJB5Q92fikKwdRZU3/SSUQWpIJc15XdQSqdsDAkWJuWExyowRPG5enUBvXUCLN2WHSmtCskq0P9ihLqudzZCNgAPlnnXVWPd+NlbS/u/l3jz/++NNPP73kkktSZ5qv3ppnYS3GBUnRl5VJCwLwaMAQRIEeKrI7V3hywy6EFT/0Ir2T59zknbBggB6BsQNdg+YmUM3woYOsNsOAsugGKgXBCM0YZx8op1SUbyCf9vjEjq8wMeQuMKMMUGtXLIkNU+MC9xsmUQOvjPAt/NUEkP5THGUL40XyNhqHmrI0DS5+NzBKv++++77zne8ww7G732vKoq0TgRl6E7IrC0gsLKFrICbyjFTH3wCftbYxI7J2lorjda25VEmHsLnVVlvRHKxc4nCKnRRQLz+rJI9NNPUCVs6ofvDBB6kaux0bZuBNrXMuOyBTiST8hfUl7qy5ZX5hfhYC9qdJ78XbcAMBXcW3dgzGoAykmdsXQYXcRQ/SMnoIhtHDzdnUnEwn0onU/LlznBZFR83YbFbiZJS2OhcW42QBunB7acxanq7o6YKd2v7K05ShzzSyob1JZ8MEmGhKcZK1KZpZXjKXNwMO5tFFuhAHzrCDwEYXos8wCadsVkg4XS9sIJpZHhL8o48+imaHQQjuwMoAg1BABdpBgvykO7Fb0OQMA6fV5VwxyIAfQ6iYEkN5vQ1/ghK8Oz+UzMtGlfxQrvKdy+Yr3sOml94OMPj2FAFH4EynZZKPsl20If4kVnbQk0SmtHIFhJaq2UyaNGn//fdHMiYFFmhaopKfKT8MGKoCF4EXiI/rrrsuZ9QpLLRXLZeoA+Ew/vlwSSarXekFCxdkEEaHkMCgSXHHaDwyKt832XsMzeNmZGJZXoHFwUmxzUnIrZEKT3HYbEX1uKJhgGQluSkzlPyhxUVIfuaCBKV4xrX4KzG1mcqfiqW6nppJySc2Z9s3UZuyUsdtmZh9ZRN3AnHBgG07xiZGXtpUxdka4bfraS7ZuN9Wi3fTxDesoy/wW10DYVgNtR5K5AeHstwDuQETME/C3k9e5YrvbJZxEbuiCmMtkSW3jscaWT42lySWnwOJbE+P+Gfp7uoeEngt7AZkER5Kr/K2Q5JhBegLX/gC+XBxQFjpN2P1N2h55ZVXWEJnDWP69OkrrriiIkcRpc9wcKMiFGIV0p1IVnIhYslMu8KF9BXgnYfsC3dxcQiLWeRLIpuNByq9xx57fOxjH4OVPXD/A+h9KDGEcptdL60FW8vnz59P1fbaay+RrSMmw8ARkNhwU4l/r3GZdE86wd2dSnSnc+NSCe6eVGp8MsXT3vy0d1caybHCnc6kKt5uYpshAa8sU3pXJiF3OtmVYSaC1JfvznSJAqRFV2ATb+Z1AwtxDzPA/qBscqFR2om2BstfWzWJVBMO0a8Qra8qsHvemRzspxooZGU+18jKn8NuZNY3uGhg0MjWqfS4rq40SI/Mpb1l+nenM/cDKFTmTMh1b1VkYBwVIKwAqACHuoF6sX+McchKaaPK2vsYepDWR5WgEWY2hdUKOoQh+BH/Kpdi9XzM9SvRWeWvaog1YopoOrogeyaBrCfnGGkGMCcczM4TSCsJNzVkPKokrL/tvffe559/PnpudMM8e3t7Ea8RoUaV75j/GPJKpTLMP1NdmQk9fZD3xMkTxvWM60KJVOWqNEaWU2mB0RUTjBtfpQCXwNKJZfp6UK4yDRDFl3xRFbBq2bV9fKUW8SrNKzgYihp6yo9//OPtttsOhRQxxHO1PWbasoII32rYyJMJEroGxqZ777134y9sHOjY1JbIq1wpuoKMJuO6piw++aP/TELZj37Ul+4KIl/Zx8LNWHySeF8sLKTxx+hCjBuqmr7AS+F4XPPmLVhiqSV7J0zqkllA45ft7x0icZF42GGH8ZNJ4Be/+EX4pE7tLa7clCGE1WSZJXDMCylOCaBSuQUiqfS2zjgdUYvIVcRRbAuwnzG21BjTyCJTUy/IkkGKo5TmzJlz08034X+draC0SFMLDSFzuDQcG3UwT+hq8cUXj2QHzE2ZML6vJ9PXlVhi0vikcSiO5YIxLlAkFTVENcp06XKYeYkz+ZXMi7KWCMlGlG0stYIzMsomerpTXeMy6IETidbI/IEpGrq7UtnswBKLLd41vnuIGsLlDaPPp4tkW0VfCaYEOWVXBUQ7QnIZcsu/lwZIDmSX7UlM6B2X6M7Iyja+auQkM1pi5AzKcgwmooQL021wo4oHF2QdHBl85jOfCXo2HgzYDeSC5ySOg+JwjX333ZclQY6+C3j/ntBDJpGEiUvXYl8SdAe3xWiBhV12UCiqVctuLIsqVCJZNAY4VFEuyujXRSOKk6GfHgtSlBfi19OoP9IoEjNZlImZLoAUOL2LjhEWETLqLL/c8rhpwFk6OyoJbLnllgjZ8CIfms7fxjFAe2fS6f/MmnXDzTct7J+fSnJYPaOjDJA4iynJF0urkphgf7otOm78uIUL509dYurrb7+G2iufNi9hvm6iYItvu9zgIcxF//KXv3BQBRe6hp/85Cef/vSn266iY6VCNCjmddRWdfr33HPPTjvtxDyZo2f03Mexgojg6skaP4s473/w7tXXXD1n1iwQzPqeyd4ZT92xzxn4isUhTW95pg1YWN0MiayUXlmuNxynFi4ceOq5p19//bVBNP5M/2xOncCoMUCXueWWW8gGfrjFFluoTpafOiccdfY1ZUCbWiqZMmXK1L4pmM+kuiAM9ME15RBEIgsCFOmFMdFO5jK5wcSE8ZMGukT1gB4miLKq5sHOAtYscb5On8Jh7a677koAMa/qB3F4Aa+mFptuuimnaaDrR3vCgiXrZFHRoWiTsqa5aP6Vl15031/ve/uDj2R3Vbo7lWf3BDMR+oOwKZdzFciluAlcBUQ16i0nIinCv0wpouDIJYXdZYUPJ6dMWWzWW69PnTIhwUK7sEcXFv/LJv8NRtFAxeb397/28gsfzFm0MJsfUPma2RYbVKRSilinegb1iiAXuV5lzVBR3BjOtwZNxegupLVIR8inmfODiz5YYfmXn38mMbgAu1dfy9BkpNaZ/cc//vG1114b58bo7ZB+1EBXxaA6c4pEch1mGIEYeKjRZpttBqcgDHBMyWR7TVCXDCR+08PJ8xmMlSaOXwIAKEUdQ6JloHTSmUHHT+wAINtqyulzhK5Yno9Hn1KWoW0cmhqjHeqd6+uZCrfv7Z3gFBtqEBUP5aHPOuOMM2DQHDhKo6i0HSoc7VgYBroM8LQ7J3ocdPBBmQQKTbmyicFcAl5fSireFoamocIOOmzgoZBMIj2QWESgK81+M9ltAUFqj2gaCJUFTSmdbhg3QwCcrSCr3X333You7IS5tt12W4w511hjjYo41JraV7GrsoW8LQPaHCi+H3roIc6n5JRTWCKjLZvLMCqMb5XL+xeCBKNfOBI5ptpMOw855Af9i+aDQzPa8tcV21xOWIgv5ocaryndZyF9cZ7V0mvRCImwm3RPogeDwp5x8Z5xRZAy1RQXwPDAhdUkfYplnhaxO/HD9+qrbzK1f//D9/xFb5fkmoc/1x7dTCy9olJp9k0OJnvHz104b5CukMqj/0DfbxQOfg8JECyQzzF2J598Mu7eLr300m984xtIJgHmH35WOmuAomDO++y9DwAQVmk2fGCGK5HWxIorkZg9Z67M72ls5AamoFjViNTDljKXg3k5VZj5qvSmtGElOS+596easazOfFXPRWdgnVMAISvZmzaY6Mmwp5zVL7E2a8UVgKKBunG/+9brm27wmUTXuMQQDs4zYjcu+gIkbc+sXZTJRegW1iBXaZfzuUNpvNNURfn46RV9mueQWeUe352YN1sgYZYLOCnsRrjJ18lKvwr3WcKL/9dcFgQRDlzeZV/EJIDEQy/jrB0CrBSxTAS/UJanxgWjrodtcUNgtCidJ5+48PzL77zzHo7bZR05ne8RowZzedjWhY7ispFEzF56G+sQhv+5fecFKuUjr4rSc7ZNLp/KisItlZzSOzU7wG6pvoRYMQjw2IsKF5LL04OYcLMeqtXGB8Eqq6yCmQkeOuM+AjULU/XnSzsalVYKw5UdvrzjW/9+C46eZo8Wpja0chm1lPT9+gsc4YsSRTiTjb5Jff988h9vv/uW+VKcg7DdY4RcRveaOiJ08oTM0PWTGZIfPMG15mg2HkZXg8LX6tuMgzD32WcfxLh33nmHqt1qLpaMDjzwwI022kjrglIVaZsv6dtu7fhJpBtTyL0TajIGtC3U8hY2qLPu3/3ud4ceeuiMGTPQLwgjzuXYgf/tb3+7ybA0PXvqwgV92tEWw+MmKRqckVIIHmGvr68PNL7//ofUUy0aMG0g7FO+HbWJK3wt3xYujdeU5qn8s3hsLST38ilLTwr5xNz5FC6K/vGPv8/q/w+/jfrfhcTJrBMcCQPaWDQoRl70pqeeeooYOtHhhx9OACfcgviiBh0px2Des6ojlPbN7b4jVqTYhvOkkV3KCqagSrlUK4V4bta2exND82VK1JVH1ZU2Ppsq5TO6ONBOu7CzgHYhcP/997OfhZ0Uo8u1xV/Duyw5+cqjFoNUWrw3S83MH0rtuPu+v7j0sjffnzUocj7cRywakMfgOaVfQRql01svSYn8Vv6h5FyF5OwqO2nYPS49gM6Yz05bfumDvr3/Xb+/TmcfFfNsdmRgEidj25e23/6d92ePGz9R1A1yUVM0fFnqL7plwY6Lcc+SqAyz3jBQ2hLFg42HU0+O9z7RBpCmNevnmPPNXzBnyqQJr77ywnNPPW10OVWa14AbkQdsIsXafNwuaJq5BHI5Uwuer7/+OjGM8fvusy9VYf2cn8F5JNYWFwKTfkeTJxOP3PfkI395UvRIxCmXVxwOg0s3WYAIJ1vAoFye7AxdKFmz1wyoaFzCoCrA0kbMikLR+LCNBauZAw44AOlziy22uPPOOztGDSOibsQEHFegi3JrrbnWVTddMeuNOSlx/Iz+SPidGLYUX0IARays+PUofxl+aDlnNis7OKYuM/Wnp59+3LHHmrzFW1YIOm2sB1nkpLKo9JFKkRKC6/ujxFF9n1MF1ZOipHv55ZdZKWL/0b///W9yQdvAkbE77LADJwdhIkSro9ahplJrR9qWFu9crcAADQevYyKEwMpyHyBwTiEt+M9//tOCg1EDZwpsvPHGJLORcQwomfHUmYZWIUj7wepIYXBhCN5www1vvOWm/vf7Gfl8RYNwmir0743Kbk9xSlC26TJPdxR3w/pReXriJdns2f3LLL/Ej0845fQzT3fy7wQbwQBNSXvR3NAYu8nw90QuP/vZz/hJQGQ/h+81UkB939DoRZRw3P8elellwiG5YE3AUBcK58VuoQLcJpLt5Lm+3okL5y88+qiTxfk7PsGYAAY26yqUS6PMmzePY+ywaGCrMi2FOhXXiVV6X+HDTmj0GIAQB3OpgUR6MJUYwAnj+D7yFNcIZgZgxv8iQiW+GmmOhmINGfo8U4hffHOlcgMYtqHswDtuSX8ZfcVrzyEAkqcO1GS1tde/+Irf/GfO4KJskh17BgKpMzvVVSlQvEoveNf4ShiXD4sVEG47Sdjp24avmBYlngt0YqpCtgx3Q4sWrLDMlMsvvOyoQw7KynwvgPqaQpr4iClrsGAz3rCN+YUXXmDUjtyJuQAAQABJREFUYfOeLGYaa22eAa2uQABKD9CJ6Vfs0OlNXHDVTzfcfL0PPnqXwybSzO/96ZwAVrawXGi/ilsn/G8LydxQeW5F6QU26eXJrJwok0tOSE3dYKUtesap9CUAo0oy4AHaaBiLC9MIYdoCUXu//fbDQyfCN2cFvfrqq8ydRvis87oGDIBb5tIQxQtPvPTm628h3Gdkw5ho3MoJT0T/ImqpoYDakzApdogT92wfzZq16qdXeevVtxlzlAOnm88DWVNlFyVQ4yGMfZWbbLIJqyvMw5FHw5VEa0dcaUrg5KJ7qiGGquT4SffBTvimm25iFxIsjs+Q5+64445tttmGzbG4PrFcjs95G1oHL63AmP+t+KfhoEZY35lnnnnjjTc+8sgjOgZBjTvvvDOtCYmCKkweZJOB79YnpsiD2Kg11jcbbLDB1ltvrXoW0XoKO2ri1Z3phgG+9+4Hb778xkvPz0A2M4s6cDpRNFRhOB5IAFkBMo+Jua+cKpTzzwrpPVF+APk69ykMkfBTbgpy86xQcidqeAzQm1R5d9JJJ5ES0lKnANrdhv826LdCElAaMlRajn1I7Lbn9ktOnTA3O1cn+di0livanIlDUODIqnWli4Nwmd0l+3onsHXiqGNOZgBGMCxfe6j0bd1xqkhF3YBPNJ58f/PNN6NoqDujiH0QhwFU6BAaEHlBJiSpufNlqokNp4p/FS0aqqG50nS4NK23ym6iy9KbExgAw5x6ZqQ+zHsm5WVLbzdPpsWl2YXyO5iJN6j8qH/2nAUDs+YvyqZ72Dsh9ZdxT5R4XKZ7081sJQUDlSvoCMpOB3bTqwZRl+WEv9iLBjATPFk5loYfGlK3gIPYVSwa6unuk2mpAGa/6ASCxIBaNyG0vfbaayrenXXWWXA9NWdoAsvwSIgJTG4gMWHKuL5JmcGuXm8nkhIYFDDseVQYF+jY4xCbwUkN9Ek6Z9xy6Dmfxh2kWMslEuPTOLxeomdKYtas/0CVrRiP4YBi3yjQplJHHnkkSxAsOON5tKNoMC09qgdKLNArJx8ODOGMav7sebJrAkaHU1AhKThO0WVEH4dUil6WJi566f2o9q28lp0aDv/M9GToGtjz9/X1yu7QJvuhtNBivfD3v/99/fXXh95+8YtfYFCtegebIPoBmBUXLSsn1wyJQZb+pONgj8qSEce4XHPNNccccwwbYpllYR/E6eW48oXjrbfees2e2kUfgaFBSBvRNOXFSfuZFrzgggvgeG+99ZbOaXlihHL66aevvNLKqgRnJRBrr/Ic4hhz7rnnHn/88fT6o48++oQTTmDwDcdSA9Grp6trwviJk/omofbX7Z/gHxyWT/ZcxNJ8/k+Xs/mcULlZ6VjsptSvi9ObOI1aOLigd9y4XHYoE6jFSjWq8+vStn8hJ+a08DqclxPWYxQZ/VppsAbxycCXePGlJxeussx/PnjPdH38E8C0S9X6rPwE3jau6FiY+Jljq3EG2dc7eeFcmafgKER2koujvsJEKEBgGKQYd1ZfffVlllkGzdrvf/97tOQqk4ssYq4Ai+tkVYIBWVhCBhNhG+u5VEbMljHxLrYfLWVlJXmUrK+XvtXfhUkH6yAlxtFixiPlYlZOYlU0GIrXpc1yzlm5iMBjA1E0AH1qYCA7bnxvdvYAji7F377xO0aIE2947Q8m8tfv6L7vhpI6+Ukl2g2Tp3+ZkPdOGIy9TJAXFMH5zHLGhJy3iXaHF1lZUMZ6RFxioGuQz3UgtF93AqPHAJINE5u77rqLrNA1sIFZ1av8bAq2TXeSrROsSi2aC+HN+nA2DJdlHF1UKXbBABA+9flVhTuI3QE049CR/3Lkv4U+X5LWX1teMDQ/2cu5qoms+A2BDQjxyr5+MbhvqMiSgmr46WIeJ3a4BAE/WBHjHpI5kqwz5/I6INWQWeyTiPUB5492dbloabhWRrdJTmKxLOoGljDYNiGaBpZZhNGU5iyMxyPCglCiicqIs/Rbfsvn1a/iHOiA8xbO7+LIE3PZ+oYgJaNzoUzkG9ZV2Flw4YUXquaxOugRfWPmTNK+Cp/qTAkj0tF9WBXH4wlO0ZjH0qcwFPqsubDP/3//7/9pla3vhojWMOZg2abBakb1WYp2xp1LLrnkhz/8IfWjCZgd0WTrrLPOlVdeOW3aNCL5UDtCFL2L1dwodDFWmKE9aoc/2oMPPpgwehN0YbwisuacGkpo2BGyLmUxzVF9qxl6ka/FcRIYxrKvtqwdl2mWj3nsriD7SVYF3YSfcUl6M6DTYxnTMaRfOH8+TY1MbhgvvFdx0iBmqJFfqtRaWIPPHGx8GwegKGp92WWXEaCLocUjsGDRgnFp3S4dXtV9Sc5M7xhSk4nevvF9E7oXLZBNUvgHMcYOxdM8oQBvNGwKoMh3fr7G6WMmN5Ac19vDmifRMilq5qV0SPf/+c9/zthEUddff/3Xd/06DQSLYORqZuHNytvtblHuaPk8c0tRJzEjMAbLAAuHyWdRNRVdFdiOO4moZRriU75Ig0U4sWzQ8DibjM29HMIHW8YCoAiWEH9UqHYDpVtMgSZxgwd/r61GJr2MB/auuXTQ6N1anH0Sa5QbVA03ELaCwDjEql7N+XcS1o0BjvBl/sYeZszy6QBsfCULYurOqM4PxP2BaVhpclpcGl3anSbnp3lquOLToxBLw7UXXu0TJphGxYathNA16gWUDJxzLIyh1RcyN6f0MfA8+uijeAxiEOJCy+Ay9FbD2KzyqTuqADQCDLoo+wOsMih0gYakjCbTozf3VYUww4Pe8q7A1iqFyddP7ARgud7t5K4xToQZk0IhQMhp5ZVXZgfvUkstBaVddNFFYD4EPuBWNqiwmtOXkwrSNpEoVTkc8c033/z1r3/N/ljqCHXRs5jQfu5zn3vmmWdIg+RH9YOCp5NPNQz0dHvHCjD0oGJgGJo+fTqYV+R/4hOfYB7OSROc8VQthzjGw70hRZ7oT6kvAXoc5gzLL7881Qmn0yHAMoliqPMuGXO9UdWPsn+H5W+Wp9nkFQM2mQ1UTCbin/eCABZm/BDOXDlxrbHYNKHJUvle7J3E59rYujh2h0Oyoboll1xSzwJQu4aWYMFQnW0CQ12GKgpjoh0cTQCFUxNvxmx/LsN8BGgK8yAxrReNW/lQMnq8aZ48aRRyQ8uvfODa664lgD4IPlA0Ix19kZ0cyjDAIiJ8BgNIVL80hP4hFZtl9DZfGBItEfMsHyNQ8mqknzR64RKRBLnWiLaGzOWcP48vC2G08Ko2HtQFksi+YpIkEyzBlN/XJDKQAoaHhg0a9pZmFSmfYxTzsk2fgAw2AhWsxwTMLHD4HDtvG8KAOl3nnFu+huQ5+ZaA8r4xy+bYP5gyh2I4e4XQg/kSUEN4Hs1HtNFyyy3H8EOLHHXUUYxAqIeQnEaTZ1y+pe7MgcXOkyudCkTXAJ1Xqr6yGm/+7yZgo6a93figwlbA0gwN05Mgi4saE05PhAlw5PW3vvUtptmUy0aDcMrVOgb1dGEuaWheEYMMxwSPqewe39zjvffew3DdTu3YmoSuYYsttnjllVcgPGWDQQHWyacEA6BXuzYuM/C8wEwbP0Ga5lOf+hRN8OKLLzL3pqVK2rEkn9j9pOIwM8DGQg2DGn5STbXj0Pjm1ohZOzMoU4ZyHoIhiHx1V0pkP7VoqPvTkg9QUqNM1EgGkVZuGSiBLJSfEBjLSBQFY+ecbGOnGUrBVQpBmYRlqDBqUSCxyJThTiY4ZZNxR94ZT/TIW95dJZtgo+kBJbfkzzQlHCNW/M6sueaalIi1HU+GJMYpgaBzNQ0D4owthzWr0NkoVZl1wWjlPROQubd/WQr0Izydhf0ZaiDIQcGs4oYKvRTGEOLc3kKi8BjzUrz9e2EDGeNNRC8EoHIZCLZeMT6adcA8lcMm2JWtIo4uqoxxHicb442FhbgnjcaFiyCkJcZm5kJPPvlkX29fvMhsNFhEy/Djk36MeTue54PSNYwGnhC+tWOPSGOV1SLBQ9E7vhcmwJGBa621FuUy/dttt92CL6ZFOVIjLiqItJ1Jc7BpFysHxDDFnfXRLPxEwvpQLsD66GLsoUDd8PTTT9PLWgRvuxVbMiaqahtU77jTjkccccTVV1/9wQcfLFiwAFfEzL1hcdOmTVMU0ChthgtUJ0wk0KQcdthhqtT761//Sh3D0DI4qPS0mE5MtIJVLSzqBpNuzjd4ZsHr3rrrrqs/6enlwlvdWUfyA9vXtILQ1T333AOk9CmO+0XkI761nI32UGmfDev+DB8AzdQmuHavuXEqTakMGIpAfdacW60J3WxpFLYrQplcmBMi87hva82xk65ODPjtK6N8y+bCdcIcWvJKvaKxwqUv6VRKDrmRy8yRG8tslF9JDzO7JAwMo8wsvM9dMzzwpwXruqtBqDzCg6ahku677z4GXQae/fffXzNA/U9rNJRZgx9pJ9exp3IW0Kp/C6mMdOHdo+Jd9TsxFxAK5NagdobKwIQbqxuYxVEQy+3JpGy2TMvZb/SUcAFpTWlU9pZbbnniiSc46VOayNg1jAYUS2ZQnWo5/dzgh/b24/jrE55j4eK89YL2w5JAecqyGJu/vuFnKy5BrFlcxc+/Trk5CZK5n/QLww0sQ4s4T1OAS54Wo6JrwCFMJm0NGQh///vf54R5nP+vuOKKIIHrgQceWHvttXfddVcmgcwMkfzohmNcA2txWG9ACYan2mHNmjVr9uzZnK2IlocDR9kfwSvW9DDwZusK8bZptCB4nbam/nTD9UIShfTqlgItA8CgcMHtJZtEwgZMeF+NSrQShlbyszmAGx7IqcOW5Y7eGR/Dx3PPPYe2mokcQLexooHa0aH0opq436KXEYlXabvtn07UnJarIVdngGMtBziN4MWGdLlq+L4dkmhNTY29WjPcgAo45J/++CetobZgLGqroOpAGQeAq7I+Kxy2rBZmFxu8GUiKpdOwIWqaGCr9v2mZ14Aly2UiPzcvVIZZKX2M36qDJIwgS2QhRbRDAIymHwYBmBxrRy1Ch5c+X7XbhwkM5gsKh6VD5hthAlCtLORsjL05/EzmSOn0xRdfjGhOmCVZ5e/VPmyPeFzBPf/88whMxx57LDViw+3ojT/h4JbT0Vdby9ALzdQiDqwcTJ8AwzRv++23h8AIW6MGV6NaALgtQlQc6sK/IOoGpiKYd6266qo6Jb7hhhu22mqrXXbZZdbsWXRDeh8cMiJsIUa4t/I0h5sANjoFzk/FLwazPpTauOcEz3ffffc2W28To0qNBlS8UaA8JQfcT7Bpgr6m9DaaPNvpWxGynfpYXu3E1RdEwmHFmG9gbnhW1oCyuPoyikNquhtVg6fxpL44kwZqwuizkE2jUQPAEEgQYZhS1XWaYEjw16qGCwYcxhTOP+LEZdrrzrvu1JUkeIKVRYMppjm5qOQAqLJC6U+ImlNUk3I1cxBpcd/Wvknl1Jet10f8HbT1fTz61BHhFKOvSGkORq/pji+lCaL2Wxk6k0AAY1kMVk4MvY6AhvnJFTWwXXhefvll9AsAiRi97LLLUgvWWOyUw03Z3LDt5DUUU3AoEhxqXdsAabMotRpzG13TY/8egDHR5ixSRiaZ9rDLqK0vOteZZ56pXYyjPak1I7H+DLzesju00h14QZIhBG/vphRQX6Zul4feOHLiS1/6ElkwHcKUBp4wmG3bLaPoUCAq5dsQ2J577smmCXwTMgnU7sZK+0orrcT2JXyjKJ+vD7md1AYDb7zxxo+O+RG9GL3hjBkzsJoB1ezQQcuAFpWpYP/c/rGAKvYqcowllIZDvj//+c9UGdpTFUybVt9I8MPvNxYBgEufFg3B+GggO4hts802Y+sE02/ORmXfCl3eFtOWASpIZTFnwCarr6+PDXE4oGEGG6HK4ofPF+HgtEjPLYLNpU8bDhUW5gsMu6hfaTKsCHGlQfFxke5oOy7gx9gWsAmHirtGC7MeaiBCEfxMPiUMqNG8h/3OCn4e0xs2ceteNodTOOZMrataLEtmEgjcHAfAuUFaAWSI6NdEpxb9/f2wNlgD573xJMyYBKeLPvxjB0LmNsq7EcdZa6XiHIbEk8GJJ+2oTdlOCLE1Yh6CTziq9vWvf10WZMzS36RJkxqubHXuWf1Nw4XF9sPJkyffeOONCj4rrjA0JPXY1mYEwOlfulkM0Zy5Hx4cmPXtt99+TAg5mUKPPEDVwlmMvEWcGiG7zusqGPjwww/xhYFqG53CN77xDXboXHHFFVv+95Ykx6Ib5EN1VT5tq2g2TaAspr44p4C6GHO5ILq2qmQDlWm+2M3ZgcCFNxAIrwEA4/UJRAWN3XHHHQymsK9DDz2UI1TjVYWxAy0jLBwAxStVRr8AbySAtGMFoeij4owzz8CT9LRp05hToLtHZAX46MNvVF1Gu2SOOIk+nkODMBCBWA8UNDCX2Ot2NA41t6T2Iqy/UJZvuummqMn5lEidFtacTWsSKvCsJrEwDpvjEG9iCDM+ceqYvg0fMvEEW7ysMbxlHTaWFe9qkKNrLVG3SnklUdU+Dj2eVrAXbcS1zDLL8Lz33nvZQ45lHZIEMisBm4xA6GAGWSC10+po4Pjjj8d+AV2eemWnJKrccHnKOu1GCaWcQm6wPo6XMJacZuTx1+BcDbSGC98QItfqPNnkWZRcc9X4Cm+L04byS/kVT7101Quc68neLD4jQAAIb0MBp8WFIPBxa2WxYnjsscfYrMRaKKwSfcTot+20uHqtKx6Uwp3wxsfZoldddRVe1vkJsfHkld09XhFAEtirYoLoR6KooqZ33nknhMQUgpM1fvSjH9HLYGi6VhGhKtTOnSpwMOWd1WrjclY3XC19YPEMK/Ritu2Q4zXXXMPcmyEmsNyjl5ES1dlnnw2x4UoZZt4kS8DGq14qqgw7kjZeTKUv3TG90nsvTpRfeg+TqMFXlqFpAPpceumlkcPJjmVLdjTHSLlPV8KjE5D/+9//Zp2ScyIJa70axE44nwkZiDlDySzAzikKZqcuwaCS8MmijIOU0bB+OEJ19Kuyb/kKBtuiq3kFV6pniyoZi2LpSMBJp0IYRVzAqxOzdNi6xke5CqzLAScQcsAbDI4lYg7yRQxSYy0xYhsbk4ryNhIOEuH5FNvIaRpYOUfBIUnA33lqU5bXJY4x1EUlJPR3CEasf9KzMOVAVKI6OicZfb3QNVh1g7Dy1nHzSnUxfFhA0jtscsS0GP4AaeEdAxUDs2tWAtUxfiVo2zxu8cUXBwlsplBbBrDR5hVuWvUw3oa0OFSCPXpjbXyBlaG+Z3KLQwoQzDjLUrMyuqbhu5NxEQZUk7XUUkuxmsKa0O233x4X0/SiatT8A1lU/YAwgO6zzz581971rRkxNSYsm0LW+F1DyeCHmNHhQQM5nAwwptODQhB4Gsov7I8Q2zhClefGG2+MukT7WrSZvOxdRdQ3+FW9gWgcOpfFgMwPO1dEMAA3h0FstNFGMHEY+vnnnw9g0d96oC5b8OR/6aWXwh2OOeYYhD9FKdwhbAYhauMh0R0W2zK4TawaxxK9o5tglGE/Z53ajTKzJn7+1a9+FfMZCmC7OE/ajgkhhNfEIluUNSTKlj9qxzV9+vQQoPBpoFTTpHQ5HHWOEjhXWT7KrIL4HLeIKE8hLfgAjsQwmSHXHXbYAZPIsDlDENUJKg81boca4yL8BVXxQPJRvHHggu5SCSTPGGUCi4Z+cDeDrgGwOQFhueWWQ8UfoyrUCWq9XJP0zbpQEYJqGBoF4OqVDVDwMWQeNA7NKjIC+UJyLEsACDsmVL1Frd0rAjBGAwQ7/iJ/FsLhwcYkgt1kCDyoGlit1II5+jc8CEZdEr1JdkzkcggPZKZy6ahzDSEDsWhA8DPOuYQFiUlrMy5LVwRicrWdokFQHxvslxAJqjsECM5FQ9HANXPmzOiLocg6MAKGXizwdY7KWhP1Yg2ZJ6Mvo1FJNcfIz+hrNGk1PReNhcGf/vSnEJs2ZZs1EF2Jeh133HEs+uH2f4sttmAwprLhUCaGc629WgsAqNa1VoiNBUC8tcMTOI/w9ttuby1aWli6Wh2DGchyGCJE0mohkKEVrcYdbnEjjnqKNPADDlXp4H7e9mHIhn3X6v8f6307nWj7ikehgkhoMDTtm0suuSSaesYXjBrUDY0SMwQ8Ig1HoS41wkBlcW+E+1UEPIwBMVllHtjdJd7EInIVLAojAlDrwKCNUL8ifsMkufBfAyznnXfek08+2TqgairZdpmHHnoIkkNOuPzyy/mSMByvpiw6iYbBQEstbdu6/VqK2WFavNor+AKdaoMNNoBTEMDZFbpJVOZm2PIe1b5tVbwqFwD4T3/6E2LfyiuvvM466wC8qFT5Z3bStgq2auWmmFD7d7U0bjyakoq3m6Y8HLXBT4YdX+mjYdoOf5CshvHz//7v//QtkZbjl1cqdjHUhXkdm0vRiDHXxeE/FUQ0tKhoUo1clw0wWXvXXRxMzN41fFwgbMP9WqJlUOrSp/IHAFd1wznnnLP88svDH4448giWWWgaWFwN1WqrJHBL6mMxU61uCIu6FR8aBkvlE/JqH8YiXsczQNUtuATUdq8W5gNpIXpyQVQusQ0TjgVOagGSvsOFwhQmRno6FLUGaSOSUy2ZtyCNZW6GX7UAgIaKVJzzKauvYB5SxPdKQznF4CMYkZozYI+24447Qmy943vV+Y6FvlrXswmaFEAwK5/DsKrcssvSM3AVwqGCQ1vY1kG5D3Ey5jLUEh8qHHUWppz//fffZ2shn6644orsNIzVqEeLi5c3s5dWqDKZK6fNOpEy2uR2J69C0jICaDkiRovI6t/Hr2p0KpUsd911V/R5dLybb76ZChLg0pq64ep1D++NCs1ou9UL8U477bTEEkvoRA49Q3hwRLIkb7YZVQMnBh4UQ5///OeR1/HSxy56KDDio1G97YxshAEeJ8AhDnKpgt/2pnpzi1F62F9LtAzDowiZVduCUwM4IA0PVShSmTgN/9UYfItSjLVTZMTrrr2OLgmi+Bk15h9Iu8B8YDtUDUqAC9Fh62JBdSUOBOBWZaKKGEiCFQiWlAFjyy23XGONNZSbjR08tAr/brmKbTAPE/vKV76CLeqzzz6LNlA7KW81gftJfMPMWt966y3gX3311ffff3940cDgQNQqyOwuguOd3+hhT0Zs63De/FFHHYVXRdYvbaQPVYT+yhCQy7NUySFoyAZAhq0QvA7aA+zID3wynxcVg2BUJK8IYTYaoHQwEo12cKCgcx144IHM1Qncd999zpsoBhGIAWuFFVbATyyj0fe+9z3AZkZHZJT5WmtRmc+LxUdrYaB02DocHA/StNRiiy2GhsgqwlsOW8MAqNhtP4cU//GPfyCXQ6i4BsABin3VCYSMAQRxzlnYa8+9vvSlL6FRhVHcdtttaBmQMEKGJPrF0SWRvZhS7r3P3uuvv74aZutBX2XAD7dbMIflqefqXJNVfLpZVkwwTKT7bR1hFR9ZZ+PS6RnbuFZZZRVOUqgjl7GUFJ0+TAxdDDMHrTe4UnbXGWrDJwSLc3xkIK3hCVvdJWrPDR+e5pVIDz3ggAPQL5x00kmUAl8i3LziOjkHiAHW/9hjxcARYJ7NyIohAJEYhR2TCAQDDifGAUozCurk2RIMNE/RoKJJSyoV10LpbDi4AvrPfOYzPBHBL7rooueff54wfS+atWI9SqYKZns/m/fQOEQTzpZAZT0CtqT0EQtlEo5UhBUuO35feOGFrbbcygpPI34b5QRQI5eFEPcT2AfxEzcNyEwE1I+xTTDKgPH94+ehfnr8X63/qz5rPM818OQCWsKHTcXTwewg/jiVoeGOFIf58D3lIW6rhQ9epEoEIaAFRQzaQDRlX/va19Zbb70/3PGHWoB00BiCfNl4EdAAk2eugw8+GP/B+BqA/0AStdRxDKYBOVybb745eCOAJ3mIBDwQHoPYqKPKHver44vakzKgcEr01KlT6ad77LGHdr020Ne7GIBvI4g+/vjjKIiJ10VmN0EUwozCRtzCOUHjHCkKFQkWBlQM6PeZwzuDQrAlBJMb2ivY2j//+U8WLJHW4GwYuAWTdYtySepesFjtCGseqgKZvop7VUiZ8S7JFuGk54aE3/yk05fcbmXUttx9um8bC2NDRaeK6eBLZ6PWYO473/mOVv/dd98lQMcTfJpa6bMx5DTpK+Vi6iRWizDAeo9mFGoamYdMnaR0/snKKH9AYNo8U+IAtrhspTSXJGuhPVOIFlXh6ZZgczORSvju+6JwIH2vKMcafpS3C2IE/jvRfFvxKIIEVkPNCklM95cFYY3Ss8e22247LD/Ri/FW9Q6FDxoKqThjXX6WKpUQd2SLntxu9taHAgFvD6f72mOWTpTJp+C/ulSKgohgGV5BpiyX3Tr5RCOIbgt644TRK664AnUPbG377bcHNOV70YAxElBoH+QIdAyOwBIUi6C/yy67YCrPDFP5LevbmNMPDXGQB3Ikq6piXJbL8RNGyGYE2Y8g9FHgd3jBrni7Y7SbwI0fJixGE84tKRcsGhw0/Q+rKQDMDSaGBvIL5gqE9mKShmUvunUcDegwRzW/8IUv2ASdgMWACgA4AuCwOsIbbrjhpptuqhoHmyaiAZeDlYPovtVwURql3waeXi5mvBPZNEke+SFuOoWIC0Fc5ApDw8/RmmuuqVpUzjZC40C8lOJfQRTV+jxWW201dILAQe1aD41hazRpUha55FLApKWxFU0xIQlR42CHcmY/zmX0gd5vrOqTiXQqgXP0UP2jo62GMqPPKKTVEomHH34YtQg96Ac/+AE/aVYHnZGgOkNrZQ8OmuCS9mW0TaUTyTRhoURZ37JjrifvOdSiMS53I71zqSznRJR967yLdJA6NuEqlYabUMSwWUbND9+wwFZ+iR2pCt84jCWgezJJKvQcsQvwuCIGVIvBQeMgk0O9RNovvSRB9C7loNGDqw6I6CBKjayc8xkjlu5tRtFgcwmqE/kNbDO2gepvbJIwAjnRfEGJZvegkFyrqc4yCubPeCFFTGduiVGDFSnCwEocykAjBpg88ZwCcvDBxk9mmP/61784NoXNFJg5yGHpbGJNkzJ5221/uOjii9E1sDmluH60v72L34z8y344TMDNRZNJTCpD0yaG8vi8TKYziSf//o90dzLDykMWjTCbtobOPvscJOATTzxRuyefMHPGblaPfHcz7YTBAFuNXnzxRT0kiJ9t7HowXs2tQwknhekECWkNnkaHjVctYg1twTuDlbhiXZ8xCTwDGXpzLBqoPeMCm8dRN9CnYoiMwiAYQ+CbBTJI6VxRxABe+hAr6X7a99izBJRtMA+MIq5Dh0mnVaEXO4YK1H2ks2bNuvTSS1nqpeZf/vKXx1D941DVc889FxaHPLHtttvedNNNgMzJIHEAPAwYIWDELCtpXX/99dnBLH5bKZu5DdYN7KT4wuc3fvjBR7KLss/967m9vrnX9O9+/8tf3o7T95L5NHdC7zJgVd0U3FN0Wf5dMPjqSSdQeKSNVnynbbbfAjuFbKIrnUlmEr+68EJEySOPOFK1TjwZ7HA2ftddd4mbmI7OuqzJiEDlhItolDKsKnOGzn+t+l+VUnXiWoABDuvFJSTnjFL2M888w/lNttu2AJpOkR0MxBMDKFLRn6KnYwjo6+vTRYh4VqUDdSkG2lXREJEVxVJ01/UbrR6COEc0I1byoW4vD2oxti5IOok7GIgRBug1WK2hzcGXHr0G0XzvvfdmDtOkKsjuUD9rmb9FxuRITBiietFGqH6YW2LRQPi0006jpdy9V1EFPCS4MFZgNt6VkdVR8MNqDwX/5S9/mTlzJhtPQBrxjzz68Fe+/GV+fm3Xr89bsAAzgnXXXQ8pTZUIupcHyuR21QohVIAS5+WGMBslcOYZZ915910Lcouuvviyh/96X0+ya/r077K/A5Nn6rXJJpsgX7788sv490GyZM4WAnhxKQJlk93/ddVVV6m3ps9+9rO4tMAiWkkiLnVpVzjZA6+LQHgCoo6oTTmBK+7by+PbWML0IgO9GhJ64ER4LI4CwuBmDG1AQg9iBcIa9EUBttphcE3p8dHg/qw9k7ZM2Y6KhrYwoKK/ffzjH0dqZCczHY9+mMECtXMFgQFmgvYOIr92yAMFVsUrjnXDhIE1JSauTz31FPMxOhFSIOoGt4JB1QvJRi/0CyUqBpEzyu6gyq2WjxneGOGiPsjRFrQLy4DMmZlhshHg+OOPp7Gq1WusxYMfqsx8UolW1nnSKfCD2zl2muC6Fd8WoG7BwIIHH3nwuReeHUpkV/mvT5546knI2Yvyg9yDGIvkBgfy+EYYwk+CufMcM+Pd7F+oeNsEErAf1hHAIQM3+5MXSiB/8OGHdE0eT/iAg6dvtPVmA2LoIK285Zabo0D/05/+hDkD9aKCPFE3dGjAkvpQboibn9izcKwgPA0sYc5ADAoIWt+mjE8A8iy54wN7JUi1V0K0OPBm7w+NwvG9JKTb6hkx2pErfdqJCwADhbmcM3TYQTmAAurMotwHE2NxlDX+ddavWcnRLFxyySWau57EGa+BIJVneJbZtGfdh9csc/HHi3HoU1+NqWd7z12llWPanKlk6tBDDz3zzDPxkoWOXDV8LAF1xq0YNahymRgB3B6goptD4KPXPProo3SZ3Xbbbckllwy5agUBKOSCC8VFmvWJFG48Zz300EPsAkDp8MQTT2A/v9RSSxVq0AmVYQB9Gfbz06ZN+/3vf4+/BoQzPClOnjJx9qz+V154cavNNuILGSZ8sQYcF+dRO1U0uDRoNCPqFM1ze9b/0UeUmktlcZM1dcrUXXbZFZcTW265JdpzxjW1OYqXWFmM0mb9Qq1ANyF3WBlP+gguANZdd120DMxv08bTWLPK7uRbGwZUHuMJAe+5555//etf+U4lN7SELBcRr8Jbbfl1UtWPAaPNr/+zzhcRwgBa1N/97newuI997GNLL7008husDy7Xme9EqJFGAUp7KxpGgZhWf8oohQTG3j8ESixmuTbbbDP6YavhaofyfSG8yXWp5AOyyUV2shcMZLNZZHROy2Piys9jjz127AxXJVYVkSUI+JvOM3Fm/sc//pF12ttvu51zYyILcEQAY96CYTYTGMYCTlHhVEhoe2F//1I9iY02WCeRG8AvSYZ3hseJQQ0aa5ffsbxWa00abgtTwhAKjhSPoVRir+23vf3uO2fPH+jJpNZZa61zzj27u2scrY/ShOrwZKTrTMbKm4WW1cnqD3/4Q1SlN9xwA24awFhH/i7HVctjaCx0ZzxRc59//vk/+tGPFl98cWzriGk5bB0AwsOAtaeG6xKm9W1MeEDEr6RXX32VzoKKHO/dustV1yHiUpNckpUTGTHFmkbuXLrm8bPmhHFBRgU4m6No6EyxKqC67ijExf322+8Pf/gD3Y8nigaEjCiMW4DBVaL1IIYaRgG8uhHd+aA6BmjW2LUpWoZnn30Wp1zMYZiS6SJ5uBWpeTZXHfOjfyMbVs2eVdnWYYZAcc7nTjtHX8YocmBuydoscjkbKNCoMqfiUKhR5DcmPtXOqFb0Tz755JFHHinVzuHBJ3Hmj3+88qqf7J85s6er2yoXMCqweJHjVuu76hWBTP6M/owFOTlwGHUI98J05pGHHvxw3gDHmd59z71PPfnUeuttgDYQAsA6g+MtAYp66QhSH4BjIDUdBEXM17/+dRbMx0B1Y1xF9rpythErQ4hG77zzDooGArHToMmunEw8+XBn3lF/72mt3M7URucRu+++O4oGJDfMGagE4ThuDTPLPAya3Km86h3qb5G2/CIQRYM5EN5DD6KGI810ev4oqIa1KDbiYlf897//HSNwOELJ3H4UeQfwqS5I2oxKxMQozE6Rtunx7sRKN+9ZKdwC3wlYDNCOtu1oYsRc+9OmiXiA0evaa6/lCAMgP+qoo8aPGw/AYdfCZX3umoYbroBHh3lWeFs5ioOa7YtCyEZFL2CFG1YtOIeAnxMnTqTVaKOwmyl6yCmHyMUJWEIIA13MB9Zee20S4+Ng6802u/7SS99784OVF1uya8GiRH+/cL18BnaXd5idEywvpOGYChSXylNydkiclmTyqe7BdFdPOrnaKh9/6c03EwtyP/nJT1mcR79Au6vbPBSCFO9Ws2Fo2ulD7RHUSCer2ms6iGqwiYUhV6DVBnOr8tl1112HD1ca7uijj77lllsYPUloG45wZIkcIBU2zMpU4Ys2k7pUqWj0ol05T6Az6s5QwCx2QFldRyNDPzYOPFt2uUI7VMqEQomzVWSp5QKDeoLE0Qnq1HwOF1Mx3DRByxpHXWaWISeLl7S1ch+3+cfOfKR5nDcMtt6y/tr8glnEULNJDnphVZa9fw8//HDzi62pBLiDVdUj7wIeFzFcNX3fSRRhDLA1jgsAaVMUzHFsUwatU089VSHHEo9+RG8KC+Wu1jWsMiuVI0KN2C8YPuwJ2YQL69uVPgopzooXrnxjI125PCSA4lMMkiK9EnhP/+npCjVr3aeefArtitcx9AvdQ7ku7EQGhzLZoa5sPsM9NKR3mmUiuQfLbo1v6Mn6Z9md5EBZSsljwpDtymUnjx83Z9bsTb642dZf2gaYmYB985vfZBqj8HeaW/FQ/nR7R/nbTsxwGChwPBVEQ5JMUJlB2wCGtPb666/Ha/RElmPoh71ol1T133BIjuI7be6IQWamoMw84dI8m6PwHbnKNO5FF13E0svJJ59Mape90OJ6jZxLcCmgN2BgIPjzn//83nvvkTH7KLXLxG13mOiPUKsbJYKnS9KdFMFhK945hcR/442kVkAPl2d2hEb8iCOOoO/xExETXtAKWErLnD9/PlFM4WATv77814xM5Zr70m+i9JsJmL2bCVdU5px11REWT3rGJGyb3aGorkxam/ill14CfjrLiSeeiCUe4xk9KCJ9p9mYEf2CubUgVZk3u9BO/qFhQOWwZ5555pRTTqF7YhGATg2CrwaAuGnIyREk1j1kUUpvec1SjZGUbArRXIx028TlAZnsyTW3v3/+wqEVVvj4DTfcvNJKK6LBvPHGG1mljyl70Up1nh0MVMTA8ssvD21jT/erX/0KSYmRqGKyqEXSGdHIw14AWDtmp3s21EY+LxXWau+Gcgr6I0zh8FfFGXYcjMJsouXtOzA4ALFhNeNtAOQoov/eUvuLhS1WYptYNCBxiTWhGVhr1DXYyUgbS2sdRUPQvTm4/OhsdDN2L3/605+m+x1++OF2FSi4QhrJSXnB008/zYB6wAEHcDQdoDKdaySvzjcRwwBNiYrhrLPO4sB2NuwodLFi94nLLrsMsIEZFydioIEpnrnCxLTZrRdmgZ2y2hYDym+1empFf8ghh6je+fTTT0cf/d577yLcNHj+sadraCL2+iZMwHph7px5zGW+/vXdUEwDM4YYtsh4sRcLdicQXQy0YpoHGaNE+8lPfsIZtHRPAq+99hqB6GKpGDIV4QB4iy224FwYduxSoxCNAYuh6fwKGgN4PWAvNuoGMmbzGk+1RLaz+qALHCE/tFpa9Jw5cwhzns5uu++G8KlrXXzcGRdGwGB8XncUDdFtK+2EbJ2YMmUKHIFVLF3OajnECLscuvnyyy/Ds771rW9h71QCUqs4VwkY1X6q9VoLbdiqARaR+AceeACtFoqkiy++OCIg1QXGmmuuiSMuzBmWW2458bqfZOSSnZPNGLesGWT5ojG6hvK7poq4y8g1fVBIBEPXW6MseIUU0QjBIkouhUsjowFjJKCwRMs0QMOsR913331oH/A8d+CBB7IiNG7ceIRHDMuAmDTgMJGSWwLDX0ppw6cZ9VuUfWz2YP0qnUgdftSRn/u8nMF56623brLJJoxrABmjydiokVFHBtJ+la46sugkDREDtquyz5xJHeu0K6ywAp3TbcMQwWmwKI7MuPfeezlrEDN7amQr1WB2Y+4zfwQWey57RwILTB+waFBLVYzgosB1AeaCCy5gKkF/0TPIoTf6i8WXG7aRUQ3I1kUkLm8bo2yTgQAKl9q3FH6PpVARIsZSxaNeV/obEySF8rTTTqMfYs5w/fXXRwRuFo3feustONcee+zBUIq8GFdPxSEgtPlrhsFWYqONNlpnnXUgOSyccQtP5rB7JjZcwRbUjNwAktkXxIk7LhWSVNOgI1YzxCZUDJaNlqsbmlHHWvIEpMhqGWqBv5NGMQDdwmah23nzsQhIPv/88+ywRUaE6zIZUBu3IeQ16aQp2fIU7U46aeKkP9z+B93+ff/996sGMJ67wTsU2sFAZQygCnzjjTewaFAft5UTRS92KCdr3Yyb2Bwx65s+fboOmtGDNG4QiT43EjCvuuqqrMEg2p100kkAREO3UN0AdQHA3XfdzQAHSBhHA1IL4RlFC8kGQ18ONJsNR5FXW35qJeS2rF28K2UdosD0F1tsMSrzs5/9LApVQjqcMWMGbAJhd6WVVgIk+AW6BiPuFvmYiQK0HRjqxQDNuu2228L9P/roIw6JZJJD4/KTq96swk/v75PIY3cDwFApV/hgdEpsBgYgRb2akXlk88SWob+/Xyct3/3udxXO4447DvJGLONSEhc6l1BEqB1eUYFdzOmfM6G3d++99wZs4N9k000+/PBDtdmOLP7DBwwiD7/QTomjxAD0jCBEV+WJbyA2HSiRjzLbMD+nJzLWU+Lmm2/OoTZUAWEgTAA6ZTUbA9/4xjdoYvTRDz74IGXpTopmF1oxfyQ01rFu/8PtjFkTJkzYeeedgUr3BpK+wwMrIi2mkRVEgYBqMpbtRAJCoZ/NKp9cZfXVV8fv17PPPktcC3ugLpc99/xzV199tTIFXB8pmKgkuXyQO3/jjYFvf/vbtC+S08EHH4y0oc4+Y1El1HOMVYhHCHkA3NR5VwXuifVKpAxYdK+yPsU3VWcCEwsqLgISY7G+3j7UuAcddBBWzfB/dqux8EgiBgWovYUjQhGg7o8ydQcLPlw93T3zFiy48MILMS+Hw/ztb3+jUu53YzOsszu37iAnis3qghibcEiyKO3FRB2FIIMOMyh8pqjBTlzwBCc56uijgBZq/N///V8CCABxAd6HM6S29our46+6CVTPf3V8FmhSLOBwwIGgDoneeeed5N3aJsYPyA9/+EMUcx//+McBhjGubazbSvZNBNqM8cssED7iytbF/TxSMnesWsdOkGAEDFh4/2LownzgiSee0FcqiDC26RVC5aSgnEiLSIdqsrvvvvvacoETwBQ2G9mMgCXZEX20Kmd3YcCYPER7crdfuFBEPUzj4t2AY7qgMWQO7GhCaNYAkaJjp1WNk7MhTO8RVEFKhzp9Is9S0oL1sd1E73rdT5tvRWFRG//EYt7eQdUuzHyGaR0oED0XTwsPTMiGx05A/WNRd2QyyPuOO+6g7qjSLGZAimCG/xY/doNoKRVBufZWFNqfStQ+Xl06rD2sh3z5eZSQcSaTRg3IyyuuuIK6sJ6GWzI2N9nkYzBAkyFh24ZjskoYzPC01xhES8BVll5QLJ0GXIB0PhwPkyt6+e6ubt2yR2TQ5QSZn2q41KQIUM8880x09FjXr7jiivAWKqIcJgZ0WMpCah09R49NI1Tq+J8Sg0pzew6o9adw5kReDiFI8mwVSah9yjbbbKOtecYZZ3ACKxP7VsED5jfeeGNUWkxtHn/8cX66C1oqFYy+dYLNwZVVCmGalUukPXgMf/LJIR55guWlq6ZJn+Vv2zWmWLAIqpbC0ztXYBiA1+PfbubMmf96+l9snidfBgYVRAIrY6SM6EcIQ6g8AAZFAxyBAQnXMu53dDz3Z/PCzaHaoOG1vcBK/EGX0KT8mMCga2ClUU1UIDzoTQWOJpUYYLahEaGFuZJfhqgwQBF/0LP4J3jHzqBh9uzZGNc89thjFttjLSACjDlwjmn5eeed99RTT1155ZXLLLOMiwdkGvdnJMJlTM9oeM3xXz58u+++O2etwWpUM+hHj7m/8FtdY2Q8xRUfg6yiIHxW1j6oF/Kzd0jVor2Qi+iwBNAy2Cuk4hsqRqd2g1nZLoFjb570R93Ab/OjRjYc6YCVuCINZcuAYwRhKKHFv/Od7wAEKzGzZs2KlFzXFgOBsh1pZZW+WtbeUSoYpHSuiGJAByqAo/thWbTUUkvpyg+zPmQRXdoKDXS0DMqSZsyYcc0116AIx2o3UkwqNFS0fUG0LIIvWzS/9rWvUVlkXzzGQ40xrXh8IY8pwoMC+/LLL8c3Dc9bbrmFRQ+yVXXnmGpQKuuKX3jzYpMtJtmKkKBQ3ZR8SnUNpcIGLYvfVopmHBnLQwmLilT/3//+N+4DUe9ymjXnvTG+N6VROpmGggG6LVcoRY2qEIBEnmTRSP0xaV777LPPqDLtfBxJDDCOsDyJ6L7LLrsg4zGCsIUtFlQaSXR2gKoDA6Vjfx2fdpKGiAFOCNPSZG3ZmOchmhAODQTkHi4KfeWVV+bOnUu5Z599Nh5cQgOgU1BoGKChKYthSTXfKB3YR6cuD4gfy1OCEZvALNuOmCqkBJECpoE6c5Y7X/X29jIjZSW/gRza4xN6HBoWDF+pjpo6o2hGUrS1i82SozFet2BrvfgJq+Gy8WMkYFuNNkXXgEmzmCkODe26666TJ0/usNlRkYEsbtt7VDk19nFc5m+QGctXkCLOZXky1n//+99vrMqdr6KPAdoXQ4bNNttM9dRsW3v11VcBm6aPC8VGGcmuxNXx0eC21Jgb3d3KxyUsAwB+hfRiMcv4XGQnmBo4hFYLLf+6667T3e8cgqgz0tAACKogFhrsHVSebZYPR6sigmDUALEh+1588cX85IIUIbw2q2wg1YGidJjhaR03BJJzXZnoPo5KuznqyiYSiSdNmoR/kPnz53NY6ec+9znmY7J7K5WGBiMBX1hAIAJy4VuOAoP2lYUA0DIZgAGFeoWFxSiWo5TMMHrMMce88MILgLjDDjuceOKJoAWuayEe41iyeAgqAItuBt3TTPYKCtRm58NoDoNlHYv9sPAWRLtzzjmnK9OxpmkQ8aht9EY658hDEQokpsHcAv8M+tQ8YTgEUDfAguDDym3s28DLbfsMUSuoZkHFv2SpNV/bI2CECrZMyBgBrs5rBwMl/Z+fwh3SoUpp7NwDIiR+zjtkcRtngaw0OjB2gm2FAaUuZnqssyF8MA6xVZ4BSf10tFVVO5WJMAY49OR//ud/0HbB9K699lpI0e5gjzDUAYOm/D9+FU8m8tzFVgygRvxgBYyhWGZHs6I7wyyRTROnnXYaYzoxTPOojB6RqO2uz1jWMBpAQ2/QYeeqiAG4CnapnCP24osvYjMFs2WUjx+rqVi3VkcWCI9pZzQo0DKTz3/+8wjw6JjUH4cuXrYaYZ3y2xYDHUVD2zZtsBVjFx+S0BtvvKHZnnzyyWyfjsE+4UpYQPa1d6X3AcXFWanJgKTXrbfeSgBZBBd077zzDqiBDAJCUFtlIxMqI0wUxItW1E+mdv7divKDLJMtWmi49ttvP2Rfdgqg6sJLtt3CE2RJ7ZGX5wvNd7feWtdoLOhVspXoTPksraFiwDEbkzqUuRqJugFnTMz34LfQOQngvTZ9JzAaDAhz7mgcyjCIlSJxp59+OswWyxqOANCYsoSdiLbCwKabbqrKhaeffvrDDz9khFV71baqZIiV0fPFKFDFv3xrB98QK15jUc1RNMR5ilUj4sZaMnSfVPmII46AJRFYccUVEf01PNZQMXbqi5iLWmGrrbai9Qlz6DGyiHroiCYSwvRaYjGgE3v9qbqGilMsmz60AMDIjW4ICVv1ILGatjDdgt422GCDSy65BFMaJmCHHXaY7tsKDYdxKUg0p7KzCQ8+qgdUowFf6dCKakB1SoEe+XkwYFEcKypsGupQMWDRwIri888/TyHbbrsth1gTYFSF2aKD0DNNm1b+mMi4OQJu+6AOSnvuuefYq8+0k0uoLpVS89X4VbIz76i5zWjo6dOn0/pPPPGESnSMsJ01pJrxVzmhEf8YeWXwNaqHju2eh6hA+LBuRtIcydDJs9PzKxNk47GwhopX4znW9iV2VpSLJ0gUn6utthpbpvku4ooG2S5VXDsVfIvjOr+GwwCNjs9/HYF+//vfk5QBabgPwn0HYGznQV7XYrND4cGmMzk7aSolLVif2banm/fssxnoySVS9m5G/q3NEwrca6+98AgD50Egvuyyy4gBJJRfrPq2mWxUV3XAg6JCnwlvFWUIdQN2R4k8AVU6hNuANE4J2w23/BiV9vLLL59wwgk0Ewo1jpxAiUaY+R5VMG3bwWPjjWn91CAGwJxxyyC75V3ptPG86/jS9kH41QcffBA1I1DI7Be/+AWaBcbQU089FQosWdn2eEsdNW5R0lJW585KWgRShItF0bDOOuuoawaWD4GUGCtHhQM4XcNe4ZTY3FIYf43NlIiCcBq59FlUrDiI8e+iF239w1EKBFzPYo1DwJl3sgsbAww/d95559tvv43GAaGfnRRwJRWJwgalU16IGEDOoKEPOOAAhoTf/OY3aJqmTJkS8oA0THUBD9gwrmEKComqpD5M+mBfWV2DHU90BAm2lMZyAyQGPPVH5T1FyI7r1IXFXnyVwXD233//9957D9MVGBHNHRs5uIZWtFKXDdTwkUmSFAetxlOrXZWyJFlrHk1Kpwo4oLEquSYVFK9saWJo+Mtf/rKCjZaBUy11WxAkzdt4VSdy0MoSV/OE2zqqqwyK9sU+a5VVVona2gynWj722GPKSDlkSshySE671BrGg7sW7YhpbbuXklzBLXTEOjQty9kTK6+8Mg39/vvvK8MJ2tNwaTeB41mbU2TISC1ZlcJa32+xHDTaTD4TyYs/nd0TLgpLO4b7rhPuYMBiAKaA3TIjEIp59ncRD6eIxzhk6+AHPNnXWPb6cZ2/VTHACRT4/qStafpjjz0WD9WqCK/6QYgvZsyY8Y9//AN49t13XywAAZIrxPLNZN6UJ/bhfsktnOTpKGcBEF2DuZKqYvB/erHx+bP99ttjWaPwYlmDVXnHWYPbejQsp9QlOYiI5RJvIaXF+yYAz/YIF9ROGAzAps459xzcQBLeeOONcXpqWFds+2fkGzWfauW2abbBM5ebPXv23XffHSlUPfDAA08++SSwnXvuuahuEfMwrlEIQx5JA0GLMD//qtiXOA7A3kYVpYqJqk8ykdt85WdcNTEJ7OkDJixfoGvgg6hdSO9LL720uoT8y1/+wunRxLBU01Q4GZ1y+ZxVNzBWNbW4MDOns0BY0IqoG+QWTOY9u4YwAYloWU1q6SZlG1EkthAs0UBnC+dgNQ8SOhLTOYpj4WX33XbnJ6ZW/GxeiZ2cI4IB9hIfffTRNDeGAxyC9cGHH9gVj9ZCCOUzRrK+DTVefvnlEydOZLDkahVUHBWmFwAAUsWr2bC5haYScjINE1AuykVDxEAY0z5Ls66//vocg0Jdvvvd7/7qV79SK99m4zPM/KWBiq/aG6v0y+J8Yv2rxJY71nWxwMO78MDHT8j4oosugqqhcAx2am9xm1XLAwAP2M2epTRWTfyWyIet3sO76qqrAgVjKOf14muGcGvRRXupohalLYQHYBjXsK1DR0+eOmQI6uJz6fmCwKum6dUBZ3pS410tj2E+L/rEVXwUvWj1D06JBoStt96aXTNY2Vx66aXNNmegOAqC0ub0z+GG1yFYKuuII7GVNCB6Bo2x/RofDSVpmvFTZM5hbvp5NOZoHnYCQIHsSTYMvYinB5d/ACC2VRYwCJZwISN0hEpMQlPmCqqejIiqxWDg4VTLZ599lpilllqK/AnAHSLIIJhRwdz1GRQeRp+PqjlHn0/4OSjfZHhALEYQeffdd3UBJHxIyktkWfvmm29G/YGKgVVBqBHyj4KaXLuhXTnR3cL6TCUzhZuBN5ArzYlk3l0sQhXhDGGIqHQAAEAASURBVKiKfsfqBxQ4aeKk22+/va+vD43nz3/+cygT5MWqEsMBq60zb948yHjzzTc///zzCQ/3AYOtuRh2GQFIyfntkr5gNN7KXcpQO5c+JSRXrY3FFIjjjW677Tb9jOYuV8Hoq3g9aS6IVueZ222/HRbLwI/KjFmo15RGQRmvSim01As7N8K0lZi8ycO/xQLOv22kDdhXwQYEILkAKY9+VQx9vIPuW4Je9vdtuOGG2AugGb/nnnsMVK3kxjAZkIMX0uuuu44w9mLTpk1j/YDBFFK0Rg0twVVdhfo2UwXeYub2hZ/FuREvZCAqiVpuWBaJGWc8piqfy13922SuULRqGRxdQ+FVMVQt+EVvQ5zbYYcdWDiEAB5++GG4ro4jTYWGsk455RSI7bfX/ZZtO5RFTFNLDCdzrQUSljB5cwtLZ26WHSq5zfYR5lVyl1/u21rCUkqlqwCDgFMQFcLBRnkpwZN+a6kG5i23MV9RC3nqbO2Ha5d1yjEVkRiGB+jqkUce2XvvvVnZg74ZFZTSAodQjt7KyBoLRXCIPfkTOPzww3G5h4hPuMU8wh9kiiuObIHEw1NuMWHyb3HQ4oebHqB0hnKhPVMoIBKI20XTAzJPGpoNnITZPYHGISJW63/+85+ZedIjvvWtby02dTHUYdAkFBsymt1eYE85oo8sGlzo3wOLsgu9e4BlI/8eWLSAn6O+Fy1asHDA3ExiCoXi4GuAmwtPXznZwi8zAaA1AMOn9Q4ZWw0WR+PSsuutt95BBx1EFnjRO/7442n6BrOL2GfKwKkgKrNf/vKX9957L7b011xzDS2lDF8TaH+sBHvw43ilUhqJQ852RO2acvjjH//IKY9f+9rXGGv4QAaaVGvFiprAHjER80zImJVDTlG54447aM0vfelLjOP6Iaq0EXOIbAKWJ2kj5OY8qga5s7kE9jkSGMKUSp7mxnDahgmU/HRfBRMu8Ae/79BTWtNZYFxYr7A9gQ5OO6IaVpB8wMJrW109Qq3AlE8XqzhC2AwK4cEQYEmIgSLhC4fQxi0oAhxdv7djwqgMpHCSugsAlcOiYlBIRdfgpcn5ARa0ym5So2uQxKJxkELNU6YlJOZHGq2Xl6fm3JonpAgB0G3htHAeDhSbOXMm2ocmQWOJnOGM/WL9/f1777P3rFmzEE7iS3gursRs1DQx3C6FL8h8Lm1uCRffmXxOb0mTK72JLL/tJyUBSUmTie/nopvIZF7uFM7RCeSQ4cUaHYZsuokLeEjhpoxtdCSd24dkPVLAVUrLpUcToMHhQTz1siymkDyGIeiZC49oL7zwApus8M64zNLLqDog8NpothRHzu+88w7PyZMnb7P1NujpEP74Cddo2aKiahlg2VYElZm8aBYYCGDzOTUXF72Td5XrJWANSjB+kgD/CjBmKscza9YaORDBl3vyImQoYgMsMtisdGwQajOWAqx1oGvAyvf++++H6qZNmxZscQ3kxqHfjJeY9uyyyy7QJOMloDaQzyg/sYX2stre15dJClPNw+ELFgR+u0MBLoAB6Z4KuUtNPH5nqD03lB7o6Rs3rq8nlUgi8fM6l8im8pgsSlJzFUJ+TBT/0tCqSOI4QLbJvPnmm2eeeeZnP/vZ7bbbLorg1gMTdGJJha1J3/ve9/iaGOxaCaDag6gIWDKzeXsxuN8wIdklKv8Z9oopwn4QkwASMAMNG9rxh49q6Stf+Qpdm8iYgF8VTNoULQNkfPHFFzOAEjjwwAMZQG3rV/0y8i9oICyhB4cGesZ3Z5LdhJU4pWpFXK6cMn35LPA6yhwv0ZPvyvRkULcuHJINCy25QALXrrvuil0DE3skN46fgMLpqohY4YPEfDKdkAUkvDtTOgY1HCHMMBqC8XyzKyvT+3zGWHWh3yzIfoVyDVXoz5EpT0i3kKowg3AyKeTshxji+c4oJpgJITwZxYfoG5A3K4Hkfxjy3//85z+LLbYYUwkWbOBC+HrfZ599mgQDxKYMgVIwVWPicN555yFDomjQYb1J5YaTLSQCzTEjQrnQlctNHtdNa6NfENXDkJGvinigB1SRKGgBLSctaLoK1ZBD+aRGZExjwgAMQ2AdpYNk7q932oLCDQSsaKCGKGly6WQa1CSRaJteG1bqXElZSyTKdO6sKHPcSyZ8zYfJLTHoMD2WLJnvfeELX8D2kkELR31NGiTQfOtAiEzPYctwitVXX50YHaUUkpboGoob1Wx3ZGO+dFG5QRAEkPZGiEJzQ5zmKvlahoSKreQTVml6U0rFL0oiJVvbBTy7ZpdYS5JH7CfNTeNieEWLq+L5q1/96oUXXsjAgA9tAtr0dggJH3zMUCmUM1DWXXfd1moZELOHBoaYGs2aNTuTyrCPgZ0MBTVrCRdyMFWLa2KzKlJxRClk5JWFnSdxhp41BnMJjB0weSAulRbis5sJCx9HPqSshuUX6JDNpUgqn/nMZ4hkwmYVDS1hRKPHHLXgsvmoloFZ6AUXXMBaE/E4q3cT2JRVApaV2UCVhOFGF0EjjHg4Pvjf//3ff/rTnzbZZBNYEIv/zNDiK4/Sdiphg2+UZTxPO+20Rx99lPD3v//9zTffHLq1CcJtkyBLkw3XmTS2vrPnzkbTarmxS7oFflhcso6//mhb/E5+lYy/3iJz9fSFHMh5YMHg1CWmoKWa2CPaOnORYRE9+vFN/IuEBgxXX301mxQ4j/mf//wn8lsqw4rjkFEPDtcdmgQWB0jROgxU06dPx86CLhZ7RYMMfMz4MmaS7yn6fQFMW1yOBnAv/60b54Tx5lc0dnu0Nwz9kL/mKVQtN3NBMXDwpx2yCOYU0Mrg1KlT4TzLL788hkhwIczHsAxtHkDKCTFnoC9QCjoOnhh56W6dmPPAXCYldi4Z5r753NxZbAmh0cWWwZ+eljCxqmguP39XMqlwaaQsqJfoGpJiE6HEnxpE3MsNLb1Ynxy4mfFJs0JuTY8KRtEAinu68Q2Y6EHBkMFC3evP4AiDXapd0r1HWS3XjSuetm1ueTOVZKsqHDyZTgxm8wtzgwYaWUzGdsSsMMebpGEKjOJwBwRQxoYTTzxx0002ZbSwSAgwwCwTnQIZspePJ5zijDPOaLlgpD2MJVlh6KbeKs30ZPoSuXE93X0i8+BmhkFiSPTbZQgp7bdMwcrSSIRPtF56S2g68av4iROJBnEQCHsyvalcBi0D00/hPmnKYiGjQLTOJ5ELSlcxuCEATpnPf/KTn3zppZeY4GG4jilNFcyFUZEjjzxSi8HIXCX4MEotK0NPc8h0dT3+xD/WWGMNuszkcZPnL1wwmBiA7cEJ7RfKBZPwRxslQnQhgY12tQ8Qm9JnhXT+B4YyvfdJ1nPMlU9kIX1W85AiFmXnd6d7JBoWGAkvFgpjHU/t4xAh36y22morrrgigjIahz322OOKK66AAaIRayE11lGT4qT0LKoG8AigOIp7/PHHeQ8h7bP3PkxC1L6aNFzF35X+oq2xY9Cckgx+kly5TDkDLP024N9aYBm8CECmIJ5uD6hQOGPcep9ZjyaG1eClf88997zyyisrpIt8lB0rCVApZuN33XXXCSecAODQMH7+CdCySttamxEbOpqVBuyHH3qYAWLOvH74FVwRRT/OaF1oLa+zIylvxWGtIRh/tHW/0DB9oxAp/HCE9G7iZFci0ze+9+0Fby/eN8XwUiFCWVr2VF1KkyMQZCHHhkIgh5k8n6IVXWuttZ5++ml6Ok6FsCyg6TPMjZtbfinQykXZeMsLJEkMFYkBkhjSHs0nt7RiPrH00sv2dk8ct1RvOt0NkYBenkotPs/B3JW5QOHy39oYpyXQDohhrFKIl2CY9LLwnNL0EqLEfDaVHUxO6J2wqHdQRl5ZfuWf2J3Z8loY0FkDVoHQJJovTOfeeOMN1W4HDpWOzlT817/+NdIaC5Y77rgjXBGWSFnKACOClsbqnkmmb7/11o8hEg8Yy1FRLbkmzE6u2viGiYnpiwYEC4YnyYIkt0N1NoHkYeM1AIma9B6VmkhJD6+lGF6hh8gnJvQm5vVPnNxKk8AAFA1oE7gXm9SXzg0uMWFcIt2N7bB0NLmosOLJ+62xZd1Vo2t6mgFpuI5KeQxxDHTZ8b0DPenFexJLTJwIxs02FdFC1FRMVBPRJ5k/L7vssp/61KfYWIVGcNHAIoYx7avBQq0DEvu+f/vb36J3xHkMC4mwAxgEBVm+0LIhCiowxJXJdNOhxncvvlh62UXj8jCvrhTWm8MwdNtdqYdoIitejnzjKsIr0E8levZMlTKZnp7ERHZLdaEHQduQkt4OV/L5SsWSIxFpuT8BpQT8gCKUHHXUUQjNaLhQN0AJavIQGsTW0I7SKXSdddZh30RopZcXlB0aZA7PNAI9Aoian5ifXJjMJuQnArehLCE2JTGeac+uvUCBVv4uZO4QJAOF7KwzV+GbEslU0nvfsFdMSFrU3KJizS7KovLoNtoN6bCeNOkUUCg10iGAV4IESjo4Z5riXA33tPiOYTMF4nILlU2jRJwIfKnEX//6V5R3VBObasyFNE9b5UaLMIrWYlm50azq/g6+Cq+kDxBQqYcsJHIkjRB9nBENp7MMN+garr/+ekyodIdg3UC09AMV5QGBMZQBFM2RNjG0etxxx9HWxIsHimhMPEaDKqwY5i1c0J0YRPJS3SviNjyQPB2uJSU4vMr76XE3mYSVpJUEcjnsChY2cnrzEQzQ2LDnhxbM6Um0wHePgaL0gV0DzY2+STkYa0V2Oac0adN+Q5ZwTrZAEkCMxJCBDR0sXDWtwCZlXEwtycRXt/tq3+I98+YtlFFOqE/WOLlQMCkEZl8ttq/6oaexkld+Ak0ms4u8mQCL9wdDboaF4hVSBnB/OCaVOw9Jsd1ebNVFkUGJ6VRXMoH3tOSE8RMWziuchKUb3LSgiDxZs8FqjLHmqquu+tGPfhQsVGTLJTv6zMyLvYEIjfQCVbzqFuz4M8BUFnobyi297HJzFg4O4fCbxe58FvKxyPRkQZ9i9CdvvdmH2R/B3BYDJ4bH8g9tPs4risSiQdL729OkOB1hRRpNikUDyF9i8qQ3n34S9104DutOBTDlt8DUHgimVBQNjz/04FJ94xJM+aisYlO6nCyyFLqxHTBsoHZIbUq1Qbc/iwKGoQjXQHRDU5xKDPR3900eWIj77qxsG5Q1Ho/LFH0Xnx9M+VAuIKlwHBGKBowwGbTwKaU9Nth6MPzAEW699VbkIXrOTjvuBEdACtRpp5bVCh5BC+qtlIaleheyyNe+smdiwKc93kNjfq8OAC0jZlVO0jq60QnoZINdg4u6AGpgIDtufGYwNygLPpJAEwUAYAhZ0PSYWeIP/7XXXmMCwE+orsI8uZmgKO3hSo19CsjurKFRGuJaq+aZaBnAAzB8bt3P3X/Pg+++/S4yN04QRAHDxVhgZBS7Xof2y0EPFFHX5VBLsWzkjzSOzJTMZfNZnEGusvq0M0/72fEnHMcyCxBRngtBXcVHJDHHMXDO5WGHHcZGA7SfmKDrIW3N4IHNrrJ2IkrBcpU1JYT+U089lSWmWspFeNPeV0xUtXwa6TRoGZiTM/Rwqu7+++9P70a/yTAE0BBwK0acBtFFLeADkCWciiwOPuRgxmvgxwEkC3q8jb+xumBmaHBofv+CHbbZ4fprr5/55lvI2EzrpZlw9uiKfzRfGetpdmvOnd2/8horHXrwIb+4WOxHuJpdopZS7YmanqbnLYH77ruPYQsyqJa4efF77bUXQh0aW/wcUQo9rnllNSFnd9xkTEz5dGV2xSPmp5MyCstqo+gL/IuUWDQYB+GQgW8aI29LBlOJ8oZaBm5jYCgDOKap8sabQZigeTD9sT/UYgKVhBQzhEWD37govSRduZhoP21ZAEkUauQEClx1BA4E3Y2VUd2Cfe0116oVwyGHHCKiY7Ev53jxdgdRjMKJ+QO5Hb65188vvmTuYILpCJcQjJvItLzKgVbLIMmKKcKnZOfL4qCbnsRWsLSpKJQ0aj4oSq9cYsnexHcP+MHtv70KdzU2WciBYBQNAE2dP7HyStmhDB1rkKM78tm8MR0R6hFUeupDF02jqKrbgoXRy9NcGnmabdLjxvfMmz+hj2d/etZHC7PZgaEheKvsk4zvBVPQkXKzzTY75ZRTQC/nY7F5nkCwlSLD3vG9yEnsgX/11VdZgUHsowg1dgq2rFHmhoBODhMXmzRuQqZ/1myknB5MJnGFYtbbh8m8iIyGSVc8tpTzApeqbZ5D2NElEuP7JqXz42a+8aGwnYCbaFiIm/BSVdGI/hADQhL+QVdYYQXVlzShtMpZyriVyzHx4MlYxWyzhVoGQITRofsQoS2R+fDDD2e+OZNAF6sZXFZ8cZeU0VYXroKAUogbLmSJq6JsVPwlhaYSc+bPWXKxxefM6meLGYoGXBPlxX1OvC+dniGvc+Yu63Ls599yyy05qiAWigYZEI2ySQMoC2iMY489VjdNTJs27dv/8+3Gm8eSXJGQ03h+4X+pAxmCKSoYDp5Q77McLoN+E72Soi58qBorUf0ZMZMkwAo2/s8YPbG+UYsVVvngXRBtvCpVigrccuHTEIuGOfPm989/5+13MskuqilqTTniEm5T4FrlYnFTXcbICnY+N+fdWdCSusItBT7030znYFYsDnFwGF0emx27PSo0WDBnwFSegXvxxRfHE2Ro5Ta3oGTi1lt/t/jHJrO2zOZZmYIMoXco0J6WLloDfzj2xLayNC7F+svFMlL72xIJFwbu4gJ06wSTHYZ56QIcuzJx/KT58wdXWPLTCkAEOztsdqeddoIXNQk2GJ0Odsccewzsju23O++8c3OJIfTcsSyYN28Rst1HHy1YxPKSUItY+g8DiKtuGCbZMK8MO/WKsKxVC82b33BfTHvGL78YfNjYWCRkYtSKKxhFQzaRWWv9jf/+8L0fLEwku2VzsDcZM1u8xbbDF27dKVkg9S3M+vwiwLAaPdDcWA4vPz5z2W+u3X+fPaB1RsRACm1tJsim1OWLX/wi7rIYrjjpDXjgESqiBQUbGYoaMptgmwZ5XnrppZoz41OwBdUPMLzdsnd0xCkU2GTyxztvWv9Ta72TfSOdSXUnurBpweZ8RFDdaV81SHzKkve2P5ckthROPHnqJ0KKia7k0PhPLPOpZHqBWUoWvoCVnZhHFWpRklkkfuqoowjUsE7ksLLDCyN743GdjSPSEnrQlM2rACvYrG2i42AtiCOgsfxU30JuQzcbBrd2cmZQ0jjAtSRJ66P1o+3NeGMS09beeMAc0yGhQrybZ5UwBVhKdMNFyR06RMoaSmCZO5DjWdh/gctxszGy6LNo/6BBQaltVjQ7/GSLAdqulVdeGTUoAfs22lUpLKgqwPBYhH62BmBKuuSSS+J1ghhkPrXcsZVyq1+9goYErdxMAPpq1QWpjq50MMCFFmnFFVd87733WHNjGZYDODDoAxtxsQWQhjO6RfSzsnw9NHTEEUcw77V9UBwJieMe77It7kfE4S9jLVXgWEtsu/BxLlKf+GhIJDOyId0wLbik1qRU9LYsLfCKomVIpAcWzWWyt2j+AnGEgNhdpOcNvMiRM8RKFF0D/Iqk55xzDgfo0OIozcNsd/zwsVTAKZto7jhPF0jCLH1kHI2cotBfzMAqYy0fvfPuzK7JeQ5NLCx+qMd+nyXKAef+QKwBN6PyYh3PfEq2ZrnU11NIepOzndTIZIS3EslMI53EAiKbmt87T7dOsPJqHCc1j+LLa1BTjA4ucNTaRpma8nQTYdeELTlEPmPGDOIZuKE3ytI0WmjcKNCpn+iiEpl8enx6XHcyMTh30fi+yVgaiqAnui6PSJwPvODwtFeevjxGiVIVZ16Y0gyj5eg90stEPDmEcf8Axw3TCmaFozyfEGICUDTISS7J1PsfffTGnMQbMz/MJpIDKmKb3Uq4paLGIv76vT2gWmkzVdk8T3nJbD43MLRo/tzllpn54YewF3buG32ObZGAAAk9GwZLpRh8hjFioWjAHRpCWDMAafnAPEKlDFGJQiSRePa5f05ZNv3qGy+iaGBJBT8IhfHG5FLQSTEcVOX2w3V/NwdnxPJgrJQn2yO6pvYts3ARWgaxY4rvJTRnhges1llgRGjWoSJkIQkj5EMPPRRJndIR2cFnV0YOimNO0hLc6oocmKlUOqzGY1PuW9UFOOoG92WNYZtzUXpHy0B8EaOzRpuymyPml4vt3Xbb7fTTT4cIN99885tuuokTf3UBOeJVxAQGwU6NcfAOiApP/Z6w3A0x07moUS1VsNyKRoWiihhURZKsJdOg0jAIG22DBdJkzK/hINPGtWIo/Z2h7ayzzmKGxmkUbPHlVVy0DNQXtQiNi+M9hGyae9NNN2X3mbWWbxXXCqqFvXyGa8+Ai6o9uxIGKz4XI3DhCmG//fbjLJUnzAW1h8+vlllmGXgOmjstHay4qq4IIKkhEOCAiFjeXeCEwkZ9+hT2WFATyJvhB8MidmqAKk3v52xGXjkCQIZdebKI34XwJ66cmYlQqJ/SZDO2HnKcaiaNGZcK5HhChYez/1E37ECEXO2BETsFIMA4J4e6yJKTDICO0krqKvEB1Fnnv94A6wl2ZpGLwoUaRZujm3dkqUn8ObSIEANjvqK6Tic42JIZfd/4PpCou5XU/7VgorqioWRIsPgv7+f2lZdbUjZlOZcpB1yyvyCRTbJBKtPT2zdl7ryBxBBErpX1WsX5KmZBO3/G3omTxvnJsk+TFA22LBdHUeMLIreNxylHcsqkSVOmTkTH0G22SXHaFutFFnKXzMzAYN+4geHIw81BWEfxVSlP6BB3kJkuvFXKlFTs1/ko6uqb4nrZX+B5wcIFKBqYCBFpJwMaCIcqgOHNN9+k9LXXXhv6p2jGMI6WRKCPkNQurqhobB5CJK7nao7DUZZVLPRYHA8T0KUSm6CU/Hghq4k+P5STucR6L5vHURFP3xrCtprNKNaBk086GYuGm2+++aGHHmIzBc/BrDg9iuxcFPzTUwAPJyNMQW+44QYcjqBcQGvMFJS2sCoGknHV0zpCErS7UJ2MtixhVPV0W0+2rUwLZjjqiA0yqBgee+wx+j66mFYCVGfZmB/+61//YnsjPBOlCYQKAWDPX7x7vM5Mo5bcEClcjvFRHe+zbZBRDvYjJuRG8FO2UzyANlKN4WTCCvnRBYgVwKRoWeXz+WOFxGFE0esZQNkqhfU45+Y8+OCDbIwXS/v6enoAoKLZ7Onu0UWaALJrWRa+wGYa2rT1IMd6AY4OkNLm/mVi/PQm0n3rpyr81RwKv5WC3N9+2KRUYtMoSA7XkBIto7+naOAnd4sp0Ac5vL8YkiO24YSIoQ02uOaaa0LtkR2g68aLMadBlwTrG2LfggQGjSm1tLXqF4QLFTc7olrF6/+zdx6AkhRFA963+969ixzHwQkceHCIBJEkQZCgRxAVQZIgqEgSRQVEQZJkBBEVkIwgQcLpj4ooCGIAORARBESSkj2ShIOL773dff9XXTO9vTnN7s7sztzcvN6eDtXVNdXV1dXV9XFI6KrcZU57hOuaUzZhxixziijIjh6ouFym1sUHo2gAuSMjHOuRGBgcM9A/dgk+kBhpBAlirqEjjf/hl2hLfhc4Ccq+EClK0kmCQlzTtfR1iooBwnzsctZlsj+bkZ0UzPqcCqIdxE0DuydwVz579myOaBZcYKrj2ztEu231QC+D5TBa+dSSkUXsCuXry2C6aQwaUJRY0YQvzH7GpZQCVFnlC3RLIDHfcCUwgYP9q6OpoaHFw0OVEkbinfCsVHLc2HFgmwEDMQUZuj0aE6qW2lkcSCQuvOhCpmdMPzgcjicx/ETF0H5BrUyvQRIoj9F0Imp4/MvM/Wzy3IG8IoIEfTmSkwIg8JhKTKDt4mzQ7StRHvZcqvliLoqP0pmrzkS/WCJdOKKUUFnchm5Z1dlnn30IYE3KOY6iMMUJjnFPYE3rK0NNp4pMbS4YnVIUZIebSGOaamRcP0EU//Lh68f+wQ9+EA7AkHfLLbdEaGP5goUL2DQB5ulxWBZn12OawQYQ2hXF7igBsycxC5MRnwgyGZMwpMiOClm3MwlwlmASlCig1VFWAGh1RbWUz/wKDrDKKqvAshhDcemN6kHtm2rJHmAa1pO7hQg9sU06GtoT8mMF0qAqf+jNx57kEkqt5ypKLxEUVIrGRNfgx0sy+SlPqdSDuJ6qo5tW5MRkEr5HE1gf0vNNiOMn3wJXdJvmQE53Y7rCZDdNR3tcj59Qoxo1GK7opC8drHFU8KibMvKLVZETtqsLS9RtyE3q8vcNWXmhNAAtig2O4GmwyNaq1JH2GRMHPI/JTE9WcsvfbCa2t5yrXP2GSL1bMCgCVu5WTJm1Y1pnNuahaDCXridruDueK6ywAg1h4scJFAhkLJUwejF+2Ks7mlmtFZ5WyywGCk+H5AQL8pF5mMjIsVgS1kCZJ9s57Z1L6RXhl2TOSaV41VrmvXSK9coxGi9hC2aU4a835PBHQqG/dCTQJ8CqjSU/mdsTLnjbotagPkNMB9GUf8455+gcbIstthAY/OGqRVU3WCxyDhyNhSpzY9Vlb4fA8r7TPDJyfuQBYHisDC35N2p0e8sg5915WaP+w6U0Nzw8IjN2/NRCFaDt9ttv1zU6pZZwthrYmGYg6AM2wMO3sWRmnVN0xMZvVt2KM+jBv+Q8Nv3pRPovI/PXdjGogKuz6suGKcIsAuuGKVqCc5Yw97Li+oknnmBoJsy+j6997WsEkLNt6zSgKbvpqdM8nkayM4trSIIs/lQSA2t9i0lYDTeWY3BdHlzGIxIqWJkA6GXlXiMithH1nKUCPXNYEu6NGNTmz5+PTRPah45QMqhpY9NbU5Wxl7HrRoh8eunYW1ylHYgJ1HK56UuFcXekNCZkxs1qjLnN/Mfwczi64Jl/YmctvtyLoepsjIuHVkBC+ZygSckMdj/72c8IGEG9a7QMwlQ8oUstGmTCaxBpln2EMuS4VO9GMrS3aGLz79xM2E6JSwW0NEks5tG5+TMMFt7IU6jOPGUeDu3JA1d2ndEygAvLcA1emniwfkxbkJ3T2REZUfzPyXxrOb2AVRC4AcGCf7kg5HdBQY/ITy7yud+/V4zA4qFfsWySZlPsnqhRZeTCEeLwYYcdhsxKY8WLWDLFl1zjUliI29QQaObD5tMisygRzSyewZt1Pb0Jc6muQQOOUsDTKZgk5sFitM2J8sYvhAA7nzSZbO+T3/lv9Z15MqXkaF0+CORj630vsO+tISQFkonGBVJO7YVgCY9MBpGzVRvbaZ14aHaUHe2Hp3bIi1P6Vg7Fb4KJsTwzmOJCXwqCO/5BOaSNg0hOOukkNsMDMqQSesATHMl53XXXseWHKSjHfRXAXPCzcnM8fgfTM8oFmeCZOZUJVM4a9rcshTGoASUbKHD8yeiGx/7nn3+e9TEGPrAX8gZgJ4wTJcjyxhtvDDmoQYHnWwu3ZKxTqa8WULGogNmW4rctAawWkEjD4KWmeaeeeio/GbxefPFFOFiN2eNkJTAgszpZUDRP8YsQ4LymRHU1RwERN0yYu0Nb42uGtZUJGcuuv/562DUqdVW08QlETnKriCFv5CWNrG4a6UOfkitf4+9ysFLcqWI95qUtQQLlCyetA4l+FB37NALhuSLnMMeTaR7NltU8/zIaHaIbu6uiXCo0n7D/zEpFsBlhOt5Fx0PU3o/8XvGTRPUvSzqbbbYZRg2IYshhqLZoCRNfsB/VJjUDt3ZuURdDADIS+V+dcn/7dCtEQvJuYRbscZfbUFOOfoXSjAKxYDBza+Hzli/cN9tD2WlqoSj5Lgj3aAe5uK4nzIIP673oGq655hqeXOx5Dj8OVcwteMoaHxJwzbeuzFR+FuBSa1SSLXjVfT8hA2QXNh1wduAJJ5yAAIMoH3JlKzDDuk855RQmGMw0CCCBFe9ZbYDCDRfy1Q7CdpTzRLjbsSrXPiXASTfY63GY8eqrr47CkXh+hrxt+GXA9x4e/uFgIQc1UPCMYMmUL2fXpdZdDYqCdgCuH0iBREbkEHwMfOz2o2bPFIozfh5xxBFvvvlm/e2KcxRhAIlL5iEpUXWVoD2hQDePJaoKATd9PWEqKrhFIKynhO5Jy/Fk77zzDmLbzjvvjLdm+DZtg/I10D3tLJh6GApEGBPqcqiB9lp6EzwYZah91osNW5QGbHad8lCV+RakziRnoHSOAoMhfcGjQSg7J3DA57J04tt5hWNAaVOLdT0HXQPfMOIXx6RRsVoONwwBYyFXw9k7nbGsYK26vQYaphlrbJeb2NVu1Jg9TlYSA8wxiOck10ceeQS9OAe7rrPOOiVThiQSpsfI0ZHL1msD8OaOQNKeSlErwAZ1R4/WiKYVCcYK9O0Bo8ZagFZTsmkCH4HAedxxx+F9AE0xW7VrLCTiycBAHd8G/ai6Bj58pmToksAVPY5bIvAQzl52O4jBdAyugLv9kpm80eaLRNs5cbYGNOdJpzWkDzIJ5KoUC0kjs3HuA9aOVMDuiSCr6bGyZIMO3rI8Z/s91vigmxus8K/jHWMcoxuUzw5B4CWShYFgKwoaDQGU1+aZLxDrHLw06J3myYHJoMpAaWSYCCiw1pXuvHDE7rvvvgoIyyZN4p++C1P31Yvf6jKE2COIEjG3raneOtz0jnTlRnthZ6YnLEB8Y0X8Eimp2tWKJupwhfWdDFGZDGePI5+p57xWVFdXmXY4obtzYbFbYa4rmzYLbqbAdd11AUNiZ6OzZo081VXFgPrpsIxLDbuq5mpzAsCDevl6qPeFF17A1QizZQ60P/jgg4mEmDnQXr+tZgCDCGFunmrJBpopMbi8srRS/4V+QW09QA4qhpNPPlmtP8466ywO2VV81l9q+3IAYUnVv/Z18bN9kAVfk6dEo+Cksdhyayhuqca4aQzdNkIk+YUU/PKhcg180HTpXZC2XT8haQh75syZEDPaRnZ+KfviqYF2ARL5eszKom2Fr8ftoCUXXNfeupItfDgyF+Q3b948Nqap0NUw3ErGKP0pAZU6T2KWW245FeHMdmR51VWX6WiVA3UUpuNZbpRpgll7rz4/yUdHJcVBfsrw/wrsG2DYoLVm03o4CAjXG91+8ekyUG200UYgn+Hq8ssv5yeactqt3dEAAuzwT1FN8poGag8oC1QdGGE3A5LAEY6voZlWdDwv5sdY33G6CpBgNb3yyisLlZqNQh2HDQDM7M4DRNRYYYBJYNCNP2GBpkVwCCHgPti5iHF+hSIIo0atwDImsLFdYtasWWzugLviuhJiRssAMTdjhSF8Rhqq+5MlJOIOvztnWSNABH2p2Mp57GASC76ddtop6Bri8hrGgD+fb7iAlmVU0b9lxTde8JZbbrn33nuTH5eQF1xwgRbkHsjdeNE9lTP8XE5UD9HoEoahpZdeGoe1MNtm5H/GOxqMjvVxczHe4c1XTyZmrOMnFUUDIzVBKSOvDriemklzqcqpphK6PFFgnS2Cvy/hEZALgvJjuhyLHWoei2CIXFOWnoIjbmUKTz75pOxdsYrdOgHTftNMLB91Fy+oExeBJBcdZ3glsECa2J5COMKQAQuCxNxUa1SFWntqj2uJEAZgYmGDFpBgp/iSQOOARwaOTgDCz33ucxtssIHVMoQN5hbBAwYa7iBOLUX9/cUvfhEf5iwIUxTGTS2CMy42xkCrMQBPOPLII5HWGMvOPfdcquPTKFCbthqGbirfqGtBYej4f7SQzKIOa5bfO/t7zYDN5A8/RJSABg3dOrOS7bff3spscR81g9so5g1M0RDFxncBzOPGjkNriFchpFjGLQ6hwKpctYnNfMycyPXd737X4qdhzYUtoS0BTJPDM62XL8ucedOWpnd1Jdjycd4EWgZImoPHVTKD4Juh8EAQVp57ln8TSMU1F8I6Coou8REo1Gj3UdecP04YEAYwRoVcUQQrX2WSfPzxx1O2EY4DphbXisp3ChVQMzpaDMoF5FdQx25BZFbszznNQc9LU7hQQ3QUwLjyUGLA3TQRMgDXX399fGxB0szusDCHgDs+qIUMQ/WCU699er3ld3l6xqn999//wAMPxLECh/vQWoizgTYzK1HF+muvvYbGgTXRD3zgA1qUrD/3gDLIHYUbQGCXZQlMxBHaUasGzpU0l57/J/gye1fahjgs5agfam5bjR2sSPd/HnDAAXzMaA0RZJHGLDz0iA3XFUCSw10Za26aq+Fy6qq0rsSmk3lAXWafiNguCQVqITqnEhfEak6MXROnSuElociWSeO9p9lmL3nk5gQT7zaTNR3AZM+ZvY3BspdcS7al2bYYsx6By8AmHwavlD7db09emMtmjFAAoR/LmgcffLBFMEPVL7/8MuS91157sc0PjYMlcunyoqtFYBQUq90Ht9GLnzZMT0IPchJq0V1QSCA/vaNS1MOwU6JHUuaPHqWsqHKSRD7odn6YG4OxKAs7W221ldBGInH66aezCYh5RYHVmG1OfW1xRlgO0hZepHspKAU6tOH6Cg0mtbZIADLMWZvfcNE6rK+xxhq33nqrDnl77rmn6hfQP6qGveHC44x1Y4Dx1ooYxum6MBv/BGg5f8wPS3zuQkT0bh1D/afWr9Tr0HAeWO7bCuG8POaHN4IXv+hUDBQLStT3EG75OaFcMRQsPJSJmp4yu1INR+tEnDJM1eJNprLmFDzLTm3ApgkyoFJlkWwp3aubyQVIwBTRUVbDQnwxTjFIocMFpf/73/+AVHErKPavWsAnLclw4A1VExg/fvzUqVN1rahgyKultGikEQKQsZYOVoe4dD6EZ4bjXAv8uUlu1cefcei8I/fUPO7bXClRC4GWFl8i9LT7shJ/uyvuUH1QM16yeM6dO/fOO+8koN95w+A89thjlPDPf/5TzZ8osOGi2p6xA/RW3Ea4g3/CLfCEAqRiIIOKufvuu3fccUec5//xj38Mqky3nNtuu01tanA778aHJWw0Ux4wRdJGp4E05CdM2AY6DVFP1o+fkbPPPluPlth9992P/OaRoAGprklkuJyFUS/3M/z7lutvuTuozZo1a4cddtAyOPWWAEMVSK6/1DhHoBjohLwXaAPaXRheWqBkaPt3v/sdRqkB+mjQQRPh7dvf/jZPPaiFJYF2t7B99aFLiq+mMAAL3W+//WbMmAFBnnHGGZSFXkAJqa5yRQuczjz88MOo1xnmWLYkO+RdVyGRTOxIgB3W8YcJfTnJJExQxbDUjYEpU6aw5MsnzVpZ3ZnzMzDgEQF/wXoK8yfcbuW/j3/FGMjDwMKFC+fMmcNy4qmnnpr3IqAfuCA59thjv/a1r4X8VMuAmhsX04UY4IgEfMvrJlUWGLFEQ/TvyjXGyp2H/NqM1tqqvLFpYvrEyhsxGPQx6iHFUnjl2uO3MQbCgwFGTKiXa5VVVkFH/+c//xktg9qoBgIkIhwM58Ybb0SE43thPyzFIiXymdgrkIpCUogik2dI4IkiGBAGkwiOQwL4O+64AzUBAWim3rZAe+lM+t577yUjegq2YxCg8Lh36sVkd6QPXNGgBQZebL3Y7jnVJtMwpC54BB+zftL1osymZ8cEbGKttdbaY489EIVhDVqmTRDegLHeLDBVCi+03QLZxz/2cTaa4hwEZwq2TQ1owW1eNwDtISQxkeBEwPBrxMWSJRyXMRQux4fLxYcD9KahgGaCIr+mYfEKePPNNxUk/Gyxhsm6Ij/7U/1Ble+VY9aTwyZoS3cgbgatBdh8883ZrkXhsAX89oOBMEixwONeAfdvWIsTLzBCdmIXHjbyCyvOEroBEKK96KKLtthiC8gGd6cBQkuBVHHooYdSJpvkCcRWPwGit2pRjLK5byE0gkFlsBH4OXXisEMPY7MD/hqeeeYZDBPY/1s5V/FbCI99E9iawZynTZumGnZ9Fifumphcd9P1sW2X0691E5CTtyBoi7KBggTt+qmzzXbVFpJ6YAeHH344X/If/vCHW265RUWuxqTtN954g4xwCgYqAigvwiDAVcdzrt+hQL0LM/n7nZLsodK7MEU9vxk7IjJ81NOq+tOyCHPSSScxRP3rX/9CCw7ZUEaA+6UhP8oMORFaMzkC/tatUnSolnWOfV39+K6Uw/prMJ9Abh+gk0f5cynYnESRDkItehQircCjFXP7M888k3AHLQjYLoFygb0/rO3AVGX5PRu4IyHp2T7riQanNXJbauxMl6prBupWt5TmM2mQa8IB7CUFjo6iDefkjptuuomzPDrTPFMrkJx11lnas0DYQUg6VzV7z73t5zgqgvB0l3J98JTlipZZCYV35QVDgHJQ1gfbuiuuuAJxjjI5BH3Gu2cg1DF7tFX0Kq1aBAQfsIKl0L+5zbcQfEUtKjGVlN18G2+yMSdcEkA5ZRxe1M3TYImUAD0z2J122mkYXFMa65ctArvjxXqzCTPayhjHKNz0pVyvcjFSl39XTtnZtwHztVxj5BvjV0ctC8qOWzkwuybEZG/99dZHKYCZExdDCEoHnvV+2xdeeCGWEeTlvCU17Qt88GsxzgP4woOCEFD8OWdQRYaxHMYSNvXNnDnzxRdfxArmrbfeChBKaJhBK8ACe62ocDufaklvCNPz2QD7bn7961+jYpg8eTI29tBS+8VrKoWLYoaKmwYarCSt59gpbTcJkmgTEONK6BTM+JvTwDaA7XIjuOK33Nu8isQjGnoBZqJ50Y3/0E4EaXQr62YIxxz6zlSNnzwbL7f+nOj3GXlRIV166aWcWvrII49goO72qRuuv/jI5OhBJtN83xTTRr3SWmUY+EDY/crozAbYgw46CEJFrpswYYLmapLnVK46fhtRDMBIM8MZjrFjuESQY0eP0qTSau2NIhfzkYceeuiyyy7bbbfd+AlzrreQ2quLU4YcA744FnIwY/BqwABzPA7/u/666/HMx+iC9hq9A4EasuaSnHjiidjXIa5tuumm8AVGo2AHv1xNnQyBE3u3DA70XL1xQSHTV5y+wgorMJawe0L9DEN+vdF6r5XiTNgECfhnCpaiMWZb9m4JgmylCk5L6gh/ofAuZX0oGiBLwpdccgk0GaCvtdqRABeFo+Jni9pV3rIGF7UXUpzSnqzkBwp63PyU2X21m50N9i5IbGnVDZBG0xNw40uGpcCALzTg6tNO1QqqZaCL26xlsK1iye6FF16gl5dddlkbGQdiDHQWA6gVOFwQhsPJgrvuuivAdKk411k0F9TuMFvLDwuShPgnLBTWiv70e9/7HpQDpNiLNQYvo9Laa6/9/e9/f6lJS2kJXSwTGnsuRkOR/UQUTAY/6pXsBakr/yYZMWG7WjMX6pkpVki6E74g9gvJPtbudtt9Nx1OELzEOrdOS07Oc6ZReN2DKZAXVUVI2hiDEVoM6LLeN7/5TSBka9/OO+9MAILsFQW2YfTaOzLAIF6E5AoPJG1HiPJAKBMhe/r06dSPB2z1fd0RstRKWVRUjuqrBprFi1VYlLD5kXO1GAC4Rfpx7sJIMYfQ20uvCcxTtjw4P03YmE+YPToUS4L8O1eaeatiEO1Um4tmG2zyYx4iOiOjQ8ecQbcQgwoZ8tpo/UR1dCh6Vdx8IpRjKswmnUAaGBcSY6BhDNhP4LzzztNvROkTEuXD0bf1ioUNA9PejK2ZzrS3DR2vDfsXhicclCLLYd3MrjSWHhuASlcoUQrDmMkeNcvoBlpsjPhVu2TGTRn7GimmqTxWy0DVNtxUiUFkDuTLNJuRFBpRMThlxhqHIDqpahnwBd1bxSjC96yftIjaKekLO/BUKIc0jEmYM2ga7J00EO0BqYAaaZLPBSqgIn5VLwZ0IPnUpz7FCczYh6MUxwlQgNpriNBe9cLWuvQ6hDB/04ufNiwxSmnFTy95oH/cWgItONKFKd+DreE6BM0pZMkqzcuvvNyRRkHACo+KX/xUMJSwGwPJK5M9CWaDFi3tGzMIHY5k0iOjw8PZ4aHs8JLMiL2H0yPmzgyjmNZwZmg4g19K/86MDHPzVgP2p6TP3UOZEXt76c3bJZkM92JzD6Uz3PwcTlP6EK0dSKZGA3J15yJNdDf94gJZB7vGMNlwLnD+9a9/Xfv0uuuuw6qLolzw3HDDtXRdRpGBi26fZ+a1Nl+ezHtV7w8typFO6y0g6PQtog2V5XjqIVAYUuFpkrpQcWqNPINuSgjKK1C1Ivu1a/bhrihXRISheQbrUF4QjF46fXj/+9+/+uqrw7pxuIvqgVeNQY0caOcjjZUQmVwihglfA1P+NsESfW1dKhDQS3XxBU9eGXLJa73mdVO6rzXeK9N9EYJwy3w0eOqGEogOQau7DQQZP1J5W9nhC3UNJ2i74Qg/+clPkJkIvOtd7wJHrBTVVUi3oTVuT20YQIJResN10F133cXg9Nxzz7GYrMu2XUxCyt10rVhRxdhhhg/ehMQUyBmt2iV41UY1LUylUpHQZFJEapaazz///K233prwnnvuib9ShG81w2khEEVFt+JDyCRG5VDHPrEXGBw/YWR4JJscMzBuLHouxA6hRrOapLBU8ReDnORRCKRbUiVQjqS9yZuraBOflCJSc7TGmEyin5zjBvo5Jy2oNR5lOBbHittWYNhWURxgrMQB83/+8x9efexjH2PHImZcASpYi2vs6hioJTgtgFCylqYTzuBKjkIfIM5hsv7OO+9AjVtuuSWE2oBxaxQaWgyj9rsZhItfdirGTEHFhw5Eieu+ToFRZ70YAH7hC19AnLv66qs/+9nP1pk7l7zNbDlXcftCYscqox0dLH3NSJoSXUMTZEhRVhPRvna0rKao0HzLEBAXbDDAch/DEt5fWKJBIl9uueWI7sgaUTs6RHmBPltYX/t06i1sRLWiVeLnSUK8/nDOJQEOoeCkLgYYja9WRoTfy9BiLmZZNmwidIZf/PTSt+pPm2i7VeC3olyIEFubj3zkIxR+3333/ehHPyLAKd+tqKuWMoMSvPxy8HjdP5DoGxpNDiw1dXDSlHRyHPdIanCkf3Cofyz3cP8Y7pH+sfbOpMY69/hManwmOZHniNyD5klgor3lbXJC8T2SmjCS9O7h5ATuoeSk4eSkoX6JJP1o32Cib2Dy4JhkhmA5VUUtaAtFGpehsWkCl8mAxXCJe38CvEUu74gfkFBgJwYiBBhArcC5uXgnBRY0Xz/+8Y8JmDUjf6wKAZBdC0Le+GtH/0g2l2OY2TTBKHPttddGsgFtBpquZwgwciD7DSXcek8NyJz2lhr9u81Nr1pd6ywaqlYdJwgLBjB3Z4nv2GOPXbhwIavTHEWD3gHgfEE2LHDGcIQTA4jXHHQCbIg4+++//7333svWPvaFnnDCCUSiuiK+RsgpylIdyzJKhzXmDVUyWd1tQp8dYFu6SS/eGFrYJgoh4UCbDRRso2B++MUvfpHTwrU0d+poaa+xijqSC0pjkWw4MXrcKadMnzwls2j+2H6IL5sWhwu6b06EHplk+CYthiTcT9INu9tKvfjC9H452l5XvyZk76/Y9Y1mU6NpNvVlksl5Q2zl6JhmR+EM5IkSgZ0aWhSbJhgu4W9M6rABJMAEL57RBYLnZgtB0PfYrxB/71wMmtDkXX+5i2N9afV2223HdBEBD+v33kFC3NJAMLD55pt/8IMf/Nvf/nb77bdrgVEcHwNBRVxIkxiIFQ1NIjAU2fn+3RlaY+yAo7kohCnixRdfTKvcAkPRyDqAUFMCZN5wTPXqgDx6SZX22GWDceZo36haNKAIZz/8t4//tteevIlMpTZSmopKBBDcNWmYp4IyB5PJlZxjmj+lT4KNSk1tzTs5vttcZnKZzQepNVWGr1SlSZ4FoG2//faPPfbYPffcw6nyavGupGV5nf4szlhQTrmfZEepwYXetlwa4hsuv2SZzPMzycRwMnHnAw+MxxWCoUd6n2k9T/2EFBHuV1iIGr/ociRbS3qtS0sgPdXx1En5O4nEEmPOYDQRfmVN/K0Fh8pA4CcMak1UlZcVfzTDi4fp36uuukpJCANA3NN4lAMfxEy90f3MeTXFPywGfJ4mEWap0L7p1oD49jZCHRrSWkjd4gFqh/zYN8GmCWvOUJkd2bxxIAAM5GgV/mfvkjvRAqgtwCKUzDw+5g+dX/rSlzg/mFquv/76vfbcy+pYA6y3e4oyXa9yYFbCbJ7IHbMdYDPLCXVIAnaYLjeOBwhGXUXFioa60NWdiRmHkL/RXNK8d7/73RMnTCSACWj0tOB83jlGH57OUgk8PPAEDAlDFC5CtND3ve99m2yyyT/+8Y9FixZdfsXlB+x/QI2V2RHOWjHo9IB4rrqErRprDCoZQ4uoFAwCeFheH1T5jZXDQGf0bCrrNFZGN+QS2kyl4GaQ0w9+8AMsGv70pz9xGCErfjNmzIC0aje3qYoO6oJ6mWziAEIOQ/DFtaoZm04gHOaQg/dfa9VVF897czCFjiuL9oFZfcZMeo3KixmwEIVejrCSi+RVOVbl5rU2C5LeIXdP0aAfgmxFRs5Kj/YnRpL93zr5e+m+0SHMm7z62/GHntV+D9Afh5gt9Pf/+9//xnQLisKQgZOerEqUHoei2tG2uI6uxkDDnj4Gxwy+M/+df/7zn2gZOKEccS5A4u9qlLeycSKXtrL8lpWNG1FmAawbsQzJCaljEmNiXUMpZMu8QyQtejmkc5BSULcxLlY0tBHZIa5q9uzZjEwAOGvWLHbXpxII55HfTxtifHczaDfffLM6E73xxhsPPPBA2VWRTlQdn5DRdXrGAavovD7xiU+AI9ZnmLbxL+S6hm7uzoi3jUmgOkLDAQ0Hep9xxhkowqDJbbbZBkJ9z3vew3QUGwRaqc6xm2kuBMw/Jgk64aRqq2uwgWbKL5kXU6LUaHZMNvHV/b6w4uRJg8zlR80mBaP08pxBjhqvmJ6ioVjqVV2DKgHEdXbhVSgl5+km3PS+wULWqDbQuqUT/QNvj4yecMr3EiOoGvIzFlYT/G/VWlZlPrVXrJ2LS1E6Fy0GzmiWWmqp+fPnT5gwofZC4pQxBipgACby2OOP4VD58ccf59DoujShkDo+aF599VXktyOPPBJ1f2zOUAHV8avKGEBRxdabp59+GhsZyGncspUs9SoXFb/tZQy0TtGA1NLO1Yte7sS8tjMl4yoYnJCKCmLy8iQSWDSwPoMUdd6556lYZheWC1LGP2MMVMAAQtK0adN23nln9vVxMBJDFIckocNCdVUhl74aSY+M7R975pln4mKNEe7fT/0bIoSYq2bsVAKmTWV4XLsnVDVhQKyOq/dCTUVFJxEz/GR/EvKDuTEh3HDDDb/1rW9997vfffHFF/fYY48HHngAim2SzCBR1SPAOb+w3xdeeuklNu3PnDmzdcoFF/1qUINt7txnn11u5sz5b7w+bkA2MmHRwOoK+5lsYt8qoQRxqoGDMU9QitanpiymcYwjpFTXnMH5Kem9BEkMAAYSk6bIz0wfviPSbdQ13HTTTVdfdfWNv7hRYA3owkfDhRddiF0MnYs/f7yyU7COle3p7oDa0UvFCN8rpuHwYgA2su6660JU6Nn33nvv6dOn105azzzzDGeTowJDv/DJT34Spkc7YXHtNbDqIG61r4t1pR0EyasabgkX5AmIJVhw5wEshAAqggi33XZbiIoZBGqvzTfbfHhENo4VJmWo8QfB4le9GcNomy19eFMv4iMQgjfneSj2Cnh6b2yoCwPhMBTpxfKdeuZjdgenYKwCPCLRbcML7KUwYxNFAEM7Fp9VGaFahjC0qBwMlmQLxNxy6TsXL98FK4oG5+qVTboIeHgS7co+2nf6tnMAN1izBZ6A0s/BBx+MAyrOMeHAVAotoLpy1WCk9/e//x37dtL/97//XbR4ESldF+6hwo/Sobdya0QHG5YGGgu6bILBJu8u1/Zm4hnS7N1MOd2U16VJ265JkyYR5nh5OCTskekiSgGvioYHAABAAElEQVQ+RGi1GdIiL8yTkufMmXPdddf9+c9//vjHP65WErZq+wkQsJFBBZjGQ41jU8mx/Sl59iXHJpPjEslxfckJydSkPu+ekOzjHpcarXiTgDvF7aeXcP6taQqfk/r65E7KPUEK6Ruf6gOe7PCQWFeg8QmqweXL0T3qIHn99dfHdcKvbvoV8nH55DW90b7TpMjZp59+OmHsX+hoArxVMwdNED8VA7kPKqdackc8iyeIwt42Mj+AGGnv/Df1/KJ2vevJ1KG0nN8EJUNX+AFRTKrBaTlwkO5Ijzj35JNPMnRizsB5hK6JVq47yhURzXhpF/95YqflX9IUJZi2NMp4ZxIebNhwhSoNnec+hwopO/bK4NJ7AATLkxdeeCGjG6i98sorsS0l0kez/HUB1bmGG9NbYRH5hJeBowo6fdE0+XcxfjwyMsRU/LZyjObVNIARqqs18MT6hc51MjM9XWBhlMJIgTDsgMjx4/EUlncxdCmnuOuuu1RJeeKJJ+alCOuP1lBt0K3NfQXRgDeQ9jNnY/2EU+WnTp0KgXHqG3M56LDGwnHuAMUysGHiPnHiROgTM4ca83YsmTsqGP1CxyApqliXu+Upg59r5F6UtDcioEnEKAxBoUkCa6yxBvvt4X5QGj8bxgECGVrdL3/5y6px4FQLNvw0XFoDGbUyT0xBkhnFP0KCLRUcikDYvSXSv40bBfmpko+N14DmciNrSY/SRW/JmBUYgKptF9xD1UYcaMp0i+5gp4y6H2oYBnRGnMdEdrp4v/32wy4dKtJTA7XMZiinYaiilLH9kyup0d5RQpXC+n//939QL2SG4pLx9O2331bGUq4lSHdQ6UD/wAUXXEAauJndc0HGrj2nvBAdJTVZhYni3zViwIptq666Kll++tOfYtpQ0pyBt/BAeC9bZTfeeGOkuBqr6MJkjgQow3HnWijsr40jb+WGdhAPlQGL3zaLAcYnimAfcsHami2XZT0GM7TguGHnyeCEj1n7Ng7EGGgAA2MGxFaTC9dBjD0I5ZdccgmkVXxpMvdJGnZboBfHxQO+1vhJCdYjaSzNu7iKw41hAPUrdMXBhJy9SgDeiEoLWZxwYwXaXEcccQSmYfzEvcjhhx2Obpcy7WWTtSiAiD3al1SFgVjTdFrC8ad5NFdkDJCrq2Etar5bLJMueAi7Y0477TQGOHQE559/Pku+bpq6wuiMxo0dh5YBAZoZIHlhUHvttVddhcSJYwzUhQGsAkl/zTXXPPvcswyCXJaZECguihng0PDQLbfcwkDJfO+9730vaTRlPHQWoyuOqR0DCGMkhpdio1qcy1Lja6+9BrnCJNmfWJwsjullDMSKhi7sfZZ0+PiP+tZReKjaaaeddG3NsgPbYEYmxO5nn30WrTmiNkvQ8YBkkRMHGsAANMbiCVMs8p511ln8ROj/5S9/6Y5P0Fg5MmPD/OWXX45enNWYKVOmkAyyZFWnmHQbgK09WcKjQqa9vv1ee5oemVqwPoC0vvrVrwIxAVSxrHgTCZnxs0Zi02TQpzb70Ucf5VRgNSJjuxCfAHTbNoygX3Cvgp/uq06FQVOqvfY02I0ffvjhKNPpFMRfPaStseajImF/zYIFC3bZZRct4e6776bYxkqLc7UDA2JOaO92VBh4Hbg6QkJDZfbDH/6QteUK/EQ3wALAd77zHeVLnOOr8ChPCxy2uMDuxgBkw8XopquV3/jGNxgfmVacc845PAvaTkqNueiii6BScrFtpyBNj/x0x+H2ez5WJIdKBLX9HisaLCq6J4C4zLXMMsswSjFEnfej82gba3eGe3gPYpSVsFBDGnYv21GqexARt6S9GFDCU9KaPHky4xMLiWwZhQ4ZqLhIoBDZgP5kPCMNC488uTgjk3i8MzBP0I2mBek1V6ie1kyOgO+sAe6qtwOpWtY59nXOuwCCHN1s7wCK694iOCxAd59iljxr1iw2UEBjKrIX8MliHJCASJ7QJ0+IHO+nqhRjIR2NLZEo3GzG8FOvBVVsD+ydiy0fsokFJWWvNssZKCu5WARmksYAx1oc9iYKnIrOZQEt9QLGRQlHHXXUK6+8Qo9DNhxWUiphHBdjIAAMKIkeffTRTOqU3jBZR7GlbKe4AtgLr5jjqfcQtoPB0EhGJK+4irPEMS3CQFbMyrxb/TRhPh9CzW/l5hfQDBOElVZaCR6Ih2/0rTavJoNENQZOSwzcEl/I0J69bPouDqiTLIZOZD8RBWXnYjsuqSv/plZiwna1CR1ha3Z3w4OARQN1kx4fPw72+Mln77aan7yCKWBFTAB/7Mzu+OmmiV64jR6AooectkCso4tWdcwxxxCYN28enhos+UFsqBIsLBg76E/OPbn66qsZqFZdddUPfehDCFusIjK26dBl08eBGANBYQCXChxxAtNDv4DdDSSqnLNq+aRECwZlQp8khtNyhgUBTj1k9xmkK6+MXQ+RhKsW2KIEcthkxyoXhYVKXb7erUWtrFTsYYcdxh4ZRGF2taAggNXYfqmUregdJgxXXXUVpLLccsuhVOK9NWYpShtHxBhoFgNoDdCUwZ20ILZ3uYNmQenMA2FH0DmsBiOID3/4w9gDxvRZgKX2/0S/4B/3m+wkH26o5dASl/XuwTQBLormi82wWh5vbcEMiE899RRKdoiWlSGI0L6KAzEGwEDEJ5ZxH5bCAJ89ekeGn8022wzRmZ0Rc+fOLTD1ZCTjFWtxmA3DMpja4TSrVGFRjHOdVHu2zR1ohuyU7ukLkltvvfWgRhZnXn/9dUQfpCUiGY0sXlhYZpqH9M+GauR4EjNVI9JqGdzxzOYKYQCpQknNES9K2e6qLqxlGjE50NC/Q4ilUIGEzmufffaBLKE6zrzEqzbgVVW2kphk0CfEzIWIjx4NKsWQgQNT4LocTEACTdZe6vUI0DEaNzGqbsiJhUBX/rKJG0ivecuX3eY3MBaGOXHMmeSYzSxGDQ8//DBdVi8YDKYf/ehHkbMpBGF62WWXbaCQeiuN0/csBhgNoTSIFtJVBoIWHsOrcgiB1ZAFymRsZa8ipjekhPJ5tpf/lAMwjo8wBnQgO+CAA6AuloWOPfbYArrSrdl/+MMfIFpeMZI2YDUWYQQZ0NUBMuokVEsiCnLUWHw5GGjNXKjnp1gOhjsQZHRB3kWM3m677RC2GLEwaij4+HUE+sUvfsGiHByE3acIT665bwfgjqvsLgywHwff77SJsYrDvZHUdR8EP+3FW5JBnHhZI8Fqq62GEyxGLLVliIacZIYW7ToZYGR7cCguBj+GPe9m9mnO8Q4FZOEAAurCIhRbBlSu8EDO3IEsVUAHQKU9JVQXXuK5iEdfBpvdZNNN4LTEHH/88csvvzyimJo5kMDN1eKwJTkbaHGFUSiePuWaPn36rbfeyiBIN+G1oUbA3e4jF3nJuPfee3PqBGXS9UoDNZYWJ4sxUBcGoDEoEB9bZ599Nhlx111Z0YAfPpJBpfgofffK7yZACXXV2BWJWzOd6QrUNNkIOB7u3nC+wP5WWJ9bmnr5feihhzTyyCOPtHYQbrJeCMuQ768hjcrZS97iUy+0vXIbA/kyOT7UL0cCfpiabXxlKOK3QWNAhWC0j8jQTNtw0+I6/R6VNTz5KLbcckvW9JCtWaUhxk0TNESdK6/Fa8ida1gYa2YQshfwHXrooayxEGAaBk0SYAW4GG4ca2sk/o1x7oDOi0KIETJt64StGLSyMdoM3zZSRhQbljyG6vQ4QPdZtrgmXnjHCZpDBZsopueyQlpQGkexYkQDD2QHPsdeggWUs5bqlJiLUYNOliyYNOtRXjvuuCPTUWazaM3QptnsxRlbHmPZnW9hkzNw8GQgSNW79QDL8iDlj+YQtTkpExHK3rYoAh79l7BrcESC8pUF/obuoPvoC3zjqeN0zPfYQKH9Xrk624N33nknFiv6E20pgykXeYlRHlW5nPhtaDCgxNwZUqwXCZAuBIayAN0WshnZ0TjAcIi0zrZsmagVsGIgC0MnZjsqxSnl2zTdHyjQ6zL1iGcfzfW6jn36ZB7x85//HK+67373u91SYYaaACdHxEOEnEpOjJumV8IyvBo5kLGQsFz6zEOA61Ih70VX/2gd21W23tXIi0LjVCr6/e9/X+B2W0x8h4dnzJjBwcuYhjKeMarBIBjJuKLQshjGsGMAxx8MS6wB6hrvDdffAIFhc640pk+lT6YBDFEYn7PJIkLkp8OImgxoZ/DlmI+nxADT5t4CAhTqZqjzgYkFr/w+gAcitfPERJk3UCCrMU8++aRuMVVXC/k5cr+wVUahgCZXyZUjJ3gHR+WpYlYnhC3p8xyIEir4mf+yBb/ydG2F5bdO2Cisyf2tHcEhI8jKdDceatEuqdLTTVYyTIeyaUK7+Nprrx0cM6jJlGuVzBJHhgIDwuv82wuHAq7agYAdkRjdAXZSyGZwGLbHY/QHDfPKZS9PPPHE7NmzGWTx/73KKqvwCp5GXgIRGkxrx0xhypwrBN5opxcmiX+3CANQI2T2pz/9CS82VIHz3Z7kjWLHKqOvKNk9m1Zf19AixEesWOFl8dXFGDj55JMZlvj433zzTdtMNW1C2ELkYuhCsGZAQqiCZehlU0YwoNK2//Q0ix1pR6/r1MePG4+E9JnPfObzn//8yPDIXp+Rk+d194TtD8jvwQcf/Mtf/kI87rKhVehQ5SSbJswBM5kXAJli2bAzw/fpUKZ8ere2Nda6QQY8HflaW2G0S4fqUChwiAB+3VVNgF6MJsEGOfOkctvWWWcdTYCbQOYDrCsWyFgUUrmE1rz1Kc0QgGrBXF1Y5Upbnb5y7a17u/baa5977rnYm1AF5yvR6dx0WbkaUYfySk1dCODmE118ucRxfIyBFmEAHgLhKWvCjREyG9JaAZ/ZYIMN8NLHYtJXvvIVxlNlOzz1ahFg4Sy2g35nw4mQVkOl4hynkjOVQHjDPFDmFOkqQ2eroepM+f6Ai/iHxR8w4C2rM5CEr9auVjTQ2aa/E33QvYpf4euBFkPEriodlnBKbKtSPTdG7PhxYehiQGJCyEW8XjZlHIgx0BgGICTNiJ/Ryy69DEMGpCUke41HjmdYIgED1SGHHEIA1biK8gxUjdUYnlyhOV5Ixjmr/ggPfkICCfIQ3A/bBJ4rrrjipptuCid84IEHcL1GoCSQljhxKcJCItuCcFyKHo0S4J8RUpCVbF0XR9JBn/vc5+hlRkPc5n37hG/T7xX6i7dM3ubMmQO/glOdd54cEV1V99TFCIyb1hEMYIaD6lPt1fVoG7iWDp0uPFD1Fh/aAmsd7BrsyOsm6IVw284U7AVk1thGhDpoT9VbM2fOhMFWYKo1ltkdyWJ/kG4/dqOiIbYQdnoYb2e6Sf65555jhypvmOOpGI0sxe2k9YLlhOzilKGMsUpEaNvYFEAPFe6WtSE0s82WtbBawWwWRejhwoIGAQidAqTFhZykGygI8PP+++/niYEoCzIUyR4/npLOv6rV08n35XqZpZWSdydhjevOx4DL/RCPME7eeuutf/azn6211loQbbFnXER8hCo8ODAF5bgv0nBqD8d6EcmVX3YHfxm+p6yvfihcoi2X203jhsulD0M8HcSc7Y477oDn0IOcDwJUVm1UDkL20WASyObkadOmkYtkPk+Sv+VyxfEhxIAcdC929eai6/QOIaBFIEF76MjQY0K9v/71r0tqu6Bq5WYEigro5ggssMyVa7Xsgc/mfnZz40PQNpgqOvcbbriB0VD1XACFdGdB6wE+CbGVpTd95z4tZtoXEF6X65H21evUVBZBTpooBru1XY30BfumyDZv3jy8nS1cuJDT18p9/CI9dYn8FCoC6PBH3gjRNJ2ngJAMZfWxrqjx6B3wDMLM7ayzzmKI4sJbZNN1xgXkMGDs93I/JdTpwSYfmtD9ev/733/bbbftscceChnaMcjShXJoeAh9BKTL+RTq5h0fDe9617s0jaHivPRu3raEVZ5pS1VRq0QnYJxAgZ05sGNahWMO2FGFdqhVMJaAu+66q+opiCkgiQrZ41c5DAjnae8g6NUo9eZ3WcS+EXRhkC60yowO8sNbrRoG5nDb8yF0DWK1pxqHkPrjaC/xt54k3G/qkUceUUa60UYbQatVtbethy4UNZRbggoaOGVoztMutNtA0FU2UB7wdeHFOmkXtqrRJs2aNUu9MBx33HFsUtVwo4WFPR8cUFbLzQ7bsMPa7fAV6Bq0uXQQFwMSpMjM7aabbuLnqquu+uUvfznS+GBp1x1a3L3ubhjB17pRyAsYSVzl8bynOUsiL6XGOOnBG5JWwZ2HzFjFkIeO0j90Lgo1ln6dSKhp2M0338wRX4j7eHaAo5ZLHMeHCgN0K10GSJzojEDM+jBHSJx++uklgRQDlWwW6RmhmVxqrK5PJZKSueJIg4EsNsPmaDeH0ZViUB6/cvhYHt9rLh5IlOtqpzASRVQkQK1JE9g6wVHlUONdd931xhtvVLZOLznsKh66/WnNOZIedeGdrJ23TDu8E3kYsh0SVsSbmK4Yi+0oCZ989NFHocyPfOQj6623Hu1EHaZES7iHSFGNpmmykQN5SvNVlBBXDbnb91fqqAbEIMK7+/pS3PZn9YDW6z4pygeGgBj4KEioeoGnrHQjALfuap2iQT+z1kHulwxOi65SH3lRop6JYM1tlVVWQUrmFIDnn38eplDyiMEI4QPaKnGVooQSydoTFSpg2tPkmmth+EFqJzn+q/AESWCXXXaBLGsuICwJlfUwnMjoYsYVu7JSDkQEoMCvcmV645xXX+mPJnBgIlqglZzwFVKuCWo8z8kUGkDjgNKW3RPl0ocgvsTg2FKo8kjOeIOHOJnydfxCX8D0DLGYLVq//OUvldtcd911FrBi/gOPQteASkKN0vVp6cRmjANggI0JIt2a6b3Mrzo9m3KZXVGXuS/D3nvoR9AsACVu9qBe9hV+6lOfUqCL2hX2tnQ/fHLub66VbjgXG/2QEp4+0duyVoS5DWZfykKR7rii38p6W8CUni3BcECWufuTRl8gz0RKIkpdCTzksY84/9ZaSyUviGMbsuxELih8NKnxoq2QDKZ0Uya7iTrJlgMRRJwGeGo8aRqfmSMBU1GFW9FrnpRQ8naS5BclXeve9vP2ewa9Grvz5BehvGJ64wfO/znci33FOBO67777EJ7SmTRswl7RQoOKCfRlCms5+lN6Vbo1lbC6QBONu0FmgPK+0l24EAzJBnEb3GZGR7m5shhY8AdKNU8NRwvrjUDrf4CFfykLDoj1Mn7gdZkR4Umt7xqppkN5lHXqzMpTG4vALdAI+ze3ciqXoBKZPnuPZlln828hjRKXy9nywk6r2ZJa4i5UXQfC6p1auyuoPIRZJXTo0iutpFeYcyJFETj88MP/+9//ErnzzjuvueaaxNgjDzWXYsUtoTjcWszZ0VAG3BwpQpD546b7swxEOhDnvXRzaTj32tMyCJ8zPDf3ps12807FftBaIrDght+iD3zgA7zB6+c222xDz9K/zOI0LX1Kl/n5Svwt7tAKMSXyd1OU9rW0SORsQ2DicZ2VngzHtpjFZK+5lizzA2C7RVcmkeFG8OMp3hnYNy5uGvSyugYCNuy9C9UfBkpOfQakAw44ABpGinvooYf0qHKWi1Tj6SIwVMC3DBjttVzHmZVFapPR1FTqv8ontrwBtOSrPIi1Fo0qF87L4FetiWXYL9K4efq44mzRikH/BdNDhIMggRxnkBjdoHZ3W1GZi7opuyJsR0PhMTrFT6RSEAET0tG+VPGdTaT0lrmLTF5ER+CGNcaLT/Yn3FsnsyYG5YK9ZYbLLfE8KZMVMBylyf4xYYY8ZIbegctnvMFW3aHGBNuI7igN+WkkPbLllluqpMUmZOSqyqZ30W040628JbXOtSQPDIa0+HIwwODEujELwhy/jJXdjBkztthiC+d91ILulF602n53o2uAINECiAGbd9M2DfPX6iMqBGzGRgO+7oP6QvN1RKiD4Z+IU/i1AeY//vGPF110EQFmqmyWJoCAVdI3W9sb6JNc2yuuvUL3K6k9V1Ap1b2Cyr6oFXBAu9VWW1E4x7+zIoe1QvGYqIdAFwDQY9JzQevL/hRG57I+4XLmzud+xUysAutr/pWCyzy8LNyhf2EVZEB6ySWXaFuefvpp0Y5lM6wkq64h9O1oEYDOrElWndJmHiVPj/z8YbeY8ErHuCp7L69Ssg7fTthN6YdRZFii5Qvw9Bra9AjTYGHfwQPRfxHLX3xsEdh8882xuFHeGOnPrbCptf3WvpX9EUatkBnlZOw0+7e5jbqT+T0WvIX3aCKtt0mMqpsveoTw6GjhnTWRXglm9dLoDGQZkxsFqr1lQdMsZ6pmgXiSGKjMZvJK+vPamtpoqggIKA00TUwY4stgAEmY0Qg/Z+zxI+Lee+9966237OpN9yGJD0uu2EdDiLsWIQm5n6PjgJHBCQ/wSEtI/yEGuSxoYj9lBl2zfiHJCHC5ckwq05/KJvXuryZ5uxkJW8GlxoCtyK1UIoEoygK34LQTF4I+SjHWangedthhGOBwXXHFFautthoBzB9gsNAwl0LnhjsBb1xndQzoJggVkenEM844gzxm2JCH5rcdWr24OIXBgDArDxW+XjUL3+uvzLgK2F2wP6laIGLGKEt7kZ/q7feF/ThMlwaddtppKOgxvxIW1D9gidZDf/f/oVvtTWvFLYIxW2XXm+oaxJSgMuGVeQu5+hQL6epdPAoXUrXm6k+OcvvEL7Tn3USaPpEntNgd/QPVIcL961//YhDccMMN1bRBSbGnBsGsMYkyZgtiq4r4N5wZRpxdkh0eyuDgh+eSofRw5Xs4Paz3SJad7SPkcm/KkaI0TWbxcGbIvYfSQ4U3aaiRXOmhJZkhMaJNJkZSwNYx2tMPIGDKFzozWxXkwX8TVuIr/dQ0lVOWSeNG5wogZEGQJhpbJrGzKLZlCrjtYStOLBrM7ndlAY8//vhf/vKXCvuQwwZ/LfBI00SKaAkx1wJAlTSy3IMPQEf7XiVDl79WaemBBx6gneussw67+1hODMfKcN2YlxUn+LgYxyUZbIzyWIzlKlzYeeY7kalKGIZxKRPznqWL140bJd9hSIeSu+SrOLIyBvS81QMPPJBNEwj3s2bN2nbbbRHx9YsusBetXFTr34aIB+rnYBVwtF3ouOKn0Xr85GrYdNNNd9ttN37jJoajKKxkog6M+JlLGoeqYSBEZFcEKsuGxHWBooEdr5yqS1ueeOKJ66+/HrEHKkVrFtOqYS0gRsgQlZIqERo0anA0BUZrIJaAhbdY6+QUCqYi+YkpvNVfaAL9CVTQH8ngKQa8yA/EkNzLL7/MUdAQIfMLLFIZJXWK0avUaKeWWaZXmcxwOjOcySzhHk0vGc0sGiVQ5k6glMh4d2JkSZaf5iasP/VJOX6yRTaNE2nfmuwZkzczlE2L7zORS1m08r8TIcj2XuwJCeqy4rKeJ4RNB4IF2/R0DKr4abkSsGCj1OWmcZTTGIcUpJYvWsSZzCg3S9tiSmJMSMxGefnWe+nqT/WLDU82y7rx+973PhThv/vd75jawQ6I5JNAdI46PoTBeTN5WbbVi8kc7cqjmlLtJHGp6ObjBApK6R8YwLzJFMcHAtYFKp6tqrZ5wNtSAkfTo/CCGjmmngqhw1Qy5faFIqotsDRYifI7PZs93TfKPu/JU5ZOjX114rhJo5nMGLzy2C0zzlYyYYulr9KyuhkdSmYond6yTxZ5Mtjg9WcnL7MU9nsYlso2PeMiCDyHH70l29ypyDvvvFMdB+JKcPbs2YARSrapJMGzjYNcOXLO76raUuXnCfRXSYLnBHg2UMCI2Pf+7LPPsiOG1TmGS/2oqV85Usm8gUIX8cKyImmlxiSXWXbq5FdeTSb7B1IIlknMh5G5mHFVaF5LbQ+RPhOLM1OmLMPuJyMNtPXLqNDqxl6hjseQirxot6+88koO4mXchEQxDyRSqbQ3KVaG2tEEq7fZTD++dFKiWhLtQONfrjNkl5qdFZG0ErnJ5etS+/syydF0MtPP4rSouhD9DF+2E6XGqCAUubAC49xfZhDQ5FprrcWEgk+MMbFxhIeiWY0AASlgi9vfl5w6ZensSGLF5af1j5Vy6GtUS3iDrHxZgY1kpLfioRuvC1dKV94ilhlQ3TS2FiVNyiE9Mw/SLJ1ILD1pKfFhJ84gqgFkCwo0EIyigcbg05IbXywp9qUMLaGRosAzajwDsPdluqhx1v1y3x4TxtINxLVfDkXud16YPtsnukXhDux4GV6cHh6RjRR9o7KtqFvMlkqjqFSsKLyzfdj34roMjvDUU0/9+Mc/fvvttz/5yU9+7nOfC6W4XKoZFePgd4l+PqDUYGocWxf7Ib6UGMCRScddP3chqfgJXHLy08rfwvT6qfspCt/68eYv/oeTI2gRF2UWLT12aWxK+gf41gDHQpWj5ryMvfFD7ZYhS2iSFqNlKLkjOgrIoEP7kv2pd95Z+Ojjj82dO3dwYCysT+wcvEvoxPKufIL0k8jfAiL0qKv8yF2QPleUz2OzeBd6Z8H8vjHwZbwOoW1NJ5L8yKWMQ7VgAPai+85IjPvSZZddtpZccZoCDOQzz4KXHfv5mc985p577oERrb/++pgBo2tQbw58p+U/vY5BG86KhbOkh956562H/vnQ3BdeTCYROsSjqsiDnGRcUdFQl5mVWMjXc8EJU8nkE09kJ0yc4DDkeooIU1qQuc8++xx00EHoF+644w5cxmy37Xa4j1FFQ5ggbTMsDIWiaFht5hrTJkydNG6KmBkaA1IGYnGUUGF2VZI4c2SmmzKKqC4vV8FArCtcTOn6MVjPZpITx05esmjYrIcaOI0UGHV7KU6yhGeCWEiRY54xamDrRHfMJhqgXUS9gf6+h//+98cefuTJF+YmxwxSSEoIIZvypbFyxbIqzisrLJo1ci+tUC+TEP+dlSG91/ZPHjVqUbrYj8AHz8iusdpqz/zn3/hq6CAPDEDRICoGQU8WzA6IBi/ZP3Ycs31RNMgriw8JuGi3iMOLBq/4qYXkHGMqBvnsTcCkl5R+elb7RLuRPyGUBJgx09EoyzN9AxPHDvRlFeFGXyTdRhZJ1iOXsF3jt4zzw5977jk4Au6vMP1F0QAZoonEiUOkUSENXJIYP2E8rVhp+XenUgMDybEpPK/S066zBmf8kPYaukKeVOqSmIKrIL1sBXRSYBGiv3LJnLfUnBQ19mByYmLx2LHj+hYvXkiV2hcIYVZz6ebpkTAWd5grgwSsl70lGvYdGIxES7iHtw2Njgz2jRkzbtzzz7yw26f3GFo4BJmIt1/4Ok0S2hAysaOFUo3+dAaSYnbkE5c4IpYCzFOTES5Ob97zwue3qFuJGk6PTJo04bU3XieHrO/hN4iNPOWze6XEfxwMLFy48JhjjjnllFNYQtx9991xJqKb/G2SaBGtBTsOgIGDDz74hRdeQO+p/CfGSX0YMAMiBg3Jsf33P3T/rnvu8sZrb6Qw5+9ngg8XZL2nylUX2gsUDSygVCg9OYoUmBqDhj/Vnx6FDU7OifMVsoX4FZt6Fi9ZjD/v7bffHp7zve99b4cddhjXP04WWnrx0labJxOE0cSlF1/ZP0lIjsVbHX4ZA1XPZeYdzqCZm5s5kXk4NMXKchFugEui18+YVxRSn5eYxS6zwpecOG7i0KIhwOtLDECQ4i0k+hcUiC0DcwfUDZxNjiHYhAkTenYcRLbCD8Oj99/3yS23FisCCBBN0ijyP/spYJEl6ccnAqNo8H+YaTA/imnEnXpoak0j8T4peqWY6nBDySuMe7T80aEJk5dCKK2FJ3vFBPonAEWDwIPXlGwWg6UBPrS+dHbxYpDAIEAHyO4JD9Oiesi7zAzNYEUwhfBttDvaKzwVfX5uH5s2PVlMeZo+V7DkFEUDEKTTw4syS5agv0D6l/R4w6g+9uWK6pqQzm+/8Y1vXHvttTAFtAw3/t+NSMyow0O2x7hOlBsSEk3qYOKO3/8xOTbz3/++MJAcM5Ac9BQNhRN6h1rIy3KLJFBKK1l1fnpIKO+Dd97m55Z9+EbRkB3pG0xMQpUDXyaJqY4HXIBKK9SbX1x3/cJ1v7rLPvbYYxctWjR+/HiGKG9rdFVTs5ChQvZmIdfA20ZHX3pp7pjUIF8V8gQbg8WBcN4YI9RitgpX6XefqviLCVZ1x7Z+ekGNLTojjLePnVNvLHyTeACT2vOoV9LHV1UMsGD46U9/GkXDAXrC3NBihPuqueIEkcAAI+PRRx993nnn3XrrrdOnTwdmGJEqkgyvjkQjOgwk6GIIxt3scy8+j3xtuRa8S6Zbufm9x6iM2ObB7C0iVWyBy99cqd0tp6AA+JypXfgnbA/Vaqo/8rwPPLMTnhPE9KSJV155BcfexLADsaD5vfdTxr3zzviJUB+rZnQ1szxZ6DFXPgH5sbX9NZRUU1KqdkmMMPUCA+tfi3IDs6zJRry7VLHFJAK0QHvrrrsuFg29qu3ySGPSpInLrjB92512fWP+khEjZplVdiwaNEFZErR6J5XcdJu1P1PO5bIrVT4peoJeUTzvZXVfphiJZGZUbMqmLTPlrjtu+98Lz3Rw9huAogFM0bCnHnt85sorDaXxhzKGXQpgSLQM6PSQwGH2fIS+5s9HoqgnfKx5nWGwltsioX2gwnGuP2R4yWWkgvxC5JeKCOPHjVu88B1meBw3bPQ6DDcuJyjO180xSFQzZ85cY401/vOf/2g7UccgMbONYvLkyRFseY4GREmcSVz6w9mXnjvb4/VCb6ZNuVRlmqjJyrzMiy5OWRxjM0BoECZPBpWhxNgJMuUzdCsAMa82ChDCJajXltGtAcyV2ReNbMoJl2zhoZn6wbK5iUCE9OJ0r2zIol/T6eWmTr35z7+Z+8Jc4O83W7eMVbB0t8/xOP1Ito2WGhvyutoaG5PSYKYqEedltz/Y0LFw0YI11l7zu2eeec111/WJMSeK1n41HrPJ4kBlDMAhcRZImkMPPfTJJ59EtGIlR/WGlTPGb8OPAeTjfffdlw7dZptt2EOB+3R8NKA5BPIIMaKO4VlHQAS8kcQH1t3w+utveO65Z1XSkwFQlvWcAc6X96wsJ5KhrPhVuWx6ky5XoM9XS2U3JwXwgjn5e9ZY7fSzTr/quisROP0R1xZiA6UKCWUc/rwVrq9+9atTpkzR+R4xOnTWgs9QNqsBoLTv5KmUcPWvLlz5vSvOe+sdNOxo/pVseMWwW1h6XkzR21xqijGnVOZiSoT8Ad0jLs+Gog+T6j6me+PHLYVFww5b7aI5VdQpUUp0olhcSZppF0tEcM4f/ehH559/PuDDS3VFMzpNCQxSunWjTTY56/QTH5/75uCEKYhYYmNjtk6gcfAnCpW4DVQkZExaS5wu8zSQwjA9YtNX5hhX8mgzfDo05UB5oyx1kiG78nLjDps377YXnw2stfUXFICiQRUBVL1kODOyYDGraLjGoPEZwRdmxIJkH9ECoMW6/Mhd/tfuKA7cXDaMHCCZ+E35oNvsC8gV44QWol9YtHDRxPGJhYtlRoB7tt60Z/BxgssrRimGItjB3XPu3nrrrWET0dQy+E0y39j8BfOhh0OO2Gfl96zw9vw3UfyzdaLf2JyXZuvqgJXvEdM4MS5QiqrABdQlX34C3A4rFEXsgGjGttEkirb02AmD/dnB4w8/1dQCOBAuN1ktRdu29EoAZ+9nn302PvxxF/Ktb31r1VVXRVrS/Tul+yvEiMFOGEk5m85MmjSJFdE+WLunaCDW52m+JCQjgZGJdEgwQdpWYoFDfehih8mZzNJ6Q6K+Y92y6LB2m6SAvLBgXrB4/sorrLzi9Ol4zulx7lcWa9VeTJs27aSTTjruuOPwF3jmmWeyAI4ZjkuovSTcV0NWFN7Td2wtBlKM+9jrzkZCBkQ0R7hpIBJjYMRotPBxt9bYmaNDHOuXXGHaCjNXnTmyBCfnMjDC3JTh5KsJPJaokzGZATprP/kp8yq3ulc3tmSknyApW8S4UqPT3jWNzs3IEp9e3rjt/4zMX+gWmmSzD+TKitGXv/xlGJEMN74zyB6n2JkzV5k+413zprzF14vxuifXSffmxlCns+1O2Nww7bzVIFQkh1YWxWuEZLSzO1OLaiWEwABAHNalkxPHT1yycIRvAmmTDFgakiWqJGjajYC91157QY2QH6R49dVXH3XUUeyBxa4BCuxNInx7wfz5CxcsSCcWLFicHh3U3f2yXRVBjLmAalOLZgoO8ShFiGoqxwbz02shmkWcgHGxaCXeA7zLpWNRcUFsopkYXXpo3PxFCxYtYhbcsavcJ1QHQOA03Zdc54Ob/Omuv761cDjRN5hGkQ1mBbli0cAfX6SuUKz9aP1NTk5ai0pTlKTUznBR7yaH0UgaNs4sWLjCskvdcMX1hx50AH5ZZDtHCaneydrVQbVlgDVwXXXVVSgakLTgDjwj3W72gCQGEh/9xHabbLreq2+/1MfeieRAMimNkgNHSl2WfsyEQdO4VFaYJ/fx2zf5XMBG2wCKBsIDA2Ozi5PHfO1ULOoBRwYZCFOchojBvU3cU4GpU6eiX+C8QMak008//YorrkDLYDoiemjQEQS4oZDFCxctXLiIfkXshuO5LbL0RpOd0aVse60g7hZSNrX/omDpBouGxWzqXbhk8YIlDHcie8VX/RhgAYdNZ2ydQB2GumHHHXfkTNa6+qX+OuMcLcQA3yCjHstx6DpxVERNrMUhLstXOzqqGs8WVt91RaNr5dOY9/bbr7362quvvKan7cDBZEWjWJ1udKaahqlXjdOS3Ak+NWLPnD4oaZN9YyYM0t3snSgtCtRYYDiSzZkzB2NACHWtNcWJMhdbX3vcal2wAFH1Jd5+543x85Nvvf0m4y+0Z2b+8rL0Zeiw9Cs31hKSG6lhU0IRUVGvyHUCQRpFQ9/wyJIhFA3AKLbafA9FOYpLDncM8jZLRJw68cUvfhGC5OP63//+h7k0zJOrxi863E2sDzp6dCST4dyx4TQHEKRTmQzkKI4b6HSR/KzIV8wNbUX6SvBnowoXI/3cIkDqlnBTuEtRmptoLYdFfZY7RzKJEQBj0tG5KwBFA8BjJzI0PLoknXlnwWJ0LOgdpME6ojBE+Aiq0ExX+HbDmsUtwQ3LW2ZtbtcI1pP4IhJhYsniJQsWTZyw1Fvz2SNFBLsIK4DQ/a9uueUWGsmKDSrwGTNmEIZHQNpRVzSIG8i+xMIFCxdmFr4x/61+LNe51BmkfpDl+5bml3/ZzBtzrC7CViY1rm/yOPFT6VxoGXr4AucYNWBx9/DDD7NKDCbUFr1lfdFyXMOUGGzQaqmUIYYy1OnYbnlExgCADFQDxZGQAjgqp4yiLK9FqtLS9HkvsJ5Do4V36LRqxXqb/eWhptYf0CS7Ywb7B9nGzxIiMv0RRxxx++2315o/Thc+DDArRssAXIcdethrr71G4JBDDvnYxz6GAE1fcykjIhA+2EMJEQdZokKH/WWy5swdj8/gqgZw8wW23CwLLUQDjam1U/z54eJh8c7rVKQANFK1U0hngrSdqR11w4W23W5bApAxIhzCDq+4OgNWp2tlPBUak7GV4+ZGUSjxFaeqz+drp4Ec0Ra3tUCSM5MRSa/Ej1kFxhVm3VMWHmQC2CqBsxi0VsWor5C11177nHPOwZ835kKXXXbZxhttzMlhTC56kxSZcooLXNY3+znZnDVOfOFy8B02LMJ8EPpq64y8I97LZskVBvVBbDkatGoKRFCyi9oHU9Z+1r7G4EinbIGtfxGIokHshYaH0/3J/iWLh/vHjRXdgho10FSz1SF/sClslqspsJhyE4lmyB8swKtbmvHRkGME6h4Cs3VMjodHUmxJZk43xIq3HiJKIblOcmvo5jBDEaoEVI8cG47rIOZ4rCHDDmB53XEgjQyxo+gU0yMc540KT75rzw+KlTEQIEv2sU1Q8m0TkUwSBazBMQPj+tUNJETIxfBWGpIm6opY1gULFrBhB+EeK9A///nPeH1fYYUVZAQuMwYLIsN6CS8y0BGwPMoAW/psh3rbUos0zrKgRY81JzY8MWd1Y3ZDa+UEfaBttjhQHgOsH/ISEf+GG264y1w3XH/DXp/Zi0gkfsxHcbhtc9fbvzZjHGgbBuAz6BSeefaZX/zyF9gvYGB1wQUXoOtku4SyIJ5xP9bRHYxzWAcbsUz2nPiTfI/d+JKbFOhwKi0/f1zWuV9OnKsDhoKk1G3EStlkT5j/Ap5fsgUpxzgL8ofop8pvAPToo4/iI4bWrL766l/60peUVu3ZlvpTcR4i6FsMij/mSs/SdtwjCQVivOdOKloMg1u8WcGWCBFJAU7EwEJmIma2USA8t10lwxxjpyPg008/jfaBsNiM9+QFz5MzCGBgLCTBbWQpUXgiWxfAh+U3VXFTy/frai1MRV6p7txZT+QRZyXC/MynUcR7qwITYALl7MEUCEZlp4TxVyGYRcoVawa5swns1sreo4l+e5dLZhMQcNOY8nPZpVLxdsYtWiXz07SOrgeK2js8GJSEohQdgV584UWgYY631VZbqegcCuCCAEIOLNCeFQs6IUMpNRc2Mfqz+CmJW3B7YHhVi6bLu/SLE4bkx/Tc3/HGwOPDH/4wu99pPLboKMI4RDC6iPDFHWF2NtzZ5gh5ySIjcwBvhbaz8ES0dnfgx+UVnJMZ6d777P3Pf/4TP3Ns9Ud1G9Gm9SzYLMEhFn/+858HA3QiZ1sS0DGR7lYtg9vvPYuo2huON2ZFnc0SI9CiopmAahmwwSHwwx/+kKJQjbElvpkyuy2vHXPNTpyQEh42jyKI87+rJiF77rkn5HTnnXeyo8c6iew2AqveHm+mibhvBPucbC/9Xc9VvapopshhpEn4xdOJqBjwhJB3p5PZNL4R8iMLfpo0JJO74JX9WS6NVkq9/q2zTKZxGaNs4Ex7wuxOYT+H75OjyaZGKvvixYsxW4DUr7r6KgBHy7Dv5/eNVAsqAwsB+zRs5vbGOUqmj+42N12vt40pCNgEgQfEERCENzpszlLQVoiHMUIhHQsrYzq4t+lMGgy8973vxa4B+Z4524MPPqjGzMFVEpcUYyBgDGAsyvHAkC7sFOsGJH4UZIQDriYurvUYOPHEE//6178yMuIsZu+992Ytrur+wbijy3aLWZ7VQU1Um/4A1+PDXFl01fMCgwW0DBAq9IlFKrskll9+eXRk4NZe9ZTXzWlBSDc3L5Rt22KLLYCLofDnP/95KAGMgQoFBvxJWnPAWDMldayK5CUxunosJasOQiJL3rZy1/bDRmrAfZVfSEHhuqYt64q6umha6C90h2S1saBtrfyJryCKf+65537/+98TWHPNNVnMaWWFnSxb7WeAQAPy9BWMuRhDGLmfNkGwAb4BcXstR9pyGekrmG+tk/gNqG7EJj0JnO3uCAcsMJ566qlRtLIp36Pl3wSEw0aKEYbcSL44j8XAHnvssfHGG/MTlw3nnnsu1BvPPy1ywhwQs17/4rxSGA6/YEQnn3wya8V6CEWFiYpmj/vaR2H1vxWQWT1zsCk868JgC21TaaBRMfmTn/zknXfeQenw3e9+1yXmNsERmWr4zHNfemSgjiygnLGKuzfEOc/TvNl9FvNJsev3VkBDKQq2nd4CwYJMqPwpPAVyXLzXDp3tm0kdM67qN8vRFe6SJeAGR2+dOoobNlliYtsykHhKB/bMyOZAVpZ7VeX5xBNP6OCE7xbwg/WvpTQdxuzPqATEXE6+ZrVIkw1xOHllOYUNSamEnGPq3cY3Rx/uUsxGnuInr1pyG/mAB9uBFcMCgzPPIzKQby8q/eXCSdtZmWGb9AEHHMCRkFDmr371q7fe4lQquWSztHiNzl1u3vCEtft8vufptnLgGXpjsCm4hT3Vc+UKLB/STdF2a7RNaDDoYVKMNnUnRbwCbxGUH4AgmXPmx+X9Uu0YDrfZ1c8eNNRk0CpYzUvUaz8YUsvckF/uM5ZVbndvabvR5HYTh4awPgx4TN548jnqsxxMlipIVi5Nr8V7PAyuYnYtghnGMx+N0u0gBI4kRyvJYaH+bUiCIdre+ep9xaLIkP5djFf7qnKgOKNOQSM2C4UpQas4gvn+97+PXgylPLpOnm7zBPn+5cb3Vrjg08wtc7YeDVqXeZYb2xH9ZP4hfvPhNpBul1y0d8UVV4T6br311vv/fj8BbZiLhy5pqmmG265cWHbE4IyQySsnSo7KPMTsVyXGZBJHMfZ25yAFwiE/K86Avckx29Ts7abXSJHyGNHMTFrDSCgKagc7IkiK18VbxSzjBE4R6m2YKgvKPesrTdTYXP64Yga++krootRf+9rXaM2WW265//77wwu6VyPud3fn+84lftgHALkxnYevsxAgPGFZw/OYY45ROf63v/0tILHTR21wOgtevbXnWJ/qs+rN37L0ykthftzQn457LastqgUzDMuyNgdTGf1XOfaI/otk73//+/fbbz+4qLcenq6km4gqRroObrgNczaahVNPDmNjZfiggw7afffdiaErVe8AGWi7bYCfhHlL1+ur+FkNAx6iwGq1lPH76hjIZEX7yWHk//rXv0i98sor63BZPWcvpgiP+Ncr2OczP+yww2gtyq/bbrsNbkkMV6+0v0Q7YYChGCwEjtAsggSCEfGEgcJGcC5Pwt76nhdZojMCi8rVld+9stWCFRRP3WF8ddSv+AgMyo4WxEIcB4bDAtQ7OqtwYwa8k06izxGE3hS70uPhu/ALwmUkWA9OA2M8InpdhTOhSZMmIcerxgEtA4LU/Pnzw9eTMURdiwG+UCaTP/3pT9dYY43f3vLbclwRzQLJwAIHpkC0HOV1xhlnHH300V2Lly5qGFwFXcPrr7/OnA1FEgznzDPPVLEY7RINJWybazi26J6IYSX50EMPZdAUl8PxVQ0Dikb9gsp9R9XKiN/nMIBGDJ5z9913Q7GEL7roImVBuRRxKMZARzGwyy67YCMGh/zjH//IXEOZZ0chiisPHQbcyU+zwDHnt4bEcrYnOxbMJvlmy60nv27WKMghU1DjHqIgvhd+8v3jwEyHf7X1RfW4eMnibmx7aAVBtWjoRpRXbFMtay9LTVqKTafI+ujCZs+eTXlQrHteYMUaQvcyVMquNuh5Q9cBjQLEzp1vfOMbbDTda6+9WPEuLobZqS6JE2BX6uOPP46iAaEfTw02MWxWOa2NiQMhwYD2C5Z9uGNAFD7++OOnTJmC7kCM+stYK+CHj7HyyCOPvPDCCwmMxKYr5fsSG2EjZYlPIm6ujm6UKQ9oFN4YLuI9ONTmqaee+s1vfgPb2XDDDbfddtuqo2rMgqLQydGG0ZIoyi/072qFev/990Or0W5YkNAHObmuHa4kngzCdwUDk6NfCEUTPXi83ROhAKlTQPSn+r/5zW8iE6+//vrbbLMNkjEjlvXwr/yiU7AFX28EetxqQ2wgeDSEpESoDkiYnnEuF9puwsUyEMuMq622mq7SHHvssSRAtCon+oekXSXBsCpOWKrPDwnq7eTQvRX6dKKDCuKB1N5BldnF5ajugAbCJNFwEfj0pz+99NJLFzfZLs8S4GJjKis5KvQfeOCBGonURaA4bxzTWQzQy2gNLr/8cs4KYfjjvBu1Q+Hgczsf1h60T3QQhDmCF8hRg9LF6Bo624pw1y7HfqlNCHtmZUNyA/JuWa5oGSmBLr90iFTBTMNz5szBDSTUiDkDDAeShYZdLOAv5t5772VLFxtj3XjCbjkFr+KfMQbqwoAdK4tJ9KSTTkLdwL7XP/zhD7yFVusquSsSYxbncic3XKV9Oav8nFFdlSz2tXDa/JtXjfBeW2JrAnWgozUAxKW2FgNM5ObNm8eUb6WVVmptTXHpMQbyMTDQP8DgdNzxx3GG3Pbbb6/SUoGQxOxuu+22Q/OFruGll1566KGHGKjU615+YfGvGAPBY4BlarQDTEGvvPJKpTqOk0BmKq4J4Yl5qcpYvIWMzznnnLXWWgulGMfOoacozhLHdBwD8B8c6TEIYrHy9a9/XeH529/+xnlsyojKycS49iLxNddcgxoUCsFtpM3e8UaFEIBw7lsMIaJqAQkmI/7czM2gyeYdcqEp22CDDcQAR87IFg+mXFoa0t2HPvQhFpNhRGgilKShfDszrKXSOE2MgRoxAOGhh1X6VPUrBz8vs8wyZMf4qxxHrbHwOFlXYiBWNHRlt+YahV06P5CGsbuDBdgrlyLaIbMrRpsg5gx5mv5otyz60CPfv/LqK9dddx3SD3IS8zfUCgXWCvyEJm+//Xaai8CE9R1jWIEyIhKYQNRWH8OQoC92K0Hm0yRUau8WNGw0mbV3C4rvtiLRhTEFZUFGG8YZwBBqjY1EdYteDFrFpcjVV19NLqavNeZtTTKlNEt1GoAcy9+V4aiQ0b7SEpQNu8/KJbfr7dDw0OAYsURg26B6fiGAoS+yMuvAarZQEhb0nqTBtuW+++6DcfETXUM8cyuJqziydRi46aab1A3kl770JTG/KXVyy+TJkz/ykY+gDmOcPeGEE3jKCMopb6lYvG9dz/RWybC+004/jdPrWLak5dCh3oThk+wlxMSP8NNPP00aYnoLO9JarHddSc8Lu4Nx7+Ek1+KYE+Vw0R0hPnJ3nsbAg5xEzEc/+tGqDSQvV9VkcYIYA+UwoCSkTySe888/H+cLJN53332ZhiGvFwjrOq9b9/3roghDO451KAOY3dpTrpawxdsDfTlWSPQI4bgwZgYw2T5tZpqAFX/bBT2DLuzmm29WX7n77LPPrFmzaqQ91ZfBWllLhNqZxLIx7e2337bli6jfPl5qSc4GBBDZwtPDXU4X0Jt0MSeS4ukThCANn3jiiYKZvj62aPFK0FR0kRHeRffBnXDlsPnmm6MhZW35oC8eVJQ2jogxEDAG4C1qUEO5uCyFyRBgAC2pZeAVtHrLLbdoABsc1ebXrjAlY7dc8XSmVT350ssvnXzyyVjwffvb3y6oA683xOAXWeP/9Kc/LVy4sI1jXwE4Hf/JECyjcI86ZiuD/vjLLIOYiEfb6RxcAFU3G48322wzt00wAuLvuecejH6RlefOnctPdZTlJovDMQZqxAAUxaWJNYys89e//hWxCf2C2paLQU3+mgxURxbWXrC+4+Ujjzxy6aWX1lhjZ5PplM43XpD5vFU3CGBG45BMsFM5724FzEahIHukuWsq3wJdU+puToT3Rz2da7nllvvRj34EBdJapV59uo0X6vUvG49D+B122IFtF3ghgZ3qjJS3mOewYG6TtS+gqi6hBL92VTfYZzOgOAZkclaqvYvLFER2+KILgADLBURkdAeEOUN3qaWWUjZFT2qgJJSsHdt4ZnE44eMnRg26vExGVSQpkVQoxxYSBxwMKHEWPD2e6SQjSC/kOiL/Vb2/tKigSqu39lrTQ5YkhfzQgj3176f+/e9/Q10777wz+yZsET4T8r4xhld09OxAhODReL7yyitkUfK29OkGbDldFfBED79NZf19+AkC/Fur3yVDgSSO2sVs4tFHH4WEoLStt97akp+lVRKssMIKqqNnPEWHG7UmBgIvi0z+eYs6+ObLY8rv3Jqs61w3sivD0SP6ruyGABvFx8/UjnMlEHk5MxzuQOEcxqZMwVbEDisGJ9z7I17/4x//YFkPxbke9GXTxIEYA/ViwBNozP7Sv/zlL0y9kH44sv4973mPyvq6FGyL5S0LMhAnpqFQLCMZ58+p9sGmCW1A5i7msjN3MR8Q8c++8RK0/w8QCCQ67fSqj7l9Xj9AkEwg2cNPLCeesHat3DIvUQ0/oFhNhTaNEmC2/Cxn51xDeQ0mQc2EQU2htqvVpCg05twO7JCffg7263BetiMIb0EC/upXv4rFCuZUOHTEI3ItFcsY6pids9XiiiuuIJILlT12R3ziowAAQABJREFUKwSiwqNqaW840gTKM2U6Z25vwhkx1oeWAb7097//ncERqUyVoXSToUG+t7xLuRZaMGLReP785z8nGRnzEvXKD+33Xmltq9sJ/8TFI9R4+OGHI7lBiq6vNwgPmY3taYCx9lprf+ADHyAA4T3//PPML1oNW5jKd+xYrcSVr2UIE7QdgCVi/LcDGIpmlSgRmLP97ne/gxegQVB/QjpKuWPV9OnT4Quwkosvvti1+41mo0MGtSfrhAyqNoKDC2JEfSpklIIOXcKzUKAFh1CZ8m2yySasyTBsPfbYY4hKNkHIA3Ze56sYBF4Tidxc8m5tg6x1g+7gCFR4by3k7S/9+uuvZwoKWbJgqOcLIEs14Ih02rRpGDUA/3PPPYfLQApEPsOeoUCn1tIGqliXR3hW0HEVATpJsTFGG4UioPgWaG0yG9A26M9SeXNZTErg6SAFoly44idXqBpo5ZVX1oNIdVam7ajxSW+Sfdddd6VD4U6nnnoqhTDC1pg9ThZjoAEM4FuEA3fJiB9lXDAwcys5gJJAdV7IcrvvvjtkyXZFjBoaqDHOEmOgAAPDI8NjBmTnDq5GIT/08h/84AeVDnnCD6E32Cw+lbFU5cxgzc7AWmC4WlBsd/60A640T8c9ecogWGDu2p3tr9SqWNFQCTsRfYdghOqR3RBsKyXAKIW7oIK2sGLD7A6DYXYmwzJIyXyvnZJxATzxz+7AAJoFS0WnnHIKPzFnWH311XVwKm6jricjKkGNbPODXCHFSy65pDhlHBNjIFgMsF1fFWHf+c530LdCihAh7iEbqOXWW2/95Cc/SQlobB9++GGWgNg31MCctoGqbRaVZlzzAdE+oBRwL19HQJwqF9yXbrjyW5sykxgdMTcBvFJkaLN/iT86thhYLxF5cpgtIPgA9dOPWu6RRx4JS0EU5mARdJpvvPFGA/WpdQMu/XHWQHY2IaOU130x1FWOszVQUZwlMAwIsZm7XVQXGOS+4uDyKy5//fXXMY/i3ErIbOGihVBayVpU5wUHw3cpG4U4vAlqd/f+lMwVR7YaAxjG+wddw21bXVvw5cNFMeZiLLv22msR6vi51157uYp4yJJaeQWDJR4POCxqQo2/+c1vgocmLjHKGIi8osGqi3RgUTHLFbai3DsNwo5+kZwHH3wwHz/cgdUYfhKAL9iLQUsV4YhihF9++eXLLruMNA1WGWfreQyowK3yDYMTp1qCEgaeddddl0CxkKSkqB7gSUYa/EF+6lOfIiUHAfATyYknlyVaAhoTnifChF6uVGEi1Yaz+Nla2O2hygmzF7Q0f8+JP60FJoSlW4t31gkxZ0BIOu200/APAutD0wrArs18jfBrmeybQN4iy1ZbbYWq16rbaiykyWQwblUNMCC6w5+EnVvTeCm9qVhpwxs7nrpZ3DDDCWqFTF8ik0yk+0TdgE5BLrNtiic/0T6IAqL0/KjJFpfNjgqA2nmt7jnRJX3iE5/YYostAE3PYCub03+hzEoZmj71zU477YTagp2G66yzDiyOV9LY8DElvx3d8le3P3ibILqlUeXbAUUx9iG/EcDnAlQHpTFECqn5l81NBLyLC83mxRddzE8SY9Tw1jwZQ+OrzRhg6NfbjrEEjDzQZkACqE6HMEhx//33h8CgKxYsGeN8GhQeS1hrQpCDK8Jml19+efckYHIFAEokijASlw+pSl7ytCThvwryr7VgtQFKzyZDN48rLYgGiYm2l9Vmsabt7ateIQyCa80110QqYnzCCwN5iCnIqTGozEmGSgJ7dV3fs7yjIH38M8ZAVQwgf0M/DE56rirDTy3HndhijzvuOLI/+eSTuG1j4scUzqobbJoQBqxUwaBiw52Fk69dIOkZ6bxGbCP3oAXgyFV8N0Ja6LaOOeYYNuzADGGDjUlFqib70OYfYgM/YeYGt/7uVqXbtvFSupsmcPUlB/pS/Yn+fp6j/UkOTmAKYu9RIv2bNFVvm7hEAJMN7mQK3cxon3macDaVItK7+8yrwPz51dTJarWLHe/f/vY3MrBzmO6uKWepRC5J4Efm85//PKmwFpwzZw4B6oJ4SuWL42IMNI4BdbhA/h133HHjjTeWffKp0iekaB2i18tkocavfOUrON569dVXOSi68erjnDEGDAaYF6DAUk363nvvjSxXLI8xxnHBJ3n+8pe/ZP/gLrvsoqpel3l2NUaTuq4jbXQkLtExtb3ZIdQygIP246HtiO+9ClMIfInED37wA0yY8OGv62x2Kc/iAw6CzD116tT99tuPSPxp65qeTRAHmsOAw32aKyhCuRmZoDRc8QMzstEXvvAF9k3ULovjE/69730vgxkCVvGQFiE8xKCGEwOwOAy+cBqCUpVVGuzhOWoHa8+GtQzaTJQLSPlnnXUWxI90hfKCilT2agsekhj/DI1khkbSS0bSi0fSC9PpBZn0Iu502Zs0Ve/K2Xm7eCS7ZDhNvQS03oXp7CJzEzMkdxo7CFZY2r+shxk53cqGLEa6hnuBIRI6ITtPvKlj/6LjKVbEmDag2eFV7fytYTDijL2DAdgRKi1tL6cJwlIg48qyGcyHmyy4dYDsmfJFyM9R7/RstFoKFcHr8O+mHG/VVVeFCOGljGv2cltEJMuWDIVEqsGX+zYO9zIGGh+AexlrIW87H7laOuGdgdkaohIxxcIWQtJIZmTihIn4EEIfMW/ePNaTTz/9dPgL2nE1IYZ3hLyxIQZPTrsxmk4TCDGgAYIGwSAnnXTSSVAXOqyjjjoKOYmYqlWQEVplMFOn/ZxSwWnMbKsuptuqRbUtAZMMz3CwsMpwKnBlulcIabf/hptx6YRQ24oh/f/+9z/CF1xwAdtKobomtQw6BVUxC0b62muv4TgQrqsslNotjlvETpnJTxqTmDJ12cnLvIsvbawsfgrPyWLl4NpzumEDk7H+yyMJPf1brQIrqgaSpl0yA8ftKBsIYHQmfa40U4g4vXhrcXrcmCQ/zVhisliMtCbApAuNAA4+f//737OBgt53e6GuOq2JFkI2fQrB4AySbTLsu2F4ZdCktFr4W12VxomDxIDnryHIIltaFvyEYybgTu973/vwkYwaCzspy08qV80A+rGPfez222/HJuLHP/5x5cTd+Bb2wh1LrcH0LWzzhBNOUObJuEmh6OUr6Lx0EISAg6k+0qWMJuUQqM41Qb4Ec9xm50DI1RxJRYOKQdqIpHOMisSLKNdHACvYXCt7MqRCrZ2nlZRxkbARwmbMmIH8hJsGPYFZrZ5gLiWzdByXSLL69Uov52T4QrgAXv11Fb5o22/kGwGvBKsBtw1sBW8b4M1UhMyNp7S//vWvtHG11VaDrlA0lCxQqUvHMA1j9k1iTq0nEnUDkWqbUzJ7xyN1RiXbEwynYZaVNzEzkzqXUynAyVL00GRb8iaTTZbVddmhJWFoSVl2Rt06+2ez2TSB4D5z5sxDDjmE5iI2qVq2saZTvGoxCLBRiDMUKUcr5amErc/Gyq+aCy2DXKPJG//v18+vteYbL704OBZnE9lMn1Bo/kYeYpRsTRbzKLDuRDthdA25ZMU0LPmQomh4IotKm/qlvSLWmFxso/DHYRZZ+YYTEyb2M1NKpEuwQgNDix74SOfSwuvtAjc9YZWtVenADmR2I3KEG4zu3nvv3XTTTVsEf6SLRdeT1Q6HTDyPjCX7P0dpZdsbjENHalcAaqixLChtevHDH/7wiCOOgC/xYcGs5Ftzzlt1gVBCNd+fRBNAy8l5T+RFI3b00UeTHTNDWJObqwvDBfK+0kyRarUVDTcGW37BygOFzzLJk1uMTZiMjCbEVW5C3NowIEVCEwLYuDdm2w4k9NnPfnaDDTagkQVaBiU/v/HeX1etX/Aq6j9LtVemnKabUzIUmk5PJPqZfmDYkUjIsWt6Kd9xmaCOrXlyo5+4wl93c0Qym+NmbjwuG6QETzioUFibXkVS0VAZN+C2gOdUTt+Vbws+dTsOuY0lku+DJ8bq6BrQMqAFx18xB7PBWdyUYQvzbeU+37ABlwePy1XyXghz6VIyxewTuuLiBHvaXJL28nBhfpAeOZ5b7fRqzFVcTkdi6E2PIEWyERkjJBeAcQMON5DxFN1XL13CCZNyvDx0hSGDnhgHdelhh4qJAm5ZF3qgW01vA/yUcE4AqKu8RhJjubBoJHv2hZdytpBCAzUqQWq/U2gxOAW8iZ+GdqsAYHNBSKRHgECYIsxNjfpWa6dqFB7EL8SxK4ClUukqZQfz2u2IYEp0SsEuHR0oBu0cpsMoyTkU6Bo48s1JEgeLMJDTNRS9alGE8mEKb8tUM8BGwJqUHakP73rHQRacZ82aNWXKFNw0cJgOe7j4HLpfy+B1gGVOAXZIYEXpQCw8Fp4YhQva0xMrISoONIHdEdNS7hoFrFSF0RtFUSJEpaOrNqn5BF2oaGgeKb1WAlZ5G220EQITDecQCnXj12tIaE17YTq9dWE3fsMNN9BmHBTjPYiRiWXkWlAAEYqjeqYuZkM141m9MlYttcRpehADmMlgukXDMTdA/mZNZt999+XUie5Aha6K8OXMWHHasmPHp0bTff1oHvr48GigWjRg8+KYNnhMqeRqh5OsNHpsLtblUtkE1iBk4VMlPi0HXsgWCVmzk1Cmn7ep5Py+0fv+M7d/zFhPJVO64GjE6sauAw44AL0VT4DGLubBBx+MBvQxlKHHgE7kdKMEjKvGJR87/WNOiIYCD0fXXHMN2dlAxCHToW90DGAYMQBRsTWMJ2aqqFahSTXpssQWRqBjmEKJgZrmAKGEPAaqKQy4szjYx6GHHvrTn/6U5Ro2UKBQx+6umVW+piCLM0cQAzLRMEu7iN0E+In7a554BsK/I1brlbcxk0XMGdgKYza06zOCaPDWk0MCebylQjtCNkdksnfcccevf/1rYtjOw/7n2oX4kPRmOTBk2aQvy0D+059cteF66y1+ZW4ylR2FhbOggnGfWT3LVzRQUgAKUHQJZuuE7F/jg0XTwTmX3PL5UzX7oEYTLIEl+lPvjI7OXG/DzKLF5uMOoOpyqNB4ZUSV0zT21h0WOWEEc2LKwYnybbfdpmfr4O+jwLS4sYriXM1iAMthXTcOZttFs+DUmx+de0qGxLrlc8ZZJDd2TFxxxRV8CG+++SaMDt19LM7V2wXNp4cBGo5nSoqOFYPlnyjlmRQAPbsnpi4zFXKsKsg1j7QuKgFFv5oVdlGbGm1K3Yys0YrifJ3BgOUaFaqHj+B7D9d9bDq98cYbL7vsMmztkB1ryVuh2PhV72AAalHrdPXOQMPxKopcjp92ngjfru7ApSs3TC5+WsIreBVyZJrZlcDIRErWeL2gQu2YdLqmvC0QgvucPXuVXJgoXL3xhJBefe3V3XbbjeayEH3xxRfXuE4YCfRAbAOZBFvdRua9lXj55aHXXh03wD5Rjj2XHZpiiG2oMc+awCXCRhtJgTi7QsHAzmPvYpOKqQZFA38BLJ3IjiSTmYHBCdnE4PAoQGacT8HPFpm/7mwNnobpFmdPQF077LADnmVwwudyuci0KgY0fBhQCyymdrWAVjBQMhtEnNt///3ZBnv11Veff/75DMG1lBOnaR4Dls3aAOwXThiVC408VMdEAIvUs88+G80pY+XBBx+MixAsGhrQfEWl4U3Didzn6NDF8E963YlquoYoFxAkHrCZFPQ6Fz87xOGotkM1O82PSlAHKsztGJCYLuKICMgZrqICfwxnZzEA2XBY4JiBMQ8//DAnzKOlwlyTYalhqAokp4bLiTPGGAADrOlxniXGNUwOcefOtBAfV92BGRVhkWWZkfQz6R/NjsnIPZjOjk1nx49kx6fTYzNy47Y+d2eHx2SHBzOFN5E13l7edLo/m07pPSrlD3JWqNQ4LDVm0wNyZ7kBbcA4iewOtGsr9txzz9mzZyOLo71iUkdkNymwuqmnItcWV6VVF/B26Nxuu+0oBKa300471VVCnDjGAFoG5H8WIEEFo+c555yjKtRyHkljjMUYqIyBIBUNtiY0DjLRz1c62LdtDODcVdUNqnfoLdVD7asrqvDmyC6s3Omde+65h6fq1NvYWd1aVUs+sVAhC4EG40x8iHJkPWHIielcw2OSikpWYApVSysAI0pVw/HgMr6+tRTbgSPZu0Jxjb7CZt7ejZbRbfnuvvtuTpWHH6600kqnnyaGNswMu6mRuJtm9UQcTXOeFmcuyT4GeRqSLPs0unhLoiaLkHAtN14dnVvp2Yz5UqYc+sSdTvRx46tBzqWgdHZVhGtnUdMUAEWxgYInatbf/va3HEXRdJFxATEGmsWADp3s5YHRsZPi5ptvRt3QbKFx/towAB+2t+aA+/nyQG1FdDQV5gw6cWBHGKbNwKKbcRSoyEllbcSljqR+hb4Rizu++u968W8wsyAVslW+EHuRjl5+F3cUiPZWDmtQ7uBWWxzjvnXD6BRQXrJ1WSORmeAyLNSoAsJNGYcbwkDS2y/aUObwZ0LnjcUdmyZ+9rOfQTPowtFYBTgmGer2HuHEBqxPL6ZVokcIx8Wc0/JkmYiqMV84YGsPFMrBMGHAloHVZtzcIkgxLUQdpvTUHjBqZ8X1wuOKsEKEQntKgSZgNA7FCgWJKfmqZKQQTtGtdO6RetFbP705ZEuUINx63KYLcL2Ntekr9F3rUG1rtwGta86cOeitYHr4OWJXczOWXLbkOBBjoGEMcCY0dqkMweyegCzhdajDGi4tUhmDmc4E1WQdfL3SjPJVR2ErLQRVUbDlQDMwMRzcItdR8gknnKDDaMNLRxSiJQQLZ1hL09EQN0WNm/SGtWmNw9WSL9OTbs2n1Tho5XNiKWpvN5VGQtVOq+h1I36pYOSm7qKwFbwYYPik9ZLT482lbytIYEjhTAvxV8zZbzo/xAZ+cMygLaGLUBU3pSUYYP72/e9/H0KidAKMVSiqAqlJ6dZSrxJzICU3WYjhLLnFijypgqINw8FbXsHdZKUlsxuFgixlm3ldyST5kYHM9vKLDOcvdApI25AiF+fSY0UM/eixJpai2gA5TBWerKSLDNeKqpUatS257vX1X0G3kQHWv5M4nfTvEtWIqosL8MSiIeiLpVoXmW446KpKlMdoS+xmm2121llnadUnnXQShl2E7VUiWxzVGQz4FNuZ2ttXa19SjgmA51x44YWowOA8d95559NPPw0E/8/eeQDaVRQN+JZXkhdIgpAQEgiE3ot0EMRQpCqISEcBKVJUBH4QsAAiIB0pKtJ7leZPlyaCdKSIlND5pYQW0t675f9m55y957b3bjnt3ndPTu7bc86W2d3Z2ZnZ2Vm8fbfNrrHSBnW4Xfc1bmj88ETjZjfYX484MjiNawEMRLmARh527r777tM6H3XUUeiqQCeuwVqh0jdy++9///vFF18gSjAFV4rSRu+E5TMuuuD+VMvgKOKdOjIJeqdp3roSa32NkMJTv3t7U+pLfSOOwMPCfy8M1cKDD4xqqSq/h7/hjoe6ztOndY+OyrWL81tLBZhXIApc8EBwPDUytcSHuCAlcvwbYWqKrChLf6xTd9wIxbnj4wEbyPPRRx+hnEKuw/sj++FhdFSc8wVA4dodMa2Em/Al+8YzsdOGFe2gfiZsvzSeeZMpgUAgKVKw+kntmwQv6OQq2FMKB/fiNATCiN8Q0FLLBaEszQwaEs0fugqnJYWqv0RfS0XQd/OzAbrefRfoX8vN2EBRcQKPDAqXx/KFPdC+oxNHjRpFWAt0mtd9LIIimAfwSjPeYYcdWEBmAn3yySdV2RpMgZ1ca24BwUZzE3DCNadt5YhK2bQGkLt99tkHhETv/8477/CSmbqvr6+V6zck7NrvQ0brRKjcAuAPvNY999yD9xli4KyBF5WjDvUWy2io4qRJk5566imc9KDhAg+HStSi3712rK7sWaxlCL9iMhIa7Dr/gQUYfy7LapMdteNR2Qt/cm8sl0JPa9+7GNBYbjFOBbOFToCRfPjhh99+++3Tpk1THkg0BUO5+VaOXPULEyZM0Fq+9NJL8G1MUYiOMa53vEEbTvzN3Xff/dZbbzEnoaJSXMJAJt7d4w90VnAScueKduall+Z4w/6UWy0Xa92gOzgoeHheICHKVigY562+/PLLONAmrLagkTQIRbOxaOmllwYwKxv7DYnM5vmkkaz4hc3giEnP7Yhe1hhhqIA3bbWwVMGR5VBtcLillKtFm9+ihQcf2R6mLVU3bLTRRvfff7/fLVlTftqPaPM5sIkFwC233BLeOkIcqwnoTqTh1AJoV3Ul+ayzzqLejBp9HE5tEGpdmfrtrQVD9Aw/ECoYDReGzQtLjBAxVcr/4fw/QOV4bGDOWn311VX9evzxxzP5orNo90MrPNyWI3vKmxJ8aLhrWjqhsCZtf4nXBh95nFi21+w5sx966KHTTz+dU9zYiqxMGMN7yI1VlvFFSiTMjizSonq8+eabGyAusWybDlCBt8Add9yhKLfddttpYeBe4KV2Cui0QKUWUH6aY1AI8B0fkKzsEWBBT7E0fMp25513ckjYq6++yoGICrLCVgn8Zt8pX2vVXs1m13R63/lsGF804ExYZ5999j//+c+pU6eibvDRhKqWGtN9utaHiQoBDGeYMcePHw+DHj521QLw8IojjL65C6tNcWyA4IiA1pYFp/33358wLiHhD/HGBWuneBvH5mgvmMC/gtjhLkLEvIroTEGPc889F8yEjk3deGpjAKv6ddlllwXfcMbML29QYTSWWydVq7eAb4oGrwtGxhSPmG0ENLhkxcS9B+sAc4S4sR6R6LJrRdK154VF3GeffgZ1gAnbddddqWQKDqhmO1I796y99tqEudBW8Bsy9+ZH3yiH4UdOnTxqa4Ebb7zx6quvBlvYDz927FhNxKNNXTse2iQ2QFp2Yp92+mnshcZUh0cu+zXygDVOE3Ln1tjSHJdKWXIVCP2BspXfukOvcnkW0MibLxgAwBCon2Vr0J+iYigpKmQswpZBrcxuuOEGIIFKt7cHHEWxiogmO1mbu5jm0GNCDdilBQtL57JyS4fCyzaXcR2pLf4QsGGbXl+Wv7cR2jeg54zU7C+mfRuilpqBIZgY7LHHHqio8KSgkya/9qolE28ci3gEGBqovZZccknCKOauvPJKRk27b53wNkYn3EgL/Otf/2LdiBlqxRVXhJ2DqEJgLUISqCXTgYwYROtebDAc3LbTcS3JWzOOh9sSYZNLfi2LaN5U+CGSvSt8rvDKRpf8q1zxkoMGAbQK/NVfw1VUZCyqpwjui/ZEcPnHLufp06fvtvtuzCjrrbfeBhtsAHXAloERPuTWCa2Jzk+EN9tss8UXXxzW/OOPP8aMive8JLcwebjYNW4HoEFbYObMmUxFXHi3ZnYhrk5LmkhRaNAMhvh4yimnHHnkkc8++6ya8w0RO4rPlu551Q1RAFIoEwooUFmz9sKX9g+BcsrWQMdiUlsUDRdddBHAMDT05LlhwHhJ2zMiLKfFXELPNNkj7BDE4wbNiHdPPMJAdg455JCvfe1rvGGjYJOZ157czpi1J+nE7LRASQuARXBu7IfHydHGG2/87rvv+ri1gaFBcXj45nffffdlXy2lkD8jpQSMzmOnBbQFwI0333xTubijjz6aE0z4h8aq3vZRVg1bQmX/Lr74YnJQ77n1ZtUK8b2eFwuyJwxYXPiPqBux0w5R94BP5b///vus80Amll9+ecY2Oyn4bWBGIRWurciKLffXXHMNgc8//xwGrlXYYqqM4xn+NVB3n7rCZuOlPvZlGwZYkMGpxznnnLPSSitRPW15nWD0t8k64/eYGQsV+2WXXdZkVp3knRaIpAVQeXDK5tSpU7ERw6EJ2ygYJhwqHAkwrVgozcVkhL5bnb9w0MOtt96KKhxbX8wZWLCF1MAfh0/2fSFxrdgjFWFm4nVm3440W7GBPC+Z0Xg64YQTVLnwi1/8YpFFFgGTPVGaCipmUspA/8Af//hHTBvGjRun2oem8u0kbt8WAGfwJQRCgieTJ0+G7YeiNrzAw462rbfeGr3wE088Aekm0L4tV6gZFLDw0AmZFvBf0WCXLzotHGYL4AMSGoF1LtaklAs7C1sGsWhAQcCCm05RV111FfwxecZtcsIqqMIVu60x1hVtBWNh3/ctV2iQEF9NmTKFbfDzzTefl9FXLGoeCtYwJ06cyCx1zDHHaG7gdvPZNp9DdepZ/UvzpTaeQywarXHwWzwlVPTuu+7GRpp6sIECgbmEgfOOnRavawXwOeeVt2i/K3yr4ZUSE4TYUX2jODKNgyRNbsl7772XT7oER4Crhsw6UXxugeTgllPROkpw/DX4XOXms1O26s9//jMDf8EFF2TjIXn6KIwh2pEzcyWaBi7nMSuKoOaBj2sOEJnONNd454AwrOug8GLRiM2qPCICqEas3kzZ8YrWDAtrDBk49OSCP1+gCrV682nF+OogSY5EiA56dUIZXflFJQfCEHutJbU0rXO9v15IAdTeSkvKKYrbsuKbII3/a+E60jAeKXkikA6ktl4oww0z/iEEiGEYHSCDMaMstNBCK6ywAi+VhSXQAETf+MY3Vl11VRK+9957qq1oIJPgkujQTdKj7PUVY1yYVwmkEtLFlGu9oIEBwYExdM7WH1VZVABum8nei2NgnfSFexGueLnfa/27wAILgJOqXDjssMNgmGpNGWQ8JSZWbioleobz5txBz9GDpdCABBVvx42ZcWZmT5GQgOeDsydC+Xvvb2khnecYtQAc/ym/OwWAINqrrbYaC/KEQWxQOiAmzItgHvQxOlAIVPntaS1we8jbE90NMueUTTsQhXReKDcEoaFJSTInLQoFVOdsQlFqwKYJ1mnRqkOFvITIBaXzN6gWcAg7fSJzcCKdSHfl4a90/tUpGhIFgbPadmJFy39Z8hlUm9SbLxbp7JvQ1WN2HXIQILIZmXgnzXrztPHtcGBSRnnBwFG/4MZ7TbQdYWH0NVDgO0y2RYjna0HlmXmpaPnXwpvYYWABNDcE2iBBvP3225zU674rQkiQ074fPKDkmk1tRAPJ0TVEYnE2OJB+fhU2zDBtpoVcaVRKQMkuenYvnnjCXh6vKE4RcIxZ5y6K74mTS+W49QWlm0tRjqC+L5uYNVbwv/5QHNgR5bM75gzBd1mhBDTiDGa8+2CCiz9hVI9f/epXrfZRKYKdbwrJagjhxAVDvhdeeEF3VcHVNZZPDUW1dxRn2LdxJcsnnvI3zVefLabzzjsvExUerZA0QPXm8/QxhwLdU5nfx6yby0rJMpMON03mzD7N5dlJXW8LQDxRJUBFOQjj29t+mwty/eKLL3LmHC/1RDFwO4iBUy+oPsb3cgXCIhkmh8M9Gy6C9oFh/fe//83mYcKrrLIKZyTRbjbDziRlmyLkgKEz6m875JKrFAcdVr5cAjG9wOE999yTuQwWjmVkoCwxcWoSbjQLJSOCEimO3yZzjmvy9me3gm75Es5KUcVLY2sBAKzDXm/kiJEIJmuuuSarRLz57W9/W0va1o+jlCet3rjjVJ0oR0d8qXCceii+sMB4wariKg8jJWapCy+8sF6iUF43eGLmPxQN+IHRxaIWmpygaPYqr1rob4bp+GJ+0inKlwZn+XfKlCnoGsB2rKZxauVLtp1MOi0QTgswFmD6IdEsJ7IT7YorroBGUbQ6gWMvADQ8HEhauhRtNGxxqQX69Msvv5yz2a1ivaWr1jbA28m3bUz2gusa5jLctTCpLbroomydAJNZOoqJvV5wte7kPBxaQM68S4leGHlkww03RIJAt/6HP/xhONRd56nhUNPa6+i/IGSVpSxoRHXR07IEbzwCisEme9JMOCp4gisXRQAGeBwuyHgmMHr06GawXNoMO95cFm6YnFk6ZuZDN9lMnsHVvXrOXnPN6rE6X1qkBcBG0JLzwFGlMWN98MEHsQXcUr8iCCNaVTMH+hYB0n4P5SI6b2JIr9A1iNLW7I7mrITvf//79AWL8yzLfznzy5J1pNbsppp4iWb0j6Q9++yzP/vsM+jAjjvuyEEeTHlow1uouWKImT62Hva6dt+ij9k2khWWycaKWUyR43qxnCMInE5vscUW4DZYDXroBoe4ghxLuJJyomosIbNAxRw8C6c/AZBZzU7Zs4OugW1BIlZks//3f/9nC2hvSmirGUSgYD87VO41TclDZeLL90AgETs6A13tLVJjZXT2IPMSuL37YRw/HLJklMJHkeg7jD1zkm0ybXehBUAvTrUYybrSq9v8GqsoBAJWGNZNqYD+oplsKaIwvGh6Yx3dWqlASyQxvOhts8024Dk+Sln84YpVLdRQHJAslckllVCZF4YEBWRNR9GFO5eC0OlduX0cnkxJaWsPFtEp5PKwMtSUXx4JqDBfue6RvhU0Nm6CIKes7aBrAGC0Zvfff3+QBNYioQ342wqebBXJNftqywz1K91Y42UHHyIZe0x+/vOf04yEMeIjgC48zoIZ/atbZhQz/W33OOfGLuK8u1s4fnDGZR3iueeeY30ID8rHHnusbSWw2oabCWg+NjcCNtxMtvFLWzSFCbevl5cWue/C+Mv0Wn6HUXAcywC9uZZbbrk11lgD+DgeiGVMBRRsDHLWC781dB4sLhdPmrqDi4DcBQ8DNYU1rZODSau+HhKF3JzyFOWcB1o4Z/KXpvZdBi+uYa1PtENAVzY4XbI0pAdq1++FvFItQzbPGXuUr0YNxBaLBh6LknlyaNEgOgWGMXsclNU+8MADkb7mGTVPM9WBb/OybtACHtt0imqmnWpKW7bCY6fBmpJ3ItkWUC8knKgCwqMa33777WO+kuklSrYWsQl4yWdsgKofEDHI7EqDEl988cU666yDo9CPP/6YN/XnFFIKJaRAS3msZ7KYSeCggw569tlnQ4IgzGJU/9W0ZxCUSayMMeo5KU0bEEctMK9QAO9UFWbNaiwL/MQ8UE8zff7550ml8DOr6lVjPq0VTRxAxuQSSHJls3DEwOmhtuedd96nn34K53bAAQcMNz1UIB2AO3CHvQpOqAkE8DbO1JI7dk9gl4rNzqWXXQq2K8Lr1zaufnhVK1Lfi0bDXCUDoeQxPOgoyb+ydXYxlN3WQKYcnKWj2zYBkfVrvjWh40iTTMyt+Xg/EcGbJ5OKZ5ZzFD82VQV32BbWFgzgHg+o33jjDbgW+DAedb9DC1bFF5AFmVXAE/4ChDGPghKD3MZTq6JZ0W+1JDXHL1RJh4YiphMufOyEamkBZinkSXydMlcR/4477nj7nbdrSRhaHFScFvfcQgUBzY2E4S50uN+a/WszLA6wkGjvZouId3qIngWQE3mffvrpM84446abbuKlsvL2a9wC4DDI/PWvfx1fhsDG4T7wYX4ACbJxKcrZXz8ybjYPC1gjGdFWJLvgggsefvhhLPhoq5122in+WgatKs7bt9tuO44wRBGmGIuNA+Il4Q6fXYYNFmkHCZQlKnphExqSW/QJ9sDD7mI34I/pQFEZQz5AmlA/IWjddddd/MKzYc6AHk1xo4MSQzZgSQRdWdSZl/51dgsyBTr8G4GiuyS5/48ctWLWnuW0KbWqKBIC/S8w/jmC8EcccQS4Dd3717/+BQ0frniuJKj231r61iVrWEzkzC1skTHk56/gHre8MWMkCpKnxZvfZn+EyTZ5uPbDQu6R/3ln1Nsu9ffyxEOVWeAiJRPJyd4mT01vSnGzdclNIeviNwJPO11MWn/729+eeOIJKnX++eePHTtWllDicfhfhO2MvkDkuqgvnQI9UCgCe160WhBmSPkhL+AVX3ojNBmGGwOlKQXzaRiyffbZRzOEUWsyZx+TK53BNB4Kb7x6M5N6rpQIFcFc5Fw1c3geHDLJLZs42uSy+KZGmDxy2OGrr75K9TiUl+NyCKBvjXNt9bQgIMcYbf311+cRd6cPPvhgEzBbcmcDTWTmW1Klw8Ug1UmZ9dgjtgfSVsAFNbj++usJxNyWwTYh+oXVV18d1RJdjGmDVEH8IOUYkzZOGwQggIYGwuoi7WGpAycippE1Ej0Te4gfzWqISIXPHH/NseYpdtcAhxxtLkdfC9s9yHnDIXQEC0IgA4MdZADY3XbbTVCi4wu28aZnkiOxTHA6NSQ5M7r6VaJ38OMRyUSQSu+sbN2TmyuXyPPIjVJRwHOlpMbrGkBKgPPmWvLo/dRwmDzZ97rMMsswhHGy8/nnn/NGrbAbzjNuCama3RViYZMjLeXit3gStDFqD6iQa+LrYZkuS1chc7VocIVx0XsBW7RbSl1oa69whZjKTzhZyW5hN47qGtyG9h4aTxydm5wZiiTlt5uNNKWJ59o16EYIMY5wo5T9Ra2oM4qZ//hciekpS9VaL1jmveGGG3QCXnvttRX4Ji2HNbfy3zi2TBGFrASgq4EqrCeXv6mUTt6Vx/QM9QqJvPH1s+qzVbFdmoDB4svQK8030GewgvxRbwmnnMt5vYEwc+gVEAAQSkqniFNPPRVGDRZN5Q22bQdU4pDZ6tRhSRB0xoZJq+samomyIPlkmtubLWqAirfBDXh0wx87u/uMcZZ+cPbpFSYYNekqKtJbjAmnzGHYEFntxLLvrfRCq8DMqUCDEjNnziTMorciScwVDWAv0AIqF4bTsFycb7fZZpvh5pBhpZVyx5P81Te1/lpCVJW5UeLTBAmyK3WDLdY5FTFgmzCA1X+hW2Q/IN7yoDY40cQGZNy4cbr3pP7MIkhBX//kJz9Rn3/f+ta3MMUCiO6uwpGcEcAUZJGgNJpWWdYVlkv2z9LrntvIY4b5E/dZOJMxlqrZRNbcjmCm4lnhN5/M5omc5kbl7LnlvfNYIQfJE8WvLO/lOG8PZHVw3iCiAKWABdkepXmPGjWKV6eddhoowajfZJNNGOA0Gi/1tzRBE89kaK+SbCgUFV7dtKUkl7g8mtVaMM6gFEAZuwbpaw/iOUiYyeUyuURQN3hm2AD9tc3jyiD2RbwCFg1swF/4QEIy1EMuKQLXJDy2oI/5wVoFsgS5Mw4oQLoMPqPy3PlsMpGB2uX46uuN5kDuivTSdRRAU6PwQlbGCJgdfINBH/w3n1Z+DMNheGthiPOsKRsVgDS08EnmoagyvIQQuJeXQ3ffFf46un/6z8lHMrfsl4fXUQZOiIuJmBUr4gL37/iJqQhOobBWCjFVsJQHH7bAAgsstdRSgA7PqirzVqqGj7DmRZAzTI7za9HEx0KGzorRjUQHplXWMgydQWxjMGeonSdopjwT5Ax6FtwCo7BKaVExdKeEO+cJP3DHH3/8o48++tRTT7FaiGwGMGpfHXK7WZKkBIyROGegP51g5z3shtA3JUReqKiK97GhMMW6lwpvHhpYRFdTif7M3P5MP794raHpRNEgiNk8DC4Aof+VWphLZfK9996bsxt4gf8OkAE8IYJOBKGD1kiBO++8MxXZfffdgRkjanZ/kAuI1BA+l1tyKap4ptpGYIwyDdZMF154IV1M+yy55JLqNo9TLaOEqZ6y6Vxc2P7xj3/cb7/9SIcH0Hfeeae7t5v3FpPryS/WcSGDcwf6qVo63S2WVDlZq0dDUAZ0gYLV1gjlOZgsNWdHgVXI01tcJp9jNM2di6vYud73UYWpL8qmv/71rwDATIp6kTchIwPFIeah7XrhhRdWWmmlqJrCz3LBBJna5JIJ1vhrYPItwRtQxOCbv9OfKdZBQuZ9t1rONKWPlZHTjRrxX500AcIGfAeI1SmUa+eccw6s2rnnnot+rbaB7zsgAWXIgBpgQySIl8v3p7sw7EIRIJKwgwWCFc3jgIPOloXkuUR0tp8QfPjKI+wSfHM6nezpRdgHhpIxEVCDlGbri6JBQNfNUWkcnovmuYvq5ZOZfKILRU5pmTwXzT1D1VwimwZyBnMiLVyzybU4H1GPC3FB0hRVUlY2SeUy/BLXZU8rANOyrx566KF7770X8FnTQ+qDJ4M9VQ4VktGy1WoIcKqbB5lFz4VUKrouM7BFpedc1dCs2vhvKj4oWDh0QADjqpahA1/8/7AIgzXBP/7xDwzU/+d//gezT5XogtMylLcJMKBTg1Vi6sL7KTao8JEsdZbHDOcNBEetFAj09o3sGzkylcBMt9DX7lQj4IAVPsyvgzHWhXIpjrKyiQx2T319Ix3lgvzxmJyF00YBlKIs0Y033njppZeS/YorroiIrvy6Dy0cAMDVsgRaxtHBBx+MOcOZZ545depU5NLmFGdK0FA6lMyz1UAI+n25+qOOEv/98r/ZNMGoJ41qYepIHIOoQM4A3HfffbHF+PDDDz/55BPkzG223gYxPAbQ+Q9C36iR4p41DQcoZ1flWdtzLi9pKsy5RaPVZfDcJOZvEY/HG28+GnYRno/eHExCtNAjR43qA6zuPs0WhoCmL0Cgb0P8Vc+vmDPgD5L5lMUhtpoUtUPAwLz00ks4DTnrrLNQ4bW6osFYC7J7UJpsVN+8847sG+ifQ2PK/tmyy/KCZV+ae+HFOjcn2a2TSeczqVF9I7vyasEkIMVtJ6O0VTY3d0AIrBzi0+UdX25lmv4LDSQPdk/gExctG4rjZZddtulcY5QBh19xejVLTBMmfmUAPYOYdbHKnjarnimsrvyA1ekak6eTXwl7yVvVNeTzaT7Jadr5FMezjZ1vnv6BWSh+TRaBdPHgFWxW0SCVMSVohW1zMvIx7OALAQzXCKgmogI0Nk2Fb+4rnZRNTC3I0dx40jqqB1LIsIeZhr9hluM2Yd4z8dDwRcbLbv6t+VdnLLb4ckIS9IJNEzKzZzK+WA6TlbRlsYIGXYZuvIxFg4EVBgF0Cxwolsp1JXNd4h401WXU2egdXBQp5VfcGlSaJORbU/Gx6JEzBgEpmeuBgPf29NA/xUySKs9cMFrhL9uMYYxwb/bKK6/stddeEydOxAkfJFVRJZwasAq06667wqKh7/jnP//JLyZ5cPO67z0cGLyl6DoKnGImkQWM2f2zsWhgNY84HlcyhRRMQIWHBkMOxZXUir1FuFqYRdC3zcrM5AYqWVxM5oBMh0yDJccjGaolAEGjyiIJygUwUEVQiFUJvYoHvINBwd4f1GScdrnLLrswjjhynBXv+eeff7A09X0r4EN96XyPXYSlteaOMnH69OmM+qOOOgo3kDoBMceFLJvVCm5ZPCBX5zJsTsaHJWQKGxbUDRBSjduK2rHSWootunjLgsLMGZidzvVnsj0IVFi6uTuBiudTz5xbPGCVspVmXzRvFmGR4rZJ5eRZyMEwnCl0dpBlroEB5zhk5Rqap8LlUNb4Bls8sILZc4cddiAJCACQELEakzcfDbXsJZdcQj6/+tWvmMcJKBVtPucoctAel+XEzz+d/dlnc7/4dMBo8eS9u0k+ULgKKEcxKp4QkKkoK4qGxOzu2bMG4PbSsKZAVITAgQI2ROZMN3b0MUCI3dXXrDxYsUglcXz67ne/i6KBR4rzS06pWGLIL0GzBedf4L13377h6qvf/vjjfLo7hcJGditwzKSMa3Z4+QiSV9FQRBvBOivuGMGH4xfRd0ycf77XX/53Hz6Vs/2J9AgfIak9K18QiwmG+uZzSFI9qWR3asBsVcIVSoIVtRy2BeaS/SSWmNIHFcMVIdeRbH6ZTsiuUj5s1pPFRbGkkCWUZLY/mZ3dnc7P250c0aMKRfqBCJqbLb1iiTF9yRBVM3X1O/LAAw/AuGAwzBZWFfb49UXLQP29ZIhHLVr2RWcTTbqBCKhxwb9UcsT4r0xMplAlSv+ytoxFA5vdhyjRQQlPrAKCeV7a4BDxHdSC68KooW/EvH2p+dKpPjG4KJAb8RaEQZPNsiUCSEQsvyDkgwxLLLEEWgbAVsQLDn7mwhJUpCxWCNF38AlWab311guTS6tYU4Te5599YenlloLojUqPmpOdM5Bg5ma0FHEhpHUNPCtmU+PL0jy9ydyJBkaHO4UA0NPTxd4JCJ8QRnRxWXifFkM8bwUJgw/IafiAhGWn5TmLZIMNNuC9ZZtK4sf5ceQIWerhYAJ8jnBoK0LpKaeccvLJJyvOo9pj5bNO+KE/hgQJKmjAoUhOPuXkq84C6ozu8NaWBa8luVb/mquvYZgTf/nll8euAbIj4iuLNeZY0FryiTwOOIlUCRiokDhiYMsttwR4fLD/7ne/ozos9LUi0hZa1UytA5mBdE/q7/98ZMlllkCnWfgaSMg7mytiKz20lM8pFWN65LpUApPh3hmZz3oSI2BIsTWDJnuzCATG4kwxvmOGkiuZuuPOO2677Ta+q3NTAsLReSzgipMG8gTiYZB49NFHv/vuuzgR33effTPZjNV8BVJkIJmafoeayUomRCa59dd3lnJY2QEXRMoLpNShM1X04hcAMonuUYkB8SCUSGd70hgNYGkNeCGjYCWgoTywc0ygL//n5dVWWw3Jn/nU+nqrlKLxdwgsyA4sEZ144omMg2OOOeb222+HkjeeY8xS9uSSj9/zwOP3PJhI9+KMOpGC26f74QeVRhVPwX4CLyq2oksfaVtwDIlDvDmgY50zcf6xKfYYRMT6+aJooJq5sfONxt/FV+YfPWNWZr7RNDvsYE8i2QMRQMvi6mA8zV3SOkVNVenBGZlCPISsVLzMa51GkvmeVG5kcmAuJ0D2jegRtxmsJaal7SsmbYmXDFGmSRbBYFlYy+XICRb3YE/tiA2Ia8H51l/+8hdoEPZOKCNjo2gwWie4adiJTOKSiy+7/a5bZs7+PK0CPV70kuzcwbKggC1W4ed0tzls1eeud5UUclgm6649I3sSo7/44kvWVCjINbQXoQgjdqW0AfWaz/Uy2WFsiTkDQYy9wQT0XGAgv0GUNUierA3+4Ac/IMLLL78MPMsvtzxNGUkzclZdT1rMX/uz/QuMWmDWzNlo+npysBQou5RYFdVDbAqavSRbV2Fqwi491AdVZwmCoWXL5cbMO+aj6R83W2ac0mPh+cwzz+CdAaBgmh/5+yPIn5HgYfOtorSUEbT11luTG2wf1hmoTljtxMNlnXuCYrRc1kzLQFgQe9hAvsuuu6iUzrbekgwjGewlMNT7iGYWsFm+xoBl00035RGkbUEBr7TeIC0MyYddH47o6Z3bPxcJX2ywsXQT210Pu6UzY7GeS5aGCpcSsMKzGyrM4I7uTD8UZahpbQ6yiRLesyvZNaKXwyPHJP1idV2Yav+LOglLHGgUbaKYzHhnjJODcgBh7j2kUDAQ7TwB4Dn99NNxEAM8KnPWXqkYxtz7oD26ZU0eZWQCa3XmP6QPV/QIBV7WltxyRCXK0msm0dc3D5qms0+/QL+ECo8LTMlfyywBGBPocccdB9MCVjCQS2L68iisiBG7Jk+eDMWjRLxrTZs2bcqUKb7kH4NMUrNmzfzO93Y84NAjXn/3/Xy6h6NuIH70tUijgekaFJfKMUpFC4o2vkmzS0yedNA+e34w7aVEOmxG3XZNs9SXStKY6XzuX08+uedOO346c24On22pXuYYTjARPwk5FHiMPu9UYZtewSj6RBtxKa9soRTdApdnilJtAu+kld2ORLTkjUmLdQPMfmpg9pfj5hs77T+v9vaOgH2RTEogMa9a64ed4IxVLGz5BXI8QQZBIJiE0GgwA6GAP+GEE+Dsd9xxx2uuuSY2jBF1N1iRSAEkWHj/9f9IfMVospW34aMMcvNrO1g/2Uci6G3f+BLQQhWvQTog6UfbNQ/AMP1QgpjWK6L7UlxYmbC+Cp8ET8m6K2KesJKIsubX8v12DgsOKPVFwlZnlgc/+uijDz74gI2mOhaCK7RazmgZmDv5usF6G/zmVyd8+H8f9eAIzeqQ+OBQJycDNlZUy6rW9w4ZNMivYU8REFwnH8P0ZLLZhSYteM7551x/4/VStHgwaRqAWgENMN4hhxyCCApJv/rqq/GozNZSiEAIuBdclYAfnzvsDiBw5ZVXQmxh/lq6Rg23FVMMFUeVyS+sMCYea66xJsRHN/o2nG3kCSGV+JRhqxdCHRpbccbRFmdRZzP5OTPnrrfGuiccf/y0V6d1p3pQn6U5VBJRH0VDgTrpjOgSKNMfRfTQw+AVdVYhB1572EV9X04PNTFfk7mZs2ctvdzSvzruV3fff5d5XVR6USmBPdDL0Kierh56X09lXmyxxdi/QIFympI5AzSwwitkjMZjo402mjRp0vvvv4+898gjj0B2WnBwGUxwcCADQ3XIofuMmzD2y/5Z1FnxKmwmy4PAsqqJYJRJzTNyHrZOnH3WBdk0h13kmH6hAxV6JcRXyq3BMtHpyA4YtsDDYGmlHuWDAERUaeIeMffb3/6WTXAcVwznNqVNFA3Sm3OQSPtGrbnWal9ZYtl8F1snUl0i9uqiuCJqEO0q1LAcyUUqNzoO3ToxbnR6nY2+cf27r83FcCmiA4+aVTTQeNRKKjZr9n13/m9iTkZMR5LiflNsNmhn/S2aKrTFvYPNG642ExS/F3nNiJGamfwyr+hb3hsTEXZMzP4i0TMi0Y+0l+8e0cvQinyQF+BtKARvDYcNpWDhC36FsbrtttsyXQWhFIcGwS5gMQHXS7s99thjgMysGQOLBpChgA+iQkonfn364etsuNaHH36QTHbJzijsbJNd2RKLhtI2x2yx4K6q9KPzXCjIvDDLhp4ZpWIqgQ9DJYhPzzxduVHbb7a7rO2gfRP3LHLJ7iIftutrZiH9ssbIRWFbbbUVa60q26uSSxjKsC7QDyTcfPPNF1988TfeeAMXeuuvvz7CSSSjO5PPMEb6+we6kv2rrLLyu/O+h3cO5F50SewbK79SCcfbUglWlces+qaAezYPSz9Fhe0qYcVmLpsdWGyJxZZadGmsiGU/UXi9VBX85j/ccOMNsOzkA2mC+sEhtTpVpy5gL2i85557XnbZZbjLOvzww1HvQufbYKWxgR5//fXX2RhIQnSIHA8JhxqEMr0BwJpMwh4ffH/Sxbfccgun55x00klNZhiH5Hh+7J89h+PbVl5hldE9YzkeCB0ZpgQCm7Nx1hIoC6/SLhGydRuzd0K3kSoFvFlpWLOyxFASKQ1ks9jsgdkLT14IL3T3PnhPpdzCeCdaBmP09+tf/5qJEnqFuEXBOmmq1BcGHG4Zyivefffd6gkSI3YOv4gHX+eCWPdfMW6d9uZL+RETp386nd7HJZa4Yyw+T9rkWoQn5UJa3SVXSSAAGB8N84wcPWfmANIJe2mR/qpEj+A1ygVOHkG7zY4J8BM+qk4DuvpghiFK5VJrrrkmrBocO+oG9WQfCdtWH+hDxaZTZw9kZvUPfDQ38eGnn1mLBkknkm+BZJWtoA+V9aDfFXtd+qlRHQRT2iJ+EnPZcfMv8P706XMyud5e2acZyeWDokHhXnDyYg/845/T3v2ge9S8+WS3NGheNiPhjUctOIqq59E7GOf88pEkNBzGEaTSy5ktnCdUN7LjxZ1CeGuMk0x88xJrFVE0aATRnnFibqZ/icmLXH35Jb84/GfS9MWrrzbjFgrAUjMzIV898cQTBNZYYw3O+oIZdRzvmpp4py5vuN5qQhFE05lIf+c737npppuwoYD5W2uttUamo91Z6iXWMobFY0UqscwKSyyz4uTF5o7D5bVIHiLqySHbRLCjzsEN2xCsx3sIQZVZxy3OoJ+hGpZwyKeKtEMmFVVhZNO92dG9I7oFOQU9JS1AAZI+N9NBth6BBuxGcXwFqzyPFKQllgNf/qZ52Lx5Srsxh7PVMZFgIw/KeKar++67D8tzcFX4W3M1X2iNOXC2J2e54c9r5ixM3fk/C4yCycYPAqAqKXOxThEJlBNS5mJVSTmKWlU+lsRVjQOFCF45l8Fhg2PiIiSRyfYbuHA4DDiYkmJPymuLwG6yVvirIjc7ZfQwSBgUmBV+YdwBnwA9Tx21KoRboU4FGKFY8Pp4ZwCfwWp8wiORrrLKKtSrEKmWkKiTjEbJNkBBM1WeviFMGCxDWwQ5W8ilFDut2wX2EBQAAEAASURBVBiDBOza2p/+9CdJK1yqrY+ka7n+BWaV5fDOcN1112EURl9jF7booouqFCq1armL0cYNuglhScyaMevLz78wioYeln0EDx1lqzAtnso5iOESRv1iscUTsdGgJYlfzvxi/vnn+/zT6bJ5VvCW22BjiDRQ6840+vDDD7NKtOqqq8JQgQzCoEjDeFum0QrXk05LxO8JUh8OlTGyYG8sbKRMEKEDUw/gJXEtwohTNtBtxMguued0gVcs44B+lWpTJPJUYflKCqr50SGMApiQKwSgTKprRKo7o4Uq+lmwa842sIjwUVdccQX9juS/3HLLBdT7ZEsRshiQSmxkTGlee+21F1988b333lt44YVD9oTqe1vSncpzpFPIHdgsd6V7RiCAyLYZrrxZ9XRLLen7YgIokeqaJd1cC5M4coe+zJvTHpEzOFNkLuKRuI0Im8644MnfolHn/VBXmPb6ygILjJs4Pjmasz04RdSkVlcKbJ2omJerMuCjNLfwwDI1YXBiBz+KgUJShzWXN9o9Ep/zMyW+rA7z3hknIjoatjorioaFFhwFRWcdcUC8oLX8kh6SHoMWBw3IVEhW8NxUFi6M3yAumpQZkQ2lN998M4w+Cg4ohTJMQRRXT55U2Qwq093Srxwykuz/4MN3WGBhkVkncefoE2NHQBjMsSyIlCWW5LiHLbReIVQEio5efpm9nCGjSOjA4EQupBZzhlQ/+JnK9cyTGsc2IvGiaVHUEygqJ34Pynwg7bCPBkc+TEhY/KrHIHCDC5CJo4DrY6CV8BbBofR33nknsOHRHUUDn9TBuzdOoMDYWkuJThsUFegiCS+dCYCAvDTzQREqajrNxJ0tivKq9iCEsYB4hCz9JIUtgowpV3zUtOzFmjYk6NBDD+UXMsiCMLyRKl4tBrZs5QRw2L5x48Ydf/zx2DVQR3aFsOTIdvcaKlVAgBoihxbF6DtMaYLylQZIRVDwPcR7upuFX1wdM7vpGqz2csijuyKEjb2kf5E22erIGvLGG29MRX7/+9+jd2gstxilggClRK6rBhJdbwmPuKR1L7hqyxnzTp3auB+b/lsMD7rgpnNsMANYNVJihMVUxQyFJ2OWRnTDVyTIrHM6U/mPfvQjFA1IfWyPRa0JkPqpwXrGK5nMsyXaSQHQQ4VAPzs/NgO7xW3JXtBbMRz1twaMHqSZAgJICypizsAsg1IbTT3n1kFpAyinNEtKZJ/O22+/jf9jWEo+MzriIVCUglrXs8U07EYdZYENeDKCRBbIn+d9w0G7Tu/mICWA1Yrm/ErYICgWPhRdlUa76QP6WwsTM0TRhs1NYTfy2Zdz3//gs+SIPnHTwCUqBtmUVE1J4+HCaQ/TApgdewhBScFF8d1v3vhKtbXpWVHOZwcSAwNj5xslCYtnHTd16/2FQOAedp999kERCPS6GNI/0K9Os3ysD1MORIFS4OlZeNlvv/1gJrD9gwkWD4uiLfYSWB9LHjIrHSzFo0ZsBGgQtsAh3KeBDrUT+nk73pxMRZnlufguXkILLIgrMnviFAVRVrgIqhl52KYiHEtlUNGQtKcrKSexsG8V/4CiXyga6dG1YVGtBnlgjzQb+YCTfeNvvfUWMTGzBNnAQ2EuGVi2QQbJJZhPOMzD9vi5557D/A9PDePHj4+PfTUMB7hVqLcXTwpvy0I1RitLZ19A64TqMhKMRYP++sJO2SIiCcAWI3L/8Y9/REgDAA6HUztk5YwVCfU3/mNqkAZEd4x1PQueOKVfeeWViRnQweaDwBD5p3XXXXfGjBloGZh3vLYM2rMt2r8gJ1dvTy9kE6tArLFeffXVc845h8M7Ia2Rt7kPABjaBf0xNz9ywpfZp2y4W5db8GoWtFCf9QvFNQFbDCMgvxDF4o8hPVE2JaEhBQHwAYnvfRg2ZlXmUP0UEhxuMRSKaMdcyUmrUFQs5xlrKBqkjaKbzV3omvmr2i6EGu1oHovYvWayrjGt4ThVliEFAb0LHGaN+QQdjcVZVF333HMP9iwIEWi39UyxgDgoxXMhgL29uKfBG64tCJUbGtig6xtc/nQw2Ea/yy397ciwwobxHLDgaRwgFlUOygsNhvcUuoohidBjeQN4hjM0yFmUIowHf0oVrYlsOe9KdnexLpPAgkTcDpsb5xP4Mq10p/CZYW+8AXBXj0wOhcg2VaX4ycLXHsIGh8WOrz0u2BSG6K233spZ69/61rfwXM10FYQjH0gDusae7h41bWJLPG8Q51huAgCUHVANe0XctrA4qmjA/Q4NxAKY7M6Tm0cJdMld+YLEuTeCs5x/am4snyreHGShdyqdS3RxOHIhuTy6t6Q1h2t19aTRLfQPzEFip5UwXI+4reosntP1dJLgQCwwjdTIP7Ap+tKbWfkb79cgwsDz85//HPxECo0Vn1TKTDetPqin9eBe5ZYkLutfT/KYxoVBeevttw477DCFD6+09D6bVcA6pUIxhbtOsKBYi05e9NprrsWwGT2v1k7HXW05+TOh11ZWjbEEJIQ8h+evgfFiOuPUT1xUsM7W0jxoSQPJxNQlO6ogqi+99BI+2AmDySXRWuzRcFaWvZa+dsiOkCAlREqRCnTJU8NAtQyecgrBSEYIJgNsPAQIjLAY4zpXhj9jFloBiSglXrTRzrOgDWw33HCD/QrBqYfm2HRRB4S2mO4lUAOdCRRckevcm/VTKCCPsZJC6H1cbtHRcFDqncEK/wG1jCK8KrlsEW1D4bW7pV6yXi7UDxyI4HJ5P1F8mNvFQ8MTRgCQFOkb1UWCgnLhBYiVbpXRZMlbPP1U2XaitKD8N6KGaKFiadVNNtkEmR+HUhXBNg3v/FSMUONL6T6R1MV39P77768SHZ6KSa4veU+YaDVm6H80M50IALlERjZhOlIH5ixcOHvm+F40pqhvuW3p7sArzARVsdSmqRSolk9JbqJRhP3yAKCZlVhXVCohLu/YNIGdG03KuZLsm2BusJ1OQK/wYUUSQ9cGv86UyVKw6fNCL4cDD32tl7fTfaOq4dQhrqUos4uxsQZQdaFRAlg2TbAmDNahfeCRfufXRcPoaJEfzQi9ZXBxw/OB1Vo7qqa4rTWtXo7iHb/eu3p0f77AvugtDJbqtsoyrntAWApDlam+ZqhdXJZ5a7ywtbDoiu4e0MFtfC0rhg/Vv61R01hBabwg4MQFzT8n2zt4KJvFw71++MMfMqJRMGEPyIocrDLlW5QIFxYpTS29wbpjjjlGcQ/jmhKQwEa9wgevjhJVrKojQSeqtAATDZOprhpyAArrNKAB3R1C60DbQyilhYrwyhF+gW35UjL0sqZ+5V9vPv4QXFWcIHzIPhAhTiLYmUAYiFtvndssvk5awVUKkkTmW225lRbxy1/+kg6GWFAuV4STZVGVsWgok+SLIkTxQMMZNQcWkiI/RAGCP2XCjpARrNIBBxyA/IPs50++zeWCLT3A4KZkwoQJaEBUDkHj0Fyu9af26FKh6dGosatAHYc5pgpoQ79GT0gktpJCZ/Ccd+ONN/LIpgnUXuiYtKOVN4oLFRq6TkPEKK+I0ljF7fKvQ2QX6ucoF0xCrWhzhWmHsqx37LHHctQlmeEbEvLFm88//7y5vDupY9oCuE9eZ511pk6dusceewCiem2IBFZhz408qfoFvInjdAmc/M9//gNRBSR+eeSKBLxOoeG0AFMJe07ZOkF3o2XQrsdOOZzSO6WE0ALC+4VQTG1F+AmJu/BQW8lhx/KzpmHDXkN5lhMNiBlFTmabBjatnH/79NNPAxGMkZKnGqALIwqKLYrBfEA28phAM6WSQ8Vbi6AUtVNopohWSWtZE44SZAM5inDfHYI00BRApbqb3/zmN9Nen4YPEc1E14EbyNDnJLq6W3WNt/bS7IrxIIJcLXFqLzEuMWGFuejl559/nj0yEB+IG57b6XreY86gpp4BUby4tEL7wNHmU3DtHQXGgtU4/mRFUcLpNIbrvBk7dmztmbRYTC899IYDrgbNKxcLIvDdYnoTzcWeLxwJsYGC4hHnVIxXgT8agNxSgeHggw+GnP73v//F4TevlajGATYXxij/Kqc3OL/nZRSjhLWesunxl19+WVNY51ba9fVk04k7dAtY5mzoqO0bw/+5X+h51Jd3SoFGcGhm1BC1Q/ksJLKKiN/sxx9/nPpksplhSJiGiX6BeciirJoz8Ig7K37joGUADLhHhRBQcWsEZqIBsS/1U9C/FainD5qFoKFumfylZ83mJ3xH0bMw6CwCL7744gQwZoEVjsB6Ja6NJ8yu2hCGYv4a12ZoGbhAXc4UBMNZTkTRADJz1iBzq7cCHWHP2xotHcYG0C7MoAonTHVCnq0qNiBzJ24jOGMV3cf3vvc9jaPgVYzfNi/jIKdE2JgQnzPOOAMKwwYu3brVsWGJsDvavugKrLIPdTYcj+ZjdYES4LA/e5s1Yf1a9GsjEKgWp8p7Wcr2bNElecmjD1UbTlkwEdqLenOo+6hRo5gm8VEMnVJeP87toZpmL4RiTVRdEQbueNBH0ikGlucweD7e+C0a9u5D4WQHJiG2J6gb/FjViJkS2MBJ1rd1puQNVwhAKum0GyVACRsWxCl6rAKOeqgp+qjq76JXngerHC8KAEn57UnVwkFUmVh4nnfeedRhhRVWOProo1En8ZL9XJAm+t0SqJJAC9e5HtCptfjLcZVumjQc/K8HzMbjeru18VzilxJfHKNHj4ZkXXbZZZz0BoDPPPPMQQcdREDVZ+3TiUW61yLCFaGJQcgYIdMTvs7NOZchF11SnFAMo+PQkYWOns1oSy+9NDov3lx88cUgnhAUz1WSQ8s9KrdW8iuNUAO/561sNXnEG6dVwh9//PGzzz6LinPBBRek6xVsup6rVarQcnAq7fOCHYQcUZInj3oFI+p7azNYOKjS4bNtDSmfIRraFWZZoVUq8oIgRiwkbrnllmyX4IJOMRvxJnLAOgAE0QLY8aojBtaQ5aDjbBb3e9iuB1FWM3nCDtnkwKnTpPel/RpgAJ1CgLk3mLWoHtw5RrQe+ui+aTDTcJNhO/PZZ59ttNFGWuwVV1yBYMbFY9hdHG7FGyiNjgX50fDrDrIGcugkCbMFILC2uMsvvxysZoa9++678bmL+oxPTLKsNts4nUB9LSB+H+1dX1LfY5cLb97e9724ujJEqwUt5ahgiC1w4rSSeV9pbF35dCKXtABrnFYxEf7RKiXAlD+ivofOQHM4SpnuBgfKsbQ8VedNXS2gOFBXkmAjC5cQbAnVchemrXN1WmDIFoAYQZg23XRT6BEHXlx//fXKGw2ZMPwIDn1vrmDNRPOwEwaBeq7oz1iqB1onLnMPPC7GkyzCIOadeuqpqJM4S/Xss89uILdhnMS7dldvM9Sbtt749cITTXzwkGPeMZ6i+J/97GdYNBAo4Yc6Godo+qZTqn8tgIXOzjvvDGK/99572LGjxCdvEDs+4qgfdVUaRcUIeK/2pF3eGsY5zLmG6LbYtrPeeuuBhyDhlVdeidVYnGEOGrZG+T2BC9lSbwOkxW0FOS6aL6D56U9/Cl9Hj+MXBrsG7zRaMsMq6J3fhlvAolPDObRBwuGhaEjKjrjWvWIy8hE+sWjQZrzppptwkc3uidZt1Vog98wZtURvhzjoj5h+cMDB3IPjPbgQVA+sKrN1oh2qF0EdIqaxateQ5xw9sWGOSKHdULNzujsnWYKQ6667Ltu1IINovnS9t6H82ioRrcHF2Mxj/WzWvvWNd9NTKBUeCr3dMwUtMHjesOFOQFsAa53vf//7oPq7776Lh0i6EiLcQo0jEhVLuGyNRZNQ1uOFipRqGQpffA5JQfb2Oe92yg4CwqGbiJqsKAgxMdou1hioI+F2qmmnLt4WgM7Q3Wzawr03nF4c+hp4vBB2wu3UAkNxCQHWlaIr3v4W6eJuaDOcv+B7covcQw97JZA24YeYlt5++23ogp586YGxNYLYJVS8y6EffKd9xUz0ZXlWrfiG04/oayBnYRnmg9nIXnGrDpOlveIBmyVusGzGtkV/wwWupRQLpU0zffp0XtGtp512GoiHlS/b2ksjDYNn6q61ROtnFxtZj3IHo4gHYJuD/ymeXEl+EJGv3nbz4rA3LPl4UL2QrYp5fDL4775PpQtMLTMIRit8GeYsplqQ4XMXYsuFVbO2lu13t/Hi+pc5Ty6X15JdWilufev9NRtqU3bVl8BwuOzERCAO9S2BB/UWUK266qqbbbYZn4477jgQEtzTsRkHgFscBqWEVKLCiAizalaCOPPMMykXVeYWW2xR3sthYqmSOACTO5vlUd+E2SyRlwVa2DtyYAICIGLUD6hWVbItTIRVIsT0NSMfctA/0B8tfKyVweZy8DKb4TkM6ZRTTgmTJEVb93pLR8CzO+TrTRt5fJCNdeM33njj0ksvpa+//e1vjx0zVl02RA5bB4Dh0wJTp07NDGTOOeectddeG/4Dxkh54uHTAlpTJbO0AHbOBx54IA7beI9ESmvIL0PUGNnzh4sfpTzJXIwmdytgdaWA3LGDQ6T5xS9+8frrr1vtyXDrWa0vvUZgySWXxGznlltuOeGEE3TGb6XWaP2FnFZqbf9gVYqqvzvttBNEBq3f4Ycfjh5zmI9KX9o4n8xZBw2pHBqdKGnyQEa0ulzsQ+QXNo/TRqK1EITQgXKffPrJ9E+miyFtRgxpDYydn7ZqgSjxvq0aMuDKQP3Vni3gcobOHiWozkCPPPJIZyoaur2IUYkJwzykprRRRILiQ+6xpWR7PF289dZbY5sdk1Mto2iPTpmRtQBy8/777w82DlstA00PRwg3RiMggl544YU4bMPUSLdI8D6REweQ2Dzwi7kzf6S3zA/qTu/qcRDhWjAD6AQcYxn72adfoGtg/er9998//vjjITIbbrjhzJkza8mnXeOIwsj4hjzssMOYXjnkkvZBfdThuVuxxxXZLeT6KEPAXPZ93AIgG4pdhYpTfkC/eeedl8eYgx23ZiyGp9LSZiVLn+JUQT2pv09oC31KGXvvvTeOOUoKC5PmINQ89thjeKPE+xKTGpCAdSXwdB7bowX8UTQoB+NtEd7U6TnPm7qpcOWVZMN4NZVvpIlfffXVN998EwKh5sRKKfQ3BLgoCKIAM4SyY8qUKVAoCn300UfhgAlYYAhoOASQYl5EhPjffMswFU2bNu3aa68lKyaeb37zmxzG1ipKpQgwEP9PpY3uD10tzbWh58G2TDeUYZiJ6E18wYCEuuYWZtHxKQsliwIzZswYEUrTaQ4/V/WCEFy5hKNl8UyUDagkytAx1LpYDarneGnGg87LMLvZfIKp5Bvf+AagUjX0mMNzR4x2ChOrnmfJI2F+2TOvp7eG2mvNFQazx5Q36BUdSXT8NQwKXZAfwfMgs/ct74UXXlh1DSwsX3DBBeRrVZy+lTHcMnJwL2dYBKGC0TYAM+kxvzhGYRg/fjyBCJGTuWzy5Mk333wzrpceeughgAE8pYHRtlLblg6JHoJKB1V1P/HeahaKphyzn1OshridLXrCc6TyuSq3fjW/mmrQ32Q2ae9ENskNr2VWeAhlDePF/C1LPdjoBNWKwed7//33r7TSSpjOqqLBHnwFmdAraBAgAbYIDK5gEJmNWJLipaUOBLhstEAD0qN0qLm1UDmip/5hBE6U3Aq24qo37H2j70sSeh9NBHzvGZV2mQ5bII98ztE6VPrF2gKHZIpphx566MSFJhIrtJ6tBFF973Su0kauL2U9sXW1wtK6ElZbSZ3jnaFatqCHYkghgvIizfx68ireKY0ZJ9/EJWTrXGAd0nXrwBsUpDr6DjroIA6iYy8rsuidd97JS1n3ZidCHuJkTBqY5rDVJSzPciUTaXMLbaz/1rTml8IqXQkss6rdTnxnK4fCg76yv38A04xXXnmFEbr66qvj/yVa810FLKpfGslWH86bS3rNTKNBUzAfqlzEuTqExXpnYPMO4VxCbuhkPilVM1c1+uZ+9+8vbWjYQf5kE+aWN8GwgjZnzV/1R4Qtt+ZftQLJCYDvuOMO9JjAfPLJJ+OHi2LUciqQ8oLJVLtbaY+WwNRsb2+ZyrB53yiPV++vNweUvHojg8TTalWPD0Phu8suu0B56GuHTps/3roEHZ49e/ZCCy3EPg5ECcIvvPACGKiLl0EXHUn+JUhoiaC/wGgpNk9baLRsnyX9FrCmAiWVbCqvJhJDKeyqjlCNJrKKQ1JoAQo/+EsGoaohdbeVsiMhQOil2hSHsRMqD3jc1159TUsHttCACaG+/hQxqEQHWsb2gtl99tlnAY8+5Rgklh9ZPmVOaokuViARY7DKs0uFATV1Bdpt9KoBFddwtpb1V11Dw/l0EkbeAl/72tfQozERfPe7373uuutQ+CqSY3KKzwYx/EhXme6Urfb3l+aomCHvXREUCROuwF5jxs47srcb5QJvAPbyyy8nEPQ4taXHMDAIUR3kUwwroiBZUhMxhKJts3cYsMCk6UWvYYTFGaXMmAxVHuMpcFZsFJg6PDUwMN955x2qAHlh6q8Ys/NyqBag3ZzbJYB63rmi5VCpg/k+Y8YMdmaBknvssceKK66IeI+QH0xRQ+fa0y0s5eKLL05UpgBOmCIwTCwaQIJhdQ23+rZk595999033HADIxD9H8feUgfGJ49MbJHUhy1V66+/PszuTX+RQy6BgTCUYpjQCF/anB70JZ8gMnn88cfPPfdccj7qqKMmTZpEwPZsnMG2TYEibJllltl9992HswBjW6NVAlFRs1ZpH4WTHQfMBdgFQHJ1X6vKM8K9shieTiXTesagp1oVdQGBvjSFo2LAxBAm2ywlyy+vZ8+au+POuyDGEMb34WKLLcZGLargAbcT7LRA67UAMyNr6fyCzxdddNHEiROx99TVdSpjiZsqI+JZPYYhNGTbbbdluoe8qNNZ1pDiCW2rQOVqGQprnxFCjsMjSgcJR44cyS/dHSHtVZ04G7HVG8jzzz8PsgFYhO3TKTqgFugoGgJqWH+yVQGPIx6wZ4P0n3XWWbzhws04msgITfIweVXvgHhqoKqAxxTL5U+1O7lE2gIYZqNfYDVjrbXWAhA4J/ANrIszk+RtMPT0PHJqxoMPPkiAgcNvgMB71mzhKuxmCi9IkYdznsPnIgemIgB0UMX3nZfeFkDFsP322y+11FI0F8tTHPzJVzCctbOsOGfID2Agzqxuzans0i6G60PfriWCxiysCYseo9JN4fa9F0xErlwmJVoGVWjI0p4B6Ywzzrjppr8Q9etf//oRRxxBLVpovddbw064vAViZDAlyGbvckgDeYPpHyxQJpvh1AaG5JNPPsmbllN2YyrFfsm77rrr97//Pc0EaxdIYw2XTM3uSLBR6KCSyshqDk5eddVVFA+Dx3E/4Gq0cy4AABJyBFsCUXmwVUcNaSNroE7BgbUAqN+mV9Sj2pdmVdH93nvvVYqwwQYbiI4cLk4cEkRmkgd1+N73vofkSR05AZFf1KLxmZCcLXZ+dIBm5UdOrZQHNnXvvvsuHNK3vvUt4AbZlIXSOhjMi7VGCX0coOK4lCXToNvdrlcQKISdUivJYNAlewcNnMnfGjPnzFJ3KGU2UghkrZFkwyyNnj3E2RPwZ1BdZHW0aVOmTJmLoiGVyGDnnM7nkugc5DGLZyQ0THKnxE8R7G7tdxYfSt746lLJ+6ZKGMfB+Vx/IpfJZQbyiX5cP/b0YJ47auxo+urKqy7P9M+BhqAi0UlNDDE6V0u3gMwGgmJaCQgO3hmqnK4asawVQjP/7ne/gzVinHIoL8VZWU4D8Z89gZmzYDbbbDNtq85BAA3jjLtrzBkXYnNmXDc1nGGTCTmhHJxk4th0003xv8sCEky7xc8mM28gOUpzVrMAYOmll1ZpgkkBqBrIqpMk5i3QmeNj2kEMPOR5piXkvQ8//JDHddddF02kqBjMyYhqRBAJ9FAHNlapRcM111zDIYjA2eEXI+mLEArVnm0JDklbY4kllthqq62wa+CgFgYO6MqUFqH5Twh91OpFKJ8RIdPTQg3IjIDG+emnn8bQlHbDRmDkqFFM5NlkavZA/xezZyW7uxLd6SR3V3e21717enOV7+5cT3euN21uAuaWNxqwqUw0ctD3NhoxTfJ8T3e+J21vAaAnnepKp7rTia4ewJswceKtt9+imybQYK66yqq0eWfWaCHEGwTUeJpxDQJwEJ9g2BiPv/zlL8mcMLI6BM0uwLTQBOptHMD2PnbCDbWA1TU0lNqnRDhWe+Tvj3z66afsucOdD/9gjSLsX5RxjBcAwC2lHjyEW5CqPoZ8aoRONpG0QOfY0kiafehCcdkCXWCi4pyh++67j9GIWz7dWDV04oBjMImiZTjppJNwGMumLygXZAL2F7IVcMn+ZB/SyqnY1LTbFeG0VHtTIoB9//vfZ9S8//77bDRlE6CuA9eeg08xK7EX1qbdpzJaPRtWMPiHwAlHjjKo5Q72C7n9mRdQNzMv4IWEALsnHnjgAezLZiUSc7IZ5L10d/eMWV+mk+l8oiub7BrAwqE6iEaGUCzNQa0IOUcDFGhXERETF8suAovBA5fzmPN84j0Gw7Iwlc/3DqQGZn45QLeeeuopX8xB4zd3wQUX5EgzWExq0RL0pHr7db7U2wKKbN5URQjm/eBLWDUgjrmZ7mvjEFiGiS+5F2cC/4PtN6wRS8fbfnvbNdZYAyTH7Ijf4oidp2HSArJvwuwnEnQzpoVge7AIP0jLQmwxTYUUb7H5FqJniIEpGTAw/TMR7Lrrrueffz7WeZymtMUWWwxSi86nVmwBnxUNYhokSyvSFITrP20wyDYsME9BluJT3iNHiE8URiBbzdUfD36zfMq72WxUoYCKAQYXEU6nUghYh3FstmU76f1oAVi9jTbaiCOaX3vtNZT3e+21F5iJZT6zrB/ZD5qH7saMjpkYFLg4fkTLgI4SMiI8R65zeM0QfdTb00sMHPRAhFExbLPNNh999NHTzz8/ZmTP+EUWHrPiSmO+XDQxd7a4Y0h0YTAgLkMGW5L0ikCEDRNM/PK5Ut+4WgZRJVgFhvclwDnKCJNzviuR7Mlm04svvczzb7ye70qhpD7nnHOINXPmTPUBNkSFh/dnmqtV1Pex7agSawu7wc0vgHVVFikOkQlzBrQM5Hzsccfya/VoBGzYr3I7+bRSC1hqGTXQzLa43IoJNkLfYM3YnoPLnj/84Q+Er7322o6iIWoc8b98PxUNolkwl/doSS8j4z/4lXNUlaFu9dEw8Wygcpq4vUXvyKiD+YabBDZsTfHMp1OaBTUSYgFUWi6B+eabD2AAUgAzbiN4qeBFApttGQcG88fFSnlQJsMiqkbr/LZfC4wbN26llVZ6/fXXUTqw8e+www6DAFjUDaO+In15US+MMlurDIgGpANCwUYAfjnBtyN5VutB2opP6sZCmTOkmq9+9atsoJgwYcK8o/o+nznrm9ts2z939pdfipyjsx1pCCgWDomLGpm0omeoBofnvQBU6dL3fenEzGyih1UHNk1MGP/fDz4cOWbezz+bceKJJ+JtDuDpazY3qY9xxYRKmQ3rd9ArvRgdrPhhwHLeeefRVhHZZzXYF7gEJWVSxH1WdxvMpMlk4LNZAiswgV7sLbxtqBgZlThaTSZnzZ51++23s+7CAsxyyy4HkitT1FCunUSdFvC/BaAn4Cf5gq7+515/jtYDyI477njyySf/61//Yrsr2TA1wLnpCkT9uQ7rFCrd2BmcR+eOtFX8UTSgWbDKhUirU6Xw8vWZKhHj8xpCAEtxxRVXPPXUU0DFmUPQCFaB2E8ROZDK/VgwlAm2j51ApwWibQHmJwD49a9//Ze//IX1pX//+99gbKgzq2gZgreeiLaVmysdE3o6BWaCQ62ee+45Mtt8880fevChMKxOmoM8/NQ0lBaK3EIgMzej8vmZZ5557LHH8mZO/wDC0rIrrbTQpEXw5KrmIaLEyefSSewLM6hzKoJtFK9GznKnSDkpAreRBSMFTefIYoXVYCe+vJeXbnIedfMF2hDAAHImrGOOOQqXkPk5/SP6RsJN4qZBHdpTC6Jhwzu3fy5SdEUIh/NLSJZKBSeccML111+PYSMX636t2SZe0T74GghCpsDMnPxKOIgiQW8uu9vrJz/5CY9YG2299da2uA53ZJti2AaEQlYmwKE2CcgZann1FAZsaMzhBNg98fDDD6+//vpwCLAH9eTRiTtUC2j/R4GK/igahqpf53uDLfDll1+SEoZsySWXJNA3si9UealBqOOerMAuw4AESHtZQ497U7QlfKjJmaWQuHbYYQcs8a6++uoDDzwQAwerPg+l1jDWFbs/EJY3lBr5XAhsBOyF+maHrCEzd7QMQzax7ABKpy+//PI99tiDAJInSRDX+V1s8cUvvPBiAiylusIPSEgEfgdhLhQhS9CSJNz2KvnKe/vGBmxkCShTq7PVJZde9M477w3MmQuUuUz2j+b68Y9/fPrppwN/PiX6pqLEnQcaytiwoC2iMfbdd9+LLrpo2rRpuF5mmCyyyCIt20KKVF6cGRzN/KmozvjYU2hh0OVBxkPtRYLeXAw3CNfnn39+2WWXaVrcQKpOkK+159aJ2XYtIFaUllSa831BQC/Ct12NG60QDBszwt13300GeJdHW8fsxsvWst5qtPbtn85L8du/tq1VQ6auI488kvH2zW9+U62LdfZqrVp0oI1tCyAMlFyxBbUuwODOGTUkOeCAA1gSZLqCTbdahs4SU12N6W9kbXxYc3aeE8aVhubP9pZNNtlEtzf7W2Lr5sbYLAf+jjvvGDt2LFoGK8Ng7IYjElZtL7roEj2PFrHH/cr8zloCPnr5ZURUvJGFKt7eyBUj6EuAtHcBXgadwnD00UezSNXb27388sufffbZ48ePl3X5bJbDMhiS5557LiNUxelC4k4I6cRctAQtyUawY445hvaE/4Yd7zTP0C0gJjnmLrXNGTppXTEYpAMZUfMdcsgh/LImhEsgRmhdmXQit3kLeAy+2rymjVYPXfPGG2+Mh2My4FRyJghmB3cWazTTTrrYtEBH0RCbrigGBC788ccfR7fHkINF47Ei31mcKIwnHfz8ll9hFO9HGYVtSxU4eT8K8OYBo+PwOkaT7Q17o4UeVnQqQSoeS96EDpc/BTJLMWSQanAjQgB7Y9BVa6cb3f0pxmMRo0il2RozGUhrxduvkmvNxyykSOSUHCwQ8QU1AwIUpmxveeaZZx555BF6B75c2XTCEcMXp+KxU1DNC6jLpjn2AaGdQZVAK6lwvsoqq9CGrHKfddZZm39TPHWvttpq/Dp4bv54zGoGURb49alC86204kp5dmPkk7fddtvBBx/8n//858ILL5wyZYrCifMUaod7SI5w1sQdZZO3EelDPLfxBuMsFhtAiVtuueVPf/oTqjrOH/HGbJGwksSwgVW7RfYEKQUE3f266CDd3vLoo49C3CBrHBNG5sodaSnesF/lhpwP1cS6tkOfh2x2rGbs7fQ+ntacZHgyc9yEDZlPEBEUD2OFjRYkXXtQp2//+Mc/WCjik3ILQTRFJ8+QW8AdAiEX2yluqBZgPeOVV15hsKEj32qrrXgk7K+MNBQIne8+tkAcJSiRRER/Vbh8rHCEWVEfOHJW/zioBZsgIGH44E2NXzHJ8/3sCeNuR+sLR+sjF+tjG6YKtu4+5lp3VojKrNDSEaxg7LnnnnASdBaeaMiIQGejfkmD0kocHvznP/8ZXTOiJow+GjQakMWfxx57DIaM9zQm7Yb8+d577x111FElOVTRdqm85/0tSef9VDFcEr/0EchVZbDTzjt98skngIpyQc/mZMkXdQN+DZdddlnwgYuTmznzEnXDW2+91bGV9TYlk77SK6RZ7IrRnDJMsG6YPWc25490lDLetioPe5W/5V99ecOEwgDkeCNOByNDbOj4VSdBvuQfh0wQAsE0BqZu0YoDSK0DA7snzC26XGNi0zqghwlp/4D41TrllFP4Ze646cabCCD7hAlDp6zgWgAeonPFsQXgJw499FD4DPYwr7feeqpLhufgfRzB7cBUZwt0+rHOBqsvOswfY4cZa7fddiMlw+eqq67673//S7jT8vU1pd+xlZT97Gc/e/HFFxEyETu33HJLGFlYdr+LauH84OlnzJjBaXn4FmGL/vvvv4+oSXNts/U2CJz/+7//u/LKK4PheOdWfObTxIkTEQa0eSOvOVDpSi+BMWPGrLDCCrlsTrUPyC1c++23H+YYqBtWX311XbnCumGJJZa4+OKLIwc+hgAwOujinXfeGdiwc2Q3JcxARykTeU8pkv/qV79C/Qcwxx9/PANQZx+Gp16RA9kMAEpPEPlw2qqVbSa34Zw2qiNXWqLNoW/MFFg0LLfccsx9p5x6CgOqzRR2LdERAQHpj6JBjYUCAtGHbB17dR9yCiELZRwZbMxSUHncQDLkrJ0kL0OAockiWgLIJuvYZHKvQ8oms+okL2kB0A+mnAuOfP755+c4PeWWnnzySV4ymnSIlaRq4LE69az+pYFimk5id08ko9ssSpvrxZo854Bg7U+nwF5gSM+vFUqbrmubZACigrr33nvvRx99pFUCjW+99darrr5q0003BYcRzomDDJ9OiTsSLpQ1iuf6GPkv4hY6BToXFQMiMftlmNTQNWRzWUYoahQ6He8Sf3/475hsrLHGGqAHldp7770jhzyGAGC0QuudduppuOSgSbEQwQ6Iho0hqFVAiowkpvLBriS/8MILbGvSWtNNoL16CKrSDi32WqrTlUYnePbvz37iiSeAnkHdYnWIBFw1ZJAJ12K+7qINFhsjqWvzhTKXQf+xcVtggQWYBTi2GcRrY8VWNIRbzGqa76tGcrBjoJHEJWms7GT1DvZNSUwfH2FZChermOhdObI5xb4ofc2xdubMWF62gojOYFM42bQMY4EimVP64NJiNeQKDe4JwTiigPzss89gg3zs3/KsLMoOiV3YfpSYf4RgS1kAOJlJcKtLKt56tF2mi9mPrlNORDTHbPwGrrvuuotN8rvsssv555+v/VmoQouHRo8ezYDiZDh+Yf5uuOEGKqSST/M1Uzy0x/rKpgkPtiVzKW7HbNLyHOHK+egXrIqB+qqWAdLYfN0byAHUYvmdX1iKffbZR0Xi+++/n6zU7VM7cecNtE9JEjCWNzQUXNd2226H9cell1yKN3tdxOaXqcGQkQQHTGhaZHiYMw3rQNYIJTmH86hFI6LQ3fzyCGwKPG+Yzuadd14kFua4XD6Hb8vHHn2MA2IgRL/4xS/CgbDlSqH1aElkWlAC4LEJmv7JdFROWpHYbqOQAyY53En2rlvKQ6DiHVSfGAa7aLb1Ft9kqVjlQNnAauwa+CW3NtviyrYs7GoxsL3kkkuoHYrCJluszZLDiNrbVg05hG2p9lED4bN6zCNMtTqblAATq0eIG3MBIB133HEKmD3DJVZwNgOMSh/86lVEj5rJtzitoqJ9ZzEzfNyzMBCA3vp5eStJOF6XR9KLF2AuNFAE5RsIsMgDdeAQ8kmTJvE9VooGF175q9QBgBHqYCWxfULV440QRDjwAnwD2h3dcRsLpoKjRo3i79/+9jfsZf76178i6em05FvtY5ARTDljav/99yfAoYBsAocXZJ3VT9C8fhlUp+Bn7s3mlY8N3VMXDOwwZz2WWrGIveGGG9rqRSgVWxjiE6A1IK3f+c53Xn311QsvupClHoRMq0fwwlnSbiWP3pgxCQMhF3VRNQTjEU0T6hLW6nFLZBnNmEAbQzCgZkDFoVToGpQxgG7DqfPLFUOAIwBJ1Kn2DrD8559/HgU9UycofcQRR2hJIHaARYaeNVvboEWgFooGPJSDcvB7oUPRogXKYkMOljiFMoJGC7vdkCNQ6dKDenJkbBuR4aNKOtzQoENnQKnNY2wB7gBWVwu0jshWV7VaMzKDjVUpiDhuvXR70jbbbBPzqkAdEOSAnKmIMItUzLIddmfIXotDE8Hrs5z43HPPIYR/5StfgX/Fs3RFYWbI6sQ2gqq9Fl10UeoFlup6qWXK6YU4dERsW893wFD0cHgVbY5e8vbbb6dHfC+iDTKkfSCkkFOmgylTptR7Wl7LtSoAczHxYTfLbNKxzR4ch5Fs2ckMh4DwMG3aNJpLp2BtxsHTdr762wLMnjQ7svdJJ53EaCVzTDv9LSLy3NADQrQBA8MNjr8hwG9n3qyhXyqJV+Hq/bFDgTO/88477ebrGsCOJgrjCJrGVvF11lmHuQAVXmyttKJpoFYutdJIaOX6VIBdWNmw9YgVwKjtFQMMiYiVCqLr7FVbushiATBwIqCyUMkK+cE/Phi6BsmIDKBOwfW0AOYM7AMH5fD3q10Jz1pPBnGPy0op+Mlak+Lkddddh6xrXej5Dn0H7wdpUhCMVWsi0BdsDRs3bhxvVKM6SKrW/QQvbq9qtdAIJV9pH/CWT7SPfqKVoKsl0So+ti7thQpRZdQrWveKtRvkpW2rQeK0xydINLoG6oIPmq997Wvvvf8eIi4GDrRee1TQh1o4q8eyhuzd0UbO1nTZh1ISCYyzaHYWjfHTyaY8GCGsUH3JOT6ZQFI4VZfhydhUTb1udosPhLGFxGxUhK4JgFg05HFLHZY0ovRQe4oFy8033xxEtVfcWgzAmOOAGYARJTBqAEI9hIJPhBXyuIHdgafGFmhjRYMZ0QX1ocOx1dguUUWDmqPGUzMnBh4MBL+WOuiQiwq2iuUCMPNr38g+5FWUpkA+ffp0YsYZZqRBe1es1PB5edppp9GDGKphxE4/EkYsb7/qwyqpJgUeHY/9Oo35W017SDtchcvaQl319hSleyvit8PCA6KfQThvsoOI8XvCCSc8++yzUAbcGbKfmU7hgnn1s7zY5KW02pJB4AL3YKS4CKuqS8ODgGwRtS1HZXnFoT9cihjlX+0bbVV9pA1Z0pc35qReXnrb3CZppwAGWV//+tePPvpohhUn6eB6BtTCLSgVj3k11V+Dylr6GzTASoqhySLsuZRZBb9mitamxqmtanxQm06YMAHDQMYp2NtMzvFMu+wyyx5++OHoOjlVF6dOPd098Ue2aFrSO7+bsOgauLkKwkgYoEEPb7vtNk5Hhkp89atfVXMbW7AlkjHpR8BQ4g+ECy20EAQNxgD4AZ4BpUC25ciyPdLegTZWNJR3XGvoGpi9GGZAf+KJJyLAW6fi5fWJwxvmHigCm95XW2014OE0aU5NjwNg8YAh1uML2/V77rkH9ghzNbZOQMfbb6WCSiHUgQwHHXSQTmannnoq3Hmb+euqhu34YMNXDr+oPAzHEzYTzPoeciD899tvv83ZbwaO1AUXXFAN4HZ6D76J9GtukFBVKnB+oJ8daMpCScxKUqK+11+SVIzTTi1WY12U9cTbP2oswmzWo3n1MIsac2jpaKqf+vGPf6wYxbBCf0eNhglNi0PfMRLhfM444wyV32B+WGURVHSPgIkDkH7BQE1xbbjwwguj4UL1ib6YEae6Y7+KaL98RLelC1pI0KpoCKuSyvDQZdOmTaNMuozTcAl4Z5OwYKm1HBk76TRzHAnYF7bjjjvy5qmnnsK7PG9U3VBrXp148WuBWAtC8WuuYCESbimTPfbYYxljlMRgg6BzB1tqc7nD68D3MAMBNgaEhHGLrZxQcxkHmNqsbciBFKEsAMV3iGGEoq3MDkxQzgo/ATZ9uFnrzKo+CEHUH/3oR7zhvMCPP/7Yd0BEpDeZuoI9D7poV6zfZFnD3r4D4SmyuNQASqotS1gEpWBTp06FLLBqgS3D5MmTa0vdPrHg9j788EPs21dccUVcc2E9RN0YdFztU8lwa4JPmbXWWovphkFN86LMYvbkDheKCEpTGY/Z9uSTTwZ/4MXXXXfd1qi4JX02EEH7NVskNqfQsXfffffSSy8lvMQSS6y66qpgIH0Rc26tsZozdVJfNPWIgqj28CCGBKuMX2MZDpdUastQ0DIYfgDMD/gCFSmBBb8jjzwSqgiRhD6orC7zjbkCBqGR7F3QZELcaKONYEdxTvT666/zSC0aybGTJjYt4LMU5Jiomeo1b58Wm1YKCRDoOKYBWDQwezG0FltsMQqOOQMBeExCUDHOm1h66aWZfi688ELgh/kLqdVaoBhs5+IIJX596S/mIXZgtp+WgRa3cyoiLoi63377zT///Mh4HEjGV0VRzqJvFlcxGXC7V454CJ6TcEsb4q+eAWRP/5FA6FItXQCUWJHAMYBsLK388pe/bLbBh6h3HD9T/V133ZXDIzBnYP159dVXpxFUYowjuLGHifM4HnnkEYS9HXbYATYa3yu0J65t8e9QAru8b4UD3krArvZIdeAN+EWcOOCAA37wgx8Qk8drr7u2WpLOe39bAD6NDP/0pz8xflli+clPfrLgggtihdqW5gy26RhEHEALPWfQ4eeIALaQvOSycTqBOLQA1AAw3nzzTfhwrFFgy0FUrjjAViMM66233ssvv4w/SGg7SbRGNabtRIthC5TOys2AaDUL7IuzW+OayXC4pYURhxwgsVNxNs/rBgrvUXzKtcekWRj8XBho8YuYig3hN77xDaQ4dk/oxuzI4SwXrFT0Cgkw0WR7xldBsW3Kd74WxwkeMjpLC8ERMQG4BExw9Y10p7mChyK8EqgQhSGCIIGsuMKKKO+oMhtrv5wpRtd8BYEbg0bZK7MrQTIAtWxYSuRAD86Ptwt3NtBYYYOmMgsljvnEoBEj+AhNo1SWVviF70HjAOsDlaPxK14RgOhrkda5N7VDA8sK58N/f3ivvfeC20MwxqiB0hCSr776aiIgpdjCebRhAhUbpySON/5wC//mN785+OCDwSjahCP3cMWHwhSaht5QLL1NYzpjnyM8ssaJgyFu+rJ1m4uqgVdaQTQOKE95pDp77LHH1ddcrSp+lKpuXeVv1JVV+gQUkUqkhQm32fZQmnb//feTER5t99lnHxqZaaVcydVsSXFKD5ptvfXWK6ywAqOJPSNgIAoXxlpnww47I+w9aI8ZZq+EDxw0QWMf6Rqw8aqrriI5JOLnP/85v1ze3IhjL+/7qMIA4y0aNFtkkUXYraMv+RoDOuYFML5h72I/UOqj/EYKsj+lW81CpHVp+cIZTrCeDz30EKte8A2I7tALXsI8UbeSoRiT2nqh2n777Tl4gumHbRSd6ScmHVQCBpogkIqXhx12GL9rr702C63KqpbEbJtHpihlAdEpbLfddlSWkYVrK1AXPolHLw7XVWuHcTZ+EDShqBskFClLbUBxIXGgiRAgrBh0DRDhcPnllwegrrRsxWzLS/fpUDWUKbB6mG+wZ+Syyy5j0KE4xgvmBx98gPc+2kEVLnGm7XHuIDhRdPF//etfYUYR/BjCTz75JBPQqqutirM6hVz3pyhxa9f5aM0117zllluoPkMMngFxAnIXSwP+cClQQa0QiHT3yiuvPPHEE6AWRiXMJgzt3p7ehueROA+0EtgmTZqEogHvTg8//DDV58KMqCRO57GsBUTOQthDmDboWPbd7xfs0VNl0JQpUyYvMpnsSxQNfhcYbH6MrHYdXCUSeMljo81aZUjCm0akdvapXo22RyedtwUYS6J47OnBqAHDSHhT6DiP8A0tMcyWWmopfAoCNsZ13nrFLax2DfobN9iChkdXY2699Vbd/MYyIIyp2s4EXXTk+SNssPrEGhTrflQfRGVwNakpB4v08qKTeQmtr3gH2wwKhoXKa2QRbMFVcsd4hBPgEPk22WSTI48QuwYwMJaCUJUK1PlaLVRh8jgHbrfddsP7CfUFzTgbHBUDh/Kw71SzVPF4+LgwrLMhB4vOmAWjMKMDqbAQhpphqcRcicjHxkM2grFnngVnVW8RuY1RDprGOjPYBeLRAtdccw2/0LfBmi/Kb0oSvRB4iaT3fazDe+65p7JkmHZjP8IE2sY0zdsTe++9N4thrCfhEpL3jKxUMh5CRKkEBVSVAWPG1ttbr2phG9md56tFdN7b+EPEC/gzS5WKnxtvvDFTjDJ4vLFXwOU3kn2cYRuyPmmzK7XYLIODdXMp7uJlJ8VL+0vOuk5vV+vtp1oCCpjGlHDSlujS1Xjs5AVCPy8aC72dXj5n7SeY8c2LSYuVLuCDPWLg8Ys4xG98IfZAhod53TJ6xx13eF6HGrToF2qpQxcWo9EAJ6pWx/DlcOSg2dDgt0UM1l7QMpx99tnf/va377vvPirug4OG2LdMJLoGqJY2DPwoK2A09e9//3s4Hn0Zk61VQXTdFVdcceihh/7sZz9D10D+tAMy8DvvvPPoo4+OHz9e3/ALg65UHWk5CDDaPk/EG5CKi5qyZe/FF1/EugEFPcoFrueee45z4yFunKqjNiOtMofW23HYaoFjJ510EsosBhq6LY50gbDXm08nfu0tAC498MADHJOMfRwHB6Ji4GxRu2eq9nxaMSZaLayxIODYDXEoAFVoeO9h+NWHOWTJDsKhdyHU6PbJilWgFO5kyRaJcFl4tkswxQDe7373O6WT7UoAK3ZBJC9xWEK5tLM2uPwaPEMHUSKVWJ0C8Ql7L/uocQb/9SbUsCCeai7QbojSoTxKNG/8lH9sG1G9+NQwmnZtqFTB0ZQ4/4dGxJ8B1eGkFdUwqu5lllmGWqArOeroo3gJk6dLfA21R62JRJRKygBTbJbBaQzVak0faDzmGzbrywZ+A50z/fg57hoAn7MGScWhXLvvvjtnnWDKTq9VvBrIPFZJFDP1FwkE2L73ve/dfPPNVJawzATDRs8SZr+w0Ir8gy03LsqRf7BrwA+iSte0PAtiYQITXFkQN10sorIsI1988cU//elPUTFQU2gg+5JwZ8UaO3KIhcEKgSAe0dD62U+dQO0t4F1ARsamVdEs4KOOLuB0NN7QI2gf2Laz3vrrcbyOUgDsIEA/tPlakA3UXm7cYlIvKsW6OposcIlHdusAJGhJTbW+cYM5fHiMnRcDLaVaV2vw1TAkjNzNNtuMPVCjR4/WTOyeqYbzbImEWNr+z+H/A6cHpp155pnADNbpZBpn+HX46w7KFNO+6wPHwuxVOnjDjCgERZEVyYL/FW/N3cRMYYXsuGwA2wTl+LWSkS0u0ABjn+1UzE3YFyvP095uSgNtzKEzd90fMAqUl2REpNJpg2ToEUTgR9Og+CN6LoNOglEuHiqC6a9gmXnvfVktLFmBdTaJwcN0Ip9Gy2WwkXJJK1hoPoH1Q1cnmBhtyOWYtnTW02g0mVTMAZHNzy7BdEFRruCDPhNQ8q3cQ1GkuD7AW+PnbMqUKQD49NNPw+qxhsmcFAy8QsHJOZIF2zpqpF4ARdMZl3PX8P4IUlEFhCICPd09ZhZ1EK+OqrVaVK21F2o73Lwv/QsbCi98hnND8CVzo3vSX5TOg9zemNXi856BoLeyNW6YkqQ45XKYb0JjB5HfkAPxfXj55ZcDAJsF2LHSBkIddfFecHLIG1zsiUC+xVjmhz/84YwZM2hnBD9W/JD91HdawGjmBWq4h/EKiXIHpxgrr7wyTDZ9wV56XGNwYfLAGyYpkBNsJGC0YQVWoUXbTsf1SiutNHHiRCqI1fSf//xnpl3W2EHOcqIXcjWFHAkVElrkUiob4KX3lkW4wW5ZKC6+q8U30SrUVJT+Akkz1zAfztt9ZztshUCtF154QdWs6LOaaU//0sqiTgGjTFhXPZM5zoPKu8ZNTIXiVymZTYpeYNDb4K3kSaBqTFlYddhRT10EzXTBVX5DXFvGgA5NK6UfcsghDH9oAhRvmCOtp1+CCgqO0ecG04Tbz2WSuQz7JtA1CIl23gse6qU4aR8HeekkKPvjps2yosttCpWCErlsCutRgUFuoZnyMo8QElTlh8pXBkPzlwp7VuQz+mM3VyNo0dxh3GZLjNkaTatmpXXNBhVRNMFyGD9tLaFuoO1AKkhDa1EHCBpTcvwhAABAAElEQVQEjlPigZ9FJHQNPMLSeQeIixZN/7XYVpaTDGXc7ppfQQDCFf3/u5/4OvhNDvYePKZ+lchF+7JMcn0jnyK+YLjRAXHQ4/bf2V5BgRJGDFMbFu9wzC5vLYyIw0O7egfhXQa9iVByl8f3RvB+7YIFN9y2cFow+mLlE9IFW4PszQqzUrD77ruPgnkZUvGhFMMIgrhxVvm5556LQ74DDzwQc2LM0DhtEXN9jj9gwRNAYMThyJXtCwWuYVFItWmRTlGZB6slZp8rr7xyww03ZPah/UFCrL5RALHWRxshdGhLRS6HN99h2KNRcaqJMb/azuy77764lGYnYwjmhBXh9xIiCZtlXi+lqqZNKE04KHkcKrKoJDqXvy0AlQPTkGD55ZhePLCCYzB+/pZSd25GjeVoGfJdqpRn1mPuK8K6fCqfUeFLfhERhryNpOYkGSxyJkHORAAntXSqQNFG62FrI9IWtIums6+CCOBHAytCiN7RRx9N17QBiQuilfzNM52QLZAMA5wyZAb6swNzcxmWWQeyEs4k2A6fGeADxt6ouAn4ctusCNiwzVkKHcjIjJhJZDL9stITnUJQUN+Xy+oakOQZSFzIl8x+vmRefyYi2hkQsqwjw86HyGbXD2ylFMqjV/oS33dQNC52LULjCPztb38DuYMTMOhjxTrZHMUQF7KuG56F1kPxhdCbG0bHBqqqpasrtjVt7TlITGMyZwLSXwCYS4hFQ/hqBkaiF2POOeecadOm8WbHHXeka1jW4/xUy3Z7Y3bCtbSAIXWyTaCWyEHEkdUS97KMtQZExWC+8phmTUZU2m7UIP/SJlgx7LTTTgRYYV5u2eUoLSqZp/mKVutczm3Blyr2QawgMY7Yusyb6667burUqRTKTgqoHwvLBErGYPMgdXKo2AJMOqh1VLWNaQmHUNx22234R0QTpLoJDsJkOw+HtLN5Cq0QV8V8WuWlEh/IOGjGnItRA34xAR49y+TJkwlQ62rY62cdhbzgK8JMuCZftPx6u6UInykymF424H62f3WNqmilyn5rMABlFuKczdlV98hodYM1iFMytFdg2mKLLaaWqltssQWPsVmogM1jRINmXaJfEGuXrlTeuZPZNHc+x83qL7oDRMJ0IjvEbeJrKvmtGh/7CCkRlV+aZS5gMFoGmX8N2ruYH0pXciSK+kWiNEhiKGUO90Kw3obLorsxlUnkBtiel8hm8nDWrLNmBxLZuYmM3Mms3Plc1Tubm6v3IHHMp9n53OxkdnZCbic3DSRycxPZgWTG3AaMRFYY1Ew+K6vs4a03FaGEnxOtVMNcMtAMR0tAxxukvcBsOYu61ci9Z2BUn5CKKuE+uKt2pjSZXtBYarFCenKJlCgdomppF8hB/iozNEiEGH4qgZnm3muvvY466qiPPvro2muvxengyK6RQYMtuJbizLwR3Ym+bJbtSNLp8pJfFyeDhqEsfxBNfLGMGNHXl5Yd6Wpco9Gw7iHgQfSy1P69oIN0DGhPnXzyyZo3a7AaYCpC7U2ckq70D4Q45uRjZdUgc1TfKPJkwWd032gxqk/psdWi5PR0Nfw3jwXSZ76Wt4+LGti7FZvAVI5viC3zHNhuiXCCNdtUMpNFC4fKVUrkbxoS2bTlcDm4JW/AJVZZsd9mVR99ln6FHy2J1iqPMG2MESolVy4/kBnA3SPbsz/99FP0C3DeG220EQfunHrqqapMITI7mW3trFMG+6YTCK4F6CLdia1+QBiV393+u9y3//V2TgBB0UAfYeyA/gsHikceeSTKCPoX9URwIAWXMwSH+nrzx2TjxBNPPPyww7URvJ8CCWvhA4k0tnxzE/2z+ikFkiWHHiLbQ3syQnkUyLxRtQsYXr4uXQS/95NqS4nuUkNJWkIPvfHlc/HFPok5/bMHkCm706xkm5wEHtbCWKUwceUxVVRCcRadp7IWgL7hfWbChAmvvfbazJkzOUkHOk8X+zillpVZ04uJCy08b9+YEX3z0KG5hHhjIZkKIDa9B0hBq8rzqY0t+OZ5qCE+q5uaQApCMZHJ940YM2f2gCt2yGb5ohz9e0DByhr6Y489Bu9NrvhFYnpqUcpmW0X1pKCcfYPiPpZTKo7G5x+RTqywzCSIIH0s1g0u0CVdXkzynEjE4b3QI3PZtO4L+evNhwhELsnKRlDimYUnzCf60omJEyeku2jFyCyPfFI02JlDAiK4CN1hcwqUHmWKkfYcradXy+DloW0OMhvZRkZTYMPeBq8choVWcY5EsNR5fLik2JtkJjaWuNFoVk7XeetPC6gDKqgALrhYR3rmmWcefPBBjFe9bLc/JZGLjiSRxBKYJif6E7O/HJg9N9OdHNmVTHHLbpniYeik8CBBySxSAlv5JFRHfMFtYWZy/V3g3YheGRGoOpVuOmOhDtQuAa3uR8ynmHJOP/30999/H3qD1Qku0wBJnLcbRzE6K9ed77BP0JXuHpg7gBTNNX6hBTJzl+7r7ZNulrYtoJqyO1AnL6NcWOIra8ba41t2XPPw6rNYxIP0jl9ygUmLTMiLVyJ2k4UkUGEm09vVy8pqWc1a6YUOCkYIlI0VPDRK+LjFjzdDSXm4XXfdFXJ33nnn0ftIsLrK10o1bF9YlaHHU5ZWkTMvOWsGxxmHH344K370LOoGzmhALD/iiCNAVDo3OOO74JrZyi3UiCpAzNGe2OLCMZxGwGFOmTBpoUmLTkLBgZKRfwIYq3qs8ArLjX5THF0XSKLh69BOFHg9AbowI3rJGhNx7fTQ1p0A8/Vnn346fqFxHPjS0SZ4W6bhsDIwJOc4IWzWeOSUE90m1nCefiXcfffvjxk/6osvZ8Bf4QYPDDRoo7p+V72lii2DfoXpuRgCMYA1F/jjnU+rxvdIe1lXmyZO+ljkzibmGTnP7FkDsKKgMfk6eojiEpt/QsvQP9APp81+MaYnBj7EjUDzOUebA30I7yq6frNAAnmJpZYBGaTr4vPPuuovN82dMUNkk55uMWMWxGAjD7hnFQhGW1CKSe5X0FJQz6CfpLXv3U6wIrP9qutG+l4KIn+THB2D5COahvkXmjD9jVfmn38Mql6zGG8iuFmG89cHRQNLZDKr5LGdk/mApmKeQY9CiJZmzKvy2KFQTkuZFrStRl3NyHfr7GmIovfu9yp/me1ySbEPkdNqUKunM8DVzUSX7Enke3uy3WlGeWQ6nSpAt9Fr+Gxhd3K5rbbaCndcMN/4nMcbs3Y9fUFdiaCB5uoN/shQM94QUpkB9sYl5hszcXTv+Px8KVR3/Cv3fheqokG4HEHy/v7M6HkW7Er3YXBhRr7B7aRONzSIB9Wba5HBU0OpodcY1tL4DBM2UPDo0G6PtnjwTDpfy1tgbv/ckb2yfP3wPx7+ypgFZvXPEiuuRJ7tcbpZxkFURxltkLY8lwpvdC6qPX6FLMCtnnT3nOyAePvs6srDY4njEiabAIkggx2aDylAOAfTWprXYbBAr5CauH/9618fe+yxcDlUirGD4IqlOkLdyBGO8QLkrkIfdF7FoAWQwPlHB6EB52LRD7ednEmBtghtOPLSTTfdxLmkKhLHAN5GQABXwVJSMu6oqU6y+ttIdjWmMVSqa2R65Oi+m267cdTYvtmZORWTElFv96tMfGaZgAUp953716vi14/KgMsGxNou7Uo2TkPrEE5mZGeMHjGPJnXtXmvLqBOruAWUJPKLbg4XABg1YNvFRiS0eMURQ3gyvJMICCaQSLz88rQxn3R/8eUA0gfMJrcgkjOFOkgG+6P4JmJ/DcIFkRXnpJiqs3EBLfENp1a0goHIP5nEqL5558wyg8IUh67N8MA+tw9IrrbDr776KswefmFbeubV1hFpIptjgxv45siP5gNaFeobOHGrsYsMn9+LMUNvau7H/5fo7hJRf85cwRtBP6NokK21BkmUCDoI6ynAopZG4It9UxKWRAV8k7BNIqXocED2Jj1W1aJomP76v8HdgcyXsIGSOoqrKUWD9j3Ay9aoXLY7l+iWNbRkL39TiW5qylhPy0RiZEzlbj08rlfRoJWvYeQP0kqUkk92URyEJpfDpzRdkES868nLQl5S9quYjoistQeB3fnEEFJ2YeioMYsBUQAifiFzMHOEYePYQ8EBilAKWHMlfH5QBx1m9jfV1dVLT2+/xY5941KzPpNFEnBSkMEzVr197nldahpnG7XapKKzlI1mA974WpZjx85MM0/3zI8GXP8RNkV4AYi1qoE51RJVN+YMHGxJp/Dekh76RXswPLDiURK1bgYnFas5kbYn3YOZbm+iF8871MxoGcSGxTDYtqpeNLQvBwnUFL942nKwW+ecnq4UPoAG8rlu8ZkDYAEdAVOoAnI4AxA6RstIuMUvcINpjuU7tAxUBbtNxg6HaEDiFl98cR0yjCPFoiZxqcWbKr7gC1eaSjIf8YfpFQL41FNPPfDAA/vttx8kEUTFswbahzfffLMZUhB5/ZUf85rVhFkdypqdmeuyd0K4ZHKRMx7g8yBKBQ8xhkLp9F11/i1pzNpVDJpQTWHR9uYTmf9n7ywA5CqSBrw7a8mGeEJwEoIECX7YJUAguLsdHA4HBHcn6A+HHXLBDrmDBLfjcA0W7C7oQTiCQyDEbWV2/6+75vX06L6ZeeP9Mnnbr19rverqqurqajSrDWyszNwfB8PZ8C1x7anaR74yF5BhNLEXSWzWvv/++1IAyMSJzw9cpk8rKyhqp5vyQe65bIhOQzTeNFWvPEVfmXg7YPOK0Zx2ihipD54zYrKA3i+ESp+tE829Fy5oGTxoNTkDgKkiJndAD6gX6Rp+vjnsiU/DhFW+ooQBCT1auGgh9Plvf/sb/kfXWGMNnI/ytoS0DLqt0LLW9raDDz/q6htvmrUozMZRGB+WddiuzwWa1XmWW2FYI0UUVTx3W2ogbOLVa33ZCbw42KvopY608ArEiQgL9tpuRjGgXDB8kN1u9TUXnXnKvbfdpGM9Ch0toxChnBQN2kIJqtPRUBv+6vMPD95vt3mtjLPGGm2xQ7cVmJRmJe4S8Ot7ZJRaoIvRNVjxcWUkexQiouCupN1we8uihvraPj16zJs1s7Gmpa6WWacNQ3s166WgGclKzW+c8KZypyYWXsp3XUV6gSiL7+uVV14ZC9VffvmF08VwmcbnCHbFLx4zwjW9llyssRnsmV9XX8s/GgN+csn305NK5FOKylkeMHCPxMb+STrCSeJH0SBtw6KBxAz7/n0WnzrzB2VJAInUohdhGhdbYd6fLr74YjhpvsJ9990H7RYtg5l3DaDy3o7Sq4C+R0iHhy3SRgOcNE0mDdlxBbTyKitPuGfCN199y9YJ9kqCY3zkVNiSpsBMX2lEjeKSjedtbYto3XJDBl9/w3V33H2XcoysfFYVYqZBxqgALYN8CzrCPiPkNwxT77jjDigbFyPIoI1RDftBmEy/r0ufEQRSfQLiDULKTISqCHqII6FLLrmEg/qgihlVVIKJU/U9j02V2ZUpNFw7etRWYy+8cNZvM6iutlYxlqo9SpWt/HKzegyVlJbEUEVLF2nTLnt+t+fimLw+OoZP0FVXH3bDzTfcfd+d0qQIQ+AjLwMcRYPZfqLovLe/hrAUUASY+2h5AZJAAEePHo2y9auvvjr//POPOeYY/BM1d29GCCwWZGbM+rnPkvXTZ0xX0lbk0l7AvQf+MilbT10YNUSnVS+PjZZeXEwqCzHqQu11ne2hHq3zFs3XPhpqldyXJ9mjob4ByE+ePJkdfBA6PNSUtSjhwbaGjixcuJBplxiECMDLiCsx9Z9CipbW2l+mz8GK9Ztvf6trasaOql5z+KJhwMBAiBtsYbsWOEQGEWoWi5Km6ypgk8ToC2uFHqpKvFWOIDyOAhSmo2/F6+ngpXr+8vO8mo5uNZ0suBfHS2FOigZsZNV2JlquNiTVPP/sM5zfUcMcAxQVIAGB/KIg0iEFGv3K3O3xmyqsM3VxM3lFesQrR113TPdbWzrDixqbG1FA1OG4QV0mZRcl5vs1msirr776p59+Gjp0KP754IGIsRcl8t2AoMoHB7ggBMLP4RoN62LC0L7f//73xHORIIjq+HZKdaeK0uW149C1puahR+/fYP01F7ZzyBwoiD2NUjPY+mMZzzanwpyTpkmJKCKIm6oLcell64Qy3azpseJyqzH0yYgdp2q1gkNc8lSlBhAPrwcTcOuttwIQ5qEhQ4YQYBd9Q6ghTfcDqLgcihAIiIchwEKTM4KJZGETIRn79es/d9ZC7aNBPSLaFAAAGqujiGkPsNbWRSztDOwzYEC/QcxYSoMZw2XlsXVGqMtjHQUses8999x8880/+uijUaNGUS2DqICVu6ryCAGcle6+++5YN3AURR6rKUjRGRGuIFvUXjN33tz2lvalBi3VqIxH0S1AeJRtM9YEzD5qRQrhz9Ot21Ow2mHrXTbDbXPYdnxMXi9jmr+D+g/q338gCeYumpcmWeIrzEMQbLAEhB+Tt9A0Bj5bpYxiMTFXVcX07duXM3dOPPFE3OJef/31J5xwwoIFC5AMi4aHsHy1bR2hVr13Wn8KuER7RiQO1suS05Isg1qfUKRBKyLZsqn9Wpv0SoRa0QRx1W5urV9QNuzIyfziGhSbP9sncBJW5M033wRLcYsmvHe2hZVQPnCJzdcXXHABPeI41b///e8H7H+AaPlNK4uHb6YJWK6wZQbnAUgmbNfi0JN67X1WycGQLEXBvM/Oo3BhhpRh42CTuGihGlvtRx2OMnvURl5zRcK19UjlEFweIbwgfKRVUObE4WAy5zmQk6JB2SPKxgnmlKbu1948bsYCnPx21qttq8psTqlblL0u4y86nXjj3IaX3UsrpZ3LTpIiLJy9cjqkBdqO9tbG2pq+PRd76emnHn3o/nBnXV0TSp3oJ09RTEGjmcnwpsMBadTKyfMrrrgiWgZIRikMniwAYZpNX8iOff7YsWMPO+wwOoUpFzEBTdIgiR6teuTU44ejpua7H6YMHtb3hx+/wUcDhwerQ4yAo7fyQAIZzGZ4E6NwIYVFA29tXodHrvRzRGx6tk4pDGcLUb/FBs2cNautfSH2n7isb6pramuD/4qusOmy83ijrptuugl/GRBoNiRTEzG2gYkNpTy2oySLhqEEGgxDgCDicdbQaGvFH1NrK2qusPr0FFuYHscgnsVF0RjU2m2qTUowLlh7CtPrAtQCJhig9e3Td9ORm0qMjJ2s8aQALXdV+IcAk9QWo7aQL2t/cf8lVHvKTqV6w+9jc3OP9rZpsL8YDuMVD80m8JS7Mtb0pDp7GrUtCm1u2+ICYyZiO68fsM+ZP6dn/14Yk6vEiiALk991VuYCLpgW2XVIBvrCwFcKCL1FvOsiKjeFoYocP4H5KvoFTrPCuJ0jeIppsc/c501/WrpAy2DjkfoerIUppkwu/LhFQsn/2LyipIhPnyChoE+IlK3VDNqOHalPsb66XgrIF1fw4osvvvDCC/B4u+66K2v+NoMnTSrHO2o+Rly/fv3EsIjjijmfuMQGILjGZ+XeATp0htqRPNA51HkGXIasgXqQLx4Fr2xcSoUTJm/023kYrmM0mkffSagdtIep5YHjXEPaI3iHahXx0tT4DAV4zknRIO2D/mLHsPLw4QcdctACbEg0yJUIp8EfgaBN3lMBNbG7dq7EtwkxwFZ9bT2ZcGpyeyvuOWr6dqupb2h4+IEHarBlkGYlZCx8hGFoCECmGUtI5hjoivrc0PHCNyyoGqF36623HlthsV1EE8lJ5pQcpDWXcBxqoaQGSYq95+yWqGvqrOuO83C1Q4GNC8BW2Q7FXmLQInFq2GWiaIgtKf1TZM5Thy83qTaoL85/S/GRPn+Ab0GnZ555hqqZfnC6DrKp9nDYoXYeFmBF5VgUqEKzOUpg2LBhW221FRqxrEcf+q16jllVjnhUmUnLyRQBkhYSB2d7xrLLD4XqlRPsqN0KCf0T37hKKvlRgBYHaonkLvEYphLmQmiBuFUyOKqpb/JBkZNZN4tDgMoAg8ZZNQEJoctLp/CDppT9tQsXwgCiF1DLyMqigblV2zVIpcZ2QU0/3mXD3KZNqcKZEjA2PEHSWXgQd5Jetb7+MtIRb0jKYSVMoOw9RJCmweYoE1+lVG4iGImNNtpo2WWXlU2ynB0Lm6fMnK2PXpDea17LxhhVq3b6qBFfywSRhtjsH1GJqoRIuhR/0qaX4y2MTiG2iPjmxb7N+QkKxh4WioHHW3755VGeEmbs2+Mr50qKUABjkEHH5mtONnn66adRphShEX6rVIRNaRmUW0J4QL1dIva786R0DRojY9aHklWhmchkL5SMm+ZiOFCLaoQi/noHt6owqoazGcY05QT5KidFg6wGo73BamTOvPmzW8Lf/janvbMOdxjKWE7BXG2qEfN1AskaLiALpucMKswZpBbs3jrD6HHahy6z1Lc//oRPypYOTndL1oRixCkE0DonVph/++03moCfWO7lThcMLCEQ5513HrpVdlihbkDzjZYhkN4JxohqUKrDgkWNIxgcpUVkOxzWcYSR8QGyaVGSgHqbOkGXhECyJkVrigX7wUUKwSsQdSv2y1whPMUocd+KMu+CD+AiCJs6gI/rJhzqyEhk8KYYksE3oMRLPPfccznlbvXVV8c8T7QwuTaYT6890SaWY1Y8El8lj0mOXjFpY5NEcQp+j3PslXVPctobU0gWDxQLf8PKiYxrU0sgwzyL9vjMwjYZdrSC/5hGK1cS3qbiuPabThFPWC6qoL+SkhifNbpkJQsBPiIflCujFkIl1OSisCc63DIqoZCJaaoS/0IhJmWaLFbWBqsDaImGAQVywexBbuRkG8VyK402N2WvDqhl7raHDRlMA6IhcpnY2LD9QkqzEsYEEXd5ptL6evbMYlhh28ybz5bu8+nPG+Jbsw+UtWK8eHDCwsiRI+lmTE3V+qDoZyjEUVZs+x00aNDs2bM1tHOSKXKFpc1lRcpKbbaQJHF8/UmWlOOTmGeFj4oXVRdFK6t1hX/6UtiHdKJ+Kk6nCfIG8K+66ipK3m3X3eC6gyy6qGWh/2XbL7qGvfbaC0UDm3Suu+46duuU2hjUwkI8MUFIifnW+rPLt49TeGUI4zS5lWM4LXZQOTIIpEorNtD5qiVVqTzD2oJInhNRAIgyt7BurNQnyv1ECNqDTIW8r/4Y1UvqdeMgehEpQ31Uj3ZgqFeLtUi4rXsPfH8qm5XWMK46CiTapemUmozRgGjunwZPnz6dmWzgwIHoy5XVmd7DT5oYBE1TXKm+AgO23357FhNgbnBCAe2jjwE3FqqtjZHUcgV6ZMXGyE8NJzR5xieQ+e4E4i5F+JNdxOoRq94lnWyMGoKUakxb6dWD1mh2dDDveHUqzGRM6JeFvW222WbwSTfffDMnxlNzuaNW4MB76aWXGIPswGcm23qrrStpwQq6jJcQzevHz4K5gxFEQkIrMc9M6brFV5Z915IILhmSC7EVcZFA0syJ4yUxJmlGF1mpEFAIgyShrxJHBpqHZkEtb9XgpDtiicOkTNt5DKrx2nywo74Wt+AwW6w9KVZYBpSeGwuPCGq2pVZWl9T03e7NwqohfimhWPzxrT///HOy9enTBy2DKsBdHgT4xIMHD4a1OOSQQ4SKglHy3b0k+ftrf9NILfBXbFBXav5SuhTXl+cWIYH/+OOPQL65R3MpdT3XtoBOQrU47Al1A0oHzu/MtdC85PdLVfJSebRQaQZDI0J3PbyLOIk08dEcBQnlBB2QgCvSTqXC0+HOUH0Dx72xX6Ghob5Jfo11TQX6qYqx42tqqudXzzHyzK26kUqhnlNvA/oeNIMJjJVAAlOnTkXIoWDc6mDRoHYWeNbshSLWAfUqoRhWewA9nr1h7tG2cvwEj4QTEmYbkU5kV/K80nYh56PORo5QyGlGGlgQ/alkyX6R9CajFzCJKYRi9bKJVqkR1nVFKyJLpCI16EPIr8oxVhplZLag6CofWmFMHO+9994111yzq7TV+P6aa66RbuNPpBr7n1WfIVAMZ+6Ma6gZoktWxRQ0E6wwraXN6Bc4m43x+M+n/kmk0CUe5bLbRIz96MJVDoEJ4ycwWXMcJhseZY6We8mCheaB3qCx6NRoJwEsqznvkXCeGl9Jo4YvzvGN9IgjA4EY371kv3WBGwYtBSwomo8++miWlHiUBhT065uFHOqOcFwSUAomVjr1YidsZ+SnIjP5ZQ5SU5de+qJ5dgszL85nDg5C4gwdTmdgVc9kKeiHMLUGHQCvMEJce+21xV/vP/7xD6mhZHoHny+XCiARKPtqvcSOF1x5wdkT6vgJT4iIC4gcEZEmUqSJy5LuUVXJ5jVBPHVHySw/aUxR7gZGQdau5Cl2zGNIoBbvOeuNqa29AD887fHjFm5VPx7a29vCmNJHvnfkqwfZ1azKYuJn8HBnhZlNgAwYFv/xziDmDKbIPDEBpvy8BoA+Hdx///2pZcaMGZjugwSB1mgGc1ypeUHpuDr8PXotUfMfF4TGX77gUoFC6sK2Qxn5qqtkqHNwncy5JJTlnNRFMYBKCgNKJpxz8ZVWgMIoz+QKc2KQ6ptvvsFSAKBxlXhv0Ymw/oY6+pxzzoEIcyobTExZaElKHLBV0rw7/nbHrFmz/vCHP+DShSPrGQh0XO6lCQEZlfBhOKpF47zTTjvRThgj7vIqkGazxqR5WcViqWN0c7u8iTO3Ujx2P66UjArny5551pmQuMUXX3zdddflERjGFVjNjzB1UFG2x4pzMagrXF/Rh4Oyayg4rxWHBgwIo9GIexX4owC8f//+hx56KIgaePlFLJCuMfqEXsnQw6jBrAwVsWGJVevVzUg0RMaTOhMTVmNMRlTXL4CUZOPpciSPjpDopHc+ivklTZBJJLipk+sblrG6CRG1YrHJj24LfC1zPOMHLQMMOq299tprUQnLbmEeSRUgEyCfoPB3Pvpuu+224447trW2HXXkUbJNVHoXbGOUcYOCWVSRBN+jIvTGJIFkqrvdEpkY7Jj04fRqQtUqdSmdIuKXR3ekkdGmSqI83ek1JWMmYyxleEwFiqTxeWpYSRVLx8866yzukyZNuuL/riBA85jbwFVzpWqwYWhs5NGRkNakv1QllVM8cgsXSygHHnggINpuu+1sWZ2YUuiMaDa5yyIkDeZ8H8jscccdB+Fl0wdtXm211WgqvHIpNNi1ofQhwIy20korgTkcFMUxcsOHD2ePuvDBNJ6NkPZAKJHuQNBYfunZsyc6tddee23yh5PhOlpaI6c2BtdIa1LjaDXv4DUhoZEluCSVRYmkWdkjsVgjJt5jlvKSE1gpMElNRtFPCp/Xo48++u233wIuNNFLL720UDbgaS6f5VRqMpYU6Vr37srjJgGWygQyldrf0uwXMAczEShKs3k5toqucWYq99NPPx0ihjEWluCotCjWIVsq2Bq+VJRuNneaKkte4/OCmooJ1ZwoxgzSemXikOZnyDaBNMl8vtKlkVaXxd0A0JoITVwxAgwSmsWmiQkTJlA/mkgZNoTNTFaMdgVWJ72Aj1efsrb2kUcekXLVonphNq+I9VFgvcm+oKIPb5puD4Dse1LROcFVVquQH5iq77rrLukrzKXPTgspl8R22Gf2sksGRikjtfZ2jjSj8UAP6Yv9k4j0rGsx9ksB5WgMS7g0DxUbTb3nnntgUDDx5RPTYD4uS7s4S3/++ec5vq5SWbSyQ63SbzAmMPg2Zkc6LofAczYM434I6vHwww/T+AEDBjDHoWuAA+KRld5S6BFjASTHVxyNmTNnDqdBzZ8/P+CV+YLYh2cLzNSqh7Ql8vmgGMKS4bSbzSZu34QNsERSD7EtBeJvN7JKwpUKdrtfG/xuA2ZqZKXbb78dnWmVfNmsu6mkj6wzB52xdFoSdM9KuDyYchgRPM/JStrZZ58N7wt3Yg+qEm5+102jI1xIHTD3MF628ojMMnN3XYpL4SBQEAgwdeHoa43V12BZ5ssvv2TRD9acVZqCVF42lTBsuZQSuaMDiH3yySe33norEz8LfVdcfkUJdqOxoRF25MYbb0SFhGQIRZLG//GPf2QnFw7kiccO07EsJfjtSrNJ4A+UgeW1cePGffjhhzg/h27Q1I8//njPPffceOON0WfxyLTOuEBMBQPjOiKDyNzj3ubpEfGPGk866aTzzz9fdGrjx4+nI3mqrtyLBVamC5ioMBFsvvnmbG4FjPAz9luTzAUcBBwE8g0BBqDs/EJWevzxx7kzd7vpO99gD6T8ClQ0eCbrsfCJzh2x8cV4guVlvv/ss89gXKj/5JNP5g53InMYb4vRqODrFFFNH61VIOirHROlBLz0eyuCh7grMSsIMO6YtM4+52xZgWSzj7DmXRaWmnqmflNCWuYu+xeTALrEhS0AtAuNzFZbbcVrKBim4xg4cEYvQCOet6XAiy9YuODiSy5ebrnlxowZgyda9krQVDZNoGJAP4JvVL41shY6X7wHx/TTPTgIpIAAuA2SC4Zjh8iph2y24i4LBu+++y46rPXWXw81BJo4pj+xqUlRWOGiaYxsoT/ggAMYBbT2tNNOe/+D9wNsAXxMSc28sV2jdYrR8nMJSyYp//rXvxKAUFxyySV8dMIYhkAD/ZTj0jgIOAgECwGI2HnnnUeZqHqfe+452AxsFRmSwdYSUGnaF2NJ23kF1FF/xaRhiLsugC8duazDkMmmOVJ9YzHbdvHKWc5Jf37SJM2YLFLaTcV1nBCgLccx2ecvRl00reteFSQFY+Y///kPXDvrgXaFlTeNwdZg0UAfo1iR21RtUFZ5PrAu8xjH8Yi0b8v8MThp4Z4UZr+VGFMCj6hMzE/eymNcXvVY286P3XP8FAJSrnesrtrUw4bU+JKkDHcvNARYqUIvjkWxCM9z585lZbLLkSh4aNSa6vNa+jR13EnY+J2JBnTfyJq/X36hJzLVueeeC5RgvvHRgJsDLIoxGjcVdwk6kzLHALMPygJoKQEpCjmBU76eeuqpgw466KKLLkL9gbEYr7BowMr9hhtuYNmZR1qIHIjQSFg840h2d3cQ8AMB2ZEO4i2zzDKnnHIK/hrwsqQwqq5u8uTJ6LM22GAD1A2iaqdAhFUwk8sgqtQi7JOfGnNJwxAQdhzX9BjyMIRh0HFEzf5WKZaGZVl+DEsVKUSxXfoSDwvi/7yzJqx+CZecCxA5HUA8pet7ZwgPxkl+EW/qOo2dNz4coQeRlujmeG7Y47UONDvyg5oob2KamACTyy+/nDDbwjfacCOJzBJKFZpNOLridi7ijY1Pxj992e2RFtp3+60Llz4E7G8H1R26wlD2fLH/Cy/UuKBmnYAuyHdP/Pr57p1Vb0xVest4xIybF+q8CX3ZcgQRhgWUt0HdRX4xpWmxQ05gMXFFCNiEuAjV56lKm+m3w3mqLotin3zySTnxURyogrKymppFUVWYJR3WelxOPsACpfB5gXX68hg43xl9lu+SBQgBRE3mMMYgEoIUi5tDHv1WoXVVXmKZPrynivsL/42z/SuvvJJlUqZ5pClYATEgL3xfqRpjCr4dAb4XZikP3P8A4gE+Ix577DGJ32WXXThjnMXJoUOHJm0heZPGu0gHgTQQQFxHehe926BBg4499lg0XOxNQLnA+oGoG1ZYYYU777yTQsAxWRJPU2BhXu2+++6MDhrzyiuv3Hb7bVJpgG0zWobCdCdtLcnZhI4Y6wZvgtYF1eLMkG0metkMQxWO5eb7jhgxQjSSUBi50lbqXjoIOAjkBQKsBjEScZvCqOTCJSTVoO3NS2V5LjQ5bcpzpap431xtsG0pWn+D7UZ5lQZDzM5h7nDte++9N4tyMCIwKJXH8kqPuEugvD6Ta22VQEDwE8l55513ZsWbOQzHaQzPKul+Rt1EJtljjz0kCxbFyFrM9EWc7OVIYASAm266CWc3Bx9yMKSV5rG2zFkYOL279957e/XqRbNlVTmjzrrEDgJpIADdAPGYxIVWoHdD0YDXD+x7eYW6gQMLDj300CWWWOKZZ54xfgQj0qr+k6bwPL3CluFPf/qTTMfHH3/8p59+SvvzVFc5Fgs0WIvkji6Vz8r1l7/8pRw74trsIFBhEIDZQNeAcSKkFW7t6quvRnSCjgo1q7DOVlh3AlY0YDxs7IdNoMggU+tVRZ5KhbcwMzpMMAMG3pcVD2YytHTmVZFhVd7Vi21kefch2NZDiIMtsCJLY3gyALkDLlymMSSZxkRepb8yeLn76btnyeInbdmksakTu7sxxaLpONsXAxCmf2wHCtCZpJ8A4smGCMzX8cXAIiQqD0wr119/fdQKHE3HphiYEigtYh7xBArQTldFxUMAVGRQQC5k9wEb4bTH41piwLGxY8eCh6gYSMPQYGF8n332WXXVVSdOnCiQYc0c5tjwx0kRO08wZDhssskmKD4oH0JHANVDUNNER+nsSVYtiXB99q7JLqHKx+K7sDUM82w0zttss82QwUOCgk+XtVdeAnDbXJXXO9ejAkOAXWCrr766MCQ47X73vXdhPwrchjKqrnTY0SAZL2OBSvekXNVPtRXd+6X8RCQ3v5SJsnlhZj4TyKaUAPKY/ZDM7ngjw3EUowVlOX6MAyi9tIvQPFXkVoCW1naqrddZXCCwwWE7u8Jn72fHK+y2n4sdZka3mwB7BCMskW6ytyGTGEYixSkgUqv4TAFcyK7c0TggOcjG3fQwRK+qyB0oFFW2JqNphhjmc49PYgdziaH7sh77z3/+k61ewAFVKeK96qxe1CUml/L95zWmEwT4NFgr7LfffhgysEOeQuA5cLTx3//+94UXXkBIIIYPKoxIwIf5+W+xS1mJEIhMZmzG9fbjgmmCb9Jd0I9dReDhOuusQwy4ih8HDi9YfvnlOW2KxGZO5FXBhg8tgVNn7Gw6clMhdFA5DqnFYbPs3IQn4ZIuZHbHCCAUyYj3IfklKQE2LO6XJFGAUdG+RENpi4ey8Wk4UgSyD6t2yy23kJxpNG0m9zIKAZAZuPGciEu8Mlc0gwvlAAGoh0A7hzLKJivII2QT70uMUx7pOxAomw4UsKGGCxXJxTwWsAkxVYlCICYqiwdhqLPIWNgsPuea4BulRoha9VD+2GFBXnzxRZnOWXmjMiJ5FXytrsRiQ0DxWwVsg6G5LJ3hKQd2FsNyNFxc4FgBG1J+VSlYaVEBSXXttddmGuO64447UEDQGREMyq9XAbUY2iWC+lFHHSWE6+6778YNXkDF+y0GJoP1YcHk9957b+TIkeecc86ECRMQnCCqbHt5//338R+BBCWN9FuuS+cgkDkEwEZmbZm4CZsCwE9BUQgItgMg5AMPPMCGLFlRwI8DLhKwbsApI+QaXTAoLYWYEvIaoC6aB0HjfDjxk3rzzTfDm7BzE65d9SerKSvOAXNeu5BZ4RmuMLHNig0v7CiBquy7775LLrkk1cn+rMzqrdbUjAWo8SOPPLLppptit6tHQ+RWVSBhRBjnf2p0RClEYGBQAzlUB7QpkXBg5ZZqQWrzo/Zfu+222zLFY4p1+umnCxEr1Sa7dkUgUEg5KA3QUQGYX5pk5fpK5m9ogXAkv/76Kz0ZNWqUuGq32ZRy7aFrdyoIFHDhGpoL8/q///2P0xMGDx7MXlwYSjwdgmAOx1J9H+Jt4MD3s+QIh4SNMeI0B9QJl5Qmu7wSxoIwAcVYqCsZTbMX9CRVyd8hXwABg3DEJML4PuCcvGK1+ssvv8StIxs10TWgTaMZnCn4zjvvIDgNGzaMz8eHE2fUxWqhq7eqIKCJa1SM4BFuWCkRwmGsaZBXd9hhh1dffRXNwhZbbAGJRp7H6GavvfbabLPN7ht/3/fff19gcMlZGGhUL7jgArQh7BEYc/wYWoXEguKDBhe4PUWpLhlpjjQEKwb8zPHtLr74YqWpZ5cMv7BT1vv9UHg6x48PBwkfeeSRfvNUSjqZ/T0GIL+9whbp1NNOxe0xg9fmYfJba/FKZ7VDVoNYoGWKhydhnH7yySfFa1Hp1my4UNF2mcditThgRQPFmRLFkLhYHSu1eo2WAf/nsB3QBVwxVdXePyBgf5S4R/tVxYX1QZb575XMN9iVCbPIAYTUWQ0zUICgZUjivI3VcspkNXLSpEkgKrrzdFXotQtJoHcpw8RWyGVMA9jLjViCKMLCLBSsKIOXT/PEE0+wRZMzQTirkrbhJOLtt9++/vrrh68xHCd8AF0axquitLBCvrrrRm4QEAMoGTvIq2w7YuDstNNOzz//PFLBjjvuSPGQZbRjRxxxBOe05VZbZrlpFZeMjjNOP2ONNdZgsmBEc6AvBbW0tqCqy6zEMk8txBqYCOuKAxoM2dDFoGtedtll+ZRcfD75pmXe1wI1H0MekQPZ2maqzM5SxmSvgAAiHxwwIh+BrC97XmP34nXXXXf44YeDn1kXWC4Z6TgMgHQfXIIbgRWZNm0azABdEGNex+vGfc0kmJYD7sUVntGjUQtklCt5YqNZMBtCTIyXwVZE6DhZ3/NeV/BfQyDYq8lczpDgUGvRz1Vwr6Vrok8BAm+99RZ2pGeeeSa8lwFIvrsvm5TkLnWJki/uzis1DcS2xs4V+8bHk7JlsMZXnGmDciTBoJefj9J8JIFbArCsJECIYY/+8Y9/GPoLvsnlo5iqToIJA+gKlgIuAIGugZVJrkSgCIdq1i5AJxMmsRwCH78hOUM73sRKCxPD2JThiXcGagQgJ598MhiFJuvBBx/kMD8BTmEaI7VQNbY5a621FmYLtIT2YLkDp/W73/0ONotN1N27RU7tgf9wqF7IT+PqSoQAmxHYdSUiPQtxEoAs41zwsUcfw43Z6NGjQVSEfOxxErPnL4Y2cMn4hf1g55EQN3G5QpNkss5fA4pbcoRoxzYiXNPZ3gmFh9aF0Lmga0CzzHmlAIqEcjc5Ck/6TNXlEujfvz94LqB76aWXAKBo6oUsVx5xtjnJfH8joCcjFJ07BoZUx3Sc70pLpHy2cJqWbLnlljC3cALjx4+Hugqja95Wc0Cw0UBA8aXY1qLeMlHFCFiCUA7VG81CDmVUeFYIBINh1qxZaHkhu5xqCccs3HyF9xx1Y1srtAAIHHjggThvw+IL3itu/i46EBiK5X5BdjEgRwAD2njgB7uw/KxsxjHwT8ZaFvw3Rg277bobheNlEGBi8CybA+OqM7oGgzzeekVxqXpcM7N/BBTIQs89/xzG3pTCpomtt95a3CsWmHZRHQQEX3qs4aCr5WgrjuyhbdBShDq35Jj9N3Y5g4aAwlWQUu9Zs8uGFENMGFMYTLGJHVEBb9D9+vWz0xQ4zGoHLmkYWQzwPXbfg+axTaDAbShwdUp1GnvV1tQ21jW2hvEq1/nss8/CmaDWPPHEE/mGQuUUWLLzkRlbUZU8gedsFIImAzQMdqDS4qyhSrov3RTRDmQTMS9AHw2YHVEFjpkZtnAm+Cko8FxclO9IZxmY3KV2qNaGG25Ix9lKSWRz92Y4AfCtGkDhE/5FVy7Y7QxG0WCXmE24DPctZ9pNBgBEgQ2ZOGhgDeH3v/89egdR9GZaVNmlF0dTkIOzzjqLNRPaP2H8hCqYufVhK4X9Wvg5R92AHofD1QA4TC38bmGbUN61yUTFfYkllwBvZ86cefnll6s1mXDy3RNGxaDIuqer0gH49aS/MoAPmMMlDSWw/fbbE8b7I8flBKVlyJQhgHjSBnKNGDFiQP8B0ja+C/QEJDetlXh3dxAoLgSSIiTaB0yEmPe5INGs+nImK+S6iFMhI+jf//43tu6I1o8/8fjHH39cDWbY4IZIgNxrMVmo4cAM/DbXzZwz64P//Lu2vu6448esuOKKfC/5jmLYRa6kn7W4mFaCtYt6mm1BtA2zyqlTp/bs2bNKQCdsgOEEAv86zIBMeYzWyZMnM3gXX3zxAptEBd6j7AqEZu6+++7c2dyKlSUox6WGq8e3ZFdsxeQyGChLX4KWReydE0IKBHyIAjVx3gRMBoENNtiADRRF5DAK1G2vGvHez4KkdB+eRgDivXd/A4AAq1KYwjIPsezco7kHsxEI5ihvppBlQYbpHP0CuMrsxTQ2ffp0ELiqIMkaFHADAsOHD5ehims02BoiM9URJMKfEuRK+ioxUmLIQoCvwKKrfCOEIkdGUoHLxZcUBMBbcFUxxPgX7OiAOEvziOTKqKkyEDLKkjQx5ciplmx4pkno8lgJSJqygiM7azraOlrRNUDrsJPC5oTO4muWO58MEFUPkxbgV4a5ZQMF2jTK5ERkRbSdSUgQ8GVtEgLChhQ8vICc4lEF8AZRdpmVgVGDtPiDDz4IiiSWGQjKp7mZzXDZ9ota5JdtARWRD1J70kknYc6ABTKKBvoEh1ENI4SOw2DRXzamrrTSSogHLLwX4JNCfZMS4ESvDekbI+WkKi19XoX5Ee8MMtbse1dZM3yPIS45sFQEzRDGQK1MWdgMK6yo5ICLy3QJd1bHHHMM8sD8+fOVxkEjsHlLIFRTq35eDtudh440RC8uYJdRumEsF+g7viq++OILBiwe8jlpIkCGRhYJqQKNhhG6/DCjNIaLz5G0MUTKVbqQdS2rdAh4OBjz13QaEzPEA6T6OIJjEsQFDFFCuSZW6Em3ccXl8vNI+zDaYtBtvvnmHI2B0uHll1/GIFnyig7CTznlmMbM6arxyhNNR3tL6xWXXcZ95IgR6669DtEG8lHTBv3NyrG/hWwz9Byk4pQiDgdFMP7b3/7GJFIl1ruGt8wTwFlGgrWTZQCAjI2hhPNUXckWC2e75RZbPvzww+wUvuKKK9ykH/elDF+KW4N842Rc1UkfRexJ+iqQSGGyAymqvAuBKPz8888ydYkZMOwCHHN598pf621xl7M2AAVLxLfddpu/3C5VOggATLhP8AoHvH//+9+Z4/v27cv2dfJUCXalg05W78xmk7PPPpuhyhmK5513Hlw+P7glzWp62gVdvpByqcoOZ1V5CWUCf3CqwuAFwTh1ggD2HULBcmylKQTsRaMhiEot6dkF3uZYr8vuIFBECCSSDhpjxkLShhk1HMlEQ8EoaGtXglzS9FlEMq4ZgGz5xtMqxbKTAkYF1aroINI3L4vqSjCLuO3EnIHONnXvtvfe+7S2ty1oUSZd1dD9wL8IWCQs3z777AMAWVrD1VG1QVJJd3qhi0lLSX0xLEP2IEdqwOE3ShzmYkYoGwmrDbACO3qNwoXdE8LrZg/Qys0pGFgi/cu3oqFEuln8ZkB50e+ef/75nEaDETK0mHHCVfyWFbYF66yzDiSSOl9//fXC1ly82vJ81gDrY/CjuP9AVKOTSMVspyfgBLMcP3nv3r1BVwphoymjlVV0eCYGcjUAFo2VbP6ERuEXraG+gRVOup8jSCU75SAswSuAur/99tspp5yCi0fCgsBpqqgGyKfpvntVVRBggEBtGICI/TiP5ByE999/X4aALBoHC42jjz5ahBZ8ssjRyCIuEilXsNWVTmnQIhqDj2qoHOAdtcUotMp1Ge5nKZ3uFL0loI3gD2cDEeaSw5uK3rAKaABMyIcffshmAebQNddcExUh6oYK6FemXWCc0nfoEuSRO9mJ4cq0HJe+MBAIRtGAwwn7aDer6eIOzYqo4iDj4cILLxz313EcXg3xVZN3h3KlXlUgwQvmqFGj6PKrr75aVR3PU2cFf5hsLrv0MpbfcWGFiTt1YWKXpxqrp1iguu2228oyPtuYkbTFFaIPCARDV31UFFgSma0VUdLTNoek4u+D0pdbbjnWozg/kr6LijD3KmERIIAIS8hO7KViEy+bfShWTL1yL9+V4CBQ7hBgGELbGREMOg5c/MMf/sDJiyxg4uV3ypQpvBVdA3x2Rpca3t5lg4g4FIusgjAwMWrg9DjeUrKdpjLCifxWY0O3z7/4vKWljR8QWGnFlZqboHVuAs3yg0Pee/ToAfL06tXryiuvJPDpp59+/fXXWRbnssVCgKProAyMU9kywMiNfV/5T0LxpJ/AodpkqHL8wEEyxLajS9E7eDGibjB3C1BsX4/7WS8rLMh44DKbvfHpwt6/Cuujn+6IE5d58+ZNnDiR9MLNeMyP+uunED9pKCiuLNmtZOeVmMR4O42Uk1ianSZZuKMGv3VcHfzRRphyVwaZxAfAxFE2GAUA2aj26GOPsvCFG0g89hEgPlmTXFxKCKjB6QFNwiIJM5ORB+ZbdpmygcIrQmEEeyXkAoVMmJjajhC/eOIW8dbhFVBKf+myoBONko5zJ/L5558HwdBbCUwybTJlCsTASWw+5XHOnDlHHnkkSrFbbrmFcz1Q6AwYMICNP0FZTGTaSJfeQSBPEJBRk/6evmrRyv3ud7+DEDEkUf9xSMRee+31ww8/ECNX7hvg1cAMh1kFIUCZHD8h5dtt45W57Pj0YUUG9cU0mD5lAd5KUxKnxilfTGnu0Y0GnDDmeEmDDxjTHj6fCbuAHwiAQgAN1EV7xV5Ostx4441m6jQBP0WlT4PELYMLd1yRgJXBoKsJWC8DC/rhIYOqjDkUT6WAF/0jh3AHCMmgWliAcsyHJlCA6sqrijhZRvGlevNOhAoXqTMB1y66BOmLp2UoUs9Ku1oIH1suq3OcnHrqqZhJz5gxgzM4+ErwN0CjtD9XVq0rFF/FrGOsXtmxhv256G6yarTLFIUAfNLAgQM/+eQTBONHH32UM7p4h6IwHrza3Y6XDYoaMFH1Ss77X3QB1AFRwo6DXjMqr7/++qErDJXI7AYpohHWEJSAXhUn5BR+6GGHLr300nfffTebfRj7YO+f/vQnjkDDH0Tee+gqcBAoEwjYvAGi2iuvvIKreewBGTLo6HGpgCkQB2SKjTqJGWjpe0YarlRpGIZYT+CF5qKLLkKGoRb230H3UqXPKN7oGjLKVcjEKOjffuvt8eP/gfaTeqHwhtylAVohW1h2dQnc8Kk8dOhQ0Am3hdN+mUYvAKxZbCu7TuXSYBH5cvTihMafIQ88MXEaNmyYQJKxH8+T5NLQks+rCFlqUlbyzS9qA4skaZUrT1zUb5Vl5UnHRtLILCsoh2wQRFYvmX5oLOuZP/74I1Qy9wWZcuh6vtr41VdfyT4UWE8OZodf9G3hn68mVUC5MjBBTlhP4GlACgJX6phF/YdZJt+OY3HkC4JOsDK59Bds5EL5xcoT+yOQZ+655x4O8iCSKljvam1Rp8oJQUCzY/j7CkAh1wUHgUAggMkPA4eTX6DzbHcXk0AIEdoHdBCoBRk4auyo0xOSX10OYUpjSFLI6aefztZ6Svnzn/8sqtXkJVZQLMYcULnhw9fad1+16xBQmM51CTeT0gWSQoA5ZaeddgKvkI1xrlydRyQkhUx2kSjohVvGxAnmRK0qhTvQDIKoburMDqQuVwEgUFWKhqrqbAGQJ5sqIIhM5AgYME/iUpsAFDObsqo7j1jNcce3MwBksuE4RqZzoCJL09UNnsB6L1gK0spEDsefhvtk1aKsL/xf0n5sjjCcZtNEUH3B0Rr4icdHFmSURNTZyWIsgL3rrrtg8WX4cweZ08A2qMa4chwEygsCjBcGixB89k289dZbr7322vDhwxE5IPjPPfcco2bvvffGYW3SfvkZUzJxcIe+XX755ZdddhnqBj8Zk9ZYRpHCFHLwRFs7hzfFGFdWQ/fz/aWAIdrqQYMGUREOTZXGubaWe77rrZjyGftcRvl1xhlnEAaGOHpjJkU5yAQqPIlD14r56EF1pHTY0cqVvdV2aL1HWj5aKe2OhiKYKyiUKpdy0L8iUcDK4FqP5VMkELglCKXQ08R7PvqF/aj55aP8wpSJr2ywiGlm8uTJTDnM5RwdygICtYssV5hmVFgtcQMTwRhFGEJyl900JpEEPGcNmgTF7aSwXdJ0WWjBE9B9qXOppZYaPXq0hE1k+uYgC8EDiS8GUhLGyJNTbIEe9tiSF/zEXIKUjz/+OEySjHcSkJg7Vg/EaIGnbQAAQABJREFUpK/FvXUQqDYIMACxqILUS8cZRCNHjuQECvYebrTRRkLzn3zySQYUC55MpiTjLpdPa0FGH0NP7lR31lln+Rz1ZfotbNIsFLshVC90m47LVaZdK6lmw+b16tkLjzzYrDEd4L8QNBP+RIh/hRF8wSWPAcj1Uwhw4JmlIBQ0gI7xjosWKACRIGqudbj8lQgBwUN6hq5B7dmJMqXF6a2akyr8UoxrFXSzTL4iu0BhgCCg7NyjyRh/4qzB2KWXSScyambyVaaMirATAzqkMu6AkXiWs1iGQndz2mmn2ckIyywVF+keM4KATOTCslf8pA7CpLq6BBo4CRIKJ4QvBgwixo0bxyYpWExecQFDFl3Z5oM3OxGN4so0zD1tiHvlHh0EHAQMBIQQIXKMHDGSCfTBBx/ceOONZTrgTAp8KxCD0zgGHVnkbvKmCkiZcs+aCKQq3MVXLQREQYYFK+gKEHAyCo4JrlY2TBDtRMYTMS/eLbm/zgMrLuGZycGyHKdBM8muttpq/gpwqRwESgICTgIvic9QVY0I1YZEKcvcg0M45JMCm/ojx5hfHiFvjGgiXiFF42Dfs69cbcwL1bFccMopp8BKsup+4oknZl+cy5kzBDhnR9zfWmf98q3lZ5UOMpifFV3uQXhHBjV4iNcV+CEOxcS+BhUYfBLLpBwa98UXX7BRol/ffj6XWMsdIK79DgL5gwA0H+K/YOECxteOO+74+uuvc2gLAWpkAO677774cbjzzjtJk6NQpwQd78pfd1zJlQoBYe2YDlCB0UecmP7888+V2tk89Ut0NBSOvRJTKrare+yxh08FYp6a5IotcQgYLhSmlJ95LFazA1Y0UJwpEU1eaV1G8CutZlVLa2SdBKIph3oy8cDAYE134YUXIjNXCxRy7idAE96RHbn4Bvv8889ZNJCt9TmX7QrICgKeioHM+gg30SVlVVQ5ZDKWnHZj4XvQIODucf3117/gggtQLsAScWGDzcmguBwfMmQIFICxXyUe5mzguLCDQLAQEL1ejx492C6B8QKPe+655xNPPPHAAw/06tWLur755hsOc1lvvfX+8pe/BFu1K81BwD8E0D6zUY5FeJzySC58mnJsiikBfsaEXSApBBjdoq/56aefSIBzFhw0FHhxLmnDSiSSpcoSaUmpNSPJyY9FGm1GLRAAiIxmwWwIMTEBlJ5BETHqDi9fkD31yuz6Lw6coLNdp6uCFN66iMJ0RI7+/fsvueSShD/77LM89Z6a4oaVqPfyVF18scqQwZI5I3YNXqrI29g03ks/f1HZoKOByHLht99kEYWOPNphk8AFAoGAfFpUxXKJ2tiU3Bnq4Bc1XigHKwYzQtMH0HChayCN3AnAR3LAKker4u6RnVA8onS45JJLPvzwQ+5wRYCFZNg7cE/ESSK5BHR22ADTBRwEHATiIID4wUUkO5XkFWOHzdtTpkzhOCfkEOYF5tYzzzyT9eQrr7xSRhZ3hid3hqFdoHnrM2DnLftwqLbG+/npftn3t7AdYCEEdcNWW20F/admVGAffPCBwJnjhwrblnKtjdGKmRIGSnQAVT53NPsykybOp+XayWzbDaEj68MPP4xV7xZbbAF9y7akSsgXJ+MovlQtgNmiSBG6GYz4bTQLReiBnyo7i7ZgjgMncUAQN6/7aXUFpwEagIUlF1Y+8aH924zfKrizMeqGIPqJ6oqVYRjKd955hwmbtWL4yyAKdmVkBgGja4COy4XeQYctBVNmRZZ6akYuDjJBP6bzcIc64uTggw/mjL3bb7992jR1TDoX/urx84otw8orrywx7u4g4CBQGAgMHDgQxygoGu644w5qZMD+8ssv559//tJLL33DDTcQIwyJKCkK0yRXi4MAEDjvvPO4M3GgjAYtQVE8RLotAF3iBoI0mhqZXvv06SObZN34NXBjyQ0sYlXj+uuvx8j3zTffFNWDSVCdgaIrF2ywB6NosEt0YRsCHB3MjmWGAQKhHV+1YdFk033YHRznYlyN3AKUCgkQNQK9Xx7rVVt1+Fkypyxro2GMs27IsBGQUaYZ9uWSjwmbo8jQ1zjDmQyhGExyrVZQRQlGSaE6ku+e9BdMvbmUkgstkrygH6wP8gyPbA5nFGPCTZOOOOIIVl04U6ZHcw9Jk0tdufTR5XUQqHIIsDmRXRUYGQEHhDp8N3BsLf5Z2cfEDJKndb9OTq/ObXbL91cLxZs55rtCV34EAqiexajh2GOPRfklUwZzhwNQegjg1IzB+8Ybb5Bs+eWXZ58s41fUhekzVslbFA0wGyhSudNlGGNkiirXNRi+VJa+bO60KFjhFA3Bg93W0f7rX/9Cj3vAAQcEX02Zl4iEvMoqq6y44opQBFzpSm8QSzo7Ykw6y7yXeWk+6wCwiffddx8wXHbZZTFnYLYmnJfKXKGZQaBEKWoHtpZKzxVBknC4PbNuealBPDBt4sSJnI6ObyqiIXcMYXbegoTIMMQw8Zuj+Hh0ugYPeO6vg0CBIMAYpCa0gYxTxuwZZ5yBZIK6AVOjo446Chlv+vTpDGRzFahZxa8mfr0HCBS/UVXTgosvvhhpELH57bffptOsjgiiVg0AsukokylbEUUjs/nmmwNAFpYc3hpQsj+dsCx7wI08//zzsByAyCRwgaJDoETZ4qLDJZcGICrLxSlT//3vf+HC33vvvVwKrLC8UAEuOoWbBsz+CXAGOE7pIaDE2yJKFh3HUiHUGVZ7ebLInO8s2DgoMwd2TeU67h5//PGXX34ZPpIjA0E2ZmsBqfTADue7T9VZPix7iLPqsVgxnzLycYGHRPKJk/5KAWC18C26eaw/dtAXWzeavn0gG+N0xIgRnKInKXfeeWci//XUv3gkAO4lop9E+rmnr929dRBwEPAPAYYqiWHEL73kUkyNjjnmGKYMYpgvMG0gwICV0giYS2IS7ySAVkiuOIqhh7aavyUXOk1+ET81iQUVM0a1kHOuRGGKvlUTKyLNr5iNq/i68QSsAV6D0xDTWYN4BEykC9gQ2H///WXEXXPNNSzX8yqpS2Y7S/WE2cgpneWId0xmWAVB1wDpKyg6GT6weuCeSU9zFXgyqata0ooCEnr66aefClV97LHHqqXzGfZz1113hfVBWXvttdfCD8WxLxkWVi3JcaF00UUX0Vu4pVGjRgk9bahXS1juKgAEsCXB9+Gzzz8TqUudHVRmhLS+nlMhWmfPnoUaFMbF/5GTrIsySCFrp59++sYbbzxnzhx0XmBgqE5pCbkKAH9XhYOAg4B/CMCQMDxJf9NNNzF3bLvttmx34lEmDu4SSF+gJMMUgoOiKETWV9NnKb23Ic7GoVU4I8QCixM6OMFJ61tLr6WV2CImDpad8QpJ59hRzyEpMC3s7gmmrz50FEVyTo8WC1vC7HuJdk/2TXCwpSnFSNcmpmoDYgQNJ7P66qujToUJ+eijj4oADbXUpKtV3GDRGMLUSF60JgGUYtZdBFQoSJVMyeyqwncLLDi2YejYVltttYLUXDaVaJFEkV40teytIjB58mTFDxVYDZlfgCUMrtpgzMg5iZrNJrB6nF623XbbCZ0FegJVJ+zl96vW1Bx33HH3338/up5wTVnuL+3s7Kira/z11+nrrLMO/uG+++47/zijTDlC8OtK1zDxtYmsUCnXpPoC7CKxiEyS76/gyncQcBDwAwEZoaSEF+f+1FNPiX5fhq0ZvOmLIhkW7xxvhOfXc889N43FO24a0hdVrLcL5s0JhXAlU3vVVX+eNGkSvMdzzz1XrMZUYb1MHCwmsXMHj4a4C4F7gWkxZ6YEAZAYjguJK4R9q/VD4Pd8c+UrENsL46QpNjrDJwyiAR1yxPHHH5/RmM2wnnJNjhZV1kv+8Ic/DNGnaD/00EPEACthSPLbMXPwmFRj1pyU3kEhpMLD2J9KKG8T7mRI/sOQNOkvIb1XY8Sutla3wVsJy0HdpRqd/RUzMrMuRi3pFa0LPlpdWzh5AMyGF4eAQh04QJ4VANb9fDSxGpMAKKYcfNuwn4qJH4exQIFV+i5hUaKMTHy7pZnBDDG7bI4r+/zzzznuCPtDZiBZrbITuHBeIYB8zjrMW2+8zcoYFZW4vWdkprEgwv4kWn3Z5Zd98+03GCZAqdKIDVY+FUTFILoGxunCReq0LTHGLsSMHtcU9+gg4CDgAwJYIcmsKsN89uzZyopQb2z2kTuahDGOcTITTqKFpiIywtFGk0dD6qDfErjodUvr/BmzZzz68MP0Yt111/3dBr8rgXZVSxPAH9bhd9hhh/fff//cc85lhYnZhKsi+49AZJQZOQpH6GIYcbvssgtH1VYkrHLplEKgcAeUDezC2GrTTTclzEl2L730Ui7FZpHXkoLTEcMsSs4ui1JYWGY+Su1WvCvIuhlXchmIqxhlNVRgUkJ1psbEQH6BjRaNOYw6UP9DIGDK//rXv/oRnvPbrNIrHUABHBZb8FYFrYA63HPPPTTTllhIk9hw+aIYB9RhjkYCEql7TV1NHTe1TZS9oupfkryJpWUaQ/MyuDrEXweK1ZpOMzwYE1hsdkIEaCGokvEYRHuFdmb8+PFbbLEF7Y+DkoZHzC3TPrr06SFw0kknAd+6hoYb/nJja1trqK6mva0NrIjLJYf+xt87QrVd/exyZLdz4t1OYxdox6MCN1qGbk3dF7Uu5Lj4UH3dwoXz77jj9htvvInjKbfccsvdd9+dXAgemB0yEhM7YpeJ0AL6EQPXjlNSAjb62Whn53JhBwEHgSJCQLSB0gBMEgiwFmK3xx65TMqGY2GO5iIlxAHHw5tssgk7rb755pvddttN0rS3qM0IDaGGphA+YBvgfph6lWahLkIPjZYh6Yqc3YZ8h0MN9U2NzWxohQZC6PBY0bdX74521Tt3FQYC8Mbgz+DBg2V1ROYOG/cyaYbmnWLUWx63HytxeFNwh/I0rtdEMbqx7G7I5WWMHBQlrfATH99er3yJF+4uYwYvrtC11157p512gt8TycK8zRZupoBKCCggaB8N/MW+kuU3AnQMO03ukDLppM20B9VtVTUXx9goRh6DYn7w9XX89CN+0zSNjcXG9LULziW5W3oro8BSAY279l2sJ8B5fWmVB9JHbcgkTt+APL3NdQzENUs6LJGWYBWXqkiPmXzvrJsIQiMz//DDD9iGsezJBN+jRw97ms+65MrLCFiQW7beemsmnjXWWOPGG2+EdyEm255GxapsSwg0H/hWEJQzjYbombAL5A8C22yzzVprrRVub/v73fd8PXUqFTHVIX6zJUGJ6RybAhsTr3bIX3O6LhnGf3bL7IbGxrb2FnYp1zfUj714LNmYEMeNG0eAVsP5MR4FhbLwlqJmXId+XX8Kl8JBoAwgACnA9pjpGIFcFIuNDUq9+PTTTw8fPhwLxGeffRb/zWgZ6pvq2ZnctqildVEbv1Lum95j2HHKKacgHEDADz74YHrX2lrSbS5leGbXNqYJBGaZLCScXTlWLk+K0eqDjpDSJnBpyQqNl2e3XhtC3PJyEeBRGWITqX6SLDFsp7HDptjYAIImNXo/RoYqmUdpD0wBNZacZOQBpRz/GhQSjEKawFMDHbn33nthY+Q4VdibvHZNShdJXn/xqDFLpN4EQUAlTvbLtZ3KC2/yMhTiJX9TiFgz8ApRWTXUoQSNjg7W+nCYRH/33HNPZSEWLuInLl2oo5GhccDns88+Y+sERIEYQxSEcJRu67NqmShfs8rqK1NFAs1XzwuYCCzFTYPWY9dcetll1NyrVy9053Us1tSpG38K2BxfVWHzQwtZ0mNJ6ZCDD0ETSrY9dt9j6NChsnMbzMHyELcyxLPaGbd44qsOl8hBwEGgfCDAkDeX3Wp0/dAEqBzqhksuuQRnNLA0GOeRhjXDu+66C/dAGDhgikj2jjZlmadpHmSvXsXwr1R1jpPem/TOpHfoCNIIfQyHO7o197D77sL5g0B+sMITYZR8xdIO9jX81KEnWtqv76ipb68NqR+BmvpwLTFGEWAC9R1qyZdHlT4mrGJMMhPw0ti5dN4wW+m1tiKs7yavsl6NmK96Dc4K0MDQXFkVUOGZGNRDBg+hk6+88srUr6dKb4XDyVPPRbQD37CRYYu+Nj5VSoSwUnhpPJQ7jq3MT6kYrFcxYXyKWCkl7D+9V5SqWjdGtSqkGkOT8qtuSQvfnJDelKy6ZB5KLSAEqFCtEgYdr85MxlCEHXfY0UjOhWpCedQjGhlpKwwKBpeE9ZpDzu1X6sPSwMdOjDQbQrVqzUfQIJgO5gwhV0AuEMBS6cADD1x1tdUaujVFLBpqlUUD+sT2jpp2/pTivtNOlAeMsHvuuWvC/RPAxt69e912220EaDmUiv0gHDe7zz77cDSU4GouIHJ5HQQcBMoXAkLB3nnnnUsvvfTCCy9cYoklOKNBDghYeaWVt9Bb9vBQc9ZZZ7FDmm5GTLm8lUNZVYOZLi4EhLfu7AyLaSFa/lNOORXWgMUgtoxB91hZ1y2EAy8iE15cIFVe7SLUGNGGQPSnLQ2jj94rvr8YmSfcFXi6Sm8nMOUQaW/rcPhVEERjUJ9/wfnwMwhiH3/8sdkCVpDKNbmD6EUEECug5BHrJ2lS3e2UEk6VUuJj0+P9NNIA3uqwpnEd0LoiOi9XJ7IGdZmhpKaZTvbMK70OlkPFuTwoF7h2zHWYz8444wzMC7mGrzlcIb0+XKrALSmL6kSkEfgQBnRc0nJ5ZR7LojtpGun5aGDS4sqTB4k09btXgUFADexQHVt8xxxz7KRJ77W0tPXu3WfOzPlw3CAtWMy3jqF7ihZFrg4/B1XACrN2Ihf7MLy8kb+4ZOgMmTmDJxwsRJNYqXv06I76rr5WWQnhwWSx3ovBYeMxHqVeW3t40tuTujd3Z2Vy7Nix1113nZmSX3zxxdGjR1fMuItCxoUcBBwEfEAAcoEtAwnlOHqUDr/++utBBx201FJLoXfApQvxnFvB6RXXXHMNZHDw0MEQFsniFa8W0HxdtjBm0cmUeX2mJ5luAAZmFAU1Gzhg4BuvvfHGa2916964wpAVZd+EWhZCURJVN6i0Kat2L0oRAnrC43N31rQsYjtjHe6StPWNZrgMtmjUYlJuV7owa46M9Ei4slTdS5veVOHlFv1aPWtMnQ014bpwfWu4rR20Qg5AHoK9ZXw5LPOgFfBfVnlWWWUVNkbhLv2KK67YfPPNFUOW+viJ3PkcYb9qO1llamtvq+EeqmkP4ShO2zcIYqkvDpMWRRUYtlQol4hsgMhfekzJ9Iom1TEG+K8WvDrDnVjysIYbxl0F28Sgb6mqDvhb2MUFpWiINF1vdtLaQW4MLT3+7fryHZbpjTvgVA3Q+1IIFOzC6hjFP5IIxoR//OMfZb8Q2B/ne6lg7SnlihIHeWJMF+1n/oiOXpU2FGrQXlka65QhnBq0tqYrCfejdGH2wEs6zqOtYCDHDtTk6TF/UoWGaubNW9i9Vu0CZWO8imBbvLprwyZC7ipDCMBSQ8K3GDVq8SUG/fLzL/gfmjljhuwD8tUbm5+OwT2VO6piSMYQydKcsDKCt5JeqxtiK6/taG9vD/ND99nOzFeLKuG4Mcd9990PTU0Nx405ilXK//u//zv/vAshVnSH+8CBA9l3jeE01lhu60QsNN2Tg0C1QIA9VrK3+cwzzsQRHSfg4pEBEsF+K1ias88++5FHHoHovfDCC6gpt99+h08//6Rfv37IUfVNdUoByjFsmuOC6408poOcta2U/c3pUso7n+lphhI1Ozvb6+pqW9paMdc68sij8FcZbu888sgjpawIv4FXHa6IukHeuHt5QUAhzvLLrziweWBTY0+bzYvT+CuTUnv+lV7Gz8J4+1IvMDtXf7pIr6oWp8uSXmZnNPt1NQ2d7aEe3XotWtgG28h8rYQ82cRfQJFEdaFqLuQsfksuueR//vMftmPDychGsLwCgI/Z3rKoR1Nj74aaQX161tR3q8V8tKNNsIiqtaJBmuCDwiVRhJE3VUZLANGICoIpH/RK2VYbRsHR0dmjoaYJh+Vz5iCBpColr/Ch8EAUDarxel+TYpNxjIa6QbVbj9WUY16lSHLZ6e3XMfTCepGYPjLaSaP2CiqrisQ0VgHBBzmEht3O4PfgwYPFnZJM28HXVM4lMsdbwy+jnpihJdOAlxfc66zH2x2sBYSfj44qU955f3myV1rIrg6njfgOkqRgTMxl6lJ5Sa94Eo3YkVRxya28IgE21HdvxF9WHS65lUOKCGdTq5SsVloXLCcIILkzma02bNXu3ZvxNPzEE08sufgSK62wChrt1va2hlBbbbtSLUcvj1MBDzUXYiNVPOVHDR3NqOiq/UQ4+tYoyITLiUtHyva2lgULWxYu4N9Cjil56OGHJoyfgKYL88KG+oYBAwagTQAt8UKPFPHkk0/iaYI92DiZJ0FCaS7CQcBBoCogALvCrmZWSliC5TBCrmeeeQZXzZgw0H/UDZzYvfzyy+OIqr0l/O233/z6w3SyLFy4gD1j4U5kKrUhOEK3lAgXjmPebHoFt2aTuLiUAu5M0+tc1KsWBdo62nBSyWyLUca0GdN5tdJKK+BdIlJynXc2BlOzRLl72UAgOhWqJnfWvPj8a916N7KUK/IUUzDoFDMRq/k03venl8CaiNGU6elb8ZD4dvAuL6U8e+m1kkLmYpVeOYnwErDS1Vbbo3mxRfNbmLfr2LxYgybO5gy8tO5vQBBA4GId6LLLLoNkIV/gc+rQQw+FyclW1vDTLNYUQ3179vrlhx9/+3XOz9/+UFvfjePvUDSARYIl7CzzCvLQxntO+Gthtcc3qjQazRISKyE3SSQDQBPgsOI4O3p1LN6zvmHJfv3xLFIsTUMgigbVUw70MJOENSCx8o3CoUsYq6RW+mhOoRz2swnHpvcmLdmmQiJPioSVjuXgTQHBBkBomY/R8XMEFEjPnE2kEyyDhbMtcSmFgjIUAJnr+/VdvAFrktrGUG2dnnKi1Ub002qyiUQq5NRq5miimFBkDFt6CnVKpYXeMantB81rYS/X0VDXvWdT/+YePQUB4MRQdaNuVCiRlrchAQVyt9eWJZJ4h042tAscRsvAoh+axBNPPAHXBo0NdT/8/P1aa629xNIDW1oW9mhsxoyOJiWSO1nrMHjop9kGV1Ml1gQ2sgKj03gUDzxvb+++oKUfM8wSA2bPm3fyySdj20AaNA/XXns9Gof+A/qjAeNY2XXWWUfUDaIYhVSCYA7HUsHcxTsIVDAEzNTDPBVmwqqv23bbbbcavdWLL72IV5eHHnoI+sAJlyTjCoVD2+ywzdQpU0dvsUW35qbe/foBGbUDUk1fhieMoYVmAtUTa1TRIKQsEbCZpqcEyUL5Le0tDd0allxmSdEyNDTUnnTyCbBkGRigJTbIxeQNAmBU1vPOSYddqCZdc2qZ4u5iBQoYP4WWXV1GdotB2xS54qowWYhHp8GvmbNhI0u61B/HlKYoNBqdC0CipVRNSAQuLBoGDx789ddfsyeUTVL0Pmuk8gU5vnVH55uvvb7R7zac+cNPNeHamu7da5TKFYNrXYD2pJuMJUws3iAfr+ywQay4LHYa/Uq54lIdruFwTZb89Q6ObjXtS/XpURPWe3Y88SeuoLw+BqJoUNoEDNbZDNK2cEELB6U1MbYY0sTHCPcJIMmga37yyjfFal1pMgF1uA1XoPXsmGlh6wymJAJgKSnVZ8ugSYlJIQpz5sz56quvsENmmxC4ToxaGdDqpcT0VR4TBFj4mupT1tbXv/na++xC/+mnb/EsiaKhNszHV1dXQE6KWTHoobFJFQMGpVAzxKTXNeLrVZXc3t7Ws2kge2d0pGdDpdSQEqMUJKkucqltfcxWbUoNL7wRkbi0MP0KAoap6nfxMRCAvUbpo3GqU+0vaA8ff9yYB+9/4M033yTdu++9889/PTp71lz4cr6Rp8PmK2Noo9FD76AjpUGnmNKthy6VC1baJEGjF2toaJw1a9Y7n707a9GM1nDLL9NaSI3xcHOPZtyvct7ECSeesM9e+xEJmtFi/ZbDn/FT3BGCbrrLQcBBoCohANMCoTO+k4ABYY6O43rttddwzYABFDw9tIL9WVP+98W8BfM+/Oyjp1949ucffsbSAVVmn559yNXRJu5jbGIiE19E3W8mMo8q2ikF9DHpzddInV4lgfPkDiHFPqu5Z/dp06Y1herqGusWLGo98ogjCLGnA40/9/a2tqYGJZu2d7bTnXTzsSrYXfmCgHg34xMYlCDcdWURVkp97nH3Xbn8SkvOmD1LZ1QztUYDT5PF9kL+RXZ3pyo4gjbqNSWridsoy1JmMfO1xskIAoNdCg3bQ4t1X2zhgrY9dzmYAmFGlWtSfxft5/IFBH8FVkkq+ORBgwZxCu/UqVM/+eQTem2v1QUPBM/WYIUVhjT2Wbxbr/6tygd8XW1Nez1UKGrLEFezTegiJE6lSGGhEJc5zaOUBeaQBosGGLsl+/b+77/fxbKL0tNkzOurABQNjC5g2r0h1Lt77TKL923Dx74yEmfdVrHUNjjz2hMKFxAz7OGZFZzR5bS19ayt6derNyZN3evFHpgWWd810DbxdQ895FC0DFxrrrkmMzHFO0oRKIxNYeY7diA1dbaFb/3zXbf+5S6lQQSpGVB8ZEXXTXovYGIIkCxx6JkEXg71VyJJbCN00pSSS0rmzrQCFswGGZXLCBn/2DV0iRVqjsFrn5ZszfILuUTL0GV2u+0unCME5KsJzA3kf53+65QpU2BkKZy9Br/+9ste++/dWN/gnTohiAIWEpCfctuB7Zq88JoUT4vin1Xm2BzRnNG0cZjovcDVQoNSGdQqFwzsVyQrClc8JI0cMRJzjDacVCW7RMBI9sbFOQg4CFQFBAyhM73F3ImZaFN9ffTRR6eddhpeIVFJyEne33z/zQEH7a8OjAx10xMcClb9z5szPaJEeSoojxibe+9NPYmBaHpNCpPTw9iZnCzQW72sBy/aGGrv6MBZIGZcV155FYdlSB1KGlTMKm1Qk21La0t9bb2ZbRPb4WLyBAGlxNfLJ/ZUm1ldtTXrr7/WcoOXmrFgZgikrMdVFxihsSwivBHmoL80qiSdmFrt9J1pRKQk6cU3l2p5R224vTbc2omiYdH8NjBRdk1KB/10TUyhRYjwk96lAQIADVxSZ0+cfz6m5UDvkksuIZxv4CBrbrTx7/9y6y1TfpjR0H0x+HYWuNUG7pSKBlokdMzDooCaqBbaoWhRRUPnMv17HXvwwc899gCO4gKqJONi0owi32XVhtHcfPnp5CvHXjQfo5GGbu34OFeKBgg9fY7C0ZjApSnaLMfFpUmV104f0Syix2TiCNc21TcsWrCwb8/Fpk75rHu35nY8snApllxNQIFffFpMDX/6+SdKZssi24QIJM7WgddbfQXGj0/MOxlYJ140ZthaQ3769RvsGepDYB+qcWXTEAefeERKqkH0lJQmr6yfgGCxxwl47xPSi/aa9Ki0ezb0OXvM2DAHjnPkQLhDLRVBfZIzS16B4KgWSEkbh0I8ciX2K5rThYKGAEoiNh3wRRDXuV911VVoytlxINI75mmibujRrfuFF1241z77LLvssq0tWoYHu/SqiGkR6Ce4FIlJQL8IETMZwASloEhyyYwiL+KxXMVyMkXnZptu+u//fNDRrnYpE3X00Uf26dMH0oTMoBb46hpBJIdLAkN3dxBwEEgDAbh2CB3kgsBKK63ELmhRN+AqklyQFOy8+vXpRfy6664zd+a8xRbrIaXJnGuzaqaWTC3JTcbEQAxdVa8V5znm+DGT3p303XffLWpR9lx1odqzzz73ggsuWKx3L+4nHHcCugZWANrDrRxE3b2xO2lQSUDkk9NcXrsrPxB4//33119//SwnIz2NTvv1x259wlg0sHWHj6iKSpxe4w6EsvuSkFi9TGTtTJak6Zl39WSMcUx9TaO2aOgligblvE6JH1GByJSUGBADQ5bQEl+5mDQQQEuIM34+PSIYplWzZ89mk1ea9MG8UvrM0Jz5C+e2gHJ4EWBhh0/Nf9zLKGxISvo81ErUfOVEeyL7c7XhDBwgmo4FyKHN3Wu7NXa2tdQ2qsXvwl8B4LHMIt27dbt87FgFOzxhqAGlRxSiVdxo1IMwZT/Ty/+JeZOk5yNpW+X6bqGmbh1z59XU43BzHpsGFQcvJSTJlbJFPl+I3des2bPeeustJqkVVliBKZl512f2Kk8GXRBBKBM4mNEYwgkfVgwjR268/qZr/Tb3JyUMinZc751IU2a80iFNUu+Vd/K295zyrzbY6wx1b+zRHOp1wcljsbrA1qc+rBTtOJFA2ZAyq/dCnA7y9Ne//pX7n/70J3mT5WTsFev+ZgoBdkOAUVdeeeWMGTO4i6KHYS7cwFprrjn5ww/5ng8/8girfXUNaqsUh6hlWks+0vfqvVhHR1t9U2N7S+thhx0GIuEoHrMYWSdpa2+BYqMuyUfVrkwHAQeBioGATNDQQdT6LBsKb8P2K9QK48aNO+6445qbmzFtaOhdP3yNNWDBevZdTFnzWZfivzTrZesdYqfgKGcm0ppoXe2wx8NJufHpo7XB6WnV6o033/jjjz+O3Hwz8dGgtyIqxxMzp888+eSTTjzxxP4DB9x2y639+/dbY43h/Xr3JRuFYIoB52o4jGixLpQHCLATh8MIb731Vs48WnHFFeGcZXrKrCqWOBtCDY11uOGoqQ81Kn4rWQHpVpiTpc8wTvbskKm2sx4tA21QB534WEgWpo676LgefPDBNdZYg/ElZq0ZtqJ6k4M8ECI4ZzShMGPs8Prb3/526y23wsIZoGQlbpjcyQOtHO5V09mrZ+3Uaa1z53LSDW6zFIdPXQoP8dmWFBsjhVmUJkaxZcUnrzYxVkncyo5Cr7By6kRtZ1v/vj1wRtqG6NKIQJquHYnFBRUTgKKhU6mEQ736Lz726vN+mjm/tltza5sS6LEVUj4alCFHVKaSOSNV69N+jNiVQF2ETh/5GJYWQrlGq2flEDC3tS279OIPTbj35Wee5nhT1Salhwj+wiAZKofyTLT+O+20EzOxsgdzmvGsgG2DTsYMxQivY5Vnfclwzez5Mxe2zZ0x+xd8NOiLAWchhZXNDlpF2NEpwwltSJ7Sc/gXWrhgblt9SztLJh1qRcUziIiOiOT5NYWSVyhoTz31VLwPovK//fbbRbhVRWlGSoDjs1Wp6nLxSSEgoL755ps5KonpyuAhAUY3rteZyS688IK33npzs802a+sI77Pfvr9N/w3bBxhx3wc3JGJCpigZQ9Ls4igIo8FJk95eMG8eR1fSEbAGTT/IpRGmg7MnOOzN4FJSILhIBwEHgaqCQKrZBLqHvpWfgQY8PeGjjz762GOPnTdvHkRv//33b2hq0A7IUi4e2BJgCN9epjgvYHjhaE1Rti1deq8AKBxBtaCIb4ljjz/mq6+n8vzv995dYejQH3/84bAjj/jgP/9WZwHU1Mz87bfdd9kdpnCT32/M+ZcD+i++w3bbQUXVEb9OAxsFaB5DaLrRMlABZwTA6gB5m/1LXbFCE3CVz6xv+g8h7RlZuUkyaGQVwTqP9ZSPoIWzEaGRmFiVW4pqGV8K60IhWL4DDjiARxy9cUaj6p132WEvzv2NQgCLBuAGDmDWPGrUKBQNwPOqP1/FUYAkQgGBpMa+mmiG3ENaqAx3huubGhZRmjpgrlGdOqHkXzgtFBxK35TySqlZiGpGUuZN8gK/bkrXgNE0+K+W3DvalLeQiN4tTTuSlBVgVDAQV2db1oT2O/CPs1trFoah7coNT+TgIIwb1PYk1eb0WgbTq0QCkTKjNUfZuUivPEQwuttbluvX1Nq28OXnOOwk4pkyhjE3teYWkH19nKSCjIGu4aKLLsqtvOrKLdSTtVakIOYYFkY4jGrIkCESzx3akQQiKk6RdSW981HVwULqoAe8gCr7JYZXEp4koZhkBScksiN8jVVQHl2DPpeLFrazO76jg+bZYqBdZvKwaGEvvfRSOQtg2LBhpENl61uITV6si/UJAZCQU9wAvqQHD3v06IF+AbUCK3gcGEk8vMGGG27IOsxnn30+evTo32b81r9ff1t9nraupPiQEX1KWkKkTt6NGDHylVdf2XmnnS+9+GJoFOdvolywmyS2P3aMCzsIOAg4CGQEgfvuuw/v7nvvvTen8CDkK+GPhWX2jaWjT6qGpNMvkalmWb/pVX6VNtzWglu4N954u7l79+FrDoeGD1ttjVdfeplt/C+9+so111zNroopX0xpWdj65sS3Jk16hwZfc821J4wZs4iDip2iQX2ivF9MqTB+sH9spz/++OMJZ2nUkPeW5rECMJNLyYcdHTNnzoTjJYCjN2dvmAXQgSS59ttvP07aAozffvstj2Ig3LqwVYS1LIpNkwXxU8m5EB0doH61ygkZVFKv8lYT1ZSmKUW9yk6/ECk0IgUr5YWn24pRZHRRd/5eB6BokL7VNzbRo5+mTWvrwBmkMhsWBw3KigOg63nD1hekkgFJnPRKld58P8loKiJ9qKM91IpL4UFz586vCbc0NzYtmruguac+ESNpHblFotHHAAy0ZmtQbiVVY27EucaGRiaYu+66i2VYDtsbP3782muvnVzFkAxCmM+AcpBq9Icca6y87lnqCRv3kuWOxqXGNJ1GHwMhqdOl1OSGZGzX8nYP2TwXhMB+jNZu+ksAcvnLL79wopi48dtjjz1IFycoRnO6UNAQQPOt0LKxEdUhh8lrJ2ibrr766liXMP2z7ABLxDcCaSe+PpEvNWyV1WSGk28XdHMyLk/NNp2dmF9++eWXjfrsG73nEwobw65LmzMu3WVwEHAQcBDQENh999333GNPtYrWUdvW2saJkspUqqOjS5VrDCXKAzDrGprGXnLJ2Esum/jKq+3t4abuzTisoR7WOTfZZJPRo0bPXzSfZc+XXnrxv//9/N1338HMYcvRW5IAs7SaJrf1NQ+fJKHI9dZdb99994XxY0f92LFjL7rwIsMFJaSt8Aj0/thiYN8BBDBCPPfccznVJR+CcYXDUXdviSWWgG174YUXgOfZZ58NPNkzBc+Wj77zvdiuAE+v3CKgYZCFdkRipWuA4YIXS6VB8DQCkWbFPWbWWC10U71aU1du6hSnpwwr+LHumllZgaYOQNEg7YHzbmtVMnZLZ30rBirKuTqdVbIf3TXSWTrZLPuO8RXVxReOhAC1smjAgIZz2uQDM/OBZHnRMmDFAJ69/fbbBGjG1VdfrZvjbhlAAGLK3I9nps8++4yd8NOnT0d+Q2vjTwrSQjsWDbXsiuPT46FBjSpLz+DLuME0l+xcorQykRKIQ2BJGZfGyqsbFnltcDMxefIYMPbhhx/mwFReH3bYYayua3LWoXxluyv/EOjfvz88KEtesKQsi4GffBH0idA6dBBof2ThBdTt329An969YRGw2SsdzqAtzIbP2n69+yxoWUSDaa22hck3b5//D+NqcBBwECgZCKCNlel7/pz5WCyzltPS0oZC3GO9itxQNo41NXUfMXKkKPdbW9vw3BZuaUF9PGP2jH69++2919577bUXvRh/3319+vRbZeVVmLYXW6xnqnYzC3MxHSRNwCt/TEvS3NUYyWEfF154IYoGQHrLLbegaKhO72awu/L50baAQksvvTQsHyMLjKpGtAiiz1ibvvzyy3A+uJIRe3OAnGrkZl2hkua1+EkJBJB2tS2zKi8iIKT7gDDzKZQLcf4NE9qXKKEoqUfnEl8hVKsEYf0zTUoophARgSka0Ofw/eBoQw09oON1aklZXZ2dyv4sMoCAe9CdsmEdqVFXoZw/0oRQvapSfXnM78N448vHheBB3zEalB1WBx10ELX4FpLz0aLyK9MMfjwDPfDAA1itM/FssMEGiUSW7ynopBRY1gBWe5Nq8PbKrgm+PD/0i+p1JIkgRwJg7GhKE3SySo3PoE7ssi4b/axoCUawHvMK/awQMSVNUSMlWjJhBhTLQbj1hgEi+xlnnAGIiAbZbD7GDic0oKojBJ4GkubRYJof6Oy44452Mr6IsVcyzFAIIoP+uC7i/TGTjS2CGHYNmYbjS7Cf2f8sxTXrpTmvYURGsV7bGGZaqUvvIOAgUHUQSDXXILELLHr16iWBpqbI/iyhumkglarMVFmilCtVCuJjp/zmHr08gqeoY7fmyFkYhPv07sckLecHNzc1H3bI4WlKlVf0CIWy7DizG88iE8pcM910WY5LYCCApw/OaUKbjyd1APj2pLd///vfm7fpAwrginVSfJn6w6WO9EqJJupt8Jc161orUdoDPTiiLnhWdQ4acl/q+kkGarH7kgbSDxw0wOwRVtnd5Q8CNqwQwTAJkbMtX3vtNdbqgCoJ5O6vvPhU5I2LQgTQy5qRaFABBl0JIMqsgY9OeoWaalu3vtKKDKSISA0qbXxVOr99S5pAY6C0U3WX+pRgRCBparu4PIatEZJDLZgStOvjNCgjFo76AAhUEGiR9E/ZkAT7S2g2XzfJuAQbUlOfhDIyixDRRfT6UEwys/LJZSN9ZiVWX2oGhlx0HZMnQHfnnXdijp4UEkmwVn11s0khaSYVqQZdEuSISc9wlJ9JL7mS5jWJ7YAuzqYXeo9WRN0QU1f6h6lTp/7jH/8gzemnn44TXeADWEA2h1fp4SZvgRIX4FLmiBy90NbGI6/wOIUay08JLo2DgIOAg4CDQBlDgIk50ItJBHUz93/9619jxoxhZpHiWXlGSmSWYbqB/TPxgVZesYUBT0RBNDVz586dOHEi0BNDzkrpcBKONbFrAKG1rRW3ArxaaqmlsIyGeQGXElO6GD8QwLYUkOJA/dVXX+WwMLKwOESMn7yZpuEDazVSNJ+lcYpGxoVsySK7cFyB1mMKlAuaHlo1pgumaE26LCnfAanCX2IWYt/1fpiIuYi94i1OHPLUwmuvvfZ///sf0ww76hEImYqYePJUV8UXe/jhh2PsxE549uxl0llLtvedzSgIyOEHgW1y4KuS6ME2vsZaRN2ivcbefffdGOqj72ddXbQMvmp0iSwIgEhMLUzY+E9Bpc0SAduJ4+wUrOQu6CDgIOAg4CAQDASgvemvYKopeClMxxwxxt5vzMQ4y3zC+AmwK6gYMO5AvCHAVfBGlWuFzNE0HYjttttuhDmD+eeff8Y6JsIL6T/l2rcM2w3+3HvvvWTCH6TYB4FLGZbhkkcgICIY53eMGDGCxUuJRYcFQgUII2RMdApYLkTwFJ+EeqsCVUS/nCyCRpZClYFB6l9mTbPlkUgY54SRmqP1Z1ZoHlIH3RRZttXCVV4F+/SgUNYT3gJyRLGklSDZSKLpa9JvZec2nufB7K222oqZNVhU9tGEikrCfLPyyivTpXPOOadgHQuS9sQ3OoNRhiGMyX355ZeDSDgdFBMPiXeoZeDjJ8DeE3Y8ckIE23A4olKtO1XaaokfMLg0DgIOAg4CDgLBQAAej9kZqZjZBMPD/fbfDydzyy233EsvvcQEzdsuqyEZV5fJqiGBCIRYBDNNE+aMgHHjxsmO0TLuvieA+O8CuITbZtKjvTrhhBMIoL3yg0v+q6iqlIAOkGLFgBaANWD6jqRGZJ6UgLLa7UG4a/NqL2XB/mYghgTepgDrVkUp5UJ0CTfw1iYrkPEc9zOpYoa6fPi8qBpYfmYvEEumP/74o1h8mVlEAm5GMd8kVYDxby4OESQZj88++yygE6N3HlPlzSWeQs0vVTm2vYwdTpXeR7xoNGMSSvehjNBBLGLYWiaafrwzrDBkBeJtpIrJWWUPBg6m3wBHwkwkYmr4yiuvcCwoIF1//fUvuOACDDKZs1kuwKLhyCOPxGTG5HUBBwEHAQcBB4HKgYCZ0U0gRd/gWbPmgNnVuM8++2y77baIhVzMNdOmTdtuu+3QOHA+0XvvvSfTNzUL+8ckFXPhMN17laJ1VREtwOGOiuHmm24GREiGd9xxB/CUV2UGBSOMqHYLfvlFMeRh4UzAHLgXeBXwCoAAB3OVGTSK3VywiCbATrMrpxht6ULetKWJWD2F38Z2VYKWjlVhfpHQb8UZpgse+lrXoDum1LVdADrD1pZockgk4g02TgMGDIAiQBpoKIESbW7JN6tnz54nnngiGyDR2qC+4Xw+wAuxKPmGRxvIKMCQCSqQ6SWUEd3KFVdcAV7BtSAY03eZb6Q0SZNpyRWTnu6DD2j3MFJl/gBKoqIGaBxl9Prrr//973///vvvecU8zR2TENJsv/32QHLdddcdNGhQxYDCdcRBwEHAQcBBoMAQQBTcTF8EmJpffPHF0047jX2OLDXxiJcBDOho0hFHHMFm+5133nmdddZhejJeMwnDH+IyLk+LqwWGRo7VKSna81THSj7uwNk6gR7n4IMPzrHkImaHAxQmED5QyURK4O3iwkEDBpgkMnb+XWRwr7uCQByr7PahdAWwfL0PXtGQr5ZmUC7Km8IpOBSV1E4ZuDN/MHfwL4PGuqQJEACMuCDu27cvPho4y+OJJ57wr2VQ4r1yA6sdLviQ8+0kQX02u8z4zsVY2cS/5FkoI2sj7NCDcYFTYQvowoULDY+SJE81RcHGYZ6Asp/TH1Af0HUsV3EaigslsOWLL74AeXgLGAkAutVXX/2UU05ZfPHFhw0bRqSsMjFUBWZx81A1AdL11UHAQcBBwEEgGwigwsa2n4VSAuTfYYcdUGQzm9x0000PPfTQDz/8MGXKFOJx4sD6PGc3slG8X79+xx13nOwJRf3NHBQ3+yTGZNOy8sxjeGYYnhtvvBH4ALpyVDR0sEFeX2zf1mIITKVfYeTDDz+cNWsWuTfZeJPy/Iyu1SUAAXX6Hgvd0hKzg8MvEuapA3lSNBTRTkOqlnt0u3uewEexzBZcssSKhJO/iqqnZKYZjvZhMX/69OmceCRzOfN6NQjb+GiA4cCcAeUCBjLHH388372a8QpocJmVHwLs5ERf8Mknn7AbgnUknHJ99913KCBAG1QPol9Ycskl2UMLAIeuMFQWjmSEAkxSypitngHleuog4CDgIOAgkDsEmD4ohGmISQSruroaxfIx6YjW+xh9MT3h0o/99igdROPw4IMPkgbvzhtttBG6b4whuOPVy25P3Exnv6qeMOsB6BrQ17D3RHptz/5lBQfwxIgDCmeUgYPWOqjFsGQ9Oe+884iGy9181ObJ3ru4yodAFkbQKYCiUC7WkEbHBLWamqLWVNGBKBrMPhBqYQTZg4jDRC1pv6vl3FStTIi3q0h4qWoRKNuNkUamzZhQkv8IJcx0uHMH/QMsXUpM4pm2L7vssgMPPJDdE5xHuMsuu5CBKYfZPV3O2HeJlgXph3Gm6aUpdi6mEK70tcS2Mf6JBRBM/ZloebHmmmviX4oAqhb6Hp+0ap6xq8STr6jzPvvvZ6DEVVdd9dRTT6GUge0TPRRahlVXXRU82Xjjjbfeeus42ChmBU/gMaQpLol7dBBwEHAQcBBwEEgHAaYhmYtl3pEwsw8BXnGRGVnx0EMPZW466qijPv7448mTJ4sMyczFSXvPPfcch5T179//2GOPZZeorCVIRrhImaOkWIqS+HQNKvN30kHTX3oDWDh4gskdKBEu/eWlDllD1h+iLt0eCaQSs8Ic+WxYrWKbyQPKKfEEyU5h+9jUSDr3JzcIlM44skVQEVPjemYniHsV95g8uxYUItIC3jDVD/tudS/iFYiiIVn7O0WZlxQUydIHHIdagRI9HUdgCo7krYzMMXVF/ZLJm1aWsbJoIPp+lg6wjWd5HwMHezYqi46pEZ5hQ9EycAojTAwTLQ4FyM1KCHxMhsVUTnKZITBG/eCDD9hEc9ddd4EGAASwwIKInctFF13EShF+H2VZqezwpHK+luuJg4CDgINA5UKA+UimJOmiHbY7DcfS2NC4/PLLs2OCXRUIzF9//fUDDzzA8okkQ8LE4R+zPDMaG0UxvmOHRV1DHRlZbLCLqqowvB/nebPn8auvvjr11FMRuYEwQriodcoIFEqu094ZxFNDKv0Djs8xzOSLP/roozC6dFC4GuFkyqi/rqmBQMC/loHqSJyJfJFJ2kA6YxWSUb+sfC7oIJA3CEBqmW/WW2895mmERiZgqsL03WeFWoHndwRGEmvfjT7L7zKZlJmQzNdYY8qZMGECUjTT7WWXXsYU66acltaWLbfcEmsFeDKxYuAOeNEvsLOGZSI8SGHIIPoF2eWYAHwX4SDgIOAg4CDgIFAICLAuIo6lWK9mYkJOZjcomgUWEn766SdcNqAiZxZjUyR+hW677baHH35YZGkyFqJ9JVaHUt54F1wfmhfZLgrvB+NXRloGvXSslpE1gLnLLwbcdNQ8E2YtDXb3scceg++F2Tv9tNPLqL+mIy5QShCIRT+l85KYorXRl/BTtNZlX3G8hVL2JXWVU8SbrlK595lBAGt5aC7umsnG3PzII4/gkjezIoqdWrka9nHF4Q97O5l4uNipGO4Is4uEMjr0UVg+Civ1JHGd9dlceK+1116bKVn0C6effjrMGUXhKBRDBpQyMnPL9IwHB5/FumQOAg4CDgIOAg4CwUKAuUkuipW5iTumCsjMzGUce8S5SMiW//vf/5CopWqmNrIQlnum7YnUV/77K4XhYacJgGLRBQfPbe1tmUKjJNL7EO3kWwvfgsELuifOKNl9j90FZ0qiFxXUCEac3ZvsBppdggv7h0AlKhp8jHD/AOoypSMKXYIoiwT4vGAx/49//CPkAEmSRQAmnnyQBvwpmF+qdmojOGX7FvdLlV7iaxXbkDC+kiGnTQHxGoWGBbbj6quvJiC9Nj6Z09dYsm/NhxONiTxyh/Eyr9I0niGG2mWPPfZgvyuQwcvj4MGDARobKOROXsqRokjs50pTnXvlIOAg4CDgIOAgkB0EZAIyeVk2QGZmKhcbB97KKzYIPP/88/A5L7/8Mqc5minMZLQDMrsRw6QJR2R4hrhc8hgXaZdTgmF7vmZOp4NABqMP5vpx48a9//77pu8l2Pi4JnXJTJK+1ttHQb/4lMRgqvn2228T4Kwx9A52fw22xFXkHjOCgIBUBg5jBxwjuw3njErLX2IxgPF/T9ESkTu46x+SSJ69B6RoRjQ6QRCKvnKhdBAAU1lplxSOFqSDVFbvmGO4hg8fznFQgPf8889nPpaSSpA6JOmiGtghmXWSvLWi6B1dk/kGQfrJJ59Esb3aaqvZ3awYBENjwo5E1nboMp3CQaPdTQsq0SAJuEaPHo0KBk/d0Rcu5CDgIOAg4CDgIFCSEEg/a8tbDsik7ZtvvjmmeenTs9QPY8BUyAo/KxAoL8iIyMRMyis9SapbSUIig0YBhIWLFtK7fffdFw6QDrKvhAOtTRHl0kfF+6FT6FQ/wt5xg6ofeMjnrr4WXq71J/v222+JQRuFxoGA4XUJuyt3CABk8Aqk4hz0zz//HGmC3bjph1vulboSbAgoauWuTCEA+UNGYhc9IhOXSImZFuLSp4EAO/TQbaPc3XDDDZlWmWJxBCjphTSnyVuKr1IzAHSHCYY7qisOa5TGX3nllZVEB+kLYwR2gZmVzvJBGUFvvPEGipUuPxZ5AQ7Z0VAAIh7jri5LcAkcBBwEHAQcBBwESgQCTGEy42PjwFTIvMY9fduYNNnDjwj6z3/+k2Myp02bRnpEcdnYnz5vubwFJlzduykvFRgtDhkyBD6QzrLOn6wLseJ7shSlHwdni1tQjkGFL+JiaQ1MkM0UNB48Kf0ulH4LDRivu+66ddZZB1cp99xzD812Cp2Cfbu8KRrUqZZdkM68djKm7mT26rnUjm4M3EVMgspjEYdInEtpLm8iBIAwMyviJUSBt0D4ww8/lGRMrgTyhriJbckqRqGcnCuTPrvCU0xj6kJ177737rPPPssjSMUCPjNu+pwl+DYNt8TX5Cgv+KqPPvoIM9GDD9DXP6UAAEAASURBVD545MiREH1mWelpmryCDIBFTE9LsOOuSQ4CDgIOAg4CDgIZQQAOh9mNeY27n4xMo/vvv/8hhxyywQYb7LrrrscddxzMQ5qp00+ZpZMGppqLNTyaxMHehx56KAHEbzndXNpZjnxRGgjTuzfffPOTTz5hHQV2iO9LABMV4JAml3uVKQRkjOy5556SkcPRMy2h/NP7ojB56mZOdWuywK2zFgtoZQWEORCbriN2XNosKKw2h5hfYJ1QUlyqn7JT0pc6YCaq7BD3kEquC+QCcdE+brHFFnfccYddoAeT+L92Ghf2CQHI7rLLLos+G9UjPgtmzpxJRnVckM6fNe6CF+bnpyWiOU91t0uQvRIeBmoU1RoHO43XdtN81Rv8Pt53330MIRiOsWPHci9HB5AQATT0DA06woWxD3fp+5QpUzDTgGPARAUG4v7772eEkPKmm26SBOSVQNK7lGNKs9PYI82Od2EHAQcBBwEHAQeB0oQA+gU4HNM2eyIzYfNWAjAG6CYIY2z/+OOP33nnnRzqvOqqq15xxRW84uKVWP9RQtLpMq7A0nyUlnO8JatK8H4vvPAC/YVb4KLBci+dlhuuz2P8umhah7WPgp7iBlI+Nw4p0DIQDvFPX10U5F77hgDgJC3MJ9uUCDz99NNAnvGS6RiR75JwrxGWFx5WC8Jyo+wkPK2WCiLiq+/mZ5Aw5sw7uqh+bN5R9yJeRtoJtg2KHFT29euvv9LBV199FQmqsnta3N5hVbjbbrvRBg4chiITYPRGJpzitixt7d6o9jUQ2IODsRwnKVAkhyZwmAIWHGW0es/ngLnhLiszkG8hxBj7oHc499xzR4wYga+NM844A7NPtA+c+NW/f3+cO7L9EmaCXsNMpOlvUnqdFvzupYOAg4CDgIOAg0BFQQB26LvvvsO689hjj0VJwbQLUwTnwDENHGaBHHX44YffeuutzLx021iGm0BZwEKYB2nqmDFj4Cuw2vjyyy8xfpSOkKAsOhLXSOWsAVNcfTevOFbjnHPOEYkXFpcA345lJ1EnmWQukCMEWLdj7FDI0KFDwR8YbNa9qtEUvUjqhjwpGnLEijLIfthhh0EUuAYMGFAGzS3bJraH29lYxVY9NDvjx4+H/irvDf7sDEur0yn8vooUPXHiRNk3wVYRGAhIYblI17QT/S0fiMUHGAIaD2cA5PG4A9/DrHnNNdfgjkG+BW85SQQSzwLFAw88wCBS6cNqUyK5/HRZuBD7Xlpf2bXGQcBBwEHAQcBBIC0EmMLSvk/+km0FyyyzzKrDVr3hhhuQuhG/t9lmG0mKo7v/Z+88AOMorv9/RcWWu2nuxqa40DuYUEwPLTQDIZDQjfk7xKH33gIYQk0gQOihl4TyCx2TYIoxNXQwptjg3mWVO/0/s0+3Wp1Op5N0pztJ30Ws53ZnZ2c+Ozs7782bN++8886dd96JcM6qmWPHjmVJC0bIOevP+U+daKEeJds33HCDfeuvu+46LFvpZtDlJr+u1+FthZr3dPki4/5pRmLYOIKGaODAgRSZMJ2izigD+1ByEMBGpLLKuZOn/0mnFDni008/lTYnB6RTJylFQ2ouTR5lkg8tYP/+/f846Y9NRlaEFhNwM/OjUT6upHD//ff/8MMPBMorK1qcYD4ubOItozdw+eWXW8YOOOAAPjyEW9YRyWnpgh9I/0aoCej9sPGYeDrXX3890w75+ac//entt98mGsXhw3nCCSdcffXVS5YswdpzwoQJpSWlpGYbtgxOy5B26oR/OwVEQAREQAREoLMRSHwwnZhqMyuZWPrEE09wfPr06RtvvLHfZ3j//fffeuutffbZB3XDDjvs8MknnzTJyhJvMlobR0AUxA8F3QlsIVnnko5EBxMOcXWJpQb9n1133ZWioTziQZhupY1Rd+DbYcvAZmY+ODfB9ofC4inMf19g3uGKj9CRJHck/WzTEufz3jkrqM2CyVnyodC0adOoo9RdmvI0Jt85zEFnShpdA5wBjtW9c5PjzWBLA8CbkpT8kqWJ37an6k2msI+K02l7ygWsY3r37k152zZLGd3NOiJ8Ec0Cza7hIPUfV0YMsKy++urrr7/+pEmTOIU+nueF90c8V91+++0LFiz4y1/+wiwJFPbWuHMVAQtbUjzWxvJhMW3fWBwdFwEREAEREIEOSSAoCznNvrcyNIK3Hd9oo43ee+89eqQszoXvMI4jsqL6R9c/ZcoU/Djw9fztb3/72WefLVq0CD7ItNbr8OX2Avy8kjf6ev369WNOwauvvrrZZpsV/pOt7XymlVuDj5IHh4sNjDXWWWcdOlcUkAfBPhin8Etd4Dmkb8m74GfSXK3zsjAqZrRtb9gh32bw6/lT8PJnRzLf+4WqC5hLxNrfnodEF86zpJ/n29fRaSch2j5qId7saMrJ8tChQ9usUrYTQtnPJoTxFktbwHj4mWecGQ2FS4tLsn+bHKToXLDEm37F8Mvw8ssv4+2SRXd8LUOh1Ss3yS0Wc5YLRVGbKsmECHxQbbHFFvRsTjrpJLQJ1mvhSTGQ8thjj02dOhUjFFxk44QiDV1K6jf0aaLplAiIgAiIgAh0QgJpPpF8QP2NIRk8ODDz/5prrmHq4vDhw2HFCA17hnCxeujbt++ZZ57Jp5kFMulsIMlzyj7oBUWVEiEcYuh49tlnL5i/gDWqyB6iOBnmVEFlNWVmnEcGc8qA7oD8psoyAvDee+/NOA3PC40DMoWNxLSLAqYsdWEepNfK5ucNJ2jWzWbdQGoUG6dgHhxC8yMrkBUCWpexeRhpC5CmaKNpmtFB0mRzvdqF5kFsTmxow5xWGMhsKPKXLFvas3uP5qSRHDdVm58cp81+V1VXxard4MPJJ5/s35SS+uECCTjLnWqXFxz24gUaO0Y8ofLT14xgizF69OhLLrkEE7Wtt96a+SDWuLunFmjlGysOHak0fanGrtJxERABERABEegMBJr8RGKnwAeXXtPvJ/4e7T9jAzfddBN7fBzMmjWLs6SADoKJjWj/x48fj8U+Xh6QbwuNnusNhMMUBPGP2fV0IegHciTfQ7PZ5MR0UYZvSJFOIAVkjfN4KO7KqC2XBDAhocLTQcXpG/1VXGNQx+jf8pf1jje2Cb6OqU7VkcvSFWbaOVI0dEykNNOIVRinzZs3j8e59tprs6cplOOW3FVu07gjxL7yyiug3nbbbUtx5RKLBU2hcnf37KTsbJlob1K/FCwvwSrafHJo+PyZBdm5b9pUrEnNUAtgKfElfPTRR8eNG8dHETWwKd14C7DIuPTSS/v06YM+iMETjhPff0D6cKZ9DjopAiIgAiIgAlkgwMfXyeRFxYsWL2JpJ/oVrE9Bv2K//fZj5BwHeBdddBE9De7EHjfbLPyEKSIzUtPrGugn2HfcD2Qhr2mTsBvZMEbXIreIhm1+d6LwxmISWcz4Xzd4421+gFJnfLUitpwAK9nde++9jBbjgh03nJXVlfGqeCHIcVjB5HbLkxYrK4oGfx4IyptAGGCNeNrPLcpcpo7qi4k9SFnIUdjz01jTpufyhp09bazLaHzZ8Gnks0Dr64fTBCxS8M11+kVvhaE0V7XxKT6cLJxcE64pjTr/iG1zd7sRe/+zncl9iczsFfbUf6ZLsErlWWedhU0jMwy53JIyjzv8NHVDk8k2KwNNpqYIIiACIiACItDxCKT/Vto3nTjusx4Jo/fnM02gKFJEYK211mJIYLfddkPjwEf82WefZdiM2ZqY7jOWU1FV0ZiigWuNJMmij2DU3ZeK80jYCpvHDGTl1rBFKxRMKv0jDsZUuMUEwM7AGG44SeGpp55C0UB1or9qfs1bnGyTF9Zzz+bFTqlWqFch0ibaMEGiW5q+IOHcPTDniH1GMlPa+7XiZFYUDanuX6tiSIkiVfz2cAz1KnV04cKFZHbEiBFHHHEEtZNh9kgjg9XtoUyFnkdaXtvc59PTL1ClklrnQBncOxX4WQDBcDzk/vwXP0WW/K+mFTBFjGwf4kbUZIY7IkXONsEFopFM7n7ggQcydYKJoHiWQsvGAhNk3s9/trOp9ERABERABERABFpFgG4qOgKGAYYNG0Y/dpNNNqHvivskrB5QHHSNdvU/4kndAKZd0EMwy4KkU63KUFMXN7yXn8OmLi2g89Yfdfas2gqGAN3dAQMGkB3kCFYzIYBZcYHkLnMtg8t/wUk7jVJsVrkaTaXDn7A2jsYaF/pYoCFfYcxPS+0sGjxD8Q5PIF8F9L83fiCDnDShazD1Xu6VfAX9ctHI0sOgD2GBJvESE3MeFpJ45JFHfnPYbwjzFmB7xigHDTd/9lxIp8mkMniCiiICIiACIiACIpAFAhgsmCWCqQys14rSwbdGtK92w283Me0SMkHYN2egS2y94ixkrsMm4Q0y1SsdfcKC7hbWy2wH/eG5NY8xhwizdCo8I2fU6qVLl+aouL64kaP020WyqvRNPyZkKmtSaWcRq5Cv0C9cddVVXKl5E03jU4wCJkB9Jnfm1tGyiUIhZX5NH8H4Bmdpl/lD70b3xTofGESkvEoHRUAEREAEREAE8kgg6bPuaQlqmDRhPrDIWJNag0GDBjF99fvvv7fI2JnnsTiFf2usGDIxVm8Se+GXtN3l0HqtGDXQg4X/ueeeSxH8Ob85Kk7KWRI5ulcBJivxoImHQkWsRtPgyVcrVqxAv2DhMWPGcAr5Si1FEwTb9jTvM3Zq/HXyFzuJOrU02NUgzBHcmh533HHrrrvuzjvvHNQ1JF0b/Ilawf8ZDDccCfGjKSACIiACIiACIpA7AnyC/a+whYN7xgmCt7ZTNmBgx/1rg9H8MJ7z5s6d++abbw4ZMqR///6HHXbY8hXL/a5vMEBnOdjT8FNofSBYHD/c+mSVQuckgGsGxowp+08//cQ+2JvNLhAkERNGeAPrvYS5cZ2Qe0vtluBJKnhLkujY19CiWRtNA/rMM8/88MMPtKo77rhjxy61StcBCFBRUdni84kPP9XYFGSsCjFt2rSDDz547Nixv/rVr+hAfPnll7jbvfPOO63IST2SDsBBRRABERABERABEWgBAQbYXn31VToGplCYM2fOgw8+OHz48G7dumF//tFHH/3444/0NGz0gmE5C3MjXwHRgps265I2u1GzcuUIhL0Fx7zLkLUa+mog5/7W3MQVv2UEmCUBc6497bTT2LPsK4up2ZGWJairmiRQKD4wmsxoHiOg66LpXFG+ghYWaY329IYbbshjfnTrdATwvFhwWxNZQgvgZzkr7R1VlIqKVaSpafGk8PPPPy9evHjixIl4nP7www9N6cBNUTFsvvnm22+/PaYN/CRmY96n/Rw2FgiWorE4Oi4CIiACIiACIlBoBBr7gtOL+Otf/3rLLbdcc8010991/yGbzZ8/n/xfeeWVF154Yb9+/f74xz/iH32bbbZZffXVrVz0NLgw1xbphcawfn7o+NUuYFl3vMOtxFdXtHYSoovLxB/MeUaPGk0VpTPMOq+sIdhOst8us5kzRQPyXk0T8lU7AoY8xsjwFVdcgVqX5pjpau0o850mq6iMM1o6oS2A8DnxtAeBeXq8DslvRDjMdyj5YPrsWW/AjBQai+kGH+I1GDcuWrRo2bJll1xyyVtvvTVjxgyu9RUZ9AkOP/zw9dZbz1831EYkGktTx0VABERABERABDoPAToM+MyjOxEtjp55xpmIZ1j1PvHEExhC3njjjXgrAwXG52eccQYBFtEcN27cOuusQ9eC1a99F9E5xsU8UOc6qiA3/EFWh8KxUE0oFnMzepMyCaJwtG6cKemsfmadAJbpdIOpxlTdQ3996OlnnI7bEYQ7FGe9e/U2BUTWb5qXBJ28EW5Q4fKSFUY0s3HfgG9VW8yvJqHGc+qG5FcrG3dMlwZrByNmEcP26aJmfI6q+e233zJLDWFswoQJvXr1yvhSRcwyAeoTT5c9jTdOY2nB3SB8cahLUZeSaKmrbwzlB2wEsnz7jJNr6CSixvlPwtdi9aJFC1ftsxrmA9ES3pR6L4jpEZq8ifuEU1Bv4iWtJAF0YSRoDqL58N91111MkWC5bJKy2RPobqm9m2222R/+8AcGHwiY0sFXPRCTZX74mWEemsykIoiACIiACIiACLQNgVx8u103wzopETcbYuDAgZhGUpzjjz+en7feeivGDtyX3sU333wzefJkhoivu+66jTbaCEtJ4piZJGfxPE1XDUkPMa+VNKZPn87wyUMP/6Nbl7JYyDm0LtANLUNkZYjuaDgUq8S/VdGcOT/QTe3bd9WysjLEWtdVLYDOaoHSy0G2AF5SVELC5qBhr732wlrngw8+eOyxx1iy3ex5qavuqWTjuXhSgMkr9QoTGICsfzzwq55gEDjuhNtUWzDNxuKkuq4tjjVWllbeO+/FrO/6phWloVnkjwRwhEOLSeDQQw81Aa8VqerSVhGg1pZXVvA4VixbRguOIB3iv4qKqsqKiFuxhvlXztthq+6RtYvr3gX0qMuWL6+OVe233/477bQTM3FacBMaQTY+1Xy2rWZSGxlzICm0DBjd4HlhzTXXPOuss2g60drilIHWc4MNNqBDwASKV155BU9O/CR+UkvKTy0e0YInoktEQAREQAREoOMRaNhJ4IgdxByS7aabbvruu+8+/fTTfffdl8Uy6Y3QP/n4448feOCB8ePHDx06dLfddmMFQbQPjIWgmGi9luHAAw5kmOTJJ5987dXXDTjJFh55G3+l+1cdD8ejzok2xQ+XlfU4+OBDcW9x/fXXg0KiRBs/uCTgeCsjA/SfX3rpJVQPnKUuOScjrV1UBZ2c99fGxSvU22XFoqFQC5eNfCF60arOmzfPHIeQJE0nLak1tdm4g9JoCYHSklKnJCsprShf6dqOaIgnZVoG5GXMHApnCydsG+LVlXyJMaBD8T9n9hxmhaG9am4+rSm0UQLaRDa+3zgNwZrx9ddfp2aSIJUT5QLN5ZFHHjly5MhJkyZx3DS4xEf1wMxJP2ZzM6D4IiACIiACIiACnZaAdYDphzC8gY0DHJ566in2TNLEO8N5551HgJ/oIGbPnr3nnnsSZrYmox19+vTp369/a9QNG2y4weNPPG6Jsy/wzcaZ0YREvcm0zz337NSpU8kzcgQ9NOxMCzz/HTt722+3PWN+9Jy/+uornJ7Su6afjGEvhg8du+BtXLocWTRQisB8ijYuU1ZvR3vKuLGz+PJWuDz77LNpIMy0Iav3UWKZEjAJ+dJLLzlp0qQPpr9X6i1R4y72JunY969gzBlC0UidlWCkqBibgfvuu2/2j7OpTtQlM0nItOSJeJTRivnII4/gYQFFA6oE1qdEicB3C5M8RhIuuOACTDywCjv99NO5zrQMBLgQ9S1fOAwi2moKZSLf+lcEREAEREAERKCdE6AbRi/CpmpSFPoeVqAttthi1113ZQTlnXfe+de//jV48GCimcUlBzfccEOO7Lrbru+9917wqmbBwP2kuZxk9oRdaD0ZsoSvdvuvWQm2QeT4yhBu5Ol9XXvttXY7LE+lZWgD8ulvwfAkHWaq6Pvvv4/djd9PdoOX2rJHIHs0nbd/a2vqbMWzl898poTy9aqrrqIu0i4wPkxWTMzLZ5468b0R0f/2t79dcN75N910I8vSoCZevHipUxeHw5UVbrae/80rBEhUHjLWs2cP1tSJs/BTxcobb7yBF4WGbJ999uGsm/fR/O3ll19G+XXIIYcErRj4wDM38uuvv2bOJFoMFBDUWL6+BNiDxTYOsnHQrHXIXtLW/OzoChEQAREQAREQgU5BgD4DvQgrKmFfMPMDrGa19957z5w5E4sGDBkGDBiAVoJOCxvzN9FBZIKJXnfDaHRm6N5wIxK/9fbboqGiquoq+oGJDo77t+FVbX8EW4a6afPVobKuXXEIWV3hSrTpppsyWtmycaa2L0gHviNVlz6zFfDFF1+kahGOZ28dA6ZxB/+SSLL6qbudV0/q1ZakeI3/TLrWEvSjY0udMKf2j+UnkD1FQ23+TcuQd10DGchOHmizaBGYh0a7huprzJgxtA6+Hjc/D60T39XEZmzw8PiIb8Wrrr4aoR3/xjgr4hSScwGyYe4NVYhZeUztwMRg+jsfkMktt9ySKQwEgl/HDD+QlHT99de3bzCfbb7ozFdkuID9iSeeyFpTtJ51yeI2MtVGfSZaqjM6JgIiIAIiIAIiIAKpCWTYeSAaHZKjjz76u5nfLViwAENL5lmMGjUKH1J0itInQgTTZdDhqevPEIrXrL322hwkZ1dffSUDf/SC6AGGMXnnz/svdabzdzTcLVTWrdvTTz/DOBC5uPnmm8lnFgXa/JWs3d95yJAhNh0eNw1UUcqTYT+8BSVH6WAiisn/zptcrjbuU0DSUNv4aEhT4PTqgDQX+s/HS8HZU3ibs59PhP0orQjQFDLNbPHixaRBczls2DBfZduKVHVpCwnwjYkURUaNHIU/xScffxy3DLQKvXv35V8+PLTdLt3atU4S1SAoTQfDXhbigbcxE+VfPJJINrMSxGJVLO2ycmVFjx49qqurmOzgrouE7rjjDvRWfEox1mpuu0YNRNV11FFH4TKXBaX4bJvmCxUYEyJI3iwVNTMis0ekWCIgAiIgAiIgAlkmQK8Mm82S4hIGgboWdcUS4ZRTTqHDg0ElXWtOpRm041q6NOgN8GmNVyk/Z0yOwOkDa8zPmv3Dd9/PfH3qK865ddytmUXPh104GqnyZjr7l2Qa8OWIxAXB/mHiWPK/wX5jnQkDsdzAOAtLuPg1y0NVKyv+8583WB+suDjCQBFeAIojctCQDLONf1O16IGzGuusWbN23333Pr37kIH0+q+UOfQWD3GOHTJx7mAyKtXG6kaw/qRMPM3B4LXIJl5d86VmJxxR9Vg+NcrEcjPT4EADIShN+tk61TaKhmzlNoN0XEvhg84gflNRaOzeffddYiHdrbbaatjtI7/R0vlmY00loPPZIcDHiY3PFU8ETTCNwpNPPI5k/fBDj26x9ZaMzfNk+MAkbIcyrga1WonGM5n87WlM0ZC61qFKQNFQVRFCrcDsialT3+Kl32+/X40ePZpb8rmlUA3vTRE5mFKlRXzawVVXXfXOO+8kGnG4BcYR1Ez7ZrO4p1QMDZHqiAiIgAiIgAiIQNsQsL4KOgK6KPSZXW8n7lz601GhD8NZetEWJ2V+kACZHGGjzcEIXEuHh/HnkaNGVJXXXHXVlUcdfTzrjlXGKipqqlbEKqMh1i+sSTkklKKzFUy6vhdxr6vX1BXcyYti0mmcFerCCVfxlC2xlfakgxpjFgnFZ0aJM8JlRNTrv9W7v360IQH4O1uYUOjcc89FQ0Vt5AgbFbX1uXAqAJMdage/UwsIrb9RbQrJckqD22Ha7HQNDY5nLQfpEsoC0HTJu3MULKlsjYlqTaXkztu1lmAinSTEtU838ZgzSbXxOAwRMw2eKkgD8Zvf/IbqSFuJhz9rVhq/TmdyQsC+TzQE+/5q7wnjnY3T669POfjX42jfu5R2X76c9UepG/GEuqE2D0E1c9J3w2n3EvUnGC2Q+0Q1SyQWOOUHUXAEonluKe0cqhB0AZGiUO/evV988aVqNOTFoXPOOZfmjMN8e6hRBIiMr+YpU6bMnz+f5XypY2hGKV3DahY8YhdaW0lkErG9ny0FREAEREAEREAERKCNCVhvjZuaQsHuzogLAfo/7P0IwV6NRbM9CwGsu+66LKLJmPP+++9Pv4j/GEohESwahq255rfffbtgwfwPPvwg1D20rHJZVXGM+aj43+7q5tYGU3LhRrqF1nML9N+SBZbadFJaN7juZmCI2C145rp/3iHXJeOA+8PAAml2Gcuxh0LHH388e8bSredWm7r+aXMC1DqreGi+kO8wn3EDmbE4RtPNzYtZNDDeibM4VAxmaODVQFZF8BJLqo3ewdrbBOpPvfsmXZI4F8xcvbpn8b29Kb9iribHeB1qtR4cbexeicRz9G8bKBpylPNEskEBL3HM+zfYcNQ70dwfZ5xxBl735s6dS9PGtZhmNTcFxW89AWsRfNl7wBoDfnfUUXf//e+sovTkE0/XxEKVFVUlxV3i5bxaTNfz2nf/rok31v/S1H2EwnFrFPy4DQIkGKxLwde87hsTxjTJu9K9+dzd2zhUUuQcIK255qDp06e//OILHB44cMDQIUPQEdC08QHmyGOPPTZt2rTJkydz5Igjjjj8N4dbHfM/xpZa+r3xsX36mDorAiIgAiIgAiIgArkmYIMftf0TzLjrbxy3U/UPu1/YQeCo78svv8SvAc4jhw8fjkfJiy++ePvtt3eGoqEQi1xuvNGm703/oFv3VVbr37ukSzESVaQYOTGMFXnDbnpQKiOc6MlZB49OY203LzGP3rnxC2wZrUMQdZJmIh1nXxsNI7VGI13KipbNdVoGOn6oTggwwtRYwQM3VbAtCPAg3OCxt6RlYyN8TeaD3j6Vmz1rY/LYefjhmto66MkdtTWffwJ1r4lUa4WKRmKlSKd2jDOCIxMuQrHgyhQNMasixg+MGhq+FY0knt3DbaBoCAppfuYDBxOvpX+uNuCQBaLVO504Xntt4me9OHU/3PvM/7jGbb4+BymR9XjYUHeRoo0h1yWtUJ4IVNdUDxu2Jjf/4IMP//GPf3TvWVZRXV1UWoolADMSPF0DJ+s2+2b4X466F7gmhea77jIL1aui9Spb7bcKMz2qmKcy50vnX87tSqKlFSuqFy5a9NBDD5V4KirWN+rTty9xHn74Ydx/oOc26waUDlTRe++995577uEsn1J/YgXH/TQVEAEREAEREAEREIEOTIC+99SpU1nJcs6cORTzq6+++vbbb5kzi+Ziq622euqppzbacJPd99zl1VdffeG550Oloe5lZUWhmq7FNbh/KKopjsTremJGKdiNCoS9Hp3Xx6udx55QQXi9xEAiXtcu2BWrp4jw7uGGsxM2Dk7DEq6OFpfWlOCNwjnPYmPp8eHDhhOwQSbvmHb5IWD9ahu8bLkjeU+Yd4YMoXCxC9dU4eU0xixubBlY0dQVzathdRXJH+9ssti+wJIyZop0fOnDu3GcO2PsHS1DcHU5c3JwypRyfjAnigZPhYkFEe9ZQ8k+IKfVk98aFNXO+uA4nyp+DWPZdZtLnGdtuhtMz1tJ1YaU3Z3ZPOOYulsplG8CZ599zsXnX1QZq/zf//63xhoDUEq6HFFhEjUn+JbWveU8zcD3oU7jkKY4gfjJFbrOoYu7Q23lTlRaDq0sr+7ZgzbHrf9UWR3rt3pfXH7Mm7fwpP93EvExYWDPKWoarwtzczARNL0DP/0JFMSRrgEI2kRABERABERABDo8AXpAH330EbMnHn/8cdZ9mzFjBhoHukw4fcCOtU+fPnSKiBNGiGEIpzIWqepaFuqzMs5cVH4X15ks+KSCHTk/XNtdtL6bp5xI9N+862p7jslKB+9cQJipvQd9Syd1eIkzyyNWEyutKcO8tlf3XguWLSXDuLEsX1luPhq4xuU/X8JfbZb1jyNgdanFLMJ4qffsm3FDskrfPk4WccJnbQUxdUOLE8/0wkC9tUrtZON4LIqfVCplI8vPZZp46+LlRNGAzOQEPt4hSunmYgWlvMC7mUpxkFycALuUigbEf+9xcgunQEpczk/ebm9KF1pF4tSqCZqneTAJ0BoC9hZI3EL/tjWBIP+aeLw4WnL8ccfffffd//vkf6FIdVlpl5UrylcsW0I1CGO+hMLJTKG4zP+oUC0C4eDxJgpTV7WI6Fez2os8M4raoxa2E8ynQLVZHl7Jq4D6oFvX0nnzFlxzzXXVldV8isgXc8OwXGCpy+uvv36TTTZBye00Dp63UVwoS7fVxEPRaREQAREQAREQgYIkEOyzNTeDruNWU4NagQGYcePG0Vn6/vvvn3nmGdatYLkKUnMSBvvqUJeyLhXRqv12PTpaEl64cGloKQYD3t04nxD7G9492BV0ZwPCQa1kGOgr1l4eiJOcoB85ccdoKQ4gGU0KrbZG70Xzl9K1Y9S7T59eTJqwa8k/PuBk2pBMsm1/J1XRpJ8Z5aUmVBwtilVVzvt5YUX5itk/zgwzL4b5Cm4yhdun9O6RUcrNj+REX6oicndNpBoJJ15Ttvpq8fKKskiRs7q2N6L5ybbyiuwoGigYc1KYGVWEbIdUH3WahajT7LkpS/U3dyohqgXD9WO5X3Y2eDx4JHit3aNWicSFMadzQJkYZ3EZNnc7Z+cQTCrTcEuqXaZpK17LCUQjRTzZvn37lleWh0PFLJ00oF//Ncr6Fw+iGsS9ahfBnIkbNHyCQf1CRrUirZbBvknUL+6V0FzWVdQu0S41laU9e/Re/NOy5eUVxdFwFUZ0kdCQwUOYcHjZZZftuOOO6LP45OAciKmFTrlQqxRrORxdKQIiIAIiIAIiIALtnQC9I/pIKBdY9+3II49kmXnMHM4666xHH310ZWX1smVLV65YTkdvyazKSFlJaHnonIsvXDR/ISPMFDzYwUvR8fO6dmGb0B5cpsKbDJvgVtedc+OnjegaGCCiA2hnTQNCz7OsW7fb/3b7/NmL41U1JSVFlZXVe+29F/atFOHUU0/t379/VhY4SORT/+aNQNey0n8++ug/n/u3kwHQf+H+3asP3t7LVXDI3LLZSEVqVRkQck3O9ZytOusaJ5Mg/5YP679GCJEoT5tTGbbm1lzOtsPYnb/58ce33//w258W1JR0LV/p5iOZLidV4oEB4XoinIsbOEcbEXjDkxOyU8Ho3NL9ZJFDmhdz/lkTrxwyYNWH7r3rrJNP+uHbGQP7D0hOpqnflM6P0lBk9U8p0MYEVlSsKCt17ot5KMVFXauqy8++atL2u2zz7bffeE8/6qwJPJs0/kmTt+C3J020wKn6Vc7VN3fSKavdPhDRO9yrd7eiWMnvxv2/pfNXUCc32XSjVddY/YCDDjr6t0fj8dGWiTZ3DHxKuSJY34Jpqe4FaSgsAiIgAiIgAiLQgQlYd4i9TSO1IRn6QhxgH6sJLZi/6Oyzz77nrrur4pWMdtZUVl121eWnnHpKcdhMGhjTSe6T1cdVK5Z5B4Ndu6DoEQw3nhpXMwXWE/SYP0GCK1au6NalWzQUOfvcs664/MrSLl2qKipxPOGGlLwRWYxY9913X7w2rNJ3FfQUQdMGK7hlVX2/+o8sV79aytzZDvQfMDBc1GW73faYMeun0u7dw9EiN186VG3aLpfj+sJsA0mhhYUKGlBzC8QZ7uiUa7gUYK2TGvemrNp3lamvvVi9bP6PM7+JlnRp4oVoYUaauCxLFg2heNfi6Grduyzv0ydS0rW0m7tr1DMi8IEGNYu1mWrwzhK5TqxHhgz+SBQkKR0//cR5968pBxzq6prB3bz5Mt6gsafHCLYawYsULiACttJMmhb2p59mxyrjN1z35949ei9auoSsX3HJny8//88hbNWs2rh23ytRkw88VTXLlEVSHU75szpU2tW9aPvsuxc6eJwDseAzmTT3okuXLqUt6FbWDbct9vkJtneZZkPxREAEREAEREAERKBjEaAfiL2079CKwtFHKq+oisWq+67S+2+33fLPp55YvHgh86O332PHX+62OyON1fQOHYS6zl9QcAhKFnWiYGCUs5ExzrrUGgJ2TrZYSzASR7i0Lly0uHhFRXmP0m4IgPT3hg5d86jf/W7evHm333471hmoG1iJjOXG7rrrrg033HDttde+6qqrrMfrJlmkHR5reHcdaT0BmLe4742JzSFHHHz1TdfNWxKLlrLUiJNe/eqSUkptfYZTCi7uvpzwar/bxUM9u4bOOPWcpx64K1rapfU3bVkKWVA0YHdE2Rb8/POVf7p6/pLKcHGXSo8rC2qg1DHEPL/ge16b14bmDFhB0IjUSmv1lgPwL/cDxHI83UI2kPW4JhigyCFICxKrKl9j9VX/88qL1CE29I0ZrVGTSEf/5ouAs0PzvHwy7M87zLNjqh61CJ8fs3+a/ac//en2O+/oXtaNVrvI+QJy2/BBw8899+yRo0YQjsRTV+yUL7xfo7xkmrdLStCsJ8gtqTADIh6ufuGVf5571pUVS6u7dOly1VVXo2Woqq6ojqGLd26HidmjRw+7pdM7eC2TXd68fCi2CIiACIiACIiACHQUAsG+UHDAnyGZsi4loVDJsmUru3fvggeHm2++kfUpLjzvwiGDBxeFi6sjMTpTtQ7CPRresmB1sl8qQgEhov74c6rIycecVbo3Cd43T0fYjDHcGgr96fI/sUe5cOaZZxI46aSTPvjggylTptxyyy10bnFyyWoaKBeefPJJ7B1++ctfXn755cHCBqXfIBCS0pZ1AnjNALhtdNoJNMXcPfXqeHz+wsXlVaEffppbEYtH8NHAcg9OzEc+rV1sMutZdckHhWiv0iLOUPPx88btnGheE9twxNCf5sxbsdKtCJuvLbU81vzcxBcuXHjBmWeFirqESrq7F453NsYEigQIlH3ujWtqQ0CzPyLaq97wKv8IAdMnuH2gjXC/8M5RFCotCS1d7DRLuIsJ1RQVlTZ1e53PPwF7sWlnaXn5nKD3LS0pRd1Azo4//niWUy4vL1+yZElp1y5UOQ7yIq2zzrosfbT2muv+etwRxTx3p2PylYn1SlS/ltQ7la0f1DbM/NhIMB6pGrHeWkXh0rNPuWibbbZZZ8RIdF8oI4qKolG3BpPb/PtaGf2fCoiACIiACIiACIiACKQkgJaB45MmnfSLbcccfMjBy5Yt69mzZ/2l6FJel/JgoNNY1y9LGbPRgwiddindVzx+47Susrrqmxkz3p02raoaJ+BVDDINHjwYncg+++zz+9///ocffmCZc3yBkyKdWDqEs2fPRtHQ6A10ImcEeGSxWAzRgzG/mFvGwIkLrivvuQhJf9swy1lituIE30hZ955ILpG4W1GuGl9yjIObjX36JFpwlqHMekPstRUYXYO9Au6+FMRbC6GqqnaB1Rbcp/WXZEHRYHyHrjV85z33nvHTXBbtLC4rQ03gLBqc70u3uUflKwjq5zppTJifnjmCRWrCqIFIicvtPgbaqx9UmVgVeRg2eNC/Hn989oxvItQF2TPUh1+Av0z2Ni0D2eOR3Xf/fXfeeed77723fPlye/k5jnr4sEN/PeH48eutv94ev9wb1eP8ufPQKbm1Yqlvjai1Wvr5aA4n925T310lpMKVRcomTjxp0oTT0XvFq51OMVrES8frEPiqNSd5xRUBERABERABERABEYDAWt6GSIXdKz+rqrBpjtPRCgodgTGdHDLze3W+rIGPhoH9+vffcy+sFbgxP/HJZTkY4m1bb731pZdeOnPmzNNOO+2ll17661//Gs2f074coin4pHlkbIgYjPmde9a5m2222UEHHWSqhwzyjitR502UfaSoxEvISRssksC8n1wpGly2TPIl4Fc9N3XCBBBP0VCFjIxEEpgi5C5r4y0LigZyHAtHcIBxwaWXLq2qqWQB2xJnO+BNnagtThrQPieiBlQMdmEdO34jRvpbsAVxB82ApNZVbNwtahqNlBRFKlBwlnUZPnTNM08+uaLCaZi0FT4BagttMe/qI4888tvf/tZXLnCEzB944IEjR448+7xzWf4Rn704+zn22GNPmHACSxNj/pDfNppWhRxS9zCpQGMSC1VFQtFu4d6hItd+xavjTvGJJqItFB6F/5yVQxEQAREQAREQARFoNgG6VFiPmiko3S0CbmVzJlSUlMRCsTRCR7Pv1PwLuLsbS3ZiZphZwIyT2xCayySLnrt1L91oGXuW7ezWrdvIESOfffbZ119/fbvttuNgY5nnuKWTPkf+5ZlETp9UZzv79ttv77DDDgggf/zjH1E0ZFj8xIA3MqyzYUFUKfIeMQtM1uC0NEcWDQi+dfozJOlaedmbs+GkZd6CCNb9AcHZhfMhfWRB0eAUObxK4UhJ9x7zZs6uKekSr3QljTr/DHFetPSPyn9CRAuGa6dOBC4Oah2SFQ2JaJYCTj3CFTU1VVCOrb5qr4oqBpkjTL1xsQx6E5lKJKd/80eAr8i///1v9paFsWPHsrIRXnM23njjYKZ4h4899hj+6imigjHShgMvatp4LTrJQq/4fIyFKsMhp+MMFTmVNi86tTen921RZnWRCIiACIiACIiACLQDAvSpMHE3KY49fSqOWIAFzn0BK+99LcbDgnkgk/wkn2bjYPlndIrAL37xC8t/SvoWk1NczgziaDRKCvjIJGAKF7uWPVvDFIJ5aHhWR5A1eDQYmBi9V199FSawbYqMJ0w6C+VaCRXzAZ6uiaI8agSY3JFP+aCTMkxdcBWi3iSLpCg5/xkU3ltzs0gF3jDQK+Ca0RmQeH/hSCxcVJ3uL1KdiI+egj/PXyb7IvfHT3fEC3v7eIjpLm7GC39cmPKPU15S7r6WZsybNoNbDPfkeftSvIDJBf/kk09YpPf555/HU0vyOf3OPQFeS6ZCoOhFszBq1Kh77733jTfe+L//+z+sG4JaBu/lyeBx5j7Djd2BtyscKuLPXjP7wDQWWcdFQAREQAREQAREQAQyIYB8nhTNhDo6h0nH2/gn2SBvKSXMJvNmPVt/7+ecDiQbPznFJJHbbruN4XcclnGkfGU5ngW4HXvnzpBxbG+zRCysfXoCGERbhHPOOYcAk7WfeeYZYKa/quFZ99RtQ36tm9rQMGIWjiTulPpfcsJfFm7T6iSS39IWJ2hKAk+pg1LR/Tk3DZn9cVXYLQHaxF8EfRN/adN0GfB0S+gsbEEKcyFBuUzDlEkBX3jhhSuvvHL33Xe/6aabMomvODkisNNOO6H0Ofzww9EyYnvmt7zJrafnmyFHeWhdsm4OheeZsvZF89uD1iWrq0VABERABERABERABAqXAF2+FmTO7ygGL6ffS1LsOci6FfiSHDNmzJprrnnGGWfgVNKmkGDiEQlnTaxrQc7b7yVlZWVmQL3eeutZKd5//32zOmlOoWpNsPES0pyrsh6Xu+c3A/VKlKUaaXYjvAWefqF2bzJ/8IiLFtySfnKKI4mDdmEiutMN2ZacSOK49y/R/NfaZcepGzjB4hcMLntrbSaSD17Gq0sNsz3Hv/76azOY2XPPPYPRFG4zAkyRsmVmbPFk7MRaoFlss9zqRiIgAiIgAiIgAiIgAiKQIwIIKebf4Z577kFIQWyhhzx58uTtt9/+kEMOmTBhAh7Tc3Trjp0sYN3mpMCavfbaa/To0ZT36aefNtUDewtkBiGx2GJmsXMcyxN+ndKBP8Tj/GgfUondzS63JeLvCTT+5+sdAncJGCmgGPDMFpzlAs4z6+wXAtHrqzP8BH0FhB0JmDCYrqFOw+SrIgKJ8vYiyqIp/P7775977jl+Tpo0CV+2gSgKpiPgXtCAuZq9lhyhHbTj7GfNmnXttdfuvffeL774Yrq0vElNppdlehsXYidGIP0lhXrW3oVCzZ3yJQIiIAIiIAIiIALtjUDKkX8K0djxtixfY3kIHm9umM4wHWP2J5544n333nfJJZeQAoVasGABrtNZsWKTTTbZfIvNz7/gfJtnYXsicAmzKhjAI9CWENrRvYwMLjyZtY0/SHKOnQgbAWRDtsbK4iT4GnyFVIOWgKeswCMCR5yzwjbcTJtge7ttftQKDYts2oGGx1txpFbgtykMDdPJwR2DN3F3d+qJOssIzqLFcYocN28pJXjeVSzzEYkJvPnmm9999x0XYZJEhQumrXAaAryl9iqC0bQMNmfMpjxceOGFmIeguDnllFOY+PTpp5+mSYpTbjmJBrPv0l+isyIgAiIgAiIgAiIgAiLQ8Qg4xYHnNnLYsGGH/vpQvAl8/vnn06dPZ1jUhuJYJpPe9eWXXz5o0CBmHDMHHAjINVxHgEXZCNNXb874fMejmLpEkHGsvJXjcA8HMbQM//nPf8DFKTYCwS11Ki6JoKjfaKxOdcJVvo66ebMtnGLB2TK4eRO+ksEP1BWdCoQDQn7jXgVRGaukHXfc0XepUhdPoUYI8FqassZpFuLOvosG8ZZbbkGzgD0CCwXjzdEtP+l5ykUloZauEZA6LAIiIAIiIAIiIAIiIAL1CJjQSwcbmYUTQ4YMGTFixDXXXIMtNh7NDj74YHrXdLNnz56NLINXe9bOHDx48N///nfcRJofB0uhXqLeD0uw4fHOc8S0DJR3yy23NAkFmxF4osRhw9ghHSITMOuJmSkkzTaC6cm89e7lMpa3/ORO0VBQ01RMyWTYU7NGuYCojG0/DiAxZDjhhBNYSdFey3pPq1P+4O2yF6xJ7QBaBgjd+fc7zz33XDCedtppf/7zn7mWZpHjxx57LKrWRYsWsUStaVg7JU4VWgREQAREQAREQAREQASaRwBNAXIvey5jNgR9aXrma6yxBgqFhx56iCOsmLDttttap53hvTlz5uC+gQG/DTbY4PXXXzdb45S3tEtSnupUBysqKg477DCKjGCI8AJqyKCpaa9jz3Xqj/w8xizOezcB3vQL/tKjHPTDKUuYVU1H7awNdyO3EAH7lPdMdZD3lsOYytgaJ71796Zu8fbay5zqis54LMiEMG8jFPzVgB988MH//ve/999/PyvumHWD2YagcB07duxll13WvXt3a8jYpweb/mxnRK8yi4AIiIAIiIAIiIAIdG4C9JCtL21je8DgJx1sAugdUChMmTLlyy+/nDdv3u9+97uvvvqKs4z8ffzxx7iNJM5WW22FO8l1112XLrqlgLCDFN1pJywbT1/ugNWpp576wAMPsDIAoI4//njfhKTRemfiZ50QmlXZttG7to8TWVQ0BAuMfqFQKLt1KDLwfsLLiYrh3nvvpRhIzuPGjSPAEbSAwYJ1zrD/+hGgYaJJ4j0Ehe2nTZv28ssv33777d9++y1nDRFa1e222w4DpPHjx6+zzjp2EE0qjRlmXfy0VpKAn7jF0V4EREAEREAEREAEREAERCBDAn6nmgASDR3vtdde+4svvvjoo4/wjPbEE08wmYKk6LfTaWdhhT59+uA8kkUrVlttNQwiquO1cysyuR236NhddwabmU2PDMgAKtYNLH6JRgbZJxM4ipNEIEeKhqS7tIOfJiHfdtttVCYUDSZCm5lDO8h97rNIs7J06dKePXsCx5oYfC689tprr7zyCm2WzYxAKUPTg5Z0wIABeKlBdWqvJe8qVxEHtvjp5PLc51d3EAEREAEREAEREAEREIFOQcB61/TDbVTPrBWwKR49avSZZ56JpwbWv7zjjjvef/99Bvywd2ABOHQNoMHwgUUrOgWjzAo5dOjQww8//O6778a5ppNcIhGERBslzSwBxaojkDtFA4qfQtL9NCXbsmbMzB9mMg5PTcJz4Tprr7Ns2TLf1L8OWGcN0X716NGD0qNWYHv11VeZDMa7hyrB3j30o7169brooot23nlnplTQ0pnuBmUNWgb8qbDnco6zEZC6obNWJZVbBERABERABERABEQgmwTMiUCERduKWJ3CjerR36YTjmjDbY466ih67BMnTrzzzjsxQLapARwhGgtk2oryDXPDWTZ6+8FTHOGndeaDxztGGDUNAE3R8NNPP7F4x3nnnYe6wYZOmyhj7aKHhST/NpHjnJ/OiqIh4PfR82wZjrhkw9Rwl/9GcNtUFvNRUTetJanAjVybFKvBT96bcNz+CHovQzgcJUMh7lTvbfEvpVadcfoZ/GTgnVUY+cnLybvUUV8kv+AZBlAuQIZVKp9//nkzKOJC3rrNN98cr7ZnnHHGFltsQQClA8TMHoQIRo8WyrQMwXsJbJCGwiIgAiIgAiIgAiIgAiLQJIGUXeigkwUiOA2BJ8XQJ/d746R8xBFHIDafeOKJb775JmtV3HfffSgdGrujSyJeY5ITQaIF9/5VKfPjny38QDD/sXgsGooyo4TRU+w+MGpI0rM0KA5ypYmWCQETWRNUmbsJbJBiRzqQFUVDQyAoCDzc6BEaVSI0vConR3DQ4K1z6RJPVIHUN3r8icc5wfKzZkrEm8nrFKx8qS/ruEeZ8mBLfjJLYt9998UqASCoDNiji9l0000PPfTQAw44AMsFM9CChClHCXRmbh23RqhkIiACIiACIiACIiACBUcgw443I4IrVqzo16/fft6GJzUmPrvZFvF4Q4naHYmEbJCf9InDoKONKRZc+bOUoWgkSjFHrDvikEMOYUI95tt4auBIlpLvdMmkF707EQ4ctFJaBGYmMg0cOJCXijeKrRMhaFBUdApw4DBqBVCYFpMlKm+44Qbc2LK6BFoGImD6gUoCdYO9hx62Ts2tAUgdEAEREAEREAEREAEREIE8E0B3gHdDMkGnnY79KqusYhlKs3wjVt4mDnAtghL7DiwfYRiCUEORx4wZAxlKimCIeiXPj63d3r7TKBqa8tGAKRFyNdIy/gXY5i+Yb9Wr3T7ZLGScpoQ3jYRwi8L7hv8FmqSbb76ZtV44aFYMROAghg+oG6RiyAJ0JSECIiACIiACIiACIiACuSQQFJ7pzAdnXgRviz4C4Yizt9xyC8sxMMLPWdM7BKN1mDCFtQUHMW9nbQ5knLlz5yLygAvLbtOwcLCR8jJpotNI1o0gSDqcbxxuHkuu8sCkieDClv4EiiACe3848s477+AuhVp18sknM2li9dVXD0brGGFeDGsa2GfeRnAVKhgWsMQbCmG2JBroIzhiWoaU+6T4+ikCIiACIiACIiACIiACIpAjAsEOecpbIDyb2Gxng+FgfKRrU0nMmTOnvLycSdPEHDFyxNSpU+OxOGHkAl+mwI8+gjrylH8kmFS7CKNxsUKNGjVqr732QsbBfeb8+fPBBQqK0ACUL8n68qwfaBclzm0mOyYLdAq+ioESplQxwBUtAy8PVefpp58mTIAVEwjkFnmeUqdcvPkVlRXcH81lOBKmvVi4cGGT2bE3ir2/NXmJIoiACIiACIiACIiACIiACLRrAnT+MVtm/9lnn9kCFhTn66+/3nbbbQcOGjh27FjWyzQzcGYcYN2MMIWsXh2rbqfylJlyU17yv95661kpbr31VkqNcwr7ydmmnqmTPpuK0ynOd2oKqKlQNFBpbrrpJl4MAhtvvHFHfewUFksNMweiUXjssce23HLLyy67zN6ZjlpqlUsEREAEREAEREAEREAERKAFBJCPzELh/vvuZ4kKJlDvsMMO5ir+559/fv3111l+bo011sCz5Icffsjcc3NwgMRh4/8tuGOBXILcdOSRR5qF+/Tp07HRQI1S+IVyKyx6apC4t8cKPb/j5zlSNOQo2ezXPXtVzJchqzMeesihhV+HWkwBZSSLU26//fYsGIFPio8//vjxxx+nvN58iNpdixPXhSIgAiIgAiIgAiIgAiIgAh2GgM2YxkcDRgq//vWvxx8//pVXXvnHP/5xzz33MODPeK1FePbZZ7faaiuGMBHOWTWT4jPtur1DwFPmL7b9BWO0iEtffPkFhcVYw8pr+4ItoK9lyHsOs7K8pT87hfVVUTEEtAw587/QYnDBmoHiDesX5t6gkCPBwYMHW7LBOBmYx7Q4L1m7kAwn5RPtI2+47VkD9qeffjrrrLO++eabxYsXm+ET+gUcUgwYMCDpwnZX9qxBVEIiIAIiIAIiIAIiIAIiIAIBAkjX/MK0gX28Jh6KhVjzngH/cePGoV9gHbq//e1v3333HWdZwu+LL77417/+hYiBVIX/SPwpchxzAJu7zYIXCBo4d7AZCpwq5I1s77TzTo8/8Tiy0ttvv43XBjMDbyh2FUIpTLnguw6wn+zNwCFfOQwoBbKbhSSNQ3YTb0VqVA5/o7pg28MrgTEMSV533XVV1VXtUQPHC0CheB/YUxAClII3/6mnnmL21Prrr7/TTju99dZbixYtwkcLU4wwanjttdfeefsdnFPQcPhA7PJW0NWlIiACIiACIiACIiACIiACHYEAIoZYI+HOAABAAElEQVQtSEGADSGCUjG2jzE4VuF77rnnpEmTmFnw3nvvTZ48GTkCBQR7XEU++OCDa621FjLIaaedhpahtKTUVBVIIhhH8F/h00Grsuuuu5JP5ESKg6tLwsahkDPPXAlfy5D3fOZM0ZD3kjWSAasftjeHBZgz2CD/LrvsgnzOmH8jlxbuYVMQmHaQsnz8v4+ZKzVy5MiDDz74jTfesHzz5m+00Ub//Oc/sWVAy8CUKj9+B54tUrjPTDkTAREQAREQAREQAREQgfZDAAEKvQPqBrQMOJUn44hOPXr0GD169IQJE5YvXz5v3jw0CzaCywDn//73v2uuuWbgwIFDhg659tpr0S9wCo2DySD8LOQxTuSjddddlwKiWGH1PYwaTI1SyI/L98hgawRizmBKh3zludMpGoKgmTrx17/+9d133+WFwW2Be3kikV69egXjFEg4/XtIznkZKMj48ePRnuDSElsGck5x0DtguIEejgUnWMJz77335icvNqYcROAsb7s1BwVSUmVDBERABERABERABERABESgMAkgd9gkAgQNZAqMFBCpkCyYGcFciYsuuojB/wULFhx33HGWfwZxZ82adeaZZ5Z2Ke3du/fdd99tx1E3kFRhlpFc2YD0JZdcMmzYsHPOOQfzcMSx9BJZwZYlXxnr1IoGtHEo20CPpur222/P1zPI5L5MZ0qKZlYYtueV3mKLLTBSYJZU8AU477zzmC61dOnSQw45xHSHJEKj4IfR0vGGB7eku+inCIiACIiACIiACIiACIiACPgEkB0QoxitxMCBAUsTKHwZhONoHFgVEvdwrIW52267EZ9hXQQupJKjjz4aB/x4WPRTK+TA6aefzmz0888/3zJJQQo5t75gb7nEZUPEbBvylOmsOIPMU95bfdsff/yRCUUkw+uB2xJLz39JWp189hNAfWhKgSVLlvAO33HHHVdccQX+V8zzAvfjnWfS1CmnnLL11lsfdNBB2c+BUhQBERABERABERABERABERCBBgSCYhQyC7YACCa4h2SP0uH3v/+9OeDHAuKAAw5ocHWhHyhwLYPhQ9fguewrCJidTtEQrCJY9WDG88QTT+C8wJ5G8PUoiOfjZYJcmSMWTI9QBz766KOslMHqtd9//z0vKlEwUkDvcPjhhx9zzDHDhw9nKpQVBAUEugnOomvkiJU9SKBwyqiciIAIiIAIiIAIiIAIiIAIdAwCiB5IIhhfd+/eHT8OG2ywwZQpU1A6fPjhhxzvGGVUKdITyJmiIRwPsQJKYW/UeJx8MP5v2TThvACzTMZQFpAxtAa4eJw/fz6KA5s0gaaQI/hZYR4RYQ5yiulS+HdlohTaB64yVyvoF6RiKMCHqyyJgAiIgAiIgAiIgAiIQAcjwLxvJBE2G/jE7BqJZs9f7onDOAK2bkX6IiPC+NO908fU2YYE8jtpwvKTFUVDPIRawTYXSIQ54tQN/myR2ij5+4ecMGPFslebK/OJ6GepMEVxP1doBNdZZ525c+cy1+NXv/oVs4ZYpXb11VfnPWQJFjZeZsoSKYqUFZX5hWryFfXT9y9RQAREQAREQAREQAREQAREoJMTaJmYgCrBF0AQW4whSdlBAqZ3SJk417IxyBqLx6pWVhEHAYefSDo18ZrKqkpz09jJn0tS8U2t4DtksJ/scdOQxy1HWoCAriGPhWvmrVPW9WamkZPofsZQizDX4z//+Q+rxdxzzz2bbbYZWgZuGY44Dyt+tJxkQomKgAiIgAiIgAiIgAiIgAiIQFMEMpFKGovDcZtbgYYCw3NUEkceeeScOXO4J1oGfjLC2tT9db4gCORI0VAQZeuQmcDcaMyYMQP6D6B0OGgwYySUfMF31RSBHbL4KpQIiIAIiIAIiIAIiIAIiEAHJuDLNdOmTaOY99577xprrDF06NBTTz21uroa13V51zUgbZEx8hl2UpgLOGEsse/Aj6ZZRZOioVm48hzZ6jSZoC4XRYswcDBTItMs+GfznEvdXgREQAREQAREQAREQAREQARaRABbBjcnPB7feeedzVEdyfzwww8smckpxJ+rrr6qRQln7aJCFrts3kQhrMMpRUPWKlzDhHDNSC1ctmyZf8reGfbmypFTZ5111o477ojjRj9OmgB6MtMpOIVZtN6zczq0xMquwXCa1HRKBERABERABERABERABERABAqTwOWXX/7dd9/NnDnzuOOOI4fIQYg5sVjs7LPPRgHRs2fPO+64Y8WKFZxCvPKLQDinJg9OHIs7N4Qmc5lFgwubVUNCIvPz02YBPDKYUwZf11BPXGyzfCRulN+7J3LRQf+NRmr9JixdutSKyItBgBfjggsuYBJEr169rrzyyrfffru8vPy1117roBhULBEQAREQAREQAREQAREQARHIlADiEhuxBw4cOGjQoNtuuw2FAktj7rHHHuZdEmEfCevYY49dbbXV1lprreeee27evHnE57i7MhqxCeaZ3i/jeGgxzIVEnWIj6dqktRGSznamn1lZdaIzAWtOWauqq8pXlrNUBBdR9dEyMMXokUce+eijj3Cv0K1bNzw4cor9xIkTd9hhh+akXS8uGjReqnqH9EMEREAEREAEREAEREAEREAE2jkBJB1KwGITo0aNevbZZzFweOuttx544IGnnnqK44zXfv/99wcccADqhREjRlx//fWrrroq6/SZPiK7RUfgIjOWcmU1sl1VLFYZqw5Vx6pCsZKoOx3DmiAeZudiZvfu7S61HCka2oehhNWVVj4z1Fqmb2uYDm5ROYiK4eGHH77xxhuXL1/Oa4BagVrJ5CKsfX7zm99ss802v/jFL4YOGYqFj78MTMOkdEQEREAEREAEREAEREAEREAEOhsBJHbmmyPem+iOV0i2gw466NVXX0XO+sc//vHkk0/ChAgffPDBTjvthHQ2duzYc889l32StI8xQlFRUdLBJnmSIJewLV68eO7cuag8MKPwnOWRWHFRcai4uAjzi2hROBKKuMkLNVg1dHYtA1SzomiIhNwsFW9zgUSYA/7x2tP5/2fpsqU9uvckHwj2SW4OWpY5puhUVFVQ1VBbrFy5EqWDOWgktT//+c9o2r7++mv0C/ykdrKnpo4bNw6zH7bevXubMsJuTQoW8CP7P/2AJeL/9AONHfcjKCACIiACIiACIiACIiACIiAC7Y6AWYibrGR7ZC50ChTk4IMPfvHFFzFzuOeee6ZMmcKYLhHQQRBmqT40ARdeeCHDusTnOIKYXe4TyFyGQnhkoc2nn376D3/4wzXXXEMK8ZpYTSiGAFdTE8NBg5emzahwRuvasqJoSIWxVsXQ6OyVVNe0xbFuZd3wD2LaEKtnmVevYP6ophgmUJUxQzBLBNJBv0Wcxx57DL0ay7F8/vnndgtqNmf333//3XffHdUa84gsKeqrJRJMWWEREAEREAEREAEREAEREAEREIEggcakNhaniMfihx9++Pvvv8/47sUXX/zxxx+jYnj99dcZ091ll11YHXOTTTa54oor1ltvPRJM0jUEb5EyjCiH6PfZ55/9+9//Rna77rrrSCpSVMJceLdVhthFS50lu7YggZwpGoI3KaTw1KlThw0f1r/fwEhRwPKimTmkdmLIYJMgsGKgBlP/8OZ40003UaF//vlnXgMqN6liw8OiEv369bvsssvMxsa/FWmgoYh4GpnmVnc/EQVEQAREQAREQAREQAREQAREoNMSQMuA0Tqm4lt52/777b9g4QJMD0444QQkNbAww4KfmDnMnz/fZLTmsmLFQJQULH7BdHgEN0aUtx4zBpP20tLi0tJQly4liQS9ORM1smhwPFoubCdopvq38GZMkEv0AuwXLlo4cMBgwlS1VFnP6BjVi2kXbKTDmq6ozbbYYgt0af/6178WLFhAEkQYPXo0K6+wosSzzzz797//fcCAASjALHVTK5hOjr39zOjGiiQCIiACIiACIiACIiACIiACIuARwNaAaQtoGfhFmI2hXMZ3jzrqKDw7YGuAAQJSGALX73//+0g4glYipfDFwZTHSZbjJkgeffTRLBrIERxDsJ5mUdSNKzM53ps3gYqh4Gz5yV4etyxaNJjOIuCvwRWLg4VBPB439dV5515Atqh8TNoJVqbGTHFSPhsiz5gxY/LkybgDYRUJUxZg10BVxjLnvPPOw0PJrrvuai5JLQXuxcQKu2PwXhwJ/kx5Ox0UAREQAREQAREQAREQAREQARFIIsDQr38kGOYgjvPYT5o0acKECYhpzgg9Gqmuro6Gai0OkqQw+4mqgnTYY8XAJZY4GgqkNkaOTZq79dZb/3zDjfiA5Gytc4bmz8iwlHO/h0+dH8Dc367uDllUNLhE3VIetUXB4QYH8qBlqL1vkkrJ+WUIVVZXffbZZwSGDx9uNY9wCzZqIX4WsJWhCpIO/ktZ35X1KfG/cOTvjjSXDS1IVpeIgAiIgAiIgAiIgAiIgAiIgAhkkYAzPSh1jvlRE6AysJRTahk4ZfoFdA1oGYjvFBPRKDYLhMvKyjCLwEED0aZPf6+ouBivffH6Ujy/4viFrH/Qu2OdQsQykNinl5cbXtVo/BrG+BGB8ySDJ4pT9282FQ1uMQ9v8wNO5vdWWqi7Yc5DkXhQu+FN4ojXhEPRLquvvtrU/061hSFQa1lVa3F27rzzzmOOOWbgwIFYLmy++eYk6Cqup1FbsmRJjx49SDmp+rb4XrpQBERABERABERABERABERABESgBQQYBuaPEWJzw4+MllJMMysG1qvE4x5O/bfffnvuZYPTCHqoFLp164b3veuvvx7tAzMysGSvqqpevHglp7xZFLVZc4qGhg4KGvMtYKtVNFaqhlc1Hp8lMNy4fzhma2siiDeqk2jsdlk9nh1FA5oWz44gmDXKlZeZKolVSxNaD/JRUtoV7OXlsdNOO6OkuAvP4Ne//jU1KWUNC5YhTRiPDBMnTrz88stZbYWaR1IY5GDjgHUDC1vwc+nSpaZuSJOITomACIiACIiACIiACIiACIiACOSaAEPCKBoQ09hS3gtn/1gxoGXYa6+93n33XRQNJ598MiPK/fv351ozath6660R95D7HnzwH1179V1z5Ab9eneZu3iFb9eAxwhsCmo84/rE6LtnlRAwt2cQ3G2mRMDFg5NbgzqBgBWDd1X6+LVnPSuKcI1Lp1YwD8er3c+EVOxu2aZboBgtv69x8fcWIDk/4BQRWfoL5pL0/VtY2N/XRjPuzqKlqKSkS/Srr76JxeOsAYFqiuqCLop6hgqKP1aa5BpUBmzBezQWHjJkyA033GBrulplpV4SwC+DTZ1I0jJwyt+CafoHkwLBOAqLgAiIgAiIgAiIgAiIgAiIgAi0hgA++5C5GkuhqrqKU48++ihaBgJTpkwZN27cpptuyux4FrbkCPIj0h/aB8IMNK8oj0Wi3ZZVhWKVsWg4HEVjECkiEusKFkdDxW6f0V9RpKYoEm7OX2PxWf0gUhKJY73Ru9jNo4iEMW+oXY6APLfxlh2LhngY8wA3QcHb4jVMX2CrwRkn+4SDjoaGH17sZuycRsfXjATDpMFxdzfPUsX9i40B+SFH7JcuX96ld++LL7l6waJFnLrnnnvQJlDJ0GlR23AKYhUO1RSLX+JqoRlZUlQREAEREAEREAEREAEREAEREIGCJ5BGy0DenclDdezAAw/s06fPLbfc8uabb2ICj+//OXPmoHQYNmxY3759H3rooSOPPJJFLpetqEDZ4FQJeEWoWFEVrwxHI/Gok0CR8I1ExOTfBlJwQmI1wTYerqmvC2gqfqRurN3dx8nAbnPuKt0/sVgkXlVR1bdbSXHX0i6xmPMx4UVo6112FA3kGl78Uax4hCkJteWtZewVGZVKbgvn3Szx2LARiRFG5VHNoy4O9x8y6Lob/+wmrsTiQ9YcapWMGTgEeCSPPfbYwoULTzzxRPQO06dPHzlyZG6zqtRFQAREQAREQAREQAREQAREQAQKhgCW6XiCxPj9oIMOQt3AFInnn38eT3zz5s1D48Cag2wjRoxADUG0EEsNxMtX6VnUJVqz/toDWT8zHI3iatINdSeMJhJTJ5JLSBy3mRk94YSqIDle4rdFTPxKqDESv11qXgyzy4/EQpF4Td/iUK+S0lLM7aMliYht/W/2FA3xKlfocLW3gqhHC3WD833JVqutyVXhEnoNdyfMKOzJuZvVOokoKo7O+OLTUPlyjCx22W234WsOs5y8/p/X2e67775Zs2atWLGCCsSGK9EXXnjBImgvAiIgAiIgAiIgAiIgAiIgAiLQsQmYwTu6Boppe7zv7b///gcccABH7rjjDmZMLF++HIv4xYsXo4NA8mWGwl+vu/pvt90YW7GyTmtAbH/FS8LOPCH9cDsic5Imgcv8reG1QbWEhe0W+IYwMZiDmEjUlLIIJNoQdCIlpX5ybRnIlqIhjgnHsiWLaqqqunYtWopIHwpFa+I4okClkuDbEFOLS9qQr5eUr3FgDRJsUKpWhqoq4xWxUHVlaBmTJmqwqrj++mtx03jBBRd8/PHHQYUCJiU9e/bcZeddjjr6qBZnSxeKgAiIgAiIgAiIgAiIgAiIgAh0AAK+bQKrDbJRoksuueTCCy90qoOaUHVVBUdiy1eGSsL77bMv2+wfZuG2r6raHfc2T/5tMBUicdb71xdg6x1N/Gh4bcP4ni4DJYONt+PPIFQTY1+E2oFJGZxFUidsWohEwm3wr5s40JrbcDmGIjvuNHb+0sVT331/1uKl4eKyMO4vQqEi5Ho/bT/Qmpv51zaFKezpT2JV8RVLl/Rbtff9f7/r1IkTazz1B7NrMHphBg7GC5YeC6KussoqeHbcZ+99zI+jfx8FREAEREAEREAEREAEREAEREAERAACCL9suHMYMmzwj7N+dPqGSgfmk8//N2rdUbGqSrz/MXeiUSMFUxw0VBY0BrfJ+BbBM5pwswlcDt1gfxi3lGgayKoTnAnkQdGQBYsGlCQoUOb8+MMH777z3U/zw8VdKqqc7sQdp5xGrRZBYwjrjgcmPtQdbBCqs2jgLoGzLsyaIvAlgLkL9iILV13l648+Ni1Dj25lM2fMKI6Gq+M1OGJA4TR+/HiWuuzVq5clYmtPOAed3hZIWUEREAEREAEREAEREAEREAEREIHOS6AKOdfNrYhMmPD/zr3gXCbmY8Z/+XXXrrXuqEoWNIyx7kM0FMmP80W3rqbbMOxnTcXqcLUTivkvUlzEKLt3qk13WVA0kF+0JgvmzN9x8y0j3brHq+KhWA3LPjgXCU534ikCMlY0ZFT6ekqgoKIhcbUz04i46SiV5aGSLkW10EMrlq/o378fUyQm/L+JePhgTVQu4DmsXLmSALNxeBSsfJlIRf+KgAiIgAiIgAiIgAiIgAiIgAiIgCNQUlJUUeFWwdxww41CK2Ohbl0m33TtCcceX4LVQFFxqCgUr44x1h5GsA9KwcGwgawnz9qhBvugBJ1JfCeAO0cN/EVqF/KMOvuLPG2tnTrhClMTe++995YtX4YRibMmwP2FTV0IQTmhaGij4pnSwSlsenTv9fPPPx9yyCHLly7uUlJaXr68W5eu199ww2677bLKKn3LuvdEFRJj+gRKnpqaKEtcRuvUPP5snDbKtW4jAiIgAiIgAiIgAiIgAiIgAiLQfgj8/d57Bg4aNGrUyMH9Bjh7/oRE37ayZN2ge0Kn4KTamsTKF3UibpuDzYKiAZsASuUBjdQJ+m1eEu+G9njdTBSMKsD7vw//t8cee8ya/QN2CqXFxUuWLGWBU86iY6iuqmKaRlG0CBUDmU88GJdM21YOd0dtIiACIiACIiACIiACIiACIiAC7YIAwiN28QxaR5h1782797PdtrJkE4oGcpUvXUMWpk5A2QONvqGKAIVpW7j+M60XYNERlAsbbLjexN+feM4555ClI48+Gi2Dl1m3C0UjRRi1eFoGy7Nlvl4q+iECIiACIiACIiACIiACIiACIiACAQIm8CI/xljiocZTNtRXNwTitlmwTqVQF2qzmze4Ub2R/AZnMzzADAS0OSyiUatQQauT4ZW5i8bTZlYEq0jg33HmdzNZVOLUU08dNGhQmjv6ioZCUJSkyadOiYAIiIAIiIAIiIAIiIAIiIAI5J0AIiRbUH4MhnOfvaBFQ93SjG2bh9SlzIqiITlpWCcfKpjfhQC9YGAoIyIgAiIgAiIgAiIgAiIgAiIgAiKQZQJZmDrRMEeFIMwXsrKjITEdEQEREAEREAEREAEREAEREAEREIGOQSD/cxw6BkeVQgREQAREQAREQAREQAREQAREQAREAAJSNKgaiIAIiIAIiIAIiIAIiIAIiIAIiIAIZI1ATnw0ZC13SkgEREAEREAEREAEREAEREAEREAERKBdEZBFQ7t6XMqsCIiACIiACIiACIiACIiACIiACBQ2ASkaCvv5KHciIAIiIAIiIAIiIAIiIAIiIAIi0K4ISNHQrh6XMisCIiACIiACIiACIiACIiACIiAChU1AiobCfj7KnQiIgAiIgAiIgAiIgAiIgAiIgAi0KwJSNLSrx6XMioAIiIAIiIAIiIAIiIAIiIAIiEBhE5CiobCfj3InAiIgAiIgAiIgAiIgAiIgAiIgAu2KgBQN7epxKbMiIAIiIAIiIAIiIAIiIAIiIAIiUNgEpGgo7Oej3ImACIiACIiACIiACIiACIiACIhAuyIgRUO7elzKrAiIgAiIgAiIgAiIgAiIgAiIgAgUNgEpGgr7+Sh3IiACIiACIiACIiACIiACIiACItCuCEjR0K4elzIrAiIgAiIgAiIgAiIgAiIgAiIgAoVNQIqGwn4+yp0IiIAIiIAIiIAIiIAIiIAIiIAItCsCUjS0q8elzIqACIiACIiACIiACIiACIiACIhAYROQoqGwn49yJwIiIAIiIAIiIAIiIAIiIAIiIALtioAUDe3qcSmzIiACIiACIiACIiACIiACIiACIlDYBKRoKOzno9yJgAiIgAiIgAiIgAiIgAiIgAiIQLsiIEVDu3pcyqwIiIAIiIAIiIAIiIAIiIAIiIAIFDYBKRoK+/kodyIgAiIgAiIgAiIgAiIgAiIgAiLQrghI0dCuHpcyKwIiIAIiIAIiIAIiIAIiIAIiIAKFTUCKhsJ+PsqdCIiACIiACIiACIiACIiACIiACLQrAlI0tKvHpcyKgAiIgAiIgAiIgAiIgAiIgAiIQGETkKKhsJ+PcicCIiACIiACIiACIiACIiACIiAC7YqAFA3t6nEpsyIgAiIgAiIgAiIgAiIgAiIgAiJQ2ASkaCjs56PciYAIiIAIiIAIiIAIiIAIiIAIiEC7IiBFQ7t6XMqsCIiACIiACIiACIiACIiACIiACBQ2ASkaCvv5KHciIAIiIAIiIAIiIAIiIAIiIAIi0K4ISNHQrh6XMisCIiACIiACIiACIiACIiACIiAChU1AiobCfj7KnQiIgAiIgAiIgAiIgAiIgAiIgAi0KwJSNLSrx6XMioAIiIAIiIAIiIAIiIAIiIAIiEBhE5CiobCfj3InAiIgAiIgAiIgAiIgAiIgAiIgAu2KgBQN7epxKbMiIAIiIAIiIAIiIAIiIAIiIAIiUNgEpGgo7Oej3ImACIiACIiACIiACIiACIiACIhAuyIgRUO7elzKrAiIgAiIgAiIgAiIgAiIgAiIgAgUNgEpGgr7+Sh3IiACIiACIiACIiACIiACIiACItCuCLRc0VDjbfF4PFhejgV/KiwCIiACIiACIiACIiACIiACIiACItCpCLRc0QCmeKxWy4C6wTQO4XDYFBD+vlPRVGFFQAREoL0QiFXH+CO3tm8v2VY+RUAE2phAwx5dwyNtnCXdTgREQAREIBMCNNeZRMtRnKIWp0u+w5FwJBJxgXAYpUM8FLefNfGaSLRVKowW50oXioAIiIAIpCdgQkK8xmmKY5W03fFoUTT9JTorAiLQaQnQwauqrqL4xUXF6t112mqggouACLQjAvT0qqurq6qqotFoSUkJonpeMu8MEFpzY9MyWArl5eVdu3YlTLfVL48faM1ddK0IiIAIiEC2CATbbUuzsrKStrq4uDhbt1A6IiACHYxARUUFfdbu3btTLr/rqD5eB3vKKo4IiEAHIOB382i0kcpLS0vzVaiWWzSQY78Yf/nLXxYuXLjpppvusccefIpQnOSrPLqvCIiACIhAGgK0237Tff3119Nib7fddltttZUEhjTQdEoEOjmBW265ZdmyZRtttNHuu+9OA9LJaaj4IiACIlCABKxxpjuHGRqWqsjmjzzyyJIlS8aOHbvZZpvlJcOtsmhgZi/FePHFF/fee296qxMnTrzxxhuxa+jSpYsVRj3XvDxU3VQEREAEGiPAdwhrOowX3nrrra233prmes9f7vnY448R35cf1HQ3Rk/HRaCzEaCn98yzz/zqV7+i4CeffPLkyZNpKNREdLZqoPKKgAgUPgEaZzb8GGDIUFRURDdvm222IduHHXbY/fffn5f8t8qTQvnKcjJdVlZGt5XAokWLVq5cyVQQK4m+Q3l5orqpCIiACGRCYPbs2USj0T7woAMJ8HHK5CrFEQER6CQEMLilt1pZVbnKKqvQtUM7OW/ePMqu3l0nqQAqpgiIQPsi4NQMcdeXo/Vm//3339Nu02IffPDB+SpIqxQNTNXjIzRmzBi0JhTjvffeY3AM0wYKo+9Qvp6o7isCIiACaQj4jfNXX31leuH+/ftjiZbmEp0SARHobATop8ZiMXp3+N7adtttCdPf++ijj6SR7Gw1QeUVARFodwSsoUY1TLtNuFevXvkqQqt8NJB766daMbBooDBy0JCvZ6n7ioAIiEAmBBAeiDZr1iyEB7TDO+64YyZXKY4IiEDnIYDxLRudOqZO+CtNYAblayo7DwqVVAREQATaBQEzZyCr1lBPmzbN5PQ8dvNaZdFASfgO8REyL8SY4DKHQoqGdlEXlUkREIFOS4AvEKZn2KBBAKNofrJ1WhoquAiIQBoCtg4unT3imI4yTWSdEgEREAERyBeBcMT15Rj+Rxinm/fBBx/QdK+22mqI6vnKUqsUDUz8QNuNP8hdd92VfioWDXPnzqUkHMxXeXRfERABERCBNATQCPP5ocU2yaFHjx42aJnmEp0SARHohASsL2fKhQMOOAACP/zwwzfffNMJUajIIiACIlD4BOjXIZUz6u+Gj8JhWm/CvXv35mC+Mt8qRQMqE3MDaX4ZsML9+OOPKRiF4ftkW74KpvuKgAiIgAg0JMBcCVZU/vzzzz/55BPO4iKIRjuPH6GGOdQRERCBQiNg3TyscPEuVmh5U35EQAREQAR8AiaJ44fLvOqw5IR/qu0DrVI0YNFgRg0XXXQRagW+QNZzbfti6I4iIAIiIAKZEGBFZaLx+fnpp5/4GmHR4Ly+5c+sLpM8K44IiEB+CZx//vlkgJ6eTbnKb2Z0dxEQAREQgcYIFBcV06979913V6xYgWw+dOhQW4Sisfg5Pd4qRYOfs0033XSXXXahGFdffXVlZSW+KFJufnwFREAEREAE8kKgZ8+e3HfGjBnmIsj8ydu8vrzkRzcVAREoTAIoIv1t880332qrrZj3e/PNNxdmbpUrERABERABCGCjyhyKzz77jMUmmG0wevRomyqbFzhZUDTwHSLrG2+8MQHcmN9///15KYluKgIiIAIi0CQBTKBZ8ehvf/sbn58jjzxym222wTAtjx+hJjOsCCIgAnkngNETa5nTaGCO+9BDD5EfvL3kPVfKgAiIgAiIQJAAVgwM/ONP54EHHli8ePGECRM222yzYIQ2DmdB0WA5PuWUU8xjEFN/OeIvsGGmDW1cKt1OBERABEQgJQEcNCAnzJw5k9WCUDQgLbDhcCdlZB0UAREQAQgwRHb88cc7T2PRKJ7MOUK7ITIiIAIiIAIFRaCsrIyG+uGHH6abR8YOPfRQ7ADy2MfLmqKBNdL23HNPivTggw8acXQNKL8Jm8mDHdReBERABEQgjwTQcJ999tk0y+i8+/fvj29IwqiJ1VDn8aHo1iJQ4AQYNBo5cuQ+++xDv+6JJ55glTGMcjlY4NlW9kRABESgwxMI+iugsAsXLrz88sut1GuuuSZahjy21VlTNGB8269fP4bI0KBMvnZyLB4zN+bqvHb4+q0CioAIFDgBdArmNJ6PzZw5c8rLywkcc8wx66yzDjkvitZqGWiubSvw4ih7IiACbUyAloFmZNVVV8VNA928++67jyN5HCVr4+LrdiIgAiJQmARMicDeNjLJvAlz+33CCScMGTIECT2POQ+Trazcni/QggULBg0aRHcWjcN///vf4cOHk3IwfT5LWbmXEhEBERABEcicAM0yMybQL2C5sPPOO7/11ltY1j3//PPbbbcdidB6y0dD5jAVUwQ6IQEmSuDJ/Oc5P9Ntxahh2LBhL7744uDBg2UM1Qkrg4osAiJQOARqFQzxWnGeYX6cfL/xxhvk8OWXX95hhx3oATLlDR1xXvKcNYsGJkqg6p40aRLFQONwxRVXBMsjFUOQhsIiIAIi0JYEsFngdl27dsWa7vXXX2dtoC222GLMNmMsD9IytOWz0L1EoD0SQFNJ/3XAgAFMvKLP+u233954440MlFVXV7fH4ijPIiACItAhCVxyySXvvPMOrTTLQY4dOxa7M/p45kUxL+WNXnjhhVm5McWgMLvvvvv3338/bdo05u/RkcW0wbqwKBqka8gKZyUiAiIgAhkSwFTBGl7aYcI///zzQQcdZLLBk08+uepqq/IpyjApRRMBEejMBGhJGDfDHPeXv/zlJ5988vHHHxPecMMNmQAMFnXwOnPdUNlFQATyTyAxP2HPvfZEHqfLx9yCrl26VseqzRVXvnKYPYuGmhpTbGPUQKf2m2++wXLDerH+7Ila6w7vn3wVWPcVAREQgc5AYNmyZVg487GhxTWV9vbbb08AE+jTTz+dBYnzZUfXGeCrjCLQIQlg18A6l+PHj6d3h7oBo1xUDGx+N69DllqFEgEREIGCJUALbIP6GJ2tv8H6TJKlfcb0zA32RyNoGfLbPmdH0UBfNh6LY5dL8UaPGr3fr/Zj0IxiY7+B9gEEPB6miNDHLdjnpIyJgAiIQEcigGveSDiCroFvDA3ydddd9+WXX/L5QQX8pz/9iWYZgaEjlVdlEQERyCkBGg0aEFx9Y467xx57cC/altNOO80CtueIbTnNiRIXAREQAREIEkCnMHnyZIb5UQf/4he/uPiii4NnaZaDP9synB09tBs0i9egSmGsjFGyKVOm4G+Mg5Tkn//8J2FW9eSnaRyseMFwWxZY9xIBERCBTkIAXwyoGGhsUTGMGTOGhS3R/+IJkmXqONWjR49OwkHFFAERyBYBunmoGzDKtW4eCs277rrrwAMPJP1gX1Z9vGwBVzoiIAIi0CQBunmbbroppqz0+qZPnz5ixAgChdAmZ8eigc5rOOIWOsJCgwG0HXfc8ZFHHqGEHN93331ZctkMd31MUnj7KBQQAREQgVwQQLdrBmWfffYZq0vMmzePu2BNN3r0aBTe3bp1y8VNlaYIiEDHJsDaE2x08x566CFKunz58sMOO+y5554L9mg7NgGVTgREQAQKigDuEbfZZhu0DOTqggsuWHfddWmlCySH2VE0UBhfe41nS743BxxwALYM9HQxcDj88MMff/xxzHSJg8bBj2nqBn2cCqQqKBsiIALtnYA1pww5mpYBUzJ8tmHkjBtIinbeeeedf/759vlBC9zeC6v8i4AItD0BDHSxh+K+WDE89thjtDn06/bcc0+6efTu2DjCvu0zpjuKgAiIQIcnQAPLRjFxVmCF/eKLL7BlmD9/Pv26c845h/Ekunl+tLwDyWZf01QMFImvDiXE8RhKborNduihh953/32cIg6dYOZZ5L3kyoAIiIAIdDACjC5SIrQMWJah5H3zzTdxET9z5kwOsrAlioYOVl4VRwREoI0JeMoEp0cgsP/++z/66KPW6x03btx997luHsfp5rVxrnQ7ERABEegMBHwNQklxCTrfN954A6c5mKwysHTZZZdddOFFhQYhOz4aKJV9aSyAZsECfG9QeGNWhwUvEY4++ujbb78d0wa8OfjxiUk09tpEQAREQARaSWDFihV8b2hmn3/heYYZSY2JEii50TLgkZfGVotNtJKwLheBTk7A+m9+z+2pp57ab7/96Pjhu+G444676aabGG1SO9PJK4mKLwIikAsC9PFIlm4eEyVeeuklLMsYWKL5Rctw2qmn4ay3sUkTfoudi1ylSTP7igb/Zv6X5plnntlnn30oOSw22GCDK6+8ktl9wY9QvgrvZ1UBERABEWhHBOjos5lKN5htPjz4ZsPpI0tL4HwYyzoaXlQMbERD+R1seIMXKiwCIiACGRKg8SEmezp1mKnShWNI6aCDDqK1YVRp1KhRV1111V577ZUyNbvWen2E1f1LSUkHRUAEOi0BGka2hh08gCxatKh3794W2GyzzebMmWN+GRhMwpYBLQN9PK5NiS5fjW3WFA0pS+UffOGFF0466SQ6vja175hjjrn1r7dyFheS5sEYoI3pYPxEUgYaA5oysg52eAL5epE6PFgVsKAI0O6xojCL/Vgvn5/UfHS7bGhyMWD+8ccfrbFlth567oLKvDIjAiLQ3gnQ5vjNjqkvn3766TPOOOOTTz6xomHacNttt5lyk24ensI5zqwu9qgnWtbfa+/QlH8REAERaJIA7aSt5EhMmlnbm88BJgQwgESn7u677541a5a1qJdeeik9vSaTzZd8lFtFg/clqtPKHHHEETaFD7UCns/Hjx8/4YQJ6Br46vAdatlaa/YMmuSrCJ2EQL5epE6CV8UsHALWy7f82Mfmzjvv/Mtf/sKyRhyk68/06ZNPPnmzTTfDeZvei8J5cMqJCLR3An6/yxoW9JsoDkybcOyxx95xxx1WwGHDhp111lm4A+/atStH/CbL5s+2dwjKvwiIgAjkjkCwnbRxI/p199xzzy233EI3j1aXWbGHHHLIH//4R1ayNE1u+szkqx+YW0VDwzLjnIwVOFA02IdqdW/DEfr+++2PnqZh/CaP+B+8JmMqQmcgkK8XqTOwVRkLkAANIFrtm2+++frrr7eZe5ZJtLp8kCzM54pAyxrYAiyysiQCIpBfAn6/ywJm4uubLbz22ms77bSTfYsZRurZs+fAgQMvueQSVJ8Wk8wH+9D5LYvuLgIiIAKFScDaSfYzvp1x6623XnfddbSouMKh4WV//PHH4xDH4vhtcpqC5Es+yq2iwcw/bDzNdN4gAAfzh6dOnYoChi+TlXzNNde84YYbmNq31lprpcHU8FQmcBtepSMdlUC+XqQkntR8v0eVdEo/RSArBPi63HrbraeddpqvX2DYELU3DiAfeeQRVN3cxRxDmr2DKmRWsCsRERABv9+V9MGlm0f315qaMWPGTJs2DVYcZM9xht0wuRo6dGj//v2jOI5s0diS4ItA3glY/U+q/HnPlTLQkQj4QgT2CxMnTqRRNf2CNad4wHnqyacqqyrp9eGjAUuHTCaj5avG5lbRwFOnN+wb7vofJ46//fbbOIl88MEHv/zyS0j5y4HiyWzIkCH4usCPA9Mrli5dylW77rprvgB1pIrbGcrCy4kzKpsy6peXN5NaxHvIqQhzdZwhudv8CFkPmIqRvbubZ7+Dw38CfsZQsXFTdG346+7WrVvDDNCmrLHGGiPWHUFvjHRw8cJs/IbRdKRjE/ArMysYPfzww3379v3www9xdvPBBx+Y+sBqtTWtu+2227PPPOsDoe7VhQO1Pac137+jAiIgAp2ZAFrOjz766Elv++yzz7xPbhgFqNkAb7nllnz+jjzyyEGDBvFxpKHDZXhnxqWyZ0jAPnZ06ugRmUrLLrQj1sfzhY4M02xWNMsAl/hfUvPBHEyEOP5ZpBg+3xafg3Tt7F3gReDzjdKNcdZM7N6D6SvcIQn83//937PPPkuT+M477+Bmi/F4RAbqSXDPguXEsUroV0WrXU0y8etkkzGzGyHnioY02TWFDb3nK664AutfnGemjGxymp1qiCkIOuXlOtipCKy66qo02dQTFlVdJ7H16dPHBnhB0WYr/FnrwB2p54R//vlnvjf4KKGqf/rpp/TA7DPDd9EkxpSPicrPZ4k4qucp+XSGg/aNsZIG1Qq+fpZFfFg5mClpa6+9Njopn4kUDT4KBURABNqSAB8sNuu88TlGn37BBRfMmDGDbxkb3z6+yATs20eENB/Btsy27lX4BKg5q6yyCh05+nVMUB8+fPhGG23EnkFKvo9UJLMtp1Kx5aI4VGw8MaPLIGD3op5TpVF80LubN28eXvrYv/feez/99BNrBHCWXBGZzLC3gGWMHHKV5TkXWVWa7ZQAvT6qFhv5ZxlLlGhjx45lWR86e8OHDTdbMA5yFgHByphJbc8kTi6I5UfR4N66eI3Bor2wnjQ2dd9//z0uLr777jteRfZGORfFVpodmADfISqVFRClA3XpqKOOYukXAoMHD+blxMgg10abGCxQq/nAUJ8vvvjiuXPnotK2em59LzJDA5GmhhPNVzHQOhC/Az8yFS09Ab8CoFygbq+22mrovKkSZ555Jpo1vkC0qMShr8M+peULx/1bBMP+QQVEQAREILsEaKysRWJvstnkyZMZqcOglQ4erRaCWXbvqNQ6AwET3ZNK+oc//AGveOgaBgwYQNXiLLWOLSla63+izUeA4UYkxTeXft1bb72FC5LZs2fT3+M4nUwO8oFGldaw52ZZ8rLm+nX27fa7ha3PnlJovwSsbmDjTMtJZ4+5ZpTl9NNPp7+HCRhnTX4JGmU3rGBpim/pp4mQo1P5EWDMFJxX0QK8aYRBADJTQGCJ9NJLL3311Vfocj7//HMmVvDGpkTQLMopU9DBDkOAl3P+/PmokOm+LFmyhGpD9TC1H2Wkgp1yyil4T8HQoWGRrSJl/h6mj49mgdEb1hJ//PHHLSbadyRA8mb54buCi6xNNtkEv9wp67ZTZnprgHEVATLGJQ2zrSMdmwDKJptls/3226M1Y9lkTGP4/PTr18/qg314bH6Nqc+sj5WEJVixg+GkaPopAiIgAtkiQAePRokvIN84X2/OTwYDrBViXsUXX3yB1wZWQEdma/K+aruaRNSxIyDG081jSJJiMoUQK1Gqje+liINULdQNdPOoVPykwgTrDHUv+LNlrEgErRnCHlbY5OHqq6+mGpMUefPuFiaHfsr4u99www1xhspZLmSjO2cBIhMgMn08pErCKbuCflIKdHgC2KWiZdhggw2o0hjp0M2zIlM36Aeyp55YBaabZz1ADmaOpfWVP/N7BWPmR9GQhCbpJ/kL4giGg1lXWARSEuAVRc5HV4Wo/+2339rEHItpbyZ25mgcRo4ciZBG3bPPg4lnmQvzXMjHJmgZQWok9eKLLzJo8/XXX/MFQn1G74rjwQ/P+uuvj9C4xx57kIGUKo+UhdJBEbAqBwe6ONZZoW10PkDqr15JNDYXrZHBnMaOi7AIiIAI5IjA/2/vPOC0KM4/DneHJkoTxJYIQWxBEEUEIYhKVIwVQRELBDugBCMxxBrB2KKAPZoIEj/xDypiRaMiYI9GBQFbjBghGBQjEEFA4Ph/937nsL7luP6W+y1+1tnZZ2ae+b57M88+O0WNEpnHWye3RTVEO1+z5eHRM4NZxQgCjhUrVjAXdezYsYyRCbVmbgWzKhjHuv1228sLz62EjjIIlz+AIaehCuyrgpk3Z84chueQnA+i+Ag4gpeft0ReGtnYdffddydc/iIsaQLhIQ9tZnI7WR6Z7CGZGUcD9Q8EE8IBTXD7IamDv2ECQUCB5B8gQcCXdYEA7/P4CHgYWMua+vL6xVleA7zODGxjTQR6nehWiReZ3oIpo5dccolcAOo89CyV84nSM4kwB5ngR6fvYRgOI+hwN9ALagaHtCLMdl8HHHAAnR8LnTKWodS9XbyRZWPpn1I+2OSsfotAXfgRXccEAnoqeIQ0G4KHnMBmHwY9mXrUEzLU5WZzSJnKkSZgAiZQaQKhj4saqOLIkGP8OWfFcy5PuyThSuvghLlOgPELPAONGjXiaYk/M5988sn8+fPxOLANk25RUwJXX301AxwwwEjIuerVZ+gNlt4111yj0vVAythDq8MOPaxrt64s18dYd8w8FMAUxIrjdUa2nIxSqcFdAtziTLwEytYwnrxsSd/NRQL8vhyy9/R4qBbxcEK99AQSWYZMQpLav8xSRwOs440IXESz9FzSSwmWXilrH5xLzEICas35e+M1HvX0hxfad6Yt4G7Axxw83+wQw+wGNjepdF0YzhSWC8arTWem4vSgMtC9devWl112GftHMAiKu3g0VBa+CWS45FGvdOlOaAIpCejxS3mLyGzukNLp7HgTMIGcJhAaJQJqgtwQ5fQPminlw/ODraXvkTKrZIyx/iJH3759GVKKhphnxx57LGaeZlJURWctzaCyOMtzgSHHUpSMTr3wwgt/9KMfsWQSRcioQ55/+CBkE/K0p3zg9XfBrfAHUoaSKXMoQ963coVA/NcPj0RQPtd/93I93KG21RhIxhrPPN1fnXDH0+b6DxCvtcNVIRCeCh6J+FNBK8+QAXyEav3pjUaNGnXdddepLFwShOkkqlL0iy++yM6C6uc4szhfr169+vXrh5chIVuUDJ51Kcw5rm2QV6RkUgoESQdMIIGAHpuEyHDpxymgcMAETKB2CIRGSQENatCHong4nTLlkYmnlbxiNHQiftfh7CTAqL1yKhZe5rUICKl4ruja1LthZY0cOfLWW2/VeAFmtuNrYOEGuQnKWUSC2NNPP92nTx9m5lI0+TAz4rDDDhswYABrT1KcPjiVPtux3SikVUJWvjSBchLQE6WnupxJslAsY46GOAuhjMeEsP5KoZxOJtd/gFBTB6qdQPyZiT8nuB64POigg9gJgn6I8Lhx437xi19ogR/UUB+WTh89k3QtTNOgk7vppptYFRZHBmtPkoTtWvGmp0wb1wEBLsmKQ+HkJAnyyQKOMQETMAETMIEcIqAuTwqHMH6BskenqjcM8putb7z3jIc3m9ACGSSgj//pFIj/jvFwOnniMfNeffVV5i8Qvuuuu/A16MtTOT0OrCnOkASOK6+8klmxpCUhH66mTZvGLIn4El3SIf58SkNiyqlqGbXwLRPIaQJpX+Brs1bxP85QbvIfZznFQg4O1HEC8Qcm+XFi6MG7777LtrQsXAyoPsf3mTR5Er0IHmuc0+l8DeTJoSUYSHX77bcPGzaMGPIfP348+2gSma6zTNah7B+oovJl5+a7JmACJmACJpBZAnSXKRVwf5cSiyMrTUCW2OzZszt16qRMTjnllIkTJzKOlQ9F5fQ1kPDmm29m6QetpTV58mRW2mLGBMl5khMMxfiz7ee50j+cE+YZgSydH57yT5RIHXn2G7g6GSGAN6Fjx44vvPACayugwNSHp9IJEdBlOpV4Aulv8HAjwFgGeRnocu68885BgwbhgKADS5fW8SZgAiZgAiZgAiZgAjVEgLd9VkfgP7wAHOwuySrdstlwE/A1CDtN6+2VRwG2LcPLQBKyvffee/Ey4L8oKizC0iPz8uRgGROo4wT8d1LHH4A6V/3gcsYnTYfRvl37l19+Wb7tqVOnnnDCCfQf8emdCYDobyTMdkrMmCA3Lu+44w6G5JEhew3iLMcZkZDKlyZgAiZgAiZgAiZgAjVKAAOMSQ38p9msWGUsm4WvgW9ImHyTJk3im1A5fQRMl2C3MibY8l3qvvvu69+/f7TA4/r1TPPBc6EREzVaF2duAnlAICumTuQBR1chVwjgGog7Aug26JBef/31Ll26UAUWDRo8ePANv79h3fpof0okE+RVzccff5yljAnT/eBxOOuss8rjII+Xmyu4rKcJmIAJmIAJmIAJ5BYBzY+QjYdTYPr06UceeSQuBj4Xsbs5yy6UUR2+Gz311FPHH388ZhvWHRMu8DIEeSI5yFOBEO+ACZhAMgE7GpKZOKZuEQi+BtZrYOEGPAu33HILIxQYm0CfxCU46E4CFHZR7tmzp8Y1IIl3HEmJBZmUgXgmKQUcaQImYAImYAImYAImUHUC8Q9FWHovv/LyMcccs2rVKky7sN1YXEYlEvPMM8+wcRiLQRLDWIaTTz45buPFbbl4uOoKOwcTyD8CdjTk32/qGpWXAD0HB/0E+00wruG111474ogjWBsSRzUHucgjHs9uxYoVTZs2bdSo0cqVK1kJkrEMDKtjTyaPaIhTctgETMAETMAETMAEMkgAA4/Sgy8Ai27evHnHHXfc4sWLmUmBxwFLj0iGpsaVxLrDxsMZwd0///nPAwcO5C7hkE9cOGVkXMBhE6jjBLxGQx1/AOp09ekh6EtAgJeBXoTZExdccAEBOqcRI0bIy8Aouzij888/n0v6IfZPxsvAWAa8DJzjMg6bgAmYgAmYgAmYgAlkkAA2HofcDdHY1YLC/fbbj6EKqMT+Ypdddhl35WWQDPFYgOeddx7xBIYOHcrqjyQknMFauGgTyGkCHtGQ0z+fla9OAvgUPvjggx49eixbtozFGuhsbrjhBgqgB+LAJcFwhmbNmqnLWbp0KeFQPN1SCKcLlEcmXVrHm4AJmIAJmIAJmIAJVIgA9hvyGGBMhWjcuPF7773XrVs3viQxVXb48OEss8Vd7DouGeaA+SfTjm9Ia9esjRIWRNad7DdbcaDwYQIVIuARDRXCZeE8J9C2bdv58+czao71im+88cbgxtbAB4YzaJMk9pto0qRJnrNw9UzABEzABEzABEwglwngHZCDAC8DRl2bNm34pIT3gTDLbHFmzAIC2tqcPcslzH4TuVxp624C2UKgsOyVV7NFTethAjVPIHJgr13LWIa5c+fi86ZAhjAwwIF+iA2Nbr755nHjxtED0VexfLG6oqBUwmWIjwfKIxOXd9gETMAETMAETMAETKBaCGCGsTQDYxZmz5798ccfM46VYQ6HH34421byPen6669n7S0W3tpuu+2mTZtGicFsIxDC1aKJMzGBOkLAUyfqyA/tam6eAA4F9SWsB9m8eXMS0AkRbtiwIWEGO8j7wIbM7DpBDPIh0/L0QOWRCRk6YAImYAImYAImYAImUI0EmCJBbsuXL99xxx2VrWw/wu3atZOZN2vWrP3331+bi0lGxqHCPpuACZSfgKdOlJ+VJfOcgOZHMKAOz0K0AtCGDVT4scceI4Y1ilkAkksWiTzkkEMIJCwSmedoXD0TMAETMAETMAETyHECTIDl2GGHHXr37q2qPPzwwwSWLFnChyXsvcGDBx944IFhKwq5GPyhKMd/dqufMQJ2NGQMvQvOZgJ33303/Q3+7JkzZ6LnyJEjFy1aRGDIkCH0N3jEvdNENv981s0ETMAETMAETMAEUhJgXQb8CzLknnrqKWTwLyxcuJDJs1dccUXKJI40AROoBAE7GioBzUnynwAOb3od1iW+//77izcUs3wDB9VmGaEwyi7/KbiGJmACJmACJmACJpBfBAoKo9efQYMGMZR18uTJ2HVNmzbl2xLbXm7bfFtsP+56ukR+/eauTWYIFGWmWJdqAllPgNWA8HYzlG7PH++57bbb0g+NGjUKremWcEPQDxUV+c8n639FK2gCJmACJmACJmACMQKaCtGqVSumSLDLWMeOHfmYhGnHAvkbijcUFhRi43ngagyYgyZQSQJeDLKS4Jws7wkwsq6oQRFuBRwKrMjATIrXXnutc+fOVBynA2d7u/P+GXAFTcAETMAETMAE8pUAzgWsO2w8ApznzJnTvn17AngZiJc/Il/r7nqZQC0Q8NSJWoDsInKPQNTBFNQfPny4eiDOLB205557UhO8DBuLN+KAcA+Ue7+rNTYBEzABEzABE6jbcgR/3AAAKqhJREFUBDQ5AtNuwIABciuwu3mTJk20FQXxfGrirKNuo3LtTaBKBOxoqBI+J85XAnRC7DrBskB4tbXFEXP5uJSXobAoWq/BhwmYgAmYgAmYgAmYQG4RwK7DlcDnoq222gozT76GM888k63NcS4wjpVPTblVI2trAtlJwI6G7PxdrFWGCRQVFuFTYJNLPA6NGjVCm8aNG0dD6YqjkQ4ZVs7Fm4AJmIAJmIAJmIAJVJwArgQMPL4YcT733HPxMvAZiTPrQeJ6wOpjyKoOLjkqXoJTmIAJlBKwo8GPggmkIEAPxL+2bdvqHl3ObrvtRphZE+51UvBylAmYgAmYgAmYgAlkPQGsOD4aoSZnlvomoLW999hjD3wQ2HtZXwMraAI5Q8B/TjnzU1nR2iQgbzezJ+iQvvjiC5YjbtGiRW0q4LJMwARMwARMwARMwASqnQDbW2LmcWbqBJmvWrUK/8L222+PowF7j3M4qr1oZ2gCdYqAHQ116ud2ZctLgC6HlYHY96hXr16kWb9+fcOGDel4Cur7T6a8DC1nAiZgAiZgAiZgAtlGQENTOWPaYeYxogG/Q7NmzaQnxh4B7nJkm+bWxwRyi4DfmnLr97K2tUQg2tuysAj/At2M3NsK11LxLsYETMAETMAETMAETKBmCGDdyaGAi4HhqxTy2Wef1UxRztUE6i6BorpbddfcBMokwCIN69asY8bETjvtdOqpp3bp0gVxVoK0h7tMbL5pAiZgAiZgAiZgAllNIDgaWrZsyf7lAwcO7N69uzS2mZfVv5yVyykCpf68nNLZyppALRFYs2YNsydCYaxFzMYT7oECEAdMwARMwARMwARMIBcJlC7EULKbGBNmGdrA8pAs3CAfhI29XPxNrXO2EbCjIdt+EeuTLQSYPUF/Qz9EZ+P+Jlt+FethAiZgAiZgAiZgAtVBAOcC2cjLwLk6snQeJmACmwh46sQmFg6ZQCCQsBRQ6IqCgAMmYAImYAImYAImYAK5SyA4FwgEwy93q2PNTSDbCNh7l22/iPXJCgIMYWA4Q1CFSwbUyd0QIh0wARMwARMwARMwARMwARMwARNIJuCpE8lMHGMCiQTk5ybWcygS0fjaBEzABEzABEzABEzABEzABL5LYNM32+/G+8oETMAETMAETMAETMAETMAETMAETMAEKkzAjoYKI3MCEzABEzABEzABEzABEzABEzABEzCBdAQ8dSIdGcebgAmYgAmYgAmYgAmYgAmYgAmYgAlUmIBHNFQYmROYgAmYgAmYgAmYgAmYgAmYgAmYgAmkI2BHQzoyjjcBEzABEzABEzABEzABEzABEzABE6gwATsaKozMCUzABEzABEzABEzABEzABEzABEzABNIRsKMhHRnHm4AJmIAJmIAJmIAJmIAJmIAJmIAJVJiAHQ0VRuYEJmACJmACJmACJmACJmACJmACJmAC6QjY0ZCOjONNwARMwARMwARMwARMwARMwARMwAQqTMCOhgojcwITMAETMAETMAETMAETMAETMAETMIF0BOxoSEfG8SZgAiZgAiZgAiZgAiZgAiZgAiZgAhUmYEdDhZE5gQmYgAmYgAmYgAmYgAmYgAmYgAmYQDoCdjSkI+N4EzABEzABEzABEzABEzABEzABEzCBChOwo6HCyJzABEzABEzABEzABEzABEzABEzABEwgHQE7GtKRcbwJmIAJmIAJmIAJmIAJmIAJmIAJmECFCdjRUGFkTmACJmACJmACJmACJmACJmACJmACJpCOgB0N6cg43gRMwARMwARMwARMwARMwARMwARMoMIE7GioMDInMAETMAETMAETMAETMAETMAETMAETSEfAjoZ0ZBxvAiZgAiZgAiZgAiZgAiZgAiZgAiZQYQJ2NFQYmROYgAmYgAmYgAmYgAmYgAmYgAmYgAmkI2BHQzoyjjcBEzABEzABEzABEzABEzCBqhLYuHFjVbNwehPINQJ2NOTaL2Z9845AcXFx3tXJFTIBEzABEzABEzABE9hEwL6GTSwcqhsEiupGNV1LE8hSAhvWb4g0s8cvS38fq2UCJmACJmACJmAClSeAf6F+/fqkjzsaFFP5TJ3SBHKBgB0NufArWcc8IkA3Ex3F0T+OosLoXx7Vz1UxARMwARMwARMwAROIPAvrS47CwsIGDRrYueBnoq4RqM/fQF2rs+trAiZgAiZgAiZgAiZgAiZgArVGIP7OZadDrWF3QRkk4BHbGYTvostLID9WMaAWpRMl6tVbvXr1yy+/fOyxx55//vlQUAXpgeKdUHnpWM4ETMAETMAETMAEMkQg2DZVLD8vTaAJEya0b99+ypQpeVm7Kv7iTp73BDx1Iu9/4iyq4DfffLNhQ7Qkwfe//33O0Yv3hg2cGU5WUPAdnxfNMb7edevWIcZdphlsrB/FhGY6Fz3B1GJD8YaZ02dee+21M2bMoGpbbLEFvgYC3ArLNOR0HamLDxMwARMwARMwgTpCQEYLthyGWdw2w/tQv6D+Rx999M9//hMbD4uO6QOdO3du3rx5MhnsQyKLioriOSSL5UoMTMaMGfP73/9+6dKl6Iwdm6B5flQzoVK+NIFkAnY0JDNxTDUToMGlSV2yZMkZZ5zx6aefLlu2bPny5dtssw3FMHNtyy23XLlyJR4H+iG1vDp/9dVX9EZr16599NFH6ZnoopJb6mpWtIazo8ctKij68MMP8S+0bNly4cKF1LpFixYUy3oNhfWilRrEqoYVcfYmYAImYAImYAImUA0EijcUs9TUHXfcMXr06P/9738M2FSmuBVat26NkYNhgxsCcw5774033uCSY+edd+7QoQODOnv16oU8dhFnLD08Dt/73veqQa2MZgEEbN399tvvlVdegUmCLvYyJADxZR4T2PSJOI8r6apllgAdDE4Eml36GALvv//+aaedNnfuXA1YuPHGG/fee+/PPvssKEkPxC1c4A8++OCCBQuuvvrq3/zmN9wlPsjkaDONg19LPxIoahC5+YYNG3bLLbfIjRL1vd8uTcytHK1j+I0cMAETMAETMAETyHsCeAfwFMiVwCele+65hyo3bdoUEw7Db6uttvr66685Y+Eggx8BA2/8+PH33nsvH5+QvPzyyy+88ELkNQWDrzKyf3LdCsK0mz59+pFHHsl3skmTJvXt2zdu4+X9U+EKmgAE7GjwY1DjBHA0RFMD6tUL2yt88sknP/7xjxnOQCtMeIcddmC8nPRAsqCwdGgDMXQzF1xwwbhx4+jG4iMacrT7UR+Dz4XJI1SBjnno0KHUzo6GGn8KXYAJmIAJmIAJmEC1EsCqIT/sGXwEGG+MbuDMQSR2zgsvvNCpUycGsW699dYF9QvWfrMWY49DAsjceeeduBjwPmAoTp48+aSTTiJSFhGB3D1CFR544AEqRX0J9OnTJ0dt19z9Iax5xgl8Z2J8xrWxAvlKIOp4CgvokOSuxp9Nv0JDTOP75Zdf4kGg8dUhMbocHSxhsN1225GKS5KnPHIFGsqjKuf4sEAtWhFu4WdJeeRKHa2nCZiACZiACZhA3SEgAwbjLaHKfEzS9yHGMhDgU9OWW2zJ95XgZUB+8ODBf//734nEAuzfv/9FF11EJMLKMyHDHLqUaRcUpjpUkMu4gRfuOmACeUwgsV3I46q6apkioOa1cqWf3P/kv/71r6vXrM71Xidl9emK8rJeKSvrSBMwARMwARMwgXwioE9ElbZk+Ia0++67v/nmmzDBAcFcWg4tDJnTlOLOFCoCH2IqTSmnUVj5Ok7AjoY6/gBke/V7/rTnqaeeijtcG1Vku7rWzwRMwARMwARMwARMIIkAiy8kxPH6zZSKtm3b3nrrrRrcyppc8+fPTxCrxGX2vNXrY1tVPrlVovpOYgJZQsCOhiz5IeqKGqzWQFXpAOJ9QDwsEFHXU3Iwb+Kcc84hUnMuGImXQKo8bbdmYZSU+Z1yE7IKl5qmoctk3YJYGQFyQOHNpmVN5uQaUfcycq7orVBrBSqa3PImYAImYAImYAImUEME2PYbk4n1qrRaJCM9b7jhhnhZ3I1flh0O1tdmDTDlg3w5JZPLJWFVkidnmDKmnOohpiNlJo40gUwRqM5XmkzVweVmOQG5DHSOq0oMzSIrEscXJggv3qzg8B3hEs8D8kQiw914hiyvWNrElggw7o73fPkmkI/yL1mNMgp/u06EMlcqSdJhsHJEiA+BuCZIIqZbCoRLReqMbus3rKf3VM5BE+KlOakI06Eqc9WLSzliIh9/bLNPJJWtFkwKBZFWCcmNwJqSI9zVrXDpgAmYgAmYgAmYgAlULwFMlE1H6UeiyGghEktGgVBismVSWFCIJAJsdak1HVgV8j//+Y+SYD7F7TesHY5wC6uHMHlGYiWv/bKjyJBU2EgcEg5hxHSEWxJIeUZSRXCXcoOJKGHuUhxhlc4lYc5IsuqEkkQm7rfxSiWZKG2SPRkEEspSDsowLhOfYxJkQiBIOmACGSRQutR/BjVw0XWKgFpA2lACrIlIC84Ow6whFPwLgQYbXrIFMXsgIaYmm1s02Zx1ydoNTz755JgxY+iZNLFixYoVf/vb32h5W7duvcsuu+AdJ4ywdrugRA6SRy6AjcWkevfdd5999tnZs2eT8Ac/+MEBBxzQr18/JYm6jfrR4j10k9INX8arr75K/4cOq1ataty48ZlnntmuXTvuBvXInCJIpUPlUtnlK5Z/8MEHr7322hdffNGqVauuXbvuscceSNJFkTZSq0QxBhCGHAgwenDatGnvvPMOZS1YsOBXv/pVz549iQ8HVVBCFJs6dSp7SrGXB3epFFp17949Wue5oAAFiEQlzpJXOOTjgAmYgAmYgAmYgAnUMgFskrCK5KGHHnrVVVdhYuECeOmll7DHUGbd+nWM/SxeV4yBhE2FxfjQQw+99dZbvPMT7tKly9lnn73V97fCqFv/TTTiFbtI3g0+9Mg2Y2fNF1988ZVXXvn4449JtfPOOx999NEHdj8QSS5lVcrwwyLF4MT4vPvuu7HZGjVqtHTp0oMOOujnP/851hRmFcIyoihIdhRaRfZeQfTZjHIx2/7xj3+Qlrhdd931hBNOIOdkpKVpiwqowtZbbf323LepFOViW7IL2y9/+UuULCosfUFDGCuOD1dU/49//CP5jx49umHDhsTPmDED67dHjx7KkIJQI4STy3WMCWSAAA+lDxOoNQI065S1ePFiPes0iP/+97+jJjR20NZzhR8BGSnGJY01YfUxp512mjoSztwiOasW//rXv952223Dn9A222zz0UcfkYq769cxBIIRDqUH+dBLnXjiidtvv/0VV1xxxx137LPPPiRUnzRz5kwVShLSKnzNNdcgsNNOOzGib+LEicwhpL+hdNwZNPTkKzHOIQmRlE5dWEiZnoDOgwCLHklD3A0URw4MF6QgehcSalwGSTjon/B93HTTTW+88caDDz6Ir4FO8Yc//CFu/lAWRePsUDfJmY6ZEn/yk59QnOpCB4lrQ9Um/3CEHBwwARMwARMwARMwgWohIHNI77rYIXPnziVb2W8EZITECwpmCQE2O+c9H7cCB4YZYsoNi4gA1uPll1/Oyz8DH/j08vjjj++7774yqILZJklsKpmapBo7diyWYZs2bcaPH89e6eSvJB07dsSeDJpgfZGKGCxDlD/44INvu+02dJCzgxh2QMNYxVQLSRQglWqHI4MXfsrC94HHJBSELaoSH3744XhlCZPDnDlz2rdv361bN/YBxaLje5LQHXXUUQhQHWQIXHfddcSAhbvk/NVXX/3rX/9StrJFR44ciSS1psoqRer5bAIZJ1D6IpdxPaxAHSEQdzTwms0r8f3334/fgWEF4WD94c8///zwww+nSRUWmk415QxwYJgDzStv3ZwZuUBDzGs/fcbpp5/OcAbGOIwYMULtL809yRHgIIdw8N5OSz1gwADiA3Y8xErFqIFQKF0ITTZdDrduueWWIKzAsGHDqAKtPJ1f/Bap6AYo7r333qMKdDncjTf9ffv2JUP6DM7nnXced4WFhMoHx0fz5s0VltpUn8qCC9dG6LPZnJlM0IF8OONYQRO6djot+BBAt1BrKRBXQ/n7bAImYAImYAImYAJVJ4DJQSZ6W66oowHDiVGZpOU45ZRTpEx4k2fkAvF8O8FyC7Nlhw8fLmsKcwt5hFEgeBAYicDdRx55RPHYP8gcccQRRHJMmTKFS+RlgC1ZsoRIisDOJB5hbpGQ3BibwC3MrSeeeCIYYMjoQOx3v/sdleVD1Ldx0f+ffvppPg4RHxVWr16yowHfBPFUIaSiOG3AgfGG8iqLKmP4IYluWHosnIm/Y6+99mratGmnTp1kSfbu3Zu0xJMkGHshWwdMIIME7GjIIPy6WHTc0cDLcDhoPWlDdURNcr16+AIYcaAWk3acQ+2+wgwNoJFFjFdrDiLjNI855hga3yZNmuAkVkvNGRn6p1mzZpEKpzXyRKIPrbPSPv/887T4OJW5VLkEcGMjf+WVVyLJIUmdyXDgwIFq5a+99loiieFQF7hs2TIS0iUEYd3VWdnSl+CbV72UnDOD+uiZPvzwQ5WoM/E4YnA0kOeQIUOIXL58ufS5/vrriWS0BQ4XfN6Ui/Bzzz1Hzn/6059CoapRqBcyPkzABEzABEzABEygughgcpAVthxmSbKjgVsYIWWUxZuz0mIjSRI7jQNjBnORUQnMgwgH+SxatEgv87yWK/Ng0fGRH5NJIz3JCvMPq0nqsYMmBhJzZkmCHYXNtnDhQtlXfLAhkltEUi65kQQZzFFm42KpLliwAAWQ4VBumH9UNm5uKR4BZkMwx4G7VIrJESWJSk9kQjxDLdCNsrhEPVwthC+99FIZlsy3DUnQUMMrOnTogK8Ee48aoSG7deCUYY4tkmSCwuKmc0jugAlkioAdDZkiX0fL1buxpk7QbdDOsurBvHnzXo4dvOpP+r9JTBygaaatTHnIUY0ATmjypMGlgYapzoxGI2c6ubvuuitEqvshnoPVGegJou6r5CCH0DrTypNEwrgeEG7RogWdGTIcyT8brgRkcBnQM5GK5BJjVAIK6LUf9SguoSKRHvXqMXWCW+Fgah89Gf4CCkqQJ4YhdiSht0OMSxWEbuqYcb4wtE99J3epmWSUeTw34n2YgAmYgAmYgAmYQDUSwN4gN9kkFXU0YL/ttttumEDYObgJMFrICrOKhRLIMFoS69vPQkFhrCCmzUa2VL16zDPF7JEXgIGr5BMGqCIvxWQXheQEZEexjToegcGDBxOjHJSEEknCmeEJZMgHMEw+RSohKyagm4agBkNOZemMFYpuyMjRgDdBpTNvFxuY0Ra6VHEhTBKKO+SQQ+JqP/DAAyAlFZ4FShcNAgKFnankwdgLuTlgAhkk4MUgo+bJR0YI8HJOS/rTn/6UNpp1boIOWkNx4aKFjEYLkQkBliqg4WY6HIsXcIu2WG5jWmEaWfVVrCfElD/dJXLtN2tvG3sbAnjKGZ5HX8Ibe9RARzs9RKXzd4gadCREogPniy66iHhc7LjMyY12nOTE6GA8G5qzBCPT6mjrWb8ncrev/po5hHRyODtYXZJhF2TLoj7UlFSElRblFdC6x+jAekjcvfg3F1Mu0xoZ14DPQjKcqSDu9v/+979kiCvhvvvuY5AFVWC5ZvrgoqIi8sFDj3dm5cqVjPSjK6IiJBSQkI8DJmACJmACJmACJpBtBLBwsGf4mI8VxAxQzCQMGy55ycc64rNNn759sH+C2kSyA/rrr7+uGEZ38iqO8YM9pkGmvMyTA5dhYWzuKk8sJVKxECM2EoMUNI1i//33J1LWmgLRouD8K97IZF7K4ssWn6le//vrnffvvC7aNGzdueeeixqyxzQmQsqEfBhtSjiYfMigD0UwXZeqsf7XhAkTiCETPBrYckRiaqIna0+ymhhGbOvWrYnEIPz4448RIyuKQ23WLOc/kihzLGruhtIdMIEsIWBHQ5b8EHVRDS2rw0A1/AXsBBEQrPp6FW3xySefzI4SITIhwFs0TSrNMZK09VwS5qx9kmigmTeh0QQkbLh1NHSNlprWmVu8/3NJ00yzHskX1C+sV0hHwss8TTa36APo5zirA6OLIpK7eBAIhAO1CeNypn2nn6C30Og+Ih999FHOTNDgjJ7UiG5jiwZbRD6NkkMF0VWgFRHEF9QrQIe/Ph0tgYnPok+fPqgnYc6UjuaUyMJC1JQS0R/FcIiQCQS4ZKEKJOVwIYZyo6N4IxUM+ThgAiZgAiZgAiZgAtlGgG8kfPDHpEGxPffckzMmDGfWfcTm4ZWeebLvv/9+UJtI7CLOLVu2JFK+A5Iwz5SBBlhZfArCxCJDDCSMImIw7TDG8FZgIob8GQpBPAJyNCBPJiRBgDMHrgZe6VmGnOUh+cAzbtw4FhfboiAasspK5IhhVfLhStpyyRFyiH8x0i0yZClHLEzWCOejFFWQfHA0IMAtjFvyb9yoMYqhM8rLBGW9hmbNmqGPvodhEFK0cvbZBLKQgB0NWfij1BWV1CjrwzvNaKg23QZhdvfBx6y+gRaZmIRGnBilpf2lXSYVXQUbGtH44qXWC7zeunmNb1DQAAFaZDIhN7JFQF6GUC75qBRy48We2XHKBHna9CAWAupIeL1nHAHdBqXjbiAJ8xfefvttxMhHwqhBGOW0gSWRGsFBv0Lm0a3i4ui8MZpGyF36M0YuxJkoH84Uql6QJFJPWDhTKc7RUbxxU9qC73ALKoUMHTABEzABEzABEzCBmiOAYSLzI9kIwZiRjaeXduwixpzyGZ8k6IM9g02FcaWhAWHrrriq5MClXvVJheMAd8Dee+/dqlUrisMcIhLrS3ZXvYJ6jIRV5lKG1R8x+chBX3eSNSQGw3LQoEEs7oAYn8c4ozOWnpLoMw+R4SAJRXDmIJKz6oj9hrbsqkYAFwOjZbnLLelDWAFqjdmJlyH6UFRifmLvIY+Zx0EAfZQw7mWgFBXKLR8mkCUEovc3HyaQnQS6de2mtli9SBlKqmmOWvSSNv2bddG+ksgTr4Mwe1Jwxjehd3j6FbXUyjb+2Z8k9EwUjUcZMfJEskHRJleIktDWM6WQMIs4oCHyn376KWc8Gtwq0SXyICBAL8IlkyMoRf8ph3CmRMKc5ex46qmnNnkKgpADJmACJmACJmACJpBHBLCXVJu//OUvkblVWMg+lMTIjuIDDGtjc8nn/ZSffOIkZEoxtAHTizGzcdMxGGCSiafSHFsVEY9PF8brgZ4og0cD6w6FVWI6+Xg8KpGEyb/4CxRPVvJWxMXiYfKPVyR+y2ETyH4CdjRk/29UtzSkzVWFaVjpYAgz7iC0yJtlQYuMDN2ABtFFmWyI1hMikrOOL7/8ksvN5rnjjjuq9cd9QIbKgXPKA78AwiQhW7wPnImhL0GYTiVlkngkiulSJTLwL3m4XVzeYRMwARMwARMwARPIaQIYS9h4VGHatGlsE8anGsYssNo3n2XCf5pGwXiH+MehMmotM5LlHlhIqwyxcIuxD5iKXOLLCJFlBPgEhamGMkxf5VsU9turr76q70kUrSNdctl4jNglIWYexl46ycrFU3rlEjqVCdQQgc2/AtVQwc7WBFK+gZc6A751DYgS6/qow8BrUHYzqrtqyklLbkymiM7rN3Tp0oUYXAYstahbch+oiPhZmTBOj3wIsz8zPRarSCQUHabMcZfOkp6DMWx0jUwLZPofaadOnZqyjiqL3JLvaiDDk08+SU8WVyk5nJw2yMRHZ4RIB0zABEzABEzABEyg5gjELZMEk0mFYo/FS8fm4Y2d9/yzzjor+jJUXHz77bdr/mwQY34B2TKb9fPPPw+RKQPRolT16zNZlRxY7oGvRCnFEiKZ/UoMn6kYTJpwC30SYrjs2bOnnCOMZsX2Q+aZZ55JFksZg2VIEhZB5y5rUjz77LMAwb2SUliRGldbhoBvmUA2E7CjIZt/nTzUTe2p3qI1TUCv1rSkTHbgTHdCs6updBJgwNuoUaPoPPAL6BWaZho0xNBM08QTCKQIc5cVE4mh28BZoF4NdwM5sxMyxTE7js2NmUNBjA46J1KhAH2AiiZbwueddx7JKYUFe0iYMMqAJMSwXrE6Pw32o1ym/7EPJfkz8U/zNaQDtyiOUqLdIooKiURbxBjgR02Jp+s69NBDQcQ6RjgpyF/jMkiIPvhKUIkkHCx9TKQOIsVTGZIPAoTD8a2g/28CJmACJmACJmACNUVAhodyx2rCjCFMZLy8MAOCALYNt84++2y2nMAQuvPOO7t37x4sH6U6/vjjEWOCA/ttReZNicGGNUUYAQKUwhlTKpqaWr/+QQcdhLmIBXjzzTdr/gUKRDdKvu7oexVhAthL5ECJLL6I6XXPPfewTqTUk2LcDcrPmztP+pxzzjkab8sWZtr4jLUhseuCpAw2CaMYgcjGW72GeJwgYGHUBupxC68Kdi9pqUKJgtFJc0mwElevWc1lyZzbaOEwRuOiJKkSHDEqyGcTyE4CdjRk5++St1qp16HppKlVY8p7spr1LbeI1oCkeyDmq5Vf0QGogb7kkksIaPVgAjTlNLXqPMhEMmQoZLz5E8lSPTTEHMxlYGtJbpGEon/7299yxi9A98NdJOU7oEEnntd1Fje+5tprkJdu7M/MIAX0uemmm2jlo20jYu5t/AIkYYMJOkWGMNBTUhE20UQZ3NWkoiNhc2ZyU63Jk+Sc6WmUD2dGQ6jjweOAGiw1REKSsMAyd7lF5mRLNVl4glrg9aBTpApEciBDKUBDAB0oiCP0duTjwwRMwARMwARMwARqgQAGTLDHSqywyK6LH9gnfD1SDDYPpsuZZ545ceJETJeHHnrojDPOwDDDqoknOfjgg1laixh2HJ85cyZv3mSCPDFyOlDQz372M+ZWkBumUbt27Xr06IGBxECDefMi70BkGJW8yeszDzH9+vVjl0rsLsLYTmyEKavv0ksvRSsZXSSJTK+SMQjYWmPGjuGyf//+LFRJ5mRFWgrizDxZNteU8YY9hvnKTuf6asXAChlp27aI9imjFJRHgG3RyZN1Lln8GwHZqFISMXSYPn36VVddBUzCJMFVwc6aFAEfFY2YDxPIAQI8vj5MoNYI0LCuX7de/QF/HrSh8+fPTy5dr9DE4zLgZZ5NfVj4gEuSS1i7GTFCIaTlFgcJyX/y5Mnq6l566SViaJo5uEsHgN+aPomiGSVBDPHKAbEnnnhit912mzVrVsiTAJH6M+7VqxeNOznE75Kc/o++5/nnnyeeovmPbAkfffTRJKRLOOmkk7hUQSrxrrvuGjZsGHfp5NCHJKyBxOxEyTA7USWyafMjjzyitJTLXcZH7Lrrrmy8jCa4SLhFd8XQO/XK2ngJp750oKzkgyQ+TMAETMAETMAETKAmCGCByPjhLIMK+w1rJKEsYhYvXrzPPvvwQQXXADMjMGywxLBqyIFAODB+xo4dS25IcsbUISuSy2RiDAKeCDYvJ4YkpOWuJkFgGrEp+JtvvoldpCTS4fzzz8dCoyAuSSK7Dn8H8phzWH3Ey46SDJe4QrjLNhYS5sxXHxTQsg5YeticKKn8OSOAkTZ48OBu3bqhMwd7cxKPM4WEEmDsLf4FbvXu3ZtvTjLqlANuC7bMhI8uUZLaYUwijJlKlRXvswlkP4FoUJAPE6hpAqEBpdVm8Zu99tpLjmQaTSa54YoePXr0hAkTeIXmfZ42Fxcv53333RcBjsMPP5yEat9RFTHFc8anEJSnISbMAsWacUeHcfrpp9N1Ealb9Bws0EDHQ69AWl7ab731VoYk0D0w1o5Gnw4JYbXp+KGVM1MtEKaHw3WNDtxVfFip+LnnnlMMvQ41VVmIaTcm0lLiyJEj6aiGDh2K32TIkCGIEc8hDp06dfrggw+UOUp26NCBW3KIkPbEE0/s27cvCuBYwR8hDRGmINw0jPVAmE6ua9euxHBIf4UTztLTZxMwARMwARMwAROoOgG92ysfDKcDDzyQd3JeoTFLBg4cSLzskFDQjBkzML06d+6MGJ+RGKRADvIaSIYwB5aMDr3tM2FBr+WYdgw1xVbEUMSYJJMxY8aolOAXIPm9996LpL45HXfccVzy5YZxBERyqYKwoyga9Xj5xypjPIXsrosvvjhowt27774bAwzDTPakylKlcGG89dZbpEI3DEvMMJTBFdK2bVuUxEDlMxIl6i7KjBgxgoTKHBsSg5BDJhx7ZDIXg/mzXGKRyjkCAYQ584GKeA6MRoa+KgefTSD7CdjRkP2/UT5oSCPOWom0xbSzKQ81oOGMDE02/YdicDpAgdb5D3/4A40s8dwNB5d0HggwrowGPRzxglhLUq02YvRADH4LZSFPDsOHD1e3wZmOh2Y9XCKP0xoXuPTBnUxfwkgEcsCJzqy54EYhFcIkJKBjwIABUgNhNKfzYIyGemVyY3afep1Qlm6hJDP3SIJu9E+cSYszm7Lo55ChNyUJnvJ4HUONCDBcMHjNyc2HCZiACZiACZiACVQ7AV7XyZOvKZg0eBn40sM7ufYRx3RhYgLfkxiFiu3EyAWO/fbbD68BU1mZcYBhhtUkleKGEBZUuFQYs4fxm2SFRYSRgwXFcdRRRy1cuFDWDpnIglJCwtxiNQSZUrIq0Yo3fyRD5gkBltbi05esKVwhuCQwNSnxtttukxqcpW04oz/fz/hwJT8IaQkwPoJxHBiHDGGgaAzgBx54AJUSiqP6FKG6SE8YTpkyRXVBmCRsacEtZFAjHFxKyaCGAyaQnQSimU56WH02gZogoOeexpEWk9aWS5pp1rahLCbaceZWOcslB/ozvL/KKqQiT6a9hSY+Hh+VXhw94dGkvuLSHSjksebOnDlzGIFGp9imTRtyoDsJaeMBegKc2cydIwnDJRYtWkSAXhNXOhP5VAu98MdTKUwPxK133nmHLocBGmyGRFqqzPmxxx6jP2OhhzBfkUiUJMPAhFUh+T7QrFkz+jAUQENECITKgoKCKCIULchMDuQfWTO7L9xywARMwARMwARMwASqiwA2ibLCPMMywQLBmuI1mDNhArJPdE5XaGT8lKzXmFKAtHgZ8FlQBPYb342YZ8Gel61bt8ZkwsjhfV7jAkiurAiEcvk8wyxaBNjGEq8HmbB0V7C7EkpE7WjxxYL6L774IrYWl3vssQcFIYYOVAcLFk1CKqnEJTpwd8GCBYR32WUXziiAzca2ZSzEcNhhhxETLf3w7foUEsAyRKZBUYO58+ayCgPG3g7b70CN0JkBEchwoIZUCpah4uNnaq3LsjnHkzhsArVDwI6G2uHsUiIC8Y5EnYHeq2kZy9840v7SYcTl5XegD6AVjo6STqIky02v3yqdLoE3f/pC+gkpgLh+GzqJeOehyHAmNwRYDJJOgjd4vb2rOpQedatpXunpV4IwYiShdLoNOiQyp3SSA0F9T6iUckaA5PgL6H6QJLxu/TppTlZEIhCSEFaNCHBInrTBJaF4n03ABEzABEzABEygWghgeJCP7CIZIZgfwYZJLkImkywTpU2Wicdg5CCGzcMbvkbFYgthNmECyf4JVpZSKc94ziTkXV53sb44SBuSJ5TFZUKGKEyNiCeAnRne/+MJQ1jlSjEZpRStS5UbJBUZp0G5+s4UFMBeRb5sK06plBvCIRAKcsAEMkvAjobM8q8TpdPy0sLS2tJVqML4AgjQpOqgqeVlm39EqpnWe7vaZWRor/WCrWZXQxLi7NiagbY4oeeIN7ihCPJX5mRFgEJRTM26io5nGykZGyzAJSpFIzJKZjQkSKa8jPciwekuX7Uc4fFCKUugdFaGCBAfOp7gPtdd4qlCgm+FJPT6ipebI6VujjQBEzABEzABEzCBShPAyCGtnAvKRAaMrLW4BaWw7DTJyDCLf+RPqYasoHhWQUy3wiUBYsIZa01h7EPKkpJYYjgLJBZPqDClSBKzk1RyMegWSVAbu5F/QRmqGRwBIRwCJMRm40w+GMB8JIuXSybiIJsQc45/ITdukYTt2DCVEWBcrZBKGc7cldcjIc8g4IAJZAMBOxqy4VfIcx1oBEOjXJWqVlc+VdGhRtPmfQVrlJ4zNwETMAETMAETqE0Cesutio2nHNA5ZSaVyL/sDGsTjssyAROwo8HPgAmYgAmYgAmYgAmYgAmYQG0TqHa/QLVnWNtEXJ4J5BEBOxry6Md0VUzABEzABEzABEzABEzABEzABEwg0wRKV8LLtBou3wRMwARMwARMwARMwARMwARMwARMIB8I2NGQD7+i62ACJmACJmACJmACJmACJmACJmACWULAjoYs+SGshgmYgAmYgAmYgAmYgAmYgAmYgAnkAwE7GvLhV3QdTMAETMAETMAETMAETMAETMAETCBLCNjRkCU/hNUwARMwARMwARMwARMwARMwARMwgXwgYEdDPvyKroMJmIAJmIAJmIAJmIAJmIAJmIAJZAkBOxqy5IewGiZgAiZgAiZgAiZgAiZgAiZgAiaQDwTsaMiHX9F1MAETMAETMAETMAETMAETMAETMIEsIWBHQ5b8EFbDBEzABEzABEzABEzABEzABEzABPKBgB0N+fArug4mYAImYAImYAImYAImYAImYAImkCUE/h+gymHPsKQm2wAAAABJRU5ErkJggg==)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 331
    },
    "id": "JexlcC4QBM94",
    "outputId": "1b8c6a7f-732d-4714-9405-948e1a3aa9dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " encoder_input (InputLayer)  [(None, 32, 32, 1)]       0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 16, 16, 32)        320       \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 8, 8, 64)          18496     \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 4, 4, 128)         73856     \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 2048)              0         \n",
      "                                                                 \n",
      " encoder_output (Dense)      (None, 2)                 4098      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 96,770\n",
      "Trainable params: 96,770\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:50:36.640081: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n"
     ]
    }
   ],
   "source": [
    "# Encoder\n",
    "encoder_input = layers.Input(\n",
    "    shape=(IMAGE_SIZE, IMAGE_SIZE, CHANNELS), name=\"encoder_input\"\n",
    ")\n",
    "x = layers.Conv2D(32, (3, 3), strides=2, activation=\"relu\", padding=\"same\")(\n",
    "    encoder_input\n",
    ")\n",
    "x = layers.Conv2D(64, (3, 3), strides=2, activation=\"relu\", padding=\"same\")(x)\n",
    "x = layers.Conv2D(128, (3, 3), strides=2, activation=\"relu\", padding=\"same\")(x)\n",
    "shape_before_flattening = K.int_shape(x)[1:]  # the decoder will need this!\n",
    "\n",
    "x = layers.Flatten()(x)\n",
    "encoder_output = layers.Dense(EMBEDDING_DIM, name=\"encoder_output\")(x)\n",
    "\n",
    "encoder = models.Model(encoder_input, encoder_output)\n",
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 413
    },
    "id": "-TE7Y8Z3B4mY",
    "outputId": "0ba6750d-4607-4916-b4c2-e565e67f9052"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " decoder_input (InputLayer)  [(None, 2)]               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 2048)              6144      \n",
      "                                                                 \n",
      " reshape (Reshape)           (None, 4, 4, 128)         0         \n",
      "                                                                 \n",
      " conv2d_transpose (Conv2DTra  (None, 8, 8, 128)        147584    \n",
      " nspose)                                                         \n",
      "                                                                 \n",
      " conv2d_transpose_1 (Conv2DT  (None, 16, 16, 64)       73792     \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " conv2d_transpose_2 (Conv2DT  (None, 32, 32, 32)       18464     \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " decoder_output (Conv2D)     (None, 32, 32, 1)         289       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 246,273\n",
      "Trainable params: 246,273\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Decoder\n",
    "decoder_input = layers.Input(shape=(EMBEDDING_DIM,), name=\"decoder_input\")\n",
    "x = layers.Dense(np.prod(shape_before_flattening))(decoder_input)\n",
    "x = layers.Reshape(shape_before_flattening)(x)\n",
    "x = layers.Conv2DTranspose(\n",
    "    128, (3, 3), strides=2, activation=\"relu\", padding=\"same\"\n",
    ")(x)\n",
    "x = layers.Conv2DTranspose(\n",
    "    64, (3, 3), strides=2, activation=\"relu\", padding=\"same\"\n",
    ")(x)\n",
    "x = layers.Conv2DTranspose(\n",
    "    32, (3, 3), strides=2, activation=\"relu\", padding=\"same\"\n",
    ")(x)\n",
    "decoder_output = layers.Conv2D(\n",
    "    CHANNELS,\n",
    "    (3, 3),\n",
    "    strides=1,\n",
    "    activation=\"sigmoid\",\n",
    "    padding=\"same\",\n",
    "    name=\"decoder_output\",\n",
    ")(x)\n",
    "\n",
    "decoder = models.Model(decoder_input, decoder_output)\n",
    "decoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "id": "FME5yZHLB8Xt",
    "outputId": "bcf5ff3f-9dd4-4bc7-a1f4-5b9d48032354"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " encoder_input (InputLayer)  [(None, 32, 32, 1)]       0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 16, 16, 32)        320       \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 8, 8, 64)          18496     \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 4, 4, 128)         73856     \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 2048)              0         \n",
      "                                                                 \n",
      " encoder_output (Dense)      (None, 2)                 4098      \n",
      "                                                                 \n",
      " model_1 (Functional)        (None, 32, 32, 1)         246273    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 343,043\n",
      "Trainable params: 343,043\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Autoencoder\n",
    "autoencoder = models.Model(\n",
    "    encoder_input,\n",
    "    decoder(encoder_output)\n",
    ")  # decoder(encoder_output)\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dKXNATeLCA2V"
   },
   "source": [
    "## 3. Train the autoencoder <a name=\"train\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "pQ3oijLXB-fT"
   },
   "outputs": [],
   "source": [
    "# Compile the autoencoder\n",
    "autoencoder.compile(optimizer=\"adam\", loss=\"binary_crossentropy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "-b_ydIWdCCeC"
   },
   "outputs": [],
   "source": [
    "# Create a model save checkpoint\n",
    "model_checkpoint_callback = callbacks.ModelCheckpoint(\n",
    "    filepath=\"MFoDL/content/checkpoint\",\n",
    "    save_weights_only=False,\n",
    "    save_freq=\"epoch\",\n",
    "    monitor=\"loss\",\n",
    "    mode=\"min\",\n",
    "    save_best_only=True,\n",
    "    verbose=0,\n",
    ")\n",
    "tensorboard_callback = callbacks.TensorBoard(log_dir=\"./logs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E5SHG7tdCDy9",
    "outputId": "497a1983-1bd4-438a-9d1c-182b67576864",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2557"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:52:56.607112: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:52:56.629356: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:52:56.645262: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:52:56.726226: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:52:57.170312: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:52:57.179188: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:52:57.194721: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:52:57.211230: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2557 - val_loss: 0.2560\n",
      "Epoch 2/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2537"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:53:19.651604: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:53:19.672719: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:53:19.688702: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:53:19.769894: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:53:20.345670: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:53:20.355122: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:53:20.377055: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:53:20.394218: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2537 - val_loss: 0.2566\n",
      "Epoch 3/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2522"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:53:42.765864: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:53:42.782567: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:53:42.798485: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:53:42.878740: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:53:43.296459: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:53:43.305863: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:53:43.321468: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:53:43.338356: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 38ms/step - loss: 0.2522 - val_loss: 0.2528\n",
      "Epoch 4/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2509"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:54:05.700877: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:05.717626: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:05.733523: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:05.814897: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:06.235988: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:06.244859: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:06.260446: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:06.276222: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 38ms/step - loss: 0.2509 - val_loss: 0.2525\n",
      "Epoch 5/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2500"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:54:28.752523: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:28.769983: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:28.786253: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:29.058352: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:29.483560: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:29.492701: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:29.508511: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:29.524812: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2500 - val_loss: 0.2523\n",
      "Epoch 6/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2493"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:54:52.104831: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:52.121416: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:52.137539: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:52.217630: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:52.634659: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:52.643792: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:52.658961: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:54:52.674381: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2493 - val_loss: 0.2508\n",
      "Epoch 7/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2484"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:55:15.207158: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:55:15.224143: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:55:15.241233: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:55:15.321931: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:55:15.908567: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:55:15.917834: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:55:15.934105: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:55:15.949675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2484 - val_loss: 0.2506\n",
      "Epoch 8/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2480"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:55:38.532501: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:55:38.549616: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:55:38.565754: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:55:38.648286: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:55:39.070772: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:55:39.079912: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:55:39.095770: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:55:39.111487: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2480 - val_loss: 0.2497\n",
      "Epoch 9/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2473"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:56:01.702356: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:01.719456: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:01.736246: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:01.815834: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:02.233419: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:02.242603: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:02.258386: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:02.273641: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2473 - val_loss: 0.2490\n",
      "Epoch 10/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2467"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:56:25.013300: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:25.030611: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:25.046421: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:25.127427: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:25.545128: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:25.554253: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:25.569541: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:25.584861: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2467 - val_loss: 0.2489\n",
      "Epoch 11/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2463"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:56:48.089842: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:48.106681: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:48.123420: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:48.204140: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:48.624798: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:48.634517: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:48.650030: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:56:48.665670: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 38ms/step - loss: 0.2463 - val_loss: 0.2488\n",
      "Epoch 12/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2458"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:57:11.246572: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:11.263730: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:11.279456: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:11.360039: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:11.973168: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:11.982240: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:11.997714: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:12.013037: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2458 - val_loss: 0.2490\n",
      "Epoch 13/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2456"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:57:34.553595: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:34.569968: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:34.586362: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:34.665448: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:35.082244: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:35.091578: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:35.106946: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:35.122222: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2455 - val_loss: 0.2480\n",
      "Epoch 14/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2450"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:57:57.641435: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:57.658997: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:57.675368: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:57.756745: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:58.189290: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:58.198483: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:58.214133: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:57:58.236296: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2449 - val_loss: 0.2477\n",
      "Epoch 15/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2447"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:58:21.083762: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:58:21.101797: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:58:21.118130: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:58:21.199258: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:58:21.620480: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:58:21.629633: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:58:21.645122: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:58:21.660553: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2447 - val_loss: 0.2475\n",
      "Epoch 16/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2444"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:58:44.456386: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:58:44.475869: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:58:44.491585: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:58:44.571779: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:58:44.985735: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:58:44.994650: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:58:45.009939: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:58:45.025349: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2444 - val_loss: 0.2475\n",
      "Epoch 17/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2440"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:59:07.611852: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:07.629621: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:07.645808: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:07.726379: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:08.341316: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:08.350330: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:08.365985: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:08.381558: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2440 - val_loss: 0.2471\n",
      "Epoch 18/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2438"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:59:31.012148: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:31.028751: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:31.044765: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:31.125735: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:31.546785: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:31.556030: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:31.571698: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:31.587704: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2438 - val_loss: 0.2480\n",
      "Epoch 19/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2436"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 22:59:54.196359: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:54.213450: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:54.229645: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:54.309601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:54.896431: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:54.906199: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:54.921987: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 22:59:54.937590: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2436 - val_loss: 0.2468\n",
      "Epoch 20/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2432"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 23:00:17.456502: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:00:17.473324: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:00:17.489502: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:00:17.571272: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:00:17.992157: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:00:18.001514: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:00:18.016903: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:00:18.032275: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 38ms/step - loss: 0.2432 - val_loss: 0.2468\n",
      "Epoch 21/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2431"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 23:00:40.742222: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:00:40.759362: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:00:40.775293: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:00:40.855251: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:00:41.282075: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:00:41.291294: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:00:41.306696: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:00:41.322075: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2431 - val_loss: 0.2467\n",
      "Epoch 22/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2427"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 23:01:03.874474: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:01:03.891194: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:01:03.907123: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:01:04.174144: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:01:04.594219: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:01:04.603146: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:01:04.618479: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:01:04.633802: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2427 - val_loss: 0.2467\n",
      "Epoch 23/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2426"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 23:01:27.259494: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:01:27.276187: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:01:27.292018: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:01:27.371802: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:01:27.793955: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:01:27.803239: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:01:27.818777: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:01:27.846809: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2426 - val_loss: 0.2465\n",
      "Epoch 24/30\n",
      "600/600 [==============================] - 22s 37ms/step - loss: 0.2431 - val_loss: 0.2463\n",
      "Epoch 25/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2424"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 23:02:12.656615: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:12.673398: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:12.689340: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:12.771553: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:13.190803: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:13.206774: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:13.222509: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:13.240406: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2424 - val_loss: 0.2465\n",
      "Epoch 26/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2421"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 23:02:35.892625: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:35.909570: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:35.925653: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:36.007403: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:36.434158: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:36.443111: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:36.458372: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:36.473743: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 38ms/step - loss: 0.2421 - val_loss: 0.2459\n",
      "Epoch 27/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2419"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 23:02:59.002536: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:59.019128: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:59.035332: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:59.117006: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:59.552857: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:59.562026: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:59.577907: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:02:59.593987: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2419 - val_loss: 0.2460\n",
      "Epoch 28/30\n",
      "599/600 [============================>.] - ETA: 0s - loss: 0.2416"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 23:03:22.266746: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:03:22.284900: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:03:22.302536: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:03:22.392457: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:03:22.998581: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,2048]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:03:23.007616: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:03:23.027862: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:03:23.043179: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,64]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla, _jit_compiled_convolution_op while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: MFoDL/content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 23s 39ms/step - loss: 0.2416 - val_loss: 0.2461\n",
      "Epoch 29/30\n",
      "600/600 [==============================] - 22s 37ms/step - loss: 0.2420 - val_loss: 0.2461\n",
      "Epoch 30/30\n",
      "  1/600 [..............................] - ETA: 20s - loss: 0.2389"
     ]
    }
   ],
   "source": [
    "autoencoder.fit(\n",
    "    x_train,\n",
    "    x_train,\n",
    "    epochs=EPOCHS,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=True,\n",
    "    validation_data=(x_test, x_test),\n",
    "    callbacks=[model_checkpoint_callback, tensorboard_callback],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5tOlQ_bZDT0C"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iQZEDP-qDsXd"
   },
   "source": [
    "## 4. Reconstruct using the autoencoder <a name=\"reconstruct\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "ksSky9mNDnO3"
   },
   "outputs": [],
   "source": [
    "n_to_predict = 5000\n",
    "example_images = x_test[:n_to_predict]\n",
    "example_labels = y_test[:n_to_predict]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 335
    },
    "id": "CxfGBtGsDpJu",
    "outputId": "c178d180-330f-4307-e9dc-7524e6f4cd0d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 1s 5ms/step\n",
      "Example real clothing items\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reconstructions\n"
     ]
    },
    {
     "data": {
