{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f543de9b-3858-45dd-bddf-cddddcc60da3",
   "metadata": {},
   "source": [
    "# Part 1: Getting Started with Sionna"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c55670f1-3ee6-481d-9661-d59e61074818",
   "metadata": {},
   "source": [
    "This tutorial will guide you through Sionna, from its basic principles to the implementation of a point-to-point link with a 5G NR compliant code and a 3GPP channel model.\n",
    "You will also learn how to write custom trainable layers by implementing a state of the art neural receiver, and how to train and evaluate end-to-end communication systems.\n",
    "\n",
    "The tutorial is structured in four notebooks:\n",
    "\n",
    "- **Part I: Getting started with Sionna**\n",
    "\n",
    "- Part II: Differentiable Communication Systems\n",
    "\n",
    "- Part III: Advanced Link-level Simulations\n",
    "\n",
    "- Part IV: Toward Learned Receivers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b226f39",
   "metadata": {},
   "source": [
    "The [official documentation](https://nvlabs.github.io/sionna/index.html) provides key material on how to use Sionna and how its components are implemented."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c4211f-beee-4142-9289-c385d0180877",
   "metadata": {},
   "source": [
    "* [Imports & Basics](#Imports-&-Basics)\n",
    "* [A note on random number generation](#A-note-on-random-number-generation)\n",
    "* [Sionna Data-flow and Design Paradigms](#Sionna-Data-flow-and-Design-Paradigms)\n",
    "* [Hello, Sionna!](#Hello,-Sionna!)\n",
    "* [Communication Systems as Models](#Communication-Systems-as-sionna-blocks)\n",
    "* [Forward Error Correction](#Forward-Error-Correction-(FEC))\n",
    "* [Eager vs. Compiled Mode](#Eager-vs-Compiled-Mode)\n",
    "* [Exercise](#Exercise)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a184ac-1f64-407f-9a24-c53d40799be2",
   "metadata": {},
   "source": [
    "## Imports & Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d84bd69e-59f2-4a42-8dd3-730c8c1d821e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:13.454911Z",
     "iopub.status.busy": "2026-02-13T14:08:13.454728Z",
     "iopub.status.idle": "2026-02-13T14:08:16.264354Z",
     "shell.execute_reply": "2026-02-13T14:08:16.263125Z"
    }
   },
   "outputs": [],
   "source": [
    "import os # Configure which GPU \n",
    "if os.getenv(\"CUDA_VISIBLE_DEVICES\") is None:\n",
    "    gpu_num = 0 # Use \"\" to use the CPU\n",
    "    os.environ[\"CUDA_VISIBLE_DEVICES\"] = f\"{gpu_num}\"\n",
    "\n",
    "# Import Sionna\n",
    "try:\n",
    "    import sionna.phy\n",
    "except ImportError as e:\n",
    "    import sys\n",
    "    if 'google.colab' in sys.modules:\n",
    "       # Install Sionna in Google Colab\n",
    "       print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n",
    "       os.system(\"pip install sionna\")\n",
    "       os.kill(os.getpid(), 5)\n",
    "    else:\n",
    "       raise e\n",
    "\n",
    "# Import PyTorch\n",
    "import torch\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# For plotting\n",
    "%matplotlib inline\n",
    "# also try %matplotlib widget\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# for performance measurements\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42d471a3",
   "metadata": {},
   "source": [
    "We can now access Sionna functions within the `sn` namespace.\n",
    "\n",
    "**Hint**: In Jupyter notebooks, you can run bash commands with `!`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec4eb0ab-a34e-45e7-9055-a1555d6f0775",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:16.266357Z",
     "iopub.status.busy": "2026-02-13T14:08:16.266078Z",
     "iopub.status.idle": "2026-02-13T14:08:16.504753Z",
     "shell.execute_reply": "2026-02-13T14:08:16.503773Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri Feb 13 15:08:16 2026       \r\n",
      "+-----------------------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 580.126.09             Driver Version: 580.126.09     CUDA Version: 13.0     |\r\n",
      "+-----------------------------------------+------------------------+----------------------+\r\n",
      "| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\r\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\r\n",
      "|                                         |                        |               MIG M. |\r\n",
      "|=========================================+========================+======================|\r\n",
      "|   0  NVIDIA GeForce RTX 3090        Off |   00000000:01:00.0 Off |                  N/A |\r\n",
      "| 30%   44C    P8             29W /  350W |    4108MiB /  24576MiB |      0%      Default |\r\n",
      "|                                         |                        |                  N/A |\r\n",
      "+-----------------------------------------+------------------------+----------------------+\r\n",
      "|   1  NVIDIA GeForce RTX 3090        Off |   00000000:4D:00.0 Off |                  N/A |\r\n",
      "| 30%   35C    P8             30W /  350W |       4MiB /  24576MiB |      0%      Default |\r\n",
      "|                                         |                        |                  N/A |\r\n",
      "+-----------------------------------------+------------------------+----------------------+\r\n",
      "\r\n",
      "+-----------------------------------------------------------------------------------------+\r\n",
      "| Processes:                                                                              |\r\n",
      "|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |\r\n",
      "|        ID   ID                                                               Usage      |\r\n",
      "|=========================================================================================|\r\n",
      "|    0   N/A  N/A          289332      C   ...cts/sionna-2/.venv/bin/python       4098MiB |\r\n",
      "+-----------------------------------------------------------------------------------------+\r\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "865f1607-afc4-4f89-a79f-676c61591bb7",
   "metadata": {},
   "source": [
    "## A note on random number generation\n",
    "When Sionna is loaded, it instantiates random number generators (RNGs) for [Python](https://docs.python.org/3/library/random.html#alternative-generator),\n",
    "[NumPy](https://numpy.org/doc/stable/reference/random/generator.html), and [PyTorch](https://pytorch.org/docs/stable/generated/torch.Generator.html). You can optionally set a seed which will make all of your\n",
    "results deterministic, as long as only these RNGs are used. In the cell below,\n",
    "you can see how this seed is set and how the different RNGs can be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "953a834a-349d-471b-af47-0e7af410e8c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:16.506894Z",
     "iopub.status.busy": "2026-02-13T14:08:16.506735Z",
     "iopub.status.idle": "2026-02-13T14:08:16.646592Z",
     "shell.execute_reply": "2026-02-13T14:08:16.645564Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "5\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "sionna.phy.config.seed = 40\n",
    "\n",
    "# Python RNG - use instead of\n",
    "# import random\n",
    "# random.randint(0, 10)\n",
    "print(sionna.phy.config.py_rng.randint(0,10))\n",
    "\n",
    "# NumPy RNG - use instead of\n",
    "# import numpy as np\n",
    "# np.random.randint(0, 10)\n",
    "print(sionna.phy.config.np_rng.integers(0,10))\n",
    "\n",
    "# PyTorch RNG - use instead of\n",
    "# import torch\n",
    "# torch.randint(0, 10, (1,))\n",
    "torch_gen = sionna.phy.config.torch_rng()\n",
    "device = sionna.phy.config.device\n",
    "print(torch.randint(0, 10, (1,), device=device, generator=torch_gen).item())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6294919-0e78-4d6b-835f-55631e6264f6",
   "metadata": {},
   "source": [
    "## Sionna Data-flow and Design Paradigms\n",
    "\n",
    "Sionna inherently parallelizes simulations via *batching*, i.e., each element in the batch dimension is simulated independently.\n",
    "\n",
    "This means the first tensor dimension is always used for *inter-frame* parallelization similar to an outer *for-loop* in Matlab/NumPy simulations, but operations can be operated in parallel.\n",
    "\n",
    "To keep the dataflow efficient, Sionna follows a few simple design principles:\n",
    "\n",
    "* Signal-processing components are implemented as an individual Sionna Blocks..\n",
    "* `torch.float32` is used as preferred datatype and `torch.complex64` for complex-valued datatypes, respectively.  \n",
    "This allows simpler re-use of components (e.g., the same scrambling layer can be used for binary inputs and LLR-values).\n",
    "* `torch.float64`/`torch.complex128` are available when high precision is needed.\n",
    "* Models can be developed in *eager mode* allowing simple (and fast) modification of system parameters.\n",
    "* Number crunching simulations can be executed with *torch.compile* for acceleration.\n",
    "* Whenever possible, components are automatically differentiable via [autograd](https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html) to simplify the deep learning design-flow.\n",
    "* Code is structured into sub-packages for different tasks such as channel coding, mapping,... (see [API documentation](https://nvlabs.github.io/sionna/phy/api/phy.html) for details).\n",
    "\n",
    "These paradigms simplify the re-useability and reliability of our components for a wide range of communications related applications."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fce869c5-5169-4c66-833c-8144414cdda4",
   "metadata": {},
   "source": [
    "## Hello, Sionna!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b52449ca-532c-4c45-8e03-464832717200",
   "metadata": {},
   "source": [
    "Let's start with a very simple simulation: Transmitting QAM symbols over an AWGN channel. We will implement the system shown in the figure below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "576ea7ca-3dda-4c09-868d-1e2440d35b00",
   "metadata": {},
   "source": [
    "![QAM AWGN](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "777704cf-0855-4984-86df-b615890811ae",
   "metadata": {},
   "source": [
    "We will use upper case for naming simulation parameters that are used throughout this notebook\n",
    "\n",
    "Every layer needs to be initialized once before it can be used.\n",
    "\n",
    "**Tip**: Use the [API documentation](https://nvlabs.github.io/sionna/phy/api/phy.html) to find an overview of all existing components.\n",
    "You can directly access the signature and the docstring within jupyter via `Shift+TAB`.\n",
    "\n",
    "*Remark*: Most layers are defined to be complex-valued.\n",
    "\n",
    "We first need to create a QAM constellation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21ccb7b5-b590-4abb-95f7-d939cb283f02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:16.648383Z",
     "iopub.status.busy": "2026-02-13T14:08:16.648248Z",
     "iopub.status.idle": "2026-02-13T14:08:16.772860Z",
     "shell.execute_reply": "2026-02-13T14:08:16.771989Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NUM_BITS_PER_SYMBOL = 2 # QPSK\n",
    "constellation = sionna.phy.mapping.Constellation(\"qam\", NUM_BITS_PER_SYMBOL)\n",
    "\n",
    "constellation.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ded88c1-1576-42a5-a180-5bd8da533470",
   "metadata": {
    "tags": []
   },
   "source": [
    "**Task:** Try to change the modulation order, e.g., to 16-QAM."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcb7b6f1-5dbc-4335-b633-177da8e8a5e3",
   "metadata": {
    "tags": []
   },
   "source": [
    "We then need to setup a mapper to map bits into constellation points. The mapper takes as parameter the constellation.\n",
    "\n",
    "We also need to setup a corresponding demapper to compute log-likelihood ratios (LLRs) from received noisy samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ae9da488-bd0b-40d7-b9fd-31bc6ad7ca3d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:16.774670Z",
     "iopub.status.busy": "2026-02-13T14:08:16.774531Z",
     "iopub.status.idle": "2026-02-13T14:08:16.780411Z",
     "shell.execute_reply": "2026-02-13T14:08:16.779572Z"
    }
   },
   "outputs": [],
   "source": [
    "mapper = sionna.phy.mapping.Mapper(constellation=constellation)\n",
    "\n",
    "# The demapper uses the same constellation object as the mapper\n",
    "demapper = sionna.phy.mapping.Demapper(\"app\", constellation=constellation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "980fe9ce",
   "metadata": {},
   "source": [
    "**Tip**: You can access the signature+docstring via `?` command and print the complete class definition via `??` operator.\n",
    "\n",
    "Obviously, you can also access the source code via [https://github.com/nvlabs/sionna/](https://github.com/nvlabs/sionna/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10f6b1c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:16.782044Z",
     "iopub.status.busy": "2026-02-13T14:08:16.781918Z",
     "iopub.status.idle": "2026-02-13T14:08:16.818184Z",
     "shell.execute_reply": "2026-02-13T14:08:16.817283Z"
    }
   },
   "outputs": [],
   "source": [
    "# print class definition of the Constellation class\n",
    "sionna.phy.mapping.Mapper??"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41c23abb-5163-4e08-a2c8-a4b912046b30",
   "metadata": {},
   "source": [
    "As can be seen, the `Mapper` class inherits from `Block`, i.e., implements a *Sionna Block*. These blocks can be connected by simply feeding the output of one block to the next block. This allows to simply build complex systems. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5d88fc6-f18d-49e7-b66c-98c6361f4a31",
   "metadata": {},
   "source": [
    "Sionna provides as utility a binary source to sample uniform i.i.d. bits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8c64222f-1a4b-42da-a37a-3379034de9cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:16.819898Z",
     "iopub.status.busy": "2026-02-13T14:08:16.819764Z",
     "iopub.status.idle": "2026-02-13T14:08:16.822483Z",
     "shell.execute_reply": "2026-02-13T14:08:16.821612Z"
    }
   },
   "outputs": [],
   "source": [
    "binary_source = sionna.phy.mapping.BinarySource()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bdaf6cb-5de8-4306-8701-b0e75bbc3fcf",
   "metadata": {},
   "source": [
    "Finally, we need the AWGN channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2665b1a1-945a-4b8c-bc35-49af1b0c26ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:16.824009Z",
     "iopub.status.busy": "2026-02-13T14:08:16.823892Z",
     "iopub.status.idle": "2026-02-13T14:08:16.826417Z",
     "shell.execute_reply": "2026-02-13T14:08:16.825626Z"
    }
   },
   "outputs": [],
   "source": [
    "awgn_channel = sionna.phy.channel.AWGN()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "261ad816-d4cf-424a-9dff-61444637e4dd",
   "metadata": {},
   "source": [
    "Sionna provides a utility function to compute the noise power spectral density ratio $N_0$ from the energy per bit to noise power spectral density ratio $E_b/N_0$ in dB and a variety of parameters such as the coderate and the nunber of bits per symbol."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4feb569f-dcc3-465f-94c5-e5d25fad97b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:16.827942Z",
     "iopub.status.busy": "2026-02-13T14:08:16.827817Z",
     "iopub.status.idle": "2026-02-13T14:08:16.889240Z",
     "shell.execute_reply": "2026-02-13T14:08:16.888220Z"
    }
   },
   "outputs": [],
   "source": [
    "no = sionna.phy.utils.ebnodb2no(ebno_db=10.0,\n",
    "                        num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "                        coderate=1.0) # Coderate set to 1 as we do uncoded transmission here"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efcbe1b0-39d9-4d0f-98bc-84343a46099c",
   "metadata": {},
   "source": [
    "We now have all the components we need to transmit QAM symbols over an AWGN channel.\n",
    "\n",
    "Sionna natively supports multi-dimensional tensors.\n",
    "\n",
    "Most layers operate at the last dimension and can have arbitrary input shapes (preserved at output)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5747f93e-c89e-41b5-bc77-b6eb13aba4d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:16.891051Z",
     "iopub.status.busy": "2026-02-13T14:08:16.890916Z",
     "iopub.status.idle": "2026-02-13T14:08:17.066734Z",
     "shell.execute_reply": "2026-02-13T14:08:17.065881Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of bits:  torch.Size([64, 1024])\n",
      "Shape of x:  torch.Size([64, 512])\n",
      "Shape of y:  torch.Size([64, 512])\n",
      "Shape of llr:  torch.Size([64, 1024])\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 64 # How many examples are processed by Sionna in parallel\n",
    "\n",
    "bits = binary_source([BATCH_SIZE,\n",
    "                      1024]) # Blocklength\n",
    "print(\"Shape of bits: \", bits.shape)\n",
    "\n",
    "x = mapper(bits)\n",
    "print(\"Shape of x: \", x.shape)\n",
    "\n",
    "y = awgn_channel(x, no)\n",
    "print(\"Shape of y: \", y.shape)\n",
    "\n",
    "llr = demapper(y, no)\n",
    "print(\"Shape of llr: \", llr.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aca7a98b",
   "metadata": {},
   "source": [
    "In *Eager* mode, we can directly access the values of each tensor. This simplifies debugging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "abffb7f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:17.068653Z",
     "iopub.status.busy": "2026-02-13T14:08:17.068513Z",
     "iopub.status.idle": "2026-02-13T14:08:17.104754Z",
     "shell.execute_reply": "2026-02-13T14:08:17.103856Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 8 transmitted bits: tensor([0., 1., 1., 1., 1., 0., 1., 0.], device='cuda:0')\n",
      "First 4 transmitted symbols: tensor([ 0.7100-0.7100j, -0.7100-0.7100j, -0.7100+0.7100j, -0.7100+0.7100j])\n",
      "First 4 received symbols: tensor([ 0.5900-0.5200j, -0.8000-0.6700j, -0.6300+0.7200j, -0.8800+0.4300j])\n",
      "First 8 demapped llrs: tensor([-33.3600,  29.5700,  45.0800,  38.0200,  35.8000, -40.6700,  49.7500,\n",
      "        -24.1700])\n"
     ]
    }
   ],
   "source": [
    "num_samples = 8 # how many samples shall be printed\n",
    "num_symbols = int(num_samples/NUM_BITS_PER_SYMBOL)\n",
    "\n",
    "print(f\"First {num_samples} transmitted bits: {bits[0,:num_samples]}\")\n",
    "print(f\"First {num_symbols} transmitted symbols: {np.round(x.cpu()[0,:num_symbols], 2)}\")\n",
    "print(f\"First {num_symbols} received symbols: {np.round(y.cpu()[0,:num_symbols], 2)}\")\n",
    "print(f\"First {num_samples} demapped llrs: {np.round(llr.cpu()[0,:num_samples], 2)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d92339a-44ae-45d9-a59c-bde99e3bd656",
   "metadata": {},
   "source": [
    "Let's visualize the received noisy samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "682509f0-e734-4aec-bc05-773bd576de16",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:17.106477Z",
     "iopub.status.busy": "2026-02-13T14:08:17.106340Z",
     "iopub.status.idle": "2026-02-13T14:08:17.336443Z",
     "shell.execute_reply": "2026-02-13T14:08:17.335656Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.axes().set_aspect(1)\n",
    "plt.grid(True)\n",
    "plt.title('Channel output')\n",
    "plt.xlabel('Real Part')\n",
    "plt.ylabel('Imaginary Part')\n",
    "plt.scatter(y.cpu().real.flatten().numpy(), y.cpu().imag.flatten().numpy())\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d415e3e0-65ee-488f-a29b-5909dce48ede",
   "metadata": {},
   "source": [
    "**Task:** One can play with the SNR to visualize the impact on the received samples.\n",
    "\n",
    "**Advanced Task:** Compare the LLR distribution for \"app\" demapping with \"maxlog\" demapping.\n",
    "The [Bit-Interleaved Coded Modulation](https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Bit_Interleaved_Coded_Modulation.html) example notebook can be helpful for this task.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfab1249-a6c1-430a-a204-a9c631a82700",
   "metadata": {},
   "source": [
    "## Communication Systems as Sionna Blocks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc184ba-c090-4443-9cd6-c217b3f64052",
   "metadata": {},
   "source": [
    "It is typically more convenient to wrap a Sionna-based communication system into a Sionna Block acting as end-to-end model.\n",
    "\n",
    "These models can be simply built by stacking different Sionna components (i.e., Sionna Blocks).\n",
    "\n",
    "The following cell implements the previous system as a end-to-end model.\n",
    "\n",
    "The key functions that need to be defined are `__init__()`, which instantiates the required components, and `__call()__`, which performs forward pass through the end-to-end system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ee71bdbf-14c7-464f-b90d-450cb7b070b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:17.339070Z",
     "iopub.status.busy": "2026-02-13T14:08:17.338929Z",
     "iopub.status.idle": "2026-02-13T14:08:17.343379Z",
     "shell.execute_reply": "2026-02-13T14:08:17.342486Z"
    }
   },
   "outputs": [],
   "source": [
    "class UncodedSystemAWGN(sionna.phy.Block):\n",
    "    def __init__(self, num_bits_per_symbol, block_length):\n",
    "        \"\"\"\n",
    "        A Sionna Block for uncoded transmission over the AWGN channel\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        num_bits_per_symbol: int\n",
    "            The number of bits per constellation symbol, e.g., 4 for QAM16.\n",
    "\n",
    "        block_length: int\n",
    "            The number of bits per transmitted message block (will be the codeword length later).\n",
    "\n",
    "        Input\n",
    "        -----\n",
    "        batch_size: int\n",
    "            The batch_size of the Monte-Carlo simulation.\n",
    "\n",
    "        ebno_db: float\n",
    "            The `Eb/No` value (=rate-adjusted SNR) in dB.\n",
    "\n",
    "        Output\n",
    "        ------\n",
    "        (bits, llr):\n",
    "            Tuple:\n",
    "\n",
    "        bits: torch.Tensor\n",
    "            A tensor of shape `[batch_size, block_length] of 0s and 1s\n",
    "            containing the transmitted information bits.\n",
    "\n",
    "        llr: torch.Tensor\n",
    "            A tensor of shape `[batch_size, block_length] containing the\n",
    "            received log-likelihood-ratio (LLR) values.\n",
    "        \"\"\"\n",
    "\n",
    "        super().__init__() # Must call the block initializer\n",
    "\n",
