diff --git a/notebooks/software_testing/README.md b/notebooks/software_testing/README.md
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+++ b/notebooks/software_testing/README.md
@@ -0,0 +1,9 @@
+# Stress-Testing Software
+ 
+Concerns have been raised about the robustness of FNO software implementation across different hardwares in the `neuraloperator` package V2.0.0 (avaliable at the time of experimentation). Therefore, we upgrade the `neuraloperator` package and rerun a subset of key experiments (found in `demo.py`).
+ 
+An indepth discussion of zero-shot super-resolution vs. multi-resolution training on different hardware architecture is provided in [Appendix N](https://arxiv.org/pdf/2510.06646). TLDR: We observe a wide variance in error magnitudes across different hardware+software versions. Further, we observe the predicted spectra of the zero-shot model consistently diverged from the label spectra after the maximum observable frequency in the training data. The core trend of multi-resolution training outperforming zero-shot super resolution is consistent across computing architectures+software stacks. While implementation differences in hardware-software stacks give rise to different levels of error in the model’s forward pass it does so consistently across different types of model training regimes (e.g., single-resolution vs. multi-resolution).  
+
+ - `demo_updated_neuraloperator_package.ipynb`: same expriments as `../demo.py`, run on NVIDIA 100 GPU w/ updated `neuraloperator` library (commit: 98cd305099f4a2b232ed85773984f3e5991f9b1a)
+
+We thank Dr. Valentine Duruisseaux for bringing to our attention an updated version of the `neuraloperator` library (commit: 98cd305099f4a2b232ed85773984f3e5991f9b1a) which addresses issues w.r.t. enforcing Hermitian symmetry when taking the irfftn in the spectral convolution layers in FNO. 
diff --git a/notebooks/software_testing/demo_updated_neuraloperator_package.ipynb b/notebooks/software_testing/demo_updated_neuraloperator_package.ipynb
new file mode 100644
index 0000000..b756d23
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+++ b/notebooks/software_testing/demo_updated_neuraloperator_package.ipynb
@@ -0,0 +1,1953 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "66ae15a6-5e61-43d0-bf68-01897ba0214e",
+   "metadata": {},
+   "source": [
+    "# Demo Notebook\n",
+    "[paper](https://openreview.net/forum?id=hkF7ZM7fEp&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3DICLR.cc%2F2026%2FConference%2FAuthors%23your-submissions) | [blog](https://mansisak.com/operator_aliasing/) | [code](https://github.com/msakarvadia/operator_aliasing)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "12d46b1a-11fc-4ee1-815c-b16ada70b3f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import colors\n",
+    "from matplotlib import ticker\n",
+    "from matplotlib.colors import LogNorm\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "data_folder = \"/pscratch/sd/m/mansisak/PDEBench/pdebench_data/2D/DarcyFlow/\" # replace with your file path\n",
+    "file_name = \"2D_DarcyFlow_beta1.0_Train.hdf5\"\n",
+    "initial_steps = 1 # 1 timestep for Darcy flow (change for time varying datasets)\n",
+    "\n",
+    "FIG_DIR = '../figures'\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daa39f20-9d61-4087-ac6b-2ec0d9881a76",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "# Utils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6677f479-aa8c-4157-b827-8fa3108c90c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_energy_curve(\n",
+    "    data: torch.Tensor, normalize: bool = True\n",
+    ") -> torch.Tensor:\n",
+    "    \"\"\"Calculate 2d spectrum of data.\n",