      "image/png": 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RnlnmMZlMUKlUMJ/PXbajvn57e+vuB0apeW9oyRUQIsHAdh1jn6ibmxtXdgHABRps5hVJM2AbzLA6VTNE1akjeUDZ1jkJcnkHJWgYINByJ2YfaRaNkqlcGxlk6Ha7br7q9TpWqxU+f/7sglZc27i7E8u0X79+jdevX7tAEu0dZtlxreT8koQD7u4Rdf5Vp6vs2UybYwF/F8d2vV5jNBphMBg4Um00GmWaBms5qe7SxewnlpL1+32USiWXTcjSUu21p6VnanM2Go2MPgXg9GgURahWq47AqVQqmEwmrreYErgsjSKObf4CAgJ2EYiagAdDDRqN9pKQ4QLIngrD4dA9ZwSREQOSNGpE2OwLfd23MPFcdHqA7RaXfE7DS0tsGFXhIhsyNvLBueac6a4yvhR8GpJ2Xn1EmDY+1B4MwDaKbJ2J4PA9HBol1vRtjj9lQ5s/83M+ooYZNZr1pr0W9L6wTlBAthyNJUx0/JghyPHSxr90QOjsqaOm6fRakqrZaSpjzHzSLYG1b4ZmcfCaWTIDwGXvMJOg1+u50ifNEgklplnQwSMRyjUI2BLXNruN8wdsm69bnaqf8WXd+Hq/BfJ7t2+aypDKmO+PGTaUR5Il1KetVssdx0xeloOSNKvX664vSafTQbvdzuwYBGwDZMCWXNM+OvqaLTMu2rqp86fZTtPpNEO4UfaUfKMu07UM2JbN68YI1Mcc11qtlmmszp5eOh8kemxDYltKzO9g9haAHVs5ICDg+BGImoAHgQYBF7Xb21tcXl5iMpm4DvmMGjClW5u26ZawapwCW2NyX/PZfdfFRVmNoeVy6eq+kyRxuyfMZjO0Wi3EcQwArvO+Op8Bd7CZGEzjzus3oo6gJWq0DMP3uqbnA1sSzjp9AQ8DjUTNUNK+QgB2elro5whrIFLuNHKrjoKSBdxVqOiEqBJm6/Ua0+kUw+HQZRuqA8Hx03I1OhFAluTUcVeHsVQqZfoMUSbpFPCRuo+NSemQqCMIINP0mxF96vLhcOjKdpIkcc6o9s7gdRcRKhPT6dSVArM0QzOfVO50voGtrFqdquUYPkKc16CZVkqEFx2+jBotV7HPJ5OJk0vKGOejXq9n5JwkgJLlmlHT7XYdGcDtn+nAc645Z3T2tU+Nli1qM29fSesxy5+SoAwkUq8yoKhla9RVtk8bgIyO5C5cOp7MXKLsMAuG2Y3NZtPNhSXcaOtojyjujMigGDMVmf2o24KHMv2AgGIgEDUBB4OLE9N8F4sFLi4u8I9//AO3t7e4urpypU82fZTlE/zfRnps5sx9UIOV59G+KaVSCePx2KWhcqvuWq2GwWCAOI7RarVc+jIbvXU6ncLVdO+DpukPBgPc3Ny4iD93jPGRauo42owaW9bG19Sp1Kwn7jYVmgk/Dr7ooh1P6/Bb51ydP3UKfWVsvpInbn8KYIegLQrU+dZeF6PRCJ8+fcJwOMRgMHBOhe4GpaVPKgOM3PP8NuuMToGSo5RHdQA1m4blTfyf36n6lSUBLBltNBqup8JyuUS328XZ2ZnbDYpZjUXXqTono9EIHz58wGAwcE2jSc5R7vgZQh1x1Zn2ffudVkerw79YLJzcFx1WV2oPE5Zya+mhBqF8Pb8AuAxEbrPdbrczJFmpVEIURc4h153TtJQG2BJx0+k009uEZLlmDrPvDX/XsZOkSnAyYMfNLD5//uzmikSNbq3O9UnPkabZflxaVqVlZ8DWtuG4l8tlt806A4BKts3nc5TLZUf8lMtlF2DkDlDNZtOVOq3Xa6dnSQQFsiYgoBgIRE3Ag6CGJhc37hwzHA5d7wtGKzSarlF9G/XLy2LZZ9zb420kUp0ULpA0oGgkjUYjt1W3bZp5rAbNY2DTg60D6IMdR0vQ2OwaH0FGg1ZL5AJZ83D4oujqmFmS1Jc2r+/refNKFvm+LSEI85ft2aN9ERgF1pIK26dJ5QjIktWUOS2L0YivZixqtFjLoPQ5z0sHkI4Fe54wS4fnJElPwsbea0XXq9aZVBLap9/2ZbTZ+8DCl1Fj51TlM8jlHazO4n3PjAvNSNRSYEugUhZJlmhvP1uWyDIZZrLpDk5arqY6nMSQZvNYe8fq8aLInmZEaU8ZPlK3kqjReQGy66GS2frclopyHmljknShnWNlT8lYzu1qtdo5L6+bdmuwgQICioVA1AQ8GNx+MkkSDAYDt03z7e2ty7TRzBk1GPZFdazD5zNCddcn+z4XOzVIdNcGLtBMMZ3P57i8vASwzRKioRNKn7LOPVOFOYZq2NteJIDf0OGxNrqrBqidP0Y14zjOlOoEQ+VwqKGutfmM5lpjUrd3Za8MOv95PYZsGYYatrrzTNEdQv527YXAvl3cEUZ3VtO+E8CujrRp+JwrLb2wmVGEOoBWjypho/cA51d3hqJOZSSfx41GI4zHY5e+r6VZRXEYLTgX+qe76PmyzThm/Ly+bp/rsT4ClfOkOxSpo19kKEHD+5pjxEftF0IdRzkjgWIzk5S04f+qD5mBw5JDZk7Q2Wepi81CJfi9tHNsfzceQ5lV+Ts2WeQc0t5jmdpwOHS9FMfjcab8TOXFlmWrDVStVtFsNjPzawMVmo1IwkXL0vjc9oCzRByzJ5vNJobDISqVCpbLJc7OzpyNGrJpAgKKgUDUBBwEXbCSJMHFxQWGwyF+++03/Pbbb464YUd9zZzRcwDINdb3ETT6um2I6Gtaqt/FhZVRYHbWbzabSNPUZQL91//6X3d6KhQd2iODW1iypE0dN86ppgITalj6DE11QqyTwT4OjUbD1fcX3aF4DDi2TPdmSj+jvYwCWqcOyJdXnX8aoLaGXptEaxPjY3MQDoHqJu6iNpvNcHt763b/oYwtFgsA2NGllvgEdrOXALiyCb5OudRIvC1DJPR19lcA7kqleE5Co9Hcgpb9Hq6urtDpdBDHMTqdjstc5DXptRcFmmFKIoBZGuq4KxFm9aOPVPPdD3xOkpX3BuVfSUGWVxQJdlw104EEGnc4JIlK8hTAjsOthIl+x2azcbYGt9zm8Sqv/GMZFEtmNLsNQEa30r7hHCoZw4w2/Y38/DHKnxJt1K9JkuD6+hqfP392QcTJZLJDsOhcaq826rdSadu7i6SXbfgNZMtJNaNGew3pRhfWnuG80dZhydRisUC328X5+Tn6/X6GQD+mOQwICNhFIGoCDoZGg5mqT4OPafva6NJ+Tp/7SJr7HHA1SPelfNvvopPITByN7E4mE9TrdVfPzNKCQAZkx08dDDUwge1cHDp/h5a40WjWZsIho+Zx0HGzjS9tRgyh2TV6L/jkzjr3PkKBny8ylPDW3WJ4jzNirwQNP2dh5YjyQf2m71On2ayW+0hxJVg1M0BlUUva2DCa5SHsW6OlT0V3Lmzmht3BR8c87/MKXzZN3uetDtCsrYDdElGuebrToY4xHXyfjcPnNqNGMyeUqNFsC5uJoeezMmtJJpbI6P1UFL3r06+aEcUm7VpiBGybAque4/loM/JY7Vnjy56y/aP0nFqSCmRlVnW9Ztcxk7nRaGRKtIoypwEBRUchiRqr4HyOny9t+KHGZd7ifQjJ8BTBhWmxWLituCeTyU6dPZ0BH/b9bt+Y8//7FiUaI1rba2EX11KphOl0imq1ilar5X7LZrPZiRwXHdxCmBFYa9DahnvAtrGl3u/qfPI9IOtI6nv6HaF05nHQ8VKHENim79O4tFlkdp6XyyUAuCwLfoYybzPdNKMmzN8dlIBker4S30x7t1AZApApAdVjLOmmMsd5A+CishbWIdG5tb+Dj/xj6YWWHDCDyEcgFQ2qy9jjjWun3W0GgHO6+ZywWVR5a56WHfJzdGJ1N6K8HfyKAtVzHA8lTpXQ0nWKx/McQH5Gk/0+K1tWB/sIGT5q9hWvw94fmv3j07nP0QbdB6uH2DyYdov2puH85ZGi9n8eZ3ct1O+0mU6aUaN6mkSMnteeT8HAaLPZdIQ+gELLa0BAkVA4osZGh5XN1oXW/unrxL6FThW4T5kfco6nBDVkZrOZSyflLiWs++U45jU6PPT3HkracBHUlF9f5oVvztP0ruxpuVy6WmDu5sD05IA7sIePlj5xvrn7gSXB8jIqbGSxVCq5LSr5uhqgNJq1r0PRnf2HwueIpGnqHHX2Wshzou3nKOO2hwmw7UOkdfoACh29t+sAey+xN432qGHpkJZ4MmXe57hZZ0OdPoJzpDKpKfg8n+pTyidT/7VpMGGzeEjIVCoV3NzcII5j1xdJsxX5XUXLrtFIP/tlDIfDzI4zhK51HCPNVuPYqc5VIkxJUp03kjPaHFf7GRVhPvKCaAxEaSaG9vKxxIct5czLNrRlNWqnAMjIlm0m67tu9oXStVN7i5Ec1Yw2e28dG3SNY+kTN7mgjGnpJ7AluFQnAf4yfC0t4xzpd3LutOebtYMpr6r/1HbVOSJRzkbC9XrdZa77snkCAgKOE4UjahTWeNbIlRo4RF5E0fee7/yWNHhui6WSWjRibKq+LlqPNcTzCB4fkabvE1pfrNduyRo6FqwB1uhZWASzY6qp4JYIU6PDzpEPPmckj9BTufSRbQEPg9VFakD6mgWz4bBmN2kmhzoAvnI4m9nxXHXf14Jdb1Tn6B/H1e744nPeFXqcpvXzPXXkNcWf1+a7Xht9tuuYPZbrgZY/+XYrKQopYKFyZDM1iLy59UXggWx21X0Opzrz9rGosGuLzpEtTfN91o65yqiSpj6SzZKjPG7f+pmX6abBDL3mQ+b2GHSzDUjYzDG7A51PJ+VBdaqPdAaQyajxracE587qaN81qO0VMosDAoqHwhA1SjJQyVGJaxTFx4rbXUwA/w5FCrvA8zl34/ClRD5l2AWQdbPaBFEzHWx5Sx75kheB8kWOrTFko4x8XT+jTTh9WK/Xbitxpsra6w/IZlT4yjKA/FRtfc77hMcD2fIZvm7ru+33BDwevtKnZrOJbrebMT5pTHK+eQ/4nBpfxgXJCB6fl2VXJHC8dFcSljsxWkoZoxFvy9FUnyrZkidDSrCo/NnMGdsvg+B9oDpRHQt1IBjd5/qQJIlrBL5cLt1W4UVt1G6JALt9ucLqPp9uVdKcZA3n1DqTeQRb3vpcNPgCd7aPlF27fIE4Yh/R4iPMlUD1nUNl1Pca9Qo/xyxU2x8n7xqPYf65tulueiwtZBaS2oZqH6p8aFlZHmnKLEXdBUzlT2VKyXa7bhJ6bWpPc4fGer3u1gySfAEBAcePQhA1vsV3s9m4XQ/W67UzlkulEprNpuvwXq/X3Y4ofA7kl0cRXCyUCFqv15keAc/NceEiyLRtjp867+oocCGykVsdM5/Bnufw6+JpG7PlETX8XF60mARdkiQuRVYNnoA7aMq+dfR17H0RenXY+Vw/a3tX+AyegK8DjcYCWzlqtVo4PT11O/domj+dFMpLmqbOSKVsaT8a/S6d730RxiJAHWqWEg6HQ4xGI1d2yX4Km83G7aClZYHA1knII2o4P/oZptHruVhOxWvTe4JzxM/o/zxGPzefz9018LrG4zFubm5QrVYxnU5dWZSugUWEkjS2Z4a1J+zaaT9vZYzvcw7ynDkbwCiqTPqg48vMYZKOlAVfVpraGPq6HVvVA5bo9h1vrwvI2jSaFQUgY3Nyp0tLPBzjfOu8zedz10eRu+txJz3NuF6tVhk7UsvHCEug6VxxDrQ8VHWqEmT807VVz2F/B21qkvbVatWVx/L1gICA40chiBqFZriwTps19GSq87I2bJRfjSm78NkmfVTOXDR9EZinDBsJVGcubwtIvnYfNJphDVVr/BwSnaeBqnOVlyHDBZm/g1lWz2lu/ghY5wDIjzTxeP2szcI4BMdqUD4lUPbYo0azBpUsU2eEjqHO9SHzFOYzSzrbnXdsWaEl9hW+AIE65/q6OiJ50Xof4apOoZZr8DVCnUbqWd/veo7r3u8BS2Brz4pDMhysjrWReF+JWhjzw8vsdCxtRrRmqPiyJTRrgu8del0PvT79n+eh/Fkirwjw2ajMKtJSMVv6ZAN5vvHyZcjY133EnLVr8+bO91v0epm5k1cqGRAQcLw4eqJGI8jMoplMJlitVri6usLt7S1Wq5Vr4lipVBBFEZrNJsrlMhqNhsuuYaaNjR77HBCSP8zMoLHcaDQQx/Gzc1o0G2k6neL29hY3NzdIkmQnnRPI1syr8ajn4+NDjCjfddmF0To79nX7/YxwTyYT3NzcAIDLHHhOc/S1YTMAKDvs67PPUdd7gdEgAC6DLU3TnYwZrf+2WVdaohEMlMfDyimAnXFO0zSTyaHNgTkH1AXMloiiyGVx1Ot1d15gW7fva0ZbJNiI73g8djvnMfqt5DejrySafRFaABlCxZLm1jHRZpq6rbAlU1VXHrITGHd74h8j+rpTHEuLiyy/HC82qbUlNdpnxkfe6DxpVpsNRFhH0vZA0Tm05z1WHLKe63hqQI9BPWY36A5NvqwnYCuXdmc1S4jmBQf1egiSRLaxMX+bXo916o/dnlH9pv1plADncXxkGabaG3aMfKRMmqa5pLh+zsqbzpNeg/6laepKYHnOUqmUKcFj8++AgIDjx9ESNWp06II7Ho8xGAwwm83w7t07fPz40fULYHf1OI7RbDZRqVQyRA1fZ8q4b2tTguVU7LofxzEajQbW6zV6vd4fMQRfFXTWF4sFJpMJBoMBPn/+jNFolNmeUlPj86IUfK6ZLtaIzIM6H75u+XoOS9jYVFFdsOfzOW5vb/H582cAcKn8RYYaEbPZzJVpcGzynApLklWrVbfdOQkwNTK0H5R+txqe+7ZdDzgMPrnSsplqtep2FqITwQwbJWsoL7PZDOVyGc1m0+nJer2euRd0/tTBKSJUD81mM9zc3GAwGGA4HLoSUu2bBmQdbc1i0nMqOa7f43PwNbOT1+ILMvicST0Py5g0C1HJvvV67Xp+tVotR9ZohLho8sx5IUnDLdlZ6sb+PdYBVEJO1znNevI5gnaMrRNqs0WOFQ8hoDTTTR1jztdqtXI9vQjqNLU3dJ7s93MObO8YlXXfOqpzztcp00oKqWOvGW0PHYvnBjt3Sn5reRhljHMJIKO78qBZM5pVZWVHdSWPs+XG2t+G8qdEvbYT4P2lu5ApUVNEXRoQ8BDk6b3nIjdHS9QQvswJLrqTyQSj0cgRNYwKchEsl8tYLpeOqKEC1YbAeVitVq4un70GeI7nuFjqYqM7evjKhGzkwjoM+/4/9Fry0kv1f3stvnFXw4qLu/YMCMjOPef7kDlUp6FWqyFNU+dA6Pu++4TIi3IFfH340rvVebD9GOhAUKf50rFtpDLMY5awUX1jHT2O6SFZKHb8rb5TGbZr4iEp+z7YHWaUnNPr19KDImRu7IONoGvpMJCNyPv0qgY5rFOvx1idGuTvcNggD5AN9un9rXN0aLaYb+30zQ/Pp7Kln7cyRvJVr7doMmf1nO+3U//astJ9srEvE/Q+mVLSzfcZn6707TxqdUZR5jQg4LGwvqgPz2FNLARRo+z6zc0NLi8vkSQJ3r9/j3/961+Z7aUBoNFouMgEI83lchlRFLnSJ9usURdpjYRsNhu0Wi0XrbaR0udwkwDb5shk9Pnc9lXg71uv1zup9UD+bll5DoP9X6NRlphRw0mvZd+CrXM1m80wHo8Rx3FoJvz/Q7NkNDrF93wNYvV5pVJBv9/Hy5cvHVEznU53srDyFKk1iJ+b3DwF2Eit/fPpLZIHpdJduWalUnG7Qi2XS1xeXmI4HGacBCVzbF8ULRMo2tz5yCuuS9YgVz3KueA4NhoNJwdaHpgHWxqh5Jq+7tvJhrAlvsA2u5K7keguVQxG+EpHdNcnfk8R7wV1trShqc0qzZtfZv12Oh2kaYqbmxt3HiUWtC8HsLvLnv0r2lxYWBLNEmpc/3gsdRkDecAuMW2h2VJaHs5zAvDaLersa+YIM7QYZGTGtz1OM0mO1cHn79UtubVpdqlUcuNBW4RkDV9ndq+dOytL9v7ga4A/kKj2KOfABsAYQFb9z2vhb6Hdzez8IurQgIA8WFtHsw7V9qFccYMgtY+/xjVYfOl5j5qo4YSxr8psNsPV1RV+++03TCYT/Pzzz/jll18cqZCX9khHQ0sD8rr6a605F/CTkxN0u1202+2Msn5OJQA0CJiuzR2f5vO518DgZzQ9m9FWn0DoQpa33fa+m31f9JhCax1SfV93fmq1Whmjt6gLob2vtU7fZlrkfbZcLuP8/Bx//etf3Th/+vQJpVIpQwb44ItsPje5eSrwkSiEPqfRqH0zoihCmqao1+tot9tYLBYYj8fuPqDTYv+0X0JwCLNQp4Lbxup71Fe6nrBHGp0yyqAvs1Nlxma06Peobrb6UY/T76NTkSRJpqQA2OpuXrs2TLb9Iop4H+i4cO7pIDIgZLM0CH2t1+vh9evXbp4mk4krj9DsCs4lz8vvob2jz4tEgOdlIWmATQlHEpPa94Tjp6Sprk0++5DH6JxYp16zrNQGof1EIp1zTVtM7VTV45VKZaf86fdwJr41GExklqKSMjru8/nc2R0MnuqYUZ+q7crzEzyvJdOsDWyP4WuqN+lETqdTjEYjANnd86jjSdRMp1PU6/Ww61NAAPxtThhYZok2EwyU/KxUKmi322i1WpmSRuDhulBlWx/z7OyH4qiJGkKNo8Vi4cgG/q1WKxc15vF2EdcsGhudBrLpjWTrmZkTRVFGsfP45wSNBGq0V9/n42ON8H2fu+98+0qVfAaZXVBtfXMofbqDNSKts6eEDKFjR9a63W47Z5+Go573kCjfc5OZp4I8Zy8vo8aWZDDVn9mE/Lw1Uu/73qJnQ91HJvN1q5P0sxoVv4/E9jkGgL/0wp7PnnOz2WQCGUp+W33qc1KKVoKRB9+823nZJx+Un1qt5ghU2iW8N3x9h+z5bbZbkeVSYYNOfE31oupNtet0/PKCCffNsZVVu9bq9Wm2DwkGXzaOjyzI++7nfA/oPCkJ7dNlSl5rhst9+snqcPvaodfpe02vGdiuu3ZN8JW0Ped5Cwh4LHy2q2apsU/XfD7HdDoFsK3G4AZBmjzxpdeg1+LDY+X06IkaLqbMBrm9vcXl5aXrT0PGTZW5GsjWwL3PuOH/tVoNrVYL9Xod0+k0s03gc4SmXTKrQn/TPmMgz4mwUS2+ro6IPc4am/oZIDtffI/QFFXf97JRMpsGFn0RVKNRd3fRBoUce43gAnClGs1mEy9fvsRPP/2ENE3x7t07/P3vfwewlbN9Sq1Wq6HRaDgiNTgVjwN1F9O92WOL8kodydK08XiMNE0RRZGLOPDYJEnQbDZ3SARgWyrD7EM2kmaKqZI9RQPliLqTa5Km6uv64Evd9WXQKFnqI1EtmWJ1Jz/L15SQ0QwozZah7KphVC7nN9m3ZRhFJWyYjTYej12QaL1eZ4gWGpEAMvcEs6oajQZev36N//Jf/gvSNHWZwtTBhBKjdu5tKWJRZZKw5CjXOWbNaAkg5ZLl8Gon7GtGS2imhiVI+T6h86/HqTzRLrOOviVIDyEWnitZw99n+2apfQpsd11Te5bzrLrLZtP4oPapj/zmeWgjWRnUeeE9N5vNnI6nPuCayWxGzRTwEe0BAceCPF2lek1tJyZfrNdrTCYTTCYTl3U4nU4zgQ1mpTHTu9ls7iRfHHJt1DvUv9QvlGPyBexT+xgcPVEDbBv7JkmCq6srfPjwAUmS4Pb21qVD6aBrfSsNawAZxchaVoVm3dRqNSwWC0RRhDiOMx3dfcr9qYM9XHS7VS56eQaBGv9qDOaRNDyON7kPeenEijyG06YT289wZ6N+v+8WQl96clGgDh4deRJ12kBWj+cY01GPoghv3rzB3/72N2w2G/y///f/EMcx0jR1fYD0/rHzyh02Go0GarWat3QnYD9sCrUSX8DWOWH6/Gg0cinYURSh2+1mFhwlaiwBoAsSm6hzNyiW7jx2sXruUEeCEZ4kSZyTZUluLvp8ZC8TlTmVBSWr+T91mH3U4yl3dptaBd/XrFOuZ7xGNXZ8v5uN+YtK0jAYMBqNcHNzgyRJvFsok0QBto4lALRaLWdPvH37Fv/xH/+BzWaDq6sr/PzzzwDuiCB+F5BdL5UEUnkuKvltbRDqMpbOpOm2j4n2oFGbsNVqZdYujqXVi/qdPM6X1cv3SaJTX5Bs47kt4UvnxJepqvfWcw0UHgIdFw0sWaeO5IzqNC0Z4xw/VC5U/6qc2bHX82oWALP9Abh1kvqSuplrh9pgRbVP78O+QOshweRDzhPw+8FHLCvhyvuffslqtcLNzQ1ubm52njM4AsD5ElEUObKdvoWW6993T6je1gA2NxHSqho9L/Dwe+noiRplu6i4ObHqbOqj1rZqrwaNHFKRK3QR1eM108DiuQg/x8eXdplHfNx3vn2ZNvuOsbARqbzv23eMJeiK6kxYWPLNvgfsRug1Yluv19FoNNBqtbBer9FsNvf2g7II23N/PaiTYBcjS7RqHyLtY8GyNRKYvuwIS87mfWcRoQa7lpjt00v7ZNAH2yeDjgP/16wLwN+83feaGhr2mpQA8v1e/SuybiUpqs1/8wIWhJIu1KlRFLmed41G40GypXpA/y+yXBLWqfZlS9DG06wIQh3wx46nby7sPaKydKhsHbvcqU7KKx/T+bPlUVanPcT+vK/Uzfe6tVv1epjlw2vQY22W0LHP62PwkPWSxx9C6AT8MdB720fUqC9Kn57Bxul0ivl87sgZEjWTyQTAXeIGCVAmHfB16o48ubX3g+ob2nLsH6ZZmL7PPgRHS9Rondp4PManT58wGo0wGAwwHA5dYzgqQU3j5kAraQNs01DJlnMS6HTq1sPAtpM8P8PIzHNMM9ZohSU0fL0KgF0DkFFXPadPIPRz+r4vfZTnyYPvXNawIgnAhsJMSf/S2sVjgypNJWN0TmnAxnHsmmifnJyg3W5jtVq5ndO027qdF/2OarWKOI4dC17UnYO+FBznWq2GOI6dXtQGlIyya6S5Xq8jjmNXKsVj6vW6N4tOnUCVO00tL+Lc+RZz365Pmu3A6CnHj1FWJUyA3Z5Q9jnng9dAefWRdTYDQ89lm6eqs8B7g7+VQY7ZbJbpCcfPFxW6jlq5oWwwuqefoU59+fIlOp0OXr58ifPzc6xWK3S7XTQaDdenROdU54pzz7mtVqsZXVzUdS6PCNXXNZOBY2kzcoDdbAnfdymsfaMyC8CVxOURaZZgsGSFlgE9lPB9btCyNWZEWVsPyJLldo3S8jbVk8DuDngMZnB8LfFp/9eyKrtO8vrpk3A3KrWz+J3M+NLs/KJCbUYNiltH39onPhvFvq/nJ37PsfYREkDx/A8bAKKsqj+dJInLSL25ucHt7S2WyyUGgwFubm6wXC4xHo8xHA4dIUN51Yya9+/fu0zg169fOz3QaDQA7PYU0/uKcr9arVxm9GKxwPX1NZIkQblcdrYzA9Qq1w/BURI1ulCtVisMh0N8+PAB4/EYg8HA9abhxFEQSM7ogqxGFZ+rMcUu/CQR6vV6RvFqDfpzdlQ0JVgXfptWa52G+8qdfAspn1sh0ee+SJeFRrZ4LXq9dnFm/4DJZJLZkajoiyGxb8HTYwAgjmO8fv0avV4PL168QK/Xw3K5RBzHiKIIy+XyXsaZSjWKokx6YtEWri+FygwbOzO7iY4AiQDqNM4Fe22xbIlkGRcyK/P8HvunO8wUiWizRoduH6s7twDZ0jEey+dcOzgHwNaJ4/lt6RLHuFqtumADHQslqKnjuMYp6aLn4ut5zr/tYbNYLFAqlRxJkySJu5Yi6lS1SbQMGsiWTfMe0PuGRt+bN2/Q7Xbx5s0bvH79GsvlEt1uF81m021/TlKOgSZdd9M0defnY9GJGsDv0AHZgB933CFsYE6zBn3Oon4XoYSML8uU94Uea2EzalSWNai2L1Jsr+c5Qe1P9m+x5JTqQ81oBLbzprsScvxUR+r7lvTJI38IJag1GKKEAe8z3j9210QSNdxtVUmJoupTHXeupzbbyG6MwLG0u91pSbANUuh32te+5m+xOsN3Lx07fGTzZrPJlDhdX19jMBhgPp/j4uICHz9+xGKxcEQNW3XorsTAtncm+yZWq1UsFgvEcezeYwDZtxOiXptuPjMej12Q/+LiAjc3N5k+td1uF91uF3EcP8qnPEqiBsgyrJxgpjlpaZMez0cVfrvw+SIXed+fp8SfIywZs+/33xexybtJv3SxeexndQHXlNhjjj4divvu8zzGmSy19iXJMz7s99nz+9L6n6scfUtY0sTWy1oD1Dro+mfH/z6d4CMQigi7LljDT8fGF/EjHjqG6qzrd2kvFN9nLCHLz2pJm+8ada3wRfb1uKLdD3kRw7y5VtK02WwiiiJnaAJw8phXA79PdxdZJvet7dZZUPvPHpMHX2DqPtjP2LmhE38f9P7aFxw7Jvh06yG/9RDyyqef8z6X97qv1NDOry9Ty67LPj1aJPj0Z5qmO0QNj9HeTUrUbDYbr+7cR4z+EXpS72HKexH0s0+3cu6YbcbMGG54weA6ky/YQFj7dwHZpAGVp+l06kqilNhhPz1r96iOUaKG/IJmDzM7jqVW2j/soTgqokaFlxG86XSKT58+4bfffsN4PMbNzU2meSM/l8eU6qQyGkhhJ/OmTYh0EjVDRyOpz03BUmBms5lL8bLlT3YMS6Vt1pFvfHXs9XVgS5iocPFYLmSaakpFlucg8lGbvem9wqwoCjWFLC+9uSiwjLbPAOL7+rxaraLb7eL7779Hv9/HycmJcyqYIcMazvvGlgpad7/gtRVh8fra0F2fmGHGe11LGTX6x/II9huaz+cus8YSuFo2yswP69QXEbou2LR17T3CQAKPBbBjJKgRo2SL1Ze+TCclPbVXFA0KvV7fZ5nhQznWxp38TjViyuWyK0XQXeOKWlZqCQDfe8wM0KhduVxGq9XCmzdv0O/3cXp66pohkhBnliLPRViZ4443vjLmIsLOiW4koRtKaEa1BnfyCAFfsA/IJ3B8RKoep6Qo9a9de222s37/sYP3NQOyvkwj6j3NBASyupFZTJZMoTwC2TnV6Ls6gypTvAZ+P7+D+pR2k5aDW3srTVO3UxVt8KLoUSUvNBOVftZ6vXZN2m3vUR9pVyqV3K6kauNQz3KnSyV2vnbA0Pqfeq9ybpm9fKzzq2PgK3Fi+ZJtDnx9fY2rqyssFgtcXV3h+vo6s+HJZrNxxAuAHTmmv1epVDCfzxFFESqVCmazGZrNJs7Pz9Hv953NQ/JOA0/j8dh9x+XlJUajEZIkwYcPHzAcDlGpVFwlwOvXr/Hq1SucnJy4cz7k3jkqogbYDuR0OsXNzQ3G4zE+fPiAX375BUmS4Pr62hmLth8Dn+uEUglrVKtUKmX6BdjMAAqbNjhiT5znSNQA252zJpOJIzLymiHanhc2GuUTTpsOSoNInQklBTRN+JCICJ1SJXcI/gZulZskiVP+RSZqtHQvL9NInTTeE9VqFb1eD3/6059wcnKC09NT17zLEjXAfqJUDWU1PAMeBxICNEqsM8JyGpVLEjT8WywWO9tsa/8q6gWWRxE+Q6dIUCLLljvV63X3npI5PAbIRpk0q8kXKfKVpKkjwnWNu5IA2awan4zp8WxoS32puoLfy3uCv0t3KykqCa4GuQ0WEdoXT0m9breLH374AScnJ3jx4gWiKEKpVHI7QVF+FZa8owPJEhHdGr5Ic2F/q86L6kMtU+RzYFv2pOuhDST4iBJfwIpyoO9ZO4XyqkSCltNQJ1A/5GWVHFv2lCWudWdKtV14fysp7is1U+JcCRj9Lj63n/EFFm2moiV21AHUsla9J3hP0qksl8vOnyhKvy8NJM1mM4xGI+dbMYPi8+fP+Pjxo8uK0GAI55C6tVS66/nVbrdRqVQQxzFarRaq1SpevHiBNE3dfcKAsy0H1uy2h8iUz/dQuaVNrWXIGlA5Fqg86fzSVlitVri6unJzenl5iYuLC8znc9zc3GAwGDj7gzaIJkwwwwXIEjUkdEjMDAYDt4vpcDhEs9nEZDLBy5cvUa1WXV9NYOuPLJdLXF9fYzQaYTqd4v379643zcXFBW5vb939U61WMRqN8O///u+uPJn30qE4KqJGb3imSemW0kybUsbV9i3RcxE27VEFdR+7qsrC/j03geNvsUaAhXUauEvMQ79LxyfPgHzoON7nGNB5sKREkaHGQh7R5jNeNNLLhY73gxKcj8Fzk52nBGuoq870Gfk20mjr9ClTKi9cMAPu4NP/+3Soz/HT9/h4iBxwjfOROXb9OiRqp9k4Nkjh0w+61trfXGTdum/e1XjV46rVqiNLtZ8NCbS8PieHfHfRdarv3s17fshY5X3W4pAgk7U79c9+p713fITUscqd1TO+9wG/PQ/4mznnfY/FQ7MslBCijs6bU16b/Z12TTk26O+iT7fZbDJtLNSpp6/HYIA2edaMKNu3ywbitfE9oUQcCT+9nx7j091nFxyr/2H9B2Ab1EnTNLOLE5MD5vM5JpMJRqORe85NX0hactzY303HUeXLFyRJ09Sdf7VaYTQaOeJOs5u1Xw5Lrmazmdthitc7mUxQqVSwWCxQq9WQJIm7fx8zr8+eqNEJ0ajdr7/+in/84x8Yj8f49ddfXeOh2WzmIsnqmOiuTxpdsmmo9zHYvmjIvujGcwCFZzKZuHQvjoPPgOEY0EHXMT3UMPEZJHT0Saj4HASbDUDsy6jh/3ovMIoGwLGpRYOVB1089JHzAWznr9lsugZaun1sFEXo9/sAgMvLSy95phFnjWo8JIsqwA/KJqOv1JlJkuD29hblctlllDG6x4wa9htifww6iED2XmH0T4kedSyLNHeq95nJwDWK65HdTYb6y7c7kxJjNBipp3TtUWNAgwr2Od/3GaVK0tkdT6rVKprNpmsYzDnXLAJmJDI9XVP27fpx7NB7QEsL1fjX7AidP45/FEVOpzJln69Tp37+/DnTD0yzFplFrLs9qVwXkVy1Bj2hmWnWLtDdB5Ugs+NniVa71tEm4XN1BrU3ha+3iU8uaaclSeJ0u2YCaYZwni32GIfzW8PqWM3s47za8jWOO+97Rr45jxwHe19YOxfIZnfb7DXrR+i8pWnq9C77WOjOUKrj7Tzr77ovAPmcoLa8ljUNh0N8/vw5Y6toeXaapq5PCe953RCGjrxm5Q+HQ5cZTAK8Uqngw4cP6HQ6qNVqODs7Q7/fR61WQ7fbdRk4tVot0/oiL7tmH2HI38h59gXJeJzv3E8N+36r/m/9dbbVYGbUaDTC7e0tFotFJktKS9t4X1CGNHtbx8iWktEesaVHlUoFy+USV1dXaDQauLy8xMnJSWbXWQCZfjnX19euT87V1RVGoxGWyyVub2+RJIkrW6tUKuh2uxgOh0iSxPkzD8mqefJETd4Natkw1qRNJhMkSYJ//vOf+F//639hPB7jt99+w+fPnzNp2WTWNHtCjSi7gFJwrDFso5D2f1UOz5Eh5fVqA6fZbObNpLCGBBc93th6rIUvcmSdE83S8V2nEmM8j84da7r1fPY+Arbd49kMyhfZLgLU8NEx0IVFnTNgW/ZGp6LT6bgUfaaYnp6euowbwhJvaqSwp4qvC3vA4bCkCRdL6k2W05D9B7Zd8pkd1Wg0sFwu3UKjERHtP6JGVJ5jceywBDLHSEkabXBPZ9A66MC2Yaw6kHy0utM+KhGTJz+2xNSSbNaRZAljpVLBcDj0Ogz8Pdpsz6bsPxcj9GvCF+3l65YEB7bEWBRF6PV66PV6rj9NuXy3GxSJGs4J58N3btWpWtZYRKIG8GeNamBOSRqW9tJZU5LGrmHqcPnuba6Jqh99ASqffWU/w+OozwG4Um4fUXNs4PxpuZra9pakYYRcSUvOqT2ngv+rPvaRN3aN09d1/eVrdB4bjcZOMIz3iZZ1qG12DHNq1w0ArjnscrnEhw8f8J//+Z8uW4FrDsuXyuWyW0/pCzKgzEwbX1ankm0cX90V6IcffsD333+PRqOB169fI01Tt7Wzrq06//vmwtoEWiquf3ptPP6pro/6e/OeE+oLT6dTR24Mh0MMBgNHzrDE6erqCpeXl85GWiwWALK75Cm5qsEu1Ytatsq+UCRw+PpgMHCy2el03E5Q7XYbzWbTHU+5u7m5cVk9zPrhvccyZK6r/X4fw+EQ4/HYlSo/BE+eqCEsw8hHnRhb7sQtszRaaW8km2qm51dY8sAHjXzwPJZAeI5Ovy8qYUkawo6TLlg+YstCFzQd530K0M6pVWp6TfucAqssnyu59jWx77f75lLJNDbH07mkk6C7DvmgcqKZGE91sXpOsI4AkDVKbWTZOgZWtvNIdL5XxLnz6Uddr3xlpJb8VuPPOmXUpb6IlXUaCB8Z/tDfpPpTt/3W9/I+Zw3lfZ85NtiAgG/e9JGgLtUSJ93i3gZFDiFB7fp87FluebbDfU6Ffc/Om0+G7ptn37Xp40Pg+277nftk7Sk7fw9B3pjfN79KlFlbVeGz2X1yqo8+2PVWX7frLIlsH1Hk81t8du9TR54jr/Y3iX1uZJIkiQturNdr1Ov1jFOuzjuwtVHVT9TgEq9ByVZmbDQaDUcMkfBhk3ebIewLluzTOfvWRB0HJeb4uac0x1bW8vQMX1OylKTycrl0JUOLxQJJkjjig/PN4I620XioPaPXoFmsmsnKhvylUsmR8pvNxjUlZvkSt+QmUaPXqKV3pdJd43ftB/eYPrXPhqgBsqVEFDKmUy+XS7x//x7v379HkiT417/+hZubGydcmjZoBZV/VJDKdgPbhV4Fk8aRRh31dY0s683HpkXPBRwbZtRQcHzKRRc9Rio0Kkz4CLe8G1cXUCqpPMPDl2ps08s04sJzcT6oNDXa7TtvkaDGA50ElT/rTHLeNQtDo08siVqtVi6dcJ9Rq1tCF9Hh/9rgHJEs0+gwx5iN1jSTgnLMlF9GnNRood7UnfE0WsnzFA3W8NTMEk3P1uMoHywbZFq8NgylIUCjQ4keX2qtkiw+3WkzOkqlbT23JYmArWzydV3zLJHErBqWeWlNeRHAMdeov83w1UfaI5z7OI5dRhvvBV1royjCdDp1c2YbO6tjwO+xJOqx6tVD7zG99212DeEjR1QW+cfXKEfqMBKWWNtHCBE+x47znHdt+puOLfDkc3x1PHzkiAaSaK9wzlTXqZ5U257/EzxOg1FAtjm7yhqdPQtei2ZxsWG0ZjXTyaUe5Wv8jU9Vjnk/2mvMc+Svrq7w22+/uZ17P336hNlshmq16nZliuMYvV4PpdK2ObDa8Cyb4riqQ03HnNfAa2J5Ltda+m1ct2u1GtrtNuI4RrVaddkXSqrznPob7e9l9oWtNtBxor1lM+6+NSwBbGUP2N6TOqda4nR7e4vPnz9jsVi4sqblcumeM1NFy8OsrZF3bfZ9ny7QOdHgGWU0SRK3vtpsYx854wtCkX9IkgQ3Nze4urpCt9vFycnJg8b7WRE1wHYSqOy0wdCvv/6K//t//y+SJMG7d+/cxDOtUFPLbb0n4TNafMJhSRplcllfqqnuzPDh7gw+I+CpQW84Co0tfVKjhoqEY8DUYKar5X0HsN0m22dE+AwlJVh0UeX16LUA23khO28zfXg+daZo+N7Xl6gI4Fiqw61zDsA5hkyrj6IoQ9Sw3IlEDdOMrQxag4pOZ+hR8+VQfcVxVRKgVLrb0W4+n2eiRjaiTzKORI2SMJxPpn5ax7Joc6dG6Hw+x3Q6zRAWPseLn2HqrG6prlEgdb4Z6VGHgutUXjYj9SaNDZ7TBkV8DiXvDz2/Ejt6DupdDazwvafsXHxtcCy0xp7QdUx1KlPtlajRoAiJU+padcw1Ms81lt+lQaeijL/CBn3UfrAEmn1OaKaxfZ3nsLKjgS2OvS9YYefGR0r4rl2P199jSR7f73mO94GOi/6+PMeWr2n/O5Yfaam8nhvYkt8+Oc1b12wfFQ0O8hr4OV4PiVz1IfTaGEzR8vynTr7tI9F03tgcdr1e4+rqCj///DNGo5HrUbNarXBycoJ+v+/Kb7vd7k6wQvufUC+qD6PZOtYXG41GGI/HAIDJZIJPnz4hiiLnH2i/GhJAwHZXRO0L5rNrdZ33ETU6TvV63V0/bapvDR85ozYE54Cvc+1fr++2UNdttdl/JkkSVwbFoA4JSSUpOUdK3vCagOzuwfuCIJa01nuG5yF83w9sS5j1fFameX6ST4PBwH3XQ/BsiBpryGoHcO7qxNQ49lhQgxbY3+RJhVyh0RB9bd8CYD9DBfSct+e2kSZlK31jZg3BvHMqDjHYVZk9BBoR2Xc9PuPmOc7X14C9n9XAYdaY71guWD450cwcu+jkkXR5KaYBj4MuJnZMbTmTPcbeE7ooWafGlmQUfd7y9Io15tSR0jH0RYXUSPc5mgp11H3wkd36P8+hhq2dU+s46nfr71ZdWyRY+yDPWebcag8N2yuIx6rDqfLoO69+xur1Y5XPxzqyvvtT1zjrbOrxhzy/77sfc7y9Fp8sHit8pJUde0JlwWbb+3RanmxYUucQ+5V2k48ktfrdklCqk7UHz1OeX5/f5iNqADhHXXvLJEnittzmGHNNpE4kQW3PSR3KoKBm/qpTr+QbyYdSqeSup1KpuPIcbbzeaDTQarVcc3fdetlmM1pZJFlkAyT0F3kdrVbL6fa8gMsfCSVhbN8nbTPCY5iosF6vXQ9ZkjMMWmmvPtu3B8ivwriPDCPuI9wtNBBmiXgGpHx2V145Os/Be/uh8vpkiRo7CCrkw+EQt7e3WC6XuLi4cKzc+/fvXROiyWSyk/rE8/mMRU091+Pv66WhCkOVDxUEMR6PXUdpsoWWvX+q4PioQKkg2oUwTe9S9sgEz+fzTOqv3ujWmNdsJCpUJQR8iy6Q3UEB2E0hZ2fwxWKRcVbU2VEhI7PPTKwigvcwFzqWYqRpumPoA3DR3lar5TIBrFPBNP7ZbOZSjvm+zjMdkmaziU6n46IXAV8GNSY0CqULrxo3WtZkM2vYDJrRHt4XdCyZihzHscsGKSphw3HWnY9Uh9myI2CbCm97PVnnUA3MfcaL75r4nupE25uLGTVqrOg6qc4O12neZ7p2axSYEWI1Znm+YwXlis0Euc22lhdawrvdbqPf76Pb7aLX6+309yqXy+h2u/juu+/QbDbR7/ed06DrmdozURSh0+m43aNsCdsxYt9vs84xA3w0rrXxrM1eooOo2dpKeNrv0JT8vHIGH+mj59DIrQ0+2etXp0l7MzxkfJ4DqP/oDFHH+Ha+ApBZ42jjqXO2j/DkMfo8T8f6CFnNKrFlxbRxmE0AwK0Z9CeYHcIMhlKp9GQzE6n71+s1kiTB9fU15vM5SqXSzs47AJxTv1qt8P79e7x79w6j0ciVgDabTcRxjCiKMjtQqt8AbOeH/UGYPaNEgJaH8j5I09T5aLxHSqW7fiWXl5duNx9mN9ZqNbx58wYvXrzIlIaTbGHGjpapEZZs4/VoFlW/38df//pXvHr1ClEU4fT09EG7BX1N8B7jDkzMDE2SxMlakiQZ3UmbhyVMk8kEt7e3rpXGaDRyWaZq/9N2ALZEuy2B8tk4liyhbPL+0ddtdmkesabX4JN1X8BD259wbFj902w2H1yl8bvMuO+HPObzanzq3urX19e4uLjAdDrFr7/+ip9//hmz2Qy3t7du2zY78VZR+gxbZbo0G0R3zNBr5A2gqeY8zn6WRE0cx44dfgrs6H2w7C9T9Wz2CY/R7QWjKHJ7yNvxt+fX9yhoWvcNZLN6FDre6jQoqORtBCUv2kbhIilVRKhTrgaqKlY1NhqNhnPOuXha8pJEzmw2c84nsBvNouw0m0202220223vwh7wMChRYx1+LXGx284qUcNFT0k7nodEDdOSWb/tI+6KBhovXMeAbCqvJaUpU7Y0zRLk1E++bM28NcZGqLR0jYYpz8k0fT2PGiUq3/obuB7QodCyJ0YplWw6ZnCcqNOiKHL9h9SG0HGoVO629Xz16pUjVjSzhnPS7Xbx5s0bNJtNnJycOAJVo8XAVp+3Wi30ej1H1BzS3P3YYMdGX1PHSctQSMrojmdKRvoCfXpuYLsTGuVYswvsWsjnPhkDkLl3NDtAnVAlbDT45bu25wq1Q5WooXOsOkdtDLVtdEcZwpfxYr+Xjz4b0X6W16lENb+fzl2tVnP3kjYwVRKQTiV/IzMtniLoqLMnyfv37zEejzM9LHWcWRnBnqPv3r3DZDJBv9/Hq1evHFFDvaW6S0kazUDhZ5SosY1dlXRloAnY7u612WxwcXGB9+/fA9iWLdbrdXz33Xc4Pz93BA4bHGvT4/F47ObTyjN1gfY84c5Br1+/dr0cWW710B2DvgbUX57NZri+vnYZRmwxMpvNMBqN3LjSd5pOpxgOh872IbFjofJCPaWZTqVSaUdG8wJTNuhF+bLv0XZdr9cucKxkNz+v9pmPZFFbSP0eXUtITLXb7Qf3qf2qRM2+yN0hi4EddB0spsGtVivXmJdCzfQpbdBnoxE+Jswesw92odT/fcrcGrHK7ltW8KkvlOrI2fH1wUZ77GLle37f9/PRRmAP/Szn6b5IiX7PIb+1KLCLiw/qcPhKNXiMvm9hx9oXdXzq8vJUYZ3rfVFcIH+LUbsg5X1HXsS4yNASoPvKf6hH7yO4dB25bz2x+led+PvkS0kVXxTJEn/6XB3gvMyfoiBPl/lK00qlUiazzTc3thm4km7Arv21Tw8UGT7bJC96Sifbjp0N/hzi5PO4ffbovjkk9hFtRZAzG3wFdglp6lQga1voOR46VtbfIBm0Txf7vkP1vV6LdS5tkPQp26j0GzTjYjKZoFqtuv46qq94DB1/S6roufQ3M4PBF8RggFmDr7YVhY6nZr3p+dhAlt9HgmUymbjsyMVi4TK02JPNEjV6z/G5JWroz7bbbUyn04zv+C1gyVDdFIGElProSnDzOH6O5JW1Ffk9FprtYnUgP6NZilZu0jRbLXEfDl0T82Q87/Nq/zwUX42o8bFaCp9h4jted8Jg46flcuka8SwWi8zzwWDg2DptGqwOvUYXNN3Nsm7KuOkEMNLsczqscgW2EU4t3bm9vUWapmi325hMJi6i+JSjWDo+VFLa/Iogw6i9R1hqxAgg58SOXZ5RwkdlL1WB6oLoY6h5bkYuGJ3mdfJ9+11U1OxOzjTTIkINAWCbjQFk50LnodVqZdL6NVJLpyOOY0yn00xEJc9A0sjmU40aPQdwjMvlcsbxI3yRXl/mGnWWb8com3Vjo2U8R1FhM0v43GaS8Xmj0UC73XapsvuIEn7W6mYga8j40ns5ryrbPMZHypVKJafXeZ2aVuz7fqY/t1qtTP84n1N1jLDrjM3etQ5YmqbOrojj2PUpsODcsSyVc6Gp9DyO0IxivhcImzvo/Fg9SDlgVhRLVZRItQ6HvbdVlvhdPoNfZc9HqmnJPXWw2qvWeT/2+bWEAOVHy0Y5NxwrygozQxmtt0EGvgZsfQG+RllVmeJYW79gHzlNnRpFEQDskDWUY9qiWkrqy5J6KuAuPrPZDBcXF/jP//xPDAaDjKwA29+rMnd9fe2a0LKpL+fz3bt3BwWBNpu7VhncRUjXXPYzTdNtRg3XKu58yfUKuKuKSJIEADJ+znw+x+fPnzP3Cku9SEqQHOLvtj0C0zTN7ISomRsfP35Eq9XCarXCd99997vM030g4cLMqI8fP2I4HLpKFvqH4/E401+Hv2s6nWbKQwkf2aLPbdNgLbO3n7F2jjYHLpW2rUisbctz63pp31e5PTSAqeV4Noj1UHn9XTJq8pxv/UHWSeaf1rwNBgNcXl5iNpvh8vIS7969cwYfbwiSB3RCSAroTa//a92gCsMh12aVtjouqlh5Dirt9fqu2/ViscDJyUkm9f2pKljCx6KSSNOx4vhTeWn9KBcSpuEfYjDoOKrw+IgaG2m0wk9FbEs17PXb3zqdTtFoNJzCOWZDxwclaji/NsWaoFHCLbm1uaUuqOxdxLI4XWh9DpuSq09dVp4DqCN15xhgu7BxcfGNtWZDqRFsG/vZxYookvz4SGg1HpSs0Uw0PjIFu91uI4oid7wtB/U5A3yd/+ufkiNqRDDA4SPUfFke1O8AHGmjTqf+djXWaOw99Ujw7wFrhOt66FvnSGQzdV93yVPoesu+CWp0+ubDEhBFkk0fOD42mGcDFbQ3WB6hDZztOPoCSZbI8RE1PkfAkqWqi3kNPK86SRbHNs9Wv2mJk6+3l65RSmxqrxcAGRvRzofeD/yftpAGFn3EqmbTK9Q2ol7g79PsDr7ObJDlcumI2ac4t+xLMhqNcHl5iZ9//hmXl5c7zrKuOwyk6prHCgogm5lgiTm1aTift7e3uL6+dkQJcDeu9B0B7ASugLv5VT9tOBxiOBwCgNOzpVIJNzc3ToZJzLDUhb6p+pAaFMnzh5Tsu7i4QLvdRrlcfvBuQV8LDF4vFgvc3t7i4uLC9Rti4kFeWZO2ItFx8NkaQHZ9Utmi7aoEN+eIcqUkNe8fAPeWB9rvsu/5CBar1/mc95L26rM+zkPxxUSNEhqW3bLH2IiDdZDJ2jH1bTKZOAGdTCbuxrdlTg+9VsLnSOhNYLHv5rLH8WZktIxKQpuaPSQd61vAR1bdN942GpE3jnr++67BJ8j2s/vOpXNxaAbToWVeRYHPEAWy94hGC3zNhoGtsrX3yFM0Mo4Rlv3X1+7DQwx/1ROHkrPHDl8Wp0ZprByog2b1lm8t2/e9+jmNOnGt29cvJs+xsL2LLPQ3Wsc36NTdOaODlndv+PSlvUc0cFH0UphDYcebz/PGyN77fM13nP1/n3OipPm+ubNzrvKnZENR4LNTCZ+NrzKyzy70zamdJ/v9+llrL99neypZ7zvWZvjYteQpwo4xSQpfiwrfuOr8aZYFP0+CgIFEzSzjGNHn2kdmbTabDDlH4oyEbJqmmeAEs4h5nfp7940F7V/7XQAyDr11+p9Cfz+Vrbx51UbHOi4cezu31jfQz3L+aJvo7+eYsO+WJlzQrlFC7L41cb1eewMh9h61ukTLKJUM1u9eLpc7weuHzuMXETWqKLQmjYyiJW50YjT9V/dZv729xefPnx1zNxgMsFwuMR6PMRwOM1kwFMy81GFey740UF3glKixDYM4KTzePuYpa74/nU5dU6XRaOS6P3PynirUuM4bTyCbgs3fpHX1eg9og65DMiX2OYi+BVGFgJ9lFIwNLn3H6nwxAhxFkbd5VBGgJS55xJslXPUzvtInbSbM9HFbr6rwkT0BXwZ1/GxaOLBd1KiX1LnOMwxV36ru39ePqChQ+dBae10T1ZCwz+39r4ENILt2WQOeelbnT/+n7HHeeD69H+g42kgy55aN2rXEmL+X0bTZbIZqtepq2ItG2KgRxwwpX48TnRcgS9Ywe0KbsANwpTj8Y4aNL+CkhrZ9rahQ3WXtON96xOxcNoPWtUszpNT20DVQG2XqeX3X5SMegGz5tu7GaH+TRoptyd0xwNr5eRnABNc16i1mRuguiHnOnOornpM63Udy29fUsVPdT93Ka2I5k3Xs+T7l+yE29LdCrVZzTdCXyyX+9re/4c2bN0iSxO3cq+B6wXuW97VmzvB/rks8xhKhzL5otVoAdmVCbXslQ3hP1Go1nJ6e4uTkBABcsoDP1wO2/XC4lmoWEFGtVl05M8/Be45+k/qx5+fn+Mtf/oIffvgBZ2dn7nN/NFQX1Wo19Pt912dIf7NyAJwv7njEOfWRZpaQU3m2WcAA3PwAu0F1/mnpk+3xpvLLR14bz6lcAO9TJXi1/Fv5AWbycQyWyyVev36NP/3pT3j9+jXOzs4c4XcoHk3U2MWNZUj8UZw860QTOmHT6dT1ovn06RPevXvnsmi4i5NOBBUWlTAFkmmPen150UsddL7GhY9pkD5yR5lQPvqinWrUMlNosVg4kmY8HmOz2aDdbj92Cn536BjqQp8XsaGyYX8S66T50koJ32Kjjgf/5+d8xN99xhWFRxdvu5AqC69d24sI6zjmGZNkr4HtgmcbXPJ8tVoNrVbLlZUpoaPjr9dgyRprEAU8DDYaYjOcNEqiZYt5GRG6iKquAPwNG4sEGzjgmqk6ReWMcmCj5TqGHGMaO3YNskaI6mxf6ROQjQhZGeR30pGxBBN1fqVScTYAP0PdwG1XdWvyp+pc/F7QYAYDGnk6lb3V1OHUqKXqVBIHDPzU63VXvltUuTsUVjZ95KG170iM0UFUW08DU/p5Gy1+yL3vs39oX1miRoly/W0+e/hY4FufgO2uThpMpQxqbx/dSY16zjqO9vvs/zpHKnPU0b7MYvs/CVjqUkvYa8kddyV66lnf1WoVnU4HURS58aVf9/79e0yn08zxunOQEtYkPHTt5FhrQInjoNkdPIYZOFwDdQ44/+xL02q1UK/XcXZ2hrOzMwBwpcr0EX07ENHvXS6XSJJkh6jhOdvtdoYIqFarmT5kvM7T01P8+c9/xps3bzIEz7cCiZper+faQlh9w/FhmfN0OnV9bLgu5SVCANl7mrYOsOUM+B0kO1T+NDmEfYEA7MiTyraSf/qc16G2lhJ63LmR5+d56dco4XR2doa3b9/i1atXjrh8CB5F1Oig8AdxYjSCpqyUHUhlTrnNF8udaNDRqFPj0l6DNYSVictbEH2OvqJcLmdqh32f10eryH2KXSNlz2nBtIvQPqgQ2LHJ+6xvjNUZyHPKfQSO79r1MS8rRI/R637qi+DvDTUwNJ1Po0J85J8l8ux478sU0HPZ6wj4OlBnnFD9eV96tn7e59j75j7M365DSFjDga8B2e13Dx1Dny4l8nTxIfpQf4MNeNjjbLTMtzbvW5+PGXTINNBE6JqXRyDcJ7+HjqsvwFRk+IhnHVcfdK587+37rjy7hlBi1H7OXi8dCF9AyWczHxPsXCms82gzYiw5zdeJPNvUPrdzcd/c85Hnt+sBQQc+7/fZ3/8UwXuTDj6bJS+XS3Q6nUx2O7Bt9Gydf+29RZJb+5QA2f5AWnqiDj6zPoAssarP2+22I036/T46nQ5KpZIjZ0j42GygzWbjyIHN5m5bcCVqGMjmOXk9lHVuO65+da/Xc5tzcM34luBYMehtKyaA7fqfpmkmiYLzQBIjT240AUTXP1+lDh99/ADnmvcg5V2TB+iz8Dmvi9wE54Lyuo+o4diQaOW5V6uVI9ls8PpQPJioUeIlSRLc3Ny4JkI3NzfuZmYmghIyJHCs4aYZOGw8pYwWkI3MciCBrBBSeNJ0y2zmpQbmsdxa62YVsTVSeQx/Y55jwslibx2OD5uAPVXwJtV+QDYVTcdEsygqlcrO3vNKVpFd9zkPfM0XLVbYKFXeI4XM5/SocOpneK+yQzxfL5LTyWyLer2OOI7R7/exWq2QJIk3O4CNxWq1mtvVTLNx0nS7E4NGtCjLPI+WRPI6fEZVwMOgi6xGbphqbfUR5cISBjQ4Wq2Wmwsts9AMAFsCWbT5U2ODWafswaZrkEZyuchT9pgOr7pMsxJ1TdbvVX3mcwas0aKftccCW7m0DqQaUFpeQaPMjoHdGrUoKJVKiOMYL168QKPRQK/X85YHq8HL4BX1pLVbaOswU5dbudL+0nuG52V2qa/Ze5FhCS/drEKDhipLPgLA/vF1YDunXAv3lYVau0Mz4HRNVX2gO6rZrJrnFCA8FGpf2j+OF2255XK50/PDZpFSD+eR49aZ5Gua4WbnnHNAfWej+LPZbEcXa6kPMzmYFULwt1k5f0qgvaHr1Wq1wvn5Od68eZOpgADg5kp9Bb5OW9z6dD7/zDr+PJ9uJqMlZGrjaFkjy0l5DZZA4Pn53TYbxGZUVSoVRFHk+t7wGNpUmsGcpimiKML5+Tna7XYmk+qPRqlUcgRLs9l016NEBo8DtvevcgAqq1YHqX6y5yTUjtG10vIEGkjm2Cpxx/cs9D6yWc+qZ3mf6I5//O16jGZwtVotvH79Gr1eD1EU7RCU9+FRRA0HczabYTAYOHLl06dPmE6nO3VrfK6ZMzroOnn8DLBlRfnj+VyJA2beqBFobwgf48YbipOqE2V/730LG69HmVl+BxdYzTwiofTUDVWdD7vlWt6YqvLTxZKwUT8fUcXv5bzbCK3P+PEJtRKCPF6FSt+3To4tfXqKi+DvDc4nF4x+v4/lconBYJC5NziH3CmL9ciaOcPzkfyhE69NwbTDvt4bRS+f+ZqwTd9I1GivGtVbAHYMGS5SURQ5WWGkCcg2RQw9arI7CnDnPDaX114TWoYEbNO3tSeJkuKHRm/zQMLFR9j49CkAF0XzGVo0gukQ8XO6DlDOtcFjkdBsNnFycoJGo4FOp5Npfkmok8KSMZYzWRlN020PgPF4nNlswZaYKZGqZLnN6CoydO3hfcooeV5ZkWY7qWPGMdWgHo13IJvunwd1Quh0AMg4A/Y81umxf8cEteH0T/tgcR7n8/mOY66EmmZV2ACujr06/cBuE1hfab/aqGrfKkmfpqlzxNWH0t1rVO/6sgyeGkjUpOldRk0cxxkH25It6repbOnrasdbotRH2uijZsKQkLH3g9oseh6VRb1HfJn3XNt5HrWDVR8owWbvuzTd9q5R2+tbQMmONE1dlo/NOFEZ0Wwh+5uBrHxQz1KueE79zTo+Ou9KqtBeUtJE5wLwz6Mdfx5j39Pf4qsuUGw22wwrlrZpmfhD8CiihosW+8iMx2P3RyNUU8T43Bfp0UE5hBThNehEUWGp021JBd859NGX/pQ3+Grg6rVYB+ehk/HUcd/82Jtax+i+8+57zxfxPdTxU6VgCZ5933+sEaiHQOdOjXzCLoqUd3UQfGSedeR9Dqcd8yI7+l8bdsHLIzl9x+uCRWfPEu88R975iwi7Zu0bF0sm8zifMWjlws6TOgJANvNJj/ddry8ynPfdaqTZ36zvWwO1SLBy4zO68wiwfWW4eTrVd/48w7ToyLsnldzQe/ih43eoXcvvIfadXx0HJU/VyT1E3xwrLJmmyJu7+8aIY27LzO6zSe3cKKxDmBeUsuSDL8j4VGHtRzrhfM7fwMCsJWr0t6rzfghRQ5CoIcHGDBXNqlJdqucH/LqARIHv/sojaiwZo0SNvV5fsPNbIU/fMNBqMzN94wnsbm+vUGJHyR+rb1WnaU9EZnDrmNvx1zJR33rIcefv0++3v8kG0PS5HvelGeUPJmoWiwVubm4wm83w/v17/O///b9xfX3tGgaR+dWL1iiEkhg6MJoJw5tAHUX9LFlkZd+UvaJAcTJ8C5a+b1k5jZDYLcd4LjWCKfy+BVaZUN4wZP18uz48JdjxslEZH9uoys/OuYU1PPeRKnYhPMTwUNJO55Ljz1Ry/W4ALgLD6GRRM2qAXYNGZcsukGygVqvVHGG7Xm/rrIFteRzvk0aj4aJHWvNr7yu+FvB1kUdGqpzrYsPnjUbD7aZAvav6mOmuLKuyhu2xwxqMXGO0KbsPHGcALtWY48hooGYYqnzoukX5KpVKO7tM2cwd6xzw+mzKMH+DJevYyJY6k+XNvAcYVdWUb/6epxoJ/r3AXZ/SNM00HFT503uDzRht+RtRKpXQaDTQbrexWq0QxzGiKNoJWmnGlPYaCzo1S2xYZ1iNdbtBgl2frEOhBKs6JfrcyjKhr1Ef6Pco2cfda1h6zLJj6mLb1+E5OPYPhY880yg9CQDbOJ+fBfzBObVfda7X67XLFFHy2RfIsufRc6nzS7uIZYnqe/jaB7AvaLPZfNLrq+++9tn9fG6DE3zdl81yCAlpdav6oD4HWu+NfcSPL+hgnXSbDZJHwOXp4adCqPP+5NjQDlDCQ0F/Sz8PwMmi/c0auFDf3P52u/apXQVk59Rmc1udbK/b+rsq2/Ye9hGEPl+ZtjHtscdmNT6YqFkul27XosvLS/zjH//A5eUlFosFkiTBarVyzhhZKXXSdKtfpp1x0jmJvtQ3m3qoDqOtR7XEi05EHktLwxHIkiu6bZ9P2TLixcXRMm88nwquph1/awG8D9bR8Dl1arRrqj4/43MCD0nh80Vo86BCaL+LgkbBVYfT5yyQdCB5oIvFU5+vr428RdH+Acik6efV9Ot9QqOEToj9Xv3Mc5CV5wJ1JFQnEvq66i3VY41GA3EcO2LdlgZoLxI6pEWFXXM0AkSoQ0iChSRLuVx2Cz4NEl86sEahKFdWfq3BYUlxa6gA28xQjZrZwAOb6lljS/vtsCEkCb3nEg3+miDJSfJEm1v6QKKm0WhkSqXVIWC/qOVy6RpS8nPWudGI6LdKo39qUFvQp6dUpngv28gqnXTrqCsJAOxmnlHefM4sn/ucILWxufuK6gxt3qmBUovnvqb6rt86cLQ39f5XnQnsZiFRd/nIFQA7TX7ziAdCx1/1pyXctXeUllaor8NrpY2qwcS8MXkKyLvH9+GQ9SHvmDxSRMfpMdeU9533yddTnZdDQOKFNobdpdWSVlbP8Rx2/Ane+0A+KUfbCUBm/VKiRnWwLU1SP9Y3L5bMvu/ey7OZ1e/RDC7bNuQheHQzYS4AakCqIamRAI3M2oEn2aFMse8H0bgDkHEGVHHpsXnZGla5+sqjLGvHxzxH3bf46ns+xfxcYOchb8EHsiwy5/WxyBPyfdf5kNe1943v3JbUKzp8BKQPlMu88bsvQpC3AD5GuQXcDxuxypMz1YP2OT9rG+FZHV1E2HUlb62xjoOukap76UDoHCj0fyXErQz6yHbfHOedW/+3jTnzxsGu8UW8LyzxycBB3jqjQSib8aHnzNOreXZP0cb9ENy3Nln5yJOZPNkEsvNh5fA+29BHzvp0sf2OL3FInzrs/W2f5/12X0aND/vITP2c+gYPHV+fXr3vHHm64Nhw3zjk+WT7Pvs1ZMGnZ/ed6xhkTn0AW8pkiRoeD+za9DpWh5CLKtf6Gm3XvAw4zVb0ncN+r8qvzXTcp9t9RLv+2Yz0+3woHx5M1LA3TZIkrnGwpmZrYyGrSLR2Ok1TlyLNCICy2Wnqj1Ck6TbbgZ/VcicdRHUY7ETxPPwujXDalLh9mR0+o1cJAKZ/NZtNl/ZcKpWehQPDsVPD2mc08jiyouyYru89ZmGxRob9Lh6jjwp77Rxv3R51Pp97F0beY9xmvuioVqtoNptuSzqf0ch0XP7ZtGs7xuq4qdxb8iwv0hnwOPjIapVrbXAL7Dri1tnkvJdKJRfhq1QqSJIEk8kEpVLJZToWGRxrLX/SyK3PMGBUxu4WyGxUnpePlBNmv/DzPsJUP6u6VpthMiqlelyPZ6ZMq9VCpVLBaDTK1IjzPmJZRhzHrjzqqafs/x5gRiHXoGaz6baatTLC+2U6naJarbpefz6D2JchozqVepVb3GrDxqLDZ1SzIaVmZfgMbyCbDWfLoPi+r4RDs2As+apzafWCliQyeEnZ1NJTm71tSzGOAZb41fvaNmIH7n43tzrWUg5gtweJj+SyNqx14jSKz+P5nu/c/F+dX/6vdpAtIQe2uz495WbCvzceS4rdR6z83tfx3KBrDO9P66/b9Ubtdz0G2C0VUmjmDL8bwE4Jtx7vSwZhNrKVYbWFfJUvvD49J69jH8HLMaItDGwz79hImzs+/e7NhDebjXNgdbtpNUD1ohUkX7gA2p2hNArLH6kLJieJyon/624x1qm3Nwivja/5HAi7ePoIG73J9Hdqfb81VlnuweOeeraGXQStkegzGLkAWifQRjzyvs/HXKpA8P7S9DWfUcPz6TXwOiqViiNqarWa66ukAsn7U9OGj10Z54H3Mbcq1NRvhab6kXy9j2SxCtvKk1XsAV8OO95cYLV8U8totFQib+Fjjy4lauiQq0NRFDmyBL6ubbY5rBrhahzwPRod2oifc6Jlw/p9uo7azDZL0vA1EjJ6HOfct0byeO4ABiBjzOl9QuNlOp263fR8BNKxg86cln42Gg1nC1niin1/2MvLZzf47BNgS9To+uW7//Q8RYWPrKFzbQNq1rGmzPjIbCC7UYX+UW71WJ0DawspOa5/PLeSNdQjNpJ7aCbJc4KuYZaoYeCBUPLK9ojUufHNo3U07bwA2SDvPptFfQyVW18AzNrZhP29AYfjWO79Pwp6P1KGgN2tyXVd4aNuLgTs+mVA1p/jfU0ZoyxpQoeFtW3UZs3T1yqDADJkuZ5zHzFrSR616fR6aCM1m81HlR0/mKixhkYcx2i325mttfLSeyzRYTMddHJ9ism+xmjuPqHTxZD/6+dpIPvYbfsbfOy6NWL5fbpAAnCEky4OT50FtzfqIc4yx8/OKd8jfL/9ayhPNYB8JADf5yLuY0V5nrx7sIhKnpFz9pnQbAofVGH5nDyFb5yt0Rx6Knw9+BYbK6ckK3Vx1UcLW/pk5z8YkrtlL9bZ0oiNb63R9RHIphzb4/ddg88Ysc9V/vIcOzV0bN81+1ssbJalXaePFdbOYNNQEqW+cbjPLlJnUjNZ80C7J+jUXfB+tve+kjd5Bj0/z8c8EkBfU0LX6uV951NYnZEnd8coWw+xTYGsfZ83HjqWvjHcR77oY977+h36XJ1KH7HmQ1hXA/5oqI/EzBr1uUhY6Gv0t3g/K7Gtz/X8FnlBIxtc4uctUaNrHa9FMyPtMVa3WJ1guQT+bv2fx7Hv1D6baB8eTNTU63X0ej1HcEynU7x8+RKz2QzD4dCxZ5PJJBNFBeBIinK5jNVqhdlsltnalaw4G4uWSiXXEFEHhgaOMt8cTLunOx91ADnZ2ixMUyR90UcONplEdXB8DQErlYrba14NrVarlbmepw6dFxqTPhIGyJJ4Ni3O55zrDZtHpinsDe4TKj63JKA6Bizj4b2h16IExD4Gt0golUqI4xgvX75ErVbDb7/95hoGA37SjVlyuiOQNVR8UAOFpYKdTgftdts1Gwt4POho0Lnmfc/MSGBLygHwllv4jGKNHi8Wi50sGvt4jE6DwkeAMCuNKbB2dx4lkZWc5DpD/Vur1TK9aigXqpsV1vHwrXMaBdLP8Dp0TbXn1B3AWFag66Xd5ZD3iM24KwppwHGr1WpotVrodDoolUpO/rheaY8/zcay5aTUlQwG8Rz6Pv+Ygt3r9dDtdl0Z67HL46HgGDEgobue8b5uNBpONlV21d5U2wLYzRxVHWiNd5UDS7zocbwvNEuEu4toxNuuz8c217oeqe6zwTlgu7YxM1g/o7LiI2oeYr8A2ZJt+1l+B7OT7fxFUYR2u+3O6Qt45q3HRVhfA74t1P9Vm8Bm5VrfS+9jX3AQ8Oss+/q+AJO9xjzSlcf4stjsee1n8l6zwTQljbhu/CFETbVazThN8/kcJycnbnvuxWKB0WiEUqnketDwT9krllCR4OCFa9pTnmPGm0E/Z0kDfU8nQ40SLeGgoQNst5vV0gB+LxdrKtfNZuO2oLXj1Gw23Tn5/VEUuZT1p572rYJl06h9x3JcaMTsS7mnkOv9cMhio3Pvg94DdD50HvmddHbsNVkCzjozRVwAS6USoijC6ekpyuUy2u323m0ugW0ZlKY88pj7FJXeR3QC4zh+8tvZP3Woo24XDJKS1LuMiFhiNi+iD2x18GKxcPKV95kigoGHZrPpCI3FYoFyuezG3upQNfytHrNrqtZ1EzbrVFOAeU4g6+xQpnkNDMpwlyKVezoXXB/oJCppb8vmbAlYke4RlUHu1sQdmzQbxjr7NmCiOlUjgxxfX6CJc9psNp1OJblTRL3qu99I1LAc0ZJcnDdd93yyY519NeAtEZcHSxzwNR8Zp1lt3DkI8PddOTbkkRU63kA2i03JLEKzA/LIS+tT2KxsS4T7PmvJIA2c8K/RaCCKIrcm7LO79e8Y5zfg6eIQnZK3rj90vbfHH/J5H7n6EALmS67N+kVfon8fXfq02WzQaDTQbrfda2maZgzPZrOZcfAtUaPRdipWbWTJRntUZtr416eY1+t1JtpomTZlsNUQLZVKbnFL09T130nT1PUp4QLNz+gW1Hp+ZdB0i1R+bxRF7rueQyRrnzDoYqXOhRqM92Wj7GNIfYvPfcKp0Qx7reoE8T6xWTl5f0UHScpGo5GRId/9S1n29ULQMd2X3quG72PTBQN2oeNoDVKrLzU9fJ8c6OuawRbkZos8x1nnw+pQX4YJj6WDYCPGesxjr4vXYh1NhS1bsmQBr/2Qe6BoOlYDRbVazUX3feucrfm3wQ8em0ee5n2/zlVR4buvgV3ixRLb/Jz+WVLArnf8Pt816PdSplV+7rtGJXR0vczL6DlWWfPZCL6MPV3zrGz5SI994+XT0b57wOpnvVa1Q9Uv0ewEn33ss60DWRPw1JB3P1r9m+dL+N5/6H3+WPvosbD21Jd+74OJmlqthna7jfV67dK4uUPOeDzGarVCkiS4ublxRAwb5Slms5k7frFYuGO4o9Rms8nsbqHKlMeTnKFzEEWR+x464+Vy2UUw9XmpVMqkabOx33q9xnA4xGQywXK5xGAwwHA4zChdEi6NRsPtysBmiUyX5TlJ6rRaLTSbTbx48QJnZ2cukmZZ/acIn7GhxAfHWbMfWDJkd0yw8BE1JH/0+3gNtkmThTo46oiq8xPHsfseLX9brVaOEPQ1UC7qAlgq3WXUnJ2doVarodfrOdnh2JGQ1SyzyWTijgd2+2ww62y5XGI6nbrzqBOq0cKilEb8nrCEpe4eQj3LiJ6S0T7ixRqbwLaJra8Ep2iw9zth5wCAi4BTh7L8QvUtM1ooX5wbbV5pjRnOi8qOdU74vpIzPgKV2a6aLcnPMvjA4IQSDWqQ+aJLRbxPuA69evUKpdJdk9+bmxunR5kdpfaRPqp86Wd4P3Du9B7ksZRv2idFX98s4aINWqkfdQ0qlUpOrvRY3UhCZd7XR9EGHziH1k6ivalyqd+vRCmJP7VrfZlYeQTCc4MvSGozDG0rBC0r5YYRGpClDUgddl+g0n6/yprv8zZL0s6JkrdxHLvKA11f1ecJNmrAc8Z99+uhJM+Xfs+XIi+Z4Gt974NZAqbOAnD9I2g4TqdT13tmPB47AidJkkwZSZqmzjnbbDZIksSRNtPpFKPRKFN7a531yWTiHGx7XmbTcIeaSqWCdruNdruNSqXiiAT2kOExNDCXyyWurq4wHA4xm83w7t07t40olTnPSWKIOweRrCD5woW90Wjg5OQErVYLp6en6Ha7aLVamW2Onyp8N53efEqK0PFuNpuYTCZuUeEC4jNCfEQNz2sNeRtVsO+rMeOLYnBe2DdIjSoasMB28dQF0XeNRUK9Xne9FOI4zpQ42Eii6gMSMb7Injp4mgZuo1q+xt4Bj4fKoi+13mYD+qL4erzOv8qN7XlSFFh58EVFdex5HABnpLOni+pXzg3XT56Dax6Pu09P6bX5iJx9Te80q0MdS+2pw7WZusC3a45vLSiKfuVvbzab6Pf7WK/XuLm5yWS4aPCAZYncUY/liIQ6bDy/hRKG1Ls+/V102HtWnXvfRhA+p9mWXyvUodfX1BayNiFtYasLlJxlQ2qupzq3PmL2WJx6n06lHOX1c1ISk5ug6DbXWgWg32HtXp8e41xZgl5JNhvg0CAJ7zOWPuk18Vr0PrNrS5DjgKLgqeqv3+O6HpXOoUY6lY82s0vTu/4eul+4LhZUjFoLzGghI7laTqMLW5qmjgzQRZXfQehWwnEco9VqoVKpuCaOJHNIlnCBYwQLuHPokyRxypEZQkr4rNdrRFHkPqPlUXxsNBro9/uI4xidTsdriD9FPET520XIZ+QTlkDxkSr63EcQ+c6nY2kXRs20sc0t7Wd9OBbD5rFQI8i3+4XVCcBuYy17PjtH+l7edwZ8OXxjb4kwK8OHOP82wqfnKjrydKmOP7Dd3lWJDz3uIfrwPlCG885vr99HPul57DWTyPGVZPjI16LdJ0qYaGPvPKizbSPo93320GOKiEN0nbUj8vSbL4CUR1Lqcfvm5j7bxJK+1oH3/fmu9znCXvs+Paa6VvtE2iwjxb6x8o2brqk+ct6SbDyvDTaSsGFW8T5bPJAzAQHHjS8ianyRWBIn7XY7k5JrFw8SItzJYjabZXYW0nIZuxAyumSVqILd02kMaa8YGkXW6WR2xenpqduV4ocffsB4PM701CmX7xqqkizSVGSbLVAul102ArvNn56eOuLoqZc+2awYX+8ELn7aud7X3dqXTq+LkL6mvQ4sAcMxJpmXtzjr5xml4D3ArDASdfsMFlt/XkQomakNUZlJxh5UaoRQTvMiS9qs1EeysZkwv+u+5osB90NlgpkzJKiVyLTRfV8ETx1y6mObTZNnMBcFdkxshoka5SypZcYaZQ6Ay9SkzuPaYmWHukwJHl/UWZ+rvtX7Q+XYRuZZ0qxlcmwmrD3KeE1cF1Se89aJoqDRaKDb7WK5XCKKop2sCT5yLmiD0N4g9q1//F/XU57L9rh47k77l8De89Z5Z8CNNqMeo2NGu1VhHXH2QtSsNC3Z4Wd8draFflades0KoQ1uM4SPBaq3aJ/YBuYcF+qhZrPpNjPxlYQpbKbaPkKb66oex9d9RBHPr9dQKt21Zeh0OpjP55jNZk7nakDk2OYxICDAjy9iCawi4gJE8Lmt2aSioQGoO474DAgLPSZPaWrGhHX27/stusAlSYL5fI7NZuOUupZNqdK0ZA6Na24zzBIPNVCfekaNjd75rlcjpXQcfNkWejwAr6ED7Kbek8zSXU30HsmbXxudUOeGpWy6pemhY1JEY5ZzqmScLVlSQ0QdVJ0r6wzaudbXNQ2YMhPwZbBOnZIE6uSr854XjSX0Pe1RA+SniBcFOi6W6NI50L5m2j+ETfq195ndSYmw+lYfOW95WVQ+p57/23VAZRuAyxCljLLnEbNkec22j4021C/avaEBg1ar5eab7+lxnCvaTNoD6j5n3hI+dp0Mzp4/08H+KbGtWeL8PGEznfQ1Hks9a3WqZqLqa/fZiDbDVXWNLUG0QadjkTslQGwGrpUB6lcSH1Y3A9lsKCWcVZeqftTX1F61elefM8DFkjX9LSx94rzm9b0J8hsQcPz46ukcqrxUMfF/qwxtPa99zQc9xmfkqQPC7/ctdjbSqAqdkUtmYbCHCfuaUNnbxZAGlaY1q4NrWf7nAmvM5BmFdOLznAk9lguVwo6N77k6OfZ9PY8uiL70+7zSM3Wg9H4uMnwkii1HsnLNjJp9W/DmOYQ+x7boc/C1oURZnjPgc1oOMfKtzD4nXfe14XMANANRx19ljMfvKx8Edvsi8Dz6/YTuiOjTsXmOvP1+ex9Y0kmPsXrD3gtFle1yubxDeNv1xjpleQ63by7s5/m/JYKKOv4WPjuC6x6AjK5UUkVh5Wrf9+R9p0WerNpr9MmV7xqPEfY3+spvNQihBKjahfvgI7jtNZDE9l3TodfvI1/zfKMir6sBAUXB71Z341tMCDXegGyGjC4ueUrI5ywo0w3slmXlKVa9PqsU0zRFo9HYcR5JSFiDVH+LLgI83hrjzwlKPJEAswYE0zW73S6SJEGr1UKpVMJ4PN5L7PC5vs7/7ThrSRzH0Ee4kFgjacRINR0aYFv2ZO8TdaIY9eX3FxEcE0ahWFaou4Ywgk5ZmE6nuL29RalUwnQ63dkKXc8LZBub2hRuLZ0M+DKortKmwfP53KVjq8HJ8dcUcducUUFnXWXINsUtCnQts85wuVx25S48hjo0jmNXjsZx16aYQHZ3O9sAc7PZ7ET+KT+q/5SMtin7et36W5SI0XI5fp4ZIsxYXCwWqNfriOPYZaHq7jk+ErAo9whLnzabjcu41TkFdkm26XSKRqORKYPQ0kQNUimRZ4kbXccD/Paqkotc72xjYVvaC+w2n80LEmq5Uh45pCSs7Vnlu04LLZF5bjbnobC2N+dDS710TWL5pa5t8/k8Q8JQD9mybV8vKe2TWSqVnF58SIDC3jPa55Lv+0gaSxwVRXcGBBQJf8hKnUfa2JrcQ8/ztRw2X1Twvu/0RUz2Xc+xKE4agGpMWtBQj+PYbV9OBx6A62Oi59znwFkHwpbQ8HUbpVWyhU2trcMIwFu6o2n/1qEoMjieWr7A8bEN+ZiinyQJarUaFouFN+Xal21Bg8Se974su4CHg+TbvlJMXxp9XvTezum+LIqiIC8jjOQxj6GhT9niznTcaTBv7IGtXqTjASBDjOrrSoZQxlQHW12nZIDOq43wcq5JiKs+YA8be6/Z7ygSSqW7XiXciIC7T+qc2jGhXmV5td5XeryWk/qyKjSIxPeKNv4+5BEmWoarZdQAMmsVsOtw73PW1cneF9hUos3qWJVJGwC0c+rT788ZVofw0ScXSprpHOoOrQw0+c5JWDLMHrNcLjOlaEq02Dn2kbE6l5ZszZtPnx0VEBBwPHgyIZU8Z3gfeeLDfQbHvkVz33n3LaRFUJDW2dDFgYahOvHNZhPdbhfVahXj8RjtdtuRPLYESc+n4Dnp0CwWi0wtLz+ni5WSB2wa1+l00G63EccxyuWyc3zsziNqxOQZAUWFGoTsrRBFkXO8WFLB+4FZNhrVypNvm+Kr95bNFgj4OrAGPvXvIWWneg5gN1Jr5bjIcqPIc66sI6AkaJ5jBuQ3vbTZn/YafLCBCEsClctl73br1klUgtzn9Fpiz/c7ipJVo83SqU+5Ns1mM5cBZefDloQC2b4XvtIMO18+oizI6S7suFmbwR7rk9H7bE493mZU5elWvS+s/PnmV7/vWODLxvPdy2pnUj9pxgrJNjsXPnLMrpX6HuXV4hCdZ4+1fRXtb84bi2Oa34CAgDs8GaIG+DqEx0PO8RiypUiK0EYl1EjkgsLoL7dB73Q66PV6qFTutjBfLBaI4xhpelcOY6OwXPg0oq/fwcwcYBsZJgmwXC4BwC1mzOjh8SQKWq0W/vKXv+CHH37AZrPBaDTCZDJxfYZ0O3hgN2rCsdDHokGNh06ng1evXqFWq2EymeDi4gLANhuAxE2SJK6sZrlc7hgx2rsJyBqUjCbRadGynCLJ4NeGJVg5Z6VSKUOGcQ6sY0KdoBlWPMY6+cDujmk24lwEqJFu+5XZppdxHKPdbrveaIvFAkC2PwblQp0HjquSNZQ51a/WabD6XYl3HqOkj49sIjEPbLNn1Dni9Ws/HpvNUaTSRv7OZrOJk5MT1Go1vHnzBj/++CNub2/x8eNHt7OTltwAcH2/uLb5HDtmNWlEntlzKq9KCvBeKCryyColaDgPOnaWMLEN2HlutW14Xmv30O6gTNmMEJ/c0QarVqtYLpeZvojAdmOMPBLnuYPZiKvVytmELBcFtusY9RNJ0Waz6cozgWwpoOpIXf9oL+r36np2Hymndo4NeiqBlKYpoihyv8FWHtg11Y5HQEDAceFJETXfAse0aP0eUGODmQ1qOKgRU6/XEUWR60MQxzFWqxVmsxkGg4HrVUKyRQ0VrfvW72i1Wmg2myiVSpku/YvFArPZDMC2hKlaraLdbqPVagGAW+jiOMarV69wdnbmDNzFYuF1RPV32XEo8iKocx1FkeutwH4aLLXgWNo0fUsC7ItQ8jyMdtExUSc0yO3joc4b07Pp7HOMrWOvkUSVXY1MasaFlseEiP1uBpPe3/oeswCbzabTUcxW4/bcJE+s3iI4j8yCURnbF1XXc9JRXC6X3vvAkk/UASQL7Fzrb/YRRcdUknEIOMbs49bv9/HixQvUajVMp1MMBgMsl8vMVsMAMlstqyzaEh1bBmNl3Ue+Fh37xkFLZpQc9Y2xj/T23d++bGJL2ugOeiqHNitES7N8c6u65xjWTmuX0jbVP4IkSLm83YlVsxY5fiRJbKBOdRfnX0sPfdflg86JJXn4SH3KdYBrAIAMoe4LeAS7KCDgOFF4oiZgP6yRroY2F30ueFwAGXloNptYrVY4Pz/H27dvMZvNMkamTde10ShGMUjUaEbPYrFwGTW6ELM/Do9P0xTNZhO9Xs8RR8y6YTNcn8Gqhk3AHThOjFAxen5f2rUaUda5VEPJGiz7yJyAx0PLJTQrhgahGq42FV/nxh5j58jn2BcNvnvaEiY2G0azYEh6WCdM54rjbwkSS8jpa3xunRLbEyqPoPGd3xJ3miGgv9Neh32vKNAAhfYmUjKAYDYG9ahtnKrH6XpIQoH3jzr02j8jYAubDaP2iLUVdE6szWCz0gjba88nX1ZW9ulPq1PsenwISXssyMveVL1Ekobyptlm1K18zs+TWNWSVMqkJex0fVXdrPqQpBCPpS1VrVaRpmmmjUBe/zifjRpKoAICjg+BqAnIhS4EmrKvDgPT3huNBtrtNnq9Hvr9foZg+f777/Hv//7vLj2VhmZeuqkuNvp9PE4NJL7GR20Qx0W0Uqmg0+kgiiLM53N37vl87po42mab1tkoetRRnfM4jnF6eopyuYxOp+N2g7IOAA1Y7qoAbMuj6DzwPlEjhY4GG67y/qLTodcU8DCo46FZbyQ4l8sloihy/6vBCmx1AssraGAyO0R1gyXhijpfVoeq0W7LI4Btnxqmvy+XS0ynU6dPlWCh3JRKJZeBYckWno+wEXYAGWdTS2p4DOeWGT36myijm83G3RO8NhIP+h15KJJ+5brEptHtdhunp6eoVqv49OlTphxX52g+n2M2m2E+n2M+n2f6d6lO5b3A82gzZ/bDYeYrrycgC2beshk+sM0U1IARM4U1G8MGgPh5lQFbCqifUaJUSVbVEz6ChrJeLm/7+/k2ADgm+IhvzbRRnRvHMVqtFjqdjluXmK3LEm2Om133KDfUtXktAXzXZ4NN1KnaTLzX66HX62VKntbrdWYNtuu3BssCQRMQcJwIRE1ALmx0xkfU2EyaKIoQRVGmV42SKiRrgG3fAhtF8kUI7XN7vEZPNCtHI2GlUgmz2Qyj0QjT6RStViuTVWPPow5mKN/Yjm29Xken03GGBu8Je78Ad3PD2nFNLwayfRNoQNIA0XuORmYwRr4O7PhrTwtLwmjGhXUKqA+s48DnPgekaIRNXrTbQvWKZj7QmaZBrkQJiWk6FUqqkOABsmSOEiUqj5qZ4cvS0O9U0lyzQlimwxItW4Lj0+++/4sC1W+6rTlJaZUZ4G7tZCYpe9hwbdJz6jbSXKtVj3Ku7K6HRZ0HH6yzbxtza7CI5b2VSsX1QgF2GwPr5/jcl+2idoZPX/jmyZI1GuTSvjXHlplqZUTHRsdRbVQGfjh/q9XKEeFaxk97gzYHS1I5tiTn1L4F/POutqSWGq9WK7fexnHseueQCNIMO0viafDU2q8BAQHHg0DUBOyFLzXXGhfWCdE6eBtN53GMPjGiB+wussBusz5rLPHREjW+13m+QxxIazwFZKM5tk5ayyt88NWRk7TTnkW+81mSLJA1Xw++xoTWQfGVAQD+8hWfoeyT6yLOHx1mJah9xBafk5hh5kW9Xs9su81zWvLMGusqS0p2ammMOiSqv/UYJV60KSedfkZ/lSCw64I9vx2DIiLPwfIFMKyjz0erX31NvK3+1r9jy7J4LHw2jb2HKSeUTcqWZqtYgpIEqp7b9rxRR5wknF6XvRZrx6gsWXLV4lgceh/5q/e+3bhCAwxK1sxmM7TbbZcZrOQHda6Wy2vpUl5Gja6dlqjhPcPPauYwr8dmdGt/KlsCeSzzGRAQsItA1ATkQh1mjcxZIoYLm+46obtV7HP0LLHiuwZ97iNq9sHngPLamKWR9x3W2CryYkhDwRoLjOrbWmo1HrlbFyP8aZpiNpthOp1iMplgMpm499RJ1ciTdSYDvgw6n4ze0Zlgs26WBNomhioHlhS10WNrUBaJ/LS/kxH3VquF+XyeMd6pizTDjBk1m80Gk8kEvV7PGfKUFy0tU6KU2RckxPW7qNNUvpilAWAnQ46fi6LIyWa323W7U/V6PXQ6HVdKx/IAjeRrDxYtydC1pGiwek0JFnvv8HVbPkzdqs3bWRalDUt5nP3jDjk2M6dosOSMlinSZtDeJSo30+nUZUbYBrU8p25Xz9fonOv3cp54T1gi1tpcmjmjcqREkPZVOcZ59gXsdL2y8xlFEVqtFuI4RrfbRZqmiOPYlQHrOSljAFyWqc3q9gU0gG0jaJvppOX5nLNarYZ+v49ut5uR7yRJAMDt4KcbaOhGDcc2pwEBAVsEoiYgFz4DwUY/bRaNNpXUbSL1nN8CXMSZouwr4VIn00Z7i74Q2qitGjA2Wg5kI4DqsHNM1akgUcCx1vuH3+3Lygj4Mtj+FsDdvNmInXUofaTlvqwcXxbAMcM6Q6ortfzERlh5z6tOZeSWjgQAR9Iwos/za88vkjRagmQdBpVPhWbg6BrA7Bnurtdut1Gv1xHHsYtO8/08Ql8fVf8WicAD/KUavv/1OJsFo+SoL0tRZc6XdaOlG0WOylubxhI2fNQSXTrrtgmttYOArY2k46u6wBI12p8m73rsc1+Wsy9z2OJYAh/32QgaAGKpkSW2ADi5oWxoTyESNZbwtmsor4VbhQPIbLqg866N/LvdLuI4xnK5dNk77C1Gok/l267RAQEBx4lA1ATkggvJZnO3DTO3t+Zrs9kMrVYLJycnri+NNhfV8zwVaCSL135+fu6Ig81mg16vh7OzM5ydnbko9j5jpyigocF66s1mg06n45pHa6+Kk5MT9Ho9t106nTwasOxzc3Z2hkaj4QgbAOh2u25uWq3Wzm4LRZ+HL4FGWqMoQrvdRpqmmE6niOMY6/UacRyj3W675om2jxAzQzqdDgC4BuK8H1qtFtrttqu5p/NepLkjAaJOHuWGfS2oWzQjkdkpzF6hHj05OcGrV69c9J5N0fldQLaBqWbUWJKG59SMGjZNBbIZNXocnQcSNZTNdrvtsmi63S6m06nL6GAUu9/vo91uO9nmveHrnVGUe0Tno16vo9VqYbPZuKb8lD+uqycnJ+h0Om68NTLPda3b7eLs7Mxt7csMAc5dt9tFt9vN7Nqn2R9FBHVimqbOiU/T1OnHWq22o8u0QTazYLSXmpIzWt6rmYWcAxsQUhJA1z3NouF8KiFDO63dbrvsNl63Zq/ZDOLnCOpXXc+4oQUzxcbjsbNRKUO899UWSdPU9YeyJKYSNdoPTOcsr0RYt1ZXGdPMKyWtdU4pv1EUodvt4uTkBKVSya3LXCe4zhZtfQ0IKBICUROQCy729Xod3333Hf77f//vePv2LQaDAT58+IDZbIY4jp1R+dNPP6Hf7+806X0qoLHTbrfda3/961+dkzKbzbBYLNDtdvEf//Ef+NOf/oR+v4/T01P3e4qYog9ko+zdbhdv3rxBkiQYDofOINLGtP/2b/+Gn376Cc1mE99//z36/X4melgqlfDDDz/gv/23/+bKOnq9HkqlkiN5er0eXr9+7e4nLVV7SvfVcwLLaSqVCk5PT/H27VtMJhO3E9dqtUKv10O320UURXj58qUzbmnwl0olnJ2d4ccff3SlazRKeVyr1cLLly/R7/eds1CkedMsF+7e1O/38fr1a1c21Gw2MZ/PHYlTqVRwcnKC169fO3KYMnd6eorvvvsOy+XSkTBazmJL0zQlns6MZu9oRg0Ad14AOxk/6hzSWYyiyF0zn8/nc5TLZZycnGA+n+P8/Bzj8diVStHx/f7773F2dubICS0nKcr9AcARNKVSCf1+H2/evEGv18N0OsV0OsVyuUS328Xp6SkajQb+8pe/4M9//jOiKMLZ2RniOEalUnHOZLlcxo8//ojpdIokSXB2dob3798jTVMnl+12Gz/88ANevHjh5oPzuK/H2LFCMy3K5TLiOMbJyQkWi4Vzpkmq0g7QkqUXL15gNpvtZBrSyQeQS9RoNg5l1Zb42rJzHk9nn9/FcuLJZOI+R0KP+tiWKD93WaNu4np2cnKCt2/f4uTkBKenp+h2u1gsFojj2O1O+fbt24zscAw7nQ5OTk4yDYa1LwywDVTZccvLMNXsKCXJdN5VH3Nu+N0kk/7t3/4N8/kcpVLJ2eO9Xg8//vgjvvvuO7RaLbRaraOZ14CAgCwCUROQC0aCK5WKc84ZSa1UKi5a0e12Ua/X3QJIw+8pLRgagWHEbLFY4OXLl3j79i2WyyUmkwmm06kzmrkIclEv8iJIg5ZGf7fbRa1Ww+npKV68eIHFYuHKIOr1Ol69eoXz83NEUYRer+fGkNhsNm6ckyRx0V+SAP1+H51OxxlYNFqKOv5fCzT4afT1+33U63UkSYLb21us12sXwaNjZ6PvANBqtZwDeX5+jsFg4HasoSOjWTXq+BdlDtUJJLlBXVkul11mDXf9qVQq6PV6ODk5yUR8AaDdbrusJWb/pWm6s03zcrnccRKUBLmPqPE5EnRQlCzV6C8dWvbPqdfrTh+Mx2O3fnBHQGbX6E5RRbovgO2c0FFkwIM69fT0FKvVCmdnZ3jx4gUajQZevnyJ09NT53hrY1OSAiT0kiTJlDaRTCMRQYJGo/tFGn+FEpkcI2YNc13SDLJms4k4jjOlSjZzRolSZnhoLyLOvW/repVNzW5TuaRO4fnZY6zT6TjClLqX86yyfCzy5lvPuFYBd3qNvWjq9TpOT09dhi/tW2CXWON8aXYN4M9A8vVts8SdZllp6RPnhJ/jnJMcog549eoVSqWSy3Btt9suoMXXjmleAwICtghETUAuNAU3iiKcnJxkFv3FYpGJ1jBr4qlmnnAR4wIdRRHOz89dOcF0OsVisUC73caLFy9cCYJmghQVei8wxbhWq+HVq1dIkgTL5TLTpPn8/By9Xs9bDw7cZRp0Oh2cn5+7iCTLJ5jWS+dFnQleS8DjwbmkTEdR5Az99XqNdrvtCDKOv2ZZ0CknYfvq1StHFug2qGdnZ66PCWWoaFC9wRIK3VFkuVxmUupV56hTpeUNlLM0TdFoNFyPJ1/fESDbb4uOgL7uS/HnMRoJ1vI3vR/UAYzjGKVSyTW/JEFLApfb4x5TZP+h4Jj7nMwoitx6xOw2yqDKky3BIAHXarVwdnbmyqjq9ToAZDJB+H7RsxR1HpQYIbGsGRVxHKPVajlykeOvmWx0sLVvkMoW5ZEZNUqaqvzpfFj54/EkcNL0rlk4z9nr9dxv0h3Z6MwfW5mhkmwMIGlQYbVaORuV2dR636sO5CPPy7nLa/BN5PWHsUQN59S38QK/k99Rr9ddxuTLly+xWCwckch1RLPXqUcCAgKOD6V7mlCFDlXfDl9T6z5qHjUqMJ/PXZRuuVw6x4zpozQ2ufvHUzX81IharVa4vb1FkiSZxqrad4G1w1/gaH6tQfimsqj3gvbzYekLHX0aIIyeMxJPh4HgZ3lPsa8FgEyKNnta2OjTH4xvLotfE5oqz3t/Nps5mVaHnH0ONCII3DWr5Y4x4/EYSZJknJ5yuYxWq5W5Bxi9/Ib4JrLINZYlS3TKKEPWUczrg6AyqE6fNo21u24R6pipHuNzfla/V4/lc86/z8GwpVi6o5CWcLCEyjoqB+JoZFGdeTZWpy6kTtXdsVi+ZKPynOfNZuPKplSm6cznyaX2ufkD8WTWRStXmhXDzArb6FsdfJ4DwE4DZ3t+hcqXvm/lc9+ffnaz2WA+nzunXufcR6x+BXxzWVSiTO1Szp21UUlWWvLaN1/2+WOujdA5tcSoT+6YJbVarTAcDjGdTt1nSfCS/LZy/Ag8GVkMeDS+uSwGfBV45zEQNU8XQfCOA2ERfP4IsngcCLL4/BFk8TgQZPH5I8jicSDI4vNHkMXjgHcen159SkBAQEBAQEBAQEBAQEBAQEBBEYiagICAgICAgICAgICAgICAgCeCQNQEBAQEBAQEBAQEBAQEBAQEPBEEoiYgICAgICAgICAgICAgICDgieC+ZsIBAQEBAQEBAQEBAQEBAQEBAX8QQkZNQEBAQEBAQEBAQEBAQEBAwBNBIGoCAgICAgICAgICAgICAgICnggCURMQEBAQEBAQEBAQEBAQEBDwRBCImoCAgICAgICAgICAgICAgIAngkDUBAQEBAQEBAQEBAQEBAQEBDwRBKImICAgICAgICAgICAgICAg4Ing/wMoxy7I6WPStAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = autoencoder.predict(example_images)\n",
    "\n",
    "print(\"Example real clothing items\")\n",
    "display(example_images)\n",
    "print(\"Reconstructions\")\n",
    "display(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nIBh_B8oDrFN"
   },
   "source": [
    "## 5. Embed using the encoder <a name=\"encode\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qCYqXQWlDrc5",
    "outputId": "3bbf8c77-8a21-40aa-a732-5cf82c6933d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "# Encode the example images\n",
    "embeddings = encoder.predict(example_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "S-TMAYbADu09",
    "outputId": "942d0415-efe3-482b-95a5-3f272cad4e9c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  3.6008904    2.8011243 ]\n",
      " [ -0.6928706   -2.429704  ]\n",
      " [-11.838154     7.3077908 ]\n",
      " [ -5.7599945    4.5702424 ]\n",
      " [ -0.36855716  -0.928926  ]\n",
      " [-11.940271     6.405218  ]\n",
      " [ -1.1613183   -0.46176255]\n",
      " [ -0.7512114   -0.9489039 ]\n",
      " [ -1.6341313    8.67006   ]\n",
      " [  0.38973334   5.9436626 ]]\n"
     ]
    }
   ],
   "source": [
    "# Some examples of the embeddings\n",
    "print(embeddings[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 676
    },
    "id": "aazkksr0DxnJ",
    "outputId": "e79496d7-097e-4c4a-f5ce-3a6f10038323"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the encoded points in 2D space\n",
    "figsize = 8\n",
    "\n",
    "plt.figure(figsize=(figsize, figsize))\n",
    "plt.scatter(embeddings[:, 0], embeddings[:, 1], c=\"black\", alpha=0.5, s=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 682
    },
    "id": "idWYWpNRDzgw",
    "outputId": "cfdca3fb-5777-42a1-d2f4-20f03d3e445f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Colour the embeddings by their label (clothing type - see table)\n",
    "example_labels = y_test[:n_to_predict]\n",
    "\n",
    "figsize = 8\n",
    "plt.figure(figsize=(figsize, figsize))\n",
    "plt.scatter(\n",
    "    embeddings[:, 0],\n",
    "    embeddings[:, 1],\n",
    "    cmap=\"rainbow\",\n",
    "    c=example_labels,\n",
    "    alpha=0.8,\n",
    "    s=3,\n",
    ")\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CCtBItpVD28z"
   },
   "source": [
    "## 6. Generate using the decoder <a name=\"decode\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "JD90cjP3D2R1"
   },
   "outputs": [],
   "source": [
    "# Get the range of the existing embeddings\n",
    "mins, maxs = np.min(embeddings, axis=0), np.max(embeddings, axis=0)\n",
    "\n",
    "# Sample some points in the latent space\n",
    "grid_width, grid_height = (6, 3)\n",
    "sample = np.random.uniform(\n",
    "    mins, maxs, size=(grid_width * grid_height, EMBEDDING_DIM)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_6dpwceED4ge",
    "outputId": "ed4ef3f2-5e8d-4b7b-8763-d95e0b3cc44b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 21ms/step\n"
     ]
    }
   ],
   "source": [
    "# Decode the sampled points\n",
    "reconstructions = decoder.predict(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "e4I56yb0D51F",
    "outputId": "a20811c2-f7b7-4ae2-b782-1b8a573385d9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0oLKyEh0dHSJtiucRc1YDi8WCiooKRYtIuHn1wc7LiV24aZpDkZrFgplYl8MM/N3uWdQPzmHGd/t39YNzAICffi5F9jtWq5UWzC8qKlq2sRG41s0JRDDTutzflZp3KtZN9hxluCDmUKR/6OEsKEthcl7AcYkQBoBpH3B8cA4/zk+WNVNXVVWJfi7H2FhW0w/NNwGxj9zfVTTENnBWDy6IOWHDFq2PRO/UkVkf4hXcwfFxi5+btPF+nxUVFVFNWDq25WA1g7HW6oLNbjAArPnNRijFQPjma+3DBTFnyURKOOiT47Cg4Ape8C1+Hg2sZh/jtRqRzb5DANbkZoMlWAET9p1aq5svzm24IOYsmUgJB22CBk/mJKF+cA7TjHk6NQ44mJMUUvT0SrKSC+Va9RPKvUNrbbPBEqyAiVRQu1wuuFwuDA0Nca14DcLTlzhRxbxPwHe7Z3F8cA7xcYua8JM5SfjJjmQkxCkL4tU0bXLTIScQwd4Puc+lv+OpS7FPoPQlrhFzVpxAC1NCnAY//VwKfpyfHFIecbimzVCFqNzxa1VL5SgTynsTzGIi9/6obVLBN4FrAy6IOSuOGlOuNkFDA7PCrVSkxrQZqlmZ++tWnmgUNmreAzJus9msmP+r9t6UNnv8fVwbcEEsQzT+4S8Xq3GvofqUw6mHrbYRQ6hjWavBUtFMNAobNe+BzWZDQ0MDKisrceTIEdljAt1bJBuycKIbLohliMY/fDkiIURX415DNeWqWWyCzYXSfYY6Fm6GXnmiUdhEqutWoHuLZEMWTnTDBbEM0fiHL0ckhOhqtTgMBTWLTbC5iJZnGgmf9HojVoSN9B0sLy+nnylFOwe6t+V6Z/k7FX1wQSxDrPzhR+IPdbVaHEpZ6uIQbC6i5Zlyn/Tahbx7ZrOZFrjJzMzEyZMnkZmZGfLzW653lr9T0QcXxAyxslNkx7lSf0jLrVGul8WB+6TXLqRCVk1NDZxOJ4DofH7ROKb1TnSUKooSwu2KstKsxjjJ7jzYBiXcjkxKnWXUni8Wnl04Gz21885ZfYaGhlBTU4Oenh7k5eXR5xzs+bHv+HJ0NJNeg79T0QfXiBliZacYzeMMV7NVMsORyFOXy0UjT+UEWiz4uldC648Vq85KsZLz0draCqfTifz8fNTW1qq+XrAc+GB9wtXc43qxOMUqXBAzRIsfMRjRPM6V2CTILSqR8HWrWdBCWdhDKewfKfiCK2Yl54N9vkoVsgiT8wItWMP6ltvb21FSUiJ6R6T3EOzfwcbGiT64IOaETSSrTCnt8ouLi5GZmSlbqCOcRWWp6SJqF3ZipiS+QmIKXElhwFnZ+VDT3lBUwlUDLAjAkzlZ+MmBSvziRANaWlpQUVERMNc92E+1Y+NEDxERxBqNRgfgfwHYAUAAcFAQhHcjcW5O9BJJbUNply9XW3cpi8pS00XMZjMMBgPMZnPA6xAzJfEVLhfSDcx6WXCXWpFqpZC+U9/tnkW9pOd2/eAcJiYmcGd/P3Q6nd+7FaxP+GrfI2fpREoj/icAzYIg/LlGo0kCkBqh83KimECCK1TfXKi7/OXw/alZ0BwOB4aHh+FwOPx6ILPImSnDJdC9rldTdKzcN/tOTc4LOC4RwgAw7QP+z/V45L3ejLi5maDvFmftsWRBrNFo0gF8AcABABAEYQ7A3FLPy4l+AgmuUBfKUHf5kV6I1Qp2tabOSGgpZEz9/f1obGwUBayFOp61Rize98isD/EK/UvifD7se+wvkJcihHVPPEgvtomERpwL4DqABo1Gcy+A3wP4r4IgTEXg3JwYZbkXykifX61glwrY5VwAyZh0Op3q8awXYvG+9clxWFDoOuv1CfgPhiz8Xwf/Mqxzx4qFgCPPkvsRazSaXQDOA9grCEKHRqP5JwA3BUF4RnLcIQCHAGDbtm33DQ4OLum6HE4kURKowQRtfX09Tpw4gezsbBQWFqK8vDxiApnt3uNwOLi2E6WE0gv7b/4wg/rBOUwz5uk47yxMV+xo+XpBSO+e0hj4OxKdBOpHHImCHh8D+FgQhI4//vtXAD4vPUgQhGOCIOwSBGHXli1bInBZDuc2gQohDA0N4ejRozh69KhioQS5Igck8vmFF15ATU2N7HctFgvy8vLQ1dWFhoaGiBYUIWMqKiriBRhUEmpBDLXHBzqOLSajVFimo6MDlZWVqJj+Aw7mJCElDtAmAMkaAfvmP8Ybf16g6P9X807xIh2xzZJN04IgjGo0miGNRvMZQRA+BPAQgJ6lD43DUU8g01xraysaGhrg9XrhcDj8ii0oaRMk8jkxMRFOpxOtra2yEdy1tbWw2Wxwu91wuVyKBf45y09raytOnDiB9vZ2VUU11Jp0Ax0n5yaRukysViuampoAAA0NDfhxfjLNI9Ym3Cd7TaUIfa79rj0iFTX9NwB++ceI6X4AlRE6L4ejCLsgBfIZWywWuFwu2O12WYGqtMiyhRaIaVhpDOXl5bQKGADF/rNq7iVYMYhg31vLBLtfi8WC9vZ2xY2T3PHsz3COC9YLGwCqqqpEP7UJGpi08QGvqRShz/3Ba4+ICGJBELoAyNq+OfKspQV0te5FuiAF6tl65MgRP18eQWmRJdWwAvmOXS4XWlpawr4H9jxNTU0iTS6SBUbWCsHul1go5J6zHGqDvoL1HyZjU/obKCoqCjklSem9jMWIcU5geGWtVSKWF1Cp4F2tewl1QVJadMNJwyK/LykpETWrkFYBCwZ7nry8PJEmp+b+1tuivFJVpEKt3wz414heKuQ+SIwDABoMGGtrBicwXBCvErG8gEqF02rdS6gLUjiauxqthD1XqAuk9PyshqXm/tbborxS9xtu/WY1xW2GhoZgtVpx6dIlFBQU4PDhwwHfx2AxDqGylqxxawZBEFb8v/vuu0/gxC5XrlwRjh8/Lly5cmW1hyKL0viOHz8uFBcXCwcOHKCfRfu9cCIPeebnz59XfPahvBfBjn3uueeE/Px84bnnnhMEYfE9/NSnPiUkJiYKn/rUp4Tnnnsu4PevXLkifP/73xd27dolFBYWCsePHw95jCzHjx8XLBYLPQ9nZQBwQVCQiVwj5oRMtGthgYKvpIE8q1WhixMZwplv8swNBgOGh4cB+D/7UN7xUN8hi8UCp9NJNWIgsFnbaDQiNzcXKSkpotrl4b67sWyNW6twQcxZMVZKSEnTPtjrklQjkmaktCiFW0wj3MUxlMAfzm2CzXeg3tWBouFDQe4dYq9bXl4uih0wGo340Y9+JDoWgGzqG/seHjhwQPY+lN5dpfcn2jfS6xEuiDkRR0mIrVRQlzTtQ3rdzMxMnDx5EpmZmYrR1mq0JjnC1TbkAn9cLhddwMMRyOtBOw8238F6VweLZFYzh4HaHwZ7huT8ANDS0kLfSfIZaaV54MAB1Vp7LAeCrle4IOZEHCUhtlImMel1gv0MdA6p1tTR0QGr1YqqqirZRTxcbUNuTC6Xa0kL6npYkIPN91LfuaWaf6XPUCnjgETfm81m1NfX07S5cFppctNz7LHkWtPhsGvXLuHChQsrfl3O0hkaGoLNZgMAxbrKrEbc3t4e8NhYo7KyEk1NTSgtLaXFO5YLNXMd7PtrXSNeTuTmP9Q5lZ5D2mdber76+nr6uVwOO3+mscty15rmrCNsNhuef/55HDt2TLEGLlsjOTMzEy0tLRGtwRwqodYfDkRVVRVKS0tphaTlvJbRaFzS/PH6w0ujtbWVmovJHMrVfw5Uy5x879ixY6iurobZbBblnUuxWCwBP1dTfzqS7yBnZeCmaU7IaLVaFBQUqDJ9RYOZTI15Ua2mIVchif2uWt+gWiwWC/r7+9Hc3Ayz2cwbxq8ggfKE2d+1trbi2LFjGB8fh91uR11dHf09OW5ychJdXV1wOByK5VWlGjDRjoHb762av6f14JJYa3BBzAmJ4uJiOBwOVFVVqRIwqxWhKVeHmvW/yXW6CaVZgPS77GIKLN2/SzAajRgeHsabb76JtLQ06PX6dWuaXGmzrNy7K/c7i8WC5uZmnDlzBl1dXVRbJc+/vLwcAODxePwio1nBqlQoR/reRqp+Nid64KbpEJmcF9A7uYDJ+ZX3rUcDbETyahLM/Maa8Mji5XA4FM16FotFVGIyFOTMicXFxQFNjKHAmsNDaY231gj13sMx0Sp9J9C5jEYj6urq8L3vfQ+HDh2imz/y/Emt89zcXD83A+s+kL5Hgd7bYOPhLonYgmvEKpn3Cfhu9yyOD84hXgMsCMCTOUn4yY5kJMRpVnt4K0a07LaDmd/UmhUJoTQLkGpmrJbCBtssRRNmr8Gaw/V6veI9rHVCfffCMdEGqy2udC65xiLscUNDQ+jv74dOp/Nra8ieQ+27zM3PawylklvL+V8slrj89qVpIbXJI+C12/+lNnmEb1+aXu2hrSnUlu1bzdKUgUoEsuOSG6PacfMyhEsnnHdE6TvS3wcqoyr33I4fPy7k5+cL+fn5EXmmvDRr7IEAJS55+pIKJucF3PHbm5jx+X+WEgdc+0o6tAnrRysOB7X+vUhplMtJuPcSrEBDONfgrA5K76lSMZtQ0v4CPXPeozp24elLS2Rk1od4BTkbH7f4+XKwltIQ1Pr3LBYLSkpKaFBLNKLWByf1+YVSoEFq7iZzsZbeiVhGKc1Iya9LTNdHjhwJ2lYxkC9Yzd/Reo4jiFW4j1gF+uQ4LCgYDhZ8i58vB2vJD6TWv0dyZ9kSlJFitaNu2TkItUEBgGVpUhErRJuWtxwVvaRR0mazGVarFT09PTSaP5SqcOsxjiBW4YJYBdoEDZ7MSUL94BymGeU3NQ44mJO0bGbptfQHFUoa03LddyQaMgQSAnLHSZs5hIpcWU6XyyWyGCyl+lassNwbkEgLeqX3PdB1yHeI2dtgMMDpdCIxMVHUMYz3qF57cEGskp/sSAaAxajpuEVN+OAfo6aXi/X6B8XedzgLpNJ3whXwaoWA3HFyzRyCnYdF+g4Qi8GJEyfgcDhgNptpqc1IWxCiiVCeXTids5SecSTfv0DXYb+XlZUFg8GAsrIyjI2NBe0SFW3WAk7ocEGskoQ4DX76uRT8OD8ZI7M+6JPjeIDWChDJFJSlNmQIVBCEPS5YytRSNP2hoSG4XC5kZ2fD6XTCbDajsrJyyeeNdkierRqBY7PZcOzYMWzatAkpKSkAgr87SoJeTaU0qSC02WxoaGhAf38/cnNzRd+RtuiUXuvEiRPw+Xzwer0AQIvLFBUVUX+xdAzr1V2xllgzwVorVWhDm6CBSRvPhfAyIQ1OUQqKCRS0FKxeb6gEKwhCxgJAFMSllG+sFDGrJgiLCJnp6Wl84QtfALBokg4UBLRWAryUgpDkaj1PTk5ibGxMdecipWdD3iUAigFQSuPq7e31+71SQRx2g+X1ekXmaILNZkNdXR11RbDfKykpWdMbsbVOzGvEvNBG9BKOyUy6uw+n52oo2lMoKGlNRANyuVyiog4ulwstLS2yYwzlfqRMTk6it7cXqampdEE/cuSI4vHS8cUqgbTWhoYGeL1eWn6VsFS/OXn/Ojo6qCsg2LjKy8uRmZkpa1IOdA8tLS0oKSnB/v37g5qjpd+rqKjgZukYJuYF8Xe7Z1E/OCfK8a0fnAMA/PRzKas0Kg4QuskslN290oIWjhBUi1rTtrTHrNIY2U2CWh8oqVsMLNYudjgccLvdOHr0KDweD3Q63ZoN2gpUeaq/vx+nT59GT08PHA5HxDccrCYrbbwh58cn/ybHKlXcYu+B/BwZGaE9r9nnSAS82mpxnNghpgXx5LyA4xIhDADTvsWgqh/nJ3MT8ioSTklCtbv7YJpyYWGhoi8u0kgXSBLZTP5fTcCOWiFP8lGBxcU9NzcXLpcLDQ0NcLvdiI+Ph9VqxfPPP4/S0lLZ8a01jEYjcnNzkZKSIjJFSyPWl2IhCSdYjL0Wa5UgfYnZYh/sv2tqavDaa6+hr68PL730kmjspDBMKE0gONFPTAtiNYU2TNr4lR3UKhCtUZOhLhKR2N2T77pcLtjtdlkNZinIzbWcKTxQLnQgbT6UVCQyv0NDQ3C73bh06RK6urpw+fJlPPvss1QQrwfkcrTZDQ/ZrIRrng/lXZZutIaGhmC322kAVrDc8KqqKvT19cHr9fp1cjp48OCacTVwbhPTgni1Cm1EG2slajISu3tWOC2HFhisKQCJrg3WAF7p3HKpSME2WkajERkZGRgYGMCXv/xlfPTRR3j66aeDjnktodSekPxkA5yA5d28Sjdara2tGB0dRUFBAQD4vRvSn0VFRXjppZdgs9ngdDqh0WiCumuU7idaN+kcMTEtiFer0Ea0wf1E/kgX5kgtSEpzzWriwWplKwlGqUlb6Xi5e3G73XC73TAYDDhx4oSqMa8VghVSMRqNfub5cOIX1L4/ShXVSNyC1Eqi9K4CwGuvvQYAqK6upteVczWE2jWKC+joIqYFMbA6hTaiDe4nCo6afFA1KM11KJo4m0sqXRDlTI1yGpZ0cc3IyKD/qR2zGmJhwQ5WSEUu+j6c+IVQrQrSAC3ybsjlo7PHEtNzWVmZX454qMVqguVHh3I/nOUj5gUxL7SxvlErKKQaKxB4AQpXAMkJPem52AhckpscaDxqhMhSArLCrQQVLcjNR6StAOGcT2kzQEpYkt9LjyWQCHg2l5gU/SC1p9k4hUAbxEjcD2f5iHlBTCCFNjjrC7WCQq3GuhzpT9IxBhIcalBaXF0uF2w2W8jpS3JzyJaJjGRxlOVAbj6CWQFC3WCw70+g6mos0uccaE6lxxLNuaamhjZ9qKqqElVVI7Wnw4Fb0aIL3o+Ys2Ish5kz0uck2kpJScmSTNjLOUY56uvrUVdXB2DRn7jUphax0BdaieXs2VtfX48TJ04gLy8PVVVVqmtZk++qnVPSu7qnpweJiYmIi4tDXl4enE4nsrOzsX37dmRkZKzZnPG1SKB+xGtGI+ZEP8th5oz0zj5YDnAoBCviEMr3lYKQlMYdCsEijqMJNQKUfc+kaWWEpdQdb25uxrlz5zA1NQWPxwNA3fscKDZA7h6cTify8/OpwDebzairq0NXVxeAxYIua7nRx3pifeT3cKICiyWyNaCXA5ID3NLSQn1z4dZqltYglp4n2Hnlahgr1TUmgV6Bak6zBLt2oLrYq4nS/bOw75ma40PBaDSisLAQiYmJMJlMAd9n6RyzsQGBxjU0NIT+/n5kZ2ejqqoKRUVFsFgscDgcMJlMSExMxNatW2EwGJCVlbUm6oivd7hGzFkxVsIvFQkzsJoo5UicJ5Bv1mKxqA5CikRN71hBjabOvmeR0uzZOWYD4wLNt1xsAOklXVxcjIqKCtkI6tbWVjQ2NsLr9cJqtUKv14vKplZWVsJut2N0dBSNjY0YHh6m11Aac7RtqDhiuCDmrCnUChg1DdoJahdzpW5LSueRO680KlZNScNgAp0cw95rtJqeg6FmMyd9DpHYaJDn0tzcjMLCQlW+WWnLQ7mKa8RvTNwKAGAymXDXXXdhaGgIXV1dqKmpQVVVFRXcVqsVg4ODyMnJgV6vh9lsln2OavLPOdEBF8ScNYVaAROKRhhqswelc7Ln6ejokC3sb7FY0N7eLoqKDXZeVtMaGhqC0Wj0S4cJt7Z1LKKU4rMUiG/4zJkz6OrqClj5jJQqJVorW2ZVaRNGSnB6vV7ccccdGB4exvT0NLZt2wan0wmHw0EFN/Edm83mgLXZI2XZ4Sw/XBBz1hSsgAmkAZDFSc40GC6haJlWqxVNTU0AIKqFbTQaUVVVBavVSjWpQM3kyXekmtZSU6SWympqX3KbGbkxhVoty2Qy4cyZMzCZTLBYLIqpbqRUqdfrxd69e/3eMbmqWsTH+9vf/haXLl3Cli1boNFocMcdd+DBBx8UPU+iPRcXFwdMxVtqERPOysGDtThrlkABMWSRam9v92u2Hi6hBDhVVVWhtLRU1DuXIG0er9RMnkUaCMcuwsslEAMFfEU6SCqU8QBAbW0tDhw4gKysLFRWVqKjo8NvTOTfNptNVcCTTqdDVlYW9u3b52d1YOfebDbDbDbj61//Ompra2nRFqW5IAK9vLwcmzdvxszMDPLy8vDFL34RExMTyMzMFBXuyMzMRFNTE6xWa0jPNVoD8DhcI+ZEGZHUpFZKAwhnzEVFRYpdodT4koP5ownLaY4MdO6V1r5I3q3T6aTjOXjwICorK9HU1ISpqSkUFhaKmiewZmE15U/l2l2Sn+zxDocDHo8HeXl5MBqNAeeio6MD1dXVtDPTE088gZGRERw6dAg7d+6kz5jtzFVcXExzipdS1IMTPXBBzIkqIik41PhBI9GrN9iY2YpKagpAKJkvQ7kmIRICMdT6xkpjXk5I3i3bjxgAtTgYDAY0NTUhLy9PNEaLZbG2c0lJCQB/XzqL3HMhKVLss5XOS6CqXFarleYIWywWvPDCC+jv74fdbkdpaSn9Xk1NDc6dO4fExERkZmaitrZWFIzHiW24IOZEFSutSUVCYAQbMxGaBoNBMdWEINV82tvbAfj3J1Y7T5G4PyWhzwqi1YzEHRoagsvlQmlpqd88EcvD0NAQhoeH/bRI0mChsrIyrE2Z0rMNZp0gGwCtVguLxUK7K/X29mJ8fBy9vb2i7zmdThQUFKCwsFCkyYdT0pQTfXBBzIkqQhUcoVSfWi6CjZn1HRKtSQob+EN6EjscDtjtdgBQbJ0n1bKCpS1Jr6dmjgIJ/WiIxG1tbQ0YPUzutaqqCu3t7aLocpZw3j2Xy4WSkhIUFxfD4XBQnzQpxMHCziMJ6AIWS5KSY6urq5GWliaKHZCLfmbfE15dK/bhgpgTkwRqzhANwkEpl1XJLywt2AAsasQkUlpJSyMancvlwpEjR0T/JpHUgH8eqfR7gQgkoKIhEletRaKkpAQOhwNOp1Ok+RJtOBRYn/SBAweg1+vhcDjw4osv4s033wQgftbS94FEP7vdbtHGQK/Xo7i4GHq9nn7PZrPB4/HQn42NjbItEjmxCxfEHEWiuQAAu7gG62SzGqipd8yiFPijJLjVECiPNFJEQz5ysM5IrCmXNE0gZt1AmnQgpD5pMreFhYV+Gi05XtoS8ciRI7SgB9Fqpe8N8Q1PTk4iIyODbsx0Ol3QDRQnduCCmKNINGiWSigJLiBywmEpGxGpGTJSTQikYyJ+TZKrWlxcDABUg2L9h1LBvNQgtWhDTYWxzMxMakWR28TJIZd/LPVJS90PRKMlyOWtA6CmbRIZzf6b9Q2bTCYIggCNRkMre3HWDlwQcxSJBs1SiaUK21C7+IRax1ep3jE5J5sqQ64VyJdLFngiRNjvs6USKyoqkJmZKes/lM7ZSm2uVsqyIve+Sp+htCd1sLgCubSoQD7pU6dOobGx0c/kT+aePCcC2RAQNwsb2S3dbLLPONx5XOqziGYrWSwTMUGs0WjiAVwAMCwIwp9G6ryc1SMazI7LhRptX02QUrDcU0BeKJPcVYJUY5YK3qSkJLz33nv48z//c1RUVIi+z1bSMpvNaG9vx0MPPYTR0VHFalwryUpZVuSqqmVlZcm2HpQe19/fTwVoeXk5PY5opcScPTQ0RP27ciVFdTpdwDHKvVMulwsnTpzAxo0bMTMzg56eHhrZzVpQlLTqUATjUp9FNFvJYplIasT/FcD7ANIjeE4OJ2zUlLiUClm1DQPkBKoarZn1Z0pNw6zGTNJhiPn01Vdfxfj4OEZHR0UlEVlhQDSulpYWGAwGeDweUZ1jtXMTaVbDssIKRpKnKw1ekx4n/S47ZrIhIhYGpZKiaiLj2Tknz9LhcODcuXMAgL1799Lvk6A6OX+0y+WiwWfsPQUi0LNQ805Es5UslomIINZoNHcC+I8AfgTgqUick8NZKoF270utRBVIoLIR3U1NTbLNB6Q5uFKNWVr8w2w2Y8uWLaIgIDYCOND3Q52bUFCzeK+GZYXcN2mY4Ha7AUBUWYscR6KXpb5XdjMmfc7Sn0qR8ez8BMrHrqqqwtTUFEwmEw4fPgwAqK+vh8fjAQD09fWhs7NTdE0SfCYtYhKIQM9CzTuxlq1kq0mkNOJ/BPA0gI0ROh+Hs2SCab1yla7U7vjlzJwENqI7UClC6cInFWrsgi4tialUSYoIbbnOTmrmRukew/GjryasAM3Ly6MaLfGvsveWmZnp5/MlKV7A7WIqwaqdySGnXcvNOSmLmZubS/3BJ06cQHZ2NsrKyjA+Po7p6Wk4nU76zsn5upcC13ZXjyULYo1G86cArgmC8HuNRrMvwHGHABwCgG3bti31spw1wnKaSINpvWoqXSmNMZQ6y0qlCAOlFwUqlSn1F0rHptTZKZDZPdR7VLqHaEOq0RL/Kpt/rhTktZSCGWz+L9HCQ8nHtlhud5BKS0uD3W6H2+3GwMAA8vLyaApUJK0ZXNtdPSKhEe8FUKrRaL4KIBlAukajeVEQhCfYgwRBOAbgGADs2rVLiMB1OWuA1dCopIFNSn18AxUICdSaUG10sjT/1Ww2B0ynaW1txQsvvID6+no8/fTTcLlcqKurw+DgoKhhPTFfB8tlDfaZGiEbC4u3nHmYTV2S25S4XC6UlZVBp9OFtcmQVs4KVPFLagIngViknjTp5uTxeGTHw/a2DiXvfCXdE5zALFkQC4Lw3wH8dwD4o0Z8RCqEORwlVkqjUtIGSYs6NX182d+xrQlDWfyUNM8TJ04gLy9P5EuWq8ZUX1+P7u5uPPvss3C73bSRfFdXFxwOB70PUl+ZLXARaK7lPosFIauGQOZhOQHCpiepvX9pvrJUkMuVHpVq5XKV4sj19Xq96PtHjx4FsGg2V7KABCNSf3vR6p6IJXgeMWdVWanFXmmxCBR0E2iM4Sxicjmp5BxyjezlqjHV1dXBarWirKwMvb298Hg8tNCDVFuS+/5yBuFEq2bEWi/Y+2Rzetl0JraohlrIZqq9vR0GgwGNjY2orKykucTS/GGpVm6z2XDs2DGYTCbcc889aG5uhtlsphuq6upqdHZ2UssHazZXsoAEI1J/e9HunogFIiqIBUFoA9AWyXNyOJFAabEIthgpCZdwFrHW1lb09PQgMTFRZNI2Go2ybe3kxiwN2mKLPEhNmyu9QEajZjQ0NIS6ujp0dXVRwUZQ8g3LFesIVPCDmI+zs7Nx7tw5GI1GuN1uGvFMrkFyj4uLi0VmccL4+Dg6Oztx9epV9Pf3AwD279+P/v5+nDlzBjMzM+jq6oLJZILZbIbJZJIN7Ftp1orlZDXhGjFnXRDuYhFJ4cJqvsSkHSj6Wq5AhXTxZv3d0iAk1rS5EkSjZtTa2oquri5MTk76fRaKpUPpPWB9ztPT03C5XIiPj4dWq6W5yeTZARDlIrObpvLyctjtdnR2diI9PR0PPfQQtm/fjpMnTyIpKQkAsGvXLnz1q18FAOovjkYLBCd04lZ7ABxONGOxWKj2QhbOjo4O1NfX06IaaiH5onl5eVQjJgs5WaiVUDqOCBPi6/Z4PIpBZJGCzIP0/slYpBpjOHMVKSwWCw4dOoSnnnpKVX1mkv5VU1ODjo4O0XlKSkrQ39+Po0eP0vsh7wcA9Pb2Yn5+HrOzs9i7dy+9HivEyTl+8IMfoLq6GidOnEBrayt1O+zbtw9erxf79+9HVVUVCgsLceXKFSQlJSErK4vWEi8pKQEAVe+OHIGei9xnq/0c1zpcI+ZwAiDnU1Sb9sTCFvlgg7zUapHBjmODhOx2e8hBZOwYg2lYoaRZrba5mnQ5CgY7ZhL8NDU1hf3799O5tdvtOHPmDDZt2kS1WjbaGbitqZLcY+JzLiwsxODgIM6fP4+xsTHcunULWq1WVEWLbNSsViv1Zw8PD2NoaAibN2/G6Ogo6urq4HA4aOvGcBt3hBpBv9rPca3DBTFnWYjWwJ2lwJqB2apVoRS+kHb8UTKZkzxUQL6ghJRAlb7UonaxDbYpCBSlHK3vBTtmEvRkMBhEAVhnzpzBwsICCgoKZO9HTuATn7PBYMDp06fh8XhgNpvx8MMPiwQ2QRqNTypuZWdnY9u2bbRsKeD/7oQyt6FG0Eej22EtwQUxZ1lYiztopTKGoRa+CLZIkujqc+fOITExMWBBiUgWZVC72AbaPJDAJaUc3Wh6L+QKpJCfxcXFMJvNGB4eRk9PD7q7u5Gamordu3ejrq5OVdETNgK7uLgYer0efX19qK6uVrRWSMdRVFSE/fv34+TJk9i2bRt0Oh3VhqWomdtAMQkEtZkDnMjBfcScZYH1ra515PzIavynSpDylQUFBXj00UdpwRGlY8P1E0qRjjFUvyAZi8PhULzXaHov2Llj7529j9raWuTn58Pr9SIrKwsmk4l+X5rqJJ0vog1nZmaiqKgIP/rRj/Dyyy9Dr9eHNK9msxk6nQ5vvPEG/uVf/gV2u132ODVzq+Z9CdV/zFk6XCPmLAvraQcdLDc1VFitiCycSlrxcpoMQ9Ve1Ywlmt4LpfFKf282mzE1NYWuri68/PLLGB4eRm1tLe2MVFlZSetDsyZ5qZAmrgYAfj2lgcXSpC0tLYiLW9SPiKvBarWiq6sLLpcLU1NT6Ozs9KtdLi0YwhZyYY9RkyMdqv9YbgzR5HaIBbgg5nAiCFmASXSt1AeoBlZYBRNuoQi2UBbKcApbRJOQVYNcWUmpeZ+0lSwpKUFhYSHsdjstvCKFzRW22WyifOT6+npahKOystKvpzQANDQ0wOPx4POf/7yoAhexjty4cQO///3vaT4xKctKjpPrby11Cyg1vpBLiVPrP2bPHy1uh1iDC2IOJ4IYjUZkZmYuqWGA9HyRWtRCWSiVClusRQJ1wSLCFVgMmisvL6d+8Pb2dpG/lsxTQ0MDysrKUFFRAbPZjKNHj2JwcBDZ2dlITk5GcXExzSFnNWKn04lLly7h3nvvBbC4GXI6ncjOzsYTTzyBf/qnf8Lk5CQmJyfxi1/8Atu2baMBYoECrKT/ZoW8UlvGcCqwxUpgXjTCBTGHE2HYxXu1fKFyiyCrsbHalByRXlSjeVEOJKAsFotf3+eDBw9STVlpo6LT6XDw4EEcPXoUzz//PBYWFgAA8fHxaG9vR1FRkZ9Q+9GPfkTN26R1Y0NDA7RaLQDQfsRJSUlITU2F3W6nz1FNoxHpMZF2a0RzYF60wwUxJ6qI5gVbLWpzV5cTuUWQaOuBfM6ESC+q0bwoBxJQxDQs7fustKmRy+3VarU0yKu3t1d2DHIR5zabDVqtFgUFBTCZTHj33XcRHx+P5ORkGAwGjI6O+vmKl3LfgcYVzt8jT3lSDxfEnKgimhfsWEJtIJIcStp0sO+FM55oRMlHL601LrepId/t6OhATU0NysrKcOjQIQCLKVFs/jkLee/Zjk9Sod7b24u33noLPp8PDz/8MHJzc1XPp9wzlf5Orp2i3N+jXI57sHnkBIYLYhVMzgsYmfVBnxwHbYJmtYezpomlBTsakGuvJ5e/Kz021MpZwXKH5c6p1HoylgjmE+3v7xd1SiKw1bnS0tLgdDrh8XgwPDxMq2YRiC9Yp9PR0qRy81pYWIiuri4UFBTg8OHDqppSENRUy5Jrp8hGjpOAttbW1ojFQHAW4YI4APM+Ad/tnsXxwTnEa4AFAXgyJwk/2ZGMhDgukJeDWFqw1WgZgY6NxDXVRMsSIlU5S+05Y9G6EcpzIiUo33zzTaSlpYkEMVud65133kFeXh56e3vx5ptvAhD3F25tbcVrr70GALSiFttWkfSoJho1OTcb6R1srtVUy5Jrp0j+HuVSs6Tn44QPF8QB+G73LOoH5zDju/27+sE5AMBPP5eySqPiRAuh1ORVK5RC1WzURMuS86pNRwplMxROqksw0+ZqxglIA7WCbbSUegGTdpUdHR3o7e2FXq9HUVER0tLSUFVVRa9DBNqjjz4KjUZDfc4WiwXNzc04d+4cbDYbjhw5Iip/SZp8kFxk1rcsh5pqWdIWmyxSE32kYiDWQkxIJOCCWIHJeQHHJUIYAKZ9wPHBOfw4P5mbqdc5arSMYL+XEqpmoyZaFoBf8YlIEahVo5JAD2baXE1NWhqoJU1rqq6upsU1jhw5IhJecm4Cl8tFBWdeXh69b71eDwC0bSXp4HTixAmR1qs0NgLJRWZ9y8sh3JZS1zoQsWg1WQ64IFZgZNaHeAU5Gx+3+LlJG7+yg+JEFUpCSE3qiBKRLOCx0oRi+g5k2lzNOIFAxVRaW5V7G5PPpW6CkpISVFZW0vNIrQEARAFZpF+11WrF6Ogobacorcwl7frERm/LmbXlWIobRXoN8rtQBTOPCVmEC2IF9MlxWBDkP1vwLX7O4RBCLbgf6UjTYOdmI3DD0WbUfEftohrMtBktmw1p5S2z2YxDhw7B7XYDgF8uNrnvrKwsvPjiiygsLPQzvZO8Yq1WKxLAIyMjcDgcKCsrQ2NjI8rKyjA2NkY/V2oCIhe9bbFYqEBn/dDSZ8eax8lY1Pibyb2y1wD84xOW831fa3BBrIA2QYMnc5JQPziHacY8nRoHHMxJ4mZpjgg1Qmg5zXDBzr3Uethqxm40GumiL20VGSt+QDnhwaYWHTlyhM4fEXxs2s/BgwdRWVlJA7ikmmZbWxvm5uZgMplE5m+dTgeHwwGz2QyPx4OxsTE6z0ePHsW5c+dgMpmwb98+0Tsm5/s3Go2ora2lz6GmpgZOpxOA+NmR49lSm0pxB3KYzWaYzWbF+ARudlYPF8QB+MmOZACLPuH4uEVN+OAfo6Y5HBY1O/vlNMOFcu5wcolD9XEbDAYMDw/T38fKgixn1pXeu/SnNO2H9BDW6/XUdGyz2WC32+FwOJCUlIR9+/aJzt3f3w+Hw4Ht27eLiocMDQ3BbrfD6/UiNTXVT8NWKkXKRjvLFSRhj5GW2lTzLsvVrZbCzc7q0QiCgv11Gdm1a5dw4cKFFb9uuPA8Ys5KEg2RpETrY4OA1EB8mR6Phza+B2JLIyYa5IEDB1S5GUZGRvwKYdTX1+OnP/0p3G438vLyMDQ0RFspPvzww0HzgMm/XS4Xmpqa4PP5EBcX5zemYH5e1jKhFPkdCVdFuO/LekKj0fxeEIRdcp9xjVgF2gQND8zirBjRYNILR2sGbvssSceiQAFs0Qhr1pWagFnBRqKdgcVnxKb9DA0NoaurC729vZienobL5cL+/fsBAKOjo8jNzQ0qhMlm4Atf+AKys7ORlpaGqakp2YIfcnPLRslL/fFS37D0XtTO03LWrV5vcEHMWRWiQeuLVpZzUVM772rNk3IbBiXfY6wgd+9SX+6jjz7ql7fLarG/+tWvMD8/j8zMTHzjG9/A008/DWBRQErrU0vnka1vTa7ndruRkZEhKvgRKDhKDmk9a/J8SkpKUFJSoqoZSChzxlEPF8ScVWE5tL61ItyXc1ELdd4DmTGVNgxKvsdYRurL1WgWXVQjIyOiyGQi2A4ePIje3l488cQTGBsbAyCOcAZupy2Rc5MykllZWcjLy0NVVRXNN/Z4PBAEAS6XCx0dHbLFWdhnKxclz+YsS58P+a40r3u5/qbWyt9qpOCCmLMqLIfWFw0mXSnRtuCEOu9y2hr771A6OMUy7OYiNzeXapNsUBo7t+RZSyPUpdYCtjKW1WqlmjCpoFVUVERNy+RcpMKWNECLFehy/tvPfOYzmJ6eRlZWluiepGNnWa6/qWj8W11NuCDmrArLsUhHo58q2hacUOc9WMQwIdo2HMuFVNuXBkNJc3al2i7x8RYXF1P/LBHoRAhXVVXRVKajR4/iypUrGB0dxcMPPwyDweCXY8yOzWKxoKamBj09PTTymxz36quv4uLFi3jxxRdRWlqqqimH3POORD31aPxbXU24IOasGaJRA1trC06gspXRtOFYbth5YAO15IpkEOHIaruseRhYND2bzWaanlRUVIT6+no0NDRgaGgIs7OzGBgYQEZGhijHWArxLycmJtJiG8Ry4XQ68d5772H79u2KY1WziVpqPXV2/kixlLW+gQsGF8QczjISjZsDJeTS9JajY1O4Y4lGlPKt2UA1AH7aLjtPDoeDpktJTc39/f145ZVXcP36ddx///3YuXNnwDlmNXDpdaqqqkT5xGqC6uSev8WyWKK0v78fR48epZsH6XnVaM5LjVlYK3BBzOGsc+TafT6eeQt7P3wdnwvS1YcQqQ3HwJUh/NXvruF3qXlIiNNEfetRuW5NZrMZTU1NtL8wCbgiwkOqQRMhzfb8JU0zcnNzsWXLFtx///14+umnVbVmlJrH2c+kWmiwoDqz2QyDwUBN6uQ8mZmZfo07pO+AGs2ZCHW1Edtr1fLCBTEnJlirO+GlEol5kWv3+dL1eLw1nIL/B44VXfD+6nfX0JZwJ3yCBlhY/F20th6Vlpdkq4qRalh1dXW05jQAPzMsq0WS3F/S2Yn8njTIUEswYSX3ucvlgs1moz2P2Xzp4eFhtLe3+/nCg/UkVrKSSH9PLAJqIrbXmquHwAUxJyZYrp1wrAv4QPmkweo9d3R04Kc/r8evHvkH3IK4icmcJgGj/8GCPymYXrE5mpwX8LvUvEUhzBBNrUelZlW2zCNrFjabzbDb7ejq6kJXVxcVbtLCGXKWBLfbLRLY0qYOwQimZbLCjBQPIQ0lSEQ2iQYvKSkR5RyTsQdr3BHonZHWPVcqwSm3MVGyvMT63zEXxJyYYLl2wtFo6lpq9Kmaes+kt+4fxqaxsH8OSPSvn74hMQFxWXq0vvy/VbXVUzP+QHnJ/bNxEBY2AHGJfueMltajUrMq+1MawEXKfdrtdjidTlpIgxzPNosoKiqiub/9/f2oq6ujAiiUd5/MJwC0tLTICm85QVhQUIDCwkI/jZh9TiRKXCm4So3vN1AN86UI0Gj8Ow4FLog5McFyBT1Fo6krnOhTFmnADtuWj9WOvV4vzJ/KxHuJ/oIPWGxy4hsbgcvlQnZ2tigKNxBymozSvbH/nkU8vJu+AolyTscSDa1HpYJDSTtjew6Xl5fLbkykzSKIYKqurobX66XHSa8TaKPDFhWRq/zFjktJEBIfNuvLZptIKEVaB9qkSMcH3NaslXzabFGSYCzl7zgatGkuiDnrmmiMag4ld5N8Jm02DwB6vR5FRUV4/PHH8frrr9Pfk3zWw4cPw2Kx4MC5Abwdb4CQdNsHmyTM4/HMBfzurVZaNzoSVbKC5SW/cm4I7ybmYYYxT0dT61E1ptHW1lYcO3YMk5OTAIAjR47Ifqeqqkr0E1gUVKOjo9i7d6/oWbLIBYhZLBZkfOpO5BU/jEcRj0csX5IV0sECrIJBnpPT6aSbLbLRYDX+UPKSlTaeoYxtKX/H0aBNc0HM4UQZgWodA/IpJmSB9Xg8aGtroxqVxWLB9PQ0UlJSMD09DZvNJipzCAC1d43gwDu/w+VPfwEbEhMwt7CArf/eir2GmbBMh4E0Gem9Sf99+vE7b0dwx1DrUalwfPXVV9HZ2QmPx6P4naKiIpHWSb5Lfip1S2KPaW1txf+xvYxfpt6L36VuhLCwAZqsP8P77g34iUEQRZqTdKje3l5RFLRa2HGwAWTk3tV0XgpkwVktq9RqXx/ggpjDiQmUcjTNZjNcLhceeughjI6OigKDyEJ98+ZN3HPPPbh+/TrsdrtfjeIPexy4+99O4rtZE/jy40/ANzaC37mm/UolEuRMnJEiIU6Dn34uBT/OT46JPGKCVIDu27cPAwMD0Ol09Bg1JlA1mzBpacpfpt6Ld1PzMCtoFv3rwmKk+cTEBL7w4W/80qFIX2Rg0TzOVuoK9CyVallL50AJpfsPR5uNpDk5GqxiXBBzODGAUo4mCcgiXXoKCwthNpvxxS9+EX/xF3+ByspKlJaWwu12o6+vD4ODg0hLSxOd+7YQ+RKM2nhAa0Sa5XY1KEAckc1G2RIT51KKNMjh/uRjnP3j+bRREAWrJgCN3bjICSqlSlbBhEogjS3jU3fi3bR0UeoZsBhp/v+NAv/2P/5f/OxnP8MzzzyDsbExZGVl0bxg4qPu6+tDamoqAPVFW6TvoxoftlKEv9KmTo0vPNiYYwUuiDmcKCXQQkQKLTzwwAM4ffo0hoeHMTY2hsHBQVy7dg1WqxXj4+O4desWduzYga6uLiQkJCA9PR1nzpzBt771Lezbt48ufnJaWE9PD3w+H/r7+2kajM1mw7lz52AymbBv3z4aRcv2tI2EqS/aFtrW1lbFyHG5KHW5hhhkPqSpQMHuNVA5yJFZH+IVDAbCvBfXfQm40d2NZ599FqmpqXTDZjabqW9aqXY1IVjvY+l7qjRXSv5hqd9aOq9y8xIN5uRIwgUxhxOlBFqISL7n6dOncebMGUxNTSEtLQ2pqalwuVy0y056ejp6enowMTGBsbExbN++HbOzszh37hzOnj2LwcFBWjaR1c7MZjPy8/Nx7tw5NDY2Ijc3l44hMTER+/btw5EjR2gULRulGwlTX7QttBaLBe3t7bKR42azGTqdDlqtFrt371bM3yX+XZvNJnIPqL1Xufdh+N9/j5m5u4H4JL/j4xM3oPLPvgLX8BXs3r0bv/71r7Fp0ya43W54PB7qo2ZTnuRy0INtFKRR8kpzpeQfVioMEmheosGcHEm4IOZwohSySElr+rKf/fa3v8X09DQSEhKwbds2HDp0CFevXoXD4YDRaITX60V+fj7KysrQ2NiIsrIy9Pb24he/+AX6+vpw6tQpXLp0CYBYO6uoqEBtbS01G5LFUGpyJZp5cXGxX+BRqKjpBrRaGI1G1NbW0vEBt82qpHBHYmIiCgsL0dnZqVglyuVyoampCXl5eaJzS1N45ASinGD6xbGfIUG7E9hXgYWEDfT3KRoBT346DT997CgA4PHHH8fFixdx8+ZNZGRkQKfT+fUpJpw8eZJqziQqWnpd9lmpmatA86pUGCTa3oHlhAtiDieKkAojuZq+BIfDgf7+fqSmpmLbtm1ISUnB2NgYnnnmGfzwhz/Ejh07RB19SktL6XeLi4tRXV2NyclJ2UYA5PpHjhwJKCCJZk5654Z7n0D0maOlSE3ELpcLDQ0N8Hq9KCgogMlkgiAINFAOgJ+wKykpQV5enp+2qJRfHczcXVZWht7njiLjy1/GW3EGGmn+ZM4GUaS5yWTCW2+9Ba/XS8dms9nQ1NSE7OxsvwC+/v5+GtAVLIBMzh8eTIhGQ+5uNMEFMYezQqhZfKQLspLpjjQL2L17N62I1NTUhObmZmzfvh3T09Ow2+0oLi6WvVZRURFeeuklv/GEmjqlFM0dSgRusOb00QZbNONLX/oSzp8/jyeeeAJjY2PUmiDdXEgLbEi1RSUzNVuUpbKyklbhIoyNjSEtJRmP3TiPo1+y4NW2d/HYvj245y6jKBCqsLAQJ0+exMcff4w33ngDer0epaWldFOQmZlJx0w2G7m5ubLPQlpjO5APmxwfa5uuFUcQhBX/77777hM4nPXG8ePHBYvFIhw/flzxmCtXrgjHjx8Xrly5EvBccscdOHBAyMzMFL7+9a8Lzz33nPDcc88JV65cEc6fPy8cOHBA+PWvfx3w3ErXZn8fbHxK9yj9ntr7XEnkxhTsd2TODxw4oHgs+yyCXU8J9tkqzSM792Rs27dvF/Lz84UDBw4IhYWFQl5envDZz35W2Lt3r3D+/Pmg9yf3ObnOc889J/pM6dnL/T4an/9yA+CCoCATuUbM4awQajS+QCX/pMdJfZB6vR579uzB1q1bAdxOB6mpqVGVphIoMpg9JpAmQzR4acASm7pDCCVdZSWQuze537EaoF6vx0MPPYSqqipFc6y0u5CcbzZYNHJZWRn6+vqwdevWgDnFwKIWXVNTg4sXL2LTpk24//77sWnTJpjNZhQXF6Ourg5dXV1ob2+XrfrF3jMxv0s7QpHPpNYb9nOC3O8j7f9V8+6s9vsVCC6IOZwVQu3io9ZsJ83d7ezshMFgwFtvvQXgtk9ZbZqKUrQrGxVLAneUiv8rdQtiF+9w0lVWAjmBEWjz1Nraivb2duTl5dGew3LHSLsLKZmrpYKCnffMzEykpqYiJyeHRrlLYetBO51OaLVaxMXFYXJyEh9++CEqKipQVFQEk8mEM2fOoK2tTbYgC3vPxLQtdx25RhCB0q+WEzXvzmq/X4HggpjDWUVC6bkqLX4gV2SftOBjfyeXpiJ3PjXRrtLi/+Q6SiUYpffIEkq6ynIgnXupNQLwbx0pHW9zczPOnTuHF154gVbRIh2MpJHO0udL/L9kLDU1Nejp6aEWCem12J+Biou4XC6UlpaKOim1t7fTiO3Tp09jYWEBvb29fqlYgFhwqilXyr4LqyXg1Lw70RyDsGRBrNFojAB+ASAbgA/AMUEQ/mmp5+VwooFImLOkgoj9f7nqVUoahLSZQHFxMa2SxOaoArcFa6CKV0pNAKTnUSpnSEzQL7zwAhobG6n5Uq4KGGvyjpZ0lWCmaACKnYbIvG7fvh0OhwO9vb1UqJISkmQ+lAQd28nI4XCgp6cHMzMzeOONN2AwGHD48GHRddnWhYHqjpNa4mwt6/b2djQ0NCA1NRW9vb3IyMjA448/viShRDZybrfbL+paetxym4TVvDvRnA4VCY14HkC1IAjvaTSajQB+r9FoWgVB6InAuTmcFUWuStBSd/usIGKbwwMQmS3VlDpsbm5GZ2cn2traYLfbMTo6SlOH5ASrktZMOjBVVlbSz1jfpfQ8cvdO8mdNJhMA/4b27LiVimGsJmpN0cQXygpk1rxcXV1NtU5gsfEGEcpKsJHHwOJ7kJOTgw8//BATExPo7e0Nadzk2bElLNn3ye12w+12Iz8/H263Gxs3bkRubm5QwRjo/Wfft+rqasXSn8F84ZwICGJBED4B8Mkf/39Co9G8D8AAgAtijiKT80JUFvWXSx8ClmbOYgWRtDk8+dxoNAY18RmNRlRXV6O6uhoOhwOJiYnYu3cvFaIulwtlZWXQ6XSyaTBEyz116hQt7kFSVNjrk3rV27dvV7zv1tZWdHV1YXJyEvfeey8ee+wxuFwuuuGoqqoSNZZXU+BhOZtJyCGnIcnVUCa+UDY4Sc7kTLRPkvqj5EcHxJorMVEbDAZ0d3dDq9Xi4YcfVhSCcgF90rxjh8MBh8NBv5+RkQGtVovLly9j8+bNyM/PV3wWHR0dsFqtqKqqogVb5Lo1EasIIB8zQHzcZWVlfu88i1qNOZqDrZZKRH3EGo3mLgA7AXRE8ryctcO8T7jd5k4DLAjAk39sc8e2bFstpAIsFHNWoO4yrCBiP2PPrUboOxwOxMXF0fxhIrDq6+tpxabDhw/75QXX19fT4hObNm2C1+tFV1cXXn75ZTQ3N6Ouro4urETT3r9/v9+CRxbpsrIyHDp0CIDYDE4ihK1WK5xOpygCO9g8Eg3L6/XC4XAoRm6vNNLgJKmpWOn4QBsrVsC1t7fDbrejrKwMmZmZ+OSTT3D27FnqJ1YKFJPLN/d4PKKCIuz3HQ4Hurq6kJaWRqO85SDNIIBF94dSwRa2Klage9XpdAGfvVqrUzQHWy2ViAlijUajBfAKgP8mCMJNmc8PATgEANu2bYvUZTkxxne7Z1E/OCfqFlM/OAcA+OnnUhS+tXIsxY8UaKFQ68MKlLpEtN6CggKMjo7SYh3k99nZ2dT8KxdARYTs4OAg9u7dC71ej/feew9dXV3UZJyZmYnR0VFRlC8Lu0gTsyQ7frLhIN19gpmj2c0LCTSbnp6OCjO2XDBXoMIV0u9JrR/s+diKZCw7d+7ErVu3kJ+fj5qaGkWBmZWVReuJA7ej1YmWzQaGkbGYzWZMTU1hcHAQdXV1oo0cC4myr6qqotHgwSwZbIEPAomwJ8coCX41G1Cla6wVIiKINRpNIhaF8C8FQXhV7hhBEI4BOAYAu3btEiJxXU5sMTkv4LhECAOLLduOD87hx/nJUWWmDpVImLGlwlwabNXS0oLp6Wm8//77SEtLQ1FREZ577jn84he/wKOPPooDBw7QPFJpBG5mZiaqq6upL7O4uBgajQbvvvsuXn31VWRlZdGIWyXTMLtIs7DCBwD0er0qc7Q0OMrj8aCkpEQxUjfSsObw4uJitLe3w+Px0AhotqMUOc7j8YiC06TnIwF4BQUFeO+995CVleUXbyD3rtjtdnR2duLWrVt4+eWX0d/fDwCypUNffPFFXLx4ES+++CJKS0vR0dGBV199FUlJSSIzMolPmJmZwfj4OB5//HFMTU3RWANpChm5HntNNZYMsgEAINqkyKWySVFrLZFuMtYSkYia1gA4DuB9QRCeX/qQOGuVQC3b4uMWPzdp41d2UBFkqVGZQ0ND6O/vh06no4up3OKdlZVFfbz19fVoa2vDxMQEHA4HTpw4QfNIExMTqWYJACdOnEBeXh4NGMvMzERubi7q6+sxPj4Oj8eD1NRUv8WOCCuPxwNgUci2t7dDr9f7FewwGAxwOp1obm5W1LhYlAKmVmqxZQOOHA4Hzp07h2vXruGOO+7AoUOHqEbLHifnLyWQNpEFBQV499130dXVhWeffRalpaUiczSrXZPnMzg4CEEQsGHDBuzZswc7duygGx72GQiCgBs3bkCr1dJAOavVirfeegtJSUlob2+n6WrEUnLhwgXMzs5Cp9NBo9FgZmYGGzZsUOwURQjml5VqqtL3Va0Wu5SezGuBSGjEewH8JwD/rtFouv74u/9bEIQ3InBuzhpCnxyHBQVbyIJv8fNYQdodh+2So1aIyEVoNzY2AgD1yUmDgoigLy0tpX65ffv2IT09HU8//bRsHik5BxswVlJSQjWuvXv3orOzE3/2Z3+GzZs3+y12NpsNzz//PBYWFhAfv7hR0mq1Ij8u66OcmppCV1cXHA6HrCY0NDQEq9WKvr4+VFdX+wVHKc3PcmCxWNDf34/e3l488MAD6O7uxuzsLAoKCqhplUQjm81mZGdnY+PGjXSTIb0vu90OYLG28xNPPIFnn32WPhdiqifPltWeS0tLodVq8e///u/41Kc+RZtHENOw9BlotVp89atfxSOPPIL6+nqUlZWhu7sbN27coOMhWmRJSQkKCwsBLJqLyRi1Wi3dkIVbBEOqqbLvq/SzUNP4WCKxyY3mQK9IRE2fBRC79kTOiqFN0ODJnCTUD85hmjFPp8YBB3OSYsosLY1SlXbJkUMqvNm0DiLIysrKMD4+jubmZpjNZhQVFSmeTy5yt76+3i+PlFyXjWRmc5I3bdqETz75BM3NzfjmN7+Jixcvorq6GiaTCY888gjsdjuSk5ORn5+PgoICCIKAvr4+OJ1O2Gw2akYmPkp24WfToogmr9fr0dDQgPHxcWpel5sjIrgCzelSMRqNyM3Nhd1ux+nTpzE+Po77778fdXV1okhyg8EAj8eDtLQ0dHV10TaG0vES/7u04xWxUrC+d1Z7Li8vh81mw+zsLAYHB/Hyyy8jKytLJCSJBnzvvfciIyMDxcXFdI4OHDiAV199lY7l6NGj1MzPbsiMRiMGBgbg8/kwNTWFTZs24R//8R+RlZUl6s5FCKaJBgpulH4ml6NNkM5NpJFq6tEmlHllLc6KQlqzHR+coy3bDv4xajqWYNOCpBqxElLhzZY4ZLWXtrY2OBwOPyElRU5LkFs4yXUrKipEi2RzczO6uroALDZ/Id2DpqencfHiRZw9exbDw8MYHR3FF7/4RVEUM5sjGiilB7gdUUt823v27MHOnTuh0Wj8fM1SE/dyLs4Ecn7S+q+wsFC2ClZ7ezvcbjeuX7+OtrY2VFdXo7CwEC0tLXS8+fn5stHeSnOTmJiI7Oxs1NTU4PHHH8f169cxPT0NrVYLn8+Hrq4uDA0N0aIq7HvGCnJ2jtrb2+km66mnnqLFPFwuF4qLi5Geng6z2Yx9+/bhF7/4BSYmJvDUU09h586dIQsmNdHjcnMg/Z3c3EQSqaYebdHXmsWmECvLrl27hAsXLqz4dTnRQ7TmES8ncs3e5QTbiy++iMnJSTz//PMoLS1dsllN6fskFemBBx7A2bNnaS3qrKwsvPjii1QjVjK5q837ldOIOzs7RRsDJVN/OCb/cOcl2DyTDYVOp8PZs2eh1WppChcJ8GLnIdj5yPxfu3YN58+fR2lpKWpra1FdXY033ngDs7Oz2Lx5M3784x/7VdWqqKiA0+lEQ0MDKisr8aMf/Yh+VlJSgrfffhsXLlzAX/3VXyEjI4MeZ7fb8frrr+PBBx/Evn370N3djba2Nuh0OnznO98RRVmzVhv2Wam5t5Uk1LGs1tg1Gs3vBUHYJfcZ14g5q4I2QRPTgVnhwGoIcjmZJHiHVKwi1ZWWuoNn06JYwUZSaDQajV8qEmumlNPKWf/mgQMHAi5oUt/20NCQn6Yrp7UXFRUtax1jJXMlcDvyF4AoqpoU4GB9rnIRvez8SM9PjiE51+np6aIOTtnZ2Zibm4NGo8GOHTv8tEZSWlSj0SAjIwMZGRn0M/ZnX18f9cMTUlNTkZiYiJmZGTrmb33rW7BarTCbzQGtNuS+5KplyQm3lRJ4of59RGOpSy6IOZwIEemFx+Px0FKUSpWJAmnZLHJRzSaTCbt37xZFzqqJkrXZbDQfOScnB/39/Th69KiiNkiuL83HZVGq4BTJaFnpvbHXDFSGlGxSPB4PhoeHYTabceTIEVEkMxsZzDZwyMnJgcvlgs1m8yvzaLHcrrj22GOPUX/+mTNnMD8/j5SUFGzevBkjIyOicWdmZuLEiRPIzs5GZWUlDRpj57W8vJwKelIwpKurC0ajEampqcjPz8fOnTtFvnjWtaL0PpF5ys7OFt2znDBcKRNwsHckmrR3Jbgg5nAigFQDCtZfVqkur9PpRFdXFwoKCgAAdXV1qKysVGyUQBY7nU4najQgRSmqubCwUJRPygokOV8nW/1q7969MJvNstWw2BZ+wKIw6+/vF5XUZGELXLAaeCS1F6lgIALKbDajvLwc7e3t6OnpwdTUFEpKSqhf+KGHHsLo6CguXryI8+fPA4CotrfX66XPi2jIxF9sMBj8yjyy74E017q1tRUTExPYsGEDfD4fzp49CwA0dYw8x+zsbIyOjiIzM1PxXSLBeS6Xi5YjTUlJQUZGBnJyckQtE4mFQmq1kRYvYTcP7LUD+X+X28cf7B0hz12ueUe0wAUxhxMByOKrFFwUTFCzAjUxMZGaPuVgF3JyLaLRKMFWXiJRzR6PB+Pj46K8ZXahlVa2IqlRRDCxlZfsdnvQalhdXV2KhTBWYtEOdA1SFYw8o8zMTDgcDjQ1NdFiGPv378cdd9xBA8yIUCTaJknXMpvNyMvLQ1VVFS2ewpZ5rK+vF2122GpdWVlZ2LVrF373u99hZGQEbrcb27dvp+8VG9SnVPREauYfGhqin0lT2pSCyAD51oxGoxFVVVXUlM3OXzjV5NhrLUccBHuPbGAhN01zOGuQQAsaEFxQy5kEAcgutnI7fIvFEjDCmC0WUlxcTP2vdXV18Hq9sFqttECHUkUsIgRIKg/RXomZlv0O2zpxZGQEDocDGzduxMDAgN+4yPeCLY5LXaylgkHa3lHu3pubm/HWW28hPj4e27ZtE20gjEYjNQubTCbabKO1tZVq90otJKWbHfJMk5KScP78eWi1WnzmM5/Bl7/8ZVGZS6X3TLo5c7lc1GVQXFws0gSlmm6gHGJpYZiDBw+KrBd6vT4iZt+lmLGDbXLlaoVHm7maC2IOJwIE2/0HE9RKgVxy55Tb4QfTJJWKhTidTvzmN7/BmTNnYLPZZPsJS6/LbhbUCNL29nacO3cOJpNJ5NMcGhpCdXU1urq6FE3q0ntYikajVDta+jnxGVssFhQWFlJXgbSABxmTNKpY+qzlAuVqa2vxwgsvoLm5GVlZWXSTNDw8DJfLhfHxcTzxxBP46U9/Krqe0rORbs4A0OfN9kcmpnM1rQnZ593e3k5jCZYjFWgpFpFgm1wCO3fLGQQYDlwQczhhEOqOejkiNVlNJ5gfjGhJ5P/JmPLy8jAzM0MLSVRWVqKqqsovUlp6v9LKUOS85Bhp8YbJyUn09vbiscceE5XFJL5LpV7GLEs1XysJDZJGZDAYYLfbRf724uJi2O12WkpSzZikz1oaKEfaRLa1taG7uxsDAwMYHh7Ghg0bsHHjRuh0Otxxxx0wGAwBy08C/g0mSCvK7OxsPProo8jIyIDb7aZuC2lrQrZ9IRmr3EaFtFSU0+6l9x8O4f59sJXk2C5gwf42V8p/rRYuiDmcMFiqKS1Y0FagRUTOB8g2mVfqYUu0IRZWQNvtdrS2tqKvrw8vvfSSX7SstBkF8R+S3rZsoFdVVZVspLd0ESflJTUaTdD5XOpmRmnxJR2l9uzZA51Oh4GBAQwNDWFwcJD6ih0OB3JzcwPm0io9O1azJBHKVqsVXq8X27dvR1JSEubm5pCVlYWUlBR8+ctfBgC88847fteUwgrW3Nxc6gPu6emh7Q4BUG2RpGIRn/XRo0dpUB1p0AD4PwOpFiz1cUeSUDa5cqljav42g71LK2265oKYs+4J549OzY5a6bxKC4WayGtgcUHX6XTo7++n1yCLkcVikfWDAfL1fI3G2z1lOzo6qHZGzNRKLf3YyGC23jTxfTocDtH45czObHlJnU6n2DIwUguh0uJLhJVer8drr72GoaEhzM7O4urVq8jKykJqair27NnjN6c2mw3Hjh2j/ZyDCQC2IxVpX7hx40YqHLds2YKUlBSMjIzggw8+wL333hu0KQOht7cXdrsdFRUVoqAz8uyam5vhdDpRVFSEwsJCFBcXAwDcbjeuXr2KX/ziF/j7v/97v8hu1pwul3YVKDgvXJTyu+XmYLmitVcq9YrABTFn3RPOH50a7UzpvHILhZyGqQTR0IiWJvVJyvnBAOV6vqxwKS8vFxX3IMKG1DgmfkZWy2MXSjWtD9lrsgI+lGIYS4W9Z1KTu6OjA319fSgsLMTU1BSqq6tRV1eHnp4e3H333TAajVSDJJr8+Pg47eesJACkggVYFJw9PT24cuUKJiYmkJOTg4GBAYyNjeHWrVuYn5/H2NhY0KYMcqUvSWRzXV0d+vv70dzcjFOnTmHDhg3o6upCX18frl27hgcffBAajQazs7Po6enBCy+8gNdffx0ARDW2pTXUQ3nO4RCKD3qp0dpqxrAScEHMWfcs1x+d0nnZhYKtVBSoVrH0vKy/V+58bPQs8XUeOHBAVphJTcpSP+Dk5CTOnz+Ps2fP4o477qCCgeShKi2UgbRaOfM68VVKaygvh3ZCTLpsbnN7ezscDgcNKLPZbH7tBgm9vb0YHR3F7t27AYB2sgoU5EburaGhAY8++igSExNx/fp1+Hw+mgZGKp2lpqYiKysraAtBpSA/drP26KOPYseOHbhx4wZmZ2cxOjqKTz75BH19fTh06BCMRiM+/PBDamEBQF0d0nQnuetGGvbcq+XLXenqW1wQc9Y94fzRqTGdKkXlst8hQqakpIQKSgABA5ek/l72vFKhFagxO6uV5uXliaorsRoyALS1taGnpwcmk0lkLpUulNIgLTU+R6kG7PF4MDk5CZPJJHuNSNLb24t33nkHzc3NmJ6ehtfrpfdBinV89atfxeHDhwHc1kAFQUBDQwPuvvtunD59Gu+99x50Op2iCZ69f6/Xi76+PlRWVuLy5cu00MbMzAx8Ph82b96MT3/607h06RJSUlJko7WDQfzvFy9ehEajwT//8z/D4XCgv78f3d3d8Hq9SElJgcfjgcViwSeffIJbt27Rd4rt4BVKipJal4Ka46ItxWg54YKYwwmDcLQ0pfxfqXYr1TIDCXCCXAck4HbpyKysLD/hTrSzyspKkalRakrNzMzEM888Q0smEtO1NNVJ2iQeANXIpX5OqQmdNZvbbDZkZGRAp9PRY5dqnmbncGRkBG1tbbj77rvxxBNPoLGxEefOnQMAWiHLbDajsrISbreb1nJmx11ZWYn3338fer0eBQUFtItVMOHBlp48e/YsDAYDbty4gY8//hgejwfz8/OYmJjA5cuXcfPmTXR2dopaTaq9d6PRCJ1Oh4sXL6Kvr49q/TabDX/913+N3t5eWmClsrISP/jBDwDI+1pDedcDxT+wPmen04nXXnuNpq3JNRBZaT/tasIFMYcTBuFoaaxwCqS1BtIyA5nspGZq4HbpyMbGxoD9kpXMgVIT8tGjR+H1evHb3/4Wdrsd1dXVNJWJ5AQ//vjjIv8hK7iDzSXR9qXm8VACeORgv9/e3o63334bmzZtwtjYGGpra6kQAED9skeOHKGbIjZNzGKx0AAvUl1MySIhh9lshtlshslkQl9fH7RaLbRaLbUIzMzMwOv1QqfT4fHHHwcAnDhxAs3NzSgsLAzY6Qq4LfTcbje0Wq3IxE803cOHDwfsnBUspkGJQH7yEydOwOfzIS4uDtnZ2aLx1tTU4Ny5c0hMTFT8O1jLcEHM4YRBOOZsuQo/gLjhgZxGJbcgSa+vpiiD1NcnJ/Ck52b9zKQvrsPhwKlTp3Dz5k0AwMsvvyzKCSYRvCykgYWc8JTeCztP5DuhBPAEmgeLxQKz2YypqSmYTCY6HlYrKywshMvlQkdHB5xOJy0BKrVo1NbWAhDn3ioF4rGCmghDu92ODz74AAaDAZWVlaivr0dfXx9u3ryJW7du4caNG9i0aROd83PnztEymoH88WSc99xzD+644w488cQTMBqNomh7QLwxCtTSMtC7rtZ8THzwJBiR1MEm43U6nSgoKEBhYSGdO7n3YK2aqLkg5nBWGKmplS0ZSAonEHM12+cWCOw7VrqWXDCP3DiUzklMiUQA1NbW4vr16+js7KRBTKyJXRrgk5mZSQtNyDWTUJvmtZQAHnYejEYjXn75Zb9jiJA0GAx455138Nprr+HatWtITEykvZENBgPcbreoOxM7RjmhRVwATqcTGo2GCvbm5mZMTExgeHgYDQ0NuH79OhISEpCUlIRbt24BWPRhkyhlIiiJX52dM7bJBnlXmpub8f7776OxsRGlpaV+0fbsOG02G5577jn672BVztg5Y+9fqbCMNNKaFIUh90N+yr2D68FEzQUxh7MKyKW0kFKChYWFsNvt6OzshNvtppHKgHLwU6C2g2rHQQpOlJWVYWxsjP5bLqVq37592LdvH4qLi2mrRmk9YwLRaohAJ7mnSr1tCWqizsNFbr5IKhV736yW1traStsglpSU0Gh0NfnPXq8XLS0tND2psLCQ9gmenp7G9evXUVBQAJPJhFdeeQUfffQRMjIyMDU1haamJoyNjYk0VaVodY/HQ+/l4sWLMBgM0Gq16OjoEDXskLabDDZHSgVLpBYTJfcLoPzclH5PrpeVlSXbInMtwQUxhxNB1ApEqRZAUoGIRjY6OkqLPphMJqpNyaWzSGs2B6qQJIUdR01NDZqamtDX14e4uDj4fD54vV5qSpT6UUtKSqjASkxMRFxcHD0Xa+Yk81BWVobGxka6oLIR48HKLUYaNmWL9B9m60Xr9Xo/Uy27YSIVskgOb01NDTW3SjV/Ylru6elBVlYWxsbG0NbWBmCxzSUZT1ZWFhobG5GXl4fLly8jPT0d58+fx/j4OFJTUwEgoMuAtTz4fD5cu3YNk5OTeOuttzA5OQmn00k7SbW3t1Pfvs1mg9vtxl//9V+LrC/SzaK0IAx518n7wKa1dXR0UCsCoK5vtlTQk+uTXGZpi8y1BBfEHE4EkS7wTU1NdFEmn8sFVQH+/lw2wrShoQFarRZ/+7d/67eA2Ww2nDlzxu88aky37DhIABIRmEQTJiZFtvcu0doHBweRmJhIBTbRHNmiIOQe8vLyRAuqdJzsQg/4a/+RTGchPkun04mNGzdienqabnzIvGRmZuLEiRO0zzKBfI9EeZMNDADU1tb6VZ1iTcsej4dGLA8MDIiE1+OPP47f/OY32LBhAwAgISEBe/bsQW5uLrZt2+YXIyAN8mMtD0Sb3759OzIyMlBcXAyr1Yq2tjZq9gYW38djx45hcnISTz31FE2LM5vNogh44sdlC8JIN1Ls+KT9paVClX2u7FjYZx4ovmGtwQUxh7NEpEU02Abz2dnZdFEGELAxg5zPtqioCD/4wQ8wNzeH5ORkRfPcpk2bFDsEqYVUmBoaGkJvby/MZrNIGyS9dwcHBzEyMiLSllkth/UXA4sVvbKzs6l5URqMA/inMBGkkdNKfuZQYYWjzWbDRx99RH2pwO1UrPT0dJw7d46mEBHBQ44bGRmBwWDAQw89RNsVSvv1spqj3W6n/aABICsrizbaMJlMSEhIgM/nw9atW7Fp0yZs2bIFH3zwAQoKCgJaVaT3JbdhkWr55LvNzc00/UoqMEkNZzk/rtzviIbt8XhE1hs1QlV6bKD4BrXESi4yF8QczhKR7uRJrd+enh7k5OSgtLQUFouFLtoej0exBZ2csNHpdEhKSsLs7KzIPEcWGWm/WTn/YaCoWLn7kRbSZyOL2VKcZIzsQkmOJdclJlP2nNIFUmmhlx4j1TbDMXmy48zMzITX68XmzZvxwAMP4OjRo/TzlpYW6HQ6JCYm0vGw4zp58iTV/EjxC0BZG2RN8GSsNTU1ePXVV/H222+jtLQUX/jCF6g1hPjglQRXqL5yYiYnmw9y7urqar9rSAWm3LXkfsdaQ6qrq0XvTzChuhzVrGIl0IsLYg5nibALNFn0iVZE/IjE50WCfciCfPToUVFktNlshs/nQ09Pj8jXfPDgQQiCICqOQepAFxQUoLq6WqSVs+MCxAtkoNrF5HuBCnEoaVxyAk8pZUvNAik9Rk7bDMfkKb1XItzPnj1L064qKyvpM2pvb6fHSyO35Xozs/Wz2SInRPC1t7ejpaUFLpcLGzduREJCAkZHR/GrX/0KTz31FLZs2ULfG2KlCAU2gloa/cy+B2TDUFJSQoMBCXq9PiwtlLWGRIMpOZwI+9WAC2IOZ4mwwob4OQ8cOOBXGF+q9dXX16OhoYFWbyKLYVxcnMjfSgKjyCIOLApTj8eD8fFx6h8eHR1VbE3HLpDBgqLk/I/S+5UGZAWqhCQnoLOyskR+WbVFS9rb22G322E2m0V+5lBMntJ7qa2txQsvvICuri489NBD2LZtm8hqQFLKyLwT6waJAyDPngTMHTp0SGQ9IO8DCeRKT0+HTqfD4OAgXnnlFaSlpSE3NxcPP/ywKFAqXOHh8Xjgdrvh8Xj8PjObzbj77rsxMzODjRs3+rXOZP8/HA2StYZEA8uhZS8HXBBzOBFCGtASrFBFVlYW7r77bmRnZ9M2eyMjI8jLy6P+RjYdhJwbADV1fvGLX0RXVxe2b9+OtLQ0arYlQpwVUCQQh2hkhEAdolihLc3xJUE+wGLeqZLAkxOyx44dQ2dnJ44dO0ZN91ItXE7gS4mEydNoNGJ4eBjvvvsutmzZ4idIyP309/dTTdPhcNAAraKiIr+AOXLf7PswMjICn8+HS5cuYWFhAZ///Odp5au6ujrR/ErN8oB6f6dOpxOVCGVxOBwYGBiA2+3G6OgoqqurYbFY/Aq7sBp+OP7V1fpurMIFMYcTIaQaL+uXZX19RDDpdDq899570Gq1+OY3vykyX7e3t1PtlwggdrEk/0+Eq1S7lTPZAoulErOzs/3SoOQW3kDdlaRBPoCywJMT6gAgCILou3JauNSkrlQNbKmwJSulkA1BdXU1bQohd7w0YI59H0ZGRlBdXY2pqSlotVoMDw9jy5YteOyxx+hxrJsCgF8cgVp/p3SO2OdKymtu3boVOTk5fhtG0n1pZGSEulaUricnMNnccDZjIBSBGit+3UjCBTGHEyGkgkjqj3M6nbTVYEVFBZxOJ7q6umgtYEBcj1rq05WWtJRbCAP5MIHbqTfEb81+p76+3i9QTEnLNRqNqKurU2VClRPqzzzzDLZs2YKqqipF3yr5XWVlJR2DnLAPVYOSO17JF8sKltHRUezdu5eardnjWeHHmvjJpqi5uRnd3d3YsWMHMjMz4XQ6cf36ddHcHzt2DKOjo0hKSkJRUZHsZknuWSjNN4Ft+Tg8PIzR0VHs379fdExHRwfq6uowPT2Nmzdv0m5ccv2rCXICkw1MI+cggXVqiRW/biThgpjDWSaIudXtdkOj0WBqaoq2GiSClDVjA2LzNXseOQJpDkomW6WG7kNDQ+jv78fMzAwNFFMq2Sh3DXKOQBHMrPlZr9fTjQYR0GxBDfZ30prIUiGqVFZRCTUaV0dHB6xWK/R6PTo7O0VtKuXOLXU7kPklPuLi4mKkpaWhqqoKTU1NeP/993H33XfTY81mMzZt2oTr169jbm4OfX19+NrXvuYXKBdIoAXbkFy8eBHvv/8+tFotLl68iKNHj9JNhdVqxW9+8xskJCTgC1/4Ak01M5lMtFiJdKMiJzClvwvH1y03l2vdRM0FMYezTJDAFSJUSHQqmxLCVmVSSgEiBEr5CWVMchol6X4DAHv37hWdc3JewO8HRuBo/zc8YvkSAP8ALzatia2yJfWRy5mf1SzohEBBXXJlFeVQM29WqxVNTU146KGHqJauRhhIC7qQHOqMjAxa77moqAgjIyPQaDT4h3/4B9pVKS4uDtu2bUNWVhbuv//+kAWY0gaD7Sk9NTWFqakpvPLKKzRA8ODBg6iqqkJ3dzfGxsaQmpoKu92OiooKNDY2inzh0spxUqTv11JMy+vJRM0FMYezDEiLfADyRe2tVisaGxvR19eHl156STbHlqBmYQon0IUEFZGKWURLmvcJ+G73LI4PzmHBm4i5jftR/1sHvn7zIn7RUC9KjyHnkFbZkiInBOUKmQTStqVVnJRSpJTmQ+ncrG+fFOkgbR7VYrHcTocym804cOAADfKy2+3weDzUb6/T6WjUe1paGm7cuIFbt25hZmYG27ZtU33NQHNL7jczMxM3b97EF7/4RZhMJoyPj+Pq1aswm81U+3/mmWdojXFi0SBpYsQXzr6DwNIirMO9n7UIF8QczjIgFZpKC1VVVRX6+vrg9Xpp9SZpEwSl4veBfHTs74JhNptppDYrdL7bPYv6wTnM+ADEbwDiga7NO/5YgrFedA7WDJ+RkUGFuVTIKZlXA41bmgakVPJSqcBEqPnKra2ttAKWw+GAXq9XHbkszbMGQIO8TCYTcnNzaW6y2+3G9PQ0ent78e677+Ly5ctITU2FyWRCT08PampqFDtVyZn/pUFX7HFZWVmiZ0ysNCQljmi9xF1A3gPWF87mRMtZL5QINwo6VlKPIgEXxBzOMqB2N19UVISXXnqJBgTJ1e6VRkAT4cD2yg31uiwkkIz4RI1GIybnBRwnQphhIWED7Bs/g//n4F+j/PGv0d+TBfa1115DZWWln/8WCM1cLO2vKxc4JD23mu5AcoJAeu1Apm4198MKkPr6ehrk9cgjj9Bnl5mZSfPDH3vsMfzyl79Eb28vjSPYsWOHbKATMX37fD5Z8790bgwGA5xOJz1ers63VOtVgq24Rv4dSLgG667FuQ0XxBzOMhDKbp4c29TUhNdeew0mk4nWMgZuCwbSnYc0qX/ttdcAgC6urBYUigbCmlPJwj8y60McBAAav+N983PQbNarPjf7kxBMS2IjfXU6HUpLS/20bDUWgmDFScgxSvneAERCPNhGR6qJEvM02VAQ/zGJnCf3bzabUVtbi0uXLuHGjRu4evUqysvL/a5DnpVca0q5eVdqZcneszQCXAn23kOxNMg1hYgEkcg3jpacZS6IOZwVIlDepcViQWNjIxwOB5599lns3LnTz+RYX1+P4eFhnDp1Cr29vfjSl76EnJwcmp9LNI9A5R7lkDOn6pPjMLfgAzTxfsfHJ27AY/v2+P1eLsc3UHAYm6MqLcvodrvhdrtx8eJFeL1eUd1rOQuBXDcnQri+RqkQl/qx5ZCOjY0EZzc8JHK+o6ODBuu9/vrr6OjooKZsNsWMHZNSiVHpceS6er1e9GzZSGS1giicQMFAsRGRIBLBXNESEMYFMYezQkjTbFhtpb29HWVlZdRfLJd7SRa25uZmvPnmmygtLfWLypY2FVCLVGBqEzT4xpZ5vHRdwJzm9jKRGgccvDsV99y1mf4ulIYSZB4C5agODQ2hr68PWq0WO3fuRG5urqxPUq4xgZywXEo6jJIWqCSUlcZGxiHd8JDobGBRYDocDtTV1QV8foFyqaXXJdeSpoUBt/3hgXzz0gIx5DiluQ42zkgSiWCuaAkI44KYw1khpL5H4r9LTEyE0+nE2NgYXnrpJdhsNsWGCwcPHoTZbKY5qex52QWRmKoDCZ5g2tD/2rMFG7tn8UL/NHxeL+ISE3EwJxVP6cZQXy9u4C4tPhJIOMuNl9WmW1tbqV/18OHDslqhUllLqfmX7escyFcZqEOTXJqVkgALNDa5a7AVuohVoKysDLm5uX7PIxBKldSkGwdp1HkgQcRuHAH4BWmttjYZSNCr1fSjJSCMC2IOZ4UhbQuJz7esrAxjY2N00Qjm05RWgQonElnN5wlxGvz0cyn4L+ljeLXtXTy2bw/uuWsz6uv9G7hLO+60tvrXog40XjmBp8bXLWcyJeZfq9WK4eFhKpwC+SqV5kKu7Cc7xlA0KbngMofDQSOjSUnT3t5e2gUqmJCQpnXJaeLsxkHaaUnp3WEjpAH4tcWUzkG0+FoJq71JCBUuiDmcFYIsDsRvSHy+Y2NjITVcUEswYaFWmNxzlxH/9wH5BRgQ9ypmc6dJLWqPxxPSfbA+8WDCUaq1kTxds9lM63srmWuVhAqLGs1XjRCSpv6wBVBISVFiFQjFtSB9pwCxJh5u0ZPW1lY0NTUhOzsbJpPJTxuWzkGwZxWoX/RyEC0mZ7VwQczhrAByOZhKi4WcIFITKCS9nrRIRaR8p2o0cIvFgsLCQhQWFsLtdqOurg4ul0vUpCLYtZQ0Lul1gEUB8y//8i/45JNPkJKSgr179wKAX11oqcBgzyt3T4EWdPLd/v5+NDY2igqcSOebFWwejwe9vb0YHBxEYmIizRl+4IEHaK/lHTt2KF6PnTu1AidQ0RO5a5Dc8nPnzsHhcKC6ujpgLnWwjUyoAYTBUJPPHQuaMIELYg5nBWBzMJUqPElR8kmSIC+5+r/s9dQG5agN2AkWVUs2GqRsJynAEehaagtUsALUbDZDp9Ohq6uLCndgMeXp448/xszMDDo7OzEwMOAX7SwVGMFMmFLNl/V7k+/KtRskvmpSvrK4uJgKtrNnz2LDhg0wGAx4+umn0djYSBuCDAwMoLu7G52dnX6uCaX0rECm9ED3IwerYdfW1tL7DeYbltvUAaDvBNt9LBLEmuk5GFwQczgrQDDNJVgpRvb7NTU1ovq/ga5HzNtypSHVjE1uwVNqvEAKVMjlrcr1u2XPH0xjkm5KHA4Hzp49K6qX/Mwzz+CHP/whzGYzDAYDMjIyZAOr5NwASn5OUv6RBH6xQWnEN+7xeGhpUPa87e3tOHPmDM6ePQsAVLARjXh0dBRjY2M0kloQBJFGLH0earRftQJKSWBLA+mk9c6DjYO9PnDbt6zU3SoclCp8xTJcEHM4K4BaTQQIrpmRNKeysrKg15PrbBTK2OQElVz0MRFKdrsdg4ODyM/PF5VnlAo/qYYayC8qLVRiNpvx6KOP4vz580hOTobZbMbQ0BAaGxuRkpKCL3zhC4rCNtC9y/k5SXrR1NQUCgsLUVZWBp1OJwqsI8JmZGQENTU1NPiuqqoKU1NTuHDhAux2OzIyMmCz2bBnzx5UV1eLLADkek8++aTovpeSbsXOnfQcanzfSt9X+75Ifxcp5KxLsQ4XxBxOFBBKcAnpkDM2NhbUFCl33kDfkavfTAQnafaenZ3tp40QoTQ6OioSwoHaFgJiDTWYmZ2Nfh4ZGcHAwABmZ2dptPG5c+dEvZ2D3av0c7m5IulFGzduRENDAx599FFkZmZiZGQENpsNg4OD0Ol0yMrKQnV1NS5duoSmpiakp6fjG9/4BkwmEz744AOcP38eb775JtxuN/r6+lBQUBDUDRDMvaBGSCoJXLXvWygmYKX+2IE2FOFEW8daIJYauCDmcKKAUIJL5HzHSr145c4bqr+YTX0hzd5J1adgXaakFbOk45dDujgTU/MDDzyAs2fPwuPxoKenBz6fD8nJyfR7iYmJKCwsVKX5ketIK3zJ+YNZX2lfXx86OzthMBhw7tw5XLt2jQrmyclJJCUlYXx8HF6vF319fXA4HLh27RomJiaQlJSErVu3Ij8/H1lZWX4VrqRjUQrqI9YJcnygPszEUiHNS1f7voWykVOa63DjE5SItUAsNXBBzOGsEJHKtZTzHatNS2G/IxWEcr439nfEB8pWhgrkfwUW04ncbjc8Ho/s+FmIP1ar1eKtt95Cf38/cnNz0d/fD7vdjqmpKXg8HpjNZuTn52Nubg5paWkoLi6mjRSk90SEONsYg9Da2kr7BUsFlTQPmtw76S5VXFxMzc6bN2/G5OQktFotSktL8cYbb2D37t2orq7GL3/5S7z66quYnZ0FANx3332Ym5tDY2MjhoeHqRB1uVx+1cak8yQ1o5PjAeV2hOR+jh07hrfffhsPPvggTCYTGhsbAwb7BXpWoWrZgTZea1G7DYeICGKNRrMfwD8BiAfwvwRB+IdInJfDWUuEs/tXGwUbLC1F7jty45P63tjfkX+Hkj6j0+mQkZEBQRCC+jtJb2adTgeNRoPTp08jJSUF2dnZ8Hq9mJ6exu7duwEsmoytViuNNmY1QtanTI4h9ahZzGYzsrOzMT09jaamJlGEtdlsRkFBAbq6uuicsR2TSEoPaWfY19eHwcFBTE1N4Xvf+x4V3GfOnMHo6CiysrKQkpICQRBQUlKCjIwMNDQ04MqVK/ScpDlEsOh0Mj5yPOAfDCdlcnISFy5cwOXLl5GRkYH3338fgLI7IBCB/NDB4htC+Ww9sWRBrNFo4gH8CwALgI8BdGo0miZBEHqWem4OZy2hNpCGIGeuVFOKMVLjk/5OyQzOBl9J74MIJLvdTn25SpWctFottFotbt26BZ1Oh4GBAdx7773Izs7Gpk2bcP36dWruBUCjjaXWANanTLRGueIoDocDDocDXq8Xe/fuFd1jRUUF6urqRNo/+dnf349jx46hoKAAJpMJ//qv/wqj0Qifz4fe3l4Ai5aAd999Fz09PVhYWEBycjJ2796N0dFRZGZmor29He+//z70er2oC5MS0kA5Enynxt9fXFwM4LY2z2rEga4VbPNHkOZ1R1OFrVghEhpxIYCPBEHoBwCNRnMSwJ8B4IKYw2GQW8BIXWRpA3jyudRcuZz5k2pLTyoJPnKMNC+YBHGxJmAANJVHEARcunQJ58+fx+zsLNLS0iAIAiYmJjAyMoL3338fGzZswL59+2AwGOBwOODxeERCktUI5SKx5ebNbDbDbDbDZDLhkUceoeNmBaPUNGyxWPCXf/mXuHLlCiYmJvDhhx/i448/xsDAAOLi4qDRaPDRRx+hsbER8/Pz9Lujo6N455136DXNZjOmpqawfft2VUKL9dPL9apm70v6nYqKCr80JNJmU67wi7TiV7AAK7nNmtx4Ik20ldVcCpEQxAYAQ8y/PwbgZ+/QaDSHABwCgG3btkXgshxObGOx+PcBln5OfoZaSSmURSpYFS5pQJbNZlOsEEY2F2zjelaAt7S00FrHDQ0NcLvdAICFhQXqR929ezdyc3Pxq1/9Cnq9HgsLCzCZTLRsZW5urt9mgI3OJf5gvV5Pm1/I5Z0Sga7T6agJ+8CBA4oau81mg91ux4ULF3Dr1i2Mjo5iYmICXq9XJHTn5uYgCAIAIC4uDlu2bIHb7cbVq1fh8Xjwwx/+EPv27UNhYSFaWlqQl5cXkl9fTWnOYJ8RpMF0NpsN586dw6ZNm+B0OmGz2fwCwaTCVinffblZS0U9IiGI/TuHA4LfLwThGIBjALBr1y6/zzmc9YbR6N8WT/p5IA01EHJaKmkyIQ3SUUqTYQOJSN6wy+WiZtni4mI/3yDZXJCCHmzjhpGREVr7Wa/X02IYgiBAo9Hg3XffRXd3N+69915UVVVh27ZtGBwcxObNm7F161ZaTKO4uBh1dXW45557REFW0jQn6fiJ71vaKEEuUEpuPhsaGjAzM4Nbt27R309NTSEpKUl0LBHCBK/Xi/vuuw/vv/8+BEHA+fPn0dnZiR07dmDz5s2ygWRSlJ57oDKlS3FX3HHHHXjwwQcB+AeCkUjs/v5+HD16VNRdayV9vmsp0CsSgvhjAOyW+04AIxE4L4ez5lmuhUvOXDg9PS0bpKOUJkO0zsLCQhp5/Mtf/hKDg4MYHh7GjRs30N/fj+eeew55eXl45plnUFRUJNpc2Gw2HDt2jJZ6HB4epoFT5eXlopKROp2O9iIGFs3N//Iv/wK324377ruPjtdqteLNN9/EZz/7WXzwwQe0spbUJE3GL2fOPXHiBPLy8lBbW0uvFch6QISP3W7HJ598Ivpsbm5O8Tn4fD64XC5cunQJO3bsQE5ODqanp9HZ2Ynu7m5kZWXJBpKpgbVUyKWJSY8lc82Wm2TbTwKLz8HhcNAUNeJaYIUdCVyTtr5cadZSoFckBHEngO0ajeZuAMMAKgB8IwLn5XDWLMvt35IzFwqCQPvdsmMgGtnIyIjo36RdI0kfMhgMaGlpwdzcHLxeL8bGxuB2uzE5OQmn04nU1FQUFhZScy/B5XKhubkZ2dnZVCB2dHTg29/+NgYGBpCeng673Y7t27ejoKAAo6OjdG7+8R//kZa+rK6uppr11NQUtm7dipycHNl0H2KSlgpYYqbOzs4WuQTktErpMyIdpgRBwK9//WvMz8/7ab9KzMzMYHBwEIcOHYLb7cbdd99Ng9HC1eik5SSDHUvSsex2O00nk7v3QFYaAtmYkP/nLI0lC2JBEOY1Gs23AZzGYvpSvSAIjiWPjMNZw6jxb7GCgHwnFMEtTSupr69Hamoqent7UV9fT022xJTL/iT5tYWFhfR8vb29uHbtGgBg69at+M53voPGxkYajbt161Y8//zzmJubo+ba8vJyHD9+nEY7/8//+T8xNDSEb3/727h48SKSk5Nx9913o7OzE2fPnsXXvvY1pKWlwWw2w2g0orS0FGNjY9izZ4+owcD+/ftx8uRJ5OTkiLRqNcVMSHWw0tJSUQR7dXU1urq6RPnLbFclMp8ajQYbNmzAhg0boNVqMT4+DkEQMDs7i+TkZCQlJcHn89EcZGBxE3T16lX8+Mc/xoYNG2A2mzE3N0fzgMNpdym1YARKYbJYFttSdnZ2Ympqys9frpQPrtTeUKkOdSRZS8FYwYhIHrEgCG8AeCMS5+Jw1gNq/FtSjSfUDknSxVVqciYm2xs3bqC9vZ3m6Obn56O9vR2XL1+Gw+HAo48+Suss37hxA52dndi/fz9Onz6N3t5efP3rX6cVt7RaLeLj4+FyuWggFrBoomXHNTo6ioSEBNx555145pln8OKLL6Krq4sGNJ06dQpWq5XWdib3STRVtmm9tBGDdC7Y+TGbzfD5fBgcHMT+/ftFAUidnZ24ceMGXnnlFczOzmLjxo3wer1+83nPPfdg8+bNmJubQ1xcHDVNa7Va+Hw+bNmyBQ888ABOnjwp8icDwPj4OO644w6YTCbodDq4XC7YbDa/2t1qkG40An3XaDSirq6ORkSTFDCC0vuo5j1dLoG5loKxgsEra3E4q4Aa/5bcIsjmwwIQ5ZYCwSsd9ff3o6urC5/5zGfoZ3/xF3+By5cv4+c//znuuece9PX1YWxsDJ/61KeQmpqKiYkJDA8PY2xsDF/5ylcwOjqKqakpdHV1YXJyEpcuXcJrr72GsrIy/O3f/i3VJDMyMtDa2oqpqSls2LAB27Zto1HNe/bswZkzZ+Dz+TA2NkZzdol/t7m5GW+++SaARRP5yZMnqS+SLTIizXkOVKLT5XLRvOGcnBw4nU4abGQ2m7Fx40Zcu3YNg4ODSEhYXBr37dtHc6FJla6NGzciLi4Ok5OT2LJlC9LT02E0GvGd73wHVqsVN27cgEajocVCMjMzMT4+juvXr6OwsBBf+9rXRH7dhx56SLH6VzgEyjWvra2luelspL5SPria93S5BOZaCsYKBhfEHE6UIqfxHD16lAblZGZmUs22pKRElKMrV+motbUVP//5z+HxePC5z30OH374ITIzM1FZWYn3338fGo0G6enpyM3NhU6nw5UrV/DRRx/h7rvvFgm9zMxMmM1mWqDD7XZjYGAAOp2O+htzc3OpP3fr1q0QBAHd3d00Raiuro6alNnIaqPRiKKiIiqUDAYDsrKyRIKKDcqSmqWVNi86nQ5tbW24fv06DZg6duwYTbHKzMzE7Ows4uPjkZSUhPvvv58KYbYAiNPpxNTUFD772c/iwoUL1LxsNpvR2NiI9PR0jI+Po7u7G+Pj4/Tazc3NEAQBaWlpVAi3tbXB6/VidHQUHo8n7KAtKXKCkRXOxAcsLXISrkCNlMCUaziy1jVhAhfEHM4qE65pj2hoJAqWaI2A2IzNBmXt3r0bnZ2duP/++7Fz506qBZF6yampqfjggw9gMBhw/fp1mM1mVFdXiwQEWRz1ej2t3CRN/SEmVwBISUlBTk4Opqam4PP5kJWVJSrmUVdXh9HRUdG5i4qKqB94eHgYw8PDaG9vp9G+xOfd0NAAr9cLh8NBi09INTtSQevmzZvYsGEDioqKcOrUKdy8eROJiYnweDwoLy+Hy+VCd3c3HA4HDh06RIteEEhqVldXFxISEvCVr3wFDocDo6Oj6O7uxvz8PDIzM/HVr34VZWVlNFXs1KlTOH/+PAwGA6qrq2kqFKnoRXodWyzyPZGD5XlLkROMcj5gqf83XIEaKYG5nkzRUrgg5nBWmWAdgliTLYlkJlqV3W6H2Wym5lOyiPb39+OVV15Bf38/dDodNeX+7Gc/8wsAy8rKQkFBAbZu3YpNmzYBWAwuMhgM0Ol00Ov1ABabMtTV1SEtLQ1TU1MwmUyw2+1UW2brO587dw6JiYmorKzEgQMHRHWfGxsbYbfbMTY2hlu3biEzMxNf/OIXFf2T0nQkYg0wm82orKyE3W5HT08PampqqLYnFTD9/f04ceIEhoaG0NLSgo0bNyI1NRWCIODEiRO4efMm7r33XrzxxhuYmprCs88+i61bt4p6JLe2tqKqqgrf/va30d3dDWBxk5GRkUGjuB977DGUlpaivb2d5ksfPnyY+rnJXJrNZmzfvp2WmSQ+20CmdfK7YAJLTjCyc0ncAy6XC7t37xblYq+mAFxPpmgpXBBzOKuEtLCE3AIkLVJBagxLYRfRoaEhnD59GpcuXcKFCxfwve99z69sI1vHOi8vj6azvPXWWzS1xmw2w+PxUN+s1WrFb37zGxqk9MADDyAnJ4ealklFLa/Xi4KCAhQWFopMu0SDJiUe29ra0NPTg127dsmWUlRKRyICmZRu7OjoQHV1NXp6euj5iUmbmF5zc3ORlJSEuLg4XLt2DYmJidi7dy+6u7tx5coV1NfX0+peCQkJcLvdqK6upqZrl8tFU7+2bt2KK1euYMeOHXSsY2Nj+NrXvoa///u/R319vahz05EjR6gbgVgsRkdHkZaWJnrGQPB630rHBIPMJXFtkGdrMBjQ2dm5arnAnEW4IOZwVgm2FrDUnMoK6ZKSEtr+j5gvbTYb3G63KCeYnNPlcmFsbIw2GpCm9ZDjiBAmplHSpYiwdetWpKWlISsrCz/4wQ/Q19eH9PR0TE9PAwBSU1Opydjj8WBmZgZerxcmkwmFhYWiylvs9fV6PY4cOYLy8nJR1a2ampqArfmUOk05HA7ExcUhPz+fzqPT6cSzzz4rKrNJgrVICtK+fftw6NAh/OAHP8C1a9doahYAxMfHw+v1QqvV4mc/+xn1T/f29uLmzZv46le/CrPZjJaWFtphCgDVNtnOTeT67E+2tCnbqlFNNbWlaq6kk1VJSYmouEc4RDJimpumORzOiiNX/QoQmx9LSkpgt9vR1dWFQ4cOwWg0Ut8oABQWFsJut4t8wyUlJfjmN7+J3t5eVFdXAxDnqXZ0dKC5uRnFxcXUjAosNgIggUenTp3C6dOn4fV60d3djQ8//BCzs7NIT0/HZz7zGezZswcbN25ERkYGPB4Pfv7zn2NhYQFf+tKXaA1lMu7m5mYaFU2aXLB+UaPRiJqaGjQ1NQEI3ppP6gdm55F81t7ejosXL0Kr1VIBd+TIETz++ONwuVwic/59992Hc+fOwe12Y25uDvPz89i4cSOefPJJ/OxnP6MCtbq6WtRIYmRkBHa7HVu3boXJZEJvby/eeecdGozGugCkY66trYXNZoPL5UJ7e7uo4thyIq2cVVRURC0V4eSry5VSDVcoc9M0h8NZcQIVy2d9vSTN5+233wawmM5TWVlJ/1+q0ZAF0W6300AlVsjX1dXh9ddfx3/8j/8RDoeDCnVCe3s7ent78dFHH2Hz5s3w+XxISkrCXXfdhW3btuH69et47733EBcXhwMHDgBYbNowNzcHk8lEx6TVajE5OYmuri66QBNN0Gq10opZFosFBoMBDz30kGJrPilKBSjIvFZVVaG6upoGchFhk5aWhoSEBKSnp9Pz9PT0wOPx0FxnjUaDiYkJmmZ169Yt3H///TRS3OPxwGq1oq+vD11dXUhMTERhYSFGR0dp0JpSMRG22xYb9a7kmog0bH1zs9mMo0ePwm6302A5YoIPVCpTrvOS2WxW3bIz0NjUFLdZi8U9uCDmcFYJ6eIiZ348evQoNm3ahE2bNqGnpwcXL14EANEiKa0bzZq0XS4XTCaTKP1n+/bt2LRpE7Zv3y5qBQiA+ja/9rWv4dFHH8UDDzyA06dPIzs7G9u2bUNxcTGsVitt6kAWYqL96nQ6OBwODA8Po6SkBE899RQdFysESAMK4l+22+0oKSmhQjNYdHAw7Yk1VxMh0dPTg9nZWfh8Prz33nuorq5GdXU18vPzMTExgbi4OCQkJGB+fh4LCwuorq7G5OQkbt68id/85je4evUqHA4HLVSi1WqpL5zdECn1lpaW1pRq8qESrnAi7xYbdb53716YzWbU1dWJiphIUeq8VF9fv6wtO9e62ZoLYg5nlVCzuJCi/FlZWfjhD3+IsbExAPKLMBuARTTVY8eOITk5GR6PB93d3fjnf/5nlJaWoq+vDxMTEzR1aP/+/bBYLGhra0NnZyc2bdqEn/70p6ivr4fH48HU1BR+9atfwe12i2oRk2tL84KVmt0TLYqMk3RjMhgMcLvdosIkgeZHSXtifesHDhyAxWLBCy+8gFOnTiE5ORlerxezs7OYm5vDhQsX4HA4UFZWhvb2dlqkIz09Hbdu3YLL5cLVq1cxPj6OhIQEbN26ldbSFgQBGRkZIv872RDJPRtSvKOsrIzOtVIRDTnkzrlU4UT85gCov350dBR79+6lZnu577A/5X4fastOtWON1LmiES6IOZxVQs3iwmocKSkptMiEdBFmG7rn5OTQFoOTk5O0McP169dhtVqp9nb27FkkJydDr9cjKysLra2tKCgooMU52LH98pe/xNjYGC5duiQSIKzPNDMzEy+88AJee+011NXVKQoWNlCMnGd4eNgvelzN/EgFFBsAR4RTb28vJiYmACxqsRqNBklJSdi1axfNk7569SqSkpKQmpqK2dlZXLt2Denp6VQ73LBhA3p6enD16lVRXrVc+0Gr1YqGhgY4nU786Ec/AgB4PB6qSUuLrKgRpnLHLVU4SetFq9HQg7VjVHNsuGNdi5owgQtiDmeVULO4SDU8YsolpmfSE9bj8eDcuXPUVEoaGxw8eBCCIGBiYgKjo6OoqqoS9QLu7e3F6OgoGhsbaaOHyspKqhGRMfb39+P9999HQUEBgNuCgZiiXS4XysvLUV9fj+7ublitVlEQUKBm9qRutJqmDVKU6mmzVaNIwFpaWhr6+vqQkJCAnJwcVFdXw+FwYHJyEikpKUhLS4PH46GpXDdu3EBKSgrS09ORnp6OS5cuYWZmBmlpaTSd6lvf+hbOnz+PoqIiWK1WGI1GXLp0iW5ayDPs7e2FVqsVdaWSmwsl5I6LtHBa68IumuGCmMOJIoJpeKQaUkVFBQDg5z//OQBgx44dmJycxNatWwEA2dnZ1ORMimmwmhzRhFhBTwpuABD5MFtbW/HII4/QspWAOJiMTXnat28f9Ho9qqqqFLW9kZERtLe3Uz+x0+lEaWlpWP5OdjwdHR2wWq00IpsU/wAWo8vb2trQ0dEBQRAwOjqKU6dO4ZFHHoFWq8WWLVtoqUlCYmIisrKyMD8/DwBITk7GPffcA4PBQOft/PnztE8xqd1cUFCA9957T7RpGR0dpf8mxTNCYTmFZCQDoaI5qCqax8YFMYcTRShpeNKfJLBmYWEB8fHxtMLT1atXcenSJaSnpyM7O5se9/rrr2Nqagovv/wyvRbbLF6v11PfLymYQZCaegFxTm9ubq4oavbAgQM0LYYdM7BYneub3/wmrl+/jr6+PsTFxdFWgGrSYALVI2ZToGpra+FyuUTCV6fTQRAELCwsYGZmBi+++CJu3ryJ4eFhGpRFiIuLQ1JSEq3+lZSURBtBvP766zh58iT++3//77j//vupRkzu8/Dhw7KbFtKcg+0S5XK50NTURCOpV1pAsHEFQGSCqtjI8EjcT6QEaDQHfHFBzOFEEWxxB1bDk3bD+f73v48zZ87g3nvvhdlshtPpRFlZGR555BFRiUmHw4HU1FQsLCzggw8+wOOPP041Y5vNhueffx5arZZWVmILZpB0ncLCQmoCVzIfS6NmWSHPYrVace3aNWRmZuLTn/40pqenqbmcFVBKC2agz0jqE1s28g9/+AMmJiaQkJAAl8uFDRs2IDk5GfPz87hx4wbeffdd3Lp1C263GwsLC/RcGzZsgMlkwpUrV+Dz+aDVapGYmIjZ2VlcvnwZN2/eRENDA1566aWgHYvkCpG0trbihRdegM/nw9atW/26Ia0UxF+fnZ0tKnUpLa2qVgiyKWqRup9ICdBoDvjigpjDiSJI6o/D4UB7e7tskYuOjg788pe/xPj4OACgqamJlkwkx5Fa0BaLhXb+6e3thdPpFB1HUnCknXiIcG1paYHBYIDdbgcAxVKI0kAftugI+c7Q0BD0ej2+8IUvYGZmBr/97W+RlpYGq9WK2tpavyAk6Zik15FSVFRE74tsDLZu3YqEhATcdddd+OSTTzA0NASfz4fExEQYDAbcddddmJiYwPT0NMbHxxEfHw9gMZe4t7cX8/PzyMnJwcTEBMbGxjAyMgIAuOOOO1BZWalYDY0USSkvL8fIyAjdUJF7NJvNuHr1KkZHR3Hr1i385//8n1dFQJBrOp1O+ryOHDniV1oVUCcE2RS1SN1PpARoNPvAuSDmcKIIdtEheb9lZWUigWS1WuF2u3HnnXciOTkZV69ehUajobnADocDg4ODGBkZwSOPPILq6mpMT0/D7XYjIyMDVVVVGBoagtvtRkFBAQ1aUorKJbnG7O+kSBc5NjWG9TV3dnbCYDDg3LlzAIDNmzfL9saV6w4kdx3AvzGGxWIRjd3hcKC/vx8///nPkZKSgqysLLhcLhopPjU1hcTERGi1Wni9XiQmJiIlJQWbN2/G5s2b8dFHH2FsbAyCIEAQBDoOjUYjakJBzMw+n4+Wy7Tb7RgYGEBfXx+A2xsqh8MBn88Hn89H63mvht+SzVf3er2w2+0YGhrym79QhCAPIgsdLog5nCiCXXSMRiP0er2fD6+qqgrXr18HsCik09LSkJaWht7eXnR0dMBisYiilxsaGvDYY4/h5MmTeOyxx1BUVIT6+nq89tprACBaaOU0Y0C+7OTQ0BBOtb4Fc/GXcd/demgTNKL7kFZmItfIysrC9evXIQgCDh8+jLGxMdmFXq0mpKS9sSlWzc3NSE1NRX5+PmZnZ+kmITMzE8nJybh58yZmZmawsLCAW7duwefzQa/X48aNG7h69Sri4uKwc+dObNiwAVevXkV6ejqysrJQUVFBfeolJSXIy8tDT08PCgoKMDU1hTNnziApKQk7duwQVQ2zWCx49dVXMTk5iYyMDLrRkZvjcPOMg8F+hy19STZFgZ49J7LErfYAOJz1AMk3Jc0Z1CLNuQUWF8YtW7bg7NmzsFqtKCwsxNTUFN58802aQlNXV4eSkhLo9Xqq4UhzdCsrK1FZWSkSukQzbm1tlR03+ffAlSEcOHcNf7PpK3i4ewPu+O1N/M0fZjDvExTvhVxjbGwMAwMDuHz5MsbGxmiFJqXjlSpVkXFlZWVhenoa+fn5ogpiREBbrVaMjo5i37592LlzJy5evIjx8XHMzs7iG9/4BsxmM3w+n8hHPDc3h8uXL+PKlSsAFk3V+/fvx/PPPw9gUdN9+umn8bOf/QwZGRkoKSnBlStXcO3aNRQVFaGwsBA7d+7Epk2bsGfPHrz00ksigWY0GlFQUID5+XnaOENuvsk9kOcR6D1RcxyLzWajhViISZkUQeGsLFwj5nBWgHADTsxmM+2QxAokUg1qdHSUVmxKS0ujWldRURH279+PkydPIi8vz68eMwBRByOiBROTMgnckY6bVIg6Hv8ZXMwwwydocAsAfED94BwA4KefS1G8H1LqsaysDDqdjgZ2harNseNqb2/H+++/D2CxIxRpnkDuhXSuKi8vh81mw/z8PLxeL3p6ejA8PAy32435+XloNBrEx8cjJycHo6Oj1AefmJgInU6Hixcv4vTp07T4h9PpxPz8PP7u7/4O/+2//Tf86le/gsfjwaVLl+D1elFeXo6//du/FeV+k0piDocDgiAgJSVF8b7kouaViIQfdT2YgKMVLog5nBUg3IWSDd5iC2T09vYiLi4O9957L+69914AoI3o1VyTCFTi32QXf7Z3LhvFTfAlpcCefg/mBY3onNM+4PjgHH6cnywyU7O0traipaVFlA4l5wsOBhGyFy9exJUrV2jA2bZt20T363A4aF3skZEReDwe7Nq1i2q8pNkB8Q97vV5cvnyZasdarRaf/vSnaTGQ2dlZbN26FZ///OfR2NgIALh58ybsdjv+/M//HG1tbejr68Pc3Bza2trwve99j8412wOY1OL+3ve+BwC0gIr0makVjuEIUVI+lWvAqw8XxBzOChBsoVTSCtmFmaS81NfX4/Of/zwSExOxb98+ZGZmoq6uDoA4qlnt4sxeg2isJSUlovKTRMssLy/HXNY2/I+EeMz7/M8VHweMzPpg0sYHvVag3wXDaDQiMzMTzz33HFwuFwwGA7xeLwoKCkT1nc+dO4eUlBScOXMG3d3duHHjBqamppCdnY24uDiYTCbcc889aGtrw+TkpMg8DQCf+9znUFZWhsHBQQwMDGBmZgb9/f344IMPqPC+++67afGU9PR0/OM//iPi4+MxMjKCZ599Fv/2b/+GoaEhFBYW0gIrSilBkdRKg1kauAYcPXBBzOGsAmyerVztaHIMu5BmZWVhYGAAU1NTuHXrFiorK1FcXEzLWaakpPgF/XR0dKCurg4mkwmHDx+mCzKrDZEFuaOjA9/+9rcxNjaG//Jf/gsAiIQysLh4f+eJO/H3v70JORZ8gD5ZOfQkEjWJ2Sjp3bt345133sHCwoLIj06YnZ3F6OgovF4vPB4PjEYjpqamcPnyZczNzWH79u0YGxujwW8sGs2iVt/d3Q2bzQadToc777wTExMTmJycxJ/8yZ8gLy+P+vBJAJher0dKSgpmZmZw/vx5OJ1OLCws4Pe//z0MBgMtB7rcUdKRyr8NN6eYox4uiDmcVaC1tVWUZyunFUoXUlIJKjk5GTdu3KB9cc+dO0ejb6XN5a1WK15//XWcPXsWubm5AbVlq9UKh8OBpKQken1iRmYXXm2CBk/mJKF+cA7TjFacGgcczElSNEuHA7sZIWMiFaoqKirwzDPPoK6uDtu3b0dVVRVGRkZQU1ODqqoqlJeXw2q1YnJyEnFxcUhPT8cjjzwCYLEr1c2bN/Hee+/BaDQiISFB1AIxLi6OCs/33nsPt27dgsfjQUJCAgwGA2ZmZvDggw/Cbrejra0NQ0NDWFhYQFlZGfULt7e3Y3BwEOnp6bh+/TomJiZQX1+PjIwMxXzsSBKp/Ntwc4qjhWgubUnggpjDWQWkebZyglG6kJI+wpmZmVhYWKANGwoKCmAymWjwEwBalSs/Px/5+fnYs2dP0AW5qqqKFgIhPkuXyyVbVesnO5IBLPqE4+MWNeGDOUn096EufkrHs5sRADRNqKKigtbHJmZho9GIb33rW2hpacH169fxs5/9DA888AAmJydx5513Yv/+/Th8+DBaW1shCAI0Gg3S0tLwX//rf8XZs2cBAP/6r/8KYDHF6sqVK5ibm4NGo8GGDRsAADMzM7TutiAI+N3vfocNGzZg48aN+Oijj9DV1UUtD6dOncLLL7+M3bt34+c//zna29vh8XhEz2k5CdYqMpRqWUB4OcXRQDSXtiRwQczhrAJyebZyx7ALB9H4SPARaW4gt6BarVY0NTWhr68PKSkptA1gIIqKikS1qAn19fWiMpgAkBCnwRMzf4D71/X4s8rD+ErRTpEmHOrip3S8nFZHBAIppZiXl0fzn0mpyo8++gg2mw2/+93vMD09LUoHIu0WT506BZ1OB41Gg9raWjz22GOYn59HVlYW9Ho9bt68CY1Gg7m5OWzatAkjIyNITExEYWEhtm7dikceeQQ3btzApz71KUxMTGBubg6dnZ2w2WwoLy/HqVOncP36dbS1tWHfvn1Bn/dKEeqzYUt0sg0+QmW1NNNoLm1J4IKYw4kRjEYjqqqqUFdXB71eD71er1hsgaQxlZWV0Q5HSrV/5cy/7GJJymBKFzKr1YrfNjUhcWEO92+rxcvM90Jd/JSOZ3sfWywWWnHrxIkTyM7ORmlpKU1LamhoQHZ2NjZu3IipqSnY7XaYTCZcu3YN4+PjePvtt9HY2IjKykrk5OQgLi4OPp8PTqcTLpcLcXFxuPPOO3Hr1i309fUhPT0dPp+ParwajQZTU1N46qmnkJOTA4/Hg/j4eGRnZ9P2iqmpqfB4PKiursbg4CBtHhFNqHk2gVwCwO0e2KEI1tXSTGMhKI0LYg4nymEXPIfDQf/LyMgAAGruZE3Her0excXF2LlzJ3bu3AmbzSYq6s/Cdswxm82ixVYa1MXCNlmQLrKhLn6BjpfLrSXaMPnc7XYDWNSWN2/ejOnpaQwODmJ8fBzj4+OIi4uj+dFvv/02du7cidnZWUxNTeFf//VfYTKZkJOTg+9///s4duwY2traMD8/D7PZjImJCdx5552YmZmh9aoBwOdbdJBv2rQJJpMJV69exe7du6HT6dDV1YX5+XkkJyfjT/7kT6ipPxr8laH2eQbELgFWOIciWGNBM10tuCDmcKIENX5S1rcMAA0NDbSGNGs6li6SbG6wnC+aCDZitiWLJds7WDo2tsmCXMvDSCFNr2ptbaVm+YsXL+L555/H1772NVRXV8PlcuGDDz5ASUkJ7HY79fkKgoDNmzdjeHgY77//Ph588EE89dRTuHLlCt59913apWpsbAwFBQV45513AAAfffQRrl69CofDAY1Gg/n5ecTFxSElJQXx8fGIi4tDcnIy7r//frz22msoLCxEcXEx7HY7bQSxb98+AIsmfqlWGa0opZkppdapgTVxS5t5sETDZmWl4YKYw4kS1PhJWd8yadxw6dIl3HvvvbK5ucR3KhWw5PtkwWM75rCLH+trTk1N9RsbYTnNf+y5SfEPUhDk8ccfx/j4OAYGBpCTk4MrV65Ap9MhIyMDAwMD2LBhA27duoUdO3bg2rVrSEpKwubNm1FcXExrbnd1dWH37t0oLCykdak3bdqE+Ph4eDweAMDCwgJNZ0pMTMTHH38MYLGS18DAAK5du4aysjKaiubxePCVr3yF3sMLL7yAxsZGlJWV+T2H5WCpwkz6PCP5zINp0sE+X4uCmgtiDidKCOQnVVoI8/Ly0NnZiby8PL8+wRaLhTaMOHDgQNDevnLXKCsrQ19fHyorK6HRaGQFyEoujNI5qq6uRlpaGvR6PY4dO4Zr167hjjvuwMjICHp7e5GVlYWCggIcOnQIvb29sNvtGB0dpWleZrMZ2dnZyM7OhtvtRl1dHQYHB5GTk0NLX8bFxeHmzZvYsGEDLfgxNTWFjRs3wmg0YnJyEp988gl27NgB4HbudXFxMaxWK3p6euDz+eD1eqHT6WA2m2mK1XI1VIjmSOFgmnSwz6P53sKFC2IOJ0oIR8MItGjJNYwgkApahYWFfr5jVrCOjY0hNTUVGo1GlQ+XDawKVSgHKxwhDSA6evQoAKC2thYjIyNoaWnBzMwMjEYjZmdnsXHjRsTFxWFgYABjY2PUT8vOl8PhQFdXF65du4YNGzZg06ZN2Lt3Lx544AEcOXIEN2/ehNFoRGpqKm7evIlbt24BABISEnDPPffAYDBg9+7d+PWvf42ysjJR7jXpZpSYmEirfgFAXV0d3nzzTQDL19komv2xwd7zYJ9H872FCxfEHE4ME2jRkpq0CUNDQ1RTzs7OpikpxOQtFayAuD0iOUYuQnop2kqgwhHsmAlsQRSXy4WxsTFkZ2cDADo7O5GamgqdTocvfelLdGzSAiUWiwXNzc24cOECPvvZz+LBBx9EcXExvvGNb1Cz9JXrLqTemYtbN6eor/k//If/gKGhIVy/fh0DAwMYHx9Hb2+vqGb0yMgI8vLyUFZWRrXxpqYmFBcXixp0LAexECkcLmvx3rgg5nBihFBNwEoLFqsp6/V6v9xQqQAnKUPSKFrAP0I6mLYS6B7kCkeQEp03btzAwMAAdu3aRYWc2WyGyWSCxWKBzWbDrVu3MD4+jq1btyIjIwOJiYlwu924ePGi3/nZYKG6ujqRpv2tb30L/f39QFw8cPB/QLD8Z0z5Fhb//W+/gPfV57B582bk5+fj/PnzuHr1Kubn5+mcE6HvdDpht9thNpuRmZmJ0dFR2U5a0cZyuhqW240hPX+s+JO5IOZwYoRI+cakwlJqulaq8kXSf4qLixUDjoJpK4Hugf0uMdnW1NTg9ddfR3x8PLRaLQoLC2E0GmGz2dDZ2YmBgQE88sgjKC8vh9vtRl9fH5544gk89thjyMrKwrPPPovJyUnU1NSgtrYWBw8exPe//300NDTg4sWL2LlzJ81PBhaDwTo7OxcH9OT/AB76T8AGplXhQ/8J0GXgwq9+jM2bN8Pr9WLr1q14+OGHqTZM0sFmZmbg9XpFc01aIkazYFhOH+xy+3el548VfzIXxBxOjBAJ35ichqC2ulKgFCi1qL0HMs4HHngAv//977Fx40Z8/vOfBwA0NTXBZrPh6tWrGB4eRl1dHaqrq9HS0oIbN27QFKK6ujrs3LkTV69eRU9PDxXGfX19GB8fx5kzZ/DWW2/RJgw2mw1utxvl5eW41OvEO5b/DCRJeisnp2Ki6FHMNzyDGzduICMjA1u3bkVubi6dT7PZjJmZGYyOjtIWlQBElgXSfjLaBLK0+1akWW7/rvT8seJP5oKYw4kR1PrGApnj2D7EoZZclFvUwjWXS3NJpedh/cULCwvweDy4evUq3nrrLaSmpuKjjz5CZmYm1Uirq6vxhz/8AXFxcWhra0NbWxtaWlqQkZGBv/7rv6alQVtbW2mk9caNG/Hmm2/C4/GgpqYGZ86cwdTUFHbv3o1UQy4gaYlI8M17cSstA5qbbvh8PuTn59M5GRoagtVqxY0bNzA3Nwen04nOzk54PB78/d//PT3O5XJFpaam1OgjUiy3f1d6/ljxJ3NBzOGsMZbLHCe3qIV7Len3bDYbjh07hubmZlqYo6SkBBkZGeju7saePXuQnp4Oh8OBPXv2YMeOHSgrK8PY2BhcLhfOnj2LlJQUzM7Oor29HSkpKUhJScHWrVsBAHFxcVRgGo1GFBUVYWhoCAUFBejv78e5c+ewceNGeDwetLa2IiUjC9j/ffnBx8Vj/vowkuPjcf/996O2tpYKLeIb3rVrFwDgrbfewsTEBHp7e0WnKC4uFnXdihZiRYNca3BBzOGsMVh/rrSkJVuyUo5wO/OEunDLfW9ychIXLlxAdXU14uLicODAAbS3t6O/vx933303tm3bhrKyMlrOEwA1JxsMBgwNDUGj0WBychIzMzPQ6/WYnZ2FIAg4cOCA4j0JggAAuPPOOwEspjTdGnch/q3/g4V9FUBy2u2DZ6ex1fFv8KYkQae7A9PT07h48SKdM+l9vfDCC+jt7UV1dTUdb0NDAyorK0UWiWgJKooVDXKtwQUxh7PGCOTPlTZRkKs7HU5nHrWwAodEOwOLGiIA2O12WlDD5XKhrKwMAGAwGNDS0gKDwQC73U5Tlp5//nnalCIzMxPJyckYGxtDVlYWtmzZgg8//BATExMBKziVlJRg7969cDqd+NM//VMYjUa89dZbWDj2NDA/D1j+E+IgwIc4xLe9BP2Fk5g3GOB0OjEyMgKPxyOqOsY2RDh8+DCARQFM2iACi/XBWdN8rAQVRTvRsqEJFS6IOZw1SLBCH0qL/nKaJoeGhlBdXY0LFy6gubkZhYWFolzg8vJyKqhMJhNaWlqQmZmJhoYGdHR0oKurC1euXKG9lW02GxXC1dXVaG9vx+DgIK5evYrt27fjhRdewPT0NLq7u0U50KzwJ9HfIyMjqKurw/DwMK5evYpNmzZhZnQU+F//P+CXP0TGp83wDHyAhclx/CEhARs2bMDs7CxycnJQWVmJs2fPwmw2y84xcDvnuaysDIWFhQAgCtqSK0G63glHqMbqhoYLYg5nDaK20Eco3wsXsqA6nU6cOXMGMzMz6OrqQmFhISorK+lYWltb0djYSFN+SkpKaM6vy+XCe++9h/HxcWzbtg1Go9GvM5TD4aCBRhaLBZcuXaKFPeSEIvkuafHX1dWFoaEh+Hw+JCQk0NrSKRofUtzDMOTm4OrVq3C73ZidncXCwgImJiZw+vRpUdlMYDFy2mAwwGw2Q6/Xixp1kNziiooKGrRFamdzbhOOUI1VHzcXxBzOOkNJ2C6XWY8sqDqdDps2bcLu3buxb98+UdvGjo4OvPLKK8gy3oVbqRl4r+ffUVhYCIfDgZMnT6KwsBA7d+6ERqOhFanIfXR0dKCmpoY2VCC5us888wwcDoeoQMjIyAjuvvtuALdzek+cOIH09HTEx8djw4YN0Gq1mJ2dxczMDJKTk/H/b+/+g6I8z0aPf28QIrIqyKoEXrRq2E5c29BoMDOGY9MEYjJJXkyaQzrHmWPsVOmcd87MG2x7+rZxzpDMvD0ptH/0PeeQdEQnY8+ETBsMSRtla4oxnigYg29ccgRZFQIVxQUVVn7uff7A58mzy4KowMOP6zPjCPvj2XsfdvZ67l/XlZiYyOOPP24mEPnZz37GmTNnAOjo6MDj8XDfffeF9Ii9Xi8tLS1mcLYW6rBePFh/F6HuJKhO1zluCcRCCGBihvWMABsbG8uWLVvYtGmTGRit/tcbb1K5/DEGHv1PzFEwCLx9xcsL/s/JyckBoL29nWAwyPvvvw9gBtfi4mL+/Oc/093dTXFxMbt27aKuro7ly5eTmZlJSkqK2VP1eDycO3fOfL7b7SYYDHLq1CmuXr1KXFwcW7du5U9/+hNXrlwxaxC/9dZbpKWlsWbNGr766itzgVd0dDTXr1/nq6++oqSkJOS4kYaap+v2mrt1Jxd5s+XcgARiIcRNEzGsV1JSQlVVFQsXLqS9vX1YukxjaPjKPxYQ7E+CmLkM3HzuqcXfxufz8S+LO8nLy2P//v2cOHGCL7/8kn379rF06VL8fj+BQIC4uDgCgQBlZWV8/vnnNDc3U19fT21trblgzUhW8dhjj+Hz+fD5fPj9frq6uggEAsTGxhIXF0dmZiYfffSR+R6CwSAAFy5coLOzk66uLgCcTidOp5Nz587R19dHXV0dxcXFeL1eNm/ezMqVK81jTNdFRONlus7dTpa7CsRKqV8DzwB9QCPwkta6cxzaJYSYZBPRA8nPz6e7u9vMCQ2hAd/j8bDvT/v55Ee5BGOiQ547EB1Dx7qnafv3oZ7mgw8+yPnz5+no6KCtrY3FixdTXV1NS0sLCQkJXL58GQCHw0EgECAmJoaMjIyQQhUVFRUEg0EuXbrEuXPn+N73vkdrays9PT3MnTuXtrY29u3bR1xcWEYthnq/g4ODzJkzh/j4eLTWPPzwwyQlJdHV1cXy5csJBAL09/ebRR6MuWGj/CLMzkA0XeduJ8vd9og9wM+11gNKqf8B/Bz42d03SwgxE6xfv5533nkn5LbwIhEXoxxUR0fRHxz+fBUMcqC6lnv0Gxw6dIh58+aRkZHBjRs3uHz5MvX19TgcDmJjY1m9ejVZWVk0NTVx7do1HnzwQbNGsNEbTk5O5sKFC2RkZJCZmUl1dTW9vb0sWLCAwcFB4uLiSE9PN0s/GkPQCxYs4N5776WxsZHBwUF6enpQSnH58mVyc3PN9lZUVLBhwwby8/Pxer34/X727NlDf38/GzZsmLWBaDYNM9+JuwrEWutKy6/HgO/fXXOEEDNVeD1h4+f/uuUFXvvwWsTn6Kgo6o7+jSUD180V1llZWWzfvp26ujqSk5MpLCykvb3d7GEfOnSI/v5+zp49i8/nY9GiRQBUVlaSk5PDpk2bzCHiiooKWltbuXjxIj6fj2XLlpGfn28mFRm8mebyW9/6FikpKZw9exatNb29vaSmprJy5UozQUf4Km4je5fBujhNCKvxnCPeBpSN4/GEENNYeOANrydsnTP84fJYdp/v5YZW5v2xegD18TsM9HTT0tJCR0cHiYmJvP/++zQ2NhIMBgkEApSXl5tpJrOzs/H5fBw8eJCuri6zupIh/CKgvb2defPmsXTpUlpaWsjMzKSsrIylS5fidrs5e/Ysvb293Hffffz4xz/m3LlzfPHFFwwMDPDAAw+wbNky89gj5dG+3ZzeYva5ZSBWSv0VSI5w1y+01u/dfMwvgAHgD6McZzuwHQj58Aohpjcj4Fq3CRnZovbu3cuRI0dwu91mDWRrYDT2Cb/8eDZ1gUtUzUkjJjqKqDkx/MdFg6QsvMwHq1fT2trKm2++SUxMDPfffz9z584lLS2NVatW0djYiMfjMWsjr1y5kri4ONasWROSB9pa6tC6WMzv91NVVUVaWhqBQIBf/epX9PX18dxzz3H16lXa2tpob28330dHRwcwVD7S5XKRmZmJy+WiqKjIfF+VlZVTtsKSmHpuGYi11o+Pdr9S6j8DTwOPaWNCJfJx3gTeBFi3bt2IjxNCTC/WSkktLS3AUNDLzs7myJEjNDY24na7zXzP8HV2qyNHjlBZOTTDtTc7m/c9H+LOepylMTf4vx95cGY+hOfgAWJjY4mKiiIYDHLmzBkWLFjA888/z7Vr1/j000/RWpu9UCO4ArS2tg67SLAuHDLSgV67do0NGzaQkpJCVVUVPT09nDx5koSEBObMmWMOQbe0tBAIBIiOjubAgQNcv36dlpYWysvLqa6uBuCll14KSdZhnA8hRnK3q6Y3MbQ4a6PWOjA+TRJCTCfWovfGHlojKBYWFoYEQhgK3EZ2KyNoGUHxmezv4fEcpMbvp7KykkAgQENDA06nk6SkJK5cuUJbW5s5lGwEupKSEgKBAD6fj5UrV9LU1ERZWRlvvfUWCQkJZs/5yJEjFBYWhgTG8BW9p06d4tixYyxevJikpCQuXrzI/PnzWbFiBQkJCVy8eJGOjg5cLpe5KMvpdJqrw4254IqKCvbv34/T6Zy0v4WYnu52jvjfgHsAz810cMe01vl33SohxLRhXRG7fv36YUO/ENrzdbvduN1u0tPTycrKCknuYS3E8OKLL+J0OikvLzdLHjqdTl5//XX6+/s5efIk/f39zJkzB601gUCAgwcPEhcXx40bN+js7GRwcJC1a9eSn59PSUlJyDC2tf3GQi+n04nWGofDwfHjx/n+97/P1q1b8fl8nDx5EofDwdq1a/F6vTzwwANmL9vj8dDZ2cnKlSvNYejy8nJzz7OxmEyGqEUkd7tq+r7xaogQYvoJT1RhbBPKyckxA5QRWHNycvD7/WYVolWrVpkpLMOLHxg9aGPRVHl5OZ999hnf/e53zaQZn3/+OWfOnKGvr4/z58+zYsUK2tvbeeihh9iyZQv79u3D5XKZFZCMCwDj4sDa9pKSEvbs2YPT6eTs2bPcuHEDgKqqKvbu3UtRUREOh8OcR87NzSUxMdGcA8/Pzx+WSctIxZmamipD1GJUkllLCHHHjPq6fr+fnTt34vF4zMILgBmU8/LyhvV2rUHLWvwgOzubgoICamtrycjI4MSJE5w/fx6tNQ0NDSxbtoydO3fS3NyMz+ejurqazMxMlixZQnl5OfHx8bS3t1NQUGD2tq11gI1eqTXbU0NDg1nO0OFwmIE4EBiacTO2Jh04cIBDhw6xePFic1jaGPI2XscYml6/fj0pKSmUlZWZFyZCRCKBWAgxLsJ7w+HBL3yRlMHY8mOthlRbW0tXVxfp6emcO3eOlpYWoqOjSUpKMl/L4/Gwa9cuc3i4oKCAvr4+/va3v9HY2EhycrKZVMNgrQNsbY/T6aS6uppLly7R3d0NgFKKp556KqTn7Ha7iY+PJz8/n7S0NDZv3syrr77Khx9+SHNzMzExMSE1oK0XJjIsLUYigVgIccesSSxuFXSsc8nhQ9rh2bb8fr+5b/iVV16hvLyc1atX89577+FyuUKCvNE7drlcuN1uuru7CQaDzJs3j46ODi5cuGDWDYbQ/cvWgBkbG0tvb6+ZW9rhcPDcc8+ZRSSMGsqFhYXA0DaoAwcOUFdXh8PhYOPGjWRmZkbctyy9YTEaCcRCiNtm3TtssAYdI6OUkXHK+pzs7GzeeOMNSktL8fl8vPbaayHHbm1tpbq6mkAgwLVr13j22WfJysriwIEDnD59mtdff52NGzeGPMfj8VBdXU1eXh5er5e6ujq8Xi8Oh4O2tjZOnTrFqlWryM/PH1Z20OjJv/DCC1RVVXHs2DGCwSBRUVG8/PLLOBwOYmJi+Oijjzh8+DAwVMv47bff5pvf/CarV6/m4Ycf5qc//emwCxBJ7SjGQgKxEOK2WWsM19bW8u6774bUGC4tLTVTShrB1zonW1tby5UrV6itrTWPaQTqAwcO8MEHHwBDAe/DDz+kv7+frKwsWltb6e/vJzExkYKCgmE9TuPCoLu7m/r6epYsWcKWLVsoLy+nsbERr9c7rFfu9/upqKhg1apVpKen8+mnnwLQ29tLW1sbAE8++SRNTU1mW43X8/v9nDlzhu985zsy9CzumARiIcRtMwKRz+fjk08+oaamhnPnzpnzo9ZAFb6VyUhDefLkSTIyMsxjGpm4BgcHueeee+jp6eHSpUv09/fz1FNPkZ+fT35+/rBV2sac77Zt2ygqKmLPnj1s3ryZ+Ph46urqKC8vNxdWWXvCxvB2bm4uycnJHD16lKioKLTWREdH8/TTT/P3v/+d/v5+li9fzk9+8hPg65zR4XPbhtle8lDcPgnEQojbZg1ECQkJ5nyuEZAiBSrrMO2OHTtYuXKlOYxtDHMvWLAAj8fD4OAgTqcTt9vNww8/bC6OshZRaG5uNvNXG9ufOjs7AUhISGDHjh3m/daecLjExEQyMzPN7VLt7e2kpKSYOaJLSkpwuVzmXmDADP4wdLFhXS0ttXfF7VKjZKWcMOvWrdMnTpyY9NcVQtgr0r5jI1hu3bqVd999l7/85S9ERUWRlpbGK6+8Ygb08GFkt9vNvn376Orq4sknn+TMmTPk5OSEBP7ReqeRqkEZCUOMghFut5vKykpSU1PNXNnGbTk5OXi9Xo4ePUpMTAwFBQUhbZUesbBSSn2mtV4X6T7pEQshxmQsAaa5udnMIx2p7F+kfcd1dXUEg0F8Ph8rVqxg0aJFOJ1Onn/+ebPHbKxcXr58OcnJyTQ2NpKSkkJXVxeXL1+mra0tJFWmIXyxVPh7sAZOt9tNSUkJ/f39xMTEcPToUVJSUswEIyUlJdTV1dHd3U1OTg4dHR0cPXqU9PR0Hn300WGjAbd77sTsJYFYCDEmtxpyNQKm0UO07qcdidvtJhgMcvbsWZqamvjRj37EM888Y/Y+09LSKCoq4ujRoyxcuJCLFy/y7LPPAkNZr/r6+nC5XBQUFLB+/fqQtkQKfJHeQ/jCs4yMDFwuF++88w4NDQ3msHhKSorZezdqHMfExPDoo4/estShDFeL0UggFkKMya32xHo8HhobG0lPTyc+Pj5ka5PBuu8YhnJQNzU1mTmj6+vrzVXO1ufHxMTwxBNPmPPKHo8Hr9fLtWvXyM3Nxev1kpKSEjFr1mgFHqw/+3w+vF4vmZmZZGVlUVVVxenTp/nBD35AcXEx69evN4tYGM8JX6h1p+dOzG4yRyyEGBfWeVwjsceten+//OUv+f3vf8+aNWuYO3cuDQ0NZGRk0NnZaT4/0nB3c3NzSBpM6+OtbRnrULD1NbKysiguLqampgatNVevXiU3N9esGDWWcyBD0CKczBELISZc+EppaznESEGpubmZ+vp64uPjSUpKwuVycf78eVwuFwkJCfj9fpqbm82awW+//bY53J2WlkZxcbG5wCq8B307iTSam5vZunUrNTU1PP/883i9Xmpqaujt7eWJJ56goaGBRx55JOS93M7QtxC3EmV3A4QQM4sRBI3KSh6Px7zP2Pdr9EBra2txOBxmYo+CggKeeeYZvF4vFRUV5nPdbjepqak4nU6KioooKioChoJde3s7LS0tIeUUrayvGUlZWRlHjx6lq6vLzFPtdrt55JFHSElJYd68eXzyySfmezHmwvfu3Rvy3mBo6Dm8oIUQtyI9YiHEhIg0L2pdNQ1Dc7/Lli2jvr6e+vp6duzYYc41G1uFSktL8fv9tLS0UF5ezuHDh/H7/XR2dvLaa6+Nae7aWmoxUg99yZIlREVF8corr6CUMofXExMTQ8oyGsUsDh8+TFJS0rB58JmW0jLSdjMZeh9/EoiFEBPiVkHJWLjldDp59dVXOXHiBGVlZWRlZZGcnMz8+fMpLi42V0obW4bmz59Pa2srBw8eZMeOHbfcLmRNvGHUDy4sLDQDSfgCMo/HQ1ZW1rCgbV2V3dPTw9WrV/F6vSG3TyQ7gmD4ULsMvU8MGZoWQkwal8tFYmIiLpfLDKDt7e1cvXqVnp4eALxeL16vlz/+8Y/U1tayatUqM1jW1NTw9NNPs27dOqKiooYNDRuMgOHxeMzXycrKIhgMUldXF/I8435rViwjE1ekgJeXl8fLL7/M9u3bh6W2HG0I/G5Z39NkCR9ql6H3iSE9YiHEpCkvL+fLL7+kvLzc3A/sdrvJyMggPT3drNRkDD3DULpKCB3qtuacjiTScLXX66Wrq4vOzk5qa2vNhWC3el6kVduR9g1PdG/Rji1Q4aMNM23ofcrQWk/6v7Vr12ohxOxz7NgxvXXrVn3s2DHztt27d+vs7Gz961//Wu/evVs3NTWZj92wYYPOzMzUu3fvvuvXbmpq0hs2bNBxcXH63nvvHfMxd+/erVevXq3T09P11q1bzfZFOr61/ePten9Qn7k+oK/3Byfk+GJiASf0CDFResRCiIi6BjStPUFS5kbhmKPG5Zjr168fNqcaqVLTtm3bKCkp4fTp06xZs8Z8jDUdpbF46nbmSzdu3EhCQgIZGRnDjjnSsbKzs/H7/VRXV9PY2IjH44nYK5yo3uJAUPPPp3vYfaGPaAWDGn64PJbfrpnLnKjx+bvMJBPxuZ1oEoiFECEm+4s/LS3NXI2ck5OD2+2mqKiI+fPnk5OTQ0FBwbCMWampqbS0tABjHwb2eDzU1NQMSzQy2pCyEaTz8vLIy8sbdTh8ovzz6R5KL/RxI/j1baUX+gD43bfjJrUtU9l0vmCRQCyECGHHF7/H4zGzcXm9Xvbs2UN/fz8bNmwgJSXFfJwRBK1JPMa6mnik+V+/309OTk7EmsLGNiYYCtKjBf2JWNXcNaDZHfa3AAgEYfeFPv519dxp0+ubaNP5gkVWTQshTMYXf2CEL/6ugfFNiWusNHa73eZq3OzsbF566SUyMjLMoWCDdaW1kcRjrKuJraujDcYFgPGzseLZ2kse6yrhiVjV3NoTJHqEOBsdNXS/mPzP7XiTHrEQwjSWL36XI3rcXs8IXuHDxTt37hxWL9hqtOINIzl+/DglJSXk5+eb89QjzU+HHz9STze8BzwRq5pT5kYxOEIMGQwO3S8m/3M73iQQCyFMk/3FHx68ItULjiT8vrHME5eUlFBRUQF8nZwjPD92pJrCpaWlEeeQw+eWJ2KxlmOO4ofLYykN6+3Ni4Jty2NlWPqm6X7BIoFYCGGa7C/+tLQ03G43u3btIj8/38xPDbe3F3cs87P5+fkh/4e3Y6TXG6mnO1n7en+7Zi4wNMQaHTUUWLbdXIQkhkz3CxYJxEKIEJP9xW/tqRYWFgKRg9vx48cpLi7G5XKZhSGMwHurZBrNzc14vd6Q1JbW+0YL4iMF6clKbjEnSvG7b8fxr6vnTrttOZNpOl+wSCAWQoSY7C9+a091tLzRf/jDHzh8+DAJCQm0tLSEbF8y9vpaSydajRaop0v+ZMccNaXnOe02nS9YJBALISKarC/+SEk+rIxA2draitaahQsXkpqaitvtDpnTXbRoEXv37o3Y8x1tGHm0+yL1lqUC0dQ2HS9YpvYMthBiVgvf57t06VLuv/9+Pv7445D6w8bjkpOTh215gq+Thng8Ho4fPz7m4gyRtiTZUXxBzGzSIxZCTFnWRB95eXksW7aMjo4OGhoaQtJNGo/LyckJWf0cfqxIWblGG5oebZuUVCAS40UCsRBiyhhtb64xDLx//35yc3PZtGnTsKA42nCx8Rhrnurw54aLNGctFYjEeJNALISYMsrKytizZw9+vz9iqUFDYmLibZfnsz7GOictgVXYTQKxEGLKCh82zsvLG3HoeaJFqkssxHiQQCyEmDLy8vLMn5ubm4cNG9vZey0rK+M3v/kNDoeDRYsWSS9ajBsJxEKIKcGYHwbMQgxG73eq9D4dDkdILWMhxoMEYiGEbayFGIz0lg899BCpqal0dnaGlCC0m3VYfKpcGIiZQQKxEMI2kdJb+v1+ampqQkojgv2JNGRRl5goEoiFELaJVIghKysrYs9zuqSiFOJ2SSAWQtjGmt7SKDcYXpvYIIk0xEwlgVgIYbvwVJaRyNCwmKkk17QQwjbNzc2UlpZSVlZGZWUlixYtAhiWC9p43FjyQwsx3UggFkLYxjrvayzMkkILYraRoWkhhG0i5YiWQgtitlFa60l/0XXr1ukTJ05M+usKIaYGu7ciCTHZlFKfaa3XRbpPhqaFEJPuVkPNkeaEZZ5YzFTjEoiVUjuVUlop5RyP4wkhZrbs7OyQZB3hRpon3rt3L7t27ZJgLGaUuw7ESqk0IBtouvvmCCFmMqNXC0NJOUarHRweqLOzs1m1ahWNjY2yaEvMKOOxWOu3wE+B98bhWEKIGWys2bEi7RlOS0ujsLDQnFsWYqa4q0CslHoWaNFan1JKjVOThBAz1d2ufpakHmImumUgVkr9FUiOcNcvgH8BcsbyQkqp7cB2gGXLlt1GE4UQM4UEUiGGu+PtS0qpbwGHgMDNm/4BaAUytdYXR3uubF8SQoyVbHUSM8GEbF/SWn+htV6itf6G1vobwFfAg7cKwkIIcTvKysooLi6mrKzM7qYIMSFkH7EQQghho3FLcXmzVyyEEOMqLy/PrE8sxEwkPWIhxJRmLPCarPlhyeAlJpsEYiGEsJBKT2KySfUlIYSwkEpPYrJJIBZCCAvZ6ywmmwxNCyGEEDaSQCyEEELYSAKxEEIIYSMJxEIIIYSNJBALIYQQNpJALIQQQthIArEQQghhIwnEQgghhI0kEAshhBA2kkAshBBC2EgCsRBCCGEjCcRCCCGEjSQQCyGEEDaSQCyEEELYSAKxEEIIYSMJxEIIIYSNJBALIYQQNlJa68l/UaUuAxcm8CWcQPsEHl+MTs6/feTc20fOvX2mw7lfrrVeHOkOWwLxRFNKndBar7O7HbOVnH/7yLm3j5x7+0z3cy9D00IIIYSNJBALIYQQNpqpgfhNuxswy8n5t4+ce/vIubfPtD73M3KOWAghhJguZmqPWAghhJgWZlQgVkq9oJTyKqWCSql1ltu/oZS6oZSqvfmvxM52zkQjnfub9/1cKXVWKXVGKfWEXW2cLZRS/10p1WL5vD9ld5tmOqXUppuf77NKqf9md3tmE6XUeaXUFzc/6yfsbs+dmGN3A8bZaeA54I0I9zVqrTMmtzmzSsRzr5RaDbwIuIEU4K9KKZfWenDymzir/FZrXWR3I2YDpVQ08D+BbOAroEYpVaG1rrO3ZbPKo1rrqb6PeEQzqkestf5Sa33G7nbMRqOc+38E3tZa92qtzwFngczJbZ0QEyoTOKu19mmt+4C3GfrcCzEmMyoQ38IKpdTnSqnDSqksuxszi6QCzZbfv7p5m5hY/6SU+nelVKlSKtHuxsxw8hm3lwYqlVKfKaW2292YOzHthqaVUn8FkiPc9Qut9XsjPO3vwDKt9RWl1Fpgv1LKrbW+NmENnYHu8NyrCLfJUv27NNrfAvjfwKsMnedXgWJg2+S1btaRz7i9NmitW5VSSwCPUur/aa0/trtRt2PaBWKt9eN38JxeoPfmz58ppRoBFzAtJ/btcifnnqHeQZrl938AWsenRbPXWP8WSqnfAx9McHNmO/mM20hr3Xrz/0tKqXKGpgqmVSCeFUPTSqnFNxdUoJRaCaQDPntbNWtUAC8qpe5RSq1g6NxX29ymGU0pda/l180MLaQTE6cGSFdKrVBKxTK0OLHC5jbNCkqpeKXUfONnIIdp+Hmfdj3i0SilNgO/AxYDf1ZK1WqtnwD+A1ColBoABoF8rbXfxqbOOCOde621Vyn1DlAHDAD/RVZMT7jXlVIZDA2Pngd22NqaGU5rPaCU+ifgIBANlGqtvTY3a7ZYCpQrpWAonv0frfUBe5t0+ySzlhBCCGGjWTE0LYQQQkxVEoiFEEIIG0kgFkIIIWwkgVgIIYSwkQRiIYQQwkYSiIUQQggbSSAWQgghbCSBWAghhLDR/wdQxyVQmYQ9oQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4HDZpIb/fD7/fb4zISSm2Ow2nTAFJ0PM6CQ7DfuB00GKxGCKRyFBfXavVGhoezbYASTwgOUjq3+MkRyekcSiXy9jY2MDVq1fxzjvv4Nq1a0Yn0+k0UqmUyYhEIpGhYdyUYSAQMLXQZDKJycnJIZKb2+BMEbJUwr0nGAzi0qVLZpyeZN7ydwCG0HaSwXF7MkIHdgImZ68mv8u1KiG5MO1229RNj43hHPWBI5GIydsD/X+8LM7ycZ5vCOw0vwaDQbOhAcNHN8nJRABMyoO1FNZPeWJKPB7HxMSEYZmmUikkEomhiMzNYJqa4wyZsmadWOv+1BsqD3Dv/+ukL0jgXoIJUz9aa+NQOGtJkogmHTwbbQ4zaLkpvfLKK7h27Rpee+01c/4kN6pyuWwOe2CdGejvFTzyiRGZz+cz6/bSpUuuLafI1CGjTJ7wxNa8ZDKJ559/HpFIxJSvtNYmhc2s3fnz5xGJRFwng72Ce//t27extbVl9Eprbe673W4P6QkAI1fqB9er3+9HOp1GrVZDsVgEsGMrmLE8SFk+VDuKDKslIYUGKhKJGDYZ0I+CnIOL5aZ1v5Si9DiY3uAiJSMtHo+byIKRAlN0JylaYIrcSXSh4aQsZYvA4whp7KgvzoPRAQyN33O2ncgUkvz+uEK2ENy9exerq6vY2NhAo9EY0keZ5aFDwsxRLBYzzgllnslkTK+tU6/dBDqwzvacXq9/nFgsFkM6nUYmkxly7JwESNZ6TzK63S7y+TyazSaAHedMlkqcLShOUH/YTUGCHzMjss/zILFvw0nPKhwOI5lMYn5+HpFIBLFYzAzYDQQCqNVq2N7eRrVaNWPhJNlnVIuEPKeO18sJ+IykmELLZDKIxWI4deoUQqEQ4vG4OZfzqaeeMsMCTsIxPVQCzt/lhlStVg2pgoQgpqmbzeaJP2nBCad+cQwkJ1oBO83UXJRk9AWDQTNEXzp+jyPkpsMU7d27d3Hr1i28+uqrWF5eRrlcBgDTVgHA8B7q9bp5PmVcrVaHnBqPx4N8Po9arYb19XWkUqmju8EDxtTUFObm5szpRIlEwsji8uXLOHfuHCYmJjA5OTlEkgwGgwgGg+h2u/D5fOY4wJMMZn9khwBr3ZwCRDvAth3u/SQHATtlPk6aI0moXq8fGknooSNOUv0nJyeHCvz80H6/H9VqFdVq1Xj7JO3Qe+XvhMxhywjA6cExFx6JRBCNRpFMJhGJRJBOp01dJRqNmoHdJ6HYLunYAIzsJAVeGgE+R/7tcQNTh9RJALt6sLyetXtGTsDjKTtgR+darRZqtRqWlpbw9ttvY3NzE/l83swIlWQN5+QcYIeXwLUth5k0Gg2Uy2WUy2UTgbgRZPVLcL0mk0kkk0lTmmLfsJx6xn3wcRi4wf+9s4TGlK0k8dGhZRofuLeeTMeOZbnDijaBh4w4fT4fksmkIeGQYbu9vY1yuYxoNIrt7W1T66xUKqbWwXQijxwDdpSFC5HXATtC4heNLgDEYjFkMhksLCwgnU5jfn7epGpTqZQ5Bdytww8kuAgrlQqAvswajQaAnabgWCxm56wOwAW4vr6OXC5nPFtn/ZO6x8Wcy+WglEKpVMLExMQjvotHA1keabfb2Nrawp07d/Dbv/3beOutt3D16lUTMXCdOg2m3BQl+YqZJBqMarUKr9eLra0tVKvVke1CbgADCZkqZE2dA0vI/A8Gg4b5Xi6XTZTlJMacVHAvk3pGm0BHDdgJ0BKJhNn7eIgIX4NZN47YY33zsHgtDx1xsgUkkUggFAohGo0iGAyaPrhut2uMmEyLyQUEDB8TQ6Xhc6QXxvflNYw40+k0zp07h0QigXQ6bWoo/DwyLeJmhRyVZpR0d17DRdhoNEzt83EE5VAqlVCr1YYOBwCG++TkYQJA32krFArGMXkc4GQvkp29vr6Oa9eu4cqVK7h79y62t7eNc0aCCzA8tHyUzjnZywQ3OqbU3aqvgUBgyHDKw5Sj0ShSqZQpp6TTaTPMXdbfGWG5eZ96EGQWwzn5x8mEdUac1DfpXDHgkrI7TBnu23DKojaVhcQgGs56vY5Go2FqkbKWBOywGAEM5aKdc2llwVj+naBCzs7OIhaLIRaLmdeWc0ZPiiJKZZF9YtIY0Gmh/N26ET0suHmVSiVUKpWh4RrcpOSox2AwaK7xeDymXv84gcaNJYB8Po8rV67glVdewZUrV8xAbva5yl5ESaZyGkf5uk5uA9+rWCyaOrQbdZZ7IQBjDGkYotEoEomEYcFnMhkzU9s5b/uklJbuBzq10tFyztimDnAQvmQi8+9s95HHWMrBOYeBhzacNIqy5YNFWobPMo3DU9EJ0rTpqXJDkxElFyQFQ4HLkwc4ek6OmuOXjFDdbjyll8WUD/P+VBjKp91uI5VKDXlnjxva7TYqlQpyuRzy+fxQxoOROFOD3MAlKaFQKJjUkRtTh07spgOSO8AZouVyGX/0R3+EO3fu4JVXXsHm5iY2NzeHmIvSSZNMd8kQHaV/fB5TttTfzc1NZLPZe8ZqugV0JjjekYYBGDacwWAQp0+fRrlcxtbW1tCwd5mhO8kIBAJYXFw0dkKSQyXIup6ZmcHMzAw2NjawsrICYHg0K20IdY8j/er1umnROygcWDsKMBw1Mk0qW0G4MJ1ehaRuSyMpF6d8bRnOUyllTxjTtNz8ToLBBIbJUdzEOfhYOhDc/JkGcW5YJ0EWDwLvWQ4gd6ZcnSMbgR3mKPWH5CC3Ox1OHZCQAyC42W9vb5upQKurq3jzzTdNVLRbFCmJHjK7NGoN830ZWdHJlSQuN8pcDmznWbnAzv5Eg+jz+RCLxYaIRLIf+6TXOGUb0vr6ujF2wI7hlPs2OzYikYgZtiMdYI5z5f5GnWMf7UHr0kMfK8aFIT8sPSdOA6FCMGXIlBiwQxjgghzFwgN2aifSaNBIcqA0mbRsxziJ015Yt5TpQ6YX+b/gNXLknhs3oYeF1tqcFsOWANlawto3r2VGRGttmNgsObg1Yudndvb0SkPGYwDL5TLW19dx584dfOUrX8Hm5iZu3ryJer1u0l5y0pc891W2idGZk7IFYKIvRpM0IkopU17JZrPY3t42ze9uA+duh0Ihs0YZhcr9yOfzYWZmxvAxuJ+lUilzoowbI+5xEAqFcOHCBZRKJayvrwPYGYxASP2Ynp7Gs88+i6tXr+JTn/qUcUQ4f1qekEW7ks/nUS6XMT09faCf/aENJ3CvUZIjk3ZjddITIyOP37mgnUrjHI1GD1UecC1PVpDveVKMJjd3Z3O1jNCBHa/XOZbKjRv/w4Jpa/ZjOokIUiaSVSvbA9w661fqC+9dZmroWLEJvVQqYWlpCSsrK9jY2EAulxtizVJesm3C2XIyasqSZMjTsXaub0anUu5u1FeZhZO9xOSCSOIZnXyCKe6TxsnYDSwJNBqNIcKPrGE69WhUlO4s6cn6+ihdOwgciOEE7p2sovXOIcCjPjgXBifZy5QGa5yMSmWPDsETFuTIJXkG5UlJzzrBTR0YPvGcAw8YgfNoNdnL6caNaL+gbHjkHUk+ckOWTdQyrcjJOBzrKJmRboG8R6ZeGVXyBI67d+8il8vhxo0bxogyU5HP500zeSAQGJru4uQX8GfJ9HZ+DmDHWZazankNnV9Gv7I26CY4s2XNZtMMimEkKqNLjpdjipz10cfBcFarVXzhC19AuVw2de5er2dIpjLtz9ITZ5HL07GA4WMsnVnPg65vAgdoOJ2QrE/m/CVhRyoYb8rJtnM2tzrrc3LRyoV8EiE9+kAggFQqZTYbJ3uMUQFHmFF+MnV20hclsHOf9OLlZBtnytL5PKYguZG7Ta8YZS4vL2Nrawvr6+toNBoolUpoNBpoNpvI5XJmWg+NoTRa0gnma8o+TWA0exaASbUybebkKPB/48wWyXqp22QO7PSiU68oB9l5QIefPfByvcpxfScZ1AHyMJRSQz2ssp+ftdC5uTnDm5GT0aTxVEoZx8zv95sI9VgbTobMhGwJkUVvWbtwGk4AJkpi3VMqoiQi8T2cBvMkGwU5eIKbGNMRlAk3TQ7DpyGgV3eS5UNIR0M6cDK9zfSl1CWCGzjH7snXdANIELt69SquXr1q6obNZtMYzlqthmazaYZs08gBO+0QzhKJTKOOSoVRRiRU8TVlbVm2H8g9gXrpTP+6CZSJ1Ctmz3g6D6+j4ZSsZEmSceP97xVSn6gP/L/L8h4N5+TkJBYWFgyPhcEXI0/qIAMJMpfZdXDQOLSIE9gRDjcf2ddEheGNyxSPJA7R+MrFS9AzLpVKKBaLQx7tSTMOkr3Y6XTM6ROtVguxWAzBYNAc6UZ5VCoVc2pNvV43TsxJk839QAdCNk5LBijlyQUqHTtOdOGUJjeBBvHKlSt46623UKlUhqKgZrM5RPSRBwcDO6lGMlwlG146JUyLyb9zQ5TRJs/kVUoZR4TXM0JwMiPdpqdKKXOANyNHRk3VatWkYWUUxWEJ0iGRfewnFZRBOp02mQ/aBjpTnK/t8XgwMTGBqakpJBIJTE1NIZVKGcIfj6uTvcTSyTuMlP+hGk4A92xU0ruQKRw+xrQin0uMunmmN5heOsmQUZJk1rKwLjcZtl9Uq1UAME3q4yrQSYjiqX+ScCFrc84UIoAhogLZom7z/judDmq1GvL5PLLZrBltSRYiNxynPGTdnAQiGaUD9xI2ZJ3YKVOnsZVlF0nokHCrrgE7A2Fo9KQTIYMD9p3LE1BOwnobB0xX12o1o5+yRklHi0aUAxBCoRBisZhx+MilYVbDuZ4PY+0eesQpoz9n8dbJgHI+R9bwGF3SCDvPU5Svc9KUTm4yhUIBW1tbWFtbG4ro2TjNOauVSsU0Fcv68l69WKeyuS2KlwYBwMj6OttTJJFI1trcZiwlaDhl3Uh68c4jv3w+H6LRqMkO8bmskct0Fx1c6hLbwgCYzJJkNcqZycCOPne7XYTDYaTTaTMRhhHoYUUKhw3JnuUwF07wkuPlPB6POQaRrU+jyFUnFUopRKNRvPjii3jzzTdRKpWMEyH7NDlClcPxqcMLCwsmQ5FOp81JXJKoJicIHTQOPeIEhs80lEZA9nTJSFMSC/h8ZwuGjKDIuHocwIiTZB9ZQ6pWq4Y5KcfwOentezGAznq1GyGdKJkGc96b83cpN7duZDRY8XgcmUzGTEcCYOq9zsEljK5ldmiUc0tIAiB1kVkg1qFoNOUIuUgkYl4/lUqZXkYAxnC6VfdkXQ4Y3ruczoB0ZknMCoVCrr33cUDSHo98DAaDpsNCtjrREQGGR43KATeMNClf/g8Ow2ASh/LKcqFRYcgGBXbSF7LG6fP5hhhWNKhSiFIBZcRZrVbN6QInEdJT58bEjc/v95vDfzc2NlCv17G+vj5ERuAmNm7E6KYIczfwHur1+hCFXTpi8jrqVygUGhos4TYEAgEkk0lcvHgRXq/X6EYul0MgEDBjMalPjUbDnFDCdSeNJteojNhlrZP6Fo/H4fF4MDs7ayJI6fUzRUlGJckesubMMZFuBI0m9Ub2vMoWG0msUkqh0Wig2+0iGo26lhg1Lvx+P+bn57G8vIx0Om1kQJBgxvGFzA5J++D1elEqlUxZQepqPB6/hxdzUDgScpCcYykPsQZ2Nixew+dQOIRkkAIYYgDSoJx0ZdNamw2uVquZmbztdtuk3th7J8cPOqcoPU6QtUrnGEdgZwOTG5k0lLK9wE365ff7kUwm8eyzz2J6ehp37txBpVIxc2CBfh9ds9lEJBIxfavUGd4vh7nLo/n4XfZMs7+OxjaVSqHb7aJcLpu1S1nKtC8nfsmpQplMxrWTc6ThBHbGx3m9XtRqNVMzBobPhGUwIQ9Od5O+7QdMV0vCorxnRuGcNSt7YOPxOEqlEoAd2VH2gUAA8Xh8aCrTQeNQDadk2skwWtaQnOlbfpeRgTN8B+49WkwSDtxWj9sLeF/NZtOMkOMmRKeD9SXZAiSHQZzE+u+DIJmksk9OevWS/u6cdCUNp5vg8/kQiUSwuLiIqakpTExMmMlAXFO5XM5Emc1mE4lE4p60Kr141pe8Xq8htZAEw/diM7/H40EymTTZD5LYeD31ktmQaDQ6VP+bnp4eIti4CbIvVSll6nU+nw+1Ws2MbwRG72Fcw48DWOcc5STJzGKtVjOkIMosEokYNra0Ldz7YrEYUqmUYTEfNA7NcDrZtLwhPkZl4Q0DGGKYcbMD7p0KQU+ExqFard4zFeakgbJiWi2fzwPoz3usVCqGfOD3+40HXywWzZDjZrNp+hgfJ+Mp21GUUqhUKsaIAjsHqnPDk5sam9YjkYjrInZuyKlUColEAhMTE2i32/iar/kaw6olG51p7EqlgnK5bGrlzBIFg0GkUqmh+5dRKEsBMgsUCATQbrdx6tQpsz5JgpEGVJ4EwvaERCJhIhE3ghG4rNUq1W9JkYd0Uwc517bX6w0da+cmfRsXToLY7OwsNjY2zGMAhtLcMhNE3QNgugtkO4/X68Xs7CzOnj07NJ7vIHEohnMUgYcLQ7L5JHORG5eEfK6zxun8nanak244W62WSddGIpGhQ3A5RSMWiw01F7N2cFJlsxuk18pIkpGNTPvL6/ldsvLcuIEz8uORc5xdTJ2RJCD+zGwG2wPIIwiFQkilUgAwtLFxk+fmJB1crnWSPui8ONsM6JAw0s1kMggGg65N1QLDJzUxaGD0KQdBSBIasEOGcZuT9jCgrJyHAsgy3aieVjmgg3Il9wPoj2SdnJx0X6pWGjh6pbFYDI1GY+ikc2DHaErB0SNz0thlxArsKNtJHlVFWTQaDdy+fRt37tzBtWvXAACxWMykz8iklY3AsVgMyWTSvA6/n/SFKVtOarWaWXwynS8nVZGsAmAojehWchAwzCrmPfj9/iHmprNEstugA5nyd9bonLpE9vIoR1Y+Jv8mHRVZd3YrYrGYOXuT9wUMH71IxjEdCemoPQ5lFUbm5GlwX6JTGwqFTLZEBk8ATLZDssFlyW56ehrPP/884vH4voiRD8Kh1zi5iORikG0R8mcqlCyOOzd5emjycc4UdWvv114ga3Vsv2GEIEd2ATs9erIZ/SRH4w8Ca3SMYpxtFsDwhsZFKh00t8tv1DoD7jVyMls06rlSHs71y8fl707sZji5NzD6crPRkGQpmTXbbR066+1uddTGAXWMhy/IjKHUQWbYisUiqtUq0un0PTVgqXuyY8N5GtJB4lANJzd0KoKsJckTPpwRJT1/mbqVbDVZH9BaI5/PY2JiAvV6HfF4/DBv6cjhVCSyDz0eD0qlErLZ7BAxiidZsEYViUTQbDbN8x6HaJNgyjAUCiGZTKJUKiGXyxmHgnVPLlqpi0yHu9lY7ganI2pxcGCAkEqlRrbVOI2DTDXS4LqxNDAOeN+1Wg1Xr17FxsYGqtWqMXJ0WhuNBgqFArrdLm7cuIFIJIKpqamhE4uYqZBjH3mYwcbGBlqt1lBr1UHhQA2nU0nkYAPOxwR2ZtA621LoSUiShvTYZLQpUyDcAGmoT7JxoBzr9TpKpdJQkz4dDY6vkqy9er2OSCQy9vu5XZbBYBDRaPQeOTkHSbPWQiePvY7AzkI+zIZqi5MFnoRCPSJZyHnQADBc05PteifRaZPwePozaGOxmCmLOEt27BbgoHw5W3q3dHav10O5XMbKygpKpZLpFT5IHOpOQOPGKJIjzZwblrM26ayPyGhAFoXJypORg+ztPI4b/oNSWfd7DoAhRmS5XDbED7/fj2AwaJwSPiaHddNx2c9nPo6y3AsCgQCi0ajRH1lvoo5RT1lbokzD4fBQzfhxi9gt9gel+sPeeTIHDScNqXNvk4ZTjoU8yYaT0eLU1JQ5M1Oep8xraChZ+2VwBNxbY5cllkKhgI2NDRSLxUMZqHHghtOZWnS2ngA7BnVUTUVGoUztylqTjDx5Tb1eN0xAOXrvOG5y+/k8MpXN2iYPHq7X60gmk4hEIpiYmDAGk0pULBbNz0f1eY8DpG6xtUQ23kuHTWZG6PEzKpXj6R6H2pPFw0MphVOnTiGbzSIejw9NVHJGm+yB5fF/bM8ZxSQ9iRhV85ZtJ8wo9nr9mdzb29tYXV3FysoKNjY2DLeF65wpW6ZzZRbyIO3BkZCD+GGdp55I4yo3djk0wQkZITASYM1U9uedJEivinLhhCD2QbEZGBgmVbHP6aQyju8HJ2vTOS5ulFcvF6DzmDELi71AqX5jP6MoRlLA6FYLRlnMFrmdUbxfOIlm0oFlzbNWq5mj/uRQd5mVlHul7AU9SBxaHyewMzkI2BmfxA2cZJVRRzYx6pSnLBAcpMA0GtCfNKK1NuezOYkebgZlwQXGqLNQKJi/cyZjNps198waC+ezssn9OEbhhwGpg0wLyXq7ZN1JT5cTcoC+bBltSl22sNgN3MS517FOziwRM0Vc18yGUOf486g+45MEGkVG2FxnMuPIfS8YDGJ7ext3795FMBhEtVo17YeS+Mg9PxqNmkAin88jnU4feJnlwA2ntPrBYBCJRAKnT58eOraImxeJLrJ+BGAoxy2NKllnTG8wKpifnzeHm3KixEkCI6BQKITz58+j2WziiSeeMIpw6dIlcz4do/VQKIRwOGxOn0gmk4c2ReM4QjoQqVQKCwsL8Hg8qFaraDQapufVGQGEQiFEo1HMzc0hk8lgfn4ek5OTj03qzOLhwL0vlUohk8ng9OnTZvPPZDKIxWKmHBAKhTA7O4vLly+bwGJmZgaTk5MnxvEfBe5nfr8f09PTuHjxoiH8bG1toVqtDvVjTkxMYHFxEadPn0Ymk0Gv18P58+eNfKThjEQimJ6exrlz53D+/HnMzMwgHo8fuDwP1HDyg9FzSCaTUErhO7/zO7G1tYWtrS3Ts8M8NCeUyCZWeUyRc8ZtOp024TuL7tPT08hkMrh06ZIZfH7SlI6e6Ld8y7fgueeew8LCAoAdZprP58P29jYajQYqlQpisRhisRje/e53GwNwWHMbjyPo0U5OTiIWi+Hbv/3bsba2hsXFRTMkX3q49F6j0SjC4TCmpqaQTqfxvve9b+i8yMdFfhb7AzNDi4uL8Hg8WF5eRiAQQCgUwrPPPovZ2VmEQiHjCD///PPo9XqYnp5GtVrFBz7wAVy+fPnE6xrP1XzyySdx+vRpvPjii7h9+za2trZMZF4ulzE/P4/5+XmcO3fOsG8XFxeRTCbx5ptv4vLly0MkU86pfde73oWLFy9ienrasJkPUp5qj3WvsYpjfE0aw1KpZJig9CQYZUrGorP1xEnLZhQrm6VJU/b7/UNGdUwhHZaGHlhRkTLjCQvb29vmbzSIlG+73TZ1FUaadCgOeEEea7mREUuWXbPZRKFQGBp95iSjkQzE+lQmk0EgEEA4HD5Ir/VYy+0Y49jLjfsV59Kur6+bvWp+ft4cV0e9q1QqyOVy2NraQqfTwdzcnDnZwwX6BjyE7JgdIz+lUqmYFDcf597FSJ31zlKphHK5bE5IkalvngzEcz73aROIkU86FMPpQhz7BXlMYeW2P1i57Q9WbvvDsTScLsFI2dmijYWFhYWFxRiwhtPCwsLCwmIM7DVVa2FhYWFhYQEbcVpYWFhYWIwFazgtLCwsLCzGgDWcFhYWFhYWY8AaTgsLCwsLizFgDaeFhYWFhcUYsIbTwsLCwsJiDFjDaWFhYWFhMQas4bSwsLCwsBgD1nBaWFhYWFiMAWs4LSwsLCwsxoA1nBYWFhYWFmNgX4ZTKXVWKVVXSl3Z5e8fV0q9ppS6opT6klLqj+1y3a8qpW4NrruilHpuD+/9fUqpryqlekqpF/f4eX9fKVXZ6/WHgT3I7M8OZPaaUuoLSql373LdP1FKvTq47t8qpWIPeN/nlFJfHMjsNaXU9+9y3c+L/8PbSqnC4PELg8cq493xweBBchPXfa1SqquU+t5d/v4vlFLXlFJvKKX+qVLK/4DXc7XcnBglx4EcskqpN+7zvL8m7u+NgYwnHNdElFK/qZR6ayCvv/ugzzD4+iXxt0e+RiX2sF7TSqnfGOjGHyqlnnnI9/vQQCZfVUp9dpdr/quQ3apS6j8MHv9+pdQNpdSnHuYzPCwOSmb7tAu/pZQq3E8GSqlFpdTvDt7/M0qphcHj469VHrw6zheAswDeuM/fY9gZIP8uAG/tct2vAvjeMd/7KQBPAPgMgBfHeN5Y1x/01x5k9gEA6cHPHwPw8i7XJcTP/weAv/6A970M4NLg53kAawBSD3jOXwbwTx2PVY6j3AbXeAH8HoBP76ZPAL4N/bP1FIB/CeD/c5Llthc5AvhGAM8/SL7i+u8E8HsjHo8A+PDg5wCA/wrgY+P+Lx/1Gh3zs/4cgP9l8POTAH73Id4rBeBNAGcGv0/v4Tn/DsAPiN8/BOBTJ0Fm+7QL3zTQz11lAODfAPjBwc8fAfDPHX/f81o9lFSt1rqiB58EQBQHe8L6Va31tYN6veMCrfUXtNb5wa8vAVjY5boSACilFIAwHiBbrfXbWuvrg59XAWQBTD3g4/xp9I2LW/CX0d9IsrtdoLX+tB4AwB9iF/mK60+83LTWnwOQG+MpI+9Pa13TWv/+4OcWgC/jAfI9AXgawO8CgNb6LQBnlVIz+3ytPwPg32utlwavt6seA4BSKo7+xv8f9vl+jwoHKbMhaK1/F0B5r+8P4PcBfHy/73doNU6l1Hcrpd4C8JsA/uJ9Lv3ZQej880qp4GF9HpfhhwH8593+qJT6FQDr6Htt/3CvL6qUei/6EcE797lmEcA59CO4Yw+l1CkA3w3glx507eB6P4A/D+C3xniPEye3caGUigD4VvQdlPtdl0Lf8//dXS45p5T6ilLqs0qpbzjYT3mkeBXAnwCMfixi/87CZQDpQfrwFaXUDzzg+u9GP1or7fP9HhXGkdlh2IVXAXzP4OfvBhBXSmX280KHZji11r+htX4SwCcA/Mwul/0N9Df/rwUwAeAnDuvzuAVKqQ+jbzh3lYXW+ofQTx9eBTCy9jbidecA/HMAP6S17t3n0j8F4N9qrbt7/tCPFv8AwE+M8Xl/EcDntNb/dS8Xn2C5jYvvBPAHWutdI1SllA/9iPT/1FrfHHHJGvrpyPcA+B8B/LpSKnEon/bw8XfRN3ZX0M94fAVAZ5+v5QPwAoBvB/AtAP6WUuryfa53VWZDYK8yOyy78OMAPqiU+gqADwJY2eX9H4gDMZxKqR8Thdx5+bdBOuiCUmrS+Tyt9doge9YE8CsA3jvitX9l8LqfPojPelwwSmZKqXcB+GUAH9dab9/v+YMN+v/Bjgd1v/dKoB/5//+01i894PI/hWO8KEfI7UUA/0opdRvA9wL4RaXUJ3Z57v+Cfrr1f9zje50YuR0A9nJ//xeA61rrfzDqj1rrJvVaa/0K+hH8/QzEsYFT77TWJa31D2mtnwPwA+jr1a19vvwygN/SWle11lsAPgdgN3JgBv198jf3+V5Hhv3KbC92YT/QWq9qrf/EwHH7m4PHivt5Ld8BfaBfAPAL/F0pdRHAO1prrZR6Hv001z2GQCk1p7VeG9TrPgHgHnbfILo6cRghszMA/j2AP6+1fnvUcwZyuqC1vjH4+TsBvHW/91FKBQD8BoB/prX+Nw+49gkAaQBfHOdejhJOuaGfHgXQZ+OhTw74D87nKaV+BH1v/pseEDny+hMlt4eBUiqJvof+5+5zzd8BkATwI/e5ZgpATmvdVUqdB3AJwKjI9NhhxHpNAagNaro/gn4WY7+p008C+EeDiD0A4H0Afn6Xa78PfR1v7PO9jgz7ldle7MJ+MAjecoP1/zcA/NP9vtZhpWq/B8Abg5D8FwB8P8lCSqlPi6j0XyilXgfwOoBJAH/nQS88qJ0uA/g6AL+plPrtwePzLo9KfxJABv2I6YpS6kv8g5CZAvBrQmZzAH76Aa/7J9FnT/4F5aB3K6V+Win1XeLaPw3gXwlil6vh0LVfAjAD4IsDGfzkA55+4uWmlPqX6Bv7J5RSy0qpHx48/qNKqR8Vl343gN/RWld3eZ0F9D34pwF8eSCrHxn87buUUtTRbwTwmlLqVQD/FsCP3i/1e8zxFICvqj6P42MA/gr/4NC7B0JrfRX9mvtr6BPXfllr/cYur+XmzMZeZbYfu/Bf0WfNftNAl79l8Lhcqx8CcE0p9Tb6e8HP7vdG1H7WulLqLPpez0P1Lh0llFKfAfDjWusvPejaQ3r/s3CZzCSUUhWt9X17Rg/pfc/Cyu0gPsdZHHM5Puo16vgsZ3HM5SWhlPoQ+rL7jkf4Gc7CRTJzYpy1ut+IswsgqR7QlH5coJT6fQDnAbQf4cdwlcwINWgOBrDxiD6CldvB4FjL8ZisUYljLS8J1R/O8YsA8g+69pDhGplJ7Get7ivitLCwsLCweFxhZ9VaWFhYWFiMAWs4LSwsLCwsxoA1nBYWFhYWFmPAGk4LCwsLC4sxYA2nhYWFhYXFGLCG08LCwsLCYgxYw2lhYWFhYTEGrOG0sLCwsLAYA9ZwWlhYWFhYjAFrOC0sLCwsLMaANZwWFhYWFhZjwBpOCwsLCwuLMWANp4WFhYWFxRiwhtPCwsLCwmIMWMNpYWFhYWExBqzhtLCwsLCwGAPWcFpYWFhYWIwBazgtLCwsLCzGgDWcFhYWFhYWY8AaTgsLCwsLizFgDaeFhYWFhcUYsIbTwsLCwsJiDFjDaWFhYWFhMQas4bSwsLCwsBgD1nBaWFhYWFiMAWs4LSwsLCwsxoA1nBYWFhYWFmPAGk4LCwsLC4sxYA2nhYWFhYXFGLCG08LCwsLCYgxYw2lhYWFhYTEGrOG0sLCwsLAYA9ZwWlhYWFhYjAFrOC0sLCwsLMaANZwWFhYWFhZjwBpOCwsLCwuLMWANp4WFhYWFxRiwhtPCwsLCwmIMWMNpYWFhYWExBqzhtLCwsLCwGAPWcFpYWFhYWIwB3x6v04f6KR491CG9rpXb/mDltj9Yue0PVm77x2MpOxtxWlhYWFhYjIG9RpwWLoXWOw6hUofpeFpYWOwXdp26C0dmONvtNtrtNra3t9FoNNBqtaCUgsfjgdYavV4P7XYbvV4PWmvzN5/PB6/XC6UUer0eSqUSvF4v4vE4pqenkUwm4fF4Hjtl40LTWqPb7aLX66HVakFrDa01Go2GkbPWGn6/Hx6PB36/3zxPaw2Px4NIJAKv1wu/32/kTbhRrpRJp9NBuVxGs9lEt9uFUgo+n8/Iq9FooNvtot1um+cGAgH4fD54PP1kTLVaRSAQQDqdRiwWQzgcNn87aZA6xXXY6XTQ7XbRarVQr9dRr9fNOuUape74/X54vV54vV6Ew2HzOK8j3KhTwI58KBs+prU28mk2m+h0OqjVavB4PAgEAkY2wWAQHo8HHo8H3W7XrNFGo4FerwcACIVC8Hq98Pl8QzL1eDxDzwfcK0eCsuv1euj1emZdSp3jNc7nUV5KKQQCAXi9XvPd5/MZnVNKma+DxJEYTipWuVzGV7/6VSwtLaFUKgHob1QUVKVSQavVQq/XM4oSCoXMZtZut3Hz5k0Eg0GcO3cOH/7whxGLxQ5FMMcZzg2u0+mg3W4jn8+j0+mg0+kgl8thc3MTW1tbaLVaSKfTCIfDSCQSQ0rq9/sxPz+PcDiMVCplnBa3y7TT6aDRaODOnTvY3t5GvV6H1+tFNBpFs9lEvV7H+vo6arUaarWaue94PI54PI5AIIBer4e7d+8inU7j+eefx7lz5xAKhcy1JwlOo8A1WavVUK/Xkc/nsbm5adZuu902RjMSiSCdTiOVSiEcDiMYDGJubg7hcNg4ZQBcrVNyzXU6HbNxcw1ub28jm82iUCigVCphaWkJPp8Pk5OTSKfTSCQSSKVSxhg2Gg1sbm5ifX0da2tr6HQ6UEphdnYW4XAY8XgcsVjMOGuBQACZTMYEEW6VI0GD2O12zVez2USz2TTObrlcNnpI+SuljFGl/sXjcYRCIaTTaQSDQUSjURMA0HgedHB16IaTAlpZWcHnP/95fOYzn8Hdu3dRKBTuERwFJIVED5ZeVrlcht/vx/T0NHw+HyYmJpDJZBAMBg/7Vo4NlFJmAdNgVioV/MEf/IHZ3MrlMsrlMkqlElqtlpEhvWAqViAQwOnTp3H27Fl85CMfMVE8F6jbwI3szp07+PznP48vfvGLxkBKR6PT6aDZbKLdbqPT6QAAut0uAoGAkQ8A1Go1RKNRfO5zn8MnPvEJfOQjH0E6nUYgEHClfCScxqDRaCCXyyGbzeLGjRvI5XLY3t5GpVLBxsYGSqUScrmccbpkZoJGMhqNIhQKYXFxEbFYDJOTkzh16hQymQzOnz+PcDhsIgK3yY9ZjNXVVRQKBWxubhrZrK+vY2NjA61Wy2z2NAbBYBDBYBDT09OIRqPwer2oVCpYWlpCoVDA9va22djj8Th8Pt9QpDo3N4dMJoOPfvSjmJ6extzc3JBRcBvkOmRAVSqVsLy8jM3NTaysrKBYLA7JWWY5aDei0Sii0SgWFhYQj8cxMzODdDqNCxcuYHp6GplMBrFYzMjRmfl4GBxZxFkqlXDz5k1cu3YNt2/fRrVaNenZ3cJx6dnzhrXW8Pl8KBQKWF9fR7VaRTqdPorbOHagArZaLVQqFaysrGBtbQ2vv/46SqUSyuUyGo2GMRRUOEbyXKA0KpVKxUSdbobWGvl8Hm+++SauXLmC5eVls6FxAXIR8nrKko/RcdBaIxgMYnNzE+9+97tRrVaRTCYf5e0dKOQmVq1WsbGxgatXr+KNN97A9vY2CoUCisUilpeXTfRJB4xllna7bRyxeDyOSCSCUqlkItGNjQ2cOXMG09PTJvXoxg2fmZqtrS0sLy/jxo0bKJfL2NjYQKFQwNbW1pDTX6/Xsbm5aZyxxcVFRCIReDwelEolXL16FdVqFZVKxZQH6JBRvkopnD17FvPz8zh//jyUUpiZmRkqc7lJljKzwf2/Wq0il8vh5s2byGazxmDevHkT6+vr2NzcNJlIea+xWAzRaBSVSgXpdBqFQgETExMA+us3FAqZ9DYd4YPCkdU4ucHLOqYzLcj0h8xfE86aAtO59CQeN7Be5/F4EI/H0e12kUqljBLR2+12uwBgoiqfz4dgMLhrXVN6sm6UK3WjWCxiZWUF29vbqNVqQ46D9DypT84NiDoIwNSjtNau3fRHgbKq1+soFot466238NJLL+HVV1/FxsaGMZTNZhOVSmUoRQkAfr/f/E4Og9Ya9XodjUYDfr8f4XAYyWQSqVQKk5OTuHjxIk6dOuVK/WLq+o/+6I/w5ptv4s6dO2g2m6YUMj8/j0AgAK011tbWTKaH66zdbqPRaKBSqaBUKqHRaBiZcZ1yXYdCIRPZLy0tmXTuCy+8gIsXLxrH120ylClaaTSXlpZw9+5dbG1tIZvNolqtmgjc4/GYNSyzkLFYDIlEAkA/E0kHMJVKYWpqyvASWPeUGZKHxZEaTi48pmQlEQjYSUHKn0cpBgXPwrrbI6T9gpsPDR3TQ3ROAJg6QDAYNHKi10/CAolDsujuZrCmzo2JcuHfgPsTK5wOG2XKDW96evpwb+AIobU2hvH27dvIZrPY2NhAPp9HvV432Qrp8FJHnOsY2HHQaDxrtRqazSYajQZu3bplSH0ADixtdlRghFSpVFAul03NfHJyEhMTE5iYmECr1UKr1TIp2k6nYzZ6Ohf5fB61Ws3opCSzyA1eZgO63S7W1tZMHXViYgKBQACAe0lCskSQz+dRKpVQqVSMw899i+h2u0NOfSQSQTgcNjKiPeD/h+ufe98oB3m/OBLDSYVrNBqGlSdvBMA9aVlnROoM8UmAWV1dxalTp47iNo4dnMyxZrOJWq2GbDaLZrMJACYqj8fjAPqRAaPKaDRqlM/v9xuFdbMjQkNXrVZRLBZNHVPqmRMyZSv1TXrHTIW/+eabOHPmDCKRyJHe10GDm3K320WpVMLa2hq+/OUv4+bNm7h7965J8VMfpJGkzklGKbDjyHF9AjD/i0KhgJdffhn5fB6Li4umluemTZ/OeqlUQr1ex/z8PJLJJM6ePYuFhQVMTU3hxo0b2N7eRjweR7VaRSKRQLvdRrPZxPr6OiqVCjY3N00QQQYy05DRaBQAjMMnHb/V1VXcvXsXb775Jp5++mnDXAbcazxbrRa2trawsrKCbDaLfD5vIm2muLXWSCQSxpiy1hyJRBAKhVCtVs0693q92NjYQCaTwezsLDKZjDGeznX+MDjSPk5pEEdt0DJFK/P8FJazBaNYLOLWrVt47rnnjvI2jg2crFp6usVi0Xi5zO2zDUPKnEzaM2fOYHJycihCO8i0xqOA1LHdjKb8XWY9ZLZDlgZY//vQhz50ZPdxFKhUKibSZIqWqW3nlzMalzKUKUfJOmVbwcbGBsLhMDY3N03E4KYaHdtMer2eKZP4/X7DmJ2YmDCp6KWlJeRyOQBAPp83JDSnkxEIBBAOh00akqxtlrYksY/XvfPOO5iZmcHExIQhW7kRzHaUy2UUCgWUy2VUKhWz54dCoaF1yfUs0/xsJVNKmdKBbPMBcE/pyRURp5Mly8ecaVo+Lq+T+WwAZlHy51wuh7t37w714T0ucDIiWT9utVqo1WoIhUImBQv0a5eytqy1RiQSQSwWw5kzZxCPx43XdlLStU5na9Q1wL1G0xl1An1DsLKyguvXr5vNz83gPfd6PZTLZWxubprWnWq1OpRGdRKn+Jgzte9M2xLMEi0vLyMQCGB1dRWhUAhTU1OuMJq8V9bkABhugN/vN20jsVgMoVAInU4HPp8PyWTS9FIXi0Xj4BI0nLFYzDweDAZNuwUjKxrOSCRiWqSy2Szm5uZc1U0wSi9oOIvFIgqFAiqVimEh+3y+odQ15ccyE+UEYMhOsC7Pvx0GZ+PIUrUbGxt46623kM/njQcq4WyQZsQpW1HYOMxrisUistmsSQs9TpBp7Wg0ina7bSjs0WgUgUBgKLdfr9fv6ZliO4rP50MqlcLp06eRTqddTbhianBtbc2kHEc5AnIhSQajBNOS1NVCoYBbt24Z/XWrjAhmK958801cu3YNd+7cMVkHZ5O9k4sgIZ0TWVvnewA7jm4oFEIul8Ps7Ozh3+ABg73lZ86cQTKZxPvf/354PB7U63WUy2VTgwN26pbFYtGsPRLzGLVy4282m6ZcQmPZ6XTg9/sRiUTMGmV6l+vcbTViWXIDYIhpW1tbWFpawvb2NprNJhKJhCk9MRInMQ/YaQui48HXqlQqAGBKA7lczmRODhpHFnHWajVsbW2NjGicLEeZppXXyO8ATO3gJERI+wWjcr/fb2QXDod33eQYeTLdJKeacJG6bUFKaN2fmlStVlGtVu/rVEnDKen9fMwZkbLt56ToGw3d9vY28vk8qtWqqS/JdTjqedKg7vW7JGw5U27HHdIhmJycRCQSwfz8PLrdrmnVqdVqpv1BKWUyQMx8yNIJa8GMZKPRKDwejymVMMKX05iYXeKXG/XQmbVghowOhiSQssbuDJ6oU7JlDICxLe1220y5kmvYdREnlYNMMid7lgrlbEOR6SBnCk0pZejIbq/HPQyUUuh0OsZzY59hqVRCsVg0Kdt4PA6/349UKmWYjvF43BCE6Dk7iTRu2diITqeDbDaLXC43FG1KnWImg4/Jn6W+0cng48DoVim3ghvx1tYWcrnc0P9eOhXEbpsPs0CjonZniUAphcnJSUOCcQNICmo2m9Ba48KFC/B4PEin06hUKqjValheXsb6+roxdPF4HLVazRhO1nQZhVK/uH5rtZqJKpkVYg1VspkLhQJu3Lhh2i0SiYSJutwGku5oOElolAQ/mQ2iPFjvBXbSva1WC6VSyaxPMmvpOLvOcEqGo7MwTnAjA4bnQEqPyjltZLd6yuMGSRCibLjo6KmRaNDr9cxiloQioC/rarVqvDa3TiXpdDq4ffs2crncPWlDaRSckZSTBMPrpJPnJMOcBDCNT+98lLM0Knuxm1N1P2eL05pqtZrreAnczJnlAmBqcoVCAfV63awhyrRWqyGXyw31bTJDxtejftFAyL9JfeSa7PV65rUk58ONoLxYBwYw1JLj3IecETZlJbOTdAbZluJM1R7UnnbohrPVauGtt97CnTt3UKlUoLU26QqmDNmvQ49O1uLMBxXD3qlI9DRO2mY2DpjuaLfbZqYlPbVoNGrkmc/nobXG1taWUcb19XWTMqrVaiiVSkilUq6exFQqlfCpT30Kr7322lDrDbBT02NtlykxblLS+ZC9ddRTXnuSwChme3v7HqKPM8sjnyMh5eucZsONv9vtol6vo1Ao4M6dO66scbKH88aNG2bKVr1ex8bGhpnZy+Z9Gstbt26ZfYrBAdu+2KIHAI1GY0h2jEj5mJx+s7W1NRSduQlSl7rdLrLZLEqlEmq1mmkvYdq62WyaMpJzn5f9rZKrwboxR0dKPoKrIs5Op2NmD3Kj6vV6JuSm5ylTYEx30JBKgTHXr7U2ReCTtpmNC6a6eTpAp9NBKBRCOBw2nhw3L0508fl8qNVqhradSCQwOztrUt9uqj9JtNttLC8vm1nIhKybO43BbpETgCFCAlmVbqwtjYLTSZVycEbloyJ0p0EFcE+UIDct2X9dLpddUwpgxmZtbQ0vv/wyVlZWUK/XzdB17mdkgQaDQTQajaFTnZyD4QnuZZ1OZ4iQJcsJXMPVatW8TyqVwuzsrDntyI1gPZIGkLJjXyvT3GT7Azv7v2S906FIJBImm8G9jjbnoHHohrPdbhvDKWso9KRkfZI3yH4lSSSSNRcaB6ZCrOHcGetF2dADllESa6GUKdNJ9OxmZmbMyQJuJQi1Wi3cunXLnL5DOL15id2IVHweX5fyk/1kbgZ1Q5JRiFHlFEL2eO4GZ1qX78fJOYVCwfztuMuRhLNbt27hK1/5ClZXV9FsNnH27FlEIhFkMhnjlHHz9/v9xpEFMJTNcMqNToszeAAw1PDPtUvDKU9LcSNktoy2gHO0afikjjL7I6ecMWPp9XoNG5ekQA7xcC2rlh6T3+8fOh+SfwcwNCh6enrajOyS3gjbJFhnYH/USYkA9gO5aOQwBFm/lDN9OQaMxfRoNGo2ScmodMOGNgqkpfO8V94PG8t5X5JwRmdDOmeMHkjYkKkxOfXFrZC1oNXVVZPK5707o3LnyELg3vYC5+s7+QhA34BUKhXTD+kGsBa3ublpGLRerxfJZBJzc3P42q/9WnMkFgOEixcvIpvNolarmYEPZBTT+ZLOCgMCOsA8ZYYTbzqdjjlZBQA2NzexurqKxcVF101gAoYPqGDbCUHWMG2FXGt0ImhApcw4s5vpWpIdDyOwOhJWLVMPTmaj/E7jyEODfT4fNjY2hno3geFmVnqwbsvzHzScm5lUMplypOzYR0ZvTtaynK/nNtBRk2kxyoT6BsAsRpmS5Bl/wI68JNtbvoebZUTQ0WLWQUbkTkKV/NkZme4Gp/NFuXEijlugdb+drlwuI5/Pm5paPB43x6bVajVUKhUzzN3r9aLRaJjoU/IzuPFTPk7WKM+UZG8nSXvycPBms4lCoYCFhYVHLZ59QZKtZKmA61RyDwgnUU/uW1yrbKejnF2bquVhrqFQaCiaoaAkBTsejyOVSuHSpUsoFou4c+eOMZrcDDlRgnD7bNWHBR2IbreLcDiMdDqNZ555xjD4SEQIhUIAYBZ2pVJBNBpFOp029dBkMmn6Qd0acXLROZmxUmd4f+xbVUoNEc246ICd+rGsVZ0U0DNnLQnA0CYEDI80k8x3wukE0yFj6pL6JCPWYrE4dHj4cUez2cRrr72Ga9eu4ebNm7h06RJmZ2fx4osv4ty5c1hcXESpVEKhUMDc3Bza7TZeeuklrK2tYWVlZYhRS1lThsykyaAhGo1iYmLCyJNzWmdnZ82Q/FAohDt37uDJJ598xNIZDzKDwcwhS0vxeNzsQYwsZQqczqwsS8m2nmAwiEQigYmJiSGC0WEwuI+kHYWHKUswTSFrLDzFe35+/p40GI2D3BRlCu1xhXQauAgDgYBRGKk4fr8fyWTSbJbBYNAYDkmBdytkJOj0MuUMVd6n3+9HKBRCKBQy0QQNL3VTphn5+k6v161wRs5OApXzOhpE5+O76Yxkhsrn0Ii4BXSk5KxZjtSLRCImK1Eulw3hkWPfOIDcGWVyzTnlLOukDBJY54zH4wiFQqbnmsPi3QjuN7LPlfuRHCH6oDS0zFjKGbeMPFm6Ouh97dANZ7PZxI0bN7CxsTGUCuTCkQYxmUxifn4eX/d1X4e33npr6G9UELnxNZtNlEqlx5Ic5NzwfD6foXNTYSqViqm5cJg0T6aIRqOIx+Nm1iWn4tCbczNkzxuw06vIn5nhCAQChp24ubmJu3fvDmVFAAydECJ1z80OhgQ3a6c37zSKcpyc/JtTFk4mLjdCPq/X65kzUt0iQ27qzslSPO/R4/Egn8/j1VdfNY4qG/ALhYIhlsnauCxBUSfptHDz51Fl5XJ56HisRqOBjY0NbG9vm2MV3eTAUQ8oJ47/jEQiiEQiWF9fN5O/uG9Jp82Z+SE/gSfu8HswGDSvc9A9/4duOFlYd564AOx4uFSgcDiMSCSC2dlZFAqFkd6qFACF4yalOSjIfD+Vi/WjSqViFBLY2cwajQbW19fNIgRgaPZ+vx+ZTMZMN3ErZB1Twple5GbD0y1GRUDOhSZrKm5t15HgCDzJEmbWQpJXnHVOGXFLOFt85KQXPsaeYdlqdtzlqLU283vZZ0hDSvh8PjPkvdfrIRKJoF6vD031kYZS6in5HVprM/aw2+0ikUggmUyawfEcFNBqtQxj97jLbjfQcDIbxswXABOFygHv8tQm/h9kDVNyE8iu9Xg8pt3uoEmPR8Kq5WSSUalAuQmFQiHEYjFMTk5idnbWtEXIG5X9UKQvu7V14qDAlCOVkecFklnKBdZsNnH37l3EYjFzPmez2UQ2m4Xf78fCwoKrhx8QzoXhdNYIpsWSyeTInmFg9FF3bjeavE824O9W33zQa/C73Lhkaw9fy7k+5fi64w6ZeWBUxBSt1APW1/icdruNYrE4dHqJ01mQvYgEuwkqlQqefPJJJBIJk4pkapaG023HiUmdodFzGk452EDqTrvdHtIpmRXjtQDM/0hyYzit6SCZ8IcueW7IPLZIQpIGPB4Pzp07h6effhrpdNqkKdgAK1mhFGCz2TQHnz7OoIyYFpqenkYmkzGtGeydY02YbTw0ktlsFj6fD1tbW5icnDTetRuiASeYqnHWbEdNxbl8+TIuXLiA7/me78Hrr7+OT3/608az5aLlguPz2E/XarWGzgt0I9gHzUiGjhdrcnJjA+6tgUodcRoAyk3W7RgxcJNjiu04O75K9U/gePbZZ+HxeDA/P49v/uZvxvz8PE6fPm0OSZibm0MoFML169eRzWZRKBRQrVZNTzoAc6oJiXrSgPJ3rj2Pp3+MGHurOTii3W5ja2vLDF1gGtgNJxrJjASzGdFo1EyRYioa2JEF1x4zizJrIY8dk0GWdHZZR5WZyoPY146EVTs1NYVEIjFygfAmvF4vJiYmMD8/b47TyWQyqFarKJfL9xTRgZ1ctxs818OELLT3ej2THpLpCg48pjHhBunz+cx5eIxUuRCP84a2G7xeL2KxGJrN5lBdc1Smg0S0hYUF00rgHLhBA8BFz94z6t1x36x2gzPilPVKJxFP3qczLeaUj3x9YNjY8nXkBueGtcuezcXFRfR6PVy4cMGckMJ7YXtKp9NBsVhEuVxGo9EYYoMySopEIgB2jIdSykzQkWQ0lhEYQdHxZX+7HAjvRl4CnYNYLIZkMmlIUJQB5SMzjMDOvs/SwijnizJ3lvoOCoduOCORCL7xG78RGxsbeOmll0y6QkYAPNLq2WefxVNPPWWihg9+8IN49dVX8fLLL5u2CiqLUv3TBiYnJ13diP4wcG7azj4x2SfF9Jg8LaBQKKBUKmFjY8OcMci+szNnzrguFaRU/0i1F154AW+88YY5IxEY9jLpqT7zzDP4+q//ekxPT6NYLCKRSJjasJQhdZXEjXw+j0qlYjZAt4LrUDI3+bhco5SbzPrIx+V3GZ0DMMQOkloAIBQKmX49NyEUCpnWOjqWsk5Zq9Xw8ssv4+rVq6bXMhgMIhaLmQOqlVImpSvH8nGMXzAYNBFUPB5HOp1GOBxGtVrFW2+9hc3NzaE+Rc55dRPXg45GLBbD4uIiwuEwQqGQWVfSNjALwolm1Bk6JcDOem61WvD5fKa9jgSreDw+9L86CDkdekjB3Hy1Wh3yNp03QpKQTLvKDUtODuLzwuGwGTv1OEOeOsEIwllP5ublTF+SLcmNjWxAt0QDTrD9iUOzqW/ykADnFw2ijLxG1TG5oXGYvls2qvthFFnvQZmG3chBEjJKYI8iDauM2I+7jsl6MFOLo+RD57RYLJqUqowigX6qlizjTqczFI3yfchqJkOe04MCgQCKxaI5t5LOHdf8cZfjKLAeGY1GEY1GjTMiWcdOXZPBkzNTAuwYZRImOYHpoNfqoYcUpVIJn/zkJ3H9+nUAw2w7J604m81ie3sbi4uL5pxAzhzl+WtyiG8mk8HFixddex7dw4IKU6lUsL29jbW1NTP0XiqhPAi32WwaYgGjjUwmg0QiYQgNW1tbru0Pq9Vq+PznPz9EJiDxjPdPuTBFDcD04bHnS453ZHsPN7Tp6WnE43HXG07qj2yToJGTRwA6n0M4HQ1eL6NVRgBKKVMu4Mi9TqfjirXb7fbPjWS06Gx1UkqhXq8jm81ic3MTW1tbyGQyADA0M5mtYtvb26an2ilDTg2amppCJpMxvZtKKeTzeWxubppe7Wg0imw2i3Q6jZmZmUcim/2AOsQUOEtMpVLJlI8oY+fEJc7rlYxaOcvW7/cjHA4jFoshFoshk8mYNLCTaPowOJJ2FJ6OPoo+zPomJ0EQrFUlEgmT7ul2u4ZFS7r2xMSE61KKBwXK5M6dO3j99dfNbEYA5nxFynVmZsZ4qzI11+l0zGgv2Xgsx2C5yUAwOuBG5Ew3SmZeOBxGOBw29yijTl5H3WO6KJFImHSam+QyCs6JLNLblz2bTgKL/E5I4ob0/umgyTQw5yiTYHWc0ev10Gg08NWvftWsr8nJSePIE6FQCJlMBgsLCybrw2iQulSpVADA1Chl2w9JVNzb8vk8yuWySd/6fD4sLCyg2Wxia2vLHABNEpIbI06Pp3/0YblcNvOimb526qQzO8b1Km2GXMd0LBgkHKTRBI7QcLJG6ZzRCOwsYLlgPR4P4vE4YrEYIpGIGQpNw9loNEwN4HFN1dLzeuONN3DlyhUz0xKA2ZxSqRS8Xi8ymYxhzlLOdGAoU6Z/ZLO22yCZ2lxwzoiIaf9wOGzaBeQmT1ICyQt0IMLhMFKplGuJUwQjHG5KMgske+fk9fIx2b6y23e5ifGLhpVpTMmaPI5g5Fyr1fDWW2+Zg6s//OEPY2pqCsDOPfv9fqRSKSwuLqLRaODOnTvGcDIwoEPK9gh5iAWwcxiD1toQjOr1OpLJJDwej6nFr6ysmMb+Uqlkzjl2E6gb8XjczP+VHRSsW9LgSZ2TmSRJGJJlF/bb0nAedABw6IYzGo3i67/+6xEMBs2mrfXOGWos5HJuqkyBSS+BShWNRs2BuCsrK7h+/bqrxncdFLTuD57OZrO4du0abty4MUSg4kIql8umqE7iC40JvToe88Q0biaTMRur28BMhdPw0zCyJYD3mUwmTV3k4sWL2N7eRrFYNMaRGx7ZjNls1vXtT9xEcrkclpaWhib7yM3IWSMnRtWd5HNkCrhUKhk9o1PC2lyxWDT9xMcVlEcikUAmk0EgEDCzYuVGHAgEkEwm8YEPfADz8/P41//6X5t2EtY6OR2IxjKfzw+tMY/HYw6h9/l8yOVyePvtt82pKHRsOORkfX0da2trmJiYcI3hlNkIZ9TIzgA511cSgmQtkxFqrVYz61R2DQA74/icWaeDwKEbzmAwiEuXLuGdd94ZKvpSYej9Sw8BgEmT0cAyzy1TSFx8bt/IxgUVqF6vY3t7G/l8HrlczvRBycHcJGL0ej00m03jvTFF6/V6jcEtlUqoVqtD9S23pSO9Xi9SqRQqlQrq9foQyYA1JKZpyY6kUZ2dnTV1FDnDFxhOAR/nKGkcMGKRm678nzvv07k57zbDV/591ExkGlU5XP44g5FRKBRCIpFAKBS6Z4YqszgTExNotVqG9MLDlavVqjEQ3PRlzyZfj6Qhj8eDVqtl6sLONp5Wq4VarTZ0nqWbIKNDKUeuM7nWZFuUJIxSj6izkgHP95CG+iBx6CFFNBrFH//jfxzPPvvskIfBfzpTsvPz85icnDTU4XA4jPe///1YWFgwdZB2u4319XUz9MDJwn1cQCO4ubk51HYh60wySqrX6yYSm5qaQjAYNEX4QCBgTl2Px+OGzefWGl4ymcSf/JN/Eu9+97uHIijOECUZanFxceh8w0gkghdeeAEXL140LL9wOGxOvWCdnuO73A6tNQqFAgqFwtBBwsBO2mtUymw35q2ztkndabfbhlDFkoDH4zGsRzeUWcLhMN7znvfg1KlTQ5GzNFbsiQ0Gg5icnMTzzz+Pr/marzGDEdiKEwgEkEgkTIsKU4k8wFk29CcSCczPzyMSiRiHj/+LbreLer2O2dlZXLx40TXZIalDJI7RgWUQxQCJjFg+LuVF/WIQwJp5r7dzghZlchjr9dAjTnrzslHVSTwIBAJDSsPnycZpCohfQF8J3T5bdVzQKObzeayvr2NpackcrMu0ovTaKFc+F4BhhyYSCXOQNbBTP3ab9yrBtBqb02XEyS+m/CXJR+obvX3J3mM60s2yIWSKVUbjTGHzZAng/odV7/a68ncaZBpJed6iG2TJXmjZXkInjPfC7M/m5ia8Xq8xohz3xp/p1LLn0jl3mw4FjYRzWhqdDZYR2B7jxj3QmaGQJFHqpUyxjhrK4Tx8XrbcARiyJfL7QeBIZtVy86EnJckaWmszo5bKAuykeZjvloOh6RGThu3GqRn7AZWiXC7jC1/4Al5++WW88sorqNVqJnKkkvFgXZ7rx+fW63WTFr948SIikQiWl5fR6/UMCYE1Y7ctRmAnxc9jmWQai7oYDAYRj8fNJgTskNiY2mbK2tku4IYIaS+QqS86t8w6hMNhFAoFAMNsWflc/k3KllkPeY3W2mz4HExOA+QGchDTpe+8846RVS6Xg9frxfT0NJTqtyzdvn0bv/d7v4fLly/D7/fj1q1byGazuHPnDnK5nMkKcZ2yNUrOUPV6vZidnTWOC6MvoC/rVCplnF2mOScnJ5FOp121VqXuSceNesJsmWxNYRRK+yEnDEnyEAlGstQnddI17SisKyWTSSSTSXPEi/T0ZZjOjcnv9+P06dOYmpoaCrudG5ebFOYgIIcd0KNlRFmr1VCv11Gr1Yb6mmQaiB5vMBjE9PQ0wuGwqZM4/y9ulK3f78f58+dx7do1BINBc7gA74WUd7YJcGEFg0GcO3cO6+vrAHZSj/w7N71wODxEiXcrtNaGlBcIBNDtdk2f4W4RIdegc8IQgHsMJrBTUuAB4dRHTo3hWZbHEZTB9vY2VldXcefOHfM/f+GFF4ai9nq9jqWlJbzxxhumxaZYLKJSqZjWFMkuBnYa9RldMSsXiUSMc8GZ0nzNVCplyJFkw8vuBDdgtyhTMtUZTUuH1TlrVhKAKAMaU5kGplwPGoeutUw/pFIpZDIZZLPZkQuSRpOC8Pv9mJmZwdTU1NCUFqdwHycwBdtoNMwgiE6nYzY81uKksaCHKwkasjmYrT6c7erW2ibh8/kwPz+PiYmJIb2RNRGe1sMFxuedP38eb775plm0HBABwGxUu02OcRO48XDUZTAYHCKLyYhgt3uVmxp/d0agrD2R5c2auvPs2OOKbreLjY0N3Lp1CysrK2ZTlie7MAP0zjvv4Ctf+Qq01ojFYqadhFGl7Jnlc7nncWAJ/x/AjoPMGnQgEEA6nTbHlTnLWm4EDR4Np7NcIp0ySSgFdo4PlE6+TNFy33POBzgoHIm7x3QNz0mT9SLW67a3t+/x4pna5YKUOW4AZkTcSSYI8V5Z27x16xZu3LiBl19+Gfl8Hj6fz7Bm5aHBlB1fg7W/mZkZ0x9Lr5Z1Y6amOC/SjVEVU2AeT/+g4VwuN1QT0brflsNWAC4qj8djGJPctGTKOhAI4PTp07h8+fLQKQ5uBueoXr58GQAwNTWFQqGAzc3NIUYjwQ1K1oudaVtJyOCapd5dunQJ+XweiUQCZ8+eNfNHjyuYjq3VaojFYoaLQR2TToJkugIwhpM6R4PbarXM4QMMEEKhkNnoOU2oVqthfX0d9XodZ8+exczMjKk/U+7dbte0TzkdmeMMZnqYfWCLDuXHflc6WfLkF8mP4Xc5xpHMZjq4MnviqlQtQWEBw8YA2GGkyRuT9Gte6ywo00s7zl7rXrHbRsx7ppHLZrNYWlrCysqKOSOQHq3c5CQxptvtIhqNIplM4uzZs4jFYohGo1hZWRnq6zwJxkDeh0zxywiBixTA0IJkakcytul0MAOysLDgijFxD4JS/YEOiUTCHOs0MTFhRl0So3Rit8fk2pWbeygUQjKZNKMKye4+zoMk6CTIqFxGhNJZl5sy63M8V5OtYc4JSkzbMpXI149Go2ZCGssx29vbiMVi5nmMorxer2HNu83Rle0ozkhSpmHl3/kc1jWdesjH6YgcZvbsyAwn0w4AzObkJFs4N28aTjkCjsLwer145pln8J3f+Z3Hvon6fnDm7eUXWZ3tdhsrKyvY3t7GZz/7WaysrODu3bvmhHje/82bN81ii0QiiEQiKJVK6HQ6ePHFF/E1X/M1eN/73mcWdrvdNilLyjmZTOLy5cuuNQ500Hh+IZl3TPXQs49EIkMRlcfjQSqVQiQSGaK3M5UUDofxtV/7tfi+7/s+pNPpY7vh7wX8fz/11FOYn5/H7Ows2u02/H4/XnrpJayurgLA0GbsjDQlZD1cblS8bnJyEk899RSee+45M9ibJZjjTrbi/35mZsZENSyHyKja5/MhlUqZMkA2m0W9Xke5XEY8Hkc0GjXP53PJNuYaTqVSeM973oNWq4VwOIzNzU2sra1heXnZRE+hUAjPPvusyTK1223kcjmTHZEHZ7sBvKeJiQkkk0nUajUsLS3dk4qVexQjTEbgTMlSV+kQkjl/GHvZkbBqORGC0Q2AoXStjEadz3WOQJOGptlsolgsHmtm3l4gjaSkXTPKbDabyOVyZog7z/pjWpERJ38mi5nzMGOxGE6fPo2ZmRkze7XRaCAcDhuChvSM3UhvJ7rdrjkcgD2X1EH+na0FzuZxZ1pHRk281o3N5k7wf8t5q+fOnTObejwev++IMikr599HXc86MQ2DTEm6gacgJ/aQ4V+tVk2GTG7is7OzZh1Go1H0ej0z+IAdA3J0HDNmwWAQU1NTmJiYwMLCAsrlMm7evDlU85MkNk4KYglB1qXdAGeKnzVJyllmGZ3RpgwqZOaSNsL55VzTB4UjMZztdtvMVJQ0Yho8UuFHkQuYt2bYLlNs169fRyKRwAc/+EGkUqnDvpUDh5SD1tp4sjSMbIloNptYWlpCLpczJzDI4QYkwTBKovfZ6XSwuLiICxcu4F3vehcymQyUUqbdIpVKodPp4Pbt20bOrLm4MaLSWqNareKzn/0s3nnnnSEClewL43Ds7e1tlMtlJJNJ9Ho9Ey3IM0tZ983n87hz5w7efPNNnDlzxvVncdIzZy90u91GuVzG3NwcYrGYSaM6MyL8WX7n6xFOR4SHPM/OzhpSkDzm7biCxo11OGbNNjY2EIvFjFNVKBQQj8fx1FNPmYlVvV4PW1tbaDabiEajiMfjpqxEvWw2m8Z5ffLJJ3H27Fk88cQT2NzcxBe+8AVjKDmTGwAqlQqmpqbMqNKpqSnX6OIow8V0NXWR0SEzjKxv8jtlLo0rZ/4CO+ldXsvpTK4znMBO+szJgqLQKBRnvpvPo8I5p5OMmmDiFkhvlYvo9u3bKBaL5vft7W1z/erqqhkyzdF57XbbHM8knQufz4eJiQmcOnUK3/qt34onn3wSU1NTJsKoVCq4e/cuSqXSEPuPaR5GY0yBHOfNzQmfz4fp6WkkEomROkd96XQ6ZhMHdjIYTAXJiJPOxurqKt5880188IMfNDNu3QxZZ2LKjG0iJGbIPlb5POfPzghVypyRFU+jcZ5qcdzR7XbNOpN9hFy/6+vrhqAja+hK9Y8ho9Hka5FFGolEcOrUKUxNTeHy5cs4ffo0otEoqtUqYrGYOXeTE71oSKUxZmZqtwzBcYOMGGWUKB8Lh8PG6FF3CK5plmCkbsqofrfRewdVBz6SPk5Z5HXmrenN7WYIGQGNKga7bQE6wRRguVxGqVTClStXsL6+bhiv29vbJpWztrZmonaeIED2HZl8TNt4PB5MTU3h4sWL+NjHPoZz584hGo0C6BvFSqWCO3fumJ5G1kFZK+WkE6Z63QSfz4e5uTnE4/EhEgW9VNmLyHFfwI7n6jzFR6aDVldXce3aNTM+7SRArk+ybNk4LlmJvFY6rk6jKQlq0nCyDYWkDfl/OO6gMaxWq6aWCWDIcK6srKBUKt0z3Yx1c0aHtVrNROCMsqanp7G4uIjz589jcnLSXBuLxVCtVlGv1834R2k4PZ7+qFKttelJ5v/nOBpQZ+aCRpJ7v8y8BYPBoZ/ZLkWmrZOYRXDUniT2AaN7jB8WR3KsGFODPKiUSsXjX+QAaOdIJa31UM1NerGzs7M4f/686wriBO8pn8/jzTffxLVr17C9vY1AIGDS28Tm5iaq1aqZ08vnezwes6jZED05OYn3vve9+KZv+iacOnVqqNFcKYX5+Xm8733vw9raGjY2Nkx7BSc11Wq1e4hbbgHvg2xYPkZ9o2MhR7/RaN64cQNbW1sjF92o93E75Jqi0xqJRMwUL9lUPuo5hCQQycyQs94ke/TcECGxhsh1xfpmOp3G9PS0ifa63e7QtCoS7hqNBgqFgiGqsC7KTEcikTClFGaDGJG+5z3vwe3bt7G2toZ0Om1O/6BDTYeW871lD/xxh1OfSJgCdlK3TLeyLYw2gmuac6cZvTvPyHWSipzv/7ByOlJyEEeY0dOQTcHA8KBomSZjwVgyILn5uS2VSMgNpdvtGoPFc/ba7bZpfqbHSxYsAMMikz1MJPbQi71w4YLplZNtGaFQCOl0GsVicSg9Lnup+BndBulwyZ5CCenl0jB0Oh0Ui8Wh9hwJXs9rydJ1o4yccGaA6GhJI+jc7Lg2R9U7na87yoC6RW4ykuP64CQu/p3ZHLJkm80m/H6/GYUpWbVaa5N6zWQyOHPmjIk0uQ8GAgHMzMwAwNAEHMmkrdfr5mSbwzhv8ihB2Tp1g7bDOXuW6486ypYeGc0S8v/nqoiTxfN8Pm+KuBSUZIORHEOlA3YKvYFAYIhtCvTP8bx79y6+8pWv4Nu//dtdN68R2PFoFxcXkU6nsby8jFKphKWlJeNo8J+dTqehtUY+nwfQX1DNZhOVSsUsoLm5Obz73e/G933f9+HZZ5/F6dOn7zmYWEZdpLsz6iJ7d2lpybBs3QbOnOXnl/oF7LD4gsEgNjc3kc/nzcaXTqfNEAn5HJKmAKDVamFtbQ2xWAwzMzOujMolpDHzevsHnj/55JN4+eWXsbq6OiS/+5GBpKPLv9ORk/2azjV6nNcs9ykSzCTxTnIJPvrRjxoHQxKJODKPf+MpIDSETInTAMhSFeuefM92u41isYhOp4PJyUl4vV7TUiZTnscV0tly3isziPxiBohHRsp9n+uQzGW2j/HsV57FS/Y7ywQH7Vgcyci9cDiMTCaD06dPmxx9KBQy/Vxnz57F4uKiScnyy+v1YmFhAc888wxyuRxu375toiG/34+5uTmcOnXKdXU4Qm7iyWQSzz//PBKJxFB7ANmzoVAIvV7PbPKRSATlctmcEu/z+fD888/j4sWLuHTpEtLp9D1Gk+9J5ZU9amSUzs7Omib/47wQd0MwGMQTTzyBcrmMVquF69evo1gsmlSO3+/H/Pw8pqamzBFrHH6wsLCAxcVFPPnkk8jn80b2QH+jvHDhAi5fvoxkMula+YwC74N1swsXLuDd7343otEobt26ZeYf09OX5+TK15Bp2G63a8gvCwsLiMVirpuDzM8ZCATMPZRKJcNk5f0mEgnjONAIhsNhIytu/vIIMUZKkt8B7GRDZH2Yh1cnk0nMzs7i1KlTQwGEm5w3ubez7s3JUvl83szOLpVKWF5eNjrHIELqHYdpcKpTOp3G1NQUZmdnzfQgecTYqDLDvu9jj+HrvmNchttf/vKX8clPftIYv7m5OYTDYaRSKTz11FO4ePEizpw5Y2oFwM5c0XK5jE9+8pO4du0alpaWUK/XUalU8PGPfxwf+9jHcP78+YelZB/WKt6bcPXOVBuZbmD6kJt4u93G5uamWYj5fB6FQsEMzf7Qhz6EeDyOZDJ5X+JUpVJBoVDAO++8g62tLWjdPxZpa2sL73nPewxRYQ/TNx6p3EaBqeuNjQ0sLS3hi1/8Ira3tzE5OQmgL+OzZ8+aL7JI6eW+9dZb+PznP28IH6urq6bt5xOf+AS+/du/HadPnx46rm0fOFZyk+lWRldLS0vIZrP4/Oc/j+3tbSwvL5tWC55lKtncwE7koFSf8Z1KpfDMM8/gueeew5kzZ4aOw+L1Y+JI5UbiTbPZxNramvl5cXHR6I0sgThn+3Itc71KYwhgT06ErMNzPOlLL71k/hfnz59HJpPBs88+e79JTIfppYytc5KVLCctFYtFM2own8/j9u3b5joOy5eGlyxtnuhD52ZqaspEo8wAPEQ6e+QTjsRwaq2xvb09xD5jHYUzbJn3d85Y5UJeWVkxJw4wFD979uxQ39lD4FhsZM60mByCQAXijEzKhn1KPp/PjDGTTOVRYBqYURmww/DlFBN5HuB9cCzkNvTEwaLkHGNOVZHsWR5Uzfov68VM92xtbZkJTGxD6Ha7OHfuHObm5oZqgPvEsZQbsNNMzib/ra0t01PMtcghCaNIFpKvwMMd0um0mR36kNHmkRtOMmW5cXN8JdOKzutHEVFGtdHxZ2KUTEYxUVutFra2tky9MxwOG87Cfdb8sTKcwA6fhSU4DiYhUYitdtL5cDK6ZTsjiaayJszgYTeZ7xGPxnC6BMduI3MJrNz2Byu3/cHKbX84dobTRRgpO3c0U1lYWFhYWBwTWMNpYWFhYWExBvaaqrWwsLCwsLCAjTgtLCwsLCzGgjWcFhYWFhYWY8AaTgsLCwsLizFgDaeFhYWFhcUYsIbTwsLCwsJiDFjDaWFhYWFhMQas4bSwsLCwsBgD1nBaWFhYWFiMAWs4LSwsLCwsxoA1nBYWFhYWFmPAGk4LCwsLC4sxYA2nhYWFhYXFGLj/KcVKnVVK1ZVSV3b5+5NKqS8qpZpKqR93/O22Uup1pdQVpdSXxn3+OFBK/bhSSiulJnf5+19RSr2hlPqqUuqvisd/Tim1/jDvvY/P+iCZfkYpdW0gtytKqekR13yzUuqVgXxfUUp9ZA/vuzi49spADj+6h+d8v1LqhlLqU3u6uYfEfvVNKRVSSv2hUurVwb397V2en1ZK/YZS6rXB9c+M+fl+ZvDcK0qp31FKzY+45rRS6veVUlcHn+WviL8dR337uLinLyml/tgu131EKfXlwTr6NaWUbw/v/UB5jXjO7yulKkqpFx94c4eIPcjtZ5VSd5VSlfu8xnvFOn5VKfXde3jfY79O7/M5DkrXvmmga1eUUp9XSl3cw3v/oFLq+uDrB+9z3Z9USr05kO2vDx67MHivXf+X94AnbI/6AnAWwBv3+fs0gK8F8LMAftzxt9sAJh/w+rs+f69fAE4D+G0Ad0a9H4BnALwBIALAB+D/BXBJ/P2n9vve+/y8D5LpZwC8+IDXeA+AeXF/K3t43wCA4ODn2OD/M7+H530IwKeOiWxG6gv6h83GBj/7AbwM4P0jnv9zAP6Xwc9PAvjdMT9fQvz8PwD4pRHXzAF4fvBzHMDbAJ4+xvoWw84pSe8C8NaIazwA7gK4PPj9pwH88EHIa5fnPXANHAO5vX/wv67c55oIAJ/Qiyx/v89zjv06PUxdG/ztbQBPDX7+7wH86gPedwLAzcH39ODn9IjrLgH4Cv8GYNrx913/l86vh0rVaq2zWus/AtB+FM8f4OcB/E/Y/STypwC8pLWuaa07AD4L4IGe33GG1vorWuvVwa9fBRBSSgUf8JyW1ro5+DUIF6bpd9MX3Qe9Rf/ga5Q+PA3gdwfPeQvAWaXUzBjvXxK/Rke9h9Z6TWv95cHPZQBXAZza63scNbTWFT3YNbDLPQHIAGhqrd8e/P5fAHzPHl77gfJyK7TWL2mt1x5wDfccAAhhD/d/EtbpbtijrmHweGLwcxLA6i7XEd8C4L9orXNa6zz6+vmtI677bwD8wuAaaK2z43x+icP8p2gAvzNIO/y3h/EGSqnvQj/aevU+l70B4BuVUhmlVATAt6EfpR5n/MogdfC3lFLqAdd+D4CviMW2KwZpxNfQjx7+njC+rodSyjtIEWXRX0Qvj7jsVQB/YnD9ewEsAlgY831+Vil1F8CfBfCTD7j2LPrZgVGf5dhAKfXdSqm3APwmgL844pItAH6RPv1e7HENjSOvkwil1PuUUl8F8DqAHxWG9H7POcnr9EG6BgA/AuDTSqllAH8ewN99wMueQl9WxDJGO6uXAVxWSv2BUuolpdQo47onHKbh/Hqt9fMAPgbgx5RS33iQLz4wgn8TD1iMWuurAP4e+l7Ib6G/eT5QeR8h/qzW+lkA3zD4+vO7XaiU+hr07+2/28sLa63vaq3fBeAigB8cJ9o67tBad7XWz6FvCN+7S/3y7wJIDwzsX0Y/bTOWLmit/6bW+jSAfwHgL+12nVIqBuDfAfirjsjr2EFr/Rta6ycBfALAz4z4uwbwpwD8vFLqDwGUsUe57VVeJxVa65e11l+DfonhbyilQnt4zklep/fVtQH+vwC+TWu9AOBXAPwfD3jZUcHFqGjWh3669kMA/jSAX1ZKpR78qe/FWIZTKfVjoth930I/vaRBOPwbAN67nw94H1wAcA7Aq0qp2+hvmF9WSs2O+Cz/RGv9vNb6GwHkAFw/4M9yYNBarwy+lwH8OnaRm1JqAX25/oDW+p0Rf3+f+F99l+M9VtFP8X7DQX/+g8Q4+kZorQvo18ju8Sa11iWt9Q8NDOwPAJgCcGufH+/XsUu6UinlR99o/gut9b/f5+sfCu4nU6315wBcUCNIdlrrL2qtv0Fr/V4An8OINaSUYqbk0yPeeld5PQ4YOPBV9DkJBidhne6G/eiaUmoKwLtFxuj/AfCBB7zVMoYzIAsYnd5dBvBJrXVba30LwDX0DenYGMtwaq1/QWv93OBr1/SBUiqqlIrzZwAfRT9lemDQWr+utZ7WWp/VWp9FXyjPa63XR3ye6cH3M+in6v7lQX6Wg4JSykdFGmy+34ERcht4Sb8J4G9orf9g1GsNPF3+r/6jUmpBKRUePD8N4OvRV5xjizH0bYqe4+Ae/ziAt0Zcl1JKBQa//giAz40TDSql5CL7rl3eQwH4JwCuaq0f5CkfOZwyVUpdZDlAKfU8+uSUbefzxBoKAvgJAL804rV/aPC63za49oHyOslQSp1TA/axUmoRwBPok30MTsI63Q371LU8gKRS6vLg929GnydwP/w2gI+qPms+jb69+e0R1/0HAB8evP8k+qnbm/u4NTyQUn4/DKK7L6FfyO2pfqvH0wAmAfzGQEY+AL+utf6twXN+FAC01r+02/MfNrU18G5+mQsYwL9TSmXQJ5X8GIvDxxBBAL89MJpe9BnA/zdg6rkvaq1/Ev2U10UAf0sp9bcGz/3oA4rdTwH435VSGv3Uxt/XWr8+eO2fBvAlrfV/PIybOijcR9/mAPyaUsqLvjP4r7XWnxo8x+gb+jL4Z0qpLoA3AfzwmB/h7yqlngDQQ5/F/aOD95D69vXop9dfVzu0/P9Zaz0qCjsO+B4AP6CUagOoA/h+EjgGkeOPDJyWv6aU+g705fuPtda/t4fX3ou8XAml1P8G4M8AiAxqcb+stf4pxzr9YwD++kC2PQD/vdZ66wEv7fp1eh/sSdeUUv8N+nt2D31DulstFACgtc4ppX4GwB8NHvpprXVu8LpSZjSwbwLoAvhrWut7nMS9gNTg0X/skxs+pbUeq9/NTVBK/RT6NOS/f0TvdxYukqlS6kPot098xxG811m4SDb7gdW3B0Mp9Rn0dW5k//cRfYazcJHcjnKd3ucznIWLZOaEUqqitY7t5doHpWq76IfNVx76Ux1DKKV+DsCfQ7/2cFRwjUyVUt8P4BfR9/qOAq6RzX5g9e3BUEr9PoDzeLgWtYOAa+T2CNbpbnCNzCTUYAACgI09P+d+EaeFhYWFhYXFME5Mc62FhYWFhcVRwBpOCwsLCwuLMWANp4WFhYWFxRiwhtPCwsLCwmIMWMNpYWFhYWExBqzhtLCwsLCwGAPWcFpYWFhYWIwBazgtLCwsLCzGgDWcFhYWFhYWY8AaTgsLCwsLizFgDaeFhYWFhcUYsIbTwsLCwsJiDFjDaWFhYWFhMQas4bSwsLCwsBgD1nBaWFhYWFiMAWs4LSwsLCwsxoA1nBYWFhYWFmPAGk4LCwsLC4sxYA2nhYWFhYXFGLCG08LCwsLCYgxYw2lhYWFhYTEGrOG0sLCwsLAYA9ZwWlhYWFhYjAFrOC0sLCwsLMaANZwWFhYWFhZjwBpOCwsLCwuLMWANp4WFhYWFxRiwhtPCwsLCwmIMWMNpYWFhYWExBqzhtLCwsLCwGAPWcFpYWFhYWIwBazgtLCwsLCzGgDWcFhYWFhYWY8AaTgsLCwsLizFgDaeFhYWFhcUYsIbTwsLCwsJiDFjDaWFhYWFhMQas4bSwsLCwsBgD1nBaWFhYWFiMAWs4LSwsLCwsxoBvj9fpQ/0Ujx7qkF7Xym1/sHLbH6zc9gcrt/3jsZSdjTgtLCwsLCzGgDWcFhYWFhYWY2CvqdqHgtbafNdao9frodvtotvtotlsolaroV6vo9vtmr8BgMfjQTAYRDAYRCwWg8/nQyAQgFIKXq8XSikotRNJy58tHl9Ifev1etBao9PpoNPpoFarmS+pP1prtNtt+P1++Hw+pNNphEIhBAIBeDweo2/AydazdruNTqeDYrGIRqOBer1uZMj77na75nelFDwej/lZKYVer4dyuQyPx4N4PI7p6Wkkk0lznYXFbtBao9lsotFoYG1tDa1WC51OB8FgED5f31x1u1202200m020Wi1jJ5544gkEAgEEg8FD/5yHaji5gQEwi4/GkYtyc3MT6+vrWFpaQrVaRafTQbvdNsZxamoK09PTOHPmDOLxOFKplBGgc8HKxW3xeMJpNOmg1Wo1VKtVrK6u4s6dO1haWoLX6zU6RKMaiUQQjUbx3ve+F5lMBhMTE/foG3HSdI0ORq1Ww40bN7CysoLV1VV0Op2htdxsNtHr9eDxeODxeIxzIWV5584dBINBLCws4CMf+Qii0Sj8fv+Jk5nFwUJrjUajgfX1dfzn//yfUS6XUavVMDExgWAwCI/Hg1arhVKphGKxiFKpBK/Xi3Q6jR/7sR/DxMSECa4OE4diOJ0RJo1hq9VCPp9HuVzGjRs3sLq6itdffx2bm5tYWVkxz/d6vSYaTSQSSKfTuHTpEqampvDiiy8imUxiZmYGsVgMoVAIPp/PbILAydvQLO4P6luv10Ov10O73TYeaaFQQK1Ww/LyMpaXl/GVr3wFS0tLeOedd4wX6/P50G63US6XEQqFEIvFcPPmTZw5cwbvec97kEgkkMlkTATq9/vh9XqNQT1J+pbP53Hjxg188pOfxO3bt7G8vIxWq4VmszkUwRO8dxpNr9cLACiVSggGg5ibm0MsFsPMzAzS6TQCgcAjuS+L449ut4tWq4XPfvazeP311/GpT30KzWYT3W4X6XQawWAQgUAA3W4X1WoVlUoF1WoV7XYbyWQSTz31FN797nfj+eefP/TPeugRp9zMarUatra2kMvlcPXqVaytreGVV14xj/l8Png8Hvj9fnQ6HdTrdYRCIYTDYTSbTczOziIej2Nubg7BYNAsVBl5AidrI7PYG5jNoMNVr9dRrVaRzWaRz+fx9ttvY21tDVevXsXt27exsrJiIiWpbz6fz2z4jUYDyWQS09PT8Hg8iEajJiIFcI/euR309jc2NrC0tIQ33ngDm5ubaLVaaLVaI40mnWPKgIaz2+0iGAyiUqlgc3MTjUYDvV7vkdyXhTvAIOv69eu4efMmbty4YXSmUCjA7/cjFAoZPWWqlmv91q1bmJ+fNzp6mOvy0AynTJU1m02USiVsb2+bDeyrX/0qNjc3kc/n0W63zSamlEIoFDJ1FK/Xi16vh+XlZZRKJQQCAWxsbJgoQWuNZDIJv99vnm9Tto8PpMGsVqtoNBooFosoFovI5XJ46623kMvl8M4776BYLKJQKEAphUQiYeqWPp8PvV7P6JDP58M777yDXC6HZrOJiYkJXLx4ETMzM8hkMshkMohEIshkMvD5fCcmBUlH1O/3m00MgJEJr2Hqttfr3eM40HDKv8k6qIXFbmi1WqhWq9ja2kK1WkUsFkOn00G32zXZxWAwCK01QqGQMZ5AXy9v3LiBubk59Ho9o4eHhUNL1UrD2Wg0UKlUUCgUkM1mkc1mkcvlUCwW0W63jVexm8FjwbharaJYLCIUCmFjYwOxWAzhcBjhcPjQBWVxfNHtdtHpdFCpVFCr1bC5uYnNzU1sbGxgfX0dhULBlAg6nY4xltzM+V2m/FnrKxQK6PV6CAQCqNfrqNVq0FojHo8jFotBa31iUrZ0QprNpiEJjYLH40G32x0iVo3K+HAfYOpcRqsWFk7QWavX62g2m4aUJyGzFl6vd6hER5LpUeBQU7VM0RaLRaytreHatWu4du0astksbt++jWazadiOnU4HPp/PCI9pIS62druNarWKu3fvotVqIRgMmsUdiUTg8/lMGO/2Dcxib5C6UavVcPfuXWxtbeHGjRvY2NhANpvF9vY2KpUKstmsKRsopYZqbZL8Q+PJyGplZQUbGxtYXl7G9PQ0Jicnsbm5iZmZGYRCISQSCUNacLPeca21Wi1DumB6Va5DZ6Qp02KSeSxflw7vzMzMkd+XhXtAtmw+n0c+nx8qxTEAI3HU4/EYI0o9JJ8B2D0IOygcaqqWXvva2hpu3ryJpaUlIxQaPcnYcy5GmXqlh1utVlEul1Eul7G1tWXqUWxZ4etYPB7gxkxSSzabxdLSktn8K5UK6vU6PB4PtNbGiw0EAvdEQFykJP5IY0hmbi6Xg9frRbFYRCaTwdzcHNLp9InRuV6vh1arZdLfdGK5SVGOwHBalvIChsmBdHgZuVtY7AauOzqvsqZOe8G/Mcji8wAM6aArWbVEp9NBtVo1RnNlZQXZbNakaNmaIg0nFyY3OaLX6xlDXC6XUa1Wsb29DaDP4GPazOLxAaOfSqViIs3t7W3cvXvXtDsxq0HvtdvtGlYs6+jsG5ZEM6aBpMdbqVQMO7xQKJga5+Li4si0kttAWbRaLZNa5Xe5ETkJQpQTa6M0kCzVkKRFOVtYjAKJetJwAn0do+7IIIp6yUwH9fAonNhDM5zc0DY2NnDlyhWsrq7i5s2bKBaLpsGVBlK2E7BmJL1Terwej8eE8m+//TYymQxSqRROnz4NpRRmZ2dPxAZmsTdwk87lcqY3s1gsYmtra6iHc7f2Cb6G/BsXH4dtyN5j/kzjefPmTYRCITz55JNmQIebI0+lFAqFAq5fv26YsNKrd/ZMU4ZMcbOXk+QMOsEkfVjH1uJ+oNPWaDRMOY5rkQNyOGCjWq0OObYejwfhcNhEoq5M1fKDN5tNlMtlZLNZrK2tGZairGESMgW0G1mImyGNJ4kM29vbKBaL6Ha7pl5l8XhAa416vY5CoYBSqYR8Pj+04QMY8lwZaQIwm7wkCMlIUzpgkuxG3S0UCkanI5HI0d74IaHdbqNUKpmJLTSQlJHX6zWPO8lA8jpnzVc6yBYWu0E6aqFQyKxDqXMsAzpZ28wkHYWeHWqNs1QqYXl5Ga+//rrZ2OiVS0MpG6el0QR20me8jmF7p9NBo9FALpfD3bt3EYvF8P73vx9+v/+wbsnimIF6wOlTm5ubKJVKqNVqQ4ZQbvp0rKSeVatVw6qVLSkyapIZENZb1tbWkEgksL29jWg0avo73QpnukvWhZVS8Pv9CAQChgnPNUpngiMLna8XiUQwPT1tnVqL+yIQCCCZTGJqagq9Xg/JZNIw2QGYfmvJjaF+hsNhZDIZpNPpI/msh2o42YJSqVSGGHqycZweP0Ej6YxIna/Njazb7aJUKqFQKKDVallm7WMEpk6r1arp4eSkEfYAOyEb9SVDFBiefiOjp263a4wIp2DR4HJht1qtI733w4DsiSVkzYjOh6wNy2lCTtkBO0b1fuvZwgKAyVSEQiH4/X70ej00m01UKhUzuSoej5vHGIRRF+PxOOLxuHtrnLyRGzdu4O7du8jlckNGkx69XHwAhuZfSnYtNzduhjJtprXGysqKmS60W+/Z4wbnJnUSHQlS1Dl5qlwuo9FooNPpDA0lcKZkgWHCgSShkXErJ+Dw8UajgUajYfSOIyRXV1cxPz9/lLd+4OBalM4Ho3DpYNDrp4ElC5eOMCd6Ueac4lQqlazhtLgvqDfxeByBQABbW1vY2toyQ0sCgQCefvppFItFLC0twe/3w+/3o91uw+Px4NSpU5iZmTmSaV6HZjh7vR7y+TxKpdLQ486fZSpWbmCjyBsADHFIeruFQsGc5vC4Mvck0Up6+QCM0knD8aDXkjiuRpebdrVaRa1WG0rjyGhRpiAl6KQ5nS2mHWXkRIYpjQrBdgtnW5Ub0ev1UK/XUalUhuqbwPBQExrRcDhsjKhsQQF2OAt0MEqlkm1HsbgvuGYnJiawublpCEEMljjMXZYQGJkGg0FMTk4iFou5O+KkQatWq/f8XW7esh/H2c8p00JOdh+vZy21UCigXq+j3W4fxi0da8hNjeSpTqczNH2DXw9KY4/6XxzX1Lc0nJwKxLonjaYcp+e8B+phIBAYcjRkypLGlgaAxCO+VqvVMkb7uMppL6D+NBoNlMtl0y7m7IuT9xmJRMwAeCd7mbpGLkKlUnlsnVqLvcPj8WBychIrKyumvOfz+UwbWTqdHspecu0Gg0FMT0+bXv7DxqEYTvZccjYoWbSjhmLLeZiSITXqNQlnW0C1WkWpVDKjwniNWzex+8EZtTMS6nQ6ZnJGNptFuVxGoVBAPB5HIpHAs88+a2Y9AvdGRTItzoiLSktSzXGTZ7fbNTXGcrls9EKe7UqHQbJhJVmI6UiC11PfZCTZaDSGzvH0+XxoNpvI5XJmke8loj+OoBPC2Z+8Z0n2YerV7/cjGAziwoULpqcagEmR8wQLTl/iuExrOEfjpO5V44Lr8sKFC+Z0Ha7bUCiEZDJpTsOSNoVGdGFhARMTE+5N1QI7i4ybGCH7wJz9NpLKzteQrwfcazgYZZG0IQ/ZPWlwRoOMiur1ujnVolQq4datWygUCiiXy0gmk0in0zh37pypUe02XUPKk1FEKBQCgGPJiKSBp8NEw8g0q8xmAMMtEXIQtGxHkQZVRvKSGCR7hTnur9VqHUn/2GGCm5SELKNQN8LhMEKhEKanp009mIZSOhWSm3ASyFN7wV7ruDJrxu8Pozdu1juC6y8Wi5lDGKh/fr8f4XAYgUDgnnUNwLC3j+IsTuAQDCe9fkaC9Eb5OCeMkFAgG1iZTpPN686/mQ8uzlEEYCa70GM+aRgVEVYqFRSLRbz22mtYWVnBzZs3zYHNvC4ajSIejyMSiWBxcRFPPfWUiQokpMGsVqtYX19Hs9lEMpk0558Cx6d+RwNJxna9XjcGTzpPcoD7/foNaWjlNTJlK4kwJMAEg0F0Oh2srq4ambsZUn4SkgHfbrcxOTmJ+fl5PPXUU8hms/jSl75kMkc85FqOLZRp8JMKOk0yQyEdKamLzv5X58/jvi+wE1ichOEvPD4sGAyamcmpVAqRSASTk5Oo1+umZ1My4Tkw4ShwqKxaplKd3oFsJZGekoykZETqnFsIDHtsjASq1ap5v+OywT8snAaTZyPymLaNjQ0z6SWfzxvDR9Djv3PnDpRSmJ6eRiKRMHLl35khKJVKyOVyWFpaQrlcxvT0NDqdDqanp4+dTGnonZOoAAw5Ww/63E4WN7BjLGQvqFNPWZLgaD83g46ILHVQL4Cdntler4dYLIZ0Oo2FhQVjJKUe0UGWTh4N6kkD75Ff9XrdzPolqC+Mzln+YBqcOsafx4E02PJ13AoSGUOh0FCWkdlEjuTj2iOLXjooR4FDMZyMOMvlMur1+hDRRKa9JM2d9RA+HxjeoKQhla/FzZMTihYWFlyfMiPkPbOOuby8jM3NTVy5cgXLy8tYW1szBjMajZpxVMDO2YnVatVcX6vVcOnSJZw6dQqxWMyQOJjqvX37Nq5fv47r16+jWq0ilUrh+eefx6VLl8yCPy5oNpuGrl4ulwHstDQFAgFz3BwdORpIbvZOFqxk0jJakl4t00QADHu3Xq9je3vbRJx7TdUdN/R6PRSLxSGniylWrfVQOnxqagoLCwt44YUXDIuReirniFJv8/k8tra2TlyrGPcfjohrNBq4efOmmWQG9KMntjFlMhlEo1HMzMwgkUhgZmZm6NABubYetHfJvZHlGhpjN+97SinDy+C9ATA8BtY4JZudTr8c8n7YODTDSS+TNyZngPp8PlPzkCkM+fuoDUgSh4LBoDl7jYY4l8uhUqm4avMa9VnpUHQ6HbMgt7e3USqV8Nprr5neQW7WoVDI5Pa5oXNkGtMX1WrVDClfWlrC/Pw8pqamEA6HzZi1paUlcxzX2toa6vU6JicnMTc3Z47zOQ6GU27KdM7IpOVnpGcqrwd2vHs5X1VCGk1ghx3KCD0QCAy1anDR0vN1psDdAjpctVrtnpYbYKd1R2uNSCSCRCKBVCqFTCYz5FA4MyRKKUSjUSSTyWOhOw8Lmb6n7m1tbRnnn61xdBQ4LENrbSanra6uIhwOY2pqCul0GslkEtPT06bxX/I87kfik21nuzmDbsNuBFHuh/LQBn5FIhEkk0n3Gk7+02g06aUy+nMO5ZWH4Y7K/Y8KvWU7itzUyCrl8UXHnd3oJK1IyDREsVjE9vY23njjDaytreHNN980Hi6NA09FpzNCwkqtVkMkEjGGUymF27dvI5PJYGJiAhMTEwiFQiYqXV1dNcdx0SgXi0UsLi6i1WodK4KQNJzONCDrH5xxKdtSqHuyTYeOinyM70Fdk/V559AOkrNkfctt6PV6RmfklC9gOG2tVL9/M5FImEktzmhJZpRoOEn2cCtk3ZKO6crKCgqFAt5++22kUimkUinTglcsFs3gB65TRu/SKJ49e9Zkc5LJpMkCSRLlqM8iU+cyM3USMm27zTpmJpN2g9dFIhGkUqkhMtFh49DIQUx7yUlBsnjLfziNBFsfpMDkZsjHSHkPh8PGO+PrMKqSEcZxg9Mjl4rPTZ6DI27fvo2NjQ1sbm5ia2vLbGrcxOmgUFno4QIYqqFIh4aL+u7du+YzybYBRqzE9vY2stksstks5ubmDMv2UUL+v7PZrCGasc7IrAbbRahbvCYQCJgjsBhFUu+4IRFSB4PBIJLJpKnd0zFsNBqGxODWebVerxeZTMZ47qPquXQsJicncebMGYRCIcTjcUPYKJfL9xhZn8+HUqmEjY0NV/VYO9Pu3Nd6vR7u3LmDra0tvP322/B4PJiamjLklVAohHa7jUwmg2KxiJWVlSEORrvdRiQSQaPRQDabNfO8S6US5ubmDBlvbm5uyLFzfjbqoIx+Jev0ODm544KZSd4fbUmr1RraL4H+3hUKhRAKhe4xtoeJQ+vj5IYjCRZckKPSD1IgTk+DkLUTeYyRpLw7B1AfN1A2cjA2FyXHna2vr2NrawtvvfUW8vm86ZXjvQE7sqBS8bWZYnOmHIF7CSBytJoE5auUMpNkSqUSMpnMUYhoT+C91Ot1IxPJLOQm79QtpnTlKSlOgouUqRy5R+aeJMLwenmGpRvBQdlMF45yXim3eDxuPPxAIIDJyUnkcjnk8/khOfI5TmfkOEOWAeT/UjL9s9msMZ6RSARzc3MIBAJDrTl0yhh5SqILnf1yuWyuv337tmkfm5ubQzQaNa8p24Rk5EWd4+jHZDJp+hjdajglw13qodz3ZeaS3IOjru0eWsTZ6XSGJtawaZoLi5uOTJvx+YTT+AIwEVYkEkE4HEY0GjWKyraE47h5yTpHrVbD1tYW6vU6stmsSfVtbW2hWq1ic3PTRH6UDdOPnKLRbreH5AsMH+8UDAYRDAbNgpeKJdsr5NmnTuZor9c/U3VzcxOrq6uYnZ19BJIbDeqZTNNKZp3f70c8HjeN941GwzyX988NnqluKRe+JqNZGpVoNIq1tTWTrgNgUsaSIOS2dJnX60UqlcLc3Bzm5+exsrIytJa01qZWeeHCBczPz5v04kc+8hEjaxkl8Lmzs7M4f/68K+q//OxLS0solUoIh8PGkLKW/fnPfx65XA6nTp0yZBWuTa5zkqYKhYJZn9FoFMFg0KRjJycnEQ6HEQ6H8dWvftXocjqdxpNPPomJiQkkk0nDZyiXy0NG2ePxYHt7G7VaDZubm7h8+TJ+8Ad/EGfPnsXU1NQjluT+oZQyAzYYTft8PkQiESwsLKDT6WBiYsI4tel0GolE4mSwamUrinPzdw4zcKZWnbT13YhCVB5udqyrHsdULQ0Ypymtr6+jXC6bfslms2nm7XKuJw2AnPs5KpqW3rxMtTINTvk4v+/2erIGwyiNda/jABk9ypoHACOzYDBo9A0YdsLk68hIQspH1uMoD5/Ph3A4PBRxAvfquxvR6/VMxkPW2OR6lZG6zGwwPcj0t+yB7fV6hrzhhhon/5fZbBarq6sIhULGQWOEl8/nTRaG9xgOhxGJREzZqdfrGcIdDS6nqHFgRqlUMjLlIQVaa9OnuLGxgVgshrW1NWM4+b+gzHO5HGq1GorFItLpNHK5nOsPHGDUydqwc22PmmJ21I7qoRhOtqLQA2UExCNhJGNPbuDOHLUzTSQNraxjsceHrNrjmKql8WGrx+bmJmq1GvL5vDEElI1zCgtrkFzAwE47Do0HZcW5oLLWLFuA5Eg1+TwqKzdNmXJzvsdxQLfbHRr2AOykXDl5JB6PD9XACbnw6HBRtozgnfIAgEgkYl6TMmU0y17O46Z3e0W73catW7ewtrY2xKDm/TMa0lojl8uhUCgYvaVT5fV6TYTG12y1WohGoyZCPe7odDqoVCp45ZVXcP36dcPYrtfrCAaDCAQCZi288847hlhHwyrLIzScjEYZMUajUeP0T01NYWZmBsvLy6hUKvD7/dja2sKdO3fMZ8pms6b1gmlYvo8sf62srOCdd97B+fPnH5X4DhT3I+vJfn0nL+MocOCsWhqAWq1m0jVyiojcjJyhNaMs5+/OFCKwI1S5MfL7cTSc/DyJRAILCwsIhUJoNpuYmpoyLNlyuWzaT2RdiAoiWwKk5ylrMiROUQY0Cqz3yRSabKvgyCrKlOmkQqGAZ555BmfOnEE4HH40wnNA/p+dniZTOqw38V5lmlD2dpIQJNOzhGRvs57HSFYaT76Gmw2njOJJepIOHNCXLYkYlKfX60UymTQHeTcaDXOgtVLKGBNG6scdPp8P8XgcX/d1X4czZ85gbW3N1BP5xfVJx0AaLzqqdOAkv4O9xLFYDMFgENFoFFNTU5icnMTly5cNM1sSB7vdLjY3N83ewJoq9SwWi5nXXlhYwHPPPYdEIvGIpfjwoGPPjJLz7E2Z9WB0epQ4lBqnPDFCsmVlQVd+OZlShGTyScUEhvs/nfUpZw/acUIymYTH40EikTBsO7aAbG9vGwo7WXjO8XC8T9aQnTNBmVqTfYicOStJRDL1y40tnU4bOScSCRPNs2Zy3AynTBfKmaqsJcmoksPteb1M71Jm3PA9Hs89k1jo6cteO0lmo8Fxq+GU9W8ahlHZiGAwONQ37PF4kEqlEIvFEI/HzQQnGlYpNzcYTqb3n3jiCZw+fRp37941zub29rZht3OPI0ub026KxSLa7bbp941Go2ZN0ekiPyOdTuPUqVOYm5szoyN5Mg2dl06ng83NTfN7LBbDpUuXjGMzNzeHSCRieq7Pnz+PeDz+qMX4UOD6ZmpcGkiZ6ZEzAo46m3Hg78ZNqdFomM2MJIpAIDB0bqbTyDlThHITojCZNpRtBrJ+WK1Wj2WNk2mVTCaDeDw+NBScC4X3yIXKhUPPa1QkrZQyBBhuTh6Px2xuJHjIBUxIo+CcOsKfG40GIpEIZmZmhozPowSdM5KqarWaSal5vV5MTEwYw0l5yFFyMhVLfXU2kxN0VmgQZJ1O/g85pOI4O233g9/vx7lz57CwsIB4PG5kRMIL5xv7fD5jKIG+od3e3ka5XDabPvXN4/GYE3u2traOVap/N1AvotGo6VflmnOWPkjUkZkxuUblNZShDASchD/pzMrpafI16bzwca5T6qYcRuFWyHXL/RDoZwMmJiZQKBSGSI0nglULDE8N4e/ATvTDxeU0BLuRBxhBSSKH86BdWas6bkYTwJDH7vP5TIpVRsvOHlcaT6aEZCQp20zoRJC1TKIGj99hBECFpHycQ82l/Hkd635HOUB5L2BqVKYVGf2Fw+GRDdT8LvVpt4Zpp9PmbEsBhmvwMuKUtRi3wOv1GtasZL8Dw61NjJicp1A4jYSz5OKWaNxZGuI6JaQe3Q/O50iHje/j/Lt8XD7mnKwmnyevH3WNW0Fdk0NuGLUzQJCR/FEztg8lVctNWHoCjBJkIVeOlpLkIOcGL5WQRpltAjIlR0gFPS5gxCkdCpkqHbXJA8MnU0hHwVl/4rXORcZ0xqgFSQPA15SLT2YEnPXlRw1u0rJ1B9jp36TuyDqRTB3KsgHBKF16unKABwBjNJiulYdaO1tj3Ab2cU5NTQ1F7MwcKdUfbcYRccxgBINBvOc978HGxgbeeOMNw23IZrNDTlsqlTqyqS4HAee6cmK/a2Ev+9Jur+187oM+o5sRCARw+vRpbG9vY2lpCQBMRokGlXXNubk5LC4uuj/ilNNYJDOKm5fTIx1lIEelafkYN0OPx2NIIMd9UdLIyXqtvCeZnpZEJ2fqWl4zynDSuDqfIz8DMHzk0aiFyr9L43GcFqfMPjhTq0x9ybYbea/SkZCPSb10TrCSjsuoYfdsSj8uzsW44L2y3i1bx6TuyuOc+Dx5xqszo8Q1SjbyScFhroXjtM4eBahvzGzI9LWTNAnsrHlXG06llDmtm+nCcDiMWCxmWGZcUDIFBuxMaXFu+k4jA8AM1GaYzs3zONY3geG+U2AnHSuVwMmok0MOWBembOSgY4KyklEVQcWSmxcjAskKlNE+DTczB8clVSujSRkRAn2nLRQK3TMMQ7asMEKU/w9mROiIyawFe+/4v2I7gFy4nCvs5nm1QH+sIFt5crnckFMrDeeoVKPkHwA7RjWRSGBycvLY6I/F8QdZyRy9x+wH17WsNz+KTopDiTgDgYChp7NnSbI6geHz/kYZSWe+3lk7oZFl+oykkEgkcmzrnMBwVEijBQyP4pJeFg1ZKBS6J2Xt9LCcdQ7Z+yojBHm9fEx+ZzM7sFPXO04Rwygyj7xX9tdxcY2671HtJySgSTYz34/y5bFHpVLJ/A9peI9jK9RewVpmJBIxeiodMRKEZEnF6/WaWa2SoOEsGxxXh9bieIKlE5ZNGHGS70Hdkuzbo8ShGE4as3g8jlqtZiLDUV6qjB6AHcMiISNOGQUotXPoKUdYRSKRY795OTcXiXFIB87HRtVARslyt9e53/XHKX3ETIUzy+A0nDxGTTolxCijSSICsxnOdC2v46zW1dVVYzi5gOVnOk4y2ytkz6Xsn6MzJdOyvJ4ngziZjfJnt9Z+LR4NWD/nWubMXpIlZdaDjx8lDsVw0iDE43HTasHJ/VrrodYARgNyk3K2qDjTi0yN8WgssmnT6fQQld6NeNDnvp9xG3Xt/f52v8d3M8aPGvxcMuPAz8jmfLbNyDGBMoonZOTE31m/lClJEtpoDGOxGCYmJszn4GQZEmOY4nYjZLsD09msLZVKJTP2bVS2wzlIgvVNDvewxtNir2CvuxwcwdnkyWQSgUDAsOmnpqZw5swZd9c4geHWi2g0amqdsp4kvwP3Elf4mKy1cXFy/Bzfg71WsVgMkUjk2BFZDhpHcW/HWX7O6JL/b2Y2GCXJmrmTQSyNJcHr5AkMo94zEAiYPkZJWpDtGG6GXIcygt5tHq+Myp1rm86HNZoW48Dj8ZgSHB1WOrYcOFGv16GUMhwa1xpO3hzn0iaTSWP8uOjkImNNBbh/H5KT6MIFHYvFkE6nkU6n4fP5cPbsWSwuLh45w8ri0YARDRdTIpEwAyYYdTKNyt/ZNM6hCDI6lAaBryuJLryG05+YPpIG1/k6bgOJUMBO3zUw3LLkdHhlvUn+zjN4M5kMTp065frGfIujg8/nw+nTp5HJZAwZTxrRaDSKSqVibMpRZ3gOrcZJZi0As/kwzcp6k6wr7eaROqNHGWFwLqtcoCeN9m4xGtyYZetEJBIx4/acnuooBq4sA1AHuUCdjhcXZqfTMfomj5Pa7dQGN4KyJaMR2CGvcfCBrCtL4h6dXBnt21StxX4gnVXailGZokeRYTw0Vm0sFsP58+exubmJjY0NkzrjKDjnnFGv12uIHE5DKadHyFQa891syF5cXEQmk7lng7Q4WeAiikQiSKVSCAaDaLfbyGQyZhyc3++/ZxQaN27W4rjg5Og8ACYyktOC2PLUarUQi8WgtTbHl4VCIcTjcTNmzq0g0Yl1YkbcTMEGg8Ehxi2fw9GQlBMJHJTf2toarly5ggsXLiAajT7KW7RwCbTW5pg76hYJpnSEpYN31Ovu0GqcPp8P09PT8Hg8pvZTq9WGPAcnAYXXjooKJBlEHikWDAYxOTmJVCqFZDLpmlMYLB4O1DGfz4doNAqtNaanp5FIJAwjFhhdR5dsbvk3GVXK62V5gc4fDQwN6KNYvIcBOVyELUhsRaExdd6r19s/Tiwejxv5ysEZzECdBPlYHB2kntEOSJ6LU8+O9LMdxotysXDMVrvdRq1WQy6XG6p/OGtBZNvKIQHOzUumxbiYJyYmkEqlkEgkXHMKg8X+IfsKma4H+ifPxGKxewznqBShc04tDTEAcx6ls3bCHk+SkNgzzFSxrJW6tR2FGxNw76ko8kg1Z0tVKBRCLBYzmxszQyypMFK1sNgLqFdco3IaGteh8zrXkoMIbkI0nM1mE1tbW0MTWZxtBfLmnXVPp6cB7Gx8JIUkk0lEIhHT+2MX6cmGNJxM/adSKaTTaUSjUcRiMaRSKZMypEPFiIpTmgCYOjlHQvIaydxlv3AkEkEmk4HX60U6nTafh8w+WYdxo/Gs1WrIZrOoVCqmV5qEP9ma4zzNg1EnZcz68+zsrJklaslBFuOC6X8SQcljkBnHiYkJw6c5qjV3qH2c9Mrj8TjC4bDxWEkekHUS6eUSzokw8md6tKQmy7Fw9+tftDg5kDWOQCBgzjnkeMd0Om3OTKRDxZmywI5+cYBGMBg0eknDKhv+o9Eo4vG46RWORCJmFOKoIR9uBCcuyYH1dGalAyvXriQG8TE+j+vypBCnLI4Wco0zdSvLCMw4HfXaO5RZtbwx1pt8Pp8p9C4tLaHRaJjDmuV8VhpPOauVhAx5hl0sFsOpU6dMXTMajZq6k5uHH1jsHXIzj0ajiEQiphH68uXLpr/rxo0bKJVKpjRA4opkeNOoer1eczwYjSmdsng8jqeeegpPP/00YrEYGo0GUqmUMSSMct3uuFWrVRNxchKSk+rPtcwaryQIcVYt2wRKpRJu376NL33pSzhz5gwikcgjujMLt2BUjzbLfTzwOxqNmulgqVTqyPXqwPs4nc2qoVDITHe4fPkytre3kclkzMKs1+vmoGYaSm5yzWbTGEOm08hgnJ2dxaVLl8wJ6GxBcE6DsTh5YCQzOTlp+gO11lhcXMTMzAzi8ThOnz4Npfrj8Th8nedCOlm1NHZkdrNuSVJLKBRCKpXCpUuXMDMzY/RwcXER6XQa7XYb8/PzmJ+fH/J83aiD0WgUMzMzmJ+fN2uP6bDTp0/j9OnTZv4015nH48HMzAwuXryI9fV1kzqPRCKGfzA1NeXaaUoWRw+l+iP3kskkZmZmMDs7i3Q6bWrpp06dQiQSQalUeiTEs0OLOHnj3IASiQTOnj2LJ554AtVq1QzgrlQqqNfrqNfrpq7CAeOkIXs8HkxMTCAcDiOZTCIUCplaVjQaNWQQLmZb4zy5YOomkUjg/PnzCIfDhq194cIFUwOZmJjApUuX8IEPfMD0EXY6HTQajaHXAoab+mXbk8fjMa0t4XDYnMNJ3fz4xz+OarWKYrGIxcVFzM7OIhKJuDYtqZTC/Pw8PvrRjyIajWJrawvxeNzUkp5++mk88cQTmJ6eNpsVI/annnoKMzMzSKVSuH37Nmq1mhmT9r73vQ8vvPACQqHQo75FCxeATmwqlcLTTz+ND33oQ0gmk5iensbk5CSCwSBeeOEFZLNZ5PP5IUfuyD7jHqecjDUKha/J/i9ZE+Fh1owuGQk4KcZ8Pr1aGkfWMZkCloQjSV0eU4iHJXH3jpDZGx6J3KgvzWYT9XrdsGC5ycsJPowupZ4Bo0+JkT9Th1hXoSFl1NTtdk0k2263EY1GTa1lD4v42OpbuVxGoVDA1tYW6vW6qfGy9JJIJIbIGQBMOaXVamFtbc04KdwAZ2dnMTExYdLfD4FjK7djjsO0KIciO67vzc1NrKysIBAIIBwO49y5c+h0OlhZWTElA/YHH1LkOVJ2h2I4XQi7IPcHK7f9wcptf7By2x9cZziPEUbKznYkW1hYWFhYjAFrOC0sLCwsLMbAXlO1FhYWFhYWFrARp4WFhYWFxViwhtPCwsLCwmIMWMNpYWFhYWExBqzhtLCwsLCwGAPWcFpYWFhYWIwBazgtLCwsLCzGgDWcFhYWFhYWY8AaTgsLCwsLizFgDaeFhYWFhcUYsIbTwsLCwsJiDFjDaWFhYWFhMQas4bSwsLCwsBgDYxtOpdRZpVRdKXVll7//WaXUa4OvLyil3v2A1/uHSqnKHt/7+5RSX1VK9ZRSL97nur+ilHpjcO1fFY//nFJqXSn143t5v8PCKBkqpb5VKXVNKXVDKfXX7/PcDymlrgzu7bO7XBNQSv1fSqm3lVJvKaW+Z8Q1GaXU7yulKkqpf+T4Gx/fVcZHhT3om1JK/Z8Dub2mlHp+l+t+VSl1ayC7K0qp5/bw3nvVt9tKqdcHr/sl8fiR6dse5PSkUuqLSqmm/DxKqZBS6g+VUq8O7vVv7/L8pFLqP4nrfmjMz/dTSqkVIf9vG3HNrp/lqGS5XzmOuG4/+vb3BvvWG0qp79/lmr+glNoUr/sjg8cvDH7f0156WHiQ/AbXPHAP2+N7/TUhhzeUUl2l1MSI6z6ilPry4JpfU0r5Bo9//2Df+NTYb661HusLwFkAb9zn7x8AkB78/DEAL9/n2hcB/HMAlT2+91MAngDwGQAv7nLNMwDeABAB4APw/wK4JP7+UwB+fNz7PsgvpwwBeAG8A+A8gACAVwE8PeJ5KQBvAjgz+H16l9f/2wD+zuBnD4DJEddEAfwxAD8K4B+N+PuuMn6Ushrx928D8J/RP3D2/bvpG4BfBfC9Y773A/VtcN3tUTI+Sn3bg5ymAXwtgJ+Vn2cgt9jgZz+AlwG8f8Tz/2cAf2/w8xSAHIDAGJ/vgXJ40Gc5ClnuV44Pq28Avh3AfxnsWVEAXwKQGHHdXxi1XsXf97SXPkL57WkP28f7fieA3xvxuAfAXQCXB7//NIAfFn//EIBPjft+B56q1Vp/QWudH/z6EoCFUdcppbwAfg7A/zTGa1/VWl97wGVPAXhJa13TWncAfBbAd+/1PR4R3gvghtb6pta6BeBfAfj4iOv+DIB/r7VeAgCtdXaX1/uLAP7XwTU9rfWW8wKtdVVr/XkAjYO4gUeIjwP4Z7qPlwCklFJzB/HCe9Q3V0BrndVa/xGAtuNxrbVmlOIffI06a1ADiCulFIAY+oazc8Cfca+f5ZFhNzkeAJ4G8FmtdUdrXUXfef7WA36P44C97mHj4k8D+JcjHs8AaGqt3x78/l8A3JOBGxeHXeP8YfSjgVH4SwD+o9Z67YDf8w0A3zhIRUbQj0hOH/B7HDROoe8VEcuDx5y4DCCtlPqMUuoVpdQPOC9QSqUGP/7MID3xb5RSMwf+iY8P9io7APjZQTr355VSwQP8DBrA7wz+J//tAb7ukUAp5R2k1rIA/ovW+uURl/0j9J3SVQCvA/grWuvemG/1lwby/6dKqfRDfBa3YBx9exXAx5RSEaXUJIAPY/d963sGr/tvlVLHfW9z4oF72LgY7PPfCuDfjfjzFgC/KLV8Lw7AHhya4VRKfRh9w/kTI/42D+D7APzDg35frfVVAH8Pfc/it9BXyAP1jA8BasRjozxtH4AX0E/rfAuAv6WUujzimgUAf6C1fh7AFwH8/QP8rMcNe5Xd3wDwJPpptgmM0MuHwNcPZP0xAD+mlPrGA3ztQ4fWuqu1fg59vXmvUuqZEZd9C4ArAOYBPAfgHymlEmO8zT8GcGHw3DUA//tDfBY3YCx901r/DoBPA/gC+pHTFzF63/pPAM5qrd+Ffhnq1w7wMx8F9rKHjYvvRH+/yzn/oPv52D8F4OeVUn8IoIwDsAcPbTiVUj8mCrTzg8feBeCXAXxca7094mnvAXARwA2l1G0AEaXUjRGv/SuD1/30OJ9Ja/1PtNbPa62/Ef2U0vUxb+uosYxhL2gBfc9+1HW/NUizbgH4HAAn+WobQA3Abwx+/zcARhJm3IgR+rYn2Wmt1wapwCaAX0E/Pe587f3q2+rgexZ9ud/z2keNUevyQdBaF9Cv545KEf4Q+ik2rbW+AeAW+oZhT9BabwyMYg/A/40HyOgBn+XIsB85AnvTtxHP+Vmt9XNa629G3yG8Z9/SWm8PXhPoy/GFvX6mR4Fd1uuD9rBx8acwOk0LANBaf1Fr/Q1a6/cO3u+h7cFDG06t9S8M/tnPaa1XlVJnAPx7AH9e5JWdz/lNrfWs1vqs1vosgJrW+uKI635o8Lr3MPDuB6XU9OD7GQB/AvcR6jHBHwG4pJQ6p5QKoK8I/3HEdZ8E8A1KKd8gPfE+AFflBQMP6z+hX/QGgG9Cvxh/IuDUN/Tl9AOqj/cDKI5K/7PuOajRfQL9lL7ztcfWN6VUVCkV588APjrqtY8aI+Q0EkqpKab3lVJhAH8cwFsjLl1CX5cwSP0/AeDmXj+Po+783RghozE+y5Fhr3J0Yi/65rjeq5TKDH5+F4B3Afid3V53gO+CY/0fN4yQ3wP3sHGglEoC+ODgdXe7hvYgiH7k/0v7fT/C97AvMAI/iX5B9hf7OoOO1vpFABh48j8yjgJKKKW+G/307hSA31RKXdFaf8vAk/llseH9u4EStgH8mN4hKx1LaK07Sqm/BOC30WfY/lOt9VcBQCn1o4NrfklrfVUp9VsAXgPQQ/+eRy3InwDwz5VS/wDAJvrRApRS34U+O/QnB7/fBpAAEFBKfQLAR7XWbjOyn0a/jn0D/UjbtEk49O1fKKWm0Pfkr6DPJr4v9qhvMwB+Y6DrPgC/rrX+rQO8vwOBUmoWA6YmgJ7qt2k9DWAOwK+pPlnPA+Bfa60/NXiO0T0APwPgV5VSr6Mvw5/QI0hn98H/pvotGRp9FvJ/N3gPKctdP8txwW5y1FqXHlLf/AD+60CPSgD+nO6TG6GU+mkAX9Ja/0cA/8NgHXfQz6b9hYO9w8PF/fawfdqH7wbwOwNClYHjtf6aUuo70Nepf6y1/r2HvQ/VD1DGeIJSZ9Gn77qy9qCU+in0KduPrO7nBhkqpT6DPt3+Sw+69pA/x1kcc1ndD0elb26X015wFLJ0uxyVUhWtdewRvv9ZuEh+SqkPob/Pfcc4z9tPqrYLIKnu0+B6XKGU+jkAfw5A9UHXHjKOtQyVUr+Pfk/pQVPu94NjLav74Yj1zbVy2guOUJaulKMaDEAAsPGIP4pr5Kf6QyZ+EcDYGcmxI04LCwsLC4vHGXZWrYWFhYWFxRiwhtPCwsLCwmIMWMNpYWFhYWExBqzhtLCwsLCwGAPWcFpYWFhYWIyB/z8wcKxfh5aK3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a plot of...\n",
    "figsize = 8\n",
    "plt.figure(figsize=(figsize, figsize))\n",
    "\n",
    "# ... the original embeddings ...\n",
    "plt.scatter(embeddings[:, 0], embeddings[:, 1], c=\"black\", alpha=0.5, s=2)\n",
    "\n",
    "# ... and the newly generated points in the latent space\n",
    "plt.scatter(sample[:, 0], sample[:, 1], c=\"#00B0F0\", alpha=1, s=40)\n",
    "plt.show()\n",
    "\n",
    "# Add underneath a grid of the decoded images\n",
    "fig = plt.figure(figsize=(figsize, grid_height * 2))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "\n",
    "for i in range(grid_width * grid_height):\n",
    "    ax = fig.add_subplot(grid_height, grid_width, i + 1)\n",
    "    ax.axis(\"off\")\n",
    "    ax.text(\n",
    "        0.5,\n",
    "        -0.35,\n",
    "        str(np.round(sample[i, :], 1)),\n",
    "        fontsize=10,\n",
    "        ha=\"center\",\n",
    "        transform=ax.transAxes,\n",
    "    )\n",
    "    ax.imshow(reconstructions[i, :, :], cmap=\"Greys\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "bvJ13Cb5D9Yy",
    "outputId": "a5fc797a-0d3f-43a9-80bb-0f65d3e28b22"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/8 [==============================] - 0s 5ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 225 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Colour the embeddings by their label (clothing type - see table)\n",
    "figsize = 12\n",
    "grid_size = 15\n",
    "plt.figure(figsize=(figsize, figsize))\n",
    "plt.scatter(\n",
    "    embeddings[:, 0],\n",
    "    embeddings[:, 1],\n",
    "    cmap=\"rainbow\",\n",
    "    c=example_labels,\n",
    "    alpha=0.8,\n",
