diff --git a/Autograd13Metasurface.ipynb b/Autograd13Metasurface.ipynb new file mode 100644 index 00000000..72f9066a --- /dev/null +++ b/Autograd13Metasurface.ipynb @@ -0,0 +1,1350 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4ddde2f6-0a95-4d80-98e9-a2d99bb8b6ae", + "metadata": {}, + "source": [ + "# Diffractive metasurface inverse design with topology optimization\n", + "\n", + "In this tutorial, we will use inverse design and topology optimization to design a diffractive metasurface that generates a desired intensity pattern when light is transmitted through it. We use the `autograd` feature from Tidy3D to perform gradient based optimization of a mask to minimize the difference between the measured and target intensity distribution.\n", + "\n", + "With Tidy3D's `autograd` feature, we can optimize objective functions that involve arbitrary functions over measured field patterns. We define our metasurface using an arbitrary permittivity distribution as a function of (x,y) and minimize the loss function with respect to this pattern. We also include a penalty for small feature sizes.\n", + "\n", + "\"Schematic\n", + "\n", + "If you are unfamiliar with inverse design, we also recommend our [intro to inverse design tutorials](https://www.flexcompute.com/tidy3d/learning-center/inverse-design/) and our [primer on automatic differentiation with tidy3d](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin1Intro/). For another example of metalens adjoint optimization in Tidy3D, see [this example](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin7Metalens/).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "91836104-8a1e-47ac-91ab-0c4324293ffe", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import autograd\n", + "import autograd.numpy as anp\n", + "\n", + "import tidy3d as td\n" + ] + }, + { + "cell_type": "markdown", + "id": "3c57a46c-641a-4d48-896a-e436e6095243", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "The setup is simple and similar to other examples, such as our [metalens](https://www.flexcompute.com/tidy3d/examples/notebooks/Metalens/). Our structure consists of a slab of dielectric in the xy plane sitting on a substrate. A plane wave is incident from below (-z). We define the slab using a custom medium, which gives us full control of the permittivity value at each point in space.\n", + "\n", + "### Set Global Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3b9ed675-6b49-452f-a827-c4edb63c9371", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# wavelength and source properties\n", + "wavelength = 1.0\n", + "freq0 = td.C_0 / wavelength\n", + "fwidth = freq0 / 10\n", + "run_time = 100 / fwidth\n", + "\n", + "# permittivity of the mask and substrate\n", + "permittivity = 2.0\n", + "\n", + "# side length on x and y\n", + "length = 20\n", + "\n", + "# thickess of the metalens, enough to apply a relative phase shift of just over pi\n", + "k0 = 2 * np.pi / wavelength\n", + "delta_n = np.sqrt(permittivity) - 1\n", + "thickness = 4 / k0 / delta_n\n", + "\n", + "# distances between PML and source / monitor\n", + "buffer = 1.5 * wavelength\n", + "\n", + "# distances between source / monitor and the mask\n", + "dist_src = 1.5 * wavelength\n", + "dist_mnt = 6.1 * wavelength\n", + "\n", + "# resolution\n", + "min_steps_per_wvl = 17" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b3c7abb1-6d18-4a0b-a5ed-35adeb459dc6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# total z size and the center of the slab\n", + "Lz = buffer + dist_src + thickness + dist_mnt + buffer\n", + "z_center_slab = -Lz/2 + buffer + dist_src + thickness / 2.0" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d1f2c4d4-bf35-41fc-a97c-3429aa7f4a6a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# resolution of the design region\n", + "dl_design_region = 2 * wavelength / min_steps_per_wvl / np.sqrt(permittivity)\n", + "\n", + "# number of pixel cells in the design region (in x and y)\n", + "nx = ny = int(length / dl_design_region)" + ] + }, + { + "cell_type": "markdown", + "id": "0efd2b8d-408a-490e-a877-d3dc7520cf8e", + "metadata": {}, + "source": [ + "### Define Simulation Components" + ] + }, + { + "cell_type": "markdown", + "id": "f2db7723-a7e2-4517-bfad-df1bee96afd0", + "metadata": {}, + "source": [ + "We start with defining some \"static\" components, which don't depend on our design parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f609d704-2f5e-4af3-8be2-678a0b44f961", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# substrate of the same permittivity as the mask\n", + "substrate = td.Structure(\n", + " geometry=td.Box.from_bounds(\n", + " rmin=(-td.inf, -td.inf, -1000),\n", + " rmax=(+td.inf, +td.inf, z_center_slab-thickness/2)\n", + " ),\n", + " medium=td.Medium(permittivity=permittivity)\n", + ")\n", + "\n", + "# plane wave\n", + "src = td.PlaneWave(\n", + " center=(0, 0, -Lz/2 + buffer),\n", + " size=(td.inf, td.inf, 0),\n", + " source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n", + " direction=\"+\",\n", + ")\n", + "\n", + "# monitor we use to measure the intensity pattern above the device\n", + "mnt_out = td.FieldMonitor(\n", + " center=(0, 0, +Lz/2 - buffer),\n", + " size=(td.inf, td.inf, 0),\n", + " freqs=[freq0],\n", + " colocate=False,\n", + " name=\"output\",\n", + ")\n", + "\n", + "# monitor we use to inspect the field pattern from the side for visualization\n", + "mnt_side = td.FieldMonitor(\n", + " center=(0, 0, 0),\n", + " size=(td.inf, 0, td.inf),\n", + " freqs=[freq0],\n", + " name=\"side\",\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "ae5aff19-0a41-48bb-9296-f705417eb223", + "metadata": {}, + "source": [ + "Next we define the mask as a function of our design parameters using topology optimization + filtering and thresholding methods." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "85ca201b-2ef0-4eb6-9220-5bdde41f0493", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from tidy3d.plugins.autograd.functions import rescale\n", + "from tidy3d.plugins.autograd.invdes import make_filter_and_project, get_kernel_size_px\n", + "\n", + "radius = 0.20\n", + "beta = 50\n", + "\n", + "radius = 0.120\n", + "beta = 50\n", + "\n", + "filter_size = get_kernel_size_px(radius, dl_design_region)\n", + "filter_project = make_filter_and_project(filter_size)\n", + "\n", + "def get_eps(params: anp.ndarray, beta: float) -> anp.ndarray: \n", + " \"\"\"Get the permittivity values (1, permittivity) array as a funciton of the parameters (0, 1)\"\"\"\n", + " density = filter_project(params, beta)\n", + " eps = rescale(density, 1, permittivity)\n", + " return eps.reshape((nx, ny, 1, 1))\n", + " \n", + "def make_slab(params: anp.ndarray, beta: float) -> td.Structure:\n", + " \"\"\"make the phase mask as a function of the parameters for a given `beta` value.\"\"\"\n", + "\n", + " # construct the coordinates\n", + " x0_max = +length / 2 - dl_design_region / 2\n", + " y0_max = +length / 2 - dl_design_region / 2\n", + " coords_x = np.linspace(-x0_max, x0_max, nx).tolist()\n", + " coords_y = np.linspace(-y0_max, y0_max, ny).tolist()\n", + " coords = dict(x=coords_x, y=coords_y, z=[z_center_slab], f=[freq0])\n", + "\n", + " # construct the data array for the permittivity\n", + " eps_values = get_eps(params, beta)\n", + " eps_data_array = td.ScalarFieldDataArray(eps_values, coords=coords)\n", + "\n", + " # construct the permittiviy dataset\n", + " field_components = {f\"eps_{dim}{dim}\": eps_data_array for dim in \"xyz\"}\n", + " eps_dataset = td.PermittivityDataset(**field_components)\n", + "\n", + " # construct the phase mask slab\n", + " custom_medium = td.CustomMedium(eps_dataset=eps_dataset)\n", + " box = td.Box(center=(0, 0, z_center_slab), size=(td.inf, td.inf, thickness))\n", + " return td.Structure(geometry=box, medium=custom_medium)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "be4d9460-f52e-44e3-ad01-1f7faa3b56c6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def make_sim(params: anp.ndarray, beta: float, pml_xy:bool=False) -> td.Simulation:\n", + " \"\"\"The `autogradSimulation` as a function of the design parameters.\"\"\"\n", + "\n", + " slab = make_slab(params, beta)\n", + " \n", + " # put a mesh override structure to ensure uniform dl across the slab\n", + " design_region_mesh = td.MeshOverrideStructure(\n", + " geometry=slab.geometry,\n", + " dl=[dl_design_region] * 3,\n", + " enforce=True,\n", + " )\n", + "\n", + " return td.Simulation(\n", + " size=(length, length, Lz),\n", + " grid_spec=td.GridSpec.auto(\n", + " min_steps_per_wvl=min_steps_per_wvl,\n", + " override_structures=[design_region_mesh]\n", + " ),\n", + " boundary_spec=td.BoundarySpec.pml(x=pml_xy, y=pml_xy, z=True),\n", + " structures=[substrate, slab],\n", + " monitors=[mnt_side, mnt_out],\n", + " sources=[src],\n", + " run_time=run_time,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "bcb0c957-b748-4dd4-9fbc-9311d0fd487d", + "metadata": {}, + "source": [ + "Let's make a simulation with some random starting parameters to inpsect our setup." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "8e87837c-a990-415f-a121-28ed117b7d74", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "params0 = np.random.random((nx, ny))\n", + "beta0 = 1.0\n", + "\n", + "symmetrize = True\n", + "\n", + "# symmetrize the starting parameters (optional)\n", + "if symmetrize:\n", + " params0 += np.fliplr(params0)\n", + " params0 += np.flipud(params0)\n", + " params0 /= 4.0\n", + "\n", + "sim = make_sim(params=params0, beta=beta0)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "84b13caf-9509-47aa-8478-5a2149b37b6d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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cn//852FZFl566SVMnDgRCxcuBADsv//+uPXWW3Httddil112QXd395iIMwB4PB5cf/31OPPMMzFjxgyccsopUv5qp512wte//vVNnTYN06dPRzwex+mnn46LLroIDocDd911l0asif333x+/+tWvMHfuXBx44IEIhUI4/vjj13rdiy66CIODg/j2t7+NX/7yl9rv9tlnH+yzzz4Axp8jvT584xvfwH//93/j+OOPx/DwMO6++27t91xTADBv3jz8x3/8B5YvXy71sI899lhMnjwZBx98MLq7u/Huu+/izjvvxOrVq/GrX/1Ku9a//Mu/YPr06bL2V65ciRtuuAFHH300jjnmmC3WJ4MPKbapZ7iBgYGBgYGBgcF7jnfeecc+7bTT7FQqZVuWZe+88872nDlz7FqtZtv2SPmrtZXIsu1Ouavdd9/d9ng8dk9Pj33eeefZmUxGfv/3v//d/upXv2pPmzbN9vl8diKRsD/1qU/Zjz/+uBzzxBNP2P/0T/9kT5w40fZ6vfbEiRPtU045xX7rrbfW2/aNPa9er9vXX3+9veeee9qWZdnxeNzef//97fnz59u5XE479uc//7n9sY99TI6bMWOGvWTJEvl9X1+fPWvWLDscDtsApBTW6PJXxK9+9Su5XiKRsL/85S/bK1eu1I45/fTT7WAwOKZ/V111lb0xr+TPP/+8/YlPfML2+/32xIkT7W9/+9v27373uzHtKRaL9v/7f//PjsViNoD1lsKaMWPGOktZXXXVVRts06ZgffccPQ4sGbZ8+XL57Oabb7YPO+wwu6ury3a73XYqlbKPP/74tZa5sm3bfvbZZ+3p06fbPp/PTqVS9pw5c+x8Pv+e9M3gwwWHba9lK8vAwMDAwMDAwMDAwMDAwGCtMHWkDQwMDAwMDAwMDAwMDAzGAUOkDQwMDAwMDAwMDAwMDAzGAUOkDQwMDAwMDAwMDAwMDAzGAUOkDQwMDAwMDLYqnnnmGRx//PGYOHEiHA4HFi9erP3etm1ceeWVmDBhAvx+P4466igsW7Zsg9f9yU9+gp122gk+nw8HH3ww/vd///c96oGBgYGBwaZg4cKFOPDAAxEOh9Hd3Y3Zs2fjzTff3OB5999/P3bffXf4fD7svffe+O1vf7sVWrt+GCJtYGBgYGBgsFVRKpWw77774ic/+claf//9738f//qv/4rbbrsNf/jDHxAMBjFz5kxUq9V1XpPlfq666ir86U9/wr777ouZM2diYGDgveqGgYGBgcE48fTTT2POnDn4/e9/jyVLlqDRaODoo49GqVRa5zkvvPACTjnlFJx11ll45ZVXMHv2bMyePRuvvfbaVmz5WBjXbgMDAwMDA4NtBofDgd/85jeYPXs2gE40euLEifjGN76Bb37zmwCAXC6Hnp4eLFq0CF/60pfWep2DDz4YBx54IG6++WYAQLvdxpQpU3DhhRfiO9/5zlbpi4GBgYHB+DA4OIju7m48/fTT+OQnP7nWY04++WSUSiU89NBD8tknPvEJ7Lfffrjtttu2VlPHwL3N7mxgYGBgYGBgMArLly9HX18fjjrqKPksGo3i4IMPxosvvrhWIl2v1/HHP/4R8+bNk8+cTieOOuoovPjii+u8V61WQ61Wk5/b7TaGh4eRTCbhcDi2UI8MDAwM3nvYto1CoYCJEyfC6Ry/6LharaJer2/yvUd/Z1qWBcuyNnhuLpcDACQSiXUe8+KLL2Lu3LnaZzNnzhyTFrS1YYi0gYGBgYGBwXaDvr4+AEBPT4/2eU9Pj/xuNIaGhtBqtdZ6zhtvvLHOey1cuBDz58/fzBYbGBgYbD9YsWIFJk+ePK5zqtUq/H7/Jt8zFAqhWCxqn1111VW4+uqr13teu93GJZdcgkMPPRR77bXXOo/r6+sb19+ErQVDpA0MDAwMDAw+lJg3b54W5cjlcthhhx1w+umnw+v1wu/3IxwOAwB8Ph/i8TharRaazSYAoNVqoVgsotlsSmS7WCwiHo8jEAjIi6XD4UBXVxcsy5I8b6fTiXa7jUajIT8TzLorFApotVoIh8MIBoNyXKVSwfDwsNyzUqnA6/XC5/NJVKhWqyGRSMDn8wEAyuUybNuG2+1Gq9UCAHg8HrRaLaTTaclPjMfjCIVCADpKAKATWUqn08hkMtL3WCyGWCwGj8cDAMhkMsjlcvB4PNKu4eFhOJ1OWJaltdXn82GHHXZAIBCQc7PZLJrNpjbejUYDyWQSADBlyhQ4nU4sW7YM7777LgAglUqhq6sL+Xwe77zzjlyrq6sLU6dOlQhXs9lEuVyG2+2G1+uVecrlcsjn8zJH4XAYkUhE+t1qtTA4OCjHAIDX60U4HEaj0ZCXeMuykEql4HK5tL7n83khJ+FwWOa43W7L/FYqFUQiEWmrZVlyX16rt7cXHo8HhUJBixhGIhGkUikAgMvlkv4UCgVZR+wP57Rer6PZbCIQCMDtdktbly9fjqGhIcTjcQDAjjvuiEgkgqGhIQAd+e0OO+yAXXfdVdq/YsUKpNNpeDweWdeFQgFutxuxWEyuVS6X8e6772pkjWuC10okErAsC41GQ8Y/Ho+j0Wggm80im80CANxuN+LxOJLJpIwPI5rFYhGZTAYAEAwGkUwm4XK5AACNRgMulwvNZlOekUAggGq1iuHhYYmc2rYtkVm1rYlEAn6/X9pbKpVQKBTk+uFweExEtt1uw+PxyLMOdNZ1rVbD0NCQPOehUAjlchmZTEbmybIsuN1u+dnlcsHtdsPlckkfq9WqrCHO7X/8x3/IMzQebGokmigWi1ixYgUikYh8tjHR6Dlz5uC1117Dc889t1n331YwRNrAwMDAwMBgu0Fvby8AoL+/HxMmTJDP+/v7sd9++631nK6uLrhcLvT392uf9/f3y/XWhnVJD30+H9xutxANYIT8BAIBeQG2bRter1cINdAhba1WC06nU87nZ+VyWV7+fT4fgsGgXKvVaqFaraLVasnLaCQSQblchs/nE8LaarXgcrngcDjk5dfn8wkB4D19Ph9cLpe86IdCITidTjQaDZTLZWl/IBBAvV6Xdvh8Pni9Xti2Led6PB5YloV2uy1k0OVywev1yv28Xi+CwSAsyxIC4nA4tDECOi//0WgUPT098lmpVEIwGITL5RJyzXHjuLdaLTgcDliWJZGpaDQqklJuNLTbbSEfJBgOhwNutxuBQED65HA44PF4pP2VSkXaxbFWNyu4IREMBhGJRFCtVuWejUYDtVoNlmUJmeIY8Frtdlvmi/cMBALweDzw+/1yXqvVQq1Wg23bcv1gMCj354ZHtVqV47hWeB1uzvj9fsRiMZlT3rtcLqPZbMpxXB+NRkPuybkjGeaGiG3bMm/tdlvmhM8RN2e8Xq/MZTgcRqvVQi6Xk364XC7U63XpdzQahdvtRq1Wk7Z6PB44HA64XC5tE8KyLHg8HtnU4XE+n0+7J4kyMPKsBoNBWVO8ts/nk7kh0VY/47NEIstxtCxL7mfbNvL5vFyPfXQ4HCiVStIO3pubWhzbVqsFn88nz34oFILL5dKeJYfDoW0+sB1er1cbo81NSxnv+eoaVIn0hnDBBRfgoYcewjPPPLPBCHpvb++4v9+3BgyRNjAwMDAwMNhuMHXqVPT29uKJJ54Q4pzP5/GHP/wB55133lrP8Xq92H///fHEE0+IaVm73cYTTzyBCy64YNxt4MuxSjQajQacTqeQbN43EAigVqtpL58ktTy/2WwinU6jUqkIiY3H4+jt7RUC0mg0UCqVYNu2EMFEIoF4PC7X41gwKs7rkzwVCgXEYjFpRzablXZFIhFYliWRNH5G8st2tFotIUgkbbZtw+PxaFH2arWKbDarEQmSA5KxQCCAZrMJ27aFlPj9fiSTSYRCIYmstdtthMNhbWxLpRKcTqfcb3h4GMFgENFoVK5fr9eRzWYlCgp0NlXcbjdKpZKMNeeJZIjtDYVCQnQHBgbg8XgkYsixbjQaaLVaGiknkeM8MRKsEqtgMIhAIKBtBIyOSJP8OBwO+Syfz6NWq8HpdMr1GeUvl8vSDqoZGC2PRCKy4UIil0qlEI1GUavVxmwKFYtFmRPbtmFZFrq6uoQk53I5tNttWU8TJkyQceW6KBaL0n7Oic/nQ7Va1TZBotEoUqkUnE6nEFGv1ysbHGofq9WqELNyuYxyuYxqtSrX4pzxd2qfWq2WrGPLsuByueQZyefzsG0b4XBYm19uDPD5ymazCIfD8Hg82vPbbDZRKpVEIcEINNs1PDyMSqUi5BzoEORqtYqBgQFZ64FAAH6/X54LAFqf+bx6vV6J0AMjSoJqtSrjz+8AHsv7bg5I2MeL8XhX27aNCy+8EL/5zW/w1FNPYerUqRs855BDDsETTzyBSy65RD5bsmQJDjnkkHG3dUvCEGkDAwMDAwODrYpisYi3335bfl6+fDleffVVJBIJ7LDDDrjkkktw7bXXYtddd8XUqVPx3e9+FxMnThSSDABHHnkkPve5zwlRnjt3Lk4//XQccMABOOigg3DTTTehVCrhzDPP3NrdMzAwMHhfYmsQ6Tlz5uDee+/Fgw8+iHA4LCkS0WhUIvCnnXYaJk2ahIULFwIALr74YsyYMQM33HADZs2ahV/+8pd4+eWXcfvtt4+7rVsShkgbGBgYGBgYbFW8/PLL+NSnPiU/M0/59NNPx6JFi/Dtb38bpVIJ55xzDrLZLA477DA8+uijEmkDgL/97W+Svwl0yqMMDg7iyiuvRF9fH/bbbz88+uijYwxqNgaUSuZyOYleud1u5HI5FAoFyUEMh8MSmWWeJqNzlmVJ9JDS7nq9rkWzHQ6HHFMqleB2u9Fut+Vafr9fIoqqpNflcknUDBiRhdfrdYlgtdttNJtNiVJVKhWRq6uSbUa42E+n0wmn0wmPx6PlnjKKzAhcsVhEPp8X6a/b7Ua9Xteiw5R5Mteb97RtG7lcTiK0wWBQIoJsWyAQ0F7qKStnvjYwEr0tlUoSXVVzWVUZLcdIjQYzig6M5K729/dr0tloNIpKpSJRfEYsuUaAjrqAkmB1PMrlsvQxkUhItJqR1OHhYbTbbYncs5/Mj2XfnU4nms0mstmsXC8cDmvmUMxHr9Vq8pyoygiuM8qIVZdlyvQLhYLMudfrhdfrlYh3NBpFJBJBo9EYk+bg9/tlLBhpDQaD0tZcLgfbtuHz+eRc5poTxWIRhUJBcpmBzjORyWTgdDol4h0KhWDbNgYGBjRZOJUDXMd8nviMuFwumUeOdb1eR6vV0nwDLMuCz+fTZNZsN1UjbL9lWTJeuVxOFAZcK+12e0wKRL1eh9PphG3bMj5US2SzWflOazQaiMViMhYcHzW9gn31+/1wu93jIrPrwqYS6fHg1ltvBQAcccQR2ud33nknzjjjDADAu+++q/lGTJ8+Hffeey+uuOIKXHbZZdh1112xePHi9RqUbQ2YOtIGBgYGBgYGBugQs2g0inPPPRc+n08jQrFYDM1mEz6fT8g5iVexWJSXz2AwiHg8Dp/Ph+HhYQAdAyyaOzH/k+ZSJIXMqSYhAjoyZb5k8+WfMl3mOwMjeZ2lUklMmer1OsLhsEhda7UaGo2GRsBJXiuVihDRcDgs5b8GBgYAdIgfjctIrAqFAhwOhxhDcaOhVCpJ36ZMmQK32410Oq1Jc0m2SCxLpRJWr14Nt9uNHXbYAUCHhJD8ASO525RzAx2yYds2yuWyjEUwGBQTNMrCy+Wy5G6TMNFwi6/BvFelUtEMpPj/HKtmsykmVszZpIlWIBAQ8sV2UhodiURkDkmq8vm8HMN1kc/nRcJNQsx88nQ6reXAA5D+cLNCJdfMmabRGjBCKGu1mow/86Oz2awQRUr5ua49Ho+0k2uFY+L3+2XNvvvuu2g2m5g4caKQ33K5LDJuzonT6UQymZQxWbFihSbf51iThJN0Mz99eHhY2t/d3Q3btpFOp4XE0ixQXT9Mf+D6p+FZoVCQ8Y/FYuJdoMrOuTHF54nkm5siQ0NDaLfbSCQS2qYIpeD8LqBEu1wuy6ZLIpFAtVpFJpPR0ilCoZB811iWhf7+fs0HIZvNykaMbduo1Wq47bbbkMvlxpWrDIx896kbOBsL27ZlrsZ73/c7TETawMDAwMDAwECB1+uVPGHVLMqyLEQiESGFg4ODGBwchMPhkBdIkrVKpSIv8sFgUEyOVKKrRpCDwSCKxSIGBweFIJB4qE7ajLTSyAgYMQ2Lx+NCOLLZLCKRiJxHZ/FAICD3rNVq8Hg8iEQiQnDcbjcajYYWvfX7/fB6vWg2m/ISn0wm4fP5hDTbti0kgEQrm83K/dV882QyiXq9LhE1RhM51gAkt1Q1HyuXy1q0mHnHEyZM0NQK9XpdxojX5xyppmeWZQnZY/upHgA60TlGQNUNCbpSkzAtX74cQIeQsu+FQkGiokCHwHJjoru7G0AnylgqlURhAIy4stfrdUyaNAlAJ+LNsVSJnMPhkI0NGsa1Wi3pk9/vF6MrNd86l8tpkV+6S/N+HJ9cLqcpCZgTTdJcLBZRLpfR09Mj8+RwONBqtWROgQ759Xq9cl8A4mDPdacasdFAiuoOdS02m00hyWpeOaPIHO9QKKT5FHADSd1c4vPl9Xplw4PR7XK5LOSaUXw1/5wO4VwDhUJBzuFYl0oluT6f1Z6eHjidTpRKJWl/pVJBu92G3++Xccjn86hUKjLWEydORCQSgW3b0iePxyNrnUZwm4utEZH+IGH81boNDAwMDAwMDAwMDAwMDD7EMBFpAwMDAwMDAwMFjDqpebaNRkPyexl5rVQq4mjN4xgRKxQKEplKpVIIBAJIp9OanFqt5csc59H1pEeXtQIgkWxGjihtZi1eACJPVfNZmeeqRlwpVx0dWWMJI7bf5/NJzWCgE1VlmSCgEx1LpVIIBoOaM3a73Ra3Z7adUTVGI3O5nNRTZv9LpZImb67X66hWq1JeiWNGubHqOp7NZrWIq8vlknarEvnRkVq6brMNzHNmDW4AWpSS/8820q1abS/B+sfsG/tdrVYljYDtozu8ei+1b5w7ls/iHNG5mp85HA4pk6VG7DmGqiycUm41Su31erX6zZw39fmoVCoolUoyjl6vV2qKqznejJ5yfKjQ4LgmEglEIhFNHs81zZxo9on55oODgzKe1WpVaoHz+moJu0mTJsHlcokTO+eL+dxcP/QoUEvC8TmkI7l6rhoFdjqd2ry73W40m03k83ktHz2ZTCIQCIjJVrFYlPaq3yP1el3zUFAVFmwro+Rq6bXNgYlIjw+GSBsYGBgYGBgYKCChVcmr2+0WCSaJim3bSCaTWu4tzbxUUpXP58Wci5/5/f4xUk6fz4cJEybIy7MqEyaZUvOX1dq0Xq9XIzlsn1rzF4AmzQ0GgxgcHJQ8UbbD4/FopXSq1SrK5TLy+byQO5IXEgmWJYpEIvIirkrE1XzVgYEBrbYuSa9KiEOhkJbzy3JK6uaAx+OB0+lEuVzWclBJBjiOlLDT8I1j43Q6JUed9/T7/SI5p3laOp0WEtzV1SWSaR7H39XrddkcoCkUzag8Hg923313AMAbb7wBoEOk/X6/JoP2+XxyPdU4i+OwcuVKAJ28+2QyKcSRY6cao3G+1fJjJNC8Ns9tNpt49913tc0BEmC1P+rmEnOoVck5Tdjq9bqUfKpUKmI+xn6wz2rNaBqgkTTTe0Ct6cz5VTecaASmpg9wDTDfWk0bUEuSjc6Rz2QyKJfLMvcci+7ubtkMAzrPpDqunDeSaWBE0t5sNrXrs8Qcvwu49lVZfiQSQaVS0Ug/65WrG1XAiEcC77s5MER6fDBE2sDAwMDAwMBAAd21nU6n5AA7HA6JtPEF1u/3I5VKwe/3y4syc4w9Ho+QhFarJY7EjNyFw2GJOgEdAkKjohUrVgCARHNZBxkYcZsul8vyUs/IZL1eF0LDGrdqHibrWPNFuVgsjnG9drvdqFarUh8YGHFiVl+yORZ80Y9Go0JaOD6sV9xut+V3JNdqlJdGVvF4XEhCNBqFz+fTXItJnFWVgNvtht/vFwKbTqclH5ZE3el0IhKJCGkFIFE8uiQzZ5W56zxPnUeORbFYhG3bGulkX1USrhpWcVz5//wvyRgJpmVZCIVCWj9LpRIcDocW/fR6vTLnQIdoeb1eVKtV6VMwGERXVxcSiYQQT7/fL6oK1SmdRlypVErWFAkw54iu6WoeMuuT8zPWPS+VSrI5wGi7uslDB3I1h55rS3XC9ng8iEajGrljfXRenxtcqlM+SSkj6qPrmQMjihNuNqjzptZ8TyaTsG0bLpdLnkPW4SYZBjpRdRoGEqzvzHVcr9eRy+UQCAS0dUUlAZ8Tfqcw6k4vBtVN3LZtDA8Pr7VvmwpDpMcHQ6QNDAwMDAwMDBQEAgF5oebLLg3I1CgR5amNRkMIB81/KPnmuaMjcDSrYtQuEAggGAxqZIbldlwul5AGRrVVYuVwOOD1ejWzpXa7LbJkYMQwSX1553mNRkMIAftJR2wAGokmIVCJNq/F+6rO2LVaTcpd8Ti32y3u3fyM7tKqU3coFNJKOXEMeX2v14toNIpgMCjXonsy7wN0yB3Hm8TN4XCg0WgIwST5C4fDMpfNZhPValXIIdCJptNRmaSq2WxqBlD8zO12CzGKx+NyDM20WD5ptKkdVQFsf7lclugsj4vH45r5FTdl0um0tNXpdMLlciEajWqfxWIxLWLMTRfKpoGOdL9UKsm80bxNlcn7/X4xJSO4VlTzK24uqZtQjCxzPVLJsbZ1xTWprjNVVk2ViKrwYPkurn81tUCVrzudToTDYW2jgetRfb4o0eZnfCZpUsaNBrWUW71el2eSGyxsq/oc1mo1GX+uPX7GdnFzjuud12d7VFn+5sAQ6fHBEGkDAwMDAwMDAwMDA4MPOQyRHh8MkTYwMDAwMDAwUECpaqvVkpdKRtgsy5KIKCOAgUBAopM0rWJkERjJG1VNyWguptYnpnSUea+Dg4PI5/OoVqsSqaUM1+/3S/SQUM2VGLXm9VkqirWwgU4Ui/1hn9jOVqul1T1uNpualDUUCiEYDEqENxQKyb05ZoyYqaZJ1WoViUQCXV1dEkFjjnM6nZZoWzgc1qL/jFaqkmegE2FljWyONXO1mWvMyKQqofb7/XC5XHJMNBqF3+9HJBKRdmWzWbjdbpkPAFKr2OVySbSTZl1sI9tl27bcjznA6vj4/X6px82IZaVSkfJRvC/nkTW+CbXmdTQaRavVklx+toGmZfyM5ZgikYhWi5pqBbU0mpqHrEbDiUKhIOXCmE9cKpVgWZYYgvH6/f39yGQyck+WVOPPXq9XxofSZY5hq9US6X6tVoPX65U1yeMYwefY+nw+BAIBGddyuSy+BVw/LpdLcrdVuT3bw+eLkffRRnetVktSKVKplNQOV43jWBJLzesHOsoSRs/5nWHbtlb7vFwua32k7J/3bDQa8t2iyuYNth4MkTYwMDAwMDAwUBAOh+H1eiU3FYBmcMSX23K5jEAggGKxKEQiHA4jFouhVqtJDrNlWUgmk/I50CHJzEXmNWkqRfLLXFzV1IsEgmQRgBBctc4ySQiJEUkW/599IlFRiToJofoST1dhtiOfz8PlcolMGeiQKNWojDJdtc62ZVlSj5ukOZPJSL4zyVcwGNTMoNT+s/25XA75fF7yk9knEiTes1wuI5fLaRJ51uglsaELd6lUEiI02skaGJH+shYwMCIjdjqdMt6VSgUej0c2U2h4BkAjq5SFk3CrNczVFAISX9UAjykIANDX1zcm75YpBiTn7GetVkM0GhVZcj6fF2fp/v5+mUvVmC4ajcraU+eEa4PX5xhFo1HNpTybzSIajQoJ5EYPr8+UADVNIpFIoK+vT3Ot5+aPmo5ACbvaT5/PJzJzALIxpG4QcXy4SQSMOK+rruncFFPJO59jrrtwOAyHwyG1yNmu/v5++Hw+6ZNlWchms0in03INunhns1n5HqHcnmtRfa7V7yQ+R+pa3hyYiPT4YIi0gYGBgYGBgYECl8sFt9utvZiqJZzUF1mv1wuHwyGEqVKpCEFW85Xb7baUIgI6kc1GoyFENBKJoFarIZvNyou4WsJJdfJmbixfrhldzWazWv5qNpsVUu73+8VAjW0oFAqIxWLweDxaLnVXV5dG5Gi65vF4ZAx4DRLfWq2GYrEIn8+Hrq4uaXOz2dQij4xg12o1Ld+VZaxIaJrNppRD4vHMD2e7SFrUnFPmdjPPm+dyPtlPRnN5Px6j5lFHIhG43W4UCgUhOJZlIR6Pa2Wm1Pxmjg+jhJyjTCaj5e8CHYIcjUY1okhSxL7wnoyA09E6HA4jHA7L+sjn8xgcHNTInmVZ4gZNoh4IBMT9WW03jdH4GUusqQSW9+L4WJYlbu4k19FoFIFAQAzxOLZ0qVcVGSTTQIf4Dg0NoVqtapsu6v9zfBqNBgYHBzXHb6/XK/nxQCfHu91uy7zVajW4XC4hqABk44mbDRzzWCyGVqslc+l0OsU1W72nOkfpdFqM79QNir6+Pm1ziWuTRoK8Rl9fn1YmKxKJaO1Sjf9IytkOPtcmR3rrwxBpA4P3EE899RQ+9alPYenSpTjiiCO2dXMMDAwMDDYClJKSaAIjdYeDwaCQkmq1KlJsvlj39/ejUCigu7tbq/9brValtA6v12w2NflovV5HuVyW81jSRiVXlALTzAqA1JClszjQiZTRoIjX9/l8WkSRkWdGXgGIwZpaxocv/nTXJlSpeq1Wg2VZ8Pv9QnpIDEji2X6OHUkJy4el02mtjBg3H4CRDQpV/sqyXx6PR5Ohk4Co0VuWvuL404WZY9FoNMT1nESiUqkIWeS4MjLcbrc1aTTP53iThPN+o+cVgLix0yGb92Sf1OvTjI0bNi6XC5ZlyYaGatimXp/nkxSy36oBFuuhk5BxHLnOOG+M8I6uE84NILaL//gZDfgAaHJ1rlGgY75HR3HC6/Vi8uTJWlkrrjW1BjUj9erc0fyL5NLj8UjbVQdt1mDnPfgsq+PTbDbFOI/td7lcWlkyRrXZD6BjfMdINtdxIBCQZ1Ft28DAAFqtFnp6egB0zOQKhYIWYSehVw34crkc2u02AoGApgrYVBgiPT44N3yIgYHBhnDLLbdg0aJF27oZm4R7770XN91007ZuhoY77rgDe+yxB3w+H3bddVf827/920afW6vVcOmll2LixInw+/04+OCDsWTJkrUe+8ILL+Cwww5DIBBAb28vLrroInlBMzAwMDAwMDD4MIFEerz/PqwwEWkDgy2AW265BV1dXTjjjDO0zz/5yU/K7vL2invvvRevvfYaLrnkkm3dFADAT3/6U5x77rn4whe+gLlz5+LZZ5/FRRddhHK5jEsvvXSD559xxhl44IEHcMkll2DXXXfFokWL8NnPfhZLly7FYYcdJse9+uqrOPLII7HHHnvgRz/6EVauXIkf/vCHWLZsGR555JH3sosGBgbbORj5UWvhOhwORKNRJJNJiapmMhkpWaNGpmh+pZZpYo1fNUqq5kBWKhXUajXUajWJDjOHlEZDwIgEXM3hZNSXMmEAUsqJ0lhGCZnjy3ZRks3r85hGoyERRUYAVfM1j8ejRQpZE9jn80kbGHWePHmyRPdKpZImo+W12A+17nV/f79EROv1uvSFx9AUjvdTr5HP5yW66vV6JarIKCBluGr5Jf5XLXXFPHK2lVFCtXYy14plWVLuirJitVazGsnlWIy+FktOBYNBiTxyTTF6CnQUB6VSSatpHg6HZR3yM0Zm1XQBmuYxSs08c9XIjaWb1FJRLDm1cuVKGVeWtuJ5HD/m/LP9bA/zpi3Lwt///ndtrQQCAS0vmyZoNNjj2DEKznv6fD54PB4tVzufzyMUCsl8qMZ3HOtYLIZ8Po9CoSByaZrOVSoVeQ5plqemGrB0mprmweeX65sqBHV++ayq9crZB8uyMHHiRACd50l9LmOxGMLhMJrNplw/nU6jWq3CsiythNfm4MNOjMcLQ6QNthhKpZKWt2Ew4phpsHGoVCq4/PLLMWvWLDzwwAMAgLPPPhvtdhvXXHMNzjnnHPkjvDb87//+L375y1/iBz/4Ab75zW8CAE477TTstdde+Pa3v40XXnhBjr3ssssQj8fx1FNPycvZTjvthLPPPhuPPfYYjj766PewpwYGBtsz6PCsEsVarYZgMCiyWwBCjvnSDnReeIvFouRUAh0zoVAoJC/oQOf7rl6va5JqEhke4/f7hVCpBJBkbrTBUzwe13Kp1b9BJPZ0pgZGTKD4ck+wTZQzVyoV5PN5LWcWgEZMSewpOQZG6myrpmrMKVcdjyl1bTQaGBoaAtAhCTRMA0Yk1WruNokI87wBiIM0XZXZTpIN1XzN5XIJkaPLM2XaPI+EZ7QpXK1W04g9DdsofXe5XJqMXnUWZ1sptQ8EAtoGAskiCShzw+m8zutxgwboEFPm4nJzptVqCUFTXeVpTsfxaTQa8Pl8Ws51o9FAtVqVn71er0ivuZGUTqdl44JzSZMxutKzrXSDJ0njphLbT4dtNfCQzWY1kzyOP58drlmSZRJ3zkmlUtGey3w+r9Up9/l8KBaLmnEf89/VjbBWq4VIJIJwOKw5kXPNct64GcE5D4VCYmKnusXToHDVqlUyJ6oDPuecJJnH0Nmcm1L8jqnX6yiVStqGksHWgZF2G6wVq1atwllnnYWJEyfCsixMnToV5513nnwpLlq0CA6HA08//TTOP/98dHd3Y/LkyXL+Lbfcgj333FN21+bMmSM728SyZcvwhS98Ab29vfD5fJg8eTK+9KUvIZfLyTFLlizBYYcdhlgshlAohN122w2XXXbZBtu/MefVajVcddVV2GWXXWBZFqZMmYJvf/vb8gWl4u6778ZBBx2EQCCAeDyOT37yk3jssccAdMjX66+/jqefflp28pgP/dRTT8HhcOCpp57Srnf//fdj//33h9/vR1dXF0499VT5QiXOOOMMhEIhrFq1CrNnz0YoFEIqlcI3v/nNjcqDefDBBzFr1iyZw2nTpuGaa67Rzj3iiCPw8MMP45133pG277TTTuu85p133gmHw4Gf//zn2ufXXXcdHA4Hfvvb326wXevD0qVLkU6ncf7552ufz5kzB6VSCQ8//PB6z3/ggQfgcrlwzjnnyGc+nw9nnXUWXnzxRaxYsQJA5w/UkiVLcOqpp8ofT6BDukOhEO67777N6oeBgcH7GzSGisfjSCaTSCaTaLVaGBgYkEgVzZcKhQL6+vowNDSEoaEhFItFMTHy+XxC/mi2xO9av9+PZDKJVCqFVColBDQajUpOMl/gI5GInO/xeCQanMlkkMlk0Gq1hHjFYjGJXpFs86W/Vqshl8uhUqnIiz/zo9lWGn4xH5wv+NVqFWvWrMHAwICMA1/gGWVWTZWADpkcHh7GsmXLsGrVKqxatUoidOFwGNFoVNyjmTvKyDnQIYfd3d3o7u5GKpVCMBiUCCvzX4PBIFKpFCZMmIAJEyZo1+SYhcNhJBIJRKNRMemiyzIj1dxkiMfjMhZdXV2YMmWKNpbACIFhhDYUCslGCdvmdrsRCoVkvpg/zhxyEl+WDeN5fr9frsfrk9QCI1HacDiMKVOmoKurC11dXfD5fIjH45JvTndqh8Oh9TkajSKRSEj0muZYHDeOI8dbdXEn4eOa7e7u1pQMVDMEAgHtmuFwGJZlwel0yjpYtmwZhoeHtfkm2eb6LJVKEg3muluzZg2q1aqUXwsGg0LaaWrHTRHm8JNU53I5UXP4fD5tLPncsDxWq9WS54teAx6PR8aMzyWv0W63xZWcfUqlUkgmk2Oee/7MdlCZUiwW5Xukr69PyqyxLB7HgfdMJpOIx+Oo1+sYHh6Wja/NgZF2jw8mIm0wBqtXr8ZBBx2EbDaLc845B7vvvjtWrVqFBx54QOrhEeeffz5SqRSuvPJK2Qm8+uqrMX/+fBx11FE477zz8Oabb+LWW2/FSy+9hOeff152Q2fOnIlarYYLL7wQvb29WLVqFR566CEpkfD666/juOOOwz777IMFCxbAsiy8/fbbeP7559fb/o05r91u44QTTsBzzz2Hc845B3vssQf+/Oc/48Ybb8Rbb72FxYsXy7Hz58/H1VdfjenTp2PBggXwer34wx/+gCeffBJHH300brrpJlx44YUIhUK4/PLLAUDMItaGRYsW4cwzz8SBBx6IhQsXor+/Hz/+8Y/x/PPP45VXXtGMXFqtFmbOnImDDz4YP/zhD/H444/jhhtuwLRp03DeeeetdxwWLVqEUCiEuXPnIhQK4cknn8SVV16JfD6PH/zgBwCAyy+/HLlcDitXrsSNN94IANqO6GiceeaZ+PWvf425c+fiM5/5DKZMmYI///nPmD9/Ps466yx89rOflWP5crchqPVXX3nlFQDAAQccoB2z//77w+l04pVXXsGpp566zmu98sor+MhHPqKRYwA46KCDAHTk3Gxzs9kccx+v14v99ttP2mFgYPDhBKNlJMEAJOJTr9fle5KRVp/PJxvN+XxeMw0CIG7WjB4BIy+sahQNgGY8lcvl0Gq1tO9lGjepUWSSMrX0TjweF8kuAGnjypUr5YW7p6dHHIrZLrVUFCOngUAA5XJZq1scDoe1+zGKWq1WpexXoVCQTQX2qaenB5FIRIv4ZTIZIcaskxwOhxEMBrUaznSC5j29Xi+CwaCQOQBiHEXyShQKBTFs4s9qRJ3zxDJfAMTlmWQUgGyQ0CgM6Pyt5mYAr6eWReJ8c744FiTK9XpdjuNYM1LNOee7l2pYphpncQ7V9cToNDeFgM6apWEVx4fqC1WBYVmWJpVuNBool8ta2TIS2VKpJO1zuVzIZDJCYNlmy7LEWZzjrzrIZzIZJJNJrZ40N3qCwaBmEsf3Bra3XC5LtJ5jRnUE56G/vx+lUgk9PT2i0qhWq0KKVek41xTni30plUraGBWLRbl+KBRCIBCQjRmCGxqqMoHl2lQVJ9MY+P5CtQI3UNLpNIrFIiqVirSfahXKyVWp+Kbiw06MxwtDpA3GYN68eejr68Mf/vAHjWgsWLBAc1MEOjX+nnjiCfkiHxwcxMKFC3H00UfjkUcekS/83XffHRdccAHuvvtunHnmmfjLX/6C5cuX4/7778cXv/hFud6VV14p/79kyRLU63U88sgjUkpjY7Ax59177714/PHH8fTTT2t5s3vttRfOPfdcvPDCC5g+fTrefvttLFiwAJ/73OfwwAMPaPknHIvZs2fjiiuukMjy+tBoNHDppZdir732wjPPPCNfhocddhiOO+443HjjjZg/f74cX61WcfLJJ+O73/0uAODcc8/Fxz/+cdxxxx0bJNL33nuvJtU799xzce655+KWW27BtddeC8uy8JnPfAaTJk1CJpPZYNuJn/3sZ9hzzz1x1lln4aGHHsLpp5+O3t5e/OhHP9KO+9jHPoZ33nlng9e76qqrcPXVVwMA1qxZA5fLhe7ubu0Yr9eLZDKJ1atXr/daa9aswYQJE8Z8zs94/po1a7TPRx/77LPPbrDdBgYGBgYGBgYfNIyXSI/mBh8mGCJtoKHdbmPx4sU4/vjjx0TrgLEP19lnn63t5j7++OOo1+u45JJLNNJ59tln47LLLsPDDz+MM888U/JZfve73+Gzn/2s7CCqYGT2wQcfxJlnnrnRJgobc97999+PPfbYA7vvvrvkYwHApz/9aQAdifH06dOxePFitNttXHnllWOusyk7di+//DIGBgZw9dVXa7nTs2bNwu67746HH35YI9JAhwCrOPzww3HXXXdt8F4qiS4UCqjVajj88MPx05/+FG+88Qb23XffcbcfAHp7e/GTn/wEp5xyCg4//HC8+uqrWLJkyZgo8D333KPlIq0LO++8s/z/+ozZfD7fBq/HUiVrO5e/V/+7rmM3pt0GBgYfXDACnE6ntbqxlUoF2WxWvl99Ph9CoRDC4bCkJdXrdZG9Mlo1PDyMYrEoUVZ+NjAwINenLFTNCW232xgcHNS+2xjR6urqkr+djJyr0SS3261FLEOhENrtNgYGBrSSTIw88vrFYhG5XA5Op1OipIzOhcNhiRjH43G43W75vnQ6nZLXyc+azSb8fr8WkWOkn5FYgqWnWA6M0W31+5rRPZ5XLBaRz+cxYcIE6efAwIDk9jLaWS6XUSgU0NvbK3PHmsupVEraypJkau42o+WTJk2SdjCvXCUQ7JcahVVzmtUa0wRz4lV5s5rDrNZmZq1tNYJbLBYl0lwoFDTzMrZpcHBQi7xXKhX09fVp+b6M6FYqFc3oa82aNZphnmVZ2rWY+6yWesvn82NynSm5LpfL0icacDF6y2eLcmcAIj8Ph8PauFJCrxrAUVrNNet0OlEqlbS17nA4RDbP81g7WzUVdLlc8jzy3KGhIc30b7RJH03X+DugoyihSR7XtdfrRaFQQKPRkLaGQiGpjc3x4HsyI9LZbFa+Bzhv+XxezNPC4fBaUxPHi02JSH+YI9iGSBtoGBwcRD6fx1577bVRx0+dOlX7mRHI3XbbTfvc6/Vi5513lt9PnToVc+fOxY9+9CPcc889OPzww3HCCSfg1FNPlS+Pk08+Gf/+7/+Of/7nf8Z3vvMdHHnkkfj85z+PL37xi+sl1Rtz3rJly/DXv/5V/oCOxsDAAADgb3/7G5xOJz760Y9u1HhsCOsaH6ATtX/uuee0z3w+35g2xuPxjcqDef3113HFFVfgySeflLqThJqHvin40pe+hLvvvhsPP/wwzjnnHBx55JFjjjn00EPHfV06Wa4NlGBt6Py1/SHhHyLV7APAOo/d0H0MDAw+2GA+Z6lUkr8brKFcq9XkxZG1nFUH4UgkglQqJbm8QOdFmY6/6gs7nX+BEafwSqUiL9MOh0Nyh3lPGhqp9XxLpZL8TAKjOjEDkJf3VCqlybjptk2CTImwWqeX967VavL3JBqNoqurS4jK0NCQOCOTqPj9fnFj5ia33+8Xcyq2lS7MqjmUw+HQXM6DwaBI40m0isUistms5L1yTigP7uvrk+NI+lV3bpWMkSTRDI39tW1b8tmBkdq9fX19ci3mtedyOSFWrVZL+xvDvF+Ogfpfy7I0Yy7bttFqtTA8PCx96u3t1QypWFOb9+CmiVqzmetLJXlsR6FQ0Iy4uPFAv5ZqtYrh4WGZt0gkItdWN124WaKapXHdqjL0drsNv98vSrBsNotmsynzxpreai1ubhaVSiVZd2rdbK5PPmvd3d0yT9lsFrlcTtZnNBqF1+tFo9EQszRuZIw2jqMbOvtJ93xueqjrmP2mAR3z/DlvjUZD8qG5jtl29bsllUpp3yO1Wk02joARV2+mcXCNcWNhS1WHMUR6fDBE2mCzsDmE44YbbsAZZ5yBBx98EI899hguuugiLFy4EL///e8xefJk+P1+PPPMM1i6dCkefvhhPProo/jVr36FT3/603jssce0SPjoNm3ovHa7jb333nuMHJmYMmXKJvdrS2JdfdwQstksZsyYgUgkggULFmDatGnw+Xz405/+hEsvvVS+qDcV6XQaL7/8MgDgL3/5i/ayRgwODm5UjrS6OzxhwgQx9FHl3fV6Hel0WspCrAsTJkwYY9oGjEi5eT7/kPPz0cdu6D4GBgYfbDA3k6WmAEj+Ll+8gRHTKUbDgA7h8Hg84twNQByR1dI+kUhE+9nj8SCdTmN4eFg7hqZZjGa63W44HA6JsgIdchcOh9Hd3S3f7ySwfMll3moikZBI+dDQkDhX8zOgozxSSb5lWchkMigUCnJPl8uFiRMnynnlclkM00hw6IwdDoclcspc7tEkn7mfHNt2u41Go6HlhzNHlNG9np4eKbXEvzeMDJNQA5BIZTKZ1MgXDa14DI2/1FJdJHvM+7ZtW/LgSaRJ0NhuoPN3i+7kxOh3JhIzHgtAy/8mWq0W8vk8LMvSyo+x7BYAURV4vV6Zk0KhIO7WJF8kmjSy4rVYtkp1Oo9Go5Lr7/F4RFmhKjIymYxsyBDMpVfzyp1Op5jmcZ4KhYK0i/nozWZTyG+hUIBt21i9erX295pmaWq0n9dh37kpxfHp6upCKBTSnq9YLCZrhUTX6XRiYGAAhUJBxlo1XOO5NL5jYCOdTsvz1dvbC6AT+Gi32+LezXFk9FgtjUbTW3XDQN2UIpHmhhvQIbDqBsDmvtvxmoZIbzwMkTbQkEqlEIlE8Nprr23S+TvuuCMA4M0339Qku/V6HcuXL8dRRx2lHb/33ntj7733xhVXXIEXXngBhx56KG677TZce+21ADpfaEceeSSOPPJI/OhHP8J1112Hyy+/HEuXLh1zLRUbOm/atGn4v//7Pxx55JHr/QKYNm0a2u02/vKXv2C//fZb53Eb+yWijg9l5MSbb74pv99cPPXUU0in0/j1r3+NT37yk/L58uXLxxy7KV+Ac+bMQaFQwMKFCzFv3jzcdNNNmDt3rnbMgQceOO4caY7xyy+/rBmXvfzyy2i32+udA56/dOlS5PN5TWr+hz/8Qbv+XnvtBbfbjZdffhknnXSSHFev1/Hqq69qnxkYGHz4wHJTlIOqaLVaQnKoolFNjlifN5fLyQsvI9ejpdY02AIgL+k0yuK16N7MiCVTYNSSWLZtw+fzwe12y8s0jazUFBaWDiJZolt3oVCQ6CddpdV6ys1mE06nE8lkUoge3b+JSqWCaDSqkWESFLXsEd2JVSMlNfKsEk+v1yv3oOyaTtgcMxJ0fue3Wi309/cLqeF8sm0kVpQlk+wxwkgXcl6LEUQStFKpBI/Hg56eHmlHu90eo6ZiSSu2nwQKgBYRpQEax4zX8Xq9okhjJH5gYEBSAzh3aq1mkju1DjlN4/gZo9rq+sxms3A6nejp6ZE1RUKnunKHQiE0m01RtbXbbYn+q/PWarWQTqelbVybDodDS5UgieVY0JBMTZPo7+9HvV6XjSoqCehIzs84N0zXG319StfVDRaWr7NtW67BOVGVJ9ysUWtQh8NhhEIhIf3c+FFLznm9XrRaLc2EjqXM6O7PfkajUc1cz+VyabXi6dStblg4HA4poce1aLB1YcpfGWhwOp2YPXs2/ud//kcijio2ZChw1FFHwev14l//9V+1Y++44w7kcjnMmjULwMgXiYq9995b+2PCP+oqSITWlweyMeeddNJJWLVqFX72s5+NOVbdwZw9ezacTicWLFgwZqdP7V8wGBxT3mttOOCAA9Dd3Y3bbrtN68MjjzyCv/71rzI+mwv15Yqo1+u45ZZbxhwbDAbHJfV+4IEH8Ktf/Qrf+9738J3vfAdf+tKXcMUVV+Ctt97SjrvnnnuwZMmSDf477bTT5JxPf/rTSCQSuPXWW7Vr3XrrrQgEAtr4DA0N4Y033pA/RADwxS9+Ea1WC7fffrt8VqvVcOedd+Lggw8WpUE0GsVRRx2Fu+++W15oAOCuu+5CsVjEiSeeuNHjYWBgYGBgYGDwQYApfzU+mIi0wRhcd911eOyxxzBjxgwpDbVmzRrcf//9eO6557TyTKORSqUwb948zJ8/H8cccwxOOOEEvPnmm7jllltw4IEHijP0k08+iQsuuAAnnngiPvKRj6DZbOKuu+6Cy+XCF77wBQAdl/BnnnkGs2bNwo477oiBgQHccsstmDx5sua0PRobc95XvvIV3HfffTj33HOxdOlSHHrooWi1WnjjjTdw33334Xe/+x0OOOAA7LLLLrj88stxzTXX4PDDD8fnP/95WJaFl156CRMnTsTChQsBdMoz3Xrrrbj22muxyy67oLu7e0zEGejskF5//fU488wzMWPGDJxyyilS/mqnnXbC17/+9U2dNg3Tp09HPB7H6aefjosuuggOhwN33XXXWjdC9t9/f/zqV7/C3LlzceCBByIUCuH4449f63UHBgZw3nnn4VOf+hQuuOACAMDNN9+MpUuX4owzzsBzzz0nu6WbmiN9zTXXYM6cOTjxxBMxc+ZMPPvss7j77rvxL//yLxJd4H3nz5+PpUuXSt3ugw8+GCeeeCLmzZuHgYEB7LLLLviP//gP/OMf/8Add9yh3etf/uVfMH36dFnnK1euxA033ICjjz4axxxzzLjbbmBg8MGBbdsSqWJ0iZEwVToLjLx4MrpXq9UkeqoaNzHdRZXF+nw+iTCWSiX4fD4kk0lNJsxyOtw0ZJRXzZe1bVuiU9ykbjQa2sawbdvyM6OTzOVWI8I8jmZZvGcwGEQymZRN4L6+PlQqFekP69mm02lpA6NqqjQ9nU6LHJ6fMfdTjeCyfBGvVSgU4Pf7EY1GZVxZUzcUCsnfh0ajgWw2q/WJ0Tp1Azsej2slssrlMgYGBqSMF+eWeef8jKWLVCltrVaTXFiORzgcRqVSkXmjWRr/n+Pq9XrFkI3toAJBLU/Fsmtcj1RDqHPENjA1iipDtWQTx8Dj8Wi5+JZlIRaLyVg1Gg0tasqa0blcTjbfA4EA3G63Vj6NOdT1el1kz8FgUDwHKC1n+7kGyuWyRK2pMmANab/fL3JpKj7y+bxWpowl4VSjvnA4LOM1ODgosm72kXJst9stzy9TA1QZPcdBfQ6ZBsA1wDJaqlIkEolI7jTHjLJ3VSbebDYljYDvaaxTrs5dKBTS+mhZlpQzUxUkmwMj7R4fDJE2GINJkybhD3/4A7773e/innvuQT6fx6RJk3Dssceu1V17NK6++mqkUincfPPN+PrXv45EIoFzzjkH1113nXx57bvvvpg5cyb+53/+B6tWrUIgEMC+++6LRx55BJ/4xCcAACeccAL+8Y9/4Oc//zmGhobQ1dWFGTNmYP78+SLxWRs25jyn04nFixfjxhtvxC9+8Qv85je/QSAQwM4774yLL74YH/nIR+R6CxYswNSpU/Fv//ZvuPzyyxEIBLDPPvvgK1/5ihxz5ZVX4p133sH3v/99FAoFzJgxY61EGgDOOOMMBAIBfO9738Oll16KYDCIz33uc7j++uvXu0kxHiSTSTz00EP4xje+gSuuuALxeBynnnoqjjzySMycOVM79vzzz8err76KO++8EzfeeCN23HHHdRLp8847TyK8/OJMJpO4/fbb8U//9E/44Q9/iG9/+9ub1fbzzz8fHo8HN9xwA/77v/8bU6ZMwY033oiLL754o87/xS9+ge9+97u46667kMlksM8+++Chhx7SJO4A8PGPfxyPP/44Lr30Unz9619HOBzGWWedJZsjBgYGH16sWrUKXq9XSALQISDRaFRzGiZ5UI3FKKnu6uqSv3m2baNYLCKTyQi5YH4mX5SZN6wSdb7kq3JO1QSM7fB4PHC5XEJSgQ65UGs108yJ/QA66pzREljm/8ZiMY2UMN+XbaFrN6WowWAQw8PD6O/vlzYkk0mRppMgs6402wOMmDmpBl2sWUxpK92zmUdLcNODRMXj8aCrqwvZbFbkySRA7XZb3mM4/uwjTbJUsyuVnKj5vsPDw5pZF3No3W63kF/mTaubHaPNxnh/OmLzPOZ8UzFFd2jm2qvrgG0lkVYJoGVZkgNP1RzzmVutlkYKY7GY1IoGRszMCLp1c6OIY9hoNJDP5zVXduY7q/WgWadarfMcCoVkrWezWZHMc1OkVCrJGlM3pbheVDOwbDYLl8slEn/OBdeiakrHZ6Jer4v0XZWEJ5NJtFotjcSqGywcx3a7LWus0WggGAxqG0TcDMrn8zL+9F4gKea5rK6iGt2p96cJXrPZlHWhOs1nMpltVkf6w0ykHfaHufiXgYGBgYGBgcH/j3w+j2g0ivPOO0/K3zBdyOFwSMROJbY0imLki27czKEEIMSoVCrJS/DonEfmRw8NDckLdCgUEvMuldB7PB4Eg0Gt/JXH4xlDaOr1upavXK/XEQwGJQrOHEy32y2EhoQ1Go0KYW02m+LCrJYhqlar0i7mldLxGBhx9qZZGTBSFSMWiwlpY24piQHQIaeqA3K5XJboLV/cW62WRG/VKB3bphIRki7VyC0ajWp52oVCQSKjxOh5YtRfjSgyB9bhcGjllxhRBdaeGscIthrxpsGWSnQZ/VZd2UeXzVLniW2g6iGXywnhU6PoBDdTVCM9RstVwy1uBpCw0YVezbtnfr1qABYIBIRIMloej8dRq9WEwOZyOTgcDq0UG8uYjS4r1m63tXlyu93I5XJot9sSzWY+N9tKY7ZSqSRjSLLNzQvOCaP4XBvlchmlUkk2KjgeDodDxqtYLMomjpo7P3qeWAKPGy88l8ZrXCfcyFBN57ghwbG0bRuJREKMy2q1Gm699VbkcrkxJUk3BH739fb2bnS5WbVdfX19m3Tf9ztMRNrAwMDAwMDAwMDAwOBDDhORHh8MkTYwMDAwMDAwUDBx4kQprcSIE6XCsVhMIrqUGrNWLzASsWQ0C4BE7NQo8urVq1Eul0Xm29PTg1arBZ/PJ/mgdNSuVqtjSvawNi/vycgXI1h0BuYxjLxZliVR0qGhIQQCASSTSa18F3N7VXdmRjtHR9sYiWREUHUoZm6omnsbi8XEvVnNQWXdXEbx6YjM8afLebFYlLFgWSs1xzWbzcp1eFyxWJRSQmpprqGhIc052e12a+7HzWYTq1atQrlcljlnLiwlwZyner2OXC4nigNK8tUIL8eY46rWIGdElKXU1DrMlOjn83kpw8Vc90mTJsm1PB6P9AvoyIF5HVUxUSwWRVrNexaLRSnxxj6pMmLObzAYlDVGB/lUKiWKhkqlIm7xHDOv14t4PK5J0TleHJ9IJCJReDVfmeuca6Ver6NcLmslt6h8UNUKxWIR6XRa5jcYDI55biifZr1qXp9yaTX/2efzSdSXY+bz+USGTvm8Otb9/f2oVCoIBAJSVpMls6i44LlUOXBsGYVnuygjz+fzEmWPxWKYOHEiHA6H5sJusPVgiLSBgYGBgYHBdoWddtppreXzzj//fPzkJz8Z8/miRYtw5plnap/RiGdTQKkt8zGBEZks6/4CENMs1oQGOi/nfr8f8Xhc7p/L5USCzHNJfNQqC16vF93d3SJ/5fFqvialpy6XS+TShUJB5N0kZP39/chkMiLznTBhAlwuF4rFopBfklmVqIRCIcnvVfNxKY2ldNPv92tluUj21T4Vi0UMDg6iUCgIiYzFYsjn81i9erVWM5klxEi+PB6Plg+u1vJVS0UFg0H4/X6Ru9KwrNVqaaSN+e4kR+l0GtVqVY6hjFytCV0ul2VcSXp4P/Uzrgl1frkGCDV/VyWKXq8XmUxGM1njfUjIKpWKGImpcmZ1LVqWJURalYkPDw9L+S9gRBrNjRauY5ZR4j1jsRgqlYoQ91qtJrJtrh9KwlWDLW5q1Ot19PX1yWepVArRaFSIeX9/v0i32V+n04lCoSDtDwQCkhvO8aEpl5o37fF4JBWD+clc02r5KACS/wwA7777LgqFAlqtltTL5nhXq1WZ31gsJvn7ah1yy7JkTru6uuR7Q12zrNGtmo0xF5okmeuk2WxqdbXz+byMF58rzg8AyZlmHWpTR3rrwxBpAwMDA4MPNBYuXIhf//rXeOONN+D3+zF9+nRcf/312G233dZ73v3334/vfve7+Mc//oFdd90V119/vVbf3OC9w0svvaTVpX3ttdfwmc98Zr2l6SKRCN588035eXNe7pinqua10jG5VqsJgW232/D7/eLgC0BqwbIuMdDJiyS5IEmjiRL72Wq1JP+SL90qeeJ5jUYDtVpN8iwJ5oOqjt/JZBKTJ0+WNjCfmwSBub/tdltyl0mM1Fxb27aRzWalPTy3UChgzZo1ADov+lOmTEEqlRLSw3rRJCu8FutskzQkEgmEQiG4XC45rlAoiHs47+d2uxGNRiXiSqKh5qqq9XpJMn0+H+LxuFYDmTm5alSZZITzbtu25LrzWlwXam1sRmETiYSW887rAh0yRsJEckfyn0wmNVdw5iJzPXg8Hpk7zkkymZRIO9cTyR3HgoSwUqnI9el6XiqVhCSrdcl5LnPGmVvNvHk1X58meqqSIJFIoNVqoVgsCpFWneL57DDHWd3I4ObMihUrZI7YPrVmtN/vF2UD10a5XMbw8LBsUtDUi/1WzeDUHOmVK1ei2WyKF4LqgcC1xPWl5twzWs7x8Xg82sYNz+d8jTYOU2tEc+0XCgXNyZtzx/GnU79acz2fzwuh3hIRaUOkxwdDpA0MDAwMPtB4+umnMWfOHBx44IFoNpu47LLLcPTRR+Mvf/mLRL9G44UXXsApp5yChQsX4rjjjsO9996L2bNn409/+hP22muvrdyDDx9SqZT28/e+9z1MmzYNM2bMWOc5DodDJNGbi2w2i0qlopk7MdKWzWY1t2xidJSa5XLYtna7LVFXoPOizFI4vH6r1UI2m5VIIWWiHo9Hc4im6RZfqMPhMPL5PIaHh+X6PT09iMfjch6jml6vV+SutVpNShipBJAEmy///Fkluo1GQ4ukBgIBRCIRTTpO0kPDLqBDkF0ul5SfAjrkLhgMauWWKOsmEVVLVbGt7LMaBXe73YjFYqhWq0KOuMGgkiRGOdlHfq5GfX0+n5RaIigPVk2rKPmNRqNSfaPZbGJgYEDWQDQa1dQH7KPb7UZ3d7fcM5vNioxadZceXWatWq2K1Jr9JqFRJeHc7FENpBiB5j3VdAG2sVKpYHh4WNai6qCuSqNpisXrh0IhUUyQlHMDRt1QYQqAauzGCC/XRT6fRyaT0dqvqhJUosvnS13Harkqy7IQCAS0zZNJkyYhGo1qbvrlchmJRAKpVEr66/F4xJVbLTfGtAuOCd3P2ScaD6rPKhUDDodDnhM6mquO8QTvx404NU2CbuVcY6qB3KbCEOnxwRBpAwMDA4MPNB599FHt50WLFqG7uxt//OMfx5RFI3784x/jmGOOwbe+9S0AwDXXXIMlS5bg5ptvxm233faet9lgBPV6HXfffTfmzp273he2YrGIHXfcEe12Gx//+Mdx3XXXYc8991zvtdVSPgA0wmRgYGDwYYMh0uPDh4pIt9ttrF69GuFw+EM96QYGBgabC9u2USgUMHHixHGXygBGdtc3tw2jv8sty5Ko1bpAuSBNYtaGF198EXPnztU+mzlzJhYvXrxpjTXYZCxevBjZbBZnnHHGOo/Zbbfd8POf/xz77LMPcrkcfvjDH2L69Ol4/fXXRdq8NixcuBDz588f8znLMDmdTjk/EAigr69PonwARGJqWZZE7oaHhyU3U40c0ZSMuaqlUgmZTEYixgA06TTPYySYkWaaQQHQopH5fF6TGzOiqUYwGdFkJIztcrvd0o5Wq4Vmsyl1iXmubdtoNBoS/bIsC729vSJdrdfrkvfKY8rlshhPqSWZaKqm1tCu1+taRJpSa1XSy0iwGunM5XLac8+IOqXCAMR0rd1uy3EsnUWwhjdNz4BOJNLr9UoOOuecueEcf5fLJZFfNbKcyWQkKs5IPAC89dZbslYSiQQCgQC6urrk+jQ343drqVSCZVnw+/0yd9VqFeFwWNZYq9UStYK6OUQJMfvNcbBtW8aHfWLJLmDEKI5zVKvVpF1qqTHmBavpCPzHe1K6zJrWXBu1Wk3ux3arkv8ddthBxpj9Zh5xvV7X1Ap85lQDL3WOK5UKfD4fgsGgtIG10VWPALVkF9tfr9cRCoUQCoU0szfVQ4HXo+qDP1erVcTjcekH/RJUIzdGo5kewDGjsoL9ofkhUwTK5TIGBgbElEytr76pMER6fPhQEenVq1fj8ssvRzweR7vdXu9LnNvthtvtlj8mWxo01lCNC0bD4XBIvs6WKLI+Ghvbx41p66ZiY/to5mN8bd1UmPkYgZmPEayrjz/96U+xYsWK9RKVtUE1cdkchEIhIS/EVVddhauvvnqd57TbbVxyySU49NBD1yvR7uvr08xngI5Uljl/BlsPd9xxB4499lhxvV0bDjnkEBxyyCHy8/Tp07HHHnvgpz/9Ka655pp1njdv3jxtwySfz2PKlCmau6/qxl0ulzWyy7xIlZyqZkAk3D6fb611nVlDGIC4KHd3dwspIZlXSQmJXalU0oy5KG+m2RJrGJMYkPSoebY0q1LrNzOvmXnePI4uyfyOoWM07/f6668jk8lorsg0hlJrV/O7Ss3vJnmloRnbGwqFNDJPgyy2ld9NhUJBI4+s602ZNetuMw8V6JAX1ZU9k8nIvJPMk1xGIhHZyHC73ZLLyrlTZdK8HtvE9tMJnf/PNtRqNRQKBZkT3kO9PglZOByWPpXLZSFuXE+WZYnknm1hfj3vqZI6dU44vqqRm9qGWq2GYrEo+bgcf4/Hg1wuJ/NGB3s15581qFX5OWszq+ZpTqcTq1atknU3bdo0BINB9PX1yT0p3w4EAtr4FwoFbUOItdu5cUXSq9aWHh4eRqPRkBrRwEhN9larJc8hn7VwOCx9qlQqUhOa65W119XNAb/fP6Y+Op9DldCr1+V4qHnX3Bgrl8uaYaHP55N5+jAT2m2FDxWRDofDiMfjmDhxIuLx+AajKHzI1XIVmwva1/PhVnND1gYaY9DtcVMiP2vDePo23jZvLMbbtw/+fBQR9DQQsJRr+lKAQ2+TmY9Nb/PGwjwfHayvb3wpUF/+NhZbauOjWCxixYoV4poKYIPR6Dlz5uC1117Dc889t0XaYPDe4p133sHjjz+OX//61+M6z+Px4GMf+xjefvvt9R63LgUDI6FqBI1kod1uy7oPBAJCMEaTC4fDIRtPJAyNRkMrTaRGFH0+HyKRiGYYRHKhOvIODg6iWq1qkXGaQKnRYBpWqSWOaDQ2uuwOS1sRjUYD+XxeNgVcLpdEqEfnhXMzi8ZcXq9XI4B0viZRGRoaEkdtPruBQEAM3kiwmdfM8ajX6zIvbBfdqNXoOcc/GAwKieLY07AM6GyaqIZyJLPq5gbvz/xafsYNBRKtVqslRFJVDkSjUe0YEmlGn3t6euS7lQSqXC6LiRjHotlswu12w7ZtuT7TEjgfdDmvVCqyWcDos+oQzShtNBrV3KKpOFAN2lRjKyoa1M0gNYdbzf92OBxi7saxbjQaKBaLWv652h+a4YXDYZnLYrEo/VPzvhlJ5trjOlZN+TjWqts6j1UdwFn2S81FZikujiOfN44B0NnIVR3Suc5CoZCkiHCDpVgsyuYG1+Roh26Ov7rRFgqFZHxKpZJsoPX398u12A6u2c2FiUiPDx8qIs1dUtV8Y32grCOfz2uypE1Fu90WSUhPT89GL3qfz4ehoSFUKhUkk8nNJgvcJU0kEhvdp0AggHQ6jWKxKBb/m4N6vY5sNgufz7fRffrAz0csirB7VH6e3wc4x7bLzMcIzPMx0oZtMR+b8wd0U/5gE3wBjkQiGpFeHy644AI89NBDeOaZZzYYRe/t7ZWXFaK/v3+LmVkZbBzuvPNOdHd3Y9asWeM6r9Vq4c9//vMmu6zTmXh0XVdGA1UTIlW2DUAiXKwDC3S+V1jDmC/sjEKpxHFwcFAjpw6HQ6TjfDkn4VTr+aq1b0lUuAmgRspHl4UqlUpCFPkZI8aqzJpmYerGA2sns12UD6smXKzFzRq8ALBmzRqJBquRNZISEh+alqlEnQSWY0YCpJq4qSW9CJYKU2XhLpcLiURCxqtSqaDZbGpE3el0CjHkvFmWJaktJFo0sFINsJLJpNRxBjobCHRGJ5Fm/eJ6va6VmfJ4POIEzrb6/X6JOI/+TO03CRgAiYZmMhkMDg5K+4PBoLQBGJG1s1STOufqWHN8OJdUKambUaNN0tT16fF4JDWhp6dHI6bcDKYqAOgQcJYBU9diNpvVpPKsER4MBuVcqg1U8zFuFLB9NJtTUwgYPa7X61rprFKphHK5LH9bI5EIarWaOK9zrtTIMM3tKAPnWHBTgddyOp2oVquisOCaUo3RgsGgtunGezHdwuPxbBFVnCHS48OWCd+8j0CpysYiHA4jEolotdw2BXwpZeH38bxoe71edHV1odFoIJ1Ob1adOJYyiEQi43rRdjqdSCaT8Hg8GBoa2qyIUr1ex9DQkHyhmvngfKzdPXhtMPMxAvN8jGB7mI/xgH+wN/XfxsK2bVxwwQX4zW9+gyeffBJTp07d4DmHHHIInnjiCe2zJUuWaPJhg/cW7XYbd955J04//fQxSo/TTjsN8+bNk58XLFiAxx57DH//+9/xpz/9Caeeeireeecd/PM///PWbraBgYHB+xbv9d/jDxo+VBHpTQVfqLnrOt5Iz5Z4KSVZGBoaQjqd3qTI26aSBIJkIZ1OY2hoaJP6sjkkgfjAzkd7fOTLzMcIzPMxgu1hPjYWW+sP8Jw5c3DvvffiwQcfRDgcljznaDQq0a/TTjsNkyZNwsKFCwEAF198MWbMmIEbbrgBs2bNwi9/+Uu8/PLLuP3229/z9hp08Pjjj+Pdd9/FV7/61TG/e/fdd7XnI5PJ4Oyzz0ZfXx/i8Tj2339/vPDCC/joRz+6SffO5XISCVNNsiinVQ2TWF9aXctqdBOAlN1Ro1U0qFKjY5Tzque53W7kcjmJWLLkkcfjkfXrdDqRTqe18jnhcFiTprPUUH9/v0huma+pljaq1WoSCVNr2jLHlZuV9Xodfr9fJLOMMDNiDnTkr16vF/V6XTNoG53TyUhuKBTSxpFtASDRRkqoAYj8nnm57FO1WpUcYmCk5vXKlStlfHbccUetzBojyoVCQSvz5PF4EI1G5TzmfLfbbYlG+v1+iSKr+e2JREKLbnMuOK40iVPrH7NkmJrXHIlEpP+qrJ1lsgBo0VaqdBiZrtVqeOedd+S4yZMna/nnnFvmB3NsVRMurhlKvoHO+qdSgePPNazOHcdTTVFgWSg1X5kRaDWH2+PxoFwuy3mUb6vRc7WdvFckEsHg4KDMUTqdhtvtRiqV0uqER6NRBAIBmTd6BqiKEqogVEUDo+BUHJTLZVFQsC1+v1+ee0bPGUXmdwLbHwgENO8Qqq4YiXc4HIhGo5oqhLnc1WoVuVzOlL/aBjBEeiOxqS+nW/KldHPIwuaSBGJzyMKWIAmEmY8OzHyMwMzHCLaH+dgYbC0ifeuttwIAjjjiCO3zO++8U5ygRxOz6dOn495778UVV1yByy67DLvuuisWL15sakhvRRx99NHrlCo+9dRT2s833ngjbrzxxi12bxoTqXJJt9st8la+YLfbbTSbTc2h27IsdHV1aTWKmc9MY0Deg4ZgBE2f+OzxRbxSqchxNCAKBAKSb+lwdOrJDgwMiBolFoshFovJ94Bt2yI1Zp62ZVmIxWLwer1aLVrKT3l9ylPpiA108leDwaCQNofDod2b5zE/lGMRDoeFDKpE1+12a1J3OnmTXMTjcZG9q6SJruZsP8e90WhohmZer1fqJQOdTQo1B5ubJHRoBjqbbUyxIXnJ5XKSZ8w+qYSeZKZUKslnvD/l1Kokn5skXBckXWqNaG7WuFwumRPeR83hLxaL8Hq9GlH0+XwIBAIiqeb6UpVTlPurOfAq8ePPzGnmZ5RPqxsqlIRTcg9A8vKbzaakzHg8HsTjcZmPWCwmub78mxUOhyXNgOvO6XTK/fgc5nK5MbWlOd/cOK3X67JhxL6z9nmj0dBk+lzXHEeS1Xa7LeNOszGuz3K5jGq1KuMIjGwWcG0DnWeu2WzC6XRq+e2jTcm4MaJ+19C9X5WvM72E5mpbAh9mYjxeGCI9Doz35fS9eCndFLKwpUgCsSlkYUuSBMLMRwdmPkZg5mME28N8bAhOp3OzcqS35LGjiRkAnHjiiTjxxBPH0yyDDwhILmlEBHTWEXNJadLk8XjEkZvrjKWFXC6Xls/Ka6g5kLwmfwd0CJJq/FUulxEIBIRwMCdV/Ux1+iWxKhQKkoMLdL5n6vW69nyTdObzeSEXqpmYmr9N4yQSB0YKSQD5PPf09KC7u1trv1ruTiVBqtMzHahVd2x1M4I53qrBFtul5rgWi0VxdmZ+NUlpKpUSwl2tViWKz3FV88OBDpFmNJvX5waLmqvN/O5QKCTzyb8BJDeBQGCMP8/Q0BAymQyazaYQ5FAoJAoAXp+bJ2peeaFQ0IgTDe+8Xq+Ma7lcFmI+adIkGddSqYR8Pi9rMRgMitEV26iairGPJJxqWbFms4lkMinrt1qtSm45546lvNScfY4Z55vqi2w2q/kNsE8cf6o9VFM7tY0sN8ayXKr5WyQSQaVS0Yg0Xd85lnym1Hz9QCCAarWqqUWcTqcY6XFcuU5UHwSeo5Jfbv4Q3KTgxgcwkt/On2lyV6lUtO+QYrEIy7JEMWOwdWGI9DixsS+n7+VL6XjIwpYmCcR4yMJ7QRIIMx8dmPkYgZmPEWwP87E+fNhzqwy2X0QiEZGQkpQwyqaWQuLLrc/nE4I2ODgoBJzPM0mR6tzMWspqrVq6Z6uf0Y2f9WVZg9a2bWnb0NAQ8vm8di5JtWr4xKgcj2k2m+IorkqvKbFWHZabzaaUwAIg5ml8eXe73VIRhe0i+aPzOFGtVqXOL8FNBVUirJbbymazMkYqkeCGhWrmlEgkxGAMGNmMCAQCchxluiSAJKt+v19IDqPpDodDyq8lEgmR546WRrNUFtuRz+dFMqwSY45ZPp9HrVbTTBPZN9URmhsMjUYD7777LoBOSVfbtmVTh/W51Y0QkmMajAEd0lYsFmW9AEB3d7eQUBJdpgJwvFhfXC01xrlQCT7XiPrdzrUOjFRW4EYSz6OCgP3hOHHtcHOjXq9LJFmVm5Osq88XS4Lxvvybr0Z5uea4keFyuZDP5zEwMCAKAtbcDofD0q9sNisqDV6fxnFqVJxrnZ8FAgFJP1BN1VqtFkKhkKQbuN1u6QvPq1QqyOfzmgGcahKnOv5vKoy0e3z40JmNbQlsyNBna7yUbozB0ntFEoiNMVh6L0kCYeajAzMfIzDzMYLtYT7Wha1lNmZgYGBgYGCwYZi/x+PD+yoivWrVKlx66aV45JFHUC6Xscsuu+DOO+/EAQccsNXbsq5Iz9Z8KV1f5O29JgnE+iJvW4MkEGY+OjDzMQIzHyPYHuZjbfiw/wE22H7B0mostcPPaEDEiGupVEImk0EikZDI3eDgICqVCiKRiFZSyuVySUQVGIlmM1Lo9XolCk5jMVU6rp5XqVTQbrelpNGKFSuk5rxamqtWq2kRRpohMfrJKLDP59POY91cfi8lEgk4nU784x//kL673W5NXuv3+2W8Vq9eDWDEJIs1g4HOc5/JZESmzc8o+VbrAKvyaeZ4B4NBiVqzBBgjrARzyBlRZO6qmo/Ocknc5HS5XJKDqioO+vr6UC6XtXmyLEurSc3zKf0HIHJnNeLK6Cgjh6xJzlJZwEhNZjWyzPJLtVpNItzDw8MIBALSrlgshnK5LOPE8yiLVjdzPR4Puru7JUocCoXE0Ivzm0gkEAqFNOk763Wrc9lsNrXoNhUJavkuy7JkftX8eIfDgTVr1gCA/E2OxWIy54VCQVQVbJfD4UBXVxdisZjk+rNNHo9njDRdzYFnNJfzUKlUxOROVYHk83lks1lZn8FgEKVSSUzlgM7693g88v3QaDQQDoclr5vzy1x/nsc5ob8Cz83n89IerifK1NkG/pdz4nQ6ZbxKpZIpf7UN8L4h0plMBoceeig+9alP4ZFHHkEqlcKyZcs0CcjWxuiX02AwuNVfStdGFpj78l6TBGJtZAHAViMJhJmPDsx8jMDMxwi2h/kYDUOkDbZXsJ4tJaPAyItzrVaTl3qaD61evVrWcqPRgNvtRq1WE6IVCATEBVut+9toNIQY0QirUCjIC7RlWSIpJiGgEVi1WhW5K3M13W63tIPHEYlEAo1GA/39/XItv98vOd1qbihzX3l91i9WpbPDw8Pi+M02DA8Pay/0brdbpPAcM44L78cxo3yW45PP58Wwi22IxWJaXnm73Ra3ZPaVEms6MgOd77tcLodSqSTXp6M4+0Mps3rPQCCAHXbYQTMD4yaDajDHa6jkiJsDqpnWaHkz82fZD2BELq3mizMXViXvqVQKyWRSMx9zu90IBoPShlarJXJjziXJYTKZHCOjJ1kGILm4qsM4STnJI+sjB4NBzUGehnD8u8K1qZqBUeLMfvPvtJrGwHWgbkBR4l4oFKRPXKvMFVb7yfZzE6Wrq0vmbXBwEI1GAw6HQ+vn6OdL9TtQ0xGcTqeMBXP3w+GwPF/ZbBaxWAwej0erme7z+bRnn3PXaDSk1jhzprlWmOev1hLnmJRKJRSLRePavQ3wviHS119/PaZMmYI777xTPtuYWqBrQ7PZ3Kw6ryqYE5HNZpHNZuFwOITcb6l7bAyi0SgymQxWrVoFALK7tjXbEIlEkMlkNEdGNYdla+B9PR92A2iO2k1sNIBN/H4y8zEC83x0sCXnQ31B31QYIm2wvYLpGIlEQogKS1ypqSI0cioUCvJynkwm0Wg0kMlk5Fg1csgXetu2tahsOp0W52F1Y2toaAjRaFTaEY1GJUeaG2S9vb1yPF/0STBVU6NUKoV2uy1pHoyUM+IJQHK++SIPdHKFeS2SOxINNTpfLBYRDocxYcIEGR+g47jNa3EjMxKJyPgMDQ3JBgSJUDQa1QiUZVlCotWSTNVqFYVCQb7DSCRVwkNH82AwqBmhMcIKjDgsl0ol+Z5mek4sFtPKljESS9AEqlqtCvFhvjojpIzOc6w4/iTIJJS1Wk1TCLCdzAfnOiNBY1Q2nU6Lu7eaK6sSe44/CTDd1ev1upA2toPGWWoEud1ua5tBdKSmuzkAIdaxWEw2jp1OJ/L5vBa5jsfjKJVK0s+uri6sWbMG6XRaNpKCwaDkn/M8Pm98BrjOaLbFNcWyXKo5WCqVQiqVEvJLl2y/368Z/HFc+HyRoHMzAOg4hZMIsw2lUklz9W82m6hUKvKcAZC5LZfL8hw2m03E43F4PB5tc4Al7Ajm8aubOrlcTsqnbYl3GkOkx4f3DZH+7//+b8ycORMnnnginn76aUyaNAnnn38+zj777HWew1IGRD6fl5p3WyIhn2A9RADyB2lLWdCPBzSaYJu2ZB/H0wY+UJZlbRMHwfftfNgtoD46l7cOODa97WY+RmCejw621HysLe98vDBE2mB7BY3CGKUERgyHSN4ASLkglqABIJFsABop4Quw+plqSEbDL5fLJS/yNIsCRty01aggn91UKoVaraa9xKtSUmDEhXjChAnycl4ul6UcD1/qo9GoyJxJ3klI1Bf7dDqNbDaryVij0SimTZsmBKRWqwlBZvklSmvD4bAQStYhZh+AkTJZqqEXXZxJeliXWI2ou1wu5HI5kc8CHVVjPp+Hw+EYI9elPJ7Sa9WMiuXCGo2GRN7Z7mKxKCTZ5/NhxYoVKJVKMu6VSkWrDaxKtfn/fr9fNil4z1KpBIfDIQZgAORewWBQ5onqInU9UVI9es34/X75rFwuI5/Pa4TM6XSKpFxVJlBKz7mkS7talovrn6Sy1WqJYZsqwadMn2ubUV514yQUCuFvf/ubtlHLjQyuH/7NCofDQuiHh4c1OTjPU4lvLBZDd3c3LMsSoq6adamEm4Z83PQql8uycaH+7WPEn/dTjfc4hkynUOvRl0olef65pvh71agPGFGitFotmR/OOQk6v6tU0r2pMER6fHjfEOm///3vuPXWWzF37lxcdtlleOmll3DRRRfB6/Xi9NNPX+s5CxcuxPz587XPAoEArr322jElCAwMtjkaOWD1I/pnE48FPNFt0x4Dg/WgWCxizpw5m3UNQ6QNDAwMDAwM3q943xDpdruNAw44ANdddx0A4GMf+xhee+013Hbbbesk0vPmzcPcuXPl53w+jylTpiAWi8nOooHBdoO6AyiMeiTjMcC77XwADAzWBdVkZ1OxtepIGxiMF8FgUHJ7GRGiUZIqKa1UKhIpYmS52WyOiWhls1nkcjnNKIhlpxiJisfjYqrESDCvp0YUaTrV19cn0bGenh5EIhE4nU75LBqNavJgRpUZhQNGlHvsA9B5tlhXV61jHIlE4PP55DOWDeJYxGIxKZ2ltpX5obwn5ev9/f1abrHD4UA+n5fjKFPm9dU6xvzeYO65z+eTyCBNFNWIKGtzU4UDjET7OR/NZhPDw8NotVoilafhnJq77XQ65RpqRJd1kNk2qgtU46+//e1vACB570wNUOXqrGGu1izm+aNl1SzHxj729fWhVCrJmmJ5MrWMFeX5tVpNkxa3Wi0kk0mJGJfLZXg8Hplvll5SI8GMkLZaLa08FdOWmO8bCARg27YYzfE4tYQY53zq1Kny3KhKCDUiXa1WNQM+KhnC4bA8h/V6XVvD6phS3s/nrF6vyz2dTic8Hg/y+bxI/JmzTuUE14Ya1Qc6zzkN8IBOpJwydNVorlQqod1uS6krRvDz+bwWzVbl2kx7UCPS1WpVFBOqEmFzYCLS48P7hkhPmDABH/3oR7XP9thjD/zXf/3XOs+xLEurW2hgYGBgsP3ARKQNtleQWKgy30ajgWKxiHK5LC//lLX6fD6RYjocDlQqFfj9fnmpp4y1XC4LMa9Wq5okmbJsNU+YxJsu/zyPObp8+a9Wq4jH45oUmi/0fMbK5TJKpZJWv5lEqVgsCmGlWzb/n23jC71ap9rn80kbaMzGcQM6BIX5y2wXXa9V92RuWKj3pjGYmltN6bIqYeWYUkKt5jCrsnCfz6cZTTWbTfh8PjG2YqlEv98vub2qtJ33zOVyaDQaaLfbkmsbCAQQjUYlfZDjQcLK9pNIq2RMzTln+9XxADqkiuuHbVKvzT7mcjmtZnEikZDNHzUXeWhoSHN0p9kWST3bw9xbYCRPnu0BOqSfmzFsa71eH0P4mIagblx4PB5xqgc6BDkej0tetDpvo0kzpfujHcXV9cO5VDe4hoaGtNrt4XBYnNX5nHg8HjEbo8za7/dLtQ8+m11dXYjH4zKuaj121Y2bGz8cR35/qJtvdNR3uVyyCcI2cCwajQZWrlyJQCAwxhCONbO3hOeKIdLjw/uGSB966KF48803tc/eeust7LjjjtuoRQYGBgYGmwNDpA22VxSLRbTbbViWJS+7uVxOM7QCOmZUoVBIIkJA54V6tBO/x+ORMkG8htfr1Vyd+RJM4gx0InR8yVajoF6vV4gg0ImYNZtNLYJYrVaFfPL6NI9i+/gMer1ezSuBGwNqnnU+n8fAwICkxgWDQdRqNSnVxXYBI2Sb5zEayXbQq4GENRAI4N1334Vt29IOOjirUeV2uy3RTQAYGBgYk7drWZaQMRI+ljiiyzr7HgqFhIAzqskxByDO2aoygXnfnAP2ieOqRoxVslqpVGQMVNd3l8uFYDAoc0KSpfp40OG9VqtJ3wOBgOb27XK5kEgktNztZDIp0WKVdEejURSLRW39OBwOrVwXc6lJ9GnIRULMNdZsNsW8DBgpqdbV1SVt6+vrk2NVd3uabHHeeG/VDCyVSsHhcMg6Y7tpzgV0CDGd33l9biKpuc/tdhvDw8Oaqzw3nNTNGa5Rts3lcsHv90uUm/Pp9/s1l3yWueK68Pl8iMViqFQq0rd8Pg/LsrSceUb76cwNdJ79eDwukf5sNou+vj7pG9vFNb2ljJQNkR4f3jdE+utf/zqmT5+O6667DieddBL+93//F7fffjtuv/32bd00AwMDA4NNhJF2GxgYGBgYbB8wRHp8eN8Q6QMPPBC/+c1vMG/ePCxYsABTp07FTTfdhC9/+cvbumkGBgYGBpuAzYlIf5j/cBu896jVaiIPZtTO4/EgGo1Kri4AqTms5iJTZqlKbOmuq0aM/H6/Vl6LUSvmEAMjucNOp1Mkn3RYjsfjmgw3nU5rUmE6cVNm3W630dPTg1gsJu1ghDMcDmsS8Hw+j2AwKJHNQCCAQqGAdDotOa5Tp06VevRAJ1JIubY6Zm63G36/X6J7rE0cCoW0sl1DQ0OabLtUKmn5vnQlpss4x5olmXhP1sbmOUBH4lyr1aTMFtDJSQ8EAjJ34XBYc7bmnKhyYY4Z82U5PpSXq7WlGY1X5cejS6DxnEQiIe2gDJjRcWAkn1itOFGtVpHNZqXN3Fz0+/3amDElgfPm8XjQ1dWFSCQypjZyIBAYE8FVq12MrgVdKBQkz1qttx4IBJBIJEQ2zzJf7XZbc1KvVCpaZJaRc0ZhWTauXC5jxYoVADrreMcdd9Sk7aztHolEZJ0xP5z38/v98Hq9yGazWu5zu91GOBwW5UOxWBQVBdtPtQBVAPxMLYdXq9Xkeuqa8ng8knvPuWQ+vbqOVXk5r1+pVDRFCdUSXFOUfzPV4P0UkX7mmWfwgx/8AH/84x+xZs0a/OY3v8Hs2bPXe84999yD73//+1i2bBmi0SiOPfZY/OAHP5D8+W2B9w2RBoDjjjsOxx133LZuhoGBgYHBFoAh0gbbK5j/SxMgoEO8/H4/BgYGNLlloVDAzjvvjMmTJwPoyFj7+vowODgoMl9Kd9X8RpIukhkSFLfbrRFdyrVVgmZZFrxe7xjix1xRAHIvkja1pi1f/inDVfOVWWe6WCzKPaPRKMLhMOLxuMhuWe6KxIUEJBKJyJipJbl4LRJhmikBECIwWmKuSuRJ5FRTJdu2hYjSQIolpwqFgpwfDocxODiISqUihLNer2s5u5lMBqVSSZMHezweFAoFNBoNMeFSS2SpxrXlchlOp1PL73W73UI0h4eHZQxIahOJBAKBAIaHh+WeJGdqDnA+n5cSXFwvNLDjBkKj0UAulxNDOY4Z5do8j/LqaDQqbSOZjEQi2ibC0NCQVkuc647jSgO00bXVuYbYX+b+q+WomEusSs5rtZrmSxCPx0WGznbE43GEw2HU63XZJCoWi7KZokr1uckCQIy71M0abvIEAgHtGVLz3Dnn3OQiSWbZNc5RvV6H2+3WNsLcbjc8Ho/IwvkZUzFIkmkI19vbK5tL2WwWq1atku8apoF0d3fLM53JZOSZqNfrW6Qs5dZCqVTCvvvui69+9av4/Oc/v8Hjn3/+eZx22mm48cYbcfzxx2PVqlU499xzcfbZZ+PXv/71Vmjx2vG+ItIGBgYGBh8cGCJtsL3Csix0dXUhkUhoxj65XE7yJQFoBlx8YSeJYNQYgOTONptNiXyFw2F4vV4hM3QY5s9Ah4AzMs5IM++rRqAYuR4eHtaIhErAG40G8vm8ECKgQ3qYH8rz+GypOcZAh1REo1GNkLH2MDBiHqXm96r343kOh0NqAbP2cLFYRKFQ0PK3eT2OP4mSavbWaDQQCoUwadIkiR4yb7RYLMq51WpVyAbJEaOTHG9G51UXZm52MGLIcaG7ulqzmySN5IjO77yew+GQa6i1w0kI1fP8fj9s2xYSxbrPKnFjLWU155j/ZZ1kRvTVHGCOBaPeHDPWqVZz9qvVqrRBdUdX56Srq0tzZef6V/PpWSebpmm8Pq8NQDZgfD6f9JHR12azKRHvaDQqBmrsU3d3t+S8q+1nbjkAid6r5Frd2Bidy86INud8tHlZtVpFLpfTzPCAzuYIiTjHX920ACAmemwbzdO6u7vl+qozOjDi7J3L5eSY7u5uuN1uDA8PI51Ob5G/i1srIn3sscfi2GOP3ejjX3zxRey000646KKLAHQUMV/72tdw/fXXj/veWxKGSBsYGBgYbBNQ4mlgsL2BZlWUZAOdSHOz2URPT49m0hQIBJDJZMQIiA7FlmUJOaUREmXfwEhpJVU6y0gwo5+tVksioqpbNl/C+aLvdDpFRsxnyuv1IhAISFsZqWQpHwBSzkeV9JJIRKNRrbwTpatsm9vt1kymYrGYmJQxuseyUYlEQuTGwEhZLJIQkpRAICDkq1QqIZ/PyxiSXI4+hsRILcvFSLNqGsZxJHkiuWO/R5f34j1VJ2ue12q1xE2aYGSW57tcLpGBA0Bvb69WggrQnbrV9qtEG4CQy0qlIn0PhUJaKa5Go4Hu7m4pDwV0NlwYKVePKxQKssnD42q1GoLBoBbNVtcwSbZKtCqVCiqVCmKxmBDdYrEoUXXOr8fjQbVa1arpcFxUUks1AdvfarXEDZvrjpLqUCikpTHk83kxAAMgZaFUB+3RJcUAYHBwEOVyWduU8nq9iEQimjEd1xivR4UB17Xb7UY2m5UINNvfarXgcDi0KDVTR3gc0wf6+/vlOD43fJai0SicTif6+/tlXcRiMSH9atm5zcHmEGluJhBbsnrSIYccgssuuwy//e1vceyxx2JgYAAPPPAAPvvZz26R628qzBuMgYGBgcE2Af9gb+o/AwMDAwMDgy2Hzfl7PGXKFESjUfm3cOHCLdauQw89FPfccw9OPvlkeL1e9Pb2IhqN4ic/+ckWu8emwESkDQwMDAy2CYy022B7BUvnsE4rAIks1et1ibRFo1HU63WsXLlSokHhcBjJZFIzIKMck9FhoCMn9fl8chxzc8PhsES5GMlSpcVOpxORSETyTgHIMcwjBkbqVDPCyNrYHo9Hi26HQiHYti0R9eHhYclzVQ3OWApKlc6yrjbbT0m8Kidn7WpKZ10ul1Ybl6DclTnY2WwW1WpVIrCJRELyavmZ2+1GuVxGJpORa/p8PpRKJcm75lgwQq2W5rJtW0pwJRIJuFwulMtlOY91uUdHvJmnO7rkE3PC1blTr8V2U9JMAy7mzXLtMadejZYXCgVtzkfn8TKSzRJVnEvmObPfjNbTHI7XYuSX0eBsNgvLsiTqG4vFZH2xrX19fcjlclrkXR1jdX4pS2fbLMsSwzpgpH7z0NCQjA9z1tXIuKqo4NjSVE8tHcfSdJwPRoIZmVbXopo60Ww24ff7RU0BdKLbjOqrRl9qnjzXg5qjTtVGtVqVeWPddvX5SqVSMjYrV66U8UmlUmJG2Gg05G+malxGVYXX690i1Sw2JyK9YsUKzTdgS0WjAeAvf/kLLr74Ylx55ZWYOXMm1qxZg29961s499xzcccdd2yx+4wXhkgbGBgYGGwTGCJtsL2CpHS0DJqO3apcmrJY1e2Yuask18FgUGSrvN7g4CByuZzk9no8HpFnksyQHKnuwHxZ58sz0HnRZx4yj6tUKlp9Yua7qi+6rIObz+fFybhcLkv71bxmj8ejfcb7qGTJ4XBoebbMQVZzUNvtNhKJBHw+n0YISALY91KpNMbYyrZt+cdx9Xq9KJVKshEQDAalpjbbNjAwgEajgWQyKZLmcrmMSqUiL/vxeFzylXnPer0uua0kR3TfVqXpJE/xeFyTk9Ngi+uHY892Md9YJVWRSAR+v1/6y7URDAZF7s7xAKBJ5ukyz3VRKpVEQkyi6PF4pCY4Ddoox2fuN9sbi8VkU4Tjrkq2ucGg1k5Xnw01NSCfz2N4eFiOo5M7xyQSiYj7vOqSzvFifzmvdNbmmqlWqyLf53HqZgefq3w+r23EMDdbJeqUmaubInQw57p3u93isM61wueQ/aYBnNvtRiqVknbRDZ7fGZRA5/N5+YwbHuqmFwm5usY8Ho84+atGfZuKzSHSkUhE+37Zkli4cCEOPfRQfOtb3wIA7LPPPggGgzj88MNx7bXXYsKECe/JfTcEQ6QNDAwMDLYJDJE22F6hGvvwxVktBUSQAHZ1dWnkly+4ai4jI64kEiRGaqTW7XYjk8loJYU8Ho8YOAEQkyXmMgMj5Mvv9wsRarVaQpQBSO4m81yBkVxl/h4YyfVV3Zj54q66P7tcLo2os8/lcllMxPr7+5FOp+F2u4UQlMtllEollMtlIXeM9vn9/jFmX7w+Nx5cLpeQEuYp53I5iYyzL6qDcyaTgcvlQm9vr7Sj2Wxq5l2FQkGifarJFJ2ROT4ul0siuBwLuj/T2AvQHcu5ptRyWRx/9oHtoimdGt1mNFJ1uWb+r0rmaV7GY2iuxpxf9fpUNrCf1WpV5oX9HG2uNTg4iFarJVFSt9stDtrsG4mi6oHBOVcj47ZtY2BgQCO6Ho9HNpTUNavmMNu2rbmBj16z6u9yuZw8Dw6HQxQGPMbn84nfAD9jhFx1BbdtWzY31A0hl8slCgoaiwWDQW0zy+12IxgMaoSYEXZG3tmGdrst92fbVLdvYGQjDRhxKyfx/yB7jpTLZe27AdA3t7YVDJE2MDAwMNgmMETaYHtFMplErVbDwMCAZjTFl2e15q/f79ciaHzRV02fWP5JlVqTpPBckhuVbJMQZrNZeekOhULweDwa0apUKuJmzJdNkma2X5XmkuhS8hwIBCSySfk3o9D8jKRbJSqM/LKtQIfwkrxQ0j5a7k0HbVXuyo0E9smyLM3oi8RGnY9kMgmHw4FCoSBj7fV6xZCN49jT06O5XXMc1Zq8qrs4x9rlciEWi6HVaklbGQFUS3pRXksZONCR/ZfLZSGm6mYI+6BGi1XJMNcBr0+zKxpoASO1yUnsWq0WQqHQGCJNJYS6CcTUA36PMspMp24iHo8LSUmn00in03A6nVr0lpFZXsvtdsvzoZaGikQi6Onp0dbBwMCA1CGnMzdN8giuBzUyPnottlot6Y9q/sX63jw/FoshHA5rbutUnahRXjpy81y3241Go6GtDcuyhPRyLDiGatTa5/PBsixtnrhWOE806VPnhOuYYOqAmm5CJcCkSZMQi8XkHpuDreXaXSwW8fbbb8vPy5cvx6uvvopEIoEddtgB8+bNw6pVq/CLX/wCAHD88cfj7LPPxq233irS7ksuuQQHHXQQJk6cOO77bykYIm1gYGBgsE1giLSBgYGBgcH2ha3x9/Xll1/Gpz71Kfl57ty5AIDTTz8dixYtwpo1a/Duu+/K78844wwUCgXcfPPN+MY3voFYLIZPf/rTpvyVgYGBgcGHE4ZIG2yvYDQwEoloUmnmiI42MFLzNcPhsORsMkLE6KrL5RI5LWXbag6ubdsIh8OSNx0MBlGr1TTJLUsvWZalmWnVajWEw2GJFjLylslkAIyYlDWbTXl+XC4XGo0GyuWytH90WS4AkjvL3GZgJH9bzallDrlaB5hyZzUKS+kuo4DMMVfzgjm+qqR6woQJKBaLWp42TbzUSPVo6S+j65VKRdrPUk+cI7fbLVFjRiInTZqEaDSqlS3jeKhlqnidarUqffL5fNrP5XIZQ0NDACARV7XmtlryaXQZLka8Kd9ne5l+AACrVq2CbduIxWJaXrbf75c8ct6HioPRY1atViWaTOUD29VoNBCNRhEKhcYYf6nzxjxiVcbN49X8asrNuf5Zi1rtN0tyqTJx5vqz3jbv6fV6pUQax1uNNNNTIBAIiGQ7k8kgHo8jEAhIOyqVCgqFgqZo4PNbrVa1XO1AICA595FIREt7ACBlqTwej2Ywx+dLlelzztmOcrmMSCSipSJwHajrTs3X39ZmY+PBEUccsd72Llq0aMxnF154IS688MJx3+u9hCHSBgYGBgbbBKaOtMH2ikajga6uLoTDYSEq2WxWHIpVySVdivlSSJMgNd/Rsix5AeZLJw3NSOxUos7zIpEI0uk0HA6HvLCzbqya/1wsFkXOrNYLdrvdGiFWzcKAkdzMVqslBJ+ycZXM5XI5zUwNGKljrDpw84WepDOZTIocWa15zXPUvFf+zHtS7kwCGAqFpLa1Kvdl/WqSVMpj1Q2PSqWCRqMh0mHOiVrnlkQbGDHwYs62mqtdrVYlp5vXr1araDabmlydhFL9eXBwEAAwefJkACPyeHXjgnnPlAWzTy6XS6T+PJf1t9lm1itXyTb7y3mn27rq3k6Jsyox7+rqgs/nE+l1OByWfF+2lc7f0WhUk5OT1PFaKtFSjda6u7u1muCWZcHj8ch5gUBAHOrVHHia/DFXnrWnKc0HIGkAKlnjGh2drsE5ACBjT5d6oLMpUqvV4PV65Tkk0VbNtYaHh7X++nw+WSvqZ2yX6rDfbDbh9XplTmhgxzVWq9WkljvvGYvFEI1GxQlcfbY3FVuLSH9QYIi0gYGBgcE2gYlIG2yvIMGlORHQeTEn4VEjqh6PB9FoVCJ3+XxeIoeqURkdu0nMy+UyotGoRE8DgYCYZqkRP7r+jo6y8v4AxB1aNThj/ipf/Fn6R312QqEQQqGQZpI1urQX0Ml7ZoSPZCGXyyEWi8m1WC6oVCrJedFoFLZtCwkh6DCsliGqVCqoVCpCYmlcxkge81/VyFu73Ua1WpVNA7a/VqtJiSEAEmFkdJZjpkbnmc+s9qler0suu+pUzeiwmr/NHFrVTVsl4M1mU9aSGpXlZoeaS83oKTdUGPkd3Q61HBOPpakXr8++8v+LxaJEzwmn04lQKKTl5Y52Z1cd07m5kU6nEQqFRJnBseb651hwrtU1wA0p3o8RaTWHudlsolQqIZvNyrWY1x4IBKR0mdfrleeK8xmNRlEoFGT9VKtV2UgiQabDtrrhQWKt+hnwutxw4PjkcjkhvnTitixL1jDXQy6XE0LMZ75SqWjPeaVSQbPZFJJM8q4653OjiW0IBAJIJpOiftkSG9OGSI8PhkgbGBgYGGwTGCJtsL1ieHgYLpdLjL4ACHmgiRcwYsxF2TZ/H4vF4HA4MDAwAGCElKjlqWhWxZ/L5bLIXXkvkkG6F/OedGzmc0AHcDWyTPMyHsMX8VarJeY8NLoKhULSRho5qYSdkVuWHQJGiANl6IlEAh6PB+l0WsgdzalIPNkuvvST0NDgyel0CjHh/6tELp/PCynmWNTrdS2iSLdmRvn4GSN3HNt2u416vS4ErFKpaJskHDNGtzk+vB8wUuopGo1KzWx1c4B9VscRgCa95r15TbZB3WThZoG6iTC6HBmJrFof2u/3IxqNwuv1au7wlmWJuoLt4cYOyRiJLgk+yzWpCgeWe1LnTa2brLq9M4LOvlcqFZRKJW0zwrZtBAIB6ffAwAAGBgYwPDwsY0m5s1qjmBJ/qg44PqFQSNpfqVTEhV81cWs2m/KP40dVCJ8dOuXzWScajYaUjXM4HIhEIvB6vXJPjg37rs51uVyWdjAyn81mtfXD9QBAXOhVlUY2m5USXGpKwubAEOnxwRBpAwMDA4NtAkOkDQwMDAwMDN6vMETawMDAwGCbwRBig7Xh6quvxvz587XPdtttN7zxxhvrPOf+++/Hd7/7XfzjH//Arrvuiuuvvx6f/exnN+n++Xxe5LWMVDFiZlmWRFwpo2UuJNCJ7gUCAa08FaW7jGzxODW6x2imZVkiR6W0mPmSQCfKyKiWGjFjJIxmRcxRZdSUkudgMChybxoosQyWei1VmkvDJzWaxxxvynVpKBaJRDSDLeZ0s12BQEDyplUDtXa7LeZYAKTsFCPg+Xwew8PDkifKPnGe2K5SqYRSqYR8Pi/RT9bxtW1bIrOcOzUibVkWstmsZuZUq9U0UzKn04lwOKwZrbXbbfT29iIcDsvcUSKsSu05F6oElyZSrM0cCoUQDAZRrVYlisnaxKrpHOdnbTnB6voBRspncd1R0sz2U1IcDAYlosuxGq2OqFQqokJIJpMy31RuUOZdLpe16Cr7rprrqe3q6upCMBiEbdvamvL7/ZgwYYJci5Fm5kXzWiypxvFhrrxal5m1sjknLPfFvHGOWSAQkHJsHAvWCh+d983xoSydygZ+xtJpakSakm/Vd4DPPceDY831zzVMDwb1WvV6XQwHNxcmIj0+GCJtYGBgYLBNYCLSBuvDnnvuiccff1x+Vg2+RuOFF17AKaecgoULF+K4447Dvffei9mzZ+NPf/oT9tprr3Hfmy+qbrdbXmxjsZjkwZJUkaiqbaMJkcfjkRxO1QmYhM/pdGoyXJfLJbWnVdJAOTXvGY/HJedUrTdNokzyTpnr6FrHqjyXOc+FQkHLQaXkWm0rpbSUPtMcTc29LRaLQp7V9mezWZEkM68zk8kISeCmACWqQIekOZ1OIUasTx0MBiWPWR0DNXeYxI3kLZlMikO5SlRoVsZ2+Xw+5HI5aVcoFNIk0zyPjtE8rl6vI5FIoLe3V6S+6XRak/SqNaX5X+YDO51OMc7q6elBPp9HLpeTsfX7/SiVSshkMmP6SyJdLBYRiUS0fH2alI2uk8y1yXnimlNzsDmW3Njwer3IZrPiFQB0ng+1ljevRcm5OufxeBzpdFrGzOFwIBaLyRpj/etqtSprjKkO6kYS7622tVKpIJvNolwua+tYdcBnioFK3vncqNJ6bmapDuDtdhutVguVSkVc8EulEvx+v+Yqz80PtpWkmt8HwIiZXDQalbFl7rXqceB2u4Ugq+tVlfwzB59jx++tzYEh0uPDh5NI1zNAvbXh4wwMtibqWaBZGvuZgcH2iHp+sy9hiLTB+uB2u9Hb27tRx/74xz/GMcccg29961sAgGuuuQZLlizBzTffjNtuu23c9+YLLKOdADBlyhTYto01a9bIyzRfwvv6+iTyFQqF0Gq1NGOrTCaDWq0Gy7LkxR6ARixoxqSWEiIRVSOPzLuOxWKag3ClUhFDJJ6rGjwxMsZIIDBCkEulkhB1knHVAblQKMDpdGoGZMPDw2NymovFIgqFghDxeDwuucIqQSuVSuJyDUDIhxoxHh4ehsPhkPFxu92IRCLi5Ew0m014PB4hUPV6HcFgUKKlbBsjxGpkeXS5pXa7jVqtNoZAOZ1OibjmcjmUSiX09PQIyeY1q9WqnGvbNsrlspazS6hRfebG87yBgQHZZCDRWrVqFfr7+zE8PCyRa0apSdTD4bBsHnD8eQ81+klzMM4n0IlmU5WgluZSHeR5LXX95PN52SThGHDDhioDzpHqMQB0vsNVJUcmk0G5XBb3cXU+2u22bEpxs0M112o2m8jn8ygWi7IeGcHlMSTjqsEZnzePx6O5m7Nt7Gcmk5H5V8dCfd64VhwOh4wFy3Qlk0k5b3h4GLZtr1UZ4vP5ZMOGzyTXBc32JkyYIO3q6+sT47h6vW6I9DbAh5NIr1gMhP0bPMzAYKuiWQKG/zj2c3dw7GcGBtsahcpmX8IQaYP1YdmyZZg4cSJ8Ph8OOeQQLFy4EDvssMNaj33xxRcxd+5c7bOZM2di8eLF670HS8oQJG0sAUWpMjAipQ2FQtrLLs2VeB2SPUaUAEjEUa2nXC6XUa/X5aW71WpJ+R+VDNTrdZE5AxAS7/f7hQSqxmVqLVvVRbrdbiMWi0lJJ2DEkMzv92sRxdEGVel0GpZlaRFEuicTlNcySgl05LqUYlNCTWdytbyTGuFk2zKZjCYl59iwRBjHlRFGtaa2bdtIpVJyfYfDgXK5rM2T6o7NNjQaDUQiEdnsoEuyGnlvtVpIp9Oak3qj0cDQ0BAqlYoQPo4H108ikZB5ZRSchNDv90tEdGhoCKVSSYucFotFiXCrztcul0uit4lEQgjr6DlS1wHlzmrJNkrv1XQB1aiO40VzOpXoDg0NiUM81yUl7VyfzWYTw8PDWmQ8Go0iGo1qGzF8HkbPiSrd5+YKS7TxniSjPI5u6+yD2+0es65J9vmssE+MEHP8/X6/EGmS366uLqlfDYyYrKml3mhQ1mw2ZYNi4sSJkibCsVAjy3TFV1UiwIiknTWtgc6mQiaT0UrPbS4MkR4fTAFPAwMDA4NtAr6obOo/gw8uDj74YCxatAiPPvoobr31VixfvhyHH364kL3R6OvrQ09Pj/ZZT08P+vr61nufhQsXygt9NBrFlClTtlgfDAwMDN5vIJEe778PKz6cEWkDAwMDg20OE5E2WBeOPfZY+f999tkHBx98MHbccUfcd999OOuss7bYfebNm6dFsvP5PKZMmSKGYfF4XKKrzFVmlAkYiV6pOaKFQgHxeBzValWisKpEdmhoCEAn4ur3+yX6mU6nkU6nEQ6HRf7qcrkQDAa1esTMDVXlzHyWbNvWyiGp9XVpmOTxeCTyDozkNvP6rC1tWZbkZ7LOcCQSkagXTZoYkVPLTY3Op2apLLaV8mCOido/SoALhQLcbrcWZfN6vQiFQlokmFFAtd8ej0eTKddqNTHqUr87VEk7ay2r5ZdcLhfK5TJqtZpEloPBoJQuYhR29FwAnagzc1c5hqqsn+0KhUKa4gCAmF2pfe/p6ZHPgc5atSxL+sjyT8wzBkaMzNRoJeeBucDAiJFevV7XVBm8Ln9miS+1zBJLSKmRcWCkvBivz5/VGtcs+8T55vrhmvL5fKjX6xJN5v1Yro1jbVkWQqEQSqWSrCN6EqibrqzHreZph8NhrV6zGsXmnHg8HiQSCTgcDolIsz+qOqJUKqFQKGgqBIfDgTVr1sh53d3d8Pl8kvMOjNSWLhQK8j3C54TtYqpGNpuV6yeTSbRaLUkdUUvWGWwdGCJtYGBgYLBNYIi0wcYiFovhIx/5CN5+++21/l41eSL6+/s3mGM9uh4tQYKj5odSzk33XqAj8ySJIgmo1WrIZrMoFotCYkn+isWivDy73W45H4CYbTWbTSFflHOqtY0pw6XLNDBiKhWPx+VlmtdTjcVYa1Y1NOILvZrXTDdr1bTK5/OJfJZtU3NQ1bxqktN8Pi9u0rw+axVns1np54QJE4QUkDjQIZnEjxsWJHYApC/M/+b1m82mln9LCbs6jiThJHuUO7NuNO/JOQ2HwwA6a5GER807tiwLrVZLI3zpdFo2BoaGhoQAkciVSiUEAgGEw2GtNjbdwtU1F4/HEQ6HRWJMIj3aTI7Ozhwfyr/VHG2OCdexz+eTTQSOI4mcWseYGwtqXfRAIKD5AXR1dcHlcsnGEK8Vj8cRjUa1zaVsNqu5WY823GL/mN/OdrC9KqG3bRvxeFzGNpPJaNJ91opWpdcej0fql5Po0qyO/eP8u91uhMNhLf+c0nwew40lyvu7u7tlk0d97kOhEKrVqswvDdZqtZpcn9J49XngWud5dFmnm73Jkd76+HAS6SmzAcWowsBgu8DajMUmzwa8sa3cEAODjUA+D+CSzbqEIdIGG4tisYi//e1v+MpXvrLW3x9yyCF44okncMkll8hnS5YswSGHHLJJ9+OLtEpE6TIMQDNWYp6kSoiHhoYkUgpAXt5pOAaMkFiSvWg0CofDgWKxKC//NG1imSegQySYp8sXZ0ax1bzlZrOJcrksRIU5oyrpZx6vSh7Vkl3qczY4OChlkoBO1LRerwuxY36q1+uVfg8ODgpp4dj19vZKiSZG30qlkuSPqy7RjUZDfmb+rmpixY0HGmgBkJxdlq1iX+r1upBUXp+EhmPNMkUqqS0WixLZBSCbEaoDuNpHEu5isai5vrvdbhkDbg6oucU8j2OqOrC3223NvZnjUSwWNXMwqg7USK3P55MIPftEVQXbzTx21YCM1+f9qBpwOBwa2SPZ5bhS3aBGrsPhMLq7u7Xv/GKxKLnOvB/LX3FdNBoNxGIx+Hw+eeb6+/vFvVx1HSfZVjeSKpWKlofPvHOuqWq1inK5rG0geL1eeL1e1Ot1aUe73UYymdQ2cWzb1p4ROoxTuQGMlCRT85qpmGDZKgDIZrOyKcSIPl3PeU+fzydrRN1MUT0RVOO8TYUh0uPDh5NIe+OA1xBpg+0Qo43FvLHOejUw2N7g3TZ/sNVzx4NnnnkGP/jBD/DHP/4Ra9aswW9+8xvMnj17vefcc889+P73v49ly5YhGo3i2GOPxQ9+8APNgdXgvcE3v/lNHH/88dhxxx2xevVqXHXVVXC5XDjllFMAAKeddhomTZqEhQsXAgAuvvhizJgxAzfccANmzZqFX/7yl3j55Zdx++23b8tuGBgYGLyvYIj0+PDhJNIGBgYGBtscW5NIl0ol7LvvvvjqV7+Kz3/+8xs8/vnnn8dpp52GG2+8EccffzxWrVqFc889F2effTZ+/etfb1KbDTYeK1euxCmnnIJ0Oo1UKoXDDjsMv//976XW7rvvvqvlPk6fPh333nsvrrjiClx22WXYddddsXjx4k2qIQ0AqVRK3LfVSB7llozIORwO1Ot1rQxOsViUSKlaT9a2bfnH4ygZBTqR2kQioblxM7e33W5rDtSjr6/mojLaOToSpjoo8/hGoyF5towMsgSRmrdMN2FVtsqcXDWnmWCUjDm9dEHm7/x+v8wlAK2mNKNuLOdEOXY2m5UosJr7nM1m4XK5JNrLiKbqAN5qtVAqlZDL5WQc4/G4Jt2npFiV+mcyGdTrdYlwc3w49hxTRvrpTE0w6s3/V6PhQEcS7vV6JU+d48jcXjXCWCqVtCg7XZ4Z9VVLobGPzLEvl8siLaYrO/OP2adCoYBYLCbXLxQKKBaLYuLH6D3XDNCJwDNHWvUI4JhzLlOpFDwej1ayisoKtp+u6irC4TDi8Tj8fr985zOvXXVBb7fbkofPOQ8Gg2i1WiIlp1pCnWNKpSnvJvhcU42i1ndXPQLoUg90FCWhUAh+v1/6QRWHmkJCNYZ6T+Zaq+t9tFu83++X9AEqFfgsJJNJreb55sAQ6fHhfUukv/e972HevHm4+OKLcdNNN23r5hgYGBgYjBNbk0gfe+yxmoHVhvDiiy9ip512wkUXXQQAmDp1Kr72ta/h+uuvH9d9DTYNv/zlL9f7+6eeemrMZyeeeCJOPPHELdYGyoVZjsa2bSF7JD0+nw/JZBL1el1MxFjGKhgMajLpWq2m5WySXPLFnPWPfT6fkBGWyKGMFBghDSpRpOw1GAzKyzTrcPPnoaEhOBwORKNRedEnqVLLZvG6Kmnwer2YPHky4vG4liPq8XhExsqc1UajoZUlcrvdkqMMjMigg8GgjOfQ0JBIqtlPmqzxeJUE8Vo0LVNLJrG2stqner0Or9eLaDQqfQqFQppknoRQldvzvq1WSyuZxHxqbp6Ew2HU63X09/djzZo10n7mOgPQSN7g4CCAkY2MTCYjGxKBQAA9PT3wer2axJmbKiS66lriGuAYqn2iORevb1mWSJd5nDpm3Iih5F8l7qPXNcdHlfjncjmRUKs1ryuVikjIOXeWZWnydRqjqXJmPidcdzvssAMymYzWBtaHVsvLhUIhrewac6tzuZysga6uLliWhb6+PvmMGw+VSkVL1+