    "        self.num_bits_per_symbol = num_bits_per_symbol\n",
    "        self.block_length = block_length\n",
    "        self.constellation = sionna.phy.mapping.Constellation(\"qam\", self.num_bits_per_symbol)\n",
    "        self.mapper = sionna.phy.mapping.Mapper(constellation=self.constellation)\n",
    "        self.demapper = sionna.phy.mapping.Demapper(\"app\", constellation=self.constellation)\n",
    "        self.binary_source = sionna.phy.mapping.BinarySource()\n",
    "        self.awgn_channel = sionna.phy.channel.AWGN()\n",
    "\n",
    "    # @torch.compile # Enable compilation to speed things up\n",
    "    def call(self, batch_size, ebno_db):\n",
    "\n",
    "        # no channel coding used; we set coderate=1.0\n",
    "        no = sionna.phy.utils.ebnodb2no(ebno_db,\n",
    "                                num_bits_per_symbol=self.num_bits_per_symbol,\n",
    "                                coderate=1.0)\n",
    "\n",
    "        bits = self.binary_source([batch_size, self.block_length]) # Blocklength set to 1024 bits\n",
    "        x = self.mapper(bits)\n",
    "        y = self.awgn_channel(x, no)\n",
    "        llr = self.demapper(y,no)\n",
    "        return bits, llr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2397775-1a9d-467c-8048-0949a25bda02",
   "metadata": {},
   "source": [
    "We need first to instantiate the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3212d737-3ae0-4158-983b-6d0667cc1dd8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:17.345384Z",
     "iopub.status.busy": "2026-02-13T14:08:17.345265Z",
     "iopub.status.idle": "2026-02-13T14:08:17.348581Z",
     "shell.execute_reply": "2026-02-13T14:08:17.347752Z"
    }
   },
   "outputs": [],
   "source": [
    "model_uncoded_awgn = UncodedSystemAWGN(num_bits_per_symbol=NUM_BITS_PER_SYMBOL, block_length=1024)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e449509-4ba0-4e14-8a6a-91e55a7efec4",
   "metadata": {},
   "source": [
    "Sionna provides a utility to easily compute and plot the bit error rate (BER)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "34534be8-947d-4d29-8e58-1c7d0dce96c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:17.350501Z",
     "iopub.status.busy": "2026-02-13T14:08:17.350381Z",
     "iopub.status.idle": "2026-02-13T14:08:17.621634Z",
     "shell.execute_reply": "2026-02-13T14:08:17.620849Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -3.0 | 1.5825e-01 | 1.0000e+00 |      324103 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -2.579 | 1.4700e-01 | 1.0000e+00 |      301059 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -2.158 | 1.3469e-01 | 1.0000e+00 |      275847 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -1.737 | 1.2324e-01 | 1.0000e+00 |      252386 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -1.316 | 1.1249e-01 | 1.0000e+00 |      230371 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -0.895 | 1.0066e-01 | 1.0000e+00 |      206157 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -0.474 | 9.0547e-02 | 1.0000e+00 |      185440 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -0.053 | 7.9997e-02 | 1.0000e+00 |      163834 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    0.368 | 7.0215e-02 | 1.0000e+00 |      143800 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    0.789 | 6.0873e-02 | 1.0000e+00 |      124667 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    1.211 | 5.1930e-02 | 1.0000e+00 |      106353 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    1.632 | 4.4062e-02 | 1.0000e+00 |       90238 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    2.053 | 3.6719e-02 | 1.0000e+00 |       75201 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    2.474 | 3.0021e-02 | 1.0000e+00 |       61483 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    2.895 | 2.4250e-02 | 1.0000e+00 |       49663 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    3.316 | 1.8932e-02 | 1.0000e+00 |       38772 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    3.737 | 1.4715e-02 | 1.0000e+00 |       30136 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    4.158 | 1.1146e-02 | 1.0000e+00 |       22828 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    4.579 | 8.3711e-03 | 1.0000e+00 |       17144 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "      5.0 | 5.8662e-03 | 9.9950e-01 |       12014 |     2048000 |         1999 |        2000 |         0.0 |reached target block errors\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "EBN0_DB_MIN = -3.0 # Minimum value of Eb/N0 [dB] for simulations\n",
    "EBN0_DB_MAX = 5.0 # Maximum value of Eb/N0 [dB] for simulations\n",
    "BATCH_SIZE = 2000 # How many examples are processed by Sionna in parallel\n",
    "\n",
    "ber_plots = sionna.phy.utils.PlotBER(\"AWGN\")\n",
    "ber_plots.simulate(model_uncoded_awgn,\n",
    "                  ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 20),\n",
    "                  batch_size=BATCH_SIZE,\n",
    "                  num_target_block_errors=100, # simulate until 100 block errors occured\n",
    "                  legend=\"Uncoded\",\n",
    "                  soft_estimates=True,\n",
    "                  max_mc_iter=100, # run 100 Monte-Carlo simulations (each with batch_size samples)\n",
    "                  show_fig=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70e75524",
   "metadata": {},
   "source": [
    "The `sionna.phy.utils.PlotBER` object stores the results and allows to add additional simulations to the previous curves.\n",
    "\n",
    "*Remark*: In Sionna, a block error is defined to happen if for two tensors at least one position in the last dimension differs (i.e., at least one bit wrongly received per codeword).\n",
    "The bit error rate the total number of erroneous positions divided by the total number of transmitted bits."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c8f75c-65c8-4440-a8bd-fca7e3d8f35f",
   "metadata": {},
   "source": [
    "## Forward Error Correction (FEC)\n",
    "\n",
    "We now add channel coding to our transceiver to make it more robust against transmission errors. For this, we will use [5G compliant low-density parity-check (LDPC) codes and Polar codes](https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3214).\n",
    "You can find more detailed information in the notebooks [Bit-Interleaved Coded Modulation (BICM)](https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Bit_Interleaved_Coded_Modulation.html) and [5G Channel Coding and Rate-Matching: Polar vs. LDPC Codes](https://nvlabs.github.io/sionna/phy/tutorials/notebooks/5G_Channel_Coding_Polar_vs_LDPC_Codes.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b6800065",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:17.623782Z",
     "iopub.status.busy": "2026-02-13T14:08:17.623647Z",
     "iopub.status.idle": "2026-02-13T14:08:17.635623Z",
     "shell.execute_reply": "2026-02-13T14:08:17.634866Z"
    }
   },
   "outputs": [],
   "source": [
    "k = 12\n",
    "n = 20\n",
    "\n",
    "encoder = sionna.phy.fec.ldpc.LDPC5GEncoder(k, n)\n",
    "decoder = sionna.phy.fec.ldpc.LDPC5GDecoder(encoder, hard_out=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39673cbe",
   "metadata": {},
   "source": [
    "Let us encode some random input bits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6e940732",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:17.637332Z",
     "iopub.status.busy": "2026-02-13T14:08:17.637213Z",
     "iopub.status.idle": "2026-02-13T14:08:17.666684Z",
     "shell.execute_reply": "2026-02-13T14:08:17.665830Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input bits are: \n",
      " tensor([[0., 0., 1., 0., 0., 1., 1., 0., 0., 0., 0., 1.]], device='cuda:0')\n",
      "Encoded bits are: \n",
      " tensor([[0., 1., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 0., 1., 0., 1., 1.,\n",
      "         0., 1.]], device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 1 # one codeword in parallel\n",
    "u = binary_source([BATCH_SIZE, k])\n",
    "print(\"Input bits are: \\n\", u)\n",
    "\n",
    "c = encoder(u)\n",
    "print(\"Encoded bits are: \\n\", c)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99a5303a",
   "metadata": {},
   "source": [
    "One of the fundamental paradigms of Sionna is batch-processing.\n",
    "Thus, the example above could be executed for arbitrary batch-sizes to simulate `batch_size` codewords in parallel.\n",
    "\n",
    "However, Sionna can do more - it supports *N*-dimensional input tensors and, thereby, allows the processing of multiple samples of multiple users and several antennas in a single command line.\n",
    "Let's say we want to encode `batch_size` codewords of length `n` for each of the `num_users` connected to each of the `num_basestations`. \n",
    "This means in total we transmit `batch_size` * `n` * `num_users` * `num_basestations` bits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e89a7fc4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:17.668550Z",
     "iopub.status.busy": "2026-02-13T14:08:17.668417Z",
     "iopub.status.idle": "2026-02-13T14:08:17.699252Z",
     "shell.execute_reply": "2026-02-13T14:08:17.698638Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of u:  torch.Size([10, 4, 5, 500])\n",
      "Shape of c:  torch.Size([10, 4, 5, 1000])\n",
      "Total number of processed bits:  200000\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 10 # samples per scenario\n",
    "num_basestations = 4\n",
    "num_users = 5 # users per basestation\n",
    "n = 1000 # codeword length per transmitted codeword\n",
    "coderate = 0.5 # coderate\n",
    "\n",
    "k = int(coderate * n) # number of info bits per codeword\n",
    "\n",
    "# instantiate a new encoder for codewords of length n\n",
    "encoder = sionna.phy.fec.ldpc.LDPC5GEncoder(k, n)\n",
    "\n",
    "# the decoder must be linked to the encoder (to know the exact code parameters used for encoding)\n",
    "decoder = sionna.phy.fec.ldpc.LDPC5GDecoder(encoder,\n",
    "                                    hard_out=True, # binary output or provide soft-estimates\n",
    "                                    return_infobits=True, # or also return (decoded) parity bits\n",
    "                                    num_iter=20, # number of decoding iterations\n",
    "                                    cn_update=\"boxplus-phi\") # also try \"minsum\" decoding\n",
    "\n",
    "# draw random bits to encode\n",
    "u = binary_source([BATCH_SIZE, num_basestations, num_users, k])\n",
    "print(\"Shape of u: \", u.shape)\n",
    "\n",
    "# We can immediately encode u for all users, basetation and samples\n",
    "# This all happens with a single line of code\n",
    "c = encoder(u)\n",
    "print(\"Shape of c: \", c.shape)\n",
    "\n",
    "print(\"Total number of processed bits: \", c.numel())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f19f80a",
   "metadata": {},
   "source": [
    "This works for arbitrary dimensions and allows a simple extension of the designed system to multi-user or multi-antenna scenarios.\n",
    "\n",
    "Let us now replace the LDPC code by a Polar code. The API remains similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b05b3fd2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:17.701325Z",
     "iopub.status.busy": "2026-02-13T14:08:17.701206Z",
     "iopub.status.idle": "2026-02-13T14:08:17.707861Z",
     "shell.execute_reply": "2026-02-13T14:08:17.707115Z"
    }
   },
   "outputs": [],
   "source": [
    "k = 64\n",
    "n = 128\n",
    "\n",
    "encoder = sionna.phy.fec.polar.Polar5GEncoder(k, n)\n",
    "decoder = sionna.phy.fec.polar.Polar5GDecoder(encoder,\n",
    "                                      dec_type=\"SCL\") # you can also use \"SCL\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f00d4b2",
   "metadata": {},
   "source": [
    "*Advanced Remark:* The 5G Polar encoder/decoder class directly applies rate-matching and the additional CRC concatenation. \n",
    "This is all done internally and transparent to the user.\n",
    "\n",
    "In case you want to access low-level features of the Polar codes, please use `sionna.fec.polar.PolarEncoder` and the desired decoder (`sionna.fec.polar.PolarSCDecoder`, `sionna.fec.polar.PolarSCLDecoder` or `sionna.fec.polar.PolarBPDecoder`).\n",
    "\n",
    "Further details can be found in the tutorial notebook on [5G Channel Coding and Rate-Matching: Polar vs. LDPC Codes](https://nvlabs.github.io/sionna/phy/tutorials/notebooks/5G_Channel_Coding_Polar_vs_LDPC_Codes.html).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708efce7-2145-4637-981a-5a38a315a9b6",
   "metadata": {},
   "source": [
    "![QAM FEC 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xVivowSUQHgJqEgLL3+9uhJQAk0kwALmLGzunVjWCcsV1i4sUkybgaWMBdJZrgnr2e23327X3zx27JgVUFjKcJWyigBLPCH2xowZYy1hLKTOJLXMmca6nkxSyzU4n6+UlpZmrV3En61fv14QYMSgMaEsrk2Wk+rQoYMVhs6SUSwvxYS5WP2++tWvypo1a+yLgQlY1MjzxRdfLPPnz29QHPrKi25TAkogsgmoSIvs8tPcK4GoJYCgwsrlnXAp4ubEmsWC6UyhwcACEtsI7EesIbIQcH/4wx+sleyJJ56QWbNm2Rgz4swQVsSKIco43+jRo+Wcc86RH/zgB8IC6g3NmcZSUM8//7w9Bpcqa4ByPqx9uGKd0Zyex+/du1e6detmrYEITPbDtco7+SfPbGeVA7ZpUgJKIDoIqEiLjnLWu1QCrY4AAmjRokXCyErEC++vvPKKnHfeeXa5JoL5t27dat2FF1xwwSn37wglZyOxXljAWJ8TtymxbggnhBxuTkRejx49hPNgETv//POlXbt2dvkorF24OIlF4zPWLlYluOKKK+w758DtiUDE7co+3q5S3LIITqyDuD3J34ABA05ZToptKtCcEtN3JRAdBFSkRUc5610qgVZH4KqrrrJB+cSLIYhYGJ2RmZdeeql1gxL0f9ttt9mpNBBBJO9RlFi5WJ+T0aEIMcQXwf7Ei7F4OnFpV155pT3/448/bs9HDBsi7qKLLrKCC6sZxyG++Ix7srCwUB588EG7DijWPNypuE+5PvliYAGuVifhfmXpqeuvv14eeughex0GCrC/Z549PzvH6rsSUAKtl4Cu3dl6y1bvLEIJVNaUyCOzMm3uk+Iy5GcTiiP0TvzPNuKjKVYirF2IGyaTZYHzPGPlwuJF/NiePXusJYq/+/btawUTLkdiz4gFW7hwoQwdOtRuZzJcxFS/fv3sOyMziSnD+sV8aZwLSxnB/GwbNmyYFU/EjsXFxdkF1hmwwGeWkeL6iLCcnBxh0ltcs7ycmDaEIPthPeP83DvHIBRZfor84mJdvny5nSyXGLaioiJrGWSpKm9LYGOk4dLaF1jfXDhLZqz7nhQcXSs9sybIdQOflY4pAxtDo98rAVcTUJHm6uLRzEUjARVpgZc6E9B6L3xOHBjJn+kqfM38770NIcXL+3xscyxcvj57H4erEzHnK3lf09c+TdmmIq0p1PQYJRB+Ar5rivDnS3OgBJSAEvCbgLdA40BvMXWmk/myTHlvQ4g5YszzXJ7bfH32Pq4hgcY5va/peR39rASUQPQR0Ji06CtzvWMloASUgBJQAkogAgioSIuAQtIsKgEloASUgBJQAtFHQEVa9JW53rESUAJKQAkoASUQAQRUpEVAIWkWlYASUAJKQAkogegjoCIt+spc71gJuI7A8OHD7QS0rstYK8gQU3uwZqivwRWt4Pb0FpRAqyagIq1VF6/enBKIDAIslD5nzpzIyGyE5ZJVGRDBLC2lSQkogcgioCItsspLc6sEWiUBZvhXkdYyRcuqCqmpqT6nD2mZK+pZlYASCBYBFWnBIqnnUQJKoMkEsKSVlJTI1KlTm3wOPfB0AtOmTbPrmrIUFUJNkxJQApFFQEVaZJWX5lYJtEoCLMl01113yaNmHc4VK1a0ynsM9U0Ri/bkk0/KxIkTpX///g2uchDqfOn1lIAS8J+AijT/WemeSkAJtCCBa665xi5ajlhTi1rzQGNBu+eee+yAgT59+qhAax5OPVoJhI2ALgsVNvR6YSWgBDwJYE175JFHZMeOHTJ79ux6oUbQOy9NZyaA5Yw1TBFo69evl0mTJgnClwXhNSkBJRCZBFSkRWa5aa6VQKskwELgvHJzc+tFGgMKtm/f3irvN5g31a1bN2sxGz16tBVnPXv2lKysrIDWMA1mfvRcSkAJNJ+AirTmM9QzKAElEGQCCDXcniQGFBQXF9vP+l/DBNLT060gQ6ypOGuYk36jBCKJgIq0SCotzasSiCICCDVNSkAJKIFoJqADB6K59PXelYASUAJKQAkoAdcSUJHm2qLRjCkBJaAElIASUALRTEBFWjSXvt67ElACSkAJKAEl4FoCKtJcWzSaMSWgBJSAElACSiCaCahIi+bS13tXAkpACSgBJaAEXEtARZpri0YzpgSUgBJQAkpACUQzARVp0Vz6eu9KQAkoASWgBJSAawmoSHNt0WjGlIASUAJKQAkogWgmoCItmktf710JKAEloASUgBJwLQEVaa4tGs2YElACSkAJKAElEM0EVKRFc+nrvSsBJaAElIASUAKuJaAizbVFoxlTAkpACSgBJaAEopmAirRoLn29dyWgBJSAElACSsC1BFSkubZoNGNKQAkoASWgBJRANBNQkRbNpa/3rgSUgBJQAkpACbiWgIo01xaNZkwJKAEloASUgBKIZgIq0qK59PXelYASUAJKQAkoAdcSUJHm2qLRjCkBJaAElIASUALRTEBFWjSXvt67ElACSkAJKAEl4FoCKtJcWzSaMSWgBJSAElACSiCaCahIi+bS13tXAkpACSgBJaAEXEtARZpri0YzpgSUgBJQAkpACUQzARVp0Vz6eu9KQAkoASWgBJSAawmoSHNt0WjGlIASUAJKQAkogWgmoCItmktf710JKAEloASUgBJwLQEVaa4tGs2YElACSkAJKAElEM0E4qL55vXelYBbCNQdr5HNhz+WY7VHpKq2vD5bdcer5YuCaebvGGkblyW920+o/04/KIFoJ1BVWyY7iz+TippCKShbLZU1pRbJ0eoDsvHQB2bbWslsmyc5KQMlPjY52nHp/UcgARVpEVhomuXWR6DueK1sL/lUVhdMl+PH6+pvsLquUj7c8jOJjUmQ4Z1vUZFWT0Y/KAEIxMj6Q+/L1qLZUl5dKEerD1osRRXb5dNdf5CENikyNu8H0jGln+JSAhFJQN2dEVlsmunWRiA2Jk7yMi+Q4oqdcuDo2vrbQ7BhISg7tl/ys8bWb9cPSkAJiCTEpkinlP5SXnVIiiq2SU3dMYsFC9vh8s1SayzU7ZPzJb6NWtH09xKZBFSkRWa5aa5bGYGYmFjJzTzPWMomnnZnsW3ipU+Hy6Rb+jmnfacblEC0E+iXPUk6pw2TuDaJp6CIkVgZmnOjdEjuK23M86VJCUQiARVpkVhqmudWSSDB9PZHd/+2JMamn3J/KXHZdrs2NKdg0T+UgCWQnthZBmZfLWkJOacQyTYWtj6m05Mc3/6U7fqHEogkAirSIqm0NK+tmoAva5pa0Vp1kevNBYmAtzVNrWhBAqunCTsBFWlhLwLNgBI4ScDbmqZWtJNs9JMSaIiAtzUt2wwUIHRArWgNEdPtkUIgZKM7d+/eLTU1NcL79u3b7WcgOdu7desmcXFx4rzzXU5OjuTl5UlSUhJ/alICrZ6ApzVt/aG3NRat1Ze43mCwCGBNW3twhpRVHTSxaJMlW2PRgoVWzxNGAi0q0hBg8+fPl8rKynox5txrTEyM81H4vGfPHvv3jh07zBQEx+3n/fv3S2ZmpowePdq+Dx8+3L7XH6gflEArJIA17bwe98iBI6s1Fq0Vlq/eUssQcKxptXXVakVrGcR61jAQiDGC6IQiCtLFHWG2efPmemsZIsyxkPGem5trrWZc0tnuWNScd75bsWKFbNiwwQqzoqKiesE2fvz4ViHWiouLZdu2bcayuFUKzefSkhIpKT4xGSP33xpTRmaGDBkyWFKSUyQ/v6e1lrr1PrH8YvXl5fwueQ9Fqq6rkDWHXpeh2beFdGQa1uwxY8ZImzZtJM9YsXv06BGK223SNZyy4Z3kvNs/Wul/lI3jcaB8+Oy2xDNCWfByvCe8hyKVHtsnhcc2mGk5hkrb2HahuKS9BmXBC6OC8x6yi/t5IYwllMm2bVtl3/69UlF5TA4WnJhXzs9TRNxueOFGjDxbkhKSbHuD3oi0FFSRNm3aNPnoo4+s5ax79+72x0qlkpiYWC/GAgWENY0fFi+sbPzQ0JVY1SJZrC1fvlxmzf5ICg7vN5MwlklNbZUwoSmv1pxizVD4hLgUMdMXSWpCuky+6SYZcfYI191yWVmZvPrqq/UW3nBkMDZepLY6HFc2162tFZ69O++804q28OSi4atOnTrV1gns4WmVb/iI1vMN9R9C6Nprr5WJEye6KhyENmD9+vWndNBDTT7W6Fbz8xUJqvmh8bugXOh4I9IoG97dklasWC6fLvxUdu7eLuU1R82qJrSjtVJbFxrxHC4OMWay46T4NIk5HiN1FW3kllunyAXnjwlXdpp03aCItEWLFsmcOXME61nnzp1tpT5u3LgWqTywrnGtEmN1ikSxxkNM/md+9J4cT6ySnv17SOdu2ZKanvLlK7VJBRkpB5WVlsmmtdvkSOlR2bZup6QkZkrf3AG2UiMGMdyJHj+NzGuvvSaHDh2yv2Usv47FNxJ7YoEwRZwRogAH3nnGEhIS5Pbbb3eFVY3OGgKNDhudNMrGaQyd90DuN9L2dcqGOvfYsWO2fG688Ubp379/WK1qiEY6NTw7I0eOlN69e9d7TJxnJ9JYB5JffpfOywnZcYMhwbO9Kasqlv7DekmnrtnSPjtTEhITpF12ViC3GXH7Vh2rkjXLN0jVsWpZu3yjaW8ypFuHPJkyZYqt0yPhhpol0ngwqTQQHVi4Jk+eLBdffHGLiDNvmN5i7fLLL7fWNbcPMnjzzTfl5X/9Tbr2yZYLLz1Xcrp18r61qPkbwTb3g8XyybuL5CuXXi0/+tGPwn7vCLMHH3xQUlNT5bbbbrPxkGHPVJgygGDDMv7rX//aioDnnnsuTDk5edm77rrLWs6++tWvWpF28pvo+4RQ+9WvfiVpaWny1FNPSYcOHcIG4YEHHrCdmlC2AWG72UYu7HQkVq5cKfxOsaqFK9He/PP1v0uX3u3lrNFDpc/A/HBlJezXRbAt/mSZTP/b+zL23PHym9/8Jux58icDsY+a5M+O3vtQQVBpr1u3Ts455xzb0x41apTtdXvv2xJ/Y3VxBhTMnDnTikUn9s2tQm2riQV4693p0rZDrHzlxomtvhfTWLnTk8vp1lFiYmNkyaKlkpXRTvr07tPYYS32PZaJ2bNny5IlS+Sxxx6TgQMHtti1IuHETlxaly5d5L333pPY2FgTTzgkbFmnM0ij8/TTT9sOWdgy4pILY6HCejhr1iwpLy+XQYMGhaz+9URAWzBjxgxBqBHeguU1mpMTl0a4AJZFvEvh8BJsM7HOb74zXZJNe3OFaW86m7o2mlNsXKxlkJaZKp/Mmi+pyWkyaOAg1yNpkkjjofzHP/4hR48elRtuuEGuv/56+yOkUg91cqbpwKqHWIuPj7cVl9uEGr2rf78xTcpqi+SCS84x5ubWbWb293eQ+KVQq66plsVzl8q5o861Vix/jw/Wfgg0rEb/+c9/5J577ol6geZwdYQaz9kLL7wgY8eOlYyMDOfrkL0j0F566SW57777VKB5UHeE2t/+9jcr0jp27GgHfXjs0qIfnbbg5ptvtp3maBdoDmxHqCHSCgoKrCU6lG2SbW+mT5OjdUVy/kRtb5xycYRacmpbeWf6f2TsmAslPf3UFV6cfd3yHrCqch5KetQPPfSQ4GYM5Y/PFzh8/1TemJUZLUmFji/eTYl8bdyyTjp0zjBqPnpdnL7KJC09VQYO7yN1sdWyfPkyX7u0+Dbc9fy2+S1jodV0kgAjCC+88ELp2rWrtVif/CZ0n3imiY8jDk3TqQT4vRIDhgWY33EoE88M1zz33HPD3g6E8r79uRZWTtomZiZANIUybd9u2pvN66W9bW+i24LmzR0PztBzBkjb9HhZaAZTuD0FJNJ4IKks+eERrxPuYFVPuPT0sYAg1MgnFhE3CbWi4kJJSI6TTmaQgKbTCaSYgRPtO6fLsjCJNALlDx48qALt9KKxW3B1IgaIQQ1HcgYKhOPakXBNyoY4XSzCoUx4MBgoEO6OeijvOZBrIdRI/H5DmQqNMExIjZUcM0hA0+kEEGr5/bvJgoULTv/SZVv8FmkIH1ycWNAY6UVgtRuTM6Jm4cKFrhJqO3bstMOe1Yrm+1eTZkRadpcOsmrVKt87tPBWrAE0cmpF8w0aa1o4RRqdQ7Wi+S4btjoiLRyWNOpcFWm+y8YRaaG2pO00orCq9lhUD0zzXSIntiYkxkuvAfmy5LMlZ9rNFd/5JdIQaIwiGjBggLTU1BrBpOGM9HzllVdk8eLFIXcB+LqXkuISOwcaU21oOp0APZvU9LZSsP/A6V+GYAuWNAJ9W/sUG01FSWwabHbu3NnUUzTrOBo5p8Fr1ola6cGUDb9ffsehTFjS8GK4cVLdUHJo6FrEppFC7dUpKSm1c6DR+dV0OgFi05iGZM/uvad/6bItfk1XzWgqhnkTHOpWC5o3V4QaFTtCjV6exkx4E9K/lYASUAJKQAkoATcTaNSShhUNE/q9994bUUsxOTFqCLQ33njDjrBxc0Fo3pSAElACSkAJKAEl4EngjCLNcXN+7Wtfk8GDB0ecSdsRapiaGQod6ngNT9D6WQkoASWgBJSAElACgRBoVKQhbCLZVeg5kID5ajQpASWgBJSAElACSiASCDQo0hw3J/OPOcGPZ7oh5jCqqqqS6urTV4Tmu3Am4tOc2Z/VmhbOktBrKwF3EqDeak49xdQXoQ7adydJzZUSUALBJNCgSJs2zcxWbFYUYGi3P8OrX3/9dXn88cfl5z//uTz//PN2sXXW/tuyZYv83//9n5SWlgYz3wGdC7cn98G0HGpNCwid7qwEooLABx98YEMi6urqpKSkRJ544gnbsUO4UY8xoz+dUBITUz/55JN2KSZGu1K//eIXv7D137///W+7H/PJMbksx5I4JyPkWb5JkxJQAkrAXwINijQsaYHMf4OoQwTRm/z444/l5ZdftiKPig1h5FRW/mYs2PupNS3YRPV8SqB1EMCK9uyzzwodTccT8OKLL8qCBQtsvbV582YrwPbu3WutbSwf9tprr8mePXvs4vMffvihPa6wsNCsmLHcHjN37lwrynbt2mWPoZOKSKPjq0kJKAEl4C8Bn1NwINA6dOggF1xwgV9WNOdiLB3z7W9/W5566ilZuXKlncGdSSjbtm1rF2eml8lnKr3k5GS56KKLpEePHnbx5o0bN9qFebF4nXXWWXafTZs2CRUfvVDSlVdeKT179rS90XfeecfGyuXm5vq1Vh3WNCyCuD1xefpjHXTuS9+VgBJovQSYxBjr2L59++Q73/mOZGdn24XCqbsmTZpk13Rlxng6ocxHxmomTKxLZ5SR41jS+BsrGfWLs34lx/Pdj3/8YwsPq1xzXKqttwT0zpSAEmiIgE9LGlaxK664IuA50Zg49rnnnrMTyDqDDdasWWMrNSqwv//97zJ9+nQ5cOCAILKo4OhZfvHFF3aSTITd7373OyvKli1bJs8884x8+umnVqi9/fbbtlJEsLE/eQzUdYllkMrYEX0NQdHtSkAJRA8B5lJkku6ysrJ6SxjiDJcl9dZ7771nV1qZOXOmrXOWLl0ql1xyifD30KFDZcKECXYeSYi1a9eu3lrWr18/K+zefffdegtd9FDVO1UCSiAYBHyKtEBdnU5GcBUw3QVWMmI1WO8P6xquTnqQVIL0RP/rv/7LVoqffPKJXWsOQTdixAi7Gj2uBCahZV9cpV/5ylfkv//7v+Xqq6+24g9hNmvWLOnevbt07NjRLyuakz9cnvSaeekAAoeKviuB6CVAfYK7csqUKdayTyeSemfs2LG2LsMaRlwt80QSY8a+WMoQZ4cOHRKEGPUciWNxac6bN8+GffTp08cuoYfrdPXq1WEP+WhqKW8+/JEcKN9gVkwJ7WoGTc2vHqcEWhOB00Sa4+rEjRjoUh+ILUaDPvDAA9ZShmWMc8TExNQzY2mXrKwsu8QUlRw9VdwIW7dulc6dO9t9qTipKPPz84WKDtfrZZddZt0RWNjYH6sYIi2Q5O3yDOTYaNu3prpGSgpLpbIitAs2Rxtnvd/wEsB1ST2EmzIlJUUYQED906lTJ1vHEKvWq1cvW/+kp6fLn//8ZzsIiTqMUe+4QZ14W45BoGHpd7Zde+211gr3pz/9KeSLnweL7NK9f5ePN/9CFu/+kxQcXdtssUaHndhlBmlocjkBU1Z1tXVSa8qrJedoOF53XCrKK6T48InQJpdTCWn2fIq0QAYMeOaWHmViYqLEx8dby1lFRUWDMRjsQ0KcEbB7zjnnyMUXX2wrSo6jkiNuzOml9u7d28ajsS/uStYRpVINNHFvnL8lLGkbDs2UjYf/I0WVOwLNlt/7b1y7VV578S158el/1r8+n7/CPEQnRpH5faJGdiw6XCxzP1gkWze23L00koWI/prfGFZh3Oskfm9YgLHKaDqVwLqDb0thxdZTN4bgLyz/DHA6++yzrdhChCEeCPrnHSs+1jNcn3xHCAiuTjqMdD55//zzz60wo7N5/vnn2/08O6V4Fb773e9agRap8WhHju2TLwr+JbO2/FI+3Pxos8QaDKZOnSqPPvqoPPbYYzZmb+3ateoObsLv/VD5RtlZskgqqouacLQfh5iy2rv7gPzlGdPWPPOK/PP5aTLvw0VypKTMj4MD24VncdOarTLzzdmBHejCvavrKmSj0QLFlTuDkjufAweacmYqJnqRuDtpkHBzDhkyxJr5PSst73Mj1tq3b28rO9YHzcjIEB5aFnT2TFSKEydOtEG4DDjo0qXLKRY6z33P9BmRRiwJQg/LWjDThsMzZX/pSslqmyc92423r6yk3GBewv6Q5/5noWR3ai8dOrWz5+YHHuxeDg/i8sWrJTm1rQwc1jeo9xDqk/HQbCv8RDqmDpTMpB4huTzuehoj3GYMpmF03wsvvGAbeiwzmk4S+HTnc5Ka0NE8L+Okl3lu2rXtefLLFvyEpZ+Y2T/84Q/Wak+dgxWNjuANN9xgY82oI4g5o5665ppr7FQcDG6iPsJKRhwuUw7Nnj3bljHl3rVrV9v5dLKOR+DBBx+0g6HOVBc6+zf1Pcc8pkv2/1G21WU19RQ+jyus2GE627VSWrXXiLXXZUfRAtlSOMeWVVL7Mok54e31eaz3RkTaSy+9ZAeQYXlE5DIi9nvf+57teHvX+97H698nCewqWSKrC96QTqZeo73pmnaWtI0PXtnTpuzfXSAz/jlTzh0/QmqqamT1sg1SsOegXH3rZfUDZE7mqOmfaoyRYdfWPfLZ3OUy5RvXNv1ELjjyWM0RWbDzWUlP7GbrtLzM860maGrWThNpu3fvti5GKqFA0o033igbNpi4BWPCZgkpRmgycpMe6Z133mkDa3knloMKj4rr7rvvtn8To4bFgcoNdykWNFwJw4YNs65OJx+4Q+nRIgARdk1JVLqOkGzK8Y0ds/fICtlSNEe2mkrMEWq8BzN1ze0sl15zkQwcfkI84U3esGqT7N6xT6qOVZsyqDXCqp/k9+shRYeKZe3yjbJ3135JTEqQIecMlPbZWbJu5SbZs3OftOuQZc7TRzp17WRMzcWyY/Nu2WSsdUWFJcbdecL0XFFeacXhtk07bOPUd3AvyenSUTat3yqH9hdKedlRSctMlwmTxkhMG5MZlyXvhyY/a0yLizXc9TRAWFi+8Y1v2MafQTD8xunEEI+JW4znjGcFMcfzgxUHazQDarDwEKuJ9c17+8iRI21HBqGBdQehzrPFdq7L1A90RHj+7rnnntM6PW4qopLKXcYCPdM8M7Nla7uLQibWiC2jvmEQABYvEmX12Wef2XIhNIPQDeoqxNXAgQOt2CKulr+Ji/3Rj35kLaZMz0FYBqzHjBlj6z46qY7HAKH3wx/+sEnWf3/Lqkt/kYX7npakI8F9BuvqqqTO/HOSp1gbdFWxbC9/X3Lq7pCENv57Nq666iobA8hyfcwxh9UZ0QZHrJe0I/CmU81UKPAmzpm2gXaAuGWeLX7vtDeEwPA8sR/PE88NswPwTFHfI/7YjzaFmQR8bUdArlq1ysYsYzDg+cODw7mLiorsgDee1/POO8/mzeERrvfKmhLZXjRPNhd+aJ6dT+xz44i1YOYJY8ANd15p6vY2Mvf9T+WDt+bI4LP6S+cenUw7slF2b99r2pR20ndIb9MuZMv+PQdkxaIvDLMj0iE7U4aMHGifg2ULVpo66Yj06NlV+g3pI5lZaVKw77AsW7BCDh8qkr07TqwIhFfoUEGhrFm5QQ6b9x75XaX3gHxJSk6UmdNnGQNFB9m/74Ccde4Q6TMwNB26QHgSu3mofLP1qm0zdVpu1tgTZZN1YZPE2mlKDJFGgxGoSJs8ebLP+yCwlhcJkeYkptLgRbrppptsI0OFxoNCA0cFygPnJCpOeriDBg2yDy5TeTQ1VdWWyWe7/iwHUzs09RQ+j9tVsliq6yrtd4WV26Vw79R6sba1aLupxLr4PC7QjYf2H5ZFsz8zgmqH9B/aR/L79pDP5q+QtSs2SJ8BPY2JukAKDxYbUZYoK5eskaXm4cjKzpCUtBQpKSqVXdv2yvyPFktqmhngYXovPCAXTBhpeknrjTl7odkvVSB/tKzcxqR9sWStfPzuPGnXMUuqKqpk++adpmc1UhYYdyju1yHnDJD4xASxBwV6MyHY/5SHxgjo/CysnOPMe8uINRqYw4cPC88SLnkaC4QUs9LTkCCoaCTYzm8c8UZjwIAWRjcjFGiEGF2IBYZ9vbcjDkhMd4NY4LkhnhSBR1zVm2++aa12WKb5rrmpzlhS9pSbuROvOSZztv26uac75fijVYfs38XHdsmyfX+TraaMnEanJS1riFpenom4Wl5O+vrXv26Z8jd1EusY8+4kGn5eWNAQEE69mWcEnmdiO+Xqeazn98H43MZYtGrrzMoHJ/VUME7b4DmO1ZVJfLK5WBvzMjFFcqrzo8Hj+AIO/G6xKlOvY9Hkd4tVzfme5wJXMSP5eU7wnvDM8PzAF0GHyGNQB8KLjj6dGzopTKOCuEL89e3b1x5Dx+j++++3zxuWT+/tnItjyRttEM8qovv999+3+aIdQ7DzXSAp3YROby+aH/TnZmfxQqmqLZcqUw7biufKvrKV5tkxYi1rnOwpXWWy6L9oPtP9IM7STNvRJTdHjpkY5aULV8oS82q3IV1WGwNAZrt06xbdvGG7TLrhYpn17gLT0d8snUxH3hSb7cjvMkJugWlzOvfIkf27DlhhN/HqcTLPtCHLzLnaG8/QwYKDJhtt5NCBQnnv9Q+NoaDUCLMk2bOrwLQz22TiNRfKS8+9LqPGniVduufYDuiZ8t3Yd7Em4iqzZ2nQy+WYEc/lxgVNnXmwYpMcqtxs67SeX7Y7PQMUaz5FGtYmp7Jp7EaD9b3T4+RhpKHxTlSC5AuXAw93U5NjSVuw7S3ZnNDyo5VOijWR/h1ubWq2Tzmu2gT1F5teCsLoaBlxf2KsWeXSzvRarph8sSw3vZi1qzbah2mLiSnr3rOLXHXL5aY3E2fjCRZ9skwystLlhq9eKWuWrpMVRsjFm0Zkx9bdUlVZK1+/7xpjgSuR16fOsKJuwxebZLN5SC7plyvFdWYKlM/XSVLbJCM6qiTDPKDX3zHJWtI8RfUpGfbzjwRjzIjvvKtlHxoTx3G4Yots8xJrfmbRr91oRIi1pKEgdpLpZrCGYbFxxBoxmDTqNC40TlgPOI5td911l22AEGg0EPzevbfPmDHD5mXOnDny/e9/335mqgeuhRikLHCzEuDuxHX6lfkGdkLo7i6bJyMmV8qHWx5tYK+mbUZYeCZiOTzF2qCO14StA+BdF3n/7eQ7NTXV+djge0sKNC66f4PItZO/Y6wa6Q3mwfMLf6X7yv2vSWnFznprWmJcunTPGGXEwHh585H/lbxbL5eEuMbv3/PazmeY4B1B+DA6lrnqeE7Yxm8coYULmgFkt9xyixV0bKMdwEL261//2naIsJBRBog9RB0dmyNHjtjwGYwAWEqZJYDOPh0oOi+e2wnVeeutt+xzRmw0ljM6OoTWEKpAZ4sRwFj3Ao2FzuhkhMrROea5me/cdlDeeSZxQzsJy9q2IiPWjiDWRHIzvuJ8FZR3rJFJxhuT2DZR9hmvzefzVph2IEF69c+T7Zt2yuql620IzvLFX8jl119kxNTZ1iJasO+grFy0Wgad3V8uuWa8LPx4icz5zwLp0auHLP10pZw3YYSxig2VWe/MkxWfrZEdW3bLh+bz2ecONlazdvKFaaOWL1wlI8cMM+1TlQwe3s8ItnE2H825sTjT12rXuzjo9ZmZDdHc90khTyeZmNuiym1WrPXKMl4wU6cN6Gisk35YNlwj0hqDPWrUKDu5Lg91c8QAIq38SI1xC7a8QGvsnpr6fSdjUh4/6QLpb8zLyaanQU+HlNkuQzp1zpaOpleyyQzi2L5pl1Qb92d+31zpmNPe7lNqLGlHS49KXp8e0i23szUnixFp640Qg2teX9O77ZcnW2W7pKSmmBE3lXKg4LCkGdM0D2lapnEDnD9E0jPSrEDMN8Kwo7lmMFKSqefjO29v8YfG9nA8xFrPdhMktXx40IQADQ49ftwuxFEyoSmdnocffljWrTOi2FjMaCxoZGg8cN1jBcBdxt/MtYWlmJ4+8ZmEDXhvd1yl/J4pF6x3BLrjMmImfCzOeV7WnOaVkVkeyTQIVGw1X1qLm3e+xo+m0SmvKbQ9UtMh19QIgb1GpI3OuU9yc3s0sqfztX8ybY+JfTpSuUcSY1PrxVmvzHHSNXOElO56QY7XBhCU5lz6y3c631icsSTzbOBmpB6iY8FADVybJMIEeE74mxfPCKP7cedzDo5FzNEpocPPiF2eQ8QeggyLHc8RzwadGO/tdKKwXjP4g2eJDhbPrmOJxlrK88T5A03mduR4TI15bkLT5vB81tQF/4nBDVls2o+y0jJJTG5rLF+HrdsTXp26djQd/zQpNcYD2peh5wyyFjZYFew9aOY6LZYJ/S+07VCPXl3l6JFy0z7tkGJjLRs20ogx0z7RRlGehNlUmpGeGe2NF8Ac39MYBzqaNq/WjDJNNIaJYecNMV6gIFkJY81I4xDVZ4g16rSK2hNWNmtd4cfRSDpNpBFvwXB0HopArGlkAFOzs7pAI9cN+Gt/eqr+nJR7I97tgp73SN9eposTxERczV4zcMCz0O0gAtPj3NduW9CuFBvbRtoaV2ZKSlt7TqbL8ExtzPcxxveRmpFiK6oDpifDg8VDVmfcEknmuGLzIDDcGZ8/2zp1zTYBoQeMafqwjU3jgaiuqjam/3hTUaUZd4bYGICEhFgTC3LMVoQFb8ySRPN90JL5vcYY14knv6Cd28eJjh+vkwrz0FTWGLEktX70aXycxMcmGgdW1SCeBosZMTe4SohRoqLH7cLqG4g2/qYXz7NDI4Q1jZHN/E45B7E1PFve2zk3sW00ToQn8JvG/UMIARNKB9rb93Ebp2xqExMnXVJGy4q3EuVeE8cVzLRk91+Me+CEy5PzJsVlSJ6J4+hlnhtcN13Sz7L1WTCviZUF6yQNOo0MjTd1HoI3FAmhgEUHwdGcTqdnXk0oqsTHmtH1sUmem5v9OSk2S3p3uMRYZs4XR5zFtzlR9zT15MSTIaqwIPO7ZaAGq8tgwcLdzHOB+EJYYeHylZyyQuSxD5YuLGqONZnniGeRd2I0KWO+P3jQ1Ifm3J7bmcwY0Yd44zOdIn4XuDf5jfBbaapFuvSAsdhUjZFx+Rf4uo0mb9tbuty6UavrTqwHG9cmUTqkGJds5oWyL2tjk8/rfSBTcBwx7UflxkpZbLwwySnJxprVRzaajn1GuzTrfqwzPvZa8wM8bFyVn89faUNiiDmrMHVUbXWt6dSnyoE9BcaTc1T2GfdlWyPyGPwWG9fGuj4zszJMWVVLGyPO25r2KTk1WXJMnPTwUQOl4miFxBsLXlysmc7LxDwnmbYvGKmmWqR4iynv/O8E43T156iqMYPu9r8ildWF9duS49ub8Bpi08ZLfsaF0jl9qHnu/atrGhRpKFp/RRq9etwuPGw0NN/61rfsj70+hy764Ii0Mb3ukWG9hgU1ZxWmsT9YtsGKDEec9TKxT7gF1mZNld0lm4JyPQYIvP3ah7JwzlJ7viEjTMSwSaeYTo3g6Z7fRdq1zzSxahvlH8//25RnrAweMUB6EsM2d5n89dlXrFuT/cZOHC3LjJt0yYLlZt/p5mGJsT0a3Jn4/z96+xP5aMYn9qFqZ4JER405K2iWJ5t5898xs6xh9b7u5qEJjlvYOa/3QwOnFDOa8ORDM1YqC1ONEHjSOaRZ7zQMjnsTSxeVPgKNjgYWA1wxbCdImWBorMNY13Br0pA8/vjj1rpAQ8JSaAgx7+3XXXednUoG986rr75qO0c8s7hIg9Xoe0JApHVPHiNLX0+SS373iOdXzf687sAMK9J8ibM2gQwd9CMnsMQtTD1Ah/LSSy+1ZcMktIhpLDShSMyvNtWM/v3pT39qBUAortnUa5zd9XZJje8kXTKGS3PFmZMHhBSdECxelMH48eOtYGVNVFz8iCSsVrfeeuspwtnXbxsRRblhdUbkId6witG54Vmj04IoRJRh3SZ2k3hPz+0sgcg1CUegA4VAY1+eSUcMOnkP9L3EiLQOdePkkp4nlgcL9PiG9l+8+/+MUWC5sTbV1osz2pseGefJiow/ya6SzQ0dGtB2gvjf+Md71r2IBewrN10iQ0cOMRayQ8YDs9kG8yNge5ng/oHD+tgY5VnvzZdVn62VlPRk6W08MwOMi3LB7M9lp4mHPrDvkJw9erD0NwPfBq3qZ9qWeXZwGxa3uPhYyevdw3w/xITVrDUD3vZKQryxno0cJDndA5sXtbGbrDUeyaItmaZcgluflZopa5iK65iJS6Odyc087xRxFmiddppIa+zGvL+nQfrjH/9oHwJ6PfSGaCzcmhyR1lBsSXPz3Tl9uGQl5Znh6SfEGWItmKnPoJ5y4ZHzjGXs5ELNBFeONMGU9DJijRBj9Od5488x7zm2F4ircr/pvbAfLtEePbuZ7XFmNOhe6dw1R4aeO9CMBM2VZDOQIMeYrXdt220HHeD3ZxRPB2OGTjCxB9s37pQaU9Fl57SzVrpzzTWDGWdzzEy/U70/r8UfGsRZPuVjejSd0gYJD82Ooh1BKyasWAykwYqG+4bgczovNAKILhpotiOwsN5885vftI0KGXAsO4g4YmNwWyIsvLcT8E6DxehDJnimEaLXT2VJHA2WuWCn2DZxZhg+1prmWVG889U2rp30zzbr8hqrGdYzLGeBVmTe5/T1N/FOP/vZz2zjjVjG3cwIWOa0+8c//iEPPfRQyEQa9RBigAEgWGncnAZmXxM04c9v9i4Tc4mI4hlgOhNGw/Ic8JtHOCOU6KDguqeeZqoTLI48P1iQeZYIDUA8MRiD0ZYIOkQa1jCeJ+olRj7zjjhj0AErzuC2xHrnvZ1OVP/+/a1blM4Tx3AdDBUcxzPdVG+OMdib33N80J8bYgM7pw2TbGM9w0KTa8RZeiKWWeNJ8dNKc6bfnennG2HUSa4zMcdwSM9KlVzTdgwybUKcaT8uvmqcaRvaGUvYPhOb1layTIeemQMuuXa8rDZxy4cOHJZM47Ls1K2jHXRgNLOUFpfKWecNtZ38Dmbfy667SFaZfbHUDRrRz/wmUs2Agw5y7W2T5Itl6+Sg8exkmvjpFOMVSjPWuJvvvk7S0oPj6uTe62pjgl4uCXHJkhbfWbp0Gm7bmbz0C6zlrKl12mkiDXcnlZm/Qov9GCnzgx/8wAZ20jjgx8ekzBIqWAAI+uRhpJdDz4aeEw8cPSYeKvz/c4wljgeUhxcrAY0OjQ/rfPKwMKEkPxRG32Cl4AHmnDx8gZihHZHWEhVjv/aXC6+OKf2bNNT2TA+M811fM+SY15kSIo2Xk3KMK7POPCFYkNp8OUVGZ/Pg4NKk0sR9SiJGrasZfYP707pMnROY9zEXm5jACaNMA1dXz7sd1rQgp+O1bVr8ofEUZ0HOvj0dLhOC9p3E75QGhRcNEi8SMTeILxKNEmvZ0ljhBiXxHW5OjvPebncw/xE3M8FM70Cj4pyLvyMpndfDLGqe3K/FxJnDgsB06hnWAaaxp0OJCIAxiUB16h1nhQE6oGzDQ0B9A9c844bDcokoQDxT1pQBddi//vUvW/dhIaLDSuNOXUgdiPUGcU5diNUmkpIv61VT8++INF/HU/9jdXb4OJ4c2gMnYd3i5SQ6QCTc/LQrTltAmcKdMANGaMLdOR/7N7SduE4GLrAvzx2J59SNqXvGSCPKukhOqokR/lKcBTWfpm3o0q2TfOM+354NxNQlV4+37QhtiPM7ye/dXfKMmKs1bUWc6TQ6abJpX3CLxiYYt+WXG5lag4EHWC6dsuMr4tDy+nS3bRGGB2f/W+6+1jmda98TY9Pkgrz/J9lt+9cbAZqT2dNEGg0Kw5axBPjTc6BhYD4ZhBo9Ix4UftyMjEGkUVlRafHAcD5cDQRnItKoNIkRYATc//7v/9pYBCo1YjVY9JieLpUjPV22IeoQkDzMuFgRcOQToeeG1K/D5W7Ixml5sELMPHCeiW24P72T3de4Or0T20+c4vRjvPd129/BfmgCvT/PxsHzWEdUsY3fNOLAU7ixnWfJ13a+cxL7OA2Ksy2S3ofn3FpfwbdkvqmTsLowtxaJuoVEh5LEFAzUUdR/iC6sbUxWSznROcQ1x7xqLA1Fo88+rCJBfBsjBZ944gm5/vrr7bmYMBtrDzGFv/3tb+390TFEiNNZRXRr8k2goefF994nt3oeh6UMqxll7FjEnD2xTtNmeG93vufYSEiIs04pg0Py7DTEo8F2xBgD4uTUtsLWUwknhK/n+U60Oafuy/cn6jXPPSPjc0JsigzpODlo5XIaMWfggL+WNCowejqIJ4Y+01NlyDJijN4NvRiEGgKLyglLG6qZxGd6q1yLCpDhzUxsi1UAVxDuImaiRohxDL1aBBsjPbkuFaRTwfpbfFjS6AW3hCXN3zzofqEl4Dw0BGs21eTc0jmmQqKzwqTQnqmh7Z77RPpnKulQJALVGTHbUMKCw7xcxDIhyuh00gFl+SfcXazzSV2F1RMxR92Gi4z4Jiw31C24oTkHiThDPAMvm6WnOBdWIuo8gtwd611DedHtzSeAxQ3mDKrxTA1t99wnUj6H6tmJFB5uyWcwy+U0kYb5noYBoeWIqcZunIbl97//vY0bwFXDJITEFWAVyMvLs2Z/3JwILCpAX4lKkLlviKthtBs9IGJrEI2INyxxxI+QL2agppJjhBSCzt9E5UrAKq4O4h80RQ+BYD40LUGN/NF58BYRDW1viTy09nPSMcPq1VBCpBEoTueQKVSw5LM/nUE6mMQ4OZ1X9qGO4hjCL5x6iL+psxACbEfs0RHFtYpnAXFHPdRQPdhQ3nR74ASwdFIW3paxhrYHfgU9Qgm0PIHT3J0IIn7UTLBJgCfiqbFEBYb5n6BcphfAvYmli8qISg1XJe5NXvQkEVhMH0D8hyMEuQ7HkLg++3C8sy/+ar7HisayUGyn0sUl4W+iZ8sxLTVowN986H5KQAmEngB1B/UaVn/cXVjhqUe86wPcZtRLuCz/8pe/yNNPP23FGJOoOmLMyT31iS/BxXbqL96ptxgEQgeUbXSEqTO5Nh1NTUpACSiBhgicZkljR3ofxE5QoTSWqKAw3+POxB1JBYcLgOBL/sZFQHwZZmcqSaxpxK+xP71Vp2fqeR0Cbjkvx1Kpcn6GWCMEmckdF4KzCLtnHILnOXx9xi3hWOd8fa/blIASaL0EWNKJhOhCgLHUFqM6sXT5Sli/iAekg0i8GgmLvtOx9HWM9zYsbogzLGq4Q7HGIdIY8ETdRafWVx3ofR79Wwkogegk4FOkMWKGisQflye9QZa2oeJD2GF9IziXuAx6ic8884x1GTDfDUG0xJcxFw7rtRG/QbwaAZx85/RoEXnkgVFVzGXD4tL0SBlBijXuT3/6kx10gGDj+v4kBCeuTtyc3jEK/hyv+ygBJRDZBKhjnnzySRvv+stf/tKGZBDcj+WeEbdO/YOowu3MIAA6h6wYwcAOYtNYGYJwCSxxuKL5noEI1E/e53DWemQONgY58U7HE0seYSCsZfzee+81KBIjm7bmXgkogWAQOM3dyUlxeVJ5EUNBj+9MLk8qOCbfRAQhoIhnIyHApprJGtnO8U5MEJNtMhiACtHZxv6MmHIS27GmYdjQUDkAAArYSURBVI2j10qP09mXChFrHNf1HLLrHNvQu+PqZGQPcSGalIASiD4CzLlF7CtWMjprTh3CQCcnOdM68PeLL75oLV3UN1i8cHdeffXVzq7WO4CHgOR5DubvctL48eOFF3WhZ93zk5/8xAo0zq1JCSgBJeCLgE9LGjsylQa9PKxZ/pj3qXwcgeZ5IbY7AsvZTq/Te5vznee7E4fmvS+9Wqdy9dy/oc+4GRhWf9VVV9kebEP76XYloARaPwHqE6z3/tQh1GmOiKKz6FjbmkLJU6A5xzvndv7WdyWgBJSAJ4EGRRojNnE7fvzxx9Zy5XlQpH3GokdcG2vC6ajOSCs9za8SUAJKQAkogegk0KBIAwdmfwL3/bWmuREhVjRm+8bFkWfiQDQpASWgBJSAElACSiASCJxRpBGbxoS0DAgg2NUft6ebbpoYEGLdiGO7++671YrmpsLRvCgBJaAElIASUAJnJHBGkcaRWNNYSYDRmATsR1Jiyg3ctbm5uTqiM5IKTvOqBJSAElACSkAJSKMiDWvaD3/4Q/nss89kyZIlfk95EW62TKjLcHcWzGUBZF9Bu6HMY2KSGc1q/lUd8z0nUyjz4sZr1dbUWjYpqY1PntwS+SdAnN8I085oOp0A8xbChrV1w5EYickKAJp8E6Bs+P36Grzl+4jgbGWqJjwWkeZlCc7dN34WJm0nMVgulMmZPUHbG9/Uj9cdl/Kj5ZKWnuZ7BxdtbVSkkVcGETBnEPOTsbC52ydfJA7t+eeft4sY33777a5wc/bI7SHxsYmyb/cBFxW/e7JypPSoHNhbKEOGDg5LpqhEWcqH+EVNpxNg6gnYMBdYOBKTYc8x6wJr8k2AsmH+tuaMPvV95jNvHT16tCxfvtyuIHPmPaPzWyZeJ+HNCWXqkdvdtDdJ2t40AP2YMZZsWbdTzhk1ooE93LPZL5FGdh2h9uqrr8q6detcK9QQaMwizoSUbhFo8Ms3gxbaxqXIfhVp4DgtVZZXSumhMunTu89p34ViA40bjZyKNN+0EWms8jFkyBDfO7TwVuYZY3Z+Tb4JUDYskRfqKT0QacxBqctb+S4X2iNSqGcVyM/LN+1NsrY3votFaqprZN+OAzJ44KAG9nDPZr9FGllGqGFSJz4N96e/s/2H6nZxcb700kt26RZmBw/1g3Gm++yZ30sy0zrIrq17pay08eW2znSu1vYdJvnCQyWSnpIlXbt0C8vtYUk7++yzraV49+7dYcmDWy+Kq3Pnzp12bkNWCAlHQqQhBhzLRDjy4NZr8nvFu8FyfoEskxeM+0GksfILL7d7WIJxv4GcA/c8L1z1oV7lJj+/p7T7sr3BS6HpJAFCaw7uN+uJJ6VJbo/8k1+49FPsoyYFkjfcDiyYjhiiYWNZFCaX9Z5wNpBzNndfYiJWrVpll28hb/fdd19AC6839/r+HI+4JW/z5y2Qmrpq6ZbXWWLjYv05tFXvwwOzZcN2mfvuYrlswiT5yqQrw3K/TGyalpZmrTWIAZY2C7XrKCw33shFEWgFBQXyi1/8wi7dxvJu4Uh0uHB3Yk1DsIU6xicc9+zPNYlFe+KJJ+zKMJQN9UwoU3Z2to1VpmwIFyBmMZxtQSjv/UzXIhaNZQjXrFljV8/BSh/KVN/ezP3UtDdV2t58CZ9YtP17CuTff31PLh57qdx805RQFkuTrhWwSOMqDCag8v7Zz34mR48elU6dOklWVlbIg1bJC+ZkVkZgjdABAwbI/fffb9cD5Tu3JRhtXLdZliz+XJLTkyWzXZokJEbvkjBY0Dav3y6z31kg1SUx8uOHfxLWIsMKQSA0azWylBm/a/6O1sQzvmXLFiHE4f3337fr6IaTBfMcsv4lnUJEW6itE+G8d1/XxoLG9EishUxfm6X8wpGw4L3wwgs2Li2cbUE47t3XNbGeMbMA5dKrVy9hKbJwpKzML9ubz5ZKSlpbaZvSVpLNK1oTBoEdW3fLLNPebFu9V5767dMRgaJJIo07c9b3ZL06Zw41elA0aqEYYYT1DBO7s1g7a+WxYDGWPbcmejcdOrSX3Tv2yZoV6yQuIU7iE+Ol2ixEj1UtGixrPCjlZeXW5btm+SaZ+e85UnG4Vr71rW8a62d44tGc3wvWNBoZ3mfMmGGnnOnatasd1chvOxosawizkpISe++rV6+2gpUl1R544IGwxaM55eMIM0QjDSGijXdStFjWKBusNE5ox/Tp0229h+U31PFoTrlgTaMD6rQFrPdcXl5uR3zyzESDZY0yoU3CaMCsAi+//LJdeowQoXCF3ZxobzrI7u17ZM3KDVJhRjOmZ6WZUY0V0iY2RuLj450ibLXvWM6OHjlq7r1CNq3bLu++9rHsXLtf7r33v42GcX88GgUTYyrl480tIeZR+93vfifEJ0yYMEH69OljR7O0RHwEDwI9SMzI9FaIi7vyyivtqLNQm/qbw+3zZUvk/Q/flf2H9kjb1ETJ7tJeUtNbfy+n6liNFJn4s4ojlbJ/22Hp32+Q3Dz5Zte5p/md0QHgt8bvGEGAxaC1J6ZSIP6M52rBggVCA3zHHXeEXaB5c59qlnpz4tMYJET5REOibCgjBrjQyN500012kfdwCTRv5rQF69evr4+Ro2xaoh3wvm64/6azwIvfJEYL2sLLL788bALNm8fSZZ/LJwtmy9adm6VNQox07JIlWR3CM52Od95a8u/amjo5VFAkNVW1sn3NHhuDdsdtd1ojU0teN5jnDopII0OMLiKW58knn7TzkjE3GWINq0RzA1odYUZMF40mFVSkijPPwoPX0mVLZe/+PfLFyi/kQMFBz69b5WfmQcvvmSddOneRm1wozjyh87uj0amqqrK/bX7jrT3xvDKCk0bfjeLMkz9CjcRzxCsaEmVDGWGhGTVqVNisZ2dizTPDs+O0CTw/rT1hLeNF7JmbxJknd6x87898X4pLimTlypWyfesOz69b5WfqsT79e0tGWobcftsdESXOnAIJmkhzTug8oLwPHjzYuiHoVTiCjf0cl6jz7hxLEKwzKSIPOT0TzPuOMGMyXYaZT5kyJeIsZ8496rsSUAJKQAkoASWgBPwhEHSR5nlRX4KN77GsYar3trAhxpxh3Kj+bdu2WfMx+zm+fURaJLk1PXnoZyWgBJSAElACSkAJ+EugRUWaZyYcwcY2zOCYwJ13Zz8GIzixFYgxTMe8VJQ5hPRdCSgBJaAElIASiBYCIRNp0QJU71MJKAEloASUgBJQAsEgENCKA8G4oJ5DCSgBJaAElIASUAJKoHECKtIaZ6R7KAEloASUgBJQAkog5ARUpIUcuV5QCSgBJaAElIASUAKNE1CR1jgj3UMJKAEloASUgBJQAiEnoCIt5Mj1gkpACSgBJaAElIASaJyAirTGGekeSkAJKAEloASUgBIIOQEVaSFHrhdUAkpACSgBJaAElEDjBFSkNc5I91ACSkAJKAEloASUQMgJqEgLOXK9oBJQAkpACSgBJaAEGiegIq1xRrqHElACSkAJKAEloARCTkBFWsiR6wWVgBJQAkpACSgBJdA4ARVpjTPSPZSAElACSkAJKAElEHICKtJCjlwvqASUgBJQAkpACSiBxgmoSGucke6hBJSAElACSkAJKIGQE1CRFnLkekEloASUgBJQAkpACTROQEVa44x0DyWgBJSAElACSkAJhJyAirSQI9cLKgEloASUgBJQAkqgcQL/H9DhU++tqriMAAAAAElFTkSuQmCC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "39b597a6-8eef-4ba5-91e7-f030c5fedae4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:17.709669Z",
     "iopub.status.busy": "2026-02-13T14:08:17.709552Z",
     "iopub.status.idle": "2026-02-13T14:08:17.713570Z",
     "shell.execute_reply": "2026-02-13T14:08:17.712953Z"
    }
   },
   "outputs": [],
   "source": [
    "class CodedSystemAWGN(sionna.phy.Block):\n",
    "    def __init__(self, num_bits_per_symbol, n, coderate):\n",
    "        super().__init__() # Must call the Sionna block initializer\n",
    "\n",
    "        self.num_bits_per_symbol = num_bits_per_symbol\n",
    "        self.n = n\n",
    "        self.k = int(n*coderate)\n",
    "        self.coderate = coderate\n",
    "        self.constellation = sionna.phy.mapping.Constellation(\"qam\", self.num_bits_per_symbol)\n",
    "\n",
    "        self.mapper = sionna.phy.mapping.Mapper(constellation=self.constellation)\n",
    "        self.demapper = sionna.phy.mapping.Demapper(\"app\", constellation=self.constellation)\n",
    "\n",
    "        self.binary_source = sionna.phy.mapping.BinarySource()\n",
    "        self.awgn_channel = sionna.phy.channel.AWGN()\n",
    "\n",
    "        self.encoder = sionna.phy.fec.ldpc.LDPC5GEncoder(self.k, self.n)\n",
    "        self.decoder = sionna.phy.fec.ldpc.LDPC5GDecoder(self.encoder, hard_out=True)\n",
    "\n",
    "    # @torch.compile # Activate compilation to speed things up\n",
    "    def call(self, batch_size, ebno_db):\n",
    "        no = sionna.phy.utils.ebnodb2no(ebno_db, num_bits_per_symbol=self.num_bits_per_symbol, coderate=self.coderate)\n",
    "\n",
    "        bits = self.binary_source([batch_size, self.k])\n",
    "        codewords = self.encoder(bits)\n",
    "        x = self.mapper(codewords)\n",
    "        y = self.awgn_channel(x, no)\n",
    "        llr = self.demapper(y,no)\n",
    "        bits_hat = self.decoder(llr)\n",
    "        return bits, bits_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "056e25da-3d0a-416d-ac6c-98b15bc042e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:17.715364Z",
     "iopub.status.busy": "2026-02-13T14:08:17.715249Z",
     "iopub.status.idle": "2026-02-13T14:08:30.128037Z",
     "shell.execute_reply": "2026-02-13T14:08:30.127054Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -3.0 | 2.7954e-01 | 1.0000e+00 |      572498 |     2048000 |         2000 |        2000 |         0.2 |reached target block errors\n",
      "   -2.429 | 2.6426e-01 | 1.0000e+00 |      541204 |     2048000 |         2000 |        2000 |         0.2 |reached target block errors\n",
      "   -1.857 | 2.4687e-01 | 1.0000e+00 |      505587 |     2048000 |         2000 |        2000 |         0.2 |reached target block errors\n",
      "   -1.286 | 2.2690e-01 | 1.0000e+00 |      464691 |     2048000 |         2000 |        2000 |         0.2 |reached target block errors\n",
      "   -0.714 | 2.0228e-01 | 1.0000e+00 |      414272 |     2048000 |         2000 |        2000 |         0.2 |reached target block errors\n",
      "   -0.143 | 1.7119e-01 | 1.0000e+00 |      350601 |     2048000 |         2000 |        2000 |         0.2 |reached target block errors\n",
      "    0.429 | 1.1315e-01 | 9.9000e-01 |      231741 |     2048000 |         1980 |        2000 |         0.2 |reached target block errors\n",
      "      1.0 | 2.0587e-02 | 4.8650e-01 |       42162 |     2048000 |          973 |        2000 |         0.2 |reached target block errors\n",
      "    1.571 | 1.9622e-04 | 1.1833e-02 |        6028 |    30720000 |          355 |       30000 |         3.4 |reached max iterations\n",
      "    2.143 | 1.6276e-07 | 3.3333e-05 |           5 |    30720000 |            1 |       30000 |         3.4 |reached max iterations\n",
      "    2.714 | 0.0000e+00 | 0.0000e+00 |           0 |    30720000 |            0 |       30000 |         3.4 |reached max iterations\n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 2.7 dB.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CODERATE = 0.5\n",
    "BATCH_SIZE = 2000\n",
    "\n",
    "model_coded_awgn = CodedSystemAWGN(num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "                                   n=2048,\n",
    "                                   coderate=CODERATE)\n",
    "ber_plots.simulate(model_coded_awgn,\n",
    "                   ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 15),\n",
    "                   batch_size=BATCH_SIZE,\n",
    "                   num_target_block_errors=500,\n",
    "                   legend=\"Coded\",\n",
    "                   soft_estimates=False,\n",
    "                   max_mc_iter=15,\n",
    "                   show_fig=True,\n",
    "                   forward_keyboard_interrupt=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df537cad",
   "metadata": {},
   "source": [
    "As can be seen, the `BerPlot` class uses multiple stopping conditions and stops the simulation after no error occured at a specifc SNR point."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e7902e8",
   "metadata": {},
   "source": [
    "**Task**: Replace the coding scheme by a Polar encoder/decoder or a convolutional code with Viterbi decoding."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c02ebb11",
   "metadata": {},
   "source": [
    "## Eager vs Compiled Mode\n",
    "\n",
    "So far, we have executed the example in *eager* mode. \n",
    "This allows to run PyTorch ops as if it was written NumPy and simplifies development and debugging.\n",
    "\n",
    "However, to unleash Sionna's full performance, we can use *torch.compile* which compiles the model for optimized execution.\n",
    "\n",
    "We refer to [PyTorch Compilation](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) for further details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b3bc7a86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:30.130579Z",
     "iopub.status.busy": "2026-02-13T14:08:30.130439Z",
     "iopub.status.idle": "2026-02-13T14:08:30.720999Z",
     "shell.execute_reply": "2026-02-13T14:08:30.720024Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.compile(mode=\"default\") # enables compiled mode of the following function\n",
    "def run_compiled(batch_size, ebno_db):\n",
    "    return model_coded_awgn(batch_size, ebno_db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "64a7c186",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:30.723146Z",
     "iopub.status.busy": "2026-02-13T14:08:30.722870Z",
     "iopub.status.idle": "2026-02-13T14:08:44.263391Z",
     "shell.execute_reply": "2026-02-13T14:08:44.262284Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[0., 0., 0.,  ..., 1., 0., 0.],\n",
       "         [1., 1., 1.,  ..., 1., 1., 0.],\n",
       "         [1., 1., 0.,  ..., 0., 1., 1.],\n",
       "         ...,\n",
       "         [1., 1., 0.,  ..., 1., 0., 1.],\n",
       "         [1., 0., 0.,  ..., 1., 0., 0.],\n",
       "         [1., 0., 1.,  ..., 1., 1., 1.]], device='cuda:0'),\n",
       " tensor([[0., 0., 0.,  ..., 1., 0., 0.],\n",
       "         [1., 1., 1.,  ..., 1., 1., 0.],\n",
       "         [1., 1., 0.,  ..., 0., 1., 1.],\n",
       "         ...,\n",
       "         [1., 1., 0.,  ..., 1., 0., 1.],\n",
       "         [1., 0., 0.,  ..., 1., 0., 0.],\n",
       "         [1., 0., 1.,  ..., 1., 1., 1.]], device='cuda:0'))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 10 # try also different batch sizes\n",
    "ebno_db = 1.5\n",
    "\n",
    "# run twice - compilation happens on first call\n",
    "run_compiled(batch_size, ebno_db)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e76ffd1d",
   "metadata": {},
   "source": [
    "In compiled mode, the model is traced and optimized by PyTorch's compiler.\n",
    "\n",
    "The compilation happens when the function is first called with specific input shapes/types."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3664c47",
   "metadata": {},
   "source": [
    "We now compare the throughput of the different modes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d3f22327",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:08:44.265795Z",
     "iopub.status.busy": "2026-02-13T14:08:44.265666Z",
     "iopub.status.idle": "2026-02-13T14:09:02.740056Z",
     "shell.execute_reply": "2026-02-13T14:09:02.738992Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Throughput in Eager mode: 9.052 Mbit/s\n",
      "Throughput in Compiled mode: 49.663 Mbit/s\n"
     ]
    }
   ],
   "source": [
    "repetitions = 4 # average over multiple runs\n",
    "batch_size = BATCH_SIZE # try also different batch sizes\n",
    "ebno_db = 1.5\n",
    "\n",
    "# Use CUDA events for accurate GPU timing\n",
    "# (CPU timers like time.perf_counter don't wait for async GPU operations)\n",
    "start_event = torch.cuda.Event(enable_timing=True)\n",
    "end_event = torch.cuda.Event(enable_timing=True)\n",
    "\n",
    "# --- eager mode ---\n",
    "# warmup to ensure GPU is ready\n",
    "_ = model_coded_awgn(batch_size, ebno_db)\n",
    "torch.cuda.synchronize()\n",
    "\n",
    "start_event.record()\n",
    "for _ in range(repetitions):\n",
    "    bits, bits_hat = model_coded_awgn(batch_size, ebno_db)\n",
    "end_event.record()\n",
    "torch.cuda.synchronize()\n",
    "\n",
    "elapsed_ms = start_event.elapsed_time(end_event)\n",
    "throughput_eager = bits.numel() * repetitions / (elapsed_ms / 1000) / 1e6\n",
    "\n",
    "print(f\"Throughput in Eager mode: {throughput_eager :.3f} Mbit/s\")\n",
    "\n",
    "# --- compiled mode ---\n",
    "# run once to compile (ignored for throughput)\n",
    "run_compiled(batch_size, ebno_db)\n",
    "torch.cuda.synchronize()\n",
    "\n",
    "start_event.record()\n",
    "for _ in range(repetitions):\n",
    "    bits, bits_hat = run_compiled(batch_size, ebno_db)\n",
    "end_event.record()\n",
    "torch.cuda.synchronize()\n",
    "\n",
    "elapsed_ms = start_event.elapsed_time(end_event)\n",
    "throughput_compiled = bits.numel() * repetitions / (elapsed_ms / 1000) / 1e6\n",
    "\n",
    "print(f\"Throughput in Compiled mode: {throughput_compiled :.3f} Mbit/s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "749d2355",
   "metadata": {},
   "source": [
    "Let's run the same simulation as above in compiled mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1de9bbf6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:02.742480Z",
     "iopub.status.busy": "2026-02-13T14:09:02.742345Z",
     "iopub.status.idle": "2026-02-13T14:09:20.310639Z",
     "shell.execute_reply": "2026-02-13T14:09:20.309625Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -3.0 | 2.8016e-01 | 1.0000e+00 |      573758 |     2048000 |         2000 |        2000 |         8.4 |reached target block errors\n",
      "   -2.273 | 2.5949e-01 | 1.0000e+00 |      531433 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -1.545 | 2.3648e-01 | 1.0000e+00 |      484311 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -0.818 | 2.0812e-01 | 1.0000e+00 |      426224 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -0.091 | 1.6690e-01 | 1.0000e+00 |      341809 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    0.636 | 7.5483e-02 | 9.2300e-01 |      154590 |     2048000 |         1846 |        2000 |         0.0 |reached target block errors\n",
