{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f543de9b-3858-45dd-bddf-cddddcc60da3",
   "metadata": {},
   "source": [
    "# Part 2: Differentiable Communication Systems"
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28fa5595",
   "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](#Imports)\n",
    "* [Gradient Computation Through End-to-end Systems](#Gradient-Computation-Through-End-to-end-Systems)\n",
    "* [Creating Custom Layers](#Creating-Custom-Layers)\n",
    "* [Setting up Training Loops](#Setting-up-Training-Loops)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a184ac-1f64-407f-9a24-c53d40799be2",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d84bd69e-59f2-4a42-8dd3-730c8c1d821e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:24.954390Z",
     "iopub.status.busy": "2026-02-13T14:09:24.954229Z",
     "iopub.status.idle": "2026-02-13T14:09:27.732892Z",
     "shell.execute_reply": "2026-02-13T14:09:27.731976Z"
    }
   },
   "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 as sn\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 numpy as np\n",
    "\n",
    "# For plotting\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# PyTorch imports for the implementation of the neural receiver\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# Set seed for reproducible results\n",
    "sn.phy.config.seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dacb1e70",
   "metadata": {},
   "source": [
    "## Gradient Computation Through End-to-end Systems<a class=\"anchor\" id=\"Gradient-Computation-Through-End-to-end-Systems\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f617618",
   "metadata": {},
   "source": [
    "Let's start by setting up a simple communication system that transmit bits modulated as QAM symbols over an AWGN channel.\n",
    "\n",
    "However, compared to what we have previously done, we now make the constellation\n",
    "*trainable*. With Sionna, achieving this by assigning trainable points to a\n",
    "`Constellation` instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7f9b5bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:27.735590Z",
     "iopub.status.busy": "2026-02-13T14:09:27.735311Z",
     "iopub.status.idle": "2026-02-13T14:09:27.911357Z",
     "shell.execute_reply": "2026-02-13T14:09:27.910553Z"
    }
   },
   "outputs": [],
   "source": [
    "# Binary source to generate uniform i.i.d. bits\n",
    "binary_source = sn.phy.mapping.BinarySource()\n",
    "\n",
    "# 64-QAM constellation\n",
    "NUM_BITS_PER_SYMBOL = 6\n",
    "qam_constellation = sn.phy.mapping.Constellation(\"qam\", NUM_BITS_PER_SYMBOL)\n",
    "\n",
    "# Make a trainable constellation initialized with QAM points\n",
    "# In PyTorch, we use tensors with requires_grad=True for trainable parameters\n",
    "trainable_points = torch.stack([qam_constellation.points.real.clone(),\n",
    "                                qam_constellation.points.imag.clone()], dim=0)\n",
    "trainable_points.requires_grad_(True)\n",
    "\n",
    "constellation = sn.phy.mapping.Constellation(\"custom\",\n",
    "                                             num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "                                             points=torch.complex(trainable_points[0], trainable_points[1]),\n",
    "                                             normalize=True,\n",
    "                                             center=True)\n",
    "\n",
    "# Mapper and demapper\n",
    "mapper = sn.phy.mapping.Mapper(constellation=constellation)\n",
    "demapper = sn.phy.mapping.Demapper(\"app\", constellation=constellation)\n",
    "\n",
    "# AWGN channel\n",
    "awgn_channel = sn.phy.channel.AWGN()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b8e774",
   "metadata": {},
   "source": [
    "As we have already seen, we can now easily simulate forward passes through the system we have just setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6afe9194",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:27.913866Z",
     "iopub.status.busy": "2026-02-13T14:09:27.913711Z",
     "iopub.status.idle": "2026-02-13T14:09:28.143984Z",
     "shell.execute_reply": "2026-02-13T14:09:28.143086Z"
    }
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 128 # How many examples are processed by Sionna in parallel\n",
    "EBN0_DB = 17.0 # Eb/N0 in dB\n",
    "\n",
    "no = sn.phy.utils.ebnodb2no(ebno_db=EBN0_DB,\n",
    "                        num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "                        coderate=1.0) # Coderate set to 1 as we do uncoded transmission here\n",
    "\n",
    "bits = binary_source([BATCH_SIZE,\n",
    "                        1200]) # Blocklength\n",
    "x = mapper(bits)\n",
    "y = awgn_channel(x, no)\n",
    "llr = demapper(y,no)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39061033",
   "metadata": {},
   "source": [
    "Just for fun, let's visualize the channel inputs and outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3538928a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:28.146327Z",
     "iopub.status.busy": "2026-02-13T14:09:28.146194Z",
     "iopub.status.idle": "2026-02-13T14:09:29.223311Z",
     "shell.execute_reply": "2026-02-13T14:09:29.222462Z"
    }
   },
   "outputs": [
    {
     "data": {
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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(1.0)\n",
    "plt.grid(True)\n",
    "plt.scatter(y.real.cpu().detach(), y.imag.cpu().detach(), label='Output')\n",
    "plt.scatter(x.real.cpu().detach(), x.imag.cpu().detach(), label='Input')\n",
    "plt.legend(fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc0b529",
   "metadata": {},
   "source": [
    "Let's now *optimize* the constellation through *stochastic gradient descent* (SGD). As we will see, this is made very easy by Sionna.\n",
    "\n",
    "We need to define a *loss function* that we will aim to minimize.\n",
    "\n",
    "We can see the task of the receiver as jointly solving, for each received symbol, `NUM_BITS_PER_SYMBOL` binary classification problems in order to reconstruct the transmitted bits.\n",
    "Therefore, a natural choice for the loss function is the *binary cross-entropy* (BCE) applied to each bit and to each received symbol.\n",
    "\n",
    "*Remark:* The LLRs computed by the demapper are *logits* on the transmitted bits, and can therefore be used as-is to compute the BCE without any additional processing.\n",
    "*Remark 2:* The BCE is closely related to an achieveable information rate for bit-interleaved coded modulation systems [1,2]\n",
    "\n",
    "[1] Georg Böcherer, \"Principles of Coded Modulation\", [available online](http://www.georg-boecherer.de/bocherer2018principles.pdf)\n",
    "\n",
    "[2] F. Ait Aoudia and J. Hoydis, \"End-to-End Learning for OFDM: From Neural Receivers to Pilotless Communication,\" in IEEE Transactions on Wireless Communications, vol. 21, no. 2, pp. 1049-1063, Feb. 2022, doi: 10.1109/TWC.2021.3101364."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03d7c7b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:29.225749Z",
     "iopub.status.busy": "2026-02-13T14:09:29.225612Z",
     "iopub.status.idle": "2026-02-13T14:09:29.233308Z",
     "shell.execute_reply": "2026-02-13T14:09:29.232358Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BCE: 0.00020045811834279448\n"
     ]
    }
   ],
   "source": [
    "# Compute Binary Cross-Entropy loss\n",
    "# In PyTorch, we use F.binary_cross_entropy_with_logits\n",
    "loss = F.binary_cross_entropy_with_logits(llr, bits.float())\n",
    "\n",
    "print(f\"BCE: {loss.item()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393dc7d2",
   "metadata": {},
   "source": [
    "One iteration of SGD consists in three steps:\n",
    "1. Perform a forward pass through the end-to-end system and compute the loss function\n",
    "2. Compute the gradient of the loss function with respect to the trainable weights\n",
    "3. Apply the gradient to the weights\n",
    "\n",
    "To enable gradient computation, we need to ensure our trainable parameters have `requires_grad=True`. PyTorch's autograd automatically tracks operations on such tensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6654b8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:29.235413Z",
     "iopub.status.busy": "2026-02-13T14:09:29.235287Z",
     "iopub.status.idle": "2026-02-13T14:09:29.240435Z",
     "shell.execute_reply": "2026-02-13T14:09:29.239581Z"
    }
   },
   "outputs": [],
   "source": [
    "# Forward pass with gradient tracking (trainable_points has requires_grad=True)\n",
    "bits = binary_source([BATCH_SIZE, 1200]) # Blocklength\n",
    "mapper.constellation.points = torch.complex(trainable_points[0], trainable_points[1])\n",
    "x = mapper(bits)\n",
    "y = awgn_channel(x, no)\n",
    "llr = demapper(y, no)\n",
    "loss = F.binary_cross_entropy_with_logits(llr, bits.float())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12d20342",
   "metadata": {},
   "source": [
    "Computing the gradient is done by calling `backward()` on the loss tensor. The gradients are then stored in the `.grad` attribute of each parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a68657af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:29.242461Z",
     "iopub.status.busy": "2026-02-13T14:09:29.242344Z",
     "iopub.status.idle": "2026-02-13T14:09:29.297563Z",
     "shell.execute_reply": "2026-02-13T14:09:29.296807Z"
    }
   },
   "outputs": [],
   "source": [
    "# Compute gradients by calling backward()\n",
    "loss.backward()\n",
    "# The gradient is stored in trainable_points.grad\n",
    "gradient = trainable_points.grad"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "318aa681",
   "metadata": {},
   "source": [
    "`gradient` is a tensor with the same shape as the trainable variable it corresponds to.\n",
    "\n",
    "For this model, we only have a single trainable tensor: The constellation of shape [`2`, `2^NUM_BITS_PER_SYMBOL`], the first dimension corresponding to the real and imaginary components of the constellation points.\n",
    "\n",
    "*Remark:* It is important to notice that the gradient computation was performed *through the demapper and channel*, which are conventional non-trainable algorithms implemented as *differentiable* Sionna blocks. This key feature of Sionna enables the training of end-to-end communication systems that combine both trainable and conventional and/or non-trainable signal processing algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7437d3f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:29.299675Z",
     "iopub.status.busy": "2026-02-13T14:09:29.299557Z",
     "iopub.status.idle": "2026-02-13T14:09:29.302532Z",
     "shell.execute_reply": "2026-02-13T14:09:29.301714Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 64])\n"
     ]
    }
   ],
   "source": [
    "print(gradient.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f7a8cc",
   "metadata": {},
   "source": [
    "Applying the gradient (third step) is performed using an *optimizer*. [Many optimizers are available as part of PyTorch](https://pytorch.org/docs/stable/optim.html), and we use in this notebook ``Adam``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "26b73d64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:29.304440Z",
     "iopub.status.busy": "2026-02-13T14:09:29.304322Z",
     "iopub.status.idle": "2026-02-13T14:09:29.306975Z",
     "shell.execute_reply": "2026-02-13T14:09:29.306145Z"
    }
   },
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam([trainable_points], lr=1e-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2b15d3",
   "metadata": {},
   "source": [
    "Using the optimizer, the gradients can be applied to the trainable weights to update them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "54565c6b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:29.308907Z",
     "iopub.status.busy": "2026-02-13T14:09:29.308780Z",
     "iopub.status.idle": "2026-02-13T14:09:29.348073Z",
     "shell.execute_reply": "2026-02-13T14:09:29.347163Z"
    }
   },
   "outputs": [],
   "source": [
    "# Apply the gradients to update the trainable parameters\n",
    "optimizer.step()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9ef8110",
   "metadata": {},
   "source": [
    "Let's compare the constellation before and after the gradient application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31dd793c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:29.350438Z",
     "iopub.status.busy": "2026-02-13T14:09:29.350300Z",
     "iopub.status.idle": "2026-02-13T14:09:29.656807Z",
     "shell.execute_reply": "2026-02-13T14:09:29.656055Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sn.phy.mapping.Constellation(\"qam\", NUM_BITS_PER_SYMBOL).show()\n",
    "fig.axes[0].scatter(trainable_points[0].detach().cpu(), trainable_points[1].detach().cpu(), label='After SGD')\n",
    "fig.axes[0].legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a936c10b",
   "metadata": {},
   "source": [
    "The SGD step has led to slight change in the position of the constellation points. Training of a communication system using SGD consists in looping over such SGD steps until a stop criterion is met."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008a93d5",
   "metadata": {},
   "source": [
    "## Creating Custom Layers<a class=\"anchor\" id=\"Creating-Custom-Layers\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "244c594f",
   "metadata": {},
   "source": [
    "Custom trainable (or not trainable) algorithms can be implemented as [PyTorch\n",
    "modules](https://pytorch.org/docs/stable/nn.html) (`nn.Module`) or Sionna\n",
    "blocks. All Sionna components, such as the mapper, demapper, channel... are\n",
    "implemented as Sionna blocks which enherit from the `nn.Module`.\n",
    "\n",
    "To illustrate how this can be done, the next cell implements a simple neural network-based demapper which consists of three dense (linear) layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9670872b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:29.659353Z",
     "iopub.status.busy": "2026-02-13T14:09:29.659220Z",
     "iopub.status.idle": "2026-02-13T14:09:29.662818Z",
     "shell.execute_reply": "2026-02-13T14:09:29.662158Z"
    }
   },
   "outputs": [],
   "source": [
    "class NeuralDemapper(nn.Module): # Inherits from PyTorch Module\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        # The three linear layers that form the custom trainable neural network-based demapper\n",
    "        self.dense_1 = nn.Linear(2, 64)\n",
    "        self.dense_2 = nn.Linear(64, 64)\n",
    "        self.dense_3 = nn.Linear(64, NUM_BITS_PER_SYMBOL) # The last layer has no activation and therefore outputs logits, i.e., LLRs\n",
    "\n",
    "    def forward(self, y):\n",
    "\n",
    "        # y : complex-valued with shape [batch size, block length]\n",
    "        # y is first mapped to a real-valued tensor with shape\n",
    "        #  [batch size, block length, 2]\n",
    "        # where the last dimension consists of the real and imaginary components\n",
    "        # The linear layers operate on the last dimension, and treat the inner dimensions as batch dimensions, i.e.,\n",
    "        # all the received symbols are independently processed.\n",
    "        nn_input = torch.stack([y.real, y.imag], dim=-1)\n",
    "        z = F.relu(self.dense_1(nn_input))\n",
    "        z = F.relu(self.dense_2(z))\n",
    "        z = self.dense_3(z) # [batch size, number of symbols per block, number of bits per symbol]\n",
    "        llr = z.reshape(y.shape[0], -1) # [batch size, number of bits per block]\n",
    "        return llr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6552c1a7",
   "metadata": {},
   "source": [
    "A custom PyTorch module is used as any other Sionna layer, and therefore integration to a Sionna-based communication is straightforward.\n",
    "\n",
    "The following model uses the neural demapper instead of the conventional demapper. It takes at initialization a parameter that indicates if the model is instantiated to be trained or evaluated. When instantiated to be trained, the loss function is returned. Otherwise, the transmitted bits and LLRs are returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "502066d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:29.665216Z",
     "iopub.status.busy": "2026-02-13T14:09:29.665096Z",
     "iopub.status.idle": "2026-02-13T14:09:29.669379Z",
     "shell.execute_reply": "2026-02-13T14:09:29.668641Z"
    }
   },
   "outputs": [],
   "source": [
    "class End2EndSystem(nn.Module): # Inherits from PyTorch Module\n",
    "\n",
    "    def __init__(self, training):\n",
    "\n",
    "        super().__init__() # Must call the PyTorch module initializer\n",
    "\n",
    "        qam_constellation = sn.phy.mapping.Constellation(\"qam\", NUM_BITS_PER_SYMBOL)\n",
    "\n",
    "        # Trainable constellation points as nn.Parameter\n",
    "        self.points = nn.Parameter(torch.stack([qam_constellation.points.real.clone(),\n",
    "                                                qam_constellation.points.imag.clone()], dim=0))\n",
    "\n",
    "        self.constellation = sn.phy.mapping.Constellation(\"custom\",\n",
    "                                                    num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "                                                    points=torch.complex(self.points[0], self.points[1]),\n",
    "                                                    normalize=True,\n",
    "                                                    center=True)\n",
    "        self.mapper = sn.phy.mapping.Mapper(constellation=self.constellation)\n",
    "        self.demapper = NeuralDemapper() # Instantiate the NeuralDemapper custom module as any other\n",
    "        self.binary_source = sn.phy.mapping.BinarySource()\n",
    "        self.awgn_channel = sn.phy.channel.AWGN()\n",
    "        self._training_mode = training\n",
    "\n",
    "    def forward(self, batch_size, ebno_db):\n",
    "\n",
    "        # no channel coding used; we set coderate=1.0\n",
    "        no = sn.phy.utils.ebnodb2no(ebno_db,\n",
    "                                num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "                                coderate=1.0)\n",
    "        bits = self.binary_source([batch_size, 1200]) # Blocklength set to 1200 bits\n",
    "\n",
    "        # Assign points to constellation\n",
    "        self.mapper.constellation.points = torch.complex(self.points[0], self.points[1])\n",
    "\n",
    "        x = self.mapper(bits)\n",
    "        y = self.awgn_channel(x, no)\n",
    "        llr = self.demapper(y)  # Call the NeuralDemapper custom module as any other\n",
    "        if self._training_mode:\n",
    "            loss = F.binary_cross_entropy_with_logits(llr, bits.float())\n",
    "            return loss\n",
    "        else:\n",
    "            return bits, llr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af122bb3",
   "metadata": {},
   "source": [
    "When a model that includes a neural network is created, the neural network weights are randomly initialized typically leading to very poor performance.\n",
    "\n",
    "To see this, the following cell benchmarks the previously defined untrained model against a conventional baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6eda9275",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:29.671736Z",
     "iopub.status.busy": "2026-02-13T14:09:29.671619Z",
     "iopub.status.idle": "2026-02-13T14:09:30.834361Z",
     "shell.execute_reply": "2026-02-13T14:09:30.833402Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     10.0 | 2.7044e-02 | 1.0000e+00 |        4154 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "     10.0 | 2.7044e-02 | 1.0000e+00 |        4154 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   10.526 | 2.0892e-02 | 1.0000e+00 |        3209 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   10.526 | 2.0892e-02 | 1.0000e+00 |        3209 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   11.053 | 1.7057e-02 | 1.0000e+00 |        2620 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   11.053 | 1.7057e-02 | 1.0000e+00 |        2620 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   11.579 | 1.2350e-02 | 1.0000e+00 |        1897 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   11.579 | 1.2350e-02 | 1.0000e+00 |        1897 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   12.105 | 8.9062e-03 | 1.0000e+00 |        1368 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   12.105 | 8.9062e-03 | 1.0000e+00 |        1368 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   12.632 | 6.8815e-03 | 1.0000e+00 |        1057 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   12.632 | 6.8815e-03 | 1.0000e+00 |        1057 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   13.158 | 4.4727e-03 | 1.0000e+00 |         687 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   13.158 | 4.4727e-03 | 1.0000e+00 |         687 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   13.684 | 2.7604e-03 | 9.4531e-01 |         424 |      153600 |          121 |         128 |         0.0 |iter: 0/100\r",
      "   13.684 | 2.7604e-03 | 9.4531e-01 |         424 |      153600 |          121 |         128 |         0.0 |reached target block errors\n",
      "   14.211 | 1.8034e-03 | 9.2188e-01 |         277 |      153600 |          118 |         128 |         0.0 |iter: 0/100\r",
      "   14.211 | 1.8034e-03 | 9.2188e-01 |         277 |      153600 |          118 |         128 |         0.0 |reached target block errors\n",
      "   14.737 | 1.0221e-03 | 6.9531e-01 |         157 |      153600 |           89 |         128 |         0.0 |iter: 0/100\r",
      "   14.737 | 1.0449e-03 | 7.0312e-01 |         321 |      307200 |          180 |         256 |         0.0 |iter: 1/100\r",
      "   14.737 | 1.0449e-03 | 7.0312e-01 |         321 |      307200 |          180 |         256 |         0.0 |reached target block errors\n",
      "   15.263 | 5.8594e-04 | 5.0781e-01 |          90 |      153600 |           65 |         128 |         0.0 |iter: 0/100\r",
      "   15.263 | 5.7943e-04 | 4.7656e-01 |         178 |      307200 |          122 |         256 |         0.0 |iter: 1/100\r",
      "   15.263 | 5.7943e-04 | 4.7656e-01 |         178 |      307200 |          122 |         256 |         0.0 |reached target block errors\n",
      "   15.789 | 2.9297e-04 | 2.7344e-01 |          45 |      153600 |           35 |         128 |         0.0 |iter: 0/100\r",
      "   15.789 | 2.7995e-04 | 2.7344e-01 |          86 |      307200 |           70 |         256 |         0.0 |iter: 1/100\r",
      "   15.789 | 2.7778e-04 | 2.7865e-01 |         128 |      460800 |          107 |         384 |         0.0 |iter: 2/100\r",
      "   15.789 | 2.7778e-04 | 2.7865e-01 |         128 |      460800 |          107 |         384 |         0.0 |reached target block errors\n",
      "   16.316 | 7.1615e-05 | 8.5938e-02 |          11 |      153600 |           11 |         128 |         0.0 |iter: 0/100\r",
      "   16.316 | 9.7656e-05 | 1.0938e-01 |          30 |      307200 |           28 |         256 |         0.0 |iter: 1/100\r",
      "   16.316 | 1.2370e-04 | 1.3542e-01 |          57 |      460800 |           52 |         384 |         0.0 |iter: 2/100\r",
      "   16.316 | 1.2370e-04 | 1.3477e-01 |          76 |      614400 |           69 |         512 |         0.0 |iter: 3/100\r",
      "   16.316 | 1.1849e-04 | 1.2812e-01 |          91 |      768000 |           82 |         640 |         0.0 |iter: 4/100\r",
      "   16.316 | 1.2912e-04 | 1.3932e-01 |         119 |      921600 |          107 |         768 |         0.0 |iter: 5/100\r",
      "   16.316 | 1.2912e-04 | 1.3932e-01 |         119 |      921600 |          107 |         768 |         0.0 |reached target block errors\n",
      "   16.842 | 7.8125e-05 | 8.5938e-02 |          12 |      153600 |           11 |         128 |         0.0 |iter: 0/100\r",
      "   16.842 | 6.5104e-05 | 7.4219e-02 |          20 |      307200 |           19 |         256 |         0.0 |iter: 1/100\r",
      "   16.842 | 5.6424e-05 | 6.5104e-02 |          26 |      460800 |           25 |         384 |         0.0 |iter: 2/100\r",
      "   16.842 | 5.6966e-05 | 6.6406e-02 |          35 |      614400 |           34 |         512 |         0.0 |iter: 3/100\r",
      "   16.842 | 5.9896e-05 | 6.8750e-02 |          46 |      768000 |           44 |         640 |         0.0 |iter: 4/100\r",
      "   16.842 | 6.2934e-05 | 7.2917e-02 |          58 |      921600 |           56 |         768 |         0.0 |iter: 5/100\r",
      "   16.842 | 6.2314e-05 | 7.2545e-02 |          67 |     1075200 |           65 |         896 |         0.0 |iter: 6/100\r",
      "   16.842 | 5.8594e-05 | 6.8359e-02 |          72 |     1228800 |           70 |        1024 |         0.0 |iter: 7/100\r",
      "   16.842 | 6.0041e-05 | 6.9444e-02 |          83 |     1382400 |           80 |        1152 |         0.0 |iter: 8/100\r",
      "   16.842 | 6.1198e-05 | 7.1094e-02 |          94 |     1536000 |           91 |        1280 |         0.0 |iter: 9/100\r",
      "   16.842 | 5.6818e-05 | 6.6051e-02 |          96 |     1689600 |           93 |        1408 |         0.0 |iter: 10/100\r",
      "   16.842 | 5.7509e-05 | 6.7057e-02 |         106 |     1843200 |          103 |        1536 |         0.0 |iter: 11/100\r",
      "   16.842 | 5.7509e-05 | 6.7057e-02 |         106 |     1843200 |          103 |        1536 |         0.0 |reached target block errors\n",
      "   17.368 | 1.3021e-05 | 1.5625e-02 |           2 |      153600 |            2 |         128 |         0.0 |iter: 0/100\r",
      "   17.368 | 1.6276e-05 | 1.9531e-02 |           5 |      307200 |            5 |         256 |         0.0 |iter: 1/100\r",
      "   17.368 | 1.7361e-05 | 2.0833e-02 |           8 |      460800 |            8 |         384 |         0.0 |iter: 2/100\r",
      "   17.368 | 1.9531e-05 | 2.3438e-02 |          12 |      614400 |           12 |         512 |         0.0 |iter: 3/100\r",
      "   17.368 | 2.6042e-05 | 3.1250e-02 |          20 |      768000 |           20 |         640 |         0.0 |iter: 4/100\r",
      "   17.368 | 2.7127e-05 | 3.2552e-02 |          25 |      921600 |           25 |         768 |         0.0 |iter: 5/100\r",
      "   17.368 | 2.8832e-05 | 3.4598e-02 |          31 |     1075200 |           31 |         896 |         0.0 |iter: 6/100\r",
      "   17.368 | 2.9297e-05 | 3.5156e-02 |          36 |     1228800 |           36 |        1024 |         0.0 |iter: 7/100\r",
      "   17.368 | 2.8212e-05 | 3.3854e-02 |          39 |     1382400 |           39 |        1152 |         0.0 |iter: 8/100\r",
      "   17.368 | 2.7995e-05 | 3.3594e-02 |          43 |     1536000 |           43 |        1280 |         0.0 |iter: 9/100\r",
      "   17.368 | 3.1960e-05 | 3.6932e-02 |          54 |     1689600 |           52 |        1408 |         0.0 |iter: 10/100\r",
      "   17.368 | 3.0382e-05 | 3.5156e-02 |          56 |     1843200 |           54 |        1536 |         0.0 |iter: 11/100\r",
      "   17.368 | 3.0048e-05 | 3.4856e-02 |          60 |     1996800 |           58 |        1664 |         0.0 |iter: 12/100\r",
      "   17.368 | 2.9297e-05 | 3.4040e-02 |          63 |     2150400 |           61 |        1792 |         0.0 |iter: 13/100\r",
      "   17.368 | 2.9514e-05 | 3.4375e-02 |          68 |     2304000 |           66 |        1920 |         0.0 |iter: 14/100\r",
      "   17.368 | 2.9297e-05 | 3.4180e-02 |          72 |     2457600 |           70 |        2048 |         0.0 |iter: 15/100\r",
      "   17.368 | 3.0254e-05 | 3.5386e-02 |          79 |     2611200 |           77 |        2176 |         0.0 |iter: 16/100\r",
      "   17.368 | 3.0020e-05 | 3.5156e-02 |          83 |     2764800 |           81 |        2304 |         0.0 |iter: 17/100\r",
      "   17.368 | 2.8783e-05 | 3.3717e-02 |          84 |     2918400 |           82 |        2432 |         0.0 |iter: 18/100\r",
      "   17.368 | 2.8971e-05 | 3.3984e-02 |          89 |     3072000 |           87 |        2560 |         0.0 |iter: 19/100\r",
      "   17.368 | 2.8522e-05 | 3.3482e-02 |          92 |     3225600 |           90 |        2688 |         0.0 |iter: 20/100\r",
      "   17.368 | 2.7817e-05 | 3.2670e-02 |          94 |     3379200 |           92 |        2816 |         0.0 |iter: 21/100\r",
      "   17.368 | 2.6891e-05 | 3.1590e-02 |          95 |     3532800 |           93 |        2944 |         0.0 |iter: 22/100\r",
      "   17.368 | 2.7127e-05 | 3.1901e-02 |         100 |     3686400 |           98 |        3072 |         0.0 |iter: 23/100\r",
      "   17.368 | 2.7083e-05 | 3.1875e-02 |         104 |     3840000 |          102 |        3200 |         0.0 |iter: 24/100\r",
      "   17.368 | 2.7083e-05 | 3.1875e-02 |         104 |     3840000 |          102 |        3200 |         0.0 |reached target block errors\n",
      "   17.895 | 0.0000e+00 | 0.0000e+00 |           0 |      153600 |            0 |         128 |         0.0 |iter: 0/100\r",
      "   17.895 | 0.0000e+00 | 0.0000e+00 |           0 |      307200 |            0 |         256 |         0.0 |iter: 1/100\r",
      "   17.895 | 4.3403e-06 | 5.2083e-03 |           2 |      460800 |            2 |         384 |         0.0 |iter: 2/100\r",
      "   17.895 | 3.2552e-06 | 3.9062e-03 |           2 |      614400 |            2 |         512 |         0.0 |iter: 3/100\r",
      "   17.895 | 2.6042e-06 | 3.1250e-03 |           2 |      768000 |            2 |         640 |         0.0 |iter: 4/100\r",
      "   17.895 | 2.1701e-06 | 2.6042e-03 |           2 |      921600 |            2 |         768 |         0.0 |iter: 5/100\r",
      "   17.895 | 2.7902e-06 | 3.3482e-03 |           3 |     1075200 |            3 |         896 |         0.0 |iter: 6/100\r",
      "   17.895 | 4.0690e-06 | 4.8828e-03 |           5 |     1228800 |            5 |        1024 |         0.0 |iter: 7/100\r",
      "   17.895 | 3.6169e-06 | 4.3403e-03 |           5 |     1382400 |            5 |        1152 |         0.0 |iter: 8/100\r",
      "   17.895 | 3.9063e-06 | 4.6875e-03 |           6 |     1536000 |            6 |        1280 |         0.0 |iter: 9/100\r",
      "   17.895 | 4.7348e-06 | 5.6818e-03 |           8 |     1689600 |            8 |        1408 |         0.0 |iter: 10/100\r",
      "   17.895 | 4.3403e-06 | 5.2083e-03 |           8 |     1843200 |            8 |        1536 |         0.0 |iter: 11/100\r",
      "   17.895 | 6.0096e-06 | 7.2115e-03 |          12 |     1996800 |           12 |        1664 |         0.0 |iter: 12/100\r",
      "   17.895 | 6.5104e-06 | 7.8125e-03 |          14 |     2150400 |           14 |        1792 |         0.0 |iter: 13/100\r",
      "   17.895 | 6.5104e-06 | 7.8125e-03 |          15 |     2304000 |           15 |        1920 |         0.0 |iter: 14/100\r",
      "   17.895 | 6.9173e-06 | 7.8125e-03 |          17 |     2457600 |           16 |        2048 |         0.0 |iter: 15/100\r",
      "   17.895 | 6.8934e-06 | 7.8125e-03 |          18 |     2611200 |           17 |        2176 |         0.0 |iter: 16/100\r",
      "   17.895 | 6.8721e-06 | 7.8125e-03 |          19 |     2764800 |           18 |        2304 |         0.0 |iter: 17/100\r",
      "   17.895 | 6.8531e-06 | 7.8125e-03 |          20 |     2918400 |           19 |        2432 |         0.0 |iter: 18/100\r",
      "   17.895 | 6.8359e-06 | 7.8125e-03 |          21 |     3072000 |           20 |        2560 |         0.0 |iter: 19/100\r",
      "   17.895 | 6.8204e-06 | 7.8125e-03 |          22 |     3225600 |           21 |        2688 |         0.0 |iter: 20/100\r",
      "   17.895 | 6.8063e-06 | 7.8125e-03 |          23 |     3379200 |           22 |        2816 |         0.0 |iter: 21/100\r",
      "   17.895 | 6.5104e-06 | 7.4728e-03 |          23 |     3532800 |           22 |        2944 |         0.0 |iter: 22/100\r",
      "   17.895 | 6.7817e-06 | 7.8125e-03 |          25 |     3686400 |           24 |        3072 |         0.0 |iter: 23/100\r",
      "   17.895 | 6.5104e-06 | 7.5000e-03 |          25 |     3840000 |           24 |        3200 |         0.0 |iter: 24/100\r",
      "   17.895 | 6.5104e-06 | 7.5120e-03 |          26 |     3993600 |           25 |        3328 |         0.0 |iter: 25/100\r",
      "   17.895 | 6.7515e-06 | 7.8125e-03 |          28 |     4147200 |           27 |        3456 |         0.0 |iter: 26/100\r",
      "   17.895 | 6.9754e-06 | 8.0915e-03 |          30 |     4300800 |           29 |        3584 |         0.0 |iter: 27/100\r",
      "   17.895 | 7.1839e-06 | 8.3513e-03 |          32 |     4454400 |           31 |        3712 |         0.0 |iter: 28/100\r",
      "   17.895 | 7.3785e-06 | 8.5938e-03 |          34 |     4608000 |           33 |        3840 |         0.0 |iter: 29/100\r",
      "   17.895 | 7.3505e-06 | 8.5685e-03 |          35 |     4761600 |           34 |        3968 |         0.0 |iter: 30/100\r",
      "   17.895 | 7.1208e-06 | 8.3008e-03 |          35 |     4915200 |           34 |        4096 |         0.0 |iter: 31/100\r",
      "   17.895 | 6.9050e-06 | 8.0492e-03 |          35 |     5068800 |           34 |        4224 |         0.0 |iter: 32/100\r",
      "   17.895 | 6.7019e-06 | 7.8125e-03 |          35 |     5222400 |           34 |        4352 |         0.0 |iter: 33/100\r",
      "   17.895 | 6.6964e-06 | 7.8125e-03 |          36 |     5376000 |           35 |        4480 |         0.0 |iter: 34/100\r",
      "   17.895 | 6.6913e-06 | 7.8125e-03 |          37 |     5529600 |           36 |        4608 |         0.0 |iter: 35/100\r",
      "   17.895 | 6.6864e-06 | 7.8125e-03 |          38 |     5683200 |           37 |        4736 |         0.0 |iter: 36/100\r",
      "   17.895 | 6.8531e-06 | 8.0181e-03 |          40 |     5836800 |           39 |        4864 |         0.0 |iter: 37/100\r",
      "   17.895 | 6.8443e-06 | 8.0128e-03 |          41 |     5990400 |           40 |        4992 |         0.0 |iter: 38/100\r",
      "   17.895 | 6.8359e-06 | 8.0078e-03 |          42 |     6144000 |           41 |        5120 |         0.1 |iter: 39/100\r",
      "   17.895 | 6.8280e-06 | 8.0030e-03 |          43 |     6297600 |           42 |        5248 |         0.1 |iter: 40/100\r",
      "   17.895 | 6.9754e-06 | 8.1845e-03 |          45 |     6451200 |           44 |        5376 |         0.1 |iter: 41/100\r",
      "   17.895 | 7.1160e-06 | 8.3576e-03 |          47 |     6604800 |           46 |        5504 |         0.1 |iter: 42/100\r",
      "   17.895 | 7.1023e-06 | 8.3452e-03 |          48 |     6758400 |           47 |        5632 |         0.1 |iter: 43/100\r",