    "    s=5,\n",
    ")\n",
    "plt.colorbar()\n",
    "\n",
    "x = np.linspace(min(embeddings[:, 0]), max(embeddings[:, 0]), grid_size)\n",
    "y = np.linspace(max(embeddings[:, 1]), min(embeddings[:, 1]), grid_size)\n",
    "xv, yv = np.meshgrid(x, y)\n",
    "xv = xv.flatten()\n",
    "yv = yv.flatten()\n",
    "grid = np.array(list(zip(xv, yv)))\n",
    "\n",
    "reconstructions = decoder.predict(grid)\n",
    "# plt.scatter(grid[:, 0], grid[:, 1], c=\"black\", alpha=1, s=10)\n",
    "plt.show()\n",
    "\n",
    "fig = plt.figure(figsize=(figsize, figsize))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "for i in range(grid_size**2):\n",
    "    ax = fig.add_subplot(grid_size, grid_size, i + 1)\n",
    "    ax.axis(\"off\")\n",
    "    ax.imshow(reconstructions[i, :, :], cmap=\"Greys\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODULE 3 — Variational Autoencoders on Fashion-MNIST\n",
    "\n",
    "A variational autoencoder adds a probabilistic structure to the latent space.  \n",
    "Instead of encoding each image as one deterministic vector, the encoder predicts parameters of a latent distribution.  \n",
    "The model is trained with two objectives:\n",
    "- reconstruction quality,\n",
    "- regularization of the latent distribution, usually toward a standard normal prior.\n",
    "\n",
    "This makes sampling more principled than in a standard autoencoder.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9FRmcyOpE3Tc"
   },
   "source": [
    "# 👖 Variational Autoencoders - Fashion-MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "l7XF23BZE4AP"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras import (\n",
    "    layers,\n",
    "    models,\n",
    "    datasets,\n",
    "    callbacks,\n",
    "    losses,\n",
    "    optimizers,\n",
    "    metrics,\n",
    ")\n",
    "\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "PM5H9VxsRZKe"
   },
   "outputs": [],
   "source": [
    "IMAGE_SIZE = 32\n",
    "BATCH_SIZE = 100\n",
    "VALIDATION_SPLIT = 0.2\n",
    "EMBEDDING_DIM = 2\n",
    "EPOCHS = 30\n",
    "BETA = 500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JCCV_iAtE_3H"
   },
   "source": [
    "## 2. Build the variational autoencoder <a name=\"build\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Sampling` layer implements the reparameterization trick."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "fkqKY1kGE7Os"
   },
   "outputs": [],
   "source": [
    "class Sampling(layers.Layer):\n",
    "    def call(self, inputs):\n",
    "        z_mean, z_log_var = inputs\n",
    "        batch = tf.shape(z_mean)[0]\n",
    "        dim = tf.shape(z_mean)[1]\n",
    "        epsilon = K.random_normal(shape=(batch, dim))\n",
    "        return z_mean + tf.exp(0.5 * z_log_var) * epsilon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Task 1 — Build the VAE encoder then compare the VAE encoder with the deterministic AE encoder.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "id": "wLHaaMBUFBjA",
    "outputId": "9cf61584-961b-440c-e460-5cde2fbfc5d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"encoder\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " encoder_input (InputLayer)     [(None, 32, 32, 1)]  0           []                               \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 16, 16, 32)   320         ['encoder_input[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 8, 8, 64)     18496       ['conv2d_3[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 4, 4, 128)    73856       ['conv2d_4[0][0]']               \n",
      "                                                                                                  \n",
      " flatten_1 (Flatten)            (None, 2048)         0           ['conv2d_5[0][0]']               \n",
      "                                                                                                  \n",
      " z_mean (Dense)                 (None, 2)            4098        ['flatten_1[0][0]']              \n",
      "                                                                                                  \n",
      " z_log_var (Dense)              (None, 2)            4098        ['flatten_1[0][0]']              \n",
      "                                                                                                  \n",
      " sampling (Sampling)            (None, 2)            0           ['z_mean[0][0]',                 \n",
      "                                                                  'z_log_var[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 100,868\n",
      "Trainable params: 100,868\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Encoder\n",
    "encoder_input = layers.Input(\n",
    "    shape=(IMAGE_SIZE, IMAGE_SIZE, 1), name=\"encoder_input\"\n",
    ")\n",
    "x = layers.Conv2D(32, (3, 3), strides=2, activation=\"relu\", padding=\"same\")(\n",
    "    encoder_input\n",
    ")\n",
    "x = layers.Conv2D(64, (3, 3), strides=2, activation=\"relu\", padding=\"same\")(x)\n",
    "x = layers.Conv2D(128, (3, 3), strides=2, activation=\"relu\", padding=\"same\")(x)\n",
    "shape_before_flattening = K.int_shape(x)[1:]  # the decoder will need this!\n",
    "\n",
    "x = layers.Flatten()(x)\n",
    "# TODO: Modify the encoder so that it learns a probability distribution\n",
    "# in the latent space. Instead of returning one latent vector directly,\n",
    "# the encoder should return the mean and log-variance of a Gaussian\n",
    "# distribution, then sample z using the reparameterization trick.\n",
    "z_mean = None\n",
    "z_log_var = None\n",
    "z = None\n",
    "\n",
    "encoder = models.Model(encoder_input, [z_mean, z_log_var, z], name=\"encoder\")\n",
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 413
    },
    "id": "IMjoN1NPFDD5",
    "outputId": "4f7e2fe2-d16f-42e4-bc98-10fd8eaafc6e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " decoder_input (InputLayer)  [(None, 2)]               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 2048)              6144      \n",
      "                                                                 \n",
      " reshape_1 (Reshape)         (None, 4, 4, 128)         0         \n",
      "                                                                 \n",
      " conv2d_transpose_3 (Conv2DT  (None, 8, 8, 128)        147584    \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " conv2d_transpose_4 (Conv2DT  (None, 16, 16, 64)       73792     \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " conv2d_transpose_5 (Conv2DT  (None, 32, 32, 32)       18464     \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " decoder_output (Conv2D)     (None, 32, 32, 1)         289       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 246,273\n",
      "Trainable params: 246,273\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Decoder\n",
    "decoder_input = layers.Input(shape=(EMBEDDING_DIM,), name=\"decoder_input\")\n",
    "x = layers.Dense(np.prod(shape_before_flattening))(decoder_input)\n",
    "x = layers.Reshape(shape_before_flattening)(x)\n",
    "x = layers.Conv2DTranspose(\n",
    "    128, (3, 3), strides=2, activation=\"relu\", padding=\"same\"\n",
    ")(x)\n",
    "x = layers.Conv2DTranspose(\n",
    "    64, (3, 3), strides=2, activation=\"relu\", padding=\"same\"\n",
    ")(x)\n",
    "x = layers.Conv2DTranspose(\n",
    "    32, (3, 3), strides=2, activation=\"relu\", padding=\"same\"\n",
    ")(x)\n",
    "decoder_output = layers.Conv2D(\n",
    "    1,\n",
    "    (3, 3),\n",
    "    strides=1,\n",
    "    activation=\"sigmoid\",\n",
    "    padding=\"same\",\n",
    "    name=\"decoder_output\",\n",
    ")(x)\n",
    "\n",
    "decoder = models.Model(decoder_input, decoder_output)\n",
    "decoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task — Inspect the custom VAE training step\n",
    "\n",
    "The VAE class typically combines reconstruction loss and KL divergence.  \n",
    "\n",
    "1. Locate the reconstruction loss.\n",
    "2. Locate the KL divergence term.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "-dEy7kbnFEpt"
   },
   "outputs": [],
   "source": [
    "class VAE(models.Model):\n",
    "    def __init__(self, encoder, decoder, **kwargs):\n",
    "        super(VAE, self).__init__(**kwargs)\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.total_loss_tracker = metrics.Mean(name=\"total_loss\")\n",
    "        self.reconstruction_loss_tracker = metrics.Mean(\n",
    "            name=\"reconstruction_loss\"\n",
    "        )\n",
    "        self.kl_loss_tracker = metrics.Mean(name=\"kl_loss\")\n",
    "\n",
    "    @property\n",
    "    def metrics(self):\n",
    "        return [\n",
    "            self.total_loss_tracker,\n",
    "            self.reconstruction_loss_tracker,\n",
    "            self.kl_loss_tracker,\n",
    "        ]\n",
    "\n",
    "    def call(self, inputs):\n",
    "        \"\"\"Call the model on a particular input.\"\"\"\n",
    "        z_mean, z_log_var, z = encoder(inputs)\n",
    "        reconstruction = decoder(z)\n",
    "        return z_mean, z_log_var, reconstruction\n",
    "\n",
    "    def train_step(self, data):\n",
    "        \"\"\"Step run during training.\"\"\"\n",
    "        with tf.GradientTape() as tape:\n",
    "            z_mean, z_log_var, reconstruction = self(data)\n",
    "            reconstruction_loss = tf.reduce_mean(\n",
    "                BETA\n",
    "                * losses.binary_crossentropy(\n",
    "                    data, reconstruction, axis=(1, 2, 3)\n",
    "                )\n",
    "            )\n",
    "            kl_loss = tf.reduce_mean(\n",
    "                tf.reduce_sum(\n",
    "                    -0.5\n",
    "                    * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)),\n",
    "                    axis=1,\n",
    "                )\n",
    "            )\n",
    "            total_loss = reconstruction_loss + kl_loss\n",
    "\n",
    "        grads = tape.gradient(total_loss, self.trainable_weights)\n",
    "        self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n",
    "\n",
    "        self.total_loss_tracker.update_state(total_loss)\n",
    "        self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n",
    "        self.kl_loss_tracker.update_state(kl_loss)\n",
    "\n",
    "        return {m.name: m.result() for m in self.metrics}\n",
    "\n",
    "    def test_step(self, data):\n",
    "        \"\"\"Step run during validation.\"\"\"\n",
    "        if isinstance(data, tuple):\n",
    "            data = data[0]\n",
    "\n",
    "        z_mean, z_log_var, reconstruction = self(data)\n",
    "        reconstruction_loss = tf.reduce_mean(\n",
    "            BETA\n",
    "            * losses.binary_crossentropy(data, reconstruction, axis=(1, 2, 3))\n",
    "        )\n",
    "        kl_loss = tf.reduce_mean(\n",
    "            tf.reduce_sum(\n",
    "                -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)),\n",
    "                axis=1,\n",
    "            )\n",
    "        )\n",
    "        total_loss = reconstruction_loss + kl_loss\n",
    "\n",
    "        return {\n",
    "            \"loss\": total_loss,\n",
    "            \"reconstruction_loss\": reconstruction_loss,\n",
    "            \"kl_loss\": kl_loss,\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "VWoxQlNtRMsf"
   },
   "outputs": [],
   "source": [
    "# Create a variational autoencoder\n",
    "vae = VAE(encoder, decoder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KwtYTMvXQmQD"
   },
   "source": [
    "## 3. Train the variational autoencoder <a name=\"train\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "id": "ocyu6pytFJvM"
   },
   "outputs": [],
   "source": [
    "# Compile the variational autoencoder\n",
    "optimizer = optimizers.Adam(learning_rate=0.0005)\n",
    "vae.compile(optimizer=optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "4R749HIxFNfy"
   },
   "outputs": [],
   "source": [
    "# Create a model save checkpoint\n",
    "model_checkpoint_callback = callbacks.ModelCheckpoint(\n",
    "    filepath=\"content/checkpoint\",\n",
    "    save_weights_only=False,\n",
    "    save_freq=\"epoch\",\n",
    "    monitor=\"loss\",\n",
    "    mode=\"min\",\n",
    "    save_best_only=True,\n",
    "    verbose=0,\n",
    ")\n",
    "tensorboard_callback = callbacks.TensorBoard(log_dir=\"./logs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QmgZ6eGxFN1b",
    "outputId": "810455b6-ee2d-4103-dab9-0decb81d3884",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 136.2306 - reconstruction_loss: 131.4096 - kl_loss: 4.8210WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 136.2319 - reconstruction_loss: 131.4107 - kl_loss: 4.8212 - val_loss: 138.2330 - val_reconstruction_loss: 133.1853 - val_kl_loss: 5.0477\n",
      "Epoch 2/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 134.1082 - reconstruction_loss: 129.1657 - kl_loss: 4.9426WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 134.1144 - reconstruction_loss: 129.1720 - kl_loss: 4.9425 - val_loss: 136.4394 - val_reconstruction_loss: 131.3959 - val_kl_loss: 5.0435\n",
      "Epoch 3/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 133.1655 - reconstruction_loss: 128.1678 - kl_loss: 4.9977WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 133.1649 - reconstruction_loss: 128.1670 - kl_loss: 4.9979 - val_loss: 136.0918 - val_reconstruction_loss: 130.7275 - val_kl_loss: 5.3643\n",
      "Epoch 4/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 132.6267 - reconstruction_loss: 127.5456 - kl_loss: 5.0812WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 132.6192 - reconstruction_loss: 127.5377 - kl_loss: 5.0816 - val_loss: 135.3079 - val_reconstruction_loss: 129.8600 - val_kl_loss: 5.4480\n",
      "Epoch 5/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 132.1906 - reconstruction_loss: 127.0508 - kl_loss: 5.1397WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 132.1929 - reconstruction_loss: 127.0534 - kl_loss: 5.1394 - val_loss: 136.0633 - val_reconstruction_loss: 130.9284 - val_kl_loss: 5.1349\n",
      "Epoch 6/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 131.8897 - reconstruction_loss: 126.7180 - kl_loss: 5.1717WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 131.8880 - reconstruction_loss: 126.7163 - kl_loss: 5.1718 - val_loss: 135.4441 - val_reconstruction_loss: 130.1115 - val_kl_loss: 5.3326\n",
      "Epoch 7/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 131.5650 - reconstruction_loss: 126.3426 - kl_loss: 5.2224WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 131.5713 - reconstruction_loss: 126.3489 - kl_loss: 5.2224 - val_loss: 134.6518 - val_reconstruction_loss: 129.2540 - val_kl_loss: 5.3979\n",
      "Epoch 8/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 131.3165 - reconstruction_loss: 126.0720 - kl_loss: 5.2446WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 131.3063 - reconstruction_loss: 126.0615 - kl_loss: 5.2448 - val_loss: 134.5915 - val_reconstruction_loss: 129.0282 - val_kl_loss: 5.5633\n",
      "Epoch 9/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 131.1229 - reconstruction_loss: 125.8581 - kl_loss: 5.2648WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 131.1264 - reconstruction_loss: 125.8613 - kl_loss: 5.2651 - val_loss: 134.6215 - val_reconstruction_loss: 129.0347 - val_kl_loss: 5.5868\n",
      "Epoch 10/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 130.9229 - reconstruction_loss: 125.6242 - kl_loss: 5.2987WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 130.9150 - reconstruction_loss: 125.6162 - kl_loss: 5.2987 - val_loss: 133.6487 - val_reconstruction_loss: 128.2047 - val_kl_loss: 5.4439\n",
      "Epoch 11/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 130.7838 - reconstruction_loss: 125.4652 - kl_loss: 5.3188WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 130.7729 - reconstruction_loss: 125.4543 - kl_loss: 5.3187 - val_loss: 134.4010 - val_reconstruction_loss: 128.9000 - val_kl_loss: 5.5010\n",
      "Epoch 12/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 130.6206 - reconstruction_loss: 125.2870 - kl_loss: 5.3337WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 130.6205 - reconstruction_loss: 125.2869 - kl_loss: 5.3337 - val_loss: 133.3951 - val_reconstruction_loss: 128.0555 - val_kl_loss: 5.3396\n",
      "Epoch 13/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 130.4395 - reconstruction_loss: 125.0844 - kl_loss: 5.3549WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 130.4436 - reconstruction_loss: 125.0885 - kl_loss: 5.3551 - val_loss: 133.1550 - val_reconstruction_loss: 127.6859 - val_kl_loss: 5.4691\n",
      "Epoch 14/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 130.3215 - reconstruction_loss: 124.9307 - kl_loss: 5.3908WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 130.3108 - reconstruction_loss: 124.9203 - kl_loss: 5.3905 - val_loss: 133.5979 - val_reconstruction_loss: 128.1968 - val_kl_loss: 5.4011\n",
      "Epoch 15/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 130.1614 - reconstruction_loss: 124.7546 - kl_loss: 5.4068WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 130.1630 - reconstruction_loss: 124.7560 - kl_loss: 5.4070 - val_loss: 133.0215 - val_reconstruction_loss: 127.3277 - val_kl_loss: 5.6937\n",
      "Epoch 16/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 130.0524 - reconstruction_loss: 124.6332 - kl_loss: 5.4191WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 130.0462 - reconstruction_loss: 124.6275 - kl_loss: 5.4186 - val_loss: 133.2728 - val_reconstruction_loss: 127.9502 - val_kl_loss: 5.3226\n",
      "Epoch 17/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 129.9022 - reconstruction_loss: 124.4673 - kl_loss: 5.4351WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 129.8915 - reconstruction_loss: 124.4567 - kl_loss: 5.4349 - val_loss: 133.6940 - val_reconstruction_loss: 128.2332 - val_kl_loss: 5.4608\n",
      "Epoch 18/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 129.7977 - reconstruction_loss: 124.3479 - kl_loss: 5.4498WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 129.7925 - reconstruction_loss: 124.3426 - kl_loss: 5.4498 - val_loss: 133.3792 - val_reconstruction_loss: 127.8490 - val_kl_loss: 5.5302\n",
      "Epoch 19/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 129.6250 - reconstruction_loss: 124.1622 - kl_loss: 5.4627WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 129.6262 - reconstruction_loss: 124.1634 - kl_loss: 5.4627 - val_loss: 132.7942 - val_reconstruction_loss: 127.2612 - val_kl_loss: 5.5330\n",
      "Epoch 20/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 129.5662 - reconstruction_loss: 124.0871 - kl_loss: 5.4791WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 129.5599 - reconstruction_loss: 124.0807 - kl_loss: 5.4792 - val_loss: 133.2428 - val_reconstruction_loss: 127.6264 - val_kl_loss: 5.6164\n",
      "Epoch 21/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 129.4707 - reconstruction_loss: 123.9778 - kl_loss: 5.4928WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 129.4643 - reconstruction_loss: 123.9715 - kl_loss: 5.4928 - val_loss: 133.2308 - val_reconstruction_loss: 127.6926 - val_kl_loss: 5.5382\n",
      "Epoch 22/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 129.3560 - reconstruction_loss: 123.8529 - kl_loss: 5.5031WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
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    },
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     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 129.3595 - reconstruction_loss: 123.8564 - kl_loss: 5.5030 - val_loss: 133.1164 - val_reconstruction_loss: 127.5287 - val_kl_loss: 5.5877\n",
      "Epoch 23/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 129.2872 - reconstruction_loss: 123.7717 - kl_loss: 5.5156WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 129.2819 - reconstruction_loss: 123.7661 - kl_loss: 5.5157 - val_loss: 132.8228 - val_reconstruction_loss: 127.1887 - val_kl_loss: 5.6341\n",
      "Epoch 24/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 129.1917 - reconstruction_loss: 123.6612 - kl_loss: 5.5306WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
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    },
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     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 129.1881 - reconstruction_loss: 123.6574 - kl_loss: 5.5308 - val_loss: 132.8641 - val_reconstruction_loss: 127.1189 - val_kl_loss: 5.7452\n",
      "Epoch 25/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 129.0642 - reconstruction_loss: 123.5258 - kl_loss: 5.5384WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
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    },
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     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 129.0672 - reconstruction_loss: 123.5287 - kl_loss: 5.5385 - val_loss: 132.8758 - val_reconstruction_loss: 127.1915 - val_kl_loss: 5.6843\n",
      "Epoch 26/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 129.0458 - reconstruction_loss: 123.5041 - kl_loss: 5.5417WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
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    },
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     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 129.0474 - reconstruction_loss: 123.5057 - kl_loss: 5.5418 - val_loss: 132.8215 - val_reconstruction_loss: 127.1435 - val_kl_loss: 5.6780\n",
      "Epoch 27/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 128.9794 - reconstruction_loss: 123.4245 - kl_loss: 5.5546WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 128.9826 - reconstruction_loss: 123.4277 - kl_loss: 5.5546 - val_loss: 133.1829 - val_reconstruction_loss: 127.5237 - val_kl_loss: 5.6593\n",
      "Epoch 28/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 128.9185 - reconstruction_loss: 123.3481 - kl_loss: 5.5704WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 128.9243 - reconstruction_loss: 123.3537 - kl_loss: 5.5707 - val_loss: 132.2086 - val_reconstruction_loss: 126.4460 - val_kl_loss: 5.7626\n",
      "Epoch 29/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 128.8132 - reconstruction_loss: 123.2325 - kl_loss: 5.5806WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 128.8194 - reconstruction_loss: 123.2386 - kl_loss: 5.5807 - val_loss: 131.8267 - val_reconstruction_loss: 126.2051 - val_kl_loss: 5.6217\n",
      "Epoch 30/30\n",
      "599/600 [============================>.] - ETA: 0s - total_loss: 128.7852 - reconstruction_loss: 123.2029 - kl_loss: 5.5823WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Can save best model only with loss available, skipping.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 22s 37ms/step - total_loss: 128.7949 - reconstruction_loss: 123.2121 - kl_loss: 5.5828 - val_loss: 132.3774 - val_reconstruction_loss: 126.4082 - val_kl_loss: 5.9692\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f4d787ce970>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vae.fit(\n",
    "    x_train,\n",
    "    epochs=EPOCHS,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=True,\n",
    "    validation_data=(x_test, x_test),\n",
    "    callbacks=[model_checkpoint_callback, tensorboard_callback],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ohfo_2FMFUmf"
   },
   "source": [
    "## 3. Reconstruct using the variational autoencoder <a name=\"reconstruct\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task  — Compare AE and VAE reconstructions\n",
    "\n",
    "1. Display the same Fashion-MNIST examples reconstructed by AE and VAE.\n",
    "2. Compare sharpness and semantic consistency.\n",
    "3. Explain why VAE outputs may look smoother.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "-v7hHe1pFV1_"
   },
   "outputs": [],
   "source": [
    "# Select a subset of the test set\n",
    "n_to_predict = 5000\n",
    "example_images = x_test[:n_to_predict]\n",
    "example_labels = y_test[:n_to_predict]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 335
    },
    "id": "pu_yydzMFWPH",
    "outputId": "112c20ca-f631-4863-efff-b8ad03354d65"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 1s 5ms/step\n",
      "Example real clothing items\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reconstructions\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create autoencoder predictions and display\n",
    "z_mean, z_log_var, reconstructions = vae.predict(example_images)\n",
    "print(\"Example real clothing items\")\n",
    "display(example_images)\n",
    "print(\"Reconstructions\")\n",
    "display(reconstructions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "id": "PJAVPuTgFaqw"
   },
   "outputs": [],
   "source": [
    "## 4. Embed using the encoder <a name=\"encode\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task — Visualize VAE latent structure\n",
    "\n",
    "1. Plot `z_mean` or sampled `z`.\n",
    "2. Color the plot by label.\n",
    "3. Compare it with the AE latent plot.\n",
    "4. Discuss differences between AE and VAE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PvTIMsWmGbk1"
   },
   "source": [
    "## 5. Generate using the decoder <a name=\"decode\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task -  Generate from the standard normal prior\n",
    "\n",
    "The VAE decoder is designed to accept samples from a known prior distribution.\n",
    "\n",
    "1. Sample `z` from a standard normal distribution.\n",
    "2. Decode the samples.\n",
    "3. Compare generated outputs with AE-generated outputs.\n",
    "4. Explain the role of the KL term in making this possible.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aibXdsJWFqFP"
   },
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UHHNks5qFqcr"
   },
   "source": [
    "## 6. Explore the latent space <a name=\"explore\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 682
    },
    "id": "wOCcNSTZFnCd",
    "outputId": "c2637ef7-cb2f-496a-efa0-26367856d1cb"
   },
   "outputs": [],
   "source": [
    "# Colour the embeddings by their label (clothing type - see table) compare with AE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "5NP07mx3FsFu",
    "outputId": "8e3ba0a8-5293-45f4-9a06-99a07440cdba"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODULE 4 — Variational Autoencoders on CelebA Faces\n",
    "\n",
    "This module transfers the same VAE idea to a more complex image dataset.  \n",
    "The goal is to show that latent-variable models can represent semantic factors of variation in faces.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "163aXCaMGjlY"
   },
   "source": [
    "# Variational Autoencoders - CelebA Faces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "id": "AW4k3y2mGi3C",
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras import (\n",
    "    layers,\n",
    "    models,\n",
    "    callbacks,\n",
    "    utils,\n",
    "    metrics,\n",
    "    losses,\n",
    "    optimizers,\n",
    ")\n",
    "\n",
    "from scipy.stats import norm\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting kagglehub\n",
      "  Downloading kagglehub-0.3.13-py3-none-any.whl (68 kB)\n",
      "\u001b[K     |████████████████████████████████| 68 kB 2.3 MB/s eta 0:00:011\n",
      "\u001b[?25hRequirement already satisfied: pyyaml in /home/akaluza/anaconda3/lib/python3.9/site-packages (from kagglehub) (6.0)\n",
      "Requirement already satisfied: packaging in /home/akaluza/anaconda3/lib/python3.9/site-packages (from kagglehub) (21.0)\n",
      "Requirement already satisfied: requests in /home/akaluza/anaconda3/lib/python3.9/site-packages (from kagglehub) (2.26.0)\n",
      "Requirement already satisfied: tqdm in /home/akaluza/anaconda3/lib/python3.9/site-packages (from kagglehub) (4.62.3)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in /home/akaluza/anaconda3/lib/python3.9/site-packages (from packaging->kagglehub) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/akaluza/anaconda3/lib/python3.9/site-packages (from requests->kagglehub) (2021.10.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/akaluza/anaconda3/lib/python3.9/site-packages (from requests->kagglehub) (3.2)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /home/akaluza/anaconda3/lib/python3.9/site-packages (from requests->kagglehub) (2.0.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/akaluza/anaconda3/lib/python3.9/site-packages (from requests->kagglehub) (1.26.7)\n",
      "Installing collected packages: kagglehub\n",
      "Successfully installed kagglehub-0.3.13\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install kagglehub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vO2_fKRxIEsc",
    "outputId": "f37f799e-9f65-4faf-8a76-b16bb2c560cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Looks like you're using an outdated `kagglehub` version (installed: 0.3.13), please consider upgrading to the latest version (1.0.0).\n",
      "Path to dataset files: /home/akaluza/.cache/kagglehub/datasets/jessicali9530/celeba-dataset/versions/2\n"
     ]
    }
   ],
   "source": [
    "import kagglehub\n",
    "\n",
    "# Download latest version\n",
    "path = kagglehub.dataset_download(\"jessicali9530/celeba-dataset\")\n",
    "\n",
    "print(\"Path to dataset files:\", path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "id": "C-eM9EN4HgCt"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def get_vector_from_label(data, vae, embedding_dim, label):\n",
    "    current_sum_POS = np.zeros(shape=embedding_dim, dtype=\"float32\")\n",
    "    current_n_POS = 0\n",
    "    current_mean_POS = np.zeros(shape=embedding_dim, dtype=\"float32\")\n",
    "\n",
    "    current_sum_NEG = np.zeros(shape=embedding_dim, dtype=\"float32\")\n",
    "    current_n_NEG = 0\n",
    "    current_mean_NEG = np.zeros(shape=embedding_dim, dtype=\"float32\")\n",
    "\n",
    "    current_vector = np.zeros(shape=embedding_dim, dtype=\"float32\")\n",
    "    current_dist = 0\n",
    "\n",
    "    print(\"label: \" + label)\n",
    "    print(\"images : POS move : NEG move :distance : 𝛥 distance\")\n",
    "    while current_n_POS < 10000:\n",
    "        batch = list(data.take(1).get_single_element())\n",
    "        im = batch[0]\n",
    "        attribute = batch[1]\n",
    "\n",
    "        _, _, z = vae.encoder.predict(np.array(im), verbose=0)\n",
    "\n",
    "        z_POS = z[attribute == 1]\n",
    "        z_NEG = z[attribute == -1]\n",
    "\n",
    "        if len(z_POS) > 0:\n",
    "            current_sum_POS = current_sum_POS + np.sum(z_POS, axis=0)\n",
    "            current_n_POS += len(z_POS)\n",
    "            new_mean_POS = current_sum_POS / current_n_POS\n",
    "            movement_POS = np.linalg.norm(new_mean_POS - current_mean_POS)\n",
    "\n",
    "        if len(z_NEG) > 0:\n",
    "            current_sum_NEG = current_sum_NEG + np.sum(z_NEG, axis=0)\n",
    "            current_n_NEG += len(z_NEG)\n",
    "            new_mean_NEG = current_sum_NEG / current_n_NEG\n",
    "            movement_NEG = np.linalg.norm(new_mean_NEG - current_mean_NEG)\n",
    "\n",
    "        current_vector = new_mean_POS - new_mean_NEG\n",
    "        new_dist = np.linalg.norm(current_vector)\n",
    "        dist_change = new_dist - current_dist\n",
    "\n",
    "        print(\n",
    "            str(current_n_POS)\n",
    "            + \"    : \"\n",
    "            + str(np.round(movement_POS, 3))\n",
    "            + \"    : \"\n",
    "            + str(np.round(movement_NEG, 3))\n",
    "            + \"    : \"\n",
    "            + str(np.round(new_dist, 3))\n",
    "            + \"    : \"\n",
    "            + str(np.round(dist_change, 3))\n",
    "        )\n",
    "\n",
    "        current_mean_POS = np.copy(new_mean_POS)\n",
    "        current_mean_NEG = np.copy(new_mean_NEG)\n",
    "        current_dist = np.copy(new_dist)\n",
    "\n",
    "        if np.sum([movement_POS, movement_NEG]) < 0.08:\n",
    "            current_vector = current_vector / current_dist\n",
    "            print(\"Found the \" + label + \" vector\")\n",
    "            break\n",
    "\n",
    "    return current_vector\n",
    "\n",
    "\n",
    "def add_vector_to_images(data, vae, feature_vec):\n",
    "    n_to_show = 5\n",
    "    factors = [-4, -3, -2, -1, 0, 1, 2, 3, 4]\n",
    "\n",
    "    example_batch = list(data.take(1).get_single_element())\n",
    "    example_images = example_batch[0]\n",
    "\n",
    "    _, _, z_points = vae.encoder.predict(example_images, verbose=0)\n",
    "\n",
    "    fig = plt.figure(figsize=(18, 10))\n",
    "\n",
    "    counter = 1\n",
    "\n",
    "    for i in range(n_to_show):\n",
    "        img = example_images[i]\n",
    "        sub = fig.add_subplot(n_to_show, len(factors) + 1, counter)\n",
    "        sub.axis(\"off\")\n",
    "        sub.imshow(img)\n",
    "\n",
    "        counter += 1\n",
    "\n",
    "        for factor in factors:\n",
    "            changed_z_point = z_points[i] + feature_vec * factor\n",
    "            changed_image = vae.decoder.predict(\n",
    "                np.array([changed_z_point]), verbose=0\n",
    "            )[0]\n",
    "\n",
    "            sub = fig.add_subplot(n_to_show, len(factors) + 1, counter)\n",
    "            sub.axis(\"off\")\n",
    "            sub.imshow(changed_image)\n",
    "\n",
    "            counter += 1\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def morph_faces(data, vae):\n",
    "    factors = np.arange(0, 1, 0.1)\n",
    "\n",
    "    example_batch = list(data.take(1).get_single_element())[:2]\n",
    "    example_images = example_batch[0]\n",
    "    _, _, z_points = vae.encoder.predict(example_images, verbose=0)\n",
    "\n",
    "    fig = plt.figure(figsize=(18, 8))\n",
    "\n",
    "    counter = 1\n",
    "\n",
    "    img = example_images[0]\n",
    "    sub = fig.add_subplot(1, len(factors) + 2, counter)\n",
    "    sub.axis(\"off\")\n",
    "    sub.imshow(img)\n",
    "\n",
    "    counter += 1\n",
    "\n",
    "    for factor in factors:\n",
    "        changed_z_point = z_points[0] * (1 - factor) + z_points[1] * factor\n",
    "        changed_image = vae.decoder.predict(\n",
    "            np.array([changed_z_point]), verbose=0\n",
    "        )[0]\n",
    "        sub = fig.add_subplot(1, len(factors) + 2, counter)\n",
    "        sub.axis(\"off\")\n",
    "        sub.imshow(changed_image)\n",
    "\n",
    "        counter += 1\n",
    "\n",
    "    img = example_images[1]\n",
    "    sub = fig.add_subplot(1, len(factors) + 2, counter)\n",
    "    sub.axis(\"off\")\n",
    "    sub.imshow(img)\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "id": "iKBYp7RKFtxG"
   },
   "outputs": [],
   "source": [
    "IMAGE_SIZE = 32\n",
    "CHANNELS = 3\n",
    "BATCH_SIZE = 128\n",
    "NUM_FEATURES = 128\n",
    "Z_DIM = 200\n",
    "LEARNING_RATE = 0.0005\n",
    "EPOCHS = 30\n",
    "BETA = 2000\n",
    "LOAD_MODEL = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y3FNNRuXHSTL"
   },
   "source": [
    "## Prepare Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AI7TZ4sLHPrw",
    "outputId": "8954bb8f-94c6-4f7d-94cb-48b823bbdd4b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 202599 files belonging to 1 classes.\n"
     ]
    }
   ],
   "source": [
    "# Load the data\n",
    "train_data = utils.image_dataset_from_directory(\n",
    "    f\"{path}/img_align_celeba/img_align_celeba\",\n",
    "    labels=None,\n",
    "    color_mode=\"rgb\",\n",
    "    image_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=True,\n",
    "    seed=42,\n",
    "    interpolation=\"bilinear\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "id": "Xr91_6u5HUD3"
   },
   "outputs": [],
   "source": [
    "# Preprocess the data\n",
    "def preprocess(img):\n",
    "    img = tf.cast(img, \"float32\") / 255.0\n",
    "    return img\n",
    "\n",
    "\n",
    "train = train_data.map(lambda x: preprocess(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "id": "aDFC6LyhIik2"
   },
   "outputs": [],
   "source": [
    "train_sample = sample_batch(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 149
    },
    "id": "EDydhyvHIiyT",
    "outputId": "6940e524-3e15-4ab7-95a2-877379c189c4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show some faces from the training set\n",
    "display(train_sample, cmap=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lQ8F259cIl4M",
    "lines_to_next_cell": 0
   },
   "source": [
    "## 2. Build the variational autoencoder <a name=\"build\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-05-05T20:31:19.777762Z",
     "start_time": "2026-05-05T20:31:19.659891Z"
    },
    "id": "1lbCYo3tImKr"
   },
   "outputs": [],
   "source": [
    "class Sampling(layers.Layer):\n",
    "    def call(self, inputs):\n",
    "        z_mean, z_log_var = inputs\n",
    "        batch = tf.shape(z_mean)[0]\n",
    "        dim = tf.shape(z_mean)[1]\n",
    "        epsilon = K.random_normal(shape=(batch, dim))\n",
    "        return z_mean + tf.exp(0.5 * z_log_var) * epsilon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 875
    },
    "id": "Y5U0Yp9_Ing4",
    "outputId": "3d78c9d6-f624-4bdf-8671-0c25a9666f3c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"encoder\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " encoder_input (InputLayer)     [(None, 32, 32, 3)]  0           []                               \n",
      "                                                                                                  \n",
      " conv2d_10 (Conv2D)             (None, 16, 16, 128)  3584        ['encoder_input[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_9 (BatchNo  (None, 16, 16, 128)  512        ['conv2d_10[0][0]']              \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " leaky_re_lu_9 (LeakyReLU)      (None, 16, 16, 128)  0           ['batch_normalization_9[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_11 (Conv2D)             (None, 8, 8, 128)    147584      ['leaky_re_lu_9[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_10 (BatchN  (None, 8, 8, 128)   512         ['conv2d_11[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " leaky_re_lu_10 (LeakyReLU)     (None, 8, 8, 128)    0           ['batch_normalization_10[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_12 (Conv2D)             (None, 4, 4, 128)    147584      ['leaky_re_lu_10[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_11 (BatchN  (None, 4, 4, 128)   512         ['conv2d_12[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " leaky_re_lu_11 (LeakyReLU)     (None, 4, 4, 128)    0           ['batch_normalization_11[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_13 (Conv2D)             (None, 2, 2, 128)    147584      ['leaky_re_lu_11[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_12 (BatchN  (None, 2, 2, 128)   512         ['conv2d_13[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " leaky_re_lu_12 (LeakyReLU)     (None, 2, 2, 128)    0           ['batch_normalization_12[0][0]'] \n",
      "                                                                                                  \n",
      " flatten_3 (Flatten)            (None, 512)          0           ['leaky_re_lu_12[0][0]']         \n",
      "                                                                                                  \n",
      " z_mean (Dense)                 (None, 200)          102600      ['flatten_3[0][0]']              \n",
      "                                                                                                  \n",
      " z_log_var (Dense)              (None, 200)          102600      ['flatten_3[0][0]']              \n",
      "                                                                                                  \n",
      " sampling_2 (Sampling)          (None, 200)          0           ['z_mean[0][0]',                 \n",
      "                                                                  'z_log_var[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 653,584\n",
      "Trainable params: 652,560\n",
      "Non-trainable params: 1,024\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Encoder\n",
    "encoder_input = layers.Input(\n",
    "    shape=(IMAGE_SIZE, IMAGE_SIZE, CHANNELS), name=\"encoder_input\"\n",
    ")\n",
    "x = layers.Conv2D(NUM_FEATURES, kernel_size=3, strides=2, padding=\"same\")(\n",
    "    encoder_input\n",
    ")\n",
    "x = layers.BatchNormalization()(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "x = layers.Conv2D(NUM_FEATURES, kernel_size=3, strides=2, padding=\"same\")(x)\n",
    "x = layers.BatchNormalization()(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "x = layers.Conv2D(NUM_FEATURES, kernel_size=3, strides=2, padding=\"same\")(x)\n",
    "x = layers.BatchNormalization()(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "x = layers.Conv2D(NUM_FEATURES, kernel_size=3, strides=2, padding=\"same\")(x)\n",
    "x = layers.BatchNormalization()(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "shape_before_flattening = K.int_shape(x)[1:]  # the decoder will need this!\n",
    "\n",
    "x = layers.Flatten()(x)\n",
    "z_mean = layers.Dense(Z_DIM, name=\"z_mean\")(x)\n",
    "z_log_var = layers.Dense(Z_DIM, name=\"z_log_var\")(x)\n",
    "z = Sampling()([z_mean, z_log_var])\n",
    "\n",
    "encoder = models.Model(encoder_input, [z_mean, z_log_var, z], name=\"encoder\")\n",
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 892
    },
    "id": "JcfJKqkJIrI5",
    "outputId": "be87a3a8-d4b9-4805-dd46-c5bdb3d4d0ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " decoder_input (InputLayer)  [(None, 200)]             0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 512)               102912    \n",
      "                                                                 \n",
      " batch_normalization_13 (Bat  (None, 512)              2048      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " leaky_re_lu_13 (LeakyReLU)  (None, 512)               0         \n",
      "                                                                 \n",
      " reshape_3 (Reshape)         (None, 2, 2, 128)         0         \n",
      "                                                                 \n",
      " conv2d_transpose_11 (Conv2D  (None, 4, 4, 128)        147584    \n",
      " Transpose)                                                      \n",
      "                                                                 \n",
      " batch_normalization_14 (Bat  (None, 4, 4, 128)        512       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " leaky_re_lu_14 (LeakyReLU)  (None, 4, 4, 128)         0         \n",
      "                                                                 \n",
      " conv2d_transpose_12 (Conv2D  (None, 8, 8, 128)        147584    \n",
      " Transpose)                                                      \n",
      "                                                                 \n",
      " batch_normalization_15 (Bat  (None, 8, 8, 128)        512       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " leaky_re_lu_15 (LeakyReLU)  (None, 8, 8, 128)         0         \n",
      "                                                                 \n",
      " conv2d_transpose_13 (Conv2D  (None, 16, 16, 128)      147584    \n",
      " Transpose)                                                      \n",
      "                                                                 \n",
      " batch_normalization_16 (Bat  (None, 16, 16, 128)      512       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " leaky_re_lu_16 (LeakyReLU)  (None, 16, 16, 128)       0         \n",
      "                                                                 \n",
      " conv2d_transpose_14 (Conv2D  (None, 32, 32, 128)      147584    \n",
      " Transpose)                                                      \n",
      "                                                                 \n",
      " batch_normalization_17 (Bat  (None, 32, 32, 128)      512       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " leaky_re_lu_17 (LeakyReLU)  (None, 32, 32, 128)       0         \n",
      "                                                                 \n",
      " conv2d_transpose_15 (Conv2D  (None, 32, 32, 3)        3459      \n",
      " Transpose)                                                      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 700,803\n",
      "Trainable params: 698,755\n",
      "Non-trainable params: 2,048\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Decoder\n",
    "decoder_input = layers.Input(shape=(Z_DIM,), name=\"decoder_input\")\n",
    "x = layers.Dense(np.prod(shape_before_flattening))(decoder_input)\n",
    "x = layers.BatchNormalization()(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "x = layers.Reshape(shape_before_flattening)(x)\n",
    "x = layers.Conv2DTranspose(\n",
    "    NUM_FEATURES, kernel_size=3, strides=2, padding=\"same\"\n",
    ")(x)\n",
    "x = layers.BatchNormalization()(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "x = layers.Conv2DTranspose(\n",
    "    NUM_FEATURES, kernel_size=3, strides=2, padding=\"same\"\n",
    ")(x)\n",
    "x = layers.BatchNormalization()(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "x = layers.Conv2DTranspose(\n",
    "    NUM_FEATURES, kernel_size=3, strides=2, padding=\"same\"\n",
    ")(x)\n",
    "x = layers.BatchNormalization()(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "x = layers.Conv2DTranspose(\n",
    "    NUM_FEATURES, kernel_size=3, strides=2, padding=\"same\"\n",
    ")(x)\n",
    "x = layers.BatchNormalization()(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "decoder_output = layers.Conv2DTranspose(\n",
    "    CHANNELS, kernel_size=3, strides=1, activation=\"sigmoid\", padding=\"same\"\n",
    ")(x)\n",
    "decoder = models.Model(decoder_input, decoder_output)\n",
    "decoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "id": "6fx3K1ecZX9c"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.losses import mse as mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "id": "qlonMkJJItRG"
   },
   "outputs": [],
   "source": [
    "class VAE(models.Model):\n",
    "    def __init__(self, encoder, decoder, **kwargs):\n",
    "        super(VAE, self).__init__(**kwargs)\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.total_loss_tracker = metrics.Mean(name=\"total_loss\")\n",
    "        self.reconstruction_loss_tracker = metrics.Mean(\n",
    "            name=\"reconstruction_loss\"\n",
    "        )\n",
    "        self.kl_loss_tracker = metrics.Mean(name=\"kl_loss\")\n",
    "\n",
    "    @property\n",
    "    def metrics(self):\n",
    "        return [\n",
    "            self.total_loss_tracker,\n",
    "            self.reconstruction_loss_tracker,\n",
    "            self.kl_loss_tracker,\n",
    "        ]\n",
    "\n",
    "    def call(self, inputs):\n",
    "        \"\"\"Call the model on a particular input.\"\"\"\n",
    "        z_mean, z_log_var, z = encoder(inputs)\n",
    "        reconstruction = decoder(z)\n",
    "        return z_mean, z_log_var, reconstruction\n",
    "\n",
    "    def train_step(self, data):\n",
    "        \"\"\"Step run during training.\"\"\"\n",
    "        with tf.GradientTape() as tape:\n",
    "            z_mean, z_log_var, reconstruction = self(data, training=True)\n",
    "            reconstruction_loss = tf.reduce_mean(\n",
    "                BETA * mean_squared_error(data, reconstruction)\n",
    "            )\n",
    "            kl_loss = tf.reduce_mean(\n",
    "                tf.reduce_sum(\n",
    "                    -0.5\n",
    "                    * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)),\n",
    "                    axis=1,\n",
    "                )\n",
    "            )\n",
    "            total_loss = reconstruction_loss + kl_loss\n",
    "\n",
    "        grads = tape.gradient(total_loss, self.trainable_weights)\n",
    "        self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n",
    "\n",
    "        self.total_loss_tracker.update_state(total_loss)\n",
    "        self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n",
    "        self.kl_loss_tracker.update_state(kl_loss)\n",
    "\n",
    "        return {\n",
    "            \"loss\": self.total_loss_tracker.result(),\n",
    "            \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n",
    "            \"kl_loss\": self.kl_loss_tracker.result(),\n",
    "        }\n",
    "\n",
    "    def test_step(self, data):\n",
    "        \"\"\"Step run during validation.\"\"\"\n",
    "        if isinstance(data, tuple):\n",
    "            data = data[0]\n",
    "\n",
    "        z_mean, z_log_var, reconstruction = self(data)\n",
    "        reconstruction_loss = tf.reduce_mean(\n",
    "            BETA * losses.mean_squared_error(data, reconstruction)\n",
    "        )\n",
    "        kl_loss = tf.reduce_mean(\n",
    "            tf.reduce_sum(\n",
    "                -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)),\n",
    "                axis=1,\n",
    "            )\n",
    "        )\n",
    "        total_loss = reconstruction_loss + kl_loss\n",
    "\n",
    "        return {\n",
    "            \"loss\": total_loss,\n",
    "            \"reconstruction_loss\": reconstruction_loss,\n",
    "            \"kl_loss\": kl_loss,\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "id": "KFMqbyOxYdi8"
   },
   "outputs": [],
   "source": [
    "# Create a variational autoencoder\n",
    "vae = VAE(encoder, decoder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Train the variational autoencoder <a name=\"train\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "id": "Aw-D-h-NIxBg"
   },
   "outputs": [],
   "source": [
    "# Compile the variational autoencoder\n",
    "optimizer = optimizers.Adam(learning_rate=LEARNING_RATE)\n",
    "vae.compile(optimizer=optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "id": "pU6L1DWdIxqz"
   },
   "outputs": [],
   "source": [
    "# Create a model save checkpoint\n",
    "model_checkpoint_callback = callbacks.ModelCheckpoint(\n",
    "    filepath=\"content/checkpoint\",\n",
    "    save_weights_only=False,\n",
    "    save_freq=\"epoch\",\n",
    "    monitor=\"loss\",\n",
    "    mode=\"min\",\n",
    "    save_best_only=True,\n",
    "    verbose=0,\n",
    ")\n",
    "\n",
    "tensorboard_callback = callbacks.TensorBoard(log_dir=\"./logs\")\n",
    "\n",
    "\n",
    "class ImageGenerator(callbacks.Callback):\n",
    "    def __init__(self, num_img, latent_dim):\n",
    "        self.num_img = num_img\n",
    "        self.latent_dim = latent_dim\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        random_latent_vectors = tf.random.normal(\n",
    "            shape=(self.num_img, self.latent_dim)\n",
    "        )\n",
    "        generated_images = self.model.decoder(random_latent_vectors)\n",
    "        generated_images *= 255\n",
    "        generated_images.numpy()\n",
    "        for i in range(self.num_img):\n",
    "            img = utils.array_to_img(generated_images[i])\n",
    "            img.save(\"./output/generated_img_%03d_%d.png\" % (epoch, i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "id": "8JMVYnpOIzpR"
   },
   "outputs": [],
   "source": [
    "# # Load old weights if required\n",
    "# if LOAD_MODEL:\n",
    "#     vae.load_weights(\"./models/vae\")\n",
    "#     tmp = vae.predict(train.take(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/"
    },
    "id": "5hUuISyuI1qW",
    "outputId": "77294f67-6d00-4751-ee3b-9988c74345a0",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 68.5144 - reconstruction_loss: 51.4839 - kl_loss: 16.2338"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 23:40:33.837785: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:40:33.929415: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:40:33.966147: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:40:34.002177: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:40:34.038205: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:40:34.074957: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:40:34.240466: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-05 23:40:35.233462: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-05 23:40:35.288097: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:40:35.296797: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:40:35.336391: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:40:35.375893: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:40:35.416125: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:40:35.457077: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 248s 157ms/step - loss: 68.5139 - reconstruction_loss: 51.4839 - kl_loss: 16.2338\n",
      "Epoch 2/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 66.1914 - reconstruction_loss: 49.2413 - kl_loss: 16.6423"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 23:44:41.592058: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:44:41.685874: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:44:41.722777: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:44:41.758748: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:44:41.795593: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:44:42.429911: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:44:42.604639: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-05 23:44:43.619044: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-05 23:44:43.673358: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:44:43.682146: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:44:43.722493: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:44:43.761907: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:44:43.801477: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:44:43.841979: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 248s 157ms/step - loss: 66.1912 - reconstruction_loss: 49.2413 - kl_loss: 16.6423\n",
      "Epoch 3/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 65.2796 - reconstruction_loss: 48.1978 - kl_loss: 16.9001"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 23:48:50.944061: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:48:51.036191: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:48:51.071701: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:48:51.107113: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:48:51.143460: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:48:51.180614: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:48:51.347615: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-05 23:48:52.348916: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-05 23:48:52.402712: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:48:52.411276: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:48:52.451161: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:48:52.489976: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:48:52.528927: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:48:52.569331: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 249s 157ms/step - loss: 65.2795 - reconstruction_loss: 48.1978 - kl_loss: 16.9001\n",
      "Epoch 4/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 64.7678 - reconstruction_loss: 47.5395 - kl_loss: 17.0758"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 23:52:59.843840: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:52:59.937863: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:52:59.973580: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:53:00.609406: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:53:00.648465: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:53:00.685846: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:53:00.860423: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-05 23:53:01.873991: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-05 23:53:01.928851: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:53:01.937835: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:53:01.977404: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:53:02.017114: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:53:02.057491: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:53:02.098450: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 64.7677 - reconstruction_loss: 47.5395 - kl_loss: 17.0758\n",
      "Epoch 5/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 64.4012 - reconstruction_loss: 47.0389 - kl_loss: 17.2437"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-05 23:57:09.311960: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:57:09.402810: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:57:09.439044: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:57:09.474750: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:57:09.510944: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:57:09.548054: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:57:09.715655: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-05 23:57:10.724214: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-05 23:57:10.777729: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:57:10.786260: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:57:10.825652: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:57:10.864681: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:57:10.903492: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-05 23:57:10.943599: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 249s 157ms/step - loss: 64.4011 - reconstruction_loss: 47.0389 - kl_loss: 17.2437\n",
      "Epoch 6/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 64.0846 - reconstruction_loss: 46.6680 - kl_loss: 17.3350"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 00:01:18.629960: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:01:18.724232: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:01:18.761740: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:01:18.798091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:01:18.836716: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:01:18.873538: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:01:19.046062: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 00:01:20.055979: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 00:01:20.110490: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:01:20.119842: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:01:20.159347: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:01:20.198954: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:01:20.238432: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:01:20.280490: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 249s 157ms/step - loss: 64.0845 - reconstruction_loss: 46.6680 - kl_loss: 17.3350\n",
      "Epoch 7/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 63.9278 - reconstruction_loss: 46.3836 - kl_loss: 17.4327"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 00:05:27.481885: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:05:27.574901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:05:27.611007: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:05:27.647609: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:05:27.692335: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:05:27.730885: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:05:27.899386: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 00:05:28.914672: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 00:05:28.970796: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:05:28.979541: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:05:29.019439: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:05:29.062858: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:05:29.102783: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:05:29.143236: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 249s 157ms/step - loss: 63.9277 - reconstruction_loss: 46.3836 - kl_loss: 17.4327\n",
      "Epoch 8/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 63.6845 - reconstruction_loss: 46.1185 - kl_loss: 17.4859"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 00:09:37.678573: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:09:37.771307: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:09:37.807465: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:09:37.844366: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:09:37.880060: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:09:37.917559: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:09:38.087100: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 00:09:39.083514: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 00:09:39.137178: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:09:39.145785: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:09:39.184880: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:09:39.224296: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:09:39.263117: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:09:39.304054: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 63.6844 - reconstruction_loss: 46.1185 - kl_loss: 17.4859\n",
      "Epoch 9/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 63.5270 - reconstruction_loss: 45.9336 - kl_loss: 17.5271"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 00:13:47.105019: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:13:47.197602: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:13:47.233253: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:13:47.270103: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:13:47.306502: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:13:47.350074: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:13:47.518174: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 00:13:48.517027: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 00:13:48.570877: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:13:48.580090: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:13:48.619619: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:13:48.673813: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:13:48.712438: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:13:48.752918: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 63.5270 - reconstruction_loss: 45.9336 - kl_loss: 17.5271\n",
      "Epoch 10/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 63.4101 - reconstruction_loss: 45.7996 - kl_loss: 17.5456"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 00:17:57.275123: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:17:57.368630: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:17:57.404473: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:17:57.440977: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:17:57.478589: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:17:57.515815: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:17:57.691620: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 00:17:58.698112: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 00:17:58.753130: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:17:58.761889: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:17:58.801832: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:17:58.841435: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:17:58.881390: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:17:58.923040: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 63.4101 - reconstruction_loss: 45.7996 - kl_loss: 17.5456\n",
      "Epoch 11/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 63.2425 - reconstruction_loss: 45.6033 - kl_loss: 17.5729"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 00:22:07.113050: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:22:07.205232: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:22:07.241042: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:22:07.277319: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:22:07.312795: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:22:07.349935: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:22:07.516528: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 00:22:08.515783: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 00:22:08.572261: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:22:08.581026: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:22:08.620531: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:22:08.660715: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:22:08.700113: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:22:08.740970: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 63.2425 - reconstruction_loss: 45.6033 - kl_loss: 17.5729\n",
      "Epoch 12/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 63.1698 - reconstruction_loss: 45.4819 - kl_loss: 17.5936"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 00:26:17.264386: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:26:17.356392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:26:17.393308: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:26:17.429046: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:26:17.465906: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:26:17.504639: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:26:17.672576: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 00:26:18.677773: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 00:26:18.732631: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:26:18.741370: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:26:18.781477: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:26:18.820901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:26:18.860268: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:26:18.900862: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 249s 158ms/step - loss: 63.1697 - reconstruction_loss: 45.4819 - kl_loss: 17.5936\n",
      "Epoch 13/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 63.0948 - reconstruction_loss: 45.4108 - kl_loss: 17.6320"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 00:30:26.569442: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:30:26.663212: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:30:26.699125: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:30:26.735080: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:30:26.771694: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:30:26.808345: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:30:26.976806: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 00:30:27.994322: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 00:30:28.048052: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:30:28.057204: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:30:28.096292: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:30:28.135293: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:30:28.174306: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:30:28.214891: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 63.0948 - reconstruction_loss: 45.4108 - kl_loss: 17.6320\n",
      "Epoch 14/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.9633 - reconstruction_loss: 45.2520 - kl_loss: 17.6568"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 00:34:37.157120: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:34:37.250358: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:34:37.286176: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:34:37.322610: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:34:37.358303: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:34:37.395594: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:34:37.565991: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 00:34:38.561126: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 00:34:38.615584: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:34:38.624317: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:34:38.664336: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:34:38.704335: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:34:38.743742: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:34:38.784111: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 62.9632 - reconstruction_loss: 45.2520 - kl_loss: 17.6568\n",
      "Epoch 15/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.8452 - reconstruction_loss: 45.0751 - kl_loss: 17.7053"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 00:38:46.816405: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:38:46.909480: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:38:46.945532: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:38:46.982635: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:38:47.018559: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:38:47.057361: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:38:47.229776: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 00:38:48.238464: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 00:38:48.292331: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:38:48.301102: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:38:48.340032: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:38:48.379399: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:38:48.418625: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:38:48.459336: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 62.8452 - reconstruction_loss: 45.0751 - kl_loss: 17.7053\n",
      "Epoch 16/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.7769 - reconstruction_loss: 44.9567 - kl_loss: 17.7566"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 00:42:57.359128: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:42:57.458138: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:42:57.495499: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:42:57.531757: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:42:57.569850: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:42:57.607553: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:42:57.778948: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 00:42:58.785459: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 00:42:58.840329: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:42:58.849396: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:42:58.890643: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:42:58.930108: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:42:58.969763: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:42:59.010323: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 62.7768 - reconstruction_loss: 44.9567 - kl_loss: 17.7566\n",
      "Epoch 17/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.7194 - reconstruction_loss: 44.8833 - kl_loss: 17.7766"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 00:47:07.021832: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:47:07.114661: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:47:07.150899: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:47:07.186993: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:47:07.223705: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:47:07.260993: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:47:07.430027: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 00:47:08.432091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 00:47:08.486691: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:47:08.495998: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:47:08.536941: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:47:08.577168: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:47:08.617081: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:47:08.658411: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 62.7193 - reconstruction_loss: 44.8833 - kl_loss: 17.7766\n",
      "Epoch 18/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.6580 - reconstruction_loss: 44.8019 - kl_loss: 17.7679"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 00:51:16.961725: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:51:17.055457: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:51:17.092217: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:51:17.129326: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:51:17.167625: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:51:17.205181: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:51:17.376704: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 00:51:18.386975: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 00:51:18.442261: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:51:18.451067: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:51:18.490519: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:51:18.530005: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:51:18.570693: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:51:18.612781: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 249s 157ms/step - loss: 62.6580 - reconstruction_loss: 44.8019 - kl_loss: 17.7679\n",
      "Epoch 19/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.6037 - reconstruction_loss: 44.7538 - kl_loss: 17.7998"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 00:55:26.016848: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:55:26.110072: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:55:26.146164: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:55:26.183177: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:55:26.219380: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:55:26.257910: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:55:26.426790: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 00:55:27.427314: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 00:55:27.481673: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:55:27.490771: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:55:27.536819: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:55:27.577432: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:55:27.624381: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:55:27.664817: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 62.6037 - reconstruction_loss: 44.7538 - kl_loss: 17.7998\n",
      "Epoch 20/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.5480 - reconstruction_loss: 44.6668 - kl_loss: 17.8161"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 00:59:36.110119: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:59:36.202766: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:59:36.239984: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:59:36.276302: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:59:36.317994: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:59:36.355783: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:59:36.531560: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 00:59:37.530564: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 00:59:37.584887: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:59:37.593627: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:59:37.633204: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:59:37.672128: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:59:37.711284: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 00:59:37.751235: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 249s 158ms/step - loss: 62.5479 - reconstruction_loss: 44.6668 - kl_loss: 17.8161\n",
      "Epoch 21/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.4993 - reconstruction_loss: 44.5766 - kl_loss: 17.8486"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 01:03:46.692009: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:03:46.783465: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:03:46.820070: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:03:46.855651: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:03:46.900300: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:03:46.936834: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:03:47.106620: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 01:03:48.104504: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 01:03:48.158760: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:03:48.167610: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:03:48.207550: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:03:48.246543: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:03:48.285624: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:03:48.325659: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 251s 159ms/step - loss: 62.4993 - reconstruction_loss: 44.5766 - kl_loss: 17.8486\n",
      "Epoch 22/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.4262 - reconstruction_loss: 44.4861 - kl_loss: 17.8748"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 01:07:57.535941: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:07:57.630294: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:07:57.666581: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:07:57.703456: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:07:57.740587: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:07:57.778707: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:07:57.951206: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 01:07:58.958675: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 01:07:59.013846: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:07:59.022670: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:07:59.062387: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:07:59.102023: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:07:59.142314: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:07:59.182791: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 62.4262 - reconstruction_loss: 44.4861 - kl_loss: 17.8748\n",
      "Epoch 23/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.3833 - reconstruction_loss: 44.4201 - kl_loss: 17.9134"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 01:12:07.901095: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:12:07.993749: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:12:08.029499: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:12:08.065900: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:12:08.101524: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:12:08.139111: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:12:08.314070: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 01:12:09.318124: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 01:12:09.372392: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:12:09.381083: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:12:09.421297: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:12:09.461695: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:12:09.501302: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:12:09.541811: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 62.3833 - reconstruction_loss: 44.4201 - kl_loss: 17.9134\n",
      "Epoch 24/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.3133 - reconstruction_loss: 44.3601 - kl_loss: 17.9125"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 01:16:18.171770: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:16:18.264191: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:16:18.300851: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:16:18.336711: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:16:18.372404: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:16:18.410021: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:16:18.578582: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 01:16:19.574949: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 01:16:19.634149: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:16:19.642863: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:16:19.682815: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:16:19.722538: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:16:19.762008: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:16:19.802766: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 62.3132 - reconstruction_loss: 44.3601 - kl_loss: 17.9125\n",