+    "\n",
+    "    data dim: batch x time x X x Y\n",
+    "    \"\"\"\n",
+    "    signal = data.cpu()\n",
+    "    batch_size = signal.shape[0]\n",
+    "    time_points = signal.shape[1]\n",
+    "    n_observations = signal.shape[-1]\n",
+    "    signal = signal.view(\n",
+    "        batch_size, time_points, n_observations, n_observations\n",
+    "    )\n",
+    "\n",
+    "    if normalize:\n",
+    "        signal = torch.fft.fft2(signal, norm='ortho')\n",
+    "    else:\n",
+    "        signal = torch.fft.rfft2(\n",
+    "            signal, s=(n_observations, n_observations), norm='backward'\n",
+    "        )\n",
+    "\n",
+    "    # center FFT\n",
+    "    centered_fft_signal = torch.fft.fftshift(signal)\n",
+    "\n",
+    "    # compute energy\n",
+    "    energy = centered_fft_signal.abs() ** 2\n",
+    "\n",
+    "    # define wavenumbers\n",
+    "    wave_numbers = generate_wavenumbers(n=n_observations)\n",
+    "    max_wavenumber = n_observations // 2\n",
+    "\n",
+    "    spectrum = torch.zeros((batch_size, time_points, n_observations // 2))\n",
+    "    for j in range(1, max_wavenumber + 1):\n",
+    "        ind = torch.where(torch.tensor(wave_numbers) == j)\n",
+    "        spectrum[:, :, j - 1] = energy[:, :, ind[0], ind[1]].sum(dim=-1)\n",
+    "\n",
+    "    time_avg_spectrum = spectrum.mean(dim=1)\n",
+    "    batch_avg_spectrum = time_avg_spectrum.mean(dim=0)\n",
+    "    return batch_avg_spectrum\n",
+    "\n",
+    "def get_model_preds(\n",
+    "    test_loader: torch.utils.data.DataLoader,\n",
+    "    model: torch.nn.Module,\n",
+    "    device: torch.device,\n",
+    "    initial_steps: int,\n",
+    "    # data_transform: DataProcessor,\n",
+    ") -> torch.Tensor:\n",
+    "    \"\"\"Return model predictions.\"\"\"\n",
+    "    model_preds = []\n",
+    "    model = model.to(device)\n",
+    "    with torch.no_grad():\n",
+    "        for _idx, sample in enumerate(test_loader):  # resolution 128\n",
+    "            model_input = sample['x'][0].to(device)\n",
+    "            model_output = sample['y'][0].to(device)\n",
+    "            with torch.no_grad():\n",
+    "                if initial_steps > 1:\n",
+    "                    out = autoregressive_inference(\n",
+    "                        initial_steps, model, model_input, model_output\n",
+    "                    )\n",
+    "                else:\n",
+    "                    out = model(model_input)\n",
+    "                model_preds.append(out)\n",
+    "    return torch.cat(model_preds)\n",
+    "\n",
+    "def generate_wavenumbers(n: int = 6) -> torch.tensor:\n",
+    "    \"\"\"Generate the wavenumbers.\"\"\"\n",
+    "    # n = 7  # Size of the square array\n",
+    "    center = n // 2  # Center of the array\n",
+    "    array = np.zeros((n, n), dtype=int)\n",
+    "\n",
+    "    # Fill values based on distance from the center\n",
+    "    for i in range(n):\n",
+    "        for j in range(n):\n",
+    "            distance = max(abs(center - i), abs(center - j))\n",
+    "            array[i, j] = distance\n",
+    "\n",
+    "    # For even-sized arrays, ensure the center area avoids 0 directly\n",
+    "    if n % 2 == 0:\n",
+    "        for i in range(n):\n",
+    "            for j in range(n):\n",
+    "                if i + j >= n:\n",
+    "                    array[i, j] += 1\n",
+    "    return array\n",
+    "    \n",
+    "def get_energy_curve(\n",
+    "    data: torch.Tensor, normalize: bool = True\n",
+    ") -> torch.Tensor:\n",
+    "    \"\"\"Calculate 2d spectrum of data.\n",
+    "\n",
+    "    data dim: batch x time x X x Y\n",