      "   17.895 | 7.0891e-06 | 8.3333e-03 |          49 |     6912000 |           48 |        5760 |         0.1 |iter: 44/100\r",
      "   17.895 | 7.0765e-06 | 8.3220e-03 |          50 |     7065600 |           49 |        5888 |         0.1 |iter: 45/100\r",
      "   17.895 | 7.0645e-06 | 8.3112e-03 |          51 |     7219200 |           50 |        6016 |         0.1 |iter: 46/100\r",
      "   17.895 | 6.9173e-06 | 8.1380e-03 |          51 |     7372800 |           50 |        6144 |         0.1 |iter: 47/100\r",
      "   17.895 | 7.0419e-06 | 8.2908e-03 |          53 |     7526400 |           52 |        6272 |         0.1 |iter: 48/100\r",
      "   17.895 | 6.9010e-06 | 8.1250e-03 |          53 |     7680000 |           52 |        6400 |         0.1 |iter: 49/100\r",
      "   17.895 | 6.8934e-06 | 8.1189e-03 |          54 |     7833600 |           53 |        6528 |         0.1 |iter: 50/100\r",
      "   17.895 | 6.7608e-06 | 7.9627e-03 |          54 |     7987200 |           53 |        6656 |         0.1 |iter: 51/100\r",
      "   17.895 | 7.0018e-06 | 8.1073e-03 |          57 |     8140800 |           55 |        6784 |         0.1 |iter: 52/100\r",
      "   17.895 | 6.8721e-06 | 7.9572e-03 |          57 |     8294400 |           55 |        6912 |         0.1 |iter: 53/100\r",
      "   17.895 | 6.9839e-06 | 8.0966e-03 |          59 |     8448000 |           57 |        7040 |         0.1 |iter: 54/100\r",
      "   17.895 | 6.9754e-06 | 8.0915e-03 |          60 |     8601600 |           58 |        7168 |         0.1 |iter: 55/100\r",
      "   17.895 | 7.1957e-06 | 8.3607e-03 |          63 |     8755200 |           61 |        7296 |         0.1 |iter: 56/100\r",
      "   17.895 | 7.4084e-06 | 8.6207e-03 |          66 |     8908800 |           64 |        7424 |         0.1 |iter: 57/100\r",
      "   17.895 | 7.5035e-06 | 8.7394e-03 |          68 |     9062400 |           66 |        7552 |         0.1 |iter: 58/100\r",
      "   17.895 | 7.3785e-06 | 8.5938e-03 |          68 |     9216000 |           66 |        7680 |         0.1 |iter: 59/100\r",
      "   17.895 | 7.2575e-06 | 8.4529e-03 |          68 |     9369600 |           66 |        7808 |         0.1 |iter: 60/100\r",
      "   17.895 | 7.2455e-06 | 8.4425e-03 |          69 |     9523200 |           67 |        7936 |         0.1 |iter: 61/100\r",
      "   17.895 | 7.2338e-06 | 8.4325e-03 |          70 |     9676800 |           68 |        8064 |         0.1 |iter: 62/100\r",
      "   17.895 | 7.2225e-06 | 8.4229e-03 |          71 |     9830400 |           69 |        8192 |         0.1 |iter: 63/100\r",
      "   17.895 | 7.2115e-06 | 8.4135e-03 |          72 |     9984000 |           70 |        8320 |         0.1 |iter: 64/100\r",
      "   17.895 | 7.1023e-06 | 8.2860e-03 |          72 |    10137600 |           70 |        8448 |         0.1 |iter: 65/100\r",
      "   17.895 | 6.9963e-06 | 8.1623e-03 |          72 |    10291200 |           70 |        8576 |         0.1 |iter: 66/100\r",
      "   17.895 | 7.0849e-06 | 8.2721e-03 |          74 |    10444800 |           72 |        8704 |         0.1 |iter: 67/100\r",
      "   17.895 | 7.1709e-06 | 8.3786e-03 |          76 |    10598400 |           74 |        8832 |         0.1 |iter: 68/100\r",
      "   17.895 | 7.1615e-06 | 8.3705e-03 |          77 |    10752000 |           75 |        8960 |         0.1 |iter: 69/100\r",
      "   17.895 | 7.1523e-06 | 8.3627e-03 |          78 |    10905600 |           76 |        9088 |         0.1 |iter: 70/100\r",
      "   17.895 | 7.0530e-06 | 8.2465e-03 |          78 |    11059200 |           76 |        9216 |         0.1 |iter: 71/100\r",
      "   17.895 | 6.9563e-06 | 8.1336e-03 |          78 |    11212800 |           76 |        9344 |         0.1 |iter: 72/100\r",
      "   17.895 | 7.2142e-06 | 8.4459e-03 |          82 |    11366400 |           80 |        9472 |         0.1 |iter: 73/100\r",
      "   17.895 | 7.2049e-06 | 8.4375e-03 |          83 |    11520000 |           81 |        9600 |         0.1 |iter: 74/100\r",
      "   17.895 | 7.2814e-06 | 8.5321e-03 |          85 |    11673600 |           83 |        9728 |         0.1 |iter: 75/100\r",
      "   17.895 | 7.2714e-06 | 8.5227e-03 |          86 |    11827200 |           84 |        9856 |         0.1 |iter: 76/100\r",
      "   17.895 | 7.3451e-06 | 8.6138e-03 |          88 |    11980800 |           86 |        9984 |         0.1 |iter: 77/100\r",
      "   17.895 | 7.5818e-06 | 8.8014e-03 |          92 |    12134400 |           89 |       10112 |         0.1 |iter: 78/100\r",
      "   17.895 | 7.6497e-06 | 8.8867e-03 |          94 |    12288000 |           91 |       10240 |         0.1 |iter: 79/100\r",
      "   17.895 | 7.5553e-06 | 8.7770e-03 |          94 |    12441600 |           91 |       10368 |         0.1 |iter: 80/100\r",
      "   17.895 | 7.6220e-06 | 8.8605e-03 |          96 |    12595200 |           93 |       10496 |         0.1 |iter: 81/100\r",
      "   17.895 | 7.5301e-06 | 8.7538e-03 |          96 |    12748800 |           93 |       10624 |         0.1 |iter: 82/100\r",
      "   17.895 | 7.7505e-06 | 9.0216e-03 |         100 |    12902400 |           97 |       10752 |         0.1 |iter: 83/100\r",
      "   17.895 | 7.7359e-06 | 9.0074e-03 |         101 |    13056000 |           98 |       10880 |         0.1 |iter: 84/100\r",
      "   17.895 | 7.7974e-06 | 9.0843e-03 |         103 |    13209600 |          100 |       11008 |         0.1 |iter: 85/100\r",
      "   17.895 | 7.7974e-06 | 9.0843e-03 |         103 |    13209600 |          100 |       11008 |         0.1 |reached target block errors\n",
      "   18.421 | 0.0000e+00 | 0.0000e+00 |           0 |      153600 |            0 |         128 |         0.0 |iter: 0/100\r",
      "   18.421 | 6.5104e-06 | 7.8125e-03 |           2 |      307200 |            2 |         256 |         0.0 |iter: 1/100\r",
      "   18.421 | 4.3403e-06 | 5.2083e-03 |           2 |      460800 |            2 |         384 |         0.0 |iter: 2/100\r",
      "   18.421 | 3.2552e-06 | 3.9062e-03 |           2 |      614400 |            2 |         512 |         0.0 |iter: 3/100\r",
      "   18.421 | 2.6042e-06 | 3.1250e-03 |           2 |      768000 |            2 |         640 |         0.0 |iter: 4/100\r",
      "   18.421 | 2.1701e-06 | 2.6042e-03 |           2 |      921600 |            2 |         768 |         0.0 |iter: 5/100\r",
      "   18.421 | 1.8601e-06 | 2.2321e-03 |           2 |     1075200 |            2 |         896 |         0.0 |iter: 6/100\r",
      "   18.421 | 1.6276e-06 | 1.9531e-03 |           2 |     1228800 |            2 |        1024 |         0.0 |iter: 7/100\r",
      "   18.421 | 1.4468e-06 | 1.7361e-03 |           2 |     1382400 |            2 |        1152 |         0.0 |iter: 8/100\r",
      "   18.421 | 1.3021e-06 | 1.5625e-03 |           2 |     1536000 |            2 |        1280 |         0.0 |iter: 9/100\r",
      "   18.421 | 1.1837e-06 | 1.4205e-03 |           2 |     1689600 |            2 |        1408 |         0.0 |iter: 10/100\r",
      "   18.421 | 1.0851e-06 | 1.3021e-03 |           2 |     1843200 |            2 |        1536 |         0.0 |iter: 11/100\r",
      "   18.421 | 1.5024e-06 | 1.8029e-03 |           3 |     1996800 |            3 |        1664 |         0.0 |iter: 12/100\r",
      "   18.421 | 1.8601e-06 | 2.2321e-03 |           4 |     2150400 |            4 |        1792 |         0.0 |iter: 13/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   18.421 | 1.7361e-06 | 2.0833e-03 |           4 |     2304000 |            4 |        1920 |         0.0 |iter: 14/100\r",
      "   18.421 | 1.6276e-06 | 1.9531e-03 |           4 |     2457600 |            4 |        2048 |         0.0 |iter: 15/100\r",
      "   18.421 | 1.5319e-06 | 1.8382e-03 |           4 |     2611200 |            4 |        2176 |         0.0 |iter: 16/100\r",
      "   18.421 | 2.1701e-06 | 2.6042e-03 |           6 |     2764800 |            6 |        2304 |         0.0 |iter: 17/100\r",
      "   18.421 | 2.0559e-06 | 2.4671e-03 |           6 |     2918400 |            6 |        2432 |         0.0 |iter: 18/100\r",
      "   18.421 | 1.9531e-06 | 2.3437e-03 |           6 |     3072000 |            6 |        2560 |         0.0 |iter: 19/100\r",
      "   18.421 | 1.8601e-06 | 2.2321e-03 |           6 |     3225600 |            6 |        2688 |         0.0 |iter: 20/100\r",
      "   18.421 | 1.7756e-06 | 2.1307e-03 |           6 |     3379200 |            6 |        2816 |         0.0 |iter: 21/100\r",
      "   18.421 | 1.6984e-06 | 2.0380e-03 |           6 |     3532800 |            6 |        2944 |         0.0 |iter: 22/100\r",
      "   18.421 | 1.8989e-06 | 2.2786e-03 |           7 |     3686400 |            7 |        3072 |         0.0 |iter: 23/100\r",
      "   18.421 | 1.8229e-06 | 2.1875e-03 |           7 |     3840000 |            7 |        3200 |         0.0 |iter: 24/100\r",
      "   18.421 | 1.7528e-06 | 2.1034e-03 |           7 |     3993600 |            7 |        3328 |         0.0 |iter: 25/100\r",
      "   18.421 | 1.6879e-06 | 2.0255e-03 |           7 |     4147200 |            7 |        3456 |         0.0 |iter: 26/100\r",
      "   18.421 | 2.0926e-06 | 2.5112e-03 |           9 |     4300800 |            9 |        3584 |         0.0 |iter: 27/100\r",
      "   18.421 | 2.0205e-06 | 2.4246e-03 |           9 |     4454400 |            9 |        3712 |         0.0 |iter: 28/100\r",
      "   18.421 | 1.9531e-06 | 2.3437e-03 |           9 |     4608000 |            9 |        3840 |         0.0 |iter: 29/100\r",
      "   18.421 | 1.8901e-06 | 2.2681e-03 |           9 |     4761600 |            9 |        3968 |         0.0 |iter: 30/100\r",
      "   18.421 | 1.8311e-06 | 2.1973e-03 |           9 |     4915200 |            9 |        4096 |         0.0 |iter: 31/100\r",
      "   18.421 | 1.7756e-06 | 2.1307e-03 |           9 |     5068800 |            9 |        4224 |         0.0 |iter: 32/100\r",
      "   18.421 | 1.7233e-06 | 2.0680e-03 |           9 |     5222400 |            9 |        4352 |         0.0 |iter: 33/100\r",
      "   18.421 | 1.6741e-06 | 2.0089e-03 |           9 |     5376000 |            9 |        4480 |         0.0 |iter: 34/100\r",
      "   18.421 | 1.6276e-06 | 1.9531e-03 |           9 |     5529600 |            9 |        4608 |         0.0 |iter: 35/100\r",
      "   18.421 | 1.7596e-06 | 2.1115e-03 |          10 |     5683200 |           10 |        4736 |         0.0 |iter: 36/100\r",
      "   18.421 | 1.7133e-06 | 2.0559e-03 |          10 |     5836800 |           10 |        4864 |         0.0 |iter: 37/100\r",
      "   18.421 | 2.0032e-06 | 2.4038e-03 |          12 |     5990400 |           12 |        4992 |         0.0 |iter: 38/100\r",
      "   18.421 | 1.9531e-06 | 2.3437e-03 |          12 |     6144000 |           12 |        5120 |         0.1 |iter: 39/100\r",
      "   18.421 | 1.9055e-06 | 2.2866e-03 |          12 |     6297600 |           12 |        5248 |         0.1 |iter: 40/100\r",
      "   18.421 | 1.8601e-06 | 2.2321e-03 |          12 |     6451200 |           12 |        5376 |         0.1 |iter: 41/100\r",
      "   18.421 | 1.8169e-06 | 2.1802e-03 |          12 |     6604800 |           12 |        5504 |         0.1 |iter: 42/100\r",
      "   18.421 | 1.9235e-06 | 2.3082e-03 |          13 |     6758400 |           13 |        5632 |         0.1 |iter: 43/100\r",
      "   18.421 | 2.0255e-06 | 2.4306e-03 |          14 |     6912000 |           14 |        5760 |         0.1 |iter: 44/100\r",
      "   18.421 | 2.1230e-06 | 2.5476e-03 |          15 |     7065600 |           15 |        5888 |         0.1 |iter: 45/100\r",
      "   18.421 | 2.0778e-06 | 2.4934e-03 |          15 |     7219200 |           15 |        6016 |         0.1 |iter: 46/100\r",
      "   18.421 | 2.0345e-06 | 2.4414e-03 |          15 |     7372800 |           15 |        6144 |         0.1 |iter: 47/100\r",
      "   18.421 | 1.9930e-06 | 2.3916e-03 |          15 |     7526400 |           15 |        6272 |         0.1 |iter: 48/100\r",
      "   18.421 | 2.0833e-06 | 2.5000e-03 |          16 |     7680000 |           16 |        6400 |         0.1 |iter: 49/100\r",
      "   18.421 | 2.0425e-06 | 2.4510e-03 |          16 |     7833600 |           16 |        6528 |         0.1 |iter: 50/100\r",
      "   18.421 | 2.0032e-06 | 2.4038e-03 |          16 |     7987200 |           16 |        6656 |         0.1 |iter: 51/100\r",
      "   18.421 | 2.0882e-06 | 2.5059e-03 |          17 |     8140800 |           17 |        6784 |         0.1 |iter: 52/100\r",
      "   18.421 | 2.0496e-06 | 2.4595e-03 |          17 |     8294400 |           17 |        6912 |         0.1 |iter: 53/100\r",
      "   18.421 | 2.0123e-06 | 2.4148e-03 |          17 |     8448000 |           17 |        7040 |         0.1 |iter: 54/100\r",
      "   18.421 | 1.9764e-06 | 2.3717e-03 |          17 |     8601600 |           17 |        7168 |         0.1 |iter: 55/100\r",
      "   18.421 | 1.9417e-06 | 2.3300e-03 |          17 |     8755200 |           17 |        7296 |         0.1 |iter: 56/100\r",
      "   18.421 | 1.9082e-06 | 2.2899e-03 |          17 |     8908800 |           17 |        7424 |         0.1 |iter: 57/100\r",
      "   18.421 | 1.8759e-06 | 2.2511e-03 |          17 |     9062400 |           17 |        7552 |         0.1 |iter: 58/100\r",
      "   18.421 | 1.8446e-06 | 2.2135e-03 |          17 |     9216000 |           17 |        7680 |         0.1 |iter: 59/100\r",
      "   18.421 | 1.8144e-06 | 2.1773e-03 |          17 |     9369600 |           17 |        7808 |         0.1 |iter: 60/100\r",
      "   18.421 | 1.9951e-06 | 2.3942e-03 |          19 |     9523200 |           19 |        7936 |         0.1 |iter: 61/100\r",
      "   18.421 | 1.9635e-06 | 2.3562e-03 |          19 |     9676800 |           19 |        8064 |         0.1 |iter: 62/100\r",
      "   18.421 | 1.9328e-06 | 2.3193e-03 |          19 |     9830400 |           19 |        8192 |         0.1 |iter: 63/100\r",
      "   18.421 | 2.0032e-06 | 2.4038e-03 |          20 |     9984000 |           20 |        8320 |         0.1 |iter: 64/100\r",
      "   18.421 | 1.9729e-06 | 2.3674e-03 |          20 |    10137600 |           20 |        8448 |         0.1 |iter: 65/100\r",
      "   18.421 | 1.9434e-06 | 2.3321e-03 |          20 |    10291200 |           20 |        8576 |         0.1 |iter: 66/100\r",
      "   18.421 | 1.9148e-06 | 2.2978e-03 |          20 |    10444800 |           20 |        8704 |         0.1 |iter: 67/100\r",
      "   18.421 | 1.8871e-06 | 2.2645e-03 |          20 |    10598400 |           20 |        8832 |         0.1 |iter: 68/100\r",
      "   18.421 | 1.9531e-06 | 2.3437e-03 |          21 |    10752000 |           21 |        8960 |         0.1 |iter: 69/100\r",
      "   18.421 | 2.1090e-06 | 2.5308e-03 |          23 |    10905600 |           23 |        9088 |         0.1 |iter: 70/100\r",
      "   18.421 | 2.1701e-06 | 2.6042e-03 |          24 |    11059200 |           24 |        9216 |         0.1 |iter: 71/100\r",
      "   18.421 | 2.2296e-06 | 2.6755e-03 |          25 |    11212800 |           25 |        9344 |         0.1 |iter: 72/100\r",
      "   18.421 | 2.3754e-06 | 2.8505e-03 |          27 |    11366400 |           27 |        9472 |         0.1 |iter: 73/100\r",
      "   18.421 | 2.4306e-06 | 2.9167e-03 |          28 |    11520000 |           28 |        9600 |         0.1 |iter: 74/100\r",
      "   18.421 | 2.3986e-06 | 2.8783e-03 |          28 |    11673600 |           28 |        9728 |         0.1 |iter: 75/100\r",
      "   18.421 | 2.3674e-06 | 2.8409e-03 |          28 |    11827200 |           28 |        9856 |         0.1 |iter: 76/100\r",
      "   18.421 | 2.3371e-06 | 2.8045e-03 |          28 |    11980800 |           28 |        9984 |         0.1 |iter: 77/100\r",
      "   18.421 | 2.3899e-06 | 2.8679e-03 |          29 |    12134400 |           29 |       10112 |         0.1 |iter: 78/100\r",
      "   18.421 | 2.3600e-06 | 2.8320e-03 |          29 |    12288000 |           29 |       10240 |         0.1 |iter: 79/100\r",
      "   18.421 | 2.3309e-06 | 2.7971e-03 |          29 |    12441600 |           29 |       10368 |         0.1 |iter: 80/100\r",
      "   18.421 | 2.3025e-06 | 2.7630e-03 |          29 |    12595200 |           29 |       10496 |         0.1 |iter: 81/100\r",
      "   18.421 | 2.3532e-06 | 2.8238e-03 |          30 |    12748800 |           30 |       10624 |         0.1 |iter: 82/100\r",
      "   18.421 | 2.4802e-06 | 2.9762e-03 |          32 |    12902400 |           32 |       10752 |         0.1 |iter: 83/100\r",
      "   18.421 | 2.4510e-06 | 2.9412e-03 |          32 |    13056000 |           32 |       10880 |         0.1 |iter: 84/100\r",
      "   18.421 | 2.4225e-06 | 2.9070e-03 |          32 |    13209600 |           32 |       11008 |         0.1 |iter: 85/100\r",
      "   18.421 | 2.3946e-06 | 2.8736e-03 |          32 |    13363200 |           32 |       11136 |         0.1 |iter: 86/100\r",
      "   18.421 | 2.3674e-06 | 2.8409e-03 |          32 |    13516800 |           32 |       11264 |         0.1 |iter: 87/100\r",
      "   18.421 | 2.4140e-06 | 2.8968e-03 |          33 |    13670400 |           33 |       11392 |         0.1 |iter: 88/100\r",
      "   18.421 | 2.3872e-06 | 2.8646e-03 |          33 |    13824000 |           33 |       11520 |         0.1 |iter: 89/100\r",
      "   18.421 | 2.4325e-06 | 2.9190e-03 |          34 |    13977600 |           34 |       11648 |         0.1 |iter: 90/100\r",
      "   18.421 | 2.4060e-06 | 2.8872e-03 |          34 |    14131200 |           34 |       11776 |         0.1 |iter: 91/100\r",
      "   18.421 | 2.3802e-06 | 2.8562e-03 |          34 |    14284800 |           34 |       11904 |         0.1 |iter: 92/100\r",
      "   18.421 | 2.3548e-06 | 2.8258e-03 |          34 |    14438400 |           34 |       12032 |         0.1 |iter: 93/100\r",
      "   18.421 | 2.3300e-06 | 2.7961e-03 |          34 |    14592000 |           34 |       12160 |         0.1 |iter: 94/100\r",
      "   18.421 | 2.4414e-06 | 2.9297e-03 |          36 |    14745600 |           36 |       12288 |         0.1 |iter: 95/100\r",
      "   18.421 | 2.4162e-06 | 2.8995e-03 |          36 |    14899200 |           36 |       12416 |         0.1 |iter: 96/100\r",
      "   18.421 | 2.4580e-06 | 2.9496e-03 |          37 |    15052800 |           37 |       12544 |         0.1 |iter: 97/100\r",
      "   18.421 | 2.4332e-06 | 2.9198e-03 |          37 |    15206400 |           37 |       12672 |         0.1 |iter: 98/100\r",
      "   18.421 | 2.4089e-06 | 2.8906e-03 |          37 |    15360000 |           37 |       12800 |         0.1 |iter: 99/100\r",
      "   18.421 | 2.4089e-06 | 2.8906e-03 |          37 |    15360000 |           37 |       12800 |         0.1 |reached max iterations\n",
      "   18.947 | 0.0000e+00 | 0.0000e+00 |           0 |      153600 |            0 |         128 |         0.0 |iter: 0/100\r",
      "   18.947 | 0.0000e+00 | 0.0000e+00 |           0 |      307200 |            0 |         256 |         0.0 |iter: 1/100\r",
      "   18.947 | 0.0000e+00 | 0.0000e+00 |           0 |      460800 |            0 |         384 |         0.0 |iter: 2/100\r",
      "   18.947 | 0.0000e+00 | 0.0000e+00 |           0 |      614400 |            0 |         512 |         0.0 |iter: 3/100\r",
      "   18.947 | 0.0000e+00 | 0.0000e+00 |           0 |      768000 |            0 |         640 |         0.0 |iter: 4/100\r",
      "   18.947 | 0.0000e+00 | 0.0000e+00 |           0 |      921600 |            0 |         768 |         0.0 |iter: 5/100\r",
      "   18.947 | 0.0000e+00 | 0.0000e+00 |           0 |     1075200 |            0 |         896 |         0.0 |iter: 6/100\r",
      "   18.947 | 0.0000e+00 | 0.0000e+00 |           0 |     1228800 |            0 |        1024 |         0.0 |iter: 7/100\r",
      "   18.947 | 0.0000e+00 | 0.0000e+00 |           0 |     1382400 |            0 |        1152 |         0.0 |iter: 8/100\r",
      "   18.947 | 0.0000e+00 | 0.0000e+00 |           0 |     1536000 |            0 |        1280 |         0.0 |iter: 9/100\r",
      "   18.947 | 5.9186e-07 | 7.1023e-04 |           1 |     1689600 |            1 |        1408 |         0.0 |iter: 10/100\r",
      "   18.947 | 5.4253e-07 | 6.5104e-04 |           1 |     1843200 |            1 |        1536 |         0.0 |iter: 11/100\r",
      "   18.947 | 5.0080e-07 | 6.0096e-04 |           1 |     1996800 |            1 |        1664 |         0.0 |iter: 12/100\r",
      "   18.947 | 9.3006e-07 | 1.1161e-03 |           2 |     2150400 |            2 |        1792 |         0.0 |iter: 13/100\r",
      "   18.947 | 8.6806e-07 | 1.0417e-03 |           2 |     2304000 |            2 |        1920 |         0.0 |iter: 14/100\r",
      "   18.947 | 8.1380e-07 | 9.7656e-04 |           2 |     2457600 |            2 |        2048 |         0.0 |iter: 15/100\r",
      "   18.947 | 7.6593e-07 | 9.1912e-04 |           2 |     2611200 |            2 |        2176 |         0.0 |iter: 16/100\r",
      "   18.947 | 7.2338e-07 | 8.6806e-04 |           2 |     2764800 |            2 |        2304 |         0.0 |iter: 17/100\r",
      "   18.947 | 6.8531e-07 | 8.2237e-04 |           2 |     2918400 |            2 |        2432 |         0.0 |iter: 18/100\r",
      "   18.947 | 6.5104e-07 | 7.8125e-04 |           2 |     3072000 |            2 |        2560 |         0.0 |iter: 19/100\r",
      "   18.947 | 9.3006e-07 | 1.1161e-03 |           3 |     3225600 |            3 |        2688 |         0.0 |iter: 20/100\r",
      "   18.947 | 8.8778e-07 | 1.0653e-03 |           3 |     3379200 |            3 |        2816 |         0.0 |iter: 21/100\r",
      "   18.947 | 8.4918e-07 | 1.0190e-03 |           3 |     3532800 |            3 |        2944 |         0.0 |iter: 22/100\r",
      "   18.947 | 8.1380e-07 | 9.7656e-04 |           3 |     3686400 |            3 |        3072 |         0.0 |iter: 23/100\r",
      "   18.947 | 7.8125e-07 | 9.3750e-04 |           3 |     3840000 |            3 |        3200 |         0.0 |iter: 24/100\r",
      "   18.947 | 7.5120e-07 | 9.0144e-04 |           3 |     3993600 |            3 |        3328 |         0.0 |iter: 25/100\r",
      "   18.947 | 9.6451e-07 | 1.1574e-03 |           4 |     4147200 |            4 |        3456 |         0.0 |iter: 26/100\r",
      "   18.947 | 9.3006e-07 | 1.1161e-03 |           4 |     4300800 |            4 |        3584 |         0.0 |iter: 27/100\r",
      "   18.947 | 8.9799e-07 | 1.0776e-03 |           4 |     4454400 |            4 |        3712 |         0.0 |iter: 28/100\r",
      "   18.947 | 8.6806e-07 | 1.0417e-03 |           4 |     4608000 |            4 |        3840 |         0.0 |iter: 29/100\r",
      "   18.947 | 1.0501e-06 | 1.2601e-03 |           5 |     4761600 |            5 |        3968 |         0.0 |iter: 30/100\r",
      "   18.947 | 1.0173e-06 | 1.2207e-03 |           5 |     4915200 |            5 |        4096 |         0.0 |iter: 31/100\r",
      "   18.947 | 9.8643e-07 | 1.1837e-03 |           5 |     5068800 |            5 |        4224 |         0.0 |iter: 32/100\r",
      "   18.947 | 9.5741e-07 | 1.1489e-03 |           5 |     5222400 |            5 |        4352 |         0.0 |iter: 33/100\r",
      "   18.947 | 9.3006e-07 | 1.1161e-03 |           5 |     5376000 |            5 |        4480 |         0.0 |iter: 34/100\r",
      "   18.947 | 9.0422e-07 | 1.0851e-03 |           5 |     5529600 |            5 |        4608 |         0.0 |iter: 35/100\r",
      "   18.947 | 8.7979e-07 | 1.0557e-03 |           5 |     5683200 |            5 |        4736 |         0.0 |iter: 36/100\r",
      "   18.947 | 1.0280e-06 | 1.2336e-03 |           6 |     5836800 |            6 |        4864 |         0.0 |iter: 37/100\r",
      "   18.947 | 1.0016e-06 | 1.2019e-03 |           6 |     5990400 |            6 |        4992 |         0.0 |iter: 38/100\r",
      "   18.947 | 9.7656e-07 | 1.1719e-03 |           6 |     6144000 |            6 |        5120 |         0.0 |iter: 39/100\r",
      "   18.947 | 9.5274e-07 | 1.1433e-03 |           6 |     6297600 |            6 |        5248 |         0.0 |iter: 40/100\r",
      "   18.947 | 9.3006e-07 | 1.1161e-03 |           6 |     6451200 |            6 |        5376 |         0.1 |iter: 41/100\r",
      "   18.947 | 9.0843e-07 | 1.0901e-03 |           6 |     6604800 |            6 |        5504 |         0.1 |iter: 42/100\r",
      "   18.947 | 8.8778e-07 | 1.0653e-03 |           6 |     6758400 |            6 |        5632 |         0.1 |iter: 43/100\r",
      "   18.947 | 8.6806e-07 | 1.0417e-03 |           6 |     6912000 |            6 |        5760 |         0.1 |iter: 44/100\r",
      "   18.947 | 8.4918e-07 | 1.0190e-03 |           6 |     7065600 |            6 |        5888 |         0.1 |iter: 45/100\r",
      "   18.947 | 8.3112e-07 | 9.9734e-04 |           6 |     7219200 |            6 |        6016 |         0.1 |iter: 46/100\r",
      "   18.947 | 8.1380e-07 | 9.7656e-04 |           6 |     7372800 |            6 |        6144 |         0.1 |iter: 47/100\r",
      "   18.947 | 7.9719e-07 | 9.5663e-04 |           6 |     7526400 |            6 |        6272 |         0.1 |iter: 48/100\r",
      "   18.947 | 7.8125e-07 | 9.3750e-04 |           6 |     7680000 |            6 |        6400 |         0.1 |iter: 49/100\r",
      "   18.947 | 7.6593e-07 | 9.1912e-04 |           6 |     7833600 |            6 |        6528 |         0.1 |iter: 50/100\r",
      "   18.947 | 8.7640e-07 | 1.0517e-03 |           7 |     7987200 |            7 |        6656 |         0.1 |iter: 51/100\r",
      "   18.947 | 9.8270e-07 | 1.1792e-03 |           8 |     8140800 |            8 |        6784 |         0.1 |iter: 52/100\r",
      "   18.947 | 9.6451e-07 | 1.1574e-03 |           8 |     8294400 |            8 |        6912 |         0.1 |iter: 53/100\r",
      "   18.947 | 1.0653e-06 | 1.2784e-03 |           9 |     8448000 |            9 |        7040 |         0.1 |iter: 54/100\r",
      "   18.947 | 1.0463e-06 | 1.2556e-03 |           9 |     8601600 |            9 |        7168 |         0.1 |iter: 55/100\r",
      "   18.947 | 1.0280e-06 | 1.2336e-03 |           9 |     8755200 |            9 |        7296 |         0.1 |iter: 56/100\r",
      "   18.947 | 1.0102e-06 | 1.2123e-03 |           9 |     8908800 |            9 |        7424 |         0.1 |iter: 57/100\r",
      "   18.947 | 9.9311e-07 | 1.1917e-03 |           9 |     9062400 |            9 |        7552 |         0.1 |iter: 58/100\r",
      "   18.947 | 1.0851e-06 | 1.3021e-03 |          10 |     9216000 |           10 |        7680 |         0.1 |iter: 59/100\r",
      "   18.947 | 1.0673e-06 | 1.2807e-03 |          10 |     9369600 |           10 |        7808 |         0.1 |iter: 60/100\r",
      "   18.947 | 1.0501e-06 | 1.2601e-03 |          10 |     9523200 |           10 |        7936 |         0.1 |iter: 61/100\r",
      "   18.947 | 1.0334e-06 | 1.2401e-03 |          10 |     9676800 |           10 |        8064 |         0.1 |iter: 62/100\r",
      "   18.947 | 1.0173e-06 | 1.2207e-03 |          10 |     9830400 |           10 |        8192 |         0.1 |iter: 63/100\r",
      "   18.947 | 1.0016e-06 | 1.2019e-03 |          10 |     9984000 |           10 |        8320 |         0.1 |iter: 64/100\r",
      "   18.947 | 9.8643e-07 | 1.1837e-03 |          10 |    10137600 |           10 |        8448 |         0.1 |iter: 65/100\r",
      "   18.947 | 9.7170e-07 | 1.1660e-03 |          10 |    10291200 |           10 |        8576 |         0.1 |iter: 66/100\r",
      "   18.947 | 1.0532e-06 | 1.2638e-03 |          11 |    10444800 |           11 |        8704 |         0.1 |iter: 67/100\r",
      "   18.947 | 1.0379e-06 | 1.2455e-03 |          11 |    10598400 |           11 |        8832 |         0.1 |iter: 68/100\r",
      "   18.947 | 1.0231e-06 | 1.2277e-03 |          11 |    10752000 |           11 |        8960 |         0.1 |iter: 69/100\r",
      "   18.947 | 1.0087e-06 | 1.2104e-03 |          11 |    10905600 |           11 |        9088 |         0.1 |iter: 70/100\r",
      "   18.947 | 9.9465e-07 | 1.1936e-03 |          11 |    11059200 |           11 |        9216 |         0.1 |iter: 71/100\r",
      "   18.947 | 9.8102e-07 | 1.1772e-03 |          11 |    11212800 |           11 |        9344 |         0.1 |iter: 72/100\r",
      "   18.947 | 9.6776e-07 | 1.1613e-03 |          11 |    11366400 |           11 |        9472 |         0.1 |iter: 73/100\r",
      "   18.947 | 9.5486e-07 | 1.1458e-03 |          11 |    11520000 |           11 |        9600 |         0.1 |iter: 74/100\r",
      "   18.947 | 1.0280e-06 | 1.2336e-03 |          12 |    11673600 |           12 |        9728 |         0.1 |iter: 75/100\r",
      "   18.947 | 1.0146e-06 | 1.2175e-03 |          12 |    11827200 |           12 |        9856 |         0.1 |iter: 76/100\r",
      "   18.947 | 1.0016e-06 | 1.2019e-03 |          12 |    11980800 |           12 |        9984 |         0.1 |iter: 77/100\r",
      "   18.947 | 1.0713e-06 | 1.2856e-03 |          13 |    12134400 |           13 |       10112 |         0.1 |iter: 78/100\r",
      "   18.947 | 1.0579e-06 | 1.2695e-03 |          13 |    12288000 |           13 |       10240 |         0.1 |iter: 79/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   18.947 | 1.0449e-06 | 1.2539e-03 |          13 |    12441600 |           13 |       10368 |         0.1 |iter: 80/100\r",
      "   18.947 | 1.0321e-06 | 1.2386e-03 |          13 |    12595200 |           13 |       10496 |         0.1 |iter: 81/100\r",
      "   18.947 | 1.0197e-06 | 1.2236e-03 |          13 |    12748800 |           13 |       10624 |         0.1 |iter: 82/100\r",
      "   18.947 | 1.0076e-06 | 1.2091e-03 |          13 |    12902400 |           13 |       10752 |         0.1 |iter: 83/100\r",
      "   18.947 | 9.9571e-07 | 1.1949e-03 |          13 |    13056000 |           13 |       10880 |         0.1 |iter: 84/100\r",
      "   18.947 | 9.8413e-07 | 1.1810e-03 |          13 |    13209600 |           13 |       11008 |         0.1 |iter: 85/100\r",
      "   18.947 | 9.7282e-07 | 1.1674e-03 |          13 |    13363200 |           13 |       11136 |         0.1 |iter: 86/100\r",
      "   18.947 | 9.6177e-07 | 1.1541e-03 |          13 |    13516800 |           13 |       11264 |         0.1 |iter: 87/100\r",
      "   18.947 | 9.5096e-07 | 1.1412e-03 |          13 |    13670400 |           13 |       11392 |         0.1 |iter: 88/100\r",
      "   18.947 | 9.4039e-07 | 1.1285e-03 |          13 |    13824000 |           13 |       11520 |         0.1 |iter: 89/100\r",
      "   18.947 | 9.3006e-07 | 1.1161e-03 |          13 |    13977600 |           13 |       11648 |         0.1 |iter: 90/100\r",
      "   18.947 | 9.1995e-07 | 1.1039e-03 |          13 |    14131200 |           13 |       11776 |         0.1 |iter: 91/100\r",
      "   18.947 | 9.1006e-07 | 1.0921e-03 |          13 |    14284800 |           13 |       11904 |         0.1 |iter: 92/100\r",
      "   18.947 | 9.0038e-07 | 1.0805e-03 |          13 |    14438400 |           13 |       12032 |         0.1 |iter: 93/100\r",
      "   18.947 | 8.9090e-07 | 1.0691e-03 |          13 |    14592000 |           13 |       12160 |         0.1 |iter: 94/100\r",
      "   18.947 | 8.8162e-07 | 1.0579e-03 |          13 |    14745600 |           13 |       12288 |         0.1 |iter: 95/100\r",
      "   18.947 | 8.7253e-07 | 1.0470e-03 |          13 |    14899200 |           13 |       12416 |         0.1 |iter: 96/100\r",
      "   18.947 | 8.6363e-07 | 1.0364e-03 |          13 |    15052800 |           13 |       12544 |         0.1 |iter: 97/100\r",
      "   18.947 | 8.5490e-07 | 1.0259e-03 |          13 |    15206400 |           13 |       12672 |         0.1 |iter: 98/100\r",
      "   18.947 | 8.4635e-07 | 1.0156e-03 |          13 |    15360000 |           13 |       12800 |         0.1 |iter: 99/100\r",
      "   18.947 | 8.4635e-07 | 1.0156e-03 |          13 |    15360000 |           13 |       12800 |         0.1 |reached max iterations\n",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |      153600 |            0 |         128 |         0.0 |iter: 0/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |      307200 |            0 |         256 |         0.0 |iter: 1/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |      460800 |            0 |         384 |         0.0 |iter: 2/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |      614400 |            0 |         512 |         0.0 |iter: 3/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |      768000 |            0 |         640 |         0.0 |iter: 4/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |      921600 |            0 |         768 |         0.0 |iter: 5/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     1075200 |            0 |         896 |         0.0 |iter: 6/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     1228800 |            0 |        1024 |         0.0 |iter: 7/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     1382400 |            0 |        1152 |         0.0 |iter: 8/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     1536000 |            0 |        1280 |         0.0 |iter: 9/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     1689600 |            0 |        1408 |         0.0 |iter: 10/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     1843200 |            0 |        1536 |         0.0 |iter: 11/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     1996800 |            0 |        1664 |         0.0 |iter: 12/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     2150400 |            0 |        1792 |         0.0 |iter: 13/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     2304000 |            0 |        1920 |         0.0 |iter: 14/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     2457600 |            0 |        2048 |         0.0 |iter: 15/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     2611200 |            0 |        2176 |         0.0 |iter: 16/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     2764800 |            0 |        2304 |         0.0 |iter: 17/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     2918400 |            0 |        2432 |         0.0 |iter: 18/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     3072000 |            0 |        2560 |         0.0 |iter: 19/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     3225600 |            0 |        2688 |         0.0 |iter: 20/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     3379200 |            0 |        2816 |         0.0 |iter: 21/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     3532800 |            0 |        2944 |         0.0 |iter: 22/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     3686400 |            0 |        3072 |         0.0 |iter: 23/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     3840000 |            0 |        3200 |         0.0 |iter: 24/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     3993600 |            0 |        3328 |         0.0 |iter: 25/100\r",
