{
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   "source": [
    "(mmm_budget_allocation_example)=\n",
    "# Budget Allocation with PyMC-Marketing\n",
    "\n",
    "The purpose of this notebook is to explore the recently included function in the PyMC-Marketing library that focuses on budget allocation. This function's underpinnings are based on the methodologies inspired by Bolt work in his article, [\"Budgeting with Bayesian Models\"](https://bolt.eu/en/blog/budgeting-with-bayesian-models-pymc-marketing/).\n",
    "\n",
    "## Prerequisite Knowledge\n",
    "The notebook assumes the reader has knowledge of the essential functionalities of PyMC-Marketing. If one is unfamiliar, the [\"MMM Example Notebook\"](https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_example.html) serves as an excellent starting point, offering a comprehensive introduction to media mix models in this context.\n",
    "\n",
    "## Context\n",
    "The emphasis of this notebook is on enhancing marketing budgets. Contrarily to broader issues addressed in prior notebooks, our primary aim here is to unravel specialized knowledge on budget allocation tactics using the functionality.\n",
    "\n",
    "## Objectives\n",
    "To elucidate more efficient ways of resource allocation across diverse media channels.\n",
    "To deliver data-driven, actionable insights for budgeting decisions.\n",
    "\n",
    "## Introducing the budget allocator\n",
    "This notebook instigates an examination of the function within the PyMC-Marketing library, which addresses these challenges using Bayesian models. The function intends to provide:\n",
    "\n",
    "1. Quantitative measures of the effectiveness of different media channels.\n",
    "2. Probabilistic ROI estimates under a range of budget scenarios.\n",
    "\n",
    "## What to Anticipate\n",
    "Upon completing this notebook, readers should get a comprehensive understanding of the budget allocation function. They will then be equipped to incorporate this analytic tool into their marketing analytics routines for data-driven decision-making.\n",
    "\n",
    "## Installing PyMC-Marketing\n",
    "Before delving into the specifics of budget allocation, the initial step is to install the PyMC-Marketing library and ascertain its version. This step will confirm support for the budget allocation function. The following pip command can be run on your Jupyter Notebook:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vWMGdRlmyjcI"
   },
   "source": [
    "## Basic Setup\n",
    "Like previous notebooks revolving around PyMC-Marketing, this relies on a specific library set. Here are the requisite imports necessary for executing the provided code snippets subsequently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "28_D6j7jjQTy"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from pymc_marketing.mmm.budget_optimizer import calculate_expected_contribution\n",
    "from pymc_marketing.mmm.delayed_saturated_mmm import DelayedSaturatedMMM\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "az.style.use(\"arviz-darkgrid\")\n",
    "plt.rcParams[\"figure.figsize\"] = [12, 7]\n",
    "plt.rcParams[\"figure.dpi\"] = 100\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%config InlineBackend.figure_format = \"retina\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9AlvbqZ6yqhs"
   },
   "source": [
    "These imports and configurations form the fundamental setup necessary for the entire span of this notebook.\n",
    "\n",
    "The expectation is that a model has already been trained using the functionalities provided in prior versions of the PyMC-Marketing library. Thus, the data generation and training processes will be replicated in a different notebook. Those unfamiliar with these procedures are advised to refer to the [\"MMM Example Notebook.\"](https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_example.html)\n",
    "\n",
    "### Employing ModelBuilder: A Feature in PyMC-Marketing\n",
    "The ModelBuilder feature, introduced in version 0.2.0 of PyMC-Marketing, empowers users to easily save and load pre-trained models. The capability to load a pre-existing model is especially advantageous for accelerating analyses, mainly when dealing with expansive data sets or intricate models.\n",
    "\n",
    "### Saving Model\n",
    "Once the model has been trained, it is easy to save for later use. An example of the \".save\" method is demonstrated below to store the model at a designated location.\n",
    "\n",
    "```python\n",
    "mmm.save(\"/content/budget_optimizer_model.nc\")\n",
    "```\n",
    "\n",
    "## Loading a Pre-Trained Model\n",
    "To utilize a saved model, load it into a new instance of the DelayedSaturatedMMM class using the load method below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "6eM1cTtMys1O"
   },
   "outputs": [],
   "source": [
    "name = \"/pymc-marketing/data/budget_optimizer_model.nc\"\n",
    "mmm = DelayedSaturatedMMM.load(name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X3NUoJ1bzBvK"
   },
   "source": [
    "For more details on the `save()` and `load()` methods, consult the [pymc-marketing documentation](https://www.pymc-marketing.io/en/stable/guide/benefits/model_deployment.html) on Model Deployment.\n",
    "\n",
    "## Problem Statement\n",
    "Before jumping into the data, let’s first define the business problem we are trying to solve.In a progressively competitive scenario, marketers are tasked with distributing a predetermined marketing budget across various channels for optimizing Return on Investment (ROI). Consider a forthcoming quarter wherein a marketing team must decide the division of its operations between two advertising channels, represented as x1 and x2. These could effectively symbolize any medium, such as TV, digital advertising, print, etc.\n",
    "\n",
    "The task lies in making decisions that invoke data, comply with factual evidence, and align with business logic. For instance, how can one incorporate prior information like budget restrictions, platform trends, constraints, or even distinctive features of each channel into the decision-making process?\n",
    "\n",
    "## Introducing Budget Allocation Function\n",
    "The updated budget allocation function in PyMC-Marketing aims to tackle this issue by offering a Bayesian framework for optimal allocation. This enables marketers to:\n",
    "\n",
    "* Integrate the outcomes of Media Mix Modeling (MMM), quantifying each channel's effectiveness in metrics like ROI, incremental sales, etc.\n",
    "* Merge this empirical data with prior business knowledge and logic for making holistic and robust decisions.\n",
    "\n",
    "By utilizing this function, marketers can guarantee that the budget spread not only obeys the mathematical rigor furnished by the MMM outcomes but also incorporates business-specific factors, thereby achieving a balanced and optimized budget plan.\n",
    "\n",
    "## Getting started\n",
    "Media Mix Modeling (MMM) acts as a dependable method to estimate the contribution of each channel (e.g., x1, x2) to a target variable like sales or any variable. The function `plot_direct_contribution_curves()` allows for visualization of this direct channel impact. However, it is crucial to remember that this only unveils the \"observable space\" for values of X (spend) and Y (contribution)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 828
    },
    "id": "_QF9nHcqyycl",
    "outputId": "af09b2a1-1c30-429e-ab1f-a4af0c163434"
   },
   "outputs": [
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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "response_curve_fig = mmm.plot_direct_contribution_curves()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iG_KsGWPzsrp"
   },
   "source": [
    "The observable space only encompasses our data points and does not illustrate what transpires beyond those points. As a result, it is not assured that the maximum contribution point for each channel lies within this observable range.\n",
    "\n",
    "## Understanding Saturation Functions: Sigmoid and Michaelis-Menten\n",
    "What do we mean by saturation functions? We assume the effect of spend on sales is not linear and saturates at some point. Two prevalent functions deployed to comprehend and estimate the saturation effects in advertising channels are the sigmoid and the Michaelis-Menten functions.\n",
    "\n",
    "### Sigmoid Function\n",
    "The sigmoid function is formulated as:\n",
    "$$\n",
    "\\frac{\\alpha - \\alpha \\cdot \\exp(-\\lambda x)}{1 + \\exp(-\\lambda x)}\n",
    "$$\n",
    "Key Elements:\n",
    "* α (Alpha): Denotes the Asymptotic Maximum or Ceiling Value. It is the point that the function approaches as the input x becomes immense.\n",
    "* λ (Lambda): Influences the steepness of the curve. A more substantial value of λ renders the curve steeper, while a lesser value makes it more even-paced.\n",
    "\n",
    "### Michaelis-Menten Function\n",
    "The Michaelis-Menten function is formulated as:\n",
    "$$\n",
    "\\frac{\\alpha \\times x}{\\lambda + x}\n",
    "$$\n",
    "Key Elements:\n",
    "* α (Alpha or Vmax): It represents the maximum contribution (y) a channel can make, also recognized as the plateau point.\n",
    "* λ (k): Denotes the elbow on the function in x, signifying the moment when the curve adjusts its direction.\n",
    "\n",
    "## Which Function to Use?\n",
    "The preference between the sigmoid and Michaelis-Menten functions ought to be steered by the data's goodness of fit. But it really comes down to your assumptions about where the peak might be and the speed at which it saturates the curve.\n",
    "\n",
    "To determine which function fits your points better visually, specify the parameter `show_fit=True` in the same preceding function to obtain a plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 828
    },
    "id": "47RuJ_epzm2U",
    "outputId": "5df16f8a-ef66-447b-8e7d-a093ca7c1504"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigmoid_response_curve_fig = mmm.plot_direct_contribution_curves(show_fit=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "--4LSceFihbn"
   },
   "source": [
    "### Handling non-fit errors:\n",
    "In some isolated cases, when X is very small the scipy optimizer may not find the optimal setting. In those cases, you can use the functions separately by directly importing the module from utils.\n",
    "\n",
    "```python\n",
    "from pymc_marketing.mmm.utils import (\n",
    "        estimate_menten_parameters,\n",
    "        estimate_sigmoid_parameters,\n",
    "    )\n",
    "\n",
    "estimate_menten_parameters(x=df.X.to_numpy(), y=df.y.to_numpy())\n",
    "estimate_sigmoid_parameters(x=df.X.to_numpy(), y=df.y.to_numpy())\n",
    "```\n",
    "\n",
    "Use the parameter `lam_initial_estimate` or `alpha_initial_estimate` to play with the initial estimates given to the optimizer, better prior initial values should make the optimizer find the answer quickly. Otherwise, you can use the `maxfev` parameter to increase the number of iterations by `curve_fit` before giving up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Sjo33xGt1ZHu"
   },
   "source": [
    "The plot by default shows the sigmoid function fit with the quantiles estimated by the posterior distribution of the channel. If you need to change the fit method, you can use the `method` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 828
    },
    "id": "pgRn7yI71UPb",
    "outputId": "08c116e0-a9e7-43bd-c95d-4be9a60f526c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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n7d69W/v27dOFCxcUERGhtLQ0lSlTRrfffrseeughp120UlJStHjxYu3du1cHDhzQxYsXFRkZKZPJpHLlyqlx48bq3bu32rZt69J9On36tB588EFbUaQJEyaoV69eTseHh4fru+++08aNG3Xq1CklJyerePHiqlOnjrp27arHHnssX9t6Eu7Kg9jYWD3//PP6559/bPsaN26sL7/8skhUwcpKQkKC/v77b9u2l5eX6tatW4grus7Dw8T/zC5C6tSpo4CAAMXHx2v79u25Cndl9xebjFXkcqNUqVJq3bq1Nm7cqN9//13PPvusrl69qk2bNkmyb8koSQHXnmTe3t629owAAAAA8i4w0KT27Q2tWZv3uTq0t8wHAABQFKSvXpLqoAViakr2lbXSh7VSUzNX2ErNENRy9IG6h4d9MMvHx3FI63pYi79PAcCNKi3NyNSm0Bq+ctTOMGNFrdQUxxUab2SeGdsYOqiGZRfa8sk8lvbAuNnMnDlTa9assW0HBATIx8dHERER+uuvv/TXX39p1qxZmjFjhsOWf9mZP3++pkyZkuv1jR49WvHx8bbt4sWLKzU1VefPn9f58+e1atUqtWvXTpMnT7Z9jm8VExOjd99917ZtMplUokQJxcbG6vTp0zp9+rSWLl2qnj17aty4cfLyyj4GZRiG3nnnnRznFDZs2KChQ4fa7oOHh4cCAwMVGRlp+/7Onz9fX3/9tcqXL5+jOV1FuCuXEhMT9eKLL2rfvn22ffXq1dP06dPt2tgVVbNmzVJsbKxtu3HjxoXWbg/5Ly9/OfH09NS9996rn3/+WYsXL9ajjz7qclU6axnBiIgIh8czVpHLrfvvv98u3LVq1SqlpKSoUaNGqly5st3YqlWrysfHR8nJyTp16pSqVavmljUAAAAAkHr1MGnN2rz/+rjnw/yiFQAA5K+0tOutEDNW2EpJtQ9xpaZKRoa/4ti1QcyislZqag7CWtf++Ppa2h5aWyB6eliqg9ACEQBuPNaWu7bWhcnXb1tDWXbH0lXUsr4v3Uw8PNIFrzK0L/TxyaZyli2YxfsgkFGbNm3Url07NWvWTNWqVbNlPyIjI7V06VJ9/PHHOnPmjIYMGaJffvlFHh4eLs1vMpl02223qUGDBrr99ttVvnx5jRw5MsfnP/LII7r99tsVGhqq2267TT4+PjIMQ2fOnNGsWbM0e/ZsbdiwQRMnTtTYsWPtzvXx8VHfvn3VtGlTNW7cWOXLl5e3t7fMZrOOHj2qL7/8Ur/++qsWL16smjVr6oUXXsh2PT/88IO2bt2q0NBQhYWFZTk2KipKr732muLj41WpUiWNHj1ad955p7y9vRUbG6uFCxfqP//5j44cOaLRo0friy++yPH3xRWEu3IhNTVVr7zyil3lq+DgYH399dcqWbJknuc/c+aM7rnnHtt25cqVtXr1aqfjDx065FI1pQ0bNmRKVT7zzDMurxM3DmslOWf9WbPTr18/rVixQqdPn9Z///tfvfnmm1kGxjZt2qSyZcvaqsFVrVpVknT48GGlpKRkCoctWrQoV+vKqF27dipevLhOnjyp/fv321oyZqzaJVnaPLZt21Zr1qzR3LlzNXz4cLesAQAAAIAU2kQKriEdP5H7OWoGW+YBAABwhbNWiCmplrBVSqp95a3MYSv7sJYttJVmH9iyVt1yGNYySZ5e16ppeVk+rPb3u7aPsBYA3HDS0gxLACtDCCt9OMsW0kpXVetmrJzl5eWgOpaP5Xb6cFb6ylk+PtfH0PoXyB/O8h6lSpVS37595ePjo1GjRuno0aMKCwtTs2bNXJr/pZde0pAhQ2zbZ86ccen8d955J9M+k8mkqlWrauTIkbYQ2pIlS/Tuu+/a5QmKFSvmMEjm4eGhunXr6pNPPtG5c+cUFham//3vf9mGu86fP6+PPvpIQUFBGjFihB599NEsx//555+Kjo6WJE2cONGuBWWxYsXUv39/RUZG6osvvtCaNWuUkJBgK37jToS7XGQYht5++227knZVqlTRrFmzVKZMmUJZ09NPP63Q0FA9+uij6tChg9OWkNHR0fr222/11VdfKTU11ba/ZcuW6tKlS0EtF4WgcuXKOnHihLZv3+5yr1nJ8hwfMWKExowZo8WLFys8PFwvvPCC6tevbxtjGIYOHjyo77//XqtWrbILEDZr1kx+fn6KiIjQ1KlTNWzYMHl4eMhsNmvx4sX67bff3HI/fX19dffdd2vJkiWaOXOm9uzZIx8fH917770Oxw8aNEh//fWXlixZIl9fXz333HMKCgqyHU9JSdH27dv1yy+/aOzYsfL09HTLOgEAAICbnclk0sgR0pBhhhISXT/f3096Z7iJX/gCAABJlqon1jaHKSn2bRDTV9eyhrYcnZ+pslaq4wCXOS3zh/Am0/WqWl6elsoh1upatEEEgKLPMAy7VoVZhbQcHXMU5L0RmZR9xayMga2Mt3mPA5xbunSp5s+frwMHDshkMql69ep6/PHH1atXL3l6emr+/PkaPXq0HnvsMb3//vsFurbGjRvbbl+4cMHl8/P7c/LGjRtr6dKlSkxM1NWrV1W2bNkcn2symdS4cWOFhYUpPDw82/GjRo1SbGysxo8fr9KlS2c7/tKlS7bbDRs2dDimUaNGkiSz2azExETCXUXBuXPn9Msvv2Tad9ddd7k0T+XKlbVy5Uq3rMkwDK1du1Zr166Vv7+/QkJCVLt2bQUFBcnPz0/R0dE6duyYduzYkalnaHBwsCZPnuyWdaDo6tatmzZu3Ki5c+dq7dq1KleunCSpe/fueuCBB3I0R5cuXRQQEKAPPvhAmzdv1ubNm1WqVCmVL19ehmEoPDzcllitUqWKKlasaDs3MDBQgwYN0v/93/9p/vz5WrZsmSpXrqzw8HBdvXpVw4cP1wcffOCW+3rfffdpyZIl2rhxoyTpzjvvVPHixR2ODQ4O1n/+8x+98847+vHHH7Vw4UJVrVpVxYoVU0xMjM6ePWsLQr733ntuWR8AAABwqwipa9L4cdKIka4FvPz9pPHjTAqpyy+MAQC4mVnbIaavopXiJKyV5qAlVVpa5gpbqdcqa6WmZd8K0VpVy9r+0NsnQ1jLK12FLT7IBoBCl5ZmZGprmKMKWte+3gzVs7JqaZgppJUhsOXjY3lP4z9RAe6XnJysN99801bQxNfXV4ZhaO/evRo5cqQOHjyod999V8uWLZMkh4V33n77bS1evFiSpXObu23fvt1229p1qyjZtm2bJCkgIMDlokpms9nWWrFatWpZjv3pp5+0bt06tW7dWr17985RBbIqVarYbu/du9eucpfV7t27JVlyQKVKlXJl+TlGuMtFhpH5rd+ci7h2mqN/jbpBQkKCdu7cqZ07d2Y7tmvXrnr//ffd0koSRVvnzp0VGxurJUuW6OTJk7YXqaZNm7o0T7t27bRo0SItXbpUGzdu1LFjx3T8+HGZTCaVLVtWLVu21F133aWOHTvKy8v+5eWJJ55QUFCQfvjhBx0/flynT59WvXr11L9/f7Vo0cJt4a7GjRurUqVKOnfunCTHLRnTa968uebPn6///e9/2rRpk06dOqXTp08rMDBQ9evXV8uWLdWhQ4dM9wcAAABA9lo0N2nKJGncBEPHj2c/vmawpWIXwS4AAG5M6QNbKan2LRGtVbasFbgy/4rcuF5Zy0lYK321reyqa/l427dCzFhliw+3AaDgWQNayUnXQ1mZ/jgJcOXTR6sFymSyhKx8MrYy9MlcRSvTtg8tDYGi6tNPP9Vvv/0mLy8vffDBB3rwwQclSf/73/80evRozZkzR/fcc4+2bdumEiVKqHXr1gWyrsTERIWHh+v333/X1KlTJUktWrTQHXfcUSDXz05MTIxOnDih77//XsuXL5ckPfvsszl+nYuMjNSxY8c0c+ZM7dq1y3a+M5cvX9aECRPk6+ursWPH5nidd911lypWrKjw8HANHz5co0ePVtu2beXt7a3Y2FgtXLhQM2bMkJeXl0aMGJHjeV1FWuEmMGjQIG3YsEG7d+9WfHx8lmN9fHzUvn179e3bV23atCmgFSK3vvjiixyPbdasmbZu3WrXVjC9nj17qmfPnnlek7+/vx599NFse886ct999zkNW23ZssXh/lGjRmnUqFE5vobJZNKiRYtcWlepUqU0cOBADRw40KXzAAAAAGQvpK5Js78x6cjRYpr3Q6L+WJ1s90t5T0+pQ3up58MmhTbhF9UAABQ1WVbYykFgyxrWsn1Ny9wSMdXhuZYPse3aIfo7DmpRXQsACo61xWH6MFZ2YS3rcUetc280Xl7pgle+9pWxrCEsZwEuL8LFwE0nIiJCc+bMkSQ9/vjj6tGjh+1Ynz59tGPHDv30008aMWKE0tLSdNddd8nb2zvf1nPp0iW1a9fO4bG77rpLEydOzLdr58Svv/6q119/PdN+Hx8fPfPMMxo8eHCW50+bNk2ffPJJpv0BAQF65ZVX1Lt3b6fnjh07VlFRUXr99ddVvXr1HK/Z399f06ZN05AhQ3Tq1Cm98MIL8vDwUGBgoGJiYuTh4aHWrVvr5ZdfVosWLXI8r6sId7moSpUq+VIGLy/XsIZSzGazTp48qZMnTyo8PFwxMTFKSUlRQECASpYsqeDgYDVo0EA+Pj75uHoAAAAAAOyZTCa1bOGtli28dfZspC5eMhQfLwUESOXKSoGB/HIbAICCZDYbWQa1sm6JaNhX10rNHNhKTXMW9rrWDtHzekUtHx/7kFb60BaBLQDIH4ZhqZboLISVVUjrRm9xaNK10JWDAJZdQMtBgMvbW/L05L0JwHUbN25UcnKyJEu4K6P7779fP/30k86fPy/JcUtGSZo4caJbgleenp4qW7asJEtlrKSkJElSt27dNGzYMKeFYgqKr6+vbX2RkZFKS0uTp6ennn/+efXt21eenp5Znh8QEKCyZcvKbDYrMjJShmHI399fr7zyih555BGn5/32229avny5QkJCNGDAAJfXHRISojlz5mj48OHauHGjzGazYmJiJFk6/cXHxysiIsLleV1BuOsm4uHhoeDgYAUHBxf2UgAAAAAAcCgw0KQaAYW9CgAAbj7WD+ozhrWchbYynH29upbZck6aOV1LxNSsK2x5elwPa3l6SgE+lm27CltelnEEtgDAfcxmw+XqWdZ9ZnNhrz73rOFgZxW0bKEtBwEuLy+qZwFwn8OHD0uSgoKCVKdOnUzHQ0NDbbcDAgLUvn37fF1P6dKltXHjRkmWfx9cuHBB8+bN0zfffKM//vhD7777rvr06ZOva8jKvffeq3vvvVeSlJqaqmPHjmnatGn6/PPP9eOPP2rKlCl237OMnn76aT399NOSpOTkZO3fv1+TJ0/WhAkTNG/ePH3xxReqWbOm3TmRkZF6//335eHhoXHjxsnLy/WY1JIlS/Tuu+/Kx8dHI0aM0N13362yZcvq9OnTmj9/vubNm6dhw4bp9ddf16BBg1yePycIdwEAAAAAAAAAUESZzUamYFZyiuPQlpGhlEpammHf/jBdWCtjta2MVVg8PK6Fs7KqsEVgCwDcIi3NsIWukpKlpCQp5drt9CGtpOTr+1OSLe8DNypre0MfX/vKWTmpoEX1LABFxdWrVyVJlSpVcni8RIkSqlatmk6dOqWOHTvK19e3wNZmMplUsWJFvfbaa7r99ts1dOhQjRkzRo0bN1a9evUKbB3OeHl5KSQkRJ988olKlSql2bNn67XXXtPvv/8uPz+/bM/38fFRkyZNNGPGDL388stavXq13nzzTS1YsMAuxPvBBx/oypUr6tevnxo1auTyOnfv3q233npLkvTVV1+pTZs2tmN169bVqFGjVLJkSX3++eeaNGmSOnfunClg5g6EuwAA/8/evcdHedZ54/9ccz7kSAgEChUoEGwFCbZdqwRXXbW2rgVcdVepdkX8+TxWqo+ubZGuPkqprutqKXV3rbRPT+varVDtY6122wXC0+5Supx6MEBLK5RTApkkcz7c1++Pa+6ZeyYzyRzuOST5vF+vec1MMnPNncwpyf3J50tERERERAUKBCTO9QGhEOB2A9PaOVaSiIhKE4/L/C1b0XRwa7SxiNkjEfXz+uezw15CJMNZyWCWy5XVsGU4zcAWEVHxNE2qQFYkM5CVcdr4uWSb1shGxfHBYkkHs+wOwGk8tgPOZCDLmfV5BrSIaKIZLbTV0dGBP/7xj3jf+95XxS3K9MEPfhAzZ87EqVOn8Oijj2Ljxo0125ZcPvvZz+LBBx/E6dOnsXv37rzjK3MRQuCzn/0snnnmGRw+fBgvv/wyLrvsMgDA3r178fjjj6O9vR1f+MIXEAgEMq4bDodTp6PRKAKBAIQQ8HjSYwfuvfdeSClx2WWXZQS7jNauXYuf/OQniMfjePrppxnuIiIiIiIiIiKqNikl9h8Atu+Q6NmjxlTprBagu1ti9UqBrqUc70FENNlJKUdt2NIP8fjIcVj5Wrb0oFaqbSvXWERDYMthB9zG0FbGeES+TxERFUJvTdTDWKmAViRHWMvwufHapGVszsoZ0sr1cYd6f+HvQEQ0mTU3NwNQo/9y8fl82L9/PwAgGAxWbbtymT59Ok6dOoU33nijptuRy/Tp01OnS9m+7Ovr4a6TJ08CAPr6+rB8+fJR1/jWt76Fb33rW2hsbMS+fftSH3/11VcBALNnz8573YaGBkydOhX9/f2p2zQbw11ERERERERERHn0HpHYtFni+Ou5P5/QgJ27gJ27JObOATZuADoXcucGEdFEI2XmaMTUIZ4Z2Mo3GjFX01bcEOJKJEaGvSwiPRLRZgXs7nRISx+LqB9zxzoRUW5SyhFjDY2tWbEYAEQRjUoMDUtEI8nLx2q95cWzWZOtWMZDjnGH2Z+329nWSERUqgULFgAATpw4gYGBAbS2tmZ8/re//S1i6s0Gvb29Vd8+nZQyFTryer012458Tpw4kTpdyvaVe/3R6L9rnTp1Ku9lotEohoaGKnL7Ooa7iIiIiIiIiIhyeH6fxIaNEqHw2JcFgOOvAzeul9i8Cbjicu4cISIaD/KGtnIEtzJDWzId1soObBkbt+JAVtYLVku6TctmA1zOkS1bNht3tBMRZUskJCKR5CjDZDgrEjWczzEOMRYd+TpsJCBhs6tKxHhMjnrZajEGspzOdFNWvnCWw8kxh0REtbJ8+XLYbDbE43H84he/wBe/+MXU5xKJBB566KHU+cOHD1dkG+LxOGy20aM/v/zlL9HX1wcAuPLKKyuyHfkUsn0//elPU6ezt2+s68fjcWzbtg0AYLfb0dXVlfrc6tWrsXr16rzXPXnyJN7//vcDAO64446cl73sssvQ29uLl156CYcOHcKSJUtGXOaXv/wlotEoAGTcvpkY7iIiIiIiIiIiytJ7pLhgly4UBjZslNi6hQ1eRES1ZByPmApqRXOPR8wObelNWrFYnhGJyc+PCG1Z08EshwPwuA3tWobRiAxtEdFkJ6VEPJEccRjJHH2YHdwyXibXWNp6Zrclg1d6UEsPayXHHOqf00/rIxD5PkFENH5MmTIFK1euxKOPPoqtW7diypQpWL16NTRNw6ZNm3Ds2DG0t7cjFovh8OHDeOKJJ3DNNdeMWOeWW27Bjh07ABTf8PXCCy9gy5Yt+PjHP453vvOd6OjoSH3u9ddfx6OPPor77rsPAHDxxRfnDDCNdfuapsHn86XO6y1VADA8PIwLFy6kzjc0NMDhcKTO//SnP8Vrr72GP//zP0dXVxeampoAqFDWSy+9hHvuuQdPPfUUAOAjH/kI5s+fn3Hb3/3udwEA11xzDd72trelmrGi0SheeOEF/OQnP8HevXsBAGvXrk2tb5ZPfepT2LFjBxKJBL70pS/h1ltvxXvf+1643W5cuHABP//5z/GP//iPAIA5c+bgve99r6m3r2O4i4iIiIiIiIjIQEo1irHYYJcuFAZuv0Pi/ns5JouIqBLicYlgUCIWlzh/XiIakyNbt0aMR8xq2tLHJBo+li+0ZTOMRnQ5Aas3HeKyGtq2+JpPRJOR3oAYMYw6zBnQygpuZY+irWc2m2rGchqCWnoYyxjcyv4cQ1pERJPDzTffjJdeegmvvPIKbrvtNmzatAmapiEWi8FiseBv//ZvsX//ftx777346le/ik2bNmHr1q1YtmyZaduwb98+7Nu3DwDgdDrh8XgQCoUQDqf/uLVo0SLcfffdcLlcRa9/6tSpVMNVti996UsZ57MbsBKJBB5//HE8/vjjAACPxwOHwwG/3494PJ663DXXXIPNmzePWD8SiWDHjh3413/9Vwgh4PV6YbPZMDw8jEQy+S2EwPXXX4+bbrqp6K9tLIsXL8b//t//G9/97ndx7tw5fPWrX4UQAh6PB4FAIHW5mTNn4ic/+cmYLWWlYriLiIiIiIiIiMhg/wE1YrEcrx1X6yyrTBM7EdGEpGm5Q1rRGBDXj+OApgk0NKidAH6/CnvlDG7FVVNXIpF7POJYoS39mKEtIpospJQZ4w2zA1k5RyFGssO09ctqHRnGcrkEGhttcLkEtEQcdoccEdbiuEMiIhpNU1MTfv7zn+O+++7Dk08+iRMnTiCRSGD27NlYv349PvjBD6banP7t3/4N58+fRywWM+32L7vsMvzd3/0d9u7dixdffBH9/f3w+XxwOBy4+OKLcemll+JDH/oQPvShD8FqtZp2u4X62Mc+htbWVuzduxdHjx7FhQsXMDw8DI/HgxkzZqCrqwsf/ehHcfnll+e8/rp167BgwQI8//zzOH78OM6fPw+/34+GhgbMmjUL73jHO/Cxj30MixYtqtjX8MlPfhKXX345/uVf/gV79+7FyZMnEQ6H0dzcjEsuuQTve9/78Fd/9VdoaGio2DYIKcfLj1xUrwYGBmq9CZQkhEBLSwsAwOfzgU9vIiIqFd9TiIjITOPtfWXjtzTs3FX+Ou/9U+C737aUvxAR0QQQj6vAQMaIxBzBLSNNywptJdRlEwkBh8ODeEJieDgETct8XzGOR7Ta1DFDW0Q02eiNWmG9SSuSO5iVatdKBrrq+yd1RSDdopV9bAxmOZ2APXnszBPSEkKkdsT6/f66/12FiIjMt2rlJRge9mHhwoVFj0QkMsP/+l//Cz/60Y8AAM8++yyuuuqqEZdhcxcRERERERERUVIgINHTY85au3er9bxehgeIaOIarW0rFlNhAdW2ZbyWTLVrxWKG8YixdOtWbMR1AIslGdKyA3Y74PZY1NirZGDLZgOsFo7BIqKJKZGQKpgVTge08h7GUaOWEOnwlTGopX/M4UwHtvSP2+0M6BIREdHkwnAXEREREREREVHSuT4goY19uUIkNKCvH/B6zVmPiKjaEgmZDmpFM0NbxoORpslUy1ZCH4tobN6K5x+RqDdsORxAY47WLT20pVpW1DgRv1+Mi/ACEZGR3qoViahmLWOrVjisgrHhsGE8YkS9ntY7iyUrmJUjsOVwZIa5bDYGtYiIqHRSSmia+gcRTQM0CWj66eQhoWWe17Ium0jU+qsgGhvDXURERERERERESaGQuesFg+auR0RklowxiYYAlz4eMRoduZMjkZDpEYmGMYnGIFfOtq1kSMvhADyedFhLH4/IEYlENN6lWrVGa9MaZ61aNqtqzdLbs7IDW9lBLYdTXYev50REVCg9mJURwkpkBrTyBbMSGiCTAa18hEg2+1ozT9ttyfNW9fuKxVK9r5moVAx3ERERERERERElud3mrufxmLseEVEhRgtu6R/PNSYx5yGRPp0dRDC2ajncQIMts4HL2LZFRDReGFu1coWy9IOxcaveW7VstqzRh/pph+G0M/O01crXbyIiGp2myXQgS8sMXSW0UQJahhBXPqMFs/RAVsbBqo9oTx9GCxzr/2RitQIiGe6S9Z68pgmrkMcew11EREREREREREnT2tUfA80YzWi1Au1Ty1+HiMhID25Fs8ckRtOns4NbsWQbVyKhAgh625Ye2soekyiEIaRlA1zOzPNWG9tZiGj8kFIingAi4XQoKxJOHhvOp05HRrYQ1hMBQxBLD2O50qEs48HlZFCLiIhyGzHOMNcYw+xwViIzvJWPgApb6SErPXRlsyfPJz8nLOnQVqnBLH2tjI9Zxvh81u8yXo8Lw0NAyOw6d6ICBQ3V/+48/3nKcBcRERERERERUZLXK9DdLbFzV/lrrehW6xERFSpX41Y0WkJwKzkiMZFs3jKyGIJbDjvgcWcGt9SoRL52EVF9SyQkwlmBLP288bT+uewxs/XEZs0MZzkMoSy9WcvlzByFyHAtERFpmhw5zjDXeZkOZRXampURykqGo2xWwOLI/JxxrGHmYfRgVq6wVaGhrEqMdG9ubsaZM2dw4cIFU9clKpTxsdfa2przMgx3ERERERHVqUBA4lwfEAqpMWHT2hkSIKoEPtco2+qVAjt3lV/Fv+o6Po6IKC2RkCMCW7GYGvOlh7kywwclBLcs6YCW0wnYvOnxiAxuEVE9SyRkZjArOnq7VrxOxyAKGBq1nLnbtLKbtmw2vi4TEU1GhYaz8rVo5ZviJqAasbIDWI6s1qxUSCvHebVKjrXF2IGsjHCWVf0+UslglhlaWloAqPakYDAIj8dT2w2iSae/vz91muEuIiIiIqJxQEqJ/QeA7TskevZk1ltbLUB3t8TqlQJdS+vzF2Gi8YLPNRpN11Jg7hzg+OulrzFvrlqHiCYHKdONW1FDy5axdSs7iKCHvfTRiHpoK9+oxHzBLbtd7SSx20f/D3kiomrSNIlINHeb1ojTEfWaWY+s1szxho5kOMvlSrdqGT/P12IioslD0ySkTI81TJgYzhox0tAK2G35A1mZ4azCxhnma8XKDmPpv4eMtfZ4Nn/+fPzXf/0XAGDPnj344Ac/WOMtoskkHA5j7969AICOjg40NjbmvBzDXUREREREdaL3iMSmzTJvmCChATt3ATt3ScydA2zcAHQunJi/UBNVEp9rNBYhBDZuAG5cLxEKF399twv45q2CwUCiCUQfl5gd4DKOUDTunEkkZM7QViKurpdIZF6eoxKJaDyIxaVq0wqnA1ph/TjrY9Forbc2N4tFBbFcrsw2Lf28y6latVzJIJedrVpERBOWlOnmrBGBrBzBrELHGurNWakAVQHhrMzzY4ezbMlWrHyNWfmCWvw7RW7vf//78fDDDwMAnnzySYa7qKr27NmDYDAIAPjQhz6U93nKcBcRERERUR14fp/Eho2FhwiOv65CB5s3AVdczl/KiQrF5xoVqnOhwOZNKOrxAqhg1+ZNgoFAonFE02Q6sJUnvGUclyilTAe2YrlbtzQt8zZsNtXmYrMBjc7M8wxuEVGtSKle//RwVr6glj4esR5HIVos6VCWw5lu1HK50m1aTkNgy2bjjm0iookk32jDXOEs/ePGkFY+xtas4scaFh7OMh5njzIcLyMNJ4I//dM/hcVigaZp+O1vf4t/+Id/qPUm0STyu9/9LnX66quvzns5IWW+wj+iwgwMDNR6EyhJCJGaCezz+cCnNxERlYrvKdXVe0SW1Q6zdQtDBESF4HOtdsbz+8pYTW9G8+aqxi4+Tojqi966lR3aMn7MSG/d0kcmxmJAPJEMecXVaSOrNRnWMjZtGYJbNu6EMZ0QAg0NDQAAv98/rt5XiCotexxiOJwOZ2WHuCKR0Xds14IQhjYtQyhrRIAred5u52sslYfvKUS1p2mysNYsQzBLv8xoow31oFT2eENLjlYta1ZYS60w0lhNWWzOGr+uvvrq1Gi8J554Ah/+8IdrvEU0GVy4cAELFy7E+fPnIYRAX18f2tracl6WzV1ERERERDUkpQoNlBI2AYBQGLj9Don77+UfB4hGw+calapzocAD9wH7DwDbH5Po6VF/TNZZrcCKbmDVdQJdS/n4IKo2vUVLD2tFjCGu5CFX65YxvKWf1pu38o1LdDoBmzcrxGUbfWwKEZEZEgmZ2aJlDGsZPhaJqEM9RVMEVCArFdbKGomYHeByOPjzFBHReGQMaKWOE1kjD/VQliG8lUjkXzM7lGXN055lteZq0Mr9XiJEjgCWbfRAln6ZsUYm0vi2du3aVLhr/fr12LdvH5qbm2u8VTSRSSnx9a9/HefPnwcAfPKTn8wb7AIY7iIiIiIiqqn9B1BQG8xoXjuu1lnWZcIGEU1QfK5ROYQQWNYFLOsSCAQk+vqBYBDweID2qYDXyz/uElWKlDIjqKUHtyKG88bWmURCIpHIat0yjlDM0bqlh7W83swGLrud4xKJqHKMga1wKPdoRP0Qq7NxiFaroT0rGczKd97h4I5wIqLxIldAyzjuMGdAK5H5D1DZco03tDszg1n5xhzmC/sW0pCVP7zF9yTK7S/+4i9w//3349lnn8WxY8fwoQ99CL/73e8Y8KKKkFLi5ptvxn333QcAaGhowA9/+MNRr8NwFxERERFRDW1/zJz/qd7xK4llXfzjBFE+fK6RWbxeAa+31ltBNHFomkQslg5rpUJchhYuY5NWIiFHBLaMx8aglxDpsJbDrgKZNqsKbbF1i4gqwTgSMRxKB7VCoazQVkiNiK0ndlsymGUMaWWd1kNbNhvbtYiI6pWUMnOsYSLdkJVzxKEhtJVvxGGxAS1jk9ZY4w1teUNY+T/Gn+GpEoQQ+NGPfoRrrrkG58+fx3/9139h8eLF+Id/+AesXr0aFoul1ptIE8Thw4fx5S9/Gbt27Up97O6778bMmTNHvR7DXURERERENRIIqPFeZti9W63H9hiikfhcIyKqnXzhLeMhTWa2bSWbtoznR4xMtKcDCQ3J0zY9vGVl+ICIyieleh0bEdAyHEL6WMRw/YxE1Mchplq0DOGs1ChEw8fYZEJEVF8SCZluySoiqKXleSNKjSPMHnHozAxnZYe0igpo2fKczhHU4s/pVI8WLFiAX/3qV7juuutw/vx5nDhxAh//+Mcxffp0fPCDH8TVV1+Nd77znWhra0NTUxMfxzQqKSVCoRAGBgbw0ksv4Xe/+x2efPJJvPzyyxmXu+eee/CZz3xmzPUY7iIiIiIiqpFzfaPXlhcjoQF9/WCbDFEOfK4REVVOseGt7HGJ+ulY8rQxvGW1pINaHjdga1LhLb15i0EEIipHPC4zgln5AlvhcGYrYC0JkWcMYq7GLY5DJCKqOSllxujC7HBWRkirgDGHAiqYZQxh2ayA1ZEObGUHswodcZivQSu7OYsBLZroLr30Uvz+97/HN77xDTz99NMAgLNnz+LBBx/Egw8+mLqcxWKBy+Xi84DyisViiGb+USTDvHnzsHXrVnz4wx8uaD2Gu4iIiIiIaiQUMne9YNDc9YgmCj7XiIhKJ6UcEdbKDnIZLj0ivKUfR2NAIp7ZaGO1pINaDd7kCEV7+pihBCIqlqZJ1a4Vyh3SMh5i8VpvrWKxGBq13IbTOUJcDgd3pBMR1YKmZbVo5TjO2ayVL6RlaNHSw1cOR2Z7ltWaK7CV+z3AeJ2MYFYBYS3+zE000ty5c/HII4/giSeewMMPP4yenh4EAoGMy2iahiD/SEhFslgsuPLKK7F69Wp8+ctfhsvlKvi6DHcREREREdWI223ueh6PuesRTRR8rhERjS4WUwGuSCQzwBWJjByFGI/nDnDF4kA8lhXesqbHJDY6M4NbNht3JBFR4RKJZMtWKB3WCoWyjutoLKJAMpBlCGu5s4NbLga2iIiqLVdIq5DAVr5Rh6nQlSUdxrI7Mpu1rMawlmX0xqvRmrNSxzlbtPg+QmQ2IQSuvfZaXHvttYhEIti7dy+eeeYZvP766/D5fBgcHEQkEsl5XauaZ4pEIlHNTSakv/f1wGq1oqWlBa2trejo6MB73/te/Nmf/RmmTJlS0noMdxERERER1ci0dvUHHjPGxVmtQPvU8tchmoj4XCOiyS4elzlbt/Qwl7FRIJGQqcBWNEeIK9fYRHtW8xbDW0RUqFhcIqwHtrKCW8aPjTLNpKoc9pHhLP3gzhqPyNdAIqLKkVKOGdAqKaRlaNNyZI06zG7VUvmBka/1eitXvlDWiI9zzCFR3XM6neju7kZ3d/eYlxVCoKWlBQDg8/kgZT3868Hk0draWutNqBiGu4iIiIiIasTrFejulti5q/y1VnSr9YhoJD7XiGii00cnRqJANJIOcOnhrbhh9JimqfCW/vHs9i1j0MtiSTdveTzqNMcmEtFYpFQNf8ZGLeNpY5irHkYjWq0qmOXMCmhlB7ZcLjajEBGZTUo56ljDESGtMcYd5gpp2R3p88WGtGy2scNZVlt2mxbfK4iIyHwMdxERERER1dDqlQI7d5X/3zurruMfjohGw+caEY13evtWJJIZ4ho5OlGmglqxWDK8ZThvnAohRDq85XIDjcnTDru+s4qveUSUJqVEJJK7ZSt7PGKtJ9AIYQhlOZMjEfO0bdlsbEohIjKDpsmRAa3s08YQVyJ/w7befpUx7tCeGdzKDmiNFdLSA1mFhLT4szAREdUbhruIiIiIiGqoaykwdw5w/PXS15g3V61DRPnxuUZE9W5E+1ZWiMsYlMg5OjGqAlzxGGCMsuotWw474PWkxyja7Rz9QkSKsWlLb9nKeQhnjmatBYslHdRyuzJPu9zpY6eD7YJEROVIJOSIsYajBbTyjTwUSI42tKYbsxx2wOoaGc4yns/1M+po4w6NDVts0iIioomI4S4iIiIiohoSQmDjBuDG9RKhcPHXd7uAb94quGOWaAx8rhFRPUi1bxlGJkYio7RvxZDZwhXNMzoxGdjyetOtW/r4RIYbiCa3REIiaAhohUNQ58Pp86EQEK9x05bNlhXQMga3DB9zOBhKJSIqRsbYwxwjDhN5Rh/mkt2mlT3yMDugpU7nfs22Ghqy8rVqjWjW4j8mEBHRJMZwFxERERFRjXUuFNi8CdiwsbjQidsFbN4k0LmQf9giKgSfa0RUDbGYTAW2ItHMAFc8nr5cvvataAxIxDPbt/TwlssFNNjT51X7Fl+biCYjTZOpEYjZDVtBQ2grGqvtdjrsmY1a7hwtW24XYLfztYyIaCy5glqFtGrlYskKY9ntmaEtfRSitYA2rYwglh7GGiWwxXG4RERExWO4i4iIiIioDlxxucDWLcCmzbKgsXHz5qoWIYZNxrdAQOJcn9rx5nYD09oBr5f3aSXxuUZE5co5PtEQ5DI2HcTjMrN9K5pu4TJezti+1diQPG1o4OLOL6LJQ0oVEDU2awVzjEuMhDNDoNUkADideVq2koEtT/I8A6hERLkVHNQyhrXyBLWyG7NSYw+tGNG0pR/Uq/nIdfSAltUQxMoVzuLIQyIioupiuIuIiIiIqE50LhR44D5g/wFg+2MSPT2Zf7izWoEV3cCq6wS6lnJH73glpVT38Q6Jnj1Z97EF6O6WWL2S93El8blGRGPRNJnRuBUxnI5GRxmfaDhEM8YspoNaDgfg9aQDXGzfIpo84nGZatbK2baVDHBpeXbeV4PDAXg8yYatZEBLP+9yJz/m5MhXIqJsiYQc2ZqVa/RhgY1aqaCWw9CiVUpQKyuklQpnZTVtcZw3ERFRfRNSylr9gw9NEAMDA7XeBEoSQqClpQUA4PP5wKc3ERGViu8p9SEQkOjrB4JBtUOlfSpbnca73iOy4MaouXOAjRvYGFUNfK5VHt9XqB5pmswZ3opEVDhLf5hKqQJc0WTrVjwZ3IrF1Gnjo9lhB2yG0JbD0L7FnWVE5hFCoKGhAQDg9/tr/r6iv06EQurnCT28FQymg1uhYG1HJNpsKpjldgFuQ3hLP7Bpi4gmq1zvKZomR4S04jnatIwBrlyyg1p6MKuUoFZ2SMuaFdTKCG5Z+bMnEVGt8G9gtdXa2lrrTagYNncREREREdUpr1fA6631VpBZnt8nsWGjRChc2OWPvw7cuF5i8yY1SpAqh881oolrtABXNJp5OWPjVtx4Op6+nEWkg1terwpvGUcosumPaOKRUiIcUeEsY+uWMcQVCqqd/rVgsRgatgxhrezzdjtfn4hocipk/KHTmUAiAQz7JRJxCS3HfnghMgNZNivgdIwMZxkDXbl+NiykUcuW9Xn+jElEREQMdxEREREREVVY75Higl26UBjYsFFi6xawwYuIKA99hGI4XESAK6pCW9EYEItmhjIslnRgq8mVOT7RZuNrMdFEomkyo1krO7wVquGYRAHAaWzXymra0j/mdHKnPxFNLrlatfIFt/K1agmk27NsVhWIdbpUgCu7bUs/n6sJy2IxNGkVGNZioxYRERGVguEuIiIiIiKiCpJSjWIsNtilC4WB2++QuP9e7rgjoslLShXgikTSIa5weOQIxewGrlhylGJ2gMtqUeMTHQ690SY9RpEjyYgmhnhcjhiPmD0qMRLOHK1aLXZ75jjEXG1bLhcDAEQ0OWiaHDukFTc0beUI3Ga0aiVDVLlatXKNP1RjGa0AgHBYpFu19GP7yCYt/TL8uZGIiIiqheEuIiIiIiKiCtp/QI1YLMdrx9U6y7rK355AQOJcn9qp6XYD09rVWEIiolqTUgWzwhEgmmzeChtauPQAl365aHSMEYrJBi4GuIgmnkRCYnBQQzAo0dcvEQyoIFdqVGJQvSbUgsuZDGt50kEtj4cjEolo8tDDWvFEul0rnkiPQMw+n2sEYnZ7lkMPatnSYxEz27VGadUapVHLblM/H7a12WCzAYODIvUzJxEREVE9YbiLiIiIiIiogrY/Zs5fhnf8SmJZV2k7AqWU2H8A2L5DomeP+o9nndUCdHdLrF4p0LWU7WBEVHnxuBzRwBVNBrnSTQzpAJexhSsaU80N+ivriBGKDga4iMa7RCI9KjEYTDdtBYIqtBUIqtcDmz0CAIjHZFXat4RIjkbMCmplB7f42kNEE02uZi1jOCue9blc4Sg9rGVLhrNczvxBLas19++lVkMwy24ceZgMbakR2sW3agkhGLolIiKiusdwFxERERERUYUEAhI9PeastXu3Wq/Ylq3eI2osZL72sIQG7NwF7NwlMXcOsHED0LmQf9gmovJomsxs3jIEuYztWvG4TAe39BGKyfP6fkEhVFjLbgcaG1Rzgx7ostn4ekU0nmiaRDgCBAPphq1AVngrXMCoRLOf+VarIaCVI7zldqsgAsckEtFEoGlSjTiM5xmFWGCzlh6ksloAu8swFjFHw1ausJYxqGW15QluGUJb/EckIiIimswY7iIiIiIiIqqQc32ZLVnlSGhAXz/g9RZ+nef3SWzYKBEKF3b5468DN66X2LwJuOJy/uGciEZnHKMYCaeDXOGwCmrpEgl1Ob2BKxpV4a1Y3NjUlW7c8rgBR3MywOVQO/W4M4+o/kmpQp36aES9dSsYMoS5QrkbXSrJbs8cj+h2J88bTjscfJ0hovFLytzNWtkjEPWDluN3VOMYRJs1R7OW4bTNBmTHbIVQn7Pbk6MP7VljEO3p03Z7/sAXEREREeXGcBcREREREVGFhELmrhcMFn7Z3iPFBbt0oTCwYaPE1i1s8CIiJZFQgQ09uBU2NHElEuoyetArmhydGDcEufTLAOkdei4X0GjPbOHiDj6i+haNylS7VkZ4y3A+V2CgkpwOAa9HwO2W8HhVUCs1JjEZ3rKz4Y+IxiFNk4jHkbNhK24chxjP/Q9FethKPzjsgNWVDmmlWrfGaNbSw1rWrFYthrWIiIiIqovhLiIiIiIiogpxu81dz+Mp7HJSqlGMxQa7dKEwsPFvJX7xLxIWi6W0RYho3IlGZUYLVzisAl3ZLVzR5AhFfYxiNKrCXHoZj8WSbOFyqICF3RDi4kgzovqkaRLhsBqNqAe2AoHM41h87HXMZLMmRyR6DKGt5GmvV2DaNBfsdgG/P171NjAiomJlt2ul2rQMAS5jw1b265oAYEm2aqUCW47MVi1jYCvXz1xWw4hDY7uWcQxiKrzFMYhEREREdYXhLiIiIiIiogqZ1q7+yG7GaEarFWifWthl9x9QIxbLcfoM8MlPAZu+I9ngRTSB6AGOglu4kk1c+vlcYxS9XnVsd6hjG1tyiOpOLC7VaEQ9uBXMDHIFg9Udl2ixpANbbg/g9aTHJuoBrtFGJQohYLfztYaIakvTsgJbWUEtLWskYjaLRR30sJVxFKLNNjLMlT0KMXVd28hxiMaQln5gyJ6IiIho/GK4i4iIiIhoHAkEJM71qXF/brcKD3m9/ANtvfJ6Bbq7JXbuKn+tFd2F39fbHzNn7+zpM8CN6yU2bwKuuJyPM6oOvs6ZIx6XGeGtUFiFufK1cMUMYxSNLVxWQwuXHuJyOLiDkKieSKla94JBIBhIh7YChvPG536lCQG4XYagVvLgdidDXB4VYGAjDBHVo9Q4xOyWrXhW41Yi9yhaPYhlK7FdK29QK/mxVPOWDbBa+TpKRERENFkw3EVEREREVOeklNh/ANi+Q6JnT2YLlNUCdHdLrF4p0LWUO8nq0eqVAjt3lR+2WnVdYfdtICDR01P2zaWEwsCGjRJbt4ANXlQxfJ0rjZQqnBWJqOeqcZxiPDU+zdDCFQNiUdXSFYtlNkhkt3A5HPrOSH6/iWotkZAZjVvGUYn6x3IFDCrF5co9KlE/7XIx/ElE9SWRkKMHtQwjEXONQ9TDWFab+jnJ6lKnja1aemir0HatVLOWnaMQiYiIiGhsDHcREREREdWx3iMSmzbLvCP2Ehqwcxewc5fE3DnAxg0M4FRLoe1CXUuBuXPKG5M4b65apxDn+swZA2kUCgO33yFx/73c2UDm4+vc2DRNqjGKEUOAK5Q5SlHTMkNc0agKckVj6Z2UFpEcnehQ7Tn6GEW7nUEMolqKxVR4yx9QoS09uKUHucLh6m2L1ZoOaXn1Y29miIuhTyKqB2UFtkRmKMvhANzJsJaxWUsdRr7mWbMatOyGVi1rsmnLbtfX52smEREREZWP4S4iIiIiojr1/D6JDRslQgXu0Dv+enEj9Dj6rHiltAsJIbBxg7pvCr0vjdwu4Ju3ioJDVaFQ8bdRiNeOA/sPAMu6KrM+TU6Vfp0bbxKJZIjL0MAVDqtmLn2nZGqUYgyIRgyjFOPpdWzJVgmXG2hqUjss7Q7Azp2LRDURi0kV2gqmw1t6gMsfqO7IRKczHdrKFeByclwiEdWQcSRiPAFohuBWvMjAltMJeHKMQswX2MrZrGUYi6iHtRiKJyIiIqJaYLiLiIiIiKgO9R4pLvCgG2uEHkefla6cdqHOhQKbN6Ho+9TtAjZvEkW1FLndha9frB2/kljWxccFmaNSr3PjQSIhU8EtY5ArEklfJh6XGQEufZxiIqGCIH6/2qnp8QBtbUDblHQTF1t1iKorFpPwB4BgdoArebpa4S0hMoNbxtCW/nE2yBBRtWmaREIzNGvFM9u2jE1bWq6RiLbMwJbbWnjDViqwlSO4ld28xd9/iYiIiKieCSmz/7+BqDgDAwO13gRKEkKgpaUFAODz+cCnNxERlYrvKbUlpcRn/jp/iKgQ8+YC99+b2fY0VjjJSIWTigsVTWTFtgsB6WCWsV2omPtg3lzV2FXsfRAISFzz59L00YyACgA+8bhgwxsVLft9RdO0irzO1Zt4XDVxhcJqnGIoGeYyBj1isdxNXFryOSxEcnSiAzh1Ctj3AvDyy4DxndkigMVLgBXLgQXzAbUrlIjMEo1KNSIxkB6dGDSEuKKx6myH3Z4ObI1o3/ICLufkaZMRQqChoQEA4Pf7+fsKUZVJKaFpowe1UqMSs34vETAEspKjEDNOGz82RmBLb9XKDmzpx/X8cyLVD/4NjIiIzMT3ldpqbW2t9SZUDJu7iIiIiIjqzP4DKCvwAIwcocfRZ6Uzs12oc6HAA/ep+2b7YxI9PVntaVZgRTew6rrS29O8XoHubomdu4q+6pgSGtDXr3YqE5WjEq9ztWQMcYVD6SaudIhLIhZTzVx6eCsaBWKxdIjLIlSAy+lQzzGHPTlO0Q6cfBO47/8AZ87mvn1NAgcPqkPHdOD6NRKzZ03u126iYkSjOcYmJsNc1QxvuVzJ4JYhvGUMcTkcfF4TUWVpmkyFteJxQ9tWVnjLOA5aZ0mOP9SDWQ5HOqhlM7RvWa1AdhBdH3doDGqljrNCWwxsEREREdFkxHAXEREREVGd2f6YOf/No4/Qm8yjz8olpWraKvZ7pwuFgdvvkLj/3vROCCEElnUBy7oEAgGJvn7V/uHxAO1TYUor1uqVAjt3Vea/woLBiixLk4zZr3PVoo9TzN/EZQhxZbVx6f+oabGonZ1OJ9DYaAxx5f46ensl7tlWeLjkzFngzi3AurUSnZ2T87WbKFs8LlOhLX/WcSCggpbV4Haphi09vJU6blDHHKlKRJWSSMiMli09uJXdtKXlatkyBLNczsxmLWNoKzt0pYe9skNb2e1advvkaR0kIiIiIioVw11ERERERHUkEFBtTmbYvRvw+zVs2gxTw0mTSaXbhbxeUZEWrK6larRmuduei8dj/po0uZj9OhcISNNHhWqaCnGFDQGuUGiMJq48IS6XC2hsUo1cdgdgtxW+rSdOFhfs0kVjwD3bgJvWs8GLJgdNkwiF0qEtvz/ZwuVX58Ml/hxULLfbENrypscl6qcZ3iIiM0kp06MPk4GteNZIxHgBLVs2a1Zoa5SxiEKMHIWYL7RlK+JnHiIiIiIiGh3DXUREREREdeRcX+aYvnIkNGD3nok1+qzaxmu7kBACGzeo0ZqlBvtysVpVuxgpgYDEuT4V+nG7gWnt5jSvTXRmv86VMypUyqwQV0gdR6PpkJbfr+7nQACAANxOQFgM4xRzhLgcDjN2aEo8+FDp4+CiMeChh4Fbb5bIHn1ENN5IKRGNJtu2ksEtvz/dvhUMjmybMZsA4MoR3tJbtzwMbxGRSVKjEQ0hrZzhrUTm9QT0QJYKXDmdgKfAli3943owy2HPH9qajP/0Q0RERERUawx3ERERERHVkVDI3PWe/L0561Q7nFQPxkO70Gg6Fwps3oSSRnLms6Kb4SUpJfYfALbvkOjZkxlSslqA7m6J1SsFupZyx1c+Zr/OFTIqVA+G6A1cehtXOJwOcSUSMjVOMRIBjr0K7Ptv4LVXAWPMUwBY9FZg+buARYuKa+IqxtFjasRiOU6fUessmG/ONhFVUiIhVeNWrtGJfiCWo3nGTALJ5i1vsm0ru4GL4S0iKpOmGUYjjtKyNWI0okiHr2xWwOFOhrj05i1bOriVHejOHoloDGzpB3Vdvr4REREREdUzhruIiIiIiOqI223uegcOmLNOLcJJtVZP7UKluuJyga1bgG/eJssOiQDAqusmz/2fS+8RiU2bZd42vIQG7NwF7NwlMXcOsHGDCtlRJrNf57JHhcbjEsGQauEyjlXU2y0SCRX0Mh4i0fTn+/qBp54Czl/IfXsSwCuvqEPHdOD6NZUZfbh7jznr9OxhuIvqQ/boxNRxFUcnupzJsFYD0JAMbjUkw1weN8MNRFQaPbSVHdLSQ1z6x7JDWxZLZqOWx2EIbNlHH42YCmcZA1uOkU1b/GcDIiIiIqKJgeEuIiIiIqI6Mq1dtf+YESqyWMwbUVSrcFIt1aJdqBI6Fwo88nOJT35KtfiUat5coGupaZs17jy/TxbVgnb8dTUWc/MmFbKjNLNf56wWiTffhAp0hYFYcoyh3taVauNKBrnihvYfR3KEYkuzOn7jDeCXvyy8IejMWeDOLcC6tRKdnebdz+GwxOFD5qx16JBaz+Xi45AqT2/f8geSYxP9meMTKz060WZLj0pMhbca0gGuSjXtEdHElKtpq5jQlj0Z1HI6DGEtW/7QlsWS2aaVPRpRP1itDG0REREREU02DHcREREREdURr1egu1ti567y1+paCrzw3+Wvo6tVOKlWKt0uVE0WiwWbviNx4/rSRjS6XcA3bxWTdidS75Higl26UFiNxdy6hQ1eRup1Dqa8zi1ZrIKnsZgcEeKKRdPjFG02tWO1qTEd6LLbAYslfb+cOClx3/3Fj36LxoB7tgE3rTevwcvnAzQ55sUKoknANwh0uMxZjyY3PTSZK7zl95sfjM4mROa4xIaGdPuW1ws4nQw8ENHYNE2qgJYxpJUjxDVa01Z2aEtv38oV2tKvkxHWcqRbtxx2fbQiX7+IiIiIiCg3hruIiIiIiOrM6pUCO3eVv1f/gx8wN9xVy3BSLZjZLmS1Au1Ty1+nHJ0LBTZvQtEhJbcL2LxJTNpwkpRqFGMpoThABbxuv0Pi/nsZODAy63WucyHw6mvpna9WC+BwqvFqjmZ12unIDHHlJvHgQyqoVYpoDHjoYeDWmyWA8u/nSLTsJTLXi5i7Hk1sUkoEg/kDXLESnyeFyjc60etVP4uM/XwmoslKShXaSoW04iMDXPFEehSzTohk05Y9czyi3TZ601Z2aCt/0xZft4iIiIiIqDwMdxERERER1ZmupcDcOWqsW6nmzQXe0w383Q8mTjip2sxsUVvRrdartSsuF9i6Bdi0WRb0+Jo3VzV2TdZgFwDsP1DecxEAXjuu1lnWZcIGjWN+v4YzZzWcOyfR3CwxexZw4mTp67W1AfMvUS1cTqc6LrXx4ugxNWKxHKfPqHUWzC9vHUAF0szkdJq7Ho1/tRyfmHN0YrKFi6MTiSgffUSiMbCVHdqKZ7VvCqhwlj4i0eVOj0a029LhrFzjEW02/WeLkeEtNm0REREREVG1MdxFRERERFRnhBDYuAFlj9BraBDo7tYmVDip2sxqF1p1Xf187zoXCjxwnwobbX9MoqcnMwBotar7e9V1Al1L2Ta1/TFzZuPt+JXEsq7J972UUmLfCxK/3AE8+9xARmBECHWQJXyL7TbgczcA06eb8z3d3WPKMujZY064q6UFsAhzRjNaLEBLc/nr0PgTjcoRrVv6IRRKjy2tBJcrHdhKHZJtXC6OTiQiAynlyHateFb7Vnzke6LFkm7WcjgAjz3dsKU3cNlsgLFRUwhDo5ZhNKKxZctuZ2iLiIiIiIjqD8NdRERERJNMICBxrk/t1HO71ei5yRjaqXdmjdCbiOGkajKrRa1rqTnbYxYhBJZ1Acu6BAIBib5+IBhU467ap/I1QRcIqPCbGXbvVutN5O+tlBLRqHp/CYWAl/8g8U//rBqtcl++tNtx2IF1a4HZs8z5Xh57VeLgIVOWwqFDQDgs4XKVt20ul8DiJRIHD5a/TUsWo+ztofoVjUoM+wH/MDDsR8bpqMnjPY2EMIxLbBgZ5GL7FhEB+du2sgNcRsYRiTZrsj3Lnt20NXJEq96wlS+wZU+2bTFcSkRERERE4xHDXURERESTgJRStfTskOjZk9XSYwG6uyVWr2RLT70xY4TeRA0nVYtZLWr1/LzyegW83lpvRX0612fOWFNArdPXjwnzvdY0iXAYCIbSYa5QSO2kBYCXX5bY9n+AWMzc253RAaz5tHnBrt5eiZ9uM2UpAKpVxDcIdLjKX2vFcpgS7upeXv4aVFs5A1x+YHi4sgEumy3duJXdwOXxjAxWENHkkkjIEWGt7CBX9ohXqzU9FtHlTAe11IjD3CMS9c+PCG5lhbj4mkRERERERBMZw11EREREE1zvETlqOCihATt3ATt3ScydA2zcACzq5B/G60W5I/QmQzip0sxqUaPxJxQyd71g0Nz1qiUelyNCXOGw3ryl2rqiUSAcASIR4MRJ4JFH1I7dYgkBQGaOi7NYVPtU93J95KE5z6kTJyXu2WZ+AC0SMWedBfOBjunAmbOlrzGjw5wxkVR5+gjF4WRoy3i6kgEulzPZvJUjxMXxiUSTk3FMYr5DIpHZwCmgAlt6QMuTbNuyZQW3sl9T9GCWw64u77CPDG5xRCIRERERERHDXUREREQT2vP7ZFGBlOOvqxDQ5k0SH/xARTeNilDuCD2Gk8pnRosajT9ut7nreTzmrlcJkcjIIJceLNE0FeSKRIBIVAW8otH0zl2bDXA6gN8/VVqwC1BrTZ8OrL1B3YbTCbQ0V2KsoMSDDwFRk4NdgNpmcwhcv0bizi2lbafDrlrOzArDUfmMAa7sFi6zQoHZhAC8njwBLi9gt/PxQTSZaJpMjUIcrXHLSAj1Hq+Hrdzu9IhEuz3drGV8v7FY0qGt7LYth2FMIgOkREREREREhWG4i4iIiGiC6j1SXLBLFwoDG27TMGtWHJe+lT8u1ptSR+gxnFS+clvUaPyZ1q5G15oxmtFqVWHMeiFlcqxiMB3iChrGKiYSEpGIauOKJsNcsWi6UcvpUCGmxkZ17HSoMUpHj0n095e3bWfPqsDLgvmVew4dPVZeI1Y+FosKo5ll9iyBdWtVw1gxAS+HHVi31rzxlVS4WExieDhzdOKwHwj41fOpEoRQI18bk6GtxsbkcYP6OEeVEU0OmiYzAlr5GreMLJZkUMum3s+9ydPGxq1cYxIdDkN4yxDastnU59i2RUREREREZC7urSMiIiKagKRUoxhLGcMHqJ38Gzb6seNRE/dQU80xnFS+clvUaHzxegW6uyV27ip/rRXdtXt8aJrMCHAFg6p1S0s+/2MxmdnGFUk3b1ksamevxw04W9Rpuz1/WGT3HnO2uWdPZccJmrWd2ZYsNr9lrLNT4Kb1qmmskEDa1KnAp/4SmH8JX48qJZGQCARUcGtoOB3gGh5Wz6FKYICLaHLLGdyKZQW3ssLoVmsypGUHXC5DYMswLjH7tcM4JtGeFeBSoa2RYS8iIiIiIiKqPIa7iIiIiCag/QdQUDvTaI4eS+D5fXEsXGDGFlG9YDjJPKW2qNH4snqlwM5dcuwLjmHVddV5bsXjmY1cwZAKm6jRickQVyQ9WjESSYe89LGKTU2AI9nG5XAUvt3hsMThQ+Z8HYcOqfXMH8do7nZm615emXVnzxLYcIvE0WPA7h71/cn3qOzvB+66G1iyRGLFcj0kx9f2YkmpQpF6cGtoSJ32+wF/ID2O1EwCyQBXY3p0YmOjCnB5PAxUEE1U+qjEWK62rVgBwS332MEtIdLBLWN4yzgicbTwNhEREREREdUWw11EREREE9D2x8zZ4/ivj4Txt980ZSmqQwwnEY2taykwd055gdl5c9U6ZovHJYJ6I1dQHSLJsW9SZga5whEgGk0HUhx2FeBqbU2PWCx3hJLPB2gmBV40CfgGgQ6XOesZmbmdRjM6Kts2Bgi4XBJnz+YPdumkBA4eVIeO6cD1ayRHNOYRjcpU69bQkGGU4nC6wc5MeoAru32roRHwMsBFNOFIKdNtW1lNW3qYK9+oxGKDW/lGJerBLbbyEhERERERjV8MdxERERFNMIGAGrdnhn9/OoqvfcUCj8ec9YiIxhshBDZuAG5cX9qoW7cL+OatouwdqvG4VAEuQ5ArGlWf07SsRi49yJW8rh7eampUx05nZZo5IlGT14uYu15qXZO3E1A7z9d8GqhkQ1Zvr8Q924BorLjrnTkL3LkFWLdWorNzcu7YTySkCm0Zglv6IVyBxxkDXESTRyIhEYuNDGwZD0Z6cMtqA1xOwOZlcIuIiIiIiIjGxnAXERER0QRzrm/k2I5SJRJAXx/wlreYsx4R0XjUuVBg8yZgw8biAl5uF7B5k0DnwuJ2uKaCXPohlA5yJRIS0agKpETCKqgUSwa5hFA7f10uoLlZhbgcjuqNWHI6TF7Pae56qXVN3k67DVi3FhVtxjpxsrRgly4aA+7ZBty0fuI2eOljFIeGRwa4AoGx285K4XKlxyY2NqoApT5OkQEuovFP06QKbMVGBrb0EJdxRKsQKpxlt6nglcejAlt2u/q41TrytUEPbWWMSmRwi4iIiIiIiLIw3GUCn8+HI0eO4I033oDP54OUEs3NzZg5cyaWLl2KxsbGmmxXIBDAvn37cPbsWfh8PkyZMgUXXXQR3vGOd8DhMPmv2URERFQ3QiFz1wuavB4R0Xh0xeUCW7cAmzbLgkY0zpurGrvGCnZlBLlCmY1ciYREJJps4wqrQFcsGe6xCDVW0e0GWlvSQa5a7gBuaVHbZcbIQ4sFaGkuf51czNxOAPgfXwTmX1LJ77vEgw+VHuzSRWPAQw8Dt94sUcmGsUrTW7iGhlSQa2gYGE6ezm7IMYPNlg5vZRwaAIdj/H4fiSa71LjEWFbjluG8ZviHGQHVtmVLHhqc6dPp8Fbma4LNZghqJUNcTmOYq8bv20RERERERDR+MNxVAk3TsG/fPjz11FP4z//8Txw5ciTvZYUQuOqqq3DDDTfgPe95T1W2r6+vD//wD/+AJ598EsFgcMTnW1pa8NGPfhQ33XQTGhoaqrJNREREVD1ut7nreUxej4hovOpcKPDAfcD+A8D2x9QIXGNTotUKrOgGVl0n0LV05A7beFxmjFXMFeQKh4FoJHeQy+sBnC61Y7gedwi7XAKLl0gcPFj+WksWq/UqwcztXPr2Sge7gKPH1GhFM5w+o9ZbMN+c9SopEpHpANeQauAaGgYCfvNbuIQAGrw5AlyNqoGv3p5rRDS2REJmNm3FgHgis4XLSB+XaLMDLjfQkGzgMga4jMFYqzVzVGKqgcuRDnBVqzmTiIiIiIiIJj6Gu0pw9dVX44033ijoslJKPPvss3j22Wdx7bXX4jvf+U5FA1XPPvssvva1r+HChQt5L+Pz+fDAAw9g586duOuuu7Bo0aKKbQ8RERFV37R2wGoxZzSjzQa0t5e/DhHRRCGEwLIuYFmXQCAg0devQloeD9A+FfB61Y7cREIiFJIIGIJckYhaQ9MkIpH0aMXRglwu5/gaybRiOUwJTXUvL3+N0YyX7QSA3XvMXa9nT/2EuzRNIhDICnAlA1168NFMblfuAFeDlyEMovFEb90KhSRiMQnfoDoeq3VLb8zSxyXqoa3scYlCpJu1HFmNW/p5m42vGURERERERFQ9DHeVIFdwas6cOViyZAmmTp0Kp9OJM2fO4LnnnsOZM2dSl/nNb36Dvr4+/OxnP4PT6TR9u1555RV86UtfymjrmjZtGlasWIGpU6fi9OnT+I//+A8MDQ0BAP74xz9i3bp1ePTRRzF9+nTTt4eIiIhqw+sV6O6W2Lmr/LXe/z4HvN4EpDS7I4OIaPzzegW8XhVQCYeBQADo71ftXOEwIGU6yJUKc0XSoZXxHuTKZcF8oGN6eU1TMzoqHz4aL9sZDkscPmTumocOqXUr1YyWSywuU6MTjSGuYX9mAMMMNls6tNVkGKHY2AjY7eP3uUU0mWiazGzciqdDW/p5AcDtTgAAItFkWMuuWowbbKpNy2pNj0w0tm7ZbIa2LUd6dKLDMDJxPL8XExERERER0cTDcFcZLrroInz84x/HqlWr0NHRMeLziUQCjzzyCO644w5Ekv+ivXfvXvz4xz/GzTffbOq2RCKREcGuz33uc/jqV78Kh8OR+pjf78dtt92GJ554AgBw7tw5fOUrX8HPf/5zU7eHiIiIamv1SoGdu8oPZP3lJ1wAAuVvEBHRBCGlCnIFg0AwpAJdoZAKcgHJRq4wUsfRqBohJ4Rq/XC7gdYWwOmsz9GK5RO4fo3EnVuAaKz4azvswJpPq3Uqq/bbGQ5L+HwqlOB0AC0tI0dR+nyAZnK+WpOAbxDocJm7rpQSoTAyQlxDw+p8MGTubQHqudTUpAJc+nFjE8coEo0HxpGJsZgKbRlbtxKJ9GWNrVs2K+ByqXGJdjvQ0mKFzQaEQgL6/6IIkRXWyhHeMrZ0EREREREREY0HDHeVYObMmfjsZz+LlStXwmq15r2c1WrFX/3VX2HmzJn44he/CC3576gPPvggbrjhBlPbsh5++GG8+eabqfMf+9jHcgbIGhoa8MMf/hADAwN47rnnAAD//d//jaeffhrvf//7TdseIiIiqq2upcDcOcDx10tfY8F8K6643IbBQZM2iohoHIpEVBNXMJAOdKmdzhLRqCHIlWzl0ncuOx1qB3RzszqemEGu3GbPEli3VuKebcUFpxx2YN1adf1qqM12Shw9BuzuAQ4fzgxuWQSweInEiuV6I5hApAKjCYH0iNBSSCkRDAKDQ8kA11D6dKyEoNxoLBbVutXUlGziakq3cbGFi6h+xeMyFdSKx7KOs0cmiuR4RJsKPnu96XGJ+rExSGu1qvdYh0Ng6lQrHE4gHEo3brF1i4iIiIiIiCYiITljp2jxeBw2W3G5uK9//et4/PHHU+e/9a1v4VOf+pQp2xOLxbBixYrUuMjGxkY8/fTTaG5uznudN954A1dffXUqcHbZZZdh+/btJd3+wMBASdcj8wkh0NLSAgDw+XwcoUVENMn1HpG4cb1q0SiW2w08cF8zLn2rje8pRDRpxOMqtBIMAoHksR5WicWyWrki6Z3TDnt6rKLTpXY6WyzcsXzipMSDDxU2+nBGh2rCqlawy6ha21nM7XRMB65fo8IOm79f9E2NacMtQMf00b8GTZMIBDLDW3obVzxu7vY4HOkGLmOIy+vlc4mo3kgpEU9kNm1lB7iMvzpYLOq1zGbPOrblbtHS27UcWY1b+sdtNnV5/g2MiIjMwvcUIiIyE99Xaqu1tbXWm1AxbO4qQbHBLgC49tprM8Jdhw8fNm179u7dmwp2AcBHPvKRUYNdAPCWt7wF73rXu7Bnzx4AwEsvvYQTJ05g9uzZpm0XERER1VbnQoHNm4ANG4sLeLldwObvWnDpW/mjIhFNXJqmxisGAukgVzj5WplIpINc4QgQCQPx5Igom02FuFpbVSOX08HxTvnMniWw4RbVVNWzBzh0OLOtxWIBliwGug1NVbXezt17gMOHshq1TNjO3t7iGsLOnAXu3AJ85nrV6GXmaEaLBWgx/MlA0ySGhw0tXIbTxvurXAIqrJUKbxlGKjqdfA4R1QtjeCtf81au8JbdDng8mY1bNlvme2T2yMRUaMuh3k/tdgY6iYiIiIiIiHLhHrsqufjiizPO9/f3m7b2M888k3H+Qx/6UEHXu/rqq1PhLgB4+umnccMNN5i2XURERFR7V1wusHULsGmzLGhE47y5wDdvFVjUyZ0qRDSxRCKqlUsPc4VCKrgipQpyRSJAKKwCXXpbl8WiAlxNTckglzPdGEKFElgwH1i4QMBq9WDAJzFwIQiHU6KlGXC56uX7qbZzwXwgHJbwDarHhNOJsrfzxMniRz8C6vIPPAjMnw8cOVryzY8w/xLg6DFgaEhicAgYHs4MapTLZgUa9eBWY/p0YyODkET1oNjwltWaDms1OEeGt4xhLIslM7TlMIS29OYtjkwkIiIiIiIiKh7DXVUSCAQyzpfS/pXPvn37UqetViuWLFlS0PW6urpGrMNwFxER0cTTuVDggfuA/QeA7Y9J9PQACUMTh9UKrOgGVl0n0LWUO1yIaPyLxyWCISCYDHIFAukxcrGYauxKtXJF1E5sIdQOaI9HBblcTsDh4OuhmdxuC9xuoKlRmBomMpvLJdDhMms1NYqx2GCXLhoD+s+btS2K3Q689LIJ69iApmaguSndxNXcpJ5D/FmCqLbicZkObJkY3rJaR4a3jAEuu53PfSIiIiIiIqJKYLirSnp7ezPOd3R0mLKupml4/fXXU+ff8pa3wOv1FnTdSy65BC6XC+Hk7JFXX33VlG0iIiKi+iOEwLIuYFmXQCAg0devRpB5PED7VMDr5Y4YIhqfpEyOVwymw1yhkPpc9njFcBhIJMcr2u0qxNXYADiT4xU5CorMdvSYGrFYjgsX1BjQgYHyt8fjBpqbx76ckcOhrqOHt/Qgl9vFEBdRrSQSMjO0lRXgKje85UyOSjSGtxwOtlcSERERERER1QrDXVXy61//OuP8O9/5TlPWffPNN1PhLACYOXNmwdcVQqCjoyMVDjtx4gTi8biprWJERERUf7xegQKz4EREdScel6nRioGACqqqwJYhyJUMc0Wj6jr6eMXmZtXI5XJxPBxVx+495qzT4AWGh4B4ovQ1LBZg/oL8n3e70sEtY5CrfkZnEk0empYMb2WFtmIxdVozhLcsFtWkZ7erf9zIDm8Z3+9Ga95ieIuIiIiIiIiofjHFUwV79+7F3r17U+cbGxuxfPlyU9Y+ezbzX4CnT59e1PWnT5+eCnfFYjGcP3++6DWIiIhobIGAxLk+1SbjdgPT2tmWRUQ0FiklQiEV4NIDXfr/tiQS6nPhCBBJhrm05MhZp1O91ra2qCAXxytSLYTDEocPmbPWiZPlXd9iARZ1qpCYx5PZwKUHufg8IaoeKVV4K54MbMWywlvGEepCpINaHjdga0qHubLDWxZLMqzlZHiLiIiIiIiIaCJhuKvCgsEgbrvttoyP/fVf/3XBoxPHEggEMs4Xu2725bPXKwTHMNQP433B+4WIqPaklPjv/cD2xzT07EmPAgPUf82vWC6xaqUFy7rq73Wb7ylEVAvxuEQwCPgDyTBXQEDT1OtpJAqEQ+lWrlhUvTZZbSrA1TZFHTudHK9Yj4xvJer0xL+PfIOAZpyNViMtLcBHrgHmXyLQ1ATY7RP/e09UD+JxmRHYisbSDVzxuAD0lweRbNpKvp/ZG7Lbt4w/l2eFtpzp4JbDMbme3/x9hYiIzML3FCIiMhPfV6hSGO6qsG9/+9upZiwAmDdvHj7/+c+btn4oFMo473Q6i7p+9uWDwWDR29DS0lL0dajympuba70JREST2suvxHHrN/049mru+UmJBPAfu4D/2KVh/iVW3HF7Ay59a33+aMb3FCKqlFBIwh+Q8PvVIRRSe7rjcXU6FFbtXJGIhJYAINSIuPZGAbcbcLvFpNqRPVF4vQ2jfj4U0nBhQCISlnC6BKa0CrjdliptnXmsljiA4n/HNoMQwOXLbHjfex3o7LTyj4lEFZAenSgRjertW+p0PCZTbZKAPg5RvXfZ7YDDLmB3AHabgN2e+Qd/1bQl4HQJFdxyqmOXa+RlSeHvK0REZBa+pxARkZn4vkJmqrs9iAMDA3juuefw8ssv4/Tp0/D7/QiHw5BF/rerEAL3339/hbayMPfddx9+9atfpc47HA784Ac/KDqANZqwPpPEcBvFyL589npERERUvGefi2L9V4eRlcHO69irCXzmrwex5UeNeNdVxb2XExGNF5qWDHEZwlzxuLGVKxnoCqkd44Bq5fK4BRraLHC7BFwutnJNVFJK/KE3gaefiWL/gXhGKMJiAZZ1qaDSojoNKsXjEoNDEr4BFUwb8Gk4cUIb+4omsdmAj3/MidmzLWhutqC1ZXwG4ojqTSyWDnDFYkA0qtq4olGJRDx9OWFRzVsOh4DXA9jsIhngEnDYM9+7rFYV1NKDWy6ngMOZDHSxfZKIiIiIiIiIcqibcNfp06fxgx/8AE899RTi8fjYVxiFlLLmf+x94okn8Hd/93cZH/vOd76Dt73tbabeTnZQLBaLFXX9qL7XJM96hfD5fEVfhypDCJFKAA8ODhYdiiQiovL1HpFY/1Wt4GCXLhQC1n91GHdvsaBzYe136PA9hYjKFYtJNV7RDwSCQDAISAkkEhKRiHrdC0fUmEW9lcvpBNwuoLk5OZrK0MqlaWoNGp+ESDd2BQJ+GN9WTpyUeOAhiTNncl9X04B9L8Sx74U4OjqAz6wRmD2rNu+VUqrRob5BwOdTx4M+ieFhIPutMp67vLMi4nFg564Ibr1ZQAiBRALw+/NfPhyWGPAB0QjgcAKtLSpsQjTZaJoKGseMYxOToxNjMUAa27dsgCM5LtFhB+we1cKVPTrRYskcm5g6nTw2XjZFApGIOlBh+PsKERGZhe8pRERkJr6v1NZEnjpXF+GuPXv24KabbkIwGEw9uGsdzirHs88+i2984xvQDP9q/LWvfQ2rVq0y/bY8Hk/G+WKbtyJZfzXKXq8QfEGqT1JK3jdERFUmpcR3b5dFB7t0oRCwabOG++8VdfWzEN9TiGgsUkqEw0AgABXoCqR3UMdi6nOhMBAOAdEoIAFYLSrA1dKijl052kr42jORpO9bKdP3bW+vxD3bVKiiEGfOAD++U2LdWonOzsq+V0ajEoNDKsQ1OJg+jhX4/2hWKzBlCnDhQiW3Mu30GeDoMYkF8/NdQuLoMWB3D3D4MKAZnl4WASxeIrFiOZLXr5+fQ4jKlUjIzOCWIbxl/P9SIdJhLbcLaGpMBrkcGDEO0W7PDG85HTJ1fqzRiXxvMx9/XyEiIrPwPYWIiMzE9xUyU83DXUePHsWXv/xlhJJ7QYUQ4/pBfvDgQXzpS1/KaNBau3YtvvCFL1Tk9rxeb8b5YJH/yh4IBEZdj4iIiAq3/wBw/PXy1njtuFpnWZcJG0REVCGaptqL9CCX3w8kEgCQbuUKhVUrl77j3GEHXO50mMvhYHhksjtxsrhgly4aA+7ZBty0XprS4KVpqnlrcDDZyDWoTmf9ulySGR3VC3cBQM8e5Ax3nTgp8eBDwJmzua+nSeDgQXXomA5cv8ac7y1RNUiZbt8acYgjY8yr1ZoMcNkAt1sdj9a+lQ5vZZ63Wvn8ICIiIiIiIqLqqXm46/vf/z5CoVDqP9qEEPjwhz+Ma665BosWLUJbWxvcbneNt7IwR44cwRe+8IWMgNXHP/5xfOMb36jYbU6fPj3j/Jl8cyzyOHs2/Zddm82GtrY2U7aLiIhoMtr+mDnh9B2/kljWxR1GRFQ/4nGZ0coVDKqd5ZqmWrnC4fSYRU1T7SdOJ9DYoHaeu1zcEU7ZVNio2GCXLhoDHnoYuPVmiUJbpqSUCEdGNnENDmWGP8rR4FVjRZubVZCxpRm4627g7Dlz1h/LoUNq5KJxxGLR7WhngTu3oCrtaESFyh6fqIe34skAl/4/ogLp8YkuN9Cgj1BMjlM0vhfZbOq9yhjg0k+P1b5FRERERERERFRNNQ139ff34//9v/+XautqbGzEP/7jP+Lyyy+v5WaV5I9//CM+97nPwefzpT724Q9/GN/5zncqersXXXQRnE5narziqVOnCr6ulDIjDDZ79mzY7XbTt5GIiGgyCAQkenrMWWv3brWe18sdSkRUG9GohN+fDnPp42bjcZkKcoWyRyy6gSmt6tjpGDlikcjo6LH8LVKFUmMIczdV6W1cAz4V4hrwAYM+FUA0g8MONLeo8FZLczrQZbePfNx/5nqJO7eUHmQrhiZV81iHS52vl3Y0okJomhwR3kqFuLLGJzrsgM0OeDzpMJZ+0AOf+phFPcCV3cJlbOoiIiIiIiIiIqpnNQ13Pf/886nxi0IIbNiwYVwGu86ePYsbbrgBfX19qY+95z3vwQ9+8ANYLJaK3rbFYsGcOXPQ29sLAHjjjTcQDAbh8XjGvO6rr76KcDicOn/JJZdUbDuJiIgmunN9QMKk1o+EBvT1A5yWTETVEg6nw1x+vwptASrkFdJbucJqBzugdpa7XSrM4nZzxGK9CIclfD4gElXhBTUCsz7vm917zFmnZw/wloulGqfoMxwGzWnjEgJoalTfS2Mbl9tdeKvP7FkC69aWFrIqRSQVYKt+OxrRWBIJOSK4pYe51HhfxWJJjkx0qPZHu93YwJU5PtHpzGzdMga52L5FRERERERERBNBTcNdxjCU2+3GRz7ykRpuTWkuXLiAG264AW+++WbqY1deeSXuuuuuqrVgXX755alwVyKRwMGDB3HVVVeNeb39+/dnnL/iiisqsn1ERESTgd5qYxbDlGciIlNJKREKISPMpRpRJCKRdCtXKJze0e50Al6PauVyu9h2Ul8kjh4DdvcAhw+r5iadRQCLl0isWK63W1XvfssOmrW2Ag0N6c8dPmTO7Rw4qB6fVmv5a7ndySaulnQTV1OjOSNFOzsFblqvwlblNpaNxelUx5VuRyPKJ5GQI9u3oskAlyF0abWm27a8nsz2LePzzmrNbN8yBrkYLiYiIiIiIiKiyaCm4a5Qci+oEAIXX3zxuBsJ6Pf78fnPfx6vvfZa6mNvf/vb8U//9E9w6n9NrYL3ve99ePjhh1Pnn3zyyYLCXU8++WTG+fe///2mbxsREdFk4Xabu14BJZxERAXRNBXmGvarIFcgoEJbUmaOWAxHVNOREKolpblJHbvdHLFYr06cHD0spEng4EF16JgOXL+m0iP2RgmaWSSWdQXxvvc6YLPKjM+VKxIFPEW8D9us6fCW3sTV3Aw4nZV9nM+eJbDhluT3aA9w+BBM/T4AqsWopVmdNrMdjeEuypYrwBVNBriMrXk2q2rfcjhUK60+QtFmywxwpcYnOkaOUWSgmIiIiIiIiIgmu5qGu1paWlKnrWb8m20VhcNhfPGLX8RLL72U+tiiRYtwzz33wFvlGUpXXnklWltbMTAwAAD4zW9+g6997WtoamrKe5033ngDzz77bOr8ZZddhtmzZ1d8W4mIiCaqae2A1WLOaEarFWifWv46RDQ5aZpEIJBu5goE1I52TUuGuQxjFqVUYRC3SzUruV1qZ3p2mGs8jfubLHp7ixvzd+YscOcWYN1aic5O8++7MYNmGrDvhTj2vRBH2xRzb1tL5P+cxw20tKrAU2uLeuw2NNRyVJvAgvkqLBUOq3GSkQjwf38D9B4pf/Uli9Vz08x2tEOH1LbyOT/55GvgiuYJcLmc6vnlMDRwGd9PHA5D61ZWC5cZDXlERERERERERBNVTcNdixYtAqD+Y/zs2QrPJTBRPB7HTTfdhOeffz71sblz5+Lee+9Fc3Nz2eufPHkyo0XroosuwjPPPJP38g6HA5///Ofxgx/8AAAwPDyM733ve9i8eXPOy2uahm9961vQDH+J+5//83+Wvd1ERESTmdcr0N0tsXNX+Wut6FbrEREVIpGQCASTrVx+IBBUO90TCZkKcQVDQDQCSKgAqdsFTG1TrVwOR76gS32O+yMVpCom2KWLxoB7tgE3rTe3wavYoNn5C6bdNADAYlUhxaYmQ4grGeiqdBtXOVwugQ6XOv3BD0hTwl3dy9Wxz2deK5gmAd8gUttKE4umyVTjViymnse5RijqAS6nE/COFeDKMUIxV3CYiIiIiIiIiIgKU9Nw15IlS9DR0YEzZ87g/PnzOHr0KBYsWFDLTRqTlBK33HILdu7cmfrYrFmzcP/996Otra1m27VmzRo89NBDOH36NADgl7/8JZqbm/HVr34VDocjdTm/34/bbrsNzz33XOpjXV1d+LM/+7OqbzMREdFEs3qlwM5d5e9JXXUdd3wRUX7GMJc+ZlHKdJhLP0SjKsxls6kQV3OTHuYa+zWm/sb9UZq6b4oNdumiMeChh4Fbb5YwI5RXatDMLEIA11ytGi/Hc/PPgvnquZTvOVeIGR3p8YmRqDnbpYtEzF2PqkvTZDq4lTVCMWFovrNaVTgre4QiA1xERERERERERLVV03CXEALr1q3Dd7/7XQDAz372M3z/+9+v5SaN6dSpU3j88cdHfOy9731vUetcdNFFeOqpp0zbLpfLhbvvvhuf/vSnEQqFAAD33nsv/u///b94z3veg7a2Npw5cwbPPPMMhoaGUtdrb2/Hj3/8Y9O2g4iIaDLrWgrMnQMcf730NebNVesQEekKDXPpYQ67XTVztbSoMJfdXtyO9nob90eZjh4rLwAEAKfPqHX0IFDpyguameHtS4CO6RPhcSdw/RqJO7eU9v102IE1n1brACpsYyan09z1yHxSypztW9EYEI+nL2exqPcJhwPwetKnbbbMgGRqhKKLAS4iIiIiIiIiolqrabgLAD71qU/hqaeewn/+53/i17/+Na688kp87GMfq/Vm5SXlyDYO43jDQiWM/xppkssuuwx33XUXvv71r8Pn8wEAzp07h3/7t3/LeflZs2bhrrvuQkdHh+nbQkRENBkJIbBxA3DjeolQuPjru13AN28VecajEdFkoWkS/kD+MFfQ0MwFJMNcbjWGzu0G7LbSX0PqbdwfjbR7jznr9OwpLdwVi0sM+oABH/DKH8oPmpVLH0M4EcyeJbBubfHPQYcdWLcWGc+9lhY1PtWM0YwWixpxSfUhFk+GuKIjQ1w6IZJjEx1Aoys9QtHhyAxw2e0qrOVyAg6n4bRjfDfhERERERERERFNNDUPdwkhsHXrVvx//9//hxdeeAG33XYbjh49iv/xP/4Hmpv518NidXd34/HHH8cPf/hD/O53v0u1eBk1Nzfjox/9KL7yla+goaGhBltJREQ0cXUuFNi8CdiwsbiAl9sFbN4k0LmQO9KIJhtNkwgYw1xBQNMym7mChjCXIxnmmtIKuMoMc2Wqr3F/NFI4LHH4kDlrHTqk1nO58t9X0ajEgA/wDQAXfMDAADA8pMZ9AsAfes3ZllIZxxCOR+GwhM+nWvecDhXI6uwUuGn96GNRjWZ0qMau7FClyyWweInEwYPlb+eSxRj1cULmSyQyxyhGo+kQlx7YEwBsycCWxwO0OFSYy2EHbIb3BasVcLnSwS29fUu1dfF+JSIiIiIiIiIaD4TMVUVVRY899hgAIBaL4ac//SlOnDgBIQRcLheuuuoqvO1tb0NbWxucJcwAWLlypbkbO84EAgHs27cPp0+fxuDgIKZMmYKLLroIl19+ORwO82Y0DAwMmLYWlUcIgZaWFgCAz+fL2TRHRETV0XtEYtNmWdCIxnlzVWNXPQW7+J5CVDlSSgSDwPAwMJxs5soOc2WPWfS4VaDL7a7czvijxyTuurv8db78JWDB/Pp5PZtIzpyR2Px989bbcEt6pGE4LDEwoBq5BgbUwR/If91EAvivveZtS7EcduCm9SNDTfVP4ugxYHcPcPhwZrOWRQCLlwArlgPzLwGOvaqa2g4fyrqcRQWuupfr4bbc3wM+p+tbxhjFaGaQy1j2brOqIJbdng5v6S1c+n1vsYxs33I6VaiLAS6aqPj7ChERmYXvKUREZCa+r9RWa2trrTehYmoe7lq0aNGI0UP6JpU7kuiVV14p6/pUGIa76gffLIiI6ouUEvsPANsfk+jpARKGSc5WK7CiG1h1nUDX0vJ/7jEb31OIzCOlCm0N+9PtXImECnOFw0AwODLM5TaEucxr5hrdtv9jTsvP0rcDn7uhvl7TJoo3/ijxwx+Zt97MGcDChYDNph6HxQgGgQMmPF5KoY8h7OwcX4+zEycLb+TqmA5cv0aF18JhCd8gEImowE5Lc6FNWhKbv1fe6MwZHcCtNwNs4ytdPDlGMTrKGEWLJRnYsqeDXPqxxaK+90Koj+nhLZdLhblcTnW5evtZkqjS+PsKERGZhe8pRERkJr6v1NZEDnfVfCyjTkqZ+kNUvrBXIYQQGWsRERFRdQQCEuf6VEDB7QamtQNe7+R+PxZCYFkXsKxLIBCQ6OtXO8M9HqB9Kr8/RBNZOCzh9wNDwyrMFY8nQ17GMFdYjbez2VQzV0tLMsxlr/5rQ7XH/VFpnOYVMAMATp1WB7cbWLAAaPAWdj0BdZ1amDoV+OvPjo/GLuPYxTffBB7drl4LCnHmLHDnFmDdWonOToEOVylbIHD9Gok7t5Q2btVhVyMfGewam6ZJxOOZAS79tJYM9+tjFO329BhFPcBlbNey2w3jE13qee9yqcvqQS8iIiIiIiIiIppc6iLcpYe3zEgtMvlIRERUPalmqh0SPXuymqksQHe3xOqV9dlMVW1er4C3wJ3mRDT+RKMSw8kg17Bf7dQHMscshsKAlKq5z+0GmtrVscNR+9dHny9z7Fs5NAn4BlFiGIVG09KiRveZdV/pQiHgxReBRZ3qNowsFqC5GWhtAVpb1XFLC9DfDzz3n+ZuRyE+s6beg135xy4WKxoD7tkG3LRelvw1z54lsG6txD3bigt46e1o9f29rr5EQqrQljHElRynqLNYVBDL4QC83vRpY7uWcYyiy2UIczk5RpGIiIiIiIiIiEaqebjrjjvuqPUmEBERUQl6j0hs2ixx/PXcn09owM5dwM5dEnPnABs3AJ0LubOKiCaGeFymglzDw0A4rD4eiUgEQ6qdKxxWjS0WiwpxTW1Tx05n/b0W6iMhTVsvYu56pLhcAouXmDM+M5umAX/oBZa/G5g7Jx3kamoCrNaRj9mWFgkB1T5XTa46Dg0WM3axUNEY8MBDwOduUKEip0OF64ppxuvsFLhpfeHbNqNDNXZN1mCXlGqMonGUYjSqglyJrBYuhwPwetIBLocj8/mSGqOoB7iSTVwOB//xgYiIiIiIiIiIClfzcNeqVatqvQlERERUpOf3SWzYqMaLFeL468CN6yU2bwKuuJw7soho/NE0iUAgHeYKBlULVywmEQwCwWQ7VyIBCKFCXK2tatyi01n/O/HNHvfndJq73mQXi6vxfgMDKiRYKZoGHD0K/MVqYKxRfH39qnWomkE+iwVoaa7e7RWjt7f4dqxCnT0L3PH99HmLABYvkVixHFgwHyhkbOLsWQIbbkm2iu0BDh/KbBWzWIAli4HuItYc7zRN5hyjGIup13dAfV/sOUJcxhYuq1W9hjqTAS6XK93ClSsYSUREREREREREVKyah7uIiIhofOk9UlywSxcKAxs2SmzdwgYvIhofgkGZCnMFAiq4lUioUYvBIBAIAvG4uqzLCTQ3AW6POm2xjK/XOTPH/dVzAGc80DSJwUHgwgXg/AXgwgAw6MtsyHK7VZiwEk6fAY4e0wM+uVUyyDSaJYuLa6yqlhMnq/v90CRw8KA6dEwHrl9T6NhGgQXz1X0bDkv4BlU4z+lUz9l6/N6aIR6XIwJc0Wj69RsAbDY1itLjBhzNgN2hzhtHJLKFi4iIiIiIiIiIaoXhLiIiIiqYlGoUY7HBLl0oDNx+h8T993InGBHVn2hUYng43c6l2lvSYa5gMD2+0OEAGryAx6N29I/3dhYzx/3VawCnHkmpGuHOX0iGuc4DAz4VJBzNggXAiy+qpq1K6NmTP9xV7SCTUffy6t/m2NS4w1p8PwA1ZvHOLcC6tRKdnYU/71wugY46HnFZrJyjFCPqWDOMUtRDW42N6tjpVC1ceiDXYkm3brmymrjG++s8ERERERERERGNXwx3ERERUcH2H1AjFsvx2nG1zrIuEzaIiKgMiYQK1gwNqUCX3oQUiUgEgkAoqEKpUqpWF7cbaEmOWjS2uUwUK5bDlHBXfQZw6kM4LHFhQIW49GauaLT4dRq8wKJO4A+9lQl4HTqktnVkSK92QaYZHaO3idXK0WMqYFVL0RhwzzbgpvWFNniNX5qmQlx6E1ckmmzkyjdKsUGFuPRxivq4SavVEN5yp0NcbOEiIiIiIiIiIqJ6xHAXERERFWz7YybM6wKw41cSy7q444yIqktv4RoaTo9a1DQ1sksPcwVDqjXJIlSYq61Nhbmczon/mrVgvhrxVk5QpV4DOLUQj0sMDBjGK14A/IHS1xMAmpqBKa3AlCnqeNgP/MvPzQ8XaRLwDWJEs1OtgkwOO7Dm04AezKknu/fUeguUaAx46GHg1psl6vH7VCw9xKWHt4whLp3VqsYiutxAU1M6wJVrlKLblW7gcrkAu338f4+IiIiIiIiIiGjyqOtw1+uvv44DBw7g9OnTGBoaQiAQgNfrRVNTE2bOnImlS5fiLW95S603k4iIaFIIBCR6esxZa/dutZ7Xyx1rRFRZsZgatagHumIxFRpIjVoMpZuTXE6guQlwe9RpfUzX5CFw/RqJO7eU1sxUzwGcStM0icEhFeDSw1yDg+kmoVI0eIEpbUBbMszV0grYsxrj2tqADbdIHD0GPPl74OjRMr8Qg0hk5MdqEWRy2IF1a1GXjVThsMThQ7XeirTTZ1QAbzwFLDVNjghwRaJAPJ6+jM2mHgceD9DiSIe49DGJQiRDW07A6coMcnGUIhERERERERERTQR1F+7q6+vDz3/+czzyyCM4f/78mJdvb2/HJz7xCXzyk59Ee3t7FbaQiIhocjrXByRMGv2U0IC+fsDrNWc9IiKdpiVHLQ4Dw0MqvAXkH7Xo9ajgjMfNEACgAjTr1krcs624gFc9B3AqIRiU6D+fHK84AAxcAOKJ0tdzOlUTV1ubejy2TSmmLU5gwXygsUFi8/dL34Zc22RUiyDTjA4VGKzXx5XPp1rO6knPnvoMdyUSEtEo0oeYOs4OcTmdQGNDOsBlt6dfmy2WdIDLOE7ROSnDuERERERERERENJnUVbjrF7/4Bb73ve8hHA5DZv2LsxDpP9QZP3fu3DncfffduPfee3HLLbfgE5/4RNW2l4iIaDIJhcxdLxg0dz0imrwiEYmhIRXo8vvVWMVEQiIYBAJB9XqTGrXoAaa2qQYYh4NhgFw6OwVuWi/x4EOFjeCr9wBOuRIJiQsXgP7zSB2X855otaZHK7ZNUcdeb+bvvKVoaVGPcTPCRhYL0NKc+bFqBpkWdQIf+DM9pFS/j6tItNZbMNKhQyqI53LV5vs2IsSVDHLpIS4BwGZX4xQbG9WxHuLSA1pWa3qUorGJy+Eo/3lCREREREREREQ0HtVNuGvjxo345S9/mQpu6X+w089nh72yLxMMBvGtb30Lhw8fxne/+90qbTUREdHk4Xabu57HY+56RDR5JBKqnWtwCBga0sfHqVGLgSAQDKRDF04n0NQIeLyTddRiaWbPEqlxf7v3AIcPZQZ7LBZgyWKge3n9B3CMwmEJn089PpwOFYgyhmCklPAHVCOXfhjwlT5eUQBobkm2ck1RYxabmyrzOHS5BBYvkTh4sPy1lizGiHBQNYNM114DvOXiyjymxnoMFGPggqmbZgpNAr5BoMNV2dspJMRld6hWv6Ym9b222zMDWlar+vnS5VThLX2UYuGtdURERERERERERJNDXYS7fvSjH+HRRx8FoP7IJ6WElBIdHR34kz/5EyxatAitra1wu90IhUIYGBjAH/7wB+zduxenT5/OCHk9+uijaGtrw1e+8pUafkVEREQTz7R2wGoxZzSj1Qq0Ty1/HSKaPMLhzHYuTQNiMdXOFQyq8Yuapl5fPB6gpVWNWrTZGBIonRr3t2B+MhAzqIJ0TqdqdapVM1DxkiG1HuDw4cyQmgCwsFNi4QL1dV04D4Qjpd+S15tu45oyBWhtBexVfAyuWA5Twl3dy0d+zOkof91CZY+ELF/+x4BFAIuXSKwoOqgo8Zsnzd5Oc0TKeAxn0zSJSBSIRtKjFI3jFIsKcWWNU2R7IhERERERERERUWFqHu46evQofvazn2UEtBYuXIhvfOMbePe73z1m5f6ePXvwgx/8AL29valg2M9+9jNce+21WLBgQTW+BCIioknB6xXo7pbYuav8tVZ0q/WIiPJJJCT8fhXmGhxUYQIpk+1cARXmiiabhFwuFaLxuFUohGO7zOdyiYo3AVXCiZOjj5eUAHp71cHtBhYsABq8ha3tsANtbZnjFWsdeFswH+iYXtg4zXxmdOghp0xmjn0cTa6RkOUY6zGgSRWIO3hQfe+uXyMLGjF69BhwtozvcyWVEo7TNIlYDOkgV1SdzjVOsalJPf4djswQl82WbuAyjlS02/maTEREREREREREVI6ah7vuvPNOJBKJ1B8DV65cidtvvx1Wq7Wg6y9fvhzvete78M1vfhM7duyAEAKJRAJ33XUXtmzZUslNJyIimnRWrxTYuav8vbqrruNOPiIaKRKRGBzM3c4VCAKhZDuXzQZ4PSpM43EDVitfU2ikF1+UuPf+dDhlLKEQ8OKLwKJOFWQy0scrtrWpINfUNqCxsR6DhALXr5G4c4tqWSqWww6s+bRaJ5uZYx9Hk2skZKn+0Cvx03uAeKKwy585C9y5BVi3VqKzc/Rt2L3HhA2sgLHCcVJmjlOMJI9jhseLzaYCYo2NKsyVHeIyNnExxEVERERERERERFR5NQ13RSIR9PT0pP5A+I53vAPf+973il7HYrHgjjvuwIkTJ7Bv3z4AwO7duxGJROA0f54DERHRpNW1FJg7Bzj+eulrzJur1iEi0rRkO9cQMDikRonla+fyuIEprWrkotPJAAFlklJi2A/096vDa8eB//wvFQYshqYBf+gFlnWp97u2tmQ7V+v4Ca7MniWwbq3EPduKC3g57MC6tRi1tcqssY+jyTUSshT7XpB44KHirxeNAfdsA25an7/BKxyWOHyozA2sED0cJ6Vq4soOcemvqQBgs6rQltcDOJzpIJfFkg5x6QEul4vjFImIiIiIiIiIiGqlpuGuF154AZFIBID6D9C/+Zu/KWu9r3/96/jLv/xLACo49sILL+Bd73pX2dtJREREihACGzcAN66XCIWLv77bBXzzVlGHTSdEVC3RqMTQkAp0DfuBRGL0dq62KSrQpYcNiAA1tvPCQDrMdf48EI6kP7//QPHBLp2mASdPAp9ZA+RqsBoPOjsFblo/+jhCoxkdqrFrrHGEZox9HGs7co2ELNYfeksLdumiMeChh4Fbb5bI9Rjw+So/nrJUb7sMOHFCtXPp22ixqCYut1u1eulNXHrrocViCHG5k21cTgZpiYiIiIiIiIiI6kVNw12nT59OnZ4yZQre/va3l7Xe0qVL0dbWhvPnz49Yn4iIiMzRuVBg8yZgw8biAl5uF7B5k0DnQu4oJJpMpFTBrcEhYGhQtXEByXauIBAMqEYZQAUPWltVqIuhAjIKhWUqyNV/HhgYyB/eGhxUIcFynDkLHD1mTtCoVmbPEthwi8TRY2qE4OFDmYEkiwW47FJg8WIV2LLbVCPV6CMRyxv7OJrRRkIW48RJiZ/+rPztOX0m/2Og92j561fClFZ1XzocQINhpKLNpr6nQiQbuAxtXG535shFIiIiIiIiIiIiqj81DXfpISwhBDo6OkxZs6OjI7XuhQsXTFmTiIiIMl1xucDWLcCmzbKgEY3z5qrGLga7iCaHeFxieDgZ6BoC4nHVtBQMqnGLgaAK5litKsjVOkWNXdRbZGhyk1JicFCFuPQwl99f+PVPnzFnO3r2jO9wlyKwYL76OsJhCd8gEA4DZ88CBw8DL70EHH4xfWmLABYvkVixXP/aRz4nSx37OJpCRkIWRrWVxeOmbFbOx8CJkxK/ftyc9c1ktQKf+2vg4tnqe+h0ZjZxuZJtXGxBJCIiIiIiIiIiGn9qGu5yOp2p0+FwCbOdctDHPAKAw+EwZU0iIiIaqXOhwAP3qdFX2x+T6OkBEoYWFasVWNENrLpOoGvpxG6ECAQkzvWpphi3G5jWDni9E/frJcolElGBnMEhFcSRUn1MD3NFwoCEChe0tOjtXBP7tYEKE4tLXDAEufrPA7ESQ0M2GzBg0v/4HDpUSJPV+OFyCcT6Jf7l5/nHKmoSOHhQHTqmA9evkTkDV8WOfRxNoSMhC3H0mLkjI0c+BtTXXOrjs5IWzAeWv1ukglwMyxIREREREREREU0cNQ13tbW1AVD/mf3mm28iFArB7XaXvF4oFMLJkydHrE9ERESVIYTAsi5gWZdAICDR1w8Eg4DHA7RPndgBJymlCrbtkOjZkxVsswDd3RKrV078YBvVh1oEDKWU8PtVM9fgkGoDklKNW/QHVENXPK6agDweoHmaOtbHg9HkFQxJ9PcBfckxiz6fCv4VSwBobgGmtgFTp6rj4WFgd48526lJwDcIdLhKu344LOHzqbGjTocKNdYyKNbbW1zb1pmzwJ1bgHVrJTo7czd4jTb2UQhg3jx1+vhrI0dCLlkMdI/SEFaK3XtMWSYl+zFgdnjMTEePqoauifyzFxERERERERER0WRV03DX/PlqvoEQApFIBE888QQ+9rGPlbzeE088kdEApq9PRERElef1Cni9td6K6ug9IkcdSZnQgJ27gJ27JObOATZuAEdSkulqETCMxyWG/cDgYOa4RX8ACAaAYEiNW7TbgQYv4PWqBhmOAZu8pFSPGT3M1denwn+lsNmAtrbMMJfdnvnYOn+hlJhYfoZi6AIlw049wOHDWYGmAkYeVsqJk6WNUYzGgHu2ATetz93glWvsYySSbOhrTofZwmGJwUEBi8UNl1vAYQ/CUORtinBY4vAhc9cEMh8DZofHzJTQ1HMs189ibBglIiIiIiIiIiIa32oa7lq0aBGmTZuGvr4+SCnx93//93j3u9+Njo6Ootc6e/Ys/v7v/x5CCEgpMW3aNCxatKgCW01EREST2fP7JDZslAgVOFH6+OvAjeslNm8CrricO1LJHNUMGEYiEkNDqr0me9yiP5AOPrhcQGurCnU5HPX1WK+3BqWJTNNUwKevT7Vy9fWrVrdSeL2ZQa7m5rGDgk5HabeVd70iAkgnTo4+prDQkYfmU9tVbLBLF40BDz0M3HqzxGiBNJdL5G05c7kE3G6Bhgb1Jwi/X/3ebiafLzNMZxb9MVCJ8FhbG3D+vHnrBYPp02wYJSIiIiIiIiIimjhqGu4CgFWrVuGf//mfIYTAwMAAPvWpT2HLli1429veVvAaL7/8Mr785S9jYGAAgPrD5KpVqyq1yURERDRJ9R4pLtilC4WBDRsltm5hgxeVrxoBw2BQYnBQNXQFQ8lxi2Eg4FeBLuO4xZYWwOsBrNZ6e2zXZ4PSRJNISJy/kA5z9fcDsXjx6wihwoGpMNdUwOMu/n5paVH3rxkhH4tFtU8VwuyRh2YyY5Tg6TPAkaPAwgXmbFMlRKLmr2l8DJw/b254zGEHvvJl4LZvm7emx6OO2TBKREREREREREQ0sQhp9r/LFikQCOADH/hAKpglpYTVasVHPvIRrF69Gu94xztgs43MoMXjcbzwwgvYsWMHHn/8cSQSiVRr19SpU/H73/8eHv0vm1RR+n1HtSeEQEtLCwDA5/OZ/t/wRESTmZQSn/nr/DtKCzFvLnD/vWJcNGTwPaU+9R6RuHF98QFDAHC7gK1bRM4d+JqmRivqga5oVIV2gkEgEAACQTVu0WZTbUoNdT5ucawGJSPVoIQqNSiNf9GoTDVy9fUBFwbUY6NYdpsKcLW3q0DXlCmAzWbOfbDt/0gcPFj+OkvfDnzuhrG36cRJiTu3lNaM5bADN62v7OPPrO+Hywl8+cbSt1UIgYaGBgCA3+83/X3lzBmJzd83dUm8dRHw0Y+ohsJTp4FHHjVv7a+sBz78IYFr/lxmtGqVymoFnvi1wMuvoOggutsFbN4k2DBKROMKf18hIiKz8D2FiIjMxPeV2mptba31JlRMzZu7vF4vfvzjH2PdunWIRqMQQiCRSODXv/41fv3rX8Nms2HevHlobW2F2+1GKBSCz+fDa6+9hlhM/fVcSpkKdjmdTvz4xz9msIuIiIhMtf8Aygp2AcBrx9U6y7pM2CCadKRUTSylBLsA1SB3+x0S99+rfsGMxyWGh9W4xaEhIJEAYjE1bjEQAEIhQEKNJGttUaEup7P+d/zXW4PSeB8JGQxJ9Pclw1z9wKBPPS6K5XIB7ckwV/vUwkYslmrFcpgSZupeXsilqjPysFRmjhIMR4AfbwG+UIW2sVKY2dqmu+yyZHtXC0y/ey5/h4DXK9DdLbFzV/nrregGTr5ZfLALYMMoERERERERERFRvat5uAsArrzyStx55534xje+gaGhoVSbhZQSsVgMvb29GQ0XxnSjECIV7GpqasIPfvADXH755VX/GoiIiGhi2/6YOXuLd/xKYlkXd5xScQIBiZ27y2uOA1TAcOcuidmzAb9fNS5FIirQ5ferAJIQgNutQjgeL2A3qU2pGk6cLC7YpYvGgHu2ATetlyY1KI3fkZCBgMS5PuDcOdXM5Q8Uv0YiAdisQGMjMK0dmDMHaGtD1VoLF8xXjWzljCKc0aHfP6Mza+Th0WOF3V6xfD5zw04x058r5nG5BBYvMaelDFCPoRXLAUDAZgOmTZOmhcesVhVyBIDVKwV27ip/0ZUfhakBYCIiIiIiIiIiIqofdRHuAoA//dM/xa9//Wt8+9vfxq5du1IBrlx/VDSGv/TWrve+97349re/jenTp1d1u4mIiGjiCwQkenrMWWv3brWe18sdpzQ6KSX2HwC275Do2QNTxnYBwKM7gL/6pETAr4I78bhqpvF6gdYpgNdTfKNSfbRT1UeD0lgjITWpWqUOHtRHQlY2JDPafSOlCvalwlz9qrWtWAJq3XgCeO014NixzHYvFWgDViyXVQq0CVy/prxRiWs+rdYZy+49xa+fS8+eyoS7IlHz14zGgAceAr55S2XaxsphVmub3Qb8r68A8y8RcLkAh0MAEFixQjOtZUv/OaBrKTB3TnntoPPmqmM2jBIREREREREREU1MdRPuAoCOjg780z/9E44fP45HH30Uzz//PF555ZXU+EUju92OSy+9FFdccQX+4i/+AnPmzKn+BhMREdGkcK7PvGBNQlMBCq/XnPVoYuo9okYwlrujPpdDB4F3Xgl4POpx2OBVTV3FN7XUVztVPTQo1c9IyPz3jQBwyXyJt8xWLW3BUPGrWyyqiat9qjqEwsC//qJ+Am0AMHuWwLq1xTe5OezAurUoaPvMHHl46JBaz+xQpNNh6nIpZ88Cd24Frrm6WoG93DRNIhKBOkTVGNkprcCFgdLXFAL4/h3AlVdYRnzOrJatVdelv19CCGzcANy4vrTWLbcL+OatAg8+zIZRIiIiIiIiIiKiiaquwl26uXPn4m/+5m8AANFoFP39/RgaGkIgEIDX60VTUxPa29tht9trvKVEREQ0GYRKCD+MJhg0dz2aWJ7fJ7FhY+mjtcYiATQ0AG+5eOK0UwG1b1Cql5GQY903EqpZ69gxFepbsEAF/EZjtwFTp6pRne1TgSlTAKtVbWv9BNpG6uwUuGn96N8PoxkdqrGr0PvBzJGHmgR8g0CHy5z1dC0tMG2UYLZXXwXuurt6z/FEQiIcViGuSASIRtKPOyFUMM/lAj7xcWDbfWqEZLGEAG7bkDvYBZjXstW1NPNjnQsFNm9C0a/9bheweZPArIvAhlEiIiIiIiIiIqIJrC7DXUYOhwMzZ87EzJkza70pRERENEm53eau5/GYux5NHL1HKhvsMkM9hnlq36BUHyMh//AHdd/E4oVdPhQCXnwRWNSpQkA6hwOY1g5Mm6bCXM3NuUd11kugbTSzZwlsuCXZZLYHOHwoq2XOAixZDHSX0DJn9sjDSMTc9QA1gnPxEmnKqMJ8KvEcj8Vkqo0rkgx0xZOPa4tFNXR5PECrU512OgH9vrv4YqC5SeLHdxX3PXU4gO/dnj/YBZjXspWrKfGKywW2bkHBrY3z5qq1OhcKHH9dsmGUiIiIiIiIiIhoAqv7cBcRERFRrU1rB6wWc0YzWq0qLEGUTUo1irEawS4VhChevYZ5at2gVKuRkFJKDA2p0bG9R4B/fxrQinyd0jTgD73A+98HdC4Epk8DmpoKGdNZH4G2wggsmK++t+GwhG9QhX6cTqClGSWPQjR75GGpz8uxrFiOioa7gNKf41JKxGJIj1ZMHvT3W6tVfV8aGwFXMsRlt6v1LRbV1OVxqxC2x6POW60Cl75VYOHCwsfbGoNSYym3ZWu02+hcKPDAfcD+A8D2xyR6ejJ/9rBagRXdaqxj19L085QNo0RERERERERERBMbw11EREREY/B6Bbq7JXbuKn+tFd3gqKMJJBCQONendqy73SoIWOr9u/9AeaO+CmWxqEBL8eo3zFPrBqVqjoT0+yXOnQPOJg/hZLhk/4Hig106TVMNXn9+LVDofVOrQFu5XC5h2uhDM0celv68HNuC+Wp0Yrn311jGeo5LKRGJSITDEhcuSIQjqp1Lf9zabCrA1dKSbuOy2dQ6Vms6xGUMco0WQiw1KFWIclq2xiKEwLIuYFmXQCAg0devwlYejwqH53qPYcMoERERERERERHRxMZwFxEREVEBVq8U2Lmr/D34q65jsGu8k1KqsMAOiZ49WWEBC9DdLbF6ZfFhge2PmVQ9NYYli0trKqrnME8tG5QqPRIyGFJhLj3QFQiMvN7gYPnNPcb74ODnyQABAABJREFUJhyW8PlUaM7pUGGb7MdMNQNt9crMkYelPi8LI3D9Gok7t5QeziyU/jiaf4lENKqCkuGIGq0YjQm4nAkAQDyhxiC2tqYbuaxW9fXbbCpcpIe4PG512WJeT3WlBKUKVcnwmM7rFQWNR2TDKBERERERERER0cTGcBcRERFRAbqWAnPnlNesNG+uWofGr94jo4/5SmjAzl3Azl0Sc+cAGzegoKaWQEBi925TNzWv7uWlXa+ewzy1bFAyeyTkuT5AQKaauYaHx77e6TPm3P4TTwINXonDhzO/JosAFi+RWLFcD3+hooG2ejNa2M2skYelPi8LNXuWwLq1Ej/dBsQqHPD63e8B8SFAStXfZXckRys2AW1TLHA6BUIhASlVaCu7kcvhqMxjodCgVDEqGR4rBhtGiYiIiIiIiIiIJraKhbtuvfXWjPNCCGzevHnMy5kl3+0RERERlUIIgY0bgBvXS4TCxV/f7VIjmUpp7qD68Pw+iQ0bC7//j7+uHi+bN6kRXrkkEhJDQ8CLL0vTAkKjmdFRWrCq0u1U5aplg5LZIyF//xTQ0FD45RMJ4MIFc2771Vdzf1yTKsB08KAa73fN1eYG2nyDMG1Uonkkjh4Ddvdg1LDb/EvKH3lY6vOyWJ2dAl9ZL7Fla/GjR4tx9CjwFx8DmptUqMtiUc8np1OgY7oVHq9APKbCXPrYxYmgEuGxYrBhlIiIiIiIiIiIaOKqWLhrx44dqZ2XUsq8YSvj5cwy2u0RERERlapzocDmTSgq4AOoYNfmTaKgBieqT71Higt26UJh9XjZuiXd4KUHugZ8wNAQoGnAqVPmb3M2hx1Y82lAdekUx+x2qkqEeWrVoGT2SEiLdezLuJzAtGnA9Gnq/H/tNXcbRnPmLPDgv5i7ZiWDRqU4cVLiwYfyB7ayw24f/ADwr78obeRhOc/LUugNXlt/UrnbkFCNXDNnivR4RTdgtwu0tKg/Qfh8qrmLzMOGUSIiIiIiIiIioomLYxmJiIiIinDF5QJbt2DU0XxG8+aqxi4Gu8YvKdUoxlIa2wAV8Nq0WeLOH0n4fCIV6IpEJIaHgWG/ec1L+TjswLq1KthRCrPbqSoR5lkwvzYNSmaOhARyh8XsdmBaOzB9ugp0NTUh9Q8yb/yx+gkZs8f6OZ3mrleO3l6Je7YVHtQ6cxb4l39VjW8HD6kmtUKV+7ws1cIF5T9XxjJzhsDcORPvfS8QkDjXB4RCKrQ2rb1+RhiyYZSIiIiIiIiIiGjiqmi4Sxb4r7iFXo6IiIioHnQuFHjgPmD/AWD7YxI9PUBCS3/eagVWdKvRRl1LwR2l49z+A+U1oQDq+k/9O3DxbIlhPzA8DMTjgNWiRvAtXKB6eyrxU/GMDtUMVE6AxOx2qsqEeQSuXyNx55bqNiiZORKybYp6/bBZgfb2dDtXS0t6tF02s++barNYgJbmWm+FcuJkccEuXTwO/Pf+4q5jxvOyVFICf/lJ4O6fALF4ZW7D46nMurUgpVTv9zskevZkvd9bgO5uidUr6+P9ng2jREREREREREREE1PFwl1PP/20qZcjIiIiqidCCCzrApZ1CQQCEn39QDCodmi3T62fJg8q3/bHzIlcPfl74OoPpgNdDQ2q+UUPAyx5uzkBIUAFZpYsViMGVRNVeY9HM9upKhnm0UfOFRvQKbdB6Z1XmjMS8qqrgK63A1OmAFZrYdtidnNYtS1ZrAJytadGMZYSDCyU2c/LQsViEuEIEAmr1rxwRLUHXvth4De/NT/gZbWq98GJoPeIHLWpM6EBO3cBO3dJzJ0DbNyAmgek2DBKREREREREREQ08VQs3HXRRReZejkiIiKieuX1Cni9td4KqoRAQDWzmeHYUaDtr4DWltztLiuWmxMQ+vRfAW9fYm5gxsx2qkqHeTo7BW5ar4I6hYydK6VBKR5Xgc6zZ9Vt+HwqqBcKlb7dMzqAD/4ZYAz8hMMSPp8ai+l0qCBX9vfOzPumFrqX13oLlKPHKjem0GoFPvEXQNfSygfZ4nGZCnCFwyrQpTdN2WyAywW0tqrjeXOBBQtR8HOlUCu6J0bA+fl9sqgGrOOvq5GImzepgFUtsWGUiIiIiIiIiIhoYqnoWEYiIiIiovHsXF/mDvFySADRaP6d6AvmAx3TywtZzOgA/uRKoBKNQGaFz6oR5pk9S2DDLRJHjwG79wCHD2U2WxXboKRpKmR15qwKdPX1q+YjowULgBdfHPnxQmSOhExudw9w+HDWdgtg8RKJFVnbbdZ9U20zOvSvo/Z276nc2okE8Oh2YNYsYPaswq5TSLAvkZCIRNMhrnBEjYgEVHjH6VTXczpVmEtvg3M4AI9bNU12LgSuuVri8IsC2+6TOHio/K931XXjPyjUe6S4YJcuFFYjEbduqX2DFxtGiYiIiIiIiIiIJg6Gu4iIiIiI8iiniSmXSGS0zwpcv0bizi2ljYbLDAiZz6zwWfXCPAIL5qvbC4clfIPq++90qrGQYzUo+f0y1cx19pwK5o2mwQss6gT+0FtcwMs4EvLEydEbxzSpQlwHD6r74vo1ErNnCVPum2qr9OO1GOGwxGETQk2jicWAhx4Gbr1ZIv/XnD/YJwBc9jaJK94BzJyhAl3664TFAricQGODCnE5nYDdrm7DZlNhHo8nHehyOLJvX4WAupYCa26QeOON0r/OeXPVOuOZlGoUY7HBLl0oDNx+h8T999ZPIxYbRomIiIiIiIiIiMY3hruIiIiIiPJwu81dz+kc/fOzZwmsWytxz7biAl7GgFDl1Hf4bDQul0CHa/TLRKMSZ8+l27n8/uJuw2YDLr0UeNtlwH/sVO1eYzGOhOztLe5+P3MWuHMLsG6tRGdnefdNuew2IBYv/PLVebwWzufLDFJVyukzavxjroDjWME+CdUM9+KLQNsUYOVKYO5bVJjL4QAAAas1HeDSD05n4d9jIQT+9ptqtGApwSa3C/jmraKsQFMgIHGuTwVr3W5gWnv1G6b2H1AjFsvx2nG1zrIuEzaIiIiIiIiIiIiIJr2ah7ve+ta3AlB/SN62bRuuuuqqktd67rnn8LnPfS613ssvv2zKNhIRUWXUww48IqJc4nGJgQFgaEjCIswJflgsqjFqLJ2dAjetHz3oYWQMCFVafYfPiiOluo9Pn1Hf5/5+QBZxPwsBTJmiGrOmT1eBG33s3fJ3FzcS8sTJ4r+ngLr8PduAm9bLku8bM1y/Bnjit/X3eC1UZIxWNjP17BkZ7nr5ZYlt9xUekDt/AXjwIeDG/wlc/g4BbzLI5XKV3xTVuVBg8yYUPZLQ7QI2bxIljSKUUmL/AWD7DomePZmjcK0WoLtbYvVKga6l1WnC2v6YOUm/Hb+SWNZVP49zIiIiIiIiIiIiGr9qHu6SxexBqcF6RERkrnrbgUdEpEskJIaGgAsDwNCQer0KBIAFC4DeI+Wvv2Tx2KMAdbNnCWy4pbiAULXUc/hsLKGwxJkzUIezY43JHKmxMR3mmtaea7ydrpiRkOp7WWogK2oY91fsfWMGi0WNo1z6dtTl47UQTkf1buvAQWDAJwEJhCPAH/8I/OIRIJ4obp1oFPjHfwK2bgEunm3u9/OKywW2bgE2bZYFNVjNm6sau0oJdvUekaPeTkIDdu4Cdu6SmDsH2LgBJd1OoQIBiZ4ec9bavVutx39cICIiIiIiIiIionLVPNwFcOc9EdFkUW878IiIpJQYHlaBrsFBIJEAQiEJvx8Y9qvzb19iTrire3mx1ygmIFRd9Rw+M0okJM6fT7ZznQEGfMVd3+lUQa6Oaeq4lJDGWCMhjx4rP4hlHPdX6H0z7AdefbW82wUyQ4v1+ngdS0sLTGvoK8SRI0Bbm/re/O6p4oNdulAYuP0OifvvNf936s6FAg/cp0YLbn9MBZ4yAvlWYEU3sOq60gP5z++TRTWEHX9djYzcvEkF0CrhXF/m11mOhKbGs3q95qxHREREREREREREk1ddhLuIiGjiq8cdeEQ0eQWDEhcuqLBPLAZEoxLDftXYFY8DNptqaWpqVGGV3T3lBXBmdIwcxVaMsQJCtVGf4bNhv2rnOn0GOHdO3Z+FsliA9qlAR4dq6Gppqfw/ouzeY846meP+xr5vjh6TuOvu8m83V2ixPh+v+blcAouXSBw8WJ3bmzIFmH+JCuT195e31mvHVQBrWZcpm5ZBCIFlXcCyLoFAQKKvHwgG1QjI9qnljdLuPVLcz4W6UFiNjNy6pTL/ABAKmbteMGjuekRERERERERERDQ5TahwVzQaTZ12OKo4W4OIiEZVrzvwiGhyiUQkBnzAhQtAOKxanYaHgaFhFXyxWICGBhXqcrsyQz3Xr5G4c0tpo/McdjWWsN5G0ZmplmGeWFzi3DmkAl1+f3HX10ctzpgBtLcDdlv17qdwWOLwIXPWOnRIrZcdqst33yyYr77uWoYW68mK5ahauGv2LDVK8ZFfmlMVtuNXEsu6Kvu49XqFaQ1UUkp8d7NW9M+Fuko2lrndpi4Hj8fc9YiIiIiIiIiIiGhymlDhrtOnT6dOezn7gIioLkipRjHW4w48Ipr44nHVWjRwQY2h0zSJQEAFukJBAELtfG9tBbwewGLJ/Toze5bAurUS92wrLuDlsAPr1qrrkzmklBgaUkGu06fV2DOtiDFqdhswbboKJnVMBxoaanff+HzmjQLUJOAbRBEhO8HQooEZYbdCWK3A25eo79lzz5qz5u7dQCAgy2rSqqa9z8dx/Hh5a1SqsWxaO2C1mDOa0WpVDWdERERERERERERE5ZpQ4a7f/va3ANTO/9mzZ9d4a4iICFA73o6/Xt4alRw5REQTj5Sqkev8BWBwUAW6giFgeAjwB1QQyOVSLU0NDYDVWlggorNT4Kb1Eg8+VFgAZEaHCr8w2FW+eFziXJ8Kc50+re7HYkxpTY5a7ACmtuUP8VVbJDr2ZYpaL1Lc5RlaNCov7FaoFd2qBev469KUABGggkh9/TCtWavS/vWREhP/WYptLAsE1OtIKKQauqa1jxwt6fUKdHdL7NxV/vbp9zURERERERERERFRuaoS7nr++ecLulxvby9stsI3SUqJUCiEkydP4ve//z327t2b+tzixYuL3k4iIjLf9sfGz8ghIhrfQiGJCxeACwNALAZEoxJDw8DwMBCPq0BKa4saw2e3l/Z6MnuWwIZbJI4eA3bvAQ4fymxesliAJYuB7uX6uDq+bpXK75c4lQxznesDEonCr+typsNcHdMxYlRhvXCaPEne6Sz+OgwtqvBgOKzGsa68Dti+A4gX8Xgrxqrr1PctFDJ33WDQ3PUqxe/X8PQz5qQaC2ksk1Ji/wFg+w6Jnj2ZjVxWC9DdLbF6pUDX0nRD7OqVAjt3lf/zq35fExEREREREREREZWrKuGu66+/ftRRWlKqP5x+//vfN+02r732WtPWIiKi0gQCEj095qw13kYOEVF1xOPJQNcFIBgCEgnV2jU0rFqMrBagoVEFutymBXwEFsxX4a1wWI19jERUsKaluX6DRPUukZDo7wdOnVaH4eHCryuEGn/W0aHCRy0t42OUb0sLYBHmjGa0WNTjrxSTKbQopUQ0qsJV4QgQDqswKADYbMAllwCfXwvseAw4e87c2543F+haqk673eau7fGYu16lnDmrFRXUHM1YjWW9R9Ro8HwNsgkN2LkL2LlLYu4cYOMGoHOhCnrNnVNe86zxviYiIiIiIiIiIiIqV1XHMuohrlI/PxrjzpuPfvSj6Ori7C4iolo714dJO3KIiCpH0ySGhlSga3BInQ8EVBgoEAAkgAavGsXn8VR2BJ/LJdDhqtjyE14wKHH6NHDqDHD2rGpYK5TbDcycAcyYAUyfVnobWy25XAKLl0gcPFj+WksWlxssrF1oMRyW8PnUmEqnQ4XezLq9REK1chkPmlSBQKcT8HrUY8nlAmw2dZtvuRh497skjh8Hfv/vwLPPlh/AczqBb94qUr+3TmtX4VMzfk6yWlW4cTwIBs1pdE2vl/vjz++T2LBRIlTgBMjjrwM3rpfYvAm44nKBjRvU+UKvb+R2Zd7XREREREREREREROWqWrirnOBWoeu3trbi+uuvxxe/+MWK3hYRERVmso4cIqLKCAbTYxfjcSASUSGvYb8a2ed0AlOnqpYuq5U71euRpkn0n1ejFk+fAXy+wq8rhLp/Z3SoQFdL8/ho5xrLiuUwJdzVvbz8NXTVCS0mm8J6gMOHs5rCBLB4icSKEprColEV5golg1zR5ARAm1UFuFqnqPCNy6UePxaLCoF6PSpA7vXqQUGBuXOA971XNYf29QP7XpC4+ydArIgQIgA4HMD3bhfoXJj+Orxege5uiZ27ilsrlxXdGDfNph6PuduZq7Gs90hxwS5dKAxs2CixdYtq8Nq8CUWv43YBmzdl3tdERERERERERERE5apKuOvGG2/M+7mtW7emdsp89KMfxezZswteVwgBj8eD5uZmzJ8/H5deeilstqqWkRER0Sgm68ghIjJPrrGLQ8PA8JBq+bFagabk2EWnkzvT61E0KnH6DHDqlAp1RWOFX9flVKMWZ85Qxw7HxLuPF8wHOqYDZ86WvsaMDj0ENT6cOCnx4EP5v2ZNqsDbwYPqe3P9GonZs0be95omEYmkg1zhkGrCElCBKrdbNfi5XOlmN4cjGeLyAA0N6nNjtft5vQJeLzDnLQKL3yZx27ckTp0u7Gu9aCbwnW/nDvusXimwc1f5/wS16rrx87zomG6B1QpTRjPmaiyTUo1iLKVxC1CPpdvvkLj/XtXgtXULRh3taDRvrmrsYrCLiIiIiIiIiIiIzFYX4S7dypUrcdVVV1Vjk4iIqAom68ghonoWCEic61PNem63ep7WW+OLlKqR6/wFYHBQnff7gaFhIBgAIFQ4o61NhT4nQnvTRDM8LHHqFPDmaaCvDyi0xFdANSvp4xantE6G+1fg+jUSd24pLvimc9iBNZ9W64wHvb0S92wr/Gs9cxa4cwuwbq3E/PnqtSuUDHKFI+oyFosKAqpxjqrFz2oVEEK9zjV4061c5QYEOxcK/OJfgP/eL3Hv/blb14QA3r4E+NwNQNfS/OP5upYCc+egoOBQPvPmqnXGi4YGC97/Pgd+/1S07LVyNZbtP1De9xMAXjuu1lnWpe7vB+5T57c/JtHTk/lzrdWqtmPVdQJdSyfD6xURERERERERERHVQl3UXFV6ZCMREdXGZB05RFRvpJRqx/QOiZ49WTumLUB3t8TqlbXfMR2JqJF9AwNqlFr22EWXE2hvV207HLtYX/Rxi6dOqcPQcOHXddhVK9eMGaqByuUa//dtOCzh86l2OadDDx3l/7pmzxJYt7a40BMA2O3AurXI2WpVj06cLP5rBNTl//ke4GMfU4FUm02Nv2tqSoe5AAGrVb0+6M1cHk9lXiuEEHjHMoF3LFOB2TdOSJw+pT43cwZw8cWioJ9ZhBDYuAG4cX1pTVNul2qKGm+Bor/8hMuUcFeuxrLtj5nzt4Udv5JY1qXWF0JgWRewrEukRnQGg+rx1T6VP58SERERERERERFR5dU83PXAAw+kTi9atKiGW0JERJUwGUcOEdWT3iNy1JFSCQ3YuQvYuUti7hxg4wZUdaRUIqFCMBcu6CEu1dI1OAREIqoVpbFRjV7k2MX6Eo1KnDmj2rlOn1aBvEK1tCTbuTpUA9tYY/HGB4mjx4DdPcDhw2q0oM4igMVLJFYs18cnjvx6OzsFblo/+rjCbPE4sOc5wGKVedetH+prK6WdDADiCWDnTuAbX0+PWHS5VIjL26DauZzO6gdUvV6BSxcJXFrir7KdCwU2bwI2bCwu4OV2AZs3jc8RgFdeYcPcucDx46WvkauxLBBQzVpm2L1brZcd3NJHdBIRERERERERERFVU83DXVdeeWWtN4GIiCpoMo4cIqoXz++TRQUGjr+uGmQ2bwKuvKKygYFgMN3SlUgAwZDE0CDgD6gRfl6vGsnn9XLMVT3x+yXeTLZznSti3KLFAkyfBlx0kQp1eTwT6z49cXL0UJYm1fi+gweBjunA9Wtkzrat2bMENtySDIntAQ4dBEb7FssC160HR48VHlrL5+w5wDcI/MkV6rVBD3mNd1dcLrB1C0YN4hrNm6sau8ZjsAtQr+m3bbDgS1/WTG0sO9dnzihwQK3T1w8GuYiIiIiIiIiIiKgu1DzcRUREE9tkHTlEVGu9R4oLdulCYdUgc/ddEn9icgY/HpcYGADOnweCIXV+aEi1dMXjajzflCmqpctm43O+HmiaxPkLKsz15ilgaKjw67pcKsh10Uxg2nTAPkHv097e4kYNnjkL3LkFWLdWorMz1/dEYMF8QEtIvPwyEDNt3drauducdfb8P+BDH6jN1xcISJzrA0IhwO1WIyLNGsnXuVDggfugRug+phqoMkboWtWI6lXXmTtCt5Jf02hUY5kwtbEsFDJxA6FGLxIRERERERERERHVA4a7iIio4ibjyCGiWpJSjWIsJVAJqIDXps0afr1DmhIgGB5WASGfT4WF/H5gaBgIBgAhgIbk2EW3m8/1epBIqHGLJ98ETp1W4zELpY9bvOgi1bw20YO5J04WF+zSRWPAPduAm9bnbtrS1y002FXoutUUjUqEwypwMzgIvHjYnHXzjcurFCmlClztkOjZkxW4sgDd3RKrV5oTuBJCYFkXsKxLIBCQ6OtXASOPB2ifal7oqppf02jMbixzu83dPo/H3PWIiIiIiIiIiIiISsVwFxERVcVkGzlEVEv7D5Q3ChUAXjsOPL8vjiuvsJd0/VhM4sIA0N+vwkHRqGrpGh4G4gnA5QTa24HGRsBi4fO81iIRiVOngTffBM6cUfdRIfRxizNnqlBXtQI39UGNYiw22KWLxoCHHgZuvVkCMH7fKrVu5UgpEY2qIFcopAKiiYS6dYdDNfMVOMFzTNUcl9d7RI76c0tCA3buAnbukpg7B9i4Aab93OL1iop8jbX8mnIxs7FsWrsKp5kxmtFqVYE6IiIiIiIiIiIionpQ83DX+9///oqsK4TAv//7v1dkbSIiKk2tRg4RTTbbHzMnRvGvj4SLCndJKTHsV4GuwUFDS9eQGsNotagwV1MT4HTy+V1rgYDEm6dUoOtcHyALfNi4nMCMGSrQ1dExccctjuXoMTUKsRynz6h1Fsyv/LqjCYclfD4gEgWcDtXA5nLlv1+llAhHVJArnAxzaZpq4nO51HPc7VKnrVaBP54wK9qlVGNc3vP7ihtte/x1NYJ68yYVaK9H9fo1mdVY5vUKdHdL7NxV/jat6J5sYVUiIiIiIiIiIiKqZzUPd7355psQQkAWujepQAwEEBHVp2qNHCKarAIBFZw0w78/HYXfP3YFSiymxi6eP59u6RocVKMXNU2NyuqYrpp22NJVO1Kq++XNN4GTp4CBgcKv29wMXJRs52pr48/aALB7jznr9OzJDGFVat2RJI4eA3b3AIcPA5rh1zGLABYvkVixXK2haUA4kgxyhYBwWF3eYlFhv9YW9Tx3udRjw2pVz/cGrzpubjbna9JVelxe75HiQlC6UFiNoN66pbJtV6UYL19TuY1lq1cK7NxV/t8WVl1XX/cfERERERERERERTW41D3fpzNhBJKWsSFCMiIgqo1Ijh4gms3N95oykAtRYtbPnNLRNGfm5XC1dw8Mq0BUKqSa+5ibV4ONwcCd5peVrXtI0if7zKtD15puAP1DYegJqbOZFFwGzLmLwNls4LHH4kDlrHTqk1nO5RMXWzXbipBr9mK8hTJPAwYPq0DYF+MAHVADbYlEhrilt6WYuQMBmU0GuhgZ1cLszf7+zWACrRY6LcXlSqrGFxYagdKEwcPsdEvffWz8hyIn4NeXTtRSYO6e80cTz5qp1iIiIiIiIiIiIiOpFzcNdM2fOLPo64XAYQ0NDiMfjANJ/YG5uboaXKQEiIiKaxEIhc9cLBGRGuCseVy1d/f25W7o8bjWqz+thS1fl5W9eEgBmzVb3XaEtR1aruu9mXaQaujg6Mz+fL/P7XQ5NAr5BoMNVuXWNensl7tkGRGOFrXP+AvDL7cANnwEWvw0ABByOZJAr2czldo/+WBlP4/L2HygvGAQArx1X6yzrMmGDTDARv6Z8hBDYuEGNkywlzOZ2Ad+8VdR9iI2IiIiIiIiIiIgml5qHu5555pmSrpdIJPDKK6/gN7/5DR555BEEAgEIIfDtb38bK1asMHkriYiIiMYHt9vc9fQQhd+vGqB8PiCRkPD7gcEhtnTVyljNSxLAiRPq4HYDCxaoIE42h0ONW7zoIjU602bj/VeISNTk9SKVXVd34mRxwS5dLAY88CBwx+3A25eUFvwbL+Pytj9mTrpu230Sy7rq4/lk1te041f18zWNpnOhwOZNKHoMpdsFbN4k6m6kJhEREREREREREdH/z96dx0d5nvf+/9wz2kajHYQECIMwILyAEQanSRFxnKZJm6TGbpImMc5SDjnnNBT/nKSJTWjrkxCSpvFxTWh6Ggc7duy2aVNvWdokjcOWNIkds9lgATY7CCRA22hG29y/P24kJJDELM9oRuL7fr14aZu5nnueeWYeyc/X1+VL9wIS5ff7ufHGG/nc5z7H888/z/XXX09zczN/9md/xk9+8pN0L09EREQkLSaVg9+j3/D8fjAG9u2z7D8Ap09bzpyxHDrsQkUG1+lpxnSYONFcFuyKRCwNDZYjR93HSESjs71QX295eMPwwa5LhcPwyisumAeu09Kc2XDbrbDsj+BNtxiqphoFu+KQm+NxvdzU1u3ttYRClse+HX+wq09nF2z8hgsEJqJvXF4yUj0uLxSybNvmTa1du2HnLo9m5CbBy8e0daurNxYsXmTYuMHEfMzNrIaNGwyLF+l9UERERERERERERDJP2jt3eWHq1Kl861vf4s4776ShoYHPfvazzJ49m+rq6nQvTURERGRUeTn+7OaFWTQ2RjnTZGlptnSEXXCssMh16hq6S9fwowJ9BubNtyxdArNngYuHSTwOvm75x0egpze++0WjsH8/rPhTuP46NHIsSSUl7nj2YoSizwclxd7XNQZ6e+DoMUtnJxw/AU1nk6uZzGi+sTAu70wj9HqYx/rK38I/f8em9fXm5WPqjUJjkwuIjgU1cwxPPOaO2aefdSG3gfvC73djPu+43VC7QO+LIiIiIiIiIiIikrnGRbgLoKysjD//8z/n85//PJ2dnfzt3/4t3/jGN9K9LBEREZFR59X4s4ULsnj9jV7aWl0HoIoKN9rP5xv6AviVRgVGLeza5f5VVsDdyy3TqnQx/Uo6OizHjrtwzk9+Gn+wq09PLzz/fbjhem/XdzXKyzPMm2/ZtSv5WvPnuXpe150503Xpyg+48NjmrcnXhORG82X6uLxw2Nt6x48nHobzitePqaPD23qpZoxhYS0srDWEQpbGJvcY8vOhfOLF0cMiIiIiIiIiIiIimWzMjmUcyrve9S6ysrKw1rJ582aamprSvSQRERGRUefF+LOyUigpMRQEDddcA9OmGYoKzbDBrnhHBTachoc3uPvJ5draLftes/zkvyzP/8AFRA4eTD6ocaoBDhz0ZIlXvaVLvKlTd0kdr+q+4+1QPcNQUWEoLoZXX/GmbrKj+TJ5XF4g4H3NZ55L73uc148pP9/beqMpGDTMmG64/jr3UcEuERERERERERERGSvGVbgrGAwybdo0AKy1vPTSS2lekYiIiMjoc+PPDIG8xO6f5Yc/+QDMmpVFZaWf3NyRL4AfO255ZJPrEhSPrm54ZJO7v0BLi+XVvZYf/8Tywx/Brt1w7tzFn59q8GY727Z7U+dqN3uW60CXjMmVfSNKva07ZYobNzez2nUGKykxnox6hIuj+ZLhxuUZNjxkuPWtbtzrQH4/vO1W2PCQ4fFHU9+xq8+k8svXkqxkw3DJ8vIx+f2u25WIiIiIiIiIiIiIjK5xM5axT2FhYf/nJ0+eTONKRERERNIn0fFn2Vnwif8Bc+f68PtjCVS4UYzxBrv6dHXDk0/B/Z+zwNXXRaWlxXL0GBw7Dq2tw9+ut3dw0CsZu3dDJGL7RwFKogx3L3cd6xI5/nOyYfldrs6ldZff5ep298RfNy8XvviA4ZppF+uGw96Gi7wYzZeJ4/KCQUNdnWXzFu9q9oXhgkHvasbDy8e0tE5jDEVERERERERERETSYdyFu84NuOrV05PA1ZA4hUIh9u7dy+7du9m9ezd79uzhxIkT/T+fOnUqL7zwQsrXcd999/HMM88kdN9169bx/ve/3+MViYiISLq58WfwhXWWI0evfPvJlS5sMq0q9ov3Bw7GPopxOH2jAi/tYDRetbZeCHQdg5YRAl0DRaPebT9qobkFKhPs7CYXTasyrFwRf+e6nGxYueLia81aS2enG7vZEYbuLnj3H8AP/gPi+ZMmkAfr113e6SrTR/MFgyZt4adL3bnMsHlL5oXhkuHVY7rjdgW7RERERERERERERNJhXIW7jh49yvHjxzHG/Ufn0tLSlG3rscce4+mnn+bgwYNEvbzaJiIiIuIBay1tbW6M1qfvhfp6eGELvLYPBl7iNwZumg91S/rCVfFdvN/q0Yi/bdvHd7irrc0Fuo4eg5aW2O6TlwtTp0LVVIh0woseThw/cRI6Oy25OVBSgrp4JaGmxnDPatfBLpagY1+IsmISNDdbwmEX6uqNgs/nnvfSMqiqglmzXWe7WOrOrIbP3z/0CMO+0Xy9HvzZMt5H89UugKlT3GvEK16H4eJVuwCqZ8Chw4nXmFnt6oiIiIiIiIiIiIjI6Bs34S5rLevXr+//3BjD7NmzU7a9F198kf3796esfrL8fn/Mt+0Lw4mIiIwHoZDlTKMLSwQCLtRwNY2R6umxnD8PZxqhs9MFeJqbwZ8F73i76wYEYC3k5kJJceLBnkjEsme3N+sej6MC29otxy506DrfHNt9Anku1DOtCiZOBJ/P7Y+GBm87CT3+xMXPfQbmzbcsTTDgJ64D15r7LAcOusDjnt2uQ1ofnw9uvAEW3ezCXR1hOHLU7em8gAvYBQKQl+d+N8/OhsJCmD0b3vVOy969hqeftWzbNjig5fe7UXl33G6oXTD87/UazRc7Ywz3f86y6h5v6mVCGM4Yw9o1sGp1fGN6+wTyXHBQfzeKiIiIiIiIiIiIpMe4CHcdPnyY9evXs3XrVowxWGspLS3lpptuGtV15Ofnc8MNN/Dqq6/SkcbZG6M1ClJERCRTWGvZsROefsaybfsl4Qcf1NVZ7lw2cvhhrOvstDQ2wtlz0NtraW93o/fCYcjKggllUFQEfr93j7+5eXCAJRnjZVRgKHRx5OK587HdJxBwYa5p02DihKGP0ZISF8Lyan8PFLWwa5f7V1kBdy+3cY3mlD6G2bNcQC4SsZw778Zu2qh7DfY9rZ2dUFjgujnl5bkAn9/vvldY6P4NDjkaFtbCwlpDKGRpbHJj/vLzXWgo1qCVRvPFbsFNPm6aH2WXB+HVTAnD1cwxrF8Ha9bGF/AabtSniIiIiIiIiIiIiIyetIe7Nm7cmND9Ojs7aWpqYt++fdTX1/d/v69r1yc+8YmUXrzNzc1l/vz5zJs3j3nz5nHjjTdy7bXX4vP5uO2229Ia7hIREbma1O+3rFtvhx031RuFzVtg8xZL9QxYu4ZxdZG6tc2FulpaXKirpcUFSnp6ID/gugQFg6kJtXV2eVyv09t6oyUUshw77kYunjsX233y8lyg65prhg90Db69Yd58y65dHix4BA2n4eENsHKFpaZm/LxORkM0aol0QrjDha8inZDlh+w8F8TKD7ggn99v8PmgoMD9Kyp034/lNRoMGoLBxNan0XzxWfFxw+p7x1cYbvEiw8YNjHjOHGikUZ8iIiIiIiIiIiIiMnoyItyVzMVGa91/cDfG9Hfteutb38pHPvIRr5Y4pIceeiil9UVEROTKXnzJxtWF5NBhN5Zq/Tp3kXusikZdV6DGC+Mn+0YvtrW7nxcVQnEx5Oam9jHm5nhcL9fbeqkUDrsOXUePwdmzsd0nL9d15+rr0NU3cjFWS5eQ8nAXQFc3PLIJ7lmtDl4jsdbS1eWCXB1h91q01o3hyw9AUbH7mJ1tMMaFLAsKXIeuYDD+5z9ZmTKaL5NG5460lvEahquZY3jiMVy3yyRGfYqIiIiIiIiIiIjI6El7uCtZff+x2VqLz+fjrrvu4rOf/Sw+ny/NKxMREZFUqt8fX7CrTzjixlL97d9YiotNRgQMYtXd7UayNTVBT48bvdjS4oIlWVlQVgbFHo9eHImXowJ9PigpTr5OKnV1WY4fhyNH4cwZiOVh5+ZCVRVcM82N0Esm0DN7lhub2HA64RIx6+qGJ5+C+z9ngcx+XYym7h5LR+hCmKvDhWJ8PteJbcIEF+bqC1UGAhdHLQaDkJWV/v2YrtF8mTQ6N561rF1j0h6GSwVjvBn1KSIiIiIiIiIiIiKjIyPCXX3dtxJhjGHGjBm89a1v5X3vex+zZs3ycGUiIiKSiax1oxgTueAOLuC16h4YGM8Z7YBBPDo63OjFc+fd6MXWVmhuge5uFyqprISCFI1eHImXowLnz3P1Mk1Pj+XkSRfoOtUA0eiV75OT40YuTpvmQoPedWgy/P47LE886VG5KzjVAAcOulDZ1aq31xIOXwxzdXW7qFtuHhSXuDBXXp577eXkXAxzFRa6jl2ZaLRH82XS6Nz412JYv86MehhuNCUz6lNERERERERERERERkfaw11PPPFE3PcxxpCbm0swGGTy5Mnk5+enYGUiIiKSqXbsTG5U1lBGM2AQC2tdiOvMGTdusbvb0tICLa0uYFRQABUVEEhzIMqrUYF1S5KvEYm48ZSdXW5kZElJYoGxaNTScNoFuk6cgJ6eK98nJ9t16JpW5Z6X1Izcs/zkpykoO4Jt26+ucJe1lkjnhVGLHRC5EOjJznZdjSYEXEcuv9/g9w8Oc2ViOHE4ozWaL5NG5ya+FsPGDWbUwnAiIiIiIiIiIiIiIpdKe7jrlltuSfcSREREZIx5+lkP5gCOIJUBgyvp7bWcPw9nGl2wJBKxnG+GUDsYnxu7WFwC2Rkw4g28GRU4uTKZAJHlwEHYug327Bk8ItJnYN58y9IlffWH32fWutFkR47CsWPQ1XXlLWdlQdVUN3KxoiL14zAPHBydkYwD7d7tjsGxFFyKV3e37Q9zdYRdeNLnc2GuSeXuY3a2wRg3XnHgqMVM6vAXr1SP5kt2dO7GDd4FbJNfi+GJx0zKw3AiIiIiIiIiIiIiIkNJe7hLvNfe3s5f/dVfsXPnTk6fPk1nZyfFxcVMmjSJ2tpa6urqWLJkiS46iIjImBQKuQvrqZaKgMFIurstTU3Q2ORGAba3w/lmF/DKzoaJE6GoKFUdoZJhuHu55eENbmRdvHKyYfldrk68jh23fOfJ4QNPUeu6iu3a5QJody+3TKu6uB1rXaevI0fh6FEX7LkSnw+mTIZrroGpU1If6Bpo6/ZR21S/qHUjQCvzRn/bqRKNulGLoQuBru4LoxbzAlBa4sJNeXkAhry8C525Cly3vKwMCVV6zevRfF6Mzv3Sly2PP5p8UMq7tRgW1pqUheFERERERERERERERIajcNc41NLSwne/+91B3wuHwzQ0NLB7924ef/xx5syZw+c+9zmWLEl+BpJCYplj4HOh50VExqvGJuiNprZzV5++i/pPPGZS9r4aiVhOn4Hz5w09PW4U4/lmQ0+3G/82ZXJ6OgQN3Jz7fPjtXzPNsHKF5ZFHbUwdr/rk5MDKPzVcMy3+x/baa5ZHHo2twxa4ANjDX4eVfwpTp7pA15Gj0NZ68TbDrsJAxSTD9OmuU1dOzuifYyMRy549o75ZALo6U3f8jwZrLZ2dLrzXEYJwxICF7BwI5rtwTt+oxaysi2MWCwsgN3fsPu50enkHHDqc3Pv0G4dg5y4Xpsq0tRQUGAoKkip5VdLfKiIi4iWdV0RExCs6p4iIiJd0XpFUUbjrKrV//37+x//4H6xatYpVq1YlVaukpMSbRYmniouL070EEZGU8Pu7gdYr3s4rbxyCAwcLuGVxtqd129qinGqI0txsXaeukPs8GoUJZYbSMh+BDBmFFwxeOcWwaBGUl/fyzU1hTp6MXvH2U6f4WLkiwPTp/rjXc+RIL488GoorSAYuCPb/HrEsuCmLggK3b7NGeFrLy33MrPYzY7qfQCC9z0VLay/RaCgt2y4ty6egIP7nKZ16eiyh0IV/HZbeHvD5XSinogKCQR85OW7UYkHQUFxsKC72kZ+vP7i98IMftQFxvkCHrJPFbW8rHDdrkYv0t4qIiHhJ5xUREfGKzikiIuIlnVfESwp3jSP5+fnceuutLF26lOuuu46pU6cSCARoa2vjyJEjbN++nX/6p3/i7NmzgOti8PWvf53CwkI++tGPpnn1IiIiscnPH/3gxb/8a8STcJe1lvPnLQ2no7S3Wzo7LefORWltsxigpMRHaakhO3tshkumT/ez7v8Eqa/v5Wc/7+LlHT1EB+S8fD64eWEWt92aQ02NP6EQjbWWb24Kxx3s6tPbC/te62HxoqGfz5ISHzOrfVTP8FNY6EtsIynQGRmdbnWX8vmgtCQ9x2M4HOXceUtnxJKbZygrNQQCQz8n1rpRi+0hS0fIErmwv3LzXHCrIGgIBFwHsrxcKCryUVxsKCw043bUYrq0t0f52QvJh6kA/utnXbS3RykoSOy1mElrERERERERERERERFJVEaGu6y1/OIXv+A3v/kNu3fv5vjx47S1tREKhQgGgxQWFlJVVcX8+fNZvHgxS5Ysuer/D/sPfvCDrF27loIh5oOUlpZSWlrKggUL+PjHP86aNWv4z//8z/6f/83f/A1Llizh2muvTWjbzc3NiS5bPGaM6U8At7S0YG16LgSLiKRSIM/i97uQzmj5r591ceLEeYLBxH7fiEYtZ8/BmTO48XAdlvPNbkRcVhaUlEBRkRsL19npbpNuxlzs2BUKtRPPKaWqCj56N/zJ+w3Nze7x5Oa6x5mXFwUihBJsQrX/gOXkyeTObx0dcLapm77/aSY/CNOvMUy/BkoGBJna25PajKdGaxTppW6aB729HaO2L6y1HDgIW7e7MZSXhgPnz4O6JYbZs6Cnxz2XoQ730UbB73djFouL3ajFrCyDzwdFF0YtFhUNHrWYSc/xeHHosPXs/bm3Fw4ebGHGjMTeezNpLaK/VURExFs6r4iIiFd0ThERES/pvJJe43nqXEaFu3p7e3n88cd58sknOXXqVP/3Bx7wLS0ttLS0cOLECX7961/zyCOPUFlZyfLly/nYxz6G3z+2RsZ4ZcGCBTHdLhgM8tBDDxEOh9myZQvg9vuGDRt4+OGHE9q23pAyk7VWz42IjEv5+VC3BDZvGb1t9vbCmUbLjPz47tfTY2lqgsYm6O62tLfDufMXwk45MKkCCgsujoHLrPftiwEGaxNbW24uVFQM/l6yj3Hrdm/20elGuPlmmH4NTJjgwmzg/XMQiVgXcOtyz7kLuMUfDikptvgMjHbGa8mS0Tsujx23fOdJaDg99M+jUdi5C3buskwog9/7Pagoh7wAlJZAMHgxuJUfgMIiKCq0BIPg8w08njPpdTb+dHR4u39DHTaucGmmrkUG098qIiLiJZ1XRETEKzqniIiIl3ReES9lTLjr4MGDfPrTn2b//v2DDnBjzLBdufpud+rUKb72ta/x/PPP87WvfY3Zs2ePyprHKp/Px1//9V/zzne+k+7ubgBeeOEFwuEwgUAgzasTERG5sjuXGTZvGd1fiDs6Yr9tV5elsRGazrqAV2srnG+G7m4XOpkymYS7gF3NIhHLnt3e1GpqhBuuTyxodWUXuk9tw3WfGnCo+gzMm29ZugRmz4KBIbqR5OUZ5s237NqVguUOY3Jl3xpTr77e8sgm6OqO7fZnz8HTz8CKj8OsWYasLNeVq7DAfRyro03HA6//nMiPM1Q7UCatRUREREREREREREQkUb50LwDg1Vdf5e677+4Pdg0MdPWlGY0x5OXlYYwZlHDsu621lvr6epYvX86rr76azoczJkydOpW3vOUt/V93dXXx8ssvp3FFIiIisatdANUzRnebsVzUj0QsR45YXt0LJ09ZGhsthw/DmUbXxWpaFUydahIOdkUiloYGy5Gj7mMkcnX9Hx/Nzd51rrJAc4s3tQY6dtyy/ivw9b+HXbsvX2/Uwq5d7ufrv+JuH6ulSzxe7AhysmH5XRBr+CwZx47HF+zq090Nj30bfH7LvBthxnTDhAlGwa40m1QOfo/+yvT7oXzi+FiLiIiIiIiIiIiIiEii0t65q62tjf/1v/4X58+fHxTUmj59On/0R3/Em970JmbPnt0/lxTcaMb9+/fzm9/8hueff54jR470h8FaWlr4X//rf/HDH/6QoqKidD2sMWHBggX9oxkBTp48mcbViIiIxM4Yw9o1sGq1JRxJ/faudFG/o8PS0ODCQj09bgxfS6sbZ1hU6Ebx5eQkGjjxvgvUWGOtpeks7Njpbd3OTm/rxdt9quE0PLwBVq6w1NRc+bmbPQsqK4YfW+iVnGxYuQKmVY3G8WR54jvxB7v6dHbB/30IHn/U21VJ4oJBQ12d9WR07tK65LocZtJaREREREREREREREQSlfZw14MPPkhjY2N/qCsQCHDffffxgQ98YNhxjMXFxSxevJjFixfzZ3/2Z3z3u9/lb/7mb4hE3NXdpqYm/u///b888MADo/hIxp4JEyYM+vr8+fNpWomIiEj8auYY1q+DNWtTH/Aa7qJ+a5vl9Gloa3OjGJubobUNjIGSYiguhqysxMMAx45bvvPk8GGevi5Qu3a50M/dy+0oBXJGR0eH5dBhOHwY2trjG40Zi9xc72ol2n2qqxse2QT3rI7luTPcvdzy8IbEw1BXMrnSdexK5XFkrSUSgVAIXquH02eSq/fGIRf8W1jryfKuOqGQ5UwjhMNujOGk8uRDTF6Nzr3j9uSPw0xai4iIiIiIiIiIiIhIItIa7opEIjz//PP9wa68vDwee+wxFixYEHMNYwwf/OAHmTt3Lh/96Efp6urCWsvzzz/P/fffT66XV+3GmXA4POjrnJycNK1EREQkMYsXGTZugHXrXQgoVQZe1LfW0tLiAimhEHR2Ws6dh1A7+PwwoQyKisDvTy4IkOouUMmKRFyYrbMLcnNcd7K8vOS329trOX7CBXbOnHbjE/t4+Wudz+cCeN5wIbxEA1dd3fDkU3D/5yxX6r42rcqwcoXlm5vcWEKvzLsRbn1r6jrA9fZaQiEIdbiQXjQKWX7Ytceb+s88Z1lYq/BNrKy17NgJTz9j2bYdeqMXf+b3QV2d5c5lhtoFDPs/3Iykb3RuMu/LM6tdnWRl0lpERERERERERERERBKR1nDXr3/9azo6OvrHMa5atSquYNdACxYsYNWqVTz44IOACy796le/4q1vfauHKx5fjh8/PujrSzt5iYiIjAU1cwxPPOY69zz9rGXbtsFBhWT1XdS31nL+vAt1hcMQDrtQV0cHZGdDeTkUFoLPl3zAJJkuUN/cBP/7f1pmXZuKoEtqRkRaazl7Fg4dgaNHhw8t+f1QVgbnziX1IACYP8+bMBrAgYPJj0o81eDquH03spoaw90ftjz6eHLbHOi974HKCm+Pmc7OC4GuEEQujMDMzYXSEggG3djSA/u92dbWra4DlcbmXVn9fjtiILY3Cpu3wOYtluoZsHaNe5+NR7KjcwN58Pn7TULBskxei4iIiIiIiIiIiIhIItIa7jp58iTgLuhlZ2fzgQ98IKl6f/Inf8LDDz9Mb2/voPpyOWst27ZtG/S96667Lk2rERERSY4xhoW1sLDWEApZGptc6Co/H5qbLX/xORK+qH//5+DsOTh9Gjo7XYDkfLMLeOXmuHGIBQWJdbcZWnJdoLq7YcNGuGm+ZWmdd52YUjEisiNsOXIEDh1y4yyvpLAQlrwFnv9BAg/gEnVLkq/RZ+t2b+ps2x5buAugtMybbfbp7Ey+RjRq6ei42J2rp8d1SMvPh4piF+jy+w1+v+tu19JiBwUEk9EbhcYmtw0Z3osv2bhG2R467EJR69e5TonxSHR0biAP1q8zcQfKxspaRERERERERERERETildZwV0tLC+AuhlZVVVFUVJRUvaKiIqZNm8ahQ4cwxtDa2urFMselH/zgBxw+fLj/68mTJzNrVoxXE0VERDJYMGgGBzymG9aviy/QAJCXC5/9C+jtNRw9amlvdx2jOrvcz6ZMJiVdgrzoAgWwa7f7F2vQaiSvvebdiMjeXsuJEy400tAweOziULKz4Jpr3Fi1CRPAGPjNi8nto8mVsYeoriQSsezZ7U2t3btdvVg6iuV6PE070ZGX3d0Xxy2Gw64bV04OFBa4oFUgAGAIBFygq7jIfd8Yw959cOUjIHYdHZ6VGpfq98f/PgguGLtmrWXjhvg7eMU7OndmteuSlYowVSatRUREREREREREREQkHmkNdxUWFvZ/HvTof7MfWKegoMCTmulQU1Mz6Ov6+vphb1tfX3/Z7Ueyb98+HnjggUHf++hHPxrX+kRERMaSeC/qXzMN7vowTCiDpibXqaurC/IDMHUq5AdSd7Hfqy5QfUYKWsXiyJFeHnnUJjQi8pFNcM9qFyw7d95y6BAcOXLlkJgBJlXAzBlQVeW6PQ1093LLwxsS626Wkw3L7+rbSvKam/Gs+1TUQnMLVOZd+bYlJW4Uphfb9vmgpDi221priUToD3R1dbk9GQjAxAkuuJWdbfD5XKe14iL3MTf38v3tgl/eyc/3tt54Yq0bxZhIB0NwAa8vfdny+KPxdym80uhcvx+W1sEdtxtqF3jZBTGz1yIiIiIiIiIiIiIiEqu0hrtmzpwJuIsNDQ0NntQcWKev/nj32c9+lkAgwIc+9CHe/va3Dxtqi0QifPe73+Whhx4iHA73f7+6upq77rprtJYrIiKSFle6qO/zwZtugTcthupqNybw8BE35jCYD5MmQSCGjkrJ8LIL1ECXBq1iZa3lm5vCdHUlvt1Nj8Kbf8cF5K6ksABmzIAZ00fuijatyrByRXzdxMAFu1auIKkuZpfqTHDfDFsvxvGIeXmGefMtu3Ylv8358xixW1g0at2oxZALdfVGXQgmmO8CkIGAC+Dl5FzszlVQcHko71KTysHvG/w6TJTfD+UTk68zXu3YSUzB1pG8cQj+9XuWD7wv/tDTSKNzyyempgviWFiLiIiIiIiIiIiIiEgs0hruuvnmmwkGg4RCIc6ePcu+ffu47rrrEq63d+9empqaAMjPz+fmm2/2aqmXOXHiBO94xzuG/Flvb++g211//fVD3u7b3/42t9xyiyfr2bFjBzt27CA7O5s5c+YwZ84cSktLyc/PJxQKcfjwYV566SXa2toG3W/ixIk88sgj5OR4PNtHREQkAw11Ub+tzXUiikYhKwtaW12oq6cHCoJuhN9QXYdSwcsuUJfq6oYnn4L7P2eJtWvVa/W9nDyZXPLm3Hm3P4uH6QyVleU6pVXPgIkTYw+N1NQY7llt+c6TsY1onFzpOnZ5GeyC9I5HXLoET8JddUsu/95Q4xZzc6Co2IW6+sYtBoMuzFVUBPn58e3bYNBQV2fZvCX5x7C0TqGckTz9rDdvLF//e/jBDy1r18Q/orHPZaNz0yiT1iIiIiIiIiIiIiIiMpy0hrtycnL44Ac/yKZNmwD42te+1v95Ih588EHAXRT80Ic+lNLAkrV2UIhrJMPdzlrvr952d3fz6quv8uqrr17xtrfccgtf/epXmTx5sufrEBER8UIoZDnT6MIlgYDr9ONVgCM3F3JyIBo1+HyWtjY43+xCXYWFUFoyeqGuPl53gbrUqQY4cBBmz4rt9i/83JsFNTRcHu6aNMkFuqqqIDsrsf08rcqw5j7LgYNunOWe3YPDcT6f60pVt6TvMXv/fKZrPCK4x1RZEVu4bTiTK10day2dnRfGLYbcsdg3brF8outqlJ1t8Ptdh7XiYhfoys5Obp/eucyweUvyO++O2xXsGk4o5DoVeuXQYVi12rJ+nRt5KyIiIiIiIiIiIiIiqZXWcBfAqlWreOGFFzh06BC//OUv+eu//mseeOCBuEZ9WGt54IEH+MUvfgHAtddey6pVq1K15IyzfPlyfvrTn7Jjxw5aW1tHvG1WVhaLFy9m+fLl3Hbbbfh8vlFapYiISGystW504jOWbdsHj2zz+6CuznLnMkPtgvhHgwH09LjAWGOj607U0grN56GnF4oKobQUcnLSE1jwugvUULZtjy3cFYlYXt7R48k2z56D3l4Xmque4f4VFHi1jw2zZ7nHFIlYmlvcaMPcXBeUGmncoBdGczzi5Qx3L7c8vCG+8ZR9srPgj++EM2dch66eHjfeMD8fyi4Zt1hcfHHcos/n3T6tXeCOh2RGBs6sdnVkaGcavRl9OVA4AmvWWjZuSLyDl4iIiIiIiIiIiIiIxMbYVLSPilNDQwMrV67kwIEDGGO47rrruPfee1myZMmIF22ttWzbto2HHnqI1157DWstNTU1fPOb36SiomIUH0HmOH78OIcOHaKhoYGWlhY6OzvJz8+nqKiIadOmMW/ePAJujo5nzp8/72k9SZwxhpKSEgCam5tT0h1ORCSV6vdb1q23MQU9qmfA2jUm5mDBiKGuItepK12hrj6RiOW+NakbzQiuy9RX1l85RNRwGtZ/xbuFrFwBN1zvbTAoUxw4aPn63ydf588/CbNnxb9/6ustj2yKL+CVlQXvffeFzmnZbtRiQYE34xbjVb/fsmq1JRyJ/76BPNi4Ifb3gavR3n2WT/zv1LypzKyGxx81CQVt5eqjv1VERMRLOq+IiIhXdE4REREv6bySXqWlpeleQsqkrHPXs88+G9ftP/ShD/Gtb32LkydPsnfvXj7xiU8wYcIEbr75ZmbPnk1paSl5eXlEIhHOnz/P/v37efnllzl79mz/C2Lq1Kl88IMf5L//+78BWLZsmcePKvNVVVVRVVWV7mWIiIjE7cWXLGvWxh7wiHU02KWhruYWaG52naSKiqCsNPnRcl7xsgvUcKIWmlugMm/k23V1ervdoqLxGewCb8cjJqKmxnDPast3noxtDWWl8J53w8yZUBB0oUafz3VW6+vQlerXxKUjV/9yLXxxHXEFvAJ5sH6dgl1X4vH/1zHIG4dgx05YWJu6bYiIiIiIiIiIiIiIXO1S1rlr7ty5Cf8f3AOXdKXOXSPdbt++fQltX+Kjzl2ZQ0lgERmrUtG5p6fH0tjoRpJd2qmruMiNX8yUUNdAXnWBGsmn74Xp14xu564190FlRebtb68cO574eMScbLhnNUyrSnb/WA4chK3bYfcuGPjsGQNzZsPvvhluvBGysgxZWe61UFzsgl1+f2qfnyuNXF2wAE6ehFMNV641sxo+f7+CXbEIhSx/+F7r+WjGPm+7Fb74gEa9y5XpbxUREfGSzisiIuIVnVNERMRLOq+klzp3JSGeg7UvoDVUUGuoINdIt9NoEBERkbHBWjeKMZFgF7hOP1/6suXxR935v6fH0tQEp8+4gFdzy9gIdfXxogvUleTmjvzz3l5Le7sLBHnxd4fPByXFydfJZNOqDCtXxD8eMSfbjaxMNthlrSUcdkGt226F37nFdcXy+aCkxB1T+fmG3Fz3XBQXQzA4er8zX2nkam8Ufvuy+7yyAqZOhZ07LwmA+WFpHdxxu6F2gX7fj1UwaKirs2zekpr6W7e6AFkwqOdDRERERERERERERCQVUhruijeFGOvtlW4UEREZP3bsZNjAR6zeOAS/fdlSVQVnzlzo1NUC5y+EujJt/OLIDHcvT7wL1JWMFLRqb7e8/gYcOgSdnTBhgqGpKfnfu+bPcyMnx7t4xyNOroTldyUe7IpGLR0d0B6CUAiiUcjKcqMWKyogPwBgCAYvjlsMBEb/eYh35GrDaWhpgXVfgGnTDB0dkJ8P5RNRgChBdy4zbN6Smr+heqPQ2OTCgiIiIiIiIiIiIiIi4r2Uhbu+/OUvp6q0iIiIjCNPP+tN4OCJJ+Gjd1taW+HceejpgaJCKCsbK6GuixLtAhWLS4NW0ajlxEl4443B4/AMMHWKn6amnqS3Wbck6RJjxrQqw5r7Lo5H3LMbogMOcZ/PPQd1S1yXNrenY9fbawmFXKCro8N1VsvNcd25CoKQm2vw+aCg4GKHrnQe//X74wt29QlH4AvrYOMGuP66sfX6zUS1C6B6RvJB2uF0dKSmroiIiIiIiIiIiIiIpDDcdccdd6SqtIiIiIwToZBl2zZvau3YAW/+HReeKSx0nbpycsZuKCTeLlCx6gtahUKWNw65rmfh8NC3LSkx5OcnF9yYXNkXYrqaGGbPco87EnGjQTs76R+JGG8Xs+4eS6jddefqCLs4WF4AJkxwga7sbIPf7zrUlRS74z8ra/SO/VDIcqbRHUeBAEwqdx22vB65KokzxrB2DaxanfjzMZL8fO9rioiIiIiIiIiIiIiIk9KxjCIiIiIDXRoCCYctvVFvaltcl6ua2WM71DVQXxeoV/fC5i1Qvz+5epUVEMyHrdstp066fTaSigofH/5gHo9/J0J3Ah3EcrLd2MF4u1ONJ3l5hsq8+O/X1WXduMV2iHS6PRjId8GpggLw+w3Z2a4zV0mx+57PN3r72VrLjp3w9DOWbdsZ9Dr2+6CuznLjDd6MXN2xExbWJldHoGaOYf06EuqkNhK/343MFBERERERERERERGR1FC4S0RERFJqpBCI11mUosLxE+yCC6P9tsGePYNH+yUiyw/Tp7tRgSPJyYYZM2DWtYapU3MByM3p5JFNNq4RkTnZsHKFC6hJbDo7XaCrvR26ulwXuvwAVJa4zkh+v+nv/lVcDMFg7B2thuuulYj6/a4j13DBrd6oCyNu3pJQ+cs885xlYa2OIy8sXmTYuIERn794La1L/FgSEREREREREREREZErU7hLREREUuZKIZBkA0uXys31tl66HDvu7ThGnw/mzHEddoYzYQLMuhamVbmRfgNDQ3PnxjcicnKl69ilYNeVRSKW9nZoD0F3t3uugkGYUOYCXT6fIRC4GOjKz499n8bSXevOZYbaBbGHxF58yXre+elKtm514TQFiLxRM8fwxGPwr9+zfP3vk693x+16XkREREREREREREREUknhLhEREUmJ0Q6B+HwuADPW1ddbHtlEXF2yRpIfgFmzoSB4+c+ys1w3r2uvhdKSkQMafSMiDxx03b/27B4czvP5YP48qFsCs2fB1TyKcSTWWiIR+gNdPT0udFcQhIJy11XLGEMw6MJcpSWQmxv/voy9u5alegasXeNCP1eqOdrBLnBrbWxyobdU8bKz2VhgjOED74Mf/DC5Dl4zq6F2gVerEhERERERERERERGRoSjcJSIiIp5LRwhk/jzIyxvbYYxjx70Ldk0og8pKFxC6VFmpC3Rdcw1kZ8WzzwyzZ7nwViRiaW6Bzk76RwWO9f2fKta610J7u/vX2wtZWRcCXQUXA10FQSgpcc9ZMuNF4w1WHjoMq1Zb1q9zY/uGewzr1o9+sKtPR4f3NVPR2WwsMcawdo177hN5XgN58Pn7zbjcNyIiIiIiIiIiIiIimUThLhEREfFUukIgM2YAWMZuxyg39jCZYFduLlw31328dASj3w/XTHPBrLKy5PdRXp6hMi/pMuOWtZZw+GKHrr5AV2FBX6DLYAwUFrpAV0mxG4eZrESDleEIrFlr2bhh6A5eO3aSVIenZOXne1svFZ3NxqKaOYb164j7mAnkwfp1ZlzuExERERERERERERGRTJOycNd111036GtjDHv37r3i7bwy3PZEREQktdIVAnn2OfjVr+Du5ZZpVWMvcHDgIDScTq5GZyd0dw8OwhQEXZeumdWJjfeT2Flr6ehwYa5QuwsIZWVBUaELdOXlGXy+C4GuYtehK9FA11BjBPPzSSpYGY7Al75sefzRyztVPf2sHeZeqef3Q/lE7+qlorPZWLZ4kWHjBkYMuw00s9p17FKwS0RERERERERERERkdKQs3GVtbBeAYr2diIiIjA3pDIE0nIaHN8DKFZaamrEVPNi63Zs6DQ0uNDS50nXpqqwEn29s7YuxZKhAV3Y2FBW7Ll25uS7QVVTkAl1FRYkHuq40RnDevOSDlW8ccgHNhbUXvxcKWbZtS65uMt7yZlyQ7ajtD7IFg4ntw1R1NhvrauYYnnjMPfdPP+ue70HHlx+W1sEdt4/fMZUiIiIiIiIiIiIiIpkqpWMZ+/6j/5UCXMYYz0JeXtYSERG5GgzVASjR4ER7e5StWz1eYJy6uuGRTXDP6sQ7eEUiluZm6OyC3Bw3Ni8vL3VhhkjEsme3N7XOnYPfuw0mTlT4IlWGC3QVl7hOabm5Br/fdegqLXWdu/z+5J6PWMYI7tyV1Cb6PfOcZWHtxfWeaRwc9Bltv/glbNt+8fd7vw/q6ix3LosvaJTsyNiROpslysv332QZY1hYCwtrDaGQpbEJOjpcR7jyielbl4iIiIiIiIiIiIjI1S5l4a7Fixd7ejsRERHxzpU6ACUSnKjfb/mr/wPRDMhYd3XDk0/B/Z+zQKyBBMuBg7B1G+zZM/hx+AzMm29ZusR1w4q9Zmyam73bbxbo6fWmllxkrSUchrb2kQNdRUUuDOhFoKtPvGMEk7V1qwsd9YV5wuHR2e5wopcEy3qjsHkLbN5iqZ4Ba9fE1k3Li5GxQ3U2i1cq3n+9FgwagsG0bFpERERERERERERERC6RsnDXd77zHU9vJyIiIt6IpQNQvMGJ0Q6fxOJUAxw42BfGGtmx45bvPOnGOg4lamHXLvevsgLuXp54V7ChdHZ5VsrV6/S23tXKWndMt7dB+zAjF1MV6OqT6BjBZPRGobGJ/nBPIDB6247XocPwydWW1asss641I3a/8mpk7KWdzeKRivdfEREREREREREREREZ31I6llFEREQyS7whrEOHYdVqy/p1sHjR0AGDdIRPYrVt+5XDXfX1lkc2uW5fsWg4DQ9vgJUrLDU13oQucrI9KdMvN9fbelcTay2RiOvQ1d4Ovb2QlTV8oKu4CHy+1IRvkh0jmIyOjoufTyp3HaXSOZpxJJEIfPVr4PrWDd39KhSybNvmzfYu7WwWq1S8/4qIiIiIiIiIiIiIyPjnS/cCREREZHQkGsIKR2DNWkv9/su73qQzfBKL3bshEhm+W8+x4/EFu/p0dcMjm9z9k9Hd7fbrr19MqswgPh+UFHtX72oRjlgaGy2Hj8DxExAKuU5c10yD6hmGSeWGigpDdTXceIP7XmmJSVmwC7wZI5io/PyLnweDhrq69KwjEX3dr1bfa/nIx91r7Eyjd+G0vs5m8UjF+6+IiIiIiIiIiIiIiFwd0tq5q7Gxkd27d/d/XVNTQ1VVVRpXJCIiMj4lG8IKR+BLX7Y8/qjrgtMnneGTWEQtNLdAZd5QP3WjGOMNdvXp6oYnn4L7P2eB+AI+be2WAwfg0CHo7nHfKyuDc+cSW8tA8+dBXp66/MSis9PS1ua6dPX0QJYfCgrcv0DA4PO5gFdpqevU5fXIxSvxaoxgvPx+KJ84+Ht3LjNs3pL8ev78k7DnFdi2bXDYyueDaAo6g/V1v/qfn/C27sDOZleSqvdfERERERERERERERG5OqQ13PWTn/yEdevW9X/9/e9/P42rERERGb+8CGG9ccjVWVh78XvpCp/Eo7Nz6O8fOOhGLCbjVIOrc6XRj+ACHmfOwP4DcPJk3wC5iyZXehPuqluSfI1MFolYmpuhswtyc9xoxHjCbF1d1o1cbHMBPf+AQFd+wIVnioqgtMQFurKy0hOm8XKMYLyW1nHZyMHaBVA9I7n3kZnV8IH3Gf7k/W5MYmOTC0nl58M/ftOy7RfJrHp44Qj8wz96W3NgZ7MrSdX7r4iIiIiIiIiIiIiIXB3SGu5qa2vDWndps7y8nFmzYrgyKiIiInHzKoT1zHOWhbUu9JHO8Ek8cnOH/v7W7d7U37Z95HBXb6/lyFGo3w8tLcPfrrgYCoLQHkp8LZMrYwuajT2WAwdh6zbYs8d1ZOvjMzBvvmXpkr7HfnkYq7vH0n6hQ1dnp+sSFQxCebkL6RhjKCx0QbGS4vQFugbycoxgvO64/fLHb4xh7RrXBSuRDlSBPPj8/aa/81QwaAgG3c9CIcsv/zuZFV/ZcCHPRAzV2WwkqXj/FRERERERERERERGRq0daw10lJSWAu1hUUVGRzqWIiIiMW16GsLZudfWCQZPW8EmsfD4X1rlUJGLZs/vy7ydi925X79LuUZ2dltffcJ26IiOEYQwwdSrMmQNveTNs+HpioyJzsmH5XX0Vx49jx934zOG6rEUt7Nrl/lVWwN3LLdOqDL29lvZ2aG1z+98YF+gqK3UfjTEUFLgOXSUlkJ2dWfstHE7PdmdWuy5dQ6mZY1i/DtasjS/gFciD9esMNXOG3sdj4b1koKE6mw0nVe+/IiIiIiIiIiIiIiJy9UhruGvSpEn9n4fTdQVLRERknPMyONEbhcYmyM21HD2a+SMZ588bemRfc/Pg7k/JiFpoboHKPPd1e7ulfj8cOgQ9vcPfLzvbBWnmzB4cFFm5wvLIpvgCXjnZsHIFTKsaX6GP+vr49kXDafi7h+GP77CUT3Ixt0C+C30Fg+DzGfLzobTUhbpycjJ3fwUCadjmJd21hrJ4kWHjBli33sY0anBmtas5XLAL0hdkS9RQnc2Gk4r3376OZyIiIiIiIiIiIiIicnVIa7jrhhtuwOfzEY1GOXnyJF1dXeTk5KRzSSIiIuOO18GJk6csbW2GtnZv66ZC3ZKhv9/Z5e12OjuhqcmFuo4fh5FyY4WFLtA1YwZkDzH+r6bGcM/qkbtVDTS50nXsGm/BrmPH4w+5AXT3wL8/Ayv+FGrmgN9vyMtzHbtKSyE3d2zsp0nl4PeNXkerK3XXGqhmjuGJx2DHTjdycNu2wev0+113qztuN9QuYMSwGKQnyJaoqVOG72w2FK/ffzs6vK0nIiIiIiIiIiIiIiKZL63hroqKCm655RZ+9atfEYlE2LJlC+94xzvSuSQREZFxx+vgRHMzdHdZ2lpdZ6RM7d81uRJmzxr6Z7keZ8lf3uECXiOZVA41NTBl8pXDLtOqDGvusxw4CFu3w57dgzuN+XyuK1ndkr7HODYCS7Fz4bZExlOCC3g9/334v38LZWWQnz/29k8waKirs2zekvptxdJd61LGGBbWwsJaQyhkaWxywaP8fCifGPvYQhj9INto8vr9Nz/f23oiIiIiIiIiIiIiIpL50hruAvjYxz7Gr371KwAefPBBlixZQmAs/e/7IiIiGc7L4IQx0N4ObW1QVAQ3zoM9e5Kv67WcbNfNarjQU0kJ+Ix3oxk7Oly3oksZA9Omwdw5UFYWb8DIMHuWC29FIpbmFhcgy82FkuKhx02OFwcOxta1bCQnT8GZRkPVGO5oducyw+YtyR+kN90Er+xJrrvWSIJBk9SowNEMsiXrxEnXsWxhbWy39/L91+93wTkREREREREREREREbm6pD3cdeutt3LXXXfx1FNPceTIET7xiU/wd3/3d0yYMCHdSxMRERkXvAxOzJzpglMTyyE/YLh1qc24cFdONqxcMfKYwrw8w7z5ll27kt/ehLLLg13ZWW5fzZkdXwej4eTlGSrzki4zZmzd7k2dZ56zLKwdu+Gu2gVQPQMOHU68xsxq2Ph3ho4OkuqulWpeBdlGQzzHlZfvv0vrMus5ExERERERERERERGR0eFL9wIA/vIv/5JPfOITGGN46aWX+MM//EO+/vWv8/rrr6d7aSIiIuPCncu8CQS8tc51osoPuHqzZ0FlhSelPTG5Eu5ZDTU1V368S5d4s83KyoufBwJw03x473ugdoFRECMBkYhltwehO4CtWyEUGhuBoaEYY1i7xhBIMNgXyHPjFo1xx+KM6Ybrr3MfM+3Y7AuyjQXxHldevf/ecXtmPWciIiIiIiIiIiIiIjI60t656yMf+Uj/5yUlJZw7d46Wlha+8Y1v8I1vfINgMMiUKVMoKCggKyv25RpjePzxx1OxZBERkTHHiw5AlRWw4CYYPOrQcPdyy8MboKs7mRUmzueD+fOgbokLmw03ivFSfcG0ZMb/5QeguBhKS6CmBq6ZBj6fAhiJsNbS0QFvHAKv4li9UdetKpmRgelWM8ewfh2sWWsJR2K/XyAP1q8z1MwZG8ejC7LBqtXxPc50iPe48qoDW+2CxO8vIiIiIiIiIiIiIiJjV9rDXb/5zW8w5uJFp77PrXWX9drb29m/f/+g21yJtTau24uIiIx3yQYncrLh7uUwVHBqWpVh5QrLI5tGN+D1sY/AlClQUuzGFsYvuWCazwdv/h34nTfBpEnod48ERSKWtjZoa4feXjc20Ete10uHxYsMGzfAuvU2poDQzGrXsWusBLv6JBpkS4d4jqtk338HdmATEREREREREREREZGrT0aMZRyKMWbQPxEREUmOC07EP+ItJxtWrnAhrmFr1xjuWT16IxqNgeuvg8oKk2Cwy3HBNPcY4+H3w0fugtv/yFBRod9V4tXdYzl33nLkqOXYcWhvh8IC1/ls+jXebis/39t66VIzx/DEY4YNDxlufSv4L/kt3u+Ht90KGx4yPP5obMGuUMhy6LBl7z73MRNGWLogm8n4EY3xHleJvv+OtQ5sIiIiIiIiIiIiIiLivbR37oKLXbpEREQkteLtADS5EpbfNXKwq8+0KsOa+ywHDsLW7bBnN0RTdIq/aX6i3bouN2sW3HknfP8HEApd+fYVk+Ajd8e2T+SiaNTSHoK2VugIu4BeQRDKJ7qgjM9nKCqC6ddYfMabY8fvd/XHC2MMC2thYa0hFLI0NrkOUvn57nEGg1c+Jq217NgJTz9j2bbdjRjs4/dBXZ3lzmWG2gXp60bngmy4dT5r2bZt8DoTkZfrOvRFk6wDiR9XV0sHNhERERERERERERER8Vbaw12vvfZaupcgIiJyVZlZDV94AH7zIvx8C7z6CgzM0fh8MH8e1C2B2bNgqFGMwzPMnuXut2eP5dtPQHePl6t36pYkX6Ory/L6G1C/HyIRFxhraYFTDXDu3ODbGgPzboS3Lk1kn1y9rHVj6NpaXXeuqIX8gBtjWRAEv99QUABlpVBSAllZBjAsXRpl85bkt7+0LrbA01gUDBqCwfjuU7/fjhgs6o3C5i2weYulegasXUPagkXDBdneeMPydxsg0hl7rb7uV89936b9uLpScM3vd/XvuD29ATsREREREREREREREckcaQ93iYiIjFWhkOVMI4TDEAjApPLMDpJYa2lsdOGlri4oLoK3vdX9y852AabcXCgpTr4r1rHjlse/k5pg1+TKvoBVYsIRy/79cPDg5esrLnb/enshKwuqqqB6hgsfedUp7GrQ1WVpa4PWNujpccdXaSkUFkJ2tiE31+3TsjLIzb18v965zLB5S/Ktu+64Xc9ZnxdfsqxZ68J2sTh0GFattqxf5zpOpdPAINv11xlmzx45pDbQ5MnwxQdgbo3B7ycjjisvOrCJiIiIiIiIiIiIiMjVQ+EuERGROIyFkWZDaW+3HDsO4bClpQWazrrvl090YSZv12r5zpNuBJrXcrLdmMhEOmeFQpa9r8GhQyOPZisrhevmwtSpblQgQCRiaWiwdHZBbo7rMqWw12C9vZb2dhfoikRcB7jCQigsgEDABWtKS1ygq6Bg5H1Xu8CF6mIJ7wxnZrWrI65jVzzBrj7hCKxZa9m4IX0dvIZSM8fw+fsta/8KGk6PfNtTp+BLX4a1a2xGHleJdGATEREREREREREREZGri7HWJv+/r8tV7fz58+leglxgjKGkpASA5uZm9PIW8daVRpoN5EaambQHIrq7LSdPwtlzLqB0phE6O6GoCCZOcGPxvHbgoOXrf+95WXKyYeUKqKmJb82tbZZ9++DwERjpbbGiAq6f60YGurCb5cBB2LoN9uxxIwX7+AzMmw9LExpdOTYYYygoKACgvb19yHOKtZZw2AW62tsBC4F81xUuGHThuMJCmFDmQoR9YblY1O+3rFodfyAJ3Bi+jRvS//rrk84uf9ZaPvLx2N63hjOzGh5/1GRMYDXeLmRwcTRjURHj5rgSGWv0t4qIiHhJ5xUREfGKzikiIuIlnVfSq7S0NN1LSJm0h7tefPHF/s/nzp1LYWFhwrXa2tp47bXX+r9evHhxUmuT2CjclTl0shBJnWTCBOkYaWat5exZOHHSjcg7exZaWt3YxfJyCKSw69Smb1t27fK25uRK17FrWlXs625udp26jh2F4d4NDW704nVzoazsYu1jx133sSt1BQKorIC7l8e3trFgpHDXpWMXc3Jcl66iQsjKMgQCrkNXWakbw5iosfa6G+jKXf4YlS5/L++wrL43+d8HNjxkWFib/mPci9Bfaytj9rgSGcv0t4qIiHhJ5xUREfGKzikiIuIlnVfSS+GuFJo7d27/Ba1HH32UN7/5zQnX+u///m/+9E//FHAvmr1793qyRhmZwl2ZQycLkdQYax2EOjosR49BRwe0tLpgl7WuU1dRUWqDJJGI5b41g7tcJWPejXDrW+PrjnXunOXVfXDixPC38flcd7WaGigqHFy3vt7yyKb4xkom2lUsk10a7urtjdLe7kKCA8cuFhW6EZVZWVBa6rp05ed7tx/i6Zg3sxo+f3/6OytlUpe/tX8dZfOW5Ou87Vb44gO+5AslwcsuZPsPMOaOK5GxTn+riIiIl3ReERERr+icIiIiXtJ5Jb3Gc7grK90LAHehxqsLzXpxiIiIl6x1IY1Egl0A4Qh86cuWxx9NbagKoKfHcqoBGhuhs9ONgYtEXPhmwgTXUSnVmpu9C3YBvPc9UFkR27obmyx798KphuFv4/fDrGtdqCs/cHndY8fjD3aBu/0jm+Ce1XbcdfDq6LCcPu06dVnrxgpWVkBBgRuzWFTkunQVF8U3djFWNXMMTzyG64L1rGXbtku6YPlhaR3ccXvqu2DFIt5uY4cOuzGB69fheVeoUMjtLy9s3erqjdY4yaHs2ElSwS6ANw65Ogtrx9ZxJSIiIiIiIiIiIiIiV6+MCHfpYomIiGQqb8MEHixoGOfPW46fcKGus2ehpQWyc6BqKgSGCDGlSmeXx/U6R/65tZYzZ+DVvXCmcfjbZWfB7NkwZ7brMjVMNb7zZPzBrj5d3fDkU3D/5yyxdhnLVD09lrZ2OHOml64uS0+v68pVWOjGLObluUDXhLLkxi7GyhjDwloXyAmFLI1NrjNdfj6UTyStgaOB6vfHP0YSXAh0zVrLxg142h3qTOPgwFIyeqPQ2ATBoDf1EvH0s94kR595zrKw1oyZ40pERERERERERERERK5uGRHuSgUFxkRExAtehwm81tlpOXYcWluhvd2FE6K9rlNXScnonw9zczyulzv09621nDoFr+6Ds2eHv39ONsyZ40JdOTkj74sDB6HhdBKLxXUNO3Cwb4zk2GKtJRRyx1Kow41dnDQJKit9RKMGn+/i2MV0hl6CQZPWgNFwMrHLXzjsSZl+HR3e1otHqruQZepxJSIiIiIiIiIiIiIiMq7CXR0DrjjlDnc1WEREJEaZPNIsGnUdqxpOu4BXYxOEQq6rTvnE0emmNJSSEvAZb0Yz+nxQUjz4e9ZaTp6EV16F881D36+3F7BQVQXXzoSJE68c7ALYuj3pJQOwbfvYCnd1dVlaWqGtze273FyYVO66dBUX+yksNOTmQFER+P0Kzw8nE7v8BQLe1OmTn+9tvXiMty5kIiIiIiIiIiIiIiIisRpX4a433nij//OioqI0rkREJD1CIcuZRtetJRBwAQ2NlUpcpoYJ2ttdt65w2HLuPJw/74JQkyuhoCC9z3denmHefMuuXcnXmj/v4gjFWEJdLS3uOWtqBAv8+kX3fZ+BefMtS5f0ha4u30eRiGXP7uTXDLB7t6s3/PjH9ItGLe3t0NIKkQj4/VBYAMXFLgiXkwMTJhhmVmeRl2dobjZYb5rYjVuZ2OVvUjn4fd68j/n9LjiaLuOpC5mIiIiIiIiIiIiIiEg8xk24q62tjX//938H3CibmTNnpnlFIiKjw1rLjp3w9DOWbdsHX8T3+6CuznLnMkPtAo2sjVemhQl6elzAqemsCw+dOQOdXa5b1oQy8Pky4/ldugRPwl11Sy6Euk5dCHWdH/p27SF4/aAbJTiUqHXr2bULKivg7uWWaVWD91Vzszfdxvq219wClXne1PNSJGJpbYW2dohGIT/gQoHBoDt+ioth4gTXtcvnMxkdUMskmdrlLxg01NVZNm9Jfl1L69IbFh5PXchERERERERERERERETiMSrhro0bN8Z0u+eee47f/va3cdUOh8McP36cX/3qV7S2tvZ//+abb46rjojIWFS/37JuvR12FFhvFDZvgc1bLNUzYO0aqJmjsEasMilMcP685fgJF845ew5aWyAnF66ZBrm5mfWczp7lQlQNpxOvUVnh9tdP/mv4UBdAVxfsfRV6emOr23AaHt4AK1dYamou7rfOrsTXOpTOTm/rJaO319LWBq2t7nFmZblAYFGhG98ZCLhwYFkZZGVl1rE0VmRqlz+AO5cZNm9JPrl4x+3pPTbGUxcyERERERERERERERGReIxauGukbjH2wpyf5557LuFtWGv7t5GVlcXtt9+ecC2Rsa69Pcqhw5aODqvRfOPYiy9Z1qy1hCOx3f7QYVi12rJ+HSxepOMhFpkQJujsdKGulhY3jrGxCaK9MHGiG6GXmd3YDHcvtzy8Abq64793VhbMng3btg9/m8JCtz+/+6+xB7v6dHXDI5vgntUXO3jl5sS/zpHk5npbLxHh8MUuXVgXFpowwX30+w2lJX1fZ+IxNLZkWpe/gWoXQPUMhg0Bx2JmtauTTuOpC5mIiIiIiIiIiIiIiEg8xs1YRmNMf8DrvvvuY9q0aeleksiostby8g74/g/beOHnXfQOCDtoNN/4U78/vmBXn3AE1qy1bNygDl6xSGeYwFpLYyOcanABr8ZGN34wGITycsjO8A5L06oMK1dYHtkUX8DL74M5sxn0HjZQYSHccL3rWPaVr0J3T2Lr6+qGJ5+C+z9nAUNJCfiMN6MZfT4oKU6+TiJ6ey2tF7p0dXVBdrbryFVc5AJdfQGv0hL3tXgjk7r8XcoYw9o1Ltwb7zkDIJAHn7/fZMTvDuOlC5mIiIiIiIiIiIiIiEg8Ri3c1dedK9nbDCc/P583v/nNfOxjH2Px4sUJ1xEZiy6O5rPA5bPFNJpvfLHWPd+JXKQHF/D60pctjz+qoF8s0hEmCIctR49BKAQtLZams2AMVFZCYcHYec5qagz3rLZ858nYRjTmB2DWbCgYYhzdwFCXz2d45VWb1NhHcMG5AwfdGMm8PMO8+ZZdu5KrCTB/nqs3mjoudOlqb3df94UA8wMGv98FvCaUQX7+2Dl+xpJM6PI3kpo5hvXriDsUHMiD9etMxvzOMF66kImIiIiIiIiIiIiIiMRjVMJdTzzxxJDft9by0Y9+tD9c8NnPfpYbbrgh5ro+n49AIEBxcTFTp05VSEGuShrNd/XZsTO5C9sAbxxydRbWerCgcW40wwTRqOX0aReE6uy0nGl0496Ki1y3pbHYaWlalWHNfZYDB2Hrdtize3B3LGOgrNQF14qH6HZVWAA33NAX6nJhrK3bLLt2e7O+bdtduAtg6RI8CXfVLUm+Riz6u3S1uE5k2dnuOCkqdMdKQQFMnIDrSuYbe8fOWDIWRgYuXmTYuIELYfAr335mtevYlSnBLhhfXchERERERERERERERERiNSrhrltuuSWm282dOzfm24qIRvNdrZ5+1oO5ccAzz1kW1ur5v5LRChOEQq5bVzhsOXcOzp2H7CyYOtV1XxrbDLNnuRBVJGJ54xDs3QetbZCb4zoVXaqwAK6/HqZf44JJx47H3gEsHrt3uzXl5bk1VlYkt43JlRfDYqkSDltaBnTpKgjCpEkQCBiyslxYbuLE0e8edrUbCyMDa+YYnnjMhXufftaybdvgbmN+vwuX3XF75o5xHi9dyERERERERERERERERGI1amMZR5LMOEaRq5VG812dQiF3Md4LW7e6eqnoEDPepDJM0NtrOXUKzjS6bl2nT0NnlwvolJaOr45L585Z9rzixiGCG8N4qUtDXQD19ZZHNrnuVF6LWmhugco8AMPdyy0Pb0hsWznZsPwuV8drvb2WtjZoaYWuLtelq6zMdXXz+w2FBa5rl7p0pc9YGRlojGFhLSysNYRClsYm6OiA/Hw3DnIsnBPGQxcyERERERERERERERGRWKU93PXaa6+lewkiY5JG812dzjQO7rKSjN4oNDZBMOhNvfEuFWGCtjbXrSsSsZw9C83NkJPrRhDm5o6fEEJLiwt1HT8x/G2GCnUBHDueumBXn87Oi59PqzKsXBH/NnOyYeUKd38vRSIXunS1gbUQLIDyctfNze+HCWXq0pUpxuLIwGDQjNlzwHjoQiYiIiIiIiIiIiIiIhKLtIe7RCQxGs13dQqHva3X0eFtvfHOqzBBT4/l5CloanIj9k6fgZ6ei52XxksIoa3N8speOHoEhnvHys+HG6+HGTOG6jjlRjGmMtgFkJs7+OuaGsM9q2MfAzm50nXs8irYFY1e7NLV2QlZWa6LW3Gx69JVUAAT1aUrI2lk4OgaD13IUikUspxpdL87BAIwqVz7RERERERERERERERkLFK4S2QM0mi+q1dgiDF2ycjP97be1SDZMEFr68VuXU1noaUF8vJgymTIyRkfr8OODsure113wOEmL+flwfXXwbUzXWBpKAcOxhauSobPByXFl39/WpVhzX2WAwdh63bYs9uNcBx4v/nzoG4JzJ4FXoxi7OqytLRAaxvYKOQHXWeuYNDto7IyF+oKBMbHcTJeaWRgeozlLmResta6APIzlm3bLwkg+6CuznLnMnUzExEREREREREREREZSxTuEhmDNJrPe2Olu8Wkcndx1ovn3+93YSRJXDxhgp4ey8mT0HQWOsKW06ch2uueg+Li8XGRPRyx7NsHB1+H6DDHaE4OXDfXBaKyskZ+zFu3p2CRl5g/b6SRhobZs9xaIxFLc4vrpJWb6wJhXoxCtNbSHoLWFmhugY6wG/FYVgZlpVBWZpg4EUpLhg/BSebRyEBJh/r9dsRQYW8UNm+BzVss1TNg7RoUKhQRERERERERERERGQMyNtz1+uuv09DQQFtbG5FIHHNtBli2bJm3ixLJEBrN542x2N0iGDTU1Vk2b0m+1tK6zAywjUd93brCYcvZs27cXn4AJk2F7Oyx/xx0dVleq4f9+6Gnd+jbZGfBnDlQMye2DmWRiGXPbo8XOoS6JbHdLi/PUJnn3XZ7eiwtrdDaCoePwKuvwoEDg8dX+gwsXerehyaUebdtGR0aGSij6cWXbFzjQA8dhlWrLevXuW5zIiIiIiIiIiIiIiKSuTIq3LVt2zb+9V//le3btycc6BpI4S4ZrzSaL3ljubvFncsMm7cMM+suDnfcnhmPZzSNdoe2Qd26Oiynz7huXZPKobg4vu1GIpbmZujsgtwcKCnxpnNUMnp7LfsPwL590NU99G38ftf56rq5kJsb+3qbmwePQUyFyZV9IxVHT0fYjV5sb4emJvjpz9zHoURt5r4PSXw0MlBSqX5/fMGuPuEIrFlr2bhB7y0iIiIiIiIiIiIiIpksI8Jdra2t3Hffffz85z8HXDedRBljsNZmTJcdkVTQaL7kjPXuFrULoHoGwwbTYjGz2tW5GqSrQ5s33bosBw7C1m2wZ8/gsJPPwLz5lqVL+gJKo3dsRqOWw0fglVfcGMGh+Hxw7Uy4/joIBOJfW2dXkou8gpxsWH4XjMZ+i0YtbW1u7GJXlxtN2doK33sauocJxV0q096HRCQzWOvC6vEGu/qEI/ClL1sefzRzupSKiIiIiIiIiIiIiMhgaQ93hcNhVqxYwSuvvNIfyuoLaCUimWCYyFih0XyJGw/dLYwxrF3jgh6JXMwN5MHn7zdXxUXcdHRo6+21nDjpujF1hC2nTyfWrevYcct3noSG00P/PGph1y73r7IC7l5umVaV2ufUWsvJU7B7twurDcUAM2bAjTck996Sm5PwXa8oJxtWriDl+6ury3Xpam0DG4VgEMrL4exZ+Ofvxh7s6pNJ70Mikhl27Ewu7A3wxiFXZ2GtBwsSERERERERERERERHPpT3c9Y//+I/s2bNnUKgrNzeX3/3d3+W6666jrKyM/KtxZpzIFWg0X/zGU3eLmjmG9euIO6gWyIP168xVEQxJR4e2tjbLkaNuhGLTWWhpSaRbF9TXWx7ZNPyow0s1nIaHN8DKFZaamtQ8t41Nlt27oXGYEYIA06pg3jwoKkx+DSUlrjuZ16MZJ1e6jl2pCnZZa+nocF26Ojpcd8TiYigphqwsQzBo+cY/uA5eicik9yERSb+nn/XmTfKZ5ywLa/WeIiIiIiIiIiIiIiKSidIa7urq6uLxxx8f1KnrAx/4AJ/5zGcoKipK59JEMp5G88VvvHW3WLzIsHEDI3amGmhmtevYdTUEu0a7Q1s0ajl5Es40ujGMp89Ab0/83brAdeyKJ9jVp6sbHtkE96z2toNXS4tl9ytw4sTwt5lUDjfdBBPKvNtuXp5h3nzLrl3e1FtwE9SlcIRlb6+ltdV1NOvuhtxc11GtoAD8fkNZKUycCK/VG44eSy6MkUnvQyKSPqGQZds2b2pt3erqXU3dXEVERERERERERERExoq0hrt++9vfEg6H+7t23XHHHXzhC19I55JExgyN5ovfeOxuUTPH8MRjLujx9LPuIm9v9OLP/X43evOO2w21C66OTj+j3aGto8Ny+IgLdTU1uY5NeXkwZTLk5MS7v90oxniDXX26uuHJp+D+z1mSDTB1dFheeRUOHYLhXjklJXDTPKisTM2xtXQJnoS7PrESbrw+Ncd+Z6cbvdjW5vZTQYELdeXlGXJy3BjGCWWuaxfA089GRy4Yo0x6HxKR9DjTOPicn4zeqOvMGAx6U09ERERERERERERERLyT1nDX0aNHAXch3ufzce+996ZzOSJjjkbzxW48d7cwxrCwFhbWGkIhS2OTGweXnw/lE8mYdY6W0erQZq2locGNRIxELA2nXcemiRNc6CmRsNOBg65eMk41uDquQ1X8uros+16D/Qegt3fo2wSDMO9GmH5NagODs2e5oFQy+2RyJdx4vXdrAvfch0Ju7GZHGLL8UFrqxi/6/YbCQtfNrKho8P4Zz+9DIjL6wmFv63V0eFtPRERERERERERERES8kdZwV3NzM+AufF5zzTWUl5enczkiY5JG88XmauluEQyajFzXaBqNDm2RiOvW1dFhOXcOzp2DnFyYVgW5uYm/trZuT/iug2zbHn+4q7fXcvAgvLp3+M5hOTlww/Uw61oXYko9w93LLQ9vSKybWU42LL/L1fFC3+jF5hbo6XEd2iZXDhi9WOYClXl5Q2/vankfEpHREQh4Wy8/39t6IiIiIiIiIiIiIiLijbSGu3Jzc/s/Ly4uTuNKRMa2vtF8O3cZvv/DLH72QtegjjtX42i+S6m7xdVhNDojNTZaTpy82K2rsxPKyqCsNLnXViRi2bM72VU7u3e7esOFjAay1nLsuLtPe2jo22T5oaYG5tZAdvbovn9MqzKsXGF5ZFN8Aa+cbFi5wt0/ErE0N0NnF+TmuM5qseybPl1dluYWaGu9OHqxtMQF+XJzXaCrbMDoxeHofUhEvDSpHPw+b0Kjfr97LxMRERERERERERERkcyT1nDX9OnT+z/v6+IlIolxo/kMt72tkPb2KAcPthDqsFftaL5LqbvF1SGVnZG6uixHj0FrK7S0uPGX2VmuW1c8QaHhNDdD1JumY0St6y5VmTfy7ZqaLDt3QdPZoX9uDFw7E264AQIePMaB4glc1dQY7llt+c6TsY1onFwJd33YBe82PWbZs2fwvvUZmDffsnRJX4ezobfb0eHWGOq4fPRiUZF7b7109OJI9D4kIl4KBg11dZbNW5KvtbROvyuKiIiIiIiIiIiIiGSqtIa7Fi9eTHZ2Nt3d3Rw/fpyWlhZ18BLxQEGBjxkzDNajoMh4oO4WV4dUdUY632w5dswFkk6fcd8vLoaJE8Dn8+ZieGeXJ2Uu1usc/mft7Zbde+DoseFvM60K5s2DokIvL/ZbDhyErduIO3A1rcqw5r4L998Oe3Zfcn8fzJ8HdUsgNxeefGr4IFjUwq5d7l9lBdy93DKtym0vGrW0tblwXNeF4FnFJCgsdJ25Skvd+0kigT69D4mI1+5cZti8Jflf+O64XcEuEREREREREREREZFMldZwV0FBAe95z3t45plniEajPPfcc3zkIx9J55JEZJxSd4urg9edkfLyLEeOwNlzLhB1ptF9f8pk74+B3BxPyzFg8nG/ri7L3n2w/wBEhwkYTZwAtQtgwgRvH9+x4yN33hopcHWRYfYsF/6KRNyoxM5O91hLil3gqr7esuHrsY9wbDgND2+AP/2YpaICWlrcvgkGXRgrEDDk5EB5OUyIYfTiSPQ+dGWhkHudhcPu9TypfHw+ThGv1C6A6hlw6HDiNWZWuzoiIiIiIiIiIiIiIpKZ0hruAvjUpz7FCy+8QGtrK9/4xje47bbbqKqqSveyRGQcUneL8c/LzkjGwN59UFLsOjm1tkFBECZNcmP5vFZS4rpXeTGa0edzYac+0ajl4OvwyquuG9VQCoJw03yoqop9zGCs6ustj2yKP3C1coWlpmboteTlmcvGTh47Ht92+nR1w7cehfe/D2Zd656L7GxDQYELdZUUe7dP9D50OWstO3bC089Ytm0f/Pr1+6CuznLnMkPtAu+PTZGxzhjD2jWwarUlHIn//oE8+Pz9Rq8tEREREREREREREZEM5kv3AsrLy/n7v/978vLyaG5u5mMf+xh79+5N97JEZBzq626RDHW3yGyuM5I3tayFr3wV7v88/Mu/QSgEkyenJtgFLqw0b743tebPc/WstZw4YfmPH8PLO4YOduVkQ+1N8AfvgmnTvL/An0zg6pFN7v6xcZ3B4t1On55e+PlmF+aqqDDMrYE5sw2lJd7uE70PDVa/3/KRj1tW32vZvPXyYGZvFDZvgdX3utvV79e8YZFL1cwxrF9nCORd+bYDBfJg/TpDzRwFu0REREREREREREREMlnaw10AixYt4sknn2TatGkcP36cD3zgA3z605/mv/7rv2hoaKBruDYjIiJxcN0t4r/42UfdLcaGO5d5+/xY4PXX4dFvw/qvxBM2it/SJd7UqVsC585bfr4Ztv0C2touv40xMGc2vPsPoabGpCi0llzgqqsbnnzK1bmSAweHH/kYq4bT0N0DM6Yb8vNT8zrX+9BFL75kWbXaxjxO7tBh153oxZcU8BK51OJFho0bTMzh0ZnVsHGDYfGisf9eIiIiIiIiIiIiIiIy3hlrbVqvkF133XWXfc9am/RFS2OMOoCNkvPnz6d7CXKBMYaSkhIAmpubSfPLO2O9+JJlzdr4xhf1dbfQRdDMZ63r8BNrYCReOdmwcgXDjgtMjmX9V5ILKVVMgt97Oxw+PHwkqmqqG8FYWJja4/nAQcvX/z75On/+SZg9a+S1bvq2Zdeu5Lf1tlvhiw+kPvs+Ft6HUnlOqd9vkxojt3GDug2JDKV/zOmzlm3bLhlz6oeldW6sq8acSjrobxUREfGSzisiIuIVnVNERMRLOq+kV2lpabqXkDJpD3fNnTu3//OBFxiSXZYxhn379iVVYyzr6urit7/9LSdOnODcuXOUlJRQWVnJokWLyM/P93RbCndlDp0sYle/37JufWwBoJnVrlOOggRjRzLBkVjkZMM9q2FalffHxLHjloc3JNbtKssP8+dD3jBdocpKYcECmFQ+OseyV4GrBTfBn35s+DVHIpb71kDUg7c8vw9+9H1DMJj6fZTp70OpOqd4EcCcWQ2PPzo+OpiJpEooZGlsgo4OyM+H8omMynubyHD0t4qIiHhJ5xUREfGKzikiIuIlnVfSazyHu7LSvQBwB7i11pMDu6/WaAiFQuzdu5fdu3eze/du9uzZw4kTJ/p/PnXqVF544YVRWUuf9vZ2Hn74YZ5//nmam5sv+3l+fj7vete7+PSnP83EiRNHdW0imaRmjuGJx1B3i3GqZo5h/Tri7owUq75xgfd/zgLeHhvTqgwrV1ge2RRfwMvngzlzhg525Qdc6Gv6NaN3LEcilj27vam1e7erl5c39Nqbm70JdoF7H2hsgmDQm3ojuVrfh3bsJOnOem8ccnUW1nqwIJFxKhg0o/JeJiIiIiIiIiIiIiIiqZX2cNfixYvTvYS4PfbYYzz99NMcPHiQaDR65TuMkr179/Lnf/7nHD9+fNjbdHR08PTTT7NlyxYefPBB3vzmN4/iCkUyizGGhbWwsNaou8U4tHiRYeMGYu6MFK9TDXDgIMye5X3tmhrDPast33kythGN+QGYNRsKLrmIn5UF118HNXPA7x/d49nLwFXUQnMLVA7Tkayzy5vt9Ono8LbeSK7G96Gnn/XmwHjmOcvC2vG3f0REREREREREREREREQGSnu46zvf+U66lxC3F198kf3796d7GYOcOnWKT3ziEzQ2NvZ/r7i4mLe97W1UVlbS2NjI1q1b+39+9uxZ/uzP/ox/+Zd/oaamJl3LFskY46m7RShkOdMI4TAEAjCpfHwGRGJxpc5Iydq2PTXhLnAdvNbcZzlwELZuhz27B4eljIGyMqisgOLiwfc1QHU1zJsHgWG6XaWa14Grzs6hvx+NWjo97s7m8fTimI2n96HhhELudeiFrVtdvav1/U1ERERERERERERERESuDmkPd40X+fn53HDDDbz66qt0jGbLD8Bayz333DMo2PWe97yHL3zhCwQHXCXu6uriwQcf5Nvf/jbgunh98pOf5Ec/+hE5OTmjumYR8Za11gWYnrFs237JaDcf1NVZ7lw2vka7xWpgZ6SGhigvvgR/8zVval9pXGDyDLNnuQBZJGI53wwHX4fDh90YRr//8ntMnOBG1ZWVpfd5zvX4tJKbO/jrnh5LS4vr6NXT4wJtXvSD8vtdxyxJjTON3gUsR3OEpoiIiIiIiIiIiIiIiEi6KNyVgNzcXObPn8+8efOYN28eN954I9deey0+n4/bbrtt1MNdP/nJT9i1a1f/1295y1v42te+dlmAIycnh/vvv5/W1laefvppAI4dO8a//Mu/8JGPfGRU1ywi3qnfb0ccPdgbhc1bYPMWS/UMWLvGdbS6mlhrOXkKTp82dHV5NCuQK48L9FJzC+zcCS2tkJ19+c/zA3DTTXDNtMwI8JWUgM94M5rR54OSC93JurpcyK2tzXUvKy6Ckmtg/k0w4FSYsKV1V2+nu9EQDntbb5R/5RIREREREREREREREREZdQp3JeChhx5K9xIG+Yd/+If+z30+Hw888MCIF/bvu+8+fvKTn9De3g7AN7/5TT784Q+TlaXDQWSsefEly5q1lnCMY+kOHYZVqy3r18HiRVdHgKWz03L4iBvf1tQEJ056Xd/bepcKhSwv74QTJ4b+ud8Pc2tg7lzIzsqc5zQvzzBvvvUkcDV/nuvKdeqUpT0EWX43krK4CLKyDGWlsPxD3mzrjtszZx+OR4GAt/XSNUJTREREREREREREREREZLT40r0ASc6RI0fYt29f/9dLlixh+vTpI96nuLiYd7/73f1fNzY28tJLL6VsjSKSGvX74wt29QlHYM1aS/1+7zpYZarmZstr9e7jseNwvhkmlXu7jUvHBXqlt9fyyquWH/3n8MGua6bBH74L5t1oMirY1WfpEm/qzK2B48ehqxsmTYIZM6B8omHKZMMN18P06YbfeZOhekZy25lZDbULPFiwDGtSuRsV6wWN0BQREREREREREREREZGrQcaHu3p6ejh9+jT79+9nx44d7N+/n9OnT9PT05PupWWEn/3sZ4O+fuc73xnT/f7gD/5gxDoiktmsdaMY4w129QlH4Etftlg7PgNe0ajl+HHLG4egpcVy9Bj09EBVFVxzjRsX6IWB4wK9dOKk5T/+E155FXp7L/95aQnc9jZ4y5tNRo8QnD0LKiuSq1FW6p63yZUw/RqYOMEwdaoLdU2dasjJcY/fGMPaNYZAgiMyA3nw+ftNRoy0HM+CQUNdnTe1NEJTRERERERERERERERErgYZOYfv6NGj/Nu//Rsvvvgi+/bto6ur67Lb5OTkcP3113PLLbfwvve9j2nTpqVhpel3acet2tramO43b948/H4/vRdSA+rcJTK27NjpRiwm441Drs7C2N42xozOTsuhwxfHMDa3QEHQdXzy+10QxMtxgXl53oVL2totO3bCyWFGR+bmwvwboboafF4l1FLKcPdyy8MbXNeteGVnwfIPwzXTDHl57jksKx3+sdfMMaxfR9wd7QJ5sH6doWbOWNinY9+dywybtyQfLNUITREREREREREREREREbkaZFTnrsbGRlatWsW73vUuvvWtb7Fr1y46Ozux1l72r7Ozk507d/LNb36Td77znaxevZrGxsZ0P4RR9/rrr/d/npeXx8yZM2O6X0FBAddcc03/14cOHRq3HXxExqOnn/Xm9frMc+Prdd83hrGlxY1hbGl1Y9smTzb9wS7wblxgnUd1enose15x3bqGCnYZXBesd/8BXHutGSPBLmdalWHlCsjJju9+2dnwif8BN91kqK6G6+a6rl1XeuyLFxk2boh9ROPMati4wbB40djZp2Nd7QI0QlNEREREREREREREREQkRhkT7vrFL37Be9/7Xn72s58RjUb7g0bGXD4iaeD3rLVEo1F++tOf8t73vpdf/vKXo772dOnu7ub48eP9X1dWVsY1TmrKlCn9n4fDYU4O1ypGRDJKKGTZts2bWlu3unpjnbWWEycGj2GMRqFqKpSUXP6+6MW4wMmVrk4yrLUcP2H5jx/Dq3vdmi81cQL8/jvg5oUXRxCONTU1hntWx77PJ1fC5++H97zbddMqLYlvXGLNHMMTjxk2PGS49a3gv+S3Hb8f3nYrbHjI8Pij6tg1UChkOXTYsnef+9jePsRBmSSN0BQRERERERERERERERGJXUaMZfztb3/LJz/5SSIRN0PJGNPfoSsrK4vq6mpKS0vJz8+no6OD8+fPc+jQIXp6egbdvrm5mU9+8pM8+uijMY8nHMvOnj3bvw/AhbviUVEx+Cp7Q0MDU6dO9WRtIpI6Zxqh16O8RW8UGpsgGPSmXjp0dV0cw9jYOPQYxsslNy4wJxuW3+XqJKqtzfLyDjjVMPTP8/LgpvkwYzrjIsQyrcqw5j7LgYOweSu8sgcGxgqNcY/3D94Jt74VgsHk8ufGGBbWwsJa446NJujogPx8180tGBz7+9Qr1rpxoE8/Y9m2feD7i8XvP8/bb8vhPX9oqV1gPTsWNUJTREREREREREREREREJDZpD3d1dHRwzz33EIlE+kNaAO9+97v54z/+YxYtWkROTs5l9+vq6uK3v/0t3/ve9/jRj37Uf7ExHA5zzz338OMf/5hAIDCqj2W0hUKhQV8H40xnXHr7jo6OhNYxHkIH48XA50LPy/jlcrDeddsKh8duB5zWVsvhI4Zw2NJw2hCJwKTyobt1XeqaaYaVKyyPPGrp6op9mzk5sPJPDddMS2yf9fRY9u6D1+oh2nt5PMwYmD3bcOMNjNlOXcPp6oLiInjbW2Hp77pjOCcXgvlu9OKM6Ya8PO8fc0GBoaDA87LjQv1+yxfXWw4dGvrnvb3wk5928ZOfQnU1/OUa74JVtyw2/P3XLV9cHx12+wPNrIa1a3wKdomIjFH6W0VERLyk84qIiHhF5xQREfGSziuSKmkPd33rW9+iqampP9hVUVHBww8/zIIFC0a8X05ODm9+85t585vfzEc/+lHuueceGhpc+5PGxkY2bdrEqlWrRuERpM+lYazc3Ny47n/p7RMNd5WUlCR0P0mt4uLidC9BRtDeHqXhdJSODkt+vqGywkdBQWydiiZN6gFaPFtLRUUhJSVpPx3ExVrLyZNuH/b0RmlqipKTDTNm+AnEEQ5atAjKy3v55qYwJ09euR3a1Ck+Vq4IMH26P6F1Hz/Ry69/3U1bu8XnA98lT3lFhY/fuSWb0tKMmZrsic5OS9PZKG1tFr8frrnGUFriIzvbMHGij8mVvnEXZBsLfvnfXay+t41wOLbbHzoEn1wdZcNDhbzlzZcH7xPxplvg+89YXnyph3/+boSfvdBFb+/Fn2dlwdtvy+GDH8hj8aIs/SEkIjJO6G8VERHxks4rIiLiFZ1TRETESzqviJfSfjX/3//93/uDXSUlJfzzP/8zU6ZMiavG/Pnzeeqpp/jjP/5jmpubsdbyb//2b+M+3NXZ2Tno66E6nI3k0tv3jcUUkdSw1vKbF12A4YWfDw4w+P0XAwy3LB45wFBZ4cPvZ9D9E5WVBRWTxlaQqLvb8sahXpqbXajr7FlLsMAwudJHVlb8wY/p0/2s+z9B6ut7+dnPu3h5Rw/RATkvnw9uXpjFbbfmUFPjTyhc0tFh+fWL3Rw5MvSTlh8wLLo5m+pq37gKr4TDlrPnorS3WbJz3LFbXGzIyjJUTPJRUeECXjL69u7riSvY1ScchtX3tvHEY8Vcf503v0YaY7hlcTa3LM6mvT3K6TNRQiFLMOiOk1iDryIiIiIiIiIiIiIiIiLjUVrDXa+99hqnT5/GGDcS7DOf+Uzcwa4+U6ZM4VOf+hR/+Zd/CcCZM2d47bXXmDt3rpdLziiXhrO6u7vjun/XJXPI4u381ae5uTmh+4n3jDH9CeCWlpb+MaeSfm702fCjxy6OPuu6MPps5NFjS5fAz7ckv66lSww9Pa2MlZdxKOTGx4UjllMNLmgyYQKUFEMkklxIqKoKPno3/Mn7Dc3N0NkJublQUgJ5eVEgwiXTcK8oGrUcOAh79lh6ei7/uTEwZ44bwZid3RN3/UzlQl0Q7nBjLEtLobAQsrMNxcUwcQJkZZlx83jHGmstn70/Gnewq084DJ+7v4UnHktNGHFCmfsH0NPDmHl/EhGRkelvFRER8ZLOKyIi4hWdU0RExEs6r6TXeJ46l9Zw18GDBwF3kTEvL493v/vdSdV7z3vew5e+9KX+DlQHDx4c1+GuYDA46Ot4O29d2vkrPz8/oXXoDSkzWWv13GSIF1+yrFlrCcf4Ej10CD7551HWrzMsXjR0cOKOZYafb0n++V12e2a8hkMhy5lGFxoJBGBSOQSDgx97U5Pl2HEXHDrVANbClCmu6xV49zhyc6GiYvD3Eql99pzlpZfgfPPQPy8vh0ULoa8jayY8D8nq6LCcO++ex9wcqKyEggLIyTFMKocJEyx+v7fPl8Tv5R122KBprN445OosrPVmTSIicnXR3yoiIuIlnVdERMQrOqeIiIiXdF4RL6U13HX27FnApRerqqoIBAJJ1QsEAlRVVfWHxvrqj1eXhrE6Ojriun/okpYpiYa7RGR49fvjC3b1CUdgzVrLxg0M2cGrdgFUz4BDhxNf28xqVyddrLXs2AlPP2PZth16B4xC9Pugrs5y5zLD/HmWEycMZ89BS4ulsRFy81xwKDuBMYyp1tVl2fMKHDwIQ/26lpMDC25yz994GcEYClnOnYPIhW5nkyuhoMCQkwOVFVBWBj7f+His48HTz3rzh8Qzz1kW1up5FREREREREREREREREUmltIa7BnaOSnQk4KUGjiq8dOzgeDNhwgSysrLouTDr69SpU3Hd//Tp04O+rqys9GxtIuLCS+vWxx/s6hOOwJe+bHn80ctDQMYY1q6BVasTqx/Ig8/fb9IWLqrf7/bNcOG03ihs3gKbt1imTIYPf9CSkwttbW4E48SJ6QlGRSLWjWzscp2p3MjGi52ojh6DHTthuEaKM6vhpvmQmzs+AjGhkBu/2NnpOq5NnQL5+Ya8PKiY5EJd4yXANl6EQpZt27yptXWrq3dplz0RERERERERERERERER8U5aw11lZWWAuyAebzBpOA0NDf2fl5aWelIzU+Xk5FBVVcXhw4cB99ittTFfSD958mT/53l5eUydOjUVy5QxKpYxeTKyHTuT66wFbvTZjp0MOfqsZo5h/Tri7gwWyIP168yQHcFGQ7xjKk+egoe/Du95Nyy6GQoLR3vdlgMHYes22LMHogOaHvkMzJtvWbQQzp2HSzKz/YqL3NrLy8f+a8haSygE5865kFt+AKZOdeMxAwHXqaukRKGuTHWmcXCXvGT0RqGxCYJBnTNEREREREREREREREREUiWt4a7Jkyf3f37+/Hl27drFTTfdlHC9Xbt2ce7cuf6vp0yZktT6xoKZM2f2h7sikQhvvPEG11577RXv197eztGjR/u/rq6u1oV4iXlMXu0CBTdiMRqjzxYvMmzcwIhdsAaaWe06dqUr2JXomMruHvjBD2HWLCgsTM3ahnLsuOU7T0LDMKGtqIVdu9y/QABmz4aC4MWf+/1w4w1QM2fsjyW01tIegvMDQl1VUyEQMOQHoKLSdVXTe0NmC4e9rbdjh+Vbm3TOEBEREREREREREREREUkVXzo3fvPNN5Obm9t/we9rX/taUvUefPDB/s9zcnK4+eabk6o3FixatGjQ1zt27Ijpfrt376a3t3fYOnL1qd9v+cjHLavvtWzeenlnl74xeavvdber3+9NcGm8SsXos+HUzDE88Zhhw0OGW9/qQhUD+f3wtlthw0OGxx9NX7Ar2TGV3T3w5FMAo3Ps1ddbHt4wfLDrUuEwvPIKNDe7r6dMgT98F1w314zpYJe1lrZ2N3KyocEdT1VTYepUw4QJhpnVMHeuobQkfWM+JXaBgLf1Hvw7dM4QERERERERERERERERSaG0hrvy8vL43d/9Xay1WGt56aWXWLNmDdFofPOCrLX81V/9Fb/5zW8wxl1cXrJkCbm5uSlaeea47bbbBn39n//5nzHd79Lbvf3tb/dsTTL2vPiSZdXq2Do/gRs1uGq15cWXdLF+OKkYfTYSYwwLaw3r/o+PH33f8OTjhm/+g/v4o+cNX3zAx8La9IZvvBhTeaoBDhz0YjUjO3bc8sgm6OqO737RKLxW7zqM1f3u2B5Ld2moK+uSUNe1M2FujaGkZOw+xqvRpPLLA6CppnOGiIiIiIiIiIiIiIiISOLSGu4CWL16NT6fD2MM1lqeeeYZ3ve+9/GrX/0qpvv/+te/5n3vex//9m//1l/D5/OxevXqFK88M1RXV1NTU9P/9S9+8QuOHDky4n1aWlr40Y9+1P/1xIkTWbx4ccrWKJkt0TF54QisWatuLMPxevRZR0fstw0GDTOmG66/zn3MlICRV2Mqt233pMwI3CjGeINdfaJR+OlPYaw2seoLdR0bItQ1caJh1rUu1FVcPEYf4FUuGDTU1Y3+dnXOEBEREREREREREREREUlMVroXMHfuXD7+8Y+zadOm/nDW3r17+fjHP87kyZN505veRE1NDaWlpeTn59PR0UFzczOvvfYav/nNbzh58iTgLkb3de36+Mc/PijwNNZcuvb6+voRb/+///f/5v/7//4/AKLRKA888ACPPvrosB16vvKVr9DW1tb/9cqVK8nKSvuhIGmQ7Ji8cAS+9GXL44+icWyX8Hr0WX6+t/VGm5djKnfvhkjEkpeXmmPuwMHYRzEOp6/D2OxZ3qxptLS3W86dg84uyA+4UFcgYAgGYXIlFBXpdd4nFLKcaXRBzkDAdcTKlCDlldy5zLB5y+iHrHTOEBEREREREREREREREYlfRiR6/uIv/oKmpiaee+65/ot91lpOnjzJs88+O+z9rHUXJvtCXdZali1bxmc+85nRWHbGeNe73sW8efPYs2cPAL/85S/5zGc+wxe+8AWCwWD/7bq6unjwwQd5+umn+783depUPvShD436miUzeDEm741Drs7CWg8WNI70jT7zYjSj3w/lE5Ovk05ejqmMWmhugco8b+pdaqtHncG2bR874a5QyHL2HHR2KtQ1EmstO3bC089Ytm0ffEz7fVBXZ7lzmaF2QWaHl2oXQPWM5N//E6FzhoiIiIiIiIiIiIiIiEh8MiLcBfA3f/M33HjjjTz44INEIpFBF0X7QlwD9QW6+n6el5fHX/zFX3DXXXelfK0nTpzgHe94x5A/6+3tHXS766+/fsjbffvb3+aWW27xZD3GGB5++GHe//73c/bsWQB+8IMfsG3bNm677TYqKipoampiy5YtNDY29t8vPz+fb3zjG+Tm5nqyDhl7vBqT98xzloW1mRtkSAc3+syyeUvytZbWjZ2OQMPxekxlZ6e39fpEIpY9u72pleoOY14YLtSVn+9CXRq9eFH9ftfpcLhAVG8UNm+BzVss1TNg7RqomZOZ+88Yw9o1sGp14p0bk6FzhoiIiIiIiIiIiIiIiEjsfOlewEB33303P/7xj/nEJz5BWVkZ1tohg11A/88mTJjA//yf/5Mf//jHoxLs6tt2b2/vkP8uNdzthntciZo6dSrf/OY3mTp1av/3WlpaeOaZZ/h//+//8b3vfW9QsKu0tJSNGzcyd+5cT9chY4eXY/K2bnX1ZLA7l3kTXrjj9rEfgvB6TGWqMqnNza4zmBf6Ooxloo4Oy7HjlpOnwOeDqVNh6lTDhDLDtTNhbo1RsGuAF1+yrFo9fLDrUocOu+DUiy9l7vtizRzD+nWGQIo64I1E5wwRERERERERERERERGR2GVM564+FRUVfOpTn+JTn/oUr7/+Ort37+bEiRO0trbS0dFBfn4+RUVFTJ06lZtuuomZM2eme8kZ48Ybb+S5557j4Ycf5vnnn6el5fJUQSAQ4J3vfCef+cxnKC8vT8MqJVN4OSavNwqNTTBgCqjgzeizmdWuzljn5ZhKnw9KipOvM5Smc97WS1WHsUSFw65TVzjsAnJTp0B+viE/AJWVUFKiQNel6vdb1qyNv8NVOAJr1lo2bsjcDl6LFxk2bmDEjmSpoHOGiIiIiIiIiIiIiIiISOwyLtw10LXXXsu1116b7mVcpqqqivr6+pTVT6Z2YWEha9eu5bOf/SwvvfQSJ06c4Ny5cxQXFzN58mQWLVpEUFdTBe/H5HV0eFtvPEh29FkgDz5/vxk0pnasCgRg4c3w4ovJ15o/D89HHUajlr374KXfelo2ZR3G4hWJuFBXRwfk5riRiwUFhrw8mDzZheXGw3HmNWvdKMZERxeGI/ClL1sefzRz92/NHMMTj8GOnW5U77Ztg0OYWVnw9ttymHdDN197yLtuWzpniIiIiIiIiIiIiIiIiMQmo8NdkricnBze8pa3pHsZksG8HpOXn+9tvfHCjT4j7s4/gTxYv85kbMefeHR3u65AtyzyJtxVtyT5GgOdP2/59YtuJGN2tnd1U9lhLFadnS7UFQpBzoBQV26u+7y0NHNDR5lgx87kOu8BvHHI1VlY68GCUsQYw8JaWFhrCIUsjU0ufBXMN8yaVUxBgY8dO88D3oW7dM4QERERERERERERERERiY3CXSJXKS/H5Pn9UD4x+TrjVbyjz2ZWu45d4yHY1dFheeOQGwcYCEBZKZw7n3i9yZUwe5Y3a+vtdd269u4DeyGz4vdDWRmc82A8YywdxiIRS3MzdHa5jlolJd50Jevqspw7D21trvNSxSQoKoKcHENlJUwoU6grFk8/602Y6ZnnLAtrx8b+DgZN/7hEYwwFBT5A5wwRERERERERERERERGRdBmVcFdXVxd33XUX5wZcLb///vv5vd/7vaRrb9myhS9+8YvYC1fmJ02axJNPPonf70+6tsh4Fgwa6uosm7ckX2tpnasnw7vS6DO/3+3HO2431C4YH8Gb5mbLkaMu4HXylPve3cvhH/4fdHXHXy8nG5bfBZD8vjl3zvLr30BL6+U/mzLZm3DX8B3GLAcOwtZtsGcPRAfkh3wG5s23LF3SF2KL77F291jOn3OPKyvLBXKKiyE721BZARMmgM839o+t0RAKudepF7ZudfXG8vukO2egc4aIiIiIiIiIiIiIiIjIKBuVcNe3v/1t9uzZA7jAwh//8R97EuwCeOtb38qvfvUrHnvsMQBOnjzJP/3TP3H33Xd7Ul9kPLtzmWHzluQ709xxuy7Sx2K40Wf5+a6LzXgKOzSctpw8Ce3tlobTrivV5MmQlWVYucLyyKb4Al452bByBUyrSm4f9fZaXnkVXntt6AFzxUXw+78HTU3QcDrx7QzXYezYcct3nhy+dtTCrl3uX2UF3L3cxvSYe3tdp66WFjcOcuJENxIyO9tQUQETJ4DfP36Or9FwptGbLlXg6jQ20d8Ra6zSOUNERERERERERERERERk9PlSvYFwOMw3v/nN/i401dXVPPDAA55u49Of/jRz5szBGIO1lm984xt0dyfQFkbkKlO7AKpnJFdjZrWrI/EJBg0zphuuv859HC/BrmjUcviIC3adO2c51QAFBTB1qgt2AdTUGO5Z7cJLsZhcCfesdvdLRlO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ieQ/YOc75MPUVbVzXmFQMK0UbV3n08bAyDHK5ck0Ki8dqJlIBAAAAYIifdgAAAAAAgN3JOT+rmPggl/otGZsVIa9eEeTq+IlFmxcTi7EUT/lWrqhG0APYaWl3YxtXZ8HfT2+FKQ3DW8MgV2WGKTZgJ9ls45Ri1pPJEt/SpU1MKpaiy9q4iklF2jMBAAAA4Ib4CScAAAAAANgdbC4lLT+vmBRhLmf9+7OuD42kxUtnfZgrqkrVWVmeJAZ2Xtq9clrxVoNcQTQMcdkiyKXKNPdtYKc4K+XJaFIxG0wqZv7DxvgQVymWi5vrJhWj4uNXm1SsSaVokn8rAAAAANjTCHcBAAAAAIDJsFkxr9iS6a/6lq7B+9Puumaunn9/UPJPFtcP+zBXWGG2CdgpwyDXujBX2r612wwiH+KqHy4auQ5L8RT3a2Cn5KmU99dNKvb8pKJzkiQXRMWk4kwxr1jxL4v7qAtjKar5ScVBiItJRQAAAAAYO8JdAAAAAABgZ+SJXHte6q0puHRaSoqGnzyV0k4R5ur6J5klqRT5J4wrMz7Ute4JZQDbKOsXAa75UZgrGUeQ65Bs7bBcfRDkanKfBnaCsz68tS7IZbKeb8ZU0bYVxnJRVa466wNapXg4feqCUhHiqg7DXIqqTKMCAAAAwA4h3AUAAAAAALZHlhSNXGsyvTWZvCfXaMhliczqJbmkLZN2ZPJUkuTCshTVZGtz/knjUnnCfwHgAMj6V04rJq1bu80N04o+zKV4miAXsBPy1N+vc9/E5du4Usk5OcnPI4YV2eqsb+IK4+HjrZM2TCn6l3Up5PEYAAAAACaJcBcAAAAAABiPrO/DXP01md6qn3aS/JPLaVcm7ci1JdlUptuVShWp3JAt1/yUU8CPKYBtlacy3UXfyNW+5INc/bVbu80glKsekqsflh1MK1aakgnGc2YAVzdo41oX5DJZf9TGFQRSKZaL6nLVeDSpOGjjKkVFkKu24SX3XQAAAADYffipKQAAAAAAuDlpbxTm6q8Om0HWh7mUdmRsLhkjF1Wk6mGpXJetW8kw5wRsG2dluks+xNWe90Gu7rKKbp6bY0pytTn/XxHmUmWaMAiw3W7YxlWWwvjqbVzGSGFVrnxZiIt2TAAAAADYMwh3AQAAAACAzUm7vuWnvyrTX7sszNWRSTpS1pGxtghzVaXqrOzgieQgkGk0/G1lrVvKmABYxzl/v2xfUtCeL2YWFySX3/xtDoNch+Rqh2Xrc1JlhiAXsJ2clfJk1MKV9WWy3hbbuGqjNq5yTQqrTKICAAAAwB5HuAsAAAAAAFzd9cJcSVsm7V4W5qpJ1TnZqMq0E7BdnPNhynXTikF7XrLpzd+mKclVZ+Xqh+Vqc7K1w1J1hvswsJ1sNgpvrZ9XdD757EqRFFY20cZV82HqqCaVokn+jQAAAAAA24RwFwAAAAAA8G4Y5ur4yzgrmcA/mVw77MNcYYUgCLAdsv4wxOUDXfMyWfcWbtD4IFdtTq5+xAe6qrPcf4Ht4lzRxjUIchWtXDbzHzaBFJbloopcdeY6bVw+wEUbFwAAAAAcPIS7AAAAAAA4qG4mzFU/7GcWwwpPLAPjlqfFpOK6IFeydks36eLmxiBXbU4K+JEgsC2c9Y+hl88qDtq4grBo45qRSoNZxci3X0p+SjGqSeWaXFiVyrVhWxcAAAAA4ODiJzkAAAAAABwUaU+mvyr112T6q4S5gEmyuUx3aV2Q65JMb0XyEY+b4qKaXO2wD3HVD8vVDvspNwDjl6cb2rj8rGIiqZhNLMVyYSwXN/39MIyHwUoXlIo2riLIFdWYMwYAAAAAXBPhLgAAAAAA9qusPwpz9Vb9k86EuYCd55yUrMm0LykYBLk6i5LLb/42S2XZy4Nc5fr4zgzAG84q9oogVxHosv7+64KSFFbkynW5aG7UyFU8hrowHs4p+hBXXQpp4wIAAAAAbB7hLgAAAAAA9os8kemta+bK+qMwV9qRSTpS1pGxhLmAbZX1fSNX61IR5Jr3YZCbZUrFtOLhYaBLcZP7LDBuNpfyvg9ypYM2rv5oVrEUFbOKs/5xczCrqKKta9jGVfePsVFNCkqT/BsBAAAAAPYBwl0AAAAAAOxVeToKcvVWR+GRrCclHZm07UNd1krG+Cecq3Oy5ZoUVgmGAOPgrExn0Ye42pf8xGJ/5RZu0MhVZ4dtXK5+WK46y1wbMG43mlUMY7mwIleZlqKKb+QqglquFF05q0hIGgAAAACwTQh3AQAAAACwV9hc6q/KDGYW045/f5ZIgyBX0pGxWRHmqkrVQ76ZK6oSDgFulXN+0rQIcgXtSzKdhVuaV3Rxc2OQqzYnBfzIDhibzc4qxg25sCKFsQ9yGSMnFZOLtQ1hLpWYVQQAAAAA7Bx+UgQAAAAAwG7lrNRv+Wau/qp/XfJtI2nbt3Ml7VGYK6xI1RnCXMC45Ilv4hq2cl26pXlFF1Z8gKt+xP9XO+yDJADGw1kpK2YVhy97o1nFIJKieN2sYjwMajkTSFG1CHLVfZArqjKrCAAAAACYuF0X7lpaWtLf/d3f6eGHH9a5c+fUarXU6/Xkim/AN8sYo/e9733bdEoAAAAAALaBc759q7dShLnWZJyTbCYlg2autkye+ouHFanSlI3qvkmEMBdw85yV6S6NphXbl2R6K5K29jOpIVOSq83J1Q/LFmEulRvMtgHjYjMp78u1/KyiWV2UyRLJuVHjVhjLxU0f4gorzCoCAAAAAPakXRPuOnfunN7+9rfrz//8z5Vl2S3dlnNOhm/EAQAAAAB7Qdr1E4v9FR/msrmfiko7o5nFoinIhWWp3JAt5qGYbgNu0iBIefm8or35n0n5ecWikat+WK56iMYfYFzydMOsorKeDzobSbW6D2ZFNdnKukauIvDswtgHuco1uUEYmllFAAAAAMAesit+Cvw3f/M3+sEf/EF1Op1hQxfhLAAAAADAvpQnMr1Vqb/qG7ry1M9Ipd1hM5fSrp9fLEVyUU22dsiHuUrRpE8P7E02k+ksyLQujuYV087N314pHrZxDWYWmVcExsA5KU+krC+TdYuXPR96luSCkm/kiptFaKsqTR+SMZJrteXCdbOK5cGs4q74ETgAAAAAADdt4t/ZPv7443rzm9+sbrcryYe6nHNbnmEEAAAAAGBXsplv5OqtyvRXZVL//e8VYS5npaAkF9Xkpo7Lles0iwA3wzkpacu0L/pGrtZFme6iD1HeDBPIVQ/J1Y+M5hXjKebbgFvlbBHk6smkvVGQq7ivuiCSoli2OmjjqgxDzs4EvoGrXFdw+IRvtez05cT9EgAAAACw/0w83PW2t71N3W532NRljNHXfd3X6dWvfrXuv/9+zc3NqVqtTviUAAAAAABsknNS0lo3tdjyTzXniZS0h4EuY3PJGLmoJtUPy5brUikmMAJs1ZhbuVx5atjG5RpHmVcExsHZYk6xKzOYVcz6knNyklQqy0UVufiwb8ELK8PGLVeK/KxiVPPB56hWzC4aGWNkpmb8n9FN/WMwAAAAAAD7zETDXfPz8/roRz86bOuamprSf/yP/1EveclLJnksAAAAAAC2Ju36icXB3KKzvrEr6Uhp24e58tSHucKKVJ2VjYq5KBNM+vTA3jHuVq5SuWjjKsJctcP+fgng5tm8CG/1ikBXTyZPfJDLGKkUy4UVuXhaiio+2FwEKF2pLJV9iMtFfl5RIS2WAAAAAICDbaLhrk984hPD+UVjjP7Nv/k3BLsAAAAAALtfnoyCXL0VH9xyVko7UtKRSdtS2pOR5MKyn4satI3QAARs3lhbuYxcdbYIch2VbRyR4mna8oBbYbNRC1fa9dOKeSKpmE4My75xazitGA9DzS6s+Caucq0IctWGs4sAAAAAAGBkouGuS5cuDV+vVqv6hm/4hgmeBgAAAACAa3BW6q8N27mG4ZK0O5xZVNqRcU4KQt84Up2Vi+o8UQ1s1tVauToLkm5yZq0U+1auxlHfylU/LJVoAAJuWp768FZWhLiynn+fJBcEUqlSNG4V04rF1LCT/KxiEXJ25RphZwAAAAAAtmCi4a5utyvJt3bdcccdiiJ+4A0AAAAA2CWStkzRzKX+mg9u5amUtv3Hko6MzfwT11FNqh/x7VxhZdInB/aGy1u5Whd9aOSmDFq5fJjL1o9IcZNWLuBm5ckowJX1ZNKeb+mS5IKSFFZk46YPcYXV4XSiM+ayIFedCWIAAAAAAG7RRMNdMzMzw9dLJX5TCwAAAAAwQVebWrS5b+dKWjJpx89OSXJRRapMF1OLPGkNbErSlmldVNC+OJ5WrsYRuTqtXMAty5NRgCvr+UCXzSVJLgh9kKs6M5pVLA2CXIFUrvuAc7nuG7nCKqFKAAAAAADGbKLhrvvvv1+S5JzThQsXJnkUAAAAAMBBM5xaLMJc66cWk7Z/ezC1WIrkynXZ2mGpXJOCiX47Dex+zsp0l3yIq3XBN3MlrZu8MVq5gLFwTrKplPY2TiuuD3JFFdnqrA9yRRUpiIqPlYogV10q1/zLMOZ+CAAAAADADpjoT6Nf+MIX6vjx4zp//rwWFhb0+OOP6957753kkQAAAAAA+1na9UGuQUOXs/6J7uTyqcVArlzzrUDl4glsANeWJ6NpxdZFBe1L/r51M2jlAm6dc6NGrsG0YtZfF+SKpCguglxVKYrXBbnCoolr1MrF4yAAAAAAAJMz0XCXMUZvfOMb9TM/8zOSpPe85z1629veNskjAQAAAAD2E5sV7VzLMt0VmTzxjV1pZxTmynqSmFoENs25YmLxwmhisbukm5tYHLVy2cZRufpRKZ6iDQjYiqsFudKef7zTIMhVka0eWtfI5X8s7EqRFBVBrnJNiupSSJgSAAAAAIDdZOI7Et/+7d+uP//zP9fHPvYxPfTQQ3rggQf0zd/8zZM+FgAAAABgL3LOTyn2lmV6K1K/JSNJWb8Ic7WLqUUrBWExtXjIt5IwtQhcnbMynYVhK5dpXxzNmG5VqSxbPyrXoJULuCk3CnKVIimsyNbmfIgrvE6Qq1zn/gcAAAAAwB4w8Z9cG2P07ne/W//sn/0zfepTn9JP/dRP6fHHH9cP/MAPaHp6etLHAwAAAADsdnnqg1y9FT+5aDM/O5V2pKQl02/L2FQyxs9L1Q/7dq6wMumTA7tT1vcBrtZFBa2LMu1Lkstv6qZcPOXnFRvHZBtHpcoMrVzAZg2DXH2ZrLuJIFdVCkqjjw2nFetSuUaQCwAAAACAPWri4a4/+qM/kiS95jWv0cWLF/XMM8/ofe97n97//vfrZS97mZ7//Odrbm5OcRxv+bZf85rXjPewAAAAAIDJc86HtgZhrqTt3592N7ZzSXJhWYobsuWGf2KbqUVgI+ek/moR4iqauXrLN3dbJpCrHR7OK7rGUT9xCmBz8kRKe6MgV9b3YWWtm1a8ViMXQS4AAAAAAPYt45xzkzzA/fffL3PZb2wOjnT5+7fqkUceuaXrY3OWlpYmfQQUjDGamZmRJC0vL2vCd28AwB7H4wqAXSVPLmvnyiWbSUl7GOgyNvPhkuIJbuamdiEjNRoNSVKr1ZJ4aNl5w4nFCzJrF3ygK+vd3E2FlWGIyzWOytXmmDcFNmswrZj2RhOLlwW5XFghyHUDfM8CABg3HlsAAOPE48rOmp2dnfQRts2u+Ymbc24Y5rpW2GszjDEbbgsAAAAAsAcN2rm6yz7MlXb8+7Kef3/S8u0m8gETVab91GJUpZ0LWC9PZdqXZFoXFLQuyLQu3vzEYmV648Ri3GRiEdiMPB0FuNLuZUGu0DdyVWf9rGJEkAsAAAAAAGy0K8Jdg/DWOFKKJB0BAAAAYI8q2rlMd1nqr17WztUq2rlyKSjJRTW55qx/srsUTfrkwO6RdmVaFxW0zvuJxc6CbqoizZTk6usnFo9JYTz24wL7js02TiumPf8+FUGucBDkKlq5Av8Y5kqRFBVBrnKN9kkAAAAAADA08XDXz/3cz036CAAAAACASXDOTyr2inaupH3tdq6oIlVnfTtXWKUtCJCK+9CagrULfmaxdVGmv3JzNxVW5RrHRhOL1UNSUBrzgYF9xmZXTivmqSTJBaUiyDXjg1xhZRhGdkE4auQq16WoLoUEuQAAAAAAwNVNPNz12te+dtJHAAAAAADslDyV6a1IvRUf6LLZtdu5ynW56qxcuTGcqAIONGdluktFkKsIc6Wdm7upyoxc46hs45hv5So3CE0C1+PsaFIx6/nwcZ74DwWBFFZl4+aokato3XJByQe5ilYuleu04AEAAAAAgC3hp+MAAAAAgO01aOfqLvvXJSntXtnOFdLOBWxgM5n2/DDMFbQuSja9iRsyxcTisWE7l8LK2I8L7BvOSllfyrrrWrn6/kMmkMLYB5Cjw0UrVzz6WHkU4nJR3Qe9AAAAAAAAbgHhLgAAAADAeNlc6q/KdJd9qCtP/fuStg93JS3f2BWU5KKa3NSsfyK8mKsCDqysP2zkCloXZDrzPmSyVUEkWz/ig1xTx+TqR2i/A67FOSnv+6Bx1vOBrqwvOSdnjA9yRTW52qFiWjGWjPEfGwS4BoGusEIwGQAAAAAAjB0/2QMAAAAA3Lq054NcvWWpvybjnG89KaYWB41dLoylSlO23JCiGk+C42BL2kUj1wWZtfP+/nMTXFiRaxyXaxz1ga7aIckE4z0rsF/kyWheMe3JZF0f5JJ8kCusyFVmRo1cJvAfK9flyjVpMK/IYxgAAAAAANghhLsAAAAAAFvnnA9xFXOLJuv5hqGkM5xbNHnq203Kdd8eVG5IpfKkTw5MTr8l0zqvYO28b+jqr97Uzbi4OZxXtI3jUjxFyAS4GputC3IVL20uSXKlSC6qysVH/XRiKZaCkv9YVC0CXIOJxRqBSQAAAAAAMDG7Otz11FNP6f/8n/+jc+fOaXV1Ve12W/V6Xc1mUydPntSLXvQi3XnnnZM+JgAAAAAcDHkq01uRessyvRUZm0s29YGVpCUlHRln5UqRVG74di6eEMdB5ZzUXx0FuVrnfYvdlhm52iG5xjHZItClqDb24wJ7ns2lrLcxyJWnkiQXhFJYka3OSmHVh7mKqVJXKsvFjQ0Ti4OQFwAAAAAAwG6w68Jdly5d0u/8zu/o937v97SwsHDDyx85ckSve93r9K3f+q06cuTIDpwQAAAAAA6QtOObuXrLPsQlSWl31M6V9nw7V1iV6od9oCuMJ3xoYAKck3rLozDX2nk/97ZVpiRXPzIMcrnGURrvgMs566d/s65/HMq6MlniPxQEUliVjZt+WjGqSqXIf6wU+TaueF2Qq/gYAAAAAADAbrWrwl3vf//79fM///Pq9Xpyzm34mFk3L7D+YxcvXtSv/Mqv6L3vfa9+/Md/XK973et27LwAAAAAsO846+cWu8sy3SWZPPFtKGln2NBlbCYFJblyXbZ6qGg52VXfXgLbz1l/H1k7X0wtXpDy/tZvp1SWbRyVaxz3U4u1OVqDgPWck/LksiBXX3JOzhgpjOWiur/vhNVhwNiZQIob/mPlIshF+BgAAAAAAOxBu+an7//23/5b/cEf/MEwuDUIcw3evjzsdfllOp2O3vrWt+qzn/2sfuZnfmaHTg0AAAAA+0Axt2i6S1J/1c8t5qkPeSUt397lnFxYlipN384V1aR1v4QD7HvOyrTnh61cQeuCnyXd6s2EFR/iahyTmzouV51luhRYL0/9vGLaHc0s2lxSMaEYVeXiad/IFcaSCXzIK6oNQ1yu3PCtXTxOAQAAAACAfWBXhLt+8Rd/Ub//+78vyQe2nHNyzun48eP68i//ct1///2anZ1VtVpVt9vV0tKSPv/5z+vjH/+4zp07tyHk9fu///uam5vTD/3QD03wbwQAAAAAu1zalektF4Guy+YW+y3/ZLoxclFNrn5ULm4wDYeDxWZFmOu8zNoFBe2Lks22fDMuqg2DXLZxXKpMEzgBBpz1j0dZb/Qy96FJF4RSVPENkVHFt3IVrXYurPjHpWErV42QJAAAAAAA2LcmHu56/PHH9Z73vGdDQOs5z3mO3vKWt+grv/IrN8wxXs3f/M3f6O1vf7seffTRYTDsPe95j77+679e99577078FQAAAABg93POB7e6S35yMev5J9WT9lXmFhuytblibpF5OBwQeSrTvlS0cp2Xac9LLt/yzbhyY2OYK54izAVIxbxi34e4hq1cfsrUmUAKK7Jxs2jkqkilyH+sFA3buIbzikwBAwAAAACAA2TiPwl517vepTzPhyGu17zmNfr//r//T6XS5p5AePnLX66v+Iqv0E/+5E/qD//wD2WMUZ7n+g//4T/ol3/5l7fz6AAAAACwu9lc6q0UDV3LPrxlUx/m6rektM3cIg4um8m0Lvow19o5mc68DzxukYubRZDrmFzjuBQ3tuGwwB6UJ1Lak8m6o5fOyUlSWPHzirVDRZAr9m2RJpDihlxUHzZzKaQ1EgAAAAAAHGwTDXf1+3399V//9TDY9eIXv1g///M/v+XbCYJAP/dzP6dnnnlGn/zkJyVJf/VXf6V+v684jsd6ZgAAAADY1fLEB7m6y1J/RcY5KesV7VxrMulgbrEq1Y/4QFfI9004AGxWNHOdU7B2C81clZlhK5ebOuYDkcBBZ3PfxDVo5Eq7wxlTF0Ry5apcfLSYV6xIJvAhr6gmF9elckOu3Cg+RsAYAAAAAABgvYmGuz71qU+p3/f168YY/diP/dgt3d6P/uiP6vWvf70kHxz71Kc+pa/4iq+45XMCAAAAwK6WdopA15JM0vbTV2lH6q/5ucU89U+kl+uyzVmp3GDSCvufzWTa8+vCXJduIsxl5GqH5BrHRmGusLItxwX2jOvNKwaBFFZlqzP+vhJVpGAwr1j2bVzr5xVNMMG/CAAAAAAAwN4w0Z/mnzt3bvj6oUOH9KVf+qW3dHsvetGLNDc3p4WFhStuHwAAAAD2DeekpOXDXN0l/6S6zaWkLSVrMklbxuZSKZIrN2TjwdwiT6JjH7N50cxVzCzebJirfngY5HKNY1KJSTgccHkqZeuCXGlPcvYq84rVYROkC0rDENcwyMV9CQAAAAAA4KZMNNw1CGEZY3T8+PGx3Obx48eHt7u4uDiW2wQAAACAiXNW6q34hq7esm/jsqmfW+y3pLQt45xcWJGqs35uMapO+tTA9rG5TGdeZu28b+dqXbzJZq452akTclPHizBXtC3HBfYEZ0ezimlXJuv5cJckF4RSVJWtH/aPL+vnFdeFuJhXBAAAAAAAGK+JhrviOB6+3uv1xnKbg5lHSSqX+Y1AAAAAAHuYzYZzi+qtyDgrZf1ibnHNt6cY41tT6kf93BXNKNivnB02c5m18wpaF24hzHVcbuqEXOMo9xkcbFm/aOXq+ZdZX3JOzgRSWJGNmz7IxbwiAAAAAADAxEw03DU3NydJcs7pzJkz6na7qlZv/jfLu92uTp8+fcXtAwAAAMCekSW+mau7KPVWZSQp7fpAV39NJk98oKvckG3OSuWGFEz0Wztgezgr056Xaa0Lc9lsizeyPsx1nJlFHGw2H80rDlq5rA9IulLZB4UrM751q2jeciaQ4oZcVC8CXcwrAgAAAAAA7LSJPgNwzz33SPKzjP1+Xx/84Af1zd/8zTd9ex/84Ac3NIANbh8AAAAAdrW0K9Nd8v8lbck5KW0Xk4trMjaTgpIPdDWO0pKC/ck5qbukYO2czNpZBWsX/PTolhi56uzGmcUwvvHVgP3GOSnv+8eXtFu0ciX+Q0HJt3JVZ6Ww6pu5gpL/WFT1s4rlIswVVplXBAAAAAAAmLCJhrvuv/9+HT16VJcuXZJzTu94xzv0lV/5lTp+/PiWb+vChQt6xzveIWOMnHM6evSo7r///m04NQAAAACMQdIeBbrSrm9PSdpSsiaTtGSslStFUqUpW25IUY0n2LG/OCf1Vn2Ya/WcgrVzPoyy1ZupHpKbOu4DXYS5cFDZTEp7Mll3FOhyVk6SwopcVJOrHfZhrdA3b7kg9NOKcX0Y6KIJEgAAAAAAYPe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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigmoid_response_curve_fig = mmm.plot_direct_contribution_curves(\n",
    "    show_fit=True, method=\"michaelis-menten\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EbpwQ9421gLN"
   },
   "source": [
    "Additionally, the plot will consistently return the value of `alpha` (Ceiling). This means you can visualize the maximum possible estimated value that your target variable will achieve.\n",
    "\n",
    "## Curve Fit\n",
    "Libraries such as Scipy offer functionalities to fit these functions to the data and extract these statistics. We use `curve_fit` from Scipy to ascertain the optimal fit for each saturation function, the method used by them is based on `least-squares`.\n",
    "\n",
    "## Estimating Curve Fit parameters\n",
    "Estimating curve-fit parameters, particularly α and λ, is pivotal for optimizing marketing budget allocation. These parameters determine the shape of the saturation curves for each marketing channel and function as the input to the `budget_allocator` function.\n",
    "\n",
    "The PyMC-Marketing library proffers a straightforward method to calculate these parameters for each saturation curve. You can employ the following code to estimate parameters for the Sigmoid and Michaelis-Menten curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "5wDjp4Xn1dGJ"
   },
   "outputs": [],
   "source": [
    "sigmoid_params = mmm.compute_channel_curve_optimization_parameters_original_scale(\n",
    "    method=\"sigmoid\"\n",
    ")\n",
    "\n",
    "menten_params = mmm.compute_channel_curve_optimization_parameters_original_scale(\n",
    "    method=\"michaelis-menten\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tN5mDugj1weE"
   },
   "source": [
    "## What You Get\n",
    "After running the code above, you will receive a dictionary containing α and λ for each channel. For instance, for the Sigmoid & Michaelis Menten curve, you might get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0QT8JTAk1ssS",
    "outputId": "905231db-158a-4bbd-d524-d250f3b27f5c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'x1': array([2.40931096, 6.11360947]),\n",
       "  'x2': array([1.92067375, 3.02135665])},\n",
       " {'x1': array([3.1334831 , 0.24731411]),\n",
       "  'x2': array([2.6305693 , 0.50773317])})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigmoid_params, menten_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V8oZ3-Bk12lB"
   },
   "source": [
    "Once these parameters are obtained, if desired, you can recreate the curves for each channel independently. More crucially, these parameter values are indispensable when using the `budget_allocator` function, which leverages this information to optimize your marketing budget across distinct channels. This section is fundamental to budget optimization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GEGsiGlf142o"
   },
   "source": [
    "## Example Use-Cases\n",
    "The `budget_allocator` function within PyMC-Marketing boasts a myriad of applications that can solve various business predicaments. Here, we present five critical use cases that exemplify its utility in real-world marketing scenarios.\n",
    "\n",
    "### What are we optimizing?\n",
    "Before jumping into the examples, we need to understand the basis of our optimizer.\n",
    "\n",
    "We aim to optimize the allocation of budgets across multiple channels to maximize the overall contribution to key performance indicators (KPIs), such as sales or conversions. Each channel has its own sigmoid or michaelis-menten curve, representing the relationship between the amount spent and the resultant performance.\n",