+    "    \"\"\"\n",
+    "    signal = data.cpu()\n",
+    "    batch_size = signal.shape[0]\n",
+    "    time_points = signal.shape[1]\n",
+    "    n_observations = signal.shape[-1]\n",
+    "    signal = signal.view(\n",
+    "        batch_size, time_points, n_observations, n_observations\n",
+    "    )\n",
+    "\n",
+    "    if normalize:\n",
+    "        signal = torch.fft.fft2(signal, norm='ortho')\n",
+    "    else:\n",
+    "        signal = torch.fft.rfft2(\n",
+    "            signal, s=(n_observations, n_observations), norm='backward'\n",
+    "        )\n",
+    "\n",
+    "    # center FFT\n",
+    "    centered_fft_signal = torch.fft.fftshift(signal)\n",
+    "\n",
+    "    # compute energy\n",
+    "    energy = centered_fft_signal.abs() ** 2\n",
+    "\n",
+    "    # define wavenumbers\n",
+    "    wave_numbers = generate_wavenumbers(n=n_observations)\n",
+    "    max_wavenumber = n_observations // 2\n",
+    "\n",
+    "    spectrum = torch.zeros((batch_size, time_points, n_observations // 2))\n",
+    "    for j in range(1, max_wavenumber + 1):\n",
+    "        ind = torch.where(torch.tensor(wave_numbers) == j)\n",
+    "        spectrum[:, :, j - 1] = energy[:, :, ind[0], ind[1]].sum(dim=-1)\n",
+    "\n",
+    "    time_avg_spectrum = spectrum.mean(dim=1)\n",
+    "    batch_avg_spectrum = time_avg_spectrum.mean(dim=0)\n",
+    "    return batch_avg_spectrum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4cee131-42c6-425f-bcd8-018371066cb1",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "# Data\n",
+    "\n",
+    "Download Darcy Dataset from PDEBench: https://github.com/pdebench/PDEBench/tree/main/pdebench/data_download"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bba24120-a43a-4259-94b3-a45c18b47223",
+   "metadata": {},
+   "source": [
+    "#### Transformations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "90cfc82d-126e-4b2a-acc4-116fb9b7b24b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Transforms to apply to data prior to training.\"\"\"\n",
+    "\n",
+    "from __future__ import annotations\n",
+    "\n",
+    "import typing\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn.functional as f\n",
+    "\n",
+    "def filter_batch(\n",
+    "    filt: torch.tensor, batch: torch.tensor, ndim: int\n",
+    ") -> torch.Tensor:\n",
+    "    \"\"\"Apply (low-pass) filter to batch.\n",
+    "\n",
+    "    filt:\n",
+    "            filter for the batch\n",
+    "            (already centered i.e., torch.fft.fftshift)\n",
+    "            dim: (channel x X_dim x Y_dim) 2D\n",
+    "    batch:\n",
+    "            input batch\n",
+    "            dim: (batch x channel x X_dim x Y_dim ) 2D\n",
+    "    ndim:\n",
+    "            number of spatial dimentions\n",
+    "\n",
+    "    \"\"\"\n",
+    "    dim: typing.Any = (-2, -1)\n",
+    "    if ndim == 1:\n",
+    "        dim = -1\n",
+    "    # fft batch\n",
+    "    batch_fourier = torch.fft.fftn(batch, dim=dim)\n",
+    "\n",
+    "    # center batch\n",
+    "    fourier_centered = torch.fft.fftshift(batch_fourier)\n",
+    "\n",
+    "    # apply filter\n",
+    "    filtered_batch = fourier_centered * filt\n",
+    "\n",
+    "    # convert batch back to spatial domain\n",
+    "    filtered_batch = torch.real(\n",
+    "        torch.fft.ifftn(torch.fft.ifftshift(filtered_batch), dim=dim)\n",
+    "    )\n",
+    "\n",
+    "    return filtered_batch\n",
+    "\n",
+    "def generate_wavenumbers(n: int = 6) -> torch.tensor:\n",
+    "    \"\"\"Generate the wavenumbers.\"\"\"\n",
+    "    # n = 7  # Size of the square array\n",
+    "    center = n // 2  # Center of the array\n",