      "   19.474 | 2.4113e-07 | 2.8935e-04 |           1 |     4147200 |            1 |        3456 |         0.0 |iter: 26/100\r",
      "   19.474 | 2.3251e-07 | 2.7902e-04 |           1 |     4300800 |            1 |        3584 |         0.0 |iter: 27/100\r",
      "   19.474 | 2.2450e-07 | 2.6940e-04 |           1 |     4454400 |            1 |        3712 |         0.0 |iter: 28/100\r",
      "   19.474 | 2.1701e-07 | 2.6042e-04 |           1 |     4608000 |            1 |        3840 |         0.0 |iter: 29/100\r",
      "   19.474 | 2.1001e-07 | 2.5202e-04 |           1 |     4761600 |            1 |        3968 |         0.0 |iter: 30/100\r",
      "   19.474 | 2.0345e-07 | 2.4414e-04 |           1 |     4915200 |            1 |        4096 |         0.0 |iter: 31/100\r",
      "   19.474 | 1.9729e-07 | 2.3674e-04 |           1 |     5068800 |            1 |        4224 |         0.0 |iter: 32/100\r",
      "   19.474 | 1.9148e-07 | 2.2978e-04 |           1 |     5222400 |            1 |        4352 |         0.0 |iter: 33/100\r",
      "   19.474 | 1.8601e-07 | 2.2321e-04 |           1 |     5376000 |            1 |        4480 |         0.0 |iter: 34/100\r",
      "   19.474 | 1.8084e-07 | 2.1701e-04 |           1 |     5529600 |            1 |        4608 |         0.0 |iter: 35/100\r",
      "   19.474 | 1.7596e-07 | 2.1115e-04 |           1 |     5683200 |            1 |        4736 |         0.0 |iter: 36/100\r",
      "   19.474 | 1.7133e-07 | 2.0559e-04 |           1 |     5836800 |            1 |        4864 |         0.0 |iter: 37/100\r",
      "   19.474 | 1.6693e-07 | 2.0032e-04 |           1 |     5990400 |            1 |        4992 |         0.0 |iter: 38/100\r",
      "   19.474 | 1.6276e-07 | 1.9531e-04 |           1 |     6144000 |            1 |        5120 |         0.0 |iter: 39/100\r",
      "   19.474 | 1.5879e-07 | 1.9055e-04 |           1 |     6297600 |            1 |        5248 |         0.0 |iter: 40/100\r",
      "   19.474 | 1.5501e-07 | 1.8601e-04 |           1 |     6451200 |            1 |        5376 |         0.1 |iter: 41/100\r",
      "   19.474 | 1.5141e-07 | 1.8169e-04 |           1 |     6604800 |            1 |        5504 |         0.1 |iter: 42/100\r",
      "   19.474 | 1.4796e-07 | 1.7756e-04 |           1 |     6758400 |            1 |        5632 |         0.1 |iter: 43/100\r",
      "   19.474 | 1.4468e-07 | 1.7361e-04 |           1 |     6912000 |            1 |        5760 |         0.1 |iter: 44/100\r",
      "   19.474 | 1.4153e-07 | 1.6984e-04 |           1 |     7065600 |            1 |        5888 |         0.1 |iter: 45/100\r",
      "   19.474 | 1.3852e-07 | 1.6622e-04 |           1 |     7219200 |            1 |        6016 |         0.1 |iter: 46/100\r",
      "   19.474 | 1.3563e-07 | 1.6276e-04 |           1 |     7372800 |            1 |        6144 |         0.1 |iter: 47/100\r",
      "   19.474 | 1.3287e-07 | 1.5944e-04 |           1 |     7526400 |            1 |        6272 |         0.1 |iter: 48/100\r",
      "   19.474 | 1.3021e-07 | 1.5625e-04 |           1 |     7680000 |            1 |        6400 |         0.1 |iter: 49/100\r",
      "   19.474 | 1.2766e-07 | 1.5319e-04 |           1 |     7833600 |            1 |        6528 |         0.1 |iter: 50/100\r",
      "   19.474 | 1.2520e-07 | 1.5024e-04 |           1 |     7987200 |            1 |        6656 |         0.1 |iter: 51/100\r",
      "   19.474 | 1.2284e-07 | 1.4741e-04 |           1 |     8140800 |            1 |        6784 |         0.1 |iter: 52/100\r",
      "   19.474 | 1.2056e-07 | 1.4468e-04 |           1 |     8294400 |            1 |        6912 |         0.1 |iter: 53/100\r",
      "   19.474 | 1.1837e-07 | 1.4205e-04 |           1 |     8448000 |            1 |        7040 |         0.1 |iter: 54/100\r",
      "   19.474 | 1.1626e-07 | 1.3951e-04 |           1 |     8601600 |            1 |        7168 |         0.1 |iter: 55/100\r",
      "   19.474 | 1.1422e-07 | 1.3706e-04 |           1 |     8755200 |            1 |        7296 |         0.1 |iter: 56/100\r",
      "   19.474 | 1.1225e-07 | 1.3470e-04 |           1 |     8908800 |            1 |        7424 |         0.1 |iter: 57/100\r",
      "   19.474 | 1.1035e-07 | 1.3242e-04 |           1 |     9062400 |            1 |        7552 |         0.1 |iter: 58/100\r",
      "   19.474 | 1.0851e-07 | 1.3021e-04 |           1 |     9216000 |            1 |        7680 |         0.1 |iter: 59/100\r",
      "   19.474 | 1.0673e-07 | 1.2807e-04 |           1 |     9369600 |            1 |        7808 |         0.1 |iter: 60/100\r",
      "   19.474 | 1.0501e-07 | 1.2601e-04 |           1 |     9523200 |            1 |        7936 |         0.1 |iter: 61/100\r",
      "   19.474 | 1.0334e-07 | 1.2401e-04 |           1 |     9676800 |            1 |        8064 |         0.1 |iter: 62/100\r",
      "   19.474 | 1.0173e-07 | 1.2207e-04 |           1 |     9830400 |            1 |        8192 |         0.1 |iter: 63/100\r",
      "   19.474 | 1.0016e-07 | 1.2019e-04 |           1 |     9984000 |            1 |        8320 |         0.1 |iter: 64/100\r",
      "   19.474 | 9.8643e-08 | 1.1837e-04 |           1 |    10137600 |            1 |        8448 |         0.1 |iter: 65/100\r",
      "   19.474 | 9.7170e-08 | 1.1660e-04 |           1 |    10291200 |            1 |        8576 |         0.1 |iter: 66/100\r",
      "   19.474 | 9.5741e-08 | 1.1489e-04 |           1 |    10444800 |            1 |        8704 |         0.1 |iter: 67/100\r",
      "   19.474 | 9.4354e-08 | 1.1322e-04 |           1 |    10598400 |            1 |        8832 |         0.1 |iter: 68/100\r",
      "   19.474 | 9.3006e-08 | 1.1161e-04 |           1 |    10752000 |            1 |        8960 |         0.1 |iter: 69/100\r",
      "   19.474 | 9.1696e-08 | 1.1004e-04 |           1 |    10905600 |            1 |        9088 |         0.1 |iter: 70/100\r",
      "   19.474 | 9.0422e-08 | 1.0851e-04 |           1 |    11059200 |            1 |        9216 |         0.1 |iter: 71/100\r",
      "   19.474 | 8.9184e-08 | 1.0702e-04 |           1 |    11212800 |            1 |        9344 |         0.1 |iter: 72/100\r",
      "   19.474 | 8.7979e-08 | 1.0557e-04 |           1 |    11366400 |            1 |        9472 |         0.1 |iter: 73/100\r",
      "   19.474 | 8.6806e-08 | 1.0417e-04 |           1 |    11520000 |            1 |        9600 |         0.1 |iter: 74/100\r",
      "   19.474 | 8.5663e-08 | 1.0280e-04 |           1 |    11673600 |            1 |        9728 |         0.1 |iter: 75/100\r",
      "   19.474 | 8.4551e-08 | 1.0146e-04 |           1 |    11827200 |            1 |        9856 |         0.1 |iter: 76/100\r",
      "   19.474 | 8.3467e-08 | 1.0016e-04 |           1 |    11980800 |            1 |        9984 |         0.1 |iter: 77/100\r",
      "   19.474 | 8.2410e-08 | 9.8892e-05 |           1 |    12134400 |            1 |       10112 |         0.1 |iter: 78/100\r",
      "   19.474 | 8.1380e-08 | 9.7656e-05 |           1 |    12288000 |            1 |       10240 |         0.1 |iter: 79/100\r",
      "   19.474 | 8.0376e-08 | 9.6451e-05 |           1 |    12441600 |            1 |       10368 |         0.1 |iter: 80/100\r",
      "   19.474 | 7.9395e-08 | 9.5274e-05 |           1 |    12595200 |            1 |       10496 |         0.1 |iter: 81/100\r",
      "   19.474 | 7.8439e-08 | 9.4127e-05 |           1 |    12748800 |            1 |       10624 |         0.1 |iter: 82/100\r",
      "   19.474 | 7.7505e-08 | 9.3006e-05 |           1 |    12902400 |            1 |       10752 |         0.1 |iter: 83/100\r",
      "   19.474 | 7.6593e-08 | 9.1912e-05 |           1 |    13056000 |            1 |       10880 |         0.1 |iter: 84/100\r",
      "   19.474 | 7.5703e-08 | 9.0843e-05 |           1 |    13209600 |            1 |       11008 |         0.1 |iter: 85/100\r",
      "   19.474 | 7.4832e-08 | 8.9799e-05 |           1 |    13363200 |            1 |       11136 |         0.1 |iter: 86/100\r",
      "   19.474 | 7.3982e-08 | 8.8778e-05 |           1 |    13516800 |            1 |       11264 |         0.1 |iter: 87/100\r",
      "   19.474 | 7.3151e-08 | 8.7781e-05 |           1 |    13670400 |            1 |       11392 |         0.1 |iter: 88/100\r",
      "   19.474 | 7.2338e-08 | 8.6806e-05 |           1 |    13824000 |            1 |       11520 |         0.1 |iter: 89/100\r",
      "   19.474 | 7.1543e-08 | 8.5852e-05 |           1 |    13977600 |            1 |       11648 |         0.1 |iter: 90/100\r",
      "   19.474 | 7.0765e-08 | 8.4918e-05 |           1 |    14131200 |            1 |       11776 |         0.1 |iter: 91/100\r",
      "   19.474 | 7.0004e-08 | 8.4005e-05 |           1 |    14284800 |            1 |       11904 |         0.1 |iter: 92/100\r",
      "   19.474 | 6.9260e-08 | 8.3112e-05 |           1 |    14438400 |            1 |       12032 |         0.1 |iter: 93/100\r",
      "   19.474 | 6.8531e-08 | 8.2237e-05 |           1 |    14592000 |            1 |       12160 |         0.1 |iter: 94/100\r",
      "   19.474 | 6.7817e-08 | 8.1380e-05 |           1 |    14745600 |            1 |       12288 |         0.1 |iter: 95/100\r",
      "   19.474 | 6.7118e-08 | 8.0541e-05 |           1 |    14899200 |            1 |       12416 |         0.1 |iter: 96/100\r",
      "   19.474 | 6.6433e-08 | 7.9719e-05 |           1 |    15052800 |            1 |       12544 |         0.1 |iter: 97/100\r",
      "   19.474 | 6.5762e-08 | 7.8914e-05 |           1 |    15206400 |            1 |       12672 |         0.1 |iter: 98/100\r",
      "   19.474 | 6.5104e-08 | 7.8125e-05 |           1 |    15360000 |            1 |       12800 |         0.1 |iter: 99/100\r",
      "   19.474 | 6.5104e-08 | 7.8125e-05 |           1 |    15360000 |            1 |       12800 |         0.1 |reached max iterations\n",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |      153600 |            0 |         128 |         0.0 |iter: 0/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |      307200 |            0 |         256 |         0.0 |iter: 1/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |      460800 |            0 |         384 |         0.0 |iter: 2/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |      614400 |            0 |         512 |         0.0 |iter: 3/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |      768000 |            0 |         640 |         0.0 |iter: 4/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |      921600 |            0 |         768 |         0.0 |iter: 5/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     1075200 |            0 |         896 |         0.0 |iter: 6/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     1228800 |            0 |        1024 |         0.0 |iter: 7/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     1382400 |            0 |        1152 |         0.0 |iter: 8/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     1536000 |            0 |        1280 |         0.0 |iter: 9/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     1689600 |            0 |        1408 |         0.0 |iter: 10/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     1843200 |            0 |        1536 |         0.0 |iter: 11/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     1996800 |            0 |        1664 |         0.0 |iter: 12/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     2150400 |            0 |        1792 |         0.0 |iter: 13/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     2304000 |            0 |        1920 |         0.0 |iter: 14/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     2457600 |            0 |        2048 |         0.0 |iter: 15/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     2611200 |            0 |        2176 |         0.0 |iter: 16/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     2764800 |            0 |        2304 |         0.0 |iter: 17/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     2918400 |            0 |        2432 |         0.0 |iter: 18/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     3072000 |            0 |        2560 |         0.0 |iter: 19/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     3225600 |            0 |        2688 |         0.0 |iter: 20/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     3379200 |            0 |        2816 |         0.0 |iter: 21/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     3532800 |            0 |        2944 |         0.0 |iter: 22/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     3686400 |            0 |        3072 |         0.0 |iter: 23/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     3840000 |            0 |        3200 |         0.0 |iter: 24/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     3993600 |            0 |        3328 |         0.0 |iter: 25/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     4147200 |            0 |        3456 |         0.0 |iter: 26/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     4300800 |            0 |        3584 |         0.0 |iter: 27/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     4454400 |            0 |        3712 |         0.0 |iter: 28/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     4608000 |            0 |        3840 |         0.0 |iter: 29/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     4761600 |            0 |        3968 |         0.0 |iter: 30/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     4915200 |            0 |        4096 |         0.0 |iter: 31/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     5068800 |            0 |        4224 |         0.0 |iter: 32/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     5222400 |            0 |        4352 |         0.0 |iter: 33/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     5376000 |            0 |        4480 |         0.0 |iter: 34/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     5529600 |            0 |        4608 |         0.0 |iter: 35/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     5683200 |            0 |        4736 |         0.0 |iter: 36/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     5836800 |            0 |        4864 |         0.0 |iter: 37/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     5990400 |            0 |        4992 |         0.0 |iter: 38/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     6144000 |            0 |        5120 |         0.0 |iter: 39/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     6297600 |            0 |        5248 |         0.0 |iter: 40/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     6451200 |            0 |        5376 |         0.1 |iter: 41/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     6604800 |            0 |        5504 |         0.1 |iter: 42/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     6758400 |            0 |        5632 |         0.1 |iter: 43/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     6912000 |            0 |        5760 |         0.1 |iter: 44/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     7065600 |            0 |        5888 |         0.1 |iter: 45/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     7219200 |            0 |        6016 |         0.1 |iter: 46/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     7372800 |            0 |        6144 |         0.1 |iter: 47/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     7526400 |            0 |        6272 |         0.1 |iter: 48/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     7680000 |            0 |        6400 |         0.1 |iter: 49/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     7833600 |            0 |        6528 |         0.1 |iter: 50/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     7987200 |            0 |        6656 |         0.1 |iter: 51/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     8140800 |            0 |        6784 |         0.1 |iter: 52/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     8294400 |            0 |        6912 |         0.1 |iter: 53/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     8448000 |            0 |        7040 |         0.1 |iter: 54/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     8601600 |            0 |        7168 |         0.1 |iter: 55/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     8755200 |            0 |        7296 |         0.1 |iter: 56/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     8908800 |            0 |        7424 |         0.1 |iter: 57/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     9062400 |            0 |        7552 |         0.1 |iter: 58/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     9216000 |            0 |        7680 |         0.1 |iter: 59/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     9369600 |            0 |        7808 |         0.1 |iter: 60/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     9523200 |            0 |        7936 |         0.1 |iter: 61/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     9676800 |            0 |        8064 |         0.1 |iter: 62/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     9830400 |            0 |        8192 |         0.1 |iter: 63/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     9984000 |            0 |        8320 |         0.1 |iter: 64/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    10137600 |            0 |        8448 |         0.1 |iter: 65/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    10291200 |            0 |        8576 |         0.1 |iter: 66/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    10444800 |            0 |        8704 |         0.1 |iter: 67/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    10598400 |            0 |        8832 |         0.1 |iter: 68/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    10752000 |            0 |        8960 |         0.1 |iter: 69/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    10905600 |            0 |        9088 |         0.1 |iter: 70/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    11059200 |            0 |        9216 |         0.1 |iter: 71/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    11212800 |            0 |        9344 |         0.1 |iter: 72/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    11366400 |            0 |        9472 |         0.1 |iter: 73/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    11520000 |            0 |        9600 |         0.1 |iter: 74/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    11673600 |            0 |        9728 |         0.1 |iter: 75/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    11827200 |            0 |        9856 |         0.1 |iter: 76/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    11980800 |            0 |        9984 |         0.1 |iter: 77/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    12134400 |            0 |       10112 |         0.1 |iter: 78/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    12288000 |            0 |       10240 |         0.1 |iter: 79/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    12441600 |            0 |       10368 |         0.1 |iter: 80/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    12595200 |            0 |       10496 |         0.1 |iter: 81/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    12748800 |            0 |       10624 |         0.1 |iter: 82/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    12902400 |            0 |       10752 |         0.1 |iter: 83/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    13056000 |            0 |       10880 |         0.1 |iter: 84/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    13209600 |            0 |       11008 |         0.1 |iter: 85/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    13363200 |            0 |       11136 |         0.1 |iter: 86/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    13516800 |            0 |       11264 |         0.1 |iter: 87/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    13670400 |            0 |       11392 |         0.1 |iter: 88/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    13824000 |            0 |       11520 |         0.1 |iter: 89/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    13977600 |            0 |       11648 |         0.1 |iter: 90/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    14131200 |            0 |       11776 |         0.1 |iter: 91/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    14284800 |            0 |       11904 |         0.1 |iter: 92/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    14438400 |            0 |       12032 |         0.1 |iter: 93/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    14592000 |            0 |       12160 |         0.1 |iter: 94/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    14745600 |            0 |       12288 |         0.1 |iter: 95/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    14899200 |            0 |       12416 |         0.1 |iter: 96/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    15052800 |            0 |       12544 |         0.1 |iter: 97/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    15206400 |            0 |       12672 |         0.1 |iter: 98/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    15360000 |            0 |       12800 |         0.1 |iter: 99/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    15360000 |            0 |       12800 |         0.1 |reached max iterations\n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 20.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     10.0 | 5.2380e-01 | 1.0000e+00 |       80455 |      153600 |          128 |         128 |         0.1 |iter: 0/100\r",
      "     10.0 | 5.2380e-01 | 1.0000e+00 |       80455 |      153600 |          128 |         128 |         0.1 |reached target block errors\n",
      "   10.526 | 5.2522e-01 | 1.0000e+00 |       80674 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   10.526 | 5.2522e-01 | 1.0000e+00 |       80674 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   11.053 | 5.2592e-01 | 1.0000e+00 |       80782 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   11.053 | 5.2592e-01 | 1.0000e+00 |       80782 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   11.579 | 5.2693e-01 | 1.0000e+00 |       80937 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   11.579 | 5.2693e-01 | 1.0000e+00 |       80937 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   12.105 | 5.2683e-01 | 1.0000e+00 |       80921 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   12.105 | 5.2683e-01 | 1.0000e+00 |       80921 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   12.632 | 5.2392e-01 | 1.0000e+00 |       80474 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   12.632 | 5.2392e-01 | 1.0000e+00 |       80474 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   13.158 | 5.2674e-01 | 1.0000e+00 |       80908 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   13.158 | 5.2674e-01 | 1.0000e+00 |       80908 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   13.684 | 5.2599e-01 | 1.0000e+00 |       80792 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   13.684 | 5.2599e-01 | 1.0000e+00 |       80792 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   14.211 | 5.2521e-01 | 1.0000e+00 |       80673 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   14.211 | 5.2521e-01 | 1.0000e+00 |       80673 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   14.737 | 5.2607e-01 | 1.0000e+00 |       80804 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   14.737 | 5.2607e-01 | 1.0000e+00 |       80804 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   15.263 | 5.2429e-01 | 1.0000e+00 |       80531 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   15.263 | 5.2429e-01 | 1.0000e+00 |       80531 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   15.789 | 5.2418e-01 | 1.0000e+00 |       80514 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   15.789 | 5.2418e-01 | 1.0000e+00 |       80514 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   16.316 | 5.2365e-01 | 1.0000e+00 |       80433 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   16.316 | 5.2365e-01 | 1.0000e+00 |       80433 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   16.842 | 5.2637e-01 | 1.0000e+00 |       80851 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   16.842 | 5.2637e-01 | 1.0000e+00 |       80851 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   17.368 | 5.2438e-01 | 1.0000e+00 |       80544 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   17.368 | 5.2438e-01 | 1.0000e+00 |       80544 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   17.895 | 5.2335e-01 | 1.0000e+00 |       80386 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   17.895 | 5.2335e-01 | 1.0000e+00 |       80386 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   18.421 | 5.2558e-01 | 1.0000e+00 |       80729 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   18.421 | 5.2558e-01 | 1.0000e+00 |       80729 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   18.947 | 5.2402e-01 | 1.0000e+00 |       80489 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   18.947 | 5.2402e-01 | 1.0000e+00 |       80489 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   19.474 | 5.2444e-01 | 1.0000e+00 |       80554 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   19.474 | 5.2444e-01 | 1.0000e+00 |       80554 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "     20.0 | 5.2452e-01 | 1.0000e+00 |       80566 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "     20.0 | 5.2452e-01 | 1.0000e+00 |       80566 |      153600 |          128 |         128 |         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 = 10.0\n",
    "EBN0_DB_MAX = 20.0\n",
    "\n",
    "\n",
    "###############################\n",
    "# Baseline\n",
    "###############################\n",
    "\n",
    "class Baseline(nn.Module): # Inherits from PyTorch Module\n",
    "\n",
    "    def __init__(self):\n",
    "\n",
    "        super().__init__() # Must call the PyTorch module initializer\n",
    "\n",
    "        self.constellation = sn.phy.mapping.Constellation(\"qam\", NUM_BITS_PER_SYMBOL)\n",
    "        self.mapper = sn.phy.mapping.Mapper(constellation=self.constellation)\n",
    "        self.demapper = sn.phy.mapping.Demapper(\"app\", constellation=self.constellation)\n",
    "        self.binary_source = sn.phy.mapping.BinarySource()\n",
    "        self.awgn_channel = sn.phy.channel.AWGN()\n",
    "\n",
    "    def forward(self, batch_size, ebno_db):\n",
    "\n",
    "        # no channel coding used; we set coderate=1.0\n",
    "        no = sn.phy.utils.ebnodb2no(ebno_db,\n",
    "                                num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "                                coderate=1.0)\n",
    "        bits = self.binary_source([batch_size, 1200]) # Blocklength set to 1200 bits\n",
    "        x = self.mapper(bits)\n",
    "        y = self.awgn_channel(x, no)\n",
    "        llr = self.demapper(y, no)\n",
    "        return bits, llr\n",
    "\n",
    "###############################\n",
    "# Benchmarking\n",
    "###############################\n",
    "\n",
    "baseline = Baseline()\n",
    "model = End2EndSystem(False).to(sn.phy.config.device)  # Move model to GPU/CPU\n",
    "ber_plots = sn.phy.utils.PlotBER(\"Neural Demapper\")\n",
    "ber_plots.simulate(baseline,\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 occurred\n",
    "                  legend=\"Baseline\",\n",
    "                  soft_estimates=True,\n",
    "                  max_mc_iter=100, # run 100 Monte-Carlo simulations (each with batch_size samples)\n",
    "                  show_fig=False);\n",
    "ber_plots.simulate(model,\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 occurred\n",
    "                  legend=\"Untrained model\",\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": "4982feda-3b93-46dc-a936-bd225d7260dd",
   "metadata": {},
   "source": [
    "## Setting up Training Loops <a class=\"anchor\" id=\"Setting-up-Training-Loops\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257d7e00-7341-4542-bc75-d9a612fba091",
   "metadata": {},
   "source": [
    "Training of end-to-end communication systems consists in iterating over SGD steps.\n",
    "\n",
    "The next cell implements a training loop of `NUM_TRAINING_ITERATIONS` iterations.\n",
    "The training SNR is set to $E_b/N_0 = 15$ dB.\n",
    "\n",
    "At each iteration:\n",
    "- A forward pass through the end-to-end system is performed within a gradient tape\n",
    "- The gradients are computed using the gradient tape, and applied using the Adam optimizer\n",
    "- The estimated loss is periodically printed to follow the progress of training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c53960db-28ee-4f33-8840-7b1373348162",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:09:30.836509Z",
     "iopub.status.busy": "2026-02-13T14:09:30.836369Z",
     "iopub.status.idle": "2026-02-13T14:10:04.601178Z",
     "shell.execute_reply": "2026-02-13T14:10:04.599895Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0/10000  Loss: 6.95E-01\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/10000  Loss: 3.87E-01\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200/10000  Loss: 2.35E-01\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300/10000  Loss: 1.74E-01\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400/10000  Loss: 1.23E-01\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500/10000  Loss: 7.66E-02\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/10000  Loss: 4.08E-02\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "700/10000  Loss: 2.26E-02\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "800/10000  Loss: 1.53E-02\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "900/10000  Loss: 1.13E-02\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000/10000  Loss: 9.11E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1100/10000  Loss: 7.36E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/10000  Loss: 6.51E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1300/10000  Loss: 6.16E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1400/10000  Loss: 5.38E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1500/10000  Loss: 4.76E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1600/10000  Loss: 4.71E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1700/10000  Loss: 4.23E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1800/10000  Loss: 3.93E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1900/10000  Loss: 3.76E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000/10000  Loss: 3.39E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2100/10000  Loss: 3.51E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2200/10000  Loss: 3.17E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2300/10000  Loss: 3.24E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2400/10000  Loss: 3.00E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2500/10000  Loss: 3.03E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2600/10000  Loss: 3.03E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2700/10000  Loss: 3.16E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2800/10000  Loss: 2.93E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2900/10000  Loss: 2.88E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3000/10000  Loss: 2.61E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3100/10000  Loss: 2.65E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3200/10000  Loss: 2.28E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3300/10000  Loss: 2.66E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3400/10000  Loss: 2.37E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3500/10000  Loss: 2.31E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3600/10000  Loss: 2.54E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3700/10000  Loss: 2.55E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3800/10000  Loss: 2.28E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3900/10000  Loss: 2.54E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4000/10000  Loss: 2.54E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4100/10000  Loss: 2.47E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4200/10000  Loss: 2.38E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4300/10000  Loss: 2.31E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4400/10000  Loss: 2.33E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4500/10000  Loss: 2.13E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4600/10000  Loss: 2.28E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4700/10000  Loss: 2.37E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4800/10000  Loss: 2.36E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4900/10000  Loss: 2.08E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000/10000  Loss: 2.35E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5100/10000  Loss: 2.35E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5200/10000  Loss: 2.41E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5300/10000  Loss: 2.18E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5400/10000  Loss: 2.05E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5500/10000  Loss: 2.36E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5600/10000  Loss: 2.27E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5700/10000  Loss: 2.13E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5800/10000  Loss: 2.58E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5900/10000  Loss: 2.27E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000/10000  Loss: 2.34E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6100/10000  Loss: 2.27E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6200/10000  Loss: 2.07E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6300/10000  Loss: 2.11E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6400/10000  Loss: 2.37E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6500/10000  Loss: 1.99E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6600/10000  Loss: 2.17E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6700/10000  Loss: 2.19E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6800/10000  Loss: 2.46E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6900/10000  Loss: 1.94E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7000/10000  Loss: 2.16E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7100/10000  Loss: 2.27E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7200/10000  Loss: 1.96E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7300/10000  Loss: 2.03E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7400/10000  Loss: 2.09E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7500/10000  Loss: 2.07E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7600/10000  Loss: 2.36E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7700/10000  Loss: 2.15E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7800/10000  Loss: 2.39E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7900/10000  Loss: 1.95E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8000/10000  Loss: 2.49E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8100/10000  Loss: 1.90E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8200/10000  Loss: 2.16E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8300/10000  Loss: 2.20E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8400/10000  Loss: 2.06E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8500/10000  Loss: 2.02E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8600/10000  Loss: 2.06E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8700/10000  Loss: 2.23E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8800/10000  Loss: 2.14E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8900/10000  Loss: 2.16E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9000/10000  Loss: 2.29E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9100/10000  Loss: 1.98E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9200/10000  Loss: 2.24E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9300/10000  Loss: 2.34E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9400/10000  Loss: 2.34E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9500/10000  Loss: 1.73E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9600/10000  Loss: 2.10E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9700/10000  Loss: 2.15E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9800/10000  Loss: 2.18E-03\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9900/10000  Loss: 2.02E-03\r"
