{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "430cdb67",
   "metadata": {},
   "source": [
    "(Root_tutorial)=\n",
    "\n",
    "# Symbolic Root Finding\n",
    ":::{post} June 12, 2025 \n",
    ":tags: optimization, root finding, worked examples, tutorial\n",
    ":category: beginner, explanation \n",
    ":author: Jesse Grabowski\n",
    ":::\n",
    "\n",
    "\n",
    "When faced with problems involving systems of nonlinear equations, it is rare to actually have access to analytic solutions for the zeros of the system. Nevertheless, these zeros are often important to downstream tasks. A common application is in perturbation theory, where we seek to linearize a nonlinear system around the fixed points of that system.\n",
    "\n",
    "To find such fixed points, numerical algorithms such as Newton-Raphson and Broyden's Method are typically utilized. Once you have written down your system symbolically in Pytensor, it is always possible to compile the function (and, if desired, the jacobian of the system), then pass these compiled functions to a numerical solver of your choice.\n",
    "\n",
    "This solution can be incomplete, however, in cases where one is interested in using the roots as an intermediate computation in a larger graph. Compiling the function breaks the graph, causing:\n",
    "\n",
    "1. Pytensor to not see optimizations, such as re-use of computation, between the two halves, and;\n",
    "2. We cannot get end-to-end gradients, because the optimization step happens outside of pytensor.\n",
    "\n",
    "To address these limitations, pytensor offers *symbolic* root finding via the `pytensor.tensor.optimize.root` function."
   ]
  },
  {
   "cell_type": "code",
   "id": "d746079b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:39.521039Z",
     "start_time": "2025-07-28T14:29:38.748257Z"
    }
   },
   "source": [
    "import pytensor\n",
    "import pytensor.tensor as pt\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "id": "037051ac",
   "metadata": {},
   "source": [
    "## Basic Usage\n",
    "\n",
    "To use `tensor.optimize.root`, first set up a system of equations. The first test function we will look at is:\n",
    "\n",
    "$$ \n",
    "\\begin{align}\n",
    "x^2 - y - 1 &= 0 \\\\\n",
    "x - y^2 + 1 &= 0 \n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "This system is analytically tractible. Two roots are immediately visible by simple inspection (aka experience-based guess-and-check): $x=0, y=-1$, and by symmetry, $x=-1, y=0$. \n",
    "\n",
    "Remaining roots can be found by solving the first equation for y and plugging in the result to the second:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "y &= x^2 - 1 \\\\\n",
    "x - (x^2 - 1)^2 +1 &= 0 \\\\\n",
    "x -x^4 + 2x^2 -1 + 1 &= 0 \\\\\n",
    "x^4 - 2x^2 - x &= 0 \\\\\n",
    "x (x^3 - 2x - x) &= 0\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "As already noted, $x = 0$ is a root, and we see it here. We also can see from inspecting $x^3 - 2x - x$ that $x=-1$ is also a root. Remove the root $x = -1$ from the cubic expression by dividing it by $x+1$ to reduce it to a quadratic factor:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\frac{x^3 - 2x - x}{x + 1} = x^2 - x - 1\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "Which leads to two roots:\n",
    "\n",
    "$$x = -\\frac{-1 \\pm \\sqrt{5}}{2}$$\n",
    "\n",
    "Plugging this expression back into equation 1:\n",
    "\n",
    "$$ \\begin{align}\n",
    "y &= \\left ( \\frac{-1 \\pm \\sqrt{5}}{2} \\right)^2 - 1 \\\\\n",
    "y &= \\begin{cases} -\\left ( \\frac{-1 + \\sqrt{5}}{2} \\right)^2 - 1 &  =  -\\frac{-1 + \\sqrt{5}}{2} \\\\\n",
    "                   - \\left ( \\frac{-1 - \\sqrt{5}}{2} \\right)^2 - 1 & = -\\frac{-1 - \\sqrt{5}}{2}\n",
    "    \\end{cases}\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "Whichever branch we choose, the value for $x$ and $y$ are the same. So the four roots are:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "x &= 0, & y &=-1 \\\\\n",
    "x &= -1, & y&= 0 \\\\\n",
    "x &= -\\frac{-1 - \\sqrt{5}}{2}, & y&= -\\frac{-1 - \\sqrt{5}}{2} \\\\\n",
    "x &= -\\frac{-1 + \\sqrt{5}}{2}, & y&= -\\frac{-1 + \\sqrt{5}}{2}\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "In the next cell, we plot this system of equations, and mark the four roots."
   ]
  },
  {
   "cell_type": "code",
   "id": "e9b609af",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:39.995863Z",
     "start_time": "2025-07-28T14:29:39.669229Z"
    }
   },
   "source": [
    "fig, ax = plt.subplots(subplot_kw={'aspect':'equal'}, dpi=77, figsize=(14, 6))\n",
    "\n",
    "x_plot = np.linspace(-2, 2, 1000)\n",
    "ax.plot(x_plot, x_plot ** 2 - 1, color='tab:blue', lw=2, label=r'$y = x^2 - 1$')\n",
    "\n",
    "with np.errstate(all='ignore'):\n",
    "    ax.plot(x_plot, np.sqrt(x_plot + 1), color='tab:orange', lw=2, label=r'$y = \\pm \\sqrt{x + 1}$')\n",
    "    ax.plot(x_plot, -np.sqrt(x_plot + 1), color='tab:orange', lw=2)\n",
    "    \n",
    "ax.axhline(0, ls='--', c='k', lw=0.5)\n",
    "ax.axvline(0, ls='--', c='k', lw=0.5)\n",
    "\n",
    "quad_root_1 = -(-1 + np.sqrt(5)) / 2\n",
    "quad_root_2 = -(-1 - np.sqrt(5)) / 2\n",
    "\n",
    "for x, y in [(0, -1), (-1, 0), (quad_root_1, quad_root_1), (quad_root_2, quad_root_2)]:\n",
    "    ax.scatter(x, y, color='tab:red', marker='*', zorder=100, s=150)\n",
    "\n",
    "ax.legend()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1078x462 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "id": "adcfacb9",
   "metadata": {},
   "source": [
    "To find roots of our system using pytensor, we first have to symbolically set it up. \n",
    "\n",
    "Currently, all variables need to be provided in a single vector. So we first make a vector (called `variables`) of length 2, then unpack it into `x` and `y`. I use fancy python double-assignment to do this.\n",
    "\n",
    "`x` and `y` are then used to type in our equations. Like scipy, we need to rewrite the system so that the right-hand size is always zero. In this case we already had that, but in general you will need to keep this in mind."
   ]
  },
  {
   "cell_type": "code",
   "id": "4ad8a428",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:40.025838Z",
     "start_time": "2025-07-28T14:29:40.017110Z"
    }
   },
   "source": [
    "x, y = variables = pt.tensor('variables', shape=(2, ))\n",
    "\n",
    "eq_1 = x ** 2 - y - 1\n",
    "eq_2 = x - y ** 2 + 1"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "id": "1dcba2cf",
   "metadata": {},
   "source": [
    "To make a compute graph with a root finder, use `pt.optimize.root`. The function expects:\n",
    "\n",
    "- A vector of equations to solve, `equations`\n",
    "- A vector of variables with respect to which the equations will be solved, `variables`\n",
    "- Configuration arguments, like `method`, `jac` and `optimizer_kwargs`, which are forwarded to `scipy.optimize.root`."
   ]
  },
  {
   "cell_type": "code",
   "id": "c992e50d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:40.113631Z",
     "start_time": "2025-07-28T14:29:40.065336Z"
    }
   },
   "source": [
    "solution, success = pt.optimize.root(equations=pt.stack([eq_1, eq_2]), \n",
    "                       variables=variables,\n",
    "                       method='hybr',\n",
    "                       optimizer_kwargs={'tol':1e-8})"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "id": "1ecf771b",
   "metadata": {},
   "source": [
    "Looking at the graph for the `solution`, we can see that the outer function takes `variables` as input and returns the first output of `RootOp` (the solution).\n",
    "\n",
    "It also has an inner graph with two outputs. The first is a `MakeVector` (this is `pt.stack`), combining `eq1` and `eq2`. So the first inner graph simply computes the equations we provided. The second graph is a `Scan` -- this is the $2\\times2$ Jacobian matrix of the system of the system:\n",
    "\n",
    "$$ \n",
    "J = \\begin{bmatrix} \\frac{\\partial f_1(x,y)}{\\partial x} & \\frac{\\partial f_1(x,y)}{\\partial y} \\\\\n",
    "                        \\frac{\\partial f_2(x,y)}{\\partial x} & \\frac{\\partial f_2(x,y)}{\\partial y} \n",
    "    \\end{bmatrix} \n",
    "$$\n",
    "\n",
    "Pytensor happens to compute this matrix using a `Scan`, so that's why one appears here.\n",
    "\n",
    "So notice that we don't have to compute the Jacobian for this ourselves -- it's automatically by pytensor! Also pytensor can see all these inner functions and optimize across them. "
   ]
  },
  {
   "cell_type": "code",
   "id": "61498784",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:40.163920Z",
     "start_time": "2025-07-28T14:29:40.154022Z"
    }
   },
   "source": [
    "solution.dprint()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RootOp(method=hybr, jac=True).0 [id A]\n",
      " └─ variables [id B]\n",
      "\n",
      "Inner graphs:\n",
      "\n",
      "RootOp(method=hybr, jac=True) [id A]\n",
      " ← MakeVector{dtype='float64'} [id C]\n",
      "    ├─ Sub [id D]\n",
      "    │  ├─ Sub [id E]\n",
      "    │  │  ├─ Pow [id F]\n",
      "    │  │  │  ├─ Subtensor{i} [id G]\n",
      "    │  │  │  │  ├─ variables [id H]\n",
      "    │  │  │  │  └─ 0 [id I]\n",
      "    │  │  │  └─ 2 [id J]\n",
      "    │  │  └─ Subtensor{i} [id K]\n",
      "    │  │     ├─ variables [id H]\n",
      "    │  │     └─ 1 [id L]\n",
      "    │  └─ 1 [id M]\n",
      "    └─ Add [id N]\n",
      "       ├─ Sub [id O]\n",
      "       │  ├─ Subtensor{i} [id G]\n",
      "       │  │  └─ ···\n",
      "       │  └─ Pow [id P]\n",
      "       │     ├─ Subtensor{i} [id K]\n",
      "       │     │  └─ ···\n",
      "       │     └─ 2 [id Q]\n",
      "       └─ 1 [id R]\n",
      " ← Add [id S]\n",
      "    ├─ Blockwise{IncSubtensor{i}, (i00),(),()->(o00)} [id T]\n",
      "    │  ├─ ExpandDims{axis=0} [id U]\n",
      "    │  │  └─ Second [id V]\n",
      "    │  │     ├─ variables [id H]\n",
      "    │  │     └─ ExpandDims{axis=0} [id W]\n",
      "    │  │        └─ 0.0 [id X]\n",
      "    │  ├─ Add [id Y]\n",
      "    │  │  ├─ Mul [id Z]\n",
      "    │  │  │  ├─ Mul [id BA]\n",
      "    │  │  │  │  ├─ Subtensor{:, i} [id BB]\n",
      "    │  │  │  │  │  ├─ Eye{dtype='float64'} [id BC]\n",
      "    │  │  │  │  │  │  ├─ Subtensor{i} [id BD]\n",
      "    │  │  │  │  │  │  │  ├─ Shape [id BE]\n",
      "    │  │  │  │  │  │  │  │  └─ MakeVector{dtype='float64'} [id C]\n",
      "    │  │  │  │  │  │  │  │     └─ ···\n",
      "    │  │  │  │  │  │  │  └─ 0 [id BF]\n",
      "    │  │  │  │  │  │  ├─ Subtensor{i} [id BD]\n",
      "    │  │  │  │  │  │  │  └─ ···\n",
      "    │  │  │  │  │  │  └─ 0 [id BG]\n",
      "    │  │  │  │  │  └─ 0 [id BH]\n",
      "    │  │  │  │  └─ ExpandDims{axis=0} [id BI]\n",
      "    │  │  │  │     └─ 2 [id J]\n",
      "    │  │  │  └─ ExpandDims{axis=0} [id BJ]\n",
      "    │  │  │     └─ Pow [id BK]\n",
      "    │  │  │        ├─ Subtensor{i} [id BL]\n",
      "    │  │  │        │  ├─ variables [id H]\n",
      "    │  │  │        │  └─ 0 [id I]\n",
      "    │  │  │        └─ Sub [id BM]\n",
      "    │  │  │           ├─ 2 [id J]\n",
      "    │  │  │           └─ 1 [id BN]\n",
      "    │  │  └─ Subtensor{:, i} [id BO]\n",
      "    │  │     ├─ Eye{dtype='float64'} [id BC]\n",
      "    │  │     │  └─ ···\n",
      "    │  │     └─ 1 [id BP]\n",
      "    │  └─ ExpandDims{axis=0} [id BQ]\n",
      "    │     └─ TensorFromScalar [id BR]\n",
      "    │        └─ 0 [id I]\n",
      "    └─ Blockwise{IncSubtensor{i}, (i00),(),()->(o00)} [id BS]\n",
      "       ├─ ExpandDims{axis=0} [id BT]\n",
      "       │  └─ Second [id BU]\n",
      "       │     ├─ variables [id H]\n",
      "       │     └─ ExpandDims{axis=0} [id BV]\n",
      "       │        └─ 0.0 [id BW]\n",
      "       ├─ Add [id BX]\n",
      "       │  ├─ Neg [id BY]\n",
      "       │  │  └─ Subtensor{:, i} [id BB]\n",
      "       │  │     └─ ···\n",
      "       │  └─ Mul [id BZ]\n",
      "       │     ├─ Mul [id CA]\n",
      "       │     │  ├─ Neg [id CB]\n",
      "       │     │  │  └─ Subtensor{:, i} [id BO]\n",
      "       │     │  │     └─ ···\n",
      "       │     │  └─ ExpandDims{axis=0} [id CC]\n",
      "       │     │     └─ 2 [id Q]\n",
      "       │     └─ ExpandDims{axis=0} [id CD]\n",
      "       │        └─ Pow [id CE]\n",
      "       │           ├─ Subtensor{i} [id CF]\n",
      "       │           │  ├─ variables [id H]\n",
      "       │           │  └─ 1 [id L]\n",
      "       │           └─ Sub [id CG]\n",
      "       │              ├─ 2 [id Q]\n",
      "       │              └─ 1 [id CH]\n",
      "       └─ ExpandDims{axis=0} [id CI]\n",
      "          └─ TensorFromScalar [id CJ]\n",
      "             └─ 1 [id L]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<ipykernel.iostream.OutStream at 0x7f9c8dd689a0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "id": "4fedca48",
   "metadata": {},
   "source": [
    "Since we're not doing anything with the outputs, we're ready to compile a function. We don't have any parameters, so we just pass in the variables -- which are treated as the inital values -- and pass back the solution and success flag. "
   ]
  },
  {
   "cell_type": "code",
   "id": "7d770466",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:41.266325Z",
     "start_time": "2025-07-28T14:29:40.232897Z"
    }
   },
   "source": [
    "fn = pytensor.function([variables],\n",
    "                       [solution, success])"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "id": "aa89c9e5",
   "metadata": {},
   "source": [
    "Looking at the final graph, we see how both outputs -- the system of equations and the jacobian -- become simplified."
   ]
  },
  {
   "cell_type": "code",
   "id": "3adc6558",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:41.291421Z",
     "start_time": "2025-07-28T14:29:41.283661Z"
    }
   },
   "source": "fn.dprint(print_shape=True)",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RootOp(method=hybr, jac=True).0 [id A] shape=(2,) 0\n",
      " └─ variables [id B] shape=(2,)\n",
      "RootOp(method=hybr, jac=True).1 [id A] shape=() 'success' 0\n",
      " └─ ···\n",
      "\n",
      "Inner graphs:\n",
      "\n",
      "RootOp(method=hybr, jac=True) [id A]\n",
      " ← MakeVector{dtype='float64'} [id C] shape=(2,)\n",
      "    ├─ Composite{((-1.0 + sqr(i0)) - i1)} [id D] shape=()\n",
      "    │  ├─ Subtensor{i} [id E] shape=()\n",
      "    │  │  ├─ variables [id F] shape=(2,)\n",
      "    │  │  └─ 0 [id G] shape=()\n",
      "    │  └─ Subtensor{i} [id H] shape=()\n",
      "    │     ├─ variables [id F] shape=(2,)\n",
      "    │     └─ 1 [id I] shape=()\n",
      "    └─ Composite{((1.0 + i1) - sqr(i0))} [id J] shape=()\n",
      "       ├─ Subtensor{i} [id H] shape=()\n",
      "       │  └─ ···\n",
      "       └─ Subtensor{i} [id E] shape=()\n",
      "          └─ ···\n",
      " ← IncSubtensor{:, i} [id K] shape=(2, 2)\n",
      "    ├─ IncSubtensor{:, i} [id L] shape=(2, 2)\n",
      "    │  ├─ Alloc [id M] shape=(2, 2)\n",
      "    │  │  ├─ [[0.]] [id N] shape=(1, 1)\n",
      "    │  │  ├─ 2 [id O] shape=()\n",
      "    │  │  └─ 2 [id P] shape=()\n",
      "    │  ├─ Composite{((i0 * i1) + i2)} [id Q] shape=(2,)\n",
      "    │  │  ├─ [2. 0.] [id R] shape=(2,)\n",
      "    │  │  ├─ ExpandDims{axis=0} [id S] shape=(1,)\n",
      "    │  │  │  └─ Subtensor{i} [id E] shape=()\n",
      "    │  │  │     └─ ···\n",
      "    │  │  └─ [0. 1.] [id T] shape=(2,)\n",
      "    │  └─ 0 [id G] shape=()\n",
      "    ├─ Composite{(i2 + (i0 * i1))} [id U] shape=(2,)\n",
      "    │  ├─ [-0. -2.] [id V] shape=(2,)\n",
      "    │  ├─ ExpandDims{axis=0} [id W] shape=(1,)\n",
      "    │  │  └─ Subtensor{i} [id H] shape=()\n",
      "    │  │     └─ ···\n",
      "    │  └─ [-1. -0.] [id X] shape=(2,)\n",
      "    └─ 1 [id I] shape=()\n",
      "\n",
      "Composite{((-1.0 + sqr(i0)) - i1)} [id D]\n",
      " ← sub [id Y] shape=() 'o0'\n",
      "    ├─ add [id Z] shape=()\n",
      "    │  ├─ -1.0 [id BA] shape=()\n",
      "    │  └─ sqr [id BB] shape=()\n",
      "    │     └─ i0 [id BC] shape=()\n",
      "    └─ i1 [id BD] shape=()\n",
      "\n",
      "Composite{((1.0 + i1) - sqr(i0))} [id J]\n",
      " ← sub [id BE] shape=() 'o0'\n",
      "    ├─ add [id BF] shape=()\n",
      "    │  ├─ 1.0 [id BG] shape=()\n",
      "    │  └─ i1 [id BH] shape=()\n",
      "    └─ sqr [id BI] shape=()\n",
      "       └─ i0 [id BJ] shape=()\n",
      "\n",
      "Composite{((i0 * i1) + i2)} [id Q]\n",
      " ← add [id BK] shape=() 'o0'\n",
      "    ├─ mul [id BL] shape=()\n",
      "    │  ├─ i0 [id BM] shape=()\n",
      "    │  └─ i1 [id BN] shape=()\n",
      "    └─ i2 [id BO] shape=()\n",
      "\n",
      "Composite{(i2 + (i0 * i1))} [id U]\n",
      " ← add [id BP] shape=() 'o0'\n",
      "    ├─ i2 [id BQ] shape=()\n",
      "    └─ mul [id BR] shape=()\n",
      "       ├─ i0 [id BS] shape=()\n",
      "       └─ i1 [id BT] shape=()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<ipykernel.iostream.OutStream at 0x7f9c8dd689a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "id": "feab3dd9",
   "metadata": {},
   "source": [
    "Checking some points. We see that starting at $0, 0$, we converge to $x, y = \\frac{-1 - \\sqrt{5}}{2} \\approx -0.618$."
   ]
  },
  {
   "cell_type": "code",
   "id": "1b4b47e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:41.485954Z",
     "start_time": "2025-07-28T14:29:41.372073Z"
    }
   },
   "source": [
    "fn([0., 0.])"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([-0.61803399, -0.61803399]), np.True_]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "id": "aa1df7d0",
   "metadata": {},
   "source": [
    "Starting at $1,1$, we converge to $x, y = \\frac{-1 + \\sqrt{5}}{2} \\approx 1.618$"
   ]
  },
  {
   "cell_type": "code",
   "id": "aff1d6e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:41.522901Z",
     "start_time": "2025-07-28T14:29:41.517783Z"
    }
   },
   "source": [
    "fn([1., 1.])"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1.61803399, 1.61803399]), np.True_]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "id": "7ebde90a",
   "metadata": {},
   "source": [
    "Starting at $-1, 1$, we converge to $x=-1, y=0$"
   ]
  },
  {
   "cell_type": "code",
   "id": "f50a5ff0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:41.628965Z",
     "start_time": "2025-07-28T14:29:41.622223Z"
    }
   },
   "source": [
    "fn([-1, 1])"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([-1.00000000e+00, -1.26918883e-12]), np.True_]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "id": "ae7a4b57",
   "metadata": {},
   "source": [
    "And starting at $1, -1$, we converge to $x=0, y=-1$"
   ]
  },
  {
   "cell_type": "code",
   "id": "48b0142d",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:41.697969Z",
     "start_time": "2025-07-28T14:29:41.691275Z"
    }
   },
   "source": [
    "fn([1, -1])"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([-1.2693032e-12, -1.0000000e+00]), np.True_]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "id": "eb9cbae7",
   "metadata": {},
   "source": [
    "## Graph manipulation\n",
    "\n",
    "Since the `root` Op is fully symbolic, we can manipulate its graph as much as we like. \n",
    "\n",
    "For example, we can vectorize it. This will allow us to test many points at the same time. To do this, we create a new variable with a batch dimension, then rewrite the graph to work out the resulting dimensions."
   ]
  },
  {
   "cell_type": "code",
   "id": "1cfebb4a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:41.831905Z",
     "start_time": "2025-07-28T14:29:41.824311Z"
    }
   },
   "source": [
    "from pytensor.graph.replace import vectorize_graph\n",
    "\n",
    "variables_grid = pt.tensor('x', shape=(None, 2))\n",
    "grid_of_solutions = vectorize_graph([solution, success], \n",
    "                                    {variables:variables_grid})\n"
   ],
   "outputs": [],
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "id": "bc21773a",
   "metadata": {},
   "source": [
    "Compile the new, vectorized function"
   ]
  },
  {
   "cell_type": "code",
   "id": "bdc1182f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:42.474359Z",
     "start_time": "2025-07-28T14:29:41.939432Z"
    }
   },
   "source": [
    "fn_vec = pytensor.function([variables_grid],\n",
    "                        grid_of_solutions)"
   ],
   "outputs": [],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "id": "7f7d3e24",
   "metadata": {},
   "source": [
    "Now that we're vectorized, the input will be a 2d array of values, with the first column representing `x`, and the second column `y`. \n",
    "\n",
    "To quickly get a bunch of pairs of values, we can use `np.meshgrid`."
   ]
  },
  {
   "cell_type": "code",
   "id": "51f7145c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:42.499151Z",
     "start_time": "2025-07-28T14:29:42.494827Z"
    }
   },
   "source": [
    "x_values = np.linspace(-2, 2, 30)\n",
    "xx, yy = np.meshgrid(x_values, x_values)\n",
    "grid_values = np.c_[xx.ravel(), yy.ravel()]"
   ],
   "outputs": [],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "id": "3eac6e42",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:42.904056Z",
     "start_time": "2025-07-28T14:29:42.570142Z"
    }
   },
   "source": [
    "solution_grid, success_grid = fn_vec(grid_values)\n",
    "\n",
    "unique_solutions = np.unique(np.round(solution_grid, 3), axis=0)\n",
    "solution_ids = {tuple(v.tolist()): k for k, v in enumerate(unique_solutions)}"
   ],
   "outputs": [],
   "execution_count": 15
  },
  {
   "cell_type": "markdown",
   "id": "024ed40e",
   "metadata": {},
   "source": [
    "Across all the solution, we found only the four roots we expected, which is great!"
   ]
  },
  {
   "cell_type": "code",
   "id": "d6434b1d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:42.946383Z",
     "start_time": "2025-07-28T14:29:42.941239Z"
    }
   },
   "source": [
    "unique_solutions"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.   , -0.   ],\n",
       "       [-0.618, -0.618],\n",
       "       [ 0.   , -1.   ],\n",
       "       [ 1.618,  1.618]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "id": "3b856dcb",
   "metadata": {},
   "source": [
    "We can make a nice plot to see that roots roughly correspond to the four graph quadrents. But there are some exceptions, especially near the origin. "
   ]
  },
  {
   "cell_type": "code",
   "id": "4d2e5d20",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:47.983463Z",
     "start_time": "2025-07-28T14:29:43.317791Z"
    }
   },
   "source": [
    "fig, ax = plt.subplots(subplot_kw={'aspect':'equal'}, figsize=(14, 6))\n",
    "\n",
    "x_plot = np.linspace(-2, 2, 1000)\n",
    "ax.plot(x_plot, x_plot ** 2 - 1, color='tab:blue', lw=2)\n",
    "\n",
    "with np.errstate(all='ignore'):\n",
    "    ax.plot(x_plot, np.sqrt(x_plot + 1), color='tab:orange', lw=2)\n",
    "    ax.plot(x_plot, -np.sqrt(x_plot + 1), color='tab:orange', lw=2)\n",
    "    \n",
    "ax.axhline(0, ls='--', c='k', lw=0.5)\n",
    "ax.axvline(0, ls='--', c='k', lw=0.5)\n",
    "\n",
    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:purple']\n",
    "\n",
    "rounded_solutions = np.round(solution_grid, 3)\n",
    "\n",
    "for root, color in zip(unique_solutions, colors):\n",
    "    subset_idx = (rounded_solutions == root).all(axis=1)\n",
    "    subset = grid_values[subset_idx]\n",
    "    ax.scatter(*subset.T, facecolor=color, edgecolor='none', alpha=0.25, label=fr'$({root[0]}, {root[1]})$')\n",
    "    ax.scatter(*root, color='tab:red', zorder=1000)\n",
    "    for x0 in subset:\n",
    "        ax.annotate(xy=root, xytext=x0, text='', arrowprops={'arrowstyle':'->', 'linewidth':0.5, 'alpha':0.5})\n",
    "\n",
    "fig.legend(ncol=1, bbox_to_anchor=(0.65, 0.5), loc='center left')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "markdown",
   "id": "26e65d23",
   "metadata": {},
   "source": [
    "## A function with parameters\n",
    "\n",
    "Our first function was really simple. More commonly, a function of interest will have both variables and parameters. \n",
    "\n",
    "To keep things simple, we can add a coefficent in front of every term in our system of two equations:\n",
    "\n",
    "$$ \n",
    "\\begin{align}\n",
    "ax^2 + by + c &= 0 \\\\\n",
    "dx + ey^2 + f &= 0 \n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "Although this still looks quite simple, we no longer have a general analytic solution! If we are faced with a parameterized function like like \"in the wild\", we have no choice but to resort to numerical methods.\n",
    "\n",
    "\n",
    "To get back to what we've been looking at, we can set:  $a=1$, $b=-1$, $c=-1$, $d=1$, $e=-1$, $f=1$"
   ]
  },
  {
   "cell_type": "code",
   "id": "fe60c766",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:48.084061Z",
     "start_time": "2025-07-28T14:29:48.075515Z"
    }
   },
   "source": [
    "x, y = variables = pt.tensor('variables', shape=(2, ))\n",
    "a, b, c, d, e, f = pt.scalars('a b c d e f'.split())\n",
    "\n",
    "eq_1 = a * x ** 2 + b * y + c\n",
    "eq_2 = d * x + e * y ** 2 + f"
   ],
   "outputs": [],
   "execution_count": 18
  },
  {
   "cell_type": "markdown",
   "id": "074a63db",
   "metadata": {},
   "source": [
    "Notice that we don't change the call to `optimize.root` at all!"
   ]
  },
  {
   "cell_type": "code",
   "id": "48e9291e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:48.291352Z",
     "start_time": "2025-07-28T14:29:48.246028Z"
    }
   },
   "source": [
    "solution, success = pt.optimize.root(equations=pt.stack([eq_1, eq_2]), \n",
    "                       variables=variables,\n",
    "                       method='hybr',\n",
    "                       optimizer_kwargs={'tol':1e-8})"
   ],
   "outputs": [],
   "execution_count": 19
  },
  {
   "cell_type": "markdown",
   "id": "97064e08",
   "metadata": {},
   "source": [
    "Unlike `scipy.optimize.root`, pytensor is going to automatically figure out what additional arguments are required. By knowing `equations` and `variables`, pytensor analyses the implied subgraph, and collects all other unknowns as `args`.\n",
    "\n",
    "We can see now that the inputs to the `RootOp` are `variables`, then all the parameters. Otherwise, the graph is unchanged. As a user, though, you will never interact with this inner function! You just pass the parameter values and pytensor will figure out the rest."
   ]
  },
  {
   "cell_type": "code",
   "id": "5a900fdc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:48.386955Z",
     "start_time": "2025-07-28T14:29:48.379153Z"
    }
   },
   "source": [
    "solution.dprint()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RootOp(method=hybr, jac=True).0 [id A]\n",
      " ├─ variables [id B]\n",
      " ├─ f [id C]\n",
      " ├─ e [id D]\n",
      " ├─ d [id E]\n",
      " ├─ c [id F]\n",
      " ├─ b [id G]\n",
      " └─ a [id H]\n",
      "\n",
      "Inner graphs:\n",
      "\n",
      "RootOp(method=hybr, jac=True) [id A]\n",
      " ← MakeVector{dtype='float64'} [id I]\n",
      "    ├─ Add [id J]\n",
      "    │  ├─ Add [id K]\n",
      "    │  │  ├─ Mul [id L]\n",
      "    │  │  │  ├─ a [id M]\n",
      "    │  │  │  └─ Pow [id N]\n",
      "    │  │  │     ├─ Subtensor{i} [id O]\n",
      "    │  │  │     │  ├─ variables [id P]\n",
      "    │  │  │     │  └─ 0 [id Q]\n",
      "    │  │  │     └─ 2 [id R]\n",
      "    │  │  └─ Mul [id S]\n",
      "    │  │     ├─ b [id T]\n",
      "    │  │     └─ Subtensor{i} [id U]\n",
      "    │  │        ├─ variables [id P]\n",
      "    │  │        └─ 1 [id V]\n",
      "    │  └─ c [id W]\n",
      "    └─ Add [id X]\n",
      "       ├─ Add [id Y]\n",
      "       │  ├─ Mul [id Z]\n",
      "       │  │  ├─ d [id BA]\n",
      "       │  │  └─ Subtensor{i} [id O]\n",
      "       │  │     └─ ···\n",
      "       │  └─ Mul [id BB]\n",
      "       │     ├─ e [id BC]\n",
      "       │     └─ Pow [id BD]\n",
      "       │        ├─ Subtensor{i} [id U]\n",
      "       │        │  └─ ···\n",
      "       │        └─ 2 [id BE]\n",
      "       └─ f [id BF]\n",
      " ← Add [id BG]\n",
      "    ├─ Blockwise{IncSubtensor{i}, (i00),(),()->(o00)} [id BH]\n",
      "    │  ├─ ExpandDims{axis=0} [id BI]\n",
      "    │  │  └─ Second [id BJ]\n",
      "    │  │     ├─ variables [id P]\n",
      "    │  │     └─ ExpandDims{axis=0} [id BK]\n",
      "    │  │        └─ 0.0 [id BL]\n",
      "    │  ├─ Add [id BM]\n",
      "    │  │  ├─ Mul [id BN]\n",
      "    │  │  │  ├─ Mul [id BO]\n",
      "    │  │  │  │  ├─ Mul [id BP]\n",
      "    │  │  │  │  │  ├─ Subtensor{:, i} [id BQ]\n",
      "    │  │  │  │  │  │  ├─ Eye{dtype='float64'} [id BR]\n",
      "    │  │  │  │  │  │  │  ├─ Subtensor{i} [id BS]\n",
      "    │  │  │  │  │  │  │  │  ├─ Shape [id BT]\n",
      "    │  │  │  │  │  │  │  │  │  └─ MakeVector{dtype='float64'} [id I]\n",
      "    │  │  │  │  │  │  │  │  │     └─ ···\n",
      "    │  │  │  │  │  │  │  │  └─ 0 [id BU]\n",
      "    │  │  │  │  │  │  │  ├─ Subtensor{i} [id BS]\n",
      "    │  │  │  │  │  │  │  │  └─ ···\n",
      "    │  │  │  │  │  │  │  └─ 0 [id BV]\n",
      "    │  │  │  │  │  │  └─ 0 [id BW]\n",
      "    │  │  │  │  │  └─ ExpandDims{axis=0} [id BX]\n",
      "    │  │  │  │  │     └─ a [id M]\n",
      "    │  │  │  │  └─ ExpandDims{axis=0} [id BY]\n",
      "    │  │  │  │     └─ 2 [id R]\n",
      "    │  │  │  └─ ExpandDims{axis=0} [id BZ]\n",
      "    │  │  │     └─ Pow [id CA]\n",
      "    │  │  │        ├─ Subtensor{i} [id CB]\n",
      "    │  │  │        │  ├─ variables [id P]\n",
      "    │  │  │        │  └─ 0 [id Q]\n",
      "    │  │  │        └─ Sub [id CC]\n",
      "    │  │  │           ├─ 2 [id R]\n",
      "    │  │  │           └─ 1 [id CD]\n",
      "    │  │  └─ Mul [id CE]\n",
      "    │  │     ├─ Subtensor{:, i} [id CF]\n",
      "    │  │     │  ├─ Eye{dtype='float64'} [id BR]\n",
      "    │  │     │  │  └─ ···\n",
      "    │  │     │  └─ 1 [id CG]\n",
      "    │  │     └─ ExpandDims{axis=0} [id CH]\n",
      "    │  │        └─ d [id BA]\n",
      "    │  └─ ExpandDims{axis=0} [id CI]\n",
      "    │     └─ TensorFromScalar [id CJ]\n",
      "    │        └─ 0 [id Q]\n",
      "    └─ Blockwise{IncSubtensor{i}, (i00),(),()->(o00)} [id CK]\n",
      "       ├─ ExpandDims{axis=0} [id CL]\n",
      "       │  └─ Second [id CM]\n",
      "       │     ├─ variables [id P]\n",
      "       │     └─ ExpandDims{axis=0} [id CN]\n",
      "       │        └─ 0.0 [id CO]\n",
      "       ├─ Add [id CP]\n",
      "       │  ├─ Mul [id CQ]\n",
      "       │  │  ├─ Subtensor{:, i} [id BQ]\n",
      "       │  │  │  └─ ···\n",
      "       │  │  └─ ExpandDims{axis=0} [id CR]\n",
      "       │  │     └─ b [id T]\n",
      "       │  └─ Mul [id CS]\n",
      "       │     ├─ Mul [id CT]\n",
      "       │     │  ├─ Mul [id CU]\n",
      "       │     │  │  ├─ Subtensor{:, i} [id CF]\n",
      "       │     │  │  │  └─ ···\n",
      "       │     │  │  └─ ExpandDims{axis=0} [id CV]\n",
      "       │     │  │     └─ e [id BC]\n",
      "       │     │  └─ ExpandDims{axis=0} [id CW]\n",
      "       │     │     └─ 2 [id BE]\n",
      "       │     └─ ExpandDims{axis=0} [id CX]\n",
      "       │        └─ Pow [id CY]\n",
      "       │           ├─ Subtensor{i} [id CZ]\n",
      "       │           │  ├─ variables [id P]\n",
      "       │           │  └─ 1 [id V]\n",
      "       │           └─ Sub [id DA]\n",
      "       │              ├─ 2 [id BE]\n",
      "       │              └─ 1 [id DB]\n",
      "       └─ ExpandDims{axis=0} [id DC]\n",
      "          └─ TensorFromScalar [id DD]\n",
      "             └─ 1 [id V]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<ipykernel.iostream.OutStream at 0x7f9c8dd689a0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 20
  },
  {
   "cell_type": "code",
   "id": "877783c0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:48.689077Z",
     "start_time": "2025-07-28T14:29:48.491298Z"
    }
   },
   "source": [
    "fn = pytensor.function([variables, a, b, c, d, e, f],\n",
    "                       [solution, success])"
   ],
   "outputs": [],
   "execution_count": 21
  },
  {
   "cell_type": "code",
   "id": "aa07dee7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:48.730932Z",
     "start_time": "2025-07-28T14:29:48.727619Z"
    }
   },
   "source": [
    "arg_inputs = {'a': 1, 'b': -1, 'c': -1, 'd': 1, 'e': -1, 'f': 1}"
   ],
   "outputs": [],
   "execution_count": 22
  },
  {
   "cell_type": "markdown",
   "id": "c72129d4",
   "metadata": {},
   "source": [
    "We can double-check that we still get the same answers:"
   ]
  },
  {
   "cell_type": "code",
   "id": "5a653a4a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:48.826491Z",
     "start_time": "2025-07-28T14:29:48.821123Z"
    }
   },
   "source": [
    "fn([0., 0.], **arg_inputs)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([-0.61803399, -0.61803399]), np.True_]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 23
  },
  {
   "cell_type": "code",
   "id": "7a345308",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:48.920120Z",
     "start_time": "2025-07-28T14:29:48.915206Z"
    }
   },
   "source": [
    "fn([1., 1.], **arg_inputs)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1.61803399, 1.61803399]), np.True_]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 24
  },
  {
   "cell_type": "markdown",
   "id": "96f86022",
   "metadata": {},
   "source": [
    "## Gradients\n",
    "\n",
    "Since `root` is symbolic `Op`, we can backprop through it. To do this, we use the implicit value theorem. We have a function $f(x, \\theta)$, where $x$ are the variables, and $\\theta$ are the parameters. There's some optimal $x^\\star$ that depends on $\\theta$ such, such that $f(x^\\star(\\theta), \\theta) = 0$ \n",
    "\n",
    "If we take $\\frac{\\partial}{\\partial \\theta} f(x^\\star(\\theta), \\theta)$ and use the chain rule, we get:\n",
    "\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\frac{\\partial}{\\partial \\theta} f(x^\\star(\\theta), \\theta) &= \\frac{\\partial f \\left ( x^\\star(\\theta), \\theta \\right )}{\\partial x^\\star} \\frac{x^\\star(\\theta)}{\\partial \\theta} + \\frac{\\partial f(x^\\star(\\theta), \\theta)}{\\partial \\theta} \\Rightarrow \\\\\n",
    "0 &= \\left. \\frac{\\partial f \\left ( x, \\theta \\right )}{\\partial x} \\right|_{x = x^\\star} \\frac{\\partial x^\\star(\\theta)}{\\partial \\theta} + \\left. \\frac{\\partial f(x, \\theta)}{\\partial \\theta} \\right |_{x = x^\\star}\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "The zero arises because, by definition, $f(x^\\star(\\theta), \\theta) = 0$. All three of the terms in this expression are matrices, and we know 2 of them. As a result, we can directly solve for the unknown quantity of interest, $\\frac{\\partial x^\\star(\\theta)}{\\partial \\theta}$:\n",
    "\n",
    "$$\n",
    "\\frac{\\partial x^\\star(\\theta)}{\\partial \\theta} = - \\left(\\left. \\frac{\\partial f \\left ( x, \\theta \\right )}{\\partial x} \\right|_{x = x^\\star}\\right)^{-1} \\left. \\frac{\\partial f(x, \\theta)}{\\partial \\theta} \\right |_{x = x^\\star}\n",
    "$$\n",
    "\n",
    "So we just need the jacobian of the objective function with respect to the variables $x$ and parameters $\\theta$, all evaluated at the optimal point $x^\\star$. "
   ]
  },
  {
   "cell_type": "code",
   "id": "90a3a4f2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:50.611554Z",
     "start_time": "2025-07-28T14:29:49.018275Z"
    }
   },
   "source": [
    "dx_dtheta = pt.grad(solution[0], [a, b, c, d, e, f])\n",
    "dy_dtheta = pt.grad(solution[1], [a, b, c, d, e, f])\n",
    "\n",
    "d_theta_vec = pt.stack([dx_dtheta, dy_dtheta], axis=-1)\n",
    "\n",
    "f_d_theta = pytensor.function([variables, a, b, c, d, e, f], d_theta_vec)"
   ],
   "outputs": [],
   "execution_count": 25
  },
  {
   "cell_type": "markdown",
   "id": "01d4fc9a",
   "metadata": {},
   "source": [
    "These values show, evidently, the effect of a nudge to one of the 6 parameteres (on the rows) on the value of the variables $x$ and $y$ (on the columns). "
   ]
  },
  {
   "cell_type": "code",
   "id": "725c23f9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:50.690469Z",
     "start_time": "2025-07-28T14:29:50.677027Z"
    }
   },
   "source": [
    "f_d_theta([0., 0.], **arg_inputs)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.89442719, -0.7236068 ],\n",
       "       [-1.4472136 ,  1.17082039],\n",
       "       [ 2.34164079, -1.89442719],\n",
       "       [-1.17082039,  1.4472136 ],\n",
       "       [ 0.7236068 , -0.89442719],\n",
       "       [ 1.89442719, -2.34164079]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 26
  },
  {
   "cell_type": "markdown",
   "id": "5851e416",
   "metadata": {},
   "source": [
    "Note that this is unique to the root associated with the $(0, 0)$ point. If we shift the point $(0, 0)$ slightly, but still in a zone that converges to the $(-0.618, -0.618)$ root, we will get the same gradients"
   ]
  },
  {
   "cell_type": "code",
   "id": "4f35bcbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:50.842414Z",
     "start_time": "2025-07-28T14:29:50.819363Z"
    }
   },
   "source": [
    "f_d_theta([-1.0, -1.0], **arg_inputs)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.89442719, -0.7236068 ],\n",
       "       [-1.4472136 ,  1.17082039],\n",
       "       [ 2.34164079, -1.89442719],\n",
       "       [-1.17082039,  1.4472136 ],\n",
       "       [ 0.7236068 , -0.89442719],\n",
       "       [ 1.89442719, -2.34164079]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 27
  },
  {
   "cell_type": "markdown",
   "id": "cce26caf",
   "metadata": {},
   "source": [
    "On the other hand, if we evaluate at a different root, for example the $(1.618, 1.618)$ root, we will have different gradients."
   ]
  },
  {
   "cell_type": "code",
   "id": "9737f793",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:51.123385Z",
     "start_time": "2025-07-28T14:29:51.114228Z"
    }
   },
   "source": [
    "f_d_theta([0.8, 0.8], **arg_inputs)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.89442719, -0.2763932 ],\n",
       "       [-0.5527864 , -0.17082039],\n",
       "       [-0.34164079, -0.10557281],\n",
       "       [ 0.17082039,  0.5527864 ],\n",
       "       [ 0.2763932 ,  0.89442719],\n",
       "       [ 0.10557281,  0.34164079]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 28
  },
  {
   "cell_type": "markdown",
   "id": "5803a46f",
   "metadata": {},
   "source": [
    "## Using roots for downstream computation\n",
    "\n",
    "Often, there are quantities of interest downstream of an optimization problem that researchers are interested in studying.\n",
    "\n",
    "One such example comes from labor economics. The [McCall Search Model](https://python.quantecon.org/mccall_model.html) is a relatively simple model of how people look for jobs. Every day, an unemployed worker wakes up and gets a job offer. The wage of the job on offer that day (at time $t$) is drawn from a known distribution $w_t \\sim Q(\\cdot)$. Offers are IID across time.\n",
    "\n",
    "The workers can either:\n",
    "\n",
    "1. Accept the job and work it for the rest of his life, earning $w_t$ forever, or;\n",
    "2. Reject the job, and wait for another one to come along. In this case, he earns unemployment benefits $c$, and gets to see another offer tomorrow.\n",
    "\n",
    "The agent's objective is to maxmize expected discounted utility over his lifetime. We assume he discounts at rate $\\beta$, such that:\n",
    "\n",
    "$$ U_t = \\mathbb E_t \\left [\\sum_{s=0}^\\infty \\beta^s y_{t+s} \\right ] $$\n",
    "\n",
    "Where $y_t$ is the the income the worker will earn at period $t$, either $c$ or $w_\\tau$, depending on his choices up to that point ($\\tau$ is the period in which he accepted the wage, if he did).\n",
    "\n",
    "Interested readers can check the quantecon link for details. For our purposes here, it suffices to say that this is a dynamic program involving a search for an optimal **value function**. A value function maps states of the world to expected utility, allowing an agent to evaluate actions. With some manipulation, it can be shown that the worker in this model has the following value function:\n",
    "\n",
    "$$ v^\\star(w)\n",
    "= \\max \\left\\{\n",
    "        \\frac{w}{1 - \\beta}, \\, c + \\beta\n",
    "        \\sum_{w' \\in \\mathbb{W}} v^\\star(w') q (w')\n",
    "    \\right\\}\n",
    "$$\n",
    "\n",
    "Where $w$ is a vector of all known wages (or at least some kind of sampling over the support of the wage distribution, $\\mathbb{W}$). So $v$, $w$ and $q(w)$ are all vectors. By $v^\\star(w)$, we mean the value of a wage offer $w$ under the optimal value function, $v^\\star$.\n",
    "\n",
    "Because of the special properties of this value function, it can be shown that it defines a **fixed-point operator** $T$. Starting an arbitrary vector $v_0$, iteratively applying the following function:\n",
    "\n",
    "$$\n",
    "Tv_i\n",
    "= \\max \\left\\{\n",
    "        \\frac{w_i}{1 - \\beta}, \\, c + \\beta \\sum_{1 \\leq j \\leq n}\n",
    "            v(j) q (j)\n",
    "    \\right\\}\n",
    "\\quad\n",
    "\\text{for } i = 1, \\ldots, n\n",
    "$$\n",
    "\n",
    "Will eventaully converge to the optimal value function, no matter what $v_0$ is chosen."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "941cf87e",
   "metadata": {},
   "source": [
    "### Where's the root?\n",
    "\n",
    "What quantecon presents is **value function iteration**. We can, however, just jump to the end by interpreting the definition of the fixed-point operator $Tv$ as a system of non-linear equations. In particular, we just require some vector $v$ such that:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "Tv - v &= 0 && \\Rightarrow  \\\\\n",
    "\\max \\left\\{\n",
    "        \\frac{w}{1 - \\beta}, \\, c + \\beta \\sum_{1 \\leq j \\leq n}\n",
    "            v(j) q (j)\n",
    "    \\right\\} - v &= 0 &&\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "Such a vector will contain all the **roots** of this equation. We can find the answer directly, without using value-function iteration."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89b8f8d8",
   "metadata": {},
   "source": [
    "### Where do wages come from?\n",
    "\n",
    "This is a free choice in the model. Following QuantEcon, we will assume they follow a *Beta-Binomial Distribution*. Pytensor implements this random variable and can draw samples from it, but it doesn't give us the PMF out of the box. We have to write it ourselves, using the definition from [Wikipedia](https://en.wikipedia.org/wiki/Beta-binomial_distribution):\n",
    "\n",
    "$$\n",
    "f(x\\mid n,\\alpha,\\beta)\n",
    "= \\begin{pmatrix} n \\\\ k \\end{pmatrix} \\frac{B(x + \\alpha, n - x + \\beta)}{B(\\alpha, \\beta)}\n",
    "$$\n",
    "\n",
    "Where $B(x, y)$ is the Beta function.\n",
    "\n",
    "For numerical stability, we will actually compute the logpmf, then exp it."
   ]
  },
  {
   "cell_type": "code",
   "id": "f065a891",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:51.461035Z",
     "start_time": "2025-07-28T14:29:51.432526Z"
    }
   },
   "source": [
    "from pytensor.tensor.special import betaln\n",
    "\n",
    "n, a, b = pt.scalars('n a b'.split())\n",
    "w_min, w_max = pt.scalars('w_min w_max'.split())\n",
    "\n",
    "w_support = pt.linspace(w_min, w_max, n+1)\n",
    "\n",
    "k = pt.floor(w_support)\n",
    "ln_n_choose_k = -pt.log(n + 1) - betaln(n - k + 1, k + 1)\n",
    "q_probs = pt.exp(ln_n_choose_k + betaln(k + a, n - k + b) - betaln(a, b))"
   ],
   "outputs": [],
   "execution_count": 29
  },
  {
   "cell_type": "code",
   "id": "543052e6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:51.995551Z",
     "start_time": "2025-07-28T14:29:51.741018Z"
    }
   },
   "source": [
    "dist_args = [n, a, b, w_min, w_max]\n",
    "f = pytensor.function(dist_args, [w_support, q_probs])"
   ],
   "outputs": [],
   "execution_count": 30
  },
  {
   "cell_type": "code",
   "id": "b90d037a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:52.284051Z",
     "start_time": "2025-07-28T14:29:52.058204Z"
    }
   },
   "source": [
    "dist_params = {'n':50, 'a':200, 'b':100, 'w_min':10, 'w_max':60}\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 4))\n",
    "ax.bar(*f(**dist_params))\n",
    "ax.set(title='Wage Distribution', xlabel='Wage', ylabel='P(Wage)')\n",
    "ax.grid(ls='--', lw=0.5)\n",
    "[spine.set_visible(False) for spine in ax.spines.values()]\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x400 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 31
  },
  {
   "cell_type": "markdown",
   "id": "a4e368ba",
   "metadata": {},
   "source": [
    "### Setting up the model"
   ]
  },
  {
   "cell_type": "code",
   "id": "ed92df70",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:52.381751Z",
     "start_time": "2025-07-28T14:29:52.341604Z"
    }
   },
   "source": [
    "c = pt.dscalar('c') # Unemployment benefit\n",
    "β = pt.dscalar('β') # Discount rate\n",
    "\n",
    "# initial value function guess\n",
    "v0 = pt.dvector('v0') \n",
    "\n",
    "# Fixed-point operator\n",
    "T = pt.maximum(w_support / (1 - β), c + β * pt.dot(v0, q_probs))\n",
    "\n",
    "v_star, success = pt.optimize.root(equations=T - v0,\n",
    "                                   variables=v0,\n",
    "                                   method='hybr')"
   ],
   "outputs": [],
   "execution_count": 32
  },
  {
   "cell_type": "code",
   "id": "fdc49be0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:52.907099Z",
     "start_time": "2025-07-28T14:29:52.448626Z"
    }
   },
   "source": [
    "fn = pytensor.function([v0, c, β, *dist_args],\n",
    "                       [w_support, v_star, success])"
   ],
   "outputs": [],
   "execution_count": 33
  },
  {
   "cell_type": "markdown",
   "id": "9e6e77f4",
   "metadata": {},
   "source": [
    "### Solving for the value function"
   ]
  },
  {
   "cell_type": "code",
   "id": "e70e2bae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:52.969094Z",
     "start_time": "2025-07-28T14:29:52.962622Z"
    }
   },
   "source": [
    "c_value = 25\n",
    "beta_value = 0.99\n",
    "v0_value = np.zeros(dist_params['n'] + 1)\n",
    "\n",
    "w_values, v_star_val, success_flag = fn(v0_value, c_value, beta_value, **dist_params)"
   ],
   "outputs": [],
   "execution_count": 34
  },
  {
   "cell_type": "markdown",
   "id": "22af8580",
   "metadata": {},
   "source": [
    "This plot shows the optimal value function. Below the reservation wage (which appears to be around 38), the worker will not accept a job, and gets constant utility from being on unemployment. After the reservation wage, his lifetime utility is increasing linearly in his wage level. "
   ]
  },
  {
   "cell_type": "code",
   "id": "29760ad2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:53.271982Z",
     "start_time": "2025-07-28T14:29:53.063939Z"
    }
   },
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 4), dpi=144)\n",
    "ax.plot(w_values, v_star_val)\n",
    "ax.set(title='Lifetime Value of Wages', xlabel='Wage', ylabel='Value Function')\n",
    "ax.grid(ls='--', lw=0.5)\n",
    "[spine.set_visible(False) for spine in ax.spines.values()]\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2016x576 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 35
  },
  {
   "cell_type": "markdown",
   "id": "792a3381",
   "metadata": {},
   "source": [
    "### Studying the reservation wage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb63e54a",
   "metadata": {},
   "source": [
    "While the shape of the value function is interesting per se, it is not the primary object of interest in this study. Instead, we are interested in the reservation wage -- the minimum wage at which the worker will willingly choose to exit unemployment and join the workforce.\n",
    "\n",
    "This wage can be computed as:\n",
    "\n",
    "$$\n",
    "\\bar w := (1 - \\beta) \\left\\{ c + \\beta \\sum_{w'} v^*(w') q (w') \\right\\}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "id": "b318e23c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.025778Z",
     "start_time": "2025-07-28T14:29:53.334235Z"
    }
   },
   "source": [
    "w_bar = (1 - β) * (c + β * pt.dot(v_star, q_probs))\n",
    "\n",
    "# We want to study the impact of change in unemployment and patience on the reserve wage \n",
    "w_grads = pt.grad(w_bar, [c, β])"
   ],
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Only tensors with the same number of dimensions can be joined. Input ndims were: [3, 2, 2, 2]",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[36], line 4\u001B[0m\n\u001B[1;32m      1\u001B[0m w_bar \u001B[38;5;241m=\u001B[39m (\u001B[38;5;241m1\u001B[39m \u001B[38;5;241m-\u001B[39m β) \u001B[38;5;241m*\u001B[39m (c \u001B[38;5;241m+\u001B[39m β \u001B[38;5;241m*\u001B[39m pt\u001B[38;5;241m.\u001B[39mdot(v_star, q_probs))\n\u001B[1;32m      3\u001B[0m \u001B[38;5;66;03m# We want to study the impact of change in unemployment and patience on the reserve wage \u001B[39;00m\n\u001B[0;32m----> 4\u001B[0m w_grads \u001B[38;5;241m=\u001B[39m \u001B[43mpt\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgrad\u001B[49m\u001B[43m(\u001B[49m\u001B[43mw_bar\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m[\u001B[49m\u001B[43mc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mβ\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/gradient.py:747\u001B[0m, in \u001B[0;36mgrad\u001B[0;34m(cost, wrt, consider_constant, disconnected_inputs, add_names, known_grads, return_disconnected, null_gradients)\u001B[0m\n\u001B[1;32m    744\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(g\u001B[38;5;241m.\u001B[39mtype, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdtype\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[1;32m    745\u001B[0m         \u001B[38;5;28;01massert\u001B[39;00m g\u001B[38;5;241m.\u001B[39mtype\u001B[38;5;241m.\u001B[39mdtype \u001B[38;5;129;01min\u001B[39;00m pytensor\u001B[38;5;241m.\u001B[39mtensor\u001B[38;5;241m.\u001B[39mtype\u001B[38;5;241m.\u001B[39mfloat_dtypes\n\u001B[0;32m--> 747\u001B[0m _rval: Sequence[Variable] \u001B[38;5;241m=\u001B[39m \u001B[43m_populate_grad_dict\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    748\u001B[0m \u001B[43m    \u001B[49m\u001B[43mvar_to_app_to_idx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgrad_dict\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_wrt\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcost_name\u001B[49m\n\u001B[1;32m    749\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    751\u001B[0m rval: MutableSequence[Variable \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(_rval)\n\u001B[1;32m    753\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mlen\u001B[39m(_rval)):\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/gradient.py:1541\u001B[0m, in \u001B[0;36m_populate_grad_dict\u001B[0;34m(var_to_app_to_idx, grad_dict, wrt, cost_name)\u001B[0m\n\u001B[1;32m   1538\u001B[0m     \u001B[38;5;66;03m# end if cache miss\u001B[39;00m\n\u001B[1;32m   1539\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m grad_dict[var]\n\u001B[0;32m-> 1541\u001B[0m rval \u001B[38;5;241m=\u001B[39m [\u001B[43maccess_grad_cache\u001B[49m\u001B[43m(\u001B[49m\u001B[43melem\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mfor\u001B[39;00m elem \u001B[38;5;129;01min\u001B[39;00m wrt]\n\u001B[1;32m   1543\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m rval\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/gradient.py:1496\u001B[0m, in \u001B[0;36m_populate_grad_dict.<locals>.access_grad_cache\u001B[0;34m(var)\u001B[0m\n\u001B[1;32m   1494\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m node \u001B[38;5;129;01min\u001B[39;00m node_to_idx:\n\u001B[1;32m   1495\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m idx \u001B[38;5;129;01min\u001B[39;00m node_to_idx[node]:\n\u001B[0;32m-> 1496\u001B[0m         term \u001B[38;5;241m=\u001B[39m \u001B[43maccess_term_cache\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnode\u001B[49m\u001B[43m)\u001B[49m[idx]\n\u001B[1;32m   1498\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(term, Variable):\n\u001B[1;32m   1499\u001B[0m             \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\n\u001B[1;32m   1500\u001B[0m                 \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnode\u001B[38;5;241m.\u001B[39mop\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.grad returned \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mtype\u001B[39m(term)\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, expected\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   1501\u001B[0m                 \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m Variable instance.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   1502\u001B[0m             )\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/gradient.py:1326\u001B[0m, in \u001B[0;36m_populate_grad_dict.<locals>.access_term_cache\u001B[0;34m(node)\u001B[0m\n\u001B[1;32m   1318\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m o_shape \u001B[38;5;241m!=\u001B[39m g_shape:\n\u001B[1;32m   1319\u001B[0m             \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m   1320\u001B[0m                 \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mGot a gradient of shape \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   1321\u001B[0m                 \u001B[38;5;241m+\u001B[39m \u001B[38;5;28mstr\u001B[39m(o_shape)\n\u001B[1;32m   1322\u001B[0m                 \u001B[38;5;241m+\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m on an output of shape \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   1323\u001B[0m                 \u001B[38;5;241m+\u001B[39m \u001B[38;5;28mstr\u001B[39m(g_shape)\n\u001B[1;32m   1324\u001B[0m             )\n\u001B[0;32m-> 1326\u001B[0m input_grads \u001B[38;5;241m=\u001B[39m \u001B[43mnode\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mop\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mL_op\u001B[49m\u001B[43m(\u001B[49m\u001B[43minputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnode\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moutputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnew_output_grads\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1328\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m input_grads \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m   1329\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\n\u001B[1;32m   1330\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnode\u001B[38;5;241m.\u001B[39mop\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.grad returned NoneType, expected iterable.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   1331\u001B[0m     )\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/tensor/optimize.py:915\u001B[0m, in \u001B[0;36mRootOp.L_op\u001B[0;34m(self, inputs, outputs, output_grads)\u001B[0m\n\u001B[1;32m    906\u001B[0m df_dx \u001B[38;5;241m=\u001B[39m (\n\u001B[1;32m    907\u001B[0m     jacobian(inner_fx, inner_x, vectorize\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[1;32m    908\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mjac\n\u001B[1;32m    909\u001B[0m     \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfgraph\u001B[38;5;241m.\u001B[39moutputs[\u001B[38;5;241m1\u001B[39m]\n\u001B[1;32m    910\u001B[0m )\n\u001B[1;32m    911\u001B[0m df_dtheta_columns \u001B[38;5;241m=\u001B[39m jacobian(\n\u001B[1;32m    912\u001B[0m     inner_fx, inner_args, disconnected_inputs\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m\"\u001B[39m, vectorize\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[1;32m    913\u001B[0m )\n\u001B[0;32m--> 915\u001B[0m grad_wrt_args \u001B[38;5;241m=\u001B[39m \u001B[43mimplict_optimization_grads\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    916\u001B[0m \u001B[43m    \u001B[49m\u001B[43mdf_dx\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdf_dx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    917\u001B[0m \u001B[43m    \u001B[49m\u001B[43mdf_dtheta_columns\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdf_dtheta_columns\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    918\u001B[0m \u001B[43m    \u001B[49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    919\u001B[0m \u001B[43m    \u001B[49m\u001B[43mx_star\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mx_star\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    920\u001B[0m \u001B[43m    \u001B[49m\u001B[43moutput_grad\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43moutput_grad\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    921\u001B[0m \u001B[43m    \u001B[49m\u001B[43mfgraph\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfgraph\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    922\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    924\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m [zeros_like(x), \u001B[38;5;241m*\u001B[39mgrad_wrt_args]\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/tensor/optimize.py:333\u001B[0m, in \u001B[0;36mimplict_optimization_grads\u001B[0;34m(df_dx, df_dtheta_columns, args, x_star, output_grad, fgraph)\u001B[0m\n\u001B[1;32m    290\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124mr\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m    291\u001B[0m \u001B[38;5;124;03mCompute gradients of an optimization problem with respect to its parameters.\u001B[39;00m\n\u001B[1;32m    292\u001B[0m \n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    329\u001B[0m \u001B[38;5;124;03m    The function graph that contains the inputs and outputs of the optimization problem.\u001B[39;00m\n\u001B[1;32m    330\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m    331\u001B[0m df_dx \u001B[38;5;241m=\u001B[39m cast(TensorVariable, df_dx)\n\u001B[0;32m--> 333\u001B[0m df_dtheta \u001B[38;5;241m=\u001B[39m \u001B[43mconcatenate\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    334\u001B[0m \u001B[43m    \u001B[49m\u001B[43m[\u001B[49m\n\u001B[1;32m    335\u001B[0m \u001B[43m        \u001B[49m\u001B[43matleast_2d\u001B[49m\u001B[43m(\u001B[49m\u001B[43mjac_col\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mleft\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m    336\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mjac_col\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mcast\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mlist\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43mTensorVariable\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdf_dtheta_columns\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    337\u001B[0m \u001B[43m    \u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    338\u001B[0m \u001B[43m    \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m-\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m    339\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    341\u001B[0m replace \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mdict\u001B[39m(\u001B[38;5;28mzip\u001B[39m(fgraph\u001B[38;5;241m.\u001B[39minputs, (x_star, \u001B[38;5;241m*\u001B[39margs), strict\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m))\n\u001B[1;32m    343\u001B[0m df_dx_star, df_dtheta_star \u001B[38;5;241m=\u001B[39m cast(\n\u001B[1;32m    344\u001B[0m     \u001B[38;5;28mlist\u001B[39m[TensorVariable],\n\u001B[1;32m    345\u001B[0m     graph_replace([atleast_2d(df_dx), df_dtheta], replace\u001B[38;5;241m=\u001B[39mreplace),\n\u001B[1;32m    346\u001B[0m )\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/tensor/basic.py:2991\u001B[0m, in \u001B[0;36mconcatenate\u001B[0;34m(tensor_list, axis)\u001B[0m\n\u001B[1;32m   2984\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(tensor_list, \u001B[38;5;28mtuple\u001B[39m \u001B[38;5;241m|\u001B[39m \u001B[38;5;28mlist\u001B[39m):\n\u001B[1;32m   2985\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\n\u001B[1;32m   2986\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mThe \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtensors\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m argument must be either a tuple \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   2987\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mor a list, make sure you did not forget () or [] around \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   2988\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124marguments of concatenate.\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m   2989\u001B[0m         tensor_list,\n\u001B[1;32m   2990\u001B[0m     )\n\u001B[0;32m-> 2991\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mjoin\u001B[49m\u001B[43m(\u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mtensor_list\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/tensor/basic.py:2805\u001B[0m, in \u001B[0;36mjoin\u001B[0;34m(axis, *tensors_list)\u001B[0m\n\u001B[1;32m   2803\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m tensors_list[\u001B[38;5;241m0\u001B[39m]\n\u001B[1;32m   2804\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m-> 2805\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_join\u001B[49m\u001B[43m(\u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mtensors_list\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/graph/op.py:293\u001B[0m, in \u001B[0;36mOp.__call__\u001B[0;34m(self, name, return_list, *inputs, **kwargs)\u001B[0m\n\u001B[1;32m    249\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21m__call__\u001B[39m(\n\u001B[1;32m    250\u001B[0m     \u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39minputs: Any, name\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, return_list\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs\n\u001B[1;32m    251\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Variable \u001B[38;5;241m|\u001B[39m \u001B[38;5;28mlist\u001B[39m[Variable]:\n\u001B[1;32m    252\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124mr\u001B[39m\u001B[38;5;124;03m\"\"\"Construct an `Apply` node using :meth:`Op.make_node` and return its outputs.\u001B[39;00m\n\u001B[1;32m    253\u001B[0m \n\u001B[1;32m    254\u001B[0m \u001B[38;5;124;03m    This method is just a wrapper around :meth:`Op.make_node`.\u001B[39;00m\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    291\u001B[0m \n\u001B[1;32m    292\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[0;32m--> 293\u001B[0m     node \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmake_node\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43minputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    294\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m name \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m    295\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(node\u001B[38;5;241m.\u001B[39moutputs) \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m1\u001B[39m:\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/tensor/basic.py:2498\u001B[0m, in \u001B[0;36mJoin.make_node\u001B[0;34m(self, axis, *tensors)\u001B[0m\n\u001B[1;32m   2495\u001B[0m ndim \u001B[38;5;241m=\u001B[39m tensors[\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m.\u001B[39mtype\u001B[38;5;241m.\u001B[39mndim\n\u001B[1;32m   2497\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m builtins\u001B[38;5;241m.\u001B[39mall(x\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m==\u001B[39m ndim \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m tensors):\n\u001B[0;32m-> 2498\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\n\u001B[1;32m   2499\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mOnly tensors with the same number of dimensions can be joined. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   2500\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mInput ndims were: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m[x\u001B[38;5;241m.\u001B[39mndim\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mfor\u001B[39;00m\u001B[38;5;250m \u001B[39mx\u001B[38;5;250m \u001B[39m\u001B[38;5;129;01min\u001B[39;00m\u001B[38;5;250m \u001B[39mtensors]\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   2501\u001B[0m     )\n\u001B[1;32m   2503\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m   2504\u001B[0m     static_axis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mint\u001B[39m(get_scalar_constant_value(axis))\n",
      "\u001B[0;31mTypeError\u001B[0m: Only tensors with the same number of dimensions can be joined. Input ndims were: [3, 2, 2, 2]"
     ]
    }
   ],
   "execution_count": 36
  },
  {
   "cell_type": "code",
   "id": "a77aa3d8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.038374074Z",
     "start_time": "2025-07-28T14:28:22.016693Z"
    }
   },
   "source": [
    "fn_2 = pytensor.function([v0, c, β, *dist_args],\n",
    "                         [success, w_bar, *w_grads],\n",
    "                         on_unused_input='ignore')"
   ],
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'w_grads' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[38], line 2\u001B[0m\n\u001B[1;32m      1\u001B[0m fn_2 \u001B[38;5;241m=\u001B[39m pytensor\u001B[38;5;241m.\u001B[39mfunction([v0, c, β, \u001B[38;5;241m*\u001B[39mdist_args],\n\u001B[0;32m----> 2\u001B[0m                          [success, w_bar, \u001B[38;5;241m*\u001B[39m\u001B[43mw_grads\u001B[49m],\n\u001B[1;32m      3\u001B[0m                          on_unused_input\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m'\u001B[39m)\n",
      "\u001B[0;31mNameError\u001B[0m: name 'w_grads' is not defined"
     ]
    }
   ],
   "execution_count": 38
  },
  {
   "cell_type": "code",
   "id": "fa568587",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.039512358Z",
     "start_time": "2025-07-28T14:28:22.929234Z"
    }
   },
   "source": [
    "success_flag, reservation_wage, dw_dc, dw_dβ = fn_2(v0_value, c_value, beta_value, **dist_params)\n",
    "print(f'Reservation wage at c={c_value}, β={beta_value}: {reservation_wage.item()}')\n",
    "print(f'Change in reservation wage given unit change in c: {dw_dc}')\n",
    "print(f'Change in reservation wage given 1% change in β: {dw_dβ / 100}')"
   ],
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fn_2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[39], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m success_flag, reservation_wage, dw_dc, dw_dβ \u001B[38;5;241m=\u001B[39m \u001B[43mfn_2\u001B[49m(v0_value, c_value, beta_value, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mdist_params)\n\u001B[1;32m      2\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mReservation wage at c=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mc_value\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, β=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mbeta_value\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mreservation_wage\u001B[38;5;241m.\u001B[39mitem()\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m      3\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mChange in reservation wage given unit change in c: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mdw_dc\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n",
      "\u001B[0;31mNameError\u001B[0m: name 'fn_2' is not defined"
     ]
    }
   ],
   "execution_count": 39
  },
  {
   "cell_type": "markdown",
   "id": "86110c8c",
   "metadata": {},
   "source": [
    "We likely want to study the effect of many pairs of c and $\\beta$, so we vectorize the function"
   ]
  },
  {
   "cell_type": "code",
   "id": "798abcb6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.053678627Z",
     "start_time": "2025-07-28T14:28:24.075241Z"
    }
   },
   "source": [
    "c_grid = pt.dmatrix('c_grid')\n",
    "β_grid = pt.dmatrix('β_grid')\n",
    "\n",
    "w_bar_grid, *w_grad_grid = vectorize_graph([w_bar, *w_grads], {β:β_grid, c:c_grid})\n",
    "\n",
    "fn_grid = pytensor.function([v0, c_grid, β_grid, *dist_args],\n",
    "                            [w_bar_grid, *w_grad_grid],\n",
    "                            on_unused_input='ignore')"
   ],
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'w_grads' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[40], line 4\u001B[0m\n\u001B[1;32m      1\u001B[0m c_grid \u001B[38;5;241m=\u001B[39m pt\u001B[38;5;241m.\u001B[39mdmatrix(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mc_grid\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m      2\u001B[0m β_grid \u001B[38;5;241m=\u001B[39m pt\u001B[38;5;241m.\u001B[39mdmatrix(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mβ_grid\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m----> 4\u001B[0m w_bar_grid, \u001B[38;5;241m*\u001B[39mw_grad_grid \u001B[38;5;241m=\u001B[39m vectorize_graph([w_bar, \u001B[38;5;241m*\u001B[39m\u001B[43mw_grads\u001B[49m], {β:β_grid, c:c_grid})\n\u001B[1;32m      6\u001B[0m fn_grid \u001B[38;5;241m=\u001B[39m pytensor\u001B[38;5;241m.\u001B[39mfunction([v0, c_grid, β_grid, \u001B[38;5;241m*\u001B[39mdist_args],\n\u001B[1;32m      7\u001B[0m                             [w_bar_grid, \u001B[38;5;241m*\u001B[39mw_grad_grid],\n\u001B[1;32m      8\u001B[0m                             on_unused_input\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m'\u001B[39m)\n",
      "\u001B[0;31mNameError\u001B[0m: name 'w_grads' is not defined"
     ]
    }
   ],
   "execution_count": 40
  },
  {
   "cell_type": "code",
   "id": "c9dc5bb7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.055374337Z",
     "start_time": "2025-07-28T13:48:15.524167Z"
    }
   },
   "source": [
    "c_values = np.linspace(10, 50, 30)\n",
    "β_values = np.linspace(0.1, 0.99, 30)\n",
    "\n",
    "cc, bb = np.meshgrid(c_values, β_values)"
   ],
   "outputs": [],
   "execution_count": 40
  },
  {
   "cell_type": "code",
   "id": "46c5a937",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.063046781Z",
     "start_time": "2025-07-28T13:48:15.644965Z"
    }
   },
   "source": [
    "# Use the answer we already found as starting value to try to speed up convergence\n",
    "\n",
    "w_bar_grid_vals, *w_grad_grid_vals = fn_grid(v_star_val, cc, bb,\n",
    "                                             **dist_params)"
   ],
   "outputs": [],
   "execution_count": 41
  },
  {
   "cell_type": "markdown",
   "id": "b2010d3f",
   "metadata": {},
   "source": [
    "This next cell reproduces the final plot of the quantecon lecture"
   ]
  },
  {
   "cell_type": "code",
   "id": "a5434ef6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.064048539Z",
     "start_time": "2025-07-28T13:48:16.909317Z"
    }
   },
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "cs1 = ax.contourf(cc, bb, w_bar_grid_vals, alpha=0.75)\n",
    "ctr1 = ax.contour(cc, bb, w_bar_grid_vals, colors='k', linestyles='dashed', )\n",
    "\n",
    "ax.clabel(ctr1, inline=1, fontsize=13, colors='k')\n",
    "plt.colorbar(cs1, ax=ax)\n",
    "\n",
    "ax.set_title(\"reservation wage\")\n",
    "ax.set_xlabel(\"$c$\", fontsize=16)\n",
    "ax.set_ylabel(\"$β$\", fontsize=16)\n",
    "\n",
    "ax.ticklabel_format(useOffset=False)\n",
    "\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 42
  },
  {
   "cell_type": "markdown",
   "id": "9c3bab4b",
   "metadata": {},
   "source": [
    "Since we have the gradients, we can also show a vector field of how the reservation wage changes at each point.\n",
    "\n",
    "From this perspective, we see that the reservation wage increases more when $c$ is increased by \\\\$1 than when $\\beta$ is increased by 1\\%. The gradients primarily point in the $c$ direction, except when $c < 20$."