      "    1.364 | 1.8838e-03 | 7.0000e-02 |       15432 |     8192000 |          560 |        8000 |         0.2 |reached target block errors\n",
      "    2.091 | 4.8828e-07 | 2.5000e-05 |         100 |   204800000 |            5 |      200000 |         4.2 |reached max iterations\n",
      "    2.818 | 0.0000e+00 | 0.0000e+00 |           0 |   204800000 |            0 |      200000 |         4.2 |reached max iterations\n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 2.8 dB.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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NwmI5+BxVBax5aM+ePVRXV4/1MERERERERERERMa1Xbt2MXny5IMeo4A1D/l8PiBTAIWFhWM8mtGXSCR44YUXuOSSS7Db7WM9HBkHVDOSK9WM5Eo1I7lSzUiuVDOSK9WM5Eo1I7nK95oJBoNUV1dnc7aDUcCah3rbAhQWFuZtwOrxeCgsLMzL38Ay+lQzkivVjORKNSO5Us1IrlQzkivVjORKNSO5OlZqZjjtN7XIlYiIiIiIiIiIiMgIKWAVERERERERERERGSEFrCIiIiIiIiIiIiIjpB6seSyRSJBIJMZ6GKOu957y8d7k8FDNSK5UM5Ir1YzkSjUjuVLNSK5UM5Ir1YzkKt9rJpf7MkzTNA/jWOQIqq2tpba2llQqRV1dHcuWLcPj8Yz1sERERERERERERMaVcDjMkiVLCAQCh1xEXgFrHgoGg/j9flpbWw9ZAONRIpFgxYoVLF68OK9XqZPRo5qRXKlmJFeqGcmVakZypZqRXKlmJFeqGclVvtdMMBikrKxsWAGrWgTkMbvdnpcF3ivf709Gn2pGcqWakVypZiRXqhnJlWpGcqWakVypZiRX+VozudyTFrkSERERERERERERGSEFrCIiIiIiIiIiIiIjpIBVREREREREREREZIQUsIqIiIiIiIiIiIiMkAJWERERERERERERkRFSwCoiIiIiIiIiIiIyQgpYRUREREREREREREbINtYDkMMnkUiQSCTGehijrvee8vHe5PBQzUiuVDOSK9WM5Eo1I7lSzUiuVDOSK9WM5CrfayaX+zJM0zQP41jkCKqtraW2tpZUKkVdXR3Lli3D4/GM9bBERERERERERETGlXA4zJIlSwgEAhQWFh70WAWseSgYDOL3+2ltbT1kAYxHiUSCFStWsHjxYux2+1gPR8YB1YzkSjUjuVLNSK5UM5Ir1YzkSjUjuVLNSK7yvWaCwSBlZWXDCljVIiCP2e32vCzwXvl+fzL6VDOSK9WM5Eo1I7lSzUiuVDOSK9WM5Eo1I7nK15rJ5Z60yJWIiIiIiIiIiIjICClgFRERERERERERERkhBawiIiIiIiIiIiIiI6SAVURERERERERERGSEFLCKiIiIiIiIiIiIjJACVhEREREREREREZERUsAqIiIiIiIiIiIiMkIKWEVERERERERERERGSAGriIiIiIiIiIiIyAgpYBUREREREREREREZIdtYD0AOn0QiQSKRGOthjLree8rHe5PDQzUjuVLNSK5UM5Ir1YzkSjUjuVLNSK5UM5KrfK+ZXO7LME3TPIxjkSOotraW2tpaUqkUdXV1LFu2DI/HM9bDEhERERERERERGVfC4TBLliwhEAhQWFh40GMVsOahYDCI3++ntbX1kAUwHsXjcf70pz+xePFi7Hb7WA9HxoFEIsGKFStUMzJsqhnJlWpGcqWakVypZiRXqhnJlWpGcpXvNRMMBikrKxtWwKoWAXnMbrfnZYFf9tAiOkjxg0e/iRsDJxachhW3YcVpWHEadtxWG06LA5fFgcvqxGl14rI5cdncOG1uXHYPLpsHp92Dy+HNPOw+nE4fLqcfl8OH0+XH5SjAbsm/X8NjVb7+npDDRzUjuVLNSK5UM5Ir1YzkSjUjuVLNSK7ytWZyuScFrDLuRM0UcYtBHAhhAqnMwyTzoGfXKLGaJk4TXBi4ACcWXIYFV0+g6zJsuCx2nBZ7T6DryAS61r6BrhuXzYPLXoDT4cFl9+FyFOB0FOJyFmYDXafTj8XmBMMYvRsQEREREREREZHDRgGrjDuz0hYCyQQxwyBmmEQNg5hhEDUMzMMQTKYMg7AB4eyedM8juT/UTY/e5znTJk5MXD2hrhMDd88s3cxMXRtOiw2nxY7b4sBpzTzcVhdOmxOXNRPoOm2ezExdhxenvQCXvQCX05cJdR0+XK5ibC4f2D1gzb9/aRIRERERERERORIUsMq48z+3rua5557j8ssvx26zQSoByShmIkI8FiQaDxKLhYjGgkQTXcTi3UTjXUST3cQSESKJMLFUhFgySiQZJZaKEUvFiKQTxNJxoukkUTNBLJ0iaqaIkiaGSZQ0USBmQNQwSB6mWaYxi0EMg2C/vb2hbqL/5gdkM02cponLNHGZmTDXhbF/dq6lz+xca2+7BRcuq2vwENfhw+0sxO0qwu0swuMuxu0uxenwYVgsH3zAIiIiIiIiIiJHGQWsMr4ZBtgcYHNguApx+ipxHonPTadIxruIRTOBbjQWyIS68S6iiRDReDexRDfRRJhoIkwsGSGaihBNxoimosRScaKpGNF0glg60RPqJomZ+0PdKGYm2DUgdpjC3GRPUNw94J2etgvE+03YHSmLaeI2yTww8GDB3dM3122x47HYcVuduK1OPFYXbpsbt82Nx1GA2+7F7fDicRRmwlunPxPeeopxu0qwOwvB7lZbBREREREREREZEwpYRUbCYsXm8mNz+Sk4Ah+XNtPEk7FMmBsNEIsHicZCRGMhYoneYLebWCJMJNFNLBklloz0zNCNEu0JdDMzdDOBbiydyIS5ZooYaaJmmqhhEiPTFmFUx28YdBv0CXL7zMg1o/vz3BGwmSaedPqA8NaCG2smuLXYcBkOkpEYex7/DR67G7fNg8fuwW0v6AlvfbgdhXichbhdxXhcRbhcRVicvkx4a/eAxToavxQiIiIiIiIikmcUsIqMAxbDklkoy+6GgsrD/nmJdCIzCzfaSTQWIBrtJBbrCXXjgZ4ZuplQN5oIE0tkZuhGklGiqRiRVIxIOk44lSBiJomYScKkiZgmEcMkYkB8lELcpGEQtFqHbqnQ2yfXCURDEB3+tV3pNB7TxJ3OzMD1YGTDW7fFisfiwG1x4La58FjduHtCW4/Di7unXYKnp12C212Cx12C21OOw1WM4fBo1q2IiIiIiIhIHlDAKiID2C127K4ifK6iw/YZyWScSLSTSLSdcLSDSKSTSDxAOBYgEgsRiYeIJLoJJ7qJJMJEUhHCyWgmvE3FCafjRMwEkXSSCGnCZjoT3mKO2gzcqMWSyWMHTF7tG+B2Z1rjJhh2eGsxzUxw2zPr1o0FT0+7BLfFjsfqxG11ZYJbW89MW7sXjzMz09bt6mmT4C7B7S7B7SnLzL61ubFZ9LUuIiIiIiIiciTpb+IiMiZsNgc+bwU+b8WoXtc0TeLpOJF4mEgsQDjaTqi7lTfeXMmsOTXEU91E4iHC8a79AW4yQiQZIdxn9m0knSBsJomYKSJmmghpIqM04TRtGHQZBl37R02myW0S0pF+65nlyt4707anz63HYsNtZEJbr82Nz+bBZ/ficxbidRRS6CrG6yrG5ynD5y7H563A66nAZfdgaIatiIiIiIiIyCEpYBWRvGIYBk6rE6fbSZG7GKghkUjQsDHCOfMvx263j/jaaTNNNBklnAwTSUQI98zAjUTaicQ6CUcDRGJBIvFgT4AbJpIME05GiKSiRFLxTOuEdKZtQoQ0YTItExKjFGYmDAhgEuhtbJuOZ97oWbNsuGymSaEJXtOCz7DgNewUWhx4rU58Njdem4dChxefoxCvsxCfsxifuwSfqxRvQRneggqsziJwesF2RJaeExERERERERkTClhFRIbJYljw2DMLZOEGCqtH7dqJZIxIpI1IuC0T2EbaCcc6iEQDmZm48SCRntA23NMyIZKMEknHenrdJoiYKcJmikhPaBu2WIgYBukRhLdJw6DdgHZMsquQmdHsRNvhKEin8aXT+NImPgx82PBabPgMBz6rMzOb1uHD6/RT6C7B6y7B56nA563E552E01sJ7uLMQmMiIiIiIiIiRykFrCIiRwG7zYndN4lC36TRuWAyDvEuzGiQeLSD7nAbXZEWQpF2QtEOumIBQvEAoXgXoUQ3XckIwVSUUDpGVzpByEwSIk3IMOkaYUjbbbHQbbGwr//Aeh5hSHZkX9Ix8Hy7aeJLpylMm3ix4DNseK1OCq0uvL3hrMNHoasYn7sUr7sMX0EFPu9EfL4qCrwTsFj1vzkRERERERE5vPQ3TxGRfGRzgK0Ew1OCkxqcQMkIL2Wm04QjrYS6mgiFWwiFW+iKthPsDWpjAUKJTFAbSobpSkYIpeOEeoLaLtJER9ABIWEYtFuttGcXGUsDkUyf2njHIVseGKaJ1wQvBj6s+Cz7Z8567QX4HIX4nH587hK87lJ8ngoKvRPweifitheSNIc5VVdERERERESOaQpYRUTkoAyLhYKCCgoKKpgwwmskUglCiRCheIhQpJNQpIVQuJmu7hZCkVZC0Y5MUBsP9QS1EbrSsUxIS4ouTMwcZ9GahkHIgBCwlxQQgVQEUp0QG941vv/QdzKzZy32TEBrc+OzFeB1eCl0+jMLhLlK8HrKKSyoxOspw+cspNBRiNfuxWP3YDEsuf5yiYiIiIiIyDiigFVERA47u9VOibWEElcJFE7N+fy0mSacCBMKtxLs2kNX1z5C3c2ZcDbSTlesk1AsmJlJmwwTSkUPaHVgELfkPo02ZkCMNG1mDFIxSIUy4Wz38M63AAVYexYIc2VmzzoKKHRkFgbzuospLphApX8qFQUTqPBUUOYuw2bR/55FRERERETGC/0NTkREjnoWw4LX4cXr8DKxqCb3C6RTxLqbCYX2EArtpSvcRCjcSijSlu1JG4x30ZXMzJ4NpmOEknG6LCZdFoOQxUKXJfeZqGkgRIpQuqe1QaIDIgc/xzCh1LBSYXFSYS2g0lFIhauYCncFFd6JVPomU+6vweerwigoB/WZFRERERERGVP6W5mIiOQ/ixWnbyJO30TKhnF4IpHgueee4/LLLsNOAiIdpMJtdHftJdS1j67uFoKRVrqi7YSiAULxIKFkN12JMKFUjJCZaW3QG8yGLBaCFgvJYbQ5MA1oJUVrOszmdBgSLYPOmHWn01SkUlSYViqsLirsPipdJVR4KqnwTaaiaDplJcdh91dDQRlYrAMvIiIiIiIiIh+YAtY8lkgkSCQSYz2MUdd7T/l4b3J4qGYkV9maSSbB7gBPJXgqcZfNxQ1UDOciqThEAxDpxIh2YIbbiYVb6Qo30xVuJRTNtDZoiwVoToZoTkVoNhO0GGmarRZarVbSBwlkIxYLDRYLDQDEId0G4TYIb4XWzDGGaVKS6glisVFhdVPhKKTcVUpFwUTKfdVUFE3HWzQNwzcRPCWgnrEjou8ZyZVqRnKlmpFcqWYkV6oZyVW+10wu92WYpmkexrHIEVRbW0ttbS2pVIq6ujqWLVuGx+MZ62GJiEguzDS2VARrsotoqoXuZBuhdDuhdJCA2UXADNNhxGgnQZslRXgU8lBX72zYZJpS00Kx6aQIN4WWQryWIgps5bhsFSQdZUTtfhJWL+S46JiIiIiIiMh4Eg6HWbJkCYFAgMLCwoMeq4A1DwWDQfx+P62trYcsgPEokUiwYsUKFi9ejN1uH+vhyDigmpFcjaea6U5009y9j+aOrbR0bqc5uIuW7n20RNtoTgRoTkVoM5OkRiEPLUmlqEymKE+bVBpOyu1eyp3FVHgqqPBmesMW+qeCbyL4JoCz8JgJYsdTzcjRQTUjuVLNSK5UM5Ir1YzkKt9rJhgMUlZWNqyAVS0C8pjdbs/LAu+V7/cno081I7kaDzVTZC+iyFPErPI5Qx6TSqdoi7bRHNpLU8dWmjvfpyXUSFP3Ppqj7TQnQjSno3SRPuhntVuttFutbMnu6YJkFwR3QXA17AFndjZsioo02bYEFa5SKgsmUlE4mYqiGdj9VX2CWN+o/XqMtfFQM3J0Uc1IrlQzkivVjORKNSO5yteayeWeFLCKiIjkOavFmpll6qnghMoThzwunAjTFG6iOdRIc+d2mjt30NzVSHO4meZYB02JLlrNOKmDfFbMYmGXxcKu7B9GTCAA8QDEt0MH0JCZDVuRTFGRSlFuGlRaC6hw+qlwlVHhnUilvwa/fypG4aRMCOubAI6CUfxVERERERERGR0KWEVERAQAj93DNP80pvmnweSzBz0mlU7RHm2nOdJMc3A3zR3baQo20Ny1h+ZIK83xTpqTYUIHjWH3z4Z9d/+VgXaItUOsDtpewZlOU55K7Z8Ri40KWwGVrmIq3BVU+Kqo8E/D0Xc2rHcC2F2j+csiIiIiIiJyUApYRUREZNisFivlnnLKPeXMK50H0wY/LpwI0xJpoTncTFNwV2ZGbHAXzd37aIq20ZII0pKKkmToVvAxi4XdFgu7+/1oThJogUgLRDZBMxT3BLDlqRSVqRQV2Kmw+6hwlVBZMIEKXzVF/qkYhRP3z4b1TgCbY1R/bURERERE5NikgFVERERGncfuYap9KlMLp8KEUwY9Jm2mM7Nhw82ZR3AnTYF6mkO7ae5upjnWTnOii6CZOOhndVitdFitvNdvbxzYB937oHsdjj1mz2zYZLY1QaXNS0XpbComLaKi5nwqJi7EadPsVxERERERyY0CVhERERkTFsNCmbuMMncZc0vnDnlcJBmhJdyS6Q/b3URzaBfNgd62BC00xzppTnYfdDZs3GLQaLHRaD/gjz7RrbB9K2x/GIAiw0a5s5hJ/hrOn3YZl067DK/DOyr3KyIiIiIi+UkBq4iIiBzV3DY3UwqnMKVwypDHpM00HdGO7GzYpnATLcFdmVmx3Xt7+sMGCaRjB/2sTjNJZ7SFrdEWXml6mx+88T0uLprD1XNu5tTjrsRisY727YmIiIiIyDingFXGne0t3bRFIRBJUGy1YbUYYz0kEREZYxbDQqm7lFJ3KceXHj/kcdFkNDsbtqVrL8371tLUsonmYENmJqzFoNlmJWFk/t8SJc0znZt55o1/YeJr3+QqdzVXT7uC6tkfgaKpYOj/QSIiIiIixzoFrDLufOq+1ewJ2PjXtS8B4HXaKHTZKHTb8blsFLp6nvtt2yl02zLPLlt2u9Blx2mzYOgvyCIixwSXzUV1YTXVhdWZHTOv3P9mMg571mDu+Aub6//Mk6FtPOtxELRmZq3utRr8T3w3//Pe/7Bo3U+5OuXkkoln4Jl2Pkw+/Yjfi4iIiIiIHB0UsMq4E4ol+213xZJ0xZLsCURHdD2H1TJIIDtUUNt/u9Blx+vSLFoRkbxgc8CU0zGmnM68877GvGScr+x+k5e2PMKTzW/zmhkm3fMPcqvcLlYB/x54nQ+/9ieu7urm4rQPa+o5mHYuTDsHioZuaSAiIiIiIvlDAauMO1fMn8B723dSUFxOVyxFMJogFE0SjCSIJdM5Xy+eStPWHaetOz7iMfXOoj1wpqxm0YqIjGM2B46ac/hwzTl8GGgO7ubpd37NEzv/RH0yCEDEYuEJn5cnfF6qEwmu3vkcV21azsRUKhOw1pwDNWdnHgpcRURERETykgJWGXe+e9VcnnuunssvPxm73d7vvVgyRSiazAauoWiyJ4BNEIwkM889+/puZ95P0BVLkh56Eeoh9c6i5TDNoi3y2PF7HBS57RR7HBR57D0PBwUOq8JZEZEjoKJwMn97zp38jfkd1reu54m6x/jDjufpSmW++3fZ7fy8uIjaIj+nRaNcE2rlwneW4V73UOYC2cC1J3Qtqh7DuxERERERkdGigFXyitNmxem1UuZ1juj8dNqkO57sE8weGNRmtoMD3t8f1EYTR3YWrd1q4HdnQtdij73f66LeMNbdP5Qt9thx2xXMioiMhGEYnFh+IieWn8g/nvbPvLjzRX6/9fe8te9NTMA0DN5wu3nD7cabTnNpVzdXd3VzYudOjHUPQTZwnbo/bJ12Dvgnj+l9iYiIiIjIyChgFenDYjHw9fw4/yTcI7pGPJk+YGZs70zZgwW0+7dD0UROs2gTKZPWrhitXbGcxumwWvD3BrEHBLC9oWyxx46/93VB5tntsOb4KyIikr9cNhdXTL+CS6ovYdkzywjXhHl6x9PsCu0CoMti4XeFPn5X6GNaIsnVoRBXdoWpSKWgswHWNcC6BzMXK67paSfQE7oqcBURERERGRcUsIqMMofNQqnXSekIZ9Gapkl3PNUveA2EE3SE4wQimefOcILOSILO3tfhzOvueGrYnxNPpWkJxWgJ5RbMOm2WAbNiiz2O/UHsIEFtkceOy65gVkTyW5GliCUnLOEzH/oMa5rX8MS2J/hj/R+JJCMA7LDb+ElJMT8rKeFM08k1rfu4oCuIo/cCHfWZx9rewHXaAYFr1RjclYiIiIiIHIoCVpGjjGEYeJ02vM7cf3vGk2k6I/GeQLYngO0TxHaEEwQifV6H43SEE0QSww9mY8k0TcEYTcHcglmX3ZIJYt37Q9lsEOu293tdXLA/nHXYLLn+MoiIjCnDMDi58mROrjyZfzr1n1jRsIIntj3BqqZVAKQxedWI8mp5EYUTJ3G5q4prgiHm7l6HkerTKqZjR+ax9oHMtgJXEREREZGjkgJWkTzisFmo8Lmo8LlyOi+aSBGI7J8J2xvEdvTMjg1E4nR0J+iM9JkxG4nn1G82mkizNxBlb44LgRU4rNnZsH0X+Mq87j9jtrgnoC1027Fa1F9WRMaex+7h6plXc/XMq9kV2sVT7z/FU9ueYk/3HgCCyTCPdG3lEQvMnH8W15R+iCtiacp2rYbdb8HBAteS6f0D18JJY3CHIiIiIiKigFVEcNmtuOxWKgtzD2Y7w/vbFvQNZbPtC3r2BfocF08NP5jtjqfojkdo7IwM+xzDIDNT1p0JXv1uG93tFtY89y6lXle/xb56Z9UWFzgocGjhLxE5fKp91dz+odv57Imf5e19b/PEtif4U8OfiKYy//C0LfA+dwfe5yeGjbOnns015/0955pO7DvfgPpXYffb/QPX9u2Zx5r7M9slM/oErmcpcBUREREROUIUsIrIiLnsVib4rUzwDz+YNU2TaCK9v5dsTxuDfttDtDhIDnP1L9MkO9OWtnDPXgurWnce9Dy71cDv7hO89iwC1jtbtqh3UbADZtQ6beovKyLDZzEsnDbxNE6beBr/fNo/80L9Czyx7QnWtawDIGkmeXnXy7y862WKncVcMf0Krrn6x8z2TcmErDtW7g9c04n9F25/P/NYc19mu1/gejYUTjzStyoiIiIickxQwHqU6Orq4u6772bVqlWsWrWKpqYmli5dyr333jvWQxMZVYZh4HZYcTvcTCpyD/s80zQJxZLZmbAd/cLYeL/2Br2hbEd3nGA0OezPSKRMWrtitHbl1l/WbbcOGrz27TN7YCsDv9oYiAjgc/i4ftb1XD/renYEdmRaCLz/FM3hZgA6Yh08uOVBHtzyIMeXHM/VM6/mijNvp+jCb0A8nAlZ618dXuBaOrN/4OqbMAZ3LCIiIiKSfxSwHiVaW1u58847mThxIosWLeLZZ58d6yGJHFUMw6DQZafQZae6xDPs85KpNG2hCE/+4U+ceMqZdMXTg4ezvX1mR7DwVySRIhJIsSfH/rKFLlvPgl6ZALakb1/ZggP2FWQCW5dds2VF8tU0/zT+YeE/8Pcf+nv+uvevPLntSf6888/E05m2AFvat7DlrS3cvepuLqi+gGtmXsOZNWdhm35e5gLxcKZvazZwXdU/cG3blnmsvjezXXpcT+B6tgJXEREREZEPQAHrUWLixIns3r2bqqoqotEobvfwZ/aJyNBsVgslBQ4q3bBwShF2u31Y5/Uu/DVgdmz29f4Fvzr6LAyWSA2vjQFAMJokGE3SkG1jcGguu6Vf6DognC3YP0u2xOOgqMCOz2lTb1mRccRqsXJ21dmcXXU2gViAP+z4A09se4KNbRsBSKaTrGhYwYqGFZS5y7hy+pVcM/MaphdNh+nnZx6wP3DtbSnQuPqAwHVr5rH6nsx2b+A67RyYejb4Ko/ofYuIiIiIjFcKWI8STqeTqqqqsR6GiPQYycJfpmnSHU/R0R3PhrOZBb4yzx3h+IAWB5lgNnHoi/eIJtLsCURzmi1rsxjZ9gQlfdsX9MyK7e0zW1zg2N/qwG3HZrUM+zNE5PDwO/18bM7H+Nicj7GtYxtPvv8kT7//NG3RNgBaI63cs+ke7tl0D/PL5nPNzGu4dNqlFDoKweE5IHDthl19Zrg2roJ0nzYqBwauZbP2z25V4CoiIiIiMiQFrCIio8QwDLxOG16njeoczkulzf2BbPf+MLY3gM3s6x/KdnTHh73oVzJt0toVp7UrfuiD+ziwhUFxz8zYvi0MsvvUwkDksJtZPJMvL/oyn1/4eV5vfJ0ntj3By7tfJtkTkm5o3cCG1g384K0fcNGUi7hm5jWcNvE0rJae35eOAphxQeYBPYHrm30C19X9A9fWusxj1W8y22Wz+7cU8FYcwbsXERERETl65WXAGg6HeeWVV1i9ejVr1qxh9erV7NyZWT3829/+Nt/5zncOeY1QKMSPfvQjHnvsMXbs2IHVamXWrFncdNNN3HHHHTgcjsN8FyJyrLBaDEoKHJQUOKB8eOeYpklXLNmvRcFQQWxHn36z4fjwe8uOtIVB39C1bzhbWuCgzOek3OukzOekzOuk0KX2BSK5slvsnFd9HudVn0dHtIPndjzHE9ue4N32dwGIp+M8X/88z9c/T6WnkqtmXMXVM69mauHU/hdyFMCMCzMP2B+49rYU2LPmgMD1vcxj1f9ltstmwzlfghNvOgJ3LSIiIiJy9MrLgPWtt97i8ssvH/H5DQ0NnH/++dTX1wPg8XiIxWKsWrWKVatW8dBDD/Hiiy9SXFw84FzTNInFhrcCucViUVArIiNiGAY+lx1fjot+RROpPqHs/uC1/8zZ/vtybWGwNxBl7zBbGDhslmzgWu51UOZ1Ut4Tvu5/nQlm1UtWZKBiVzEfP/7jfPz4j/Nu+7s8ue1Jntn+DJ2xTgCawk38esOv+fWGX3NSxUlcM/MaPlzzYQrsBQMvdmDgGuvqP8N1sMD1938HsRCcetvhv1kRERERkaNUXgasAMXFxSxcuDD7+OIXv8i+ffsOeV4ymeTKK6+kvr6eiRMncv/993PxxReTTqdZvnw5t912G2vXruWWW27h2WefHXB+Q0MD06ZNG9YY582bx8aNG3O+NxGRkXLZrUzwW5ngH35v2d4WBu3dfdoW9LQwaO/ev+hX78zZ3n3DaWEQT6Zp7IzQ2Bk55LFOmyUTvPbMgi33OfrNht0fzDrwKoyVY9CckjnMOXUOXzr5S7yy+xWe3PYkKxtXkjIzM9fXNq9lbfNa/uOt/+DiKRdzzcxrWDRhERZjiH7LTi/MvCjzgJ7A9Y1M2Lr9lUzgCvDcV8DmgoWfOAJ3KSIiIiJy9MnLgPWcc86hvb29376vf/3rwzr3vvvuY8OGDQA89thjnHHGGUBmtunHPvYx0uk0S5Ys4bnnnuPFF1/koosu6nd+WVkZ99xzz7A+a7AZsCIiR5t+LQyG6cAWBu3dcdq64rR2xWgJxTLPXTFaQ5l97eE45iHy2FgOYazLbjlgFmzmecAsWZ+TAodVYazkFbvVzsVTL+biqRfTGmnlmfef4YltT/B+4H0AIskIT29/mqe3P02Vt4qrZlzFVTOuYrJv8sEv7PTCzIszD9OEF++EV3+cee+pO8DmhAU3Hua7ExERERE5+uRlwGq1jnyRlfvuuw+ACy64IBuu9nXTTTfxjW98gx07dnD//fcPCFi9Xi+f/OQnR/z5IiL5INcWBslUmvbuOC3ZAPaAMLbnubUrE9YeSjSRZndHhN0dhw5j3XYrZb6e4LUndC1x22jaZ2Dd1MTEYk82rC1w5uX/NiWPlbnL+OQJn2TpvKVsatvEE9ue4LkdzxGKhwBo7Grkl+/8kl++80tOmXAK18y8hounXIzHfojft4YBF30bknF4oxYwM+0CrHaYd+3hvzERERERkaOI/qbYRzgc5rXXXgPgsssuG/QYwzC49NJL+eUvf8kLL7xwJIcnIpK3bFYLFYUuKgoP3bog0RvGhnpnwWaC14FhbIyO8KH7x0YSKXa1R9jVfmAYa2X5jnf67fE4rNk2BAN7xfa2LXBR5nPgceh/sXL0MAyDE8pO4ISyE/jqKV/lpZ0v8cT7T/DXPX8lbaYBeHvf27y9723+zfZvfLjmw1wz8xpOqjhp6BnehgEf/jdIRjMLX5lpeOzTYHXCnJH3whcRERERGW/0t78+tmzZQjqd+UvGCSecMORxve/t27eP9vZ2SkpKRuXzf/7zn9PZ2UkymVlAYv369Xzve98D4Nxzz+Xcc88dlc8RERnP7FYLlYUuKocZxvZtTdBywGzYllA0O1u2cxhhbDieYmd7mJ3t4UMe2zsztrQgE8iWFjiz26XeTP/YUm/mdbHHgdWiNgVyZDitTi6ddimXTruUpu4mnt7+NE9ue5L6YD0A4WSY32/7Pb/f9num+KZw9cyruWrGVUwomDDwYoYBl98NqRisfTCzCNbypXDTw3DcxUf2xkRERERExogC1j727NmTfV1VVTXkcX3f27Nnz6gFrHfffTcNDQ3Z7bVr17J27VoAvv3tbw8ZsMZiMWKxWHY7GAwCkEgkSCSGv/r3eNF7T/l4b3J4qGaObaUeK6UeD7MrDv4jz/FkmraeXrH7Orv5y9vrqJgyk45IirauTPuC1q44bd0xApHkQa8FB5sZO5DFgGKPg9ICB6XeA54P2FdW4MTtGHkrHDk8xuv3TImjhKVzlnLr7FtZ37qep7Y/xQsNL9Cd7AZgZ2gn/7X2v/j52p9z2oTTuGr6VZw/+XxctgP+gePSH2FNRLFs/B2k4pi//TipG5dhTtM/Dg9lvNaMjB3VjORKNSO5Us1IrvK9ZnK5LwWsfYRCoexrj2fov4j3fa/vOR9UfX39iM77/ve/z5133jlg/wsvvHDQ+xjvVqxYMdZDkHFGNSO5OL0CiG4FA/D1PHok0xBKZB7BhEEo3rttEExAV8IglICuBHQnDz0zNW2SCXe749B86LE5LCY+O3jt4LPvf+21mxQe8NpjywS4cmSM9++ZkzmZ+QXz2ZzYzJr4GrYntwNgYvLGvjd4Y98buHAx3zGfhY6FTLZOzrYQMGxXcHJRA1Wdb2Mko/DITbwx46u0e2eP5S0d9cZ7zciRp5qRXKlmJFeqGclVvtZMOHzon1zspYA1D/zTP/0TX/rSl7LbwWCQ6upqLrnkEgoLC8dwZIdHIpFgxYoVLF68GLvdPtbDkXFANSO5Gs2aSaTSdIQTtHXFe2bIxrJhamvPvvaembOt3XHiyfQhrxlPG7TFoC0GmQR4aH1nx5Z5HZRkZ8JmnksKet9zUlrg0OzYEcq375lruAaAPd17eGb7Mzy9/WkauxsBiBLl7fjbvB1/m2mF07hy2pVcMe0Kyt3lkLqU9GOfwrL1D9jScc5u+AmpJY9hVi0aw7s5OuVbzcjhp5qRXKlmJFeqGclVvtdM70+ID4cC1j58vv3Tkw6WUvd9r+85Y8XpdOJ0Ogfst9vteVngvfL9/mT0qWYkV6NRM3Y7eFxOqobRTcY0TbpiyZ4wNkZLKPPc20c2+9w9/L6xfWfH1g1jdmzvQl6l3v39Y7PbXidlBQ7KfJkwtki9YwfIt++ZqUVTuX3h7Xz2pM+yumk1T2x7ghUNK4gkM60vdgR38LN1P+Pn7/ycsyadxTUzr+H8j/4vjuWfhG1/woh3Y3v4Y7D0KZj0oTG9l6NVvtWMHH6qGcmVakZypZqRXOVrzeRyTwpY+5g0aVL2dWNjIwsWLBj0uMbGxkHPERER+SAMw8DnsuNz2akpKzjk8YlUmo6embCZ4LU3hO0NZHvC2FBs2LNjc1nIy2JASYGTCp+TykInE/wuKnyunkXInFQWuqgodFJa4FQQO85ZDAunTDiFUyacwj+f9s+8UP8CT2x7gjXNawBIm2lWNq5kZeNK/E4/l8+4hGtSIY7f8SZGLAAPXAOffBYq543tjYiIiIiIHAYKWPs4/vjjsVgspNNpNm7cyGWXXTbocRs3bgRgwoQJo7bA1eGgRa5EMlQzkqvxVDPFbivFbjfHlbsPelxmdmyK9u79s2B7F/Xq99zzujMyvNmxrV0xWrtibN479HFWi0GZ10GlLxPGVhQ6qfC5ssFsZc++Irc9289zvBlPNfNBOXDwkZqP8JGaj7AztJNntj/DMzueYV94HwCBWICH65bzMDCrZjpXt7fwka4AxfddRfITT0HZrLG9gaPEsVQzMjpUM5Ir1YzkSjUjucr3msnlvgzTNM3DOJajRk1NDQ0NDXz729/mO9/5zpDHnXvuuaxcuZILL7yQF198ccD7pmkyc+ZMtm/fzq233sp99913GEedm9raWmpra0mlUtTV1bFs2bK8XuRKREQOn1QaupL0LNa1f9Gu0CCvgwlImx88GLUaJn4HmYfdpNABfkdmX2HPPr8DXFYYpzls3kqbabYnt7MmvobNic0kSfZ732aanBuOcHkYqP4KUZd+AkhEREREjm7hcJglS5YQCAQOucaRZrAeYOnSpaxcuZKXXnqJN998k9NOO63f+8uXL2f79syKurfeeutYDHFIt99+O7fffjvBYBC/369FrkR6qGYkV6qZ3KTTJh3hOE2hGE3BGM2hGM3BGE2hGM2haHa7tTvOwf5ZN2UatMeg/RCLd7ntlp5WBPvbE1T0mR1b2TM79kgu2KWa2S8UD/HCzhd46v2n2NC2AYCkYfDnAg9/LoDicC2XT7iaq+Z+nOOKjhvj0Y4d1YzkSjUjuVLNSK5UM5KrfK8ZLXIFdHR0kEqlstvpdKbvXDgcprW1Nbvf5XLh9Xqz20uXLuWnP/0pGzZs4Prrr+e+++7joosuIp1O89hjj3HbbbcBcNlll3HRRRcdobsZmXxtMtwr3+9PRp9qRnKlmhm+CU4HE4q9Bz0mmUrT2hWnKRjNPEIxmntfB2PZ/R2HWLwrkkjT0B6m4RB9Yn0u2/5+sD4XFX16w/buL/c5cdpGL4hVzUCJvYSbjr+Jm46/ie2B7Ty57Ume3vYkLdE2ADosBg/VP8VD9U8xt3QuV8+4miumX4Hf6R/jkY8N1YzkSjUjuVLNSK5UM5KrfK0ZLXIFnHTSSTQ0NAzYf9ddd3HXXXdlt5cuXcq9996b3bbZbDz11FNccMEF1NfXc/HFF+PxeEin00Sj0ey1H3roocN+DyIiIvnEZrUwwe9igt910ONiyRTNwczs1/3Ba08Y22dfKJo86HVC0SShaBfbmrsOelxJgaNnJmzfxblcVPr2h7FlXgc2qyXnez7WTfdP54snf5E7TrqDv25/nide/iYv2VIkeno8bG7bzOa2zdy96m4uqL6Aq2dezZmTzsRmyds/ooqIiIhIHtKfXgdRU1PD+vXrufvuu3n88cfZsWMHdrudefPmcfPNN3PHHXfgcDjGepgiIiJ5yWmzUl3iobrk4H3Ew/FkphVBz2zYpsD+mbFNwSjNwSj7glGiifRBr9PeHae9O867+0JDHmMYUOZ1MqEncJ3YExRPKMw8l3lsxFJDnn7Ms1lsnDPzSs6pWETnvZfyfKKVJ3wFbHY6AUikE7zQ8AIvNLxAubucK2dcydUzr2a6f/oYj1xERERE5NDyNmCtr6//QOf7fD7uvPNO7rzzztEZ0BhIJBJ5uZJbvq9SJ6NPNSO5Us2MD3YDqvwOqvxD/6OnaZp0xZL7e8P27RPb++gJZROpoRvEmia0hGK0hGJsaAwMcZSN777z5+ws2An+TGuCCf6e7Z79JR47xrG6Spe7jIIlT3DTA1dy855d1NntPFk5hacLCuiIdwLQEmnhNxt/w282/ob5pfO5cvqVfHjqh/E5fGM79lGm7xnJlWpGcqWakVypZiRX+V4zudyXYZoHW25CxpPa2lpqa2tJpVLU1dWxbNkyPJ6Dz/4RERGRTIAaTkJnHIJxg0ACAnEIxA2CPc+BOAQTYB5kAa7hsBomRQ7wO6DIYWaenWa/bb8d8rkjgSfWzNlb/w13ogOAVvdUflNzPatSm3gv8R5p+s86tmFjrn0uCx0LmW6bjsXI418cERERETkqhMNhlixZQiAQOOQi8gpY81AwGMTv99Pa2nrIAhiP8n2VOhl9qhnJlWpGhpJMpWntjtMUjLEvkJn5ui8QZW8gwrsN+4jbPDQFY8SSB29LcCiGAWUFjn4zXycU7u8TO6HnucA5jn8YqW0btgeuwuhuBiBddQqpmx+l3UzwfP3zPLX9KbZ2bh1w2gmlJ1B7Qe24n9Gq7xnJlWpGcqWakVypZiRX+V4zwWCQsrKyYQWs4/hP5XIo+bqKW698vz8ZfaoZyZVqRg5kt0O1y0l1af9wL5FI8NxzjVx++TnYbDYCkQT7glH2BqI0BXqeg/2fA5Ghf+TINKGlK05LV5yNe4Yej89ly/aBzT73fV3ooqTAcXS2JJhwPCx9Gu69HMJtWBrfxrL8Fio//js+Of+TLD1hKVvat/Dktid5dsezBGKZ1gwb2zbym82/4SunfGWMb2B06HtGcqWakVypZiRXqhnJVb7WTC73pIBVREREZBQZhkGRx0GRx8GcCUP/S3cknhoQumaeI+wLxtgXiNASipE+yM8ahaJJQtEutjZ3DXmMw2bJznqd4HczodDZ87w/kK3wObGPRU+Cijlw65Nw70cg2gkNr8EjN8PNv8Wwu5hbOpe5pXP58qIv8+ddf+abr36TWCrGQ+8+xA2zb2Bq4dQjP2YRERERkQMoYBUREREZA26HlZqyAmrKCoY8JplK09oVZ28gkg1h9wWj7Av0PHpeH6wlQTyZZld7hF3tEaBj0GMMA8q8zn4zXycWuagqcjOp51Hpc2I7HCHshPnwid/D/VdDLAjbX4ZHPwEfewhsmQXMHFYHl9ZcSl17Hb/e8GuS6SQ/WvUjfnbhz0Z/PCIiIiIiOVLAmscSiUReruSW76vUyehTzUiuVDOSq8NZM6UeK6UeLydM9A76vmmaBCJJ9gUzM2D3BWP9nwMxmkJRApHkkJ9hmtASitESirGhMTDoMRYDKgtdTPK7mOh3Mamo53WRm0n+zGufyzaydgQV8zFu+i3WZR/FSHTD1hdIP7qU1HX/B9b9P5q1dM5Sfr/197RGW3lp10u8tus1Tp1wau6fdxTQ94zkSjUjuVLNSK5UM5KrfK+ZXO5Li1zlkdraWmpra0mlUtTV1bFs2TI8Hs9YD0tERESOAvEUdMYhEDd6nqEzbmSeY5nnYAJMRt6v1Wk1KXZAsdOk2AnFjp5nZ2Z/kQMONgm2NPQup79/NzYzDkBj0amsrvkspmHNHrMmtobHI48DMMEygc/5PofFGIP2BiIiIiKS18LhMEuWLBnWIlcKWPNQMBjE7/fT2tp6yAIYj/J9lToZfaoZyZVqRnKVLzWTTKVp6YpnWw/sDUTZE4iyp7P3dYT27pHPUDAMqPA5e2a8upnYMws2MxM2s69o32vYHv04RioGQHr+jaSu/Dn0hKhpM80tf7iFdzveBeCbp36T62Ze98Fv/gjLl5qRI0c1I7lSzUiuVDOSq3yvmWAwSFlZ2bACVrUIyGP5uopbr3y/Pxl9qhnJlWpGcjXea8ZuhykuJ1PKfEMeE4mn2BuIsKczyp7OCI2dEfZ0RtjTs6+xM0J8iJ6wpglNwRhNwRhrdw3eisDjsHJtwde4M/J9bCSxbHiUhkCKpvN+QFVxAZWFLr5+2tf55B8+CcAv1v+Cy2dcjs8x9JiPZuO9ZuTIU81IrlQzkivVjOQqX2sml3tSwCoiIiIiw+Z2WJle7mV6+dA9Ydu645nQtTNCY08Qu387QmtXfMjrh+MpHoofT6vlDmrtP8VmpJm28zFeuSfETcmlGIZBhc9JQeVCum1raI+28w/P381NMz/LpCI3VUVuijz2kfWCFREREREZAQWsIiIiIjJqDMOgzOukzOtkweSiQY+JJlKZlgN9Z8B29p8V+8fkKXwhcTs/tf8cq2HySdsLxLHz78klNAVjGJGLKZj+DoYlxVvtT/LSIzMwE6UAuO3WzCJcPYHrpOzDRVWRmwl+F06bddCxiYiIiIjkSgGriIiIiBxRLruVaWUFTCsrGPR90zRp746zp/NsNr0zmflvfx0Dk/9nexZ/oY+7EzfQEioh3n4OzrKXMSwpnBXPE228BYBIIsX7Ld2839I95BjKfc6eADbT+7W6xEN1iZspJR4mF3tw2RXAioiIiMjwKGAVERERkaOKYRiUep2Uep0w+TMwwQVP/wMAHws/wscumEnsrC+xvfUUPv3ndwgmOrAXbuScyi6ioZrsLNhoYvBesAAtoRgtoRjv7Br8/Qqfk+oSD1NKPFQXu5nc+7rEw4RCF1aLWhCIiIiISIYC1jyWSCRIJEa+0u/Rqvee8vHe5PBQzUiuVDOSK9XMYbbg41hiEawvfD2z/dL3sFlszDz97/mHk/6e7771XQA6Xb/jgasfwGqxYpomnZEEezqjmXYEPS0Jel/v7YzS3BXDNAf/yOZQjOZQjNUNHQPes1uNnlmvbiYXu5lc5GZKz+vqYg9+t+2QPWBVM5Ir1YzkSjUjuVLNSK7yvWZyuS/DNIf6Y6WMN7W1tdTW1pJKpairq2PZsmV4PJ6xHpaIiIjIqJjR9Dwn7Hk4u71+8i28X3Yxvwj9gn3pfQBc676Wk50nD+t6yTQE4tAeM2iLQVu0/3MoMbJZqi6rSakTSl0mJU4oO+DZbhnRZUVERETkCAqHwyxZsoRAIEBhYeFBj1XAmoeCwSB+v5/W1tZDFsB4lEgkWLFiBYsXL8Zut4/1cGQcUM1IrlQzkivVzJFjefU/sb7y79nt5GU/4q2qufzdi38HQJmrjN9f+XsK7IP3d81FJJ5id2eEXR0RdndE2NUeZnfv644I3fHUiK5b6XNSVeTCGulg0dzpTC0toLokM/u1wudU+wEZlL5nJFeqGcmVakZyle81EwwGKSsrG1bAqhYBecxut+dlgffK9/uT0aeakVypZiRXqpkj4IJ/BDMBf7kLANvzX+HMa37BRVMu4sWdL9IabeX+d+/n8ws//4E/ym63M7fAxdyq4gHvmaZJRzjBzvYwu9rD7GwPs7sjzK72CDvbw+zpjJBMDz6PoSkUoykUAyy8/Zf6fu85rBaqinsW3ep5zvSBzTz7PaqvY52+ZyRXqhnJlWpGcpWvNZPLPSlgFREREZHx5YJvQDIKr/8XYMKTt/PlK37AK5ZXSKaT3LfpPq6fdT1V3qrDNgTDMCgpcFBS4OBD1UUD3k+m0uwNRNnVEWZ3T+i6qyPcE8hGaO2KDXrdeCrNjtZudrR2D/q+z2XbH7iW9l+Aq6rIjctuHc3bFBEREZFhUMAqIiIiIuOLYcDi70IyDm/9D5hpqp/9OrecfhP37ltJPB3nx6t/zN3n3T1mQ7RZLZlZqCUemDHw/UB3hIefeoFp809hTyCWnfmamQUbHrL9QCiaZNOeIJv2BAd9f0KhK9NuoCeEzc6ALXFT6XNhUfsBERERkVGngFVERERExh/DgMt+AKkYrL4XzBT/763lPDV9Bu3Jbv5Y/0eWzFnCwsqFYz3SQXkcNiZ64MLZ5QN+/Mw0Tdq74+zqiGRbEOzuM/u1sTNCaoj2A/uCUfYFo7xd3zHgPYfVwuSetgNTSzPB65SSzEzYKSUePA791UBERERkJPSnKBEREREZnwwDrvhxZibrO8vwpeLc3ryX75ZkFiH44ds/ZNkVy7AYljEeaG4Mw6DU66TU6zxk+4Fd7fv7vvZut3bFB71uPJVme2s324doP1DuczKlxMPUnpm32RC21EO514lhaPariIiIyGAUsOaxRCJBIpEY62GMut57ysd7k8NDNSO5Us1IrlQzY+zyH2NNRLBs/j3XBTp5xOthq8PGprZNPFn3JB+Z/pGxHuEAH7RmJvjsTPD5OWWKf8B74XiSxo4oOzvC7O6IsKsjwu6ex66OCOEh2g+0hGK0hGKsbhg4+9Vtt/QstNW78Jab6mI3U0o8TCpy47SNrxB7PNL3jORKNSO5Us1IrvK9ZnK5L8M0zcF/vkjGndraWmpra0mlUtTV1bFs2TI8Hs9YD0tERETksDPMJIt21DIpsJo3XE5um1gJgM/w8YXCL+A0nGM8wqODaUJXElqj0Bo1aItCa8ygLWrQGoVgIvdZqgYmRQ4oc5mUunqenfufC/JvUWERERE5BoTDYZYsWUIgEKCwsPCgxypgzUPBYBC/309ra+shC2A8SiQSrFixgsWLFw/oWSYyGNWM5Eo1I7lSzRwlUnGsv1uKZdsK7qgo4+WCzD8033bCbXx2wWfHeHD9Ha01E4mn2N3T+3VnR4RdfZ53dURIpHL/q0Ohy0Z1iZspxfsX3MrMgPUw0e/CqoW3huVorRk5eqlmJFeqGclVvtdMMBikrKxsWAGrWgTkMbvdnpcF3ivf709Gn2pGcqWakVypZsaY3Q4fexAe/hhf3vkqr3rcJA2D+zffyw2zb2Cid+JYj3CAo61m7HY7cwtczJ1cPOC9dNpkXzBKQ1um12tDezc72yPsbOtmZ3uYjvDgP0YXjCbZtCfEpj2hgZ9nNagqcjOltICpByy6NaXEQ4FTf1050NFWM3L0U81IrlQzkqt8rZlc7kl/YhERERGR/GF3wU0PU/PQR7k5uIkH/IXE0gl+/Pqd/PCS/x7r0Y1rFovBpCI3k4rcnDGjdMD7gUgiM+O1PUxDW+Z5Z3smfG3siJAeZPJrImVS3xamvi086GeWeR3ZsDUbwpZmFuIq92nhLRERETk6KGAVERERkfzi8MCS3/J3D1zD06m9dFqtPL/3NZZsfYYPHXf0LXiVL/xuO/4qPydUDVx4K5FK09jTeqChvWcGbFt3djZs9xALb7V2xWntirNmZ+eA91x2y/7wtaSAKSVuppYWMKXUw+RiN06bdbRvUURERGRQClhFREREJP84ffhveZzbH/ww/2btBuCuV77OAyXHYymdMcaDO/bYrRZqygqoKSsY8J5pmrR1xzMzXtv6zoDNzH5tCsYGvWY0kaauqYu6pq4B7xkGTCx0MaXUw7QyLzPKC5hWlnlUl3iwWy2jfo8iIiJy7FLAKiIiIiL5yeXnozc9xSPLF/O+Jc16u8Fzj1zDRz7+HBRVj/XopIdhGJR5nZR5nSycMrD3a2bhrb5tBzKzX3f2LLwVT6YHnGOasCcQZU8gyhvb2/u9Z7MYTCnxMK2sgOnlBUwr8zKtrIAZ5QVqOyAiIiIjooBVRERERPKWzVvBV8/9Pp959R8B+LEryYX3fQTPp/4AhUffolcykNth5bhKH8dV+ga8l06bNIWi+8PXtkwLgszr7kEX3kqmTba3drO9tZsX3+3/XoHDyrTyAqb3hK7Te17XlHnwufJv8Q4REREZHQpYRURERCSvnTXjcs7Z+hgrm96i2WbjPrOdz95/FXzyWfBWjPXw5AOwWAwm+t1M9Ls5ffogC2+FE2xv7WJHazc7WrvZ3pIJVne0dhFNDJz52h1PsbExyMbG4ID3KnzOfqHrtLICppUXMEUtB0RERI55ClhFREREJO995Yxv8PqT15Iy09zjL+Ta3e8z4f6rYekzUDAwmJP84PfYOWlKMScd0HognTbZF4z2hK5dPaFrJoDd3REmbQ68VnMoRnMoxps7+rccsPZpObC/7UAmhK0sVMsBERGRY4EC1jyWSCRIJAb+WNR413tP+XhvcnioZiRXqhnJlWrm6FftqebG4z7Gw3UPE7FY+FlxEf/evBnz/qtJfvz34C46ouNRzYy98gIb5QV+Tp3q77c/lkyzqz3MjtYwO9q6qW8L98yADdPWHR9wnVTazM6QPZDHYaWm1MO00gKmlXmoKStgWqmHaSNoOaCakVypZiRXqhnJVb7XTC73ZZimOci/z8p4VFtbS21tLalUirq6OpYtW4bH4xnrYYmIiIgcFcLpMD8O/ZiIGQFgWeM+5sfjdHim8/rMfyRpdY/xCOVoF05CSwSaowYtEYPmKDRHDFqiEE/nNlPVZzepcEGF26TcZVLhzrwudYJNHQdERETGXDgcZsmSJQQCAQoLCw96rALWPBQMBvH7/bS2th6yAMajRCLBihUrWLx4MXa7FhuQQ1PNSK5UM5Ir1cz48fB7D3PX6rsAODGR4oHdjRhAevJppG7+LTi8R2Qcqpn8Ypom+4Ix6tsyM113tHazoy1MfWuY3Z0RUoP1HBiCxYDJxe5My4HSzKzX6WUeJvsdrH9jJZdcopqR4dH3jORKNSO5yveaCQaDlJWVDStgVYuAPGa32/OywHvl+/3J6FPNSK5UM5Ir1czR7+a5N7N863Lqg/W8Y7fyh+IKLutoxrL7TSzLPwEfXw72IzeTVTWTP6aUOZhS5uPc2f33x5NpdraHs/1e+y621doVG3CdtAk72yPsbI/wygHvOSxW/qd+FdMrvEzP9nvNLLjld6uOZHD6npFcqWYkV/laM7nckwJWERERETlm2C12vnrKV7n9xdsB+PGEKi6IxHBFA1C/Eh75ONz8MNicYzxSyRcOm4WZFV5mVniByn7vBaMJdrT0LLDVJ4Dd0dpNOJ4acK142mDLvhBb9oUGvFfmdTCj3MusSh/HVXo5riLzXFrg0EJbIiIih5kCVhERERE5ppxTdQ5nTTqL1/a8xt5oG/ef9Sn+