     ]
    }
   ],
   "source": [
    "# Number of iterations used for training\n",
    "NUM_TRAINING_ITERATIONS = 10000\n",
    "\n",
    "# Instantiating the end-to-end model for training\n",
    "model_train = End2EndSystem(training=True).to(sn.phy.config.device)\n",
    "\n",
    "# Adam optimizer (SGD variant)\n",
    "optimizer = torch.optim.Adam(model_train.parameters())\n",
    "\n",
    "# Training loop\n",
    "for i in range(NUM_TRAINING_ITERATIONS):\n",
    "    # Zero gradients from previous iteration\n",
    "    optimizer.zero_grad()\n",
    "    # Forward pass\n",
    "    loss = model_train(BATCH_SIZE, 15.0) # The model returns the loss\n",
    "    # Backward pass to compute gradients\n",
    "    loss.backward()\n",
    "    # Apply gradients to update parameters\n",
    "    optimizer.step()\n",
    "    # Print progress\n",
    "    if i % 100 == 0:\n",
    "        print(f\"{i}/{NUM_TRAINING_ITERATIONS}  Loss: {loss.item():.2E}\", end=\"\\r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "185884d2",
   "metadata": {},
   "source": [
    "The weights of the trained model are saved using PyTorch's `torch.save()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c1eaf2c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:10:04.603738Z",
     "iopub.status.busy": "2026-02-13T14:10:04.603507Z",
     "iopub.status.idle": "2026-02-13T14:10:04.610118Z",
     "shell.execute_reply": "2026-02-13T14:10:04.609155Z"
    }
   },
   "outputs": [],
   "source": [
    "# Save the weights in a file\n",
    "torch.save(model_train.state_dict(), 'weights-neural-demapper.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc9252a-47ee-4c2b-9224-194c90d7f4f2",
   "metadata": {},
   "source": [
    "Finally, we evaluate the trained model and benchmark it against the previously introduced baseline.\n",
    "\n",
    "We first instantiate the model for evaluation and load the saved weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6a0e6e6b-3650-4524-9c83-46c715493e3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:10:04.612324Z",
     "iopub.status.busy": "2026-02-13T14:10:04.612109Z",
     "iopub.status.idle": "2026-02-13T14:10:04.622189Z",
     "shell.execute_reply": "2026-02-13T14:10:04.621373Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiating the end-to-end model for evaluation\n",
    "model = End2EndSystem(training=False).to(sn.phy.config.device)\n",
    "# Load the trained weights\n",
    "model.load_state_dict(torch.load('weights-neural-demapper.pt', weights_only=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e6372e-d5a4-40ae-90fb-2bb1694dd533",
   "metadata": {},
   "source": [
    "The trained model is then evaluated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fc0e47a5-7d61-4e8b-b077-8fc63a33057f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:10:04.624798Z",
     "iopub.status.busy": "2026-02-13T14:10:04.624584Z",
     "iopub.status.idle": "2026-02-13T14:10:05.537033Z",
     "shell.execute_reply": "2026-02-13T14:10:05.536127Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     10.0 | 2.5781e-02 | 1.0000e+00 |        3960 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "     10.0 | 2.5781e-02 | 1.0000e+00 |        3960 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   10.526 | 2.0052e-02 | 1.0000e+00 |        3080 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   10.526 | 2.0052e-02 | 1.0000e+00 |        3080 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   11.053 | 1.6204e-02 | 1.0000e+00 |        2489 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   11.053 | 1.6204e-02 | 1.0000e+00 |        2489 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   11.579 | 1.2161e-02 | 1.0000e+00 |        1868 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   11.579 | 1.2161e-02 | 1.0000e+00 |        1868 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   12.105 | 8.1771e-03 | 1.0000e+00 |        1256 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   12.105 | 8.1771e-03 | 1.0000e+00 |        1256 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   12.632 | 5.7161e-03 | 1.0000e+00 |         878 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   12.632 | 5.7161e-03 | 1.0000e+00 |         878 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   13.158 | 4.1732e-03 | 1.0000e+00 |         641 |      153600 |          128 |         128 |         0.0 |iter: 0/100\r",
      "   13.158 | 4.1732e-03 | 1.0000e+00 |         641 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   13.684 | 2.6693e-03 | 9.4531e-01 |         410 |      153600 |          121 |         128 |         0.0 |iter: 0/100\r",
      "   13.684 | 2.6693e-03 | 9.4531e-01 |         410 |      153600 |          121 |         128 |         0.0 |reached target block errors\n",
      "   14.211 | 1.8034e-03 | 8.9844e-01 |         277 |      153600 |          115 |         128 |         0.0 |iter: 0/100\r",
      "   14.211 | 1.8034e-03 | 8.9844e-01 |         277 |      153600 |          115 |         128 |         0.0 |reached target block errors\n",
      "   14.737 | 1.0026e-03 | 6.6406e-01 |         154 |      153600 |           85 |         128 |         0.0 |iter: 0/100\r",
      "   14.737 | 1.0579e-03 | 7.1875e-01 |         325 |      307200 |          184 |         256 |         0.0 |iter: 1/100\r",
      "   14.737 | 1.0579e-03 | 7.1875e-01 |         325 |      307200 |          184 |         256 |         0.0 |reached target block errors\n",
      "   15.263 | 6.3802e-04 | 5.3906e-01 |          98 |      153600 |           69 |         128 |         0.0 |iter: 0/100\r",
      "   15.263 | 6.8034e-04 | 5.5859e-01 |         209 |      307200 |          143 |         256 |         0.0 |iter: 1/100\r",
      "   15.263 | 6.8034e-04 | 5.5859e-01 |         209 |      307200 |          143 |         256 |         0.0 |reached target block errors\n",
      "   15.789 | 2.7344e-04 | 2.7344e-01 |          42 |      153600 |           35 |         128 |         0.0 |iter: 0/100\r",
      "   15.789 | 2.9622e-04 | 2.8125e-01 |          91 |      307200 |           72 |         256 |         0.0 |iter: 1/100\r",
      "   15.789 | 3.1250e-04 | 2.9948e-01 |         144 |      460800 |          115 |         384 |         0.0 |iter: 2/100\r",
      "   15.789 | 3.1250e-04 | 2.9948e-01 |         144 |      460800 |          115 |         384 |         0.0 |reached target block errors\n",
      "   16.316 | 1.3672e-04 | 1.6406e-01 |          21 |      153600 |           21 |         128 |         0.0 |iter: 0/100\r",
      "   16.316 | 1.3346e-04 | 1.5625e-01 |          41 |      307200 |           40 |         256 |         0.0 |iter: 1/100\r",
      "   16.316 | 1.1719e-04 | 1.3281e-01 |          54 |      460800 |           51 |         384 |         0.0 |iter: 2/100\r",
      "   16.316 | 1.2533e-04 | 1.4258e-01 |          77 |      614400 |           73 |         512 |         0.0 |iter: 3/100\r",
      "   16.316 | 1.4193e-04 | 1.6406e-01 |         109 |      768000 |          105 |         640 |         0.0 |iter: 4/100\r",
      "   16.316 | 1.4193e-04 | 1.6406e-01 |         109 |      768000 |          105 |         640 |         0.0 |reached target block errors\n",
      "   16.842 | 3.9063e-05 | 4.6875e-02 |           6 |      153600 |            6 |         128 |         0.0 |iter: 0/100\r",
      "   16.842 | 4.8828e-05 | 5.4688e-02 |          15 |      307200 |           14 |         256 |         0.0 |iter: 1/100\r",
      "   16.842 | 4.9913e-05 | 5.4688e-02 |          23 |      460800 |           21 |         384 |         0.0 |iter: 2/100\r",
      "   16.842 | 5.3711e-05 | 5.8594e-02 |          33 |      614400 |           30 |         512 |         0.0 |iter: 3/100\r",
      "   16.842 | 5.8594e-05 | 6.5625e-02 |          45 |      768000 |           42 |         640 |         0.0 |iter: 4/100\r",
      "   16.842 | 6.5104e-05 | 7.1615e-02 |          60 |      921600 |           55 |         768 |         0.0 |iter: 5/100\r",
      "   16.842 | 6.7894e-05 | 7.4777e-02 |          73 |     1075200 |           67 |         896 |         0.0 |iter: 6/100\r",
      "   16.842 | 7.1615e-05 | 7.9102e-02 |          88 |     1228800 |           81 |        1024 |         0.0 |iter: 7/100\r",
      "   16.842 | 7.2338e-05 | 8.0729e-02 |         100 |     1382400 |           93 |        1152 |         0.0 |iter: 8/100\r",
      "   16.842 | 7.1615e-05 | 7.9687e-02 |         110 |     1536000 |          102 |        1280 |         0.0 |iter: 9/100\r",
      "   16.842 | 7.1615e-05 | 7.9687e-02 |         110 |     1536000 |          102 |        1280 |         0.0 |reached target block errors\n",
      "   17.368 | 1.9531e-05 | 2.3438e-02 |           3 |      153600 |            3 |         128 |         0.0 |iter: 0/100\r",
      "   17.368 | 1.9531e-05 | 2.3438e-02 |           6 |      307200 |            6 |         256 |         0.0 |iter: 1/100\r",
      "   17.368 | 2.1701e-05 | 2.6042e-02 |          10 |      460800 |           10 |         384 |         0.0 |iter: 2/100\r",
      "   17.368 | 1.6276e-05 | 1.9531e-02 |          10 |      614400 |           10 |         512 |         0.0 |iter: 3/100\r",
      "   17.368 | 1.4323e-05 | 1.7188e-02 |          11 |      768000 |           11 |         640 |         0.0 |iter: 4/100\r",
      "   17.368 | 1.4106e-05 | 1.6927e-02 |          13 |      921600 |           13 |         768 |         0.0 |iter: 5/100\r",
      "   17.368 | 1.4881e-05 | 1.7857e-02 |          16 |     1075200 |           16 |         896 |         0.0 |iter: 6/100\r",
      "   17.368 | 1.8717e-05 | 2.2461e-02 |          23 |     1228800 |           23 |        1024 |         0.0 |iter: 7/100\r",
      "   17.368 | 2.0978e-05 | 2.5174e-02 |          29 |     1382400 |           29 |        1152 |         0.0 |iter: 8/100\r",
      "   17.368 | 2.4740e-05 | 2.9687e-02 |          38 |     1536000 |           38 |        1280 |         0.0 |iter: 9/100\r",
      "   17.368 | 2.4858e-05 | 2.9830e-02 |          42 |     1689600 |           42 |        1408 |         0.0 |iter: 10/100\r",
      "   17.368 | 2.6042e-05 | 3.1250e-02 |          48 |     1843200 |           48 |        1536 |         0.0 |iter: 11/100\r",
      "   17.368 | 2.5541e-05 | 3.0649e-02 |          51 |     1996800 |           51 |        1664 |         0.0 |iter: 12/100\r",
      "   17.368 | 2.6042e-05 | 3.1250e-02 |          56 |     2150400 |           56 |        1792 |         0.0 |iter: 13/100\r",
      "   17.368 | 2.5608e-05 | 3.0729e-02 |          59 |     2304000 |           59 |        1920 |         0.0 |iter: 14/100\r",
      "   17.368 | 2.4821e-05 | 2.9785e-02 |          61 |     2457600 |           61 |        2048 |         0.0 |iter: 15/100\r",
      "   17.368 | 2.4893e-05 | 2.9871e-02 |          65 |     2611200 |           65 |        2176 |         0.0 |iter: 16/100\r",
      "   17.368 | 2.4233e-05 | 2.9080e-02 |          67 |     2764800 |           67 |        2304 |         0.0 |iter: 17/100\r",
      "   17.368 | 2.3300e-05 | 2.7961e-02 |          68 |     2918400 |           68 |        2432 |         0.0 |iter: 18/100\r",
      "   17.368 | 2.3112e-05 | 2.7734e-02 |          71 |     3072000 |           71 |        2560 |         0.0 |iter: 19/100\r",
      "   17.368 | 2.3872e-05 | 2.8646e-02 |          77 |     3225600 |           77 |        2688 |         0.0 |iter: 20/100\r",
      "   17.368 | 2.4266e-05 | 2.9119e-02 |          82 |     3379200 |           82 |        2816 |         0.0 |iter: 21/100\r",
      "   17.368 | 2.4343e-05 | 2.9212e-02 |          86 |     3532800 |           86 |        2944 |         0.0 |iter: 22/100\r",
      "   17.368 | 2.3600e-05 | 2.8320e-02 |          87 |     3686400 |           87 |        3072 |         0.0 |iter: 23/100\r",
      "   17.368 | 2.2656e-05 | 2.7187e-02 |          87 |     3840000 |           87 |        3200 |         0.0 |iter: 24/100\r",
      "   17.368 | 2.3287e-05 | 2.7945e-02 |          93 |     3993600 |           93 |        3328 |         0.0 |iter: 25/100\r",
      "   17.368 | 2.3389e-05 | 2.8067e-02 |          97 |     4147200 |           97 |        3456 |         0.0 |iter: 26/100\r",
      "   17.368 | 2.3251e-05 | 2.7902e-02 |         100 |     4300800 |          100 |        3584 |         0.0 |iter: 27/100\r",
      "   17.368 | 2.3251e-05 | 2.7902e-02 |         100 |     4300800 |          100 |        3584 |         0.0 |reached target block errors\n",
      "   17.895 | 0.0000e+00 | 0.0000e+00 |           0 |      153600 |            0 |         128 |         0.0 |iter: 0/100\r",
      "   17.895 | 6.5104e-06 | 7.8125e-03 |           2 |      307200 |            2 |         256 |         0.0 |iter: 1/100\r",
      "   17.895 | 4.3403e-06 | 5.2083e-03 |           2 |      460800 |            2 |         384 |         0.0 |iter: 2/100\r",
      "   17.895 | 3.2552e-06 | 3.9062e-03 |           2 |      614400 |            2 |         512 |         0.0 |iter: 3/100\r",
      "   17.895 | 2.6042e-06 | 3.1250e-03 |           2 |      768000 |            2 |         640 |         0.0 |iter: 4/100\r",
      "   17.895 | 2.1701e-06 | 2.6042e-03 |           2 |      921600 |            2 |         768 |         0.0 |iter: 5/100\r",
      "   17.895 | 3.7202e-06 | 4.4643e-03 |           4 |     1075200 |            4 |         896 |         0.0 |iter: 6/100\r",
      "   17.895 | 4.0690e-06 | 4.8828e-03 |           5 |     1228800 |            5 |        1024 |         0.0 |iter: 7/100\r",
      "   17.895 | 3.6169e-06 | 4.3403e-03 |           5 |     1382400 |            5 |        1152 |         0.0 |iter: 8/100\r",
      "   17.895 | 3.2552e-06 | 3.9062e-03 |           5 |     1536000 |            5 |        1280 |         0.0 |iter: 9/100\r",
      "   17.895 | 2.9593e-06 | 3.5511e-03 |           5 |     1689600 |            5 |        1408 |         0.0 |iter: 10/100\r",
      "   17.895 | 3.7977e-06 | 4.5573e-03 |           7 |     1843200 |            7 |        1536 |         0.0 |iter: 11/100\r",
      "   17.895 | 3.5056e-06 | 4.2067e-03 |           7 |     1996800 |            7 |        1664 |         0.0 |iter: 12/100\r",
      "   17.895 | 4.1853e-06 | 5.0223e-03 |           9 |     2150400 |            9 |        1792 |         0.0 |iter: 13/100\r",
      "   17.895 | 4.3403e-06 | 5.2083e-03 |          10 |     2304000 |           10 |        1920 |         0.0 |iter: 14/100\r",
      "   17.895 | 4.4759e-06 | 5.3711e-03 |          11 |     2457600 |           11 |        2048 |         0.0 |iter: 15/100\r",
      "   17.895 | 4.2126e-06 | 5.0551e-03 |          11 |     2611200 |           11 |        2176 |         0.0 |iter: 16/100\r",
      "   17.895 | 5.0637e-06 | 6.0764e-03 |          14 |     2764800 |           14 |        2304 |         0.0 |iter: 17/100\r",
      "   17.895 | 5.8251e-06 | 6.9901e-03 |          17 |     2918400 |           17 |        2432 |         0.0 |iter: 18/100\r",
      "   17.895 | 6.1849e-06 | 7.4219e-03 |          19 |     3072000 |           19 |        2560 |         0.0 |iter: 19/100\r",
      "   17.895 | 5.8904e-06 | 7.0685e-03 |          19 |     3225600 |           19 |        2688 |         0.0 |iter: 20/100\r",
      "   17.895 | 5.6226e-06 | 6.7472e-03 |          19 |     3379200 |           19 |        2816 |         0.0 |iter: 21/100\r",
      "   17.895 | 5.6612e-06 | 6.7935e-03 |          20 |     3532800 |           20 |        2944 |         0.0 |iter: 22/100\r",
      "   17.895 | 5.4253e-06 | 6.5104e-03 |          20 |     3686400 |           20 |        3072 |         0.0 |iter: 23/100\r",
      "   17.895 | 5.7292e-06 | 6.8750e-03 |          22 |     3840000 |           22 |        3200 |         0.0 |iter: 24/100\r",
      "   17.895 | 6.0096e-06 | 7.2115e-03 |          24 |     3993600 |           24 |        3328 |         0.0 |iter: 25/100\r",
      "   17.895 | 6.2693e-06 | 7.5231e-03 |          26 |     4147200 |           26 |        3456 |         0.0 |iter: 26/100\r",
      "   17.895 | 6.2779e-06 | 7.5335e-03 |          27 |     4300800 |           27 |        3584 |         0.0 |iter: 27/100\r",
      "   17.895 | 6.2859e-06 | 7.5431e-03 |          28 |     4454400 |           28 |        3712 |         0.0 |iter: 28/100\r",
      "   17.895 | 6.0764e-06 | 7.2917e-03 |          28 |     4608000 |           28 |        3840 |         0.0 |iter: 29/100\r",
      "   17.895 | 5.8804e-06 | 7.0565e-03 |          28 |     4761600 |           28 |        3968 |         0.0 |iter: 30/100\r",
      "   17.895 | 5.9001e-06 | 7.0801e-03 |          29 |     4915200 |           29 |        4096 |         0.0 |iter: 31/100\r",
      "   17.895 | 6.1158e-06 | 7.3390e-03 |          31 |     5068800 |           31 |        4224 |         0.0 |iter: 32/100\r",
      "   17.895 | 6.5104e-06 | 7.8125e-03 |          34 |     5222400 |           34 |        4352 |         0.0 |iter: 33/100\r",
      "   17.895 | 6.6964e-06 | 8.0357e-03 |          36 |     5376000 |           36 |        4480 |         0.0 |iter: 34/100\r",
      "   17.895 | 6.5104e-06 | 7.8125e-03 |          36 |     5529600 |           36 |        4608 |         0.0 |iter: 35/100\r",
      "   17.895 | 6.8623e-06 | 8.2348e-03 |          39 |     5683200 |           39 |        4736 |         0.0 |iter: 36/100\r",
      "   17.895 | 6.8531e-06 | 8.2237e-03 |          40 |     5836800 |           40 |        4864 |         0.0 |iter: 37/100\r",
      "   17.895 | 7.0112e-06 | 8.4135e-03 |          42 |     5990400 |           42 |        4992 |         0.0 |iter: 38/100\r",
      "   17.895 | 6.8359e-06 | 8.2031e-03 |          42 |     6144000 |           42 |        5120 |         0.0 |iter: 39/100\r",
      "   17.895 | 6.9868e-06 | 8.3841e-03 |          44 |     6297600 |           44 |        5248 |         0.0 |iter: 40/100\r",
      "   17.895 | 6.9754e-06 | 8.3705e-03 |          45 |     6451200 |           45 |        5376 |         0.0 |iter: 41/100\r",
      "   17.895 | 7.2674e-06 | 8.7209e-03 |          48 |     6604800 |           48 |        5504 |         0.0 |iter: 42/100\r",
      "   17.895 | 7.3982e-06 | 8.8778e-03 |          50 |     6758400 |           50 |        5632 |         0.0 |iter: 43/100\r",
      "   17.895 | 7.3785e-06 | 8.8542e-03 |          51 |     6912000 |           51 |        5760 |         0.0 |iter: 44/100\r",
      "   17.895 | 7.3596e-06 | 8.8315e-03 |          52 |     7065600 |           52 |        5888 |         0.0 |iter: 45/100\r",
      "   17.895 | 7.6186e-06 | 9.1423e-03 |          55 |     7219200 |           55 |        6016 |         0.0 |iter: 46/100\r",
      "   17.895 | 7.7311e-06 | 9.2773e-03 |          57 |     7372800 |           57 |        6144 |         0.0 |iter: 47/100\r",
      "   17.895 | 7.7062e-06 | 9.2474e-03 |          58 |     7526400 |           58 |        6272 |         0.0 |iter: 48/100\r",
      "   17.895 | 7.5521e-06 | 9.0625e-03 |          58 |     7680000 |           58 |        6400 |         0.1 |iter: 49/100\r",
      "   17.895 | 7.6593e-06 | 9.1912e-03 |          60 |     7833600 |           60 |        6528 |         0.1 |iter: 50/100\r",
      "   17.895 | 7.6372e-06 | 9.1647e-03 |          61 |     7987200 |           61 |        6656 |         0.1 |iter: 51/100\r",
      "   17.895 | 7.6160e-06 | 9.1392e-03 |          62 |     8140800 |           62 |        6784 |         0.1 |iter: 52/100\r",
      "   17.895 | 7.4749e-06 | 8.9699e-03 |          62 |     8294400 |           62 |        6912 |         0.1 |iter: 53/100\r",
      "   17.895 | 7.3390e-06 | 8.8068e-03 |          62 |     8448000 |           62 |        7040 |         0.1 |iter: 54/100\r",
      "   17.895 | 7.3242e-06 | 8.7891e-03 |          63 |     8601600 |           63 |        7168 |         0.1 |iter: 55/100\r",
      "   17.895 | 7.1957e-06 | 8.6349e-03 |          63 |     8755200 |           63 |        7296 |         0.1 |iter: 56/100\r",
      "   17.895 | 7.6329e-06 | 9.1595e-03 |          68 |     8908800 |           68 |        7424 |         0.1 |iter: 57/100\r",
      "   17.895 | 7.9449e-06 | 9.5339e-03 |          72 |     9062400 |           72 |        7552 |         0.1 |iter: 58/100\r",
      "   17.895 | 7.9210e-06 | 9.5052e-03 |          73 |     9216000 |           73 |        7680 |         0.1 |iter: 59/100\r",
      "   17.895 | 7.8979e-06 | 9.4775e-03 |          74 |     9369600 |           74 |        7808 |         0.1 |iter: 60/100\r",
      "   17.895 | 7.9805e-06 | 9.5766e-03 |          76 |     9523200 |           76 |        7936 |         0.1 |iter: 61/100\r",
      "   17.895 | 7.8538e-06 | 9.4246e-03 |          76 |     9676800 |           76 |        8064 |         0.1 |iter: 62/100\r",
      "   17.895 | 7.8328e-06 | 9.3994e-03 |          77 |     9830400 |           77 |        8192 |         0.1 |iter: 63/100\r",
      "   17.895 | 8.1130e-06 | 9.7356e-03 |          81 |     9984000 |           81 |        8320 |         0.1 |iter: 64/100\r",
      "   17.895 | 8.1873e-06 | 9.8248e-03 |          83 |    10137600 |           83 |        8448 |         0.1 |iter: 65/100\r",
      "   17.895 | 8.0651e-06 | 9.6782e-03 |          83 |    10291200 |           83 |        8576 |         0.1 |iter: 66/100\r",
      "   17.895 | 8.1380e-06 | 9.7656e-03 |          85 |    10444800 |           85 |        8704 |         0.1 |iter: 67/100\r",
      "   17.895 | 8.0201e-06 | 9.6241e-03 |          85 |    10598400 |           85 |        8832 |         0.1 |iter: 68/100\r",
      "   17.895 | 7.9055e-06 | 9.4866e-03 |          85 |    10752000 |           85 |        8960 |         0.1 |iter: 69/100\r",
      "   17.895 | 7.9776e-06 | 9.5731e-03 |          87 |    10905600 |           87 |        9088 |         0.1 |iter: 70/100\r",
      "   17.895 | 7.8668e-06 | 9.4401e-03 |          87 |    11059200 |           87 |        9216 |         0.1 |iter: 71/100\r",
      "   17.895 | 7.9374e-06 | 9.5248e-03 |          89 |    11212800 |           89 |        9344 |         0.1 |iter: 72/100\r",
      "   17.895 | 7.8301e-06 | 9.3961e-03 |          89 |    11366400 |           89 |        9472 |         0.1 |iter: 73/100\r",
      "   17.895 | 7.8125e-06 | 9.3750e-03 |          90 |    11520000 |           90 |        9600 |         0.1 |iter: 74/100\r",
      "   17.895 | 7.8810e-06 | 9.4572e-03 |          92 |    11673600 |           92 |        9728 |         0.1 |iter: 75/100\r",
      "   17.895 | 8.1169e-06 | 9.7403e-03 |          96 |    11827200 |           96 |        9856 |         0.1 |iter: 76/100\r",
      "   17.895 | 8.0128e-06 | 9.6154e-03 |          96 |    11980800 |           96 |        9984 |         0.1 |iter: 77/100\r",
      "   17.895 | 8.0762e-06 | 9.6915e-03 |          98 |    12134400 |           98 |       10112 |         0.1 |iter: 78/100\r",
      "   17.895 | 8.1380e-06 | 9.7656e-03 |         100 |    12288000 |          100 |       10240 |         0.1 |iter: 79/100\r",
      "   17.895 | 8.1380e-06 | 9.7656e-03 |         100 |    12288000 |          100 |       10240 |         0.1 |reached target block errors\n",
      "   18.421 | 0.0000e+00 | 0.0000e+00 |           0 |      153600 |            0 |         128 |         0.0 |iter: 0/100\r",
      "   18.421 | 3.2552e-06 | 3.9062e-03 |           1 |      307200 |            1 |         256 |         0.0 |iter: 1/100\r",
      "   18.421 | 2.1701e-06 | 2.6042e-03 |           1 |      460800 |            1 |         384 |         0.0 |iter: 2/100\r",
      "   18.421 | 3.2552e-06 | 3.9062e-03 |           2 |      614400 |            2 |         512 |         0.0 |iter: 3/100\r",
      "   18.421 | 5.2083e-06 | 6.2500e-03 |           4 |      768000 |            4 |         640 |         0.0 |iter: 4/100\r",
      "   18.421 | 4.3403e-06 | 5.2083e-03 |           4 |      921600 |            4 |         768 |         0.0 |iter: 5/100\r",
      "   18.421 | 3.7202e-06 | 4.4643e-03 |           4 |     1075200 |            4 |         896 |         0.0 |iter: 6/100\r",
      "   18.421 | 3.2552e-06 | 3.9062e-03 |           4 |     1228800 |            4 |        1024 |         0.0 |iter: 7/100\r",
      "   18.421 | 4.3403e-06 | 5.2083e-03 |           6 |     1382400 |            6 |        1152 |         0.0 |iter: 8/100\r",
      "   18.421 | 3.9063e-06 | 4.6875e-03 |           6 |     1536000 |            6 |        1280 |         0.0 |iter: 9/100\r",
      "   18.421 | 4.1430e-06 | 4.9716e-03 |           7 |     1689600 |            7 |        1408 |         0.0 |iter: 10/100\r",
      "   18.421 | 3.7977e-06 | 4.5573e-03 |           7 |     1843200 |            7 |        1536 |         0.0 |iter: 11/100\r",
      "   18.421 | 4.0064e-06 | 4.8077e-03 |           8 |     1996800 |            8 |        1664 |         0.0 |iter: 12/100\r",
      "   18.421 | 3.7202e-06 | 4.4643e-03 |           8 |     2150400 |            8 |        1792 |         0.0 |iter: 13/100\r",
      "   18.421 | 4.7743e-06 | 5.7292e-03 |          11 |     2304000 |           11 |        1920 |         0.0 |iter: 14/100\r",
      "   18.421 | 4.4759e-06 | 5.3711e-03 |          11 |     2457600 |           11 |        2048 |         0.0 |iter: 15/100\r",
      "   18.421 | 4.2126e-06 | 5.0551e-03 |          11 |     2611200 |           11 |        2176 |         0.0 |iter: 16/100\r",
      "   18.421 | 3.9786e-06 | 4.7743e-03 |          11 |     2764800 |           11 |        2304 |         0.0 |iter: 17/100\r",
      "   18.421 | 3.7692e-06 | 4.5230e-03 |          11 |     2918400 |           11 |        2432 |         0.0 |iter: 18/100\r",
      "   18.421 | 3.9063e-06 | 4.6875e-03 |          12 |     3072000 |           12 |        2560 |         0.0 |iter: 19/100\r",
      "   18.421 | 3.7202e-06 | 4.4643e-03 |          12 |     3225600 |           12 |        2688 |         0.0 |iter: 20/100\r",
      "   18.421 | 3.5511e-06 | 4.2614e-03 |          12 |     3379200 |           12 |        2816 |         0.0 |iter: 21/100\r",
      "   18.421 | 3.3967e-06 | 4.0761e-03 |          12 |     3532800 |           12 |        2944 |         0.0 |iter: 22/100\r",
      "   18.421 | 3.2552e-06 | 3.9062e-03 |          12 |     3686400 |           12 |        3072 |         0.0 |iter: 23/100\r",
      "   18.421 | 3.1250e-06 | 3.7500e-03 |          12 |     3840000 |           12 |        3200 |         0.0 |iter: 24/100\r",
      "   18.421 | 3.0048e-06 | 3.6058e-03 |          12 |     3993600 |           12 |        3328 |         0.0 |iter: 25/100\r",
      "   18.421 | 3.1346e-06 | 3.7616e-03 |          13 |     4147200 |           13 |        3456 |         0.0 |iter: 26/100\r",
      "   18.421 | 3.2552e-06 | 3.9062e-03 |          14 |     4300800 |           14 |        3584 |         0.0 |iter: 27/100\r",
      "   18.421 | 3.3675e-06 | 4.0409e-03 |          15 |     4454400 |           15 |        3712 |         0.0 |iter: 28/100\r",
      "   18.421 | 3.4722e-06 | 4.1667e-03 |          16 |     4608000 |           16 |        3840 |         0.0 |iter: 29/100\r",
      "   18.421 | 3.3602e-06 | 4.0323e-03 |          16 |     4761600 |           16 |        3968 |         0.0 |iter: 30/100\r",
      "   18.421 | 3.2552e-06 | 3.9062e-03 |          16 |     4915200 |           16 |        4096 |         0.0 |iter: 31/100\r",
      "   18.421 | 3.3539e-06 | 4.0246e-03 |          17 |     5068800 |           17 |        4224 |         0.0 |iter: 32/100\r",
      "   18.421 | 3.2552e-06 | 3.9062e-03 |          17 |     5222400 |           17 |        4352 |         0.0 |iter: 33/100\r",
      "   18.421 | 3.3482e-06 | 4.0179e-03 |          18 |     5376000 |           18 |        4480 |         0.0 |iter: 34/100\r",
      "   18.421 | 3.2552e-06 | 3.9062e-03 |          18 |     5529600 |           18 |        4608 |         0.0 |iter: 35/100\r",
      "   18.421 | 3.1672e-06 | 3.8007e-03 |          18 |     5683200 |           18 |        4736 |         0.0 |iter: 36/100\r",
      "   18.421 | 3.0839e-06 | 3.7007e-03 |          18 |     5836800 |           18 |        4864 |         0.0 |iter: 37/100\r",
      "   18.421 | 3.3387e-06 | 4.0064e-03 |          20 |     5990400 |           20 |        4992 |         0.0 |iter: 38/100\r",
      "   18.421 | 3.2552e-06 | 3.9062e-03 |          20 |     6144000 |           20 |        5120 |         0.0 |iter: 39/100\r",
      "   18.421 | 3.1758e-06 | 3.8110e-03 |          20 |     6297600 |           20 |        5248 |         0.0 |iter: 40/100\r",
      "   18.421 | 3.1002e-06 | 3.7202e-03 |          20 |     6451200 |           20 |        5376 |         0.0 |iter: 41/100\r",
      "   18.421 | 3.0281e-06 | 3.6337e-03 |          20 |     6604800 |           20 |        5504 |         0.0 |iter: 42/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   18.421 | 2.9593e-06 | 3.5511e-03 |          20 |     6758400 |           20 |        5632 |         0.0 |iter: 43/100\r",
      "   18.421 | 2.8935e-06 | 3.4722e-03 |          20 |     6912000 |           20 |        5760 |         0.0 |iter: 44/100\r",
      "   18.421 | 2.9721e-06 | 3.5666e-03 |          21 |     7065600 |           21 |        5888 |         0.0 |iter: 45/100\r",
      "   18.421 | 2.9089e-06 | 3.4907e-03 |          21 |     7219200 |           21 |        6016 |         0.0 |iter: 46/100\r",
      "   18.421 | 2.8483e-06 | 3.4180e-03 |          21 |     7372800 |           21 |        6144 |         0.0 |iter: 47/100\r",
      "   18.421 | 2.7902e-06 | 3.3482e-03 |          21 |     7526400 |           21 |        6272 |         0.1 |iter: 48/100\r",
      "   18.421 | 2.7344e-06 | 3.2812e-03 |          21 |     7680000 |           21 |        6400 |         0.1 |iter: 49/100\r",
      "   18.421 | 2.6808e-06 | 3.2169e-03 |          21 |     7833600 |           21 |        6528 |         0.1 |iter: 50/100\r",
      "   18.421 | 2.6292e-06 | 3.1550e-03 |          21 |     7987200 |           21 |        6656 |         0.1 |iter: 51/100\r",
      "   18.421 | 2.5796e-06 | 3.0955e-03 |          21 |     8140800 |           21 |        6784 |         0.1 |iter: 52/100\r",
      "   18.421 | 2.5318e-06 | 3.0382e-03 |          21 |     8294400 |           21 |        6912 |         0.1 |iter: 53/100\r",
      "   18.421 | 2.4858e-06 | 2.9830e-03 |          21 |     8448000 |           21 |        7040 |         0.1 |iter: 54/100\r",
      "   18.421 | 2.6739e-06 | 3.2087e-03 |          23 |     8601600 |           23 |        7168 |         0.1 |iter: 55/100\r",
      "   18.421 | 2.6270e-06 | 3.1524e-03 |          23 |     8755200 |           23 |        7296 |         0.1 |iter: 56/100\r",
      "   18.421 | 2.5817e-06 | 3.0981e-03 |          23 |     8908800 |           23 |        7424 |         0.1 |iter: 57/100\r",
      "   18.421 | 2.5380e-06 | 3.0456e-03 |          23 |     9062400 |           23 |        7552 |         0.1 |iter: 58/100\r",
      "   18.421 | 2.4957e-06 | 2.9948e-03 |          23 |     9216000 |           23 |        7680 |         0.1 |iter: 59/100\r",
      "   18.421 | 2.5615e-06 | 3.0738e-03 |          24 |     9369600 |           24 |        7808 |         0.1 |iter: 60/100\r",
      "   18.421 | 2.5202e-06 | 3.0242e-03 |          24 |     9523200 |           24 |        7936 |         0.1 |iter: 61/100\r",
      "   18.421 | 2.6868e-06 | 3.2242e-03 |          26 |     9676800 |           26 |        8064 |         0.1 |iter: 62/100\r",
      "   18.421 | 2.6449e-06 | 3.1738e-03 |          26 |     9830400 |           26 |        8192 |         0.1 |iter: 63/100\r",
      "   18.421 | 2.7043e-06 | 3.2452e-03 |          27 |     9984000 |           27 |        8320 |         0.1 |iter: 64/100\r",
      "   18.421 | 2.6634e-06 | 3.1960e-03 |          27 |    10137600 |           27 |        8448 |         0.1 |iter: 65/100\r",
      "   18.421 | 2.6236e-06 | 3.1483e-03 |          27 |    10291200 |           27 |        8576 |         0.1 |iter: 66/100\r",
      "   18.421 | 2.5850e-06 | 3.1020e-03 |          27 |    10444800 |           27 |        8704 |         0.1 |iter: 67/100\r",
      "   18.421 | 2.5476e-06 | 3.0571e-03 |          27 |    10598400 |           27 |        8832 |         0.1 |iter: 68/100\r",
      "   18.421 | 2.5112e-06 | 3.0134e-03 |          27 |    10752000 |           27 |        8960 |         0.1 |iter: 69/100\r",
      "   18.421 | 2.5675e-06 | 3.0810e-03 |          28 |    10905600 |           28 |        9088 |         0.1 |iter: 70/100\r",
      "   18.421 | 2.5318e-06 | 3.0382e-03 |          28 |    11059200 |           28 |        9216 |         0.1 |iter: 71/100\r",