      "Epoch 25/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.2812 - reconstruction_loss: 44.3007 - kl_loss: 17.9307"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 01:20:27.862448: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:20:27.962030: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:20:27.998120: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:20:28.034760: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:20:28.071806: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:20:28.108970: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:20:28.277383: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 01:20:29.277300: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 01:20:29.332380: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:20:29.341115: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:20:29.380423: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:20:29.419790: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:20:29.459439: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:20:29.500436: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 62.2811 - reconstruction_loss: 44.3007 - kl_loss: 17.9307\n",
      "Epoch 26/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.2195 - reconstruction_loss: 44.2521 - kl_loss: 17.9340"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 01:24:37.401039: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:24:37.495549: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:24:37.531783: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:24:37.568658: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:24:37.605638: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:24:37.643080: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:24:37.821082: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 01:24:38.817870: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 01:24:38.871082: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:24:38.879687: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:24:38.918979: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:24:38.958864: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:24:38.998031: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:24:39.037960: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 249s 157ms/step - loss: 62.2195 - reconstruction_loss: 44.2521 - kl_loss: 17.9340\n",
      "Epoch 27/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.1630 - reconstruction_loss: 44.2144 - kl_loss: 17.9515"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 01:28:47.732094: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:28:47.823995: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:28:47.860946: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:28:47.896962: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:28:47.932928: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:28:47.971244: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:28:48.146703: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 01:28:49.139935: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 01:28:49.194783: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:28:49.203353: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:28:49.242245: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:28:49.281746: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:28:49.320516: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:28:49.360516: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 251s 158ms/step - loss: 62.1630 - reconstruction_loss: 44.2144 - kl_loss: 17.9515\n",
      "Epoch 28/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.1748 - reconstruction_loss: 44.1756 - kl_loss: 17.9501"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 01:32:57.472390: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:32:57.565531: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:32:57.601251: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:32:57.637238: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:32:57.675007: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:32:57.712401: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:32:57.881067: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 01:32:58.883029: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 01:32:58.936655: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:32:58.945835: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:32:58.984838: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:32:59.023847: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:32:59.062767: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:32:59.103551: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 249s 157ms/step - loss: 62.1747 - reconstruction_loss: 44.1756 - kl_loss: 17.9501\n",
      "Epoch 29/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.0922 - reconstruction_loss: 44.1182 - kl_loss: 17.9567"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 01:37:07.883408: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:37:07.975355: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:37:08.011049: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:37:08.047522: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:37:08.083070: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:37:08.120372: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:37:08.287739: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 01:37:09.282608: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 01:37:09.337755: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:37:09.346609: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:37:09.385542: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:37:09.424619: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:37:09.463631: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:37:09.504834: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 250s 158ms/step - loss: 62.0922 - reconstruction_loss: 44.1182 - kl_loss: 17.9567\n",
      "Epoch 30/30\n",
      "1583/1583 [==============================] - ETA: 0s - loss: 62.1004 - reconstruction_loss: 44.0892 - kl_loss: 17.9603"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 01:41:18.560416: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:41:18.653138: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:41:18.688810: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:41:18.725013: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:41:18.760874: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:41:18.798527: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:41:18.966142: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 01:41:19.960837: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 01:41:20.016098: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:41:20.024830: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:41:20.063950: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:41:20.103525: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:41:20.142673: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 01:41:20.182628: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: content/checkpoint/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1583/1583 [==============================] - 251s 158ms/step - loss: 62.1004 - reconstruction_loss: 44.0892 - kl_loss: 17.9603\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f4f283494c0>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vae.fit(\n",
    "    train,\n",
    "    epochs=EPOCHS,\n",
    "    callbacks=[\n",
    "        model_checkpoint_callback,\n",
    "        tensorboard_callback,\n",
    "        ImageGenerator(num_img=10, latent_dim=Z_DIM),\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "id": "0mGO93TMI3A7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-05-06 07:18:16.352072: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:16.443094: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:16.479848: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:16.515247: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:16.551749: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:16.588468: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:16.753814: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
      "2026-05-06 07:18:17.740601: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "2026-05-06 07:18:17.795227: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:17.808546: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:17.848162: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:17.887251: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:17.926315: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:17.966925: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ./models/vae/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ./models/vae/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
      "2026-05-06 07:18:19.814998: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:20.160355: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs_0' with dtype float and shape [?,200]\n",
      "\t [[{{node inputs_0}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ./models/encoder/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ./models/encoder/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
      "2026-05-06 07:18:20.558947: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:20.600644: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:20.639013: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:20.675522: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:20.713381: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:20.884163: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:21.191756: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,512]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:21.201354: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:21.241824: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:21.281501: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:21.321158: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "2026-05-06 07:18:21.360794: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,128]\n",
      "\t [[{{node inputs}}]]\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 5). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ./models/decoder/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ./models/decoder/assets\n"
     ]
    }
   ],
   "source": [
    "# Save the final models\n",
    "vae.save(\"./models/vae\")\n",
    "encoder.save(\"./models/encoder\")\n",
    "decoder.save(\"./models/decoder\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZDr9FtfYI6J6"
   },
   "source": [
    "## 3. Reconstruct using the variational autoencoder <a name=\"reconstruct\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task — Reconstruct faces\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "id": "SeDs3CZyI6Vr"
   },
   "outputs": [],
   "source": [
    "# Select a subset of the test set\n",
    "batches_to_predict = 1\n",
    "example_images = np.array(\n",
    "    list(train.take(batches_to_predict).get_single_element())\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "id": "ATJwT0_yI7zR"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 12ms/step\n",
      "Example real faces\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reconstructions\n"
     ]
    },
    {
     "data": {
      "image/png": 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15zw23+Ci1iE59qPjjVe98JTpa4v+TYqaKxHLrc/6xRAmsplkW8TrPq0BU2D6y0xjCIjrh/ib1+9xffEJATlpbjBvIH9PJWxU6XRYadMEf0boUrjxhJa55keRTcUVKQwO7794a0Bk82fupx20/V3TStV26o4gd7wB1PZY+Sfopn+iQw1z5Z9qG6NlhisRFKHgirHRb8EGqyTumna5+xUOPEG1kz6x6EugT9ldjkniKRP4FNONOvdaf4iEUZM+0vUabk67zZk8zYc1XHdrfUuDLDyz4xkKwLkvUrvO+kRvfW+eb09v+3RFL7bWws/BE3vuC7T0WRsBV67WuP76ZNvCR9VZZUtZ5W76ygCfTovG3zYfQ0qUmh228ZT6+5UPDrXXtLjIjaFuOPhwP56DLST7PM051Tfdaa/xJysx9uDow3TJ0RbCtR3L472aQPwLQ/P70OFlU4vJftOEf5BzwfqleHtbCPGKmcpsVPqsXzen35MH39/24PqnF2rbMnoL3U9eQhsw3swtYZlnTNOIkjMe7u9x9+k95mnC3ccP+PjrL5imER/ff8SHDx8xTRMejyc8PLIXzXgZcRkvEjKVseSlep6Vyk942lNhhY9iYKVhKQUhBCyF0M0Lui4hIPB7phn7uwNiStgdDhhu3qAbdshDxu5mwrDLCAgohZCaVb3eA9TwAk9M64u1rUUhtMqFLdzkxuSH54WBb8DR7WXuCtoClKY7V89co7ymPxvnyP3VnzwevF4M5WfqgaNHwZ1eA/gzN1UaxALzgNFVMl1729mr3tSiHNrWjCoqy7eBO69V3F8Y52rOnr6D2mNq+0tAywNt8FclsKxCqEoX9ZxRJWou2UIUiyQTLlSw5IwsSt1lmVEy/z5PE3LOT4/vb2hNz78E645P+a3Pr4/98s2qzLjeP7/tnZsdeKp9hWn8La/+ku44PHH8XG1L39CWQfDKmq3e0dWp6lkW7JrgfvP8UkN5eYMwLS2rED/ob/w7SuRrhM6FEBBKBEWs8GY9XkfIcF+qp6gPlVrPzReX2wsYT1y05vG+1L6iqGkpiOIlm293WbtJzRlIvitDt4FiiX+P7rq6Qm0f9CiGWqXIHnx1H64mu71G3Z1qFy3kQ65XJKpr7PuuLuDGqsbK/Jq84UfauD1tRy2/OA9DvgerhQxXlwhB8yExMIGC4TDYfJIxQ0rVFQKUmZRva6kAcIPfYPqUKV1Js7Suo+CWl+T5hdz70K7H1jlfhQGr8/VicoxRpQY+g8SXjHN/a2ss7K0M0RxeIY/CNbZCICA6sWuNkJSAXuFY/+T121ZE04W9eKUmr4m46UsFApJ59JWfPHtWh9sK8QZZpsEm1Fo3Ioa2mjrUEBgdhVtPD3crKfql92MdmV8Dp+F/grk3Aif3tpFPtAp7cRvDtnqFgYqvtgmZvo/sPgf7cmnbRbe/VxYPv6atMkZzCVG92L3L3ukZdnWndfDRQGrD3L9Mu84Dcx1GW3u/hX9WokFoZ9pv9fUyamWrAOIQKn3Hilgpza53M14EVXf4Nd2qFVXaVahWLUc0AYQQKz22Dj/t5qvr04Ldei4373rWtt7h25U2KuZQBZbRRGzgXaWHJBVXSsHlcsHp8R7zPOP9zz/iL3/6J1zOZ3x8/x6//PgTpnHE/f0DPn+6wzIvGKcJl2lCKQXzsmBeVXdq11MZSTlDjtcg3VvAGCNXxwgBKUX7Ox7PHBa16zHnjMfjEYebG4TUASDsD7fYHd5g2N+ACOhKRiFCtPVaM7A6NzIjDa16aYzqQza1d45+NN4R1BjoaievAd+EJrfYT4bPKskJq5MILQ1fvaPCF1Y0r66h36N+Xiv9lL8VbHoDYEsz6g+ex5PebJGeLST2LK0UVhqQhB0UFY6i9Fd7dD3d7fdmjdbXuR0v4yVaqduveKs6t/UpzT/tfK/5zQ3jE4iqNxyhCpxw+Nu9L6osAkIuNeQp56qcmfNix3lZUERxMy2LhDxxhZ2SF66sM83PrqhZ91vbFRzR1lVfe1ZoeYgNHpi/1pX+TfbSFfHZ4nYJaGzRvj3RHTtpeSL9DU90sOKrNU1193zBsP3727UcZnTf0HrtW2MIV95MtMx+XgLgZHEJcQIrIkNguR7BvQu8noWK5FXKKAt7eOZcK4cyQ1MQAqHEaLhD3oKAyIoctHvLIEv6FGMwGhmjVpaTz7iebKWx7TzxGIPx701YI3CNjO36Ly7It4c+2YTXNbDuuowXrrPrO33H0Gw6xXn+FqKah+HJ/qyfE66usInTeab1b/Zu9awJraIGXilT+xNW72XAUFHDcU36Tmr31npj+/E8f3P9tu86B9fssREYscSRS3awsYXrTTWhzfUlbvxGHNcIew2vgszIFpncdc6TZGO8LaPj1uSJpkTS5BzHNNVObnjc6BoHB68eoz9bc8/zoR6e0QgBFnZny6okXvNmfOUVaxh88nrjdNwZj6whXml1ozRWfEBK2HpE1iK/dh1bJsw+go5O1rhRbrQAFp4gji9G77baFyl6vcjGsyIu/LOJR9velhutoflrBLz1DusJXYOFg7ltxuSJ51297/ruK0XpBo7YfKR+22JgXgSnXnfnCo628PlTON4W0l3w5LUwRmTzvdocDrA19IrRUizPCYnwwEQxIgrBbAwMMTTzW2mnL/ULaN6sYG/e7uV6m24NdwslvUR7Wrm+YkxwveZrGDRaIhbAnBdM04R5HnE6HXF/9xnn0xGfP37Exw8fMY0jHh+OuLt/4JKly4JpWeTejCVns7ZvGY1irIarQpzfSToBZQxLJIRQEADEFI0ptdCnZcHhcERKCbkQzqcTpssFMSYW/HJBicWUBT6hrNfRmTfNFdy3tOLl2sb+2QKqb33K1bXk2SesvnxD28B533LdUxvDdWnrQa261N+w2seeHmywcF9To/7eRkVeHtUwVxl2443X96w+v9w7JwB43im0HPDTz6Dm6Gskhtdjc0GaxdLrFGe053XbVO6oOANkUeGVuPR2LlnKHquihjAvC+ejKRmLeNGUzGGU5cUUNX8HnL2xCN549O16DH7Qk9D9jANp4WzDkLHaqqYKf9mt9/W2KZetr3H4PQDr8GA+FexSWyAhF16GaZryJqhhe2Q5mPQacntIEt/DPCdqv5Qm+odL79QIU2V56YooXaqcKXvRj6/Bo3W+WFdynfLBQ0Id7ZcX+IuKGloBjn9fi9haSNL5MGu9uzb4Qaw4PvLX+N9XnYgBZlFsiYpPWFUt06ytW0+MwUkFRH+NY3yiYk2qyhivsQ3uId7gRUFmZoNvqbh6FaDwAu4YLWzyl9D8Q6IYgxxLV4hYAaGD2KLguj8V0PU3+z3YGqxv3eqohe7IPvDEq3ZX1qkpLu/UhfV2PdNOgnTGJy+tfO0GI+W9DhqrXDum9uxLkqkKUNs7BFfIdWvO6xJq72Ux/cXutL/PAlxkPlpBQr/z/YUALjBAYt1RK75T1NCKqXTgZvvRNOXBhI2AgBjJLHEk8FHxUoWbCterObIxt7P0ErRxKyKg2S7uxWvZpqpnsIkn3Cp+M1u9nhv3ekVPljza8IMbQfNOAizrjMEBOQJZ3Ivc+SuCuvrumifOa28gHgtVJsG8kp53JRvvzCu8xkBl8c2hnq9JvWuX7NNNIjXPc8dyIqLCDN9SLe71j5l4CENPRa2wM4rkOVlyZssUVXd6AGxVUvpq/Q2IXYeYOvHKSEgxcpWg1CGlJJaoKNandUz3NY9Qj2njLCwh89Zvz9O2ZnrVwrrXoemK7oWKz3g+52nC5XJGXhbcff6ED7/8jHG84Ocff8L7X37F+XzC3afPOB6PmKcZl3HE4oSqYm7e0o0nNvfW7ETX6S22tCgJK4QlF8SQAQSczheEmDDngo8fPyOmDjdvRty8+yN2h1v0w4JhdwANBaFEIDmDy6o/CrANPb0mkr+7rcWdp/24Wn89O7tichu2eo2r1mO0e1Dv2WiNd3mDMFrG8BrXP6UQr0TEzl/hyg0qQE5hG3DlHWDL84Sy/Jv2y9/YslQgigBKjIgibIXgqQ0kcajwiSti2vSa4JSJ63WVs44u+LySrexB23NiHr3Ku/hXu/VZwRCBQLl6xmlVJn0Huetc54w/WnJBJpLKbzNKycg54zKNmBdWvkzTBVk8aaZlxpILqGQsEn5ZSkFeJBn5CzWlc5VP/Vth5gtIY70k2xv0q8/fYP3aK/7GrrfPXeOJjXFdvWdrPMrbvAx/ut0c37lqHsN47EBQ9kswWWD+p/JFmuFzVQFKZCwO38vIecYyz8h5wTJPWOaZeZu8iEcYSXJgSQBMBZQz8yClB3WZkwZHdtagmJC6BMoRoAgqHahk2XfCnwa/+5428a95dVP2NPNA9gySC5vZ/EaQ/fZkwlswFdYX1E4oDx5dR/zfU11T7xfv3bJ+cxTXJL4evhaB9atJFiSLFNzzAWEE64vboTbIEyZU6BIWAoqMoXp8MABWYVXEFgLyipg4H4BmbC/SyjWio9AGD7Xu0tzDyi5w39hR45r9bpkPXRfnLqZ7s15k7/Eu056KthbZNtCM72uVcvXJDm3QBv8iD7dRknNDLVSVTu7TW6FBBVueXopsbF88u1zxJSZJNMDSZ4ZzOWuIgZq5qWFPLSPnP0m0qiZ0uyfVW0hcENXCIzHTmSSsgrAshZkF4mol2TTiXtFT3QQ9fIUYENQdMSXEyAJh3yUTDlPXIcbE98Q6ZsUfClY2el0ny2uyvujlSWBojl1lrE0C3CZK3DA6rJ68QVzWuJraH0OouE0ZxcpjsOKlmKeExxFypFNHhEDOS8NgqlRFBRX3riLlh67/qhzjmdb6XO+RpTDvJZBqLHjezei9YMmerhDnO8MXWoi094JRwu4VEWp9cUjLGzvU0hNDQNLbSgFk/3EljwwiwjRNnBelFEwjVwsqJWM8HTGeTyDisJolL25O5Z0xIaSIgIAUq/dFvz+gG3ZIKWK/32PoB8QUsdvtubxzCOj7HqlL4s7LihyAeA9rl3Xi3OzVWZRP268+KfRLNcKVCfZJBorqn/AEhSpzmZcFhQpOx0fc333CPI346a9/xT/9h/+A8/GI97/+ih//8meM44jz6YzT40kqOs0YpxGlEDJpFRcIvWkZYtlotUfElSTZICVGKaNxwffY4KtkxilxBkjy35TPwOPxgt1uh0LA/f0D3n73HXY3b9EPA3b7G+z2t9jtbkAJ8EaLsJ68wL+3ymYnXD9Ts6c53ozxpe45uLkgB+drmgNDYrYjPe8nz+FhhM3CAeHqX/db8LxUnTOKcHjx6ibnmFvH1vCncuCxnFVldF7enucBQptgeKNVZXvLt70EPgWARbw7EpEYYCKziQ1vh6qDB7W9MNLl9kVZ7Rt9zsog24xI1j96vLzZqpV9fUVloyqeKFSTfxcXtsFeAq7vHjbBsFq0WhMRpiVjycxnTRMrd5dlxsPxEedxxLLMOB+PmMaRFTUzK2pQMso8mvLeeN2Xbpv8jM5Pe139YeMhq5/C6me/Pbf68IWv7txX3rPR1pzQGr+FqwP/o/Kp7du+uCzCYLwkNQzrb7qJ1jwkcL13yKFL5WnBxgM+LwoTCiY/q/MGy3QEogzKE/KyYJkuGM8nS74/C09TxDOMQEgSeh0CEFOVC0rfo+t79hwVxQ8bmCTvU0ooKYJSQKHEChvztKjja+dE8vzpOogxid/feA5soPOAxnfLOZN8rf2mqk+b6/VkE6CjlrE15Eh+IJUrMu+XK4TpNkNURlcYV70XTmkTXdmtGAT58/NUabOlJVftdrX0CyhS5UtIrrV+UbsoQay6xS2Khur4caxni9xUPGdbkei2E54LXAnqds4RpHY1WsSyCW8bnkxPYyI/QZr4sjKD6krW1mT3BxvI4+r5TsykliDymyqm0TK45ilCkph3Bblb7t4vJ1esWY8rtFo/N4jkul/G0DYcqX+XIFzA4MKjMK9wMQRK7Iq7ZK4ssMwZ85JhcdUiTHpGmVwVAlP3irLPcit0PWIi044jADFEhMJFikMIQGH8YHuJ3A60fb+eumt2rrXmv3AL9VVX64UtpuHpfl3DvpJHbJzlf5rtfvWQ9scvhaOSIMlavn2FTxwjCr3G4E49ZAQGbDut3lc36mogeqzv/AKH+DtbY7nbZDavqeTVvpNrDH8AqPODBu82TzVYWeelYcWV7j9OYDuh5Izz+YLz+cKW1+MDLscHdn/PiySnhVNSi2JUFKJdiugil7Mc5gX9fkFKyRRlKSXeg+JNo9YrUmOKWNG075Xkelzv4QCNkqZd3BdqX0apT9+jB0ZDpKJKKViWGdPljHG84PjwgLuPH3E8HvH50yfc391jmkZM44xxHAVXLqZk83pL36WrYlNKykUjoYoJnutguS+0u7XLLKRHYmW6eQePE5aFFemPD49I7LrswqC6Bnc/KRkF7W+oe9Lh4b9X+xIfcK2kkQ+n+PXnmyet9uY1xbzuR8vwK3STmye6uoc2Pttxre5xW+pq7M3NZBbuzR22ZmecsQeCI567kZRZJwpVmaZGz+DnQf91ZXmb2SDr5ppUyNC2yH1zL0KovMiTPAIZjWkgxinEKm9DluQUqAYtoM2jaApEvUf+sihmciHMS8YsCYHHcWI8M884ns44Xy6Y5wnHxweMl4soagSvlAyaJ1BZriflX1hb6wK3ukru2uCOv3TP5rvWRxt82Bbv9C0v+G34ru3J+vG1SMjfE4NetytcsaZR/rMxGDC3Y2sUKi/Toj4CzDOV+Zm8zOJtytUSiYrJEfysYswsSWiUygjqRZO7BSkvCMTGYipZ6Clfz7K/5LExgWgTk0LV30qTFb/r2Bg+tis9GVQ4hj/U00+236So2Vokvzn4hS3AWX8aHEcO+QcnqFSGThfVEhuK1gpATfoDsSDpM4DmGq6HDmhyILt+U1teqUHNxg5jwkDgJKhUfTzUY8aQrUOuem9RK02pC1oq5Farz9+pXRFm6V8lwKtkuCuirMRELddahciQl1yuCSv9LiQRJkg3GJERaN0cq7chgCy3Sq1xD8TUIaYk65tMm1n0tdqnVoozglwVcrD1auIeYSTTfnuK+VnZdvxPz9bCE1/s0FEs3YfmUaOJR80LrVqD/Gb2jBDqafebnCJCES+nvCxWiWSeRDikgmlaMM0zx0nPnHeBPWo4ltr2mUx48d4Rsuc5xClxmFMI6PpevGcihqFH37MnTT8M6LoeGpLBHjgcwmH4IASLF+VUG1UbbsBrgsXVjL9sI57hEILBmadgHqlr28JgvlVmJ6yGcm1Js2cFZfbr7vEMrH9k+5D1cd15NYyRmmcBVaEjWAeahE2ZdP+OK4Iu+EA18zVBssL9i8gT9m4eSqU7Da4J25f736sHlVPWyLhVYXJ1vzL0hd3fAcI8XjBeLigl43I+43xiD43z6WjHlzMz8SVnjGfxqNEy0WphJfe2GBmfiqImqaLmcEC/2yOlhMNhj92wQ+o6HG5usdsf0KUOh9tb7PY7pJQw7A/o+x4hRnQd42tAsToaWlvhgo+NRIpQ+aKChRLmjdNtM+oBVUqWUpAF/03jBZfzI/Ky4OP7X/HTX/6My/mMn/76I96//xXn0xkP9/e4XC5Y5hnzvGBZFraWC/5Tunml0Fr1o1G9hkrG9V/7HsIm/MlPFZQL42YAWOYF5/MFKUXE2OHh7jPuP3/EMs/47vs/4nD7xsLdIqWKn76EM22T/H0kRH0LkVs1wvXrFf4UfQCoru+hxaPuZtI1oOp7WHnXr/esQWV+D2wMwtizrSfYRvKYV/FJ7ZualxjnNgDjOqIGGbQ5zeQ5nlQ+d5vGEQBMCZxS5pCGVNgiHtgoA7WcBw0AbXlGn/Puau2haxSujhucAye3PDVYpTPwcw4HT633lipbiIAsCX0J1MgU5DyNNQFwKYRpmjFLqNJlXizvzOV8xjxPmOcZd/f3OF8uWOYJx+MjpvHC4VHiwYxSUJYJKFlkqejG/twtPD1v/iqVt1bd8CLJ9ZPXB0/1oL2ImvNPren1e7deE4CrytS/ZSb9PU/dd92Pvw/e3OyINsKGsYA2p1N5WaM1FCwUkdlB4e9IYDMvEpq3YLqccTkdscxz5WOWhWF9GuGNwoBzvjBjEdOkZVkwzzNijJzrbVmQYkQpC/IyMU+zzOh3e8SUZP8V8c6X0CznFex56aq9EBlL8IHKUKZ83+DZm/aNwPObFDVbLzAEWE19TtC6lnlaZKjXo3rHxIAkA9cQpxBgVQrYHZtdsgEgqQInqFKHn5liRErOo8ZZ9eIWggqVEGk8P9wxI09BrBBvffD4sysbptmoC3HcbVDmK1IlyKYYaOfQz89ztjWS0u1SwxIgMEgO8NbPWG1Is3p4UukQI/FcAeAyolpGULSi8JpNU3KxRrOxrNuGAWJkoV3DXVLH+RJi6pBSJ/2OgBFxP+o6dlW8gCT5ohzXnBeOGaI69mZPCQH2c0ioxOe5aWB9nhtXqAgDDvYRgylj2CslQkOdQnSBgoZ4V8K74wgr7FRm0gsW0zRiHCfkknE+nXE8snB4Ol9wOp3ZRXfOmKRyiSUwBaqQRrp/JE7dXBkDuq5D6hJiDBhEIRNTxF4FxZSwv7nBbrfn8AwJvYghoBMlD7uYVyUfwNnmzaroGDeb0a8y3L+/XT1fFEc6J/734NeaL26eQ80XGUmUdXMwvL5Xvwd/ayMVeA8yUeISoYZ4wphSezZV8SDKvcXOk7yvKv8q01LLVFu5bT9RAZagOlCoNUshBF/nSba/4o3n34umHWrwwmrFNmGo0jy3XyHhQDoZTqiw+wzPiFfaMqOUgof7O9x9/oh5mvH50wd8fP8eyzzj+HCHx/s75GXBeLngcua9OM8jFmV2iHMXXL0tCCUIAUlwbowRw26Prh94z+33GIYBqetx+/Y77G9u0Q87fP/DH3H7lkNlvvv+D7i5vbWSz30/VKZGiK3Hs74XBk8vhE/9y9S7qV2s1WKgHvpwz5wXLAtXaDod7/H5wy+YxjP++s9/wr//N/8Gj48P+PjxM37+6RdME4egnc9nVljnjEU8DTXkqQm3aV8rgoHQRQVuvUBDgYlhKYANWNd0A83eAMCpowJhWTJCLii54P7uAeNlxDTN+PWnHzF0AW/ffYd33/8BNzc36PqBcXPqxHsqbryLVm/eMmk8X6uBPi1TVel3Nbq57hntV+VLFaDIeNnm+o3RKJ6qAX6VpjQh9q4/iisBlnfW+Dm4d/guXO2RFfGm5sqGoTFszLcJjqosRLPRGrpis6Odfd52Op0AsKJmGNhrL0r+K626oiHQ1WDn5lj7qHy7TC3ze3VfBbT5Klt7laNzOgfrsRpDFK5PE4zPVR5Tc9HkLJVqqLCCVjwZVRkD0oTALD9M84x5mZFzwel0xmXkRMDnkffksiw4H48YxxHzPOP+/h7n85lDn05HEWh9fwiUObQjxYhewlafs21zJ2tssLojtHC+df/mC4wbegoWKx93rZP0GKnuXYWnq1mhFjrg7hHUe9WLLXLl91Pzuz5k3UuPeDw9oDYU/tnbFs2wl9s/9fLA59Z2T30G85XU7NMgPBsV5kHyPGG+nLDME84P93j8xIaBh4cHPNzdW4jfPE0C0/ISUh5P9Qe+umFinUGMGIaBw7NjxG6/x24YhC+5wbA/sMHpzYT9MjFNowygIEY1MHV1NI6HU0BQ/Ucb+rWFKq9VbsH9+1T72xQ19CRsrcjU9fG6KYNumwUw4qhKFf3sdAFEUaNIN4bgEDjqNUmu0VAJVM3bdUcqQfZeMQxIQqZy4TwNAArV8t0IhFAKEz4iFBGUSqhE35QgTohZ8RNOdPl7NLf5RfghMak38/NEd5RsV2XN9itUAWOJLTWPgsUZLnYNmQCv1SVqCc0QJP6wSPwhcXWLECI6Y7RFsRfl5U8Q08q0VMbNi6PSiWaezPLvBOhGOPW3MWbanrjnal6uWB23B9XDwCxm+vkbGg+LJ49IrUOc24AZioxxHHG5nLEsGafjGcfTCTmz+61X1HjGqSotOfRJka0qaru+Y2t8iFiWjL5fkITBICJ0qeOQqJjYw0oUuxSrAjeEgECqFYeDC4Hiv+e2+0JrELsyAcAX+9Yy0+68wYSILyshQI/X+WYMdFfwX68h99Jr+L8SNkLdM+qhVsWpVlmDgMbysh5QsOcYdyeMtKhzHTL19ORFm3EmzUkArVDFl1bljKd5V8yeW9TGw02EScOdJWMaR5xOJ0zjiIe7e3z++BHzNOLx/g6Pd5+QlwWXyxnjhRU1yzwjL7M9h5yCy+MMkv6acjxGDMOOGfyYsNvvMAw7dF2HeV5wuIwYduxhgxAw7HbY7Vk5kzpC12ekVHj80bHCtJ6nFg+TrDG9ND5t+vD0u6q6uhpw1Lq3zBPGywnj+YTHhzt8/vgBD/f3uL97wPHxAdPEHjTLvFj4Qy4FVVHtpekrNcDVtwbV+/lxOMSuDSa6yHdhnB3wGT8DYJ5nAITh0uN8OuF8fETf9ZjHEXmZEWI0Zf01tG8j1A0e/3c3/8Yn24YgtcZfpjxxcxG+RCerhRKVc2u9xL/aGY9Kv3FiPL7fxP309Nw3oV4Nfq488dYq2jmitgjIM7ZlmaX7hJgifwqeKCEglIiYCJXPJ+NjlD+s1k/+MO9qZ4zz6FplBdtrVOlSZR0rXWxnpZ4lfYK8S/PPFMfr5IUVNYUI88yKFsYd2TxtFgnnKIUwTqNcl/F4PLG3TC44ny8YJ87fcXx8xHi5YJ5nPNyJoiYvuJyOmOfJ6IXOq+L7lJIZuV6qee+Ceu5pKP/Sb1vPXt+5eb9cdq082H7/k1Ctm+SJ+3wl5PUzrniAp7pBq4s2X/RUB/+lNMN+zFk/PW21sZDNyppl5iTY4jkzTxPmkfPSLEvGsrD3WM0jI29VnBScogYBuWSkrMUNyHBKAIBSjK9BCEi5Qz/skIdePHx2rNhEqAZ8hSf51ytqFBfVSk8tfbian9+4lt+sqNl67mb+vTWNcl+8dYKVL/WcJjZlJYxY01NEl9hS03UJvVjKWQmTECCeM4LQWfPOz+rU6yY4T5sgFTNCZZJrvysT3YZkUK2YoZ4EALIkiC5EmE0TTlhy5FKWREgSV0pEWEqwhGBLqQkIg7dMPzXRv7PRCirMMdHgrxGXLGavuSOsniNWuMp3hNW81VwkmrmbSsEyT5glPKYs6l2jXkg+CakmEJbwJwRJFCXKOg2DCZFDX/qBlTZ9j5gkDKbrmRgFcfVclZw1Dw9y42imyjFybq5o82KdBXnHM1udQgus9ewVUq/som3G4AXDFvavMIZ3QXDrqYm2fIhTLgWn0wnH4xFLzjgdT3h8fBSPmpHzYhQSZc46L42HnXbPecVrtyyWHGyaZwmhiJjmGf1lQJc6TPOC3b6GZFiS02Hg0AtGIEgRXLUPAFlyU4+wdD4dw/2CrUHfOs8Or9s6eUHDM5BOrjNGU895izLhmmkDoG6njeRg95Ixrl6pJg+zZ9r19gjZw6jX2nm/b66UnO76LSpG9Z01VACb7zIZivCy+lJ9N5RVtB6A8aG70K1jFX4co2/rhmb8VPjJXKGJcejldMTp8R7LPOPD+1/w608/YppGfP70EZ8/fMAyz7icjjifHqUU6ySWKELJiyXhI61yYK3iDQKjgRIzYpZQqELIy8KuxMuEqbsgpoR5ZutuN/SY5xGP958x7PassHj3Dv0w4O33P+Bwe4sUk3nGMW12ueTCUw7ewgk/d3N7rjJPa46YmusJkKTB7NE0Xs44PT5gmSd8+vAL/vrnP+FyOuKXn37G5893OB0fcTqfMYkVPQudawwDitpJ93JNMOpbI0C3aHqrt/wsz3/pp+PBgn+q7E3NAYAAjOOEu/sH7HYDpqXgHz6+x+HmFoMklu6GnmEj9LXAA4LjDRySesHGsENV0evpsyh5bQ503qCSRFizO1eCW0Mzsf5SqQVfR83vHjO0fAOv9hPRA3bv2ttgq7Qvfe3bJl+DipCUf/EIyv3MW0Mtxs+/mHd39wDYGDf0PYc8q6JYcYTzFGYljhpxWxoKIhTU6mbqrUukqRK48XPqRNT8RFTzaobV+rmmymNVsqrSVo1R9TywzBlLziApma2Kmlwyh0GRJIMv7NE2TiMnAhZFzeUyYskZ54sqajLnjpJqcY+PjxhHruo0Xi7Iy9T2miq+jzGh5PL8ihq/Jcjviu1LfKvBnFQvcvsiXG9A90yvIKjX1yd6hqpe5Ng+oUGVF7zCW08pbRVnGgxW6m7vUHwSamj7FTStHh5WCKnZcy/pTdO0Fo9dfafK/aD59F+p/hGBKBsPMo0XXE6PyAt7AD98/sQ5lu7v8PjwwKFP5xPG8cJ53JaFeSCiq9fplETVB4SAVCJK0mpQrDiNIaLkBfM0IqaEZckYxhGp6yy3nB7nvCClHupdzNEbvXiiBSBEeKjYnAXHAm8BkHniPL0IAL6iqFnB9RVwBTdByji0cqQXLn3nYAqXGp7BG6VLAX2MiAHou4Sh57CHvksYJASiSwmdVJZIMaHThYmwXDQpRX5+3SUAXE4bsGGvJjoMV4wiK/uKhDtxpYRCqv0mC3maZknwRSyUZk3oKAqcUgjzwse5ELBkLBIe1ahj8TKRiOapgsrQWIURKK8iRCpwfzTmDrGiFV3bJpeLI0ZUhMGjgnmeMY8XZmbHM2fuzgumcRTrbkaeRYGjCpPC5dZKUcWI5KchRt6aKyHENl/Jbn/g0JeU2Io77BBTwn5/wLDbc44EcdWWQdp8qIKtkuhgR3UC6w4wIRbt5tJcHC/lFdV6OV0ziQi6dnrs9qNeZudDy0Q7RrUym6pJFkWjWIxLKTifzzg+HrHkBfd3D/h8d4dlWXA8nXE8HpnRmBdM08whgOr2C4Dn0A/DEVM7HyxBeHTuzuoOHWOQvDScJ+Hm9gb73R5d3+HNm1sc9nt0XYfb21vsd2I5GnZATwglIHaV8UQkIaQVT2wxGM/V1kpT2Yr1fQLrLYfsurNi9MmdIvd8fkxlXP3cq0JULRlKROGu1+NGSWPXuWeuEWbx1/jqTm3oZx1iHUuQ/eUTiNf++H6R+/BE2zEOIrBF3pTP28jegpqjq+57antichA5icz2ppyyunYyTyZ4CL2ZxgvOUv3g4/tf8P6nv2K8nPHzj3/Gj3/6TxjHC87HI46PD+apWEQY8EpQVZhRM186f55ee0GTOxtHl/9JFN+qHE+pl734Brv9HsN+jz/847/Gm3ccFvWP/8V/B+++/yP6ocfbt29xOBwQQ81dE1BDG9bvfalmW8sr/rB+pdsHpHm5Zq6olTOO95/x4ZcfMV7O+Okvf8G//7f/BsfHR3z6+Ak///gzxnHEOM24jJMz+hTReVKlu1RZAX1rxZO1d8b4Kx5HRRNkY6keHnZfoOrS75hDTdzMECceAJkwY0HOBcAJP//8C06nE767u8ebd98DINy+eSc5i3ZmDKl0JKBqiPw+/pZV+W2tDQ8IzR6zvaZjBhCDF8ghoZYO2YYV5Huy2/xy3Syfor38+nrjaazPK97K3+XhUnN2+fF51nE1uVe4GQ6e/DN13JZ0gwwn1c5U2kQhvojO9KeffwbA9L4TOq8GNs/QKK9evW6BFNs5g5ChrHwpqZcL74EYqueupkdgJYaGXoMTaUMmYUOMYVLHE1H5G1Zkq4GyuOvmacEy83nNMcOeNhlZwv8XeQ7zzBdMEyf3fjydcBlFUXMe2UgmHsyzJAvWECgqxZTIgMo1SjqZ7sckIeLxeRU1zfw0X64vWv/cSBkKfG4vrmnBmi1a98GJfO6Z9bnr6yOApmhPm6QJm83tjyZUd8VB+r5wkndySnkbXe2T490rHaib/QVJImDv23iPm4s2mkAGE9ar6nhF9RzNGcssCsXTAx4+swfw3acP+PjLT5imEafHRzxKuBN76V8s3UKhfG3js/erjOhyVMrxnCakMUHz1UbBLcOOQ6JSSjifHrG7uUXX95jGM24ub9H1AzQlR4wd+h0hBDFOkCyUg0bj/WgNNaH5tKkKjmZ+of3tOWpW7ZuAJ3iArUjEjgE3kYwsO00kmiK6To7Fu4Y9Z6qXRYysrAFgSRCVGOsGEqM6L6opiBzj4yaNqJbGAwEl1YRgMZaam0YGZF4yqIAcApe1LVRJsCmnbEOE6yRNL9SqHEgOAcAAPUg/yTCCzzde+0irTUhQF/CFiYVYcXPJmC4XXFRRcznjcj6ZG/4yT8IctooaS0YMMgKaXI4aVtSwdjMvXBY1pSTrlTnZoVUhYatMUWIv60wEyZ/zJQuV51q0T+0VQRksz2U991peEbjQvObaHfT6W3B/zXr6jrcUrvK8Mq85sxJunCaOk75wLhqOjT7jfLogF/agmZfMZeyzKCjRTovudz2vvbA/25Oizc7Z4tVzKbzGHQuPVAjd0qHvOqTAbvm7YYclsVamdLyPY9S96URrhf2reXzhRn76r1kPsn45IaT5UR4CB6JOEdQohUyBI4KKMG7Vq4JWz/TX1w4HnbuGSIbmvqrMpPps19dvP/bdp/ozGYZqxutvp1CZoRdbU5l0C4ZcLWHLgPI8tV4QBISwWjbvm8Pzp9bWeZqxLDMupxMe7u9wOZ9w//kTPn/8gHHkMpaXE+NWVfIAdY9VWfR6L3oUp0K39UeYMPO0dBagEALmabLQQ04GOKDf75H6wVz9b999j2F3QCk77Pd7ERRqPDkjUcfgqkGhESSfuemeCMHo3lYcZOthJkYFqdI0z6PM+xHHx3vc393h8f4ejw8POJ/PnE8iZ/Oyhe0nvy/qMG245L4qKZYpcjzeajz1Hj2hlmpb0ackG9uyApPECU2XJeNyGc04cjo+4nx8REqdKO6zWYp57yuPs43vn7s5KLW3fY2VYp5QjhW0BMZro6t7/OeWsLf+9OyVLs639K1tGzCyddMGCm2Ux1f3eezkP6+fZRguKOf4/Ct5vlwAiAFXvGkAVgTXSeTPuKWoUdwmfc+W8wmmqAFWipokKRQCGlpoXv0A1lopBREzJjoFCyte1Orfhl5NoqhhY25V1Cyi2DGDloRDjpeqqDmeTziPI3vRXC4YR/HmGydJRp4FzyySFHwRXF09FqG4B8TpA8DyzHO2r7K+VzwsDOW2tLK9yi39l5/fPPcpGF1vFmP8rvbt9Rd/F29mI+PXT70axdZu85zRJn7+Yt9ftnGfrunh+qLKumzlziE3RpEVNXnwMlto03S54HI5mSGfvWjYG1gT7vvKsNZBOPqMCudKwyyfD8EZZXk1YgwgOHlRwrbz0mPa79H3HYhYTi3LAiRIZEEHpjjtWmytzDYVqd9D8/l0+92KmgbwjOjpgtVF9qzfutMaqhQQ0KfInjMBGPoOuy5JktCEXd+xR02X0HedIfUuJQDsxijRTqLsESSswjlq1acAX1EK1YNEj4OU6i41IZh3cZwXYq+YQogpo5fY0jlFZswKIS5BvGrUlTkAKEhJSSjntWkqFb2AuUKT+vIUVEJbGUYnQEBdiPVmaq1UAWZdJNGQ5mWRsKYZ0zhK7oRr5QwLG6NkpC9M1DSxsCgCmFd0iYidoKcEGkrMO1bATOOE4bLjhJenE+dKSAn7wy32+wN71xxuMOx24l3TS/JhAIiV8WgElEpVPKPVYia5R4EfaDnpZ2wNwt8gQgrnDL9PM4/+gQbva3IiyLCUzPkUiBmHk6zn4+MRD/cPWGY+ZkXNgsuFY0g194KG/fn8C2vo9l49utLRfmMVIaMUeVYICIX3ZkqFwzLAGd67jrMVzfOMvu8BApZlQd8xYuX47IgYB/O8c5MHt9h1Ll6q0Wrs5HkLvyfJ39A+wDVlofU6UmkPcEyaEDXNAVWKlOsUG7wPW/LPJ39Qn6n4wyoWwSmB5FpjLUi9RJQxoxUPTHWtISGh2vf1+Br25un53ZI7nqNVWHacGlzmHaWDikttBB6NkpsdzaEmDAQ4CX3OCwvDueDh7iM+/PoLpnHE+59/xM9//RPGyxmfP/wqySMnzNNsuaOUvumrvbKmtZjpeuqJDZgP60NytxGoBE1PKkks2aBxvP+MkheMlxOG/R7LPGF/OCAQJ0Puug43N7eS9yYidUCIyZW8fMH9B9SJAKqk5+PLFZ4h1eoyx8dfzic83H/GPE34+OvP+Ouf/hmn4yN++flnfPr4Cacj48R5nqXCi4Yd2IttLtest7IJHodz9wQXOt7FfgNcqEawB6r+S7/oe9ST2F6omiD/QqFpVDj5aRonnE9n3H36jJv9DvO84I//+g43b99xxT0XosJhKVuJSp+ft9FwK91zpuAKKxGD6qQoudZc5AF+zSvHGkI7LV7fad89COlfqBihfWbDfck3Wl1Rv6mH1ZVAoLBh/PbTIlyrrF+95spA5WlMO3/V2+tl+JuPHz7KW9UrXl/i4MjYtKpICQFVqeL7a/y6jEvGxvw/r0FK4r0jC6kcSJe8tz9sz3nvlELFKjblJWPxihrJt8PhVywwTrPyUuJ1L7JDzktV8qisUArGkUOftLrTKF400zgantdcNxyyocofrqpaqMBCEL11QDZIiBHp2bej7pwVr/cleFn1wUjQF/rm6al/3/qK5own1f4JwX9rcSofrPkuf0VLP3hPXu/5LeZb+RvzpFxfYjzEV9qTCqm/vV0/ka7e0+AFueTqRlXel4y8TJwk+3LC6eEOyzLh4e4j7j69xzyNeLj/hNPjUfK8saeYGoaLGkhqbxz/WV8VwHK2GhMDRTHCE4LTSUaReYgC4pIREMyLDQFY5g5dSih5RjfsEEJCyRmp73FTvsNAXImuG3ZIgMxNdDTHLeiTPJXnzb7O63xRUWMTsQFInufQbVmUsHiLG6jtOFoBKQT2bOmklO6uS9j3HVIM2PUJ+52U4+07DL0cdwlD37GVR8pEVkZG++sIrYVPSJUoIe7RGBkn/AbRsseasHSd5K8QGPFmRrbTwtUbSikYJSN7LoTLFDE7JK7v6VUQDvx8VtgoDn3+jacu26RIygsaAEIgyd/CyKd6yzCi9zwd0OafmecR84W1n+P5jPOJy5Neziecjg9cVm0aLZllXhYJdyrVWwnq1lYVYpo3xjMP5mEBNBUAOqsAxflq+p6Tyx6kGlDXdbh9+w6Hw40rJ8shUbHrEWPnuNpaGcgzvTw11aPAAJrIIVreAC8hYNgzm73o3OYCbA2vmHz3p/NpiU2NwOivBLXiL0vGOI3IOePx8RF3d5+xLDMeHo64+3xv4U6PjxzuNM8zK2roOka8rKYN+tUJfNqyI3qWONDhGSAgxsXGMI4zUscK2/FywW43YOh7zPOEm5sDhn4AUUYuC/quZ+881eo2E2TI4yV4URnoinFW8T4ARDzWij9XKhPySXmbh9qHD1fj0CN5Z5OoW0IcSgZlDY8pgChKPQPvicg6HNDCexpJ85rzMkJeTzVKGt5mso+ce68PizKmxo2ZK6X4M2gIoypSrip3/M5mvaK12OX74Sjgas09WtHvRpOIrb4BhHmZcTlzmcoPv/yIf/pv/h3OxyPe//IjfvrLP2McL7icTjgdj5Ybwdz3Y6Vz5rFiffFMZl3bNYvJsFCv9YI/e2TyghQQUAJKCKCyYJq4Wsu8zOjvPrOSZhxx9+473NzeIs8Txj/+A3bDDigFNzdcGcozLSGk1rDwAhvyKgxxzXyTGmk4nGAazyg54+HuE3756S+4nE/4+S9/wX/4r/8NHh/u8enzPX7+5Veu0DIvmOZJ8lUomCoPoHNY/1H4kZmuMOt4KStDqued8ani1srh81xWpKsV1UKIVcFRivXDaJj2jtijeBpHlCUDhfDrjz+ijGcc//iIH/7xX+Fwe4tht0fqBkSt0IPO3Jb9sm3x8r+3acJZ0rEEF7KLytQ3k26kPcgwqyeQzkBDErTf7jiuByYHQSZzBUn1/rroq/m4xlEBaAWhevbqUI0ahpWfIBGrbS+yVH1e/TnY96DzJPj5JWjjX//8F+mPrp3Q/GYcFQ9FKHyrcZb3VRSDK7lnaC6agIAYyAS1rksYpHJoinJNYG/8XnJjcvVYxsi+EEkWIVL5pEWKJWQNOYUoahBQCJjnXEOfSvXA4dxjUrZb+OFCRUpyc06r0+WCy1S9aGZJRq7Vn/Q56rFXpFiH4ghL6SBzEkmSNMctZervaKEerCudffXWFu18GVEoHlu9+Gq/rY51P21d4x/Ybu2Nja4fbhta91WT1zyo7V9ozq32k42fms6u8QmffBlTxrbuZ4W9da6ca2LFakx3tNKnL719Ot7j88efMU8XfP7wAe9//isXRDge8Xh/LyW1F0zjDPUktqTcqDTb01T9DIDEkBdU/yXeBxQKohj/1UFDnUlyYU99ooJ5GpFixDJecHoY0A0D5mnC5XTkZMN5wU3h3DV7BISdyKHJraryXcTw0K5bS1BMEfwVhdu3edQoccMGsKyuI2wTt6aboQVurfCisWNdkjAmEahi5E+OXWUE22liYdGKQwQ6dUkzJiFIuV/hcFKIFn9aFTXaKbFWaYysDGrN0HEkVACCavv4OaWwZi6AEEvhxMLyO/eBGQJluCDjpiss9bxti9i1R6sM7foR1JuhvUOrOBERypK5kogkaJou7F2h1S/ysmByXjTmgaMeEiJAmoUCer5lmIFKJJVRhQB5zgvizIo7tkJ3iDEZ0VJFTgychLjve/a+iFLKO0Qp9xshEaQG7MYsmSKjRbEteHtk9swodIOIIHhWuMJ7/azlRjcfqZyOC8lgwUsYXxLPmMyVnaaJPWbGy4hx5EzskzIOmZPlZUOqbQ6kqux073LCoSdXgZxwRm7GTWlKotQA1EJaSkFJWZgqhqv9OIq3HTAvC4aFEXIpmjvFk846wQplL0EE2+a55y+8rWHuN7gEfyHVZ1aiVsObmupqLvEhUTb3cBVe+PlbgAdHKSvO8PjEPhqZoiqgVlfK8ZeEAD9Xa6nDP2N1ywss4pM86JrJ3CSYDnbdZaE5VrybTbE9ns84Ptzj9PjAFZ0e7sS6OlluAjNMOMJbq6RYYWfYxHi4Cmjyo9iVTxF9E/DUA5NAISBDKiAWQogXq051Oj6wIlk8Ug6XCwIgluCFu0PVoOGZDv72/IRRn6mhXZu/q+eoJBdUenY5HXE6HfH4+ODCnR5xPl84N5dLErp+qoYSrylFI2i0G0PWNbSeM5VHdrBWF5JhSXZcC2DGpF4JFlfdZW+iBSyMXs5nnI899ocDpvHCSRljtHALihGaMFNnOLgHP/cqVs8gkkkTxl15BTlPG2NsDYhwfEbdk6akdvMXsFqfFd901Ud3mZ/i9bpfoVh/jdsS/j2bp1eKYc++healFVYUFXh2+Elz+QvwqFqeG7LXjHYVv/Pr+kThMFR20P2RjKep+7vmw6w5KkPgHJil602pnRLnxiwpgjo5jskSkqoiCIAl/mVj5fKEoiaIooYwzxxGSOSKj1AbNsV5OFhRwyW8madaxPM8qxe6CK+KWy0Hj/BHWiCDsUy0tTUjHAglEp4mZH9ba0ErXOX++rYn0NW++NK7vnZu/eOmg8yVwsPxoluU5ymauNmRjV6t8PbWMjTy2N+BE/2trdKVOqFXqNSMhsWM+1xM5sLhTZcTLmcOd5ouF06VkTPyUpUzTTVE/3woPNcTBJhXJSQXma8GWqSPBZqntsqfiAF5WfhsjJhiYP4rZ4znE7p+QCGOCMmzeMxJgRUgXmWP0D7CqH0r43jqr4WUvtS+7FHjoXGLFnkGx3WUqDIGihSN0RA3Rc7OLB4RiUObYgjYDQn7gfOL7PqEw44Rad/32PUsXGuSYU0w2qk17qqHfEYVQAjsJqlad28hNKtjCBY7rwOrfG8woh9iROoYsXYpikBbkGLAPPN3CgFxZm+bJbPFOMaARRBYCUXyCYiwuZ7fZ2pbcX2VSDvmxphuGbdj+CFMK8BxtfPE8enj+Yzz48OVF8008mbMQmiYSLHQX6uOkJU+J7Neot2cXpgQDwsDe8vbEUC0IBQWPnIuVhUg5yzZ7Wt4DAnBiylx9YqeQ6Ji6iy0KgT2qlJGU2cqwM+Jm1M/V0Rf23e/uQWX6axqX1WQkU+B2arQCpbwU5lzRQohVObDSAKxwiwv7KI7jiNXdFo4xOnx8Yh5nnE8nXG+sKJmnCUHQ6n5m1Y9RxVOBL2uGE1uVTB7Sr5Wa7sbOl9cCEWCL5ZZkC0RLpeLKGYyxv0efUoAVEtfKzuowtaqw8G955lbVbTU7/VdVYBmuPZzVfclf9TnVM+WwmEWoKqEEeYMJcv6ZomzJXaRzjWZN1FuuXqgMjFBPBah8149avyaXKlabF8odq6wZhc4AseXa0JdHSNBSbO3T9D6X8+Ehbprn5kfvWpXQ/Eoc4XRK42q/avzylYdDSU9HY/48OE9xssZH97/go/vf8H5+IjHhzsukzzPoJyRoN5I1UKTInupmuVI4Fr1azZf1+TSH9gKAx7vQDwmohunnxCq4xBceDkeGQPkjPtPHxACOBy16xEAdD2HzzBeEgEsMSPhdBLP27wnAakVrs4Nu16zEux8fMD9p/eYxjM+/Por/vxP/wmPD494/8sv+PDxE47HI04nzh8xqyHCKqpVJZkKi9qC7JkIAmJwcCy8i+eZgk9y2irClcZrPqsGDqG7R/qA1svLwtQ3EC9BQ6cL5mXB+XzGQ2JP1I+//oL9fo/Dm7fo+j1iTEipw7A/VDzqF+8F9qHRxSD8CQXEWPkFDfFkfVQwrxvfnRqyH8xbA4DxiPYu+UfXQl6yGpbinDrjzQVrRbq6mq40czptlmQc7IENque3dPXMZ2O1t+sGNdZEiafzvvJP0UTXgbwRL74IbwMAjw8P0r8iCs7SdN3Dc51/EqOnyhQu3UG9HCmxjBHAe6lTPFk6wZ0RSAGBmE8qFFGQ2AMtcQiFKkmLPNyUJIWseqmmAmD+FiAXVh/UWEJujbSPqtgkhWH+tOpRmhagVMHVewwpnannYQrvRpgVoKgG0pchjEGfrLyDwzVPv1EnJNj112Cm/C7cP+6JTz18pYQ3zOkNv01rEWFFj/V9W69yGPWJjmw+fj2KzSd4HuMKXfxnbLXv7Sh4Hy8gKpguZ5wePmOZJzx8/oDPH95jvJzw8PkO5yOHbY/jZMrIkktV1JRqtDfYhvIx9c02HQRD6BpOCKg3HPParIBl3MZrS2xYygXAIgZ8kYVywenxAUSEfrdD1/WgktEPO3kvIaaEbgCS8lrQoggVWFcQVWHP5LEvz/NXQp+2N1hriVDA1YVy9hPSjcWTo5prYyRTQojg/DMDhzsdhg63+wEpBez7DofdgBQD+r6TkBa+r+8EeabE2csDGnc7Y0YRLAEpI3NF8s6V0T6vs0UzYyXjEg8MAtDlgpxZkNKcNFxWL0km9oKYEqZ5wbRkZFEyLLnwMYBcmHEIxM/JBaCvLNjf0lRraGsCBlpmSURQpCoIembCVr4UE/DmabScM+fHBzzcfZLYwgtOp0dJ7iuJgkv1yrCcNmWR11RBtBIeahjPpj8NEq9cSynEDD6AJWaop9I8TUhdhxQTxnHE7shhUPM0Ybyc0XU9bt68xW5/wwrAYYfU9QgxIMYOUZmSwOVpSbrgdaTVkFf3hGmTn7EFyZIdmu3ebvggHIAXzJoQJ3dsAkAIVQ4GCxdaleB4OuPu8z2mecLD/QM+fbrDPC84nc84Hk+cWFhcFZuQG5uOpp6GtWY5sSJKcmJDZnCKpdpffh4rKEopGMGKxGVmZS+H3u24+lNkb5rbm1u2OBGZIKvhAMr4v5RwuMU8NxXXVLQX5suPU282t3D9Jkwt59GoFja1tkHCmthauaCI0pQTos5CqDSZ4lpmqMyMZzDhFClNtR5nHWjcjENkcVQZrODhOchW5tw0ehwFJwRUBjTC+0LVfxWXeKL9MmzoCpbd0XWghx55hiKsrhfmAYyTNWlwXhbcff6Mv/75zzifHvHjP/8TfvzzP+NyOmK8nHE5nUwRkNy+jxL+2KVWUaPvLQ6uLCRR+tUysHUs6/HqOGKo864oL8szSeAQS2SPxwBMF066m7qE8XzEze1bdKlDLoWr9nU9EKNUd6s0u5HSnrNpSLCNXuBWBlTyjGVir6Dj3Uf88qf/iNPjA3788Uf82//63+L+7h73D4/49dePVpVlnGZQqRCq88nKcq5Cpp4AyhsZ+dBtbvPMuftqGAZ7DIQQZB35wqWQJWsPWCU5DH7dK4OrhsS6r9WzdyWIqKBIhDAB9/ePWMYR85Lx4z//E8o84u13f8Bud4Ou69D1gyXvDyGY8eClWkw+RL3iAigDTiRWVvk9eCWV9/ipeM7Cy1Y72t7pw3+o4l8QZO1FwSIT7lF4g/eLU+Z5I0GoNDMB1X1e9i4Jrxv8s6Rd7dzKYDW0tY5Q5yq4JzglQPBYSvMsPf+afvzwXrpZJHSnDmyt9FM6pHCre0I97L3iJgBIIi9EUV5byd6+R6LMuZUS/1EMCF1CpML4p0Sjbz6XiIYXlUIoy4wsPJAZrAAAyegcCpl8olx3ge5XVDotihoztBTJOaPCa9GqqCxcGs8lsERoy4Jzhyu8B7A8oIrL52ye7gb3eS2FVkRHq9/1+mtPlituUP4Nzan2ro3xhad29VfmQvDzNu3/eru6dtXNZpnC9mX2U7i67EWb4cit95p3MMHyuxGxkWNhz9LL6QF3H3/FNJ7x6def8ctf/oTxfMLpdMLjwwPLi7mGDy4iWxdR1GSDc8NMDWrT6BQvGxAAytWYGCW1SgjsRRolzQDvRvW1WVAy45KcF6QpIqbEeaIk9ImWBdP5EcPuYLQ2dT1i0BDJaPxWCFWx28pATiYD4OW1p9rfmEy4IvEnfm0Ws+X7q8AYIkwjzpnWebAcziRluKXMdpcia8XlOGnWd8nQrsSGmXoIceY3q0DKSpdqDamCqwgSksSs9cBxAmIUgZ2EcGk1p8DKghICcuaQG86dElEKhz/xuAIKBRcGVV03C8g0t8/dvM5AWROF9mpNC9W9dkNJQqbhL+Ketkg84cSZu+cJ0zSa+1rJzsrQhFhQdWc17b/0zAuiaD0vbCCtpF6RJ4mlrHByKLOaEaHELHmMgJJ7TOOIvu9BhbDMM7qOvWtKKYhUgBKBwImeFUG1ng9fmmy8CAZtCZJ+q5tduZdqqamCAJygVi/YRg4EGLOR88J5Z6YZ0zxjmuX7vEgZerF+lapke6LzSpEN0XrCRHrN5r2VOjRMqA6anPAJVdoV8eQSb66UJMlfRuk50Z5XEtbZCav3PP9CXjvSVlHh6hrP4SvDL7iX3KcPcbIk36TeegRQNkUNyd5kpWlV2ihzaEKDdsYdMIjVfQfBWYHIcl5Aq0zAgYPn2hTPgOpvbuTBfZL7rOe2Z7XOnBNAXpyVCdffQtujFlvpUbveCm/BhOjC1fKmib0UTyeczxxKOp7PmKW8JRGZsaEaFYTmheB+c0IgVZnN9yt6/CbP8wKd4mlVAnD1FL62FFgyYXEE461JBKKMkIFlnll4ShHT5YyxH5BiZ+FbMSZkDZ8xwZRsUl90JVWiCeQsdaLszFnw4ITz6YjT8QHHhwc83N3j4e4Oj8czzhcOd8qFLIxBYcHjFSGzNp9ueh0OkrmWc+wZVY1bmji1QJJWE0CoigKPI6nB72TsaDaZXVZYDCBPsR6m2CsFy7xgCsB0GXE+nXB6fETXD5jGC5Z5YiWSKohtHH6en7k1jHA1DXjvLzJ81uIEAlBzrcq+geMP7dqKeQTkZZ6dCnY1fxVvtcf1InJ/WPWPoF5RHo9c8YcOheqdAcD6suYG5du9tWT17uYV5HhEe+fzr+M8TQAkBF5pElDxFoLk4NP550WIgXNjKd9ORb1fqicaQEghARQsfx5fm0ElgQKxB2sEqAT+o8BFC4AaHhjcGii9VIWJOyaHT2zOUSHP4Ge1X22C3d4xAZU8bvJbSXBV89Xx0cZcOVoa6nNesm3h7EqdqblmvXd09232cuPBfg+sn/cvuTUY+umN+/87zRTTZOFOeZkxTxfM44Vp//mEy/mMaRwlbYaECXsZUZiUagBWXtdeA48dPaa+MkohuD0hGMGV+yP9LOy1GAIhZBkLkdA1/j6NZ/QX9gJe5gl5mVmuFP6aOaFa9l5Y3msAV97gG5Q0wNcUNSva4nhtP1vw26S6qyvjE9ykVMQUgyQQjgF9FzGIgmbo+K9LAUMfMfRS9anj0rtrjxq2hLOrUiQH+KE6lPsqByFY1KabuNCEiEQX+hQjmQeO5jNB4KUIEewyhcxhTACKVJ4JIXOoljANu74DFUIMLECqRTrHCEqEIMJuk5vlmVr7xBVLID8WSJ4B1E3CO4UJUJEKJFQyxssZp6MkDT4dcbmcscwz5ok3Hrtp1k131YxRcATEeJZKXNokspuDaWGShFXT+6gI/STM8wSAM/Sf+yOAgtT1QABrT7seu1IwlB1iTKB+QEzsqRMJgFYi8Jpb7QOxlU1Jz4sIic4y2Wq3BTUFOKUjw6pdp4JPqGxBZfIhVhper2macL5csCwLTqczjqcTnztfMEk5SKtaYDklVvOh/1g3g1XYqKzpdT6S4P5x+pkrS6NdBl73aO9TRS2J2+KCeWLF6GUcOewtBIzThGGa0fVAXwb4GiVVWPVveca2BcxU4bf5Rf5RgcJmzgiZ7tdagc0SdWe2UrALNScNBkmViXmuihpxT62Kmifohgkn3GIFLcaXiUt+qjJbH6RyAVsWRYkTIkJUJS3vKYNF5VOpwNR6EnJpMLvGCTZLii+cwLPBCv/e1oQAkT9fT1QRoa6fFzDWzLni3GWZRRkz4fRwj/tPH/D4+IDTwz0rwZcZKAWdbChOjO0Fefa+6GK0Y8vR1rKFTfgEQObRgY3hsdFWDBNwzBMBOZJUeDMphu/RvQyBTQAhRpweH0EELLng9tNHAAGH21vsbw5IXcc5xaQaiwpdL7EXzRPCjgN86MV4OeP48BnzNOLDLz/jx7/8BfefP+PX9+9xd3+Px+MJ58uEeclS2dGFE3hcFVg0CSGYIkDxmsKSGqPMUCRr2ktiUw57kvx6sl/Ua0OrxVSLPq8BlyhmGGSvGxXQioTcVH6t4uY6zwYOiocKYV4WBBDS+YJPHz8DFDCOGd//8Rd0qcfu5gZdNyAlTu7fgYR2vYyyLUZlijlERfdSCNEY7RCyg9c6Xo9v27Vy0EZ1J9e9jUrjK+Ni15AgMsvRQbBKdw6xs4cF1WdWfkitvV5Y5X+LMDsRldlVBWqD7YKzFSusaC+rhcJNgdDO4B4q56uSiZns5+dQOVcVv7aYErqFl2psVaWNCjpWWAJiBAiQfINsRY+QIiJBPfklF2bgeVSldgoql9TiJikl9miDJhyWNSmV7vYpYk7s66lVYsmghPfqUrhKLB8XKR1OmCIwB04EHAMhZ0IOhLQIz6RKp8DGQy00UGFUIMTzYYIvg5s7bmRg/BKKmiflTbo+rAbQtodr7BOaI3evf+/Gt6uuhM3Dei60d27NzlrZGp64bqtt9odWT5HlqflOXthA8c1tPfd+1JUfKLJ3VaFxfryXcKeP+Pz+V4znI+4/f8L5dMR4GTGpvChFeVjBzzpPq1QMzitr3mwb66iOFZavSgBRPUH12mqOJUOMVCLU0FFQ8QuIUGJAjAVhDOytvizoHnqUnDHuL+h3exAV9MMeWvwgxIRuIKTUr6ZwbQxueZqvrfQ356jxpKjCVRCgcjH2hucreSni6RJJr2KGsk8JKUXsug77vkOXAg59h/3AnjS7ocN+6ERRw0lgY6jJhBtmJwQkuJw4ITU5O4zXU2sZYDGfQRU16nnjqj6ZOz4Cx7LKhMcumBttWALHpkZmDmIMSJnVQSkt6FLEsmQEInQxIi9aPpWZLALHzJWl4CXI4FNPrIr/Nv9FrQRRK4ks84TpckLJrJw53n3GMnNVkvPjA5bMZQmXabLs81WQXMGPzOx1Zi/v5XB9Da3+rduOr/GiGQGmpQ0hYJIxxJRAxMqmru+wLDP2lzP6fsBtzijlBjF1GIjQ9wMQIlIiBOKNSFESDwe3uQLYAiM9q4zW87XqQq5KDB19lQhU0cjKpOR+80wXt0JBlIySi2Fh5drlcsHx8RHzPOPh/gH3d/eYphnn0xnnM1f3muZaUrIKbtcE1frulFssYCiT66+B3W1KJ7nXkJwXftzzveWYNd8FVGBlLAsRjpKsMFPB7fktun6HngjDbo+OmAGODfJ8GRLp80XZG7Z8aoWJ1v1Te+WYQRJmUSzYbLmYJNGgKmSKeM7Mdg0rXAlU2LvGBG9V1NhEcme89UvV3+rJyHnCOqSOc4bFmCzPE+Q5hJaZDikBSFCxRIM19JgHzsojvqbG97PCQHpGKlhWnL6GRf3vOZvPMdLu8vrWFltx74JnBAzepb+iqJ/HEafjI6bxgrtPH/D+5x/x+HCHh08fMV/OyPOECEKfAiIC+i5h1yXEAPSJDR4WBhAZZyUJJwqherACEKFGjtW9mDtVR+AsXHOpCoHFEmICUy5YisBpJlNaa7gaCMjLbKVoQ/iE8/mMw+mEfhgwTiPevH2H/c0N5w0bBnRdV0OQY7ya6edoVJbVynFIwyJ44/Rwh4+//IjxfMRf//lP+G/+3b/Dpw8fcf9wxK/vP+IyspJmkios6lVoUOCEKlXOJA1/CkAnirUYgF3f4TB0iCFWA5UoajpZ0y7VCjQEZixZ+arKJeJQ65kFvykXzJmNFWPOmLIoBBcApBXeNBSI957ZiQxElSaTVJpZLOde+vEXPNw94Pu7R9y+eYeyzHjz7nvs9zdccj0lhLADUleVm99gPfwtLXXKwnqvAqqGokIoJZpie73e/kul3/K9MVhVOiczBYBd22s4IRnOslwjdrsPcZH3mDJCIYbXIQDCY2z3l/dosOcQgNXlleKHqopTxZ4pjBwZDmA6WsPf9XzlyRCYrn6tQsnf0hbxqPGJcdf8oA/hjrEqNRHF6ycGRCrsLQP23ouoXmkxRvQxYhAlcBcDusBr2YWIXgS+TmQTLmyS0Avu7Ds9FspFonhZuMJdDb8jEziZRxJFjfy+lIJFFKpjDJhi5EpPAVgCsGTCHDMWRFAgxJAQA1evicgNVWNYgc0dz1Vs2Be/L9rDZ+ZRlWdrX2U8qPIx65+C/+L6Rf52fkH9vgGCazbqKTh1rI29IGxdT0132nN6H/2GWWye//Rduh//ZTTvrbLule7RIjwMe89QyRhPj3j49B7TeMGnX3/Er3/9Z/ZIfXzEw90dlnnGsnCYN0khmVy8MU72sEtDol5ygK+AqIaN6mShOK8s7A2rCnqN5DCvnaAKbOWBYLDg06WUJWOOzNvmecHp4QHDfg8QYbycsNvfyPs5DAoAwgCTw8y4r8pk907lUb+24N8U+nQFUgbgiiFCRfhXuwvXkMf41jRhyvSrxloT/iZZgCSeNzXcqVoLzcsBojVXZjhGs7Y0Ce1QvQC0fBjPYU2u2sSPwf0TUK2EgT1wCMUqN1keHgogYutXiRElcpLhlCInMXXjVg2gIornFvBt/vXQBIT6mw9FaJKRkW5CrXqRLXHaMs/stiZVOywbPRUXX3yN6QzVegznsPc2KlORzj3OY+sGvoztcAQKtcQbccLZKEq2ZZqwCLO3LOyGR4DEHxf2mhJLnaUUDzDOqCIvtii/AA8jU+CIlEl5brM7YFUvrjqxT3eK1xqMLCVcZtGwtoWrGSxSgSC7JF+tt5TMhcao+j7qDDlqbOCmMOC2p+4/VUa5Icp+DM33OrqW+qpXTShBkoJmLLmGbOWSkUoHUSW2ROhFqeSa+Q7G/Fdwdkx45fXNQqteb+2flrsvNl7OVyPVISSsJC+LxNNrgsJsAoZk9pa5VRGCmj6z4hoABUnGJpXrCqBKGU3MKRcCCAjsb27j0H2kjO32zjcO1HpiRJXW165mmVAV7N+0Lt/ePKQFhxLc2938tX10pARX4xM8uwh+nacJ03jBdLmYNyOIFc9J6I3mMGGhPlRFTYyWJ0QFfLuvxv4aDnnK+9FKuRMh5mAChmKXQoREkeEJkISbzQMASE6UQCiBvb4QAlLXW8WHaRgE/y7IOTXvZa+I56eL7XiFSVR37ZLFOHHG5XzC+XzE8fGIx8dHnM4XjNOMaVkanMhPuW7Kfii/ouVBVVhUhczQJSuisB86ERS1VDDzD6mrihq21nNuvLKwQlq9AzMB7L8dUCIhEyGLh6lZHQWfKAz7/tclrMSZCKKcC1hixjiOCAjY7Tln0uV0Qt/vrAokAqQSlOCQEH9z2d6vteAIhKPEwmNykvmgYUSm3Fjtvwb1B+MJEULVCuvlmp9GjI+OPNUnrpTxa+NbaPDBmjeiZu5XbHPFH+TQq3+3v3b1e3shVY7Kl5TyVaAMM1ec66bwWZtVIlTjA1XPJIbRahSOIdoaqxCmnVZ+pmp4yObCK6p9qXv1cosQHl7wZHByCXsscoVZ5tclBTixosfnUdNPTYRKBISiXvMAsoBWIGQpRBKKyAvBlRAXfGEyCQ9oNXHribye26fA4EUWUt9ZWZjaC8s/t3XDNzzziTMOQ7lfwtal7dnVvm/oOozF//I0PTm51xe2HOrX7g2b315wyX5nE/gv2aVNGDFPFy4sc+aQJw6RlTQErvR2KWTGKk1ZEiDecLHymZrmRD3kWI9Qo2o0hQkIkgs2itK0iAdx60lssggE2wmSIxBQXC7SEkCxRmaEEIQ3OyOGWMOgECxFCCtMVxJKgCVo/y2Cxjcpajw8rYmBl7LUvfdKWA31T3PEqEKmS+Le27OL4a5PGPqOPWr6Druek4IOfY9h4KQ9fZfQ9x2afDeCXBOvlljiqqJG+1ST1rIngSb+Uo2XYnVLrKcKCzADWhFCnRWL1Y9k5YFjCCjUmdJnN3DJvhgjxiUz/Y8B01KQxEys1S5esl0/XTFRFRo0t0UpGUVc7efxgul8Ql5m2XAu3GlZmk0HD4aC8dTzPkCz8leqSgAoFlCpQoP1yI4rs7g5qo1ps00Y3DOpYFlmIABdKbhcOhCArp/RqQdN1yOGjklx5I0eieEoarKowGikJph7QSUNNgi1KRKDDF89y9w1ILvuiihYX6kK86VglqTQ0zTL52TJTUWqRgBb6U2Z5y3oK5Psem6qYN72R5koHauGG9J6PBtNoc3zZwDHo6MULJnLXU7TjNRpvp0ZIcZaTlzhI+icvsx6XgnDqrBAuFI+EMRjidy1EGFSvGh4j7LiRZNkZzm3TJzMuywzlkWPF4Z/yWcDyUsTbAZVkKvVMxQ/2p8oaAJF2WMW3AuzutragZ+s5UANt8r5K1HF5SfxjK+NvXppGKZZT6kdCNA/8zpa960iyurNoT3vX1/7XgUyRpkcjjJNE07HR6mg94jL+YjxcgLlGX0K6JCw7xNuhg4pBBx2PW6GHikGDhtO6tkkxgwEyfkmFuhUDR4hRAupJFOwe6FHQyJZCTDNC+bMifGneZGQn4LTlDEuGbkAl6UgLgUFbCEz+kpVCMt5AU1AShdcTo8c5pQCLsdHXA4HUClYbm7Q9z2AhEQRL5FlP2cJtyhaoYIwjSNOxyOWacbnT+/x17/8GceHe/z848/4fHePh8cTTuOEyUJANdmuLrTsIYEDXn9N7Byw6yL2HSdLPvQdbnY9uhRxu9/h7e2ePYyHHvvdIGHefu2iVDBE3TfKhEq4yDjOGMUj6DItGOcFuRCOlwmnccZSCk7jhPPIlf2WUj2jlNlum+5BQL0rMgdK43SZsBRCfOjx6/v3QAw4X0a8++M/oBt69P2A8I4wCF8YparXc7aucyys5/GKWHmpoCitsiFV1YnimuB+U3xohF8nQPOmaIibKQj4HpJKIsrzkPud3PPN88HRU6809c2jfo8zzKtPDE7kpUrLuaD4j9oyUVfrrKaKraqNjl4juOc/bysl87sI1RtKT/AoWBgjGL/B01UT1NMaTxCJko7APpwc3tSJsnpIyVIr7PoO+6FHjBHDbsBut5O0DD2Gnj3d+p7TL1RFDbcsil2jV/Juq2BDwFwKlszGIy4wogVHRsQoBsKinnFCg8F1o1j5SmZQVXpY3B95cHTzumLtBWxfRtZY0zlVdhhZXF+07qf+7tJkbD08bDxI95A/21zn7/fwDBP5bD4U9rjrvA78Pdge5HnVZ9PmvvF9C6H93mbcq3zKFs8AtGu6fcXztXYsgqsEz1Tlr+A8KlY4YJlGXM5SJfbus4U73X38iMfHR86x5/PSFM2jyM8LAYiIiF1ClzqEGLDr617suoRevDNrvrYgxV+0Wi+gWkJ1HuA8M7XyMHudMr3P5CpMEStXCcTehUG9IMVDJxZgVE+dgof7e+RScLmMGA43ICL0uz00T2MICakfEJPgTUPajjsMevjl9fyiosYDbl2y9UUO0FfnfUdqAuHqNdOnYLlo9gOHM+13PQ77Hl2M2A8DbnY9K2qGAfthx4i07zBIGJS6RrOiRtykgKqosf7pWJjwEJElvWU6XMOOCtTbRpQ54uJYCMhuC9ucBF6bSAGQePIiAgUzoQwwzHhxFShCQFhYaZPZVIWYC547EztqL+Et3O3PpOwYC0JaoWlZsEyjlOE+4Xx8wDLPGM9HXE6P5lWzLJO5Xqtrb/BwWP8BQgEornpBHCso1EYJT2URSLvpGB53Hmsipf/yWzzDVIiwgNd1iQkEDpHphwEhdsiZuHJFSGBFTUJPQCISF/wa2kExmHfB1b77AuL+W1rQLCoO63vlTPWiqZdZn6qWzOaBm7osFrFkZxZUTieM44Tz6YzL+YJlYTdFSIWyxBuMZ9gxAURiPW3Grhah2qGWDjCBUg05AkxTzj+r9ViY7hYA5KgqaSSbCSPWQiiUEULEOLFiJqQO4zhiHCeEEMULjGqJVqcsehGlqa/ApoxvYOvauqlXkFoIrbqTEyyLhDKVkjGNF5xPJwt7WsaRrRvLhGUam+SoVSCRMEz17kNAUmHeFFasXEkWHgpW5IgAFkxJEwBkGx+hMjiFosTZS7ULSqLQqGxT9dOoe12FruKUNvCyk7FO/FdnkXNVvYSDovfq8ohsnVWBUGUnr6g0/GTnCCRhmuPlgvv7O5yPj3i4v8P58QGX4wNCnjEkrnL47rDDD7cH9Cnhzb7Hu5sd08iuE6FCaazmVoimKEidhkEFxNSZ1ylbsbODNcbFWar0lUK4zLMI/gXnccJlnrHkgvvLhNO4sNA+LrhEUewsGTmw142uIcRTJeQFIQDHh3tLYn18uMOw24FyxvzmDYZhABKBUmpydD1XyzOHW3ClLa7udD4e8fnjR4znC97/8jP+47/797i/+4yPHz/jl/cfcTyeMC8LLuMsebrUW9PTuUoDguC2TvLLHIYe7/asnPnusMMPb/YYuoR3b2/x/XdvOJRtv8N+v2dlmyhqEDSsUBU1lQRqov9SiJXr42QM5GVi3P354YT74xlzzvj0mHAfR+RCuMwLJqlaSYZr4MJkKl5VWkFEWMqMTIQ0TpgL4fDXv+J0fMQPDw/47ocfuGrn4Qap00qaiQ0gz7yO/TDYsZIYHz6jXmqtUkVwq/uuSpgq8UpokjEdQiCIaZKW1Q4AoswbUeRKkYohVsohObDD4ErCC5NsfdNXWgUfh1oMrwZR7uuYhZ5YwvBAxsdyiOVa8e2RE4+vcrbXmfZCIPOcfO5WclXUNB5qqtQKMF5BBXJeDuKw5RBBgcumsjFB164gUhQFDdDHwHg0Ruy6hP3A8sVh1+Nmv0NKEfv9jvNlidyx61nu6Pse/dDDQngdP+VlJF1p5amoMC5cMiu2z5dRqsQVpJSQ4oWVvktBnrMoamqOIfX4Ke5dFtbnPQ8b2STYets8FoKmFPc5756rNQm89ZzCLtV1g7+K0PSab9p4NiqfS+6a60uVf1vLv1VI9r3QPqt3FQBJaeGkCBH8yR/7PUnN4+vYVx1seQD9sV7d8OxuRO1SNTP1Qm2LFxX9hy6mVkwU2YGoYBovOD7cY55GfP7wK3798c/Gx9x9/oxpHKUK6ywVzaohR/MwhQDsB96LXUq4vbnB27e3SClhNwzYDTtodbdOvWhi5S1t1QjmSV4KYRxHpou54Hg+43S+MI0cRzZsCL+icrv5rAYg5KpjyUvGFAPSyCk+To9HxhUpYZku2B1umc6lhJh6aF7bEIKkfgEM1xs8X/l/XbVvq/rEK7T909fuQ2VsQ+2fuRjanzAyymAmqfaUxDLYRWZYorgf9l21HEaNxQ/sOgxhDGqemWuLviLDLASgUACJRSFAwjiq9GaCxzXj7wgJqncNomSWh/aRxxRLlO+r0Cf7+6YV+Z2tIvK68Q0tQRVZFk6RRcBbuJwvE6BFNkG2TPe02uAmdhlQytkrZBTAiZ3YUheK5j4J0iMIH+EEMdK1EWLtx+aQWnO9aIULFZUnuXRsjAhLtEpWJsCblYcqoYtbVkdPD56kIL+vOYLTzGOoREzf3zJX7nflSa/2sxL+Iog3ixIzS+b2Nmkwu+9Ti2ACOBcASkPAFKaj9EGZQLtAlFwakmHbJ1Tli0JnoVgJBAW04MYwpPwcXyMoV9Y8W2iQatF9yIJ7WKhz+9yt6bIxo/paB++A24dkzAHcOqkQwsyghDtp+Mg8Y1mYIKpClauvZa6qAVaRxKACSs3LRS4ZsKZZDs1fMBxef3P5FtawBWbYXDA9PJPWXO35EHLXNhfXO6hO3vVM+3qqL9DWLNNTLFS1ybWtMtH6x1ZYrqbH66dWoUTEYTII6FPCru8wdAmHocfNMEgi/uQMGNVzJgWvqEmWTyipoiZA4ClbPyCWJcb10Uq6xhgkPxUrV+ZYMCwZ81KAQFIggIAi15LHWArv7HWlFSGWeeK/ZbbwJx9iCdCzh8wALBACwtBJCO8iHoWX8xnn8wmn4xHHx0ecz5xUfV4WlzyYLBWJ4hrov55BR+V1uhgk10XEfmCPmqHvcLsf8OawQ9912B122B8O5hWlYWvhK4oaIkIfAyYJp+jEI2deFkzzjHmekZaAXcf8U8wFSwnIRfgVEUBVwenNJKpgrMqPml9vnmdcLhechx7n8xnj5YxpvCBJtT2d50gJeGZDVAzVgKHz3oSgOIE/wAbiFBUqeERhi0QIEWmQQ1LJvttZcWnXqkMAhGd0sK602LFZJHTKaKPJaZ7PhJv5qiTbJEmKdISmB+tz9fpwfh6AzkEF1zo30OUJzdrbbxra/fxbsaHZujamNEbtU91h7EVk9zU9bZvRraBKD0keLEmDWRnKhuIuceGSQRKa7/oeO/G06fqeDXpBjRq1L3AgojOXl06EWEKcs4WPKx8SYkbfdehSAhHMEBKLp7WC/zZGV/e/J4srvtnOyxndxy8sZ1yBSdj6rcoFtnrUUkrPXzoWt32gPa2VLcLWzQjCi8hxc32oj5ONS3I9O6xJT4Of79Vo6yVtX9YTtNVeeE3+5ibzwSIU1XGvlYZSzEIjLaYLhwZpdadlkSqjWb10W5BVvr+LEgbcJex2PQ67HbouYTfssN/tVnQRbDSUhPW2+4hEUcOeOylwxE1NgbBIZdiEJS4c0QKs8AnseYVRKkrJbJgEG/gR2NN1GjnEK6YOeZ5F8cwh4V5B/qTx9ytr/5WqTy1GduKVHXgEaZV3QmXmTTkTAAtPihrvGa2qwdBxAq9BEnZ1iT1ndn3PWu5hwGHHlsN+EOQZImLqkMQdMcaIpG5HWn1kJbh6oSfnmZNsgjhcQBhVX9FmCUApoSbIrZUwjWxwjhp1rw0IkeNRE4Et0QRW0qSIVJJZybRsd5cCQBJ/9wIYtBJacmca8agi8FJAmcfP+SwmZmCnkf9mts5niUPXqjFWThEw1zStRuJzCqnLms5bVNjRcsKAJao0ADfSF6yr1WLG65VFM1ts7dQaZSOEYgQqxG5smfPV6LXj5QKEiJwXDLs9Wzs6hi/20AI0CarFDutncN4toQonz7aG3mVcGGroe/WcZwzrxfL/tdCq2vFSyJQzs+SkmSUvjeWnABAETvk5qT5fn1eq14d/v08CFqwCTe07CzJO8BflDq+nuCSCxCIlggKRab/Z/Zfhg0pph2mCRa2EpLHjpcYrQENBVOkbQ2ySxj5Xq8Jqe17Ri1883ROVGGp1p0WStkmS7/HEmejPZ5ylGlueZ/OiqQmE23CnFDkUJgQOGdV8GZ3g3xgCQkqIseO96xKbxhRRc4ZVLzPFu7wuRSrhyDSbkFRAWWbDKV99as3iGFCzKtvE1E8XRSyvMXbOMUrPLFmYgFNPXb/BcWkmEBqA+yugCrlChGVZWFFwuVgybBSuLHLbDehiwHe3B/zw9hZDl/BmP+DdgS3BQ+KQYI7lrp4zKaaaoyYlDp+R46ihNKWgkCimc63apwxNoYJ+6C3caRgSLtOMeeGECzFESV5LmDIhoyAVydFWlEER0UOAXfPxTNOEeZwwnjnXSUqJvcSWRSq6rX2VnqdNlzMAYJ5Grqo1T3i4u8dPf/krjo9HfPjwHr/8+gH3UuHpdJkwTktV+BpuaagLtMiCejkE8QLuUsTbmz3+8btb7PqEP7y9wT9+/xZD3+HN2xt89+4tUpcw7HYYhCGNso5oFDWh7gciQK3qpWB/OFhC8ctl5PLnS0bqewy7AfO8IHYduj5hXjLuTwEB7IGTc8Ai1uSFuEqUCcwKxoC9N0vc/zQvOB55LlPX4+P7XzH0Cbdv3+Fwc0BKAV3qua/dqhrG72wcHidN6YbDlf6vSrWVbkDGogkxWUkj4aBSdplRF3tr8P1M5EmFNt3jhb2FrwRrh6c8EmPlo9JLYRxI6ZnwMI63WQohEisoaiQTVVwZIApWpushQEpACx+m5ecVF3uZC/6AvJTr5hdOiH3eZnSCJZorYf3qlSvhLjhjaBKLu895mSLLHl1X9+LNfoc3N8znHfYDbg8ceng4HHC4veGKT/2A3TAgBFbUdH0vcxmd0FVpTZVha3liIsIsHjUlF3RDj2G4YF6yKL+C8V3LkhFDREoTe69STY5qaxo87UBzZDgBq7VVXtgzxM+8jus+mbKCrq9pZDL/XTWBur6NhsfRzabvLT3lS50SZiUDqkyoc8n3tkZynqbQyBBF9p52unjljGcKfJfl33aMaNK7Xm/CLX7ierxXg37RJtRN8YPS8Jw56iJnXE6PeLz7iPF8wv3nj+wRfHzE+XTCNM1Y5sV4cFVacOgS0Hedebe9fXuLP3z3Dn3f4+3bt/juu+/QdR2GYcAgHjW614PQV0gkjfcUVEMzlcK58MYJOWfsHx5xezxiyQvuH484nhJyzjieJwSwp4zJlI74sYGalVWUA/NnIvMfHx5kH2fcvL1HN+zQ9TvErmf+OUYuRONmtMpweFqBI+0rVZ/8Q7eQgkhT4nlSQIZUggyoVmGqWmzLTdNFy0uz6zr0HVeA2vWsZd73PQ67AV2K2O92uDnszR1xGAZerL5H1w8mMCStOBJqjhrf7Wqh5pAAroZSuGpRyXKc5ZgQl2ACayi6CUlKiAkRidEEhSIeF5kkaVHmHCcpdUipIBWw5j4zo9eliJzYmpNjEEL/3E2FGGUhPNMgnjDmqVDMorksE+bpwtVkxjOmywnLPGEeObmlXqdl1bx3UJfYtTSEUF3yI1uDh75aepMAb/VwAFsts1h34YU0IdkkVUfE04OvXypBLFXhloWJtjhefV4GKDCqVSVFSkcWQmZBCLES565jqwdr44MRS6+kCbEinudW1Jhng7xUlUJautb6gsqIKc2Iig2kBdTQPgIrQ5Ylm4JmHGcuxT1Xy3YMXCYvBBiStPfKp7dgKnzp9ZqNnV19axZ3U9opcysoRZbZ1rAQYZQcGUXW2SrQaMZ4E4gFUUPngRNuKsyooGVheqhKmpgSJxWMz7+CkP7oGni+ySYMMFxCrp8+3CkvM7Ikl53GC8YTK2cu5xOODw9iuZiRVVGTFynbLV40Otd9hwDelylFDJEZUM4ZxnH5qetZER6r1dGvKUL1iOSBiRBDEJzKg7SQNAIrgjVpZfC+OJ4pqkJhGx5l01RPhJUypoF1aq9/gXbdnWC8O/e3rcIhHWuaJvKeZ66wdjqeMF1GkJS7HFKHt4ceuy7ij29v8a++f4tdJ54Y+0GqKEbzNO2cAaMT5Qwr2EQ5owq2pDlqRDkDDnmlJRtuzVnzmWTLaXJz6XGZuOqRwsZlzjjPBZe5sKu+CsQoyIU9D0wWRpGQIw5BvFzOOB+P2O2Yxi/jiLzMrCyVUu3P3cbTEQBwOR3x+eMHjJczPn74iH/+j/8J93f3+Pj5M/78l5/weDxinGYcTxcsi4ZKC/yKkhhGFwIzchRNBE+SA2PoEr5/c4P/4o/f4bDr8cP3b/GP//A9hr7D4fYGt2/fIKXE9Gboq+I4sXImWKiaKLssX5MoYBU3LIuF0XEOuYyb2z3ePjximmcMQ4fDkDDOC/e1ZMw5YMoZcy5SxrsaTqoQXzcl001WIo6Ycf/wiGmaUAj4+a9/BS0jvvvDD3jz9hZdF9D3OwnBfd51HFzoE4Q+qTdw5ffWVSyN6xHhlZUzBIiyRRKsl4IgITmQKokmpGjG7FLMwhAFThWjtUy5O1YaQMWO2UGUr6r0CY4XlTwnZpyAKQJrrhJZHlHWaESqJphW5RKJkFlxqdJLFSiFhwmKl+sYqlfL8zaFNUP6T8FJI/Fyr0yhKZ73aiRkD/yqtEkpmBdi13V4c9jj+7dv0HUJh8MOt6q0ubnB4fYWKfJe7Puh0kJR1KgVn6coNjRHcYMKiQRCXsTjtRTsTnsczmfMy4IY2PgxTrN4vS2IcUGvxkET/Ot8mIKhssMG0yZDr6dNFZQNf/YyhDGsv7miEV+/HjXPU3PS0XRV2BizWB8S3OU1rEl5c2kypyqb6vVmNLY3iZynPEmpofVeMVfpPDa81T1cuCkJXwPz1cC2WngJOeNaiWYHyrMJbmS4njGNLCOeHu7w+f0vOB8f8On9r/j88SPOpyMXRbiM7AyhHuCEGiUTAnY978e+S/jjH77Dv/5X/4BhN+Ddu+/x3R9+QNf1theDU8xCFDVWFdfl2qOssmnBMk5YJnYueLy/w/HxAfM8Yz/0uOvY65QVcywjjLPiWlX08/hTUE/hjEITwjJjyRn9p15yRI64efMduq5HvzugGw5I3YBIwncpv9ooCMPvU9RsLWALV/JWvwmpXhTcv3rEwliQDOsbfyIs1HAopynXT/nTMChlSJNYdoMoahBWihrArC2QMIpIRcKeCJwUSnOasHBUqzgFBCqIUTec2S14bKtp0HGaIB+xKgEemt+jXvdbFuRvahWLX5NcRfTC3KgSRpKXmlLGhY2QUQVhPoKGRaiHk1Su6Hhtho6ZVrP6mqKmxtvGGBAX9lAq8MQlQL1AYijIIYswUEBI3L8kLukgFNWq+/6hwjDTLq6EESzkh6uOWNhPie1Y0UJ0hWk5H+rme9YW6oERKlWTuE3PdFEsuaBNw5he2zhGF+el4j6VJgXAlBfqEWcacfm9naO6+2NSTxWtQMOlwxvvKhXHlQAKvVtyQZT8C1ngIRYShWhNBG7r3PAF5JQ1rn+yvys9rYQ/CBbV/fv3bo17MnyfXTluYmsd+ZBErcYmglpZZvO60TA29oyqbs+aG0RXUV3CY2QlqlW9UM8MVz3Il00Mgq9lEsEzT6yEEQHEr0kd4/Xo9SfPbK4/Gy7Jf/7dlkvHaoi06cbVtR7pKJLYuJqXWZNQ5ppbQ3Bin5JVB1LlNx8n56GaEGIwuhiDhDtJEr6qqGnDg5nuSRJ5EoZUcECAhKKGgBALUslYuk6EKxZ+upTQFZIKDZ72QfbTWnTlKSiajFerLWVJTK/w/hQn+wwtLzMArvanrtrjhfM8nU4nXM4XjOOEcWIBipW8vj/VSl1pv/AQ7qqge0ro4G5g6+F+x0mDh6HHbrfDMAysyO47sdyzsSmkTuY+IcROcC2q4OUUNUp3a+UJQkwLdkOPedcjxoDd0GE3dAAIfWJvqyL3Ki2T5Vkd+JHrgXhGiTfmPE0YRw4d2x/OmKcJeZ4RA9NUre7zXK2Gt2OlqAk2P8oz8LoEWy+DLtlmgSCJQcjtbTJ66sHXaEMMCGCmMISIiFJp4lreJPedBO86D1TNjaHj0b6T8KeKn0Vcqr448lxNQKu00L87bKAco/6eIcJGbi/j7UNVDDx3o/bLF+T6ppnBTI+dsVB/swpKwfGlQte6LqHvOvtLXUKnxymxgChKk5g6w6MmKAblF2LtuShNouMdVbETS0Hf91ycIcASFJdColCPSIUVTBpitSVDARvz4+gQKS+0mlXHSX/D7P7G5mTANclbV5vdVmXIb9+w8OHqoMIAH6unuYMR4xFbjxr1plnPK8n5CKgenufNVUkzD0pAUmX8hr7jSU5gdRyurtugps/XthapacrDkRiYJGx4YS/ueRwxTzWcWRMHa14a48VR92yKUiSo4/yzO0norfnaUtez8qPvRXZ2ETMWPSM8rfBMGurPfC/LmnlJmHc75HlEigG7gUOPEcCySYwCv3XG2SYi0lKoOU9LYVxflox5mdFJeLGGf8XUW9EPU/ZszfM3tG/2qKn4vHa+lWMUycN+r/G6ulFqXhZzSRTmv+sEaUr4E3tkdBh6ZgKHvuNwp5Qw9AN68XhI/YDUD2Z90qoImsSnTkowDKZAFlNE6bgUaFxmLBLGE2JEWNh6zcR/YWtFYDcahgNJAmyT4yVFIZ4hIoZieXRSLEiRvWi6GFHkMzMnhxQ5Qe1zN69F18P2nDB7gCkrNN5wHkcsy4xJN97M81Rs0xEUOKLE/MYQsR96vD3sOCn0rrqV1uz6gXMKiWeGd1We8yJuodR4P7AigdHEMi+GAKaFs3gXKjhPM8Z5RimEac6YREBFJuS8GnOA5V5AliSXgYXKeRoxj+yuXcRCafCsGlATuuRPFRcmoTxfa5UzqlSon/pP8IKgY1quZCQv/BfxNFmKxG0ulnA2WV6MiEG038rMKCOheQLUe0GZxijIkxkQUaaKUGeZ21OU2RMlAmqoi3rOzAuvs65tJsJlmjHNrKjDkoFc76nVEeSTR6zSTYM0yah1rFYy3cMvQAnXe3GTHyYXxic5oErhdaFSkMWrreSM6XLCdD4iLwumyxnz5cz7c16Q51GsH9mSNcLh4C4G9KIsVYE/xmiVLkKMYskQz4yuE4+aqqhBqHm2bFzCzccAJKGbWta5JvBUgu3mRv6M4bTfgmM6g7smoP6wMZkvJOPXx7YAcg0uKy6SgFopij0vqrDE+7EUyduyzKCSoSWa9xLmtOs73O53nNtEvE/7lNiK3HXiURNlrXrZc/U4pISgLsOpMjiNV0aIoDlU5YwwJyEvCGJVHLpOcH7GflgwZ062vusnDH3mSmuF8W5wyxQgzgi6N4sk/FsWLIsyd1OtItjQmedtD58/AgCOj4/48MsvOB+P+PjxEz59+oT7+wfcP55wGUdMs8S0O1dor6iocq7Sf7Ilj4E9h292PfZ9h7eHHb57e4ub/YA3tzc4HHbo+x67Xc9W9JTEi20QS2HnFDWdFUiAU6SDsq1diAFhCdBw5ABCygnLzR5EBcM8Y5pmgAiXccZFrIzjnHGZC8ZZksaHus+qR+tqDeR8KcWqaHTnCz59ugOIPR3/+OED51Q63HByxWdeRg19CgZgjHvMg0D6aXhflNPKfDMMFjuPItUniYBQ6vNKkWS1vE+DKEUglwRiY0MM0brRWOxRPw0rUEFQTx5UAduH1c0Lh+wUIoRcEALDYKYaHpqlCmwhCcMnSF0u83W9lv3shbD54MqDIoTYvFUeg0JBKGj56mdqa09ciADc5kFr+Z7qwQ1TwliYkxp5hVZ1+ieK0r7rsN8N2O0Gzgu122G323FejGEnHvvi3SZ7MSb2LoUYMdSibwhuNR41dqoSLwTO9TUMA0AF3dJhvuF9M0wzLhKyOE4zLvPMHm5L5oTf4smHeJ1MNwZIQmVRG5AoDSC0GFhtu8obPWfzyhPS766z/vdGp7HeI0r/V8+/7i0ZhNtzUXGB97xJofJ00a518BUcDrH3My9Jhed14SozUCOUwqhuL1XbXPe65QOuxnGlGPEn2vULq0tegEV144fh0ODPSz4arjQ64vz4gHm84PHuM+4+fcTp8QHHh3vOTTNJbpriE9bzhLGnqYQ73d7gj99/h2Ho8cMfvsN333+HYRhw8+YNdocbSUMhe1Hk5aDhh6Kskc5Vjxp1KigFMXDkChuDZgRIPp1lBsD0S1NuLEtGJo7MsDDUoh6XwYqOcBEShpnxfDFYf7z/zHjmZsRweCP0XBIMqxdQo7m8xh/r9pUcNf7Du0q63zeeXxkXWLlbzVzeIFNV0khy4L4ThUzfSUiUC4MaWMvGrsE79Ls9e954RU2SGG5DnH4XOuAXZQ0tHUiUM3mZkJYZhYrkXJD8ADEi5MRx6CGz0FM09EmTzdYCbjb7gRADC7pUgihm+Dl9SsgdA1QfI7K46XQxAukFpAtlLpXpcsS5Cq48J3nhMImSMyaxji3zxImhpolz1iwLJ5htwkdg+S1SjLjZDfj+9sB5FG4OePfmltdx1+Ow30kcbkISV25o1S1y8bzEORIWUZrNmQUAIqoJUokwzjMmqUZyvIw4SXbvU5yAia22zGxVVzaeDz2WEKppMgXGdDlLkreCZblFydmYAmMQ4GNPWyXNc/tGVQUN4D0+VCNtPTCQr+/f9AwRWEDRsKBsc71I6VkqnJASIWLfdzgMXRUIXdU1rS7jmdNOvN1MwJdye5rzJwRW/qSoHlgmgmPJRQR7ZlqmiSusnMYJl0kyt8eEc5w45ClEkHhXZSrIhqiyI/q69soQK/yHRkkTY7L+vISkb3kRdA2guFqTJxfLh6WJnAtJWe2ZhfdlumC8nFHygkkqsOVlxuVyxuV8ZAXmsqDMDM+BCqKMJcieiyGgD8CQOPxl6BJ2UpWl7zsMmkBRLIqstOmqgs4pahrXTbe3uggOeSFCWjIWKeyLTCZMGU7SKQmV8anciedu1iIP/7GMrGzaS4SPumZj5X88aald3M69Q+4a6yXBpK4iCW3neQSVBZ0QzsPQ4d1hj0Pf4e1hj9vdTtYsYTBFTRKLk64be2WkXo6jeGVo2OLKZZgnviCHyGFKRLa2qtgOC1BCBPVSyWfJuNnzusaYcNhN2I0LYiy4LJmFOqluo94A0fgCkiTYC5bMeZRmyYOWszJ3qc119Izt7uOvAICHu3v88te/4PHhEZ/v7vHrr+/x+HjE6TLidJaqEKLs1P3rvVE9evUecUwrwDRw1+Nm1+P7Nwf88N1bzo/x9oDb2wNS12EYdhiGHiFKSc9+b1XqkETJZmFQzCwrMiPK4HCdgpIDKLFHTQTvwZL59y4GU/Z2MeAyThyutCw4TwuO44zTOItAk21f+jwN0Y1cx1pKkUTLDEvv33/g6lPzgj/+8AMSCDdv3mEYDhZe8FxNQ59MyNK+bXjueI/EGh7VWl45S3IwPoByFN5BlN1ECBQRNY9NDIgSBpXArvHKC6m3qPfu8F7TVHjNFGSUH/NV0lIImFUoCJmLZhIbnXIpHN4kytMC8L5VRaHJncIvkEOsG4qbGsYHeGWj8RVOKfL8rQobFUWK+OvnLsAqx2555Cf1lnGKGg2x6BJXxtsPA4aeFTWH/R591+Gw32O/P7CiZr/j6jKqNFX5ousRTWkqSm60/BW5g0ZRE7loBcPcHklyIZZSkGLEJEJjBOEycUW9ORdcphmnccRlYjOWKlAJVVlVbG6Em7Bk4HAKsK+rPX5vs1A5cgkWTHmyvnhDP6EctdOX+N5fFXIhQMuMG1dgypn6DvUS1uMk12gUg17veXaV56iQlIMP0HxSxkNK6oviYHc9TrJ/w8Yk2FTYQStjtwqesLrnuWUMbY0xUf7x0WhExQrJTJcTHu8/YTofcffxPT59+BXHhwecT0dczudailtoJzkmqBNFTd91+O7NLf7VP/wB+92A7374A3744Qf0w4Dd4Rb7m1umi6lHFFoI23+BQ2qVjym10AFX0mScnbsJpe9AJSOEjKGDFI4h9F3kHDqSc3WaF0x5wTjPCAVYMlePlU1nBnsEADlI5M2ZFVI54/7jB6BkHN5csL99h64f0A0Dun7gQg4SjOr9jL+GU7/Zo+ZKSfOVtq3DqavduijWv2vkG+uf88aJJlhFQ5pt9mdHUfyuJTJEVqIUvyuVaY0E9ywglMjIIBZmdEsVhtXaUtHKVguNMOPH2bhoGjF//s1X9x35L1BlDSNELzxpiexVlRyz9Khb+vVYrbqFuHn3fWfeUH1K2A8D9ppfyDygdB7ZW6nrsnjUFMwpYklsiYg5IYmlNYI9MHz1nlhqItQASNicQ8SyTEHHC0At3G24l7jiO42sMnW6Wa/RZv38lo33m5tXvKzm3D6D+6T2tm1tP6Az4YVm5RqrwiIYw5MkrEJDLJKsozKnUWDYwjBUUSMW/a73uTNqiT22RvIUp5yRck3oDZJqODmjlA5LyKbkJRBiLNAqUcrY8dCEadlci7AifsEBCc/LSxFCeXxLBMlT+eqk7GPLVYlj1ZuUAGoFNqvSVUMVVQBnoRucnFDGpsO9CkMVr7jgvnvcHBxehuGullkNAEqsJQkVx0VhrgwPWYUV+PRCFVV9EbXKOwWnt5La71ib39Su2U3/bYtN3qaN1UPCh+cBskaSz4v3zMpiHOBoyIq2OFqpLvvmNWaKGgKsoppcF7g4rBcGtMKNCk2+upSGKEexNAdNIOxI73ZThYPSlTbM1E3Ds7d54vLc08RJd8fxwsl3p1m8aLIUFiDHZAp6FHgzlgJY89V2LgZUi76FW7Bnoa92ec0XBJgSWZhTBGHvIhseEEiqULBPorqDQ8KsYwzgQgVsGCEiNohJ6JoayBYRGKMoHhwquhaYmqXjX0ohBLBn5jQvGMdJEjiKK/zAOYfKsjzrGtZy3x7voxZxctc2iuGV0GBhdpHAYe4AFZ5Dvi4iBFcwQY7YMNEy3AGAVvWseNbzBorUYgV/VMNBKAVF8pOkGNiQ58PwARShaxHsTbFm+M1UFMJVxbTgF9MdNzYEY/iZhsLB+Et4tz21x/0+0hkM7kdP2yu62VDuKG5UJY/HWWn1GR2+VDrn8CfkPOdcuub96oCCdSoWKR+O+i5CMnxQqHD4VZ+QS2cGz85wvJMVmve0c/TUPG6ffhnEah6YT/7ewlpYjea3hmX5FQib55RmqZG1hQ19q55XzK6svqIJDS307/oai/K3tuaZX2JDX5BF5Ub1wwGZyUo5I8+zhTrN04R55nBX84h1in74+QuoPI2EPA3DgL5nL+6u66QyZWTv3ybcsOag1T0JQOLUIAsm7yPi+1ICAUgpoSQO/WWjcyd0kT3Gi4Rwc5GgloPb0oOQGLtzECO3FNxZxKkh5wUxJ+NtPD/+re035ahZN1p/CWt+2QsEkv9AFS4yEckJAFpCqxOhsDI3ilST+5P4bWNkosv+rNyh4xJ1x4UarsNMagJQGg15TIkTwxGXuYyS0yZY/DG1zwag2cB9Hgy1ygRZlJrkLCDHgJJUoJX3xogQX4II1qSABnbKrACy6diiVNQV3XJfzFzhSULDOCzKJWNFJZCDuHj3KeHdzR4/vHvDFS1ub/Ddm1t0Ys3Y7/cSs8+eFrWfzCxx0ldJZimWhlII88K5AsyCN0t1i3HCMM3IJSOA53mR0qEEmMu65l3RhIJ+Roh8ZaBFSsxxoktN0MgESCzPjvi3gqoyzc+NQY2igEJlwgCzOwH+naG9txGC9VyVkaoOSvZrDFzZp0OHQMDNrsebgyQt7TkDewwS5+3ylqh3TW+JZwN655Xhw6Y075RXGBCIw7CyetRM7FGTM3bDBWepYqIJZpdcMBM7aYRckFN0gh6hFN7Xije4v0nKYnbm4aMhWV1KMnUvkzRxKwyxxosKw+7yBHEMsFTlmtmKMc8sBGkit3G8IM+zuJpyZnvKGWXhEIcYWBHGSKggIlmCSfN0hMl3cuyYQ+DqT/fOVeK/UNkuTWIdABTxGizE4VAFGgYloS2AVTIhmxLN2VD3FgUP5wWatrM+Yc2qPX8zyAj8jVGJWDSJaUGR34k0AWHbLd/bAsm/RGQJ+f1+Qqz5Z/qUpAR2tS43Rg6nVKvKGXesShhdNlOY6cQrkqjCWf2T98DhPjfPnvEiqmGTnk5WvFXDI5MqBhFM+G/wdYu4nq3df/7En3f3uLu7w8P9I+4fjzidzxgvI4dZZs3NRs06qnC9ZtihcwMgRaALwC5F3O4SbncJhyFh10X0HVd7TKpoA8TjoYbXMJAUIGTB+QRQMlxeS2izQjaAwN41C4cvUeY/FKRA6FJAQMRu16PkHVIX8Pb2gGma0XUdbh5HLgAATVKvIMH7VsfJkUFkdCMIM8zWx4xpnHCOAcfHEz5//IQuBrwdJ9zcvGlysjxH6/udzDkM13hFH2wUMNpf+bMa+lviIsexJuPPBQWL8CWA5aKhwgY8iHdN4LVj/hXGgySvqDHYEPigSpMN7zvcVyuKsXKtRMFxVESRJFnoiJrKMRHsMWB4xGlYqrEmSN4dOB7QbX3FaTp3yhsEIKDgJUKf7JHk9xNPXCMjyiTW8PJqcECdTplH3Vgi5Enern7gXBf9IH9dZ4Kh5vAyhbYqs73C1MkcbgTwcMYKMvjBmIKOcTsnFu36HrvCXvyHwx7LsiDFEYcdh7kSsTKVlTZcZjiKa6KGPFnThbxqsrgGbAZ0z9paltMtyMaLNr287TnuN3IHa/bW7ytUOcQr5pSnScZjSr1SxbvK3ziljb2RAIpkqDjLc0j4aRXiixB6sQted309N9i+BlCcfn1PcPDVXP/Ec5+nUdMfDSkqC+ciy3nG5XTEw90nnB8f8Hj3GefjIy6SQLh60dS/4OZ91/e4vdljN/R4c3uDmzdvsN/vsD/coO936Pq+VtwVvYEpRxsZyIFbRXeeWa3rK7mpqO8QIjDsBuRlj5gibm8PmGfOSXeeJozTxPLFsmAytkj4XvdMLQLBaTQijo+PAIClAG8f7tAPO+T9AcP+IONhvUUB72XF519q366oeeI55KFKNmW1VAT7SQem2mxj0ISgeWVNFyO64CxQImAlrcqSEmLqxDU4icImyGeqSNwQqdvRRZ1EUd2lQkAoCSGyexMLdFyJI+aMGAoQnIWdhEFV5Q8/TMbuLIKFDLhT4PwzJbJipqQIKnycO9H0RVbgPHdr4uo9MVYiXapnSnE5A0rWxEiTJSnNiyR9LFoNqK7dzuVR+MPbG/zD9295E97c4O3bN+hSwm63E0VNZEVN18Hr0FVhwhWdisURqnJmEjd0tX6WUnDqEi5dQhar4NB1XDVKnrlIVacs5Z2DMv/wtE3eiwVhCZinyZKosiv+LJtKMw8qgxAdvPHfi/hiCCybXBxU4+8EoeCVNtdNM9J7xkw96IXyAGABOwVWWKbIhI7LAO9ZYBykZGVsFTKseNMcGanNlyGhUswQaQUasV7BKbtISuNlFuCnacQ8z8hLxuF0wvl8wTjPWAqHR805YyqcDyGEIDmNnICHYhZ/LU3MSclYk85hWQmdwGLXsaZ9m+H5/a0NfdJ1ohX8c99ZcVgkE322PTiPI8bLGXmZMJ5PGM9nziM1jpikVCJKsYpBCJBicgGhi+b+GwNMiaMMzJU3I4I5aaogDTBDEfyxl0SkxaCMNiuoAfa0iCGLsaNYP4vMjYlxwQdZ6HMDKCRj3lVY1neSqfp4rGg+n7817wkVZJTCcP8CR3pBGUkh7rZTWQjOAsOCWliwCLw/SGjfIDncupScZymgThQhOgWNswgrjrIcTJ7GWKepIgXS0sFVEeFDl8Xvpu7bqsZBFzmMjkg8GoPDWSqwAKZMsr1pCj6y7Vc9WdaC9/O0zx/eAwDu7h7w8eMnPDw84ni64Hg643wZOdR2qbSuhgBVoVoFA4WEKIxQACtp+gjs+4C3u4Q3+w63Q8KuTxg6NUCplZdxlRBkmQBW0lj5nqoZgeetSD3nqACFw7NDKQhlQaAFkQgxsqImhojDfkAMQN8nfPfmgrIsGPoJ7+9P2PdspUwapyZjLlSM7mjpcV2bIMAfwDnuLuMo4VURH379FWUeMZ7PeHN7iyjh4s/Vht3O5sE8uKgqILyAWhU1PMdVmZ9RFkn8mqMoogpKyKaEYhgWRY3kpQFxuewkMNEFZqhZYRnMCAGHydSiDwAoEaplMVxGfNpoGEUEJAn9Y2yscx4ALoQhMBHAeF3Jinr7OBTZYkOiZq5YqewkHsWlBnKieA3PH4YYHX+jsORxq+L/VlmjRp7K8WivFXsRQlXSpISuZ0XNMPTod/IpypvUd+zl3XWISRN5O0WNKWzEsyamKwGadJ94wV26HQNEOBMv5BAw9DsEBKS04PbmBigFXZdws3vAYegAKhgkiTyRhHUZrudn1lDvLdWAWyvFH3gZ9maLrKjBpWrb/GFYAeQGfNr0thd6pYqCh86xfupei6pA9UYGwGiodyZQGqRTWiQnSQlAR0GqVPLaknrbwc1pqLS/6bUb6nqV7DttnHM8gzIZa7rz7G0FHLwlq2zLPOgFyzLhfHzA/acPON7f4f7TRwt7KmJsL1QaOp6E5scYsN/1eHt7wH63w9u3b/D23Tvsdjscbt+g3+3NiBqTd8hQPOCM40FlHqowRjCpksA8UpT1i11CKhwivtvvQJTRzQnvxhEoXPX2dLngMl4wzxnnMRh+Vh4gAOYJWwhY5mLVFh/u7kVuzXj7h0/o+gHLMvO4hgEUO4TAPFyRiJ2v8Te/y6PmNzXVXKLhM0yotN9MANZjJ8QF/4TKHHqkXoE31E95FgjV+hCCcZCa2KciDz3v3/B1zNZsuI2Jb7yLnKtzVGbXxve8jbYQOLVXKBL32s9a3am055VhbRQW7BHUO48FzjfUW/hTSpzbpOs0/0VCJzGHa0VNSpoESpaqyeYdObeQhHj0XYecM0JmxcHcMXHqNDyGyM03NcNuDtz4asiXJMFyyY59a5eszVnzcq2+x39idW6r0epz69lBqEAEIQUR7KVak8Z6s9t+MIWMetckCXFi1/5OrumliklA11fFjroAA9V7gwiIuSDGGnIWQFhCQN91WPoOBdfVSthNUa0jNpKWCIfg9l4N71E35nUIz4u0Jyber4vnu0whQS7Mz0KfGEazhDv5MD1SwU0lfwconrGxY8eoNPPwJUbqqjmG2fhq5VYV/6nLZx3xVc6oEGBZOpXcEgHgrJnMgDuFjAploKoktY4+P0f6LU+k1Z+10B5fXbNB99qQs+DWz9FAR0PWoRjXLwbUPb0dEW2Mbc20ebW66zPQ7LO2n081hynXF6km5LoLz9aWeQbJ5yylcWcpo6teNKSCusP7db0UbtcwV+ejGp+04l0NGfNbLDRP1r/QCFe8j6iZD2ruk2NaQ5V4V8SAQMEqUKWkHo7iTahKs1hWeNC9RfbneklMpiIY7VwW9v4bJQRqEff452w1T0iFPfWYCdoxXUMQSgQbaiiyd49I0xQDAkVWSImrgj0PZPuK3xUaAdw40Kt9UHGqKkvqjgXsBwRT3ALspajT7/PhKR4o+ix7H+9J2+8V5dZ5QqWvX8bhFb78tYpZTTH33E33AqEpQrK+hufN9eAb6TWHDir9j82f9zasz6s4FFefwV2h37f4/SpveBKs7wkULClx9MakyHnj2pAnz8tckZGnxw1duy3c/oLN8Rqk391PwLcsXX3Ik54moV0bTxtNLml4m5bniQJUSq900cwTLQQOM6T6fMYFtTJawMYabIPE5k9PXhqeePbfuzkcClHYqEFfc5ouExcBsKqNliYDphDX5r1/NT9tJ5Up1YMm+j1p9wFQHuc3dJ/3niw4hYYWajhVFPmGZcliaRmy0sLgp8DRQIFLziEm0QA6J/Nijg7dMlQePbhUGkrTv7I7f5OiZutRa9HUGEgZXEOs/KTrtZt/MpHqsh29VTA4AUv/NEbNYcOmj+03JThiKqqTBkCtiXXz8ySyZYUtwLxakrGfAB97ZvyVDZLXIoYAiuoankAdW85jcu7pv2UxfkszJlMYAcAYUHaXdnlYcmYlSFmarNleUeG1q5xALyGlgMOux7s3NzjsBnz/7g2+/+4ddkOPw/6A2xvO3N0Pg2TUbyuT+P5ZThyChR0VKhz/OLF3zdh3mIYBuWSLM8w5cynay8SVoyRPx5wyJ2ubF+QgyWqVcDVWBnYrZo8eLjeXlo7DvuYZMUYLCwtE5r7qJhovhVaf1Lh6v2cFP58/QS6zXjVCbUUSFPgvBJiXWxcSdomtdJx4b5CqCQP2O666NvQcW8rVoGr5yk4UNUEUNcmuSZKjBsYg8R6p88ZhTwxjKSUssrYAK4yGecZpnLCUjHFeMC5c3Sss1CB2Vchw1Ti2nu36no/Vo0ZLYjqmzRDoC5idyMIQdXWUAujvDP+8DyQpcCmu5PYi8DhVbzeJCV4kRFGTXqJkYXAikGL1oNGwUw0rFQWczoP+GUGVXloJdXhcD3ghRltlaiCMqihJHcPDyWRLU8JQqx+T4PT6NgAonIlWvG2K/h40E3805lj786KZhjyIPAUq1P5sq14lDc71lBJAxN5ewx673YhJ4rUzCV6LATEFs/KGKF5p5mGTan6hqFUPZR4cfbOMoaHtI+BPqTIs1GMKDl2S0dAYNKyX8+f0QZxAiGrFJmpkIIOB6D+Flte5cbj5G5fkt7SHxyMA4PF4Yk+a84jzOGGaM2apAFG9FULjnqzeD4ZLA5CoCuddCtj3nOz5ZtfjcBhwOAzY73p0fULqEnsROJrKc8MzY8Kcxw+lOHHLQzUrZdtnRCAkIGTJV0P8h8BlhjtCR2AL5s0CChG3Nzu8PfToYsBdN3KIdmEXbg19KoB5W5G9y6AMRIRlKQhhwThNeHg8ydpHfP70Edcqnt/XtMqnKeANVlHnTTyVNOceoIaXIH0jgDiHQKCEEAtIcIlW+eSxRYNH5QsTkSVYTgCSVHJLlig/oPKQ1yun33yoGecdEut1TEiJ8xDlUCRfCiGReNYQh3YHyWkTiL0jqbLg0iq/VyUOfaMehxpO5/edR3Iroeu5muEd4Ze3fte9Ff3n1ZWeASfDtVq0RKvLqnLS59fS3RYA3pcOd1Gg1VxWWu7XtlGsNmK2Cop8HAJAsQqsMUWuztf36HLGIIbOXLJ4wnkvEcENbvDB8AbqHjP8dD1Lnjd8rtbAsC1DDZ9peuHXLtS+BHtA28kGLkIzq41SU71oUgQ6UYZrYu/grg/Q8/wU9WzU1+sSZojiFBx+WDRHFLGXDgWyPPywPqq8WweufBI/X7g/x/4p32JzWWfjpUSKrzYyAKKm0tMyTxLedMbp8R7Hhzs8PtzhfOSQJ67yRI0nahHGrgtcBTGlhP1uwM3NAYf9DvvDHv2OQxJZPqjJgiu3GAzObdENRupeBGSrBVW0y/4Ikcli5CicSLBwSAAcKbDbAQg47Ha42e+QYsTxPKKLgQsIgfkCBtrglMqKLwMu48h0MnU43t+h63rkZcGbd39AP+yQ+h4hcQVHhseIr+3GLypqPB939Ri38xotJVomVCfVFBHGgLRMWrSfqpLGEhN6ZY1L6lVz0/AiBN9heKFN+0HNQLzXSI0BacfO/RMXX1PuVO8OH1dOSuTc7jSAEderJOEiIHZ3T5rAbJ0M8wVaaAePIMl7GQKzKGxYQQPJc3GtrKlJXpUxTCmgiwmH3Q5/ePsGN4cd/vjdO/zw/TvshgHDbo/97sA5T0Spwt4VA/p+MHhQplSrHoCAUjgvDhUyy2cpBdOuxzhymId67ixZlDZdh3lZsCws8E/zYrlOlgJMi58LYTpJwlJKQSg1T01KHWYRimOMyGJxTXqraw7vPnszpoCCMQ2EgKiKF2U4AbaaOslI++QZ2HXCUm2cN49hsu8C9j2HJRz2O9wc9ui6hP1uh8N+hxQTdkOP3W7n8tV0Jnhq0uDUq0eNxokneZlDuETWnVxqSd7c9SiZc0XEEND3ictXThNKybhMM47jjPNU87GoNTUFzjegipr9sOOSnEPPcNn3lrAsSWLPGKvwtMkx/s5moU9CpamBQwhhEya9lJoxf1lqrqhpMuv0Mo0cL7zw3lgkb1MgZvYZ5yaEJDg3QNxPwTkyzEsqWS4w/QyBvQGU+dM/AIiBapnLBulXHGt7QZhMLzxwfgdi/LIsHC5UyOanhGhEsBFxIucWq4oa/1DN74IqaClhf8ZmlcTMYtS+gZpjslmrbKDMQFAruMx3SlKqeYc8HzDudhhEUdN1ySW7dAaMmJjoh2BVD+Mq3ElfW2FacEmjYHODCIovHFPkuUiCVB1iupcCV3DLpaCPQB8DssCM0o02a5fSf56ZWN/kRaXKpG9zIL+7Pdw/AADuH454OJ7xcDpjmhaM4llTqO5OXidh2L1saxMCy4kRAPQp4jB02PcJN/set4cdbm922O0HdEOH1CfEJBNdWr6kCmLyMAEwIgIyHLBVzA4TGsFIHFyxEpEVgEiyf0tEzAWJGC73hz2HA6eEtzc7vDsM6GLA/jFJuGLFSw5yrRNBB2xonLBI2HGME+4fHtlziQjfv/vA4ZjP2DphstVTwtM64wNVsURV2KAYpeQqIWSIYC4jE0NUIALF7JQ+frMr7ALJ6I1T1AgeBSruhH9Os1atQB1C4RAL4rCqIvs2loxYmD5FikgESThMEv7iwj0g+4vI+mr4hwhmpnFbqyoiqzGn9hl1bnw4zXM15VWovt2agJv3Kowt2K0ul/8EP7PhKJrlvhce0Yw04r2i//F2KpLNQDA4hbpuKhhqkjEitJjCLlqNzwtK4gWlShpKpqjpc0Y/dNgNbKDqYmTYimgM1NdTGGpffXlurzgyfBKenb1Ze+Hxsmmf9PUK49ajhjLwb26eg9tyqyEHOWm0I8AUWl0I6KMqbYJVeqocRTADg+5XrdJGRBa+FCggs25UzgX7hFxTguQKUqW+zat+qd49vJ1kToI+h5rxrIe64tDtzEtJjI1R2HCnGNRKxjxdcDk9YDwfWUlzd4fHu884HU+cPH6eUQhmbHccBxCAruMw7v1uwO3tAYfDHofDHsOwQz8MksNSg+7XeaAc7NvgqUGpdnWoXvac5oQXMUi+ygKwcbnvgRCw2+1QlgUhRNwc9rhcLkgxYNcn9DFiQcG8QJT9kltG9rF2oBAhXiKWzNfcf/6MACAvC9798A/Y7ffoyg6p3yGmjnULMZqB8qn2zVWftr5/CUg8L2EIdQu5fO39G51oTrnN2j5t48kezyrxMQWL0uGvYS/ZVEbh3M3Nu+v3IAgq+O82L44he4ld5/rMhNadc/02K4r8VQ8h73lx3aobG1voeyGE6lGROiaGTUZ9U7S5T1REHwpVTWUWpUTUxH+EEgpy16FbFmSwVS2myDk2RMjMMZpVy3IgRa6aoFNNWK0UsVdJhYvqOUSa9NRCweIKoeEF16+Cl3rLCK2FJuiyPgBGHK4kyOaip861nmpm8VHlaYi21jGqN0ZqkkOHIHGgkpSvS9XTRoVJvxmY76kwFoJSRsnWTpLzIiXJa0QWcpVLMcWCuinaTlOFcIjOcpZsPBaCEL0FGxW5v0RrQOaaISVDRrB9WOGwwmIhVZqK5a/Ua+s+ri8MDYBS81Kb+ZUM0XY61Oei7g/5AhYUPVPo8Ia/FteojlDHCtK7KvElkGPkZZwgoCHieheE5rAgusn4/+52/bynt3+4vm4laJuBIuj+qknzG7f8KmqZ4Kyri0Zp5Zh5z+Fu0N81JqxUrYaXkTKpcGC1eoSFO6kQFerIm7c6XN+EaMk5fVbT7c15/f1tkQpEWas7FUJeGSG2m3pJXPMoTg4ThahWXFKDTB1N9SLjXCcsGFShT/8NzRq591WNYfsJXT/vDdVsX6hqzIeCpBRreFZUBW1YgY0Kv0KHcA1BhVhBoOFPy7JgnhdM84z52UOfgn0azIsS3KoVMVKHVjFr9gPB7ZW6d3RPBVNShrp/qN7rF99zSbpHG2PhylC4YhPtPmPN6hmsD3VdlK8OqvSFjtcNkXSPOUxoffdKhuv3+FMrKHzWtkWZvnSdHW90xtOXuh+rkseHiV5RIkdzUcTrXYVVoPJdEOi/6nA95+mxp89N+gB3ve9rFN4rRLfOq7E9NUM1ZfQKxNYg+EKIdZO3fmKd7FjndAMAbEtv3OtJqcG+7g3FX3Zer69GKzU2qYIHUK/B+u7mnVudWT33epNvtY3N/5vai3Gornnc42QhiThg724Jf1oWCRkW2C4wGK88isC2GJqieAQnJxcaC0l1/1AhUJR9SI7H20RG23PqeRefk8srqoEWT3jP8ipXuH0rS8hwK9haPIlikZyoy2IFcWoFqO4KF3xNafrl0CcT4qFTfcWYQAaOEIw+6sXBbR5l4HzcpWc81EDHLlNAzMCyEKYloyMgpIzYZaQEoGNLg25EqOZM3b2N0DprhSxKyVxKC1SYQVv4uCwZZVEXfKcUIDQMcAxSZs9vYB0nquawMqOcpM6gC2EFHMoWK/F9gQ3o3lV5Oj9OST5q3jO5/pFzq5Y/W1Nw3P2u7zB0HW4OrCG93e+x3+/YvTsmABFZ4/0zVw0IVBAiIUqm7C4lRMldApkLELky2YSY2MMll4xMzASWEICYQIGrYXBGdl4rVhx1IIKVIQ1A3XwCeEQra5PGYIo3zyIMZogR8zyjWxZWHHQZha7SrTrB6hmXsEjIjOwlKPPpsnWadXNTkoKtH+8HxYYtkKhnDLoOfceWqBQDQkxgH6wqBEAUL72EsvU9uy4GjbXuOrmGE4IBwbzfAE5QmXVcRd0jneLB+s7W5363R0gJaVnwbsmIXYfzZcLjlHFZCrp5wTQzglQmO4aIfuhxe9jjcHPAzc0BN/s99rsddrsd97nrTOHErvSOWXv25hh7l0zYmHMye2CjpNGKZCUv5mVj3jYaKqieSEVxim12WW4WnthbABinGTGNSBLCF8DKMEZVgk8lmSUgCXDFcr8sJhYwTNi+hXE3IRQ0Aq2Mr+I7sfiWygCUwpWcStCKTrBPZZICcR9J8tVo/gFEIIBL2VKQJG2S3vXv0RqrqmcSZfzsiusukWtiDKDEiZC7vsOw3yEvM/phh64f2LMxJEyZBc1uyhjGGd1SsB94fyQpMdSFBMSImHoE8WiLMXHSfeFIfeUEErdbhiFen4UCFlnqJZNV4CuS7LgQO3YU4jXpUsIw8DoNfY++X5AJEmIQkUtAknu5C75KXLVsWiEByefSRc7tojzDc7fj8QwAOF8uzExJRcO6juTgt3oxhkCOTlWmR8cRA3vN7vsOh77DzpKWMy2aphlUCpbUYekKhwETMMg6dTGDUhFWJvKnJbuoQoFtb9tNVBW3RFiy5BYuwDRlLFIxcZnYMy/nwnRZ+JSh73CzH1AIGDr2sCMUxIXXiBx4m6CKyvAq75Mz8ztzCLiM7E2TujPuHx6ffR1j9KFPmmzSMdPK+IGFhyC/USHmF0jWOMZa1SsRIGFOlKODABJeKJp6o5SCRUKhAxUkSSar1TyDU9bxfnc019FgrfoGkjwHkuNhXjihdSGphqkVAXn0vPpBDcbETlRUQFH3GdUcHaoAlfE0wpDy4B5fG3y9BB1sW6PIF1hrhHCFeztZe6pCeYz6F9jLu2uLCCRNDCw8IhXCsmR5fwTRwrxLLkhLZlybC/rCYe6pELpBDFj6sjoCmU/nDSxFOYiKVLLMIJDlwGKap1VU+ZxONfOuPfqUufKfKSJlrJbrrZ0TyBqawUfxluMLQwh4Gd6mNgs58Zot6WtdV8czQPlXxbQ6nmuGVnlsfUSF6wa6LZeTVh70choCzGBX5S6Pmxi+OPST9+ZSCFmO1QmS/r/EvWlzI0mSJfjUzNwdABl51NHT3TsjMiv7/3/QyIrsys7O0T1VlZkRJAF3t0P3gx5mDjIis6rIWg9BAMTpboceT1Wf+vkMPzw+Pnzj3RgYkfgbMvGrszNsgn+EXcMqP1urqjMKtvWG2/MTri9PuD4/4Xa7YV037ZJUUauuf/V7vfua8qMZf+k0TdJ+OyYwNHBCUmJUK4MoICptAlHQzPwFHvCN8ej34LgCRsoOA0m4VeRtR9lWaUpzW7GvN7RasW479lxQimWEDlxWuvcaA0V9FuLB5/NV21ymhLjjen3xzOfbyxNO5zNabZjPD0jTBEYEcxx8gbeP35RR87VFMwIzxyXeN0RPVTzexs1litNAGpkk1tZYoqSoFMRSvQ1vYha6AihZlJAvgIJd0pDsOSij2tg7CtXSUHLFCNTAlXGDR3WtnamWWQV3zBW59o5QY5K+CQD5nFg3dKcc7wQQ4UMM0tepbKZQxmyRI0gD/VvKorqxAx9z2RxTjDjNE5Yp4eG04NPDGQ9nqTu0VmQgcgHHxOAqCCIFRqjSkQKTbMqx2wkgoJoBRsICPmkHHOGdCURASGAKyrOiifRaZjMlcbxnBZMICtRg1G+D0aYM3E3LTgQ5ljKTEAKKlpgwA6lVhBZVX0fYqvfo3DsezbplDAqbiRCarPOg2qLnk4iW8pRTXXduVRt4aGtRP0OBFNCSLiEpBW0JKe3k7GYWQ4wJ0zwjxoh5WTArd43VflptKDk40wGJVmW/dyfQOnKIsyGjaV1qJP0wzTOmKu1ql9OC67rh823Ddc9IWhKVVeBH5e6YlwUPlzMeHi44n8+4nE84nxbMpxnzPCFNCtQkLcv6yv58z8OvU8e9A6hH+cAa6TfSYAdpit7UqD90uzLnY3Ax7DeqOhZCfiYGYAhR5Zg4FAwCWUeumDzNnLk6P3FVo4WhMluVgZF2E4mxHGPoRpFZ4Ab2+gXLl1o3OUC6Y7ibbEafGXz6W2xRfQq2KsX2CWNUsQ1S+Z3m7u7e/hod2cNxcJD0HM3ohjr9UeSXlD6dpBPPckKcZuHFooCtMhgVYS+IMSPFioaAkCbhypgJS0hACOCUEKzc0LiXAHEu3GINAtQwo3JFAcBoyEzIykWdK6OWAaiHzr+lgBMQY8IcIhgByzRhmTIqsxKKRxA1VCX+HqPFQcEYa2ucQo9iCVhDPSPyPSdQj+cX4ai5rrt3lmtN7AHRA1B9NzivfUq7xtcwtVyHnO+spU/nuQM1MUUwGNu+o9aAGCpirKBAmBHQ0oTQGC1UIAm/VIyEoP4KAT0iPoQThzADGgdUlZ+lkqZqM7atYd8UIMoZrWS0xiiVAUgW1zJNuJxmVAbmqZO1G5hmW++AM+hfzhDQACKV41Rw2zaUWhFixOfPTx5weK/jyFGjusJkKCxSqaXpNHAtUdNSJ3Vkm4CgMs5aLtYaWGWjgedg6eJmgF5rjJqLbooKqrKL5hQxJVnnwjcS3ea1m+sABkqrapcewZlcqpeSWfaVrkDvqBOCZgY0A2rEnqbQEBqhqbM6Vu/AromHAInZCG/Jy3+EVzj+3J1f0bMDAZuN/t5e8hpCJ8uetKwppQ7WBJN/IFQFv5gJtQm4bBH/ELLsS+XLCzFiAok+DPLbwU+0b4pWmwdP8i4E2k2DigJ4C8+FZ9qY7lN7s9MJROlMmbXrjREhk3aP6o7GAZwZ7Yc+QOjJd+N4fjAARzY+B9TtrfeNc0uH0zIt3m0ZuF/l4IuBF3pvckr6pOm3jiCXvjcEQnTfbjgHNpnKqMzI6rt4Z0YMGab6XSKOFTTkb4+s/4yt8Tfef5i+u7E7/PkB/qKPtdpmoleqkAXngn294uXps/DTPH/B7XaVYMduOrT5eIkCtWYd2nBmSt3uTgkhSRA4awA8UwVRUaBGSpNCkG6z1gI7TRMSJrVh4TbWoUKbmwOipRTZi61hWzfs6yrgzPWGfb3Kft12lF2BGpYgQIg9A58aqc5UmFzliJicAohzI5DyShIFvLxcZfmHiOuzAjXMOOfvMNUTACA2SV741vFtoOZgmej06X+2YcapfbXY0N/b79980+FxjxApmzJbpo0Y3YJOy2MjXQMr5wGGbW2GZeu8KhaJZo1cNBOUPFwv92u9P2dPQ75TJMMw+TmQOspKg9Tfc1A6XXF/yJ576xjQ9uNFdmn+VUHjly8PwmBcG2N9NEJLAy2G+XRjoFl3iKoGz+AMKMW6OOls3lcfe1dKA8igUtg7+LQeyaoG/tjrZHNz0AjDvSlRduPIo/2jE2/r6x2m5LcfsrpULxwXIN2tMXs83L91rjS+R8fISp4OaeH2Lu7p+vftcz11UMuMTHmO588WldAoEwAHanxJDgapg6AqlAPDW7unVJVfJaLGdiivS/p47FQVBwLBaNdJnaDQjQvcD+47HfeG8uG54340B8HAjbdSpzv4dpSwttfsD9vyo2HubdBZohm5FLQQEDVrBwzJbOGuRJp+UW+/CI80ARi6aAAMAUltXDmEYd8cNtzhRO3V/o6+5k3OHz576IZjoya1+qK4P3qH2nl8+6do+N8eeR21G55971AweSVAf2uMSgq2qcObLZgRgVKkXX1kDBksOhZ2pjb2TOIw6L9SCrJ2JcilqQNjIGBff3bv+96uxQBkK4/0vQUBlKlzTES9rkiWYnzsahVMlrv+wIdsxaoGVxsj3La2Rv341nE0A1yImm6x64hEB1lse7Y1zU+khsBBwIxcQY1BoYBiRtBy35TeBv8NjBxlQVUnX+ZRjM5madi1DcDvEHW35UviuESfw8550qPd33ZEXMdrwKM2RlQntNSCkstfNUe/drwqfRqdPj3ZbnuNY9jfR8P+wzBf/vww9kcJi4N9AL0RMwr1ZNcarA1r70DT5032VHNZzDpWaqM2I7Xu9rT//sGG7Nl6FkTsOh2uhzv8fbT7uoMPjF367k1DA8n/YcdbP/WGSHDseXw8yJRj+QJg1z9et1XvqmsqRM2DvxCT7Kum3+3d3oa1wNaBkZt3xWljRg2bdjI5Iz/cyzHY7Wsjhe/UAP26D7Lx7vim7f7NN7zf8Vf91DecKOfjJXS859X8dwAUQN+zh3Po6/awgod1Yy920GvYF7/pWroBYOfJ3/jQQUwdzvU3HncBoI87dK+ojW7Z3VXLnWq1DrndnhiDg16WqWL0mKVyzPTr+kV+iyS9RoCPEBVAFeCkxQgiVnu/f9YO9y9bQ9PyLGnMoefs523XdhxbUPc+Rskx2uejHLZ5dDt7wBsk4C9j1mpRDtYGcPhNc/4rQM2gGkZHwO6Ie2trYud2kMnpBksNckJT1fotMuJIHEopZDolTQgUwYhoHIBGWPeK3FZQCNiypCnFGHE6n3G6XBBiRJoXTJqC3WoFq+LblXCTG4vxkjU7wdF1JV5T4j0WD8UNqlatTbA5sXChzyTvBfMxhc066Kh2jjH4lKdQwOpATiEgx4BWGxIR6gegNWPEzRStR5YOQmgwUPTe2gYmvVkqI2tBp0ThTjjNE87LgnmelRBqkpKXIOzWIUoZjEWMAGmLul3FkcvnE+r5hBgi5tOCeTnL6QyExnm7Yr1eUWvF9fkJL8/P4rSU6iVWpxNLp6BSXdnte8Z12bBuGwjQVqSEqiVZtQ/UHUeNGrbaASpoG3AD/ho6au8M4ySx/fcWoewzNGoxqGE5GKrD3HV9JY8FXNFuFxDjkt2xDk6ylSbJokjRotrQOlK5rlIbbrdN+WNkrFKMeGjNeWtEEMt5jMbOvu3YNcq054Ld0gSVid3SGqNGMgMPRo2mcyMA8zwLqSwFPFzOeLzeMMWIdd3RtERk1kyZZZ7x/eWMy+WE03nBZZEMsGmKEnUL3aHqxs/Yfeb9Di/1QjcARgXQPApgpU7d0WrjTUvzLCvOQQ4D0dCdTuOKsAgAc0aggFwqtr2AiHC9rZjTC0IIeLhc8Pj4gBgjHs5nhMtFxmXgyLneVtxuq5N170XKzVKavMXi6TRhWSYlgRbyRsDKZmT9eZcSqMHaLHoMzZHxRQyPOnDUdFqGlDbpfZD3mLPW5/OdJ/Le+tItORqRtj9Fo4kMtlNxh+JwZqqHQidcT8r11WJEbRUva0ak3hUtxYDrVvDl+SbzdprxcD4hxYDvHh/w3XePSCE6wTogkXvLqNq2XUm5GVvO2NWJHveBl6aBnVweYDE2uAIE56IKWmq6zAmVNasgBEQwKCVMVEEkPGZWlz4vknm5nBac5sn5zcKhhPkD5hBS8gQA65aRS0GuBbUOQYPRAcMw7YNdROitXYVMtmcHTTFIVgVYDMVMqFNCYwIhoDXS+rGKur+g/vIMAD7/IUQ8PD7g8fGTZPzNRso+7kVGXqX9dW0VLy8rrtdV5YDtq/GqGdSqy20DdlqpCNC09FixTAnnaUKkir02iSSPzrzJF7MFrVTVgQuxCXORDnTbnvFy277qXP6tx6uMGiK/Zr92lvw8ZnLST9PtYNX/nJT/TjOYuEnLbvUuHIBSUKWoHVBy8U6UXDJqFlL7RL3rzHmeUBaNCiuHHwHa4lZsiW3P2K2jZa7YtQy/cs9W7HreAB+bUegYaEYJAwC5XjMbpY32Ow9rm4/rvGMIr4Safu79yYTfWhcj4OKde8j4EDvo6wGX0K853j221vOSedTEN/A1TQMQJkGIUmT/5yzlERQCzpcdrRaxh5X4lIZJYG7I24q8SlnFum7Yt13gcFa7kMg79snIjiCPlbxJF680JUwlaanIBGaTtRHU4EAwSLRg1akagdSD7znooI8A3PocDvKRj3PrQLg752/Mu/5nrpWcbr+wgC5zLcOIIGs/6aIx2xMktkZWtm1ZE2oRU3AiewdloF1ha88K8SCk/sY4qKOzzuiVBgpT9Le+Ao761dKALnx1avQa+2b/CI346ic1g88ASOk4WvOOvK/I24qyb96Zt7bqZWEyJHJtlj1LRJiirGVv5mGdRlNCmhfEmGSvaMrSnivapo1CwlWCCCHgdDnjfHmQrNx51pIoDMGkhn29Iq831Fpxu65Y19WDFB5oaoQQZ3BomNjA0YwYrpIaQJ0YvrWg/OIuIGEIEY9Zv+Yjt4ZSCvZd9PN6e8HtegICIeu4yfu96ParxzeBmp5maQjX3RsULZPHfcFFAKidj6aGAHBPz6/U6/zsxk7Ypm0lQ0KjiIqAxgGcG9omhlVKG6Z0RYwBD58+AWCkmABm5x9puyjNVivWlxfcrpLetOeKXJo4FacT0rwo4ak4mCAGV+NlgQM1dvEhGJO/1o2a0aLjYQ4CWCMqQTotIHRFGWMU5zY0B2taDG7kvfeh/VlgsVVZzAbemFSxH+73BMlIQQgSaTegRhANEBFOszgHp3nG+STM3fM0I6X5ANJEBWqyRu65NbS8o+UVBKBsZ3C+IKUIwidMaQICgVsRRLVW5PWK9fkLSsm4Pj3j+flFhGucQHFCDMASIuZ5kRbaEMN5SzueXq64pgSwlGvFECHkglWvthsxjqhbyUmxVt2TGFfcECzKxeggo2Z8yJh+tBg1MHQAaYJF7WjoYtCPoHvOEGsKwufRu6kFKWVKExqRdHCJxiFh3WRICCIV7GxVUulTiiAClnkCpgk8Tx3YaRU1b74Xry8vaLVhzQV7KQCEfyZNC0KImOYFNIdBgSpJsJUe6nviNAMU8HA+49PljClF3NZdOkSFII7flDDPM75/OOHh4YRpEaDmNCekKToXRgg9q0bG9mPmz4AaoAv8US+zRiss+88zV6zcqUqtbRu6so1OW890MAODUSssSW2o/1XQBhsAaZ0dINGJT48rct7FoWDGoqmnI2j09OULvnz5IqDdumPdMwDCvJyEWyUGPD4KOBZDwDJPmGcthQvCo8KqjYNG350na9ALjjYSgUhBrqAvhKjtMQMAcQgtetM7QIUPm0s7Rl9GMBt2507A4mOfHOjIk5zm8EXWMj0puDJ5R7JSCp73DYGl/XGrks1ocooAXJaEh2XCFCP23/8o3ZimhGWegXkGwNi2Ddu+odaKL1+e8OX5WeZw23HbM4gCTuczltMJxh2WrAy1ZDQt7RDjooECYVmkXDIEwjwJMGo8J1OMYGrSyjSKITRPCSkIAfy8LIjKyyPGm3C5SDaetRi3+/c9brcNALDl4rXpneOlHZzW7tja3/IfEanjMJQ+kXDrzDFgjhERLMZsAGpZ0JrI6tak3p2Zcb3e8PzygtYqIhiRxJb5/R9+D/7jHzDNE06XC+jhQcAIixTWiuvTF1y/PCHngp9+/oKff3kSWzHNCFpOvJzOWBYxZlOQ7kSNgVoaahaejEACtM2p4TQlnKaEQIS1NOyVHdxx4Kr1bpeVun1oDk9tAtRwa4hbxsv11o3cdzpitK5P0M6id3ud2W0c5qalUUegxnltGiNUaPY1wFWycRvEYzSZ2mrzFP89G8BSUfaMsu1gbgjcENAQiFBOE1pZEGPAaZkdgPAIa2u4KfBdG2PNBVtWOajd76SUNKjDY2WCKksUtGA2QAMAOniB1nlcejaNgVjqiLnd40t7cLRNetkaeH95GsLwnWxOb3e474GasRmAlzuRZMna2EiAiQ6BxgAo5xfBWrQPNMxgJtTKKCUr31sFt4JAhJZ3oEln0aUWBDTlcdPTbg379Rnby5M4h+uO27rLOMYJCNIRM3kLYg0Qi0c8lE0xQpT3TUW6mk4pSRliTCDKCIHdzrOMTF3Gsib6Q33CsqlMQ716x98/hz59xxMxgIH1PA4HHe4Oj5mMX2YAyukOnCEDXgQoT3HIitapacxi8/h3aNZmA0K0IKZtB8lo20sHappfSgfGBKscAvUkZxlJBqL7BT2fdcBYDuDq67K0YyPAQ3b8HajzEYf9Hrts0EyamoUGouzI+4a83aS8T4EaW7s9u17tnxAcYJFAzoyT63rdmykhTYtSWzBaYc0KzdhuN/HBWwaqBBY/ffcJqFmCPJeHzk/WpINxaxX78xfcnn5BKQVPT1e8vNxkB6QFQbluUgyIaRYgiIIHnWSfyfpKgcRHV//KysFEr7CW9OO4tnXMSskIO2HbVqy3Z9xeZlCIKFmAGom5VIC/TRf87VftN+9AGhPkgmDZ5Apq7Jth0AMdIDjaPeOSeztyIfwHREqwVrtjY8Zv1WhQhQleM6g6QVdrnXTTUrVABM4FVTkwaopITYknjYFPicFgkQYMMAaNG+6w9WRs0B0wHj4o6oCG7+gpmR+WyOao/1Ew8/g64RVYc8jSMOFm32WIdrDOP8HLSjwVrTGYWFPspa9o1lbZzBKBQqmSYeVZAjqPYOdVkfbpTQ2k4kaSOHTsZGx2VcRGzNfHWxwg6RJkqfbtAGa8oTEwCqrBSDVD1RTI8G7LmnpvXoxvHXb+49p663VWRe1tgfXFwzyrQAWHTs5n+1Ov28oQCRDysBZBlTSdsKIGmUP/fkAFl5JD19LfW7XfbC6oLSCECu0RjgBCgiBLBEKElICQK3Bbf720ye5DIOkMpTxFSfky0tB9pfNl9THw8x1G7j2Pt+To6zepkvR7W2Xsd6NMGr5Nj6MRZlFgiSJpfvcgawGzaxghNAdTiWjILjgerbG3qi+lImcB3JiyEMnGiDQN7U+HKCgldSDudkl/rNcOBrivaHEcdDyIhrG05+wzo9zlt8f4HY57U/dbq4XGN5Dt09cnJjL12IknhE6ybvKncgO3oHtIAKwpEooSspdadQ5lLtCEAN+ceyPXK1kiYcahAIjzycpt02ICG2+TggNalCxSbnDaTI+FMDhXRGLg6NWGINwRSa/L7q1Dn9WD99Dvrwzs33E040OysicbX/e87J1f2aR60HCahxspd5jaBOYEbDkjNLGXqkbz173guu6orWIixkRAjEG5c4SLzUoqgOAReEnr1vlU/rScs5gvFeDShIOK5BYIKEGAIDDr2pHH4vgEz44SXW4OYcBB4gwgc7/6o53kdhybs9RLXd/r6MB6t6VwdyZkBD8Y1hQCrGRylP2wx9Z61b53+E2GBhot8Gi2SW1eshQgxMKNlJ+vtSHiC6Ur7PaDOYVWjlqrZewyGsn+aVAmPIITWYJIOlnSuO6GG761fbpOMV3Ed/Mqz5Pb92b3v/dxGN+3dDDd/9mttlfXPdhC6q+70zWasLYfDWxs+sPWpQwsTh+4IgRSEuCKWklKKrh5cpGs9wYesl6tNKSxfBWTBJwaA4mNr4u1wkBpGNCvwctBzdEdbBa3Xek4NAef4+65e1377mrR5kZBioM+pzdU8a+I9673BWQyh7Ov6297TCNwrGfhYM/ouY1jYy/1zEHCoVvaUQreL8tug1tm/qDpzZ+3L7g/94Od8ysT9EEq8e1j9HtGvdM6cbabqV853MOlbttYEE3wFRbO0coSwKgyB9uecVt3CbxzQWgFIZCUMdWqQYvmNuEIwgsBctWsUek6yExAC0CVc5kULAIYXDK4Zi+RGlfZoVrhTte9NV5in/Zgv2EPRUufzN/lFu1D35yC38RRMzqjbkDb+QxTYXf36bFV2fYryyS0YCY6QYjsNPUpRTQGrntGzBUxMaJ2F1m3HbdVImDff3rED98tSBSx5YbrdRXnLE6YT3JStVTPqKm1CfkhEwoTdo1kPX35Bc+btItcUsCsDsUpEWYlMjwtQgTn4s6Uuf6TeN9r1BRQp6KxdgSQiHWDRq6hKXzh3mH85nz9TUfPqLGbPjJjgRiA8Ik4GS8kGh1jVOeoAnWSWmt04fZwXvDdwxnnZcGnhwseHi44LTNabXh5eQEzozQgNyFve3654vPzM5gbfric8ePjBVOKWKqkSxupNNeKRgGtFJ/Hbd1wva6SmtgCaDqhlYo/f37Cz19ewACWlDCrY1H3FTWL8btMCT9894h1z3jZMq5bRlCDqDYB7kx42Jx41gHDN9ur+n4f407fKjbhx4rSrhhsrgBPmziKfHd6VN25JjCC36iINkCgOiOcT+BWMRPjpIYEmIUXg6VUYN0kUvT9IxDThAbG7bbiC7EKv4SHx8cjEAIxWGoxoxSoHFFrw+fPn/F8lRTjTw8XPF4uSFHKOS7LJGWOlzOWWRjiqUl2VwwBp2nCw2nGFAj5ckJEk+dPi7LLz/j0cMH5ckaaZpy09Cmm5E4jqTF05IJ4/zkcy+yAUYZyd+bUcbQuIG44NPa23K4wTdZKOzAQRXBoYlyrM11rQ67FozxNnesG8qyVKUXMKUma+F4wrTtKZXyqTQgUNeU6MINqBcIzKhNKBfbKWLOAB/v1CXttCIHw6XLCw3nBlCJ++O4Bnx4kY+5yOWMJJ1g0ImmWYiDyrC8oOAsijygLuDRYg6ZgoQq6WfvdJmAkEUyuvefxyqC6m1PAnLCR0tkMuMGQ6zvSDynVW0Ct4XI+4/HhEXMg8Apwy0CrAn40BqOKIbPtIBCWecZ0esA0JeTK+Pz5C6YU8MN33+E8TyCQlHFqRg1ak/IMRMxLBKYTSm345WXF85+/gADJPpsm0YVTwDKJsz9PUtbj+0bHRcCZgCkQTlPCwyImRrDXQ8A8SxkchQiaFlBMSKcTlnmRdOg0SUeHECT7ij4mo6YoaFDd6GzoAKkZ9kfQxu1nsgj10eQ3WWcglXC+GF9XwJ8/P+PLv/2M2oD5dMFyeQAo4JcvX/DTL59Ra8UPlwU/XhbME3C67rg8vWCed4RpwvIghLG1St29AD8Ft61IKWML2GhG5oqfPt/w+boBRHi4XHA+nWW9lQ2oO6YU8ccfHvH948VtsDg1pMqY5xmn04yQpaNeaXBy25Hwux3AGBkXm6mmur8xI+SC65Z7OeM7HUFLn0DHRgzOY0XwDBACgZsa2a15pgwFaKltk1L7ECSaXxUkBaRMTee61Ya8S+bmum243mQ/7duOfdsAZkyaURUDkBujqv5t6E62gGcsPDYIyArc7RywKXCzFsmuAdABbyKcpoh5krW1LBKdhl+l2NuWhQLWOeE7h13XeRsDUfa8ozc2huY8vpFZ/w5HjGqjDg6hu7i2H5k9+8BI2AWAsSziDtjYtSfVL5OSC1vpWYwR+77hxcoEKaKR2Bb7vmNdNzAYj+cFny6L8NvNBfNekBKLv5IznPMQkuWWNcOqlIp1y7iuO0pt+PKy4um2goi04+QMIZrWrOUQMC8z0jwB0EYOmtF8mgIuy4RAwHmK2KeATIwSCVXJkCINBWnDFutmqlPz98l/7zk0kJTst0e4SH5RSrLtKeqvDufc16foyf4uWYS+rtH1ih3Nv7xnwozv965vw29ZuRJBwLoQmoPs5ucxi89YajvoBdfyenqByJ9t9nu4G30asSJ16v3vYRzo7jEP4/CRh10bcwdimgXJJSBQ9h15V1Cj3gE5eOWBuI+bYtSGA9IhNsQJoIS//PQLPv8//wO1MSgtoPkMgPD0/IwvX4SE/tNpxg/nWZqdTCecHwomkDa1MF+NwRoQyLli2ytyqXi+FXy+ZuTS8Pn2BU9rBoM8Ww3c0LYrWl4RCLhMAUuKiLFpdnNEY1aey+A6bwS2G1uWNInf31g6ApaAfd9xu16RpgSKCev1BfPpDG4N8+kMpOmbU/JXdX26RyiP79UUPiVwFJ4Ecse2ojcQMgDMjZsQJcVbgRozPCkyKMsif3q+4suTOOOVZpweAxZO2PaGwDdpEXo6CT9FUIM07x7paE2UYG6ErUnt2//8yy/4n3/+CWDGKQUsMWCKhO8vCx7PUjbxux8eMU0P6LtLN68aZP73YK6Z4yAAh5CSBRaQhrgz8litpfN/0Adl1QxGrpFrOh5owBpVnZPevlDSbaUDEHFCmCewAzUyBg+nEz49XHA5KVBzOWOZZ7w8v+Dl+RmlVFz3iue9otSGv/zyC/7XTz+hNcZ//pd/wmn6VzACSlUAxMEa6bxhgFspxYGa2hg1TqB0Rms7/vL5iv/7v/1PMDM+Xc54PJ0kkswFSV3jZZqwLAvWbcfn5xueritCqMi1IqnhKWnFVl7Ts22cq8jQUeMR8RknF8Y43D7i6AZMjxy9fo9Prlku9k6fdsmeQZMOTSklEAhxmTGxRM5nNMxUEcDY94Jtl5au13XD52dhMw8x4Xy5gAm43lZQXjGlhMvjo/y8rWvbL026yAhJIqFxwF4Y//6Xz/jv//4nEBH+8P0n/PDpAcuU8IcfvgN9ukg3nNPiBl2BgEYpBpxm6Tg2h4D6sCNRQ4xRS/GE1+HycMZyPiMmqY+dpoQ4Jc+6wX13HPqYOTSQpJcfjo4gd6ByVJLo6ac8rMMDv4DNdwggzUIktUhqEw6E1hpyBfbS+cGqRhHPpwWXk7ZF3gqmtKNolwOKEUGBPBBApYJCEqCtyfetRZy4n56e8fnlBUSET+cZl9OMZZqw7T+g5IJ5nrRD2ARzKqTTVE9nJjA8mmkADZE67BrFdL4siPNAOj4gEFcY+Xjg95/Hr4Ez3a84AjTyNjMsO1jTC6L6F8YYxYgH43w+4/HhAXsgVGoo+QYjImia6bnuGU/XFQDh+++/R1KgppQbPn/5ghQIp3kCf/eoGVJFwOsqQFjU9s9zmkBhAvaCz3/6jP/33/4EgPHdecanZcKUIn78dAE9LHKO03zITutADbzN9jkl7LM4GJOXGAblMIsSFIgLOETEkzgvaRYDjmL0Ejk4X837Hg7UDCnbtudGy/lg7xANZNZmEwwAAQgBI1Bjde/CofaXX57xX/7rv+O2F3z/4+/x4x//CSFG/Onnz/j3P/+E1ir+9XffgeKEcws43XY8PL9I6eDDA2oDQJp5odHCfa/C4VcaVgVqVs74759/wX/9Xz8BIHy6XPBwPgGtodyeUNcXPJwXxP/jP+KHTxdQIMQUEVtCaoxlmXFaFoRQcMpVCMPrWGJpQBacq62BJJDsNga0LI9BJeC27cq38n6HAzWwJWKbsP8OOeigcgQs98zgIKT4UO4zYtH/zARW/oQGSYs3/VnVIS+l4LbueL5J8GjfdmzrBmbGeUpoS0LigFOTbSu96TRMR6QgTZSsGwVqCgfs3LC1gNIavqwFT7cNAB+4Vx6WCec5YdI2tQbUmFwJwzrkYJkX7GPj5Ra25jE49Q5U9nIRNe1lLj/AtLHSJwmKHcOezNDkJ+nUBS3/8mAnHW/mGEayTGqhFxCwRrs7xoDbbcXPP39BLhWZAzao/3Fb8XK9AQD+w+9/AP74O8zzhHkvOOeMxuK8lZzdTgxEygVXDkDNbc3Ycsb//F9/xr/95WcAwMP5jMtpQQyEyxxwngjTPOH3f/w9TqcZBPFDUgTmKODMw5IQwDhNEVuSgMkeA4oBceoIw9e6zi8Z+HZ03hRTfd85HL6QcTRv7LnG/Xddit6dBw0POgdqf83ASscsBjC96QI+lA5F6oCa2VEqD8yGjhYQYladZqTRwrfYmJF36QDF5pDbOfHAU6kAIhioYPT8U+CefFp8L/cYD+DROCh9PEaQ9KP8C5Pcep7qw46ZuCUXkX95R8kF1uWJh48BJi/kXG2PpBC0C96MKSlQEyJ++ukX/Jf/8//Cbd0wPXyP+dOPQIj45fMX/Pnnz+DW8K+/+x7/6Q8/4rTMOF02/JilM1SrFqwzqhKpssm5Ydsr9lzxvBb88pKx7hn/9U8/4X/85TMaS1n/nCZwq6i3L2jbC07LhP/9X/8D/vWPPyIEVjqI5LyAUfUDWh8pcA8YNgj4jipADYiQ9ozb9YoQA0KccLu9YFnPACSphOdv8379ptKn+2m8/+vVkuHjO81oHVF7j1jpe8ZIlClFUseJmby9HgOaJh01A0IX0hAVOLQi1jOkEECNNJW3t4AOyp9zaBtutbBkG1zTs/TEzSAg9Osnv9ajG22bX4TOsP/6XkX/ttfj+77H/UzdSVIAllfoaWoazSSNbpqAl3RS8o473lEn9nKHGAJaYMTAiCRpwJZSTcTSIcrIpMzgdyluSnpsZTiuF2u5qK2VnffHnAdGQpAFTvC1VGrUyFRAaOwZHy603Ym6d8hGQdQjT8Ndf+ubz/6dx+AcsJYC2XiQ/ZydFFlZSN+d1m52gOj0eZ1PCsL9YfVOJGnWKWiWQxJDndDnnCFlFd7py9qgDoYgMKQNEmn9p7Qyj9CMtNDXwEhgbWUQYXhs6yMEEk4B6p9vWvYkZKXR2wMbCv4m27yBra/CF/R6u7zD8SZHw+BXHNfY8CQPbx4X3lvLTGWOZ3CovANBUq2DfY5cTllpoEX/rfPXsfxRHYzAuucSWgNSlDbDjeH7WeYxDvMo/ArWQtfn0Qwk4HA7DIgaPj5Qbo+OViAfN+ygmN5bot5HCg+/aQ+89+bbv//6uT7OQXWbydOUIihGUAxgRJNOIu9idKJgX+sKNkrWgOyTt87R+B4AyfwwHjKbQ3EOx3041psPpWyEV/Nn3520DNZq0a0LW4xBnNQQwK7L+xobx/ajNOJxz3X5LtuKj+/5ytl0w5vunu//rF2w60eXbwO/RgiSUdxISaQl40/a2w+AkN3fGU8SYBCdmlJDbOzlnoyeuWsRaTiPR1CglA9y9iB3aSy3uBvD+0F99QYtl+Xe4vY9Dz+jw53NyrBPVWaQ60ZVfKovSZ0mz6gcHMQemOt/m53qe6KxgyjM0la9z7N2wQwaHDEb6062hhCF1yZExAidN50fZtV/o53cbbSux6Qc8Xi+rwbN7962CPvikpXNbtd+1G6ku8d/7a8cPm/yaJBNPl6+rmPXSY0ROCCwyDx7niBAYNQMHLJOpq5YWNWOrikVFl2O656vzdv8Ascs+hDISw3NJ2E27qtBJ9DYHa+T6Y7X6+Ngy/3Oxxj15d8yxr92HO3lfiJdXfLxvEaT6zd85xiYtH14IHlz8TiuVDpwHRmIKWuiZ2NZMMjWinGP9U1Cx3WFsauaGyRue7kPpc+bDAE6OGNfP1oTX7Es/n87GEO2nYNcltH9NQN0PEzW2RgQRrtefMD4StdY+bOVfzNJEMv2opdOhTGI0w2+vsasSiJqIKL1zzO77CQisPo+bgNFqcwY5bjtywZWneE/+doG1blnttLWzjd5KH8yH/cbx68ANV//8AGg8EU5vK4/bLW8hOCZCJJ1LYSIHARz9CjcsuDhchYBGS01KqCUT9ohhvDp4RHfffokgqtlhLrLhmNp5QUmaXMJJS9cIuKktaG5YCnCkZGmiD/87nuAGVMAEokQPc8RS5LJOp1mLMsEMCuRZ0WlhkhF01khxGQcfFQ8iKtEiE2L7hprGYm6yzxs1q97Xe9xuPeHTm/FYHWsnU6NJMsnqdPLnJB4BrcImgIwBYCrK5BAhE8PD3i8SEbNw/mM8+mEZZkxxQmX80UI90rDnqXN6/X2Bzxf/wXMjB+/e8Dvv/uEKUVBSZfZIyCoVeq4lYNIyA4Jc5rQAEynE9LpjFIbAv4j/vjDd+K4EJRcTOrEA5p2UhDi2pSSd6mKISBr+jhDpLYZalb2JNPVoxJW/uSdn7ghMPl8SkUDv/tUWrr5UYGLUmmBpBWxywmLruicahmIcZXYtZhsCCFolxGp22fpnY5LIjzOQdJqdR6YpcvPtkt3nstJ9mskQkRF5IqUIuZpQiQhII4hYUozIlU8PD4ihtS7W+SCUhvm+M/4599/DyLC48MFl/NZCGkvJ1xOM0KMOJ2FDBgs2RuhViAJKp7nGSlEtFLdIVyWRTJnUsIyL0jThJBmJC+vSNppaiivOIA0H6Eu7zw/A8+0Pr2nj7LOkaUfHo0sl7tuiELbIEsEJ1BAggCic0x4PMm4NSaPNtHQaWuZJyy6/87zhLNmUVzOZ0xp0tJSKYFqU8MPP/yAgIBSCj693PD99YZaG/74wyes+w4KhIfLCefz6VD6JDJ1wTxPspb2DJQCMGAtncECoFo0aoyOs0Z4ANLwnHJ2jI6a70ExfV67l3/nDHpUkiXyPswL9Bfhcl7/dpfH3gPRH8RuvICBaZpwOi1IgZAvZ9weLigRoFBBtAO1iK7U/V0bI2uXs+8/fcKPZylTyi1iz9piWceQmhifkTRwsUiHPmYpTc2NcA4J//mPv8PvzicALLpwEnDmcppxnhMoAFMKSMp8z77mepQ7EXCaEtpJUvyXJKTEFAghaZSSIkqa0UJCnGaEKSEoKGVg82ug5P0Ob89tMp0HMm6+A2wMHLk7DDc8OL8kJKZTCpiniMt5wQ/ffcK8TIjzjO+/+4RSG6bTCctZSp/+448XPP/zj2AAPzye8eOnC6YokfSHk5SKLacThKA/gEjB1ACczmewdjBMl4IHTfc+n2b8y+9/AJid2JnACPw7BC6Yp4R/+v0P+PT9o3S2JMlsqxppPCUpe56GKKKbNn6TQRHnRtY4+YCwlGMoQLOX+u6lT9Y95/ULqr0N4PVTUlAGBHBwJ6QZeu3cNOow6F6OJJHgFgh0WoR/plac5wmPyyz8NHlH2XaxJ7XMJhDh4TxL9lIgzJOQfRMRGnoZ48NZnIbGDZesZKat4dN5xrpn8wFF1gfCMkmJdwyEZU6YpwRmVs44y1gNHjQxEMmzLyQ+KTZEGEFLiQa3Ydj0BblrjmH9Q45Rz9mis3XXAbTXAJaTC4fozmAMEfM043y5CGn2csKn774Xgn2VgcySaVdKAYjw6fGC7z89IsaAZUo4zWLTJKUEoAZQ0OAtEeZpBl8uaI0xnRseivDgPDw+4F/+wx9UxhthOmGZAqZE2sF2wbzMKKVgXhP2FDEV5dmLESWIbRMpoobm69QcYA9+oHsTzaZO1z48wwbvLlTHjBpj+DiCQ+T4zf1xOBUeP6P31MEpyywjkiw1IxN2fkX7gIMD/bMBko0UiHCZIx60lJcVqGm6/u3cm5JNN2bEFLBQQi8L7CC0ZaWVZiUxdlX+ZW4FyLzQnZ3S3/rm+PjA8dtveM9jDJK35sBCzTvqvgsVRSmQrqNVfQzlVKYOSNlZyhyJDpk0++80z1La/d0nTNOE//SfCOfLGTkXIcGfFjAIt3XH9baDwfjh8QE/fnrAlBK+//SAy6dH7ZA4d/xBS3aIGdM04/L4iLk20HzC4/ffI5eKH3/3I/7z81VkI4SKBNyAsoPrjhQDvn+44PG8IOcd274h513s5CScNlXJv8eADrGRtgupdOfzkfHbtw0pRqzLDevthvV2BVFQYuH5m1PymzJqbHkcIL/hRbp7L9SAlXszgBSkadKe29rQNSGs8U4Jl9OC3/3wnQozcajEkE2I4+OYQGCU9YayCmliYAZald9mbdxKohwRJzCAWclomRk/fP/odZ1BwQtbdGO9q/gODXnfdEFUAYkgGy6MAuggWbS+mYzwCCCLKLkxyP4ZJ8r8sGNEcaU0wu7N4IoEcJBOW0gRoBnEFYEjYouQ5N0ezZFypwvOpxmX88mBGjprFImAVns5R6sVVduDSh2uCtIgJTgiVIOSuLEw9OstEEmNH8mmPj9ISdoPjxeUf/1nYQlXXhoxUCQhu5SKX55f8HyVNrZnbQUbAmHLCaUWd6gYHahRqKMDG7qeR8Pey0/0xg7SvK8wrUqWTAoKkhpbLRBCU5ak4SeVOhZEDI6qJEcQYHQfQ5C2yrHpaTcQNzycEn44T5iCRAmCr09ReYRuHAEAVxHgIQTM0+SZazFGcJsQQsKFEpZFajO3dcW+bWAG/vDDI6Do97TMSFrDbVkCIChPShDwwlBwZszTjNO8oMQCtIYpSneFeZ7EIEpJOy1M8jjKY4oJgay8QuadR0fjI/ThuL9d3xswo8oRx3Vlfx8+SzTIp26sGVDTyy6k+84ppYF7SWJKwgs2gYK05Z2mSaP/nXj5cjohaVlqSlJGxsyg737AeV5QS8XL87N08mLr5idt2i+PF5wuZwdnlmVWY01koZCpsZPBRY9iHeKWPePB/uAOMnNjWCeEN6frA+ya1sx5h5OWg4w49h5S03PXPWIGbPN3dOAVJFxRy7IghYB8PuNyOaMEIMWGOWSEVjARYQ7yezFNiEnaxPpeYcYt924xAaR4FqvRpCVIUeqlGVIKnHMFc8B3f/gB/IffAQSkyL49pJuKXVv1tVqqlMgFqO6EzOWSIjDPEN63pCWG8FBmowRKE2qYEKZZgFPb4za+gAMn732MZMIj95jcy0QxDO97w/A5HNzlIcSZFt6niMtpwfffPeJ8XvDjD9/hP/9vOoIM1Kap8Uy6JkRuGYdFMKA9BCzLach2C2ia0bGczojzCcyMs3a1bK3hD98/Yt/2Dm4qKeoyBcyTgGYpTYgpSic/re2vjbGkiGXSLNUYtO2tyg8egBodhmAjoLLT/BSJJords5fqvBHvdRjPmu863++E4xMjnEpAEHvRop1EAQhCoNiz/MwZlOAVxQgOhHQC5ig2XT0tyOeTdrHM0hnoLkNbQPBpyFKSci2mTtZOIAGCmJXYuyknUEGu1S+DdY1Z2aHZpyGQtHXOAGnH1ark30DPxmCVG8bBYTeTaAbQ+BAODn13UD/i4Lv70Xk1+X4Pufcgo+lDG3MrezpkNcWAaZpwPp8wTzNimpBUdrbW5UHPJBd7M00T1JAF1wIACnp1W96AkmmeHDy8aACImfHH3/9OwB/YkMraShoQFlCggbkiZ+mOk5Q/z/h1ypCNF1UGjCBNB2p6ThSPU0boftn7TNrhcI4aAFamQ5DsA/vBsbtXzxgelpmtczasRa+JNACrc5uiltIQMBH5HojaBrpn0AxjwPIdUYGbyxRxmaMGheBAjQ4VjF+zKQfKRAFNgxNN7bLGAs5YG2+C8HmxLY/BZLbzqLCAbr9OO+i4wMVO8hMa7u39f9tU/cph4MORCLeWjFJ2VO3+aOT2pgd6BrZ+x6AnLGt0SspRM8s+fHx8xHxa8Pj4gH/9l3+CEHw3L7NtjVCVH2yaZyzzLPbqlJCmJP7FJLiALXjztWUvChh/eYQ2HGL8b3lHzgXMDa3s4JoBsOtaZigQ1bCtG15eXrDdrnIOU9Js44bc+rqy4F2A+ElevKnlmq1W5H3HGgKmdcW2XrGtV4QYpcOqyoavHb+x69Ovv/56wciz4wYc/Nk+qHdCIwQaOrhoFCgE5bCZYGmiNjG+OPxnj2djzjY06yCGoP3D1Xim7o6TxnvMwZWj+XeOqXfyIfJ6dXfugONgEN4YnB6n65P8YRrQv7vvcXXSCZ784a/p+ZoDKFo8IDAjUXTHwlLGvNOTt3hWRalpn5JSxtoqntFSVPSZVWAOilbT8F8JKz1MGIB62rYI5QlTEqESqaFYu19FhIlI2sFayZs6Kva4l730i6fhz8MwDguY714ad8J7C9AxNc7BoAPw8sZnoEq7sSoF9s4Crhpt6emC6BlFCrKEiBil/Cn6OrJUa9E01u6bWdeApgkPG6UrTSMNJEnx5yqZOR2oC0jL7MLXDBMQhjRHS0E2v0/WTuCgmXjCUxW1BfCRZT54+jkN+98m+zh7H6MGffhf3XfD7/C2eyeRRidpWKt3b+pZV70TVtR0UyIFaqZJxlxJFvvelbH01NDRIIQYRClJ+955nlDzLOtLV2MIQUgT5xkxCnBnJTqtkXJQvd7rv2nE31A4X/3cB06hHabpmN+WXXx848Hp8oO61UbDejW5apmGgcRpnqJky0hby+QgnTldI5juhpOvbr23VHsAKTA4qp6jAJCC5lq6Cgh3s2WE9jbW9t1dJgG2L3UdkZX+KABDY2TVNa7+bgdNP/rwzKivqN6D8zDeA6/W1avvGIbEdNuY7g0iJ1SXt3bukmlKmCZpD6qkbZ3ra5BN/og0Q4IJQiWlmRQQgEEie9LFhgg4zUJE22VqQAjteE3D+L8tX/jNh2B0MtFx6AZQ5D2Pe0sPvtbtafOYZOOx/90/J7KUYPyK43eNdpHJWykdk7VMMQAK4EgKe+yBFNV5Xg4ahjIlc1wh+8HWBjEL/zmRz6GB725jEDn4IKfEAPGhVIass6PpisGucnuZ0NfUkIF47wzKr+rr93LrvY77L/XzBr6u49567/H+8BG296rtGqJk2ZI1jFBnS+1DIs3+U73FlVFZ0o9cJ6J7Bgzbi2LjSLl9AsBe2ggcWy/HpF3uIC3Bq3ZnPJRij6uc7q/yN8zHwbj/QD/DTol1xtTm9PV1dzrkexJ92ZkdemdLj3aZATi2b8U87PxNXipmcoiN6FyBGnQOI9OZgGSEjJxHDJLueLZvggI60O3CJl4sC63vPfDd2vuA45t74u85Rn03oE2HBhbmudt7aZjjPpEqeu/9ZrjscblHAZSSzFWt3j2PWZMGIKCp6MWhjP7g/x/XNqmslj4bAm4yW7ZrVKCG0IqWPBJAJNeYiYSDSu0XUvntMpX67n9d0ncPKOsaaZIh37RLoHWHsy5a3zp+E5kwhpMYn+BB4ZigZ5jC6O9rjYEgKbVVDfTCTdsWDgYfi4E5hYQ5SoR3WU7SbUTBGgAaha1C5kNAC2pUIrhlRTzWEKqACxKBhE9rV38EdJLAIZLdagFD2kYLWld7dox+0BTygbfSH4/oGvcJ8y5C1TtTjT3o3/foO88E0OEYztXAEA5Wgy2ZS4kSFkpOgJx0g5yXRerpowEh6siFIGRR1AdFxG+HFaQzgWz21rSbBLMbF4DW82p3F3Ey5XdTlLRyc77NICtzQi0LmJun6O0lY55WpCA8CRapaBy9200DnGBVJlW5efQ6nSCUcZhDMc6Oxp+BSe95WDRG5kbOi3VNNrfA7I76/yGIo6Xj4y0gATfmjDxT1rMKTggPyZSSENuFgNlKw0ypsiDPtUhHIaQEJCECTWlyg8gNW2YBSZRPKKaIZVlgxqcr3mmSvU7wWnI7mABuDUGdxhDE2EmTABHMLMaVAhExSilFmCSbJiTJIvBbsPWjBrQCTK9F7TseKqO6uOny5r7sybIWbNBNloUomVUhBlAM0lGujRLNDmXanye9n3E6LSpPJwVqBkNQ14DXEmsZygjegaREJ5JEKFJKuJwvh/OX6OKMNCfPhBNuIkZVgxRQ3S5Wcge9eYiU6kmZEQy4vzCYcuxyQ08fgEXDP3QmfbB7fID6eJnMQC9LDPqAWY1B3UsS9ybdfxGIymuQJsRakGrCkhJiIywp4KIgWVQuE4C6AcBNyizSBCIp27AescZdZIBcUDB9SjPOrqfIB1eyOXpprJpfqGXXBgECJIjhoZkpLOtwmjo31GwpwwzsraEwoyGgIKAiIlJEChOgNwpCJkxG4voBR9bSp1p7yZOT5OqcHswnN8RwcCwOOI5+vjJLyW9pqEVrKyqQ0oTlLJkxCBEc+v5izQ4xAxbMqHlD2VeV6Laa0PcHs3KDCdAynWZQmgFWkuSqgSaVJQQgBkYIsmdKltJfYgDVWn1Lq/bStFMn9+uSXDZWInLJpLJr73JHFg+zfN7km3RXfM8ZxKH06TXJ8zgxsq6NP4IZChiylj3odzUrXSfHC9V0VDsAGt5Xvi5QJyZOEZiTqTOXVzEGxBQOziYIaByUBF0zdpIsJk5qY0Dm0FqoO2GnnZfZUywz0hpQCd2ZHOwA68YmTa2s1LJ5mZCQvLI7WO5E+/92Tx9ro7KOm4MhOlx6veO4vtJ1w3yNRqRH6UNFq2brkMqlSbPUxiBV58jwYCUACbyL7R8O79EAFRjMndxayjm7v+F7iH2HQGxusyUruGqmu3Wh006PNqcAHLR1OQV4Bxp5zIesz9eHKdD3nUfPrKZ+jQw4+NJXkv38sEpN5bC+i4bd8spdsWwwycj3oEWUToQGwiQbJ9VJANwvJJKsuElBHpNnzOIUTyTnkaL4QtCztyETX1a+dyeSDrIspVJV/WAMczKOB9B1iMkif06PN2JzbvQcSXo/9jCbrrFm1WhFRLUsVFjCg8gym0iTFXYtfv3cb4D4mJECQoo9sD+uHe+RPHDXALAABnAcN9nC0nRCKz0BQMuqZx3vHmBqNQuWYM0rmtCibHSVznulaLlhz0QeaSei+bdkNugQAKZxfzJKLQglIu87tu2G9faCGBP2bcW8nL45D7+NTJiPD/qfYyzfrBcaJsmiKA3QKGplKUmpagS4IYA+cVMImLWe9LSc3JgMugiqcswwN6m5j9GdPANvwNr1xDc1hEBPHTYAWiY1OAx6jP3hKzNqlWwbWJp+6wYvIMasRWJMyQPsi4R004+Lvmm2h6V5NXf6P0AJ8nGWXCjaRhL7xA0MoUsgBERMqhznAJwia+ogSTkMkfDKxKQOdZ8nIXNNThBFGqGFOcPyy77Ay74D++7GpJ9hMMJn1owAmWevAdeOYSFK5KLVSVp7t4a8rSj7hrBLmn/yyLRkazVmbb0mKdllWDMcgnJ+jBFeOiCjlrEDq2+38bUl8I5HLVL61IgQtT6ZQ4/u9NmFO4my9htaNCZ6EawAe+TguDwUnQ7i+BuRaYoBS0o4TRHGIcJaiJvB2GsBg0FxAmlrXct+s+908MoEmZ4u6VQbURmBBHjQbh6kzpqDACxOgnEiCO6jRJyacRO1rXM0DpoYQGkCpSg3L7EYuso4MGfO0vvOnx+H/d1dPLs+i1yMz43dndzoDnLONSjazwypbxmMIx3rqEDNFBMeHi749PigGYoJMco8VavLR2/XKnsvAQOQZa0swzQBOken08mvhbl5m0LrokXqBIlvwMIjpk6OOZF3GvzglBrhsV2bmyc6Ln2126vke+Aj9uLw866IzXA6SNrByuwz3Q+L8hG0kRUB1n0Mg1wL2nVgTgmJCZcp4XGeeqaYzkPei/C4NSk98gwp1YsIkv1mBrXJQqKAOM1I2iKy1R7hIY3W97PXiJPPmTzdqpLltU4YO6XkWQQG5pPyhZVS0RBQ9QZEcEhAlJJE0B0I/wEmaa5ynl7KisHpMdvl1adIx7DPewdcB8OsCRCUS0OpDK6yPVNMOJ0viEkMxzjPKn9I9wS6k8YN+7WBs7R8dt3Cdi8/HNBAkA4Y0zJhujyokh8uQEE6kSVF6+YrNr4BtYqTop2kJHDE7ozwsHqPzmEH5UYnpNt+A7jAUu7+3vwmIy/GPT5zOB0AzKSZvRgceRl3IafvTvAoP8zgd3suBBBHAS6D6CuTY8QzTGIdRACZ44YeGCHtCqOlbaaPLcLMYLACr1aSZ4EWdwAh3ImtMWpgDxoavGl2sfO2jA5Pk0yqoBcmFka33TE8GkGaV4G+dzhGtShjNspusmnqIA33uXaz0ubUzDX/buGJKSGo3SIXFSlq55mkRKWxz70zyY66uYGrBJmE2Fm7T2l2tv6aykxCmiekedHvU3tDT57Va+VWwKx7Lm/uH0FlgM+93kA9U9KBQvT9Z2CzZ64NA/HKs3hnxWgBNR5kgv3uIZapr9Pd79toj+vv8OrgT1k2TAoCzoi+k869IZB0+VJaBQdqdNzNlpojYdJxrA5sMRIYk/pvpDp5PAWGlEQV/V5Suys0RrFMtvEaBv1g1wkdk9eTMugT3L3/7u8Bi/ygo9uj0m1UQJqmXRKb8fG4bS8lQWPSxkE3vNIV5FUOKU2Y5kVtH7Np5DvZgjUKqDAzWhGuHNlv4zn3vWHZvwhawrjM3lTGu7t64kVDyxtayailAKWg5YwaesVIJx4OIGoDUAMX9GL3WpaPySrFErQrXCkZ+7ZiW29I04yiHDjfOr4N1BwWjBni3/yESlDTjPCV1AXJeBscEc+sOap7q/n2xU/9dw4L351pe30gbjRLwZn8XSOCRoRCv1ceDhg2m5LtC9cv5u54reLsJPp122eP3/drA/seBx/lwmBYws+536T8SwhJDUGUTIbufAvfhX7Whlkv0s1rR0J1jjRll0cz+E7ikBkw+vmjVOojPBoifWOLczFGlO6PMWVO7rVu9GDtjZbWYdgGn3Jcs3cL/h0PAxTFWBIhIyTdtp4J5lBJS0MBj1oIIAUueDjXNsiXcV3SN9aho8X3utQNz57GeG8EHJWMfIGfu21BTXHs32Gvq9o7jPNhM8HnKpB8j62zoArEFIk+Nmfe1qSvVYwP/gHHcQMeX7hT2AAOW/SY9jm89ur73pCpw228Xh5kHZlx2ppEnTULkIiGdXJntpu8Jvsd+Piqtj7KvFEO+jfyQRwS0B28w96839v/yInrokHqzV9bTXT3mEw3updBRxVh8ngENR3s0hsarGQp+mt9M5spZBHjQ6nwuOaH33T/ZgBRrY0xycbU97bhNRt7I/3lPr/oPzVm17JK/MaSYMKAOqp2O2a3dX3/QfN6L+sGe8JVAeO4nVTUuAgcRE8PxpBHtT1r2G0dGTmJCvf5tfmWMWFwu//RNw470QPAa1Osv2IB/kYwlKJVcq4koNtexkdgWVmWHeVZUoOZ8ubQHQbl1WAflsf7HW4QfmOV2EBZlhu/euVb33z/Pd3uEF0r3Ia2JwYbYPwCHh7wsFZGI4x70MW/LRCoadYOmitu+cgRTBxUwzcv5lWg5GsjcVj7/Xf/EQfdPaL7Z796offHHWhwsN/7L8j2U7mjCozQO70ef/3uTNn0Uwcl76+l26h35hNRV27Db/Xv4ldjfv93dwm/fYge1TX8gRNpe8x/jwc7ntnbWI/r634d8rAev60ChsoJoNv0w2OR4133+g7V3yf3D49ja2vOv8MuAbbnXTX+tqX4xhZ7fW3id70lQruk+8Y+/7DjXvDzW68elzDeEIF3vv4YgKRwtHdgNgE6XcII0PqEfW0tq5yVwEbP0DO9CwYQNKGiwSsVRhl8OPlRXhwe2Crhw/qz17qKHrGO5jdL2vjW8U2gpt15CqOws8M2xKsx0o3Bd891A6Yhlwoiki4yOSMSSV/2UoTLolUl+R2NSAYaOyEwGJJNQeTp0kCfVECiEjVnKQNJ2t5U309Jh5WHMpB9Q4OQ2aK1A6FtK82jUdw66zOZkUyh70cWRWydr1oVMuVSB+I+bdHVrOznA1ThuHHGfeZRAlZxZ85ssDHS1UkQvz/21K6RmJTZMk2azF0I4ChRAXHcNJ1fI4e2XgTIrANiK2ipG7BgSGTVUFoFJyj4eAKEhI7mowU0kveAZe7NwTQCa8AUZ+hRf3cWbTfaed5vPBuzTpANkvugXWgYpFk273dkzXYgAJWMRFB4H44OjablKd8BEaGGcDBQGejOmX6/KZzIVdY8uJczAMJf4SEGKBG1rKFaZdwCEYJm1AhhYgA0itk0fXuMNAYHUkh5U/SctRWxnewIjHKtsmastd1QMwtAM3DU6IqaMRMiKE3AUPIkkXvremQG2nB97zp7/bhXtt1+7+Ted4huNxR1jq2+HrF5VoXsm6Dz3rQLmBryVeRlYHjE3MimDWatOSPvu0ae1N0IAfu2YU8TQgzgUsBFidvHMWKTXVKqRlGdxMDyCzQ4Jq1J6VPOTlBXc0FpllbbnKjvTsvAnE837ghKmAsH/Cwt1cXZR0zk8KVCWG9Pj+CCvQ5EMw4PANdg5TE0omeOl0aRonRDQmsIdROy3QYtd5q8xTNFLSvMxWWdcAwtkqG6LPI9RKCcYenFrTGg8jqmZEkdiEnqwOU0+xqsVUgEO3CqKpK1y4zqkr5OzTZm7LWCa0NujJe9Yi0NiAxOCQgzKM5AWoDpBEoLQuxyxMHcdz466DA6cxqUUJEzmk+s42GZYCZHbTMbsSSDsZWKay4gIqxbxr7t2FPEnAu4Vktb1Shd8LJpYim3bvsmsq1kWMox+foegSyRiSXvoBDQll32aAhClh60zzM1pdtrQG1AzuBaUbcded2w7xnbbcd623HbpNPGdd2x54LbXrDuBaU1sVucTH8YGNZsTd0MIxeMO074APvmYCh/bZnoBJnTa+dmolb3XQ9k9Jua34Mr3MM/dk1h2Nd69c7hBOCgn6zMn9n0a4Xte7OZY4zKo6hAp7WJheJtzJ6R3i+ieZbr6Lmwj/rxpkK5g8LDHpA9bTZsH2bXWW8CcX/f8TU57aNOAwlyoCFYeJ9xp9fIvQSoA5FaApUrSihoc1Wm655pOJY/AQCa7Ffhs1C7nxmECImXaVaFCkOx5cWvaaVIoExLJ0x/jRfM6mPAusNol51am5eYyHBT96e4z4VNn41FQ+8GJW/1DTLsDZvN953HcQ5HsGa0ejzL4Gj9+MH6KXZZ0oEV/xZzeAGMtAPEjMg9q2zY9j5mprMY1jFRPtuYJXjASiBs9jDp9xH8nhmo1DeJZbuNwINdm5nlpB3zRmJ60kG7X/o+Zzw+p/+PQNRhbD/g8EvpMsZALQ/G2ft4mJdBcNiasNdKbdhzxrRHlJxlP1W1G9xn6UDNaO+JPhQSYC7iAwCQ8mHbI8P5tlbRStG9qETgQTjFLBuOGRrYFoZa6eTGh+qZe06eg11q4A+67dn3Xz8f66DFKvNLzij7jqJdtOp7ZdSMwtkekkmK+4/ZBhn+tgnlxmgkAjPXCiJgLwV7Lkgk7V65Vm2zLQLQXF7Sle5KjvmQykQGBugg2oJprYIqg1hTHyGLIZghMwgNblUXQ+5C1IGa5mU1ZhDbfEEnajQEXPmPkSl1fq322AAHQdQ+IqkU/dpsMwG6ENkXtYkORlCwyxwJnUVNRjBnKFiqLilBZJON1KpsDk7J54/M+bbNF9QRZGsVretCmb4p2TzyEE1WAW5giCo2qx+NBoyEADQtRWL45rD5ki4xUPR2QHD1e/sulIu9T4P2OYV9p3A4OCGUOs34FYT0rz3qwApuRrqUrITDOQKa+TQIOiPB4ru9SsO9O94sHZ8C9QirIM4mDMx01SE2VnxmAEE6K8WoJHpK0W3zwM7WJOeVGCArW5TPggiUkqaTC7EYFMwzY4abgTR6P8gmAfjUkYlJs2oUwI0JCOlQ+kQDWHQw0D7AGD0cgwFs92y/y13pH80pckdNygGjk0pKOrZetxoFaADrPNYi0EytFVwaGELibGui5ox933R/WU1wQEk79rgKkFYqUIqnzgdV1F4OSpDW51DgnMyABRzcVDC3laJATUGtRWVh1bLQLqvgyg+eyTEiMK6DRuuFbJxkoN9fpvbf7qannudgGAOEoALOjE337lnNWBoNHPY5YXW0Y1pAjRHLrF3KJDARp8lr8EWeilxrWnqWtPwwhIA0LQiTtH90ABUiCyss4MFuZCQtibMTNl4o7CLnrA1z516DZ2DgbizMGCq1IjcgN8YtN9yKOEcRASFMiHEGkvCrkIO9Q7rxB8yiyQ1fa3fGtdrF+ubxTrJm/ND3NmYFahr22rDuslfWXJBzQdklrZorS9DDIYDuvjAA1IqWd9FbpejckMtJz6RTwI1bRS07iILo3loARAdqZI/q91flFVLQtaqxWBRMWtcd67Zj3TJuW8ZeKtZcsBZJdy8jlx4PLp85KdQdomE7dsfiA+XqYYWMDpAZGSSZKc7F7x7GwSdSx+5gjg8vyjMEMY9E2wzlx7qOiN/oDAktK23y291J6Q4fiLQluAHyRk7LnhTVHJjRjlUY7bjhSuh42geg5qB5VA6BugxCB+LuneSPP7oL6o4ucHCYvbzA5Ly+0X0O9HF321ttzFoqWlT+pjZycNGh9E0vuNv8FiRidF4OmCwX29E4J0GEEC3gHLodQuqQ6s4hNmqIbtP2Tjtv81b6X4Mc6OVshHYAatDXLNnIdnv/XWdt+E1Zk6YL7dfMYDhoxMM1EWzuaOig2r+1i2N2e4mVA9V8xcDoMRAHe2ye+tau1uIMVsYpyQlW0mO/ZXZOJDifU6jwwT1mh/RzPsqjwQce/M3798Df0//u099hC1uvH364XuRBtlmFhdlALn687O7gU+g3NN0fZc/IMYqvb36T23oDBYHvQfuSBq4ajKoFrBxz6LRQg57uiQMgQqqCK9gcWAWGiA0Z1QDVk8PFGPA0AnzyFSYjyE3SMZPcQQt0EN4zaNTuLVkDljkf/Lu3jt9GJjyCNK/e1K2ZcevZ4/GTjPFCx/HgnvFgN0OgWDIXBDMgV2isKFVnrjLUit349fMY1tph0TUWYWAm5/g9AwfJvSHtQ3JArO7Hyr5HP9es/d74XeyjcozmvO9xMELuZuXo4WBYfN3hsLM04dUI6ubRQGLWo0heuz4aKY3BQROuWy/D8TEax6IxGlmatkbZVVmaEDCHhIb5gW5OJ2H11DJT1M0/5/6jXeS9dU59LPylcTz983xYa+ZkvwVg/j1Hqx0UdNeQxLAauWAAaBmBCT5SxHi8zn6Ys9AdTgFqMAA1leBRVAqDUaHz15oAbhYNAkjqWatGljxjbBxH9j10GDdAft9KFwf+krFE0t8/OrjQ7Uej4u8RRBqE68EYc1dpGJz7bfJOx9tfyd989f44ghPdqHdjk9Q4UTCgZ38pT1iroCpX3ajPlwGPUEOpBaupLeAgBM4xkAMm1kYZA7GbGKp9bE1fNd2TI8h9bHM/ysYuv21UfGZ+07ay1fDRhszr77+zK/0ZX2m+R+/UBgTYMgdA5FAvBRJjgtQQ0r2GIE4GD3Nse1I79bmzoiCK844wgzyvCj4vCJq1GKp8r3NrtDfmaZT1rsXA495uEqnMlZEbIzf0dt4sGSUjT5SDpxTu9uj7H/xX7LvXmpkPD+VyLbpOAmo0Rq6aQVukk4Vn0NagARrNqAAD2q2wd4KoXdYBh33RSzi6XIQGDFoV1p9GBWQ5c1bfr50mLDvAbtUy2jyaP2Shtq4/D4arzfP9sHx1yv4RexKv1wwBrwkhTN5/zbalwz2Pb+BhTgb97wMDPuyRvm90jShQY1nE0LEEiz4egZ1u5+K1zlPdONqT3S7R06X76+oHv/XYkFUcMyL41di990Fv/0V9puz8zLnqYPzxs/eO1Vtz0YagD7fgtiKBDt1gDyUKFlSFAawCyJDPOQ7lwf6dAb6f5YLMSGGw691+e/X3G/v/Xq90893sAIB5tF152AN0+I73OsZZ4OEJhxfsP4LapN1ufuuMZNvSgQ/0cLjOsTlWkMB8lmFb3n0MYM2WbP0cjG/LE9Po9edsDkZRMH7n3fS8Pt6wK39tNmxL0t37P/64mw33CY8+Ix3edYSB5WuOdkjVzLbegECBUG5gDhLAZ2D09wEoUNp9O0uUoFpBUWggxoC8+aLmL1qrbKuIcck+7MPDbfQh2xs+7WHy0WXtvZv1yu1iT0xwrONY6/zq+DZQMxrLb6wid4p4SHGj4/vtehp1tM0mK1cRaFsuuG47mBm3bce670JCOO2Y8obYJBXb6spK3lFqVuOkiUFrXBTqhLVanMODQ0fquDFazpB04wJzJYySi1tD3VchFqqapl+ydlCoPrimeEd8o4MP7CzPtUn2zJ4zdnV6iqY0Wv26GbpWv//+hy3JYUUdjOCgvoG+HpNOXJNUSjAKN6xVyJsTMbJG1BEK5hrRwNhKwVYyQIyYAkqZZJyga4SO5yHrS8G4WjyttNYKZHk97zvytqG1ivV2Q94lxbvkjJJ3cJOsAhMQVaP1rVbs+4Z937DtGeu+yW2TdG4rP5PsRZ19uSBZQ5qdZSRwpCRXhgs68NokYNIaQE2ukUkev+exbqs8GLiXRAmHAVPrzrq1Lu8AxTDvQN+Y6Bk4pHM7K2P6Qg3XyJiiXGCEdN4y7hIwY9935JLl69YVlQkUJTOuWtphreAmiLG1zCYiUAUqm1HDIH2/MLQbObQJUO0WVIo4pLUbOO48QBzRpplNIfTLtjI3ttTHaNk0Q0bNsDbf2YbpxyhID0L1Gz9IYrBIuYReQwgAEihWhBhFkUdGiA1SclbBXFUOVWx7RglVuqXFIETRMWKahEB2zxn7nuX9RRxLAMjbjrytCCHicj6hnk7SLWOZME+addGqZpAxSinYIfsgTROitiQ1IIEbY8u7yMFakcuOXbtA5VpRNGJpES7hjjJwiLsBSBiISUV2G32dG874GKPGM9O6va3XaE8qIDMYdHbaxtmjb5LPBYmCQtvbs3XaShMwzVLqSwl7lUhe3DMSKhIZviFAzbat2PcVrTFCrIhNSuMQE1gzK663Dbd1AzN7O9Og68Fbm4ZdM9Pg+4NZOxCVjNYa9iLZFrVVKYlxnS43me+qKeVS7rTVhsqEGyfsLGSPkSak6YQwnZDmE6ZlQZoWxKQZNQPJ7vtPZL8flxIDPeticPjGj7hxzb4qURqDsTNDXwAAo5pJREFUquj1ay74fNuxloZfXlb88vyC1mSvXk4L2jwpCayUKYk9ofpPo23MEv0vRYiC6yBTSymoOYNbQ8kVpTAIFfm2ISAoOW3o86hrk1tDWW+o24ZSK9brDbfbinXLeLne8PRyw3XPuN5WXNcdWWXHXgpYM2qsHMNB1rthle05cEewLnX2E3m345BtQG+6c3C7lO3M7CxFargtxMKJYK23maKUcTNDMibEySfNIBQDXxwNAmu0t0CcdvL1Y0GL7qgoONN6Nk03x0jVtJxlG67JzsNspKp2aNV2tmZfm4/TFIo1SLZRZ150acl9jXcrGAfQx8b2w3Qi0Lu5AAe92DMUu46WVrvRu1cF0hJUsHcTFJkk3GqlWfY+IZeMPW8IxMj7hLLfgJYBngAuTjJqZ1A1Q7xxQ942bOsKZvZsfBIvEHBHS0eXBHgXlR0EIA0betaAydQdrWSUWrCvN2zrhn3fsd1WbOuKbc/IOXsnNm9EYjNFGLJqZM4l25YcjGAFacadKtvxfbXjmOFhHQ3Hw7CZt87i+F4+vA4aSkr0laaypDZCboxKkKqJ0BAbIUVCwjF/zAIdVmYsROn9F23dm84iAFF5ExmSgVMhss+CDb0hDg97a9AddzCGqH9248XBAhzH5l6SkacIvYH0vPNxlKnwvUfmD1EPpBBIMwuBwKzMmEewRmzKhhaAbS+4rTsAwm3dsN5uTqExTRM4FQkfKVs/14paC0Zwmhnq/2X5/to8kF33TcqGuaFsmzSpIULQZgaNCC3vKKYXdW1xa6jbirpvKKUg76vsw13wiNu+Y90ztlyxl6Z2dfN1gtDzY9WDRAeTOx8kKV6Sc8a2bZj2HXnfkPftm3PyK0DN4a/hMb35Hho3p+4+ZkIDI3BXDKZQqkWdSsO6Z4CBdd+x54wYA6ackUuWjBplXQYDRYETltDicSGRpQSrIvUlr0q6sSBzqH1HdVddgZwNtexaMqDO4Yh2D9ERv1T9lcamkCUabUrUItN5aMUtmQiK4kKN2w/dhHebfIw86dgSbN40q0Gvs7BGWiGdn6YmHXdSathrBQjItSCXjBAYqSSUWg4IJIEARw/ZATYGNGOmR/XNAd/WFfu6orWKbduQtfSiZGsL3RyoAXCIFOa8Yc87cs7YfaNl5GIcQc1ZyxWy6FYldX4TDGSebsz4Ooamr7Ibw6TlVe957JvUMH7LcTCgxgyYMV2+z7eqBrsAsDpE0tFpIgYHmds1MG6JULX986wZFWB4SUvOGSUXXbskzkoIjkhb1o8YMoRpmpAwgYIoNtJeg1QbQMKfEXkCs9af1iGzruSeWfWKo8ZAYGjt/qgw5B8bg7xH7hX8CKMBPx7vvxdHIKgrab57h5th3dSgTrpqPFIAdw6eAMmuCFE+UVllHKO0hj0XxEDYtoA1avezlCTlUh2/XGRst11KIAApiSp5QwwRXIvUgUcxUGNUkLA1wOSe1h0TAVOdMc3SntQy20xJZS13ygrYVM30qRqxbL7GyUfBHS7LJoLNMfVxZNmQDthoaeb7HjpzI0iDO13Y39YdVuo8CGPlDJuTyCR8FI0BqqA4AXEGUkOjiJ0JoTIiGibeNRUb3sp520XeMasMasoDFXcpAwRw23bcNinrnaIYtLEScgxSShVoOF+SEiRtqy56N2sJTEFuQ/YFd96MojJ8rwVFeWmetow1NzQK2IlQKaAm4BQSKC2gaZbOU9OCNE3aRdAIlIdBfMdjzFo4fL2tPXdieQCLZK3x8GZ3ttFQdNxuueJpy9gr4+m24+llBTFjmSdstyu4Tlq22UQHtir8P+YoaJZirVq2GNiDRSBS0Eb+LtYCHEDcd0R1FMdF6euuiQFb9x2lVuw3cQ7XLeN2W3Fdb7huRQNmGblWCWyU2ue4WdaUdezs0WvXMPf+xL3Ceq9j8F8cC/IzuTtoKNcb3uNuhYEBSmRv+pKtvHtQ+s1K81vVbCUBalrNKoN66n5RsE0AFgFWZNi6Ih+bNLQqbbYBId0+XKguytJ6hnH1bGN4u1zXicBrB9J+mrtkfa3/HB6C+sTd1viALLexexf7/69/R8opyUFcCTCRA4FNkLHefY6EV6nUihAIpUrXlUxAKRtK3gCuAFcQd+4oq4oxHkJuAtTkdUXjJiCNlxU2L3UP2mlI+DADWgqut72Bg2VJMqOUHc18g21F3hSo2TZs24Y9F9GRmt3GnvHKPhcORpA4iezrVpxny6Jvuk9lf75/MWlfF5alPU5hX5P9GX333QI7zLyDc4NcUVncGKpvWDv7NoRqgJXyY+qYOJGzyiwPBhlYQ+MOO655f34IqltgwroWO0jqnyFfx7a/zD92rlU7jNdLFc89gGby7OOz2t46hkztQ8YrQRm0zLP2+2AzaPOpNgExSyntlgEErOuOfdu0TDui5AWxaTc9+aDztzCzku6LL1P3jLILUGPZKQSg5n0IYOzC/UKEtCfUpHQQugBGvitmRt031JIVqNmR1Xfc/CZdNXMdqjRYS8aZXAdZYNFHiIKDNEGvq5QiQe59R9k76PS147e159aj6wyZiNfy+ihc73VyX8TDgubxbxEmlS3tthNzeZtQhoMw/nuEV+cixrAuJItAD5u1O5Byc/CAmzuAcLBnMM10Ug7XyHZeZrz0dKYxw+ZVCuZw3R95dAfiK78zWjh0fGE0bOxazQAQYTnU/yrQZOVQjRlB09m4Ndm77rQDvZuI/umAG9zQdGJhFkAhaqt2mz8DxSwNzsjc6lDrK23he5qZpeu/KtEahkGWFQ33w5zfDWNfRl3AvHe0wjIcGPAyhnuxbar3UMOt5+7XYNfxCqixKBAQoyi+UoMYOGDUGlFqBTj0ZWLjB9vLrgHh6fg+DOrUi7eqwMNoYHdF3VoDGVFYa2/vG1dqR8PTSOFIz4OCOK1hHC/fc/rL/PZ4fsi2HPXzb1wlo6PvpU0OutGwb2zO+7jbzzVoPbxm+AGM0ISUG0S+D+xD43nR3d9+UmY9DZ6ZrQNZp1YKZ7JxTEE9yseDjESf068NIg+ZZd0U7eYMHf7/oENP8vVpfv13faXZwn/jENxG59Q5lsT8kcxUjRyTjoNG2ZplOjDEWBrkY7XsUkAzU1l9SRoMX/ZMQJEXNvE2n905POqzvg/NQZJmAZYlxfpYItwtwgFHClFBGb3FMHSrejVw73oMfsXh648/9Ss/PFjyphcD4FnDQsBb+60UlFoQi4ByoRi5s4Kd+Io9wJK9YRk1rNm9Vt9vTlJ3flQOq150acesvFDWhnvQke7gWrZvL03sXZ+Osvcwnr82TMyveNL+3mNwg/wqxRm6d2sIg3gb7hmHSbw/56/+pt4ONmT/zW9eJR2Bj/tPmOwTE2iQy/6bAN/vv1+xI11Hor/X1tpb1zk+1yW72c/furi/8Rj24vEJe/zb1k2/RnZ5Vo3sfCjpMwBGZBqhNUJtUnZh8hvAofSwgyF33WEGu8p4bhx40g96+RqgiV26ryxT/2Cvmtw+cuu47G2vbRbfVqqXO0zQz+Fer7/30bf2vTN2fHCABNk2A7k/QOPj4fP3AOE4xLaurS35mF3WHZzXe+RANUZ0sEPtuy053nQr3+31wzfegzDDNfDxVH71GK8dMADgDcH7QcebI+bOUffTBAzTv9/Yq7aWiYRCwXRibT1A15qQ/TJZUF9/3XxxtfnI1wJ6Yoj6lLbvDRuw/UpqpzKrfSjkXvILTW0RbpLhqpUd3dZpBzqPo7/Ifl4MX7LDUJnvhX6P4XxVp//dXZ/GFcHHP4c/6NWCMtP5lZ2lRiMhOEotbV/Zkf+iadUxEOKeMG0bUixeugCQpwCCLXtAUL7oXVzUCIoCzgSNIEOFKCtgwM7oLtEQthSrVmDdiJrWdutYS/kEFHRQJZqLdROySJimp+okVI2WVV0EdQQSnFfgI+EaXfSDpXKcz9FI0Mj9mGJHQ90mk5wzxPDfSsCmBvheG0plxChpYUW7d9XWEFuFRT5I01ypDflOgTClSca0dWUUSLogcGuYUkKehRQzTROMGyUjQ9LGoeMsym/LO7aSsRVJ3d5LwVYqtlywZYlS5FKRS1WBbHlVhOibrJPyAoC1VhNyOVvDWu9cOzj43ineT0/PPof1oKj73I37zQwFModIrydY22tgIAXrANgUgCWp8G0VMzHmFMCtIqIhRkKkgGRliK2hWcsSaD2xMuJzlWhxSBNCSqCgRKXJwDb2tQUl6gOAUAvqbhekjuSwn9xggUZVGklaagO20rBXBWi4IFQgJMIyM5KlA5vigJCTUesZR2P+3ccco7cwABuuYOC/34kSDYAJkA5oQ9tljeyFCITGmv2g2Ysk5WaVlQSUgK1kpF3Itz1100BPyHoKMWJKEwjAnBKmGBFjxBQ6eXEIQUijCZL2qYSIiBVN2idgzztK2XW99XnM1vWuNS+zkm54DdnABoLrkNBUcZNkFphW7HW9Ad55g0hLn3qE470F60GOjtPp99xlrM0lLPuuYYSVMLxfdxCsnJGs6xMYLU1oMQA1YOeMkHcEaCRRvyPnimKOW2MEiaEilwzaVpEFIWC+XAAwIkl2JBHQQpDMSIxrn0G5O72sGSCNIVmJrR6iiqUx1lzxsmbk2vCyblhzQWXGrTD2BiASwimA4gROM9JyxnJ5wHK+4HQ64bQsSFHSlD1S/v5TeLxO8inoxuloD9NwR8e5HR1akcsNlQh7qbjtGaU1PG87vqwrmBrmJeLTy4Q8JaS8YtpvEoBI0pFSpl+zOAHJilJbo6w3NAXsWyngIoArgTFNYspZlhsDYmdoyVItYoRCS0hZo/TrtmPbs5QH54xbzlhLjyAaz47pnKpGa58TdWJfG4HDe3SIB0fqvY6+F1lLl62U4uj5i5xtbtc5B+GB52Xo8mHgC7p8biajm5Y/6fdB7UZ4pjBUbWlZvWU6qlwag4R2+iEM8j4M2edDea8sPOWDG2xGsThtJYwjruOCo9NvsnaMCptjOzoint1tm4H4Q2wbOVu1r14BNqMZ0AGRLj/ZnWkyEINE362aRRpDwC3tKLXhvO1YcwYTMO071n1DqgWpRExlVxmppe59IsEsmaTLchLvhnp03wK6BLFjbS5jjG7PmmMGXWfW5dX2Za1NovhFqgj2UpCr2qz7jtu2YcsF656xWum+BTsgek9KYAEOGMC7PkZEA3zzAQI1xnh8YrBx+u9x15u+x8gzaMXmH5zWUYHr1EuGjK0LvT4mz65xfaXvsbIT+V11rnVRdTsZXm7jZXMQ8vuqdsYoEyyjpg17eERhbKn2XP/j62xCetix9haHOiyxoH9KMa2Ps06BO5k62KUGysQQkGJESgkpRSTLHENACeI7so41w2SLZFnfth0v1xtKrbjebli3VXyKCMxJO96ZLckMUBDqEmhJkZYmhpAg7Ah6rqXKSNbq2f4pRsSwgEgekyohCVBI5iPXIr4Hi15sates6w3bvmPLO/ZakLX5UakVudYOjLMBRkc9aLJcxktpQmxMGkvlh2b8lL+39KlPnN7r3zQ874Hd/u7hiV4mYkuUG4NhJD1ifI9R8toa9iICNpUd+76hxoioTgKIJFORGWDLABDHz1OlA0nHi2n2qGGvgRVISJSXsEeDG+q+o+Udpgxl8x+RM3hqeN9AUn8nvDPgXpcP9Gty5L4WJ0+qraeMM0OV4nvnYdg88evHo3DhrvQwCBZLycfdmYlyF4LnvTTsRaIQubKTVRpqyhDBK91CIBFT60jEBGrqnhAhJlmOovcMqBEQgbkhxYgypUEAsHYHE6JUoEcLZR3tAtCowttrRba/1Vm0zccgNGXxJ3MSTdq6gdCzeMasESapkSSSjiiCWzjt97sc1+tNx16MrA7sjW7VOK82a51Bvbc9J4TBSQshaHtswhQDcgqy/6jhNAG59NKnFAkpRLSYFCEfzkM3O6kxi9bAypcQp1l4E1JASNF3EKvWtpIm2UMFDo4q+Cp7bSCCduPSwBqgNFmPW2lAYIl8JSAhIGpWDUENW2669ljAD2hdLIZUz3edQZ2R0VExRT3KiztHxoEjA2ucZDUIcEGkTkB3CAJI5JAaMQ1A4YbA4mBvpKTAqlkodOAHJMDdlBIIwBSTAjQG0pAav9EzHxgqJwKBM8Fi+K1kcKu6xqS2maHlHOYwKMeJOQ+lqiSkDrQwM4KOSxgMB5MDZJafjefoWI/q6P0msU9f/9NlpJy/6ifwAY+xUsmvnpPLG2kvHyYFr1NEC2K05KJ8Tlx9TROzgJXcf4t1jZciXGwhEOI0YVpmlQENwVgMWEAyN8rsGpuUBchXst8b0DbW51cG9lJx3aWM7sttx3Xb5XkOyEyIU8R8CkghAXFSXpoz5uWEeV4wT7O0FrcSgg+ETM0kaHd1/3z4qz87EkGPbzAHAyBNhRZwY81F+Gr2jOdtAxHjcou43hJqSUg5YdqlM9d8mjCfFuWWUZJl2BgAYEnN3tdVjEPzXYgQU0Sa1AZS/i+wBJmydtHM24a87wqgS8eMyg37lpGLlFcI71DGVjTFu0g2lJddQEDfexkFvF7Od2pI5DdYu6C94+FgLR3szOMZ2FruwIx8rq99GHBjdp9ZfYMNeMjAVtAS1uVH5ZBrfYvkAgBJxiqzva7O5RBwMefer0XP2jgOHUhyS7G/z51c+246rmVzECwTzzOmmA/6dLTD2+Gz8qBH9D9gT5rDrI77oVOQ34+CXc6JlUgdkH1s5KGlNoAKCgWkIABHbU0AyJJBgWS97ztaimiV0LSDZogJwTpQUgCR7a2oXfPgMlpOo7m8lMCxIwS69jW4NzqEVcpPjfeytoZs3G2adSccblXOed+l9MJK952AVeWyA1lC28mQYIdjHqYebQBpBBje54jxDZtXbUI27Whrh1m3LjnIZr5i30VGIUBGuQN7Va1xB4BBPVhAqvhZeYkMrAEU5IGuJxqeDx3oqpA1zvp9ByhFB62MoOadtlCzq8vuw3hQb9FNuJuD/u43sRga84TxlpJ6p6PreZMNYMu4l3GKUYILKcqtRc1I87bXCiCy2O1F1+SWM67bhsoNt004RKcUMEUgT4QWhA+TrbR0mhHmpZdbqR8WIoM4il6s7DJY/A6VxbZfTS/qlbVWJVjR2sBpw55hWlvDvm2y13LWrODmN+No82kBEFpTPrMOtBlv1Jh9575LLWglir+qrbq/dfym9txfe9rN0q+kcR/RfX79fb45Fazgu8eKZhsSbBu61oa6N5hbFahqirg4y9COJQgV1NTxNqudG4y7gatl1CjaTUBvx6HW9FeMalfodmsG/rDUR5qAViFtv2HRi1flHB+69+6+nMc/YJDg8UKpS5H+ygDmeNs7KzcxQ8DKYV4jxYAu0qbppQp0ABCn2sSvGwfKOVOqpn1X1NwJ1SrLGpimiJSER6Fh0AV6hm04NydwbsPYD6PxqnuOZ1nI/UH4DoabWTTW/vO9/QsvfWLj1hnM0bHG1Z/U86MBGDMwU4EaJgMxGc0qAzmAENECoaixHgAHFcUYggJ1hFoq8q6t5VLSGmlGDNICk6Kix7FKxygOnjkj+18N51q1razUuNLoGN2NxahAemlHL/Go6nCylTuGnuJPhwglPAXdlrvaBW+Kq/c/+M2HXz3MmHXE/u1bN9hN8Ol40SCTuNffdyNJF21lVXZADRVVjdNaKspe0CIjpF25m+w7xdEppaAqaA01IP3ShnHt59EdFubjun49JL9lU9Hx9sHRp6+dgv8q9TT0fiZHiXr30UHKjnKod3/SUA3QAnpGgJa1FXHkYgCSXj8b83mv7oXIyub7z1rdj+fge/HuJA8GP3C3Fy1qP0TxQQdn0trLSyaYZGuFKJxR1h73rRTq9z5GnfvV7eeT8deV0Yne6WVquUrL7tI6R4KApDKXjQXApCaAGxXVs7WBi5yh4WjMA1ADeVwGQ7oo+bPzuDVZH0QEDhAZzAxuAqoaV2CpjL02zaAZS9zgTpSN1zg898+9HgtzLj5yPgdBfve8AQ0WFLi3vUx3+0Vyf5/trX7tx9XCQJdr3KPv1IBqhMBVs28h74GCXtwMfBHZS4dJVVtJ5+L1Kn1LcvRx8N/yK++gkDn3DkaZfnCbtv/c+Isygx8tT0fpd/c04I7a8fm754ZrASQoU2tDIOpdzCzog+F7qWejGRcUO6CnDQmc70+zW/sP6m+xY4fs5XfcfQwddSs/hWaAkAUC9eqFB0wCGVU5dizAYeUYHUDsp9B/YRiMr430O0/lWJpE4wPbJIwuuJhU99h7qO+J0J+n4aNsK5ChIA0NUz/awKSl3tTfN6xetzAGe8q3PoYfRAdBx81gsp2B3lQEr2/Dxds3DONzzznz1xx0uPu4o2d0Y/CL/O/h1v+Gml3dknH7AH1dx9LLgc2fcS5Gt9u6HgMpd1fRvWLRWf1O3w8N7lM2sNMv1NrApDxheUctyiXWmu5F8U1IDWXjLZWf0SCi60TAgKvjwcP/d88cnhwXTMcHvnX8tvbcd49fP2fKoYtxZkup75PUFSUGgEOMuVKqpuNLG8/EAFNAmGfpaqLkr9wY6/MN69MN3Bjn8wXn0wUhRqS5Is1CPEwxgzxDQ6MfYLQmnRYAQ1FlUaUUEKMNRxeq1CRDgFsDl4IKa3FaUbSzVClZ06HYy5lkDAS8qbVJWnoW5vaiZLZFDbeeRvdtg+dvPTzd+KDrVZSYoNSN2NvDaoq+kp5Ju2cl0yUoIAbk1rDukrq57hl7FtK2uU3KQUJAlOgwQVj06yZo5n7bsF5XcGOkkDCFpJteou9gaDqadBrZthX7JmRuW8nYq0RGPn33iIdPF0d7KUSApdNBYUlfXEvFLWfcspQ/7aV6qmQzpSChfxxKSg73ynnE3DsfNVXoWgIFqMH8zhN5ux0zaqz0rw1Ta8CnPGTVRT2LZsyaiESIQZ1+Kx8EkGLAFCWjhtoJcwT2FDHFgMfzIjvcsuGYcX264vr5RfbicsJ5OSGGgHouaGfpOpOngjhtKoTvMtqaEFEn/c0QCPNpxjxPLrRNppjJ1FiIq/e9eFeSbc+S7rwJMzuIwLkAISDNM8K0yOPalKxUo2UD55GkVRoV2kfsxOFgcxx89u7fYNsSTOS8Q+AIjpoBBaDF5uS+0j49yNoLFSDpUtZYSMAHXx0MSCYZS8rlvmWs6w5ujEgRCWKUltOCuohs3teK28umGYsRYdKOTqTfSEBKhBRl3U0pYEoDMacC4A3snAECBkq004gTYftQ92JgGYPRIPAsHj1G1/4VePXOFo3rZ5s2Os5g0DfZ+w7p1QOIY7t1PDuLEgrPtWQTokW0lIA0a2ZiQyDJqOFcUHeRp+u6Y71JdOg0n3BeBIQFC0GfOH8FNZvRlNGqELLnIiT+QI8uxhBwPs84L5MDcgBE/gXdI6rvSpEy0r1UbEVThVsTEnqwl7IhJqR5wXy+6O0st9NJuoTF5F0geiSK4J7yOx7t7r57BsN8cP/5g/oc38c4eD0MaAcsya592Qp+uW4olfF4WbBDshJTmhBPZ0QSwPvl6SaG6S62AjOQEBE1oh9jQojWaY21zl5hUpUnVu8PAiiSg1/TnDBdzmqoZkAzT/c146U0vOwFn9cdP193KV/LRbp0Dan/8F/Tv+7nRMfqFTGstZ3/B4BvHW0f/jYHuVW0VtwwbkYC3HopSjOy32bdlJq3hbXyFfEL1ZEgM+pZA0mSKVFLQ9XuZ2RtktXxMf69scXsIZuF+yiLOSafSykgJcvioK46tBzUgFIpA2ZNarV7Aw171rN1XrXgWqnsHXHYPFc/qN9/5BTSEQwyeem23aAH7EQcxyYZEEYvlSYw9ky40YZcopNkUyCcapMuMBSAlBDnGYEIec9CGtwYeS/IuzQemacZ87QIgbGTrPdVLf5MgZGESzReZGqaI+IUEGLAcj5hPp3lTBVMbbWC94LG0pDhumc8XVesev90XZGrZAR5qXDrJYkOqgI9CDmModilOAqwd57HNJQ+mdNOQM8yc2DUxiq4D+tch8xo+vwocW1fyJmz84SaaWHvbvo5oSGRgHAgpwtHI6E18OwQsxEatIRbq24MJFD/7B5UGQHsynKzLO+3gRt6tZ3ER9AsNeDgMxi4bCAUDZ/D3fe8++H2iujh4Jnbao+FiBiT6qM0cMwp3yXr2FO//qol02vOeLqtWHPC03XF83UFUcA0T+AQgZg0W0bWynrdcH15VvksXU8BwpxmLNPsssCzsHiUhUVvTUqY8g7mBhIvHjEEXB5OOF9Ocr3WWa1WtFyRUbA34GUr+HxdseWsWbIK3KJn5Zq4tAy/psY2k3g8pJVDNJwfMwuuUSpqLt+ckt8M1IzP2oIzIKZPsAjJzvrclaYtfHFOyK+sKxcBNKTlmS78EBCStOqtnCWbojastw3Pn5/AlcGZEarwJbTM4Do4XcqjUasZpIxSd2n3RcC0JEyzttm7nBCnpAZpU1IxANSUd4PAWjvsjkYzbgWtBVflwGZAmfFUq7fXqwo4WYqVZ3nwxwE1h3Inmzv0pxwptXtb+AyVHqyGgZRbuANNcu57kSyXvVTstSKWoK0RdV8F2+RAyxl539Fqw8uXZzz/8gWtNtl4aRGDNEwIQZbmCNTcbjes6w21VVzzilveJKUtBEyLpMzHKTl/SiMBagpLpHDNFaszd/d2lm0YA2sDPzKch8Fp7A4hyxqxmwI1hKCG0/vO5LqKQ20dV+5TlmWebYoVYYYZOANQo2h1IOn4Iso0eO1nCh2omQJwmRPKnHA5zbovCVZjza3h+nLD558/i8A5P4DOVQDPSggtgGJAiMVB01ozarO9KNkXRITlNGFepJ1zSDKf3bk/Kj3pdCEdg6QrScaes0SE94w9FyEtLRUcCFNjzNuGkCYwA6UU5bMKXvcqJIHk690V5Xsf47owo4KPhsDbulj5hQILgB2EHjnG0A3G0NRi5W656l6VwAKjDT/dNG2fmfHycsXzlytaa5jjhDnOCBTAhUFV1tC+FWxJu49ZiR/YyRFDJFwuC86XWUlhZ0xaUucAS4Mrsqr8Xdb1qSiIDSJQ0hTlYADB4CiMDp+9xIBlQx7/BZdt73WMRlO3p/o6FWdV5QkAi5Qfyuno9feNMplhpODSrptiAmICUgLRBMKEwAG1MBpntMrYt4zrszj74RywhEmxdeVsIonuG5FlLsIh1FrDbV+x7isASWGPMUpqcwo4nxfzROxiAZaIMhGhNTjw5pkjrssZTdegBQHiNGOaT5gWuy1I8yyGX9AOCcHm7+MOB7lt0rprgzfYXv8q+7ixdr9iYM0Fz2tGY+CWGzITCgUginMYQ0DOV6zXDa1UrNcNt+cNaIw5zTilBSEETMuCadF9NHTDs9Klxk26HJYdCMB8npFOIlPjPCEuCwCWdvBappgB3ErDrTQ87wVPCnSvuWKvPQu4Z1/Ymu+OivxpTpR4ZyN5+7h7339ChzkzScF3r1spu5efaPdOA2oUrBlBGymLt+DgwJkwGFAEckJ8Vgd7zxmtNuS9YN8ymIFIAcHAtiDOOgBw7dmdlg3KZluy6MWk2cIhkNs6Ovx9YJsBmeQZXAbMjLwzlsndBpvTsqnMdq2NfTTvDyIrR33/XTnOIg2LZQRqOkgzfGbQLfacgSbChymZwSuAFCWok4tw0tTWJHARSPnAJpFne8a+7ai6F6/PUm54Pl1wObM4qilJqb6dn/o7JW8oWWXqesW6raAALJcFy2VGnBKm0wlxXuR8aQeHAMoFHCIaCJWBLRdcN+nGdt02XDfh2NlzOXT86nM7AB3MGpimA/BFEL39UVI16Nok6h3MiICk98bvaEHEFrrd2s8f6pTrbrYAO5P7ZDRmn/lC4EE26feQQSTwkkMLHhOxByrZ5q+ZW6qADUvSRq5DL17CIPMMhBjOH/COYaMY4ru/faDGNw4GgL+X0THJu/d91Dz6erHNqP4brC23dYkNSTNhB59JfQsOZESDYFgWm/iKtO1IteK67rhtO1JKuFTpbMkaqAmA6rYrnj8/oeSCVhityPldLo/gy6P6ZgmI8TAazIycC0reUGvF88szXq4vEJuZkRIjTQnzaZa9SADHCq4VIRdwWFGYkBm45YrndceuvqNUcuiadr1jWVk9AYWDVR4EtBHB0wVqWUC9wcfXj7+i6xMf7r7+FllZUkrUy1gOdXU0pOzhqDxGrgJPnzZ02FBQbT/M3FwgGNmwReOMcNMMXiNgkpo1OTcTtsGdcdMA+v5B+Hn2D3dh02uWRwMGfm1m4IylTq+6ZQyPX+3u9zre+M6+54+5Ax17BjScj0PbaiOOJSWNUuPAjPVSKnJUotLKUlZDDNZuwjJ/EWAgpiSkwKEhhjR0+zDFK2TCbC4XGdeQkFlNSEIUHXXu9DPtsI6q348kpj72h1HpRsD43PE97Gu63/iwHkwRvefRu1pZJoquLTWsfJ3qH7bOAlnd5DiuUOJFA28E6RW/W0ROY9IIeQERnHSZIIo3QGV4CEgpgikot0T01pk9YhF0Ho2MW9N+KYFYALw4yV6MZoz6MjxGcMU4aQ6Ujp29xtInVoOHG4FCQc4FSetAS85CLhakyErKOzonxIftQ4wKWCQG8+sX+/rpLk5flkNmSTcR79btEdyCrlH7bqvjB9TOZUnrTjFIxCkKH00kzay6iz5BDTFn9LOqnEBIkxDBjvLUfrvy0Ma59rnqhJnNZ9siKv2yZCz6/+O10t2j13/9Q46DU4FhXr5tUrlzAXM+0NfCALZZe3mJAossZjWiQhSuthQjmFnnYOikFKPuweDte2MIUhMOiYZO6njEFPW7hE/KL8Ycdh72Ymu+H8dMABkPcj0sRlz0dt8hjevkWH9u96Ndyh+0H20Gjprg6z/GZjj3rfnVw6K7pTVsuSAEwpoztr0gxYBlntEaIwz2iGTOVKSYtYRUx4sCQpqE64sCOGpwQJ3Nqs5NjAGNIxBMvwp5u7VwNyDAbKzdyPU1C8r04+h43A+JJ+2/iWe9vUOB0QF/v8PXhZ6IGc/u89iadGTjmG3Aw5foq0c9jkGfD/cdFuoXKUs4SKtttSnBVjKjwKO2lAbEkTTw1rImGaKbmYMHWVyfmn06+Hevbzxcx9E+6eDa+M67Y3QIx6c/WJS+JSFHCX7/qm+/QY9appJ1w7MrbcphWAnOS5iUTLsNDpNlbQq5dwJAiKkipSSZpmHIvh1vwwnTIYgihKsI0vwipkn2uCAEPmdmq1W1UbMS7FvmvZRtv/YdDsDhG+MDwBsq2Ts/chpTNKBGs58gHB0xqOMdWLgkD2vSOAPR7VaiYR/KeyWLXVMARvtajNvhyvp4HKwn18emmy2I0rPcTM0Fs28Af+1of3Tfru+nLosEwFXiX/8d9Kov3M2DrVv4FwxvHN5J0Ewi+YaP3pN2Km5nWgBF78cMNw+GfeWkXC6xdqwshD0XrKu05973jFoqmgVQlVMmxoQ0i86r1BwkiUPGLTmQRDrG5vuQ+z1GgMxgTBOQJgHBo9k3UFk5VPhYADiXetCLYGgW1BvXSuOdvof7xDvuMdj6Y7D9a8dv5Kh5U6QPVsvx7cxSlz6SPTXAhY20iZVoW9D67VXLiq5bxvNtQ2mM5SwEWgxCYIhDGAJOywK+nIDKOJ3PWJaTdCpZFkzLCb2ERTNgCqFWEZ4UF0BTgtMyaRYNgYKkfDMYqHBhaGhXrVVSGffsKbFj95n7SbMF2SzSWCQ9fMyosdplQTlYUpnbt2vV/pajeVqYziR3xegyCHDQpsFRFUFTWRWfWWdBJxXCtJ51Xq9bwZcXSRELIeB8mjHXhGWexShXY3OJSZ2JCct8knT+BpDyQZF5fxDulBqU14ZmhCjO3EILKjVQDLh8umipjHYN24WU7fm24ct1xXXb8fllxZeXm3d8qr5BbEuZWXB/0/XbJCrtkSp1NqlWBAYoVhlBZnFO3tmx2DZJoZUyg971zDk92DKyOkgIFgBizGjowgtOsD2WGMRASGoYBgjh8DJFJCKcU8QyJTwuM6bTjBAizqcF8fvvgMY4TSec5gUxRMynE+ZFsjLClBAmMXxkt8hYIQVPz5euJ0YEHrXzhRpbakhZp4NaK/a8Y90lyiRpjdJpZt13rHsRAl0QGhPiXlARcF03zPMsJJv7rjLjpA5SQFLQ1ozgjzjG/T0CMqNzYIK8y+7uvJrhEUii4g5Uo+eQyHcPQWAF94gZJUA4LIiECC4EIBAu5xPmmABmRGjpEwLmecYyT2q8RgW2CWmeMM3ayWtOSLPIUc1e1bPsik2MZZGJt02ynqRUTVLRGzfPrDH5HSCRwcZCJsxgRBsfM7Z8bQcfI/j/Os4fNJeH3zHjzo29wTh8+xP9z74MusNF4ry1EFFDQ40JJSa0OoFaBrGVKs6YT1JjTUxIyj9yWk44L2fEELAsoiNpCFhI7XfAVISo/Xye0SDlo2meEHU+UyKEFIbIj9xvpToR+7oXdfYlS1HK73QtsIC4SDNCSJhOFyyXR5wuj1jOD0jLIqWIaRLScD3HThFLroo+8jCH1qdFH3ebuQuFEUe9N7LsUQWUYB54Wnf8+5crTlPEMkV8d57xeJpRKuN0WrBMCTFNePx+AjdgTjcsYQJXRkozUjwhhIj58ROWx08CMA8ZINvLE6brswLVM2oroEiYH8+YHxbR4WTlhhW3dcdtXXHbMv785QX/65cnXLeMz9cV1z1rh4sjp16fD71X+9PoCS0b2IEBN+8Hpwjv3yzBAhi+/3j4HYLqDiXjdQLWdojuW4aNrXHja7Ib661VyZ6lpjcV1LY2U4qg0yyl3ClhnibfzFaq4YFEAC0GyYxi5UxpRqTOTpUYjX+PgBgJFDX4SexZswY8jbq/B6u6k2H2qC1grYYc5pR6KXxHwDroDAxOyjsf9ObD49+jw9OkbIkQ0CojBHH4EY7AibxfCEpDDbitK56eX5BzxrLM2LN0eko8qb0bJeMlJLTGmKcVS5zBzEhxRoriNEbdsw7U6JjFQGhFgPDpNOPcKigQ5ocF02XR7O9Ju9029yv2PeP5esPnL8+4riu+vFzxdF3Fmd3FWfRuXZXV/hNybglLac+vAbiSFWEKhu8G5f2PRyshUceYSErsJyVI9/2lgsKdVPSmFNwEsJLH1qVNgjxS8g8YT+NoG/L99TnAop13dJ4SEZJyEaUgwame+SpjZYCOgHdwP7Iye4mTDanZ3f48TFcJSGVNKkd0ZnCrD9PRO9V1WXp4P6Alx+McfsR8DmOp9lWIAl62OiFNM+Z5Rl1mpHWSjG6rYKHO7zLOsT3OhbEXSbD48y9f8N/+7U+4nBcwgO8/XVDLCafTGXG6IATC6dMn/C7NaLWiXHfk6wY0YJpmzGozhHlGnBZVSsbf1EBoCK2gNUK4XDBrCfd8mTCdJci/nCaQBreK0ShsOz5/ecaf//ILXm4rPj+94Pm2CshbqgY6O3G5e6zUy6ECWXrBOKoj0KqltK0psfDfUfr0reN+eRwMGRMOuFtwbEBNr+OyFOG9SOedNRfc9iyP94JcGgI1TMajQYw2JfCyAI2xzDOmSRbLNM1IWrdG3r+NUUlryQiIS0JcknSimSeEWXlsajmkwqI2qS9jdqCmlipkmdq5aawLVTF5AEaNOMnSwms1ksWeWsutd8Ay4+u9j8OGt57vuojc6TEyL7aoqy00raFsgHXHYQgYId+pBIlgbLniZd2Qa8Qyz9j2AmYgxIi5MRDYo77iZExYppM4Y1tB24tyn8AtvwrhZOAWAFRQkHa1mCNoEuGQloQ4RWE+2YoovlKw7jte1h3XbcPLuuFl3ZFzwV4sI8WmasQ/u1M8rt/GAA1RDUsxpipj0qqsU5aJf3fxmYsCNU0EyqG2XQEaqVceBQIO2ReiQNV0duvCZLEKmyCt7CmIcpsiYU0Jpynhu9OMPE+YiPDpNCMEwrLMmBXEm+OMOQk4M82ztHgOatTMk/yWlWIQIS4T4qLlGcGXlBjO3AkXPeLUdA/W6h1KipVAaUR4L9JWtjGQWTpBxVLRQJj3jGVZME3CFh9SQmNCmoRLh1vzFpNsHADvfIxliN24OEaPxujneBJmRvQIkEX0bE5V5pGZbRrV1wXsROxKYBqt5pgIaZlxmmZZA40QmuyFKU2YFMxOU0KcBKg5nRcs55PW3C+YTzOIgMYVjM7dVcouCnoo99xzFl6rJhlbUrbB2m5U9qVl1IxK3gxRcqGFvrbv/h0H/QMm8u579VQORp+DC4OlxtA3gu+MtNdfLaCFZdEE1JDQQgFRhNCvM2IUfrXAjNCApOyG8yQdlEKImJcF83LuGU5qZcbASFpaFhIQIgR4O81Ii8hZ6T5ShIsGJFHo1jzbTrIypKteKarfLcoVIRlzIQLTDPYuT8LRMC0nBWmiZO6YoYdOqmnn+/4ufh/07tS8YRnfP8EMfustGM8XAo7qHN+2jF9IOuo9LhP+/PMT1vOM07LgDz9mRCIsy4LzcgJBwLZUxFEJcUGMJ1BIWB4/4fT9j05EysySqk0AGaCAhAZxDpfvLpgfhQtj21YJYKgxetWOXF9ebvjp6ablWbt3qho58/x69cG9I2zb8TB8+lzv0GK3992MB/491St2OmLfqK53MKYb8j0j+mhAu03Gwk3TuIM33BhBu/yMwBMRCW+JIlgcE1rUEo7GVsHRgyIM6TREyo3DndtQ0hDU4Z20bTugHHhwec44llwYSDPaJx4sHLPTRyMVRxDUADhxVNFBGnvT1xb/33l8PaPmkOMtP87U96zOWWuakTRkGPcFKeMVggRyrrcVtVV82i7SYTZGtQdFL6ZJOe2YMVHEhKDfG0HQErY0IUzTIO/ldyJJcJHBmAK0rIowXU5IFy2xAANqKwpnpdis67rh+XbDdV1xvW24rZvQCYwRfc8+Zd+LBrjZ36Pd4AkYHzRv43FeZgAKjCgAEokwR82oVqDm4MCjB+3Bd13OWvMAe6kNe9CM3Kp6jFkI8VsnDB5Wtd5ZlpQ508HbdRsfowUuzb5iHbfGjKRf08AeSG/iMLkPcfQDcfgu6H4aOwX6VMjXYDxl1yH6mbutqknMg/3zAcdhmbidGRCiZr1rwC7pY1J/wWRvb+aiV+q2rPFiSZnPl5cX/OmnX3BeZnx6uOB6u4nomyaciEAxYbkkLKcL0Bj7fMVOwocZKCGEpKDpjHg6ycArnxi3BpQdiBGsgeCFZuGm+u6E+eGkuQAVDC1zbUJcvO0Zzy83/PLlGdd1w8tVghqWtME6QcyERp2DadRx3Srvo6pLHBjGxcCa2v6u0qev72xTht/c/XeLze+ZlS2bnSekNsmukTrMikBi/OWcZSBSdKUP0mwAok6YZxHMwSpwRDUQkOTTY03dfTbQWJ5kUQkDVCx7xhzh/mPdubd7B234eJMUPzbb4HiDPX5/aXpfQNDvyf/vDoSVffX3+LuUZBhsYzcQOFE3FMZoTgzk3DzyE9KCzb81BqARQmIYhTqL9JPHlFQoNkRKYtgQgETgaIY9nA0/l6q1hLLhtqzR+6L8QKbFdCHRCFy9Ctset9orC1VHciwrsfX93krRSpwc5Bsde8DXrQnEsXuOzW+AZFmQpgb3Ol81LokEZCKAGgl/iJY75Vyw54IAIC8zSq1qJCiBLTCQV+qPGk2KpW0TdeqUoHsxhmHP6pyjG+BWXsHM3tq+d226N7LZuQcqA6UwihIqhhhdma7rphkiE0ARlaUMxA0JHZOPcA5f7+8uD+wxvrZ8utWM/j9cmcpzPWPKnRX7Xe7AOIMQm+wHM0FDkIhqiArigFQpB1WIEWmSLJo4JaTJSmui8y5Qq+jcH+YcNCdRrwo01moOoWUlDlfs2Sh3++/VcJBf8Tgux9F9f3vmIAYGVeDLePi7v687kKzycrDd3IG9B6cMPnDSUgrSgrsxQmMUaijUEGwMpe3aYMGrMcBV5LZfAGsAWccwymPXpRij88eyQpHvnVuIHThTMsEglNwxEGJkyagJUUg7yXu6DTqvr0/2sfAREbPnA/SiH2T7rx9v+TbyN+F+dx7eY/vYzp8l2lpUrm6l4LplEBGu647ruqtjHDGlKqWqxKBEQOslhAJis3TG4wAD25irjHuErw+APVBlGWlVefTEKSya1l2w554Z1ZrpFB0WvVTfhdQv2K/5W9PyxuZ7773Yo9g9iqtP+Hl6MGOwtfpn2def6I8O4vTIZ7cLoePpQA2zRs0H4Mbsz9A9rzFT3t5jcgJqewTzcCJpFhQ6+D6cs61LKxTtXHsiLca2wXwYg36t+m32pcP6173vv4feTvgffphMFJvFPcC71t2HxXhEx+E2grajbq0h14KQCTmLf5FCEEL7WtUWCZ71hEAIKQoYwFYePdgxo6wnsWc1F1TKywMkuz92f8MAQAdpdsmo2fYd67Zh24RHZyx7GmDkrx5dHYltd+h39FpsvfsxTRKMMxCEiJACYbKyW7XPDuUeg79l81NKgPEghmA0Gg0N9WAXWkcfhs0vvR4lXTamogz0t31SuXPYGOfqq6Xu+vsIihrv6rhBzbaWj/W9G4avG/UK3T3vDwZZwcOrweTFN2ye9zzk98ltTCIjFFaumjFYSL9qsYH9H0lG9Z4BIgFEXm4yF9OM00Uraci4GSWrJ0xJA+GhZy0HAFbDQ6p5iZ3Xy8QCEYu/YdQKMCoJScTY9x23dcO6brhtO27rjnXbJYumGeB/tPPGQIQPGPqcsYkkMwYGOezZqrYXvnH8DWTCx9ePNcpvTJGeSAA5csoNqJWRK4PRsBep4S6N8XTdMKcXzMpZcV4WLPOE8zLhYZmdaCicJPobpwlhVoMmEgxeFkdQF880IXLUx0JWKqNM6gBzL0VilqyZIlHhfduxK/mttJ6V9DsRvgmBldWfKrgFcFWyYSaUBuQqfd5zU1b9ykdCYY80H8nc3vPgAfPjYUWZAHe5QBCUUN9HygEEcwDAyiliXgYDXHXWpdwtKyCzbhnX64oySX2vCO2IaUqYWUpyAkjKYgBgSohndCmopKOx7GhFOimkmjEZKTRX57zYW0UpQhr19HzFy+2GPRf8+efP+OnLM9Zc8HS9Yd13n7+gLYcbD0KGBkfl3tMarCsDHw1gBLECUWocUnh38blpRxZWR/fe6HKgxqbFrFR34umO4LRH/A2kIkBIJpvM8UbAMxhTDFgC4ZQClikhgHGaCHNMWGLAaY4IkChKSCJYaSKRLgGgOSAsWhqTtJMNAaTEwWJQNnfWJb1XrrHU4hGWop1KapPX3YBuvYPQvhds245SGVclxqQQEJ9fJLNrnnG7rTidz5imCQ+fHjHPC1KKOC0LUkoyHndA7nsdI6eQWwTmKMAMF33ajGydJbiiVO1EWtoGcuUZFTBphVD1/KuDXQ1bbuAqxlSZWPdCwClJPX0gwhQT5ijzNU0T5ml2wu5pXiSLZpEsmqCAW9TymFpISN9YSE63XYgV103I2MbSJ9m7TYEjlVPOUxX8Bggf1gGF12umEA/j0veu7lV3+N/vCK6GybZ8d7roTnHbYQqc+2smNxmQrEVV5h2M7cGBhoAaJtTY0Dig7RWoBTtVFKqIYCSuSJPoRwQGhQJQQ2WCdLik7vgDCEn0pzgYAtaAxLA1wvIt78hZu3LtO/Zdid23HdueZZ6rgLwUIqY54hQkRbwlgKqc+x4mABEUEhhBO9OgR/qNWLVWT52mENRR/HUn5W85rAzM9iEfZsRjBe4MH/aivWs4MZe7+qrt41wrbjuwBcKfvtwQKOA0Rewse+eyzPjd959Qf9fEqWFGPE+yr7XEjaihtRv2W+ugrMkMbIgncTfEOYxyrqFh21fUWnF9ueJ2vSHngp8/P+Pz0wtue5G07pdVCPaz6FZilsimgR8+TMd58DLNwziQGsb3MDfd3b/P4eS3xJ1Di/uu7xk1YlM0NocRsLb1rTUhnueGWgXM4ta0G49GUktGLdJdjTStXnDQJhk2unqC/bbuKbAGCs0EazZmYhPbXEaKYBK9eGgnEmxI2YNRzAL8idxkFJa91Fj4V3ItqNWyuXmwLXuHJynh7s6CrO0+39YQA9Bx0rn7AJWo89e/mNnIZGWcxIdwl1G7aN1nT/aN2m3cYQ2wPL9tG56fA7YUcZ4TfjnPWE8zuFXMKWKaJkxJSjsIBEoB03nWASLv1CO6pzvPXjqeYtcLQUlVCYDbOYycs2abZnz58oSnL09Ytx3//uef8O9/+RnbnvHzl2e83FZvmmAtiMFHfjnnUnGXmnTpCH1Bdxjp4Kx9hDz94ftPMh4hYE4CzsQQ5bFmAZq/ZUBMd1xlXddaUXJWaoHq2bZZMx1aY8mW37KUQ5Wq5L09QKwD0rutGZjLsg9JfZXaGjLrmGkZOTBkyLjulVLcykDWoofC8rf7VOb7M/nW9eA9Bl2Drkvg56ZrHGbPD5m3dwL2yIn0QZtxPNQnEtsygVOVboVp8vvOaWjUCt0GMn3pu5S73HlZd/zplydMansuKeLhfMJ/+Od/kgzS04JlXrAsUjIYlgmL+Z0NsARDCg1AFr2jPIcERpwIIS4w0LSpLA1T0NbdDfu2Iu8b9j3jz3/+CT/9/AXX24b/9m9/wv/408/Yc8HTyxU5Cy8HqTdt2WJp4OzpQGzHPNQh1Iu3MZBgaagVpWnQsr5n6ZOtGfrmU3cfESHRqGdbIMAVCBFryUJDZEkTfrqtmHLC5XzTQZrA7YSk6fozhBdBHH1x9qTVLgagBpJ6TaQp1WrQJ0G3GWZomLPH2k5RnT5t05WzIN7mDDr6pYYpmMUohkAVUl+vG7vxAMJYjaOWqJiBamnGpkA/IHLBA57b22T2+Rv3OxNLlw411syoEJyCj8gcN22Sw4ALPwahDU6atEGbUkKNzQ3wQFLHbSmSnYQNDtKAGa0EcFGCzJakdRo3IGewGk51lTreUipeblLfu+8Fn1+u+Pz8gj1X3LatKzw2kmJJhRShqDWWoNcCkPw/FzwOiLBxLzVQsw3ZXIi811GKpMaZMjpEJdCFgyjBu5NXUMbLZmBATRegpqQCm6EL7GBENJRAeEkBT3PEPiVc5ojbOqNOFXFZQLMSQUclASPJdIM6fqQ8F14Gpd3VnIwW8PRwsbU066MJOFO0o1opUp5YvUxQZoQ98616xDiXitttE8QeJgOkDWBuDafbimmekWvFcjphign5nDFrOnMIqZM3vuNx2N+D4B6BGjPourFKfm+IwEEJahRd0noFwKkekZUfsnWfITXuFpmRUjcB42AKeYraDS9ILfAkHDXz6YTpdJLHy6w1v30OxdLqirio/GytYtt2BUoZ6y6liU3ejmrXGIZ1OYYs31A4I5hjBpFc0oCWDGP0nke3wxhukoxiw36+T9LhMFPM+dlhHCAdeGyDg8kQmVxDQqWKzIQ9N6BUcCiAAjXnwEjGD0QsKAkamAm1WmpyQNRUt6glR2ROpTqWpUlZmvBhFQfYNs1UbBoJ2/ZduOe0blG63AbMQbrtzQRwJVQQKic0CLDGLJ2i2qADPdrampKpig76yhC+62FOqjk2Yxmz+vIqarpT+Gobj6iNO75y1MbYSkUgwuebEJrPKWKZJnx3WvBwEt6Ly3nGMiUsU8KkwDZVAhm423aUrUAM386NgtAQpu4QQs2SXDJKlmjhtq64XSWA8fx8xZfnK9a94HrdcFt3ZLVv3KmBiGcGCemtLfWDk6F3A0hzt1uH/YrXr73DYRmQluVgQGgHGbhzdbGVglLXl2xZqlLW5NnTI09NlbbetUrwjiCcWdB7ViQjYhy7rl+tCR8ULGhVB8X4JnSwPQvKwRm5joaeGWM2YuWGCnVuWTultobSBk4aHjPDe4a4Xfuhc6TNjGUVDGvcfruXIb2/XjSgZsxKHufxoAvd6TjqBetCeL9A+3cQcsm4rYQcA16uM56fzyhZdNzjw0UCQCf0JiNRswcAKR/2toldFghBNNTviFJDCoAjaRKj+j+QUp1SC/IuAPj1esPT8wtu64Zfvjzhp89fsOeCl+sN67ZrNtyYgeLWp+uYN7mGdKyg3Z8s1+TX/LW/53h4OAOAgjNJMyoj5jR5xnLPFtU9N2SsAYxaigcDimbHt9awp4wQSHghSdZ6qFqeV1Vak5QlMfqY2PhUSIClMVBJMzQVFAEBEUFpAXSQeJD/sAwckq6n6uu4zrY93L+uz4PZa2qjyB7WNWk6Q/8mt+FHA6Lvu3HiDrbORx5EmohgXXCl01OM2qI7RG06ERyk0bqLrx4GDq85ozTJvD3NEx7nhMtJOkD+7nc/grmBSLodBgLCnMR/B4BSgVpVfjO4FZ836w4WEwFBsrw4ElqUsWrQcqfWkHPGtt6wbRmfvzzhLz/9gpd1w19+/oK//PIkQZZtF91IrB3MJBimiY+DTyX/yWn0oPloxHSfsSnfZs9U/tbx13PUvOXDDo++CvINSGFXBMcyIyO8K0V6nW97wboJx0GKAduUpBOFdqQwFBuOavUUQynD0PcE9NEEunPbOvDSqrWq7eTBbXjdLtHqw6VTijiJYYj8HQfIZnGosz0oP/t3NOz+UceYmmenKyfR7gTD4IGwfdIcRk2FQ1MW8N7xx67HuoKAgFoDSimSdUFQngzSFEf7JXYHxggwXS3qOTa2RS6o+75n5S4pKFl4L6qmc5tz3Bne+3Jo7miZgzjc0CNe/f/uNI9z1o0ZuDJ6z+PISXO8uWNg69r/Gx+Iggno5zk+PlyH7VW2CEXwkkSAsOWKTR+XJMox2BgaEXHU6L239KOhBAou0EwhSmbZkDmj6YZWsmaRUXPqmhrdramhqu2AratDtntt9ywkvvKb67rDUi/TNKE1RlYPt5Sq0YPagcN3PHicl+GxKe435+7NtTSkvx4MAlnnDtqEgVAapEYRNELUFYRFX4GGSce3V8bRG4J9BAllvTc2YC0rSWJxTq+iHF+W3dM5okbLY7j2+/t+2cObgWEIYAAW+Xs+2Ihxce9eArzNxlvRy8OU9+wE038M1oi+RA47eX1Bydn3hXUDQasoLKVPTCwEtjpVIqbNwOoAqjgfmtE2lAyzynaL3OfauWg6kWUvkRHDWPd9iCCSzI9YA1IgLc2CduuwdcfOsWUArN2IyHWuRCXHbI6PcA37cfjuwXh+8438xuNvfO/4FrN9LDgj5OcFMZCQoG+78NIAmGJwDimKWnKBCAzZmjI0rPtTC2HIHFNW+Vl62aHaNWMmjMSxCJEtzVy+XR4bUenoMgxXdHBOqO8HQv/MIDc+xMYZZCNbWa8+z8DAN2Pls5qJ2XrWJtu4NMmoqRYQ0P3Gmhpf9bucKws9G8Cu1y53XLs+XLrXgpYnMppXI5rslv/6KFvJPI9y86D/uy15fxxMOzdzDp6enKkKjDE/pQMnfjX9Wz9YrB4POUcbmvH+rYPZhrBnk9DwvwV1Kkkny23fQSSZNuu6CRAXIlIq0tHS9CjUfuzpEn4fhueNb09+WV5nMFplFJYyi7xnbJtE8W/rhtttw03/to5P1Tj63B67v2a1RXmceTID9OCXvbU2+si83zFr6VOMAkJH5Tu0QI/pAHExGlor/Tndn1VBsdYaomY1m+1ba0Mg2Ycxi88VgpS4oEE6p1mpFA47T6+3XzMPb3g9CiZBe9Cs77FBdur3eVneuC5JgsC2LoJ1IWYrUzQlzbpWqetK4CA3x7Bcf1LX21cd7vc5CPBsoDGINmYwH32m46d97dFwvbYX2fyagG0veLltaMz48nzFL5+fsO0ZtWn3whiRQsCkHJLQrlBgkaes3cTkS63Ee9CJGgSUvdg8i2XbNtyuK9Ztx/W24eW24bpJE6NSzfc4WKp3puWg3/T6vGHD3Vjy648ML35bM/51QM2ba+K4QL9mUvkY6gnVxp7yHkvFHgtiC8qYLK3eokaF5ynhh8cLSi6YYsJ3lzPSQxIHTFuIhhARpoRoEfGoBIWAehzyu5bydCxxYtSye8przjtKLa64DUwxfht1YyW5sDWAAgpVhBqQg9TWWV0fQlBbypPgNJLMnk7n9cXv798fDxqcu7sZcwODeiSDDIiBOnsKthDLPIEDIlXMkDmbp4BpjkL8HK20SFqdbfuGSAGlZOQ9CqnTvICXpi2eJ6TEdyAJvDaRuQGNvCxAlNwNpVR8eX7G08sVpVY8vVzxcluRi4B8WQ0sgBBjAgXWLlMiHAOkM5CRAVp9srXDDHobeVYcNdWbRF2F0I7AEgV954ksSsDVWJT+WIPuRqm92QSGzq8LdUA7JQyPTbebw8ZBnHQCqGqqHzU8h03KYpKApItGhCUSfAZCwBwT4qIdguKk4y0ZGiGl3kZP91DTqD1zw7Zu2PdN95+kBUvpk3LSDGJPyi2A3Ah7Bba94rYVbFk6xj1dN5RS8awkYIxexhBjxPO6I00J0zThy/ML5mXBlBIu57OXXC7LCSn+zVzrXz34LdIwM1583vjuNRMMXUCYUR+sXhiMVqWsjEBoKSoIpdw7Gg3mWlBaATUGoYJ5RyQCmIfacvmeBu2yBKDzbhnnRQAVUYh1iDxfb1es6026y9yu2NYVrTVsQxZNqQ1FjROm/6+9d91xJEnWxD5z9wgyq7rPnCMIkiAI0Pu/1molrHa3Z7qrMpNkhLubftg1mNk9NxYw0NILLEaSwQgPv9jdPiOwCnHybMK4D5ZPbR7yfu9JoiREJ6OVDdSjSapX7pKNk+ipKasf8Y0ORlTftwooOa2SG+NmYJdj4Ha94PL+JgLF6ze8f/+O2Tf093dstxswdkyamDRQCaAmFS0aEdpSUNQr3NqCRUH2rTw2AMeQkv0xXRF89+inicv16ulr+y5GcH0CWWuloCxnUFvEYDsI+5QU2I06dgzwBOaY2PuOSQXl+o4OwuCBl9dXoFavDAgYXtQiqZRkBt/HC6TO8+4Mak66k1Emw3mBVWwj9m0pp5viG3KQbNMQZBki99AAXi83/LffXvH9InRmIeC0NPzHn76ilp/RtMpeXVfF/hEAReh+tbtOHmCWqMPBEv0xJ+N23XG5CI+8XK64Xm+uCNoALLXivFT0UrwowLT0kWGGcbjn+zB+Njb3A/qJAvRDwKCBqJbhW4+CZgKOWSY0cGKOPRlnulfdGLtE/459x367CD3rcmxGnTG60ymyqjuwdCegFqApLWqK3UUEkSNsRZh3BAg8HOu/ah6uJLI4Hrr2f8zAhLIo7czbLAqlENy5VcBeuUbwj6ZbbUhlPIO14mndO9JMBrSaJX6Ykea4VuwTixpRL7bJhTC5RWV67VqJnx0coWoZAGAecKEx31/f8d9rwbo09C4RvKd1wU8//4wxJlptOJ9WlNNZjTFFCprIlcKJZ/TJNEc1FlJKX73dbtjU2P792ze8vb3idtvx//63X/DLn3/Fbdvxy6/f8f3tgqFOMU/ZA3uao4h7lIwH9tBKpBhq5M4yoQv3d2P82PYf//4nAFL97LSePCVmbYuXLLeXpHFl0GBd471jNzmwd9xuknZ7u96wtIrRB2q9ucHfwLvFkSARiAxOYwelDeQ0wmKMQu8JBxeAiESD6anhZMgR0aSYfgAFLJzJIipLWfUrd5axXR8xDioPGF6gTNWd/JCMG9GBR83csYVBgZzXkGHEzKqp7gosXCWtLaK5xVLJmM4bOY1xNkDJnDFAA798f0Ufkn74eut4e7/i5XzC//a//M/4P/73/xWndcVPP/+Mn//t31BLFRzFNXTYGDg4nxJcPqka1ufAmEPTnW64vL+j7zt++eUX/PqXX3G9bfhP/+UX/JdffsNt2/Hnb694t4g21XvtVsbZJXcE6oiSTwp0bx6MVrETgzNFEEDOjPi99o9rIiZEZyPNB4saYHTSBEIjoD4A0Hxb3ahbB+qNUBXQshTC2ipoMtZSsS4Na1vw0xclSKWIda2owWaxMrHVPYciEAtjJfOuTMbsHX3bwTzR901yI9kUxa79nMn7JBZS25SibE4PrWU9R8+GeTQD/CyMIGK0goaO3+fAP7jR/XEWI3FchHz4Ex6FwiRGDA1Hk++kfHMlQiVGbVLiuGoKhcy3Ak/ugplRB2lahhC5WjR/1QTyUiQAymJJi5LUWRQUTOakD8lZ7b3j/XLF69s7eh94v1xwvd0kosIrdE2/vghX6s3SGfHoIE2P8eisrPT5sQ3OUbQI4MEi6+3BqU8Gxuzed1fu+aOhBvmPsPB68CUd51a84kJRmbSYOxEUrQiFpBpbuW5YasHLuuLb+YbzMvHTlxd0lmuI0bSJJ0XDI21fCogXOQaJiR1meJMQRGHQ2765ocbSAkFwfI3Jhm8hL8G4kiif6yb5y3uXkEUx1Ej48GRZV23bpPpYa7hum1aoavj65Yp1kdzbL192B8d7ZDsSZD5Mms+nvudQ4UwdQnhVAbwQJhdV5kQgqkPHnwijDpAacCyKUCQDobmFpCT7rTWMyViaePQwpQShWfNkP8saNwR8AJJvr1hC1+sV7+9iqLlerrjeLhIdopEZDC1dbFEnlj6alM8QSJFs7JkqJbqELCDFuBidEzb1YIkmz6ExAuSpTMwRpnrZCSZEhMPAcvHHFIFelOuO2/WC97dXCQd/f8P1esHsO/rtJnxrdhBJVbxKwIkquvgNhNNo2H5ZGuraRJiqVdcCpGKGrq2puHGS4jTwfpOoKMGiUcF4lwgDUoNtVewBKed9FvyPoRXD+kTrhNrhWGJjDnDv2G43DMhevl6vaOsKnhMvL2e0VjAVfJJQQ+D5AYaaz1o21iSRXOdZ+bfPeUz+4Vz48nPZx6/Pkdp52Xb89n7BdWs4t4KfloLzumBdKn7+KqXS+QSpTqng56UuYYzQ9TTGjjkJzBOjCwbVmILrcL3eMLqkHkrUqUTR2QqtlRRPYmIZBcssGMRoLMY7glTOM4XGnuHDEyfWmI2jdHfSo+WbqTSIyBQw6ZvR2UNa0xyOM8MaMSNjtqNvZqjZsN9ueu6GoZ9HBA7USKNRNCwOOwAOHhuRSZZKbanFamQwclDEuQORTlXBJ7GYKE+3lHvDNRhzhLJnUYkpDccGw1LjjEZ6KlZejP49qYKvCiTlVRzrO8jqj96Ldl/hEU7jXaGOs4ymMlHsWd130z9Q2QySMUEETJq43G747bVgaRWFBFfltC4ACtb1hEWjQk4rewESN74lwGEqJucrXdeEGYDBQz7re8ftesO+73h7fce3b99xu234y6/f8Odfv+O27/j+dsHluql8O1T/AO43TZYG7BFD+cOBn4Z7+Dg2+HjZf7r99PULAKC1hvPpLIq8wh6YXOIUwECizMjSNaKt7+hbk8ijvWNZhA+1IrKL4YTeth2F6GiwQaSdDJhepmNAdndyOmFYI76mKcZl2HVYU3TBvp7yXjgYIQDRfXSfrerUJMRnjHDOgxE4PdA97eteJyuNmj0b84+gpL/fciSNyZgWmVvMUONFCFwQO0qs6XmMeswUrPH9/YbttqOWIhFl246X0wLMiZ/OJ3x5OaMtK77+rHpiVeevXvFAkWzPT3FaMDPKzph9AhNSYe1yxXa74bffvuOXX/6Cy23Df/vzb/ivf/mGvQ+8Xq+47WIDSF1PtCj2n+37PCvMcMN7PPGdco188h/P5z9uqMnCx7Erx0E78BBOCw7HxYfIB5/MWnVGPApEhK0PXBW8cFMMCoCwpIgJmhM07lIWCGAe7rmYBk5qoeX7rsy6q8UtBGggNjkAWPlQEwr88TS8nIzLpZfNgRlmfp9IhmD/6BaGpvQZ8ra51zTSFjMvhitAhMJw74aADrIChxXftJ7HWJIBhnIfIjTbU2T0PuZBJQA8uwBMsoBZ7tumCN0yd5bi5PNVpIx7ZagyXkGD0OpErVMzE6ZXtpo6Ry4BJAONpQZ8EHB0UDPhjNxbVu/TYwmpK/gciqzdz6eO76YQxrChjMoPlHayExQ2sD6yHF/9TvR1z28fRFLide8gALe946Ze9n1v2NfhoZ1T9yTXCi5iNEAZoCHAehHRNrDdNsGCUoC40ac/p3Q3rNdDPY3bPvRlFb6sTLdVMpkHD+ScBoatYcUEtF0wH3gyWq2Yc2IZU0t2PtjaFhPz4RBIO/HemHNQe1wkE+r02VpVZnkMS1Xww4NAI4ICk5Rd7n2Ai9DX67ajlaEGS0md6VPSyYhISzM2QOexD5nHy/WG6+2GOaZ6EMUzaPhe8kRJEM+PmWjDB8bljO/jTz6npfcj9rjmZXS9cpooFJzDlnX/m2PC963TicAE2XvHVaNWLpcrLu9vbqixiJr9esW23TC70MNAHihOupjUiMmGEyTHlQ1oXKMJKZ7DUntl3wgPvG2bR9GMMfy3pQj+SSmEtixoSxNau6yoyyr3IjH4SAWbENZE8ZXS7fu+YZIAX9+uFyzrCmLGdr0KDl1tqCrkGD7Ej0hD/HsWRiK/Lm/5+qOP5D7vWCRBvZAp8sUBLIsKeuaw6n1g7ztI6VPvO0oRkNKqe95ZthrADLutayUbT3NS+leI0Jrg9C1Lw6phk0trWJvu6T7R6gSRVBSjqYZ853+K7UZJKOXYuHkLfybJfJANH9DY92LcOBu4J08dG4mimTomc0ZEzehqtJkpPWwOjcDRiM5pZb2DxzMkosbEPlHoVCmctucmoMavcAvpeCm2ncghpqSxetzNuBmGGsOtAoBcDhiqTnyQ9TPrQBhlXPFQns/3p979zk7/UfLpfbP+CQVJNPXQB5NmomdhoI8zP93iLIadjN1jqZ2lF2x9x+0mEYWtNez7KrJA5ZCFM4TaNEXdHGhTaeruqYe32xXXi4B5W9qT8UdL0feKSJMdw9L0oaPT5v55Do920Kk+nPAD27qKY6u1hmVdFMNEnLel6GyY04kLMAWgFUN5hfEm1sIsUMxLlTdak4jL1iqaRoW2MdFKkQwFZowpcqJFFd1r8AxxEjnYuAdLTBiWp2OGIkFz4G4U8/pMxprqNF2AZpum6HjqE9QnCp2rGelrhSNiJ9JnMtRCGGdNivoRUxvGvYwRl/Qg1eeOryjT7cYa35ARJfhplznhUkLkz8vthslT0qC+fcNt29BOZ3z56SuWLgUujB9FlHVcD5Aocp4SSb5vG7btht47Lu8XvL+9Yds2XK9Xifbe+4FfmjOCDhc1BzPlj/zgA3XM85zG8fP2xxP5dxhqoiN/bW3cd9vON4V2gnRiZPIKQ0CFIGFmuzK5W594v3W0ziC6ghlq/a5Y1wWnZRHhpQgo7aKMzXPxVbibWp5ULNs79r6LoWa7Yd92IdwQrVQGNYHqlagsUmrVBGNlrAzQnKha2aKgg+oClAHmIZU6uChGS4oCYEm3mWwxNhZ1E5bZh7akzPvlTSFPsyRrzuC4poA1qyBJINmMLOSsglGIsRBjLQUVwLpIxFOrhNNacT4vqIU0t1AJ1RRsBWYoo5L5avWKpS3hKVePwJwdPDp4Mrbthk0NNW+Xd7y9X9S6LjhG4umVdJw+JwYIKFWqCJUCLlXTZibKSKB6E2qkaQ4oTLXC8R3MeKP5praOeVqFElIQaU0Q0VDnRzarbiHKVlL+DoogwqCj/STCoUxo3r3ThVuWSlUEx0ggIgHPUyK494FNwdxeLzcsRUKGT+uCL6cVp3XxCDmLpmlaOajuHW3ZlHCHgUuqyGyayiYRbQ7CBel8bWK1F4+ZMO8+GK+XHW8KFvzLr6/49fsbtt7x29sFb9cb+ph4v4pANGGAz/JcpQ/BZaiyNpr293q9qkDQ8OVy/SERNUfiaeQ75sdUW1EGEtAeRxi4RX0Zw5TIQUKpE1Yqvczhn3t0EwE8BiQ8dapwGnhUc0i+97ZvuFyvKKXgtFxwXhdJYavFryPzK8KSRdNIipPs0ZmM32AVPMxIRCWivMxSSAiaK9qWP/tBOTImyqzV3JNxESGpm7HqRxjAd023EOUgFImDQGXCAkvKhUePWYj3MJB6xm274f39XarzvL/j2/dX9H3HdrvgcnkTgWPfMXeJOGuzo01CgaSQlkVC+icxNmbMSbgNRusDtTC4DHAZKKRS6S770NIq5py4bJruNAberle8X68ytiWU9WVpaCcx2p3OL1hPJ1CpKOsLaDlJiPh1x9wGBnVQFeBcE5Su1xtAN9C2g+qC6/kFRAXXywXn8xk8Oi5fv2BdFoyffsLptEro/LpoWtRj27x7P8g3plfAliUfHC0ugpJGSCTDXFxMrii0pnjqqHla11a14kUFg9VYN/B2ueD764K1VVHSJ6PWgrasaMuCEJolrbGPHaNrdbXrDdtN0w2vVwyt5LQsDW2RiDlqO5ZVog4nhL/tY3r59H1M7LxLiiQx6jSjB1S5VyrljqkYI4mACk/+nQXlIPo+ovV9i/lwMiJ7DrrnLHJGwOZ3NcJImtNMETVitNk89WJ2GVc2r/ecaVFMv6+lPrHm7WdnlKUcVY24kOLNqhpOUwzMaKqpkGqkmczY+q6RiKYwGqPI3ltLS00vHyFZyGYgIrKU9lA67DeC+Xb8pUVmhL3k4fGJ+gzpvuZsgNlnpM+HqAcfBk5GnKw4A0esMJU92AClDSdKojyvW8fbdcc2GFzeMFkMm/vWMYc4cU7rivN6Qo7YP0RdM2sKcMdk2X+36xV9DPz22zd8f32V1Ke3d7y+S3r+uwJ8b+pgEiwkw/GySKqU5hYP6tKDGRGm6VeW9nNnpMjjZiPyyPanf/93AECrFevpLHAURSo/CY+eqmMxMLXCn629aRhSHX1t4DmxbDtqbQIcTEUiMLpAS4whzoVCBTzFWE27yXlS6dCNHRzPPeaE1dcx6FJA9TxFig4jK7xSGjN875mBxmwS1dLPKSA7JBOkYWkSyWz4nT7yZE786Xuvc9x3Mt3NtWJGOmYK4LTowS22k8iaoKnO9gqqjFIbSjuhLjvqIuC/dd9R9qH7QgqrGAs0HmrOI1m3iWZBjWND9vmvbxf0KRUQb73j9f0N59OK//P7K0bfcD6f8NNPP+Gnn39WmbSpfEAOEi1pdBtG3zHHwOv373h7fcW+7fjllz/j119/xbZ3/OW37/jt9dULzsg6tApPvmvc+GOOa5GJzKBDB9uUGWak+jNgKdLNd1wyLqncy/OPJ/JvNtQkucNp3wcjFtLmpyMhMKJia8wmDXw8NmMGazWoW5/oEyi0oTBjaRVfz2cJEZyMZVmw9R1Ny29DlXyiksqtqVckRWWwCo+7MXoKBuuGAvNGa0qMhY3LA+nup4JSp4R7F/nbyiyKMSa/p3xiH7PEZOgTr8gDmrFtUoUpGQE/nGnPRszQ8vVuqClaXaZCc7EBNJriBSW1dKsQ2pYmXtdCgv6uhGx2XbwMDXcUsllrxV53eHgr1BgyujM+V+zHxNvlgvfrFdM90/DrUCkoc2Idcq8+JrY5sasgInnaZoFnibBQxderg1mUT0lAuC4+xDhJGh1hFgmrKza4D55Iz43lbJgxQse+j3wm8+3VYvOR8Oi1obns0Bx19tgbjCkEyZT6ycB12/FaC5be8f39iteLCCNNcWtq1RS4OkBU0OZEc4yd6Yz5drs5gKaBigLwNCnDs6EmHFHCUQn7ZNz2jrfLJn15v+Hb2xX7GHi/blK6cU4XgNwgx2qoMWNuFU+rlbXee0etFUtrmGNiWR6PUZMmJU1SqC98v3Z0r9jcGR22MNMISUV4N5hj7QJubARMICEwWwqZlplFFwBYIoxesGlo8bpsuCzNvUQWudN0zIRmD/da712i3CzCjVnTDqvgiCmHcwOHP6kaXJA+ZbBHdv31LUXHIzME3btZH9As5csMNeRCs9xL1poqYCkqxVMvWKI5N60mcL1e8KpC/NvbG7799k3G8XbF9fqGOYaknM4BAnAq4rmDGetq0Qo9A11zsncV8BmMMifKHB6JYXRj71LKd86J18sV79cbxhTaerndQAQsa8O6CC5K9f1dcX55wXp+EeFtPQPthDombrOgckfRShHC81mxyW5gFPDewVQx9h3LepIw99sL1nXB5IHTKphRALxowF/L4f5HWpZ3f/+keDP5hXwTwrE7IiLxjrUq/6xEqrQUAUasimGggr2kfYviclVP31DsvVYFG0Oih4euuapFDHRsdzPUXHG7XGElpecYOncNtVWRP1SW2buki445sfWBSx/YB4PKQFNQdZ5T1hoTaAqumZVPF/xG40UxXmY0uR/WT0WOf7LNmfai7kAp9z6cz4ShpqszQI3IexiUuxpnstFmji4RbIfKNDhofkeoVlLnltCInSx6ijHVkFesjDhnQ432U+9jCttMe9Ts2TM59MxAHVG10ZcP28Xl4oiO/iCfHww8/jNlPuQy/Y8w1GQFwrFfjv/9jrwacpkZaUzSsW85nZv3sRQhEJVqH1LVbrCkOQFiqKkkVYtaa8IfoU5gFvw21xP0nqPvkoo4Ji7v77i+v2PvHd+//YZv3ySt4u264f22KXbj5gUTouKYKK1SzCQiPERXZFcG7ansefwYsnaMP302Zj+iff0iqU+lVqzryQ1ZxTXYqfuVwWNi9oFwjqsxZOxKD6dWCJS9xEPS4y015rTK8RwTe6tSBGgyahmgWcBkVWtJFZ+ITrKxNDoFQIGO9A+TOQCv1psbIUqyF4LLQoWk6qFlEZwWMcoDUVgDsOgaLdeuPHqyBCgMvbfUa7zDimJGLzMivX+EsuhPeL/3VSeawotKbSh10Xd9tRoyqDvmYk2GE/ajfjLdWMF4YzFU1kICcXG94bwugt/28wu+nE8gnlgWcQa3ZQEvi+xBnqpkG5yJYM++v37H66+/Ydt2/PrnP+OXP/+KvXd8U/1lH6IvmH4CDthp6atSEQqKf6D8d/uMVVCYRrj9CkhE9LNwls/b36iJ0Ic/77c6HW76kRBkEdyUDs6bh6HhpPaDxBL0oSydok8RaohI0h401Hcqw/OIGreoBqCcpVpY5QpnCqaE+LE+lSmL9r1GJ/h5mGbZCcNOmrT8fHMew9LNAu6Elv/adP1jLQuNd0smOmkPpcKmM0AjSLAwMxM85XQpzSbpT576RBH+Z3NRNQTQ8BEEcEmNJsABMNKUVRk7K1WbQo913motGp5NCg4MkBFWIhGIWwPTRGsTy7CQ4QmQbEgaE4OEMXOpElkCwLFqiO7WNlRpTN4r0jGkiH7wkOyHtyQYH3Z+0uaPZ8sZ+pt74yrZJUkQ59kUDbsH2dpU5q/GrT4ZZbALOADhvO247rtUfpmMpco85RRG8yCIgtF9XzIQlRPcUGb7V9aEMM2JfZeKcLddwodvPVKeuqZCCjYRx75iG7oQYqApOTZGhaQvhIiaeHz7TH0BfJdmSVuVQJ+7T35tNMfSbkxgJQqaLH8X9aYaYj9ANFW41b022cHEDZsojNe6p5UujlrQhkYXzukMzjGhEIyMfOwRy9T+YHjp35xP7r+zPw/sIKtGSajwd/p8rB7U9n1P95fGsBB1DeP1yJmBfd/dYNMVI0MMNTcPi39/e0Mf4mF9e/uOvnf0/YbtdsGcU2iuKqJLK4LNBqOtVQwBwMFw0NVbT32AqKuAH8Lh3nW/KCbCULyoUiTyQ/LsF6yK1bBaqfZasWh0B5UKtAWoDRMTVAdIvBZgJAeFpk5KZJHQ/b0UbLerR8tcL++CtzQmzifxXA8V9if/QKOpL5ZPVg3Zek009rNLqEDrMSPJ62PeM0kPJjHSlOKRpk2j08zJYUZVU1L2vaMW21O6n4sJ+0A3z6FFbEH8lVXxwczIVqpExLZaMJooCosrE8CpVdzaAAjaL3WujBnA8yoPGJ10OT7RKOvnZ8P86GYYNXJ99YJOUXqZU4TDDEBgr+5kn/cIez+kjOl++IATlmkXEHPOJu+JQcVkTCaAC2sPOTz9Q7ByGAbIPp2OjCRfHMfwj0YxSeAmw5HJqXR35tHp5N/esaN86dAvfsBMflQxUjMGEW8HQ4XKe6IM6oz4NKUozDsjhfGVD2NqMsM80m/jh6UU1C6GTyAMX6Jf7L4Xb9ebGmK6GGM81UrW1ZgRORXpTbaiPnQpPe/nusKBetHdNxRrIl/vkc1A6mutUdY8GWp4SpUmZga0WhPP4sRD8BOFt8m55MboUgSYlz1aW4w4Ei0stKwOdQhjiiNqRnSrSBYfn5oPghXHZ2mUXLZwvQhJv4E6oi3dSY02mkkQVW1r6BLFocUxi9IIDt2FAQzRLNNa19R9EKZVHvtRCmMen/sPTM/1aO6cdRCQFQfqwvlCR/p5vJfpNqrHo2AMqTxJRHi/3vDtVdLCycCMWxW5ZF3lvr45tCjQvmlhhqsEaehe/mzgKL+Ubh767KJp0u/trwNpOdIZ+Sh0EVnrdHePP6apfyj9fEbAPhDUg/U7Kwv5OveX0dAknuBZvI44o6Ap0XWLnEazTCIMKiAmXPaB396vWFpFZ0YfXdHFK1YFIjTMFBmjwJyJ/+X5qgIOGygSYEYD6IKkWJytipUXCC/GILEk8kThiMAQaxo8bHFTpbaPqcem8JrFPAT9R7eSFxAbMiADWbkzxHjAFeaqwiSRAQbLTxsRFs3RXYlxKpIGtS4yB00F/VrjeG0Nls7ElpetnnjYeOom5RGGtT66e6H77LJmmFFbxZlOTuQsNHAfWp9+TkyqoLqgjwkuC+oiBrpbH9i7MEnB15hqxbZwQ+dqABWPkik2fqn/QuQLBomAXCCflwdT0A8YNR+4NHllYGE+4TXgvG9duIT/H+5PuUD2rBm49GALv52KF9XR68S39yvO396xLg37lLGvReZ7VWXPkOGF0CWqne+jygSBJEKgKBA4FQUCZlxuEiFz2zp+/f6Kv3x7w3Xf8evrG357l2oJ77cNN63k1s1YAxG6pj2ypqwRSVirhaX2JilRe62YQ/LTH94OmAoxiTmcNeei02HCgqhbpEjRNWsCkRkXA4VfALsNWHhWiXyak8RzWyz0lrGpcaoTsClTqiVXLoALpaZoytwdn8/pqxmRAJhHHiQCiYFXqyjltJYh+CbszxuGUqmwomuS4amZrgzb+YmhfsKy/un27ds3eVQO48OYYviYKtzfUkrf+9ub4jFJWdY5B/q24Xa7SDrGtuF2u2Lq7y6Xq5YCHloljLG0htOqOf+nBagnAcZuDS/nL2iFUOaG2m8gMDoPXBVHquwdpdwAiGI7NdzfjDMZH0WU9IbTIpWGXl5OUvmkFpzXE9ZlBdWK5fwF7XSSCm5tBZcFNAbqgLkDMUpFh0TA7V3K0nooPwPbTcpxvr++Yj2f0PcbXl6+4Pzygn3b8eXrVyzLgp++fnH8g0e2kMMo9hlwqPCUkXEE6JVdWbRrEGwdJ5nJNgwkKmhR4f20VHxZF6y14Kfzip/OK5ZW5fjLKilR6wKGVN/bth2suFCebmiKt15/atU1TxHUvi9rQ6uL7i2RXSyCsNUiGF5jopJgAE7Ifr+pUilpplMi9MAYZArlkCc2HsMSXQP+qMPfi6GPBsG83W5xXe2nRdSAxWBjxtE5BL/HsWhSdE3Xqk+zd/lc918YvuQeRltCEYnnsTRqIk1jmAo47A4fWyvSvHQ4JFXLQJ45ra+pdJCyBmHNWcMxasSN18WUQsPuE1PWoQw7InXGLmr02v7OxvCPERqPbabARHdMRjn+aWXj9Q+QAjFzUZVcq0mavASLQiBWfJngGS4OmwyqMuqcwPV2ExpaCi4mx5aUTqzaG2nn9j2UwW3bsOnx++XqOBjb3rErzua+DzWYTww2xZziWROh8fS0e6WP4etLKnhROD+MTjDcWSMk7PGKxvnlBdCxam1RXJrgxzwJkkWoIAEV4MI+3qr6gdAweWJhxlymRs4M9NMJtUr0Ud87FgUg7lpKmQBgToxJ2FTwtah5HhPZWGOGZTcOZIy5YAWu+9jaPxjrNDJ4UcwciUCWEtKe+lQlPbkq/TXZzJ2QpPI8JHrd09hIS3kwo/fAkpuK1ynrNArd/IgWkSPQeZT1UzSKZQzBprPUp3rbHI/UcdQMeBZmQFGsLj12Z/e0iBpx6uw0MZjxvu0+xv/Pf/0zAMZpWfAff/oJ/9Of/juW1vByXvHldJKIVUD1NMamGLRzTilsoRHD23ZT49m9Q5M9s6CqU8VsYUxBmzxlSR/NRolUPpA9x+mR5ZoWsMG6X2uxalmUdPTP2x9rIkm5S7MXAo2dppLB4V531qj4jnVIZGJmmZhcJArCFDcTvt1gA8UAkfSHbUy8bxuWUVFSNMdeC7qGSld9CQ1WAUYtgUUNKbU1sQhSyjlFVJfxxzAGWYpbPaQMlxJJY4iWIqP9njo5Y0aZNzPcCCO3V0zmR1v6P9+Oa8BX1od7+WPqss1W40JSIaIAijtjYX+MpoaaHMod6ODk5dwsIsc6kEteDvWCWfekXPlUfB8NY7ZoDDBKJSlVimBGk1m8x2NK6tPUajh14oQKLqJIoQ6Uat7jiqLpMZ3hYY4m/BwFE9t5RigVDBBA4RIMZ84PwKf/dPN+IRFnI6HHLSr0IkZaeFYI8Jlhub0gwhpg4aJm/AohgYEpVdq2MTDBuGwdr9cNax+S8qQe4rVVnNQ456jwEIHK6FytapGH7sWS96J55GVeh4a+Xm87rnvH++WG14sgs1+0nHDX6B5LIZhzBqHVB/W/1ThgRJOKzFsphFEl0mQkb+2j5zFNnwsL/iHHy5fRBwVICZNuWisHf4im8f1bUDSlr5RskCZnqBJ6qlUYADc0GvgpkHVPwpLSGYsxPVASgqFlaQEzWpvRz+gmjC6bQKqPlIfI1mwYYvy0Q3+MTiceqsIzHr4Xr5crAIn26xrJKcYZqUhhgJF9DNwuF3z//g37JukpNy1d3vcbtqsYaoZWcppThPZtl7Te/Hzn0wriM2otGLX42qilYllXMah3BljSpOYY2BWsGNydro4Evm4RNKoLgqFYNKcFp2VFrQVfzme8vJxQipRcXZYTqFa08xllPQFUMOuCWRpm7yhtB7UBdME8GUwYrLn+XQBv9z7EoKuGwW25Yd1OKKXgdtvwsm1Y1jMGM07rKntSjUiPbAePGaWFRwhjjZ8LN4Tfd0TVkePHNncUkTRFI2hOrWJtEhp/WppXmzmvq+fcQw2cvXfMLp1xo6l1yKQbxTQhQEH8ixrxCtZVHSQk+Ho8Q1iuY+Ll1MA8sfQiqR+a6vS2NtyGRBHsvWAMUemNHpja4/uTj9pNEhPTGN1/+s+30TW6LWmuPA1IUtMNk3Gma4pY4NVIdE3fNhlHM9QoH89VpYqTW/Jjk2ahx9OX0lRDzdFzSoi1xQYOrHtxzCibzgeCFr/NQrkZs6UfdBACMg8I5UJmzGQ6p6Nm9DiseT7MVj7/0fsQh+f6naYKUE7fBCgMNqzGQgeHNYcHDo4PuROlZ4+/5TZ8eO27YFgWIuy14FYFY6iqrJvnlZkVtFQrN3UpasBTnCAe7atG8aHGOXdUf5DxKM1C0pv8mY5T5usgD6fRODryT9b+PrK1ZQVgmFxVoRJE5gOMdrKIz2WCXWaWz6TCFwG1SnR3nWoMg2MHggjLsggWJQjrojgwBMxR0dVgwpOluiwn2feumYEEEDvedIj+KPXu+0Wfy8BrpbKp/L2oAd0qFJtRXo7VUJNwVHIaFJvMgwSLQeQpplK9b2AOpfO1+Ho2I+/jW1zzsERcJy/KZ5rq0C30Z097urtWsNZ0UXvgpIuqXmxwFHsfuJQd+xj49fsramGsrWG7XbFfL+LkeDnh55ezRDQRvBDBtgtI8Jwqc+zDnfPWmywrGm22+S+UUiV9CMyQTPEI98+K9H1+TEak3WW9yOwcf9D+NpfxJ9ewLf/p5Q83FQnH+Lgphm6JCv4KC1Hk9FMRwsXyVB3QUpTB2qpaKo+llIlUSSDz4suNwlCjxhxVCEU5LH5MljZFx35A06DEM59zQO0OBx0rvSL80v+eMQZ899tHt99bA4dFSvFe9Ysqlc/d4tfUAtmIlEjJuX5ONs7onGUL8jEsjmU8uTozEk+IWlwJDt5HHBVjLOw6hwXrLxQ1nTAhgKmtAY0hpd25YFKV6KUyQFWEJFkP4mmkKdUuhGhG3BWKAguTnOthfxqJQQnp/dMymI+ZRX3/jBjwH5yZZzkIpO09yl8F7QlhUr8zxm7ePuacwichm11xbAAx5hmw90FIIANhU+MmWblLUgFf8Q+mvO8K5tbHxHXruNx23PYd121ztPa9h9AzZ3gffL/p88Z+S/RID4yhi0dUrkXj8RLpvZHtg3DMh5MOXyf2lj6P+fX0TA6jMZBwlpANN9A0xSJeLbAITqTejqR85PuK904A4cXTTq7jMomRlwDHwsqwovqEadz1nLT2Iv0uxV7dDdVxCO9YLR+W8uHoUe3t7Q0AvKz2nJJCdLvtGqq746ogktv1itfX7+j7LgDB21W8/PuGfbu6sXqootiH4DWZY8GEQ5Fk2QXGqoJrWxY5LgVEC4im4Nl0SPquCXQmBWoEKZhRJmGwCdPGOwmndVUDScG6rlg0ukaqO0m6k+SnLzLPpaoWW8Csef1DU700lURSUQyDwdLjhoNDEhFu16vOGOP97RUA0NdVaEnfP8zDP9synQzqGM6d45lCJ+5B4gnkRs1MYW3ugKj+UYt4W9cWQML3r1qLHxfSlGO9pmG96Q3ijhwqhxi/xXPbFlUORCCS+SFGqQWVBaB9WRrWIUbrdW04af3i89Jw6wOVCL1LhKFgr4iiQFLMSNPoflcSlM8pePej2aIb092BYoYa8Twf052swo59HmW3bU06kKjuFzPwEwNsQ6/3jnWT1orSQpOPme6Il18homJNWfPowyw/OF+Mc7RHh/V2oINqzDgy9/SnXkd8M7GOxBhp1+d8xR/e/HHv7vlBHGYO3pLllzsu88kdfudzkw+ywG7S6EeZzg2ylPmQ8kqTGUl/r7+dCUPWTlef1xFglRGjntZCMMjgt3JOpKubEVkiA/Pc3vPD+zF5YKM4sHE6zmco+lwUd1FxSXyfeIyX0CvTx4x+GRh26BoS2cRg1w2BZNTWdO5ZSKFLIr2/IOSbDIBeEIZMn3/Aox+IghZb5oboQ6aj1jBW1RxVXr3veX+boi/FZVT20ei3qnM8SAxb7tRn9opRj2+xGUXfQqx/kzEpMkeyMVnaUXb8oOsfOSVs1bucjohumhCZfBC7HlBoeNo2Aeh9KqaN6Z5y+YiaqRHJMhllTFCZQuuS4cUN2yqb+vww3/VW1gsbbhfLcf6Z79nDhzG0nw33H7W/L/Xp/treKfNofv4Loxm2LwmiFA/1GobgVkLJgpIl3YxtaTifJDT45XzC+eXsYcJtWSJPUI0GudKJW0KJpP66GWqS0UZy7VRpTNWdshDEpXhY4dRQrSNBlCiaoYrrcOFgSvrGEPCyPgzLQSNqEuH+EduulbtNlDUjmNdc5sIIjxGj5sRIcuuJCK0Aq1qTGzHWwqgEnFrBqmBabdEcQouoMWAxQIgtIIphjfQ08y7NuUhaEUvOb+8LDF9oaAUE9jUjuD+2tkofqJoqQ22gdk1vOTEWBcPd+sSuoG1S5rsLmNfe0VXR7yOXygumUerihsFSmxtsapUShKRh5WTr58Et7zA+fMrH4jn3e5EAS2vyX3MwLCPIOhlxfdYQ9+CpYpRRuXifjNsQsMTbPnDZOlotfq1SCAsBRfPmilcOygCz0kHL3e/KTOdkXLZd0qxGx6/fpVrCtnf88usr/vL9HX10vL1fcb1JBR3x1o9Y5kZLOARqCx0mAgomwOrBmAbUV0HUBaT6wS1Kft95ebL1Qat/EAdItikMR5ZhQo0xFUsZk3muTaKKKmsYvobatxF4CDxt7xGYhion08tLMBT8OzXSseQiTG2CUIv2gkPJYK1haoK1y2KT1YgA1yvAoiQIoywqzAr2jhliyY698owcG0aPMV6vTGPS1oMF0v/8f/1nAEA3g0wXkNLL5YLeJb3ierlgdClFfb1cFA/DwIRVUTSQU48mtLUqi7PVIoDWpQCtCihwKRLu+/JFojC+fMHL16+SDz9XtHkGeIL6DdhvEgred4zelbZGdRnzkBDMUSFe0NNJUpxqLTidz1jPJxAV1OWE2hagVJT1BFpWMICdyRWFPoF9l/D+y/WG98sFt9uGTUH9puLzGGbVGAOlVGytYuwb2rLgdDpjv15xfvmC9XTC25/+hNPp9NA5BGJVSHLIvdc1lBz5SyJ0jcbCfxuRJXmblCTIn9aGl3VBqwU/nxf825cVp1bxpy8n/NuXkxRIeDnhpy8ntFpwWhvO6yJ814RNwAVRpD4lmRogjQZWPp0rXzo0IgMohDoqFjXQLEsTz38htKVi2yU1dGkFWx9oBCwFXiGuaHUV6N+MwN6zZuNZrKdmHHiwbni7XOAPZhG3llLk+8wiZ+YBy6drSrWdY2W4BeRUrze0hDARij7fpFBCJ7S8Oo46iwH0S99Uk8YhjhVI1NwdF3rsN7A60NDjHI7Pxs9Iebmm9TClHFG9FElVGzMeFF3LpmsZCLJZBSdHPz/IET+gFZe34fKH3y7pPCbgiHHXPjgaBQAnbX4NOdPGKYwlLtiM6SmhxFMdkEqDmynYqqDavBSK7ia9hYpEPJamaTOTBbcLAwyJMOzTXuxp5WaKN0cFqMCsUfZsMlfpPNJKYE6AdG5LNh+GOcKtNvKg//S85eYRNBYlgGSgAQvdQQEKwFMxtpQncdf1XEjT1FnwK6s6H+oA1Sa8qi0oraESsPQF59OKMSQNzVKPSdfJnFNAvSHT3AnYYY6pAIYdOi+AAr+7cYacbhk9J6WNzSr5LU3TnSSCcdHsgaU1T4mqLemdKv+6Nch5jQlDBKgcZ9EgQx0dwwzQzI7L+ugWRlORocRZq7oyWFLpW0Ud6ggoUaveMh7s3Zrvt+OdYCnH+XvZksI/xmTR1UASSX/d0LWowcsqlRGrRqrWSkBrqItmxtSKtYgsWduMCDZACy1AjWd3xTgAhFwttGay0fsw8HmkGIcM7dGWKtQGnUlja3S26PEHQ9fH9relPiHt6Q/XuxMcvB1nhe8OGDIZgukBDXfiEFb1qSySoRYB21u0KsuyNAfgE4VPCYSG3pdaUJttDLF0wpVDA3gSowJcUI3SzGaomZz6RKTGGMllm6x9psj4POSjuSHmCBh2/DzIqFseH9zcZqCE3O7owh0UHJhYQbBkLCuJccYNNTqerQBLMtQsRUt2p9QnBxY+AE3BjUEuBhdZ9szFvXMSdlxF2dRrMjNGrRhNc/GH5uVDczttLKkARXNTqYKL5vU2MQ5OBsqYaIqqX2pDbYJpUuqOomkz5Hn6EIapRFSqSlk55OLRVwYgRhRRRQ9ttpHNjcRB481K7fxXv8j2OB/vJIBZs7WXrcGGbi78nNK6zoSU3ShZpqDW78o8aiEpIcwFtbL3S8ImU4pTSSTI7xH4Ttd94HLbsfWOt8sN39/EUPN6ueLtesUYE9smYH3OvNQYkhlDyH8pdPhwDqvSb8NdwI+vCOypcr+7OhLHyrPEh7/NUJGJckQMEhOoMKgMrVIhFRTkOLw8PCdmHWAtpTiVo/CMYwNIPSwbW39sebmhE1iOvJyeBA/7kWq6tjajPHf8RgJBkkICkn5QGG7I3NCH0cz3QvThwdrFX/7yFwDAvm24vL9h33fs24b393f0XVKcru/vklLRd+y3m5Ypn26ANgMN1GhoAL9GP4gItDQ0goIrDpDyS0l3WrCsK9b1JKlPtaBxxcJNxnRXRWIOjELokDU/U3SiGc0BwboxYfK0nrFaRM35jGVdgVJQ2gmlLQKuv5xAbREBxrR0GpgTGhUk0UabAn6PriWRLZpBAQIF/L+g7AU8hlT/O10BMG6Xd6znM3gOnM4vD51DwISpoAWHWBqnhcE2Kf0mr7Qjjo20SoLhVAp5ulOrBeel4WVtOLWKsxpklnRc1VBzWhc39li6UylhtMmE3bzs7u2s0jNXBpTeWSQwk6SZzjoVs4bQxhCcGma0NnDdJU142zu2rWsVo4GtDsxZMEjSuCcdUMeSIUKEcB8nddI8ei/2PVKf2PeWpDwJ7kGU4Z5D8GgmxxrMx562bGCT08KeWQsfaPdT6V8YtgXd02ioghNmgmDa9+fgIKh7lLYL8brCXCFCrEt7dlf4TLE/FrkwYcH/URg6bN2XtB9MGbGr/uj2IQKZP0/qyHLKR/nmw1VhT0GHz+4uaEaWqdU/weq81IiKWnCMqkHiawjFVOfRDTWA8rYZtEZfg1OREQ4jTRqQ0Cv8v6Ohk8xwzInrWfqQ7XtMj5LKu/OPRu0fbp7qBBWXKT0VaSVZqRTphiTre0nyvxuoFBOUyTG2yKLgawGzGAyapti3McRggpBtBDuLVQ+QvTqLyhfqgHT9SwdXDOTS5yNocCrDXSTFiUhTn5R3tiqRroWEp7ba5LdVDAqSBmUG9LTH4YMmY6UGhMkTtfRkqCGPGu+l/CBDTeKLZPRCS6Srjuh6nUcHJSN0UjOcJtqxdpeMP/gWtXv60gU4MIYAYB9Dq7iy4zyBgV2ruzYuWIqsC+i8GXOmWVCqVMCse5e+T3bYEkz2/Z0NJ5xe1ksnO2kBZ6PfB6PLPSH1r0nHN3/2efsrETXpXp8SREr9ur9TJg5JqCATiBLbMCFDT/BqQRQLwtKdvDqCAjjVpi+ycDQjrmrAIQ3xr1ElwzabpQEIL9S8QWOMRnQwg82akBa6lBDpacJBAHMaeLGcxDEUaVT4/tgZ+2ObMeC8ynIIpaUwyViRl0SNiJoAzyqF9FzSMYzUqJz6RFTSPX7vmVRYMDelSwexUcygxjyl0okJYtANLZYEB62qhbTMpYA4MklEwKph2pMhjLMEhgmhuHIvxJEBKuhKIFira1hupqObl+reg1qOhprHpz593u7pdIgmH+mFnwAc1iJ9ONe8IUfmkcRNv7et+6FK11AARQe8oyDQzhT1AuJFilKDJuz0KcB6Y0xcbxsutw1bDyyabR/Y9uFgmINT9QTrHx8e8bMteNyDLE9oxpzJU5PeH9xyByh9yPcnGJGRd7r/aSL2OW3FQto97NpChUkex9L0ZqKtEfpd9NkZVm3hfsRsDRyUCYQ4ZvNrYfzu9EznfOQhwcFcCHah+MjIjFfck5TEWz+O9YOn8f31OwCp/nR5F6Dgfdtw8ygaKUUtlWUCC8MBLpVnTE6GGt0cVGLMbV9alKPl6ctrwbIsqJreUmtFBaEy1PjVgNnAHDSTmVESsL6Lh6Tlm9WJIdUUFlkfbQE1jSJsC6hqhE+pYKryLGTVarQ0uaYrjmHV16ankppSA9trk73y3xhdjFV7wXa7uUF4WVaJOHpwy3xxusRISQiDR0tkgdpXq/LRcr+yScC2lyYh8KfWcF6aRMsoLs2pVS/fuqgDKhwdVu7Vwuqrr4Mw1OiNKOYQur+pUPqO3GAg+N3skSEg0pLdMjfN06AKzmvHPiSC63zr6F1APTcFthwKCA0yrL5wOpn848K49u9HpARHMYLpGFsSbas4ZTnFaWoalBr03TttaU+y4HyvkBIzgskZoUjAxBYdU8oCcgiMn9Ied1PZ1yrIHNKd7GLZ0JIVCDaODKf5ofXKxdNVwojHFJck8/5Gr45c3hZ/VkB+jFwTvTVDKWkVWB3kNCwunVi/Ez/Ie1NoTOwR+9wNVXFzPZeDfim9mbXK2iGLNI8Iiyyjsvbn4BhgAmOGnOTyUrwiRTuNcRCcmCtjpqEoeNfDgIjgl7DzNf3OfwfgMMePbCZkHddIGMJNDkvOeP2ZU9BMs4zxmzxghoGi2Q+FQ4ZhifSwylNlmLO0SJqLzrdFlt23MExGRAThCLydo9FqDZm/1hK8swoch6U+iWMXimGTIDcSjYaNTnIIu47BBdOiIicA1upRlnL6Aww1H9KpTHD3lvsYQQ9eht2Xf9oLuq5tHo53SLyXYq0c5D/txpyMWVhxniaIoiCCGfqSYOPbiSiegYw33tMFSj+1z5hQUmGWAwW0eyBd5H6YjD7la9qz/R2k9K9g1OQrsf+fPw1ieexnTAQdGYq+AsskgeqUEEwk5LBhXRcsteJ8WvFykgoJL+cTXjT1SYQgCS9rxaoRiRfLLJ45BNitgH6si0GBkEQiqoAp54gwrmJGGH1Cy2Uec6L3CK3ynGd2FciJkRNyXUEG2PoBBOyBbW3J5+f8IBvEIFE0RB7umY/NEFO1XGcBe3nuVuRVScrGLkvVcxXLxYSSO0XZULRt5RJzMFfLvGAGcRUkblY8jVrd+2HpGzLuaoQpDAwZ88qEVcEs2wRWjRTYumzyyYxt2bU60MRt3z0yY+tSwciNDNpXC5UDoDma8hwWJQKohfbBExksVohaqPNJG81E4368ARcwQ8g5Eg2bjywwkpcrt/WKsCFAQEJ3jULa9oJbKxiqpAyNHpQIDfndkMro+vlwQWbO6YLLtg9sWiHtt7cLXi8b9t7x2/c3vL5fsY+B7+8XXG5aWUeNNmwKhPPho1XE+jzToAwrggZDRQl8ovIJQ/9nG+U+JYYYUcg2uAyLvJCPZ/ptzLkZXUCKC6XhqYanVUBAneBaFGyxgltDUYOaGQ14ai49i5d8qHd6mkEBJmwmpVSNMUzh+cugwYWP1WCMQboQatfQdSYnGRZUCGUmwMi61GMkXDJ8NOgc2oNlmf/yf/8nAAL0amlNo3dst80xMaTagJX87XdGL9bpNQNZfEUQvJegyxLSe1oXvLxIpMvXr1/w9esXnE4rXl5esJ6FFy6YWM1UuRdwqwAPjLag7ausJQNHQ6TsuKGmCW1r6wltWSTl+HxGXU8yF3UB1UVoUG1gqqoMd3Qe6EzY+1Rvl+BHbb1j793LD1skaTy7EAOJqu0gIimj2XfF4Flxu1wkqufBzaqWDYooWDOYOa1zZSHenW9BnRyI9V0UhVLAghsqEb6eV/z85YylFfz7lxX/8fUF61Lw05cX/Pz1jFYrzqeG89pQa8G6SEl0E/abG2ruIzVDrnKaTuQBGFneNuohPggF5p8SOUm1oo6Jr1RQlwW9SwTe6bTitgm0+NIa9n2gkODr9DmxbEKnB0sVy67029KHyTrBgbdEn27Qf7zdbld9VomyNdnAjDCs6U7iTJsOMjymGWrYU6EYbOHdANgj2JhlnqfyCnHkyHxPCJ2zKou4wwwx5SQr9VlwjzUVjTIho5AZmYLmivypys9B2tZSEBrFISRVHWw5epwNS674upDwDun84Ei7OUQF/T3axd/RPJrHR8bux2F0ciUHrvBZtHbx75JhDZqmxAZKkFJZkBwbKoNPLZDQFQzeDKS1miLaUEgV5UPlHktJY4lkNd49pmcLTDdcM/oY6EOqjQ6nidlYo05P7Rd4goukaEvVpizzsRrfJDoAJkdzrE9r7KNL8fsHtgyGjLT+7QPvtvG+aTIFpb4oDg0YRBVUhs6zgNYyCLUNtEWcB3NOtEUiJOZknMbAGNXnc0xxBXZd31Vdg6z99XQ1wOfTUk61N264aaqXliIgxqvqncu6YmkCg7AuEVGzLItH+ywtSnWbbhTjwiq7F30P59tkxQZTmjpHpIvto4hT+uFN55GBQ+U0fRFZUQoxSrUq2S21mm4U6ZoTcLol6e7mTMzODY5nRuCd2l63Js8sNPu27bjeNoxRcV4r9r4AtUpKbtrbKHB4EsM3un8doucKicOrGK0wIRZp14TzM0fYmbGWXH4jE8M9GvYe80qyS/ivGtz+jvqzruVl1RD3XIbS98dPkSbDDDth2T4QTjOuaBWZqtEziyJ8L4ulPokA0ZKhppqAqzlrlBQYAAl4WAQf90a7UiDKAquCKuHoskB5zlhgbIoUtLpAAkpUhSYL4HlMLEyVwZGPChGaPoRIPKBVQwc2PsBwsOWInMEnhpriqU8SpaTMEaxeRBbAYQMWtuinkgCz7FnNJQUcHE3ONChJNwS3YkpJyeLCno3RLAVlWnk60f6JgQYzjgnhqySVgzCBogYDKgNFFftCRSq3aEjlXsVQQ3WiTSvbDSemwqjLQXZHWrciUP0YfBrY/e6XiMk0+n2OYnCFw35MJtgldk3xl29louPc+K+SuskhgADwCBeCRMvoFgljFwd4nij1LOXWAfe+MzOu245tE4/u2+WG18sNex94vd7wdtVqOvuuQK4idOfUQv5s0yEZQ+++Co8PJ6PCTAB3D2wf9jd/8j2HMu0KLR+8psp7nI7GGixAkepV0xiV7l8oIyomnNeCOiuYJiaqgqKFNOV7ayYh70D8yfti2/tgjtA/iO7WIKRPpoBkwdFNLpmZ+u8oPW++1uHSSp/j70e3b7/+CgDoY8d2vbp3fmgpyKm4MFOFbEtxyn12Y41flVVhCOmWYFGO4rxY1gWrvU6rAv0uaIuACTdiNFXJNctfUqaUt0qUgBlrFJ9Cx1oiUxsEi2ZFW1YJMV9OKIsYalAbqDQIjRNnBiZj0sTAVKgHoQemhAwTllOkApKALGOg3muNaiya1lpKRWsbeEpUzaObGT14JoEL5l3lwzozYUzeyI8tTJ7sWIW0tVWcFaPtvDZ8OS1YWsGX04qX04K1FUmBWptXybMIHKuc515Zw8ggS9UGjjsteI/nvQNpnElxeAyglNVTy6imqJSJVRguupYLJipotQv+12BsteOmVTTEGEOgIinCoCleXmWYFq4u1g0ZQRNQH9mGVvA4AAib4cXkMcWCMuBgO8cNNcwegUPiNQAQ6T+E4GNZ3vVqMia20T2PpKSGZOqW1lViwnEYx4JFYnKT0cwQlw40z7pHcDnKziOVcQsHP2BSBUJBTMnlLnL0M+tFvtE9mtMjWpY7TMs/GLzsmYEYSwod4iCq5Ov5cRilMxmmNAchz4ghRXARq8s3rEaQA9/Va/mhfcnw/RZyj0XSJKeuRQOwSy1+nUI2/4wCUtQMjnsidAc1N8k60n7GrFl0azLp0Q+YxaNimHQfk2fgZMvGxHigRT7Z4Npc+YwTgaoq75otweCocgccSqZ3LRQDAsoUOR9FZCiKzZxkkxiNjEtTYLiaQtst0rFZIEApkt2xKO9szfEqxfnRlKZX16usIAnMCHcw1CQDBakDQz0BhTVinAUcWbLqfoCQkwU4Z9Ls8pyMlkbTfIC3sM1ARzphvNP//nhb36eU96ruV90jY8iu6mNi7x0Ejqhd0vQj36AmZ1oQwJEyBw05LD3f22L3pEOAGB12VVzjcBHE/OUIvDg/+MHxur/f/g5DzSej+8lg39/Oxsffk7BzjK4pSdGX91bUQFNlM3iIt+b7SaibAGiaNcxCObNBxhUZG6QSgGAWJuyGGkBAMo3pprVqi29Cc0vNQMOJ2DJcWUwqo4+NyOtpDDKz/kHKhVeK0OexzWAW3lIkbSyUAkt3CkDE4hbGwEEC7HP9riTDlyv6SfFCCA/2/PHwuo180VtkQFD2wmHXLboxwUJM2Y/FiBTRS2K5riB1FjEqi9BjyoMQxKlpUZr6VKYaG1jBiuW8ktLiTGm28YxnKZHD/uj22X6m+MrWK/uiPRLI+1591suDAHIgJuS/MD+ECbpFrfzdAJ41DQokBpxSpwqEwwXAoUB6zEJ4uwJsi6Fmxz6mpDvdNvQxJKWkd1UAY34sXNmf+5MxYvs/72fCcc+5kvxD9fzUp4jiiT5zPAR/8p2+EeJrWYKUOhz0xYTLvCcPe7QQwOJhnVxUkS/gooYbmKikZROLeSaNdiZafr8P9Fiu8QnTprw6swKTnskIRhba0jo8zBLne8fbo/di1wpEVupaqhoNByNlMyCbcINQ1rxPILiwbDTZ+JPPjVa18ChT8V5ZBQkXkCy6CICjNFPwMyqS6glWvAJOgobx4NokqpQKSmsC3FiKvNem12sSbQoxnjMpL5xR4amPgd4V/2No1IYZadLeclkwTZYpvqYwAYqHMvrDFfzcREFV3kymECEt0BC07ul9JQUMpQDfJyJNbWqolXBeBfhw0TLcp3XR6FPxuFZ1RhVNeyqpGqVXqQR5BLBoF1F6wPirPYxBPBgzIFhmsFJwWyLT0sLF6FYqo06hSqJkTIlEXRes+wQVeZYxJ+qcAihPhDInht7FcDciP5x8XH9ESnBWoh1A2BXgGZG3duzphh9f9rntyQMbMR7LalBF0JZgJYkuHeSdZOhL9NEFfBzPiY+DYvg+IUrG8vtOah/T/7A+QLG/KBRR8wgXXfMlSIMad/CBEbKt/Uc3GwdbMwQclSL4uMg7xXF6HTiNMUly7vIJrwIOD4gwHBzolBsX1AWYvvyAM+mvhEvJgUdjFW2yTpEnkg5/80e5Js9Lfr/v7x8N9w+QbtgjbzVt2sY/y1y5nybPuzPqfvzSM5DJCEgyhho2SlWWpylRemwOKcHrKxIhx4ziZcGNJppjV1ouw23RNUSGM1Pd2UWH46IBAaaTllTQJjBQCUHToUZ0x/qjI30kiC5aJkXUctHI8KnG1kdjYep8xCwFbfywonwffTTW3DvY4nr3v9cx0GgXts/vaAyl/3PvYk/l/SfRWs7UkygddISi/wi64LAAFMbsCFoKnSfL0E7jKfU9ydeUMH1sbWT5zdbaH7W/AiYcA5x5wuGShE9v4QaZdJkc9iPAS9VBl5ZlQS0a9uvpTgteTivWpeHldMLL+YTWJCR3XaUqxaq/Nc9W0/5IGj3pcVo8OkDQQfQS3mZxcKFHjTZJaTIMjMmkOXImmNrx9BA1jzDHkfDYfNvUmKVXNoSR5ce2dWk6F7EBPLyMAjSYVOjMUTTNo5LMA6uCrbIXL8+NiKgxow1MMCuJuNoQyyKJJXZYL+ZFYmAmj5IJUwhhngHQkIo2kwEeLC8AhQkTIoTwFCmE2aJlZD57b2KRZUZbhqQ7sQDZGgGYVpHqQHQAxdvX4yxU0cOVww86+8cz/P/juZz2cVwg78ssPDqBIfMkyNkHJVKvYwJIH5Ims9WCtg+Mwlj2gbYM1ClVgYYL68MVyH0M7BpFI+lOEtl0ud5w3XaMMfD9/SZI73Picr3itu0Yc+K2jwAQVkwMsFVO+zj2zPnY9rQMBKmB5iCTJsXykS36oSZfl2DyCYkx8jxE1SSR0EW8oLXkxhkX6k3AsX2nIZgMAV3jGoy5kIVvD5CX4Q2vHxXZZ0AIM/Y7XxcWlQhoSlOI0SnZBjikOuW1dfxNFsg+vEyB4eRpRlKYSISIRwulFy0dPedE1yiaXJHhoAB6f2x9WT/TXwUQbxo8nLgUwaRZ1hWtNZzWFS+nE07rCef1hNOyYG0LWm2wMuuklfsIDGoSUSPzVSQCRpVZ+BwGXpunPhUrw72CSkU9nVHWVa5RFGRfl+JgoINx6xOX247LdcPlcsP7+xWX6w192zEVRNhKhJsRK/ZBEuJ0/wotF+OMFBko6LU/dA4BwFNYwZqypPvgYAENGmi4Ao5bQOJlXdSxs2iFmELFo2haLfj55Yx///kFS6v4+csJ//bTWatXrjifTyrHSMUQqyLSlkV4swKui6IQkcG5RqSLecY3jXdmGsaAAYMXFuMZVUj0ZxX+tqoxro6JfQCMitoG+gRqbdi1dPe6NuxjorYbLrukcNCtO/0uRdKhoAZ0A2o0UNZHtl3BhC3NyXCgpqVBjenluQ/pURwRnA7sjRS1CNMzgzabgs3E4vQhM34EvWOTJzUd5iDAIwT6LOADoST4mtP/g9JHhIsoM5l/+QT7vMuvE40uxZ+lKhDrIImosstpDF48v4c+q8E5BIZ/fMJ+p1G+NrE7FEH+mChpbIzfHIxO/l3i5EQAR1T8fQpCToUSuU05sw740NQlQNOqh/DBCcGmAonibPL+mMAY6uDT86WsMKMPqfQ0GCqXUqQkau/JR//YVENIo288jn1f+7lsxQCSPpH0N1uLj25R0VIwv/wu98Irp+Ifqit5xGXC7ZHvJSJelPkiDgfj/4VBtaEsK7gMlAmUMYFSUOdE7R00JwYTGmv/iMA0lAdN59Gw9QbFOtV10SxwgMhxUUshLIqNKphuC5riuDVNgyqlCOC/RkO2GqlPhu0Sd7TUH9WX2PDrJN3NVA/JAFC5Wxd8/SERNcPnKYxvIjcYDpitVnKDlKSmlWoRRV0NZwyaaW3ibn1TmL1J05SyPOlqI8VIAVrBS6bas1XnhFdWjsJ35Dcm5YHZKOPAyMyODQcitDoxWfDbiAQz0/RHNtpfDDrFxFmzMRQ3ztTaHMNo0Ypg6yL4gu6oKX+9+MzfGFET5IP8v8PBh+PjKXfW7EwgtZM2SAKmV92DuBjY3iLo3ouBCFeLqJFzDUfFCLVFf9gkBwBskapOCIwaV2h8sLIyfnwuYZrhxbeIGivXbTnupgxmL2J+D6ZCvzuKj2qtmnCXUiDoPvWJ9FjKeVuUTK0SNi3DY4qARLEQFIRYDTUGyOzKvQot2RDgsoh9lA0ETragHmBSJUY+FYIahhph4ixKp6Y+DUyNqAESIpmnQMlGK1KqmI1AaCWUlE5VnVEESHQohdKPiZn6E16vHKL8qMa/c2yCyP0XbgBIgvGhR/TJZ0h71K6NIJixr00RCGNNlKOPNCjBjQFKmfCceQ2TZwB779jV2HLbB25dohLerzfNP514u95wuW5inNmkgoxUd5oOImY54PLEJrSQr7NsIHXPTRoG/hFhwL/bojNCInKPrZMmbMc7HTwadwK1fnoUdp3ChOEj0V9TEmyuuZR0HxwFO6TupHvR3TXi2klItqei4CLxqffQP4nj9KK7d32JNypdDCryEI53ebhyuAGApjgl48w0JRCJXth6uxeOTUBRgUfnRIzc4YUJXtiw1OYlP+3zQsVQKfwasu+tOl3RcG/tCxlIFEAajUoElNZQ1FBTlgVlWUBUQa0BdYEZvSytk1UZmVCnRRfD6a7VGPqulSrmXVSN8ca8AfXAjJHMcIBhQCr7cFICHtV8meo/Y/+HUGfnZTjyTs2hb0VC4EsBllpxUiFdqjuJoebltOCL4eudVpxPUqVLBLamQNHF5RirLChroR48sRb+L4Rtev+Dv8rxgRrq/Fu0AibDEIWp6nMVjabRKjWtLWhN+MdpXcDMqPvAyy7FbWsXfJoJoe+GVUOkQP8zRaeot9QUlUe24WDCrFWfpqei22deAcoUQdh6PBpVM8WTbZQZqlBdYbfkH7qnm4wmBQ1zfDenSUE35TjGw3ANYL/3Wxz5d9jgCQfv6f27/eCONgueS0GhqdE1UsmFEBE1bMfpksInM9X+QY2QDETpMZBkR3ueu8+AvF9VflQZKHp+NNCEnGAXUqMYx/CyvytG29QUMR2gaWwa5KybE2CwYdAcq7ween0cgrvhzX34MCi57JOz7qPOgcNfwRMfPYuOUcOSFlk4Y2PqKsqyGEe0kY8bEo/AnWJPIcPIcQALF5CU8C4lImpMz6sFZagjBGKIl3trkRKWtT3ZbmGFQQz+wYwtihdWSCuWVufTngJUq/BSBxMWjBqrhmt04ZDipO/Gy8VBJtg9ZRr8g64tfRdweIIFFDx4IuPN5cEkj7LpFZQMFjWc9GTPGA7CD7ewKbX/dRkbK3Oa9UFuUl0r6R4zdcv0AS8cdoheC3nTZC5CyLEBCzA1w4Sc3h+5g16LjnQor01/d4NN8choS9ezqBqh/X88JX9c9Sn4kXhHD4Nrg5qNMp8ckyj1JoR6xSVPsYkUJitFaQ/l55jnr9x7VWPR26IwoB+k+YmQVGOm8uW0CeOgd8Z07RwxypBbqT3P1CrODCn7KFgZ7JY9hjF0W3kxgUWJSWFGgSHnSi7qj1AZLa/dFQIka6K+W269Vc8ywdQsy57epENr5EE+Z1+0LhyQCjZ3z6M0JjZo+j5lnsA2r4/fXbPUAVlY4jWGbkxPkWJde/rsgsdGAqRH4s2oRUUrnWPrIyYcSHASKaBqOoclbxjpXraZOTOkH9HStQmMJDemL+6EOJsPwkP65s+amK0JI6TepD4ZjAnqE4Rx2AfMwN6HGmpmGGqGgJFue4+oNcd/ugtXT49CNuScexirTOUomeeELJ9lYvL/jr9/ZAvQMEbEVJoH8fg5ZYONG20A82hID9Oz4KNAa1tMPDWZic7wsrIqL8xRFaao8mLpoMxAErxccYUyuU+OXRh24Tlo4PE4+nj/ctaaHjBTcm/OiPkwj5S/fFBjMwra2Pnrs7OFTkUJVRsnTseZsZsiZaGxEWETobKW6kQ4yL6mm6S/4coB3ZMD5X9JcNF/RRNIMx80I53tecOj6l326m3fcNNS3FvfA0A4VbpKI+LzGZ/eHbEpH7IfOD/Yg5rj1qkSwYp8naOzQnAM2cWNDpCI09W8rLViVcPLaWk4KY7eqph6i1arFNwZS3eS33o1k3tBz2imdiSU+MSFKdaVVdr4lJWCEr+QM4wWyJxYVJZGHlTBNDFnWJ1QZ9kAg7C0jjEZRANra2Ct+iWVOMwwIopHjuB9ZHOFzlMO+XAc0c1Hg4zv18xEVAiMHgZ/tzF0Q0msEH83Snx/WTGWmUx0/1u7t8lF5giyj/mwB+O58zm2L9N9TdCyc/2e0auDQuRKqpxymKbMCn9P8/on2xFMOB8lSTHR/tgG5PvT+3/45fHdFPBwFieafPdcRuumK86R+pQH2o0otsYOhpmEnaeKZExH4oGIPTKZPxjNhDRRyM7MPiYCthwLlIhi1bBhdChNyHP+90/T39RCp7/nzy6Iwl1N/PG39+tZfpkij5RPWrlvKwiTi8aY058gGDW1Tsfo8XlQg9JxfwDN6DtFupMfa4SMKdwlGW1K0lVzMYRsWJLbGG9NspruK9a5i6jg1DedcPmmKJ1+vKIxZ0TUTMWzmzPSu6eleo/huq85WOw5ZX8Z0LfwE55JtjOakh7RgkVzdUOPeCFym4AVHsq06ON6iXmGH8e681HVfharjqrR5mJ7CD0DDrOgokniZ/LIkUoqdozsaLtLd0pyLwHuyPqj9oeGmmLeM1L2Y8JwWjzHhQZfSPafK/iQQWi60NdWsVq609pcwFmWRZCzVehZzOvUFg2tqpI3TwrIVKq8DlRcJsQ8BVPFTxNDWQUc++Qg7dos60dWcpTZwhqBPhhbH9i2roKqehL7cOyMMY2oWGqVlJMjEEoFGiJFYar3MDP3R7ZTTXOjqQDZw+WRM2qQ8Yiaks+5S30yZQwsixzwDUR6UmyWEGK8ETQ1In3gbzJPyqbCKsQqZBpl9XD1YuKNEgU7XzcVS0qczKlsyDEtdx4oU6zppRDGLDrvUEtqeOeQbm1M3Bi1gUeLYe8jA/qRLYYwdc7ebADy3k3TwIffyycWrSSnUtrarPPKXjnJwnoLT/TJ2KckhN3GQN06SiHshXHrQ4VQZUbMGlEjgMDXfcemETXX24bbtmEMxavZOyYz9tE9Tc2wbQ6CJ35n/yRhLI9XPLdRARkN56s/YDcSawpHSNP6uU2aSzn6Po+f2zGrcYdDNZC9iLBfEny8I91JjOCWlmTpTjwUfJgZE0OEPWYUmkqfNIKwTF8zRueLGX+Ag/B7kE0QUrQwRvnblVRjfGq8d4wxpylKn5znGGExq/gUabakvslOBh6s5M8hc5hBSEMJhG9+VyiSh9j3Ut5XyfhSa8XSFpRasLQVy7JqarAABy9eRtsMYoqpwsAgD5QQwSIBepuxRii2yfQkCqTtdBWspo+/JLgWHb/BEkU4p1Rd2LUU+dv7G76/vuJyueL1/R1v7xdcN9nDXXF8pgpxZugg0whNEEpGLx8nnVee44cAe1tK8JyMmmi8z12arywwGu80UMnTIqDBa9PKTUVSn76eT2i14OuXE376ckZrFV/OC076+bo0LOsCB+DXNGNST22EUUdEjTE3Hz/A95u1MDQdFaXIlgmaQqzV3yaDCqNUkY5qbWgNAA2syyJOjzKwD6ENS5c5bVWwxEAFSxP6vQ0FGFajiSmPjg34wDZ6Ks9tqYeeYpeiazyCZroSGYZWH0okL9JRqOcjGckKpD0Up71IriRYxI3OAeBOzwOmzb0Mmu4TERJqOPV1SskAYC9CRPciOp3OgfZZ2LngEaJopEHR8ZjT72r4f6Dj8z6yRUof4NYwAPlORPnvbGhKf/teUKeUj7Km6mfcRZOHXakM+dh+NacWRgCDxkSpkgphSIjQ+bC1JLK/Om4HB6i6QSIkOczk6FIImKIYAtCEermPZA4WlzGNppuBx3CHAAqZNUUfZswh180AV4Qf2SyquRTyyKNDc5kn8D09zclXW+xNj66xz11+lBTNAnG+1jpANNHaxLI0xUZbxJBQFWSWxNlaR0WrVu1NaQKAwSQ4WyTR9576ZJWeSCA6LKKmudGmOKA/FTtWmm5RqjgaFtzYBKhSRXd+VTu3iFyTjOmSeizpqxWR4vbINjRiODug5hhSIGFM7PuG/bZh3zdJ/R5RQQ8GMqzjI/MJVI18nEX0CCqMMqUQgcnnpHqHg/IjyqATERbNqKnFIoElSMOioaavFzGMM0WFV3cmz9iD0HkptaBCnRBD9Nm5CM+azCiGh4lYM24sVJmmakolUUqXK0WLHmlUdGtaIaupLAsV8afzsd9rfxNGDdkFXXmDK3JhmAmGkw7DgkYJUIcSRk0htzrFu6VB1UN0TVgoj5E1YbHMlDyMH9FrfemDWERNfkaA1Bggn0upXjpEDEhEDSuQZIqs8YpPyhTv76uMoNAEFzUOsBDpnBrw6OYRNS4gU2wGuj82YGELBVOGptcxxhj5wmL6yqXPcjPrcBAhHBi9+5WTnPLBeJPrL5h1JSvoktgogpBCDTGL0iJAtxry6BtYkhtl86ri4pcTijlBvuknmVENzjjcoIEgAiYAepnXf5UWQ+XtA5OmTz5LJ372nTFQe3Zb+8SSp70PARCeDFSNK3VmyyxlfHvX1KeIorlp1afBjF0rkFh6YXinVIRNgo8Puct5n0R0UZrrT4RO+r1xeFCjXE4xr5HceZ6BO5A+5/SZfW++l8MUJ9ps+8VC5U1hAltVE90TJsUbCChzhPVbv/wtr22j7UZXVFA+kOTMEOxzE1aMphj9KYg0gPS9/9BUnzSztgbAUqo0jcPhhAe1HOKdFT798MP5dOxM+jyeIz+/eWLcC+PgwXZcYjzSEzJrdd30t3mEpikU6XztBQiBl0BQMD0vsRmezOwlHmOooaZj23bcbhtu247bLq99F4wpSw82bshpXcTS1r3sFAWxh1kFox9QhbS60UP7lg1sMNwAqPCoHlrjlyq8L2pwqURYFwMQLjitYrQxQ46UbVVhTYXNqtE1zmPTK8sN9ynExz0O76dtzaAHKQLOnwku7OvDg1TmidQYeJpVRNSIQrO0piDxUqJ2smB2nPpUZVLWzVADjUUpmaHp0Tq+42JwcqjYmuNspGG36xotDQONL7YP13dWgrAfmMR4ONuMNSamJMosKZpBT+lw3TwgkbLm9+f7+1iXw9GXDTf285A/7aNEg3WfM2LOCZbWrvOVJyoPgq/Nx7bPIq3o/p0+fu/pYro/Mr+x/8VFEXzH00vvdJfMbYAk34guKfRvsvyWj/w4DGUaVW96gkcCGy02AdL2ttzXHNqsoTSSmkbieJghlwjdZ6/kJR3VaACG4ieFm9MWZJZrzED0aFNNkPPfiy6NkzK157tVfy/6xOfJyKGpp1SmG/mMXwIRBUhEqGqIL5yemRmzaMQfdA70u4joOFZrarV6oEFEQ5bEn8vhvvfgugfF2Pl30pdNxvM9xnGsBP6gW6MEntoD28zppCY/eESNRdJ0zN6jiIJuANtjIcuwO/ALmzEj8a5EEwNPC2o4hctDZjPIUTX3wrrrZgzAUhMpfWf71GkzvJ+zRMYPwKilYhZ2x5fFOUl1y4isNdm6Jl3ZM4USPEup9dB3c7IR2J0Jf9R+QILbsz3bsz3bsz3bsz3b/5jt8arssz3bsz3bs/3/qj0ZxbP9DY3+pTz/z/Zsz/Zsz/Zsz/Zsz/Zsz/Zsz/Zsz/Zs/wO3Z0TNsz3bsz3bsz3bsz3bsz3bsz3bsz3bsz3bv0h7Gmqe7dme7dme7dme7dme7dme7dme7dme7dn+RdrTUPNsz/Zsz/Zsz/Zsz/Zsz/Zsz/Zsz/Zsz/Yv0p6Gmmd7tmd7tmd7tmd7tmd7tmd7tmd7tmd7tn+R9jTUPNuzPduzPduzPduzPduzPduzPduzPduz/Yu0p6Hm2Z7t2Z7t2Z7t2Z7t2Z7t2Z7t2Z7t2Z7tX6T9f4XVJvq7KLWOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create autoencoder predictions and display\n",
    "z_mean, z_log_var, reconstructions = vae.predict(example_images)\n",
    "print(\"Example real faces\")\n",
    "display(example_images)\n",
    "print(\"Reconstructions\")\n",
    "display(reconstructions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "id": "BZnGepX3I9Er"
   },
   "outputs": [],
   "source": [
    "## 4. Latent space distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task — Inspect the latent distribution for faces\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "id": "jF8vOegPI-rd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 4ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 50 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, _, z = vae.encoder.predict(example_images)\n",
    "\n",
    "x = np.linspace(-3, 3, 100)\n",
    "\n",
    "fig = plt.figure(figsize=(20, 5))\n",
    "fig.subplots_adjust(hspace=0.6, wspace=0.4)\n",
    "\n",
    "for i in range(50):\n",
    "    ax = fig.add_subplot(5, 10, i + 1)\n",
    "    ax.hist(z[:, i], density=True, bins=20)\n",
    "    ax.axis(\"off\")\n",
    "    ax.text(\n",
    "        0.5, -0.35, str(i), fontsize=10, ha=\"center\", transform=ax.transAxes\n",
    "    )\n",
    "    ax.plot(x, norm.pdf(x))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Generate new faces <a name=\"decode\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task — Generate new faces from latent vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "id": "BDAgxfNXJDWs"
   },
   "outputs": [],
   "source": [
    "# Sample some points in the latent space, from the standard normal distribution\n",
    "grid_width, grid_height = (10, 3)\n",
    "z_sample = np.random.normal(size=(grid_width * grid_height, Z_DIM))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "id": "6Nt_hfrkJDoT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 126ms/step\n"
     ]
    }
   ],
   "source": [
    "# Decode the sampled points\n",
    "reconstructions = decoder.predict(z_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "id": "de8GRPlNJFCF"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a plot of decoded images\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "\n",
    "# Output the grid of faces\n",
    "for i in range(grid_width * grid_height):\n",
    "    ax = fig.add_subplot(grid_height, grid_width, i + 1)\n",
    "    ax.axis(\"off\")\n",
    "    ax.imshow(reconstructions[i, :, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODULE 5 — Latent-space manipulation with attributes\n",
    "\n",
    "The final part uses attribute labels to estimate semantic directions in latent space.  \n",
    "The key idea is to find a vector that corresponds to a chosen attribute, such as hair color, and then move images along this direction.\n",
    "\n",
    "This is a bridge between generative modeling and representation learning:\n",
    "- latent vectors can encode meaningful factors,\n",
    "- vector arithmetic can modify generated images,\n",
    "- interpretability is partial and should be verified visually.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2bE-wB8CJIa-"
   },
   "source": [
    "## 6. Manipulate the images <a name=\"manipulate\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task — Attribute-based latent vector\n",
    "\n",
    "Possible task:\n",
    "\n",
    "1. Choose an attribute label.\n",
    "2. Estimate the corresponding latent direction.\n",
    "3. Add or subtract the vector from selected images.\n",
    "4. Evaluate whether the manipulation changes only the intended attribute.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "id": "goc5skJzJGcW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['image_id', '5_o_Clock_Shadow', 'Arched_Eyebrows', 'Attractive',\n",
      "       'Bags_Under_Eyes', 'Bald', 'Bangs', 'Big_Lips', 'Big_Nose',\n",
      "       'Black_Hair', 'Blond_Hair', 'Blurry', 'Brown_Hair', 'Bushy_Eyebrows',\n",
      "       'Chubby', 'Double_Chin', 'Eyeglasses', 'Goatee', 'Gray_Hair',\n",
      "       'Heavy_Makeup', 'High_Cheekbones', 'Male', 'Mouth_Slightly_Open',\n",
      "       'Mustache', 'Narrow_Eyes', 'No_Beard', 'Oval_Face', 'Pale_Skin',\n",
      "       'Pointy_Nose', 'Receding_Hairline', 'Rosy_Cheeks', 'Sideburns',\n",
      "       'Smiling', 'Straight_Hair', 'Wavy_Hair', 'Wearing_Earrings',\n",
      "       'Wearing_Hat', 'Wearing_Lipstick', 'Wearing_Necklace',\n",
      "       'Wearing_Necktie', 'Young'],\n",
      "      dtype='object')\n"
     ]
    },
    {
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       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000003.jpg</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000004.jpg</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000005.jpg</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     image_id  5_o_Clock_Shadow  Arched_Eyebrows  Attractive  Bags_Under_Eyes  \\\n",
       "0  000001.jpg                -1                1           1               -1   \n",
       "1  000002.jpg                -1               -1          -1                1   \n",
       "2  000003.jpg                -1               -1          -1               -1   \n",
       "3  000004.jpg                -1               -1           1               -1   \n",
       "4  000005.jpg                -1                1           1               -1   \n",
       "\n",
       "   Bald  Bangs  Big_Lips  Big_Nose  Black_Hair  ...  Sideburns  Smiling  \\\n",
       "0    -1     -1        -1        -1          -1  ...         -1        1   \n",
       "1    -1     -1        -1         1          -1  ...         -1        1   \n",
       "2    -1     -1         1        -1          -1  ...         -1       -1   \n",
       "3    -1     -1        -1        -1          -1  ...         -1       -1   \n",
       "4    -1     -1         1        -1          -1  ...         -1       -1   \n",
       "\n",
       "   Straight_Hair  Wavy_Hair  Wearing_Earrings  Wearing_Hat  Wearing_Lipstick  \\\n",
       "0              1         -1                 1           -1                 1   \n",
       "1             -1         -1                -1           -1                -1   \n",
       "2             -1          1                -1           -1                -1   \n",
       "3              1         -1                 1           -1                 1   \n",
       "4             -1         -1                -1           -1                 1   \n",
       "\n",
       "   Wearing_Necklace  Wearing_Necktie  Young  \n",
       "0                -1               -1      1  \n",
       "1                -1               -1      1  \n",
       "2                -1               -1      1  \n",
       "3                 1               -1      1  \n",
       "4                -1               -1      1  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the label dataset\n",
    "attributes = pd.read_csv(f\"{path}/list_attr_celeba.csv\")\n",
    "print(attributes.columns)\n",
    "attributes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "id": "xfUGviprJP0E"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 202599 files belonging to 2 classes.\n",
      "Using 40519 files for validation.\n"
     ]
    }
   ],
   "source": [
    "# Load the face data with label attached utils.image_dataset_from_directory\n",
    "LABEL = \"Blond_Hair\"  # <- Set this label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "id": "lRWxKsQbJUVJ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: Blond_Hair\n",
      "images : POS move : NEG move :distance : 𝛥 distance\n",
      "22    : 3.273    : 1.362    : 3.543    : 3.543\n",
      "44    : 2.314    : 0.996    : 2.647    : -0.896\n",
      "65    : 1.102    : 0.505    : 2.326    : -0.321\n",
      "87    : 0.917    : 0.395    : 2.02    : -0.307\n",
      "110    : 0.699    : 0.302    : 1.864    : -0.155\n",
      "128    : 0.507    : 0.258    : 1.833    : -0.032\n",
      "150    : 0.442    : 0.213    : 1.762    : -0.071\n",
      "171    : 0.413    : 0.208    : 1.74    : -0.022\n",
      "197    : 0.408    : 0.17    : 1.675    : -0.065\n",
      "218    : 0.286    : 0.134    : 1.629    : -0.046\n",
      "241    : 0.301    : 0.132    : 1.6    : -0.029\n",
      "260    : 0.234    : 0.12    : 1.602    : 0.002\n",
      "282    : 0.243    : 0.112    : 1.596    : -0.006\n",
      "302    : 0.219    : 0.099    : 1.545    : -0.051\n",
      "317    : 0.166    : 0.101    : 1.534    : -0.011\n",
      "337    : 0.199    : 0.084    : 1.521    : -0.014\n",
      "356    : 0.178    : 0.079    : 1.507    : -0.014\n",
      "377    : 0.179    : 0.076    : 1.495    : -0.012\n",
      "395    : 0.155    : 0.077    : 1.487    : -0.008\n",
      "415    : 0.162    : 0.063    : 1.494    : 0.007\n",
      "436    : 0.158    : 0.067    : 1.492    : -0.002\n",
      "453    : 0.116    : 0.068    : 1.486    : -0.005\n",
      "472    : 0.12    : 0.056    : 1.477    : -0.009\n",
      "489    : 0.127    : 0.059    : 1.459    : -0.017\n",
      "501    : 0.094    : 0.056    : 1.448    : -0.011\n",
      "525    : 0.126    : 0.053    : 1.442    : -0.007\n",
      "545    : 0.116    : 0.051    : 1.448    : 0.007\n",
      "563    : 0.118    : 0.049    : 1.452    : 0.004\n",
      "588    : 0.115    : 0.047    : 1.452    : -0.0\n",
      "600    : 0.08    : 0.048    : 1.448    : -0.004\n",
      "617    : 0.099    : 0.046    : 1.446    : -0.002\n",
      "639    : 0.107    : 0.044    : 1.435    : -0.01\n",
      "657    : 0.089    : 0.04    : 1.427    : -0.008\n",
      "670    : 0.076    : 0.042    : 1.42    : -0.007\n",
      "688    : 0.087    : 0.038    : 1.418    : -0.002\n",
      "708    : 0.09    : 0.042    : 1.414    : -0.005\n",
      "732    : 0.094    : 0.035    : 1.413    : -0.0\n",
      "752    : 0.089    : 0.034    : 1.413    : -0.0\n",
      "772    : 0.089    : 0.038    : 1.407    : -0.005\n",
      "791    : 0.084    : 0.035    : 1.401    : -0.006\n",
      "810    : 0.078    : 0.036    : 1.403    : 0.002\n",
      "832    : 0.078    : 0.033    : 1.398    : -0.005\n",
      "853    : 0.073    : 0.03    : 1.39    : -0.009\n",
      "875    : 0.071    : 0.029    : 1.388    : -0.002\n",
      "890    : 0.056    : 0.029    : 1.393    : 0.005\n",
      "911    : 0.076    : 0.029    : 1.396    : 0.003\n",
      "927    : 0.06    : 0.03    : 1.394    : -0.002\n",
      "945    : 0.064    : 0.028    : 1.392    : -0.002\n",
      "972    : 0.073    : 0.025    : 1.389    : -0.003\n",
      "990    : 0.063    : 0.027    : 1.388    : -0.001\n",
      "1009    : 0.06    : 0.026    : 1.386    : -0.002\n",
      "1026    : 0.051    : 0.028    : 1.384    : -0.001\n",
      "Found the Blond_Hair vector\n"
     ]
    }
   ],
   "source": [
    "# Find the attribute vector wchich correspond with Label. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "id": "foqPLbN5JWGs"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 50 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add vector to images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task - Latent interpolation / morphing\n",
    "\n",
    "1. Select two faces.\n",
    "2. Interpolate between their latent vectors.\n",
    "3. Decode intermediate points.\n",
    "4. Discuss whether the transition is smooth and realistic.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "id": "R3dw_2EBJXnj"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Wrap-up — Key takeaways\n",
    "\n",
    "1. Simple probabilistic generators make assumptions explicit, but they often ignore dependencies between features or pixels.\n",
    "2. Autoencoders learn compact representations and reconstruct data, but their latent spaces are not automatically easy to sample.\n",
    "3. Variational autoencoders regularize the latent space by learning distributions rather than only deterministic codes.\n",
    "4. The KL divergence term encourages latent vectors to follow a known prior, which enables more principled generation.\n",
    "5. Latent-space visualizations help interpret what the model has learned, but they should not be overinterpreted.\n",
    "6. Attribute-vector manipulation is powerful, but it may change unintended features if the representation is entangled.\n",
    "\n",
    "## Concept questions\n",
    "\n",
    "1. What is the difference between reconstructing an input and generating a new sample?\n",
    "2. Why can a deterministic autoencoder reconstruct well but generate poorly?\n",
    "3. What does the VAE encoder output besides a latent vector?\n",
    "4. Why does the VAE need the reparameterization trick?\n",
    "5. What is the role of KL divergence in the VAE objective?\n",
    "6. Why might VAE-generated images look smoother than AE reconstructions?\n",
    "7. What makes face generation harder than Fashion-MNIST generation?\n",
    "8. What would it mean for a latent attribute direction to be disentangled?\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
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  "structured_revision": {
   "based_on": "LAB12 Modelowanie_Generatywne.ipynb",
   "note": "Original LAB12 cells preserved; additional markdown structure and task candidates inserted.",
   "style_reference": "MFoDL Lab 7.0.ipynb"
  }
 },
 "nbformat": 4,
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}