38r/g3gI3n8RHl0KN94PNsdYD1XyXKHLzonVRZxYXdRvv2maNAVjbG/dP+P1/eYQm3a20B63DNpyoLUrTmtXO2/uaO+3v9hj57hKH8dV9ISvFV5mVnop9zoVvIqIiIwSBawiIiIickwxDIOvLPoKbzz9Bikzxf/ufJ5rPvprKpb/DSTCUPc8PP5puP43YNUfl+XIMwyDCX4XE/wuzpxRBmT63D333HNcfMli9nVlZr72C2BbBm850BFO8NaOdt46IHgt8tgzYWuFj1l9ZrxW+BS8ioiI5Ep/YhQRERGRY87M4pncMOsGHnnvESLJCD9rfo3v3fwILLsRklHY/CRYPwPX/g9YrGM9XJEsh83CjHIvM8oHthzo6I6ztbmLrc0htjbtf24ODQxeO8MJ3q7v4O36jn77C102jqvMhK4zK/bPfK0sVPAqIiIyFAWsIiIiInJM+tyHPsez258llAjx5PtPcvPxNzPvYw/BIzdDKg4bloPVCVf9F1gsYz1ckUMqLnBw6rQSTp1W0m9/IJzIhK3NXf2C133B6IBrBKNJVjd0sLqhf/Dqc9k4rmL/TNfetgMT/S4FryIicsxTwCoiIiIix6RiVzGfOfEz3LXqLgB++NYPuffSezFuuA8e/QSkk7DuwUwv1iv+ExQiyTjl99hZVFPCopoDgtdIgm3NXWxrDlHX1NUTwIbYGxgYvIaiSdbs7GTNzs5++71OGzMrvNmZrjMrM6+ritwKXkVE5JihgFVEREREjlk3z7mZR+sepSHYwJrmNaxoWMElcy6H6/8PfvcpMNOw6jeZmayXfl8hq+QVv9vOyVOLOXlqcb/9oWgmeM3Odu153dgZGXCNrliSdbs6Wbers9/+AoeVmX17vPb0ea0qcmOx6PeRiIjkFwWsIiIiInLMslvtfGXRV7jjz3cA8J+r/5Pzqs/DOe+aTJuAx/8fYMKbvwSbEy7+jkJWyXs+l52TphRz0pT+wWtXLMn7zV3UNYXY1vO8tbmL3R0Dg9fueIp3dgd4Z3eg3363PRO89gauvTNfJxcreBURkfFLAauIiIiIHNPOm3wep088nTf2vkFjVyMPbH6AT8//NCy4EZIxeOrvMwe+9hOwu+H8r4/peEXGitdp48TqIk6sLuq3PxxP9pnxmmkzsLW5i10dYUyz/zUiiRQbGgNsaOwfvLrslp5WAz5m9oSux1V4qS7xYFXwKiIiRzkFrCIiIiJyTDMMg6+e8lVuePoG0maaX6//NdfMvIYydxks/ERmJuuzX8oc/PL3weqAc740toMWOYp4HDYWTC5iweSifvsj8RTvt2TaDNQ1ZQLYbc0hGtoHBq/RRJqNjUE2Ngb77XfaLMwoz8x4nVXpy/Z7nVpaoOBVRESOGgpY81gikSCRSIz1MEZd7z3l473J4aGakVypZiRXqpnxb5p3GtfOuJbHtj1GOBnmp6t/yrdO+1bmzQ/diiUexrrim5ntF+8kZbGTPvUzI/481YzkajzWjM2A2RUeZld44ITK7P5oIsX21m62NXdnFtlq6WZrcxc728OkDwheY8k0m/cG2bx3YPA6s6KAWZU+ZlV4mV3pZVallwqfU4tr9RiPNSNjSzUjucr3msnlvgzTPPDfDmW8qq2tpba2llQqRV1dHcuWLcPj8Yz1sERERETGha50Fz8O/pgYMQwMPuf9HBNtE7Pvz2x6hnl7Hs1uvzN5KfXlF43FUEXyUiINzRHYFzHYFzbYF4F9YYPWKKQZXmjqsZlM8sBEj8kkj8lEj8lED7ish3nwIiKSd8LhMEuWLCEQCFBYWHjQYxWw5qFgMIjf76e1tfWQBTAeJRIJVqxYweLFi7Hb7WM9HBkHVDOSK9WM5Eo1kz/u33I/P1n7EwBOrjiZX130q36z4Sx/+SHWlT/Mbiev+Cnmhz6e8+eoZiRXx3LNxJJpGtq62doz43Vrcxd1TV00DDLjdSiTi1wcV+lldqWPWZWZGa/TygqwWy2Hd/Bj6FiuGRkZ1YzkKt9rJhgMUlZWNqyAVS0C8pjdbs/LAu+V7/cno081I7lSzUiuVDPj3yfmfYLHtj3GrtAuVjevZuXelVw0tc8s1Qv/GcwEvPpjAGzPfgGcnsyCWCOgmpFcHYs1Y7fDvMlO5k0u6bc/mkixrbmLd/eFeG9fsOc5RHMoNuAauzuj7O6M8tJ7rfuvazWYUe5l9gQfsyf4mDPBx+wJhUzyu/KqzcCxWDPywahmJFf5WjO53JMCVhERERGRHg6rgy8v+jJfeOkLANy96m7OmXwODqsjc4BhwEXfhmQM3vgFYMLv/w6sdph37ZiNW+RY5LJbOaHKzwlV/n77O7rj2dD1vaYQ7+4LUbcvRHc81e+4RMrk3X2Z9/vyOW3M6hu6VvqYM6EQvyf/wgMRERkdClhFRERERPq4sPpCTplwCm/ve5vdXbt5aMtDfOqET+0/wDDgw/+eCVlX/R+YaXjs02B1wpzLx27gIgJAcYGDM2aUcsaM0uy+dNqksTPCe/tC2dD1vX1Btrd0kzygz0AolmR1QwerGzr67Z9Q6MqGrrMqMwHszAovLrsavIqIHOsUsIqIiIiI9GEYBl875Wvc+PSNmJj8av2vuGrGVZS6S/seBJffDakYrH0Q0klYvhRuehiOu3jsBi8ig7JYDKpLPFSXeLh4bmV2fyyZYntLN+/t2x+61jV10dgZGXCNfcEo+4JRXqlrye6zWgxqSj3MmVDYr9VAdbEHiyV/2gyIiMjBKWAVERERETnAnJI5XHfcdTy29TG6El3UrqvlW2d8q/9BFgtc+TNIxmHDo5CKw28/Dksehennjc3ARSQnTpuV4ycWcvzE/ouXBKMJ6rKha6gngA0SjCb7HZdKm7zf0s37Ld08u2Fvdr/HYeW4Sh9zKn3MyvZ39VHmdR6R+xIRkSNLAauIiIiIyCD+/qS/5w/1f6A70c1jWx/jY7M/xuyS2f0Psljhml9mZrJufhKSUXj4JrjlcZh6xtgMXEQ+sEKXnUU1JSyq2b+wlmma7AtGs6FrbwC7rbmLeCrd7/xwPMU7uzp5Z1dnv/1lXkdmpmtlYTZ0nVXpw+1QmwERkfFMAauIiIiIyCDK3GXcNv82frLmJ6TNNHetuotfL/71wNXFrTa4/v8glYD3noNEGB66AW59AiYvGpOxi8joMwyDiX43E/1uLphdkd2fTKWpb+vOBq+9zzvbwwOu0doVp3VbG69ta+tzXZha4mFWZe9M10y7gZpSDzar5Yjcm4iIfDAKWEVEREREhnDL3FtYXrecxq5G3tz7Ji/vepkLplww8ECrHW64Fx5ZAtv+BPEQPHAdLH0KJn3oCI9aRI4km9XCzAofMyt8fGTB/v3dsSR1TaHswlq9rQbauuP9zjdNqG8LU98W5oXNTdn9DpuF4yq8zJlQyILJfuZP9jN3YqEW1RIROQopYBURERERGYLT6uRLJ3+JL7/yZQB+tPpHnF11NnarfeDBNid87MHM7NX6lRALwAPXwCefhcp5R3bgIjLmCpw2TppSzElTivvtbwnFsj1de8PXuqYQ0UT/NgPxZJpNe4Js2hPksTW7AbBZDGZV+rKB64KqImZP8OGwaaariMhYUsAqIiIiInIQi6cuZmHFQtY0r6Eh2MDD7z7MrfNuHfxguxuW/BYevB52/hUiHXDfVfCp56B89uDniMgxpdznpNzn5OzjyrL7UmmTne1h3tsX5L19XbzXFOTdfSHqW7tJm/vPTaZNNu8NsnlvkEfe3gWAw2phzkQf86v8meC1qojjKr1H+rZERI5pClhFRERERA7CMAy+durXuPmZmzEx+e93/psrZ1xJsat48BMcBbDk0czs1cbVEG7dH7KWzjiiYxeR8cFqMZhWVsC0sgIuPWH//mgixbv7QmzY3cn63QHW7w6wtTnUL3SNp9LZ9x56M7PPabNw/EQfvoSF2No9nDS1hOnlXqyWA3pIi4jIqFDAKiIiIiJyCPNK53H1zKt5YtsThBIhatfV8s3Tvzn0Ca5CuOWxTLC6bz107dsfshZPPXIDF5FxzWW38qHqIj5UXZTdF44n2bwnyPrdATY0Bli/u5Ptrd2YfULXWDLNul0BwMLKxzcC4HFYOWFST2uByX7mV/mpKS3AotBVROQDU8AqIiIiIjIMnz/p8/yx/o9EkhGW1y3nY7M/xnHFxw19grsYPvEE3HclNG+C4O7M6089D56Koc8TETkIj8PGopoSFtWUZPeFogk27QmyYXeA9T2ha0NbuN954XiKt+rbeau+PbvP57RxQm9rgZ6ertUlbgxDoauISC4UsIqIiIiIDEO5p5xPz/80/7X2v0ibae5edTf/ffF/HzyIKCiFW5+Ae6+A1jrobMiErLc8ecTGLSL5z+eyc/r0Uk6fXprd1xoM85vf/wnP5Dls2hti/e4AjZ2RfueFYkn+ur2Nv25vy+4r8tj79XNdMNnPRL9LoauIyEEoYBURERERGaZb597K7+p+x97uvby+53VWNq7k3MnnHvwkbwXc+hTccxl07ID297Etux7HhDuOzKBF5Jjkd9uZXWRy+bnTsNvtALR1xdjQGMjOdN2wO8C+YLTfeZ3hBCu3trJya2t2X5nXwfwqP/MnF7GgJ3ytKHQd0fsRETmaKWAVERERERkml83Fl07+El/9y1cBuOvtuzhj0hnYLfaDn1g4EZY+DfdcDoGdGK3vcWb3DyF6OdhLD36uiMgoKfU6OX92BefP3t+mpDkYZUNjgHd2B7KLabV1x/ud19oV56X3WnjpvZbsvspCJ/Orijixp73A/Co/pV7nEbsXEZGjiQJWEREREZEcfLjmwzy05SHWtayjPljPo+89yseP//ihTyyqhqVPZULW0B78kZ2k3vwFXPwvh3/QIiJDqCh0cVGhi4uOrwTANE32BqI9i2h1ZhfT6gwn+p3XFIzRFGziT1uasvuqitz9+rnOr/Lj9xziH6BERPKAAtY8lkgkSCQShz5wnOm9p3y8Nzk8VDOSK9WM5Eo1c+z58sIv84k/fgKAX6z7BZdUX0KRs+jQJ/omw5LHsP3qLAwzjbFxOYlz/xHU21AOQd8zkqsPUjPlBTYuml3KRbMzM+xN02R3Z4SNjUE2NAbZuCfz3BVL9juvsTNCY2eE5zfuy+6bUuJm/iQ/J1QVMr+qkLkTC/G5FEUcjfQ9I7nK95rJ5b4M0zTNwzgWOYJqa2upra0llUpRV1fHsmXL8Hg8Yz0sERERkbz0u+7fsS6xDoDTHafzEc9Hhn3uGdt+QEVoEwB/mfUtOgpmHo4hiogcNmkTWqOws8tgV7fBri6DXd0QTx/8H4wMTCrcUF1gUu01mVJgUlUATusRGriIyDCFw2GWLFlCIBCgsLDwoMcqYM1DwWAQv99Pa2vrIQtgPEokEqxYsYLFixdnm7WLHIxqRnKlmpFcqWaOTU3hJq59+lqiqShWw8pvL/8t0/3Th3Vues2DOJ//AgCpRZ8m/eH/OIwjlXyg7xnJ1VjUTCptsr21OzPTdU+QjY0BNu8NEUumD3qexYCZ5d7sLNf5VX7mTPDhtFmOyLglQ98zkqt8r5lgMEhZWdmwAlbNy89jdrs9Lwu8V77fn4w+1YzkSjUjuVLNHFsm+yfzN/P/hl+s+wUpM8VP1/2UX1z8i2Gdm5h3Nak/fBWrmcC6+Qmsl/0HWFU7cmj6npFcHcmasQNzqxzMrSrmxp59yVSarc1dbNgd4J3dnWxoDLBlb5BEav9cr7QJdc1d1DV38fjaPZlrWQ3mTChkwWQ/J04u4sTqImZWeLFa1FLlcNP3jOQqX2sml3tSwCoiIiIiMkKfnPdJHqt7jKZwEysbV/Jq46ucXXX2oU90+tjnP4mqzrcg3ArbX4bjFh/28YqIHGk2q4XjJxZy/MRCbjylGoBYMkXdvi7WN3ayYXeA9bsDvNcUIpXeH7omUiYbGjMLbD305k4APA4rJ0zqWUSrJ3idWurBUB9rERljClhFREREREbIbXPzxZO/yNdXfh2Au96+i9Mnno7Ncug/Zu8uPiMTsAKsf1QBq4gcM5w2K/MnZ4JSTsvsiyZSbN4bZP2uTtb3zHbd3tpN36aG4XiKt+rbeau+PbvP77azoE/gemJ1EZWFriN8RyJyrFPAKiIiIiLyAVw+7XKWbVnG+tb1bA9sZ3ndcm6ec/Mhz2suXIDp8mNEA/DusxDvBkfBERixiMjRx2W3snBKMQunFGf3haIJNjRmZriu393JO7sCNHZG+p0XiCRYubWVlVtbs/sqC50smFzEiZP9LJhcxILJfoo8jiN2LyJy7FHAKiIiIiLyARiGwddO/Rq3PHcLAL9Y9wsun3Y5fqf/oOelLXbMOVdhrHsAEt3w3vMw/6NHYsgiIuOCz2XnzBllnDmjLLuvtSuW7efaG7y2dsX7ndcUjLFicxMrNjdl900t9fQLXU+oKsTjUCQiIqND3yYiIiIiIh/QieUncvm0y3lux3N0xjr5n/X/w9dO+dohz0uf8FEs6x7IbKx/VAGriMghlHmdXDCnggvmVABgmiZ7AlHW7+rknZ7AdcPuAKFYst95DW1hGtrCPP1OZhEtiwHHVfgy7QWqM8HrnAmFOGyWI35PIjL+KWAVERERERkFXzz5i/x555+JpqI8vOVhbpx1IzX+moOeY045AwonQ3A3vP8idLdBQemRGbCISB4wDIOqIjdVRW4umz8RgHTaZEdbd7atwPrdnWzaEySWTGfPS5vwXlOI95pCLF+9GwCH1cLxE33ZtgInVhcxo9yL1aJFtETk4BSwioiIiIiMggkFE/jkCZ/kv9/5b5Jmkh+t+hH/ddF/HfwkwwLzr4fXfgrpJGx6HE697cgMWEQkT1ksBjPKvcwo93LtSZMBSKTS1DWF+vVzfa8pRCq9fxWteCrNO7sDvLM7kN1X4LAyr8qfbS1w4uQiqkvcGIZCVxHZTwGriIiIiMgo+dS8T/F43eM0R5p5effL/HXPXzlj0hkHP2n+jZmAFWDDcgWsIiKHgd1qYd4kP/Mm+bn51CkARBMpNu0Jsr6nn+s7uzvZ3tLd77zueIq3drTz1o727L5ij535ffq5njjZT0Wh64jej4gcXRSwioiIiIiMEo/dwz+c/A9849VvAPDDt3/I8iuXY7Mc5I/dlfOg/Hho2QK73oSOeiiuOSLjFRE5lrnsVk6eWszJU4uz+4LRBBt7ZrH2Bq+NnZF+53WEE/ylroW/1LVk900odGXbCiyY7GdBVRF+j/2I3YuIjC0FrCIiIiIio+gj0z/Csi3L2NS2iW2d23h86+PcOPvGoU8wDFhwA7z4r5ntDcvh3K8emcGKiEg/hS47Z84s48yZZdl9LaEYGxr393NdvztAW3e833n7glH2bY7ywuam7L6aUk+/fq7zJhXicSiGEclH+p0tIiIiIjKKLIaFfzz1H7n1+VsB+Pnan3PZtMvwOXxDnzS/T8C6fjmc85VM8CoiImOu3OfkwjmVXDinEgDTNGnsjGTbCqzfFWBDY4CuWLLfefVtYerbwjz1zh4ALAbMqvRlZrhOLuKkKUXMmVCoRbRE8oACVhERERGRUXZSxUlcWnMpf6j/Ax2xDn61/ld8edGXhz6haApMOQN2/hVa34N9G2DigiM3YBERGTbDMJhc7GFysYfL508EIJ022d7a3a+f66Y9QeLJdPa8tAnv7gvx7r4Qj67aDWQW0TppSjELe1oVnDSliEKXWguIjDcKWEVEREREDoMvnvxF/rzzz8TTcR7c8iA3zLqBKYVThj5h/g2ZgBVgw6MKWEVExhGLxWBmhZeZFV6uWzgZgEQqzXv7Qqzv6ef6zu4AdU0hUmkze153PMWr21p5dVsrkPnhhdmVvmxv2EVTS6gucWPopxpEjmoKWEVEREREDoNJ3kksnbeUX2/4Ncl0kh+t+hE/vfCnQ58w71p4/muQTsKGx+DiO8FiPXIDFhGRUWW3Wjihys8JVX6WnJb5B7ZIPMXmvQHW7QqwpqGDVQ3tNAVj2XPMPrNcH3pzJwBlXieLegLXhVOLOaGqEKdN/38QOZooYBUREREROUz+dv7f8vttv6c10sqfd/2Zt/a+xakTTx38YE8JzFwMdc9DaA80vAbTzj2yAxYRkcPK7bBy8tQSTp5awt+ePS3bz3V1Q0dP4NrBlr1B+kxypbUrxh827eMPm/YB4LBZWFDl5+SaYk6ekgleS73OMbojEQEFrCIiIiIih02BvYDPn/R5vvX6twD44ds/5Lcf+e3QJyy4IROwAqx/VAGriEie69vP9eoPVQHQHUuyblcnq3sC17UNHYT6LKAVT6ZZ1fNer2llBSycUsyimkzgOrPci0WLZ4kcMQpYRUREREQOo6tnXs3D7z7MlvYtvNfxHk9se4Krpl01+MGzLgOHF+JdsPkpuPxusLuO7IBFRGRMFThtnDWzjLNmlgGZBbS2NnexqqGd1Q0drG7ooKEt3O+cHa3d7Gjt5rE1mcWzCl22zMJZU4o5uaaYD1UX4XEoAhI5XPS7S0RERETkMLIYFr52ytf41B8/BcDP1v6MCydfOPjBDg/M+QisfwRiAdj6AswdIowVEZFjgsViMHuCj9kTfHz8tKkAtIRirNnZkQ1cN+wOEE+ls+cEo0lefq+Fl99rAcBqMZg7sTDbx3XR1GImFbnH5H5E8pEC1qPEmjVrePDBB3nxxRfZsWMHTqeT448/ni996Utcc801Yz08EREREfkAFk1YxOKpi1nRsIL2aDu/2fQbZjJz8IMX3JAJWAE2LFfAKiIiA5T7nHx43gQ+PG8CANFEik17Aqyq3x+6tnXHs8en0iYbGgNsaAxw7+v1AEz0uzi5Z/GsRVNLmFGmn5gQGSkFrEeJH/7wh6xYsYLrrruOz33uc0QiER5++GGuvfZa/uVf/oV//dd/HeshioiIiMgH8MWTv8jLu14mkU7w0LsPcUfBHYMfOO18KCiH7hao+yNEA+DyH8GRiojIeOOy7188C8A0TRrawtk+rmsaOqhrDmH2WTxrbyDKM+v38sz6vQC47Raq3BbedWzl1GllLJxSjN9jH4vbERl3FLAeJe644w7uvfdeXC5Xv31nn3023//+9/nCF75ASUnJGI5QRERERD6Ial81n5j7CX6z8Tck0gn+GP0jt3DLwAOtNjjhenjzvyEVy/RiXfiJIz9gEREZtwzDoKasgJqyAq4/eTIAgUiCtTszYeuqhg7W7eokHE9lz4kk0mxLWNj2yg5++coOAI6r8LKoppiFUzIzXaeVFWAYWjxL5EAKWI8SZ5111oB9VquV6667jjfeeIO6ujpOP/30MRiZiIiIiIyW2+bfxhPbnqA92s6mxCZWN6/m9KpB/ow3/8ZMwAqw4VEFrCIi8oH53XbOn13B+bMrAEim0ry7L5RtKbCqvp09gWi/c7Y2d7G1uYuH39oFQEmBg4VTillUkwlc51f5cdmtR/xeRI42CliPcnv27AGgvLx8jEciIiIiIh+U1+Hl8yd9nu/89TsA/Gj1j3hk4iNYLQf85bRqIZRMh/btsGMlBPdA4aQjP2AREclbNquFE6r8nFDlZ+mZNSQSCZb9/jmKZi5kXWOQ1Q0dbNoTJJXe31egvTvOn7Y08actTQDYrQYnVPk5uSd0XTi1mAqfernKsScvA9ZwOMwrr7zC6tWrWbNmDatXr2bnzp0AfPvb3+Y73/nOIa8RCoX40Y9+xGOPPcaOHTuwWq3MmjWLm266iTvuuAOHw3GY7wIaGxu55557OO2005gxY8Zh/zwREREROfyumXkNy7Yso66zjnc73uWp95/i2uOu7X+QYWRmsb7yH4AJGx+DM4fo2SoiIjJKipxw+fwJXL2wGoBwPMn63YHsLNfVDR0EIons8YmUydqdnazd2cn/vpppK1Bd4mbR1BIWTi1m0dRiZlX6sFrUVkDyW14GrG+99RaXX375iM9vaGjg/PPPp76+HgCPx0MsFmPVqlWsWrWKhx56iBdffJHi4uIB55qmSSwWG9bnWCyWIYPacDjMtddeSywW41e/+tWI70VEREREji5Wi5Uvn/xl/u7FvwPgZ2t/xodrPozH7ul/4PwbegJWYP2jClhFROSI8zhsnD69lNOnlwKQTptsb+3qaSnQweqdHWxv6e53zq72CLvaG/n92kYAvE4bJ00p4pSaEhbVFHNSdTFuh9oKSH7Jy4AVoLi4mIULF2YfX/ziF9m3b98hz0smk1x55ZXU19czceJE7r//fi6++GLS6TTLly/ntttuY+3atdxyyy08++yzA85vaGhg2rRpwxrjvHnz2Lhx44D98Xic6667jjVr1vDoo4+yYMGCYV1PRERERMaHUypP4Xj78WxJbKE10sr/bvhfPr/w8/0PKpsJk06CPWth33poeQ/KZ4/NgEVERACLxWBmhY+ZFT4+dsoUINM2YE1DJmxdXd/BO7s7iSXT2XO6YklWbm1l5dZWAGyWTFuBU2qKWVRTwik1JZQUHP6fEhY5nPIyYD3nnHNob2/vt+/rX//6sM6977772LBhAwCPPfYYZ5xxBpCZbfqxj32MdDrNkiVLeO6553jxxRe56KKL+p1fVlbGPffcM6zPGmwGbCKR4MYbb+SFF17gnnvu4brrrhvWtURERERkfLnUdSlbU1tJppPct+k+Pjrro0zyHtBndf6NmYAVYMNyuPCbR36gIiIiB1FS4ODiuZVcPLcSgHgyzaY9+9sKrGrooCW0/yd9k2mTdbs6Wberk1+vzLQVmFFe0DPDtYRTa0qoLnFjGGorIONHXgasVuvIp5rfd999AFxwwQXZcLWvm266iW984xvs2LGD+++/f0DA6vV6+eQnPzmiz06lUixZsoQnn3ySX/7ylyxdunRE1xERERGRo1+ptZSbZ9/MA1seIJ6O8+PVP+au8+7qf9AJ18ML3wAznQlYL/hGpj+riIjIUcphs3DSlGJOmlLMp8/JtFLc1R7h7fp2VjW083Z9B9uau/qd835LN++3dPPI27sAqPA5sy0FTqkpYc4EHzarZSxuR2RY8jJgHalwOMxrr70GwGWXXTboMYZhcOmll/LLX/6SF154YdQ+O51Os3TpUn73u9/x4x//mM985jOjdm0REREROTp9et6neXbHs7RH2/lD/R9YcvwSTqo4af8BvkqYdh5sfwk66mH321B96piNV0REJFeGYTCl1MOUUg/XnzwZgLauWHZ269v17WzYHSCZNrPnNIdiPLthL89u2Auoj6sc/RSw9rFlyxbS6UyfkBNOOGHI43rf27dvH+3t7ZSUlHzgz/7qV7/KQw89xBlnnEFZWRkPPvhgv/fPPPNMpk+f/oE/R0RERESOHj6Hj9s/dDvffeO7APzwrR/y0BUPYTH6zNJZcGMmYIXMYlcKWEVEZJwr9Tq5ZN4ELpk3AYBIPMW6XZ2sqm/n7YYO1jR00BVLZo8/VB/XRVOLKfU6x+ReREABaz979uzJvq6qqhryuL7v7dmzZ1QC1tWrVwPw17/+lb/+9a8D3r/nnnuGDFhjsRix2P5+JsFgEMj0c00kEh94bEeb3nvKx3uTw0M1I7lSzUiuVDOSq741c2XNlTy85WG2BbaxsW0jT259ko9M+8j+g2deis3mwkhGMTc9TvKifwWrfYxGLmNF3zOSK9WM5Gosa8ZmwKIphSyaUshnqCGZSvNeUxerd3b2zHTtpPkQfVynlxWwaGoRJ08t4uSpxUwpVh/Xwy3fv2dyuS8FrH2EQqHsa4/HM+Rxfd/re84H8fLLL4/43O9///vceeedA/a/8MILB72P8W7FihVjPQQZZ1QzkivVjORKNSO56q2Zs5Nns41tANz1xl0kNydxGPtXVF7kXUBV51sY4TZWPXo3zf4Tx2S8Mvb0PSO5Us1Iro6mmikDPuyDS+ZBWwy2hwy2Bw22hwyaIv3D0+2t3Wxv7ebR1Y0AFNpNpheaTPdlHpMKwKq89bA4mmpmNIXD4WEfq4A1D/zTP/0TX/rSl7LbwWCQ6upqLrnkEgoLC8dwZIdHIpFgxYoVLF68GLtdszfk0FQzkivVjORKNSO5Gqxmtr+ynb80/oWQGWJv9V4+u+Cz2eON94Df3QrAae56Upf/01gMW8aQvmckV6oZydV4q5m27jhrd3ayqqGD1Ts72dgY7NfHNZgwWNdmsK4ts13gtHJSdREnTyli0dRiTpzsVx/XD2i81Uyuen9CfDgUsPbh8/myrw+WUvd9r+85Y8XpdOJ0Duw1Yrfb87LAe+X7/cnoU81IrlQzkivVjOSqb8189ZSv8vqe10maSe7fcj83zL6Bid6JmQPnXAauIoh2Yql7HosZB0fB2A1cxoy+ZyRXqhnJ1XipmQlFdi4rKuCyBZk2jofq49odS/HqtjZe3ZZJXNXHdfSMl5rJVS73pIC1j0mTJmVfNzY2smDBgkGPa2xsHPQcEREREZGRqvHXcNOcm3hwy4PEUjF+suYn/ODcH2TetDlg3jWw+l5IhOHd52DBDWM5XBERkaOK22HljBmlnDGjFIBU2mTL3mA2cH17R/sh+7jOKC/glJoSFtWUcEpNMVNKPOrjKsOigLWP448/HovFQjqdZuPGjVx22WWDHrdx40YAJkyYMCoLXB0uWuRKJEM1I7lSzUiuVDOSq6Fq5tPzPs3T7z9NIB7guR3PceNxN7KgLPOP/sbc67CtvheA9DuPkDr+miM5ZBlj+p6RXKlmJFf5WDOzKzzMrvDw8VMnY5omuzoirG7oZPXOzMJZ77d09zv+/ZZu3m/p5pG3dwFQ4XNy8pTMwlmLphYzu9KLzWoZi1s5KuVjzfSVy30Zpmmahz5s/KupqaGhoYFvf/vbfOc73xnyuHPPPZeVK1dy4YUX8uKLLw543zRNZs6cyfbt27n11lu57777DuOoc1NbW0ttbS2pVIq6ujqWLVuW14tciYiIiOSjN2Jv8EzkGQAmWyfzd96/y8yeMdMs3vQlPIl20lj44wk/I27Pv377IiIiR0pXAnb0WThrZzekzaFnrDqtJtO8vYtnwVSvidq45q9wOMySJUsIBAKHXONIM1gPsHTpUlauXMlLL73Em2++yWmnndbv/eXLl7N9+3YAbr311rEY4pBuv/12br/9doLBIH6/X4tcifRQzUiuVDOSK9WM5OpgNXNJ+hI2P7+Z7YHt7E7txjLPwmU1mZ+ssrjXwF9/hoU0l1R1k15001gMX8aAvmckV6oZyZVqJtPHdX1jgFUNnaxu6GDNrk66Y6ns+7GUwbsBg3cDmW2bxWDepEIWTS3i5CnFLJxaRGmBY4xGf+Tle81okSugo6ODVGr/b4J0Og1k0ufW1tbsfpfLhdfrzW4vXbqUn/70p2zYsIHrr7+e++67j4suuoh0Os1jjz3GbbfdBsBll13GRRdddITuZmTytclwr3y/Pxl9qhnJlWpGcqWakVwNVjN27HztlK/xmT99BoCfrfsZi6ctxm1zw4dugr/+DADrpsewnvGZIz5mGVv6npFcqWYkV8dyzdjtds6e5eLsWZXA8Pq4vrM7wDu7A/zfaw1Apo/rOceVc+GcCk6bXoLTlv9TXPO1ZrTIFXDSSSfR0NAwYP9dd93FXXfdld1eunQp9957b3bbZrPx1FNPccEFF1BfX8/FF1+Mx+MhnU4TjUaz137ooYcO+z2IiIiIyLHprKqzOKfqHFY2rqQp3MS9m+7lsyd+FirnQcVcaN4Mu9+C9h1QMm2shysiIpKXrBaDE6r8nFDl55NnTcv0cW2P8HZ9O6sa2nm7voNtzV39zunt43rv6/V4HFbOnFHGhXMquGBOORP97jG6Eznc8jZg/SBqampYv349d999N48//jg7duzAbrczb948br75Zu644w4cjmNnyreIiIiIHHlfWfQVXt/zOikzxT0b7+G6mddRWVAJ82+AF+/MHLTxd3DuV8d2oCIiIscIwzCYUuphSqmH60+eDEB7d5xV9e2saujg7fp21u8OkEpnljsKx1P8aUsTf9rSBMDxEwu5cE5mduuHqouxWobu9yrjS94GrPX19R/ofJ/Px5133smdd945OgMaA4lEIi9Xcsv3Vepk9KlmJFeqGcmVakZyNZyaqS6o5objbuCRukeIJCP8ZPVP+Ncz/hWOvwZ7T8Bqrn+U5On/AIb+gpbv9D0juVLNSK5UMyPjcxhcMKuUC2aVAhCKJnh1Wxsv1bXyl7pW2rrj2WO37A2yZW+Q2pfep8ht55zjSjl/VjnnHldGkWf8/Yh9vtdMLvdlmKZpHsaxyBFUW1tLbW0tqVSKuro6li1bhsfjGethiYiIiMgIhdNhfhz6MREzAsBnvJ9hsm0yZ9X9G2Xd7wHw8ux/JeCpGcNRioiIyGDSJuzqhs0dFjZ1GOzqHvwfRA1ManwwtyjNvGKTSR792+nRIBwOs2TJEgKBwCEXkVfAmoeCwSB+v5/W1tZDFsB4lO+r1MnoU81IrlQzkivVjOQql5p5+L2HuWt1Zg2BE8tO5DeLf4N17X1Yn/8KAKnTPkf64n897GOWsaXvGcmVakZypZo5/FpCMf6ytZWX61p5dVsbXbHkoMdNKHRy3qxyzp9VxhnTSyhwHp0/gJ7vNRMMBikrKxtWwHp0/heSUZGvq7j1yvf7k9GnmpFcqWYkV6oZydVwaubmuTezfOty6oP1vNP6Dn9u/DOXzr8e/vhPkE5g3fx7rB/+Hljyf5Vi0feM5E41I7lSzRw+k0rs3HSal5tOqyGRSvN2fTsvvdvMS++19Fssa18wxm9X7ea3q3bjsFo4bXoJF86p4MI5FUwtLRjDOxhcvtZMLvdkOYzjEBERERGRD8husfPVU/YvZPXj1T8m6vDAcYszO0J7of7VMRqdiIiIjITdauHMGWV844q5/OlL5/GXr17AnVfN47xZ5Ths++O6eCrNyq2t3Pn0Zs6762UuvPtlvvvMZl7b1ko8mR7DO5C+NINVREREROQod07VOZw56Uxe3/M6e7r38MDmB7ht/g3w3nOZAzY8CtPPG9tBioiIyIhNKfWw9Mwalp5ZQzie5K/vt/Hnd5t56d1m9gSi2eO2t3az/dUd/N+rO/A6bZw9s4wL5pRzwewKKgpdY3gHxzYFrHkskUjk5Upu+b5KnYw+1YzkSjUjuVLNSK5GUjNf/NAXeWPvG6TNNL/e8GuuuPRhJjoKMOLdmJufJHnJf4BNf7HKV/qekVypZiRXqpmjh92Ac2eWcO7MEr59xWzqmrp4ua6Vl+taWLOzk3TPakpdsSR/2LSPP2zaB8C8ST7O7+nduqDKj8VyeFfKyveayeW+tMhVHqmtraW2tpZUKkVdXR3Lli3D4/GM9bBEREREZJQ8HX6aN+NvArDQsZDvtrQwpf01AN6adgd7i04Zy+GJiIjIYRZOwrudBps6DLZ0GnQnBw9RvTaT44tM5habzCky8WiKZc7C4TBLliwZ1iJXCljzUDAYxO/309raesgCGI/yfZU6GX2qGcmVakZypZqRXI20ZjqiHVz99NV0JbowMHjwhH9gwdNfAiA9+yOkPnrvYRqxjDV9z0iuVDOSK9XM+JNKm6xvDPDye5nZrZv3hgY9zmoxWDiliPOOK+OC2WUcV+HFMD747NZ8r5lgMEhZWdmwAlbl13ksX1dx65Xv9yejTzUjuVLNSK5UM5KrXGumwl7BZ0/8LHetugsTkx81v8K9BRUY3c1Ytr2AJdkN7qLDN2AZc/qekVypZiRXqpnxww6cOr2cU6eX87XLjqcpGOWld5t56b1mXt3aSnc8BWSC2LfrO3i7voO7V2ylqsjNBXPKuXBOBWdML8PtsH6wceRpzeRyTwpYRURERETGkZvn3MyjdY/SEGxgTfNa/jTzTBa/8wSk4rDlKVh461gPUURERMZAZaGLm06dwk2nTiGWTPH2jo7MQlnvNbOjtTt7XGNnhAff2MmDb+zEabNwxoxSLpxTwQWzK6guUavJkVDAKiIiIiIyjtitdr588pf5/EufB+BHiV2ca4DThP/P3p3HR1Xe/f9/zySTZchCYICwJICKiCzKkgBuBTEotloFl5p6i7XV/qx20brWLtrb1va2trfVsV9rreKt0eLWukuKiFSBJCgSBIzIvgQIhAwwJDmZmd8fIUMiW64wycmcvJ6PBw+vmXPOzOcKbwf45OS6tGw2DVYAAKDkxASdNcSns4b49MuLTtXaqn3Ru1sXr9ml+lBYklTXENb7n+/Q+5/vkPSZhvRO0+QDzdZxg7LkSXDbO5E4QYMVAAAAiDOTciZpfN/xWrx1sTbv36Fn+wzSdyvXSev+IwW2SBn97C4RAAB0IoN93TT4rMG67qzB2lfXoP+sroo2XLcF6qLnfbF9r77Yvld//WCN0lMSdc6QXpo0tJcmDe2tXunJNs6gc6PBCgAAAMQZl8ul28fdriveuELhSFhPeN36ZoJbvlBYKn9JOvNHdpcIAAA6qW7JiTp/eLbOH56tSCSiFVsDmrdqu95btV2fbNytSKTxvD21DXqzfKveLN8qSTptQKYmn9Jb557SWyP6Zdo4g86HBquDWZYly7LsLiPmmubkxLmhfZAZmCIzMEVmYCoWmTkh/QRdeuKlenn1y9oXadCjWd11b9UuRZbNVkP+jbEqFZ0EnzMwRWZgisx0XSf38urkXoP0/bMHade+ei1YvVPvf75DC1ZXqWZ/Q/S8TzfV6NNNNfrff38hX1qSzjqxh7L2uzRx735lpdk4gXZi8v+CKxJp6ksj3vn9fvn9foVCIVVUVKioqEheL4sTAwAAONXe8F79KfAn1alOrog0e8tWnVJvae6wB7Q3pb/d5QEAgDgWikjr9kgrdru1otqlLUHXYc/73tCQRvZwXnsxGAyqsLBQNTU1ysjIOOq5NFgdKBAIKDMzU1VVVccMQDyyLEvFxcUqKCiQx+OxuxzEATIDU2QGpsgMTMUyM7NWzNLDSx+WJOXtr9WTldsVPvNWhSf9LBalopPgcwamyAxMkRkcy9aaWr1fsUPvf16lhWt2ar8VVoIrokV3nqPu3VLtLi/mAoGAfD5fqxqsLBHgYB6Px9Efik6fH2KPzMAUmYEpMgNTscjMNSOu0curX9amvZtUmpqi97ypmvLZy0o475eS6/B3miB+8TkDU2QGpsgMjiTX59E1vnRdc8YJqrVC+uiL7XprQam6d0t1ZGZM5uRuxzoAAAAAtLOkhCTdNu626OOHenRX/e710sYSG6sCAABOluJJ0NlDfDo7mx+Ml2iwAgAAAHHv3NxzlZedJ0na6PGoKCNdKp9tc1UAAABdAw1WAAAAIM65XC7dkXeHXGpcEuDxrEztWvGqFGInaAAAgPZGgxUAAABwgFN6nKJLh1wqSdrrdsufEpa+fM/mqgAAAJyPBisAAADgED8c/UN53UmSpJfS01SxdJbNFQEAADhfot0FoP1YliXLct6PhTXNyYlzQ/sgMzBFZmCKzMBUe2UmMzFT3x3xPT2y7DGFXS79T/USPbZ3l1zJ6TF9H3Q8PmdgiszAFJmBKadnxmRerkgkwnZfDuH3++X3+xUKhVRRUaGioiJ5vV67ywIAAEAHsiKW/lL9G213N0iSfhweq149LrW5KgAAgPgSDAZVWFiompoaZWRkHPVcGqwOFAgElJmZqaqqqmMGIB5ZlqXi4mIVFBTI4/HYXQ7iAJmBKTIDU2QGpto7M//+2K87Vj0pSRooj2Zf+R95EshmPONzBqbIDEyRGZhyemYCgYB8Pl+rGqwsEeBgHo/HkQFv4vT5IfbIDEyRGZgiMzDVXpm5YNwP9UL53/Sxx6X1svTyyln6r9E3xvx90PH4nIEpMgNTZAamnJoZkzmxyRUAAADgMK6EBN3Rb4pcB35Y7S/Ln1R1bbXNVQEAADgTDVYAAADAgYaP/b4u3rtPkrQnXKfHlj5mc0UAAADORIMVAAAAcKI+p+pHCX2UGg5Lkl6smK3V1attLgoAAMB5aLACAAAADtV75JX63u6AJCkUCesPZX+wuSIAAADnocEKAAAAONWIy3RNYI/6NjRIkj7c8qEWbFpgc1EAAADOQoMVAAAAcKruOUrJPUO37NodferBsgdlhS37agIAAHAYGqwAAACAk428XBfsC+q02jpJ0tqatZr9+WybiwIAAHCORLsLQPuxLEuW5by7E5rm5MS5oX2QGZgiMzBFZmCqQzNz8teV6L5dd+6sVmH/bEnSY0sf0/k55yszObP93x8xwecMTJEZmCIzMOX0zJjMyxWJRCLtWAs6kN/vl9/vVygUUkVFhYqKiuT1eu0uCwAAADbLX/O/6lvzsX7m66nX07tJkiYmTdTXvV+3uTIAAIDOKRgMqrCwUDU1NcrIyDjquTRYHSgQCCgzM1NVVVXHDEA8sixLxcXFKigokMfjsbscxAEyA1NkBqbIDEx1dGZcK/+lxFe+q8qEBF2Um6NahZXoStQ/LvyHBmcObvf3x/HjcwamyAxMkRmYcnpmAoGAfD5fqxqsLBHgYB6Px5EBb+L0+SH2yAxMkRmYIjMw1WGZGfZ1KSld2fV7dF1gnx7LSFVDpEEPf/qw/FP87f/+iBk+Z2CKzMAUmYEpp2bGZE5scgUAAAA4nSdVGnaRJOnaXVXq42m8C+ODTR/ow80f2lkZAABA3KPBCgAAAHQFoy6XJKVGIvpJ5ODmVg+WPqiGcINdVQEAAMQ9GqwAAABAVzD4a1JaH0nShWtKNarHqZKkL2u+1EsVL9lZGQAAQFyjwQoAAAB0Be4EacSMxmGoXnd0Pz16yL/Ur0B9wKbCAAAA4hsNVgAAAKCrGHl5dHjalx9q2uBpkqTddbv1+KeP21UVAABAXKPBCgAAAHQV/UZLPU9qHK/7j24ZcpWSE5IlSUUri7SuZp19tQEAAMQpGqwAAABAV+FyNbuLNaK+axbo2uHXSpIaIg16aMlDtpUGAAAQr2iwAgAAAF1Js2UCVD5b1424Tr1Te0uS3t/4vhZtXWRPXQAAAHGKBisAAADQlfQ8Ueo/tnFcWS5v9Qb9eOyPo4f/p/R/FAqHbCoOAAAg/tBgBQAAALqakVccHJfP1jdO+IaG9xwuSfqi+gu9svoVmwoDAACIP4l2F4D2Y1mWLMuyu4yYa5qTE+eG9kFmYIrMwBSZgSnbMzP0IiW++zO5IiFFlr2o0Nl36dbRt+q7//6uJOmRjx/RlP5TlJ6Ubk99OITtmUHcITMwRWZgyumZMZmXKxKJRNqxFnQgv98vv9+vUCikiooKFRUVyev12l0WAAAAOqEJqx9Unz3lkqQFQ36uXWkn64V9L2i5tVySdFbyWbog9QI7SwQAALBNMBhUYWGhampqlJGRcdRzabA6UCAQUGZmpqqqqo4ZgHhkWZaKi4tVUFAgj8djdzmIA2QGpsgMTJEZmOoMmXGVz1biaz+QJIXGfEfhaQ9qy94tmv7GdNWH65XoTtRLX39Juem5ttSHljpDZhBfyAxMkRmYcnpmAoGAfD5fqxqsLBHgYB6Px5EBb+L0+SH2yAxMkRmYIjMwZWtmhl8svfVTqWG/Elb+Swlff1ADswZq5vCZeqL8CTWEG/TIp4/ofyf/rz314bD4nIEpMgNTZAamnJoZkzmxyRUAAADQFSWnS0OnNY7375K+fE+S9N2R35Uv1SdJmrthrkorS+2qEAAAIC7QYAUAAAC6qlFXHBwvmy1J6ubpph+N/lH06f8p/R+FwqGOrgwAACBu0GAFAAAAuqoTp0ipWY3jz9+S6vZKki4+8WIN6zFMkrRq1yr968t/2VUhAABAp0eDFQAAAOiqEpOk4Zc2jq2gtOpNSVKCO0G3590ePe3PH/9Ze+v32lEhAABAp0eDFQAAAOjKRjZbJqB8dnSYl52ngoEFkqSdtTv1t/K/dXRlAAAAcYEGKwAAANCV5YyXMnMbx1/Ok/buiB66Zewt8rgbd9D9vxX/p017NtlRIQAAQKdGgxUAAADoytxuaeRljeNISPrsleihnPQcXX3q1ZKk+nC9/rTkT3ZUCAAA0KnRYAUAAAC6ulHNlwl4scWhG0beoB4pPSRJc9bP0ZJtSzqyMgAAgE6PBisAAADQ1fUeJvUZ0TjeVCrtWhM9lJaUph+O/mH08e9Lfq9wJNzRFQIAAHRaNFgBAAAASCMvPzguf6nFoUtPulQnZ50sSVq5a6Ve+/K1jqwMAACgU6PBCgAAAODAOqyuxvGy2VIkEj2U4E7QnXl3Rh//+eM/K2gFO7hAAACAzokGKwAAAAApc4A08MzG8c4vpK1LWxzO75uvyTmTJUk79u/Qk8uf7OACAQAAOicarAAAAAAajWq2TMCyFw85/NNxP1WiO1GSNOuzWdqyd0tHVQYAANBp0WAFAAAA0OjUb0oJSY3j5S9L4VCLwwMzBurbp3xbklQXqtP/LvnfDi4QAACg86HBCgAAAKBRapY0ZGrjeG+ltPaDQ0654bQblJWcJUl6e93bWrp9aQcWCAAA0Pkk2l0A2o9lWbIsy+4yYq5pTk6cG9oHmYEpMgNTZAamOnNmXKdeqsRVb0iSwp/OVij3rBbHU12punHUjfpt6W8lSb8v+b2envq03C7u3WhPnTkz6JzIDEyRGZhyemZM5uWKRJptD4q45vf75ff7FQqFVFFRoaKiInm9XrvLAgAAQBxxh+t1QfnN8oRrZblT9c7IRxR2J7U4JxQJ6bE9j2lbeJsk6TLvZTo96XQbqgUAAGgfwWBQhYWFqqmpUUZGxlHPpcHqQIFAQJmZmaqqqjpmAOKRZVkqLi5WQUGBPB6P3eUgDpAZmCIzMEVmYKqzZybh9R/Kvex5SVLD9L8rMuziQ85ZtHWRfjDvB5Kk3qm99epFryo1MbVD6+xKOntm0PmQGZgiMzDl9MwEAgH5fL5WNVhZIsDBPB6PIwPexOnzQ+yRGZgiMzBFZmCq02bmtCulAw3WxBWvSKNmHHLK2bln62sDvqb5m+Zr+/7tKqoo0v932v/X0ZV2OZ02M+i0yAxMkRmYcmpmTObEQkkAAAAAWhp8jpSW3Tj+Yo60v/qwp/103E+V4EqQJL299u2Oqg4AAKBTocEKAAAAoCV3gjTiwF2roXppxb8Oe9rgzMEa3nO4JGlNzRpV7a/qqAoBAAA6DRqsAAAAAA416vKD42UvHvG0/L750XFpZWl7VgQAANAp0WAFAAAAcKi+p0s9hzSO138o1Ww67Gl52XnRcUllSQcUBgAA0LnQYAUAAABwKJdLGtl0F2tEWv7yYU8b3Xu0Et2Ne+dyBysAAOiKaLACAAAAOLyRlx0cH2GZgNTEVI3yjZIkrQ+sV+W+yo6oDAAAoNOgwQoAAADg8HqeKPUf1zjeVi5tX3nY05ovE8BdrAAAoKuhwQoAAADgyEZdcXC8bPZhT8nPPrjRFeuwAgCAroYGKwAAAIAjGz5dciU0jstfksLhQ045rfdpSnInSeIOVgAA0PXQYAUAAABwZGm9pBMnN45rNkgbFx9ySnJCsk7vfbokafPezdq8d3MHFggAAGAvGqwAAAAAjm5ks2UCyg+/TEDzdVhLtrJMAAAA6DposAIAAAA4ulO+LiWmNo4/e1VqqD/klObrsLJMAAAA6EposAIAAAA4uuQ06ZQLG8f7q6Uv3zvklJG+kUo90IRdXLlYkUikIysEAACwDQ1WAAAAAMd2jGUCPAkend7rdEnS9uB2bdizoYMKAwAAsBcNVgAAAADHdtIUKbVH43jVW1LdnkNOye97cJmAkkrWYQUAAF0DDVYAAAAAx5bgkYZf2jhu2C+tevOQU1qsw7qVdVgBAEDXQIMVAAAAQOuMarZMwLJDlwk4teep6ubpJqnxDlbWYQUAAF0BDVYAAAAArZMzXuqe2zheM0/au73F4UR3osb0HiNJ2lm7U2tr1nZ0hQAAAB2OBisAAACA1nG5pJGXN44jYemzVw85pfkyAYsrF3dUZQAAALahwdpJbNiwQVdffbVOOeUUZWRkKC0tTSNGjNCvf/1r7dlz6AYCAAAAgC2aGqzSYZcJyOubFx2XVrIOKwAAcL5EuwtAo23btmnTpk269NJLlZOTo4SEBJWVlen+++/Xa6+9poULF8rj8dhdJgAAALq63sOkPiOlbeXS5jJp55dSzxOjh0/JOkXpSenaU79HpZWlCkfCcru4rwMAADgXDdZOIi8vT++//36L577//e/r5JNP1h133KHi4mJdeOGF9hQHAAAANDfqcqm4vHFc/pI06c7ooQR3gsb1Gad5G+dpd91ufVH9hYb2GGpToQAAAO2PbyV3coMHD5YkVVdX21wJAAAAcMCIyyS5Gsfls6VIpMXh5uuwskwAAABwOhqsnUxtba2qqqq0ceNGvfnmm/rZz36mlJQUnXPOOXaXBgAAADTK7C8NOqtxvHO1tOWTFofzsg+uw1pSWdKRlQEAAHQ4RzZYg8Gg3n77bd1///2aPn26Bg4cKJfLJZfLpXvvvbdVr7Fnzx7de++9GjlypNLS0pSZmam8vDw99NBDqq+vb7fa//a3v6lXr17Kzc3VN77xDbndbv3rX/9STk5Ou70nAAAAYKz5ZlflL7Y4NCRriLond5cklVWWKRQOdWBhAAAAHcuRa7CWlJQc13ql69ev16RJk7Ru3TpJktfrVV1dncrKylRWVqbnnntOc+fOVVZW1iHXRiIR1dXVtep93G63kpKSWjx3ySWX6JRTTlFNTY0++ugjzZ8/X4FAoM1zAQAAANrFqd+U3rpNCtVLy1+Wpt4vuRMkSW6XW3nZeSpeX6w91h6tql6l4T2H21wwAABA+3DkHaySlJWVpSlTpuj222/X888/r+zs7FZd19DQoIsuukjr1q1T3759VVxcrH379ikYDOqFF15Qenq6PvnkE1199dWHvX79+vVKTU1t1a8xY8Yccv2AAQN03nnnacaMGXrooYd055136vLLL9e///3v4/p6AAAAADGV2l0aMrVxvHebtPaDFodbrMO6lXVYAQCAcznyDtazzz5bu3btavHcXXfd1aprZ82apfLyxh1RX375ZU2cOFFS492mV155pcLhsAoLC/XWW29p7ty5mjJlSovrfT6fnnrqqVa91+HugP2q6dOnKyUlRU899ZTOO++8Vr0uAAAA0CFGXi6teqNxXP6idOLk6KHmDdaSyhJdO+LaDi4OAACgYziywZqQkNDma2fNmiVJmjx5crS52ty3vvUt3XPPPVq7dq2eeeaZQxqsaWlpuvbaa9v8/l/V0NAgy7JUXV0ds9cEAAAAYuLkC6TkDKkuIK14Tfr6Q5InVZI0OHOwfKk+Ve2v0sfbP1ZDuEGJbkf+8wMAAHRxjl0ioC2CwaA+/PBDSdK0adMOe47L5dIFF1wgSZozZ07M3nvbtm2Hff7xxx9XKBTS+PHjY/ZeAAAAQEx4UqRhFzeO6/dIFe9ED7lcLuX1yZMk7bP2acXOFXZUCAAA0O74FnIzK1euVDgcliSNGDHiiOc1HausrNSuXbvUo0eP437vO++8UytWrFBBQYEGDhyovXv3av78+Xr99dc1dOhQ/fjHPz7itXV1dS021mraFMuyLFmWddy1dTZNc3Li3NA+yAxMkRmYIjMw5aTMuE6drsSlz0qSwp/+Q6GTvxE9Nqb3GL297m1J0sLNCzWs+zBbanQCJ2UGHYPMwBSZgSmnZ8ZkXjRYm9myZUt03L9//yOe1/zYli1bYtJgnT59unbu3KlZs2Zpx44dSkxM1EknnaSf//znuu2225SRkXHEax944AHdd999hzw/Z84ceb3e466tsyouLra7BMQZMgNTZAamyAxMOSIzkbDOT+yulIbd0hdzVPzabFmJaZKkfaF90dPe/uxtZa9v3cazODJHZAYdiszAFJmBKadmJhgMtvpcGqzN7NmzJzo+WmOy+bHm1xyPiy++WBdffHGbrr377rt16623Rh8HAgHl5ORo6tSpR23MxivLslRcXKyCggJ5PB67y0EcIDMwRWZgiszAlNMy405eLC3+i9yRkKYO2K/ImCskSZFIRM//63ltC27T5shmFZxfIE9C/M/XDk7LDNofmYEpMgNTTs9M00+ItwYNVgdITk5WcnLyIc97PB5HBryJ0+eH2CMzMEVmYIrMwJRjMnPat6TFf5EkJa54VRr/veih/Ox8vb7mddWGarWqZpXG9BljV5WO4JjMoMOQGZgiMzDl1MyYzIlNrppJT0+Pjo92G3DzY82vAQAAALqkvqdJvpMbx+v/I9Vsih7Ky86LjksqSzq6MgAAgHbHHazN9OvXLzrevHmzRo0addjzNm/efNhrOhs2uQIakRmYIjMwRWZgyomZcZ86XQkf/E6SFPr0HwpP/JEkaYzv4B2ri7cu1ndP/a4t9cU7J2YG7YvMwBSZgSmnZ4ZNrtpo2LBhcrvdCofDWr58uaZNm3bY85YvXy5Jys7OjskGV7Hi9/vl9/sVCoUksckV8FVkBqbIDEyRGZhyUma8dT1VcGC896On9H71SdFj3d3dtTu8W0u3LdW/3vyXPC7n/RhhR3FSZtAxyAxMkRmYcmpm2OSqjbxer84880wtWLBA77zzjm6//fZDzolEInr33XclSVOnTu3oEo/qpptu0k033aRAIKDMzEw2uQIOIDMwRWZgiszAlFMzE675h9yby5RZu1EXjhss9R4mSSpZVKLX1rymBjWo37h+yuuTd4xXwlc5NTNoP2QGpsgMTDk9M2xydRxmzpypBQsWaN68eVq8eLHGjx/f4viLL76oNWvWSJKuueYaO0psNacuMtzE6fND7JEZmCIzMEVmYMpxmRl1pbS5TJLkWfmK1P9eSdKEfhP02prXJEkf7/hYZww4w64K457jMoN2R2ZgiszAlFMzwyZXkqqrq1VVVRX9FQ6HJTXe3tv8+b1797a4bubMmRo5cqQikYhmzJihuXPnSpLC4bBefPFFXX/99ZKkadOmacqUKR07KQAAAKAzG36p5EpoHJe/JB34O3h+dn70lNLKUjsqAwAAaDeObbCOHj1avXr1iv7auHGjJOnBBx9s8fzNN9/c4rrExES99tprGjRokDZv3qzzzjtP3bp1U7du3XTFFVcoEAho9OjReu655+yYFgAAANB5pfWSTjy3cVyzUdq4SJLUp1sfDcwYKElaVrVM+xv221UhAABAzLFEwGEMGjRIy5Yt0x/+8Ae98sorWrt2rTwej4YPH66rrrpKP/zhD5WUlGR3mcdkWZYjd3Jz+i51iD0yA1NkBqbIDEw5OTOu4dOVuLpxs4vQp/9QuF/jeqtje4/V+sB6NYQbVLalTBP6TrCzzLjj5MygfZAZmCIzMOX0zJjMyxWJRCLtWAs6kN/vl9/vVygUUkVFhYqKiuT1eu0uCwAAAF1IQqhWFyy/WYnhetUndNM7Ix5RxJ2oZfXLNDs4W5J0TvI5mprauTaMBQAAaC4YDKqwsFA1NTXH3ESeBqsDBQIBZWZmqqqq6pgBiEdO36UOsUdmYIrMwBSZgSmnZybhnzfI/dkrkqSGy59V5OQLVLW/SlNfbWyqjug5Qs+c/4ydJcYdp2cGsUdmYIrMwJTTMxMIBOTz+VrVYGWJAAdz6i5uTZw+P8QemYEpMgNTZAamHJuZ074lHWiwJq54RRp+kfp6+urEzBP1Zc2XWrlrpepVr26ebjYXGn8cmxm0GzIDU2QGppyaGZM5OXaTKwAAAAA2OfFcyduzcfz521LdHklSXnbjeqyhSEhLti2xqzoAAICYosEKAAAAILYSPNLwSxvHDfullW9IkvL75kdPKa0staMyAACAmKPBCgAAACD2Rl5xcFzeuLnVuD7jok+VVJZ0dEUAAADtgjVYHcyyLFmWZXcZMdc0JyfODe2DzMAUmYEpMgNTXSIz2aOV2H2gXLvXK7LmfTVUb1JaWh8N6T5EX+z+Qit3rtTOfTuVkeS8TVnbQ5fIDGKKzMAUmYEpp2fGZF6uSCQSacda0IH8fr/8fr9CoZAqKipUVFQkr9drd1kAAADook7Z8pKGbntNklTe/2qt6T1Vbwbf1ML6hZKkb3f7toZ5htlZIgAAwGEFg0EVFhaqpqZGGRlH/4YwDVYHCgQCyszMVFVV1TEDEI8sy1JxcbEKCgocuUsdYo/MwBSZgSkyA1NdJjM7Ppfnr2dKksL9xij0nTmav2m+bvngFknSVUOv0u1jb7ezwrjRZTKDmCEzMEVmYMrpmQkEAvL5fK1qsLJEgIN5PB5HBryJ0+eH2CMzMEVmYIrMwJTjM9NvhJQ9Uqosl3vLx3IHNii/f77cLrfCkbCWbF/i7Pm3A8dnBjFHZmCKzMCUUzNjMic2uQIAAADQflpsdvWiMpIydEqPUyRJFdUVqq6ttqkwAACA2KDBCgAAAKD9jLxMkqtxvGy2FIkoPzs/erhsW5k9dQEAAMQIDVYAAAAA7SejnzTorMbxri+lLR8rLzsvenjx1sU2FQYAABAbrMHqYJZlybIsu8uIuaY5OXFuaB9kBqbIDEyRGZjqaplxDZ+hxHULJEmhpf/QqMk/U4IrQaFISCVbS7rM1+F4dLXM4PiRGZgiMzDl9MyYzMsViUQi7VgLOpDf75ff71coFFJFRYWKiork9XrtLgsAAABdXGLDPl2w/IdKiDSoNjFT7454WI/vfUIbQxslSXdm3Kl0d7rNVQIAABwUDAZVWFiompoaZWRkHPVcGqwOFAgElJmZqaqqqmMGIB5ZlqXi4mIVFBQ4cpc6xB6ZgSkyA1NkBqa6YmYSXrpW7s/fkCQ1XPWS/hwo11MrnpIkPXDGAzp/0Pl2ltfpdcXM4PiQGZgiMzDl9MwEAgH5fL5WNVhZIsDBPB6PIwPexOnzQ+yRGZgiMzBFZmCqS2XmtCukAw3WxBWvaML4/4o2WJdULdE3hnzDzuriRpfKDGKCzMAUmYEpp2bGZE5scgUAAACg/Q05X0rObByvfF2js4Yp0d14v0dpZamNhQEAABwfGqwAAAAA2p8nRTr1osZx/R6lfjlPo3yjJEnrA+tVua/SxuIAAADajgYrAAAAgI4x8oqD4/IXlZedF33IXawAACBe0WAFAAAA0DEGnSWl920cf1Gs8VnDoodKKktsKgoAAOD40GAFAAAA0DHcCdKIGY3jsKVR279UkjtJEnewAgCA+JVodwFoP5ZlybIsu8uIuaY5OXFuaB9kBqbIDEyRGZjq0pk5dbo8Cx+VJHmWv6JR/UapbFuZNu/drPXV69UvrZ/NBXZOXTozaBMyA1NkBqacnhmTebkikUikHWtBB/L7/fL7/QqFQqqoqFBRUZG8Xq/dZQEAAAAHRSI6d9XdSq/dIkm6/eQr9Y61UJJ0aeqlGps81s7qAAAAJEnBYFCFhYWqqalRRkbGUc+lwepAgUBAmZmZqqqqOmYA4pFlWSouLlZBQYE8Ho/d5SAOkBmYIjMwRWZgqqtnxv2fh5Qw/wFJUtnE7+s7lW9Lki4cdKHuP+N+O0vrtLp6ZmCOzMAUmYEpp2cmEAjI5/O1qsHKEgEO5vF4HBnwJk6fH2KPzMAUmYEpMgNTXTYzp10pHWiwnvblAqVmpmp/w36VbS9TYmKiXC6XzQV2Xl02M2gzMgNTZAamnJoZkzmxyRUAAACAjtVjsDQgX5Lk2b5CozOHSJK2B7drw54NdlYGAABgjAYrAAAAgI436oroMK8+FB2XVJbYUQ0AAECb0WAFAAAA0PGGXyq5EiRJ+ZvKo0+Xbi21qyIAAIA2ocEKAAAAoON180knTZEknbprk7olpEhqvIOVfXgBAEA8ocEKAAAAwB4jG5cJSJQ0xt1NkrSzdqfW1KyxsSgAAAAzNFgBAAAA2GPoNMnjlSTl79wcfZp1WAEAQDyhwQoAAADAHslp0ilflyTl76mOPl1ayTqsAAAgfiTaXQDaj2VZsizL7jJirmlOTpwb2geZgSkyA1NkBqbIzEGuU6crsfxFDa23lK4E7VFIpZWlqquvk9vF/SBNyAxMkRmYIjMw5fTMmMzLFWEFecfw+/3y+/0KhUKqqKhQUVGRvF6v3WUBAAAAR+SKNOj85T9WcsMe/bBPb73vbdzs6ub0m5WdkG1zdQAAoKsKBoMqLCxUTU2NMjIyjnouDVYHCgQCyszMVFVV1TEDEI8sy1JxcbEKCgrk8XjsLgdxgMzAFJmBKTIDU2SmJfc7dyphyZN6NiNdv++ZJUm6bcxtKjyl0ObKOg8yA1NkBqbIDEw5PTOBQEA+n69VDdZOuUTA6tWr9Zvf/EZPPfWU3aXENY/H48iAN3H6/BB7ZAamyAxMkRmYIjMHnP4tacmTyttfG32qbEeZZo6caWNRnROZgSkyA1NkBqacmhmTOXWqRY1Wrlypq6++WsOGDdMzzzxjdzkAAAAAOsKAPClrkIZYlrqHQpKkJZVLFAqHbC4MAADg2DpFg7W8vFxXXHGFRo4cqeeff16hEH+RAgAAALoMl0saebnckvJq6yRJe6w9WlW9yt66AAAAWiEmSwTs3btXCxYs0Nq1a1VTU6P09HSNGDFCZ599thISEo543aeffqp7771Xr7/+uiKRiCKRiFwulyQpNTU1FqUBAAAAiAcjL5c+eFD5+2tV3K1xo9bSraUa3nO4zYUBAAAc3XE1WHfv3q177rlHTz75pCzLOuR4r1699Jvf/Ebf/e53Wzy/detW3X777XrhhRcOaax6vV7deOONuu22246nNAAAAADxpNdQKXuU8neuiD5VUlmia0dca19NAAAArdDmBuu2bdt07rnnatWqVYpEIpIUbZJKUiQS0fbt23XDDTdo7dq1uv/++yVJr7/+uq677jrt2rWrRWM1PT1dN998s2655Rb17NnzeOYEAAAAIB6NukKD5/xcvoaQqhITtGTbEllhSx638zbOAAAAztHmNVi/973vaeXKlZIONlab7kZt3nCNRCJ64IEH9OGHH+rll1/W9OnTtWvXrujrZGVl6b777tP69et1//3301wFAAAAuqoRM+SSS3m1tZKkYENQK5rd0QoAANAZtekO1o8//lhvvvlmtIGak5OjW265RWeddZaysrK0c+dOzZ8/X3/+85+1efNmSdK9996rpUuXRjew8nq9uuOOO3TrrbcqLS0tdjMCAAAAEJ8y+kmDz1Ze1RK9ndZNklRaWarTep1mc2EAAABH1qYGa1FRUXQ8atQozZs3T1lZWdHnTjzxROXn5+u6667T5MmT9dlnn+m9996LLgkwZswYvfjiixo0aNBxTwAAAACAg4y8QuPf+ij6sGRrib438ns2FgQAAHB0bVoioKSkJDr+y1/+0qK52lzPnj312GOPRZcMkKTBgwdr3rx5NFcBAAAAHOrUi5UTSVCfhgZJ0tLtn8gKHbqhLgAAQGfRpgbr6tWrJUnZ2dmaOHHiUc8966yzlJ2dHb179Y477mBJAAAAAACHl5Ip18nnK39/nSRpf6hW5VXlNhcFAABwZG1aIqCmpkYul0snnXRSq84fMmSIKisrJUnnnntuW94SbWBZlizLed/tb5qTE+eG9kFmYIrMwBSZgSkyc3SuU6cr7925ej29cR3WRZsXaWSPkTZXZS8yA1NkBqbIDEw5PTMm83JFmv/8fiu53W65XC6df/75euutt455/rRp0/Tuu+/K5XJp//79SkpKMn1LtILf75ff71coFFJFRYWKiork9XrtLgsAAAAw4g7Xa9TKn+gb/XtIkk5IGKjr0q+3uSoAANCVBINBFRYWqqamRhkZGUc9t013sB4Pmqvt56abbtJNN92kQCCgzMxMTZ069ZgBiEeWZam4uFgFBQXyeDx2l4M4QGZgiszAFJmBKTJzbAmap/4752mzJ1Gbwps05fwpSk5Itrss25AZmCIzMEVmYMrpmQkEAq0+t8MbrOg4Ho/HkQFv4vT5IfbIDEyRGZgiMzBFZo7i9G8p7823tdmTpvpISCuqVyi/b77dVdmOzMAUmYEpMgNTTs2MyZzatMkVAAAAALSrgWcpX6nRhyUb59tYDAAAwJEd1x2sq1ev1q9//etWndekNec3+eUvf9mmugAAAADEObdb+SdOk7bPkSSVrv+3lH+7zUUBAAAc6rgarF9++aXuu+++Vp3rcrkkqdXnSzRYAQAAgK6sz+kzNfDNN7Xe49GyfVsUtILyetjEFQAAdC7HvURAJBI55i/T8796DQAAAIAuKHuk8iIpkqQGl7R07RybCwIAADhUm+5gzc3Njd6RCgAAAADtwuVSfv8z9NKOBZKkkpWzdcbJl9hbEwAAwFe0qcG6bt26GJcBAAAAAIfKG329NKexwVq68zObqwEAADjUcS8RAAAAAADtxdd3tE6MNN4X8pk7pH2bSm2uCAAAoCUarAAAAAA6tbwep0qSQi6XlnzyhM3VAAAAtESDFQAAAECnlj90RnRcuvkjKRy2sRoAAICWaLACAAAA6NTGDZwcHS92W9KGhTZWAwAA0FKbNrn64IMPJEk9evTQiBEjYlrQE088oZUrV8rlcumhhx6K6WsDAAAAiD9ZKVk6ObWPKvZv06okj2o+fU6Zg860uywAAABJbWywTpo0SS6XS+eff77eeuuto5577rnnSpLy8/P1u9/97piv/corr+jdd9+lwQoAAAAgKj9nkioq/qGIy6Ula97VuQ31UmKS3WUBAAC0/xIB77//vubPn69ly5a191sBAAAAcKj8/gfvWC1NaJBWF9tYDQAAwEGswQoAAACg0xubPVZuuSRJJSkp0rLZNlcEAADQiAYrAAAAgE4vIylDp/Q4RZJUkZyk6tXvSrUBm6sCAACgwQoAAAAgTuT3HR8dlyZKWvm6fcUAAAAc0KZNrhAfLMuSZVl2lxFzTXNy4tzQPsgMTJEZmCIzMEVm2maMb4ye1tOSpJLUFJ23bLZCI66wt6gOQmZgiszAFJmBKadnxmReNFgdxO/3y+/3KxQKSZLmzJkjr9drc1Xtp7iYjQ1ghszAFJmBKTIDU2TGTF2kTm65FVZYpSkpcq2drzmvv6yGhFS7S+swZAamyAxMkRmYcmpmgsFgq8+lweogN910k2666SYFAgFlZmZq6tSpysjIsLusmLMsS8XFxSooKJDH47G7HMQBMgNTZAamyAxMkZm2++e7/1T5znKtSfJoZ4JL5w/vocgJk+0uq92RGZgiMzBFZmDK6ZkJBFq/1jsNVgfzeDyODHgTp88PsUdmYIrMwBSZgSkyY258v/Eq31kuSSpNSdG0zaXS0Kk2V9VxyAxMkRmYIjMw5dTMmMyJTa4AAAAAxI287LzouCQlWdqw0MZqAAAAaLACAAAAiCOje49WorvxB/FKUlOkzUukkDM31wAAAPGBBisAAACAuJGamKpRvlGSpA0ejyrDdVLlMpurAgAAXdlxrcFaXl6u6667LqbnlpeXH09JAAAAABwuLztPH2//WJJUmpqsizYslvqPtbkqAADQVR1Xg3XLli2aNWvWUc9xuVytPhcAAAAAjmV83/F6fNnjkqSSlBRdtGGhNPEHNlcFAAC6quNqsEYikVjVAQAAAACtMqrXKCW5k1QfrldpSoq0YZEUiUgHbu4AAADoSG1qsJ5zzjnRO1MBAAAAoCMlJyTr9N6nq6SyRJs9idpUt00DqtdKPU6wuzQAANAFtanB+v7778e4DAAAAABovbzsPJVUlkiSSlNSNGDDIhqsAADAFm67CwAAAAAAU/nZ+dFxSeqBZQIAAABsQIMVAAAAQNwZ6Rup1IQUSVJJSrIiGxbaXBEAAOiqaLACAAAAiDueBI9G9xkjSdqemKgNu9dIwV02VwUAALqiNq3BeryCwaDKysq0detWpaamauDAgTrttNPsKAUAAABAnMrLztNHWz6SJJWkJmvgxsXS0Gk2VwUAALqamDRYq6urVVZWpp07dyotLU0jRozQoEGDDjlv9+7duuuuu/R///d/qq2tbXGsb9+++tnPfqYbb7xRLpcrFmUBAAAAcLDm67CWpqTo8g0LabACAIAOd1wN1i1btugnP/mJ/vnPfyoUCrU4NnHiRPn9/uidqRs2bNC5556rtWvXKhKJHPa1fvjDH2r+/PkqKipSQkLC8ZQGAAAAwOFO7XmquiV6ta8hqJKUFEU2LBK3agAAgI7W5jVY161bp4kTJ+rll19WQ0NDtGkaiUQUiUT00Ucf6eyzz1Z5ebkikYguv/xyrVmz5oiv53K5FIlE9NJLL+m3v/1tW8sCAAAA0EUkuhM1ps9YSdLOxASt2b5MsmqPcRUAAEBstbnB+p3vfEcbN25s8dxX70zdu3evbrnlFr366qsqLS2Vy+VSWlqafvSjH2n27NmaM2eO/vGPf+imm26S1+uNNll/+9vfqqqqqq2lAQAAAOgimi8TUJKUIG35xMZqAABAV9SmJQLmz5+v+fPnRxuikyZN0k9+8hOdcsopqq+v10cffaTf//73Wrt2rebNmxddPmDAgAGaP3/+IeuzXn755frxj3+syZMna/Pmzaqvr1dRUZF+9KMfHfcEAQAAADhXft9m67CmpuiqjYukgRNtrAgAAHQ1bbqD9R//+Ed0fMkll2ju3Lm6+OKLdfLJJ2vEiBG64YYbtHjxYuXk5EiSPvjgA7lcLv3xj3887OZXknTSSSfpoYceij5+//3321IaAAAAgC5kaNZQpSd2kySVpiQrvH6hzRUBAICupk0N1tLS0uj4wQcflMt16FLyPp9Pd955Z3RNVq/Xq0suueSorzt9+nR5vV5J0rJly9pSmmOsWrVKycnJcrlceuedd+wuBwAAAOiUEtwJGnfgLtbdCQn6orJMCodtrgoAAHQlbWqwrl+/XpI0aNAgnXjiiUc8b+rUqZIaN7A65ZRTlJCQcNTXTUxM1KmnnqpIJNLl12C98cYb5fF47C4DAAAA6PRarMPqqpeqKmysBgAAdDVtarDW1NTI5XJFlwA4kubHe/To0arX7t69u6TGDbK6qv/7v//T4sWLddttt9ldCgAAANDp5WXnRcclKcnSxkU2VgMAALqaNjVYLcuSpOiP8x9JcnJydHysu1e/el4kEmlLaXFv9+7duu2223TXXXcdcb1aAAAAAAcNyRqi7p40SdKSlBSFWIcVAAB0oDY1WNF+7rrrLqWlpemOO+6wuxQAAAAgLrhdbuX1HS9J2pPg1qotNFgBAEDHcWSDNRgM6u2339b999+v6dOna+DAgXK5XHK5XLr33ntb9Rp79uzRvffeq5EjRyotLU2ZmZnKy8vTQw89pPr6+nape/HixXriiSf08MMPKyUlpV3eAwAAAHCi/L4TouPS+l3SnkobqwEAAF1Jot0FtIeSkhJdeOGFbb5+/fr1mjRpktatWyepcSmEuro6lZWVqaysTM8995zmzp2rrKysQ66NRCKqq6tr1fu43W4lJSVJkkKhkG688UZdeOGF+sY3vtHm2gEAAICuqMVGV6kpunbDImn4JfYVBAAAugxH3sEqSVlZWZoyZYpuv/12Pf/888rOzm7VdQ0NDbrooou0bt069e3bV8XFxdq3b5+CwaBeeOEFpaen65NPPtHVV1992OvXr1+v1NTUVv0aM2ZM9LpHH31UK1eu1MMPPxyT+QMAAABdyeDMwfJ50iVJS1KSZW1gmQAAANAxjusO1pKSEp177rkxPXfZsmXHU5Ik6eyzz9auXbtaPHfXXXe16tpZs2apvLxckvTyyy9r4sSJkhrvNr3yyisVDodVWFiot956S3PnztWUKVNaXO/z+fTUU0+16r2a7oCtqanRL37xC11zzTVyu93RO2erqqokSdu2bdO6deuUk5PT6s3CAAAAgK7E5XIpLztfb2+cq6DbrRWb/qPT7C4KAAB0CcfVYK2urtb8+fOPeo7L5Wr1ubFyPE3IWbNmSZImT54cba42961vfUv33HOP1q5dq2eeeeaQBmtaWpquvfZao/esrq7Wnj179Ne//lV//etfDzne9HobN27UgAEDjF4bAAAA6CryBpyptzfOlSSV7tuo0+r2SslpNlcFAACcrs0N1kgkEss6OoVgMKgPP/xQkjRt2rTDnuNyuXTBBRfoL3/5i+bMmROT9+3du7deffXVQ55/77339Mgjj+iXv/ylRo8eLZ/PF5P3AwAAAJxofPb46LgkJUnf27xEOuFrNlYEAAC6gjY1WGfOnBnrOjqFlStXKhwOS5JGjBhxxPOajlVWVmrXrl3q0aPHcb2v1+vVJZdccsjzu3fvliRNnDhRF1xwwRGvr6ura7GxViAQkCRZliXLso6rts6oaU5OnBvaB5mBKTIDU2QGpshM+8hOyVYfT7q2WXu0NDlZtWs/UELOGXaXFRNkBqbIDEyRGZhyemZM5tWmBmtr1xiNN1u2bImO+/fvf8Tzmh/bsmXLcTdYj9cDDzyg++6775Dn58yZI6/Xa0NFHaO4uNjuEhBnyAxMkRmYIjMwRWZiLyfcX9u0Svvdbn24/A3t3TvK7pJiiszAFJmBKTIDU07NTDAYbPW5x7UGq9Ps2bMnOj5aY7L5sebXxNq1117bqvVc7777bt16663Rx4FAQDk5OZo6daoyMjLarT67WJal4uJiFRQUyOPx2F0O4gCZgSkyA1NkBqbITPuxvrRUtrjx5oMKd5W+d8FUyR3//+whMzBFZmCKzMCU0zPT9BPirRH/f9OAkpOTlZycfMjzHo/HkQFv4vT5IfbIDEyRGZgiMzBFZmJv4oCJ0uLGcanHrRt3VUh9T7O3qBgiMzBFZmCKzMCUUzNjMid3O9YRd9LT06Pjo90G3PxY82sAAAAA2Kt/Wn/19zT+Hf3T5GTVrfvQ5ooAAIDT0WBtpl+/ftHx5s2bj3he82PNrwEAAABgv/xeoyVJ9W6XPl0/1+ZqAACA07FEQDPDhg2T2+1WOBzW8uXLNW3atMOet3z5cklSdna27RtcHY1lWY7cyc3pu9Qh9sgMTJEZmCIzMEVm2teY3Cl6dcsHkqSS6pUaXV8vuVw2V3V8yAxMkRmYIjMw5fTMmMyLBmszXq9XZ555phYsWKB33nlHt99++yHnRCIRvfvuu5KkqVOndnSJR+X3++X3+xUKhSRJc+bMOepmXfHOqbvUof2QGZgiMzBFZmCKzLSPPeF90XGp29LJ//o/7U/y2VhR7JAZmCIzMEVmYMqpmTna8qFfRYP1K2bOnKkFCxZo3rx5Wrx4scaPH9/i+Isvvqg1a9ZIkq655ho7Sjyim266STfddJMCgYAyMzM1depUZWRk2F1WzDl9lzrEHpmBKTIDU2QGpshM+3th9mPa0LBXy5KT9egJCUoZdaHdJR0XMgNTZAamyAxMOT0zgUCg1ec6tsFaXV0dvZNTksLhsKTG7nNVVVX0+ZSUFKWlpUUfz5w5Uw8//LDKy8s1Y8YMzZo1S1OmTFE4HNbLL7+s66+/XpI0bdo0TZkypYNm0zZO3cWtidPnh9gjMzBFZmCKzMAUmWk/+b5R2lD5kRpcLpWvn6czxl5rd0kxQWZgiszAFJmBKadmxmROjt3kavTo0erVq1f018aNGyVJDz74YIvnb7755hbXJSYm6rXXXtOgQYO0efNmnXfeeerWrZu6deumK664QoFAQKNHj9Zzzz1nx7QAAAAAtEL+4POj45Kdy22sBAAAOJ1j72A9HoMGDdKyZcv0hz/8Qa+88orWrl0rj8ej4cOH66qrrtIPf/hDJSUl2V3mMbHJFdCIzMAUmYEpMgNTZKb9jc4+IzouCe+RtadKSsm0saLjQ2ZgiszAFJmBKadnxmRerkgkEmnHWtCBmm9yVVFRoaKiIkdvcgUAAAAczeM7f62NCfVKiET0J9elCnQfZ3dJAAAgTgSDQRUWFqqmpuaYexzRYHWgpk2uqqqq2OQKEJmBOTIDU2QGpshMx/j9nO/rH1WlkqRHss7QmdMetbmitiMzMEVmYIrMwJTTMxMIBOTz+VrVYGWJAAdz6iLDTZw+P8QemYEpMgNTZAamyEz7Gn/C+dEGa9muck1ywNeazMAUmYEpMgNTTs0Mm1wBAAAA6PLGDZ4aHZdYu6SGehurAQAATkWDFQAAAIAjZaVk6WR3454EqzyJqtm40OaKAACAE9FgBQAAAOBY+d2HSpIiLpeWfPGazdUAAAAnYg1WB7MsS5Zl2V1GzDXNyYlzQ/sgMzBFZmCKzMAUmek4Y3Mm69ldn0iSSrYt0dlx+jUnMzBFZmCKzMCU0zNjMi9XJBKJtGMt6EB+v19+v1+hUEgVFRUqKiqS1+u1uywAAADANvvD+/RAzW8Vdrk0pL5BM3s9ILlcdpcFAAA6uWAwqMLCQtXU1CgjI+Oo59JgdaBAIKDMzExVVVUdMwDxyLIsFRcXq6CgwJG71CH2yAxMkRmYIjMwRWY61tVFE7VCdZKkuZP/pqy+Y2yuyByZgSkyA1NkBqacnplAICCfz9eqBitLBDiYx+NxZMCbOH1+iD0yA1NkBqbIDEyRmY6Rn3mSVtR8Jkla+uXrmpo73uaK2o7MwBSZgSkyA1NOzYzJnNjkCgAAAICj5eVOjo5LKktsrAQAADgRDVYAAAAAjjb2lBlKOLAyWun+rTZXAwAAnIYGKwAAAABH6+b1aXgkSZK0JkGq2llhc0UAAMBJWIPVwSzLkmVZdpcRc01zcuLc0D7IDEyRGZgiMzBFZjreuLSBWhZcLUla9Nk/dP7Eu2yuyAyZgSkyA1NkBqacnhmTebkikQM/K4O45/f75ff7FQqFVFFRoaKiInm9XrvLAgAAAGy3c/fb+pM+lCQVWFn6Wq+f2lwRAADozILBoAoLC1VTU6OMjIyjnkuD1YECgYAyMzNVVVV1zADEI8uyVFxcrIKCAkfuUofYIzMwRWZgiszAFJnpePtrNulrb1ykBpdLuZEE/fPbpXaXZITMwBSZgSkyA1NOz0wgEJDP52tVg5UlAhzM4/E4MuBNnD4/xB6ZgSkyA1NkBqbITMfx+AZrVMitjxMj2uAKaWdws7IzB9ldljEyA1NkBqbIDEw5NTMmc2KTKwAAAABdQn633Oi4dNXLNlYCAACchAYrAAAAgC4hv/8Z0XHJpv/YWAkAAHASGqwAAAAAuoRRQy9VUrhxC4rSvettrgYAADgFDVYAAAAAXUJyr1N0ekNYkrRZljbVbLC5IgAA4AQ0WAEAAAB0DS6X8lL7RR+Wrn7NxmIAAIBTJNpdANqPZVmyLMvuMmKuaU5OnBvaB5mBKTIDU2QGpsiMfcb1yZO2vCFJWrzhfX1j1Pdtrqh1yAxMkRmYIjMw5fTMmMzLFYlEIu1YCzqQ3++X3+9XKBRSRUWFioqK5PV67S4LAAAA6DTS91borvqntd/tVs9wgn6Sda9cLpfdZQEAgE4mGAyqsLBQNTU1ysjIOOq5NFgdKBAIKDMzU1VVVccMQDyyLEvFxcUqKCiQx+OxuxzEATIDU2QGpsgMTJEZG4XqdfPTY/RRapIk6Z/f+KdyM3JtLurYyAxMkRmYIjMw5fTMBAIB+Xy+VjVYWSLAwTwejyMD3sTp80PskRmYIjMwRWZgiszYwONRXkpvfaTdkqSPN72nE0+73t6aDJAZmCIzMEVmYMqpmTGZE5tcAQAAAOhS8nuPjY5L1s+1sRIAAOAENFgBAAAAdCmnnjRN3cJhSVLp7i/EqmkAAOB40GAFAAAA0KUk5k7QmNo6SdLOSL3W1KyxuSIAABDPaLACAAAA6FpSMjU+sXv0YcmmBfbVAgAA4h4NVgAAAABdTl7v0dFx6bp/21gJAACIdzRYAQAAAHQ5Qwedp/TQgXVYq1cqHAnbXBEAAIhXNFgBAAAAdDkJA8/QuNpaSdLucL2+qP7C5ooAAEC8SrS7ALQfy7JkWZbdZcRc05ycODe0DzIDU2QGpsgMTJGZTqBbtvIiyZp34OHCTR/qhPQTbC3paMgMTJEZmCIzMOX0zJjMyxWJRCLtWAs6kN/vl9/vVygUUkVFhYqKiuT1eu0uCwAAAOiUemx4WD/I2CFJGunK1ZWZN9hcEQAA6CyCwaAKCwtVU1OjjIyMo55Lg9WBAoGAMjMzVVVVdcwAxCPLslRcXKyCggJ5PB67y0EcIDMwRWZgiszAFJnpJEqe0JTPH1V1QoLS3Emad8WHSnAn2F3VYZEZmCIzMEVmYMrpmQkEAvL5fK1qsLJEgIN5PB5HBryJ0+eH2CMzMEVmYIrMwBSZsdkJZ2ncpw+puJtXe8P1+nLvlxrec7jdVR0VmYEpMgNTZAamnJoZkzmxyRUAAACArqnPcOU3W16tdGuJfbUAAIC4RYMVAAAAQNfkTlB+1inRh4s3zrexGAAAEK9osAIAAADosgbnniNfQ0iS9HFVuaywM3dCBgAA7YcGKwAAAIAuy5U7QXm1tZKkYLheK3ausLkiAAAQb2iwAgAAAOi6BoxTfm199GFpZamNxQAAgHhEgxUAAABA15XUTfnpJ0Qflmz+0MZiAABAPKLBCgAAAKBLy8k5Q30aGiRJn+z4VFaIdVgBAEDr0WAFAAAA0KW5Bk5U/v46SVJt2NKyqmU2VwQAAOIJDVYAAAAAXVvOwY2uJKmkssTGYgAAQLyhwQoAAACga0vvo/zkXtGHpVsX21gMAACIN4l2F4D2Y1mWLMt560c1zcmJc0P7IDMwRWZgiszAFJnpfPr2n6D+uz7QZk+iPt3+qfbW7lVyQrLdZUWRGZgiMzBFZmDK6ZkxmZcrEolE2rEWdCC/3y+/369QKKSKigoVFRXJ6/XaXRYAAADQ6Q2smqdX9v9Lr6anSZKu63adTvCcYHNVAADALsFgUIWFhaqpqVFGRsZRz6XB6kCBQECZmZmqqqo6ZgDikWVZKi4uVkFBgTwej93lIA6QGZgiMzBFZmCKzHRCVRV659nz9LPePknS9SOu142jbrS5qIPIDEyRGZgiMzDl9MwEAgH5fL5WNVhZIsDBPB6PIwPexOnzQ+yRGZgiMzBFZmCKzHQifYYpXynRh2Xbyjrl7w2ZgSkyA1NkBqacmhmTObHJFQAAAAC43erTf7wGHlhvrbyqXEEraHNRAAAgHtBgBQAAAABJyp2gvP21kqSGSIOWbl9qbz0AACAu0GAFAAAAAEnKnaDxtXXRhyWVJTYWAwAA4gUNVgAAAACQpH6jNa7+4B7ApZWlNhYDAADiBQ1WAAAAAJCkxGT5sk/TifX1kqTPdi7X3vq9NhcFAAA6OxqsAAAAANAkd4LyDiwTEIqE9fH2j20uCAAAdHY0WAEAAACgSe5E5R/Y6EqSSrayDisAADg6GqwAAAAA0CQnX+PY6AoAABigwQoAAAAATbw9lNXzZA2ta1yHddWuVaqpq7G5KAAA0JnRYAUAAACA5nLGK6+2cZmAiCJasm2JzQUBAIDOjAYrAAAAADSXO1H5zZYJKK0stbEYAADQ2dFgBQAAAIDmcidobG2t3JGIJGlx5WKbCwIAAJ0ZDVYAAAAAaC5rkDK8vXVKfeM6rF9Uf6FdtbtsLgoAAHRWNFgBAAAAoDmXS8qdoPz9B5cJKKsss7EgAADQmdFgBQAAAICvypmg/AMbXUlSSWWJjcUAAIDOjAYrAAAAAHxV7gSNqa1TwoF1WNnoCgAAHEmi3QWg/ViWJcuy7C4j5prm5MS5oX2QGZgiMzBFZmCKzMQB3zB5E70aXlevZSnJWlOzRlsDW+VL9dlSDpmBKTIDU2QGppyeGZN5uSKRA9+SRdzz+/3y+/0KhUKqqKhQUVGRvF6v3WUBAAAAcemML36nIs9m/a17piTpcu/lOi3pNJurAgAAHSEYDKqwsFA1NTXKyMg46rk0WB0oEAgoMzNTVVVVxwxAPLIsS8XFxSooKJDH47G7HMQBMgNTZAamyAxMkZn44J7/Oy0ue1Tf79tbknTpiZfqF+N/YUstZAamyAxMkRmYcnpmAoGAfD5fqxqsLBHgYB6Px5EBb+L0+SH2yAxMkRmYIjMwRWY6uUFnaPSHDykxElGDy6Ul25fY/vtFZmCKzMAUmYEpp2bGZE5scgUAAAAAhzMgT6lyaVRdnSRpw54NqtxXaXNRAACgs6HBCgAAAACHk5Ih9Rmh/P110adKK0ttLAgAAHRGNFgBAAAA4EhyJyi/tjb6sKSyxMZiAABAZ0SDFQAAAACOJHeCRtXVKSncuDdwyVYarAAAoCUarAAAAABwJDkTlByRTj+wDuuWfVu0ac8mm4sCAACdCQ1WAAAAADiSzP5SZq7ymi0TwDqsAACgORqsAAAAAHA0ueM1fj/rsAIAgMOjwQoAAAAAR5M7QSPq6pUaDktqbLBGIhGbiwIAAJ0FDVYAAAAAOJrcifJIGl3buA7r9uB2bdizwd6aAABAp0GDFQAAAACOptcwKTlTeQcarJK0eOtiGwsCAACdCQ1WAAAAADgat1vKyVc+G10BAIDDoMEKAAAAAMeSO16n1tWr24F1WEsrS1mHFQAASKLBCgAAAADHljtRiZLGHlgmYGftTq2pWWNvTQAAoFOgwQoAAAAAx9JvjOT2KH//wWUCSipLbCwIAAB0FjRYAQAAAOBYkrxS39OUxzqsAADgK2iwAgAAAEBr5E7Q0HpL6aHGdVhLKksUjoRtLgoAANiNBisAAAAAtEbuBCVIGnfgLtaauhp9Uf2FvTUBAADb0WAFAAAAgNbImSBJyj+w0ZXEOqwAAIAGa6exbt06uVyuw/669tpr7S4PAAAAQFovqedJbHQFAABaSLS7ALR06aWXavr06S2eO/HEE22qBgAAAEALORN00tLVygqFVJ2QoCWVSxQKh5TgTrC7MgAAYBMarJ3MqFGjdPXVV9tdBgAAAIDDyZ0g99JnNa62TsXdvNpj7dGq6lUa3nO43ZUBAACbsERAJ7R//37t37/f7jIAAAAAfFXugXVYmy8TsJVlAgAA6MposHYyDz30kLxer7xer0466SQ98sgjikQidpcFAAAAQJJ6niR5eyq/lnVYAQBAI0c2WIPBoN5++23df//9mj59ugYOHBjdMOree+9t1Wvs2bNH9957r0aOHKm0tDRlZmYqLy9PDz30kOrr62Nes9vt1pQpU/TAAw/otdde0+OPP66srCz96Ec/0i233BLz9wMAAADQBi6XlDtRg60G+RpCkqSPt30sK2zZXBgAALCLI9dgLSkp0YUXXtjm69evX69JkyZp3bp1kiSv16u6ujqVlZWprKxMzz33nObOnausrKxDro1EIqqrq2vV+7jdbiUlJUmScnNz9e9//7vF8e9+97uaMmWK/vznP+v73/++hg0b1uY5AQAAAIiRnPFyrXpDebW1ejutm4INQa3YuUKn9TrN7soAAIANHHkHqyRlZWVpypQpuv322/X8888rOzu7Vdc1NDTooosu0rp169S3b18VFxdr3759CgaDeuGFF5Senq5PPvnkiBtRrV+/Xqmpqa36NWbMmKPWkpCQoDvvvFORSETFxcXGXwMAAAAA7SB3oiS1WCagtLLUrmoAAIDNHHkH69lnn61du3a1eO6uu+5q1bWzZs1SeXm5JOnll1/WxImNf3lyu9268sorFQ6HVVhYqLfeektz587VlClTWlzv8/n01FNPteq9DncH7FcNHDhQklRVVdWq1wQAAADQzvqeJiWmKH//wZ9cK9laou+N/J6NRQEAALs4ssGakJDQ5mtnzZolSZo8eXK0udrct771Ld1zzz1au3atnnnmmUMarGlpabr22mvb/P5ftXr1aklSnz59YvaaAAAAAI5DYpLUf6xy1n+oPg0N2paYqE+2f6L6UL2SEpLsrg4AAHQwxy4R0BbBYFAffvihJGnatGmHPcflcumCCy6QJM2ZMydm7719+/ZDnqutrdV///d/KyEhQeeff37M3gsAAADAccqdIJcUvYu1NlSr8qpye2sCAAC2cOQdrG21cuVKhcNhSdKIESOOeF7TscrKSu3atUs9evQ47vf+/ve/r507d2ry5MkaMGCAKisr9cwzz2j16tX61a9+pZNOOum43wMAAABAjORMkCTl1dbq9fRukqSSyhKN7TPWzqoAAIANaLA2s2XLlui4f//+Rzyv+bEtW7bEpMH6jW98Q88884z+3//7f9q1a5e6deumMWPG6He/+51mzJhx1Gvr6upUV3dw/adAICBJsixLlmUdd22dTdOcnDg3tA8yA1NkBqbIDEyRGQfIHq1EuTS+2UZXJVtK9L1T22cdVjIDU2QGpsgMTDk9MybzosHazJ49e6Jjr9d7xPOaH2t+zfH47ne/q+9+97ttuvaBBx7Qfffdd8jzc+bMOeo84l1xcbHdJSDOkBmYIjMwRWZgiszEt8kp/dWvdpP6Ww3a7EnU0u1L9a83/yWPy9Nu70lmYIrMwBSZgSmnZiYYDLb6XBqsDnD33Xfr1ltvjT4OBALKycnR1KlTlZGRYWNl7cOyLBUXF6ugoEAeT/v95RXOQWZgiszAFJmBKTLjDG7Xe9LHTyu/tlavetLUoAb1G9dPeX3yYv5eZAamyAxMkRmYcnpmmn5CvDVosDaTnp4eHR+tS938WPNr7JKcnKzk5ORDnvd4PI4MeBOnzw+xR2ZgiszAFJmBKTIT5wadKX38tPL21+rV9DRJ0pIdS3TGgDPa7S3JDEyRGZgiMzDl1MyYzMndjnXEnX79+kXHmzdvPuJ5zY81vwYAAABAF5IzXpKUX3twP4TSylK7qgEAADbhDtZmhg0bJrfbrXA4rOXLl2vatGmHPW/58uWSpOzs7JhscNVe2OQKaERmYIrMwBSZgSky4xDd+ioxva/67NmqgQ0hrU9MUHlVuQL7A0pNTI3pW5EZmCIzMEVmYMrpmTGZlysSiUTasZZOY9CgQVq/fr1+9atf6d577z3ieeecc44WLFigc889V3Pnzj3keCQS0UknnaQ1a9bommuu0axZs9qxajN+v19+v1+hUEgVFRUqKipy9CZXAAAAgN3GrX1U/XeX6Nc9s/RiRuPyYTO7zdQQzxCbKwMAAMcjGAyqsLBQNTU1x9zjiDtYv2LmzJlasGCB5s2bp8WLF2v8+PEtjr/44otas2aNJOmaa66xo8Qjuummm3TTTTcpEAgoMzOTTa6AA8gMTJEZmCIzMEVmnMNdulmaU6L82rpog9U90K0LT78wpu9DZmCKzMAUmYEpp2eGTa4kVVdXKxQKRR+Hw2FJjd3nqqqq6PMpKSlKS0uLPp45c6YefvhhlZeXa8aMGZo1a5amTJmicDisl19+Wddff70kadq0aZoyZUoHzaZtnLrIcBOnzw+xR2ZgiszAFJmBKTLjAIPPlCSN218bfWrJ9iXt9vtKZmCKzMAUmYEpp2aGTa4kjR49Wr169Yr+2rhxoyTpwQcfbPH8zTff3OK6xMREvfbaaxo0aJA2b96s8847T926dVO3bt10xRVXKBAIaPTo0XruuefsmBYAAACAzqT3cCkpTb5wWCc2NK6+9tnOz7S3fq/NhQEAgI7i2Abr8Rg0aJCWLVumX/7ylxoxYoRcLpc8Ho/Gjh2rP/zhD1q0aJGysrLsLhMAAACA3RISpQF5kqS8YGNTNRQJ6ePtH9tZFQAA6ECOXSJg3bp1x3V9enq67rvvPt13332xKcgGlmU5cic3p+9Sh9gjMzBFZmCKzMAUmXEWd/88JayZp/z9tXrhwDqsizYv0sQ+E2P2HmQGpsgMTJEZmHJ6Zkzm5YpEIpF2rAUdyO/3y+/3KxQKqaKiQkVFRfJ6vXaXBQAAADiab89nOnP177Xb7dbZAwdIkvol9NMP0n9gc2UAAKCtgsGgCgsLVVNTc8xN5GmwOlAgEFBmZqaqqqqOGYB45PRd6hB7ZAamyAxMkRmYIjMOU79XiX84Ua5ISJflDtLnCWG55NK8y+YpIyk2fx8nMzBFZmCKzMCU0zMTCATk8/la1WB17BIBcO4ubk2cPj/EHpmBKTIDU2QGpsiMQ3iypOyR0talytu7W59nZiiiiD7d+anOzT03tm9FZmCIzMAUmYEpp2bGZE5scgUAAAAAxyu3cb3V/Nq66FMllSV2VQMAADoQDVYAAAAAOF654yVJY2tro//IosEKAEDXwBIBDmZZliN3cnP6LnWIPTIDU2QGpsgMTJEZB+o7Vh5JGeGITol4tMJl6YvqL7R9z3ZlpWQd98uTGZgiMzBFZmDK6ZkxmRebXDmI3++X3+9XKBRSRUWFioqK5PV67S4LAAAA6BLO++yn6la/Q3/o0UOzMtMkSd/yfksjkkbYXBkAADAVDAZVWFjYqk2uaLA6UCAQUGZmpqqqqo4ZgHjk9F3qEHtkBqbIDEyRGZgiM86U8NoP5C6frQWpKfpBdm9J0uVDLtfdeXcf92uTGZgiMzBFZmDK6ZkJBALy+XytarCyRICDOXUXtyZOnx9ij8zAFJmBKTIDU2TGYQaeIZXP1pjaOiXIpZAiWrJ9SUx/j8kMTJEZmCIzMOXUzJjMiU2uAAAAACAWcidIkrpFIhruSpEkralZox3BHXZWBQAA2hkNVgAAAACIBd9QKaW7JCl/b0306dLKUpsKAgAAHYEGKwAAAADEgtst5YyXJOUFdkefLqkssakgAADQEWiwAgAAAECsHFgmYHRdnRJdjf/c4g5WAACcjU2uHMyyLFmWZXcZMdc0JyfODe2DzMAUmYEpMgNTZMa5XP3zlCgpNRLRKHeaPg4FtGHPBm2q2aQ+3j5tfl0yA1NkBqbIDEw5PTMm83JFIpFIO9aCDuT3++X3+xUKhVRRUaGioiJ5vV67ywIAAAC6DHe4Xhcu+/+UEGnQn3z99Pf0xntaZnhnaHTSaJurAwAArRUMBlVYWKiamhplZGQc9VwarA4UCASUmZmpqqqqYwYgHlmWpeLiYhUUFMjj8dhdDuIAmYEpMgNTZAamyIyzJcy6UO5NJSpNSdZ1fRvvWr3ohIt034T72vyaZAamyAxMkRmYcnpmAoGAfD5fqxqsLBHgYB6Px5EBb+L0+SH2yAxMkRmYIjMwRWYcKneCtKlEo+rqlORKVH2kQUu2LYnJ7zWZgSkyA1NkBqacmhmTObHJFQAAAADEUu5ESVJyRDrd012StGXfFm3as8nGogAAQHuhwQoAAAAAsZQzPjrMD+6PjksrS+2oBgAAtDMarAAAAAAQS916Sr6TJUn5VeujT5dUlthVEQAAaEc0WAEAAAAg1nInSJJG7A8q1Z0kqbHByh7DAAA4D5tcOZhlWbIsy+4yYq5pTk6cG9oHmYEpMgNTZAamyIzzufrlKfHjZ+SRdHpSTy2s3artwe36cteXGpgx0Pj1yAxMkRmYIjMw5fTMmMzLFeFbqI7h9/vl9/sVCoVUUVGhoqIieb1eu8sCAAAAupxuddt03orbJUn/2/tkPdmtVpJ0cerFyk/Ot7M0AADQCsFgUIWFhaqpqVFGRsZRz6XB6kCBQECZmZmqqqo6ZgDikWVZKi4uVkFBgTwej93lIA6QGZgiMzBFZmCKzHQBkYgSHx4u177t+jQtS1f3SpckTc2dqt+d9TvjlyMzMEVmYIrMwJTTMxMIBOTz+VrVYGWJAAfzeDyODHgTp88PsUdmYIrMwBSZgSky43C546WVr2v43mp1y+6tfaH9WrJ9iRITE+Vyudr0kmQGpsgMTJEZmHJqZkzmxCZXAAAAANAecidKaryrZWxqH0nSztqdWlOzxsaiAABArNFgBQAAAID2kDMhOsyvD0fHJZUldlQDAADaCQ1WAAAAAGgPfUdJiamSpLwd66NPl2ylwQoAgJPQYAUAAACA9pDgkQaMkyQN3bVR6Z40SVLptlKFI+GjXQkAAOIIDVYAAAAAaC+5jcsEJEga5+0vSaqpq9EX1V/YWBQAAIglGqwAAAAA0F5yD67DOr7BFR2zDisAAM5BgxUAAAAA2suAPEmNjdW8nZuiT9NgBQDAORLtLgDtx7IsWZZldxkx1zQnJ84N7YPMwBSZgSkyA1NkpgtJ8Cqx93C5ti/XiZWr1P3k4dpdX6OyyjLV1tUqwZ3QqpchMzBFZmCKzMCU0zNjMi9XJBKJtGMt6EB+v19+v1+hUEgVFRUqKiqS1+u1uywAAACgSxu58RmdUPVvSdLMwWfoYzXeyXpj2o3qn9jfztIAAMARBINBFRYWqqamRhkZGUc9lwarAwUCAWVmZqqqquqYAYhHlmWpuLhYBQUF8ng8dpeDOEBmYIrMwBSZgSky07W4PntZif/8viSp6PRv6oGaTyRJPz79x5p56sxWvQaZgSkyA1NkBqacnplAICCfz9eqBitLBDiYx+NxZMCbOH1+iD0yA1NkBqbIDEyRmS5i8FnR4YSabdHxkh1L9D3P94xeiszAFJmBKTIDU07NjMmc2OQKAAAAANpT5gApY4AkafCmZfKl+CRJH2/7WFbYmevWAQDQldBgBQAAAID