   ]
  },
  {
   "cell_type": "code",
   "id": "1d2d756c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.064906187Z",
     "start_time": "2025-07-28T13:48:17.329600Z"
    }
   },
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "cc_grad, bb_grad = w_grad_grid_vals\n",
    "\n",
    "cs1 = ax.contourf(cc, bb, w_bar_grid_vals, alpha=0.75)\n",
    "ax.quiver(cc, bb, cc_grad, bb_grad / 100)\n",
    "\n",
    "plt.colorbar(cs1, ax=ax)\n",
    "\n",
    "ax.set_title(\"reservation wage\")\n",
    "ax.set_xlabel(\"$c$\", fontsize=16)\n",
    "ax.set_ylabel(\"$β$\", fontsize=16)\n",
    "\n",
    "ax.ticklabel_format(useOffset=False)\n",
    "\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAo4AAAHcCAYAAABYoCuBAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsnXd8FGX+x9/bkk3vZdMrhJqEHjpJCNazi13R058Fz+7ZUc92nu0869kbh6LYEBFEQHrvgZAC6b23rTO/P2IiMRuyO4tSfN6v174gszOffWZmy2e+zzPPRyXLsoxAIBAIBAKBQDAA6uPdAIFAIBAIBALByYEwjgKBQCAQCAQChxDGUSAQCAQCgUDgEMI4CgQCgUAgEAgcQhhHgUAgEAgEAoFDCOMoEAgEAoFAIHAIYRwFAoFAIBAIBA4hjKNAIBAIBAKBwCGEcRQIBAKBQCAQOIQwjgKB4E/FqlWrUKlUrFq16ng3RSAQCE46hHEUCASnJK+99hrvv//+8W6GQCAQnFKoRFa1QCA4FRk+fDjBwcF9KouSJGE2m3Fzc0OtFtfOAoFA4AziW1Mg+JPR3t5+vJvQi46Ojj/09dRqNXq9XphGgUAgUID45hQITmEeffRRVCoVubm5XHbZZQQEBDB58uSe5z/++GNGjx6Nh4cHgYGBXHLJJZSWlvbSyM/P54ILLiA8PBy9Xk9UVBSXXHIJzc3NvdZzRGv69OkMHz6cbdu2MXXqVDw9PXnggQc466yzSEhIsLsPGRkZjBkzpufv9957j8zMTEJDQ3F3d2fo0KG8/vrrvbaJi4tj3759rF69GpVKhUqlYvr06UD/YxwXLlzY0/7g4GCuuOIKysvLe61zzTXX4O3tTXl5Oeeeey7e3t6EhIRw9913Y7PZ+j8RwJ133klQUBBHdvLceuutqFQqXn755Z5l1dXVqFSqnn0ym8088sgjjB49Gj8/P7y8vJgyZQorV67s8xr19fVceeWV+Pr64u/vz9VXX82uXbtQqVR9uu0PHDjAhRdeSGBgIHq9njFjxvDNN98cdR8EAoFAGEeB4E/ARRddREdHB0899RTXX389AE8++SRXXXUVycnJvPDCC9x+++2sWLGCqVOn0tTUBHSZllmzZrFx40ZuvfVWXn31VW644QaKiop61nFUq5v6+npOP/100tLSeOmll5gxYwazZ8/m0KFDbNmypde6xcXFbNy4kUsuuaRn2euvv05sbCwPPPAAzz//PNHR0dx88828+uqrPeu89NJLREVFkZKSwkcffcRHH33Egw8+2O/xef/997n44ovRaDQ8/fTTXH/99SxatIjJkyf3ab/NZmPWrFkEBQXx3HPPMW3aNJ5//nn++9//HvUcTJkyhYaGBvbt29ezbM2aNajVatasWdNrGcDUqVMBaGlp4e2332b69On885//5NFHH6W2tpZZs2axc+fOnu0kSeLss8/mf//7H1dffTVPPvkklZWVXH311X3asm/fPiZMmMD+/fu57777eP755/Hy8uLcc8/lyy+/POp+CASCPzmyQCA4ZZk3b54MyJdeemmv5YcPH5Y1Go385JNP9lq+Z88eWavV9izfsWOHDMgLFy7s9zUc1ZJlWZ42bZoMyG+88UavdZubm2V3d3f5rrvu6rX82WeflVUqlVxcXNyzrKOjo08bZs2aJSckJPRaNmzYMHnatGl91l25cqUMyCtXrpRlWZbNZrMcGhoqDx8+XO7s7OxZb/HixTIgP/LIIz3Lrr76ahmQH3/88V6a6enp8ujRo/u81pHU1NTIgPzaa6/JsizLTU1Nslqtli+66CI5LCysZ72//e1vcmBgoCxJkizLsmy1WmWTydRLq7GxUQ4LC5OvvfbanmVffPGFDMgvvfRSzzKbzSZnZmbKgPzee+/1LM/KypJHjBghG43GnmWSJMkTJ06Uk5OTj7ofAoHgz42oOAoEfwJuvPHGXn8vWrQISZK4+OKLqaur63mEh4eTnJzc0w3q5+cHwA8//NDvWERHtbpxd3dnzpw5vZb5+vpy+umn89lnn/Xqyv3000+ZMGECMTExPcs8PDx6/t/c3ExdXR3Tpk2jqKioT/e5I2zdupWamhpuvvlm9Hp9z/IzzzyTlJQUvvvuuz7b/PZ4TpkyhaKioqO+TkhICCkpKfz8888ArFu3Do1Gwz333EN1dTX5+flAV8Vx8uTJqFQqADQaDW5ubkBXVbGhoQGr1cqYMWPYvn17j/7SpUvR6XQ9FWXoGs95yy239GpHQ0MDP/30ExdffDGtra0956u+vp5Zs2aRn5/fp4teIBAIutEe7wYIBILfn/j4+F5/5+fnI8syycnJdtfX6XQ9291555288MILfPLJJ0yZMoW//OUvXHHFFT2m0lGtbiIjI3uM0JHMnj2br776ig0bNjBx4kQKCwvZtm0bL730Uq/11q1bx7x589iwYUMfM9vc3NzTLkcpLi4GYPDgwX2eS0lJYe3atb2W6fV6QkJCei0LCAigsbFxwNeaMmUKS5YsAboM4pgxYxgzZgyBgYGsWbOGsLAwdu3axWWXXdZruw8++IDnn3+eAwcOYLFYepYfeV6Li4sxGAx4enr22jYpKanX3wUFBciyzMMPP8zDDz9st501NTVERkYOuD8CgeDPhzCOAsGfgCOrdNBVuVKpVHz//fdoNJo+63t7e/f8//nnn+eaa67h66+/ZtmyZfztb3/j6aefZuPGjURFRTmlZa8t3Zx99tl4enry2WefMXHiRD777DPUajUXXXRRzzqFhYVkZWWRkpLCCy+8QHR0NG5ubixZsoQXX3wRSZKcOi5KsLePjjJ58mTeeustioqKWLNmDVOmTEGlUjF58mTWrFlDREQEkiQxZcqUnm0+/vhjrrnmGs4991zuueceQkNDe8ZiFhYWOt2G7mN09913M2vWLLvr/NZsCgQCQTfCOAoEf0ISExORZZn4+HgGDRo04PojRoxgxIgRPPTQQ6xfv55Jkybxxhtv8MQTTzit1R9eXl6cddZZLFy4kBdeeIFPP/2UKVOmEBER0bPOt99+i8lk4ptvvunVfW3vDuPurt6BiI2NBSAvL4/MzMxez+Xl5fU8fyzoNoTLly9ny5Yt3HfffUDXjTCvv/46EREReHl5MXr06J5tPv/8cxISEli0aFGvfZo3b16f/Vi5ciUdHR29qo4FBQW91uu+e12n05GdnX3M9k0gEPw5EGMcBYI/Ieeffz4ajYbHHnus15hCAFmWqa+vB7ru6LVarb2eHzFiBGq1GpPJ5JSWI8yePZuKigrefvttdu3axezZs3s9313tO/J1mpubee+99/poeXl59bkj2h5jxowhNDSUN954o2efAL7//nv279/PmWee6XD7ByI+Pp7IyEhefPFFLBYLkyZNAroMZWFhIZ9//jkTJkxAq/31mt7ePm/atIkNGzb00p41axYWi4W33nqrZ5kkSb3uNgcIDQ1l+vTpvPnmm1RWVvZpY21tres7KhAITllExVEg+BOSmJjIE088wf3338/hw4c599xz8fHx4dChQ3z55ZfccMMN3H333fz000/MnTuXiy66iEGDBmG1Wvnoo4/QaDRccMEFTmk5whlnnIGPjw933313r9foJicnBzc3N84++2z+7//+j7a2Nt566y1CQ0P7mKDRo0fz+uuv88QTT5CUlERoaGifiiJ0Vd7++c9/MmfOHKZNm8all15KdXU1//73v4mLi+OOO+5QeJTtM2XKFBYsWMCIESMICAgAYNSoUXh5eXHw4ME+4xvPOussFi1axHnnnceZZ57JoUOHeOONNxg6dChtbW0965177rmMGzeOu+66i4KCAlJSUvjmm29oaGgAeldgX331VSZPnsyIESO4/vrrSUhIoLq6mg0bNlBWVsauXbuO6T4LBIJTiONzM7dAIPgj6J6Op7a21u7zX3zxhTx58mTZy8tL9vLyklNSUuRbbrlFzsvLk2VZlouKiuRrr71WTkxMlPV6vRwYGCjPmDFD/vHHH53WkuWu6XiGDRt21DZffvnlMiBnZ2fbff6bb76RR44cKev1ejkuLk7+5z//Kb/77rsyIB86dKhnvaqqKvnMM8+UfXx8ZKBnap7fTsfTzaeffiqnp6fL7u7ucmBgoHz55ZfLZWVlvda5+uqrZS8vrz5t6j7OjvDqq6/KgHzTTTf1Wp6dnS0D8ooVK3otlyRJfuqpp+TY2FjZ3d1dTk9PlxcvXixfffXVcmxsbK91a2tr5csuu0z28fGR/fz85GuuuUZet26dDMgLFizotW5hYaF81VVXyeHh4bJOp5MjIyPls846S/78888d2g+BQPDnRGRVCwQCwSnMV199xXnnncfatWt7usYFAoFAKcI4CgQCwSlCZ2dnr7vWbTYbOTk5bN26laqqqn7vaBcIBAJHEWMcBQKB4BTh1ltvpbOzk4yMDEwmE4sWLWL9+vU89dRTwjQKBIJjgqg4CgQCwSnC/Pnzef755ykoKMBoNJKUlMRNN93E3Llzj3fTBALBKYKYjkcgEAhOES677DK2bdtGc3MzJpOJffv2CdMoEPxJePTRR1GpVL0eKSkpPc8bjUZuueUWgoKC8Pb25oILLqC6utrp1xHGUSAQCAQCgeAUYNiwYVRWVvY8joxMveOOO/j2229ZuHAhq1evpqKigvPPP9/p1xBjHAUCgUAgEAhOAbRaLeHh4X2WNzc388477zB//vye+Wzfe+89hgwZwsaNG5kwYYLjr3HMWnuKIUkSFRUV+Pj4OBxdJhAIBAKB4PgiyzKtra1ERESgVv/xHatGoxGz2XzM9GRZ7uND3N3dcXd377Nufn4+ERER6PV6MjIyePrpp4mJiWHbtm1YLJZeMaMpKSnExMSwYcMGYRyPBRUVFURHRx/vZggEAoFAIFBAaWkpUVFRf+hrGo1GYmIDqK0xHjNNb2/vXilR0JVV/+ijj/ZaNn78eN5//30GDx5MZWUljz32GFOmTGHv3r1UVVXh5uaGv79/r23CwsKoqqpyqj3COPaDj48PANd+9yRuXvrj3BqBQCA4efDTbe6zLNE74ji0ROB2+DtGDRmMWq2856zgYCsAicneinvgaqo7KT7cTvroQLRaZVXA1hYLu3c2MGZcMO56Tb/rtbVamJD+fc/v+B+J2WymtsbIxh2n4+2jc1mve19KS0vx9fXtWW6v2nj66af3/H/kyJGMHz+e2NhYPvvss2M6HZcwjv3Q/eFw89Lj7i3mPxMIBAJHMTKt199+ug2UUdfzd7JP5B/dpD8tg1O98PNwc0kjfXSgy+3w8dGRmOQ78IoDaEREejq8/vEcZubto8PnGBjHbnx9fXsZR0fw9/dn0KBBFBQUMHPmTMxmM01NTb2qjtXV1XbHRB4NcVe1QCAQCH5Xmi0ZPQ+A/NZy8lvLj3OrBIJTm7a2NgoLCzEYDIwePRqdTseKFSt6ns/Ly6OkpISMjAyndEXFUSAQCAR/GN3mESC/dQMgKpACwbHg7rvv5uyzzyY2NpaKigrmzZuHRqPh0ksvxc/Pj+uuu44777yTwMBAfH19ufXWW8nIyHDqxhgQxnFAag+WETUq2SUNc7tRjJP8HZAlCdVxuGNOIBAcG36tQG7oWSZMpECgjLKyMi699FLq6+sJCQlh8uTJbNy4kZCQEABefPFF1Go1F1xwASaTiVmzZvHaa685/TriV3cAtn+43KXtZVlm45uLXW5H4aqdLmu0VjW4rCFZbS5rHAtkSWLf1+td1mmpqHe9LSK1UyBwCdGNLRC4zoIFC6ioqMBkMlFWVsaCBQtITEzseV6v1/Pqq6/S0NBAe3s7ixYtcnp8IwjjOCBNpTUubX/o5z0UrtrlkkZnUxtrXvrSJQ2A1c8tdNnk7PliDTYXzWNTaQ3Glg6XNHIXb6J0c55LGh2Nrax//RuXNAD2fbXOZY2WylPLwDYWOx9jJRAIAykQnPgI4zgAzZUNyJKkaFtZltn01ne0Vje6VKnb/vGPNJfVYjVZFGuUbT1I4apdmNuVzy1lbjey6e0ldDa0KtYAWPefr13SMLcbWf/q15jaOl1qx/pXv6GtutEljbJtB9nzxRqXNKxmC6ue/dQlDYC9X7puYNvrml16nwHUFVSw6a0lLrdl/3ebXNawdJpc1jhWWIxmrGbXju2fBWEgBYITF2EcB0AyW9lRWERuQ7XTj5+/X0vNgVJkm8SOgsKe5c7Q0dDKrk9XA9BWo8zkyLLM+te++UWjSZEGdBnYzsY2OhpaFGtU7Cqk4KcddDa1DbxyP2x5/wc66lswtSqvWlbtPcy+r9dj6VBuLKxmCz89/T+sZqtiDYDNb39PfWGlSxp1BRUuD4mQbBI/PPIBam3/86MNhLGlg8V3v4nOo+8cY86w+/OfKVixwyUNc7uR1c9/7pIGQG1+mctDNOqLKvnhoffQ6FwbVt5a3ejyBZNktVHw0w6Xe0L+CISBFAhOPMTNMQPgGRqErtybwIDBTm0nyzK7Pl+IWqtBstrQVfoQGDyYBnWeU+bx4H+X9VRN9uYXEejlWHfk0MCwnv8fXreXyt1FQJdxDEowOLEnXXQ0tLL9k67b+DvqlVULZVlm7b8XASg2js3ldeyY/xMAplZlP6CyJHVV+GQZswvGcfuHP9J4uBq/6BDFGrV5pWz9YBneof6KNWwWKz888r7i7bvZ+v4PVO05hFqj7HpSskksfehdmstqGXzaGMXtKPp5N6ue/ZS0S2Yo1rB0mvj69tfwCQ9QrAFQvCGXrR8u54LXb1OskfvtBlY+s4Ax18xSPK+c1Wxhx/yfKNuSx7mv3KpIo7Opjb1frmX352vQuuu4bP4DinQkm0TN/hJKtxzAPzqU5OxRTm1vs1hprW6ks6G160K0sZWOhlbChsQQmzHU7jbd5tFPt6HHPB7Lm2gkm6T4fS8Q/NkQxnEAJj92D55Bzv/4yJLMlMfv4auLb2DMbXPQ6LomAg2UHDegsiQRMaiR6sD9+MVFo6v3cXj73Iau8X+yLJO3chs6P09snWbyD5fQPsj5yVwPvPp9j4HNLymlY0hwL3PqCAUrdlC5+xCg3Dhq9W5MveMCtn24nIj0JEUatQfL8TEEUnuwDMmmrJLUWFLD1g+XAWBTWHG0WW0sf/xjZJuELCkfn7jxv99Rd7AM7zDlJql8RwEb31yMZ6DyCXo3vPEtxetzAfAMUqZTte8w3z/wLrIk4xelzJBbjGa+ueN1KnYUMOX2CxRpQJfhW/HEJ4y+aqai7c0dRlb989OeLvfBp49VpHN43T5WP7+QppIa/vLSzYrMZ+7ijfz01Pyu96pKxUVv3YlO79yk0PVFlax/5WvKtudjbuskYdpIRl+p7Nj8/PxCDq3Z2/P3uL+eTvS4lAG36zaQ3qzlies+xlRrwctXj5e/B15+ekZOjWfMLOcu8gG+fHktO1YUEDMkjNihocQMDSNmSChevs7PhvHly2tprm1naEYsQzJi8QlQFiAhyzKfP/8zgQZfUqcnEBzpp0hn9bImGssPkp1jICFRWfLLnl2NrFheSVaOgeEj/BVplJd18OF7hWTnGBg1JgiNxnmNlhYLL7+wn+mZ4YybEIybmzD7xwNhHAfAKyQQN29vp7dTa9R01rYgWa1ETR6Hu4/zGiq1msgJo9j0r9dIu/5yfKMdj+w60mBOvCmFb9bNZdzf/g/viDD8pRin25J6fiDteQ34x8fgo44iUBrcY04dpaK+Dt/BEVjbjBRXVqE+SuW1P1PqFeRLU0kNkaOTyXrwMqdev5vQlGgMIxMwtnQw5bbzFGl4BvqQ/dDlrHv1a6JGDVKk0VrVQMLUETSV1uCucLomq8mCh583WncdHv7Ov8e6aa9tQufhjleIsh8nWZIYcuZ48r7fgkqtwkuhcZRtEoFxYTQW1+CvsJLbXteM1l2HWqshNEVZ3rzFaKZ8ewGyLBMzfogiDY1OS9jQWPYv2YxhRBz+CoywLMuo1CqaSmoITYkmbtIwRW1JnDaS1c8txGa2kn7pDCLSEgfe6DcExofTWFyNua0TQ2oipz95raJhDWte/ILD6/YBXclcsx6/hoRpI53SWLfQRnluG23VTV1tM/hw8T3TGTXT8c/ijhUF/DR/Bw1VrTRUttDWZKQ4t4Y1X0D88HCyrxrFpHOHozlKPF7Bzgp+/GgbZqMVi9GK2WilqaaNisJ6VnyyA5UKYoaGkXPVaCZfMMJu7F9lUQNL3tqEJMnIkowsd/0rSTKlB2opO1gLQPTgEFKnJzI6J5mk9N7V1pb6DhY+3zWkiV+uQbtvllO3NLBy6SGeemwPcfFeZOUYyJppYNyEYHS6X/fNYpF45P6ddvfTZpP57H+HefFf+wkL15M100B2joFJU0LRe/R+Dzz+8C46O+1fkH+9qJQ3XjlIQKAbM7LCyc4xMGV6GL6+vRNWXnoul+oq++Pxly+t4K3X8/Hx0TJ1RhjZOQZmZIUTEOja8BiB4wjjOAAqlfIrGq/wEC746j00bsrjnjRubpy/6F3UGrVLcxae9cG/QZZRa5Wdcq+wELKefxTZZkPj5nz1FCAwazDDZlyIzWLF2mlEL/VvLo5mSoOunoxksrK/scbpqmc3oy7PYsT5kxWPxXP39mBQzhjiJg93unLTjX9UCBP+7yxGXZFNZ6PCCqy7jlFXZDH8/EmKNQAG5YwhfsoIxRoqtZrAuHCu+OwhZJukeAiAYWQCF719F61Vjei8lJ0b/6gQznr+RuoLyvGLDFakodO7kfXQ5Qw7ZyKhQ52/0IIu4zjy4mlEjh5EfYGysXkqlYrYjKHM/uBeOupaFHd1u/t4ct4rt7Ll3aVk3PwXxW2Z+ehVrHz6f/zlhRvRKnzfJ0xLZdi5k/j5+YVkPnAZAbHOf4YDYsMY9peJbPvoRyZfO4QrbsnG3cO5eDdPH3fiRxoYnTMIU6eFBc+sZPwZKWRfNZrEVINDx1qrVePt54EuTIubXoubXkdZfi0VhfWExQYw/qwhTDgzhajBIf3qqVSg0arRqlWo1CrUqq5/VWoVtWXNAOi93YhICiI6JQRDYpBdHfURvw89L6VSodb++rp6vQa9XoOHh8Zuxa+/XT5yuUajQqNVodaowN76qv51jtRTd++vnZ81lUrlgMYvx+o4xgr+WVHJJ9IcHicQLS0t+Pn5cf4Xb6PzcjwbU/DH0aB2rOKp1FwKBKcalk6TyzcudTa1uVTdPlZtaSyuRufpTmTEfpfHO1YW1ePpq8cv2MslHYCdKwvxD/EidliYy6Zm6btbiEgKYsj4GHTuyi76a1Z+Rlu1D1kzDUTHKNu/PbsbWfljFdmzDAwZ6qdovyrKO/j4/SKycgykjQpU3FX9yksHmDYjrE/F9EhaWy0MT/qG5uZmp/OdXaXbO+wt+Msxyao+nvvSH8I49oMwjqcGjphLYSwFgpMXP52ILTwag9U/EOaRcLyb8YcijOPvi+iqFpzSONKdPtBYTWEsBYITl2ZLRo95FAgEvz/COAr+9AxkLoWxFAgEAoGgC2EcBYIBOJqxHGheTmEqBYI/hvzWctFdLRD8AQjjKBC4gCvVSmEqBYJjg+iuFgj+OIRxFAh+R45mLIWpFAgEAsHJhjCOA9BeU49/vGt3VVuNRrR6ZRM8d9NSWo5ao8U7QrmhsFmsGBub8ApVNrddN1aTGa278rkp4ZeJjU+A+bckm82l+S1dQZhKgUAgEJxsiLyeAcidv8il7WVJYs8HC13SkKxWNj77GrIkuaSz96OFdNTUuaRRs3s/lVt2uqRhbmunYuN2lzQAGouKXdpelmV2vPnRwLPVDkDt3gN01je6pGE1melsaOr5O1AabPcBkNtQ3e/DZlUWoXiskWwSVpPleDdD8Cei2ZLRk2MtEAh+P4RxHABTq/I0DoCDX/9A3T7novl+y775X9KYf8ilqlj1jr0cWLgYjbvyCXeNjc1seOY/6DyUV09lSWLjs69h6ehUrAGQ/+1yqrbtdklj/6dfU7VtN2qN87Fp3TTkH2LD0/9BH6Asqg/AajSx9tHnUNmJI/st/RnKQGkw5T/sYNPGHXYNpaO01TRRtfeQ4n3p1vjhofdQaVz7eilctYv2+haXNKxmCx2NrS5pSFYbhat20l7X7JKOLMtYjWaXNAQCgeB4I7qqB8CV+dFbyyrZ894C/OKVRZYB1O3PZ/+CrwBQ65SdLlNzCxv/9RrIMlq9MuMo2SQ2PvsqxoYmdN7Ku+73zf+Sys07SP5LjmKNqu172PH6B4y/52bFGod+/Jk973+GYWyqYo3m4jJWP/g03oYwxXGQVqORNfOeo72qFr2/wpxoWWb/p99wcMEPnLvwLTRS7/fJ0e78PrLbu3LPIb67579c8uHfFbUDoGj1bpY//hEpZ4xHoyDHGKC1upHV//oMS6eJxOnKzo9kkziwZBN7Fq3lvFdvVaTR2dTG3q/WsefzNUSNHUTi9DSnNWRJompfMYUrd1K9v4TT/nGNoqg+q9FMXUE5NQdKiRoziMC4cKe272xqo+ZAKZ0NLXQ0tNJR30Lo0FgGzRztlE5TWS2b31qCWqtBrdOg0WnRaDWknDmB4KQIh9uy+/Of8QkLwCc8EB9DID5hAWgUfL8Zm9vZ/M73BA+KImxIDD7Jyntlqosb2f5jPkMnxhI9ONRurrQjyLLM+q/3kZgaQXh8oOL2AOxZc4iwuABCo/0Va+zf00G9V5PixBeAosJWGhvMihNfAKoqOyksaD1q4stAtLRY2Lq5jomTQ9HrlV/wC1xDGMeBUNg9LMsyRctWYTNbFH9YZVmmfMNWVGo1smRTbBxLft6EZO7qNlRqHKt37KGltAIANy9lsVWt5ZUUr1wHoNgkWTo62fvxF8iShGeIsi9lU3MLxT91tcM7wrkf4G4km42DX36PuaUNv4wxijQAipaupGZXLtFTJyjWqNm5j30ff05o2nC7P779jaU80lB2Vjay+Y530If54x3qr6gdTWW1rHruM4zN7Qz9i7L9sXSaWPrgu1TsLOTcV+Yq0pBlmVX/+pQ9n68h68HLcPN0vkIuyzLbPvqRbR8swyvEj2l3XqioLfkrdvD9/e+gcddx0Vt34hXs/Pu+8XA18694GqvRTMaNZzttGgF0Hu78+I+PaavuGlIx7q+nk5yV7rRO+fYCSjYd6Km++kWHkHn/pQ6bRoDq3GL2f7eJ5tLanmWhQ2KY8fdLCB8e55BG1d7DVO09REd9C/k/bmfH/J8A0Ok1RKeHcu2DpxE79OhjgSsK6ynaXYm504Lpl8fiNzYy/8mf8A7wYMj4GIZmxDI6J5mAMB+7GnXlzRTurECyyUiS1PWvTWL917m8cediQmP9SZ2WwMhpiQyZEGM3T7u1oYMDm0qQZZABfilWyDLsXl3Emi/2EDkomPQZSaRlJpKUHolG29t4GdvN7Pm5yG4bpdJWXnlmBZFRnmTODCc7x8CEiSF9jJfNJrNsaYVdjfY2K/fcvpWAAHcyZ4aTlWNgyrRQvL377s+PP1RgsfYtuNisMvfesQ2NRsW0GWFk5RiYkRWOf0DfC6m1q6tpbbP2WS7L8NhDu2huMjNlWihZOQYyZxoIDXXtHgKBcwjjOACDzz9T0XYqlYoRV88mLG24YuOoUqkYOecSQkcOxWY2o3FTFl+UfPZM/OOjMTa1oPX0UKRhGJPKlMfuprW8CjdfZTm1PpEGpj5+D435h/AIClCkofP0YOIDf6N2zwG8wkMVabj7+ZLx91uoy83nl69qp1FrNKT935VEjE93qSqddNZMvMJDkV0YmxiaNozJ8+7CajQ5tV0vQxkGE+/3wdTc2qc66ejNOP5RIcycdyXF6/YRkhzlVFu60Xm4M+WOC8j7fjMx44co0lCpVKRfkoksyQw9O0O5xqUzqMsvJ/Xiabj7KKuyx00cRuzEoaScNpawobGKNPyigokeOwjDyATGzjlNkYbWXUf02MEU/LSDnMeuJmlGmiKduvxyAhMMdDa1MeaaHMbOOQ2tu3PfSxU7C9H+coETMjiaCTecSfzUEU59T5Zvz6dw1U48g3zReXZdDAclRTDigimMOxtiHbiJsGB7OUve2Yy7XoebhxY3vQ7NEcMrfAI8iEwOOmp+demBWhY+9zNqjQq1Ro1arUKtUdHS0DUUp6a4iS1LD2Kzyui9dKSM69v7VFPaxP+eWQl0ve9QgYquY2Hq7LrgLz9YR9WhBg7vq2LqhSOZcPaQXserramzR+O3aKSOLo2yDj56r4itm+o5+9worrshGb3Hr+bRapV48tH+h//IMtTXm1i4oJhNG+o44+xIbpo7uI/xe/7ZXFpb7I9vNpttWCwy335dxuZNdWzdUs+tt6cQbuj9u/Tf1/MpLLA/xKSx0YTJKLFsaSVbNtezZVM9c28fTHyCfXMvOPaIrOp+EFnVAkHfrG9HTOSxuGP+RNEwtXXi7q3sYqtHo7VDsfHsxtjSgd7XNY3Gw9XIskRgvMElneINuXiHBRCUoFxn18LVeIf4kzBtpMvnaPM73xM9djDhI+JRqVT46TYomgi8ramTBc+sZNzpKQydGItWp7wr9P1HluHh7cbonEEkjDQo7vb+4sU11JY1kZ6VzMgp8Xj4ON9jVLHsU5Z82kFWjoGsmeFERTvfY7RndyOPPbSLrJkGsnIMJA/ycfq8VVZ0cPP1m5g6PYzsHAPDR/o7rdHaauHaK9YzekwgWbMMjBodZLfrXGRV/74I49gPwjgKBL1RYiIFgj8apcbxRORYXPwksxSDV6JLGpIkKza/x0NDGMffF9FVLRAIHOK3YyWPnGtSmEiB4NhzLOa6ddWsnWoaAtcRxlEgECjiSCP52wnLhZEUCASCUxMxj6NAIHCZ/iYpFwj+aMRE4ALB74uoOAoEgmNKf5VIUYUUCASCkx9hHAUCwe+GMJECgUBwaiGMo0Ag+EPoNpG/TbIRJlIgEAhOHk6aMY6vvvoqcXFx6PV6xo8fz+bNm/td12Kx8Pjjj5OYmIheryc1NZWlS5cqet32unqXJngGkKxWbGbXMmplSepJbnEFc1u7yxqSTXmsl0DQ33hIMSZSIBAITnxOCuP46aefcueddzJv3jy2b99Oamoqs2bNoqamxu76Dz30EG+++Sb/+c9/yM3N5cYbb+S8885jx44dTr/29tc+cGlKBFmW2fbqe4BrGjv++zGtZZWKNQAqNu2gZPVGlzQ6ausp+n6FSxqSzUbZ2v6Nv6M0FZW4rFGzez+t5a4dV3N7h8sacGxM/bGYllWyWrEajS7rOIIwkQKBQHBycVIYxxdeeIHrr7+eOXPmMHToUN544w08PT1599137a7/0Ucf8cADD3DGGWeQkJDATTfdxBlnnMHzzz/v9GtbWl37Md//6ddUbtmlOC4Q4MDCb8n/aim+0Y7nwf6W6l37WPfES/jGKNfoqK1n5b1PoFOYVQ1gM1vY8PR/aK10zRjkf/MDRUvtR2w5gizLFCxezqZ/vYa3QXlXaUN+ET/e/ghu3sqPibmtnU3PveHShYEsyxSvXEfFxm3KNSSJklXrWfePF1FrlY9iaa+uZfd7CzA2Nju13ZEmUpZk1q7YyLZ9BxS3w2axUrolj01vL8HU1qlYx2q2ULX3MB2N9iPQBkKySbRWN1Kxq5CDy7bSXFanuC3Qda6drfrLskx1bjF1BeW0VNRjbG7HpiDmsrGkBmNzu8sXKLIkUbwxl7baJpe1zB1GKnYWYrP0zTZ2lurDjT0xf67QVNOGzep6z0xzrevHuqnBiiS5ptHcZMbq4v60tlowmZRHqwJ0dljpaHf9PAtc44Qf42g2m9m2bRv3339/zzK1Wk12djYbNmywu43JZEKv7x167uHhwdq1a/t9HZPJhMn0a9ZvS0tL13YKM5UBSlatZ8/7nxEyIkWxRumaTez5YCEqjQav8BBFGo2Fh1n3jxeRLBb846IVaRgbm1l1/1O0VVYTODhBkYbNbGbt4y9StXUXg88/Q5GGLMvsfncBBxZ+y+R5dynW2PPBZ+xf8DUJp2eiUiu7fir9eSMb//Ua4aNTcfdTNqN/Q34Rax97AXc/XwIHK0t36KxvZOOzr9JSUsFZH7ykSMPY1Mzax1+kPvcgkx65Q5FxtJkt7HjjQ4qWrmT4VReiD/BzWkOWZQq+XcaBz78jMDmelEdO76k+OjMWcueClax/7RusJgvn/PsWRbGBJZsPsO7lL6krqCB19nSm3H6+0xqVu4tYdPPLWI1mUKmYNPcckmeOdkqjva6Zr//2Kq3VjVhNFgITwpn12NVORQeqVCoKV+5ky3s/9CzTebiTcdPZpM6ejlrj2GegeP0+Vj+3EDcvPb4RQfgagjCkJpB26Qy0Dl4cm9o6aatuZPfCnylavRuPQB9CB0cTkhJN1OhkYsYPGbCXx2o0Y2ztwNJuwtTeyfLHP6K1uhHDiHgiRyUTNSoZS3oxQ0P6zwe3mKyYOi3YLBI2qw2rRWLvusN88o8fGTQ2mhFT4xk5JZ6owSH9tsdqsWExWpFkGeSuZBNkmbytZbz34FJGTk0gdUYiI6cl4BNg/z1os0qYjUeY1SM83s+f72bVZ7tIn5FEelYSg8dGo3XrG4UoSTLmfgxv7q52Lj9tCZnZ4WTnGJg8NRRPr76fb1mW6eiwb+wqKzq57MI1TJ4WSnaOgWmZYfj5udldt70fY9fWauEvp60kfXQg2TkGMrPDCQ7R2123s8OKPa9rMUucc/pPxCV4k51jIGumgYhIkez2R3PCG8e6ujpsNhthYb1/NMLCwjhwwH41YtasWbzwwgtMnTqVxMREVqxYwaJFi7DZ+r/aefrpp3nsscf6LA9MSVLc9uipE2guLkPjZv8D5pDGlPE0F5fRUVuvuAoUkBjHiKsupi73IG4+3oo09AF+jLj6Yg6vWKu4Qqdxc2PIxX8BSSIgWZn5VKlUxGZNprHgEKEjhyjWSDwji6bCYqImjVWkARA+JhXD2DRip09UrBGQGEdo6lBChg/8Y9kfHkEB+CfGYRibpvi9pvf3wzAmFY2bjsiMMYo0NG46/BNjCRiUwOALzlKkoVKp0AcGoPP0YOzt1+Mme/f8kHbfle2IgQyIC0ej0zLl9guInaDsfeIV7EdzRX2PaVRyfnwMgag1arR6N0574hoSp6c5reHu60lbTRPGlg5GX5lNxk1no9E5/12Q98PWnv8PPm0sk249F58w5y6MDy7rqmib240YW9oZfv5khv1losOmEWDFk5+Qv3w7WveubTobWmmvayYxPBXDyASHjvP6175hx/yfAFCpVahUKiSbRNnWgzSV1mI1WQgN08NRrrWXf7SN/z1lv9di37rD7Ft3mKXvbGHmVaM5/bqx6Nz7HvMt3+fx2u3f9PsaG77NZcO3uejctZx14wTO+r/xuOl7H6v87WU8ecn8o+7vD+9v5Yf3t+Id4MFFd09j+sUje5n92tIm7p7x5lE1Pp1/mE/nH8Zdr+b/bh7EzbcOxsPz130ymSSGJnx9VI2vF5Xy9aJSNBoVl14Zzz33DcM/oPd3TsaoJTQ39V+1/WFJBT8sqUClgrP+EsVDj40k3NDbVJ9z+kryDrT0q3H4UDurVlTz0N93Mj0rjEefSCU+weeobRccO074rOqKigoiIyNZv349GRkZPcvvvfdeVq9ezaZNm/psU1tby/XXX8+3337bZRISE8nOzubdd9+ls9N+l5W9imN0dDTnff6WS92QcGzyRiWb5HBVQGg4roEsudQtK0sSkk1S9EPeoyHLSBarS8MZujQsLl2kAJiaWxRXT4+lhrGpBb2/fY3uzOyBDGTNgRJCU2JcakfV3sOEDYt16fOb/+N2/KJCCE1RVu0HyF+xA72vJ9FjBw+8cj8Ym9v56tZXmHLHBUSmK7sgbqmoZ9HNLzP6qpkMOWu8U4axpx0tHWjdtOz+/Gdq8koZeeFUhw1jj8YvXe1uXnq07jq+uf01vEP9STl9HBFpiajU6gEzq9tbjHS0GNHqNGi0GjQ6Nbnri5n/1E+MPW0wY08bTGJaxFFj7jrbTLTUd6BSqVCpuy58VCoVBTsrePf+70mdnkh6dhKp0xLw9LVfXTMbLTRWtfVe+MtLrvtqH8s/2Nqlk5XEiCnxdnWsZht15faHhphzl/H4PSVMntJVLcycaehj1KCrall8uM2OAtRUG7ly9lpGjQnqqvTlhPdr1IoPt9ntGu/osDH73NUkDfIlO8dAdo6BwUN87Z73stJ2LJa+XeM2m8wVF68lINCtR2NEakCfcySyqn9fTnjjaDab8fT05PPPP+fcc8/tWX711VfT1NTE11/3f4VkNBqpr68nIiKC++67j8WLF7Nv3z6HXrf75J//xdvovEQpXCA4keg2jyCm83EUq9mCWqNx6YLL3G5E465Do+3bXeosNovVpQuubmRZxmax9jGxAxlHe7Q1deLlp3f5Qr+hqhXfQE+73crOUH24keAoPzRa5efMt+JbBkcm9aouOt2O6k70ek2/3dOO0FBvQpLkfrunHaGtzUJLs2XA7mlhHH9fTvibY9zc3Bg9ejQrVvx6J68kSaxYsaJXBdIeer2eyMhIrFYrX3zxBeecc87v3VyBQPAHIOINnUfrpnO5Su/mpT8mphE4JqYRuqp8Siqf9vD293DZNAIEhvu4bBoBwuICXDKNAIYoN5dMI0BYmIdLphEgMMjdJdMI4O2tE2MaTwBOeOMIcOedd/LWW2/xwQcfsH//fm666Sba29uZM2cOAFdddVWvm2c2bdrEokWLKCoqYs2aNZx22mlIksS99957vHZBIBD8DvzWQAoEAoHg9+WkMI6zZ8/mueee45FHHiEtLY2dO3eydOnSnhtmSkpKqKz8dSoTo9HIQw89xNChQznvvPOIjIxk7dq1+Pv7H6c9EAgEvyfdBlJUHwUCgaCLZ555BpVKxe23396zbPr06T1jcbsfN954o1O6J/xd1d3MnTuXuXPn2n1u1apVvf6eNm0aubm5f0CrBALBiUSgNLhXpKEY/ygQCP6MbNmyhTfffJORI0f2ee7666/n8ccf7/nb09O57v+TouIoEAgEjiK6rwUCwZ+ZtrY2Lr/8ct566y0CAvpOueXp6Ul4eHjPw9mbboRxFAgEpyTCPAoEglOFlpaWXo8jpw/8Lbfccgtnnnkm2dnZdp//5JNPCA4OZvjw4dx///10dHQ41ZaTpqtaIBAInOVX8+j45OECgUDgKuvbE/FQu7us09neZRCjo3vPAztv3jweffTRPusvWLCA7du3s2XLFrt6l112GbGxsURERLB7927+/ve/k5eXx6JFixxukzCOA9BaWY3O0wOfiHDFGlaTGWN9I94Ryn+0JJtEXW4eoSOUpWB001ZZ7VI2M4CloxOdp/MRbkfSPX2oq1NfWE1mtO6uTRMhOPU5cuyjMI8CgeBko7S0tFeXsrt7X1NaWlrKbbfdxvLly/vELndzww039Px/xIgRGAwGsrKyKCwsJDHRsdhb0VU9AGvnPYdHoPK8anN7Bz8/9E9kWXlAvNVkZsNT/6atQnmXmyzL5H35PYd/XKNYA6B2Xx57P/7CJQ1jUzN7PvjMJQ1zWzubX/wvxvpGxRqyLHPox5+pyz3oUltayypd1rBZrDQdLnVJQ7JJVGzagaXT6JKOqaUVc6v9BAln2+MqVqMJyWo/+9ZZxJ3XxwbJ2n90q7M6tmOo9VuaLRnkt5Y7pXOC52EI/sT4+vr2etgzjtu2baOmpoZRo0ah1WrRarWsXr2al19+Ga1Wazd2efz48QAUFBQ43BZRcRwAdz8ftHpl5WZjUzOrH/wnpqZmvBVWLE3NLax59Hnq9+eTfuNVijRsZgvbXnmPQ8tWkf3vxwfewA6yLJP/9VJ2vjWfiQ/8TZEGQOXWXWx+/g2GzP6L4mpjxeYdbP332/gnxCqu4jYdLmX7q+9ham7ltDf+qUjD2NTMvk8WUbZ2M6e//bwiDavJzKEfVpK3aAmTH71bkYa5tY3CpSsp+HY5MdMyiBif7rSGzWKlfP0Win9ahyzZmPzoPU5ryLJM3b48KrfuovlQKWP+dh0eQc5fdJVv2ErVtj3UH8gnLG04I6+71GmN+rxCavceoLWsAo27OyOvmd3zOXa0+lixs5DcxRsxNrXR0dhGRGoCE248y6nJplsq6tn75VokSUaWJNy9PUi7dAZuno5PhNzZ1MbSB99FkmTcvfS4eXvg4e9F6iUz8A0PdEhDstpY8eR8bBYrPoZAfMMD8TEEEpQY4XRe9aa3l1C4ahfByZEEJ0USnBxJyKAoPIPsx8f1i0rFFze8iEarxpCaiGFkAoaRCeh9nZ/guXLPIVb/6zOixw0melwKkelJ6Dyc/97uaDHx75sWkTwqkpHTEkhKj1Q0AXdFYT3/e+onUqcnkpaZSHCkn9MaAMs/3EbZwTrSs5IYmhHTJ+faEdb+1MyaJVvIzjEwdUYYvr7Oa+zd08QrLx0gO8fAjKxwgoKdP7blZR08cv9OZmSHkzUzHEOE8+e5pcXCnXO3kDEphOxZBmLjvJ3WOJXJyspiz549vZbNmTOHlJQU/v73v6PR9J2UfufOnQAYDAaHX0cYxwEIHORY6dYe1k4jGnc3QtOGKTZJkk1C56EncFACniFBijRsZjM2ixnvyHACkxOUtcNiob26DndfHwxjUxVpyLJM06ESbGYLsTMmKdawGk2YW9tIPmeWIg0AS3sHDQcPMebWa1GplRXerR1Gin9ax9BLzsFNYSylzWTiwOffETVpLP5xyrKMtR56ytZuRqt3Z/iVFyjSUGs1lKzeQMPBIma99rSihBGVSkXpmk0cWraazOceUWQaATpq6ylYvJyks2Yy8rpLFX12jI1N7HrrE0LThjH54Tv6XPw5Mm2Pm7cH+75ah0qtIuOmsxlzdY7T7xWdpzvb5/+EzWQhbvJwsh68zCnTCF1pLbX55XQ2tAIQM2EIY66e6bBphK7zW5dfRs2Brqq23s+LUVdkEZHq3Pfb0offo3h9LsbmduoLKshjC4bURNIumU7SjDRUDqTKrP3PVxSu3ImppQNjaweyTaJsWz4AhtREJt16DpFpR8/S3jH/J/Yv3ojVbMFmtmI1Weiob6H2YBnbP16BWqthxAVTmDU3DOxHKrPhm1yWvrcF2SZjs0lINhlJkqgrb2H/xhK+eW0Dnr7upE5P5PzbJhMe3/d47117mC9e/BlZPqJa+cv/Sw7UsHNlIR/Mg+jBIaRlJjFrzhj8gr16aRTnVvP+I8vstrGzzUT5wTp+mr8DNw8dwyfFMva0wUw8Z1ivz2hDVSuv3PqVXQ03ayP7dnXw5eclaLUqxmcEM3NWBJdcHtcrUcZksnHpBf33SO3YVs/3i8tRqSB9dCDZOQYuvyoB/4DeQ4WuumQtbW32ewn27W3ix2WVPAgMG+FPdo6By66M75OdffstWygpbrerkZ/XwvIfKnn8kd0kDfIhO8fAJZfH9Zud/WfCx8eH4cOH91rm5eVFUFAQw4cPp7CwkPnz53PGGWcQFBTE7t27ueOOO5g6dardaXv644TPqj5edOdNnvHOC/hEKh/fKMsypuYW9P7Krji7NTrrGhQbxxNJA7qMgasaLaUV+ESGKzZ9AK0VVXiFBqPWKr9+aq+qxT3Az6Vxlh11DWg99IrNJ4Cl00hraQWBg5RdGEBXZbrhYCEhw1MUa0hWK3X7DhKaOlSxhtVk5tCy1SSdmaX4/NrMFgq++5GkM7PRDFAh7M69/q15lKw2Vv7zU5Ky0omdoGxssSzLrHxmAWHD4hh69gTFF5Br/72IglW7mHrnhcRPHq5IZ9PbS9i1YBWjrshm5EVTcfNyPv6teGMuTcU1rHlpEYNOG0Pa7OmEpsQ4pVG29SDt9S3ofT3Z9dlqag+WMuTMCQw5czwBsY71IFTuLqK+qBKtuw6tuw6bxcYPD79HYLyB5JmjSM4eRWBc+FHzqotzq8nfXo5Gq0atVqHWqFFrVCx87mdaGzoYMTWe0TMHkZaZhE+A/THdVYca2LPmUNf5UPHLhMog2WQ+eXIFKpWKYZNiSc9KJj0zkYCwvuamsbqVrT/0HurSfX6L91ezasEufAI9SJ2RyKjMJIZPicfDu/eFUEeLkXVf7bPbRreGPbz9chWenhqmTA/rqRqGhPY+/1arxMcfFNnVMJkknvnHHrRaNRmTQsjKMZA1M5yoaK8+6/7vo0OYzH27RGUZ/vXUPoxGG2PHB5GVYyA7x0BCYt9j8tUXJTQ1me225fX/HKS6qpO09MAejZShvavdJ0JW9X933YGHzzG4OabVxA2pLyrel+nTp5OWlsZLL71EaWkpV1xxBXv37qW9vZ3o6GjOO+88HnroIae0hXHsh+6Tf/4Xb6Nz4QddIBCc+BzNPKpdzGa2WW0u5zs3HKrENzLYpUzm+sIKfAyBTlc8f0tjSQ3u3h54Brpe4anad5jQlBiXM7Sby+uwmiwEJfTubjuacbSHsd3M3nWHGTElHncP5ce6pqSJ0rwahk2KQ++p/KJy16pCPHzcSUqLUHyMzDsXoTWFMGFiCHq9svfhwbwWigpbmTItDC8vZRfalRUdbNpQx/TM8D5VSkdpabGw9Ltyu8b3SIRx/H0RxrEfhHEUCP5c9GceBScvzhrHU5HB6h8I81DeE3EyIozj74u4q1ogEAgQE4YLBAKBIwjjKBAIBL8gzKNAIBAcHWEcBQKB4AiEeRQIBIL+EcZRIBAIfoMwjwKBQGAfYRwFAoHADsI8CgQCQV+EcRQIBIJ+EOZRIBAIeiOMowNYTfYnInUGV/ODAWTJ9exfgUDgHMI8CgQCwa8I4zgAGxd9z5Zlq9hfVcP+qhpFGpVbd5H3+WKX2tFeU8e++V+6pNGdpuGqAa3euQ+b2eKSRmNRMU2HSlzSsHQaaSktd0kDwNjU4rJGe00dktV+zJajyLKMzeKahuD3QZhHgUAg6EJkVQ9A5f++5KJX3sFN1RWttLuqos86Q8JD7W4r2ST2ffw5uQu+JvNfDytuQ8nqDWx9+R1G3XKNYo2Gg0Vsev4NYqZlKI5x66hrYOebH6FxdycsbZgijc6GJvZ+uJCa3bmc/t9/KdKwmc0ULP6R/G+XkfXcPEUasixTuWUneZ9/x/CrL0Lv7/zEqpJNomrrLgq++xFvQyijbrpaUVuaD5dRvHIt7dV1jL/7RkUappZWKrfuouFAIcOuvAB3H2+ntpdlmbbKamr3HqBubx5BKUkknpHlfDta22g+VErToRI0Oi0Jp2c6HY9ns1jprK2nvaYOc2sbkRPHoNY4l3jRVllNR0095vZ2LG0d6AP9MYxxLmPd2NSMsbEZfolI0Ki9sMa3OaXRWt1I8fp9uHnpcfP2wM1Lj7uPJ4Hx4Q4fF4vRTOFPO/AODcA7PADvUH9FCTLFG/dTuvkAQQkGAhMMBMSFKUqRkWwSq59biFeQL2HD4wgbGoveV1lIQtXew+z/bhNRo5OJHJWsOI1GliTWv/4twYkRRI9PwTNAearNd29tIiIhiKETYxWnx7Q2drL6012kZSYSmRysOGpy67KDyJLMiCnx6L2Upa3s2NxGR20JMzLDCAhUNiH1gdxmdmxrIDMnnLAw+/GLA1FR3sEPSyrIyjEQE9s3qtARWlos/O/jQ2Rmh5OU7KP4uApcQxjHAQgbMhw3r1/f5CNVEb2e3y1X9KlEdhvJluIyCpeswDMkiOChgxS9fkddA3s++AyAqEnjFGlYOo1sf/0D2iqqSDwjU5GGZJPY+eZHlK7ZxGlvPqtIQ5Zl9n/6NUVLVzLuzv9TnBFd+P1Kdv73Y4ZfdREeQQGKNKq27WbNI/8i4bQZhAwbrEij+VAJax97Hq/wECY+cKsijc76Rlbc9SgqjZpZrz6t6JhYjSZW3PEo7TW1TH/mQadNI4Bss7H1pbep2Z1LwmkzSDjd+feJLMvsfPNjDv/4M8HDBjPl8XsUfbEfWPgtez9ciGdIEFMeu8dp0whdZnztY88DEDNjImNuvc5pDWNDMz/ccj/IMr4xkYy78/8IklPIbchzOF1G5+HOule+xtjcDkDYsDim3X2RU8dFo9Oy6e3vaSrp+p7RebqTcdPZpF40zak4xLKteWz7cPmvC1Qqhp83iUm3nIPez7Ef8l0LV1N3sIzyHQU0Hv61+hqcHMnkv51HbMbAGeV5P2ylOrcYS4cJc4eR/OXb2L1wNQBBCQZiMoYyds4sPPz7fx8fXr+Pih0F2Kw2JIsVm9VG+fYCtr73A6hUhKZEEzNhCKnZapIz7CfHHNxWxp6fDyHLMrIkI9lkJFkmb3MphTsr0LlpGJIRS+r0BNJmJBEa499HozSvli3fHwC6sphBpjuL7af/7eTTZ1cRHOVHemYiaZlJpIyPwc2992e8rryZnz/fY7eNNcWNrPtqH1o3DUMmxJCemUR6VhLBkX691mtr6mTZB9vsari3NrHg3XzUahg9NojsHANZOYY+xstqlXj133l2NSwWiddezsN2t8yIVP8ejeEj/Pu8l994JQ+TqW+vlizL/Pe1fB59aBeDBvt2acwykD4qEI2mt8bHHxRRX2ey25ZPPiziqcf2EBvn1dOOcROC0elEB+ofhTCOAzA4+7SjPn9UI+mpJ+7B2whua1dc5fMMDmT6Uw/QWHgYrbuyK06dh56JD/yN+v356P39Bt7ADmqNmlG3zCEiYzR+sVGKNFQqFSOumY1XaDCxWZMVaQAknpGFbLWSeGa2Yo3wUSNIv/EqYjMnKdYISIpj1C3X4B8fg1avLP/XIyiA0XPn4ObthWdIkCINrd6dtBuuwNzWrtgEq7VaUq+/jLzPv2P0rdcpMnwqlYrBF56Jpb2dCX+/RfExiZ0+kfr9+Yy94wY8Av0VaQQkxeGfEEvS2TkknDZd0f54hgTinxCDYUwqwy4/H43br5+/3IZqh8yjztOdgNhQmsvrmXTruQw5Y5zT3wVqjRq/yGDaqhtJvXgao66aqaiiJksybt4e2EwWUs4YR9qlmQQnRQy84REYm9pBpcI71J/Gw9UEJUUw/NxJpJwx3uGqY0d9Cx0NLeg83PEK9kOt0yJZrESPHczg08aSOCMNd++jV7U66ltoLK5BrdOg0WlRazU9JtrN052A2DDChsQQmth/hbilvoOSAzWo1SpUalXXvyoVZmPXMByrxYaxzYzFZMNmtT+8p6PZSHHuLwZapaL7baZChfTLNo3VrVQdaqT6cCNRg0IIDO997oztZor3VdnVb2vqGhtvNdso2V9DkMGXoAhfAsJ80Gh/fR9ZzTYO77Wv4W7p0pCkrsphWJgHoeEeREV54uH5qwWQJNi9s9GuhizLdKcT5x9sJSzMg7AwD2JivPDz7/27tG9vMx3t9ofcdGscKmplz249oeF6omM8+1QxDx5oobysw66G2dx1XEtL2tmzu4nQcD0xsV5ExyirYgqcR2RV90N33uTFb7yPm4fyrOrdcu+u7f66tQUnJ7Isu9xdcqJoAEg2m6IKXy8Nq1VxNflYahibmhVfKHXTWd9ot6rtTK51yeYDhA+Lw81LmZEGKNl0gKCkCLyClGfVmtuN7Jj/E8PPn+ySDsD+xRsJiAsnbFisS++7lsp6ilbvJjl7FF7Bys+VLMtseWcpwYOjiBmf0tOVrySretG/1xIY7kN6ZhJ+IcrMSFtTJ/97eiUjpyUwckq84szinxfuprasifTMZOJGhKNWO3+sG9YsZPc6lUuVub17mpj/YRFZOQYmTQ5F7+H8d0RFeQcvPJvLjOxwpk4Pw8fH+WEALS0WHntoF5Omhh61611kVf++COPYD8fKOP4WYSQFglODBrXjXdaC44MS43iqMVj9A2EeCce7GX8owjj+vohBAX8wI1URPQ+g525tpXdsCwSC40OgNFjcZX2C02zJIL/V9ZkXBALBr4gxjseRPuMjq0Q1UiA42XB0vKNAIBCcCgjjeAJxpJH87d3awkQKBCcegdJgGtR5wjwKBII/DcI4nqAcrRopTKRAcOLQbR4FAoHgz4AwjicJvaqRoktbIDjhEFVHgUDwZ0AYx5MQ0aUtEJxYiC5rgUDwZ0EYx5McYSIFghMD0WUtEAj+DIjpeAZAMpsp2brJJQ1ZlinZshHJZnNJp6m8DKvJ2O/zR071M1IVYXeqH6vRiLmt3aV2yJJEZ739hAFnsJktLmsIBCcaYooegUBwKiOM4wD88OQ8bBblBqe5soIfn3mc2oKDihM5TO1tbPn4PXZ+Ph+tu+PpE0eayNzyStYu+Jql9z+N1kNZgoUsSZSt3cyPd8zDauzfwA5E06ESNj33BjW7cxVrmFrbyFu0hOKV6xVrmNs7KFq2ip1vfYLNYj8iayBkSaJufz77P/sGS6fyY2JqbqF07WZMLa3KNVpaqdy6i6rt9nNvHUGWZTpq62k4WITSbACbxUp7dS11uQdpc2F+UlmWsZrMWNrtR4/93lhNZqcvbgKlrsjHbvPYXt9C8cZc6goqMLZ0KDqmVpOF0i15tFY3Kj4n3eSv2EHl7iIsnfZzgB2l4VAl+T9up72u2SUdWZY5uHwbjSU1Lu9bxa5Cmkpdnw+3YGcFrQ2uvefMRgsFOyuQJNf2qWR/De0tyr9XAIqLjNTUuKZRVtpOWalrBYfaGiOFBcq/36BrYu99e5tcfq8IXOOk6ap+9dVX+de//kVVVRWpqan85z//Ydy4cf2u/9JLL/H6669TUlJCcHAwF154IU8//TR6J/NzzR3txIwZr6jNLVWVLH/6UUytLUy84RZFGqa2Vn785+M0lhRz2iNPKdKwWSw0vDKf8p3bmDL3TvJq63uec7Q7W5Yktr78DkVLV5Jy0dn4RBoUteXA54vZ9fZ8oiaPwzAmVZFG+YZtrH/qZfziosh+8TFFGnW5B1l1/1Oo1GpyXnkSjc75j0JbVQ0r732Czrp6pjx+LzoFhtzc1s66x1+kZncuqX+9DPfJ/b+n+8NqMrP2seep3r6HwJQkpj15n9MasiSx7dX3KP5pHVq9nmlP3qcoSm7Hmx9x8MvvAUg4PZNRN1/jtEbBdyvY894CLB2d+CfEkPHA39B5OZfeVL1zH1v//TYAOm8v0m64nNARQ5zSaK+sYdnc+wHQenh0ZZPffA2+0UfPdz6yy1rn4caP//iEtuquCr3WXUf48Dim3TPb4ZxojU7D+te+oWrPIbR6NwJiQgmIDSPtskwMI+Id3p/yHQUUrtpJ3vdbUKlVBMSGEZISTVzGUAafNtahDO2aA6W01TTS0dDKymcWIFlt+EUGY0hNICI1kYRpIweMDmw4XEVbTRNWkwWbyULutxspvv8dvMMCiBmXQvS4wUSPHXxUnebyOtpqmpBsNiSrDckqUbmrkC3v/YBfdAhxGUOJnTiUqNGDoJ9ku8bqVmrLmpElGWSQZBlZktm5spCl72wmMT2StBmJpM1IJGZIqN3PQ2tDB1WHf2Pof/nv2/ctob3Z2KWRmcTwyXF4ePdNEulsNVFeUGe3jfvWF7PopTUMHhtNemYSaZlJGBIC+6xnNlkpye2n0n24nYvu+o7UtACyZhnIzjEwdJhfn/2RJJmd2xvsH6tGM9dfvYHkQT5k5XRppI0KtBuBuGtHAzZbX2NnNktce8V6QsP0ZM4MJzvHwNjx9iMQc/c1Yezs20Mny3Dz9ZtQqyFzZlc7MiaFoNe7FpMqcI6Twjh++umn3HnnnbzxxhuMHz+el156iVmzZpGXl0doaF/jM3/+fO677z7effddJk6cyMGDB7nmmmtQqVS88MILTr128vRsNDrnMzUBfMMNpF14CY3Fh/EKClak4e7tw/CzzqNy326CE5MUaWh0OgZl5qDT64kZM77XF4aj0/yo1Gric6bRXlPH0EvOUdQOgIhx6VRu3smom65WrBE4KIHgIcmMnjtHcZ6xX1w0wUMHkTBrumIT7BkSREBSHIPOPU2xCXbz9sI7Igz/pDhSLjxLkYbW3Y2gwYlY2juY9uR9uDlpsqDr/PrFRqEP9Gfak/fhrXB8bEByPFoPPSOvvZSks7IVmU+v0CAkSSL5nFmMnHMJGjfnP39aD3dMra2EDB/CmFuvtZs3PRAaNx1aTw8ks4WUC89i0HmnO9yWrlSZPBLdfDG3dQLgEejD2DmzGHH+FLTuTuyTSkVLRdfFnmSxEpwcyagrZzpsPLv5+cXP6WxoA0CWZPT+3sRPHkFSZppDphFg/WtfU5dfjt7HE7VGjWS10dnUhkanJTDBgKcDGdhb3/uBop93o3V3Q+uuw2rqquq2VTdSuiUPvb8X/jGhRzWOexatZfdnq1Fp1Kg1atTaX41Dc2ktu8t/pq6ggrbqJjIusL9vm5cc4NNnV6NSgUqt6nqgQpZlZBkKtpdTsL2czUsOkH3lKKZdNBK1prdW7oZi3rxrcdcfR7zXVSqwmG3IkszPn+9h3Vf7GD45ntl/n0704JBeGiUHavjnlQvstlGSZSSbzP6NJezfWMLS97Zw+nXjmHnVaDTaX9vSWNXKk5fOt3+wZAmAXTsb2bWzkXf/W8DlV8Uz97YUPL1+/f40myVmn/ezfQ3AZpM5sL+FA/tbeP/tQs6/OIa7/z4M/wC3Xutdd+V6mpvtV+rNZolDRW2882YBH39QxBlnRXHfQ8MJN3j0Wu/u27aRn9diV8NikZBl+Pj9IhZ8fIjM7HAemDeC+ASfftsuOLacFFnV48ePZ+zYsbzyyisASJJEdHQ0t956K/fd17e6MnfuXPbv38+KFSt6lt11111s2rSJtWvXOvSa3XmTF732Lu5e3i61X7JaFRucP0rD0Qxtm9mMxs3N7nOOcqJoWNo7nK5k2dPQenooMki9NDz0Dv9429XoNCLbbLh5eynWsJnNWDo60fsfvWI0EHX78wkekuySRu3eA4QMT3FJo3rnPkJTh7p0bgq++5GI8aPwDO5b5RmI7qpjy+dbUWs1pF0yHTdPZcNEtn6wjI76FtIuy8Q33Pm2dLP/u01U7T3EiAumOm08j6SzqY3lj33EoFljSJyeik6v/LO45t+LkKwSg2aOInx4nOLPwb5vNlDw0w4Sp6eSMG0kngFdRsLZvOrv3trEntVFjJqZTHpWEiFR/k63xWaV+MfFHxMWG0B6VhIjp8bj6ev8uf/pfztZ/eku0rOSSM9K6rfyeTRaNn7Oi49Wk/1LpXDUmCA0Guc0CvJbuPaK9czICicrx8D4jGDc3Z2r8tXXmTj/rJVMmBhCdo6BSVNCexlXRzB22vjLaT8xdLg/2TkGps4Iw9e370WYyKr+fTnhjaPZbMbT05PPP/+cc889t2f51VdfTVNTE19//XWfbebPn8/NN9/MsmXLGDduHEVFRZx55plceeWVPPDAA3Zfx2QyYTL9Ou6npaWF6OhoLn7jfdw8XDMXJxtHmkhxZ7ZAoJwGdR4pvsG9KmJKkKw2lzWga0yhK0b6WOscSy3JJvWpCILzxtFqtqF1c+1YWy02VCpVr6qgEkydFtw9lPV4dRNr+p7YQGW9Vd10dljRe2hcOk/GThtu7mq73duOYjLZ0GhUaAc4rsI4/r6c8F3VdXV12Gw2wsJ6z40WFhbGgQMH7G5z2WWXUVdXx+TJk7sG2Fut3Hjjjf2aRoCnn36axx5TNl7uVKO/ycaFiRQInOdAS53LczseC9MIHDOzd6x0jqWWPdOoBFdNI4BWd2zOl6umEUDv4fpx8fB03SroPVw/Js5WOQW/D6fkXdWrVq3iqaee4rXXXmP79u0sWrSI7777jn/84x/9bnP//ffT3Nzc8ygtLf0DW3zi0n1XNtBnah+BQHB0uu+yFggEglOFE77iGBwcjEajobq69x1j1dXVhIeH293m4Ycf5sorr+Svf/0rACNGjKC9vZ0bbriBBx98ELWdMTTu7u64u7teVj5VEVVIgUAgEAgEJ3zF0c3NjdGjR/e60UWSJFasWEFGRobdbTo6OvqYQ80vcyie4EM6TwpEFVIgcJyuO6zFpOACgeDU4ISvOALceeedXH311YwZM4Zx48bx0ksv0d7ezpw5cwC46qqriIyM5Omnnwbg7LPP5oUXXiA9PZ3x48dTUFDAww8/zNlnn91jIAWuI6qQAoFAIBD8uTgpjOPs2bOpra3lkUceoaqqirS0NJYuXdpzw0xJSUmvCuNDDz2ESqXioYceory8nJCQEM4++2yefPLJ47ULpzzdJvLIvGxhIAWCX8ltqHb5JhmBQCA43pwUxhG65macO3eu3edWrVrV62+tVsu8efOYN2/eH9AywZEIAykQ9OXINBmBQCA4mTlpjOPxpKOxAY2bm0sTgUs2G1ajETcv5RM0d+sozbzuRpYklyabdgTRjS0Q9EVUHQUCwcnOCX9zzPFm3+KvWffGf3DzVGb4ZFmmZOsmVjz7hEtmramshPX/fYW2WuU3onQ0NrDjs/mUbt+qWMNmsXBow1oObXAsgQfs30zTVFRC6c8bFbcDoKO2nqJlq7CazIo1JJuN2r0HMLe1K9aQZZmW0goai4oVa0BXsk/HETnirrTnWCBuJDu2uDI1j7G5HckmudwGY0sHnU1tLusANJfVHZP3SEdD6zHZN1NbJ7J0DHQ67cflOYNkk7Ba+mYtO4vFZHVZw2w6BsfE5Pq+mEw2l98vZrOEJInvpeONqDgOwL7FX5J51wOKJqltr69jzasvUleYz7ir/4rOw2PgjX6DuaODTe//l+JN6xly+tn4hjufq2yzWtk2/30KVq0gJDmFtIsudVpDlmVyv/+W3CXfoPPw4Kwn/uW0xkhVBIc3rWfrt5+xt66BQY/fQ7TTKlC1bTd7PvqchrxCMu6/Fa2783FnTUUl7P34C2p27iXprJmK4u3aq2rZ8d+Pqdt3AI/gQGY886DTGua2dna/t4CGg0UYG5uZ8tjdeIYEOaUhWa3kLviapsLDtFfXkXLRWcTOmOSUhizL5P7vK+r2HqCzsRlvQyhjbr0OfYBz8YOHlq2mesdeLJ1GNO46Uudcild4yMAbHkHJzxspXPxjz98hI1IYMvscpzKr6/bns3/B12g99Gj17niGBDHovNPReTr+GWwtq2TNY8+j1evxCPTD3d8P/4RYks7Mcjj+09Taxpp5z6F1d8PbEIY6QkV1RCCTcyY6FdNXvb+YxXf/l8CEcIITIwlKiiA4KZLwEXEOxxjKsozVZGb+pU/h5uNB+LA4wod3PYIHRaF18PhKNglLh5HN737PobV7iUxPImpUMpGjkwlKMDh8gSxLEjaLjdq8Ur5/4B2ixgwmdsIQYsYPwS8q2CGNI+loaOWjix4neuxg4iYNJ3bCEPR+zl/w715VyJcvryN1RiLpmUkkpUc4Pbm4Sq3i2asW4BvkRXpmIiOnJeIT4Pz3/5pFe1m7aA/pmUmkZSYRNSjY6d+iHZvbuOGZH8n6JXJwZFqA0+ktxYfauen6jczICic7x8CYcUEDprf8luZmC5de8DMTJ3dFDk6YGOL0hN42q8Q5p69ixMgAsnMMTJ7qfGyhwHXEER+AoIQkDCNSFW3rFRRMQEwcFqORpKmZijTcPD3xCQvHOySUEedcoEhDo9Xia4hE5+nF+Dk3KDLBKpUKn5BQZJuNSTfMReuuLHPXKygYW20jk/96MzGhI3q6sZ3pwvaODKe1pJwhF51NzNQJytoRHkLdvjwMY9MYcc3FijQ8QgJpLi7FzdeHaU/eh5uP80MZ3Ly9aCkup7OugelPP4hfXJTTGmqtltbSCqp37GXig7dhGJvmtIZKpcLU0krV9j0knD6DUTdd45RR68bU3ELxynWEpg1jzK3X4hEU4LSGzWymZncuGnd3Rl57Cclnz3S6Wm8zmanYtB1UKhJmTSfxzGynTGNXOyy0lna9P1s9PUi54Ezic6Y5lxkvSTTkFSLbbFTv2EvkxDEEpMY5ZRplSWLxXW9iNVmoyS2hJreE6HGD8Y8Oceqi6cMLHqOppKvHorOpjebSWsp35DP0LxPxDPZzKAP7i5v+TdmW3mM1C1bsoGDFDgypiaRfOoOkrPSjfscse/RD8pZuQbL2rmIVrtxJ4cqdaN11pF4yg3HXndavKV736tfs/N9KZElCsknIR1QsDyzZzIElm1GpVQw7ZxKn3R4BPnba8cFWFj73M7IsI0sysgzIMjJdsYOlebUsfmMj3v56TrtuHKf/dRxu7r3P/eYlB3jrviW/LjiiGGYxWbFZJTYt3o9KrSJlfDSX3DuDhNTeF/8Ht5Xxrzmf2d1PWZIxdVjI31bOZ/9aTXCUHzlXj2bmVaN7pdPUlDbx0Jnv2dVQY6G9TSJ3XzP/efEAIaF6Zl8Wxy1/G9zLdBmNNsYMX2xXA6C11UrBwVbeej0fP38d554fw533DsU/oPd7cOLo72lptt8L1N7epfHhu0V4emo4/axI7n1gOOGG3p/Nc89YScHBFrsaHR02cvc28+n8w7i7q5mRHc59Dw0nPsHOSf6T88wzz3D//fdz22238dJLLwFgNBq56667WLBgASaTiVmzZvHaa6/1Sec7Gid8VvXxojtv8ozH/0lgbLxLWq3VVfiE2Z+s/I/UaCovwz/SeWPSS6OsFP8oJXXCo2t052M7aiAb8ovwT4hzKWasseAwvjGRigxSN01FJbj5eDldJTyS1rJKAHyinK8md9NeU4epqYXAQQmKNSwdnRSvXEfSmdmKNSSbjbwvvmPwBWcpPjc2i5Vtr7zL0EvOwdugbDygZLWy7ZX3SDh9BkGDleX0SjYbG57+D54hQQyZfQ56f+dzYmVZZtOzryEjM2T2OfjHRdOgznN6nGPVvsN8/bdXScpMI3X2DIKTIgbe6DfUFZSjddfx/YPv4hMWyPBzJxEzYYhT56m+sALJJqHzdCf36/UUrt7N4NPGknLaWHwjHPsMNByuwtTaiUanReOmpa26ke/ufYu4ycNIzhpF3KRh6DyOHsbQVFZLe10zarUalVqNWqvG3Gbkq7+9QuiQWBKnjyRhWioBMaH9ZlXXV7RQU9qEWqUC1S/Rhyoo2V/Dh48uZ9DoSNKzkxmVlYwhwb6pbq5rp6Kwa3jJkV5ZhYr5T/1EdXEjaTMSSc9KYsSUeDx9+xrh9hYjJfvtD0HK21LKohfXkDQqkvTMJNKzkohM7lt1NBstFO6ssKuhLlvHE/eWMHSY31GrjpIks2lDnV2NlmYzN/11E7FxXj0a/VUdt2yqw2rtayssFokbr92If4AbWTnhR6067trRQEdH3+5xWZa5Y+5WbFaJzJmGfquOIqsatmzZwsUXX4yvry8zZszoMY433XQT3333He+//z5+fn7MnTsXtVrNunXrHNYWxrEfuk/+xW+8j5uH5/Fuzp8GZw2k4NRElmWXM4yPhYa5rR03b9duaDO1tuF+RDW6++5qZ8yjqbUDySbh4a/8Bj3o6mbubGrDK8j1H9O22ia8gv1cPsZttU24+3g6VYXtT0elUuEV3Ht4RX/GsT/KC+rwDfTEJ1D5977NKlGwo5yk9Eg0TnbpHsnhfVUEhvviG6S8Le5FXxMVEEdklHKN0pJ2LBaJhETlVb2aGiP1tSZShvoqfs+0tFgoKmgdsLv9z24c29raGDVqFK+99hpPPPEEaWlpvPTSSzQ3NxMSEsL8+fO58MILAThw4ABDhgxhw4YNTJjgWA+euDlGcELRfSONSKT5c+OqGTlWGq6aRqCXaQRlN8m4+3i6bBoB1Br1MTGNAN4h/sfkGHuH+LtsGrt1fmsalRCZFOySaQTQaNUMHhvtkmkEiBsW7pJpBIhL0rtkGgGiY7xcMo0AoaF6hgxz7ULD11dH2qhAp8dongq0tLT0ephMpn7XveWWWzjzzDPJzu7de7Rt2zYsFkuv5SkpKcTExLBhwwaH2yLGOApOSEaqIsRckAKBQCA4KdneGIW7xfkbon6Lqa0TgOjo3sO75s2bx6OPPtpn/QULFrB9+3a2bNnS57mqqirc3Nzw9/fvtTwsLIyqqiqH2ySMo+CERUwmLjiVEXM6CgQCRyktLe3VVe3u3rcbvLS0lNtuu43ly5ej1yu7gdURRFe14ITH3jyQAsHJjCtzOgoEgj8fvr6+vR72jOO2bduoqalh1KhRaLVatFotq1ev5uWXX0ar1RIWFobZbKapqanXdtXV1YSHO37zrag4Ck4aRAVSIBAIBAL7ZGVlsWfPnl7L5syZQ0pKCn//+9+Jjo5Gp9OxYsUKLriga3q/vLw8SkpKyMjIcPh1hHEUnHQIAyk4FQiUBpPb4PzUPAKBQGAPHx8fhg8f3muZl5cXQUFBPcuvu+467rzzTgIDA/H19eXWW28lIyPD4TuqQRhHwUnMbw2kMI8CgUAgEPTPiy++iFqt5oILLug1AbgzCOPoIJLNhlrjXDyS4I+hx0AqSKERCI434iYZgUDwe7Fq1apef+v1el599VVeffVVxZri5pgBaK+rZdP7/6XhcJFiDavJxMEVy6jYvVOxhixJVOzZSdG6nxVrQFd+dvGWjS6FzdusVqoP5CJL0sAr94MsyzRXVmBqb1Os0d0Wc0cHgEs30Fg6Ol1qh0CgBHGTjEAgONkQFccB+O7he4mbMIngxGSntzW2tHBg2RIO/rQM33ADOQ/9w2kNS2cnB1cuJ3/lciydnZz91AtOa0hWK4c3rado7Wpq8w8w6+EnnJ6EVZZlKvfuonjTekq3b2Hsldc5nR8MUJO3n5KtmyjbuY2wlGFMuPb/nNZoKiulbOc2qvfvQ5Ykpt9+T89zjo5/bC2vpHz9VurzCmkprWDcnTc4HU1nbGymYvMOmg+X0lJcRuIZWURNHueUhqXTSPW23bRV1dBeVUvg4ETisqc4dX4kq5WSVRvobGzC2NgMwLBLz3UqO1uWZSo378TY1IzVaMJmNhOXOdnprOnyDVtpragGWQZZxjMshOgp453an9p9ebSUlKNxd0Pr7o7O04OQkUOdi8bLK6Bs3RbcfX1w9/PF3dcHr/AQ/GIdj9xsr66l6IdVeIUG4xkajFdYMJ4hwU5FVFo6jWx58b/o/f3wjYnANzoS35hI3P2dS8+wWW0sffBddB7uhKZEE5ISTUhyFG5ezk+5sfG/31GXX4ZhZAIRqYmEpESjdTJ2c983GyjfdpCosYOJGjPIoZzr/jC2dLBs3gdEpCUSO2EIwYOiFE0SbWrt4Ken/kfUmEHEThqmuE15m0vZtGQ/aZlJpIyP6ZNP7QiSJPPJEz8SNSiEtBmJBIQpmzx73Vf7qD7cQHpWMnHDwxQdl01rWti3aQ/ZOQZGjQlCo3FeY9/eJhb+7zBZOQbGZ4Tg5ub8d39FeQf/efEAWTPDmTQlFA9P549rS4uFJx/dzdTpYUydEYaPj/K4WIFyhHEcAI2bG6NmX6FoW52nJ1W5e7AYOxk/5/9QKzBaWr2e+sJ82mqqmfa3u9EriE9Sa7U0HC6iKncPYy6/RlH2tkqlorm8jMI1q0jJOYP4jMlOawAYW1s5sGwJYSnDGHf1XxV9