{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0caed09d-661f-46a0-bc5e-74c69a72fcb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import OrderedDict\n",
    "import scipy.io as sio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f27b25e5-b907-4922-bff0-ee13ed60f246",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2f9d1c8-fc8f-4829-b27e-84d9ed014593",
   "metadata": {},
   "outputs": [],
   "source": [
    "## DEFINE INPUT PARAMETERS\n",
    "# defining velocity, density, and viscocity for fluid\n",
    "u0 = 1 # lid velocity\n",
    "rho0 = 1\n",
    "nu0 = 0.01\n",
    "N = 4400 # number of collocation data points [x,y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "afa29b5a-5efc-449f-b0e7-1cdfc9089c28",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Uniform_with_Cylinder(u0, N_col=50000, N_circ=400, r=0.5):\n",
    "    \"\"\"\n",
    "    Create collocation points inside a 1by1 box with a cylinder in the middle\n",
    "\n",
    "    Args:\n",
    "        u0 : Inlet velocity\n",
    "        N_col : number of collocation points # note the acc number is slightly less as we are removing points in the cylinder\n",
    "        N_circ : Number of points along cylinder boundary\n",
    "        r : Cylinder radius \n",
    "    \n",
    "    Returns: \n",
    "        Tensors of:\n",
    "        X_col : Collocation points [x, y]\n",
    "        BC_X : Boundary points [x, y]\n",
    "        BC_Y : Boundary condition values [u, v]\n",
    "    \"\"\"\n",
    "\n",
    "    # setting up domain size\n",
    "    xc, yc = 0, 0  # cylinder center\n",
    "\n",
    "    # CREATING collocation points\n",
    "    # set random points across the domain using the random normal distribution function hence *3 or *10 to change standard dev and get distribution centred round cylinder\n",
    "    y = 2*np.random.randn(N_col,1)\n",
    "    x = 4*np.random.randn(N_col,1)\n",
    "    \n",
    "    xy = np.hstack((x, y))  \n",
    "    \n",
    "\n",
    "    # Distance from cylinder center\n",
    "    dist = np.sqrt((xy[:, 0] - xc)**2 + (xy[:, 1] - yc)**2)\n",
    "\n",
    "    # Keep only points outside the cylinder. Boolean mask for cylinder and BCs\n",
    "    valid_col = (dist > r) & (xy[:,0]>-10) & (xy[:,1]>-7) & (xy[:,1]< 7)\n",
    "    xy_outside = xy[valid_col]\n",
    "\n",
    "    X_col = torch.tensor(xy_outside, dtype=torch.float32)\n",
    "\n",
    "    # CREATING Boundary condition locations\n",
    "    # domain currently set y = -7 to 7 : x = -10 to 25\n",
    "\n",
    "    # evenly distribute BC points along the sides\n",
    "    N_BC = int(np.sqrt(N_col) // 2)\n",
    "    y = np.linspace(-7, 7, N_BC)\n",
    "    x = np.linspace(-10, 35, N_BC)\n",
    "\n",
    "    # turn those points into x,y co-ords\n",
    "    b_left   = np.column_stack([(np.ones_like(y)*-10), y])\n",
    "    b_right  = np.column_stack([np.ones_like(y)*35, y])\n",
    "    b_bottom = np.column_stack([x, np.ones_like(x)*-7])\n",
    "    b_top    = np.column_stack([x, np.ones_like(x)*7])\n",
    "\n",
    "    # cylinder BCs\n",
    "    theta = np.linspace(0, 2*np.pi, N_circ)\n",
    "    cylinder_x = (r * np.cos(theta) + xc)\n",
    "    cylinder_y = (r * np.sin(theta) + yc)\n",
    "    cylinder_xy = np.column_stack([cylinder_x, cylinder_y])\n",
    "\n",
    "    # Add all BC points\n",
    "    BC_X_np = np.vstack([b_left, b_bottom, b_right, b_top, cylinder_xy])\n",
    "    BC_X = torch.tensor(BC_X_np, dtype=torch.float32)\n",
    "\n",
    "    # CREATING the BC_Y essentially 0s everywhere except inlet\n",
    "    # tensor of zeros for all walls except inlet\n",
    "    walls_Y = torch.zeros_like(torch.tensor(np.vstack([b_top, b_bottom, b_right]), dtype=torch.float32))\n",
    "\n",
    "    # inlet velocity\n",
    "    inlet = torch.zeros_like(torch.tensor(b_left, dtype=torch.float32))\n",
    "    inlet[:, 0] = u0\n",
    "\n",
    "    # Cylinder no slip so 0\n",
    "    cylinder_uv = torch.zeros_like(torch.tensor(cylinder_xy, dtype=torch.float32))\n",
    "\n",
    "    # Combine boundary velocities\n",
    "    BC_Y = torch.cat([inlet, walls_Y,  cylinder_uv], dim=0)\n",
    "\n",
    "    return X_col, BC_X, BC_Y\n",
    "\n",
    "def Data():\n",
    "    \"\"\"\n",
    "    loads data from benchmark file\n",
    "\n",
    "    Args:\n",
    "        \n",
    "    Returns: As Tensors (dim = Nx2)\n",
    "        X_D   : coordinates of data points [x,y] for u,v, dim = [N*2]\n",
    "        U_D   : training output [u*, v*] at data points X_D, dim = [N*2] \n",
    "        PXY_D : coordinates of data points [x,y] for p, dim = [N*2]\n",
    "        P_D   : training output [p*] at data points PXY_D\n",
    "    Notes about data:\n",
    "        - flow around a cylinder\n",
    "        - Re = 100 , all data has been non-dimensionalised?? , inlet velocity u = 1\n",
    "    \"\"\"\n",
    "\n",
    "    ## Read file holding training data\n",
    "    xy_data = sio.loadmat(\"Cyl100_Sean/xstar.mat\")\n",
    "    uv_data = sio.loadmat(\"Cyl100_Sean/ustar_Aug24.mat\") \n",
    "    # pressure is calculated at different data points\n",
    "    p_data  = sio.loadmat(\"Cyl100_Sean/pstar.mat\")\n",
    "    pxy_data  = sio.loadmat(\"Cyl100_Sean/xpstar.mat\")\n",
    "\n",
    "    uv_data_np = np.array(uv_data[\"ustar\"][:,:,0])\n",
    "    u_np = uv_data_np[:,0]\n",
    "    v_np = uv_data_np[:,1]\n",
    "    \n",
    "    xy_data_np = np.array(xy_data[\"xstar\"])\n",
    "    x_np = xy_data_np[:,0]\n",
    "    y_np = xy_data_np[:,1]\n",
    "\n",
    "    p_data_np = np.array(p_data[\"pstar\"])\n",
    "    pxy_data_np = np.array(pxy_data[\"xpstar\"])\n",
    "    \n",
    "    x = torch.tensor(x_np, dtype=torch.float32)\n",
    "    y = torch.tensor(y_np, dtype=torch.float32)\n",
    "    u = torch.tensor(u_np, dtype=torch.float32)\n",
    "    v = torch.tensor(v_np, dtype=torch.float32)\n",
    "    \n",
    "\n",
    "    X_D = torch.column_stack((x, y))\n",
    "    U_D = torch.column_stack((u,v))\n",
    "    PXY_D = torch.tensor(pxy_data_np, dtype=torch.float32)\n",
    "    P_D   = torch.tensor(p_data_np[:,0:1], dtype=torch.float32)\n",
    "\n",
    "    #print(X_D.size(), U_D.size(),PXY_D.shape(), P_D.shape())\n",
    "    #print(PXY_D.size(), P_D.size())\n",
    "    return X_D, U_D, PXY_D, P_D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "8adfc689-3737-4439-9c96-5e83f62f0451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x208340e6850>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_col, BC_X, BC_Y= Uniform_with_Cylinder(1)\n",
    "Data()\n",
    "plt.scatter(X_col[:,0].numpy(),X_col[:,1].numpy(),marker='.')\n",
    "plt.scatter(BC_X[:,0].numpy(),BC_X[:,1].numpy(),marker='x')\n",
    "#plt.xlim(-2,5)\n",
    "#plt.ylim(-2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "97417112-23ad-4ca5-85d4-639a02e083a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Compute gradients\n",
    "def grad(outputs, inputs):\n",
    "    return torch.autograd.grad(\n",
    "        outputs, inputs,\n",
    "        grad_outputs=torch.ones_like(outputs),\n",
    "        create_graph=True,\n",
    "        retain_graph=True\n",
    "    )[0]\n",
    "def compute_gradients(model, xy):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        model : PyTorch neural network, with three models inside it (u,v and p) for input xy\n",
    "        xy    : tensor of shape (N, 2), # when setting up model set requires_grad=True\n",
    "\n",
    "    Returns:\n",
    "        p_grads : (p, p_x, p_y) as a tuple\n",
    "        u_grads : (u, u_x, u_y, u_xx, u_yy) as a tuple\n",
    "        v_grads : (v, v_x, v_y, v_xx, v_yy) as a tuple\n",
    "    \"\"\"\n",
    "    # create a clone so we dont change the original tensor\n",
    "    # detatch the xy data from previous gradients so it can be evaluated in isolation\n",
    "    xy = xy.clone().detach().requires_grad_(True)  \n",
    "    x = xy[:, 0:1]\n",
    "    y = xy[:, 1:2]\n",
    "\n",
    "    # Make a fwd pass that goes through the combined mini models collecting all coefficients needed for derivatives\n",
    "    u_v_p = model(torch.cat([x, y], dim=1))\n",
    "    u = u_v_p[:,0:1]\n",
    "    v = u_v_p[:,1:2]\n",
    "    p = u_v_p[:,2:3]\n",
    "\n",
    "    \n",
    "    # First-order derivatives\n",
    "    p_x = grad(p, x)\n",
    "    p_y = grad(p, y)\n",
    "\n",
    "    # Second-order derivatives\n",
    "    u_x = grad(u, x)\n",
    "    u_y = grad(u, y)\n",
    "    v_x = grad(v, x)\n",
    "    v_y = grad(v, y)\n",
    "\n",
    "    # Third-order derivatives\n",
    "    u_xx = grad(u_x, x)\n",
    "    u_yy = grad(u_y, y)\n",
    "    v_xx = grad(v_x, x)\n",
    "    v_yy = grad(v_y, y)\n",
    "    \n",
    "    p_grads = (p, p_x, p_y)\n",
    "    u_grads = (u, u_x, u_y, u_xx, u_yy)\n",
    "    v_grads = (v, v_x, v_y, v_xx, v_yy)\n",
    "\n",
    "    return p_grads, u_grads, v_grads"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4fb1aef-ab4e-424f-9937-e87a719e9d88",
   "metadata": {},
   "source": [
    "# Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "dc8538d5-9dfa-44a1-8e38-7009b7b8a3fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "## LOSS EQUATIONS\n",
    "def navier_stokes_loss(model,X):\n",
    "    \"\"\"\n",
    "    calculates steady navier stokes residuals at collocation points\n",
    "\n",
    "    Args: \n",
    "        model : whatever model calls this function\n",
    "        X : input collocation [x,y] coords as defined in models data creation\n",
    "\n",
    "    Returns: \n",
    "        tensor of all collocation point residuals\n",
    "    \"\"\"\n",
    "    p_grads, u_grads, v_grads = compute_gradients(model, X)\n",
    "    _, p_x, p_y = p_grads\n",
    "    u, u_x, u_y, u_xx, u_yy = u_grads\n",
    "    v, v_x, v_y, v_xx, v_yy = v_grads\n",
    "\n",
    "    #compute PDE residuals\n",
    "    u_eqn = u*u_x + v*u_y + p_x/rho0 - nu0*(u_xx + u_yy)\n",
    "    v_eqn = u*v_x + v*v_y + p_y/rho0 - nu0*(v_xx + v_yy)\n",
    "    \n",
    "\n",
    "    # combine into one tensor\n",
    "    # [u_residual, v_residual, continuity]\n",
    "    return torch.cat([u_eqn, v_eqn,(u_x+v_y)], dim=1)\n",
    "    \n",
    "def BC_loss(model,BC_X):\n",
    "    \"\"\"\n",
    "    calculates u and v at boundary conditions\n",
    "\n",
    "    Args: \n",
    "        model : whatever model calls this function\n",
    "        BC_X : Input Boundary conditions [x,y] coords as defined in models data creation\n",
    "        \n",
    "    Returns: \n",
    "        tensor of u,v at all boundary condition coords\n",
    "    \"\"\"\n",
    "    _, u_grads, v_grads = compute_gradients(model,BC_X)\n",
    "    u, u_x, u_y, u_xx, u_yy = u_grads\n",
    "    v, v_x, v_y, v_xx, v_yy = v_grads\n",
    "    return torch.cat([u, v], dim=1)\n",
    "def UV_Data_loss(model, X_D):\n",
    "    p_grads, u_grads, v_grads = compute_gradients(model,X_D)\n",
    "    p, p_x, p_y = p_grads\n",
    "    u, u_x, u_y, u_xx, u_yy = u_grads\n",
    "    v, v_x, v_y, v_xx, v_yy = v_grads\n",
    "    \n",
    "    return torch.cat([u, v], dim=1)\n",
    "def P_Data_loss(model, PXY_D):\n",
    "    p_grads, u_grads, v_grads = compute_gradients(model,PXY_D)\n",
    "    p, p_x, p_y = p_grads\n",
    "    u, u_x, u_y, u_xx, u_yy = u_grads\n",
    "    v, v_x, v_y, v_xx, v_yy = v_grads\n",
    "\n",
    "    # solution need to use torch.cat rather then torch.tensor to create p data loss as it retains the gradient computation graph whilst torch.tensor performs a .detach()\n",
    "    pval = torch.cat([p], dim=1)\n",
    "    \n",
    "    return pval"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7661146-5baf-4d1f-bf77-1c08c047291b",
   "metadata": {},
   "source": [
    "## Define sub models and combine into 1 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "4a15f79e-0281-40e5-881c-ddd351dccc1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## CREATING THE MINI MODEL FOR u,v AND p AND THEN COMBINE THEM INTO ONE MODEL\n",
    "\n",
    "# sub model for u,v and p \n",
    "class submodel(nn.Module):\n",
    "    def __init__(\n",
    "            self,\n",
    "            N_input,\n",
    "            N_hidden_arr,\n",
    "            N_output,\n",
    "            activation = nn.Tanh\n",
    "    ):\n",
    "        super(submodel, self).__init__()  # Create network\n",
    "\n",
    "        # Create input layer w/ activation function\n",
    "        layers = [('Input', nn.Linear(N_input, N_hidden_arr[0]))]\n",
    "        layers.append(('Input activation', activation()))\n",
    "\n",
    "        # Create hidden layers\n",
    "        for i in range(len(N_hidden_arr)-1):\n",
    "            layers.append(\n",
    "                (\"Hidden %d\" % (i+1), nn.Linear(N_hidden_arr[i], N_hidden_arr[i+1]))\n",
    "            )\n",
    "            layers.append(('Hidden activation %d' % (i+1), activation()))\n",
    "        layers.append(('Output', nn.Linear(N_hidden_arr[-1], N_output)))\n",
    "        layerdict = OrderedDict(layers)\n",
    "        self.layers = nn.Sequential(layerdict)\n",
    "\n",
    "    def forward(self, x):\n",
    "        y = self.layers(x)\n",
    "        return y\n",
    "\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self, N_input, N_hidden_arr, N_output, activation = nn.Tanh):\n",
    "        \n",
    "        super(Net, self).__init__()  # Create network\n",
    "        # creates three models using submodel as the blueprint\n",
    "        self.model_u = submodel(N_input, N_hidden_arr ,N_output, activation)\n",
    "        self.model_v = submodel(N_input, N_hidden_arr, N_output, activation)\n",
    "        self.model_p = submodel(N_input, N_hidden_arr, N_output, activation)\n",
    "\n",
    "\n",
    "    # combine the outputs of all the models into a single output\n",
    "    def forward(self, xy):\n",
    "        out_u = self.model_u(xy)\n",
    "        out_v = self.model_v(xy)\n",
    "        out_p = self.model_p(xy)\n",
    "        combined = torch.cat((out_u, out_v, out_p), dim=1)\n",
    "        return combined\n",
    "\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c185db0a-8994-4f80-87bf-4715cee2d15c",
   "metadata": {},
   "source": [
    "## Defining the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "435525da-d488-4ccf-b370-a140cf1a538a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## create neural network\n",
    "\n",
    "class PINN:\n",
    "    def __init__(self):\n",
    "        ### Need to change this to check if gpu available as well\n",
    "        device = torch.device(\"cpu\")\n",
    "        print(\"Using CPU\")\n",
    "        self.model = Net(\n",
    "            N_input=2,\n",
    "            N_hidden_arr=[32,16,16,32],\n",
    "            N_output = 1\n",
    "        ).to(device)\n",
    "    \n",
    "        # DATA CREATION \n",
    "        self.X, self.BC_X, self.BC_Y =  Uniform_with_Cylinder(u0)\n",
    "        self.X_D, self.U_D, self.PXY_D, self.P_D = Data()\n",
    "\n",
    "        # copy and seperate tensors into format for loss calculations\n",
    "        self.X    = self.X.clone().detach().requires_grad_(True)\n",
    "        self.BC_X = self.BC_X.clone().detach().requires_grad_(True)\n",
    "\n",
    "        self.X_D   = self.X_D.clone().detach().requires_grad_(True)\n",
    "        self.PXY_D = self.PXY_D.clone().detach().requires_grad_(True)\n",
    "        \n",
    "        # OPTIMISERS\n",
    "        self.optimiser = torch.optim.LBFGS(\n",
    "            params=self.model.parameters(),\n",
    "            lr=1.0,\n",
    "            max_iter = 75*10**3,\n",
    "            max_eval = 75*10**3,\n",
    "            history_size=50,\n",
    "            tolerance_change=1e-7,\n",
    "            tolerance_grad=1e-7,\n",
    "            line_search_fn=\"strong_wolfe\"\n",
    "        )\n",
    "        self.adam = torch.optim.Adam(self.model.parameters())\n",
    "\n",
    "        \n",
    "        self.loss_fn = nn.MSELoss()\n",
    "\n",
    "        # Counter for printing loss\n",
    "        self.iter =1\n",
    "        \n",
    "    # Loss\n",
    "    def compute_loss(self):\n",
    "        # Compute PDE residuals at collocation points\n",
    "        residuals = navier_stokes_loss(self.model, self.X)\n",
    "        ru, rv, conservation  = residuals[:, 0:1], residuals[:, 1:2], residuals[:, 2:3]\n",
    "\n",
    "        # PDE loss (residuals against tensor of zeros)\n",
    "        pde_loss = self.loss_fn(ru, torch.zeros_like(ru)) + self.loss_fn(rv, torch.zeros_like(rv))+self.loss_fn(conservation, torch.zeros_like(conservation))\n",
    "\n",
    "        # BC loss\n",
    "        bc_loss = BC_loss(self.model,self.BC_X)\n",
    "        bc_loss = self.loss_fn(bc_loss,self.BC_Y)\n",
    "\n",
    "        # Data Loss\n",
    "        uv_data_loss = UV_Data_loss(self.model, self.X_D)\n",
    "        uv_data_loss = self.loss_fn(uv_data_loss, self.U_D)\n",
    "        \n",
    "        p_data_loss  = P_Data_loss(self.model, self.PXY_D)\n",
    "        p_data_loss = self.loss_fn(p_data_loss, self.P_D)\n",
    "\n",
    "        data_loss = p_data_loss+uv_data_loss\n",
    "\n",
    "        if self.iter <= 500:\n",
    "            total_loss = data_loss\n",
    "        elif self.iter > 500:\n",
    "            total_loss = pde_loss + bc_loss +data_loss\n",
    "        # print the loss\n",
    "        if self.iter % 100 == 0:\n",
    "            print(f\"Iteration {self.iter:5}, Total Loss {total_loss:.9f}, Data loss {data_loss:.9f} = uv data loss: {uv_data_loss} + p data loss: {p_data_loss} ,pde loss {pde_loss:.9f}, BC loss {bc_loss:.9f}\")\n",
    "            \n",
    "        self.iter+= 1\n",
    "        \n",
    "        return total_loss\n",
    "    \n",
    "    def train(self, adam_epochs=300, lbfgs_epochs=10):\n",
    "        self.model.train()\n",
    "\n",
    "        for epoch in range(adam_epochs):\n",
    "            self.adam.zero_grad()\n",
    "            loss = self.compute_loss()\n",
    "            loss.backward()\n",
    "            self.adam.step()\n",
    "\n",
    "            # extra printing of loss for when adam optimiser in use\n",
    "            if epoch % 50 == 0:\n",
    "                with torch.inference_mode():  \n",
    "                    print(f\"[Adam] Step {epoch:4}, Loss = {loss.item():.6e}\")\n",
    "        \n",
    "        for epoch in range(lbfgs_epochs):\n",
    "            def closure():\n",
    "                self.optimiser.zero_grad()\n",
    "                loss = self.compute_loss()\n",
    "                loss.backward()\n",
    "                return loss\n",
    "\n",
    "            self.optimiser.step(closure)\n",
    "            \n",
    "    def eval(self):\n",
    "        self.model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "8baeb469-b77c-48cc-b8af-f1bcaa59b1d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using CPU\n"
     ]
    }
   ],
   "source": [
    "test = PINN()\n",
    "#print(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d2f57190-7a40-43ca-b5c5-6f042448c11d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Adam] Step    0, Loss = 5.323519e-01\n",
      "[Adam] Step   50, Loss = 5.174376e-02\n",
      "Iteration   100, Total Loss 0.043784540, Data loss 0.043784540 = uv data loss: 0.04252943769097328 + p data loss: 0.0012551030376926064 ,pde loss 0.002857223, BC loss 0.407941669\n",
      "[Adam] Step  100, Loss = 4.369256e-02\n",
      "[Adam] Step  150, Loss = 3.921257e-02\n",
      "Iteration   200, Total Loss 0.035003856, Data loss 0.035003856 = uv data loss: 0.033859528601169586 + p data loss: 0.0011443282710388303 ,pde loss 0.004175111, BC loss 0.390727729\n",
      "[Adam] Step  200, Loss = 3.493423e-02\n",
      "[Adam] Step  250, Loss = 3.181600e-02\n",
      "Iteration   300, Total Loss 0.029017163, Data loss 0.029017163 = uv data loss: 0.02791612781584263 + p data loss: 0.0011010351590812206 ,pde loss 0.008012174, BC loss 0.334290355\n",
      "Iteration   400, Total Loss 0.021949125, Data loss 0.021949125 = uv data loss: 0.02092437446117401 + p data loss: 0.0010247514583170414 ,pde loss 0.040707886, BC loss 0.276147723\n",
      "Iteration   500, Total Loss 0.015751038, Data loss 0.015751038 = uv data loss: 0.014746168628334999 + p data loss: 0.0010048700496554375 ,pde loss 0.082149126, BC loss 0.268758118\n",
      "Iteration   600, Total Loss 0.013188911, Data loss 0.013188911 = uv data loss: 0.012211738154292107 + p data loss: 0.0009771721670404077 ,pde loss 0.078456059, BC loss 0.274888426\n",
      "Iteration   700, Total Loss 0.011689182, Data loss 0.011689182 = uv data loss: 0.010731165297329426 + p data loss: 0.0009580167243257165 ,pde loss 0.075172827, BC loss 0.260430723\n",
      "Iteration   800, Total Loss 0.011049038, Data loss 0.011049038 = uv data loss: 0.01011063251644373 + p data loss: 0.0009384055156260729 ,pde loss 0.067041203, BC loss 0.250346839\n",
      "Iteration   900, Total Loss 0.010520742, Data loss 0.010520742 = uv data loss: 0.009603985585272312 + p data loss: 0.0009167566895484924 ,pde loss 0.068193920, BC loss 0.247068480\n",
      "Iteration  1000, Total Loss 0.010109163, Data loss 0.010109163 = uv data loss: 0.009203370660543442 + p data loss: 0.0009057920542545617 ,pde loss 0.076012582, BC loss 0.250374675\n",
      "Iteration  1100, Total Loss 0.009517306, Data loss 0.009517306 = uv data loss: 0.008649200201034546 + p data loss: 0.0008681060280650854 ,pde loss 0.086420357, BC loss 0.249534085\n",
      "Iteration  1200, Total Loss 0.008832856, Data loss 0.008832856 = uv data loss: 0.00801133830100298 + p data loss: 0.0008215176640078425 ,pde loss 0.082809269, BC loss 0.247700423\n",
      "Iteration  1300, Total Loss 0.008080676, Data loss 0.008080676 = uv data loss: 0.0073042321018874645 + p data loss: 0.0007764438050799072 ,pde loss 0.085654870, BC loss 0.244380772\n",
      "Iteration  1400, Total Loss 0.007552867, Data loss 0.007552867 = uv data loss: 0.006822505965828896 + p data loss: 0.0007303609745576978 ,pde loss 0.089012936, BC loss 0.239433452\n",
      "Iteration  1500, Total Loss 0.007051629, Data loss 0.007051629 = uv data loss: 0.0063681877218186855 + p data loss: 0.0006834418163634837 ,pde loss 0.092597365, BC loss 0.243242875\n",
      "Iteration  1600, Total Loss 0.006674251, Data loss 0.006674251 = uv data loss: 0.006026310846209526 + p data loss: 0.0006479396834038198 ,pde loss 0.090385795, BC loss 0.243660837\n",
      "Iteration  1700, Total Loss 0.006312294, Data loss 0.006312294 = uv data loss: 0.005695745814591646 + p data loss: 0.0006165480008348823 ,pde loss 0.086372629, BC loss 0.243957102\n",
      "Iteration  1800, Total Loss 0.006032964, Data loss 0.006032964 = uv data loss: 0.005439144093543291 + p data loss: 0.000593820062931627 ,pde loss 0.089437783, BC loss 0.241309389\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[107], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mtest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[1;32mIn[105], line 99\u001b[0m, in \u001b[0;36mPINN.train\u001b[1;34m(self, adam_epochs, lbfgs_epochs)\u001b[0m\n\u001b[0;32m     96\u001b[0m     loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m     97\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m loss\n\u001b[1;32m---> 99\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimiser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\anaconda\\envs\\workshop\\Lib\\site-packages\\torch\\optim\\optimizer.py:487\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    482\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    483\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    484\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    485\u001b[0m             )\n\u001b[1;32m--> 487\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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[0;32m    488\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[0;32m    490\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