+    "    array = np.zeros((n, n), dtype=int)\n",
+    "\n",
+    "    # Fill values based on distance from the center\n",
+    "    for i in range(n):\n",
+    "        for j in range(n):\n",
+    "            distance = max(abs(center - i), abs(center - j))\n",
+    "            array[i, j] = distance\n",
+    "\n",
+    "    # For even-sized arrays, ensure the center area avoids 0 directly\n",
+    "    if n % 2 == 0:\n",
+    "        for i in range(n):\n",
+    "            for j in range(n):\n",
+    "                if i + j >= n:\n",
+    "                    array[i, j] += 1\n",
+    "    return array\n",
+    "    \n",
+    "def get_2d_low_pass_filter(f: int, s: int) -> torch.Tensor:\n",
+    "    \"\"\"Return's low pass filter at limit f and size s.\n",
+    "\n",
+    "    f : the frequency limit (only allow freq < f)\n",
+    "    s : dimention of image\n",
+    "    \"\"\"\n",
+    "    if f > s // 2:\n",
+    "        raise Exception(f'Max frequency of image is {s//2=}, so lower f')\n",
+    "    # 1 = keep, 0 = get rid of\n",
+    "    # central of shifted FFT image is lowest freqs\n",
+    "    wave_numbers = generate_wavenumbers(s)\n",
+    "\n",
+    "    filt = torch.where(torch.tensor(wave_numbers) < f, 1, 0)\n",
+    "    return filt.unsqueeze(dim=0)\n",
+    "\n",
+    "\n",
+    "class LowpassFilter:\n",
+    "    \"\"\"Lowpass filter the image.\n",
+    "\n",
+    "    Args:\n",
+    "        filter_limit: frequencies > filter_lim excluded\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, filter_limit: int, n_dim: int) -> None:\n",
+    "        \"\"\"Initialize filter transform.\n",
+    "\n",
+    "        filter_limit: number of frequencies to keep\n",
+    "        n_dim: number of spatial dimentions\n",
+    "            typically you use n_dim to choose filter dim (1d v. 2d)\n",
+    "            here we simplify and only have 2d filter (see repo for more options)\n",
+    "        \"\"\"\n",
+    "        assert isinstance(filter_limit, int)\n",
+    "        self.filter_limit = filter_limit\n",
+    "        self.n_dim = n_dim\n",
+    "\n",
+    "    def __call__(\n",
+    "        self, sample: dict[str, torch.Tensor]\n",
+    "    ) -> dict[str, torch.Tensor]:\n",
+    "        \"\"\"Apply transform.\"\"\"\n",
+    "        model_input, label = sample['x'], sample['y']\n",
+    "        img_size = model_input.shape[-1]\n",
+    "        # assert that filter limit is less than half img_size\n",
+    "        assert self.filter_limit <= img_size // 2\n",
+    "\n",
+    "        # get filter\n",
+    "        self.filter = get_2d_low_pass_filter(self.filter_limit, img_size)\n",
+    "\n",
+    "        # apply no filter\n",
+    "        if self.filter_limit == -1:\n",
+    "            return sample\n",
+    "\n",
+    "        filter_input = filter_batch(self.filter, model_input, self.n_dim)\n",
+    "        filter_label = filter_batch(self.filter, label, self.n_dim)\n",
+    "\n",
+    "        sample['x'] = filter_input\n",
+    "        sample['y'] = filter_label\n",
+    "        return sample\n",
+    "\n",
+    "\n",
+    "class DownSample:\n",
+    "    \"\"\"Downsize image.\n",
+    "\n",
+    "    Args:\n",
+    "        out_size: x/y dim of downsampled obj\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, out_size: int, n_dim: int) -> None:\n",
+    "        \"\"\"Initialize downsample transform.\n",
+    "\n",
+    "        outsize: resized spatial dim\n",
+    "        n_dim: number of spatial dims\n",
+    "        \"\"\"\n",
+    "        assert isinstance(out_size, int)\n",
+    "        self.out_size = out_size\n",