EdosFnYunI+vIZJZDz2O1r3vj+dA4x91np7sfv8zNG46pj15n6I8Y523F7veno+lvYNxd93otGkE0Ord2fPR57QUlzHk4r8QlzXZ6WOi1mopXbORik07CByUwKSH73DKNELX+a3cupOCb5fjERzIhHtvcdo0ArRWVLPrrU8ASDwzi8Qzs53eH2NDE1v//TYAwUMHMXrutc7nXsuQ9/liZElGpVaTeEYWw6443zkJWebgoiVYjV0JDYEpSQy/4gIMY1Id1pCsVupyD9JZ19CzLGTEEEZcfREhw1N6lh2tu1qWZb698w3qDpbRXtfM/sUbAQhKMDDlzguInTDUobYsf/wjmkpqaK9tprm8jsKVuwBw9/Eg4+a/MOL8KQMe53WvfEXNgVKMze3U7C9h/3ebAPCLCmHYORmkX541oAnd/vEKyrfnY7Nakaw2bBYbdfnlHFqzh3X/+QrPIF9iM4Yyds4sAmLtH5PcbzdQ9PNuZJuMLEvIkowsyVTtO8zB5du6jk9iBHGThjHhAk+SR/SNHNz+Yz5rvtgDMsjIyDIgy0g2mZ0rC1n+4XbcPXUMnxxH2owkxp+R0idC7uDWMpa+23eiZYDi/dUse7+rLfHDw0nLSmJMziBihvT+LiovqOOLF9fY1WhvMpK7oZgvX15HQJg3aZlJpGcmMWJqPFrdr0Onmmrb+PDR5XY19JZa1vzYwhuvHCQg0I0ZWeFkzTSQNTO8l3kzmyVuu3mzXQ1ZhmXfV/De24V4e2uZOiOM7BwD2bMM+Pn1Tv25+7attLdb7eqsWlHF/I8O4a5XM3lKKNk5BmaeFkFIaO/v8H/M201FeYddjY3ra1nwyWF0OhXjM0LIzjGQc3qEy+k4AscRxnEARl1yJZ4Byq5cNVotM+68n9JtmwmIjlGkoVKpGD/nBsJShhE92nlj0s3Icy/CKziEwTNPV6yRND0bWZZJcUEjMm0UqefPZlDWLDRaZW+/wLh40i66jPiMybh7H/1Kvr/xj/oAP0bOmU1QShLBQwcpaodGp2XENbPRB/gSNXGsIg2VSsXQS87FajSSeHqmIg2A5HNOw93Pj9Fzr0Hjpiy+LT5nGp11DYy94wbcfZVVSCIzRlO0dCWjb76GsPThA29gh6CUJHxjIkm58Kyu6quCCy7P0GB8oiLwiYpg5JzZ+EZHOK3h7uONT5QBtVbLsCsuIHz0SKdNsM7TA7+YSIwNjURNGc/g888kaHBir3UCpcE9+dX2UKlUxIxLQad3o2j1LhJnpDHywqlEjk52qj2hKTEEJ0fSXtPEto9+xJCayNCzxpM8czTu3o4lXPjHhOHmpcfU2knN/hK8gv1InjmKQTljCB8e51B7vMP8CUmJRq3VoNFpUGs1tFY1YOkwEjl6EEmZaSROT8U7xL9fDc9AX4ISIkCtQq1Wo1KrUKlV1BdVYmrpIHhQFInTRpIwLZXA2FK7Gl5+eiISg0ClQqWCrqarsJis7FxZiEoFMUNCSUyNIHlUJHrvvp8rvZcbhoTf/D78cgxqShq79jfAg4jkICKTgwmJ6huJ6KbXEh5n/zemobIFALVGRVhcAOHxAYTHB/YyjQAaraZfDX17fc//Y2K8iEvwJi7BG71Hbw2VCuLj7V9wWm2/Dm2KjPIkPt6buHhvvL37XiREx3ph6rT1WS7Lv8aAGgwePe3wD+h7XCMjPXDvp6q5dXPX/gSH6Hs0goJdz4QWOI5KdmWw2ylMd1D5xW+8j5uHuJI5lsiy7HLGrRKN3XJv83i82vF7aEg2Gyq12iWdY6MhYTOZ0Hkqj9qSZRlLe4fLOdFNRSX4Jyi7YOumIf8QAUmOGaL+KF2ziYDkeLyPctNWg3rgaXkOr9tHyOAol/OYK/ccQu/r2W81zyGN3UVYzRYi05Odrwb/BkuniYPLt5MwdYRLedzmDiO532wgYepIfCOCepb76TaQ7NO34tgfRbsqKTtYS+qMRPyClb0HJUnm+7c3k5TeZTqVHqNtyw9i6rCQOj0RLz9lSSAdW7+gsdSHzJnhhIUp+1weyG1mw/pasnMMRMcoOyYV5R1881Up2TkGEpN8FH2mWlosfPBOIZkzwxl6lNzr1lYLw5O+obm5uVfayh9Bt3e4cdXzDl+QHQ1TWydvTL/ruOxLfwjj2A/COJ66/NZACgTHm+6Ko7i7+tjjrHE81Ris/oEwj4Tj3Yw/FGEcf1/EXdWCPx1H3n0tEJwIiLurBQLByYIY4yj4UyLmfhQIBAKBwHlExVHwp0ZUHwWCU5tmSwb5reXHuxkCwSmDMI6CPz2uTBwuEBwrurKrq493MwQCgeCoCOMoENBlHkX1USAQCASCoyOMo0BwBKL6KBAIBAJB/wjjKBD8BlF9FBxPRHe1QCA4kRHG0QHqDxdxaMNalzSMrS0c3rQeWZIUa8iSRG1+Hjar/TgnRzG1tWIxGl3SkCUJS2enSxrQNdnziYowj4I/GjEtj0AgONER0/EMwI//fJyWygrOeuI5p7eVbDYq9uyiaM1KynZuY/pt9zodnybLMg2Hizi8cR3Fm9YzOOcMQpKd/3Fpq62hdPsWSrdvwSswiIk3zHVao6Oxgcp9u6ncs4uOhgam3XaP0xqWzk5qC/KoydtPY2kx6RdfgX9klFMa5s4OGosPU3+okPpDhcSOm0jMGOfiGG0WC63VlTSXl9FUXoZPWDgJk6b2We9o0/ZYjUZay6tor66lvboOm9HEoPNOQ6t3POHBZrHSWVuPsakZU3MLpuZWIieNxd2JrGnJZqOpqBhrpwlrpxFrZyeeocFORSnKskxLSTmyTUKWJGRZQq3T4R8X7bAGQN3+fGxGE2o3HRo3HWqdDs+gAKeys5uLy7B0dOLm7YWbjzdu3p6onYynbCoqobHwMB5BAXgGB+IRHOh0mk1HbT01e/bjExmOT0S40/nf3ZRv3EZnfSN+cdH4x0Wj81IWKFCy6QC1B0sJHxZHSEo0bp7OJ4nU5pVSsauIiPREghMjFMU5AhhbOtj75Vqix6UQMihKcTKKLMvs+WINIYOjCRsa61IKjWST2PvlWiJHJRMYH6447aeyqJ7y/DqGTYrDw1tZlJ0sy6z7ah+DxkQRGu2vSAMgd0MxOnctiakGxccmd3cH+aYaxk0Ixq2fGL+BKMhvofhwO5Mmh/aJKnSUyooOtm9rYNqMMLtRhY7Q0mLhx2WVzMgMIyBQxAweL4RxHICGQ0WMn3MD3iHOz/Nnamtl+4KPaKksJ2XWmUSMTHNaw2oysW3+h9Qc3E/UqDEMPf1spzUkm40dn31C8eYNBMbGM/6aG5z+UpVlmQPLlpC75Bv0vn6c9siTuHs7/0NatP5ntnz4Dhqdjqx7HnLaNALU5R/kp+efAmDU7CucNo0ArdWVLHnk70g2GwmTpjLiL+cfdf2Rqgh2yxXsr6rpMY82i5VVf38Sc1s7PtERTH38HqdMYzc/P/IsrWWVuHl7MeG+uU6ZRgC1RsOeDxZStXUXAIlnZJF2wxVOaahUKvK/XUbh4h8BCBqSzPi7bnRKA6Ahr5Adb3zY1S6dliEX/4Uhs//ilEZbZTVrH32+52/D2DRG3Xw13gYnUlXUKrb++y0ka1dmrkqjYfD5ZzDs8vMcPkcqjYYdr32Aua0dADcfb6KnjGfknNkOm0hLp5GaXbkc/PL7nmVeYSHETM9g6KXn9tuW3IbqnhQZWZbZ9ekqmsvr2Pm/lV1tU6sITDAQN2k4Y+fMciihInfxRlqrGtj01hJkm4S7jwcRaYlEpCUx9OwMPAMHzicvXLWTlooGrGYLuz5dxbr/fIW7rydRowcRMz6FuEnD8DUEHVWjZNMBGkuqkW0SktVG4apdrHxmAXo/L2LGpRA7cSgxE4YcNau6cncRdfnlyLKMLMm/XOzIHFiymZXPLMA3Ioi4ScOJmzSM6DGDwI5PKdlfQ8HOrgtCZBlZ7jrWNouN/z29ElQwZHwMaZlJpGcmERrTtz3VxY3kbii228Z1X+3jzbsWE5kcTHpmEmmZiXbjB5tq29j5U6FdjfL8Opa+uwWfIE/SpieSnpnI8CnxfQxtZ6uJTUsO2NVwa2jh9X/l4eOjZdqMcLJmGewaL6tVYuEC+/tiNNr4xyO70bmpmTwllOwcA5k59iMMFy0sxmTq27MmSzJPPLoHs9nGhIkhZOUY+o0wXLK4nOYms922vPTcfu66dQtjxgWTlRPuUoShQBnCOA5AxMh0kqZlKdrWw8+fMZddTe7Sb0m/6DJFGjq9ntQLZrP7q4VM/Ostij4cao2GoWecQ2dLM5NumIvW3fkrNZVKRdK0TGoLDjLmsqsVGWmAmNHjKd64nmFnnUPo4CGKNEIHpxA+dDgRI9MVGWkA34goIkam4x0SyuhLr3Ko8vJb8+ju403kpLG0V9cy6cHbFFWkNDotUZPGUrllF5MeueOoecZHwzAmlfr9+Yy9/Xqip4xXpBE6PIVDP6xmxNUXMei8MxRVOLwNoWjc3QkeksToudfiE2VwWsPN2wudtyceQYGkXX85hjGpTmvIkoSbrw+mpmZiM6cw7IrznT62ss2G1kOPua2dkBEpDJl9DuGjRzr1GVSpVDQVFaNSq5BliJwwiuRzTiM0dWi/OoHS4J4Iwm6NQ2v3ovNwQ61RI9kkIkcPYvi5k0ickYrWzbHqzeG1ezG1daLRabHazLh5eRCUFEn8lBEOmUaAw+tzaThUhdZdhyx1DTOxmSyoNWo8AnzwCBhYp2xrHqVbD6LWqFFrNbTXtwBgbG6nobiawAQDxuaOoxvHPYc4+MNWVGoVKrUKVGpUahUdv2i1VNRTuvkAOg83vIL9CBrRV6M0r4ZV/9sJv5wGlUoFKnrOi80isW/dYYztZkwdFmZckopPYO9qcXVxIyvn7+wt/IteU00b0GX+Who6aK5rR5ZlUsb1zk9vqmljxSfb7e6n2dg1LKm1voP13+yjsbqV1iYjUy8cgVb3a+Wvo9XUr4ab1HVMWlutLFlcRlVVJ3W1Rq64KgFPr18tgM0m8/EHRXY1oMtUGztt/LiskqqqTqqrO7n62sQ+BvTT+cW0tVnsalhtEhaLzJrVNV0alZ1c89ckwg29Deh3X5dx+HCbXY22VguSBJs31lFV2Ul1pZGrrk0gPsGx97DAdURWdT90501e8O838fAPcEnL0tmJzsO1zMoTRcPc3o6bl7KQ+25M7W24eynr9uvG2NKM3tfPZQ13H19FZrw77zpWq8XdzxeNTvk1WGdDEzpPD7R65V0vxqYWbEYTXuEhijXM7R101jXgF+t8FbgbyWajbM0moqdlKK4AyLLM4RVriJ0xCbVGWbcYwJ4PFxI7YyK+0cpzivd+/AXho0Y41e3/W1rLKyn4bgXJZ890uGraoM7rk1vdVFbLvq/WMeycifhHK7vAaK9rZsMbi0k5bSyRo5IUd1VbzRZWPrOA2AlDiJ8yAp2H8vfumn8vwjPQh8QZafhHKX//ypLEj098QkBsGInTUgmI+/X4OZNXXZ5fx2fPrWZUVhJpM5LwC3H++06WZd65/3t8g7xIz0pS3NW8+rNdHNhSyqjMJIZPjsfDx/nj3PDzQn74wkh2joEZWeEEBjmvsXdPEy/8c19XpXGmoY/Rc4Tysg7uv3s70zPDyM4xEBPn/G9AS4uFW2/cxISMropl8iD7lUaRVf37clIZx1dffZV//etfVFVVkZqayn/+8x/GjbPfTTl9+nRWr17dZ/kZZ5zBd999N+BrdZ/8i994HzcPZWOSBKc23eZRxBUKjjX2jKNAOc4Yx1ONweofCPNION7N+EMRxvH35aS5q/rTTz/lzjvvZN68eWzfvp3U1FRmzZpFTY39O14XLVpEZWVlz2Pv3r1oNBouuuiiP7jlglMVcde14PdETMsjEAhORE4a4/jCCy9w/fXXM2fOHIYOHcobb7yBp6cn7777rt31AwMDCQ8P73ksX74cT09PYRwFxxRhHgW/B2JaHoFAcKJyUhhHs9nMtm3byM7O7lmmVqvJzs5mw4YNDmm88847XHLJJXj1Mz7PZDLR0tLS6yEQOIIwjwKBQCD4s3BSGMe6ujpsNhthYb3H/ISFhVFVVTXg9ps3b2bv3r389a9/7Xedp59+Gj8/v55HdLRz89cJ/tx0p82IqEKBQCAQnMqcFMbRVd555x1GjBjR7400APfffz/Nzc09j9LS0j+whYJTBVF9FBxLxDhHgUBwonFSGMfg4GA0Gg3V1b2/RKurqwkPDz/qtu3t7SxYsIDrrrvuqOu5u7vj6+vb6yEQKEGYR8GxQIxzFAgEJyInhXF0c3Nj9OjRrFixomeZJEmsWLGCjIyMo267cOFCTCYTV1zhXJKGQOAKwjwKBAKB4FTkpDCOAHfeeSdvvfUWH3zwAfv37+emm26ivb2dOXPmAHDVVVdx//3399nunXfe4dxzzyUo6OgxWEdDlmXqDxdhNRkVawCYOzowtdmfDd+ZtljN9qOYnNUR/L4I8ygQCASCU42TJnJw9uzZ1NbW8sgjj1BVVUVaWhpLly7tuWGmpKQE9W9SEPLy8li7di3Lli1T/Lq5S76lZMtGIkakMubya5ze3tTWRtn2LZRs3YRktTLjzvuc1rBZLNTk7ad813YaS4uZdOPf0Lq5OaUhSxINJYepzt1LTf4Bhp91HsGJyU63paOxgdqCg9Tm5xE5Mh3D8JFOa5jb22koOUxj8SHcPL1ImDLd6ZQRyWajva6W5ooyLJ2dxE2Y5HQKhixJdLY0015XS0d9HYbhqYpScSSrFWNbK6aWZjz8A9EfMczBXsa1/f2RsBqNWDs6AfAMUX6hoxRZkhQnifRoyLLLmbHGphY0bjq0HnrFWua2dtQ6HVp35z4nR2Izm2kuLsM3OkJRBnlPW9o7aCoqJiAhFp2Xc2ECgdJgchu6JgO3Gs2Ubc8nfHg8el9loQSyLFOxs5CwITFo9cqPDUB1bjGB8eEupcYANByuwjvUHzdP5ce4m/qiSgLjwlx6H7c2dn0GfQJcm7y5uriR0Bh/lz4PdeXNBIT5oNEq35+aKjN+Bht6vfIUptoaI55eWry8lFuGpkYzKhX4+St/37W3W+lotxIS6vp7RaCck8Y4AsydO5e5c+fafW7VqlV9lg0ePNjlytreb74gKCGJ9NnOd3V3NjWx/JnHaKksxzMwiNMffRq11rlDbjUZWfnCP6k+sA+tuzuzHvoHnk5GIEo2Gxvefo1D69cAMPmm25w2jbIss/urhez56nMAhp99niLTWLhmFRvefg0Aw7CRTLvtHqe/WKsP5PLTc09is1jwCTeQdfeDTv9QNJWXsezJhzG3t6Nxc2PSDXOdNo3GlmaWP/0ozRXlAAw761xSL7ikz3pHM49Wo5GV9z5Bw8GujNiA5Hgy7r/VqXZINokNT79M9fY9yJKEztuL0bfMITJjtFM62159j6Lvf0Kt1aLSaomdMZHUay91yuzkLVpC7ieL0Hp6oPPyJHBQAqnXXYre3/F4yMaCQ/z80D/RuLujD/RDH+BP4hmZxGVNcfi90lnfyLK5D+Dm7Y1XeAhe4SGEDE8h4bRMp6Lftr/6PvUHCvAMDcY3OgLfmEgiJ4wmNHWoQ9tLNhvt1bVsefG/tFVU4x0RRkBiHP6JcRjGphKQGDeghizLNB6uprO5jfWvfE3twTIC48MJHxGPYWQCkelJBMQOnDLTUlGPqa2TLe8tpWzLQcKGxRI1ehCRo5IxpCagc8BIttc1Y24zYjVbyP12A3u/XIdhZAIx41KImZBC6JDYAY9vR2MrlnYTks2GZJMo2biftS9/SUR6EnEThxKbMYygRMNRz7WxpQNzeyeyJCPLcldutiSz9b0fKN60n7iJw4ifPJyYCUP6Te/obDXR3tLVi9T9OyHLYGo388TsT4gaFEJ6VhJpmYlEJgfbbY+xw0zbL0bzt3z67CqK91WTlplIemYyKeOi0br1NW9mk5WWuna7GjtXFrLwudWkTkskPSuJkdMS8PLra5psVqknG/u31Bzo5OLMb5kyLYysHAMzssMJtWO8ZFmmssL+vlRVdnLZhWsYNyGYrBwD2TkGIqPsfy9UVXYiSX1/d9vbrVxw9iqGDPXr0UhItJ8vXVNjxGqR+iy3WmUuOf9nQkP1PRopQ5XFxgqUc1IZx+OBztOLKTffjsZJwwfg4e9P9Kix5K9sYuqtd+Hh5++0htZdT2RqOo0lh5l4wy0ExMQ5raHWaAgfOoLyXTsYee6FxE2Y5LSGSqUiOCEJdx8f4jOm2DVIjuAfHYPe14/A+ASmzb0LjZOVUwDvkFDcvLzxCAgk8877e1X4HMUrMAg3Ty/UWh3Tb7+X4IQkpzXcvX1Qa7Vo9XomXn8LMWPG97tud7f17qqKXuZRq9ej8+4yrIPOO52R117qdO61WqNGq3fH0tFJ5MQxjPnbdU4ZtSPbIksy7n6+jJ47h4jxo5zWQOqqnMp07U/yX2Y5vT/GhiZUahU2kwmPwABGXnsJIcOcu1Gks64ByWrD2NiEWqclLnsq8TOnOmUaWyuqqT9QAEBHTR2eIUGEjEgheHiKwxrWjk6W3Xw/6l+OQVtFNZ6hwfhEhjucCa5Sqfjk8qdAklH90v6GQ1WodVpCBkWh93PsgmfRTf+mubwOlUaNbJOo2FlIXX45bTVNaHQaIkcNfDG55P53qNjRdUxUahWyJFO+PZ/yHQUUb9rPiPMnM/i0sUf9IV/97GccXL6tz/KyLXmUbclj54KVpM2eQdqlM/p972x+ewk75v/U72vsX7yR/Ys34h8TypTbzictq6+RWfnpTv731Mp+NQ5uK+PgtjI++9cqJpw1lEsfmEFAWG+js2NFAa/d9k2/GgDL3t/Gsve3ER4fyJXzshk5tXf836FdlTxxySdH1djwbS4bvs1F7+3GeX+bRM7VY9DqfjWhdeXN3D3jzaNq/PB9BT98X4FOp+KGmwYx9/YUPI+oIJpMEhmjvj+qxuqV1axeWc2jD+7ksivjufu+YQQE9q4450xfTnOTpV+Njevr2Li+jicf3cNfzo3iwUdH9sm+vuKiNeQd6H8u5fKyDnZsb+C5Z/YxLTOMx55MJT7BvgkVHHtOqqzqP5LuvMmsex/CMMz5ytqR1Bw8QOggx39sfi+Nqty9hA8d7rJG2JBhLl3hVe7dTejgIWh0OpfaERSfiM5DeXdS9YFcvENC8QoKVqxRW3AQnYcn/pGOmQDoyrg+0jw2FZXQUVunzKj9QltFNXW5B4nNmqy8e7e1jX3zv2T4VReh81DWFWQzm9nx5kcMv+JC9AHOm1foqnysmfccSWdmYxiXpmh/ZFlm/ZP/xjA2jbisyU5X+qGr637fJ4toKSln8AVnEpTi/MWFLMtIFitb//MOOk8Pks7MxjfGuczkBnUeyZ4BaN11LH/8I9w89Qw9O4PQFOfmmjV3GNG6u/HzC5/TVtPE4NPGEj9pmFNd1uYOIyq1Go1Oy/aPfuTw+r0kZ40iMTMN7xB/hzVkSUatUaPSqClavZuNby4mcUYaidNTCRsaO+A5t3SasFlsqNRd66nUKlQqFT+/8AW1eaUkTBtJwrSRBCVGoFKp7GZVm40WzEZr1/YqFfzykp2tJh4970OSRkWQnplE2owk/ELsm3OLyYqp075Jev/hH6gsaiA9K4n0rCTiRxhQq/vul9Vio7PN/rj1rT/k8cWLa0jP7NIYOjEWvWff8yXZJNpbTPbbuHcxj9xWwoyscLJzDEydEYafX18NWZZparTfjvLyTi694GcmTg4hO8fAjKxwgkPsf0c0NZrt9vS1tlg494xVjEwPIDvHQNbMcAwR9quWzc1mJFtfDYtV5vwzVxId40V2joHsWQZi47z7vpbIqv5dEcaxH7pP/sVvvI+bh7LxRAJBf/zWPAp+pbvr0ZnqoH0NCbVG+bguAEtHJzpP17/8bWYLGjdlF0oN6jwAhgaGYbPa0Ghd3Cej2aFu6QF1Ok0uj2+Erh/GY/EDC9DZ1IaHf18jYc849kdHixGtuxY3d9c65Jpq2vAP7dsWZzV8g73sGk5HCWlYTLIhGa0L4yQbG0x4emlxd1f+3mtuNqPTqntVOZ2lo92K1Sbj63v0z5Iwjr8voqtaIDgOjFRFsLuqAkAYyN+gUqlQaVwbs9Sl4ZrBAo6JaQQUm0boukGm2zy6ahqBY2IagWNiGoFjZhoBu6bRWTx9j82NF66axmOlERisc8k0An26o5Vgr8rpLK6YTsGx46SZjkcgONUQ0/UIBAKB4GRDGEeB4DjSbR4FAoFAIDgZEMZRIDjOjFRFiKqjQCAQCE4KhHEUCE4AhHkUCAQCwcmAMI4CwQmEMI8Ce3QlyFQf72YIBIITmNdff52RI0fi6+uLr68vGRkZfP/9r3NzTp/eldJ25OPGG290+nWEcRQIThDEzTICgUAgUEpUVBTPPPMM27ZtY+vWrWRmZnLOOeewb9++nnWuv/56Kisrex7PPvus068j7m13Aktnp0sTTsOxyfIVnLo4mm0tEAgEAsGRnH322b3+fvLJJ3n99dfZuHEjw4YNA8DT05Pw8HCXXkdUHAegvqiAHZ/NZ+njD9LRWK9Io72+jvxVK/j5lReoyt2jSMNiNFKxZyc7PpvPoQ1rFWnIkkRzZQWFa1dRuHaV4hxvU3sbVfv3UbR2NZLVqrgt7fV1VO7bg9VkP/HAEWxmMy1VlRhb+o+ncrQ9VrP91IQ/mj975fFYZBIcq1wDm0XZ+/u3WDqNLmtI5mPTFqu5/zg4Z7BZbcdER7L1zSRWiiy5rnWs3jsnyvtYaJwatLS09HqYBvjdtNlsLFiwgPb2djIyMnqWf/LJJwQHBzN8+HDuv/9+Ojo6nG6LqDgOwIpnnwCVihl3/B2/CMdj5QA6m5r4+ZXnqc3vmrx31OwrnI4vtJqMrH/rNUq3bUaWJOImTCLtQudyoiVJYvv/PqRo7WrMHe0ExiWQfe/DTlU+ZVlm//ffkvfjUtrr63D38SH73oedjnIr3rSevYu/oqWqEtlmJeP6W9C6Oze5bPWBXHZ89gltdbUYm5tImpbFuKuuc0qjubKCrZ+8R2dTE8aWJnxCw5ly8x1oAwMd1jC2NLPx3TfpaGrE0tmBSqVm9KVXEZma7rCG1Wxmy4dv09HYiGS1Ikk2BmfNYsT4ieyh0qHKoyxJbH/jQ1oOl6HSqFGp1fjFRTPsigucig7c/+k31OUeROuhR+vhjt7fj0HnnY67r+MZsIXf/8ThH9fg7ufT9fD1ITR1GOGjRjisUb1jL1teegvP4EA8Q4K6HqFBRE/NQO/vWHJC06ESNjz1Ml5hIXhHhuMTEY5PZDgBSfFORSEWr1xL7idf4hcXjX98DH7x0fjFReMTZXAqlaZw8XIKFv9I4KAEAgYlEDgogcCkeHRejqdSVa/JZcdn7xCRloghNZGI1AT8okKc7sE4vG4fG17/lqjRg4gaM4io0cmKJs6uPVDCj49/TPT4IcROGELkqCRFk4I3l9ay9KF3iR4/hLiJQzGkJiqe6HzTW0uoL6wkfspwYicOwyvI+aSN1oZO/n3TIoZmxJKemUTciHBFyS3vP/QDAGmZSQybFIub3vlJ4Fd/tptdqwpJz0oidXoifsGO5ZIfycbVrXz18boBY/6ORu6+Zp6Yt5usmV0xf3Hxzr9fKis6uWPuFqZODyM7x8CgFF+n37utrVb+b84Gxo4PJjvHwPCR/i6l6vye1NT7oTO6njpnae+aOD06unfE6Lx583j00Uf7rL9nzx4yMjIwGo14e3vz5ZdfMnToUAAuu+wyYmNjiYiIYPfu3fz9738nLy+PRYsWOdUmYRwdIP3iy4lMdT5H2MPfH71f14/UoMwchpx+9gBb9EXrrsczIBBZkogYkUbG9begUjtXKFar1XgGBmHuaCcgJpasex7Czcu5LyCVSoVXUDDt9XV4+AeQde/DTuUzd+MZFExjyWG0ej3Tb7sfw3Dnc8C9goKpLypABkZdciVDTjvL6S8gT/8AavPzsBqNJE3PZuwVc5zOznb39qGh+BAdDfUEJSQx5ebb8Q5xrntZ6+ZGc0U5dYX5eAYGMfGGWwgf0pUnPhLHuq1VajXGhiZqduei1moYMvschsw+x+m0EmNTMxWbtgMQPWU8Qy89zynTCGBp76BuX9eFkkdQAMOvvojQ1GHOtaOhiY7aejpq6gAIH5NK1JRxDptGAFNTC22VNbSWVcK23XgZQkm58CxCRg51WKO5uIxdb83H3NpGe3UtFZu2EzgogeRzT8M7Ihwc8DamllZ+uOk+jE0tyDYb7dW1lK7ZREBSHIlnZhM/c6pDF1/fXXcnxqZGrO0mGg5VsffLdbh56Um7dAZjrpnlUBrM5ze8SGNxNVajGXO7kYai/2fvvOPbKu/2fWnLlveW994zy3GmYzuDHXaZYXRR6KL9vXS8LZ3QXdoXWihlFcqeAcJMyN7D2bFjO473tiVZWzrn94djE2M5luQACZzrg3B0JN16dM7R0X2+z3Oeu5MDL21AoVIy48ZqZt+2Ykrj985PHqfzYBOC043b6cJmMNPf1Entc+tQqJQUrJxPxR2XoA2Z/Adzw59f5sSmg4iCgOAWEN0ClgEjPcda2fPU+6h1WrKWzmTety4lMMLz/rfryfc4snrbSAVKBFEUQByJU7QOmGhYtw+A2PwUiq9exNyVEytVG186wFuPbGdcEevUnf5OE/W723j9/7YQGqWjrDqTS++sIDoxbJxG7boGnr1vncc2mg02jP0W1j1Xi1qrpGB+KlXXl1G6JGPc85oOdPLID97yqOF0uOhtNbD7vXpkMsgoS2DuxXlU31CGUvXxDtjXbuCPt7zoUUMlDnOyyc5HH3bxUyC/MJQLL07k9q9njktisdvdXFC91qMGwIlGE1s39/Lrew+QkRXMigvi+dodWRNSZVZe8BFGk+eqdutJM9u39vGH+w6TmBTI0uV6vnlXDnH68UPAbr9pKydODHvU6Gy3sHVzL3/781FiYrVUL43jG3dmk5bu27HqfKO1tXVc5KBmkoJLTk4OtbW1GAwGXn75ZVatWsWGDRvIz8/n61//+tjzioqK0Ov1VFdX09jYSEZGhkc9T0hZ1ZMwmjeZXbOc2Tfe5ve4RJfdxv5XX6Tsmhv8zs112mwceO1FSq64BqXGvzgsl93OgddfIv/CS9EG+5d36XY6OfDai2QuriY41r8xEoIgcOC1F0maMYfItHS/NAAOvvEKYUnJJM2Y7bfGsffXoFCryaqs8VujcdN6htpaKL36ehQ+Vl9Had27i9bdO5h1422oAyf+4B4Qp44m7Dt6nH2PPM3s732NsNSkSZ93JoytHWz59V+ZcccqYssK/dKw9Pbz4ffvJfOiGrIvvwCl1vcKlLV/kPU/uZ+AiDAKb7qKqPxsnzXsRhMbfnI/oiCSd80lJC4s9/n757LZ6Nx9gO2/f4iEeTPJvmwFkXlZPh0LBLdA56592AaN1P7raZIr55FxYRURWb7t++3b9+ActrDjLw+TMjePvIvmkrG4GKUP8YHNWw8jCgI2g4X3f/EfEmdkkr18NplVpV5XHFt31WE3WVColbhsTt756eNEpMWRVT2DzOoyItP1U2p07G9kuGcImVyOXCFHFATe/dmTBITqSF9cQsaSEhJmZJ2x6thzrIXBkz3IZHx8Ii2T0bLjKIde3UxMbhLpi0tIX1REVHYiYertE7Kq2xv6aDlyajjIJzbp07/8ALvVRfHCtJFq3xLP1b6+dgPH97R7bOPWNw5T+1EjaYVxlFZnMqM6k5SC2An7j6HPzOEtzR41Wut6eevh7UTqQ8Y0cucmT8jRtg7b2be2waOGum8Pf/ttB1FRGpbUxFG9TM/CxTEEBY0/sXS7Rd58vdWjhtXi5qf37EOnU1JZFUv1Mj2VVXGEhU/c/95+sw2nY+KQAUEQ+d97ahEEkUWVIxpLauKIiZn4m7b2g05MRs/m8/5fH2Kg307FguhTVVQ9CYnjj5vnQlb1Fa/826cehclwmi28euVX/f4sNTU1ZGRk8Mgjj0x4zGw2ExQUxLvvvsvy5cu91pSM4ySMbvxr/vkE6kDfuwdO52xcECMKgs+Vxi+yhuB2+23Ez6aG2+n0uVLpj8YBseOMxtFlsyFXqZEr/F+vLrsDuULu8/CD03E7HDgtVrRh3ncHfxJRFBmoayQyN3NaGr0HjxFdlDut7561fxBREAiMjvRbA2C4sxtNaMi0sq+HO7sxaluZkeX/egEYau1BqVETFBM2LR1jRz9uh4vw1Nhp6Zi6BzH3GYjNT5n2cbJ1dx1hSTEEx4aPWx6q2jbBOE7angELTQc6yatImWDQfGH3+/VklOgJj/W/Ela3qxVtkJrk3Bj/182R1wiS6SmdEeF3t25To4nuLhuz5kSiUvl3jOnqtHL0iIGK+dFotf4dd41GJ1s397BwcSy6M+RWS8bxY6qqqkhOTubJJ5+c8NiWLVtYsGAB+/fvp7jY+94/yThOwphxfPhJ1AHT3/gSEtNlKvMo8cVnQF5HfsT0jNqXEV+M4xeNHPl7xAb437tzPvJlNY4//vGPueCCC0hOTsZkMvHss8/y+9//nvfee4/09HSeffZZLrzwQiIjIzlw4ADf//73SUxMZMOGDT61SRrjKCFxHiFN0yMhISEh4Ymenh5uvvlmOjs7CQ0Npbi4mPfee4+lS5fS2trKhx9+yAMPPIDZbCYpKYkrr7yS//3f//X5fSTjOAWNbb3kZaV83s2QkJDmeJSQkJCQmJTHHnts0seSkpJ8rixOhjSP4xQUqaOob+2hvvXLOaeexLnFl32ORwkJCQmJzxfJOHpBqToaQDKQEucEknn8ciNlVktISHyeSMbRS0rV0ZKBlDhnGDWPEl8uIoScz7sJEhISX3Ik4+gjnzSQEhKfF8WyeKnqKCEhISHxmSIZRz8ZNZBS9VHi80YyjxISEhISnxWScZwCwe2iq/kYez54iYOb10wIWfe2+9rc38eJbZs5+t7bCMLEWfW9we1y0X+ikcbN63E7HH5pANhMRrqOHMI+7DnSyRtEUcQyNIjNZPRbY1THZbdNS+PLjDTeUUJCQkLis0SajmcKnr3/27gcNuLScrnwqz/1OHv/qHmsdfSOmcfspBisQ0Pse+m/dB87grmvl8CISJb/76+R+5CaMhpZ2Hu8joGTJ5DJFVT94Mco1N5HjQmCwJG3X6e77ihDrS1YhwaZce2NxOX7FivXsHEdXUcOYezswNTVSXxxKfO+fpdPGq17d9NeuxtzXx/D/b0oNVoW3fl9nyIMexvqadm1HathCJvRgNvhoOyaG4jJzvVaw9DZQePGdTgsFpxWC06LhdSKBaTNW+h1QoPNaKR+7Xu4nU7cLieCy0lYQjJZS2q8TsZxORwcefsN3C7nqfeVodRqya5a5jF+0BNFYhwfffACGxwO4iIjUKjVKDQq9LNKfEpwaXh7Lda+ftRBOlTBOtRBOoL0sYSlJXut0fzhJvqOHScgPAxtRDgBkWEERIQTkhzv9T7btfcgHTv2oouLISguGl1cDLq4GFQB3sdtioLAvn89g1yhICQ5gZCkeEKSE1AH+ZYCZRsysufBxwlJTiA8PYWwzFR0sdE+pXi4HQ72PPQkwQlxROZkEp6d7tNnGcVuGmbfw/9BmxtM9PwyIjPi/UoKshktbPzLy8SXZpA0O4fQhCifNWAkF3rTX18hviSdpPI8dJH+TbTssjnY8tAbJJRlkjQnF02Q/+k6de/txmYwk7agkJB4/9J+LEYbbz68nZLKdLJmJKJQ+ldfeeexnUTqQyhamEZAsO/RmzCSPmPst1BWleF3As3e7SY6jx+nZpme5FTvoiU/ydHDBtav66J6qZ6snGC/Umw62i288GwzNcv0FBaH+aVhNDp55KF6KqtjmTEzEoVieklDEv4hGccpcDlshMUksGzV/0OpOvMP3ycNZHZSDHaTCXNfL5rgEGru+Rm6SN8O0kqNFrfTQV/jcRRqNUvuvofY3HyfNORyOTK5nM6D+wGYc/NXya5e5pMGgEKlpnnbZgBylq5g1vW3+BwdqAkKomHDOgDi8gtZeNfdaHS+Hcy0IaHUffAOgttNiD6exd/5IaHxiT5pBISF0bB+LQ6LGZU2gLm3fYOU8nk+aWiCgmjaspHh3m5kMhkFl1xO5uIqn9aJUq2m+9gRuo8dBiA2r4C5t33Ta9MII1m92pYBmjZvoAvQ6WOYcccqn2P/bAODHHnu9RFNhYLMi2uILsrzSUNwu2l868Ox+8EJeopWXU1YuvfmE+D46vfhVHVfqdWQefFS8r5ymdfGb+hEC70HjzLUeHJsmS4umpLbrydxwRyvfrTM3b0cfPJFeg8epW3zzrHlUfnZzLzrNq8+k9NsYd8jz9Bde4gT760HQCaXEZGTSclXrye6wLuLXfY8+ASOYTPd+w5hX2uk7qF3UAcFkDQ7m4pvXkJkxtQXS217+E2Gu4dw2Ry07qrj6FvbAQiJjyRpdg4zbqwmIu3MWdP7nl3HQHMXgtON4HLTuruOg69sAiA6O5HkuXnkXjiHqMzJU1qOvLmN7iMtiIKAKAgIboGWHceofe4j5Ao58WWZpM4rIL2yhPBkz/OVNq6vpXVXPYgi4qkbIph7DTRtPMD6P7xAZLqe1AWFpC0sInjmxF6eI9tOsueDesZ1Ip26s3NNHW89vB1dqJbixemUVWVSvDgdXeh4w998qItNrx702MYTB7s4vqcdhUpOXnkyZdWZlFZlEpMUNu553ScH+eCpPR41hvrM7HjrKE/ASOZ1VQZl1VmkFo7PvDYNWHjjwa0eNbS2Qd54voFf/uwAWTnBVC/VU7NMz4xZ442X0ylw3y89fxa3W+SZp5r43W8OkZQcSM2yEY05FdGo1eOPd3/47SGsVrdHnReebeaBPx0lNk471o55C6IJCBxvRf75f3X0dHvuiXrzjTYefOAYEZFqllSPZG8vqowlOHh60a8S3iMZxykIi47nwtt/gjbQe3MzZiBbewiduwRzfx/zvn4XIXH+XQmbubiGvobjlF17I3F5vlUJR0maOYfWPbvIrlpG+oLFfmnE5uQTk51HQmkZ+Rde5tcZY1hiMvrCEnSRUcy5+Xa/cpF1EZEklMxAFEXmff0un0zWKCptAEmz5jB4spmFPlY8R5HJ5aTMmcvJnduZ/427iM7y74rXhLKZDDQ3MeMrN5FZWe3Xeo3LL6J5+xaiLqpm4W3XodR4X5EeJSInA5lchn52KSVfvYGQJN/316C4GFRBgSi1WgpvvJLUpYt8zgNXB+vQhoXittvJunQ52ZevQBPqWzVLqdGgi41hqPEkETkZ5F51MQnzZvtUoVOo1WjCQgiMicJhMpM4fzaZlyzzKf9arlKiUKsIiAjD0tNHRHY66SuWkLy4wqc4MplcTkBkOOogHXaDkdD8JGZcOp/spTPRhnpnpgW3gFKrJiBMh1qnxTo0TFR2ItnLZpJdM5PQxKlPat0OF4ig1KpRqBQotSP7WXBcBAkzs0lbVERk+pnNp8vuxGVzIFPIkcllKDUqFMqRfUQdFEBwXARhydFnzNJ22Zw4zLaR7SDj1F/ZuKFEcpUSpUaFKkCNzENGs8vhxmKyAyDj48dlMsZ03C4Bp92Fy+nG7ZpoPl3OjzU86QMILgHrsAPrsAPbsANRFMftP4JbwGz0bJLs5o+HJJlNNiwmOxaTDbdLQKn6+HsluMVJNdz2j02cyejCZHRiNDpxOgUUp303RREMBs9DoE4312azC5PJhdHkwuFwTzCOBqMTq8U1ic6IkNXiHmuH3S7wyVRfk8k5eVuE8RomoxOr1S0Zx88QKat6Esayqn/4F8Ji/M84rXX0Yh8aRBMWTnaS/2kf5v4+n6uVn2S4t4eg6OkljpwNDWNXJ8GxcX4ZpHEaMbE+VzxPx9DZQVBUNAqV/wccU3cX2pBQVAH+d68N9/YgUyjQRfjXtQZgHujH7bDTfCrG2J9kGbvByGDjSeJmFPndDrfDQeOadWRcWOXTcIrTEdwC9a+tIX3FEp+7lk+nc1ctygAtUQU509rXGt/5CP3sEgKjIvzWaHh7LVF5WT5XXk9HcAvUv/4OiRWzcCQO+Z1Z7Xa62PffdaRXFhOR6vsJ05iOy82eJ98nuSKP2PwUv9ex2+lix6NrSJ6TS3xpBnKlbycap1P/wR6sQ8OkLywiOO7j7eVLVrXFaOPVv22meHE6eeXJqDT+1VdW/2MboVGBlC7JJDTav/14x5pj9LUZKKvORJ8e4dc6Nm1/mYZaNdXL9BQUhvqlceTwEG+93kbVMj1lMyL86iLuaLfw74ePU71Mz5y5UahUvh+7jUYnf/n9YRYtiWXe/Bi0AZ73lS9rVvVnhWQcJ2F049/yqydQa6e/8WsdvQDTMo8SEt5wQOyQIgm/4AzI6/w2jl9GfDGOXzRy5O8RG5D+eTfjM0Uyjp8u0lXVnxHS5OESnxXS/I4SEhISEp8WknH8DJEmD5f4LJHMo4SEhITE2UYyjp8D0uThEp82UiShhISEhMSnwXljHB966CFSU1PRarWUl5ezc+fOMz5/aGiIO++8E71ej0ajITs7mzVr1nxGrfUOqfoo8WkidVlLSEhISJxtzgvj+MILL3D33Xdz7733snfvXkpKSli+fDk9PZ5/FB0OB0uXLqW5uZmXX36Zuro6Hn30URISzr3B0VL1UeLTRjKPX0yODHR/3k2QkJD4EnJeGMe//OUvfO1rX+PWW28lPz+fhx9+mMDAQB5//HGPz3/88ccZGBjg9ddfZ/78+aSmprJ48WJKSkomfQ+73Y7RaBx3+yyRLp6R+DSQIgm/mEQI/s0ZKiEhITFdznnj6HA42LNnDzU1NWPL5HI5NTU1bNu2zeNrVq9eTUVFBXfeeSexsbEUFhZy33334XZ7ns0e4P777yc0NHTslpSUdNY/y1RIF89IfBpI4x0lJCQkJM4W57xx7Ovrw+12Exs7fs6y2NhYurq6PL6mqamJl19+GbfbzZo1a/jZz37Gn//8Z37zm99M+j4//vGPMRgMY7fW1taxxwRBoL+jmaM71mKzDPv9WRw2Kx2NRxge6j/j86aqPtqMBiyDA363A0Zm8HfZPScNSHwxkaqOEhISEhLT5QsZOSgIAjExMfzrX/9CoVAwc+ZM2tvb+eMf/8i9997r8TUajQaNZmIQ/ftP/Yne9iacdhtVX7nLp+hBi2mIpv3b6G1rpLetkaHeTgrnraDi0lVTvnbUPO4ZbmPL62+gsQwx1NbCUGsLIfEJLPnePV63QxAEWnfvwNDZjqmrE2NnBy6HgwV3fJfwJO+TLDoO1mJob8Pc34e5rxerYYjCSy4nsWyW1xp9jQ0MtbdgMwxhNRiwGYZIKJlB2vxFXicaDLW1MtjSjMNixmGx4LCYCQyPIKdmhdfxdub+PvpPNOJ2OnE7HLidDmQKBRnzF3udeGIzGek5dgQRRjK5RBERiM3NJyA0zCsNt8NB5+EDyJUq5EolCtXI38DwCK81REGgbd9u5EoVqoAAVAGBqAICUAfqxuIYi2XxHBA7ONrVM+nk4B079uJ2ONGEhaANC0ETFopaF+hTOk/P/iM4LVYCYyIJjI5EHRzkc1KFY9hM66YdBMXHEhwfR0BkuF8JQT0Hj2Lp6Sc0NZGQpHi/k2xOfLgRpVZLRGYagbFRfiVvdOzYi0wuJzI3E3Wwb9nsp9OyYRsBkeFEZGeAdurnT0b9+7sJS44hOjtxWulLDR/VEpEWR3hK7LTSeZo2HCAmP5mg6DC/NQC6j5xEFaAmPNX/ZCqbxUH97rZppcYAHN7STEpBLEFh/qdKNR3oJCQykKgE33LnT6exzopJZyQjM9jvdXKyeZjhYRf5Bf4lzwB0d1lpa7VQ6mfyDIwkxxw6MMjscv+SZyTODue8cYyKikKhUNDdPX4geHd3N3FxnuOy9Ho9KpVqXA5nXl4eXV1dOBwO1D78gHQ0HgZg4ZVfJ7NsgU9tD9CFcPLoHtqPjwTHF86/gIpLV/n0xZsZlMirJ1/ixN7tAMQXl7HorrtRejC5kyGXyzF0tHPgtRcBCI1PoOqHP/U5wtBmMLDnuf8AoNbpWPTtHxKXV+CThsthZ/tjDwMgUyiYdcMtPplGALlSwbbHH0ZwOgFIX7CYksuv9ikTWanRsuPJf2E3mQCIyshi/je+7ZO5UOuC2Pfyc5i6OgHQBAcz56bb0YZ4f5BXqNUc++Adug6P7CMyhYL8FRdTdNlVXmvI5HI6DtZy/KMPx5ZFZWYz+8ZbiUzLGFs2ah4nY7izm30PPz12X6nVkHvNpeRedTEKtXexjHaDka33/f3jz6fRkHlxDQU3XokqwDunYzcYqX/tHYwt7ac01IRlpFL61euJys/2SsPaP0j/0eMcePx5AGRyGUH6OOJml1B445VeRRk6TMN07Kylc1ctrRtGvn/qkCDCM9NIWlhO2rLKKbOv3Q4HHdv30nPoGA2r3wcgOCmeqLwsogtzSV4yH4Vq6sNw+7bdOMwWOnfW0rpxOwq1ipC8BAxz8kmcmUXijKwpDWDLjmPYjGbcdieNG/bT+NF+tKE6Emdlkzwnl6Q5OYQmRp/xu9ixvxHroAm3043gctP40X4a1u0jWB9Bytx8UirySJqTiyZocrPUc6yV4Z5BREFAFEREQeTo29s58YNDRGcnkjq/gNT5hcQVpk4aPzhwohNDe99IhrIogjjSizLQ3MXWB98gNCGK1AWFpC0sJGFGFnjYfXtah+ho6B8XxDz6z2d+8yGGXjNFC9IorcqYNDZwsNvEySOeK/lbXjvEjjXHyJ6ZQFl11qSxgcNDVhr2ef5eNh/q4pW/biIpJ5rSqkzKqjPJKNFP2O/sVifHdrR41FB2mvndTz8gNU1H9VL9pJF/brfIxvWeL7gaNjn57rd2EROrpXppHNXL9MxbEINWO3H7bN7QjdM1MZDO5RT47rd2odUqqDqlsXBxDEFBEzfOzu19mM0T865FUeTHP9yHxeKickks1cv0LKmOIzTMv5NCCf84542jWq1m5syZrF27lpUrVwIjFbS1a9dy1113eXzN/PnzefbZZxEEAfmpg2l9fT16vd4n0wigUKqZc8FXyCuv9rntMrmc5LwZ9LU1kTVzERWX+GYaRynMmsGO+qNoU9KJv2KVT6ZxlIjUdAIjItFFRlH5vXvQBPle9QiO06OLikauULLk7nsIifN97JwuIpKg6FhcDhuL7voBMdm5Pmtog0MJiorG3N/HnJu/SsbCSp81VAEB6CKicAwPU3jZlRRdeqVPxhNGDHlQZDSmrk6SZ5UzZ9VXfTKNowSEhgMjZq/8lq/7VAUeRakd+aEOCAun7JobSKtYMKmRmKzq6LLZkSsViIJI+gVLKLjhSgIiwnxqh2PYjFylQnA6iZtVQuFNVxKZk+mThqG5DafFCoAmNJjslReQeXGNT5W64c5uWtZvQ6FW4XY4icjJJPvyC0icNwu50rvDnt1o4shzr6HUjnzfZHIZ0QU5ZFxUQ9yMIq8qdYLTxf4nnh+XZS6TywlJSUQ/u9Qr0wiw/7HnkCkUiIIAgNvhxDFkRqlREZoQ5VVbdjz6NuZ+I0q1CpfNAYDNYKbnaAuhCVHEDKdMqbH3mQ/pOdqKXKlArpTjtI7omDoHaFi3D8HtRhWgIXlu3qTHukOvbebEpoPI5HJkchkymQyHZWTYTG99G0NtvQy19pJ/SQWp8z2fmNZ/sJdDr24GGSPvIwOQjTk/Q3sf+19YT8e+BnJWzGbRjQEQ/Il2bDrBa3/fMnb/9OYOD9lw2l3sfr+eQ1ubObi5mZV3zSMxO3qcRuP+Tp782Xse2+i0uRAFkbpdbdTvaWfvh8e54PbZzFyWPW7ddJ0Y4N8/8jxVnOAe2d6tdb201vWy54N6Kq8pYenNM1GqPz5eGfvNPHqPZw2FaAeg+YSZx/7VwHvvdHDF1cnccVcOgbqP9z+nU+D/fW+PRw0AQRDp7LDyzFMneP/dTi65LJFvfz+X8Ijxv0c//+l+jAanRw273Y3Z7OKl50/y4fudLL8gnu//v3zi9ONPNP78+yM0Npg8ahiGHDgcAqtfb2Pdh11ULdVz9//kkZYe7PH5Emef8yKr+oUXXmDVqlU88sgjzJkzhwceeIAXX3yRY8eOERsby80330xCQgL3338/AK2trRQUFLBq1Sq+/e1vc/z4cW677Ta+853v8NOf/tSr9xzNm1xwxdfIn1sz9QvOQN3u9WTPXDytrpz63RvILFvAAffI2EZ/Mq8bN68nZc48lH522QE0bdlIQskMv4znKM07thKdlYMuItJvjZbdOwiJ0xOW6LvJGqV9/z7UgYFEZ/l/hWrX0cPYjQZSyuf5rdF/opH+5iayFlf73W1o7OqkceNHFF5y+TiT4onJsqydZgu7/vZvCm+6ipAk/y6ocdnsbPvdg+Rdc4nX1UFPHH72NdRBgaQtqxwzbr5iNxjZ9/DTZF22nMhc38zr6bRu3onhRAvpK5YQGO3fPtu5q5b27XtIW1ZJRHa638eC46vfx9LTR3LVfIQMKwWRnntdpmLnY+9gN1nIWjqT2PwUv9uz9R+rsZusZFaVklCWOWmFcCo++v3zCC6BjMoSEmdlo9R4V+H+JMc/3Muh17eQvqiYtEVFhMRFAL5lVTvsLn5/0/OkFsZSVpVF7pykcSbNW/7zi/cx9lsoq86keFE6wRG+5xZvfOkAW984TFl1JqVVmcSmhPusMbj5JZ76vyFqlumpXhpHdm6Iz9v72BEDP/zebqpq9NQs01NYHIZc7ptGV6eV227aysLFMdQs0zNjVqTPXdZms4vrr9xI2cwIapbpmVMRjVo98ZgpZVV/upwXxhHgwQcf5I9//CNdXV2Ulpby97//nfLycgAqKytJTU3lySefHHv+tm3b+P73v09tbS0JCQncfvvt3HPPPeO6r8/E6Ma/5VdPoNZOf+OfbWodvYB/BlJCwpN5FEVxWic3Z0vjbOp8kRmQ1wGQHxE7xTM/Pc617S0KgseTL1+Mo+AWxiqh00FwC1MOZZgKt0tAoZyeRobwLgnBGVM/8Qy4XALKabbjbGi43SJyOVNuG8k4frqc813Vo9x1112Tdk2vX79+wrKKigq2b9/+Kbfq86NUHU2to5f61h7JPEr4xSe7rM/GD/fZMnuSaZyaCCFnzDx+Xpxr23s6F/qMMl2zdzZ1pmsaAZTK6a/b6Rq+s6Xh70U1EmcX6bKk8xhpzkcJf5HmdpSQkJCQ8AfJOJ7nSJGFEtNBmttRQkJCQsIXJOP4BUGqPkr4ilR1lJCQkJDwFck4foGQzKOErxTL4qWqo4SEhISE10jG8QuG1HUt4Q+SeZSQkJCQ8AbJOH5BkaqPEt4idVlLSEhISHiLZBx9wGIaYqhn8sg2bxBFEZtleNptEUWRqabglMyjhC9IVUcJCQkJiak4b+Zx/Lyo/egNBrvb6GtvQqnWcsk3fu71a90uFz2txxnsamWgq4WBrlaGh/pZcu230Kfne6UhCAIDHc0Y+row9HUy1NeJsa+L3PJqcmZVTvn6UnU0oiiyfe8B7EMDhMtcmPt7Ge7rJTI1ndxlF3o999lwXy/WocGRm2EIm2EIpUZD7vKLUXgZ42YZGsRmMOAwD+MwD2M3m3E77KQvqEQd6N1kqfZhE/bhYdwOOy6HA5fdjtthJyojy+vIP4fZjH3YhCgKpzJzR26aoBACIyK80nDZ7djNw8jlCuRKBTK5ArlCjkyh9Hp9CC4XlsEBlBotSo0GhVrt15x2pu4uFGo1mqBgFCrfUzeKZfHs7j+CXReIOkjn97x6DtMwDrOFwOhInyMcT8fQ3IY2MgyNDzGDn8RptjDc3UtIUoLX0X6fRHC5MLV3EZwYP615+WxDBgSXm8Ao7/atybD09qMM0I5lbUcIORwZqPN5EnBT1wABYUEotdPL+B3uHSIwPNjvxJhRzP1GAiOCpz2fo81gRhMcMK35HF0ONy6XG23g9NaNxWgjMMS7fPZPU2PY6CZGO70J1k0mJzqd0ue0mNMxm11oNPJpzedos7qRyUGjmd7+JjE9JOM4BbUfvQ5AWEwCF3/j5wQGh3n9WrlCQdP+bRzeOpJlqgkM5sKv/oToxHSvNWQyGc1HdrP3w1dG7ssVVF5zB1kzFvqkoWs8ycG3nh5bln/hpT6ZRoDe+mNseeT/xu7rC0tYcMd3vTZJAMbODtb+4ddjmbtB0TEsvOtur00jgGVwkHd/9VPcjpEMVqVGw+ybbkcT7P2s+m6nk/fvuxfr0ODYsszFVcy47mavNQA++vP9DLW1jN2PTMtg7m3fJDx56txfAJlCwZ5nn6J1765TC2QEhkcw8/pVJM8q9/pg33n4ADuf+jcACrUGTVAQGQuXUHjxShReRkzGnjTz+t1fR65SERAZhjYinKi8LApuuAJV4JljDE9n3Q9/hW1giMDoSHRx0ejiYsi6bDnh6d6tE8Hlom3LTg49/TKa0BBCkuIJTtITkZ1B2tJFXmVNC24Bp8XKtt/+neGuHkKS4glLTyEsPZnYskLCM1Kn1BCFEY09Dz7OYEMz4ZlpRGSnj9xyMgjyENs4QUMUcVlt2A0mPvz+vaiDdEQVZBOVl01UQTahqcleGVKX3YHLasPU1smG//09YenJxBTnE1Och6JIAV74UbfThcvuxO1w0XOshXd++gTxJRkkz8kleW4u0dmJUx4PBJcbweXG7XIjugXadtez/g8vkFyeR0pFPikV+QTFhJ1Zwz16oiaO/T329g4OvLyR1PkFpM4vJHFWNqozmFpREBAEEUZ7XkRAFOk93sa7P32CtAWFpC0sJGlOLupAz8ZLEEREYaTX5vTeG1EUue+6ZwmOCKSsKpPSqgyiEjyfkI62wROv/m0zx/e2U1adSVl1Jsm5MR6/z6L4cTs+Se1Hjbz29y2UVWVQVp1J1sxElKqJpulMGsePWbnpwnepWhpHzTI9c+dFo9V61jh1WJ5Ab4+Nmis3sagylpplcSxcHDsu5/p03G7P7bBZ3Sxd9AEzZ0dSs0xPZXUsoaGet/FkGqIoclHNOjKzgqleqqeqJo6o6OkZawnfkYzjFKgDdOhCwrno6z/zyTTCiGGzmU2oNAGoNFou+tr/Eh6b6LOGw2ZFoVIjl8lZevMPSMwu9kkDAFFEJlcAIimXfoUZl6/0WUImlyOTyRBFkYKLLqPkquuQ+3hmL5fLx0xj0ozZVHz1W6h1Op80lBrNmGmMSMtgwTe/Q0ic3icNhUaNfdgEQEBYOHNv+yYJJWW+aahUWA1DY20qveo6smtW+LROZDIZpt6Pu4jT5y+m7JrrCQgN86ktbfv2jP07JE7PzOtuJi6/0CeN9toRDcHpxG13kFI5j/QVS1Cova9g1r/xHta+kTx1c08fETkZZF+2grB07zPFT3ywiUNPvwyM5E2bFHL0c0pJWljulWkE6N57gI0/+8PYfUNzKwqtBv3sEkKSvfsOGprbeO9bPxq733vwKMbWduQKBRFZ3p38OUzDvH7NN8buuyxWWnr66D9yHLvBiCYk2Kv869eu+iqC0zV2f/D4CQaPn6B143ZiqvPI+Xo02pAzn3z956pfYmzvH7esdecxWnceY+8zQRRePp/Zt61AFTB5Nvgrd/yNjn0NE5Yf/3Avxz/ciypAw8xVS5l509JJ86bf+98nqP9gj8fHDry0kQMvbUQTEsi8b11K4eULPBrrTX97jX3/XTtpOw+/sZXDb2xFoVZSdn0VNd+IgOBPtOPxXTx737pJNQAObGjiqXshtzyJG39eQ0re+MrujreP8o/vrj6jRtP+Tl75yyZiksO49n8qmX1BzjgDWb+rjd985b9n1HjnsQHeeWwXulAtl9xRwfJbZo3Lz+5pGeKHSx45o8bTTzTx9BNNBAYquOX2TO76fi6608yf3S6Qk/L6GTVefK6ZF59rRqORc9W1Kfy/HxcQHjF+fynLfxPDkHNSjfY2C6tfa0WhkHHRJQn89BfFxOnHn5heUPUhdceMk2o01Jt49+0OZDKorI7j3l8Xk5YePOnzJc4u501W9WfNaN7kzKVXk1+xlIAg77pAP4ngdrFv3etkzVxESIR/0YBul4u9a18hrXAOUQlpfrdj9/svkZBVRG/yyMHP16hCwe3mwGsvEZaYROrc+X61QxRFDq1+FYVGQ97yi/zuPjnyzpvYTSaKr7jGp4rn6TRsXEf30SPMuvEWNDr/ukRb9+zk+Ia1zLn5qwRFRful0X+ike1P/Is5N99OdGa2Xxp28zAf3HcvecsvJm3BYp8NPYx0379/371oK4qZf8MVKLW+n8k7zBYOPP4cLouNvK9cRmiKbydKAE6LFUNzK7seeJScqy4ipXK+T+YVwGWz4zRbOPiflxBcLrIuXU5kjm95vW6nC9vAIHWvrMHU1kH6hdXEl8/wqdtbFASGO7tR6QJZ/6P7CM9KI61mEdFFuT5V+01tnSg0alw2Gx/d81sS5s0ipXIeUfnZDCqPe9VVPdjSg0wuQ6FSYmjrZc2P/k1GZSlZNTNInJnlVXezob0Pt9OFXCFHrlTQdaiZj373PGkLi8hcUkpyee6U3d/GrgGcZtupPGg5yGU0rqtlz9MfkLagkPTFxSTPzZu0Ughg7jNgHRoGZMhkgGzk7+DJbt758eMkzsombVERaQuLCImL8JhVbey3YOg9bbz5aceif3x3NQ67k7KqkWphzuwkj5U+s9HGQIdng/P+U3vY9W4dxYvTKavOpHhxOjoP3c42i4Oek0MeNY7uaOGF331EXkXKSOWyKpPI+Ik9K067i86mAY8anFjHz797ktnlUVQvG6k6pmdMNFmCIFJ31PNn6e+3c8v1myksCqd6mZ6aZXpy80M8Hr/rjxk9VgxtNjc3XrOJ5BTdmEZxabjH7u+mRhN228TypyCK3HbjVnQ6xZjGzNmRE7q/pazqTxfJOE7C6MZf9cvH0QT4VhH7JKI4vfElMDLW0R8zcDpul2ucyap19AK+GUi3w+F11+dkuOx2lJrJqxre4LTZUPlhbE7HYbH41EU+mYYqIGBa29dhsaDUaqe1fd0uF6LbhVLj/zoR3G5cNhvHAg0A43KsfcFuGp7W2EQYOVgqA7TTzh52Wqw+dbN7wm40oQmZXjXD7XAiuN2oAqa5z5otKDXqcZXXAbnvYxwtgya0wYHTHps43DtEQHgwimnqGNr6CI4Ln3Z7jJ39aEN0qHXj17Mn4zgZTruLntYh4jMip/W9bjnaQ0JW1LTypjub+gmPDUar8/+YG9S+moyYDELD/Nfo7LCgVMqJjvF//+3vs2OzuUlI9P+YazI56eu1TVldlIzjp4tPe7TT6eRHP/oRs2fP5uKLL+Yf//gHNptt7HGDwcDzzz/Pf/7zH/bt23fWG/t5MF3Dd7Y0pmsagQmVOX+uup6uaQSmbRqBaZtGYNqmcVRjuttXHRg47e2rUCqnZRphZEyuWqeb9vQ80zWNACpd4LRNIzBt0whM2zQCKNSqaZtGALUu0Ovu+jNxNi5oAQiKDpu2aQQITYw6K+0J0UdOMI2+otIoSciMmvb3OjkvZlqmEUCfHjkt0wiQkKSZlmkE0McHTss0AkRGaaZlGgGCg1VSl/QZ+Oc//0lxcTEhISGEhIRQUVHBO++8M/a4zWbjzjvvJDIykqCgIK688kq6u7t9fh+f9urf//73/OEPf2DPnj2sWbOGb3/72xQWFnLixAmOHz9OQUEBN9xwA7feeiuzZs0iJyeHF154wedGSXx2SFP2SEyGND2PhISExPlDYmIiv/vd79izZw+7d++mqqqKyy67jMOHDwPw/e9/nzfffJOXXnqJDRs20NHRwRVXXOHz+/h06vrCCy+g0Wh46aWXCA0N5eGHH+a5557jqquuIj4+no6ODjIyMpg1axa1tbXU1dVx/fXXc+TIEX75y1/63DiJz4ZSdTS1jl7qW3t8Hvco8cWkWBbPAXF6c5ZKSEhISHx2XHLJJePu//a3v+Wf//wn27dvJzExkccee4xnn32WqqoqAJ544gny8vLYvn07c+fO9fp9fDKOTU1NLFmyhIsvvhiAhQsXotfr+ctf/kJtbS3l5eVs2LAB9anuzC1btnDjjTfym9/8hhUrVlBRUeHL20l8hkjmUcITR7t6/B7rKCEhISFxdjAax1+4pNFo0Jxh2Jfb7eall17CbDZTUVHBnj17cDqd1NTUjD0nNzeX5ORktm3b5pNx9Kmr2u12Exw8fnzBz3/+cwJPjRX74Q9/OGYaAebPn89bb72FXC7noYce8uWtJD4HpG5ridORogjPH44M+D5OSUJC4tND2atE1T39m7J3pL6XlJREaGjo2O3+++/3+L4HDx4kKCgIjUbDN7/5TV577TXy8/Pp6upCrVYTFhY27vmxsbF0dXX59tl8eXJ8fDwHDx4ctywkJIRZs2axadMmysvLJ7ymoKCA8vJyNm/e7FPDJD4fRs1j7SnzKFUfJaSq47lNhJDDgLzu826GhITEp0hra+u4q6onqzbm5ORQW1uLwWDg5ZdfZtWqVWzYsOGstsWnimNVVRV1dXX885//HLe8oKAAGHGunkhOTvbryh2Jzw+p+igBUtVRQkJC4lxg9Erp0dtkxlGtVpOZmcnMmTO5//77KSkp4W9/+xtxcXE4HA6GhobGPb+7u5u4uDif2uKTcbznnnvQarXcddddXHvttaxePTJP0kMPPUR/fz+qSTJyW1pazsqUNOcCI9FOk+QyfcGQzKPEKNIV1hISEhLnH4IgYLfbmTlzJiqVirVrP05cqquro6WlxefrT3zqqs7KyuK1117j+uuv56WXXuLll0diwbKzs5kzZ87YrbS0dMxEbty4kW3btlFY6Fv82blCe8NhTAM9DHa3MtjdSmJWCTOXXuX16x02C4a+Lgx9nRh6OzH0dxGgC2H2iq+gVHk3t5bb5cJs6Gd4sBfTUB/Dg724XU5Kl6xErfVuXixRFLFbhrGYBrEYh7CYhrANG8icsfCMUYqfvGjG7XLhGB7Gbh7GYR7GPjxMeFIyQdHed2WKoojgdOJy2HHabLjsdlRaLbrIKK81TtcS3W4EtxtRFM/K/I4S4/H1CuuzMeG9KAjTnsfxbLTjbOmM5iycDZ1z5TNJSEicW/z4xz/mggsuIDk5GZPJxLPPPsv69et57733CA0N5fbbb+fuu+8mIiKCkJAQvv3tb1NRUeHThTHgR1b1smXLaGho4N///jfvvPMOu3btoq6ujrq6Op555hlgpFRaWlpKbm4uL730EgDf+MY3ziR7zvLBf/409u+ZS69iRs1VPh1wOxoO88EzfxmrUqYWzGb2lV/32jQCdDQe4v2n/oTbNZL/GR6XxIpb/sdr0wjQfbKedx//HQ6bBQBNgI7qG77nVf72qHms3bWP4089iN00kvEsk8kovfp6EktneN0Oc38fH9z/C4ZPy2dOmjWHubd+02sNh8XCuj/9lv7mJkS3G4DgWD3zvn6n15F9bqeTDX//E32Nx8eiz1QBAZRdcwPJsyaO1fWEKIrsfuYJWnbvQKFSoVCpUajVpM9f5FNmdePm9dS+9BzqwEBUgTrUgTrCEpMouvRKVAHeTWDdvn8fu55+HG1ICNrQULTBoQRGRJJdtQytl2kD1qEh1v35PhQqFYERkegiowiMiCQurwCS1F6Pddz70JP0HaknKD6WIH0sQfGxBCfoiS7M8doMGls62PzLPxOkjyEkJZGQ5ERCUxIITUn0Oo3BNjDExp//kSB9DOEZqYSlpxCemYo2Isyn73Dj2x/SvHYzkbmZRGRnEJmbgS4uxmfjtf33D+G224kqyCG6MJewjFSf4gthJIFm08/+gE4fQ2xpATHF+QREhvukAWAZMPHOj/+Nvjid5PI89CXpKH2MdQQYaO5iwx9fJKUin5R5BUSkxfllSJs2HqD+/T2kLigktSIfbah/aV2dB5qofWE96QuLSPFTx+V08+j/rCG1IJay6kzi0iL8asurf9uM4BYoq84krUjvMVpvKra8fpjG2nbKqrPInZOESuP75O87NhnZ8u4eqpfqWbAohkCd7xqHDg7x74ePU700jsVVcYSE+L6vdLRb+M0vDlBVo2dJdRyRUb4HQRiNTn78g73MWxhN9VL9hIzrLzs9PT3cfPPNdHZ2EhoaSnFxMe+99x5Lly4F4K9//StyuZwrr7wSu93O8uXL+cc//uHz+0w7clAURQ4fPszOnTvHbocOHcLlcn38JjIZYWFhzJ49m/Ly8rHKZHS0f9m+nwWjsUHI5CAKVFy6iqIFF/qs8+F/H6DpwHYQRfIrljLvstt8TgnZ/tYzHNj4JgBJOaVU3/Bdn0wjwKHN77B19ZPAiPFcvur/ERLpW0zZun3v0/D84yCKaENDWfit7xObm++TRu/xOj74/a8QnE4UKhUzb7iFrMoan35sTN1drPnFj3BaRkxwTs0Kyq653qfkFIfVwup7vovNMBKvpy8sofyWr/lWORUE3vrpDzF0tAEQFB3L7JtuI6GkzGsNgHV/vp+OAyNJSwq1moILLyP/wkt9StjZ/vgjNGxcB6e+zilzKii96jqCY70fu7L/1Rc58s5q3A4HAEExsRSvvJrUufORKxQcEDumNI4Nb6/l8DMvYxscWa8yuZyUqgXkXXMJIcneRb61bd3F/n8/i7mrB1EY+TwBURFkXbacjBVLUHuRTNN3pJ7df38MU3sXgnPkhEuuVJC4sJyC6y73qi2m9k62/e5BbIMGrH0f5wCrg3RkX3EhuVddNGWSkmPYzMaf/QGn2YJtYAjHsHnsMV1sNGV33EzC3JlTtuWjH/0W57AZl82OtW8Al80+9lhUfjYZd1Yyt7z0jBrv/ORxjJ39CE4XbocLQ3sfLvvIulFq1aTOL2D+t1cSljj5cXnDn16kp65tpMrvEhDcAv2NHQiuUydwcRGkLypi9u0XoIv0fMKy89/v0Lrr2KlhPyM3t8tFz5EWAGRyGXFF6WRUFlN89WJUHrKvD7y8keMf7h25I4qndvuRvx37GkZ0FHLiS9JJW1DEvKvVFCWkjW/HmmOs/e/HCWciH/8UdjT0Y+gd2VZxaRGUVWcyf2UBKfnjj5lHtp3kjQe3evycQ73DdDT0AxAapaNkSQazV+RQUpk+7njXeqyH//5mrUcNu9VJw76Rar9Wp6ZoYRozajKZe3E+SvXHSTuD3SYe+cFbHjU07n727hjJ5NZo5cxbEEPNMj1XXJU8zkQ6HAK3XO/5IlZRhB3b+nC7RZRKGXPmRo1oXJ1MeMT449TXb9nG8LDTo87e3QNYrW5kMpgxK3JM45MG8J6799DaYvaocejgEIahEf3C4jBqlum5/KpkUtM+Pi6cC5GD1zz8JOqA6aeTOawWXvzmLedU5OC0s6tkMhmFhYUUFhZy2223ASOxNvv27RtnJhsbG3n//ff54IMPxl53urk8V5l32S2o1BpyZlX69fpZS68hMbMIq9lI6ZKVfp2N51csJSQylsHuViouWYVc4Xs0V1pROXKFkvaGg1Re8y1UfsTTzcuuwHmRkYFD+1j6/f9HYJjvlY7wlDRmXbeK+nXvseCO7xGWmOSzRlB0DLNvuJXal5+j4qvfQl9Y7LOGOiCQ2Tfcyo6nHmXm9atIn7/Y520jk8uZed3NbH747+TUrKDg4pUo/YhkzFtxMea+XsKTUyi75ga/uuzT5y+i/0Qjap2OsmtuICo902eNhJIZGLs66Gs8TvFlV5E2f9GEfW2qqmNUXha5V1/CwSdfIG15JblXXowuzrcTxNCURApvupqjL65GrpCTc+VFJC0s9ylqLzAmiuwrLqBxzTps/YNkXFRN+vIlaMNDvdbQhASTvqIK0e1m7z+eJKa0gPTlS0icP8vr6E2FRkPKknmoAgPp2nuQ1k07SJg3k9TqhcTNKPL6MyXOn4NcqUCp1VD/+rsYW9pJqJhFcmUFcTOKGFI3TqmRXJ6Ly+ZAoVaiUCnZ+s/V2E1W0hYWkVVdRsq8Ao8m7XT0xemEJEQhVyiQK+Qgk7Hxzy+hUClJnV9ARmUJqQsK0QRNXgmKyklEqVWPVPrlMmQyGdahYXqOtKDWaUmdV0D64mJS50/enojUOFLnF5z6zspG/pPJcNkddNQ2olAqSJydTfqiYtIWFKINmXjVeVRiKMWV6eOWjR4CjP0WDL1m9OkjprGsOpPE7In7cWi0boLGKPW7Wulo6Cc4IoCixWmUVqaTMztxwnFGF6qlaLFnjd5WAw37OlAHqMifl0Lx4nQKF6aNM40A6gAVRYs8a2iNNvbuGEatllM+N4rKqlgWL4mdUHmUy2HREs/FBKdDZMe2PhQKGTNnR1JZHUdlddwE0whQsSAau909UUSE2r2DAJSUhlNZFUtldSyxcRN/i2bOjiQtw/MJYsNxEwac5OWHUlkVR2VVHMkp/lWoJfxj2hVHbxkcHBwzkTt27GD37t0+zx30WTJ61nDzvY+i1U3P5bucDp+6pj3hsFl8rjJ+EptlGE2Ablpjm6zDRo4pLMgUCr+n6rEZDSi1AX6ZrFHswyaQydDo/M9Ftg+bEFxuAj4xr5UvuBwOLAN9hMRN7+rjgZMniEhJm/qJZ6D3eB1RmdnT2r59jQ1EpKROami8qTqaOrpQBQT4ZNI+iSiK9B89TmRelt+fRxRFuvcdIqYk36+TrVGMrR3IFQqC4n2r0H+SngNHCUtPRh3k/4+cKAh07qolprQQpebj78+AvI78CO/b5zDbaN1VR0pFPkqN792Ooxi7Bug73k7ynNxp6XQdOoF92ErizGyfu+9Pp/d4G4bWXpLL88ZlVoeqtpEV7F3F2+V0s/75/RQuSPW7mxpg6xuHiUoMJbM0fsRk+8HBTSdwuwTy56Wg9qObGsBR+yrOoQgWLo4hKMi/bVR/zMjhQ0MsqY4jLNy/43ZHu4WN67upWqonxs/ca6PRyeuvtFBVE0di0uTfI6ni+OnymRnH843RjX/Lr56YtmH7IlLr6AWkeR6/bHhjHCU+e3w1jl82fDGOXzRy5O8RG+C5GvlFRTKOny7Tu2xR4kuLNFXPl5NiWbw0Nc85ipQeIyEh8VkgGUcJv5HMo4TEuUGEkPN5N0FCQuJLgmQcJaaFZB6/nEhVRwkJCYkvJ5JxlJg2pepoStXRknn8kiDFEEpISEh8eZGMo8RZQzKPXy6kqqOEhITElw/JOEqcVSTz+OVAqjpKSEhIfDk5r4zjQw89RGpqKlqtlvLycnbu3Dnpc5988klkMtm4m1bKMf5MkMzjlwep6ighISHx5WLayTGfFS+88AJ33303Dz/8MOXl5TzwwAMsX76curo6YmI8zysXEhJCXd3HiQH+TCTssFkx9HVh6OvE0NuJdXiI0iUrCQrzPt1DEAQsxgGGB/swDfYyPNRHasFswmMTfWqLy+nAahrCbBzEYhoiJDyaqETf5+dyu1zYLSZsZhMyudzndowiiiJulxOXw45WFzzusVJ1NLWtPdI8j19gimXxHBA7Pu9mSEhISEh8hpw3xvEvf/kLX/va17j11lsBePjhh3n77bd5/PHH+dGPfuTxNTKZjLg477N6PfHa33+CdXgIgICgUFbc9iOfTKNpoIe3/vUbTKfmWJMrFCy88us+mTXrsIF3HrufvvYTY8vyK5aReukqrzUcNgsf/OfP9LQ04HTYAIhNzaHmhu95reF2OfnohYfoPlmP027Dabei1YWw5Ct3kpg1MfbPk3kUBYHdzz5F99FDuJ0uBLcLURAouOgysquWIfMyx/vgG6/QcbD2lOjIn4TSMvIvuNTrGLfGzetp3roZuVI5dguOiaXg4stReVmd7j52hMNr3kCl0aLUBqDSalEH6shcUuN1JONQWwsHXnsJtS4ITVAwmuBgNEHBxOTkERzj3aTODquF3U8/jlKrJSAsgoCwMALDIgiKifEp1ebA6y9jHRokKDqaoKgYdFExBEVHowkOmfTE65MxhL2HjtH07kcEJ+gJThy5BcXHjUs6mQpjazt1r64hNDWZsNQkQlMT0YT6Pvnt4f++CkB4VhoRWel+pdmYe