      "File \u001b[1;32mC:\\anaconda\\envs\\workshop\\Lib\\site-packages\\torch\\utils\\_contextlib.py:116\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    113\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m    114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m    115\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[1;32m--> 116\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
      "File \u001b[1;32mC:\\anaconda\\envs\\workshop\\Lib\\site-packages\\torch\\optim\\lbfgs.py:444\u001b[0m, in \u001b[0;36mLBFGS.step\u001b[1;34m(self, closure)\u001b[0m\n\u001b[0;32m    441\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mobj_func\u001b[39m(x, t, d):\n\u001b[0;32m    442\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_directional_evaluate(closure, x, t, d)\n\u001b[1;32m--> 444\u001b[0m     loss, flat_grad, t, ls_func_evals \u001b[38;5;241m=\u001b[39m \u001b[43m_strong_wolfe\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    445\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobj_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx_init\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflat_grad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgtd\u001b[49m\n\u001b[0;32m    446\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    447\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_grad(t, d)\n\u001b[0;32m    448\u001b[0m opt_cond \u001b[38;5;241m=\u001b[39m flat_grad\u001b[38;5;241m.\u001b[39mabs()\u001b[38;5;241m.\u001b[39mmax() \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m tolerance_grad\n",
      "File \u001b[1;32mC:\\anaconda\\envs\\workshop\\Lib\\site-packages\\torch\\optim\\lbfgs.py:48\u001b[0m, in \u001b[0;36m_strong_wolfe\u001b[1;34m(obj_func, x, t, d, f, g, gtd, c1, c2, tolerance_change, max_ls)\u001b[0m\n\u001b[0;32m     46\u001b[0m g \u001b[38;5;241m=\u001b[39m g\u001b[38;5;241m.\u001b[39mclone(memory_format\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mcontiguous_format)\n\u001b[0;32m     47\u001b[0m \u001b[38;5;66;03m# evaluate objective and gradient using initial step\u001b[39;00m\n\u001b[1;32m---> 48\u001b[0m f_new, g_new \u001b[38;5;241m=\u001b[39m \u001b[43mobj_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     49\u001b[0m ls_func_evals \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m     50\u001b[0m gtd_new \u001b[38;5;241m=\u001b[39m g_new\u001b[38;5;241m.\u001b[39mdot(d)\n",
      "File \u001b[1;32mC:\\anaconda\\envs\\workshop\\Lib\\site-packages\\torch\\optim\\lbfgs.py:442\u001b[0m, in \u001b[0;36mLBFGS.step.<locals>.obj_func\u001b[1;34m(x, t, d)\u001b[0m\n\u001b[0;32m    441\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mobj_func\u001b[39m(x, t, d):\n\u001b[1;32m--> 442\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_directional_evaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\anaconda\\envs\\workshop\\Lib\\site-packages\\torch\\optim\\lbfgs.py:296\u001b[0m, in \u001b[0;36mLBFGS._directional_evaluate\u001b[1;34m(self, closure, x, t, d)\u001b[0m\n\u001b[0;32m    294\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_directional_evaluate\u001b[39m(\u001b[38;5;28mself\u001b[39m, closure, x, t, d):\n\u001b[0;32m    295\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_grad(t, d)\n\u001b[1;32m--> 296\u001b[0m     loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(\u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m    297\u001b[0m     flat_grad \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gather_flat_grad()\n\u001b[0;32m    298\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_param(x)\n",
      "File \u001b[1;32mC:\\anaconda\\envs\\workshop\\Lib\\site-packages\\torch\\utils\\_contextlib.py:116\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    113\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m    114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m    115\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[1;32m--> 116\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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",
      "Cell \u001b[1;32mIn[105], line 95\u001b[0m, in \u001b[0;36mPINN.train.<locals>.closure\u001b[1;34m()\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mclosure\u001b[39m():\n\u001b[0;32m     94\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptimiser\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m---> 95\u001b[0m     loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     96\u001b[0m     loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m     97\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
      "Cell \u001b[1;32mIn[105], line 47\u001b[0m, in \u001b[0;36mPINN.compute_loss\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mcompute_loss\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m     46\u001b[0m     \u001b[38;5;66;03m# Compute PDE residuals at collocation points\u001b[39;00m\n\u001b[1;32m---> 47\u001b[0m     residuals \u001b[38;5;241m=\u001b[39m \u001b[43mnavier_stokes_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     48\u001b[0m     ru, rv, conservation  \u001b[38;5;241m=\u001b[39m residuals[:, \u001b[38;5;241m0\u001b[39m:\u001b[38;5;241m1\u001b[39m], residuals[:, \u001b[38;5;241m1\u001b[39m:\u001b[38;5;241m2\u001b[39m], residuals[:, \u001b[38;5;241m2\u001b[39m:\u001b[38;5;241m3\u001b[39m]\n\u001b[0;32m     50\u001b[0m     \u001b[38;5;66;03m# PDE loss (residuals against tensor of zeros)\u001b[39;00m\n",
      "Cell \u001b[1;32mIn[98], line 13\u001b[0m, in \u001b[0;36mnavier_stokes_loss\u001b[1;34m(model, X)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mnavier_stokes_loss\u001b[39m(model,X):\n\u001b[0;32m      3\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;124;03m    calculates steady navier stokes residuals at collocation points\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;124;03m        tensor of all collocation point residuals\u001b[39;00m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 13\u001b[0m     p_grads, u_grads, v_grads \u001b[38;5;241m=\u001b[39m \u001b[43mcompute_gradients\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     14\u001b[0m     _, p_x, p_y \u001b[38;5;241m=\u001b[39m p_grads\n\u001b[0;32m     15\u001b[0m     u, u_x, u_y, u_xx, u_yy \u001b[38;5;241m=\u001b[39m u_grads\n",
      "Cell \u001b[1;32mIn[97], line 45\u001b[0m, in \u001b[0;36mcompute_gradients\u001b[1;34m(model, xy)\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[38;5;66;03m# Third-order derivatives\u001b[39;00m\n\u001b[0;32m     44\u001b[0m u_xx \u001b[38;5;241m=\u001b[39m grad(u_x, x)\n\u001b[1;32m---> 45\u001b[0m u_yy \u001b[38;5;241m=\u001b[39m \u001b[43mgrad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mu_y\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     46\u001b[0m v_xx \u001b[38;5;241m=\u001b[39m grad(v_x, x)\n\u001b[0;32m     47\u001b[0m v_yy \u001b[38;5;241m=\u001b[39m grad(v_y, y)\n",
      "Cell \u001b[1;32mIn[97], line 3\u001b[0m, in \u001b[0;36mgrad\u001b[1;34m(outputs, inputs)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mgrad\u001b[39m(outputs, inputs):\n\u001b[1;32m----> 3\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrad\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m      4\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      5\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mones_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[0;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[1;32mC:\\anaconda\\envs\\workshop\\Lib\\site-packages\\torch\\autograd\\__init__.py:496\u001b[0m, in \u001b[0;36mgrad\u001b[1;34m(outputs, inputs, grad_outputs, retain_graph, create_graph, only_inputs, allow_unused, is_grads_batched, materialize_grads)\u001b[0m\n\u001b[0;32m    492\u001b[0m     result \u001b[38;5;241m=\u001b[39m _vmap_internals\u001b[38;5;241m.\u001b[39m_vmap(vjp, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, allow_none_pass_through\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)(\n\u001b[0;32m    493\u001b[0m         grad_outputs_\n\u001b[0;32m    494\u001b[0m     )\n\u001b[0;32m    495\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 496\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    497\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    498\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_outputs_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    499\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    500\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43mallow_unused\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    503\u001b[0m \u001b[43m        \u001b[49m\u001b[43maccumulate_grad\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[0;32m    504\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    505\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m materialize_grads:\n\u001b[0;32m    506\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(\n\u001b[0;32m    507\u001b[0m         result[i] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_tensor_like(inputs[i])\n\u001b[0;32m    508\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(inputs))\n\u001b[0;32m    509\u001b[0m     ):\n",
      "File \u001b[1;32mC:\\anaconda\\envs\\workshop\\Lib\\site-packages\\torch\\autograd\\graph.py:825\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[1;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    823\u001b[0m     unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[0;32m    824\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 825\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m    826\u001b[0m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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\n\u001b[0;32m    827\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[0;32m    828\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    829\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "test.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "83234dda-bea2-4038-bc04-157290073412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zyqAH5bPPPhsAeuaL73//+/jZz35m54szzjgDY2Nj+OQnP2ldogvFfNatd7zjHfjKV76CSy65BJOTkzZjdBDOPfdcdLvdgfp1ZuzlY/v85z+PVqu1rHPhYubdbdu24ec//3mB5B86dKiQhTvEI4ehZe5XEK961avwT//0TzjvvPPwzne+E8985jPh+z4efvhhfOtb38LLXvYyvPzlL8cnP/lJfPOb38SLX/xiHHfcceh2uzbl/AUveAEA4M1vfjNqtRqe85znYMOGDdi7dy8uv/xyjI2N2Se48847DxMTE7jwwgvxgQ98AJ7n4eqrr8bOnTsXPOYPfOADuPnmm3HGGWfgHe94B0488UR0u13s2LEDN954Iz75yU/2dWVs27YN73nPe/DBD34QnU4Hr371qzE2Noa7774bBw8exGWXXYbx8XH86Z/+Kd7znvfgda97HV796lfj0KFDuOyyyxBFES699NJFn+eRkRFs3boVN9xwA57//OdjYmICa9aswbZt23D55ZfjhS98Ic466yz89//+3xEEAa688kr89Kc/xbXXXmufKpfj3A0xxErDK1/5Srzvfe/Dq171KvzxH/8xut0u/vZv/xac8wX3cc4552Dz5s246KKLsHfv3oKLFVDzwgc+8AG8973vxf3334//9t/+G1atWoV9+/bhe9/7HhqNRl9pnxNPPBG///u/j7/7u78DpRTnnnsuduzYgT/90z/Fli1b8O53vxsA0Gw28Vd/9Vd405vehBe84AV485vfjMnJSdx333348Y9/jI997GN9x/9rv/Zr+NznPofrrrsOxx9/PKIowq/92q/Z9a997WtxySWX4JZbbsH/9//9fwiCYN5z8upXvxpXXXUV3vrWt+Kee+7BWWedBSEEvvvd7+Kkk07Cq171KrzwhS/Ei170IvzJn/wJZmZm8JznPAd33nknLr30Upx22mk4//zzF3L6CzBxip/+9KcxMjKCKIqwfft2rF69esHz7vnnn49PfepTeO1rX4s3v/nNOHToED70oQ/1hBEN8Qjh0c2/eGzCZDJ+//vfH9ju9a9/vWw0GpXr0jSVH/nIR+Spp54qoyiSzWZTPvGJT5Rvectb5L333iullPK2226TL3/5y+XWrVtlGIZy9erV8rnPfa780pe+ZPv5h3/4B3nWWWfJyclJGQSB3Lhxo/zd3/1deeeddxb2973vfU+eccYZstFoyE2bNslLL71U/v3f/31lRuaLX/ziyjEfOHBAvuMd75Dbt2+Xvu/LiYkJ+bSnPU2+973vlXNzc/Oet2uuuUY+4xnPsMd72mmnyauuuqrQ5u///u/lKaecIoMgkGNjY/JlL3tZT6Zsv/NalZH2jW98Q5522mkyDEMJQL7+9a+36/7zP/9Tnn322bLRaMharSaf/exnyy9/+cs9/S7HuRtiiJWGG2+8UT7lKU+RtVpNHn/88fJjH/vYgrNZDd7znvdIAHLLli2Sc17Z5otf/KI866yz5OjoqAzDUG7dulX+9m//tvzGN75h21Ttl3Mu//Iv/1I+4QlPkL7vyzVr1sjXvva1cufOnZXH8tznPlc2Gg1Zr9flk570JPmXf/mXA/vfsWOHPOecc+TIyIgEUJnd+4Y3vEF6nicffvjhBZ+TTqcj3/e+98nHP/7xMggCuXr1ann22WfLW2+9tdDmT/7kT+TWrVul7/tyw4YN8g/+4A/k4cOHC331m3OqMk+vuOIKuX37dskYkwAKc+9C5l0p1f3mpJNOklEUySc96Unyuuuu65vN+uEPf3jB52SI+UGkXKRteYghhhhiiCGGGIgkSbBt2zb8+q//eo+m3hBDLDeGbtYhhhhiiCGGWCYcOHAA99xzD6666irs27cP/+N//I9He0hDHAMYkrkhhhhiiCGGWCZ85StfwQUXXIANGzbgyiuvnFeOZIghlgNDN+sQQwwxxBBDDDHECsZQmmSIIYYYYoghhhhiBWNI5oYYYoghhhhiiCFWMIZkboghhhhiiCGGGGIF45hKgBBCYPfu3RgZGRmWEhniMQ8pJWZnZ+et9eii2+0iSZK+64Mg6Kl5O8SvNobz3hDHEo7Vee+YInO7d+/uW/tziCEeq9i5c+eCCol3u11sr9Wwd0Cb9evX44EHHviVn9iGyDGc94Y4FnGszXvHFJkzBepf9osvwR9pAACELD6p8tJ3MU+ur+xpT0CJrOx7EMw2ZRBnOT3Kh2ohi32YYyv3yyrG4o5PSAJGJCSA8pA8KvJtICFA7DsjAhSy7z76oeea6L1yqZ66MpE/fUkAqf4ec2bXZ5yCEYl25oELAi4ohMj7jRMKSiWEIOCSgDvrTDtKnWsBwGPCLmfMOQd6OaMCTG/jOdsSSHs+GZGgLD9nZpk5ZkZkfs6oKFwrcw4pkYXrQInqP51t4d+2/5b93c+HJEmwF8BOSlBVgGcGwJa9e5Ekya/0pDZEEeb67/zYyzBa89XC8sQmBHrQb/Lji2g7aJuq7crtXLEFd527HZfFdyA/HrOs6viq2peX92zT5zgGbVMFVp50ae9ySvPvroWp37Jyn0BxcmcVVqqlWGoZLZ4H89303+8cGUip9lslpGG27fvbK113RnrO/UycYctf3nLMzXvHFJkzLgZ/pAF/tNmzXkgCH4o8uDfU3nb55zKZq+pzIeTukSZz5njKx+UurxqPO347JvSOlRHZ086QN0O+KKTdT9U++8GMVziUhUsKw5+IJm9cUiScggLIJIUPoJOqn7gHIM4YPABCvxvCxjmB9DyYqpIUAGS+ngKWlAFA4DmEVS9XFXAAv4LIUZKfT+IQMAPPIXN2vf7ukjm3fxe+JtDEIcpu/4t1rY0GDKNV20gJdLNF9TXEow9z/UdrPkbrfr5iPiJV1aZfu35tbXtWva5Ayira9awfsLxA3GhxWXnMXBbvfkdD5sp9mXZV5MlFFaFzl/msuK68DS0tc/fX84TeZyyDbirzjb8fBpG5QaSfi+IxD/rtcQmYn7Lf2ww49ua9Y4rMGRCoG59rzxAlgmPeqwgdJfnvjBBpCZ25EbsErx9JK/Y3P5GbDwshRu5xlZdTIvuSTTLAmma284gokLZCm4rtK/vS7USPvS9HlSUUyK10gCJyAJBkxRuDa21LUmqJasYpiCZeUhCkfHGTgCFyAJByisjv/8eXkgy8rj0PB6W2XKjtixZW80Udz2KNBD0IWfUT+wqZ1IZYIsoWF6DalL8c/c7X3yCi2Y/gAQsjZeXl820ziJwshviVyZGxLFX1ZyxOZv0gcljVzyCYG9gjQeTMtvNZ58rod32r1pfPecrz88NItRV2IVjh894xSeZcGFLXz3o2iNCp9kXSNchSV97HQoje0aAfQZtvHIwI57PsSxSpthpRIkGIBEMvmTVu1srtBywfROjMOiFJ4dqkghSWUSrBNTHjgoJRRdyS1CF+XH2WwvTZd7eVc1/53FVZzsrzppSkQNJcd6/rygWUVZKDwDdkWWqbwyP521GmxN7lS5wjh/gVAaXqZW527lOpwUJuxAt1pxkshMgtZJ+LJQhVffdzrZbXme0GEaiqfVgLWsV457PWDVpftVyIoiu2sG4esvZIEbmqPo7mupnz2I80p7zYHnTxLm+DFT7vHZNkzrq8kL8bK50hQC7hGeRyLaNsdXGJjbHiLZXEVf0/BxEt814+njKBzfsSPfFaC0H5mHNXoeMe1GSkH4EbBHdMAqRAUI1FLtVxcC7aia8sb6L62hkSxSUBcYgfoxJckIKb1MBjwhIuSqS1yglJKomc3dcAay2X6ph8Knri81xLnrVcSgKIouvXhfotHwXZC7w+k9oj+/AxxKOAKsvb0RCnRwpVsVQLsZgNsugMWm7defOchzIBW7RFyrHEGZjPQqAQjOtuw0h/IgcsPiZnsQTOtWYspG/e5wGibwyic/6rCDilRSK3HFjh894xSeaAXotbmdBVtXdhrT+la1++7q4btur7YjAfketHEt3lxurmxr1JKDdpv35NH4aQuG1doiG0BSmV1BIP32lbJnILPRdckoGWOjU+2Ji3TuLbcRWInyDoJsxa48w15BVu1SqiRErLiDP/VRE5Qwh7fyMEgjNr0bTt+5BoRmSlC9v03y/+cDEJOAUEbEVPakPMA2OdMzFXVZa6fi7X+UzXVeuXq6+B8VZlC2MpMH6Qa3Wh6EsGF9FX2YLnomqZDfI3Vio9BnPtXBdrP/fpoFi6fvsdhGoXxcLnh35EsMda28eKyiXQ0TIiQYnscrF0N+sKn/eOWTJXBUNwZIk4VLlgWekmbMhhP3Ln3rSPhtCVMV9yhUtS+yUuGCtOlVvWJYI+VXSpauzMcbP6OvGh33FWxRkamOXzuVoBFRtXcLNKarc3FtBM5JmpScaUlVKTMs4JpCCV85B7HQ2Jc685JRJSAIL2WjhdaxoXBCi7TgWBz/S5dlzdZgpiJM+q9ZkokLV+1k2uLXvLAtbH3bA8P9khfhXQT3/LvdHO506tWr/Qm/p81hmz3rV8uX0bi41L2soZrLb/RZDAfm0WGpPWTyJgMXD3Z5I5DGnpNw73HFUt7zem+dYDOUGsIoqUOH0MiHtzfytVv4/y74HL4gOHeQDhsvh7SXgvoVsqVvi8d8xXgKiKCXNJT5W1q9c92XuzL/S3BLfqQrcpx725ruOqz1VJCEbKwv1MiYRPuc5AFdaiZ0gaIWq5r18uXLJhjoMQCY8IG1vnEQEGaV+LAddxcS7xTDntIYjGvSqlcsEaC5wQBCkf/NO3JLxExDwmlEWNqsQHSmRfd2dhzMal60qeyHw/QhI7/lQUSap7zC4IkYU5MBXL9HcOqAoGLr+Cxfd/5ZVXYvv27YiiCE972tPwn//5n33bfuELX8ALX/hCrF27FqOjozj99NPxta997WiOZIgqlAmBiaVbCBZi2ZmPyMxH9tz1rlXPJXIGg4hc36zbqoxUUXyV+19IYoQ77vyP3fty92ewUOsYl6iUKSnv30VVLBijC9+nIWzmvfzqt81SUJXQYl5lImeQLJO7dRnnvUcDK2OUjxCq3FkGhgC5RKiK9MzXZ7+YSkOI3Je7vIyF/jckSIG4VZE3d3n5GCikfZnxuOsAWAJn+0D+bkifS9bMZwCLJm72uDTZSQW1V8W6SQVBKou6cYRIrSdXPHHG4lWGe52qtNzMO6HqZXXi+sWtOeTMtQwapDwf2yCpG5d0ygpCV94eqLbALgqB1/+1CFx33XV417vehfe+97244447cOaZZ+Lcc8/FQw89VNn+lltuwQtf+ELceOONuP3223HWWWfhpS99Ke64446jO54hFBh6idygmCvzR3CD+s1NfTGYL1NxIdtWum+riCXpJUoLwUKITT/C1298/Y61bIUyY66Cz3qP05CbqrEvZhwGC72e80qt9CF3VdY8Fwv5TRgix2U1sU55vm6pCRDLNO89WlgZo1xmGCLWT44EQOVNs19fBqa/sgtWtevdtiq+rnofC9s/MFgDrip7lZHeSaQq6L6f1dFsH2gTO9XxXVVEd1AMF4MEB6l0zQq93CQ7GNexm6TAHdLDBbXnVsqFW+HKhI5QCUrlwKQNdwxVx0tRjtujhVg5d/sqTT9zbJTm25isVlb47RlPCIE5Q0uOmesnQLpIf8Nf//Vf48ILL8Sb3vQmAMAVV1yBr33ta/jEJz6Byy+/vKf9FVdcUfj+53/+57jhhhvw5S9/Gaeddtqi9j3EPKgUmHVi6crWqyrr20KJ2aAA+EEY5N5dqDVmUKzcfH3006gDqo9hPnf0fPsxbkoTG8eIIimUoiC/V3V9gNJT6FHaaQZtP59cjbneR0P6K9f3I8cifzfu2aVgmea9RwsrxjJ3+eWX4xnPeAZGRkawbt06/NZv/Rbuueeeo+pzuaVB+lnq3NfC+pm/zXx9DXKhArkFzo1Rc9/LfbkWO/MKiJoMy9a2QYkYVS+7nwGkySV4rqZcymmBtMSO9atMskzigzk3Ro5EuUvzdm6MXPn8DTq+3NKmJVIEqSRUQrt9Xetd+VhNH+p4qwlpv7jL+cY5L3ym4lDKLy3oOTMzU3jFcdzTRZIkuP3223HOOecUlp9zzjm49dZbFzQMIQRmZ2cxMTGxtON4DOCRmPd64N7Eyu7W8mRUjo+zfVRs12+9u7xstatypVZt48aUuS7WQTf8xdzkBxG5yvaDMmsrrHlV1rkq2ESH8vmokioZ8H93ras+670+buybsb5WWdT6JT+47+7++o0D6I2fMyTWJWQJV6/CuRPFFwB0stx6t1S36zzz3q86VgyZ+/a3v423ve1t+K//+i/cfPPNyLIM55xzDlqt1lH1289dWkZVDFqVFWy+voouzv4W6H7/m/nGaNDPsui6UAFlDSwTORMLF9EMAeVgOtbNwCOikNGqlnF4pPgnok5ArPlMIQrLXfCKJyA3qcHIxABAXCI37vEKqVyaWR+LXNW5IVTaFwAEPi9ovjEmC+7VQn+OO9d8NuTL/W4+u6QPUGNNMooko8iMVp4mey6pc8efSlqIs6MkJ6lCFvtfNKomNPMCsGXLFoyNjdlXlZXt4MGD4JxjcnKysHxychJ79w6qgpjjr/7qr9BqtfC7v/u7SzuOxwAeqXmvEuXqAjb4fJ6b9NHWGRyEqlizwr6drM4yBkmRDFpnyEOVK9Udi91miZagqpJkBkZ6xHx2lxfa0XysZbe4Qb/lZp3rHi270c2NqFyNopxwcTQ3LiAnbW58XEcL9XYz9Up5Lkdi2neyvJ0hcUt2sw6e937VsWLcrDfddFPh+1VXXYV169bh9ttvx2/8xm8s+/76uVvLBK4qA7ZKlLdKjNj0v1htx8VY5YrL89qoJiuykKEJiVTSyv4ZJECUK9Qj3Lo6yz+gMlGjELmuXQW5E6CV5weAlThxSV4mKVJNhkw2q0t6jNac2yfnxGaxCjH/RBN6fCARKrtDhSSQ+tAI7W1btb3Riaten1/3QRUj3MxWdY8h8B0CumS7c7/YF93hzp07MTo6aheHYdi3q3JJHSnlgsrsXHvttXj/+9+PG264AevWrVvYuB+DWNZ5r0c0uI+7rnJb95qV3KYGC9Vb65fg0G+/ZSI3SPjX7r+iTb/kiIVmq7oitlXLq9oD1edlqbGHVbpz/fpaTP/mPPusNzHDfPdZUdutnGW80OtojqVwDCRP7pivIkfCe68lG0DqF4p55r1fdawYMlfGkSNHAOARd8EsRjAYKGfCwvlcvPkvtf/FwCUdZRLnEyV5US6h5TsJDKbygDvmkKinIFfuxE26MCj3O19QftkqZ6s8oFdjzqcSCafINHHLrWC5Ncyg7IY1YEQiA+mRHTF1Uk3Gahmu1auKhEqRE7qyfp2YZ3It1uHtPV9lYle2ji4bAgZ4/S0wo6OjBTJXhTVr1oAx1mOF279/f4+1rozrrrsOF154If71X/8VL3jBCxY39sc4lmXeG5S5astHybxtFWEqy5JUaYcNyq6cr0Zn+Xu/CgjuuBckMzIP+eubATvP+vncrOVi9KbPgrRHv+012Sy3G0RAy9Y4l6ylvHq/xvpWro/qtvNZ8doVLHO0lwgCC0sWcWEsbQEDuunga+uKC5vjWyrmmfd+1bEiyZyUEhdffDF+/dd/HSeffHLfdnEcF+J5ZmZm+rYdVJt0IYSriqyU67+6++q3b1ezbj7M59I15baA/lUZzDq3FJchCa7kSNmFCgAMqqoDs1a2XmsfQ/Wx5CK4QrfLrW3lcVJIxIKhqv6qreLQR5aDEglJegk0kFdzMPVZfV+NxSVkg9BD1owl1pmsXEJt+uWcAAwQXK1zY/tMQkSm9e+oSQghEimnBXHiTNICoaMk//0ARxG269PqSXERHQZBgKc97Wm4+eab8fKXv9wuv/nmm/Gyl72s73bXXnst3vjGN+Laa6/Fi1/84sWM+jGPR2Le60/WFkiOBmnR9ROHXSiqLDmD3KlVshblNlXbLXQMVRhUYcIlWy7pcImdkMgTH5jTlubLo4pK8q5QcGE5rf7stnVJ23wlxgzpc9uZqaFseRS82MZY8dyEiKrzadyrQDHxoyvyc+rqySW8t58ky/edLTVm7ujnvUcTK5LMvf3tb8edd96J73znOwPbXX755bjssssW3O98hA6ojrcaRL6qSN5CCF7V/ga5V3ssY07bQZp1VaW4PMKRyfxHnROufOLi2oFqyJvrol2I4K9p45YWSyXtaROL3p9ownMiZ/TmzLVzddvKsiSUyJ4oUWOBKwvuzkfopCgSRCGUG7cgJSIIPCbsufCYQJrmYw8DNelwoeqvMirBnHGoY+nNcK0SWu65jkfjG+jnbljkTfniiy/G+eefj6c//ek4/fTT8elPfxoPPfQQ3vrWtwIALrnkEuzatQvXXHMNAEXkXve61+GjH/0onv3sZ1urXq1Ww9jY2NKP5zGCZZn3KhWy57F6lStFDOoLWFoM2WIJ30Lcw677dL6KEIPGNMgaVxYuLu/f7NsdT9ldS1m11c1NfuhXMSLlg122VWTMtcoZ8tLv/Ic+rHCxC4+pl8l+zniRCBnrnz0XzngoKX63bVyLn37IcJMZOmlxDOaY5vSDS42qNktNgFimee/RwopJgDD4wz/8Q3zpS1/Ct771LWzevHlg20suuQRHjhyxr507d87b/0ITGMoZqgvNVAUGJ1BU7W+h/ZvxDDoGRqTWiBM9wr0M0rpRPcLBNGUDikTOfDdac67+nE2igACDsGSvXzavLFnoXPkNV+LE6Moljts08AwZKrpSuaDWvSokKSQu9BAjO7/lmauM5TVXqyAFCskVxjqYcmozZKVDJI3gbzkhI05YoR6sGXu5nUvwXLI4UDJFHsVfe5kCgV/5ylfiiiuuwAc+8AE85SlPwS233IIbb7wRW7duBQDs2bOnoDn3qU99ClmW4W1vexs2bNhgX+985zuXfiyPESzLvFcOUh8YrEths1zNy8Tc9RNlNCgLyg4KwF+OGrA2W1TmLyFykmVffax7brbpfAkXBa052dtfP+Hhqn0aDIo3m6/ig8lMNTDJCuXlg9yPJsPVvMrbuNc89BSJmw+DqklYS55UiQ2GmFuNQNlL5FzMdHMCN+dk0h+tePAwAeKXAykl/vAP/xDXX389/uM//gPbt2+fd5swDAcGZ1dhyRmA6CVc5VqblVmUJVdsmWy4GZwuBhE217VX5V614wMpyIFUSqugSNgAFFyrZaikCGFj4IgmifYYSfV5yCSzWnMCKluTy5ykudUNjBUuK7lWhcxJ1qAyZwPJLpXWole21Jokh8yxDJrvJvaOUAmuBYF9lltspSBIJFFE0bHkenqM7nUwVsUq/bnyb6Gsbegmvyx5avOXbwK76KKLcNFFF1Wuu/rqqwvf/+M//mNZ9vlYwiM+7w3SDFtMgoRb0mk+i5bZbzn2qkxqzDKbsTmPZW2Q9a28fXnsVUXbqzJaB5Gyqu3dfZjtC9mpDrFx13HtfhUoEqx+8Y5uhYZy32Xi7VryKiVlWO+1p1QRuYwXx1jOeLbZW3q5sRymFa5RI8liduNW86g6zynPrXNcAnOJmqdcF3Ynq76WC8EyznuPBlYMmXvb296Gf/7nf8YNN9yAkZER64IZGxtDrVZbtv0McrUuFlUVJqrcpuXfrSE+VVmTZowGVWW6DFwC5lZrMDFyvEQiTebpoGMAjNVN9pBBromckT9JS4ZfQ+yozmYUIEhk75/HnCPhVDwIKEcn6/25ui5WLoiWWlFj6esyZzlJ8plAymkhbq1M6Exb970fkjTvK+UE4KYOa/9tpAASQZW714mJ44KAsjxmbqFZzEeNfuKZC61POcSy4Zc17wHovSH3TWDo43YFNDkQRQtMVXak6bv83aCqVqddV7HfMklaqE7cUhIa+rUpu1X7tqVFd2sZgWMRA7SVs8KVW46BKy8vwyV8ZdJcRdxd4uha6Uw7N7vVTawwfXL0Jky4YxlEuhgBEseFnXJF3rjICZyRkWGkmDSxVKzweW/FkLlPfOITAIDnPe95heVXXXUV3vCGNyzLPpaLxA3CYm64VYkSC962lEBg4MZWKWIlC67UPDFBuWPd2DdD9PyCxEj1fkz/OTRxdbJUq9BPCNdFLJiSIylkrRa3Y1QTVtOG5csNTIJHqN21/a6/kARpSq3unGuJS3QMHJM69o1TcBP3ywSEIBDWhVoaC+mtyuFa5FzLHNEuYzNWIE84AXSlC4GCNMmSsYJcC491LOu8V75ZueSjEK/kuu2cm3fVtm57l6BB9P/uYrFu1n5EzrwXkh4qkhMWStKqiFa5NmhVwsMgDTtr0RLokRiZL2OSy+rSXuVM037LXOJnsj6NK930734HlDvVZ0Cc5eNnBEh1f6YwvbmuPlQfcaqJIM/3HflAO8n7NkSuygXOpbLACamSIBJeHJchbgDyZBGNuQTwlngfX+Hz3oohc1Iuk9XhUUKV+9TAp6LgRvS0bAhQnSxRBTeL0cAVBOZSyYyYYHlXdqQwzpKlj0FY6RFDzsoiwy4BLC93SVsVgWMQ4CULniE56jwwPX4nsaAqCcWxphEiAd3GWLpc0uSWDDPkVpYsfO42Jq7NJY9uTJwQiliadrnhQn1PUpbHAgoCxjikIOD6HkdLBIwLAkqISgAbELs3CEKSpSdhBWzF1CN8rOMRnfcGWRxshQWJSkscc1xpVrdugDWvvN6Sl4pMx0F6ZGVUre9x0Tnfq3TiBsXHLaRP09Y1NLn7qbK+Jbx6uembEXVeTcZpmWQMilkESq7ZkgXPY3kMpElgoHAIWmlcfoUr1sZQOsuNe9Rsb6x1jNoH2L4wJdpSnse+UQJ0HJHgfphqA00nrMAle4vBCp/3Vu7IlxmPlFVuUGkr141qsinL4yi7PQ2x6kfy+u3PL1VxMESO6RnI6M658XFm38Z1WtiP04YOoA0eBDLnH8+g9N3sdyLV/38e9zaXymLVrnC1ArlV3/ZLhbVsZZyCUVEggR4TPdmuqh8JUJPM0LufjFPEKUXoC6RcjYlziizLRYnNvc3zgCwj9rM5/0lK4TNpCV3BHe5Y4xSpdedtI3pMwAxJdaxzZSyZBsxXtmeIxx6MValM8Aqu1wXE0JUJW9md6v5RF1yj1ZC8UgJDucSTu65vX6J33/1IXL/4OGNJcglb+dwZ8mHcqUCvNc7tk5bWVWnL2cxcJ7nEWtkqrpuZPMpJDS5ZC535lFIISiD0es9IfLjthYAIPSu/JCjJpZiofjoNPX1+9b5MP2Z56mS+clNtQypNObsfx1pXLunVyYCa55xjndFqiNhSY+ZW+Lw3JHOPIBaiA1e1zE166FeBYqnSE7l1Tf0xpJPMkOuTSfja5Vp2o1InC9dUdRAOWXPJZhUBdK14JtEByEmssaoZSxwjEh3u6TawbQwxM1ZNinJmvLHE5ULAXCgLXLlm63wQgiDJVDkuIVTFiW6iLYbOvJFmxdg7Q+TscaHXCmf6p0wq8ugcAxcUjKodmLg5A/M9rwBBVKygzGMzlzwF9QsEXuHW8SEc9LO29XOr2u20m67QxulrYGamI6UxSHdsPgtfGWWC2ZNJ2scSV8agJIkywStb+MruPnf/5jwaa5wrl1IYk+6D6XVVmaVuHGLk5zFwQP7ZkI+wdHuvBb3HngqIWoWOHYDMY0grsmAZF/AyDiokBKUwTiVL/ljF9TNErXwtzO+h67hgGVHu1U7FNq7unIuEKzJ3pNtrSVwoVvi8NyRz+OXEys2HQoUG56bNK4hdv0SKcl+mv9AR/DVJCwK0VF7LsQ457lIKaV2teVtF4IhDDA2hK5NNCQIf3LpSRSHb1bGUaW07rkt0MSJ65DVcy5oiR8XrVk5W8Gjxe+jlx5vp2DRKivF2ZX26blL8i6hYPf3AqcmaIXmUSqQZLRA2RiUoVeSTOckN5qFZCqIC7qASM9x9G9cxZZrgoxcu4bbyJvo3ky71d90vdmSFTGpD9IFbb9VdVtVukAXOZis6gfBCx4Fxqdx49saOoqXEEA2OXqtd+XM/DBQQdgjZfIHr82WilvtJHCLab9+VKPgi1XmyReW1GO5CKkH0kxcpZ6a61jfjqu3pyxDAfJ+ZlhyhQiLWRFBSCiIEpG7H9HWNQx9MH68idQSZx+BZD6eArbNaBUM0GwHQSorjL8crMlp8agaKJN1oy021VduZGEvCCp/3jlky168c0y8Tg3TCDFz3a2VmqbPMkhkoMmSsX+Y7UJ304MbOBSVBC2O1y12u1keSj1EKAALcyVJ1IUB7YuOMbpxbe5USCcj+yRHu8XJJctJk7wPCnidGc/Ln2YQCY6GjdpkhdcZqZ8iUFL1EzqB8/zOxcULoGq0V66l2xxIi7bjNvSzjigCyPr+HPJaPgrFqFwIl0iZP9KvNu2BU3fTN8iFWPPqVlaODrGGuG5ZLh9D1gSF0xgIH5KSuyg1bBZc09XOjDnKvlq1y7r5MxQDXlQc4hMJ1wzr9FPTlRPGcuIkkLiExhK18bC55KSc3UFokZ6ZaRD/BX0OO3BhH049ZZ64bI8gC3/4O3OvOGUXqE3CH2BWG7THrWk19ptfre5T7uwo9lThB9fqCSLJz3csWNlaKlzHr3XZcW+1MnB0q2i8FK3zeO2bJHLA0i1zZ7dkvO3XQjXQQiSuvWwixqULZDWcSH8pEzuzDyI0YeIXMVtEzNr8ioMwsM9ulpRIKxtJXdXwC1EqRuO5YLgl8TdLKFSKMuzX0uC11FWf5pBkwbhNLqqoolEuAufIjVt/OSX6QkiDLCDxPot1W+zGHKO09hQBZ/jnQJcJ4QuB7AgDp62pNMworWlyqBmE+p4IWMlp7+pHAUdef6eduWCFK6ENUQxAyb31gGwNVipOywe6uGK8LY80rrytbWyy5W4AFbhDKmavucrM/O/4+rt8qC1uP67Si//JyQyDM37JvwoM5bsf1auLiyjpzZp37bmBi5Awpqgf5mEKvv0XSZ0gCD1yPj3FhyZuxrgFA6iQBUCELxE5QAuGp9X6S6Rg7BkkpWMaR6G2DbqLLY1GgnSoX71y3d0yRY6Hrpr3rAXVeAqYyVc25NO/utagifovBCp/3jkky14/ElTNCF1IftSqL1OxjKZYRQ1DMu80QJRKZpLbPslVxvpJP86GcqaqWCUvkXPJWlcUqCm5YWbncwJTx8olAKl1nb77eHLtPBRKR/8FMLVJe0qer+ZpBebkljBAJxgQ4pz0EqqxPZ8A5QTtWfwvuWNzSlMLzVB+GyAEqmYHSohCwGl/xd2EyXj1PWkteVexeWUDYLuv30NDnwYAdrWhwlWtmISWQhljxcAkfFRLCY6Ugdyhi57pP3RuezYQtJUBA5ISniiiVkyfcHzClRTkLF2Vx4MLBzOMO7acVt5DlnSy/+RfG4ArrllyGXAAJVAC/iypZkTJCr5j4ACgiZ7ZPuSq/VVH5IdMWtWIMXL4vQQnSwEOmrzUVvdY240o168ouWO4xSxB7ji1O88QNV6LEypowRebKRMw9r81AxcS5MA8ISZ/fxmKwwue9Y5LMVcHciBdC4BaKhRK6qvqoVfBKum/ljFi73I2/c272UpoSWXlbk71atpoRh5AxaVyswsa82WMs7aMsR6LW5TF1huAxSCSgBa01Mx5jMbM1TYlAIpmVFQHybFyQfAy29iotEknm8UK1CFNaLOHM3jM6sYfAF32JHKBi5JJEZbPac6rrqqZQ1R88T9VnpVSRu0KFDZpbSZWMicq4JbptIdnEHGdJmqRc2cOed/07WJbEqxUunjnEwiEo7blxVy0zn/u6Yl23q5UqceLqyvFx5qZpKgP0E5E1RdcHYb5KE5YEVhC0ykQGZ73rSq2y+JStQ66b1pC6cjartV4KIHCSDwZpnBkiZzTbzLGZbNWUK2KnY+GEtqYJx8KahGo7oq+PIW+qHQEnFEw/tGceA9duB5/z3LXKmD2OzMtj5oJEPUwbIpcFPrwkVe9prEkmzWP4Uq7IGy2d17Jr2YUlxrzXDb4cWOHz3pDM4egJXLlslwuXbC0kRs6Fq4Fm0NNHaehV+zBVH1yYMl1VoJDwwAskrl//FBJUCghCe9b7UlhXqweuiZ4mS87AZalaA9OkNQ+JUzF/jObn2accqbGoCWqJnk94QU2B63UeU9IsrUQ9yXJt/Uwzaslj4pA0RiXaHQ9JopalnCIKeYHIVcEkRah5i4Dr72TwZhYuMZeS6Cx+nUBBB9y0KvpZumWO9nlCPQq32BC/chClWCD3e+86WIKnvhNQk/1qyJKVw3Csd7HotY71q/qwkCxWKy1SESuXlCyFVb/XMnGosgS5LuHK+qsVcXGJQ9zM+pqf9+9mjJYtdYGnx6r/c5FzW3YzVkNHeqNsRQp9QIiCFTXW5E1QYq1nAOznrKLGKicUnBVJfEzVfgcZJpLAg5dxle8SeGBcINNENWuE8DpJ70aG1JmKDuM14FC72KbmFd2rjKhz6Vrj3Dg7RoBkifPUCp/3jnkyNx+Rq9Jz6ycL0i/D1Lr0KuQ6BrlDjXXJiP8aS1MVuRsUW2e2MTFz7ngZUVIjKShqOtirfHzuuHvInFQlwJjkipSVWItL6NztVb1WY6UTyPREFhCOjvTsuTFivubcmvg5AGh4GRLB4Gv5jvL5Vw9UuVAyADAmrGWsm6p9ppxasmWqOAihPhOax8MlKQV3slWNO9eez8y12CmLWyyUe5YJ2dNeiKJMiq0CwQl8lkuquFm6PhVWaw5MFK4nKenSLRleH3dDtjImtSEWhzJxG9y2FKQP9FourPivY4VRG+TZrYwCwnl3YYiUIVsuYTT9lrGQ7NJ+wfJV2bFV27rivSaBouPEeRVqq4q8jfluLHOdVPdD830KvS0txdSZ+LiyCLBbwcGQMsqsVEg/y6qrI+eu44QuKJ7SvHuZQMqYtdoBitAZUtduKBFfX1vsvE6iLV9O2a+05CoGFJkz595kuppzbs6lOef2WmhCmPDq38FCscLnvWOezJWxkIoLbptBxG4+QjcfkTPvVUStbLWbz+pHHEJQmI9L2xkRYVaRjk0d0kYgQZ02QmeyUil6zFCG0Lmu1sL+ibHcUUcst0hYfSp64ucAVbO1fK7LbRiV1krnE4FWVtRVqqrwkGpilnFirW1pwsBFvk5yAqLdoGEw/x+ei6KbKgrzbUxSg6nPamL5GJXIBIFHZZHEG0IJYuMIlw2uKGl5+RArHv3I23xZru52VIjeZIlyJQhbCQDODZz3WuDcSgGC97eE2GxI6ljmHEuY66rtV72hyqpmlrsu1fJ+NSlBx43PcspcuX0XpExkTjpMjF1VkkMV6kHxycw8qUW+stJRmp9Ln1pLGKCscuXrOYisMSkgHPGjqraudS8N8s8+59YlCwDwlOacoNQmRojQU5Zc2wHPj9GUC5vtAqvrSlqkm2rix/Lkh5pD3pphb/xcwJZe/QFY8fPeyhjlMsNkTbqWHGWfWrxJw2w33/auBaXKwmbbVWR8lq1ipGSNqXoNgq2x6pAAU2/VFQBW7lZhK0Awye32hsjZxAkpwRwrHZUCnuR53JpUWbRE9+kS1CrrqBlb+Xgimv9ZfcoR0gx1liKkGXzK4VOOhpcotywRtlapCi+RaKVqwuNCuVc7cf4809FCwK7Ybz9IrsptZSmB5ATdDkOaEccLVN2HWU5pHpcnBUGcMRCa/07c30tZWsVgUDbzUUnuGBdO1WuIFY0qImcsNv23IYV3IHfRZR6zwfXCtWz068/cMCnpFbU1N3iT3emiynpWlqtITTUBp21ZRqTQl8itOWXpEfMy6113atWYOlluvXMtRGY8Zp1LaIKStc1cm0agXkB+HtxMTy6K2cY6Ts5LqrNBDQlTiQ3Og/QSszTd30saeIh9z2bApoGHlDF0wgBUCGSe2rd1+XoMCD1kk2PA6iawfiw/fmOhq3m55lvkq1c5njBgebuxSLteZZ4dvBQs07x3yy234KUvfSk2btwIQgi++MUvzrvNt7/9bTztaU9DFEU4/vjj8clPfnLRwz8myZyLxZA4I1nhvgahTBgNykkJg2CC9c2rqp7qQuDuh4NVEgFVmqvYf2Gs+jyVZUkYpH1RqV5li2VKqCaqxcoSRm6EIXefGk08nygNPAodCweJiGYY8WKsC1q27/I5oZAIKUeorXYG7vXqpszGyAmhKjqYRIdOzNDuMLRaHuIugxTExs4lMbVEzp5PAaSZInVVECL/HeSJFfl6UlGb1VaMKDyYF/u3Mi59fotLJnRDMveYBS25KOdzrZXbuTfyvu5Z8zsplYqylqSyVpobyG/bk5z8CDE4OQDoT/7KqExwKCdGOATMfLfVCEokL+FFV2vZdevux6wvWwQBRUaMyK5btsu814P8PxhWV2yATlwRlFhBXyBPeLDNnO/9yF0/fbmy2zbxPbvOEDrTrlULEfs+Ws0IaeBhZlUTmcfQaUQAVCwdgGJd1ZEImGgoMusS9/L13zQKrB9R7wFTma6GAM73W+mHZZr3Wq0WTj31VHzsYx9bUPsHHngA5513Hs4880zccccdeM973oN3vOMd+PznP7+o/R7TbtYqEucWZF/ozdDdptw3QVGyol/sHFAkToOsLuXsUbOsXPC+qh/u7NtazbQ1UIJAgCKURVM11eSNyVyLriyfYSx1RjjYIJAZusSHL3NRYabrR6SgCHR1Cg6KECli6etx5/viRJX38okaV01b5yKaqVqvsmhNBHLrZY1lmlRTtCVF4HEkWfHPmXGd5JBRpBm1EiJxVy3vdlT7blYkYpRKXZMVensCwvI4OGmsc7Zma+7ONTIngHLzBp5AmlKEQTGuJ9Xxcz7juRiwkV0pXVs3NhAYHLA8ECu8RuEQ/bHQ+LgeeQkHhigUiWBOJACAFix0OqvVjQ/r6VS3CT1lYTPxVOUbeicrPgn1WNVExfI+/wNrhTPZprS4rRuLBQBTnTz43u2jkxYLtBt3ao9uXCnGMOGKhACqj6YmbGY7k+hgSHHhyU5b51xpDw0j6OunHJxRqxNXhrpe1BI3Cm6THQwkpQXybohgV1veACDxPQhKUe/EdhtAkcjMo0ipB04ofM4xN1JDrV2q0LCqrs5NN81drhMNYHIU2DGljtVIuXCpiJ6QyiJ3pKvO21yivs/FSy9KvUzz3rnnnotzzz13we0/+clP4rjjjsMVV1wBADjppJPwgx/8AB/5yEfwile8YsH9HLNkbhCRK392URUL536e77pXSYmYm/Cgoun94FqlFkI+LUHSbQ2ZykC1O3X+bamUEIQU3u14SgkPQE6wUqf3DNTWf3UzW0OS5oLBOoaDS4Jx1rFWvAZJwUHQkT4yyeARAY+oEmE+URa+TCqnLieqPFgqSIHEpZwOqPCgRIFdV2nSLR6TpLLSlSq5kikBgDAoXWcBRJGOSXRKe5WtbyZ+jlkXcbFeL9Bbq9WFKN1DFo1+gcDpyggEHqIaxWzV3h/IIAJX1c5oiuXkrlfSxPboMaiUQ6oWZjwncJSpm3gVyXOJm8lmTF0CRlS3LiGrkhCpInllbTLO89g4d5+dtNpdW2iXlSxuBIAmZpznZMSMj+lkkIQDo0ZqhPb2zYU6N6ZIPRe9ljlD/BgBBQUV3F4HKqS9Pt1aYJdRYeRGhP1dpIwVxIPLEIRAsGpimDKGxO9PJzKP4YjvIYqTApmbXtWAlwlEgQ9vug2gC4zV1O/lwCxw/GrV8LDOcm0l6pyVk2bGopzQlTNiF4p55r2ZmZnC4jAMEYZhb/tF4rbbbsM555xTWPaiF70In/nMZ5CmKXy/jyW2hGPezWpQRYSMm9R9meVum/n6qWznJlGUZUMGSIZQLbbrk15pkX4u2H7CvYAitUzHsVHHElaw/mn9IUO6DIGjkPCk6EtAXa06AAglt65WH9zG5BnLoHvsPhHwkYGBY5x2AAARyRBp65xy6wqEJEWdJuqdJKDIMzwViaPKqkclAk2ShM5mNfeOlBMcmQ0KljNutOXS4rljnIBxAi+lIIKAlAid625NMwJCpX6IlmCONS6tkDjxfbFga1r5N0MLFjnY41wSTIp+z2s4XTxWsJR4qfJN3pA6zmhPpqQFG2DtKGRiVXwHinVG+1VWKAyqKjGiYv/lKgL2JYvrDJFz1yVcWYTm4lw2o6r/cnWCJFPbuPtw26SO2HBZHDjO8radRFsv9csQYS71ZwGacVAh4WUcnq6nGnUSRe70d2uR08TPd+qfVhG6zGOIfR0j55C66WYDnTBAJwwQ+579PDWq3Kpz9QiZPp5uGGB6rIEDa0ZxYM2o7pcqMeO6D6wbcbJzK66bm+UbecBkU1lLGcldrWvq1ddkPswz723ZsgVjY2P2dfnlly9tPyXs3bsXk5OThWWTk5PIsgwHDx5ccD/HpGWuPI0Jh6QNcm+qtjqcoyJ+qWpbrqs2VGW9um7Ocl9AkdT1c8uWiVqZFLkw1hxl5YFjASOFH4LbpwCB3+N2nf9GYKx2xCFq5bGaeq0UEgE4IpIhBgMjAiE42sS352YMHcTw4etsWwmCkGTgoIjBQIkq92VKlAVUS38QCUCAS2otc4fn8qepdpchThh8T2C25SkVhKRI5HiLwTeJDRQweQhczzkyIyAOUWN+L6nu52IFcgkSzgmoFh2uBTqln+YJIKnWzCu72RdrzZ0XK7xG4RCLRz+r3KCMyDIhdLNeTTsPOvNaCIB5qrQTUJIsQU5aXMkKm+VKAAHYJAdrjXP2z6giSwUdOVJhfStb+mhujXPj2eaSInk0LlcugdkY0IH9SrbCtHOsf4xoF2BYjOEKPNWX6zbsOlIbXe2iNRwxcKQ82knveQs9lfUpRH7ezHnkyk1rSJ1B6jOwjIN7uSXOWOjceq1p4CFjzC7jpEjWY99HJ8xlReLAt78BQ95m6jUEWYaUMXCHAMZRYN+pEFjbPWJj6eyhhR6oiUdsRsDOKRU7uEof50xXuVwPd5xKHGJ+kel+mGfe27lzJ0ZHR53xHb1VzoCUwpOklJXLB+GYJHPAwi1oefv+rvOFVo8whK/sauWS2qD/Mua7UZcJXRWJM324EhZS67ulMo9bM1DOVm63Uy7p0sRd8b2HnEpjxRMAocgK0iQCHExZGlEki6bdCGJlxdOu2xoSK5nSJT4aMsEMiVBHijZ85WIFAaSqOOHWcp1JAnBBCxatROvMSQEkCUWSUASBQKvFIDNlgeNMWiJHBQEVgKASVBC4Ne85YAmd0ZNz7wW5koKSc1B1tEsWWV2TlZbc+OVrapMjlpvEGZiailXLhxhCo3Dj93OxWjf4vgehp1ysXCpiYlyHbvkul7CUxXuBXquWkIqQ9asKUCZyxrpmlpWrQNgYuXZx+4QDsyVLXMCUpcx8NtUdQv15LlaEruap5Z1UuQK51HIbniJ9Y5EiJ6ORciVGpdi88s0n5aqNe65MOS8X2kVL4wxZLQAVEr5ub2LbytpzaeDDy7h1mwpKbbxzquuyGqH42PMQZr1yIG2H6LTDEOOtFjLGEHuKII7P5Qlsie9hx5Z1aHRjTByes5bDHpw4Cdy7HxgJFbFdVVPXZPOYOvaZGEByFAkQg+e90dHRAplbLqxfvx579+4tLNu/fz88z8Pq1asX3M8xSeZU/cscgwhc1fcqD4VbdaDKqm+02crJEIuFEdIt7LsiIaIKxk0aalela9WJweBbGRJhy3KZ6g/l2LhFjRmqSoRHCCCpjakjkKjJFDFRVyOUHCE4UkIRQ5E1APAkR0rU5GDGw8DhSW5J8Ci6SOFhDoFdb93GkqLmpZiLfbTiAIQCMy016cVajmS2pf4KJmvVIGpTSAYQDvtO+5xjY6GTnAAlohZWWOu4IAj0cqpJHBcElEmrMSckQPX1JkTCd2LtUkltOTaz3sVSEyAEY7YcUHn5EMcGyhY5U4fTfDYgTtIDUCRyyuqTx8VRoePHPKb+H0LkLrWCHEnpv8Jobo0bhHJiQj8C6FrvrJu1j+vTIOaKmAFOxQbkFjR3HwFTFrsMyoLHBXAkBsbCvNpBR9cqrXkqYaIzB6yt6/HJ4tiFUBYpU/aMi7yk11RLLWuGan0nARAowgyoAvc6kcJY55LAszVa3WtnXKd+kiIN/IIrNfU9JJ4HTx934uXUIfY8xL4PQSioFAg4L9wvEuZh/+gYqBSoJeqETTcb8LRbl+n3VhQiWeeh0epi/Igie8lIBC/joD5VxNTIl0S+smj6+jinVSgOJuo5CV8kHq157/TTT8eXv/zlwrKvf/3rePrTn77geDngGCVzLnpj3tCjaTmffAgA7b5UH02ZKaNxlme75nFkJgbP1z47LqkW0C26SftVjWCQxZJYOomCoZfslSFACxIkhmi662OislpVYoSp7gBA9s+cBWDFgwFloRMl6RcPwiGF6o8YyuIs7UuBVWJGyZnotuO8Y9dRSIwCaFMfnjPDE+2u7cCDBEEs1M97NlUkzgXXGawZJ2i39cRmNOBiCs56iVDJgAkvI8g82dOWOQRNClKQHvF1PUNCpCVybl1WReCqrXFV5KzHQqpPNV+AdE4/KHdLlR7Z0DJ3LEMOuP6SUnAKVWxdkwbODLmjuZvPBJibrEVGAJv1TYpisiYmzAWlSogXAALoChGlODMrI1LKZu1XAcItC1WWGmlnvcTQJZa2mgNyotURKn7LatSluXs14XmgPpCL3BoLwMGWss65JJHL3FrXSpRlqmxNjLP83HaSYu1bRyLGJW9ECAiHlBlNuE5NV2/gvEDoDDpanHi6XgdzbpScEqQ0n2ebSRfTUQO1LEZKPVBIxJ4HzmJN4HzsGpvAlulDCFN1HlIp0Gh1MT3WQBwFGJ1tI/Y9bNh9SI1x+1oASpQY05q0Hemo4x2JgF1H8gzhRWK55r25uTncd9999vsDDzyAH/3oR5iYmMBxxx2HSy65BLt27cI111wDAHjrW9+Kj33sY7j44ovx5je/Gbfddhs+85nP4Nprr13Ufo9pMlfUIEPP5wWRONOXyK0nVf3nIH1j6ICcvNnyVY5rtLe+qvOd5ORuPtFgIQlAKBiU3EUqqd2P2r+ybKWEoiazvO6qFDYmzR2vCxMr5343JnpPcmSkODkocieQEPVnH8/yp6qUqT9lKDliwhBKZY0z+x3lXdRFimlW0+cASMHgE4HprIZEMCScFs5hN2HoxmoMZXHgJKagsc6YnXNS/TMVKwcAnEpL6iQz8XPazc6KbtY0IwgGVIbgnICx3NompMrGLcqL5O1dqxxQdKmXCSAjsmzfWDBSn9kn9/LyIR77qLLK9V1P1W/CjZ8zUhjKkpfX+izHbuV9UGVJMm5XIM9W9FlRMNdFwvN1LmFzExbcsmDl5AhDrmzh+xKMS7Vcr7WTOgTQzZ7luWjtnOPui/WY3Dg5l0gaYjelY79aiSJ0QO56NXVfGVUxe8bN6p4rICdvbhkwIYGUI6v1JzntWggihNaMo5bEcUaRMWataHO1CG0/sA/Zc4EaZ8iL1yijDHsa46jxFB0vhCeUgHzXjzAVNlX4jJOpPFeL7H1j7aEjiKMAvhZBjqMAByfHMX44d81aIgfkrubIA05YDfxsX9/jHITlmvd+8IMf4KyzzrLfL774YgDA61//elx99dXYs2cPHnroIbt++/btuPHGG/Hud78bH//4x7Fx40b87d/+7aJkSYAVRuZuueUWfPjDH8btt9+OPXv24Prrr8dv/dZvLft+5rVslXTl3O9S9idUVUkSbvyc0JmlhtD1S8YoJ0zYscjclVq23JW3VYSOQUpiY+YSsLzag2PxynSSgtnWuEmNSPB8YFJAEGWhMxm5Rrsu0OQskFwlYkgBXxRjMCbiFlp+UPjzGz2kruf3uJkF1HmmRKLLtTI5J+imxZ97klDEXSVBkmgS58cErEJyxNPJEJQpYseNbIggEPoz8aSyxnECAVXiSwoCv4LQGQudS9BMGS9z3T06/7kFjkJPrg+4xyq1qfrpVQ3xyOKXNe8tBP1kKzLP0SurIHZqOUfmaXefT2E1fFypEpe8uS4SSpUlrqMtUC1NtGxMnSgmLDCqXLp2IBVErmy566T5snbm1ADVbtW2npc8AmRSvXOhyFrdV+8AMKJJkynnVfNyq19VJQnjvh2L8koSLqqkVgBtrdNzmhs755KPOFVxdCVCYhIcAJXI0NWxdt0gAJESnTBALU4wU1eZoYKQwkO6IAQdL4+L21VfBU8K1HgMXwh0mQ8iJQ6GTXhSoJGp0ltVZSJbYQhBKAJdHm3HxnWoJSnqcYxWowZOCJqtjm3vZRytTavQmNXLZrWcyWxXnYPJkerzNQ+Wa9573vOeZxMYqnD11Vf3LHvuc5+LH/7wh4vaTxkrym+yWGXlfihXZhjkUh1U9aHqu9uPkL3xdVVZsIPGOQjl0k9UV4hwhX2riN8gcC3mm8JTMWgk/8NKECSOZY2D2D+5+aOLAdk3ptSXLzIEkiOQHHWe+xMaIkE9y7+Ppl1MxOpprJEmCHhmLX2e45qti2JQcgCORDDE2pXaTT0wKtFNGDhXsiSxjo2r1zJITkBjiqBlrAz5y0sJaEVlB0P4DJHjTNp6qcwX9rMLQ+BcWNeujpez/Ze2p0RluQILr8e7VOQyE72vIX75WK55bykY5Fqtqgjgvrs3Qe4xxKFTIcBjveW8AKfKQWmdqzdnPpvJ1ZQIc8mYm+zguk2rLHBuua2Yq9eRLmy1h05aTHxoa8LVzrQbVubrEw4c6hStdx3dbrqrlnfSYlxXwpW8RsfJqk1K1jbjhjWu57IAc7mShbkuoQ/4uXSMsT4JSpAEnpIb0Ra+dhRCUAJJCGLPw3Qjl/hoBQEyyvDwyAS6no+u59u5fjpQ7TJCMeU30GXFOC+zfMpv2IfwFsszV6ejBvbVRzEbRmgFyuoX6+SLdhgiDgJMjY/g/u3rsXvjBKZWK7KW6IxYbJnId1YPcqvmIrHS570VZZlbrLLyQjDIpTpfzFFVPFuxQLyy0BWC2YmSmDCxcgQSqaCOFU5ZwQTJY9MqZUkqaniW19nyWANu+irrUzqWQIIIWSH5ISUUvs5eolIg1X9IJjkgHStgxeky+/aFsEGxntGb008vkUitmX5NexYH6yMIRAYmuN1XM+ki4ByMczw0vlo/4XF4Mk/W8KUACLAnU3/2MT9BKwkQZ1oPixPrYgWUcHBrVvXPmYSn3a6UEwgmwbQWnHSIFeWOJY4DwlPbum2SmCIIteVNW+UIzYlbGHJwQeBrtyxzYu6qrqepyVp2s7og9prnsZpLReZRW3uzvHyIXz4eiXnPoCxJspQbVxWhM59dK52kFHHoIYwzJ4ZOC+U2QhX35bphfZbH1tlOK5Ij+pXOclFObnDdsIe0hce4W+dKFQpiXtzGRSELtrTPmCsplrqvCFzNITkhyzXqGFFEbm2jdx+dNHexBjqchNKc4CXI3bk+U65XIVUb42rlElmUJzxQIdGpq4f0di1/WK93Y7SjEAdHR+AJgYyqmOWu56sqDlQqYhY2bOhNh3o95R13BuP2npWB2bjmUd7FYb+OulBz/Zxj2fMFR8x81LIYU40GGkmCvavGUY9jeFygHYaYmJm18WtK0iTOpURWN9Rr1zSWipU+760oMrdYxHGMOM7/mGUF56VgULZoFQxJdDNhy4Ru4L6kusGbm/UgYmaSGkwdh/lKkhkXr5IIySdN425NwBCAIwMDiGofON0ZyRJb8cE8KDuVItS4FIGDzAmwAAGTAp7kCHmGmHnYNDsNQQjWtWbQ9gNsmppCrINtU8bsk+Cm2Wm0/QCcErQ89RTmQWAfaaIrPVW6i/voZgyMCXAd8OaSJiEIul0K3jLByxJeSuClSgjYdbUSQSxZE0xqeRJlneNCWr25LCOIajrblkILBpOClc7zZI/VTSu3gDmacv2wkIzVheglDkJGWWXgc0aHbtaVgMXMe6aKg8HACgDzED1zo3XLernvXiaspc/cNL2kNAeagutxpuQn8oyegfsuFLc37atqrrq6c2XSZixiboxcXOGONX2V4VahaGd5/FzMFXkzenY1LyeIgZe7Xo8oV6Ryt6bFcl+Akt6IPOW6DRwx5XJdW9eqyUiPdtr0WAOdmrLCRXGKI806OmGAdhjaOTajFBllaPsBZjSJTKhnw2M61ENNKE9JSihmaG4NSzWtCLUfPYUHHxmmWQ0jIrYEsEUDNLRnplwTvIwwSdCKFPnLxpuYmJ5Tnz1WjMM0rvolYKXPeyuDci4Rl19+eUGxecuWLX3bzps00IfEGatQ5avkdi0TO1OdoAqupTCV1G5fjoMzfwL3z0C1vAgAeIQvKKYqhWfduu6xZjrzNYaPlFAkYEjAkIEiIwycUMhSxmplrJ5QsXC+4KhnCdZ2Z7GhNY3VnRYmZ49g85EpNXYp0ex0sWlKfQ+TFLVucdIlQuhEC4qQp2jrRIlVUj1hr/KUCyMR2m1qKkJwYmukzjgWOQAYmWGWyBXGzdXLVHugPHe7cioVqeN6nb6UvieLBE67YH1fWEutWl50x/KKWL1+v8uFkLUlS5Mso7vhyiuvxPbt2xFFEZ72tKfhP//zP/u23bNnD37v934PJ554IiileNe73rWk8R/rWMy8B8yjC6cxSDy4t22eFSgoscTN/Ia6tSD/TXlaj8283IoPI5ogRL4Sh428POh/PpS15LjI49Hcyg4xL27jIuYqLs6tqWpfZpnzbl7l82li68r7MS5cd13gkD6DfkRWiDxmzsBnudacc404o0hCHzMjxeoI3dAHZ7SgC2cwHdXR0e5UQQgyQjHHQsyxEJwwtKmPFg0xQyPE8O3L3P9mECEFQwceWgjRQYBpWsec3gYAWixAQj3ENL+u02EdU7UGAGCq0QQAdKIQnShEquvAAkCnHqLdCIvWWmOdXAJWupv1MU3mLrnkEhw5csS+du7cCQA2ls11sVbFygE5WTOfDcolvqpgCF2Z1JVRrhNr9llVzcG1zJWJnMmQJZpeFQieTggo3+DdsXHn59CRHjrSQwql+ZaBgoOhS3x0Sf5nSYiHhHjICCvEzpWtcp7k8IWyxI3GHYx1Owg4R5Spp7dammJ8roVmp4s10zNodGOMz8zZ/TS6MWpJilqSYqTbtfvhzlNnl/iQIDiQNpFwijhTCR7dhCHRLtNWx0Oro5MnOgx+ShB1qM1WtedZk7gqiArZEnuejQXPyNMwASlUMsQgUJJb7HytM0ccd3k5U9WOpaBb2F/YejEwgcBVr8Xguuuuw7ve9S68973vxR133IEzzzwT5557biGTy0Ucx1i7di3e+9734tRTTz36AzlG0W/e64eyq7Vc2WE+IjdI0iEndcqFZbZNQh+JJR00f6e0WqjTyo+Q3jY9FjhZFI7lMo9Hc7eJHSucG1fnkrWAKata3VeJDVyqdQHLy0i5+zLfzfg8Z5zz1b51j8mM17hZDYxQcOQpsuuSSEaVVS5UCRed0Ro6jQidmiqhNduIwD2GViNC7HuYadRxaHQEnSBAwtScOBPWMBPWsKu5CtN+HbNeDQIELRpYD0yX+JglIY5QZbGLob0n+v6hYq7V52lZQ0fm94sOPHS0Fug0rYMThoQwZIRhxo9sUkXLC7FzZAJUSkw1G4g9z2bUdgMf+1aPAVCWubmxhn1h7UiuXbhILNe892jhMe1mXUoh3EJCwwJdqm75rX4urnLVh3JZMI6KTFdXpoTkAe9VFheXyAE5QSQ6M1VaQjp4QkkkK+jQdSWB1NmuGSjqSCFAEBNmYyXMuFLiFUp/URsvl9kspsmWcvkQIdCIE8S+h0Y3Rl1b30bnlFUtFh7Gj7SwYe9hTK9qYmakjsT3QIVAO8qvqQCBLzLEVD0RjpMu9soRtFMfPhXYN1NHO/aQZtTGy00fVpOLEABLVVUHY21jPM9QdTNV7f4cIifdOZwTeA0ORtFTh9Xui+VJEMyRMfEDAZ/lkiSUSISl7asSKgwWWtt3ocg81id2ZHGT2l//9V/jwgsvxJve9CYAwBVXXIGvfe1r+MQnPlFZ13Dbtm346Ec/CgD47Gc/u4SRDwEsft4ru1rLGOR6deHGyJXj5fI2FJkHXexdasKn3a1GfT8uWVpSDoCqUlcGka+IlcngDFgxQ9W1yvXTnTMof2cUaPoqSSFwbpFcAONhMSs1hiJ7swmwrqGKvNf94v7M+JhDQrkE6iwngjWvSPaMe7UZ5MuN+7AR5KTOLKcEGKshG6nBS1KgFqhyXLqeqoGJkeNMuRRb+nfS9X3EjuacJAQpocp4YDxJxEOLBAidOT4mHgSUx2hWRPBJvi5yPs/JwJZ29InADInQQIxpUkMTMRLmoSliHAybaGS5J6btB2gm6uHdjLURx0h9D1PjTXhcYM2hUhjB6NLKbC3XvPdo4TFN5hYC11K25MLkGlVWuipxXSPwWxiHjj9zSZ+USqjXJ0ouhBgtOVkkdKRgrZO2R3f/edF7AUqATDMR059qpJ+sJAXT27l/TgCYQwCm5Uti4tu4CF+q6hFVpb9CzuEJlcXa8X1MtFpg2jS+ed9BjLS6iDoJglYMxBkObl+LRidGTZfJCbsJJjKO6bGGLTEz1mnjSC1PmwcAHxnmEIJAopsVf9qmsD3Vhe+JIAhSAu5LhLEbM0QK74VrRKVNjqhClhJrgeMZQa3GIQUQBKJPyb+qZIfemDg38cEQtaqkmEEl5xaDsgyBu3yhSJIEt99+O/7H//gfheXnnHMObr311qMe4xBHhzI5cwnd0biV+iVDuMTOJXzdWoCokyDTJaRsVQif9WZnBgzoCkWI0lKc1Fw3/26K2rsWq05SzPh0xYDNMmNpA9TyiXrR9RowIAgUadvfyvdlyNZIkCdRdFK1van0AOQlvdz+ABUj52rdTWhXqFnWcEp7NYK8ioTRk2NUnR8dK5dEgbV6poxZIWfuMXQDH5mOC3P/zyYDlROCmPloOeK/Bz3l6oyJ6n+OhEicGkoCBNNcx9VJpgTLQRBRc+9Qxx8Rda+YljXUSWJdskZDdI6GaApF5DosQCRSCJ1Zm1EGT3B4QmDa8zDeatljmB5rYHS2jaiTIAmWTmmWY957NLGiyNx8ysqLxSA9ubJVbj6ZkH5tq1xiRo9OQBZuvpmkhfqphf4IQGV1DJV7YzfWQVOnlDkCxEJXepBauFg4VkEpi1UguCWhDHPCAwfBCI3R1SZzSQgCcMTw4UFlunqymM5FpUAzi0GlwGjcQUYZqBRYpevyrd9/GADQnO2oCV5nrq35f7vzsi2UoiEkZsbUBEeFRCdQ6zyhslk5YfYc7EqV+b0epGgneoJyitwb9yfjpIewBfFgqyWQW+Yyv3QdatxmsLpWOUJV0oO5bm4WKyWyIBpsiFwmCHwmeiywVckP/QSGjwa8zxOqcTeUA+qrLEEHDx4E5xyTk5OF5ZOTkz11CIcYjOWe944GVS7WquUAwAkFkwKcKOFgpi35SeCpEk3a4mcyXJU1T29cCxQBG4lUIgQXeWZr6ljdKIGNFjKVGRjRUh9pscICR5FMMQowAPVQkbu6cztshqr8FgDs1eEeZx0P/PygInBbx4tWs3sOqGxUALh3CtjQVH3MxY4+HQVG/Fw82I7BcSG7Ga/uZ0YVkTOI/GJsmJ4vYyf5gQqJOPJBhURLF7FvhSFi34efZTjUGEEz6WKq1kQVplgDIzK27lUOilkZoEFSJf8EhgNZQ3lHiLAxymZO6grPEjopCThR6qVuuUiDORKqGt00BPeIDSeKtfvXowJcEHi6HJjJpgVUbdepVSPY2DmEuZEavKk8PGcxmG/e+1XHiiJz8ykrHw3ms8oZklRGmRCWb7gLySzkjjXOxOGFlOf9O/U3yyAlIgcUq0aUx+7G51FnuVvz1c04dY+v68Q+pJIiIErVOwOFD44WCTCC2GapAuqJb5XWjhtvt7Bp3yHMjtSxafdBdbweA903o58sfWDnVP7ECQChyZCTqHdi6zIwT0td7V7diTF0hRrfbKomtsjLsL9bQxwzdLSLlWe919AIApdR5WJ1BYMFlUCNw/Nzl6kQvRnH1n1aYYnjnMBnRQkbVyyYkTwjdqHuU/ObW6qlOXUU4MvLAfQE1F966aV4//vfX9kXKT3VSil7lg0xGI/EvLdQ1ynQ31LXr8wRd2605rMhdYbQZR7TrlYBSqnWPvPQaMUAjPWN5cHthjQZ9yoX2kIlAaGzPqc6eUybyVxlREmAmOVleR9DoGo+sGkUeOgI8GuTxTJd60dUUXcAWN9UbYCcvE11gKdtAh6aVvt48trcYjcXA1vVA6Ydh3EJm8oPXBStcYDjfvVzEreumRPZZpg/8IYehMdAnazOIyM1pIGPjFFb0SF13Kjm81StaSVCZjx1jLMktK7UWRIihocZGdlwnRmp2s/y0CbwJZIhM8l8EvB0JZsWD1CnvRU8EskQkQwpKAQCeBDgYAikssx5UiChHjJCETMf43G7UE+9FQbwOEe9q+4JYZrh0MQIfM4xM15NTufDfPPerzpWFJmbT1l5qZgvTq6fVW6h5b6MZQ3Q2nGavBmiBuRSJaZOquueVaWZ8oQIDgJlK8tJXk8tT0hQ53gYcSo4OBm0RiNOatO4IQKxExBmjjOgxYyAGRmiSRRRS8Hgg4NKYTXpAMDnCcIsQy1JlLwIJdjy8AEISlDrJIrIAaq+XjwLHO6oCe3AHLC2CawbBToJIo+hG/ngHoPPOWLPw4H6CGZYUSCywRJMIYJHBabjELF2rxpC22qxgsuzH5ErXD8GcF/YJIk0nP83GIX5uTJudVOLlRGV3SqkKudFnOQHr0KexJDAo5UcWSgkUcKhVcsBYOfOnRgdHbXLq+Kz1qxZA8ZYjxVu//79Pda6IQbjkZr3XFTFzA0SDF4orDSJvhWbdyokQAUEVeWjqK4g0dElp/yUw2trEtAM8yLr3TSXDQG0gK5D+EzVB2OBsyQORZdn3cvj4dY7FQMeNwEcP5FbvRoB8P1SAsmJa4rxd6dt1J9FMdGi5hWTI9Y28kxaY3ULGFDTc5ixBDZCoBWrMYxGikhSovpv6rqs9UA99MYZssAHFcKK6MZRAC8TaDU8HG42rCdkpl7rqdjTZT4ywtDVsiOCUKRgSLU4fAqKliZvxpWqDpsWanQDuRRWIhgok0i4h4BytPVDdld4YEQgoBwMEi348IlQBlMwFTtHPATIkEHC03W4gV5Xp6SqzNhcPQLjSt0gjgLUjizNKgfMP+/9qmNFkbnlhEvE5tNjW0q/C6mPyko6c5bkOX8QRkRuaUFufhagtuRWvzgqoitA5MkPbvyeToog1MY4mL4SwXRJLP0UrcnfVFrDhN9BIhi68FCnCaZFDR7hYBCYIClmaYhIZFo/Tk3G66enESUJRmY7CNMUYZyCTumYk26qJq2qAOw4A3YdBkYieBnH6JE2qJDYtWbCNglkhiO0hgZSNEiK/byJg3M1eExg18EGOCfoxMxazQz8WB2vLdNVESNXuDaORc4g8wWgLX3NETWJU1okcibhIfRFj3XX90RPjJwBJbklzmoMOtfZjaskpOiuP+oECMZsXE15OQCMjo4WyFwVgiDA0572NNx88814+ctfbpfffPPNeNnLXnZU4xvi6HH04sDzW+UWsw/VX8lqVvfVHJDyvCqEz7TbVfbOGTWvGONW84sSJSapwJTzWlvPLWLNAPjNXwMePATs14Tg1M3K9bt+DPjOL4CTJlXZKJMxCgCH28ADh9TnbavUA2g30zpw+vh/odfvnVX7aQaK9JkSX2WkHNg8rtZHfm81jFV1u0w0QlAhMDNWx/SYskh5GcfUWFP9jynF4WbDbrq/of63KVUWL6BIlDInQc5kpBr3qQtzr5pNVR+UFD1ArUwloCWCwSMqZETpplIkQhkGXCJYc3VOiWc9ToHICuUdE5aTziZUnKTUsW4e55hr1JB1ei2BC8F8896vOo5JMjfIolZ+2gD6u1j7YT4i58KWHpyn+6pKE36f/Zg/Qu7uU9Y311JHIZGi+CMtljhTsShlkjuV1pBJihpL0RaBJpseQpIhho+mjBFThkaqMpAaqbLcxb6PEXQUIcu4InHtBNgxpZ5+Z0oCno0AONRSk2O7GIs3PtfCzjVrEOpafmOig910DAeyfNKa6/gYaySYmgmRlVyrLqkzunIme9W2obkYsAETBJmfiwZ7Ojau0VATMqWw1jcD3+91eXulGDkgr8Pak5lacgv1c7cvV/IDAGSMIqsg11XLBuHiiy/G+eefj6c//ek4/fTT8elPfxoPPfQQ3vrWtwJQEhq7du3CNddcY7f50Y9+BEDFiR04cAA/+tGPEAQBnvSkJy39gIbogetmrUp8OBqr3Pziwrm1jmrRbd+po9qzvS2k7hfnAuN+nHGSH8ZC4Eis3ZXareqSvIAp69b2VcCBFsQFzwIA0Pv2A4xg3xlPwOT3fwGsy611P3vqCdiyYRw/ebyKT3zyL3bm2nmT42hsnkDtoUOqNuhEQ43xSF5L1OKJ64A9M2osE7W8jNeY412gTtzciLPcuFQNmTTNM46ZVU3Evo+Jw7OYWjWCKU3q2mGIjFJ4SYJWGBbEb1OqJKViyiBBkBGGmDC0EKKlw2lUaUgV3+Z6c1qZGksCZh8muVAZre6cZiobZaC2mhAlEgHlkJIghWc9TxmoIpIEqMnMKiW0WYCIEPhCoOVHqJFEeX+4QOx78Dm3FSYEpcgYQeYtjdYs17z3aOGYJHNlLNUq58aZLXhf2l1q3KxlqwpQjIMz5CuV6o8ROE+vrnSJiZNziZw1Uc8jseITAa7755Igk9S+l8+NTaaQAcb9bk9mrgeBEa6ImSFygHp6anQ0Ybtnn3aX6O0ePKyeNn9xKC9p09UukbV5/MOBNeqpcs/EKl0RQt+ETFyOHn8jTJXGnADiJJ/Aul1loQtiCu5LBDGFpBIZVRY6Q+AklZbkue7VPFYOEKESeanpig9SqAwVqqs+hAHPky2oBKESGaeWyBGaZyRXWeeEJD3lu9zYSvNkvNyyJOr4aEG/z12+GLzyla/EoUOH8IEPfAB79uzBySefjBtvvBFbt24FoESCy5pzp512mv18++2345//+Z+xdetW7NixY/EHMkQllpqt2i/OrirOyN2Xm73ab33KGHwAceTDTzJbQxQNoNbSdVJ9vf1EIy+q3ghyl+dBbe2vecr6xSWwawaAjqkLPEWiTFza0zZhZqyO0SNtfPcVz0HqeRhtt3HkBacAUG5JAGh0uti9fgJTjSYmZ47g//7aiTjl/gdRi3ML0PQT1mP80GyRWBqMhupJrxEAJ64FWokipruOKL240Sjf7rhV6t3N5BUSaOqM11qg5EZCD37K0a0FmBmpw08y7NywxlbMcUWADzZHUEsTHKjlBDWhHg54DYzo7NFpUkMChjkRwicZpnnNEi1D5Lik6HIPqSBItbXOI0LXIVfzUcyZvb8R7fEKS9n/xkXLdRKEG2PMwTBHGDwthxVIji714bMMvhDoeIE1FHSCAFmWYYRzGzdHpayUxFkIlmvee7QwJHNHCVJydy2W3FVltcqCO7T47ma8umW41J9CW3xK1huqiV0KqgznRMdfaVdrLD1tYaNIJQMlEh3uQcheGRVbGsvjmM0CRCzDGM0nsBYJkHkUT2ypWKkT9u4FkxIT07No3rNHN9Ikr5sCu2eUy+Geg+qJNKi4MTRDYKYDul7FRYy32wj9FPtGxjGedfBjf6NVQ5lKIrQT9bOenlu43lA5O5Vri5ohcmZ95klwVpZ5zmuw8oyAeSpLlbFi6S5irG90cJ1VQLv+BSmUIBvcXo97GeLqVCmfiifUJUxqF110ES666KLKdVXB+490bNgQ86OfVS4vzdU/g7VcxquqnaQUnAJMB+yngQeWccRUWVpMckSgKyEkUQDPY6At/TA4VlNk50gHGNH/8ZkusKaRa9ElmSJPJ0+q98OdQgH27Dcejzlt+dqxdRL7RlSSwlStgbuaGyBAUZMpTj6yC7vHVmFvTa1PKUPIU+xbswqCEKw5MmvLSd33+E0Yn2lh4tAs6PpR5YINfWDTKuBwSz2cHukA4zU1zk1javwpz9+NBc5ox4W+dTF3agF8R5KlWwswtWoEghC0a6ElcoD6r3qOC8LNWI2Zj31abuQIrWFaRoh1XFsiGRInXprLnLglgvV4qLo8zzY1c7ApQ8mcB88M1UoNVSB9vA8uAu64Xj0PiechTJV73GTuLhbLOe89GhiSOSxPzJwhPItxseYisUWLikdET5JEOQmCEmlTvMvZq/2gkh3Ue+pceimVGZ07ljhGpCVu5smrm3kIPD0BCwLGgFQwtEkAComIZWjKGKOiiy7zsX5OZX1xE5NRD5QLghFgz6yacA+0ioOc6jiCmaGKM5lQ7tORVhexDvLlTMmcHPSb9rx0hYfprvojdxOGwOc4MuuDZ8U/o6CAV5HVOggmszXzlWackSFxUdOxcp5TzstIkZjr65VIXOD19mOsb5TKApl2r7NrlesXL7fUcl6cMfAKa0vVsiFWDqpKE82nLeeSM9dCYV20pPpG51rxZMV2ACACz1pRuMdAhEDXCxDFuUWfCIHUZ+CMombIXOgBG8Z6teYAZekKGLB2tf7uA3tngGcch4dO247j7tmlZE8A3HX8FnDGMBPVLFkDgJ/EGwAATw8ext1jG3GvXIPtRJUX9EKB3d4Y1tdnUOMJZmo1/OyJp2IynsW0X8cLfv5TzDVq2DG5Fs+68+dI9TE2D+t57vi1wKE5YEMIHNSxea4b2RyfWV5CV4898ygOTozac3242QATwlrkBCE4EtXACUXCPHSZrzL/CSnUUS2jEGoDkrtUuf6dSFLp6cn0vcJz4sDducuK2mtLX0g4QpIVvEcB8uvZogEaIkFCGMaz3GVNpUTMfDAhEfCs8NvqhPreEFfEIS4AK33eOybJ3GII19GibCVxg9eX0od5uslLeTkyFtp0XV5u4gDdZSHhiHW1B7MPRjgynv85jBUwkxRJpp7KWlq7rREoIjcexAgIR5PqCg5CWeli6iFhDGGaodaJsWb34Z7YN9x9IFdnT7hK3Q+YInMmxqWTAqGHZM0IplY10dFPnzNRDVNhE1OkXqhZywUFoxJSEKQZReCr+qezR3ykGQHVWnLaio/MlzYJQjBpK0G4CQ9cW+WSUMDzJIJQwNfxcn6gEhsIVcStXLbLZxKBrxNVWFEA2K344H4utymTOHfbRwIrXTxziGq47qdBFR8Mqqxsdvs+JK7cXg604lFQkd9408DPBcCTDCIiCJLMWvCSsTqCI6pCDI50lIXOWLoAlTRwcM6SoJlnHq+WnwLs3LAGh0ZH0IpCMClxxfEvwGsOfhf/seZE/KQziePoNABgb6JckdNxCATAf3ZUSMAOPobDrQjPntyFqbSO/ayJU7AbO0bXAgCmggaolPi/JzwBazuzCHiGmZE6anGC3esnUB9rYPP3cq1AADr+TRM4IYDQRzISITg4CwDoTI7BTzlmxurwMg5fWyr3rxlD5qkKDlQI8EAlOghCQKXEkahmr9H+aBSRSDHt1RA5iQRHUAOXBAHhiAuSUzqLVVAkgoERibYjwN5JeykDcx4+7bXVCXR5FbaiByugSo1BqSwIu42vPU1M6pKUjqXeSISEXFnjTCnIbhDYet0e55W1wReClT7vHZNkrsoV6lrnqmLNFpME0a+sl+23IpO1qn2ZgJk4g7Ib1axzA03ngxUPhkBAVdo4oMhiV3joZupPnUlqCVwV5lIfPuWo08SOdSJRT6GNOEYYJ5jcP60a/2iXKpFzRD9hzyWKrE13lYt1xBHF7GRKo2k0AlKO1Feul2mdmcUERyAyNBCDEom9fAStzIeQwEw7QOBzxClFGHC0Djvlv0IBmRH4zrU1LlROJXzkrlUA6DQ5iCBggcpcDUKhlA48Rbq4VG5V3xPaLZonN7hWt0EE3lreaO+1Ww5L22KREVYIlnaXD7FyMcgtajBf4kP/Ml39kyeMVY8Kocp3OfWbE9/rkcswyQUdj8ELOYh2F0pKsVETu7n14xjdeQhi4zimVzUxcdfDAICZ5zweo/uV3NHedatAHVfjz8fX4/cPvxxnH6+kRv5pzbNw7+FVGI9i/KI1Ya36G0eUxezvf3EaCJUYq+cPofuSJnwqMMND/JBuBgA8GXtApUSbBWizAIf9OsazDibWziFKFOHYPzGG/f/tadi2az9GG2o+Mm7esJuitk95Mbq1AN0tqxHp6jfTqxpq3hvLk7s6YYA5LWcSphli38PhRhOkQHwYOp4PT3Ls9ZUFr8v83EuicZjX0RY+fOKQak3kYp0BlmQMqeOlAUxML7e1x32q5r8MtCBhJaRyxLhzF4VSTKiVpK6Yc++iDpHL9IMDkXm9cQGCrudbQufOTEuNCV3p894xSeYWatUoZ7aaG6tLvPoRvH5xS1U3ZOZYZcr7N2ZpY51RStoA1dIk6mmmmsBVWeeAvLpDRDJlnSsdQyqoduNKxDpegjsMhxKJTuqBEYmRmpp0EsmwmrSREoqYeghFhiO1OjYDKtZlzxFFzgKmXKf3H1aFrg1m9YQZstxlYkrx7J8FjlttS3kBKhvLnbyUfp/6PFpP0Ip9dGNmRYKDUCDVBNXXZbx0wlRBay71cmucOWRJJTxf2mQHF54TF2cyVxlV8XImGQIoxskxKqw4cD9NOUD9Tvtlr1ZhuTToMkaQVmZ1rYwn1CGOHoOscv3i4cpK+b0xdrQog6F/Y+4N1MSf2WxRSkykL9q1ELs3roagBNMjDYyPNbD5YSU+/tOzTrF97Dj1STjtvvvRDkN8Y4vKgr7qgVOxraNI3rXfezyO36IIW5pS7EMd9SjDLx4eQbvNAKyG70k0GhnGRxPsO6wsXSP1FHftVe7bjata2NY4glgyfKX1RDxzdJfdf0IYxtHBLVtOBJMC21oHUUtTJMzDwRNHMNFS+x7X+m/jMy1wj6FVC1GLVUkqY3ljUmDOiQGbadQL5/zgyIiNH5OEYNYPVfF66iF2SEhKKI4gL7k1ndWccltAi2uNOk4hJUGXexCCINGEzlSlmS8cqbdKTXF9QDm4JNbDpBL7RNFrpAXnK40WUsJzrLldz4cnOOpxrC1rdEFW5yqs9HnvmCRzBuWEhXLsXNky5mqxGSzkBtqj/9YnVg5w3Kiu6w29N3UhiXricVyobuBoMsew89ZV2HfnCKYfqCGe9eA3OEaOi7HmybPY/Jxp1FZnkCDwCAclEi3uIxYMAeVocb8nNqKf/AV30tanaR3j6GDTERVjsumB/XnWasCA23cDe3SsSMhyQhcyZaVb11Ckb9NoLt554iQasx2EYw1M1+vgVGVMPRSqzK8EDF3hQUqCRBO2wzMhwkBgNvXgBwLdrr6hCCD1JRgnMBG7mQ90axxRh9nYuMSJiYs0iaNUgjnxcAa+rzK6zHI3acH3hf1dMSqsGzi/hkW3u6n2QMjCKj6U3Rj5comlRY6o7GBR4UarWjbEygFnxRvdfFIkZYua7YcUXaeCEkhKrdXO7dfWAXWWKfHw4v58zpF4no35ovpJq96NlZhwGMDnHO1aCC/juG/tenTW+3jx7A8R+x7+ebuSGKnxBDf/38fjmd99HK5/+BQkBzy0Mx9HMh/eixjuD0eQrpZ44OeT8FNi/+d8Wxerf9BE+6lzkPsCJAAaT8iwe18Nc7MejtvSxmxbeShG6soa9MP9k5hodLGhPoed2Ri2eEewI10Fn3D8NNiACBkmuSKQ+7S+26puG1ONJiZac9g3ruL0ukGA0Vbb6meuPTJjM4Q7UYiO7yP2fYy3lYvZnKOEMcyENaSUouUpwjfDIniSY5aqNik867oEFHma5SGY8cAID0eSACHlIERiJg6tqLkhcrGOOeaC2blLaRjTAnkzXgkBUkh2iJh2neplPhHwnXg5s40EgQCFh9RefxeSEJtk4W6bUYaZWg2jnY76zS5xnlrp894xS+b6ZZ5WuVvturLidYUmXbm96dPsM99PcZ8GXJusy3CJJYOER7Tor5YmMX10Dnu485pNuOtzG9DaF8IYryhTcWRSAKBANJ7icS87gCdduBfNTbE9ZpOn2fBTdDIPXDKdAKHHIfM/sip1SME5ReRnaCHAOjmH0InNsAWw78ifXAEAbSdWDlBkbp12JQQecKANPF6dKOExzI3k6uMJ9TAVNDAl64ilj0Qya9pnVFhCl2WKLDEmwT0C1DjaLQbiGaJDwJlEGBMQQdCtcQQxhWhwICM20YFSCd9TRM7oyBniFgaKyEUBh8dU25RTu19KlOXOJ8oax/Q4jVXOypPoCbQfiXN/I8stElwGJ7QyJmpQnNQQKxN9kxNKAsHldaoUl0Tm9bpU7TYkJ3tV8iVGWZ9IiXYYwuM8J4T6tzZXq8HP1Hyyd9U4Mkqxu7kKdV0i8FOnn40YPvbETcx+tY4ffWYTHr5rHP+VngBIgDAJCUC0Ge78zmZQHwiO5xh9skDn6bm0yNZv6ASIHzYhmMSB9Sk6Px6FxwFsjnFoKsTsrIfxVSmmZwJMHVFkKfI57pxdi5EoBR/NJTz2p02s92fxM7IerZEQATgm0hYOR0qk2MiErO3MYqzdwUOTa9H2lXXs/lXr8OR9ar5sh6F1M+8eX4XxjiJ0RvwXAKb8hkp0IB7aJACIesAFlFTUPjmKBlGhLQdSLSxMBKaTMC/BBeBwp2bDf+bivK61e5+0iim6go3PJHwqwJiw8XA+5QVDhdCWOAqJQBsOzL3MJwIhlOi81YsDQSTzR1EKCUEImBA2rk2AAJ6HIMusO3YuipQ0SZXG3wKw0ue9Y5bMAdWEbj4zcj+C188d1i/OqeoGXE7JLsuClE3YVlNOZ7o+/O1V+M5lJ2D6/hq8GkdzYxfMl+AoTbgZEB/2cedVm3DfjWvx1P++E1tefrgwJgkVC5FkDIwKZILZgvVUx0BEfgYuCUb9GPuyJia9Ofgyw0RnDo04BuNC6cftVwG9mEuUjpyxzCVcWevqujbibJILaJ66XolmbhrHvnXjAIDpUTUR1bIYImzCJwIpJCKSYUdLTcaGyBEi4fsSSVI89jCQ4EK5mjmTkFSiW+OgVLlHMyrgU4AFEkRb2EyMHJBXdMitcAJ+ST7EuFPda+9msVJSrOpQVbMVKD4M/DKTdjLaJ3akYtkQKwflbNbFSpBUVXdwLXKK3PW6Wl0il+pQCcYFMl3ez5C4LvNBpUTG8hqZnSCAIBRRloJKid1NZY2/s74JCRjuaa/G7vtG8aP3boJ/N0WcMbBVHIJRoMXsw6lXk5AS8Kcp+I997LlHYuInHo57JlelUiMJwYDmNEMSCTz+zhq6DYEkkmgeqWNqMkUTQMtzHsipxE9+sQrr13Yw2/ZxpBNg/VgLa6MODnRrOJIGGA9iHPZqOMzrOAMPIGbK4+ELjkNBE4f9OvbXR8Ekx+pOC3sbah772aTKqBWg8CQvuKd318YxojM8pz1FDhOizuscAjAI66LcJ5po8QDTiNDV5bUA4GCsHo47qQcuKLrEQyYIMqfaQ8YpUk4L7lXzC6AljwLnFIyJwj3E3BsMGJHwSWY/B318B57kSIhyMddEas+XiZeLmYdalhbCbAShoJoAztZrlf3Oh5U+7x3TZA6odrUa2B/wPNIlVUSuvE2VOHAZ/SwsrE/slLHMAcBd/7Qe/3X5dmRtisaWGNQDAFKZ2UM9oLY2RbQ6RWt3iFvfezxOun8vTrlYPQ2mgsKNcU45U2O37sH83Aipnq7qNEEAjlBwBFrN/ck/eQCY1tlnj1sD/Gi3LoCdqWSHaf1HN9pydU/rR2lr3t4ZYNO4HceuiQl0dFHoOeNGkBR7uw0wKtDJ1NNkq6PepSQ2ppFQCd8DhJBQ9xvtEnCqPjAKeE7FBmORA1SJLhN/1498mf7CQB2/zwRSTntjIZ0YyUFErl/m6nJVeuiHlZ7VNcRg9HOpVmFQMLlZZ1yrBekS/VsxpMzIO2SMqYxDx92bOIr9Hd+3Y0z0Ng9rAicIxW5vDA9m49iEGdx6cAv8HQJ3XHQcsoc98AkBZBRZt5qkEgJkqwSCgILHwJE7GOiDFCefKtE+zpHFGCVII4mgSwEnHnl0ygOmPCSRxMwJikxRKtFqe5gYjTE9q8jcgW4N0+0Qke8pMscV4bo/WAMKiaaM7XweiQyzLASDxOxIDZGoLkUVU3VedjVWYVR0cX+osmglCOpSJ0sQRWJaMkREUqSS4nCqlsWcKtIGZstzmXlFSAJIUrDCmfmdcwJD4RjN64obD4177/R12S7fPOhaMfpe5YUqIsdkTvDKho2UMpip2Zd5jGCirXMAbDbvUrHS571jnswBg12u7mfRh/QBReJn+qwiZ+VlrKQRl7vdRCWBY/omL0CtztzPr1uHW/9MpeH7m7nKvtH17waBUKC5OUZ3ysNdn9oAQiW2/uGhnnY+44iz4tNJ3c8sEQoIRyYZRtFFIM0fiyJphAim28B9B/INAw/YMAIc6gAsAcYjZZULnf7X5wKXe7ZPolMLEesnekEIpn01OQbgSARDw8vQyjxMt0NrFQt9obXmBHhGkIEUiFutJtDpUBUHR5U7Joq4quQARf6oTloIAgEuiLXOASouLgq5zVwOvVzWhdGciLkyI27JLlrx+zAaTUuRrzFYjozXlFIrA1BePsRjC2WyViZlC90OKJY9cvtIfaW1ZuPniLLmxV6eyZpRCs4Yup6fW+W8APvCETR4gp/769CVHmpIMZVEaPEA9353HIf+dALxbg+1iIC0taxGMPg/kDQE0AB8j+LwFMFPfgycsJogoEBSkxg5TNEeFYhaBK1Rgs6owPE/jTC1PoOgwPgBhsaRBtJQYm6MY/++ADtH61i3LsaOA6PwmUAjUqTsodlRNPwU42GM+/hq+3C5NmijThJMsDYaugpDl/qWzBmyBgATvKjFaXTipmUEAYI9UrlsI+REsC0ClGEqOUy1Ikw0umjFAVItxdTq+jbWtxyuxnkuXq7mwTyRq5wY18+DEBKOiKaQIIi0dc7c3+oygafvZUwW71nK4ubMoZKDE4KEeQi4irtLteYowEClXDL5Wunz3jFJ5qoeNhdSyWHQjbIsNeLCr7C+MCJ0ygKxejtuLIEppWI+A3l2jy3lBIm9PxjBbX9+PCAI/A2ZPQagukByFcGLJjJICdz99xvQPCHG2LltUAJ0pftHzePlGFWxYRHLes7T2s4MZsMQs2GIpyQZsHYEeHBKVXuoecrqZgIvVtfV57qnzGI1H3jSOuA3TgB8BqHFgg2Rmwlz8/ko72LGy7O8uKBohCmm2yFqUYZO10OWKd23tiaizBOgzsVvNLitymD+r9QTyDKCek2dJ2ONi0KOSIsCd2PdH1F1Vn29bd13YgWRPyR4TFgix2jRFWFI/0KyVqt+t8sdLwcAgjDwinR8sUJS9IeohqS0xyrnJiwMInKc0EI7g8xjfW+eief1bNPx/UJlgtjPi70nzMOhWsMKw3apD18K/NxfZ9v/rLUW++bqmDkYYPqvxpHs9kFXCSRUIuyosftJ73iqCF46KuCDYnqKYNetDNuewxF0CCgnoFZvU2Ldgx66DYF1D/noNvQczFSIh4uDBwMcPBhgdCwDzwiedMK02g9nmO5G8BnHunrbtj8iIgSUY4rVEYDj3ng1xv0uNtDZgmXqABtBDIZVMo8Fm5Zq7muJEIlOWpsWNXS4D48IHOzWsCbqWImpLvfQdiSm9s3ULWnLONVx13p+SzxEQaZkR+wDcPH+5s5ngcd7qjuYrFVX8iQgHIlkqCNFBooalEWxKsGPSmmzWr2KZAhD2pjzW+JUjXe+8pX9sNLnvWOSzA1CT1xa6bshL/2SGcoYZIkjLmEbEBvXb1naorjt0hMQzzA0t8Y2Nm5QWbF+BK+2OsPswwx3fngTnnrqDniTapyBx8E5BScSKP1JqsYUMx+bp6cwMTOXL1zbVHUIuVSkbf1ILkWybkS5XWu+ssidvN5u1qkFGJ1tY3q0gXs2bAQA7IhWo00CCBDs5SNgRGImyZ9C00zFeQipKjEAKt6tritXJGleg1YIAmcugM8ETFgQo1JJjTgVwboxK5A64yL1mehxieZZX06SAxUlS12vVXahlUgeCRJnsNLdDUPMj4VY5EwbN+bNlR9RmYNFsmb6SHU8nG1LqBX8zhxSaUoltYMQLU/9j1tehIxQG0phEJEMB9s1/OTnE5Cfi9C5OwBrSphnzrgmLKErox/BS0cFPE6xby/B6nso1m2WyAIg6BBkgbLSAUDQJeg2JKKWejdyRlGbonmEITvswUsJDm5I0aJAVOP4/nfXIRhX1rLRkQxrV3Wxb7qOx08exv2tcUxGLRxAA4fTGva16ljfaEFKgt18FKNMhZpQSBzIGljttbEbo9iXNLE2UIRwSrtQDZE6HCuC104jUCrx8OyIqtyTMjsHmZrV9rroUBTOCTJCkejM1Xbs9VSnsUlyzhwVeNxKaAFAxPTcSCTqTB17RDPUibK+BUSJBfvgyMDQkAk8Y43Tk6/vDJBKgYzQHkKnfncUKVXEjhMKJgVib+mUZqXPe8csmTOWpvIyQC2vilMqL5/PUsJtfAHR6/qXbuq7vvzE4vR39z9uxKG7G2hs6vYkOSwGhuAF6zPMPhjh/k+vxZMv3Q2u6+4ZwUhjRjd1QwH1520JH5u8GYQiRcjVHzjUeklBK1YnqhEAT5oE7t4H7EqBx69WyRAGJ0wo0gdATI6iUwvQmO3gzl87Hq0whCAUU0EDHgQYBA4K5VqYzQLrPkg5Qz3M0I49cM5yMV5nUkpSx9pIJQKnJmsYcKQZRSPg+fFpV6qZ5AwRNAQuCniBwBWyTLUbImA5+XMTHwCdvbyACg8LyV5dLlHhjNBKocxshWR1DTEY5USIKmtcFZGrynZ13Vqmn7IwbcaY7ce0T5x+Z8IaYqbmGhMb5hI5Aok7Zjag4au5hT/A0PlyAwgAEUm4P/tBhK4MS/AaEpgjeOAXFBOTHAgA48AwpM5LCLyEgHKAcgHBCKIWwdy4QH2GQjBgZnWGqE2RpT78doh6JNCGrnka5f/xu3YprbqHghGsH2vb+XXX7AimteyIlCoRYjyMkQr1AJoIhpBydIWH/d26jmEDxvwE06lKcIhYZskZALRidV5nYx9RwO3ybpKf/yRlYExgruPbajVcECQZtYROSCVWH3q5aoDPhI2TA3IPVEiV/mlAOHySISQcARTpM8LAFMJa3gCASQGmCVs57s0kP1TFw6ms5+K6pdZSXenz3jFL5oDiDdIldq5Lsap9P721slq/caUal6h09HdsEDyKN2/jXh3kdqNEIp5h+Nk168EiDhr0EtNB1rl+IBTwxjj2fGUc2153EMFWjiTTT3UFUqr/tExYDaGW9BFqMceZWg3dTevBPYaJw3NoMqIqPwDAE9fmtVennBTy1TmxmxmrI/MY7jlBKaxP1+voMB+ByLCbjdlz0+Zqoqx7GQ61a0i5cpGnnCo5EqFkQbjWSxKCYKSRIU4oiABqkaNUrufaMBAgRKIe5S5YoLeGaiNK81R8JnvOv3G7+4xXkjhAPVF7+hJVXa+q3+EjaZEzUJNahXjmCpnUhqiGoKRHSqSqDbAwOYZirdacyHFtlXP1ucwNNvU8CEKQUUXwUieDcNargUKiQz3sgXpYCwjHrIjgUYGfPLwav7h7DPS6OmSHwJuQqIp3XwyhMwhqEu02wf6HKdY/TnslOuaPRyCYInQA0DxM0R5V30cPMkyvU/NI1KLY9PMA3YbE9LoMAEP7cSoeLnmghp0CSBKKLRtzV+vuww14TGBVM8aBmQh0TKKbMnhMxelOtZS1bSdGcMLENABgKo6UjqQgiAXD3lk1d47VEuycHsFIlKKbMuw7XMPEaC471U0YWm0PUcjRjRl8XyBNKShV4skA7HJ73TjNCR4nIL5EqOdCj+ZETs1dEiHlYEQg0MtHSGLVHgyR88BRk6Zsl5LYMqUoGWRuqYNysbr6cmob7ep2Eh6Mpc4NB1gsVvq8d0yTORcLvf6qhmle+cF1k7pJELawsCZxqp2szHIFqjNiy21cPHjTBOb2hGhu6g7cbrGEjo1zdB8M8PCXJnD8Hx4orDN1Qz0qcgVvwaw5vc0CzAQ1bIqnAQATh5WrdW6sgebmceChqeLO1jaAdXmyA4TE9BmPw8GJUVAhEXseOGOYClWbXf44AJVuLyVBnaWYzQK0Uh+RnyHWRaHrYYZYJz9IQdCoqxg6aHdAvZaBUYkkZQh8rp9MVWZpLcitaCmnGKmlSFJqKzgEHrcSLZHPbfyikRsxBK9cY7WqtJvZ1oV7rfs9NJSvyXKD98mCXmrNwyF+NTEoPq63bS4MbJeRooWPk9xVS4WS+nAlTIjWCQOUxY8TqspgeYG12M3QCB3iAxLYGY9jddDG/5tejVt/vA7kgA85B2Q/8cAiQPhFq9zRgFIVRLJrFzB5gsp8NfASQDCi3wFAxdVlgUTQIViTMCQ1CYCCMyBq5Rtvvj/EwQ1qfhQP1IBNMaaPqHmqGzOMjqQYH0mwY98IpCTYK+p22yjIEPgCSapqTP9k9xrUowyMCnRTD41Q9WvmnJRTpBnFbDePjWt1fBAqkaQU7U7xdt/ueMq6J1R4ikoGk1a43MDMg25cuBsnF2lrXUQz612q0wSRFgYuEzkmJXyROWTMJXL6IVjwgg6hC05IgdABKknC/rbo0mjNSp/3jlky168cl3Wj0d5arC5xqy7LVV1bkztNTaZimaBV1X4dRMZ2fFWZ6kn/sqnz9lHdHmCRwN6vjuG4PzhYOT5DRAB1LhpUPX1GIsURv461TOnKfe/Jj8MTHt6DKE7QBJA9Yzu8X+wHTt2MLPDtRP/zEzZh/cHDSBlDS5euaUfK3TAbhuiwADFl4KDoQlV66AoPAgSdzINHBTJBCxYy3xfwhArmBYBVozG6CbMEK+UUATgCT4AxabNRjR5cLcxsphZjErUgs5ayyOf2nCpRYOVOVVnGsK4IAzc2jjqEnlFRcPdXydf8MixxZax08cwhFo+FSJDk3zVhkxIQRkRYIPUHT0ap56kHtArdLnMj7ugJbYrX0c48tLNR3PvwGNaujnHwgA/6wwByloJvGHyLXYp1zg8l2i2C2WlgdJVaRrkicJ4O8Q06BElNwksALyEQDBg9oNys3ab7X/XBOPDQSQkm9ntIQrWu9gP1YMp9iRDA9JoMhzap+XNyba7JFvgcew7WUa8pC1aaUvi+wP5DEbZtmsNMy8dMy8dcy8fk6g66CcNeHQpiygh2Y4YDByOE2gORJlRLLDElmeQQN4/loTM+lBeDUWmtckAeRhLpRK9MUkvoGkxZ4ALKEepsVTeJgyJXZxgVygDBZF5/1ZO8t1KSrsU6n9yIinVjtszXUmPcVvq8d0ySufnKcZllPaWW5iFF/ZT7PSJsho1LrmyWqru/PqW/3Dp18bSHA3eMwB8tZk/2w2IJnTcq0N3ro3NviPDxqR2fz0TJckQw4bUhdXwEADSyGHsa49jQmkYtSZD4HhLfw4HTn4hN+6bAnqQSGcI4K2TS7V2zyvY7NdLEdFRHwjzsC0dApcQOMqGCeWV+w5hNfXtNYi1unCT9f9ImZoQxCcbU5yjIY0QY44gTZnXi6kGGOKOI/DyBIaICXBIEekLr6AwxM8G551oIgsjLr1FZ6sbFQjNVzXZVVr3lAiesMnakKtNriJWDfnFyResa7Vk2f7/GGqfKfhEplTvVSZAwMXK+zmTnuhA8oEjhQb+JaVJDS/qY5jXsaSvS892fqUzWqUM+tu8MMPftCIdhY+UHYjGErtuUCGcJRBeYniIYXeX8VzWfMW7WaI7Y5WZZtynRnCKYm9BzhV73uB+GiOYIpjYKxDXjfs0xftDDNIDZUY4dsx5YoMlRg6NW42h3PLRaak4zxO7BPU10Y4ooFPA9gakjIVodD+OjinGmKS2UFIy7Ol6RSsy2PPheTtyUzAgBmLTVgexxExVPFwWZ9T4AKmEulRQ+5RjxczkUE/sbSw8b6KxdzrR2nC9zQheIrCA74mnLmiFuFBWGEb2OE2LjMplD9Ixbf6nlt1b6vLfiyNyVV16JD3/4w9izZw+e/OQn44orrsCZZ5657PtZiOXNqosTkVut3Bt2qdC9IUL9CqiX4+fsfkttD93VQNKiqE+qP69bkqUfFkPoSE1AHPAw/dMaJh9fLWIZOdYnAYo6umjTAA3EoBDo+AFGOx3s1vUGGeeYbjbQCiOMt1uoxQnu3LQFADCSxmCCI6Uejj+wHwCwqzZu+4+pj1mhLHampiBQzKoy1i43tk1IgsBPCkkP9TCzVjEdm6ySFDxlbRsfj+22gCJ0g6xjtaB4fpiOL6SQoKxIvMrJM26c3aB9VP0WHykiBwAC1en9S035H+LosRzzXjnj1F1msgGL7XIXqfu9Cm7SgyREub207lc5w1AQWvgtTXu55BAHRSxUpQKTmCV+XsdxhxkyKtE+QMBHFv5LXAyhIwQAAY5Mqxg5Q+Io791bnhgBZAFQP0KQBUBzyhA7ivoRAiMesPlnHh4+KcOm+9Sk05yiOLiFozUq0Jhm+H9PVXF0tMVQn2OQx3fQ6TC0DgbwMgJ/nZrr45RibtYDo0AUJtizN8LoWAYhgFbLU65TTxQq3xj5vzihyFICnaOGwKk/nXFd+pCq+0Tgq5hhnwkEfl4BYjxSVjWfFDXmGFGlukZoFyE4fGRI4SGSKSgkQql2OsJjW63CIBQpBCh8HStnXO6+EPPqxrm/O27drEsjcyt93ls0mXvDG96AN77xjfiN3/iNR2I8A3HdddfhXe96F6688ko85znPwac+9Smce+65uPvuu3Hcccctqc/5xIDLStTlddZqRUWxnwpiZpZV/Tiq2pugUBdCEkzfX4PMCKi/uBv6Qgmd+X+0duQZZeVKBYbYHuERRpiaaGZIBBpIUCnQSBPsHluFKDN/4g5aoSJku8dWIWHFn950WMe0V4dYR3BPY9Kmpx8i9UK7FlfxJrYCBYgt4QXAimAGnrAp+c1aTrgyTguxHz6TVvbOJYJuzJurBZdKCgaVsQUo10PKi5OHcZ9aCNjMMwDWhZ8rpfda3B5Jy9t8SAkFq3i6TVeIu+GRwqM19y3nvNfPIsekWHDwuLHAud95Kes1K9ViDThHwliB2BkrSEYYpkkNu7JRTCURDncUubtv1ygOHwgwptvzaUVEvAB9CkFVYzGEjjKJ1gxsYoVgcEhdnhThWuW8RH2vH1Fs0Fjn8nZAt6kIXab17rpNiTU7GbxJRQpP/HEdma8kT/ZtTkHuq4NxoOlLdOsCs7MMs7MMlKq5OOlSTD1YAxocc7tDhGtStDOKdH+AmEp4EynEwQCxQ9iCqGiMSGKKAAC0dU6ooq7wvLy2qhD5PFQPUptRDKh7XkSVtTAgHJ72zjSldhfrmDlD5AwikYJJrrTkeKYTYBRxSynVJE5YC1uZ0LkWujL6yYssBCt93ls0mZudncU555yDLVu24IILLsDrX/96bNq06ZEYWw/++q//GhdeeCHe9KY3AQCuuOIKfO1rX8MnPvEJXH755Yvqa5DLy2AQkQMqgtoHZKi62zAdFGpIXT8i5767aO/3AVIM0l1uEADdPblLsxwUy7U1kEKiKzzERrWdeGjIBD9vTGJUdLEmnkMz6WI2qlWknAv8orYGCBUpGxEx7mlMooUQLW02m8kiTCURKFGTBwAc0bpyjEi0kmKcTugJS1rrQWYDgw0inyMTBB6ViHWMyXhNTT6poKiZeBCRa/aZ4zT79IiwOnKAiZszZMw5PpPRSlXfPnXi5XS2M5BbeAlyV8ajReQAIANDhooU/YplxxIerblvueY9Wk65HgCX1BEhICmtLttVQf6o0L/zUrJERhlilv9fKQT2BYqqtaSPWPS/HQkKjN/j4xAHjNdrMX+RhRI6SqGsV1w/ZDlErgzjbi1jfC9BtwkIpjTrqmCsduvnGKbXa9ezPq7Jh7WQMpVojwhEbQrAs9p2mS8xtS7F6r0BZldlSEKBbE+AMKbIQgHGCeieEJ1mPugwpuiah0ddn5rRnNB5Oq7O8JaUE3isWFPacwTPfeeE1Kl6mDehNqFMrbIBAJudaiRGavoB39wPallikx0CI2TsuGCFTpRRn0l/Eqd9xAJLI18rfd5b9FF//vOfx65du/D2t78d//qv/4pt27bh3HPPxb/9278hTatdcsuBJElw++2345xzziksP+ecc3DrrbdWbhPHMWZmZgovYGE3SreIvQHTFhpGJHydlm1i4gAUlKzLLlmznWnnti+TuCoC5yKdY5inSf/jWugM6Emk00xp5dHiOTAuVnMeuSQ4xBs4zOvoIECX+OgSH3M0xI7aavx0bBO+N74dd45vxp3jm3EwGsEPa1tsnzOI0EGAB8kqPCRWYUeyCqn0MMPzCg8pp0gFxVyqdZsy1kPkzPFRIhEwjoAp12kzTO0LAJphisjPMFZLUA9SBB5H4HGMBEl++FTJrqji0EZmRCBkvOBi9qlA008RUI6AckSMI2QCAeX2N+QRgRrLbJYzJRIB5fb3QFDMfrbHgt71vwxIu8fe17GMR2PuW855r0onzsDEyhnCtxDiV9aXc/vOb8RaqoQQcEpQy/Ig/1ntXt1LRtCWAUKaoZUULe9+ShDEFEkkwVNtMVuioSSu9a/SY0CI2oeMi8tdSxyQW+i8BIjmgKCjP88CXgw0DwGj+wnWPEgRdAgmdhFEc8DELtrTR3OK4rifepjcwRB2CFbtYQg6BEGXYmSKgQgCIorXqz7HEHYJxg96GD/kI+owRG2KscMexg579vPIDMPIjCIjXkoRxBRxQpBlBIRJEMczUYY77zdC5S7taO3RiGYYY12Msw5qJMUkmcUqtLFRHCn0UeNJ4X006cIX3GatWkNG6UGfSpXZSqUslPMClBHARY8lbolWjpU+7y3pb7F69Wq8853vxB133IHvfe97eNzjHofzzz8fGzduxLvf/W7ce++9yz1OHDx4EJxzTE5OFpZPTk5i7969ldtcfvnlGBsbs68tW7ZUtgPQc/mA/okSrkvMLtOkzLzMMlc3rly2xG0LVFvhyuAgEJyqUS5SdsTdbz94zjqpXZauZYtCIuEUCTdPQaRwbmZkiPvFauzmo3gYY9hPmthPmhAg2CEnsAcj+L/YBgD4Qe043Jetwf6siYfTMcyKyLpRb9uzET85sBY/ObAWe2cbmE0CtDMPScasW7WqgH3ocSXoSyUYEzYxAVCBu6NhbGsljocxAsoxHsQYD2IlhEm5fdW8zJK3hiZsNZZZclZjWU95tJCqlH2fqldAuSX9hsC5pW/cBwR7fR5lcsdBkVW8jkaY+rGCX/bc90jPewasomSSQbkEmAsqJKgQ8Ll+wHNutFSouplGkiTM8qD3qaBhQ0hSSe1cVvdTTM2F2Hu4ho4um1efpVi920O7IdVD7FHcWxdC6CDRo18nmLK0uW5XL3/2A+VANEsUCesQRHPmXZG6+jTBxMMU0awidGseohjfS9CcIgg6+T6O/6GHDfcxHH+Hj433ehg7yDC508P4AUXWojbFqn0eRg8bgkYwfoBh7W5NstoUtTkKWiJ/QUzg6dKEQUxBBIHkxM7xWUbgaXkmIYBGlIExicAXiHw1bzIqMOrHan4kwlriAKAmMzRFjFBwjPAYkUjR4F1QCPjaSjeSdgtyI77IwASHz9UryJxkMTfe3CF0xkJXlCZR1jhJiO1/KVjp895RJUDs2bMHX//61/H1r38djDGcd955uOuuu/CkJz0JH/rQh/Dud797ucZpQUoXS0rZs8zgkksuwcUXX2y/z8zMYMuWLVCRDerHYGqjDkI5q9Va1pyEhvI6VrGseBwlN+oCLGZm8qOhsGNeSPJDFeaLn5MSoKWJr0wCY04RUGpdrgAQS4aQcMSS4UgSImIZphMVe7cpUrpzD82Oolv37BPP/VNj2DQ2i07m4+6HVFYrYxJjjQSBFrA8NBvZDC03ns1jxRJZZXdww0+trEo5tg0A1kYq8DgWHhpehpBmaHMfIeWIdc1DF8ZNGtLMiX2khd+CT5RFsDI2khRlXYDqLOkqqRqgmtwt95NjCgJaMYGlK+QJ9ZeBX/bctxzzniFewnGZDoJpQzRBk07WKgAIJy5Ole/KrXuJ79kbbV66i+lYKFVQnlIPsQ6niCXDdBIik6ocnw03EKpE1pbZEMTLLWePFCTUPlz+auLe3EQIV7LEnSJMW8FgSZpxtUZzQFIjOmlCW+0TgtEDitCtvxdI6kBSM31KrNvBMDeR69oBQLeRlxlT+nZA5gNROz8x9VmKoKvGOzfGIZhE1KZoa9erdObIKBL2AXikoSzMjKk51mjZ+VQiFapazQRTcyaBRA2KgNW15S0SKXyRFbTeKASaqTJ1GrJVSxMbP+nGxEVpipQ5Wal9fuN8QGzckmPmVvi8t2gyl6YpvvSlL+Gqq67C17/+dZxyyil497vfjde85jUYGVGq3Z/73OfwB3/wB8s6oa1ZswaMsZ6n0f379/c8tRqEYYgwDCvXGZSJXJUFru+2ZctQicj1EwKukhtZDKJV2VE9nbrj6EfoJCcI1uZPSlwSUKn/QHrYFKoUWDv1ejTTKFGWsA7PXaE/n5nATDfAkXaAAzM1NKIUndhD4AvsmBrD4ZkQsy0faUKxdk0XO/c1kGXEBv2uHo/RqGWYbfuoh5mSGhEEXDCEnrCJDaYqBaDcoMYS5lMKIQnG/S4SwRDRDKmkCPSTpoCqUzjudwvnhxFhY9wMceWS2OsrdfyjW1SaI19vrrfZ1pWqUefKjUV0An01oetH7B4p9HMtrBR3wyOFR2PueyTmvX6EzmazVsQIlQWDi/3pmFGdCKF053TCkCZ8AVcB7y0W6n1xCPiYIRHGECP0OFLB8IvD/z977x43SVGfiz9V1d0z885723ffvbILC6gLgkYFLxAvIAkqxhCPMRpzSIjKCQGNhpicmJwTL79zQmI04XgB4/nwgyiR+DkHNRrRHyQRb4ARFa9xlQjuwu6y1/c278x0d1X9/qiu6uqa7pl5L8vysvV8PvO+M93V1dU9PdVPfy/Pt4l6xDHXChEwlRAweSgADyXIqIAMsLTshxL0i5+TAiBMIghzkuaWs9aftWgwTYvrtPWOpoAIFImjaZZAsQh0xlS8XdwAAGnKhGliNrlfu1/zHaeRypBNI0AwYYgl5RJ2RI8mfN26gM7zChKCFMqySAVBp8FBBAELORjNqt8wYMO6TP+NFQXPTxpdQECE0ZyrI63M8NQJCKFI0UxjECnRSGK0wwgh50YLLhACQRyDSomUUqSUFZMchAQDB6UCnKqsaCGlqRbC7DJymVUuJbQQi75UrPV5b8lmnS1btuCKK67AKaecgn/7t3/DfffdhyuvvNJMZgDwkpe8BJOTk6s5TkRRhHPOOQd33nlnYfmdd96J888/f8X9u66uQRIjrjWuzCLnwt1uKbCzWptbu5CSrMoTatV4CAHqW5MeqxG3LE5CEiwm+fNAKwmRCAYuKFpJiAOLI3jo6DgeOjKB+3+2AQBwcLaOOKF49HADR+frmG1FODhTx+GZ4s3n4R+NYWEhKDwhP/SzUezep0rXpJwW6gsCSpW8HqjqDvVAuUdtsj4edLE+WkREOOpUuUibLDExcYwIQ+z0E+i6YBHjrGNiIBlRGVx6u9Bs03uH6XGjEwFGBMoSY4AskUJnzmav4yEanEha+TqRcTzmvmM172nrWllsnHaZ2uuJEGApB0utdSUTENPu1hK3bTsMC8vrIsHWVMVY6d/p5EgXmyda6HYZ4oQizrIxF8cEggkJEgIyXVryQxmq3K1CAPWRomUO6CV0mjS5bWwCqMkbULTeRYsZ2ePFJIqoTTB6mGSCxHk/k/sppndnbtVYuWn19iNzxNSYnTyg4vOCmKDeomBZwgQVBEGirHuCSgSBNJmtXAC1UIBl2nPa+9GsJdg+OY+phiJ42gO0kSnvCoVEDQkIJDanc+YYRlJF0Jqp5YOGssQ1u3m1Itd6RqVAxFNEPEXDij9lwrnPWpUedKKDtKx0K5ERWe157/rrr8epp56Ker2Oc845B1/5ylcq2951110ghPS8fvSjHw29vyVb5v7mb/4Gr371q1Gv1yvbrFu3Dg8++OBSux6Ia665BpdddhnOPfdcnHfeefjIRz6C3bt348orr1x2n1UZqS76Zaja/egnAzc2rme/y7TKSUkw8eQ2grpAukhBmyu/2bsWOpmq/dRPSzJ3YeYeFMpBDSYMuQyJQCcN0MySB1JBEVCBhFM8clgJf+rf40MHx8CoxOGZGuZnQxza00Awnqp6qD9tGBcAFQQjixSdyRSt+d5LtJPF0sSpKnPDqCJIXKpyLCO0aJkTkmAs6CIkIhewJAAHQ50kiCVDk6YqCDtTLxcoamHVSIquzNwCEBBQ6uchScHB0BGBIfpSEmPpc58UtaVNQ3s7dFtVRWJ4t+uxAK+IE1krsSPHCsdr7jsW854NTdhsy9twblgBUKrEXo1VjuYEkQrU0hRdAN2sOgSnDJFQv7HFQP2Ou8oGY3TlAGD7pgX84IF12HbGPOaOqtALMg0EoxLdOYJlZ4BZKLPQSUEwNikd8iYh2ODfnnav2kRMEzpX4kRwlSghGJBaz7F29mtkla7ujCrip8c19QjDkZOsRKw408YTqs+kLsE4QWdE6eXFNYluTYAlaj5noUAU5Xp0I43UZK42a0nhPjjCEizy0MiQAMDWdFZV/AnUb2EsVYNtJh0woSo7aFkqAAizeLjG4qJyyWvyRQgCqIcAnSgTCIFUqwMQAiKlcc/SrGpEQikCyQsivxQSKWHLdrOu5ry3XDmhXbt2YXx83HzesGHD0PtcMpm77LLLlrrJquE1r3kNDh8+jHe/+93Yt28fzj77bNx+++045ZRTltXfIKtHlZvUlRqx4RK5wv6WMQHZFjkdhzXypC5q0wm6hwLUmsNVgRgEm9ClCwxsVGD07I7ZL5cEyFLTOc9djXHmkpnrqhlpMQ5QCwQenVWF7xfbAfbvVz/4qfUxDj9aw1nfaGIrB/Y8OQZmAmz+WYikLnFoixpLYyH7gT5cQ2OBoT3K0RnJil9Pd9FqB0hSionRBHOtCOvG1Dg7SYBmlGQyIJnLlaZZbUBpEbls0kICDoI6UfpxG2gLQP50Z597tb22zkl0JEVAlBWPS2aCgTlIFjcnCjcnILtuSO6iLbhah7g2+sXRraYrQGTFd8qWn8g4XnPfas17S5EmYVIAoty16hI9XdaLUVnQ7ReEmhtrLbO2pFRgIaohEByhSDHPahhHB12EqLNUxdh2a6gFAuunVJxV8Ix5zDw4goUJjuRcBtzRP3RmKbAJnZSAJMDYuoxIWARMfZbGvZlGirTFjbydjp/TP3sRAHGgSJsNbVWzIYKcCI7MENPPwnqJaFHFy1FOTNkwylUW7OKENELFcUNn3Oo4OgnGgbmJfGedEYGI5kLCUSSweYMiYuMj6jsar3cxXWsbI8QIjU2cXACBGrI4OpFiMslIXBqjFUSopSlqSVJImmFZgoMLKmVhOWcMjHMkQYBAcKNFSqUYqgqD8Rgtcy5czXlvuXJCGzduXLZlf83NzldddRUeeughdLtdfPOb31w1AU+33mpZWa2qyg2FfiwLnu1mWyqRs4v+KjKlzL1cEtAA2PqLM0ja7JgEA8ezAUZ/ro1gvZMCLglizhBzhkRQ44JNMoGkbsowvxhh/9ERdLoMQhAcPlRDs8lBuxStBxuZZpKaqE7/Xg2nf6+GTT9TP9pn3DWC539y1Oxv48MhmvMU05neHeHAgQP5RN5NKJqNBHHKCt8fIxJNFqPJ4sylmmCUdnO3KDgiK/BmBAkmSD7jNhCjhtQE92o0SYImSRCBo0m6xjVUJ2pyC4iqSxgSNS0EmQs2Iuqlrx89Dm3J0+5XIK8OYl7Ow4IbDqCxmtmtaYWrYbnJNh4rx2rNezaho0KYl16n19v1KPuRwGL2qjCkDVA34YSxPNM1m6xGEmW6YlJijHexjrcxKdt4cngIjAhsbrSwZVS58zZMdrBlehGbds6jtrkLeXYCwgC5ikow2uWaJEqDbXKDNITLEDOGLMbNXZZnuMYj0rQTDFl8W/4qgyZw0WLuktWWPWXVI+a9Xq7dsEGcW++CmJjYOwCotyiIgImbS4LsvhRIsCyprF7rdTVvHm2BQprEryZNME66mEAbY+ighgTjvGMscTaaaYxmV82jo+0OxhcXMb64iGa3iyhJ0eh0MdruIOQcjThBs9M1GaxUSoRpqoSEC9dQNqcLDmZp1zEpwaQ0GnYrJXLA6s17y5ET0njmM5+JLVu24KKLLsIXv/jFJe33hJydB8UiVdVHHdjvKlnkXGucCwGCbS+ZAasLpK3V+QoTQZFKgrir+pt+2XxPG24ROBWzR0xJl8U4wOH5OlodRcxarQBzCyHiDsXCbIDRTOto/YEQY4cpxg6r7UaPUoweoTj7SzVMPUIRxMBz/6mB078boTnDMDJHMXmA4dQf1NFcyGdEnqpC0kfmagUB34AofbhEMjRoihEao05SROCoI8UoYmNNrCFFTVvrMuJWQ4KGTOHWBgwgEGZtAwgwSIyRGBE4GARGaRejJLtJZe1qxCn1BTeGLtewA/LrrOx6KyN1LlaL0HFJK18eax9LsdAtRXdOw5aYaMSJSYJgdiweJALBwbKbcV0kqEmlXTZGOwipwBnbjmIkSjHZjDEzq/yP8ukx0pNS0KOrcy2KQEIEEoujHElKsH4TEDaLBCyN8hg5N1YuiEmBwNnWPLVd3tb9bBM8/d614inXrSxt7xJERUAJunWJ1jgHDyXm1nFQTsAyqRIhgPGxFLU6R8oJNk0rr0Y94jhpbB7NIFaSTVYMsK7oMMnbaMgUWzqzaKYxxuM2NrTncdajjwAA1rUyAj43j4Bz83BAhQTj3MiJjLY7qMV5TJ0RD45j1LJrJxAC9TQxCROActEzqapD2BIlTPKCduuy3awD5j1Xu7Hb7Zb2sxw5oS1btuAjH/kIbrvtNnzyk5/Ezp07cdFFF+HLX/7y0OP3szP6Z626mnEaPQkTjkXOtFuFG6zOstSQkmD9s1qYPreFzsEIcSaou5KXRnwwQOOUGOsuyFyOkpiXRioIUqGWzbYjzHdCHJ2vmxqoC4sBugk1BZ61NW79gRA7fhCBcWDioBLKHD+oahiOHiEYP0gxuV8F8W7dFWD7vwfY8V1llWvOUUweCjC2r4Zuh5nzwTnJMlpVfVUNO77DJjmTYhFj6KKGFE0ZY0TGCJGiJjkaiNGQWWyHTBEixQgSjKJryJ4pFG1Z9uzvuE5S8wIUSdRWw15CJiofAIp1XGXhfxVWy9XKM7d62WupWEoQMAB86UtfwjnnnIN6vY7TTjsNH/7wh5d7GB59YJMzV/SXCgkmhcluHUTobMue7otlN3Mgj3My0iYyy/4WHDXbzUYINhH1EFmnKUaCFOO1LuphitO2z4MFEtt2tLHuPy2ABxL1kcSQseW+DNqqPOL6MwQEk4gbEmkkC1Y4m8ilkXJtLk6otuoFpDVprHXm/AaZy3VEvRbWA3Mbc+tdPJLHzun4Ob0OUATNdstqi19nVJrPLsnkJaUeNz51HvWGQKPOMTkeY/1kF4xJNOsJTp+aMSoA2kswxRYxShVhqWWke0M8ZwR/bWyencHU/AI2HVUJLeOtRYy2O4rElbhYa0lqrpGAcwQ20ZcSROSErZEkCLkwDwnCshonVLlGTYWIFcyBg+a97du3F/QbB1VfWYqc0M6dO3HFFVfgWc96Fs477zxcf/31ePnLX473vve9Q49/RTpzJyoGacjZ4CBDiwH3LCuxhOiLVRKCM67aj0PfaoLPMETrOHp/YksDX6QQnGLLbx8FqVuTvTV8LggYlYpAWdt2EyX8SYjE4aNqFgoeqWEyI3jNeYqwS1BfUJ+1+rmdzaWLVtcXiEn7B4DJRxlmNmUWtIRgsUsRdynWT6uJhnOC+U4IQiTq9dS4LU2tQORPUClhmBBtEEgkJEAAgSi7uTRFjJQwY7oPJEeHZIHbhICCoyZTcEIQSoFFoo4zBkNQcvZDIrJartaZynJI7AwpOxbOjqUjRILK4gRla9O5MXSrFTtXZYVbqmVuqUHADz74IC655BJcccUVuOWWW/C1r30NV111FTZs2IBXvepVyz4ej/5wa6260PFxS63bqitBNOIYaRbcHuli6Iyam/JU3MJc2DB9NEiKBltAvZ7iodYEAGDz5CKEJJhfDDHx8gV076gj2ROgeVKKVmtltzEpAMxRjJ8iUNsqEAs1P2mh4CBLLlByIrlbVblIVXKEXrY4oS1zeXwdULSiBbE7ghzawgcU4+o6o7rGa5FQ6qSLuCER1yUObksxlgkKd0YkZtYr70AtI3unnbIAKQgIlahHagebx1qqsk/2AFwnCaaIcqOOaatcuggqJU6an1H7ptS4RjVqSVanNftPhcDkXGYUyK4dniW9pJb7nZTMKyHnWLTkdVQWtBKcjkSKmAZI9MMBBHjmbVmuVQ4YPO/t2bOnkJxQJf+zHDmhMjzvec/DLbfcMnT7E57M2fVU7eSFMiHgMvRLeBiEKqLnCs5y2StAKyTB+me3cPKrjuCnH9uAoClAI/10snRIDnQeDbHuRS2sz1ys7oM4t1TF45QhSakR4uVc6cHpTFMAEBQYn9dCosCmnxXLb43MqjiPqF2cuMYPEBzZlusuRW1g40MMh05SkwRvMbAmB08JeMqAcWB9s4Mo4JgIY6UNlyUsjCBBggCTsg0mORKiLvma4GggBZUCMQ1UhhShiCRXVTaI0hxqipwI6jiiGCpztS4TcEIMWROgCLIsWJHFPGpCl9fhVZmwtlh0LJmp2Wvr14ksmcIldGY8jqVstSxzqaSgJZPaUmNHlhoE/OEPfxgnn3wyrrvuOgDAmWeeifvuuw/vfe97PZlbBWiXl76xVmWsusuKVrzybUuLoScpkjBQVR9EMdi9RinaYYQaT8AJxZRYQCA5ZoIRNGWMA3QMk6SN8ahhSkhtXddCZ1S9T68Iceh/TCE9StFcp+aF5ZA6KQEcpiCTApvPEQAjSLVOHM+JkiZN2gqWEzJiiQNLU/JrbqOKuxuZVVa40cP5A6xKWiDo5Io2oAxYmJJIa6qSBJDte0SiM6r7V/8XpjLB9Ozh99B2dfyz65Vr9cD2BIc25yEeU+sTIwY81khQD/NyjOsaavCTgXK31kgCAYKaTLE5UZIjo0kHNZ4goQFqSWKI3OYjR9U4uIq7DOMUIefghCp3qJDohtl8m6TohoGRttHXDxUSacAK9VY1yWecG9062xonoB4SQkEModPXXpnVcFgMmvfGx8cLZK4KtpzQK1/5SrP8zjvvxKWXXjr0eL797W9jy5YtQ7c/YclcmcuqjMi56JEyGZLIaeLmWuB4SVkuk01Z4tZKBCvIWzzlLY9i5vsjOPzNJhrbYtAw950Pe1lLASzuqaGxI8GOPz6IQYlD7VhdNsIid0lKEScUaUrAE1X/bzIh6IyoQtGTB9Q2I7Mky8TKt63PW4G7C2oSG5nRrgxgZJbiwKkc9UWlaj55kGFmA0fytBRjzQRH5moYbajJapZGmIo65jzOywhjJDb6VqFMK0lPTXKkhGIyXUSX9tZ91WnwhOZWPSoJuk56vP7+FGGTSJCLWZbFtdnkzUW/Kh36Gqhyf5ZVlhgGosKlqvvTtT41ykRqdRDwH//xHxeW9wsCvueee3qChl/ykpfgxhtvRJIkCMPe78Rj+ShKkYhCBQd3vQ0iRGWJL9fCF2aEDgCiNEs0YAy1JEGQlflKKUMnDDGSxhjJ9MlmGiNIQbEjOooYDBE4FuoRujLAvx+dxlmXHsRPHwJ2/7/rwUOAjQo0m0sndWyegDOA/VoHhIfAAsli1OwMUvV5doNAc5YWrGxpJDGzWVrJBxIjswRppIjckZOk8T4sTkoTE5dGdqUHtQ5Qn+c2SvMgq9tqAje3QYAzoDWpPut2nRGBbuZNWZjgGJ1jWBjn2L6tXaiIo9GsxWhnta1Pqi+gLQK8QP4U85mo83Ss4t9CIbBlbka95xwJYwiEwKbDR5FmKbFByhHGqblemBSI4hRpwAyJo0Ka94C6hkAZAs6RBgxMSuX9EEItsyx3SZB/nzwTDbY1DjlRosIgyzNkaAya95aCQXJCb3/72/HII4/gox/9KAD1oLtjxw6cddZZiOMYt9xyC2677TbcdtttQ+/zhCVzQpKCVW4YLFfAVRO4Mldqz7hcC1wJ2ZMg4NlQwjGBs/9qD779plMw9+8N1DcnYHV9XIMv7qRLEe8NUdua4vT/uR/hpnKpk1TXYs3G0o0VqZSCoBMzhIHAYpuh3QrQjQlG5wIIJjFylKHeoug0BaYfzkzsWcxH1CYYmVET2MhM7jIAgKhBMH5ATZZppIQyF8cp0khicax4VOsn1Qw5EiYYy8p3aRcrIzKPd8smgFBy1EVinuam4hYSSlHjHAmlWaBuYrZJCUOSTSKB5BCSQEJpHFFIhOZpUCAhec1aAEhBzXWjzx0DByUkT3nPvkstXQLk8iWmhJtFEu3J5Vhoz6lqH2Xuhjx2xMY73vEOvPOd7ywsW04Q8P79+0vbp2mKQ4cOLekp1WMwhtGSqwIRwpQ+UhYWAcEYgszyIigBN4kPQgm7UqKyFdMUCWNmPaBqdTbSGO0gApUSW9NZLGYPVIJQHCJNbCALmCUNbBldQJ2lEFcD8ijB3k+vUw/Ko+pYhiF1yiLHwIkEe2UH2ycpcBhYHFdabPOZta85pwR647oESwhaEwI8lIg6xBC9+gIF5bKQ3EA5MLNZhYosTtiETs1pC+uL811nVKIzKrMQk6LGnIrf63XPzq1X51rPh50R9XlhPMXoXIBgPMXBwzWcul25OqOQY/1o3nE9TPHkkSPYKudQJ0ogfox3sXVhBpOLLXSiCPU4Bs8I+nhrEXPNEWw6dBRJFGJyrgUqJKKMyOmHAJZyJFFgYikb3RiCUgQpN9dHGjDwVKBbD1HvxujUIkPoSIlEgy1VUgUqpRIXWabEw6B5bykYJCe0b98+7N6927SP4xhve9vb8Mgjj6DRaOCss87C5z73OVxyySVD7/OEJXNLJXKPFaouHNvFFRCh0qgFhZBAY2uKZ3xoN77/x9tw+BtNsBGJaH0KQm2rTxFSAt0jIfgcxchTYpz+/+xH8ylqtnBj5DRsIgcogU2zThDEsbLIAXlKvKCqTmB9gWJxXGJktjhR6bgSV6NJP7FOPUwQjwBzG4Cwo56WZzZwzKxPMAogDAU0l+pmG3NJkEqGgKgM1prkqIsUgeQIBTcClDWemAyrZhKrYuBcFEz6GjWRIGWa0GWad5JjkUaoZzoJHRIqYkcAkglQBqrYDDoIMk06nUKfu1wjyk2xcf19aRKnrcR63WMhD6JLt5UtB4aPHQGWFgRc1b5sucfqoyx2jklhwgtcd6u20GlXWb9+beJWWJcVUW8kMQShqKUpOmGIQHLUBUEgORo8xmjQxaPBKBgEdtSOogaOU6ZnsPXPW/jO+hgPfGwDxAJBY1MCGkm0kwDNZnk8HeNAeoCBNCXYr7ZRP4fjYLYujAni7GE4SAg6XKLTFJjax8BDifl1AvUWRRJJjB3Nr0mbsOnP9ny2MKUscdojMbNZIGoT87BqeypUXJxatzghMT8lMJaV8tp9ppqj9bz58GldbPtpDYtjAh2rqgXb3sGpW1ugVCIMBE5eN4eQSswnITbU2xhnXWyi89iUziOSShJkMl7EaLdjSmYBQCeKEKYp1s+p0JtGp4tmu4s0i4ubOjiLhYkmqJCot3O2ybhys3YaiuEKKsFSlfCQRAGiOEUcBah1EnTroSF5CBggVMZqwAFOKcI0RTtS/TDBIWiQVYOQCK1rKyW0ICK8VAya95aKq666CldddVXpuptvvrnw+Y/+6I/wR3/0R8vaj8YJSeZsF6uwLB/A8DIk9jYaw2yrkwTsz2XWuLL1JoOz5IZe35zimTf8DA/evAG7PzaF9u4ItCYQjAnQmgBhABESaZcimQ8g2gRsVGDza2dw0u8cQThp6UWRnNDpZAcgJ3aUSsQJQ6IFNFNSIH1RJm8SdShGZxl0smyQqKdMU3JmQbkigJy86afP+rxK+z+yTWJuQ36+iBVUzFOCg4fqmNwRox0HCKjEDBFohgkiyjFGEtSQYFR0CzVliZQYS5QrNhQpOGUQIHm8Rfa/W3EDAnJCp5MlgPx6UFa0IhgEeGaly0WDizF0qS1SbFnz+rlaNVzX8XJdrID6nqkomdSyZcPEjiwnCHjz5s2l7YMgwPr165dyCB5DwrbO9UuCGNyPqgZhPnOZVYNQBFFIRVBsjC+20Y5CIAiQUmoqBgSCQ0C5XwPB0Q5CjCdt81vbF46jC2W1o6HE8//kp3jus3fjjvedgUd/MgYQiXCcgzUE6uMp2nGA1lwAdAholyCVBGRnirHfnYPcLsACgcX9NRw8KVEELhMoTwOJM741AgCYm850IDnQHlfvJZNIQyBYJ9CYo5jZxDG1lxkCtzihwizGD1IcOUlg9AhVcXFZbPORkzhGZjMFgCnlWl2ckJjZxLH1xwH2PiXF5KOqM50ABgBz67gJXxnd2MXDABpTCUiXot7gOHPbAuYXQ0yPdzBZ76CeZfkLSXDh6INYIDWclhzCHOqIZIrRpIsaT4wsiEY9jtHoFs2B2/cdzr5viTBW7UdnW+q7zyxxaRRm8ZkC9XaMJGSFar/6oSBIOXiYuWo5R5y5YO14SxeqvFeKhBbXuXF1y8Ggee/xjhOSzNk3umFFgIeJlbNvumXETlpkrCxOyk10kJL0WGJ4JhzMiISQGdnT1p46wSlXHsLGV8xi36cnceD/G0e8P0AyEwJcqZvTmrLaTb14ARteMYfGk4ZT33TlSQpjEgQ8JRCCoN4QEF2KTkNg6tHMTcKAhXUCo5k2FOXExMZpuG4E/VRbnyeozxN0xiQaCwTtcYH1ewPjUrCxsalUNxmRmKS9opaAIk9aDiHMygrplHdOiYnJaKRdADUklKKZdpFShinewlxYV65WHRdnWS4imSIlDKFUOnTa5ZogQJhp06WgBXe7rS7ullVzXa2AehBZCVEbBqmkpRlmS7EKLicI+LzzzsNnP/vZwrI77rgD5557ro+XO4bIg9HzRxCb2GmJkjIhYZE9tlBQUMELFrpcskSAEpIFx6fK5cY4BKFoxIpEdIPAWFgCITDZWUTMAiyGue9y2/wRPNocx/p0EZOJ+q2fxg5hERGaF3bxnOc+iG/dfjJu/6en4vD3m4gPhRApAQgwFnLIMWDixYvY8EtzSJ8mcHCmjm6WbR/XBNZtb2Mkm1f2/bvKTvjRsxYxOsfQWGCYOMIgrTmrW5doTSodt5kNHFGHYHGnQNShCDsELJuilDWPYGFKoDUuEcYwc+HMZkXoBANaEwKtSYE9T46x93Q1Lz90Voz1e9Vt+j+e1oagQDCeka6tWbb+xi527pjD/sMNw6dP2bhgiFxABEZYYiRHdiSKkJ28eAQAMN5tm5CTTUdnMNccMcc4tqjm0TgMMH1kDlE3I9wpR7dm/SYTDnRTgBEEXAIph2jWEMQJgjhBGoVIwvKHY+WWDRFwYWTaOWOIg6AyO9W2HjLJQSQ1npPlZrSuxrx3PHFCkjkbbjaqaznTcGtqDspi1Tdl202mt7O3t024bnycS/Sk+9npU++nvjXFKVcexsm/cxitPRG6e0OkLQpal4g2pahtT2A/2PQjB1zk+5FCtU04BaES3Cp0r+f5TpsioEDYVfEknRGBOiiCmIAIW3xTtY8byu0aj+QZZHGuUoD6Qp4ZNr2b4chWjv2nFAlomNUU3D0zhpMn5xEQgZZUrr9R0kWHhphKWllcTlLIeLKfRiOuYuViFoBTop5WubqJNdJYZd11W2gHIdosAoVU+ktZ4gQHKZj5Q6li6EKkxpJAIAvWOW3DS1b4U1zNkl6r5W5YahDwlVdeiQ9+8IO45pprcMUVV+Cee+7BjTfeiFtvvXXlB+Vh4MbK6Xgn10JXFlM3KDkiSDkEJUgZK2wfcg6RxaNyQhAmaVbLVSUOUCFBMxmMVq2GOucYQRdN1oEgBHMNNSlsnzuKkW4XP12/ER0WYn1nHusIxUw0gk3BPH755d/FC171AO5t7cDsgw1MH27hSDgCTAOTp7fxQ75BlQzr1HHSdAsz7RrmWhG2bMhKAmZzWpxVrUGDY6HB0d4CPJqqY5l+uGY8CVE3806Eaq4TDNi4J8vwZ8p6N7OBY3SGod5SbZMIeHinmsPiutou6hIc2ZgizLwarTGB+Uk1Nx3ZkCAYT5VXosWQZvFwADA1mT8Bb17fxjM2HgAAzGcT7JNrhzEtFsCkQJiR61BwbJ8/AiolRjLh2/HWIhayc7zt0UMAgGa7i8m9R5XrE0BrcgRRq4u0EYEeacFM01wAmtglXL0A0JlFYLRmrosg5WhnbteomyANamh0Y8wHdXPdmDpjGXTSBZVKZLob5CW+XEsclSK/ES0Dq+1mfaxxQpK5QeKrpt0Aq90wsiRlhEvD1horc7Xa2TXS+Txw7FQi4RSN7Qlq24rm8yq3XT+rj21q1rFyjAmT0dpuM6QJQRBIIAEkU8WdgTxYWAlZkkK5GlscMx4BLK3fQkDxXFZiJ2oTrH80wOFNKWbnA2zftohHj6pp5clbZvNjyUhSQ6YIRYqUUISyaM1rJNqtk7kHKEUgBAIRY75WMxOGtkxoK1yNpybWTutj0ewmFUiOmOQ/K524EIKjm/3cGKxJRwolBUBSpJKBkzw+TtdxdS12g6xzK7XcCU5N/V13+VKw1CDgU089Fbfffjt+//d/Hx/60IewdetWvP/97/eyJKuMftIjLgYlSej1doYrFRKBluuR1LKUKJdrPY7RzSytevuRbhedSC0bX2yjXVOJEGPtDgQlaGbuvoWGqvN82uEDaIchkiBAK6phMl40Y2qmMV5BvodvnLsDT597GPePb8eY6CImAWZ5HUfiOs6Z2ovvzW7CWD3GWD3G6eMzOBw3cHixgdl2hOh0jiSl4Fx5HMJQmPi79pHAFK6fWB9j8t5iyMHDT1IEKQ0lNu+O0BkRGJ1h+PGz2tj6YA2CSozMU+w9LUFcU+7SmWk18SU1gWRazUsMQJISVQ6RSoSRQDTdxdYNi2hEKUZrqt3MYg2NKEWrq87fKewItog53B9swwYxj8mkjbmgjgaP0WYRmmnX6MNNzrcgKMHkXAuTcy0cXDdeyDpVB8KBgKH5HwcARhHMLALaypbwQhsDIVW8zkIXaESA9oYEHGGiiFsYp5CUGn26xUZNWeeYIv31OEEnCs31o2q5iiymUqCTWedCIZBQIBRAsgIj2mrNe8cLJySZs9Ev7s0la5pIuRa6qr7ceDy3nyq16tRypar2eWF7e94VmVvWED7nJm4sQE42pT5O3b5UpsWKmwOy2LksXo1QaQidrSunUW9TBIkS0mSJCiiePJA9wUbAkZMEKKcYPWKXwlEyAEHWRtcpjBuypyg1ANQ6BNMP1zA7oSa0nTvm0EkDcBljMii6WLs0RENkxE0KZaFLYoRpWgjMDiw3UzOOs+wpFYzbttw9Gpq8dWgIKjOdOMBUC+k6wbiBJW6pY+iK51wa8gYA0NeABATpL1Pi9rMSQpcICilK3A0lywZhKUHAAPCiF70I3/rWt5a8H4/hUUXQXHerK1Wis5p4phvnas65hE73RYE8eSjlKulBxOiGgbLWZan5Ix1FghLGMLnQyiRMUhNPRYXEhiMzkJTiwNQEammKkw4fxVyzgT1T6ws6do04xssf/C7iMMCGkRZ2zB9CPU2wo3kYCaU4KMdwyvhRRJLj+2QLRmkXNJLYUT+KlqihKxnu+sk2rJ/sYsNoG4wJtOMQP3xwEnJ7F9s2trHQCnHoUISZnW0ECUWQFs9pGkgsZEXuf/yMRYgmx5FFBsqBI5tStEbVOr6jY8oohodC1Bsc42MJkpTilC0LqIcc9++aAgA8edscdkzMYi5W89HG+iKePt5G3SpHqNEkXRMnvKUzi+nFBTDOseXIjIpVzDA1s2Den/kj9XDVbtbROJIvBwDUQ6CTKOvZfAcYqxfXd52QHQ7Vtpsq8kcJgnasLHvWzUVZ5ShYypVmZ6T0B4iUhdJwWqIkSlPEllyJ1prjK4j5BFZ33jseOOHJ3CCUWd/KSJ3dtiqhoUfk1bG6aGucvR/bdaav/37WPqCo/1aGshqgPVp31qElnBZi5hhRwfqLbXX5xAlFGAk0AczOBuBUgtckBJOYPKQkSjrNTAC4o4J85zaQLLtL7aO+ACyslxiZIVmpmnz/C1PS/E8ildUqpvL1U5MxOjFDPeQ42BpBSAU2R/OYIm0soIZJsQgKiYlEkTxJCALrxiMIMU+q+ikwlKqeoKQUKaXGHWvH8DApVUasEOCUItVETQokNDBuVjshwtYa1NY3+zsPiVAW2IprqWzZakOWyOHo5R5PDJQJAfe2Ka8MoS3VOrPJdtMS64