      "   18.421 | 2.5863e-06 | 3.1036e-03 |          29 |    11212800 |           29 |        9344 |         0.1 |iter: 72/100\r",
      "   18.421 | 2.5514e-06 | 3.0617e-03 |          29 |    11366400 |           29 |        9472 |         0.1 |iter: 73/100\r",
      "   18.421 | 2.6042e-06 | 3.1250e-03 |          30 |    11520000 |           30 |        9600 |         0.1 |iter: 74/100\r",
      "   18.421 | 2.6556e-06 | 3.1867e-03 |          31 |    11673600 |           31 |        9728 |         0.1 |iter: 75/100\r",
      "   18.421 | 2.6211e-06 | 3.1453e-03 |          31 |    11827200 |           31 |        9856 |         0.1 |iter: 76/100\r",
      "   18.421 | 2.6709e-06 | 3.2051e-03 |          32 |    11980800 |           32 |        9984 |         0.1 |iter: 77/100\r",
      "   18.421 | 2.6371e-06 | 3.1646e-03 |          32 |    12134400 |           32 |       10112 |         0.1 |iter: 78/100\r",
      "   18.421 | 2.6855e-06 | 3.2227e-03 |          33 |    12288000 |           33 |       10240 |         0.1 |iter: 79/100\r",
      "   18.421 | 2.8131e-06 | 3.3758e-03 |          35 |    12441600 |           35 |       10368 |         0.1 |iter: 80/100\r",
      "   18.421 | 2.7788e-06 | 3.3346e-03 |          35 |    12595200 |           35 |       10496 |         0.1 |iter: 81/100\r",
      "   18.421 | 2.7454e-06 | 3.2944e-03 |          35 |    12748800 |           35 |       10624 |         0.1 |iter: 82/100\r",
      "   18.421 | 2.7127e-06 | 3.2552e-03 |          35 |    12902400 |           35 |       10752 |         0.1 |iter: 83/100\r",
      "   18.421 | 2.6808e-06 | 3.2169e-03 |          35 |    13056000 |           35 |       10880 |         0.1 |iter: 84/100\r",
      "   18.421 | 2.7253e-06 | 3.2703e-03 |          36 |    13209600 |           36 |       11008 |         0.1 |iter: 85/100\r",
      "   18.421 | 2.6940e-06 | 3.2328e-03 |          36 |    13363200 |           36 |       11136 |         0.1 |iter: 86/100\r",
      "   18.421 | 2.6634e-06 | 3.1960e-03 |          36 |    13516800 |           36 |       11264 |         0.1 |iter: 87/100\r",
      "   18.421 | 2.7066e-06 | 3.2479e-03 |          37 |    13670400 |           37 |       11392 |         0.1 |iter: 88/100\r",
      "   18.421 | 2.6765e-06 | 3.2118e-03 |          37 |    13824000 |           37 |       11520 |         0.1 |iter: 89/100\r",
      "   18.421 | 2.6471e-06 | 3.1765e-03 |          37 |    13977600 |           37 |       11648 |         0.1 |iter: 90/100\r",
      "   18.421 | 2.6183e-06 | 3.1420e-03 |          37 |    14131200 |           37 |       11776 |         0.1 |iter: 91/100\r",
      "   18.421 | 2.5902e-06 | 3.1082e-03 |          37 |    14284800 |           37 |       11904 |         0.1 |iter: 92/100\r",
      "   18.421 | 2.5626e-06 | 3.0751e-03 |          37 |    14438400 |           37 |       12032 |         0.1 |iter: 93/100\r",
      "   18.421 | 2.5356e-06 | 3.0428e-03 |          37 |    14592000 |           37 |       12160 |         0.1 |iter: 94/100\r",
      "   18.421 | 2.5092e-06 | 3.0111e-03 |          37 |    14745600 |           37 |       12288 |         0.1 |iter: 95/100\r",
      "   18.421 | 2.4834e-06 | 2.9800e-03 |          37 |    14899200 |           37 |       12416 |         0.1 |iter: 96/100\r",
      "   18.421 | 2.4580e-06 | 2.9496e-03 |          37 |    15052800 |           37 |       12544 |         0.1 |iter: 97/100\r",
      "   18.421 | 2.4332e-06 | 2.9198e-03 |          37 |    15206400 |           37 |       12672 |         0.1 |iter: 98/100\r",
      "   18.421 | 2.4089e-06 | 2.8906e-03 |          37 |    15360000 |           37 |       12800 |         0.1 |iter: 99/100\r",
      "   18.421 | 2.4089e-06 | 2.8906e-03 |          37 |    15360000 |           37 |       12800 |         0.1 |reached max iterations\n",
      "   18.947 | 6.5104e-06 | 7.8125e-03 |           1 |      153600 |            1 |         128 |         0.0 |iter: 0/100\r",
      "   18.947 | 3.2552e-06 | 3.9062e-03 |           1 |      307200 |            1 |         256 |         0.0 |iter: 1/100\r",
      "   18.947 | 2.1701e-06 | 2.6042e-03 |           1 |      460800 |            1 |         384 |         0.0 |iter: 2/100\r",
      "   18.947 | 1.6276e-06 | 1.9531e-03 |           1 |      614400 |            1 |         512 |         0.0 |iter: 3/100\r",
      "   18.947 | 1.3021e-06 | 1.5625e-03 |           1 |      768000 |            1 |         640 |         0.0 |iter: 4/100\r",
      "   18.947 | 1.0851e-06 | 1.3021e-03 |           1 |      921600 |            1 |         768 |         0.0 |iter: 5/100\r",
      "   18.947 | 9.3006e-07 | 1.1161e-03 |           1 |     1075200 |            1 |         896 |         0.0 |iter: 6/100\r",
      "   18.947 | 8.1380e-07 | 9.7656e-04 |           1 |     1228800 |            1 |        1024 |         0.0 |iter: 7/100\r",
      "   18.947 | 7.2338e-07 | 8.6806e-04 |           1 |     1382400 |            1 |        1152 |         0.0 |iter: 8/100\r",
      "   18.947 | 6.5104e-07 | 7.8125e-04 |           1 |     1536000 |            1 |        1280 |         0.0 |iter: 9/100\r",
      "   18.947 | 5.9186e-07 | 7.1023e-04 |           1 |     1689600 |            1 |        1408 |         0.0 |iter: 10/100\r",
      "   18.947 | 5.4253e-07 | 6.5104e-04 |           1 |     1843200 |            1 |        1536 |         0.0 |iter: 11/100\r",
      "   18.947 | 5.0080e-07 | 6.0096e-04 |           1 |     1996800 |            1 |        1664 |         0.0 |iter: 12/100\r",
      "   18.947 | 4.6503e-07 | 5.5804e-04 |           1 |     2150400 |            1 |        1792 |         0.0 |iter: 13/100\r",
      "   18.947 | 4.3403e-07 | 5.2083e-04 |           1 |     2304000 |            1 |        1920 |         0.0 |iter: 14/100\r",
      "   18.947 | 4.0690e-07 | 4.8828e-04 |           1 |     2457600 |            1 |        2048 |         0.0 |iter: 15/100\r",
      "   18.947 | 7.6593e-07 | 9.1912e-04 |           2 |     2611200 |            2 |        2176 |         0.0 |iter: 16/100\r",
      "   18.947 | 7.2338e-07 | 8.6806e-04 |           2 |     2764800 |            2 |        2304 |         0.0 |iter: 17/100\r",
      "   18.947 | 1.0280e-06 | 1.2336e-03 |           3 |     2918400 |            3 |        2432 |         0.0 |iter: 18/100\r",
      "   18.947 | 9.7656e-07 | 1.1719e-03 |           3 |     3072000 |            3 |        2560 |         0.0 |iter: 19/100\r",
      "   18.947 | 9.3006e-07 | 1.1161e-03 |           3 |     3225600 |            3 |        2688 |         0.0 |iter: 20/100\r",
      "   18.947 | 8.8778e-07 | 1.0653e-03 |           3 |     3379200 |            3 |        2816 |         0.0 |iter: 21/100\r",
      "   18.947 | 8.4918e-07 | 1.0190e-03 |           3 |     3532800 |            3 |        2944 |         0.0 |iter: 22/100\r",
      "   18.947 | 8.1380e-07 | 9.7656e-04 |           3 |     3686400 |            3 |        3072 |         0.0 |iter: 23/100\r",
      "   18.947 | 7.8125e-07 | 9.3750e-04 |           3 |     3840000 |            3 |        3200 |         0.0 |iter: 24/100\r",
      "   18.947 | 1.2520e-06 | 1.5024e-03 |           5 |     3993600 |            5 |        3328 |         0.0 |iter: 25/100\r",
      "   18.947 | 1.4468e-06 | 1.7361e-03 |           6 |     4147200 |            6 |        3456 |         0.0 |iter: 26/100\r",
      "   18.947 | 1.3951e-06 | 1.6741e-03 |           6 |     4300800 |            6 |        3584 |         0.0 |iter: 27/100\r",
      "   18.947 | 1.3470e-06 | 1.6164e-03 |           6 |     4454400 |            6 |        3712 |         0.0 |iter: 28/100\r",
      "   18.947 | 1.3021e-06 | 1.5625e-03 |           6 |     4608000 |            6 |        3840 |         0.0 |iter: 29/100\r",
      "   18.947 | 1.2601e-06 | 1.5121e-03 |           6 |     4761600 |            6 |        3968 |         0.0 |iter: 30/100\r",
      "   18.947 | 1.2207e-06 | 1.4648e-03 |           6 |     4915200 |            6 |        4096 |         0.0 |iter: 31/100\r",
      "   18.947 | 1.1837e-06 | 1.4205e-03 |           6 |     5068800 |            6 |        4224 |         0.0 |iter: 32/100\r",
      "   18.947 | 1.1489e-06 | 1.3787e-03 |           6 |     5222400 |            6 |        4352 |         0.0 |iter: 33/100\r",
      "   18.947 | 1.1161e-06 | 1.3393e-03 |           6 |     5376000 |            6 |        4480 |         0.0 |iter: 34/100\r",
      "   18.947 | 1.0851e-06 | 1.3021e-03 |           6 |     5529600 |            6 |        4608 |         0.0 |iter: 35/100\r",
      "   18.947 | 1.0557e-06 | 1.2669e-03 |           6 |     5683200 |            6 |        4736 |         0.0 |iter: 36/100\r",
      "   18.947 | 1.0280e-06 | 1.2336e-03 |           6 |     5836800 |            6 |        4864 |         0.0 |iter: 37/100\r",
      "   18.947 | 1.0016e-06 | 1.2019e-03 |           6 |     5990400 |            6 |        4992 |         0.0 |iter: 38/100\r",
      "   18.947 | 1.1393e-06 | 1.3672e-03 |           7 |     6144000 |            7 |        5120 |         0.0 |iter: 39/100\r",
      "   18.947 | 1.1115e-06 | 1.3338e-03 |           7 |     6297600 |            7 |        5248 |         0.0 |iter: 40/100\r",
      "   18.947 | 1.0851e-06 | 1.3021e-03 |           7 |     6451200 |            7 |        5376 |         0.0 |iter: 41/100\r",
      "   18.947 | 1.0598e-06 | 1.2718e-03 |           7 |     6604800 |            7 |        5504 |         0.0 |iter: 42/100\r",
      "   18.947 | 1.0357e-06 | 1.2429e-03 |           7 |     6758400 |            7 |        5632 |         0.0 |iter: 43/100\r",
      "   18.947 | 1.0127e-06 | 1.2153e-03 |           7 |     6912000 |            7 |        5760 |         0.0 |iter: 44/100\r",
      "   18.947 | 9.9072e-07 | 1.1889e-03 |           7 |     7065600 |            7 |        5888 |         0.0 |iter: 45/100\r",
      "   18.947 | 9.6964e-07 | 1.1636e-03 |           7 |     7219200 |            7 |        6016 |         0.0 |iter: 46/100\r",
      "   18.947 | 9.4944e-07 | 1.1393e-03 |           7 |     7372800 |            7 |        6144 |         0.0 |iter: 47/100\r",
      "   18.947 | 9.3006e-07 | 1.1161e-03 |           7 |     7526400 |            7 |        6272 |         0.0 |iter: 48/100\r",
      "   18.947 | 1.0417e-06 | 1.2500e-03 |           8 |     7680000 |            8 |        6400 |         0.0 |iter: 49/100\r",
      "   18.947 | 1.0212e-06 | 1.2255e-03 |           8 |     7833600 |            8 |        6528 |         0.0 |iter: 50/100\r",
      "   18.947 | 1.0016e-06 | 1.2019e-03 |           8 |     7987200 |            8 |        6656 |         0.1 |iter: 51/100\r",
      "   18.947 | 9.8270e-07 | 1.1792e-03 |           8 |     8140800 |            8 |        6784 |         0.1 |iter: 52/100\r",
      "   18.947 | 9.6451e-07 | 1.1574e-03 |           8 |     8294400 |            8 |        6912 |         0.1 |iter: 53/100\r",
      "   18.947 | 9.4697e-07 | 1.1364e-03 |           8 |     8448000 |            8 |        7040 |         0.1 |iter: 54/100\r",
      "   18.947 | 9.3006e-07 | 1.1161e-03 |           8 |     8601600 |            8 |        7168 |         0.1 |iter: 55/100\r",
      "   18.947 | 1.0280e-06 | 1.2336e-03 |           9 |     8755200 |            9 |        7296 |         0.1 |iter: 56/100\r",
      "   18.947 | 1.0102e-06 | 1.2123e-03 |           9 |     8908800 |            9 |        7424 |         0.1 |iter: 57/100\r",
      "   18.947 | 9.9311e-07 | 1.1917e-03 |           9 |     9062400 |            9 |        7552 |         0.1 |iter: 58/100\r",
      "   18.947 | 9.7656e-07 | 1.1719e-03 |           9 |     9216000 |            9 |        7680 |         0.1 |iter: 59/100\r",
      "   18.947 | 9.6055e-07 | 1.1527e-03 |           9 |     9369600 |            9 |        7808 |         0.1 |iter: 60/100\r",
      "   18.947 | 9.4506e-07 | 1.1341e-03 |           9 |     9523200 |            9 |        7936 |         0.1 |iter: 61/100\r",
      "   18.947 | 9.3006e-07 | 1.1161e-03 |           9 |     9676800 |            9 |        8064 |         0.1 |iter: 62/100\r",
      "   18.947 | 9.1553e-07 | 1.0986e-03 |           9 |     9830400 |            9 |        8192 |         0.1 |iter: 63/100\r",
      "   18.947 | 9.0144e-07 | 1.0817e-03 |           9 |     9984000 |            9 |        8320 |         0.1 |iter: 64/100\r",
      "   18.947 | 8.8778e-07 | 1.0653e-03 |           9 |    10137600 |            9 |        8448 |         0.1 |iter: 65/100\r",
      "   18.947 | 9.7170e-07 | 1.1660e-03 |          10 |    10291200 |           10 |        8576 |         0.1 |iter: 66/100\r",
      "   18.947 | 9.5741e-07 | 1.1489e-03 |          10 |    10444800 |           10 |        8704 |         0.1 |iter: 67/100\r",
      "   18.947 | 9.4354e-07 | 1.1322e-03 |          10 |    10598400 |           10 |        8832 |         0.1 |iter: 68/100\r",
      "   18.947 | 9.3006e-07 | 1.1161e-03 |          10 |    10752000 |           10 |        8960 |         0.1 |iter: 69/100\r",
      "   18.947 | 9.1696e-07 | 1.1004e-03 |          10 |    10905600 |           10 |        9088 |         0.1 |iter: 70/100\r",
      "   18.947 | 9.0422e-07 | 1.0851e-03 |          10 |    11059200 |           10 |        9216 |         0.1 |iter: 71/100\r",
      "   18.947 | 8.9184e-07 | 1.0702e-03 |          10 |    11212800 |           10 |        9344 |         0.1 |iter: 72/100\r",
      "   18.947 | 8.7979e-07 | 1.0557e-03 |          10 |    11366400 |           10 |        9472 |         0.1 |iter: 73/100\r",
      "   18.947 | 8.6806e-07 | 1.0417e-03 |          10 |    11520000 |           10 |        9600 |         0.1 |iter: 74/100\r",
      "   18.947 | 8.5663e-07 | 1.0280e-03 |          10 |    11673600 |           10 |        9728 |         0.1 |iter: 75/100\r",
      "   18.947 | 8.4551e-07 | 1.0146e-03 |          10 |    11827200 |           10 |        9856 |         0.1 |iter: 76/100\r",
      "   18.947 | 8.3467e-07 | 1.0016e-03 |          10 |    11980800 |           10 |        9984 |         0.1 |iter: 77/100\r",
      "   18.947 | 9.0651e-07 | 1.0878e-03 |          11 |    12134400 |           11 |       10112 |         0.1 |iter: 78/100\r",
      "   18.947 | 8.9518e-07 | 1.0742e-03 |          11 |    12288000 |           11 |       10240 |         0.1 |iter: 79/100\r",
      "   18.947 | 8.8413e-07 | 1.0610e-03 |          11 |    12441600 |           11 |       10368 |         0.1 |iter: 80/100\r",
      "   18.947 | 9.5274e-07 | 1.1433e-03 |          12 |    12595200 |           12 |       10496 |         0.1 |iter: 81/100\r",
      "   18.947 | 9.4127e-07 | 1.1295e-03 |          12 |    12748800 |           12 |       10624 |         0.1 |iter: 82/100\r",
      "   18.947 | 9.3006e-07 | 1.1161e-03 |          12 |    12902400 |           12 |       10752 |         0.1 |iter: 83/100\r",
      "   18.947 | 9.1912e-07 | 1.1029e-03 |          12 |    13056000 |           12 |       10880 |         0.1 |iter: 84/100\r",
      "   18.947 | 9.8413e-07 | 1.1810e-03 |          13 |    13209600 |           13 |       11008 |         0.1 |iter: 85/100\r",
      "   18.947 | 9.7282e-07 | 1.1674e-03 |          13 |    13363200 |           13 |       11136 |         0.1 |iter: 86/100\r",
      "   18.947 | 9.6177e-07 | 1.1541e-03 |          13 |    13516800 |           13 |       11264 |         0.1 |iter: 87/100\r",
      "   18.947 | 9.5096e-07 | 1.1412e-03 |          13 |    13670400 |           13 |       11392 |         0.1 |iter: 88/100\r",
      "   18.947 | 9.4039e-07 | 1.1285e-03 |          13 |    13824000 |           13 |       11520 |         0.1 |iter: 89/100\r",
      "   18.947 | 9.3006e-07 | 1.1161e-03 |          13 |    13977600 |           13 |       11648 |         0.1 |iter: 90/100\r",
      "   18.947 | 9.1995e-07 | 1.1039e-03 |          13 |    14131200 |           13 |       11776 |         0.1 |iter: 91/100\r",
      "   18.947 | 9.1006e-07 | 1.0921e-03 |          13 |    14284800 |           13 |       11904 |         0.1 |iter: 92/100\r",
      "   18.947 | 9.0038e-07 | 1.0805e-03 |          13 |    14438400 |           13 |       12032 |         0.1 |iter: 93/100\r",
      "   18.947 | 8.9090e-07 | 1.0691e-03 |          13 |    14592000 |           13 |       12160 |         0.1 |iter: 94/100\r",
      "   18.947 | 8.8162e-07 | 1.0579e-03 |          13 |    14745600 |           13 |       12288 |         0.1 |iter: 95/100\r",
      "   18.947 | 8.7253e-07 | 1.0470e-03 |          13 |    14899200 |           13 |       12416 |         0.1 |iter: 96/100\r",
      "   18.947 | 8.6363e-07 | 1.0364e-03 |          13 |    15052800 |           13 |       12544 |         0.1 |iter: 97/100\r",
      "   18.947 | 8.5490e-07 | 1.0259e-03 |          13 |    15206400 |           13 |       12672 |         0.1 |iter: 98/100\r",
      "   18.947 | 8.4635e-07 | 1.0156e-03 |          13 |    15360000 |           13 |       12800 |         0.1 |iter: 99/100\r",
      "   18.947 | 8.4635e-07 | 1.0156e-03 |          13 |    15360000 |           13 |       12800 |         0.1 |reached max iterations\n",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |      153600 |            0 |         128 |         0.0 |iter: 0/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |      307200 |            0 |         256 |         0.0 |iter: 1/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |      460800 |            0 |         384 |         0.0 |iter: 2/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |      614400 |            0 |         512 |         0.0 |iter: 3/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |      768000 |            0 |         640 |         0.0 |iter: 4/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |      921600 |            0 |         768 |         0.0 |iter: 5/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     1075200 |            0 |         896 |         0.0 |iter: 6/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     1228800 |            0 |        1024 |         0.0 |iter: 7/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     1382400 |            0 |        1152 |         0.0 |iter: 8/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     1536000 |            0 |        1280 |         0.0 |iter: 9/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     1689600 |            0 |        1408 |         0.0 |iter: 10/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     1843200 |            0 |        1536 |         0.0 |iter: 11/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     1996800 |            0 |        1664 |         0.0 |iter: 12/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     2150400 |            0 |        1792 |         0.0 |iter: 13/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     2304000 |            0 |        1920 |         0.0 |iter: 14/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     2457600 |            0 |        2048 |         0.0 |iter: 15/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     2611200 |            0 |        2176 |         0.0 |iter: 16/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     2764800 |            0 |        2304 |         0.0 |iter: 17/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     2918400 |            0 |        2432 |         0.0 |iter: 18/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     3072000 |            0 |        2560 |         0.0 |iter: 19/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     3225600 |            0 |        2688 |         0.0 |iter: 20/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     3379200 |            0 |        2816 |         0.0 |iter: 21/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     3532800 |            0 |        2944 |         0.0 |iter: 22/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     3686400 |            0 |        3072 |         0.0 |iter: 23/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     3840000 |            0 |        3200 |         0.0 |iter: 24/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     3993600 |            0 |        3328 |         0.0 |iter: 25/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     4147200 |            0 |        3456 |         0.0 |iter: 26/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     4300800 |            0 |        3584 |         0.0 |iter: 27/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     4454400 |            0 |        3712 |         0.0 |iter: 28/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     4608000 |            0 |        3840 |         0.0 |iter: 29/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     4761600 |            0 |        3968 |         0.0 |iter: 30/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     4915200 |            0 |        4096 |         0.0 |iter: 31/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     5068800 |            0 |        4224 |         0.0 |iter: 32/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     5222400 |            0 |        4352 |         0.0 |iter: 33/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     5376000 |            0 |        4480 |         0.0 |iter: 34/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     5529600 |            0 |        4608 |         0.0 |iter: 35/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     5683200 |            0 |        4736 |         0.0 |iter: 36/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     5836800 |            0 |        4864 |         0.0 |iter: 37/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     5990400 |            0 |        4992 |         0.0 |iter: 38/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     6144000 |            0 |        5120 |         0.0 |iter: 39/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     6297600 |            0 |        5248 |         0.0 |iter: 40/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     6451200 |            0 |        5376 |         0.0 |iter: 41/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     6604800 |            0 |        5504 |         0.0 |iter: 42/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     6758400 |            0 |        5632 |         0.0 |iter: 43/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     6912000 |            0 |        5760 |         0.0 |iter: 44/100\r",
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     7065600 |            0 |        5888 |         0.0 |iter: 45/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   19.474 | 0.0000e+00 | 0.0000e+00 |           0 |     7219200 |            0 |        6016 |         0.0 |iter: 46/100\r",
      "   19.474 | 1.3563e-07 | 1.6276e-04 |           1 |     7372800 |            1 |        6144 |         0.0 |iter: 47/100\r",
      "   19.474 | 1.3287e-07 | 1.5944e-04 |           1 |     7526400 |            1 |        6272 |         0.0 |iter: 48/100\r",
      "   19.474 | 1.3021e-07 | 1.5625e-04 |           1 |     7680000 |            1 |        6400 |         0.0 |iter: 49/100\r",
      "   19.474 | 2.5531e-07 | 3.0637e-04 |           2 |     7833600 |            2 |        6528 |         0.1 |iter: 50/100\r",
      "   19.474 | 2.5040e-07 | 3.0048e-04 |           2 |     7987200 |            2 |        6656 |         0.1 |iter: 51/100\r",
      "   19.474 | 2.4568e-07 | 2.9481e-04 |           2 |     8140800 |            2 |        6784 |         0.1 |iter: 52/100\r",
      "   19.474 | 2.4113e-07 | 2.8935e-04 |           2 |     8294400 |            2 |        6912 |         0.1 |iter: 53/100\r",
      "   19.474 | 2.3674e-07 | 2.8409e-04 |           2 |     8448000 |            2 |        7040 |         0.1 |iter: 54/100\r",
      "   19.474 | 2.3251e-07 | 2.7902e-04 |           2 |     8601600 |            2 |        7168 |         0.1 |iter: 55/100\r",
      "   19.474 | 2.2844e-07 | 2.7412e-04 |           2 |     8755200 |            2 |        7296 |         0.1 |iter: 56/100\r",
      "   19.474 | 2.2450e-07 | 2.6940e-04 |           2 |     8908800 |            2 |        7424 |         0.1 |iter: 57/100\r",
      "   19.474 | 2.2069e-07 | 2.6483e-04 |           2 |     9062400 |            2 |        7552 |         0.1 |iter: 58/100\r",
      "   19.474 | 3.2552e-07 | 3.9063e-04 |           3 |     9216000 |            3 |        7680 |         0.1 |iter: 59/100\r",
      "   19.474 | 3.2018e-07 | 3.8422e-04 |           3 |     9369600 |            3 |        7808 |         0.1 |iter: 60/100\r",
      "   19.474 | 3.1502e-07 | 3.7802e-04 |           3 |     9523200 |            3 |        7936 |         0.1 |iter: 61/100\r",
      "   19.474 | 3.1002e-07 | 3.7202e-04 |           3 |     9676800 |            3 |        8064 |         0.1 |iter: 62/100\r",
      "   19.474 | 3.0518e-07 | 3.6621e-04 |           3 |     9830400 |            3 |        8192 |         0.1 |iter: 63/100\r",
      "   19.474 | 3.0048e-07 | 3.6058e-04 |           3 |     9984000 |            3 |        8320 |         0.1 |iter: 64/100\r",
      "   19.474 | 2.9593e-07 | 3.5511e-04 |           3 |    10137600 |            3 |        8448 |         0.1 |iter: 65/100\r",
      "   19.474 | 2.9151e-07 | 3.4981e-04 |           3 |    10291200 |            3 |        8576 |         0.1 |iter: 66/100\r",
      "   19.474 | 2.8722e-07 | 3.4467e-04 |           3 |    10444800 |            3 |        8704 |         0.1 |iter: 67/100\r",
      "   19.474 | 2.8306e-07 | 3.3967e-04 |           3 |    10598400 |            3 |        8832 |         0.1 |iter: 68/100\r",
      "   19.474 | 2.7902e-07 | 3.3482e-04 |           3 |    10752000 |            3 |        8960 |         0.1 |iter: 69/100\r",
      "   19.474 | 2.7509e-07 | 3.3011e-04 |           3 |    10905600 |            3 |        9088 |         0.1 |iter: 70/100\r",
      "   19.474 | 2.7127e-07 | 3.2552e-04 |           3 |    11059200 |            3 |        9216 |         0.1 |iter: 71/100\r",
      "   19.474 | 2.6755e-07 | 3.2106e-04 |           3 |    11212800 |            3 |        9344 |         0.1 |iter: 72/100\r",
      "   19.474 | 2.6394e-07 | 3.1672e-04 |           3 |    11366400 |            3 |        9472 |         0.1 |iter: 73/100\r",
      "   19.474 | 2.6042e-07 | 3.1250e-04 |           3 |    11520000 |            3 |        9600 |         0.1 |iter: 74/100\r",
      "   19.474 | 2.5699e-07 | 3.0839e-04 |           3 |    11673600 |            3 |        9728 |         0.1 |iter: 75/100\r",
      "   19.474 | 2.5365e-07 | 3.0438e-04 |           3 |    11827200 |            3 |        9856 |         0.1 |iter: 76/100\r",
      "   19.474 | 2.5040e-07 | 3.0048e-04 |           3 |    11980800 |            3 |        9984 |         0.1 |iter: 77/100\r",
      "   19.474 | 2.4723e-07 | 2.9668e-04 |           3 |    12134400 |            3 |       10112 |         0.1 |iter: 78/100\r",
      "   19.474 | 2.4414e-07 | 2.9297e-04 |           3 |    12288000 |            3 |       10240 |         0.1 |iter: 79/100\r",
      "   19.474 | 2.4113e-07 | 2.8935e-04 |           3 |    12441600 |            3 |       10368 |         0.1 |iter: 80/100\r",
      "   19.474 | 2.3819e-07 | 2.8582e-04 |           3 |    12595200 |            3 |       10496 |         0.1 |iter: 81/100\r",
      "   19.474 | 2.3532e-07 | 2.8238e-04 |           3 |    12748800 |            3 |       10624 |         0.1 |iter: 82/100\r",
      "   19.474 | 2.3251e-07 | 2.7902e-04 |           3 |    12902400 |            3 |       10752 |         0.1 |iter: 83/100\r",
      "   19.474 | 2.2978e-07 | 2.7574e-04 |           3 |    13056000 |            3 |       10880 |         0.1 |iter: 84/100\r",
      "   19.474 | 2.2711e-07 | 2.7253e-04 |           3 |    13209600 |            3 |       11008 |         0.1 |iter: 85/100\r",
      "   19.474 | 2.2450e-07 | 2.6940e-04 |           3 |    13363200 |            3 |       11136 |         0.1 |iter: 86/100\r",
      "   19.474 | 2.9593e-07 | 3.5511e-04 |           4 |    13516800 |            4 |       11264 |         0.1 |iter: 87/100\r",
      "   19.474 | 2.9260e-07 | 3.5112e-04 |           4 |    13670400 |            4 |       11392 |         0.1 |iter: 88/100\r",
      "   19.474 | 2.8935e-07 | 3.4722e-04 |           4 |    13824000 |            4 |       11520 |         0.1 |iter: 89/100\r",
      "   19.474 | 2.8617e-07 | 3.4341e-04 |           4 |    13977600 |            4 |       11648 |         0.1 |iter: 90/100\r",
      "   19.474 | 2.8306e-07 | 3.3967e-04 |           4 |    14131200 |            4 |       11776 |         0.1 |iter: 91/100\r",
      "   19.474 | 2.8002e-07 | 3.3602e-04 |           4 |    14284800 |            4 |       11904 |         0.1 |iter: 92/100\r",
      "   19.474 | 2.7704e-07 | 3.3245e-04 |           4 |    14438400 |            4 |       12032 |         0.1 |iter: 93/100\r",
      "   19.474 | 2.7412e-07 | 3.2895e-04 |           4 |    14592000 |            4 |       12160 |         0.1 |iter: 94/100\r",
      "   19.474 | 2.7127e-07 | 3.2552e-04 |           4 |    14745600 |            4 |       12288 |         0.1 |iter: 95/100\r",
      "   19.474 | 2.6847e-07 | 3.2216e-04 |           4 |    14899200 |            4 |       12416 |         0.1 |iter: 96/100\r",
      "   19.474 | 2.6573e-07 | 3.1888e-04 |           4 |    15052800 |            4 |       12544 |         0.1 |iter: 97/100\r",
      "   19.474 | 2.6305e-07 | 3.1566e-04 |           4 |    15206400 |            4 |       12672 |         0.1 |iter: 98/100\r",
      "   19.474 | 2.6042e-07 | 3.1250e-04 |           4 |    15360000 |            4 |       12800 |         0.1 |iter: 99/100\r",
      "   19.474 | 2.6042e-07 | 3.1250e-04 |           4 |    15360000 |            4 |       12800 |         0.1 |reached max iterations\n",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |      153600 |            0 |         128 |         0.0 |iter: 0/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |      307200 |            0 |         256 |         0.0 |iter: 1/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |      460800 |            0 |         384 |         0.0 |iter: 2/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |      614400 |            0 |         512 |         0.0 |iter: 3/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |      768000 |            0 |         640 |         0.0 |iter: 4/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |      921600 |            0 |         768 |         0.0 |iter: 5/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     1075200 |            0 |         896 |         0.0 |iter: 6/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     1228800 |            0 |        1024 |         0.0 |iter: 7/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     1382400 |            0 |        1152 |         0.0 |iter: 8/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     1536000 |            0 |        1280 |         0.0 |iter: 9/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     1689600 |            0 |        1408 |         0.0 |iter: 10/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     1843200 |            0 |        1536 |         0.0 |iter: 11/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     1996800 |            0 |        1664 |         0.0 |iter: 12/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     2150400 |            0 |        1792 |         0.0 |iter: 13/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     2304000 |            0 |        1920 |         0.0 |iter: 14/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     2457600 |            0 |        2048 |         0.0 |iter: 15/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     2611200 |            0 |        2176 |         0.0 |iter: 16/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     2764800 |            0 |        2304 |         0.0 |iter: 17/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     2918400 |            0 |        2432 |         0.0 |iter: 18/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     3072000 |            0 |        2560 |         0.0 |iter: 19/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     3225600 |            0 |        2688 |         0.0 |iter: 20/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     3379200 |            0 |        2816 |         0.0 |iter: 21/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     3532800 |            0 |        2944 |         0.0 |iter: 22/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     3686400 |            0 |        3072 |         0.0 |iter: 23/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     3840000 |            0 |        3200 |         0.0 |iter: 24/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     3993600 |            0 |        3328 |         0.0 |iter: 25/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     4147200 |            0 |        3456 |         0.0 |iter: 26/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     4300800 |            0 |        3584 |         0.0 |iter: 27/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     4454400 |            0 |        3712 |         0.0 |iter: 28/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     4608000 |            0 |        3840 |         0.0 |iter: 29/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     4761600 |            0 |        3968 |         0.0 |iter: 30/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     4915200 |            0 |        4096 |         0.0 |iter: 31/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     5068800 |            0 |        4224 |         0.0 |iter: 32/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     5222400 |            0 |        4352 |         0.0 |iter: 33/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     5376000 |            0 |        4480 |         0.0 |iter: 34/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     5529600 |            0 |        4608 |         0.0 |iter: 35/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     5683200 |            0 |        4736 |         0.0 |iter: 36/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     5836800 |            0 |        4864 |         0.0 |iter: 37/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     5990400 |            0 |        4992 |         0.0 |iter: 38/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     6144000 |            0 |        5120 |         0.0 |iter: 39/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     6297600 |            0 |        5248 |         0.0 |iter: 40/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     6451200 |            0 |        5376 |         0.0 |iter: 41/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     6604800 |            0 |        5504 |         0.0 |iter: 42/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     6758400 |            0 |        5632 |         0.0 |iter: 43/100\r",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |     6912000 |            0 |        5760 |         0.0 |iter: 44/100\r",