2ljtBkuSygsrLPEmSFGwIasXOFXZWBQAAYoAGKwAAAAC0twMNVknKjyRHx6WVpXZUAwAAYogGKwAAAAC0t+YN1t3bo+PFWxfbUQ0AAIghGqwAAAAA0N56nyolN+5AnLN5qfp4+0iSlm5fqvpQvZ2VAQCA40SDFQAAAADamztBGpAnSXLt3ab8HsMkSbWhWpVXldtZGQAAOE40WAEAAACgI+ROjA7zXd2i45LKEjuqAQAAMUKDFQAAAAA6QvN1WGt2RcdsdAUAQHyjwQoAAAAAHaH/WMmdKEnqt3mp+qf1lyR9uv1T1TbU2lkZAAA4DjRYAQAAAKAjJHmlvqc1jqs+V76vcVwfrtenOz61sTAAAHA8Eu0uAO3HsixZlmV3GTHXNCcnzg3tg8zAFJmBKTIDU2Sm63L3z1PC5iWSpLGubnr1wPOLNi/SGN+YI15HZmCKzMAUmYEpp2fGZF6uSCQSacda0IH8fr/8fr9CoZAqKipUVFQkr9drd1kAAAAADui7u1T5ax+RJC3qM1XXe1dJknITcnVD+g12lgYAAJoJBoMqLCxUTU2NMjIyjnouDVYHCgQCyszMVFVV1TEDEI8sy1JxcbEKCgrk8XjsLgdxgMzAFJmBKTIDU2SmC9u7XZ6HT5UkhQeM18U9krR+z3oluhM1/7L5Sk1MPexlZAamyAxMkRmYcnpmAoGAfD5fqxqsLBHgYB6Px5EBb+L0+SH2yAxMkRmYIjMwRWa6oKz+Uo8TpF1r5N76ifKH/UDr96xXQ7hBy3ct1xn9zzjq5WQGpsgMTJEZmHJqZkzmxCZXAAAAANCRcic2/jdUr/wkX/TpksoSmwoCAADHgwYrAAAAAHSknPHR4bh9e6JjGqwAAMQnGqwAAAAA0JGa7mCV5NuyTCdmnihJWrFzhfbW77WrKgAA0EY0WAEAAACgI/mGSKk9GscbFykve5wkKRQJ6ePtH9tYGAAAaAsarAAAAADQkVwuKXdC43h/tcZ7c6KHSrayTAAAAPGGBisAAAAAdLSmBqukcfuDcskliXVYAQCIRzRYAQAAAKCj5RxssHbf8qlOzjpZkrRq1yrV1NXYVRUAAGgDGqwAAAAA0NH6nS4lJDeONyxSXnaeJCmiiMq2ldlXFwAAMEaDFQAAAAA6WmKy1H9M47h6rfIzh0QPlVaW2lQUAABoCxqsAAAAAGCHZuuwjq2tk9vV+M8z1mEFACC+0GAFAAAAADvkTowOM7Yu07AewyRJX1R/oV21u+yqCgAAGKLBCgAAAAB2GJB3cLxhofKz86MPyypZhxUAgHhBgxUAAAAA7ODtIfVqvGtVW5cpzzcyeohlAgAAiB80WAEAAADALrnjG/8bCWlMfVgJrgRJNFgBAIgnNFgBAAAAwC7N1mHttmWphvuGS5LW1qzVjuAOu6oCAAAGaLACAAAAgF1yJxwcb1zUYh3W0spSGwoCAACmEu0uAO3HsixZlmV3GTHXNCcnzg3tg8zAFJmBKTIDU2QGUd36KTGtj1x7tymysURjz/mJ/nbg0KIti1SQUyCJzMAcmYEpMgNTTs+MybxckUgk0o61oAP5/X75/X6FQiFVVFSoqKhIXq/X7rIAAAAAHMW4tY+q/+7GNVffPflXutN6RiGF1NPdU7dk3GJzdQAAdE3BYFCFhYWqqalRRkbGUc+lwepAgUBAmZmZqqqqOmYA4pFlWSouLlZBQYE8Ho/d5SAOkBmYIjMwRWZgisygOXfJ40oovkeSFJr6O31n90J9suMTSdJb33xL2d2yyQyMkRmYIjMw5fTMBAIB+Xy+VjVYWSLAwTwejyMD3sTp80PskRmYIjMwRWZgisxAkjT4zOgwYXOJxg8ZH22wfrLzE13c/eLocTIDU2QGpsgMTDk1MyZzYpMrAAAAALBTn5GSp1vjeMMi5ffJix4q2VpiU1EAAKC1aLACAAAAgJ0SEqUB4xrHe7ZoVFIPJbmTJEmllaU2FgYAAFqDBisAAAAA2C13YnSYvPkTnd77dEnSln1btGnPJpuKAgAArUGDFQAAAADsljv+4HjDQuVn50cfchcrAACdGw1WAAAAALDbgDzJdeCfZxsWKb/vwQZrSSXrsAIA0JnRYAUAAAAAuyWnS9kjG8fbV2iEd4BSE1MlNW50FYlEbCwOAAAcTaLdBaBzC4fDamhoUDgctruUKMuylJiYqNraWoVCIbvLQRwgMzBFZuKT2+2Wx+ORy+WyuxQAaJucCdLWTyVF5Nm6VKN7j9ZHWz7S9v3btWHPBrurAwAAR0CDFYdVU1OjQCCgYDDYqZqrkhSJRJSdna2NGzfyj2i0CpmBKTITvzwej9LT0+Xz+ZSQkGB3OQBgJneCVPJ443jDIuVl5+mjLR9Jkkq3lcorr43FAQCAI6HBihYikYi2bdum6upqeb1e+Xw+paSkyO12d5omQzgc1t69e5WWlia3m1UucGxkBqbITPyJRCIKhULau3evdu/erf379ysnJ4cmK4D4kjvh4HjDIuWPeiD6sGxbmc7ROTYUBQAAjoUGK1qorq5WdXW1srOzlZWVZXc5hxUOh1VfXx9t/ALHQmZgiszEr7S0NGVmZmrDhg2qqqpSnz597C4JAFovo5/UPVfavUHaXKZTM09SN0837bP2acn2JTo7+Wy7KwQAAIfBvxoRFYlEtHv3bqWnp3fa5ioAAMeSmpqqjIwM7dmzh01hAMSf3ImN/22oVeL2FRrbZ6wkaWftTu0I77CxMAAAcCQ0WBHV0NCguro6ZWZm2l0KAADHJT09XZZlybIsu0sBADM54w+ONyxUfnZ+9OGahjU2FAQAAI6FBiuimnbKTkxk5QgAQHxrWnu1s23UCADH1HQHqxTd6KoJDVYAADonGqw4RGfZzAoAgLbizzIAcavXKVLKgZ8o27BIQ7ufrPSkdEnS2oa1Ckf4xhEAAJ0NDVYAAAAA6Czc7oPLBASrlFC9TuP6jJMk7Y/s1+rdq20sDgAAHA4NVgAAAADoTHInHBxvXKTxfQ+uy1q6rdSGggAAwNHQYAUAAACAziSnWYN1w8IW67CWbSuzoSAAAHA0NFgBAAAAoDPpP0ZyexrHGxbrpO4nqXtyd0nSku1LFAqH7KsNAAAcggZrJ7N9+3bdfPPNGjhwoJKTk9W3b199/etf15dffml3aQAAAAA6gidV6nd643jnF3IHd2lc78Z1WPdae7Vq1yr7agMAAIegwdqJfPnllxo9erTeeOMNXXfddfrLX/6iW2+9VV6vV7t27bK7PKDTePrpp+VyueRyubRu3Tpba5k0aZJcLpcmTZpkax0AAMBhmq/DumFRdKMrSSqpLLGhIAAAcCSJdheAg7797W+rd+/e+uCDD5Senm53OWhn1157rWbNmiVJWrt2rQYNGnTMawYNGqT169dr4MCBtjcWAQAA0I5yJkh6pHG8cZHGjfmv6KGSyhJ9Z8R37KkLAAAcgjtYO4l58+Zp8eLF+vWvf6309HTV1taqrq7O7rIAAAAA2OErd7AOzhisNFeaJOnjbR/LCls2FQYAAL6KBmsn8c4770iSunfvrq997Wvyer1KTU1Vfn6+FixYYHN1AAAAADpUN5/Uc0jjeMtSuRpqdULiCZKkYENQK3ausLE4AADQnCMbrMFgUG+//bbuv/9+TZ8+XQMHDoyu13jvvfe26jX27Nmje++9VyNHjlRaWpoyMzOVl5enhx56SPX19TGvuaKiQpI0Y8YMZWRk6IUXXtBjjz2mrVu36rzzztOSJUti/p4AAAAAOrHc8Y3/DVtybf1EgxMHRw+VbGUdVgAAOgtHrsFaUlKiCy+8sM3Xr1+/XpMmTYqucen1elVXV6eysjKVlZXpueee09y5c5WVlXXItZFIpNU/2u92u5WUlCSpsaErSaeccopee+01uVwuSdKUKVN06qmn6r777tNrr73W5jkBAAAAiDO5E6VPnpUkuTYujt7BKjWuw3r9qOvtqgwAADTjyDtYJSkrK0tTpkzR7bffrueff17Z2dmtuq6hoUEXXXSR1q1bp759+6q4uFj79u1TMBjUCy+8oPT0dH3yySe6+uqrD3v9+vXrlZqa2qpfY8aMiV6XmpoqSbrmmmuizVVJGjJkiM444wzNnz//OL4acLr3338/epf2+++/L0maPXu2pkyZol69eik1NVVDhw7VHXfcoV27drXqNd966y1dffXVOuGEE9StWzelpKRo8ODBmjFjhp5++mkFg8HDXhcOh/Xss8/qwgsvVHZ2tpKSktSrVy9NnjxZjz32WKvuAK+urtZdd92lU045Rampqerdu7fOO+88vfjii63+mkhSbW2tHn30URUUFGjo0KFKSUmJvtaTTz6phoaGY77GokWLdPnllys7Ozv6Nbjhhhv0+eefG9UCAABgLOfgOqyujYvVw91Dfbx9JElLty9VfSj2P1kHAADMOfIO1rPPPvuQJtJdd93VqmtnzZql8vJySdLLL7+siRMnSmq82/TKK69UOBxWYWGh3nrrLc2dO1dTpkxpcb3P59NTTz3Vqvdqfgds//79JemwjeC+ffvqgw8+UCgUUkJCQqteG11XOBzWf/3Xf+nZZ59t8XxFRYUefPBBvfrqq1qwYMERv+mwc+dOXXnllZo7d+4hx9atW6d169bplVdekSRde+21LY7v2rVLF198sT788MMWz1dVVen999/X+++/r0cffVRvv/22Bg4ceNj3X7lypc477zxt2bIl+lxtba3mzp2ruXPn6jvf+Y7OOeecY34dPv30U33zm9/U+vXrWzy/Y8eO6Gs9/vjjev3119WnT5/Dvsaf/vQn3XbbbQqHwy2+Bk888YSKioo0e/bsY9YBAADQZj1PlLw+KVgl1+ZSuU65WuN6j9Ob695UbahW5VXlGttnrN1VAgDQ5TmywXo8TchZs2ZJkiZPnhxtrjb3rW99S/fcc4/Wrl2rZ5555pAGa1pa2iFNp9bIy8vT448/rk2bNh1ybOPGjerZsyfNVbTKL37xC3300Ue65JJLdM0112jgwIHatm2b/H6/3nzzTa1evVq33HKLnn/++UOuDQaDmjx5cvSbDGPHjtUNN9ygESNGKDk5WRs3btQHH3ygf/zjH4dcGwqF9I1vfEMLFy6UJH3ta1/TzTffrMGDB2vLli36+9//rn/+859auXKlpkyZoqVLlyotLa3FawQCAZ1//vnR5uqVV16pmTNnqnfv3qqoqNAf//hHPfXUU1q+fPlRvwarV6/W1772NdXU1CgjI0M/+MEPNGLECJ188smqrq7Wa6+9pscff1ylpaX65je/qQULFsjj8bR4jVdffVW33nqrJCkzM1N33nmnJk2aJEl677339D//8z/69re/rV69erXidwUAAKANXC4pd4K06g25amuUXrtFedl5enPdm5IalwmgwQoAgP0cu0RAWwSDweidd9OmTTvsOS6XSxdccIEkac6cOTF7729+85vyer3661//Ksuyos9/8sknWrhwYfQ9gWP56KOPdP/99+vVV1/VpZdeqjFjxmjatGl6/fXXNXXqVEnSSy+9pB07dhxy7c9//vNoc/Wmm25SaWmpbrjhBp1xxhkaO3asLrnkEv3xj3/U2rVrD/l/5P/9v/8Xba5ec801mjdvni677DKNHTtWF110kV599VX97Gc/kyR9+eWX+u///u9D3v+///u/tXHjRknSb3/7W73wwguaNm2axo4dq6uuukofffSRpk6dqtLS0qN+DWbOnKmamhqNHj1aX375pX7zm9/o61//usaOHaupU6fq0Ucf1euvvy63263Fixfr6aefbnF9fX29br75ZkmNzdWFCxfq7rvv1sSJEzVx4kTdc889+vDDDxUOh/XFF18c67cEAACg7XIPLhPQc1+FxvUZF31cWnn0vxMBAICO4cg7WNtq5cqV0R8FHjFixBHPazpWWVmpXbt2qUePHsf93j6fT7/97W/1k5/8ROecc44KCwu1c+dO/fnPf1ZWVpZ+/etfH/d7xNJFj/xHO/a0bjOv2IsoHInI7XJJch3z7OPRKz1Zr//wrHZ9j1gbO3ZstJHZnMvl0q233qo5c+aooaFBCxcu1MUXXxw9vnv3bj3++OPR13j44YdbrAfcXFJS0iE/Vu/3+yVJvXr10qOPPnrYa++77z698sorWrVqlZ544gn9+te/VnJysqTGpuaTTz4pSRo1atRhl/XweDx68skndcIJJ7T4RkRzCxYs0EcffSSp8Y50n8/X4kf8m1xwwQW67LLLNHv2bD399NO6/vqDm0T861//it5F+4tf/ELDhg075PoRI0bonnvu0Z133nnYOgAAAGIi9+BP1fXYW6Hsbv3UP62/Nu/drKXbl6q2oVYpiSk2FggAAGiwNtN8zcemNVEPp/mxLVu2xKTBKkk//vGP5fP59NBDD+n2229XamqqpkyZogceeEAnnHDCEa+rq6tTXd3BZmcgEJAkWZZ1xCbU4ViWpUgkonA4fNiGVHM79tSqMmBXg7UjRY75tWjzK0ci0XFrvuZf1fz85uOrrrpKkUikxes3GT16dHT85Zdftrju3//+d3Tjqptvvlkul6vVNW3ZskUrV66UJF1++eXq1q3bYa91u9269tprddddd6m6ulplZWXRpThKS0tVXV0tqfEO2CPNoV+/fiooKNBbb70VnXvz9/rXv/4lSRo6dKiGDx+ucDgcfZ2mfDc5++yzNXv2bJWWlqq+vl6JiY0ficXFxZIam9L/9V//dcSvw8yZM3XXXXdFX7+9soKOd6TMIH40/b9vWVaHLLHT9OetyZ+76NrIDFrNN0yJiSlyNdSqx74KWZalcb3HafPezbLClpZsXaL87Hy7q0QnxOcMTJEZmHJ6ZkzmRYO1mT179kTHXq/3iOc1P9b8mlj49re/rW9/+9tG1zzwwAO67777Dnl+zpw5R53HVyUmJio7O1t79+495k7vPbyJCh+m+eU0PbyJ0YZ1rDX/H3Xv3r2tep+mRk84HG5xflNjVJJycnKO+FpNDUSpceOp5uctXrw4Oh49erTRvEtKSqLjkSNHHvXa5neHl5WVafjw4ZLU4sf+hw0bdtTXOO2006IN1q9+7Zrm8fnnn7e6qWJZltavXx9dT3Xp0qWSpIEDByopKemItSQnJys3N1fr169XQ0NDu2UF9on1Zzw6Tn19vfbv368PPvhADQ0NHfa+Td+gAVqLzKA1zkwZJN/eVepWX6V33/yHPDq4dvwLH76gqtQqG6tDZ8fnDEyRGZhyamaa91qOhQarA9x9993RzXikxjtYc3JyNHXqVGVkZLT6dWpra7Vx40alpaUpJeXoP2b0xo+OvYt7e4lEItqzZ4/S09OP+CPs8SApKSk67tatW6t+r5rmm5CQ0OL85o10n8/XqtdKTExscV7zRtKQIUOOmYHmamtro+OBAwce9f2b3429f//+6Ln79++PPj948OCjvkZOTk50nJaW1uLcXbt2tbru5pp/TZsapdnZ2cf8Wvbt21fr168/5OuJ+OaUz5murLa2VqmpqTrnnHOMPs/ayrIsFRcXq6Cg4JBN84DDITMw4X5/qfThKknSpBNTNeLE7+mlf74kSdqdsVsXFlxoY3XorPicgSkyA1NOz4zJTVQ0WJtJT0+Pjo/WpW5+rPk1dklOTo6uY9mcx+MxCngoFJLL5ZLb7Zbb3Xn3P2u6i7Op1njVvClaV1fXqrns27dPUmNDtvn5Xx235rW++vVr3kQyzUDzcxMSEo567Vffs+lx8/c3eY2v1hoKhSQ13uX67LPPSmrMzN69e5WWlnbE183JyTnkmGnG4jmPaMkpnzNdmdvtlsvlMv6z8Hh19Psh/pEZtMqgM6QP/yhJStxSpv5jvqVBGYO0LrBOy3culyVLXk/rf3INXQufMzBFZmDKqZkxmRMN1mb69esXHW/evFmjRo067HmbN28+7DWAieZr91ZWVurUU0896vl1dXXavXv3IdfGis/ni463bt2qwYMHt/ra5vVs27btqOdWVlYe9rqsrKwWr3HyyScf8TWO9h49e/aU1Lh0QNNyBE1LKmRkZLSqWdZUy7Hm0tpzAAAAjsuAPEXkkksRuTc1LoeUl52ndYF1agg3aOn2pTqj/xk2FwkAQNdFg7WZYcOGye12KxwOa/ny5Zo2bdphz1u+fLmkxh8fbo9GV6y05yZXdnLK5jNfXYt00qRJRz3/k08+id6dOXLkyCNuctXa37+vfv1OP/306Pj999/XwIEDj/kaTZo3hxctWnTUdYSbr/V66qmnRmtoWotValzT9cwzzzziazRf8/Wr8z399NP10Ucfac2aNdqyZYuys7ONMzNixAgtWrRIa9eu1Y4dO6JN26/asWOH1q1b16IWOINTPme6Mja5QmdHZmAksZsSep0i146V0rblsvbu0pheY/RixYuSpIVbFiqvd57NRaKz4XMGpsgMTDk9M2xy1UZer1dnnnmmFixYoHfeeUe33377IedEIhG9++67kqSpU6d2dIlH5ff75ff7o0249tzkqjOI981nxowZo8TERDU0NKioqEjXX3/9Udd6fOqpp6LjiRMnHnGTq2Aw2Kp1Qurq6lqcN27cOHXr1k379u3Tn//8Z1188cWtbkqkpaVp6NCh+vzzzzV79mzdfffdSktLO+S8UCgUnUf37t110kknRWsYMmSIunfvrt27d2vWrFm67rrrDvv12LJlS4sFtL+6ydWUKVP02GOPKRKJ6MEHH9SvfvWr6LHWZuaMM87Q3/72N0UiET3xxBP6wQ9+cNjzHn/88Wgjjk2unCneP2e6Mja5QrwgM2itUZG+GqyVckXCKn31L6pJGxQ99u/P/62TtpxkX3Ho1PicgSkyA1NOzQybXB2HmTNnasGCBZo3b54WL16s8ePHtzj+4osvas2aNZKka665xo4Sj+imm27STTfdpEAgoMzMzHbd5MpOTtl8JiMjQ5dddpleeOEFffrpp/rLX/6iu+6667Dnvvfee9HG5KBBg3TllVe2aH42b6R7vd5W/b4nJye3OC8jI0M33HCD/vSnP2np0qX61a9+pYcffviwX2PLslRdXa3evXtHn7v55pv1wx/+UFVVVfrFL36hJ5988pDrfvWrX2nVqsYNGq6//nr16tWrxfHvfOc7+tOf/qTy8nL99a9/PeSbHA0NDfrpT3/a4hsAX93k6pJLLlF+fr5KSkr0yCOPaPz48br88suPmJny8nKtW7dOF110UfS5q666Svfcc4+2bt2qP/zhD7r00ks1dOjQFtetWLFCf/zjH6OP2eTKWZzyOdOVsckVOjsyA1PhT/dIb7wnSRrfL6K8c67Q7Ddna03NGm0Nb9U5BecozXPoN7jRdfE5A1NkBqacnhk2uZJUXV0dvZNTOviju8FgUFVVVdHnU1JSWtxpN3PmTD388MMqLy/XjBkzNGvWLE2ZMkXhcFgvv/yyrr/+eknStGnTNGXKlA6aTduwyVXn98c//lHvvfeetm/frnvuuUfz58/X1VdfrZNPPlmJiYnatGmTXn/9dc2aNUsNDQ1yu936+9//fsjvayw2uZKk+++/X//+979VXl4uv9+vRYsW6fvf/75GjhyppKQkbdq0SQsWLNDzzz+v+++/X9dee2302htvvFFFRUVauHChnn76aW3YsEE/+MEPNHjwYG3dulV///vf9corr0iSTjzxRP3yl7885P1/9atf6cUXX9SmTZt011136dNPP9U111yj3r17q6KiQn/84x9VWlqqcePGqays7IjzLSoqUn5+vnbt2qWrrrpKzz33nC666CKNGjVKHo9H27dv1yeffKLXX39dixYt0k9/+lN985vfjF6fkpKiRx55RJdddpmqq6t1xhln6M4779SkSZMUiUT0/vvv6/e//70k6aSTTtLq1asP+X1AfHPS50xXxSZXiBdkBq1lDTq4xmrCphIleDzKz87Xmpo1CkVCKt9VrnMGnGNjheis+JyBKTIDU07NDJtcSRo9erTWr19/yPMPPvigHnzwwejjmTNn6umnn44+TkxM1GuvvabJkydr3bp1Ou+88+T1ehUOh1VbWxt97eeee67d5wDn69u3rz744ANdeumlWrlypebMmaM5c+Yc9tzu3bvr2Wef1eTJk9utHq/Xq/fee08zZszQBx98oCVLluiGG25o1bUJCQl64403dPHFF+vDDz/Ue++9p/fee++Q84YNG6a33377sEsIZGZm6p133tF5552nyspKPf/883r++edbnHPttdfqa1/7mr7zne8csZYTTzxRCxcu1IwZM7R8+XK98cYbeuONN454/uHuPJ0xY4YefPBB3XHHHdq9e7fuvvvuFse9Xq9mz56tBx98MNpgBQAAaDcZA7Tf00Op1i5pU5kUatD4vuP1wucvSJJKtpbQYAUAwCbclnMYgwYN0rJly/TLX/5SI0aMiN4BM3bsWP3hD3/QokWLWux4DhyPoUOHatmyZXr22Wd12WWXaeDAgfJ6vUpKSlJ2dramTJmiBx98UOvWrdPXv/71dq/H5/Np/vz5euWVV3TZZZdpwIABSk5OVkpKik444QRdfvnleu6553TVVVcdcm2PHj30wQcf6JlnntEFF1ygPn36yOPxqGfPnpo0aZIeffRRLV269KgbaA0fPlyfffaZ7rjjDg0ZMkTJycny+XyaPHmyioqKWqxFezQnn3yyli5dqqKiIk2fPl0DBgxQamqqkpKS1LdvX02aNEk///nPtWTJEv3yl7887Gvcdttt+s9//qPp06erd+/eSk5O1sCBA3XdddeprKysQ34/AAAAJEkul3Z2G9I4tvZJ28o1rs84udS4lE1JZclRLgYAAO3JFWnaoQWO0bQGa1VVVZvWYB00aBBrsMJRyAxMkZn4V1tbq3Xr1iknJ4c1WNEpkRmYsixLq4vu0KhN/ydJChX8RuH87+tbb31LFbsr5JJL7814T5nJmTZXis6CzxmYIjMw5fTMBAIB+Xw+1dTUHLO/RoPVQfx+v/x+v0KhkCoqKlRUVNRi86NjSUxMVHZ2tnJycpSUlNSOlQIA0L7q6+u1ceNGVVZWqqGhwe5yACAmMoLrNfnzX0iSNnfPV9ngm/XW/rf0Ud1HkqRCb6FOTTrVzhIBAHCMYDCowsJCGqxdFXewAi2RGZgiM/GPO1jR2ZEZmLIsS8Vz3tXFK26Wq36vIml91PCj5Zq/+QPd8sEtkqSrTr5Kt4+73eZK0VnwOQNTZAamnJ4ZkztYHbvJFcx3cQuFQtEdszvzrtns7g1TZAamyEz8c7vd0TXUO/Ive07dQRXth8zAiMutyIA8udbMk2vvNnn2bVF+/3y5XW6FI2GV7SgjTzgEnzMwRWZgyqmZMZkT/2oEAAAAgDgRGZB/8MGGRcpIytCwHsMkSV9Uf6FdtbtsqgwAgK6LBisAAAAAxIlIzviDDzYslCTlZx9supZVlnV0SQAAdHksEeBglmXJsiyj8yORiMLhcPTHYzujpmWDm2oFjoXMwBSZiX/hcFiRSESWZSkhIaHd36/pz1uTP3fRtZEZmGrKSn2v05TgSpArElJkwyI1WJbG9Bqjp/SUJGnRlkWa3H+ynaWik+BzBqbIDEw5PTMm82KTKwfx+/3y+/0KhUKqqKhQUVGRvF5vq69PTExUdna2cnJylJSU1I6VAgDQvurr67Vx40ZVVlaqoaHB7nIAIKbO+fxXygqulSS9NfIx7U3w6Dc1v1FYYfVy99KPM35sc4UAAMS/YDCowsLCVm1yRYPVgQKBgDIzM1VVVXXMADRXW1urjRs3atCgQR2y43Jbsbs3TJEZmCIz8a+2tlbr1q1TTk5Oh/yZ5vQdVBF7ZAammmcm+f17lVDyuCSp4YoiRYZM1cx3Z6p8Z7kk6d1L31Wv1F52lotOgM8ZmCIzMOX0zAQCAfl8vlY1WFkiwMFMd3ELhULRHbM7867Z7O4NU2QGpshM/HO73XK5XB2+o6lTd1BF+yEzMOXxeJQw8AzpQIM1cUupdOrXNb7f+GiDdWnVUl14woV2lolOhM8ZmCIzMOXUzJjMiX81AgAAAEA8yZ1wcLxhkaSWG12VVJZ0dEUAAHRpNFgBAAAAIJ6kZ0tZgxvHmz+WGup0eu/Tlehu/AHF0spSG4sDAKDrocEKAAAAAPGm6S7WUJ20ZalSE1M1yjdKkrRhzwZV7qu0sTgAALoWGqwAAAAAEG+aLxOw8cAyAX1ZJgAAADuwyZWDWZYly7KMzo9EIgqHw9ENXjqjSCQS/W9nrhOdB5mBKTIT/8LhsCKRiCzLUkJCQru/X9OftyZ/7qJrIzMwdUhm+o5T09Yb4XUfKZT/A43xjYmev2jLIk3LndbBVaIz4XMGpsgMTDk9MybzckWa/hWJuOf3///t3XlYVOXbB/DvDAwgMCAILigCaqmppKi4b7lvaZqmaLmkmWKlqWVZilav/qzMSjQ1Q82tLHdLRQL3FdRQS01BUUTFhX0ZZs77B81pkJlhDgwMDN/PdXl5Zs5zzrnPzD3bzXOeJxShoaFQq9W4evUqNm3aBEdHR5O3t7W1Rc2aNeHt7Q07O7tSjJSIiKh05ebmIiEhAUlJScjLy7N0OERE5ido0Dc2GHbqDOTYOGNfs1CokIfPUj5DHvJQVVYVM11nWjpKIiKiCiszMxNBQUFISUmBi4uL0bYssFqh1NRUuLq6Ijk5ucgE0JWdnY2EhAT4+vrCwcGhFCMsGUEQkJaWBqVSCZlMZulwqAJgzpBUzJmKLzs7G/Hx8fD29i6TzzSVSoXw8HD07NkTCoWi6A2o0mPOkFT6csbm51GQX9ufv37SCcDjGUyKmIQz9/Inudr94m7Udq5tsZjJsvg+Q1IxZ0gqa8+Z1NRUeHh4mFRg5RABVkyhUEhKcLVaDZlMBrlcDrm8/A7Pq71cVxsrUVGYMyQVc6bik8vlkMlkkj8LS6qsj0cVH3OGpCqQMz7tgH8LrIrEM0Ct59CmVhuxwHou+Rx83XwtFCmVF3yfIamYMySVteaMlHPir0YiIiIiIqKKqG67/5YTTgEoONHVqaRTZR0RERFRpcQCKxGZLD4+HjKZDDKZDGvXrrVoLCEhIWIsRERERJVSreaAzb9zJ9w6AQBoWq0pqthWAQCcuXsGHBGOiIio9LHASlRO5ObmYvPmzXjttdfQqFEjVKtWDQqFAh4eHmjZsiUmT56MgwcPckZzIiIiIsqncAC8AvKXH90A0u9DYaNAi+otAAD3s+7jZupNCwZIRERUObDASlQObNu2DQ0bNkRQUBB+/PFHXLlyBY8ePUJeXh4ePnyImJgYfPfdd+jZsycaN26MvXv3WjpkIiIiIioP6rb5b/nWSQBA65qtxbtOJ50u64iIiIgqHRZYiSzsk08+wdChQxEfHw8A6NmzJ7799ltEREQgOjoa4eHhWLZsGXr37g25XI6rV69izpw5lg2aiIiIiMoH3XFY/y2wtqn5X9H1TNKZso6IiIio0rG1dABUelQqFVQqlaT2giBAo9GU68vQteNIaWOtyMLCwjB37lwAQPXq1bFlyxZ06dKlULsXXngBkydPxsWLF/Huu+8iOTnZIueue0xL54nueGJFxWFNOUNlgzlT8Wk0GgiCAJVKBRsbm1I/nvbzVsrnLlVuzBmSymDO1AyAdo5jza0TUKtUaODSAE62TsjIy8DppNPIzc3luPWVEN9nSCrmDEll7Tkj5bxYYLUioaGhCA0NhVqtBgAcOHAAjo6OJm9va2uLmjVrIj09Hbm5uaUVptmkpaVZOoQSSUxMxNtvvw0AcHJywu7du/Hss88iNTXV4DZ169bFzz//jK1btxptV1rS09PF5ezsbIvEoJWTkyMumxpHRc8ZKnvMmYorNzcXWVlZOHz4MPLy8srsuOHh4WV2LLIOzBmSSl/OvODgBWV2IpB4Aft3b4faxh51UAdXcAWPsh9h/Z718LTxtEC0VB7wfYakYs6QVNaaM5mZmSa3ZVwLKcsAAFiYSURBVIHVigQHByM4OBipqalwdXVFr1694OLiYvL22dnZSEhIgLOzMxwcHEox0pIRBAFpaWlQKpUV+i/xn3zyifhinT9/Plq1amXythMnTtR7/9GjR7Fq1SocPXoUSUlJcHBwgJ+fH/r164e3334bnp7Gv1ir1WqsWrUK69evx19//QWZTIb69etj5MiRmDp1KpydncW2Dg4ORvNrx44d2LRpE06fPo379+/DwcEBDRo0wIABA/DWW2/Bzc3NaCy3b9/GokWLsG/fPiQmJsLd3R0tW7bEW2+9hR49esDe3l5sW1SeW0vOUNlhzlR82dnZqFKlCjp37lwmn2kqlQrh4eHo2bMnFApF0RtQpcecIamM5YyNEA6c/xFyqNGnmQcE3064dPYSrly9AgAIaBeAZh7NLBE2WRDfZ0gq5gxJZe05I6VTGQusVkyhUEhKcLVaDZlMBrlcDrm8/A7Pq71cVxtrRSQIAtavXw8gv/fqG2+8UaJz0Wg0ePvttxEaGlrg/pycHJw/fx7nz59HaGgotm7dip49e+rdR3p6Ovr164cjR44UuP/cuXM4d+4ctmzZgu+//16831CePH78GC+//DL++OOPQrFER0cjOjoaK1aswM6dO9G2bVu9sRw5cgQDBgwo8GZ29+5d7NmzB3v27EFISEiBoldRj5015AyVLeZMxSeXyyGTySR/FpZUWR+PKj7mDEmlN2d82wPnfwQA2CZGA8+8UGB4FFtbW+ZZJcb3GZKKOUNSWWvOSDkn/moksoBLly4hOTkZANCpUycolcoS7W/27NlicdXPzw/fffcdTp8+jcjISEyfPh0KhQIpKSkYMGAALly4oHcfo0ePFourgYGB2Lx5M86ePYu9e/di2LBhiImJwaRJk4zGkZOTgx49euCPP/6AjY0NXn31VWzevBknT57EkSNH8Nlnn6FatWq4f/8++vXrh5s3bxbax61bt8Tiqlwux5tvvomDBw/izJkzWLNmDZ555hmEhIRg7969JXrMiIiIiKxGXZ0/Wt86Ybk4iIiIKin2YCWyAN0iZ8uWLUu0r9jYWHz55ZcAgKZNm+LIkSOoWrWquL5r167o1asX+vfvj9zcXLzxxhs4depUgX3s3bsXO3fuBAD069cPO3fuhK3tf28P/fr1w4IFCzBv3jyjsSxYsAAxMTGoWrUqDh48WOjcOnbsiFGjRqFdu3a4e/cuPvzwQ2zcuLFAmxkzZog9Vzds2ICRI0eK61q1aoVhw4ahU6dOOHv2rImPEBEREZGVc/MDnGsA6feA22cAjdrSEREREVUq7MFKZAEPHz4Ul6tXr16ifa1YsUK8nPn7778vUFzV6tOnD8aPHw8AOH36NM6cOVNg/fLlywEA9vb2WL16dYHiqtZHH32Epk2bGowjPT1d7EX7ySefGCwc+/j44OOPPwYAbN26FRkZGeK6pKQkbN++HQAwYMCAAsVVLaVSiVWrVhmMg4iIiKjSkckA7zb5yzmpwP3LBVYLECwQFBERUeXBHqxUPCu7AOn3LXJoGQAXQQOZrAz+PuBcHZh0yOy71Z2Z3MnJqUT7OnjwIACgSZMmaNOmjcF2EydOFMdQPXjwIFq3bg0gf+zdqKgoAECvXr3g5eWld3u5XI4xY8Zg1qxZetcfOnQIKSkpAICXX37ZaMydO3cGkD8gdnR0tHg7MjISanV+j4tx48YZ3D4wMBBNmjTBpUuXjB6HiIiIqNKo2w74a1f+8q2Tlo2FiIiokmGBlYon/T6QlmiRQ8v+/VeR6Y65qtuDU6qcnBxcu3YNAIwWVwGgRYsWUCgUUKlUuHjxonj/9evXkZmZCQBi0dWQwMBAg+t0L9mvVatWkbFrJSUlicuxsbHisimxsMBKRERE9K+6Ot8Fb50E6j5ruViIiIgqGRZYqXicS3ZZe0kIAIR/e7CWeqG1lM6zWrVq4vK9e/eKvZ/Hjx+Ly0UNNaBQKFCtWjUkJSXh0aNH4v26y0Xto0aNGgbX3b9fvB7N2uKuOWMhIiIiqnRq+gMKR0CVyQIrERFRGWOBlYqnFC6bN5Wg0SA1NRUuLi6QySvmMMLPP/+8uBwTE2OWfcpkJS83l2Qf2kv7gfxzUigUJm1Xp04ds8dCREREVOnYKIA6rYC4w0DqbSAnXVwlCByDlYiIqDSxwGrFVCoVVCqVpPaCIECj0YiTJpVH2i+I2lgrosaNG8PDwwPJyck4cuQInjx5AhcXF8n7cXV1FZeTkpKMPh55eXni5Fpubm5iWyn7uHv3rrj8dJ64u7uLy9WqVTNYONVHux/dCbru3r0Lb29vg9voDi1QVB5YQ85Q2WLOVHwajQaCIEClUsHGxqbUj6f9vJXyuUuVG3OGpDIlZ+S1W8Mm7jAAQEj9bzivvLw85lolxPcZkoo5Q1JZe85IOS8WWK1IaGgoQkNDxZ6EBw4cgKOjo8nb29raombNmkhPT0dubm5phWk2uhNFVUQjRozAsmXLkJGRgdDQUAQHBxdrP/Xr18f169dx4sQJpKamGmwXExMjvjk0aNBAbOvp6YkqVaogKyuryH0cPXpUXM7Ozi7QtmHDhuLywYMHMWTIkGKdi9bhw4cxcOBAg21PnTolLhuLWVdFzxkqe8yZiis3NxdZWVk4fPgw8vLyyuy44eHhZXYssg7MGZLKWM54psrR/t/l9MSrgF3+8okTJ3DH9k7pB0flEt9nSCrmDEllrTmjO6RhUWQCrxexOqmpqXB1dUVycrKkXpHZ2dlISEiAr68vHBwcSjHCkhEEAWlpaVAqlRX6MvI7d+6gUaNGyMzMhJOTE06fPo1GjRoVuZ1Go8HmzZsxatQoAEBwcDC+++47APlfng1NRPXmm29i9erVAICTJ08WmESqf//+2LdvH+zt7XH9+nW9k1RpNBoEBASIE1GtWbMGY8eOFdc/efIE3t7eyMzMRPv27XH48GHJz8/du3fh4+MDtVqNgQMHYseOHXrbnTlzBm3bthVv6w5PoI+15AyVHeZMxZednY34+Hh4e3uXyWeaSqVCeHg4evbsafIQKVS5MWdIKpNyJicNtl/Wh0zQYHGdBvhRkd9pIqxnGJ73fF7/NmS1+D5DUjFnSCprz5nU1FR4eHggJSWlyPoae7BaMYVCISnB1Wo1ZDIZ5HI55OV4bFPt5braWCsqb29vLFu2DOPHj0dGRga6deuGn3/+GV26dDG4zeXLlzFt2jTcv38fr776KgBgypQpWLVqFTQaDd58800cPny40Av/wIED+OGHHwAAgYGBaNOmTYH1U6ZMwb59+5CTk4PJkydj+/bthS6pXbhwoVhcBVAoT9zd3TF16lQsXrwYx48fx4wZM7BkyRKDz9G9e/ewe/duTJgwQbyvdu3aGDRoELZt24bdu3fjl19+wfDhwwtsl56ejsmTJxe4r6g8sJacobLDnKn45HI5ZDKZ5M/Ckirr41HFx5whqYzmjMIdqNEESIoFMh8CrkoA+VeqMc8qL77PkFTMGZLKWnNGyjnxVyORBY0bNw4LFiwAANy/fx9du3ZF7969sXz5ckRGRuLcuXOIiIjAihUrMGDAAPj7+xfqet+sWTPMmDEDAHDhwgUEBARg9erVOHv2LA4dOoSZM2diwIABUKvVsLOzw8qVKwvFMXDgQPFy/N27d6NDhw746aefEBMTg3379mHEiBH46KOP0KpVK6Pns2DBArF4+/XXXyMgIAChoaE4duwYzp8/j8jISCxbtgyDBw9G3bp1xZ63ur788ksolfk/BoKCghAcHIzIyEhER0cjLCwMLVu2xLlz54qMhYiIiKhSqtsOACADL1QkIiIqK+zBSmRhH3/8MZo0aYIZM2YgPj4eBw4cwIEDBwy2b9KkCRYvXlzgvkWLFiEjIwPLly/H9evX8cYbbxTaztXVFT///DOaN2+ud78bN25E3759cezYMZw6dQojRowosL5FixZYuXIlWrZsaTA2e3t7hIeHY+zYsdi2bRsuXLiAqVOnGmyvr4u9r68vdu3ahRdffBFpaWlYvnw5li9fXqDN3LlzIZPJcPbsWYP7JiIiIqqUvNsAp1dZOgoiIqJKhT1YicqBIUOG4MqVK9i4cSNGjx6Nhg0bws3NDba2tnB3d0dAQACmTJmCP/74A7GxsejVq1eB7eVyOUJDQ3H48GGMGjUKdevWhb29PVxcXNC8eXN8+OGHuHbtWqHtdCmVSkRFReHbb79F69at4ezsDKVSiebNm2PhwoU4fvw43N3dizwXpVKJX3/9FUeOHMGECRPQsGFDKJVK8Vxat26N4OBg/PbbbwYHwu7atSsuXbqEyZMnw8fHB3Z2dqhRo4Y4Vuz8+fOlPcBERERElcW/PViJiIio7LAHK1E5YWdnh6CgIAQFBRV7H506dUKnTp2Kvb2trS2mTp1qsNepr68vTJ0Xr2PHjujYsWOxY/H29i7Uc1VXSEgIQkJCir1/IiIiIqvkWhtw9QaQJt4lcLgAIiKiUsUerERERERERNakblvILB0DERFRJcICKxERERERkTWp29bSERAREVUqLLASERERERFZE28WWImIiMoSC6xERERERETWpHpjwMZevCloNBYMhoiIyPqxwEpERERERGRN5DaQKWv+dzst0XKxEBERVQIssBIREREREVkbl1r/Ld+7ZLk4iIiIKgFbSwdApUelUkGlUklqLwgCNBoNNOX4MiJBEMT/y3OcVH4wZ0gq5kzFp9FoIAgCVCoVbGxsSv142s9bKZ+7VLkxZ0gqqTmjcaoFpFzOX066yFyrhPg+Q1IxZ0gqa88ZKeclE7S/IqnCCw0NRWhoKNRqNa5evYpNmzbB0dHR5O1tbW1Rs2ZNeHt7w87OrhQjJSIiKl25ublISEhAUlIS8vLyLB0OEVGZ25+5F0dyTwAAViTn4WGDRRaOiIiIqGLJzMxEUFAQUlJS4OLiYrQtC6xWKDU1Fa6urkhOTi4yAXRlZ2cjISEBvr6+cHBwKMUIS0YQBKSlpUGpVEImk1k6HKoAmDMkFXOm4svOzkZ8fDy8vb3L5DNNpVIhPDwcPXv2hEKhKPXjUcXHnCGppObMV+e+wo9//QgAWJt4D/5T/gQcq5V2mFSO8H2GpGLOkFTWnjOpqanw8PAwqcDKIQKsmEKhkJTgarUaMpkMcrkccnn5HZ5Xe7muNlaiojBnSCrmTMUnl8shk8kkfxaWVFkfjyo+5gxJZWrO2MoL/tRT3I0BGvUrrbCoHOP7DEnFnCGprDVnpJwTfzUSERERERFZu1snLB0BERGR1WKBlYiIiIiIyNolnLJ0BERERFaLBVYiIiIiIiIrJsgAJJ4DVNmWDoWIiMgqscBKRERERERkbZ6eo1Gdm19kJSIiIrNjgZWIiIiIiKgy4DisREREpYIFViIiIiIiosrg1klLR0BERGSVWGAlIiIiIiKyYoK9S/5CwilAo7FsMERERFaIBVYiIiIiIiIrI9MdhLX6c/n/Zz8Bkq9YJB4iIiJrxgIrERERERGRNavR5L9lDhNARERkdiywEpEk8fHxkMlkkMlkWLt2rUVjCQkJEWMxh7lz50Imk2HAgAFm2R8VFBUVJT5fUVFRhdab+/ksiRdeeAFubm544YUXLB1KuVLUc1hSgiCgWbNmkMlkCAsLM/v+iYgqLW0PVoAFViIiolLAAitROZGbm4vNmzfjtddeQ6NGjVCtWjUoFAp4eHigZcuWmDx5Mg4ePAgNx80qFbdu3cLnn38OAJg3b16R7TUaDXbt2oVJkyahWbNmqF69OhQKBdzd3dGsWTOMHz8eO3bsgEqlKu3QiayGTCbDnDlzAABz5sxBRkaGhSMiIrIS1eoBtg75y7dOWDYWIiIiK8QCK1E5sG3bNjRs2BBBQUH48ccfceXKFTx69Ah5eXl4+PAhYmJi8N1336Fnz55o3Lgx9u7da+mQrc6nn36K7Oxs9OnTB61btzba9tChQ2jevDkGDRqEVatW4eLFi3jw4AHy8vLw+PFjXLx4EWFhYXjppZfg6+tr8Z6+RBXJ8OHD0bBhQ9y9exehoaGWDoeIqMIqMAarjR3gFZC//OQmkHrXMkERERFZKRZYiSzsk08+wdChQxEfHw8A6NmzJ7799ltEREQgOjoa4eHhWLZsGXr37g25XI6rV6+KPbzIPO7cuSMWQWfMmGG0bVhYGHr27InY2FgAQNu2bbF48WIcOHAA0dHR+OOPP7B69Wq89NJLsLOzQ2JiIqZNm1bKZ1AxdO3aFYIgQBAEdO3a1dLhUDkll8sxffp0AMAXX3yB7OxsC0dERGQl6rb9bzmBwwQQERGZk62lAyCqzMLCwjB37lwAQPXq1fHzzz+jS5cuhdr16NEDwcHBuHjxIqZPn44HDx6UdahWbfny5VCpVPDy8jI65mZERAQmTJgAjUYDJycnrF27Fi+//HKhdt26dcOECRMQHx+P2bNnY9++faUZPpHVGTZsGN566y08ePAAW7ZswdixYy0dEhFRxVe33X/Lt04BTV6yXCxERERWhj1YiSzkzp07mDp1KgDAyckJhw4d0ltc1dW0aVPs378fM2fOLIsQKwWNRiP2Xh0xYgTkcv1vi5mZmRg9ejQ0Gg3kcjn27t2rt7iqy9fXF1u2bME333xj7rCJrJq7uzv69OkDAFizZo2FoyEiqvgEQQC8dYZA4jisREREZsUerFZMpVJJmmBHpVJBEARoNJpyPZGSIAji/+U5zqIsWbIEmZmZAID58+fj2WefNfl8goKCDLY9evQoVq1ahaNHjyIpKQkODg7w8/NDv3798Pbbb8PT09PovtVqNVatWoX169fjr7/+gkwmQ/369TFy5EhMnTq1wHGLypUdO3Zg06ZNOH36NO7fvw8HBwc0aNAAAwYMwFtvvQU3Nzejsdy+fRuLFi3Cvn37kJiYCHd3d7Rs2RJvvfUWevToIeaCNhZDjOXM4cOHkZiYCAB46aWXDO5nzZo1SEpKAgBMmTIFnTp1Mvn50hZm9YmNjcWyZcsQFRWFO3fuwMbGBnXr1kXPnj3x9ttvw9fXV+928fHxqF+/vhjb2LFjsW3bNqxcuRIXLlxARkYGGjRogNdffx2TJk2CQqEQH4PNmzfj+++/x+XLl5Geno5GjRphwoQJmDRpEmQymd7j2djYAADmzp2LefPm4eDBg/j2229x9uxZPH78GF5eXujbty9mz56N2rVr691HVFQUunfvDiC/N/DTwwSY+nxmZ2djzZo12LFjBy5duoRHjx6hatWq8Pf3xyuvvIIxY8bA1tb4x9vJkyexZMkSHD16FE+ePEGtWrXQo0cPvPvuu2jYsGGJ32fWrl2L119/HQBw/fp1eHl5ITQ0FJs3b8a1a9dgY2MDf39/zJgxA/379xe3S0tLw3fffYctW7bg+vXrkMvlaNWqFd5//33xsTNm9+7dWL9+PU6dOoUHDx7A2dkZzz77LF588UUEBwfD2dnZ6PZZWVlYunQpfv75Z/zzzz9wcHBAo0aN8Nprr+H11183+fWvVquxYcMG/PLLLzh37hwePnwIZ2dnNG7cGC+99BLefPNNVKlSxWgsL730Enbv3o1jx47h5s2b8Pb2LvL8dWk0GgiCAJVKJeZvadJ+3nJiOzIVc4akkpozuu/Reeo8qGydYevZGLIHf0FIikVexmPAzvjnAlVsfJ8hqZgzJJW154yU85IJur9oqUILDQ1FaGgo1Go1rl69ik2bNsHR0dHk7W1tbVGzZk14e3vDzs6uFCMlQRDwzDPP4OHDh3BycsJff/0FpVJZon1qNBq8//77+P777w22cXFxwdq1a9GtWze969PT0zF8+HCcOKG/V8Pzzz+Pb775RuxpGxoaiqCgoELtnjx5gjFjxuDw4cMGY/H09MTGjRsNTih1/PhxjBgxAmlpaXrXz549G4Ig4H//+x8A4PHjxwaPZcz//vc/LFq0CAqFAgkJCbC3t9fbrmvXrrhw4QJkMhmio6Ph5+dXrOPpWrJkCT777DODRSp7e3ssXboUI0aMKLTu1q1beP755wHkPw/R0dH44Ycf9O5n4MCBCAsLQ15eHiZNmoSdO3fqbTdmzBgsXbpU7zptMfz999+HTCbDokWL9LZzcXHB5s2b0b59+0Lrjh49ioEDBwLILwR27NixwPpFixYV+XzGxsZi1KhRSEhI0LseAAICArB582ZUr15d7/rly5fj448/1vu4Ozk54YcffsA333yDY8eOoUOHDtizZ4/BYxmyadMmBAcHAwCOHDmC6dOn4+zZs3rbfvbZZ5gyZQoSEhIwfPhw/P3334XayGQyfPfddxg+fLjefWRnZ2PixIlGY61VqxZ++uknNGvWTO/6e/fuYdCgQbhy5Yre9d27d8eUKVMwdOhQAPqfQwBISEhAUFAQLl68aDCWevXq4aeffkKDBg0Mtrl27RoCAwMBAEuXLsWYMWMMttUnNzcXCQkJSEpKQl5enqRtiYiswYGsAzick/9dbLzTeNRT1IP/rTD4PYwEABxr8D6SlU0sGSIREVG5lpmZiaCgIKSkpMDFxcVoWxZYrVBqaipcXV2RnJxcZALoys7ORkJCAnx9feHg4FCKEZaMIAhIS0uDUqk02NuuvLt48aJYHOvduzd+++23Eu/z/fffxxdffAEA8PPzw6xZsxAQEICMjAzs3r0boaGhUKlUsLOzw8mTJ8Xj63rppZewa9cuAEBgYCDeeecdPPPMM7h37x7WrVuHX375Ba1bt8aZM2cA/NdzUldOTg46duyImJgY2NjYYOTIkejbty/8/PygUqlw5MgRfPXVV3j48CHc3NwQHR0NHx+fAvvQFg9TU1Mhl8sxceJEDB06FK6urvjzzz+xePFiXLt2Da1atRKLVmq12uBjYyxnevXqhYiICAQEBIjn9bTU1FRUq1YNGo0GjRo1wqVLl4w8E6ZZsWKFOESEp6cn3nvvPbRv3x5qtRoRERH44osvkJGRAZlMhl27dqFfv34FttftwdqmTRucOnUKffv2xeuvvw4fHx8kJCTgf//7H06dOgUAWLlypdhbduTIkRg5ciRq1aqFa9euYcGCBWJRb+/eveKl2bq0PQC1j3nDhg0xc+ZM+Pv7IyUlBb/88gu+//57aDQauLi44M8//yzU47CoHqzz58/HggULAOh/Pv/55x8EBgaKH25TpkxB69at