GIiVzxn0qpkiJITvAEJYBT9itlFCHnqHcT1JGYoPipmQUkjCENKOrdGKft2Y+ZCTVJjLfaOKv1MOIwQN0K2NexXz//0C5MzikL1PrROTy6bhKnHz6AKE1x77Yn4SQ6g1k0MMnaeFZ7D/6jMY0uQrzkKRxdybC/3QQANKIEzWaKyYkYW6cWcSiqY914jIAJLLRD7HlYWenHJ1IsLjIEAGZSgnQsBW0xTE/HmAkl6g/XMHXuDMYkgRAqI18IAkoluhsZNkx1cPBIHRNj+bE8Y+cRTNY7mKwpwruxvoiNbAFtGYBLghpJMC46aIgUjwbqvGzjMxjjXUx1FzDeVnPf9gOHEcYpap0YM+NNTM3MF5IZAABHF5UL9eiicqFq69tUU1naALVsoaue+rVrtBaoG0ctUOtHeh+AASBox0DIkIQMnOYPDUEqkAYqaQbZddCNwszFKpBAuVKTTKKkzhN0WGjko5Q0iVy2cPBan/dOSDJn3zxd61lVzJyLKs0590brxtq5sLNV9bjKAi6TVcioKbPQlbURkiwpg8ctK9UZEWguqCzWuCbRWFA6c82Z7Ok8zoU4F6aUyKaGYEBnTIKmKlFiYb1Ow8+zwHTdwc4Ih0wztXAmMd8JMVZPUKfKZXkUdawjHRAo0tUKIqzrLJqsOca5kbzQQdi6jiSQ3VygfiQ64LaeJuqpMJsvOAsQSG6sbEEWtyEkV5pHYEprDikWSVSwzhHIgm1OZ7YCKGS3MsvSZq5Xx/q2mhNOKogyBZYt91izIEL0FfwFeq12g0p92dv0bqsC4dX+sh+MZbmrdxOkASvso9FWZEFSCpKVgzKJGYwhSAXqnQT1dmySLYJUJV5M7z6krEAA0KxhZuM4FpoNsJRj04EZLDbraLa72HLwKFoNFct1LnsQ3920Hb/8s29jz/Q05moNjPMOQrmIKdpCXaZAA7hb7gAAhNsFZjrKIrVt/QIIkQiJwHd3T2N6OsaW6UWjyVnLymUtdgMcmVH727C+i61nHAUAHDjaQBjmxOPkDcoSNrNYw/ZNCwiZRCdRxzjVaOP0kSM4mOZPuQf4KLaxPLTklMUjECB4UudRpJRi22H1pBwlKcbnF5Ewhma7i3o7xsPbppVr9cGDSKdVskew+3D+pR6YBxZjRcw0Hj6aE7eYA5HjlVmM1XqeHdNC11jkMFoHGClkPDfaMRbGGpnAMDXfr3kAELnINKA8JzFjYEJCez4jnqKVPWATKcEpXfbD7lqf905IMlcG2wI3DKEbRNKKbtL8RlxV7UEXuy+TmNBEzljkLNfpILhECyh3w2kLYT+ix5hEklAzWZkardY2I02OeCY34UddgqQmISkQ1SXStip/k0ZKzXz8IEVnVBoNprmNtvK5/V65XHWGWGeEY3E6wfqG0pJq1lNMj3ewebQFlunwTdG4WBcVSvYgpRSNOM4+9yZD2DelVD81Cp5Z7SiiNEUEoBOEJnaOCQHNzFJdjkYKVesVAikoallpMds6R6BuBnGWJqcIXZaV5bjwC1mwxzAoN+UUKIkTSddI7IhHNfoRuSposuUmP9hZrjahK4OuEMDAzc1aLU9MmTENQQkgeMFtGsapsdzoklAAMHV4XgXau5mSB+cxeXAek+sV+RGNEKPzbcysa6JdizB1dAFH1o3iWV/9Pp41+hM8fPJGPHX3w7jv9NNw+vxBzNUamGovYDFSJOyXyffwlZHTsS5YxEnNOfyAb0KHB5iJ1foLnvwwAOChuQkAwKG5OrZMKLHeuU4NmycXVRiFoGhECQ4vNPD0k1WiwXwcYSKzuI1HMU4bnwEA/GRmCvWQ44LphwAALRliQ7CArgxRIwnGSIwRGaMhYjRFDCa4kQCpJQnqmVgvAIzOt7PvUp3nHd94AGKTivULDs0DR1rKjTrbzokaJYqg6e+mFSuL21hdyZB0LILOMgsdgyLU2mKXcEUIu0mWJCFUbF3CC99ZkApkIX/GGisoRS1OgCgndOYacYwwWix42FCUMqz1ee+EJXMuIRpUf7WszaBkBHt9mTacbZGzSVc/hf8q691Syz71WHas+DkhiSFqMnsvtLgvU8K3cUIRBBI8EwkWWfFmgEDUBHiXIExVhtbUAeVqjesCaUSQRsrSBuQxcaqItc5mzYtaR22V0k85MaVrxg8zzE8GWJxOTG3YkXomM0I5IsrN8YyJLmoZEWsmsdGuKgbV8lKrglasB7fb50rkWrgSAGoixSJVQdvazK/Jl03KGDioI09ShX5u1mMpUVIlQ3OsJVE8jj1sN6gb36ZRnsVaXe5LJwbpzEMbQSogsnJShrBZZMwlciHnoIlAkOq4PWKI4Oh8O4/TSzmmf7I/txwtxrlbT1uGOgnwk0eBkQh0qgl0Ekx2U0weXkD79I046UePmHiubQ8+itbkCJ7xs59hZrSJp84ocvazTdMAgIOj4/j59oPoMoaZYAS/wHfh69Gp2BQt4IWLD+DLwZNQJyl2rD+KXd1pXLT+p/j6wjaElGPHxCwoJI5065jr1lBnaq4aj2JQIjFZ6yIWzPy+tgTz2JeO4cmTR8AlxajsIoASI3/Wwm7U0wSHG01sWZg1cb+dIMRUawGdKIIgBCcdPoqpowsYaXVAW4ooxhMjSpz3oBIApg8fVedqrK6I3MEFdc50bJz5kqgiciy7JuY7iohxqZY1M3LGZX7utXQIJbmbthbmSRIl11iz3UUrS4IQhEBQgGeWQCYEull4TEoZQpFCEApJiJmDRVY9IpIlQdZDYK3PeycsmRuEMutc2Y21Cq5cSNU6naEqS+p22q7VKqvZUtKmXVJnEwI3Y7IMOhTBtUgGTCIWxFjQo5pAUqNggoAKndUKAALjhxniukRcB0bmCOY2CEzuVxvaljgluKlqEqri1llafYfgobNitCZS8LiXIB/sNJBEWfkZplysghDMBw3zo5/sJDkJS3SpL2EkEnSwtlscHAC6YZhZ9xiY4KCE5oWhs9i5IJMqEVmCBKDquurnZgqJEAIJKCSIym61ZDUpkYVMZ3XmiokRLvS1o7OeV4KUU8iSp9GyTC+PtQs3YUGj2sJWrkMH5FY6eztN8kyZJ6lleJRsiS7GnjCWx+FZYwOAMJVotDpGlJYuJIpILHSU5efQgnLn1QJlVWI0JyIJBzqpIiJ7ZhTx2J4C++fQmO8AWyaURYoSYFzFu+24/8F8+3VNbPv+bvzgeWdgsrWIhUYdOx45gG+c8STMhzX80sz3AAALUQ3Pjn+GGueYiUbAagJTvIWXNXZhjtUxhzr2JeM4Y/QQ5hp1zKY1XLDlZ9gbj6PJYrR4hMmggyjL9IrAsSM4iqZU3oOT20fQZQynJQfN+Tl55jCSIMBYp4OZZhMT7UVMzSk37Wk/fqT4tXVTYDFGRCmwP3PLLsbqOCkF9mbLNDnrJOrcxdbcVw8zspa14db10clc25wDuuSWkHmyhI6ha8fKopfJmNBuilHRxsKYOvdxFKg4Obs0XBSiFifoRiHCNEUSBKinCVpRpOLnSIBGquSkUsJWlASx1ue9E5bMDeOmHCQCbFvs+hVJr5IwEbIoClx1Iy4IAztac2WgVPZNgihzvQLqGHlWIswGF8QQOdfkHAYqeDdOKJKMXFEqkYYCLSZR61KMzGfj4gT7dyTYsCdEra0SHVSyQ15YWjBg9AiFYNLUcNXL0xDoNiTCbK6N6gKt+QD1BkcnZuCCYP2IcifUWYpYMiQkQE10EUklJxIzJUSZBAFGOx2lTaTPS1ZD0l6WiacbiwOVihwGgoPTXPOISqCbfRYgiEQK0MAIBwMqYYJBghN1vejs11iyjNApvTkhSSFmjmauWF3y61iDcwC85MFheQ+8HmsMwyZHwIr4dF2v2rpXZq3TD0g6i1FZxoUVAJ/vv9Hq5HFwXCp3XTdRBI6L3AU431VEQZONVqwISMyBuU5uHeJSkcFOCjx4WLXddQBoBGieOl08yD1HgJEIZ/3rd/Hwc56Es+77CR49bRPOeOQRnLznIOYmRnD/k04FoMhVsxvjyGgTZ8aPoFWr4yfjGwEA4+hgfbCIAxjFVjaHcaYsiE+KDmMDn4fILF4zZARTXLlm7ez78W42p6UJUkox2VpEqxZhem4eUZpi2+JhjC8sYm50BDt+9qga+1yW0T+b/a+HwE8PqvMx2QAOt5wvVFdwyFyjbcsyFzFFzLjM4+RaMdAIgCQ73/XMR9pJgbpF6ITlTtVWO+NyVTVa6+1YaRMGFGnAwFKudAqlxOhiBwsj9fyBgHO0oxqYkIiDIHezSlmIXV4O1vq8d8KSOdfNOoxmXBlklnnoigPb/QK5W1TvMxG0KHFSoiHXz6LnosqyRjMXh0vuejJxpXKpqvERU6rL9J/9RkIqkJSI+VAqQXUMXbaeBBIpl6b0TKoTF5oC7XGlGTf9MMOjOzg27FEp+51RiTQSGD+o9t8ZlWhN6DIx2Q86Ubp1cp4hbQhEusxUIwYjEiEV4JIiIhxzqGMcHaSEYTGIsL6Tp9uXKYUzzo0KOLcELPUNiGeTDKBKy4yIGHEQICXKIpdYFrYgy2YFcisuJwQhUpDM4crBnOuAgxICkdV1TS1ZGjduDqgm5iuBEASk5GFgUJa0x+Mfrgt1UEJEv37sig+amLltAC0YTAr717U4NYnTCFJVAipIuSIXic40ksqyY7vtiin36n8nAZqZu/XAgmqvLUQAcKiVuQazesJzSiwYjVARmt1HgadsUKSkkyjSAmDb5+4HGMG6qSbWH5mHoATjs4t4yiN7MTPWLAjYbj9wGD/bNI0nzx1AxFMcaKrYtE10DhNJG/UkwWIYYardwk8nNxih8Z2zDwIAfjK+ESfPH8bUQgthmmKmOQLOGEa6XWw5cATzYyNG7HckSxhpLHYx/XAmDXBgTv23493mu+pYmhHw0JHcZarP7dFWMXkhzN2hBYLW0iKhRBFDRotWOv1d2OK/Oj5OV4nQSRGAuQ7SgGXudKU7Jyk11xbjAmlGIiWl5uE5LqEvdgLbUrHW570TlswB5YQOKHeflrldXfLkVm8oi7GzEyP6lQkp05NbCjSJc5cNc2FSIrPfda+lUIrcIkiIBGP5xR5GAoQStBNqXK6cScyuS0FEiCBR7VqTmTuhHeDoJjV5LE4IpCFQb6lM15nNSnduYZ0AEUBrUoAKVUIHyHTpsnT+MBKoRxxcSyVIgjBzWdSQ4GDQxIa0hZQwJJQhyLL6OKWIgwBRmpa6lRgXxvsZcCgxS87BOM/06VK0w1BJymSTeSg4JCGIrRIbEsraFxACbrtTSzQMAZiM1qq4umMtT7LW3Q0e5ahKTtAuzTJS5+rJ9a4XhfcuodMu26AQJ1c9RrtYO3QWrLbMaUKwmAXi6/HMdYrkYa6bkw5tUYptDbTMFbsQK0tTO1HEZLYDjEbAg0fU8nqeyIVWDLRiRF//KbBtnVrWiHDykRbCJ23G5GwLj2xdjyc/+AiOrhsDlRIXfOP72L19A7YdPIxv7TgVO2YOYsuRGdS6MVrNBg5MTmCqqyrT7Dh4EEfHVLLGztn9eNLe/WjVa9h0eBYh50afL4kCNFttVew+YCpusBEpMjaaJSYAyiKXcMtilh3/4VbuPh2N8mMDFJHTZI5bXxKjeRv9GchInMjXh0ydN5vc6QcHLvI4Oocw2iXgdGay1igUllWXSllIZxuJuyZBZTWw1ue9NUPm/uf//J/43Oc+h/vvvx9RFGFmZmZV+i3LDK0idf1cXGUJDrY1rpgM0ZvUULiZy959DXMD7xf3NiyR0+Mpgy7nFTJhlNG1kHCjxjGfKaTXGxy8xZCmxIy4PcqR1ATSUGLjI6GSLWlIIHOvtsdVvcJFJiEYstqGshBHtzApMDfFkYQSggKdBsdITUAIYL4doh5yHGiNYNvYPCZYB7Fk6BI1mQWSYzTpgkqJmcYI1rVyC517k6qSY6BSGgkT/ZkJiYSqbFdAWcq6NEAkUsSW1hwnTLlLjfacKyDsZGtVCEC7Ys928oybCb1ci10qiEl4KYxpjTyhPpFwLOY9O4HBfl+WHGG31+2q+staWG0poix5oWob4z6zLHUGNrHT0KREVxjQ5aN0AD6QW9tcYqJdsJq8AUV3IgAcaSv3oW6rrU+jkSJ/DeuWeWAO2DiOLXsPo92s40n/9hO0Tl6PzfuPYvP+owCA036oRHhP+9qP8ND5OzE+v4h2LUSz1cY5Dx9Etx6iXYvQ6MZodromk3Ok3TVWt6fs2oMjGyYwtfdoZvXKYtYsgozZttKFG4kKFkW0YjXu8bqSG7EJ6qMLvaQNyPvX/znP2zXCjPTBssqJfLsYitDF2TgpKxI5nd0qJNBNIRpqPEmo4igTSnqul5BzyIRAEAIWx0AUIXZc96atSFFelXsw1vq8t2bIXBzHePWrX43zzjsPN95444r6cm+Uwro5Vm7Tx2rXbx9cFktyVY3Bhi1BUoWqG7UrBFy4+TuEzu6jbJ/G7UoBwYlytXIlIkwJUAsFOpIBkOjGDLVMM6mbUDQaAguz6vIigQS6MG7WmekU40cCHNqSYPKQahN2CObW6wmdoFsXYJwgSAgIV65Zlv3QkpraT9SliAOJTRuybK2UImACc7EKKJ6mKi5kSrQwz9QTXI0naKYxJKVgaYqAFy0L7nsqBJKw+DPRLtc0DBFxZTXoBKEx/wNAh4WIRIqURT0ki2VxdwBMIoRdr5UjfwDQ3x9fonV2Ja5XUaG3tFYmtScSVnPec9H7EJNb4bT71bXauW5ala1KCn3k73nPMnd/LM1dsI0sdooKmYvTCqFIWzfNXamAIgixzJdp0qVJGhdFQmKWZW1htY+YstzpmLDZbN/aFcmkstoBwENHFTk6ZZ0iJf9xEOgkSmQ3YmgemAdOmkQ6ZameH1XlbHZ8fzeOnKLi8qaOLphzsGW/Immj8x0Ej86qcU2OFCxWUw8ftkpjyZzo/uxI7lYGVEKHPt5WnMe46Rg5kWWc6uPRYJkV1LaqsZLfuyZpgDr3lMDETmriByirG6M56dbvQ6L+L3SA0TpoNwUNlSUuruVEU2cw62Q0IiUCLkzlCpXVSk01iEgoRdCEUgSiWL5yWKz1eW/NkLl3vetdAMpLAB0rlN08S5dVSH4MI12ib+JudYdB+wQGx0vZlR4GBc4XKlGUJEAAyjJHSVYonqoarSSLsatFHN24+LTUGEshBEGnXbRyJYGKowsSgriekSbLlJ2G0hA3XQ6nM1L0zYgtXXPxMiYwUkuRcIp6mE/4nazFDB3BOqEms2aq9Ji0mzXg+c2IcfSA64LhOoTEibPTYsLuZyY5EqpEiEHQUxhau1KHJWi2cHBhfBXfKyMSSemaweCcQJY9oZYs8zi2WM15j3FRua6McNmWOpvU9SOBZf25ZM/uN0x473Z6WdfKSu2mVgB9mmdQxplFTsd72YkOej2QkzsbXFjWN6I+t0XRjRhllqV2Rv4aAXC0rYjSeFbOShMmbZHafQTB0UVlITttQ76/wwuYmmur4vO1EEg42FTTrA6OLOT7/tlhNXuFrJidOyGAR+fzzwCwby7ThaN5FmomR4IoUGRKn7t2klsj7fOh3dD6WLTVjYuiODDPkiRcwWC9LmLqXIpihr6RPNFjbmQu4ZoiolRI1NsxFps1kzAjKUWUpGjXcrJKpQRNU3RDVflBi7gLqKS0ZAXZrGt93lszZG456Ha76GaBogAwNzfXt31PDJ0s6nm573Ubt4+CxavCGscdwjds5QkNHXdXpiHnYpgMSDtjVnkvipY6StSELYWy8Gn3qvseAIJAmd6pUOVq0kCi08gy2BIKBMBcmGJkgSENCagg6Izkk3p9kehKQYjr5bVZw8CKN+MU84shNq9rm3M4k9YxGuXfvYpbkyBSGlLVDUNlZSupvafFgIWU4Iwa6ZJ2lE0+pkqEmtxCkaLLctmSJIuZCyRHlJ3DGEBAgNTKuIrAEYMBUJUjuFQJEon10yREApKYjFb9GSgSegJZqOO7XCQpBU1LdBFLlnk8/tBv3mNcFBJ7XJSROjdhQqPKFevCFvkFigSu4FbV6KbKhWj3qclIkrn8mpEiVGXbA73yGYz2piUWyIxj9dPbxJaL0W6rX9PN3KW5d059BoAHDgHbJoFv7c4JjE00dkwBCUd0eEG5Rh+dU+WyAEV85rPvrx4UBZF/alVp6GTuYhM72FLkzS7NFadAO82PL2Lqs3uu3HMSJzlhc6s9cGFlfdICZ1PZwtk2RquK5S7xkKlXN0tKobRQZcLWHaRxim49xNhiG616DbU4RsooOFWxy8hI32i3g4WaItahEAirrokBWOvz3hOazF177bXmydZFZX3VChesC3d5TyZqH4sLr7CADarA0A/9SF0/V6saRy9Z1dtB5qRO65/Z24dMgnPVNpNsQ8DUPuw5MUX+29YB0N2aQGck2xcHmgsMVBAkITC/LkXYpUhqKhOWCoK5dWoHaSARxBS1SGByIgZjqsP5TojJkS6masqFMCvqqJMEIECdhOjQFEdrTWxcnMsSGJSlTTCCgCt3KuOi5wajb4BUSFXpAQCn1GROxdnNkUppLHOhSMEJM5Y8DmIqQlBI1GSKjo7ns+PnLI05XSlCyt46rccyCWKtuxtOdPSb94BqC10VySuzvAFFYqcxqFSYC1VZQunNoZsqcsZINmEQE1vVg7mOIg6U5laljtWujLzZ6+KKdYATL2cdt0v4Yp4nUfDMord3Dpio5UkB7SSPt9OWvFYM/OjRfF8nT6r3ugrDgUy8d53SXzMaeVpWReNoOx+f/n6OLPYeqx4rI8CCZak0623SnK3Xx++20ZUe7PMAFMdlJ17oNmFYHKdGdg0FKUe7oR6Ua0mCbta+1kmQRDlNqccJWvWaEXMHABEEqCfqGohry6c0a33eO66U853vfCcIIX1f991337L7f/vb347Z2Vnz2rNnT08bO6bNfl8G17Jmv/pt81gqSLPMDepiGKufsEgmo0XXbMEqSdU+CM3duC6iUCCMBGp1jkYzxUiTI6oJsKZKYEhC5WqNmxyCSlNvVS/vNATmJ9XkPLM+xcx69WPt1gRETYBSiWYzLdQ2pERiLIrREdaTHiTqSJEQim5GtDjNgmlLNLCqoG+Adtq7Jmom/s2K1QiFyISDc2Jmiwj39I8iGddlyEoLPzvxczmRX52fc8pJ5ctj5Xg8zHsrQT8rnK7/ar80qlyxNOXFmDgui4kNgBG+RSftdZdq2HpmLmHVLlWNsniwQnuZ/9cvILfIVVn1ABVzd7AF/MfhTLB4Vv3fNwfMtHMxY32cgPrfilUm7YHMjfrwLPDjg/k42omKfTvcyttotOKixc22HurxVVkZdUJIIZtV9raz4xDNeayYc4yrVZ9PkZPzQuyjiv8TlIBxgahbHhzS7HSNwHuz00Wz2y2UYhREzenRMuPlgLU/7x1Xy9yb3vQmvPa1r+3bZseOHcvuv1aroVYbLnW5LNu0jBQNcl9VWeQSy5VXZpFbbdjut34uXK0v54LreDipynnpslma1Nnu1TAQSFJlKUvTjOAIgoBJlQFLCep1gcVFNdlGdYG4Y52PJkeaEhzaHCNIKKgAOFNxc51GpjHXEKBdChJI1DOXbZxQjDSUdbAepZgcUa4JAok6TQ1BAoAwEzOt8wStsI5aVgYnyJ7odEwbJVJZ7XieXWdntgZcCVrq6ciNoWukXbSDWrZOuWe1SB+VEpwoK5tOpLETa3RWq76GKJGw823Krj1KpLm29HezUt05zglEWkIi18ik9njH8Zr3bBdWmaVNP7BwRnvcsWVxclX9uG1sS6Aag/VZu2CFRSLSzB2nCUBiW4gswudaBnV71z3KCABaJDJlAf9mHSlf7qKQZAGVKKHj5nQ/s5kFUVvrZjvARGahO5QlJXxnr1pnZ4Fq1y2jimzZWnmAsvQByup30LLG2RY1AIitfszxW+fTPhZj2xG96+zP7bSY1Qtk31EWLhKGKIWQuUtW95dyQKiklyhO0W5EhTqtgEqGaDXrSlCYc5OQFnJVZzsQAjGDCaPRygJLxVqf944rmZuensb09PTghqsMaf73ltCyYd8Uy5IN+rlSgTKXbclYhrjxuhm0bmbtMJIlLgolvZxxqAQlWVlg2JbEAFPtVLycBCFK0DdJaVavVf3ItKmaMCUuHAFIUoJ6g0MIgqgmsNhiQIMjFQRRFhPHWwwiy15tTOVPbc1mikZdbSuoGk8jTM256IgAoywGB1UkiRA0eIoOU7PETL2J6cV5FTeXprnFTd8rmJo4NCTJU+ZpNik14hjtKAK3bmgJDUzcXIN30aWh6TuhylKXgkKgGBunx82IBKQwSuZVYtTHErLC3SDXiLvh8Y7jNe/ZGIbUaULXL86uX6KD3ZdelmeJZ9vpoH1NnmyCprXktL6cJimdJLP8WAPhsjjBagIH9FqjylDmOuwHnRmrDiJb5lqvnHOmXbILXfVe77MRKMKmCRojOeHTyQrtRMmCAIoQ6hi22W5RQqWdVlsmbasaoyqeLgp62zGWkzvXreq21dZQO3Ei4XkiBJBnturrQ8fO6XUhU9m5tcBcL7XsIbsbhsbNGqQcCJhJ7LIfpCOeIqUMtTRFnC4v9Wutz3trJmZu9+7dOHLkCHbv3g3OOe6//34AwJOe9CSMjo7237gEg4iYi2GsHWUJDn3HsMwbtEvuhiF1smJs5YWFrb4zMqH/c+QSJTYYlUiyQFHGVPycToiIqESny1Cv87xqTCTQABDH1LSrN/QEJBEGEnGXGgLXajG0WgyT6xJMjCUmTg4A6hFHPeJY6EYYrakJkZHshgSBbsllLgjBfK2ORhJDPwS61gdtkZPWpGFnpCZB0FdtnEqJUHAIazJkWd1W8xkcHAwJtDtWWfJE1q9OehhE2Jd6PfdDkhKQsifUkmUexxarPe+VoSomDugldoP66U/8LIIhRE7kgNwapmt62q44nc2p27iF4DUR1KTPDL7EwlZwr2bB+2Xxc2VJAVUoECdHpsMmXTp2zYxNOBbEzKLYCHKL3qglO7J/oTg2W3IF6HWVVo3f1oaLUyt7t4QA2gkOPLOs2YTQ7lMfk4khZJbFNbPY2YSu5DoJUg7OKGqdBN16aPQPoyRFpxaqCjpJaqxztpCwfoheLtb6vLdmyNyf/dmf4e/+7u/M52c+85kAgC9+8Yu44IILlt2vvglWWejs9fYN023fj8gN61ZdaWzdagTF9wmHgZCKyOnSpTqZgtC8GoNGLVJivioRgqBe46aGaxBI447VWamESvCUggUCnY76QTaaKTodhjCQaDY5RhppXlaMSSScoFkvxkjEKQMPMssXUZa6lgwRZOSOZCSpSwMElANhhFqSWIkQ+cSukx7M8ZPym1QjUYWeASChtjyJhD0P8KxsTyTVmGMEWCRRoa8yUqZruGp5EkKyGq86UYJIpKsYm8kTClJSsk2WLPM4tjhW855GUReuWO3BJXhlRM1eZhO/sv57ltlVHLpO8oJerz8LnrtZdVUHDU0eSkmL7k9bnBwCBPSSI3sMOtauhxT2I0DI3Zpa6qRM3kPDaOQluXVNj0tb61zCaUppOXFpbsybGxtoxwKa47bGbLdx5UcYyS11mtjp7y/qQ/D0cv19aqucdqFTmp9ji5GEcYo0YKCJxGKjhno3QRAUr606VEIELCH3YJnZrGt93lsbo4TSWZJS9rxWY0ID1E3Ufull9vqCoO4AAWAh81dhP0u44VIiC6/lolBmbEiTsYmZy8BoPg5CLYsdlQgzKxmzyFyU6b1RKhFmP8AwEGg2iuSLWRIjLGs3OpoW3o+NJRgbSzA5EWPdeGz6jkJhjicKuPkfZVom+rusEQ4KiYNBE0lGtCiEiWnjjJlEiJQxxGFgxCpFpkiemvUUaTZhESHAOFeJDYKDUxUPp2M2urT4rMQkB7MSILSrVYCAQRaSIMrEqe0YSPc66hcusFToe0LZy+OxxWrOeyprtPxLdCsy6Pd2nB2giJr9spe52/Xuu3c5gFxPTkMnPmiBWw07u1LDJidAHjPH6HCB+i4YKZIYuyqCfg0Dl2S5lSbcZARNRm1dPJ0tO9tRrln9I7QTMHSCQzspJ7Rl+yk97iHOj5v5qrcJLZJcsGiKXMLA3q+dCBFa1jsAtJui0Y7zhLPsoYIY97yAIMQkQwAwyWz6tVys9XlvzVjmVhN2hmkVSdJkzdWa08uqsFK9N42ycbkaeP1g3/irIPq0YVbMXNm+XFdrwIQSQWYSUigR5HqNI0kppCSF5IhGjQM1RRh5SlCvSUMEtUBxFMKQQKAo0aQNBhMj6qk1CjSZVP+1FQ7I3Jb6vZRYYDVElGMsVUVf2kENjSwVnjOGehyDU4qaSAuWOCoFUsZU/F+WOWXLMJisVcEBypQSueRICVOVIKy+CCQSkv/0FKHLarwiF5LWLli7RiuFhCAYKnO1n5W1HzgnQJlrYY0EAnv0hyZ0ZSXr1PrlXThVSRKBrvJQiIcT5QkMmsDZRM5OZLDdsuiTxKBR6WrN3IcmRsxyPZo2qLDW8d5l7nGUQVee0NtVyaO0K6x9hb5SReBc6ZSqfksJnUVUbQxLWHXMnfl+tMUtI32aFOsqEHaMnYZ2t1Kat8sQJhzdLHRQVRqRQMDy8J1UETpdkUc/kAdyeexrrc97JySZq0KZaHBZG125ARguNg4YMtFhCdY3l2AOgyqrXLHep55HLQLh7MtktFrCyZTKfC5kORGshcKQCpIRmsQyW7MgJ3KUyuJ+aR5aA6hYPH3ew1AYy6GKkRaIAo6xUBG8Jk0gQFEn+RNclwRoii4ikaJLQ9S0ThEhmZVOglfc4FJLl063MU+LlCKlBExIJXtika9AcnSpisrTRBAoxjlqjTnbGqddqkIqq53MNOceC7CEVLgbiBsq6fEEwCByp9r0Jjq4btmyPsssfiYT1VjTLDeq/p9YNViBovtQvxcO+dKVIHQfOshfr9fkz4YhY5br03XB2u5Z01cF9BgKy0rIxXLMPWWyIa7beKVmJGOJdBJCTILDAGtnzIF6BUnU0FmtOjHCXQcgilPEUaCSHoBCFQiNKEmNlwRQpE7XzV4O1vq8d8KTuWFEgocpo9TvYfZYZSJWVaEoQz/3atX43DJgzCFb9jjcMRAqTRaQftjSMXZaGy6Qee1RQlSWK2O5WzZOGMJQIGS5pp3up1lTk30t6P2ZdQXDuOW+rUNN6AEUsapJnkmRKPdoNwgQcpFnnToTAhOiVF+OSoluGJaW+NIq5Lq8TEoYQinQIcW+dS1WQBG7vCJE0ZWv4+SA6u+bEWG2WYk8CRUAKbknSIE1Mal5lMNN1nFdrvbnYYldTtZyCZ9iP1mt1TL3rl6mrXA6bk5b38omVS0DAuRPey5R4yTfljkEzo0zs5e7lrrSdnpfGYbNlB02Q3ZQ+2EkU1YK20ppx9GZRAiRZ9Zq2K5t+wlcCPV90BLipvtiVLnas9qsKrRFta23Y3QaEZrtLuabdVPLV68XlGJ8sQ1OiPK8NOpIrOonS8Fan/dOeDI3LAbJlDze4I6xrPIDsHSiaRM67TnUNVsps8iGAMDUGDSpYzS3wml3LKBKh4VMQkgVB8c5QRQIRIFAyilIJlJcizg4J2jUUmNBtFGjHCEVoJCIBUNEOQIIpKCoaUKXZZPSjPS0ghoCKRBygU6gJpMyZXtBSI/ljltuV0CZ+bmTJKHrs0ZIMc9y/TlAE7ncUmdb9Lgsyua431NZsstqiQarJ9SSayUhy6736vHERhn563HXGvkRd7nIi7Ybl5zdeUnygUt2mLMNdQkdYFyyNnGzx+X2xxzNuNL9usdd0afedhhit1QitxxrnB0L6C7rBy6LGnMmRtGJJ4xYngBhJzzo8YZMkfdaUHCtghLQbgoaMsRRAM4owjgFD1ghTi6JArCUI86yWpmUSBhDlKTG/bpUrPV574Qkc1wSUMdVagsEDyJqw1rllmORs61hdgKCtKxYxX33txrqbVZakqTM1ar3b2e4mnUmvIQYq1qkY8EcotmIuBk3IxJhlLl9BEE9Ss1+ooCDRRIB1W5eRVrrgWozHsU9456TdUySNhIwUAjEJEAkVUJEh4Umu1UjZgw1IZBqK4OUEJmL1bZs2OXACucJsmcZoKxuWjRYf04s4qeJGYdVkQQsd71L1XdiETab0EkU3d7LjZcDgLBPin5n+d16PA7QK79TPi+UJUq4hM215FUlVxSyVm1xYEqVaKzpRPa2t+G6FF2tOQ1NmKhlRTPCtyVkqxC07/QVWVY63UdfORPbbYuc0ADWOpS7YqsyZAdhWMtfz3YV9wS3UoY9rsiKLyxk/GbEt+5Y7LhU1S7qJXp2ZfFztQCiFhREhAGo+r6Z3lwtSY2Lf6TdRRqofhLGeubzpWCtz3snJJmzYZfyKqv40A8rda26grD6c5nI76ByXFWSJO44eImERT83bRmBtJMVDIGgBLrYgU3sagHPy4SJ/ByHTBizvaoWIRBkY2GsmL0bMqESLARBoOPriEQ9TBFRDgGC0TB/dtIac7FgWMcWkYAizCZXAYI5Wse46KAhuXGHdsIQKaEY77aRBAGCrBQRZwwkc7Nq65wo6M6RHm0jTfoEIfnkQvQ/qTJaSbkeEoNERDhiWb5e685xK0FntUEFASkJ+l0r4pkew6OfxlxvW1FJ2nrEgDV0W0qKxM1e1w8F16NdCaLCVQqodvp9v2oAOg7MdqmWEaqquLl+5Muu5lAYY0lflcdxDNIoh83qdcfhxtJpt6rbp+1iBVDIDtbJDiNRbo1l1LqRqsQX+xoKE44kI31RrGtzU4iwl7o0O10IQrCwzPO21ue9E5rMuRa2/jVW8/dlLj4b/Yjccoia29atmarGt3IX6qB99ku6oKR4U9DjIlQRFClQdMNabVhoBf4jJ3KaNOpsVdDiedISJAEREJIgFgwBEWBUZlmhAh0ZoEkSU8y+Bm503gAV0xZYpdZSyhAIjpgx6JBbSSlEduPR5E5ntAJFsUpdTqYd5E+oWnSYWZY0m3wTSAQQJlZOx8YxcEgQpBmxEygv1aVdsna83JD351KwhIAGJdbFEheEx9rHUgldVR89KIgEa6taiYvV1nCyLWh6eZnkhrG+0eJ+tPVIW850WTBubQcU3axlFjVmET2gl1gxx8pWRegK4y5zDQ+Y++2++7UfRADdMQ5rldPb2uS1X1UIrRmn25nYucx9Tq0+7QxXvbybZK5bAhHkD7RanoQHDFRINNtdtBrFknVlHpGlYK3Peyc0mVsulkPklkLYgPzaLttXmbWsap9lLtZhMmHdZIcyQufKiZgyX6yXYNqhZJqw6dqudp8hE0rLrpBVnM8DjAqETKCTOIkEVqUKRoQhRBIEMZiJmesShkimiAlDO4thC4VAIHghpZ1KiZTS3LVquVWpReRsaNKmEyC0PEmXhkgtS5wEUbF70MkR5TpxNpED8hg7gdzlHhABSkih9u9KEazxSc1j6Rim1mq/9oVEB13ZgRElQaLhEpGkwmo2bGamttZRm4g5sWBudmkhq7bCuldKepw4O923aWORRLvvqnFUZtWWoGxMg0idDdfqOMw2WlOuoLlHcpdqoW12jFGmGafLgbk1WrUVTsdHUstiV+ulIuohI5MoydazlINnJK+WpBCco4NIJUVk4wqHsfiWYK3Pe6t3B3iCoEzodyUgDjFxYTI9rYcWd/2g+bWKmLlErsrySB3iNAiuhIotFkyJNIkOrqtUrzeWNyYRhfny0CrRFTKJkEnUApGdAxUbFwUcARUYrcVgVKAecERMIKIcdZoiJMUbRJ2koJBoIeqpriBAEAlVr1Vb0uIgKLhNYxaUZKvSgmWuGwRG48hFkgkH29ZAACirJhFl5oOCrlx2roKyNCu9D4vIrYbblQhS+TpWOHr0KC677DJMTExgYmICl112GWZmZvpu88lPfhIveclLMD09DUKIKXXlUYEhbnLDaMwNEhY2GaZ2vVW3lJdrTTPrypIRrAD7Plm2lRUgdB+l/dJi/1Vty/opbEOKn6vau8v7CRHb44uC7D8rbjeMJtwwx2OLLOtlbgUIm5DqmMRh3bZ2fNzgGxq0fI2WJikIV6dcxdBlIEKYzwEvrlsKjse8t5o4IclcYd6Rxdeg5cNAE7hhrXFOMs+KXGQagyx3ZVUE3LG4qDoeTcTsbFVAkbyQiQKR08v1f0aVPpy2yDGa68fl7YURBAYUsQmIAM9ITER5XubKGmNgEbsAAgwq8UHX+QBQIGUxDRBTRcxaUc2QOhUXRwtJEWo57dE1iq2qD4IQpESVFksJQyBziZO04qdXpiVHSX+NOfuYqXMOloMwBcKk5LW8JLGh8LrXvQ73338/vvCFL+ALX/gC7r//flx22WV9t2m1Wvj5n/95/MVf/MWxG9gJiFKCZq0bCv2IlRaIBYruVZvU2Va7ssxV+71NqMxyWk50tEvPnejcqhGuG9Ule+7LHV/EyolSVRtdmN4lh+4Y+42tH7Gz22g3aVl/NmzrYkF3zrUyuvtyCLJdSmEpN7fsmtGELnQsuTQje41uglqSotnqQFCKKFneRHU85r3VxAnrZl1N65uNpbhTV4O0udDWt36u2EEJEDomsExXTvfLqOhLGG3XqXGBMot0sOJ5IkRb84r9MCoMOdHK313BlAwJEybZAQBqGXljkIbIdSVDkyQFkV5toevQABGv/qV2gwC1VFWC0MkPVGqhYcsaRigkKYoFC0IgQIeulyvhbA8VB0eNO4aCEJUc0RHFn+1qy+SwhICW3BjK0vZXA//+7/+OL3zhC7j33nvx3Oc+FwDwv//3/8Z5552HXbt2YefOnaXbabL30EMPHZNxPSFhEhKGe44fmrwVLG0WURtkDdSB8P2g3YxuAoImFdw6JmqNhaOPmxPFbQvLLdeq29aWKBkY71a27zJiapGqKuJaVRvWfV+a3luxnbufQuZtts9oyN+8tuLp68rNVBVSuVKr9q9jafR2znXXaMeIo6AQ35lrzeVjDFJusluXisd63lttnLBk7rGAG/fWQ1T6ED/3Bq0J1jDSJECvi9VFsepDdQxdFaGz/0uHtOVjLk/20DIruv+y+MCQZcTMIXKMSNQYBxcEYUb0KJEYob1KQAQSYUb2KKQhdHb8mpIqUYQuoRRMSrQDlfoQCG5+IbpkjLbWdYMANIux09pytjs2oRQUAjwjdJDCkMJ+dVQZJGKwLD6ufOIzxJQAiWCWxA56XMnLAeMEtCSrqyzTazVwzz33YGJiwhA5AHje856HiYkJ3H333ZVkzuNxCFdLTsfPaQhRLj1iW+iE7CU2VZa+fppshWzMElKk47bc5ItSDTvZS6AKMXTOb7UQp2bLlNBiG73exN+VxObZY7D33zejVxTbDRMv16+sl10FIspi4mgFadLnW6BI6nTlB71fNylCu1cL3LR4jGGiRIOTkJnsVl0/GwHyih/LwGM97602PJl7DGCTuGEFh6va2SRsKfFRfAnbLMetXKWN5y5TLlZFEJmTnRoY96swx88lMW7aIBMEZkRVilDB/9JYvwxZk9RY5iJHOKomi58FIYBUsW1MCAAcTLtCKUMo8rp/0pYagWudI+ZzQilCIbAY5CVobAKZkKBgsaNZpq12vUbgaFs/TWHNbrHonUB7ib9EsoI4D5YQsJInVJ49oc7NzRWW12o11Gq1nvbDYv/+/di4cWPP8o0bN2L//v3L7tejD8osdEux2ulEB1s3zqxz4ufK3pe11USOkiLh0+TALv9lWawryVoZtCWsX3JFPytaZeKBs/8yy5kmaW4bTeA0uJXIUdhH9sclkP1I3bAkrsxVPExbu13ZddPPhWvkY2Tx+BPea9mjtGAltutia2tdkHIE4BjtLF80uN+893jHCRkzdyyhHzTKXKjagsKGjGuy2/QjWMKSpOiH5RI5l3SVwY4RdGPnypbp90GWKKFj5ezYOGYlRQTZcr1ey5LY710Xd52kSoQ3Oz+pNWMEkiPIiF1KmHK/EmISFjQSyswLyDNWNXorPtBseaZJl8XL2dCk067FalvrOAgiwsGgrYoqO1c6Wog6uzikfW5My0CQEARxySub1LZv324SFSYmJnDttdeW9vPOd74ThJC+r/vuu08df1l2sJSlyz1WEf3KbJUtXwo569lWKtV/IzciitvYN/eljllD34xDltX+LCEqel96uS2XYZOgyHILViU6RKw39m0QTLxcSaxbIU6tLG6OFN+XEjHaeywahVi+iu11m6pjMfVubfew5VKPWObytvq15Uc0qPVd6c/ud5uIUpIYdRNE3dWtyzBo3nu8w1vmVglV2nMrDUavQqEag10JoLJuZ9GVWtWuShKljNBp92sV2Qus7FS7Uk/eZ/GHa7tlqdUnIbKQzUmJzKxvmZtTkh5CRzI3JYNADTkxapEINZkaEV8mM7FiQiEgQWVeGSKQHJwFqDlxdZrA2VmvutarC/XdKOiKD1QKpFmNVl0RQmaWxcR6vlLWyZ4uS7XqdB/5OerdblhQrl4utFFzz549GB8fN8urrHJvetOb8NrXvrbvvnbs2IHvfve7ePTRR3vWHTx4EJs2bRp+4B7Lw1IInbuu9AJ1rHXms9OnJnXaEgdUT6RAbhmzLYJ2zJkdO6dhrHgVLlejV2dNfGXkyBxviWyJa2nryQK1j3tArFuZoLCuOFGW4KCterr4TZWFUhM028pZZWFz3bLufrNKDEa/r1FClm3x4MJNJXPPuufUtcSVQGscCmvejboJ4lqIIFXr0mD59qlB895Scf311+Ov/uqvsG/fPpx11lm47rrr8IIXvKCy/Ze+9CVcc801+MEPfoCtW7fij/7oj3DllVcOvT9P5lYBtrxIdZsszmlIwd9hURVc78bL8T6iv2XLBgkjl7lJy6BlRXT5LSMmXBJf58bH6TahReRCWiR2jIhMTJeCEWHahhVSHjXZa4K3XbSAEv6lECbpIaVMkS6L1LnSIprYMSmRUAruWOMEIWCQSDISp2vGAsiopvoOQggkoCZeTpg2WZyhLCftIRVIhMrVXak8CUsIWNnFnD2hjo+PF8hcFaanpzE9PT2w3XnnnYfZ2Vn827/9G57znOcAAL7+9a9jdnYW559//tIG7/HYYZii7zbx0/FhgxIeloqyZIjlwn2aLY1xc/ePfL+lZcKscQ2T0FBWcqyg9yaL/dhVFsrGVIYqYli1zBDVjDLQEvKnyZudSBHzjEii9wYpZE7iXNeqTQaddUHKISg1iQ9RNzFVIsJ4+d//oHlvKfjEJz6Bt771rbj++uvx8z//8/jbv/1bvOxlL8MPf/hDnHzyyT3tH3zwQVxyySW44oorcMstt+BrX/sarrrqKmzYsAGvetWrhtrnCelmrZLlWCr6uVOrXKlaZ61ghXI+94ObbEArguk1IdJxZ0CRtBFn/y4GeToGEbmc4Erz3z1m/d8kMlBZIHKhRc6GkXrRheZDKG25CFwJFGfVEagTAxNIDk6osZgplygFpxQJDcAJgyAEXRqAZaQupayHyLmJD3qZcDJcOYhJmhBO9qrZPvtJJpIikRQMeYYukCdyMCJ6vnvbMrcSi3AYV7+OBc4880y89KUvxRVXXIF7770X9957L6644gr80i/9UiH54YwzzsCnPvUp8/nIkSO4//778cMf/hAAsGvXLtx///0+zu6xgEtGbA2nstiphOcxcaaPZdx4B+mqFeRFHJekXl+2zCUyVfIlw4yvSlbElSope9ltjQvX6c8V8i3btyZ4rhtYb2/3a8qaOVbHKnmXKmh9QSC/PqIS9+rAm0vJPoXokcuxP9vLa91lSpOs4rz313/913jDG96AN77xjTjzzDNx3XXXYfv27bjhhhtK23/4wx/GySefjOuuuw5nnnkm3vjGN+L1r3893vve9w69zxOSzGloUrdccjeMW7WKqPXLaBxu372utqUcw6C2y3HTufGCZX3YpKwq29XWi7PPn6ryIgxBDTJCo8mNIjjlNwh9vjnJrXCCUFUrtUI+RBBiLGxdGhiXaEJp4cUzwtelAQRoT9wdoIicG19n9mO5TSVIIYu1zNLGSO94V7MCBBEqq8t9HUvxzL//+7/H0572NFx88cW4+OKL8fSnPx0f+9jHCm127dqF2dlZ8/kzn/kMnvnMZ+LlL385AOC1r30tnvnMZ+LDH/7wMRunx5AYWkoDRdemWzdRt+8hW7QYl1UVW8ZIb7uCRlwh7mMwYavUlqN91jlEzf7f894hWPZ6m5yVETS3L3cMZdu54zdjKGtDVLycJuW6Pw03Rg7oJfCMqu+VCyVVQkn1eddWXUtEuKeJXUIyI3dkmRUgVmvei+MY3/zmN3HxxUqFg2gAANU6SURBVBcXll988cW4++67S7e55557etq/5CUvwX333YckGS420LtZMyy13BZgzTVL3FYTC/2/Sk6iKqHBvbn3S3yws2K1u7NcLLjXBduvpNggsldF1MrGBxRj5BiRpRZHRpSFrZAsICnqVD2JaSueS3Tscl7apUqdcy8IUTIlIn+q0wK/dhartrxRKXsSG3RbvR8321X9p0iRWwMBZUnkYMb1SjNJlTgr5RUQjlQyMEik1vjd+LrU+v5WUp81SICSqjbA6sYbFzA1NYVbbrmlbxspi9fE5Zdfjssvv/zYDcqjHGVWOfO+SirEiZtzRYNd9HOZVmWVlrk1S9cP6K/fvu34k2EyNQvLueUSJiiNn6t05VbF7VmEdCneazexwy5N1o/QlrlZTZ+WNU+7SUOWEzh3u4TDuN6HiJmzZUvyGsF6rFZM9TJFZAfNe8Nm8R86dAic8554302bNlV6Dfbv31/aPk1THDp0CFu2bBk4/jVhmXvooYfwhje8AaeeeioajQZOP/10vOMd70AcHyO/Tx+41idXfd99Ab3krbrvfBuboOlrs4yEVRK+VbSi2Mfcr0JFP1dxlVvXdqsGtkCw1YYQiVomEEwgUaMcNcqLGbR9zq3WbNMivi7s6goxDZAS1kPUbNcrt9Zr8uZmuQK5KzXNiBupsADyLMu24JKV5Xp0uk87zm81SnjZKM3oyl4ejx1Wfd7jMn+tZHsN27U6qGRXGUoJjyhmuq6Wsrodg+WOwXZ7usu1kK1+2agaW5WlqZARa1vBXBdr9t7NeC1zn9qWMdPeshDamanufoEiietntXMhpIqHc4/N7TPhg2MkXRd9v2tUCNCUZ3IkWUjLKl0jg+a9YbP4NdxM/EHZ+WXty5ZXjn+oVscZP/rRjyCEwN/+7d/iSU96Er7//e/jiiuuQKvVWpJPuQrLKbtV5k7t6bdEemK57tUyIidAMKxuXWFcjtjvaoCWkDB7X3kSRG+ig22Vs8mWrvMKKDeiLUdi+nXOp4CKM0usbNcgc2DmbYjZVrtaIXNCqNenTpWHSKYmKcK21kldtktPLk5CRaHWakbomJTghBTlUrLkB22RA9T1kkj1M02lZc0jqq1OiFiKjuAgVGV1lS3zOHY41vPekrAUAsglemLj3FqJQO9NvkwseFBm67AwmZWZJUiPs6zf4ob526USzKrkCTcZomzftpXRhi0GbJIcHDdtgWRlx2v2bWXFliZBuGNyyF+cKutcUZYg61vCTGdcAqE+BFJMdtAkeZgYRL192WIhzJyrPmcP9stMghk07w2bxT89PQ3GWI8V7sCBA5XZ+Zs3by5tHwQB1q9fP9T41wSZe+lLX4qXvvSl5vNpp52GXbt24YYbbljxpDYMketXucF2T640Dg7Ib8aapFXKNi1D6X8YaZLlYNjkjUF1Q43+WoU7VkLFyQkQk9gAZIkLkqJOkkI/QK9osG6vEUiu4tIIjFiwaUdoJjmiJoyUZOW59JOSznYltDcpAiRPrCAk20fehltPWyRL0AAUodTfvbYoajdrYLlegWIyTG7Jy6+f7jIvxyAhCMqurzWit/REwbGc93pKN1W1Acrb2Ra5QWAUKIv70Td5myS5E96gtHogJ2pu5uzADFKLELpEq+o43PVVZX56tZ3y/svi5mwMKunVE2/ntDcJDSXtdfUGm6SVklo3js/67MYy6vEJAXCS68xpuKLAepkoEQd2ITKyGvaeK2Wd63WrLt/N2n/eGzaLP4oinHPOObjzzjvxyle+0iy/8847cemll5Zuc9555+Gzn/1sYdkdd9yBc889F2EYlm7TM/6hWj0OMTs7i6mpqcd8vy6R0//LiJxrNVuOFU2jqtbqoP6q1pdZ5/qV9arCMLFxZW0oLWanlpE8QqRV8aV4jqVUBIxC5rIr2ROq1pTTZbFoRnL0ci3oa/ajkyMykmjImgTgSpBk7lVqlecC1DhsSx4HKWS5Citj1s1k1XVZU9BsDMK4XtU2w0dDLOc7dMFiICiZD+UxjJnzGA6rOu+VyWhokmevG9YCZuvK2SSszNLmWuvsdcuFrT8HVMe+ue1s9BAsR1OvyjVcJYhb6KrP9mVjcGvBAkAjLHFvlhBRl+SVyZy43/8whNYeHyPZGDPy5iZu6P5D61yUETu7HmvVVOeONXObK5mS1XnIXM1575prrsFll12Gc889F+eddx4+8pGPYPfu3UY37u1vfzseeeQRfPSjHwUAXHnllfjgBz+Ia665BldccQXuuece3Hjjjbj11luH3ueaJHP/8R//gQ984AN43/ve17ddt9tFt9s1n3UA43Kscfny3F1oww7MZw5J4Y5yfxWE4zLr16ZvP2L5BNI+rt6EiGKM1iAi5653z4Gu0eout8+vymAdfO4CR1euahvbxQooksX6qELapEtoImdqvNKedtqlyrX71oLWlauSk9GQjvucQkA4M6D7naoEiKx/IleU3Uo5AS35AZTVLfR47LDSea8vbEtdP/LWYznrQ4zsbYbRgBtkhXP7KLMquW7UMgsd8iY9VseyLFx7+yorlpu4MEwChduH3q6sH5ucVSZy0N7Pur8y3TlD6jh6rX1Wm0HJJJrIAb0WX5cklln4bOhrqcpitxSplCViNee917zmNTh8+DDe/e53Y9++fTj77LNx++2345RTTgEA7Nu3D7t37zbtTz31VNx+++34/d//fXzoQx/C1q1b8f73v39ojTngOCdALKXcj8bevXvx0pe+FK9+9avxxje+sW//1157bSFgcfv27WZdlW6ZG+Bva8aVBecDyAQleomc3YfbXz8wQ5qKy8vkSAahH5EbLE8iURYLVym3UnFcw5Yvq+qrLBmAWBmvjEhwECSS5kQT0ljidAybtIicHddmZ7Ta8W1JZnmzX7q/mAamVJj90v3ofiWUvIktPyJAEEoBJayS78/et/7ebKshBynoBupjcZGT7f7nuApBXP3yWDmO57zXF8tJjOhH5OxEhn5tTF8lbkEXZbIflW0td6ALV9pk0NjsPqt05NzPbiKEndFZFnNW9j6y7C36vdt36XhIbikr09MrkzVxt3flThgtlkjTLyHKrZauVdFGpbXE+U7s9+4xVlx7dJnSJKs971111VV46KGH0O128c1vfhMvfOELzbqbb74Zd911V6H9i170InzrW99Ct9vFgw8+uKTqD8BxtswNW+5HY+/evbjwwguN2XIQ3v72t+Oaa64xn+fm5kontqWW4tIxTjbBOBZlu+xx6TqcIoulGpbQrcS1m4+j/7EtVdaF9hEc1vuSslhhwj3fUhLAIolGVJeoODPIXKqEWmRbZbTy7D0xSRDaOicIQT/DWUJoZeYst7NWnXi7YjvL2ud8j65MDXcsciERSGRJn467fCXfeZAQBCXby9Rb5lYDj5d5b9VQyHB1bqR2TJxpv0QX60CLnWMxK7MW2tahfqS1LI6uyv1oy4y4bkBX6kTD7sOOPdPnxCY5+jMvGYvuq0D+SvZhj4nRvCJDWfxgP7hEzd63bZnTun6rBTsLuSzOU6gZ3Xxcgct1rc97x5XMDVvuBwAeeeQRXHjhhTjnnHNw0003gQ5xwVTpwJSh7Bpw3aMFuYwhiFw/l6UNOwDeRpUkSRmRGyahwY2nWo2s1ioip4+9cP4cEqddrK51qazPQjaqJADJPAd9XNi2FIie53R8WkIoapZ7VZNFKmUh1s0emm2Zo7L6hmBi4LLSXYXMVqtvWxxYCwbr93q9crH2Xutl14C+jlYaM0d5+e/BZ7OuDh5P897QqCJTVcSoTI6ijNhpVMXYlWHY2K7K7Qe4kwdlypa5bgv6cX366EfGquLuBlkqC65YVH+2x1sYe4mb2N1ngXhqAlviEmYlYscuuABoiTWQWufOhSG2DqFzfg/LTX4A1v68tyZi5vbu3YsLLrgAJ598Mt773vfi4MGDZt3mzZuX1edSCLwOvnetQ/Z6oBjrtRQMugHbyQ9LzWIdxkJTlVwxaJsq2Odg2HjBMtgPqu65pwUyLUzyArVcq3YJrzxbtPqXqVyiRfdm3/Hp5AeLAJZZ4gR6v7sULEt0KCZG2Nu4JN+9BtzvzS3ntdx8hSAGgpL7ZElZW49jiGMx7y0ZrrXIhk3YCmr9Q5AsSqr1x4bJGu3ZxiJPQpQvt/tXDfsTOqDawtZvm7LPZZa7KvJUdewaZe7okPV3U7uWPR1Dpz9HfajAoJulcEhcZTsJoITI6dqrZQkQRlKmguhV6QcuA2t93lsTZO6OO+7AAw88gAceeADbtm0rrHNV4VeCQaSjX1wcMNgduRSUzVnDVn6gVJaKBg9LGPuhX7ulHP+wSSj6XBeInCbPmZtU12RlmYQHAIRE/QJ1lQS76oPeVickBBBA5hatgm2t01ImmjxxQpAS1tO/jYTQQpxeFYnLkx5kwXIHlJA3J1M6XaFFzvSVEARl149PgHhM8VjNewPR4xIdIkN0GCubTdrKpDyWaqHTJMmVKuknQeJWYtDkwN22nzyI3V/lWEtcsWbbCrLpWtj0sh4LXkaOtHVzGP22fla4Qt+kt52d1FCWqOASLJd0AijozpXtawXo5zXph7U+7x3XBIhhcfnll0NKWfpaLnrmjtJkCNnjLlyulamK6JS5V7lzw16KVU4IUln9YaVkUxOwYWPkyhIfClUbBiRR9DvX/c4FJaoclt2mmzlaORg4GEIpeghe6X6yxAedzKC/L/t703IlRmzYyVrVenMcDAkCdDM1TfcYuPU959Y82hMjZ4/bZOcSiYAUxZGXCy2eWfbyeOxwLOa9VYeu9CBK4uCAkqB4ioLiP9DH2raM25Prgquq3VqF0ioRQ247jOWuSqjXfen+qhIrqvZbWmaL9r6q+rLRrzaulk0p3a7CDW6TN12fVS+3x63J7bAWt2GqjQyBtT7vrQnL3GqjLGNVLe+1ApVvn7WvSEQoW74qyvxLSHxYDqpi6AaRtyqSaLtY+yU9lPWjwiPKXbTanW2sXFk7kw0qVfanFhoGiuLBBKr6gv6aBUiPdc5265bGM1q6cTZiTRqdJAcd92YTvYJFDkuv5kGINBUg9HkAlNtZy5ssB0EMBGUP3GtkUvNYISqVym3JjyGvUzuwf6lyHcO2ta14w2TkViUIDLNtZdJDSYJCv/0Nu5+qsVURujLLnX2+hrF+DUo2AcozYIEiedbfi1vtQRN6PRYdQ9ePvPVz95s2ovh/iVjr894JSeZsMNJbQ9Vdr0VpC2QDRUI3yMKzXDFX7VqzJTeOJaHT+1wKqmIF+5GJQUSj32/XdbOW9ZUgQCKBOklAIRGDIQFFDRxhRuw4UWW17O9QOISuZ1yOtlxZPFwVkasCtyx+jEhA5tvp711r8pFsPYBMbqX6elhuRutan9Q8jgHcG+RSZUzKCIKb2bkSQqcxjFZeqasVRdJUVlXC3ccgjTp3f4W2onx5YX3B4jB4X7bLuEAGrT71eKr2X0YGy/avYbtUC+7gLD7Odce6osGa6Ol4OrvtsGNYJaz1ee+EJHPa/ccrSIhuoxHS/kx/EJFbCtwb8DCZrEvBalQIcPsaJA5sQ5OSpcJkm1pWUQCmtFfP2LKJIZEUNcJNH2obCjNxkN4YC03YNKkrS2rQ+3bBSS+5K4O20Gki5yY/uBgU0yhkbtmTICsUDQbKDKkDfgYeTwSstApDmezIscCw5G8V3G9DCSMDw1v3gCFcsgMydqti8KqSMLgsSpJU7b9gQXPiBV3YkiQaOpnB7auTAHWrLBUlg0t52W0fA6z1ee+EJHMaLkkry1ZdzaQGDU3YDLlwCVuVl2MIImcnP1QmSKwyoRs0Ho2lkrhSi5ugCKnI5EGKfWqywwkAJxFCI0VWXzWT/QByC18KqtytFqqIXIqihc5+r/sH8u+YZP277e3P7vclZFHEuMwKWIWVZLOymCAomUB5RSymxxMAKyFgrhvVrblaJgy8mvsHitamgsXP/v062atDVaWgxf9Af2LXz3pku2eHsSDqMRb6sI6xnyivO5aq9/3q1QLFWq56P6Uiw67bl1Rb/+zsVQA95b2OE9b6vHdCkzkbdqaqySgsS4oY0go3rDt0EKmyLTJVfVZZbDQZWqlo8EogBCmNl6uKh7Mtd24bCYKACAip/ut2iaQm4WHY78d1odrJC5rQ2RmqdhmwMiLHSbnLtV8JL47+36teP+j7F1ZVCFPlYhlVNzSCGAhWKfTH43GOpVi3+hGPgkyJLL9Y7H0NezEtRVOuzKpk9l2S2aq3KSsR1o9sDWup6xlfifWszPo1jIt4GMveoDJchTFZ59ldZuRL+rAuTdzKrHIu7Fi5MpdvoQLEgHtXP3f4ErHW570TnsxpLS+35JaNKlFbfZOtcoHZN99h6q5ySQrzncliLdlmtao6rJaFTmOpBMLEHi5Rt861zNnu1GI1CAZdOAtAsWQWGAQkBFHrWeZuza13JcSpwrU6qJJDaWwdaJ68YWWycrAecWcpVbky5lgijSiykzxhE7ylYq27GzwqoCcX/X+oYPgyUiaqCc1yLWwDdeRKSN0w2nNALlfSj9DZfS8nNmu5BK9sfz0yKAMI5rBZv67AcJn0ybB9lSUrcJGTOZeghay4zCQ9ZMdk/tNeaZOljGEFmnNrfd47IcmcTmYgJni+xALnxGZp2G499/1qxLeVhYKUuUVXo0yX7hsYjtTZ4xjW/Wxb5ZYSL2ce3gpaar2/Kt2nLUWSSmoseJTIAtEDcrIGwCRDaCKXu0n7W/mMxh0pnje3/JZNCrXmnasfxzNSxsFUxYcBhF/36WazAvnDyUrCA4IECEqGsJzSnR6PMwwjCTJUP0OKBC9lH49FrJ1GlSjwsGaYKtJQRupWaskrez9U5m0W0xbzcoteZdxcSf9Vkio2IlZOxOzs1SrplIQDtbB3nY1B8XsrxFqf905IMkfQX35kKUXkl4KCfhyKVR9cq5zep53JWkUOBwXHD0P8+sXRFSVDhj8PZeW7qjBM9Qw7ptFur4SDi9ZVXRFCQ2u1RYQjAUUIgQACHAwMHAnJCZ6dLWrG55x7TahsPbjC8VhuW+MuRa8Fz+1bGMI34PuyxJI13O95UOJOFYLu2p7UPI4B7OoKg8iJTYiW4qMaJqlhKYRrGAws3TUkcQJ64+tsYrMSkldaa3bI2Dwgd4/alj77cxn69V9G1nT7UmuZ1ZeOl9NEUme3MttyWkIKl2ItXSaxW+vz3glF5rTYZjLfAqDdcA5ZgURqxTItlcTZpKpwk7ZImd1jmTvMrcnqkrlBGa+yJGBzKVY8l1gNmm5sEmWnG5A+ZE65SouEyUjEEFn8/UMRE25tCwDCstRpq51d65VkQroM3CzrQhG6NNs3Qy60Sy13LACE/SpCWN+tqu5QTHiw66vqcfesk3kbmwxKJ/FBL7PFgwWIcbPaJFzI/DGlO7eYbbu0azjhc6ViKgnmltSPx+MD+vufW1xiSoxNIHjJMiHLl/EKq90wMXPDXKvutqWyJ7L8fRl5Krtbr1SKJe+oYrFFivslV1Ar834p1r3U+V+2btC2LtzxMqLeJ457Wme5GtKolQOyzxIAeC5L0k3VNnrSD3nejyb44fAu1bnFWO3mBJv3TigyNz8/DwD49Om/fJxH4uHx2GF+fh4TExMD20VRhM2bN+O6/dsr22zevBlRFK3m8DyOMfS8t/2/3HacR+Lh8djhRJv3iHxc1YY5thBCYO/evRgbGwMhgy1Vc3Nz2L59O/bs2YPx8fHHYITHDk+UY/HHMTyklJifn8fWrVtBh3Q9dDodxHFcuT6KItTr9dUaosdjgKXOe4D/nT3e4I9jeJyo894JZZmjlPYUrB4G4+Pja/oHZOOJciz+OIbDME+mNur1+uN+0vJYGpY77wH+d/Z4gz+O4XAiznsrTwHx8PDw8PDw8PA4bvBkzsPDw8PDw8NjDcOTuT6o1Wp4xzvegVqtdryHsmI8UY7FH4eHx7HHE+X69Mfx+MIT5TgejzihEiA8PDw8PDw8PJ5o8JY5Dw8PDw8PD481DE/mPDw8PDw8PDzWMDyZ8/Dw8PDw8PBYw/BkzsPDw8PDw8NjDcOTuSHx0EMP4Q1veANOPfVUNBoNnH766XjHO97RVzX68YLrr78ep556Kur1Os455xx85StfOd5DWhKuvfZaPPvZz8bY2Bg2btyIX/mVX8GuXbuO97BWjGuvvRaEELz1rW893kPx8CiFn/eOL/zc5zEsPJkbEj/60Y8ghMDf/u3f4gc/+AH+5m/+Bh/+8IfxJ3/yJ8d7aH3xiU98Am9961vxp3/6p/j2t7+NF7zgBXjZy16G3bt3H++hDY0vfelLuPrqq3HvvffizjvvRJqmuPjii9FqtY730JaNb3zjG/jIRz6Cpz/96cd7KB4elfDz3vGFn/s8hob0WDbe8573yFNPPfV4D6MvnvOc58grr7yysOyMM86Qf/zHf3ycRrRyHDhwQAKQX/rSl473UJaF+fl5+eQnP1neeeed8kUvepF8y1vecryH5OExNPy8d/zg5z6PKnjL3AowOzuLqamp4z2MSsRxjG9+85u4+OKLC8svvvhi3H333cdpVCvH7OwsADyuz30/XH311Xj5y1+OX/iFXzjeQ/HwWDL8vHf84Oc+jyoEx3sAaxX/8R//gQ984AN43/ved7yHUolDhw6Bc45NmzYVlm/atAn79+8/TqNaGaSUuOaaa/D85z8fZ5999vEezpLxD//wD/jWt76Fb3zjG8d7KB4eS4af944f/Nzn0Q8nvGXune98JwghfV/33XdfYZu9e/fipS99KV796lfjjW9843Ea+fAghBQ+Syl7lq0VvOlNb8J3v/td3Hrrrcd7KEvGnj178Ja3vAW33HIL6vX68R6OxwkMP++tPfi5z6MfTnjL3Jve9Ca89rWv7dtmx44d5v3evXtx4YUX4rzzzsNHPvKRYzy6lWF6ehqMsZ6n0QMHDvQ8ta4FvPnNb8ZnPvMZfPnLX8a2bduO93CWjG9+85s4cOAAzjnnHLOMc44vf/nL+OAHP4hutwvG2HEcoceJAj/vrS34uc9jEE54Mjc9PY3p6emh2j7yyCO48MILcc455+Cmm24CpY9vw2YURTjnnHNw55134pWvfKVZfuedd+LSSy89jiNbGqSUePOb34xPfepTuOuuu3Dqqace7yEtCxdddBG+973vFZb99m//Ns444wz81//6X/1k5vGYwc97awN+7vMYFic8mRsWe/fuxQUXXICTTz4Z733ve3Hw4EGzbvPmzcdxZP1xzTXX4LLLLsO5555rnqp3796NK6+88ngPbWhcffXV+PjHP45//Md/xNjYmHninpiYQKPROM6jGx5jY2M9sS7NZhPr169fkzEwHk98+Hnv+MLPfR7DwpO5IXHHHXfggQcewAMPPNBj5pZSHqdRDcZrXvMaHD58GO9+97uxb98+nH322bj99ttxyimnHO+hDY0bbrgBAHDBBRcUlt900024/PLLH/sBeXicIPDz3vGFn/s8hgWRj+dfpIeHh4eHh4eHR188voMfPDw8PDw8PDw8+sKTOQ8PDw8PDw+PNQxP5jw8PDw8PDw81jA8mfPw8PDw8PDwWMPwZM7Dw8PDw8PDYw3DkzkPDw8PDw8PjzUMT+Y8PDw8PDw8PNYwPJnz8PDw8PDw8FjD8GTOw8PDw8PDw2MNw5M5Dw8PDw8PD481DE/mPI4JDh48iM2bN+PP//zPzbKvf/3riKIId9xxx3EcmYeHh8exg5/7PI4HfG1Wj2OG22+/Hb/yK7+Cu+++G2eccQae+cxn4uUvfzmuu+664z00Dw8Pj2MGP/d5PNbwZM7jmOLqq6/GP//zP+PZz342vvOd7+Ab3/gG6vX68R6Wh4eHxzGFn/s8Hkt4MudxTNFut3H22Wdjz549uO+++/D0pz/9eA/Jw8PD45jDz30ejyV8zJzHMcVPf/pT7N27F0II/OxnPzvew/Hw8PB4TODnPo/HEt4y53HMEMcxnvOc5+AZz3gGzjjjDPz1X/81vve972HTpk3He2geHh4exwx+7vN4rOHJnMcxwx/+4R/i//7f/4vvfOc7GB0dxYUXXoixsTH80z/90/EemoeHh8cxg5/7PB5reDerxzHBXXfdheuuuw4f+9jHMD4+DkopPvaxj+GrX/0qbrjhhuM9PA8PD49jAj/3eRwPeMuch4eHh4eHh8cahrfMeXh4eHh4eHisYXgy5+Hh4eHh4eGxhuHJnIeHh4eHh4fHGoYncx4eHh4eHh4eaxiezHl4eHh4eHh4rGF4Mufh4eHh4eHhsYbhyZyHh4eHh4eHxxqGJ3MeHh4eHh4eHmsYnsx5eHh4eHh4eKxheDLn4eHh4eHh4bGG4cmch4eHh4eHh8cahidzHh4eHh4eHh5rGJ7MeXh4eHh4eHisYXgy5+Hh4eHh4eGxhuHJnIeHh4eHh4fHGoYncx4eHh4eHh4eaxiezHl4eHh4eHh4rGF4Mufh4eHh4eHhsYbhydwTCA899BAIIbj55psfs/7vvvtuvPOd78TMzMwx2edjjT//8z/Hpz/96eM9DA8PDw8Pj6HhyZzH0NiyZQvuuecevPzlLzfL7r77brzrXe/yZM7Dw8PDw+M4ITjeA/BYO6jVanje8553vIfxhMbi4iJGRkaO9zA8PDw8PNYQvGVuBfj0pz8NQgj+5V/+pWfdDTfcAEIIvvvd75Zu+53vfAeEENx444096z7/+c+DEILPfOYzZtlPfvITvO51r8PGjRtRq9Vw5pln4kMf+tBQ4/zqV7+Kiy66CGNjYxgZGcH555+Pz33ucz3tHnnkEfyX//JfsH37dkRRhK1bt+JXf/VX8eijjwLodbO+853vxB/+4R8CAE499VQQQkAIwV133YU3vOENmJqawuLiYs9+XvziF+Oss84aOO4vfOELuOiiizAxMYGRkRGceeaZuPbaawttPvOZz+C8887DyMgIxsbG8Iu/+Iu45557Cm3e+c53ghCCH/zgB/j1X/91TExMYNOmTXj961+P2dlZ044Qglarhb/7u78zx3LBBReY9d///vdx6aWXYt26dajX63jGM56Bv/u7vyvs6+abbwYhBA899FBh+V133WXOjcYFF1yAs88+G1/+8pdx/vnnY2RkBK9//esHnhcPDw8PDw8bnsytAL/0S7+EjRs34qabbupZd/PNN+NZz3oWnv70p5du+3M/93N45jOfWbntxo0bcckllwAAfvjDH+LZz342vv/97+N973sf/umf/gkvf/nL8Xu/93t417ve1XeMX/rSl/DiF78Ys7OzuPHGG3HrrbdibGwMr3jFK/CJT3zCtHvkkUfw7Gc/G5/61KdwzTXX4POf/zyuu+46TExM4OjRo6V9v/GNb8Sb3/xmAMAnP/lJ3HPPPbjnnnvwrGc9C295y1tw9OhRfPzjHy9s88Mf/hBf/OIXcfXVV/cd94033ohLLrkEQgh8+MMfxmc/+1n83u/9Hh5++GHT5uMf/zguvfRSjI+P49Zbb8WNN96Io0eP4oILLsBXv/rVnj5f9apX4SlPeQpuu+02/PEf/zE+/vGP4/d///fN+nvuuQeNRgOXXHKJOZbrr78eALBr1y6cf/75+MEPfoD3v//9+OQnP4mnPvWpuPzyy/Ge97yn77H0w759+/Cf//N/xute9zrcfvvtuOqqq5bdl4eHh4fHCQrpsSJcc801stFoyJmZGbPshz/8oQQgP/CBD/Td9v3vf78EIHft2mWWHTlyRNZqNfkHf/AHZtlLXvISuW3bNjk7O1vY/k1vepOs1+vyyJEjUkopH3zwQQlA3nTTTabN8573PLlx40Y5Pz9vlqVpKs8++2y5bds2KYSQUkr5+te/XoZhKH/4wx9Wjres/7/6q7+SAOSDDz7Y0/5FL3qRfMYznlFY9ru/+7tyfHy8MB4X8/Pzcnx8XD7/+c8343PBOZdbt26VT3va0yTnvLDtxo0b5fnnn2+WveMd75AA5Hve855CH1dddZWs1+uFfTSbTflbv/VbPft77WtfK2u1mty9e3dh+cte9jI5MjJivv+bbrqp9Hx88YtflADkF7/4RbPsRS96kQQg/+Vf/qXyXHh4eHh4eAyCt8ytEK9//evRbrcLVq6bbroJtVoNr3vd6/pu+xu/8Ruo1WqF7NBbb70V3W4Xv/3bvw0A6HQ6+Jd/+Re88pWvxMjICNI0Na9LLrkEnU4H9957b2n/rVYLX//61/Grv/qrGB0dNcsZY7jsssvw8MMPY9euXQCUa/fCCy/EmWeeudxT0YO3vOUtuP/++/G1r30NADA3N4ePfexj+K3f+q3CeFzcfffdmJubw1VXXQVCSGmbXbt2Ye/evbjssstAaX4Zj46O4lWvehXuvffeHhfvL//yLxc+P/3pT0en08GBAwcGHsu//uu/4qKLLsL27dsLyy+//HIsLi72uHaHxbp16/DiF794Wdt6eHh4eHgA3s26Ypx11ll49rOfbdylnHPccsstuPTSSzE1NdV326mpKfzyL/8yPvrRj4JzDkC5WJ/znOeYmLLDhw8jTVN84AMfQBiGhZd2wx46dKi0/6NHj0JKiS1btvSs27p1q+kfAA4ePIht27Yt4wxU49JLL8WOHTtMbN/NN9+MVqs10MV68OBBAOg7Hj3uqmMTQvS4h9evX1/4XKvVAADtdnvAkaj9DXMel4qyPj08PDw8PJYCn826Cvjt3/5tXHXVVfj3f/93/PSnP8W+ffuMZW2Ybf/P//k/uPPOO3HyySfjG9/4Bm644Qazft26dcaSVkWCTj311NLl69atA6UU+/bt61m3d+9eAMD09DQAYMOGDYV4tNUApRRXX301/uRP/gTve9/7cP311+Oiiy7Czp07+263YcMGAOg7Hk3Mqo6NUop169atYPS9+xvmPNbrdQBAt9sttKsi3FWWRw8PDw8Pj2HhLXOrgF//9V9HvV7HzTffjJtvvhknnXQSLr744qG2vfjii3HSSSfhpptuwk033YR6vY5f//VfN+tHRkZw4YUX4tvf/jae/vSn49xzz+15uRYnjWaziec+97n45Cc/WbA+CSFwyy23YNu2bXjKU54CAHjZy16GL37xi8btOiwGWbfe+MY3Iooi/MZv/AZ27dqFN73pTQP7PP/88zExMYEPf/jDkFKWttm5cydOOukkfPzjHy+0abVauO2220yG61JRq9VKj+Wiiy7Cv/7rvxrypvHRj34UIyMjRrJlx44dANCTxWxnJnt4eHh4eKwmvGVuFTA5OYlXvvKVuPnmmzEzM4O3ve1thTiufmCM4Td/8zfx13/91xgfH8d/+k//CRMTE4U2/+t//S88//nPxwte8AL87u/+Lnbs2IH5+Xk88MAD+OxnP4t//dd/rez/2muvxS/+4i/iwgsvxNve9jZEUYTrr78e3//+93Hrrbcay9C73/1ufP7zn8cLX/hC/Mmf/Ame9rSnYWZmBl/4whdwzTXX4Iwzzijt/2lPe5oZ42/91m8hDEPs3LkTY2Nj5tz85m/+Jm644QaccsopeMUrXjHwnIyOjuJ973sf3vjGN+IXfuEXcMUVV2DTpk144IEH8J3vfAcf/OAHQSnFe97zHvzGb/wGfumXfgm/8zu/g263i7/6q7/CzMwM/uIv/mKo8192PHfddRc++9nPYsuWLRgbG8POnTvxjne8A//0T/+ECy+8EH/2Z3+Gqakp/P3f/z0+97nP4T3veY/5zp797Gdj586deNvb3oY0TbFu3Tp86lOfKs2u9fDw8PDwWBUc5wSMJwzuuOMOCUACkD/+8Y+XtO2Pf/xjs+2dd95Z2ubBBx+Ur3/96+VJJ50kwzCUGzZskOeff778H//jfxTawMk2lVLKr3zlK/LFL36xbDabstFoyOc973nys5/9bM8+9uzZI1//+tfLzZs3yzAM5datW+Wv/dqvyUcffbRv/29/+9vl1q1bJaW0J2NTSinvuusuCUD+xV/8xZLOy+233y5f9KIXyWazKUdGRuRTn/pU+Zd/+ZeFNp/+9Kflc5/7XFmv12Wz2ZQXXXSR/NrXvlZoo7NZDx48WFhelnl6//33y5//+Z+XIyMjEoB80YteZNZ973vfk694xSvkxMSEjKJI/tzP/VzPuZBSfZ8XX3yxHB8flxs2bJBvfvOb5ec+97nSbNazzjprSefEw8PDw8PDBZGywo/l4bFK+IM/+APccMMN2LNnT6VL2MPDw8PDw2N58G5Wj2OGe++9Fz/+8Y9x/fXX43d+53c8kfPw8PDw8DgG8JY5j2MGQghGRkZwySWX4KabbuqrLefh4eHh4eGxPHjLnMcxg39O8PDw8PDwOPbw0iQeHh4eHh4eHmsYnsx5eHh4eHh4eKxheDLn4eHh4eHh4bGGcULFzAkhsHfvXoyNjfkySh5PeEgpMT8/j61btw4tYt3pdBDHceX6KIpMyTIPDw8Pj8cHTigyt3fvXmzfvv14D8PD4zHFnj17sG3btoHtOp0OTm00sL9Pm82bN+PBBx/0hM7Dw8PjcYQTiszpElP/z88+hPp4Y8nb05LsTIbeZbodtdbZ25YuN9uIQlsmpfkcCG62J3Z/UhT3L8qzSAVV1khBlJVGEgIBtSylDCKzVnJCIAiBAAXMe71t9h/EvE+y/gRhZl3Z/4RQ1WcGDmrOhW4jke+LW2OXKLek6rYU0rwfFvb3QEq+R1bSzm6vl+ttKZzvAcXvUSOw2tljto/Fht2mS9RPVjrnFlDnU/1X67tzi/jQ9svMdT8IcRxjP4A9IcV4yfo5ANv370ccx57MeXh4eDyOcEKROe1arY830Bgfrgh7GYHTKCNy9jbuTVmTrjIyR6Q0ZMAlc5rE6XWGREhp+mQiJ32CENVOCkPc9HIASJiiKdIhZilVyzWZU+/VstTqJyUW8QNBQIgiYZrUaYIICk6IIUX5O2TbWmMrJSf5+zIy14+8cUnAyGBpFPc7ckkdQ+93GTikzd7GfIcVRK5sewFSILUuMbXfExKAQoCDmf3a55FCou38rJcaUjBeYxgv20ZKIBG9yz08PDw8jitOKDKn0Y+gDYt+Frne5f1vgP2IHJXSkKRAcIcI5kTOhk3++o1HgBpCVwYBCiIlJFEEIyU5GVPjykmf7tduo4+Bl+zDtiy51jnukDSbrLjQpI1LtY39flhCtxRookVLCJy2OtrrQnOu1Zgi8B4SSvsQxtQ69gACPNuegRf2p4mdBEEIgQS01No4FEIG0JLrQkgAyfL69PDw8PA4ZjghyRyQkxH93123evupdr25+6n6TKUoWPBK+y0Zs1qWkQnLshZygYTRkrboIV5lZM+2HFEpwQkt9O+2Y1L2uAdtCMu16qLKvWpDkzf3vf1ZkzyX3LnErKdvkB7i7rpnBahyjyt6bNaFliXWtsa5lrdAcnP+3LEEKJJB3WeXMFAIRbhLXcQruIYZLSdzq0yMPTw8PDxWBycsmQNsspQTu5X2ZT6XWe4sq07ZvsrGUGbVq7LIAcoqZ/djk1XbDcuEhGAZOXTWc709Ue48F2mJ67bnWDKCljgkj0AW4uLKoNdXkSyXsFXBjkErs9bp93Y7TR6HsWoxK6qvyq1Krdg6G6FMkZAAgeTZNty4tIHe710QViCBoRTKHV4yrgCix9W6JET9LHMeHh4eHo83eJ25DMMQOQZpXlXb9gSvO2SmnxXI7EfKIiHI3rMSa1wguHGrmgSJUitdcVtOSSGGz05+qEKVu9glPq6lTVRcZm6cHAcxbkWbuHBJCq+yfuwXBy2Nx9Pvq/qpOp5hvjNAfW+hFAUCp4i7AJUCTHLzH1CEjklh9sckRyhSs55k7lNBaM8YKNS+AnAEGam0rZjqOl0mIlb9WiKuv/56nHrqqajX6zjnnHPwla98pbLtXXfdBUJIz+tHP/pRod1tt92Gpz71qajVanjqU5+KT33qU0sel4eHh8cTCZ7MDYmqGLlS4lQS11aV9ODGyxWyHgU3CQ+GHFhWuTzRQfYQLdsyZ4+zypI2LGxi5FrdXGtbj8s2I1o2ibO3GSbJwSVu7vb9sl5LkywsYtfP3Qv0WtnKYuTstrZrNSdsucs8lAIsI2XutRRJblyqmgQCyupWZgUM0D9OcklgBAho74st7dr5xCc+gbe+9a340z/9U3z729/GC17wArzsZS/D7t27+263a9cu7Nu3z7ye/OQnm3X33HMPXvOa1+Cyyy7Dd77zHVx22WX4tV/7NXz9619f1qF6eHh4PBFA5AlUDX1ubg4TExN435Ebh85mBQZnrRaWDZnBapM4u68qKRKWvbddq7Ylrp9l0ciJkFyGBFDWOfWfIc3i3hJadKEKUHCaZ6nqBIdcjiTPerWzWNUyTbhoIeGhzCIH9JI52zLnLrNRReC0S7fKZVogRVY8GLO+F+YQOJegaRKlSVWZVc5IlzjfUZmFNyEUoRQF0q3PtT4WCYKYBCopJcsYNm0z92oMhu7cIt4z8auYnZ3F+HiZ2EgR+vcxe+Y0xlnvc94cF5j490ND9/fc5z4Xz3rWs3DDDTeYZWeeeSZ+5Vd+Bddee21P+7vuugsXXnghjh49isnJydI+X/Oa12Bubg6f//znzbKXvvSlWLduHW699daBY/Lw8PB4IsJb5oAe12nVexvDxtcVrWrSEDkXgpACkdPLKvstiYlzl1eRPE0ChZW40O94cqtefrno86KtYWVWOZfI9YNL9mx3qL1MlrzUdkUNO3u//Sx2VWOx27v6b3rPKgFhOLe0bY0DLMkT63vSrtPefmRmtS13c9vWXAYOBo4a0gIpXRIGuFnn5uYKr26329NFHMf45je/iYsvvriw/OKLL8bdd9/dd/fPfOYzsWXLFlx00UX44he/WFh3zz339PT5kpe8ZGCfHh4eHk9knJBkziZvLnGzly81I7DMKjeskG0h7i6LsdJ9Mkdnzn3vEjcqJYjIb+SBEAiE6Nm2lqYF4WCgVxPNzmat0oCzY7pcS5GNKjemS7RcEucSt7w/Wkri9Gd3XZXlT+8TqHaZuu1tq2qZVS5PbBA9RLkmlfs8sL5jV55Ef59B5oq1r0XiuHCBzGUrpZVBK5ZEYAtgtPoFYPv27ZiYmDCvMivboUOHwDnHpk2bCss3bdqE/fvLa0xs2bIFH/nIR3Dbbbfhk5/8JHbu3ImLLroIX/7yl02b/fv3L6lPDw8PjxMBayab9dprr8UnP/lJ/OhHP0Kj0cD555+Pv/zLv8TOnTuX3eeK5BsclMXJFdZbN3RJSI91zk14EISYm32/vgKLtBEhIC03qU3oJKUIhECcCQZrQWEmOEBZD6HjugIEitmrQO724wNi5lyUCeFykJ51VX1VJVL0tJME1HKbaukQtT+WRe0VCZrJeM12W4yNI4ZwDZPlahM5DQZZ+D5t0qeTPYyeoCVVAgB1kUCAIKaBsd52KAGFipVLncobGivSmQtKznWq+t+zZ0/BzVqr1Sq7cgWLpZSVIsY7d+4s/J7PO+887NmzB+9973vxwhe+cFl9enh4eJwIWDOWuS996Uu4+uqrce+99+LOO+9Emqa4+OKL0Wq1HtNxlLku+2U7lpaCcpIeXKucXq7bAipWLuRZELyVwarBuLL2MM7N+8I+hd7WKiWVSZSUgWViwfoYKGROUtCrzZdaiQ2amA7KYi37nEha+NxjTZNEtZEEQicuZO/LluX7KFrs+lnpqvbtLnMTImzCZxOpsMQ6F0iOSKQIsnWh4KjxBDWeIBTcWp/HTdrXSl2kiKx19vhDqfTnjpWbdXx8vPAqI3PT09NgjPVYzA4cONBjWeuH5z3vefjJT35iPm/evHnFfXp4eHg80bBmyNwXvvAFXH755TjrrLPwcz/3c7jpppuwe/dufPOb3zzm+7bdl4Pb9lpf7O2qqj2UkcSy2LqytkSIHnJVmWnruGQ5JdmYihmxXRqAWPF42nKkP7sxZZrIUOQVH7SrT4AUKhkAivylloSInVWqte2q4uE0OBSx4yDm5cIlefmYqyVj9DgIJDiKene5Ra9IOt0sU43eJAdROM86o7lnO9F7jWhip5GQXDA4gECEnNytCIxUv4ZEFEU455xzcOeddxaW33nnnTj//POH7ufb3/42tmzZYj6fd955PX3ecccdS+rTw8PD44mGNeNmdTE7OwsAmJqaqmzT7XYLwdlzc3ND91+VWNDTzhC2/i5R94ZtZ6+Wba/7DbmdserESaVpDyEsG7O9TMuZpJY71ljgMt26LgsKfZQlYjApkNDyy0cnPpQtr0JPAoWlkua6TQEglY4YMZGQkiDN+iFWeykJiK4AYVvSCvGAlqtVb5dljyagCJHHormSJIXqDpYVjiFPbNDr8nYliSkOWcstnTDnOpC8pGRarjMXZDGQK5IqCZl6uSBL6/Oaa67BZZddhnPPPRfnnXcePvKRj2D37t248sorAQBvf/vb8cgjj+CjH/0oAOC6667Djh07cNZZZyGOY9xyyy247bbbcNttt5k+3/KWt+CFL3wh/vIv/xKXXnop/vEf/xH//M//jK9+9avLP14PDw+PNY41SeaklLjmmmvw/Oc/H2effXZlu2uvvRbvete7ltT3oBJbhXUD3KuVBNAhckCWRFBStsvdbpCV0PQtBDilffXlAsGRsEit11YwQrNx8R6yJpCXP2OQ4FncnUZCaClh6yVqxc+JpAUrnLsOGFz1gcteYV05YJuclOUls+xkB9tNaeLaoEt39braTc1bQgqlvIAsDg7F6h9MFt3hbh1bm0gzIVCTHF0WIpKpWi8kUkItmRhqjXNpGbwFRFVkbmndvOY1r8Hhw4fx7ne/G/v27cPZZ5+N22+/HaeccgoAYN++fQXNuTiO8ba3vQ2PPPIIGo0GzjrrLHzuc5/DJZdcYtqcf/75+Id/+Af8t//23/Df//t/x+mnn45PfOITeO5zn7usQ/Xw8PB4ImBN6sxdffXV+NznPoevfvWr2LZtW2W7Msvc9u3b8f7D/xuN8ZFSgjPIlVqlI+fCTV4wNVUhCmQuJ2dFt5oOnteWOSMgbJE5xqsrPmgyF3KObpCTsiQICrpz7VCROZ3MkFJmtOY0mROEFILsBSGIs89ppi+nCZshFln0mO1idfXkROYmLbpss+2NyzXbxhL2HRaFGrLW9xYQi6QRWUiIoJBGc84mcyGEscAx8IKEiHFvSmHi2AB1DQQWYbMJek2kBeJW46nR/3Nht+uy0LzXSRGAOu/6PKaEoU0CdOYW8e6J1y1dZ+6Xd2K8hMzNJRwTn9k1dH8eHh4eHo8N1pxl7s1vfjM+85nP4Mtf/nJfIgeoLLt+mXbDasXZcGuGaosaUG2pK4uTczXlzJiQCQpLASaqqzu4RI6J3iB7arUxFh7OAcbM51qaImHqGBIaFAguk8o6V6jYQFRsmhav1cvt8adgA61CVUROyN7Yt35EzrXY2XVXOUhBDLhnW+u7zI9FEcka4cptWREL5x6LdpFykELmqn295FmuWRk2qOtH19N1k1psq6cmdKFIc+slpaiLRK2nUYH0NWQKXvGgMRCrZJnz8PDw8HhssGbInJQSb37zm/GpT30Kd911F0499dTjMo5hgss1eVoqWTS6Y46VruCKszJVwz6WORsh50gySZKVlPOyLXqa/ASSAySv+lAmPwI4unQghSQDCYJUFsmD6yIdhsgJ5IkOPTVyLVKVSlqIqbOlS3ScnG7vJkDo9pzIgr6chiZyLgGvSmaJRJodMApyNKnldheEgGdxjCoGTxPHXPCZQYJDEcBQpmiTqHR/QyFcXh1WDw8PD4/jgzVD5q6++mp8/OMfxz/+4z9ibGzMyBNMTEyg0Wgck31WEbc8NqrcKmcTObdsV1k/QE7kVAapvpEXA+a1NU5/LvwXRQuetuoIWu5KthMgmJCG7AF57Fa/uDxuiF0eJ2eXnTLjsFyrbnkuWZIoYSc22KTPBsuSGYZxuXJJTHtNKEMiTFJEIqmSWgFDQHqzQSUIYjDjSjVjsOMdQQCirGRUaiKYwyVytgVOC0JH2XegNQhDmbtuuyxEKIrXkHaFm9JvUiCQAu3M7bqijFZLILi4fM1FZHh4eHicEFgzZE7Xd7zgggsKy2+66SZcfvnlq76/4Sxw5XVXNcoyWFmJtc0t8RVa1jfjRrWscCaOjhezKF0EnCPNskJrIkU3DBTZEAJgRctLyDl4QAsuWTcBoiyztVhrlZYu11Y7HcPGQXvau31WQVvftOVOZ7G62avC7kL29qGOR22fSoqACGOhE5mrVCdBuPIidqaoHS+nrXjanWrHTTZ4UugjFLw3O9ki65wqDUBOCULBAXBIQkwNXQCo8aTwHdnEGVgBoYu8Zc7Dw8NjLWHNkLm1kKcxTH3T4fsqIWdCFGLjBCWgQpM9J66OFBMr4qD4VQeZNl1KCTglhhRoS5AN4VR6cCs/mH1msh0FEWDrsy39YRO5RNK+macCveRNLxdCETF7ewECSAJGBLikypFLZJ4QIZV1T7fT7lRIQIAhJGlu0QMFQ4lkCMnrrFIpCtUebIstk7xgVQsEz0p8ZeszwmavV32qPjgNUEtTdIMA9STJklk4AkHRDkKEmZuWSImEMnQz8h7JFIvLDXILaXnM3Br4DXp4eHiciFgzosHHGrbobT/36iA9uWKfotLFqhFIFWAvCTH997hQs2QJZpECKqQhcmGqpSqKYrSF/XCBWqLaSZpb4HiFG1ZDyZHkNWa1KzUlzGSy2sv7JT50JSu1ukmrDqt2rXJd6SEjcmY/giHNlgurhisAdAVDV+QkhMs8jk86btky9612w3KUjzNFLtKrx6qtjtp6lyc7CPMdJDQXWw5FapJbmJCIeIpGoqx2tqCzJnW17LvV/zXKrtFQcDR53LfNUKAVdVmpny48PDw8Ho9YM5a5Y4GyAuqVbStEfatQqurvkDQ7Ts60EdKyzuQZr5oM2FImVCj3rJ3goF11nBAISkEFkOrkB5pntApCAMZQS9NMjgSF/s24CAqVH0Ip0KFFy50mwEWXa5E8Gb04RwxYt3XbAb1ZrGXfEyMSiXC06QRFSAViwRAQAZpZ4bRblUsKRvqTbBsxGBgEauAFssqzhAUjI5OR3kZGqCRR1r5QCEQ8NVY0DftcRzwt/a4FIYh4ipTSHumSGlf9dVmAINOgo5AY5x3EJMDMSnTmytys3jLn4eHh8bjECU3mNFZc/siCrSenYbvZbKsLkblFTtdIdeUpgNzyphFYsVWavNWSBFRICKpIHJMSsParCV0tTdEOQ0iaC80mjJlz0KlwsWpiEROmiK2V+GDHzBkLGsotdfq8pJKBW+5TQFnL+rlbbdKmEyV0Nqy2mOn9xYKBOBUdQJC7VaUqNqbGJBE51Q3KLIwpKCJwcDAw8EIShCHZKJJhY23r0SeURjvQzXx24yQ5YwiEQEqZim2kiiSCqqxXnbASirQQQ1eXMZaFqgoQFXV8PTw8PDyOL05YMjcsgRvGIldVA3WY/VdJVgQZESNOnJwmctrFSp0sR7VcGELHCTGkIKaBIm5SyVgogqAIXicMjQAtywiGJpqa0CVWrByVAinpDb534Wak2rFyvKK6gy0xkrclhrQZTThJAKhSXzLbxi77pS1wpuZr9j+kmWs7i6sDgI4IEBKBkKaFfQKo1KoLIBDKFILkVkiaEXQN7UIVICBEiThrEmf6sWrr1pLEZBqr74ea768OIGYBIGDc44EUCLgwiREJDRCTwFSJWBaq6rAuoTarh4eHh8djhxOSzK0mkbNRZpUrzV7tVwZMSnNz19twSkE5N3FygiqCZvp2LCaqHVcJEoBxwVIhwYiAYAxECIjAzVZVFUqH0aIz0icghuQlmQuVQiKEQCe7vIwkCZghaalD5Ny4NrdGqv3+/2fv36Nly+ryfvgzL2utql37cm7dfbqhgQZFUDBAI9I4yE8TbcEYlUTR6K8NI4AvUWMAHW+CJgF8k/RIogFvICRGkkiUN8MkSsKLtElEfwEFuWg0iKBgN/TldJ/LvtRlXeac7x9zznWrqn32Od1tn8NZzxg19q51q7VW1Tn17Of7fZ5v7JWLaP8uheut94QuHkPgy7KRoIkeUYtRJYQh9olo8ueiLbZtiOiQst7nZbuY179H04ORglG1XOLNyrLuiaykrIlcVlUYKWtSN6kq9kdjkmB4MVL5OJtAABNb1Z2wVlymI3VdztygzA0YMGDAFYmho3kFVhkd1ilyMVZkncLWHcG14rjOkhiLtoY09EAJa1HGIMKX+7go6hw54RxJWfkJEnZ135cO0Sbt6BLhXKdc2zZX2JBt1p040P0yb4+LamfLxXtTout7lKMoaWalOkRNwqAhcu2exVheLa3slFPb+8V1xomaV5iQQWKsX7aoFIWRzXonKFqmiHgu8RxK273O2jQRXzPMfI1l3Kr3TyY6e/tELrEVc90tWYNXQWNmYHw/NvK8E0syyQvGhS+RxpFs7c/RqGpiTlSIOEl7/XiVUGh7mepcLLOuegwYMGDAgCsO16Qy92gizl6NqllU2CKJa5semkkPXSLgpESE0mp0MUpnl+NHbPMFr6uGDLSDgqV1tDwHpOF4puWw9CHATZyGFV7tMVKG8VzdaQ19Itef7qB6y9ql0ZpEuS4hjBMX+v1vbZXN2K5iaFqEzroQMxLHeVmJEw4lHcYKjFWkytbKHVCP+4rl2NJJEmEpnCIN47xUyJ2TgIFOeHCFJHOeWOVCkbmmFzJCOVsTvnFZ1uXzeN/bBFsb2ylrby0Wda9jG3EfK5I6aNgfzyKdJDMllVZHUlhXQok1ocFDmXXAgAEDrkQMZK6HddEjy9sd3vO2Cqvcq0vbWFsrckkorYLPkZOt5dpYdGU6xE1Yi5MSVZnmnQ3KTgl1Kc5ISal1Xc6dJ83HoJJqqe/rsLJwLhQGFUifrF2tDlE7U/tmgrKlzPmeukaNE8GcEHPeIgrjR3C1lbS2sle7adv9eU4gAsGyTrCoFEpaEhmNEZ4+psLUipzEhXw5Ud9CE0hiFgOCW/cjlwmZbZSy1FZILOOgnhkhSWxFVvneOtFTU5NeDyS0wqit88HAWneUu1IpkqrqTPEotO4oq34SxCNsgDBH+7cxYMCAAQP+fDGQuRZWEbnDDA+rSNm6KQ8RolVyVf1et9b2sRwHsQfOn1tWVp3tpHVexbOR2DREIBK9aIZQxnRIWvu6pbPMdYZ0jrlaLg/G88tl0weXh1JsW3kDr1jFaJLDcuf6Jc3l9e0SqljqCSid7IzqWmVUML3YEv88BPaKQPKMRgpHKj2pS0SI/HDBFEHlrxfFmKqeqqGofIlZahJbkdoK4RyZMVRS1aXP2N+WGoOTslbWIlmPn7ukbJYD5EnzPsSSuZWiJoYUBfujcZgWYdAhEzBXD/OftV5D5lb0+g0YMGDAgMceA5kLuFQi10cc4aQOUd36eXLt8msavsCFtR0ip42ps+SkdbWbVbXKqjEMGADj1SArHQooU420tjZBtM9BOufdkTSju0opfWZZyJKrRCQuy+XUxNmOwzUu70x8wM9ftU5QuHCsMBM1bh8RiV9pBVFwLE0rAmUF6Ssqf8y2kielQ0t/X9u5dSpMgSitIpEtE0PYz7bCi/sjvLw659clmM70h3h/rBAgBHOSpfFd4N9b3VJXN+cLABZpskTkpPXvuRGCrGgUtjKoqDFSZmsxrwld/R4IwZbJyS83F04KWBEmvXLZgAEDBgx4zDGQOY5eWm2jQ4qwK7PC2sfVztZD1OP6ZrvljDGAtOxOdjiMyNXKXPjCrcuzlYFE18HCbWWukjIYMMDIeL6yvpZKdAnXxXqw4kQESZus+Z+FU12H6hrnaiy3RjWuP7XBmGiA6MaQdM7D+vMojSRRobQdCVt8zaDQSeGPm8jmvTJOIPHzXgurUNKsLTXH0V3a+QgZ7Uwd5qusqdXX+L5WUjIOmYARmzNP6uJ4NitDn6W1ICXmIvfdZxMqlC0ptCYzZsnAcklYV2YdlLkBAwYMuCJxzZO5S+2R6yyLJc0Y8nqIo7X/enFsV3tdEspn3olq6viRtms1kgAdvlij8aFN5qT0v5dKoSufQ2aFQEkHxpAYQ54ktZMVAhGTCuVcrcb5PrSGxEWCsG7UVVTu+uvLOu9NUbqGJETi1ihyspN+EV2okYhFFS4iEro2sYvHlM51lLyiUjWxi6qdcwITpkLkRtZEOJHGmyGI6p5GCxNKrBJFU6oFT+gqIal0xqTKGQdTRD8IWq9xH1daoSvfBymtJXosUmuplEJDEwZdlCzSBOkskzz3M3eDcpcnCUYKdLif0ZBxyRjI3IABAwZcVbjmydwqrErrXwWLrAldn8itIom+XNomAaImdKJjbvAl1XaOXHvqQ626hZFetlf+ktZSaYVylkWWIq3XxyqtyNO044KtpPKEoHWdElvPJ20TM+UMlUh6LtRGvVPB+RqVthKNdX55U2K1teEhOlfbJM5YiZLWz18N7lUpvdpmWxMihHC1YxWCate6B3lQ5fJAAKPytig1m1lRkzUpfHiwEM20COMktMZ96d50iBLlyZ3zc1ZF6/MyNmVr7FZTatXWdkqoG3neOWZUYa0UqMpgdJjYkRcYrUjmFfkorbedp0ldco2qa13CvdxIkog4m3XV8gEDBgwYcMVhIHMXwWEmhqPsE0c5xb43v6yZ6NAmdPU+rekOkXhFo0Ncn5Vlrc5J61CVCf1xrlOuG+UFs3FWH1ubrgM29lpZ0TTPG9HEWtSO05aZIS7LwwSItslBYymDAUJisailEmt7H9cpe3osjK7LqQBlJTtjvvIqDLeXjjgBIq8kiXKUJiiQwKJU9e/x+FI45qUmUV6ttE50+ucAplXCdpLXLtmFTRjLgqlLyIQhxVChqLBo4UkueCfrXjJiu1zU99bP2o2RL4LtmQ8SrhXVdh9lZRgZgxESXTUzX8sVYb2JMTWZ6y73+4xMye7S2iNCK/9YWj4ocwMGDBhwJeKa/1Pbiku7BX0it3qUV7f8GjPl2mXN9v5xXFMMh1WhxArdOaxO+i/5cV6gqybCJM29ApQUFaoynX4sIyRJ4ZWaQmsqpbBC1q/RdtTmSq8sFRuEj9XAoWNpGYfGLG3XD9Xtq1pRletGiISfIc6k3RcX1TjrBJX1j0jQ4r55JTFWUgTSVxlJZZrf43bWQRUUvrxSzEtPhoz128Ycu5g9V1hF7lRd5s2Eqa8/okJihOqE9uZK1aXVjdKXQJUxjPOiNqJUOk7lsIxnOUlRdeJHsrJR9ZJQGs8WjRHCtlQy6RwmTvloBVNvBVJ5yVA0I706j8s73IABAwYMeHQxKHM9HJaptkTkeiXWVYpchBUSK1wgdBLpmgkAWVXVBKwfIguNipMUFeO8qA0QSWmgRaiS0pBnGlUZVGWYbzSKHMDGImeepTghSIxkbzxeukYjBMoZrFj+aLhOebVR1voErtmmNelB+B64SOKUsCxMcKJaSelkXe6EZedqVNyKStX9cUXVc9K2R3xZUffIFSFfrk0CI6ZFQiItqQ730VoMglEgLnGcV+k0mTBUSFIMJYqxLyT715C6JnRbZVNCjdl/phX+2ze0RPjcQInEE/K0qLBSUGnl1VjrCd/BZExaVhghSMrK72MdeVDq9rOsJpOXhbXK3MDmBgwYMOBKxDWvzLVxsV65bpBud9JDs49dInLeMdp8ucZB69paxmW5pNa1o0ai2tZ2sC6dt3UkpUFaG376sux4lqPCa0vrmGcpJvRC1fNa1/YDrjYzxFmsZRjpFdW6dhm2fY/a0SBtOHz8SLu8KkL/WpMvJ1mUqi6rVkZirPBxJU5QlN6xWhrJolAY65cVpazVt7wKyyvFvNDMC42xXsnLW4aKolIUlaJyPpx4WvnewIVdJrVFawatdLYuswJktqzvWVQ9RQiClqFvzkoZzA4u9Eb65UlRkS2KWqkDb3TRlVkyugBMFnlHoesjZgJeMh7BcV5vectbuOWWWxiNRtx666381m/91tpt/9N/+k983dd9Hddddx3b29vcdttt/Nqv/Vpnm3e84x0IIZYei8VlqpADBgwY8AWAa57MtY0K/Yb/w6I41pGgPpTtk7ig5rUNDc73t+lQTpPOMVnkJEWJlYJx7strsRcuyyuS0pAWFboyobculFwXRT32y5dlDXmi65KsMpbpKKPUemUkihUCIxQ6jJXqq4vglcjEWSzNvNZ2Lls/LDiaC+Ior8Kq2vQQS6sulFM9+fJkqzQilD/987byFgmdMYKy9GaJvFB1abYsZf0AMMZvCzDNddc1axRFVAmdWJoyYZx36pbOK4hxpNdcaCqhyAI5t8KXXCWOtKrqWbvgR3Mp50jLqjY7JEXFxjzvhEKDV1itFDWRzxYlG9O8LrOOZznHdg8oEk1WlkwWOdJZxkXBKDhbsxU5d0eFFSK4Z3uPSxwP9q53vYtXv/rV/MiP/Agf+9jHeOELX8iLX/xi7r777pXb/+Zv/iZf93Vfx3ve8x4+8pGP8DVf8zX81b/6V/nYxz7W2W57e5v77ruv8xiNRpd9vQMGDBhwtWMos7ZwMWVOOFeXViOMECTRmNAjhhKHkaomdFbIzpD0SsomJy7sa4RAWttR4qyUjPPCl97yEmktughKTemaprPEE5d0UTDdGvuQ2p6x0SjJZJGTJ0knlqRoTQ0QvXFezQxXC4f0GGosc+ePI3AYlCdowcmqha0JHXjlzbQcquDjQirre91iyTXGj5RGYozAWq/eJcrWjtfKSHTruXECZwVCOma5Rgnn1zvBolCM04ppnjDJSnSMcrESKR2VE2TKMK0StnTTpzZzKcfknBLJmO6NbX92lDUYKZjLBG0typhA9JrrTIqSrCyxUrK9O/P3OZB1gMn+nPmkS1Cy3DtaYz/diQsH7G1tALA9nTMdZZhUsj2fM08zshXGiaOg0qru6esvvxT8y3/5L3n5y1/OK17xCgDe/OY382u/9mu89a1v5c4771za/s1vfnPn+T/9p/+UX/mVX+Hd7343z372s+vlQghOnz59SecyYMCAAV/IuKqUud/8zd/kr/7Vv8pNN92EEIL/8l/+y8M+5qUaINpQLTUrHqvJnLMI5+o4El9WNfV8Th0mPURnqTL+S39cdBUV3cr2Gs0Lr8RVBsqwvDIQVZ2y2XY89ySk0r6fyklZE7Qi0SRhykTeiiVJWupQFcJwq979affHZa4hNHE2qxJ+tJehyYhrE53KeUWuDMaESORsKJ9GIgfUalxeqXr7olS+3GoERakoKllvH3+fLrQnfY667BqxKPx57c3S8BqePMbXi6XXMpR0q6DG1fs7XcesxOvOpWIudStcWXqTifWGl6zqTneAplyaFlVnmZ4X6HkBxjG+MGM8LxiH991Kweb+nHUotfbBxdZ2/mi4VJSJWvsA2Nvb6zzyXswKQFEUfOQjH+H222/vLL/99tv5wAc+cKTzsNayv7/PiRMnOssPDg544hOfyOMf/3i+8Ru/cUm5GzBgwIBrDVcVmZtOp/yFv/AX+Omf/ulH7Jh9Na1ZvqzKrVqnWrEi0tma2HmTQSyb2rohPZZXVStmJJoe+gHB47xkMvfltWzhv5xlZcA4KA3sLZrf89I/Sj81IDbFRxSJZmPR/dKtCeWqea2BgJmlPriW+7VlkpChdy6ujwpmDP6NEx/WzWGNiMQMqPvjYok0lkxr96vzsSV56R9l5XvnnBMsckVRKu+MNZKDecL+POkQO2ME81z7XjnryZ8LCuCi0iyMZm40e2XG3CQUVtUkdeZT6ur+ucwaRrbsZLxFQhXf41FwHevK1KXzUSDdujLoaXh/8gqmuSfpYT2hrJ5Oc46dn9b7nDi/T1KUJEWJNgYTeuiEtYyrRlW8FDgp1z4Abr75ZnZ2durHKpXtoYcewhjDDTfc0Fl+ww03cP/99x/pPH78x3+c6XTKS1/60nrZ0572NN7xjnfwq7/6q/ziL/4io9GIr/qqr+JTn/rUZV3rgAEDBnwh4Koqs774xS/mxS9+8cM+TrcvTq4M+O2P57rYuv4xIvlruwrreat1WdY30BslUcaSllWdD6cr3wfX7nVLF16xAbwiF1GG3xPlf5cCtKr7rtoD2yulajVuoZvl0tk67DYSAiskibPkYfJDnAyhsVgEmauYibS+nxWNKSKSnlSajokgmhs674EVPkS41xPXRmlkXTbFRCUt5NCVEq1dXZaNmC8UWWqwNk7F8KXbRa4wWqCUqx2vsUzbPs9+2d04ycJqrBCkYsHENWSpT+TaaOf66cqwOZ2TFBVOSqS13sRQkzbnf0+UJ3WZbtbNCxh7RfHY+SnTiXcrj/OS2Tjj2P6UjUXOg8e2AdgslhWzo6DSkkov/50Xl91zzz1sb2/Xy7MsW9o2QvT+UHDOLS1bhV/8xV/kDW94A7/yK7/C9ddfXy9//vOfz/Of//z6+Vd91VfxnOc8h5/6qZ/iJ3/yJy963AEDBgz4QsRVReauFBxmfli3rr28CmQpLWIGmWWyyNGVQVhLVnSnBYznhVdp8iqOQ/CkLVH+C76ZSg8JYB16mpNn/u3NypK5yzrnMk9TtDUUSpNYw0GSNWO8nKNolQ0TZymDGaJvEoGG3KUYFmjfK0gz5D4RlkXopRurimkV8t1iKHBwtC7KMC7MNQqes7402g4Obk/FKqs4KcL6sqwVVKbJi1ssmpLoxkZDgItSoayte/C0sowzH32SakOmDXnlM+ayVqiwcYJMeoVtLnQ9MqsSkkiNS6lIYs5cKEG2FVeAMtWNY3Vd6TSosZ0B95Xxj1b/WntqhDbLfzxcKoySK/vjohN6e3u7Q+ZW4dSpUyilllS4M2fOLKl1fbzrXe/i5S9/Of/xP/5HvvZrv/bQbaWUfMVXfMWgzA0YMOCaxlVVZr1U5Hm+1N/zcNH+gjT19IbunFW1QukD6j61rKq6ZC1fLofpEP4bnanMwhd7JHLgf5Zmad+IyTQPWXQNsqKsyaQVgoVOKMNM1qVrXVFqbatVpZAkVAjckorVRukaV6wfau9/j+7V+HsbxnijwqJY//dGXjSEY3+qQ2k1XJtdVn9295O6ZFtVgjz3+xeVZFFHlgjmha7J5CIEC89MUhPZqfXE+ICMXKja/VtKTWYqMlOSVVXHsRwxygs2pjkb05zJhRkylsqN82Q9lFC7N9DA7jwmJPtHZZg8tO+PGUhfVABP7u1fNpEDHzS97nFUpGnKrbfeyl133dVZftddd/GCF7xg7X6/+Iu/yMte9jL+w3/4D/yVv/JXLvo6zjk+/vGPc+ONNx753AYMGDDgCw1f0MrcnXfeyRvf+Mal5X3iEUut6wjJuvFdbQIUS6+qlcAfoa2tU/pHRdH02Nlm+oOuDKPYR7UoEdYymfbKZMb5vriIRelnaM6KZpZmLLUCZJoyUd4xuT+rnY8RudYktmKus5Vf/hbRcrI6wOAQaOFLqlGpi7Ncy9bfBgqDFJbceaWupIkWKYz0zlYaE0R0q8a4EWubKBFjW8uiWaFq1uWlxFQSA0jpfIxIJbC+IklRSLYmXgXb3U/YGBsWuSJRliRpFL80USRhZFUs825mpT8nJ5hWCRNdgjBMXcaGCMqqM+zqEcfKrsJmlIKqQhvDdef36h7GMlFdkh1V1z5mBTx04H/fGvn3exT0v7wCKZjszznYmfjpEqkmW5R19lwhL819GnGxMutR8drXvpY77riD5z73udx22228/e1v5+677+ZVr3oVAK973ev4/Oc/z7/7d/8O8ETuu7/7u/mJn/gJnv/859eq3ng8ZmdnB4A3vvGNPP/5z+eLv/iL2dvb4yd/8if5+Mc/zs/8zM9c1rUOGDBgwBcCvqDJ3Ote9zpe+9rX1s/39va4+eabL/t47ViStvFhXd9df+Zqe7pDWlb1vFbpHElRYYSPIBG9klxdV4x9cruBNFjne6pK48mcCapd1nzpTqY5RiuKVHMwHlGFsOAkGB9i/1wbVU1uoUKB8OqcdI4yEASL8GRWUPfJZRgWrTmutkXuIpGbhxJrjCiRrQH3SjrKotmnDGHBi7yZsRrLqFF5i9uXlcAZwWhsyMOyopA1x92f+tdNU8tsrnBWAJJFUOey1DJdaCajqiZ08fUW1sciZ6pVwgylZYA9OeLGcq9TXi2VZFwY0vZEj8qwMc3rmbsdGAvnZ7CZwQN7sJF6MtdGJHyR0FkHs5JRWrAYp7VJZjzLuU7tsZcsT/g4CoxWmFVl1kuMJvn2b/92zp49y4/+6I9y33338YxnPIP3vOc9PPGJTwTgvvvu62TOve1tb6OqKr7v+76P7/u+76uX/82/+Td5xzveAcCFCxf4nu/5Hu6//352dnZ49rOfzW/+5m/yvOc97zKudMCAAQO+MPAFTeayLFvZnN3v+YpELGbD9eGEWKnO9Y0QkpDP5lZv13awFolmc74+tT4pDZVW3uHYJncHQa2zzhOAWWiWX5SNKpco7LENrJR1nARAWlVUStWjpayQmJZ644RX4qLitvK8bEUpNRrbiSkx4XeNpUSi8KHCibC149OscLKWIax3UTYl0mh2aMeWtHvhAPZ2NUqCUA5nBHkh0NoxnSqqSjAa+8y5xVwG84M/TpFLJhNPuEwlmFaaUWaYVQqlJWkS77VmklV1P1+KQQlHIgylkwinGYkKi0RiuS/Z5nHlbv1Z2p53VbpSKdKQTWelROaFJ2c2kLhI3M75zDn2cz8PFWAa3uNI4uK2GynkJXqasznN2RgvePDUDvko8cHECZcFG9zQq5ZfKr73e7+X7/3e7125LhK0iN/4jd+46PHe9KY38aY3vemSz2PAgAEDvpBxVZG5g4MDPv3pT9fPP/OZz/Dxj3+cEydO8IQnPOGSjtUmbm1lLS5fcqsGVa5N4NqRJHFfFSYndMd3+Qw5I2VN6CKR08aShGy5cV74kU55K4YE/Jf+/qLbKxfJHDS/j5J65JKfKuDHQJWp5mAyxgjBNEtRxjBPM3KtWaiEsje0PWbLxQw+GRS42DuXWK8iIgAnUTissJSBtCXChj654MQVrlbgRtr46Q/BlZsoU4/ciupdzJCDhsc62/TBTfd1LURqBcVCgnRUlUCXEqEc1Z7GZsGpGpY75SB15IWkKj1BHI0No8D3N8YV84WmUJZjmwXWdWfLS+GYmpTjyRzTyp6zSI7ZWU2AJ0WBUYoT+wcI59iazZnMPQnX85baVoVeuEjOjPXEzVpfVr0QCGGiYBFUuVhal8KrslF6jHmF8bN4GcSrPi0lqdTy/quWDRgwYMCAxx5XFZn73d/9Xb7ma76mfh5LqO0yzMPFKmVuXcbcumXREBHXWSFYV6BKWiHB8yytHY6AJ3Gl8V/2kcj1y3PRBJEoTwZsyJ07yH3JLhwXoEx0fT6elK6OZOlcS7wfDtpinQ3Lo5NVY9AY5qQIHImwKFzH/JBIS2n9LNWiNTKrnS1XlAqlHPQybxe58n1wdcBvWD6XCED0DA+6ElApiolBWIGuBEY5TCExBai0ufb9qWZrUjGba6wVJFrwYDniuuMLjPb3fSfL2S1STmYLjOuOOduxcxyCxBqyqqKSEm1tPUkjW5RUWjGe5cy3x4z35p7I9cuoe0F1lRLOHDTL5+EzMS1grH3v5Fbm++luaFylsjKceuAC060x842MYuNwx+k6VFLVs3v7ywcMGDBgwJWHq4rMffVXfzXuYbj01mEVqekjltOg2y+36lhJYBpx4kPcNo7uykIkycY8xlZ448R4ljcjnQ5y/4Wf6YakxYiKWcibM9YrM0r458p5BS/b9OcsBbP+SKiqIte6/rLW1lBKiUV60toiczLOZw1jvJbK061yrJ/VKpDCoXAU0dyAIJW+NGkRHZMEwDT3tUDrGqIW+9hmM4XSrlblilx2XKoyl6QWVBkH2weiJx1OgS4F6Tn/ES8yR5ZLrHSUSfPezaaKra1giHAWrRxlJclSw3SeUCaSycibIDJpOCgTkhBVkjvFjjCMQr7cXKWcG29yYn5AJRt1No7fAhrjQzQ2xPf0vuC0Lownbdut9600cH7ul5kVn7u9oOBluibwpVI8+f4Hlrc9Avws1lVl1kubzTpgwIABA/58cFWRuSsJVog6EDgStqjWlErWkxViiTUxfhxTWlZYKVHGsMgSJtNFKIk6jFZs784CobP+iz6W4dol1iJMgWjnzY1C3TGqJ4lCzkuYjJhvZExHWRgzJZhmq4eSu1WTIILLVzo/yaKQGu1sKLkaEIrMllghOJCeSMgQVeJtEKtLc0I4nBOM04p5K36kDH1xIhojKk/u5nNJVQmk9CQOYGMqkaZ7zlY5kmhqWAhmW4Z0IRnNBFY6qsRhlWMxFpjEgXQUuSTNfI9dYUWrb84rhRtZRekkmTD19bWxp0ZsmwUTswj3zHFsOmOWZVx37gLTccZknjO+EPrhDkKv5KzwvXGLFaO39hYwLyFV/v0+seFJ3kLA8WBs2M/hhGlIvXVYrcjyimN2ym5yeU1zRsk6U66/fMCAAQMGXHm4pslc7I9resPWK3RtJ2u9f6vX7rCctTgGqe9gHM/zepkO/XGVVvXvzAqv0JWtEV6F8UpcaZoGeQjPw9u5O4fNEST+dbOFVwL3JhJt/HU6IZjqtN7dtPvmYi9hzxAicYxs6OcLql0lYlOb/5FRUqLr8isC5k6ghMMEM8EkKblQZAjh6jiSRNl6XBdQx5LkrWUxLzmtugROWMFoJikzi57JuuSqDEx2NdGEWowgzT2psxLSHBZjS46lWGi2j5coHbICVW+aRJGQjbq5cRNRMiVjInImZsG4KtHOsj2f1yqW0YoT5w/8TNW8R9rOBnJnnH9f4/vbRnx+bgabqVfn9vKG0H/qQbh+E3bGUPq5vVK6OjD6clApVbue+8sHDBgwYMCVh2uazLVxsVKrRaLd+nT9aH4ASIxdW4bto9KSnf15neKvK9P0yUGrOSwQgWlYHhUbqE0Pdal1lMDZA+ZPuZ5Ky6D8WfZHo5pYtlHK1R8DHZvpO1lzrF4mYOwq71wVFXnPSqmEBRSJtCyMJpOmM6fVWNFxu3riJiiK7vmmQZUbzSS6FFSJwwW36mgWFLv9YOCItyUQM6OgzCwbB5Ikl+wfrxjNJdOwfjpV6MSxkBJnIdnx9zqe536RcmyUk1vNhmqIWTtv8Pj0gHmSMC5L0rLi2O60KU9GNro79+9xfE/3FnAQ3+/W56ZoqW5j7XvnihAqvD3yn4FJIOTRzZxXME7J8gqdXp6SFnsqVy0fMGDAgAFXHoa6SQ/rIjkOg3a2ExbsVatWibWqMLJR8RLjSVuepYyjc9U630+1arqDcf4LfdpqmG8rOIvKr0tVQ+wyTR6iLKwUTEcZRilmSco88QQgsf48E1vV0w3i9TfTLEydlyecI7E+H29sCrQzdUnVByYbUkyjeCLqyJJM+L4yiS+fKulQ0tazUZV0IfvNhwjvT3UdKbKYK/S+JttTpLlkfKDQoU8uPoeG4EXInsg12Zds7gbHbQmbu4rNXcXpezKyXKL3NVXY3zjBhd3Uz3G1gv1FwqzQzCpdmzemLmHbLdBY5iqr75t0Dm0Mo8K7k5MiRJBsrCl7tt/LYoU6Z6wncget93+vFWtzduoV3FXBw5eBSnkDRP8xKHMDBgwYcGViIHM9rCuXSjzxiSrMKrVK9cqoVgiMUvX4rHoCgFJI55hMF5RKIaxlY7poyqt55aMpZoV/TAuvvKSqW1qdFl0lJ5bWNrOa1OVJQj7y5G0jzzHSu1grIcmVwiLJZUMy4vWLQN7AR5EktmJsSpRzZKHhX1lPYLUzpK4icZaRLTlpfPlQY1FYxqJEYTmmfaN+Kg2pNDjns+RSbeqMuaRV3qwqUSdv5JnFKZBBqDTB4KBKwdYFydaFMDf0rCJdxPst0KVgsqeY7ClGU8nmBYU0AmkE44OmJLt9XqNLgSkk831NWTSBwqk2pMHVmgjLSPnrdwjKUKLXzrCXjjm7ucX2bM7GwptbLuxMfNhuHMd2364n6TF2pDCNWzXCWE/cdns5hG2SZ1xD4mvSb/3x9+boecGJ8wdcDkzIIlz1GDBgwIABVx6GMutFIJyr++WOUjpV1lFJVZsjmuX+GGlVkZZVJ5Kk00tXGd8gH7PkRknzZR/7pNbNY82rmtAVp7bIQwP83mTMNMtIjMFIRSF1nS1n6jmtsW/QH9tFxa6OMfEzaLVtyGxGxVwn9aSHic0x0rtWjwlP6Aq5RYUkFYa56/7tEKdA6KDAaWUpKllPcIhKncxlXSqt72e4BTvnGoIRK5/SwGgqscr/Li1Y2ZRdx/sSp6BKHJN9wd4JUztiO7ezkJSVrHv9NtIKKRyllZzQxdJM2sfvn2OS5zgh0MbW83U3P3/Ov5dnFk1eXOyDfGjqjQ7zspniAbCb+x65eem3GwfCfS6QwM3U99GlCp58slFtE+Vfa5qzOLG1dE1HwVBmHTBgwICrCwOZC7Ci2xPXWdeKJTkKIpGTziGsRQciVymFMoYi0Uhr0VVzzDxLGM8Ln4LbJmtReTE2qDG9L/02FqVX5Xbn6GPNHNZ56pW5WRZLgZZcZRSiefvbpgfd6h+UzpGZqr5HSTRALMWU+IkPsUw7shUX1Jhtt+BAZH42K5JtnXOhGtV9aJtZwd4iq+efAiTaUpQSIV09g3VrzytqsYy6dV6hovPVQFIEhe2spEqgSgNBDMutcqQLQZXA7qkwDzdGmpSCbCFwu4ois5SJDxZOghni7F6GMZJbTu9hnWCkKnbNiGNqTuYMp8oDtos5udaMi8KXo4uSyTz3I7aypCHo7VL5olxW5cATNiU9kSvCqLaDAnYyT+oK47c5MW7MEwDF3PfQHQ/B1Afz5WMfAZWQtZrcXz5gwIABA648DGSOo+XMRZhQau2rdE4ISqXqWau6N1+16UELmXOr4ijmxfIy8F/QD0294qIktIOFS+PXb4cxBomCzVGdXffAyWP16y90QiE159LJ0kvUvYJCgrMkoZSaWOt7Aq1BWUepFFkVyZ3gmDW1gWKuE0r8fNIE3093oDJOuBkPiQkJTd/cwvp9tLCdHOSykkznfl1U5qrEYqUkXQiSvOVuNZ6sSYNft2gIpi4EG3uC2bZDl1AEVU6XcOyMf7J/0lKljknLMKFKgZVQ7WnYrihKSZZKjm/nXJhlbKUFF4qM06OpjypxTah0KX0o88F4xI0PnmdjmnfjPB5qlT0PCk/CUgUPhnLquWBuULJxLR8UnsBtpn7ZQeiNHAfFVgk4sw+phmNjHya8O4cbd1YT/iOgknJlQPAqgjdgwIABAx57XJNkru53643z6kdxxC/ptirX7pmLClx7QoSRAnoCXz2T1VlKpdiazclHCdnCqzdWCrJ283qcsRq/jPcWy5MfoPkCB6/4TFLYHDG9YRtpHQeTMfl9ij89OMlDbov5dsro8RXZTRVCQCkkibMUQqGcJXGWNBgcImHtE11PVuP1gLYwdiWF0nV5Ljp/nRUQjAHbbsGcTQySsSqxCIow+UEKODYpeHDP59+NM8P+1H80F3OFLiXS+h44kzgmu2Fe6iL+FIymAl0I0rl/b3UcX7orscqxd52tVToz9vdy87zf/9yNFWXa3N/RXDLdNPU/jpM7nmwd28iZlgmP2zzwJgjpP0Nn0022ygU37O8CcOPZ8xxMRmzuz1HG+kiS9vt3Ye4NC7u574uLylpUXOPzeYvw19uEgOh0Rf9a/ANha+SPP768nDkrhf8cr1g+YMCAAQOuPFyTZC6imcO6WsFwQiCcq8usbQPEKsTpD5EsikDijJTI3jQAaR2VDk7PSOTGKdy/600PcSzXOnWlX2qdpF6N2cyY3Zvwro8+j1/5+LO5++4TFIXGWIkUlnJTs33TnGe86D6+/CWf5/gTfSnOk7DQKyUEOZqx8eeaVlVN7OI4sHgtJpSO47KT8ykH6YhSyto44S8YTogZD7pNCquQOLbTgr3Cl4CLSpIoSxHUn61JxZkzntyN5j4c2ErHsYc0RjlGU99Hd+yMqvvnvFK34n0xgs1z/rhVCqMDwcEJSyW9Uje5oHDhmAB7xyuSUrCYS6RUzHLNddsLEmXZTgsSYdhSOblT9fuugoO5rdimId+PvII/O9cNBz437zpXYwzJ2XlrooeA8Bnh7AxOhtK5sX7/nVFzrBNjr9jeuwuTOTzhBMwuz91qpMKsUOZWLRswYMCAAY89rmkyF9EO/22ChEWjqK3ol5MthU655VKtFQIC0YlIwjivItFs7c9RziKsbUZ4lcaHv+aVn/Aupc+NUzIMW2+RgRhBYi2cnIAUlJXi3/7aC3n7/+8v8sDeNi4TbE5yjh2bUWUSZwRFYTj72Qn//Se+hA/8uyfz3O+6m7/4//o02WbVuS4XVLZoeEhNVRO2tHVNyhi0teTAdj6nkorUVpQyxJ84SykkU+FVohPCb3NgUn+fneDYaMGZgw3K1ozWea5QiaWsGgIRe9yyhSQpBJMLEicBQ03WovqmC0/ibDBOpHOv2lUhli2aIRabjq3zggvXG5yCEw9o9rcryqwhZecuZIxSw0h7A8TUpGypvFZxb5k+VG973YW9poRuvBGB0nh1NRK0edmUSw9auYHx95rghe1npY81iSHDmfJELmbQ7Yy8Sgs+UHiS+tLrTce4HFRCruyPG3rmBgwYMODKxDVL5qKK0p4CEUdXRfSVueX9m+0sfhZne/kqxL66SktUaclHKUkZGtWrXn02Vc24LiUaxaYMvVZKerdrpnlovsP/++dexq9/9lmkieH0TXsUda6ZQONAO5KsYLJVcKBSpmczfuunn8Kf/q+TfPOP/29uvGW3fmkVzCCjqmRUeXKShF65rcWCSikfvRKUtGMzP77KCsmoLKmExArJiWLKXCVMZYYRAosjERXHdcXn8x0SZdnPU7SybI1LFkqRh0iQjQ3DfF/XJG580OTISesdrWt8vXW5tRg3fXXgSV6VBpdra+fRVCAtLCbNm7cxCVM5jGBrVLKoNALHjs6pnEIJy4YpyFVTztybbDCZLjh5bp9ikpFemDWGlhgrE8naQRGMLbbbB9nexoTPQb7iSiOZm5fdkuq08J+Ly4QN792q5QMGDBgw4MrD8L/zJcK04hnil1tULMo1OVxWiDp+JA5fd1LWy+omeeP8I1HNAPaR7rpblfDLRonfzljOFCd5xX/4fv77J57J5g0FW4/LW0RuNTZNweapnGOPm/H5jx3jl15xKw/8sY+yiEHB2po6Ey8qjJPcq0faGCbzBccPpmzOF2RFyfZsFuJLDJuFz1krpcRIyfXVPhNbMHaNujgJNVHZY79JcvHGfU/QBOMDr7jpQjAKv2+ea9S5qNTFdVb55aOD4KY9JyhThyoFwvhZrtfdn7BzXjPf15w8XnD9yQX7i4T9hb+nsbw6DkHI2hpu3DsPwDxN2JzO0ftz0nNTr6a2y+F7C98nl6qmJy5mBV5YrC69znvHMM4fYzf3JLAwcP9+mM378PvaSinXPgYMGDBgwJWHa/p/535ulhWyMwWhv77vYO2XVmNPWRvt8Vn90NVSNXNYy0Q1EwIy3fTEKel7pRLlRzhtj3wZTfmB67PRDq/+pb/Fx+55MuZpCXpiMYFAxcc6TKqCHbngxBMOOPenG/x/f+A5PPRQ43SVzjEpciopyaqKcVnWJeHN2YKsrEKWmkVay8Yi59hshraWUVVyYn6AClMjADZsSS4Uo0CCjql5HSAMTY4b+MDg/d2ELPfBvjHcNx9ZRlNBlTrK1JHOBRu7EmlgY1ewsStqJU4a/zySO2hInF8uGR0Idh6UTHYlJnEcO9OI1a5FMrdGnnhlIez4wKQcc3NOFFO28znzNOXk/gFf9if3YLSi2ho3Ic7gS+HtWJJ+IPAFT37rzLmlKRCuUfGgcbxGIpiqZt2xsf/9nnNr3/vDUAm19jFgwIABA648XJNkziAwrZy0NoFb2lZKnBAYoWpyZ4TACEElVSjPOkytzgW1TvqsrmiCmKdJPSbJSkmlFfONrP6pjMVq5QnA5sj/PLXpTyISuTaCi/UnP/jNfOBPv4SbTl5ApY5dNV55DYcRvC1XcPzmKQ98cov/+P95NhWKcVU25oUAaR3b01k93SAS18SYWnHUxrCR52zkfptTs32/ja3QznDSzDjumvwzh0AKx6mNOfuLBBumQmRBnXM7zTkoI1DGk7XxnmTnweY6Yll1dAAbu7D9oGBjFzbPCjYuCDbPCjbPNutimXW2s1wTV6XPsxvN/SivUWpqVe5CkSGEQwjH2BbhvVZsz+dIa6l0j/DklVfN4sSHz5xvFLmzc6/G7RdeYWsrcPXPkCN3kDfPY8k1Too4N/cxJTGzbrom4uaoEAK34sEQGjxgwIABVySuSTJnO6XSJu3euzm7t6QzXL51u9oRJZ7UCUqpsUIyT9L6OKVu1Bkr/Xgvs+JL8WBrzHycMt8eY8eJD5oFb4iIGGlP7EI/1Md2v5Rf+MhfZntzwb03naw3W0Xo+uiTu4ksueHEPp++6zp+7z2PY64TlHUUSi/dB20MOvQHZnnBKC+R1tWK3XiR+1mzocfuutkB46pku5jXgcQ34kneMb1ACMfCaBJl2cga8jbZ8r9nC8HWrgzjuGSn100XXnnru1h1LkhnYmnbPtrLTtynamNEMbJwoqzHjPXxRH2BB9UWufLTPkZFvAee0OkLM3hwv1Hn7t0LRK5FujZ0o6wZB1Ugarlpfm+XV/tRJVGdi48YNgy+NH+Z5CsaIFY9BgwYMGDAlYdr9n9nK0St0C2pdL1ya9wefAnKBmUukrJ2bp0Lv+eBxNVqXiixWikoE83BeISuDOeObdavkY/SbpZXbGKPCt0ogccfg60M90XX8zPv/wYOyhHbJ/Kl6zsKoesj3fLZcO9769MpQzjvJF+wuVhghWB7OvNhxJVBVYYsLzxpKyq0Cc9bpO/YdMax6YxJvvBBwtYwqfxg+oixLLghmyFEizRbn108mymfwnF9iTQCoxxVuCXjA7FkYojlVBnTUML6dO4f0sDGhXawcHS9NseQYZpEupBsBofv+T2flXfD9oyF8e+rwvIli/uZlJ5FRrUSQFrbuI0Pcq+UHRSeaMVeuSKQtlR5Va4fQRNLqpHc9aFE1zSxu/DBwg/sw5kQTtw31BwRnritKrNes/9dDBgwYMAVjWvWzerwZEzgw3FLIVHByeqz1EK5NGwvcRgpUdZSSk1mlic4RPKnAqnLtUZZR54kntRlGZvzBUlV4YTgwtYE4Rx5ov3cVCFhlFKmmg2tWIxTtuOw9EgOxikc3+D3P3Mz/+tPn8axE3PO7GyvvMZdNWbHXNpIp83TOQ98eos/+vXTHL/9Mzgp/ZxRY8gTTRbiVXRlycqyLituzHOMkOgqR1eGrdmCh45t1/fTCEmhdK1oaiyJCCXo8NpCOJwTTDYq8kIxGhkKKUkfSLlwqgI022cV2w8pqtRRjD1Jq1LfA9c4VhulTvb4TCR0GxcEDz3RsnlOcO5xDmkgHzv2jxuUEUw3DbF7UClLWcr6/E7rfXasP+sYFAyQ5QWb+wv0friiWeEND+dDpIgSXXNDdLMWKwhbVOyU8FEk7WURUYUrDIzDP+V5BSck3Le/Omj6CBhmsw4YMGDA1YVr8k9t11Lc2r9HhW5dScniozikc5RSk6uEmU7JpfYD5+ueOtkp2dqWgre3MWZvw6tmizQhMYb9rQ0WmQ9Ai47YIktqt+tDNxzjoafcwANfdCPz7THzyYj/+pG/wEE1Yno6O/RaL1Wh05nFWsGH/tsTkc4yS/x5VeG8xvO8JnTSOkaLsn5kpZ9okRQVSVGxPZ2RFQVWCDaLBSfmBxgh2DYLtDMcZ0bpJGNZkEnDKDE8uDuiKGXH4WqVCyYI38929qaG+ETiFvPkZNWocDFEeLQP6axR4dLAtaQRzHZcXWqN811NONZspkh7ztqnbnlTQSE0k6pgnqaMi4KDjRFGK/JMMz21BX/yoO+TU7Ipq0by1R7x1SZo0c0cXaxtoleF7aKKN6t8r10bhfHzW8GT/6ded9H3exVKIdc+LhVvectbuOWWWxiNRtx666381m/91qHbv//97+fWW29lNBrx5Cc/mZ/92Z9d2uaXf/mX+dIv/VKyLONLv/RL+c//+T9f8nkNGDBgwBcSrkkyB02WViRcRkivHrUce+1yUyF8P1wlFIVcLWhOdcY0SZlrXwt0QlCEcmvsq7PC981d2PS6z/nNCec3Jzx0bJtzxzaxUvC506coUs3u1pi7n3RDfX4Af/rE0+yNx/yXP7qVcVY+Kj3p4+2ST334Bh68sMnWYu4VuaLABuOEtM6P9YqBwnlJmpckRVVPtQCYTMMYrAM/czROj9Ahw25iC64XB1wwY45nftsnXHfA9sSrnhtjg30orY+XLZpjLyauo7pt7PrYkehW7U+DkMYTOvCBwTE8WBofWbJ/wjLbboibUzAaWRa5YpwaNkYtIwauziM8yEYUWnPd2d363kzOTX1pvDDwp2ebk6hNDLZxKhvriVodT7MmlkWLZp+201W1PgCxp06GbafL5fejwAqFWfGwl+hmfde73sWrX/1qfuRHfoSPfexjvPCFL+TFL34xd99998rtP/OZz/AN3/ANvPCFL+RjH/sYP/zDP8wP/MAP8Mu//Mv1Nh/84Af59m//du644w5+7/d+jzvuuIOXvvSl/M7v/M5lXeuAAQMGfCHgqiNzl/qX/ioYobAIKryClsukY3yIhM4gyKUil4pSSPKwn0WwkJ6wSefIVVIPm7dISqnZT0YsVMJUp5wbbzJLUvayMfMkYT/z6sm5ySa7443aJDEdjzh3bItKKe6+8ToeOHWcvckGn73peu658RT33HiKeZbyXvEX+NzucfLrj/bleqnq3Gi7ZHaQ8Ge/f7LV6+fvz3zszz0N/Vpp7gmErgxJadCVZZwXVFqSjxImi9w/8pxxUTApCxJbsWGXy9RP2t7FuTDlIbUk2pJnliJMY8hHlsWkITt904M0bZIWVLpZdxudN0QvncOx+2Mgsf852VM46V20UjpuOLnghmMzNtKKG0YzJiInxfCk2UO1ghvJahJ72OaFd7FGV+lYdwODjWuiSMATtYuh7WCNx9kIf1TEMGmA+w/g/KWV1vuIn/FVj0vBv/yX/5KXv/zlvOIVr+DpT386b37zm7n55pt561vfunL7n/3Zn+UJT3gCb37zm3n605/OK17xCv7W3/pb/NiP/Vi9zZvf/Ga+7uu+jte97nU87WlP43Wvex1/+S//Zd785jc/nEseMGDAgKsaVxWZu9S/9Ndh1ReTRXSytCJZi2XYWCrtl5ridpVQ5CrBSFlPRlgEkrdQCXOdME3SelrA7mjcCRk+s73NwcjHj3z++HE+f/w4syyjSDSfO3aCc5NNLmxMeHBri9+970mUc0U69l/wORdP+78kQpf4+/OZz5z0Ll2tkdbW8SN5orFSIKytXawRkdxlZcXG3BOW6cgTwFJrTsw98dHOMA0jvyayZCQrSqs4uRFmxQbOsn28hBMlWyEHDiCbN+9dlXqFrhhDOuu+p5HsRaNDu4/u4KQ/VhFuy3zTP59uG2Zblp3P+vdCK8s0T9jMCpSwnKkaw8qJ+ZQvOvMAk+mCcV74x3Thg4L/5CGvjEWnqXE+5BfgjL8HtYEhN01p9TCoNf9c+8vjRIjpMmE+Cirk2sdRURQFH/nIR7j99ts7y2+//XY+8IEPrNzngx/84NL2X//1X8/v/u7vUoaZxuu2WXfMAQMGDLgWcMlk7mUvexm/+Zu/+Wicy0VxqX/pr4NFUIa+tqjOtcldIXRHsfNmCdWob6FcupC+T64Kql08diSGsRxbSM1cZRih2EvH7KWeQRghKZViP8s4P9qgUJrPXncdc+1JzpnJNp86dQOVVOxlDRk7/7kxRgoutYXpkgidg3Ofm3QWjfICHQidtC5MsfCBweDVOl0ZRvOCycHCjy4LhGV72khk4zAebOwqLIITckYiLIk0nJ2NybTBWoG1grzwF7nYCGTrmEFYENaXSzd2m2kPkdjFHjnoGiDq2JExbJ9pvd9jh0masq2Vjv3tChUcttFpm1vNMTXndLVXT7gA2N/aAODYWR+3wqwlGR4UjdFhsykZA42alqmLE7mI6IhNVdNf10aq4LqJN17YIx6zh4v1zO3t7XUeeb5czn3ooYcwxnDDDTd0lt9www3cf//9K1/3/vvvX7l9VVU89NBDh26z7pgDBgwYcC3gksnc/v4+t99+O1/8xV/MP/2n/5TPf/7zj8Z5LeFy/tJfBz8j1BM6I0T9JRXJXfy9/bzZV7KQSefLLRonZoH8FVLXJbiZar7A/XLFVI+4kG6wn4zYS0fct3EcgM9unQJgXDXzPh9Mt1iohAeyLT65cwP3TE4yPZ+Bu7xmuaMSOiFh9/4Re6NAPJVib7KBlbITnxKVOdmKwWiPLkuM4brze5SJrgOUI45Vs1pVVGHK6mbmiVCWheebFfZC06OoSoFJPPmKJdXRgaiJ2saFhrStgy68MhdntAKc/hNNmcL+cb9AWcHGhuFgnrCRVsxLfw5jUdX9coXyvXI7u1OyRcl8MoLf+xzszmFRLbtPo0rXHuWlZNMzt05520j8o11iBX+cPk6E99fYJqj4EmGRax8AN998Mzs7O/XjzjvvXHss0WvqdM4tLbvY9v3ll3rMAQMGDPhCxyVHk/zyL/8yZ8+e5Rd+4Rd4xzvewetf/3q+9mu/lpe//OV88zd/M0ly+QO+D8Pl/KWf53lHNdjb2wOgQhEpVlTkcqHqL2mDIHEWi0DiqMI671ANCpzQaGcoQmlWO4MVkgOVMbIlBoHCYfBhwg4fgwKQOEulMyZV3gkinsmUuzdOoHBoZ2ujwJ+lJxi7krs5DgKqXOIcPFRNOKWnl3wvjxRZoqCYaYRz7GcZSVUxLjzR2gnRG31yRmmQSmBTGfrnDJX2Uy8iwRPW1sHBhdBsupwD0Thyt5KCCyrDWkGWGfanGnmswp7XzDYd133Of77ShQ8LbrtRo/kBGkJXq23heZXBwQlXl1er1HFwwlKMwxixUMo92K5Q51O+/KnnyCvFs06e4Ua5yzEzXxrjNs4Lkjg/94Zt+NOH6rm5Xj0L5dSDosmYiz107XFcMQgYumO7cuPVu7guju6alRANJ+2evLjNZcIgMSv+zovL7rnnHra3mzicLFt2VJ86dQql1NK/zTNnziz9G444ffr0yu211pw8efLQbdYdc8CAAQOuBVxWz9zJkyf5u3/37/Kxj32MD33oQ3zRF30Rd9xxBzfddBOvec1r+NSnPvVIn2eNS/mr/M477+woCDfffHO9rq00NM+74cF1KHCrHAtNT9FCJBQiuFUDyauQHMiMuUx9KVcISqEphKYMjwOZcSAz7s6Oc3d2nAtqzGdH/svKCMlMpnxOH+OM3uKC3CBH8wlzPVOTcPdsmz+2px7ubVyr0I1Cfp5zIFVvFq116MpwYWdCkWrKNPwtYG13kgE+HFlXllIp5uOMjUXOJC/83NbSjwqbmAXbdkGBIhOmzp0DOH3C10m3Jp4IVYljc1eRLQTF2CGN4NzjbGeCw+yY4yAMwojkzarGvVql3gBhe3/CRCI337acvtvT/JsfP2d7qyTThq1RQeWaz8pcJpza3+fJ9z/Qce/qaQ77Yebq+ZkP8t3JINX+0Q75jVAXUZT66/vq3TjxJDGiTeImvbLuEWHW9MtFMre9vd15rCJzaZpy6623ctddd3WW33XXXbzgBS9Y+bq33Xbb0vbve9/7eO5zn1v/kbhum3XHHDBgwIBrAQ/LAHHffffxvve9j/e9730opfiGb/gG/vAP/5Av/dIv5U1vetMjdY7A5f2l/7rXvY7d3d36cc899wDe1BAfbVLXJnexhBpdrLGs2i+7RqIXyV7cziJq0lbhl82F7vTWARyQcVZs8JCbMBUpF+QGDwrfq3a3O84Zu8kFO8Y4yQMLv1xPLCLksB3F/HA5cEYwOu6JnbKOeZoyCspcUlSUSrERoy96s1514fezUtSmCYCsKKikZFzkJNaSywSD4ISbsXAaJSzGSY6NczbSiiSQyY0Nw2LDE72DY5YqoY4RkabJmINuibXKGhIHsNjyj3TmCeGF05bPPquiGDkOjlvO3VBx7oaKCydLksRyw8k5s0LXvXMTF3r9Wk7ccV4ymheeyEVIuURuayLXVuRiz9sqN2ssu2aqWd8mdqqlyMVePCWbSRMPAwY601Gax6Xhta99Lf/6X/9r/s2/+Td84hOf4DWveQ133303r3rVqwD/7/O7v/u76+1f9apX8Wd/9me89rWv5ROf+AT/5t/8G37u536OH/qhH6q3+bt/9+/yvve9j3/2z/4Zf/RHf8Q/+2f/jF//9V/n1a9+9cO65gEDBgy4mnHJZdayLPnVX/1Vfv7nf573ve99fPmXfzmvec1r+K7v+i62trYA+KVf+iX+9t/+27zmNa95xE60/Zf+S17yknr5XXfdxTd/8zev3CfLspWqQRvt8if4EiyAxK6MYjBCoJyr++7Al02NEJSt2+nHhSkEjiocRmKpUOyKMRMKZiQYJCqMt7rgRkxtxlgW/El+kpFq+rPuOdjCWMn+ImG2KZmWKZtcXo5YxGHlVufghtP7GCFJ8HNYL2xOuL7cpdISXVmMkkwn/v5O9pvjWK18VElReYXOmDp0OG+V4bWzWCHIhSa+BSNVURjJhZk/bpJY7NRHhVw4JTl1X7P/xq6gSl2YAAFVKgDHYquZANEmd8UYqsyxCIbURXCwlpELlYL5puGJT5hhjH/Tbr3uAaRwjEQJzmfMPf7gPHmiGc9zn8GXhLL9Zx9qXmxnBOdmvqy6pK6JbpwIhAy51nbGeZWtTdpiLh0SFLCVNduNEx+BMg73Z3L45/4wXKzMelR8+7d/O2fPnuVHf/RHue+++3jGM57Be97zHp74xCcC/o/BthP9lltu4T3veQ+vec1r+Jmf+RluuukmfvInf5K//tf/er3NC17wAn7pl36Jf/AP/gH/8B/+Q57ylKfwrne9i6/8yq+8zKsdMGDAgKsfl0zmbrzxRqy1/I2/8Tf40Ic+xLOe9aylbb7+67+eY8eOPQKn18VrX/ta7rjjDp773Ody22238fa3v73zl/4jiajSyUC0Vs1pjdvlQqKDblGiUVgM0s9qbZHFEk0eyOJZt4FFMhIlF+zYuzmpWFjN/YvoItUYJ5iW/gt6USoSZclurkCALXnYA9lWEbo4XnX8lJL9ZMRGmD+6SFOmo4zJIkdax97WBpvTeRNNIpe/7HXl75+PM5Gk0Q3rHIltyo4jUXGu2qCwCusESjryUnLuQlMqvHCyRJeC09OUZCHYvc6ysRuUzlBOLcZdE0TbzdomcsUYZjuO6Xa3lLx7vOKEciTaooL6aZ3gJny/5UImHKQZW4t5bQSZ3H12WQ3bXTTlzwdbfY3tsV1KeBlMSVCuq9BV4XkkeO1+OSWb5Ztp00MHgRSGD8WK9+MoKJ1EuuV9yxXLLobv/d7v5Xu/93tXrnvHO96xtOz/+r/+Lz760Y8eesxv/dZv5Vu/9Vsv+VwGDBgw4AsVl0wF3vSmN/Ft3/ZtjEIm2iocP36cz3zmMw/rxFbhYn/pXw7aqtylwiLrHLoSXRM+13PCVkhK50uvDsFINCTmbLVBKg1nisnSuVxYZFyYZ5w57/vb0sSQasv+KQkjh5kquLQ84JXoE7pyrtCZ5XFfuksZYlGOLTwhydOUrdkCoxVJXpCPUjb35xSjtA4SBpiPU1xwvsaJESVwfP+AB3e2GVc5e+MdJsYTngfFJiPp9991nsBdd2xBWUrKSlIUlpESbJ33c1kvXG/YeUjx0BMspz+tqNJGadu7HjbPhn5HBUWINblwOpgcUjg4YSlTR1LA/nFLOXLc90Tf71aGcWKnt6ZsSq9+ztGcrGacKKbN1BCtOH3/eX/B+wvYCIPuY99a27FqrFfNIpmLyyJJ65das5ak2CdycX0kdAfFcuwJXHY0SWxBWLV8wIABAwZcebhkMnfHHXc8GudxZBz2l/6jgbZCZw8pM3Vy6micsRHtEtU5s1GrHBLH1KTkJuSpGc28SHhob8Sp7QVlWH7ufEqSWs4+EMpnNznKT6ScOPXw0v4j2oRuvpuwdTrnhi/bY2wKSimZpRnpfIZ0lkorkqLECIlsdVIVwRBhAuEQ1lKMMqwUpGWFlZIHt7colEZZx1SlmNBDeJ044Kz0eW3zwseB7C8SkjAb1VpBkUvmm5bR1B8/D8aFgxOW0YFgsQmb5/z7cOHGMKs15M7tXR9Dgh2zHf/7YtK8R2ceX7C15a/lxE7O6Z3e6AjgWDljs1igrSErK07ff540Gh4ASgMjDZ/b7ebGpQpQfkZrpvwjBgXXhE81JdPodo19dW1j6qq+uTYi4RtrP4XiMmCconLLblizYtmAAQMGDHjs8TCLdFcnov3hUtAncvVkiJ4DtmypcRG5U+SuudWRyDknuFBlGCvIrWJeJMwKzf48YZErimqDB8+NKAtJWQnmZ1IyK0hzgX2qYfYHCbaCNaNiLxm7asx2NSefa77iG/6MTBkqE2JXEFShbGeFIM9SoEA5y2KcMpoXlIlqIjoCjJBki5J85EvFp89f4LPX+QHwJ8opRih29QgtDLnTlE5yy84FPrN7rD5GJHR6LrEKypF/75RxHH9Asths4kmq1JdQ/VQIHwR8cKJR7TZ2BXvXOarUsX/SoEvBbKs558mk4qbjU0aq4paN851r2c492d2ez9HGR6/YcYLcC4R61psvluoQT9KKCyla+XNKerUuxonE7caJ3y72yfVLq1G1G7fe+IOiyZibl3B8DNnlmWOqNWXW6jLKrAMGDBgw4NHHNUnm1uFi6lvEOiLXXhfLqqXr3mLjJKVV9RejdZBbxaLUzApNUQYXbSlJtK0rZeLBBBKHMLCxr5BfZMk3HZ/77DabN/dIxMPAmd0NtrcKvuxb7mcmEyaVV56sEBRKs7exwebML3NSYoRknqV139xiHAwk1mGl6AQMAyzShI2y4KGNTYxQVGHCRoViS+YsrObBxYYfnyUtZ3dHVJVgZ6tkXzo4m3D2hopjDymMctz35Iob/1RTpaG/TYmauOmiGdcV8WfPbNSq6ZZlulNhJcyOVVw/qXjGLecYqQolHZ+ZHedpk4e4ye4CcJCO2Cz8tW/OW4rc9tircqXx6lsss6YK5r2g3z7iJAeAlFZJNXxuYik2U1DZhsilusmli4aKg8IbL05NgqJ3eWXRdXNYL3U264ABAwYM+PPBQOZ6OIzQrSJx8Xm7R650ktwlaGFqxamwqo64qJzEOkERSqjzImF3nlIGIrd/4BWVsxcynBUc7Gp2cgk5COtfW27A9rMrLrw/4eBCwuaxy5vD2bk+C+mB44tfeoYbn7rHyFTkKiGxFbnSbJQFsyTlwtaEE3sHVFphpUBVhuk4IwlqFcBilNSzZ2MeXakUexu+lLpRFlghmasEFGSu4kE22VIFcuR4YDFhVmhuPDllukg4t5uh5t4Ecfoe3x9mEocqBXunLPNNEWa2WtIwu/XghGN0IJDBmbp3neXBJ3gyd93dmulOhSolBzd4MnzT9b60OtL+Gp4w2uN6d4BylhsXntBtLbwKd9O9Z/37EMukbUXy/LzpiWvnvhWmcZvG/fJWgHD/cxfLsxAIW1TkkkbVi6O94vJIGhMF1RoCeRGUTiIeIQPEgAEDBgx49HFNkrk4nKiNwxS5dY3fbVJnVqh0se+oCNbKhdVY592ts8p/qRdVULKEI0ksi6L58r9wrzeZKCDNJUVmSUqBDo/N51bM/7dmdk5yoBIQsLl5+aSuOqPZvn7G87//zzgQGZkoSZ13ngJo25CDvcmYzdkCG+avVlqSGMM8S1FhQoLRzbVUSuHCTFsAExS7qfY9gO0ew9IppkVTIlTKobWjyCVl4rhwqiJdSE48oDGJYzHxD7MnWEwEo6k/9sauV+nOn/bn/eATKjb2JLNtyx99xYIic6RYnnzLQZ1pN0n8/dvWOVObdfjVjXsXGBcFT7j3QWaTjM39BTKRfuJDxKJ1/8eJJ23tqQ99fpW1lTmxPNFBya6qtxl6JiOBMxYI0SSxzLoofWBwf5zYEWGcxKwgbquWDRgwYMCAxx7D/84t9KdCQJfItctP8XeLoEBhgmO1dHKJ/Plya5gQ4ST7ZUplJfPSR49UQW0rS4kxAikd990/grFhYyrZmEqsdKS59GOsdhWjmWSM5NRfqkidgAtBjTpIODi49F4pNXVk0vC019zPsSfOURimMqsnWyTW1i7OeeJn0MYS6nzsCUapvFKXJxoTti0STaUUlVb+dylZ6IRcJfVYrDSMLdsWC6YmYWE0W2lBpkMwsHBsjktGY8P28RIRXKdnHl9y7oaKvZMVRjn2j1us8gHAi4njoZsND91sMImrFbkYNjzfNCzGBndzzj33TCiNYG+aIIVjUSmME9yk9nj84jyPm19AOsfmfMH2dM6Fnc06coXPX+jeyEXodWsbIKKKFn+a1izWVMOxEWylDZGL+0SME0/iIpFTwvfL1b10vf0S5Qnd6PL+VrNOrH0MGDBgwIArD9ekMndU9IlcG/F527mqhPPkrOX6m9qE0ipPEoxmP2TGlUZirGRWaGYL/zbMC8XBNKEoJds7FdN9v3w0U0gDm7v+ZywjAugbHeb5hjP/j6KUFoJT8+AgObJKp2YOsyd56svO8Py/8VkUhiQQrVwopPJmBwArJEYpKiUplSLB+NmrztXhuwB51r1fhdbMQ2CwtobEGuY6ZcMUXNBjLgivKo1lRZJY7q02SZVhd94cdDw2OAuLXHFwy4LR5zy5mW1aVClICl9y1YWgOm4Z70kWE8vW+eAUDlMkipGrybGZGJ71ZecwVrA1Krl+NGNLLhiLCotgoRIWKuFLz3zev5+jjBsf9MYI/cCujyM5yD15iwpaLKfuLgLh6gUEKwHjlnJW9761CGBU9cYtYh7z5FLV9NTVJdZWj138g+Qyc+b8Z3iVMjeQuQEDBgy4EnFNk7mjllbbKtwq5E6RCItxfgqEQeDCF5/E+S/Gdth/IHKrUJSS2UxRlc1rSQOjmd9eheWy9b2/9ZyKQlsu/D8JZWHhpAUpaoVuHalzDvR5sJXki//vMzzr791DGuajlkKSOYPCUUjNdrlY2n86HqGNJakq5mlCYkxthIj5cvMspdTaT0qoKkrtP3JOCLQ1WN2UZects4gKF3jd5pyz026mYV507520wvfPGVHfl0jcrHR89ssKJruSJBfc95wpRe73f9ITpyRB/VPS8dRj59iS/jqfWpwB4Hg+rRVEgK3Z3PcCpngFrOfe5aTvCaSouj1s0aQAPhOuHT8SCV9EVPUieYtRI8YFha+1bcyXa2fQTVKvyl1mzlxpJaz4fJZrPrMDBgwYMOCxxTVL5i6nRy4i9sfF7LhEWEonMShfZnXdUqwIkyDAx5FEhaOygtJItLLMck2eK0yY/aUTRz5VqFLWpgejHGbiUEZhFejI0TQcf65B7zjO/kZCcUbB2MKmzyjrkzpnQU8d1Z5Cn6x4xt+5ly/6v89wXM9ZhPDjcVDiDKJW6RZBWUtNxSzLSCpfukyCQbRUCiUduvJqXezdz5OQPycllfQL95Os7sWLGIvK96kBbROsdYIksYxHhtms1VN4MvS3XdAUmQAMo5m/X9nCH2Axscw2DeeuK7GZRZaCNLMkunntm4/ts53kpMIwFhUai3KmzsCD7tQPgBN/9pBXvs7vN2XNfo9aHNG1M/IuV2hcpjFPLi6DlvKWNM+hpc5ZuG5CB8Z6QtcmcsnDy4Ozzj9WLR8wYMCAAVcerkkyd7mKnEGggsTm8KTMIuoyaxkax+NyF37GZaWRdQhw7JNbFKqeA6pbBKOtzJnEwaKr1EEjnqRzwWLiyJ5pOPYlhtn/0hz8HwlnlVcEpQMJBw+lYAQbowq5U/Hkb32IL/meB9h+yoJM+iw5hauvR2MppQZbkSuFdBbtLJWUaBvmqmqNCeW8ySKnlAKbZWSFJ4MH4xE2GB9MOIYVknFVMtcJ+yqrX2uBRgdlcEsXqNC/tjMuyCvJKNXkheSm03P2DhKSYLDYt829mW0Kjp1N2N2wXDhVkWeWfLtRz3Tir+3JT9gH4HEnDthO/JSHLbkgo0JjOp+R7fkc6bzKuDULmXKrYj/GGh4KYcOpJpam6/iRWq0LxK6eAiG6hC5my7UjTg6KrlIXX09JCBl+/vdW+fUySZ2xErlChVunJg8YMGDAgMcW1ySZWwXXIm0St5LIxZ8O0RnPZVqqW4TE1ctjUz14lclYiRRQVMEkUCryQmKtQEhXEzllBE46QBA4DqOppAyZak6BLgTFyGIVzLb8RqNvySn/mmX0oCH/4xT7eYXKIds0TJ6Uc/LLpxy/bcpNTzlgrEpGsqpJan9yhXSWsS0ppcZqiXaGUVlSKMnIuVqxks6xCIqSkZKqN51AOkcpBJVULHRSu1jHtmJPjcgoWYSPY7xvSnin5mZWsJnB+f0RG+MKa1crp046isyRP31Kfn/GaCYpj1UQtn/cTXM2NyrGowpjBCc2c05mTfn4OjftHE85w6TwRC8rSo7tHQCwOQ2ELpZYS+NVukUZSF4ov+6MvPoW++k2s+W8uXifFN2euL7KN+6ZWmKPXL+nDjy5SxWI1ffpYiidhCGaZMCAAQOuGlyTZK4/e3KVuaHqqXeqRc66M1ib7UongwlC1ttNQwSJFG7JDbgoFM4KlGqa1k0lGY8t83kciSUoE8diwxFSM+p8tfp8WwKMDcPhkxEsnm2Z/EVPQE5fN2dr7A9wcnNOIiyp7BILg8KGyJZ16mVmqrrcCoS+N/8xks6hgmJnlar746RzVFJSqObjFtW8mUy8IugcI1GxEBrjNGNVklvFJCnZL1PmpeZxJw94cG/M3tT3iW2MK2ZzzQ03LXjooRQlIQ+TIEY3LdjZKjm4d8zWVsV1pzxpE+H+qBBFooVlRy0Yi5Iej+WG6R5WCKRznLiwT1Z6RS3LK9CBsBnb1ITbBLbvZt1MYTf3Pw9opjvMy0aNm5eAakqnkfhFlS4GBB8UjTsWGkKXtJS8hwHnROcPk/byAQMGDBhw5eGaJHNtrHOpthGJXa3SuWUzRMySW6deVE5SVMqXXh0YKxCdRAmHtaLDAdooM4syknLkR1RFw+xiYqkShy4F0vjSa2z+NxbKQnLqZM4iVzWZA08uSytB+vy7HeXJToqpr20uNNt2QSUUqatwQnCQjNgMZohca6h8P1lqKvIWyUuqqi6vSudqIrfQSd03t5AJqTN1/EkfDh+HsaErKtuUqMejiu2JL2Huz/xrXn+9I9GW++/3rtidLX+tx477n9YKJqMKrSyTrGQjqbhp4onuSFRkGBJXkYUsvccdnKeSinGRsx1Kq3miycoKPS/83NPoFl3EfrhQAi1ahpPYG5cq2Ml871ybqEVDg3GNkheJ3mZwvMYPxThMfYimhySQvFHP2fowURkJZsU4rxXLBgwYMGDAY4+BzLV63uJzaMqpffSJXJu8JcKSO1Wvj+4/6/x+0fhgrFc+pFjuKN/YMOzvJr6UmFmm2xXb5/3bVIwcunSQgAjd6PHlp1tdFlhkzXMRXmd/nrA1LlHCO2xVUAu3VYlFLgUpA8xlSmIWLGTCyHqSMtUZY1NihPOEDm9+2MqbkmWpdV2CnScJyjqKsKyUEum80pnLhgDG+5YIS2EVWlgMgsIqUmkojeTYpKA0ou7f2p8lTEYVibaUleSmm+aMUsNs4YnNyWM5Zy9k9Wuc3PLnOEkLTuopuUs4sBkj6QlZLlVN6LQ1yyaNmS+7kmk4e+AJVXS1xmy3aIgowoivWD6Fbh9cVNni876rFYJZIgm5cuEhWzNb+9hIm6y56vLcrMZJxKqeuaHMOmDAgAFXJK5ZMrdqrmp7WX90l8TVZMzRZMm1y6/Rydovp7bLUz5yw6tMZSVJE0NpNFJ6ZclFAhi+h+N3qlE+jmS2ZRnNRO0UlUZQJQ4nHVUS56NajHIo/OD4fnlsWiTsZDmVk2hhOTApx/QcgaNC1u5bi6AUkoXUCBztgJC8NjMokkB+zo0nbJRFhwCVoRRYBNI31w15M0LW93cmUu8iDY9SSLZ0ztlio1bkIhLlam40GTWzVkeZwVnBKK0YpRWz3L/m9mbJzddN2R7lzEu/7ES64IIZc0zNyV3CpstxCDZNzhddOFMfc1yUnDq3xzgPOXtSeFUuL+Mb6omadQ25KozvW4tl0NLAJAOzAFojvozzUxsOCq+2zatun108diypxnJu7Kkb6UYVjC7WWGqVwhPOy8C6gOAhNHjAgAEDrkxck2TusMy4dShdt9TahmupdUK4uveqsApjG3NEND/40VEGYwR5pUgTizGyzk9TiWUjgSKXVFhMS4ApE8eodQ75yKF6PfW6EqiJYbJVURmBtbAx8kRHScsoqUIorGndD4nCUDpJKrwu2blGBHtqxKbJkUJQCo1UjsRayhbXOkhHZKasJ0DEkip4AphLPz2iEhIrJBpLEZhpgSIVhsIpMlGxbzLf16e9srklCnKrWJQaUB2Sl2qLlI7R2GCsINWGURrUvI2ccchPeerOOZwTHFO+dLrDHCsEJ8opmS25bnaAFYKsLNmezpgscirt5+KqqsnRq4laXnV75dqIxKqdN7c9akZ+KeGVtp3WNIh6nmu8b6rbCxeJYN+p2j+HUeLP7TJQGoEzK9oNViwbMGDAgAGPPa5JMreqfNp3rR4Gi6wNDXOrUaGMGctQlZNYJ7wZIip8VpBIi7ES62Ced2+9UpZEC/anmkQ7yqpVys0sSZjNKqxYynOtEodVvleuShzFxBD1LyWcN1oWitMnZiTK4ZygiuQSQSINC6vZkDbEr1TBpasAw1SmTKxXpiqh6h46KwRzlZDZqqPGHSQZSSuwNg8D4kup63ttEChnmIsU03Ndxvy+VBpSaThbbJAERTNWgrdGBdYJFqU/dqYts0IzSgylaYh1qg3HRjm5UTx+vF8v36Bk5AKp6r3lWVmSGEOZaFjkGO3HkUnrGE/noadNw6xo1LJMN6XUwjT9bbF9LlWeyMUy6rrQtqjGgVfbpq0yLHhF7uRG43YdtcwP8Vw2Un8+l6mkGbumzDpEkwwYMGDAFYlrksxFrCJw/fIqQOGWe5Niplwc4WURiOBk9apWq4wbe+WcV8awkjSxFKXfvzI+liRJLBtjwzQQusVcICXMjlVsntOBxDkOpGM0k1jV9MblWTPxYFXwv1KW3VnKsUlOXqlggBCkusnIi31zFZICxSYFOQljitqkcKAUiauwTlAJhXaGA5l1+g4TW3EQQoEr0SUAlVC1MaIPGcq8qTAsXFLHpWwEZ2tU6ZKQ07JfpGxlni0VRjFKDFK4wGscqTKcGC8QuNq5G1W5hAojBMfNHO0sifXZcqnxapY2fvukqMjKirSoSPPeJI2+GpYoX/YcJb5OrqQnZIXxz2OciJKepCUtla0/SWISTA7Hx+HmtLLlwJPCUeIJXDxOaTyRq7fvHfOI8EacFX/wrImEGTBgwIABjy2uSTK3qsy6isTBsjvVIutlMY8uIipzDlFny9meYUIKhwFGiQmOVsu8UEjpKMv1ysdiHOaPVgKrHLMtgzSiJnEmcZShZ25jYki0I1G245h1VoQSJWykvpdubnxQbzRJ+HCSYFxAk2DJSaiEYuxKHyQsNA7jDQxCMQ7GiEjupmqExK00VFjhVTnpHLlM0NgQidJMy2gT4an1ylMdaiwcpUnrcnFcp5RlmqcI4Rgpr8ZJ4dDCsqH8+Y1kicAxCWG+22bBicJnyy1Uwk0H51HGoKxtyqkBwlrKRJEWLRcrtIJ/ZbdvDtVV1ZTuzmLdzvzzSWugbdx+Oxg2zoc8u0Q12XFSeNIWiVz7PKL5IfbKXaa51RgBK0qqZiizDhgwYMAViWuSzEVcrKQaDQ8Ch0EhsR1yJ4TzZdMw5aFejmNhVOif6yp0puVwBf8FORlVTBcaKX1EySjzvV5pZnFGUFaCamyxUuKUV9+kFQjj6ucAQjukdJhSMhpVlEayNSq9SKR8pEeiugTLWMHMJEx0wV6Vsa3BOMtI+FJriawVsgPhScaGKzpO3LlMSJxF4ihE85GKal6MJ4n33AhZb9cOZ5Zh+sTCaU8EhSQTxp+DcCxsMDSkBdNKM1KNuSMRlmOjBSNtQthwIHnCMpF5M04NgcagnGPL5PW5jkyjuhkpGYUJFpHU5aOUbFEsf0g2UthfNMSqTbDavW7Ggqkak8L+oiF0cbtJ2s2nOz7269rmB/BELhK2vGrUuDa08o/LQGUEbkUMyUDmBgwYMODKxDXZBLOq+NRX60yLhBm8whYdrG13aNwnEgiIylxz7FiCjATIOiiCalcfx3ZHeplKkKWW0bg5W5vZWomDMOYrrpPU80ZVYikLiZS+X05K2BqXtObFY51gVracpYGMTk3CzKbMnH8olnu7jPChypGQGSEphSQP5K0UkrnU5MH8YBAcqIwyyIQLkTROWZFgUOig4i3QSJxXCJ1AC1v3JEq80iaFYyspyZRlpA2pskySEiW7QchK2LokK0JU9HXiAOUcx4w3PuShj2879ypYVlVsT2eUyvfI2fBGyrZal4Uh9rpVJo3LlezOSW0bFuLzVMHWqFkeZ7VO0qC4yYb0jXqTH6DbG3d8Y3l9xOWO8woxOqsejxbOnz/PHXfcwc7ODjs7O9xxxx1cuHBh7fZlWfL3/t7f45nPfCaTyYSbbrqJ7/7u7+bee+/tbPfVX/3VCCE6j+/4ju941K5jwIABAx4LXDVk7p/8k3/CC17wAjY2Njh27NjDOpZv62+UGtcjcbEHro4siT1vLceqc6IOCnZOUFpPzkorlyIc+mVWLZcJUie+RHfXp5klDSROaF9OtdJRjT25sxLUxJOYejqUtmgVpx3Y8NOFaJTmWisnmVW6vq5+mXnulsXbnKTerhCaSij21AgjJLn0jlWHd7weqKzOkmtnykViF+9z7NMDb9qwCBJRhede9RPCP+JzgJGs2FAlibTsJEVdVt3RXnUby0ZNOy4WGBQTm6Nd6KFr9e6lxpBWFcq5etpDYgxWCqR1KGMp0nA/dC8TLpK4TDdqWkQkeVK0pkWI1URtpLtjuaAhdZHgxRy5PtrLLpPIgVdrjVnxeBR75r7zO7+Tj3/847z3ve/lve99Lx//+Me544471m4/m8346Ec/yj/8h/+Qj370o/yn//Sf+OM//mO+6Zu+aWnbV77yldx33331421ve9ujdh0DBgwY8FjgqimzFkXBt33bt3Hbbbfxcz/3cw/rWAZQK3rk+spDe1xXf/7qumiT2C9XtoKBbU/VsM5HZ5RGopTDWRilhkXRfAFPJhXTqfbELvCRRId8tdRRSIHEzyOtpGMcyJ4MRFFrR5aamsjtz5M6ky3Vxpd7A7HTwtbTIOp7YRJSaVBCMHVJTZ6i0oWATZdTCokKJdRCaEohyVZMdciFokIxVSmZ8+dR0i21QtOH6Gfbdv/WyITvmyudRAjHRBW1opgIUyt4USU9Juf1vifEDIBtuwgGDMm48gHCxxZ+XVNatXVZeJGlbMxzVNUofpVW6DgBItO+hy0RXnJtmxEWZUPkrOk6VTdSXyJtB/9GohdJWzx2JIYnJv7nrPDLStM1UYxTUAIb3LdFeXmhwVUlsdXy33mrlj0S+MQnPsF73/tefvu3f5uv/MqvBOBf/at/xW233cYnP/lJvuRLvmRpn52dHe66667Osp/6qZ/iec97HnfffTdPeMIT6uUbGxucPn36UTn3AQMGDLgScNUoc2984xt5zWtewzOf+cyHfax1ZoeIjpLWauJvE7lIbmLGXH+aQyJjSdVvm+qonDXHGyUVUrjapDBK/TZZGpQ07Z2po5FpSFoorcYe/Cx1ZKmjyCVKW4R0COlIE4u1on6kiT9mqg0qzm+Vtlbn5pWuw2KN832AzgnmNqV0uiZcsWdwgeYhMWFOykIkVChy4Y0MuVDMhaZCUiGZi+7fDAuR4K0UYqm8DUEd7X00oyoncSRBmUuEZSQrRrLys2aFaZaLkkl4jEVVk/Jc+P4+7QyZqdgu5khnkaEGXWjNIk1DWdWyMc9ZiSSmObuGsLVDe5NQSo1ELC7fSGEza36Pz6Oit9EL/43P2wHAG6knbjvj5vhZUPGkRAbiWWQrlL8jIM5mXfV4NPDBD36QnZ2dmsgBPP/5z2dnZ4cPfOADRz7O7u4uQogl5f6d73wnp06d4su+7Mv4oR/6Ifb39x+pUx8wYMCAKwJXjTL35wUbyq4Ct3bYfNyu83zNF50Ujtyq3rbNnEslHQrHvLefJ2H+d60dzjrAes6gl8u0Stt621hehUapuxiEcMyNJpUG44Tfr+7pdyFzzqua1hky0e08bKuYMQyuFJIc71gtW8ob+Hm3ckU/Xt89LEMIc7vELV1Dip3zkTCe4Pl+OkX3/NLQJbllPTEb2ZIsGB6SoJRFJW4U5qoWiSYtm9BdoxVJ2QoNltJfZxICfkvTjO3IdJjdKjxRs2G+aiRkpfFEL6p00Z0aofDr2r14bWStUm+ma2Zfl4CBxTiF/TVE9CKoKoGslj/PNizb29vrnk6WkWXZ0vZHxf3338/111+/tPz666/n/vvvP9IxFosFf//v/32+8zu/k+3t7Xr5d33Xd3HLLbdw+vRp/uAP/oDXve51/N7v/d6SqjdgwIABVzO+oMlcnufkefOF1v8SiiU9s6J0GslLDNaNuNjkiHamXB+JspRG1tU0rzThnauJwVqBEHGYvFfjTOXPL0l9aG40SkjpsFagtA0mB0dVCarKq3BKemdrorxSl2mDkD74VbVMAs4JDJAJ3yvV56+W7minqEDmTuEQ5Ki69CpxtZGhXS5tq29V6wVir1z8vf8+tMl024jRNlVGIgfe5CBpTA9tTJwvoSpnyUxJZhqiFomcNsv75YkmCZlzZdIQOisFMqpzpfU9cO3dk9Y8VilAqm5pNFHNhIZ+Xp3sfXa2x54QlmZpdqsNYcZtIjebZB3zxqWirCTykDLrzTff3Fn++te/nje84Q1L27/hDW/gjW9846Gv9eEPfxgAsSJ30Dm3cvnS+ZYl3/Ed34G1lre85S2dda985Svr35/xjGfwxV/8xTz3uc/lox/9KM95znMueuwBAwYMuBrwmJK5o/5n/9znPveyjn/nnXeuPH4kF50+tg5hu3j1OSpI/fgRJWwnpsQ6QSKaSJNE+SkQolWWtaG3Dkk9MF4IR5ZaciSmDI5Z7aBqDBKKJiDYWkEWSqmJjv1zfjTTWK3unTLON7b7HjqvcC2MIpGOmWmuLypoFoHCgvDXn4X4kngflbA1OUsxPgAYQ45ChUDgoyISM/8kxrmI+rrBq3WZWB5ZNcIvkzgSKp+Th0RjSJytp1P4ObIWKwSpMVRKYqVgc75AGx8FI63DCEmlFVYKnJShf04xrrPownn2TQeRrMXeN1gdXRKz4+LzqORtpI1jNs5aDSVUO8nC+VkqrSgThQnbWimxEmajFZElR4CzdJzP7eUA99xzT0f9WqfKff/3f/9FnaNPetKT+P3f/30eeOCBpXUPPvggN9xww6H7l2XJS1/6Uj7zmc/wP/7H/+ic1yo85znPIUkSPvWpTw1kbsCAAV8weEzJ3FH/s79cvO51r+O1r31t/Xxvb29JVYCGyK0a89U2PtQTI/pu1d7zaIBoE71+v1EcP+UnFlgK60eErRJT4rQIKV39jiXK97uldQ9dIHiy6alTyit0WvmYD2kdtKY+RVK5rhcqt4pEWhahlAmesEVimjtNJqq6v61wYXwYjgLlp0qIeG9Xl3sjqV4X4ty/r9CQ31Vl2k1RdHoaE2cZM/duWBzaGXKl2AolVCsk0tna/JCWFYVuSqyVlujKBkcrXu3SClWZJrYkkRAiTrDWK3U2lFW18gQsUU3kiHFeYWvPTo1qnQ6K3Thl5SiPcVqrcOUKx2pc52fjXt4EiMpKxIqcORcyEre3ty9KmgBOnTrFqVOnLrrdbbfdxu7uLh/60Id43vOeB8Dv/M7vsLu7ywte8IK1+0Ui96lPfYr/+T//JydPnrzoa/3hH/4hZVly4403XnTbAQMGDLha8JiSuaP+Z3+5OKyXZ1UZ9GKKnMR1errWkSCBJ2XWUbtZI2mKZVIbR3sBOFETNmiUtaJUZKmlqgQ6uF5Vqx8umhraY5YiqUuTOHDeUhmJ7qlzkRjJYCyonB96LwIRTaRbSaiKVv+fEo4cTSJsmEVrO9MxwCt43pkqOuXPMoxCg+XStbuISuqJnO9ujP2NEsdElJRIEiwJBh3ITBYmVIxtiQ7yUl3KFYK0VV6NBG2eJmzNFs1rWheUMFM7W/MsQRmfN5cWlXe5Vv7oXo2LTY9tx6oC6Tyj1sp/SPISNjNs2E4CJKqVcedLrMUkwyiJMrZD5Ewgl/MspdL+funq8pys4DMOxYqeObdi2SOBpz/96bzoRS/ila98ZR0b8j3f8z184zd+Y8fJ+rSnPY0777yTl7zkJVRVxbd+67fy0Y9+lP/6X/8rxpi6v+7EiROkacqf/Mmf8M53vpNv+IZv4NSpU/yf//N/+MEf/EGe/exn81Vf9VWPyrUMGDBgwGOBq6Zn7u677+bcuXPcfffdGGP4+Mc/DsAXfdEXsbm5ednHPawHrj/tYR15i4Qtwrj1pFBKh7AuEBlZV9RihpeQDoUfvaWUxRivyGWp6ZA20yNwkQACJIHkjdOuMtMmgvGabHQpisadK8K8WSkchVVoYeteuHbUmMLPoo37zJwv641E2XGj+m4/V/fZNfeJML5rWYk7SqkbqImcEq4mchpbT3lIW+qUn8EaFDepAE/ypHOY1oiuaHKI5KzS1H1zAPkoQVcWVRmMkiSloWoTtkx7JQ9irdsvX5X9llfYY93gX5vpUCoV6MownWQk4RhlqrGVqQlchOlNe6i0ZDoZHeke9mGtQKzIlHOPYs7cO9/5Tn7gB36A22+/HYBv+qZv4qd/+qc723zyk59kd3cXgM997nP86q/+KgDPetazOtv9z//5P/nqr/5q0jTlv//3/85P/MRPcHBwwM0338xf+St/hde//vUodfk5fAMGDBhwpeGqIXP/6B/9I/7tv/239fNnP/vZQPMf98OFQyCxWGSnN2tVmXUdAYxqnwxkKCLOFK1aylwbSlpMULwS5SNFUA7rJKZXKYvKm7Gi0xtnrf8phGuVWt3S67UjVJwT9XzT7jbN7wKv2sV9ozlWCNdR3RSuzndbuASFQwdHqR8LplCHlP0scuUs16VzozE7RKg1JdcIIyRqRRNYoTVpFYKJQ0mzPZO1UlHlgnmWkJUVRaqDSuc65da4X1TTrJSg/U/ZUtg8QWxKtL73zZM28GpfJG5ATRL7Slz8WaQaIzzxM6GvL5I8J49GiPso/5yVOfBq2i/8wi8cuo1zzXvzpCc9qfN8FW6++Wbe//73PyLnN2DAgAFXMq4aMveOd7yDd7zjHY/IsYwTyBYha3LnZN2PFV2srqO4rf8yiySpaqlyUoANDs+aEElXuyed8EPlDYSZqRJMa0SYcoyEIQ/9cjYk80PX5ND9GcqsYX2S2PrcEuXLqP1MvIjKSRJp6rmx/l70rj38ULjghBU1iYsKXXM81elzs+ia4EVcrKTaxiqy1ydxfhRY9zVsIHNWiDrgOPbyGSkQrd60SimyoqiVuEjoksJvY4TEKE/a0qLCphpjHWle1oRLWkdSmlpdi9X0qLC1e94iCez/HhEJmn/ImvS13atAx7k6H3tzBAeXGU1SCkS5gsytWDZgwIABAx57XDVk7pGEXUHkOut7cSQRKpYfQ3kxHqsdRxL7uPxxgqLXmgBhraiPUx9Xus58da1snUMnpKtdqrkVjDITxi11S8DgCZ5pRZcAS8StTU7bxMs4UfewtZfF/b1rtb1dkxPnQ4ZDREi41nqeqgsjuGK+nFM1oYtkr02gI9YRTmhUuRgQHJ9nLRIXS6zS+fWJs1gEC5WgnGFSFgjnSIzFSelnsTqHNpY8TTHG1KYIgDJN6riPVf1oMQ5EWoeRDQmT1tV9btDEm5hAEo1WNZmM25eBqLVfy7ZUtuk4W9oubuMduX7b8jKVuceizDpgwIABAy4f1ySZi1hF5NahH2USIfHhutb5sOG+s9XPEnVgoUSGkqgnO1Y0ZdBYGhXSR0BoZTGVCssCWUlsrZpFg4OSXQesDLlyfSjpOuQtKnT166umZy6uByitNyrIHvGDZcJVWNWJMjFOeCXPiY6bNRJlRUPg1oUuH4Y4w1UCGk/WMsqVJVeJw4abO67KoNJBrjWjqsQohbWWaJdJq6qJ/lByZW5bVMfSoilVR0LW3jb22AEIazsqXCRyJuTFlXqF6SSUdkulKEOvV9lS5ioVI0keGbLljIBVZVYzkLkBAwYMuBJxTZO5jorW6pWLWGV4WJr80HruFaL1X3iJsBSuKaFJ4dCyITRxGgQKZgvdUcraRDJNrDdIhJ998hbJl1JN/1x0zkZSt075sm55NFlEdI6uuvb2KXjVsqte9UegCdH02gGd3y92HkAnqFiEvr12Ht44ZM2J1rJ6fZyfFpS8Skq0tWRVQ8pKpUiMYZGmpFWFtIY8S0nLijKVUFR1H16R6roPznaMFLYmWJWWnTJp2VLT4rJVZGw69vQyz2Snn6/dp9csa1670JpyxaSQoyApWVNmjZaRAQMGDBhwJeGaJnPrsKrE2od3fDZfnu3gXFMbBvy6fkSJNzh6xavd3eWNEH7fjVHFogiKS1DyKnwPW5vAxZ9JiB6JBEjIRu1rQ8sQkYKfzSp7it1RYVqlUSmaaQ/tvjmLgJA9twrOCSqa7duKnb9vhxM66Boh2vAhwZbENaRPrjBBxNBggEWaMioKtLE4ISi0RjiHET5GRFfdXryy5YjsEyugVvbAGxm6ZEwuEbKo9EUVrnOerbJqEpTANnmMRolC6/q4VX+yxBEhhzLrgAEDBlxVuKbJXHee6OHol1ida0ZVQUNYGlLX7YtrBwiLEBRsbcias3E6hHfsRaUuToMA3wOnsZ3jx+V9x2o/giSebx0evIYgrSpP9olYW52LJdiIeC+k60aYxB66dUpev7R7GPrkzSHq2JT+KDHw82Ez2+6lM1ghfByJkDghkK7Rm2TPIWmU30ZVVU2YvDqnO9Eg88z31CXGdJytVjakzEpRl0wjSqXIk+4yGRVDLXFS+tJs6me3SmvJRylFouvnpVKUSUME42tdLvQhBojLs1QMGDBgwIBHE9ckmfOFuTXxIq0okogYLdInZ5HQRafqYW5XY2UTUWIv4tpsmQGkcEgVFDC5OoJD9susredK2iaqRDTl1kyb+jqgVba8DJWuTRDbpVT/AsskF1rEr3U97fIrND11MSS4vl7ckmFDt9ZHx2qBYuKq+vUkYITCKMVmuUDiGJXdwmGpFJmtqJTqKHkmOGGtFCyyJIz7CufZKpPmUncMElYKEmNqpa39exsx7Df+blpKXjtiJJoc2ufbRjynQmuqFaT+KNDVGjL3KEaTDBgwYMCAy8c1SeYOQztbbhXaqg90+8Zqt+uSM7M7WKkmgtKFRF1fXpXCkWpvcqiMxCDiWNJOP1m7Wlj326344q4nTPSetwnbuoy2diRJIpfLk3Hfzj6944iWGtc2Rawru67CUcqsBoHGvxcCiYmjx4L6BuH+tQ5lhUSZklL5cV5FJFtV1eTLxXGqGIxSYEyTAWNW3xOgznwDT/Si8tYP9m27VaMCB7RmrPpyaZFopHPMgzqnXFOGlStGflXBKGFXzK09CsSaMitDmXXAgAEDrkgMZI6uk3IdkVtVZoWum/UwxHLqqsBh2y4zto0O2tSvU7ZnZarlofPrX9ev162oEiWaPrl2/Ejdb1fPYe32rq3rT7scNe+osE4sZdPVr9s6n9gj52iy5MCTtpGtajdrhR/vlSuFtrqeCAGgraUKZE0FF+uoaJS7mtAR8ueUDCXXpgQKnrivKnfGua7xd1WZjgrn40xkfSyvwgWlLWn+qUYiF0vAtckiLM+1xgrhz/cyoEuBXOGqtUPO3IABAwZckbgmydw6UrIKh437aq9flZNWhw/XblWLtaqeBgGtEGHhavOD/863nZFdfv9AwKwnV/F5aSSJsstkTHq37FF60dr7HqXdqtMr1zNBRPRDhC8FkaT643anQ/RfB3yZdWXPnzPkUpFZg3ZdUlhJRWIrrJArzREAi9SXVHUgcUYphHOU+JJpJF9WCGybPCnfW1dPa4jn3rq5bRLYHgcWiWI8thFdgurPQ3aOV/WI2+VOfwBQa8jcqtLrgAEDBgx47HFNkrl2HEkbR3GxXg5UewZqgJZdM0NhlCd74SdWQggTdk54w0QrwqSNSByPei4R7cH3q0hXn6BeDG0ytY7E9UusbWJ2GPFrE7o4i3UV1hH1UviQ47HJa5ODd7JKFomqx3pJ5yil9GQtLNPG1mRJG4MLqpdyjqJFmtoRI9I6FllCWkbnacysU2hjfOk0qncrcuLKcHwjgmM1KHVtJS5GrMTns8zHmChjaofu5UAZkCvE0DUC6YABAwYMeIxxTZK5NtaRlbbBYV2PHCz30LVVt1UBwu3RXvUxpJ9WYJzo9LW1y6/9zLv2mKw4pqs+B9klVRcjeqvW127ZQ8qr6/Y9DP2eucNmqq7NwguZdPGnwlEhSTF+DqyQZC0Vru1QLaVGWVsTQ9cjPYVSpL2BuJGc6d7yQmuUMbVqlie6GRcW3uJFlvpzsBaCmlbI1f/s2lEm4AmdtD4mpUx647taRM4K0TFCGKWwQlCJy1PndCWQK1Q4OxggBgwYMOCKxDVL5i4WSdImT3GYfPz9YoQuhhG3CVe9LhA6G6YjGCc60SJxH9NT4Yztqlp1z16rpOqfd4/TRsyi09KuVNE6kyRi2bU30L6/vn0PHk303ax9pL15rKWQjF2FEQowSOdz5iyCJBxrrhO2yiZso11utULUYcK+hBpjV7ya1y67Wud8fIkxPpMuICpr3gUr6z64+hzDMbOy6ozkktbW/W7xp11BzKwUzJPkKLfvkqBLgVRDz9yAAQMGXC24ZsncpeAofXNtwuZdrd1xYVJAO1e2rbwpZf3QetMlfbJFDHw8CXUf3aqeuj7aqlx/mkS7NNvvbVMriFwbFyNy/XKpXEMI122v6KqJbSLXPoasJ3isPs8qzI+VYZtI1tpEvhIS7SxG+nm8WdU4WyspIUSXREMESLSxFImuS6gmjPsqE01SVg1hi4aI8D6uMyTkSZyxKpeUwj6iEgdgVvTFVVIGIqqo5OXNaxBryqxuKLMOGDBgwBWJa5LMtb/Ma5NCi3j1M+ZWQbSIUPs4qyCFo7RxOzAumAYCaSldDA32QcJtw4RzDVHsR5xEJMp1Xl+23KrxmDGLTghX98qtKt/GUWCRyMW4lXbZdd39uJjh4Sjl1cPcuW1yWffZrRjXZVBIvIM1mh6sEEhrSVoBwhKvslnnEEJ6Q8MaM0SbOMm6lNpzqkInvNeuIFuxjNp2rfZJXue4LUWuUnKJwK0yOvSDjy8VqhSoFcocgzI3YMCAAVckrkky18ZhDf6rMuTaaMeTrCJ97dmvmbKUoZHKD6Pvvn5DgkRtmIhoE7UkKHRSgNZ26fwjkYsRJG0i1y+vRvIlRUPi4nm3iVebyK0iYfHc63LtRZS4oxgeVmEdAYyBwZ35qwj/iDlzzlBITSE1I+sVq0isJI5SKpwQiFCOjXNaS61xYbukqjrGgnrAvRC1gaINoyQqjAYTNQGUnedlMEJ4xW196X/VRIdSL//zfbhEDiApBWrF663qoxswYMCAAY89rnkyd6no9MatULYi+uTPttStPgFrbxuJV63+tLcJZFC3DA6dAGBpV5ZV623pb7v6Glfuu4LI9a9f9tS8VTiMyLXjSJrtg4rIcvRJ3Kcppy6bNSokmSvrGa0AlVBoZyilJrGWQmq07ZkeArGSrfFflZRI58i1rt8fFWevqmZig3TNdIgYIRJzlyOJM725qX0iF/ePxyyVal5LypWKXKHUoTErR4W0zfm28TAPO2DAgAEDHiVc82TuqPEbpqXCgSd1S0QmPF11vLYZQgpCrtz6143l0fbx2uXT9muLlnLWntkKYK1X3+LypakQPUWuf862d92d9bh6XFf7eO2f6/attw/mklVEbh3WHc+7W0GHYvTKPLqLMBJvZAj3rqXCWSGQUtahwYdhpcImo7GiO91BOruUEddG3U8Xcuz651Qf3rn6daswD3aVcncU6EKgVvTuiWJQ5gYMGDDgSsQ1T+YOI3Lt8mkkJ8aJetkqZcq2XKzGiU6pNaKjxNWKl1ypZnVUMtHMKqVDDlvEqZdBl4aZVLE/r95OrFbg4nmv65E7Sm9cfL1VZKpjtIhK3kUUufYx2+fcX9cpi7tGSYxxJdJ5M4TEBzUrZyilJAmO1ai21aOynCfUkTRpayGWVq31zwn9dNaXsNsGBW2iaieRTtTlVNlywhSBcLXLo9K6TtRIPN4SuXSuXlaEWbKHlWqPClUK9CojxlBmHTBgwIArEtc0mes4UNfMZF3XDxexjtABJNL3ya0idNCb6BCVvU7JNZ7o8jix+vVWqHHtc1uFZE2ZFi5eno3HjcSqQ/z6yt4l9MatUuTWuWlrgh3eswQfORLHedmW0tdHKSTj0DNnhELVBglJISSpjeHBFmVdrXLFMmwkTwpf7oylVxvmoUrnUNZipDcrxNKoFQKk/1kmYW5rK7S541BtCXX98N/2DNfoXI3ItUZ1iOBlulmtf6xaPmDAgAEDrjxc02SujaNOf1DCgTs8rmSVolddpJQbFbp2/lzbFAHLbtf28SPaxCm6VteN6moTKN36pu704fW+wdt9cfHnYYR3lRK3Du0IkqWSb9+UwerzWkUAC6HJXIkVotM7B57QJSyzFCMVynZNDR1iFQiTOqTsGkujUXUzND12/jVkTfxWIRJFaOfNiUYdbKmJhdLhmP4cLxZxchiSQqBXGXqGMuuAAQMGXJF4+DWZPwd89rOf5eUvfzm33HIL4/GYpzzlKbz+9a+nKIqHdVx5CUQjot87B56wrFKfOmHBvVKiailq7X1XTmNQtg78ldKRhOeJsn62q3So/hSIQ9Swdo9cfPTXRUNB+/dIqFYpcUcxNqw7F09km7Jqv3x6MSLXRlsBjQRTY0N4sCftVvQULambcqXUlEGNM1LUj0r6frV+ydMoRdkabJ8nCbnuT2toXqtU3eOYnroWw4qr1szXpeiS1v6VVDWRq+9BXHe5EyCK9Y8BAwYMGHDl4aogc3/0R3+EtZa3ve1t/OEf/iFvetOb+Nmf/Vl++Id/+FF5vTYhaStO/VLnYf1g/R41JRri1F8uRbNvzJ9rrz/sHNcRyfo81pRRV5HGNpFrH7evMnb2WRNV0j+vdl9c+9HZ7xDS1ybRrlVmjcet+xjD8ziLtT7PliIXJ0EcBluTvjDsviZ0si69tkmYaZkT3BqC1iZhwBJp66+Px2ofp3OOgZSaNbl0lxtTIo1Y+3i0cP78ee644w52dnbY2dnhjjvu4MKFC4fu87KXvQwhROfx/Oc/v7NNnuf8nb/zdzh16hSTyYRv+qZv4nOf+9yjdh0DBgwY8FjgqiizvuhFL+JFL3pR/fzJT34yn/zkJ3nrW9/Kj/3Yj132cdvK2boya79kuo7QrSIHq+e2hqgK4ercOf/chwlHB2kibJdEtV7b9cqsdchwW1Vbofq1e+X6Y7ouZcZqX5VcZ4i4mEO1P9nhsNdZZ3zoo9+bWGfQ1ZEdjkLqYIZwddmzDCVNb4aQSGe8s9V2g4SbmBKFtqYmY9r2+t96apoIJgsnJdZ2r7tN0vqRI+1YlPa2dfk1hhXH8V/tkGG53iV7GHS55j+Gy2vBOxK+8zu/k8997nO8973vBeB7vud7uOOOO3j3u9996H4vetGL+Pmf//n6eZqmnfWvfvWrefe7380v/dIvcfLkSX7wB3+Qb/zGb+QjH/kI6hAX8YABAwZcTbgqyNwq7O7ucuLEiUO3yfOcPG9mb+7t7XXWd0ZqtQwQS0PtL0Lo2lgyRLRVvpa7VeBIQlxFO0zYv17Amt68fkhvPK94rqtVt9b+a0hS0w9nayfqYeXa/vm0kQi75LTtnM9F+uNWqYFtd2p7H4tYms0qcS0Ha/f4JpglKqFQ2JWkspKK1FYYqRAx9iPGulgCkVNrM93a/WzQ9LxJ5zomBmgIXJuorYog6ap63ZiSvpO1r+JdClQh0Cs+4+5RcrN+4hOf4L3vfS+//du/zVd+5VcC8K/+1b/itttu45Of/CRf8iVfsnbfLMs4ffr0ynW7u7v83M/9HP/+3/97vvZrvxaAX/iFX+Dmm2/m13/91/n6r//6R/5iBgwYMOAxwFVRZu3jT/7kT/ipn/opXvWqVx263Z133lmXbXZ2drj55ps769fFkvRLhH1CdTGC095/KeRWND1h7ZFZTfnTu1xXzTeN27UfkmY8lxZ2iTxJ4RW5JAQKt9e3lylh6/NeR+TqYN4VZdT6mMIuGS+a/W39WId2n97FEM9Hs1w2Vc6h6uBlsZJ0SRdcp2K1QlMJiUV01K5uGbOrxvVLpatKp+1SbPvnuvy47vFk52e/vLruPC8V0qx/gP+jqP1o/8F0OfjgBz/Izs5OTeQAnv/857Ozs8MHPvCBQ/f9jd/4Da6//nqe+tSn8spXvpIzZ87U6z7ykY9QliW33357veymm27iGc94xkWPO2DAgAFXEx5TMveGN7xhqeel//jd3/3dzj733nsvL3rRi/i2b/s2XvGKVxx6/Ne97nXs7u7Wj3vuuaez/qilxbYBIGIV0TnSscQhWWliedv+sdvn0law+iRLCIeSy68l6JoY2sdvX2N/fTx+H+2suGRFdsUqAtc2ORxWMu0ep2vaUK1r70zlWHG89v07TLHqE7I2IXJCYFplSz/IXnX66FYRuf6x289LrTt9c+vOI75e/Ll8LPmIlFcjdLnGABHKrDfffHPnj6Q777zzYb3e/fffz/XXX7+0/Prrr+f+++9fu9+LX/xi3vnOd/I//sf/4Md//Mf58Ic/zF/6S3+pJpf3338/aZpy/Pjxzn433HDDoccdMGDAgKsNj2mZ9fu///v5ju/4jkO3edKTnlT/fu+99/I1X/M13Hbbbbz97W+/6PGzLCPLsiOfTz9r7rBxXdAlPCYYF9rkKg54iK1qUnRDheN+SliMkytLuIk03RmwvRJuJFlLpWFxtFDgeJ2d12zHlKwwefSnNqh1pd0VClybbPVdqxFHKe3GfLlV+1skRjiSltEhUh2HwAo/5ktDPa7LCk1iK0zMkQulzlhKrXvUhMRoiXKWxKweARaHsPXLpRHtyJHu/qK3XTRaNMvWHfORhC4EesV0Elf5Zffccw/b29v18nX/xt7whjfwxje+8dDX+vCHPwyAWHFNzrmVyyO+/du/vf79Gc94Bs997nN54hOfyH/7b/+Nv/bX/tra/S523AEDBgy42vCYkrlTp05x6tSpI237+c9/nq/5mq/h1ltv5ed//uf9aKWHiaOM8epjXaZaTUDc8rYXI4XrEHPhTOuYcRxX33RwMUdrJE6xT+9SAn3720QiF3sOL0bkDlPfjqpoXuw4q6ZNXOz47fdROuf1Q+HPPRKmzJjlbaNjVikS08xurY8bDBORjMVybHu+a7NtW8mT9fb9MmnZmuWqehMiOtf0CJAUaZZV4rgcYHt7u0Pm1uGof6z9/u//Pg888MDSugcffJAbbrjhSOcMcOONN/LEJz6RT33qUwCcPn2aoig4f/58R507c+YML3jBC4583AEDBgy40nFVGCDuvfdevvqrv5onPOEJ/NiP/RgPPvhgvW5d8/OjicMcrH117WITJOL2dUk1uFzbX6ZKLs9xjUTPtsnjipfph/6uw1GI0Kq8uKYU3OofQ64lXhcjb6tCiFfto3qqnuzl0/nzaAheJFjt6JLMGQqhSV3lCRl4swMSHSZD9F2s8bgKF0qvgsQ05O+wOJA2cTusFNo3MvSPuYrIyeDSXfV6lwpVgF7xsellKF8UR/1j7bbbbmN3d5cPfehDPO95zwPgd37nd9jd3b0k0nX27FnuuecebrzxRgBuvfVWkiThrrvu4qUvfSkA9913H3/wB3/AP//n//zSLmbAgAEDrmBcFWTufe97H5/+9Kf59Kc/zeMf//jOOneZWVrrsKrU6l/n8hW2NtrzW20rUsS4JlYjjgFr1gmUdB3iVh9vxelE9e2o8SPr+uQOm95wWL/hOoPDKjPI+mMsk7OV53ERctgue0Nzj0ctZtLfpn6tEEEinVsida4mZpJcN9ElMsiocb+4DbSJ4aWpyhcjZeYQRe9yIO3qTDm5ovT6SODpT386L3rRi3jlK1/J2972NsBHk3zjN35jx8n6tKc9jTvvvJOXvOQlHBwc8IY3vIG//tf/OjfeeCOf/exn+eEf/mFOnTrFS17yEgB2dnZ4+ctfzg/+4A9y8uRJTpw4wQ/90A/xzGc+s3a3DhgwYMAXAq4KN+vLXvYynHMrHw8Hq4Jr12FVOXKd87JvMOjvX+93yGu3j9F2urYf7W0TaesHrIj7aEeXHFJiPczsEI8jO9ey3tzQPt6RDSKt7dqZce2A4FXbdpU8WztZI6rWR105u+QSPbQcK0QnCqSTC9dzmpZKrtwuHqOtyK0ySPQfcQJFH04Irwwe4mA1l6nMPRYTIN75znfyzGc+k9tvv53bb7+dL//yL+ff//t/39nmk5/8JLu7uwAopfjf//t/883f/M089alP5W/+zb/JU5/6VD74wQ+ytbVV7/OmN72Jb/mWb+GlL30pX/VVX8XGxgbvfve7h4y5AQMGfEFBuEda2rqCsbe3x87ODq+68G6y7Um9fF3v3Log4Yupc5FY9M0M3WkSsn7tfoCwQxzaz9cOCe73ra1Us4SrzRKwTOJiDEk/DLjefw35XNUX186AW1kevYga1z9e/xza2y6XgWM/YHNeWSyX4urSqcDVM1qjeiZxpEGtE2FZ1qsrthW6+jmu3t4vW1YlVZBUjRT1720jg3RuqScubr8KbVdt+zPVJ4r7+wv+1g1/h93d3SP1uMV/Hy976gVStbx9YfZ4xx8fO/LxBgwYMGDAnw+uijLrY4V+yTWiTXQebvlV9tS3emzVCtLTLsu2tzlM4TvM8LCqvNrHUWbWrnOoHhWHkbiItnu2T+LWqWp1STusr4RCO4NDNO+rAN0qofplAmUtudRoZ1DOecIlJUngajKoezi/fdNH2S2pqlW18R7aRA4aEreqZHpYSHHnmL2RYpcCaVZL9tKsWDhgwIABAx5zDGTuIlhH6CIOI3JLJMkt92fZoMJ140rau3SjSICVDtIlJWuFatfGKkJ0WGnVb9MlEqsmN/SxKo9vnfP0Unrk+iXWw8qky71wqwlRJHttw0ElFFbYVv+bCKqaXEus/HZ+fXS8RkSidthnat2xfVm1q8r1A4XBE7mHA10I9ApV0D5KPXMDBgwYMODhYSBzl4hV5G5phNeaXq7OOuEwLvRqXeJ35GFErm90OKy8GrdfhaMocnA0Z2971Fj758VUvKOaHdYtty01r14mGqdt253a3kcGp6pwriawkcSZFe7SVfvF13JCLLlPDyOB7fOEpievjUqsz6kzrdLt5UIVoFfwQXM0Y/SAAQMGDPhzxjVJ5voKU9tZugp9QhCfr3K9trFKsWtHl0QS2AkbbpVbPQlqto8qXXu+KzTu11hSba7z6CQubnPU/rh4LhdTxfq9eHH/w7COSF7stdrnGbfz5Gv1flaIOhy47Ty1NeEWSHsx9a1R3Zr3sLtdJGRiJQFbPR6svd/q124UuUc6RFga6qDrzvKBzA0YMGDAFYlrksytQp+8HCVQuE86VpXOlLA4J7xK1HqNSEziPh2yIboEs9lvuRQb1/WJ3Cqsm7faJ3LrEEncKpPDUWNHLqWn7lKVOPA9a5qjN3eZnnpnEWtJYyR9vnfO3+84IaJ7DqLuw2ujdr4il4iaCKXb9radnLwjyrcqnF98ncuBLkGveDlz9LduwIABAwb8OWIgc2twMbVuFdqkwCCWzAb1l7OARvxxa00U7R65pdc6xPywzpW6bpuldStUuVWzVNcdox38exRzQ3882LpjrwtgbiuFfdW1HVEi8W7USEala+JOltU50M50CJETAum8w7WUsjZGrEK89lXEcJ3i1pC9iyuQ8TwfDeh8IHMDBgwYcDXhmiVzEntk5aJN7Pqmgvh7n3C1e+va5dR6fZz8QDPD9VJeP/6+fF3rS6vt9RFLMSVHzN3rY5USd5TeuPZ813Xn2F4mV2xXk7PY3xbDl1coZv0ZrbjDX7tNvESrJBtLm0aI2uEK3VJpPI+LTbPwx+lm3bX3lS3CeRj6uXLrwpsvhrXjvAYyN2DAgAFXJK4pMhcj9Yq9GXBpZaj+LNRV6/vol13jNt28OVE/bx+jTwBWKVL9ElxfkWsrQxZBIiyut01XTVyVQbc6hLj9eko4+pOeDiNyKryWgqX9+tdRn+tFSq5N2bfpmStoCF2HSLfy5qKypsLvHbLoXFMudd3jx/XKOcqeOid7GXarznld2XTVuj6B9Mu6vXJ9ImeFYLqfh1O/NBZWlXusitSt2Luk4wwYMGDAgD8fXFNkbn9/H4B//YS/8RifyYABf37Y399nZ2fnotulacrp06d50/03r93m9OnTpGn6SJ7egAEDBgx4mLimJkBYa7n33nvZ2tpCHMEBuLe3x80338w999xz1Sfef6Fcy3AdR4dzjv39fW666SbkEbPnFosFRbF+bleapoxGo0fqFAcMGDBgwCOAa0qZk1Ly+Mc//pL3297evqqJQxtfKNcyXMfRcBRFro3RaDSQtQEDBgy4yvDo2OEGDBgwYMCAAQMG/LlgIHMDBgwYMGDAgAFXMQYydwiyLOP1r389WZY91qfysPGFci3DdQwYMGDAgAFdXFMGiAEDBgwYMGDAgC80DMrcgAEDBgwYMGDAVYyBzA0YMGDAgAEDBlzFGMjcgAEDBgwYMGDAVYyBzB0Rn/3sZ3n5y1/OLbfcwng85ilPeQqvf/3rDw1YvVLwlre8hVtuuYXRaMStt97Kb/3Wbz3Wp3RJuPPOO/mKr/gKtra2uP766/mWb/kWPvnJTz7Wp/WwceeddyKE4NWvfvVjfSoDBgwYMOAqxkDmjog/+qM/wlrL2972Nv7wD/+QN73pTfzsz/4sP/zDP/xYn9qheNe73sWrX/1qfuRHfoSPfexjvPCFL+TFL34xd99992N9akfG+9//fr7v+76P3/7t3+auu+6iqipuv/12ptPpY31ql40Pf/jDvP3tb+fLv/zLH+tTGTBgwIABVzkGN+vDwL/4F/+Ct771rfzpn/7pY30qa/GVX/mVPOc5z+Gtb31rvezpT3863/It38Kdd975GJ7Z5ePBBx/k+uuv5/3vfz9/8S/+xcf6dC4ZBwcHPOc5z+Etb3kL//gf/2Oe9axn8eY3v/mxPq0BAwYMGHCVYlDmHgZ2d3c5ceLEY30aa1EUBR/5yEf+/+3dQWiSfxzH8Y8TpGjWIcEdRqubB4mGFQQ7KAsJPFSHaJegYIdAJRCia3ToVCEUDHbx4Fg7DKIID9pBJITYFphXC5b0bOg6dIoCe/6nvyDjP1zl/+fPvV8g6NdH+ODpw/d5eB7F4/GeeTweV7VaNZTqz3379k2Shvq/30symVQikdDFixdNRwEAjIAD9WzWv+njx496+vSpHj9+bDrKf9rZ2VGn01EwGOyZB4NBbW9vG0r1Z1zXVSaT0czMjMLhsOk4+7aysqL3799rbW3NdBQAwIg48Ju5+/fvy+Px7PlaX1/v+Y3jOLp06ZKuXbum+fl5Q8n75/F4ej67rrtrZotUKqUPHz7o+fPnpqPsW7PZ1J07d7S0tMTD7AEAf82B38ylUinNzc3teczJkye77x3HUSwW04ULF7S4uDjgdH8mEAjI6/Xu2sK1Wq1d2zobpNNpvXr1SpVKRZOTk6bj7NvGxoZarZYikUh31ul0VKlU9OzZM/348UNer9dgQgCAjQ58mQsEAgoEAn0d++XLF8ViMUUiEeVyOY2NDfdi0+fzKRKJqFQq6erVq915qVTS5cuXDSbbH9d1lU6n9eLFC5XLZZ06dcp0pN8yOzurer3eM7t165ZCoZDu3btHkQMA/JYDX+b65TiOotGoTpw4oUePHqndbne/m5iYMJhsb5lMRjdu3NDZs2e728TPnz/r9u3bpqP1LZlManl5WS9fvpTf7+9uGo8dO6bDhw8bTtc/v9+/6zq/I0eO6Pjx41Ze/wcAGA6UuT4Vi0U1Gg01Go1dp/iG+e4u169f19evX/XgwQNtbW0pHA6rUChoamrKdLS+/XtblWg02jPP5XK6efPm/x8IAIAhwn3mAAAALDbcF30BAABgT5Q5AAAAi1HmAAAALEaZAwAAsBhlDgAAwGKUOQAAAItR5gAAACxGmQMAALAYZQ4AAMBilDkAAACLUeYAAAAsRpnDQLTbbU1MTOjhw4fd2bt37+Tz+VQsFg0mAwBgtHhc13VNh8BoKhQKunLliqrVqkKhkKanp5VIJJTNZk1HAwBgZFDmMFDJZFJv3rzRuXPnVKvVtLa2pkOHDpmOBQDAyKDMYaC+f/+ucDisZrOp9fV1nT592nQkAABGCtfMYaA+ffokx3H069cvbW5umo4DAMDIYTOHgfn586fOnz+vM2fOKBQK6cmTJ6rX6woGg6ajAQAwMihzGJi7d+9qdXVVtVpN4+PjisVi8vv9ev36teloAACMDE6zYiDK5bKy2azy+byOHj2qsbEx5fN5vX37VgsLC6bjAQAwMtjMAQAAWIzNHAAAgMUocwAAABajzAEAAFiMMgcAAGAxyhwAAIDFKHMAAAAWo8wBAABYjDIHAABgMcocAACAxShzAAAAFqPMAQAAWIwyBwAAYLF/AC4bKwjHOwYaAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import Normalize\n",
    "from matplotlib.gridspec import GridSpec\n",
    "from matplotlib.patches import Circle\n",
    "\n",
    "X, BC_X, BC_Y = Uniform_with_Cylinder(u0)\n",
    "    \n",
    "X = X.clone().detach().requires_grad_(True)      \n",
    "\n",
    "\n",
    "# forward pass of the model !!! fwd pass outside eval/inference is okay as long as you dont .backward()\n",
    "# we have to call .model() because our model is nested inside class PINN\n",
    "y_preds = test.model(X)\n",
    "u = y_preds[:, 0:1]   # u velocity\n",
    "v = y_preds[:, 1:2]\n",
    "p = y_preds[:, 2:3]     #pressure\n",
    "\n",
    "\n",
    "# convert to array for postprocessing\n",
    "p_np   = p.detach().numpy()\n",
    "u_np   = u.detach().numpy()\n",
    "v_np   = v.detach().numpy()\n",
    "x_np   = X[:, 0:1].detach().numpy()\n",
    "y_np   = X[:, 1:2].detach().numpy()\n",
    "\n",
    "# create data points to plot a circle on the plots\n",
    "xc,yc, r = 0,0,0.5\n",
    "theta = np.linspace(0, 2*np.pi, 400)\n",
    "cylinder_x = (r * np.cos(theta) + xc)\n",
    "cylinder_y = (r * np.sin(theta) + yc)\n",
    "\n",
    "\n",
    "def tricontour(gs, x, y, z, title):\n",
    "    \"\"\"\n",
    "    this is an explanation for tripcontour() and tripplot() which are two different methods for plotting contour type plots\n",
    "\n",
    "    Args:\n",
    "        grid: plot position in subplot\n",
    "        x: x-array (x coords)\n",
    "        y: y-array  (y coords)\n",
    "        z: z-array (engineering value at [x,y])\n",
    "        title: title \n",
    "    \"\"\"\n",
    "    plt.subplot(gs)\n",
    "    tcf = plt.tricontourf(x, y, z, levels=50, cmap='rainbow')\n",
    "    # Add the circle\n",
    "    plt.fill(cylinder_x,cylinder_y, color='blue', alpha=0.75)\n",
    "    plt.colorbar(tcf)\n",
    "    plt.gca().set_aspect('equal')\n",
    "    plt.title(title)\n",
    "    plt.xlabel('x')\n",
    "    plt.ylabel('y')\n",
    "    \n",
    "\n",
    "# note we have to squeeze the arrays as they are in column vector format(ML format)\n",
    "gs = GridSpec(2,2)\n",
    "tricontour(gs[0,0], np.squeeze(x_np), np.squeeze(y_np), np.squeeze(p_np), 'Pressure contour')\n",
    "tricontour(gs[0,1], np.squeeze(x_np), np.squeeze(y_np), np.squeeze(u_np), 'u velocity contour')\n",
    "tricontour(gs[1,0], np.squeeze(x_np), np.squeeze(y_np), np.squeeze(v_np), 'v velocity contour')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "def tripplot(gs, x, y, z, title):\n",
    "    plt.subplot(gs)\n",
    "    plt.tripcolor(x, y, z, cmap='rainbow')\n",
    "    \n",
    "    # Add the circle\n",
    "    plt.fill(cylinder_x,cylinder_y, color='blue', alpha=0.75)\n",
    "    \n",
    "    plt.ylim(-2.5,2.5)\n",
    "    plt.xlim(-2.5,5)\n",
    "    plt.colorbar()\n",
    "    \n",
    "    plt.title(title)\n",
    "    plt.xlabel('x')\n",
    "    plt.ylabel('y')\n",
    "\n",
    "gs2 = GridSpec(2,2)\n",
    "tripplot(gs2[0,0], np.squeeze(x_np), np.squeeze(y_np), np.squeeze(p_np), 'Pressure contour')\n",
    "tripplot(gs2[0,1], np.squeeze(x_np), np.squeeze(y_np), np.squeeze(u_np), 'u velocity contour')\n",
    "tripplot(gs2[1,0], np.squeeze(x_np), np.squeeze(y_np), np.squeeze(v_np), 'v velocity contour')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "214c5b6d-493f-440e-815e-573500df97a3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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