DPbD+fTTXNIBwOa+kVNPcLBAIy1pRsqzXe+Yzwe4Jt83q92nfOaEM+h8Mhhm1bIj+a1zREeuPxviTSL730En76059in3322dZNMTAwMDDYRGxOGastUS9zfTjkkENw2WWX4be//S2OPfZYDAwM4IEHHsBnP/vZ9/S+BtsHqtUqstksAoGAvCi3220UCgWpHQ10ooDM4eTLZK1WQzQalXxPAOLmS+LN6zFHGYDkKau5n4zeqi/wdABXc7BbrRY8Hg+CwaDmPuz3+4UYZbNZMS5j9LZYLIqhEUkeX/DdbrdmpESzKfWFXTXXmjx5Mur1OjKZjPTb5/OhVqsJKVPHV43o0tRKzXGlKZNKHBmtJ7kol8ua8zTQIYrRaFSLYno8HsTjcbRaLc2JmfnOvF+tVkMmk5FrMq+8UqkIcfP7/UL6eC3WTVaNsngM26qagHEjplaryVyoDuAqsVKvpeYPu91uiboCnc2IWq0mZnEcZ6/Xi3A4jAkTJshYu1wuFItFGR/2ybIsrfa5ZVmyftxuNyKRCGKxmKwLv98vEVg1d5sbMaqygvnEHEfW2GYEljnk8XhcCCZrKY+uV05/AV6fZmdqFNnj8cg/oEOsm80mhoaGZPwZ7Y5EItJ+PkM8htciuedzSCd0jgUVHDR94zF+v18M9oCOO3+1WkUgEBBFhMPhEMWEagCnrpVMJoNyuazNudPpRLlcFuK/JSLSBuPD+45IF4tFfPnLX8bPfvYzXHvttdu6OQYGBgYGm4gtEZFWy/gA63ZhHi8OPfRQ3HPPPTj55JNFRnv88cfjJz/5yWZf22D7R7vdRrFYhG3bQnwpHVVflPP5vJSsImq1GoaGhtBsNuUlnu7EamSQJXdUeXYulxMyAXQilzTxUg3OuAmlmj7RFZxE2rZtLbpKslQqlaS9NHGiXBmARjZVB+d8Po96vS7Eh47HaoSX0WJ+RnMwluYCIBFBdTMsFAqhXC6LczTHR5W+27aNTCYjztEAxDlbJSU+n2+MQVgwGJSSSSTOtm1rztVdXV1CzjhvJEosNwaMyLiz2azck4Q7FovJegmFQsjlcpoDNJ3l1fJgyWQS0WhUCJMqJ+ac04yNrtZcB6qZlsfjERkx28XNCbX9jPLatq2VwkokEpr5Gn+vSvZJ7lVpNOdJJYUsPcY1lU6n5fue61FdGxxXuo6rUV6ex3VNWTTN4QgSbrXMVzAYlPnl2lcN7VRTNNUEjRsv6jNBMzO1HJUqz6ZjPlMQOK6M1qtj1mg0kM1mtXQH1cEcgJidqaW6OOd8Bv1+P7LZLEqlEmzb3iLlr0xEenzYtFDANsScOXMwa9YsHHXUURs8tlarSekF/jMwMDAw2D7AP9ib+g8ApkyZgmg0Kv9oPrW5+Mtf/oKLL74YV155Jf74xz/i0UcfxT/+8Q+ce+65W+T6BgYGBgYG2xs25+/xhxHvq4j0L3/5S/zpT3/CSy+9tFHHL1y4EPPnz3+PW2VgYGBgsCnYEhHpFStWSI4mgC0SjQY6fz8OPfRQfOtb3wIA7LPPPggGgzj88MNx7bXXikzS4IMJSkvViK7L5UKz2dRqDzMnUpUIu1wuMYKi1LLRaEj+I89ltFGNXtEEitG9YrGI4eFhzXiqVCqJDJbRW7/fD9u2MTAwIPdkTjePSaVSKJfLkqsNQMptqSZZNHFSpdGMTtbrdYnShcNhTUpKaXG5XJboJ+XnlAADwPDwMAqFgqYe8fl8iMVi8Hq90vdSqYR0Oi3tz2QyKBQKYwzCaNSkRpHV3GSgk3dMcyseR+MyRucp71XbxYi7asLFPrVaLekTDbkajQa6u7vlvjSGAzplmxjQ4XdWLBZDLBbTvk8GBgakjWo9bqoXqFZotVrI5/NafWVGW3n9Wq0mUXs1os6xoDqCUWY1ck05OZ+FRqOBXC6nKRqoDnA4HJg0aZLMG03QGF1n1DocDkt5J+Ydq1Ff5oKPlus7HA6JjLOOdSAQkLax7x6PRzwNarWapnqwbVvqNHNNOZ1OKcHFte52u6VUF8e/XC4jm81KiS62rVqtSn/i8biMJyP9XG+UtrMd9Xpd5PXsH/P9uV4KhYKmjmD/2VeuC97T6/VukZQnE5EeH943RHrFihW4+OKLsWTJEq2O2/owb948zJ07V37O5/OYMmXKe9VEAwMDA4NxYEsQ6UgkohHpLQXVGIjgi/R7nZ9tsO1Rr9fhdrs1V2TKKSmjBEYMw1TXaeboqq7IJCnNZlPeYdrtNvr7++UFW83XVCWf6XQahUJByAul5aoxFF/wg8GgRoRoUAVA6tTG43G5/vDwsCYlBjpEIplMwuVyybmNRgOhUAgOh0PuCUCT6nKTQZWJOxwOpFIphMNhrV42c51J3pvNJrxeL2KxmDx3dAVX+0h5M4mQ1+tFNptFLpcT8hUOh4UQ8jjLsjSyws/YNwCS/67Wt/b7/QgEAvB4PFredKlUQqvV0vJ72T6SnEqlorlQBwIB6Ytq2FatVlEqlbT62awHzeuTVKmbCCSFbH8+nxdjMbV+Nt2kuWZVCTXz28vlssjtSe7y+Tyi0ajkcNMR3uv1yvzW63VEIhF4PB4tLzibzcozBIw4aLNGM8es1WqJ8Rr7rG4akcwXCgUZO5/PJ/nmqjEdTfrUHGnVFZyO+IODg9JH9kPNYeb4F4tFeX6ZDz/anbxer8tzSeO9XC4nBmf5fB4ulws9PT3y3FcqFXR1dckc8J7VahW5XE6+R5xOJ5LJpDzP9XpdjPzYftb5Zpu3pOGYwcbhfUOk//jHP2JgYAAf//jH5bNWq4VnnnkGN998s1YSgNhSuXIGBgYGBu8NttYf7GKxiLffflt+Xr58OV599VUkEgnssMMOmDdvHlatWoVf/OIXAIDjjz8eZ599Nm699VbMnDkTa9aswSWXXIKDDjoIEydO3CptNth2GBwclFxStdSVZVno6emRl11GnVVjsVarBZfLpW36u91uMRQiIWBeJomF1+uFx+NBqVTS3KZZeknNfY5Go+ISDnTIRT6fF2Mp3jOfz8tx2WwWfr8f3d3dQtrcbrfkhKoRs0qlIuWc2A4SVBLFcrkshlRsP/tCgkLjLNWMje7JqtMzjZRUEkXCpJo5MerI0lnM+1ajj8z3tW1bI7+5XE5yrIFOTrTaBt5HdQXv6uoSF2c1B5vRcd6zVquhWq2iUChg5cqVMi+WZY0p8wVAKwtVqVSwfPlyOcblcknpL9W0jaWgSAxLpRK6u7tls4PO56qDfLvdRnd3NyzL0lzlqX4gSQsGg3A4HNo4+nw+RCIR9Pb2yvisXr0aALQ8Zzqrs12tVku8BDhmVBuoOfUkzgRNytSya81mE7VaTQg1MPIsFQoFWZ8s86ZG1D0eDxqNhpbHXi6Xkc/nZa5jsRi6uroQiURkzRYKBeRyOW0TjW2hIznHVjXfGx4eFnMz9Zlut9v/H3tvHitZepaHP1Wn6pw6S+136+6Z6fECGLAxBIOxkcCbwCYEWRA2K3gDRyTYAkwQTEIwxug3AjvgCAwGBTwCYwWBsEFJQBiQbQgoyA4TTAgGY3uW7r5L7VVnqTq1/P44/bz3/W577LndPX27x98jtbpv9Vm+7dQ9z/c+7/Ma12EOO0uccazzPDdMzejAz+sHQYBGoyF56ZwHbsRw7m8UNiJ9OtwxRPrFL34xPvrRjxqfveY1r8EznvEM/MiP/Mg1JNrCwsLC4vbGrSx/9eEPfxgvfOEL5WeqlV71qlfhgQcewJUrV/Dwww/L/7/61a/GdDrFL/zCL+CHfuiH0Gq18KIXvciWv/ocAUsI1Wo1eSmm/FXXXKbclpFL4DgSqcvskOxoI65arQbf9w2CzGiuLvm0t7dnRGYZHaNkGoCYkc3ncyESNDXi/RjZOzg4EDkqX7w1ASmVSuj3+5hMJkIy6Q7M/2fbdAklkmBdFoqOzpPJRCTPlK9OJhMhGOv1GoPBwCjxc7LsEY2VtFEZI7Cr1cogtXQd1xHpfr+POI4lykvyp+s+R1GE3d1doxQS26GJXK1WE/ICHLuOX758WaKFzWZT5M4cYz3eGr1eT6KYURTh6U9/ulFaiXLsSqVifEandoJkWDtO07mb58VxfI3TPKOtev3QfEzL+yntZ5Q6yzIMBgOj1BgJnTYuG41GaDQaoswACgl7lmUiVWeNaaZGsD+MuPKZIAlO09RQTDQaDUPCrsvKAYXJHdMKdAkrGtBpIs2azmw/20ZTOQCiVOD3Q7/fF9dz3rvVaomxGz/L81w2ADh3jK63Wi15punoz3H1PA+NRkM2KoBjok5DOauWuvW4Y4h0vV7HM5/5TOOzMAzR7Xav+dzCwsLC4vbHrSTSL3jBCz7jS8YDDzxwzWdveMMb8IY3vOG0TbOwsLCwsLgjYSPSp8MdQ6QtLCwsLJ5cuJE60td7noXF40EYhgiCQAzGABgSTb44ssTPeDw2TMNYL1fLPsfjMer1ukTzKN9lJJL5nZ7nGQZPNIZiPisAaYfOtc3z3IiShmFoRMdY13c+n+Pw8FDOYwTwZJ1n1pgGIJEwllcidDkgndeqzZziODbKd1UqFfR6PfR6PeOey+XSMHOiZJXS6K2tLTFuYiSSeaP8tx6HcrksaRgsU+Y4jiGNZj/Z/nq9LhFuoIgUjsdjkfECheyfEUSeG0WRlCrSdcdZL5s/UwnAYygfZlSRbWVOtC6JNR6PjetRgUApMNcrc5l5HvN9dc3ufr8P13Vl/C9fviy591wvzEOmHD3PcywWC2N9TiYTHB0dIQxD+U7mvReLhRGBXywW2N7eNupIZ1km861zyHXJMOaeUwbOkmjNZlNSLGazmRyj647r+ab5GSPWbBfHX3sJhGEI13WllJnOzyZ4fa2goEEZo+f1eh3NZlNk/wDE8+BkXna5XDbKs51UZHBds43AsQokDEOj3N6NwBLp0+GOJtIf+MAHzroJFhYWFhbXiVsZkbawOA0qlYoYH2kTLspptbqBTsYE6xrrXEbgWKrJ/FhKoEkQfN9HFEWGgVStVhNnZJ3/SMKtDbNI7NgWyqx5rXq9brywA8cbUswv5bXyPIfneSLH7nQ6ODo6wmKxwNbWFoCCRB0eHgoppJSXua4cC25KkEAlSSKmXrw/Cayu0c1xJKbTKZbLpSEzZ340Zew8r1wuY7lcSroGpeTaifykKzJrWGvHb5KikyZflISTtO3v78uGAUnm1tYWms2mXIu54RrcIKERHVAQ0H6/b7jDa+k6x4T59NpEjH3gfRaLBeI4xmQykXXHHPjZbGYY31G+rtd2HMeyxrjxqQ232B79XR4Egcwrj6ObuCbhQRAYGxS8tq7NTE+BdrstppK9Xg+u62J7e1sk0oeHh7JppIm0/puO7N1uV8aV9dezLEO/35fxoYxeO50zZYDgWPP7gfn7y+XS2DTi94XOxWcbuTa45mj6RoRhKOOvn2c+X8vlEo7jSN64rSN963FHE2kLCwsLizsXlkhb3K4olUpCbElwaJSkyVC/30eSJEjTVD6vVCoYj8cIw1DIS6vVwnw+F0Mw4JjQ8OW31Wphs9ngypUrQgobjQZ83xeCwXYsl0vMZjPj+js7O5I/zT6Uy2WJgrJtNCgCIORD5wADxzm5PDcIAnEQJpFjZFmblDF/mH1kxK5er8txSZIYbsMAJKJJEy+gIJ47OztCGtI0FWJDArXZbBCGoXEt5kZPJhM5Tptc8bgoigxzM5ZTyvPcMJNrtVool8uiHGCElP8Gis0HHcEEjiOKOneYa4Tjzzz3arUqc0nkeS5zQhMrnTNbr9fRaDSEoDHa6rqukM4kSbBarYzxoXnXeDwWItfpdNBoNAzH9dVqhVKpJBFpvbHBY6IoQq1Wk3xnzmWz2cR6vRZCycg/fQEASE4z+7harYTAauM+3/fR6XREycGNJr3JkqapYS4HQNa0zuln6Si2n3NcKpVkzBzHkXXHsWUJMa0AILnWJcr4fLCt/G7gJgcAdLtdyRfnMxHHsZS10983q9VK+tntdsWkj9ei+Rg30G7G70VLpE8HS6QtLCwsLM4Elkhb3K7YbDbicE0yNh6PJQLEiJl+6WeUi6SnVquJtLharWI8HhtRapIPvsC7rovVaoVerycR19FoJJFMXSqK5lYkfDQ7u/vuu6U9s9lMyBBwHHX0PE/IC6Pi2n17sVjg8PAQeZ6LtJh1q3WEm9FmEgmgKOG1Wq2EIFA2q8tOkVDRKRqAGDvper40dmJba7WakG2WTCIxK5fLQsA9z0OWZeIEzvYzgk8i12w2kee5ETXVxmn8e7PZYLVaGcZr7XZbTNl4LvvD9nKzQ9ci5vVJyhm91WXReE9dw7zdbktUk9fX8nn+recaOHZ4Z71tgsZi7Dtl+4zwc/zX67WsgSzLsLu7i3a7bcjXacLF80jUa7WapCMwNUBv/lByrp2rd3Z2DEl7pVIRFQE3PDqdjkRvdR1m13Wxs7NzzYaBNvOjXF1H8TebDYIgMMzwqHrg87Ver+XZ5dqmLF8bkvEZZBvG4zH6/b6hRuCmANcfUJT45fzoedLO5lwTfK6BYgOt0+lgtVoZLvI3AkukTwdLpC0sLCwszgSWSFtYWFhYWNw+sET6dLBE2sLCwsLiTGCJtMXtjJPGXDQI0vLOdrsNz/OQJIlR27Ver6Ner0sULcsyiY4S4/FYTJKAIpK6XC4RBIGRGwuYMl/KrJMkEdntYDDAfD5HvV6XiCvvyWsx/1LXSW42m1L/mWWhkiQRObk2uwJgSFl5b0ay5/O55NHymMViIVFLbYrFHHRG5BgRbTQaErGkjFXPRxzHGI1Gcn2aa9HUDCgi/eyflg2vVivD3Ip/s/2U2vq+LxLtwWAg86bzpJfLJXzfl4gro6PMmwUgpc04hsPh0IhOs33MIdf/R1MvLaNP01TmVl+D7e90OiL/5meMMGuDNhrSsWQSUKgEKLdnP1l+i3OUpilGo5HUQdbrYmdnR5QVzGXnWAKQ3GFtwJdlmcieeS1GtvkssR9caxzX1WqFfr8vY8ZUjFqtJmO22WyM+us0jdOqhyAIsNls0Gg0ZH2maSq+Bzxua2sLQRBI7joAY+1yjpgewCgyc68pgddzp/PFKXOfTqfGugyCwJDTM5pOJQFVIVrxYXFrYYm0hYWFhcWZwBJpi9sVURRJfiNzFn3fl5d4olqtilyYa5L5zDSNAo4djPW6ZT4xX7r5/5pU8RgScqB4+Y+iCK7rikQ4z3OkaSpu4cCxrFrXAWYeJ1/WmfuppeMkFpVKRV7YJ5MJVqsVGo2GXL9cLovJEVA8k6PR6BrpO+XvvD5Jep7nBuGtVCpoNBpCYmlqpWtxU2ZPwzPW146iSIgcP/d9X87l38PhUEgZ88qZB86cVz1HrO2tTcQoqV+v10L6S6USkiRBlmVCfOgKzrxuz/NkU4Z9rFarmEwmIlvnvDH3m0R0Op2K8zbXAh3L2QZuFsznc2lDrVZDv9/H4eGhSNNd15XazXrumIOuxzFJErnf1tYWlsslBoOBnEc5exRFkhdcrVZl04Z94lyfrJuuTejofs9caY4Zr0MCvl6vMZlMEMexHFev14UU69SDJElkDXMzgTWneS06d/M4EnxtJtdqtaSWta4Ywfxztp9t0GZm+hlnv0mwtTs/AGxvbxtSbhqY8frcbOD1l8sl9vf3sdlsEEWRde0+A1gibWFhYWFxJrBE2uJ2BfNs4zgWotVut7FYLAwizegbACESruvKCzxJzmg0kpdqRtuYR6qNqxjR1BHp9Xotf4Bi7dPZmPekmzWj2sBxjihJU6fTEQLKl3VeXzsBk1jT4RsoCFW/30e5XJZyVDRGIun3fR+tVgtJkkhb6SauS0qRDGhjNJIllgQDIOWeSILH4zHSNMX58+dlXPv9PiaTiTEWQRBIOTL2ic7Ys9lMCGUURRLFBI7LMZE4s63cCNBO3pxnfqbNqjh3o9EIi8UCd911l6wfRljPnTsn13/00Ufhuq5RFo3X5PV1SSsdla5UKjKG0+lUCKt2gufGhY6sd7tdlEol2YjhJkmz2ZTxGI1GWC6Xsn663S6m0ykuX74s5zESys0dtp95/Pye5uYKN3eAgnAzCg4cl3Kq1+tGFNz3fUNpcXR0JCRU52DrfHauPV2Kje0CjiPdy+USw+EQ8/ncUCYAuIYAk2Br5QnXAceaDuZsvzY401F25nhzbKfTqUS0OR5cp7yW3lTjWhsOh+j1egjD0NgAuRFYIn06WCJtYWFhYXEmsHWkLW5X1Ot1DAYDDIdDo5YyTbFOGovNZjMcHBzIufw/vtQzAntSeq1l1iRoQRDIyzxNhI6OjkQi3Gq15L66RrTneYiiSI7TzspAQaRJyNgnRkSHw+E1dbCB45d44PjFXkfz6CAOHEcPtRybx2kTJBqgrVYrOVaXLNIbFbVaTe5Lwkxywj5RFUDSw5JWtVrNcH8GYNTu1XPBY3QNcKAg+KVSyahXzVrauq44y4fRqAo4JlHaLfzixYvG2CRJgkajIfJ33pPlsHRpJUbMeT1GyfmzNsLTY+j7vkiH2W+SNfaf485SShz7brdrSO0ZsacJVxiGsgGg3eLZZn7muq4Y5BG65BeP5zW5/qfTqZBvEtGjoyOMRiPs7OzInPD/NJGezWYIgkDWKzd1KL8GCun+YDAwSrZFUSSbGFr6nuc5xuOxsRGzvb0t48Oxp5IFKH5PhWGI+XxuyNyHw6Gx1vksz2YziYxXKhUpCQdA3Lq1emQ8HmM4HCKKItTrdRuRPgNYIm1hYWFhcSawEWkLCwsLC4vbB5ZInw6WSFtYWFhYnAkskba4XbFYLMQYiTm19XodnudhPB7L+mNJKNaEBoroEqNany7ypyNfjFYBkCgfo3ca2gyM0nF9jI70aekpc5SBImKWpikGg4Fci4Zfs9lMpL/ValVycbVZV57nWCwWEjGjXJjRw2q1iul0KqZsHLPNZiMmWEARGeS1tNzV932J/HGsmPPLMWy1WtIPjsX29rYRvd1sNmi1WgiCAL1eD0ARxeQYci5YH5oRRgBS/5hzsdlspCwZo+e6PJKW8C6XS5RKJRmPra0tOI4j4zoYDCR6q/OVt7e3sVqtpE+MhOp7UrJdKpUMFQIjq/yZEmfO0Wg0gu/7OHfunPTbcRxMJhMsFguR6W9tbUkNaY4jo7lcA2mawnEc7O7uSvtZDop1qfkZJf96fjmOOmdcR8qn0ynG4zGCIJAoL8eZ0mfgODecaxKA5Fbrz5hfzPFK0xSz2UyMyYBi/XPu2Fb2IY7ja56xk8+hBpUMaZrK+qnVami1WlIjmsdRbq7HghFprgPmPHP9TCYTKWfGtg6HQ+O76qQa5HpgifTpYIm0hYWFhcWZwBJpi9sVlFHu7u4aMuc4jrFYLOQFezKZGLmpQPGCzXP4otxqtSRnVzsNa+k0pcZxHIvDMokqawEDkBrJ1WpVXsgdxxEiwmvSLIkbAUmS4NFHH8VisTDkuEEQYLVaiZFXHMci2aX0dr1eSw6tlqtr52EaVS0WC5FeNxoNcdYmoWFe78may8z91vdkni5QOEM3m03JXWe/OS56XNlfnV/quq6QXH62XC7lWpSHn5RZs/40yR2dy7WMeLPZiMu4zgGuVCpCMGezmWEExzknkWM7SMZJFnn9Uqlk5FlTTn5y44R1qdlWtoNrBTh2U+f4kKxpWbvv+3AcR46J4xhBEGB7e1vmfDgcot/vY7VayZhxLlmXmtcl8SehJCnVufPlclmM7niMviZwnO/O+uScSz43XA+dTgdBEAjpHw6HGI1GGAwGkrfOWtp6w4Z11TUJ51pqt9vy7PD7gM9NEARiEMfzuGmk13qlUkEYhrJhxb6zFrb2JcjzXK7FTQsSeo5Fo9GA67o3jUhbnA6WSFtYWFhYnAkskba4XcH8U53HSGLQbDblBZjldWq1mkSqaKSlyxAxiqajiPP53CBejUYDjUYD/X5f8pwZgaxUKnLPPM8xn8+RZZkQVtd1xWmY7Z1MJkaEdLPZYDgcCjkEiqhXFEXodrtC+CaTCdrtNmq1mkQ26VrM/E62o9PpCLEgYe52u0IG2OZyuSxEjuWxFouFkJ4wDCUqSyJXqVTQbDaFwJJwMgIHFGSDY6QJGQk578/IOMs+cU40sV4sFhiNRpKLDECc0Pf29uRa/X5fCI52x16v14YpGMdXE3v9b855lmWGIoBkUysTeC6NwtjP/f192UAAIOZdmlBlWWZEW6lSqNfrMk/j8Vg+1+tMm5Rx7MMwxM7OjqwLbuDoefN9H2EYyuYA55/PBgAjss45Yn40n5G77roLo9EIw+FQ5rfb7cqGDa9Bc7xeryfPRLfbvaZ8FyPSOid/NBqJUz1wHMnW5dfobp/nuTjeM5dd51HT9Es7zZ80JSNpX6/XxneBNivj9bIsMwzzVqsV4jiWsWDpK9/3DU+GG4GNSJ8OlkhbWFhYWJwZPpd/AVtYWFhYWNxOsET6dLBE2sLCwsLiTGAj0ha3K5j3qaXGlF5SSglAorja3Zg5vMvlUnIl6VztOI5EMV3XRRzHEvWdTqeo1WqSOwocO2+naSqRKdbjLZVKEtGlm/VJp2RdXqtSqUheMiNmR0dHiOMYjUZDzmM0VEeuGXHNskxkySfLd8VxLNfm87larUR+rGtGp2mKcrks0cPFYoE0TQ338E6ng3vuuQe7u7syBsPhUGS3HGvWuGakEzguRUQwWk/3dA32Z7lcSp8ZsWQJJD2uvP/JKG+apkZEkCXKGNHV12BbkySRslDESfkucJyrrespM9+eY+g4DubzOebzuSgaWOf7ZJ/5f9q1nLXI9ZhpBQUlz+v1WqKyW1tbODg4wP7+vkicq9Uq2u22UVqMNZ11bWkt+2YfuTa0gzddqjl28/kctVrNcETn+OhyYKPRCJPJRMaf8nvP86T9y+US9XrdGH/2Wa+zMAyl7RwjlpzT+dYsbcVnmqkTrLfNOadChfelMkTXqZ9Op1KHHigi0jxPp29QRaD7fiOwRPp0sETawsLCwuJMYIm0xe0K1kLWpl46f1GTAF3CBjiuicwcU+CYyM3ncyHEfLmm/JWlfphnCcAg8dq4aWdnB5VKRe7J40hmgUI2HIahYSyWZZlIQXn8pUuXMBwO5Ro0BtNt9TxPSvPoXNX1eo0rV64AgJQK06Woms2mGIsRNLTSRLff7yOOY5RKJdmQIKngZsR0OhVpLjcaWK5qOBxeY+zGPGPek9JiEiHOk84Tnkwm2NvbMzYy0jRFHMdCKD3PQ5qmUtcaOM7v5YYGAJEGc31o4zGdG0sDKU3ol8ullDbi/FP2rKXpjuNInfNKpYLJZILJZCLHkHDqvH2OATczOOfM89elm9rttiFfz7IM0+lUCGy9Xpe64DqHnOZaurZxkiRIksSozaw3g5hDzlxgAELIm82mbA4MBgPU63VEUSRrfTqdYr1eG6W5eD+2geTZdV1pK3Ov9YaW4zgybmxrEAQyL/o51H2pVCqYTqeI41jWFI0Hq9WqPOd8rqrVqqxVfrfo8QBg5IHretucN+ZFM71Bb+5cLyyRPh0skbawsLCwOBPYOtIWtysmk4lEmPiyzBxSmhMBENOparUqL5M0FtLuvMzvJdEBChLleZ6RE1mtVhGGobz0A8fRWm1iFQTBNS/Uw+EQaZpKzvK5c+eMus7j8RhxHMPzPMmz1WRPRzYZ+eJnjExrouh5nuE2TZJRqVTQbreN6zNyxrEol8vwPM/YfOD/8bMoiuA4jjhvM3+Z9bzZVm4OcE7o4ExzNH1PnR9LAy4ec3BwgIODA6RpKuPPmuGMZnKs4zhGmqZCjNM0FVd3rRLQ+bJ6DHRNZ+b78jMaj02nU6O2eBzHiKJIxl8bWQHHBDnLMoni0yyPjty8N+eRYzYYDDCZTAzzNtZFZr9Xq5WsYU0KmTPNtc6Nk06nI4RVG25pzwFdE3y9XqPX6xmbV2maikkcyanelOH4ZFkmvgbav4C1sIGCSF++fNnYFKETfLvdlg0hbmDs7u4aag5urOmNGN/3DeXAYrEwnOc7nY64wGtTOxqqcT3wOdJqkdFoZFQI4DrX3zU0luM6ta7dtx6WSFtYWFhYnAlsRNridsVisbimfBRwHC1k9DDPc5FUc03SQZcRVqAgTEEQYLFYYH9/H0ARRdvZ2REi0Ww24fs+2u22RJFpgDUejw0ZK82RSBqyLMPh4SE8z8OFCxcAFLJbfs621ut1dDodIUvVahX1et0oCeR5nhiB8TiaazG6BhQRP03Afd/HcrlEGIZSVomRuMlkYvSTjsXcHKjX66jX64jjWMaWpJzH0D2bkXUAUiJqPp/LBkUcx2KMRZLjOA4ODg5k0wM4jtbSOKtSqWA+n8uGA9tPmbsu+aQjoFwXrutKaTKug3K5LHOkTbRIeEhWV6uVnMdSSNqper1eYzqdGs7jlIDzvMPDQyyXS7RaLVkD7XYbh4eHGI1GxpotlUrodDpCCnu9HkajkThKAxCCz3GlfFhHgmnmNZ/Ppa1UQvD/eS6j3oz8ZlmGRqMhGz80nNPO3nRudxzHiCjzvnqDh+Z02ihOz3e1WsXe3h4Gg4EQ3VqthmaziVqtJptS/X4fh4eHaDabssmy2WxkHXMcsyyTZ4Lt4lrguNZqNaRpKnMDAPfeey8ajYYxFpvNBmEYyncH+66jzHzWTkbBuZ645i1uLSyRtrCwsLA4E1gibWFhYWFhcfvARqRPB0ukLSwsLCzOBJZIWzwW7r//fvzu7/4u/v7v/x6+7+P5z38+fvqnfxpf8AVf8JjnPPDAA3jNa15jfOZ5nmEk9HjRbDbFvIdRHpqFRVEkUUyakQVBYOQ20riK65RRNeZ58pjVaiUyaJbr0bV7GfGaTqdi5jQajSQSro2+VquV5I4SrHsNFBG5ra0tRFFkmF0xX5MyXEpHdX4vI3u+7xuRzc1mI22lGZvv+xIpYySP0lMAUnopSRKJtnW7XckT5hixTjJzXClFZnSWY1ir1YyavI1GA8vlEtVq1ejnZDIx8txnsxnG47GMYalUQqvVwuHhofSR5cMo6yW63a7kMbNP7XYbeZ5LBJfSYioJqtWqkYfO8WE5JI5FFEUIgsCQY3M9avlurVYz6j5nWQbXddFqtYx1Nx6PJbec7aLigCoB1gDX0U62kZF4zpvjOLJWmJc9m82MkkxhGGI8HotZFtUEjOoCRcQ1jmOjvrjv+9I+PT40tuP40HOA87ter+F5HoIgkPnk2tTSd0Z9mS5A5YguX8e1qZ+dxWKBfr+Pfr8v1+t0OoiiyJDuO45jlH9br9eG/wDXHVUIXLNUIOjUhiAIsF6vZU5qtRqSJDHGmm1hGaybkSOtr2vx2WGJtIWFhYXFmcASaYvHwgc/+EF83/d9H77iK74Cy+US//7f/3t83dd9Hf7u7/7OyB8+iUajgY997GPy8/WuE8/zRC6qySTzakkkwjCUF2gSQEqSadAFFDmolGZubW3J9bRzL6Xf2qGbDsPdblfkxsytzvNcrr9cLrGzs4NGoyEv01mWodfryXkkCJ7niYw7DENkWWYQMt1nEpUgCCQHlsdFUYTlcimGWHT/1nLmJEkwnU4RBIHIcEulErIsw2AwMMj1crlEv98XWSzzxzmHJFC6/i7l5ZvNRjY3aEyliRDHtNVqGfnV5XLZcJvWDtI8hhsNOgeeua96kybPc4P8UvKszeq0YRSvz1rkujZ2EARwHEfO3dvbk7bq2slce7rNpVJJNhpoINbtdmVOPc/DYDAwzMaY5xxFkWwO0DSM57FN2qmakm72FYCQ5ziOZWxd10Wn00GpVBK5NCXyly9flutPJhPJ12a72u22kWJBUk5SSXAdcByZB64l4cxr51zS8Z2mdQAkr325XEr7eU+9YdPtdhGGofR7Pp9L7XKusSzLkOc5ut2uXH+xWODRRx81jOKiKJIa5HxOmH+tc+71mHOu2Xa9kXcjsBHp08ESaQsLCwuLM4El0haPhT/8wz80fn7ggQews7ODj3zkI/iar/maxzyvVCphb2/vhu9PkyVNMOmSfDLC7TiOQSizLEO73cZqtRISm6ap5NvqvE5GxHgMr82XfxJAx3GEKOZ5Lo7ZjBbSOZmmY0BBgtlmoCBa0+kU4/HYMBurVCo4PDw08pXplKyjmDR+0s+ejjAyoqhLPpHQaYdiEnVtZEVSpXPBGWnmeNFcS5d8iqJIykwxosjNhNlsZhij0UWdx3EjQJfzms1mmM1m8hnJvC7lRNJbLpdlnmgmxX7xM0bBeS2SHZKvyWQiZJ/n0WyMhmnESeUD1ybPn06nyPMcYRgKWeVYVSoVIZ16/bCfi8UCrVbLKIPGaC7Hi8+DNk1jn7g5wr5RMcExY/+YR8/xXq1Wskb0MfyM46fXIlUieZ7L+Gw2G2RZhv39fTm3XC7LMwMUOeT9ft/Y6KGx3mAwMIz76MzO52t3dxfNZtPIuS6VSuj3+0a0mc+yNlDjc6/Ls9E1ncfzmRgOh3Juu90WZQhBwzGC+eT0TbgZJpyWSJ8OlkhbWFhYWJwJLJG2eLxglIwRnMfCbDbDxYsXsV6v8c/+2T/D//f//X/44i/+4sc8XktqAQgppLMxcPxyPJ1OpZyNNqNidJgmYiQIrVZLSAgJRJZlErFrNBpot9tyz+FwiCAIEEWRQWZYRohElCW39ItzlmVCuim1ZpSXL/5s53w+F6LLY0ajkZCGZrOJ7e1ttNttOXexWIjZFZ89OmUzUr2zs4NWq2XUamaprTiOZcxarRY8z8O5c+ekT7PZDI888ohIjtnParUqxGgwGGC5XBqE1PM8bG9vG9E4Gi9px3WSQNZeBiDyWo4h+6VJP6PClBwDhdSZ9aZJ8lnKjBJbAELESLS05Fo7MzNSznvWajVxw+b1WdasVCrJJgijsrxfu902VAW8z0kDL0ZMST7Zd84bn7FOp2NI1ZMkuaaEG43r9PfxYrFAlmXY3t6W9a/LhHEdkPSz/Xmeo1qtYmdnRzYfDg8PRdrN82azmZBdtiPPc4xGIxwdHcl3RavVQqfTMSL9nF+9oUFlhy4ht1wujfJyNP7SmwOO48ixvG4YhpjNZsYzHoYhhsOhjGMURWi1WrJBxnZQhq/VCrpeOTc2tCt4vV6XdaXN724ElkifDrZ+iIWFhYXFmYDle673j8XnBtbrNX7gB34AX/3VX41nPvOZj3ncF3zBF+DXfu3X8Hu/93t497vfjfV6jec///l49NFHH/Oc+++/X9x5m80m7r777ieiCxYWFhZ3BEikT/vncxU2Im1hYWFhcSawEWmLx4Pv+77vw9/+7d/iz//8zz/jcc973vPwvOc9T35+/vOfjy/8wi/EL//yL+Mtb3nLpz3nvvvuwxvf+Eb5eTKZ4O6778Z0OpXoJSNZk8lEDIB03i7zjBllZLSYJW0ASFT28PBQNoFY41lLxbe3t1Gv1yVvdD6fS/1jHTnltRnxOzo6kmtrKfR6vTZyKxuNBjqdjmGAxag6I22bzUb6TOkso85JkshntVpNop38eb1eixEUcCxtbTQahjSX7WT7GV0vl8tG7W0aZQGFKoFRPPZpe3sbW1tbCIJAjhuNRmg2m2i1WiKLZbS9Wq3KcdPpVHKgOa6VSgXnz5+XNuj8eI7JaDRCtVo18nZ93xejOJ5bq9XgOI4oFcbjscwNo4ybzQbdbldyy9kOmqLx+svlUiLCjFwzysya0Y7jSI42o6SMOk8mExl/mpZtNhs5lyW+tJFYFEVIkkSiq0dHR6K24PXn87nUtma/p9OpyN45vzRU0woGmvFxjtjuMAxlLbI2tza543MYBIHxWZIkRr1vKhe4PvWaYHS4XC5jvV6LWRkAyR3f2tqSMm40zTv5HK7Xa5nLSqWCnZ0d+L6Phx56CEChFGH0n+eVy2W0Wi2EYSiReqocVquV8Zz4vi/rdTabSTksrkWmKrCG+80yG7N4/LhjiPT1OHhaWFhYWNy+sETa4rPh9a9/Pf7bf/tv+NCHPoS77rrrVOdWq1V82Zd9GT7+8Y8/5jFanquRJInIgbWkmnJtLbfM8xxBEIgkdrlcotFooF6vG3Ja3/ext7cnUmXKnbV5UavVQp7nxgv2crlEFEVyLdd1pS61li7TCIlEiHJggs+bzo0tlUoIggDtdlsICCW+mshRfqzNlbSEm2PBv9l+bjroXF/WNKYzMq9Psyu9ITEej6WtzGfVeap5noupmCanNGoi4aMTepIkUlfbdV2cO3fOkBaTCGtDrPF4bNQepgHUSRk3iaA2/arX63JeHMdSM5l57GEYol6vGyZZJPTMOedY0BSO/aQRFtuqZeDMx6XsutfrCWHtdruo1+uGUzvJtZZQc37ZfsqgtTyec8kNJeDYdTxNU1nrdIDfbDYyBkBBgLlWdI7yyXWl85Ap5deO1uVyGcvl0njmfN9HEARybpqmhsSf48ONJLY/CAKsVivs7e0Zz4TjOLLBxnZNp1MhuiTlrVZLPuMaL5VKsmnBzQG9icM1oMc2CAIjH317e1sMDLkxxM0sei3cjDrSVtp9OtwxRPp6HTwtLCwsLG5PWCJt8VjYbDZ4wxvegPe+9734wAc+gKc85SmnvsZqtcJHP/pRfMM3fMOpz6UDtiYbjuOg3W6jXq/Ly/lDDz0k5XNIaCaTCSqViuHUOxgM0Gg0xHkaOC4fRVLI0kLM7wWKF31GAEnaqtUqFosFxuOxnMtIs+M4ci6N0UhEScYYNQYKclSv19HpdIyXf7oB67F0XVfIKHCcY8yIXxiGEonXkVSOlSY0JFVs//b2tuSNkmSSRJB4kejp669WK1y6dOkaE7dqtSqbARyfNE1xeHgofW82m1JSi+NVq9VQLpeFDLPdi8VC+sn1oB2VXddFkiRybaDYRNB5zsPhUM5lu9rtNlzXRRzHRroKSSA/y/MctVrNMO9KkkTaBBxH9UkWCZYkY+7wbDbD1taWkHRen2SeZKzf7xsl0GiIxZJgvDbP4Vi0222JZpPsk4Brszfm57OPNM3SUWSWwprNZrI5wNxoPdaO40j+MiPeLPOmXci5JnR0nuPK8eAzt16vRU1AJYAuqcbydTpfn33kPKdpisViYRDkbrcrUXHt2p0kiag32I4sy+RaFy9eRKVSwXQ6lWeEjuPMN78ZEWlLpE+HO4ZIX6+Dp4WFhYXF7QlLpC0eC9/3fd+H97znPfi93/s91Ot1eaFtNpvywvvKV74SFy5cwP333w8A+Mmf/El81Vd9FZ7+9KdjNBrhrW99Kx566CF8z/d8z6nvT8KsXXJJzijXBAoCyAgWX6hp/KVLE9EoihFh4Lh0DUmJNmJi9L3dbmM6nRoGWzRWOjg4EFJCErFer0WKS9duEhUSYR3p5Iu+rqPrui5ms5lRl5bkVzsea5duoCCHLPfE6+v7aYMzXSsYKEghazXr8kU7Ozty/fF4LBFpblqs12v0ej1EUSRlxSgRL5VKcs/lcikmTRxvEhsdyT4Zha1UKkKCtBMzS0ORtHFc6vW6IZGnIRtgRloZAGq323AcR8zKuH42mw3W67VhlEWDM12jWEeo4ziW6K2Wq1O5oCX/w+HQiHZyHrk5AkDqQ3Mum82m1FEmuaP7d6/Xu8bUi6WfuH642cH14/u+yJKB4w0ivZHk+z48z7tGrs7yZtqYK4oikZlzvCeTiWyKsI+s1wwUzxzXHNsdhiEqlQqyLMOVK1cAFJsPu7u7srlGbG1tXVMKjwQbKMg151I/95w3HrdarWSMdB347e1tWStsEzdVOJf63icrClwPbhWR/tCHPoS3vvWt+MhHPoIrV67gve99L17+8pd/xnPm8zl+8id/Eu9+97uxv7+Pc+fO4cd//Mfx2te+9tT3v1m4Y4j0STweB8/HcuO0sLCwsDh7WCJt8Vj4pV/6JQDAC17wAuPzd73rXXj1q18NAHj44YeNKN5wOMTrXvc67O/vo91u48u//MvxF3/xF/iiL/qiW9VsCwsLizsat4pIx3GMZz/72Xjta1+Lb/7mb35c53zbt30bDg4O8Ku/+qt4+tOfjitXrhhlxc4CdySRfrwOnvfffz/e/OY338KWWVhYWFg8XlgibfFY0NLax8IHPvAB4+ef+7mfw8/93M/dlPuzBNVkMpGoUb1eR6lUQpqmIr/0PE/qD7PNQRCg1+vhE5/4hBxHmTVL2vAeuuzRcDjEo48+ikajgQsXLsi1ABgyVl4vDEO5FiXWR0dH6PV68pnneYY5GOWnjAYnSYLpdIosy4zaw/V6XepcA8cSbUa9gSKgwVxbAJLDq6Nv9XpdJN+6jrHv+4ZZ13A4lFrQOm9Xl22azWZIksTYPGEUE4BhIMUooJZGVyoVbG9vG+WX9LiOx2MxNONYuK4r0lz9wk5JLYM18/n8mrxp5q5y/YRhKNfQ+cDMldWRfubPa5k+r8Nr5HluXJ9y/+VyKe1vtVqijuA64HzQNE2P/8l+6vVKQ6v1ei31mWu1GpbLpZQ9Y78PDg6wXq8l8stSZiw1BRRy+62tLZkPqhK4lngt5iLzOeG61DnSXJdURXA+F4uFRHgpGWcuMlA8V7PZDMPhUMafJmNxHEvfT5bp4pzX63UxJKMaZDKZSDrChQsXpAyaflZZ+/zg4ABAsXbn8zm2traMHHtG5IHCxI3fRzyGhnlMQeF43AhuFZF+2ctehpe97GWP+/g//MM/xAc/+EF84hOfkCDqvffee+r73mzckUT68Tp4PpYbp4WFhYXF7QFLiC1uRzAntlKpyMs9CRXzmAlKanX9Y5o3kajpur3aYEhLl4+OjjAcDrHZbIRAAwXBoDQcKF7qO52O4VpcLpeRpimyLBNiqZ2IAQhxnU6nYgK1XC7h+75RU5gGTNoBvFarYbFY4OjoSKTjJBTakIl5tCRH5XJZcsq1iRVzgzmOut0kX8yhJTGiQZkmnfV6Hev1Wmp8854kqPoz5r2z/b1eD7PZTOaDJmw615R/cwOCIOHX+dUk3ewTN1h4/WazKTJdjgWJbxRFhmQ7DEOplczrM/+coIOzVl5yHrkZMZ/PsVwuDbOrbreLyWSCy5cvy5jneS6bMZqo1+t1udZkMkGWZVIPnf1g39n+NE3heZ5Rc5nPw9HRkfSd0nd9LRJhjiFd7fv9vkHooyhCo9EwSCcl91yX5XJZ6rcDBemkCaB+fnX/uc7o3M356na7QtbZfkrwuZ44n6PRSMh7s9mUdXJSuq/HcbFYSBqBNjPTZoTMRddEmt81XPM3w2zsRnBS+ftYZo7Xg9///d/Hc57zHPzMz/wMfuM3fgNhGOKbvumb8Ja3vMXI97/VuOOI9GkcPG/mBFpYWFhY3FzcSD1oW0fa4onEbDZDFEXodrtGBHY2mxlGXDTIouEPAIlYN5tNw30YMPNGoygyXn7X67W8+B8dHQEoCBTzP7VDMfOYeS26Vm82G7TbbflME2THcbBer3H58mVx/aX7t464AcepcYxibjYbpGmKNE2NKJ2OipPEeJ4nfRqPxxJVI7nQTuh8R2OO53Q6NUpV6Rza9XothmocC95bE9hutyuKApIeRu7orMxx1OTUdV00m02jxBE3I3Q/6QQ9n88NosXovnbM1rm9JJPA8SYE50Y7oLM80tbWlkRvmY+ty7Hx3iTSNFmjiRrnjf/Hds1mMxwcHKDf7xulrrhBpHPs9RhXKhW0220x4mK7eU223/d9bG1tGbnE3LDpdrvSJqoUmKrJjQ+tEmAJMUb3geL5ojEcSScJnM4FZ2Sf4zWdTtFqtXD+/HmZX45/vV43xozu3DpXnhslJ838aKhGU0CWQmO7uKHAueRmmjaP297exng8FmdxjiNL3bH90+kUm81GnqXd3V2Uy+VrNjhuBDcSkT4ZrHzTm96En/iJn7jhNgHAJz7xCfz5n/85arUa3vve96LX6+Hf/tt/i36/j3e961035R7XgzuGSN8MB08LCwsLi9sHVtptcbuCxj1BEAjZoMlXlmVCEhi5zfNcCMFisRCHbi1ndl0XpVLJkOjGcSzRYR2J1ARcS6d5HO9D0qEjgiS/WZaJaRhwXItYu0uTbOvAAwkmpcM813Vd7O7uGoRGuwzzHEZPgSKaPpvNRIrLtjIaqWXEjIRrUzIdsQSAvb09BEEgY3Z4eAjf9w0zs+VyKdF6LRU+ODgw6vky4kpSQhLDmsdsKwkW20F3aS295gYISRjHUUc+9WaLjsTzZ7a10WiI0zY3FeI4Fmm4Hg8dKV+tVmi1WkYknnWIp9OprM/hcIjDw0NjI4C1q7Msk/UZBAE8z5NyYaVSCRcvXpQ1xHbTRZvrgvOo55YmZixhBhTEUJctY8Sepdx4z8lkAs/zRC5N07w0TQ01AaXbvC8N4TTR5QYUFR+O46BUKiGKIiGwk8lE5oPPCdutlRM6Esz1RNd0tmEwGIhqQn9nVKtVSQcAICX19MYCN0S4GcTvCu1M3mw2EcexRLlPbtxdD26ESD/yyCNGqbubGcykud5v/uZvSurCz/7sz+Jf/st/iV/8xV88s6j0HUOkH4+Dp4WFhYXFnQNLpC0sLCwsLG4f3AiRbjQaBpG+mTh37hwuXLggJBoAvvALvxCbzQaPPvooPu/zPu8Jue9nwx1DpB+Pg6eFhYWFxZ2DW0mknyylNixuDZgbqnMgkySB4zjY2toycl2Zg8pIHuWoo9HIiOZ1Oh20222pAUs5L6OTjGhFUWTkgzKiyMgySw7xfkCxVoMgkIgqcBzhZqRttVqh3W4jDEORjqdpKmWBGFljxCvLMpHMrlYrhGEoBmwAMBqN0Ov1cP78eQBFBHOz2chxwLHkPE1TidS6rotGo4FutyvRyMFgIDmt3W5Xruc4jlG3mvJmRt4oc/V936iNzRJljIzrusc6z1hHTRnx1HJgGsTpOsCMujuOI8ZujNhraXGSJKJE4BhqwymCqgBGGSnfHo1GhsydebXEarUy5Os0nOLa5Fiwj9rMjGPMttVqNWRZZuTi+75vRNE5Bjo3v1QqyRrTQa3BYCBSfI4/jbi4zlhOjef5vo9arYbRaGSU0rp8+TI2m43MD1MX9DPBNmgJOK/N433fx/b2tlE7nOW49PN0UpXAtlK6z4jxarUyxluXv9KScM47n8N2u43ZbIZerydrcWdnR+TqbEeSJIjj2PBUoBcC12e/34fneWi321gul4ac/npxu9aR/uqv/mr89m//tqTdAMA//MM/oFwuf9ZU3ycSdwyRfjwOnhYWFhYWdw5uJZF+spTasLg1oFS61+uJAm6xWKDZbIpRFgDJmXZdV17qN5uNyK5JHkkKq9WqECY68GpzJxockTTQfXo0GglBCMMQw+HQkJiz/izzQoFjQk9CUCqV0G63EQSBvIju7+/Lyz6JKMnpfD6XTQQ6fWvpKOW62kSMOcU6n/tkPWu6K2vTJEq/2W/g2HyK12q32/A8T8ysgOO84ziODeOvZrMJz/OEMC4WC3H85nhvNhskSSJjyEhar9eTscjzHJvNRnLegYJ8TadTzOdzIbqDwQCNRkPIIO/Judd95Fjx/zabjVGnerPZ4PLly4ZxU6vVEuM2jkev1xMDMF4zTVOjLOxoNEKSJIYcmAQzCAJZx8xtT5JExpGu1VwrNLvS5HS1WmE2m2G9Xkv7gYLgMUWAx3HstWO5TkmoVCpI0xTj8Vg+40aAXsf7+/uSS83rc/zSNJX1yLx8tmtvbw/b29tIkkQ2kvI8lzrw2mmeue0cW70BoOXeJOdA8YyMRiOkaSrX8jxPiDTHv1qtSj46CbHneeIlwN9tlJNzrbBGdhzHePjhhwEUz/3e3h62traMDYIbwa0i0rPZDB//+Mfl509+8pN48MEH0el0cM899+C+++7DpUuX8Ou//usAgFe84hV4y1vegte85jV485vfjF6vhx/+4R/Ga1/7Wms2ZmFhYWHxuYdbSaSfLKU2LG4NGH06PDyUqDINpqrVqhA+vhDrkjr8m+ZfPI4lhghGgfliPplMEIah5ODyGuVyGc1mUz5j1Eo7hQMQ0s4IWbPZRJqmRlko/tFRa038geLlnM7JJFo0EnNdV67PXGgSBB2NI5kZjUZi1sWNhlqtZvRRtznLMiEvLBHEHHUqBLRZ2WazwXg8NsZC52VqAzhuVLBPdA0n6UnTVNqvncPzPDfcsblBQqdlALIRoTdU6MLMPHm9CafdmnkO1wavpU3tSNxJSDknNLfi/NKwTecO01Wb1yKh5z00PM+TcczzHEmSyAZCtVqVqLR28h6NRgaRrtVq8v8ENwsajYbhDh/HsYwr1QxcH0CxhqMoMhQfcRxLHzjXfDZ1SbNSqWTkK3PstPpivV6jXq/D930jh5zgWDDH2XEc2USgKZ12dud64fp3HAee5xku/Lrcln7OqQzRY6ZLfNGI7/DwUJ51Pc9hGN4Us7FbhQ9/+MN44QtfKD+zytKrXvUqPPDAA7hy5YpsGADFRsL73/9+vOENb8BznvMcdLtdfNu3fRt+6qd+6pa3XcMSaQsLCwuLM8HtnCN9u5basLCwsLCweKJwqyLSL3jBCz6j2viBBx645rNnPOMZeP/733/qez2RsETawsLCwuJMcDOI9BNVt/J2LbVhcWswn89FMkzw58FgING9UqmEJEnE4RsocjGr1arh5N3v940cU6CITCVJYuSM8m8tGWZZLEb5ms0mlsslBoOBRMPG47HUJKZygrVv+TywHu90OpUoKWtL6yga80zpHs120DlZP7PlclnOm8/nkiPMSJn+N6/F/GKeDxSy6uVyicuXL0tEsd1uo9lsGpFURvg4ZkmSoFQqodFoyLh6nneN6zLVAfq7gRFfXW6JkVtdVovjQ4k/yzFpOTZlurPZTOo1dzodjEYjIx+dUl6d29tut9FqteT/Dg4OJOedY8faxqwjDBxHeTkfjJguFguZX5aKojQfgETYWXccgJRs0mWgkiSR2swARAqu84ld10Wr1cJ4PBblhu/7OH/+vLSd4LzrMlP6GNZs11FlRnN1NJ951OVyWdRClUoFo9EIs9nMiHhHUSRkjWW/9BiyBBXPB4BPfepTqFQqkh4AFM9cp9NBpVKRZ5r50owOdzodQ1XA9cPa5zp3fn9/H/1+X/ofhiGCIDD6zvHnup5OpxgMBhKRZ7+Z01+pVIy87uvF7ZojfbvCEmkLCwsLizPBzagj/UTVrbxdS21Y3BrwBXu1WskLMF/KmVOswVJNQEFUoijC1taWvHTneS7ElNLRXq+HUqlklCoCjuvMAsfyThpJAYXEmfm++gU2z3PkeS4kqt/vi/QcKDadptMpdnZ2DHkzAKOs0mw2Q7lcxu7urlHDlgSTcmlKaUkkZrMZtre3hYQABbkYj8dIkkSuVa1WRdqtNySYP8vjms0mdnd3JU97Mpmg2WyiUqnIGLKEk86l5uZAlmXSv1KpJMRRm1Hp8lo0etMlmbgWmAfMdUBDtZP54Tqn1XEcNBoNQ1pMUs282m63i0ajYcwTiZ42muIGIQm1bi/byrrfruvKnCwWC5H8sh38btNlplarFWq1GpbLpVH+iiXauBaZT8wNzHq9LhsHLDPF73Ut39cSZZ2zH0WRrBVuyiwWCxmnKIpEts/nkBsGOn+7VCphf38fvV7PkDvrWs1AQU4PDw+N3Grm1/O5ofkbr8u2nST1WZYhjmM5hvMPHD/LfF56vZ78zqrX62g0GkZ+/tbWFsrlshgasm16PfEzng8Uzy9/1v28EVgifTpYIm1hYWFhcSa4GRHpJ6pu5e1aasPi1sF1XWxtbcn6mk6niOMYQRDI+mM+rY6S8gW+Wq2Ko3UURdjb20Oe50I4KpWKYbY0n88lwkqCQAOl1WolL9SMqulc4cViISZijNJNJhM5Fzg2EdNOzCQ9YRgKuVuv12JKpp22SaA0OQWOSUOz2cTW1hZ83xdyqt2+SaDW67WMkTYbY/1nTfhI+oCCrJK4kEwyV5w5skBBVIIgkLrCAGR+mE8NFOTF8zw5j87Ks9lMjmGNarpmA8f1lXVks1QqCcnnmA2HQ9TrdflOarfbMlYkPZ7nYTQaGXnN3W4Xy+US4/FYSBWNpmq1mjH+elOHY6IJMus8J0ki665arUrusTb+Yn1otl/n/HI+ptOp4R7ODRAdOQ2CQJzstQqBZJ7zydx5jr/jOGKmxvP4TOkcaZp56ZrR9XodnU5H8paBIsrL9rH91WoVYRhKH8fjMWazmeEWz7x27SgOQNQcOi+edaMByCYWzd3YLhJsjmOn08Hu7i663a6RP0+VANuv8785b9oZn9fX+e83C5/LxPi0sETawsLCwuJMcDOI9BNVt/J2LbVhcWswnU6vMa9iBExH2vhyS0ktULxg84WbL9RcQ9qZmuSYL+YkzZPJRF6eGW2dz+cSGUzTFLPZzCD0jBZ2u125/ng8NtyGW60Wdnd3MZvNcOnSJekbZav33HMPgGPpb5IkhjSaxEUTT8/zhLjX63UxOLty5Ypca2dnB61Wy3DCpgkWicRgMECpVDII8WQyMcoLxXGM0WhkbCqwzBglzBwLbizwM8qqdYkgRgC1WRTJHskMHcB935d7xnGMPM+vkXZTuqwjorPZzCCKW1tbcg32kSZoPI8mVFoWT0JbqVRw7tw5+SxNUyNCTWUBySPnsVwuG+SaZI9EkfLgLMtEYj6ZTOD7vhFJ7vV6MiZcn0mSYDKZSD9d18VkMsFms5G1EUWRtInf15STU3FQrVZx8eJFGW/gePOK9+f88pnQDtrNZhM7OzvST5JaqkLm8znuvvtuQ2kxGo1Ens4+kXxvNhvZEKLDO0ut8XpaQUGHdE2uGVUOgkCeS61s0OtAqwG4HlkKjW3Q30Gcc21sdjPMxmxE+nS4Pk2dhYWFhYXFDYK/sK/3z2kwm83w4IMP4sEHHwRwXGqDrqD33XcfXvnKV8rxr3jFK9DtdvGa17wGf/d3f4cPfehDt0WpDQsLCwsLC4vbAzYibWFhYWFxJriVrt1PllIbFrcG1WpV8lS1ZJUliLRZF+sb63rQNCXS0TDKMnWUejgcSqTT8zw0Gg3keW6U0mK5HUpDsyyTEjrc1ImiSMpDUbbKPFJGrBhl1bm3NO9qtVqGsdXh4SEWi4WRH16v17FaraRPm80GrVZLjtGlwHT5qJMGWKVSSSS9OpcXgJGTzoi7rl292WykzJSeE5YP0nNHky32ndJhnsucac6bjmxzjmgCpesY6/JKzBXmWimXy9jZ2QFQKAy0MVS1WpWxI5j/G4ahrIP9/X2RS2tTNl3+jH8HQSARe0ajdUkp1kOu1WoScWUOsM6xp4FatVqVcUySxDBvnM1mSNNU7sf2l0olmQden5FVzi9LZ+n8c0q4dfkuysa1GRuNwDhPNKCbTqcyF4wsnz9/XrwEKHvXOcdxHBuS9jzPpTQb28q64bPZTKLlaZpKbr8uzcVxYvvDMDTyufk8UJECAJcvXxaTQhqcscSdVg7wGWEblsulSPx5fSoJWDbsTqoj/WSBJdIWFhYWFmeCW0mknyylNixuDUqlkrg/k8jRQVs7Dc9mM8xmMzFAAgrSUK1WsV6vJS+VMtTVaiXEuNVqiVwZKCSv7XYbeZ7LZ3meS+1kbeoVBIEhByZBXCwWRo3ZLMuk/UmS4NFHH0WpVMKFCxcAHNcNXq1WQggoXU6SRNpKOex4PDZcosvlsshJWZeYxlUAxDV8NBrh4OAAQEHK2+22IUOt1WrI89yQDTNnlcZT2kGboLy61WrJ53EcC5nRcmbXdVEqlYzavZRVs/3z+Rye5wkZazQa2Gw2mM1mQoJZS3swGBgkeT6fw3Ec2bigrFdvgHCNkPBognzSbOxk3n2n0zHWCzdlKJVeLpdSb5qkk+Ol85Vd1xXTPJ2+wNx5jv/W1paRD82+sF9AkT6wvb2N3d1dWYscJ13jnH3n88J2kDzyGBJPblDQWV2b3JVKJTHY4poaDocolUqymcE+NRoNQ4o9HA7x6KOPSp/yPEeWZUY6Bdes/h3DetTMEQeKnPfNZiPtonxbb15NJhO0Wi2EYSjzxhrSup57s9lEGIaS9sA51+NVq9XES+BknWq292YQWkukTwdLpC0sLCwszgS3cx1pi89tkDDoSFuSJIjjGK1WyzD0GgwGWK1W8oJNd1866gJFPmie5zg4OBCCwUg3CaXjOOIMrt2OB4OB5LUCkMib53niXk0ilGWZRAwZJeeLPstj0UgMKF7OmUerc4XpXk1CXK1WhUTwXKCIOJIMNBoNDAYDZFkmn83nc8PtnPdkPrHuJ42oGG3bbDZYrVZCADkv/D/g2MwsCAIhHByTcrks5Gg8HmM0GhlO4RxLnbPq+77cFyjIIwkUx6fZbKLRaCDLMsl5ZvSTEWC2tVqtGpF4ms9dvnxZ+tHtdmUTAoCoA7STdxAEQupZZmo8HmO9XssxHIsoiiRSy80HTU5Xq5WxGcKxyvMci8VC+hRFkbEutJM114XruhJB5ryxnboqA+ewVqtdk3fM8aEPgDZLm0wmMl9cd5vNBvP5HPV63VifURSJsRr7SUMzoHi+9vf3MRwOjbUYhiEajYb0k8++Njhrt9uYz+d46KGHhBBzbNlHPrck9MDxs60VGdPpFLu7uxKl5/jwGWM7uHnDaHaj0cBoNMJoNJI+sUQXlRt6k+l6YYn06WCJtIWFhYXFmUA7rF7PuRYWTxSiKMJyucRisTAia4vFAmmaCtmo1+sYj8dGqSXHcYQY6jI7NLrii7ImnEDhCrxYLNBsNg1pNKXc2qG4Wq2KjBcoXuL39vakFi9w/JJNojWbzUTurA2w6GbM86rVKjqdjsh9AUid2jAMpU80PdNtnU6n6Pf7MmYk6XmeS2SQxl26HjEj/TSgAiAO4dyMYD+TJBFSxYjmbDaTa1EOrEuBTSYTMUpj2yjJ5/i0223UajUxAAMgcuTlcinfVev1GsPhUIylAIhCYDabyZx4niclqjjfWnYPQGoY0zyL59Gci+1PksTYIAAg61Nfq1qtCskHIKSR0VigIIqe52EymRjltehOznGk0oIRaqoWoigStQDnWKcLhGGINE0lugwUzw1dsNlPyt45/lR/dLtdQ+7NzRh+xt8bSZJIW7e3t0XyrTcJ9EbSfD6XjRqOYRRF2N3dRRAEEkkPwxCu62J/f19Iue/7shapkMiyDK1WS8g823p4eCjzTLl1lmWGSR8d109uJOnNJZqZaak6a4mTSFerVUnB0CZkNwJLpE8HS6QtLCwsLM4ENiJtYWFhYWFx+8AS6dPBEmkLCwsLizOBJdIWtyt830eapoY5FOs7u64rn7N8FP8NQMy1tJyZEdHt7W2JMDFvlBFL1vDd3t6WKBcjarrUz2q1Ejm5zssul8twXdfIoWVtZB7Duss6B9txHMkfBoroYaPRQL1el2gh+8EIHT/T0W2WyOIYccwYWeNnzIWllBiARG5p6MRzKZ/lnERRZET/GeHU0UlG53Q+MaPzVA+wHVqqrtUCvD7HS+dbs0SUNmNjVF8rbAaDAXzfl+uXSiXJmWfuue/7iOPYMPGiMZqWidPkLggCWT8npbzMfdZRd9d1RZ7Nz1imbD6fS3tY/oklowCz9BdQRN0rlYoxv4yQanXRcrm8RkVB6TJz1Qk9/ixb5rqusaa0FwCh1zQAUWJMJhNDOaCNz6gUaLVa8twwAjwajeSerVZLTL94HGt6r1YrYz1ubW0Z8niqDnS6AO/BsonT6VTWD6/FvjuOI+tA16sGimePzxefVSoWmN5gc6RvPSyRtrCwsLA4E1gibXG7gi+1nucJWanVapI7TDJWq9WEGPDlmXm11WrVkEEDwN7enpCALMsMU6PpdCrGSjyGRlH1el1IW71eR57nWK1WkqsdBAHiOMZ4PJb2sg+8Nx2ZF4uF4Uas86t5zziOsbe3J7VvaXama0uzz1qqW6vVDCm24ziSL87zJpMJZrOZGKkBx6ZnJD/AsdRXS3WZW80+9vt9GQuSTpL3Xq8nn21tbWFrawtRFEkN7UceeQSe5wmp7fV6KJfLOHfunJx3dHQktb91PWXKjbVRmeM4Mr6cT228pjc+dM4wiRfnl9J37U5eqVREmk9iyw0RtmF7exuVSgUPP/ywrMVyuYzhcCg1lAHgwoULqNVqODw8lDnv9XqSfsD2VyoVDIdDIYpsv87dZh1rvdY5V3ptMEWCxnDsJzeceH0+D7p2tOd5IpXmedxQ4Lnj8Rj7+/sYj8fSXsqqtccBa2drB/bxeGwcl2UZms0mWq2WjD/HWZujua4L13VlXXCjKggCIb/aJV5L0ymz5yYa3fD1poHeeOLfJx3eS6WSPBeLxULmzuLWwRJpCwsLC4szgSXSFrcrptOpuDNrV2oARlmiarUqUTkSyNVqJS+1+iV6tVphOBwKsUrT1HBFJhmhSRXvNR6PUa/XhYB4nofDw0McHBwYBCNNUzGU4nFJkkhOZxAE6HQ6cF33mkj2fD43SnUx55jHkVynaSrXWy6XCIJA+sOoGiPhQEHkOp2O5D/zno8++qiR6+y6rkR/2ScSNBLMJEkkr5xEkWNNd2eC+bcc/8VigcFggCiKREEwHA6lZBT73W630Wg0hAiNRiPpNwmUdlJm+5k777quHLe1tYVutyvtWq/XUhpLb4oEQYB+vy/njcdjIURsf5IkomrQ5H25XBrGU47j4ODgwFBMUPXAfpfLZXEcJ2lbLBbieK2dxXWeM+c4jmMZn9Vqhel0auTAs0waXeTZ/kajgW63K89JrVbDcrmUqLjeONHeAizxxLZub29jNBoZz8mlS5ewv78v6gEA4lSu1RHchKFSgXnunufJ88259n3fKM/Wbrexs7MjhJtrg5tZdBYnuQWO1Rf69xUVFfQn4GcsG8fxp0fASe8FjVqtJoaBdCC/UdiI9OlgibSFhYWFxZnAEmmL2xWsvavNi3QkWkeXgGOzJqAgKtpwCigIF1+y+X+UbGvDMUaQtQHTer02yhwxuud5nhHxq9fr2N7eFmnuyetTklyr1SQKSwMpXd9aR1/pEM2SSjRJ4meaNFPCGkWREEx+tl6vDenywcEBFouFROR0HV8tzV2v14YLMyP9upbxbDYz6hPTGEy7P8/nc4xGI0RRhO3tbQDAvffea5hARVGECxcuYGdnR4gyTaxIGAGIZHw4HMo4ss2+78u/O52OyKU5RzyeRA6AHENCuV6vjU0b4Li8k5ZQU6JNolWv10X6zmNorsVILFA4hs9mM/i+L+uTDueaqHmeJ1JiIo5jw0Ge64J/2FYAxqYC16yWilORwHXNtcjIO3C8UaUj+yzDNp1Opf0AsLOzI7