+    "        self.n_dim = n_dim\n",
+    "\n",
+    "    def __call__(\n",
+    "        self, sample: dict[str, torch.Tensor]\n",
+    "    ) -> dict[str, torch.Tensor]:\n",
+    "        \"\"\"Apply transform.\"\"\"\n",
+    "        model_input, label = sample['x'], sample['y']\n",
+    "\n",
+    "        # apply no downsample\n",
+    "        if self.out_size == -1:\n",
+    "            return sample\n",
+    "\n",
+    "        # desired spatial dim\n",
+    "        shape = tuple([self.out_size] * self.n_dim)\n",
+    "        # store dimention for post downsample batch/channel/time dim\n",
+    "        input_post_shape = model_input.shape[: -self.n_dim] + shape\n",
+    "        label_post_shape = label.shape[: -self.n_dim] + shape\n",
+    "\n",
+    "        pre_shape: typing.Any = (model_input.shape[0], -1)\n",
+    "        for _dim in range(self.n_dim):\n",
+    "            pre_shape += (model_input.shape[-1],)\n",
+    "\n",
+    "        # collapse time and channel dim if it exists\n",
+    "        model_input = torch.reshape(model_input, pre_shape)\n",
+    "        label = torch.reshape(label, pre_shape)\n",
+    "\n",
+    "        antialias = self.n_dim > 1\n",
+    "        mode = 'bicubic' if self.n_dim > 1 else 'linear'\n",
+    "        downsample_input = f.interpolate(\n",
+    "            model_input,\n",
+    "            size=shape,\n",
+    "            mode=mode,\n",
+    "            antialias=antialias,\n",
+    "        )\n",
+    "\n",
+    "        downsample_label = f.interpolate(\n",
+    "            label,\n",
+    "            size=shape,\n",
+    "            mode=mode,\n",
+    "            antialias=antialias,\n",
+    "        )\n",
+    "        # uncollapse time and channel dims if they existed\n",
+    "        sample['x'] = torch.reshape(downsample_input, input_post_shape)\n",
+    "        sample['y'] = torch.reshape(downsample_label, label_post_shape)\n",
+    "        return sample"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68fcf9b7-8363-4bef-9192-7d242fa3db79",
+   "metadata": {},
+   "source": [
+    "#### Data loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "24705c87-4df7-4040-9e23-9f1c96b9a09c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import typing\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import h5py\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "from numpy.random import default_rng\n",
+    "from torch.utils.data import Dataset\n",
+    "from torchvision.transforms import Compose\n",
+    "\n",
+    "# https://github.com/pdebench/PDEBench/blob/main/pdebench/models/fno/utils.py\n",
+    "\n",
+    "class DarcyPDEBench(Dataset):\n",
+    "    \"\"\"Darcy Dataset from PDE Bench.\"\"\"\n",
+    "\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        filename: str,\n",
+    "        saved_folder: str = '../data/',\n",
+    "        train: bool = True,\n",
+    "        transform: Compose = None,\n",
+    "        resolution_proportions: list[float]=[1,0,0,0],\n",
+    "        **kwargs,\n",
+    "    ):\n",
+    "        \"\"\"Initialize data.\n",
+    "\n",
+    "        :param filename: filename that contains the dataset\n",
+    "        :type filename: STR\n",
+    "        :saved folder: path to PDE bench data\n",
+    "        : train: bool (True = train, False=Test)\n",
+    "        : transforms: Any torch transforms (we will define downsampling/filtering transforms)\n",
+    "        : resolution_proportions: must sum to 1. Fraction of dataset for resolutions [128,64,32,16]\n",
+    "            [1,0,0,0] == 100% 128 res\n",
+    "            [0,0.5,0.5,0] == 50% 64, 50% 32\n",