4e3tjYcPH2L37t1YtWoV8vLy0KZNGxw6dEjsuau1fft2cWgHV1dXvPfee+IfDCIjI/H5559DLpfD09MT165dQ+fOnREZGWnsqdRLtwdrmzZtEB0djYkTJ2Lw4MFwc3PD+fPnERISgsTERMjlcpw7dw7jx4/H5cuX8fbbb6N3795wcnLC8ePHERISgpSUFCiVSly9elVv4XjEiBHYunUrgPw/hEyfPh2NGzfGo0eP8NNPP2HdunUQBAHu7u44f/58oV7GeXl5aN++PaKjowHkT7b35ptvwtvbG7du3cKKFSsQERFR4PWv7zl8+PAhWrZsKf6xYsKECejcuTN8fX2Rnp6O8PBwfPPNN8jMzES9evVw9uxZuLq66n0MBUGAh4cHnjx5gqCgIPz444+SnoPs7GzEx8fD29u7TD7TVCoVwsPD0bNnz0J5R6QPc4akkpoz357/FmGXwwAAK7uvROsarSGL/Rm2u6YAANSd3oOm83ulGjNZFt9nSCrmDEll7TmTmpoKDw8PkwqsEMjqpKSkCACElJQUSdtlZWUJly9fFrKyskopMvNQq9XC48ePBbVabelQim3Dhg0CAAGAMGfOnBLv788//xTkcrkAQGjatKnw+PHjQm1+//13sU1gYGCh9Xv27BFj6tevn6BSqQq1mT9/vtgGgBAWFlaozYcffigAEKpWrSqcPXtWb7zx8fFCrVq1BABCUFBQofUvv/yyeIxNmzYVWp+amio8//zzBWIxxlDOaDQawcnJSQAgvP766wa3P3r0qHicUaNGGT2WKe7fvy84OjoKAAQvLy/h1q1bhdrExMSIsdWuXVvIzc0tsD4uLq7A+U+bNq3QPjIyMgQfHx8BgFCtWjVBJpMJS5cuLdTu7t27glKpFAAIL774ot6YdY8VEBAgpKWlFWqzfv16sc2wYcMKrY+MjBTXR0ZGFlo/b948o89n+/btBQBCixYthAcPHuhto5vnq1atKrAuJydH8PLyEgAIrq6uwuXLlwttHxsbK7i4uIhxdOnSRe9xihIWFibuQyaTCdu3by/U5sKFC2Ksnp6egr29vXDy5MlC7fbu3Svua8mSJYXW6752u3fvLuTk5BRqs2rVKrHN8OHDC61ftmyZuP6NN97Qe07jx48vkAf6nsOgoCABgODj4yPcuHFD7350c/vDDz/U20arW7duAgChUaNGRtvpU9afabm5ucKOHTsKvVaJDGHOkFRSc2Zp9FKh6dqmQtO1TYVTiafy73wULwjzXPL/rRtUesFSucD3GZKKOUNSWXvOSKmvcZIrIgt4+PChuGzoMmYpVqxYIV7u/P3336Nq1aqF2vTp0wfjx48HAJw+fbpQb83ly5cDyL8sffXq1XrHsPzoo4/QtGlTg3Gkp6cjNDQUAPDJJ5+gZcuWetv5+Pjg448/BgBs3boVGRkZ4rqkpCRs374dADBgwACMHDmy0PZKpRKrVq0yGIepHj9+LB7b2PNg7ucrLCxMHH93yZIleseWbNGiBT744AMA+ROi7dixw+D+vL29sXjx4kL3Ozo6ipdVP3z4EG3atME777xTqF3NmjXx0kv5MwkfOXKkyPhXrVqldyzPV199FX379gWQ31NUO2atORw5cgTHjx8HAKxbtw4eHh562/Xp00fsoaqdvExr586d4ni7H3/8MRo3blxo+6ZNm2LOnDlmixsAhg8fjsGDBxe639/fX7zE/sGDB5g2bRratGlTqF2/fv3EXt76nh/ta06hUCAsLEzvEC8TJ05Ejx49AADbtm3D3bt3C6zXvv5r1KiBr776Su95fP3110bHcI6Pj8dPP/0EAFi2bJnBYTRatGghDp/w9HP0NO3rLS4ursAYvUREJI2Af99Dq9YFlLXyl2+fAdQcQoWIiMgcOMkVFcsre15BclayxY4vaATI5KU/PIBHFQ/8NOAns+9Xd1xRJyenEu/v4MGDAIAmTZroLdBoTZw4URyj9eDBg+L4p2q1GlFRUQDyL5n38vLSu71cLseYMWMwa9YsvesPHTqElJQUABCLXIZ07twZQP4lBdHR0eLtyMhI8fLwcePGGdw+MDAQTZo0KdHl+g8ePBCXjU24VVrPV9WqVTFkyBCD7SZMmICPPvpI3GbYsGF62w0ZMsTg5Ri6Q0G88sorBo+lbff48WM8efJEb5EeAJo1a2awcA4A48ePx++//468vDxERUXpHT+2OLRDVzRs2NDgGKJanTt3xs8//4wzZ84gLy9P/GOB9nGXyWRGx/McN26cOMavORh7DJ5//nlxrGJj7fz9/XHz5k3cuHGjwP15eXk4dOgQgPzXrrGJoCZOnIiDBw+Kz432jxd3797F5cuXAeQXgw2N3e3s7Izhw4eLBd2n7d27F2q1Go6OjmKh3ZDOnTtj8eLFSExMxK1bt1C3bl297dzd3QHkDz3y5MmTIifGIyKi/8ig57uyTAbUbQtc2g7kpgP3LgJezcs8NiIiImvDAisVS3JWMu5n3rd0GBWW7oRWur03iyMnJwfXrl0DAKPFVSC/55hCoYBKpSowAc3169fFHpWGJp3S0k46o4/uJD61atUqMnYt3Z6OsbGx4rIpsZSkwPro0SNx2VjhxpzPFwDxsQ8ICDA6Tk2NGjXg6+uL+Ph4oxMGPfvsswbX6RZKTW2XlpZmsMAqJT9iY2PNVmDV5taVK1dMHntZpVLh0aNHYi9IbW75+fkZ7AEL5I+J6+vri7i4uBJGnc+cz8/Tk77duHFDfO0W9frXXa+bT1Jfc4YKrNrnKDMzU28PeEOSkpIMFlh1X5cZGRkssBIRmYP3vwVWAEg4xQIrERGRGbDASsXiUcVwcaIslGUP1tJQrVo1cfnevXsl2pfubOtFXb6uUChQrVo1JCUlFSgu6i4XtY8aNWoYXHf/fvGK7toCkTljMYXuxDdZWVkG25nz+QL+O0dThhuoWbMm4uPjCzwuTzPU4xDI73UstZ2xCcOkPCfGYpbKnLllyuNeo0YNsxVYzfn8PP3cSHm91KxZU+925en1/zTd16U1DppPRGQRddv+t3zrBNBmkuViISIishIssFKxlMZl86bSaDRITU2Fi4tLgeJERaJ72XZMTIzZ9mtqz77S2odu8ScmJsbkgkidOnXMHospdMeTNFYMbNasGeRyOTQaTbl7vsqapWLW5tbzzz+PDRs2mLxd7dq1C91XER93U5SX17+HhwciIyNN3s7QWK1Awdelq6trsWMjIqrsxDFYAaBGU8DOOX+IgFsnAUHIHzqAiIiIio0FViILaNKkCTw8PJCcnIwjR46IBePi0L1ktqjelXl5eeKETdqxDaXuw9h63Z6enp6eBgunxjwdi7ExJUvam1S3wKrbE/hpLi4uaN68OWJiYnDlyhX8888/aNCgQbGP6+7ujrt375oUv3b4BN3ny5Kk5Ic5Y9bmVnp6utGJ1ozR5pYpj7s5eiqXBd3HuKiYdYfiKM3Xf1paGho3bgwbGxuj+zKF9nVZvXr1Aj3OiYioBGxsgTqtgBtRQNpd4MktwM3H0lERERFVaBWz+x9RBac7yU5GRoY48VRx2Nvb45lnngEAnDp1ymjbc+fOQaVSAUCBIlX9+vVRpUoVAMCZM2eM7sPY+hYtWojLx44dMx64AboTGJUkFlPoPnZXr1412lY74ZYgCPjmm29KdFztYx8TE4O8PMOz996/fx83b94ssI2lSXlOzBmzNrdu3LhRoFAohTa34uLixD806PPgwQPEx8cX6xhlrV69euLQAkW9/k+fPi0u6z435nrNaZ+jnJycAuMxl4T2ddmkSROz7I+IiP5Vt91/y7dOWi4OIiIiK8ECK5GFTJ8+XSyMzJ07F3///bdJ22k0GmzcuLHAfT169AAAXLp0qUAR5Wm6hVztNgBga2uLrl27AgAOHDiAu3fvGjz2unXrDO6/R48e4jl98803xZqFvVu3bmLPN2PHOnPmjNGJn0zVqVMncX/GjB8/XhzDMjQ0VJy53RQ//vhjgdvax/7JkyfYtm2bwe3WrFkjPoa6z5clxcbG4ty5cwbX//DDDwAAGxsbMafM4cUXXwSQX+D++uuvi7UP7WMoCALWr19vsN3atWuLlbuWYGtriy5dugAAwsPDcfv2bYNtta9/3dc7AHh5eaFx48YAgK1btxocjzgjIwM///yzwf0PHDhQHGJg6dKlUk5Dr9TUVFy5cgVA0RN4ERGRRLVb/rf84C/LxUFERGQlWGAlspDatWtj2bJlAPILF126dCmyaHf58mX06dMHn3/+eYH7J0+eLI5H+8YbbyA1NbXQtgcOHMCaNWsA5M8E/vRs4ZMnTwaQ3/ts0qRJeic6WrhwYYEZx59WtWpVTJ06FQBw/PhxTJ8+HRqNxmD7e/fuFeq9W6tWLQwaNAgAsGvXLr0FnfT0dEyaZJ4JGbQF1uTkZKOTGjk6OmLDhg3iWKz9+/fHr7/+anTft27dwsiRI/HWW28VuH/cuHFiIXrGjBm4c+dOoW0vXLiA//u//wOQnyuDBw+Wclql6o033kBGRkah+zdt2oTffvsNADB48GDUqlXLbMfs1asXAgMDAQCff/650UIfkF8I3r17d4H7dGP65JNPxOKdrsuXL+Ozzz4zU9RlIzg4GACQm5uL119/XeylruuHH37AgQMHAABDhgwp9NxoX/9JSUmYMWOG3uNMnz7d6ERWDRs2xLBhwwAAW7ZswZIlS4zGHRcXh82bNxtcf/bsWbHQ3atXL6P7IiIi4wr94VChM7GiYPi7GhEREZmGBVYiCxo3bhwWLFgAIP9y8K5du6J3795Yvnw5IiMjce7cOURERGDFihUYMGAA/P39ER4eXmg/zZo1E4siFy5cQEBAAFavXo2zZ8/i0KFDmDlzJgYMGAC1Wg07OzusXLmy0D4GDhyIgQMHAgB2796NDh064KeffkJMTAz27duHESNG4KOPPkKrVq2MntOCBQvE3mZff/01AgICEBoaimPHjuH8+fOIjIzEsmXLMHjwYNStWxffffddoX18+eWXUCqVAICgoCAEBwcjMjIS0dHRCAsLQ8uWLXHu3LkiYzFFv379xMm4IiIijLbt3r07vv/+eygUCmRkZODll19Gu3bt8OWXXyIiIgLnzp1DVFQUfvjhBwwfPhzPPvsstmzZUmg/np6eYpH89u3baNmyJZYuXYrTp0/j+PHjWLBgATp27Ij09HTIZDKsWrWq3Myg3qpVK5w9exatWrXC2rVrER0djT/++ANTpkzBq6++CgBQKpX44osvzH7sTZs2wd3dHWq1Gq+88gpefPFFbNy4EadPn0Z0dDR+//13/N///R/atWsHf3//Qn+wsLOzw7fffgsgf2zPtm3bYtGiRTh58iROnDiBhQsXon379gBQojF2y1r//v3FwuaBAwfQtm1bbNy4EdHR0Th48CAmTJiACRMmAMgfe1Vf4XPy5MniJf4rVqxA3759sXPnTsTExGDnzp3o3bs3Vq9eXeRrbsWKFahXrx6A/D8edOnSBWvWrMHJkydx7tw5HDx4EF9++SV69uyJBg0aGP0jhfb16OHhgY4dO0p/YIiIKjlrndSRiIioXBLI6qSkpAgAhJSUFEnbZWVlCZcvXxaysrJKKTLzUKvVwuPHjwW1Wm3pUMzm119/FXx9fQUARf5r0qSJsH///kL7UKvVwpQpU4xu6+rqqndbrdTUVKFDhw4Gt2/RooUQHR0t3g4LCzO4nyFDhph0Pt26ddO7j8jISEGpVBrcbu7cucK8efPE28YUlTNDhw41GsvToqKihKZNm5p0fnXr1hU2bdqkdz+fffaZIJfLDW5rb28vrFu3Tu+2cXFxRT4PgpD/OGrbRUZGGmwXFhYmtouLiyu0Xrtu3rx5BR73p/+5uLgIUVFRxYrFlOfzypUrJj/28+fP17uPzz//XJDJZHq3cXR0FPbs2SN06dJFACB06dLFYCzGFPV4SjlnQRCEMWPGCAAEHx8fveuzsrKEl156yejj4eXlJZw7d87gMe7cuSM0bNjQ4Pa9evUS9u/fX2Q+3b17V+jUqZNJz9G4ceMMxuPn5ycAEIKDg40+NoaU9Wdabm6usGPHDiE3N7dMjkcVH3OGpJKaM9/EfCM0XdtUaLq2qXD8zvGCK+OOCsI8l/x/Bz4uhWipPOD7DEnFnCGprD1npNTX2IOVqBwYMmQIrly5go0bN2L06NFo2LAh3NzcYGtrC3d3dwQEBGDKlCn4448/EBsbq/dyWblcjtDQUBw+fBijRo1C3bp1YW9vDxcXFzRv3hwffvghrl27ZvRSW6VSiaioKHz77bdo3bo1nJ2doVQq0bx5cyxcuBDHjx83aWZ4pVKJX3/9FUeOHMGECRPQsGFDKJVK8Xxat26N4OBg/Pbbb3p75AJA165dcenSJUyePBk+Pj6ws7NDjRo10L9/f+zbtw/z5883/QEuwhtvvAEAOHToEBITE4ts36VLF1y4cAE7d+7ExIkT0aRJE3h4eMDW1hZVq1ZF06ZNMX78eOzcuRPXr1/HyJEj9e7nww8/xLlz5zBx4kRxojEnJyc0btwY77zzDv7++2+89tprZjtPcwkJCcG+ffvQv39/1KhRA3Z2dvD19cWUKVNw6dIlcUzQ0vDss8/i/Pnz2LRpE4YOHYq6deuiSpUqsLOzQ61atdC1a1d89NFHiI6Oxty5c/XuY+bMmTh69CiGDBmC6tWrw97eHj4+Phg/fjzOnj2L/v37l1r8pcXBwQHbtm3Drl27MGTIEHh5ecHOzg5ubm5o06YNFi5ciCtXrqB58+YG9+Hl5YVz587h008/RdOmTVGlShVUrVoVbdu2xfLly/H777/Dzs6uyFhq1qyJw4cPY8+ePRg1apQ4EZdCoYCnpyfat2+PGTNm4NChQ+KYvU87ceKEOGSHdvgCIiIiIiKi8komCBVkJg8yWWpqKlxdXZGSkgIXFxeTt8vOzkZcXBz8/Pzg4OBQihGWjEajQWpqKlxcXMRxR4mMKSpnBEFAs2bNcOnSJXz66aeYM2eOBaIs37SXGc6bNw8hISGWDaYM8H3GsiZMmIA1a9agd+/e2LdvX7H2UdafaSqVCr/99luBYUeIjGHOkFRSc+bbc99i1Z+rAAAre65Ee6/2/62MPwas7Ze/3OEdoOeC0giZLIzvMyQVc4aksvackVJf469GIqr0ZDIZFi5cCCB/9nN9EzgRUdm4desW1q9fDwBm7alORFTZyGDiGKzsb0NERFRiLLASESF/kq9OnTohOTkZoaGhlg6HqNJauHAhVCoVhg0bJk6YR0REZsYJsIiIiMzK1tIBEBGVF6Ghofj111/h7Oxs6VCIKiVBEODj44N58+Zh/Pjxlg6HiIiIiIjIJCywEhH9q1mzZmjWrJmlwyCqtGQyGWbPnm3pMIiIrA9HASAiIipVHCKAiIiIiIjIysg4DAAREVGZYQ9WIiIqksAJMIiIiKwUP+OJiIhKij1Yy4mxY8dCJpMZ/Ddx4kRLh0hERERERERERERPYQ/WcmLSpEno0aNHofvXrl2LiIgI9O/f3wJRERERERGR9eHwAURERObEAms50a5dO7Rr167AfRqNBh988AE8PT1ZYCUiIiIiomIROAwAERFRqeIQAeXYwYMHcfv2bYwaNQoKhcLS4RARERERUQUhYy9VIiKiMsMCazkWFhYGABg3blyZHpeT2RARUUXHzzIiIiIiIiorVllgzczMxO+//45PP/0UQ4YMgY+PjzhZVEhIiEn7SEtLQ0hICJo1awZnZ2e4urqidevW+PLLL5Gbm1u6JwAgJSUFO3bsQEBAAPz9/Uv9eAAgl+eng0ajKZPjERERlRbtZ5n2s42IiAzgH6SIiIhKzCrHYD19+jT69etX7O1v3ryJrl27Ij4+HgDg6OiInJwcnD17FmfPnsXGjRsREREBNze3QtsKgoCcnByTjiOXy2FnZ6d33ZYtW5CdnV2mvVcVCgVsbGyQkZEBJyenMjsuERGRuWVnZ0Mul8PW1iq/6hARSVJoDFYZhw8gIiIyJ6vt1uHm5obu3btj1qxZ2Lx5M2rWrGnSdnl5eRg4cCDi4+NRq1YthIeHIyMjA5mZmdiyZQuUSiXOnTuH0aNH693+5s2bqFKlikn/AgICDMaxdu1a2NnZISgoqFjnXxwymQxKpRKpqam8tJKIiCq09PR0ODo6sgcrEVVaHIOViIio7Fhlt45OnTrh0aNHBe6bPXu2SduuW7cOsbGxAIBff/0V7dq1A5Df2/SVV16BRqNBUFAQfvvtN0RERKB79+4Ftvfw8BDHTi2Kvh6wAHDlyhWcPHkSL7/8Mtzd3U3al7m4urriyZMnSExMhJeXF2T86zYREVUwjx8/RmZmJry8vCwdChERERERVQJWWWC1sbEp9rbr1q0DAHTr1k0sruoaMWIE5syZg7i4OKxfv75QgdXZ2Rljx44t9vEBy01uBeQPh1CnTh3cvn0bWVlZcHFxgaOjI2xsbMpNsVWj0SA3N1e8/JOoKMwZkoo5U/EIgoC8vDykpKQgLS0Nbm5ucHV1tXRYRERERERUCVhlgbW4MjMzcezYMQBA37599baRyWTo06cPVqxYgQMHDpg9BrVajR9//BG1atVC7969zb5/UyiVSvj4+CAlJQVPnjzBw4cPLRKHIYIgICsrC1WqVCk3RV8q35gzJBVzpuKyt7dHjRo1DF4lQkRUGXH4LyIiotLFAquOv/76S5x1uGnTpgbbadclJSXh0aNHZr2MPzw8HImJiXj//fdN7ombk5NTYGKt1NRUAIBKpYJKpSpWHAqFAh4eHqhWrRry8vKg0WjKzRezvLw8HD9+HO3bt+fkJWQS5gxJxZypeGQymTiplUwmQ15eXpkeX/t5W9zPXap8mDMkldSc0f6uAfI7cehuJ8vLE38IqjVqaJiHVonvMyQVc4aksvackXJe/NWoIzExUVyuXbu2wXa66xITE81aYF27di0ASBpmYOHChZg/f36h+w8cOABHR0czRVb+HD582NIhUAXDnCGpmDMkVXh4uKVDoAqGOUNSmZozV7OvistnzpxBiiJFvO2Wfg2d/12OuxGHS7/9Zs4QqZzh+wxJxZwhqaw1ZzIzM01uywKrjrS0NHHZWGFSd53uNuawZcsWbNmyRdI2H3zwAd59913xdmpqKry9vdGrVy+4uLiYNb7yQKVSITw8HD179oRCobB0OFQBMGdIKuYMScWcIamYMySV1Jy5HXsbf8T+AQBo3bo1Onh1ENfJbp8GruUv+9Xzg0+PfqUSM1kW32dIKuYMSWXtOaO9QtwULLBaAXt7e9jb2xe6X6FQWGWCa1n7+ZH5MWdIKuYMScWcIamYMySVqTkjt5EXWC6wjc1/PwNt5DawYQ5aNb7PkFTMGZLKWnNGyjlxamQdSqVSXDbWDVh3ne42RERERERE5YEMnKSRiIiorLAHqw4vLy9x+c6dO/D399fb7s6dO3q3KW9KMslVeWbtgyiT+TFnSCrmDEnFnCGpmDMkleRJrtQ6k1zlPTXJlZqTXFUGfJ8hqZgzJJW15wwnuSqmxo0bQy6XQ6PR4OLFi+jbt6/edhcvXgQA1KxZ06wTXJVUaGgoQkNDoVarAVj/JFfWOogylR7mDEnFnCGpmDMkFXOGpCrWJFdnzyD1wn/jyBWc5OoGJ7mycnyfIamYMySVteYMJ7kqJkdHR3To0AFHjhzBvn37MGvWrEJtBEHA/v37AQC9evUq6xCNCg4ORnBwMFJTU+Hq6spJroj+xZwhqZgzJBVzhqRizpBUUnPmTuwdRMRGAABatWqFTrU7ietkt8/8N8mVnx98enKSK2vE9xmSijlDUll7znCSqxIYM2YMjhw5gsjISJw6dQpt2rQpsH7r1q24ceMGAOC1116zRIgms9ZBhrWs/fzI/JgzJBVzhqRizpBUzBmSytScsbGxEZdtbW0LbmPLSa4qE77PkFTMGZLKWnOGk1wBePz4MZKTk8V/Gk3+GESZmZkF7k9PTy+w3ZgxY9CsWTMIgoChQ4ciIiL/r74ajQZbt27FxIkTAQB9+/ZF9+7dy/akiIiIiIiIiIiIqFyx2gJrixYt4OnpKf5LSEgAAHz++ecF7p86dWqB7WxtbbFr1y74+vrizp076NGjB5ycnODk5IThw4cjNTUVLVq0wMaNGy1xWkRERERERERERFSOcIgAPXx9ffHnn3/iiy++wLZt2xAXFweFQoEmTZpg5MiReOutt2BnZ2fpMA0SBAEA8OjRI6ucyU2lUiEzMxMPHz60yi7oZH7MGZKKOUNSMWdIKuYMSSU1ZzJSM6DOyp/89snjJ3hY5aG4TvY4BbY5+b8Z1OkZ0Dx8qHcfVLHxfYakYs6QVNaeM2lpaQD+q7MZIxNMaUUVQmhoKEJDQ5Gbm4vr169bOhwiIiIiIiIiIqIKLSEhAXXq1DHahgVWK6TRaJCYmAilUgmZTGbpcMwuNTUV3t7eSEhIgIuLi6XDoQqAOUNSMWdIKuYMScWcIamYMyQVc4akYs6QVNaeM4IgIC0tDV5eXpDLjY+yyiECrJBcLi+ysm4NXFxcrPIFTKWHOUNSMWdIKuYMScWcIamYMyQVc4akYs6QVNacM66uria1s9pJroiIiIiIiIiIiIhKGwusRERERERERERERMXEAitVOPb29pg3bx7s7e0tHQpVEMwZkoo5Q1IxZ0gq5gxJxZwhqZgzJBVzhqRizvyHk1wRERERERERERERFRN7sBIREREREREREREVEwusRERERERERERERMXEAisRERERERERERFRMbHASkRERERERERERFRMLLASERERERERERERFRMLrGQ1YmJiMH/+fLz44oto1KgRqlWrBoVCgWrVqqFDhw747LPP8OjRI0uHSeXIw4cPERYWhtGjR+O5556Dk5MT7O3tUadOHQwePBjbt2+3dIhUjmRmZuL333/Hp59+iiFDhsDHxwcymQwymQwhISGWDo8sJC0tDSEhIWjWrBmcnZ3h6uqK1q1b48svv0Rubq6lw6NyhO8hJBW/p5BU/D1E5rBo0SLx80kmk1k6HCpn1q5dWyA/DP07ePCgpUMtczJBEARLB0FkDlOnTkVoaKh428HBAQqFAmlpaeJ9Hh4e2LVrF9q1a2eJEKmcUSgUyMvLE287ODjAxsYGGRkZ4n19+/bFL7/8AkdHR0uESOVIVFQUunXrpnfdvHnzWCCphG7evImuXbsiPj4eAODo6Ai1Wo2cnBwAQIsWLRAREQE3NzcLRknlBd9DSCp+TyGp+HuISurKlSto3rw5srOzxftYMiJda9euxbhx4yCXy+Hp6Wmw3datW9GpU6cyjMzy2IOVrEZgYCA+//xznDhxAo8fP0ZWVhZSU1ORlpaGdevWwdPTE8nJyRg8eDBSUlIsHS6VA3l5eQgMDMTy5ctx/fp1ZGVlIT09HXFxcXj99dcBAL///jsmTZpk4UipvHBzc0P37t0xa9YsbN68GTVr1rR0SGQheXl5GDhwIOLj41GrVi2Eh4cjIyMDmZmZ2LJlC5RKJc6dO4fRo0dbOlQqR/geQlLwewpJxd9DVBIajQbjx49HdnY2C/BUJG9vbyQlJRn8V9mKqwB7sFIlcuDAAfTu3RsAsGHDBowaNcrCEZGlRUZGGuxNBABvvvkmVq5cCQC4desWvL29yyo0KofUajVsbGwK3Ofr64ubN2+y91kltGbNGkyYMAEAcPz48UI/RDZv3oygoCAAwMGDB9G9e/cyj5HKF76HkFT8nkLmxt9DZMzXX3+NadOmYdSoUWjQoAHmz58PgD1YqSBtD1YfHx/xKi7Kxx6sVGm0bdtWXL59+7YFI6HywtiPFgBi7xAAOHv2bGmHQ+Xc04URqtzWrVsHIP99RF8vjxEjRsDPzw8AsH79+jKNjconvoeQVPyeQubG30NkSFxcHObMmYNq1arhq6++snQ4RBUSC6xUaRw5ckRcrl+/vgUjoYrCwcFBXFar1RaMhIjKk8zMTBw7dgxA/viH+shkMvTp0wdAfo8hIiJz4/cUkoq/h8iQiRMnIiMjA0uWLDE6riYRGcYCK1m1nJwcxMfHY9myZXj11VcBAA0aNMDAgQMtHBlVBFFRUeJys2bNLBcIEZUrf/31FzQaDQCgadOmBttp1yUlJXHWZiIyO35PIVPw9xAVZfXq1YiIiECPHj3w2muvWTocqiAePHiAli1bwtnZGVWqVEG9evUwevToAp9NlY2tpQMgKg0ODg7iLM66OnTogE2bNsHe3t4CUVFF8uTJEyxcuBAA0KlTJzRs2NDCERFReZGYmCgu165d22A73XWJiYlwd3cv1biIqPLg9xQqCn8PkSnu3LmDWbNmoUqVKuKYzkSmyMzMRExMDNzc3JCRkYG4uDjExcVh48aNGDduHFatWgVb28pVcmQPVrJKNWvWRI0aNeDk5CTe161bNyxduhR169a1YGRUEWg0Grz66qu4e/cuHBwcsGzZMkuHRETlSFpamrjs6OhosJ3uOt1tiIhKgt9TyBT8PUSmmDRpElJSUhASEoJ69epZOhyqALy8vDBv3jxcuHAB2dnZePTokTh8Vo8ePQAAYWFhmD59uoUjLXsssJLFrF27FjKZrNj/9u3bZ3Df8fHxSEpKQnp6Ou7du4cvvvgC58+fR2BgIObOnVuGZ0nmVJo5o+udd97Bnj17AAChoaHw9/cvzdOiUlJW+UJERFSW+D2FTMHfQ1SUDRs2YO/evWjevDneffddS4dDFUSvXr0QEhICf39/sSe8jY0N2rdvj/3792PQoEEAgOXLl+PatWuWDLXMscBKVq969eqYMWMG9u3bB5lMhk8++UT8Ukr0tJkzZ4o9Qb766iuMHz/ewhERUXmjVCrF5czMTIPtdNfpbkNEVFz8nkLFwd9D9LR79+5h2rRpsLGxwerVqyvdpdxUOuRyOb744gsA+Vdb7N6928IRlS2+ishiRo4ciQEDBhR7e1dXV0ntAwMD0bFjRxw+fBirVq0q0bHJMko7Z9577z18+eWXAIAvvvgC06ZNK/axyPLK+j2GKg8vLy9x+c6dOwZ7j925c0fvNkRExcHvKVRS/D1EWrNnz8bDhw8xefJkNGrUCOnp6QXW5+bmisvadXZ2drCzsyvTOKniadCgATw8PJCcnIwbN25YOpwyxQIrWYy9vX2ZD66unXDkn3/+KdPjknmUZs7MmjVL/Gvb4sWLMWPGjFI5DpUdS7zHUOXQuHFjyOVyaDQaXLx4EX379tXb7uLFiwDyx8HjBFdEVBL8nkLmwt9DBABxcXEAgBUrVmDFihVG22qvwnnnnXewdOnS0g6NqMLiEAFUqWj/gsJLNUnXzJkzC/xomTVrloUjIqLyzNHRER06dAAAg2P1CoKA/fv3A8gfq4qIqLj4PYXMib+HiKg0Xb9+HcnJyQAAPz8/C0dTtlhgJaugVqshCILRNhERETh9+jQAoGvXrmUQFVUEM2fOLHC5HX+0EJEpxowZAwCIjIzEqVOnCq3funWr+CP2tddeK9PYiMh68HsKmYq/h0iKqKgoCIJg8N+8efPEttr72HuVinqPEQRB/JySy+WVbhgSFljJKiQkJKBFixZYuXIlbty4UeCFn5CQgEWLFmHQoEEQBAHu7u6YPn26BaOl8kJ3LLMlS5bwcjsq0uPHj5GcnCz+02g0APInM9K9/+lxrMj6jBkzBs2aNYMgCBg6dCgiIiIA5A/ov3XrVkycOBEA0LdvX3Tv3t2SoVI5wvcQkoLfU0gK/h4iotJ28+ZNBAYGFnqf0Wg0OHnyJPr27Yvt27cDACZNmoSGDRtaMtwyJxOKKkETVQDx8fEFup/b2dnBxcUFWVlZyMjIEO/38/PDr7/+ihYtWlgiTCpHbt26BR8fHwD5f13z9PQ02n7mzJmYOXNmWYRG5Zivry9u3rxZZLsxY8Zg7dq1pR8QWVR8fDy6deuG+Ph4APlDB2g0GmRnZwMAWrRogYiICLi5uVkwSipP+B5CpuL3FJKKv4fInEJCQjB//nwARfdapMrj6fcZe3t7KJVKpKWlIScnR7x/3LhxWLVqFWxtK9e0T5XrbMlqeXl5YevWrYiKisKpU6eQmJiI5ORk2NjYoG7dunj++ecxaNAgBAUFoUqVKpYOl8oBba8h7fK9e/eMtmdvIiJ6mq+vL/7880988cUX2LZtG+Li4qBQKNCkSROMHDkSb731FmfbJaJi4fcUkoq/h4iotNWoUQPffvstTpw4gfPnz+PBgwd4/PgxHBwc4Ofnh/bt22P8+PHiXAWVDXuwEhERERERERERERUTx2AlIiIiIiIiIiIiKiYWWImIiIiIiIiIiIiKiQVWIiIiIiIiIiIiomJigZWIiIiIiIiIiIiomFhgJSIiIiIiIiIiIiomFliJiIiIiIiIiIiIiokFViIiIiIiIiIiIqJiYoGViIiIiIiIiIiIqJhYYCUiIiIiIiIiIiIqJhZYiYiIiIiIiIiIiIqJBVYiIiIiIiIiIiKiYmKBlYiIiIjIyhw7dgwymQwymQwhISFm229UVJS4X19fX7PtV4q0tDR4enpCJpOhY8eOFomBiIiISBcLrEREREQkycmTJ8UiW3H+tWnTptRj3LZtm3i8GTNmmHweo0ePNvkYa9euFbdzdnaGRqMxV/glotFo8PbbbwMAPDw8DJ5/aRo7dmyReWBnZwdPT0+0atUKkydPRlRUFARBKHLfSqUSs2fPBpBfSP7pp59K+3SIiIiIjGKBlYiIiIgkOXfuXIm2b926tZkiMWz37t3i8oABA/S20Xcee/fuRV5enknH0N3e398fcnn5+Gq9efNmxMTEAACmTZsGpVJp4Yj0U6lUSE5ORnR0NL777jt069YN3bp1Q1xcXJHbTpkyBR4eHgCADz/80OTnjIiIiKg02Fo6ACIiIiKqWHQLi9WrV0eLFi0kbd+vXz9zh1SARqPBb7/9BgCoWrUqOnXqpLedvgLrkydPEBUVhR49ehR5HN3tmzdvXrxgzUytVmPBggUAACcnJ0yePNnCEQFubm4IDAwsdH9mZiYSEhIQHx8v3nfo0CF07twZJ06cQJ06dQzus0qVKggODsb8+fNx48YNrFu3Dq+//npphE9ERERUJBZYiYiIiEiS8+fPi8vDhg3DsmXLLBeMHqdPn8b9+/cBAL1794atrf6vvLrn4ezsjPT0dADA9u3biyywCoKACxcuiLelFplLyy+//IKrV68CAIKCguDu7m7hiPJ79+7bt8/g+mvXrmHWrFnYuXMnAOD27duYNm0afvnlF6P7nTx5Mj777DPk5eVh0aJFGD9+PGQymVljJyIiIjJF+biOiYiIiIgqhLy8PMTGxoq3mzVrZsFo9NMdHmDgwIF62zx9HsHBweLyrl27ihwL9MaNG0hNTRVvl5cC69dffy0uV5Qenc888wy2bduGrl27ivft2LEDDx8+NLpdjRo10L9/fwDAP//8I/ZaJiIiIiprLLASERERkcn+/vtvZGdni7fLc4HVxsYGffv21dvm6fMYP3486tatCyC/B+XZs2eNHkN3eABbW1s0bdq0pGGX2MWLF3HixAkAQP369ctkMjFzkcvlmDZtmnhbrVYX+RwA+b10tVauXFkaoREREREViQVWIiIiIjKZ7mX1AMpFYVHXrVu3xJ6p7du3N3iJ/NPDAzRo0ACDBg0S79u+fbvR4+hu36hRIzg4OBQ/aDPZuHGjuDx48GDJ2x8+fBivvvoq/Pz84ODggJo1a6J9+/ZYunQpnjx5Yr5ADWjUqFGB20X1YAXyx/O1t7cHAOzbt8+kbYiIiIjMjQVWIiIiIjKZbs9NHx8fuLi4WDCawnSHBxgwYIDBdrrn4e/vD7lcXqDAumPHDqPHKY8TXP3666/icp8+fUzeLi8vD5MmTUKXLl2wYcMGxMfHIycnB/fu3cOJEycwffp0NG/eHDExMaURtig3N7fAbWdn5yK3cXZ2RocOHQAAKpVKHMeViIiIqCyxwEpEREREJtPtuVkehwfYs2ePuGxo/FWg4HloC6RdunSBm5sbAOCvv/4SJ4sqavvyMP5qXFwcrl27BiB/yIJ27dqZtJ0gCHjttdewatWqAvc/99xz6Nq1K5555hkAwM2bN9GzZ0/cvn3bvIHr0A5voGVq7+guXbqIy/v37zdrTERERESmYIGViIiIiExWngusGRkZiIyMBADUq1cPjRs3NthWX4HU1tZWnDQJMDxMwP3795GYmFhoe0s6dOiQuPzcc8/BycnJpO1++OEHbN68WbzdtWtXXL16FZcuXUJkZCSuXr2K8+fPIyAgAI8ePcL06dPNHjsAJCcnY9GiReLttm3bol69eiZt27p1a3E5KirK3KERERERFYkFViIiIiIyyc2bN/Ho0SPxdnkbf/XAgQPIyckBYLz36tPnoXuJvynDBOgOD/D09pYSHR0tLjdp0sSkbbKzszF79mzxdocOHbBv3z6x16rW888/j8jISDz33HNITk42T8D/Hv/atWtYvnw5AgICEBcXBwBwcnJCaGioyfvRLfTfv38fCQkJZouRiIiIyBQssBIRERGRSZ6e4GrUqFGQyWQm/9P2Li0tusMDGBt/Vfc8bG1tCxSK+/TpI05YderUKdy9e9fo9j4+PuKwApZ0+fJlcbl+/fombfPrr7+KBVMbGxusXr1anDDqaS4uLlixYkWxYjt06JDefKhSpQqeffZZBAcHi0XRrl274ujRowgICDB5/3Xq1CkQt+5jQURERFQWWGAlIiIiIpM83XNTCplMhpYtW5oxmoIEQcDevXsB5BcDdcflfJrueTRs2FAsqAL5kyZ1795d3Ke+SZPK4wRXN2/eFJe9vLxM2ka3h263bt2MDqkAAJ07dy7VYSE6d+6M4OBg+Pv7S95W95x1HwsiIiKismBr6QCIiIiIqGLQ7blZo0YNScXFmjVrwsXFRe+6sWPHYt26dZg5cyY+//zzYsV25swZ3Lt3DwDQq1cvKBQKg22LmqBq8ODBYrF2x44dePPNNwus1y2wlofxVwEUuHTf1B61Z86cEZd79+5t0jZ9+/ZFbGyspNjc3NwQGBhY6H61Wo1Hjx7h77//RmZmJg4fPozDhw+jdevW2Lp1K3x8fCQdQzvEwIMHDyTFR0RERFRSLLASERERkUl0C4tBQUFYsmSJWfdbkmLl7t27xWVj46/qHg/Q3wN14MCBkMvl0Gg0iIyMRGpqqlgczsjIwD///CO2LSrmBw8eYNGiRdi1axdu374NJycnBAQEYMqUKRg8eLAJZ2aajIwMcblKlSpFtlepVAV6epo6nq6p47vq8vf3x759+4zGsmPHDrz77ru4ffs2zpw5g27duuHs2bNwd3c36Ri656z7WBARERGVBQ4RQERERERFevToEW7duiXefv75582y39zcXPz1118ASna5vbbAKpfL0a9fP4Ptnj4PfcesUaMG2rZtK8an7c0KABcuXIBGozG6vdalS5fQtGlTLFmyBP/88w8UCgWePHmC8PBwvPTSS3jnnXdMPT1JBEEoss2TJ08K3K5WrZpJ+za1nRQKhQLDhg3D4cOHoVQqAQBxcXEFJuAqiinnTERERFRaWGAlIiIioiI9PcGVuQqsFy9ehEqlQpUqVdCwYcNi7SMhIQEXLlwAALRt2xYeHh4G2z59HoZ6oOr2LtUdq1R3e3d3d9StW1fv9jk5OXjxxRdx//59NG3aFOfPn0dqaipSU1Px6aefQiaT4ZtvvkFYWJjxkzORk5OTuJydnV1k+9zc3AK37ezsTDqOoUmwzMHPzw/jxo0Tb//4449IT083adusrCxxWfexICIiIioLLLASERERUZF0L6tXKBR47rnnzLJfbcHS398fNjY2xdrHnj17xOUBAwYYbat7Ht7e3gYvQR80aJC4/PvvvyMnJ6fQ9saGB1i1ahVu3LgBR0dH7N27VyxIOzo6Ys6cOZgyZQoA4KOPPoJKpTIasyl0i8qPHz8usv3T4+GmpaWZdBxT2xVXx44dxeXs7GycPXvWpO10z9nT09PscREREREZwwIrERERERVJt7DYuHFjk3s8mrrf5s2bIzc3F0uWLEFAQACcnJzg7u6OYcOG4cqVK0b3oVtgLen4q1rPPvssGjduDCC/qBgRESFp+w0bNgAARo4cqbeX63vvvQeZTIbExERERkYajdkUuhNC3blzp8j2SqWywLil8fHxJh1HO5FUaalatWqB23fv3jVpu8TERHFZyuRYRERERObAAisRERERFUn30nhzDQ+gu9/atWsjMDAQM2bMwOXLl5GXl4fHjx/jl19+Qdu2bQ0WWTMzM/HHH38AAHx9fYucrEn3PIqaoEp3mIDt27cjLy8Ply5dKnL79PR0nDlzBgDQp08fvW3q1q0rFnC1xduS0O4LQIFJuIzRLRCfPn3apG1MbVdcT/e+NWXCrtu3bxcY8sBcvauJiIiITMUCKxEREREZlZ2djb///lu8ba4CqyAI+PPPPwEAX375JXJycnDgwAFkZWUhPT0dW7ZsgbOzM548eYJp06bp3Ud4eLg45mj//v0lnUdRk2rpDhOwe/duXLp0qcD4poa2/+uvv8RJl4wVfLXrLl++bDQOU7Rs2VJcvnjxoknbdOrUSVzetm0b8vLyjLZPT08vMOFXaTh8+HCB26b0Ro2NjRWXPT094e3tbfa4iIiIiIxhgZWIiIiIjIqNjYVarRZvF1WYNNX169eRmpoKAPDy8sKJEyfQs2dPyGQyKBQKvPLKK/j4448BAAcOHNA7/qeU4QGknkdgYCC8vLwAAPfu3cPy5cvFdVWqVEGjRo30bqd7Wbt2e32060y9DN6Yzp07i8t///23SZNDvfrqq+LynTt3sGLFCqPtFy5caPKkU8Xxzz//YO3ateLtWrVqmZRruuO0dunSpRQiIyIiIjKOBVYiIiIiMkp33FHAfD1YdS/X//777wuNvwkAL7/8MgBAo9Hg+vXrBdYJgiD2qHR2dkbXrl2NHk/3PKpWrQo/Pz+j7WUyGV588UXxdlhYmLjcrFkzg5Ny6RYhHR0dDe5fu84cE0fVr18f9evXBwCo1WocO3asyG2aNm1aoNfvrFmzEB4errft5s2bsWjRohLHqY9KpcLPP/+Mrl27IiMjQ7z/gw8+gEwmK3L7Q4cOicu9e/culRiJiIiIjLG1dABEREREVL7pFiblcjlGjx4taXt/f38sXrzY4H47duyI9u3b6922du3a4rJGoymw7uzZs2Lvz549e8Le3t5oHLrnYWqReNCgQfjuu+8A5BcCtczVi9echgwZgs8//xwAsH//fpOKjcuWLcORI0eQmpqKnJwc9OnTB0FBQRg0aBCqV6+OO3fuYOvWrdi+fTsA4JVXXsFPP/0kKa4///xT71i0arUaT548weXLl5GZmVlg3dChQzFlypQi952RkSEWk21tbQsM60BERERUVlhgJSIiIiKjdHuaajQa7N+/X9L2DRo0MLpfbS9VfZ48eSIu16hRo8A63eEBBgwYUGQcuudhaoH0hRdegIuLiziUgZaxCbKcnZ3F5czMTLi4uOhtpy0qKpVKk2IpyujRo8UC644dO7BkyZIit/H19cWePXvQp08fZGZmQqPRYMOGDdiwYUOhtq+++irGjx8vucD6+PFjk3PGzs4OH3zwAebMmWOwh7Cu33//XRwXt1evXvD09JQUGxEREZE5cIgAIiIiIjJIo9GIE1EVl+4ETLq0PUoNrQf+m7W+evXqhcYz3b17N4D8S/mLmuDq6fMwtcBqZ2eHvn37Frrf2Pa6cSYmJhpsp11Xq1Ytk2Ipir+/P9q0aQMAiIuLw8mTJ03arlOnToiJiTE4xEK1atWwePFirF+/3ixxaslkMiiVSvj5+WHQoEH46quvkJCQgJCQECgUCpP2sWnTJnF50qRJZo2PiIiIyFQyQTvFKRERERFRGbl//77YI/XSpUt47rnn9LYbO3Ys1q1bhzFjxhSYAOnOnTuoU6cOgPzJqE6dOlXqMZsqPT0dLi4uEAQBv/zyC4YOHaq3XdOmTXHp0iW89957+N///meWY2/ZsgUjR44EAEyYMAGrV6+WtP21a9dw7NgxJCUlwdXVFX5+fnjhhRdgZ2dnlvjM6f79+6hTpw5UKhXq1auHa9euQS5n/xEiIiIqe/wGQkRERERlTvdy/Xv37ultk5CQgJ9//hkAMHXq1ALrdIcHGDhwoPkDLAFnZ2cEBgYCAPbt26e3ze3bt3H58mUAQPfu3c127GHDhuGZZ54BkD8x1aNHjyRt/8wzz2Ds2LGYPXs2Jk+ejD59+pTL4ioArFy5UhwX9/3332dxlYiIiCyG30KIiIiIqMzpTjilvdRfV15eHiZMmICsrCwMGTIErVq1KrBedxtTxl8ta6NGjQKQX+RMSEgotH7x4sUQBAFeXl7o1q2b2Y5rY2ODuXPnAsifAGrFihVm23d5kp2djWXLlgHIH0d23LhxFo6IiIiIKjMWWImIiIiozGl7sLq7uyM0NBRr1qwReyPGxsaiT58+OHDgAHx8fPRe5t65c2fMmzcPn332mcnjqZalN954A/Xq1UNGRgYGDBggjv+alZWFRYsWicXBTz/91OTxRk0VFBQkTsK1dOlSpKWlmXX/5cGKFStw//59AMD//d//mf0xJCIiIpKCY7ASERERUZlr1KgRrly5gvXr1yMkJAQ3btyAnZ0dHBwckJqaCiC/Z+K+ffvQsGFDC0dbPJcuXcILL7wgFgJdXFyQkZEBtVoNAHjrrbfwzTfflMqxjx07ho4dOwIA5s2bh5CQkFI5jiWkpaWhXr16SE5ORocOHXD06FFLh0RERESVHAusRERERFSmMjMzoVQqodFocO3aNSiVSnzwwQf47bffkJKSgvr16+OVV17B9OnT4ezsbOlwS+T+/ftYtGgRdu/ejYSEBDg5OSEgIADBwcEYPHiwpcMjIiIiIjNggZWIiIiIiIiIiIiomDgGKxEREREREREREVExscBKREREREREREREVEwssBIREREREREREREVEwusRERERERERERERMXEAisRERERERERERFRMbHASkRERERERERERFRMLLASERERERERERERFRMLrERERERERERERETFxAIrERERERERERERUTGxwEpERERERERERERUTP8PRr2FUy1DJjYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ber_plots.simulate(run_compiled,\n",
    "                   ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 12),\n",
    "                   batch_size=BATCH_SIZE,\n",
    "                   num_target_block_errors=500,\n",
    "                   legend=\"Coded (Compiled mode)\",\n",
    "                   soft_estimates=True,\n",
    "                   max_mc_iter=100,\n",
    "                   show_fig=True,\n",
    "                   forward_keyboard_interrupt=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57cdb153",
   "metadata": {},
   "source": [
    "**Task:** PyTorch offers various compiler backends nd compilation modes. Try\n",
    "experimenting with them.\n",
    "\n",
    "*Remark*: The effectiveness of compilation depends on the hardware and model complexity. On GPU with CUDA, torch.compile can provide substantial speedups by fusing operations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d4765a0",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "Simulate the coded bit error rate (BER) for a Polar coded and 64-QAM modulation.\n",
    "Assume a codeword length of n = 200 and coderate = 0.5.\n",
    "\n",
    "**Hint**: For Polar codes, successive cancellation list decoding (SCL) gives the best BER performance.\n",
    "However, successive cancellation (SC) decoding (without a list) is less complex.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cf855e19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:20.313383Z",
     "iopub.status.busy": "2026-02-13T14:09:20.313248Z",
     "iopub.status.idle": "2026-02-13T14:09:20.316226Z",
     "shell.execute_reply": "2026-02-13T14:09:20.315245Z"
    }
   },
   "outputs": [],
   "source": [
    "n = 200\n",
    "coderate = 0.5\n",
    "\n",
    "# *You can implement your code here*\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.12"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}