    "\n",
    "These curves vary in characteristics: some channels saturate quickly, meaning that additional spending yields diminishing returns, while others may offer more linear growth in contribution with increased spending.\n",
    "\n",
    "To solve this optimization problem, we employ the Sequential Least Squares Quadratic Programming ([SLSQP](https://docs.scipy.org/doc/scipy/reference/optimize.minimize-slsqp.html#optimize-minimize-slsqp)) algorithm, a gradient-based optimization technique. SLSQP is well-suited for this application as it allows for the imposition of both equality and inequality constraints, ensuring that the budget allocation adheres to business rules or limitations.\n",
    "\n",
    "The algorithm works by iteratively approximating the objective function and constraints using quadratic functions and solving the resulting sub-problems to find a local minimum. This enables us to effectively navigate the multidimensional space of budget allocations to find the most efficient distribution of resources.\n",
    "\n",
    "The optimizer aims to maximize the total contribution from all channels while adhering to the following constraints:\n",
    "\n",
    "1. Budget Limitations: The total spending across all channels should not exceed the overall marketing budget.\n",
    "2. Channel-specific Constraints: Some channels may have minimum or maximum spending limits.\n",
    "\n",
    "By leveraging the SLSQP algorithm, we can optimize the multi-channel budget allocation in a rigorous, mathematically sound manner, ensuring that we get the highest possible return on investment.\n",
    "\n",
    "### Maximizing Contribution\n",
    "Assume you're managing the marketing for a retail company with a substantial budget to allocate for advertising across multiple channels. Suppose you're already apportioning funds to channels x1 and x2. Still, you're contemplating ways to optimize the forthcoming quarter's outlay to maximize the overall contribution.\n",
    "\n",
    "Without, you might have considered scattering your money linearly without an MMM model - equal investments in each channel. However, you wish to explore better alternatives now that you possess an MMM model. Given that you lack prior knowledge, you impose the same restrictions on both channels. They must each expend a minimum of 1 million euros and no more than 5 million, equating to your total budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "iAtdq9uU10xr"
   },
   "outputs": [],
   "source": [
    "total_budget = 5  # Imagine is 5K or 5M\n",
    "# Define your channels\n",
    "channels = [\"x1\", \"x2\"]\n",
    "# The initial split per channel\n",
    "budget_per_channel = total_budget / len(channels)\n",
    "# Initial budget per channel as dictionary.\n",
    "initial_budget_dict = {channel: budget_per_channel for channel in channels}\n",
    "# bounds for each channel\n",
    "min_budget, max_budget = 1, 5\n",
    "budget_bounds = {channel: [min_budget, max_budget] for channel in channels}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nd5hRZu92MRi"
   },
   "source": [
    "We can use our function and see the results with this information saved."
   ]
  },
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      "text/plain": [
       "       estimated_contribution  optimal_budget\n",
       "x1                   2.409210        1.762903\n",
       "x2                   1.920457        3.237097\n",
       "total                4.329667        5.000000"
      ]
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     "execution_count": 31,
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   "source": [
    "result_sigmoid = mmm.optimize_channel_budget_for_maximum_contribution(\n",
    "    method=\"sigmoid\",  # define saturation function\n",
    "    total_budget=total_budget,\n",
    "    parameters=sigmoid_params,\n",
    "    budget_bounds=budget_bounds,\n",
    ")\n",
    "\n",
    "result_sigmoid"
   ]
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    "id": "vsqc-QEe2JaD"
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   "source": [
    "In the same way, you can perform this optimization based on the michaelis-menten function and the estimated curve according to the best parameters found."
   ]
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       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-06236324-9d80-459e-a6cb-c389194d3828 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "       estimated_contribution  optimal_budget\n",
       "x1                   2.821852        2.239459\n",
       "x2                   2.221905        2.760541\n",
       "total                5.043757        5.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_menten = mmm.optimize_channel_budget_for_maximum_contribution(\n",
    "    method=\"michaelis-menten\",\n",
    "    total_budget=total_budget,\n",
    "    parameters=menten_params,\n",
    "    budget_bounds=budget_bounds,\n",
    ")\n",
    "result_menten"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RbzCfU3x2RYs"
   },
   "source": [
    "These results are expected based on the estimates from the curve and our estimated average contribution from the posterior distribution.\n",
    "\n",
    "However, based on our main assumptions, how can we ensure this result is optimal? How can we compare this outcome to what we would initially have if we followed our first setup?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "mnlhEUUw2LqS"
   },
   "outputs": [],
   "source": [
    "# Use the function `calculate_expected_contribution` to estimate\n",
    "# the contribution of your initial budget based on the curve parameters.\n",
    "initial_contribution = calculate_expected_contribution(\n",
    "    method=\"sigmoid\", parameters=sigmoid_params, budget=initial_budget_dict\n",
    ")\n",
    "\n",
    "# Initial budget & contribution dictionary\n",
    "initial_scenario = {\n",
    "    \"initial_contribution\": initial_contribution,\n",
    "    \"initial_budget\": initial_budget_dict,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jJx8a4u-2WbI"
   },
   "source": [
    "This information will allow you to compare the optimization results against what could have been your initial configuration and budget distribution.\n",
    "\n",
    "If you want a visualization, you can create a barplot to see the contribution estimate you could obtain with the new budget and the initial one. The plot has confidence intervals, which come from the posterior distribution; these are, by default, the 95th and 5th quantiles. The estimated contribution could be within said range and not be exactly the mean value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 740
    },
    "id": "0UaDEk2d2T9P",
    "outputId": "8aefbc2b-8e9b-44ea-8715-d33bea8ac254"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 723,
       "width": 1511
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the function `compare_budget_scenearios` to validate\n",
    "# The estimated contribution from one scenario agains the other\n",
    "figure_ = mmm.plot_budget_scenearios(\n",
    "    base_data=initial_scenario, method=\"sigmoid\", scenarios_data=[result_sigmoid]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aqIaFFHy28ni"
   },
   "source": [
    "While our budget distribution changes, the contribution estimate remains the same for this budget. Could we be spending more than needed? Furthermore, how can we identify the optimal spent amount if this situation arises?\n",
    "\n",
    "One approach to explore this could be to create various scenarios and observe how our estimated contribution fluctuates according to each scenario."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dbsTmbLZ2-x8"
   },
   "source": [
    "### Creating Budget Scenarios\n",
    "Envision the subsequent situation: You're managing marketing operations for a rapidly growing retail company. The management team has allocated a considerable advertising budget and anticipates substantial results in the next quarter. However, given the uncertainty in economic trends, you are tasked with designing a budget allocation strategy capable of accommodating various scenarios.\n",
    "\n",
    "Before the advent of a robust MMM model, your approach might have been simplistic yet naive: distribute the funds equally across the two primary channels, x1 and x2. This would result in a linear, evenly split distribution of 2.5 million euros each, given a total budget of 5 million euros.\n",
    "\n",
    "Nevertheless, with the MMM model now at your disposal, you realize a more sophisticated approach is feasible. You're eager to investigate how this budget could be optimized across multiple scenarios:\n",
    "\n",
    "1. Status Quo Scenario: What if the market stays stable? What is the best allocation?\n",
    "2. Growth Scenario: What if the market suddenly becomes more favorable? How should the extra budget be allocated?\n",
    "3. Recession Scenario: What if there is a market downturn and the budget gets cut by 40%?\n",
    "\n",
    "Given that you are treading into uncertain waters, you set certain constraints. Each channel must have a minimum spending of 1 million euros, ensuring base-level visibility. The maximum cap is ±5 million euros (depending on your scenario), respecting the total budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "qbE1GDaB2sbL"
   },
   "outputs": [],
   "source": [
    "# Initialize two variables to save the results and base conditions for each scenario.\n",
    "scenarios_result = []\n",
    "scenarios_base = []\n",
    "\n",
    "for scenario in np.array([0.6, 0.8, 1.2, 1.8]):\n",
    "    scenarios_result.append(\n",
    "        mmm.optimize_channel_budget_for_maximum_contribution(\n",
    "            method=\"sigmoid\",  # define saturation function\n",
    "            total_budget=total_budget * scenario,\n",
    "            parameters=sigmoid_params,\n",
    "            budget_bounds={\n",
    "                channel: [1, total_budget * scenario] for channel in channels\n",
    "            },\n",
    "        ).to_dict()\n",
    "    )\n",
    "\n",
    "    scenarios_base.append(\n",
    "        {\n",
    "            \"initial_contribution\": calculate_expected_contribution(\n",
    "                method=\"sigmoid\",  # define saturation function\n",
    "                parameters=sigmoid_params,\n",
    "                budget={\n",
    "                    channel: total_budget * scenario / len(channels)\n",
    "                    for channel in channels\n",
    "                },\n",
    "            ),\n",
    "            \"initial_budget\": {\n",
    "                channel: total_budget * scenario / len(channels) for channel in channels\n",
    "            },\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8pErqyBw3USg"
   },
   "source": [
    "We now have two lists containing the expected results with and without model optimization. This will return the same plot as before but based on each of the scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 740
    },
    "id": "74q2NZd03SMr",
    "outputId": "485ced2a-34c4-4005-fba2-af180e42d974"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 723,
       "width": 1511
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the function `compare_budget_scenearios` to validate\n",
    "# The estimated contribution from one scenario agains the other\n",
    "_figure = mmm.plot_budget_scenearios(\n",
    "    base_data=initial_scenario, method=\"sigmoid\", scenarios_data=scenarios_result\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N7G3lVcI3idn"
   },
   "source": [
    "The graph indicates that boosting the budget beyond the levels of scenario number 3 induces extremely marginal changes in the potential outcome. Therefore, one can use the budget detailed in scenario three as a cap for our budget.\n",
    "\n",
    "However, is this the best method to invest our resources? So far, we have considered general constraints for each channel. However, since our curve saturates and beyond a specific point, it does not significantly elevate its contribution. Wouldn't it be crucial to incorporate these limitations?\n",
    "\n",
    "### Adding Business or Channel Constraints\n",
    "There may be instances where, despite the recommendation to invest more in a particular channel than another, it may not be feasible.\n",
    "\n",
    "Consider this: Given your current budget, you have already maxed out the number of people you can reach within a specific platform. Therefore, further spending will only increase the frequency without adding new reach. In this scenario, does it make sense to raise the budget? This is a classic example of budget limitations based on platform restrictions.\n",
    "\n",
    "In such cases, a dictionary can be created with limits for each channel, and the optimization function can be reapplied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "lhqA7s903ZoT",
    "outputId": "08b9dd7c-fb47-4638-889e-7a9686d49234"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>estimated_contribution</th>\n",
       "      <th>optimal_budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>x1</th>\n",
       "      <td>2.408810</td>\n",
       "      <td>1.5</td>\n",
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       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>4.288595</td>\n",
       "      <td>3.0</td>\n",
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       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4215ba86-664c-4cd8-bdb0-013451d8fe1b')\"\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
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       "        document.querySelector('#df-4215ba86-664c-4cd8-bdb0-013451d8fe1b button.colab-df-convert');\n",
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       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-58990b74-ad0c-4131-94de-08e5ee642739\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-58990b74-ad0c-4131-94de-08e5ee642739')\"\n",
       "            title=\"Suggest charts.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-58990b74-ad0c-4131-94de-08e5ee642739 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "       estimated_contribution  optimal_budget\n",
       "x1                   2.408810             1.5\n",
       "x2                   1.879786             1.5\n",
       "total                4.288595             3.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "platform_base_optimization = mmm.optimize_channel_budget_for_maximum_contribution(\n",
    "    method=\"sigmoid\",\n",
    "    total_budget=total_budget,\n",
    "    parameters=sigmoid_params,\n",
    "    budget_bounds={\"x1\": [0, 1.5], \"x2\": [0, 1.5]},\n",
    ")\n",
    "\n",
    "platform_base_optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xiIprHPQ3wPd"
   },
   "source": [
    "Utilizing bounds is crucial, as both saturation curves (Sigmoid, Menten) are asymptotic. Therefore, without a constraint on your factors, the optimizer will consistently seek to use 100% of the funds because it will always observe an increase within `y` (target).\n",
    "\n",
    "If you have yet to notice, this is why, in the previous example, the optimizer always utilized the whole budget in all scenarios. However, should you provide these limits, the optimizer can verify whether it is necessary to use your entire budget.\n",
    "\n",
    "Let us examine the subsequent plot closely!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 828
    },
    "id": "wwMqqJqA3umN",
    "outputId": "3130dd81-19dc-4a94-b36c-b1a871247cd3"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigmoid_response_curve_fig = mmm.plot_direct_contribution_curves(\n",
    "    show_fit=True, method=\"sigmoid\", xlim_max=3\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vhod0OqSUcqt"
   },
   "source": [
    "Although in principle we assign a limit of 1.5 spending for each channel, we can observe that at least for x1 after spending more than 1.2, we enter the plateau effect of the curve where we get almost zero return in `y` for each unit extra of `x`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "EaB5ok7HUcqt",
    "outputId": "c4182a42-55fe-4378-e477-8dab24d11d4a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-c1232911-d77f-4612-be7b-e320519bb620\" class=\"colab-df-container\">\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>estimated_contribution</th>\n",
       "      <th>optimal_budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>2.406174</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>1.879786</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>4.285960</td>\n",
       "      <td>2.7</td>\n",
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       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-8f10db16-1a55-4a84-b7c5-67449acad69a\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8f10db16-1a55-4a84-b7c5-67449acad69a')\"\n",
       "            title=\"Suggest charts.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-8f10db16-1a55-4a84-b7c5-67449acad69a button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "       estimated_contribution  optimal_budget\n",
       "x1                   2.406174             1.2\n",
       "x2                   1.879786             1.5\n",
       "total                4.285960             2.7"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "platform_base_optimization = mmm.optimize_channel_budget_for_maximum_contribution(\n",
    "    method=\"sigmoid\",\n",
    "    total_budget=total_budget,\n",
    "    parameters=sigmoid_params,\n",
    "    budget_bounds={\"x1\": [0, 1.2], \"x2\": [0, 1.5]},\n",
    ")\n",
    "\n",
    "platform_base_optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eNGpxrMF3-9Q"
   },
   "source": [
    "The end result is a total budget of 2.7 units on X. Is this our most optimal result? How to validate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 740
    },
    "id": "B2pXkL6n37Dv",
    "outputId": "77a424bc-ddd7-4967-c94b-e1aab86e88f1"
   },
   "outputs": [
    {
     "data": {