+    "        support img dimentions: 128, 64, 32, 16 (highest to lowest)\n",
+    "        \"\"\"\n",
+    "        self.transform = transform\n",
+    "\n",
+    "        four = 4\n",
+    "        assert len(resolution_proportions) == four, (\n",
+    "            'Only support 4 img_resolutions, see doc string.'\n",
+    "        )\n",
+    "        assert sum(resolution_proportions) == 1, (\n",
+    "            'All dataset proportions must sum to 1.'\n",
+    "        )\n",
+    "        self.rng = default_rng(seed=0)\n",
+    "        self.batch_size = 128\n",
+    "        test_ratio = 0.1\n",
+    "        num_samples_max = -1\n",
+    "\n",
+    "        # Define path to files\n",
+    "        self.model_inputs = []\n",
+    "        self.labels = []\n",
+    "        root_path = Path(Path(saved_folder).resolve()) / filename\n",
+    "        # preprocessing for train/test sets and\n",
+    "        # ...initializing/shuffling data indexes\n",
+    "        with h5py.File(root_path, 'r') as f:\n",
+    "            # num of data samples\n",
+    "            num_samples_max = f['tensor'].shape[0]\n",
+    "\n",
+    "            # list of data idxs\n",
+    "            data_idx = np.arange(0, num_samples_max)\n",
+    "            # num of test samples\n",
+    "            test_idx = int(num_samples_max * test_ratio)\n",
+    "            if train:\n",
+    "                first_batch_idx = test_idx\n",
+    "                last_batch_idx = -1\n",
+    "                self.num_samples = num_samples_max - test_idx\n",
+    "            else:\n",
+    "                first_batch_idx = 0\n",
+    "                last_batch_idx = test_idx\n",
+    "                self.num_samples = test_idx\n",
+    "            print(f'{self.num_samples=}')\n",
+    "            # grab data indexs\n",
+    "            self.data_idxs = data_idx[first_batch_idx:last_batch_idx]\n",
+    "            # shuffle indexes\n",
+    "            self.rng.shuffle(self.data_idxs)\n",
+    "\n",
+    "        for res_factor, ratio in enumerate(resolution_proportions):\n",
+    "            reduced_resolution = 2**res_factor\n",
+    "            with h5py.File(root_path, 'r') as f:\n",
+    "                # number of points in this resolution set\n",
+    "                res_idx = int(self.num_samples * ratio)\n",
+    "                # sort all indexes\n",
+    "                set_indexes = np.sort(self.data_idxs[:res_idx])\n",
+    "                # u: label\n",
+    "                label = np.array(\n",
+    "                    f['tensor'][\n",
+    "                        set_indexes,\n",
+    "                        :,\n",
+    "                        ::reduced_resolution,\n",
+    "                        ::reduced_resolution,\n",
+    "                    ],\n",
+    "                    dtype=np.float32,\n",
+    "                )\n",
+    "\n",
+    "                # batch, time, x,...\n",
+    "                _data = np.array(f['nu'], dtype=np.float32)\n",
+    "                # nu: input\n",
+    "                model_input = _data[\n",
+    "                    set_indexes,\n",
+    "                    None,\n",
+    "                    ::reduced_resolution,\n",
+    "                    ::reduced_resolution,\n",
+    "                ]\n",
+    "\n",
+    "                self.model_inputs.append(torch.tensor(model_input))\n",
+    "                self.labels.append(torch.tensor(label))\n",
+    "\n",
+    "                # remove already used indexes\n",
+    "                self.data_idxs = self.data_idxs[res_idx:]\n",
+    "\n",
+    "    def __len__(self) -> int:\n",
+    "        \"\"\"Returns len of dataset.\n",