/o4+uJqInMyicrLIighzq+T0LatuxhqaiG6KJfInEyUWu+zyE+n/vV3kSsVxJQUEJyo96stoiiy87F3iEzXkzQ7B02wfxMVO20O9j79IUmzc4grTEWu9C+hp7+pk/a9x0ldUEhInP9pLf1NnXQeaCJtQSG6KP+SiwS3wAdP7yV/bjKJOdF+Jxdtef0wkfHBZM1IRKH0PznG5XBTMD8Ftda/1JeDe83sHmhjUWUswcH+J8ccOjTEkqpYjzGD3tDRbmHDum6qlsURGzt5JOWZMBqdvPrSSaqX6klKlmIGPy/OC+PocDjYs2cPP/7xj8eWyeVyampq2LZt26SvGx4eJiUlBUEQmDFjBvfddx8FBQUen2u327Hb7WP3jUYjAFbzyN/QKD0X3P5jQiJ9S2c4vm8ztmEDAJrAIJbe9APiM/J90jh5eDdmwwAwYjznr7ydvPJqnzQ6Go9gHOgZM41FCy+i/MLrkSu83wV6WxsZ6mnHPNQPQGJ2CUu+cicBQZMfoD9pHg2dHQycPMFQWysAwXF65n/j2z5lK1sGBuhtrKf3+Eg1WR2oY9YNt5A2f5HXB3mnzUbX4YN0Hj4AjJxk5Cy9gIKLVnptGkVRpGX3DrqOHEJwOgGIyclj9o23+ZTj3bpnF52HD+C0WgEIjIik9KrrCIryPuO5edtmeo7XMdwzcoIiUyjIWlxN4aVXeK1xcuc2uo8epvvY4bFlYYnJFK28iuSZcz4O8j2NT1Ydu/YepGX9Nk6u24IoCADIlQpSaxZRtOoar4xbf10jJ9dupmX9NlzWj8aWx5eXUXbHKoK8SK4xtXXS9O5H9B6pp/9I/dhyXWw0hTddRUr1gin3FevAEA2r38duMtGyfhuNb30IgDo4iNiyQopvuXbKGEKXzUbdK2twmC1Y+wZo3bgdGFkn4VnpxJeXkb3ygilN5NEXVuOy2nDZ7Qw1naRn/xEAAqIiiCnJJ6F8BoGLws6osf+lDVgHhxGcLtwuN2276tj+8FvIFHLiClJJnptH2oJCYvNTJtU4tmYHxs4BBLeAKAgIboH693az/ZG30IQEklyeS+q8AlIq8ic1bo3r9zNwohNRBAQRURQRXG52P/keH/3ueSIz4kldUEjagkL0RWkezWjrrjq6D59ERIRT4WeiCKJbYOe/17DWLRCTn0zagiLSFhYRUjgxIK1pfydHtp302Matqw/zzK8+JCohhNKqkazqvPJkVJ+I/Wtv6GPf2gaPGsf3trP3g+MEhWkpXpxBaVUGxYvT0YWMP770dxjZ9uYRjxp9bQbW/ncfaq2SgvmplFVnUrokg/DY8T08ZoONj56v9agRYDLw5D/qUalklFdEU7NMT/Uy/YR8Z5dL4N8PH/eo4XAIPPCno4iiyMzZkWMamVnBE75HTzza4DGrWhTh//56jB/9cC/FpeFjGgWFoRM0Xni2mcEB+wQNgMcfbeDen+wnJzdkTKN0RgQKhf9RqxK+cV4Yx76+PtxuN7Gx4w/SsbGxHDt2zONrcnJyePzxxykuLsZgMPCnP/2JefPmcfjwYRITJ1b77r//fn75y19OWF5z/Xc5sOlNVtx6j1+Z1WHR8Sy+9lvseuc5Vtx6D6HRep81dGGRlF90AzvXPEfNTd8jLjXXZ42AoBCKFl7E3g9fYcHlt5FeXOGzhiYwiPTiCqwmA8WLLqZ40cVeVQhPN49qnY6YrBzMvT0kzpjFjGtvQqnx7QxWHRREcEwsuqhowpNSmHPL13wyagBKjQa1TkdofCIKlYryW79BZJpv3f4ymQylWk1Ecirm/j5mfOUmUufO97lCISISlpDMUFsLBRevJHf5RT7neAsuF6HxCQz39pBWsZDiy6/2ulo5istmIygmlu5jhwlPSaP4sitJLJvldRUYwGW1IVPI0UaE4bJYybiohuyVKwiI9H77uGw2nBYLmrAQ3HYHifNnk7VyBVH53udwuxwOzL19aENHfmADoiLIuKCK9BVLvG6L6HZjaGlDHRyESheI02whqiCHtOWLSVo4F1WAdycYA/WNKAMDUJyquMoUCuJmlpBSvYD48hleVWL76xqQyWQoNJqx7SFXKYnITid+Thn6OaUY5Z5N0Cg9R1qwDg2jUCmRqxQIwoiZkivkaEN1hMZHEpp45pOVnro2Bk50IVfIkCsUyORyBNeISXDZHDitDgS3gOwM2cyj1UWZ/NT4c7kMkI2dmBg7+hlq6cHY3kdkRjzakInV0KGWHlp3HRt7jUwmAxmc+t/I+zR0EBgeTGBEMInJAnzCx/a1Gzi8tXncstH9y2ywnXqOkaM7WtDq1IRG6UjJH/+dMvSaObR5vMZYG3uHARgesnF4azNqrZLAYA3Fi9PH7ccWo21SDbvFAYDD5uLojhZUGiUqjZK5F+WhVH9sqB0256QaWrcZAKdTZNfOPjQaORqNnMuuTEan+9gCCAJs2uB5zPJoMrEgQO3eATQaBRqNguho7YTs6m1bezEPuzzqjO5zhw4MotHIUavlREdriI0bX4Hcu7uftlaLRw2bbWR/qztmRKNVoNEqiIrWkJIa5PH5Emef8yKruqOjg4SEBLZu3UpFxceG53/+53/YsGEDO3bsmFLD6XSSl5fHddddx69//esJj3uqOCYlJXHTz/+FSq1FqfavPA8jXbxOhx1toP87tuB2YTEZCAqL9F9DEDD2dREW4/8VsaIo0tfWRHRShs+vrXX0jlUeu48dITbXt8rrJ2nfv5f44jK/u5IAWnbvJLFsJnKFf11soxr6wmKvK5WeOLljK7F5BWhD/OteA2jfvw9dZCRhicl+awy2tmDu6yGhdKZP6/X0DGvB7abhzQ9IXboItc6/LlBREDj28lskV85DF+P90JBP0r5tD8hAP7vU720suFwcfWE1SYvmEpLk/3en/1gDA/VNJC2aizbM/8zZEx9uBCChYtaE9etLZvWOf68hLDGa1AWFaIL86zp0OZxs+usrJM7MJqUiH7XOv+/AwIlO9j27jvTFxSTNzkWp8a9Lta+hg33/XUvaoiKSy3NRB460x5esasEt8NhP3iU5L4ayqkxiksP8assbD23F5XBTVp1JamEccrnvx6ktrx+m6UAHZVVZ5M5JGmcWvWVoy0tse1+gemkc8xfGEBDoe73o8KEh/v3wcWqW6Vm0xL8u7/Y2C/f/6iBVS+OorIojItL331Sj0clPfriX+QtjqFoaN8FwjiJlVX+6nBfG0eFwEBgYyMsvv8zKlSvHlq9atYqhoSHeeOMNr3SuvvpqlEolzz333JTPHd34t/zqCdTa6W98iRFON48SXxxON44Snx++GMcvE74Yxy8aOfL3iA3w/SLK8xnJOH66nBfT8ajVambOnMnatWvHlgmCwNq1a8dVIM+E2+3m4MGD6PW+dxVLnF2kqXq+mEhT80hISEh88TkvjCPA3XffzaOPPspTTz3F0aNHueOOOzCbzWNXWd98883jLp751a9+xfvvv09TUxN79+7lxhtv5OTJk3z1q1/9vD6CBFK29RcVaUJwCQkJiS8H58XFMQDXXnstvb29/PznP6erq4vS0lLefffdsQtmWlpakJ82iH9wcJCvfe1rdHV1ER4ezsyZM9m6dSv5+dMbVycxfUrV0dQ6eqmX5nn8wvHJqXkkJCQkJL5YnDfGEeCuu+7irrvu8vjY+vXrx93/61//yl//+tfPoFUS/iCZxy8e0oTgEhISEl98zpuuaokvHlK3tYSEhISExPmFZBwlPlck8/jFQ7pIRkJCQuKLi2QcJT53Rs2jxPmPdJGMhISExBeb82qM4+eFKIpYhw0MD/ZiGuxDE6AjMXtiNvOUOoKAzTKMxTiA02EnNsX7JIxP4nTYsJlN6EIjx10UdL7iKddaQkJCQkJC4txCMo5TsP6Ff9BaV4vbNZJFnJw7g6rrv+P16wVBYOsbT3Dy6B4sxiFEwU1olJ5lq37otWkURZE9H7zMySO7sVlM2MwmZDIZi6++g4wS76t1h7a8y8nDu3DYrThsVpw2K9mzK5lZcxUKpXe7QsO+zZw4tBO304nb5cDlchKVkMacFdeh0niXGtFWv5/G/dsQBQFRFBBFEW1gMDNqruCYl+ax/0QTTVvWI5OPxJ7J5QrkSiUZCysJivbOfJr7+6j78F0UajVKtQalWoNCoyYyLZPwJO/SV+zmYeo+eBdVQADqQB3qwEDUuiACwyMIjo3zSkNwu6n74B0UGg3a4FACQkPRhoSiCQlBpQ3wej9p3rEVp9WKLiKSwIhIAiMjfZ6Atv9EI/3NTQRHxxIUE4suMsrnxJXUbhlbNr9FblEuIYnxqPxIjxEFgeYPNxEUH0toWrLfCTQD9U1YBwaJyEr3KfZwXFtEkdYN2whJTiQkJRH5GeL0zoR1YIiBugaiCnPRBPufIjXYdBLB6SI8M83vtgB01DYSrI8gONa/9QIj66Z973FiC1JRaX2LyTwdm8HMcK+ByAz9tJKgzH0G3A4XIfHTSdgSaTnSTXJ+rF9pL6O0HO0hPiPSr7SXUToa+wmPDSIgyP/ksvYWO5oYx4RoQF/o7LCgUMqJifE/Hauv14bN5iYxSTf1kyfBZHLS020jIzN46idLfGpIxnEKjAO9Y6axcMGFzL34Jp8qfJ2NhzH0dWIe6gcgMbuE6uu/g8aH+MHetkaGetrp72gGICwmnqU3/YDw2ImZ25Mx1NPOUE8b7Q2HANAE6Fh8zR2kFsz2WmN4sI/BnnZOHByJeJTJ5cysuYrSJSu9NhfWYSMDXa3U79mIKIxkjqYVlVNWfflIFrgXV1o7rVYGW5tp2rwBp9UKQGh8InNv/6bXptHtdNLXUM/JHVsx9/cBoAoIoOSKawmN9y5hQhRFeo/X07JrG0NtrSMLZTIyFiym5KrrvNIA6D1eR8eh/XQe3D+2LCwxmbJrrie+uMwrjf7mJnqPH6Pug3fHlinUGoouvZy8Cy716sTA0NHOQPMJdj39OKJ7ZNvI5HKSZ89l5vWrvMoDH+7rZbClmY5nXqHNNhLhGRAVQVR+NsW3XkuQfupUE9uggYHjTbSs30rX3oMABMZEEZaeQtrSRSTMmzWluXCYhhlsPImxpZ29/3hyrB0RWWlE5GSScVH1lObNZbMx2NCMw2Smed0WOnfuQxkYQGROBlH52cQU5xNdnHfGtgguFwPHT+Cy2nBarOz++79xmMyEpiYRU5RHdHEeMUW5aELPnAgxcLwJt92B2+5guLObPQ8+gSookJjifGJLC4gtLSR4ijjEvoYOnFY7gsuN2+ni5LYj7H36QyLS9SSX55FSkUfCjKwzGsDBk904zDZEQUBwC4iCyN5nPqRlZx2JM7NInVdAyrwCwpMn/x4aO/qxGS2IoogoCCCC4HLz1g8fQalVk7qggLQFhSTNykE5SVuGe4awDpoYyT0bCT8TRXBZ7bz+7QcJTYgibVERaQuKiCtKAw8JeYY+M0M9w2P3Tw9Re+Y3a+k6MUDpkgzKqjMpmJ+KNnBiW8wGG30dBo9tXP/8fja/doiihWnMqM6kpDKD4IiJJ0A2s4PulkGPGnU7W3nu/o/IK0+mrDqTsupMohImxpI67S46mvo9ashOWLjqu28xc3Yk1cviqFmmJyMzeMJ+Kwgix456/iwDfXZuuWEL+QVhVC/XU7NMT35BqMd9v+6oAbcwMZDOZnVz07WbSUwKpHrZiEbpjAiP5ryxwYTd7p6wXBDg9pu2EhCgGNOYNScSler873U7nzgvIgc/D0Zjg4oWXkRn0xFyZi+hYN5yn3W2v/0MTpuVntYGErKKmHPB9T53Le9fv5re9iaMfV2ERMay6Kpvotb6li1bv3sDjfu3Yh02olAqqb7+uwSF+5YB3FpXy961r+J2OnHYLFRddxcxyVk+afR3nuSj5x9ELldiGuxh/srbyCiZN+4AVOvoBZjUPNqMBt77zc9RBQYy1HqSwkuvoOCilV5XTQHcLhdv/eQHqHU6+psaSJu3kBnX3kRAWJhPn+ftn98DwODJE0Rn5TDrhluJTPMt3mvtH36D3TzMQHMTgRGRlF75FVLnLfRpP9n88N8xdrQz2HoSuUJBVtUyCi66jIDQMK81dj/7FB0H9mE3mbAPm4jJyaPwkivQFxZ7XQU6/PYb1K99D5vdinvYjC42muzLLyBteSWqAO+qFSc+3Mj+R59FqdVg7u5FJpeTuGAOWZcuI6ogx6u2dO09yNbf/g1NWAiWnj4Ep4uAyHDSlleSvrwSXezUlXpDcxsffPd/UZ8ymNa+AQDCs9JJW7aY5MqKKc2nwzTM61+5A1WAFmVgAA7TMC6rDWQyYorzSFkyn8QFc1AHnbkK88rKWxFFUGhUKNTqsbbI5HJiSgtIWjiX5MVzMQW1AHiMHXxy5b2YugZQqJTIVQpkMhk2g3lERyEncWYWOSvmkHdR+aSVzJe++mc69jchl8uQKeTIFQoEtxu3wzVOZ8aNNaTOK/Co8c6PH6P+gz0gkyGTy5Cd+hxul5tTThCFRkXGomLm3XUZoQkTj1Mb//oK+/67dsJyZLIxjVGd7GUzueB7iZSlZY5vx7938ux96zy28ZNklsVz3U+qyJ45/mR925tH+Md3V3ulEZUYytU/WETFpfnj9uG6na385iv/9UojKEzLRd+Yy4pbZ4+rZHafHOSHSx7xSkOnU7Lqtgzu+n4uOt3Hx0ybzU1OyuteaWg0cq7+Sgo//FEB4RHjq6HFOasxDDmn1FAqZVx4SSI/vbeIOP3437Nliz+g7phxSg25HJZUx/GzXxWTlv5xFVKKHPx0kYzjJJyeVW0xDhEWM71B/71tTUQnTi8vtKPxMPr0/Gl15bQc3UdidhFyhf/F5qaDO0jKLvG6a9oTDbVbiM8oIDA4zOPjU5lHgObtWwhPTvW6QuiJlt07UOuCiMvz/CPnDa17duJ2uUiZU+H3tumpO0pf43FyalagUPvXpWQe6OfI229QcPFKAsMj/NIQRZEdTzxC+oJKYrJz/dbY/tg/cRalUn5Rtc9d3aIoIpPJOL76PWyDBjIuqiEwyrfPM6ph7R9k998fI31FJfo5ZT61ZVQD4Mjzr+MwmUlduoiw1CSfNABkMhluh4PNv/orsaUFJC+uIDDa++7U09syUN/E/seeJWnRXBLnz0EbNv7HZLK86tM1AA68vJGmjQfJrColo7KEgLCpe0E+qQHw/i//g2PYRuaSElLnF6INPbMJ9qThsNh44ZY/EleYSvqiYpLn5p2x8nn6z9bpWuY+Ay/e9ieSZueQvqiYpDk5qAI0HrOqPf30jWr98dYXASiryqS0KsNjlW8yjVFe/vNGDm1pHqsUJufGeDw+iKLIZDI73j7K63/fQml1JjOqMsmckYBCOdHUn0nDvu9VfnV3O9VL9dQs11NeEYVGM/F7cCaN5hPDXH/VJhYviaVmmZ75C2MI1Hn+DRE8VBsBBgccXLpiHbPLo6hZpmfRklhCQjyUgs+g4bALXLRsLdk5IdQs07OkOo6IyInd+JJx/HSRjOMknG4c1drpb3wJ3/HGPEqce4weUg7SOa0UGU8G4/PQOOfaIgjIzlCNnsw4fhLB5Uau9H/83SguhxOl2rMB8FrD7kSuVExrzCaA02pHqVFNWD+ejONkCIKIw+pEq/N/TCCA1WQnINj/sYlnSyPBvIb06Mxp7XvDw04CA5XTGvNpMbtQa+QoPRhfb7FZ3cjkeDS+pyMZx08XaWCAxDmLNE3P+YlMJhv7kZrOnI5nw2SdDY2zpXPW2nKWZlE4G6YRmLZpBFBqVNM2jQCqAM20149cLpu2aQSmbfjOlkZQsGLa+15QkGpaphEgUKeclmkE0AYopjSNEp8+knGUOKcpVUdLk4Ofp0hzOkpISEh88ZCMo8R5gWQeJSQkJCQkPn8k4yhxziPFEp7fSBGEEhISEl8cJOMocV4gmcfzE6m7WkJCQuKLhWQcJc4bpItlJCQkJCQkPl8k4yhxXiFdLHN+InVXS0hISHwxkIyjjzhsVoZ6OnA57NPSGYnscp2lVn35kMzj+YPUXS0hISHxxUHKqp6CpgPbadi3GbNhAItxEJfLwdyLb6Jw/gVea7Qc20f97vVYh43YzEZsZhMJWUUsvOJrXie4dDXXcXzPRhw2y9hNFxrBvEtvITBk6gxhgIGuVhr2bcLlcOByOnA57afypq8kJDLOK43hwT4aajcjCO5TWbVuBEEgo3QeUfGpXmnYLMM01m45NbeYbGzev/C4JGJTsqd8fak6mr3mdraufpP4mAgUKhUKlRq5SolGF0R4snftEAWBtn27kStVqAICUGkDRv4GBKDWBXk991nX0cPIZDI0wSFogoLQ6IKQ+xB/CDDY0ozb6SIgLIyA0DCfXw9g7u/DZjSgi4xGEzwxi9YbbCYjNoOB4JhYvxNsREFgqK2VkDi93xoAw109qIODUOv8n0TXZbNhNw4TGB05rbnsLL39aEJDUExjzkJRFLENDBEQ6d33dTJcNhvI5Cg105tr0D5sRa3TTnuOP6fNccaUF28QXG6QyaY9l6Pb5UaukE/rM4miiOAWPSa0+NYW4ZzQcLmmn/HhcgnTnoPxbGkoFLKzNifqF43777+fV199lWPHjhEQEMC8efP4/e9/T05OzthzKisr2bBhw7jXfeMb3+Dhhx/2+n0k4zgFpsFeOpuOIIoimgAdF6z6MYlZxV6/3mGzMjzUR/Ph3QhuFzK5nLkX3Ujhggu93vndLicW4yANtVtw2q0AZM9czPzLb0Ol9i72TxAErKYh6natxzo8EmQfnZRB1XXf8do0iqKIzWzk2M51GPu7AQgICmXhlV/32jQC2C3D1O9eT29bEwAyuYKyqpVkzVzktUauO5DVtbtoajw2tiyhdAazb7zNaw2n1Urr3l00bf74SxQUHcvM61eRWDbTKw2Xw0H30cMcfOPlsWUKlYqiy64i/4JLvDKAgtuNobODzf94YGyZJjiElDkVlF51HerAqY2TKIo4zGY++N0vcdlsKNQadJFRhCenUHrVdQTHTJ0kIooigtPF2j/+BqthCF1kFMGxcYTE6cmuWkZYYvKUGgBup4MDb7xE255dBMXEEZqQQFhCEjuyopmzdLFX+73gdjNQ38S2+/6OTh9DeEYqYekphGekEl2c51XutSiKuJ0uNv7v73GYhonIziAiJ4PI3EwistLGMqinbIvLRf+xBrb/4R9EZKcTXZBDVEE2kfnZU2ZVn94Wwelk38P/YaDhBDFF+cQU5xFTku9z9KDgFnj/W/eg08cQW1pIbFkh4ZlpPpuuoZYe3vrhIySX55FSkUdyed6UcYGe2PfftTRvOUTKvAJS5xcSk5Po1yTcL3/tz4QmRJO6oJCUiny0Ib6fMNgMZl678+8kzc4hbUERCTMyUah8/5n7v7teQxuopqw6k6KFaQSG+B6v+uoDm2it66WsOpPSJRmExwZP/aJPsPWNw2x65SBlVSOxhXFpvkeJ7tlm4s4HPhqJHFymJzc/xGfj1VBv4u7v7KaqJo6aZXqKS8N9nhC8v8/OLTdsYcGiGGqW6Zk5O9JnI2m3C1x3xUZKZ0ScMT7xy8qGDRu48847mT17Ni6Xi5/85CcsW7aMI0eOoNN9/N3+2te+xq9+9aux+4Fe/MacjhQ5OAmjsUFhMQkEh0czPNTH8lv+n9cma5QPnv4Lva2NhEbFMdDVSs1N30eflueTxtbVT3F870bCYxLpaz/BgstvJ3vWYp809m94k93vv0hEXBK9bU2ULlnJrKVX+ZRZ3VC7hY+ef5DwmEQGulpIL57LgstvR6vzPgap88RR3vznLwiJjMPY30V0YjqLr76DCL13pgTA2N/N87//DoEh4ViMgwRGRDL7xltJnDHb6wOiy27jxTtuRanV4jCbUag1FF5yOfkrLva6SiaKIq985+s4rBZEtxtREEitWEDplV8hKNr7qL01P7+HgZMnkCkUiG73mNmLLy7z+vOsf+APtO3bjUwuRxQE1IE6cpdfRO7SC1DrvDMDO//zb+rXvj92XyaTkTyngoKLVhKRkuqVxqE3X6P25efGLQuOjSN32YVY5udQ6EXO84kPN7LrL48gnpZXK1MoSJw3i8xLlhFdlDvleunae5BNP//DSCXrNDRhIaQtXUz6ikqCE/Rn1DA0t/HBd3+G2z5xWEpIcgJpyxaTeXENSu3kpsJuGubtW76Hy2pDFIQJj0fkZJB/3UoS5p75ROWN67+F02xFcDrGrZdR9LNLKfvGTQQn6ieNHHzu5t8x1NKL4HIjOF0I7vHtkSnklFxTydxvXIQmKMBjO968+2E69jee6m0QEN0CglsYt5510aHM/cbFFFxa4dFArv3tszSurx37HKIgIIrgsjnGdGQKOUmzc1jwncuJzk6coLHtkbc4+MomEEU4tTpGf8rsw1bEU59NrdOSUVnC8u8mMDNzfG/G2v/u49UHNo3dP32t2i1OHFYnAAqlnNw5Saz8znxy54w/Tu1+v54nfvqux3XlcrixmD7ed9IK41h+2yzmXVYwbv9t2NfOA9941aOGIAiYBqxj9+PSIqi8toTlt8xCqf7YMPW2DfHLK572qKHAzkDfx8OiEhIDufyqZL71nRx0p+VN22xuFsx6x6MGQF+ffSzLOjpGyyWXJfKdu3MJjxifblM1/z2MBqdHjYEBB273iEhomIrlFyTwg3vyidOP39+uvXwDjcdNHjUMBicOx8j21emUVNXE8YMf5ZOW/rExlyIHR+jt7SUmJoYNGzawaNFIYaayspLS0lIeeOABv9skGcdJGN34q375OIPdrUTEJfuVWW0zG1EHBNF14iihUXp0ob6fMdosw6jUWrpOHCUwJJzw2IkH0qlwWC3I5HJ6Wo4jkyuIz8j3WcPpsOF2OhnoasFiGiKjZJ7PZ65ulwub2cjwUB/dzXUULrgQucK3M0ZRFDENdOOwWTm+dxPqyiryMqc2JJ/E2NWBTCZn/6svUHbtjegivK/8jGLo7EAbHMKmh/5C6dXXE5We6Vc7tKFhbPjbH8mqrCFljucf3DNh6u5CFRDA7mefIkQfT+7SC72qVJ7OcO/IuNGDq19BrlCSf8ElBMf6dqJkHujHZbPSdfgQJ3dvJ2/5xSSUzkAul3NA7PAqu9o2aMDSN4BcpWTTz/9I2rLFZFxQ5VMXr8M0jLGtE21oCPsffw633U768iXEz53h9TAAl83GYEMzKl0gjmEzW3/zAEmLK0irWUR4VprXldPeQ8dQarWoArTUv/EuPfuPkLJkPslL5hEc79367d5/GLlCgUKtRq5WseWXf0EdGkzywnISF5aji4ka93xP5rG9tmEsn1qhVGAdGuatHzxCfGkGmVVlpFeWEBx75nXcdagZh9mG7FR3oUwh58TGg9Q+/xHJ5bmkLy4hfVExgRGTV9d669uwDBgZG6Yil4EMNvzpJcy9BtIWFpG+qIjkuXmoAz2b8oHmLkydAyN3xjaDDNEt8M5PHkMVoBnRWVxE0qwcIoP3TMiq7m0borNpwKP+Ww9vp2FfBwXzUyirzqKsynPFcKhnmJajnsdb73qvjg0vHiCrLIGy6pGKYXzmxCETw0NWmvZ3etRoPtTFS3/eSFJu9Fg70kviJ1T77FYndTtbPWooO7dx/09aSUsPonrZSNVx1pxIVKrxxxi3W2TThm6PGsMmF9+5YyexcdoxjbnzotFqJx67N2/sweWaeJLkcgp8545dBAYqWFKjp2ZZHAsWx44zr6Ps2tGH2TzxGgBRhB/9YC9Wq4vKqpHq5+KqWEJDx5/sfxGNY2tr67jPotFo0GjOHEnZ0NBAVlYWBw8epLCwEBgxjocPH0YUReLi4rjkkkv42c9+5lPVUTKOkzC68W/51RN+GUaJMyOK4rTHqYxq1Dp6AchO8r7KN6YhCNPOthXcbmTyaY6rEkYqOP6MbTwdl8OBchrjCmHkQDXdA57dPIxGN7Eb11vzCOA0W1Bo1NNeJ7ZBA9rw0OlpDBlR6QL96vY8HXNXL4GxUdPaV1x2B/ZBA7q4yaenmqzqeDrGrgGUatUZTZ439BxrJSw5elKT5w1up4uuQ83oi9KmlaFt7jcy3D1ITG7SuO91qGrbBOM4GaIocmhzM9mzEtEE+D+m9djOFhIyowiO8P+7dOJgF8ERAUQl+L//KhteJy4omfQM/7dzS/MwZrPbr27uUbq7rXS0WSkp872bexSj0cmRQ0PMmnPmbu5zwTieLe/gsFl48ue3Tlh+77338otf/GLS1wmCwKWXXsrQ0BCbN28eW/6vf/2LlJQU4uPjOXDgAPfccw9z5szh1Vc9V709IY1xlPhcOBuDm0c1StXR1Dp6qW/t8dk8Ttc0Aj5XTCdrx9loy3RNI3BWzpI9mUZfUU3jopjTma5pBNCGnZ0fnzOZPW9RatQoz4JOSJzvvR+eiMn1vdr/SRQqJQllvlfrP4kuMgRd5PS2lUwmo2hh2rTb8slubX9IK/Kt4u+JjOwAYgOmd3KQnDr973NsbACxsZ6HQHhLSIiKufO+nPP5eqo4nok777yTQ4cOjTONAF//+tfH/l1UVIRer6e6uprGxkYyMjK8aos0HY/EFwJpcnAJCQkJiS8qISEh425nMo533XUXb731Fh999BGJiWce2lZeXg6MdGt7i2Qcp6CpSZov8HxCmt/x3EeaDFxCQkLi7COKInfddRevvfYa69atIy1t6sp5bW0tAHr9mS8WPB3JOHpBQ4PnAcMS5xZSnvW5jzQZuISEhMSnw5133skzzzzDs88+S3BwMF1dXXR1dWG1jlyZ39jYyK9//Wv27NlDc3Mzq1ev5uabb2bRokUUF3s/zaBkHKegMCCcksAIGhq6JQN5HiB1WUtISEhIfBn55z//icFgoLKyEr1eP3Z74YUXAFCr1Xz44YcsW7aM3NxcfvCDH3DllVfy5ptv+vQ+0sUxXlISGMF+ywANDd1kZk49mbLE50epOppaPy6UkfjsONrV4/XV1RISEhISUzPVJDlJSUkTUmP8Qao4+kBJ4MhViFLl8fxA6rI+N5G6qyUkJCTOXyTj6COSeTw/kMY7SkhISEhInH2kruopMJkG2LXrTaxWIxarEavFhFyhIKHichoa8Krb2jpspH7PBhxWM3abBYfVjMthp3jxJcSl5kz5egCn3Ubj/q24HHacDtupv3aSckpIyin1SsPtctFydA9utwvB7UJwuRAEN7rQSJLzZng1t6IoCHQ0HZkQn6ZQKolLzfV6LsK+tiZEUUSuVCKXK1AolcjlSgJDwryOQTT2dyGKoFJrUKo1KNVa5Ke9/+j8jmfCOjSETC5DFahD4edE0w7LSCqPUqPxe35Kl8OBXKGY1pyQgtsNMtm4deAroiiObJdpzinpzcTqU3VXn41J4s+mjoSEhISEZBynxOm009i4h4HBDgASEnK46MJvo9OFeT3mUXC7aDqwnd7WkXmSdGGR1NzwPWJTss/4utMRRYGmA9tpq98PgEKpouLSVSRml/j0eZoO7qCxdsvY/dw51WSWLfTph/XkkT0c2rxm7H50UgaLr77Dpwms2xsOsWPNf8fua3XBVFyyisyyBV5r9LQ0su65v4/dH5m492JmLbsapfrjOa7ONDG4qbuT9+//BYgiCrUGdWAg8UUlzPjKTWiCvJs0124y8vbP/h+iKKINCUUbHEKIPp6SK671OrPaabGw5t57kMlkBEZEEhAeTmB4JFmVNYQlejfBsigIvH/fvTjMZoKiY8ZuMdm5RGd5d4Iik8nY8cS/6D1eR4g+npC4+JG/+nii0jO93sYnd27j4BuvEJaYRFhiMmFJyYQlJhMUFY1MLqdYFs8BseOMGnaDiY0/+z262GjCM1MJz0wjPDMVbZhvE3of+s9LDBw/QWROBpG5mUTkZqIJ9m1CY+vAENvu+zthmalEF+QQVZBDQESYTxoAJ97fQOeuWmJK8okpySc4Md5nUyuKIrv//hjqYB2xpYVEF+Z4na9+OuY+Axv+/BLJc3JJrsj3e0LwhnX76Dx4gtT5BcSXZPiVriOKIpv++goRaXGkLigkKDrMr7ZYBk1sf/gtUucXkDQnF5XW9/UiiiIv/mED8ZmRlFRmEBLp30T07z+1G5lMRmlVBtGJYX5p7HrnGF3Ng5RVZ5KQ5V/i0O6tJur3HqF6qZ7C4jC/UluOHB5i9autVC/TM2NWJAqF7xod7RYe/edxqpfGMaciGrXa95NTo9HJn+4/zOIlscxfGIM2YPrhCxK+c15FDj700EP88Y9/pKuri5KSEv7v//6POXPmTPm6559/nuuuu47LLruM119/3av3Go0NAtDrs+jsPE5Z2QoWLvgKitMqYvstI1mnk5nHbW/+h0Ob1xAWk8BgdxvJuTOovPZbaHXez+S/f8Ob7H7vBbS6YMyGASLikqm+4bs+ZVYf37uJLa8/jiiKuJx2tIHBLLrqG6Tkz/Rao61+P+tf+CdWsxEQkcsVzFp2DUULL/K6UtbXfoK1//0bhr5OZDIZoiiSPauSuRfd6PU6GR7s472n/kh/58mR8FIgNiWb+StvIyph4rxVniIJXXYba//4W/qbmxCcTgACwsIpu+YG0ioWeGWQRFFk/QO/p7e+DofFDIBcqSSnZgWFl1zutfHc/vgjdBysxTLQP7YsvqiU4iuu8Tr7uvaV52ndvQNjdxei2w2ALjKKgotWkrGw0itjceyDd2jashFTdydOiwUAmUJBWsUC8i+4hLDEqZMwmrdvoX7t+5j7ezH3940tD47Vk7v0AtIXVqLSjkTTTRY/2LX3IHUvv4V1YIjhji7cjpHtI5PLiZ87k8KbriQs7cxtGWxo5tDTL2M3mrD2DWDp/XjdqkOCKLzpajIurEaumHw7m7t7qf3XMzjNFhxmC8aWDtx2+9jjcTOKmPGtWwhOnHz+M6fZwq4HHsVls+Oy2XEMmzGcaBl7XBseRuYlS8m96mIU6snj7bb/8R+4rHYEpwO308VwRzeWnpH1q1CriCrMJeeKC9HPGjmR9BQ5uOHPL2PuG0JwuUduboHO/U04zDYAItLiSKnIp+TaSkITxmdfj7Lj32sYONF1KiJTRBQEHGY7rTuPjaxbnZakObmkzi8g94I5KDUTP9P+FzfQebAJURBBBE5VuXvr2xhqGRlaEp2TRNrCQjIqSz0m0xx7dxcntxxG5LSfr1P/bN5yCLvJikKjIml2DmkLCimtcTIjPWt8O9Y3sm31EY+fs/lwN+3H+5DJIHNGAqVLMpm5NIuErPHrpWFfOx8+s9ejRm+rgfrdbQAkZkeP5VVnlo0/Wehs6ueNh7Z61DAbbNSuaxxZJ0mhlFZlUlaVSd7cZJSqj4+5hl4zz/1unUeNAEc7H749BEBMrJbqpSMZzwsWxY4zXg6HwD137/GoIYqw+rVW3G6R8Ag1S6pHNBYtiSU4ePw2/un/7MVicXvUeW9NB2azi+BgJYuWxFK9VE9VTRzhEeMns/7Dbw/R2Wn1qLFhXTf9/Xa0AQoWLIyhZpmeqmVx41JpvoiRg5/HZ5mM86bi+MILL3D33Xfz8MMPU15ezgMPPMDy5cupq6sjJmbyqk5zczM//OEPWbhwoV/ve+MN96HRBNLRUU9u7rwJj091tXVq4WyyZy4iIDiM+j0bKFl0ic/RcvEZBVz41Z8QoU9h9/svUn7hDShVvp1JRyWkUXXdt4lLy2Xjy48wf+XtBAT5thOGRScw95KbSMwuYd2zf2fepbcQFuPbhQ7B4dGUVF5KYnYx61/8JzOqLic+s9AnjcCQMHLnVKFPz2fbm0+RWbaA7BmLJl2vnrqslRotqXPnU3bNDRxa/SqR6ZkUXHQpSo33ebsymYzEslnkVK+gYeM6FCo1JVdeS1CUb1MCxeTkoS8spnXvLqxDg5RccS0x2bk+aUQkpxIcG8fgyWbaavdQeMnlpM1b5FP3e3BMHJkLl+Cy2zjwxstkVdaQu+xCdJGeTYQnAsMjSJw5m4DQMLY/9k9icvLJXXYB8UWlHrePp+5qTWgw0cX5aCNCOfHueszdvaSvWEL6BVUERnlXFVMGagnPTEUTGox1YIijz79B7Iwi0pdXklAx0ysjrdCoCU6KR60LRKULpO7VNdgGhkhaPJfU6oVEFeRMWQGSq5To4mJQajUotRqcFiuGEy0ExkSRUjmP5Mp5hKYlTakTGB2JDBlytQqFWkX33oNYevoITU0iaWE5SYvKCUk6cxZzYGQwCrUSuUKOXKlArlTQW9eGw2xDX5xGxpJSMpaUTmoaAbTBgQRFh45EZCpG8tltRjOtO48hVyqIK0ojuTyX1HkFHk0jjJjLwIgQZDJAJhv57DIZhraR76gmJJDIdD1RmQmEJnpui0qrJiD8VNX4tHUnk30cIxoQpiM4LpyQ+EgCQgYntkOrnDRHWqkeMVTqABWhkTrConUEhU+My1OqFASFeY7RM/aNnHwplHKCIwMIjgggJCJwwraWK+STagiuj41xUFgAQWEBBIcHoPhETrNcIZtUI8D2sTkMC1MTHq4hLEKD6hMVP5kMwsM9fy/c7o/bERKiIjxcTVi4Gq12YsEgNEyNRuPZOI6iCxrRCI9QExAw8RgVEqrCZvOsIT9V7dQFKgmPUBMWoUanO2+szBeC86biWF5ezuzZs3nwwQeBkQDvpKQkvv3tb/OjH/3I42vcbjeLFi3itttuY9OmTQwNDflccfzWHf9Co/HurGG/ZeBTnarnbIzVEgXh44P1NDREmPZYOrfL6bMB/iQOm8WrszpPVcdRbCYj2uDpnclZBgYIjJhe9q+pu4vg2Oll0xo62giO1U9rrKSxqwNtcChqnc5vDZvRgH3YRGj8mavik1UdR+nae5CY4jzkfo4/BRiob0ITEjytnGjB5aJ9+170s0tRavzfZwfqmxBcLiLzsqb1HTz50RbCM9MISfJ84uap4vhJbEYLde/uIqOyhKCYML/b0rqrDsuAidT5BWiC/MsiFkWRvU9/SGx+CvrSDBRK//Zfy6CJg69sIm1BEdE5iWPrOFS1jazgMxvr09vy5j+3k1oQS+7cZNQa//a99S/sR6tTU7QoDV2I9yekp7P3w+MY+syULskgPNa/vGnzrlfoagikeqme5BT/vtNHDxvY8FEX1cv0ZGYF+7XvtrdZePmFk1Qv01NQGOqXhtHo5F//qGdJdRylMyIm7TKXKo6fLueFcXQ4HAQGBvLyyy+zcuXKseWrVq1iaGiIN954w+Pr7r33Xg4cOMBrr73GLbfcckbjaLfbsZ/WDWU0GklKSvLJOMKnbx4l/OdM5lHi82Eq4yjhP96Yxy8LvhjHLxo58veIDUj/vJvxmSIZx0+X82I6nr6+PtxuN7Gx4w+CsbGxdHV1eXzN5s2beeyxx3j00Ue9eo/777+f0NDQsVtSkncXJHwSKWXm3EVKlZGQkJCQkJge54Vx9BWTycRNN93Eo48+SlSUd+OzfvzjH2MwGMZura2tfr+/NNfjuY00t+O5Q7EsnqNd0vaQkJCQOF84L0aURkVFoVAo6O4eb8S6u7uJi5s4JqyxsZHm5mYuueSSsWXCqXkHlUoldXV1ZGRkjHuNRqNBoxl/Zdd0kCIKz01GL5Q50xQ9EhISEhISEp45LyqOarWamTNnsnbt2rFlgiCwdu1aKioqJjw/NzeXgwcPUltbO3a79NJLWbJkCbW1tX53Q/uKVHk8N5G6rCUkJCQkJPzjvKg4Atx9992sWrWKWbNmMWfOHB544AHMZjO33norADfffDMJCQncf//9aLVaCgvHT/ESFhYGMGH5p41UeTx3kaqO5w5TpchISEhISJwbnDfG8dprr6W3t5ef//zndHV1UVpayrvvvjt2wUxLS8u0Y9I+LSTzeO4hdVmfO3iTIiMhISEhcW5w3hhHgLvuuou77rrL42Pr168/42uffPLJs98gH5DM47mHN1nWEhISEhISEh9zXhnHzwNRFGlq2ofVasRmN2O3mbHZLURHJVNYWOnVJKaiKNLZeZxgu5mG4X46jtmJCNOg1GjJnrHI68mah3racdisuN1O3C4ngsuFIAgkZhd7PZG2xTiIy+lAFMWxqC9RFAgKi0LlZWqKw2ZFFAXkcsVIgoRMjlwu9ykRR3C7T712ehOanw2kqqOEhISEhIR3SMZxSmS0t9exe89bY0vmzL6UgoJFXpsemUxGV1cjGzb+d2xZRGIuF976fZ8SPnrbmvjo+QfH7gdHxLDkK3f6lL7S19HMu4//buy+QqVmzoqvUDj/Aq81BrpaeOuRXyK4P46Eyiidz7xLVxEQFOqVxmB3K6sf/gWCy4VSpUahUhMVn8b8lbcSHOGdiTP2d/H2o79FENyoNQGotYEEBIcxa9k1RMR5dwFUrlPDK4/9huNy0ASFoAkKQhMUTNr8RURnZnul4XY42PSPB3BarWhDQwkICycgLJzI1HTi8r0bUyuKIruefgxzfz+6yKiRW1Q0QVHRRKZner2vHXt/Db3H6wiOjSMoJpbgmDiCY+IICAvz2th3Hj5I46aPCI1PJDQ+gdD4BIJj4nxKb7EZjex94WlC4xMIS0whPCmFgPDwST9HsSyeA10TJwM/8tzrAIRnpRGRlYYm1PcJcPvrGmnfsovIvCwi8zLRhnm3j56OKIoce/FNtJFhxBTmERgb5ddJj6mji7Ytu4gpzic8M+2MWdlnom3zTgBiSgtQB/mXBiK43Bx8bTMJZVlEZuj9Ponra+jA0NZL0pwc1IH+JaQAHP9wL1FZCYSn+N8jYzNaaN15jOS5edNKsdm55hjZsxL9TmsBOLChieikMPTp/idK1e9uQ61VklIQ6/f2qTtkoZV+ys6QtDIVjQ0mujqtzJkbhUrl3z7b2WHhyGED8xfEjMvI9gWj0cnmjT0sqowhKGjybHeJTxfJOE7Bs8/9lOHhQVQqLQqFkhXLv0laWqnXrz9ydDO1te/T3d2EQqFCFAUWzL8WRe4cOrrtZHpxXDp5ZA+Ht75Le8MhZDIZoiiSM3sJFZesQq317uDY1XyMQ5vfobVu/9iy2NQcKq++g9BovVcaA50tHNy8hta62jHTqAuLZOHlXyU5b4ZXGqaBHg5uXkP78YM4badC7GUyihdfQvGiS7zKVraZjRzYtIaupqOYDf0IbjdmILN0PnMuvIGgsMgpNVxOBwc2vknPyQYcfT2YT7UlPDmFGV+52SvTKIoix957m/7mJgaam7AMDgCg1ukouGglURlZU2oANG5aT29DPb31xzB0tI0slMlImVNB0aVXevWD0bp3Fz11RxloPkH3scNjy4Pj9BRetJLUeQtRTGEcu44covvYYUzdXTTv2AqnQqXkSiXpCyopveorU0Yz9p9opPPQASwD/XQc3E/T5g1jj0Vn5jBn1e2EJ6eeUcPY0k7n7v3YBg30Hamn73Dd2GM6fQwF119Bas3CM64XS28/7dv24DANYzMYaXzrw5G4zVMa+pklFNx4xRlNpN00TNumHTgtVpwWKz21h+k7Ug9AQFQE0YW5JC+eS/zcmZO2xe1wcPKjrbjtdly2kVvdK2s4YH8OlS6Q6MJcYkrySVo094w53M0fbsLtcOB2uhCcToZOtHBy7WZkchnh2RnElRUSO6OIqPzsSU9GGz6qxW60ILgFBJcbwe3m6JvbWf/7FwiKCSOlIp+UinySy3PRBHtOvDi57QjmfiOiII5EjwoiToudTX97FYVSQXxZJqnzC0hbUEhYcozH9dK+rwFjR/9pvR6AKNK4vpYTmw4RlhxD2oJCUhcUklCWiUI18ZjQfbSFwROdjMs9O3Vny4NvYB0aJmFGJmkLi0hbWERo2sTP0t7QR/MhzwESG186yIPffoO0wjhKqzIoq84itXCieettG6J+d7tHjSNbm9n48kHi0iIoq86krCqD7FlJE3KmDX1mDm1u9qjR0djP6oe2Eh4XTNmSDEqrMimYn4JaO940WYft7P2wwaOGpt/E335bR0SkmiXVcVQv07OoMpbg4PEabrfI6tc8z19ss7r56T37CAxUsHhJHNXL9SypiiU8YuIUdm+vbsPhECYsFwSRn/2oFkEQWbAohupleqqWxhEbO/E3bO37nRiNTo9t+d1vDjHQb2fuvCiql+mpWaYnMcn/eFQJ35GM4xQUF9dQkL+IbdteYdasiwkJ8W5C8VEcdguZGTO56MK72LnzDYqKq4mLHYl/8nbMo8NmITI+jdkrruPw1vdIyZ9JWuEc39phs6LVhbD0pu/TULuFiLhkChdc6NMFRS6nA0FwM/fim2ir249KG8Ds5V/x2rzCiNkyGwYomL+C3tZGrMMG5l12KyFeVhkB5AolAx0nSc6bQVhMAr1tjcy/7Fbi0iQA+TAAAJD6SURBVHK91lAolPS0NBARn0JQeBTHD+8kseZiKi652Ot1IpPJ6GtqQBsSQvLsuRxfv5a85ReSf8GlPuU89zUeR3S7iC8pw9DRRvLsuRSvvIqwxGSfNCyDA0RlZtFdd4TwpBQKL7mcpFnlXn+egZMnGDjZTHBMLNrgEAS3i+yq5eTUrCDg1KwEUzHU1krX0UPoIiIJCAvHZhgivqSM3KUXoC8o9qrqaeroon3bbrThYaiDgwDQRoSRvmIJ6SuWoIuZ+jto6Rvg5LrNqEOCUQfrUGg1uCxWYorzSVu2mMQFc1Bqzzxvq3PYTOM761DpAlEFBowZT4VGQ0xxPqnVC4gpLTijgRVcbo6/8R4KrQalRo1Sq0GuVOC2g0KtRhcXTWRuJgGR4Wdsy/HV7yGKoFApkatVuB0jP6qiIOIwmhAFAVVAwBnX76HXNmPuMyJXypErFMiVCqyGYQCGe4boOdZKsD6CyAz9pMbx2Ds76a1rQ6aQI5fLQC4bGaYiA7fTRdueekRBQCaXkRcejDZkok7Thv2c3HoEZDKQjXyPZDIZVoMZgKGWHo68uQ3LgAnB6SZlXv6Eddy68xhH394BgAxGtE7dcVhsCC43rTvrGGrpxdDaC9ctIavENk6jsbaDNY/u9Pg5jQMWAE4c6qKreZCOxn6WXFdKwbzUcc9rP97Pm//c5lHDNuwAoOvEAB/8Zw9tdb0suGKYikvHf56BThOr/7HVo4bLOXKCPthl4qPna2mt76W3bYiq68pQqj8+QTAbbJNqqMWRbTzQ7+CVF1uoO2bkROMwt34tE53uYwvgcgk8+LdjHjVg5NhtMrl4a3UbdXUGGuqNfO2bWRPM46MPH8dk8mz6nE4Bh0Pgg/c6qTtm5Hidka9/K5s4/fjfkOeeOcGJE8MeNYwGBw6HwMb1PdQdM1J/zMjX7sgiLd3/6rCEb5wXWdWfB6N5k6NZ1aIoTns8niC4kcvHVwP2W0aqVN5eMONyOnzqmvaEw2pBHTC9DE2LaYjA4LBpaRgHenwyjJ4Y7G4jNDp+WlfU93U0Exal5xBGv8c6Dpw8QUBouNcGyxODrS0AhCd5bxg/yXBvD4aOduKLS/3eX10OB02b1pO+YBFKL8e9eqJu7XvEF5YQHDtxkn5PjF5ZfXp3dfu2PYiCQHx5mU9d5adjNxipf/1d0pYuJije/27Q429+gCpAS8L82agC/FsvdqOJg0+9SNLCuUQX5fndVX3yoy0YT7aTuKicsLRkj9t6qqxqURRZ+9tniUiLI6OyhNAE306KR+k51squx98lfXExaQsK0Yb6V/3Z+JeXEUWR9MXFxJdmolD63p1pN1lY86N/kzAji7RFRURlJoytm1kRLV5piKLIv/7nbXTBWsqqM8mZnTTOpHnLa3/fTG+rgbLqTAoXpBIQ5HvAxObXDrH3w+PMqM6keHEGIZG+H7cHNr3E6mfMVC/TU71UP8GkecPhQ0Pc98uDVC+No2aZnuTUIJ81Otot3P3t3SxeEkv1Mj1Z2cE+H6OMRid33L6d2eWRVC/TU1gU5lFDyqr+dJGM4yR80jh+mvhqHiU+HUavsJYulPn8OCBOHOco4R9TGccvImc6wQ9VbSMrOMErDWDahYKzUWw4GxrZsneJC8yY+omfcjs+Sw3JOH66nJsTH37JkBJmzg2kRBkJifObszFLw2jX+bnSFknj7GpITB/JOJ4jjJpHic+XUnU09a09n3czJCQkJCQkzkkk43gOURIYIVUdzxEk8/j5cbRLWvcSEhIS5yqScTzHkMzj54/UZf35USyL/7ybICEhISFxBiTjeA4imcdzA6nqKHE+EyHkcGRAOo5ISEicXSTjeA4jmcfPD6nqKCEhISEhMRHJOJ6jSFdaf/5IF8p8PhTL4qVxjhISEhLnKFJyjBdYrSasVhMOhxWH04bTYcPhtJGaUkxAgHez1btcTlwuOy6XY+Tfbicul4PgoAh0ujCPrykJjPCYLiMKAoIgIIrCqZQG+bQnBZeYnPrWHmluRwkJCQkJCSTj6BUGQw8vvvQb3O6RGCWNRsfyZV/32jQCDA528OJLv8HhOJXPjIzZsy+mYu6VZ3zd6eYxKsTNmw//AuuwYezx+MxCFl/9TYLDvetaNRsGWPPYfVhNQ8hkcpDJCAqLYsHK24hO8m6SWIfVwvtP/xmraQi5QolCqUSl1lJadTkJmYVeabhdTta/8A8spiFUai1KtQaVRkty3gyv4xRFQWDbW/9heKgfTYAOTYAOdYCOsOh40orKvZ7zq/ajNzD0dRIQFEpAUMipv6Ho0/MpVUePTQx+Jpq2bGTg5AkCwyMIjIgc+Xvq5m3iyUhW9BGCoqLRRccQFBVNYETkpNnDnjB0dtC6ewchcXqC4+IJjo1DqfbtpMJps3F8/YeE6uMJTUhCFxnl8/xpgiDQtHk9ofoEwpJSUGn9S1rp2nMAZYCWsIxUlBr/To5MHV1Y+waJyE6fMmZwMkRRpHvvQSKy08diEP1qS1snMoWcIP30JuYe7uxGrlQSGD11LvuZ6GvoICI1FrkfKS2j2AxmBEEgMHx6kW/Gjn6C9RHTmqvPaXPgsjkICPN/G4miyGCXiQj99CZbHugyER4bNK3PM9QzTEiUbiTe0U/6e51E6AVUKv87GAf67QTqlGi1/u8nBoMDpVI+LubQVyxmF06XQGioVCj5PJGM4xRs3vIiDQ27xkxjfHw2F6z4lteZ1QMDHdTX76D++I4x0xgcHMmK5XeQmOhdtnK6W2TD0Y3sPbEfm9kIgFKlofyiG/4/e+8d39Zd7/8/tWVJtuQhW/Lee2fHcYbtJF20pYW2wIW2QOFC4Xeh3MUdlHsv0NLyhQK3tL2MDqCD7p20TTOaZu9pJ4733pasLZ3z+0OJE8eyLdvpSHuej4ceiY6O3v58ztLrvD+f835RuHRtWP6/rjEbbfUHaDt5gLGhfnzeoG9rwZI6llz1pbAsCL0uJx2NR+k4dZiR3k6c9mEAEtLzWHbtrcRYZrbK83s9dDefpOvMCQa6WhjtD9rM6aKiWXrNl0kvWjRjjIDfT1/7aXpbGuhra6Sv7TQAcoWC4hVXkZxTOuPFWhAEBrta6G8/Q09LPW0nD4x/lpRTwpIrvzhBsIXKOoqiyGhnB8NtLfQ1nKBx67vjn+miYyi+7kayq9fM2B97Xy8jHW0MtTRz9JXn4ZxzhUJBVvUaKj7/BTSG6X+UnUNDjHZ3YOvp5vjrL+FzucY/SyytYNHf3T6j9Z/bbsPe28NYfx9N721mpKMdAKVWS0xqBuWfu4X4vIJpY3idTsb6e3EOD9H8/nv01h8HmYzIeAvRqWlkrlhNcnnltDH8Hjfu/h76egfo3n+EUy+8gUwux5ieQkxuJnFFeaStWT6tIA94vbgGhvHYx3ANDLHz3t8iBgRMWWnEFeQSV5hDfHkxWtPU4kDw+3ENjeBzuvA7XTS8+CY9+49gTE/BXJJPfHE+ccX5RMSYpowhCgLu4VH8Hg8Btxd7Rxc7fvYb9Alm4suKiC8vJKGsaEavatfQCILPh+D3E/D5cfT0sf2/fklUSiIJlSVYKkuILy1AOY1Ad42MEfD5EQICgj+A6Bc49NS7NL57kNQlBaQtKyRtWSGG+Kn747E78Xt8iKKIKIiIgoDP6eHZO35JdGo86VXFpFcVEZ+fMuV1yTPmIuD1Bx1azh7roiBy5Llt1L+5h4wVxaRXFZO6JB9VRGih73W68Xt8cIHvWbBNAs/d8St0sVFkVpeQUV1MTKY15PXA4/LhcfomfP8cf/mfTXQ3DVFek0VFbTbZ5Ykh7SG9Hv+4J/XFbPrrQXa8fJyKmiwqanPIX5yCSjP5mPX7AjhtnpAxjmxr4pmfb6F8TRblNdmUVGeg1U8WTUJAwDHqDhEBRupd3Fz3GqvPWv2tqbVgNE2OIYoiw0Oh+9LV5eILN2xjWZWZ2nVWauosmONDH2vDQx5C+dGN2X1cd9VmSsqiqTtrf5iUHPp3Z3TESyAwOYg/IPLZqzaTnKKnbn3Q/lDyqP7wkSwHp+CcbVBqajFFhSux2frx+70sXXrDJL/p6dj41iOMjPSSm7MEr9fF0FAXa9bcilYbvp/r9vefobn5MIbUQmQyGc7BJlbf9G2MceF5AAMc37GRI9teIzW/Ao3OQOPB7ay88RskhpkhBGg+toftL/yBpNxSIk1xHN/5Fkuu+hL5i9aEJV4B+toaef0PP8GaUYApPomj771B6cqrqai5AbU2PA/VsZFBnrnvHzCnZBGXlMGx7W+SXryYpVd/iajY8LaJ3+fl8R9/DZM5EXNyJg37thBjSWXJVV8kObds0vqHvP0hh6uf/4dvotRoiE5Np+voIZRqDcWf+Sw5q+tQhJnpe+Puf8U5PIQpKYWRjja8jjEyq9dQfM31GMzhDZFveeA+euuPE5lgxW0bxTk0iDk7j6LPXE9SaUVY+2f3Y7/nzHub0ceaARF7bw+ayCjyateTW7sObZRxxhhHX36ewy/+DW2UEYVKhWOgH5lCQerCJeTVXYE5J29GUd+47V12/fFh5FoN2qhInH0DAEQmWcm8qob02uppBR9A977DbPuPnyOTy1FHGvA5nQg+PzKFgsQllWSsW4l1Ydm04nOkuY2N3/pXABRqFTK5HL87+AOvjTaRunoZaTUriMnJmDKGxz7GS5//xvh7uUqJ4POPvzdmpJK6ahnZ19ShNkx9TXj2M7ci+HxTfh4RF0PamioKbvrMhIzohbaDj133I0Y7B6aMARCTaWXpN64mu7Yi5H569o5f0nWwcdoYMrmM7JoKVnzvBqIsk40N3vzhHzn19v5pYwDoYqNY8d3ryb968ujBtl89z8G/bpoxBkDK4nxW//NNZOQ0T7AcfPMPe3jyZ+9O883zWDNj+PLdaympnrivd756gt/9wythxYiK1XHzv6xmxQ0lEzKIDXva+cktfw0rhkan4vrvVnHF7Ysm+Gf3tg7zj2seCSuGSiXjjr/P5Tvfz5+Q/XO7A+SlvRRWDJkMvvSVDP7xX4uIjpko7kvzXmF0ZOpj9UKuvT6Zf/9x6ST/7HWr3qah3hZWjFVrEvivn5VNEJCS5eAHiyQcp+Bir2qv141aPfvhNkEQkJ/90XY6beh0s9/xF8bY3XcGjc5ITq51djECAWRyOTKZDNtgL7pIE0r17IbtBEFABsjkckb7u1FpI9BFmmYVQxQERFFErlAwOtCDKAQwxc/sH3sxAb8fhVKJfagP+3A/iVlFc4jhQ6FU4bSP0Hn6KNnlVVMKrKl8rANeLwq1Gp/bzalNG8itvWLWw7J+rxelWo0gCBx+7mlya9ehjw0voz0ew+NBoVYjk8k48MxfSK5YSHxueBntCTFUQYF07NUX0UYZyVi2ImwBfK4vcrkcuVLJiTdewe/1kr26Fp1p+ozahQS8XkRR5IR6EFNHNy2b3iPrylrMJflhD/sFvD4Enw+lLgK/08W2H91PSvViUldXzSg6zyEEAvgcTpQREShUSg4+8me89jHS1lQRX14U1hQCURDw2OwoNBoUajWO7l62//cvSaleQuqqZUSlhnfsu4ZGkKuUyJVKFColjr4Btv37z0lavpCU6iXE5GaGPHYvFI6OQRsymQy5Qo5MIUeuVLD7kdfoONBI1poysleXE50+/RC6c9iOGBBAJkMmlyGXy/F7fDxz233EF6SSuaqMjOriaYet3aOOYLZQdtZC7qzF36Gn3qVh4z4yV5WSubKUxIpsFFMMoXvsTnyus9mxCw4JURB5/psPnM84riwhJsOCTCab5FXtGvPgmiJb+Of/epvO0wNU1GZTUZtNTmUyCuXk7etx+XCMuEJECGYctz13dDxrWbQ8HU2EatJ6Po8f+5AzZIwj25p5+t7NlK7KpKI2m9KVmeiNk68vAb/AaP9YyBjCqQ38+52tVK9OoO5sxjFUtlAURXq6Q/elp9vNFz+3jcXL4sazhYlJoUVRT7eLULLC4fBzwzVbKCwyUbfOSt16K+kZoacU9PW5CfiFScv9fpGbP7uNBIs2GGOdldz8qEnXBUk4frBIwnEKLhaOHycOO4cAJjwwI/HBM1XWUSI0oijOa37XEbGL/ATzvP1phYAQcphx1nH8/rDnq06F3+MNZi/n2Se/241Co5kxzoXCMRRumxNt1Pyub16HG5lCjko7v3lnY30j6M3Gec9x9Lk8IYXrxcJxKkRRZKBzFHOyac7tAOjvGCE20Ti/+YldNkzxhpCiNVxi+l8j25I9r/mJA/1udDolunnMTxwZ9iKTM6/5iQ6HH5fTT5x5+ptzSTh+sEjleC5DJF/rjwapPM/smK84ulQxLoVoBOYtGgGUGvUl6ZNSq70kceYrGgHUeu28RSOAId407z6ptOp5P6Qjk8nmLRoBzMmmeYlGgNjEqHmJRgBzgmpeohEgzqydl2gEMEWr5/1Qi16vnFE0SnzwSMLxMkVyl5H4NCDVc5SQkJD4eCEJx8scSTx+uEhZxw8PybdaQkJC4uOHJBwvYyR3mY8OSTxKSEhISHwakYTjZY4kHj98JB9rCQkJCYmPG/fccw+LFi0iMjKS+Ph4rr/+ehoaGias43a7ufPOO4mNjcVgMHDjjTfS2zs7/SAJx08Aknj8aJCyjh8O0jxHCQkJiZnZunUrd955J7t27eLtt9/G5/Oxbt06HA7H+Drf//73efXVV3n22WfZunUrXV1d3HDDDbP6O5JzzCeEc9aEEh8O4VoRSsyPUlkiR8Suj7oZEhISEh8pNtvEgugajQaNZmIt5g0bNkx4/9hjjxEfH8/+/ftZuXIlo6Oj/PGPf+TJJ5+kpqYGgEcffZSCggJ27drF0qVLw2qLJBxniSAI+P1e/P5gkWK9fmY3jQ+LMl0Mhxt7pfqOHyKhrAglJCQkJD7d6Lp9aDThOehMh9ITjJGSkjJh+d13382Pf/zjab87OjoKQExMcFRy//79+Hw+6urqxtfJz88nNTWVnTt3SsLxUuJ2O3jxxfvoH2glEAjahZnjUrnyqjvDFo5+v5fX3/gtA/3tBIQAghBApdKwetXfkZW1IKwYghBg07uPMtDffn6hDEpLayksqEYmk80oHkVRZN/GZxjsakWuUCBXKJDJFSSk5VK4bN24Q81MHN+xkf6OJpQq9dmXBl2UibxFNSjCrHd35vBO+tsbUWt1qLQRqDURqLU6knPLUGnCq9XV2XiMvrbTaHWRaPSRaHUGtLpIDNHmsC0MB7pa6Gs9jS7SRESk8ey/JpSq6WuOXZh1HOvvo7/xFPqYWHQxseiiY2Zd988zNkb/6XoM5gQM5niUmtk5+0CwSHVvw0mirInoomPmXBdvsLkJgzkejSG0s0M42Pt60UZGoYoIbz+EwjNmJyAP7cEbLkIggM/pQhM5974A+JwuVLq59wUg4PMjk8vnXVtSCATCcq6ZCVEQwrYLnTLGPAu9X8o4l6otEhIfN9rb2ycUAL8423gxgiDwve99j6qqKoqLg9bCPT09qNVqTCbThHUTEhLo6ekJuy2ScJyBjs56mpsOMjLaOy4aKyuvpGr5TSiVk+2jLkYURfr6W2hs3MfgYCc2e9ArNjW1mHVr7yAyMjasdoyM9NLUdJD+/jZ6e5sAMBrjWVv3dVJSCiet3xhCPDrtI3ScOsJAZzPtDYcAUKo1LFp/M4VL14YlGr1uJ91NJ+lvP8Op/VvHl+dUVlO4fF1YojHg99HffoaBzmaObHttfHmsNY1l194WlmgUBIGRvg6GetrYu/EZOGuApNbqqKy9gaKqK2aMIYoijpFBRvo62fnq4wT85+8Os8qWs+TqL2EwzWz9d6KhmZiAg31/fRSP3R5cKJNhKShi0Ze/hjFxZreKgNeLc2iQ/U//GXtPNwARpmiMScmU33gLcVk5M/dHEHCNjnD8tZfoOXEUpUZDpCWRKIuVjOUrSS6vnDEGgNfh4Mx7mzm1aSMRpmhMyamYklOIy8ohdeGSsISG4PfTd+okO//wEJHxFmLSM4hJSyc6NYP43PywRLEoingdDk7+6G7aE8zE5mURk5dNbF4WxvTksIW5TCZjx08ewD08SlxhbvBVlIsh0TIrkdG5az/HnngWc0kB8SUFmEsL0M/S2UYUBN75h//EYI0nvryYhPIiIpOtsxY7TRs20/ru+1gWlGJZUEJ0duacxOiR59+jaesR0pcXkb68CFNa/Kzb4vf4ePWuhzDnpZBRVYy1PGtKq8DpOPzMFnqOtZC+opj0ZYVojVN7d0+FrXOQTT97kvSqIjKqS4hOndtowKP/vgF1hIqK2mxyFyajVM2+P6/8bif9HSPT2g3OxPYXj3Fka9O0doMzsWubjTee2TluNziXAtrHj41w/z3Hz9oNWrAmzr5wfFenk3+5az+r1limtRucDpvNx3e+uZuly8zUrbOSkxf5qblRiIqKmpVzzJ133smxY8fYvn37JW+LZDk4BedsgxQKFZmZFaSllrBj53Ncsf7vSUsrCTvOho0PU1+/g+TkfDIzKtix8zmqV9xCaWld2Af8++//jT17XyHenEZGZgX79r1OaUkNVVWfR6UKfRG42Jbw+I6NvP/SnzBEm0nJLaPx8PtY0vJYccPXiYwO7ynhluN7efvPv0SticCaVURP80kiDEaqrv8aiVmTxWso+tpO8+rD/wVAfEo2IwPdCIEAi664mfzFtWGJV/twP8/98p/wed1ExyfhcTlwjdkoWr6Oytob0epndo7w+7w8+bM7cTts6CKjERFx2UdIyStn0RW3EJeUEVZ/nv1/P2C4twOFWoNSo8FjtxGZYKHkus+RvrQqrKzQpvt/SvfxIwCotFp8LhdKjYbc2vUUrL+GiIvuDkOx848P0bxzO4LPh1ypRPAHb3ISS8opvPpaEvKLZjzeDj33NA3vvInPNdGvVh8bR17dFWStqkGjn/5i3/D2Bo6+8jxuu21czAPIFApSFywmt3Y98XkF07alde8u9j/5OB67jYBv4lCP3mIm84o1ZKxbTUSMacoY/cfq2fP/HsE75sDncCAKF7RFLsOyoJS8z11DQtnUHuf2jm7e+/Ev8Lvc+Fxu/C73hD6pDDryb7yGvBuvRqEOLQq8Yw42/eC/CHg8BDw+Al4vfpdrQnvMJQVU3nkbpvSUkDEA3vruf+B3uRD8fgR/gIDHi9d+3ptYHWWg4KZryb3+ikmC+pzt4Mvf+x32rkEEQUAICIgBAb/Hh3Pw/NypqKRYyj6/mrJbVocUf2//95/pO9mGKIiIoogYCHrPOwZG8Tk9wbbotaQtL2Tx164iLntyLc7tv32J1p0nQAzGQAREEb/Hx2hn8MZaJpdhLcsi/4pFFF23HPlFbTnwl03Uv7EbCH79Qoabewj4gse/KTWezFWlVH6plkTriQmWg++/dIw3/7An5Pa2DToZ7g1uX12khpJVmaz6fCkl1ROvC0e2NfG3+7eGCoFrzENf6wgAKo2SouVpLLu2kKWfKZzgKNNyrIc//PDNkDH83gCdp4PbRK6QkbcohUVX5LHmlnKU6vPbZLDLxq+++XzIGGrBxumTwXNaJoOKBTGsvzKRL9+ehf4CNxiPJ8Bnr94SMgbAyeMjCGfto4tKTKxdb+W2r2URHTPxJvDmz27FbveHjNF42obHHQySlRNJ3TorX70jG4t1Yib/23fsoqXZESoErc1jjI0F46ek6qhda+W2r2eRkXn+uv9xsBy8VHbFHo+T3z30jVn15Tvf+Q4vv/wy27ZtIyPj/DH77rvvUltby/Dw8ISsY1paGt/73vf4/ve/H1Z8SThOwbmdf8fXf4vBEI3bPYYoikREzM7Oani4G63WQEREJB6PE6fTRnS0ZVYxRkf7kMsVREbG4vO56etrJSkpb8bvXSgeHaND+DxujGYrohCg+dheMkuXzupuze2wMzbcT2xiOshknNj5FgVLapErwk9c+7xuBjtbMKdkoVCqOLDpBYqWrUOjC//uUxQEOs8cIz45G3WEjj0bniZvwSqMZmvYMQA6Th0hxpKCLiqaXa/9hdSCChKzphYSoehsPEa7ToYmJg7HrncwJiaTsbx6VsOIfQ0nUWojiLJYOf76S4CM/HVXojGEf6wNnDmNKIpExlto2bWdweYmCq/8DNGpaWHHGGptxjM2hiHOzHB7Gyc3vkbBuqtIrlwUdn9GOjtwDPQRYYpBFAS2/uZ+slfXkr2qFp0pOqwYY/19DLe1oImMQhsVxeZf/hx5moWKz15JfFlhWBlP19AI/cfqURv0qA16Tr20geHGZjLWriStZgURsTO3xetw0rX7AKqICJQ6Lc6+Qfb95o8kLqkgraYK68LyKQXjOQI+P21bd6BUq1Fo1Cg0Gg4+/AQ+p4uU6iWkrlpGdE7GjOdhy6btyOQy5CoVcqUCW2snRx59mtjCXFKqFpFctRi9JfQN4DnheHrTQfxuLzJFcKhcrpDTf7qTPb9/A3NuMllryslaU0ZsVuKU7WnefgzXsB2ZTIZMLgO5HLlcxq5HXsfWPUjK4nyyVpWSUV2CPi70NJ72fQ3YOgdBJkMmO2srKZPRV9/OoafeJSoxlsyVJWSsLCWpIhuFavL1pedYM4Nngpl5zrZVJgv6km/9xbOIokjq4jwyqkvJqC7GYDZN8qpub+in6XDoB692v17P0feaSc41U1GbTUVtNlll1klZ3d7WYU7uagsZo2FfO9ufP0ZsYtR4jIIlqag0E/sz0j/GoXfPhIzR0zzE6/+3m8hYHeWrs6iozaZ4RToRholizWX3sPuN+pAxtMOHePC+bqKiVKxak0Dt2cyjKXriVBy/X+C5Z1pDxnC7A/z3fx5BrZZTvSqe2nVWatZaiY+fnLh44dlWvF5h0nJRgP+5+wg+n8CyKjO1Z7OXySmTM8tvvt7J6Ig3ZFt+df9J+vvcLFoSS+06K3XrrGRmTbxWflqFoyiKfPe73+XFF19ky5Yt5ORMHKkaHR3FbDbz1FNPceONNwLQ0NBAfn7+rOY4SsJxCi71zv+ouDjzKPHBcMjbT07y7IYtQ3Ep5q4FfD4UqtkPi12Iz+Wa19xECA55K7XaefUn4PXi87g5ZRijwDL3h5DsXT0YrAnz2j9j3b2ooyJR6+d+PRD8foZONxObnz2vtgyfaUFjjEIXN7Nv/TnhGIqeYy1ERBswJs08JWMq/B4fLTuOk7okH7Vu7j7C7fsaiDAZphWuM2HvGaL/VAcpi/Mn+WdfLBynY9drJ8kssxKfYppTOwAObT5DrDWS5Ly5Xxfq97ShUCpCitZwEY6/iMoTz+KlcahUc4vReNpGe5uTZVXmOfted3c5OXRwmOpV8RgMc7s+2Ww+3n27m9U1k4XvhXxaheO3v/1tnnzySV5++WXy8s4nl4xGIxFnr+ff+ta3eOONN3jssceIioriu9/9LgA7duwIu02XlXB88MEHuf/+++np6aGsrIzf/va3LF68OOS6L7zwAj/72c9obGzE5/ORk5PDD37wA7785S+H9bc+KcIRguJREo4fLOcekpGesP5gOCJ2zUs4fpqZTjh+mpiNcPwkkSffSEJE5kfdjA+VT6twnOoG5dFHH+W2224DggXAf/CDH/DUU0/h8XhYv349v/vd77BYwh8JvWwejnnmmWe46667ePjhh1myZAkPPPAA69evp6Ghgfj4yT8oMTEx/Pu//zv5+fmo1Wpee+01br/9duLj41m/fv1H0IOPllAPy0hcOqS6jh88J3v6JPEoISEhMQXh5AG1Wi0PPvggDz744Jz/zmXjHPPLX/6SO+64g9tvv53CwkIefvhhdDodf/rTn0Kuv3r1aj772c9SUFBAVlYW//AP/0BpaekH8oTRxx3JWebDQ3KT+WAolU1+yEJCQkJC4sPnshCOXq+X/fv3TyhaKZfLqaurY+fOnTN+XxRFNm3aRENDAytXrgy5jsfjwWazTXh9kpDE4weP5GEtISEhIfFJ57IQjgMDAwQCARISJg61zlS0cnR0FIPBgFqt5uqrr+a3v/0ta9euDbnuPffcg9FoHH9dXKX9k8A58SjxwSJlHSUkJCQkPqlcFsJxrkRGRnLo0CH27t3LT3/6U+666y62bNkSct0f/vCHjI6Ojr/a29tDrne5U6aLkbKOHyBS1lFCQkJC4pPMZfFwTFxcHAqFgt7eiYKnt7d32ieB5HI52dnZAJSXl3Py5EnuueceVq9ePWndUIbhn2Skh2U+WCQP6w8G6QEZCQkJiY+WyyLjqFarWbBgAZs2bRpfJggCmzZtYtmyZWHHEQQBj8fzQTTxskKa7/jBImUdPxikB2QkJCQkPnoui4wjwF133cWtt97KwoULWbx4MQ888AAOh4Pbb78dgK985SskJSVxzz33AME5iwsXLiQrKwuPx8Mbb7zBn//8Zx566KE5/f19+16jueUwgYAfQfATCATIzl7I4kXXogjTOeX4iW20tBwJWm0RtNuKNllYsuR6VKrwsp1NzQdpbT2KTCZHLpMjk8lRq7WUla1Fqw3P27Wr6zSu9mP0CX7cPdEolCoUShVJOSXojeHNgxzu7aC76QRKtRaVRovq7L+R0WZ0UeE5hDhGh+hprkej06PW6tFE6Mf/H27RaI/LQX97I1qDkQh9FFp9VFh+2RcS8PsY7GpBFxWNLtI0Kyecc4iiyEhfJ4boOFTqYBHkuWQdnSPDREQZw3JHmQqf241KO/dCzHBpCpGLovip8ZGVkJCQ+LRw2QjHm2++mf7+fn70ox/R09NDeXk5GzZsGH9gpq2tbYLPscPh4Nvf/jYdHR1ERESQn5/PX/7yF26++eZZ/d2AEKC5+TB9/a10dJwEQKPRUbPmNvLzl4cVQxQFenubGehv49SpXWeXyqisvIKlSz+LUjl1BfwLGRntY2ioi8OH3x6v15SSUkhd7dfCFo0ul51RWx979rxCIODjDKA3xVJ13e1hi0a/z4vTNsyeDU/jdQX9ROUKJWWrPkN5zfVhxRAFAY9zjF2v/wXH6OD48uTcUpZe82ViLKlhxQn4fex6/S8MdZ+3/Yq2pFD92a9jycgPry2iyN6Nz9B5+ijIZEToozBEm6msvYG0wgVhxQA4vPUVTu3bilYficFkJmCMRLmimozl1WELqMbN73Ds9ZeINCcQmWAhMsFCdEoa6ctWhC3k2vfvYf+TjxNlTcSYmERUYjJGaxIJ+YUow5yOMXDmNO8/8ltMSSmYUlIxJacSnZJGlMU6yQt5KlyjI2z55b1EJliISc8gJj2TmLSMWVkpCoLA9t89gFypJC4rB2emiUBsTEgbuuk4/erbDJxoIK4wD3NRHlFpybN24hjr6ePwH57EXFJAfEkBxvTkOQn8o088i0KtIqG8mOicjDkJ9OGmVlre3oZlQSnmkgKUmvCuIRfTeeA0HftPk7a8kISC1Dn1RxQEdv3+DRIKUklZlIcqYm5TfpreO4pzYJT0FUGLwLngGBjl+Ms7yKguIS4nac43Lhsf3YslI4aCZWmoNXP7mdz+4jHUGiUl1RlERM5tmxx6t5GxERdlq7OIjJlbMelDe8cYaGmidq11kid0uNSfGGXn+/3UrrWQmh6+PeyFdHY4eeXFdmrXWcnJjZzTvrHZfDz6h0Zq6iwUl5ikG9OPiMvKOebD5Fz1d7UqAmQimZmV9PW2oDdEs37dN4iMjA0rzt59r3Hw4AZcLjvJyQWMjQ0hCALr1n2DpMTcsGIcP76NvfteYXi4h7i4VISAH4dzhJUrv0RR4cqwTp4zTQfYtetF+vpa0OuNqFQaRkf7SS5ZTcaCK8kvmFmo9bQ0sHfD0/S1nQaZDK0+EsfIICn5FSy/9jaMcTNXnh/u7WDX63+hr/UUHpcDrT4St8OO0ZzIsmu+Qkp++Yz9cdpHeP+lP9HffoaxkQEUKjUBnxe1Vkf5muspXnElStX0P6R+n5dtz/8fg53NjPR3BYW4KIJMRnbFChau/RxRsTP3Z/uLf2Sgs5nhnnZ8Xvf48tjEdGJWr8WUX0Je2vRxDr/4N/pPNWDr7sQ5PDS+XK3Xk1d3JXlrr0AbOb1jQP3bb9J97Ahj/X2M9fcS8J73eU2uWEjBFdcQn1cw7bZt3vEeHQf34RgaxDk4MKEt+jgzebXryVpVg0Y/9Q9H5+GDtO5+H9fICK7RYWy9PQg+HwAyuZyUBYspu+EmjInJU8bobzzFmW2b8dhtuMfs2Hu7cY+Ojn9uSLJQ+a1bsS4smzKGra2T06++hW/MidfhxDU4zMiZlvHPVQYdBTdfR+71V04pQl2Dw5x89lX8Ljd+txu/003fkZMEzk53UUcasC4qp+S2m9DHh7bs87vdHH3iOQJeLwGPl4DXi729m5GmoCewSq8jvrSAtNpqkqsWTbl/jjz6NH6PF8HvR/AFX21bdyIGAshVKswl+VgqS0ivW4nWNPFYOeccs++xt3AO2xEFAcEvIAoCfreXk6/vBiAi2kDa0kLSqorIrC5BrZ+ctT7y3DZsnQOIoogoiOP/duxtYLCpG4VaSfKCXNKrishYUYIxefJ2adiwl/5THWdHXgj+K4JryE79m3sAMOelkFFdTMaKEhIKJwva5u1H6Tx4JnjOno1xjmMvvY/H7sKQEE1GdTGZ1aUkL8wl1rBvgnNMw552Dm0O7RF9al8Hp/Z3oNGpKF6RTkVNNmVrsjCZJx77rSd72fXqyZAxuhoHOPBOIwqVnIIlqeN+1eZk04T1+tpH2PzUoZAxRvrH2P78MWQyyK5MoqImGCMpJ27CsWIfdvHG73eHjBHhauTZxwcAKC41UXfW37moxIRcfj6Gzyfwy5+fCBnDHxD44yONBAIiOXmR1K21UrveSuWCWBSKicfsr//fSdyuwKQYoijyxKNNOBx+UtP01K2zUrvOyuKlcajVE/fvHx85zUB/6Gllz/2tlb5eNxZrBLVrLdSus1K1Ih5txPkbsE+rc8yHxWWTcfyoqFv7NbIyF6BUqjjTdIDMjHJksvDvyqNNCaxa+SXS0krRavWcPLmd7OxFYQ9NAxgM0SyovJr09FIiI2M5cmQTmVmVGPThDQkD6CKiKMivYv36bxIbk8ShQ2+RnJyP2ZzGYedQWA/LaHUGknNLWXTFLZiTszi+YwPGOOussnKaCD1xiekULVtPQloOpw5sQyaTUbh0bdhDxGqtjshoM5mly4hPyaLj1BFG+ruoqPksWn142SyFUoXOYCKx+mriEtMZ7uuk+dhuFq67KexsJ4BWH0l2eRXRlhQ8LgeH3n2RyrrPkVa4AJlMFpabjFKjxVJYTG7tOuRKJbsf/T8KrriGnNV1YftFyxUKYtLSSV20hIgoE+89+CvSl1WRv/4ajNYw5wbKZOhi4zDn5qOPiWX347/HaE0mf92VJFUsnJDRnwpRFFHpdERZk4gwRdOwaSPu0RGyV9eSvbIGXfTMWW1REJDJZRiTUkiIiqLvVD0dB/aSXLmI7JU19BfFYU2cXowLgkDA40MTbSQy2Yrf42HkTAuGxATSa6tJr61Gb5l+LqooivjGgn7bGmMkSq2W4cYWBL8f68JSUldXkbSsEuW00wJkeIZHUWjUKCO0aI1R+J0uRppa0ZiiSFmxhJRVS4krzJtW1LuGRkAEuVKBQqVCGaFFJpcjBgJEpSZiLs4ncXHFJNF4IY6BEVwjDuQKOTK5HLlSjlx5/sc2IjqSSGsM0WkJqHShr0+OQRv23hFkchkymQyZXAZy2XnHChGQyVColCimyNQ5h+3YugeRIQOZDGQgk8vwjp2/8fLYnXjHXHidbgRB5OLksHvEga1r4OwWPrvdzm4/URDPrjOGo3+Usf4RvA43XHS/47R76O8YCdlG11hQtHicPgY6bQx02hjuGZskHD0OH/3toWOMDQf7E/AJ9LePjr9iE40TBZvHP2UMpz3YDlGEgY5R+juCMSzpMSjV5/ddwB+gry10DO3ZGzcIZv3a2xy0tTnIyo5Epz+/j0QRWlvHQsa4ML3U0+Wivc1Je5uT3LwojMaJN+ntbQ6cTv8UcYKB+vrctLc5aG9zkF8QRZx54jnU1eWiu8sZMobfLwAwOHA+xtCQh8Sky9sa+HJCyjhOwSfJqzoc5uJnfSnmsAVFwvye0RIC/jnNS7wQn9c9PjdxrridY2gi9BO2ySFv/6zmObpto6i0ESjUcxt6BHDbbSCKaKOMc47h97ix9/YSnZo25xiiKNJ97AiWwuJ5zZfsOnqImLRMtGfvtufiWz3Y0IgYEIgtyJnzMetzOGndsoOUFYvRGOd+59/yzntExMVgLimY9XD5OexdPXS8v5eUqsUYEqc/b6fzqu451kzH/tNkrS4jOm1uVRZEUWTn717BnJdC2rLCkJnKcDiz5RCDTT1kriwhNitxTvvJMTDK7j+8QcaKYlIW5qHUnj+PZuNV/fyv3sMUb6CiJosY69z29dtP7Mfr9lNRm401M2ZO/dnzRj0dpweorM0mrShhbsO7u57j2A75lBnCcDh+bITnn2mlZm3oDGE4dHU6efDX9dSsnZwhDBebzcc9/32U6tUJrFwdj8GgCrmelHH8YJGE4xR82oQjzE08SszMbMWjxPTMRTh+mplOOH6amI1w/CSRJ99IQkTmR92MDxVJOH6wXBbleCQ+PKQSPRISEhISEhJTIQlHiXGk+o4fHJINoYSEhITEJwFJOEpMQPKzvvRIBcEvLaWyRE72SEJcQkJC4qNAEo4SIZGyjpceKesoISEhIXG5IwlHiUlIQ9aXHinrKCEhISHxSUASjhIhkYasPxikrKOEhISExOWMJBwlpqRMFyNlHS8hUtZR4qPixJB0HktISFwaJOeYMOnvb6Ph1M6zNlsCgiggk8koK1uLyRheTbnR0X7OnNkfLOIqC/odyGQykpLyiYtLCSuG02mjte0oCrkShUKJXK5ArlBiMEQTGxNejTKfz01nZwNKlQaVUoNSpUal1KBWa9FqJ9vJhXKVEQJ+BrpaUGt14y+FUjWrArWiKGIf6kUTYUCt1c25ELjHOTav7wMIgcC8ilTPhlPtfVJdx3lSKkvkSI9UzzEcYoQ8huQNH3UzJCQkPiFIwjEM/H4vdvsgR49uxu0OWjLFxCSxfv03wxaNoijgcts5cPBN7PZBANTqCFau/CKxsVP79l5MIOBj795XGRzsGF9WXLyG6hW3zKJHsHfvq3R01o+/j4/PoLbmNiyWicKxTBcT0pJQJpNzeMsrNB89748aYTCy7NpbySpbHpaAlMlkHHt/A8e2v4lMJkOjM6DVR1FcdSUFS2rDFoIndr3NvreeJcIQhS7SRESkicTMQkqqrwrbUaZ+77vsf+tZ9MYY9MZY9KZYjHEW8hfXhO0o03J8H7tf/wtRsQlExpiJjI4nMiaexOxitLrgdi1Xm6e1IRxsPsPeP/+JyAQLkRYrUQnW4L+WRFTTWtudxzk0xK7HHiEy3oIxKRlTYjLGpGQ0hvDsGAH8Xi97n/gDEdExRKemE52SRmR8wqzEuSiKHH35+aAdYnomsRmZs2rDOVr37sIx0E9cVg4x6Zko5+CqY+/opnPnPuKK84nOzpjSn3o6REGg8bW3MWWmEZOXPacYAH1HTxLweDEX56PUhm89eiF+t4euXftJqCiel4uNrWeI0fZ+Esuz5twfgNZdJ0goSENr1M85xkBjF0qNEtM8bqrcNifDrb1YitLmdSNZv6eN9GILWt3cHZwaD3VhSY/GYArPNjQUrSd7McbqMcVP7Q8/Ey2NbrxGBympc983ba0OfD6BrOzZn7/n6Otz09/nprDIOGf3JpvNR+MpG+WVMRNsGyU+XCThOAMbNj5Ce/tx5DI5RqMZj8fBggVXs2zpDSiVM19UWtuOcaphJ80th3E6bZhMwYtiZmYFtTW3YzDMPJewp+cM9Q07aW09ytBQJzpd0EouJiaJutqvkpSUN2OMgcEOTp3aRXv7CXp6ziCXB7NrGo2eqqqbKCleM6UX8TnxaB/q48zhnfQ019PT2oDXHfQSlclkFCytY+G6m9Dqp/8Rc42NcubwDvpaT9Pb1oj97BCaKIqY4pNYes2XiU/JnjaG1+2k6fBO+jubGeg4w2B3G6IQwGkbHve9zl24etoMYsDvp+nITga7WxnqbmWwqxXX2CiusVGGetrJX1JLZsnSGUVj09HdDPe0M9zbwXBfB6MD3YwOdANgSc+nsu4GNBHTX7C7jx1huL0VW0839t5uhlqaGDhzGgBNZBQF66/CYJ7+x7T/dAPDba2MDfQx1t/HwOlTdB0+GPxQJiOlchFlN96CKWnqm5ThthaGWppxDA/iHBqit/4EY/3n52Qak1JY/JWvkZBfOGUMW08Xg81NuEdHcI2O0H+qgf7G89muyAQLpdd/nvRlK6b88XAMDjDY1IhnbAzPmI3Rri6ad2wLdkWhIDo1nZTKRQjrp/ZId4/YGKw/jW/Mic/pxGt3cPJvrxLweFCoVcTkZWMuziPrqlp05tiQMXxOFwPHG/C73PjdHvxuN62bd3Dgd4+j0KiJK8wlvrQQy4JSYnJDO3MEfH76j5wg4PUR8HoJeH3YO7s5+fTLyFVK4gpySagoJqGimOicjCmP2d6Dxwj4fIiBAILPjxAIcOwvz7Pz5w8SnZ2BdUEpCQtKiCvIQa4MfVnvOnwGn8uD4BcQheDL7/Hzzv/8BZlcRsqiPNKXF5FWVUSUJfR1qfdkGx6bE1EUQGR8BOb4yzt4eeuDWEoyyVhRRPqKYuKyk0Lu48EzXTgGbcE34jn/YhFb5yDv3vMU0WkJZFQXk7GiBGt5Fgrl5G0y3NbHWO9wMMSF5mcibPzRY4iiSHpVEZnVJaQuKQhpgzjYZaO3dThkP9/5ywEObWqkYFkaFTXZlNdkEZc02cJztN9BZ+NAyBgH3jnN20/sJ2dBMhW12VTUhLYedNjctB4PPZWg+WgPT9+7mcxSK+U1WVTUZpNWONl60Ov20XiwK2QMZaeDm/55A3n5UdSts1K7zkp5Zcwk68FAQGT3ztA3tTabj29/fTepaXpq1lqoW2dl0ZI4VKrJvxl7dg2M+0lfiM8n8K2v7cZoUlGz1krdOivLqsxotZP376EDQyH9rkUR7vruPvw+gTV1FurWW6lelYBeL0mZDxNpa8+ALiKSz1zzDyQnF9DcfBCdzkRiYk7Y3+/srCcgBFi18u9ISyuho+Mkfr+XvLxlYd91dfecwW4boLx8HWlpJQwPddHX38rCBVejCDOjNjjQTk/3GTIzKli18kt4fW7q63ewoupmdLrwMhYnjzbQc/IAlox8iqrWo9JEsHfD0yy/7jZireF5GrvGbJzat434tBwWrvs8kTHxbP3bQyy56kukFS0Ma5sIgQBHt7+BOTmLnAWrWJKQzKYnf0PpymsoqroCpWpmQS+Tyzm05WWMcVYS0vIoXLqObc8/QnrxYiprbsAQHRdWf4699wYqjRZTfBLJuaXsf+c5jLEWKutuxJpZGLI/5Wozhy4Yrm7YtAGvw0FkgpXEknLco6MEfF4Kr7yWzOrVYWXYzry3meG2VvRxZgzmeCItVkY728lauYb8tVcRmWCZMUbrnp10HNyPLiYWXXQM+jgzY/19WApLyKtbT1LFwilvLs7RdeQQDZs2EhFlQms0otYHRbMuJpbslTVkrapBHxNaqJ2j/3QDB599Eo0hEk1kJCptMGMjk8tJKqsgq7qGpNJyjimmftDI1trB/v99FJUuApVBj1ofgUKlJODxoDFGEVeUS1pN1ZSiEcA1MMSeXz6CQqtBqdWi1GrwjAYFT8DrQxRFNCYjeuvUoj7g9bLrvgeRq1QoNGoUKhWys+JQ8PkZPtOCLiGO6Ox0REGEKe51dv+/hxEDAWQKBXKVErlCgWck6Ek+fLoJv9OFKApojVFEpYaesrL1/r9h7xlGppAjk8uQyeXIlXIEfwDBH6Bp6xGGmnsY7Rqk/JbVGMymSTF2PvQqvcdbAMZjAPg9PkRBpPvwGXqPt9Cx/zRlN60ic2XppBgH/rqJM5sPBafrnD0/LjxPhlt7GW7tpfHdQ+RfvYSFX1mLKmJiZvbka7s48uzW8wsu+L7P4UYICJx8dReN7xwko7qEZd/+DMaMi9rxzmme++W2kNvK7w3g8wY4srWJI1ub2P5iItfduZyK2ok3taf2tfOHf30zZIxAQEAIiDTsaadhTzvvPXeUK7+2iOrPlU7IlnWeHuDXf/9CyBjCWVHcdKSbpiPdbHv2CLV/V8kVty9CqT5/sIz0O6aMIcMHQEO9jYZ6G397upWbvpDGnf+QP0Fw+XwC37x9V8gYwf6INDeN8cdHGnn+b21c99kUvv9PBUTHTNw33//OXmyjvpAxXC4/DoefvzzWxEvPtXHF1Un80w+LsFgnZmX/6z8P03jKHjKG0+nH7xd59ulWXnmpnZo6K//y70VkZM49GyoxOySv6in4OHtVi6I451T/OQIBf9ii8xyHnUMA40PWfq8HhUo9r7Z43U4USjWKKbIkYcVwORERZ8zsTYfP68ZlHyUqdu6evkLAT1/7GSzpM2eAp/KvFkWRjoP7SCqrnNecy+Yd75FYVoFGP/chruYd7xGdljFtlnIm2vbtGRd8c+3PwJnT9NafILNqFREm0/jy2XhW+xxODv3hSVJXLyO+pGDOw5hHHnsGjTGK1JVLiYiNnlOMnv1HaN38PinVS0moKEahVs06huD38/7/PEBMbiZJVYswpiVPex5O5Vc91jfCGz/8I+lVRWSvKSM63TKn8/nde5/C63CTubKUtGWFaAyzH55t3XWCXQ+/RsbKUjJXlhKbZZ11W7wON89941dYStLJrC4leWEuSk1w+87Gq/qPP3wT+7CT8jXBbKPJPPvz6OUHd3ByZ2sw21ibQ3yqadYx3n/pGO/85SAVNdlU1GSRnGee9Tax7XqO390zQO3ZTGFJWfSsh3lP1du48xu7g1m+dVYqF8agVM7uHOrrc/PFz22jakU8deutLFlmRq2eXQyX08+N126ltMxE3TorVdXxROgm/3ZIXtUfLJJwnIKPs3D8KDnsHJr0oIzE7Dk3z1F6SGZ+zEY8fpKY7c3jVMJRFIR5zQU8hxAQkCvmFyfgD4Qclp5tDLlCHnLbzEY4+r2BCRm9uXApYvg8flSa+Q0MZvrfJNk4/fSfmfB4Amg08+uL1yugUsnmlWjw+QQUCtmMwlcSjh8sUjkeiVkjleiZP1JpHon5MN8Rh/E4l0A0AvMWjcC8ReO5GJdi28xX8F2qGPMVjQCqWWb1QjFf0QigVocW9LNBpZJLD8V8DJCEo8SskFxlLi1SQXAJCQkJicsJSThKzBrJVebSIGUdJSQkJCQuNyThKDFnpKyjhISEhITEpwtJOErMCWnI+tIhDVfPj5M90vaTkJCQ+LCQhKPEnJGGrOePNFw9P0pliR91EyQkJCQ+VUjCUWLeSFnH+SNlHSUkJCQkLgck55gwCQT8tLQembRcq9GTmJgbVpkBURTo6WlCJpMhlysueMkxGifbSE3FyGgfCrkChUKFUqlGqVSNWwiGi8fjRKlUz7oI+IWIojCll7VE+MzkXy0hISEhIfFxQRKOYSKKIidPbuf06T3jy4J+01+dRW0qGfX173Po8NvjS4zGeOpqv4rJNLMl3DlONezk/R3Pjr9XKFQsW3YjCyqvDFtA1jfs5N13H0UuV6BWR6BSaSjIr2LJkuvD8uAGaGjYxbvvPoZGo0NQazhpNBKXlMGCus+hjgiv8GnH6SNsf+GPaPWRRBiMaPWR6I0xFC1fT4RhsjdsKAa6Wnj/xT8SYTCii4oefyVlFxMZHd5Q8NjwAO+//Ci6SBOG6DgMpjgMpliM5kR0kaawYnhcDna++gT6qGiiYhOIik0gMiYBfVR02PXyAn4/R174G1qjkShLIlHWRPRx5hmt/i5EFEXq33oDjd6AMTkFY2JyWLaFF9O+fw+iIBCdmo4hPvwbmwsZaGrEY7cTm5GFdo7Fa53DQ4x2dRCbmR32cXUxoiDQf7Se6NxMVBHT+49Px2hLBzpzDCr93Av7ekZtiKKI1hTe8T0Vzv7BaS0Tw8Hv9iIEhJBezrPBY3eiiZxfsWOvw41Kp5lXrT+/14dMLp93TUi304tWN/tz5uMYw+UMwOyNfCbgdPiJ0M2vRqbbFUCtmV8dRo8ngEIhm7VrjcSlRRKOM1Bfv4O29uO0th4dX6bVGliz+ith+U17PE7a24/T3HKE1tYj2O1DQLB6/oIFV7Fs6Q0zCjW/30tnZwNtbcdpaz9OX1/L+GcZ6WWsXnMrJuP07hmBgJ/e3iY6Ourp6DxJV9dpAAQhgFZrYPWqL5ORUTZtDEEQGBzsoKvrFF3dp+jqOo3H68TjDdoGxmdWsOiKm6f1ihZFEdtgL31tp+lvP0NfeyP2oV5sgz0AZJYuJady5bSiURRFnLZhBjqbg6+u4L8Bf9Af1RSfyML1t2AwTe837XbYGOpuY6innaGeNnpa6vE4xwBQqjUUV11JdML0dntet5OR/i5G+roY6euk+8xx7MPns4fJuaUsXHcT8alT+5v7fV7Shj3sPvkOkQEXXUcPMdzWMv65wZzA4q98jcTS8iljCH4/jqFBHIP9OAYG6Dp6iO6jh4MfymREWRIpue5G0pdWTXnMCoKA2zaKa3gI5/AQ3cePcmrTRgBUERFEp6STXLGAvHVXTWkRKYoiXocDt20Ut22UkY429v75TwDo48zEZmQRm5lNzqracR/rUDH8Hg9exxieMTtum43tDz2A1+nElJRCXHYu5uxcLIXF6GOn3scBrw+f04XP6cTncHH8qRfpP3KS6Ox04orzMRfnYS7KQ2OcWtAKAYGA243f7cHv8dC19yBHH32G6JwM4ssKiS8txFych1I7tfASRRHB5yfg8yF4fXhG7Wz6wY/RJ5hJqCgmoaIYc3E+Sq1myhjn2iIG/EFv6UCAo4//jYHjp7AsKMWyoJT4skJUupkVgigICIKIGBAI+AM8c+t96ONNZFQVkba8iJiM8GwHRVEEUUQURA78dRNN246SUVVE+opiLMUZsy4KPto1wKvff5j0qiIyqotJWZiHUjs70SSTyXj6y/cSk24ho7qYtOVFRJhmbxf41E/fpadl+KxdYDYJabO3l3z78f0ceOc05TXZVNRkk5I/e7vAg5saefWhnUHLwdpsMssSZy28Th51cdsPN1K7zkrdOisLF8eiUs1u3/T0uPjKLdtZXRu0HFy63IxWOztx7nD4Wbd6M0uWmalbZ6F6VQI6/ewkiCjCVbWbyC80UrfOyqqaBIzG+QlridkjWQ5OwTnboMjIWLKyFpCZUUFsbDKbtzxBzZrb0OvDyxa88cb/0tZ+grS0EtLTS0lJLuC1139LzZrbiI9PCyvGtm1PcuTouyQnF5CaWkRqShEbNj7MkiXXk521MKyL0YEDb7L9/WewWLJITsonKbmAbdv+Sl7eMiorrkSpnNkvt+HULt5440HMcSlYE3NITMxl/77XiU/IYPnyz3FGFjyUphuy7mmp55Xf3U1kTALxKVmYU7I5c/h9FEoVS6/+u2kF1jnsQ308de930eqjiEvKIC4pne7mehyjQyxc+3myK6tnzND5fV4e/Y+voFCpibGkEJ2Qgm2gh/7OJoqWX0HZqmvQ6mfOkD3xX3fgcY0RFWshOj4Jj3OM7uaTpBctorzmeuJTZrb6ev6Bf2GwqwVVpJHoxEREUaT/VD3GxGQKr7qW9GUrZvTy3vzLe+k8fAClVoshzoxSo2XgzGlUERFkr6olr+4KDObpby52Pfp/NG55B5lcToTRhNZoYqilCQBLYQm5tetJrlgwref0kZee48iLfwNAqdGgjTLhHBpACASIMEWTtXIN2Strpm1L49Z32fWnh8ffq3V6An4fAa8XZDKsxaVkV68huWIhirOZ1IutB7v3HWbbf/z8fFCZDLlSgeDzA6CNNpK6ejnptdVEZ6eHbMdIcxsbv/Wv026zmPxsMuqqyVi/BoVq8j7y2Oy8dNM3p42h0KhJrlpMyW03oY8PLYSfu+42Ah7vtHEM1gTKv/F3JC6tnHRNGJI3sPfrDzHSNvNc2uRFeaz+p5uIzbRO+uz5b/6KzoONiCLBX/Ep0MVGseK715N/9ZJJbdnwH49y6u39wTeiOGUspUZF8WerWPrNayZlM7f/+gUOPvluMMRF3xMDwvj/ZXIZacsKqf7+jWTkNE+wHNzwp708fe/mkO0XhKAgPkdSbhw3/eMqKmqzJ/Rn9+sneeiuV0PGEAURIXA+RlxSFNd9p4qVny+dIP4a9rZzz989FTIGIgT85/sTFavjyq8v5orbF01wpultHeZf1v0+ZAgZIn7f+XZEGVV86csZfPeuAvQXCDe3O0Bx9suh2wH4Loih0ym44aY0/vFfComOmXjDs6DoNUZHQx+rF8bQaORccXUS//ajEizWiTc8V9a+w+kG24wxlEoZa2ot/PuPS8jIjBxfLlkOfrBIwnEKzu38b/39/6HVBne+IAizGjIEcLsdaDQRyGTB7wUCfmQy+aziXDwfURAC+P1e1Orwxx+8XhdyuWI8uymKIg7HCAZD+HfSPp8HQQhMOBkGhzqJjTl/MZ7Jyzrg9+PzOCeIsp6WBhLSwpsnOt720SH0xpjx77SdPEhSTjGKMATwOcaGB4Ixzu6LpiO7sGYWEmEI/+S0D/eji4weF3aNB7cTY00lxpI6q3Zo9AaOYwdA1XEKVUQESaUVYQ9xO4YGUao1qPV6ZDIZrXt34RoZJmvFalQR4R0nzpFhEEW0RhNyuZy+U/W07tlBbs16jInhefy6baP4PR60UVEoNVocQ4Pseez3ZK+qIamsEvkMAhjAMzaGZ8yGWm9ArQ9mi969/6dYCovIqFqFPmby8OzFwtHncOLoH0St16HSRaCM0HLgocfx2h2k164gobJkWgEM4Pd4sXd0odRqUGq1KLQa+g4f59gTz5G6ehmpq5ZhsE4/r1cICIw0taJQq1CoVchVweNz010/JjYvi5TqJVgXl0+bsQQYOt2ETKFArlAgVyqQK5SceOZl+o6cIKVqMUlVi4jJzZzyHBqSN2Ae9CH4BeRyGTKFHJk8eB165a6HUOs0ZK4uI2t1GbFZiVPHae7G6/Qgkwft42RyGcig/o09HHthO+lVxWSuKiW9qgiNIfRxN9Leh3vUCbKz1okyGTIZOAZGefWuh0koSidzZQmZK0uJybSGbIutexDnoP38gnOriPDGD/8AQGZ1CRkrS0iqzEGpVk3yqh7pG2OwO7Q42fjoPg6+20hJdQaVtdmUrc4iMmayCLAPu+htHQ4ZY++GBt5+bB8Fy9KCmcuabGITJ19bXHYPnWcGQ8ZoPNDJU/e8S+7C5LMxcrBmTq5m4fX4aTsZ+qZA0baVH32vlfKK6GDWcb2VgkLjpO0qCCKHD4buy/Cwh69/ZSd5+VHUrA1mLssqokNmP48cGiYQmCwrvN4At39pBwkW7YTsZ6hh55PHR3G7A5OWi6LIt76+G6VSRu1aK7XrLCxdbp5kiSgJxw8WSThOwaXe+Z8WDjuHgOmzjhJTc8jbT27K9JnBywlRFOftTysIQlCkTBPnYuEYioDXO56hnCs+h3Ne8xsBfC43MhkzisWZcPT0o0uIC2v7DskbKIyZfE763V6cw3airPObKzl4pgtTanzIrGu42LoHUahV6GPn/uPodbqxdQ2GFL8XC8fpaDrcTWpB/Lz8pltP9JKQHj2vOYqdpwcwxRvQG+d+rES0vkxaXCbx8XOP0dnhRBRFklNCTy8Jh/4+N3a7j8ysyJlXngK73Udnu5O8gqhpj/tPs3Dctm0b999/P/v376e7u5sXX3yR66+/fvzz2267jccff3zCd9avX8+GDRvCbpM0x1HiknLuKWsJCWDeohGYdZZ/KuYrGoF5i0ZgXg/nXIjeMv8aoEqtet6iESA2a/71NC9FO9Q6LXHZ4YnD6cgsmzxMP1vSCud/85yUM/087XBIzdASP89jLil5/se9OV6LeR7iFSAyUkV+4fweKvuk43A4KCsr46tf/So33HBDyHWuuOIKHn300fH3Gs3086svRhKOEpecMl0Mh6XyPHPmVHvfJyrrKCEhISHx4XDllVdy5ZVXTruORqPBYgm/ksvFSM+0z0DLya6PugmXLVJh8NkjOclISEhISFyMzWab8PJ4PHOOtWXLFuLj48nLy+Nb3/oWg4Oh59hOhZRxDIOmYx1kFk9flkViItKQtcSHSakskSM9M89zlJCQkPgwUHUMolI55h1H8LkBSElJmbD87rvv5sc//vGs411xxRXccMMNZGRkcObMGf7t3/6NK6+8kp07d6KY4YHBc1xWGccHH3yQ9PR0tFotS5YsYc+ePVOu+/vf/57q6mqio6OJjo6mrq5u2vWnIicyOPbfdKxjzu3+NCNlHWdPudosWRBKSEhISIzT3t7O6Ojo+OuHP/zhnOLccsstXHvttZSUlHD99dfz2muvsXfvXrZs2RJ2jMtGOD7zzDPcdddd3H333Rw4cICysjLWr19PX1/oH9gtW7bwhS98gc2bN7Nz505SUlJYt24dnZ2ds/7beVGSeJwLZbrJZSMkJCQkJCQkZkdUVNSE12wfaJmKzMxM4uLiaGxsDPs7l41w/OUvf8kdd9zB7bffTmFhIQ8//DA6nY4//elPIdf/61//yre//W3Ky8vJz8/nD3/4A4IgsGnTppDrezyeSXMILuSceJSYHWW6GCnrOEekrKOEhISExAdJR0cHg4ODWK3hVxK4LISj1+tl//791NXVjS+Ty+XU1dWxc+fOsGI4nU58Ph8xMaGzYPfccw9Go3H8dfF8AoBUrZ+TBxpwu8fweJx4va6zRbGFEBFDI4rhr/tJQhKPs0N6SEZCQkJCYraMjY1x6NAhDh06BEBzczOHDh2ira2NsbEx/umf/oldu3bR0tLCpk2buO6668jOzmb9+vVh/43L4uGYgYEBAoEACQkTy7skJCRQX18fVox/+Zd/ITExcYL4vJAf/vCH3HXXXePvbTbbJPHYMdDEhl1/gm3B9zKZjMqKK1m27Ebk8vAykq2tR3nl1QeQyWQo5EoUSiWpKcWsWvV36HThFSrt6TnD66//FrlCgUqpQanSYIwyU1V1E1FR4dX9Gh7uYcOGh1AolajVOjSaCLQaPaVldROcYKbD4RjlnU1/QKlQo40wEKE1oNVGkpScR0J8xvh60z0o4/O42fnq4yhVGiIijUQYgq+o2IQZfaLPIQT87H/7ORQqNQZjLDpjdPDfqBjU2vBcU0RR5PiOjSiUKiKj4zBEmzGY4qb13Q5F09HdyGQyjLEWomITUKpnn6nubjqBKIIvWoMozt7fFmCkox1REIhKTJrRrnAqnENDCEIAfWx4RaZD4XU5Efx+tJFzL8Ib9L8eQ2OYuXDwyZ6+KR+Q8Xu8KDXzq+UoBAIzOs7MxDnPhUtR4zJcYoQ8TgyFLgIuISHxyWHfvn2sWbNm/P05XXPrrbfy0EMPceTIER5//HFGRkZITExk3bp1/M///M+shr4vC+E4X+69916efvpptmzZgnYKtwaNRhNywznddk53HeFMz0la+hrGl0cZLFx9zbewWLJm/Pter4v2jpO0tR6lte0YgYAPCBbfrVp+E0VFK8ctCaciEPDT3dNIR/tJ2jtOMOYYRhACyGQySkvrWL7sc2i101f1F0WBwcFOOjsb6OxqYGi4E683+MRWYmIulauunFE0iqKI3T5Id08jPd2N9PY243CMAKDTGVm+/HOY40J7cDdeUNvR4xyjv7OJgY4melrqGekLlj2SyeXkL65lQd2N07YDgn7Tw70dDHa10HXmOL2tp8Y/M6dks2j9TSTllE77Ay0KAvbhPoZ6OuhqPEbL8b3jn2l0kSy5+kvkLVg1o/Wf22FjpL+LrsajnNj59vhyvTGGkhVXUbziSuSK6U+3gN+HfaiP3tZT7Hkz6F17JEJHTFIK1uJSCq+6FuUMJ7coinjsNka7OnjvwV8hUyiIsiRiSk4hJi2d7NV1aM7a+E3bFp8P1+gIG3/6nyjVGmLS0olOTScmPRNLQTERJtOMMURRRPD7eeM//xm5UklsZjaxGVnEZWYTk56BUhN+MeC9f/4Tg81NmHNyMWfnYc7JxZiYPGG/lMoSOSJOXT6r5Z33OP3yBszF+ZhL8jEX56Mzz67o9FhXL+//5AHMxXkklBVhLi1Ea5qdKBYFkR0/+RUaYxQJlSUklBehiZq9m8aZNzbRd+QkloWlWCpLiYgxzTqGKAi89eMnMCbFkV5VRHxBGnLF7Aeijr24ncEzXaSvKB63+JstA41d7H/iLTKqS0hbVjilZeF0+N1e3r3nKRLLs8ioLkEfN7di0c//ahuaCDXlNVkk5cztxuntJ/ZjG3RSUZtNerElpD3fTOx67STNR7upqM0mpzIZRQh7vpnYs93G3ncPUbfeypJlcZPs+cLh+LERnvjTGerWWVmxMp4I3eylQ2eHk//38+PU1FlZuSaBqKjZHyM2m4+7/+0QK1bGs6bWQkysNIXsYlavXs10hoAbN26c99+4LCwHvV4vOp2O5557boJ1zq233srIyAgvvzy1MfsvfvELfvKTn/DOO++wcOHCsP/mOdsgAKMuhkxLAZkJBRxp2Y0lOgWTpQq5XDFjmZ5t257k4KGNqNURpKQUkpZWQnPTIdSaCFZWfzGsLOO+/a+zc+fziKJIojWH5JQC+vvbcDhGqFlzK/Hx6TPGOHlyO5u3/BmPx4k5LoWkpDzGHCP09JxhZfUXyM1dOuPFsbX1KBvfegSHY4TIyFgslixEQaCl9QgLFlzFwgXXoFZPLQYOO4cw6Vy88+dfYhvsRaWJIC4pA5VGS9vJA2SWLmXh+psxmad3oRgbGeTNP/6Mkf6gSDDFJ6GLNNF5+iix1jQWrr+Z1ILKafvj93l55Xd3M9LXid/nIcJgJDImnr6202h0BkpXXkPR8vWotdM7Jrz++58y0NmExzmGXKEkKjaBkb5OZDIZWWXLKV9zPTHW6X2rNz/zIF1njuMYHQp6Reuj8DjtiKKIzppMxbWfJW3xsml9ng888xfaD+zFOThAwOcLCipRRBRF1Do9WatqyKtdj8E8dbmaE2++StP723CNDOGx2yd9bi0uI7d2XdBzeoqsW9P2rTS8swG33YbbNkrA653weYQpmqzqNWSvqpmyLZ2HD3Ls1RfwOsbwOMbwOhwIfv+EdRLyi8hffxXJFQsn7Odz1oODDWc48sen8Dmd+Jyu4MvhQvD5xtdVGXQU3HQtuZ+9KqRV3lhPH/t+/Qf8bg8Bjxe/243f7cEzMooonL9spq2pouzrXyQidrLvu8/hZMfPfkPA60Pw+Qj4fAg+P66hYXxjzuBKMhmxeVmUfu0LxJcUhNwm7/34/xHweBD8AYRAANHvx+dyY28/L5RNWWmk11aTc+26kMfKew/cj2rUgxAQEIXgSwiI2DoHcAyMAhBhMpC2rJCSG6tJLJ98U7z9Ny8y0NgFiIiCeHY7iHjsLvpOtgW3a4SGlMV5ZK0uI//KxciVE4+VfY+/ReeB0wAEf31EOLs52/fUIwQE5Ao5iZU5ZKwoJv+qxeiiJwrrYy+9z5kth7kgyDh99e04B4Nz1OMLUsmoLiFv3ULSc5omWA7ue+sUW585HHJ793eO0nlqAABzipGK2mwWrsslf0nqhOOtfk8brz+yO2QM26CTpiPdAJjiDZSvyaKyLoeyNVkTRGTHqX6euW9LyBhuh5f63e0AGExaSldljbflQjvEkb4x/vhvb4aMofX3sWtb8HzW65WsXJNA7VoLV30mGb3+/HHi9Qr8/VdDT/0SRdi6uZdAQESjlVO1Ip669Vau/kwypuiJWfzvfnM3Doc/ZJwd2/txuQIolTKWLIujbp2Vq69LJiFh4k3Cf/7wIJ3tzpAxDuwfYnjIi1wOlQtjgzGuTSY17Xzi5ONgOfgPn7kHjWr+LlEen5tfv/rDj5VX9WWRcVSr1SxYsIBNmzaNC8dzD7p85zvfmfJ79913Hz/96U/ZuHHjrETjhXx5zfexmFLGLxbWmDR0mmDGpsHmmbHGY07OYnJzlxAfnzFunZaSUoTJGH69udSUYhLiM7Bas1EqgydpX18LZnPqjJnKc1gsWVx5xbewWnPGM5OdnQ3Ex6ejUoV31xYTk8ia1V/Bas3GYAjOFW1tO8bq1V8mMnLmzE2ZLoZ9wx0sWPt5zMlZGOMsyORyOk4dprLuRuJTssNqhy7SSMmKq4hNyiA6IRmlSk3HqSMULKklo3jJjNlBAKVKTeGytRjjLEQnpKDVR9LddILe1tMULlsX9hB37oKVlFRfhcmciCHazFBXCyd3b6Js9WeIig2vMn9awQLSCxcSFZtAZEw8HucY21/8I6Urr6EvNYGM1JmHF+PzColOTUcfG4c+Ng6VNoJ37vsfslfWkLliZVjZvZj0DNR6PTpTDBHRMUQYTWz6xU9JyC8kt2YdUZaZJ09HWRPJWrkGbZTx7CuK3Y/+HqVWQ/aqOpLKKmYc6tXHxpK2eBlqvQGNwYBab6Bt7y7a9u0mc8UqMqtWERk//TbRGqNIXFKBUqdDpdOi0kXg6Bvg4MNPkLi4grSaFVgXlaOYJjOmitCSUFmCUqNGodWg1GhQqFXsvv8hNKYoUlctI3XVcozpU5//cpUSc0kBCpUSuVod/FelovntrQwcqyehspSU6iUkLa1EHTl1JjiuKDcYT6FArlQgUyhwdPdR396FzhxLUtUikqsWEVeYN2XGMCrbSoyoRCaXB9eRyZAr5DRs3IdjYJS43GSyVpWSuaoMc17oPsVmWlHrtcjkMjjrHy6TyRhp76PvZBtqvZb0qmIyV5aQXlU0STQCmFLMCL4AnNNOMhkyGYgBkfa9DSCTkVCURuqSfNKWFRJhmrxdIi0xWIvPT4fhgnvE4dbgQ2XGpDgSy7NILM8iKikWaJrYDrOerPLQN6k+b4DOUwPojVqyK5LIrkgitTBh0s2owRQxZYz2hn6ajnSjjlCRWWYlqzyRjJLJmUetXk1WWegYQz126ne3o1DJSS+2kFVuJbPMOslDW6lWTBlDO2Zn1zY7cjkUlZgor4ymYkEMOt3EGHI5lFWGfgbA7xPYujk4Tz0/30j5ghjKK2MwmiafP0WlJjyeEHP5Rdi1IyjGs3MiKa8MxoiLm3xtyss3EmcOfc06dnQEgLR0w3gMa+Lss9MSc+eyyDhCsBzPrbfeyiOPPMLixYt54IEH+Nvf/kZ9fT0JCQl85StfISkpiXvuuQeAn//85/zoRz/iySefpKqqajyOwWDAYJh5qC7cu4YGm0cqDj4Lzs11/KTaEYqiOO+5a6IgTBC/h7z9s7YgDPj9yOXysET0dDHEQGDG4fHpEEUR18gwuuj5lWay9XQTGZ8wY3/OZRxDYe/oRmOMnFagzYR7ZBRn3yDRORlz3s+iKNK16wDmknzUhumnl0xH/7F6FGp12G0Zkk+e4yiKIide3UXyghyMSXP3RW55/zgyhZzkBTkhs7fhMNzSS9eRJjJWFKOLmf3QPQSHqo889x5pywuJybBM2C5G1c4JGcfp2PLMYSwZ0XMeHgbY/fpJNDo1hctSUWtnPywLcGRbE+4xLyXVGUREzu089Bx8HnuPiTU1CUTHzC1Gw8lRDuwfomatZVJ2MFy6Op28taGL2rVWUlLndtzbbD6eebKZmjorWdlTHyNSxvGD5bIRjgD/+7//y/33309PTw/l5eX85je/YcmSJUBwXD89PZ3HHnsMgPT0dFpbWyfFCLfa+myEIyCJx1lw2Dn0iRWOHwRzEY6fVqYTjp92QgnHTxOzEY6fJPLkG0mIyPyom/GhIgnHD5bLYqj6HN/5znemHJq+uOp5S0vLB98ggvUdwxmylpjIhQ/KSMzMqfY+STxKSEhISHzkXBZ1HD/uSM4ys0NylJkdUk3H8CmVJXKyRyqcLiEhIfFBIQnHS4TkLDN7pKLgEhISEhISlxeScLyE5EVppKxjmEhZx9kjWRBKSEhISHzUSMLxA0ASj+Eh+ViHjzRcLSEhISHxcUASjpcYab7j7JHEo4SEhISExOWBJBw/ACTxGD7SkPXskIarJSQkJCQ+Si6rcjwfNXbXCC/uehSv341CpkAuVxCh1rGy6Bos0SkT1j1Xpudi3G4HG996GJ/Pi1KpRqlUoVSqKSpcSUpKYVjt8Pu9bN7yZ/x+L2q1FrUqArVaS3x8BhkZZWHFEEWBnbtexO/3otXo0Wj1aDV69HoTSUl5YRUUFkWRI0ffDVr2RUQSERFFhC4SXUQUBkPMuFPOTPS3HKH7lJuMnDT0UTHoIk2oI/SzKrDc01KPz+PGYIpDb4wN2/nlQoZ7O/D7vETFxKPRza1ItGN0CCHgR2+KC7v/F+N1OUHGJLvDcrWZQ97+sGIEfD5kMtm0NoUzIQrCuDvI5USpLJEjPVI9RwkJCYkPAkk4hoHTM0ZL3ylaeusZcQzg9gY9NHOsJdSV3UCkzjTld8/Vd/T7vXR2naK97TgDAx3YbEEBEB1tYc2a28ISjaIo0N/fTmdnPX19zfT1tQCgUmlYsvj6sIWn3T5IV9dperobaW07Or48J2cxK1bcEpZQ8Hrd9PY20d19mpMnt48vj462sKLq5rAsCAVBYHi4G+1wL3v3vsrJLcHlCpWa8tXXUr7mehTKmR0XXGOjDHW3sf3FP44vU0foyVu4mgVrPx+WiAz4/diH+tnw2M9BFFFrdUTGxJOQlsPCdTeh1c9ceFUURTwuBy/+9t9AFImKTcAYZ8UUn0jhsnUYTOE5cwiiwLP334VcocAUn0x0QhKm+CSSsorBGOYpK4q8+d//BoApOY3olFRMKWlEp6YRYTSFFwPY/Kt7CXi9xKRlEJOeSUx6JlEJllk50uz9y6M4BweIzcohLjObmIxM1BHTe4BfzKl336L/dAPmnDzMOfkYk5JnLcztHd0c++vzmIsLiC/NJzI5cdaiWPD7OfR/f8WUmUp8eRGGOYrTM29uBlEgobJkzjGGTjczcLwey4IyIpOtcxL4AZ+fQ09vJmVRHubc5Dk7DbXva8Bjd5G6OB+1fm5Fj23dg3TsO0V61dydYwI+P8df3kHassJ5OeHsePk4iVmxpBVNthkMlwPvnMZgiiC7InFKG8iZOLGzFSEgkr84ZZLNYLgcP+TgmL2bqup4tBFzi3H6lI1TDTZWrk4gMnJuLjhdnU527RyYl4ONzebjjVc7qFlrJT5+/sW1JeaGJBxn4Kn3HqRvpBODNoqMhHyKUhbQ0HmEurIbyE0qnfa7eVEa3q7fwfv7/49hWztyuZzkpHzS0ko4ceI9liy5ngWVV6GcQRydaTrAsWOb6exswOt1YTanExubTF9fC/l5y6muvmXcO3oqOjrrOXL4Hbq6T2O3DxIZGUt8fDoA8fHprFr5dyQn508bo7+/lYOH3qKn5wxDQ50oFGri49MAGTpdJEuX3EBx8WoUiqkPq9HRPvYfeJO+vhb6+9vw+z2YTBZkcgWIIgVLa6msvRFdVPSUMdwOG4c2v8JQTyuDXa24xkZRaSJQKFUE/D6sGQUsWHcTiVlTC+mA38eBd55nuK+D4d5ObIM9Z63+FIhiAJVGS97C1eQvqUWpUk8Z59Dmlxjq7WC0v5vRgW68Lsf5vg50E5+aQ+6CVdOKxhM732agsxn7cB+2wT7GRgYQhQAQzGB6nGPEp+QQGWOGwHDIYuAtu96nv7EB5+AgjqFBnEODuG2jAAy3tdKp05O1qmZav+nOwwfprT+Oa2QY18gIrpFhHEMD+N1uek8eB8BSWEzhVdeSWFIeMkbfqXq6jh7CY7fhttvw2OyMDfThHBqk/cBeALRGExU3fZHM5StDCpXhtlbaD+7F63AEX84xnMPDDDWfoXnHewCodXoKr76OgiuuQREiq+rtH+TElp34XS58Ljd+pxu/y0X3vsO0bd4BgMYUhXVROSVf+Tw68+QbHfeIjZZ3thHwePF7PATcwX/7j5zk9CsbAdAnmIkvKyLzitXEFeZOiuH3eDnz2tsEfH4Enw/B5yfg82Fr76Jn32EADNYEEiqLsS4sI3FJZchtcurljQheL0JAQAwEEAIBAh4vDS+8AeKf0SeYsSwsxbKglITyYlS6yTdLXW8dwu1XIooCYkBEEAQQROrf3MP2X7+ILjaKtOWFZFQVk7okH03kZHF/+p0DjPWNIIoiiCKiKCIKIo6BUQ49tRmFSklSZTbpK4rJWFGMKUTR+pYdxxlp6zsbI7js3P93PfIaPvdfsBSnk7GimIzqEuJykiaJt86DjfQ3tJ//7gUce/F9Nt/7NDGZVjKqS8ioLsZakgkXXWZbjvdwal/o6URHtzVzaPMZoi2RVKzJorwmm6KqtEnWgd1NQxx9rylkjOajPWx/4RiG6AjKVmdSUZtDaQjrwOFeO3s3NISM0dc2wsZH9xFh0FCyMoOK2mzKVmcRGT1x/zptbra/eCxkDO2ojd8/cApthIIV1fHUrbMGrQMtE2P4/QJ/fix0X7xegZ//5BhyOSxdbqZ2nZW6daGtA598ogmPN7RX9f33HMfl8rNwcRy16yzUrQtaB168f196vo3hYW/Itjz8v6f4l7sOUFYeTe36YDsKi4yX3cjI5YwkHGcgL7GUaxZ+kdjIoO/pgK2bFYVXhW0lFK+R4YrOJD9rHZXLlqJQKOnvb2PRwmswGsPLNPj9XmKikygtqSUxMReNRsfAQDslxWtISsoLK0Yg4CdCF0X1iltITMwlMjKWoaEusrIWUFiwApls5jtiQRCQARXl67FYMomNTcbhGOHosS0sXHAVanU4w8MyPB4nuTmLWVF1E2ZzGqIosmXrnzGWraG0snjGCHKFCvtwH5b0fAqXrSfWmoouKpq3Hv8Fpas+Q2JW0YwXEblCyehANyZzIulFi4lOSMZkTmTDoz8nq2w5eYtWh5XtHB3oQRthIGHBSoxxFoxxVrY9/3/EWtMoXnEVBtPMmdfRgW4AErOKyF9UQ2RMPPvffha5QknpyquxZBSM96dcEXq42t7bg9fhxJiUjLWkDF1MHM3vb2Wks4P8tVeSsXwFSs30x+xYfy+OgX4iTNGYklOIMEbT23CC9n17yFyxipyadRitidPGcAwOMNLRhjYyiihLItqcKMYG+mh4ZyOJJeVkr6ohqXxBSLF3DtfoMAONp1Hr9Wj0BvSxsRitSQw1n8FgjiezahWZK1ZhME99/vjHHAwfOYFKF4EyQotKF4EuLob+Y/UIPj+WBaWk1VSRtGwBSm3o7eJ3u+nedxilRo1Co0GhUaPUaFBGBNePiIshecViUlctIzonI2QMMRCgc9cBFColcpUKuUqJQqUCIfjDKpPL0CfEYcpMIzY/Z8qMX9fuA4iCEPQfVyqRKxXI5HJkMhmiKBLwekEEpUaDXBX6uB3Y28iI3YtMLkMmlyNXyJHJZfhcwSk1ziE7o+392LoHcY06QgrHjv2nGWjsRCaTIZMBcjkyGQR8wRudgM9Pz7EWtFF6IqIjMcRHo9RMbE/P0Wba9zbA2WM6+E8wnigIIIr0Hm8JtlGpQBcTiT7OOLEvpzo4/c6B8wsuON9dI2MADDV1I/gDiIEAar2W6IvuI7ubhtj9en3IbTXaH7wBHO6xs/etU/i8ARRKOSUrJ/qCD3bZ2P1a6BgOuxuAsWEX+98+jc8TQBREFl+Vj1x+PoZtyDllDK/HF+zTmIeDmxrxunz4PH6qbyiZkIF0O31TxlALtuA6rgBb3u3B5fLjcvm5+UsZ6PXnz8NAQOS1l6efl+/zibz/Xh8uVwC3K8AtX0qflD3c8GYXjjF/yO8HBBFBgD27BoLtcAb4wt9lYLFO/O3YurmXtlZHyBhORzD24UPDwXY4A+h0CjIy55allpg9l5VX9YfJpfabbLB5JEvCKRBFEZlMxmHnEMCcrAgvxXw8QRBAFJBPkzGdsR2iiNftRBMx+U58NoyNDEyZpQzXu3qkow1jUsq8tsnAmdOYklNRauZe4L7vVD362Dj0sXMfOhxoaiTg9RKfmx/WcGooz2rvmIPWd7eTsnIpWpNxim9OjyiK1D/7KnGFucQV5s55aLdl03YCHg9JyxehNc3Nf3akuY3mt7aSXLWI2ILcGYdDQ3lVC/4Am3/+NAlF6WSuLJ3zEPGZLYdp31NPxspSkhfkoFDN/hyy9Qyx/YEXSF9RTHpVEbro2bcl4PPz1t1PkFCYSkZ1CdFp5/s7G6/qZ+7bgkwmo6I2m6wy65yGmt/84x4GOkepqMmZ81DzjpePc3JXGxW12RQuT0Orm3r0YypG3n+Wd1/2UbfOyso1CURFzX6o+fixEf73gXrq1llZU2shJnb214OuTid3/9sh1tRaqFlrnSQWw8Fm8/GP/98+llWZqV1rITU99Fx0yav6g0USjlPwQQhHQBKPM3DYOSR5WIdBuOLx00oo4fhpJ5Rw/DQxG+H4SSJPvpGEiMyPuhkfKpJw/GCRyvF8SEglesJDKgouISEhISHx8UUSjh8ikp+1hMSHQ6kskZM9Us1LCQkJiUuNJBw/ZCQ/65mRso7hIRUDl5CQkJD4sJGE40eEJB4l5oPkXS0hISEh8VEgCcePAGm+48xIWUcJCQkJCYmPH5Jw/IiQ5juGhyQep0carpaQkJCQ+DCRhONHjJR1nJoy3fRuOJ92pOFqCQkJCYkPG8k5ZhaIosjOhrcZdQyhVChRyJUo5SpMhjiK0xYhD8N9BeBIy25GHYOolGqGfXJaO9UkZyWTmVExrV3fhTQ1H8Q22o9ao0Oj0aFR69BoIjCZLKhU4WUzu7sbsdsH0UYYiNAaiIiIRKs1oFSGX2R2eLgbh2MUnS4Knc6IRqObdcFph2MEt8eBQR+NRjPZraKxsXfG2o5etwsh4EOjm2xfFS4Bvx+ZjHkXAAck+6uPCSd7+qR6jhISEhKXEEk4honH56atv5HekQ5Odx0FQCaTsyBrJXlJZWGJRkEI0DvSQd9IBweato8vjzVlULJgYdii0WYbYHCgg+3vPzO+TKczUr3iFuLiUsKK4fW6GR3t480ND3HOMFYmk1NaUsPy5Z9Dqw1dkf9CRFHA6bLzwos/JxAIWmMpFEoyMipYs/rLM/pnnyMQ8PPMM/+Fx+NEpdJiMEQTG5vMiqqbKYu2jDvKzMQLv/4hbocdQ3QsBpOZyGgzBUvriEsKbQcXqj8vP/gjPC4HUbEJRMUkEBWbgCUjn4S0yT7EIWMIAhsfvx+P047RnIjJnIjRnEh0fBLRCeEVfxdFke0v/AGHbYjohBSiE5KJSUjGlJAc0jc7lHc1wPHXX2K0q5PolDSiU9OJTk1DY5idG0fzzu0MNjUSk55JbEYWURbrrN1S+hpO0n3iKOasXGIzs9EYZj62Lsbe10v7vt2Yc/OJSc+c1rLwHKWyRI6IXePv/W4PLe9sI64oD2Na8pxdX7r2HCQiNhpTRuqcY4w0tyH4A0Rnpc05htc+hq2jm5jcrDk5m5yj51gz5ryUObm9nGO0cwC1XkuEafb79hxumxOv002UZe4jDYI/wEh7H9HplnndvHU2DmDNjJ1gDThbupuGMKcYUapm7xhzjt7WYaITDJM8smfVjg4veqsPg2HuMXp7XWjUCkzRs3euOcfQoAd/QCQ+fu5FscfGfIyO+EhKnpxgkPjwkITjDOysf4fOwTN0DbehVUWQas5BqVBhjkpkXcXnSTBN70TQP9pNc289bf2n6Rhswh/wYY1JQ6PSolSoWVNyHQXJFZzq8RI3hSPbyEgvra1H6exqoKvrFHb7IFFRZjQaPX6/h8rKK1m86NppvaLHxoZoaztOd3cj3d2nGRhsR6FQo1Zr8XpdZGRUUF19C7ExU/fH5bLT3n6C3t5menub6O1rxut1oVAEL0gmk4WqqpvIyV405UXb43HS2dVAf18rff2t9Pe3Mjp64Tw9kZycxSyovAqt9rxt34VZR7/PS09LPcM97QydfQ33tuP3Bt15Rvq6iIq1kL+4ZkrRKAQC9LWdZrivk5G+Tkb6uxjp68I+3AeiiH2oD1tMH+VJGdMKz762RkYHurEN9jA60INtsIeRvi68bid9bY0olCpyF64m1po2ZYyhnjZGB3qwD/VhH+7HPtTPcE8b9uF+2k4G/XjNKdmUrbqGjJKlE7ZtuTroXW3v7cHW041zeBDn0BDO4UFGOzsYOHN6fF21Tk/J9Z8jr+4K5IrJP2aOwQHsfT24RkZwjY7gHh3B3tNN+4G94+uotBHkrbuS4ms+G9KG0DU6gr23B8+YHY/djttuwzUyRMPbG8bXiUywklRWQcl1nwspIj1jY0Hvbafj/Mvh4PirL+Jzu1CoVMRmZGPOySNnTV1Iz2qfy4W9r4cxVwddrZ34XS58LjenXt7I/v99FHWUAXNxPuaSAqwLS4lKmXzcB7xe7J09BDxeAh4vfo+HgMdL977DNG/cgjrKQHxpIQnlRSSUF2NImixWhEAAe3s3gt9PwOdD8PkR/H4cvf3s+/Uf0BgjSagoIaGyGEtFCTpzaG9zW3sngj8Q9F4Wzv7rD7Dz5/+L4POTUFmMdUEZlgWlRMRGh4zh7BpicERAFATEgIAoiggBgX2Pv03b7pOkLsknvaqY9OVFGOJNodvRM4Tf5UEUAVFEFIM3XLbOAV7/599jKc4gfUUxGSuKictJCnkdGOsfwesI+jifi4EoIvgDvPDt32CIN5GxopiM6hISitJDimLnkB2PzRkMwUTzs7f/+y+4huxkVBeTUV1KUmV2SFE8NuLCPuQM2c83/7CHg5saKV+TRXlNNsUr0okwhDje7R5G+kN7Ku985TgbH91HyapMKmqyKVudicE0+RrtcfkY6raHjHFyVyt//em7FFelUVGbQ/maLEzxk88ZvzdAf8doyBjdZ5x8vuY1li6Po3adlbp1VpJTJluiCoJIS/NYyBgD/R7+7qb3KK+MGY+RlR36JrSleQxBmGxI53IFuPn6rWRlRwZjrLdSUGgMeYy0tznw+YRJywMBkS/fvB2TST3ejrKK6HkJfInZI1kOTsE526CUuCwyEwpIi88lwZSEX/BzvHUfZRlLkYWRZXzr4LP0jXaSGpdDqjmbxNh0lAoV75/YwOLcmnFLouksCXfueoHGxn0kJeaSlJRHYmIuBkMMmzc/TuWCqzAZZx6KO3JkE/sPvInVmo3Vko3Vmk1cXApvv/0HCgpXkJpSNGOMM00HePfdx0hIyMCSkElCQiYJCRls2foXrNYciotWzZg17e1t5sWX7sNsTsNsTiPenIY5Po0D+99ArY5g0aLPoNNN9hG+0IrQaR/hmfu/R0xCCtGWFGIsKcQkpNB4eAdO2wgL6m7EnJI1bTuEgJ/H/+vrREXHY4xPxGROIjo+iZ6WerrOHKd8zfVklS0PKbAu5Kl7v4tcriAqzoIx1kJUXAK2wT5O799K4fL1FC1fjy7SNG2Ml/73P3DahomMNmOICWZKvW4Xx99/k/TixZRUX01CWu6UYvyQt5/uF5+g71Q9uphYdNEx6GNikSuV1L/1BqbkVPLWXkHGshUoNVPf7e954g8073gPrdFEhNFEhCkajd7AqXffQqXTkVm1ipw1azElTZ05PfbqCxx9+Xk0kVFoIqPQGiLRREbSfmAvAa8XS2EJ2atqSKlchEIdOntx5r0t7PrTw6h1etR6PSqdHo1Oz1BrM54xOxGmaDKrVpK5YhXGxNBt6Tp6mHd/8VPkGjVqnQ6lTotSq8U9PIJ7aASZXE5CZQlpa6pIWr4QVcTk7TLS0s7Gv/8XZAoFSo0ahUaDQqsGQcTR2w+AxhhJcvUSUlcuw1ycNyl76LGP8dLnvwGAXKVErlQiVylRqFS4BofH14styCFlxWIyr1iDSj85m/LctbcS8Aaz+jKFArlCjkyhIOD1IQYCAKgNehKXLaDw5uuITLZOivHK7Xfi6h6esEx29gdXvOCHPqEonSV3XEXGiuJJMZ79+v+j69CZEFt8Imq9luLPVrH461ehMUwUS2/+8I+cenv/jDFkchmZq8pY+f0biUqcKKi3/ep5Dv5104wxACwlGaz6wefJq+ieYDn45h/28OTP3g0rRlyykS/+Ww0L1088D3e+eoLf/cMrYcWIjNXx+R+sZNXnSyeI4YY97fzklr+GFUNrUHPdt5dzxVcXTfC+7m0d5h/XPBJWDI1GzlfvyOa7dxWg15+/XrvdAfLSXgorhlIp45YvpfOP/1pEdMxEQV2a9wqjI74ZY8hk8Jnrkvn3H5dO8q1et+ptGuptYbVldW0CP/5JGRmZ54WsZDn4wSIJxym4VDtfFMWwh0ymEo+ziTHbdswm9lTrBgL+sIfZp5oD6PE4Q85vPMe54ers7IQpYzhtw+iiQmdbQrZFECb90I/0dWGMs4Q9fBgqxkBnM8Y4K6ppRNpMMfraGtEaooiKmfmm4JC3H1EQyEuzTFg+2HwGv8dDfF5BWPtYEATkF2+PjnYGmxpJW7I8ZIYxVAyZTDbh7zmHhji1+S2yqtcQGT+zV3KoGH6Ph31/fYzURUuxFJVMaufFiEIwW3FU1jM+x1EURfY+8HtMmWmkrlqK1jT5BuXiGKIgIL9oWPz0KxsZPtNK6qplxJcVTntzIYoigs+PXKWc0J/BhjMc/v1fSV6xmOSqRVNmGs/334tcqUAml4/HCXi9bPrBfxObl0Vy1SLMJfmT2noh/b5jFMQmIJPLkctl48fcOz/5K2O9w2SuKiVzZemU2cZgO3wgiiA/u3/O7qf+Ux28/k//R+bKEjJWlpK8IGfKoW+/14coiIxvDZkMZOB3+3jqy/eQUJBGRnUJ6VVFUw59B3x+hMD5jNS5WCLw8v/3IDKFnMzqEjJWlmBKDj5EdrFXtd8XIOCfnNUCePrezZw+0ElFTTYVtdmkF1tCZrUCfgG/LxAyxttP7Oe9545SXpNFRW02OZXJKJSTj1shIOD1+EPGOPD2af52/1YqaoPtKFiSikozebsKgojXHVqs+Y68wt3f66CmzkLdOisrVsaj00+OIYoiLmfovnS0O/nCjdtYsSqeunVWVq5JwGgMfePndITui83m49or3qVyYSx166ysqbUQGxf6muJy+gmlTHw+geuu3ExGpoG69VZq11oniU6QhOMHjSQcp+BS7/xwabB5QmYdJSZmHSUmcsjbH3Ke46edI2LXJX845lLcyAkBYV7zEgEEvz8oJMO8yRmSN1AYM/n88bk8qCLmVx7MM+ZCrdfOa7v4XB7kSsW85loKAQGf040mcvJN6MXCcTrsQ04iY+Y3j+6SxBh2YTDNb7tabK+TnZAzr+Hc0VEver0SZQjhGy52uw+1Wo5GM/c5ny5nUJRG6KY/RiTh+MEileP5GCKV6Jkaqa6jxEfNpXhifr6iEUCuVM75wZoLma9oBNAYIua9XVQRmnmJRghu11CicbbMV/BdshjR89+uRpNy3nMAjUb1vEQjQGSkal6iEYKCcSbRKPHBIwnHjxmSq8zUSHUdp0cqBi4xHTFCHieGpBsvCQmJ+SEJx48hkqvM9EhZx8lIxcBDUypL5GSPJKglJCQkLhWScPwYI2UdJyNlHSUkJCQkJD46JOH4MUUasp4eKesoISEhISHx4SMJx48x0pB1aKSsY2jK1WZpnqOEhISExAeKJBwvA6SsY2ikrKOEhISEhMSHy2UjHB988EHS09PRarUsWbKEPXv2TLnu8ePHufHGG0lPT0cmk/HAAw9csnb0DLdzuHkXx9v2c6rzCM299bQPnMHpCW3VFIrhsQEau4/R1t9Iz3A7Q2P9ONx2/IHJBVynGrJ2Okfp6TnDyEgPLpcdQQhdyHY6vF43NtsAPp9n1t89RyDgn9f3IVgXb7blRKWso4SEhISExES2bdvGZz7zGRITE5HJZLz00ksTPhdFkR/96EdYrVYiIiKoq6vj9OnToYNNwWVREOmZZ57hrrvu4uGHH2bJkiU88MADrF+/noaGBuLjJxf3dTqdZGZm8vnPf57vf//7l6wd/oCfMbeNdw4/R0A4a/Ol1FBdeBVJMelhxRBFEZ/fw+v7/orH5x5fXpBSyeqizxCpM036Tl6UZtxV5kJefe0BxsbOW4hZLdnU1n4Vszk1rLbI5XJeevkXDA52oFRq0EVEEhUVx/Kqm0hKzA0rhkwm48WX7qO/vw293oReZ0SvN1FQsIKMjPKwYoDIa6//lsHBdgyGWCINMURGxhAfn0FW1oJp65hd6GG945XHGexsHrfti4yJJzI6HktG/oxOI+c4su01+tpOExVrOWsjmIAxzkpEpCnsemqNB9+np+UkpvgkTOYkTPGJ6I2xs6rH1tl4jO4zJ8YtFY1xFuRhuvOcau8jNyWe4bZWeuuPE52aTnRKGmr9ZH/amXAODdFz8hixGZlEWhLD3o4X4vd46D52mLisXCJMpll/H4LnTV/DSWLSM1Fp515Ud7S1A4M1AYVaNecY7uFRVHrdvGL43W5kcsW8YoiCQMDnR6kJ7eARLgGff971EwV/ALlyfjX6hICATC6bV93CqVylZt0WQZx37cOPU4z58nHqi+RLPT0Oh4OysjK++tWvcsMNN0z6/L777uM3v/kNjz/+OBkZGfznf/4n69ev58SJE2jDvLZeFsLxl7/8JXfccQe33347AA8//DCvv/46f/rTn/jXf/3XSesvWrSIRYsWAYT8fDZ0D7fRM9xGa/9pOgeaQQZalQ6Hx05BSiVriq/FEDG1dZkoigzae2gfaKJ94AztA2dwuG2olcEdFG9MpLbsBlLipvZWFkURp2uIbe8cwi8foKv7NAMDbeOfa7UGli29gZKSmmmt/4JZyiZ6ehqD//aeweNxAhAIeElNLWLZshsxGKbO5nk8Tvr7W+nta6avr4W+3haGhrsBEa/XhVodQXHxalJTJ/vcnsPv9zI42MnAQBv9/W30D7TR39+Kx+NkeLgHrdbAwoXXkJZWPOUPgCAESHG72d/dgK3FzlBvOwMdzdgGe6D5JMhkZJUuo6L2s1OKHVEUcYwOMtLfxWh/N6P93Qx0NtPTUj++jik+kbLV15FTsQJZiG0riiIe5xi2wd7ga6iHoe52mo7sHF9HoVJTXHUllbU3TGlD6Pd6GBsZwD7cj324H9tAD0e2vTb+uVyhJHfBKhZf+QW0+siQMYRAgGynjEODTbT1NOEYHODgc08h+IKZbH2cmYT8IspuvBl9TGiLO1EU8TocuG0juEZHcY0Ms/+px/HY7Si1WmLSMojNyCJnTR1RlsSQMSBoh+cZG8MzZsczZufwC39jpKMNfZyZuKwc4rJysBSWEJ0y9U2OKAj4PG58Tideh4MTb75C15FDRKemY87JIz43H3NuPjrT9DaToiBwrKmVzKhIWt55j9OvbCS2IIf4kgLMpQXE5mWHJeBEQSDg9WJr72Lbf95HXFEuCeVFJJQXY8pKn1VRbyEg8NY3/4Wo1EQSKkuxVJYQlZo0K8Ejk8vZ+bPfIPj9WBeVYVlQRmSyddaiaf8Tb9O+t4GMFcWkVxURnW6ZdYzh1l42/uhx0pcXkr6iGEtxxqyLnPvdXl78zm+xFGeQUV1MUkX2nATtaz94BF1sFBnVxaQszkelnb2wfvKnm7ANOKmozaZ0VSZ64+xvVt74/W7OHOyiojabsjVZGONmf+O24+Xj7HzlBBU12ZTXZBGXNL1NZih2v2fnqUe2Ubc+aDl4oadzuNSfHOXf//kgtWut1K2zklcQNetjpKfbxbe/vpvq1UHbwpKy6FmLQIfDz21ffJ8Fi4K2hZULY+ZdmPyTxpVXXsmVV14Z8jNRFHnggQf4j//4D6677joAnnjiCRISEnjppZe45ZZbwvobH3vLQa/Xi06n47nnnuP6668fX37rrbcyMjLCyy+/PO3309PT+d73vsf3vve9adfzeDx4POezeqOjo6SmpgIyEqPTSI7LItWcRYIphb2nt5AclzGt2DvHxoN/o77jILFRFpJj0kmKzSApJoMjrbvRaQwUpy1GLpv+wH//xEb2ndlChMZEckouVmsWFks2ra3HcLntLFp4zbQ+zwDHjm1m23tPodHoSUjIID4+A0tCBgODnXR3NbB06Y3Exk5vx9XUdJANGx9CoVARF5dCvDkNszkVl9tB/cntLF5yPZkZlUx3Penra+a55+8FIDraQmxsMnGxKchkMvYfeIPysnWUldWimsaqaWxsmL/89d8QhABqXRRx1hRM8Ulo9ZEceOd5MsuWUbbyMxjN1iljBPw+nrrnu/h9HpRqLcY4C8Y4CwZTHEe2vUZsYjql1VeTWlA5rTvHc7/6Z8aG+0EmJ9IUG8xyxsRzav821FodBUtqyV9Sg1Y39cX6zT/dS29LAwDqCAMGUywGUyxdTSfwez2kFy2kcNk64lOyp4yx9dmHaT66O/hGJkMXHUOEKRp7bw9exxjGxGSyV9eRvmQZiik8pw899zSn331r3OdZodEQEWXE43DgczpQarWkLakiq3oNpuTQtpgN72zg2CsvEPB6x5cpI4Jesn6XC4DYrBwyq1aRUrkIpXZyW9r27mL/U0/gczonfiCXw9m2qQ0GUhctJX3JCmLS0yfF6GuoZ8fvH8TvcY8L51DEFuaQXltN6qqlyC46D22d3bx39y8IeLwEvF5Ef2gfX2WElrTaFRTcdC1q/cTz0Otw8s737kbw+4Mvnw/B7w+aKl9EwoJSyr76BQyWyTU5N975Q/weL2JAQAwEEISz//p8E2JFpSVT9tUvYC7OmxRj63/8HHtvD0pkwekhgoAoigR8fvyu8/sr0hJN5ZfXkn/loknH/lv/9QR9x9sQRQHEs9NMzma0XMP28fU0UTqyaypYdPt6tMaJgmn7r1+g5f0TwLkMoTjeB9foGMJZ72eVTkPqkgIWffUKTCkTt8n+J97m5Otnj/eLtqVnzInPGbyWy9VKkitzqPxSLXmV/WQZzt/svPfCUV7/v92TthOA1+1jbDg4IiSTQ3ZFEjW3lFO5NmeCYDq09QxP37M5ZAy/J4Bt6PwxnFFiYcUNxVRdVzRBVDcd6+b//vH1kDGEgMhI3/lpUMm5cSy5poDaL1agVJ3P8A5227j/q8+GjKESXfR2nz8HMjL1rL8qia/ekT3Bs9rrCXDNundDxgDo7nKN+0cnJkWw9gord/x9DqboiefwjddswW4Pfc719brwn7Wyjo3TUFtn4ZvfySXBMtFv+pu376S5KfT0r4EBN15PsCFGk4pVaxL45p25pKWf9zUfs/tYWvEmIyMjGI2zF9vz4Zzl4LeuuBv1JbAc9PrcPLThv2hvb59gOajRaNBMcS0/h0wm48UXXxzXTk1NTWRlZXHw4EHKy8vH11u1ahXl5eX8+te/Dq9R4seczs5OERB37NgxYfk//dM/iYsXL57x+2lpaeKvfvWrGde7++67z17BpJf0kl7SS3pJL+l1ub/OnDkzV+kxZ1wul2ixWC5pPwwGw6Rld99994xtAcQXX3xx/P37778vAmJXV9eE9T7/+c+LN910U9h9vCyGqj8MfvjDH3LXXXeNvx8ZGSEtLY22trYP/Y7lo8Rms5GSkjLp7uaTjtRvqd+fBqR+S/3+NHBuxDAm5sN/iFKr1dLc3Iz3glGX+SKK4qSpATNlGz9IPvbCMS4uDoVCQW/vxNIrvb29WCyWS/Z3pkr7Go3GT9UJd46oqCip358ipH5/upD6/eni09rvuTzMdynQarVhP2jyYXJOM/X29mK1np/K1dvbO2HoeiY+9rNK1Wo1CxYsYNOmTePLBEFg06ZNLFu27CNsmYSEhISEhITE5UFGRgYWi2WCnrLZbOzevXtWeupjn3EEuOuuu7j11ltZuHAhixcv5oEHHsDhcIw/Zf2Vr3yFpKQk7rnnHiD4QM2JEyfG/9/Z2cmhQ4cwGAxkZ0/9gIGEhISEhISExOXK2NgYjY2N4++bm5s5dOgQMTExpKam8r3vfY+f/OQn5OTkjJfjSUxMnPDw8UxcFsLx5ptvpr+/nx/96Ef09PRQXl7Ohg0bSEgI1u9ra2ubkJLu6uqioqJi/P0vfvELfvGLX7Bq1Sq2bNkS1t/UaDTcfffdH+k8go8Cqd9Svz8NSP2W+v1pQOr3p6vfAPv27WPNmjXj7889u3Hrrbfy2GOP8c///M84HA6+8Y1vMDIywooVK9iwYcOshtY/9uV4JCQkJCQkJCQkPh587Oc4SkhISEhISEhIfDyQhKOEhISEhISEhERYSMJRQkJCQkJCQkIiLCThKCEhISEhISEhERafeuG4bds2PvOZz5CYmIhMJuOll16a8LkoivzoRz/CarUSERFBXV0dp0+f/mgaewmZqd+33XYbMplswuuKK674aBp7CbnnnntYtGgRkZGRxMfHc/3119PQ0DBhHbfbzZ133klsbCwGg4Ebb7xxUgH6y41w+r169epJ+/zv//7vP6IWXxoeeughSktLxwsgL1u2jDfffHP880/ivoaZ+/1J3NcXc++99yKTyfje9743vuyTur8vJFS/P6n7+8c//vGkfuXn549//mnY3x8Fn3rh6HA4KCsr48EHHwz5+X333cdvfvMbHn74YXbv3o1er2f9+vW43e4PuaWXlpn6DXDFFVfQ3d09/nrqqac+xBZ+MGzdupU777yTXbt28fbbb+Pz+Vi3bh0Oh2N8ne9///u8+uqrPPvss2zdupWuri5uuOGGj7DV8yecfgPccccdE/b5fffd9xG1+NKQnJzMvffey/79+9m3bx81NTVcd911HD9+HPhk7muYud/wydvXF7J3714eeeQRSktLJyz/pO7vc0zVb/jk7u+ioqIJ/dq+ffv4Z5/0/f2REbar9acALjIEFwRBtFgs4v333z++bGRkRNRoNOJTT/3/7d1dSFMPHwfwb3/fesUprqmJMl8yxBfQcI3KLjZE6UKsCysvhKDQFAxKsjciCfIqqIuuirqJRywSIbrI0gnFkHxZKoWlrGbkGgmZNc3Q33MRjr827TyP5smz7wcGY+cIvx9fJl/mOe4/Kkz4Z8zfW0SkrKxMioqKVJlnJXk8HgEg7e3tIvIz35CQELl7967vnFevXgkAsdvtao257ObvLSKyZ88eqa6uVm+oFRIRESE3btwImKxnze4tou2sx8fHJSUlRVpaWubsqfW8F9pbRLt5X7hwQbKysvwe03reagr4TxwX43Q64Xa7YbVafa+Fh4fDZDLBbrerONnKsNls2Lx5M1JTU1FRUYHR0VG1R1p2Y2NjAIDIyEgAQFdXF378+DEn823btiE+Pl5Tmc/fe9adO3cQFRWF9PR0nD59Gl6vV43x/ojp6Wk0NDTg27dvMJvNAZP1/L1naTXryspK7N27d06ugPbf2wvtPUureb958waxsbFITExEaWkpXC4XAO3nraZV8c0xanG73QDg+4aaWQaDwXdMqwoKCrBv3z4YjUYMDQ3hzJkzKCwshN1uR1BQkNrjLYuZmRkcP34cO3fuRHp6OoCfmYeGhkKn0805V0uZ+9sbAA4dOoSEhATExsait7cXp06dwsDAAO7fv6/itEvX19cHs9mMyclJbNy4EU1NTUhLS4PD4dB01gvtDWg364aGBnR3d+P58+e/HNPye3uxvQHt5m0ymXD79m2kpqZiZGQEFy9exO7du9Hf36/pvNXG4kh+HThwwPc8IyMDmZmZSEpKgs1mg8ViUXGy5VNZWYn+/v4518QEgoX2Pnr0qO95RkYGYmJiYLFYMDQ0hKSkpJUec9mkpqbC4XBgbGwM9+7dQ1lZGdrb29Ue649baO+0tDRNZj08PIzq6mq0tLT8T1+fttop2VuLeQNAYWGh73lmZiZMJhMSEhLQ2NiIdevWqTiZtvFP1YuIjo4GgF/uwvr48aPvWKBITExEVFTUnC9PX82qqqrw4MEDtLW1IS4uzvd6dHQ0pqam8Pnz5znnayXzhfb2x2QyAcCqzzw0NBTJycnIycnB5cuXkZWVhatXr2o+64X29kcLWXd1dcHj8SA7OxvBwcEIDg5Ge3s7rl27huDgYBgMBk3m/bu9p6enf/kZLeTtj06nw9atWzE4OKj597eaWBwXYTQaER0djSdPnvhe+/LlCzo6OuZcKxQI3r9/j9HRUcTExKg9ypKICKqqqtDU1ITW1lYYjcY5x3NychASEjIn84GBAbhcrlWd+e/29sfhcADAqs98vpmZGXz//l2zWS9kdm9/tJC1xWJBX18fHA6H77F9+3aUlpb6nmsx79/t7e/SIi3k7c/Xr18xNDSEmJiYgHt/ryi1785R2/j4uPT09EhPT48AkCtXrkhPT4+8e/dORETq6+tFp9NJc3Oz9Pb2SlFRkRiNRpmYmFB58qVZbO/x8XE5efKk2O12cTqd8vjxY8nOzpaUlBSZnJxUe/QlqaiokPDwcLHZbDIyMuJ7eL1e3znl5eUSHx8vra2t0tnZKWazWcxms4pTL93v9h4cHJS6ujrp7OwUp9Mpzc3NkpiYKHl5eSpPvjS1tbXS3t4uTqdTent7pba2VtasWSOPHj0SEW1mLbL43lrN2p/5dxNrNe/5/r23lvM+ceKE2Gw2cTqd8uzZM7FarRIVFSUej0dEAifvlRbwxbGtrU0A/PIoKysTkZ//kuf8+fNiMBgkLCxMLBaLDAwMqDv0Mlhsb6/XK/n5+aLX6yUkJEQSEhLkyJEj4na71R57yfztDEBu3brlO2diYkKOHTsmERERsn79eikuLpaRkRH1hl4Gv9vb5XJJXl6eREZGSlhYmCQnJ0tNTY2MjY2pO/gSHT58WBISEiQ0NFT0er1YLBZfaRTRZtYii++t1az9mV8ctZr3fP/eW8t5l5SUSExMjISGhsqWLVukpKREBgcHfccDJe+VtkZEZOU+3yQiIiKi1YrXOBIRERGRIiyORERERKQIiyMRERERKcLiSERERESKsDgSERERkSIsjkRERESkCIsjERERESnC4khEREREirA4EhEREZEiLI5EREREpAiLIxEREREpwuJIRERERIqwOBJRQPF6vbh+/TqsViv0ej3CwsIQFxeHgoIC3Lx5U+3xiIj+amtERNQegohoJfT29mL//v0YHBxEREQEzGYzdDodXC4XOjo6kJubi6dPn6o9JhHRXytY7QGIiFbC69evYbVa8enTJ9TV1aGmpgZr1671Hfd4PHjx4oWKExIR/f34iSMRad7MzAxyc3PR1dWFS5cu4ezZs2qPRES0KrE4EpHmNTQ04ODBg8jIyIDD4cA///DybiKi/wd/exKR5jU2NgIAysvLWRqJiJaAv0GJSPO6u7sBALt27VJ5EiKi1Y3FkYg0z+PxAAA2bdqk8iRERKsbiyMRaZ5OpwMAvHz5Ut1BiIhWORZHItK8/Px8AMC5c+fgdrvnHJuYmEBjYyOmpqbUGI2IaFXhXdVEpHnDw8PYsWMHPnz4gA0bNsBkMkGv12N4eBh9fX0IDw/H8PCw2mMSEf31WByJKCB4PB7U19fj4cOHePv2LYKCgmAwGJCdnY3S0lIUFxerPSIR0V+PxZGIiIiIFOE1jkRERESkCIsjERERESnC4khEREREirA4EhEREZEiLI5EREREpAiLIxEREREpwuJIRERERIqwOBIRERGRIiyORERERKQIiyMRERERKcLiSERERESKsDgSERERkSL/BXvctwwJa7F8AAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 43