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AAADIpNQrA3l4eNxzm169eikwMNDcXrRokRo1amT1nAEBARo5cqS53alTJ02cONHq7bdt26apU6dq3759aT7u6+ur7t2767XXXpObm5vV+03P0aNH9dVXX2n79u1prmTu4uKiNm3aaPjw4SpWrJgkqXnz5rpw4YJ5zMaNG236B4dr165pzpw5+uOPP3Tx4sV0x1WoUEG9e/dWly5dMvwDz44dO9S7d+80HwsMDFTlypUzrCcoKMi6wu/h4MGDOnnypKHvueeek6+v733vu1u3bpo5c6aSk5PNfStXrswwPJ/esZyYmKglS5Zo/vz56d6NoXz58howYIA6d+6cYV3Z8dqPGDHCsMrNhAkTMqxj2rRpmj59urk9ZMgQDR06VJL077//asqUKel+nkqVKqW33npLrVu3tngsJSVFy5cv15w5c3T27Nk0t69du7Y++OAD1apVK936Ukv9mqR3/KV+Xvfj7tcktRs3buiPP/7Qpk2btHPnTt28eTPDfZUrV049e/bU888/r3z58mU4Ni8cH6kFBwfrm2++0aZNmwz/OHQ3R0dH1alTR/369VOLFi2s3nfq59uwYcP/Y+++o6sq0/9h3wkkVCkqotgoVlTEhl3sFTuOFaUoWBEVmRFhxjb6s4sFUbAPVkAHRZxxFDuIHcWug6JjAaXXEPL+wcv5cnKSkJMEErbXtZZruZ/slpOdHb33Z99PPPLIIxGxrHB+++23x+jRo2PRokUZ2+bk5ESHDh2ib9++WV1PAAAAQNV79tlnM2qlp5xySjWdTcQLL7wQDz74YHz00Udp9cEVNWnSJPbff/8477zzsqrVllVrefPNN+OOO+6IDz74oMRt69evHyeeeGKcc845ZYbCV0WNqLQa9aJFi+Khhx6K4cOHl1j7vO6669I+n7LqOdn66aef4rbbbosxY8ZEQUFBxtfz8vJir732ir59+8Zmm21Wrn3+8MMPafWpDTfcMF5++eWszqs8zzzKqkXeeeedZdYpS/vMKvscYfz48XHvvffGO++8U+LnGbHsGtx7773jvPPOW+l1tKKyasqTJk2KQYMGxZtvvhlFRUUZ2+bl5cWRRx4Zffr0iebNm5f7mBXxzDPPpC3n5ubGySefXOn9Nm7cOA4//PB44oknUmNfffVVfPzxx7HddtuVuE1ZvytfffVV3HrrrfHKK69EYWFhxrb16tWLAw88MPr27Rvrr79+medW/HpdUWn3keVKqoNX5HeotBr+3Llz46677oonn3wy5s6dm7FdXl5edOrUKS699NJYZ511Mr7+/fffx6BBg+Lf//53LF68OOPr9evXj+7du0fPnj0zZiMuTVnX8sq+r8oo67nahx9+GC+88EKMHz8+vvjiixJ/j5arX79+7L///nHmmWfG1ltvvdLj1oTrY0WFhYUxcuTIePTRR+Ozzz4rdb1mzZrFYYcdFuecc065XrBYrqz795gxY+Luu++Or776qsRtmzRpEl27do1u3bqt9FkOACRR9q+rAwAkSPHiTevWravpTFausLAwBg4cGN27dy816BuxrLvQ3XffHcccc0xMnTq1Use866674vjjj4/XX3+9xOB8xLIOGs8880wcccQR8dprr1XqeBERw4YNi4MOOigefPDBMoPzERFff/11/PWvf42jjz660t/r6vDKK69kjGUT6i1L8+bNY88990wbmzBhQokh3LL8/vvvcdppp8V1111XanA+Ylmnissuuyy6du1aasC4JisqKorrr78+unXrVubv0/fffx99+vSJG264IW18zpw50b179xgwYECpwfmIZUXgk08+OV544YWqOvXV6vPPP48999wzBgwYEC+99NJKg/MRy6bwveqqq+LII48stShbExUVFcUtt9wSRxxxRIwePbrM67qwsDDefffdOPfcc6NLly7x22+/VerYb775ZnTq1CmeeuqpUn9ni4qK4u23346TTjopRowYUanjAQAAAJUzbty4tOUmTZpk9YJ9Vfn555/jxBNPjAsvvDA++OCDUoPzEcvqxqNGjYpDDz00bQbLiigsLIxrrrkmunfvXmpwPmLZDHsPPPBAdO7cuUbUb7/77rs45phj4uabby6z9rkqvPbaa3HEEUfEM888U2rQu6CgIMaNGxfHHHNMxgym/J85c+bE2WefHV27do233nqr1M8zYtk1+K9//SuOOeaYuOqqq0p9zlFe9957b5x00knxxhtvlBr4LSgoiFGjRsXRRx8dH3/8caWOV5aFCxfG+PHj08b22GOPlYbPy+v444/PGHv11Vez3s/IkSPj2GOPjZdeeqnE4HzEskZXzz77bBx++OExZsyYrI9RE3z11Vdx1FFHxf33319icD5i2bXx9NNPx3HHHRfffvtt2teef/75OProo+O5554rMTgfsex6vvPOO6Nbt25r1EzaK7rooovixBNPjAceeCA+//zzMoPzEcu+5+eeey6OOeaYuOWWW8r8O1fTfPXVV9GpU6cYOHBgmcH5iGUzWTz88MNx4IEHxlNPPVWp4y5cuDAuvPDCuPjii8t8RjNz5sy47bbb4rTTTovff/+9UscEgDWR8DwA8Ic1Y8aMePHFF9PGioePa4rCwsLo169fPPnkkyV+vW7duhlTWk6ZMiW6du0aM2bMqNAxb7nllrj99ttLLGbm5eVFo0aN0sbmzZsX5513Xrz33nsVOl5BQUFcdtllceONN8b8+fNLPGaTJk1KnJ7xq6++ihNPPDE+//zzCh17dSne7aJBgwax/fbbV9n+d99997TlhQsXZlWcX7hwYZx11lkZD7xKur6WGz9+fHTv3n2NC9DfeOONcf/996eN1alTJ9Zaa60S17/vvvvisccei4hln1P37t3jrbfeSlunQYMGJX5OS5YsiUsuuaTGX58lWbhwYakPn5b/TpY2w8X3338ff/rTnzJmW6iJlv+M7rnnnlJn2GjYsGGJ206cODFOOumkCj8Afuutt6JXr14xa9as1FhOTk40bty4xCl4CwsL4/LLL48333yzQscDAAAAKqegoCCjGcMOO+wQeXl5q/U8vvnmmzjppJNKbQzRsGHDEs+poKAgBg0aFP37969wCHHgwIEZncTz8vKicePGkZOTk7H+999/H2eeeWYsXLiwQserCr/88kucfvrpGYHVunXrlmtG2sp477334rzzzssIuzZq1KjEendBQUFcd911ce+9967S81oT/frrr3HKKadkvMCyXP369UvsoLx06dIYPnx4nHvuuRW+Du+88864+eab056Z1K5dO5o0aVLizLgzZsyIM888M6ZPn16h463MpEmTMhpx7LHHHlW2/3bt2mX8brzzzjtZ7WPs2LFx+eWXp9WYl9c+c3Mz40rz5s2LSy+9NEaPHl2xk64mU6dOjTPOOCNtJoXl32dJ9+Gff/45evXqlXoWNnbs2LjkkkvSno3VqlWr1M/pvffei/79+6+C72TVK61BT05OTjRs2DAaN25c6kzT99xzTwwYMGBVnl6Veffdd+OUU07J+JuzXGn3/3nz5sWAAQPijjvuqNBxCwsL47zzzsto6JSfn5/xXHe5jz/+OC644IKVvsgAAEmT+ZcYAOAPYO7cudGnT5+0wG/9+vXjT3/6UzWeVenuu+++eO6559LG1l133TjnnHPi4IMPjvXWWy8ilnUNf/nll+Puu++OH374IX744Ye4+uqrsz7ef/7zn7jnnnvSxurVqxc9evSITp06RcuWLSMnJycWL14cEydOjOHDh8fLL78cixcvjksvvbRCxedrr702Ro0alTbWtm3bOOWUU2L33XdPTddaVFQU33zzTTz//PPx0EMPpTp4/Pbbb9G7d+8YNWpURsi1ZcuWccUVV0TEspcKVuza07Jly+jatWvW51sRxcPTbdu2LfFhUkVts802GWOfffZZ7LzzzuXa/q677opPPvkkIiLWWWed1PW1fGrZGTNmpK6vFYPCkyZNir/97W9xyy23ZOyzpnz2K3rttddi0qRJEbGsY3+vXr3igAMOSHXkmT17dvzrX/+KW265Ja3bxs033xyHHnpo3Hjjjant27VrF2eeeWbsscceqeD9//73v3jiiSdi2LBhqSD2kiVL4oorrojHH3+8yr6PfffdN9Zdd92st/v222/j4YcfThsrqQC/onr16sXuu+8eHTt2jLZt20abNm3SHpzMnj07Pv744xg9enQ8++yzqQdI8+fPjwsvvDCefvrpEoPgNeX6uP322zM6GjVq1Ch69uwZhx12WOr+M3fu3Hj99ddj6NChMXny5NS633//fZx77rkxcuTIEr/P0kybNi0uuuiiKCgoiNq1a8fxxx8fRx99dLRr1y71UOXrr7+Oxx9/PIYPH572QHvgwIHx73//u8QCOwAAALDqfPPNNxldgbfddtvVeg7z58+Pc889N3766ae08e233z569OgRe+21VzRo0CCKiorihx9+iOeffz7uvffetG7II0eOjE022STOPvvsrI795JNPpppvNG/ePM4888zYb7/9YuONN46IZTOVTpgwIW6//fa0xh5TpkyJIUOGRJ8+fTL2uTpqRH/7299S3ea32mqr1Oe09tprR8Syus+bb76ZqoVWlYULF8all16aumZ222236NatW+y+++5Rp06dWLp0aXzzzTfx9NNPx8MPP5wWMr755ptj6623jr333rtKz6kiVqxFvvLKK2mzrO67776x7777lrrt8ucXlVVYWBh9+vSJL7/8Mm28devW0atXr9hvv/2icePGEbHsZYkXX3wxhgwZEtOmTUut++qrr8bf//73rJ+bvPHGG/HRRx9FRETjxo2jW7ducfDBB0fr1q0jJycnCgsL44MPPoi777473njjjdR2M2fOjOuvvz5uvPHGin7bpSqpk3VJzwgqKicnJ9q2bZsWmM+mQcy0adNi4MCBqUDuIYccEqeeemrsuOOOkZeXF4WFhTF58uR48sknY8SIEan1CgsLo3///rHlllvGlltumbHfrl27xuGHHx4REQ8++GBMmTIl7WstW7Ys9ZxW1b364osvTs1Oevjhh8dJJ52U+j6Lioris88+iyFDhsS//vWv1Dbff/993HPPPXHUUUelXmaqV69edOnSJTp16hRbbLFF5OTkREFBQYwfPz5uvvnmtM//hRdeiDfffLNKm4Itvw9n6/7774/vv/8+tbyy5w0REW3atImOHTtGhw4dYvPNN48NN9ww9bxs6dKl8d///jfefPPNGD58eNrPeOTIkbHrrrvG0UcfXeJ+a8L18csvv8R5552X8aLA3nvvHaeffnrstttukZ+fH0uXLo1vv/02nnnmmXjwwQfT7v933nlntGrVKjp16pTVsW+//fbU3+hWrVrFmWeeGfvss0/qPjxv3rx47bXX4rbbbkv7bN59990YMWJEnHDCCRX8rgFgzeMpOwDwh1BYWBhz586NKVOmxJtvvhmPPfZY/Prrr6mv5+TkxJVXXlllRdyqNGXKlLjzzjvTxtq1axdDhw6NJk2apI2vvfba0blz5zj88MPjwgsvjNdeey1V0C2vuXPnZhTINthgg3jooYdi0003TRvPz8+PvfbaK/baa6947LHH4sorr0zrrFFeY8eOjUcffTS1nJOTE5deeml07949I1yek5MTm222WfTu3TuOPfbY6NmzZ6pzw3fffRe33HJL/PWvf03bpnnz5nHyySdHRMTbb7+d9vBlvfXWS31tVZo5c2ZGoWyrrbaq0mOUtL8VC5Yrs/xa2W677WLYsGEZ11fTpk3j+OOPT11fK07ROmbMmDj88MPjwAMPTNumJnz2xS0Pvu+6664xePDgjJctGjVqFCeccELstNNOccIJJ6QeKs6ZMycuu+yyVFejs846Ky655JKMa7RFixZx0UUXRZs2beLSSy9NjX/wwQfx2WefxdZbb10l38d2220X2223XVbb/PLLLzF06NC0sTZt2sSpp55a4vqNGjWKyy67LDp37lxq5/Xl6+25556x5557xqmnnhq9evVKvXjw9ddfx3PPPRfHHXdcxnY14fp47733Mj6TLbbYIu67776MvwkNGzaMww47LA4++OC49tpr4x//+Efqa19++WXccsst8Ze//KXcx/7vf/8bEcvu3UOGDClxJorNNtssBgwYEG3bto3LLrssNf7jjz/GK6+8kvE7BwAAAKxaJdXbqrrOtzL/7//9v7TQW0REjx49om/fvmmhxZycnNh4442jV69ecdRRR0XXrl3Ttrvjjjtin332ibZt25b72MtDeR07doxbb701ozN1fn5+7LPPPrHbbrvFOeeckxYkfvLJJ+O8887L6MS8OmpEX331VUREnHHGGfGXv/wlI9zZsGHDOOSQQyp9nOKWB2ojIi688MI499xz076em5sbm2++efTr1y+OOOKI6NatW9oMhQMHDowxY8as8u74K7NiLXL69Olp4fltt912tdTx7rvvvoyZbzt16hTXXXddRkOL5s2bx2mnnRZHHnlk9OrVK2221SeffDL222+/2H///ct97OUzPGy33XYxZMiQjKYmtWrVip133jmGDRsW/fv3T2sUNHbs2LjssstSL2pUlZJmwlwVzxxWDM8vf85RWgfrFS2vfebm5sa1114bxx57bNrXa9WqFe3atYt27drFwQcfHOedd17qJZPlMxWPGDEi43f1gAMOSP37888/n3ZP23///WPXXXfN+vusrEmTJkV+fn7cdNNNGfeR5S8h3H777fHXv/41nnjiidTXHn/88Zg4cWLMnz8/Ntxwwxg6dGi0adMmbfu8vLzYZ599YqeddopTTz017aWJ4cOHV2l4viK/x3fffXfG38WBAweWuv6+++4bF1xwQbRv377UdXJzc6NNmzbRpk2bOOmkk+Kaa65J+9zuuOOOOPLII0sM6deE66N///4xc+bMtLEBAwZEly5d0sZyc3Njs802i759+8aRRx4Z3bp1S/ub8be//S122WWXrF7qWn6v69y5c1x55ZUZzXcaNGgQhx12WOy5557RpUuXtBcyhg8fLjwPwB/Kyl/3AwBYQ0ycODHViaL4P23bto0OHTrEn/70pxg0aFBacH7DDTeMIUOGxFFHHVWNZ1+6++67L23qzWbNmpUYnF9R/fr144477ojNNtss6+ONGjUqrRNLXl5eDBs2LCM4X9zJJ58cvXr1yvp4S5Ysyei6ctlll0WPHj1W2pV94403jnvvvTfV8TtiWdeJFbuF1xQrfqbLNW3atEqPUdIUnite6+VRnuurXr16cfvtt2dcX4MHD87qWNVpk002ibvvvrvMQHjr1q2jR48eaWPLg/OdOnWKvn37lnmNHnXUURmF2OJTZa5Oc+fOjbPOOivtBZfmzZuX+KLEcq1bt46uXbuW+TkV165duxg0aFDa2PDhwyt0zqvDkCFD0jq6N23aNO6///4yX6aqVatWDBgwIA466KC08cceeyzr+0/t2rVj8ODBJQbnV3TcccdldPCqzusJAAAA/qhKqrct73a9Ovzyyy8xcuTItLGjjjoq+vXrV2a33w022CAeeOCBtDrPkiVLYsiQIVmfQ9u2bePOO+8sM9Cdn58f1157bVqo+bfffksL4q5uhxxySPTv379cXZGr2p/+9KeM4Hxx22yzTdx5551pNceffvopY8bWP6KFCxfGfffdlzbWoUOHuP7668ucCbJx48YxdOjQ2HDDDdPGK1LL3mCDDWLYsGFlzgaak5MTAwcOTFunoKAgXnrppayPtzLF70W1atUqs65fESXtL9tnDhdeeGFGcL64ffbZJ2M2gMmTJ6e9pFHT9e/ff6Uv4PTr1y/txYOZM2fG+++/H/n5+XH33XdnBOdX1KBBg7j88svTxl577bUKzQRdVUaNGhW33XZb2tg555wTp5xySqnbdOnSpczgfHH5+flx1VVXpT1rmTp1arz22mvZnu5qMWnSpLSXxiKWfSbFg/PFbbnllnHvvfemhd3nzp2b9jJZee23335xzTXXlDlrbaNGjeKaa65JG/vss8/iu+++y/p4ALCmEp4HAP6wGjRoEL17945//etfZU4pWp3mzp0bzz33XNrYxRdfXK4CaN26dWPAgAFZH/PJJ59MWz799NPLHcI/99xzs57WduzYsWlh3h122CFOP/30cm+/8cYbxxlnnJFaXrhwYfzzn//M6hxWh3nz5mWMrRj6rwo5OTkZIef58+dntY+LL764XKH+kq6vyZMnp7q613T9+vUrV7emI488MmMsPz8/+vfvX67jFH8pZ/LkyeU7wSq2ePHiOPfcc+OLL75Ija211loxdOjQaNGiRZUfr0OHDtGuXbvU8qeffpo2LXhN8d1338Xrr7+eNnbJJZdEs2bNVrrt8gdh9erVS40tXLgwnnrqqazO4fjjj48ddtihXOueeOKJacvVdT0BAADAH1lJ9baqrvOV5bHHHoslS5aklhs1alTuWlWLFi2id+/eaWP/+c9/4qeffsrqHP7617+WGVhernnz5hm1908++SSrY1WVvLy8CtXLq0Ljxo2jb9++5Vq3Q4cOGTXJxx57bFWc1hrl2WefTevkXKtWrRK7KpdkrbXWyvgd+fjjj9O60ZdH3759y/Vspn79+hk/w1Vx3Rd/5pBNA5TyKunels0zh5YtW2Y0qCnNMcccEzvttFPa2Jpy7W+55Zbl6tresGHDEmc8OPnkk2PLLbdc6fa77LJLWj2/oKAgrea/Or366qsZHeY7d+4cffr0WSXHK34dvf3226vkOJX1yCOPpC1vsskmK31xarltt902Y5bgESNGxIIFC8p9/Nq1a8df//rXlTYni1g2k8Y222yTNuaZAwB/JMLzAMAf1rx58+L222+PTp06ZQTUa4oJEyakFSIbN24cnTp1Kvf2u+++e7Rq1arc6//yyy+p6WuXKx7WLEudOnVW2kGkuDFjxqQtn3baaeUq6qzo8MMPT1ueOHFiVtuvDgUFBRljK4Zuq0rxfS6f5rQ8quL6WhUddKra2muvXe4peTfeeOOMByL77bdfrLPOOuXafvlUxst9++235dquKhUVFcWf//zntGJyfn5+DB48uFwF+YpasZP60qVLa+SLFa+99loUFRWllps0aRJHH310ubdv3rx5HHrooWljr776albnkM1UuDvuuGPa8pQpU9K65gMAAACrXkn1tvr166+24xfvttupU6esZrjs3Llz2vkWFhZmdMkty1ZbbVXuRgARmfWM//73v+Xetirtt99+Zc40uCodccQRWc1OUDw8+c033/zhuwEXv+732muvaN26dbm3P+CAAzK6z2dTx2vSpElGHbAsxa/7VVEXLv7MYVU8byjp3pbNM4cTTjgh8vLyyr1+8Wt//Pjx1dpZvbxOOOGEcq9b/JlBtttvu+22acvffPNNubetKpMmTYo+ffqkvci13377xVVXXbXKjlm8W/2HH364yo5VGcXvVSeeeGK5XjZbrkuXLmnPSGfPnp3V99qxY8esGiatjnsVANRUK38NFwBgDdGyZcvo2rVrqV9fuHBhzJgxI7744ot4++23U2/qT5kyJS655JJ49dVX47rrritXp5LVpXjYdO+9986qyBIRceCBB8bQoUPLtW7xAsymm24am266aVbH23vvvcs91e/SpUvjvffey9g+W23atIm6deumiqg1sWhWUoE4m24R5VV8n9lcL1VxfX300UdZbV8ddtxxx6hVq1a512/RokVaV6Odd945q21XNHv27HJvW1WuvfbaeP7551PLubm5cdNNN0WHDh0qvM8ff/wxpkyZEnPmzIm5c+eW+HLI1KlT05az7WC2OhTvLtWxY8esfwcOOeSQePrpp1PLn3zySSxevLhc+2nUqFFstdVW5T5WkyZNYq211oo5c+ZExLJ76Lx581ZrdzsAAAD4oyvp//mznf2xoubPn5/RZfjggw/Oah8NGjSIvffeO/71r3+lxj744INyhzd32WWXrI638cYbpy1XR30sImLXXXetluNGLAtuZ6N9+/bRrFmzmDZtWmrsww8/zLpWnyTF63jZXvc5OTlxyCGHxP3331/qPsuy4447ZvXsqPh1v7yeV5WKP3NYFc8bSrq3ZVM/zfba32+//SI3NzfVMKSgoCA++eSTrGry1SGb++IGG2yQttykSZNyz/5c0var+546ZcqU6NWrV9q10b59+7j11luzeu6yogULFsRXX30V06ZNi3nz5sWCBQtW2jTm559/rtCxVqVvv/027VlSxLLnB9nYeOONo23btmkd4N9///3Yfffdy7V9ts99Ntpoo7Tl6vobDQDVoeYkwwAAKmm99dYrdwffOXPmxNChQ2Po0KGpAszo0aMjLy8vrr322lV5mln5/PPP05bbtm2b9T6y2aZ4R4Gtt9466+NtvfXWkZOTk9bNuazjrViIqV+/flrINxt5eXmp8PyMGTOisLCwwoW6VaGkDi1VXTAvKiqKuXPnrvS4pamK66v4NVsTZdN1IyKzY0822xf//ItPpbuqDRs2LB5++OG0sQEDBmRdsC0qKopx48bFs88+G6+99lrGdVYeq+IBUWUVf9hcvGtPeRTfZtGiRfHdd9/F5ptvvtJtN9hgg6xn2mjQoEHaZzl37lzheQAAAFiNSqq3VaRWUhFff/11WqffnJycCtczVgzPF6+RlKV40G5lGjRokLa8uj6r4rbYYotqOW5ExequW2+9dVp4/vPPP89qxsQk+f3339M+i4iqqeNlc90X71q/Mqvjul8dxyipplveZw7169ePli1bZnW8+vXrR6tWrdK6qX/xxRc1PjxfmWcG2daIq/OZw/Tp0+PMM8+M33//PTXWunXrGDJkSNYzH/zyyy8xcuTIGDt2bHz99ddZz7BaE0Pexe8pjRs3zniRpjy23XbbtPD8mn6vAoCaSngeAPhDWmutteLiiy+OddddN/7+97+nxkeOHBmHHHJIdOzYsRrP7v8U71CQbeg3IruHGcWLTc2aNcv6eA0aNIh69eqVq9vS9OnT05bnz58fV1xxRdbHLK6oqChmzZoVa6+9dqX3VVVK+ixnzJhRpceYPXt2RoExm6mIq+L6mjVrVhQVFWUdCF6dsg0aF38Jo2HDhhXeNtsCcGWMHj06brrpprSxs88+O2Pq25X55ptv4vLLL8+qE1NJamLRddasWWnL2T78jVj2u12nTp1YtGhRqfstTaNGjbI+XvFrqrCwMOt9AAAAABVXUp2veB13VSlec2jcuHGFXqovqaZXXtkeLzc3N215ddbHVtSkSZNqOW69evUqVKcuHoBcXddYTVTS916ROl5JHZbLW8vOto63Oq774veiwsLCmDVrVjRu3LjKjlHSZ1/eZw4tWrSo0HOCDTfcMC08X9XPUVaFbO6Lxa+NbJ43RFTfM4d58+ZFz54902acXW+99WLYsGHRtGnTcu+nqKgo7r333rj77rsrNVvC6m5UVB5V8byhpO1W5d/o6nyGBQDVLXflqwAAJFeXLl0yOs4MHTq0ms4mU/Ewe7ZFtGy3Kd5FpHjHgao+ZjYFn2ytiilKK6Np06YZRatsukWUR0ld37PpalEV19fSpUtrZEh6RZUN9tfkFwOWe/PNN6N///5pM0Acd9xxcdFFF2W1n88++yxOOeWUSgfnI6Jcs1GsbsXvQRX5HYjILEiX92HmmnAtAQAAAOk22WSTjLGqrvOVpni9uKrqt9nUadfUekY2M3RWpaqqN9XELsurS/HvPTc3t0LXfvGfRWFhYblr2TXxui+p9l/VM8OW1Em7vC8S/JGu/cpcHzXx2iquoKAgLrjggrRu6GuttVYMHTo0607nAwYMiFtuuaXSz/Bq4vOGqvobXfx34I/wNxoAqoPO8wDAH1pOTk4ccsgh8eWXX6bG3n333Zg5c2a1daKpTvn5+WnLBQUFFdpPeber6P7LoyYWzrbeeuuYOHFiavnTTz+t0i7tKxYuVzwmfyyTJ0+OCy64IO33a999942rr746q/0UFBTExRdfnBEE33bbbePAAw+M7bbbLjbYYINU5/X8/Py0a/mOO+6IO++8s1LfCwAAAEBNs9lmm0VeXl5a7eWTTz6pxjMC/ohKqv1Pnjw5dt111yrZf1FRUXz66adpY1tttVWV7Js1R1FRUfTv3z/efPPN1Fh+fn7cddddWV8PzzzzTIwYMSJtrH79+nHYYYdFhw4donXr1tG8efNo2LBh1KlTJ2rXTo+0bbnllhX/RgAAihGeBwD+8DbffPO05aKiovjss89i9913XyXHy2bKu+IdPCrS0TubbYp3M6hoB/Hybld8+tB11lkn3nrrrQodc02w0047pYXn586dGx999FG0b9++SvZf/LOrU6dObLfdduXeviqur9zc3Ap3lKHypk6dGj179kybsnT77beP2267LaPQvDL//Oc/49tvv00t5+XlxQ033BCHH354ubafP39+VserDo0bN45p06allit6zys+a8cf8eUrAAAA+KPIy8uL9u3bxzvvvJMa++CDD6KgoCDy8vJW6bGL14tXrAFlo3gNpHidlqpTVfWm8nb7rohsnllUh+Lf+9KlS2PevHlZ16GL/yxq1aq1Rtey27VrF/n5+bF48eLU2Jtvvhndu3evkv1PmjQp4zPbZZddyr39mnDts3I33nhjjB49OrWcm5sbN9xwQ9YvaRQVFcWgQYPSxnbfffe49dZbo2nTpivdvqJ/71anqvobXfx3wN9oAFg1cqv7BAAAqltJxdHff/+91PVzc9P/EyrbwnLxokdZigcw//e//2V1rIiIH374odzrNmvWLG15xeBseU2dOrXcHeXXXnvttOVZs2bVyI7xVWW//fbLGBs1alSV7PuXX35J6/wREbHrrrtG3bp1y72Pqri+GjdubFrIavL777/HmWeeGdOnT0+NtWzZMoYMGRL16tXLen8vvvhi2nKvXr3KHZyPiJgxY0bWx1zdit9jf/zxx6z3MW3atFi0aFHamGI2AAAAJFvxOt+MGTPi5ZdfXuXHLV7LmDVrVlb15uVKqumxaixYsKDM5w2lKV6nKq1ZQ61atdKWCwsLsz7W7Nmzs95mdSrpe69IHa/4dd+oUaM1upZdr169jADzW2+9Fb/88kuV7H/kyJEZYx07diz39j/99FOFnvcU/9mWJ1jNqvHQQw/FfffdlzbWv3//OOyww7Le1yeffJL2DKpRo0YxaNCgcv98/yjPGyL8jQaA1UV4HgD4wyup+0VZHYLq16+ftpxt54BsiiXFpzwsPkVmeWSzzbbbbpux7YpdS8rjo48+Kve6bdq0ifz8/NTykiVL4ssvv8zqeGuS7bffPlq1apU29vzzz8fMmTMrve8nnngi40WOY489Nqt9VMX1ZdrW6jF//vzo1atXTJkyJTXWrFmzuO+++zJeUimvL774Im356KOPzmr7jz/+uELHXZ222GKLtOWKTLFefJs6depEy5YtK3NaAAAAQA131FFHZczy9+ijj67y47Zp0ybtuEVFRTF58uSs91O8nrHllltW+twoXUXqrp999lnacml11+LPK7KdDbKoqCh+/vnn7E5uNVt77bUzGv9URR0vCdf9Mccck7a8dOnSeOyxxyq931mzZsXYsWPTxtq0aRPt2rUr9z7mzZuXVq8uj/nz52dsk4Sf05ro+eefj+uuuy5trGfPntGlS5cK7a/484Z99903q1D4mvi8YebMmTF16tSs95PEexUA1ETC8wDAH15JYe1111231PWLT7uXTWf3iEibzndlihciX3/99azD7P/5z3/KvW7btm2jTp06qeV58+bFK6+8ktXxnnvuuXKvW7du3dhxxx3Txl599dWsjpetqujEUxlnnHFG2vKcOXPipptuqtQ+p06dmtH9Y8MNN4yDDjooq/1UxfW1/fbbl7pudX/2SbVkyZLo06dPTJo0KTXWsGHDGDZsWGy00UYV3u9vv/2Wtrz++uuXe9vvv/8+vvnmm6yOVx3Xxw477JC2/Morr2T9O/Dvf/87bXnbbbdd5VO0AwAAANWrWbNm0alTp7SxCRMmZFUbXZmSZjytX79+Rogum/pvxLJw6uuvv542VrxGUp2SWEN86aWXslr/ww8/jGnTpqWNtW/fvsR1GzZsmNY9ffbs2Vl1kv/888+z7jxfE+p4xWfNXJmioqKMOl5Nuu4r6uCDD44WLVqkjd1///3x/fffV2q/N998c8Z10bVr16z3k+21P27cuLTrKS8vL6Pp04qKX4vZzhRNySZMmBD9+vVLmzng2GOPjUsuuaTC+yw+A8cGG2yQ1fbjxo3L+pir+/po3bp1Rvf5bP9G//DDDxkvXBV/jgoAVA3heQDgD62oqCj+9a9/pY3l5eVldAdYUevWrdOW33///XIf76OPPorPP/+83OvvtttuaZ1jZs2aFWPGjCn39uPHj4///ve/5V4/Pz8/Dj744LSxIUOGlLv4/cknn2Qdfj/kkEPSlh9++OFYsGBBVvvIRsOGDdOWKzKtcWUcf/zxGV2pR4wYEa+99lqF9ldQUBADBgyIhQsXpo337t076wBvVVxf+++/f6nrV/dnn1QDBw5M+73Ly8uLu+66q9KzABS/frL5ed13331ZT8lbHdfHPvvsk/ZwcebMmVn9Dvz666/xwgsvpI3tu+++VXV6AAAAQA12wQUXRN26ddPGrr766qxmHi3Nt99+G9dff32JX+vYsWPa8rPPPhuzZs0q975HjhyZ1p28Vq1asffee1fsRFeBJNYQx4wZk9XPaPjw4WnLrVu3jk033bTEdWvVqpXxtWyeWTz++OPlXne5Bg0apC2XNLtvVSt+3b/++utZBcTHjRuX0QgpCXW8/Pz8uOCCC9LGFi1aFJdffnnWTUKWe/PNN+PJJ59MG2vVqlXWM91GRDz11FNRUFBQ7vWLz+Cx2267ZdxnV1T8WkzC/aK6ff7553H++een/dz22WefuOaaayq13+LPG7J5aeenn36K559/PutjVsf1Ufxe9fjjj2f1O/CPf/wj7dlK48aNE/GiDwDURMLzAMAf2kMPPRRfffVV2tjuu++eUaBfUfFu8K+++mpGF5iSLF68OK688sqszq9hw4ZxxBFHpI3dfPPNMXPmzJVuu3DhwgoVs0455ZS05cmTJ8egQYNWut3MmTPjL3/5S9adGzp37pzW0XratGlx+eWXZ7WP4soK7RafVWDq1KkVLiJXRH5+flx11VWRm/t//yleVFQUvXv3jnfffTerfRUWFkbfvn1jwoQJaeN77LFHxnSt5VWZ66tt27Zldp6v7s8+iW699dYYNWpUajknJyduuOGG2G233Sq97+Kd5svb2eWtt96KJ554IuvjVcf1semmm8Y+++yTNnbTTTdldMEpzTXXXJP2sLlu3bpxwgknVOk5AgAAADXTRhttFBdffHHa2MyZM+OUU07Jeka+FY0ePTo6d+4cU6dOLfHrJ510UloIcebMmaUG7Yv7+eef4/bbb08bO+igg7KacXBVS2INcdasWeWefXTixInx7LPPpo2dfPLJZW5T/JnFU089Va5jffTRRzFy5MhyrbuiZs2apS1X5novr06dOqV1dF6yZElcccUV5XoeMXfu3Pj73/+eNrbddtuV2s1/TXPsscdm1IMnTpwYF198cSxZsiSrfX3wwQdx/vnnpz1jyc3NjauvvrpCs21OmTIlY9bc0jzzzDMZz0hWdu0Xv1+sjmsxyf73v//FWWedlRYyb9euXQwaNChq165dqX03b948bfmNN94o1/VZWFgY/fr1yyqAvlx1XB+nnXZa2vKUKVPi3nvvLde2n376aTzyyCNpY507dy7zBRIAoOKE5wGAP6Q5c+bETTfdlPFQIScnJ6NLR3E777xzrLPOOqnlhQsXxsCBA8ss8syfPz969+4dkydPzvpcu3fvHvn5+anladOmRc+ePcvsVLNgwYK44IIL4uuvv876eDvuuGMcfvjhaWP33HNPXHnllaV2kJk8eXJ06dIlvvrqq8jJycmqiJqfnx99+/ZNGxszZkycf/755QpxL7dkyZJ48cUX47TTTivzc1533XXTivsLFixICx+vDrvuumv07t07bWzBggXRo0ePuO+++8rV6X/KlCnRtWvXjK7X66+/frkfxJRk2rRpcdZZZ5X52S9YsCB69+6dcX2de+65Ze67Jnz2SfLoo4/GkCFD0sYuu+yyjN/fiurQoUPa8m233bbSzmmvv/56nHfeeVl3nY+ovuvj7LPPTnuZZfr06dGjR48yA/SFhYXx97//PWPmklNOOSWaNm26ys4VAAAAqFnOOOOM6NSpU9rYzz//HCeddFI8+OCDWYW+J02aFN26dYtLL7005s2bV+p6zZs3j+OOOy5tbOTIkTFo0KAyazK//PJLdOvWLa3bb+3ataNXr17lPsfVIak1xCeffDKjllfcp59+GhdccEHaz3H99dfP+HkXV3x21//85z/x3HPPlbnNpEmT4txzz61QIHXrrbdOW37nnXfiyy+/zHo/2ahbt2706NEjbezNN9+Myy+/vMzvYfbs2dGzZ8+MrvPnnXfeKjnP6pCTkxM333xzxkswL774YnTp0qVcgeGlS5fGQw89FN27d09rFhIRceGFF8Yuu+xS4fMbNGhQPPPMM2Wu8/rrr8fAgQPTxtq2bbvS2QHatm2btvzMM8+slpkQkmjmzJnRo0eP+PXXX1NjLVu2jHvuuSdthuyK6tChQ9ossFOnTs14mau4+fPnxwUXXBATJ06s0DGr4/po165d7LXXXmljd9xxR8ZsDsV99dVX0bNnz7RnzWuttVacccYZq+Q8AYCIyr0aCABQg/z666/x2GOPlfr1RYsWxYwZM+Lzzz+PCRMmxMKFCzPW6dWrV0aXluLy8vLixBNPjMGDB6fGxo0bF6eddlqcf/750aFDh1TY/ccff4xx48bFsGHD4qeffoqIiB122CE++OCDcn9frVu3jvPOOy9uvfXW1NhHH30URxxxRJxzzjlx8MEHpx4mzJgxI15++eUYPHhwqhjcvn37+PDDD8t9vIiIgQMHxnvvvRe//PJLauzRRx+NZ599Njp27BibbbZZNGjQIH777beYOHFifPjhh6kOLyeffHK8+uqraSHbFQtiJTnyyCNj8uTJ8cADD6TGXnzxxRg/fnx07tw59t1339huu+3SZgRYuHBhfPvtt/HFF1/EW2+9Fa+++mrqhYKVBXf333//tM7YV1xxRbz44ovRvn37aNq0adSqVStt/ZV1N6mIc845J3777be0LhILFy6MG264IUaOHBlHH310HHjggbHRRhtFnTp1oqioKGbMmBEff/xxPP/88zF27NhYtGhR2j7XXXfduP/++9Ne7sjG9ttvHx999FFMmjQp7fpab731ImJZ8XT59VW889Whhx4aBx100EqPURM++6S4+uqr05Y322yzyM/PL/M+WJptt902tttuu7SxE088MR5//PHU79O0adPiuOOOi/POOy8OOeSQVKeYRYsWxXvvvRdPPfVUaurUnJyc2GmnnbKeTaE6ro8dd9wxzjrrrLjnnntSY59++mkcdthh0atXrzj00EOjRYsWERExb968eOONN2Lo0KHx8ccfp+1niy22iIsuuqjS5wMAAACsWa677rpYvHhx/Pvf/06NzZ49O6677rp4+OGH45BDDol99tknttxyy2jSpEnqJf758+fH119/He+880688MILMWnSpHIf8y9/+Uu8/fbbMWXKlNTY4MGDY8KECdGjR4/Yc889o169ehGxLKA4duzYuPfee9O6CUdE9O7dOyNcWBMkqYa4zjrrRN26dePHH3+MW2+9NcaPHx/du3eP3XffPfLz86OoqCi++eabGDVqVDz88MMZQfCrrrqqzJlyIyL23Xff2GijjdIC4v369YuPP/44TjzxxGjdunVELGtA8/HHH8czzzwTI0eOjIKCgmjSpEk0bdo0/vvf/5b7e2rVqlW0bt06vv3224iIKCgoiD/96U9xwAEHxJZbbhkNGjRIa1ax3nrrxQEHHFDu/ZemR48e8corr8R7772XGhs1alR88skncdZZZ8W+++4bjRo1iohlz4tefPHFuPvuuzNmDj7xxBNjv/32q/T51CTLnw106dIlfvvtt9T4+++/H8ccc0wccsghcfjhh6d+h3JycmLx4sXxww8/xEsvvRSjR48u8QWILl26xNlnn12hc2rVqlVMnz495syZE3/+859Tz9F22GGHqF27dixdujQmT54cTz75ZDz11FNpz3Xy8vLiuuuuy/hdL65jx45Rq1atVEOiKVOmxGGHHRYHHHBAbLrpphkdu0uqg7PM8OHDU7/Tyx144IEZDWTKq/h9ed11140DDzwwXnzxxdTYPffcE1999VX06NEjtt9++1Rjrh9//DFeeumlGDZsWOo5ZYcOHbIO0VfX9XHttdfGUUcdlWpSVVRUFAMHDoyXX345unTpErvsskvq/v/tt9/GP//5z3jggQcyXri74oorMjr2AwBVR3geAEiMKVOmxBVXXFGhbXNycuKss84qd/Dx7LPPjrFjx6YVlD/44IPo0aNH5ObmRqNGjWLevHkZhe7jjz8+dt5556zC8xERZ511Vnz++ecxduzY1Ni0adPiqquuiquuuirq1asXOTk5GR1BNtpooxgwYEB07tw5q+Otvfba8dBDD8UZZ5yRFqCfM2dOmR1rdt9997jsssvilVdeSRuvU6fOSo956aWXxpIlS9LC5HPnzo0HH3wwHnzwwYhY1l2mXr16MW/evEpN1dutW7cYPXp0LFiwICKWFa7eeOONeOONN0pcf1U9fBkwYECst956ceutt6ZNL/vNN9/ELbfcErfccktERDRq1CgWLFhQZgedLbbYIgYPHhwbb7xxhc/n/PPPj9tvvz0+/vjjmD59elx99dVx9dVXR7169SI3N7fUjlfbbLNNRpC7NDXls0+C4lMSf/311xW+B55//vkZReGtt946TjnllBg+fHhqbObMmfH3v/89/v73v0f9+vWjdu3aad3KllveuSnb8Hx1XR+9e/eOqVOnpsL/Ef835fn1118f+fn5UadOnYwHzMttvPHGcdddd6XNEgIAAAD8MeTn58dtt90WgwYNinvvvTctAPrjjz/G/fffH/fff39ERKp2vGTJkjK73+bk5MRWW21V6tfr168fgwcPju7du8fPP/+cGn///ffj/fffj4hlHWsXLVpUah31+OOPj7POOiur73V1SVINsW7dunHTTTfFGWecEYsXL44JEybEhAkTIiKicePGMX/+/FLrvhdeeGF07NhxpceoXbt2XHPNNdGtW7fU9VdYWJiqrdepUyfq1q0bs2fPzggo33rrrXH33XdnFZ6PWPaMpF+/fqnlBQsWxHPPPVfi84MOHTpUSXi+Vq1acdttt0WPHj3Sgt5ffvllXHrppRER0aBBgygsLCyxeVJExD777BP9+/ev9LnURG3atIknnngizj333LTPZ/HixfHss8/Gs88+GxHLfu716tUrsa67XG5ublx00UXRs2fPCp9Ps2bNok+fPtGnT58oKiqKF154IV544YXIycmJRo0axdy5c0uchTc3NzeuueaaMu+By62//vpx7LHHxogRI1JjZTXaKqkOzjLFnzdERAwbNqzC+yvpvnzppZfG22+/nXbtvfzyy/Hyyy9H7dq1o2HDhiU+W23evHnceOON5bofrqi6ro/mzZvHXXfdFeecc07a9zpu3LgYN25c6ndg3rx5pc5qfv7552fMbAMAVK3cla8CAJBs7du3j+HDh8cll1xS7m3q1KkTw4YNi0033TTja0uXLo2ZM2dmFHdOPfXUcgeMi6tVq1bcdNNNcfzxx5f49QULFmQE51u1ahUPPvhgNG3atELHbNWqVTz11FPl6iaem5sbp556atx7772Rn5+f8eBnebeXstSqVSsGDBgQN910U6md0xcuXBgzZswoMzjfokWLaNKkSZnHatWqVdx2220rXW916NmzZzzxxBOxzTbblLrO7NmzS32AUrdu3Tj77LNjxIgRlQrORyy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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 723,
       "width": 1511
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_figure = mmm.plot_budget_scenearios(\n",
    "    base_data=initial_scenario,\n",
    "    method=\"sigmoid\",\n",
    "    scenarios_data=[platform_base_optimization],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h6yOpHXE4OLE"
   },
   "source": [
    "Now, the result is considerably more explicit. Using nearly half of the budget with the current distribution configuration could yield the same number of outcomes as our initial setup, which dispersed the budget evenly across all channels. By default, if you need more prior knowledge about the limits, we recommend using alpha (the plateau) as the limit for each channel.\n",
    "\n",
    "## Benefits and Limitations\n",
    "In marketing analytics, curve-fitted Media Mix Models (MMMs) provide enriching insights by simplifying intricate systems, facilitating optimization, aiding scenario planning, and delivering quantifiable metrics for strategy evaluation. Each of these advantages presents compelling reasons to incorporate such models.\n",
    "\n",
    "Nevertheless, it is pivotal to acknowledge that these models do possess their own set of limitations. The primary ones are the assumptions of time invariance and generalized behavior, signifying an incomplete comprehension of market dynamics. The no-impact variance assumptions are not specific to the curves, as the model does not account for these effects. Hence, curves should always be carefully considered.\n",
    "\n",
    "\n",
    "## Other methods to explore\n",
    "Even if the method is promising, use other optimization options which includes the full posterior could be a powerful and interesting solution as it's described on the following blog [\"Using bayesian decision making to optimize supply chains\"](https://twiecki.io/blog/2019/01/14/supply_chain/)\n",
    "\n",
    "The current methodology is similar to the ones used on other libraries as [Robyn from Meta](https://facebookexperimental.github.io/Robyn/docs/features/#budget-allocation) and Google [Lightweight from Google](https://github.com/google/lightweight_mmm). You can explore the solutions and compare if needed.\n",
    "\n",
    "## Conclusion\n",
    "MMM models and methodologies used here are designed to bridge the gap between theoretical rigor and actionable marketing insights. They represent a significant stride towards a more data-driven, analytical approach to marketing budget allocation, which could change how organizations invest in customer acquisition and retention.\n",
    "\n",
    "Although it is a promising tool, it is essential to highlight that this methodology and software release is still experimental. Like any emerging technology, it comes with inherent limitations and assumptions that users should be aware of. The models can offer actionable insights, but they should be cautiously used and in tandem with various forms of analysis. Context is crucial, and while models aim to encapsulate general trends, they might not account for all nuances.\n",
    "\n",
    "Consequently, your engagements, feedback, and thoughts are not merely welcomed but actively solicited to make this tool as practical and universally applicable as possible."
   ]
  }
 ],
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   "name": "python"
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}