+    "\n",
+    "        Recall this is a pre-batched dataset, so we return\n",
+    "        number of batches.\n",
+    "        \"\"\"\n",
+    "        total_batches = 0\n",
+    "        for _set_idx, res_set in enumerate(self.model_inputs):\n",
+    "            num_batches_in_set = math.ceil(len(res_set) / self.batch_size)\n",
+    "            total_batches += num_batches_in_set\n",
+    "        return total_batches\n",
+    "\n",
+    "    def __getitem__(self, batch_idx: int) -> dict[str, torch.Tensor]:\n",
+    "        \"\"\"Get single sample at idx.\"\"\"\n",
+    "        # iterate through all resoulution sets to find batch\n",
+    "        for _set_idx, res_set in enumerate(self.model_inputs):\n",
+    "            num_batches_in_set = math.ceil(len(res_set) / self.batch_size)\n",
+    "            if batch_idx >= num_batches_in_set:\n",
+    "                batch_idx -= num_batches_in_set\n",
+    "            else:\n",
+    "                item_idx = int(batch_idx * self.batch_size)\n",
+    "                set_idx = _set_idx\n",
+    "                break\n",
+    "\n",
+    "        # return whole batch, not just single datapoint\n",
+    "        sample = {\n",
+    "            'x': self.model_inputs[set_idx][\n",
+    "                item_idx : item_idx + self.batch_size\n",
+    "            ],\n",
+    "            'y': self.labels[set_idx][item_idx : item_idx + self.batch_size],\n",
+    "        }\n",
+    "        if self.transform:\n",
+    "            sample = self.transform(sample)\n",
+    "        return sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "71e184a2-bfee-4279-8ea9-d30d05b16c53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "def get_data(\n",
+    "    **data_args: typing.Any,\n",
+    ") -> tuple[DataLoader, dict[str, DataLoader]]:\n",
+    "    \"\"\"Get data w/ args.\"\"\"\n",
+    "    seed = 0\n",
+    "    g = torch.Generator()\n",
+    "    g.manual_seed(seed)\n",
+    "\n",
+    "    # set train specific kwarg\n",
+    "    data_args['train'] = True\n",
+    "    train_dataset = DarcyPDEBench(**data_args)\n",
+    "\n",
+    "    test_datasets = {}\n",
+    "\n",
+    "    # NOTE(MS): single dataset standard\n",
+    "    # intended use: HP search, filter/downsample exp\n",
+    "    test_kwargs = data_args\n",
+    "    test_kwargs['train'] = False\n",
+    "    if test_kwargs['test_res'] == 'single':\n",
+    "        test_dataset = DarcyPDEBench(**test_kwargs)\n",
+    "        test_datasets['test'] = test_dataset\n",
+    "\n",
+    "    # multiple test_datasets\n",
+    "    # NOTE(MS): (this may not work w/ multiple downsample/filter regeims)\n",
+    "    # intended us: multi-res training testing\n",
+    "    if test_kwargs['test_res'] == 'multi':\n",
+    "        for res in range(4):\n",
+    "            resolution_ratios = [0, 0, 0, 0]\n",
+    "            resolution_ratios[res] = 1\n",
+    "            test_kwargs['resolution_ratios'] = resolution_ratios\n",
+    "            test_dataset = DarcyPDEBench(**test_kwargs)\n",
+    "            test_datasets[f'test_res_{res}'] = test_dataset\n",
+    "\n",
+    "    training_loader = DataLoader(\n",
+    "        train_dataset,\n",
+    "        batch_size=1,\n",
+    "        shuffle=True,\n",
+    "        generator=g,\n",
+    "        num_workers=8,\n",
+    "        pin_memory=True,\n",
+    "        persistent_workers=True,\n",
+    "    )\n",
+    "\n",
+    "    testing_loaders = {}\n",
+    "    for k, test_dataset in test_datasets.items():\n",
+    "        testing_loaders[k] = DataLoader(\n",
+    "            test_dataset,\n",
+    "            batch_size=1,\n",