JyHqdrODuOg/F4LBsyvCfl41xTQRBgvV6j3+9fo5golUrS3pNls/jsasLLvug681SGsG8cH6Bw5mafuCmlFRk0+NMmd3zGdCrAjcAS6dPBEmkLCwsLizOBJdIWFhYWFha3DyyRPh0skbawsLCwOBNYIm1xu2JrawthGBq5t8x91JLYUqkk0VZGvih1rdVqEjnK8xxBEBh1hVmnWUcPWY+a0Unf9yX/kVHMLMswGAyMKDhLazWbTXk2Dg8PUSqVJApKcy9KatkGRhh12SzmguroM+W7vCfzUXVNYbaJbdDRSh0Zn8/n8H1f5OpRFOHo6Ah5nstn29vbyLJM2sV6xVtbW3IMxzVNUxwcHMj4B0FgmC+xL2maSmSZ7WTUWpcdYsRyvV5LKTRGrhuNBpbLpdT0Zfs7nQ7CMDRk50mSGBFLjgEjrhx75tayrY7jYDAYyFphnnaj0ZD2sszXyRxatodrqlKpSGkyoJAu8zOuPcqSkyQR6TXLUGkzNvZZ94llw9jW0Wgka5H9TNNUxlWvDQ2W7tKmbzzf932RpnO+teyfUWRGqjn+uqyYlvnzPNY4j+NYZPftdhvj8Ri+78v6Zz134NjojB4E7MdgMBDzNLabagydAx9FkYw1x58eBYvFQkqLsawYx4u1xbX8m4oBtkP7F1wvLJE+HSyRtrCwsLA4E5TL5euuI32951lYPB6EYQjP8zAcDuXlVDt2ayJNYyidY7zZbBCGobzwLhYL5Hlu5KBS+qpl0HEcG3nOzLHUJGwymaDX66FWq2F3dxdAIcfOsgzj8dggyQBEiqqNq7RcVEthgSJv1PM8gxTS3KlSqaDX6wGAuDLzutvb21KfmH1gzVu9gdDv9xHHMcIwFFKuc6j1+HieJ+0bDAaGtBaA3J+1qoFChkuTJ44B5fTlcln6RHLHsaAJlN4oKZfLMnc6tzeKIqNeM9fH9va23Gs6nWIymQjx0nm4WpLMutW8fhiGODo6QhzHchxNuvQ9KavWudvM2+Va2Ww2YmqnDciYz045M8eKeeo813Vdg/Cu12vEcSxrpdPpoFarYbPZGHJsklBdOzyOYzz88MNCFEkoOV6u66LRaGB7e1vI79HREZIkgeM4Mj5bW1tYLpeyYcW5Y+rCyZxubi5wU4HzwbHgWuVzMx6Psdls0Gq1RMJ+dHQk+flcf5Scc41xTBuNhowrN1O0rwLHOM9zeXZYe1t7FTDFg+uT3xfNZlPumaYpfN9Hu92+od+nGpZInw6WSFtYWFhYnAlsRNridsWlS5cMozGgeJGt1WpGPihfdOM4lpf/1WolJXS0gREJAV/sPc/DZrMRYsqoIHN8CUbQdF4qr8mXbhL0xWIh59LtmH2g2zddiYHjHMtyuSzki/m0ulyT7/tC2vnZcDiE67pCcOj6TBM1js+jjz5qOD3rHF6dL14qlYycX+Y985jRaATHcdBqtYT8MGf08PBQ2r+3t4d2u224I4/HY0wmE3ieJyS5UqlgNBoJsQrDEHEcI45jIyrONvA45rw2m00jOslNFc4TDb14rTiOZd1wDtfrNUajkZEXTOdqup3zMxqH6XURRZGQ7aOjI4mQk8AyH7jVasn493o9zOdzKXHGtcF83P39fQDApz71Kezs7Eh0myXhJpOJjHWpVJJNJ7aj2Wwiz3PJXSfoHcBzWcKNbWAOMMtsAcWzROM4Xv/8+fNwXReXLl2S56vT6Ygbt47yNhoNGf8kScR9m8/gdDoVfwFdvst1XdmY4jqjGzznl67kmrgvl0vMZjOZ3zzPRTnASDld5tfrtcyT53lC3Pl9wHbosm6+7xsbAZwPboLcDLMxi9PBEmkLCwsLizOBJdIWtyt6vR5msxmCIDBMsuhmzZf6zWYjsmESFZbE0iSQ5Z5IBIFj4kmyx6jearXC1tYWgEJmSgdwvpzTGEx/BhREkOWOCP3i7fu+vPyT0NVqNaMv/IxRRv1/w+FQom4ApPQPf+71eiJTZvs9z0O/30eWZULI2u22uJOTJNA4iqWZgOOSRnQJp4x4sVgIKZlOpyI1ZqSQ9YpXq5UQLUY+SfT1POmoOOfkZP1mngsUBJbRdRIy1r+eTqfG5obnebLxwFJjvC7/9n0fSZLIeWwHza0ACMGfz+cy3vP5XOpBA8cRVZ6v51w7OvM6JMY8jps8JLaTyUTk9rw+CS3X2KVLl7C9vY2LFy/K5kyWZXj44YcNp23W9i6VSkJE6WDP58F1XVlD2g1dO4sDx8oEvT45XrVazZA302gNKJ7B5XJppEToNcjz2u02fN/HZDIxJOas032yXjk3O4bDoWyI6I0Grg32ezQaYbVaSYkw3VZNhim95+ZJpVIRR33eU5e9Yw3xG4WNSJ8OVhtnYWFhYXEm4C/s6/1zu2O9XuPXfu3XzroZdzTe8Y534N5770WtVsNzn/tc/NVf/dVnPP63f/u38YxnPAO1Wg3Petaz8D/+x/+4RS21sLCwuPPxZP19DDwxv5NtRNrCwsLC4kzwZI9Il8tl/PIv/zJe+9rXnnVT7kj81m/9Ft74xjfine98J5773Ofi7W9/O77+678eH/vYx8QYSOMv/uIv8J3f+Z24//778Y3f+I14z3veg5e//OX43//7f+OZz3zmqe5NI6GT9aEZyWPEjHmstVpN1iSl2cwVBY5l1qyvy+OyLJOIN6NgWhrNqG+lUpHIICPKjUZDorDNZhNhGEr+NK/farWMElAso6UjcpPJxIgeLhYLJEkidXiBIhp2eHhoSHNpxqZrBTOvU9e3Zq1qmp5RgsvyXEDxgstxYztYjkkbdDHqpmWyURQZpa5YVgk4jrIHQYDt7W0EQSA5uixJpssS6Yg7UETFeQzl5DTMYgQWgJjGaWktJb3aTI5jQGM09jcMQ0MOTFXAybZoKXq1WsVwOJR+c70wt5zXp8mdliQnSQLf92V8eB+uGbY/yzJRVbAut5YyU6K8XC6NvHLOnR5/moOxbcwb1tF5vQ55z0qlItFaoJCch2GIIAiMdAHOHeeTtbe1NH17e9tQclSrVbiui9FoJOPDXHea/wFFdJ7Pry6TpUt88f71el1qS6dpiizLRP3Ae9ZqNUNOzmdEf2ewfBfHg4ZqLLHFsWYOv34WbwRP5oj0E/E7+bqI9Ite9CJ87dd+Ld70pjcZnw+HQ3zLt3wL/vRP//SmNO7T4R3veAfe+ta3Yn9/H89+9rPx8z//8/jKr/zKJ+x+FhYWFhZPDJ7sRBoAnvOc5+AXfuEX8PrXv/6sm/Jp8apXvQrf/d3fja/5mq8566Zcg5/92Z/F6173OrzmNa8BALzzne/Ef//v/x2/9mu/hh/90R+95vj//J//M1760pfih3/4hwEAb3nLW/D+978fv/ALv4B3vvOdp7r3Zj5EubpArQxgcVUqXS7DdzZohz6qlaty2lKCUmUh+c0AMN/MEbl1eHUPy2X56qllJA0Hw+EQoVe8/LtuGbXyBvV6QcjCMMTBwQLVTYzyspD5JqNVIVt1HLSvlk6urAvpcrvtodstSEKjUUOptMastkLkFu3ISzkq6ymS0bHUu9PpoN3ehnu1/f2jy8jjEdphFatV8VkpX6GyLkyapMb1EtjMp5hPh8dmYL6P5WYJZ138HFQchA0Hs1mKZHQJQEG0fGeDZqeJWm1z9bMpFnmC6mYDr3aVhHgluG4ZyegShgdFO5IkhYsEu21X2l/ZzFBZrxEExWd5vkG1uoLnLbFYFESoulkDeUEqg6vndutNrFYF4Q4CElEHSTIXgrxZl1Arl5DnU6xWV9tVLqNaqaDpb3A47Rf3jGN0Oh00ait4Hl2dU6yyGbxSCVuNYjyybAYXG9Q7BXEsdWtoNosNmMgtiFaxMRAj38SoN4p2lUoJ1lmMmuMgCIp2zOcrrNIcg8EALoqF0GzsoLSZYZksr7ZhjdJ6jVqtjs28GAtns0EjbCAIHOR+cf2m30WWFUZ4x7nISVHrGUD56pqtey5crJA7m6vjtcFiMUV1k8haROiiihiT3sNYJoUE3/M8dOtlLBYllEqrq22boZSXUKkGaAXF+NTKQeGy3X+4aKvjIHKriCIPm83V+ujrKUqbFSqoIJ0X0uv5aoWg0kbZzcGV4ZSdIs8+HyOoXHUU9xzk8SHGRwW57Ha7qITAUTLG7KrcHig2icLqAtVq0c9GbYWmv0ajVkNYLZ6vfmUOr1SQ/PrVvjubGZbJsat/eTlDJypjq1vD7m6xYRLH7tW0hTUWi6vH1ctot1tCjNmG+XyO6XwGr+xdHe/iO8UtXd2MqFRQDQEsMmBxVcK9XqNWTjFfjrFcLLBZ5LhRPJmJNHDzfydfF5H+wAc+gI9+9KP467/+a/zmb/6m7M4sFgt88IMfvCkN+3Q47e60hYWFhcXti88FIv3oo4/iD/7gD/C2t70Nz3/+8/GsZz0Lz3rWs/CN3/iNZ900AEWU6yUveQkuXryI17zmNXjVq16FCxcunHWzsFgs8JGPfAT33XeffFYul/GSl7wEf/mXf/lpz/nLv/xLvPGNbzQ++/qv/3q8733vO/X9v2z7IQS1IhJcc+i6XIPv14wIYB7kyKtFNCjLr7pfuxt0o66Rr7ler7GoLZC2UlW+KILrVuE4jOSVcL47xdSdAdhInwEYZaewTcfgR+Bvis86TgeVSgWd5hAXLhaEj/m9JPir1RqhG6CxaWAxLciFv87webt+QfSTVNrRDJsIgkCidIvFAk7XQeIfu2NXKhUjYuY4FVSrFTih0BWU2AAA/uZJREFUY+QmF1HwsZDy0WiEVW2Ndrsl45hlc1QqRXRyPC82Eaq1KlA7HgPP867mmVdUNHgjJbKWzlLuuVqtEHih3EOXFmPUmAR0Fa6ujk8RXV0310KOWP5qvV5jJysi/Zv6Bp3OFGHYk+8hmsQxqg0AuVcYTWmXcXFudlfy2WQyQVyJDXfpPM8l7xcANuEGsRdj4A7knlvRkeTtc60wAq4Ntxw4QAKZp1q7UE8wwg8Aw+EIySo2zPAqlQrKflmZW5UwHo+xipaGyoFkMJ8X66JZa8JrelguV6jVjt3Jh8MRnFIZrUYxJ07bwSScyHwwGlus2atz016Kod/SX8pxQdA31B1cq+PlGOs1o+C+URKqNi2iwk13grh+daPBKaLAzpYjkfJOM0fHPcRyucRgUxDuLT+FEzlXn8FiwyBN0+LaV/3f1tU10C6hXh+i7heKg7W3waq5xGKRI46LecqrOfyaD7fiGs93Vs3Qn/dRQjG/NacGxz2ej2qpCrhA3smRpnRuz5C5GZbtJTabDdL58bq6XjzZifTN/p183TnSf/zHf4z9/X181Vd9FT71qU9d72VOBb07/UVf9EV45zvfiSAIbA6ahYWFxR2IJ3uONAD83u/9Hj7xiU/gb//2b/H93//92N7exh//8R+fdbME73vf+3Dp0iX8m3/zb/Bbv/VbuPfee/Gyl70Mv/M7vyOE6SzQ6/WwWq3EKZrY3d0VV+GT2N/fP9XxQEEEWK5Jl22ysLCwsHjy4Wb/Tr5uIn3u3Dl88IMfxLOe9Sx8xVd8BT7wgQ9cdyMeD7g7/ZKXvEQ++2y70/YXpIWFhcXti1KpJNGT0/65XYn0S17yEvzBH/zBNZ8HQYDnPve5+J7v+R68/e1vv/UN+wzY3t7GG9/4Rvyf//N/8L/+1//C05/+dHzXd30Xzp8/jx/8wR/EP/7jP551E58w3H///Wg2m/Ln7rvvPusmWVhYWJwpnkyb2k/07+TrknZz0DzPw3ve8x781E/9FF760pfiR37kR667IZ8Nn2l3+u///u8/7Tn3338/3vzmNz9hbbKwsLCwuH48GaXdH/7wh3HvvfcCAB566CFcvHgRAPBf/st/wZ/92Z/hN37jN86wdZ8ZV65cwfvf/368//3vh+M4+IZv+AZ89KMfxRd90RfhZ37mZ/CDP/iDt6wtW1tbcBxHTJmIg4MD7O3tfdpz9vb2TnU8ANx3332GHHwymeDuu+9GuVxCpVJFEPgi+SyVCmOfarUqEl2a++gavyy9NJ8fG/84jgPHcURSWxyXI01T+H5NrsFSV65byDlpZqTLBmVZJqZHlLauVqurZkPHdW619BUAqtXjclg0YJpOZ0iSWNpXtLVsSKDZjizLjDrV5XLZMJRy3aJcUyE7P5aYsgYvJaq+70sJqVKJ8Zzj0luUunMsKLNeLpfSTspkV6uVketbtIvlghwxylqt1ths1mJ4BUAMvij9Zf+zLFNzXpI5o8R5Pp9LiaxjWbtzzfcZ5eSUXgMQkzrOEfugz+VYlctlox43zdjYNraX16Q5la4zzJ89zzNKehV1u8twnLKMVRCwzFtJrr9cLiUIVdxvc42pleNUrtYTZ/mxCvJ8idVqiTheXW3HGq5bGHtR6UJzOvab5coKY67q1WNy2XDlcXwGdT9pNlapVLBaXZWaX5X4s131eoTlcmmUvGK/WIcZKMzGaEbG54dlp9i+YpzKUleebTi5Zov1lRUpD+r7geuY8zufF/ev1WpGnfPlcinHsKwWjRCB4lliWsZqtUS5fFxO63rxZJN2P9G/k6+LSGvHPgD4sR/7MXzhF34hXvWqV91QY242HusXpIWFhYXF2ePJSKS1y/OznvUsPPjgg3jqU5+K5z//+fiJn/iJs23cp0Ge5/j93/99vOtd78If/dEf4Uu+5EvwAz/wA3jFK14huZ3vfe978drXvvaWEmnXdfHlX/7l+JM/+RO8/OUvB1Dkq/7Jn/zJY5rEPO95z8Of/Mmf4Ad+4Afks/e///143vOe95j30S7CGn83fQaCZQ1+7ovTcrlcLl6k82MiGqexuDhr1243d7FcLpUr9Qaz2RTrdUfye2kwxBf4Wq2G6TTEbDaToMFutFvkV2cLITSj0Uhyd7nW2u02giDA/ngfDx8E0o5msym51eVyGZW0AnflSvurQRX7k32MeiN5gY+iCKETSq1etjXNU8R5LMShlJXEeRkAgnUgJFpymB3g6OgIq95KnIzpRp3nuRCyPM9RrVbxlKc8BX67aP/R4SEuHV2SOtKu66LZbIrbNs9zXQ+1mme4dlcqFWABIbHT6RRZlsH3falx7fu+uEvr+aA7NVCQlzzLkSTHueHz+RylpIRGoyFjRrds7Sjuui6CVYA4Lkhzmibi53N4eHi1DQHCsMjv5VjU63VjU4VzOZlMitrJy6tmVIuquGEDEJLlLo9du5fLJcqLIneaGwa9Xk8IIdcP1wPzvDmOWTbHYnHscE33b9a8XiwW2N7exoULF7B9tW+DJMEnL39S5pRjUa1W4W5c2aAZDAZwHAfb29sgxsMxHMeRNcu65HpToVwuI8xD+BVfCCvXapzFMud0IN8Lio20K1mO2WxWEN3NcQ58OksxHo9lLu/x7sGev4ckSaRe9nQ6xWw2Q7lcFlfzRqNRbHRVis2Ig/gABwcHxXM5KsY1yzI4jmNsZIzHY5TLZURRXTYtPM+Tmuxsx8mNkbBcfDdwHbP9w/Hw6nO3xnyxAPBh3AiebET6if6dfF1E+pOf/KSx8AHgW77lW/CMZzwDH/7wjU3gY+F6dqcf6xekhYWFhcXZ48lIpD/v8z4Pf/VXf4V6vY44juWFs16vY6CcYm8XnDt3Duv1Gt/5nd+Jv/qrv8KXfumXXnPMC1/4Qnl5vJV44xvfiFe96lV4znOeg6/8yq/E29/+dsRxLC7er3zlK3HhwgXcf//9AIDv//7vx9d+7dfiP/2n/4R//s//Of7rf/2v+PCHP4xf+ZVfOfW9104dy3INKydAuroaMa542FQ9zBYr5EWgE3nuYpYuJGIIFETRL3uYz4E4LkjIbBYjz4voackriGejUcem6qM3LEjVZjyD41SwKtcx3xRkcjqvXCVUwPwqgVo5DSw3SxwMFxglVw2MShEa6w1WTgNBqzCLS5IE43SJZbly9X4NjOIY8+EM58rFi+XWVgtutEEpLWN+tf1BpQnHb2Gz2WBydf2WFiVsb19EvbsRX5zZdIZKpSIbLuVaA6V1gtJqBTcqyOpiscAkGyDLcsw3BYEKgoIU5fkGlUpBCJabKlDykK19QPVzvhnKWCyXDry1D7/aQhAWRHc6naI3GiHvp9KOVquFqlcrDODWxSZFfzpGrzeB583lerXaGnmei2Gu6zpI0xKWSwfR1XZ1Wh1UVisM48vwwquR7LZbRCdXQLXMyOkadaeGjeMhXeVX27bEulJFuizami5XuNK/ahJ1tY+bpYvNoorhdI1KpVhngdNAuiphmuQol6+WKVvlqIbFBg7J9Xw+R7d5XjYyBoMBZtkKlWUJ/oaKiRXieIZqtSoE9vLlEebzOba2XFRD/3jOHQerTYb+YASgCD5Vq1W0WlfLhdXrWCwWyNYbJMviWqtNBfNNgJXTQHq1T9l6g7wUYb6ZY7WuXF3/S1SrG5RKCyRJdvWzNWo1F9tu++r4uyhlDkqOg3KtmKP1ooppMsFyuRSyf+8996JUKuHo6AibTXH9ZrOJcqmE9byC+ebqxohXgRttY1NtAQBG4x6uHMzgeR7CsPhsOpkgTsuoBNsS2V85DQxmG0wmOfYPZlfHegHXjVBxKki4PmPAzQHXLdow3wRYluuYzh15LkulEnZ2uliWK5hkxUbYOC1jtVphnMbSpxAVePCQztfIxsX4sOzdyinWzyIG5osqnGoNy/LVSHk+L74PyhVsShusrzrv3wiebET6if6dfF050hcvXvy0g/bFX/zFT1hUWu9OE9yd/ky7zRYWFhYWtyeejGZjb3jDG/C6170OL3rRi/AlX/Il+NVf/VUAwJ/92Z9dk5p0O+Dnfu7ncPnyZbzjHe/4tCQaKIjJJz/5yVvbMADf/u3fjre97W348R//cXzpl34pHnzwQfzhH/6hjOPDDz+MK1euyPHPf/7z8Z73vAe/8iu/gmc/+9n4nd/5Hbzvfe87dQ1pCwsLi89VPJl+HwNP/O/k64pInxU+2+60hYWFhcWdgydjRPp7vud70Ol08A//8A943eteh+/4ju/AU5/6VFy5cuW2rCX9Xd/1XWfdhM+I17/+9Y85bp/O5PRbv/Vb8a3f+q03fN+trS2EYYgsyySftVqtIooiJEki0WeWTNL5svP5XOScBE3ydHkhx3FQLpdFOs66sYvFQuTG/X4fi8XCyMEulUqSE6rvEccxFouFIYtN01TOY11olkgCCukp5ed8prIsE0kq/8/3jyXuly9fBnCcH04Z9NbWFjabDfr9vsib0zSF67oiowaA4XCIPM/h+8f55yx7dHh4KNfj2PDn4XCI+Xx+NR+3iKizRNTh4aHcMwxDRFEk5ZTY9zAM4XmefFapVBDHsZHzzTZyLj3Pw3w+RxiGxxLbq9fRY8fyV61WS65xdHSE+XwukvY4PpbFsw1hGIpUn3L4er2O0Wh0VbbuynFFDexA8r4rlYqxzthmrY5I0xSLxQJBEMj8hmEo48Z21Gq1qyqBXMaxUqlge3tboqa+72M6nWI+n8s92+225JrTHZ85vPWrEWz2fTQaGbnN7XYbq9UKo9FIrl+v19Htdo21uFwuDbk9x7PX6x2XMrvqS8A0C2I+n8sxLA/GXGxeP01T1Ot1GQuWU+P8A8WzlGUZ6vW64Y8wnU4NH4IoikTGznXhui5KpZJEvPkcrtdruSel/PP53JjHcrksqR/MnQ+CQPqUJAmq1Sp2d3el/zeKJ1tE+on+nXxHEelv//Zvx9HREX78x38c+/v7+NIv/VJjd9rCwsLC4s7Bk5FIA8A3f/M3y7//4A/+AO9973uxWCzwHd/xHWfYKovT4MKFC/A8D8PhUOR/JAXT6dTIjd1sNgjDUMjdcDjEaDTC1taWvCg7joMgCOSFGShegsvlsuTsFiZaMSaTiVxfm13xWsvlEtPpFL7vC/mq1WrIskzyLAEI6dQmWUBhTsV/p2mK6XSKOI7lhX29Xl+Vv4ZCWpgbW6lU5DNeQ5sokajqWtN33XUXqtWq5K72+30ZU8qqPc/DaDTCeDwWiXaz2YTneUJUNpsNSqUSfN+XYzzPQ6PRQBAEuHTpkoz/ZrOB67pCiJrNJqIokvMBSL4rv0s4jySaACQvXZOlwkhujvF4bBCXer2OSqUihKnT6RgEvFwuX7P54HkeSqVSkU999TzW506SRDZxarWi9rPeOGFOtp5X13WNuaS5nOcd55Bzw8NxHBmf6XSKxWKB0Wgk7b1w4QJarZZRx3u5XKLRaEi+crvdFhJOyexkMoHv+4Zp12QywXq9RhiG6HYLqXgURcjzXDYGODfMVefa4pxw3dHMq9FoyFxuNhukaYokSeR67B/HguZgSZLI9Zmvr+thu64rxl98vri55HmezFOappLbzDbQu4DrerVaSZ43vx84b0mSyDpYLpcYjUaIokg2rDgnfL5ocqfXEVCkbLTbbfFwsLgWT+Tv5DuKSAOfeXfawsLCwuLOAaN013vunYBKpXJTIqQWtxZ8GdcRzNlsJoZOOrpUqVQMUsuX+Tg+zoGkUROddwGIA7Y2TKLTNu/p+z7iOJZoONtEp2q+/I9GI2kDDZIYCSPZy/McjuMI+edn2i0aKAgZyRFf4mkw5fu+4QperVblpf7o6EjGi2RgvV7LdblhoN2ytWN2mqaGo/JisTCig77vSxSfKJfLEoUkUZxOp5hOp6hWq3Jv13WRpinW67Wc7/s+qtWqjP9ms0Gz2cTu7q5EZYfDIWq1GsrlspD/wWCAwWCA0Wgk4xNFEWazmZApznmj0TA2Nk66dnMjgNFNrr04jsUpmmM9mUzQ6XSkn51OB5VKRea32WyiXq/j4OBASG2pVJJcW44154SEDiiIYp7nmM/nQvhKpZKx1jneYRjKHKVpivl8jlqtJp9xEyWKIrlnmqYIwxD33HOPYZSlNxBoKMZIPtdAuVw2IuWMKGsH9uVyKRF77YheKpWkPa5bGADS9Z5z5HmeOH5zHMfjMUajYwO+breL5XKJTqcjGw2O4xg59jrKzXtyPXBziufx+4DtiONYNkr4PUIjPD6ri8VCTOn0uqNqYr1e3xQi/WSLSJ/Ezf6dfMcRaQsLCwuLJweerBFpizsflI3qCG+SJEKSNPkimSPpobST8k2gIExJkhgv9iS5fJnmtXT0ja7eYRiK4ZsmrvpalUoFg8FAiEQYhkjTVAhgYRxVXENHvFerlRGlns/nGAwGEsHjZ+v1GkmSiBRXbxQAx9G3PM+FHJH4akk7JekkozyXbSQZGAwG12wgrNdrcZ0m6MTMawRBcLUkUCL3ZCkh7XSepqkhzyaR9jxPxrVcLhsbKMAxUdcOzvo4EvVOp4MgCGQNsFSX7i8j2I7jCPklIXJdV64/HA5FCcH2x3EsGyMAJPLMMeH8ep5nOHRzrWoJeJ7nEm1l2/r9PlzXNeaY99RpDOPxWKT6nEOgcKfWEv9Wq4VarSZripsmmoAnSXK1LFpJPptMJpjNZrKuW62WPCvsLzdctOs4o8Uny4W5rivrbjabodvtGpJ5kt80TYXEVqtVbG9vo9PpyHhzo0fP9+XLlw0JP9UkJLuc381mY6x1bkq5ritzx2vrEmjcROA9eTzn0kq7bz0skbawsLCwOBNYIm1hYWFhYXH7wBLp08ESaQsLCwuLM4El0ha3KyibXq1WEhFyXVektFq6CUCkykARqaKsmP/PvNVarWZIQSk3BYpI52q1kjxRABLhzLJM8nUZMZ3P5xIFrNfrSNMU/X5f+tBqtYw6z5S+LpdL9Ho9ABC5uZaAj8djVKtV7O3tSd1j5rJOp1NpL8/T0VvHcTCbzST65rouOp0OyuWyRPym06mYMOloLXOMGcVk6gej2zT4yrLMKFuzWCwkIq/nbjwey/XDMES73UYURTKOn/rUp0RezDlcr9eGTPno6AhxHIuBGXAcUWRuNtvA/jJayGitjoRSdq9zncfjsSGp1jJgtq3RaGAymRjtmM1m0jYAImt+5JFHjPv0+32Uy2Xce++9AIDz589LhJdzslgsEIahkWpA4zudj8sosJY3M9ef/W42m1gul4aCw3Vd5HluzEmtVsN6vZb5YG1xqiTYBkZ1dQpEo9FAGIYSfX744YdxeHgokXVev1QqSYSXqQ3arItjXa1WDaVFq9VCt9s1Iup5nhcl4a62lxFjve5838dkMpHfT1EUoVKpGLn5XMNctxyfzWZjpDLQ40CnYVQqFdRqNUMRQ+ULJfw3CkukTwdLpC0sLCwszgSWSFvcriCpoWxVgzmbAETS6vu+vPBSyqlzIPkyf+7cOSEhBInKYDAQCTSvT1l3r9cTklyv14WIsp1BEAiB1O0NggDb29sAjvO06UDM//c8z8jNzPMczWYTe3t7QtqYg7xcLuV6QCE31cZN+sUfKIgKJcLcaCDRCILAyP+s1+uoVqsia18ul0J+2FbmUvP5J5lfrVaG43YYhgbpJ6HPskwk1LPZzCCFJPaTycQgkyRIJGhRFKFcLsvfbAfHitfv9XqYzWYyXmmaygYG88XL5TKOjo6QZZl8xnnQEmdem/PFsZ3P5zLf4/FY1gDXBQ2omOPOa3U6HUPa7bqupAJo2bmW5HNTh67lnCPOmyZxm80Gi8VC5peu4f1+X9rr+75xDPO29abOcrlEGIYyJjyv1WqhXq/LuRcuXBB5vJZ2a0l+kiRFjfer5wPFBgvl7FqaXq/XrzGY4wYRybXrutja2jJIf7VaxWazMWrK8zr0DciyDA899JAxr2EYIs9zlMtlmXNtdMY2sG8EU0Oazabk/N8oLJE+HSyRtrCwsLA4E1gibXG7gsSPJBEoIkQsRUXyRcIAHEcZtfOwNsoiSdNEUxtKMfcxz3MjYkajJW0WxftrR/Ht7W34vi+RtZNknvmVpVJJnJPDMJQ8W778r9drNBoNlMtluRZJUhAEQjgYYWS/wzAUR3GSh1KpZIwDACGhjHjyszzPUavVjPxtkli2gQ7Impww8ktSy7zYra0taX+e5+j1eobT9rlz5wwXZpauYvkyANjd3ZWoKL9zSNDiOJa+NxoNiaZzzTBCz/vpPG7223EcySPXTuosx8ToJ0tO0eUbKAjf1taWkKdSqYT1eo177rlHxpAu3LoM1KVLl9BsNmW82XcqHfSGEHPGgSJPm33g9bmBpM3euAGic5jpG0B3arZNH8NnqV6vS5Sd1yFZZz8BGBsllUoF586dk6g6P9NjHsfxNSXhaNCnS43Rqfzo6EiezXK5LCoHzgnd4zleq9VK8ry1GzoNwfj8TqdTeJ5nkGZGypkHzTnnuADHzvyz2cxwfaePAt3GLW4tLJG2sLCwsDgTWCJtcbsiTVNxAiZp4EtsqVSSspskk9r9mVEulukBCqKVZRmOjo4kMri9vS2RLgBCGBaLhUGaWetWS6o7nQ4Wi4VEOPM8x3q9xmKxEHLUbDaN6PNsNpMSRIzI8eUdgCFhj+MY4/FYXtjr9fo1z9xyuTRqWdNQbTabGSZfjJLyPq1WC2EYGp9paSpJCEtY6bq9s9kMk8lEyC8dtbVJFk2/6vW6kKrBYCAkmW0jwSGpJbHkhgmvDxQkjONI52ZN3lnvV28YnJS0Z1km0XYaZ7Hklu/7hhkVo79ce+VyWSKU3DwplUqyEcLr04Vak3dKnCn3pqRblzLzPA/j8VgM2bjOgGM3apbj0oZh3ITRtc8pP9b1yvlv1pPmZ5SGcz1Rmq3LovE50tFW1lFndHixWKDZbIqEnGOmn1+qMUjguQZKpRJarZY8l6xZrR3kWc/84ODAqNHd6/WM8masn812zWYz+L6PUqmERx99VK7f7XZRq9WM8mp0Uddye6pDgOJ5pvEao9tRFImyQD/rNwIbkT4dLJG2sLCwsDgTWCJtYWFhYWFx+8AS6dPBEmkLCwsLizNBqVS67nrQn8u/uC2eeDCSpGvtApAaz9ogjFJrLfGk+RLXKaO3eZ5L/iqlmkS328Xu7i7m87lE7Rjpq9VqErmbTqcSFT1pNKWjwev1GqPRyDBbWi6XRp5ttVq9Jtd2s9lgPB5jMBjgnnvuketTXs6+A8cyZ/axVquh2+3KZ57nIYoijEYj7O/vy2eMsun8WMqlGeFrNBpYrVZG1JEGW5wTx3HQaDSkjjNQRI+ZE01ZOCXFtVpNIq2U+jKSmqapSJXZLs/zZA1oyTblwJwTSs6Xy6XMOaPfuhQV515HLCmp1uexVBbbFgQB2u22YdbFtnE+WMorjmPD+I4llKguYF55o9EQZcJyuUSaphLZBYpoZ5IkEjUdDodYLpdG7jZzq2u1mkRS4ziWGuS6TBPHmG3b29tDq9WS8aGkXptp0Qwsz3NZn5T71+t1Gf+joyPJFea5lI3rnOnNZoP5fC5R6tlshoODA7RaLZnfPM8RRRHCMJR+UqnAsmf8jJFg9rHVauHuu+/GwcGBjAX7xGeO46PXCr8vVqvVp70nUDwPWrnAtlLuP5vNbET6DGCJtIWFhYXFmcBGpC1uV9A0SBMtvpSzXjNQkJfz58+j3W7LSyzzfReLhbwEszZtvV4X6ayu/wsUknDmm+oX7FarZUhzZ7MZLl++bEh/KbHVUmKSKi1JpoMwX/5pYEQjL6AgebPZzMjV5gt7lmVybhAEqNfrQprX6zU6nY4hVyeBoGyYY8b6wbqOrq6JDRw7HlNmTTmvPsZxHJFi83zm41IuDhwbN2kn9el0KkST45okCTzPE8Lruq7Ixyn9ZT3wJEkM93MSQcpu2Xbt9q3zazm/6/Xa2JwZDodYrVZGPeutrS1MJhNpH/ukN2Mcx5EcbS2zXi6XhjEdx6DZbMr40GkeOJZyc/OH64JjTndpgmNLgsc+0hGe5zJfXLtv63WXZRmiKDJqatfrdekX152u9a3X52w2M+pls+64fh48z8Pu7q60YTAYII5jw62cBHmxWMg9J5MJ1us11uu1bD60223DxZvPLeX6wHG9cF2Pm477bDPHiqkF+nvBcRxcvnxZ5oNknWuW8nM6mN+MHGlLpE8HS6QtLCwsLM4Elkhb3K5g5Go+n0s+brPZlGghyV0UReLezJfYLMvEeIokgaZWm83GyLuluRJQkJnRaGREJ+l2DMAgQjyOhIxtYcScx2myEQQBdnZ2cHBwgEuXLgEoXvC73S663S4ajQYAiKkSo9xAYVBFl2RdjkcbnzE/d7FYyD2TJEGr1UIQBJJXriNuvL52Jj46OgJQEIdOpyNEbb1eI0kSVKtVIRvL5RJxHBt50zRd0vnQJEZ0huaccIMDgDhz0xkcKAiU7/uSSwscm26Vy2W5FokvXa0BGKZpHJ+ToNMzjdq4LkjaeP1yuSwbKSTqYRhitVoJkUvTVNQGJLXMLdYmZXTKZk4ycBwFP7lh0Gg0rinFpfOoGZkfDAbSPqAg9SR4nGsd6QWK9ZimqagvuGnkuq6sRU06ue6YG82NEF6L48hzqUKg2/1isUCj0cDu7i4ODw8BFJsptVrNMA1jLv1sNjOMAKvVKlqtlvSdc8t5Ze45N6YAiOdBHMdynt7M0gZkJPO8Hk35eB6JtHaQZ946/QW0keH1whLp08ESaQsLCwuLM8GtJNIf+tCH8Na3vhUf+chHcOXKFbz3ve/Fy1/+8sd17v/8n/8TX/u1X4tnPvOZePDBB0/fWIs7DiQ4mkgkSYLlcolKpWKUhZrP50LogGNSVa/XhWTu7u4iiiLM53OJZlMiq2WmjHzxRXyz2WA4HArJAApSEkWRQbJZZ3p7e/ua0l1a6g0UBIxtYER3NBoZJlOsH00iMR6PhTwyGlYqlTAej4UYsQ60duOmlL1arRoROZqUsa3cPKDrN8e73W4bBJT1eEk6OSfr9VpISalUklrQJBY08ErTVMa73W7D8zyZN0qdJ5OJkNNyuYwkSeD7vuH0zPrcHFOWadIR9CRJrpFUc6x4T252TCYTOW5vb0/Wk3bHJtFlOyjH1mZmjUYDjUZDyClrT2sivlwupQ86HYERY210R4LNtUjjLIKR1CRJhLxzHWw2GyM6TBMvzicjqLwfa69XKhXDaTuOYyMNiM+IlqGzxJh2SWe5Kp16wPHnvLH9lIUDhZs4S7TR3Z7qlFqtJu0fjUbGZgc3evQccZwp4QeKOt6e5xmfsa60Nrrj5hw3xqhW0cZuXHMADPm7xa3D9SWnWVhYWFhY3CBIpK/3z2kQxzGe/exn4x3veMepzhuNRnjlK1+JF7/4xac6z8LCwsLC4k7Drfh9/GSCjUhbWFhYWJwJbmVE+mUvexle9rKXnfo+3/u934tXvOIVcBwH73vf+059vsWdiXq9LhJhHV2N41jyEfVnlPXq85k3CxTRMUrAGS1MkuQasyiaQGk5J3NodUkgXe4JOM6H7vf7hvS6Wq0aOcAHBwdoNBq4cOECgGOTo8FggIcffhhAEVlrt9s4d+6cIVdn/WAtZ6ZsFSg2nWjYRfkxzx+NRjIWvu9LtI0R+62tLckZ1bWZgyCQ8ZrP59fki69WK5Hb6ogopb6MDAZBgCzLjKgdI//anKpWq0mZMF5/PB4b40r5tJawsy+MqvP69Xpd+jObzSSnWhuq0VBKlzejNJ2fadOzK1euADius61z1NkHzjnXSpIk0m9Kj4MgkOPCMITrupL/zXMrlYrMr+d5khfMfsdxjFarJf0AjvOBtQSf0XQARm3sRqMhUV/HcbBYLDAej2WcFouFrDmOI8theZ4n0dogCHDlyhVRbwBFqgNVBxwfx3FwcHBg5PBTlcE1NRgMkCQJoiiSeeL1XNeVCDDVAByvVqsl48Xx0coJrsWtrS1ZD1xTjUZDzADZfqaW8BqMnvM7iH1iCbRyufxp0wdOCyvtPh0skbawsLCwOBPcDCLNlzLC8zx5cbpRvOtd78InPvEJvPvd78ZP/dRP3ZRrWtw5WCwWGA6Hhms05ZXMw9RSXp2bTEknjYLyPEe/38fW1paRt6trD69WKzHw0nJvEgB+1u12kSQJDg4O5IWdstLxeCwv53fddRfa7bY8IzRW0rWHgUIyGsexkZtMMyq+1NOMii/8PA44Nqc6OjrCYrHAzs6O8aI/Ho8xn8+lXSTveZ7L9bW5G/OmeRzPq1Qq0nb2m3nQ2gmcBFDLlLUpk97w0ERF55PrOUrT1CDXrM1Ml24ed3h4CM/zcO7cOQCFdDzPc9kIGA6HMsaUIjPvdnt7W4jclStXMJ/Pjb7PZjPpt647zjrCus3cMGC7SHBZM5oyd+3wDhRkVOfYcxNHu17TXE7XHuc86XlrtVpi+MZ7npxPbv5w3srlMqbTKQ4PD2WcoiiS3Hyu2UajgcFgYBjFJUmCwWCAw8NDIzedGy/A8QaFJr/NZlPc4Vnn+fLly7LZwPVDN/ogCCSHnNJ+rpl2uy1t5fgz93mz2cg4DgYD9Ho97O/vy2dPe9rT4Pu+YahHF3VuRujnQ+fw1+t1tNttg/TfKD6XifFpYYm0hYWFhcWZ4GYQ6bvvvtv4/E1vehN+4id+4kabhn/8x3/Ej/7oj+LP/uzP5MXM4nMHzFns9XoSkWOOJA2pAEjOIkkxAClRMx6P5dzlconJZIJGoyHRWro4nzQ0qtVqQkCYG5rnuRB6mhLpHFfgOMeU0exut2uUaGo0GhJpJxkgEWUkEyhIA0mEJplshzYLY+QXOM7XXC6XElGcTqeoVCqG2RXHgxF/ggZbPE4TFraffSdRp2lTuVyWvHISJm0aRjKpc1wZWdXmYzraCUCizq7rGu1n/jLbwTJIOmJJIzCaXWVZJm0k+er3+0I8eR5VCkEQyGdZliHLMjiOY+TMZllmlJiiqZqOwupoOedyPp8bpI1rliZkAMSxnuuC0XuWHOP1Dw4OMB6P5fqcR10mjWuH48z2z2Yz2WigisN1XTGmY0SdzwPbT5d3PjuVSgVBEGBra8twz9ebCtxsOX/+vDxLnMNqtSrt0HOoc+yZb81z+Uxoh/1arYbJZGLkfUdRZDz3LClGkzAA6PV6aDabqFarhms628i20ENAGwjSLV6vqxuBjUifDvbtwMLCwsLiTKCNi67nXAB45JFH5KUOwE2JRq9WK7ziFa/Am9/8Znz+53/+DV/PwsLCwsLiToAl0qeDJdIWFhYWFmeCmxGRpkvtzcR0OsWHP/xh/PVf/zVe//rXA4BE/yqVCv7oj/4IL3rRi27qPS1uL1AeHIahRJVd10UYhuh0OuJGzCgjSykBx+65WkLa7Xaxt7cnMlYAIsulNJxRJR3x831fotaM8qZpitVqdU1NYUp3dZQ0yzKJxjqOI/JU7YrMEj48r9lsIggCzGYzib6xNrEuaeQ4jlEzlxFRXZOX92a0Fzh2IwaO3ZMZWdze3paoWpZlGI1GMtYsUUQ3bF6XjuWUsHc6HXQ6HSOHNkmSa0pWMe+bUeJWqyURP0YxGQUMw1ByW9frtZRe0jnSnuchiiL5rNfrGdJ9z/Mkuqsl+cPhEJPJRJQEURRdkyNNx+x6vS6biFRL6Hz91WoF13WlT+w7XdIJ1h3Xx9FRno7uXEs6uj2fz6XWM1BIi6fTKdbrtTi967Jm7BPnXHsOVKtVQ3rOsk7ValXmrVqtSt62XrOLxQKLxUKi/fV6XUpnUcnA63MMu90udnd3jfQBRuYpAweAz/u8z5M5ZXSYDviO4xiO65SGc375fxwfjjFzxoHi+WL6BNesnkdd+3w+n8vPTCdI09SI0IdheM130I3AEunTwRJpCwsLC4szwe1aR7rRaOCjH/2o8dkv/uIv4k//9E/xO7/zO3jKU57yhN3b4vZAEARiikWw3M7u7q6QsTiORd7Jl1vmjWo582q1QrvdRhRFQhpZOoekgYZMy+XSMHxinjGJBF/SKW8FICRLm0MNBgM4jiNEiG1kzjUAabvruvISzpxjytt5XLVaFQIDHNcepmSVbW00GkLqS6WSSJDZVhpInZRKr9drnD9/3iCbWhLMmr+bzUYIDiW9i8XCIEcEyYnv+zIvHMfFYmGYYHEeJpOJYTBHOT/HUdeaphyY558siaVrLi8WC4O4AgXRiuPYqGdN+fZkMhES2Gq1ZI715sR8PjdkxNVqVTYe2BeSV112igZ2moRz7PhvGpLpPHCWHtMbAa1Wy5CAA8XaYw431wHTG7QngO/7Mibtdls2FrTh1nA4NNYx+6A3oZg+oGurU47Pa3FudMmqzWaDxWJhkNN2uy01qDkWfNbyPJdcdsdxhMDqPtJLgGvCdV2D+EZRhPV6jUcffdR4zjmeWiqu1yuN6/I8N2p77+3tySbP9Sq8zgJPlpKUlkhbWFhYWJwJbiWRns1m+PjHPy4/f/KTn8SDDz6ITqeDe+65B/fddx8uXbqEX//1X0e5XMYzn/lM4/ydnR3UarVrPrd4coJ5pp1Ox6j9O51OMRwO5TPmBOvopOM4aDab4rIMQCJqOmeZx2rDrXK5bNSaptkVI4i8Z71eRxzHQqIWi4UYUul8SdZUBgoyMBwOcXBwIGZOaZoiCAKEYSikh8d7nieOytqoi0QiTVOUy2UZiyAIJHJKMk/SrseiUqnI5yRprutK3wkai7Gtk8kEnU4HrusKgc2yDO12G2EYyrl02q7Vasb4AMeEmueGYSjES0dG+VmlUkGj0TA2KEiW1uu1bBgwGjoajQzHZj3f2k2bUUbtps3zwjDE+fPnsbW1Jf0kSY7jWNpZrVbRbDblu5CRXD2Gvu9fo44olUpI0xSDwUDWFI2umFvOdriuK/PNCKx2uA6CAO12WxQSBPOatdEXDcdOzvnJOs+6TzTQW6/XQh5JuF3Xlbby/3SdaxqqsV00jdP57pVK5ZqI/XQ6NSLnQLG5GoYhptOpsfGVpqmsT9ZQ1yZruqY02+F5njjgc9ODShW9YcZNI208yI27e+65R+aIG1h6Y+FGcKsi0ixJ+drXvhbf/M3f/LjP0yUpDw4OTn3fm43PSSL9pje9SQrXA8AXf/EXyw7xI488gna7Lbs7ehe1Wq1iMpmIs1+5XJYHig/XvffeC6BYIHTr9H1fvmz5i1SbMrAdu7u7cBxHfoFq+cnh4SGyLJN2aafGSqWCZrMpLpAA8E//9E/yN+8VhiFms5k8aLu7u2LowHIblHE99NBD8uXA3S/+ctVflk972tOkb4vFAq1WS740+v2+fKHzFze/EFjmAChkWHqHX5d3ODw8xPnz53Hx4kUZL8rbuBvONnmeJ2OpzULYFv5CbbVa8qWV5zmiKJJxGA6H0j8dYaAjJP/vnnvukbkYj8dIkkTuPZ1OZQ4dx8HOzo5RCoTtSNNUxqfdbuPCdoAv8P9G2pznOT6xTHDpKMFwODR+UXDdOY6D4XCIw8NDuT7no16vYzKZyG5vs9mUsXv44Yfl806nY5TB2Gw2xg6xLhVRrVZFNjUajeD7vrxocU6AYk3qEhdBEBi/8PTu6UMPPSTPys7OjjieUgrIL0r9S5+uoVzzu7u7Mj7dbtcoueK6rswHnwvguByKfinh9S5evIh6vS79nkwm0o6DgwNpRxRFEk0AiufhaU97mtzj6OhIxmu5XBq/EHu9njxTOzs7MuZBEBjfFwcHBzKW2vGUpV3YtzRNcf78eQDFs/6pT31K1lq9Xpd/r9dr3HXXXbJe+YsfKH5xc40Mh0Pcfffd8nL9f//v/5V28GXqRnErifSHP/xhvPCFL5Sf3/jGNwIAXvWqV+GBBx7AlStXpPyPhQXLEXU6HfmO22w2GI1G6PV68ruh1WqhXC5jsVgYhCiKInS7XXnmGc3i73Lg+BnmMSxvM5vNDHIURRE6nY5BQGq1mmEsRFmnfrcpl8uYzWbyfcjv5f39fcPIiy7j/L0CHBM8/TuV5JefaSMljg+dr/XvuW63a5AeljLS3yGdTkdcm9m2NE0RhqFh+JQkiWFwttls5PeqlntzfPhO0+/34XkednZ2jKid/jcjkJpMst3a5ZwO46vVSn6XnTt3DtPpFL1ez/idor+nKIXnvwmaW+lIcxAE2N3dlbUxGAxEgq+l+lpaTyOsJEmMSDz7qOX0fD/j2uaGiy5nlqapYc7GiCtNyDj+cRwjjmN5P2KagY7CzudzaQvf/aIoMuTZHGtuGgDHTuKz2cx472FpMb0hQbKt1SJpmgrp7/V6mEwm2NvbM0zf+LxyTmgkR1UG78n3JL3JojfQWM7KdV1Dps0NI+0gX61WDQf/KIrQ7/eRZZlh7qaj2aPRCIPBQEz+gOJ9J45jDAYDoyzZjeBWEeknS0nKz0kibWFhYWFx9riVRPoFL3jBZ8wfe+CBBz7j+T/xEz9xU9zALSwsLCwsblfczjnSt2NJSkukLSwsLCzOBLdrjrSFhS5DxQgpy9XU63WJAoZhKGWrqHRibdnZbCZRagBSWkmX42HpHQDY399HHMdwHEeiUjrnlp+5rit/GB1jRJp5qLwfI6c8P4oiySkGCrUKyyzpckXMHWZ0r1qtYjgcYjAYSPsZWdbqI7ZF9zGKInieJxHL1Wolii9dWo5Rez7bLCmmj9nf34fjONjb2wNwrLSi/B04jhQyJ5p9p+KJkcHRaCS1enk/qgHY7zzPRV6rx58yZLaN1ymVSiLXrVaromoC8Glzkjk3URTJZyyplqapXJ8yd90Oqto47uVyGaPRCJvNRpRENKbT65Py58PDQ0OJxtx1jiNl/zQRG4/HeOihh7BarYw66lSI6fJaWunEsWV6A+/JfG+OESXoWqnAtTSfz0VdQCVEu92We1KWrqXNNAvkMdPpVIy/dA42y3CdPJdKDc451wTP1SoNIssyxHEsqjKmIsRxLPOrlQn8bDabSZk1zpMuK6fHlYZ7nBOW3dIl+G4EN0Kk+V1AaG+BG8XtWpLy9mmJhYWFhcXnFCyRtrhdEUURXNdFv9+XF2y6djuOI2SJcldt5uT7PobDoZFLTcMqOu8Cx/m9JEaHh4dI0xStVstI1ZrNZoa0lUSxUqnIPfmir/Or1+s1oigypKIkSnxZJ/njZgBwbDamyTVJhc6FTZLEIP1aokyi0Gw2JRWNhEm7Z5M00disWq2Ke/LJl+VyuYzBYIBKpSKSak3q2X5eizJh4NixOYoiuSdzePldslgsMB6PDYIMmClLAKRmcqPRkP+bTCaysUCixY0CTYZ1vj1wbEhWqVSEqFHCPhqNDIOzyWRiqGpOks71ei1kVG+A8P/YLqazaUdxpgDozRmOLf9eLpcYDodYLpfGHHE9ca2kaYperycpCDyOMm62bW9vD1EUyVqkWzoNzTiXdG9nXxqNBnzfN+qoh2Eom1S63Y7jyKZCp9ORWuq8Psn3dDqV8aErv36m2X46l7OfrE1NXLlyBbPZTK7VaDRESq9TqGjYRik2N9D05kCapkY+d71eF+dzzhtTzdrtNnzfvykE80aI9N133218/qY3vemmKLlu55KUdwSR/tSnPoW3vOUt+NM//VPs7+/j/Pnz+Ff/6l/hP/yH/yAPpIWFhYXFnQXtqHo951pYPFFgbiLL1PCzUqlk5DWTXG82GzmOkWFdfom5yLVazYhw06EZKF7W2+02Go2G+GqUy2UhfDpKGscxxuOx4dfg+z5qtZrhJaFBoqGdncfjsRgwadJDd2NuIiwWC/i+j+3tbSMKW6/XDTLPjQX2iVH9UqkkJHK9XmMymSDPc7kWyTWdwXlcEARGBJZ5quzbdDqVduiIIV2w+f2S5zkGgwGGw6GQ2HK5jHq9LvPGnOBSqWS4InOz4SSJ1d9BdLvW5aJITLWXCzcT2C4daWZkcz6fSwRTz+FsNjNcv9lO9pv5utrUjJs32ueDubx6I4Zjpb1hGBXXOdacT84JCWySJIaigW7ijP7ToI1RYrZfX4vrTfdJt5Frqt1uS8kzHse/6ajONiwWCyHSXKtpml7z+4N+BUCxZmm+xrUyHA7RbDbFgA04LknGPHNugJRKJWPTJU1TiYbz+szD1psW3IDSPij6fuznarWSY1gijS7sNyMifSN45JFHjHKUNysafTuXpLwjiPTf//3fY71e45d/+Zfx9Kc/HX/7t3+L173udYjjGG9729vOunkWFhYWFtcBG5G2uF0xm81Qr9eNl0ISAU3uJpOJkAXtNFyr1fCUpzxFXs4PDw9Rq9XgOI4h5+SxQEGG77rrLgAwIlXT6dSQgJNgaTJHA09dZ5jEhMSI5qkkymxzo9EwCDgdwDW5K5fLch6v32w2UalUDPn0fD6Xsj3AMdFlZJrtYC1gTY5Yf5gSXhISHT2kMzihyaY2QaPRlyZys9kMR0dHcr3t7W34vm+YZDEarSORcRwbkmTKeXUNarpja1NJGk+xfcPh8JoSZjS5YqSRbeUGjTY81XPBfjqOY9QNJ7HVkUnf9w136SRJsLW1hVarJcfRzVxHdFkHW0vOWdJNqyrW67VIyoGCXO/u7kokGTiOrmp3+OVyifF4LPNB13quJc453cJ5HCP93GQCiueEpcZI3lqtFrIsM2TUlKHrDYQgCMQ0kO2nooF9j+NY2q0l61yrPK/RaKDRaMiaunTpkowRz6OKYzKZiNHt1tYWPvnJT4qZHqE3MhqNhtTV1mbDvOZ0Oj1zszH2/2bjdi5JeUcUHHvpS1+Kd73rXfi6r/s6PPWpT8U3fdM34d/9u3+H3/3d3z3rpllYWFhYXCf4C/t6/1g8OfGpT30K3/3d342nPOUp8H0fT3va0/CmN73ps5Z2ecELXnDNGvne7/3eW9RqCwsLizsft+r38Ww2w4MPPih1oFmSktUz7rvvPrzyla8EAClJqf/okpTcTDoL3BER6U+H8XgsyfyPBW3CAFybBG9hYWFhcXawEWmLT4cbUaG97nWvw0/+5E/Kzzq6cxowelitViX6tlqtJHLKiNDR0RHiOBbjJAASGdblj2gGpg2wKAHneYyIzudzKbPZ6/WMPFugeKnsdDqGeRXb5DiOSE3TNDXyMJnzuV6vJVLreR7q9bqU3QKKiGWWZWJEBhzXKKZ5FdvPSCMAydFkGSYARv6vjrgy2q3LHPFeHI88z7G/v2/IvylrpTy+1Wpha2vLyCWlLL1cLhvzz/KNOj+ZY8DzGM1m9JHReea0EpSq68gmy3Cx7+v1WqKbAGSdAMeRzOFwKGZUOs3F933JU+f9FouFsZm0XC5RLpel/SzZqA3btDGWrpPNnFqCY6ZrP1OpoM3ZGCFm+yktpiyfc0lDL441+6ANsVjXWUdX+YeRZpq4nVRRMJLPiPF0OpVrcc6jKJLaz7wW1yjHkWWtKOfWfarValKOk7nbzEPncb1eD/v7+wCKqPKFCxeM0m68lk5j4veAlqGzrBb7ARTrUpddZTv1s8pSbaPR6BrOc724Va7dT5aSlHckkf74xz+On//5n/+sv1Dvv/9+vPnNb75FrbKwsLCwOA0skbb4dHjpS1+Kl770pfLzU5/6VHzsYx/DL/3SL33W3/tBEIij843g8PBQ8pz5IptlGVqtlmFYlee5OAvrmvHL5VJqFwMQ8yLWpwZMIyigIAPNZhObzeaa69NFmJ/V63VDmptlGQaDgWGEdtKMijnaJwnser3GYDAwZNAn83NZC5fnAAXxoUEU78fNg5MyXB4PHLtGO44jJLxUKmE0GiFJEiEcrB9M0kAHc25A8LMLFy4Y9ZWZ01ytVuU4tlPPHUkayQyNp5jPTpDcaUk+c7k1KeS8U0pMwknztziOZa5Z2zjLMgkK8VqUpJO88f8ozSfJP7lJxJxjuj8DkLrNJJFAQTBZJ1w7jJOw8txut4s8z/HJT35S1gWJNseQqQlRFBnOzXx22De9CaI3hTSppeT6sQzDdA42N420+zmd2vnZYrHAuXPnxG+AbdXu3ryXnsvpdIrVaoW9vT35LmEe+HQ6Ndaz4zjy3DDHWW8kUd7PawDHNc3r9fo1GxL6e6TdbqNarcoxH/vYx+B5HmazmUjmWVOa7uefTbXzeHCriPQLniQlKc+USP/oj/4ofvqnf/ozHvP//t//wzOe8Qz5+dKlS3jpS1+Kb/3Wb8XrXve6z3jufffdJzscQPFwn3SUs7CwsLA4G1gibfF48XhUaADwm7/5m3j3u9+Nvb09/It/8S/wH//jf7yuqHQQBHBd13hRHg6HyLJM8kSBgpREUYTZbCaEiWSKZBGARCsPDw+FGDJflC/F/X4fURShXq8L6Wm1WhJ51iZQk8lEooNAERk/PDxEpVIRR2Xm7PJa1WpVIuLa4Gw0GmE4HMoLvOu6iKJIzNbYpyRJkCSJUfpIE9hqtQrXdY2SXyTSOp+b/dGOx9PpFI888gim06mQu3q9jq2tLRkvmqs5jmPkCYdhiCAIjDJKjMaTWIxGIzFsIzki6eLLPE3MuHHCeatUKmg0GjJPg8EAm81GDKN4T/aV16OpWr/fl+vzGmxXlmWoVqtoNBqyVrgRoV2p6QzPjRWujclkIlHfLMuQJImRz01yr92g1+s1ptOpURrNdV1sNhvUajWRyZJwc9PF8zxRaHBdcGMpz3MhfJzDz//8zzdM7WjIxvElqeZaZFT7pIkwvQm0CoQKhpMbNtPpVMa22Wyi2WzK88ANHL3ZwbVSqVRk/YxGIyG1dM/nfJPYs131et3YIOIcc85d1xWlAM3e4jjGzs4OyuWysdEzHo8xHA7leiwdx/llHvp8PpcxbLfbYrR30m3+enGriPSTBWdKpH/oh34Ir371qz/jMU996lPl35cvX8YLX/hCPP/5z8ev/MqvfNbr38z6ZRYWFhYWNxeWSFs8HjxeFdorXvEKXLx4EefPn8ff/M3f4Ed+5EfwsY997DP6qTxWCli9XjfKRgEQqXcURYazLqNj2viLa5svwYzSUT4LQIgd77NYLHB0dGS4CrOcEl2oAQhJj+NYXtiTJJFr63JN2pCJ0az5fC7kpdFoiGMwpcue5yEIAomI8fo8T5c5mk6nhmSYEllNMOkyzmu1Wi2RKpO8MFpL52O2Y2trS8iY53kS6dSlunq9nmEAtVqtriEldONeLpeyIRMEgSFV5/9zQwIoiFC320WpVJJ10Gq1sNlsjCg956xUKsk4pmkqBB4oiB3/zTkn6VqtVobTOaOrek0FQWC0g+3lGmCd8k6nYxie0T2e4zoYDOS6JHck2zoCyig+x3Z7e1vWCq/f6XRQLpdx+fJluT7nUW+eUCVAUyyuDe1KTVl9uVyWMSQxpiEb1xnXHMeC5DaOYzmXMmgSaxoDam6gzfF0WbT1eo1eryd94hrQzyHl2rxWqVQyNsrYBqYd8Dyas81mM2PzhOOj51wbozHqTyNA9p+KCV3r2uLW4UyJ9Pb2tlEr8TPh0qVLeOELX4gv//Ivx7ve9a7rLpliYWFhYXF7QLv7Xs+5FncWnmgV2r/+1/9a/v2sZz0L586dw4tf/GL80z/9E572tKd92nNsCpiFhYXFMWxE+nS4I3KkL126hBe84AW4ePEi3va2t4mFP4CbkgtlYWFhYXHrYSPSn1t4olVoJ/Hc5z4XQBHRfiwi/VgpYIw80VQKOC43pKPIQBE50jLZLMvQbrfhOI7IpUejEarVKprNpkijy+Uy0jSVqFS325XSULwnawxTGg4UkTzmNjPKRXkwayMDkKggj5nP59JGnedMOTjbRWm5lrBT7u26rpGL7DiORO0effRRw2iNY5GmqUiwAYg8lVJ44LjeMfNMeVye5yLNpfEVxwMo8pUfeeQRbDYbyYVtt9uoVCrXXKtcLsN1XUMOrPNstdyXUdOtrS0Ze13eidF+vo9yznRe+mazQaPRMGoc6xJm7BNLlHEcaSAVx7GsDZbqOmnWpctmDYdDWZfsI1UPWqbPHGH9GfOjq9WqzMtwOJS8euC4VNfJdAFd+oprhfXSDw8P5fNOp2OU64rj2CjvFASB1PvmeTQPK5fL0g7P88R4TZt1tVotI12AJdt09Ja55zo6zLWp0wVoyMfjuOlLpQBwrMDQuduMwuva0mmaotFoSG77fD7HaDQy1naz2cTW1paUxgIKtUipVDKUtVmWGeufknz950ZhifTpcEcQ6fe///34+Mc/jo9//ONSY5G4GYvGwsLCwuLWwxLpzy3cahUay6rQeffT4bFSwGjapAky3zdqtZpBVpk3Sckwpa/a7IokVpsB8cWXBDYIAmRZJnm5un3D4VDIBXOjaTgGFBsA8/kczWZTCCWJKOXlPEc7NlNy3u125QW+VqthNpthPB7LuEdRJOfo966TrtRpmkrOMsHcUm4EaGdmnttoNNDpdAxpMdtPYkppN+cGgMhj1+u1YUpGUy62n3WrO52OHEdSzu8Syva1DJ1EhTnXHMdOpyMbFbxnGIZIkkT62Ww2Ua/XhYxpAsc1xzYyR5bjw7XA629vb6NSqWAymRjrUJtZ1et1yatlv7kpoAk4pe/Mq2U/8zzHZDKR8aHzOX9muyjBBiB5z8yvZju63S7iODby7jnffCbyPDc2foIgwHA4NDYtGo2GsQHDsWbda6ZisB703t6ePBNZlhkkd2dnB/V6Xe7BsajVauh0OrIO5vM5xuOxbE4R4/EYvV5PyPs999yDbrdrOHRzs41jkSSJPGMnZe6+70vfKEPvdruyNuh0z2txTmlsx/ZTDq59FG4ElkifDncEkX71q1/9WXexLSwsLCzuLFgibfHp8HhUaJcuXcKLX/xi/Pqv/zq+8iu/Ev/0T/+E97znPfiGb/gGdLtd/M3f/A1+8Ad/EF/zNV+DL/mSLzl1G0iGtXM1ia82o+LLehRFEgmr1+tSQoovxTs7O3AcB5PJBL1eT87f3d01otuu64q5EnAcVe73+0akcz6fo9PpyIt4kiS4cuUKSqUSLl68CADiUMwXf5INOjQDxwRT31ObpPHlncZdOkd6NpsZGwHNZhOu66LRaAiZabVaiOMYV65cESLa6XRQq9UMEkJjKOA4mspIPwnUbDaTTQS2n9HP2WwmGw3acV1HBpMkwWg0MjYETm7uMN+Y3y801jpZ/oqf6Xz0arUqUU8AYqhGIqqdoU+WPWKkEYDMkTb1YhmwkxsxbKNu82azEXIKFOZc/X5f/p9EjPPFfg8GA8OsqtVqIQxDo3waNzN4T5qrMbcZKJQV3W7XMAjjhpDeMFiv1zh37hzOnz8v91ytVliv1xK95SYGN4GA4tlktJj3pLqgVCoZz47eSKrVajJHfIYbjQba7TZarZb0czKZ4PDwEHfffbfhOs5NA+2+7bqurCeOjTbbo4qCuehAoXKg0oHrYD6fo1arSf49UBB3nQ9dq9XEbf1kXj83k+6k8ldPFtwRRNrCwsLC4skHS6QtPh0ejwotz3N87GMfkxdi13Xxx3/8x3j729+OOI5x991341u+5VvwYz/2Y9fVhvF4LKVlTjrhzmYzIcN5nsN1XaNmseu6RqQTKEgJI0Y8l+VzWAopjmP4vi8yXuC4jvF6vRaiWavVpMYv2xaGoRg5kbjRgZlEYjqdYn9/H51ORwg+S0lpB3BKZ8vlsrzoVyoViZZroy/9M12IdcSMstlarSYbBmwr+wNAzNNWq5WQqEqlIlFeoCAbnufBdV05V5eFIrmI4xjL5RKtVkuIFmtl0ySM46NdnYfDIUajEdbrtbSVRFGXFZvP5+j3+0KueNxwODQUDqwTrMuWkbiSEPu+D8dxjPNYJ1iXSlssFmJ8xuMo69ZEa7lcYjQaCQFst9tS/kqbyVFVwTFjNJpjzs+4doFj8r9YLAxDOJpg6SgpHc13dnZkjmkuxuOyLEMYhsb4cO1wnFifW48jnwctqyaJ1KXjqCjhd0aappJSwM/q9br0l8/NdDoVhYEuI9btdlGtVmUcqYLQtda5NriZwrWuNxWiKEKn0zFSCPQGAMc9SRIMh0NZK9VqVZ55/Z20XC4lheNmlL9ieyweHyyRtrCwsLA4E1gibfHp8HhUaPfee68hMb777rvxwQ9+8AlumYWFhYWFxTEskbawsLCwOBNYIm1xu4JRZp1jyUgQI0wAJJ+TJaoAiJFTEAQSxdO1jZnDHIYhPM+TCPV4PEaSJNdEwFn2iJGwRqOBIAiM/O1arYadnR3EcYxLly4BgNQh1jnAjHBRTsuo2GazkevTUC3Pc4nMsuTQbDaTKB3NpyjVpfRam0AFQYDVaoVGo3GN5JZ1g4FCTjsYDETaDhybczHKRmmtNm6ipFWbTOl76sg+ZfQny4MxdeDg4ACj0Qi7u7vY2tqS+WXuKyPSNLoaDocy55Rd6/JUbBvnSJcY09JxypJ5XpqmYv7F6CRzidvttow/y1Mxour7Pnq9Hg4ODmQNLJdLMeLSMmtG8Tk+W1tbUqNb54dTqs+2zmYziQjznpSws61MX+h0OnIuy6DxueD1V6uV1NZmBHYymcj6iaIIrVZL7s3xD8MQ1WrVqKFdrVYxHo+N8S6VSrJW8jzH/v4+5vO5PIOlUgnj8RiTyQRXrlyRuWw2m5IaARTKDUbPdQkz3/dlfXJN9Pt9GQvHcbC1tYW9vT2jrYz+s21cB9rMrFwuG2ZvVC/o6D9TDGisdjNgpd2ngyXSFhYWFhZnAkukLW5XNBoNyU/mSyuNp+gKDRyTx1arZZh80a2aEljtFkxyEYahyI2BgjSQxPClW7svawMpSme1rLrdbkuNWqB4OQ/DUF7Wm82mkEDmArPuLs2tgGPTLS0dJ3QeJsklyRhzwnWtYJIBXeeZucKavA8GA4zHY+R5LtLlRqOBu+66S0gt8411vrImq7peMyXHJDQk93rzYblcYrFYSFsnk4mQIy3/3mw2Yo4GHEvOsywTktNut2XjhfO5Xq8xmUyMusknc6Sn06k4a2tSTuk1Zf90j9ftqFarBtGiCVetVpP5zfNcDM90DjVJG+dud3cXYRhK/jHXjzZ2Y8627/uypur1uuSn0/iLGw9sN5EkibEJxfbrmtHcUNA1o+kMrz0CCO0eTqk157xSqSBJEhmLLMuwXq8RhqGRjz8ajRDHscwTa6jTpZvjqEkr+5amqWGiB8BYd9xkazQaRq1ruviz/cy1Zr1znksjQ153uVxiOBwa65rPua6vfSOwRPp0sETawsLCwuJMYOtIW9yuYHkp5hADMMpNcf3xRRmAEYVi2RvtgLzZbOD7vpEnq8tT1et1bG9vI89zIcM611ifNxgMUCqVhBA4joMsyyTfFiiIoY5wVSoVibASjuPgwoULaDQaEplNkkQchdknmj3p8kjj8RjNZlNc0RuNhuSpajMtkkRtDMVNBraNudTD4VAIDR2QufFA4jWdToW0ua4rJli6pBEAo58kVYxqE9pZnSW+VquV9JH5us1mU9rKzRXmoAMFyWEkmcRqPp8bqoTVaiXRWG6wMMI4Ho+l3SSbmtDznvP53CjHNhgMpK3My280GrLuuOmg+0wHee3yzLFidJ9zp0uI0ZSOkXGgILIkk3reTkaMmQPs+76xDlzXFRLGsW80GtKGMAwxmUwMB3ma1dGojfeM49gwv9ve3sZkMjE2s5IkwWq1kraSSOtNET5LnDOgWOubzUZKwAHHUXa9xlqtFjqdjvEszWYzI9+aPgLaxT+KInieZ3zfcA50hJrjpteK53kIgsBYazcCS6RPB0ukLSwsLCzOBDYibWFhYWFhcfvAEunTwRJpCwsLC4szw+fyL2CL2xeMCtIZGTiWAusczvl8LiVvGAX0fV/ygBkdWywWIltm9I9RMV6LdYBns5lEXEulEjzPg+/7Ih2l/Haz2UiEkyWhdLku3pPHzOdzqUPMqOb29rbIWHUUdrFYIIoiaStdo+n4DBQROR3JY5QwyzJ5rnWEjJ9R7j0ajeT6jNi5risRuUqlgtFoJH1i3jrHltdvNBqGW/NqtZKcdF36i9fUYBSQ7QvDEOVy2chnpXJGy7HzPDe+u1iCyHEcI2+dygYAxrphdJGRRJbnYp8cxzEik6VSSVIDGNmk1JrRU6oIGLkHCsXEyTrpXNd0e+a1KClnGz3PEzd1zrfjOMb4UEEQBIG0i5Fn4DhFgVFTPhts20n36XK5jFqtZqgvmP+sc+eZosDjqEzgPACF23ij0RB5/NHREbIsw3w+l/OocNB1sBkppk8A+8H8d/aNCgEtOafsXTu8Z1mGLMvkM8/zRBHAdtB/QNchn81mRlk0pjBQGcCxpqcB182NwhLp08ESaQsLCwuLM4GNSFvcrmBO5GQykbXG3M3xeCwvrJRq6xdqlpfSpIQ5zsz35GdaXst6uQAkBzVJEjEX0qZhNOIiISMRKpfL8vLPvEzKcPkCr3NjKW2eTqdGXioJDO9J+Wur1TLMtAaDgWwg7OzsYLlcot/vG+WjTta4ZbktTYjjOEaj0UC9XjeIw2g0EjO2drsttY9JMoMgkH7pfHKSR22ExvxnkhcatpGIuq6LVquF9XptkFjXdTEcDg0TK+ZCayJKcsPxabVaqFQqRu6tLjvGv0/mojMXlxsVvD5Jra6zrXOrmReu1wDrE/u+b9Q/JwHX+fO8D9cUpfW8PmXuWvo+HA5RrVaNUmMcm8lkIu2oVqsirWdfdRkvoNigyLJMaiwDRU1wrleOb7/fx2g0Qq1Ww+7uLgCzzjnP7ff7ko4AQEiu67qSjsDn9mRZt3K5bIwFy6ex5Bv7qX+HcXNqMpkIGebmT57nIuNmqTOOL1CsWdao59jyfF1XerVaCWkGIHJ2pk5oqfn1whLp08ESaQsLCwuLM4El0ha3K1zXRZIkODg4ENLZbDYRRRHG47GsPzpoa0LJaBajiAAkwqx9ATabjdSeBY6Np5rNpkSySXLH47G8iAdBgHq9bkSfVquVRLT19SqVihDpdrsN3/fR7/cNs6jBYIDJZCIEk7VvGQFlu7mpwMiajgIDxw7gw+HQyN3ebDZwXVdIISObOm+a9Zp93xeCVSqV4DiOEGmSqjAMhbQwArxcLo2IPceJ5JFRdpImzrHneXItHqdzUBnp16ZSrGHM9vE4bjzonPn1ei0EjQ7UACQHnq7kmrSR3HMTgvcECuLK43zfh+/7sha5AaAdnOk0P5/PjYg3o7ea/DNnnedqws51sVqtcHR0JO33fR/dbhdhGArh470Y+WafTkZTS6USZrOZtD9JEozHY7iuazikz+dzY73TZVubknFDolqtGs+c4zhSU9txHNRqNXS7XflsOBzK5oSOGM/nc8RxfE3fsywTosvc+b29PRlrbrRx84RtYl12jis3YPicMxeajv9cL51OR+aNPgvNZlPW+Hg8RpqmEsXnuRa3DpZIW1jcRlhu1vjJww/hG+ufhy+r7px1cywsnlBYIm1xu8J1XVQqFbiuK0S01WqJHJOkloSzVCoJ4a7VakIQSBooxabzLgBxGGYEk+WatDMzsVgshKhXKhU0Gg3DKAs4jkgSJN88ZrVawfd9dDodgwDSpVuXlOLzxXuyLJImOcvlEkmSCHGhdJ0u0WwDUGxCkEjUajVEUSTyXF6f48PxmM1mhnnaYDDAfD7HxYsXpa00udJ9p2yckl0A4vytXbTTNMU999wj5HY2m4nplzYuYwT9pOu1Np7S64DH9Xo9KVsFQMib/neSJDg6OpLrcR65ycL1w/nX5cxITEl8SeJo8AYcm6BduXJF+l2v142NGqAo/cXNGK6N2WwmJBYoSPN4PMbBwYGMT6vVErm3VlEsFgs0Gg1j08J1XekD1ws3mAhuBnFNsVyWViqwRJou1cV7a1f22WxmyNypRtBRa26wcNz1tTRYiqzX68kzcfI4bkLwe4B9ZAqDLitGpQmPpXt/rVYzDP60a3ej0YDrusYxVJLwu+pmEGkbkT4dLJG2sLiN8LfZIf4mO0QZJXxZxxJpiyc3LJG2sLCwsLC4fWCJ9OnwOUmk3/72t591EywsrkErAO7+TmBwDviL6Sfwi3/8Cfzhh96NUfLZz7WwuBNhibTF7QpGM5mXCxyXxWGeInBsOOQ4DrrdLgCI4Vae50auMKNalAgzWvz/t/fm4ZZV1bX4OH3f3qZudbQqTZRG0ErxfBECWiIhIfKRZxMFUVQeYKDQCPkhnVFQULFHXwglicToi2IiRgWU8AwlKqY0IhIhkKKqbnvu6fvu98fJmHeuWw3c6u4tao7v46PuuXuvvfZaa5+7x5pzjsFjGDGr1+uOFzSjmkxBDofDEuFlxJc1rzoix1RTRtAotMTf6faZ9g1APId1XTMwiD6OjIxI5I4pzjqll1FrHdGluBjB6DNrfIG5SKK2zmKfta90uVyW+m0AkpZOSyTeU7vdFt9iAGJfVSwWxZqo3++jXq/LfTNNXKdjM51WR4fr9brcF6853yaN88vILH8eHx8HAKnR9Xg8klav08lpF8VrMuVcew/zuhxbrq/5Y1iv11EqlTA1NSX9S6fTklrNMWP9L++DacyMfg4NDYmdGu8pFAqJDzmvqe2a+Jmec13LrrMvWPbAFGfeE0XsuA5GRkbk3rQAGc/VNd7FYtFJaacPM9tinbPuV7lcRrPZRK/Xc7yhWdOv7cEajYbTfjqdljUNQFLEafnGfkUiEUcYjanwOiOjUCigXq87kXiml/MaqVRKnhvWmO8pjEgvDAclkTYYliL6QeDZLBBqAx0vsHlosXtkMOxbmI+0Yami2WxKDSpTYiuViry4ktQmEglRduaLOOtUtdBROp0WEjL/RVyTBgoO8Zp+v387f9hIJIJwOIxWq+UQbrZLckeSw/RZEir6/LItpolrv2PWubIfXq8XQ0NDiMfjTmo0BZh4HutKNZkhwSMh8Pl8DhkF5lKoy+Wyky6tiXoqlUKn08HU1JTUqZLY9Ho9+YwiVyQrAKTOWqfS01eb5IPX4/gBAzLDWmKtSs3NFI45x9bj8Uja9vDwsDP+xWIRuVxO1hfvSfsiA4PNAa4T9qXVask64DxRlZ3EminY9BPnv0nGeN/tdlv6QQJEQl+v14Uk816pel2pVNDpdBCJRGRzSdeKcxy52UHxO/aVtdskzpw7vQHR7XZFSIzrh7X3PI6ktlqtykYSU8C1EBdFw7hZUK/XUa1WHTV9+lqHQiFZnyTH3CDjPFG0jesRgBBbYE6MLZlMSlvc9AmHw47QHD3L+ewkEgmnthwY1GVzs4r959xOTk7KvA0PD0tdvd742l0YkV4YjEgbDEsE7cOBvh+ItIBGEHh6BOg/92kGwwELi0gblioqlQqGhoaQSqUkksTI4fwaSL4A8yWWJDoYDAqparVayOfzQpqBuSgyCQLVqHVb7XZbIns6yhsKhVCr1eQzRrni8bj0jzW0miwlk0mJ3gGDF3iqV/OlnrWrFD8DBtHgoaEhIcEARKSJfQiFQqLerKPuFAjTEV1GW3U9aKfTcSKguhYUmFPZnpycFGKsI3gkrNVqFV6vF+Vy2Yny+v1+xGIxjI4Oyqa4mUCiGIlEEI1Gt6tTZft67ljPzmgko986GklLL5KeZrMp1+Z57XYb2WwWgUBAxoybJFRkZlsUfGPGAaPk2mosEAjIZgzH2uPxYNmyZVLTW61WZd3p6PGO7lELthWLRaTTaWSzWRkTXe+viTTnV6/PTqfjZABw44XX5rOlo7fNZlMszzi23EjK5XJyXD6fR6fTwcjIiKwzRsp5j4zoN5tNx4qNtcnciOCGllZvLxQKiEajzn1S2VuL0NVqNaRSKSeTQFtYcU2FQiFkMhm5J9b/s14emLMM05tofP54zWQyibGxMRSLRUxMTBiRXgQYkTYYlgjaLxk8kF4AwQ5QCgOe5QCeWuSOGQz7CEakDUsZjPKS/DCNlv66wOAFvlgsSlQaGJCeYrEoCrvAnKewJiqpVMqJOlJRularyQu8tqkiuSPRZuQbgJCbXq/nRMh0+/F4XKx3+CJO2x0tZkaV6n6/L+SCtlf0tCaq1aq8vJN4aQVqbgAkEgkZM0ZDtagZxaRmZ2eF2KbTaRk/zkc0GkU6nZZrMlLs9XqFGGezWXi9XuTzeSGBfr9fFLrZf/oE66h7IpFAq9WS6Har1ZIx2LZtmxyXzWbR6XQkIkqhNK3Uzs0JEliv17udajcJqZ7zUCiEYDAo0VP2nxF0Eq0VK1YAmPMjz+VyIiTH8aFYmI6oh8NhmTdtI5bJZJx0fqaba/sorgOtlg0MFNV5Tz6fD6Ojo/D5fNK3Wq0m53BOSbS1CB0j7tp7OxKJIJPJyKaUVrrnNRlNbzQaTmq93iAaGhpCJBJxrOqYFaL9uFnqEAqFZBNkZmZGMhFIipkKrzMEKLSmvcSHhoacLAeOt96wKZVKzkYQ55VlHABkE0x7Uo+Ojor9m46UG/YfjEgbDEsBWaC7HIgMvnvh6wHNANB7EYxIG16wMCJtMBgMBsPSgUWkFwYj0gbDUsBLBmndgf8m0h4Avi7QfBGAHwJo7eJcg+EAhRFpw1KFx+NBvV7H1NSUEykMh8OIxWJO1JdrUQtnMcLFKCajhIywAYNokk5jZV3p8PCwRKqYQqx9gBlB8/l823nfTkxMSDSMKbCMVGkBMUbGGbVj+izB9FdGU4PBIAqFAgKBgERR+/2+CCkR+XwejUZDIq+s/dVRzHg8jpmZGVSrVacWlrWqjMjV63XH9ohpyuFwWASn2Hftb816YEa4OWbNZtNJsS0UCo4gFtN8AThp9PSG5j3RB1j7bOuUdyKdTks9L++RdbKMMlJgqlwuS0YAwdRhjivHXteaz7fqmp+q7fF4xNKL12ZEVo9ZNptFMplErVaTdczMAu05Hg6HJVsBgNSh689YOtBoNCRi32g0EIvFMDw87NTAU9yN18vlcuK9rMdai+iVy2W0223Hsq1YLKLZbIqgGseA8wdAMhf0Z8wyqdVqTqbE2NgYQqGQ1FIzsq4zPJihoUs/mGrP5ysQCGDVqlXwer0SPZ+dnRU9AN4TxQlpY8XPWPLAudR+1jxmamoK9Xp9O1G/3YUR6YXBiLTBsNjwADgWQA/Q30XBLlCNADgSwOOL0zWDYV/CiLRhqYKKzky3BiAesY1GQ15kY7EY/H6/U8fo8XiQSqXg9/vlBZxEyePxSKo4fWR1murIyAiWL18uRJpEr9vtygs8yQzTtIE5heXZ2Vl5NsLhsCPCRTEkr9frtM/fkQhRHKzdbsu5wCB1uNVqOZ7M9XpdiDVf+rWAmsfjkbRTTUDob0ySQPLs9XqFZPZ6PeRyOUnZZlprv9+XNF9uDPh8Prkm69Z7vZ60FQqFJHWWx9GnmsS00+kgFoshFosJgapWqyiVSojFYtJWrVYT4qOFrarVqiOWRrEorQDOa2sxNqpBc60wnTwWizmey8BcHTcwpwjNjRnWobPPbGu+Dzavn0wmZU7a7Tby+bxDxlnXTB911mhzM4NjWK1WnU2CXq8n98L5JSHXGyrValXmAIAIZmky6fP5UKvVhHwCgzRrrmOuz3K5LGM5XxWcYD/1PHDTpd/vy5hxcyabzcpGQzAYFAFCguUCWhwsEAigVCoJaaZAmfZMz+VymJmZQTwel/a5QaLFxuLxOGKxmPOM877YFmvzOW97I73biPTCYETaYFhsrAaQAjxNAKqMxtvHgGQfDSPShhckjEgblio6nQ46nY6jPExhrWazKS+1gUAAqVQKiURCyI6OFPIlmNGnRCIhxIFRQb741+t1VCoVETsCIFHP+TWQJLo6Guzz+RCPxx3CTSLFPgCDKLcmL5rsAgOyRmukzZs3A5irReW46OuRXFA9Wwuoeb1eEZRi9DAcDqPZbEqEGZgTagLmRMAomqUV0mk3RjLJ2nFt+8O6b7/fL3XE/X4f4+PjaLfbck1uijASzD6Ew2EhMxwLbXvE/1OtGgDGxsZQKBRENAoYEC1GEQE4EVqS8mg0imKxiJmZme0U0tPpNCYmJgDM1evz2lwDWmWcNbwk+jyPa0LPeSaTwfLly2Ustm3bhl6vJzZVvE8tnNVut1EoFBAKhYQAMlNCk37OYzQadeaSGxRcG7VaDdVqVY6h3ZRWuKbIGrMteM1ms4lyuSzkl+Ps9/tl/EmkdUYGs0IIPresZeY80Q5L9xWAs2HGdac3RdrtNiqVikSpuSmlFcC1EwDvk0JxjDizPT4/vB7nSz9f3DzQ/d0TGJFeGIxIGwyLjaMB+ABPd/tfeVpA/7+JNor7uV8Gwz6G2V8ZliqYTqqJKX2DGSUDICq9kUhEXoApeqQj1dlsVkSw5h9HAh4KhSQFnGSA0TP6IvMzknW+sCcSCRHjYrTW7/eLQjMASaMNhUJyTKlUEvVvTcrD4TDi8bgQ6GKxKEJWmkhrr2Pel9frlb4yvZxWYsCcZU8wGJS+VatVtFotBINBJ6IejUYd1ex2u41gMCjki8Ju2ls3EAhISrhOfW+1Wti6dasQ+lQqJRFWot/vo1KpOGrZTL0muc5msyiXy8jlctIWCVAymRRizvXCfukIKqO89AamNRQAiVTy2sCc9VoikRAiGIlEnA2carUqmwgcM2AgspVMJoXEVyoVjI2NYWRkRNbZihUrJN1YK6WTTLN9CrQxIhqNRjEyMiJRdX7GudTe58lkEsFg0BEva7fbzvPA73VNJtmuLrEABinSXLPpdBrhcFii7xxvPhPAYAOJqvA69Z3lGlyfs7Ozonivrd7YHs+NRqOOiBtLE3w+n/SRFnXa23t0dFSyOvQzQS9vZkOwXEAL5nGd8TuJG1dU0NdihrsLI9ILg72JGAyLiRCAFwFo7+T3LQABAC/Zbz0yGPYb+Ad7d/8zGAwGg8FgWCxYRNpgWEy8CEAEQAWDNO558AADM+ljAfwMZixteEFhf6Z2P/TQQ7jlllvw6KOPYnx8HN/85jdxzjnn7PT4b3zjG/jCF76ATZs2odls4nd+53dw/fXXY926dbvVX8OBBUb25tta0TKIkbxwOIxut+uIRTGFO5FISMSVkVYtztVoNFAqlaT9TCaDaDTqRBm3bt0qAmI8jrZa89N8I5EIgsGgk+qrfaRpEbVs2TKJ0jUaDdRqte1sm5hazOhhrVbbrpa01WptJ9DV7Xad1PdGo4Hly5cjmUw6KbcUIWPEu16vI5vNIh6PS+SOKb6MpLIOVnsnM4I8Ozsr0VvaUNGTm8cxosdxpHAU75G14xQhI1jrrFP8abOkI6g+nw8rVqyQiOiO1hSjl/O/v4aHhyVKSQsoPeeMPEYiEeezdrstqb8zMzPo9XpOXW2v15Nx1OvY7/ej0WhIRgNF8+hPDszVkDNCyv5TiA8YZEIsW7bMmfNut4tKpYJOpyNrMZ1OSwYC10symUSr1cL4+DiAuVR1LUDGemWK4gFz6fa6Pn/58uXw+XyYmJhwfMiBORFApqmXy2XH/i0QCEg0m/eka5p5zU6nIzoHenzGxsYAACtXrpT2Gclm3bmuR2cKN++NfS2XyygUCnIu17H2xS6VSk6mC/UaWKJhYmP7H0akDYbFxDH//X/WQ+8IDQBZACsBbNkfnTIY9h/21x/garWK448/HhdeeCHe8IY3POfxDz30EF7zmtfgIx/5CNLpNO68806cffbZeOSRR3DiiSfuhx4bFhMjIyNCRPVLfaPRQC6Xc8hAKBRCLpcTwkdfVy261Ww2Rc1bp90Wi0VHQbvdbjtkb3p6WtKxmaZMAatMJiPHMY22Vqs5KeDNZlPSa5mKTSE1YECgAoEAarWaEBCKoGk/YqaNUsEaGKS26p/nizgRJJ48rl6vo9VqwefzOaJn0WgUyWRSzqW/Ncda+/NOT0/LfZdKJcdbt9vtijgVxzqRSCAYDIpXMuek1Wo5aeKlUgmzs7NyzenpaczMzEgaMlGtVh0BL6b0xuNxIZ5UfWdftUozyRIwIJkrVqyQNN9SqSTEn+2zBIDzwf5PT09LX1lznslkHLG0VquFZrPp+Flz3LTPM+uT9TX1Rg/Xp6735RwFg0Eh79QSoBCZnrtgMCj3qYXB9JpptVpCHqlRwHp5tt/v9xEMBqVEIZVKyXxorYJoNOpoEPT7ffE7B+aE+/SmSCQSwdDQkIwd+5rP551nrlwuy70Dg5R/ivvpZ9rn80mZAvtB5XC9GdbtdqXGGhg8h0NDQ9L+7Ows6vW61HKzX8FgUDag9PrYXRiRXhgOOCLdbDaxZs0a/OIXv8C//du/4YQTTljsLhkMu4csgBUAnut7r4NB1PooGJE2vKCwPyPSZ555Js4888znffxtt93m/PyRj3wE3/rWt/BP//RPRqQPAlAVWNfZ+nw+VCoVxGIxRyGaEScSNEZIQ6GQkBwSr3Q6LZG7ZrOJdDrtELR6vS4q0cAgejU0NIRoNCrkpVqtwufzCfkEBi//tKMieen3+47KM1WB8/m8I0ima56BAVGhWrZWpa5UKo79lY64s30qhZMIkSyXy2UhUMViEeVyeTvxLNaT8t55H7r/9Xpd+gJAalmTyaQQRX6vtNtt2dwIBoMixsXx5u85v6wz1wJqOgqoMwm63a5EKDlmvAcd+eU4AwOyOl+8ipZSvGe2QcVstk+i22q1pE0Ki5E0JxIJ1Ot1xGIxuSctcMY5CQQC6Pf7aDab8jt+ri3PqAquiSn7p9WsmaGg540bUHxOyuUyms2mI7ZXr9fRbrfFyoxzo8XeSHDnj2O73d7umWCdsBYEI4nlMcwk4D0yAl8sFuVvCpXbOZ68JoXYeJ/RaBS1Ws0RY6tWq44NWjKZRCAQQLPZdLIcZmZm4PP5pC0KofG7hOfq2vNgMCikmv3XwmWc172Bg5kYLxQHHJH+8z//c6xYsQK/+MUvFrsrBsOe4SUY1D+Xn+tADMj0SwD8P5intOEFgwNJtZuWLoyAGF7YIGHRVkj1el3IIkGxJE0qi8WiRMz4ks3IlLbLIcnUKtAAnEh2vV6XfvCFOh6Pi6AXI1A67ZntMfrGtqanp8U7mC/iJIdU6eY98cVfR6lJDHkuX9z58j48PAyv14upqSlH+blaraJWq8lnjCTql/5qtYrZ2VkEg0EhzqlUStoEIMJRWuyNkW7dV6YHU5ANcG2CdAp7r9eT9HJ6AmvSw6iwTvfmvOrI/uTkJAKBgGwGcJ6KxaJsCIRCISGN+r4pRqU3Byjqxfap6kybKs6dtu+iSJ0W7SoUClKmoAmxTukH5oS5dER3R+NK6zKtqh2JRDAzM4OtW7fKWLdaLUe1m/+mZzPnTh9DFXv6qwMDwslSABJkpqUDc1Zm3BigvzTHX4uIMbJdKBRkTlasWCGElWMRCARQKBTQbrdl44NrT0euuWnEOWIaN++X/WKbOmLPjTr2lWKCTMUHBhsjWvyNZRvaSYBlB9y8MLGx/Y8Dikj/8z//M77//e/jH/7hH/DP//zPi90dg2H34cWg7tkLYLDBiP68sqo+gL5f/RCFeUobXlDYG0RaW5kA2C7Ktbdw6623olKp4E/+5E/2etsGg8FgMCwFGJFeGA4YIj05OYmLLroI99xzj1NftCtoCwlg+xcug2HREMYgGt3AoDbaA/TDcMTEeh6gH/nvY/oYRKJT+72nBsM+w94g0qtXr3Y+v+6663D99dfvadcc3H333bjhhhvwrW99C6Ojo3u1bcPSBKNF0WgUuVwOAETIidZYwCD9UtcsAnOWQdpbN51Oo1wuo9PpiJ1QJBJBsViUiBZrhPv9vhN1pLgSI2u0+GENNDCImGUyGaTTacdjNhaLSfSuXq9LlI4RLaYMR6NROY5iV7wWMCdwlk6nnRRVHZHW6an8jLY/FEMCBpFmfq4tmVqtlmM9NTo6KlFitp/JZOD3+50abEbwGWVkzbROTac1VLfbdayV/H6/vBvOzMxIZJZRQL/fj0gk4qQ4U+wpnU472QUAnGgnsxF0xPuQQw4BAPHnLpVK6Ha7mJqakvZjsRhGR0fh8/lkzpkezKir7hvHldkRjEpzzpPJpBNpZiSbJQJcP7RC0yncOgMjmUyKD7SusY9EIpJ5AAwis0yF51rXNcyMvOu1wX5xDTGFmTXbeoM0Go2iUCigWCzK+gwEAvD5fGInxuN09Fqvfe0dzmeE0WTavuXzeae0wev1yprhmLHGmvedTCbFVg0YPF+RSASpVEqem1wuJxZtnF/2VafaB4NBDA0NOe17vV5EIhEn5Z/zS7/2PYUR6YXhgCDS/X4fF1xwAd7znvfg5JNPxjPPPPO8zrvppptwww037NvOGQy7gxqAvwbAUp51AF68g+M6AL4JYOa/f95zHQmDYclgb/hIP/vss5J6C2CvR6O/+tWv4p3vfCe+/vWv44wzztirbRuWLsLhsBAlLf5DNW6+KDO9U4ti0bNYiykx1Zj1xwBEQEmTTq/XK2JKwIAsUVFZv6xS/VsTStZosmbS4/E4daTpdFqIjFbVTqVSTl95P9rbOBQKCYGfn9rO+5mcnBRRLAY8Op2OED7dV6p2k/SQZOm0VdYIc7OAqexaTKvT6aBYLDp+viRZ3EhgPycmJpx5WrZsmSg2c7xCoRCWL18ufSDB0iJZVNP2er1OujHFuLTfsVa91um9ukY3EAhIzTX7yvRfts+UYq5B9oOkGBik1nMTg/NLVfJms4nJyUm5T4qDaQE41gmn02n5rN1uSzlLMplEt9tFoVBwNljY9/np8HrTIhQKycYP1wHr+vl8sQRgenraqeHXgls8rtPpOGUSFMOjZgHHsdvtOps1HA/eI72aKfDGeWq3207aPNvSWgicJy2Ox2eSbfEYLUzHOm7tIc/nXOsq0Buda8Xv98ta4vzyGQ8Gg056umH/YVGJ9FVXXYWPfvSjuzzm8ccfx/e//32Uy2VcffXVC2r/6quvxvr16+XnUqm0XfTCYFg0tDHnH71jt4wBmhhEpQ2GFxj2RkSaUZJ9gb/7u7/DhRdeiK9+9as466yz9sk1DEsXlUoF8XjcqSdOJpNO7W29Xsf4+LijwNtut9FoNJyIHwWTWMsJzGXN8UWc0bRGoyFEgnZbmlSRmLIWG4C8TFPUjP0dHx937KR4X1osjZE33hNf6LX9UqvVwsTEBMrlMg499FBpr91uy6aCVqLmeSSbOsodCoUQiUQwOzsrY6Ftr0gUaXtEUH06Go06glvzRdWoNM0IIjAgOeVyGcVi0RkHWnrxmPk18Ix2MxLIa1KFWkfuvV4vUqkUVq1aJeezXhgYRJqZvUAiWq1WnbpwjmO9XhdxLF6z2+2KmjqPm2/RxD7re6IIHY/l5kkikXCsv/gf+8KaW72eKEDG799oNCrjo8kp+8z5rVarQshZI63HUY+1zi5IJBJCXjkWmzdvRrFYRDablWeOZF6LvVUqFSe7gJtFerO1WCzKMTyvWq3KMbqvqVQKwWDQycDodrvOZhMtykj8O52OqJizzVAoBI/H4xBptkOrMgCiHq8FxjKZjIjMcd644bQ3hcYsIv38sec5AHuAK6+8Eo8//vgu/zviiCPwgx/8ABs3bpTdpxe96EUAgJNPPhnnn3/+TtsPhULykrUvX7YMBoPBsHDwD/bu/rcQVCoVbNq0CZs2bQIAPP3009i0aZOkWF599dV429veJsfffffdeNvb3oaPf/zjWLNmDSYmJjAxMeGk3hn2HQ477LDt5vvmm2/e5TmNRgOXXHIJhoaGEI/Hce6550oUzmAwGAzPjf3x9/iFhEWNSI+MjGynYLgjfPrTn8Zf/uVfys/btm3DunXr8Pd///dYs2bNvuyiwWAwGPYR9qdq989+9jOcdtpp8jOzlc4//3xs2LAB4+PjQqoB4Etf+hI6nQ4uueQSXHLJJfI5jzfse9x444246KKL5GdGKneGK664Avfeey++/vWvI5VK4dJLL8Ub3vAG/Ou//uuCrz09PS1RKu1pywgTI0K1Wk3SNnXtrdfrdSJOtNSpVCqyduPxuFNz7Pf7MTs7i0qlItHFUCiEWCyGTCYj5zE6qWsitZI0wX4y0sy0Vq/X69Sb5vN5NBoNJ+3Y4/GIbzQwiIxHIhHH+ovRTu1TzTR09t/v90tdLyN3TJ2dnp6WKPbo6Kh4PDNay1pc7WVNlWKmA9MfORKJyPgz/ZlReo4ZVccZMWUEX0d9I5GIqEdzzILBIFqtloxPIBAQ1WuCll/pdFqizpVKBYVCQfrKFG5gTm2aWQ+jo6OOGjrTp3U0mP3nmioWi6LkzDbpVc570orbjAgzks6aYq4Dzh2vyTHl+FcqFXkeGDHO5XKSns7+c03n83lMTU3JGDECrm3EtD2V3++XNcy1wiwOppkDkDWoa6lZgx+NRp1U8XA4jEwmI/ft8Xgc5XaqvrNGmeuCzzzvncrhev3EYjF5djgf1CFg4G5kZETWNNc1LcY8Ho+sf84jU745d1ptvd/vO2ne/KxWqyGXyznZD3sCi0gvDAdEjTTFGQgupCOPPNJJoTEYDAbDgYP9SaRPPfXUXaa+zSfHDz744G70yrA3kUgkMDY29ryOLRaLuOOOO3D33Xfj93//9wEAd955J4455hj8+Mc/xu/+7u8u6NokE9rjl1ZF+mWXtlPdbldeilOpFEZGRlCpVBwP31Ao5KRCM12UL+1TU1PYsmWLkwbNlG693knidBpoMBhErVZDvV53LK20iFi9XhcbJb6IU4xJWyaR0JAwEiMjI1i5cqV8xhRqpvRWq1URzSKRGB4eRqlUQj6fF9EtXQtOEkXf3m63i/Hxcelvq9USwkoPaz0+4XAYrVbLue9eryeptByfVqvl1NkCA6LC9F/eN1N/dWp6PB6XewXmNnTa7bbMC69VqVTkOh6PR9J6gcFGAMdA16Pz31w/lUrFKRPgmIXDYSQSCWnD7/cjHo/L+iFJ1DX2LA3QpJmgHRswV2/d6/VkzXq9Xse7WKfJc05YD69ryJcvXw6fz+d4Xnc6HUxNTTnts+aXa2VmZgYzMzMIBAJCfrlxov2yKTiXy+XkPvUGA+eJKf9apK/T6Ti2YqyR1zXqrVZLSgU4181mU4TYtPezFktrtVqy1slNRkdH0W63MTU1Je0nk0nRUeDYplIpxONxTE9PO+Ojrd68Xi/K5bJTckHxQ84h1/KewIj0wnBAEGmDwWAwvPCwP4m04cDDzTffjA996EM45JBD8OY3vxlXXHGFI1il8eijj6LdbjuCcEcffTQOOeQQbNy4ccFEmnWM1WpVXpxJqLRqcT6fRyAQEK9eYPDynE6nJYIIzHkDZ7NZeaHO5/PI5/NyXi6XQ6vVQiKRcOopu92uRAMBiO+wrivmNbSXLfugCQYjtTwGgKiLM/qWz+exbNkyUVwGBvWajUbDqQnVoljAgKiwDZKXZDIJr9eLYrEohKBer4vSM89ttVqShq89tKkoTlBIi6SOUXBeH4CIj+lac6pIa+9kqkZrIsoabC02xnlgv8bHx1Gr1ZwNCW6akFyxb4yEA4NNBYL/JrkjQeJYz1e0JhGtVCpOrTnnAZjzz54fUWdts84kYARWR4dZm8zIdSAQEFVuAFJL3O/3Zcy49rlJAwwIMTDYJCARbbVayOfzqNfrjpJ9Op2W4BgF9Ej+AUi/tVI7dQQ0UU+lUsjlco7SdiaTcZ5BAOK/rv3Rw+EwyuWybFBwA4H3x8+YYcCx7fV66Ha7zjz4/X7U63UZA0bKO52ObA5wfPUmHTfUqJzOvumIdDAYRCKRkIg222fNOr+z9hRGpBeGA5JIH3bYYXutqN5gWCro+4G6/tmDXYuQGQwHOIxIG3aG9773vXj5y1+ObDaLhx9+GFdffTXGx8fxiU98YofHT0xMIBgMSnSUWLZsGSYmJnZ6nZ3ZZDItmuq9wCDSlk6nMTMz45BTpsqSNCQSCYkgkpCRiJJMA4MX6enpablmr9fD0NCQk/7a7XZRr9eFxAIQayxNRKnW7PF45OWfL/gkxEw/1oJajJiSDAEDUhiLxRCPxx1rokqlglKpJH0rlUpotVoOqSXZ0enATAsnkWNEjSRAj7vf75d7SiQSEiXlWFM0jMcw1TgcDjsCXhRp0krqJC0kx0xL1+nf7XZbLMEACFkLhUKYnp4GMFhrXq8Xy5Ytc9YR2+M1mdbN43q9npA1rtNQKIRCoeCoMwMQwq9J2uTkJHq9nmRpjIyMOPPGqKpWVmdkWKfpM0VfK0QHg0G5HiO/zFbgWiepJfnkPVGIjfM0Ozsrc6EF8khQ+RnT6PUY6nEH5pTDdQo7Sa22WWs2m6hUKqhUKs5zGIvFZCOG9xMIBOQevV6vZC9wHet22ddGo4HZ2Vn5PX/HDTJ+zvXE+5qdnUUymcTy5culH1oVn+Q6GAyi0+mIKwCA7aLPVA4fHh6Wfo2Pj0sWCNP6DfsXBySR3lN44157CTMsKfRme/BN95Gep4fXngG68MKTsPVqWFro9/voVXrPfeAuYET64MLzdeo4+uijHceN4447DsFgEO9+97tx00037VWLM7PJNBgMhjlYRHphOCiJdOrUFLyBRRUsNxi2Q6rcwx+lXKGIbzWDKL7K1qph6aHX7iH/7fwetbE3fKQNBw6uvPJKXHDBBbs85ogjjtjh52vWrEGn08EzzzyDo446arvfj42NodVqoVAoOFHpycnJXdZZ78wmk3W+kUjEqTGm7y2jdBRLisfjEvFkCjFTbfnZ7OwsPB6PRKQZfWXqLOtY+/2+RORKpRLa7TbK5bIcR9TrdYnSMQVUi6N1u10EAgE5r16vo1AoSKosz+v1ek6NNPutBbaGhoYkIqwte7Q3MiOT5XJZvKV13S2jfKlUCuFwGLVaTYSh6IesbYiWLVsGj8eDrVu3ApiL+mr7K30Pul6W6dJ8wacHM8WrAEiNNqN79KKmaBQA8b/WVlw+nw/ZbBZDQ0MSeaQ1GgXTOD5aBG1ychJbtmwBAKmh5fzolPkVK1Ygn8+jVCrJ2CaTSXQ6HczOzjqCUkz75/xGIhGkUinn+5HzpgW8mMbOvrLWX3sg6zFlO/V63UmPD4VCWLlyJfr9vqTm1+t1ES1jP9LpNJLJJBqNhtQAswyBx+TzeYnK6igv71OnONMSS9fAMxKvvbHpVc35ZokBo/+RSAT1eh31el2em3a7LX7UPJfrgf3kZ9pCrNfrIRwOb5d9EQwGxWccmEvdz2azjvgcPcv5/cCaey0+xog7x7/RaCAWi6Fer6NcLssY7QmMSC8MByWRNhgMBsPiwyLSBxeer1PHjrBp0yZ4vV6Mjo7u8PcnnXQSAoEAHnjgAZx77rkAgCeeeAKbN2/G2rVrd9quFqTSYCqpTll99tln5SVbe8lGIhGMjY1JWuX8l1tgTjhLi09RNIvpnUzd1OJivJau5aWXLOs92Q+qaPOFnSJo/Jn1uPReBua8gXu9npAXCi1FIhGHJFANmsQzm82i3+/L+FGkTNdqcrx0bWwqlRLlZV1fzXsg8Wf7HMPx8XHxydZ1yFQB13NXLBYxMzMjhHtoaEjqqUm0mGKryUw4HEaj0ZBrxmIxuRY3aJLJJFatWoVYLCbkMRAIYPny5Ugmk/IZxd54vlb51nXN0WhUFKWBQY09+83xWbZsGdLpNLZs2SL9Zz02f+bGAf2MeT49wjn+lUoFfr8f3W5XNjzof0yVaY5Pr9dzxKzK5bKjGzAyMoJ0Og2PxyPp+STSHo/HEbWLxWIibMf+65RqpmFHo1Ehuqy7138rqDCv6+e58cX7Yv+bzab0lWUYWkGb899ut50NCtbic8wCgYCsIRJpqqRzLikO1uv1ZA3zGczn89Iv1v6zJhwYpKEz9ZznJhIJbN26VTZr+CxojQBuxGmNgz2FEemFwYi0wWAwGBYFRqQNO8LGjRvxyCOP4LTTTkMikcDGjRtxxRVX4E//9E+FdG7duhWnn3467rrrLrzyla9EKpXCO97xDqxfvx7ZbBbJZBKXXXYZ1q5du2ChMQAOweNLNy2JksmkEJ1qtSr1jTyO0ax2uy2iQ1RO9ng8jtCRFvQiSQ2FQkJe2u221LSSEASDQdTr9e0UoZvNpiN+FAgEHOEpkikSc7aVzWaRyWQcws0X9PmkRFsHkczzHtLpNKLRKEqlkvSf0ct0Ou0oCudyOccSi4JMVKZm+91uV8jSyMgIfD4fms2m1KtqwbX5IlCNRkPmhBFAzhUAUQ5nhLRer8smgLaiCoVCzuaGjnhq4a9arYbp6WmJEJPk83iSRP6bbXm9XrTbbanlZ426Vg8n+dQRb84PSVU8Hken08HMzIwj+sYItSaFrVYLs7OzMk+cc9bLc8yYrcB+MfLOqGk0GhUdAc5brVZDs9lEIBCQ8Wk2m0LaWZ88/3mIRqOIxWIiUAcMlOxZl68zBzhuuu6bbWgbK13PzQ0k3hcweL7YV13fz+dGZ274/X5EIhGnxpuK8Vx/4XBYnkN9r8Vi0XnuPR4PisWijD8j6VoJnvegle25XtmvdDot/95bJS9GpBcGI9IGg8FgWDQczH+ADTtGKBTCV7/6VVx//fVoNps4/PDDccUVVzgp2O12G0888YQT5fvkJz8Jr9eLc889F81mE+vWrcPnP//53epDMpnE8PAwwuGwRKDoGU1lZwAi3EXRImBOqGz+izhJB6OFJNc8LxaLiXiUVg0mAebLOVPLddotI9FU9gUGL/ra8ogku9lsyj0x+hyPx+VFv1qtiv0VSee2bdtEFEtHHrPZrJAYCo/RAgiYIwj0duZ9M5Wc48gUZa2IThKn06xHR0dRLpfl3hk51xsZjH7OV/RuNpsIhULSX52CCwxIS7fbdUS/SKQ1aWu1WpIKzbaazSYmJiYc1ejh4WFHZb7X68nca9V0v9+PdrstmwOzs7PIZDIYHh4WckThOy0Ux7VBIk3SVqlU5BgSaW6+6LHQitxsl30BBkRXp0aTYGk/d/oXa+EwRv3nR+O5KcTzmaXAsZiZmRFLOM4Liers7KyjBN9qtZBOp0XIjare7XZbNpwCgQAqlYqQVXqS64wJWohpMTY+S1r0j/elsyE4LtoOi8J6esy4YaM3QAKBgCNqx3KQTqcjn7FUQm/gUKFbZ49kMhnJotB2dbsLI9ILw0FJpIsPFg/qSTcsUUT7qL3cVaMv/ryOQs3WqmHpYW84J1hE2rAjvPzlL8ePf/zjXR6zI/eOcDiMz33uc/jc5z63L7tnMBgMBgOAg5RI76nSrMGwL9DtAr15m4ndch/d2o6PNxgOdBiRNixVZLNZLFu2zBGnYn0nRYyAuZRSpiEDc5Y99EoG5kSltOjQ/FTOaDSKYrGIUqmEoaEhAINoF+1tCEa2gsHgdlFkXgNwo3QAxBKq0+lg27ZtACBCaVrgidFvnSrKdNdYLCa1wkNDQ0in0xL1LZVKEkFjW7Tzqlar21kh6VptWlgxasjPms2mZB1Eo1EkEgkn0ky7oHa7LZFH1t4yFRkY1A8z5Zap6NFo1InKtlot1Go1Z4MmEAig3+873sL83tK12cViUaKYTEumdRl/1j7b2qaIqeg6EtlqtVAsFqWUQdfEat9rndqtxdYY6efxHHNgEPFm5JTRYa/Xi3g87liGxWIxEWAD5uzNYrGY1JDXajV4vV7JdOD9MOrKZ4K12jpDgqncHMPp6Wl4PB7UajWJnvv9fqxYsULqyNkPzifh9XolGsu+MXOAqfaNRkNSobku6Ind6/Vk/OPxOOr1ulMLznT4+XZUPp9PdBuq1SpyuRx8Pp+sMeoeaGFNrT2g69tZrqHFAnm/AOS7x+fzSb8Ylae1nhZF211YRHphOCiJtMFgMBgWH0akDUsVTMOtVqtC5Fin6fP5hGyQQFOJF4D4I+fzeUmxDYVC4h3LFFz+joSARIDpysDgpb7ZbGJqakperHXKOD+jWJdWvaZAGNOsgUGNrSbllUpF1M7ZLxLYUqkkL/FMGw8EAg4R9Xq9To0xVcN1jXkwGJQaYmBAHnY0jqFQyFHRZnowCT1T3/X3htfrdTyxgbm6Ul0PDUDGhhsZeuOA9+PxeLBlyxan7tjj8Tjq0ky1J/HhZyTdem50HbgWjNNp4kyF5n2STGmhLy1kpWvB9aZFPp9Hu93GqlWrZP1wrjudjrOBQzLHTRESbS2exc0NvUHB/0jkWG+v+0/lb006Oac6xZnjreeNa4+EMBAIIBaLIRKJyPqp1WoIBoNotVqiQcCa+FAoJP1nbTXvm/0KBAJOuUY2m5VnhWOmhfnYD/o06+c8m81KKUCv15MNDJJ5ah5ks1l5vhuNhmzY6GefmzVM8adYHcGNMF6L/Sd573a7Tir67sKI9MJgRNpgMBgMiwKzvzIsVQQCARQKBUeQiS+qOtLs9/slWssXbEZ3ScSBuXpoHZWj3RNfsHVEkucBc8StUCgAGJC7QCDg1A/zGlRy5j3onym0RDIHQCKrbA+YqwWn6BXgRr/5sl4qlRzlcJKYZrMp91QqlZBIJJBMJp1IG1+8+RnFnUiQgEGUNxQKSR/Yp0qlImMdiURQKBQwOTkptdsULNMCYZwzrc7cbDYdsSiSZmCOCCWTSamd1qR8fnSSJFCPNxXA2abP5xPVej0nJKwcM1qqkcADc0J0FH3jevH5fNL/crks52mBMNbKazG5eDzuCJDxfnSdbbVaxbJly2TeqQquo/ixWAy1Wg3FYtGJms/frNCib3od5PN5GWvaRLGumGtx/kYG2wkGgxJtZu2wHu9wOCyq8YTOMADmapi1MJom91zb3DBotVpyzVQqhV6vJ+uuWq1KuzqLhc+X/owbcFoUjnPH8WBmhc4MoYaAvmahUEA2m3Wi9nsCI9ILgxFpg8FgMCwKLCJtWKrweDxotVoS1SL4Eq5FxILBINLptOMdG41G5QUagNj89Pt9WbtUN9aRZq/Xi3K5LC/wVAhnGikAiT5pH+BwOCz+zDpalUgkJD14ZmYGpVLJ8QpmanA0GhWCQFKkvZqpgExbIABimaVf9Bl1JYHiS384HHZSkCuVCnw+n0Tc9L3pZ7vT6QgRajQamJycRK/Xk9R3psczss6x4HhpYsN0e84dNyJImsLhMEKhkOPTWywWZY54nk6V1jZiVNrWY8Z54FySSHOjJJ/Pi5q3FibjZgr7T7Ezlg1wbHw+n8xHJBIRgkyCxr7rdGItaKfT0Nkux7FSqSCbzcoxgUAAuVwO5XJZRL7o2c12OYe6XY41+6Gj8fr54maETt0PhUISTddzyc0kguPQbrclyh6NRmVTiGPI9H1ec3h4WJ45zhdt74rFoowtRcq4GQYMIte1Wk0iyKVSCdFo1PGH5uaPFjjjhg03nXhNn8+HWq3mbF7pZ1WvOW1Rxs2qPfl7qmFEemGwLX2DwWAwLAp0quDu/GcwGAwGg2HvYn/8LX7ooYdw9tlnY8WKFfB4PLjnnnt2efw3vvENvOY1r8HIyAiSySTWrl2L733ve7t17b0Ji0gbDAaDYVFgEWnDUkUul0MoFHJ8e1nPqv19O50OGo2GE6nV6dK61jkUCkmdJTCIRlIkDHCtorTPLTCIBrIWk1629XpdngOdOs1oJ9NKeUyhUECj0XAirrqWWAtI6VRoACJwpFNlKT7FNOJ2u41QKISRkRGn9jaXy2FmZkYia7VaDeFwGMuWLXOitrQW4nHNZtOJmgaDQSSTSSdFmhF4bcnE9GBG0AFIJHvVqlUSUWS0mePKSGEgEJD2+v0+8vk8/H6/jG+73UY8HnfqiXX9LaOMxWLRqan1er2O9RPbZ5SZ0WxGYRm15PzTIonRTtbyMuOAtfNakKxer6NSqaDb7Yr3c6fTQS6Xg9frxaGHHirn8nxmIiQSCWet6ygz543WThRaY/utVsvJOOh0OpicnMTU1JR8xnXO7IJly5YhEAigWCxKFJ/PUKvVcsZi/lqkZZX29uba1jX0TCXnZ6lUSuZA+zUzI4Mp1P1+X3y7OU8sYdAlDxxDnZJfr9fR6/Vknrg+m82mZIEws6LVajlid+12W/rFemudHcHng571e8P+an+hWq3i+OOPx4UXXog3vOENz3n8Qw89hNe85jX4yEc+gnQ6jTvvvBNnn302HnnkEZx44on7occ7hhFpg8FgMCwKjEgblirq9Tr8fj9SqZS8KBcKBXlJJ4LBIJrNpigOA3OkVtda5nI5ERHTStudTkdEvkhKotGovDwHg0FJ8SUJC4VC8Pv9olYNDF7O6dWrib9WjW6326jX607adiwWQyqVQrlcdggBfbHnaxEwxZvHVatVERsDBmRU95/psPl83tloiMViiEajTooqx12rL2tf7JGREQwPDzuiSiRYfr9fCFosFpMNDt4T02gnJiaE0LRaLTQaDSGYiURCCCBJIUkPACell6SGadD8LiuXy0K+KG6mU8lJnEikuTkz3w+aImskrFxnfr/fUYpnv4E5Ia9gMCjnMT3Z4/FIv/L5PGq1GkKhkHxGtXGmFfPeuV7Yh9WrVzvp90z11n7cnPter+cIi8ViMdETAOZ8wVeuXAlgoEDdaDQwMzMj48R2tGq6rjfWAnBcs5xzPjP6+SWx5Vw2Gg0RJONnpVJJyh24IaTFxjifbFenkvMZ5jGRSEQUwLk+6Y9eq9Xkmiz98Hq9MieVSgWjo6PbCYzp1HmmuHMOtJL57mJ/pXafeeaZOPPMM5/38bfddpvz80c+8hF861vfwj/90z8ZkTYYDAbDwQcj0oalCgo7aYLDl9R4PC4EmfXA8+1/dM0lMCDNjHppWxsqUQMQCydd5xkMBlEul7dTQCZpIIGhzQ83AIA5USNGXFmnHAgEJKrGCHAymZS619nZWSGw2h6JEdD5dk669pmK5bpePJFIOLZQFG3S6uM6wqmJA4nO/DnhmFFVPRgMYmxsTO6zWCwin88LURkdHcXExARyuZwTUdfKyTxeRxQZBefxHB9ubmgbpVarJSQVmLPX4lhEo1EhSbwm7aP8fr9kBFCMjdkJem14PB7pj1aU5nz4/X5H5I4ieJrEs5Z3dHTU2RzIZDJIpVIy54VCAYVCQQhgOp2WfmkC2+12EYvFnHHiuGgtgWQyKRshPFc/D6VSCe1221E+16J0WozP7/cjmUw69mbUL+DGCH+vySr7p1XEK5WKI7JGMTXWRfM+qDav1z+F4divQqGAdrstGxIejwepVAqNRkNqqVnPrTMpGo0GWq2WCAuyv9qOjGru3IDjuPCZ0Pe1JzhQaqT5Pcv5XiwYkTYYDAbDosCItGGpotVqIRQKIRKJyIssRZCCwaC8wBeLRUSjUaxatUrIUaFQQC6Xg9/vl7RVppA2m015CR4ZGRGhIwAiJFQul+WFOJvNSoq1JpsUadIv/ySwOhXa4/EIUaGvNKNmPK9SqSCVSgkR5f0lk0mHvAADskNiyHRzPouNRkOsurRVUa/XQzabdV54dZquPnd+FLBUKkn/aTGm0+ir1Sp6vR6WLVsmGwG0MqKgGQBRrtb+w4lEAr1eTyyUKIqmyQjJkyY4Xq8XMzMzTpSR2QckQ8CAPOr0eE1wtad2IBBwbKFarRaq1aojNFWr1dBqtTA8POwoYGv/ZBJlHf3netDq6kzN1htCjEYnk0kZf4/Hg82bNzviZlSW5pzTlzuZTAp5rFQqQkI5v9w40ZtLs7OzGB8fl7VF0TVtGRYOh0UZnP0PBoMYGhpCKBTaLkqriTTXgF7r2k+b/eeGjra5IzHV984It46M6+h4uVxGuVxGJpNxNgsSiQQikQimp6flunxudMZKp9NBKpXCqlWrAMxZ5jHjI5VKyXeE3oBitkGr1XIyA3YXe0KkuVFEzFcd35u49dZbUalU8Cd/8if7pP3nCxMbMxgMBsOiwMTGDAaDwWBYOtiTv8erV69GKpWS/2666aZ90se7774bN9xwA772ta9hdHR0n1zj+cIi0gaDwWBYFJiPtGGpolqtIplMotPpSNSIvrG1Wk1qXFmDeswxx0hEbsuWLRKV43HZbFYiUNpDWKdjMg2aKdTAINKWyWSc8xhF09GwVquFRCIhokjAnCgZI960zWGklJ91Oh2xyuJ5lUoFhUJBIn7xeFxSnulnnclkEIvFJPpZKBQQDAadKL5OKda1thTSYj+8Xi/q9bqkIfOegsGg/Mwa4VqtJvdEsTCmGHPMmBar058TiQRKpZITpWYGADCIphWLRfj9fonKsk/9ft8ZH6bn6ugk63S19ZGuOZ6YmJDx5PfXyMiIlAVomyYex3VA4TjOLTCI/GoS02q1UCgURKgMGEQEo9EoPB6PI+DFdHu2z/IE3X96VuuU8EQiISnrPMbv96NQKDgR3Uaj4VhdtdttEdhi1HR2dha5XE5KD5iBUK/X5ZiVK1ciFouhVCrJuhseHhYLNS3W5fF4nLVXq9UcsTcKwc2v/R8ZGUG325V1RhG/Xq8n65PzS0E8YPBM66wB+nOz1p5rkXZ1OjJL4UG9joPBIJYtWyYR6Wq1iscff1z6RQ/7TCYj/ecz4fF4HAu4PcGeRKSfffZZSfHnfe5tfPWrX8U73/lOfP3rX8cZZ5yx19tfKIxIGwwGg2FRYKndhqWKZrMp6bt8cc5kMohEIpiampKXW5KOZrMpJIcpoKxpBObSjTUxZ/o0yRbTfrWqNhWEy+WykCuq9mrhL56byWTk5ZU1uUxPZX0tU5oBCGnRhLVarWJ6etpRGgYGZK5YLMq1RkZGHLJKobNgMOikPANzKcwARCU8GAwilUpJX1kHzPZYh8zxIXnV6dNerxeVSgVbt26VdFd6YusUWHoD6/62Wi2MjIzIPeZyOZRKJUc1mYJkqVRKSDMJtK4LJnkl0QEg1+d40XOcY8XzEokE0um0o77NceS9s0Zdp0f7fD4Ui0XH7xsYkD72a2hoCP1+H9PT03Lf3Oyg3zYwWIuVSsVJAd+6dat8xmNY28tx7Xa7suGi5ymRSGB0dFTmvNvtotVqbeeLHI/HZQ2EQiHUajWp7ed59FPmOLKkwO/3y3EzMzOOz7S+Jj9Pp9NSSsG2eP/6uYlEIigUCvB4PI6SeqvVkutxHLWYWTKZdMpAAIjAmi6J8Pv9QoA5PqlUStLOubZZgqC9uJnWzmd1ampKNgGWQmp3Mpl0iPText/93d/hwgsvxFe/+lWcddZZ++w6C4ERaYPBYDAsCoxIG5YqqPrc7XaFqORyOSFifMFOp9PyAr1lyxYAc6q/mUxGXqgpDEaSAgxekCORiJBOvqx3Oh0hJYxckRQAA1JFhV5NKLWFEQCJnGuyFIlEEI/Hpa1GoyFRSL6c084rHo/LS3EwGEQ+n0c+n3fskCjqxZ+TyaRTGwvMCTWxHxROC4fDMra0/tLqyeyz/jeFoNg+Ccrs7KwTiWekUo8/o/XsB6OwWmWaEUe25fF4EI/HJUINDLIQQqEQ4vG4kFqqrWvFb2AQdeVaGRsbE3LEz5ilML9+vNvtymYDMCAozHLQKubabosktdvtCilPJpNC/NkvZjIUi0VnTqhozXUwOzuLVqvlWLiRBOqIrp4jYC47QluSaZssvWb9fr8T9Q2FQkgmk1IXnM/nkUgkHOG1fD4v4nhcn9yE4XoG5mrId2T1pm2tKNDH54ZrUNfi83kJBoOOWJ0m21TYZ0YE74nPod7A4eYA56nb7aJSqWB2dlY22mKxGFauXCl9ZRtTU1PSn3a7Ler0fD4OFFQqFTz55JPy89NPP41NmzYhm83ikEMOwdVXX42tW7firrvuAjBI5z7//PPxqU99CmvWrMHExASAwfPOzZjFgOXGGQwGg2FRYDXSBoPBYDAsHeyvv8c/+9nPcOKJJ4p11fr163HiiSfi2muvBQCMj49j8+bNcvyXvvQldDodXHLJJVi+fLn892d/9md758Z3ExaRNhgMBsOiwQixYSmCit2sGwUGUSNGyhgxjsViTpQTmFMQDofDTr0m01QZTarX65idnZVzeS1dm8lIHq1egLkUYdpisR9DQ0OIRCKOdROjecBclJ1pq2yfbc+vJ9Y12Kw3HRoakprWUCgkqtnAIGJJlXJGxljvXSqVpP9MT6bfMzBIx2ZaLyODsVgMsVhMfi4Wi+h2u2g2m9J/bQXG6CHV0VkTDgyi7PMjy4yC877L5TLq9bp4YfOYaDSKeDwu7VMtmunQHOtwOIxqtSpjFolEkEwmndRcWiCxH6y39vl8EgmmfVKj0ZD2mQasI+jhcNhRm240GhL95Fhz/hlNJprNJnK5nGPdRGsrbeNG1XL+zFpu3lMqlUIkEkG1WpXzWHM/OzsrfZuYmBBVeY5Pv9+XWnmuu3g8jng8Lu1TjT4cDmP16tUAIErctA7jnHAdMEOCkWEeQ+/mRqMhkWy/349YLOaoidMHPpfLOZZViUQCtVrNSWOmzgH7UC6XHVX8mZkZWVfaE5wZB1xngUDAidZzvCORiFM6odcbz/P5fOIGsDf+nu4v+6tTTz3VKaOYjw0bNjg/P/jggwu+xv6AEWmDwWAwLAostduwVBGPx5HJZBxP4Ugksp3YVS6XQ7lcFo9mYPCC7fV6EYlEhEiwvjWZTEqqJlN1eR6JZTAYlBdlpuqSrABz/tD0ptZ9A+CQ62QyiZGREQADMtxsNp0aVApp6VpSXkOLadFPefXq1ULy6d1L8qzrd9nXbreLaDSKVqvlEKZAIIButyvWU9FoVMgciQktjtjXRqMhBIpkfnR0VMgRr+3xeITg80WdBHB4eNhJcdbp9pVKRVLFmXIbCATQbrdRq9UcwsQ0ZY4Z63UpRgcMiFA0GnU2RdgfbsQkk0k0m01JSwYg4lR+v182XZgOHAqF5LuvVquh3W7L/VSrVczOzjqbEblcDtFo1NkUabfbmJ2ddQTOSKKj0aiMYzQalY0Ejk+1WpUNJgBiFxYOhzE5OSlzToswbblFwS5tn6Zr1ml/BsxtNFAYLxwOyzpuNpsoFoviR861yLnRAnzah5mWVrSU4z1y/XPNMuVck3I+X/1+XzYfSHK5LvRzqzd/uHHFNZtMJp3ngW2xj+xvpVIRTQCui7GxMUfsrV6vC3Hn872n2F9E+oUCI9IGg8FgWBQYkTYsVfj9fvh8PiSTSSHSsVgM2WzWqe1rNBoiDkVSxUgqiRUwRyRIKoG5iCKJEF/eGTlm+6wLJrnrdDpCvkjIwuGw1LlqH2MdaaOCc7vdRi6XAzBXd6nVw6PRqEQdSWwikYj4CPM43U89FlpwqFqtCnHWPtiMRs73NtZELhaLOZFablAAEFIyNDSEcDiM2dlZ+U7odrsi9KXrXhuNBmZmZoT4R6NRIZ88j+rhJPgej0fGh9fkHHY6HSF8Xq9XalfZD5IajsXo6KgQIFr29Pt95PN5yXTgZ+12G6OjozIWhUJB1g9JY6VScYjczMwM6vW6EHFgUOdMwTZGM3O5nNQSk8B1Oh2Zc45xOBxGNpt1lMepLM+xYI2w3++XtUIhPG4AcZ4459ovu91uC6HkfJdKJSf6T8VyzqUWsNP13D6fD9Fo1HnmdM19OBzG0NCQbKAAcwS81WpJW16vF4FAQDys+VkikZB7BSBEW0f6O52OcwyzO/jscP1wjnVfI5EISqWSEHrW7HN8wuEwVq1ahXg8Lms2EAiIVoEWX9sTGJFeGA4oIn3vvffixhtvxC9/+UuEw2G8+tWvxj333LPY3TIYDAbDbsCItGGpgorRgUDASQdmpJbrj8JXmsDqqDWJYigUEmXe+USRL8r5fF6INIlVu91Gs9lENpvF8PAwgEE6LSORfHGORCKS3sl+VCoVEa4CBrZBK1eudITLKJzW6XQcde9EIuEIJLXbbVE7JjHJ5/NitwQMSEkgEMCRRx7pCE0FAgEMDw9LW4VCAaFQCJlMRkhDPp9Ht9tFKpVyxMa4kQAMCCYVqUnQSDR05I4p3MFg0EntzuVykgrLftEmi/e9fPlylEolIZ3cfKjX647quN5MAAbRwlqthkqlIuuFhJb9ikajOPLIIwHMiVcx5V+nmBcKBUlv1sTI5/NJejUA6ZPOcOAmBo/hPer77na7qNVqGBoaknmi0Fuv15P2er2es35oLaWzNCjIxbRsts+UcG7+xGIx5PN5NJtN+czn82FmZsYpbSiXy3j66afls0QiAb/fj06n45RYkNCzbyzF0ESdgl4cC6pu+/1+TE1NARhsPlAgTm/0UFGfc+7z+ZBOp+VZ57laTE6nZ7Ov7XZbNqbYPjfQZmdnMT09LWtD3yvHVm9ceTweJ9uAbRUKBZRKJcdabE9gRHphOGCI9D/8wz/goosuwkc+8hH8/u//PjqdDn71q18tdrcMBoPBsJswH2mDwWAwGAwHKg4IIt3pdPBnf/ZnuOWWW/COd7xDPj/22GMXsVcGg8Fg2BNYRNqwVMG0bIoCAYNI83xLLEYdda0q7Wh02mcikUAsFkOr1ZIU2Hg8jnK5LOmljLrRmxdw644ZpWNNJCOlACSyxegYj2eqL8+r1+uYnJyUazIFnVFoff9aXC0cDos10vyaZS1cViwW8dvf/hbLly8HMIi0saaT6ailUgnJZNKJbDabTbTbbcdXu1AoYHp6WsY/nU5LNE/7MDOdmpFCpsHr7whG3HXNMu9fIxKJIJfLSUSaomu8X2BOJCsWi0k/yuWy1Odyk49Rc86RjnryPF3jzuyFWCyGUCiEQqEg6ywWi0m0WEebdcSy1WqJ4Bvb1efOrwvOZDISRWYf8vm8k1ata4cZjfZ6vfJZoVCA3+/HyMiIROKr1apYwOlshVqthlKpJOsnm82KRzowSM8eHx9HuVx2+sVnifdEezJtT8X16vf7ZR2zBpzzVygU0O12sWzZMie6rWu2+RnnQovCtdttSckmmInA8WEUXHvFx2Ixx7tdrx2ey1rs+bXs2iKOGSg6I4Yp+fyustTu/Y8Dgkj//Oc/x9atW+H1enHiiSdiYmICJ5xwAm655Ra89KUv3el5zWbTKbxnuorBYDAYFh9GpA1LFcPDw1J/yxdnphbrF2n6Oet0S9YfU5kamEv71EJfJAR8kSdRnJ6edlKLh4eHEQ6H5UWc/r/hcFhIyMTEBMLhMJYtWybEKhaLOT7DwIBwT09PO/7HJOXsK9WoW62WkC+qPlcqFXn59/v9jtc0MCBD1WpVaozHxsYADIgCz/N4PCiXyyKMBQxIA9Oo+RmFmng/kUgEkUhEUm85riSGWliM/sQkNNlsVtrRSsG6Rp114rFYTNLoQ6EQZmZmJN2a5zAdWBMminDpWt56vS73Q0EoYI6UU4iK/t7AnJq4XmcUHtM1s5VKBUNDQ9JXpuNT/AuAI1rG+2b6N0sN2D7fmbk2EomEiMABEHE1j8fjpJczbZnXpNJ2s9mUzROK8em6eG4Scf3PzMwgGAzikEMOcXy1KQDH85gGHQ6HnbRtj8eDoaEhR6W+Vqs5Nc3T09NSssH7pmq3JteFQgGNRsPZhGIKux6fQCCAbdu2SR8ymQxGRkac+WXKud6AAubqp4G5zQeuOfatVqs5qePtdttZG51OB6lUCqFQSMQL9xRGpBeGA4JI/+d//icA4Prrr8cnPvEJHHbYYfj4xz+OU089Ff/xH//hPHAaN910E2644Qbns/m7kQbDUkEyAvjC7mepNOCx5WpYouAu+u7CiLRhqUILhmkF6mAwKArHAESwKZvNyovyxMQE6vU6otGoo7AcCARQr9elPZJhEliKd1HkCYAoHQNwouD8mdeMRqPw+XxOLSkVi/lyTaVm3ZaOkulIJGt0Se5Yj6ojm6lUylEqTiQSGB4elppN9ktH+Ng+VarZH34X1Ot1p9acdeTAgABTdIvkiBG7Xq/nRGF7vR48Ho9E1BuNhmMdBQyIKBWbOa4+n88hPcViEZs3b0atVnNsp0KhkETROU8kihyPZrOJer3u1KNzPLlRUq1WZcOF46pJr64fjsfjaDabsnkyPT0tYm7sQ6fTkcgrr1mv1+HxeBzSzJpx3tPw8LCMGduLRCKOOB43XRiZBeYyGrTwF+3J9NrjHGvLMyqhM0NjeHgYyWQS6XRa5rxcLstGAOey1+sJCdWEPh6PO+uYz55+flqtFmZmZhweQHKsySlFvPTmBgXBdIZCr9eTqDWfZ13XH41GhZRrdX7Wn7OPoVAIpVIJkUhEhOh8Pp9sRABzGSW1Ws1Zs+l0Wr5D9Ibe7sKI9MKwqET6qquuwkc/+tFdHvP444/LQ/D//X//H84991wAwJ133olVq1bh61//Ot797nfv8Nyrr74a69evl59LpRL+4i/+Qh7a3QF3rehR+FygaiS/gPYGFtomVRW17cCeYnfaXOjY7Ys2l/J8+DxA2s0yw1mHAt3nMVQ2H7vfpj0fc1jo2H3xi1/co/4ZkTYsVXg8HpRKJUdZt9froVqtOmJXVJrWljTdbheVSkU8joE5Gyufz+dELIPBoJzHiGMwGBRlaa/Xi3K5jE6nI+exjXA4LC/xjJxq9WGSTPa11WqJt7G2qaJIGb+rGHXUJH52dlYUoEm+UqmU49XM68bjcYmSBgIBFItFNBoNh3xlMhnHI5djRJIEDCKn2qfa4/Gg1WqhXC47AmEkNCQT9XpdPHcZGa/VahgbG0Mmk3FEvfR3I8kwr8FxJQFPpVJyf5w3LQzl9XqdCCut0Tj+OoLNe+T8ejweGTMKf2mixUi5VpymZzU3ZpjKrEkh2wiFQg5BK5fLmJqacvyyKRKmlbx1KnahUJAsBW3P5vf7kUwmpa/1eh2xWMxJz2aUnPZrnLt4PC5lAMPDw45oHME1zLGj+jq9vDme9KTmJgUFz0ia2adSqeRY2vl8PrGaYl+5KcQ5YTTe7/c7adXtdlueQabel0ql7SyxaL3F9rWyPPuazWYRj8dlE83v9zsp/9yI0+17PB5nc+lA8pF+oWBRifSVV16JCy64YJfHHHHEERgfHwfg1kSHQiEcccQR2Lx5807P5RegBq0fFvrS7vF4kMlk4PP5kM/nF3Q+H1568O0JYrEYotHogttiygn7vycv6n6/X/4YlUql591Wo9FAJpMR9cY9IU4vxPnwe4HevOhzowF0ertuy+bDbcuej/0zH4u1863PNRgMBoPBsPdgRHphWFQiPTIyIgbru8JJJ52EUCiEJ554Aq961asADHbZnnnmGRx66KELvq62eXg+4E6h1+vF9PT0gs4F5gQOkskkut2u7HQuFBQrKRaLC26DmwfDw8NIpVKYmZnZLbJAK5B2u71bbUxNTWF4eBjpdNqxPVgIXqjzAW8f/XmkudMB2rvQjrD5mIM9HwMslfl4PjAibViqKJVKksbNSCQjprrOk1FbHXFNJpPIZrNiywTMecIGg0GJRrKul2nQjFDpushUKiWpoIyu9vt9sfvRPretVgvPPvusXJNRa52GHo/Hxa8WGHz3xWIxOR+Yqy1tNpsSsazVamg0GggGg9K3ZrPpBCxarRZKpZKTagpALInYfiwWQzgcRjAYlM06RsF7vZ5EO2klxE27dDrt+F5zLOjdy/OYSs4IMceC86YtqyioxfsGBhFVXnNoaEii56wF51wzgs1rcp45thQg4/rRWQuM6vOedZSd19He3hSh8vv9OOSQQ5zx4Hd8sVgU8Tdeh2Pg8/lkrDkW2pebkf9gMCgR6fmp2cyESCQScszMzIxck/1nXbXOVuAa09pFfL54HtdDoVCQMWCdO9cax7jRaMgzquckGo06NmU65b/ZbIrIHaPWjBhXKhVHWG/16tUYHR2V+6RwH9PWObasr+b4swSA64HXZJ00x5/WVtqzmxkLOsWc4wdA1rSeN5YvsF97o0basDAcEDXSyWQS73nPe3Dddddh9erVOPTQQ3HLLbcAAM4777wFtbU7uyzDw8Pw+/27/WILzJm+88thoS+niUQCyWQSpVJpt19s+XLPdJWFvujTC7LT6ew20ej3+9v1YXc2NV6I81HMzwCw+dA/P1/Y8zHAUpmP5wsj0oalikKhgFQqheHhYSezjcSXz4Tf70c0GnUIcigUcupsgTkVcK1Uzd9rK7f5IlnAgJhMTU2JiFkgEEA2m3UUfbXAEskXlYK1SFMwGMTMzIyQ8lAoJErZ7JeuZ2YfWUeqCTeVn/ldRzIzOTnpKG0zHZz90t6+mhyRVOnUWQp4sf+sxeUxJPZMlwUGqdftdhvtdttJLy6Xy9iyZYuM2ejoKNLptGw8kORRRI1gqi8JU71eF8LDa7JuXmfy+P1+DA8Pi28yfaY5LsAgndnn82FqasrZVGB9vfbxpsiZrklnCjjvm+1zI4Np6eVyGVu3bpV5ymazjkgc1a315gCvxbliqYIeC9ZtczMDmEsT52YD+88x0anuXq9X1luhUEAul3P8v7nuyuWy0wYFw+arVHOuOI7ValWem3w+L9ln7D/FyBKJhCjl600Iro1arYZarYbZ2Vnpx9DQEEZHR6WvHIP52VpaXJBrZf6zr/UXtACfFlnrdDryrqP//sXjcczMzDj1+HsCi0gvDAcEkQaAW265BX6/H29961tRr9exZs0a/OAHP5AvqOcL7j4+nxfMvfVSSuzuy+neIAnE7pKFvUESiN0lCy/0+chkh4He8yPTNh9zsOdjgKUyHwvB/vSRfuihh3DLLbfg0Ucfxfj4OL75zW/inHPO2eU5Dz74INavX4/HHnsMq1evxjXXXPOc5UiGFwZ8Pp9Y3fAlsd1uSx2qjvLSjklHNEm2NCmk0rMWCtIRtHq9LpEqXXccjUZFsRqACBVRrIn9qFarUncKzIlz8cWc9lu1Wk2IhNfrlRpLEkwKaTHyx/6zxppEgUreJCpU+q5Wq9KvZDIpNcX8TiqVSiKyxXvy+Xyiaq5fyoPBoEQU8/m8RHCJVqvlKDADcASf2BZtjniv7D8tmIA5AsH+sn1tN8Wx6Pf7DmnWauI6m4CCbMCAKHIDg5H+dDqNVColtkacX1oZaess9o/rp91uo1gsyj3q2m2ttg4MiCDniRsMjFRzrTCDgeuYEVEtlsa2eN/1el3WK/9GsC1NiEnyQqEQli1bJveka8oLhYK0q9saGhqS2mb2i5kJnHO2pYlso9FAoVBwxPdqtRpisZgjAtjr9UQJHBhE56mirTeXqJjNNUsLLi1yx00ELdjGTAiuI70Jw75yzNi2/kx/1zDbYr6SN4/dG/ZXwMFNjBeKA4ZIBwIB3Hrrrbj11lv3qJ1er4dMJvOc6d17+6WUWOjL6d4kCcRCycLeJAnEQsnCwTAfoyPD8PuH0antmkzbfMzBno8Blsp8LBT7MyJdrVZx/PHH48ILL8Qb3vCG5zz+6aefxllnnYX3vOc9+MpXvoIHHngA73znO7F8+XKsW7dut/pseH548MEHcdppp+3wdz/5yU/wile8Yoe/O/XUU/Ev//Ivzmfvfve7cfvtty+4D6lUSogk1325XBayo9NYGUXiSyyVoDUhrtVqEr1j1DAajSIejzv2TjyXx5D0plIpWfPlcllskLS3LskLv4PK5TIqlYpjheTxeLBs2TJ50df+vnpzav4mAiPqnU7HScVtt9tCfqrVqqQyM8jB1Fqd7qrTkTV5r9frqFQq0g9Gmnk/lUpFouocV4q49Xo9OY6RbR4LDMh7IpHAIYccIoSmVqthenpayAw3LbTnL72Kc7mcY2XWaDRQrVYdayiOL1O5h4aGhLxxLOarpXc6HSSTSYTDYSG6TKPXGza8Fv28+Rk3Xtgvqj/r1H2m6lPEKhQKSXo9v9tbrZZ4EnNuGFXmuJLUaRV2j8cjavEc116vh0QigVAoJMdxQ0KrXFM8i2ssm83KRgnXFFPQtT0VCb4WAmS5A6/FOQfmov+JREKivDqToFgsyr1yXWvPeI4jVbtJuJPJJJrNpuMjHYlEnKwKbojoyDLVxWu1mhM9Z9mBFpiLx+Oy6VIsFp30c95To9GA1+tFKpUy+6tFwAFDpPcWWq0WfD7fLl9O99VLKfF8X073BUkgni9Z2BckgXi+ZOFgmY/87AyWjw3DH905mbb5mIM9HwMslfnYHexPIn3mmWfizDPPfN7H33777Tj88MPx8Y9/HABwzDHH4Ec/+hE++clPGpHexzjllFNEZJT44Ac/iAceeAAnn3zyLs+96KKLcOONN8rPe+LSYTAYDAcbjEgvDAcdkQYG6UGsfZr/4rmvX0qJ53o53ZckgXgusrAvSQLxXGThYJuPTm0G/uiOybTNxxzs+RhgqczH7mIp10hv3LgRZ5xxhvPZunXrcPnll+/T6xoGEaGxsTH5ud1u41vf+hYuu+yy55z3aDTqnLu78Hg8IsrECBdTRRlx4/XmCysxGurxeBxrJaaA63TaZrPppIEmk0mJegNzKcmsoQQGkbtGo+G0lUqlJK1VWzIFAgEnfToSiTheyro2mt8f9Edm1JLnVqtVEQDjeOha29nZWfT7fUdEdnJyErVazbkuU4qZjkuwBlenyqbTaZlzpnHrdFoKj+mIKH2Addv0cE4kEhIZpKUX54hCToxCc0zq9bpExzk+vD7XAf3G4/G4REYZ6WcUsdvtigUV+zA+Po5UKiW18TyuUqk4Htq1Wk2it1rgTItf9Xo9Sb1nRkOpVJLvWc5LNBqVSD6hLbe43rlmtSc1tQD0XIbDYRQKBRkTWkrx9/w/o9ccj8nJSckC4FjGYjFHJJPR6EqlIutOeyezb3wemBUBDJ5h7dNOYTGfzyfjTws2RuOBOZ2CYDAofdXPg04L73a7zjMyNDSEcDgs0XBa6PX7fWdMuD51WUE4HHbGn98rHItqtYpyuQyv1+vY3unvqr3xd9GI9MJwUBFpflnyQc5kMhgZGRF7mPmWMcBcTcy+QKPRgM/nQyqVchQVtf2MNnHfVygUCshkMhgdHZ2zYlIWPsVica9Y3ewKNh+A3wv0e0CnXoA/koE/OopAJQ94bT7s+Vh686EFh3YX9E/d3XOBufQ9Yke2h7uDiYkJqeUjli1bhlKphHq97tSyGfYt/vEf/xG5XA5vf/vbn/PYr3zlK/jbv/1bjI2N4eyzz8YHP/jB3YpKk6D0ej0h5nzRrdfrjghRMplEIBBw6jq1YBEw+N7is0LfXKaNa09qrVINDEghySVfnrPZLMrlMqrVqpAEn88nL+PsWyAQEO9hYPBsMJ2WL+c6JZzHkdxrUalWqyVqw7pGORgMSh2ux+NBuVxGKpWSPmglaZJOCnzpTYpyuSz3QEKj+6T7qr2ItdCVVu2meJn2XCYx1crZJFHAIOU2Ho9Lej3nkqJWJF96npjG3Wq1JK2b87dt2zZMT0+LiFUymcTRRx8NAGLdSpXzkZERrFixQuaNa2a+grlWjGctLuej1WrJ30eOdaVSEZEyrVbOv138jKJimvA1Gg00m03528s0cp1mTXEykl2ug36/j3w+73g483y2VygUUKvVnLGmrzafCV2vrgXIeE/62eHzx/GLRqNOGnepVJK+8jymy1erVafcgKRfp17HYjEkEgnMzs467WmC3+l0xG8bmNNLiMfj4g3fbrelnlvXxXPsdNq21+uVv++NRkPqqPXfpomJCSHdltq9/3FQEelcLocvfvGLi90Ng8FgeMEgl8vJy+TzBSOOq1ev3qNrx+Px7dq47rrrcP311+9Ru4alhTvuuAPr1q3DqlWrdnncm9/8Zhx66KFYsWIFfvnLX+IDH/gAnnjiCXzjG9/Y6TnajgeY25jRNaokG9FoVCyMdBSX0SoeNzs7K3W6JByMNGkxMIomkbiwNrpWqwkpIFnQ6skUbeLnvA/WcZJcJ5NJpNNpITMUM2s2m9I++6Mjooxq68gvFbk10e92u3j22WeFGIXDYYlQa8Eq1q9yI4HEWtfjkjTrsfV4PKhUKvKSzvvV/apUKlK3qm25SJBJYnVdurZ30uJgmUwGgUDAsf1ilLZcLgs5omJ6s9mUz1g/DswpkScSCSFb7D/bZR8Y7db973Q62wl9kYTqe2o0GkgkEk50uN/vIxgMSo0664h1ZgJrpvP5vIx/NpsVuy09jloJmhsNJKjsPzeduO6oJs9ngyDR5DqIRqOyecL2/X4/Vq5cKZsFzz77rFhncd1xbKrVqqMqn06nhWBzzLhhwJ8zmQz8fr/MyfDwMJrNJgqFgjz7nHOKAXK+OB+6FrxcLjvZEYlEwqnNb7VasgnGcW00GqJ9oFW7W60W+v2+1LLPJ/gU59Tts56egmlmf7X/cVARaS7OzZs3L/jFb3+iVCph9erVePbZZx17gqWIA6WvB0o/gQOnrwdKP4EDp68HSj+BQaTpkEMOke/VhSAcDuPpp5/eY6uOfr+/3U743ohGA8DY2BgmJyedzyYnJ8UT1LBwXHXVVfjoRz+6y2Mef/xxidoBwJYtW/C9730PX/va156z/Xe9613y75e97GVYvnw5Tj/9dDz11FM48sgjd3jOTTfdhBtuuGG7z/1+P2KxGPx+v0OWGNXVRLFQKDgv59ls1iGGwFx0r9fryYt9p9NBOBzermRE/0yypyOi9KZNJpNCYrWSM/vh9Xrh8/mEaPX7ffG55RpmFLLT6Ujki1ZV832Mm82mpGRzPCYmJuS8oaEhLFu2DKlUSsieFrHiZ0yTrtfr0hb9srX9UqVSQbPZdMS0AoEAotGoQ/ZSqRTS6bQjZsb/89+0p6KXMfvWaDSELGlrL943N0D0M0/yS8Vkzq9OAwcGxDCTyUhUc/ny5fJ9+ZKXvATAILWbdlta9Z1pyvremXKuNxa0ajn72Gq1HOE4nqNTi+nbrceK4ngkaZFIxBHTCoVCkhbOvjI7QPtlk3BXq1VMTU3JXDKNWttpaZXtWCyGdDqNcDiM//iP/wAwiLZmMhmsWLHC8XTmxo+2lCLR1RkZ+m8ECT5FwfgZsz743HFOted4IBBApVJBqVRyUt21yB0wl8Ggf9Yq8jyP6dicX5Z96PkkwdeZBLxvbgx1Oh2k02mn7T2FRaQXhoOKSPOLJZVKLfmXVADyh/JAwIHS1wOln8CB09cDpZ/AgdPXA6WfwMJtqAjtgbsUsXbtWnznO99xPrvvvvuwdu3aRerRgY8rr7zyOe3DjjjiCOfnO++8E0NDQ/jDP/zDBV9vzZo1AIAnn3xyp0T66quvxvr16+VnbmYZDAbDwQgj0gvDQUWkDQaDwXBwolKp4Mknn5Sfn376aWzatAnZbBaHHHIIrr76amzduhV33XUXAOA973kPPvvZz+LP//zPceGFF+IHP/gBvva1r+Hee+9drFs44DEyMuIIUT0X+v0+7rzzTrztbW/brfr/TZs2AZirSd4RdlZTn06npV6WKZ+MLmUyGYkEz68XBiC1lZFIRM6t1+vwer3iRQ0MNqFYfwtArKWGhoZknJgeq22zGMmq1+sS3WYtLKOUwCD7btu2bZISn0gkpP5X95X+x7wXHZVktNDv94t3LzfAOBZamKtUKjkbZExf73Q6jqhUtVpFPp936rcpHMbjWBOs0+MZSWVf6QUdiUScSD+tynQ9tI6kAxDboPmf64gr61Lj8biTZt3v9536c45jo9GQKKzX6xWBKwBO/TeJB++VInbsB33DtV/wfLsr1iVz/Cl8p8cnEAhIv/Q6Z60wzy0UCgiHw0gkEtJHji3LABjx1QJYrDHWaesejwelUgmVSkWyFdrttrTPsa3Vatt5JzebTRHoAgZR8Uwmg2g06kT/vV6vCG0Bc6Jz2hKr1+tJKjTniGURbGt2dhZbtmxxvLcZiR8fH5cyjFQqJR7p7D9LCphlML9enWuFmSlaxI3Rct57MplEvV4XrQDdhs4KicViTm11t9sVQb5Op+MIFe4ujEgvDEakDQaDwfCCx89+9jPHm5hRyPPPPx8bNmzA+Pi4CAABwOGHH457770XV1xxBT71qU9h1apV+Ku/+iuzvtqP+MEPfoCnn34a73znO7f73datW3H66afjrrvuwitf+Uo89dRTuPvuu/H6178eQ0ND+OUvf4krrrgCv/d7v4fjjjtuwddOJBJIpVLodrtChiuVCjqdDmKxmNSD8sWdZBQYCFs1m01kMhlHTEt73QJzNaj8zOfzodlswuv1irpzq9XCzMyMvLwDc37B85WXmV5O4jk5OYlcLid9OOSQQ+Dz+VCpVCSll/Wz6XRarhmLxdBsNjEzM+PUg5JUauFWpi0DkLpbnUZML2qOJ8cWGJAT3lelUpFUZo5tIpGQFHBejxsbHOt0Oi11wppcABAROI4j67tZP0yCSbAuXgtzUUCtUCgI6YnFYvB4PJJiDAxIVaPRwOzsrIw3SSB/3rJli/ybZJtCb/l8Xu4zGAxiZGREUqH1OPb7fdlQYX09x3NHtefAXPYPx5/p7bFYzKmlpjAX1yPToJkdVSgUUCwWZV54TdZN85rFYlFq6rXfeiwWQzKZdHySmRLP+yH5Jvmlr7cWuCRh5MYKMNiIGR4edja5isUiCoWCQ/rp1c5jNm/ejHK5jEwmI4SYHtVayZvP7vyyjmAwKOMTDAYxNTW1nY4AVes5rtQ7aLfbQtR53xxP9jccDstYsJRAb55Q6Z/jszcER41ILwwHFZEOhUK47rrr9loN3b7CgdJP4MDp64HST+DA6euB0k/gwOnrgdJP4MDqKwCceuqpu1QY37Bhww7P+bd/+7d92CvDrnDHHXfglFNOcWqmiXa7jSeeeMJRUr7//vtx2223oVqtYvXq1Tj33HNxzTXX7Na1WWeqLaz48qrJXjgcRq1WQ7PZlJdnXXvK6CFJeSwWk3a2bdvmkDGqTJdKJSENFLUikQIgZFK/vCYSCREJY3vLli2Dz+cT0jk7OyuRPK3M7Pf7nRpRCihRZAuAiBkx6slzSfzZV5IQbQUWj8eRTqflngqFAjweD1asWCGfsR66XC47KtqaaGnVcE1KuHHBcSVhisfj0jeST0aOAWB0dBS9Xk/WEImkJqGM2up6WQpmceNDzzPtoNhfLU7V6XRkU4afkahFIpHtIuPVatWJ9nNTh2urWq0iGo2KGnS32xWBPLZB8Suv1+tEwxktJ2nr9XqoVCrO5gLrc0mGQ6GQiKppsT22N5/gh0IhIafBYBCtVguFQsERwysUCnIe75HrkXNJMTOtgE9BPn1csVhEJBKR54JrmmuM183lck7EnvaSfG5YE69Jc6FQkEwEts9MES1QWCgUnA0crqdAICAbVRQR48YQANlICQaDsv5o46YzNBqNhvzHtmh9pcXv9gRGpBeGg45IHwhqrgdKP4EDp68HSj+BA6evB0o/gQOnrwdKP4EDq6+GAxN33333Tn932GGHORsjq1evxr/8y7/sj24ZDAaDwQDgICPSBoPBYDAYDM+Fbdu2oVqtIhQKSRoo0zh1ujEw5yHMSCTVdcPhsET86FlcrVYlAqqjhQDExqbRaODZZ58FMIjKhkIhJ6JYKBSkXpLRKqqEa5Vl1vbqiChrXBmdy2QyCAaD8Pv9UmOcz+fh9XpFDZvnsq+M5lEFm9FtpoSzlhcYROJTqZREjtl/YJCWzb4y+kbLKwDi187oc6VSkSgqI2CMRNPnGoB4SmvrIH5erVZlvJPJJLLZrET36Outswt8Ph9WrlwpkV7ON+t0deq7z+dDKpVyvIdpfwTA2fhhSrXH4xFbL44rywBqtZqMD2vYo9GozAmvqe2eOE88hmndxWLRqetnBJOeyLTH6vV6yOVyMifBYFDGml7Q2hIrGo2i2+1ienpa1ieVuDudjuNBzXlnhDsejyOVSjkp4bTR0lZRXGc6y8Hv98t/wGCtz8zMoNfrydjG43GMjY1JXxuNBqrVKqrVqhNlZ6SdY80+6GvWajVZT7oUo91uY3x8XNZnIBCQTApg8IzTv5zt+3w+ZDIZ+Z4gaLml6/MZBSd8Ph+SyaSTtbBt2zZ5DsxHev/DiLTBYDAYDAaDAoWz5lvURKNR+P1+EVHSAkKa0LB+mS/PrG3V3r2sS+Z5TO+MRqNCcGhXxXpSYEAeSSi1PZKuW+U1KRAGzPn4Tk5OSprp0NCQEHASBJIjknNgQGppvcOXeHoIkzjGYjERZNLp2IlEAu12W8aCGwaa9LPWU8Pj8SAajUofSIK11dXs7CwajQYCgYD0g/ej06qj0ahDRoE532Vd7870bpJmv9+PZDIpmxnAgPCRvHCjpFariaWRTl3Wmwra/5r11h6PB6FQCLFYTIgmxdl0DXylUkEoFMLo6KjUOnMMtRUS/Yj15gYJMUlbrVYTcqrXJz22STILhYKsP2CwKcJ0dt5TPB6XemJNan0+H6rVqqxjril9TYpz6ZpmElGOK2vUg8GgjBk3MLRQmcfjkWeA65j3xPUzMzODqakpxzGCZQhaCI5iYBwjXpMlHLouWxPwbrcrdf/sfyaTQbfbRT6fd+Y3k8k4AnZMy2dfgDk/a12+Qq9znf7N2mrez57CiPTCYETaYDAYDAaDQWHlypVCTHT9K+t0NWngiydf2BntY4QMmCMNPp9vuyg124/FYlIfrb2I6RNLwsTabV4fgPjZDg0NyUv17OysCJMBc2JUWrWY5EX7E1PIjDWrun0dMSPx58/VahWtVsvxRO52u6hUKkilUkJ6KEylSQNrnzURYjSUZIPRQz3+HG8tptVut7F161Y0Gg2pH6aqNNWNAYivtyZeVFmeXwMMQOrFG42GbGSQdLKOneQWmPPQ1mrr2iOYczK/lppZA7o+NhQKOZFwrheKnrH/VIMm0W02m9I3jj/V26maDQzqhjudDg477DC5plY9Z/85Xlqd2u/3OzXwrKHW9cRer1fuX5PfSqUia4x1/roenRF9rarNe9Rj7fP5pD5aR8HD4bCMD6PJWgyP99Lv9zE8PCz3yY2M+etM2z1yU00L5rVaLUdDIRAISM01x6Jer6NarYoOAccnHA7L+gbm1PPn+1Rzc4rjw6wEHQnfExiRXhiMSBsMBoPBYDAokJwxCgXMkQFa+QBzljp8EQbmxIS0HY3P58PQ0JDzYt7pdFCr1ZyX6UajgVwuJ4QrmUxKhI8Eh5ErnSbe7/eRSqWQzWblGiQt7NfU1JRY9pD0zMzMSPo4+xEIBESxmS/xXq8X1WoVU1NTQhzmk6p8Po9UKoVkMrkdmazX607EmKJiJKJMS43FYo4dEtNdgUFEtFarYXZ2VohKNBqV9hn97Ha7EjWdH6kLhULORkC73ZZ+RSIRDA8Po91uCxnrdruyocK5rNfrCIfDTsSehInnAAPirTcLdPRep99zjHiejpjriKjX6xUbNX7GzQ2uMVpycU6mp6dRLpcRCAQk5Zn2SYwkc/00m00UCgVpj6Jh7A8JINca10C323UIMkmi1+uVa1IUK5FIyJxzDeiNk1AohFKpJBkfTO1vNBriqsAsAZ3iTPLXbDblnrjOuJERDoeRTCZRLpdlToaGhmTDgM8JhdOYas7xoRq33tzQ5Lper6NUKiEYDDplGN1uV1TLiU6ng0KhIM9JMBgUNwCuM2ZQcLyYbaBF0CjEFg6Hd4sA7whGpBcG73MfcuDgwx/+ME455RSnPmQ+Nm/ejLPOOgvRaBSjo6N4//vf7+y47Qizs7N4y1vegmQyiXQ6jXe84x3yYO4NPPjgg7Jw5//305/+dKfnnXrqqdsd/573vGev9WtHOOyww7a75s0337zLcxqNBi655BIMDQ0hHo/j3HPPxeTk5D7t5zPPPIN3vOMdOPzwwxGJRHDkkUfiuuuue05Fw/01pp/73Odw2GGHIRwOY82aNfjJT36yy+O//vWv4+ijj0Y4HMbLXvYyfOc739nrfdK46aab8IpXvAKJRAKjo6M455xz8MQTT+zynA0bNmw3dtpPdF/h+uuv3+66O1L51djf4wns+NnxeDy45JJLdnj8/hzPhx56CGeffTZWrFgBj8eDe+65x/l9v9/Htddei+XLlyMSieCMM87Ab3/72+dsd6Hr3GAwGAwGw+JhZ3zkuf47WPGCiki3Wi2cd955WLt2Le64447tft/tdnHWWWdhbGwMDz/8MMbHx/G2t70NgUAAH/nIR3ba7lve8haMj4/jvvvuQ7vdxtvf/na8613v2qWi6EJwyimniFgB8cEPfhAPPPAATj755F2ee9FFF+HGG2+Un7lTti9x44034qKLLpKftY/kjnDFFVfg3nvvxde//nWkUilceumleMMb3oB//dd/3Wd9/M1vfoNer4cvfvGLeNGLXoRf/epXuOiii1CtVnHrrbfu8tx9PaZ///d/j/Xr1+P222/HmjVrcNttt2HdunV44oknpG5N4+GHH8ab3vQm3HTTTfiDP/gD3H333TjnnHPw85//HC996Uv3at+If/mXf8Ell1yCV7ziFeh0OviLv/gLvPa1r8Wvf/1rZ1d1PpLJpEO499eX6+/8zu/g/vvvl5911Gc+FmM8AeCnP/2p4/n6q1/9Cq95zWtw3nnn7fSc/TWe1WoVxx9/PC688EK84Q1v2O73H/vYx/DpT38aX/7yl3H44Yfjgx/8INatW4df//rXOyX3C13nBsNSQqPRENGq+QI+2vaIkT2maQNzAlKM4gKDaFg6nYbX65VoW6VSEcshACL2pL2gWQOra1eZJspoIDDY7GJkbX6UTm8gs66U5zG9d3h42Kn37XQ66HQ6Tq2wFqICgFWrVmHFihUSWKBIGUWS2AfWMPOzaDQqET4GPXTkkBHRXq+HVqvlRDVbrRaKxaKMRbvdRj6fd+qPKfrV6XScKHWhUMDQ0JBjZ6ZryPv9PuLxOGKxmPwNoQWVFo6LxWKYnp5GPp+XaOrIyIj0Sdci8z7YV/aH0HZT860EvV4vpqenZR1wY5t/Bxgl53kU6dLp5MViUUTb2J96vY5cLodyuexE+1n7z/Go1+tSCgAMIrxM02dEutFoIJ/PO9FzPQd8P6TXuvabbrfbsjZ4DFPX2T5ribdt24YtW7ZI+/F4HH6/37ERowDfyMgIAEhUmffNGvAdCQXqlH/OCX/H+2FmCOe51+uJTgDPYzaAzl5gZJljkclkxEaMa4VzRws4zrkeH6Z+a9Dfen7WhGH/4QVFpG+44QYAO/YDBYDvf//7+PWvf437778fy5YtwwknnIAPfehD+MAHPoDrr79eHg6Nxx9/HN/97nfx05/+VEjtZz7zGbz+9a/HrbfeihUrVuxxv4PBIMbGxuTndruNb33rW7jsssue88U5Go065+4PJBKJ533NYrGIO+64A3fffTd+//d/HwBw55134phjjsGPf/xj/O7v/u4+6ePrXvc6vO51r5OfjzjiCDzxxBP4whe+8JxEel+P6Sc+8QlcdNFFePvb3w4AuP3223Hvvffir//6r3HVVVdtd/ynPvUpvO51r8P73/9+AMCHPvQh3HffffjsZz+L22+/fZ/08bvf/a7z84YNGzA6OopHH30Uv/d7v7fT8zwez35fj8DgxeX5XncxxhOA/HEnbr75Zhx55JF49atfvdNz9td4nnnmmTjzzDN3+Lt+v4/bbrsN11xzDf7oj/4IAHDXXXdh2bJluOeee/DGN75xh+ctdJ0bDEsJVMnWCr/dbhe5XE7SKQFISih9bIHB371Vq1YhHo8LKaEiNEWvAKBUKmFmZkZejllrqtOP6b2ra3VJcKPRqLycs0+amPNcLXzUarVQLpcdb12mf2siPTEx4dQPM8U8kUjIcStWrJA6XfaBddLzyZ5OuWUqOTCnZJ1MJtHtdoUYAHO15tozlwJN/GxmZgbFYhHZbNYRWyOZ1z7V3NTgmLXbbUmpBea8lOcrVVM8jRuATIMmQeNYc4ND35sWpwKwndgY07NDoZB8xrR0Tfio1jw0NCR927p1qyNuRuGrVqslaywWi4mXMQlsuVzGxMSEI4rFNO/5omGzs7NyveHhYfHnJkj0uDlCUDxOb8QAgzVPkk+fal0mwVR13hOV7VesWOEIr/n9fkml5ph5vV557rgO9KYRx1Wnl9dqNVQqFUSjUSelmqSVc0lvdZJi3osWk2PpRiQSkbHO5/NSi69F7Wq1mmzQAINnolqtOpsFrOnneHFNh0IhOY+e8rFYbLvNjN2FpXYvDC+o1O7nwsaNG/Gyl70My5Ytk8/WrVuHUqmExx57bKfnpNNpJzJ8xhlnwOv14pFHHtkn/fzHf/xH5HI5eQHdFb7yla9geHgYL33pS3H11Vc7X+z7CjfffDOGhoZw4okn4pZbbtllavyjjz6KdruNM844Qz47+uijccghh2Djxo37vK8a/GP7XNiXY9pqtfDoo4864+H1enHGGWfsdDw2btzoHA8M1u3+HD++ID7X+FUqFRx66KFYvXo1/uiP/minz9Xexm9/+1usWLECRxxxBN7ylrdILdWOsBTGs9Vq4W//9m9x4YUX7vIP0GKNp8bTTz+NiYkJZ8xSqRTWrFmz0zHbnXVuMCwlNJtN58W51+uh3W6jUCigUCig0Wig0WhIlIqqzl6vF6VSCdPT0yI+RYLGF2+eS3Eo2vjwBT6TyUi9JEWhGDml8jEFkkh+eDzrrmu1mvRJR8QCgQCmp6dRLBZRLBYRjUaRSqXQ7/eRy+WQy+UwNTUlQkh8qSZZCIVCUp9KyyGex2gdX/6pUt5qtVAqlTA+Po7x8XGpn2W0vVKpiOo1I/kkmOFwWFSUuYEQDAZlDIPBIJYvX47DDjsMK1euxMqVK0WYq9frYWxsDGNjY1ixYgUikQgikYjTHjMAGDEnQea4sg6WUdxcLifkaWxsDMPDwxgeHobP55NIajAYRDAYlHURjUYRjUaRyWSwYsUKrFixAplMBplMBtFoFKFQSMYuGAwilUqJrRjbHxsbQ7/fR7ValX7oiDE3X1jTP184jfZirGWORCJYsWKFjA+j/6FQSMbxsMMOw/Lly6VfHG+v1yvjo+vpOW/cUCBJrVQqQnTr9bqsAwrr5fN55PN5VCoVeDweGTufz4dcLoeZmRmnLlpvVHB9tttt1Ot1TE1NyfiwlpvjXywWMT09LQrviURCIuWhUEiem/nPE4XHeCyfw3q9jlqtJmMdi8WQyWQQDoflmaPAGjNGaMHFDQX93TI9PY1SqSTjyHXB74dGoyHfP9wY4fn1en2v219ZavfzwwsqIv1cmJiYcEg0APl5YmJip+fMT0P0+/3IZrM7PWdPcccdd2DdunVYtWrVLo9785vfjEMPPRQrVqzAL3/5S3zgAx/AE088gW984xv7pF8A8N73vhcvf/nLkc1m8fDDD+Pqq6/G+Pg4PvGJT+zw+ImJCQSDwe1q1pctW7bPxm9HePLJJ/GZz3zmOaPR+3pMZ2Zm0O12d7gOf/Ob3+zwnJ2t2/01fr1eD5dffjn+x//4H7tMfT7qqKPw13/91zjuuONQLBZx66234pRTTsFjjz32nGt5T7BmzRps2LABRx11FMbHx3HDDTfgf/7P/4lf/epXOyw7WOzxBIB77rkHhUIBF1xwwU6PWazxnA+Oy0LGbHfWucGwlFCpVBCPx50IYLVaRSQSQTablahXpVKRVExG3YLBoChmE7T1oT0UMIiwUqlYg0SF/x4ZGXGigBSn0tE9ptFqgTBGGXUkr1KpOJZPtAfS3sAkhCSHwFwaKwW0eFy1WpWX91gshmQy6aS5k9xp0SRGjLXYGCPs3W5X0p9brRZisZjzPe7xDCymeB6vqaOfvV5P0pa1pzMjgDyOQmKcD44HCRT7EI/HUS6XsXXrVmmf86zfbXhfOjVdb0KQZAJzkWmSQZ2mz/nl/QKD6G2r1cLMzIz0X0foAYhVmxahY0o4iSgwiH4mEgkhfMAg4trtdpFIJLbzqWbUt91uC6ljvyKRCMrlMqampmROmEKto7d6znUGQ7vdlvK5SCSCdDqNYDAo/WeGQ6fTkTR3Zoloaytaonm9XnkeKQbH9qPRKKrVqpO1wTWgMz5oO1YulyWIwI0ovfbYZ20/xo0LfU22z3WmvaZ5nxTR0zZlLHfgGh4eHkalUpENPmCQydHv91EsFp1Mkz2BRaQXhiVPpK+66ip89KMf3eUxjz/++HOKCy0GdqfvW7Zswfe+9z187Wtfe8723/Wud8m/X/ayl2H58uU4/fTT8dRTT+HII4/cJ/1cv369fHbcccchGAzi3e9+N2666abtajf2BXZnTLdu3YrXve51OO+885za7h1hb43pCwmXXHIJfvWrX+FHP/rRLo9bu3Yt1q5dKz+fcsopOOaYY/DFL34RH/rQh/ZZ/3RK8nHHHYc1a9bg0EMPxde+9jW84x3v2GfX3RPccccdOPPMM3dZGrJY42kwGAwGg+HgxcFMjBeKJU+kr7zyyl1GbYBB/evzwdjY2HaqsVSP3lkd4tjYGKamppzPKGDxXLWLu9P3O++8E0NDQ/jDP/zDXZ63I6xZswbAIPq6ENK3J2O8Zs0adDodPPPMMzjqqKO2+/3Y2JjUaemd28nJyd2q/VxoX7dt24bTTjsNp5xyCr70pS8t+Hq7O6Y7A1PA5quW72o8xsbGFnT83sSll16Kb3/723jooYcWHAUNBAI48cQT8eSTT+6j3u0Y6XQaL3nJS3Z63cUcTwD4r//6L9x///0LznJYrPHkuExOTmL58uXy+eTkJE444YQdnrM769xgWEqgnYwWc2Ld5PDwsETCOp0OisUiGo2G/I1LJpOo1WooFApyHMW8arWaRLTm14OyprdSqUiNZSqVwujoqBM9LJVKKJfLTh1tOByW/vEleHJyUqK6wJww2qpVq8QzlzY8jKICc4JkjOwCrpWTtndiSi8w57Mbi8VELCqfz2NyctLxz6ZXMNN3AYgQ2+TkpAia1Wo1RCIRp7Y5FoshGo069lrVatWpMWYKMz2UAYjtlxaKSyQSYvkEQPpIX21gELFk7S7HwufziScx5zIUCqFQKGB2dlZKoJhizvNarZaMHe9b+1HzuNnZWXi9XlkvACQ9OhAISBtaZIrrh3W1PI9RZu1PzHT5drstY8H70VkUuuad48r2OSflchmlUgn1et2xhRoeHkav15OxbbVaImxHvRDWLzNzKZPJyP1wDTOVmhFzrrP5NcaJREJSxxnNZ1uMxL/4xS/GzMyMnMv2WTbBfgSDQRkbPl9MaWcGBM9NJBLCERhh1v7iLHPQntpM9280GpiZmXHGPxqNyrmFQkHKPYBBDXYymcTMzIy0RS9tXRaxp7CI9MKw5In0yMjIdiI9u4u1a9fiwx/+MKampiRd+7777kMymcSxxx6703MKhQIeffRRnHTSSQCAH/zgB+j1ekKy9lbf+/0+7rzzTlESXyg2bdoEAM7L7vPBnozxpk2b4PV6d6rCe9JJJyEQCOCBBx7AueeeCwB44oknsHnzZifati/6unXrVpx22mk46aSTcOedd+7WF8zujunOEAwGcdJJJ+GBBx7AOeecA2DwRfjAAw/g0ksv3eE5a9euxQMPPIDLL79cPrvvvvt2a/yeL/r9Pi677DJ885vfxIMPPojDDz98wW10u138+7//O17/+tfvgx7uHJVKBU899RTe+ta37vD3izGeGnfeeSdGR0dx1llnLei8xRrPww8/HGNjY3jggQeEOJdKJTzyyCO4+OKLd3jO7qxzg2EpgcJc+Xxe/h6PjIwgm82K1y0wR1SoLgxAfseaSABCeuv1uhAaplPzZT2TyaDZbKJYLDrewKwl5Wesj6RKMY9jXS1f7CluxvO4KZBKpRzPX5IG/TeSRJjlG/V6HdlsFplMZjsvbLYfDodlA4DHdLtdZLNZjI6OSvuTk5Not9vii0uEw2FHRRsYkDlNVABIKjTbr1arotrMz8rlsihAs5/AHEnicaxRB+ZIld7IIJHU48++6hRnps3rvlE9m20xlRmA9JVES3si6zVCUhUIBGQTg/0ol8toNptOmjt/z2OYWq4VuqlIDcz5axcKBdmsYaoyVbXZF24gtFotSbMulUqOsBz7T7LK1Ohmswmfz4dAIICVK1cCGPxNmJqacuabwmhcn7xnbu5wXOkbznP5XsjrsX3WGwMD4p5KpRx160AgAJ/Ph3K5LBtmFDbz+XyiGN9qteS54/PKdjiuFBxkm8BgI4mbZzqtn/Oi+8FUfRL1cDjsqKjXajUkk0mMjo5K+QO1GDKZDJLJpKl2LwKWPJFeCDZv3ozZ2Vls3rwZ3W5XSNCLXvQixONxvPa1r8Wxxx6Lt771rfjYxz6GiYkJXHPNNbjkkkvkAfrJT36Ct73tbXjggQewcuVKHHPMMXjd616Hiy66CLfffjva7TYuvfRSvPGNb9writ0aP/jBD/D000/jne9853a/27p1K04//XTcddddeOUrX4mnnnoKd999N17/+tdjaGgIv/zlL3HFFVfg937v93Dcccft1X4RGzduxCOPPILTTjsNiUQCGzduxBVXXIE//dM/lS+b+f1MpVJ4xzvegfXr1yObzSKZTOKyyy7D2rVr95liN/tx6qmn4tBDD8Wtt94qX/rAXIRtscZ0/fr1OP/883HyySfjla98JW677TZUq1URl3vb296GlStX4qabbgIA/Nmf/Rle/epX4+Mf/zjOOussfPWrX8XPfvaz3YqwP19ccskluPvuu/Gtb30LiURCXqb0C9j8ft5444343d/9XbzoRS9CoVDALbfcgv/6r//a4Xrem3jf+96Hs88+G4ceeii2bduG6667Dj6fD29605t22M/FGE+i1+vhzjvvxPnnn79dXeRijmelUnEi3U8//TQ2bdqEbDaLQw45BJdffjn+8i//Ei9+8YvF/mrFihVCkgHg9NNPxx//8R8LUX6udW4wLGUwusYXXGAQSSL5IwFptVoIh8MOESoUCmg2m4hEItuRHqr4AnOEgNHhbDYrL//8nqVQVywWE/LF9jOZjLTP3+XzeaemVVvxkGBRmAiAvOCz9pLH1Wo1J2IZDAaRTCYdlW4dHQXmCJrX63VqXDmGOoqsyQjbr9frKBaLjgoyRcF4HvutNyj8fr9EPgmKiHF8/H4/qtUqqtWqzJPf75fNBwAyLn6/36kzp/ATySfrc0OhkHyPc0NBR35JANn/+WrLwFzNNxXLgQHh48YG208mkyKExn4w+0ATdV1fzba63a6jms52Y7GYjAUj+GyL0PXu2oppvlJ1u92WiDrJJDBnAcZrRyIRJzLO+mFg7pmjAjoAyfIgiee1acXFNcVr6qg/1zbPm5qakiwQ9qfRaIjYF58bKo7rgJbP55Nnl31i3TPnlBZjzNwABn9bZ2ZmEIlEZC5DoZBs1GnF8mq1ik6nI0Q6mUw69lrcxOh2u06WDO81FotZRHoR8IIi0tdeey2+/OUvy88nnngiAOCHP/whTj31VPh8Pnz729/GxRdfjLVr1yIWi+H88893PINrtRqeeOIJ58vkK1/5Ci699FKcfvrp8Hq9OPfcc/HpT396r/f/jjvuwCmnnLLDeu92u40nnnhCvpSCwSDuv/9+eTldvXo1zj33XFxzzTV7vV9EKBTCV7/6VVx//fVoNps4/PDDccUVVzh10/P7CQCf/OQnZdyazSbWrVuHz3/+8/usn8Agwvjkk0/iySef3C4lWf+BXIwx/V//639henoa1157LSYmJnDCCSfgu9/9rqQVbd682fkyPOWUU3D33XfjmmuuwV/8xV/gxS9+Me6555596nn8hS98AQBw6qmnOp/feeedklo/v5/5fB4XXXQRJiYmkMlkcNJJJ+Hhhx/eabbH3sKWLVvwpje9CblcDiMjI3jVq16FH//4x7JDvRTGk7j//vuxefNmXHjhhdv9bjHH82c/+xlOO+00+ZnP9Pnnn48NGzbgz//8z1GtVvGud70LhUIBr3rVq/Dd737X8ZB+6qmnJE0NeO51bjAsZVAcKxaLyYtzJBIRxW4SEEYEq9Wq+EPncjlH3AmYS+ul0jbP1cJEkUhElJP5d4pqwbQ2AgZRwHA4LOJNwNxLd7FYlPeXWCzmpC7TK5gp2QAclWiKaVEtW/tIUwFcR2UZ0eVYMJrZ6/UkvZlR5i1btgiRoK0WFbgBOGRSRxBJ1ABItFgfU6vV0O12EQwGZSwoOkXiD8wJZem0Z6p2k6hQjTsUCkl2wejoKEKhEPL5vJD3RCKBbDbrRIeZlquJLNWheQzVlvk7tkVBKvaDGzN6I4ApyrOzs0L44vG4ZDEAg80abthoqzRuAujUYrarrbMKhYIQTc6JJuBM6/Z4PPJeFYlE0Ol0RM2c68fj8ThlC1RR7/V68px0Oh2HNIfDYczOzoriPDCXacENDYKbG1x7fBaq1apj1xUIBCRKzTHQmxt8XmKxmMwNSTkV89lXj8eDUqkka9bn84l6Pu+bCuq8J9rdaTVtbhrp9RMIBES5nOs9k8mIkjzXD599ficNDQ2hUCggGAyi2+3KeOwJjEgvDJ6+Nn4zGAwGg8FgOEhRKpWQSqVw8803S40wX1qj0eh2RLrRaIiVDgnC5OQk/H4/RkdHnTRiRoO1onKn08Fhhx0GAFi9ejWKxSIKhYLjBV2v1yVtFxio4rMmWkcsPR4PZmZmJDLIOliStlQqJQR5PpFut9sOkR4aGhJlcGBAmOarh/MaWvk5l8shGo0KkZ6enka9Xkc8HnfUiAE4RJqRU53qOzw8vFMizfRj1t3qyDVrbNPptOMZ3Ww20e12Zfz7/T6CweAuiTTri/P5vGwUJpPJ7Yg0U5J1CjijkLzejoh0Op0WIq2j9FSc1u37fD6HSA8PD29HpBml1T7Y84k00/11TXS5XN4hkabyO8+Lx+PweDwyPppIc3x8Ph9GR0dlvXMdc51wTpj6TtI5MjIiRHpoaEiOJZHWqFQqsrY4J36/H7lcTu5z5cqVOyXS3BQhkeazwmsODw+LRzP7ylINZpAwisz+B4NB9Ho9pNNpKY149tln8cwzz8Dv98vmAFXN5xPpqakpdDodp2acmyMARHFeE2lqMXBDptFo4KqrrkKxWHQ28p4P+N337LPP7ta5/P5a6LkHOl5QEWmDwWAwGAyGPYXP50M0GoXX63UieeFwGLlcziGDJAx86abtElNxgUHEZmhoCNlsVojP1NQU2u22kEnWxpJYAXMRb9Znsh/ae5rHURSLpVY6gs72Ga3Vda9MbWVfvV6v1AVr0ScSGkYeeR8kFvzM4/HI7wKBgPhgM4OFEXhaZQGDDYlut4uhoSEndZnEgWM43xaK6eQ6JkRbrUgkIuSl3W7D5/OJiBzHQ7fFemm90UAyTB9o3a/5Na4kvhwf3jPniNkF/DcwZ3XFlHIAToou2+c1dc07hal4bKlU2m798N58Pp8TJdVriWNGUslzWMLAftEyS88h/ZdDoZCQu2AwKFFvEspGo+GUHPAzpmlz7ehUZvaBBJ195fjoZ5Me18zy0HPOjRneG1PggcEGRTQaRTqdlvXAZ01HePm8hUIhZ20Hg0GZSx6jRQBZEhEIBERLiGNGr2uOZyKRQKlUErG9drvtbBqVSiWxJ+NY1Ot18cqenzGyu7CI9MJgRNpgMBgMBoNBQfvLarJBUqHTOVkXrBWE0+k08vm8Ux/LY/jSOTQ05Ag/0YuX0TBgTu0YgBChRCIhnrU6ct3tdpFOp6UfJBqMyPGeNKnSET+SIypba9Erpt02m035jG3xPADbqV6zhllHJJvNJqLRKEZGRuRcpqBr9WemoZNI6EgpyRfrfLVIFmtIvV6vHBcKhYS4cXxIyLR/NlNkOeb1el1IOcesVCohn8+LjzYwVwqgo6ckhYzeZjIZ6Y+uayaBZfu8H64rfZ/hcNgRd9WkkKnwPp9PPmO/5vtUd7tdp+6ea0VvIsTjccd3W280cKxZ066F14rFIlqtFoaHh2UuSS512jY3PHi9RqMhGxdaYKtUKkm9OTAonWBatxZNoz+6Js4kqLoPmiBHo1GkUilRg2c/uDmjU99ZW643ubi2AIjn/MzMjDxzXq9XnkmttUAhPE3KM5kMwuGwbNJR40B/1wBw+sDjWIahNQt2F0akFwYj0gaDwWAwGAwKtNvpdrsScdURRb7UM706Go06AmVMEaZWQzAYhM/nQ7FYlJfOkZERtFotsYmjAJTH45GX83g8jnq9LmrbwJzwko6CA3P1tnxhZ12nJkskAySlup6VRI5iYxTsAgZEggrW7FsymUSr1RIF4Xg87qgtsw/9ft9Jk6XScalUEiJaq9Wkvpfw+XwIBoMy1rpNEgnWCXu9XhHF3Lp1q6h5a1E1pjjr1GstHMe5TSaTkp5aKBTEhouo1+sol8sOEWXdOWt6gQFJ6/f70lYikXBExnieFvEC5ohuu912UtOZeq43Lrg2gAGR63a7YkfFOWfNrk6z5ljozQfWqDO1eGxsbLuaZq0Gzv9TvVoLzHGjR5c2cNNAi3PNf6aYek2CXKlURJ2efWVJBCPAHB9Gl7XFGNWx+fP8tcRjdFter1fKOrh+qKatBfaKxaKzURIOh8VuTiu3L1u2DMFgUMaR2QfDw8OOtVi/35foMjB4Jmq1mkPwAWxHrvkcxWKxvaLabUR6YTAibTAYDAaDwaDASJ9+EW82m1KDyhflYDCITCYjRA2YU5fWHrYUJGu320IkSMK18m46nUa5XJaXbp2SrEkq62U1iQVckskXbh2hZv2tvk8SIa3QzWiYjsAzEsx+MCKro59ULyfZYPRS94Nq5sVi0UlBjsfjEtUjIpGInEdyrxWuaTcViUSEjLZaLfj9fpRKJSEWmUwGo6OjjihZvV5Hu92WsS6VSohGoxgbG5MxYhRfp+F2u12ZD5KcdDrtRJnZ/nyv7/lEul6vi8ozx5XrSFtzUVSt0+lIqrPH43E2GhgJnpiYEFLL2mE9ZqxXn5qaknundVmr1XKi+Nzo4BrgeiShTiQSstaZ2p1KpcSDmuSXmzcU0OI46fIBEnWv1yubLhTSog842+LmDO+diuMUMAPmVOq1WjkF4dh/PiO9Xs/xn+Y60tkQtH7T/dcicZOTk5ImrqP/0WhUFOKBuY0kbkQBcz7htMDi/EciEZmjWq0m30EcM2bEcBNHb0QZ9g+MSBsMBoPBYDAYDAbDQQ6LSC8MRqQNBoPBYDAYFKgG7fF4nDpMpmbrlGfW4jJ6x+iT9vjl+bqus1AoOCmltVpNxKmmp6flPHr5MooWj8dFWGpHkSkdRdaRbEbEgsGg43NbqVScGmZGarWYk47M62izFvniv/V52qaIqekejweNRkMi1cBc2q2uaW02m1KrDcylDDP1lmPKtGdG52mPpa3Gcrmc+D8zykgbLrbl8XjEBolzyWvl83mplx0bG5Px0N7ArNPmnDNCqlOSaRu1ZcsWAAMFdq4BthWJRBCPx537jMViiMfj6Pf7ksJerVZFPZz3Q8s2jhkj+LVaTeY/mUyiUCggl8s54mccM46j3+9HvV530o9p5aRTpLvdLlKplETSmRLNbAceF41GEYvFZM6LxaKj3K7Xzfx1pSPXjEZrwsfa9Hq97oilBQIBifBy/Hg/HJ/5ddNc//OfL1ra6XR4n8/neJivXr0a1WpVMgK0TZ3O3GD0n2vF4/HI+HPtURFde31T84BzyT7SqmtvRKSNSC8MRqQNBoPBYDAYFCgc1ev1hFhoGyn+O5/PY3p6WuxsgAFRYR0zEQ6HkUqlUCqVJG212+0KaQTmFIpJzoG5umYtgBWJRISY8DimB2uSzHa0L7NWVwYG9lRUDme/6FVLiyRgkMLb6/VEXAoYpEuzzhWYIyqagFD1Wdd1alsnpl63222Mj487Kc7FYhHFYlF+prq1Ts8m4efGBzAn+uTxeCQtvNlsolQqOT7StVoNmUxGCDLJaiQSEULSarVQLpel9hUYEMxIJCJ1tGyfx2trMaZjs/+6fp3/n6/+rAXd9Ph7vV4hyLyWrqNmeYFW8uYY5/N5p4afGzHsD1Oqdao408lJ7Jhq3mq15DyS11QqJZZn+Xwe4+PjTrp9OBzeTtwtnU47StOsE+ba5n1zHpnirM/l+Pf7fakrZzkFLeG43qlXUCgUnPR7Eli90dTpdBCJRGTOKRKon0Mq4Gt/9NnZWXi9XpnfcDiMZrOJcrnsWHWxBIJjS7G5drstz2GlUhFSz/7Txm2+AjrLLvaGo7ER6YXBiLTBYDAYDAaDAqNktVrNqWuNRqNIJpNOpI3RTm0NRPEmksBkMinWRXwRp0c0X7qDwaDY4rCtdDq9nZgZRbSSyaREeWu1GoLBILLZrFN3yXpqAA7pZv+r1aoQsPlRQK0ATNVhLQ5F4sm+BgIBbNmyxSG1PI7RfN7n6OioKDIDA3LDyDhJTqVSwezsrNhOUUGa0TtgLsqfSCTEe7jb7WJmZgbtdtvxvWbNrLYb6/V6IibH+SA55z1lMhlnfNhnPdaMEnNM2H61WpU5SiaTjhc1+0+xMb1hQNJ46KGHSrv0kOZ4r1ixAtFo1Nm0YBRYk+uZmRnZZNHjoP2yu90uAoEAhoeHZb3U63VRFAcGpJ5jz/Gp1WrI5/Pw+XxCOimWxpp/3m80GsXMzIwQSm5IkUzSCiwajUrkPpPJiNo6jwuHw8hms2g0Go4NHceQ955MJqVWGxg8S/OfBS1IxvFPpVKYnZ2V+mZgTvFbi731ej15loHBGi4UChgaGnLU6LvdrtTt856GhoZETAxwlcLZX37G9cPNiFKpJOfR0otWaEak9z+MSBsMBoPBYDAYDAbDQQ4j0guDEWmDwWAwGAwGBZ1yzEge01RZSwsM0qVLpZLU7gJz6bo6bXt6etrxAgYGUUYqfPNnXRPJazG1VatXB4NB8QfmcUz1ZkS3Wq0imUw60UOq/jLKODMzg0AgIGnTvKdgMOhYGo2PjyOVSolyMz9jdBAYRDCnp6dRLBYl0jk0NCSprozIMf1c168y4qdrtWl5pFNumSbOF3daDmnPZWCQFcB6XgCS9gvAqWvWStqM6NZqNWl/eHgYyWRS6lWBuTRuXe/OlPNwOOwofjOln/czNTUFAOIFrbMRGMXU6eS6rhYYRFAZ5WUNPyPkMzMzEjHlPQYCAUkv1lkCtItihJZrR6u+8/pcd6zjpkc3j2EKNPvIz1KplKNAPV9lvNPpSJQbGNSxVyoVpFIpidh7PB7Mzs6i0Wg4Svmzs7NOmUEsFpO1wHXs9/vRaDRk3vv9PgKBgONlzTGsVqtOGnoqlXKO0ynpOj1f12RznnR2SrVaRTgcxvDwsESamTFRrVblXJZOzP8e0aUOsVjM8Z3mMbznfr9vqt2LACPSBsMSwF133YUrrrgC27Ztky9uADjnnHOQSCTwN3/zN4vYO4PBYDi4QAKhiSNfZEulknjtjoyMbCcyVa/Xhfiyvpo1qn6/X4gWyTBffjudDuLxOEZGRuRlOp1OS0q4Jve0NNI+vZVKBdVqVcSPSIR0HS9ravkiTvElWicBg5f6WCwmZBqAeCmHw2Eh1+VyWUgIMCAlmhiy/36/3yEqAMROStft+nw+JzU1HA4L4eTPwWAQrVZL7pHnjI+PO/OUTqcdz1/WIc+vW/f5fJJqTFui+T7bFJfiWJAA5vN55HI5uV/WTfPeE4kEut2ukMlYLIbVq1fLWHHeOp0OEomEnNdoNIQ8sX3Od6VSkX6QrM2vydZrhan9PJbrh2nAHB+KdU1PTzs10Vx7RDwelznQn+n2acWka+FZn85aXj0HHAuu006nI6nynU5HCDM3pqLRKBqNBur1uqyfZDIpnspsp1qtOv7f5XIZ7XYb5XLZsfhiDTPPo0e4FibjRgBT8dl3liiwr0zt53OgN4u4jiYnJ9HpdKRGH4CkqpdKJVmjtMJibTdtwfSmCEXRSPT1vOwuLCK9MBiRNhiWAM477zy8973vxT/+4z/ivPPOAwBMTU3h3nvvxfe///1F7p3BYDAcXEilUtsJhumIG4lcIpHA2NgYCoWC1ADrOl+SNEaKeQyPGxoakuh2tVqV+lYSkGw2K768rJut1+vSN77U0wu6VCrJS7bP50Mul5O2GA3Udd+sG+10Og4pp+IxCQfrsRn5AuY8nDWoGk2CoKPQWn2bBE0rgM+P/DLaxpd0kmEd/SwWi1KLTBKho+k8jpsAjFRzfAKBgIwho9qpVEqOYRS/Xq8L0aLatxZ7Y7/L5bIQYnqE66gySZWuk2ftN/tRLpelZlfXP9dqNUxNTck1E4mEeHIDc9F/fd+MumsxPF1nzjmnEnS73RbCx40eXeefyWREYIz9InHWYm+s/ec1Q6GQCJdp4q/XyMjICPx+v/PMcdwoCAYMiHoqlZKILY9hlF2vl1gsJn0oFAqOwjU/63a7stnDsUgmk7LRAgxILTdmtABcKBRynntqI+isina7jVwuJ5sibE+Lx1HlXNdSp1Ipp667UCjsMEPB5/PJJp2pdu9/GJE2GJYAIpEI3vzmN+POO+8UIv23f/u3OOSQQ3DqqacubucMBoNhL+LDH/4w7r33XmzatAnBYNCxjyE2b96Miy++GD/84Q8Rj8dx/vnn46abbnLSU+djdnYWl112Gf7pn/4JXq8X5557Lj71qU85glPPF3xhpYgRkUqlnCgao3bBYFCOy+VyQopJepgWqwWKKC6k7amopsx0aRI0r9crhLvRaKDb7aJcLjsCXkxNZnskAnzJTSaTCIVCTvSt3W5LSjkJh8/nk7RlXpMEtFKpSP/j8Thisdh2wkrxeNyJRNImi+2z3VQqJZsD1WrVidICEDEmjmE8Hpfx0wS/1+shFos50UduNmj7LkZmdTSPEUmOayaTwbJly+SzWq0mgls8j6RFi7YxaspINedTi7Pl83lH8I1jTULFz0iqPB6PpIX3ej34/X6k02kZf24ecK1ks1mEQiFs27ZN+kBCqC3bOp2OtE9yquef16R1GdOsmRbNPnN8qMjOtcLx5oYJMBDYyufzEvXnOpidnXWepUQigUqlImSV/a9Wq44lWSQScaLBfJa00jzTvfn9wk2qRCIh88vnjRtRHFc+Y/weSKVSGB4eFnLOflDdm8dMT0+j1+vJmHF9UUiN6ycUCjlp3LVaTa5P8TuK3HFjgZF/LbLG39frdYRCoQMqIv3QQw/hlltuwaOPPorx8XF885vfxDnnnLPLcx588EGsX78ejz32GFavXo1rrrkGF1xwwYKvvTfhfe5DDAbD/sBFF12E73//+9i6dSsAYMOGDbjgggsO6p0+g8HwwkOr1cJ5552Hiy++eIe/73a7OOuss9BqtfDwww/jy1/+MjZs2IBrr712l+2+5S1vwWOPPYb77rsP3/72t/HQQw/hXe961764BYPBYHhBgkR6of8tFNVqFccffzw+97nPPa/jn376aZx11lk47bTTsGnTJlx++eV45zvfie9973sLvvbehEWkDYYlghNPPBHHH3887rrrLrz2ta/FY489hnvvvXexu2UwGAx7FTfccAOAwWbhjvD9738fv/71r3H//fdj2bJlOOGEE/ChD30IH/jAB3D99ddLSqjG448/ju9+97v46U9/ipNPPhkA8JnPfAavf/3rceutt2LFihUL6iMtnEKhkEScgsHgdlZITBPtdrtOfS8FwrQYGCOHjIYx5Vb7KzMVlZGqYrGIWq0mqaXAINLG/vEz1vWGQiEnnTqRSDi1tIxs6/TXbreLZrPp9IvRVC2qls/nnQhxOByWGmgAkoathcsYqWX9LgARjgoEAo5fdrlcRq1WkygmACe6l81m4ff78eyzz4rAFoWpvF4vpqenAQwiv5lMBiMjIzJ3TEPXll6sl2XEksJUtKMCBhHRcrnsiMQxothoNCQNutfrIZVKSQozMCAK9XrdEaljVJJjxhr8SCTi1PuyDIDtMxoZjUZlvbCfHB8trsXxp084o9LAnP8x64qBOaG4fD4vtcgjIyNOvW8ul0MymcTq1asl6lur1VAoFJxacj4P9I0GBvXBFKLT3tU6dT+VSon4Fz9jtke325U06Fqthna7jUwmI8eVy2XxpGY/mJ2h55IlBexDJBJxIrzAICLMPnB8mFESjUYlQswoMueNkXKWDHDOGT3XUWoATjSegnBcE8BcqjjXMOu0m82mU2bCe+VzvKfYXxHpM888E2eeeebzPv7222/H4Ycfjo9//OMAgGOOOQY/+tGP8MlPfhLr1q1b8PX3FoxIGwxLCO985ztx2223YevWrTjjjDNEmMRgMBgOFmzcuBEve9nLRNALANatW4eLL74Yjz32GE488cQdnpNOp4VEA8AZZ5wBr9eLRx55BH/8x3+8oD7o1Fu+dOtaU5KLbreLWq0m6bI8lwSAn/n9fsTjcSFgwODFWys/M5UWgLysl8tlqQXlyzlrl0mUAYjCsCZMzWbTqXNmbbTf73dezpnCynsi4QbcOlaSAi2kpGtjmerKl3323+v1Oi/5VOfWaeEUcqrX6w5R12rWiURC6nuZuhwIBDA6Oop2u+0QHKb1cvOi0+mgWq0KgecYl8tlIV4ejweNRgOFQkGUsekbzhRgYE7ETdeVU+nZ5/M5995oNJzU+vmp3aVSSRS/Ofecx2az6Qic8brsB9OuOT7tdhuFQsFRuK5UKlJHy3XMNPVYLCZzl8vl0Gq1ZNOG7UejUVEaz+fzSKVSTo16NBp1vJB5Ta4X3Y/p6WlHRZtpz1qNe37Ks9/vF7VtvWlE4TLtjc21q1XHe72erHW/3y9aBZqocxNMK8gzvV77cbMP7C9FykjwE4mE4zvNPgSDQcTjcfkd51ZrFXCd87pcj1r4js+a3nTRauK6NvuFiI0bN+KMM85wPlu3bh0uv/zyxenQf8OItMGwhPDmN78Z73vf+/B//s//wV133bXY3TEYDIb9jomJCYdEA5Cfqea7o3O0wjMweHHOZrM7PQeAE4kF4AgHAXAUkIEBCZxPpBuNhkOkKTamSZXH45E62PlEmsRC2zfpqCOvwRdqClPpvrO/OpqkiRjbIpFm1JTiSKzj5HkkLSQJ7Hez2ZR7p3Ky/pnESPeL5Jft87NGoyH3rOtI9Zjp6Cqj4Y1Gw+lrvV53Pmu322IJxP4zSs7aZt1fPXYcM543P1rPuSEh1OdyjvS9t1otmbdms7kdkeYx2raJNbvz54Tjqutgef/A3FrVx9ASiffOa5Ks8jOOn74nEnx9P/NF3HY25/yZ98T29XhwTWhiyvnjefV6XX7WVmacI13frtcR/62fm3a7LcJ6fEb0s6Tnl+1oxXJuisy/d72xND8yyywIPuu6/Waz6WSj8Jq6L4yEc35JrPW46nvW8767oBr/Qs8BIJkiRCgUcpxo9gQ7+7tQKpVQr9cdjYT9CSPSBsMSQiqVwrnnnot77733OUUXDAaDYangqquuwkc/+tFdHvP444/j6KOP3k89en646aabJNVc48tf/vIi9MZgMBj2HLlcTkTjni+CwSDGxsZ2OxMyHo9vd+51112H66+/frfaO1BgRNpgWGLYunUr3vKWt+y1XTyDwWDY17jyyiufUz31iCOOeF5tjY2N4Sc/+Ynz2eTkpPxuZ+cwBZXodDqYnZ3d6TkAcPXVV2P9+vXyc6FQwKGHHorNmzcv+EX0YESpVMLq1avx7LPPSg2oYeew8VoYbLwWhmKxiEMOOURKGhaCcDiMp59+ereVv/v9/naR7L35Hjs2NiZ/B4jJyUmxzVssGJE2GJYI8vk8HnzwQTz44IP4/Oc/v9jdMRgMhueNkZERjIyM7JW21q5diw9/+MOYmpqSdO377rsPyWQSxx577E7PKRQKePTRR3HSSScBAH7wgx+g1+thzZo1O73WzlIPKWJleH5IJpM2XguAjdfCYOO1MOgykYUgHA5LjfxSw9q1a/Gd73zH+ey+++7D2rVrF6lHA5j9lcGwRHDiiSfiggsuwEc/+lEcddRRi90dg8Fg2CfYvHkzNm3ahM2bN6Pb7WLTpk3YtGmTiD699rWvxbHHHou3vvWt+MUvfoHvfe97uOaaa3DJJZcI6f3JT36Co48+WuwCjznmGLzuda/DRRddhJ/85Cf413/9V1x66aV44xvfuGDFboPBYDDsW1QqFfnuBwb2Vvy7AAyyhd72trfJ8e95z3vwn//5n/jzP/9z/OY3v8HnP/95fO1rX8MVV1yxGN0XWETaYFgieOaZZxa7CwaDwbDPce211zo1yFTh/uEPf4hTTz0VPp8P3/72t3HxxRdj7dq1iMViOP/883HjjTfKObVaDU888YQIdQHAV77yFVx66aU4/fTT4fV6ce655+LTn/70/rsxg8FgMDwv/OxnP8Npp50mP7PE5vzzz8eGDRswPj4upBoADj/8cNx777244oor8KlPfQqrVq3CX/3VXy2q9RUAePp7Iu1mMBgMBoPB8AJBs9nETTfdhKuvvtp0Kp4HbLwWBhuvhcHGa2Gw8dr/MCJtMBgMBoPBYDAYDAbDAmA10gaDwWAwGAwGg8FgMCwARqQNBoPBYDAYDAaDwWBYAIxIGwwGg8FgMBgMBoPBsAAYkTYYDAaDwXDQ48Mf/jBOOeUURKNRpNPpHR6zefNmnHXWWYhGoxgdHcX73/9+dDqd/dvRJYrDDjsMHo/H+e/mm29e7G4tKXzuc5/DYYcdhnA4jDVr1uAnP/nJYndpSeL666/fbi0dffTRi92tJYOHHnoIZ599NlasWAGPx4N77rnH+X2/38e1116L5cuXIxKJ4IwzzsBvf/vbxensCxxGpA0Gg8FgMBz0aLVaOO+883DxxRfv8PfdbhdnnXUWWq0WHn74YXz5y1/Ghg0bcO211+7nni5d3HjjjRgfH5f/LrvsssXu0pLB3//932P9+vW47rrr8POf/xzHH3881q1bh6mpqcXu2pLE7/zO7zhr6Uc/+tFid2nJoFqt4vjjj8fnPve5Hf7+Yx/7GD796U/j9ttvxyOPPIJYLIZ169ah0Wjs556+8GGq3QaDwWAwGAz/jQ0bNuDyyy9HoVBwPv/nf/5n/MEf/AG2bduGZcuWAQBuv/12fOADH8D09DSCweAi9Hbp4LDDDsPll1+Oyy+/fLG7siSxZs0avOIVr8BnP/tZAECv18Pq1atx2WWX4aqrrlrk3i0tXH/99bjnnnuwadOmxe7KkofH48E3v/lNnHPOOQAG0egVK1bgyiuvxPve9z4AQLFYxLJly7Bhwwa88Y1vXMTevvBgEWmDwWAwGAyG58DGjRvxspe9TEg0AKxbtw6lUgmPPfbYIvZs6eDmm2/G0NAQTjzxRNxyyy2W9v7faLVaePTRR3HGGWfIZ16vF2eccQY2bty4iD1buvjtb3+LFStW4IgjjsBb3vIWbN68ebG7dEDg6aefxsTEhLPWUqkU1qxZY2ttH8C/2B0wGAwGg8FgWOqYmJhwSDQA+XliYmIxurSk8N73vhcvf/nLkc1m8fDDD+Pqq6/G+Pg4PvGJTyx21xYdMzMz6Ha7O1w/v/nNbxapV0sXa9aswYYNG3DUUUdhfHwcN9xwA/7n//yf+NWvfoVEIrHY3VvS4HfRjtaafU/tfVhE2mAwGAwGwwsSV1111XaiRfP/MyKzcyxk/NavX49TTz0Vxx13HN7znvfg4x//OD7zmc+g2Wwu8l0YDjSceeaZOO+883Dcccdh3bp1+M53voNCoYCvfe1ri901g8GBRaQNBoPBYDC8IHHllVfiggsu2OUxRxxxxPNqa2xsbDuV5cnJSfndCxF7Mn5r1qxBp9PBM888g6OOOmof9O7AwfDwMHw+n6wXYnJy8gW7dvYm0uk0XvKSl+DJJ59c7K4seXA9TU5OYvny5fL55OQkTjjhhEXq1QsXRqQNBoPBYDC8IDEyMoKRkZG90tbatWvx4Q9/GFNTUxgdHQUA3HfffUgmkzj22GP3yjWWGvZk/DZt2gSv1ytjdTAjGAzipJNOwgMPPCCiUL1eDw888AAuvfTSxe3cAYBKpYKnnnoKb33rWxe7K0sehx9+OMbGxvDAAw8IcS6VSnjkkUd26khg2H0YkTYYDAaDwXDQY/PmzZidncXmzZvR7XZFMfhFL3oR4vE4Xvva1+LYY4/FW9/6VnzsYx/DxMQErrnmGlxyySUIhUKL2/lFxsaNG/HII4/gtNNOQyKRwMaNG3HFFVfgT//0T5HJZBa7e0sC69evx/nnn4+TTz4Zr3zlK3HbbbehWq3i7W9/+2J3bcnhfe97H84++2wceuih2LZtG6677jr4fD686U1vWuyuLQlUKhUnOv/0009j06ZNyGazOOSQQ3D55ZfjL//yL/HiF78Yhx9+OD74wQ9ixYoVsolj2Hsw+yuDwWAwGAwHPS644AJ8+ctf3u7zH/7whzj11FMBAP/1X/+Fiy++GA8++CBisRjOP/983HzzzfD7D+64xM9//nP87//9v/Gb3/wGzWYThx9+ON761rdi/fr1B/0mg8ZnP/tZ3HLLLZiYmMAJJ5yAT3/601izZs1id2vJ4Y1vfCMeeugh5HI5jIyM4FWvehU+/OEP48gjj1zsri0JPPjggzjttNO2+/z888/Hhg0b0O/3cd111+FLX/oSCoUCXvWqV+Hzn/88XvKSlyxCb1/YMCJtMBgMBoPBYDAYDAbDAmCq3QaDwWAwGAwGg8FgMCwARqQNBoPBYDAYDAaDwWBYAIxIGwwGg8FgMBgMBoPBsAAYkTYYDAaDwWAwGAwGg2EBMCJtMBgMBoPBYDAYDAbDAmBE2mAwGAwGg8FgMBgMhgXAiLTBYDAYDAaDwWAwGAwLgBFpg8FgMBgMBoPBYDAYFgAj0gaDwWAwGAwGg8FgMCwARqQNBoPBYDAYDAaDwWBYAIxIGwwGg8FgMBgMBoPBsAAYkTYYDAaDwWAwGPYBpqenMTY2ho985CPy2cMPP4xgMIgHHnhgEXtmMBj2FJ5+v99f7E4YDAaDwWAwGAwvRHznO9/BOeecg4cffhhHHXUUTjjhBPzRH/0RPvGJTyx21wwGwx7AiLTBYDAYDAaDwbAPcckll+D+++/HySefjH//93/HT3/6U4RCocXulsFg2AMYkTYYDAaDwWAwGPYh6vU6XvrSl+LZZ5/Fo48+ipe97GWL3SWDwbCHsBppg8FgMBgMBoNhH+Kpp57Ctm3b0Ov18Mwzzyx2dwwGw16ARaQNBoPBYDAYDIZ9hFarhVe+8pU44YQTcNRRR+G2227Dv//7v2N0dHSxu2YwGPYARqQNBoPBYDAYDIZ9hPe///34v//3/+IXv/gF4vE4Xv3qVyOVSuHb3/72YnfNYDDsASy122AwGAwGg8Fg2Ad48MEHcdttt+Fv/uZvkEwm4fV68Td/8zf4f//v/+ELX/jCYnfPYDDsASwibTAYDAaDwWAwGAwGwwJgEWmDwWAwGAwGg8FgMBgWACPSBoPBYDAYDAaDwWAwLABGpA0Gg8FgMBgMBoPBYFgAjEgbDAaDwWAwGAwGg8GwABiRNhgMBoPBYDAYDAaDYQEwIm0wGAwGg8FgMBgMBsMCYETaYDAYDAaDwWAwGAyGBcCItMFgMBgMBoPBYDAYDAuAEWmDwWAwGAwGg8FgMBgWACPSBoPBYDAYDAaDwWAwLABGpA0Gg8FgMBgMBoPBYFgAjEgbDAaDwWAwGAwGg8GwAPz/kNZBZJx59rMAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), tight_layout=True)\n", + "ax1 = sim.plot_eps(x=0, ax=ax1)\n", + "ax2 = sim.plot_eps(z=z_center_slab, ax=ax2)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d0c11ef7-1046-41da-9236-a922aa8f1220", + "metadata": {}, + "source": [ + "## Define Objective\n", + "\n", + "We'll design this phase mask to give a transmitted intensity distribution of our choice.\n", + "\n", + "### Define Target Intensity\n", + "\n", + "In this case, we'll try to reproduce the Flexcompute logo, so let's make a function to generate that." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "2a121cec-97a8-49b7-a41a-cbeff7b21cf1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting opencv-python\n", + " Downloading opencv_python-4.10.0.82-cp37-abi3-macosx_11_0_arm64.whl.metadata (20 kB)\n", + "Requirement already satisfied: numpy>=1.21.2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from opencv-python) (1.26.4)\n", + "Downloading opencv_python-4.10.0.82-cp37-abi3-macosx_11_0_arm64.whl (54.8 MB)\n", + "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.8/54.8 MB\u001b[0m \u001b[31m54.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:01\u001b[0m\n", + "\u001b[?25hInstalling collected packages: opencv-python\n", + "Successfully installed opencv-python-4.10.0.82\n" + ] + } + ], + "source": [ + "# !python3.11 -m pip install opencv-python\n", + "import cv2\n", + "import xarray as xr\n", + "\n", + "logo_fname = \"misc/logo.png\"\n", + "\n", + "def get_logo() -> np.ndarray:\n", + " \"\"\"Get the Flexcompute logo from file, load it into a numpy array, rescale it to (0, 1).\"\"\"\n", + " im = cv2.imread(logo_fname, cv2.IMREAD_GRAYSCALE).astype(float)\n", + " im -= np.min(im)\n", + " im /= np.max(im)\n", + " return im\n", + "\n", + "def intensity_desired_fn_logo(xs:list, ys:list, rescale:float=0.5) -> np.ndarray:\n", + " \"\"\"Return the 'value' of the flexcompute logo as a function of (x,y) with some rescaling.\"\"\"\n", + " logo_values = get_logo()\n", + "\n", + " # some rotations to get the logo in the right orientation for the final intensity pattern\n", + " logo_values = np.rot90(np.rot90(np.rot90(logo_values)))\n", + "\n", + " # re-interpolate the logo data at the supplied x,y points using xarray\n", + " nx, ny = logo_values.shape\n", + " xs_logo = np.linspace(rescale * min(xs), rescale * max(xs), nx)\n", + " ys_logo = np.linspace(rescale * min(ys), rescale * max(ys), ny) \n", + " logo_dataarray = xr.DataArray(logo_values, coords=dict(x=xs_logo, y=ys_logo))\n", + " logo_interp = logo_dataarray.interp(x=xs, y=ys)\n", + "\n", + " # handle any nans for out of bounds (replace with 0)\n", + " return np.nan_to_num(logo_interp.values, nan=np.min(logo_interp))\n" + ] + }, + { + "cell_type": "markdown", + "id": "d061e3cf-529f-445c-9b71-4fab1bfe58e6", + "metadata": {}, + "source": [ + "Let's test this function out by plotting our target intensity." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "070e845f-41a1-449d-9d98-6294b5f794ce", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "xs = ys = np.linspace(-length/2, length/2, nx)\n", + "intensity_desired = intensity_desired_fn_logo(xs, ys)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "fdf5eded-d61b-4259-a1aa-38f637ee28a4", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.pcolormesh(xs, ys, intensity_desired.T, cmap='magma')\n", + "plt.gca().set_aspect(\"equal\")\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('desired intensity pattern')\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "98f986be-efc7-4be6-9192-1d87bb7e8f33", + "metadata": { + "tags": [] + }, + "source": [ + "### Compare Measurement to Target\n", + "\n", + "Next we need a way to compare the measured intensity pattern to this target intensity pattern.\n", + "\n", + "We'll come up with a figure of merit for the closeness of our objective.\n", + "\n", + "First, let's run a simulation with an empty mask to figure out what the average intensity should be at the measurement plane (for normalization later).\n", + "\n", + "> Note: Although Tidy3D normalizes field values by default, in this case doing a normalization run is useful as we're injecting from a substrate, which will affect the results. In the new caching feature of 2.6, these simulations will not use credits or much time when run after the first time." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "a2306cdb-3eb9-4553-b740-5a8a803650c5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
14:40:14 EDT Created task 'normalization' with task_id                          \n",
+       "             'fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa' and task_type 'FDTD'.  \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m14:40:14 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'normalization'\u001b[0m with task_id \n", + "\u001b[2;36m \u001b[0m\u001b[32m'fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
             View task using web UI at                                          \n",
+       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f68\n",
+       "             6-4dd6-8685-f814110fdfaa'.                                         \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \n", + "\u001b[2;36m \u001b[0m\u001b]8;id=307125;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=345283;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=307125;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=517697;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=307125;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[32m-62c10ddb-f68\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[2;36m \u001b[0m\u001b]8;id=307125;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[32m6-4dd6-8685-f814110fdfaa'\u001b[0m\u001b]8;;\u001b\\. \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b1ef335f8f03492da595a384a328513e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
14:40:15 EDT status = queued                                                    \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m14:40:15 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             To cancel the simulation, use 'web.abort(task_id)' or              \n",
+       "             'web.delete(task_id)' or abort/delete the task in the web UI.      \n",
+       "             Terminating the Python script will not stop the job running on the \n",
+       "             cloud.                                                             \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
+       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
+       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
14:40:19 EDT status = preprocess                                                \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m14:40:19 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
14:40:20 EDT Maximum FlexCredit cost: 0.130. Use 'web.real_cost(task_id)' to get\n",
+       "             the billed FlexCredit cost after a simulation run.                 \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m14:40:20 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.130\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get\n",
+       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             starting up solver                                                 \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
14:40:21 EDT running solver                                                     \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m14:40:21 EDT\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db3a7f9315464c33aee9991b4bef59e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
14:40:29 EDT early shutoff detected at 4%, exiting.                             \n",
+       "
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+      ],
+      "text/plain": [
+       "\u001b[2;36m14:40:29 EDT\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m4\u001b[0m%, exiting.                             \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             status = postprocess                                               \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
14:40:30 EDT status = success                                                   \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m14:40:30 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
14:40:31 EDT View simulation result at                                          \n",
+       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f68\n",
+       "             6-4dd6-8685-f814110fdfaa'.                                         \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m14:40:31 EDT\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=520603;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=958935;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=520603;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=454364;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=520603;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[4;34m-62c10ddb-f68\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=520603;https://tidy3d.simulation.cloud/workbench?taskId=fdve-62c10ddb-f686-4dd6-8685-f814110fdfaa\u001b\\\u001b[4;34m6-4dd6-8685-f814110fdfaa'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "091abe56d6ef4938bebf93d6287a9c42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             loading simulation from simulation_data.hdf5                       \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import tidy3d.web as web\n",
+    "\n",
+    "params_empty = np.zeros_like(params0)\n",
+    "\n",
+    "sim_empty = make_sim(params_empty, beta=100)\n",
+    "sim_data_norm = web.run(sim_empty, task_name=\"normalization\", verbose=True)\n",
+    "intensity_norm = sim_data_norm.get_intensity(mnt_out.name)\n",
+    "intensity_norm_mean = anp.mean(intensity_norm.values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "88edc61c-17c0-477c-a82c-64aeb950cc1a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average intensity of '1.81' (a.u.) measured without any device.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Average intensity of '{intensity_norm_mean:.2f}' (a.u.) measured without any device.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44e5f0a1-e604-4f33-a02d-53dd71d59569",
+   "metadata": {},
+   "source": [
+    "Next let's write our loss function over the measured intensity data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "3a5a34bc-9f22-4338-9215-5d177da445c5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_intensities(sim_data: td.SimulationData) -> tuple[anp.ndarray, np.ndarray]:\n",
+    "    \"\"\"Convenience function to grab the (unnormalized) intensity patterns from the data.\"\"\"\n",
+    "\n",
+    "    # first, grab the dataset storing the intensity values and coordinates\n",
+    "    mnt_out_name = mnt_out.name\n",
+    "    intensity_dataset = sim_data.get_intensity(mnt_out.name)\n",
+    "    xs = intensity_dataset.coords[\"x\"]\n",
+    "    ys = intensity_dataset.coords[\"y\"]\n",
+    "\n",
+    "    # the \"measured\" values are just the raw data\n",
+    "    intensity_measured = anp.squeeze(intensity_dataset.values)\n",
+    "\n",
+    "    # the \"desired\" or \"target\" values are the logo function evaluated at the data coords\n",
+    "    intensity_desired = intensity_desired_fn_logo(xs, ys)\n",
+    "\n",
+    "    return intensity_measured, intensity_desired\n",
+    "\n",
+    "# range within which to consider intensity as part of the objective function\n",
+    "# eg. if the measured intensity is above int_max, we just consider it at the target value of 1.0\n",
+    "intensity_range = int_min, int_max = (0.0, 1.0)\n",
+    "\n",
+    "def intensity_diff_fn(sim_data: td.SimulationData) -> float:\n",
+    "    \"\"\"Returns a measure for the amount of difference between desired and target intensity patterns.\"\"\"\n",
+    "\n",
+    "    intensity_measured, intensity_desired = get_intensities(sim_data)\n",
+    "    \n",
+    "    # normalize the measured intensity such that there's the same \"power\" in the signal as expected in the logo\n",
+    "    intensity_measured *= np.mean(intensity_desired) / intensity_norm_mean\n",
+    "\n",
+    "    # apply the \"capping\" within intensity_range (optional)\n",
+    "    int_range_magnitude = abs(int_max - int_min)\n",
+    "    intensity_measured = anp.minimum(intensity_measured, int_max)\n",
+    "    intensity_measured = anp.maximum(intensity_measured, int_min)\n",
+    "    intensity_desired = int_range_magnitude * intensity_desired + int_min\n",
+    "    \n",
+    "    # take the elementwise difference\n",
+    "    difference = intensity_measured - intensity_desired\n",
+    "    \n",
+    "    # normalized by the 'worst case' (difference if measured was exact inverse of the target)\n",
+    "    difference_denominator = int_range_magnitude * np.ones_like(intensity_desired)\n",
+    "    \n",
+    "    # return the normalized norm of the difference\n",
+    "    return anp.linalg.norm(difference) / anp.linalg.norm(difference_denominator)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3611df44-6810-4db7-93ea-96a44ac8e9e8",
+   "metadata": {},
+   "source": [
+    "### Fabrication Constraints\n",
+    "\n",
+    "If desired, we can add a fabrication constraint penalty to the figure of merit.\n",
+    "\n",
+    "As in other notebooks, we can consider a simple penalty based on whether the structure changes upon dilation and erosion with a given distance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "92a2f4c7-1a46-47d2-ba92-1920f6a6f406",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from tidy3d.plugins.adjoint.utils.penalty import ErosionDilationPenalty\n",
+    "\n",
+    "def penalty_fn(params, beta):\n",
+    "    processed_params = filter_project(params, beta=beta)\n",
+    "    \n",
+    "    penalty = ErosionDilationPenalty(pixel_size=dl_design_region, length_scale=radius)\n",
+    "    return penalty.evaluate(processed_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83169dac-977c-4a37-8581-a1eb2af506d7",
+   "metadata": {},
+   "source": [
+    "### Loss Function\n",
+    "\n",
+    "Finally, we can throw all of this into a loss function to minimize. \n",
+    "\n",
+    "We will use a very small weight on our penalty function as it turns out to not be super important in this problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "ac5a5c88-4247-4eeb-b8a3-96e727e3fd7d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def loss_fn(params: anp.ndarray, beta: float) -> tuple[float, dict]:\n",
+    "    \"\"\"Loss function for the design, the difference in intensity + the feature size penalty.\"\"\"\n",
+    "    \n",
+    "    # construct and run the simulation\n",
+    "    sim = make_sim(params, beta=beta)\n",
+    "    sim_data = web.run(sim, task_name=\"phase_mask_example\", verbose=False)\n",
+    "    \n",
+    "    # grab the respective and total losses\n",
+    "    return intensity_diff_fn(sim_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1399efcd-21fe-41e9-b586-0b7a16c6bc71",
+   "metadata": {},
+   "source": [
+    "Before optimizing, let's test out our loss function to ensure we can run it and get the gradient of it with respect to the starting parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "7458767b-a95c-4b07-a809-9c7ec3167ab5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "interp only works for a numeric type array. Given object.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[41], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m loss_fn_val_grad \u001b[38;5;241m=\u001b[39m autograd\u001b[38;5;241m.\u001b[39mvalue_and_grad(loss_fn)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# call this on our initial parmaeters\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m val, grad \u001b[38;5;241m=\u001b[39m \u001b[43mloss_fn_val_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbeta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbeta0\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\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[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/autograd/differential_operators.py:138\u001b[0m, in \u001b[0;36mvalue_and_grad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvalue_and_grad\u001b[39m(fun, x):\n\u001b[1;32m    136\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Returns a function that returns both value and gradient. Suitable for use\u001b[39;00m\n\u001b[1;32m    137\u001b[0m \u001b[38;5;124;03m    in scipy.optimize\"\"\"\u001b[39;00m\n\u001b[0;32m--> 138\u001b[0m     vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    139\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m vspace(ans)\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    140\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue_and_grad only applies to real scalar-output \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    141\u001b[0m                         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunctions. Try jacobian, elementwise_grad or \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    142\u001b[0m                         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mholomorphic_grad.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m      9\u001b[0m     start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m     end_value, end_node \u001b[38;5;241m=\u001b[39m  \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m      9\u001b[0m     start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m     end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f..unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\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[0;32mIn[40], line 9\u001b[0m, in \u001b[0;36mloss_fn\u001b[0;34m(params, beta)\u001b[0m\n\u001b[1;32m      6\u001b[0m sim_data \u001b[38;5;241m=\u001b[39m web\u001b[38;5;241m.\u001b[39mrun(sim, task_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mphase_mask_example\u001b[39m\u001b[38;5;124m\"\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# grab the respective and total losses\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mintensity_diff_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43msim_data\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[38], line 25\u001b[0m, in \u001b[0;36mintensity_diff_fn\u001b[0;34m(sim_data)\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mintensity_diff_fn\u001b[39m(sim_data: td\u001b[38;5;241m.\u001b[39mSimulationData) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[1;32m     23\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Returns a measure for the amount of difference between desired and target intensity patterns.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m     intensity_measured, intensity_desired \u001b[38;5;241m=\u001b[39m \u001b[43mget_intensities\u001b[49m\u001b[43m(\u001b[49m\u001b[43msim_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     27\u001b[0m     \u001b[38;5;66;03m# normalize the measured intensity such that there's the same \"power\" in the signal as expected in the logo\u001b[39;00m\n\u001b[1;32m     28\u001b[0m     intensity_measured \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmean(intensity_desired) \u001b[38;5;241m/\u001b[39m intensity_norm_mean\n",
+      "Cell \u001b[0;32mIn[38], line 6\u001b[0m, in \u001b[0;36mget_intensities\u001b[0;34m(sim_data)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# first, grab the dataset storing the intensity values and coordinates\u001b[39;00m\n\u001b[1;32m      5\u001b[0m mnt_out_name \u001b[38;5;241m=\u001b[39m mnt_out\u001b[38;5;241m.\u001b[39mname\n\u001b[0;32m----> 6\u001b[0m intensity_dataset \u001b[38;5;241m=\u001b[39m \u001b[43msim_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_intensity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmnt_out\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m xs \u001b[38;5;241m=\u001b[39m intensity_dataset\u001b[38;5;241m.\u001b[39mcoords[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m      8\u001b[0m ys \u001b[38;5;241m=\u001b[39m intensity_dataset\u001b[38;5;241m.\u001b[39mcoords[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
+      "File \u001b[0;32m~/Documents/Flexcompute/tidy3d/tidy3d/components/data/sim_data.py:333\u001b[0m, in \u001b[0;36mAbstractYeeGridSimulationData.get_intensity\u001b[0;34m(self, field_monitor_name)\u001b[0m\n\u001b[1;32m    319\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_intensity\u001b[39m(\u001b[38;5;28mself\u001b[39m, field_monitor_name: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m xr\u001b[38;5;241m.\u001b[39mDataArray:\n\u001b[1;32m    320\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"return `xarray.DataArray` of the intensity of a field monitor at Yee cell centers.\u001b[39;00m\n\u001b[1;32m    321\u001b[0m \n\u001b[1;32m    322\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    331\u001b[0m \u001b[38;5;124;03m        Data is interpolated to the center locations on Yee grid.\u001b[39;00m\n\u001b[1;32m    332\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 333\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_get_scalar_field\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    334\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfield_monitor_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfield_monitor_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfield_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mE\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mabs^2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m    335\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/Flexcompute/tidy3d/tidy3d/components/data/sim_data.py:230\u001b[0m, in \u001b[0;36mAbstractYeeGridSimulationData._get_scalar_field\u001b[0;34m(self, field_monitor_name, field_name, val, phase)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"return ``xarray.DataArray`` of the scalar field of a given monitor at Yee cell centers.\u001b[39;00m\n\u001b[1;32m    211\u001b[0m \n\u001b[1;32m    212\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[38;5;124;03m    Data is interpolated to the center locations on Yee grid.\u001b[39;00m\n\u001b[1;32m    228\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    229\u001b[0m field_monitor_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mload_field_monitor(field_monitor_name)\n\u001b[0;32m--> 230\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_get_scalar_field_from_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    231\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfield_monitor_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfield_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfield_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mphase\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mphase\u001b[49m\n\u001b[1;32m    232\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/Flexcompute/tidy3d/tidy3d/components/data/sim_data.py:270\u001b[0m, in \u001b[0;36mAbstractYeeGridSimulationData._get_scalar_field_from_data\u001b[0;34m(self, field_monitor_data, field_name, val, phase)\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m Tidy3dKeyError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPoynting component \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfield_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not available\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    269\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 270\u001b[0m     dataset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_at_boundaries\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfield_monitor_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    272\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_phase(data\u001b[38;5;241m=\u001b[39mdataset, phase\u001b[38;5;241m=\u001b[39mphase)\n\u001b[1;32m    274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m field_name \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mE\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mH\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mS\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    275\u001b[0m     \u001b[38;5;66;03m# Gather vector components\u001b[39;00m\n",