      "     20.0 | 1.4153e-07 | 1.6984e-04 |           1 |     7065600 |            1 |        5888 |         0.0 |iter: 45/100\r",
      "     20.0 | 1.3852e-07 | 1.6622e-04 |           1 |     7219200 |            1 |        6016 |         0.0 |iter: 46/100\r",
      "     20.0 | 1.3563e-07 | 1.6276e-04 |           1 |     7372800 |            1 |        6144 |         0.0 |iter: 47/100\r",
      "     20.0 | 1.3287e-07 | 1.5944e-04 |           1 |     7526400 |            1 |        6272 |         0.0 |iter: 48/100\r",
      "     20.0 | 1.3021e-07 | 1.5625e-04 |           1 |     7680000 |            1 |        6400 |         0.0 |iter: 49/100\r",
      "     20.0 | 1.2766e-07 | 1.5319e-04 |           1 |     7833600 |            1 |        6528 |         0.1 |iter: 50/100\r",
      "     20.0 | 1.2520e-07 | 1.5024e-04 |           1 |     7987200 |            1 |        6656 |         0.1 |iter: 51/100\r",
      "     20.0 | 1.2284e-07 | 1.4741e-04 |           1 |     8140800 |            1 |        6784 |         0.1 |iter: 52/100\r",
      "     20.0 | 1.2056e-07 | 1.4468e-04 |           1 |     8294400 |            1 |        6912 |         0.1 |iter: 53/100\r",
      "     20.0 | 1.1837e-07 | 1.4205e-04 |           1 |     8448000 |            1 |        7040 |         0.1 |iter: 54/100\r",
      "     20.0 | 1.1626e-07 | 1.3951e-04 |           1 |     8601600 |            1 |        7168 |         0.1 |iter: 55/100\r",
      "     20.0 | 1.1422e-07 | 1.3706e-04 |           1 |     8755200 |            1 |        7296 |         0.1 |iter: 56/100\r",
      "     20.0 | 1.1225e-07 | 1.3470e-04 |           1 |     8908800 |            1 |        7424 |         0.1 |iter: 57/100\r",
      "     20.0 | 1.1035e-07 | 1.3242e-04 |           1 |     9062400 |            1 |        7552 |         0.1 |iter: 58/100\r",
      "     20.0 | 1.0851e-07 | 1.3021e-04 |           1 |     9216000 |            1 |        7680 |         0.1 |iter: 59/100\r",
      "     20.0 | 1.0673e-07 | 1.2807e-04 |           1 |     9369600 |            1 |        7808 |         0.1 |iter: 60/100\r",
      "     20.0 | 1.0501e-07 | 1.2601e-04 |           1 |     9523200 |            1 |        7936 |         0.1 |iter: 61/100\r",
      "     20.0 | 1.0334e-07 | 1.2401e-04 |           1 |     9676800 |            1 |        8064 |         0.1 |iter: 62/100\r",
      "     20.0 | 1.0173e-07 | 1.2207e-04 |           1 |     9830400 |            1 |        8192 |         0.1 |iter: 63/100\r",
      "     20.0 | 1.0016e-07 | 1.2019e-04 |           1 |     9984000 |            1 |        8320 |         0.1 |iter: 64/100\r",
      "     20.0 | 9.8643e-08 | 1.1837e-04 |           1 |    10137600 |            1 |        8448 |         0.1 |iter: 65/100\r",
      "     20.0 | 9.7170e-08 | 1.1660e-04 |           1 |    10291200 |            1 |        8576 |         0.1 |iter: 66/100\r",
      "     20.0 | 9.5741e-08 | 1.1489e-04 |           1 |    10444800 |            1 |        8704 |         0.1 |iter: 67/100\r",
      "     20.0 | 9.4354e-08 | 1.1322e-04 |           1 |    10598400 |            1 |        8832 |         0.1 |iter: 68/100\r",
      "     20.0 | 9.3006e-08 | 1.1161e-04 |           1 |    10752000 |            1 |        8960 |         0.1 |iter: 69/100\r",
      "     20.0 | 9.1696e-08 | 1.1004e-04 |           1 |    10905600 |            1 |        9088 |         0.1 |iter: 70/100\r",
      "     20.0 | 9.0422e-08 | 1.0851e-04 |           1 |    11059200 |            1 |        9216 |         0.1 |iter: 71/100\r",
      "     20.0 | 8.9184e-08 | 1.0702e-04 |           1 |    11212800 |            1 |        9344 |         0.1 |iter: 72/100\r",
      "     20.0 | 8.7979e-08 | 1.0557e-04 |           1 |    11366400 |            1 |        9472 |         0.1 |iter: 73/100\r",
      "     20.0 | 8.6806e-08 | 1.0417e-04 |           1 |    11520000 |            1 |        9600 |         0.1 |iter: 74/100\r",
      "     20.0 | 8.5663e-08 | 1.0280e-04 |           1 |    11673600 |            1 |        9728 |         0.1 |iter: 75/100\r",
      "     20.0 | 8.4551e-08 | 1.0146e-04 |           1 |    11827200 |            1 |        9856 |         0.1 |iter: 76/100\r",
      "     20.0 | 8.3467e-08 | 1.0016e-04 |           1 |    11980800 |            1 |        9984 |         0.1 |iter: 77/100\r",
      "     20.0 | 8.2410e-08 | 9.8892e-05 |           1 |    12134400 |            1 |       10112 |         0.1 |iter: 78/100\r",
      "     20.0 | 8.1380e-08 | 9.7656e-05 |           1 |    12288000 |            1 |       10240 |         0.1 |iter: 79/100\r",
      "     20.0 | 8.0376e-08 | 9.6451e-05 |           1 |    12441600 |            1 |       10368 |         0.1 |iter: 80/100\r",
      "     20.0 | 7.9395e-08 | 9.5274e-05 |           1 |    12595200 |            1 |       10496 |         0.1 |iter: 81/100\r",
      "     20.0 | 7.8439e-08 | 9.4127e-05 |           1 |    12748800 |            1 |       10624 |         0.1 |iter: 82/100\r",
      "     20.0 | 7.7505e-08 | 9.3006e-05 |           1 |    12902400 |            1 |       10752 |         0.1 |iter: 83/100\r",
      "     20.0 | 7.6593e-08 | 9.1912e-05 |           1 |    13056000 |            1 |       10880 |         0.1 |iter: 84/100\r",
      "     20.0 | 7.5703e-08 | 9.0843e-05 |           1 |    13209600 |            1 |       11008 |         0.1 |iter: 85/100\r",
      "     20.0 | 7.4832e-08 | 8.9799e-05 |           1 |    13363200 |            1 |       11136 |         0.1 |iter: 86/100\r",
      "     20.0 | 7.3982e-08 | 8.8778e-05 |           1 |    13516800 |            1 |       11264 |         0.1 |iter: 87/100\r",
      "     20.0 | 7.3151e-08 | 8.7781e-05 |           1 |    13670400 |            1 |       11392 |         0.1 |iter: 88/100\r",
      "     20.0 | 7.2338e-08 | 8.6806e-05 |           1 |    13824000 |            1 |       11520 |         0.1 |iter: 89/100\r",
      "     20.0 | 7.1543e-08 | 8.5852e-05 |           1 |    13977600 |            1 |       11648 |         0.1 |iter: 90/100\r",
      "     20.0 | 7.0765e-08 | 8.4918e-05 |           1 |    14131200 |            1 |       11776 |         0.1 |iter: 91/100\r",
      "     20.0 | 7.0004e-08 | 8.4005e-05 |           1 |    14284800 |            1 |       11904 |         0.1 |iter: 92/100\r",
      "     20.0 | 6.9260e-08 | 8.3112e-05 |           1 |    14438400 |            1 |       12032 |         0.1 |iter: 93/100\r",
      "     20.0 | 6.8531e-08 | 8.2237e-05 |           1 |    14592000 |            1 |       12160 |         0.1 |iter: 94/100\r",
      "     20.0 | 6.7817e-08 | 8.1380e-05 |           1 |    14745600 |            1 |       12288 |         0.1 |iter: 95/100\r",
      "     20.0 | 6.7118e-08 | 8.0541e-05 |           1 |    14899200 |            1 |       12416 |         0.1 |iter: 96/100\r",
      "     20.0 | 6.6433e-08 | 7.9719e-05 |           1 |    15052800 |            1 |       12544 |         0.1 |iter: 97/100\r",
      "     20.0 | 6.5762e-08 | 7.8914e-05 |           1 |    15206400 |            1 |       12672 |         0.1 |iter: 98/100\r",
      "     20.0 | 6.5104e-08 | 7.8125e-05 |           1 |    15360000 |            1 |       12800 |         0.1 |iter: 99/100\r",
      "     20.0 | 6.5104e-08 | 7.8125e-05 |           1 |    15360000 |            1 |       12800 |         0.1 |reached max iterations\n"
     ]
    },
    {
     "data": {
      "image/png": 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hpo0RAAAAAICBiARrCwcPHpTP55MkTZw4sd1yzdcKCwtVWlqqmJiY8773ww8/rFdeeUWzZ8+Wy+XSyy+/3Or6nDlzNHz48PO+D4DAkOZMU5ozTUsmLFFlQ6U2F2zW+pPrtSF/g0rrPlkixRaWL1tYvkLi3pfVF6l69yg1Vo1TY/UoyReqw6crdfh0pV7akidJGuYK989wvXRYjIZEh8lisZg1TAAAAAAA+j0SrC0UFBT4HycnJ7dbruW1goKCXkmw7tixQ5K0efNmbd68+Zzrzz//fLsJ1vr6etXX1/uP3W63pKb1XD0ez3n3rb9pHlMgjg3oiVBLqOYlz9O85Hn+jbI2FGzQhvwNOlR2yF/OZ62U/eONsiyyKbhxhNwlo+SpGiujwSXJopziauUUV+v/tp+QJCU6QzUzNVoz05q+hrscslgsxCFgMmIQMB9xCJiLGATMF+hx2J1xkWBtobKy0v/Y4XC0W67ltZZ1zsfatWt7XPepp57SE088cc75VatWdTiOgS4zM9PsLgD9VurH/7mj3Mr2ZOtw42Ed9RxVgxokSYa8qg/KVkh8tkLi/6MQb6wsNWNUXjZWnprhktH09nCqok5v7j2lN/eekiRFBBkaEdX8Jb27KlNWJrgCpuG9EDAfcQiYixgEzBeocVhTU9PlsiRYA8Cjjz6qBx980H/sdruVkpKiRYsWKSoqysSe9Q2Px6PMzEwtXLhQdrvd7O4AA0aDt0E7zuzQxvyN2lCwQSerTvqv1dtKpMgPFBr5gZzWMMXbJ8lbNVa5J4eqri7CX66q0aI9pRbt+XgVgqjQIM1IHdQ0wzU1WhOSomS3WS/00ICLDu+FgPmIQ8BcxCBgvkCPw+ZPiHcFCdYWIiMj/Y87ylK3vNayjllCQkIUEhJyznm73R6QL/BmgT4+oLfZ7XbNHTpXc4fOlWEYynHnaMPJDVp3cp12nd7l3yir3ler4/XbJPs22YdJoyLGyGWbquqyUTqYG6XKep+/TXddo9YcLtaaw8WSpDC7TTNSo/1ruE5NGaRQu82U8QIXA94LAfMRh4C5iEHAfIEah90ZEwnWFpKSkvyP8/PzNXny5DbL5efnt1kHAAYKi8Wi4c7hGu4c7t8o64OCD7T+5HptzN/YaqOs3KrDytVhySrFjo/VVdGzFO6dqP1Zocqvdaqs5pN1aWo9Xm38qFgbP2pKuAbbrJqS4tSlw2I0LSVaE5KjlBAVysZZAAAAAICAQYK1hXHjxslqtcrn8ykrK0vXXXddm+WysrIkSQkJCb2ywVVfYZMrAF0VagnVNcnX6Jrka+T1eXWg9IA25G/QxoKNrTbKKqkr0bpTb0l6S7bBNk0bPE3jnZfJ3jBeRwsc2pZbptPuTzbda/D69GFumT7MLfOfiwm3a1xClMYnRn78FaW0WIesLOYKdBnvhYD5iEPAXMQgYL5Aj8PujMtiGIbRh33pN9LS0pSXl6fHH39cy5Yta7fc3LlztWHDBl1zzTVavXr1OdcNw9DIkSN17Ngx3X333Vq+fHkf9rp7MjIylJGRIa/Xq+zsbK1YsSKgN7kCcGG4fW4d9hxWtidbHzV+JI/afpOJtcZqdNAYJRqjVV89TDluu466LSqu7zxxGmw1lBwuDXEYSg43NCTcUKJDCmI5VwAAAACACWpqapSenq6KiopO9zhiButZlixZog0bNmjNmjXaunWrZs2a1er666+/rmPHjkmS7r77bjO62K77779f999/v9xut5xOJ5tcAeh19d567Ti9Q+tPrlfmsUyV+T6ZmVriK9Hmhg8kfaBwR7hmjZilW5Ou1KjIS5Rz2qb9BW4dOFWpA6fcrZYVkKQGn0U5lVJO5SfJWLvNohFxEa1muo5NiFRkKG9dAO+FgPmIQ8BcxCBgvkCPQza5klRWViav1+s/9vmaNmWpqalRcXGx/3xoaKgiIj7ZIXvJkiX61a9+pX379umWW27R8uXLNX/+fPl8Pq1cuVL33nuvJOm6667T/PnzL9BoeiZQFxluFujjA/oju92uq1Kv0pykOZpUNEnjrhinDwqb1m7ddWaXvEbTz93qxmq9f+J9vX/ifUnShNgJuiL5Cn3zkss02XW1Sqq92p/v1v4Ct/YXVGh/gVv55bWt7uXxGjpUWKlDhZV6Y9cn59NiHZqQ5NT4pChNSIrShCSn4iLP3egPuBjwXgiYjzgEzEUMAuYL1DhkkytJ06ZNU15e3jnnn376aT399NP+4yVLluiFF17wHwcFBenNN9/UvHnzlJubqwULFsjhcMjn86murs7f9iuvvNLnYwCA/qx5o6wxrjH6/MTPq6K+QpsLNmv9yfXakL9B5fXl/rL7S/Zrf8l+/X7v7xUWFKYZ8TN0WeJlWjz9Mn1t/gxZLBaV1zToQEHrpOvRoir5zlrIJrekRrklNfrPvlP+c4MjQ/zJ1uZ/U2LC2EwLAAAAANDnAjbBej7S0tK0d+9e/exnP9Mbb7yhnJwc2e12TZgwQXfeeaceeOABBQcHm93NTrHJFYC+0lYcOqwOzR8yX/OHzJfX51VWSZY2FmzUhvwNyi7P9perbazVxvyN2pi/UZIUExqjS+Mv1aUJl2pWwizNTE2RlNJUtsGrw6crP15aoFIHT7l1+HSV6ht9rfpzprJeZw4Xac3hIv+5yNAgjUv4ZHmB8YmRGh4XLruNhV0x8PFeCJiPOATMRQwC5gv0OGSTq4sUm1wB6K/cPreONh7VUc9RHW08qkqjst2ysdZYjQgaoRFBIzQ8aLjCrGGtrnsN6XStlF9t0clqi05WNz2u9XY+WzXI0rR51pDwTzbTSnJIIbbzHiIAAAAAIIB0Z5MrEqwBqHmTq+LiYja5AtAnzicODcNQjjtHWwu3amvhVu04vUPVjdVtlrVarBoXPc4/u3VK3BSF2M5db9UwDJ0sr9XBU5X+jbQOnKrUaXd9p/2xWqS02PCmma5Jn8x2jXb0/08q4OLFeyFgPuIQMBcxCJgv0OPQ7XbL5XJ1KcHKEgEBLFAXGW4W6OMDBoKexuEY1xiNcY3R3RPvlsfn0f7i/dp8arO2FGzR3qK9ajQaJUk+w6f9pfu1v3S/nj/wvEJsIZo+eLouS7pMlyVeprExY2W1NH3kf/jgYA0f7NR/TfnkPsVV9a3WdT1Q4FZOSbVa/mnRZ0jHiqt1rLha/95X6D+f5AzVeP+arlGakOxUkjOUdV3Rr/BeCJiPOATMRQwC5gvUOGSTKwDAgGG32jV18FRNHTxV9025TzWeGm0/vV1bTm3RllNbdKTsiL9svbdem09t1uZTmyVJg0IG6dKES/0J15TIlFZtuyJCNHd0nOaOjvOfq6pv1KFTrTfTyj5dKY+39Qc6CirqVFBRp/cOnvafi3bYNb7VZlpRGuaKkM1K0hUAAAAALlYkWAEA/YrD7tDcIXM1d8hcSVJxbbG2ntqqLae2aHPBZp2u+SThWV5frlV5q7Qqb5UkKTkiWZclXqbLki7TrIRZig6NPqf9iJAgXZIWo0vSYvznGhp9OnKmUvsL3B/PeG2a7Vrd4G1Vt6zGo00flWjTRyX+c2F2m8YmRn6ccG1KvI6Oj1SonYVdAQAAAOBiQII1gHk8noDcyS3Qd6kDBoILGYfOIKcWpSzSopRFMgxDxyuP+9dv/fD0h6ryVPnL5lfla+WRlVp5ZKUkaWz0WP/6rVPjpiosKKzNe1gkjY5zaHScQzdNSZAk+XyGjpfV6EBB63VdS6obWtWt9Xi163i5dh0v958LCbJq7iiXFo0frHlj4uQMC7yPy8BcvBcC5iMOAXMRg4D5Aj0OuzMuNrkKIBkZGcrIyJDX61V2drZWrFghh8NhdrcAoM94Da9OeU/po8aPdLTxqI43HpdX3jbL2mTT0KChGhE0QiODRirJluRfv7WrDENye6ST1RadrG76N7/aopL69pcIsFoMjYoyNDmm6SuKvbMAAAAAoN+rqalRenp6lza5IsEagNxut5xOp4qLizt9AQxEgb5LHTAQ9Nc4rG2s1e6i3dpauFXbCrfpUNmhdstG2iN1SfwlmpUwS7MSZmlo5NAeb2DlrvXoYOHHM10L3Np4tETFVQ3nlLNYpOkpg7Ro/GAtHD9YKdH8EQw9019jELiYEIeAuYhBwHyBHodut1sul6tLCVaWCAhggbqLW7NAHx8wEPS3OLTb7Zo7dK7mDm1av7W0rlTbCrdpS0HThln5Vfn+spWeSq05uUZrTq6RJCWEJzSt35p4mWYlzpIrzNXl+8ba7boiyqErRsdLkrw+QzuPl+ndrEK9s79QJ8tqJTXNgN1xvFw7jpfrqXeyNT4xSosnJmjxxASNGhzR4wQvLl79LQaBixFxCJiLGATMF6hx2J0xkWAFAASsmNAYLU5brMVpiyVJJypPaMupLdpSsEVbC7eqor7CX7awulD/+Ogf+sdH/5AkjYoe5U+4XhJ/iRz2rs82tVktmpkWo5lpMXrsv8Zpf4Fb7+4v1DtZhTpy5pM1Y5vWdXXr55nZGu4K17UTE3TthARNGeIk2QoAAAAAAwQJVgDARSMlMkUpkSm6dfSt8hk+HSo95E+47jyzU/Xeen/ZI2VHdKTsiF468JKCLEGaHDdZlyVdptmJszXBNUF2a9f+mmmxWDQx2amJyU49tGiMjhZV6d39hXo3q1B7Tn6S4D1WXK3frT2q3609qkRnqK6d0JRsnZkWrSBb99aKBQAAAABcOCRYA5jH4wnIndwCfZc6YCAIlDgcFTVKo6JG6XNjPqd6b732FO3xr996oPSADDUtU95oNGrnmZ3aeWanfrv7twoPCteM+Bn+9VuHRQ3r8ozToYNCdO/lqbr38lQVlNcq8+AZZR48ow9zy+T7eFX0UxV1euGDXL3wQa6iHXbNHztYi8YP1pwRsQoJItmKwIlBYCAjDgFzEYOA+QI9DrszLja5CiAZGRnKyMiQ1+tVdna2VqxYIYeDDVQAoCdqfbU61nhMRxuP6mjjUZX4StotG2mJ1IigERpuH66RQSMVZe3+BoNVHmlfqUV7Sy06XGGR1zg3YRtiMzRhkKHJMYbGRRsKtXX7NgAAAACALqipqVF6enqXNrkiwRqA3G63nE6niouLO30BDESBvksdMBBcjHFYUF2gbYXb9GHhh9p6eqtK60rbLZsWlaZpcdM0NW6qpsRNUUpESrfWVK2sa9S67CKtOnBG644Uq6bBe06Z4CCrrhgRq0XjB+uasXGKdgT3aFwYmC7GGAT6G+IQMBcxCJgv0OPQ7XbL5XJ1KcHKEgEBLFB3cWsW6OMDBoKLKQ5TB6UqdVCqbh17qwzD0JHyI9pSsEVbTm3R9tPbVdtY6y+b685VrjtXfz/6d0lSbGispsdP17TB0zRt8DSNiRnT4RquMXa7bpoxVDfNGKo6j1cbjhTrnaxCvXfwtCpqmz6m0tDo0/uHi/T+4SLZrBbNGhajxRMTtGh8ghKcoX37zUC/cTHFINBfEYeAuYhBwHyBGofdGRMJVgAAuslisWh09GiNjh6tuyfcLY/Xo73Fe7Xl1BZtLtis/cX71Wg0+suX1JUoMy9TmXmZkqSwoDBNdk3W1MFTNX3wdE2Om6yI4Ig27xVqt2nh+HgtHB8vj9enbTmleierUO/uL9SZyqZNubw+Qx8cLdEHR0v0/X/u17Shg7T4402y0lzhff8NAQAAAICLGAlWAADOk91m14z4GZoRP0P3T71ftY21yirO0q4zu7TzzE7tObNHVZ4qf/naxlptLdyqrYVbJUlWi1Vjosf4E67TBk9TfHh8G/ex6vKRLl0+0qUnPj1Bu06U6939hXonq1DHS2v85XYdL9eu4+V66u1DGpsQqWsnJGjxxASNTYjs1lIFAAAAAIDOkWAFAKCXhQWFaWbCTM1MmClJ8vq8+qj8I3/CdfeZ3TpVfcpf3mf4dLD0oA6WHtRfD/1VkpQckdwq4Tpi0AhZLVZ/HavVohmp0ZqRGq1HrxurQ4WV/pmthwor/eUOFVbqUGGlfrX6iFJjHVo8IUGLJiRoWsogWa0kWwEAAADgfJFgBQCgj9msNo2JGaMxMWN0x9g7JEmnqk61Srhml2XL0Cf7TuZX5Su/Kl//OfYfSVJkcKSmxk31r+U60TVRIbYQSU1LFoxLjNK4xCh9c+Fo5RRX6939TcnWXcfL/W3mldTo9+uP6ffrj2lwZIh/Zuulw2Jkt32SvAUAAAAAdB0J1gDm8Xjk8XjM7kavax5TII4NGCiIw/PnCnFpYcpCLUxZKEmqbKjU3uK92lO0R7uLdiurJEt13jp/+cqGSm3I36AN+RskSUHWII2PGa+pcVM1LW6aJrsmKzo0WpI0xBmse+YM1T1zhqrQXaf3Dp7RqgNntC23TF5fUxL3TGW9XtqSp5e25GlQmF3XjI3TonGDdfnIWIXabRf4u4HuIgYB8xGHgLmIQcB8gR6H3RmXxTAMo/NiGAgyMjKUkZEhr9er7OxsrVixQg6Hw+xuAQB6oNFo1CnvKR1vPK48b57yGvNUbVR3WCfOGqehQUOVGpSqVFuqYqwxrdZcrfZIWWUW7S216FC5RY3GuUsEBFsNjY82NCXG0PhBhkL5UywAAACAi1BNTY3S09NVUVGhqKioDsuSYA1AbrdbTqdTxcXFnb4ABiKPx6PMzEwtXLhQdrvd7O4AFyXi8MIzDEPHK49rd9Fu/1deZV6HdWJDYzU1bqr/a3T0aNmtTc9XVX2j1mcXa9XBM1p7uEjVDd5z6tttFs0ZEatrxw/WNWMHKzY8uE/Ghu4jBgHzEYeAuYhBwHyBHodut1sul6tLCVbmpQQwu90ekC/wZoE+PmAgIA4vrJGxIzUydqT+e+x/S5JKaku0u2i3dp3epV1ndulAyQE1Go3+8iV1JVp9YrVWn1gtqWnzrcmuyZoWP03T4qZp/sTJ+sz0FNV5vPrgaLHeySpU5oHTKqv5+KM+XkPrsou1LrtYVssBXTosxr9JVtKgsAv/DcA5iEHAfMQhYC5iEDBfoMZhd8ZEghUAgAEqNixW84fO1/yh8yVJtY21yirO8m+etefMHlV5qvzlaxtrtbVwq7YWbpUkWS1WjYkeo2mDp2na4Gl66Lpp+tFNk/Rhbpne3V+od7IKVehuWgfWZ0hbjpVqy7FSLfvXAU0Z4tS1ExO0eEKChsdFXPjBAwAAAEA/QYIVAIAAERYUppkJMzUzYaYkyevz6qPyj/wJ111ndqmwutBf3mf4dLD0oA6WHtSKQyskSckRyZo6eKqmj5muOy+fqupql1btP6N39xcqp/iTNWD3nKzQnpMV+uk7hzUhKUo3Tk3Wp6cmKT4q9MIOGgAAAABMRoIVAIAAZbPaNCZmjMbEjNEdY++QJJ2qOuVPuO4+s1vZZdky9Mly7PlV+cqvytd/jv1HkhQZHKmpcVOVvmia4uxjdSw/Ru/tL9WBU25/nf0Fbu0vcOtHbx/UnBGxunFqshZPTFBkaOB9TAgAAAAAzkaCFQCAi0hiRKISIxJ1/fDrJUmVDZXaU7RHO0/v1O6i3dpXtE913jp/+cqGSm3I36AN+RskSXarXeNHjdfS6ZNUXzVU+45FK+tE0wZZhiFt+qhEmz4q0Xf/kaUF4+N109RkzR0dp+Ag64UfLAAAAABcACRYAQC4iEUGR+qK5Ct0RfIVkiSP16ODpQe168wu/1dpXam/vMfn0Z6iPdpTtKfpRIR0yeyJcvou0eFjw3SyKESSVN/o03/2ntJ/9p5StMOu/5qcqJumJWv60GhZLJYLPk4AAAAA6CskWAEAgJ/dZtfkuMmaHDdZSyYskWEYynPntUq45rpzW9U5XJ4lKUtySVOHT1BYwzQdOjpMZe5wSVJZjUcvbzmul7cc19AYhz4zNUmfmZqskYPZHAsAAADAwEeCNYB5PB55PB6zu9HrmscUiGMDBgri8OKS7EhWclqyPpX2KUlSaV2pdhft1p6iPdp8arM+qvjIX/aoe7+k/VKyNGHUGAXVTdXhY8NUVztIknS8tEa/ef8j/eb9jzQxKUqfnpKoT01KUFxkiAkjG7iIQcB8xCFgLmIQMF+gx2F3xmUxDMPovBgGgoyMDGVkZMjr9So7O1srVqyQw+Ewu1sAgABX5C1SlidLWQ1ZOu073WaZQb5k+Son6VTRJPk8sa2uWWRojNPQJXGGJscYCrFdiF4DAAAAQPtqamqUnp6uiooKRUVFdViWBGsAcrvdcjqdKi4u7vQFMBB5PB5lZmZq4cKFstvZoRowA3GI9uS6c/Xe8ff03vH3lF2e3WaZwcEj1FAxSSfzR8k4K9kaZrdq/tjB+szURF0+IlZ2G5tjtYUYBMxHHALmIgYB8wV6HLrdbrlcri4lWFkiIIDZ7faAfIE3C/TxAQMBcYizjYodpVGxo3TftPuUW5GrzLxMrcpbpUOlh/xlzjQclcKOKmKkFBs0XFVl41VyepwMT6xqPT79e1+h/r2vUDHhwbphcqJunJasqSmD2ByrDcQgYD7iEDAXMQiYL1DjsDtjIsEKAAD6RJozTfdOvlf3Tr5Xx93HtSpvlVblrtLB0oP+MiWNx6TIY4qI/Lec1lS5S8arsmSCDI9LpdUNWr45T8s35yk11qEbpybrxmnJGuYKN3FUAAAAANAaCVYAANDnhkYN1RcnfVFfnPRFnXCfaEq25q3SgZID/jIVvjwpOk8R0W8rXENVUTxOdRUTZTTEKa+kRr9afUS/Wn1EU1IG6aapSfrUlCS5ItgcCwAAAIC5SLACAIALKiUqRfdMukf3TLpHJypPNC0jkLtK+0v2+8tU67iCXMcV4XpXoUayKorHq9E9Sb6Gwdpzolx7TpTrh/85qCtHuXTTtGQtHB8vRzC/1gAAAAC48Pg/EQAAYJqUyBR9YeIX9IWJX1B+Vb4yc5vWbN1XvM9fps6Sr5C4fIXEZcruTVJV6Xg1uifL2zBYaw8Xae3hIjmCbbp2QoI+MzVJV4x0KYjNsQAAAABcICRYAQBAv5AckaylE5dq6cSlKqgq8M9s3Vu811/GYytQSFyBQuLek9WToNryCWp0T1ZNQ7z+vitff9+VL1dEsG6YkqQbpyZr8hAnm2MBAAAA6FMkWAEAQL+TFJGkJROWaMmEJTpVdaop2Zq3SnuK9vjL+OyFCokrVEjcaqlhsOorJqmxcpKKq+L1/KZcPb8pV8Nd4bpxWrJunJqsobEOE0cEAAAAIFCRYAUAAP1aYkSi7p5wt+6ecLcKqwv9M1t3F+3+pFDwGYXErVZI3Gr56gfL456oxspJOlacoJ9nZuvnmdmaPnSQbpyWrP+alKhYNscCAAAA0EtIsAIAgAEjITxBnxv/OX1u/Od0uvq03jv+nlblrtKuM7tkyJAkWUPOKCTufYXEvS9fvUueyklqdE/SzuOGdh4v1w/+dUBXjY7TZ6Yla+G4eIUF20weFQAAAICBjAQrAAAYkOLD4/XZcZ/VZ8d9VmdqzvhntrZOthYrJGSNQlxrWiRbJ2r1IZ9WHzqj8GCbFk9M1I3TkjRnhEs2K+u1AgAAAOgeEqwBzOPxyOPxmN2NXtc8pkAcGzBQEIfob6Lt0bpt5G26beRtKqop0uoTq7X6xGrtPLOz7WRrQ6w87kmqrZyklTsbtXLnSQ2ODNGnJiXo01MSNT4xsl9vjkUMAuYjDgFzEYOA+QI9DrszLothGEYf9gUXUEZGhjIyMuT1epWdna0VK1bI4WBDDwDAxavSV6kDngPK8mQptzHXn2xtydcQI4+7aYMsX12yJIviwwxd4vJphstQbOiF7zcAAAAAc9XU1Cg9PV0VFRWKiorqsCwJ1gDkdrvldDpVXFzc6QtgIPJ4PMrMzNTChQtlt9vN7g5wUSIOMRCV1Jbo/ZPv673j72nHmR3yGb5zyvgaYtRYOVEe9yT56oZIsuiS1EG6YXKirpsYr2hH8IXveBuIQcB8xCFgLmIQMF+gx6Hb7ZbL5epSgpUlAgKY3W4PyBd4s0AfHzAQEIcYSBLsCUofn6708ekqqS3R6uOrtSpvlT4s/NCfbLUGlyo4dr2CY9fL1xCtxspJ2nl6krb/q0xPvnVIV46K07UT4rVgXLxiI0JMHhExCPQHxCFgLmIQMF+gxmF3xkSCFQAAXHRiw2J125jbdNuY21RaV9qUbM1tSrZ6Da8kyRpc9kmy1TNIje6JWpc3Ue8fGiqrxapL0mJ07YQELRofr5QYluQBAAAALlYkWAEAwEUtJjRGt46+VbeOvlVldWV6//j7WpW3SltPbf0k2WovV3DsRgXHbpTR6FBj9SjtLBmrD98erR/+O1wTkqK0aHyCrp0YrzHx/XuDLAAAAAC9iwQrAADAx6JDo3XL6Ft0y+hbVF5XrvdPvK9VuU3J1kajUZJkCaqR3blHduceGYZFvtoUHakao4Mbx+oX7yUpNTZc105I0LUT4jUtJVpWK8lWAAAAIJCRYAUAAGjDoNBBunnUzbp51M2qqK/Q2hNrtf7ken1Q8IGqPFWSJIvFkM1xXDbHcYUoUz5PpAqrx+gvu8bqDxtHyuVwauH4eF07IV5zRrgUHGQ1d1AAAAAAeh0JVgAAgE44Q5z6zMjP6DMjPyOPz6PdZ3Zrw8kN2pC/QR+Vf+QvZ7VXKnjQdmnQdhmGTdU1afrbR2P0f7vHKtySqHlj43XthARdPSZO4SH8GgYAAAAEAn6zBwAA6Aa71a6ZCTM1M2GmHrzkQeVX5WvjyY1an79e205tU523TpJksXgVFH5UQeFHpfi35GuI0buFY/Sfj8bK1jBSV4xI1LUT4rVgXLxiI0JMHhUAAACAniLBCgAAcB6SI5J1+9jbdfvY21XXWKftp7dr/cn1Wn9yvfKr8v3lrMGlCo7ZrOCYzTJ8dm2uGqEN74/R/745VjOGjNC1ExK0aHy8UmIcJo4GAAAAQHeRYAUAAOgloUGhuiL5Cl2RfIUevfRR5bhzmpYSOLlBO87sUKPv442yrB4FRR5SUOQhSf9UVv1g7dk2Vj96f4zGDJqsxROStWhCvMbER8piYZMsAAAAoD8jwQoAANAHLBaLhjuHa7hzuJZMWKKqhiptPbVV6/PXa8PJDSqqLfKXtYWckS3kjIJj1+u4N0S/PTBav942RonBU3XduNG6dkK8pqVEy2ol2QoAAAD0NyRYAQAALoCI4AjNT52v+anzZRiGDpUe0ob8DVp/cr32Fu2VIUOSZLHVyx61T/aofSrX3/Ty8SF64cAYRRqTtGjEJbp2QqJmDnWaPBoAAAAAzUiwAgAAXGAWi0XjYsdpXOw4fWnyl1RWV6ZNBZs+Xk5goyo9bn9ZW9hJ2cJOqkGr9WZZuP7+zmgF1U/QCOswWbIKNX98osJD+JUOAAAAMAu/jQMAAJgsOjRanxr+KX1q+Kfk9Xm1r3if1p9cr7Un1utI+WF/OWtQtayDdknapaOGVd/eMlR6f5ymxs7WjeNnaMH4eMVGhJg3EAAAAOAiRIIVAACgH7FZbZo6eKqmDp6qr03/mk5Xn9bG/I1ac2KdNhdsVoOvTpJksfgU5MiVHLnK0tvau9epxz8Yq2HhM/SZMVfrU5NSNSTaYe5gAAAAgIsACVYAAIB+LD48XreMvkW3jL5FDd4G7Ti9Q2vy1uo/2avkVrG/nNVeIWv0Vp3UVj179Pf61b7hirNN1cJhV+u2KdM0Oj5CFgubZAEAAAC9jQRrAPN4PPJ4PGZ3o9c1jykQxwYMFMQhYA6LLLok7hJNGTRFE86M19jZY7Xl9Fa9k7NWB8p2y6emmLRYvQqKOKIyHdFrha/r1TyXwhonaFbCHN056SrNHBonq5VkK3A+eC8EzEUMAuYL9DjszrgshmEYfdgXXEAZGRnKyMiQ1+tVdna2VqxYIYeDjwYCAHAxaDAadKzxmHbXZutI42HVWyvaLGf4gmWpGalkyxhdGj5KU6KiFGS9wJ0FAAAA+rmamhqlp6eroqJCUVFRHZYlwRqA3G63nE6niouLO30BDEQej0eZmZlauHCh7Ha72d0BLkrEIWCuzmLQMAwdrTiqt4+t1arcdcqvPShZfG22ZdQnaWjodF03/CrdOflyOcNC+7r7QEDgvRAwFzEImC/Q49DtdsvlcnUpwcoSAQHMbrcH5Au8WaCPDxgIiEPAXB3F4Li4cRoXN04PzrpP7ga3Mo+t198Pr9aB8m3yyO0vZwkp0AmjQH84+m/9PjtMsdZJujJ5rj4/fbFGxMZfqKEAAxbvhYC5iEHAfIEah90ZEwlWAACAABcVHKVbxn5Kt4z9lHyGTzsL92nFvne0tfADuY1j/nIWW61KtU3/LNimf+T/XC7rNN059jZ9Yfoi2YP4tREAAABoC78pAwAAXESsFqsuSZyiSxKnSJIKq4r00p539V7eWhXU75GsdZIki8WnEmOHnj24Q7/N+rFmxi7Wt+Z8TmPjUszsPgAAANDvkGAFAAC4iCVExOnhy+/Sw5ffpfrGBr1xYJP+fjhTByvXSbampQR8tjJtLf+r/vs/r8plm6I7x96qL0y/TnZb4H0UDAAAAOguEqwAAACQJIUEBevOyfN05+R5qvU06Nkt/9LfP1optyVLFoshi8VQiW+3nj2wW7/N+qlmxl6rb82+W2PjUs3uOgAAAGAaq9kdAAAAQP8TZg/Ww1feog8+v0J/mf8PTXLcKjUO8l/3WSu0tew1/fd/btDVL6Xr99v/robGBvM6DAAAAJiEBCsAAAA6dGnKcK249fva+rnV+tywHyjCO1WG0fRrZNOs1n16dv/3NfOlq/XFfy3TweKjJvcYAAAAuHBIsAIAAKBLHMHBemTuTdr8hZf053n/0ITQ2yVPjP+6z1qpraUrddt/btTVL9+u3+/4m+q99Sb2GAAAAOh7JFgBAADQbbNSh+nV27+rLZ97T3el/j9FNM6QYdj810u8B/Rs1hO69KWr9MV/fU9ZRYdN7C0AAADQd0iwAgAAoMfCQ+z69tWf1uZ7XtAfr/6Hxoeky2iI81/3Waq1tfQfuvOt/9bVL/+3fr/j/1TbWGtijwEAAIDeRYIVAAAAvWJ2Wpr+745Htflz7+qzQ38iR8NMGb4g//US72E9m/WkLnv5Kt3z70e158x+E3sLAAAA9A4SrAAAAOhVkaF2fWfe9dryxT/r91f9U+OCPyejPt5/3Wep1baSf+uut+/QVS/fqOd2vKJqT7WJPQYAAAB6jgQrAAAA+oTFYtHlw4fqtTsf0ca73lL6kKcVVn+ZDJ/dX6bUe1QZWT/WnFeu0hf+/Yh2nd4rwzBM7DUAAADQPSRYAQAA0OcGOYL16PzF2nrvH/TbK/+hsUFL5KtL8l/3Wer1Ycnbuvudz+qqFTfodztfVGVDpYk9BgAAALqGBCsAAAAuGIvForkjh+r1z35LG+/6l+5IfkahtZfL8Ib4y5Q15um3+57W5Suu1uf//aC2n9rBrFYAAAD0WyRYAQAAYIro8GA9tmCRtn35d/r1FX/XKNsX5KtN8V83LA3aXpKpz69aqrkrrtezO/6i8rpy8zoMAAAAtIEEaz9x/Phx3XXXXRo7dqyioqIUERGhiRMn6gc/+IEqK/l4HAAACFwWi0XXjE7RG3d9U2s/+3fdlvgLBVdfKcMb6i9T3nhSv8/6heb+3zwt/ffXtaVgK7NaAQAA0C8Emd0BNDl9+rROnjypm266SSkpKbLZbNq+fbuefPJJvfnmm9q8ebPsdnvnDQEAAAxgcZEh+t6iBXpswXy9d/ikfrft7zpckymbI1eSZKhRO0re172Z78sZlKjbxv63Pjv+FsWGxZrbcQAAAFy0SLD2EzNnztTatWtbnfvyl7+s0aNH65FHHlFmZqauv/56czoHAABwgVmtFi0al6JF476mwoov6bkPNuvNY2+oPnSbrEE1kqSKxlP6Y9Zv9Kes32pa7BW6d+qdmpM8W1YLH9ICAADAhUOCtZ8bNmyYJKmsrMzkngAAAJgjwRmqZdfN03e9VynzYL6e+/Afyq5draDwo5IkQ17tLFmn+1avU1TQYN065hbdOe4WxYfHm9xzAAAAXAxIsPYzdXV1qqqqUm1trfbu3av//d//VWhoqObOnWt21wAAAEwVZLPquokpum7iAzpReo/+sHmr/nXsn/I4tsoaVCVJcjee0Z/3/05/3v+cpsbO0T1T7tAVyVcoyMqvvQAAAOgbAfn5qZqaGr399tt68skndfPNNys1NVUWi0UWi0XLli3rUhuVlZVatmyZJk2apIiICDmdTs2cOVPPPPOMGhoa+qzvf/rTnxQXF6ehQ4fqU5/6lKxWq/75z38qJSWl88oAAAAXiZQYh374X/P04Vd/rh/PfE2pjfepsWq0DMPycQlDu0s26YH3H9CVKxbo5x/+WgVVBab2GQAAAIEpIP+Uv23btvNarzQvL09XX321cnNzJUkOh0P19fXavn27tm/frldeeUWrV69WdHT0OXUNw1B9fX2X7mO1WhUcHNzq3I033qixY8eqoqJCH3zwgdatWye3293jsQAAAAQyu82qGyan6IbJX1VO8RL9afMO/evYP+QN3yqrvel3qCpviZ4/8Ec9f+BPmhxzqZZOul1XD71adisbiAIAAOD8BeQMVkmKjo7W/Pnz9fDDD+uvf/2rEhISulSvsbFRN9xwg3Jzc5WYmKjMzExVV1erpqZGr776qiIjI7Vr1y7dddddbdbPy8tTWFhYl76mT59+Tv0hQ4ZowYIFuuWWW/TMM8/o29/+tm699Va999575/X9AAAACHTDXOH6fzfM1bavPq0fzvirhtT/jxorx7Wa1bq3dKseXPegrlxxjX667ec64T5hap8BAAAw8AXkDNYrr7xSpaWlrc595zvf6VLd5cuXa9++fZKklStXavbs2ZKaZpvefvvt8vl8Sk9P11tvvaXVq1dr/vz5req7XC49//zzXbpXWzNgz3bzzTcrNDRUzz//vBYsWNCldgEAAC5moXabbp42VDdP+7KOnE7Xnzbv1lu5b8oXvkXW4HJJUrW3XC8dfF4vHXxeE6Jn6PZxn9E1Q6+RM8RpbucBAAAw4ARkgtVms/W47vLlyyVJ8+bN8ydXW7rjjjv02GOPKScnRy+++OI5CdaIiAgtXbq0x/c/W2Njozwej8rKynqtTQAAgIvFqPhI/eTGK7WsYY7+teek/rTjXR33rFFQ5AFZLD5J0v6yHfr+Bzu07IMndGniTC1KW6RrUq5RbFisyb0HAADAQBCwSwT0RE1NjTZt2iRJuu6669osY7FYtHjxYknSqlWreu3ep0+fbvP873//e3m9Xs2aNavX7gUAAHCxCQu26baZqVr1lS9p5S3P6drIDPlKrpev4ZMkqk9ebTm1RT/Y/ANd89o1uufde/TqoVdVVFNkYs8BAADQ3wXkDNaeOnjwoHy+ppkMEydObLdc87XCwkKVlpYqJibmvO/97W9/WwcOHNDChQuVmpqqqqoqrVu3Tv/61780ZswYff3rX2+3bn19fauNtZo3xfJ4PPJ4POfdt/6meUyBODZgoCAOAXMRg+dndJxDP/70LH23fobe3FOgF3ZsVEHjNgVF7pM1uOlTQz75tK1wm7YVbtOPtv5IU+KmaEHKAl2Tco0Swru2tj8CG3EImIsYBMwX6HHYnXGRYG2hoKDA/zg5Obndci2vFRQU9EqC9eabb1ZJSYmWL1+uoqIiBQUFaeTIkfrud7+rb33rW4qKimq37lNPPaUnnnjinPOrVq2Sw+E47771V5mZmWZ3AbjoEYeAuYjB8zdI0teHDtIR97Vae+paHao9JVtkluxR+2QNLpEkGTK0u2i3dhft1s92/kwpthRNsE/QBPsERds6X1MfgY04BMxFDALmC9Q4rKmp6XJZEqwtVFZW+h93lJhsea1lnfPx6U9/Wp/+9Kd7VPfRRx/Vgw8+6D92u91KSUnRokWLOkzMDlQej0eZmZlauHCh7Ha72d0BLkrEIWAuYrBvfENSbkm1Xtw8Wyt35avakq+gyH0KitonW8gnywSc8J7QCe8JvVP3jsbFjNOClAWaP3S+hkYONa3vuPCIQ8BcxCBgvkCPw+ZPiHcFCdYAEBISopCQkHPO2+32gHyBNwv08QEDAXEImIsY7H2jEgbphzcN0rcWj9NrH57QCx8MV/6xRbIGn1ZQ1D4FRWbJFlroL3+w9KAOlh7Ub/b8RmOix2hh6kItTFuo4c7hJo4CFxJxCJiLGATMF6hx2J0xkWBtITIy0v+4o2nALa+1rAMAAIDA4Ayz6965w/X5y9OUeeC0nt+Uq2258WooXiBLcJHskVmyR2XJGprvr3O47LAOlx3Ws7uf1chBI7UgdYEWpi7UqEGjZLFYTBwNAAAA+hIJ1haSkpL8j/Pz8zV58uQ2y+Xnf/KLdMs6/Q2bXAHoK8QhYC5i8MJaMNalBWNdysp3a/nmPP0ny6KGknlqKJkni71EQZFZcroOqt6W66/zUflH+qj8Iz235zmlRqZq/tD5WpCyQGOix5BsDRDEIWAuYhAwX6DHIZtc9dC4ceNktVrl8/mUlZWl6667rs1yWVlZkqSEhIRe2eCqt2RkZCgjI0Ner1cSm1wB6HvEIWAuYvDCm+eQpk+VNhVatfG0RdWeWHlKr1Jx6VWyBJUrMnqfoqKzVGHL89fJq8zTX/b/RX/Z/xdFW6M10T5RE+wTlGxLJtkaAIhDwFzEIGC+QI3D7mxyZTEMw+jDvvQbaWlpysvL0+OPP65ly5a1W27u3LnasGGDrrnmGq1evfqc64ZhaOTIkTp27JjuvvtuLV++vA973TNut1tOp1PFxcVscgWgTxCHgLmIwf6hzuPVv/YWavnmPB0+XdXqWlhopaaOPSHDsU8Hy/fIZ/jOqZ/gSND8lPlaMHSBJrkmyWqxXqiuoxcQh4C5iEHAfIEeh263Wy6XSxUVFZ3m15jBepYlS5Zow4YNWrNmjbZu3apZs2a1uv7666/r2LFjkqS7777bjC52WaAuMtws0McHDATEIWAuYtBcdrtd6Zel6c5Zqdp8tER/2ZSj1YfOyDCk2rpIbd49XtJ4XT7mcxo/8oRyazfrw8IP5TWaPm1UWFOoVw6/olcOv6LBYYP9a7ZOGzxNNqvN3MGhy4hDwFzEIGC+QI1DNrmSVFZW5v+ovCT5fE2zBmpqalRcXOw/HxoaqoiICP/xkiVL9Ktf/Ur79u3TLbfcouXLl2v+/Pny+XxauXKl7r33XknSddddp/nz51+g0QAAAKC/slgsmjPSpTkjXcoprtbyD3L12vYTqmlo+l100+EGbTocr1GD79L/zPqaImIOa23+e9pasFWNRqMk6UztGa04tEIrDq1QTGiMFgxdoIVpC3VJ/CUKsgbsr+wAAAABIWB/W5s2bZry8vLOOf/000/r6aef9h8vWbJEL7zwgv84KChIb775pubNm6fc3FwtWLBADodDPp9PdXV1/rZfeeWVPh/D+WKTKwB9hTgEzEUM9l9DnMF67LrReuDqYfrbzny9uOW48subfoc8cqZKT/6rSoPConTHzK/pawudOuzepvdOvKfNpzbL42t6PkvrSvVa9mt6Lfs1DQoZpHlD5umalGt0afylstsCb3bIQEUcAuYiBgHzBXocdmdcAbsGa/Oaq505O8HarLKyUj/72c/0xhtvKCcnR1arVaNHj9add96pBx54QMHBwX3Q6/PTcpOr7OxsrVixIqA3uQIAAOjvvIaUVWrRulNWHa1svaGV1WJoaoyhqxN9io+o02HPYe337Fe2J1uNajynrVBLqMYFjdOE4AkaGTRSQZaAnSsBAABgupqaGqWnp3dpDdaATbBezNjkCkBfIw4BcxGDA1NWvlvLN+fpP1mF8nhb/wo+LcWppbNTtWj8YDX46rTp1Ca9d/w9bcjfoDpv3TltRdgjdGXylVqQskCzE2crNCj0Qg0DHyMOAXMRg4D5Aj0O2eQKkgJ3keFmgT4+YCAgDgFzEYMDy7S0WE1Li9X/uuv08pY8vbz1uEqrGyRJu05UaNeJvUpyhuruOWm6Y+YCXT/ietU21uqD/A+0Km+V1p1cp2pPtSSpylOlt3Pf1tu5byssKExzh8zVwtSFujL5SjnsfILpQiIOAXMRg4D5AjUO2eQKAAAA6KcGR4XqwUVj9NV5I/Xm7gL9ZVOODhVWSpIKKur047cP6VfvHdEtM5K1dM4wzU+dr/mp81XvrdeWgi1albdKa06sUWVDU53axlq9m/uu3s19V6G2UF2RfIUWpi7U3CFzFREc0VFXAAAA0AtIsAIAAAAmCLXbdNvMFN16yRBtPlqiv2zK0epDZ2QYUq3Hq5e3HNfLW47rqtFx+sIVwzR3lEtXpVylq1Kuksfr0dbCrcrMy9T7x99XeX25JKnOW6f3jr+n946/J7vVrsuTLtfCtIW6OuVqRQUH3tJRAAAA/QEJ1gDm8XgCcie3QN+lDhgIiEPAXMRg4JmZ6tTM1KnKK6nRi1uOa+XOfFU3eCVJ67KLtC67SCPiwrV0dqo+MyVRYcE2zRo8S7MGz9J3ZnxHO87s0Orjq/X+yfdVWlcqSfL4PFp7cq3WnlyrIGuQZsXP0vyh83VNyjUkW3sBcQiYixgEzBfocdidcbHJ1f9n777Do6rWNg7/ZpJJI4VA6C1IkY6gKL2HIqCUg2Cogg2wotg9YjsWxB5QLPQAIqCAiESkCUiRrkhoobdQMoG0ycx8f+TLHDi0BJLsZOe5r4vLZO29Z94V5iHmzZ61TCQqKoqoqCicTiexsbFER0cTEKA1uEREREQKmuR0+OOkhZXHrZxJtVxyLMDbTdOSblqUdlHU99LrXG4XB5wH+CvtL/5y/EWiO/Gyx/bDjzZ+bbjL9y68LbrfQkRERORKkpKSiIyMzNImV2qwmpDdbickJIT4+PjrvgAKIrPvUidSECiHIsZSBgsPp8vN0n9OMmntQTbEnb3kmJfVQqfapRjcpCK3VSh62bUut4vt8dv59eCvLD20lONJxy85Hh4czjMNn6FZ2Wa5OQXTUg5FjKUMihjP7Dm02+2EhYVlqcGqX1mbmFl3cctk9vmJFATKoYixlEHzswFd6penS/3y7DiSwLer97Ng61EcTjdOl5ufth/np+3HaVCxKEOaVaZTndLYvKye6+8oewd3lL2D5+96nh3xO5i1axbz987HjZs4exyPL3+cFuVaMKrRKCqHVDZuogWYcihiLGVQxHhmzWF25mS9/ikiIiIiImK0OuVC+PC+21j9fFueaFeN4kV8PMc2HzzH4zM20/L9ZYxfvpdzSWmXXGuxWKhboi5vNX+LGV1n0KBkA8+xVUdW0fPHnry/4X3safY8m4+IiIiIWajBKiIiIiJSgJQM9mNkRHVWv9CW9/9VjxqlgzzHjiWk8N7if2j8zlJenredPScvX4O1dvHaTO40mfdbvk+pgFIApLvTmfr3VLrO7crs2Nk4Xc48m4+IiIhIQacGq4iIiIhIAeRn8+K+Oyrw85MtiH7oLtrXLIXl//fDSnG4mL7uIO0/XMmgb9ezIvYUF2+9YLFY6Fy5Mwt6LGBY/WH4efkBcDb1LG+sfYM+C/uw4fgGI6YlIiIiUuBoDVYTczgcOBwOo8vIcZlzMuPcRAoK5VDEWMqg/K9GFUNoFFmfA6eTmPLHQeZsOsKFtIy7UFfEnmJF7CmqlCjCoCYV6V6/LP4+XgB4481DtR+ia3hXPt3yKb8c+AWAXWd3MeSXIbSv0J6nGjxF2cCyhs0tv1IORYylDIoYz+w5zM68LO6Lf5UtBVpUVBRRUVE4nU5iY2OJjo4mICDA6LJEREREJI8lp8MfJy2sPG7lTKrlkmNBNjedyrtoUtKN1/+8ny0uPY5FyYs46jzqGfPGm2a+zWjp1xJfi29elC8iIiJiuKSkJCIjI0lISCA4OPia56rBakJ2u52QkBDi4+Ov+wIoiBwOBzExMURERJhylzqRgkA5FDGWMihZ5XS5WfrPSSatPciGuLOXHKtcPIBnO1QjomZJLBbLRdc4WbB/AZ9v/ZwzKWc84yX8S/DEbU/QObwzVotWGlMORYylDIoYz+w5tNvthIWFZanBqiUCTMxms5nyBZ7J7PMTKQiUQxFjKYNyPTagS/3ydKlfnh1HEhi3fA+Lth8HYP/pJEbM2MrtlUJ5sXMN7ggv9v/X2Ohdozedb+nMhG0TmLpzKumudE4ln+LVta8ye/dsnr/zeeqVqGfgzPIP5VDEWMqgiPHMmsPszEm/ehYRERERKQTqlAthXL/bmTu8KXf+fzMV4M8DZ/nXF2t5eMpG9pw87xkP9Alk5B0j+eHeH2hdobVnfFv8Nvot6sfLv7/MyaSTeTkFERERkXxJDVYRERERkUKkYcVQZj3SmK8H3kHVkoGe8SV/n6Djxyt5ed52TiameMYrBVfis7af8WXEl1QJqeIZn793Pl3ndeWrbV+R6kzN0zmIiIiI5CdqsIqIiIiIFDIWi4X2tUqx+MkWvNOzLiWDMjavcrrcTF93kNZjlvNRTCwXUtM91zQt25Tv7/meF+98kWCfjHXIktOT+XTzp9z7w73EHIhB2zuIiIhIYaQGq4iIiIhIIeXtZeX+OyuyfFRrnomoTqBvxhYNSWlOPlm6m1ZjljP1jwM4nK6M863eRNaM5KceP9H31r54WbwAOHL+CCOXj2TokqHsOrPLsPmIiIiIGEGbXJmYw+HA4XAYXUaOy5yTGecmUlAohyLGUgYlp9ks8GjLcHo3LEPU8n3M2HCYdJeb+POpvPrDDr5dtY9nO1QjomZJLBYLRbyK8Nztz9GzSk8++PMD1p9YD8CG4xu4b+F99KzSk2H1hhHqF2rwzHKPcihiLGVQxHhmz2F25mVx6308phEVFUVUVBROp5PY2Fiio6MJCAgwuiwRERERKWBOJcPCQ1a2nL70DW+Vg9zcU9HJLcH/HXO73fyT/g+Lkhdx1nXWM+6HH2392nKX712eO11FRERECoqkpCQiIyNJSEggODj4mueqwWpCdrudkJAQ4uPjr/sCKIgcDgcxMTFERERgs9mMLkekUFIORYylDEpe2XLoHO/9EsvGA+cuGY+oWZJnIqpRpUQRz1iaM43oXdF8veNrktKTPOPhweE80/AZmpVtlldl5wnlUMRYyqCI8cyeQ7vdTlhYWJYarFoiwMRsNpspX+CZzD4/kYJAORQxljIoua3RLSWY/WgYS3ee5N3F/7Dn5HkAYnae5Lddp+jTqAJPta9GySA/bDYbD9V/iB7Ve/DJpk/4Yc8PAMTZ43h8+eO0KNeCUY1GUTmksoEzynnKoYixlEER45k1h9mZkza5EhERERGRq7JYLLSvVYrFT7bg3Z51KRnkC4DT5SZ63UFaj1nORzGxnE9NByDMP4w3m73JzC4zua3EbZ7HWXVkFT1/7MmYDWOwp9mNmIqIiIhIrlCDVURERERErsvby0rfOyuyfFRrnu1QnUDfjDfDJaU5+WTpblqPWcbUPw7gcLoAqB1Wmymdp/Bei/coFVAKgHR3OlP+nkLXuV2ZHTsbp8tp2HxEREREcooarCIiIiIikmUBPt481rYaK0a1ZnDTcLytFgDiz6fx6g876PjRShbvOIbb7cZisXD3LXczv/t8Hq3/KL5eGXe/nk09yxtr36DPwj5sOL7ByOmIiIiI3DQ1WEVEREREJNuKB/oy+p7a/DqyFV3qlvGM74u/wKPTNtFr/Bo2xp0BIMAWwIjbRjC/+3w6hXfynLvr7C6G/DKEkctHcuT8kTyfg4iIiEhOUINVRERERERuWHhYEaL6NWTe8KbcWbmYZ3zTwXP864u1PDxlo2dzrLKBZRnTagyTOk2iZrGannNjDsRwz7x7+GzzZyQ5kvJ8DiIiIiI3Qw1WERERERG5aQ0qhjLr4cZ8PfAOqpYM9Iwv+fsEHT9eyUvztnMyMQWA20vdzowuM3i96esU88toyqa50piwbQLdfujGgr0LcLldhsxDREREJLu8jS5Aco/D4cDhcBhdRo7LnJMZ5yZSUCiHIsZSBiU/a1WtGM1uaczczUf55Le9nExMxelyE73uID9sPsKQppUY2jycQF9vuoV3o3XZ1nz919fM2DWDdFc6J5NO8tLvLzFj5wxG3T6KOmF1jJ7SFSmHIsZSBkWMZ/YcZmdeFrfb7c7FWiQPRUVFERUVhdPpJDY2lujoaAICAowuS0REREQKqVQnrDhm4dejVlKdFs94oM1N5/IumpR04/X/76mLd8azOHkx/6T/c8ljNLA1IMI/gmBrcF6WLiIiIoVcUlISkZGRJCQkEBx87f8PUYPVhOx2OyEhIcTHx1/3BVAQORwOYmJiiIiIwGazGV2OSKGkHIoYSxmUgub0hTSilu9jxvpDpLv+++NH5eIBPBNRjQ61SmKxZDRg1xxdw9hNY9lv3+85z9/bn6G1h9KvRj98vXzzvP4rUQ5FjKUMihjP7Dm02+2EhYVlqcGqJQJMzGazmfIFnsns8xMpCJRDEWMpg1JQlC5q483udRna/BbGLNnFT9uOAbD/dBKPzdxKw4pFeenumtwRXoxWlVrRtEJTvtv1HVFbokhMSyQ5PZnPt37OvL3zGHXHKNpWbOtpyBpNORQxljIoYjyz5jA7c9ImVyIiIiIikifCw4oQFdmQecObcmflYp7xTQfP8a8v1vLwlI3sOXkem9VGv5r9+KnHT/S5tQ9WS8aPLUfOH+Gp5U/x4JIH2XVml1HTEBEREbmEGqwiIiIiIpKnGlQMZdbDjfl64B1UKxnoGV/y9wk6frySl+Zt56Q9hVC/UF5p/Aqzu83mrtJ3ec5bf3w99y28jzfXvsnZlLNGTEFERETEI182WPfs2cMDDzxgdBkiIiIiIpJLLBYL7WuV4ucnW/Buz7qUDMpYW9XpchO97iCtxiznw5hYzqemUz20Ol91+IqP23xM+cDyALjcLr6L/Y4u87ow7e9pOFzm3MFYRERE8r981WDduXMn/fv3p2bNmkyZMsXockREREREJJd5e1npe2dFlo9qzbMdqhPom7FNRLLDyadLd9N6zDKm/nGAdJebdhXb8UP3H3iy4ZMEeAcAkJiWyHsb3qPX/F78fuR3I6ciIiIihVS+aLBu376d++67j7p16zJjxgycTqfRJYmIiIiISB4K8PHmsbbVWDGqNYObhuNtzdjEKv58Gq/+sIOOH61k8Y5j+Fh9eLDugyzssZB7q9zruX5/wn6G/TqMEUtHEJcQZ9AsREREpDDyzokHOX/+PKtWrWL//v0kJCQQFBREnTp1aNGiBV5eXle9buvWrYwePZoFCxbgdrtxu92e3UD9/f1zojQRERERESlAigf6Mvqe2gxuGs6YJbv4adsxAPbFX+DRaZtoWLEoL95dk0bhJXir+Vv0rdGXd9e/y9ZTWwFYeXgla46soV/NfjxS/xGCfIKMnI6IiIgUAjfVYD137hwvv/wy33zzDQ7H5WselShRgrfffpuhQ4deMn7s2DFGjRrFzJkzL2usBgQEMGzYMJ599tmbKU1ERERERAqw8LAiREU25KEW5/jPop2s338GgE0Hz9H7i7VE1CrF851qUKdkHaZ2nsqi/Yv48M8POZl0knR3OpP/nsyCfQt4osETdK/aHS/r1W/8EBEREbkZN7xEwIkTJ2jWrBlffPEFaWlpuN3uS4673W5OnjzJww8/zCuvvOIZX7BgAfXq1WPGjBm4XC7PeFBQEC+99BJxcXG8//77lCxZ8kZLExERERERk7itQlFmPdyYbwbdQbWSgZ7xmL9P0PHjlbw0bzunElPpcksXFnRfwCP1HsHXK2PDrDMpZxi9djSRiyLZcnKLQTMQERERs7vhO1gffPBBdu7cicViwWKxeO5EvVjm+DvvvEPnzp05fvw4ffv2vaSxGhoaypNPPskTTzxBSEjIjc9ELuNwOK54Z3FBlzknM85NpKBQDkWMpQxKYdSyajGaDm/MvC1H+WTpXk4kpuJ0uYled5B5mw4ztFk4Q5uH80idR+hWuRufbP6EmIMxAPx9+m8G/DyALuFdeOK2JygRUOKm61EORYylDIoYz+w5zM68LO7/7YpmwaZNm7jjjjs8DdQKFSrw9NNP07x5c0JDQzl9+jQrVqzg008/5ciRI1gsFtq2bcuWLVs4ffo0kLEUwHPPPcfIkSMJDAy8zjNKVkRFRREVFYXT6SQ2Npbo6GgCAgKMLktEREREJEelOWH5MQu/HrWS6rR4xgNtbjqVd9G0pBsvK+xz7OOn5J844TrhOccHH9r4taGJbxO8LTmyJYWIiIiYUFJSEpGRkSQkJBAcHHzNc2+owfrss8/y4YcfYrFYqFu3LsuWLSM0NPSy806fPk2bNm3466+/ADxrrTZs2JDZs2cTHh6e3aeWLLDb7YSEhBAfH3/dF0BB5HA4iImJISIiApvNZnQ5IoWScihiLGVQJMPpC2lELd/HjPWHSHf998ea8OIBPBNRjY61SuJ0O5mzZw7jt43Hnmb3nFMxqCLPNnyW5uWa39BzK4cixlIGRYxn9hza7XbCwsKy1GC9oV/Zrl+/3vPx+PHjr9hcBShevDjjxo2jZcuWnk2sKleuzLJly3TXah6w2WymfIFnMvv8RAoC5VDEWMqgFHali9p4s3tdhja/hTFLdvHTtmMAxJ1O4vGZW2lYsSivdK1F/9r96VKlC59v/pzZsbNx4+Zg4kGeWPEELcu35LlGz1EpuNIN1aAcihhLGRQxnllzmJ053dAmV3v27AGgdOnSNGnS5JrnNm/enNKlS3vuXn3uuefUXBURERERkRwTHlaEqMiG/DCiGXdVLuYZ33TwHD3HreGJGZtJSvHj1SavMqvrLBqWbOg5Z+XhlXT/sTsf/vkhFxwXjChfRERECrgbarAmJCRgsVioWrVqls6vVq2a5+O2bdveyFOKiIiIiIhc020VijLz4cZ8O/gOqpX8700d87cepe0Hyxm7ZBcVA6sxqdMk3mvxHiUDSgKQ7kpn4o6JdJ3XlQV7F+Byu672FCIiIiKXuaEGa3JyMgBFihTJ0vkXb7RUsWLFG3lKERERERGR67JYLLStUYqfn2zBm93rEBqQ8fa+1HQXn/22hzYfLOf7Pw/TKbwzC7ov4KG6D2GzZpwTnxzPS7+/xMCfB/JX/F9GTkNEREQKkBtqsN4MHx+fvH5KEREREREpZLy9rAxoXInlo9rwUIvK2Lwy9oQ4mZjKqO+3cU/U72w/lMwTDZ/gx3t/pE2FNp5rt57ayv0/3c/oNaM5nXzaqCmIiIhIAZHnDVYREREREZG8EuJv4+UutVjydCs61CrlGd9xxE6fCX8wbNqfuB3F+bTtp3zZ/ksqh1QGwI2bObvn0G1eN6b+PRWHy2HUFERERCSfU4NVRERERERMr3JYESYMvIPoh+6iZplgz/jPO47T/sMVvPPzTuoWb8Sce+Yw6o5RBNoy1nBNdCTy/ob3+df8f7H26FqjyhcREZF8zPtmLt6zZw9vvPFGls7LlJXzM/373/++obpERERERESupGmVMBY+3pzv/zzEmF9iiT+fSprTxZcr9vH9xsM80+FW+jUawN233M2nmz5l3p55AOxL2MfDMQ/TrmI7nr3jWUr5lbrOM4mIiEhhcVMN1r179/L6669n6VyLJWPNo6yeD2qwioiIiIhIzvOyWujTqCJd6pVl3LI9fP37ftLSXZy+kMZL87YzZW0cr3atxRvN3qB39d68u/5dtsVvA2DpwaWsOryKQbUGUcZdxuCZiIiISH5w00sEuN3u6/7J7vn/e42IiIiIiEhOC/T15rlONVg6shVd6v23WfrP8UT6fb2OBydvoAiVmXr3VN5u/jbF/YoDkOZK46sdX/Gx/WN+OfCLfn4REREp5G7oDtaKFSt67kgVEREREREpyCoUCyAqsiGDm57hzYV/s+1wAgC/7jzJ8l2nGNgknCfbdaZtj7ZM2DaBqTunku5Kx+628+LqF/l+z/e8eOeL3FrsVoNnIiIiIka4oQZrXFxcDpchIiIiIiJirEbhxfhheDPmbT7C+7/8wwl7KukuN9+u3s/czYd5un11Hr/rKXpU68G7695lzbE1APx54k/uW3gfvav35rHbHqOoX1FjJyIiIiJ56qaXCBARERERETELq9VCr9vLs+zZ1jzRrhp+towfmc4lOXht/l90+nglcceL8Fnrz+hfpD/lA8sD4HK7mLVrFl1/6Mqsf2bhdDmNnIaIiIjkITVYRURERERE/keAjzcjI6rz2zOt6X5bWc/43lMXeGDiBoZO2USIowbfd/meJxs+ib+3PwAJqQm8te4t+izsw8bjG40qX0RERPKQGqwiIiIiIiJXUbaoPx/3bcC84U1pWLGoZ3zVntO8v9WL/yzaS89bBrKg+wK63tLVc3zX2V088MsDPLfiOY5fOG5A5SIiIpJXbmgNVikYHA4HDofD6DJyXOaczDg3kYJCORQxljIokvfqlAlk5oON+Gn7ccYs2c3RhBRcWJi+/hDztx3jsda38Mpdo+lZpSfvb3yff87+A8DPcT+z7NAyhtQewoCaA/D18jV4JiLmoO+FIsYzew6zMy+L2+12Z/cJVq5cCUCxYsWoU6dOdi+/pq+++oqdO3disVgYO3Zsjj622UVFRREVFYXT6SQ2Npbo6GgCAgKMLktERERExFTSnLD8mIWYI1bSXBbPeJifm+6VXNQq6mST409iUmJIcid5jodaQ+ns15matppYLJYrPbSIiIjkE0lJSURGRpKQkEBwcPA1z72hBqvVasVisdCxY0cWLVp0zXPbtm0LwJ133sm777573cfu3Lkzv/zyCxaLBadTC8PfCLvdTkhICPHx8dd9ARREDoeDmJgYIiIisNlsRpcjUigphyLGUgZFjOdwOPj+pxg2O8vzw9bjXPxTVePKobzUuQblirv5ctuXfLf7O5zu//5s07h0Y569/VluCbnFgMpFzEHfC0WMZ/Yc2u12wsLCstRgzfUlApYvX47FYsHPzy+3n0r+h81mM+ULPJPZ5ydSECiHIsZSBkWMFeID799dj6EtqvLGwr9Zv/8MAH/sP8u949fS544KPNPhGe6rcR/vrn+XdcfXZRw//gd9F/Xl/pr3M6z+MIJ8goychkiBpu+FIsYzaw6zMydtciUiIiIiInIT6pQLYdbDjfmif0MqFstYosvthpkbDtHmg+Us2QqftfmCj1p/RNkiZQFId6cz9e+pdJ3Xlbm75+Jyu4ycgoiIiNwENVhFRERERERuksVioVOdMsSMbMmLnWsQ6JvxZsHzqem8v3gXER+tJDWhNj/c+wPDbxvu2ezqTMoZXlvzGpE/RbLl5BYDZyAiIiI3Sg1WERERERGRHOLr7cUjraqwfFRrIu+qiPX/97I6fDaZEdGbGPjNZpoXj2R+9/l0qNTBc91fp/9iwM8DePn3lzmVdMqg6kVERORGqMEqIiIiIiKSw8ICfflPj7oserIFzauGecY3xJ2l2+e/M3bRKZ5r+DbfdvyWaqHVPMfn751P13ldmbhjIg6nw4jSRUREJJvUYBUREREREcklNUoHM3XonXwz6A5uCSviGZ+z6TCtxyxnzY6iTO4wg5fueolgn4wdipPSk/jwzw/pOb8nKw+vNKp0ERERySI1WEVERERERHKRxWKhXc1SLH6qJf/uWosQ/4xdiZMdTj76NZYOH/2Of3JL5t+7gPuq34eFjHUF4uxxjFg6ghFLR3DAfsDIKYiIiMg1qMEqIiIiIiKSB3y8rQxpXpnlz7ZmcNNwvP5/gdZjCSk8NWsLQyfu5O6yjzGr6ywalmzouW7l4ZV0/7E7H/35ERccF4wqX0RERK5CDVYREREREZE8FFrEh9H31OaXp1rQ5tYSnvEth87Ra/waxi9J4e3G43mvxXuUDCgJQLornW93fEu3ed1YsHcBbrfbqPJFRETkf6jBKiIiIiIiYoCqJYOY+MCdTB5yJ9VKBnrG5289SruxK9i5pwozO8/joboPYbNmLCtwKvkUL/3+EgN+HsBfp/8yqnQRERG5iBqsIiIiIiIiBmpVvQQ/P9mCN7vXITQgo5Gamu7is9/20OWT9ZR29WDePT/QpkIbzzVbT23l/oX3M3rNaE4nnzaqdBEREQG8b+bi7du3M2TIkBw9d/v27TdTkoiIiIiISIHj7WVlQONK3FO/LJ//tptJa+JwON2cTEzlue+3UadcMK92eY0+t/bh3fXvEmePw42bObvnsCRuCcNvG06fGn08d7qKiIhI3rmpBuvRo0eZPHnyNc+xWCxZPldERERERKQwC/G38XKXWvS7qxL/WbSTJX+fAGDHETt9JvxB5zql+bjjNH4/+QNfbP2C847zJDoSeW/De0zfOZ1H6z9Kl1u64G29qR/1REREJBtuaokAt9udK39EREREREQKs/CwIkwYeAfRD91FzTLBnvGfdxyn88drOHrwTmbePY8eVXt4jh0+f5hXVr9C9x+7s3DfQpwupxGli4iIFDo39GvNli1beu5MFRERERERkdzRtEoYCx9vzvd/HmLML7HEn08lzeniyxX7+H6jD890eJBpnXsTteUz1h5bC8AB+wFeXPUiE7ZNYFj9YXQM74jVou03REREcssNNViXL1+ew2WIiIiIiIjIlXhZLfRpVJEu9coybtkevv59P2npLk5fSOOledupUTqIV7v+h0fqH2DclnGsP74egP0J+3lu5XOeRmv7Su3VaBUREckF+u6aT/3zzz/4+vpisVhYvHix0eWIiIiIiIjBAn29ea5TDZaObEWXemU84/8cT6Tf1+v48hc3oxt9xjcdvqFhyYae43vO7eGZFc/Qe0Fvlh5cqmXZREREcpgarPnUsGHDsNm0A6iIiIiIiFyqQrEAoiIbMvvRJtQrH+IZX/L3Cdp/uIJ1f4fyRbtvmBAxgfol6nuOx56N5allT9FnYR9WHFqhRquIiEgOUYM1H5o6dSrr1q3j2WefNboUERERERHJpxqFF+OH4c0Y27s+JYJ8AUhNdzE2JpbOn6wiJbEKUztPZXz78dQNq+u5bueZnTz222NE/hTJqsOr1GgVERG5SYY0WJOSkli5ciWzZs1i/vz5bN261Ygy8qVz587x7LPP8sILLxAeHm50OSIiIiIiko9ZrRZ63V6epc+0YkizynhZMzYjjjudxAMTN/DI1D+p5N+Q6XdPJ6pdFDWL1fRcu+P0DoYvHU7/n/uz5ugaNVpFRERuUI40WM+ePUtMTAwzZ85k4cKFxMXFXfG8c+fO8eijj1KiRAnatGlDZGQkPXr0oGHDhpQvX55x48YV+m/qL7zwAoGBgTz33HNGlyIiIiIiIgVEsJ+Nf3erxU9PNOfO8GKe8cxlAz7/bQ93lmrGrK6z+KTNJ9waeqvnnG2ntvFIzCMMXjyY9cfWG1G+iIhIgXZTDdajR49y3333UapUKTp16kS/fv249957qVKlCs2bN7/kztSDBw9yxx138NVXX5GcnIzb7b7kz9GjR3n88cfp27cvTqfzpiaVlJTEzz//zFtvvUXPnj2pVKkSFosFi8XC6NGjs/QYiYmJjB49mrp16xIYGEhISAiNGjVi7NixpKWl3VR9V7Nu3Tq++uorPvnkE/z8/HLlOURERERExLxqlA5m1iON+bjPbZctG9Dp45Usjz1F24pt+a7bd3zY+kOqFq3quXbTyU0MXTKUIb8MYePxjUZNQUREpMDxvtEL4+LiaNWqFYcPH/bcdWqxWDwfr1mzhhYtWrB69Wrq1KlD79692bdvHxaL5YqPl3nt999/T506dXj11VdvtDTWr1/P3XfffcPXHzhwgNatW3vuxA0ICCA1NZWNGzeyceNGpk+fztKlSwkNDb3sWrfbTWpqapaex2q14uPjA4DT6WTYsGHcfffddO3a9YZrFxERERGRws1isdC9QTna1izJxzG7mbw2DqfL7Vk2oEOtUrzatRYRlSJoV7EdSw4sYfyW8exL2AfAhuMbeOD4AzQu05gRt43gtpK3GTshERGRfO6G72B94IEHOHTo0CVj//v2/vPnz/P0008zb948NmzYgMViITAwkCeeeILvvvuOJUuWMGvWLEaMGEFAQICnyfqf//yH+Pj4Gy0NgNDQUNq1a8eoUaOYMWMGpUuXztJ16enpdOvWjbi4OMqUKUNMTAwXLlwgKSmJmTNnEhQUxObNm+nfv/8Vrz9w4AD+/v5Z+tOwYUPPdZ9//jk7d+7kk08+ual5i4iIiIiIwPWXDfhs6W7S0t10Cu/E3Hvm8m6LdwkPDvec98exPxjw8wAejXmUbae2GTADERGRguGG7mBdsWIFK1as8DREW7duzVNPPUWNGjVIS0tjzZo1vPfee+zfv59ly5Z53vJfvnx5VqxYcdnmTb179+bJJ5+kTZs2HDlyhLS0NKKjo3niiSduaFItWrTgzJkzl4y98MILWbp28uTJbN++HYA5c+bQpEkTIONu0z59+uByuYiMjGTRokUsXbqUdu3aXXJ9WFgYEydOzNJzZd4Bm5CQwKuvvsrAgQOxWq2eO2czm8wnTpwgLi6OChUq4OXllaXHFhERERERgf8uG/DjlqO8vWgnpxJTPcsGzNl0mNfuqU2bW0vS5ZYudAzvyKL9ixi/ZTyHzx8GYPXR1aw+upqW5Vsy/Lbh1C5e2+AZiYiI5C831GCdNWuW5+Pu3bszZ86cS976X6dOHXr27Mntt9/O4cOHWblyJRaLhQ8//PCy5mqmqlWrMnbsWPr27QvA8uXLb7jBejNNyMmTJwPQpk0bT3P1Yn379uXll19m//79TJky5bIGa2BgIIMHD87Wc549e5bExEQmTJjAhAkTLjue+XiHDh2ifPny2XpsERERERGRzGUD2tUsyce/7mbSmisvG1ChWAD3VLmHzpU7s3DvQr7c9iVHzh8BYOXhlaw8vJI2Fdow/Lbh1ChWw+BZiYiI5A83tETAhg0bPB+PGTPmiuuqhoWF8fzzz3s2sQoICKB79+7XfNyePXsSEBAAwLZtef8WlKSkJFavXg1A586dr3iOxWKhU6dOACxZsiRHnrdkyZLMmzfvsj+PP/44AP/+97+ZN28eYWFhOfJ8IiIiIiJSOAX52Xi167WXDUhxOLFZbfSo1oMF3Rfw7yb/pnSR/y65tuzQMnov6M3I5SOJPRtrxDRERETylRu6g/XAgQMAhIeHU6VKlaue16FDByCjKVmjRo3r3lnq7e1NrVq12Lhx402vwXojdu7cicvlAjLuwr2azGPHjx/nzJkzFCtW7KrnZsXVms/nzp0DoEmTJp6mroiIiIiIyM3K6rIBNi8bvav35t4q9zJ391y+2v4VJ5NOAhBzIIaYAzF0DO/IsPrDqFL06j8bioiImNkNNVgTEhKwWCxUqFDhmuddfDyrTciiRYsCGRtk5bWjR496Pi5XrtxVz7v42NGjR2+6wXqzUlNTSU1N9Xxut9sBcDgcOBwOo8rKNZlzMuPcRAoK5VDEWMqgiPHMksMudUrSsmoxPlu2lyl/HLxk2YCImiV5qfOtlA/1x4KFXlV60TW8K/P2zOPbv74lPiXjpphf4n5hSdwSOlXqxEN1H7pkoyyR3GKWDIoUZGbPYXbmdUMNVofDgcVi8byd/2p8fX09H2d1XdTM89xu942UdlMSExM9H19rbhcfu/ianDZ48OAsref6zjvv8Prrr182vmTJkuv+HRVkMTExRpcgUugphyLGUgZFjGeWHN4GlKwD3+/3Ym9ixhJwMTtPsvyfE0SUd9G2rBvb/y8wF0wwI3xHsJ71rExdyQX3Bdy4+fnAzyw+sJj6tvq08WtDca/ihs1HCg+zZFCkIDNrDpOSkrJ87g01WCV/efHFFxk5cqTnc7vdToUKFejQoQPBwcEGVpY7HA4HMTExREREYLPZjC5HpFBSDkWMpQyKGM+sORzqdjN/23HeW7yLU+fTcLgtLDrkxV8XAni1y620ql7Cc+693EtyejLfxX7H5J2TOZd6Djdutji2sD19O10rd+XBOg9SLvDq7w4UuVFmzaBIQWL2HGa+Qzwr1GC9SFBQkOfja3WpLz528TVG8fX1veRu4Uw2m82UL/BMZp+fSEGgHIoYSxkUMZ4Zc/ivOyrSsU4ZPv51N5PWxOF0uTlwJokHp26mQ61SvNq1FhWKZbxTzmaz8WD9B7m/1v3M+GcGE3dMxJ5mx+l28uO+H/lp/0/cW/VeHq73MGUDyxo8MzEjM2ZQpKAxaw6zMyc1WC9Stux/v+EfOXKEevXqXfG8I0eOXPGa/EZrsIpIblEORYylDIoYz+w59POCFzpWo0f90rz+0z9siDsLwJK/T7Ai9hTDWt3Cg80q4WvLWOLNBx8G1RhEryq9mLlrJlN3TiXRkUi6O505u+fw494f6VGlB0NqD6FUQCkjpyYmYfYMihQEZs9hrq/BalY1a9bEarXicrnYsWMHnTt3vuJ5O3bsAKB06dKGb3B1saioKKKionA6nYDWYBWR3KccihhLGRQxXmHIYb/SUMPbwo8HrNgdFlLTXXy8dA/TVu+mV7iLWqGX7p9RlrI84f8Ea6xrWJO6hlRSSXelM3v3bObunksjn0a09GtJsNV8y5lJ3isMGRTJ78yaw+yswWpx38BuUlarFYvFQmho6FXv8sy0fPnyLJ8LsG3bNs6cOYPFYvE0CnNCeHg4Bw4c4LXXXmP06NFXPa9ly5asWrWKtm3bsnTp0suOu91uqlatyr59+xg4cCCTJ0/OsRpzit1uJyQkhPj4eK3BKiK5QjkUMZYyKGK8wpjDxJR0Plu2lyl/HMTp+u+PkRE1S/JS51spH+p/2TUJqQlM+2caM3bNICn9vz+o+nr58q+q/2JwrcEU99dmWJJ9hTGDIvmN2XNot9sJCwsjISHhuv21m7qD9ezZs6xYseKa51gsliyfmx8MGjSIVatWsWzZMtatW8ddd911yfHZs2ezb98+AAYOHGhEiVlm1jUwMpl9fiIFgXIoYixlUMR4hSmHxWw2XrunDn3urMi/f/yL9fvPABCz8yQrd8fzWJuqPNTyFvz+f9kAgDBbGE/d8RSD6gxi0l+TmPHPDJLTk0l1pjJ913Tm7JnD/TXuZ3CdwRTzyz/vDpSCozBlUCS/MmsOszMn640+idvtzrU/OeHs2bPEx8d7/rhcLiDj9t6Lx8+fP3/JdYMGDaJu3bq43W569erluYvV5XIxe/ZsHnroIQA6d+5Mu3btcqRWERERERGRgqJG6WBmPdyYj/vcRomgjM12U9NdjI2JpdPHK1m26+Rl14T6hfL07U/zc8+fGVRrEH5efgCkOFOY+NdEOs3pxMd/fsy5lHN5ORUREZEccUN3sA4aNCin68hxDRo04MCBA5eNjxkzhjFjxng+HzRoEJMmTfJ87u3tzfz582nTpg1xcXG0b9+egIAAXC4XKSkpnseePn16rs/hZmmTKxHJLcqhiLGUQRHjKYfQpU5JWlYtdsmyAXGnk3hg4oarLhsQ7B3Mk7c9Sb9b+zHp70l8v/t70lxpJKcn882Ob5jxzwwib42kf83+BPuYb7kzyTnKoIjxzJ7D7MzrhtZgLQgy11y9nv9tsGZKTEzkgw8+YO7cuezfvx+r1Ur16tW5//77efzxx/Hx8cmFqm/OxZtcxcbGEh0dbepNrkREREREJH84egG+3+/F3kSLZ8xmcRNR3kXbsm5sV3nvpN1lZ2XKSjakbcDJf/fg8MWXZn7NaOrbFD+LX26XLyIicpmkpCQiIyOztAaraRushZk2uRKR3KYcihhLGRQxnnJ4Obfbzfxtx3lv8S5OnU/zjFcqFsCrXW6lVfUSV732+IXjfPvXt/yw7wfSXeme8SBbEANqDuD+W++niK1IrtYvBYsyKGI8s+cwzza5kvzNrIsMZzL7/EQKAuVQxFjKoIjxlMNL/euOinSsU4aPf93NpDVxOF1uDpxJ4sGpm+lQqxSvdq1FhWKXv8uuQtEKvNbsNR6s/yBfbfuKH/b8gNPtJNGRyLht45i+azqDaw8mskYkATa9S0/+SxkUMZ5Zc5gnm1yJiIiIiIiI/K8gPxuvdq3FT080587KxTzjS/4+QfsPV/DZ0t2kOJxXvLZcYDlGNx3Ngh4LuLfKvVgtGT+yJqQm8MmmT+g0pxOTdkwiOT05T+YiIiKSFWqwioiIiIiISI6rUTqYWQ835uM+t1EiyBeA1HQXY2Ni6fTxSpbtOnnVaysEVeCt5m8xv/t8ut3SzdNoPZt6lrF/jqXHjz2IPRubJ/MQERG5Hi0RYGIOh8OUO7mZfZc6kYJAORQxljIoYjzlMOu61ClJy6rF+GzZXqb8cRCny03c6SQemLiBiJoleanzrZQP9b/itWX9y/J649cZXHMwX+34il8O/IIbN0fOH2HAogH8p9l/aFmuZR7PSPIDZVDEeGbPYXbmpU2uTCQqKoqoqCicTiexsbFER0cTEKD1iUREREREJH84egG+3+/F3kSLZ8xmcRNR3kXbsm5s13mP5QnnCeYmzeWI8wgAFix09OtIM99mWCyWa18sIiKSDUlJSURGRmZpkys1WE3IbrcTEhJCfHz8dV8ABZHZd6kTKQiUQxFjKYMixlMOb5zb7Wb+tuO8t3gXp86necYrFQvg1S630qp6iWten5yezOg/RhNzMMYzds8t9/BSo5fw8fLJtbolf1EGRYxn9hza7XbCwsKy1GDVEgEmZtZd3DKZfX4iBYFyKGIsZVDEeMrhjfnXHRXpWKcMH/+6m0lr4nC63Bw4k8SDUzfToVYpXu1aiwrFrvxuPJvNxgetP+CLrV8wfut4AObvm8/h84f5qM1HFPMrdsXrxJyUQRHjmTWH2ZmTNrkSERERERGRPBfkZ+PVrrVY9EQL7qz836bokr9P0P7DFXy2dDcpDucVr7VarAy/bThjWo7B1ytjA61NJzcR+VMku8/uzpP6RUREMqnBKiIiIiIiIoa5tXQQsx5uzMd9bqNEUEazNDXdxdiYWDp9vJJlu05e9dpOlTsxqdMkSvhnLCtw5PwR+i/qz8rDK/OkdhEREVCDVURERERERAxmsVjo3qAcvz3TiqHNK+NlzdiwKu50Eg9M3MDDUzZy6EzSFa+tE1aHGV1mULNYTQCS0pN4bOljTP5rMtpyRERE8oLWYDUxh8OBw+EwuowclzknM85NpKBQDkWMpQyKGE85zB1+XvBCx2r0vK00oxf+w4a4s0DGsgErYk8xrNUtPNisEr42r0uuK+ZTjK/bf81ra1/j10O/4sbNBxs/YPeZ3bzU6CVsXuZbG7CwUwZFjGf2HGZnXha3fqVnGlFRUURFReF0OomNjSU6OpqAgCsvDC8iIiIiIpKfud3wZ7yFHw9YsTssnvEwPzc9w13UKurGYrn0GpfbxbKUZSxLXeYZC/cK5/4i91PEWiSvShcRERNISkoiMjKShIQEgoODr3muGqwmZLfbCQkJIT4+/rovgILI4XAQExNDRESEKXepEykIlEMRYymDIsZTDvNOYko6ny3by5Q/DuJ0/ffH16ZVivFip1upUTrosmsWxy1m9B+jSXOlAVCuSDk+bvUxVYpWybO6JXcpgyLGM3sO7XY7YWFhWWqwaokAE7PZbKZ8gWcy+/xECgLlUMRYyqCI8ZTD3FfMZuO1e+rQ985KvPrjDtbvPwPAmr1nuGfcWu67vQIjO1SnVLCf55pu1boRXjScJ5Y9QXxyPEcuHGFwzGDGtBxDi/ItjJqK5AJlUMR4Zs1hduakTa5EREREREQk37u1dBCzHm7M55ENqFDMH8hYRmDWxkO0HrOcj3+NJSkt3XN+3RJ1L9n86oLjAo/99hhT/pqiza9ERCRHqcEqIiIiIiIiBYLFYqFrvbL8OrIVL91dgyC/jDdlJjucfPzrbtp8sJzvNh7yLCVQukhpJnWaRESlCCBjjdYxG8fw+trXcTjNuSmLiIjkPTVYRUREREREpEDx9fbi4ZZVWDGqDYObhuNtzdjt6oQ9lee+30a3z35n9Z54AAJsAXzQ6gMervew5/o5u+fwcMzDnE05a0j9IiJiLmqwioiIiIiISIFUrIgPo++pzS9PtySiVinP+N/H7PT7eh1DJm1gz8lErBYrjzd4nHdbvIuP1QeAjSc2EvlTJHvP7TWqfBERMQltcmViDocDh8N8b3vJnJMZ5yZSUCiHIsZSBkWMpxzmLxWL+jLu/vqs23+Gdxbv4q+jiQD89s9JVsSeos8d5XiiTRU6VOhAmfZlGLlyJKdTTnP4/GH6L+rPO83eoVnZZgbPQrJDGRQxntlzmJ15Wdxa3ds0oqKiiIqKwul0EhsbS3R0NAEBAUaXJSIiIiIikmdcbvgz3sLCg1bOpVk8475ebiLKuWhdxs0FzjH9wnSOOY8BYMFCZ//ONPFpgsViudpDi4hIIZKUlERkZCQJCQkEBwdf81w1WE3IbrcTEhJCfHz8dV8ABZHD4SAmJoaIiAhsNpvR5YgUSsqhiLGUQRHjKYf5X3Kak4lrDjBh1X4upDk942VD/Hgmohrtaobw7z9eZdnhZZ5jPav25Pk7nsdm1d9pfqcMihjP7Dm02+2EhYVlqcGqJQJMzGazmfIFnsns8xMpCJRDEWMpgyLGUw7zL5vNxpMRt3J/40p8FLObWRsO4nLD0YQUnvl+O/XLh/BSl39TNbQqX23/CoC5e+Zy6PwhPmz1IUX9iho7AckSZVDEeGbNYXbmpE2uRERERERExLRKBvnxTs+6/PxkS1pVL+EZ33o4gT5fruPvv5oxsv5rns2vNhzfQOSiSPYl7DOqZBERKWDUYBURERERERHTu7V0EJOH3MmUIXdya6kgz/jiv47zn9kBNC/yKqG+xQA4lHiI/j/1Z82RNUaVKyIiBYgarCIiIiIiIlJotKxegkVPtuDdnnUpEeQLgMPp5sf1Ns7tGU6YT2UAEh2JDFs6jOk7p6OtS0RE5FrUYBUREREREZFCxctqoe+dFVn+bGueaFsVP1vGj8b284Hs3zYYW2o9AFxuF++uf5c3/3gTh8thZMkiIpKPqcEqIiIiIiIihVIRX29GdriVZc+2plfD8lgsgNuXM/v6khrf2nPe7NjZDIsZRkJqgmG1iohI/qUGq4iIiIiIiBRqZUL8GXtffRY81pwmtxQHrKSd6kTykftwu7wAWHd8HZE/RbI/Yb+xxYqISL7jbXQBknscDgcOh/nexpI5JzPOTaSgUA5FjKUMihhPOTSnW0sGMHlwQ37bdYr3f4llX3xDkhzF8S8/Fav3eQ4mHiTyp0jeb/4+jcs0NrrcQk0ZFDGe2XOYnXlZ3Fqt2zSioqKIiorC6XQSGxtLdHQ0AQEBRpclIiIiIiJS4DhdsOakhZ8PWUniHP4VJuPldxwAi9tKZ/+7aeqnJquIiFklJSURGRlJQkICwcHB1zxXDVYTstvthISEEB8ff90XQEHkcDiIiYkhIiICm81mdDkihZJyKGIsZVDEeMph4ZGY4mD8iv1M+iMWr9IzsAXt9BxrUqIrH7V9BR8vHwMrLJyUQRHjmT2HdrudsLCwLDVYtUSAidlsNlO+wDOZfX4iBYFyKGIsZVDEeMqh+RWz2Xi5a20GNq3Me4srsuTYRHzDVgCw9tRC2s3Yy8dtxtI4vILBlRZOyqCI8cyaw+zMSZtciYiIiIiIiFxHhWIBfB55O7N6v0np1MGeza8uWHcyNGYgj85czPGEFIOrFBERI6jBKiIiIiIiIpJFDSqGsuShkQyv8QEWVyAAVp94fk96lTZRE/hwyS4upKYbXKWIiOQlNVhFREREREREssFisTC8SXt+7DGL4j6VMsa8UvAq+w3jN0+l9QfLmbn+IE6XtjwRESkM1GAVERERERERuQGVi1bkp3/NommZlgBYLC78Sv+IPeA7Xpi7hS6frmJl7CmDqxQRkdymBquIiIiIiIjIDSpiK8K49p/yQJ0HPGM+xdbiX2Ei/5w8wcBv1zPo2/XEnkg0sEoREclNarCKiIiIiIiI3AQvqxcjbx/Jm83exNvqDYB34B6KhI/DYotnRewpOn28khfnbudUYqrB1YqISE5Tg1VEREREREQkB3Sv2p1vOnxDqG8oAFbfeAJvGYdXwB5cbpix/iCtxyzj8992k+JwGlytiIjkFDVYRURERERERHJIw1INie4STdWiVTMGrEkUqfQtgWHrAbiQ5uSDJbG0+WA5czcdxqWNsERECjw1WEVERERERERyUPmg8kztPJWW5TM2v3LjwlJiLvXq/YbVknHn6rGEFEZ+t5V7o1bzx77TRpYrIiI3ydvoAiT3OBwOHA6H0WXkuMw5mXFuIgWFcihiLGVQxHjKoVyPr8WXsc3H8unWT5m6cyoA+x1LaNL0LJwawOrYJAC2H0mg74Q/aF+jBM91rE7lsCJGll1gKIMixjN7DrMzL4vb7db7EUwiKiqKqKgonE4nsbGxREdHExAQYHRZIiIiIiIihdqfqX8yP3k+TjLuXg2zhtHMNZDfDpTgaJLFc57V4qZ5KTcdy7sItBlVrYiIACQlJREZGUlCQgLBwcHXPFcNVhOy2+2EhIQQHx9/3RdAQeRwOIiJiSEiIgKbTf/XIWIE5VDEWMqgiPGUQ8muTSc38eyqZzmXeg6AYJ9g3mv2PgePluPjpXs4mZjqOTfIz5vhrW5hQOOK+HprZb8rUQZFjGf2HNrtdsLCwrLUYNUSASZms9lM+QLPZPb5iRQEyqGIsZRBEeMph5JVd5W7i+gu0Ty+9HH2JuzFnmbn8eWP8eJdL7Ls2Z5MWLmPCSv3kexwkpiSznu/xBK94RAvdKrJ3XVLY7FYrv8khZAyKGI8s+YwO3PSr8JERERERERE8kCFoApMu3saLcq1ACDdnc6bf7zJZ1s/4PF2t7Ds2db0vr08mb3UQ2eSGRG9iTcW/o3LpTefiojkV2qwioiIiIiIiOSRQJ9APmv7GQNrDfSMTd85nceWPkaAfxpjetdn4ePNaVa1uOf4xNVxPDt7Kw6ny4iSRUTkOtRgFREREREREclDXlYvRjUaxetNX8fbmrFy3+qjq+m/qD8H7QepXTaEaUPv4t2edbH+/92sczcfYdi0P0lxOA2sXERErkQNVhERERERERED9KzWkwkREyjqWxSA/Qn7iVwUyYbjG7BYLPS9syLj+t2Oj1fGj+6/7jzJwG/WY09xGFi1iIj8LzVYRURERERERAzSqHQjortEUyWkCgAJqQk8vORhvo/9HoBOdUoz6YFGFPHxAmB93Bn6fvkHpxJTDatZREQupQariIiIiIiIiIEqBFVg6t1TaV6uOZCx+dXra1/nvfXvke5Kp2nVMGY83JhiRXwA+PuYnd5frOHQmSQjyxYRkf+nBquIiIiIiIiIwYJ8gvi87ecMqDXAMzZt5zQe++0x7Gl26pUvynePNKFsiB8AcaeT+NcXa9h1PNGokkVE5P+pwSoiIiIiIiKSD3hZvXiu0XOMbjIab8v/b351ZDV9F/Zl15ldVC0ZyOxhTbmlRBEATthTue/LtWw6eNbIskVECj01WEVERERERETykV7VezGhwwRCfEMAOJR4iP6L+rNg7wLKFfVn9iNNqFc+41hCsoN+X61jZewpI0sWESnU1GAVERERERERyWcalW7ErK6zqFW8FgApzhRe+v0l3vrjLYL8LUQ/1JimVYoDkOxwMnTyBhZuO2pkySIihZYarCIiIiIiIiL5ULnAckzpPIVe1Xp5xmbtmsUDix/gfHo83w5uRMfapQBwON08PmMz0/44YFS5IiKFlhqsIiIiIiIiIvmUr5cvo5uO5o2mb+Bj9QFgW/w27ltwH1viNxAV2ZA+d1QAwO2GV37Ywee/7cbtdhtZtohIoaIGq4iIiIiIiEg+16NaD6bePZVygeUAOJt6lkdiHmHS39/yn561eaTlLZ5zP1gSy1s/7cTlUpNVRCQveBtdgOQeh8OBw+EwuowclzknM85NpKBQDkWMpQyKGE85FCNUC67GtI7TeGXNK6w+thqX28Unmz5h68mtvN7qdYL9vBizZDcA3/y+nzMXUvnPvbXw9jLfvVXKoIjxzJ7D7MzL4tb7BkwjKiqKqKgonE4nsbGxREdHExAQYHRZIiIiIiIikoNcbhfLU5ezLGUZbjJ+pC9uLc79Re5nf3wZZu2z4sYCQJ1QF4OqufDxMrJiEZGCJykpicjISBISEggODr7muWqwmpDdbickJIT4+PjrvgAKIofDQUxMDBEREdhsNqPLESmUlEMRYymDIsZTDiU/WH10NS+veRl7mh0APy8/XrnzFbySbufp2dtwODN+3L8zPJQv+t1GkJ95XqvKoIjxzJ5Du91OWFhYlhqsWiLAxGw2mylf4JnMPj+RgkA5FDGWMihiPOVQjNS6UmtmFZvFyOUj2XlmJynOFF5Z+wp9b+3L14OGMHzaVi6kOVkfd5YBE/9k8pA7CQv0NbrsHKUMihjPrDnMzpzMtxCLiIiIiIiISCFRPqg8U++eSs9qPT1jM3fNZMKeZ/lswC2EBmQ0CP46aqf3F2s5dCbJqFJFRExLDVYRERERERGRAszXy5fXm77O6Caj8bH6ALDt1DZe3/QQ/+7tQ5kQPwD2x1+g9xdr2X0i0chyRURMRw1WERERERERERPoVb0XU+6eQtkiZQE4k3KG0RuepE/7PVQukbEB8nF7Cr2/XMvmg2eNLFVExFTUYBURERERERExidrFazOr6yyalWsGgMvt4pu/P6dmvXnULpdxd+u5JAf9vl7Hqt2njCxVRMQ01GAVERERERERMZGifkWJahvFsPrDPGOrji7DXfZjGlRJASApzcmQSRv4adsxo8oUETENNVhFRERERERETMbL6sXw24YT1S6KIJ8gAA6dP8hh/3dpWHM/AA6nm8dmbCJ63UEjSxURKfDUYBURERERERExqZblWzKr6yxqFKsBQIozhd18Se26vwHpuN3w0rztRC3bg9vtNrZYEZECSg1WEREREREREROrEFSBqZ2n0r1qd8/YwfQlVK47FYt3AgBjftnF2z/txOVSk1VEJLvUYBURERERERExOT9vP95o+gavNXkNm9UGQHz6LkrcOg6vgL0AfP37fkZ9v410p8vIUkVEChw1WEVEREREREQKAYvFwr+q/4spnadQpkgZAJJdCRSp9A0+xVcCbuZsOsyw6ZtIcTiNLVZEpABRg1VERERERESkEKkTVodZXWfRtGxTANy48C25iIDy0WBNIebvEwz6dj2JKQ6DKxURKRjUYBUREREREREpZEL9QhnXbhwP13vYM+YVtJ3AylFYfU6wbv8Z7v/qD+LPpxpYpYhIwaAGq4iIiIiIiEgh5GX14vEGj/N5288J8gkCwOJziiKVo/AO2sqOI3bu+2Ith88mGVypiEj+pgariIiIiIiISCHWqkIrZnWZxa2ht2YMWNPwLz8D35IL2Bdv51/j17L7RKKxRYqI5GNqsIqIiIiIiIgUchWCKzD17qncU+Uez5hP8dX4V/qKE0kn6f3lWrYcOmdcgSIi+ZgarPlEXFwcFovlin8GDx5sdHkiIiIiIiJicv7e/rzV7C1ebfwqNqsNAO+AOAIqf0qiexeRX/3B77vjDa5SRCT/8Ta6ALlUjx496Nmz5yVjVapUMagaERERERERKUwsFgv33XofNYvVZOSKkRy/cByr93n8K31N6slODJnk4pO+Dehct4zRpYqI5BtqsOYz9erVo3///kaXISIiIiIiIoVY3RJ1+a7rdzy38jn+OPYHFosLv1KLcPgfYsTMFN5OvoP776xodJkiIvmClgjIh5KTk0lOTja6DBERERERESnEQv1C+aL9FzxU9yHPmC14O36VPuflhUsZt3wPbrfbwApFRPIHNVjzmbFjxxIQEEBAQABVq1bls88+0zcsERERERERMYSX1YsnGj7Bp20+JcgWlDHme4qA8M/5cPVs3vn5H/3MKiKFnikbrElJSfz888+89dZb9OzZk0qVKnk2jBo9enSWHiMxMZHRo0dTt25dAgMDCQkJoVGjRowdO5a0tLQcr9lqtdKuXTveeecd5s+fz5dffkloaChPPPEETz/9dI4/n4iIiIiIiEhWtanYhpldZ1I9tDoAFq80/MtHM/mfT3l29ibSnS6DKxQRMY4p12Bdv349d9999w1ff+DAAVq3bk1cXBwAAQEBpKamsnHjRjZu3Mj06dNZunQpoaGhl13rdrtJTU3N0vNYrVZ8fHwAqFixIr/++uslx4cOHUq7du349NNPeeSRR6hZs+YNz0lERERERETkZlQMrsi0u6fx5to3WbBvAQA+xX9n0enDnJ72JF9EtsHP5mVwlSIiec+Ud7AChIaG0q5dO0aNGsWMGTMoXbp0lq5LT0+nW7duxMXFUaZMGWJiYrhw4QJJSUnMnDmToKAgNm/efNWNqA4cOIC/v3+W/jRs2PCatXh5efH888/jdruJiYnJ9tdAREREREREJCf5e/vzdvO3eeWuV/CyZNyz5R0Qx4b0f9N70nQSUxwGVygikvdMeQdrixYtOHPmzCVjL7zwQpaunTx5Mtu3bwdgzpw5NGnSBMi427RPnz64XC4iIyNZtGgRS5cupV27dpdcHxYWxsSJE7P0XFe6A/Z/VapUCYD4+PgsPaaIiIiIiIhIbrJYLPSp0YcaxWvw2K9Pcy7tFFbvRPa7x9Jl8n7mRj5PWJCf0WWKiOQZUzZYvbxu/C0JkydPBqBNmzae5urF+vbty8svv8z+/fuZMmXKZQ3WwMBABg8efMPP/7/27NkDQKlSpXLsMUVERERERERuVv0S9fmxx/cM++Vp/j63CYvFxVm/7+kUvY+ZvT6kalhxo0sUEckTpl0i4EYkJSWxevVqADp37nzFcywWC506dQJgyZIlOfbcJ0+evGwsJSWFN998Ey8vLzp27JhjzyUiIiIiIiKSE4r5FWN6t2/oWXmAZyzVbxM9f+jD8n3bDaxMRCTvmPIO1hu1c+dOXK6MnQ/r1Klz1fMyjx0/fpwzZ85QrFixm37uRx55hNOnT9OmTRvKly/P8ePHmTJlCnv27OG1116jatWqN/0cIiIiIiIiIjnN2+rN6y2fo05YXd5c92/c1hTcthM8vuIBnjj7Cg/d3t3oEkVEcpUarBc5evSo5+Ny5cpd9byLjx09ejRHGqxdu3ZlypQpfPHFF5w5c4YiRYrQsGFD3n33XXr16nXNa1NTU0lNTfV8brfbAXA4HDgc5ltgPHNOZpybSEGhHIoYSxkUMZ5yKHK57tXaUzkonIdjnsLhdRSsqXy641X+Or2Nd1qNwtuacy0IZVDEeGbPYXbmpQbrRRITEz0fBwQEXPW8i49dfM3NGDp0KEOHDr2ha9955x1ef/31y8aXLFlyzXkUdDExMUaXIFLoKYcixlIGRYynHIpc7qngB/n05HxSi2wBYOmx2XSZsZEhIfcRZA3K0edSBkWMZ9YcJiUlZflcNVhN4MUXX2TkyJGez+12OxUqVKBDhw4EBwcbWFnucDgcxMTEEBERgc1mM7ockUJJORQxljIoYjzlUOTauqd1JXL2J8QxE4vFySnLfqIufMWnbT/gthK33fTjK4MixjN7DjPfIZ4VarBeJCjov79Ju1aX+uJjF19jFF9fX3x9fS8bt9lspnyBZzL7/EQKAuVQxFjKoIjxlEORK7PZbPww4Dke+a4qf1z4CKvNznnnGR6MeYjn7hxFZI1ILBZLjjyPMihiLLPmMDtzsuZiHQVO2bJlPR8fOXLkquddfOzia0REREREREQkg7eXla/69KJ7yQ9Iv3ALAC6cvLv+XZ5f+TxJjqy//VZEJD/THawXqVmzJlarFZfLxY4dO+jcufMVz9uxYwcApUuXzpENrnKLNrkSkdyiHIoYSxkUMZ5yKJJ1r3W6nRIr3mHctnH4hq0A4Oe4n9l1ZhdjW46lUnClbD+mMihiPLPnMDvzsrjdbncu1pJvhIeHc+DAAV577TVGjx591fNatmzJqlWraNu2LUuXLr3suNvtpmrVquzbt4+BAwcyefLkXKw6e6KiooiKisLpdBIbG0t0dLSpN7kSERERERGRgmP1CQtzT+3Er8xsLF6pAPjgS6+AntT2qW1wdSIil0pKSiIyMpKEhITr7nGkO1j/x6BBg1i1ahXLli1j3bp13HXXXZccnz17Nvv27QNg4MCBRpR4VSNGjGDEiBHY7XZCQkK0yZWI5BrlUMRYyqCI8ZRDkey7G2i2vQGj5pfGu8xUvPxOkEYqM5JmMLDSQB6r/xje1qy1KZRBEeOZPYfa5Ao4e/YsTqfT87nL5QIyus/x8fGecT8/PwIDAz2fDxo0iE8++YTt27fTq1cvJk+eTLt27XC5XMyZM4eHHnoIgM6dO9OuXbs8ms2NMesiw5nMPj+RgkA5FDGWMihiPOVQJHvubViB0MC7eWRaKI6w77CFbAVgys4p7Dy7kzEtx1Dcv3iWH08ZFDGeWXOoTa6ABg0aUKJECc+fQ4cOATBmzJhLxh977LFLrvP29mb+/PmEh4dz5MgR2rdvT5EiRShSpAj33XcfdrudBg0aMH36dCOmJSIiIiIiIlKgtaxegukPtsDn7ABSjnfD7c5oTWw4voF+i/px0H7Q4ApFRLLHtHew3ozw8HC2bdvGBx98wNy5c9m/fz82m43atWtz//338/jjj+Pj42N0mdelTa5EJLcohyLGUgZFjKccitycumUCmTn0Th6Y7EX8gXL4lYvGarNz5PwR+i/qz2etP6NW8VpXvV4ZFDGe2XOoTa4KKW1yJSIiIiIiIgXJ6RQYt9OL0+l2/Ct8i5ffCQB88CGySCRVbVUNrlBECqvsbHKlBqsJZW5yFR8fr02uRCRXKIcixlIGRYynHIrknPjzqQyZvImdJ0/iX2Ey3gFxAHhbvXm98et0Du982TXKoIjxzJ5Du91OWFhYlhqsWiLAxMy6yHAms89PpCBQDkWMpQyKGE85FLl5ZUJtfPdoE0ZEb2bl7qH4lZ2JLfgv0l3pvLzmZc6lnWNg7YFXvFYZFDGeWXOoTa5EREREREREpMAI8rPxzaA76HvHLaQc6Ufa2bs8x8ZsHMOHGz/E5XYZWKGIyNXpDlYRERERERERMZzNy8o7PetSPtSfD5ZYcKcH4VviVwAm/jWR+OR4Xm/2Ojar+e6UE5GCTQ1WE3M4HKbcyc3su9SJFATKoYixlEER4ymHIrnnkRbhlA725cV5VlLSg/At/QMWi5sF+xZwOvk07zd/HxsZTVZlUMQ4Zv9emJ15aZMrE4mKiiIqKgqn00lsbCzR0dEEBAQYXZaIiIiIiIhItu1OsPDNLiuOgL/wKzsTizUdgPJe5RlQZABFrEUMrlBEzCwpKYnIyMgsbXKlBqsJ2e12QkJCiI+Pv+4LoCAy+y51IgWBcihiLGVQxHjKoUje2HPyPA9N3cSx1L/xrzAFi1cKABUDK9Lb0pv7Ot2nDIoYxOzfC+12O2FhYVlqsGqJABMz6y5umcw+P5GCQDkUMZYyKGI85VAkd9UsF8rcEc14cLIvOw4E4F/hW6w2OwfPH2SCZQKNzjeidsnaRpcpUqiZ9XthduZkzcU6RERERERERERuSskgP2Y+3Ji2t9QjKW4YztQSACS6Exn661A2HN9gcIUiUtipwSoiIiIiIiIi+VqAjzdfDriDAY1uI+nAoziTKgJwwXGBR2IeIeZAjMEVikhhpiUCTMzhcJhyJzez71InUhAohyLGUgZFjKccihjjlc7VKRviy7u/2PAvF4130D84XA6eWf4MLzR6gd7VehtdokihYfbvhdmZlza5MpGoqCiioqJwOp3ExsYSHR1NQECA0WWJiIiIiIiI5Kgtpy1M3e3Gu/Q8bEX/9Iy39m1NO792WCwWA6sTETNISkoiMjIyS5tcqcFqQna7nZCQEOLj46/7AiiIzL5LnUhBoByKGEsZFDGecihiLIfDwYS5MUzc60dy4EJ8w5Z7jvWo0oMXG72It1Vv2hXJTWb/Xmi32wkLC8tSg1X/2piYWXdxy2T2+YkUBMqhiLGUQRHjKYcixqkcBN8/chcPTfXhyPEgfEstxGJxM2/vPM6mnWVMyzH4efsZXaaI6Zn1e2F25qRNrkRERERERESkQAovXoS5w5tRN6gLKUf64nZ7AbD80HIejnmYhNQEYwsUkUJBDVYRERERERERKbCKFfEh+qHGRFTqSPLBB3A7fQHYfHIzA38exPELxw2uUETMTg1WERERERERESnQ/GxeREU2ZMjtESQdfBhXeiAA+xL20n/RAPae22twhSJiZmqwioiIiIiIiEiBZ7VaeLlLLUZ37EDKgWG40ooDcCLpOAMWDWTLyS3GFigipqVNrkzM4XDgcDiMLiPHZc7JjHMTKSiUQxFjKYMixlMORYx1rQzef0c5Sga24+k5frhLf4uX/xESHXaG/vIg7zV/l1blW+V1uSKmZPbvhdmZl8XtdrtzsRbJQ1FRUURFReF0OomNjSU6OpqAgACjyxIRERERERHJcwfPw4Rd6aSXmoZ34B4ALFjp7n8vt/vebnB1IpLfJSUlERkZSUJCAsHBwdc8Vw1WE7Lb7YSEhBAfH3/dF0BB5HA4iImJISIiApvNZnQ5IoWScihiLGVQxHjKoYixsprBw2eTGTJlPcd8JmEL2eoZH15vOENrD8ViseRFuSKmZPbvhXa7nbCwsCw1WLVEgInZbDZTvsAzmX1+IgWBcihiLGVQxHjKoYixrpfByiVt/DC8FQ9N9Wfr6an4FF8NwLht4zibdpbnGz2Pl9Urr8oVMSWzfi/Mzpy0yZWIiIiIiIiImFZIgI2pQxvTsczDpJzo7Bmf8c8MRq18jjRnmoHViYgZqMEqIiIiIiIiIqbm6+3FJ30b8Ej9oSQf7Y3bndEOiTmwhIeXPEpiWqLBFYpIQaYGq4iIiIiIiIiYnsVi4dmOt/JWu8GkHhmE25Xx9t8/T25gwKLBnEo6ZXCFIlJQqcEqIiIiIiIiIoVG3zsr8nXvAbiPPoorPQCAvQmx9F3YjwP2AwZXJyIFkRqsIiIiIiIiIlKotKpegu8e6EPA6SdxpRUF4GTyMfou7MeO+B3GFiciBY4arCIiIiIiIiJS6NQuG8KPD/egbNIonCmlATjvSGDQzw+w+shqg6sTkYLE2+gCJPc4HA4cDofRZeS4zDmZcW4iBYVyKGIsZVDEeMqhiLFyKoMlingza0h7hs8MZOuFj/Eusp80VwrDfx3B601G06Vyl5woV8SUzP69MDvzsrjdbncu1iJ5KCoqiqioKJxOJ7GxsURHRxMQEGB0WSIiIiIiIiL5mtMFM/Y52e7zHbbgvzzjHfw60dKvuYGViYhRkpKSiIyMJCEhgeDg4GueqwarCdntdkJCQoiPj7/uC6AgcjgcxMTEEBERgc1mM7ockUJJORQxljIoYjzlUMRYuZFBt9vN58v38OXfH+ET+odnvG+1fjx7x9NYLVplUeRiZv9eaLfbCQsLy1KDVUsEmJjNZjPlCzyT2ecnUhAohyLGUgZFjKccihgrpzM4skMtwsP+zUvLPsIWFgPAzN3TiU85w/ut38ZmVd5F/pdZvxdmZ0769YuIiIiIiIiIyP/r2bAC33Z/CeL/hdttAeDXQz8z5OdhJDmSDK5ORPIjNVhFRERERERERC7StEoYc/qPJODsENyujDf/bolfR5/5gziTcsbg6kQkv1GDVURERERERETkf1QvFcTCoY9SOvkJ3E4/AOLO/0PPeZEcTjxscHUikp+owSoiIiIiIiIicgUlg/34cegA6lhfwuXI2OTmdNoRev5wPztP7zS4OhHJL9RgFRERERERERG5iiK+3kwbcC8di72FM7UkAMmuc0QuHMSaI38YXJ2I5AdqsIqIiIiIiIiIXIO3l5WxPVrzcNUPcCZVBCCdZB6NGcaPuxcZW5yIGE4NVhERERERERGR67BYLDzVtgFvNfkM5/maALgt6byy+gW+3DzF4OpExEhqsIqIiIiIiIiIZFHP227h27s/x3L+zowBi5vPt43h3yvH4Ha7jS1ORAyhBquIiIiIiIiISDY0rlySeb0/xu98B8/YvP1TeGTxC6S70g2sTESM4G10AZJ7HA4HDofD6DJyXOaczDg3kYJCORQxljIoYjzlUMRY+SGDFUP9WNDvDe7/riinfGZjsbhZe3IRvefGM7nLR/h7+xtWm0heyA85zE3ZmZfFrfvXTSMqKoqoqCicTiexsbFER0cTEBBgdFkiIiIiIiIippXmhPGHdnAyeDYWqxOAoPSKPBbanyJe+plcpKBKSkoiMjKShIQEgoODr3muGqwmZLfbCQkJIT4+/rovgILI4XAQExNDREQENpvN6HJECiXlUMRYyqCI8ZRDEWPltww6XW6eWTiXFQkfYPFKBSCAskR3nUDF4LIGVyeSO/JbDnOa3W4nLCwsSw1WLRFgYjabzZQv8Exmn59IQaAcihhLGRQxnnIoYqz8kkEbENWrL+/9FsaU/a9i9T5PEkf514IBfNtxAg3K1DC6RJFck19ymNOyMydtciUiIiIiIiIikgOeb9uelxuMw51WHIB0y1kGLR7I4j1rDa5MRHKTGqwiIiIiIiIiIjkksmEDotpOxJJWAQC3NZlRq0YwcfNCgysTkdyiBquIiIiIiIiISA5qVaUys++Zgi3t/5cGsDoYu/Ul3lox0djCRCRXqMEqIiIiIiIiIpLDbi0Vxi/3TyLYeRcAFoubWXEfMmzhe2i/cRFzUYNVRERERERERCQXlAgswq8DxlPB2skz9vvpafT87lkc6ekGViYiOUkNVhERERERERGRXOJvs7Gw3/s0DBzgGduTsoT20x/kxPkEAysTkZyiBquIiIiIiIiISC6yWi1M7vUcXcs8g9ud0Yo5w590nPUvlu3bbnB1InKz1GAVEREREREREckD73QYzLCab4PLDwCn93EeX/EA/1kxw+DKRORmqMEqIiIiIiIiIpJHRtzVlS/bTsE7vSwAFmsqM+L+Q8+Zz5PkSDW4OhG5EWqwioiIiIiIiIjkoaaVavLb/XMoY23mGduduojWU/vw14lDBlYmIjdCDVYRERERERERkTwWGhDI4n7j6FByOG63FwDJXnvp+1MfJm9aanB1IpIdarCKiIiIiIiIiBjAarUytvMwXm80Dkt60YxBr0TGbBvJowvG4nK5DK1PRLJGDVYREREREREREQP1qt2UH3p8T5CrFgAWi4vVZybRduoQjieeNbg6EbkeNVhFRERERERERAx2S7FSLB8wnfqBvTxjp/mTjt/1Imb3FuMKE5Hr8ja6AMk9DocDh8NhdBk5LnNOZpybSEGhHIoYSxkUMZ5yKGIss2bQAky852Wi1tfnm13vgFcyLu9TPP37EHodeIpXWt1vdIkiHmbNYabszMvidrvduViL5KGoqCiioqJwOp3ExsYSHR1NQECA0WWJiIiIiIiISDbtTTrD5PMzcPkc84yFpTTm0RKd8PPS/XIiuS0pKYnIyEgSEhIIDg6+5rlqsJqQ3W4nJCSE+Pj4674ACiKHw0FMTAwRERHYbDajyxEplJRDEWMpgyLGUw5FjFVYMpiQcoF+P77EUecqz5ivszITIj6ibumKBlYmYv4c2u12wsLCstRg1a88TMxms5nyBZ7J7PMTKQiUQxFjKYMixlMORYxl9gyG2YryS/9xPP/LV/x0dBwWazqpXvsZFNOPp+q9ztA7Ohpdoohpc5idOWmTKxERERERERGRfOy9jg/x9l1fYEkvljHgdYGPdoxi6A/vke50GluciKjBKiIiIiIiIiKS391b6y4W9vqeYHddACwWN+sTptFm2mAOJ5wxuDqRwk0NVhERERERERGRAqBi0RKsGDCVhkF9cbstAJxjC12+78WiXX8aXJ1I4aUGq4iIiIiIiIhIAeHt5cXkni8zvOZ/wBkAgMs7nufWPMgrv040uDqRwkkNVhERERERERGRAmb4XV2Z1DEaH2clACzWdH488iFdo58iMTXZ4OpEChc1WEVERERERERECqDby1Xht8jvqGhr6xk74FhK6+m9+fPIXgMrEylc1GAVERERERERESmgQvwC+CnyE+4p+zRulzcAaV4HGPxLJOPXLzK4OpHCQQ1WEREREREREZEC7u2IIbzX9Cus6cUzBrySiPr7BQbNfZt0p9PY4kRMTg1WERERERERERET6HLrHfz0r7kU5TYALBY3mxJn0mrqAA6eO2VscSImpgariIiIiIiIiIhJlA8pxrL+k7gzpB9utwUAu2U7Xef8iwU71xtcnYg5qcEqIiIiIiIiImIi3l5efNP9BZ6sPQacRQBwe5/hxT8e4YUlXxlcnYj5qMEqIiIiIiIiImJCDzXqyNTOM/F1VgbAYk3np2Of0nn6Y5xLvmBwdSLmoQariIiIiIiIiIhJ3VYmnOX9vqOyT4Rn7HD6CtrO6M26g7sNrEzEPNRgFRERERERERExsUBfP+bf/yG9KozC7bIB4PA6xIO/9uOztT8aXJ1IwacGq4iIiIiIiIhIITC67UDGNv8Ga3qJjAGvZCbEvkL/Oa+Tlp5ubHEiBZgarPnMyZMneeyxx6hUqRK+vr6UKVOGLl26sHfvXqNLExEREREREZECrmO1Bvzc+3tCaegZ23r+e1pP7c++MycMrEyk4FKDNR/Zu3cvDRo0YOHChQwZMoTx48czcuRIAgICOHPmjNHliYiIiIiIiIgJlA0uxvIBE2kSOhC32wJAovUvus/7F3P+WmNwdSIFj7fRBch/9evXj5IlS7Jy5UqCgoKMLkdERERERERETMpqtTLhnlFM+rMBY7e+Cl7ncXuf47X1w1lz6BHGdHgEq1X35YlkhZKSTyxbtox169bxxhtvEBQUREpKCqmpqUaXJSIiIiIiIiImNvj29sy4exZ+zioAWKxOlpwYR6foEZxNOm9wdSIFgxqs+cTixYsBKFq0KK1atSIgIAB/f3/uvPNOVq1aZXB1IiIiIiIiImJWdUpXZMWAWVT17ewZO+b8nbYz/sWaA/8YWJlIwWDKBmtSUhI///wzb731Fj179qRSpUpYLBYsFgujR4/O0mMkJiYyevRo6tatS2BgICEhITRq1IixY8eSlpaW4zXHxsYC0KtXL4KDg5k5cybjxo3j2LFjtG/fnj///DPHn1NEREREREREBCDA5su8vu/Tp9KLuF0+AKR7H+GR3wbw4e9zDa5OJH8z5Rqs69ev5+67777h6w8cOEDr1q2Ji4sDICAggNTUVDZu3MjGjRuZPn06S5cuJTQ09LJr3W53lt/ab7Va8fHJ+EcrMTERgBo1ajB//nwsloxFptu1a0etWrV4/fXXmT9//g3PSURERERERETkel5pHUnTCnUYuXwkTu8TYE1h4t7XWH9sM5O6v4qfzcfoEkXyHVPewQoQGhpKu3btGDVqFDNmzKB06dJZui49PZ1u3boRFxdHmTJliImJ4cKFCyQlJTFz5kyCgoLYvHkz/fv3v+L1Bw4cwN/fP0t/GjZs6LnO398fgIEDB3qaqwDVqlWjadOmrFix4ia+GiIiIiIiIiIiWdO2Sj1+6TOHMMsdnrG/kn6g9bR+7I4/ZmBlIvmTKe9gbdGiBWfOnLlk7IUXXsjStZMnT2b79u0AzJkzhyZNmgAZd5v26dMHl8tFZGQkixYtYunSpbRr1+6S68PCwpg4cWKWnuviO2DLlSsHcMVGcJkyZVi5ciVOpxMvL68sPbaIiIiIiIiIyI0qFRjC0v7f8NhPn7Dy9CQsFhcXrP/Qa35vXr7jHfrUa2F0iSL5hikbrDfThJw8eTIAbdq08TRXL9a3b19efvll9u/fz5QpUy5rsAYGBjJ48OBsP2+jRo348ssvOXz48GXHDh06RPHixdVcFREREREREZE8Y7VaGdftaaZtuY33Nr0MXom4vRJ4c9NjrDn8IB91GoHVato3R4tkmVJwkaSkJFavXg1A586dr3iOxWKhU6dOACxZsiTHnvvee+8lICCACRMm4HA4POObN29m7dq1nucUEREREREREclL/W9rw+xu3xHgqgaAxeLit1MT6DD9EU4nJRpcnYjxTHkH643auXMnLpcLgDp16lz1vMxjx48f58yZMxQrVuymnzssLIz//Oc/PPXUU7Rs2ZLIyEhOnz7Np59+SmhoKG+88cZVr01NTb1kYy273Q6Aw+G4pFlrFplzMuPcRAoK5VDEWMqgiPGUQxFjKYN5r0rRUsT0mcqQBW+xK2UhACdcf9B+Ri8+bPUBzSvVNLhCyWtmz2F25qUG60WOHj3q+ThzTdQrufjY0aNHc6TBCvDkk08SFhbG2LFjGTVqFP7+/rRr14533nmHW2655arXvfPOO7z++uuXjS9ZsoSAgIAcqS0/iomJMboEkUJPORQxljIoYjzlUMRYymDeG+DXmIUXgljLXCxeqaR7H+PxlUNoSk+6FK9tdHliALPmMCkpKcvnqsF6kcTE/97Wfq3G5MXHLr4mJ/Tr149+/fpl65oXX3yRkSNHej632+1UqFCBDh06EBwcnKP15QcOh4OYmBgiIiKw2WxGlyNSKCmHIsZSBkWMpxyKGEsZNNbd3M2quK6MXPkMTu/jWLxSWcsMTqd0YVK3V/Gz+RhdouQBs+cw8x3iWaEGqwn4+vri6+t72bjNZjPlCzyT2ecnUhAohyLGUgZFjKccihhLGTRO22r1iCkzh77znuGk6w8AYlN+ov2sWCZ3/ZQaJcobXKHkFbPmMDtz0iZXFwkKCvJ8fK3bgC8+dvE1IiIiIiIiIiKFRYnAYGL6fUnbEo/gdme0mJKsu+m94D6mb1lmcHUieUd3sF6kbNmyno+PHDlCvXr1rnjekSNHrnhNfqNNrkQktyiHIsZSBkWMpxyKGEsZzF8+iHiE2Tvq8O7mV3B7JYBXIu9seYrfDw7i444jsFp1f58ZmT2H2uTqBtWsWROr1YrL5WLHjh107tz5iuft2LEDgNKlS+fYBlc5ISoqiqioKJxOJ6BNrkQk9ymHIsZSBkWMpxyKGEsZzD+KACOChjHh7Hek+e7DYnHx+9mJtJ60gWFhPQjxvnxpQzEHs+ZQm1zdoICAAJo1a8aqVatYvHgxo0aNuuwct9vNL7/8AkCHDh3yusRrGjFiBCNGjMButxMSEqJNrkQk1yiHIsZSBkWMpxyKGEsZzL8iHd15cOE7/J38IwDn/Xbw4blTfBXxGbeVqWpwdZKTzJ5DbXJ1EwYNGsSqVatYtmwZ69at46677rrk+OzZs9m3bx8AAwcONKLELMuJRYZdLhfp6em4XK4cqurmOZ1OvL29cTqdepuBiEEKYw6tVis2mw2LxWJ0KSIeZt1QQKQgUQ5FjKUM5j82m41Z973F2N8bMmn3O2BNwel9gkeWDeHbzuO5reRtRpcoOcysOczOnEzbYD179qznrfKAp0GYlJREfHy8Z9zPz4/AwEDP54MGDeKTTz5h+/bt9OrVi8mTJ9OuXTtcLhdz5szhoYceAqBz5860a9cuj2aT9xISErDb7SQlJeWr5ipk3EVcunRpDh06pEaHiEEKaw5tNhtBQUGEhYXh5eVldDkiIiIiIvnWM8170rhCLR5d8jj4HMfBeYb+MpR3W75LRKUIo8sTyVGmbbA2aNCAAwcOXDY+ZswYxowZ4/l80KBBTJo0yfO5t7c38+fPp02bNsTFxdG+fXsCAgJwuVykpKR4Hnv69Om5PoebdSObXLndbk6dOkVCQgIBAQEUK1YMPz8/rFZrvmmiuN1uLly4QJEiRfJNTSKFTWHLodvtxul0cv78ec6ePcuFCxcoV65cobl7V/Ifs28oIFIQKIcixlIGC4Y7y1ZhcPj7fL17NN5F9pHmSuOZ5c8wsuFI+tXoZ3R5cpPMnsPszMvidrvduViLYcLDw6/YYP1f/9tgzZSYmMgHH3zA3Llz2b9/P1arlerVq3P//ffz+OOP4+PjkwtV35yLN7mKjY0lOjo625tc+fv7ExYWRsmSJS+5s1dERDKkpaVx7Ngx4uPjOX/+vNHliIiIiIjka2lOeGOzG0fYXGxFN3vGm/g0obN/Z6wW3bQg+VNSUhKRkZEkJCRcd48j0zZYC7PMTa7i4+OztcmV2+3m4MGD+Pr6Uq5cuVys8Oa43W4SExMJCgoqFHfOieRHhT2Hx48f5/z584SHhxfK+YvxzL6hgEhBoByKGEsZLFimrzvI6IU78QmLwbfEb57xNuXb8FbTt/D39jewOrlRZs+h3W4nLCwsSw1W0y4RINlfZDhzSYFSpUrl67e9Zq4Ja7FY8nWdImZW2HMYHBxMQkICkL2Fz0Vymlk3FBApSJRDEWMpgwVDZOPKfLPmAIfiO+ByhFKk7DxcuFh2eBnDfhvGZ+0+o5hfMaPLlBtk1hxmZ06F76diuarMTcG8vdV3FxG5lswNrvLbJoAiIiIiIvmRj7eVp9tXByA9oRGlkkcQ4J2xpOG2+G30X9SfuIQ4AysUuTlqsMpl9HZXEZFr07+TIiIiIiLZc+9t5aheKmOvl9i4cgy79UNK+pcE4FDiIQb8PIAtJ7cYWKHIjdOtiiaW+Zb/7JzvdrtxuVz5+q6szGWDM2sVkbxX2HPocrlwu904HA7P3awiecnsO7aKFATKoYixlMGC6am2VRk+YwsA0SvT+WbwRJ5e+SR7EvZwLvUcQ38ZyttN36ZdxXbGFipZYvYcZmde2uTKRKKiooiKisLpdBIbG0t0dDQBAQFZvt7b25vSpUtToUIFfHx8crFSEZGCLS0tjUOHDnH8+HHS09ONLkdEREREpEBwu+GjHV4cOJ/xjrABVZ3UCUsm+kI0+9L3AWDBQie/TjT1bap3jomhkpKSiIyMzNImV2qwmpDdbickJIT4+PjrvgAulpKSwqFDhwgPD8fPzy8XK7w5hX33cpH8oLDnMCUlhbi4OCpUqJCv/70U8zL7jq0iBYFyKGIsZbDgWrvvNAMn/glAhVB/Fj/RDIvFyVvr32LB/gWe8/pW78szDZ/By6p3jOVXZs+h3W4nLCwsSw1WLRFgYtndxc3pdHp2BM/Pu4IX9t3LRfKDwp5Dq9WKxWIx7W6ZUnDoNShiPOVQxFjKYMHT8tbSNKtanNV7TnPobDLzth6nf+NKvN3ibcoHl2f81vEAzIydyYnkE7zX8j38vf0Nrlquxaw5zM6cCt9PxSIiIiIiIiIiYphRHWt4Pv506W6S0zJu+Bp+23DeaPoG3paM+wGXHVrG0F+Gcjr5tFGlimSJGqwiki+0bt0ai8VC69atr3jcYrFgsVgYPXp0ntYlIiIiIiIiOeu2CkXpUKsUACcTU5myNs5zrEe1HkS1i6KIrQgA2+O3039Rf/Yn7DeiVJEsUYNVxEDLly/3NA6v9CcwMJDq1aszYMAAfvvtN6PLFREREREREckRz3a8lcztHMav2Is95b87tjct15TJnSZTMqAkAIfPH2bAzwPYfHKzEaWKXJfWYDUxh8OBw+G4/okXne92u3G5XJ71FfOjzH3ZMmstyK5X/4ULF9i9eze7d+9m2rRpDBgwgG+++QYvL3Mv8n2tr4sZ/t7NwEw5vBEulwu3243D4TB9HiV/yvz+np3v8yKSs5RDEWMpgwVf5WJ+3FuvDD9sPca5JAdfLNvD0+2reo7fEnQLkztM5vHlj7Pn3B4SUhN48JcHebPpm0RUjDCwcslk9hxmZ15qsJpIVFQUUVFROJ1OAJYsWUJAQECWr/f29qZ06dKcP3+etLS03CozxyQmJhpdwk1LSkryfDxkyBCGDh3q+dztdnPu3DnWr1/P+PHjOXXqFFOnTqVkyZK88sorRpSbq9LT0z3/tdvtlx0/e/as5+MrHRdjmCGHNyItLY3k5GRWrlzpee2KGCEmJsboEkQKPeVQxFjKYMFWzwoLLF443Ra+XrWXshdiCfqffYX6uvsyw3sGe9P3kuZK4/nfn2eZ3zKa+TbDknkLrBjKrDm8uGdzPWqwmsiIESMYMWIEdrudkJAQOnToQHBwcJavT0lJ4dChQwQGBuLn55eLld4ct9tNYmIiQUFBBf4f04sb4OXLl6dx48aXndO5c2d69+5No0aNSElJYcKECbz11lv4+PjkZam5ztvb2/Pf7LxuxRhmyuGNSElJwd/fn5YtW+brfy/FvBwOBzExMURERJhyx1aRgkA5FDGWMmgee207mb7+EGkuC7ttt/DK3TUuO6ebqxtvrX+LBfsWALA4ZTEhFUN4tuGzeFn1jjKjmD2H2bm5Sw1WE7PZbNl6gTudGbv2Wa1WrNb8uzxv5tuRM2styC6u/1rzqVOnDl26dGHOnDkkJiYSGxtLvXr18qrMPFfQ/14LAzPl8EZYrVYsFku2/50VyWl6DYoYTzkUMZYyWPA92b46czYfIcXhYsb6wzzcqirlivpfco4NG283f5sKwRUYt2UcALNiZ3Ei+QTvtXiPAFvW370rOc+sOczOnArfT8UiBVTlypU9H6empl52fN++fYwdO5Zu3boRHh6Ov78//v7+VKpUiT59+rB48eLrPse5c+d4++23adKkCaGhodhsNkqUKEGtWrXo0aMH48eP58SJE1e9PiUlhc8//5x27dpRunRpfHx8KFmyJO3bt+ebb765qbdSZ278NXr06MuOTZo0yXM8Li4Ol8vFhAkTaNq0KaGhoRQpUoR69erx9ttvZ+kWf6fTyeTJk+natStly5bF19eX4sWL07x5cz788EOSk5NveB4iIiIiIiLyXyWD/XigWcbPu2lOF5/8GnvF8ywWC8PqD+OtZm/hbcm4X3D5oeUM/WUo8cnxeVWuyBXpDlaRAuLAgQOejytWrHjJsf3791OlSpUrXnfw4EEOHjzId999R//+/Zk4caLn7fgX27lzJ+3bt+fo0aOXjMfHxxMfH8/OnTv54YcfcDqdPPbYY5ddv3XrVu69995L6gQ4deoUS5cuZenSpXz55ZcsWLCAUqVKZXne2ZWUlESHDh1YunTpJePbt29n+/btzJ8/n99++40iRYpc8fqDBw9yzz33sHXr1kvGz5w5w+rVq1m9ejXjx4/np59+onr16rk2DxERERERkcLi0ZZVmPbHARJT0vn+z8M80qoKVUoEXvHce6veS8mAkjy9/GkuOC6w4/QO+i/qz/j246kcUvmK14jkNt3BKlIA/PPPPyxcuBCAxo0bX9agdDqd+Pj40K1bNz799FN+/fVXNm3axK+//sq4ceOoXbs2ANOmTePNN9+84nMMGDCAo0ePYrPZGD58OAsWLGDDhg2sW7eOOXPmMGrUKKpWrXrFa/fs2UOrVq04cOAAwcHBvPjii8ybN4+NGzfyyy+/MGLECLy9vdmwYQP33ntvru4w+NBDD7Fs2TIGDRrETz/9xJ9//sm8efNo0qQJAOvXr+ett9664rWnT5+mefPmbN26FV9fXx577DFmz57Nhg0bWLZsGS+++CIBAQHs2bOHzp07k5CQkGvzEBERERERKSxCAmw82irjpiGXGz5ccuW7WDM1KduEyZ0mUzKgJABHzh9hwM8D2HRiU67XKnIluoNVJJ84efIkO3bs8Hzudrs5d+4ca9eu5aOPPiI5OZmQkBA++uijy64tU6YMcXFxlClT5rJj7dq149FHH2XIkCFMmjSJsWPHMnLkSEJCQjzn7Nu3jz///BOADz/88LI7VO+880569uzJe++9x7lz5y57jkGDBpGQkECDBg1YsmQJYWFhlxzv0KEDXbt2pUuXLqxbt45Jkybx0EMPZevrk1Vr1qxh6tSp9O/f3zPWsGFDOnfuzB133MGOHTv46quvePPNNy+7k/eJJ57g0KFDVKpUiWXLll2yLANA69at6d27Ny1atGDfvn28//77vP3227kyDxERERERkcJkcNNwJq7eT/z5NH7afoxhRxKoUy7kquffWuxWpt89nRFLRxB7NpaE1AQeWvIQb7d4m07hnfKwchE1WE3N4XBk605Bh8OB2+3G5XJ5NrC5mnuiVhOfePk6oHnF5XZjzYOdy8OCfJk/olmuPf7FX+fx48czfvz4K55ntVp55JFHeOqpp6hevfplfz+Z661e6+9tzJgxTJ06lQsXLrBkyRJ69erlOXbxsgDNmze/5uOEhIRccnzVqlWsWbMGgIkTJ1KsWLErXt+hQwd69erF7NmzmTRpEkOHDr3qc1zr+TNfo1c7v0ePHkRGRl52TuaducOHD+f06dPs2LHjko3C4uLimDVrFgCffvoplSpVumId9evXZ/jw4YwZM4ZJkyZd9Y5gs3O73Z7/Xu/fCzNyuVy43W4cDgdeXtq1VPJe5vf33HxHgIhcm3IoYixl0Hx8rDCs1S28+dM/ALy/eCffDLz9mtcU9ynO1+2/5rlVz/HH8T9Ic6UxasUoDiccZmDNgVjyoG9QmJk9h9mZlxqsJhIVFUVUVBROpxOAJUuWEBCQ9Z30vL29KV26NOfPnyctLe2a5560p3Ay8drnmIHL7cZut+fa42dlwyXIaObMnDkTq9XKa6+9hq+v7zXPdzgcnDx5kvPnz3teDwDFihXj1KlTbNiwgYiICM94UFCQ5+OvvvoqW3dlfv/99wBUq1aNSpUqXfPr1ahRI89b7s+cOXPJHaSZG2Clp6df8zFSU1MvO56SkuL5uEePHle9vkaNGp6P//rrL8LDwz2fz5kzB6fTSUBAAM2aNbtmDbffnvFN/ujRo/z1119UqFDhqueaXWJiotElGCItLY3k5GRWrlx5U5u3idysmJgYo0sQKfSUQxFjKYPmUtQFxXy9OJNqYeXu03w6cxFVg69/XWd3Z9J80tiUlrFEwCdbPuGPv//gbv+78bLohojcZtYcZrVnA2qwmsqIESMYMWIEdrudkJAQOnToQHBwFv4l+n8pKSkcOnSIwMBA/Pz8rnluyWC/PLmD9Gry8g7W7HwNs+viBvi///1vXnvttUuOJycns2fPHqZNm8bHH3/M+PHj2b59Oz///PNlzXOHw8FXX33FtGnT2Lx58zWb5ImJiZfMq27durRo0YJVq1Yxbtw4li9fTs+ePWnVqhWNGze+ZqM+c1mD3bt3ExoamqV5OxwO0tPTKVasmGcss9nq7e19za+5r+/lfycXv14bNGhw1esvboSmp6dfct5ff/0FZPwD+r9LHFzLhQsXcvU1kl+53W4SExMJCgoqlL8VTklJwd/fn5YtW17330uR3OBwOIiJiSEiIgKbzWZ0OSKFknIoYixl0LwcZY/wwryMn8/WJIbxeJ9GWfqZo6u7K1/v+Jrx2zPeGboubR0+YT682/xd/L39c7XmwsrsOczODXdqsJqYzWbL1gvc6XRisViwWq1Yrdfe/2zh4y1utrwb5nK5sNvtBAcHX7fO/O7i+jO/9hcrUqQI9evXp379+lSvXp2HH36Y33//nXffffeSjZrOnDlDhw4dPOuoXk9KSsplzzVjxgx69+7N2rVr+fvvv/n777956623sNlsNG7cmMjISAYPHnxZM+nkyZPZnfZVa8h0rb/XK32dLv48MDDwqtdffMes2+2+5LxTp05lqe7/da15mFnmsgBX+vsoDKxWKxaLJdv/zorkNL0GRYynHIoYSxk0n96NKvH16gPsOXmePw+e4/d9Z2lbo9T1LwSGNxxOueByjF4zmnR3OquOruLhpQ/zebvPCfPP+o00kj1mzWF25qQGq0gBMXToUF544QXOnDnDt99+e0mD9cknn/Q0V7t3786QIUOoV68eJUuWxM/Pz/PbvooVK3Lo0CHP+pkXK1euHGvWrGHp0qXMnTuXFStW8Pfff+NwOFi1ahWrVq3igw8+YNGiRVSvXt1zXeYSBPXr12fatGlZnk+5cuVu6OuQWzLnERYWxrJly7J83f9uhCUiIiIiIiI3zstq4ZmI6gybnvF2/zG/xNK6ekms1qy9c+7eqvdSqkgpnl72NOcd5/nr9F/0X9Sfce3HcUvILblZuhRiarCKFBBWq5Vq1aqxbt06jh07xunTpylevDh2u92zOVO/fv2u2eQ8e/bsdZ+nXbt2tGvXDoDTp0/z66+/MmHCBH777Tf27t1Lnz592Lx5s+f84sWLA3D+/Hnq1KlzM1M0VOY8EhMTqVmzpjYuEhERERERMUinOqWpWy6E7UcS2HnMzsLtx7inftksX9+4TGMmd57M8F+HcyLpBEfOH2HAogF80uYT7ih9Ry5WLoVV4Xtfp0gBdvFmOpkf796927OzXZ8+fa567T///MP58+ez9XzFixenT58+LF26lHvuuQeALVu2sHv3bs85DRo0AGDfvn0cP348W4+fn2TOIzU1lY0bNxpcjYiIiIiISOFlsVgY1fFWz+cfLtmFw+nK1mNUD63O9Lunc2toxuPY0+w8HPMwP+//OUdrFQE1WEUKjKSkJP7++28A/P39PRsxXdx0vXDhwlWv/+KLL27q+TPvagWIj4/3fJzZeHW73XzyySc39RxG6tatm2cphY8//tjYYkRERERERAq5FtXCaHxLxsbIcaeT+P7Pw9l+jFJFSjG582SalW0GgMPl4LmVz/HN9m+uuHSeyI1Sg1WkgBg9ejTJyckAdOzY0fMW9qpVq3oag5MnT77iN4kFCxbw+eefX/Wxt2zZwpYtW6563O128+uvvwIZv0kMDw/3HOvQoQN33nknAGPGjOG777675jy2b9/OggULrnmOEW699VZ69+4NwMyZM/nwww+vef7+/fuZMWNGXpQmIiIiIiJS6GTcxVrD8/knv+4mxeHM9uMUsRXhs3af0bNaT8/Yx5s+5q0/3iLdlX6NK0WyTmuwiuQTJ0+eZMeOHZeMpaSksHv3bqZMmcLixYsB8PPz44033vCcU7x4ce6++25++uknFi9eTIcOHRg2bBiVKlXi5MmTzJkzh0mTJnHLLbdw7tw5Tp06ddlzb9myhQceeIBGjRrRrVs3GjZsSOnSpXE4HOzfv5+JEycSExMDZNyxWqZMmUuuj46O5s477+TMmTP06dOHadOm0adPH6pVq4aXlxcnT55k8+bNLFiwgD/++INnnnmGbt265fSX8KaNHz+ejRs3sm/fPp555hl+/PFHBg4cSO3atfH19eX06dNs3bqVxYsX89tvv9GjRw/uv/9+o8sWERERERExpdsrhdK+Zkl+3XmS4/YUpv1xgAdbZH+jKpvVxugmoylbpCyfb8m4+ei72O84nnScMS3HEGALyOnSpZBRg9XEHA6HZ23OrJ7vdrtxuVy4XNlb2yQvZd6hmVlrQXZx/ePHj2f8+PHXPL9EiRJMmTKF2rVrX3JtVFQU27dv5+DBg/z666+eu00zVaxYkblz59K1a1fg8q9d5scbNmxgw4YNV33+pk2b8tVXX132da9cuTKrV6+md+/e7NixgwULFlzzLtWgoKBr/t1d69iV/t7/dy5Xu/565xUtWpRVq1bRt29fVq1axcqVK/m/9u47rqmr/wP4J4GwhyIo4gD3eBxV6x7gwL2qtbZq3Zv2qVbro512+2i11orautC66q5oRREHFqsi7lEnKA5URAkrkHF/f/DLfRIhEDBwIXzer5cvb3LPOfd7b3IC+XLuOZGRkYU+D2tmTf2wMHQ6HQRBgFqt5oJoJAn9z/eC/JwnIstiPySSFvtg2fFBl1qI+OcJBAEIPnILg16rDFeHwqWzxjYci4qOFfHVqa+g0WkQeT8So8NG4yf/n+Dp6GnhyK2ftffDgpwXE6xWJDg4GMHBwdBqs4fMHzx4EE5O5v8VxtbWFt7e3khNTUVWVlZRhWkxKSkpUofwytLT0/Pcb2dnh/Lly6N+/foIDAzE8OHDUa5cOSiVSqNy7u7uOHLkCBYvXoz9+/cjPj4e9vb2qF69Ovr06YPJkyejXLlyYiJMrVYbtdGnTx9s3boVR48exdmzZ/Ho0SM8ffoUGo0Gnp6eaNq0Kd544w0MHjwYcrk8x/EBwNvbG0ePHsWuXbsQGhqKs2fP4tmzZ9BqtfDw8EDt2rXRpk0b9O3bF02bNs3Rhn4uWY1Gk2v7epmZmTn2q1QqcTs1NdVkfcNFvlQqVa7lnJycsGfPHhw4cAA7duxAdHQ0njx5ArVaDXd3d9SsWROtWrVCz5490b59+zxjLQusoR8WRlZWFjIyMhAZGWk0DzJRcdPfXUBE0mE/JJIW+2DZ0LyCHDGJcjxPV+PjdeHoVa3w86fKIce7ju9ic9pmqKDCtaRreGv3WxjpMhIVbSpaMOqyw1r7YX45G0MygbP6Wh2lUgl3d3ckJibCzc3N7HoqlQrx8fHw8/ODg4NDEUb4agRBQEpKClxdXcW5R4moeJX1fqhSqRAXF4dq1aqV6M9Lsl5qtRrh4eEIDAyEQqGQOhyiMon9kEha7INly91n6ei5JAoanQBnexscnt4RHs52r9Tm7Re38f7R95GQngAAcFW4YlGnRWhRqYUlQi4TrL0fKpVKeHp6Ijk5Od/8GkewWjGFQlGgN7hWq4VMJoNcLodcXnLXP9OPwtTHSkTFr6z3Q7lcDplMVuDPWSJL43uQSHrsh0TSYh8sG2p7u+OtltWw6dQ9pGVqsSrqLj7p0/CV2qzvVR8b+2zEexHv4VrSNaSoUzD1yFR83f5r9KnZx0KRlw3W2g8Lck5l71sxERERERERERGVKv/uUgf2ttlprHV/38Wj5IxXbrOiU0Ws7bkW7au0BwCodWrMPj4bqy6tAm/4poJggpWIiIiIiIiIiEo0b3cHjGrnBwDI0uiwJOKWRdp1Vjjj5y4/Y3CdweJzP539CV+dzF4Ii8gcTLASEREREREREVGJN8W/Flzss2e73HomHjKyjmoAAGYFSURBVLGJaRZpVyFX4Iu2X+Dfzf4tPrf9xnb8+/C/ka42f6EjKruYYCUiIiIiIiIiohKvvLMdJnSsCQDQ6gT8GH7DYm3LZDJMaDIB33f8Hrby7CTu8QfHMTpsNJ6mP7XYccg6McFKRERERERERESlwriONVDB2Q4AsOfCQ1x9qLRo+31r9sUv3X6Bq8IVAHAt6RqG/zkct1/ctuhxyLowwUpERERERERERKWCi70tpnauLT7+4eB1ix+jVeVWWN9rPSo7VwYAPEp7hHf/fBfRCdEWPxZZByZYiYiIiIiIiIio1Bjeujp83B0AAIf/eYIzcUkWP0bt8rWxsfdGNPBoAABIUadgYvhE7L2z1+LHotKPCVYiIiIiIiIiIio1HBQ2+KBbHfHx/APXIQiCxY/j5eSFkJ4h6FClAwBAo9NgzvE5WHlxZZEcj0ovW6kDoKKjVquhVqsLVF4QBOh0Ouh0uiKM7NXoP8T0sRJR8Svr/VCn00EQBKjVatjY2EgdDpVB+p/vBfk5T0SWxX5IJC32QerfuBJWHL2N2GfpOB2bhCPXEtCxjqfFj6OAAos6LsK8M/Ow89ZOAMCSc0sQr4zHnJZzxAWxyiJr74cFOS+ZwJS71QgODkZwcDC0Wi1u3LiBTZs2wcnJyez6tra28Pb2RrVq1WBnZ1eEkRIRlW5ZWVmIj49HQkICNBqN1OEQEREREZVJ5xJlCLmZPeChqrOAGY21kMuK5liCICAyMxLhqnDxubq2dTHUeSjsZfZFc1CSVHp6OoYNG4bk5GS4ubnlWZYJViukVCrh7u6OxMTEfN8AhlQqFeLj4+Hn5wcHB4cijPDVCIKAlJQUuLq6QiYrok9OIspTWe+HKpUKcXFxqFatWon+vCTrpVarER4ejsDAQCgUCqnDISqT2A+JpMU+SACg0wkYuPwkriWkAACWDG2CXo28i/SY++P2Y+7JuVDrskc31i9fHz/5/wQvJ68iPW5JZO39UKlUwtPT06wEa9kdx1wGKBSKAr3BtVotZDIZ5HI55PKSOz2v/nZkfaxEVPzKej+Uy+WQyWQF/pwlsjS+B4mkx35IJC32QZrVqz7GrI0GACw+fBu9m1SBrU3RfUfpX6c/KrtWxgdHPkBKVgr+ef4PRoWPwvKuy1G7fO0iO25JZq39sCDnVPa+FRMRERERERERkVUIqOuFln7lAQB3nqZh57kHRX7Mlt4t8Vuv3+Dj7AMASEhLwMj9I3Hq0akiPzaVTEywEhERERERERFRqSSTyTCrZ33x8U+HbiJToy3y49YqVwsb+2xEA48GAIAUdQomH5qM0NuhRX5sKnmYYCUiIiIiIiIiolKrpZ8HOtfLngP1wYsMbDx5r1iO6+noiZCeIehUtRMAQKPT4OO/PkbI5ZBiOT6VHEywEhERERERERFRqTajez1xO/jILaRlaorluE4KJ/zU+Se8Vfct8bmFMQvx29XfiuX4VDIwwUpERERERERERKVaoyru6NukMgDgWVoW1vwVW2zHtpXb4tM2nyLotSDxufnR8/H7P78XWwwkLSZYiajUiIuLg0wmg0wmQ0hIiNThWERAQABkMhkCAgKkDqVEOXr0qPhaHz16tEiOMXr0aMhkMvj5+RVJ+0REREREVLw+DKwLG7kMAPBr5B28SM8qtmPLZDJMbjoZk5tOFp/75tQ32HlzZ7HFQNJhgpVIIvrkjkwmQ1xcnFl1/Pz8mBAiIiIiIiIiykVNLxcMaVEVAJCSqcHyY7eLPYapTadibKOx4uO5J+Zy4asygAlWIjIyd+5cMfFLREREREREVJr8u2sd2Nlmp7vWnYjDY6WqWI8vk8kwrfk0jGgwAgAgQMCnUZ8iLDasWOOg4sUEKxGVGn5+fhAEAYIgYPTo0VKHQ0RERERERCWMTzlHvNvGFwCgUuvw8+GbxR6DTCbDrJazMLTeUACATtBh9vHZiLgbUeyxUPFggpWIiIiIiIiIiKzG1IBacLazAQBsOR2Pe8/Siz0GmUyGj1t/jMF1BgMAtIIWMyNn4lj8sWKPhYoeE6xERERERERERGQ1KrjYY1zHmgAAjU7Aj4duSBKHXCbH520/R7+a/f4/Fg2mH52OEw9OSBIPFR0mWImsRG6rrm/duhVdu3aFl5cXHB0dUa9ePcyaNQtJSUk56oeEhEAmk+HLL78Un9O3Z/jPcEGugIAAyGQyBAQEAABu3ryJ9957D3Xq1IGTk1OO8o8ePcKyZcvw5ptvok6dOnB2doa9vT2qVKmCAQMG4Pfff4dOpzN5jnFxcWIcISEhOfa/PH+sSqXCggUL0Lx5c7i6usLV1RWtWrXC0qVLodFo8r2mKpUKS5cuRdeuXeHt7Q07OztUrFgR3bp1w+rVq81q4+TJkxgyZAi8vb3h4OCAGjVqYOLEibh+/Xq+dc2hf9301zorKwuLFi3C66+/Dnd3d3h4eCAgIAD79u0zqpeSkoL58+ejWbNmcHNzQ7ly5RAYGIiICPNuWQkNDcWoUaNQvXp12Nvbo0KFCmjbti3mzZuH1NTUfOtnZGTgu+++Q9OmTeHs7IwKFSqgffv2WLlyZZ7vgZdptVqsW7cOffv2hY+PjxhLhw4dsGjRImRkZJjdFhERERERWY8JHWugnJMCALD7/AP8k6CUJA65TI6v2n+Fnn49AQBqnRr/PvJvnH50WpJ4qGjYSh0AEVmeTqfDu+++iw0bNhg9f+PGDSxYsAC7du3C8ePH4e3tbbFj/vHHHxg+fDjS0tJy3a/ValG1atVck2cPHz7Enj17sGfPHqxevRo7d+6Ei4vLK8Xz+PFj9OzZE+fPnzd6Pjo6GtHR0Th48CB2794NuTz3vzNduHABAwYMwN27d42ef/r0KSIiIhAREYFffvkFoaGhqFSpUq5t/Pjjj5g5c6bROcfFxWHlypXYtGkTtm7d+krn+DKlUom3334bp06dMnr+2LFjOHbsGBYtWoTp06fj3r176N27N65cuWJU7tChQ4iIiMBvv/2G4cOH53oMlUqFYcOGYdeuXUbPJyUl4eTJkzh58iR+/vln7Nu3D6+99lqubSQkJKBLly64du2a+Fx6ejpOnDiBEydOYMeOHfjwww/zPd979+6hf//+uHDhQo5YoqKiEBUVheXLl2Pfvn2oW7duvu0REREREZH1cHVQYGpALXz35z8QBGDhwRtYOfJ1SWKxldviu47fQa1TI+JeBDK1mXjv8HtY0W0FmldqLklMZFkcwUpkhT777DNs2LABAwcOxM6dOxETE4M///wTffr0AQDcunUL06dPN6ozcOBAXLp0CVOmTBGfu3TpUo5/VapUyXG8e/fuYcSIEXBycsK8efMQFRUlJtr0iVJBEAAAXbp0wYIFCxAWFoaYmBgcPXoUa9asQdu2bQEA4eHhCAoKeuVrMGjQIFy9ehX//ve/ER4ejpiYGGzatAkNGjQAkD0Cc+XKlbnWvXXrFvz9/XH37l24ublhzpw52LVrF86cOYMDBw4gKCgItra2iI6OxoABA6BWq3O0sWvXLnz44YfQ6XRwd3fHd999JyYQv/nmG9jY2GD48OF4+PDhK5+r3sSJExETE4OpU6ciPDwcZ86cwapVq+Dj4wMAmDlzJi5fvoxBgwbhzp07mD17No4ePYro6GgsXrwY7u7uEAQBU6ZMwZMnT3I9xqhRo8TkaqNGjRASEoLo6GgcOHAAY8aMgUwmw8OHD9G1a1c8ePAgR32NRoO+ffuKydXu3buL13bnzp3o1q0bDhw4gE8//TTPc3327Bk6dOiACxcuwN7eHu+99x62bduG6OhoHDlyBHPmzIGTkxNu3bqFXr16ITk5+VUuLRERERERlUIj2/qhkps9ACD86mOcvfdcslgUcgUWdFoA/6r+AIAMTQamRkzFxacXJYuJLIcjWK2YWq3ONfGTV3lBEKDT6Qp0i25x0yfq9LGWVvrzAFCoa/5yecPHJ06cwNdff42PP/7YqEz37t3Rq1cvhIeHY/v27Vi8eDG8vLwAAG5ubmjYsKH4GAAaNmxo1rFjY2Ph4+ODqKgoVK9eXXy+ZcuWYnmZTIZr166hdu3aOdrr2LEjRo0ahblz5+Lrr7/Gb7/9ho8//hh16tQxedzcrpnhNY2OjkZYWJg4fQEAvPbaawgMDESjRo3w+PFjLFu2DBMmTMgRz6hRo5CcnIxmzZohLCwMnp6eRvu7deuG3r17o1+/fjh16hTWrFlj1E5WVhbee+89AIC7uzuioqLExC4AtG7dGv369UPHjh1x8+b/VrQszPvZsM7p06exfft2DBw4UHyuWbNmaNGiBVq0aAGdTocuXbpAqVTiyJEjaN26tViuefPmqFWrFvr164eUlBRs2LAB06ZNMzrWvn37xFG3Xbp0webNm1GhQgVxSoZu3bqhdevWmDx5MpKSkjB9+nRs2bLFqI0VK1YgJiYGADBhwgSsWLHCKNYBAwZg/PjxWLt2rdE5vnxt3n//fcTHx8PX1xcRERGoUaOG0f5OnTph8ODB8Pf3x507d/Df//4X33zzjVGZl/tgQeh0OgiCALVaDRsbmwLVJbIE/c/3gvycJyLLYj8kkhb7IJnDBkBQQE18vid7gMeCsH+wfow0o1j15rWfh+nHpuNkwkmkqdMwKXwSVnRdgYYeuX//LsmsvR8W5LyYYLUiwcHBCA4OhlarBQAcPHgQTk5OZte3tbWFt7c3UlNTkZWVVVRhWkxKSorUIbwSw46ampoKpTL/+WD0SSCdTpejfHr6/1ZFfO211xAUFJRrm5MmTUJ4eDg0Gg0iIiLQu3dvo/2ZmZnidn4xGc5B+tlnn6FcuXJ51qlYsWKe+z/44AMsW7YMz549w7Zt28QkpZ7h3J4qlSpHW4axT5w4Ec2bN89RxtbWFu+88w4WL16MS5cuIT4+Hu7u7uJ+/ShTAPj5559hZ2eXa8zt2rXDgAEDsGvXLqxZswZDhw4V9+3evVscmTpz5kxUqVIlRxvVq1fHhx9+iLlz5wLIvpbmvAdeplKpxO2BAweKCVRDfn5+aNOmDU6cOIGnT5/igw8+QIMGDXKU69ChA6pVq4b4+HgcOXIEY8eONdq/ZMkSAIBCocBPP/0EOzu7HP1w6NCh2LJlC44ePYpdu3bhxo0bRlNRBAcHA8h+L8ydOzfXc/7qq68QGhqKxMREANnvbcNy9+7dExO98+bNQ4UKFXJtp1atWhg3bhyWLFmCkJAQzJo1y2i/vg/m1p/yk5WVhYyMDERGRpo1Fy9RUQkPD5c6BKIyj/2QSFrsg5QfFx3gaW+DxEwZ/r6ThB837Ue9ckL+FYtQD6EHHts+RqwmFqnqVEw4MAFjXcaisk1lSeMqLGvth4Z5lvwwwWpFgoKCxKSau7s7unfvDjc3N7Prq1QqxMfHw8XFBQ4ODnmWla3sDKTmfgtx0RMgCML/j5qTFe2hXCpCmHCkSJpWKBT/O4yLi1mvlX6+ULlcnqO8YTJ9xIgRRklDQx07dhS3Hz9+nKMde3t7cTu/mGxtsz9C7OzsMHLkyHzfN4Z0Oh0SEhKQkpJilGyuVq0anj17huvXr+c4vuG8rA4ODnnGPnr0aJPxt23bFosXL4YgCHj27BmqVasm7jt8+DAAoF69euK0BaZ06dIFu3btwrlz5+Dk5CReD32CViaTYeLEiSbjmDx5Mr788ksIggBbW9sC9Vc9w2s+YsQIk200b95cjGvkyJEmyzVt2hTx8fGIj483KqPRaMT6gYGBaNCgAVJSUuDq6iqOYNWbNGkSjh49Co1Gg5iYGLzzzjsAshc5++effwAAb731lsk5gN3c3PDWW29h2bJlALLf24axREZGQqvVwsnJCYMHD85zBGm3bt2wZMkSPHr0CC9evDAaYa3vg7n1p/yoVCo4OjqiU6dOBXrfE1mKWq1GeHg4AgMDjX6eEFHxYT8kkhb7IBWEUO0RZmy/BAD4S1ke095pneN7THHrrumOoCNBOP/0PDKEDGzM2ohV3VahpntNSeMqCGvvhwUZiMMEqxVTKBQFeoNrtVrIZDLI5XKTC/+IUp8AKZabO7KgivNjUJbftShsuwYf5mZd85e8XN7wcYMGDUy2Z3i7e2pqao5yL8dljjp16pg1WloQBGzcuBGrV6/GqVOn8lzh/dmzZ3meY27XzDD2hg0bmnUN0tLSjMrpb1+/fv262bd+q9VqvHjxAhUrVgQAXL58GQBQo0YN8bncVKpUCX5+foiNjc1xfuYyrFO/fn2TbZQvX75A5VJSUozKxMXFiX+9a9OmjXit9Z8ZhgwT01evXhX3Gy6q1apVqzzPt3Xr1mKC9eXXWv8apaenw87OzmQbL3vy5An8/PzEx4V5rxuWl8lkBf6cJbI0vgeJpMd+SCQt9kEyxxvNq2HlX3H4JyEFFx8ocfhGEno2styiz4WhUCiwvNtyTAqfhIuJF/Ei8wUmH56MtT3Wws/dT9LYCspa+2FBzokJViocF9NJo6ImABAEHWQyedEnWovwPA2TO4ZzQeZFXy6/v7Tllew0TCTpp5N4VYbJO1NUKhUGDRqE/fv3m9VmXslXcxT2Gpha3Ck/hrcOJCUlAUCeyVW9SpUqiQnWV2XuOZtT7uXroj8nIP/zMhyZalivIG1UqlTJ5D5LvEZERERERFR2yOUyfNSjHsatOwMAWHjwOgIbVoKNXNpRrC52LlgeuBwTDk7A1WdXkZiRiHEHxyGkRwiquVXLvwEqMZhgpcKZdEyyQwv/P1+im5tbkY0uLQ6Ojo7itrnJxLS0NACAs7NzkcRUWOaM9Pz222/F5Kq/vz+CgoLQvHlzeHt7w9HRUUzsderUCcePHzc76Wxp+sRi06ZNsWHDBrPrValSJcdzUt9yUlQscV6v0ob+NfL09MSRI+ZP4fHyQlhERERERFR2dKlfEc2rl8PZey9w80kqdp97gMEtqkodFtzs3PBr4K8Ye2Asbjy/gSfpT7KTrD1D4OPiI3V4ZCYmWIkk4uHhIW4nJCSgYcO8VwzMzMzEixcvctQtDQRBwKpVqwBkzwF7+PBhk7dkG45ylEKFChUAZE+f0KhRo0K1oR/R+/jx43zLmlOmJDB8z+UXc0JCQq71DEc659dGXvv1r1FKSgoaNGhg9lQORERERERUdslkMszqWR9v/3oSAPDjoRvo19QHdrbSD9xyt3fHyu4rMTZsLG4n38ajtEcYd2Ac1vZcC29naacyIPNI/y4iKqOaNGkibuvnlMzLhQsXxJF7hnUtrShGXSYlJYlJtyFDhphMrqampuL69esWP35BNGvWDABw584do0RhQTRu3BgAEBsbi2fPnpks9/TpU8TFxRXqGMWtZs2a4tQCp06dyrPs6dOnxW3DJLX+ugBAdHR0nm3ktV//GmVmZuLMmTN5tkNERERERKTXpmYFdKyTvSbH/ecZ2BJ9T+KI/sfDwQOreqyCn5sfAOB+6n1MODgBiRmJ0gZGZmGClUgi/v7+4qrzW7ZsyfeWeMPb1bt27VpkcRmuiJ6ZmWmRNjUajbitn+YgN6tWrTIqK4X+/fsDyB51+9NPPxWqjW7duoltrF+/3mS5kJAQyaZCKChbW1v4+/sDAMLDw3H//n2TZfWjlW1tbREQECA+7+PjgwYNGgAAtm3bZnJqjLS0NGzdutVk+/369RP/ELB48eKCnAYREREREZVxs3rUF7eXRNxCepa030ENeTp6YlX3Vajqkj11QZwyDuMPjEeSSto7PSl/TLASSaRSpUoYMmQIAODs2bOYN2+eybKHDx/GihUrAAB+fn5iErAoVK5cWdy+ffu2Rdr08vJCuXLlAACbN2/ONXEbHR2Nzz77zCLHexXdu3dHq1atAAALFizIM9EHAJcuXUJoaKjRcwMHDhSv49dff53rqNyrV6/i22+/tVDUxSMoKAgAkJWVhfHjx0OtVucos2bNGhw8eBAAMGjQIKP3EwBMmTIFQPY0AjNmzMj1ONOnT89zIat69eqJfWfLli1YtGhRnnHHxsZi8+bNeZYhIiIiIqKyoXFVd/RqlH3bfWJqJkJOxEkb0EsqOVfC6h6rUdk5+7vU7eTbmHhwIpIzkyWOjPLCBCuRhBYuXCiupv7xxx+jR48e+O2333Dq1CnExMTgjz/+wPjx49GjRw+o1WrI5XKsWbOmSOecbNeunbg9ffp0REZG4ubNm7h16xZu3bpVqBGmcrkcw4cPBwBcvHgRHTp0wObNm3HmzBlERERgxowZ6NSpExwcHFC3bl2LnUthbdq0CR4eHtBqtRg6dCj69++PjRs34vTp04iJicH+/fvx3XffoW3btmjSpAmOHTNe9M3Ozg4///wzAOD58+do06YN5s2bh5MnT+Lvv//G999/L17n2rVrF/v5FVafPn3ExGZ4eDgCAwOxceNGxMTE4NChQxg/fjzGjx8PIHvu1dwSn1OmTBFv8V++fDl69eqFP/74A2fPnsUff/yBHj16YOXKlXj99dfzjGX58uWoWbMmAGDGjBnw9/fH6tWrcfLkSZw7dw6HDh3CwoULERgYiNq1a2PHjh2WvBRERERERFSKzeheF/L/nx1vxdHbSE7POXhESj4uPljdYzUqOmXnC64/v46J4ROhzFJKHBmZwkWuiCRUuXJlREZG4o033sC1a9dw8OBBcfTfy8qVK4cNGzagc+fORRpT7dq18dZbb2Hr1q25xhMbGws/P78Ct/vtt98iKioK58+fx5kzZzBs2DCj/R4eHtixYwc+//xz3Lhx41VO4ZXVqlULf//9NwYPHozLly8jNDQ0xyhVQ25ubjmeGzx4MBYsWIBZs2bhxYsXmDNnjtF+JycnbN26FQsWLMCtW7csfg5FZf369dBoNNi1axcuXLiAkSNH5ijj4+ODffv2oUqVKjn22draYu/evejSpQuuX7+OsLAwhIWFGZXp3r07ZsyYgR49epiMw8PDA1FRUXjrrbdw/PhxREZGIjIy0mT53F4jIiIiIiIqm2pXdMXg5lWxLeY+lCoNfj1+Gx8ZTB1QElRzrYbV3VdjzIExSMxIxNVnVzHl0BT8GvgrnBXOUodHL+EIViKJ1atXDxcvXsSGDRvw5ptvwtfXF05OTrCzs4O3tze6du2KBQsWIC4uDn369CmWmDZs2ID58+ejVatWcHd3N7koVUG4u7sjKioKX3/9NRo3bgwHBwe4uLigQYMGmDlzJi5cuIBOnTpZIHrLqFu3Ls6fP49NmzZh8ODBqF69OhwdHWFnZ4fKlSsjICAAn376KWJiYvD555/n2sbMmTPx119/YdCgQahYsSLs7e3h6+uLsWPH4syZM8X2elqSg4MDdu7cid27d6Nfv37w8fGBnZ0dypcvj9atW+P777/H9evX8dprr5lsw8fHB+fOncM333yDRo0awdHREeXKlUObNm2wbNky7N+/H3Z2dvnG4u3tjcjISOzduxfDhw8XF+JSKBTw8vJCu3btMGPGDBw7dgxr1qyx4FUgIiIiIqLS7oNudWBnk/1dd81fcXiSopI4opz83P2wqvsqeDh4AAAuPr2IqYemIl2dLnFk9DKZUFpWWCGzKZVKuLu7Izk5uUCjtlQqFWJjY1GjRg2jhY5KGp1OB6VSCTc3N4sk/oio4Mp6Pywtn5dkvdRqNf7880/07t0bCoVC6nCIyiT2QyJpsQ+SJczdc0Wcg3V0Oz/M7f8vaQMy4XrSdYw7OE6ch7W1d2ss7boUDrbSfhex9n5YkPxa2ftWTEREREREREREZV5Q59pwsste42TjqbuITyqZI0PredTDr4G/wlXhCgA4lXAK045MQ6Y25wLSJA0mWImIiIiIiIiIqMzxcrXH2PY1AABqrYDFh25KHJFpDSs0xIrAFeL8q1EPozDj6AyotSVrga6yiglWIiIiIiIiIiIqkyZ0qgl3x+zb23edu4+bj1Mkjsi0Jl5NsKzrMjjaOgIAjt0/hlmRs6DWMckqNSZYiYiIiIiIiIioTHJ3VGCyfy0AgE4AFh68IXFEeWteqTmWdlkKext7AMChe4fwyfFPoNVpJY6sbGOClYiIiIiIiIiIyqzR7fzg5ZqdsAy7koAL8S+kDSgfrSq3wk+df4JCnj3ydn/cfnx+4nPoBJ3EkZVdTLCWEKNHj4ZMJjP5b8KECVKHSERERERERERkdRztbPDvLrXFxz8cvC5hNOZpX6U9fgz4EbZyWwDAntt78NXfXzHJKhFbqQOgbJMmTUK3bt1yPB8SEoKIiAj06dNHgqiIiIiIiIiIiKzf0JbV8evxO4hPysDxm4k4cTsR7Wp5Sh1Wnvyr+eOHTj9gxrEZ0Apa7Li5A7ZyW3zS+hPIZDKpwytTmGAtIdq2bYu2bdsaPafT6TBnzhx4eXkxwUpEREREREREVETsbOWY3q0uPtx6AQCw4MB17JxSocQnKrv6dsW8jvPwn+P/gU7Q4ffrv8POxg4fvf5RiY/dmnCKgBLs0KFDuH//PoYPHw6FQiF1OEREREREREREVmvAa1VQt5ILAODcvReIuPZE4ojM07NGT3zT/hvIkJ1Q/e3qb/jp7E8QBEHiyMoOJlhLsLVr1wIAxowZI3EkRERERERERETWzUYuw4zu9cTHPxy8Dp2udCQp+9Xqh7nt5oqPV19ejeUXlksXUBljlQnW9PR07N+/H9988w0GDRoEX19fcbGouXPnmtVGSkoK5s6di8aNG8PFxQXu7u5o2bIlFi5ciKysrKI9AQDJycnYvXs3mjdvjiZNmhT58YiIiIiIiIiIyrruDSuhabVyAIB/ElKw58JDaQMqgEF1BuGT1p+Ij5dfWI5Vl1ZJGFHZYZVzsJ4+fRq9e/cudP27d+8iICAAcXFxAAAnJydkZmbizJkzOHPmDDZu3IiIiAiUL18+R11BEJCZmWnWceRyOezs7HLdt2XLFqhUKo5eJSIiIiIiIiIqJjKZDLN61MPwVacAAIvCb6BPk8pQ2JSOMYpv138bap0a86PnAwB+OvsTFHIFRv1rlMSRWbfS8e4ohPLly6Nr16746KOPsHnzZnh7e5tVT6PRoF+/foiLi0PlypURHh6OtLQ0pKenY8uWLXB1dcW5c+cwYsSIXOvfvXsXjo6OZv1r3ry5yThCQkJgZ2eHYcOGFer8iYiIiIiIiIio4NrX9kT72hUAAPeS0vF7dLzEERXMuw3fxbTm08THP5z5AZuubZIuoDLAKkewduzYEUlJSUbPzZ4926y669atw6VLlwAAO3bsQNu2bQFkjzYdOnQodDodhg0bhj///BMRERHo2rWrUX1PT09x7tT85DYCFgCuX7+OkydP4s0334SHh4dZbRERERERERERkWV81KM+om5FAQCWRNzEmy2qwkFhI3FU5hvXeByydFlYdn4ZAOD7099DYaPAkLpDJI7MOlllgtXGpvBv+HXr1gEAOnfuLCZXDb399tv45JNPEBsbi/Xr1+dIsLq4uGD06NGFPj7Axa2IiIiIiIiIiKT0WrVy6N6wEg5efYwnKZlYdyIOk/xrSR1WgUxuMhlqrRorL60EAHz999ewk9thQO0BEkdmfax2ioDCSE9PR1RU9l8nevXqlWsZmUyGnj17AgAOHjxo8Ri0Wi1+++03VK5cGT169LB4+0RERERERERElL+ZPepBJsveXn7sNpQqtbQBFZBMJsP7zd7HqIbZ868KEPD5ic/x550/JY7M+jDBauDatWvQ6XQAgEaNGpksp9+XkJCQYyqCVxUeHo6HDx9i5MiRrzQSl4iIiIiIiIiICq9uJVe88VoVAMCLdDVWRd6ROKKCk8lkmPH6DAyrn73Gj07Q4eO/Pkb43XCJI7MuVjlFQGE9fPhQ3K5SpYrJcob7Hj58aNF5UkNCQgCgQNMMZGZmIjMzU3ysVCoBAGq1Gmq1+X9dUavVEAQBOp1OTDSXRIIgiP+X5DiJrFlZ74c6nQ6CIECtVvOPYSQJ/c/3gvycJyLLYj8kkhb7IBWX9zrXQOjFh1BrBaz6KxbDWlZBBRd7qcMqsBnNZkClUWHnrZ3QClrMOjYLCzougH9V/0K3ae39sCDnxQSrgZSUFHHbycnJZDnDfYZ1LGHLli3YsmVLgep8//33+PLLL3M8f/DgwTzP42W2trbw9vZGamoqsrKyChSDFCx97Ymo4MpqP8zKykJGRgYiIyOh0WikDofKsPBwjjwgkhr7IZG02AepOLT2lOOvx3KkZ2nxn/VHMMivdA4yeU14DbF2sTiXdQ4aQYOZkTMx3Hk46irqvlK71toP09PTzS7LBKsVmDNnDj788EPxsVKpRLVq1dC9e3e4ubmZ3Y5KpUJ8fDxcXFzg4OBQFKFahCAISElJgaurK2T6yVCoSMTFxaFWrexJvFevXv3KC7iVBF26dMGxY8fg7++Pw4cPSx1OiXH06FFx0b6IiAgEBATkWb4w/XDMmDFYv349fH19cedO6bu1xpBKpYKjoyM6depUoj8vyXqp1WqEh4cjMDAQCoVC6nCIyiT2QyJpsQ9ScXo9JRNdfzwOlVqHE09s8NUwf/iUc5Q6rELppeuFz//+HPvv7ocWWvyu+h2LWy9Ga+/WBW7L2vuh/g5xczDBasDV1VXczitLbbjPsI5U7O3tYW+fc3i6QqEo0Btcq9VCJpNBLpdDLi+50/Pqb0fWx1raxMXFoUaNGq/cjv4W7aJkeH1L+vuiMKztfF5FQV/rwvRDw0Rsab/2crkcMpmswJ+zRJbG9yCR9NgPiaTFPkjFoYqHAqPb1cCKY7eh1gpYdiwO/32zidRhFYoCCnzX6TtoIjUIvxuOTG0mPoz8EMu7LUeLSi0K16aV9sOCnBMTrAZ8fHzE7QcPHqBJk9w7y4MHD3KtU9JwDtaSyVIxF8e5Gx6jpL8vCsPazudVFPS1Lkw/NPyjQGm/9pyDlaRm7fNdEZUG7IdE0mIfpOI2vn11bDx1FykqDbbFxGNsu+qo6eUsdViF9k3bb5ClycKxB8eQocnA1ENTEdw5GE29mprdhrX3Q87BWkgNGjSAXC6HTqfD5cuX0atXr1zLXb58GQDg7e1t0QWuXlVwcDCCg4Oh1WoBcA7WksrV1RVRUVEm97dv3x4A0KxZMyxdutRkuYIMVS8sDw8PPH/+vFiPWdT082VqNBqrOB9LMRyZn56ebva1KUg/1P9w0ul0pf7acw5WKimsdb4rotKE/ZBIWuyDVJw6ecmwL94GOgGYvfE4Rtct3QNHOgud8cj2EW5obiBdk47J4ZMx1mUsqtiaXvg9N9baDzkHayE5OTmhffv2OH78OMLCwvDRRx/lKCMIAg4cOAAA6N69e3GHmKegoCAEBQVBqVTC3d2dc7CWYBUqVMi3jJubG9q0aVMM0ZQttra24v8F6R/WzvCPMU5OTvlem8L0Q/3tFXK5vNRfe87BSlKz9vmuiEoD9kMiabEPkhT8MzU4+eNfeJaWhXPP5PjitXb4l0/p/m7TXdMd0yOn41TCKWQiExuzNuKXjr+gXvl6+da19n7IOVhfwahRo3D8+HEcOXIEp06dQuvWxpP8btu2TVycZeTIkVKEaDbOwVr6Wfv5SY3X9384B2vBcA5WKin4HiSSHvshkbTYB6k4lVMo8H6X2pgbehUAsPjwbYSMaSVxVK9GoVDg564/Y8qhKYh5HANllhJTD0/F6h6rUad8HbPbsMZ+WJBzKt3fcPPw/PlzJCYmiv/0yYD09HSj51NTU43qjRo1Co0bN4YgCBg8eDAiIiIAZCcTtm3bhgkTJgAAevXqJa64TVRcAgICIJPJxBXeb968iffeew916tSBk5MTZDIZ4uLixPKPHj3CsmXL8Oabb6JOnTpwdnaGvb09qlSpggEDBuD333/Pcy7MuLg4yGQyyGQyhISE5Ng/d+5ccT+QPapvwYIFaN68OVxdXeHq6opWrVph6dKlZt1GrVKpsHTpUnTt2hXe3t6ws7NDxYoV0a1bN6xevdqsNk6ePIkhQ4bA29sbDg4OqFGjBiZOnIjr16/nW9ccISEh4jnHxcUhKysLixYtwuuvvw53d3d4eHggICAA+/btM6qXkpKC+fPno1mzZnBzc0O5cuUQGBgofsbkJzQ0FG+++SaqVq0Ke3t7VKhQAW3btsW8efNyfI7lJiMjA9999x2aNm0KZ2dnVKhQAe3bt8fKlSsLNB+qVqvFunXr0K9fPzRo0ACOjo6oUKECOnTogEWLFiEjI8PstoiIiIiIiEqbd1pXR5VyjgCAo9ef4nRsksQRvTpHW0cEd/3f/KvPM59jwsEJiE2OlTiy0sNqR7A2a9YMd+/ezfH8ggULsGDBAvHxqFGjjBJHtra22LNnDzp37oy4uDh069YNTk5O0Ol0UKlUYtsbN24s8nN4VVzkqvTL6/x27dqFd999F2lpaTnq6HQ6aLVaVK1aNdc2Hj58iD179mDPnj1YtWoVduzYARcXlzyPn9v7wnDRokePHqF37944f/68UZno6GhER0fjwIED2LVrl8mRixcuXMAbb7yRo98+ffoUERERiIiIwC+//II//vgDlSpVyrWNxYsX46OPPjKKMy4uDitXrsSmTZuwZcsWk+dnLsM6L168wNtvv41Tp04ZlTl27BiOHTuGhQsXYtq0abh37x769u2LK1euGJU7dOgQIiIisG7dOgwfPjzX46lUKgwfPhy7d+82ej4pKQknT57EyZMn8fPPPyM0NBSvvfZarm0kJCSgW7duuHbtmvhceno6Tpw4gRMnTmD79u2YPn260Tnmdm3u3buHgQMH4sKFCzliiYqKQlRUFJYvX47Q0FDUrVs3R30uckVkOda+oABRacB+SCQt9kGSihzA+51rYvau7O9388OuYdO4lqV2CkM9O9hhif8STD08FVeSruCZ6hnGHRiHld1Worpr9VzrWHs/5CJXr8jPzw8XL17EDz/8gJ07dyI2NhYKhQL/+te/8M477+D999+HnZ2d1GHmwEWurEtuizDpR3DGxcVh5MiRcHR0xMyZM9G2bVvY2Njg7NmzEAQBSqVSLNupUyd069YNDRs2hKenJ1JTUxEXF4d169YhOjoahw4dwqRJk7B8+fIcMRiOjFSpVDniyczMFLcHDhyIq1evYtKkSejZsyfKly+Pmzdv4ocffsD169exd+9e/PzzzxgzZkyO49y5cwedO3eGUqmEq6srxo8fjxYtWqBKlSpISkrC/v37ERISgujoaPTv3x9//vlnjqH6e/fuxYwZMwBkz1/7wQcfoEOHDgCAyMhILFmyBCNGjICnp6fJ62sO/R9aAGDChAk4f/48xo0bh759+6JcuXK4dOkSvv/+ezx69AgfffQR2rRpg6CgINy5cwfTpk0T/2hz6tQpfP/991AqlQgKCkKbNm3g5eWV43hjx44Vk6uNGjVCUFAQ6tWrh+fPn2Pnzp3YtGkTHj58iG7duuGvv/6Cj4+PUX2NRoM+ffqIydUuXbpg7NixqFKlCu7fv4/Vq1fj4MGDSExMFOvktshVUlISOnXqhAcPHsDe3h4jR45E+/btUb16daSlpeHw4cP45ZdfcOvWLfTq1QtHjx6Fu7u7URtc5IrI8qx1QQGi0oT9kEha7IMkBTsBqORog8cZMpy5+wI/bArDv8oL+VcsBQbqBiLJJgmPtI/wNOMpRu0dhfEu41HeprzJOtbaDwuyyJVMMBxSRFZBv8hVYmJioRa58vPzK9GLtljDIld50Y+G8/f3x+HDh432denSBceOHQMA+Pj4ICoqCtWr5/6XJEEQcPv2bdSuXdvksebOnYuvv/4aMpkM165dQ506xvOrxMXFoVatWgCA1atXY/To0Ub7v/zyS3z11VcAsucmCQsLE6cv0EtKSkKjRo3w+PFjNGnSBOfOncsRR8eOHXHixAk0a9YMYWFhYhLUUFhYGPr16wedTocVK1aI03UA2cmuWrVq4eHDh3B3d0dUVBQaNGhgVP/y5cvo2LGjmNjL7fqaIyQkBOPGjQOQPafo9u3bMXDgQKMyFy9eRIsWLaDT6eDl5QWlUokjR47kmNP5zz//RL9+/QBAHO1qaN++fejfvz8AoGvXrti7d2+OP+6sXLkSkydPBgAMGTIkxyjdZcuW4f333weQnRBesWJFjnMaP3481q5dKz6OiIjI8TqOGDECmzdvhq+vLyIiIuDn55ejH547dw7+/v5IS0vDnDlz8M033xi1MWbMGKxfvx6+vr7iXNallUqlQlxcHKpVq1aiPy/Jeln7ggJEpQH7IZG02AdJamFXHuP9Ldl399X3dsUfU9pALreOHMVz1XNMipiEW8m3AABVnKtgZbeV8Hb2Nipn7f1QqVTC09MTycnJ+efXBLI6ycnJAgAhOTm5QPUyMjKEq1evChkZGUUUmWVotVrh+fPnglarlTqUIgFAACD4+/vn2Ofv7y/uX79+/SsfS6PRCJ6engIA4YcffsixPzY2Vjze2rVrc+z/4osvxP0ffvihyePMnj1bACDIZDLhxYsXRvsiIyPFNi5evJhnvG+99ZYAQGjXrp3R81u3bhXbyO089P773//meX3NsXbtWrGNoUOHmizXqVMnsdx//vMfk+V8fX0FAMIbb7yRY1+vXr0EAIJCoRDu3btnso1u3boJAARbW1vh4cOHRvsaNmwoABAqVaokpKWl5Vo/JSVF8PLyEuM9cuSI0f7Y2FjBxsZGACCEhoYKgmC6H86aNUsAIPj4+OQ4zqhRowQAgq+vr8lzKS1Ky+clWa+srCxh9+7dQlZWltShEJVZ7IdE0mIfJKnpdDqh75Ljgu9/9gq+/9kr7Dn/QOqQLOpp+lOh365+QqOQRkKjkEZCn519hCdpT4zKWHs/LEh+jVMEUKEM3TsUiRmJ+RcsIoJOgKwY/jLk6eiJ3/v+XuTHKSg7OzsMGTKkQHV0Oh0SEhKQkpJiNI9I1apVkZiYmGNezYIyNYcoALRo0QJA9qja2NhYo7lC9+zZAwCoV68eGjdunOcxOnXqhK1btyI6OhoajQa2ttkfYYcOHQKQPaJ01KhRJuuPGTMGs2fPNpoL9FW8/fbbJvc1bdoUkZGR+ZZr0qQJ7t69m2NEp0ajEUcrd+/eHdWqVTPZxoQJE3Do0CFoNBocPXoU77zzDoDseXGvXs1e3fKtt94yOWWIi4sL3nrrLQQHB+e6f9++fdBqtXByckKvXr1MxgFkv0bz58/Hw4cPce/ePZMjrImIiIiIiEozmUyGj3rUw8g1pwEAi8JvoGcjbyhsrGM9eU9HT6zqvgqjw0YjPiUed5V3Mf7geKzpsQYVHCtIHV6JwwQrFUpiRiKepD+ROowyq06dOmbdliwIAjZu3IjVq1fj1KlTea7wbjgHZ2HUr1/f5D4PDw9x++W5c8+cOQMAuH79utlTPqjVaiQlJaFixYoAgEuXLgEAatSokev0AnpeXl7w8/NDbKxlVkLMbSEnvXLlyhWo3MvX5c6dO+J8Ly9PLfAyw/2XL18Wt/XXBQBatmyZZxutWrUymWDVv0bp6eliUtscCQkJTLASEREREZHV6ljHE21qeuDknSTEJqZhR8x9vN3Ker4DVXSqiNXdV2PMgTF4kPoAd5LvYEL4BKzpvgblHMpJHV6JwgSrFVOr1QVa8UytVkMQBJMriBvydDCdxCoOxTaC1cFT0tXOTR27fPny+calUqkwePBghIWFmXWsjIyMHG0aPs7tfWE4EtTBwcGsa6VWq43KPXlSuER9amqqmExNSkoCAFSsWDHfGCpVqiQmWAvz2hrWyeucDZPF5pTTarVGZQwT3p6eeb8P9YlmAHj27JlYtiBtGC6w9fJr/fjxY5P18pKammrUjuH7Rcp+ZQk6nQ6CIECtVovzJhMVJ2tfsZWoNGA/JJIW+yCVFB92rY237mSPYl186Ab6NqoIe4X1fEfwtPfEii4rMP7QeDxOf4ybz29iwsEJWNF1BRxljgCstx8W5LyYYLUiwcHBCA4OhlarBQAcPHjQ5C3BubG1tYW3tzdSU1ORlZWVZ9lfOv7ySrGWJlKtdp7bKvf61coFQcg3rm+//VZMrrZv3x7jx49H06ZNUbFiRTg6OkIuz75toXfv3vj777+hVqtztJmamipuq1SqHPszMzPF7bziMVx57+UV6vUfWI0aNcIvv5j/vnJ1dRXb0SfrtFptvtdF3z9yu77mUKlU4nZqaqrJNsy9Nvrz1+l0RuXS0tKM2sqrDcNjZWVliWUNRyxnZGQU+jXSfx5UqFBBnNLBHL6+vrm+1i+fa2mUlZWFjIwMREZGiv2SSArWumIrUWnCfkgkLfZBKgkalZfj8nM5EpSZ+HTdQXT2sb715N+xeQerZauRIqTgn+f/YMSuERjtMhoOMger7YeG35PzwwSrFQkKCkJQUBCUSiXc3d3RvXv3/Fc5M6BSqRAfHw8XF5cSvSq2IAg5Vi+3Rra2tjleP/3t2bntMyQIAjZs2AAA6NixIw4fPiwmVF+mT3Tl1qaLi4u47eDgkGO/vb29uJ1XPIaJficnJ6OyXl5euHnzJjIyMtCmTRuTbeSlQoUKuHnzJp49e5bve14/qjO/a2iKYd9wcXEx2Ya510a/0qJcLjcqZzjnqlKpzLONu3fvitve3t5iWR8fH/H5lJSUPNswnKLg5ddIP0I2NTUVLVu2hI2NTaH6oalzLY1UKhUcHR3RqVOnEv15SdbL2ldsJSoN2A+JpMU+SCVJzeYp6L/sbwgCcOypA754tyNc7K0v5dYhuQMmRkxEkioJ97X3scd2DwZoBqBv975W2Q8LMjDI+l5tEikUigK9wbVaLWQyGeRyuclkXEmgH62oj9Wa5XV+ee179uwZEhISAABDhgwxOW9mamoqrl+/brJNw8e5vS8ME2vmxvpyO82aNcOJEydw584dPHnyBN7e3ibbMaVx48Y4efIkYmNj8fz5c1SokPuE20+fPkVcXJxZMZuS3zXRM/famCpXu3ZtODk5IT09HadPn86zDf0cqUD2tdCXbdq0qfh8TExMnguAxcTEGMVheLzmzZtj8+bNyMzMxNmzZ9G6detC9UNzr0lpIJfLIZPJCvw5S2RpfA8SSY/9kEha7INUEjSu5oH+TX3wx/mHeJ6uxm+n7uPfXetIHZbF1fOsh5XdV2LcgXF4kfkCF59dRIptCnrKesJJYf4d1KVFQT5bSvc3XCLKleEty4a3mr9s1apVkt/e3L9/fwDZo25/+umnQrXRrVs3sY3169ebLBcSEmI0D2hJZmtrC39/fwDZtz3dv3/fZNlVq1aJdQICAsTnfXx80KBBAwDAtm3bTC5ylpaWhq1bt5psv1+/fmJydPHixQU5DSIiIiIiojJherf/LW58+B/rXRS8bvm6+DXwV7jauQIAYjWx2HZzm8RRSY8JViIr5OXlJa5Orx95+LLo6Gh89tlnxRxZTt27d0erVq0AAAsWLMgz0QcAly5dQmhoqNFzAwcOROXKlQEAX3/9tdGoXL2rV6/i22+/tVDUxSMoKAhA9nyf48aNy3WC7TVr1uDgwYMAgEGDBonXQW/KlCkAgISEBMyYMSPX40yfPj3Pxcbq1auHIUOGAAC2bNmCRYsW5Rl3bGwsNm/enGcZIiIiIiIia+Ln6QyFTfbAFE0pX9Q3Pw0qNMAv3X6Bs60zmts1x/B6w6UOSXJMsBJZIblcjuHDsz/gLl68iA4dOmDz5s04c+YMIiIiMGPGDHHuyLp16+bTWtHbtGkTPDw8oNVqMXToUPTv3x8bN27E6dOnERMTg/379+O7775D27Zt0aRJExw7dsyovp2dHX7++WcAwPPnz9GmTRvMmzcPJ0+exN9//43vv/8e7dq1A5B9631p0adPHzGxefDgQbRp0wYbN25ETEwMDh06hPHjx2P8+PEAAA8Pj1wTn1OmTEGzZs0AAMuXL0evXr3wxx9/4OzZs/jjjz/Qo0cPrFy5Eq+//nqesSxfvhw1a9YEAMyYMQOdO3fGb7/9hpMnT+LcuXM4dOgQFi5ciMDAQNSuXRs7duyw5KUgIiIiIiKiEqSxV2Ns7LkRAx0HwkZuI3U4kuMcrFZMrVbnOuItr/KCIECn04nzK5ZE+lu89bFas7zOL79z//rrrxEVFYXz58/jzJkzGDZsmNF+Dw8PbNu2DXPnzsWNGzdybdPwcW7vC8Pb7c2NNbd2atSogaioKAwZMgSXL19GaGhojlGqhlxdXXO08cYbb2D+/Pn4z3/+gxcvXmDOnDlG+52cnLBlyxYsXLgQt27dyjfmwp6LnrnXJr9yISEhUKvV2L17N86ePYsRI0bkKOPj44PQ0FBUrlw5RxtyuRx79uxBt27dcP36dYSFhSEsLMyoTGBgID788EP06tXL5HmVK1cOx48fx9tvv43jx48jMjISkZGRJs8rt9fI3GtSGuh0OgiCALVaDRsb/jJBxU//870gP+eJyLLYD4mkxT5IJZn+u4K1q+xYGZdll632XAtyXkywWpHg4GAEBwdDq9UCyB7xZrh6e35sbW3h7e2N1NRUZGVlFVWYFmO46rk10mg0OVas08+Xmtu+l8lkMuzbtw/Lli3Drl27cOfOHdja2qJKlSoIDAzE5MmTUaVKlTzbTE1NFbdVKlWO/YZTD+QVT3p6utF2bmW9vb1x9OhR7Nq1C6GhoTh79iyePXsGrVYLDw8P1K5dG23atEHfvn3RtGnTXNuYMGECmjRpguDgYJw8eRJKpRIVK1aEv78/3nvvPdSrVw///e9/TZ6vOVQqlbidmppqsg1zr43+A1un05kst3btWrz11lviKORnz57ByckJtWvXRu/evTFhwgS4uLiYrO/i4oIjR44gODgYO3fuRFxcHOzs7FC3bl0MHToUY8aMwYkTJ8Typl4jJycn7NmzBwcOHMCOHTsQHR2NJ0+eQK1Ww93dHTVr1kSrVq3Qs2dPtG/fPkcb5pxraZGVlYWMjAxERkZKPo8xlW3h4eFSh0BU5rEfEkmLfZBKEp3OBoAMyclK/Pnnn1KHU2ystR8a5jLyIxNKy4ovZDalUgl3d3ckJibCzc3N7HoqlQrx8fHw8/ODg4NDEUb4agRBQEpKClxdXY1WJSei4lPW+6FKpUJcXByqVatWoj8vyXqp1WqEh4cjMDCQKycTSYT9kEha7INUEjWcGw61VsC/fFyxe0pbqcMpctbeD5VKJTw9PZGcnJxvfo0jWK2YQqEo0Btcq9VCJpNBLpdDLi+50/Pqby3Wx0pExa+s90O5XA6ZTFbgz1kiS+N7kEh67IdE0mIfpJJI/12hrLDWfliQcyp734qJiIiIiIiIiIiKCO8VL3uYYCUiIiIiIiIiInpFMpS96dMoGxOsRERERERERERERIXEBCsRERERERERERFRIXGRKyumVquhVqsLVF4QBOh0OnEBm5JI+P/JTPSxElHxK+v9UKfTQRAEqNVq2NjYSB0OlUH6n+8F+TlPRJbFfkgkLfZBKokE/O97Ull4b1p7PyzIeckEgVPvWovg4GAEBwdDq9Xixo0b2LRpE5ycnMyub2trC29vb1SrVg12dnZFGCkRUemWlZWF+Ph4JCQkQKPRSB0OERERERGVAB+etIFWkKGqs4CPmmilDodeUXp6OoYNG4bk5GS4ubnlWZYJViukVCrh7u6OxMTEfN8AhlQqFeLj4+Hn5wcHB4cijPDVCIKAlJQUuLq6QibjBNJEUijr/VClUiEuLg7VqlUr0Z+XZL3UajXCw8MRGBgIhUIhdThEZRL7IZG02AepJGo4NxxqrYCGlV3xx9S2UodT5Ky9HyqVSnh6epqVYOUUAVZMoVAU6A2u1Wohk8kgl8shl5fc6Xn1tyPrYyWi4lfW+6FcLodMJivw5yyRpfE9SCQ99kMiabEPUkkigwyAAEBWpt6X1toPC3JOZe9bMRERERERERERkaWVvZv76P8xwUo5cNYIIqK88XOSiIiIiIiI9JhgJZH+Nt+yuCI4EVFB6D8ny+L0CERERERERGSM3wxJpFAoYGNjg7S0NKlDISIq0VQqFeRyOWxtOZU5ERERERFRWccEK4lkMhlcXV2hVCp5+ysRUR5SU1Ph5OTEEaxERERERETEBCsZc3d3h1qtxsOHD5lkJSLKxfPnz5Geng43NzepQyEiIiIiIqISgPc2WjG1Wg21Wl2gOgqFAt7e3khISEBGRgZcXV3FUVoyWclYDk8QBGRlZSEjI6PExERU1pS1figIAjQaDZRKJVJTU+Hm5gYnJ6cCf8YSWYr+vcf3IJF02A+JpMU+SCWZThDKxHvT2vthQc5LJnCYotUIDg5GcHAwtFotbty4gU2bNsHJyalQbSkUCjg4OMDZ2Rk2NjZlIoFCRJQXQRCQmZmJtLQ0ZGRkSB0OERERERGVMDNP2kAtyFDFScCsplqpw6FXlJ6ejmHDhiE5OTnfOxiZYLVCSqUS7u7uSExMfOVbWPWjtnQ6XYmZMkCj0eDEiRNo164dF5ghkkhZ64cymUxc1Ip/cKKSQK1WIzw8HIGBgVAoFFKHQ1QmsR8SSYt9kEqiRl8eQqZGh/rerggNait1OEXO2vuhUqmEp6enWQlW6/9WXIYpFAqLvMHt7OwsEI3lqNVqaDQauLi4WGUHJioN2A+JSgZL/awnosJjPySSFvsglUQymaxMvS+ttR8W5Jy4yBURERERERERERFRITHBSkRERERERERERFRITLASERERERERERERFRITrERERERERERERBZSUhYJp+LDBCsREREREREREdErksmkjoCkwgQrERERERERERERUSExwUpERERERERERERUSEywEhERERERERERERUSE6xEREREREREREREhWQrdQBkefrV6pKSkqBWqyWOxvLUajXS09Px7NkzKBQKqcMhKpPYD4mkxT5IJD32QyJpsQ9SSaTNTIdOrYM6Q4Znz55JHU6Rs/Z+mJKSAuB/eba8yARzSlGpEBwcjODgYGRlZeH27dtSh0NERERERERERFSqxcfHo2rVqnmWYYLVCul0Ojx8+BCurq6QyWRSh2NxSqUS1apVQ3x8PNzc3KQOh6hMYj8kkhb7IJH02A+JpMU+SCQ9a++HgiAgJSUFPj4+kMvznmWVUwRYIblcnm9m3Rq4ublZZQcmKk3YD4mkxT5IJD32QyJpsQ8SSc+a+6G7u7tZ5bjIFREREREREREREVEhMcFKREREREREREREVEhMsFKpY29vjy+++AL29vZSh0JUZrEfEkmLfZBIeuyHRNJiHySSHvvh/3CRKyIiIiIiIiIiIqJC4ghWIiIiIiIiIiIiokJigpWIiIiIiIiIiIiokJhgJSIiIiIiIiIiIiokJliJiIiIiIiIiIiICokJViIiIiIiIiIiIqJCYoKVJJeeno79+/fjm2++waBBg+Dr6wuZTAaZTIa5c+ea1cbjx48xY8YM1KtXD46OjvDw8EDHjh2xatUqCIJQtCdAZAVepR8+ePAAy5Ytw5AhQ1C7dm04OjrC0dERNWrUwDvvvIPDhw8Xz0kQlWKW+Fn4ssmTJ4tt+Pn5WTReImtkqX6YkJCAzz77DC1atICHhwccHR3h6+uLnj17Yt68eVCr1UV3EkSlmCX64Pbt29GvXz/4+PjAzs4Ozs7OqFevHiZMmIDz588XafxE1uDZs2dYu3YtRowYgYYNG8LZ2Rn29vaoWrUqBg4ciF27duXbRkpKCubOnYvGjRvDxcUF7u7uaNmyJRYuXIisrKxiOAtp2EodANHp06fRu3fvQtePiYlBjx498OzZMwCAi4sLUlJS8Ndff+Gvv/7C9u3bsWfPHtjZ2VkqZCKrU9h+GB8fD19fX6M/ZDg5OUEQBMTFxSEuLg5btmzB2LFj8euvv8LGxsaSYRNZjVf9WfiyI0eO4Ndff7VYe0RlgSX64e+//46JEydCqVQCABwcHGBnZ4d79+7h3r17OHDgACZPnoxy5cpZIGIi6/IqfTAzMxNDhgxBaGio+JyLiwuysrJw48YN3LhxA2vWrMEPP/yA6dOnWypkIqvj7e0NjUYjPnZwcIBCocCDBw/w4MED/PHHH+jVqxe2b98OJyenHPXv3r2LgIAAxMXFAcj+bpiZmYkzZ87gzJkz2LhxIyIiIlC+fPniOqViwxGsVCKUL18eXbt2xUcffYTNmzfD29vbrHrJycno27cvnj17hvr16yM6OhopKSlIS0vD0qVLoVAocODAAUybNq1oT4DIChSmH2q1WgiCgK5du2LdunV48OAB0tLSkJqaiitXrmDAgAEAgDVr1hR6FB5RWVHYn4UvS09Px4QJE2Bra4vXX3/dwlESWbdX6Yfbtm3DsGHDoFQqMXHiRFy5cgUZGRlITk6GUqlEZGQkpk+fDoVCUYRnQFS6FbYPfvfdd2JyderUqbh//z5SUlKQkZGBM2fOoEOHDtDpdJgxYwZiYmKK8hSISjWNRoNWrVph2bJluH37NjIyMpCamorY2FiMGzcOALB//35MmjQp17r9+vVDXFwcKleujPDwcKSlpSE9PR1btmyBq6srzp07hxEjRhT3aRUPgUhiGo0mx3O+vr4CAOGLL77Is+6nn34qABAcHR2FO3fu5Nj/3XffCQAEGxsb4fr165YKmcjqFLYfvnjxQoiJiTG5X6fTCT179hQACC4uLkJGRoYlwiWyOq/ys/Bl06ZNEwAIn3zyiTBq1CgBgODr62uZQIms2Kv0w4cPHwrly5cXAAgLFy4sogiJrNur9EE/Pz8BgODv75/r/hcvXgguLi4CAGH27NkWiJbIOh0+fDjP/ZMmTRIACACEe/fuGe1btWqVuO/EiRM56m7atEncf+jQIYvGXRJwBCtJ7lVuGV6/fj0A4O2330aNGjVy7H///ffh4uICrVaLjRs3Fvo4RNausP3Q3d0dzZs3N7lfJpNh7NixAIDU1FRcu3atUMchsnaWmj7j5MmTWLJkCerWrYtPP/3UIm0SlRWv0g+XLFmC58+fo1mzZrz9mKiQXqUPPnr0CABM3rnh7u6OunXrAsj+nZSIcte5c+c89+tHsQLAmTNnjPatW7dObKNt27Y56hrmbfS5HGvCBCuVWtevX8e9e/cAAL169cq1jIuLCzp27AgAOHjwYLHFRkT/4+DgIG5rtVoJIyGybpmZmRg7diwEQcCvv/5q1PeIqGjpvyiOGDECMplM4miIyp6aNWsCgMnb/5OTk3Hjxg0AppOwRJQ/U9/t0tPTERUVBcB0fkYmk6Fnz54ArDM/wwQrlVqXL18Wtxs1amSynH7f1atXizwmIsrp6NGjAAA7Oztx5AARWd5XX32Fa9euYdy4cfD395c6HKIyIzY2Fg8fPgQAtGjRApcuXcKwYcNQuXJlceXloUOHil88icjypkyZAiD7986goCA8ePAAACAIAs6ePYu+ffsiNTUVbdu2td75H4mKgf67HQA0btxY3L527Rp0Oh0A8/IzCQkJSEpKKpogJcIEK5Va+l9kAaBKlSomy+n3KZVK3g5CVMxiY2OxYsUKAMDQoUPh5uYmcURE1uncuXOYP38+KlWqhAULFkgdDlGZoh8VBwBRUVF4/fXXsXnzZiQnJ8PBwQEPHjzA1q1b0bFjR3z99dcSRkpkvYKCgjBr1izI5XIsW7YMVatWhaurKxwcHNCiRQvcunULs2fPRkREhMWm5SEqa168eIHvv/8eANCxY0fUq1dP3FfQ/MzLdawBE6xUaqWkpIjbTk5OJssZ7jOsQ0RFKyMjA0OGDEF6ejo8PT0xb948qUMiskoajQZjx46FRqPBkiVLUK5cOalDIipTnj9/Lm5/9tln8PHxQXh4OFJTU5GcnIwrV64gICAAgiDg888/x86dOyWMlsg6yeVyfP/991izZg1cXFwAZM+1mpWVBQBQqVRITk5GWlqalGESlVo6nQ7vvvsuHj16BAcHByxdutRoP/MzTLASEVER0Gg0GDZsGGJiYqBQKLBx40b4+PhIHRaRVZo3bx7Onz+Pvn374q233pI6HKIyR39LJJB9O/KOHTvQrVs3yOXZX7UaNmyI0NBQeHt7AwC+/PJLSeIksmaJiYno2rUrRo8ejbZt2+Kvv/7Cixcv8OjRI+zcuRNeXl5Yvnw5WrduLU4fQETm++CDD7B3714AQHBwMJo0aSJxRCUPE6xUarm6uorb6enpJssZ7jOsQ0RFQ6vVYvjw4di9ezdsbW2xadMmdO/eXeqwiKzS1atX8fXXX8PFxQXLli2TOhyiMsnw98uuXbuiefPmOcq4uLggKCgIAHDx4kU8fvy42OIjKgtGjRqFo0ePwt/fHwcOHED79u3h7u4Ob29vvPHGG/jrr7/g6emJO3fuYPbs2VKHS1SqzJw5Uxyx+uOPP2Ls2LE5yjA/wwQrlWKGo+Hy+iukfp+bm5t4uwgRFQ2tVosRI0Zg69atsLGxwYYNG/Dmm29KHRaR1QoKCkJWVhY++eQTlC9fHqmpqUb/NBoNgOxRdfrn1Gq1xFETWRfD+eQaNGhgslzDhg3F7bt37xZpTERlybVr1/Dnn38CAGbMmAGZTJajTMWKFTFy5EgAwM6dOyEIQrHGSFRazZo1CwsXLgQA/PDDD5g2bVqu5Qqan3m5jjVggpVKLcOV6S5fvmyynH6f4S+1RGR5+pGrW7ZsEZOrQ4cOlTosIqsWGxsLAJgzZw5cXV1z/Nu4cSMA4N69e+JzwcHBUoZMZHUaNmxo1qI5hgmd3BJARFQ4V69eFbdr1aplslydOnUAZI+ge/LkSZHHRVTaffTRR+LiqfPnz8eMGTNMlm3QoIE4NY45+Rlvb294eHhYMFrpMcFKpVbdunVRvXp1AEBYWFiuZdLS0nD8+HEA4C3KREVIq9Vi2LBh+P3338Xk6ttvvy11WEREREXOwcEBnTp1ApA9ks4UfRJIJpPBz8+vOEIjKhP0SR0g79HhhlNz8M5GorzNnDkTP/zwA4Ds5OpHH32UZ3knJye0b98egOn8jCAIOHDgAADrzM8wwUqllkwmE2/z2LJlC+Li4nKUCQ4ORmpqKmxsbDB8+PBijpCobNCPXN26dStsbW2xceNGJleJiklcXBwEQTD5b9SoUQAAX19f8TlTt3YRUeGNGTMGABAREYGzZ8/m2J+amirOk9y6dWt4eXkVa3xE1sxw3uPly5fnWiYtLQ3r168HADRp0gTOzs7FEhtRaTRz5kyjaQHyS67q6X/vPHLkCE6dOpVj/7Zt23Dnzh0AEHM51oQJVioRnj9/jsTERPGffjXW9PR0o+dTU1ON6s2cORPe3t5IT09Hnz59EBMTAwDIysrC8uXL8dlnnwEAJk6ciLp16xbvSRGVMoXph/o5V3///XdxQStOC0BUOIX9WUhEllPYfjh8+HC0atUKgiBg8ODBiIiIEOteu3YN/fv3R0JCAuRyOb799ttiPy+i0qIwfdDX1xf9+vUDAISGhuLdd9/F7du3IQgC1Go1Tpw4gYCAADGxk9dtzkRlneGcq4sWLSpQfxk1ahQaN25s9LMQAHQ6HbZt24YJEyYAAHr16oWuXbtaPniJyQTO7kwlgJ+fn1mT/Y8aNQohISFGz8XExKBHjx549uwZgOyV6FQqlbiIR/fu3bFnzx7Y29tbPG4ia1KYfhgZGQl/f38AgEKhyHcenZ9++okJWCITXuVnoSmjR4/GunXr4Ovrm+udHkRk7FX6YUJCArp27SpOBeDk5ASFQoHk5GQA2T8ng4ODxS+YRJRTYftgYmIievbsKQ64AbL7YFZWlrjgI5A9p+T8+fMtGjORtbh37x58fX0BZE+9kd/dFjNnzsTMmTONnouLi0Pnzp3F3zudnJyg0+mgUqkAAM2aNUNERATKly9v+ROQmK3UARC9qhYtWuDKlSv473//i7179yI+Ph7Ozs5o1KgRRo0ahbFjxxrNy0NElqMfVQAAarXaaG6r3GRkZBR1SERERJLw9vbG2bNnsXTpUvz++++4ceMGMjIy4Ofnhy5dumD69OlGi7QSkeV4enri5MmTWLduHbZt24bz588jKSkJtra2qF69Otq1a4dJkyahQ4cOUodKVGIZfrfT6XT5frfL7a4qPz8/XLx4ET/88AN27tyJ2NhYKBQK/Otf/8I777yD999/H3Z2dhaPvSTgCFYiIiIiIiIiIiKiQuKwPiIiIiIiIiIiIqJCYoKViIiIiIiIiIiIqJCYYCUiIiIiIiIiIiIqJCZYiYiIiIiIiIiIiAqJCVYiIiIiIiIiIiKiQmKClYiIiIiIiIiIiKiQmGAlIiIiIiIiIiIiKiQmWImIiIiIiIiIiIgKiQlWIiIiIiIiIiIiokJigpWIiIiIiIiIiIiokJhgJSIiIiIiIiIiIiokJliJiIiIiKxMVFQUZDIZZDIZ5s6da7F2jx49Krbr5+dnsXYLIiUlBV5eXpDJZOjQoYMkMRAREREZYoKViIiIiArk5MmTYpKtMP9at25d5DHu3LlTPN6MGTPMPo8RI0aYfYyQkBCxnouLC3Q6naXCfyU6nQ7//ve/AQCenp4mz78ojR49Ot/3gZ2dHby8vPD6669jypQpOHr0KARByLdtV1dXzJ49G0B2Ivn3338v6tMhIiIiyhMTrERERERUIOfOnXul+i1btrRQJKaFhoaK23379s21TG7nsW/fPmg0GrOOYVi/SZMmkMtLxq/WmzdvxtmzZwEA06ZNg6urq8QR5U6tViMxMRExMTFYsWIFOnfujM6dOyM2NjbfulOnToWnpycA4OOPPzb7NSMiIiIqCrZSB0BEREREpYthYrFixYpo1qxZger37t3b0iEZ0el0+PPPPwEA5cqVQ8eOHXMtl1uC9cWLFzh69Ci6deuW73EM67/22muFC9bCtFotvvrqKwCAs7MzpkyZInFEQPny5dGqVascz6enpyM+Ph5xcXHic8eOHUOnTp3w999/o2rVqibbdHR0RFBQEL788kvcuXMH69atw7hx44oifCIiIqJ8McFKRERERAVy/vx5cXvIkCFYunSpdMHk4vTp03jy5AkAoEePHrC1zf1XXsPzcHFxQWpqKgBg165d+SZYBUHAhQsXxMcFTTIXle3bt+PGjRsAgGHDhsHDw0PiiLJH94aFhZncf/PmTXz00Uf4448/AAD379/HtGnTsH379jzbnTJlCr799ltoNBrMmzcPY8eOhUwms2jsREREROYoGfcxEREREVGpoNFocOnSJfFx48aNJYwmd4bTA/Tr1y/XMi+fR1BQkLi9Z8+efOcCvXPnDpRKpfi4pCRYf/rpJ3G7tIzorFOnDnbu3ImAgADxud27d+PZs2d51qtUqRL69OkDALh165Y4apmIiIiouDHBSkRERERm++eff6BSqcTHJTnBamNjg169euVa5uXzGDt2LKpXrw4gewTlmTNn8jyG4fQAtra2aNSo0auG/couX76Mv//+GwBQq1atYllMzFLkcjmmTZsmPtZqtfm+BkD2KF29X375pShCIyIiIsoXE6xEREREZDbD2+oBlIjEoqF79+6JI1PbtWtn8hb5l6cHqF27NgYMGCA+t2vXrjyPY1i/fv36cHBwKHzQFrJx40Zxe+DAgQWuHxkZiXfffRc1atSAg4MDvL290a5dOyxevBgvXrywXKAm1K9f3+hxfiNYgez5fO3t7QEAYWFhZtUhIiIisjQmWImIiIjIbIYjN319feHm5iZhNDkZTg/Qt29fk+UMz6NJkyaQy+VGCdbdu3fneZySuMDVjh07xO2ePXuaXU+j0WDSpEnw9/fHhg0bEBcXh8zMTDx+/Bh///03pk+fjtdeew1nz54tirBFWVlZRo9dXFzyrePi4oL27dsDANRqtTiPKxEREVFxYoKViIiIiMxmOHKzJE4PsHfvXnHb1PyrgPF56BOk/v7+KF++PADg2rVr4mJR+dUvCfOvxsbG4ubNmwCypyxo27atWfUEQcDIkSPx66+/Gj3fsGFDBAQEoE6dOgCAu3fvIjAwEPfv37ds4Ab00xvomTs62t/fX9w+cOCARWMiIiIiMgcTrERERERktpKcYE1LS8ORI0cAADVr1kSDBg1Mls0tQWpraysumgSYnibgyZMnePjwYY76Ujp27Ji43bBhQzg7O5tVb82aNdi8ebP4OCAgADdu3MCVK1dw5MgR3LhxA+fPn0fz5s2RlJSE6dOnWzx2AEhMTMS8efPEx23atEHNmjXNqtuyZUtx++jRo5YOjYiIiChfTLASERERkVnu3r2LpKQk8XFJm3/14MGDyMzMBJD36NWXz8PwFn9zpgkwnB7g5fpSiYmJEbf/9a9/mVVHpVJh9uzZ4uP27dsjLCxMHLWq17RpUxw5cgQNGzZEYmKiZQL+/+PfvHkTy5YtQ/PmzREbGwsAcHZ2RnBwsNntGCb6nzx5gvj4eIvFSERERGQOJliJiIiIyCwvL3A1fPhwyGQys//pR5cWFcPpAfKaf9XwPGxtbY0SxT179hQXrDp16hQePXqUZ31fX19xWgEpXb16VdyuVauWWXV27NghJkxtbGywcuVKccGol7m5uWH58uWFiu3YsWO5vh8cHR1Rt25dBAUFiUnRgIAA/PXXX2jevLnZ7VetWtUobsNrQURERFQcmGAlIiIiIrO8PHKzIGQyGVq0aGHBaIwJgoB9+/YByE4GGs7L+TLD86hXr56YUAWyF03q2rWr2GZuiyaVxAWu7t69K277+PiYVcdwhG7nzp3znFIBADp16lSk00J06tQJQUFBaNKkSYHrGp6z4bUgIiIiKg62UgdARERERKWD4cjNSpUqFSi56O3tDTc3t1z3jR49GuvWrcPMmTOxYMGCQsUWHR2Nx48fAwC6d+8OhUJhsmx+C1QNHDhQTNbu3r0bkydPNtpvmGAtCfOvAjC6dd/cEbXR0dHido8ePcyq06tXL1y6dKlAsZUvXx6tWrXK8bxWq0VSUhL++ecfpKenIzIyEpGRkWjZsiW2bdsGX1/fAh1DP8XA06dPCxQfERER0atigpWIiIiIzGKYWBw2bBgWLVpk0XZfJVkZGhoqbuc1/6rh8YDcR6D269cPcrkcOp0OR44cgVKpFJPDaWlpuHXrllg2v5ifPn2KefPmYc+ePbh//z6cnZ3RvHlzTJ06FQMHDjTjzMyTlpYmbjs6OuZbXq1WG430NHc+XXPndzXUpEkThIWF5RnL7t278eGHH+L+/fuIjo5G586dcebMGXh4eJh1DMNzNrwWRERERMWBUwQQERERUb6SkpJw79498XHTpk0t0m5WVhauXbsG4NVut9cnWOVyOXr37m2y3MvnkdsxK1WqhDZt2ojx6UezAsCFCxeg0+nyrK935coVNGrUCIsWLcKtW7egUCjw4sULhIeH44033sAHH3xg7ukViCAI+ZZ58eKF0eMKFSqY1ba55QpCoVBgyJAhiIyMhKurKwAgNjbWaAGu/JhzzkRERERFhQlWIiIiIsrXywtcWSrBevnyZajVajg6OqJevXqFaiM+Ph4XLlwAALRp0waenp4my758HqZGoBqOLjWcq9SwvoeHB6pXr55r/czMTPTv3x9PnjxBo0aNcP78eSiVSiiVSnzzzTeQyWRYsmQJ1q5dm/fJmcnZ2VncVqlU+ZbPysoyemxnZ2fWcUwtgmUJNWrUwJgxY8THv/32G1JTU82qm5GRIW4bXgsiIiKi4sAEKxERERHly/C2eoVCgYYNG1qkXX3CskmTJrCxsSlUG3v37hW3+/btm2dZw/OoVq2ayVvQBwwYIG7v378fmZmZOernNT3Ar7/+ijt37sDJyQn79u0TE9JOTk745JNPMHXqVADAp59+CrVanWfM5jBMKj9//jzf8i/Ph5uSkmLWccwtV1gdOnQQt1UqFc6cOWNWPcNz9vLysnhcRERERHlhgpWIiIiI8mWYWGzQoIHZIx7Nbfe1115DVlYWFi1ahObNm8PZ2RkeHh4YMmQIrl+/nmcbhgnWV51/Va9u3bpo0KABgOykYkRERIHqb9iwAQDwzjvv5DrKddasWZDJZHj48CGOHDmSZ8zmMFwQ6sGDB/mWd3V1NZq3NC4uzqzj6BeSKirlypUzevzo0SOz6j18+FDcLsjiWERERESWwAQrEREREeXL8NZ4S00PYNhulSpV0KpVK8yYMQNXr16FRqPB8+fPsX37drRp08ZkkjU9PR2HDx8GAPj5+eW7WJPheeS3QJXhNAG7du2CRqPBlStX8q2fmpqK6OhoAEDPnj1zLVO9enUxgatP3r4KfVsAjBbhyothgvj06dNm1TG3XGG9PPrWnAW77t+/bzTlgaVGVxMRERGZiwlWIiIiIsqTSqXCP//8Iz62VIJVEARcvHgRALBw4UJkZmbi4MGDyMjIQGpqKrZs2QIXFxe8ePEC06ZNy7WN8PBwcc7RPn36FOg88ltUy3CagNDQUFy5csVoflNT9a9duyYuupRXwle/7+rVq3nGYY4WLVqI25cvXzarTseOHcXtnTt3QqPR5Fk+NTXVaMGvohAZGWn02JzRqJcuXRK3vby8UK1aNYvHRURERJQXJliJiIiIKE+XLl2CVqsVH+eXmDTX7du3oVQqAQA+Pj74+++/ERgYCJlMBoVCgaFDh+Kzzz4DABw8eDDX+T8LMj1AQc+jVatW8PHxAQA8fvwYy5YtE/c5Ojqifv36udYzvK1dXz83+n3m3gafl06dOonb//zzj1mLQ7377rvi9oMHD7B8+fI8y3///fdmLzpVGLdu3UJISIj4uHLlyma91wznafX39y+CyIiIiIjyxgQrEREREeXJcN5RwHIjWA1v11+1alWO+TcB4M033wQA6HQ63L5922ifIAjiiEoXFxcEBATkeTzD8yhXrhxq1KiRZ3mZTIb+/fuLj9euXStuN27c2OSiXIZJSCcnJ5Pt6/dZYuGoWrVqoVatWgAArVaLqKiofOs0atTIaNTvRx99hPDw8FzLbt68GfPmzXvlOHOjVquxdetWBAQEIC0tTXx+zpw5kMlk+dY/duyYuN2jR48iiZGIiIgoL7ZSB0BEREREJZthYlIul2PEiBEFqt+kSRPMnz/fZLsdOnRAu3btcq1bpUoVcVun0xntO3PmjDj6MzAwEPb29nnGYXge5iaJBwwYgBUrVgDITgTqWWoUryUNGjQICxYsAAAcOHDArGTj0qVLcfz4cSiVSmRmZqJnz54YNmwYBgwYgIoVK+LBgwfYtm0bdu3aBQAYOnQofv/99wLFdfHixVznotVqtXjx4gWuXr2K9PR0o32DBw/G1KlT8207LS1NTCbb2toaTetAREREVFyYYCUiIiKiPBmONNXpdDhw4ECB6teuXTvPdvWjVHPz4sULcbtSpUpG+wynB+jbt2++cRieh7kJ0i5dusDNzU2cykAvrwWyXFxcxO309HS4ubnlWk6fVHR1dTUrlvyMGDFCTLDu3r0bixYtyreOn58f9u7di549eyI9PR06nQ4bNmzAhg0bcpR99913MXbs2AInWJ8/f272e8bOzg5z5szBJ598YnKEsKH9+/eL8+J2794dXl5eBYqNiIiIyBI4RQARERERmaTT6cSFqArLcAEmQ/oRpab2A/9btb5ixYo55jMNDQ0FkH0rf34LXL18HuYmWO3s7NCrV68cz+dV3zDOhw8fmiyn31e5cmWzYslPkyZN0Lp1awBAbGwsTp48aVa9jh074uzZsyanWKhQoQLmz5+P9evXWyROPZlMBldXV9SoUQMDBgzAjz/+iPj4eMydOxcKhcKsNjZt2iRuT5o0yaLxEREREZlLJuiXOCUiIiIiKiZPnjwRR6ReuXIFDRs2zLXc6NGjsW7dOowaNcpoAaQHDx6gatWqALIXozp16lSRx2yu1NRUuLm5QRAEbN++HYMHD861XKNGjXDlyhXMmjUL//3vfy1y7C1btuCdd94BAIwfPx4rV64sUP2bN28iKioKCQkJcHd3R40aNdClSxfY2dlZJD5LevLkCapWrQq1Wo2aNWvi5s2bkMs5foSIiIiKH38DISIiIqJiZ3i7/uPHj3MtEx8fj61btwIA3nvvPaN9htMD9OvXz/IBvgIXFxe0atUKABAWFpZrmfv37+Pq1asAgK5du1rs2EOGDEGdOnUAZC9MlZSUVKD6derUwejRozF79mxMmTIFPXv2LJHJVQD45ZdfxHlx//Of/zC5SkRERJLhbyFEREREVOwMF5zS3+pvSKPRYPz48cjIyMCgQYPw+uuvG+03rGPO/KvFbfjw4QCyk5zx8fE59s+fPx+CIMDHxwedO3e22HFtbGzw+eefA8heAGr58uUWa7skUalUWLp0KYDseWTHjBkjcURERERUljHBSkRERETFTj+C1cPDA8HBwVi9erU4GvHSpUvo2bMnDh48CF9f31xvc+/UqRO++OILfPvtt2bPp1qcJk6ciJo1ayItLQ19+/YV53/NyMjAvHnzxOTgN998Y/Z8o+YaNmyYuAjX4sWLkZKSYtH2S4Lly5fjyZMnAIDvvvvO4teQiIiIqCA4BysRERERFbv69evj+vXrWL9+PebOnYs7d+7Azs4ODg4OUCqVALJHJoaFhaFevXoSR1s4V65cQZcuXcREoJubG9LS0qDVagEA77//PpYsWVIkx46KikKHDh0AAF988QXmzp1bJMeRQkpKCmrWrInExES0b98ef/31l9QhERERURnHBCsRERERFav09HS4urpCp9Ph5s2bcHV1xZw5c/Dnn38iOTkZtWrVwtChQzF9+nS4uLhIHe4refLkCebNm4fQ0FDEx8fD2dkZzZs3R1BQEAYOHCh1eERERERkAUywEhERERERERERERUS52AlIiIiIiIiIiIiKiQmWImIiIiIiIiIiIgKiQlWIiIiIiIiIiIiokJigpWIiIiIiIiIiIiokJhgJSIiIiIiIiIiIiokJliJiIiIiIiIiIiICokJViIiIiIiIiIiIqJCYoKViIiIiIiIiIiIqJCYYCUiIiIiIiIiIiIqJCZYiYiIiIiIiIiIiArp/wC794m5PIdjDgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Computing and plotting BER\n",
    "ber_plots.simulate(model,\n",
    "                  ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 20),\n",
    "                  batch_size=BATCH_SIZE,\n",
    "                  num_target_block_errors=100,\n",
    "                  legend=\"Trained model\",\n",
    "                  soft_estimates=True,\n",
    "                  max_mc_iter=100,\n",
    "                  show_fig=True);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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
}