  },
  {
   "cell_type": "markdown",
   "id": "564a8ea4",
   "metadata": {},
   "source": [
    "### Effect of the wage distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2feee726",
   "metadata": {},
   "source": [
    "Since our entire problem is symbolic -- including the distribution over wage offers -- we can also study the effect of a shift in the wage distribution. To do this, we fix $\\beta = 0.99$ and $c=25$, and instead vectorize $\\alpha$, $\\beta$, and $n$. \n",
    "\n",
    "We are interested in the effect of shifts in the moments of the distribution. For a Beta-Binominal, the first two raw moments are:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\mu &= np \\\\\n",
    "\\sigma^2 &= np(1 - p)[1 + (n-1)\\rho ]\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "Where $p = \\frac{\\alpha}{\\alpha + \\beta}$ and $\\rho = \\frac{1}{\\alpha + \\beta + 1}$\n",
    "\n",
    "For this analysis, it's not helpful to have the problem written in terms of $\\alpha$ and $\\beta$ -- we'd like to ask questions like \"what happens if the mean or variance of the wage distribution changes\"? \n",
    "\n",
    "To do this, we can reparameterize the wage distribution in terms of $\\mu$ and $\\sigma$. Given a fixed $n$, we simply solve the two equations above for $\\alpha$ and $\\beta$:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\alpha &= \\frac{\\mu (\\mu^2 - n \\mu + \\sigma ^2 )}{-\\mu^2 + n \\mu - n \\sigma^2} \\\\\n",
    "\\beta &= \\frac{(\\mu - n) (\\mu^2 - n \\mu + \\sigma^2 )}{\\mu^2 - n \\mu + n \\sigma^2}\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "We will re-use the graphs we've been using so far, merely replacing $\\alpha$ and $\\beta$ by these functions of $\\mu$ and $\\sigma$."
   ]
  },
  {
   "cell_type": "code",
   "id": "c8ac0c84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.065693383Z",
     "start_time": "2025-07-28T14:28:29.775080Z"
    }
   },
   "source": [
    "mu, sigma = pt.scalars('mu sigma'.split())\n",
    "a_fn = mu * (mu ** 2 - mu * n + sigma ** 2) / (-mu ** 2 + mu * n - n * sigma ** 2)\n",
    "b_fn = (mu - n) * (mu ** 2 - mu * n + sigma ** 2) / (mu ** 2 - mu * n + n * sigma ** 2)\n",
    "\n",
    "w_bar_2 = pytensor.graph_replace(w_bar, {a: a_fn, b:b_fn})"
   ],
   "outputs": [],
   "execution_count": 41
  },
  {
   "cell_type": "markdown",
   "id": "5355af67",
   "metadata": {},
   "source": [
    "To drive home what we've just done, we can look at what input values `w_bar_2` expects. Note that `a` and `b` no longer appear! Instead, it looks for `mu` and `sigma`."
   ]
  },
  {
   "cell_type": "code",
   "id": "826df86b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.068442618Z",
     "start_time": "2025-07-28T14:28:30.749036Z"
    }
   },
   "source": [
    "from pytensor.graph.basic import explicit_graph_inputs\n",
    "list(explicit_graph_inputs(w_bar_2))"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[β, c, v0, n, w_min, w_max, mu, sigma]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 42
  },
  {
   "cell_type": "markdown",
   "id": "8ef7370b",
   "metadata": {},
   "source": [
    "We can check that our formulas are right by checking that we can make a \"round trip\" from the original parameterization of $a=200$, $b=100$"
   ]
  },
  {
   "cell_type": "code",
   "id": "0991edad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.069376278Z",
     "start_time": "2025-07-28T14:28:32.019041Z"
    }
   },
   "source": [
    "p = a / (a + b)\n",
    "rho = 1 / (1 + a + b)\n",
    "\n",
    "mu_val = (p * n).eval({a:200, b:100, n:50})\n",
    "sigma_val = pt.sqrt(n * p * (1 - p) * (1 + (n - 1) * rho)).eval({a:200, b:100, n:50})\n",
    "\n",
    "print(f'mu = {mu_val.item():0.3f}')\n",
    "print(f'sigma = {sigma_val.item():0.3f}')"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mu = 33.333\n",
      "sigma = 3.594\n"
     ]
    }
   ],
   "execution_count": 43
  },
  {
   "cell_type": "code",
   "id": "448604df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.070061473Z",
     "start_time": "2025-07-28T14:28:32.699312Z"
    }
   },
   "source": [
    "print(f'a = {a_fn.eval({mu:mu_val, sigma:sigma_val, n:50}):0.2f}')\n",
    "print(f'b = {b_fn.eval({mu:mu_val, sigma:sigma_val, n:50}):0.2f}')"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a = 200.00\n",
      "b = 100.00\n"
     ]
    }
   ],
   "execution_count": 44
  },
  {
   "cell_type": "markdown",
   "id": "63ac410c",
   "metadata": {},
   "source": [
    "We can also plot the distributions we get for different values of $\\mu$ and $\\sigma$"
   ]
  },
  {
   "cell_type": "code",
   "id": "ac2faccc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.072433380Z",
     "start_time": "2025-07-28T14:28:33.845311Z"
    }
   },
   "source": [
    "dist_args = [n, mu, sigma, w_min, w_max]\n",
    "f = pytensor.function(dist_args, [w_support, \n",
    "                                  pytensor.graph_replace(q_probs, {a:a_fn, b:b_fn})])"
   ],
   "outputs": [],
   "execution_count": 45
  },
  {
   "cell_type": "code",
   "id": "5fe29a45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.083956834Z",
     "start_time": "2025-07-28T14:28:34.846780Z"
    }
   },
   "source": [
    "dist_params = {'n':50, 'mu':33.333, 'sigma':3.594, 'w_min':10, 'w_max':60}\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 4))\n",
    "\n",
    "ax.bar(*f(**dist_params), alpha=0.75, label='μ=33.3, σ=3.594')\n",
    "ax.bar(*f(**dist_params | {'mu':40}), alpha=0.75, label='μ=40.0, σ=3.594')\n",
    "ax.bar(*f(**dist_params | {'sigma': 5.0}), alpha=0.75, label='μ=33.3, σ=5.0')\n",
    "\n",
    "ax.set(title='Wage Distribution', xlabel='Wage', ylabel='P(Wage)')\n",
    "ax.legend()\n",
    "\n",
    "ax.grid(ls='--', lw=0.5)\n",
    "[spine.set_visible(False) for spine in ax.spines.values()]\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x400 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 46
  },
  {
   "cell_type": "markdown",
   "id": "b2b9f287",
   "metadata": {},
   "source": [
    "Nice! Now let's vectorize w_bar over $\\mu$ and $\\sigma^2$, and make a contour plot with vector field"
   ]
  },
  {
   "cell_type": "code",
   "id": "ba6f5ed5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.084900503Z",
     "start_time": "2025-07-28T14:28:35.994507Z"
    }
   },
   "source": [
    "mu_grid, sigma_grid = pt.dmatrices('mu_grid', 'sigma_grid')\n",
    "w_bar_dist_grads = pt.grad(w_bar_2, [mu, sigma])\n",
    "\n",
    "w_bar_grid, *w_grad_grid = vectorize_graph([w_bar_2, *w_bar_dist_grads], {mu:mu_grid, sigma:sigma_grid})"
   ],
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Only tensors with the same number of dimensions can be joined. Input ndims were: [3, 2, 2, 2]",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[47], line 2\u001B[0m\n\u001B[1;32m      1\u001B[0m mu_grid, sigma_grid \u001B[38;5;241m=\u001B[39m pt\u001B[38;5;241m.\u001B[39mdmatrices(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mmu_grid\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124msigma_grid\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m----> 2\u001B[0m w_bar_dist_grads \u001B[38;5;241m=\u001B[39m \u001B[43mpt\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgrad\u001B[49m\u001B[43m(\u001B[49m\u001B[43mw_bar_2\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m[\u001B[49m\u001B[43mmu\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msigma\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m      4\u001B[0m w_bar_grid, \u001B[38;5;241m*\u001B[39mw_grad_grid \u001B[38;5;241m=\u001B[39m vectorize_graph([w_bar_2, \u001B[38;5;241m*\u001B[39mw_bar_dist_grads], {mu:mu_grid, sigma:sigma_grid})\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/gradient.py:747\u001B[0m, in \u001B[0;36mgrad\u001B[0;34m(cost, wrt, consider_constant, disconnected_inputs, add_names, known_grads, return_disconnected, null_gradients)\u001B[0m\n\u001B[1;32m    744\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(g\u001B[38;5;241m.\u001B[39mtype, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdtype\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[1;32m    745\u001B[0m         \u001B[38;5;28;01massert\u001B[39;00m g\u001B[38;5;241m.\u001B[39mtype\u001B[38;5;241m.\u001B[39mdtype \u001B[38;5;129;01min\u001B[39;00m pytensor\u001B[38;5;241m.\u001B[39mtensor\u001B[38;5;241m.\u001B[39mtype\u001B[38;5;241m.\u001B[39mfloat_dtypes\n\u001B[0;32m--> 747\u001B[0m _rval: Sequence[Variable] \u001B[38;5;241m=\u001B[39m \u001B[43m_populate_grad_dict\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    748\u001B[0m \u001B[43m    \u001B[49m\u001B[43mvar_to_app_to_idx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgrad_dict\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_wrt\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcost_name\u001B[49m\n\u001B[1;32m    749\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    751\u001B[0m rval: MutableSequence[Variable \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(_rval)\n\u001B[1;32m    753\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mlen\u001B[39m(_rval)):\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/gradient.py:1541\u001B[0m, in \u001B[0;36m_populate_grad_dict\u001B[0;34m(var_to_app_to_idx, grad_dict, wrt, cost_name)\u001B[0m\n\u001B[1;32m   1538\u001B[0m     \u001B[38;5;66;03m# end if cache miss\u001B[39;00m\n\u001B[1;32m   1539\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m grad_dict[var]\n\u001B[0;32m-> 1541\u001B[0m rval \u001B[38;5;241m=\u001B[39m [\u001B[43maccess_grad_cache\u001B[49m\u001B[43m(\u001B[49m\u001B[43melem\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mfor\u001B[39;00m elem \u001B[38;5;129;01min\u001B[39;00m wrt]\n\u001B[1;32m   1543\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m rval\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/gradient.py:1496\u001B[0m, in \u001B[0;36m_populate_grad_dict.<locals>.access_grad_cache\u001B[0;34m(var)\u001B[0m\n\u001B[1;32m   1494\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m node \u001B[38;5;129;01min\u001B[39;00m node_to_idx:\n\u001B[1;32m   1495\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m idx \u001B[38;5;129;01min\u001B[39;00m node_to_idx[node]:\n\u001B[0;32m-> 1496\u001B[0m         term \u001B[38;5;241m=\u001B[39m \u001B[43maccess_term_cache\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnode\u001B[49m\u001B[43m)\u001B[49m[idx]\n\u001B[1;32m   1498\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(term, Variable):\n\u001B[1;32m   1499\u001B[0m             \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\n\u001B[1;32m   1500\u001B[0m                 \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnode\u001B[38;5;241m.\u001B[39mop\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.grad returned \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mtype\u001B[39m(term)\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, expected\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   1501\u001B[0m                 \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m Variable instance.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   1502\u001B[0m             )\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/gradient.py:1171\u001B[0m, in \u001B[0;36m_populate_grad_dict.<locals>.access_term_cache\u001B[0;34m(node)\u001B[0m\n\u001B[1;32m   1168\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m node \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m term_dict:\n\u001B[1;32m   1169\u001B[0m     inputs \u001B[38;5;241m=\u001B[39m node\u001B[38;5;241m.\u001B[39minputs\n\u001B[0;32m-> 1171\u001B[0m     output_grads \u001B[38;5;241m=\u001B[39m [\u001B[43maccess_grad_cache\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvar\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mfor\u001B[39;00m var \u001B[38;5;129;01min\u001B[39;00m node\u001B[38;5;241m.\u001B[39moutputs]\n\u001B[1;32m   1173\u001B[0m     \u001B[38;5;66;03m# list of bools indicating if each output is connected to the cost\u001B[39;00m\n\u001B[1;32m   1174\u001B[0m     outputs_connected \u001B[38;5;241m=\u001B[39m [\n\u001B[1;32m   1175\u001B[0m         \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(g\u001B[38;5;241m.\u001B[39mtype, DisconnectedType) \u001B[38;5;28;01mfor\u001B[39;00m g \u001B[38;5;129;01min\u001B[39;00m output_grads\n\u001B[1;32m   1176\u001B[0m     ]\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/gradient.py:1496\u001B[0m, in \u001B[0;36m_populate_grad_dict.<locals>.access_grad_cache\u001B[0;34m(var)\u001B[0m\n\u001B[1;32m   1494\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m node \u001B[38;5;129;01min\u001B[39;00m node_to_idx:\n\u001B[1;32m   1495\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m idx \u001B[38;5;129;01min\u001B[39;00m node_to_idx[node]:\n\u001B[0;32m-> 1496\u001B[0m         term \u001B[38;5;241m=\u001B[39m \u001B[43maccess_term_cache\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnode\u001B[49m\u001B[43m)\u001B[49m[idx]\n\u001B[1;32m   1498\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(term, Variable):\n\u001B[1;32m   1499\u001B[0m             \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\n\u001B[1;32m   1500\u001B[0m                 \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnode\u001B[38;5;241m.\u001B[39mop\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.grad returned \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mtype\u001B[39m(term)\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, expected\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   1501\u001B[0m                 \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m Variable instance.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   1502\u001B[0m             )\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/gradient.py:1171\u001B[0m, in \u001B[0;36m_populate_grad_dict.<locals>.access_term_cache\u001B[0;34m(node)\u001B[0m\n\u001B[1;32m   1168\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m node \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m term_dict:\n\u001B[1;32m   1169\u001B[0m     inputs \u001B[38;5;241m=\u001B[39m node\u001B[38;5;241m.\u001B[39minputs\n\u001B[0;32m-> 1171\u001B[0m     output_grads \u001B[38;5;241m=\u001B[39m [\u001B[43maccess_grad_cache\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvar\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mfor\u001B[39;00m var \u001B[38;5;129;01min\u001B[39;00m node\u001B[38;5;241m.\u001B[39moutputs]\n\u001B[1;32m   1173\u001B[0m     \u001B[38;5;66;03m# list of bools indicating if each output is connected to the cost\u001B[39;00m\n\u001B[1;32m   1174\u001B[0m     outputs_connected \u001B[38;5;241m=\u001B[39m [\n\u001B[1;32m   1175\u001B[0m         \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(g\u001B[38;5;241m.\u001B[39mtype, DisconnectedType) \u001B[38;5;28;01mfor\u001B[39;00m g \u001B[38;5;129;01min\u001B[39;00m output_grads\n\u001B[1;32m   1176\u001B[0m     ]\n",
      "    \u001B[0;31m[... skipping similar frames: _populate_grad_dict.<locals>.access_grad_cache at line 1496 (9 times), _populate_grad_dict.<locals>.access_term_cache at line 1171 (8 times)]\u001B[0m\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/gradient.py:1171\u001B[0m, in \u001B[0;36m_populate_grad_dict.<locals>.access_term_cache\u001B[0;34m(node)\u001B[0m\n\u001B[1;32m   1168\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m node \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m term_dict:\n\u001B[1;32m   1169\u001B[0m     inputs \u001B[38;5;241m=\u001B[39m node\u001B[38;5;241m.\u001B[39minputs\n\u001B[0;32m-> 1171\u001B[0m     output_grads \u001B[38;5;241m=\u001B[39m [\u001B[43maccess_grad_cache\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvar\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mfor\u001B[39;00m var \u001B[38;5;129;01min\u001B[39;00m node\u001B[38;5;241m.\u001B[39moutputs]\n\u001B[1;32m   1173\u001B[0m     \u001B[38;5;66;03m# list of bools indicating if each output is connected to the cost\u001B[39;00m\n\u001B[1;32m   1174\u001B[0m     outputs_connected \u001B[38;5;241m=\u001B[39m [\n\u001B[1;32m   1175\u001B[0m         \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(g\u001B[38;5;241m.\u001B[39mtype, DisconnectedType) \u001B[38;5;28;01mfor\u001B[39;00m g \u001B[38;5;129;01min\u001B[39;00m output_grads\n\u001B[1;32m   1176\u001B[0m     ]\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/gradient.py:1496\u001B[0m, in \u001B[0;36m_populate_grad_dict.<locals>.access_grad_cache\u001B[0;34m(var)\u001B[0m\n\u001B[1;32m   1494\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m node \u001B[38;5;129;01min\u001B[39;00m node_to_idx:\n\u001B[1;32m   1495\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m idx \u001B[38;5;129;01min\u001B[39;00m node_to_idx[node]:\n\u001B[0;32m-> 1496\u001B[0m         term \u001B[38;5;241m=\u001B[39m \u001B[43maccess_term_cache\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnode\u001B[49m\u001B[43m)\u001B[49m[idx]\n\u001B[1;32m   1498\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(term, Variable):\n\u001B[1;32m   1499\u001B[0m             \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\n\u001B[1;32m   1500\u001B[0m                 \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnode\u001B[38;5;241m.\u001B[39mop\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.grad returned \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mtype\u001B[39m(term)\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, expected\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   1501\u001B[0m                 \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m Variable instance.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   1502\u001B[0m             )\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/gradient.py:1326\u001B[0m, in \u001B[0;36m_populate_grad_dict.<locals>.access_term_cache\u001B[0;34m(node)\u001B[0m\n\u001B[1;32m   1318\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m o_shape \u001B[38;5;241m!=\u001B[39m g_shape:\n\u001B[1;32m   1319\u001B[0m             \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m   1320\u001B[0m                 \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mGot a gradient of shape \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   1321\u001B[0m                 \u001B[38;5;241m+\u001B[39m \u001B[38;5;28mstr\u001B[39m(o_shape)\n\u001B[1;32m   1322\u001B[0m                 \u001B[38;5;241m+\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m on an output of shape \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   1323\u001B[0m                 \u001B[38;5;241m+\u001B[39m \u001B[38;5;28mstr\u001B[39m(g_shape)\n\u001B[1;32m   1324\u001B[0m             )\n\u001B[0;32m-> 1326\u001B[0m input_grads \u001B[38;5;241m=\u001B[39m \u001B[43mnode\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mop\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mL_op\u001B[49m\u001B[43m(\u001B[49m\u001B[43minputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnode\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moutputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnew_output_grads\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1328\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m input_grads \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m   1329\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\n\u001B[1;32m   1330\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnode\u001B[38;5;241m.\u001B[39mop\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.grad returned NoneType, expected iterable.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   1331\u001B[0m     )\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/tensor/optimize.py:915\u001B[0m, in \u001B[0;36mRootOp.L_op\u001B[0;34m(self, inputs, outputs, output_grads)\u001B[0m\n\u001B[1;32m    906\u001B[0m df_dx \u001B[38;5;241m=\u001B[39m (\n\u001B[1;32m    907\u001B[0m     jacobian(inner_fx, inner_x, vectorize\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[1;32m    908\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mjac\n\u001B[1;32m    909\u001B[0m     \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfgraph\u001B[38;5;241m.\u001B[39moutputs[\u001B[38;5;241m1\u001B[39m]\n\u001B[1;32m    910\u001B[0m )\n\u001B[1;32m    911\u001B[0m df_dtheta_columns \u001B[38;5;241m=\u001B[39m jacobian(\n\u001B[1;32m    912\u001B[0m     inner_fx, inner_args, disconnected_inputs\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m\"\u001B[39m, vectorize\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[1;32m    913\u001B[0m )\n\u001B[0;32m--> 915\u001B[0m grad_wrt_args \u001B[38;5;241m=\u001B[39m \u001B[43mimplict_optimization_grads\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    916\u001B[0m \u001B[43m    \u001B[49m\u001B[43mdf_dx\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdf_dx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    917\u001B[0m \u001B[43m    \u001B[49m\u001B[43mdf_dtheta_columns\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdf_dtheta_columns\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    918\u001B[0m \u001B[43m    \u001B[49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    919\u001B[0m \u001B[43m    \u001B[49m\u001B[43mx_star\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mx_star\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    920\u001B[0m \u001B[43m    \u001B[49m\u001B[43moutput_grad\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43moutput_grad\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    921\u001B[0m \u001B[43m    \u001B[49m\u001B[43mfgraph\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfgraph\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    922\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    924\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m [zeros_like(x), \u001B[38;5;241m*\u001B[39mgrad_wrt_args]\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/tensor/optimize.py:333\u001B[0m, in \u001B[0;36mimplict_optimization_grads\u001B[0;34m(df_dx, df_dtheta_columns, args, x_star, output_grad, fgraph)\u001B[0m\n\u001B[1;32m    290\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124mr\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m    291\u001B[0m \u001B[38;5;124;03mCompute gradients of an optimization problem with respect to its parameters.\u001B[39;00m\n\u001B[1;32m    292\u001B[0m \n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    329\u001B[0m \u001B[38;5;124;03m    The function graph that contains the inputs and outputs of the optimization problem.\u001B[39;00m\n\u001B[1;32m    330\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m    331\u001B[0m df_dx \u001B[38;5;241m=\u001B[39m cast(TensorVariable, df_dx)\n\u001B[0;32m--> 333\u001B[0m df_dtheta \u001B[38;5;241m=\u001B[39m \u001B[43mconcatenate\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    334\u001B[0m \u001B[43m    \u001B[49m\u001B[43m[\u001B[49m\n\u001B[1;32m    335\u001B[0m \u001B[43m        \u001B[49m\u001B[43matleast_2d\u001B[49m\u001B[43m(\u001B[49m\u001B[43mjac_col\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mleft\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m    336\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mjac_col\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mcast\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mlist\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43mTensorVariable\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdf_dtheta_columns\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    337\u001B[0m \u001B[43m    \u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    338\u001B[0m \u001B[43m    \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m-\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m    339\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    341\u001B[0m replace \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mdict\u001B[39m(\u001B[38;5;28mzip\u001B[39m(fgraph\u001B[38;5;241m.\u001B[39minputs, (x_star, \u001B[38;5;241m*\u001B[39margs), strict\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m))\n\u001B[1;32m    343\u001B[0m df_dx_star, df_dtheta_star \u001B[38;5;241m=\u001B[39m cast(\n\u001B[1;32m    344\u001B[0m     \u001B[38;5;28mlist\u001B[39m[TensorVariable],\n\u001B[1;32m    345\u001B[0m     graph_replace([atleast_2d(df_dx), df_dtheta], replace\u001B[38;5;241m=\u001B[39mreplace),\n\u001B[1;32m    346\u001B[0m )\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/tensor/basic.py:2991\u001B[0m, in \u001B[0;36mconcatenate\u001B[0;34m(tensor_list, axis)\u001B[0m\n\u001B[1;32m   2984\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(tensor_list, \u001B[38;5;28mtuple\u001B[39m \u001B[38;5;241m|\u001B[39m \u001B[38;5;28mlist\u001B[39m):\n\u001B[1;32m   2985\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\n\u001B[1;32m   2986\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mThe \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtensors\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m argument must be either a tuple \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   2987\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mor a list, make sure you did not forget () or [] around \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   2988\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124marguments of concatenate.\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m   2989\u001B[0m         tensor_list,\n\u001B[1;32m   2990\u001B[0m     )\n\u001B[0;32m-> 2991\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mjoin\u001B[49m\u001B[43m(\u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mtensor_list\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/tensor/basic.py:2805\u001B[0m, in \u001B[0;36mjoin\u001B[0;34m(axis, *tensors_list)\u001B[0m\n\u001B[1;32m   2803\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m tensors_list[\u001B[38;5;241m0\u001B[39m]\n\u001B[1;32m   2804\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m-> 2805\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_join\u001B[49m\u001B[43m(\u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mtensors_list\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/graph/op.py:293\u001B[0m, in \u001B[0;36mOp.__call__\u001B[0;34m(self, name, return_list, *inputs, **kwargs)\u001B[0m\n\u001B[1;32m    249\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21m__call__\u001B[39m(\n\u001B[1;32m    250\u001B[0m     \u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39minputs: Any, name\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, return_list\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs\n\u001B[1;32m    251\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Variable \u001B[38;5;241m|\u001B[39m \u001B[38;5;28mlist\u001B[39m[Variable]:\n\u001B[1;32m    252\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124mr\u001B[39m\u001B[38;5;124;03m\"\"\"Construct an `Apply` node using :meth:`Op.make_node` and return its outputs.\u001B[39;00m\n\u001B[1;32m    253\u001B[0m \n\u001B[1;32m    254\u001B[0m \u001B[38;5;124;03m    This method is just a wrapper around :meth:`Op.make_node`.\u001B[39;00m\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    291\u001B[0m \n\u001B[1;32m    292\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[0;32m--> 293\u001B[0m     node \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmake_node\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43minputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    294\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m name \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m    295\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(node\u001B[38;5;241m.\u001B[39moutputs) \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m1\u001B[39m:\n",
      "File \u001B[0;32m~/Documents/pytensor/pytensor/tensor/basic.py:2498\u001B[0m, in \u001B[0;36mJoin.make_node\u001B[0;34m(self, axis, *tensors)\u001B[0m\n\u001B[1;32m   2495\u001B[0m ndim \u001B[38;5;241m=\u001B[39m tensors[\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m.\u001B[39mtype\u001B[38;5;241m.\u001B[39mndim\n\u001B[1;32m   2497\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m builtins\u001B[38;5;241m.\u001B[39mall(x\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m==\u001B[39m ndim \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m tensors):\n\u001B[0;32m-> 2498\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\n\u001B[1;32m   2499\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mOnly tensors with the same number of dimensions can be joined. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   2500\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mInput ndims were: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m[x\u001B[38;5;241m.\u001B[39mndim\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mfor\u001B[39;00m\u001B[38;5;250m \u001B[39mx\u001B[38;5;250m \u001B[39m\u001B[38;5;129;01min\u001B[39;00m\u001B[38;5;250m \u001B[39mtensors]\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   2501\u001B[0m     )\n\u001B[1;32m   2503\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m   2504\u001B[0m     static_axis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mint\u001B[39m(get_scalar_constant_value(axis))\n",
      "\u001B[0;31mTypeError\u001B[0m: Only tensors with the same number of dimensions can be joined. Input ndims were: [3, 2, 2, 2]"
     ]
    }
   ],
   "execution_count": 47
  },
  {
   "cell_type": "code",
   "id": "ac4b9228",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.087059962Z",
     "start_time": "2025-07-28T14:28:36.622044Z"
    }
   },
   "source": [
    "fn_w_bar_dist = pytensor.function([v0, c, β, mu_grid, sigma_grid, n, w_min, w_max],\n",
    "                                  [w_bar_grid, *w_grad_grid])"
   ],
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'w_bar_grid' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[48], line 2\u001B[0m\n\u001B[1;32m      1\u001B[0m fn_w_bar_dist \u001B[38;5;241m=\u001B[39m pytensor\u001B[38;5;241m.\u001B[39mfunction([v0, c, β, mu_grid, sigma_grid, n, w_min, w_max],\n\u001B[0;32m----> 2\u001B[0m                                   [\u001B[43mw_bar_grid\u001B[49m, \u001B[38;5;241m*\u001B[39mw_grad_grid])\n",
      "\u001B[0;31mNameError\u001B[0m: name 'w_bar_grid' is not defined"
     ]
    }
   ],
   "execution_count": 48
  },
  {
   "cell_type": "code",
   "id": "27ce3b7a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.093254777Z",
     "start_time": "2025-07-28T13:48:25.202224Z"
    }
   },
   "source": [
    "mu_values = np.linspace(15, 35, 30)\n",
    "sigma_values = np.linspace(2.5, 10, 30)\n",
    "\n",
    "mm, ss = np.meshgrid(mu_values, sigma_values)\n",
    "\n",
    "w_bars, mu_grads, sigma_grads = fn_w_bar_dist(v0_value, c=25, β=0.99, mu_grid=mm, sigma_grid=ss,\n",
    "                                              n=50, w_min=10, w_max=60)"
   ],
   "outputs": [],
   "execution_count": 52
  },
  {
   "cell_type": "markdown",
   "id": "6e9f168c",
   "metadata": {},
   "source": [
    "From this last plot, we can see the effects of varying the mean (x-axis) and standard deviation (y-axis) of the wage distribution. Since we have access to the gradients, we can also see how the reservation wage changes at each grid point.\n",
    "\n",
    "Perhaps unsurprisingly, as the mean wage increases, the reservation wage increases. The effect of variance, on the other hand, is revealed to be more complex. When the mean is low, the reservation wage is strictly decreasing in variance. But as the mean increases, there are \"sweet spots\" in variance, above and below which the reservation wage decreases."