+      "File \u001b[0;32m~/Documents/Flexcompute/tidy3d/tidy3d/components/data/sim_data.py:104\u001b[0m, in \u001b[0;36mAbstractYeeGridSimulationData._at_boundaries\u001b[0;34m(self, monitor_data)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m monitor_data\u001b[38;5;241m.\u001b[39mpackage_colocate_results(monitor_data\u001b[38;5;241m.\u001b[39mfield_components)\n\u001b[1;32m    103\u001b[0m \u001b[38;5;66;03m# colocate to monitor grid boundaries\u001b[39;00m\n\u001b[0;32m--> 104\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmonitor_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mat_coords\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmonitor_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolocation_boundaries\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/Flexcompute/tidy3d/tidy3d/components/data/monitor_data.py:312\u001b[0m, in \u001b[0;36mAbstractFieldData.at_coords\u001b[0;34m(self, coords)\u001b[0m\n\u001b[1;32m    309\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m(ncoords \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m ncoords \u001b[38;5;129;01min\u001b[39;00m coord_lens):\n\u001b[1;32m    310\u001b[0m         xyz_kwargs[dim] \u001b[38;5;241m=\u001b[39m coords_dim\n\u001b[0;32m--> 312\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[43mcolocate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mxyz_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/Flexcompute/tidy3d/tidy3d/components/data/dataset.py:140\u001b[0m, in \u001b[0;36mAbstractFieldDataset.colocate\u001b[0;34m(self, x, y, z)\u001b[0m\n\u001b[1;32m    132\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m coord_data\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    133\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m DataError(\n\u001b[1;32m    134\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolocate given \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcoord_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcoords_supplied\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    135\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata only has one coordinate at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcoord_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcoord_data[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    136\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTherefore, can\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt colocate along this dimension. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    137\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msupply \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcoord_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=None to skip it.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    138\u001b[0m             )\n\u001b[0;32m--> 140\u001b[0m     centered_fields[field_name] \u001b[38;5;241m=\u001b[39m \u001b[43mfield_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minterp\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msupplied_coord_map\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbounds_error\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m}\u001b[49m\n\u001b[1;32m    142\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    144\u001b[0m \u001b[38;5;66;03m# combine all centered fields in a dataset\u001b[39;00m\n\u001b[1;32m    145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpackage_colocate_results(centered_fields)\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/xarray/core/dataarray.py:2319\u001b[0m, in \u001b[0;36mDataArray.interp\u001b[0;34m(self, coords, method, assume_sorted, kwargs, **coords_kwargs)\u001b[0m\n\u001b[1;32m   2193\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Interpolate a DataArray onto new coordinates\u001b[39;00m\n\u001b[1;32m   2194\u001b[0m \n\u001b[1;32m   2195\u001b[0m \u001b[38;5;124;03mPerforms univariate or multivariate interpolation of a DataArray onto\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2316\u001b[0m \u001b[38;5;124;03m  * y        (y) int64 24B 11 13 15\u001b[39;00m\n\u001b[1;32m   2317\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   2318\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muifc\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 2319\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   2320\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minterp only works for a numeric type array. Given \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2321\u001b[0m     )\n\u001b[1;32m   2322\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_to_temp_dataset()\u001b[38;5;241m.\u001b[39minterp(\n\u001b[1;32m   2323\u001b[0m     coords,\n\u001b[1;32m   2324\u001b[0m     method\u001b[38;5;241m=\u001b[39mmethod,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2327\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcoords_kwargs,\n\u001b[1;32m   2328\u001b[0m )\n\u001b[1;32m   2329\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_from_temp_dataset(ds)\n",
+      "\u001b[0;31mTypeError\u001b[0m: interp only works for a numeric type array. Given object."
+     ]
+    }
+   ],
+   "source": [
+    "# construct a funciton of `params` and `beta` that returns the loss value, gradient, and the aux_data\n",
+    "loss_fn_val_grad = autograd.value_and_grad(loss_fn)\n",
+    "\n",
+    "# call this on our initial parmaeters\n",
+    "val, grad = loss_fn_val_grad(params0, beta=beta0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea8e2fa9-e672-4b78-a8ce-d6369d84d412",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# penalty = aux_data[\"penalty\"]\n",
+    "# intensity_diff = aux_data[\"intensity_diff\"]\n",
+    "\n",
+    "print(f\"initial loss value = {val:.3f}\")\n",
+    "# print(f\" - penalty contribution = {penalty:.3f}\")\n",
+    "# print(f\" - intensity difference contribution = {intensity_diff:.3f}\")\n",
+    "print(f\"gradient shape = {grad.shape:}\")\n",
+    "print(f\"norm of gradient = {anp.linalg.norm(grad):.3e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d28f684-f441-4844-8ace-b5f711a9f3aa",
+   "metadata": {},
+   "source": [
+    "Looks good! We get a reasonable loss value, our gradient has the expected shape, and it's non-zero (which can often indicate some issue concerning the flow of the gradient through the objective function.\n",
+    "\n",
+    "> Note: we passed `has_aux=True`, which means that we tell `autograd` that the 2nd return value (`aux_data`) should be ignored in the gradient calculation. In other words, it tells `autograd` to only consider the gradient w.r.t. the first output (`total_loss`).\n",
+    "\n",
+    "Let's also visualize the fields for another sanity check, which we can grab from the `aux_data`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6fdffd3-6947-43e0-ac78-341016bc8561",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "sim_data = aux_data[\"sim_data\"]\n",
+    "sim_data.plot_field(field_monitor_name=\"side\", field_name=\"Ex\", val=\"real\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f150383-ebdc-42c8-a020-c94da1a587a1",
+   "metadata": {},
+   "source": [
+    "## Optimize Device\n",
+    "\n",
+    "Now we are finally ready to optimize our device.\n",
+    "\n",
+    "As in the other tutorials, we use the implementation of \"Adam Optimization\" from `optax`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7ed00dc-0e76-488b-aaa5-ee7a4a15d9cd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import optax\n",
+    "\n",
+    "# hyperparameters\n",
+    "num_steps = 35\n",
+    "learning_rate = 0.75\n",
+    "\n",
+    "# initialize adam optimizer with starting parameters\n",
+    "params = params0.copy()\n",
+    "optimizer = optax.adam(learning_rate=learning_rate)\n",
+    "opt_state = optimizer.init(params)\n",
+    "\n",
+    "# store history\n",
+    "history = dict(loss=[], params=[], betas=[], penalty=[], intensity_diff=[], sim_data=[])\n",
+    "\n",
+    "# gradually increase the binarization strength over iteration\n",
+    "beta_increment = 0.5\n",
+    "beta = beta0\n",
+    "\n",
+    "for i in range(num_steps):\n",
+    "    print(f\"step = ({i + 1} / {num_steps})\")\n",
+    "\n",
+    "    # compute gradient and current loss funciton value\n",
+    "    loss, gradient = loss_fn_val_grad(params, beta=beta)\n",
+    "    # penalty = aux_data[\"penalty\"]\n",
+    "    # intensity_diff = aux_data[\"intensity_diff\"]\n",
+    "    \n",
+    "    # save history\n",
+    "    history[\"loss\"].append(loss)\n",
+    "    history[\"params\"].append(params)\n",
+    "    history[\"betas\"].append(beta)\n",
+    "    # history[\"penalty\"].append(penalty)\n",
+    "    # history[\"intensity_diff\"].append(intensity_diff)\n",
+    "    history[\"sim_data\"].append(aux_data[\"sim_data\"])\n",
+    "    \n",
+    "    # log some output\n",
+    "    print(f\"\\tloss = {loss:.3e}\")\n",
+    "    # print(f\"\\t\\tpenalty = {penalty:.3e}\")\n",
+    "    # print(f\"\\t\\tintensity difference = {intensity_diff:.3e}\")\n",
+    "    print(f\"\\tbeta = {beta:.2f}\")\n",
+    "    print(f\"\\t|gradient| = {np.linalg.norm(gradient):.3e}\")    \n",
+    "    \n",
+    "    # compute and apply updates to the optimizer based on gradient (+1 sign to minimize loss_fn)\n",
+    "    updates, opt_state = optimizer.update(+gradient, opt_state, params)\n",
+    "    params = optax.apply_updates(params, updates)\n",
+    "\n",
+    "    # cap the parameters between their bounds\n",
+    "    params = anp.minimum(params, 1.0)\n",
+    "    params = anp.maximum(params, 0.0)\n",
+    "    \n",
+    "    # update the beta value\n",
+    "    beta += beta_increment\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5f4797d-db68-47d3-9f5d-2c97d602a682",
+   "metadata": {},
+   "source": [
+    "## Analyze Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1e552cf-70e7-45fc-9969-d3a93ff00454",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "First, let's plot the objective function history."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb24f819-69ac-4669-97af-ad5d4b266368",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(history[\"loss\"], label=\"total loss\")\n",
+    "# plt.plot(history[\"penalty\"], linestyle=\"-.\", label=\"penalty contribution\")\n",
+    "# plt.plot(history[\"intensity_diff\"], linestyle=\"--\", label=\"intensity contribution\")\n",
+    "plt.plot(np.zeros_like(history[\"loss\"]), linestyle=\":\", color=\"k\", label=\"no loss\")\n",
+    "\n",
+    "plt.xlabel(\"iteration number\")\n",
+    "plt.ylabel(\"loss value\")\n",
+    "\n",
+    "plt.title(\"loss function over optimization\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8b34678-1d12-4c62-8ce0-f51ab7eaf656",
+   "metadata": {},
+   "source": [
+    "Next let's plot the final device pattern."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "60477482-5eca-4047-93bd-3751cbccdf45",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# get the final parameters, construct the final simulation\n",
+    "params_final = history[\"params\"][-1]\n",
+    "beta_final = history[\"betas\"][-1]\n",
+    "sim_final = make_sim(params_final, beta=beta_final)\n",
+    "\n",
+    "# convert to regular `td.Simulation`\n",
+    "sim_final.plot_eps(z=z_center_slab, monitor_alpha=0)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5196e34-ab65-4a40-a742-de3149c577ff",
+   "metadata": {},
+   "source": [
+    "Let's run this simulation to see the final field patterns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "489932b3-5676-4dca-b7ca-ddb3ac9546c6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "sim_data_final = web.run(sim_final, task_name='Inspect')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d743ed6e-4e4e-4847-940b-1cfc7c541111",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "f, axes = plt.subplots(1, 2, figsize=(10, 4), tight_layout=True)\n",
+    "\n",
+    "for (ax, name) in zip(axes, (\"output\", \"side\")):\n",
+    "    sim_data_final.plot_field(field_monitor_name=name, field_name=\"E\", val=\"abs^2\", ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb141236-58ba-460e-9b33-52615bde2174",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Finally, we can create a nice figure combining everything."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a284bac-e2e8-453b-9715-573980cc8e28",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "f, ((ax0, ax1), (ax2, ax3), (ax4, ax5)) = plt.subplots(3, 2, figsize=(9, 10), tight_layout=True)\n",
+    "\n",
+    "# target intensity\n",
+    "im = ax0.imshow(np.rot90(intensity_desired), cmap=\"magma\")\n",
+    "ax0.set_aspect(\"equal\")\n",
+    "ax0.set_xlabel('x')\n",
+    "ax0.set_ylabel('y')\n",
+    "ax0.set_title('target intensity (normalized)')\n",
+    "plt.colorbar(im, ax=ax0)\n",
+    "\n",
+    "# optimization progress\n",
+    "ax1.plot(history[\"loss\"], label=\"total loss\")\n",
+    "# ax1.plot(history[\"penalty\"], linestyle=\"-.\", label=\"penalty contribution\")\n",
+    "# ax1.plot(history[\"intensity_diff\"], linestyle=\"--\", label=\"intensity contribution\")\n",
+    "ax1.plot(np.zeros_like(history[\"loss\"]), linestyle=\":\", color=\"k\", label=\"no loss\")\n",
+    "ax1.set_xlabel(\"iteration number\")\n",
+    "ax1.set_ylabel(\"loss value\")\n",
+    "ax1.set_title(\"loss function over optimization\")\n",
+    "ax1.legend()\n",
+    "\n",
+    "# ax1.plot(history[\"loss\"])\n",
+    "# ax1.set_xlabel(\"iterations\")\n",
+    "# ax1.set_ylabel(\"loss function\")\n",
+    "# ax1.set_title('optimization progress')\n",
+    "\n",
+    "# final device (top and sides)\n",
+    "sim_final.plot_eps(z=z_center_slab, ax=ax2)\n",
+    "ax2.set_title(\"final design\")\n",
+    "sim_final.plot_eps(x=0, ax=ax3)\n",
+    "ax3.set_title(\"cross section\")\n",
+    "\n",
+    "# final fields\n",
+    "vmin = None\n",
+    "vmax = None\n",
+    "for (ax, name) in zip((ax4, ax5), (\"output\", \"side\")):\n",
+    "    sim_data_final.plot_field(field_monitor_name=name, field_name=\"E\", val=\"abs^2\", vmin=vmin, vmax=vmax, ax=ax)\n",
+    "\n",
+    "# plt.savefig('phase_mask.png', dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2be21825-20fe-4d08-91e6-c802b94b009d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3d3792e-8e84-4414-8022-e9be437afdbe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "description": "This notebook demonstrates how to perform inverse design optimization of a diffractive metasurface in Tidy3D FDTD.",
+  "feature_image": "./img/adjoint_13.png",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "keywords": "inverse design, adjoint optimization, Tidy3D, FDTD",
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  },
+  "title": "Metasurface Inverse Design with Topology Optimization"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Autograd8WaveguideBend.ipynb b/Autograd8WaveguideBend.ipynb
index 389162bd..a7808564 100644
--- a/Autograd8WaveguideBend.ipynb
+++ b/Autograd8WaveguideBend.ipynb
@@ -574,14 +574,14 @@
     {
      "data": {
       "text/html": [
-       "
16:58:51 EDT WARNING: Structure at structures[3] was detected as being less than\n",
+       "
14:02:18 EDT WARNING: Structure at structures[3] was detected as being less than\n",
        "             half of a central wavelength from a PML on side x-min. To avoid    \n",
        "             inaccurate results or divergence, please increase gap between any  \n",
        "             structures and PML or fully extend structure through the pml.      \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m16:58:51 EDT\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure at structures\u001b[0m\u001b[1;31m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;31m]\u001b[0m\u001b[31m was detected as being less than\u001b[0m\n",
+       "\u001b[2;36m14:02:18 EDT\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure at structures\u001b[0m\u001b[1;31m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;31m]\u001b[0m\u001b[31m was detected as being less than\u001b[0m\n",
        "\u001b[2;36m             \u001b[0m\u001b[31mhalf of a central wavelength from a PML on side x-min. To avoid    \u001b[0m\n",
        "\u001b[2;36m             \u001b[0m\u001b[31minaccurate results or divergence, please increase gap between any  \u001b[0m\n",
        "\u001b[2;36m             \u001b[0m\u001b[31mstructures and PML or fully extend structure through the pml.      \u001b[0m\n"
@@ -822,7 +822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 35,
    "id": "f0a6b1e7-6d87-483e-a54e-ac8ca19a03fa",
    "metadata": {},
    "outputs": [
@@ -831,50 +831,50 @@
      "output_type": "stream",
      "text": [
       "step = 1\n",
-      "\tJ = 5.6873e-01\n",
-      "\tgrad_norm = 1.4787e+00\n",
+      "\tJ = 9.2138e-01\n",
+      "\tgrad_norm = 6.2367e-01\n",
       "step = 2\n",
-      "\tJ = 7.2474e-01\n",
-      "\tgrad_norm = 1.3420e+00\n",
+      "\tJ = 9.2868e-01\n",
+      "\tgrad_norm = 5.3839e-01\n",
       "step = 3\n",
-      "\tJ = 8.4718e-01\n",
-      "\tgrad_norm = 9.8658e-01\n",
+      "\tJ = 9.3446e-01\n",
+      "\tgrad_norm = 4.6491e-01\n",
       "step = 4\n",
-      "\tJ = 9.1464e-01\n",
-      "\tgrad_norm = 6.8390e-01\n",
+      "\tJ = 9.3848e-01\n",
+      "\tgrad_norm = 4.1674e-01\n",
       "step = 5\n",
-      "\tJ = 9.3531e-01\n",
-      "\tgrad_norm = 4.4853e-01\n",
+      "\tJ = 9.4096e-01\n",
+      "\tgrad_norm = 3.8653e-01\n",
       "step = 6\n",
-      "\tJ = 9.1917e-01\n",
-      "\tgrad_norm = 5.8707e-01\n",
+      "\tJ = 9.4283e-01\n",
+      "\tgrad_norm = 3.6536e-01\n",
       "step = 7\n",
-      "\tJ = 8.9048e-01\n",
-      "\tgrad_norm = 7.6125e-01\n",
+      "\tJ = 9.4402e-01\n",
+      "\tgrad_norm = 3.4951e-01\n",
       "step = 8\n",
-      "\tJ = 8.6774e-01\n",
-      "\tgrad_norm = 8.6826e-01\n",
+      "\tJ = 9.4478e-01\n",
+      "\tgrad_norm = 3.3834e-01\n",
       "step = 9\n",
-      "\tJ = 8.5773e-01\n",
-      "\tgrad_norm = 9.0074e-01\n",
+      "\tJ = 9.4525e-01\n",
+      "\tgrad_norm = 3.3148e-01\n",
       "step = 10\n",
-      "\tJ = 8.5703e-01\n",
-      "\tgrad_norm = 8.4340e-01\n",
+      "\tJ = 9.4530e-01\n",
+      "\tgrad_norm = 3.2976e-01\n",
       "step = 11\n",
-      "\tJ = 8.6281e-01\n",
-      "\tgrad_norm = 7.5339e-01\n",
+      "\tJ = 9.4528e-01\n",
+      "\tgrad_norm = 3.3262e-01\n",
       "step = 12\n",
-      "\tJ = 8.6980e-01\n",
-      "\tgrad_norm = 6.3775e-01\n",
+      "\tJ = 9.4530e-01\n",
+      "\tgrad_norm = 3.3605e-01\n",
       "step = 13\n",
-      "\tJ = 8.7591e-01\n",
-      "\tgrad_norm = 5.2366e-01\n",
+      "\tJ = 9.4522e-01\n",
+      "\tgrad_norm = 3.3898e-01\n",
       "step = 14\n",
-      "\tJ = 8.8279e-01\n",
-      "\tgrad_norm = 4.0568e-01\n",
+      "\tJ = 9.4518e-01\n",
+      "\tgrad_norm = 3.4022e-01\n",
       "step = 15\n",
-      "\tJ = 8.8637e-01\n",
-      "\tgrad_norm = 3.7248e-01\n"
+      "\tJ = 9.4537e-01\n",
+      "\tgrad_norm = 3.4036e-01\n"
      ]
     }
    ],
@@ -883,7 +883,7 @@
     "\n",
     "# hyperparameters\n",
     "num_steps = 15\n",
-    "learning_rate = 0.05\n",
+    "learning_rate = 0.01\n",
     "\n",
     "# initialize adam optimizer with starting parameters\n",
     "params = np.array(params).copy()\n",
@@ -931,13 +931,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 36,
    "id": "09aac45c-d7d8-4fd1-a599-0a7888257120",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -965,18 +965,447 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 37,
    "id": "d782b925-2af0-4bb8-bf87-88526a3f359f",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
↓ simulation_data.hdf5.gz ━━━━━━━━━━━━ 100.0% • 3.6/3.6 MB • 10.0 MB/s • 0:00:00\n",
+       "
15:22:50 EDT Created task 'start' with task_id                                  \n",
+       "             'fdve-1b7ee63f-179c-4f2b-90d3-e233f1c19a50' and task_type 'FDTD'.  \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1;32m↓\u001b[0m \u001b[1;34msimulation_data.hdf5.gz\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m3.6/3.6 MB\u001b[0m • \u001b[31m10.0 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
+       "\u001b[2;36m15:22:50 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'start'\u001b[0m with task_id                                  \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-1b7ee63f-179c-4f2b-90d3-e233f1c19a50'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             View task using web UI at                                          \n",
+       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-1b7ee63f-179\n",
+       "             c-4f2b-90d3-e233f1c19a50'.                                         \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=622749;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1b7ee63f-179c-4f2b-90d3-e233f1c19a50\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=56568;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1b7ee63f-179c-4f2b-90d3-e233f1c19a50\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=622749;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1b7ee63f-179c-4f2b-90d3-e233f1c19a50\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=360905;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1b7ee63f-179c-4f2b-90d3-e233f1c19a50\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=622749;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1b7ee63f-179c-4f2b-90d3-e233f1c19a50\u001b\\\u001b[32m-1b7ee63f-179\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=622749;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1b7ee63f-179c-4f2b-90d3-e233f1c19a50\u001b\\\u001b[32mc-4f2b-90d3-e233f1c19a50'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30568b43d39a40fd8e95cd1f0ec6a603",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
15:22:51 EDT status = success                                                   \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:22:51 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc5521f4d5f8406c87b23ee77f24e1ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
15:22:52 EDT loading simulation from simulation_data.hdf5                       \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:22:52 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             Created task 'final' with task_id                                  \n",
+       "             'fdve-fbfacc7c-6e73-4f1c-851a-c2e1608af9a5' and task_type 'FDTD'.  \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'final'\u001b[0m with task_id                                  \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-fbfacc7c-6e73-4f1c-851a-c2e1608af9a5'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             View task using web UI at                                          \n",
+       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-fbfacc7c-6e7\n",
+       "             3-4f1c-851a-c2e1608af9a5'.                                         \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=724872;https://tidy3d.simulation.cloud/workbench?taskId=fdve-fbfacc7c-6e73-4f1c-851a-c2e1608af9a5\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=644835;https://tidy3d.simulation.cloud/workbench?taskId=fdve-fbfacc7c-6e73-4f1c-851a-c2e1608af9a5\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=724872;https://tidy3d.simulation.cloud/workbench?taskId=fdve-fbfacc7c-6e73-4f1c-851a-c2e1608af9a5\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=306117;https://tidy3d.simulation.cloud/workbench?taskId=fdve-fbfacc7c-6e73-4f1c-851a-c2e1608af9a5\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=724872;https://tidy3d.simulation.cloud/workbench?taskId=fdve-fbfacc7c-6e73-4f1c-851a-c2e1608af9a5\u001b\\\u001b[32m-fbfacc7c-6e7\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=724872;https://tidy3d.simulation.cloud/workbench?taskId=fdve-fbfacc7c-6e73-4f1c-851a-c2e1608af9a5\u001b\\\u001b[32m3-4f1c-851a-c2e1608af9a5'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a79901643cbc4fd8b87b9685fbce42cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
15:22:54 EDT status = queued                                                    \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:22:54 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             To cancel the simulation, use 'web.abort(task_id)' or              \n",
+       "             'web.delete(task_id)' or abort/delete the task in the web UI.      \n",
+       "             Terminating the Python script will not stop the job running on the \n",
+       "             cloud.                                                             \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
+       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
+       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
15:22:57 EDT status = preprocess                                                \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:22:57 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
15:22:59 EDT Maximum FlexCredit cost: 0.029. Use 'web.real_cost(task_id)' to get\n",
+       "             the billed FlexCredit cost after a simulation run.                 \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:22:59 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.029\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get\n",
+       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             starting up solver                                                 \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             running solver                                                     \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28a623e24eb04dd2b4be599f45b62318",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
15:23:07 EDT early shutoff detected at 32%, exiting.                            \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:23:07 EDT\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m32\u001b[0m%, exiting.                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             status = postprocess                                               \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
15:23:08 EDT status = success                                                   \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:23:08 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
15:23:09 EDT View simulation result at                                          \n",
+       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-fbfacc7c-6e7\n",
+       "             3-4f1c-851a-c2e1608af9a5'.                                         \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m15:23:09 EDT\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=99845;https://tidy3d.simulation.cloud/workbench?taskId=fdve-fbfacc7c-6e73-4f1c-851a-c2e1608af9a5\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=136600;https://tidy3d.simulation.cloud/workbench?taskId=fdve-fbfacc7c-6e73-4f1c-851a-c2e1608af9a5\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=99845;https://tidy3d.simulation.cloud/workbench?taskId=fdve-fbfacc7c-6e73-4f1c-851a-c2e1608af9a5\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=579786;https://tidy3d.simulation.cloud/workbench?taskId=fdve-fbfacc7c-6e73-4f1c-851a-c2e1608af9a5\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=99845;https://tidy3d.simulation.cloud/workbench?taskId=fdve-fbfacc7c-6e73-4f1c-851a-c2e1608af9a5\u001b\\\u001b[4;34m-fbfacc7c-6e7\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=99845;https://tidy3d.simulation.cloud/workbench?taskId=fdve-fbfacc7c-6e73-4f1c-851a-c2e1608af9a5\u001b\\\u001b[4;34m3-4f1c-851a-c2e1608af9a5'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb11472151de4bf8ab93e6d9aeced934",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
       ]
      },
      "metadata": {},
@@ -1008,11 +1437,11 @@
     {
      "data": {
       "text/html": [
-       "
17:54:02 EDT loading simulation from simulation_data.hdf5                       \n",
+       "
15:23:10 EDT loading simulation from simulation_data.hdf5                       \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m17:54:02 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
+       "\u001b[2;36m15:23:10 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
       ]
      },
      "metadata": {},
@@ -1039,13 +1468,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 38,
    "id": "7ec2b20e-74de-4b82-949f-591696437315",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1068,13 +1497,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 39,
    "id": "a2dddafd-8cee-42fd-a1c7-ddc94f4ef9e8",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1111,7 +1540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 40,
    "id": "9075ae74-7b0a-4336-98a6-0b89a650d0d3",
    "metadata": {},
    "outputs": [],
@@ -1121,7 +1550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 41,
    "id": "93a9b8e8-7581-4fb3-a3f4-f718a80082d0",
    "metadata": {},
    "outputs": [
@@ -1129,7 +1558,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "transmission of 89.229 %\n"
+      "transmission of 94.659 %\n"
      ]
     }
    ],
@@ -1151,7 +1580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 42,
    "id": "e6dc501b-54ef-4ef5-b54d-a45ce55af44e",
    "metadata": {},
    "outputs": [],
diff --git a/misc/inverse_des_wg_bend.gds b/misc/inverse_des_wg_bend.gds
index a4b2ab86..1738b31d 100644
Binary files a/misc/inverse_des_wg_bend.gds and b/misc/inverse_des_wg_bend.gds differ