   ]
  },
  {
   "cell_type": "code",
   "id": "1066aff6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.094016655Z",
     "start_time": "2025-07-28T13:48:27.176425Z"
    }
   },
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "cs1 = ax.contourf(mm, ss, w_bars, alpha=0.75)\n",
    "ax.quiver(mm, ss, mu_grads, sigma_grads)\n",
    "\n",
    "plt.colorbar(cs1, ax=ax)\n",
    "\n",
    "ax.set_title(\"reservation wage\")\n",
    "ax.set_xlabel(r\"$\\mu$\", fontsize=16)\n",
    "ax.set_ylabel(r\"$\\sigma^2$\", fontsize=16)\n",
    "\n",
    "ax.ticklabel_format(useOffset=False)\n",
    "\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAosAAAHcCAYAAAC+ieDFAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAqgVJREFUeJzsnXd81fX1/1933+xA2LI3CDhwgag4KqWI2tpqq63zp7bFgVa/lSqFtgpoRdGKoKg4EAFlyN57h0BCgJCQAUnIHnePzx2f3x/XRELWvffc+bnn+Xjkodz7ua97bvLOzeu+32fIRFEUwTAMwzAMwzAtIA93AAzDMAzDMEzkwmaRYRiGYRiGaRU2iwzDMAzDMEyrsFlkGIZhGIZhWoXNIsMwDMMwDNMqbBYZhmEYhmGYVmGzyDAMwzAMw7QKm0WGYRiGYRimVdgsMgzDMAzDMK3CZpFhGMmze/duyGQy7N69O9yhMAzDRB1sFhmGkQwff/wxvvzyy3CHwTAMIylkPBuaYRipMGLECHTq1KnZDqLb7YYgCFCr1ZDL+TMywzCML/C7JsPEAGazOdwhNMFisYT0+eRyObRaLRtFhmEYP+B3ToaRGDNnzoRMJsOZM2fw8MMPo0OHDhg3blzj/UuWLMHo0aMRFxeHjh074ve//z1KSkqaaJw7dw4PPPAAunXrBq1Wi549e+L3v/899Hp9k+u80Ro/fjxGjBiBjIwM3HrrrYiPj8c//vEP3HPPPejfv3+Lr2HMmDG47rrrGv+9ePFi3HHHHejSpQs0Gg2GDx+OBQsWNHlM3759cfr0aezZswcymQwymQzjx48H0HrO4vfff98Yf6dOnfDHP/4RFy9ebHLN448/jsTERFy8eBH3338/EhMT0blzZ7zyyitwuVyt/yAAvPzyy0hLS8OlBzjPP/88ZDIZPvzww8bbKisrIZPJGl+TIAj45z//idGjRyMlJQUJCQm45ZZbsGvXrmbPUVtbiz/96U9ITk5GamoqHnvsMWRlZUEmkzU7kj979ix++9vfomPHjtBqtbjuuuuwdu3aNl8DwzAMm0WGkSi/+93vYLFYMGvWLDz99NMAgLfeeguPPvooBg0ahPfeew9Tp07Fjh07cOutt0Kn0wHwGJUJEybg8OHDeP755zF//nw888wzKCwsbLzGW60GamtrMXHiRFx99dWYN28ebr/9djz00EMoKipCenp6k2svXLiAw4cP4/e//33jbQsWLECfPn3wj3/8A3PnzkWvXr3w17/+FfPnz2+8Zt68eejZsyeGDh2Kb775Bt988w1ef/31Vr8/X375JR588EEoFArMnj0bTz/9NFatWoVx48Y1i9/lcmHChAlIS0vDu+++i9tuuw1z587Fp59+2ubP4JZbbkFdXR1Onz7deNu+ffsgl8uxb9++JrcBwK233goAMBgM+OyzzzB+/Hi8/fbbmDlzJqqrqzFhwgRkZmY2Ps7tdmPy5Mn47rvv8Nhjj+Gtt95CeXk5HnvssWaxnD59GjfddBNycnLw2muvYe7cuUhISMD999+P1atXt/k6GIaJcUSGYSTFjBkzRADiH/7whya3nz9/XlQoFOJbb73V5Pbs7GxRqVQ23n7ixAkRgPj999+3+hzeaomiKN52220iAHHhwoVNrtXr9aJGoxH/9re/Nbn9nXfeEWUymXjhwoXG2ywWS7MYJkyYIPbv37/JbVdeeaV42223Nbt2165dIgBx165doiiKoiAIYpcuXcQRI0aIVqu18br169eLAMR//vOfjbc99thjIgDx3//+dxPNa665Rhw9enSz57qUqqoqEYD48ccfi6IoijqdTpTL5eLvfvc7sWvXro3XvfDCC2LHjh1Ft9stiqIoOp1O0W63N9Gqr68Xu3btKj755JONt61cuVIEIM6bN6/xNpfLJd5xxx0iAHHx4sWNt995553iyJEjRZvN1nib2+0Wx44dKw4aNKjN18EwTGzDO4sMI1H+/Oc/N/n3qlWr4Ha78eCDD6Kmpqbxq1u3bhg0aFDjEWdKSgoAYMuWLa3mFnqr1YBGo8ETTzzR5Lbk5GRMnDgRK1asaHJMu3z5ctx0003o3bt3421xcXGN/6/X61FTU4PbbrsNhYWFzY7GveHYsWOoqqrCX//6V2i12sbbJ02ahKFDh2LDhg3NHnP59/OWW25BYWFhm8/TuXNnDB06FHv37gUAHDhwAAqFAq+++ioqKytx7tw5AJ6dxXHjxkEmkwEAFAoF1Go1AM/uYV1dHZxOJ6677jocP368UX/z5s1QqVSNO8eAJz9zypQpTeKoq6vDzp078eCDD8JoNDb+vGprazFhwgScO3eu2fE7wzBMA8pwB8AwTHDo169fk3+fO3cOoihi0KBBLV6vUqkaH/fyyy/jvffew7fffotbbrkF9957L/74xz82GklvtRq44oorGs3PpTz00ENYs2YNDh06hLFjx6KgoAAZGRmYN29ek+sOHDiAGTNm4NChQ80MrF6vb4zLWy5cuAAAGDJkSLP7hg4div379ze5TavVonPnzk1u69ChA+rr69t9rltuuQUbN24E4DGF1113Ha677jp07NgR+/btQ9euXZGVlYWHH364yeO++uorzJ07F2fPnoXD4Wi8/dKf64ULF9C9e3fEx8c3eezAgQOb/Ds/Px+iKGL69OmYPn16i3FWVVXhiiuuaPf1MAwTe7BZZBiJculuHODZoZLJZNi0aRMUCkWz6xMTExv/f+7cuXj88cfx448/YuvWrXjhhRcwe/ZsHD58GD179vRJq6VYGpg8eTLi4+OxYsUKjB07FitWrIBcLsfvfve7xmsKCgpw5513YujQoXjvvffQq1cvqNVqbNy4Ee+//z7cbrdP3xd/aOk1esu4ceOwaNEiFBYWYt++fbjlllsgk8kwbtw47Nu3Dz169IDb7cYtt9zS+JglS5bg8ccfx/33349XX30VXbp0acytLCgo8DmGhu/RK6+8ggkTJrR4zeUGk2EYpgE2iwwTIwwYMACiKKJfv34YPHhwu9ePHDkSI0eOxBtvvIGDBw/i5ptvxsKFC/Hmm2/6rNUaCQkJuOeee/D999/jvffew/Lly3HLLbegR48ejdesW7cOdrsda9eubXI03VJlcMMxbnv06dMHAJCbm4s77rijyX25ubmN9weCBhO4bds2pKen47XXXgPgKWZZsGABevTogYSEBIwePbrxMT/88AP69++PVatWNXlNM2bMaPY6du3aBYvF0mR3MT8/v8l1DVXnKpUKd911V8BeG8MwsQHnLDJMjPCb3/wGCoUC//rXv5rkCAKAKIqora0F4KnEdTqdTe4fOXIk5HI57Ha7T1re8NBDD6GsrAyfffYZsrKy8NBDDzW5v2FX79Ln0ev1WLx4cTOthISEZpXMLXHdddehS5cuWLhwYeNrAoBNmzYhJycHkyZN8jr+9ujXrx+uuOIKvP/++3A4HLj55psBeExkQUEBfvjhB9x0001QKn/+7N7Saz5y5AgOHTrURHvChAlwOBxYtGhR421ut7tJlTgAdOnSBePHj8cnn3yC8vLyZjFWV1fTXyjDMJKFdxYZJkYYMGAA3nzzTUybNg3nz5/H/fffj6SkJBQVFWH16tV45pln8Morr2Dnzp147rnn8Lvf/Q6DBw+G0+nEN998A4VCgQceeMAnLW/41a9+haSkJLzyyitNnqOBu+++G2q1GpMnT8azzz4Lk8mERYsWoUuXLs2Mz+jRo7FgwQK8+eabGDhwILp06dJs5xDw7LC9/fbbeOKJJ3DbbbfhD3/4AyorK/HBBx+gb9++eOmll/z8LrfMLbfcgmXLlmHkyJHo0KEDAODaa69FQkIC8vLymuUr3nPPPVi1ahV+/etfY9KkSSgqKsLChQsxfPhwmEymxuvuv/9+3HDDDfjb3/6G/Px8DB06FGvXrkVdXR2Apjut8+fPx7hx4zBy5Eg8/fTT6N+/PyorK3Ho0CGUlpYiKysroK+ZYRgJEZ4ibIZhgkVD65zq6uoW71+5cqU4btw4MSEhQUxISBCHDh0qTpkyRczNzRVFURQLCwvFJ598UhwwYICo1WrFjh07irfffru4fft2n7VE0dM658orr2wz5kceeUQEIN51110t3r927Vpx1KhRolarFfv27Su+/fbb4hdffCECEIuKihqvq6ioECdNmiQmJSWJABrb6FzeOqeB5cuXi9dcc42o0WjEjh07io888ohYWlra5JrHHntMTEhIaBZTw/fZG+bPny8CEP/yl780uf2uu+4SAYg7duxocrvb7RZnzZol9unTR9RoNOI111wjrl+/XnzsscfEPn36NLm2urpafPjhh8WkpCQxJSVFfPzxx8UDBw6IAMRly5Y1ubagoEB89NFHxW7duokqlUq84oorxHvuuUf84YcfvHodDMPEJjwbmmEYRmKsWbMGv/71r7F///7GY2+GYRh/YbPIMAwTxVit1ibV5i6XC3fffTeOHTuGioqKVivRGYZhvIVzFhmGYaKY559/HlarFWPGjIHdbseqVatw8OBBzJo1i40iwzABgXcWGYZhopilS5di7ty5yM/Ph81mw8CBA/GXv/wFzz33XLhDYxhGIkRN65y9e/di8uTJ6NGjB2QyGdasWdPkflEU8c9//hPdu3dHXFwc7rrrrsZRWgzDMFLl4YcfRkZGBvR6Pex2O06fPs1GkWFilDlz5kAmk2Hq1KmNt1VUVOBPf/oTunXrhoSEBFx77bVYuXKlT7pRYxbNZjOuuuqqZv3DGnjnnXfw4YcfYuHChThy5AgSEhIwYcIE2Gy2EEfKMAzDMAwTWtLT0/HJJ59g1KhRTW5/9NFHkZubi7Vr1yI7Oxu/+c1v8OCDD+LEiRNea0eNWZw4cSLefPNN/PrXv252nyiKmDdvHt544w3cd999GDVqFL7++muUlZU124FkGIZhGIaREiaTCY888ggWLVrU2Mu1gYMHD+L555/HDTfcgP79++ONN95AamoqMjIyvNaXRIFLUVERKioqmoyxSklJwY033ohDhw7h97//fYuPs9vtTaY3uN1u1NXVIS0tzeuxYQzDMAzDhBdRFGE0GtGjRw/I5aHfB7PZbBAEISBaoig28yAajQYajabVx0yZMgWTJk3CXXfdhTfffLPJfWPHjsXy5csxadIkpKamYsWKFbDZbBg/frzXMUnCLFZUVAAAunbt2uT2rl27Nt7XErNnz8a//vWvoMbGMAzDMExoKCkpQc+ePUP6nDabDX36dEBVVWDS3hITE5tMagI8c+FnzpzZ4vXLli3D8ePHkZ6e3uL9K1aswEMPPYS0tDQolUrEx8dj9erVGDhwoNcxScIs+su0adPw8ssvN/5br9ejd+/e+PPd/4RGpfVLc9H22bhz5P3o33VYoMJsQn69Ff2uDO0vQiRwxqhDvwFdwh1GUMm21GBgr87hDiNoZDuqMKS7dF8fAJwTSzGkk7Rf4+VY5LkAgCEpXdu5MnZJURxB34Qe4Q4jYtFVW/DC7Yvxwc7H0aFLgl8aVpOAZ29ahKSkpABH1z6CIKCqyoajmRORmKQiaZmMDtxw9SaUlJQgOTm58fbWdhVLSkrw4osvYtu2bdBqW/Yt06dPh06nw/bt29GpUyesWbMGDz74IPbt24eRI0d6FZckzGK3bt0AAJWVlejevXvj7ZWVlbj66qtbfVxr27oaldZvs6hRaSFC9PvxbZFXZ8XQawYFXDcaUKptUGvjwx1GUFG64qCOk+5rVCjjoIqX7uvLFYuhgBbqhNjqbeiQazAstVu4w4hotAoV4hNbP0KMdfQ1FgBAx66JiEtUk7TCmUKWmKRCEtEsNpCcnNzELLZGRkYGqqqqcO211zbe5nK5sHfvXnz00UfIzc3FRx99hFOnTuHKK68EAFx11VXYt28f5s+fj4ULF3oVT9QUuLRFv3790K1bN+zYsaPxNoPBgCNHjmDMmDEhjUWt1EBw2tu/kGFiiExHJYb1kPbOMAAM7yz918j4RqriYLhDiHisJk+unyY+MEYrlrjzzjuRnZ2NzMzMxq/rrrsOjzzyCDIzM2GxeIz45XmcCoUCbrfb6+eJmp1Fk8mE/Pz8xn8XFRUhMzMTHTt2RO/evTF16lS8+eabGDRoEPr164fp06ejR48euP/++0Map1qpgeBgs8gwDMN46J8Ye6lDvmA1C9AmqCCXc2GpryQlJWHEiBFNbktISEBaWhpGjBgBh8OBgQMH4tlnn8W7776LtLQ0rFmzBtu2bcP69eu9fp6oMYvHjh3D7bff3vjvhlzDxx57DF9++SX+7//+D2azGc888wx0Oh3GjRuHzZs3t3qGHyzUSi0EJ/d2ZHwjy1wd7hAYxmfM8pxwh8BIAKvZgbgE2vEz0zIqlQobN27Ea6+9hsmTJ8NkMmHgwIH46quv8Ktf/cprnagxi+PHj0dbkwllMhn+/e9/49///ncIo2oOH0MHnmxjfbhDCAmD+/ARZrSSKxaHO4SwwfmKrcNH0N5hMwnkXEXmZ3bv3t3k34MGDfJ5YsvlSCJnMZLQKLVsFoPAgEH8BylayXRUhjuEkMD5ikxL8BF0+1jNbBYjHTaLAcazs8jH0AxzKbFQ3BJr8BE0EygsRoGPoSMcNosBho+hGYaJFfgIunX4CNp7bGYBWt5ZjGjYLAYYNosME1vEcr4i0zZ8BO0dVpOAuARumxPJsFkMMGwWGV/hSujoh/MVGcZ/OGcx8mGzGGCcbidMNj1JwyqYcfL8YVocTgHHT24laYiiG8dObGqzCt0bTpzcBgex9+TZ47thMdG+rwWnj0Bf1/qscG8oyc9CTfl5kkZlyTmUnT/T5DZfK6F1FRdRcvIYKQ6Lrg4FR/eRNASrBbn7trd6vzfNuF0OJ86u30CKAwDOrtsAt8tF0sjfug2C2ULSKNx6AJYaWgV/yf7j0BeXkzQqTuSgJqeApFF37gLKj51qdrsv+Yr1JTU4uy2LFIe51oiTPx4laQgWOzKW7SdpiG43Dn+5k6QBABsWH4dgc5I0dizLhlFnJWkcWJeL6osGksbxnYUozq0haVSW6GEx0v5GlJ6rJT2eaRs2iwFGpVTD6XKEOwzIZHKA5vEgk8khk8nIZlEmD8AyC8AIp0CMgZLJ5BBF77vet6ghl9O/p5CBKNGoE25kAWrEK5PLQP6myGTkpRaQcWOyQOkE4nvbsobX+YqyViVCTyC+pV6s11TFwTaPoAPyexfGsXaXQw1FsDihiaMdQ0fQt0OSyETqXy0JYTAYkJKSghcnzfJ7tnNpbSFWHPwEL09+O8DReWZDDxjZK+C6kUxDj0Upt87JMldLtsei1Mf85YrFMXkEbZbncHFLG7RnFpmmTP/dctz4y0G456lr27+4FSxGOx4dMR96vd6rmcqBpME7nCm4lzwb2mh0YPiAtWF5HW3BO4sBRq3UwulywO2mHYcxPyNlo8gw0Qa3zGECjZWbckc8bBYDjFqpAQAucmFinlhpxh2L8K5i63DLHN9hsxj5sFkMMGwWGV+Q8hE0wM24mdiEj6B9w2rm1jmRDpvFAKNWenIdA20W8+poVW8MwwSWWM1XZJhAYzPxBJdIh81igFEqlJDLFEEZ+RdrxS0Mw0QWnK/YNnwE7TsupxuC3cUTXCIcNotBgBtzMwwjVThfsW34CNo3rGYBAHhnMcJhsxgE2CwysU4stMxhGIaO1fSTWeSdxYiGzWIQUKvYLAaCbGO9pNvm8Ji/6IbzFRmGjq1xZ5ELXCIZNotBQKPUBiVnkZEeUq6EZqQFN+JuG85X9A+rSYBSrYBKoyTp9JXtCVBETEuwWQwCaqUGduIsZIZhGCa64HxF37GYuG1ONMBmMcA4XAIAGWlnURTdqDFUkGOpri2JCI2a2lLyPGVdTRlcTidJw1BfBYedtuNrMtTBZjGRNKxmA8w/jTH0F4fNBmNtFUnD5XRCX1lG0hDdbtSXNV0j/jTj1l2g5wDqiuka+pJSuF1tr9X28hUNJRVwCbT58KbyajgstLVqqdXBrqetVbvRDEt1HUnDYRVQX1pD0nA5XKgtojV5F91uVOeXkzQAoCqP9jsDAMW5NeT58KXnauFy0t5Xy8/Xw26jrdXqiwaYDf5vjthMAjRaJfQ1FlIcZhNPTQsmbBYDTI2hAiabnpSzqLfUI72AtqVuF6w4eGQVSUMU3dh3cDlcLppJO3RsDWx2M0kj68B6GPW0HL+cjJ2orbxA0ig8fQTl52ntQy4WnsKF3OMkjZrz+Sg8up+koa+4iNy9W0kaFoMOp7b+2Ox2X4pbnHY7spZ+R4oDALKWfAenXSBpnFm9Bja9rt3r2spXzFuzA4ZS2oe9om0HUZtbRNIoPXAC5RmnSRpVmWdRvC+D1DKn4mwpTv54lBSHrrQG6Uv3kTQsOgv2f0pb7y6HCzvfW9vsdl+PoJfNPQiLkbZW1356DDVlRpLG9u+yUXqO9mHg4Lpc5B3330BbzQ64AZzYc54UR/YJmtlk2kYmUj/eSIiGYeAvTpoFjUrrt86Ok6thc1gxafTDAYstr84ac30WY6HARYo5i7FQCR1rxS2cr9g2qYqDfATtJxsXn8CBdbl4a9Xv/dboL9sBk9GFW688Bb1ej+Tk5ABG2D4N3uFMwb1ISqIdqRuNDgwfsDYsr6MteGcxCHDrHKY9uBKaYRjmp1F/ifScxU7a/gGIhmkNNotBgM0inWxiTl80IMVdRanD/RUZJrDYzAK03JA74mGzGATU3DonIEj5CFqqSP0IGoi9/op8BN023DKHhpXnQkcFbBaDADflZhiGiR04X9F/LCYB8Un+m8X+sh0BjIZpDTaLQcCzs8hmkWEYhmHaIhDH0J21AwMUDdMabBaDgFqpgeDgY2imZbi4JTqJxXxFSsschvEGq8nBc6GjADaLQaChwCVQXYlisW2O1JFicYs/zbijjVjLVwTA+YptwC1z6FjNnLMYDbBZDAJqpQYiRDhdtM74DBNtSL24hWGYwGIjjPvjfMXQwWYxCKiVGgDgvEU/iYW2OQzDMIxnZ1FLOIbmfMXQwGYxCKiVnukv3D7Hf7htDhNJxOrUFoYJNlazg4+howA2i0FArfQsfN5ZZGKFWMhXjEU4X7F1OF+RjiiKnmNoLnCJeNgsBpiSmkJsyPgOKsIUlzpTNZbtX0CKw2634Ktlr5M0RNGNL5a8CpfLSdJZ8v0MWKwGksaGb+agtpJWjbprzUKUFpwkaRzdvhy5J/b4/fgsczXcFzJxcssaUhzFWcdwZPkXJI2aCwXY9elckoaprgYb3/0nAP/zFZ12OzZMfZkUBwBsmPoynHbaB7Ttb8yAubqGpLH3n/9D7dkiksbReV+j9OAJksapJeuQt3YXSePkj0exZ/5GkkbRoVxsmLGMpFGVV4YVz31K0jDVGrH44fdIGi6HCwsmvUnSAIBXJn4Ds5528vSvh39AeREtZeeDFzciJ/0iSePrt/bg4IY8vx5rtzrhdos4vOkcNnxxnBTHxvW01yEV5syZA5lMhqlTpza7TxRFTJw4ETKZDGvWrPFJVyYGqmRXAjQMA39x0ixoVFq/NERRhMvtxCfb3sKEq3+Hgd2u9EvH4RKgUng+bflbDe1wClApaZ/YwqGRbaxvdgztdAhQqmhxOB0CFEoVZDKZ3xoulxMymRxyuX+fs7LM1RjUqxPcbjcUSqXfcQAB+p4IApRqusYpWT2puMVpF6DUEOMIooYvx9CBiMNlF6AgaridTkAmh1zh+1ptOIIemtwFbpcbChVxrdodUGpo838dNgEqLfG9KIAalJ1Fu80BjZb2/YgUDYfdCYVKAbnc9/fV+ioznr7+Eyw89P+Q2jkBSpXC68f2l+1okq9oMAi4cuA66PV6JCcn+xwLhQbvcKbgXiQl0b6fRqMDwwes9et1pKen48EHH0RycjJuv/12zJs3r8n977//PrZt24ZNmzZh9erVuP/++73W5p3FACOTyaBUqMjzoRuMIgWqyYskDaopatCgGEUAUCiUfhvFBmRyOdkoAgH6nhCNYsA0iMYomBq+5isGIg6qUQQAuVLpl1FsYFhqN89aJRpFAGSjCIBs8gKpQR3xRzVokaSh0ij9MoqApyE3ACQka30yii1BfW+PdkwmEx555BEsWrQIHTp0aHZ/ZmYm5s6diy++8O9Eis1ikKCaRYZhGCZy4XxFOlaTxyxq4ummVWoYDIYmX/Z2UmymTJmCSZMm4a677mp2n8ViwcMPP4z58+ejWzf/8pDpHxmZFvFMcWGzyEifTEcl91dkGMZnrGYB2gSV3zuTkcYxay/E/9Q6z18sVo9v6NWraerZjBkzMHPmzBYfs2zZMhw/fhzp6ekt3v/SSy9h7NixuO+++/yOi81ikPDMh+bWOb7SUr6iVOAxf0w0wC1zmFDBbXNap6SkpEnOokbTsgktKSnBiy++iG3btkGrbV5rsXbtWuzcuRMnTtAK5vgYOkjwMTTTElIc8ydlYrG/IsAtc9qCmq/I/Iy/bXMuL26RIsnJyU2+WjOLGRkZqKqqwrXXXgulUgmlUok9e/bgww8/hFKpxLZt21BQUIDU1NTG+wHggQcewPjx472Oh3cWgwSbRYZhGGnC+YqBwXMMzTuLFO68805kZ2c3ue2JJ57A0KFD8fe//x2dOnXCs88+2+T+kSNH4v3338fkyZO9fh5JmUWj0Yjp06dj9erVqKqqwjXXXIMPPvgA119/fchj0Si1MFh1IX9ehgkl3IybYRh/sRgFxHNDbhJJSUkYMWJEk9sSEhKQlpbWeHtLRS29e/dGv379vH4eSR1D/7//9/+wbds2fPPNN8jOzsbdd9+Nu+66Cxcvhr5Zp2dnkZ6z6G+PRYYJFVItbskVaU3goxGzPIePoJmQYSPOhWZCh2R2Fq1WK1auXIkff/wRt956KwBg5syZWLduHRYsWIA336R33fcFPoZmLoWLW6KTWMxXZFqH8xUDi9UkIC6B2+YEmt27d7d5vz+zWCRjFp1OJ1wuV7NqoLi4OOzfv7/Fx9jt9ia9iwwG2ki6S2GzyFwOF7cwTPTD+YqBw2r2vcClv2xHkKJh2kIyx9BJSUkYM2YM/vOf/6CsrAwulwtLlizBoUOHUF5e3uJjZs+ejZSUlMavy/saUVCruHWOr0i5bY4U4XxFacEtc5hQ42/rHKlXQkcikjGLAPDNN99AFEVcccUV0Gg0+PDDD/GHP/yh1fFs06ZNg16vb/wqKSkhx1ClL8PhvB2knUWjVYfdp9aR4hAEK7btXkzSEEU3Nu9YBLfbRdLZufcbWG0mksbRHStgqKOZk6yDG1B9sZCkcfbEHpTknyRplGRnoDD9AEmjqjAPObs3kzT0lWXI2vgDSaNfggbHv/qapOESBBxd8AlJAwCOLvgELkEgaRz/6mvY9HpSvuLJr3+EoaSCFEfu6u2oOVNA0ijacQilhzJ9eszl+YpFh3KRvfYoKY6KMyU4+s1ukoautBZ75m8kaVj1Zmyds5Kk4XK6Mf+VLSQNAPj09e2wmmlrdcmcfagtN5I0Vi84iuLcGpLGtu9O4vRh//522kyeaujDG/NwZPM5UhxHD9NeB9M2kjmGBoABAwZgz549MJvNMBgM6N69Ox566CH079+/xes1Gk2rvYv8JTkuFX06D4bL7fDbLMapEzCw+5WkOFQqLQYPuIGkIZPJMXTwGMjltJmdA/uPhkYdT9LoNXAU4hJTSRrd+wxFUgfaUXDXKwZApWne+NQXOvToDZfDQdJI6tQVCuJs6LjkVHQfOoqkoYqPR8/rriNpyJVK9Bo7hqQBAL3GjoGcOHP7itGjoYqPB6D3O1+x++groe2YQoqj84hBiO/UfL6rL3Qc1Jc81zmtXxckdqG9luQeHdHLjxypS4nvmIj+Y4aSNNTxWgy+faTfj/fkK8px871DSHEAwE0TB0Otpf1srr29H5I6xJE0rrypF9K6JZI0Bl/THcl+xtFwDN1vRBcQlwj69k+gCTBtIimz2EBCQgISEhJQX1+PLVu24J133gnZc2vV8eiujkeVvgxOlwNut8tns6VUqNAzrWWD6y0ymQx9etEMJwD07TWi/YvaoXfP4WSN7n1ofygAoMsVA8gaHbr4nq90eXFLYlpnchxxySmIS6b9EVfHxaNL/8EkDYVKhS5X0n6+Mrkc3a+imVYAAdHoOuKn3xnCH67OV9KPyDoO6kPWSOndnayR3I1mWAEgPjUB8am0P+TqeA16XUt7T1SoFOh7I22990/sCdxKkgAAjBrXm6wx/EZ67uTga+hrpM9Q/9/PrD815e7aO5UcR5cuNOPMtI2kzOKWLVsgiiKGDBmC/Px8vPrqqxg6dCieeOKJkMei/mk+pOAUoFXzIo51uLiFiWS4ZQ4TDqx+TnBhQo+kchb1ej2mTJmCoUOH4tFHH8W4ceOwZcsWqFShL83/2SxykQsjPTIdldxfkWEYElYzt86JFiS1s/jggw/iwQcfDHcYAAC10pPXxu1zGCb64P6KDBN8bCbBp2pobpsTPiS1sxhJKBVKyGUK3ln0Em6bwzDhgVvmtE+q4iD3VwwwLqcbgt3l8wQXbpsTHtgsBhFuzM3w5BYmGuB8RSbUNLQO8qfPIhN62CwGETaLDCC94hYpN+PmfEWGCQ1W009mkQtcogI2i0FEraKZxbw6KwaMDNxUGYYJFFItbgE4X5FhQoGtcWeRC1yiATaLQUSj5JF/DMNELpyv2D6crxgcLEYBSrUCKo2k6mwlC5vFIMLH0AzDRDqcr8iEA26bE12wWQwiaqUWdgfvLMYqUixu4XxFhmECga9tc5jwwmYxwJjtRlyo9gxE93dnUXDakV9+mhSH2+1CXv5RkoYoijh77jBJAwDyCtLhcjlbvd+btjkX8k7AbrOQ4rhYdBpmQx1Jo7I0H/raCq+vb6m4RVdeitqS86Q4THU1qDxHO0K0mYy4eCbT58ddmq/otNtRejSdFIfb5caFAwdJGgBw4cBBuF1ukkbS2RI4bbTTgLKj2bDrTSSNqpO5MFfVkjTqzl2A/kIZTeNCNcpO0Uy0sUqPC+nnSBo2gwX5+86QNJx2B85uzyJpuN0i9q89S9IAgIPrc+F0uEgax7YXNBaJ+MvJ/cXQ19DeV3MzylBVovf5cVazo7FtzoWz1SjJqyHFUVpMex1M27BZDDA2wYI6YxUAmlmsMZaT4nC5nKisPk/SAIDKqiK43bQ3teqaYjidtDe1+qpSOOxWkoa+tgJ2q5mkYdLXwmysJ2lYDTqY62hvjHazEcYa2i6fw2aFvoJmJlx2O3TFNDMhut2oLywiaQBAfWERROJa1RWVwmV3kDQMxeVwWGhr1VhWTTac5qpaWOta/yPuzYg/c60R+jLaByyr3oL6Ytp6FywCagtp690pOFGV69t6bylfsSCLvrt+/kw1nA7aB5vi3FrYrbS1WlZYB7OB9uGoutQAXbXv76vWS3YW6ypMqL5obPP6/rIdbfZYrK3lU7xgIhNFUQx3EJGCwWBASkoKXpw0CxqVlqy378xG1Bgr8esb/ZtNHSvV0FJtyJ1lrpZk2xypVkLnisUxVQnN86Dbh4tbgsfKj44g52gp3vj6Aa+ub88sGo0ODB+wFnq9HsnJyYEK0ysavMPXp6YgPklD0rIY7Xh0xPywvI624J3FIMIFLoyUkHq+YiwZRYYJNzazAC3nLEYNbBaDiJpb58QsUixuAaTdX5FhmNBh5QKXqILNYhChNuVmohupHUEz0oGPoJlwYzEJiE9isxgtsFkMIp6dRTaLbZFNLBZhGCrcModpCc5XDC58DB1dsFkMImqlBgL3WWwXKRa3MNEF5ysyTGixmhw8FzqKYLMYRBoKXPwpOM+ro7XeYJhAIuUqaIZhQo9nggubxWiBzWIQUSs1ECHC6fKvF1YstM1hGCa0cL4iEwl4JrjwuL9ogc1iEFErPf2WOG8xtpBif0WpwvmKTEukKugThZi2sZqFxgkuTOTDZjGIqJWext7cPodhIhfOV2RagotbgovV7OBj6CiCzWIQUSs9vwi8s8hEM5yvKB3Mcto8cYYJBKIoeo6heWcxamCzGGDcohtWwTMnUyaTQ+XnFBe7QJsLCwBmi+/D3S/HYjEETcOXtjlWs8GvQqFLsVlMcLtos4MFmwUuJ20eq1MQ4LDRdpvdLhfsFtqca88bdtvzWL3BbqCvEZs+QjR0dA273kReq4LRDLeTtlYdFhtc9uYz2X3JV3QKTtjN1LXqhtVgIWkAgKU+AO+JdXQNfS39tehrAqBRayGvM2O9FS4nbUa12WCHYHP69Bi71Qm3W2w0i1azAJuF9r5qs9F+X5i2YbMYYMrqLmDvmQ2N/1YrNbD7eAxdb6pG5rk1pDjsdgvWbv6QpCGKbqxc/y5cLt/eCC5n/db5sFhb/iPsbducfes+R331RVIc6TtXoPwCbWcl+/BmFJw+QtIoOrYfufu2kjTKcrJwctMqkkZtcRHSf/iKpGGursGhD+eTNJw2G/bMmk3SAIA9s2bDaff+g1lL+YoH3voU5spaUhzHPvoWdXnnSRrZ36xF+bFTJI1z63ahaMdhkkbutiykf7uXpFF8LB97P9pI0qg6V44ts34gaZhqjVj96pckDafDhf/8cSXZpL3zzFqYDbQTpwV/34aqEtqGwLdv70d+VgVJY/1nGTi+q8inx1hNng8xDWZx76oc7Fl5hhTHvt3SHUcaCchE6qqXEA3DwF+cNAsalTYgmou2z8bNQydgeM9rfXpcXp1V8tXQ2cZ6yfVYlGJxi1SPoWNxHjRXQrdPQ3EL5ywGj/Kiejw/fjG+Of2c10fR/WU70Fk7sNX7jUYHhg9YC71ej+Tk5ECF6hUN3uHrU1MQn6QhaVmMdjw6Yn5YXkdb8M5ikFH7eQzNMJFApoM/rUsFzlf0HjaKwaVhZ1ETz61zAs2cOXMgk8kwderUxttsNhumTJmCtLQ0JCYm4oEHHkBlpW/v7WwWg4xnigubRSZ6keKuYqzCu4pMJGA1C9AmqCCXy8IdiqRIT0/HJ598glGjRjW5/aWXXsK6devw/fffY8+ePSgrK8NvfvMbn7TZLAYZz3xobp3DMJFELB5BM0ykwG1zAo/JZMIjjzyCRYsWoUOHDo236/V6fP7553jvvfdwxx13YPTo0Vi8eDEOHjyIw4e9z2lmsxhk+BiaYRiGYX6G2+a0j8FgaPJlb6d4b8qUKZg0aRLuuuuuJrdnZGTA4XA0uX3o0KHo3bs3Dh065HU8St/CZ3zFH7PIxS3RidSKWzhfkWGYYGAxCdBKcGcxy9ALGjetONZu8pxE9urV1APMmDEDM2fObPExy5Ytw/Hjx5Gent7svoqKCqjVaqSmpja5vWvXrqio8L4Sns1ikNEotTBYdeEOg2H8gvMVpQFXQXsHj/kLDVaTgHjeWWyTkpKSJtXQGk3LVdYlJSV48cUXsW3bNmi1geni0hJ8DB1kPDuLnLPIMJEC5ysybcGV0MHHxnOh2yU5ObnJV2tmMSMjA1VVVbj22muhVCqhVCqxZ88efPjhh1AqlejatSsEQYBOp2vyuMrKSnTr5v0HSN5ZDDKcs8gwDMMwP2M1CYhL4LY5geDOO+9EdnZ2k9ueeOIJDB06FH//+9/Rq1cvqFQq7NixAw888AAAIDc3F8XFxRgzZozXz8NmMciwWYwNsszV4Q4hoHC+IsMwwcJq5gKXQJGUlIQRI0Y0uS0hIQFpaWmNtz/11FN4+eWX0bFjRyQnJ+P555/HmDFjcNNNN3n9PGwWg4xaxa1zYgUpFbcA0sxXbGnEn9ThfEUm0rCaHejcIyncYcQM77//PuRyOR544AHY7XZMmDABH3/8sU8abBaDDO8sNifbWB/uEJgYhvMVGSa82CRaDR0p7N69u8m/tVot5s+fj/nz5/utyQUuAaas7jy2Za1s/Lc/ZtFsrcPazf8jxWG3W7BizWyShii6sfSHf8HtdpF0flj7DiwWQ5PbfG2bs/37/6GusoQUx4FNX+Ni4SmSxvG9a5CfTauYPHdoN87s3EjSKD2diYw1S0katSVF2P+1b58uL8dcU4O9c94haTjtdmx7fTpJAwC2vT4dznZ6kbXHrn+8D3NVLUnj0NufoTbvPEnjxKcrUHbkJEkj5/stKNi8n6RxetNxHPx8O0njQvo5bHtnNUmjprACP077hqRhrjXiuz8vaPV+byqhnQ4X/nH/d3C7RVIsMx5aAYuRtlbn/nU9Ki7oSBqfTd+BvONlJI3vPziEo1vzfXrM5cfQW789ia3f0tb79q2018G0jUwURdqqlxANw8BfnDQLGpV/JegutxM2wYoErWeLvUp/EV/umotX7v0v5HKFVxq5tWZ0G5CMxIQO7V/cBkZTHZISO5I0DMZaJCelBTQOf3osmgx1SEjqAJnM//FQFpMO2rgkyBXe/Rxawm41Qa5QQaVuWpnmS49Fh90Gt8sFTXyC33G4nE44bBZoE/0fNO92u2EzGhCfktrsvkxHpdfH0Ja6OsR3pK0zS20d4tOCr9FeJbSlug7xnalx6BDXIRkyuf+fxW06I1QJcVCo/D/8EUwWyBUK2BMK/T6GdtgEOAUn4pLj/Y7D5XDBZrAgIc3/Y0fR7Ya51ojEzin+a4giTFV6JHVNbfH+VMVBryqha8qM6EQ8Qg2ERm25ER27JZLeE3XVZiSmaqFU+f+eaKy3Qq1VQhPnfcHKKxO/wcTHrsadvx8JAI3GOT6p5YpfAOgv2wEA6Kwd2OL91ZVWXDtyI/R6fZOWM6GgwTv87eAcaBLpfRbnjn0tLK+jLfgYOsAo5MpGowh4xv0BgOAUoFXHeaUhk8nJRhEA2SgCIBvFQMWRmEzXiE9MJWto4hKb3eZrcYtKQ++FpVAqoSAYRQCQy+VkowiAbBQBkI2iNxre5CtSjaInjlSyhjaVns+lTown5yuqtGqotLTjQoVKQTKKACCTy0lGEQBkMlmrRtEXqCYvUBpp3ekaqZ39/8DaQFIH7/6uXYrVJCDuEmPYlklsoFC8s9EwtoQ2nu1MMOFj6CCjVnp+CbwtcomF6S1SRGrFLVKF8xUZJvxYzdw6J9pgsxhkft5Z5CIXhmEYhrGZBMRxgUtUwWYxyCgVSshlCm6fwzAMw8Q8Lqcbgt3FE1yiDEmZRZfLhenTp6Nfv36Ii4vDgAED8J///AfhruHh9jk/409xCxM6fM1XjBZitb8iw0QaVrMAALyzGGVIKiP07bffxoIFC/DVV1/hyiuvxLFjx/DEE08gJSUFL7zwQtjiYrMoXaQ2uUXKxGK+IjfjZiINq+kns8g7i1GFpMziwYMHcd9992HSpEkAgL59++K7777D0aNHwxqXWsVmUcpwcQvDMIx32Bp3FrnAJZqQ1DH02LFjsWPHDuTl5QEAsrKysH//fkycOLHF6+12OwwGQ5OvYKBR8sg/hmGYSMXbHosMHYtRgFKtgErj+15VoXgnqm2+NQBnAoOkdhZfe+01GAwGDB06FAqFAi6XC2+99RYeeeSRFq+fPXs2/vWvfwU9Lj6GZqKBTEdluEMICpyvyDCRA7fNiU4ktbO4YsUKfPvtt1i6dCmOHz+Or776Cu+++y6++uqrFq+fNm0a9Hp941dJCW2cXGuolVrYHbyzyEQ+UixuAThfkWEiBW6bE51Iamfx1VdfxWuvvYbf//73AICRI0fiwoULmD17Nh577LFm12s0Gmg07XeOp8I7ix6yjfXhDiGgcHELwzCMb1jNDm6bE4VIamfRYrFAftk8VoVCAbfbHbIY6kzVOHn+cJPbvDWLDdNbLBYDjmVuIsXhcNhxKH0NSUMU3dh/+AeIIu37d/jYWgiCFQD8bpuTfXgTzIY6UhxnT+xBffVFkkbRmXRUlpxr/Lc/xS0VeWdQeuoEKY660gsoPLqfpGGqrcbZPVtJGnajCWfW/EjScDmcyF6+gqQBACeXrYDL4SRpnP5uA+wGE0kj78edMFXUkDSKth9CfQHt+Dx/3xkUZxSQNEozi5C36xRJo6awAtlraUWGxio90pfuJWnYTTYc+JS23p0OF7579wC5Hdvy9w/CbnOQNNZ+egz6GgtJY/t32Sgvon2IP7wxD/lZFV5fb21hZ/Hk/mJk7btAiuN0to70eKZtJLWzOHnyZLz11lvo3bs3rrzySpw4cQLvvfcennzyyZDFoFZqkBiX0uw2g1XntYZCqUJyUidSHAqFEinJnUkaMpkcHVK7kjQAICW5M+Ry/wfVA0BCchqUKtqn0YSkVKjUtLnMcYkpUGt8n4V6KZqERCjUtNeiiotDXHIqSUOp1iChg2cWsr/5igqVEgmdietMLkNiV/o6S+rWFTK5rMltvuYrJnTtBIWKlk8V37kDlMR5ynEdU6CK92+dNeQrxndIhFJNe4vXpsSTHg8A6ngtEjrR5pgrNUokd0slaSjUSqReQZt1r1Qp0K0PLQ4A6N4nFUol7T2xS89kqLW0n29a90RoiUfCyWnxSEjx/oTOahYQl9j0dywxRQO5grZ3lZgkKTsTccjEcHesDiBGoxHTp0/H6tWrUVVVhR49euAPf/gD/vnPf0LtxR9ng8GAlJQUvDhpFjQqmqm4lCN5O3C++hweuvnPbV4n9bnQUmvInWWulkzbHCk34461fEWzPIfzFX2Eq6FDx7dv70P5eR1eWTDZb43+sh3orB3Y5Daj0YHhA9ZCr9cjOZn24cRXGrzD3w7OgSaR5h3sJhvmjn0tLK+jLSRlxZOSkjBv3jzMmzcv3KE0Qc2tcxiGYRgGFiMXuEQjkspZjFS4Kbf04OIWhmEY37GaBcQnsVmMNtgshgDPzmJsm0WpVUID0pncwv0VGYYJFTazQM6TZEIPm8UQoFZqIHCfRUnlK0oNKeYrArHXX5HzFZlIx2pykOdC8ySX0MNmMQQ0tM6RUC0RwzAMw/iMZ4IL7yxGG2wWQ4BaqYEIEU4Xra8WwzAMw0QzngkuPO4v2mCzGALUSk8PqrbyFvPqrKEKhyHCxS2RD+crMt7CbXNCi9UsBGyCCx9Fhw42iyFArfT0XWqvfY6UeyxKDSkVt3C+ojRoaMbNMJGM1ewIyDF0oXhnAKJhvIXNYghQKz2/GLFeEc0wTHDh4hYmkhFF0XMMzbOhow42iyFAJpND5eV8aCkitcktDMMwjO/YrU643SKbxSiEzWKAsTtsqNRdbHa72gez6HI5UVZBy8UQRRGlZbkkDQAoLTtL1tBVFcHtcpE0qi4WwCHQzHZtxQXYrSaShq6mDIJJT9Iw19fCWE3rbWgzGVF/kZaX57DbYCmmabgcTtTk5pE0RFFE1ZkzJA0AqDpzBqIokvIVq0/nw+VwkuKozS2Cw0JrlaUrKoVdT1ur9SU1MFToSBqmaj3qLtBydG1GK6ryykgaTsGJslO0tSq63Sg5UUjSAIAzR0vJGjnpF8ndMfKOl8HpoL2vFp6qgtUkkDRKz9VCX2Px6tqG57rcLFaV6FFdavDr+RvyFmtruD1dMGGzGGB05hoUVDb/w6dWamD3cuSf2axDXv4RUhyCw4ZTOXtJGqIoIuvUTrjdtDekiwVHINhpBTwFpw7BatKRNC7knYC+jmbSjualw1zV/MOAL9QWF6GqkGbk9ZUXUXY2m6SRUZUL1XnaH0/BZML5/ftJGi7BgYLtO0kaAFCwfSfcDk/HAX/zFYu2HYRgNJPiKNl/HJYaWhP68mOnYCgpJ2lczDqPipwSkkbl2YsoziggadQWVaJgPy2f0lBRj9ztWSQNm9GK7B+PkjScDhe2fpMFt5tm9HYsy4bNTOuOcXB9HuoraWv1+M5CVBbTPvyeOlSCkrwar661mX8yi5flLOZnVeBcVoXPz31p3mL+OaPPj2e8RyZy879GGoaBvzhpFjQq2jDwy/lq93u4qu8YXN13TIv359VZJVvgIqVj6CxzNRe3RDi5YnFMFrdwvqJ/cDV06CjMrsT/3fMtVhS9BLlcFhDN/rId6KwdCKPRgeED1kKv1yM5OTkg2t7S4B3+dnAONIk072A32TB37GtheR1twTuLIcIzxSU2cxYZhmEYxmoWoE1QBcwoNsAtdIIPm8UQ4ZkPzTkVDBNMuL8iw0QugWqbcyncQic0sFkMEW0VuEi5IbfUjqClglSPoIHY66/IMNECt82JXtgshoj2qqGlmq8oNaSSr8gwDBNqLCYB2iDNha6x0Svdo5EFCxZg1KhRSE5ORnJyMsaMGYNNmzY1uebQoUO44447kJCQgOTkZNx6662wWn3bpGKzGCI0Sm3M9llkGCZ4cHELEy1YTQLig7CzGMtH0T179sScOXOQkZGBY8eO4Y477sB9992H06dPA/AYxV/+8pe4++67cfToUaSnp+O5556DXO6b/VMGI3imOb70WWQYxnc4X5FhIhtbAOdCMx4mT57c5N9vvfUWFixYgMOHD+PKK6/ESy+9hBdeeAGvvfZa4zVDhgzx+Xl4ZzFEeMwiF7gw4YfzFRmGCQdWk4C4BFVQtM+LtwVFN1wYDIYmX3Z7+5tNLpcLy5Ytg9lsxpgxY1BVVYUjR46gS5cuGDt2LLp27YrbbrsN+/3ojcs7iyEiFncWs420xsSRhJT6KzIMw4QDq1naBS4l9alQC3EkDcHsySXs1atpHcOMGTMwc+bMFh+TnZ2NMWPGwGazITExEatXr8bw4cNx+PBhAMDMmTPx7rvv4uqrr8bXX3+NO++8E6dOncKgQYO8jovNYohQq2KzdY5UKqEZhmEYGlazA517JIU7jKigpKSkSVNujUbT6rVDhgxBZmYm9Ho9fvjhBzz22GPYs2cP3G43AODZZ5/FE088AQC45pprsGPHDnzxxReYPXu21/GwWQwRsbizyDChIlbzFbm4hYkmbEGshpYaDdXN3qBWqzFw4EAAwOjRo5Geno4PPvigMU9x+PDhTa4fNmwYiot9e8/knMUg0NIsZV/NosvlJMfBGoHXcAdAQ3S7Gz/xhTqWy/MV3c4AfE8iRGNoh44REUfkaLhAneYqut0QiWtVFEW4XfT17nLQZtQHSsPJGn5rWFrps+hyuelrlScXN+J2u2G329G3b1/06NEDubm5Te7Py8tDnz59fNJksxhgSmoKsD5jabPb1UoNnC5HMyPZUkPuel0FVq77LykOm92Mb1ZMJ2mIoojFS19r0fz6wupPp8Ni0pE0Nn/7X9RW0naP9q79DCX5J31+3KXNuDM3/ID8Q7tJceTu245TW9aQNIqzjuHo91+RNGrz83HgvXkkDXN1DXbM/DdJw2m3Y+PLr5I0AGD9E2/AaaPt3m+bOgfmylqSxr5/fYzas0UkjWMfLUXJgeMkjUOLd+L4igMkjex16dgzf1P7F7ZB0aFcbPzXMpJGVV4ZVjz/KUnDXGfC4ofnkjScDhem3vkl3G6aMfm/SUtg1tPSkt58dBXKi2h54R+9vBk56RdJGt++vR8H1uW2fyE81dAtFbhs/OIE1n9OW+9HNsfmyL9p06Zh7969OH/+PLKzszFt2jTs3r0bjzzyCGQyGV599VV8+OGH+OGHH5Cfn4/p06fj7NmzeOqpp3x6HpnIdryRhmHgL06aBY3K/2HgoihCJms6+1JnrsWn297CC796C1r1zwmweXXWFhtyt6QRiDh813BDJvPvM0XD9BbR7YbMx55OzeIIo8alxS2B+J5SYqFqXL6zGO0/mwZyxWIMS+sU9jhCrdHeMXTkvI8E5vcmWKQqDqJ/Yk+vrg3Ea3G7RfJ85GjUmHLL53jktVswdtLgZvdRv68Wox2PjpgPvV7v9fFtoGjwDg9uWAh1Ar3AZcWkP3v9Op566ins2LED5eXlSElJwahRo/D3v/8dv/jFLxqvmTNnDubPn4+6ujpcddVVeOeddzBu3Dif4uKcxSDQ0oJXKz3JqYLT1sQs+qIRiDh816AZRQDkP5wRpRGgP3gR83okoNGQrxjuOCJNA4ik95HINYq+EojXQjVo0aphbWVnEZDWGgkln3/+ebvXvPbaa036LPoDH0OHCLXSs1PJRS5MuOD+itLCLM8JdwgM4xM2k4A4LnCJStgshgilQgm5TBGT7XMYhgkOXAnNRAsupxuC3cUTXKIUNoshhNvnRB/cjJthGIaO1SwAAO8sRilsFkMIm0WGCSyx2l+RYaINq+kns8g7i1EJm8UQolbFhlmU0pg/qcD5igzDhJNGsxik2dBMcGGzGELUytgZ+cdj/hiGYZgGrCYBSpUcKg03YYlG2CyGEM1lx9Ct9VhkIoNLm3EzTCTBldBMtGE1tzy9hYkO2CyGELVSC7sjNnYWpQIXt0QusZ6vyJXQgUPnGotCU2m4w5A03DYnumGzGEK4wIUJB5yvyDBMuLGaHdw2J4phsxhC2CwyDMMwsYiVdxajGjaLAaZSV4q9Zza2eJ+3ZtFgrMG23YtJcdgFK9ZvmU/SEEURazbOg9vt8voxl475a2DHDx/BajaQYtm/8UvoaspIGsd2rURFcR5J4+yerSjJziBpnD9+GOcO7iJpVOSdQfaWNSQNfUkJTnyzhKRhra/HkQULSRpOu4D9775H0gCAvf/8H5x2gaRx6J3PYamhVfMf/2Q5dIW0I83T321AxQlaXmLW6sPI2ZpJ0sjbdQrHVxwgaZRmFmH/p1tJGrVFldj29iqShrnOhHWvf0vScDndmPP/fiRpAMD7z2+AxUjbOFj0+g5UlehJGt/99wAKsitJGhu+OI7MPefbvc6Ts9hyJfTe1TnYu5q23rMPxnZaSrDhsqQA0yGxM67sNbrF+7w1iwnxqbh6xJ2kONQqLa6/dhJJQyaT4abr7oVcriDpjBrzK2jjk0gaw669HUmptCPHASNuQmJymlfXttaM+4oRV0Ol0ZLi6DpwKNxOJ0mjQ88+iE/tQNJI6NIV/cffRtLQJCdj8C9/SdJQqFUYdv99Pj0mVyxudgR95cP3QKGmteUY8uu7oO2QQtLo/4ubkdSzK0mj1y2jyXH0GzMUchXtd/eKq/rCaXeQNDoP7I74DokkjdSenXDVr28iacR3SMB1j9xK0lAo5fjtCzeSNADg3meug5a4y3bXwyPRsRvt+zruvqHo0juZpHHdXf2RkNL+e6LNLLT6mkeMpRd69hvOKSnBhM1igFErNUhLavkPhbetcxQKJTp36k2KQyaToWvnviQNAOjWpT9Zo/MVdI20bn3IGh06X0HWSEqjvyHFJaeSNTTxCdDEJ7R7XVv5ikqNGim9aG/ScoUCHfr1JWnIZDKkDRxA0gCAtKH9yBodB9HXWWr/nmSN5J6tF694Wwmd3C2VHEdCR5oZAQBNohaaRNoHLIVKgS6De5A0ZDIZug+nm5KBo+iFRQNG0j5MAEC/K+nvRb0Ge/fhuS269k716jqLsfVj6I5d6essMZW2xpi24WPoEMI5iwzDBAKuhA48XBEdXLh1TnTDZjGEqFXaRrPIPRYZhmGYWMHGZjGqkZRZ7Nu3L2QyWbOvKVOmhDs0AD/tLEq4z6JUxvxxM+7IJtb7KzJMNGI1OdgsRjGSyllMT0+Hy/Vz5e6pU6fwi1/8Ar/73e/CGNXPNBxDi6IY7lCChlTG/EmhGTf3V2QYJlKwmrl1TjQjKbPYuXPnJv+eM2cOBgwYgNtuo1V8Bgq1UgMRIpwuWnUhwzAMExwKTaXon0gvUmKa4pngQutWwIQPSR1DX4ogCFiyZAmefPJJyGSyFq+x2+0wGAxNvoKJWqnxxMZFLgzDMBGHzjU23CFIFqtZ4AkuUYxkzeKaNWug0+nw+OOPt3rN7NmzkZKS0vjVi9hGpD3USk9pvzftcxiGaQ7nKzJMdGI1O/gYOoqRrFn8/PPPMXHiRPTo0XpvrmnTpkGv1zd+lZSUBDUmtdLzi8I7i5FLa824ow3OV5Qm3vZYZJhIQhRFzzE07yxGLZLKWWzgwoUL2L59O1atans8lEajgUajCVFUgEwmh6qh16LEbHpLY/4Yhgk83GMx+HDeYmCxW51wu0U2i1GMxCyLh8WLF6NLly6YNIk27i4YqJUaFNUbuMciwzBMBMJ5i4HHavLMbGezGL1Iziy63W4sXrwYjz32GJTK0G+cGq06nCvLbvV+tVIDp6vtY2ib3YwzuQdIcTidAk6e3kXSEEURJ7K3kzQAICdjJ5wOgaRx7uQBWM20AqTzZzNg1NF6KJbnnUb9RVreXG1xISoLzpI0DFUVKD2dSdKw1utQfPAQScNhtaJw9x6ShtvpxLkt29q9rr18xbwfd8DtdLV5TXsUbN4Ph8VK0riw+yisdXqSRtmRkzBerCRplJwoROVZ2kSSqrwyFB8rIGnoLtbi3J7TJA1LvQmnNx0naTisArJWHyZpuN0iNn+dSdIAgG1LT8Ih0Nbq3lVnYNbT8t+PbD6H2gojSePk/gu4WFDX5jU2809msZWcxbzjZcg/WUGK42J+2zEwNCR3DL19+3YUFxfjySefDMvzu9wuCG2YQW/MotvtDkjzbrvdQnq8TCaDYLfA7XZDLvf/c4Vgt0J0u0mxOAKhIdjgcjlbvd+bZtwuQYBLQ2t95HI64RJo5tntcsJpb3mNeJuv6HY54bTR1pnodsNpoa0zAHBYzF5d11a+omCyknuYOixWiC7aOnNabWTT6rQLcNlp68xpb32te6/hgMNKy7F2CU44bNT17oZgpsXhdrthN9HfV80Ges65xWCH2+0GoPBfwyTA5aKtd5vFAadAW++CzQm7te211rCzqIlvuXWO0+GG2078nSH+zjFtIxOl3CHaRwwGA1JSUvDipFnQqIIzlPy7/fORnDwYv5z4h6Dohwsp5CxycUtkkysWx3RxC+ApcOGcxdCQqjjIeYsB4vThEsx+cg2WnHk+aM9hMdrx6Ij50Ov1SE5ODtrztESDd3hww0KoE+JIWoLZihWT/hyW19EWkjuGjnTUSi2cEquGlsqYP4ZhGCbwcNuc6IfNYojx5hg6Gon2XUWGYZjLKTTRcj4ZD1ajnYtbohw2iyFGqmYx2vEmXzEa4CNohgkMXBUdOKxmB7S8sxjVsFkMMRqlFk4XLdmbCQ5SyFdkGIaJNKwmAfG8sxjVsFkMMQaHHJr4lmdVMwzDMJGDzjWWj6IDgI3nQkc9bBZDjFKhgV2QzmxoLm5hGIZh2sJqEhCX0HLbHCY6YLMYYpQKDQQHrelvpMHFLZGBlPMVGSac8O4iDauZ50IHiwULFmDUqFFITk5GcnIyxowZg02bNgEA6urq8Pzzz2PIkCGIi4tD79698cILL0Cv931ogOSackc6KqUGDgntLEoBqfRXlDJc3MI9FsOFzjUWqYqD4Q4jqrGaHejcIyncYUiSnj17Ys6cORg0aBBEUcRXX32F++67DydOnIAoiigrK8O7776L4cOH48KFC/jzn/+MsrIy/PDDDz49D5vFEKNUqAMynYVhGIZhogGbSeBq6CAxefLkJv9+6623sGDBAhw+fBhPPfUUVq5c2XjfgAED8NZbb+GPf/wjnE6nTyOR2SyGGM8xNJtFhmGYaMFT6MITXfzFYuJjaF8xGAxN/q3RaKDRaNp8jMvlwvfffw+z2YwxY8a0eE3DZBhfjCLAOYsBx+V2Qm9pfaC5UqGB0ynA7W59jqUoitDpK8mx1Otog9kDpaGvqyDP7DXUV8Htos3+NOlr4XTQ2hZZDXoIVtosZMFqgc1kaP/CNnAKAsy6n9eZP/mKbpcLpqoqUhyiKMJYQV8jxvKWNXzJVzSW0n9njGVV5Bnk5spauBy0ucyWmno4iTOZLfUm2Ay0tWozWmGuNZI0nHYHjJU6kobb5YbuYi1JAwDqiun9VMuL6EV95efpGpXFOrictLVaU2aEYKOt1foqc+Ps59awmdsucDHqrDDqaLn8NjNtlnogcNdq4KrRkr7ctR5D2KtXL6SkpDR+zZ49u9Xnzc7ORmJiIjQaDf785z9j9erVGD58eLPrampq8J///AfPPPOMz6+NzWKAqdSVIqNgX4v35dVZoVR4FkJbu4t6QxUOpa8hxWGzm7Fr3xKShiiK2Lrri1aNrbfzoA9u+gY2C+0PTsbuldDX0UxJ9uFNqC4rbHKbr824C47uRVnOSVIcJdkZKDpGy4GqKjiLvH3bSRr64hLk/LiWpGGtq8PJpctIGk67HemLPmv1fm/zFY+89yWcdtqHgROfrIC1zvfk70s5tWQdDMVlJI1z63aj+vQ5kkbOlkwU7MshaRQdysXpjRkkjfLTJUhfupekUXehCgc+3UrSMNcasfM92no/V1+Mha9tI3/4/WLGLliMtA8D3717ELXltPfVdZ9loDi3hqSx6/vTOH2k7QIgq0lAXFLru2JHtxTg6JYCUhw56dIqQiopKYFer2/8mjZtWqvXDhkyBJmZmThy5Aj+8pe/4LHHHsOZM2eaXGMwGDBp0iQMHz4cM2fO9DkemUhd9RKiYRj4i5NmQaPSBlw/r86K7gNTseCLKXj60feRnJQW8OcIJd6axUhGKsUtUq6E5uIWD1zgEn5SFXwU7Q9PXrsAz7/3S1wzvl/QnsNitOPREfMbj1lDSYN3+O3Xi6GKjydpOSwW/PDoE6TXcdddd2HAgAH45JNPAABGoxETJkxAfHw81q9fD63Wd3/DO4shRq32/JAEQVrtcxiGYWIBbqPjOzaTgDgucAkZbrcbdrtn59pgMODuu++GWq3G2rVr/TKKABe4hBylQgW5XMFFLkzAyHTQc/UYhmkfbqPjOy6nG4LdxRNcgsS0adMwceJE9O7dG0ajEUuXLsXu3buxZcuWRqNosViwZMkSGAyGxsKZzp07Q6FQeP08bBbDgFqlZbPIBBSpHkEzTCRSaCrl42gvsZo9ecS8sxgcqqqq8Oijj6K8vBwpKSkYNWoUtmzZgl/84hfYvXs3jhw5AgAYOHBgk8cVFRWhb9++Xj8Pm8UwoFbHwRHlx9BSGPMnlXxFKcP5ikykwbuLvtFQKc2tc4LD559/3up948ePJxdjNcA5i2FAKjuL0V7cwjAM4y+cu+gdjWaRZ0NHNWwWw4BKrYXAI/+YAMD5igwTenSuseEOIWqwmgQoVXKoNHyQGc2wWQwReXVWDBjZCwCgUcdJYmeRiQykmK/IMNEA7y62j9XM01ukgE9msbq6GllZWTCZTC3ebzQasXcvrflqLKBW8c5iuOF8xciG+ysykQ7vLnoHt82RBl6ZRafTiSeeeALdunXDtddei86dO2Pq1KmwWpsWaZw5cwa33357UAKVEiqVFoIjegtcpFDcwjAMEwh4d7FtrGYHt82RAF6ZxQ8//BDLly/Hv//9b2zYsAFTp07FokWLMHbsWFRWcs6Ur6jVcVHflJuLW8IP5ysyTHjh3cX2sfLOoiTwyix+8cUXmD59Ol5//XX88pe/xOzZs5Geng6z2YyxY8ciPz8/2HFKCqlUQzPhh/MVGYaJZDw5i1wJHe14ZRaLioowdmzTT1DDhw/HoUOH0LFjR9x88804fvx4UAKMNkprC7Ex47s2r1G3Uw1dr6vAynXvkuKw2c1Y8v0MkoYoili89O9wu10knVWfTofFpCNpbPr2v6itpDVp3vPjpygtOIksc7XfGifWr0DBEVpebu6+7cjesoakUZKdgdKVK0katfkFOPDePJKGuboGO2b+m6ThtNux8eVXSRoAsP7J1+G0CySNbVNnw1xVS9LY9++PUZtbRNI4Nn8pSg9mkjQOL96JEz/Q+gFmr0vH3o83kTSKDudi039WkDSqzpXjh6mt95PzBnOtEV/9aR5Jw+VwYdFv3obodjfepnON9fko+v8mLYHFaCfF8uajq1B+npYSNP+VLTibfpGk8e07+3FoQ16r91tNArTt7CxuWHwcm748QYrjyBbetAomMtGLjo19+vTBf//7Xzz44IPN7jOZTLj33nuRkZGBv//975g+fTpcLpq5CBcNw8BfnDQLGpV/8xNFUYTgtDd7/KXV0JnZ23Em9wAe/m3rZs4uWKFRx/kVQ7A1so31Ph1DCzYL1FracPVAaDjsNijVGpy01Phd3OIUBMgVCsh9GJN0OW6XE263G0qV/0czotuN45ZSjOjXy28NAHBYrVDF0daIw2KBKp74s7lEw9/iFsFshTqB9loEkwXqROJaNVuhitdCJpP5reG02iFXKSFX/rzOzPIcDEv1/vfOYfOsVYWKsFadLricLqi0hLUqinBYBajjNX5rAIDdbIMmwb/35QZsRiu0SbQ10pJGQ5Nub6e6mPU2JKTQXksgNKwmAZp4FeRy/9eq3eqAQimHspV1tuiNHRBsTkx5d0KrGg67E6IIqLX+t9cx1Fnw5DULodfrkZyc7LeOX8/9k3f47deLA/Je+MOjT4TldbSFVzuLo0ePxpo1a1q8LzExEZs3b8Ydd9yBN954I5CxRSUymaxFo3gpnpzFto+hqSYvWBr+FLdQTV6gNFQa2h9wAFCq1SSjCAByhZJkFAEgy1UNhYb2xxcA2SgCIL85BkqDahQBkI1iQxzkdRanaWIUG8jRVXitodKqSUYRAORKBckoAp73RKpRBEA2igDIRrE1DV9zF6kmL1AacYlqklEEAE2cqlWjCHjXOkelUZKMIoA2Y2DoeGUWH374YRQVFaG2tuXjGbVajZUrV+KZZ55B7969AxqgVGjYVQQachajt8CFi1siA85XjC0S3MPCHQLTDlwZ3Rwb91mUBF6Zxd/+9rc4dOgQ0tLSWheSy7Fw4UIUFdFydWIBNTflDhuUfEWGYZjWaNhdZMPYFKvJwWZRAgRkgsuLL76Ir7/+OhBSMYGnKbc1YAO+Gd/gZtwMwwQDbqXTHKuZW+dIgYCYxf/973948sknIQi0SsRYQaXWQhRFOJ38/WJ8R8r9FXNFWsU7w0QCvLv4M54JLtw6J9oJ2GRvURRx+PBh7NmzB7W1tejUqRNuuukm3HrrrVCr+VPFpah/KoARHDaoVPTE71DBk1siBynnK/KYPyaa0bnGIlVxEIWmUq+ro6WM1SzwBBcJEDCzCKBx1J8oio3VgGlpaXjppZfw6quvQqkM6NNFLeqfKowFwYaE+JQwR+MbXNzCMAzTNg2GkeEJLlIhIMfQjWJyOX7xi1/gueeew5/+9Cf07t0bNTU1eOONNzBu3DjU18feztTlbXMAQP3TbmI0V0RHI1nmas5XZBgmZMT6cbQoirCZucBFCgRsq0+lUmHnzp3NJr0cOnQIzz//PI4ePYrf/va32L59O7kHWbRxadscAJDJ5FApNXBwRTTjI1LOV2QYKcG7i4Dd6oTbLfLOogQI2M7ibbfd1swoAsCYMWNw+PBhPPjgg9i9ezeWL18eqKeMarxpzM0wLSHlfEWmbRLcw3xqzM2EF39GAUoJq8lTxBmXxGYx2gmIWdRqtejUqVOr9yuVSixatAgpKSn48ssvA/GUUY9arYVdiJ5jaC5uYYKNv2P+GCbSiVXDaDP/ZBZ5ZzHqCYhZ7NGjB06dOtXmNUlJSbjttttw/PjxQDxlxFJrrER6/p52r1OrtK0eQ5vM9ThwZBUpDkGwYff+b0kaoihi++4v4Xa7Afhf3HJoyxLYrWZSLBm7V8Go87+hdpa5GuUZe1BXep4UR+HR/SjPO03SuHgmE+ePHyZp1ObnI3/bDpKGsbwCZ9b8SNKw6Q3IWrqMpOESBGR8vpikAQBH530Nl8NJ0jjx6QrYdAaSxqlv18NQStv9O7d+N2rOFpI0crZmovBADknj/NFzOL0xg6RRebYUx5btI2noy+qw/5MtJA2bwYId79LWu8vhwua3foD403uiN7TUe/GLmbtgNdNapy2bewB1lSaSxrrPMlCcW0PS2PX9aZw52rIZbthZ1MS33Trn6NZ8pG8rIMVxNqOM9HimbQJiFseOHYtTp05h9erVbV6nVCphMNDeiCOdeE0iunfo1e51bR1Da9Tx6NljMCkOpVKFXj2HkzRkMhn69hlFzjHt0e9KKIktgrr2HgxNXCJJY9hVoxCX3IGkkdK9JxLTOpM0kjp1Q0rXHj4/LtNR2XgEHZ+Whg59+5Di0CQnodOgQSQNVZwWXYYPJWnIlUp0u2oUSQMAetwwEnIF7S2t61VDoUqgzYfuMnIQtClJJI0OA3ohvnNHkkZav65I7dX6iY83pF7REZ36dyVpJHZOQfdh7b8ntoU2JR49r+lP0lDHa9D3Rtp6V6gUGHDzUMjkvq2zy4+jr7qlDzRxtN6Dw27oiUTifOhBV3dHh64JJI0+wzqja6+Wu3pYzQK0Cap250/3HJiGKwbQ1nvnK5JJj2faRiYGYIxIeno6xowZA61Wi7lz5+LZZ59tdo1Op8OgQYMQFxeH4uLIbLxrMBiQkpKCFyfNgkZFH9IOeKqhLy9wAYDVG95H1859MPaG3wTkeYJNtrE+qtvmSKES+lKzKEX4GNo7zPIcDEuN3t/FWKWh2CWWei8e21GIT17bhkXpzT1BoLEY7Xh0xHzo9XokJ4fWODZ4h99+vRiqeNqHTYfFgh8efSIsr6MtArKzeP3112POnDmwWCz461//iuHDh2PGjBnYsmULjhw5giVLluDWW29FXV0d7r777kA8ZatcvHgRf/zjH5GWloa4uDiMHDkSx44dC+pztkZLbXMa8Iz84wIXhmF8h4tcoo9YnB1tNdq5bY5ECFjrnFdeeQX9+vXD888/j7Nnz+LNN99scr8oiujevTtmzpwZqKdsRn19PW6++Wbcfvvt2LRpEzp37oxz586hQwfa0SOFlnYVAU+BixAlrXOivbgly+x/riPDRBoJ7mEwy2m5iEx4iLV2OlazA1oubpEEAR2p8sADD+Dee+/FqlWrsHHjRmRmZqKyshIpKSm444478MYbb+CKK64I5FM24e2330avXr2wePHPyfL9+vUL2vNRUKvjYDTVhTsMr4nmI2gAfAQd4fARNBNLxMooQKtJQDzvLEqCgM/fU6lUeOihh/DQQw8FWrpd1q5diwkTJuB3v/sd9uzZgyuuuAJ//etf8fTTT7d4vd1uh91ub/x3KItv1CotHFHUOodhGIahE0uzo208F1oyBHTcX7gpLCzEggULMGjQIGzZsgV/+ctf8MILL+Crr75q8frZs2cjJSWl8atXL1rFni94+ixGxzE0wzCRB+ctRi8ttdORIp650LSqbyYykJRZdLvduPbaazFr1ixcc801eOaZZ/D0009j4cKFLV4/bdo06PX6xq+SkpKQxapWxfFs6BDA+YqMFElwDwt3CAyRWJjuYjULXOAiESRlFrt3747hw5v2Fhw2bFirrXo0Gg2Sk5ObfIUKzzF05O8sRntxC8D5igzDRC5SNoxWs4Ont0gESZnFm2++Gbm5uU1uy8vLQ58+tObF/tBW2xwguqqho724hWEYJhKR+nG01WjnamiJICmz+NJLL+Hw4cOYNWsW8vPzsXTpUnz66aeYMmVKWOJprW0OAKhU0WMWGSaY5IqR2aQ/GuC8xehHysfRVrODj6ElgqTM4vXXX4/Vq1fju+++w4gRI/Cf//wH8+bNwyOPPBLu0JqhVsfB6RTgdrvCHYpk4XzF6IHb5vgO5y1KCykaRpuZC1yCzezZs3H99dcjKSkJXbp0wf3339/shLWiogJ/+tOf0K1bNyQkJODaa6/FypUrfXoeSZlFALjnnnuQnZ0Nm82GnJycVtvmBAurYEZJTWG712nUcQDQ4u6iw2HH+eJsUhxutwsF50+QNACgqvgkWeNC7nG4nE6SRmlBNgSbxefHXZqvWFlwFlaDnhRHbcl5GGuqSBqGqnLUX/RuN621fEVLXR1q8s6R4hDMZlSeOkXScAkCyk5kkjREtxslB46TNAAEROPikSy4HLS1WnEiB4LJ97V6KTVnCmCpoeULV+WVoa6Y9oFJV1qLqrwykoa51oiLWedJGoLFjqLDeSQNt9OF/L2nSRoAkLeL9jsDAOf2nIbodjf+25/pLln7LsBuc5DiOJt+EcZ6WqFl4akq1JQZW7zPahIQl6RpV+Nifh0uFtD6DtdWmEiPj1b27NmDKVOm4PDhw9i2bRscDgfuvvtumM3mxmseffRR5ObmYu3atcjOzsZvfvMbPPjggzhxwnuPIDmzGG7MNiMqde1XVat+mj3d0sg/u2BBWUU+KQ6Xy4mS0jMkjWxjPWrL8+B20XY/y87nwOmwt39hG1SWnIPdD7N4KXUl52E16Ega+opSmOtqSBrGmiroK2l/gK119dATZ6zbDUbUFbT/waYtHFYbas6eJWmILjcqMmhrFQAqMs7AZRdIGlUn8+Aw09ZZbW4R7LqW/3h6S31hCaw1Oq+ube0ouq64GroS2lrVldWhOr+cpGGs0qMih7ZrZqk3oezkeZKGYLHjQjrxfdXhQtGhs02Mnj+cP5IHx2VGz9f8xZyjpTDrae+r+Scroas2t39hGxSfrUb1xZZ7FFu93FksP1+PskLah6PWYpA6mzdvxuOPP44rr7wSV111Fb788ksUFxcjIyOj8ZqDBw/i+eefxw033ID+/fvjjTfeQGpqapNr2kMmiqIYjBcQjTQMA39x0ixofjJz/pJXZ20zZ9HpcuCDhU/hsd/PQqe0yGzMmm2sj+rilixzNVdCRwE8vYWGWZ6DYanR+3vK/EzDKECpNOt+ePAH+Oe3v8XQ64M3ua0Bi9GOR0fMh16vD2lnE+Bn7/DbrxdDFR9P0nJYLPjh0SdQUlLS5HVoNBpoNO3v0ubn52PQoEHIzs7GiBEjAAB333031Go1vv76a6SmpmLFihV46qmnkJWVhYEDB3oVV8AnuDDeoVSoIJcruMglSHC+IhNL5Ogq2DBKAClNd3E53RDsrpia4KKukUEdJyNpyKyex18+JGTGjBmYOXNmm491u92YOnUqbr755kajCAArVqzAQw89hLS0NCiVSsTHx2P16tVeG0WAzWJQaG9XsQE1V0QHFd5VZGKBBPcwmOU54Q6DCRBSMYxWsyclhPss+kdLO4vtMWXKFJw6dQr79+9vcvv06dOh0+mwfft2dOrUCWvWrMGDDz6Iffv2YeTIkV7Fw2YxjKjVcTwfmmGYgMC7i9JBCobRavzJLMbQzmIg8XVQyHPPPYf169dj79696Nnz5zVTUFCAjz76CKdOncKVV14JALjqqquwb98+zJ8/v9UJd5fDBS5hJJJ3FqM9X5GJDjhfMTBwGx3p4U+FdCTx884it84JJqIo4rnnnsPq1auxc+dO9OvXr8n9FounYE8ub2r3FAoF3D4UarFZDCMqtbbFamiGBucrMrEKN+mWFtE84cVqEqBUyaHS8AFmMJkyZQqWLFmCpUuXIikpCRUVFaioqIDV6jm1HDp0KAYOHIhnn30WR48eRUFBAebOnYtt27bh/vvv9/p52CyGEY06LmJ3FqMdKeQrMowv8O6iNInWCS9Ws8BH0CFgwYIF0Ov1GD9+PLp37974tXz5cgCASqXCxo0b0blzZ0yePBmjRo3C119/ja+++gq/+tWvvH4etvxhRK3inUWmdbi4hfEHzl2UJtGWv2gzCVzcEgK86X44aNAgnye2XA7vLAaYvDrvC1Y886Ejr8Al20hrjsowTHjg3UVpEo35i1azI6ba5kgdNotBwJu2OYCnGlqI0GroaC1u4XxFhuHcRSkSbYbRyjuLkoLNYhiJ5GroaIbzFZlYhncXpUs0GUZPziJXQksFNosBRhRF2L3cLVS3UQ1tt9Pm0wZKQyDOYwZAnukcqDgcNqtX+R1t4RTs5FnZLqcTTkfbM4zby1cU3W44rPQPGg5LAH42xFnKACAYafNpAcAeAA3BFIjvB/20wGGxkecPO6wCXA4nScMpOOGw0uZtu50u2M20tSqKImwG+s/GKgGNBsNo1tN//y1GO1wu2jqzWRxwOpq/J1pNArRe7iwKNicEG22tOgTa+zLTNmwWA0yd4QJ27fvGq2vVKi0cLews1usqsGHrx6Q4bDYzvl/7NkkDAFZ9+gbZHG38Zg4sJh1JY8fK+aitLCZppK/6BuW5p0gap7auRdGxAySNgsO7kbNzI0mj7PgJZC9bTtKozS/A0U8WkTTM1TXY9993/Xpsruj5eTrtArZNnUOKAwC2T50Dp51mbPZM/xDmqlqSxpF3F6P2bBFJI+uLlbh4+KTfj09wD8OmLzfh5I/ppDjObs3Eka93kTQuHMvHrnnrSRrV+RXYMGMZScNSb8L3z9HWu8vhwrdPfkQ28sueXQC7yX+zp3ONxawpq1BxQUeKY/G/duPc8XKSxsqPjiB9a0Gz2305ht6xLBs7lmWT4jixi/Y7x7SNTKRutUiIhmHgL06aBY1K65eGt6P+AOBM7gEczViPxx+e7ddzBYNobsbdkK8ohWPoWKiE5obcwaVhBCBXRkuTVMVBAIjYCukPX9qExBQtnpx5e0iez2K049ER86HX632afBIIGrzDw+99CXVcPElLsFqw9OXHw/I62oJ3FgOIL5XQQEPOYmQWuEQrUjCKDBMIOHdR2kR6/qKN+yxKCjaLAcbbXUXgp2poLnBhLiMWdhWZ0MGV0dIlkie8WE0ONosSgs1iGPE05aYXXTAMw7QE7y5Kn0id8GI1c+scKcFmMYyo1FqIogink5aUHyiiuRk391dkmNbh3UXpE2mG0TPBhVvnSAU2i2FE/VMRTSQdRUdrcQvA+YoM0xK8uyh9IjF/0WoWeIKLhGCzGEbU6jgA4PnQTCOcr8gEgwT3MN5dlDiRlr/IE1ykBZvFAOFLy5wG1CoNAHBFNMMwIYENo/SJhN1FURRhM3OBi5RgsxhGZDI5VEpNi425Ge/hfEWGaR8+jpY+kXIcbbc64XaLvLMoIdgshhm1Oi4ijqGjuRk3EP35igwTKnh3UdpEwnG01eQp2oxLYrMoFdgshhm1WhtRBS5MeIiVqS1MeOHdxdgg3O10bOafzCLvLEoGNothpqHXIsMwTKjg3cXYIFyGsWFnURPPrXOkApvFAHDpmL/yygLs3LfE68e2dAyt01dhw9YFpJhsdjNWrnuXpAEAaz6bAZfLSdLY9O1/YTHpSRq7Vi9AfVXzNz5f8hWPrV6CinNnSHGc2rYW548fJmkUHNmLnN2bSRrlWSdxctkKkkb9+QtI/2QRScNSV4f9775H0nDaBWx/+W2SBgBsf/ltOO20nqV7Z3wEc1UdSePo+1+h7twFkkbW4tUoS88maeSu3o6ibQeb3e7L7mLO1kwc+XoXKY7iYwXY9cE6kkZNUSU2zPiOpGGpN+H7Fz4jabgcLnz71HyIbjdJZ9lfFsJuop0o/fjaN9CV1rZ6vzf5i4v/vRt5J8pJcayafxTHthc0uc1qFqBNUEEul3mlsWNZNnYso633E7uLSI9n2kYm8viQRhqGgb84aRY0P/VA9IZLK6GdLgesViOSEjt69djVG95H1859MPaG3zTe5na7YTTVIiW5s28v4DJ0+iqkprR/tNnQjLulnEV9XQVSOtJyGQ11lUju2JWmUV+FpJROkMmbfr7JMld7na9orq+FNikFCqXS7zisBj2Uag1UWu/Xx+UIVgvcLhe0iUmNt/l6DO202+GwWBDXoYPfcbgcTtj0OiR06uS3huh2w1xdjcSuvv98c8ViDO/sec2G0gok96StM2NpJZJ60taZsawKid07Qybz7o9cS5jKqxHfJQ1yhf+fxS019VAnxkOp1fitYdMZIVfIoU5KaPF+szwHw1Lb/p7bTTY47Q4kpCW1eV1bOGwCbHoLkrqm+q3hdrpgqNQh9Yo0vzUAoK64Gh17095X6y5Uo2MfokYA4qgvrUFqj47N3hMvJ1VxEP0Te7Z4X02ZESlpcVBp/H9P1FWboYlTNal8Pra9AJ9M245F6c96pWHUeTZcklLj/I6jrsKEZ278FHq9HsnJyX7r+EODd3j4vS+hjosnaQlWC5a+/HhYXkdb+L9CmBZRKlReG0Wg4Ri66SdMuVxONooAvDKKDbRW3EI1igDIRhEAkjvQ8/kSOtD+0ABAXHIKWePyNxN/mnErNRooNf4bCQBQqJQkowgAMrncL6N4OVSjCIBsFAEgKQB5o4nd6b+78Z38/xDQgDa1fYOXo6to0zBqErXQJPr/wQgAVFo1VFpa7ppcqSAbRQBkgwaAbBQDFUeHnt797nryF1s2jJ16+P8hoIHUzs0/jFhNgk9tcygmsQEtT4sJKnwMHWa4wIUBuBk3E3oajqM5fzE2CGX+otXsgJaLWyQFm8Uwo1axWfQX7q/IMDTYMMYGoe6/aOW50JKDzWKYUavj4AhjNXRDvmK0wv0VGYYGG8bYIJSG0WYWEJdES5NhIgs2i0T8GfN3KZFwDB3NzbgZhqHDhjE2CJVh5J1F6cFmMcyoVXGwc5/FmIWbcTORAjfsjg1CYRitZt8KXJjIh81imFGrtHBEwLi/aIPzFRkm8CS4h/HuYgwQ7JGAVrODp7dIDDaLYSYSjqGjFc5XjG6GyHrjTHVVuMNgWoANo/QJ5khAq9HO1dASg81imFGFsRo621jP+YoMwzSB8xdji2AYRqvZwcfQEoPNIoFLx/z5i1odB6dTgNvtCkBETDThTzNuhgkFbBhjg2DlL9rMXOAiNdgsEqFUQgOenEUAfBTtA1LKV+TiFiZSYcMYGwTDMPo6wYWJfNgsBhi9oRpncg94fb1a7RlzdOnIP4vVgMzs7aQ4HE4B6cc3kDQAIGP3SohuN0kj88A6OOw0M3w6fRssRl3jv/3JVyw8uh+GqnJSHCXZx1FzoYCkUVlwFmU5J0ka+pISlBw+QtIw19SgYMcukoZgNuPseto6cztdOLVkHUkDAE59uw5up5OkcfaHrRCMZpJG/sa9MFfVkjQu7E6HrpD2x7v82ClUZeeRNKyn1ag4TFvvNUWVOL3pOEnDVK3H8e+9f19tCcFix+Evd5I03C43Dny6lfyeeOiL7XDaHSSNY9/tg6XeRNLIXpeO+tIaUsHL0a35KDzVNPfY12roU4dKcPpwid8xAMD5M9LZRIhEJGUWZ86cCZlM1uRr6NChIY1BLldApfJ+lqpa7bnWccnOolyugEZNG0Yuk8mg0Taf2dmAt824NfH02aEabSIgkxE1EiCT05arKi4OcgVtHLpKo4VCSTteUaq1UKiJs3JVKiiIs6HlSiXUCbR1JlcooElMJGkAIjQpVA1AnRQIjXjIFLR1pk6Ig1xJW2fqxDgo1MR1ptVAqSXOD9eokaQdRNpdVKqV5PnScpUS2mTqWpUjLrX190RvkMlliEulvxdpkwOhEQe5UkHS0CTGQanyrFV/C17iEtTQaJuud5tJ8KkaWhuvgiaOtt4vjyFWmD17Nq6//nokJSWhS5cuuP/++5Gbm9vitaIoYuLEiZDJZFizZo1PzyO57+6VV16J7dt/3pVTEt+0fSUpsSOSEjt6fb1SoYJcrmjSa1GrScCwIbTWBkqFCqOGj2/zGm+KW0bccDcpDgAYNvp2ssbAkfRWD71GjiZrdBs8nKyR1qsvAKCckLOY1K0bkrrRipPiUlPR66YbSRpKrRb9xt9G0pArlRg0mb5GBt9L1+g/YRxZo/dt15M1ul83gqzRecQgskaH/p40GzNykKOrwLBU39dc6hVpSL0ijRRHfGoChk+4hqSh1Khw1f209S6TyXDtgzeTNADgmt+OIWuMmHQdWWPw7c3XWaGpFP0Te3qtMfLm3k3+7XK6Idhd0PqwszjwKnqhZff+Hcga0ciePXswZcoUXH/99XA6nfjHP/6Bu+++G2fOnEFCQtMPR/PmzYPMz40byZlFpVKJbsQ/oqGG50N7j1TyFbkZ98+cqa7C8M78vYhkEtzDYJb7bxiZ6EDnGotUxUGfDeOlWM0CAHCfxRCxefPmJv/+8ssv0aVLF2RkZODWW29tvD0zMxNz587FsWPH0L17d5+fR1LH0ABw7tw59OjRA/3798cjjzyC4uLiVq+12+0wGAxNvryFOubvUsI9Hzra4P6K0mGIrHf7FzERARe8xAbUgher8SezyAUuJC73Jna73avH6fV6AEDHjj+fcFosFjz88MOYP3++35tpkjKLN954I7788kts3rwZCxYsQFFREW655RYYjcYWr589ezZSUlIav3r1Coz585VQ7yx6m6/IMAxzKTwSMDagFLz8vLMYe61z4mpFxNUQv2pFAECvXr2a+JPZs2e3+/xutxtTp07FzTffjBEjfk4xeOmllzB27Fjcd999fr82SR1DT5w4sfH/R40ahRtvvBF9+vTBihUr8NRTTzW7ftq0aXj55Zcb/20wGMJiGFVqbZNq6FDAzbgZhvEHz0jAHD6OljiegpeDPh9HW00ClCo5VBpJ2YuQU1JSguTk5MZ/a7woaJwyZQpOnTqF/fv3N962du1a7Ny5EydOnCDFI6mdxctJTU3F4MGDkZ+f3+L9Go0GycnJTb7CgUYdxzmLXiClfEWGiXb4ODo28PU42te2OUzLXO5N2jOLzz33HNavX49du3ahZ8+fDf7OnTtRUFCA1NRUKJXKxqLfBx54AOPHj/c6HkmbRZPJhIKCAr+SOUOJWhX6ncVoRSr5ilzc0hSeER1dcP5ibOBP/qKvbXMYGqIo4rnnnsPq1auxc+dO9OvXr8n9r732Gk6ePInMzMzGLwB4//33sXjxYq+fR1L7xK+88gomT56MPn36oKysDDNmzIBCocAf/vCHgD5PIMb8XYpnPjQXuDCxyRBZb+SKrReiMZEJV0jHBr5WSFvNDp/a5jA0pkyZgqVLl+LHH39EUlISKio8H+BSUlIQFxeHbt26tVjU0rt372bGsi0ktbNYWlqKP/zhDxgyZAgefPBBpKWl4fDhw+jcuXPAnytQldCApxpaCFE1dLaxnvMVGYYJCFzwEhv4UvBi5Z3FkLJgwQLo9XqMHz8e3bt3b/xavnx5QJ9HUjuLy5YtC3cIfqFWaWE00caDSR2p5CsyjNTggpfYwNuCF89c6NirhA4XoiiG5DGS2lmMVtRhqIaORqSQr8jFLa3DeYvRDecvxgbt5S9azQK0vLMoOdgsBhiHw46aWt+qx9QqbZPZ0C6XE1XVF0hxiKKIiqpCkgYAVJa2XEnuC9UXC+F2u0ka5qqLcDoEkoau4iIcNtpxv7GmCjaj983bW6JvnBrmmhqShmC2wFheTtJw2gXoS0pIGm6nE/VF50kaoiiiUwHtZwsANWfp67027zzcLtpa1RWVwmmnvR5jaSUEo5mkYa6qhbVWR9Kw1Rtgqmh/rbZV8GI32VB7nvZBwCk4UZVXRtIQRRHlZ2jrHQDKTtHza8tP0zUqz5bC7XSRNGoKKyBYvGv2DLRc8FJZrIOx/uf3VX+OoesqTairNPn0mMsx6XjDJZiwWfSR9opb6nTlyM0/4pOmJ2fx54VuMtcjO2ePX/E1IAhWZGRuaXKbP824T+xdA7eL9oZ08vAmCDbaH76qk4dgqa8jaVw4fgi6cv+mEjRw8XQmqs/TDHRNbi4qT2aTNOqLilB6NJ2kYaqsQNHuvSQNu9GIvE2b27+wDVyCAzk/riNpAMCZpRvIJi1vzQ7YdbQPA4VbDsBURjNHxfszUJdPMxQVGadRlX2OpFF9Jh9lR056dW1rhrG6oBxnt2aS4jCU1yNr9WGShlVvQfoS2vuqy+HCoc+3QyR++D3y1S7YzTRzc/z7gzBW6UkapzcdR52PRv5yw3hseyEKTv58YuJP65xTB0tw6iDNyBed5pOJYCIT/Tm8ligGgwEpKSl4cdIsaFTaFq8J5Ji/BvILM7Bz3zd45rF5AdW9nGgtbmnIV5TKMTS3zWmZhoponhMd3ZjlOQDAOYwSJ1XRcv7iO8/8iF6DO+EPr9wc0ngsRjseHTEfer0+5D2TG7zDU68vhlobT9ISbBZ8/tYTYXkdbcE7ixGAmptyt4sUjCLTNjwnWhpwD8bYwFPw0vykxmpycFNuCcJmMQLwNOW2+lWhxEQPXNziHVzoEv2wYYwdLjeMVjO3zpEibBYjAJVaC1EU4XTSk/xbw598RSbw8BF02/DuonTgHozSp6WCF88EF26dIzXYLEYA6p/yI4N9FB2t+Yp8BM0w0YmnByPvLkqZyw2jxSTwBBcJwmbRB4JR3AJ4chYBcK9FhvkJPoqWDmwYpc+lE15sfAwtSdgsRgBqlQYAeD40w4CPoqUKG0bpU2Asgc3MBS5ShM1iBCCTyaFSapo05g4k0ZqvyCP+YhveXZQOXPAifXSusXBYXXC7Rd5ZlCBsFiOEyxtzB5pozFcEpNMyh/sr+gbvLkoPNozSp8p4tee/8trwBsIEHDaLEYJareVeiwzDSBo2jNJGMHtGB2oSlGGOhAk0bBa9JFjFLQ009FpkGMbDEFlvPoqWIGwYpYvw0whDdZyyxYbdTPTCZjEIiKLvc0MvP4b2RyMQcTTTIM5A9Vfj8pY54YojKBoR1Hw9Yr4nAdBgogfuwShN7BY71PEaGETPqD82jNKBzWKAKS3LxcZtC31+nEqlbayGrtdV4Psf3ybFYbOZ8fXy6eR50N/OexEul5MUy8pPX4fFpCNpbP/4bdRfLCZpHPz2E5TlnCRpnFi3AvmHdpM08rdsxemVq0kapUfTcWzR5ySN2vx87H/3PZKGuboG26fPIGk47QI2vPhyq/d7u7v44yP/B6ed1th+47MzYamuI2nsfn0eas8WkTSOvv8VSvYfJ2lkf7MWeT/uIGnkb9yLzM9+IGmUHc3Gobc/a3a7Ly11qvLK8N2fF5DisNSb8Nlv3yFpuBwufDzxP+QPN5/cNwt2Ey3t6JvHP0R9SQ1JY/X/fYXiYwUkje3/XY3TmzxrVTDZoE7wdPdoqWF3a6z99BjWLcogxXFoQx7p8UzbyMRI2uYIMw3DwF+cNAuanxplN+DLMbTL5YRC4VvOxoatC5AQn4Lx4x72W6OlOM5YjCSzGKg4fNW4fGfR7XJCTowjIBpuN2QyGWQymdePaRjzd2mBi9vlglyhoMXickOuoH3eC0gcTifkSurPpvU4ckXPh4ThndsuEHI7XZArqa8lQjQC8LMV3W7Ax7XaogYAmdz/WERRhOgWW3w9ZnkOAGBYavvvURHzs5GSRoDX2ekNGdi3cDP+vO71xvtTFQfRP7Fn2xqiCFEE5HL/16rZYMNjIz+GXq9HcnKy3zr+0OAdnnp9MdTaeJKWYLPg87eeCMvraAveWQwC/pirywtcqAYtmjVaaplDNXkB05DL/frje3klNNWgeTTov74BiYNoFNuLw9vKaOofzojSCMDPVubnWm2mQTCKACCTyVp9Pb7kL0bMz0ZKGgFeZ3aLDeqEphstOtfYdncXZTIZySg2aDDBg82iFwS7uAX4qcCFq6EbkUrLHCYwcLGLdOGCF+kgmO2Nx9CXw/mL0Q2bxQhBrY6DI8DV0NR8RYaJNNgwShM2jNJAMNuhuWxnEfAtf5GJTNgsRgjcZ5Fh2qbhOJoNozRhwxj9CBZbqzuLbBijGzaLEYJaFQc791nkEX9Mm7BhlDZsGKMbwexpndMabBijFzaLEYJapYUjiOP+ognOV2TagkcBShs2jNGL3Wxr8Rj6UtgwRidsFtshFMUtQOCPoTlfMTJoaJvDBBYueJE2bBijk7YKXC6lwTAy0QObxQhBxdXQkuXytjlM4GDDKF3YMEYf9naOoS/Fm5Y6TOTAZjFCUKvj4HQKcLtd4Q4lbHC+IuMLnL8ofdgwRhdCC30W24MNY3TAZjFCUP80MSYQu4vZxnqyRrjgfEXGF9gwSh82jNGDt8fQDXD+YvTAZjFCUKvjAABCgIpcOF+RiRW44EX6sGGMDlrrs9gWbBijAzaLbeBPcUt1TTEOpa/x+bnUas8vmMNhg8FYi137v/VZ41IEmwU7V31M0gCAbcvnwe2iHY3v/vFT2Cwmksax1UtgqKb9ocjesgY15/NJGnn7d6D01AmSRunRdBTt2UvSqDqTg7PrN5A0DBcv4uR3y0gaVp0O6Ys+I2k47QIOfvA/ksYQWW9seGMenHaBpHPkvS9hrdOTNDI/+wG68xdJGjnfb0bVyVySRuHWAyjed4ykcfFwFs6t303SqDlTgNNLaWvVWFqJ3I9PAvDfMFr1ZmyYQVvvLocLa/7vq8aZ2f6y7vVvYTfTNga2zlkFfTntFGn/J1tQeZZm0o4t24eiw561Klh821ls4OAGLTI3FZMM4+lDJX4/lmkf+lBXpgnJyZ0xoN+1Pj9OqVBBLlfALliRktIFQwfdRIpDqdZi2Og7SBoAcOWNE8jzg4decxvUmrg2r8kyV7d5BN332jGIT+lIiuOKK69BYlpnkka3wcOhbOe1tEfHAQPgdjlJGim9ekGTnETSiOuYhp433kDS0CQmot9tt5E0lBo1Bt39C5IGAHS+ewzyDDoM7+x/KkP/CeOgSaF9X3vfdj0Su3YiafS4YRS0qckkjS4jB5PnB3cY1AfJvWinFMm9ukEZ57uBuJT4zh3Q5/YbkODuB7M8Bzm6CgxL9S0ubXI8rvr1jaQ4FCoFRv/+FvK87Kt/O8bnHbjLGTFpNBI70dbI4NtHIuUK2vtqv5uGQJsUB5fDBafd4dfruuKqfgD6AcjxO45eg2m/c0zbsFkMMBp1HLp08u9YrGE+tFKhQveuA/zSaMhXlMvl6N5nqF8al9Kj7zCyRrfeQ8ganfr49/24lI49+5A1krt0J2vEp9HenAFAk5QITVIiSUMVp0XH/v1JGnKlEp0GDyJpAEDnYfS1Onr4bcgVi3Gmuspvw9j5yoHkODoOoq+zlD49yBqJ3WkfjAAgPi2VrKFOSoA6KYGkodCokTakHwDPkbRZ7rupkMlk6Hl1P1IcANDrWtrvDAD0uoau0WMkfZ11GUxfZ2l9Pb9rVr0ZALyuhr6UlO4dGv+/0FSK/ok9fdZITqN9iGfaho+hI4hAzYfmfEUmVuGCl9ggwT2M8xcjDMFsBwC/jqEb4PzFyIXNYgSh5l6LkiLTUck9FsMAG8bYgQ1j5GBvNIu043Vu2O0bs2fPxvXXX4+kpCR06dIF999/P3Jzm+Y722w2TJkyBWlpaUhMTMQDDzyAykrfBkawWWyFUE1uuRSVWhuwauhoor18RYbxFa6Qlj5cIR1ZCBYb5EoFlGp6dhs37PaePXv2YMqUKTh8+DC2bdsGh8OBu+++G2azufGal156CevWrcP333+PPXv2oKysDL/5zW98eh7OWYwgqDuL0dxfkWECjWckYDGp4IWJbBryF/0peGECi6dtDq2Q6XL8zV+MJTZv3tzk319++SW6dOmCjIwM3HrrrdDr9fj888+xdOlS3HGHp+h18eLFGDZsGA4fPoybbvKumJZ3FiMIjTqOvLPI+YoM0xQ+jpY2vMMYGXgactOOoC8l1vMXDQZDky+73e7V4/R6T+uvjh09hZQZGRlwOBy46667Gq8ZOnQoevfujUOHDnkdD+8sRhCe+dD0Apdogkf8McFkiKw3uUKaiXx4hzH8eEb9BXZnUecai1TFwYBqBpO4KgEaNa1dlULw9Irt1atpGtyMGTMwc+bMNh/rdrsxdepU3HzzzRgxYgQAoKKiAmq1GqmpqU2u7dq1KyoqvP+AxWaxBfLrrVApA/cJyVvU6jjY7eb2L5QYnK/IBBM2jLEBG8bwYvdjeou3xOJxdElJCZKTf+6jqdG0b8SnTJmCU6dOYf/+/QGPh4+hWyHUxS0ALWeR8xUZpnW4Qjo2aDiSZkKPYLL51WOxPWL1ODo5ObnJV3tm8bnnnsP69euxa9cu9Oz5s7Hu1q0bBEGATqdrcn1lZSW6dfP+QxWbxQhCTayG5nxFhmkdrpCODbgHY3jwd9SfN3A7ndYRRRHPPfccVq9ejZ07d6Jfv6aN50ePHg2VSoUdO3Y03pabm4vi4mKMGTPG6+fhY+gIQq3SwhFDfRY5X5EJNVwhHTvwcXRosZuDZxaBhnY6B2PuOLo9pkyZgqVLl+LHH39EUlJSYx5iSkoK4uLikJKSgqeeegovv/wyOnbsiOTkZDz//PMYM2aM15XQgIR3FufMmQOZTIapU6eG9HlNZh0Kz2f59Vj1T9XQdrsFeflHSXG4nE7kZu4laQBATsYuiKJI0sjL2g+nQ2jxPm/zFS9kHoXdbCLFcfFMJsz1tSSNyoKz0FVcJGnoLhSj9lw+ScNUWYWKk9kkDZvegIvHMkgaTrsdFw7QEtBFtxuFu/eQNACgcPceiG63V9e2dhx9fsdhOO0tr1VvKT14AjadkaRRcSIHpnLah6nas0WoLywhaegvlKH6NG2tmqtqUX7sFEnDbjSjZJ/3a7WlCmmXw4lTG46R4hBFEVlrjpA0ACB7XTrcThdJI2drJuxm2uZCwb4zMFXrSRrFGQWoK66GYLZBHe9fzmJFTikqz3p3zNzacXTFBZ1fzx3tLFiwAHq9HuPHj0f37t0bv5YvX954zfvvv4977rkHDzzwAG699VZ069YNq1at8ul5JGkW09PT8cknn2DUqFEhf26Hww6zRefXY9U/VUO7XE4YzXVePy7bWN/sCNrtdsFYT9+5M9RXks2iSVcNt8tJ0rDo6uBqxXB6i1Wvg8NOe3MVzCYIRNNqNxphN9E0HFYrbAYDScMl2GGp9X6dtYTb4YC5irbO3C4XzD5OE2gJc2Ul3K72zWJb+Yumihq4BAcpDkt1PZxW2jqz1ungMNM6I9h0BghGWsGcYDTDpqOtM4fZCmsdzZC4bHaYq31bq5cbRqfghP4ibb2LLjd0F2u9/lDSGrrSWriIZtFQXg+njbZWjdUGCBba+6q51gibwQrBYocm0T+zaNWbYdG1v1bbyl+0GGmvI1oRRbHFr8cff7zxGq1Wi/nz56Ourg5msxmrVq3yKV8RAGQi1QlEGCaTCddeey0+/vhjvPnmm7j66qsxb948rx5rMBiQkpKC557+BBp16IeSF5eewdrNH+K5/7fQp8e1ZBajAalPbuFxf5FNrlgMAHwkLXHM8hwA4CPpILP06Y/Rb8wQjHnyzqA/V6qi+XG0xWjHoyPmQ6/XN6kiDgWB9A52wYqPFj0bltfRFpLbWZwyZQomTZrUpAFla9jt9maNL8OJWuUpcJGYf28RqRtFJvLhCunYgJt2hwbBHPg+i63B4wBDj6TM4rJly3D8+HHMnj3bq+tnz56NlJSUxq/Lm2CGGpVaC1F0w+n0fjudW+YwjP+wYYwN2DAGH7sleH0WW4MNY+iQjFksKSnBiy++iG+//RZarXcLdtq0adDr9Y1fJSW0ZHAqapUnbl97LUbjETTDRApsGGMD7sEYXART6HYWgdjtvxguJGMWMzIyUFVVhWuvvRZKpRJKpRJ79uzBhx9+CKVSCZereTKxRqNp1vgynKh/ynWgzoeOdLhlDhNpsGGMDbgHY/AQLPagNOVuCzaMoUMyZvHOO+9EdnY2MjMzG7+uu+46PPLII8jMzIRCQZvXGArUKs8vWizMh+Z8RSbSYMMYO7BhDCyiKP7UlDv0Y3IbDON5c1nInzuWkIxZTEpKwogRI5p8JSQkIC0trXGgdqQjk8mhUmq8bszN+YqRzdWqrsgpY+MRTbBhlD6cvxh4HFYBolsM6TH0pfCEl+AjGbMoFRoac3sL5ysyTGBhwyh92DAGFsFiB4CQF7gwoUPS4/52794d7hB8Rq3W+lzgEk1wviITDQyR9UauWIwz1VXch1GiJLiHNfZgZGgIZo9ZDNfOIgDoXTcCWB2255c6vLMYYXh6LUo7Z5HzFZlogHcYpQ8XvAQG4aexg+o4dZgjYYIFm8UIw9tjaM5XZJjgw4YxNmDDSMNutkEdr4FMzpZCqvBPNsC43W6YzTq/H69SaWEXLDCa2p9h2l6+oklf63cckaZh0deT57FaDXq4HLRZqnazCQ4bLU3AYbVBMNNm9roEATY9beKQ2+WCpY42KxcAzDU1dI3qyNXw1TBaaurhJs79tekMcNpps24FoxkOC+2UwmGxwW6gzTF3CQ7Y6qlr1Q2Lj7OhW8Jc2fy9yNf8RX05/YN6IDQMFTrytC9TjQEuh5OkYak3wVJvJh1B24xW2IzEtWqNzdnQoYLNYoCpqCrAoWM/+v14tUoLo6kOu/cvJcVht5qwc9XHJA0A2LLsPbhb6FHpC7vXfAKLSUca8Xds5TfQV9JaI2RvXYPqonMkjXMHdqL09HGSRsmRIyjavYekUXn6DPI2biRp6C4U49SKH0galto6ZHy2mKThtAs49OH/SBoAcOiD/5ENVsbni2GpbW5KfDGMJxevhq6I1vft7A9bUZV5lqRRuPUASvZlkDQuHs5C/sa9JI3q0/k4/d0GkoahpBwnFn1P0rDpjDgyt+W16q1hdDlc+PG1r8kfXNdPXwq7ifahc/u7a6C7SPsgfuDTrag4Q1urx1ccwMWs86QeizlbTiBnywlSHOfT80iPZ9pGJsbCIGIvCeQwcH/ZtnsxRFHE3bc/2eZ12cb6qKuEjsV50JmOSgDAsB6x9bqlSq5YDABc9CJRGgpehqVG13truDn+/QFkrjyMJ5f9LWwx2E02zB37GvR6fcgHbATSO9gFKz5a9GxYXkdb8M5ihKFWtV8NHY1GMVa5WtU13CEwAYRzGKUNt9TxD8FsD2slNBN82CxGGGp1HBwSrIaO9ZY53JxbOrBhlDZsGH1HMNu5x6LEYbMYYUi5z2KsHUE3wLuL0oMNo7Rhw+gbgsXGO4sSh81ihKFWxcHexs4it8xhmMiADaO0aTCMTPsIZjupwIWJfNgsRhhqlRaOdvoscr5idMJH0dKDDaO04abd3mE32/gYWuKwWYwwpHgMHYtV0JfDR9HShQ2jtGHD2D5c4CJ92CxGGCovqqGZ6IV3F6UJG0Zpw4axbex8DC152CxGGGp1HJxOAW5380bY3DInuuHdRWnDhlH6sGFsGU+BCx9DSxk2ixGGWuX5hZPK7mKst8xhYgs2jNKFK6Rbh4+hpQ+bxQhD/VP3d6GdIpdoItbzFS/lalVXPoqWOGwYpQsbxpbhPovSh81ihKFWe37hHJftLHLLHIaJHtgwShc2jM0RLLyzKHXYLAaYi+V52LLzc78fr1SoIJPJsW1384H3vuQr2q0mrPp0ut9xNLD8o1fhdjXPn/SFze//C1aDjqSxe9F7qL9YTNI4svwLlJ89RdLI2rgSBUf3kTRqDx3CniXLSBplx08g85tvSRp1BYU49L/5JA1LbS12vTmLpOG0C9jy2j9IGgCw5f+mwWm3kzR2vTkL5uoaksahDz9CXWEhyTAeX7gcFw9nkeI4s2Iz8jfuJWkUbT+EU0vWkTQqjp9B+v+WkDR0RaXY9y/aWrXpjNg2dTZJw+10YuMzMxDvHALAf8P41Z/mwW6inR59/8JnqC+lrdWN/16O0swiksbuDzfAaXeQdhYzlu1HxvL9pDjydmeTHs+0jUwURTHcQUQKgRgGLopu2OwWxGkT/Y5j/md/wV3jn8CQgTc03uZPcYvVbEBcAm0QOUWjIV+xd5oW2kRaHDaTgaxhN5ug0mohVyj91nDYrJArVVAo/ddwCgKyhAqM6NfLbw2XwwmXIECdEO+3huh2QzCZoCEOq7fqdIhLTQ27ht1gIL+WgGgYjVDFJ0Cu8HwWzxU9H3KGd/Y+HUMwmqHQaqBQEdaqxQaZXAal1v8dH6ddgOhyQRXv3/sh4DFYDqsdmqQEvzVEUYRdb4I2NclvDQCw1ukR1zGFpGGrN0DbwbNGzPIcAMCwVN/em021RiSm0V6Luc6EhI7+/50BAKveDE2CFnKlwm8N3cVafDzxP/jrxulI7Znml4Zg8XzIo1RUm+tM+GD8G9Dr9Ugm/g77SiC8QwN2wYqPFj0bltfRFryzGGBkMjnJKAKevEW5TEaOhWoUA6ExuE8XsskDEBANTUIiySgCgEobRzKKAKBUqzE6sTcpd1GhUpKMIgDI5HKyMQJANnmB0gjEawmIRlJSo1EE/DuSViclkIwiAKjitSSjCABKjZpkFAFArlSSjCIAyGQyslEEQDaKABqNIuD/lBeqUQRANooAEJeSQDKKgOdnA4B0DK2O15Bb7yjVtN8Xpm3YLEYg6st6LXLLHOnCxS6xAecwSpdY78FoN/+0K8gFLpKGzWIEolJro74amlvmtA/3XYwt2DBKm1g1jILFBrlSwTt7EofNYgRy+c5itMItc9qHW+nEFkNkvTFE1psNo8SI5QppT9scroSWOmwWIxCNOq5xZ5Fb5sQGbBhjCzaM0iNWDaOnITcfQUsdNosRiGc+tLXx35yvKG34ODo2aTCMbBqlQywaRs+oP95ZDCd79+7F5MmT0aNHD8hkMqxZs6bZNTk5Obj33nuRkpKChIQEXH/99Sgu9r4dHZvFCEStjoMgWNu/MELJMlfzEbSP8HF0bMJ5jNIj1gyj3WTj6S1hxmw246qrrsL8+S33Ii0oKMC4ceMwdOhQ7N69GydPnsT06dOh1Xr/c+OM1AhErdLCaKoNdxhMGMgpq8KwHmy0Y4khst7IFYtxprrKp16MTOSS4B7W2INR6ghmO7ntDUNj4sSJmDhxYqv3v/766/jVr36Fd955p/G2AQMG+PQcvLMYgah/qobmfMXYgo+jYxfeYZQesdJSh0f9BQeDwdDky+7ndCq3240NGzZg8ODBmDBhArp06YIbb7yxxaPqtmCzGIGoVdrG2dDRlq/ILXNo8HF07MKGUZpI3TDazWwWG1CU1UJxsYb2VeY5VezVqxdSUlIav2bP9m9UZVVVFUwmE+bMmYNf/vKX2Lp1K37961/jN7/5Dfbs2eO1Dh9DRyDqS6qhoxHOV6TDx9GxCR9JS4uG4+gcXYXPIwGjBcFsgzaZNk2KaU5JSUmTcX8ajX+G3O12AwDuu+8+vPTSSwCAq6++GgcPHsTChQtx2223eaXDO4sBpq6+HJnZ20kaWk0i1GpawrBDsOHwtu9IGgBwcNPXjYvNX06sWw7BaiFpnNq6Fua6GpJG3v7tqL/offVXS5zPOITKc7RcpLKckyjOSm/1/obj6LZ2GGvzC1C4azcpDmNFBc6u30DSsBuNyF7xPUnD5XDgxFffkDQA4MTXS+ASBJJG9vLvYTcaSRpn12+AscL/3aQhst6o3ZWO9MMZpDhK9h9H+bHTJI3KzLO4sLv1teoN9QXFOLd+N0nDXFmL09/R1qpgsiDzsx9IGm6nExkffwfRh/fElgpeds1b1zgP2V8OfLoVphoDSePYsn2oKaTtfCq1KiR2oo0vLNh3Bvn7zpA0Lp46T3p8pJGcnNzky1+z2KlTJyiVSgwfPrzJ7cOGDfOpGpp3FgOMVpOADqndSRr9+14Fc1pv0hG0XKFE5x79SXEAQJeeAyGXe/eZorUj6I69+kGhVJHiSO3RE6o42qfX5C49oEmkvaklde4KlZY2Kze+QxrcTkeb11yt6opMR2WrO4za1BS4nU5SHOqERKT06kXSUGo06NCvH0lDrlAgbdBAkgYApA0cALmKts469OsLpQ8Vgi2R0qsX1Am0ub2DeoxARaqdtMOY2K0T5Gra9yO+Uwco42nfD01qMlJ6094TVYnx6NCfuFa1anQc0pekIVcq0WlYf8i8fE9s4PIdxm7De0Gpof1sugy5glyF3Kl/N8Sl0tbqr/75EOnxAJDUrQN+GjHtN4kd6XPdpYharcb111+P3NzcJrfn5eWhT58+XuvIRFEUAx1ctGIwGJCSkoLnnv4EGjXNEFCJxnnQ3DIn8GQ6KgGAj6RjlFzR88mfj6SlQUOFtFSPpMOJ3WTD3LGvQa/XNzm+DQUN3uHFSbOgUdEMvN1hwwcb/uHT6zCZTMjPzwcAXHPNNXjvvfdw++23o2PHjujduzdWr16Nhx56CPPnz8ftt9+OzZs3Y+rUqdi9ezfGjRvn1XPwMXQEwlXQTAPeHEkz0oWLXqRFw5E0wwSSY8eO4ZprrsE111wDAHj55ZdxzTXX4J///CcA4Ne//jUWLlyId955ByNHjsRnn32GlStXem0UAT6GjliicVeRCQ7tHUkz0qah6IWRBp6WOjm8u8gEjPHjx6O9Q+Inn3wSTz75pN/PwTuLTMDgI+jgwTuMsQ3PkpYeUm+pw0gLNosRBh9BM63BhpFhwygNYm0kIBP9sFmMQPgImmkNNoyxC+cvSgvOX2SiCTaLTEDgI+jQwYYxdmHDKC1iZSQgE/2wWYwg+Aia8RY2jLELG0bpwYaRiXTYLEYYfATNeAsbxtiFDaN04PxFJhqQlFlcsGABRo0a1TgeZ8yYMdi0aVO4w5I8fAQdPtgwxi5sGKUDG0Ym0pGUWezZsyfmzJmDjIwMHDt2DHfccQfuu+8+nD5Nm48aCvgImvEXNoyxS4NhZKIfLnhhIhlJmcXJkyfjV7/6FQYNGoTBgwfjrbfeQmJiIg4fPhyyGGw2M8orCvx6bMMRtEOwo+w8bai62+1GacFJkgYAFJ/LbLXZp7dH0GVns+F20eYYV+afhVOwkzRqLhTCZjKQNHTlpTDX1ZA0jLVV0FeWkTSsBh3qSi80/tsfw+iwWFCTd44Uh8vhROUp+oex8iz6Wg2ERuWpU3C7XCSNmtw8OKxWkkb9+fOw6nReXdtaD0ZDSQVMFbS1aq6qhf4Cba3adEbU5p0naThtdlRl55E03C43yjMCsFaPnQqAxulW31e93V0sPlYAp0B7Xy07VQybwULSqDpXDmOVnqRRX1KD+hLaWjVV02Jg2kZSZvFSXC4Xli1bBrPZjDFjxrR4jd1uh8FgaPJFxWSuR0nZWZKG3WZGcV4mScPpsKPwzFGShiiKKDh1qM3O8N4cQZecPAaHzUaK5eKZTFj1OpJGZf4ZstGruVAAXcVFkoaurAR1JedJGsbqSlQVNF1nvhpGa70OFVlZpDgcFjMuHssgaTjtAkoO0T/QlRw8BJcgkDQuHjsOwWQiaVRkZ8NaV0fSqM7Jhami0uvrWzKMtXlF0BWWkuLQXyhD7dlCkoa5ogZVWbkkDWudHuXpNJPmsFhRuv84ScPtdOL8ziMQ3W6STvGedDitzT/8+nIcnb/vNCx1tLV64eg5GCp0JI2LWedRd4F2qlGZexGVubT3VarZZNpGJrY3IybKyM7OxpgxY2Cz2ZCYmIilS5fiV7/6VYvXzpw5E//617+a3f7c059Ao44LdqiNNBxBR2NxC+crRiaZDo/R4PGAsUWuWIzhnflnHu2Y5TkAwCMBfcBusmHu2Neg1+uRnJwc0uc2GAxISUnBi5NmQaPSkrTsDhs+2PCPsLyOtpDczuKQIUOQmZmJI0eO4C9/+Qsee+wxnDnT8pHutGnToNfrG79KSkpCHO3PRKNRZCIXzmOMXbjgJfrhghcm0pCcWVSr1Rg4cCBGjx6N2bNn46qrrsIHH3zQ4rUajaaxcrrhi/Ee3lWMbNgwxh5cIS0d2DAykYTkzOLluN1u2O20wohgEo1V0LyrGD2wYYw92DBKBzaMTKQgKbM4bdo07N27F+fPn0d2djamTZuG3bt345FHHgl3aG0SbUfQAO8qRhNsGGMPNozSgVvqMJGApMxiVVUVHn30UQwZMgR33nkn0tPTsWXLFvziF78Id2gME1bYMMYebBilA8+QZsKNMtwBBJLPP/883CH4BB9BM6GkwTBmlnGldKwwRNYbuWIxzlRXcZV0lOMxjDlcIc2EBUntLEYjfATNhBreZYwteIdRWvAOIxMO2CwyXsO7itKBDWNswYZRGnDBCxMu2CyGiWg8ggZ4V1FKsGGMLXiOtDRgw8iEAzaLYSQaj6AZacGGMbZobY40E12wYWRCDZvFIOBw0ubTAoDTERkaDsHTo5Iy2s9JnNcbSA3qTFeX0wm3y0XScLtccDmdJA3R7Sb/fEVRhFMQyIbRGYA+pk7i7PBIi4M6RdUlCHC7iGvV4YS7hXXmi2F0O11wCQ5SHKLbDZc9AL+/tgD8fFuYxxytGhpLfwA0w+iwCeS16hScAVirLrgcxPdVJ+3xTNuwWQwwpWW52Lbrizavae8IWldbjk3fvkOKw26zYOUnr5M0RLcb33/8d7hcNGOz+f2ZsBp0JI3dn72H+ovFJI2j3y9Gee4pksbJTStRmL6fpJF3YAfO7FhP0ijJPo6M1d+SNGqLC7Hvy/8B8H+H0VJbi13/eYsUh9MuYMtr/yBpAMCWv/8DTqIp2fXmLFhq60gah/83H3UFhSSNE98sQfmJEySNs+vWoXDX7lbv98YwFm0/hFPf0tZqecZppH9EW6u6wlLs+9fHJA2bzoitU2eTNNxOJzY+M4P8oXPzc/+BYLaSNHb/Yx7cJR0B+G8YN/17BUpPFJHi2PvRRpzdnkXSyFi+HxnL9pE08vbQ3tuZtpGJ1I8VEqJhGPhzT38CjTouaM+TbayPqiNoyq4iE31kOri1TiyQK3o+fHFLnejHLOeWOnaTDXPHvga9Xh/y0b0N3uHFSbOgUWlJWnaHDR9s+EdYXkdb8M5iiIm2whaugI49OI8xNuAKaWnB+YtMMGGzGAaiaVcR4AroWIQNY2zAhlEacMELE2zYLDKtwruKsQ0bxtiADaM0YMPIBBM2iyEk2nIVAd5VjHXYMMYGbBilQYNhZJhAw2aRaREuamEaYMMYG7BhlAaeGdK8u8gEFjaLISKadhX5+Jm5nKtVXXG1qityyqrYNEoYNozSgQ0jE0jYLDItwruKTEvwLqP0YcMY/XD+IhNo2CyGgGhql8O7ikx7sGGUPmwYox82jEwgYbMYIqLlCBrgXUWmfdgwSh82jNEPF7zEBnv37sXkyZPRo0cPyGQyrFmzpvE+h8OBv//97xg5ciQSEhLQo0cPPProoygrK/PpOdgsBplo21Vko8h4CxtG6cOGMfrhghfpYzabcdVVV2H+/PnN7rNYLDh+/DimT5+O48ePY9WqVcjNzcW9997r03MoAxUs0zrRsKvIx8+MP1yt6opMRyVyyqp4PKBEGSLrjVyxGGeqq3g0YBSTo6uI+ZGAUmXixImYOHFii/elpKRg27ZtTW776KOPcMMNN6C4uBi9e/f26jnYLF5Cw5hsQfB/wHtlVREKzh/H2BseAAA4BRsEm8UnDaOuGif2r8Ot9zzpdxyC3Yo9Py7CLx58wavrnXYrBvbqDMH6c6yi243dn83DbU+9ALnC/6Wy/+v5uO7Xf4I2yf85l0e//wpDb5uA5C7+v9llbVyJ7sNGoku/wX5r5O7bhvjUjug1crTfGuePH4bDbsOgMeP91qjIPY2a4kKM+MVkvzXqy4pRcGQfrvv1I35rWPX10K1fgbGPPIvsggsY0r2zzxpOuwOHP5qPcX+b6nccALB/7jzc9NwUKDUqvzWOfLwQox7+PeJSU/3WOPHNt+g3/jak9urpt0bO2nXoOGAAul453G+Nwl27odRq0XvMTX5rlB7LgK2uDgPv/gXOiaXIvlCMIZ18+xnXnMpHxYkzGPEn33YyLsVQWoG8NTtw3XP+r1XBYMKx+UsxdtozfmuITif2/ftj3DrzOUDu/8Hc/n8vwI2vPA5VfJzfGunvf4Xhf5iEhG6d2r1Whb6wyHNx0nwBQ1K6Nt6+Z/5G/P/27j+m6vr7A/jz6od7gUAQULgkIGihBOJEvaOWGjDEtaK0sukmzdJp2C+tpW2G1R86rbZyjNrE7I/y5yRNZ6vkR9bQAmWIFQO6SU3QKXFBAUHu+f5R9365cN/3AsL98eb52N6b9/3j3NfhxYHj697L+/5HkqBPiBr+OL4oQ2hsOOIenDHsGDUnKwEAiY8O/+dqbXE1gP//Pe4O3Xe6RixGW1ubzX6dTgedTnfX8QHAZDJBo9EgeCg/54SsGhoaBAA3bty4cePGzQu3hoYGl/cOnZ2dEhERMWI5BAQEDNiXl5c3qLEAkKKiIodjnTNnjqxYsWJIOXJlsY+QkBAAQGNjI4KCgtw8Gtdpa2tDVFQU/vrrL0yYMPzVP2/DvJn3WMC8mfdYYDKZEB0dbf097kq+vr4wGo3o7u4ekXgiAo1GY7NvJFYVe3p68Mwzz0BEUFBQMKRr2Sz2Me6/lxWCgoLGVJFZTJgwgXmPIcx7bGHeY8tYzXvcXbw94G74+vrC19fXLc89GJZG8fLlyyguLh7y9wabRSIiIiKVsjSKdXV1KCkpQWho6JBjsFkkIiIi8lI3b95EfX299bHRaERVVRVCQkKg1+vx1FNP4fz58zhx4gR6e3vR3Pzvn1IKCQmBVqsd1HOwWexDp9MhLy9vxD5x5C2YN/MeC5g38x4LmPfYyhsAKioq8Mgjj1gfb9y4EQCQk5ODbdu24fjx4wCA2bNn21xXUlKCRYsWDeo5NP99eoaIiIiIaADewYWIiIiIFLFZJCIiIiJFbBaJiIiISBGbRSIiIiJSpNpm8YcffsBjjz2GyMhIaDQafPXVVzbHn3vuOWg0GpstKyvLadz8/HxMnToVvr6+MBgM+Pnnn0cpg+Fxlnf/nC3brl27FGNu27ZtwPkzZgz/PqCjYfv27Zg3bx4CAwMxefJkPPHEE6itrbU5p6urC7m5uQgNDUVAQACWLVuGq1evOowrInj77beh1+vh5+eHjIwM1NXVjWYqQ+Is75aWFrz00kuIj4+Hn58foqOj8fLLL8NkMjmMO9z6cJXBzPeiRYsG5LBu3TqHcb19vv/880/FGj98+LBiXE+f74KCAsyaNcv6h6ZTU1Nx6tQp63E11jbgOG+11jbgfL7VWNueTrXN4q1bt5CcnIz8/HzFc7KystDU1GTd9u/f7zDmwYMHsXHjRuTl5eH8+fNITk7G4sWLce3atZEe/rA5y7tvvk1NTdi7dy80Gg2WLVvmMO4DDzxgc92PP/44GsMftrKyMuTm5uLs2bP47rvv0NPTg8zMTNy6dct6zmuvvYavv/4ahw8fRllZGa5cuYKlS5c6jLtz5058/PHH+OSTT3Du3Dncc889WLx4Mbq67v6G8SPBWd5XrlzBlStX8P7776Ompgb79u3DN998g+eff95p7KHWhysNZr4BYM2aNTY57Ny502Fcb5/vqKioATX+zjvvICAgAEuWLHEY25Pne8qUKdixYwcqKytRUVGBtLQ0ZGdn49KlSwDUWduA47zVWtuA8/kG1FfbHm9Id5L2UrBzY+2cnBzJzs4eUpz58+dLbm6u9XFvb69ERkbK9u3bR2CUI89e3v1lZ2dLWlqaw3Py8vIkOTl55AbmAteuXRMAUlZWJiIira2t4uPjI4cPH7ae89tvvwkAKS8vtxvDbDZLRESE7Nq1y7qvtbVVdDqd7N+/f3QTGKb+edtz6NAh0Wq10tPTo3jOcOrDnezlvXDhQnnllVcGHUOt8z179mxZvXq1wzjeNt8iIhMnTpQ9e/aMmdq2sORtjxpr26Jv3mOhtj2NalcWB6O0tBSTJ09GfHw81q9fjxs3biie293djcrKSmRkZFj3jRs3DhkZGSgvL3fFcEfc1atXcfLkyUH9T7Surg6RkZGIi4vDypUr0djY6IIRDp/lpRjLTeUrKyvR09NjM38zZsxAdHS04vwZjUY0NzfbXBMUFASDweCxc94/b6VzJkyYgP/9z/Hf5B9KfbibUt5ffPEFwsLCkJiYiC1btqCjo0Mxhhrnu7KyElVVVYOqcW+Z797eXhw4cAC3bt1CamrqmKnt/nnbo8baVspb7bXtacbsHVyysrKwdOlSxMbGoqGhAW+99RaWLFmC8vJyjB8/fsD5169fR29vL8LDw232h4eH4/fff3fVsEfU559/jsDAQKcv1xgMBuzbtw/x8fHWl7Uefvhh1NTUIDAw0EWjHTyz2YxXX30VDz30EBITEwEAzc3N0Gq1CA4Otjk3PDzceuuj/iz77c250jXuZC/v/q5fv4733nsPa9eudRhrqPXhTkp5r1ixAjExMYiMjER1dTXefPNN1NbW4ujRo3bjqHG+CwsLMXPmTDz44IMOY3nDfF+8eBGpqano6upCQEAAioqKkJCQgKqqKlXXtlLe/amtth3lrfba9kRjtll89tlnrf9OSkrCrFmzMG3aNJSWliI9Pd2NI3OdvXv3YuXKlfD19XV4Xt/3Os2aNQsGgwExMTE4dOjQoFYsXC03Nxc1NTUe977K0eYs77a2Njz66KNISEjAtm3bHMbypvpQyrvvL82kpCTo9Xqkp6ejoaEB06ZNc/UwR5yz+e7s7MSXX36JrVu3Oo3lDfMdHx+PqqoqmEwmHDlyBDk5OSgrK3P3sEadUt59G0Y11rajvNVe255oTL8M3VdcXBzCwsJsbsbdV1hYGMaPHz/gE3ZXr15FRESEK4Y4os6cOYPa2lq88MILQ742ODgY999/v+LXyp02bNiAEydOoKSkBFOmTLHuj4iIQHd3N1pbW23OdzR/lv3eMOdKeVu0t7cjKysLgYGBKCoqgo+Pz5DiO6sPd3GWd18GgwEAFHNQ03wDwJEjR9DR0YFVq1YNOb4nzrdWq8X06dORkpKC7du3Izk5GR999JHqa1spbwu11razvPtSU217KjaL//n7779x48YN6PV6u8e1Wi1SUlJw+vRp6z6z2YzTp08rvn/EkxUWFiIlJQXJyclDvvbmzZtoaGhQ/Fq5g4hgw4YNKCoqQnFxMWJjY22Op6SkwMfHx2b+amtr0djYqDh/sbGxiIiIsLmmra0N586d85g5d5Y38O+YMzMzodVqcfz4cacryfY4qw9XG0ze/VVVVQGAYg5qmW+LwsJCPP7445g0adKQn8fT5tses9mM27dvq7a2lVjyBtRZ20r65t2fGmrb47n38zWjp729XS5cuCAXLlwQAPLhhx/KhQsX5PLly9Le3i6vv/66lJeXi9FolO+//17mzJkj9913n3R1dVljpKWlye7du62PDxw4IDqdTvbt2ye//vqrrF27VoKDg6W5udkdKdrlKG8Lk8kk/v7+UlBQYDdG/7w3bdokpaWlYjQa5aeffpKMjAwJCwuTa9eujXo+g7V+/XoJCgqS0tJSaWpqsm4dHR3Wc9atWyfR0dFSXFwsFRUVkpqaKqmpqTZx4uPj5ejRo9bHO3bskODgYDl27JhUV1dLdna2xMbGSmdnp8tyc8RZ3iaTSQwGgyQlJUl9fb3NOXfu3LHG6Zv3YOvDnZzlXV9fL++++65UVFSI0WiUY8eOSVxcnCxYsMAmjtrm26Kurk40Go2cOnXKbhxvm+/NmzdLWVmZGI1Gqa6uls2bN4tGo5Fvv/1WRNRZ2yKO81ZrbYs4zlutte3pVNsslpSUCIABW05OjnR0dEhmZqZMmjRJfHx8JCYmRtasWTOg6YuJiZG8vDybfbt375bo6GjRarUyf/58OXv2rAuzcs5R3haffvqp+Pn5SWtrq90Y/fNevny56PV60Wq1cu+998ry5culvr5+lDMZGns5A5DPPvvMek5nZ6e8+OKLMnHiRPH395cnn3xSmpqaBsTpe43ZbJatW7dKeHi46HQ6SU9Pl9raWhdl5ZyzvJW+HwCI0Wi0iWO5ZrD14U7O8m5sbJQFCxZISEiI6HQ6mT59urzxxhtiMpkGxFHTfFts2bJFoqKipLe3VzGON8336tWrJSYmRrRarUyaNEnS09OtjaKIOmtbxHHeaq1tEcd5q7W2PZ1GRGQEFyqJiIiISEX4nkUiIiIiUsRmkYiIiIgUsVkkIiIiIkVsFomIiIhIEZtFIiIiIlLEZpGIiIiIFLFZJCIiIiJFbBaJiIiISBGbRSIiIiJSxGaRiIiIiBSxWSQiVWpsbIRGo0F0dLTiOaGhodBoNGhpaXHhyIiIvAubRSJSpV9++QUAMHfuXLvH//jjD7S0tCA2NhYhISGuHBoRkVdhs0hEqlRRUQFAuVm0HE9JSXHZmIiIvBGbRSJSJcvKolIzWFlZ6fA4ERH9i80iEamOiFibQa4sEhHdHTaLRKQ69fX1aG1txdSpUxEaGjrguIjg/PnzANgsEhE5w2aRiFTH2aphQ0ODtZnkh1uIiBxjs0hEqmN5v+Ls2bPtHj958iQArioSEQ0Gm0UiUh3LyqKfn9+AY7dv38aePXsAsFkkIhoMNotEpCpms9n6fsSDBw+io6PDeqylpQU5OTmoqakBAMyYMcMtYyQi8iYaERF3D4KIaKRcunQJiYmJ0Ov1aG9vh6+vL+bOnYv29nZUV1cjIiIC//zzD65fv46ZM2di1apV2Lx5s7uHTUTksbiySESqYnm/4sKFC3Hs2DHExMSgpKQEdXV1ePrpp3HmzBls2rQJ/v7+MJvNMBgMbh4xEZFn48oiEanKhg0bkJ+fjw8++AAbN25093CIiLweVxaJSFUsK4vz5s1z80iIiNSBK4tEpBo9PT0IDAzEnTt30NbWBn9/f3cPiYjI63FlkYhU4+LFi7h9+zYSEhLYKBIRjRCuLBIRERGRIq4sEhEREZEiNotEREREpIjNIhEREREpYrNIRERERIrYLBIRERGRIjaLRERERKSIzSIRERERKWKzSERERESK2CwSERERkSI2i0RERESk6P8ARSCIA9eRLqUAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 53
  },
  {
   "cell_type": "markdown",
   "id": "9495f353",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61be28c4",
   "metadata": {},
   "source": [
    "Anyway, the key point is not the result of the analysis. Instead, we see how we can leverage the power of pytensor's symbolic graph manipulation to:\n",
    "\n",
    "- Solve a root-finding problem\n",
    "- Compute quantities of interest that depend on the solution\n",
    "- Use graph transformations, including `graph_replace`, `vectorize_graph`, and `grad`, to push the analysis even further"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071fee51",
   "metadata": {},
   "source": [
    "## Authors\n",
    "\n",
    "- Authored by Jesse Grabowski in June 2025"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d08d2548",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    ":::{bibliography} :filter: docname in docnames"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17360af5",
   "metadata": {},
   "source": [
    "## Watermark "
   ]
  },
  {
   "cell_type": "code",
   "id": "d22c2ef1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-28T14:29:54.094720084Z",
     "start_time": "2025-07-28T13:48:27.537664Z"
    }
   },
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pytensor"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Mon Jul 28 2025\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.12.8\n",
      "IPython version      : 8.31.0\n",
      "\n",
      "pytensor: 2.31.3+5.gacd921952\n",
      "\n",
      "sys       : 3.12.8 | packaged by conda-forge | (main, Dec  5 2024, 14:24:40) [GCC 13.3.0]\n",
      "numpy     : 2.3.1\n",
      "matplotlib: 3.10.3\n",
      "pytensor  : 2.31.3+5.gacd921952\n",
      "\n",
      "Watermark: 2.5.0\n",
      "\n"
     ]
    }
   ],
   "execution_count": 54
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}