+    "            shuffle=False,\n",
+    "            generator=g,\n",
+    "            num_workers=8,\n",
+    "            pin_memory=True,\n",
+    "            persistent_workers=True,\n",
+    "        )\n",
+    "    return (training_loader, testing_loaders)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "162d3d5b-79dc-4725-93fb-9e0ce81cc1ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=1000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This is how we graph a train dataset of resolution 128 and test datasets of resolution [128,64,32,16]\n",
+    "train_loader, test_loaders = get_data(filename=file_name,\n",
+    "        saved_folder=data_folder,\n",
+    "        transform=None, \n",
+    "        resolution_proportions=[1,0,0,0],\n",
+    "        test_res=\"multi\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c623bbf-6a52-455c-b68e-fdcf7e7f88d4",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "# Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9c143972-c648-46ea-a30f-291e87d81f4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FNO(\n",
+       "  (positional_embedding): GridEmbeddingND()\n",
+       "  (fno_blocks): FNOBlocks(\n",
+       "    (convs): ModuleList(\n",
+       "      (0-3): 4 x SpectralConv(\n",
+       "        (weight): DenseTensor(shape=torch.Size([32, 32, 8, 5]), rank=None)\n",
+       "      )\n",
+       "    )\n",
+       "    (fno_skips): ModuleList(\n",
+       "      (0-3): 4 x Flattened1dConv(\n",
+       "        (conv): Conv1d(32, 32, kernel_size=(1,), stride=(1,), bias=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (channel_mlp): ModuleList(\n",
+       "      (0-3): 4 x ChannelMLP(\n",
+       "        (fcs): ModuleList(\n",
+       "          (0): Conv1d(32, 16, kernel_size=(1,), stride=(1,))\n",
+       "          (1): Conv1d(16, 32, kernel_size=(1,), stride=(1,))\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (channel_mlp_skips): ModuleList(\n",
+       "      (0-3): 4 x SoftGating()\n",
+       "    )\n",
+       "  )\n",
+       "  (lifting): ChannelMLP(\n",
+       "    (fcs): ModuleList(\n",
+       "      (0): Conv1d(3, 64, kernel_size=(1,), stride=(1,))\n",
+       "      (1): Conv1d(64, 32, kernel_size=(1,), stride=(1,))\n",
+       "    )\n",
+       "  )\n",
+       "  (projection): ChannelMLP(\n",
+       "    (fcs): ModuleList(\n",
+       "      (0): Conv1d(32, 64, kernel_size=(1,), stride=(1,))\n",
+       "      (1): Conv1d(64, 1, kernel_size=(1,), stride=(1,))\n",
+       "    )\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from neuralop.models import FNO\n",
+    "def get_fno(n_modes):\n",
+    "    starting_modes = (n_modes, n_modes)\n",
+    "    model = FNO(\n",
+    "        n_modes=starting_modes,\n",
+    "        hidden_channels=32,\n",
+    "        in_channels=1,\n",
+    "        out_channels=1,\n",
+    "    )\n",
+    "    return model\n",
+    "\n",
+    "get_fno(n_modes=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15ca4345-f278-440b-83c5-7a101d3f07a7",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "# Train/Test Utils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d2fa64c3-ce57-4ec1-9495-34300f8efec6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.nn import Module\n",
+    "from torch.optim import AdamW\n",
+    "from tqdm import tqdm\n",
+    "def train_model(model, train_dataloader, test_dataloaders) -> Module:\n",
+    "    \"\"\"Train a model.\"\"\"\n",
+    "    # set up trianing args\n",
+    "    epochs = 150\n",
+    "    loss = nn.MSELoss()\n",
+    "\n",
+    "    # set up optimizer and scheduler\n",
+    "    optimizer = AdamW(\n",
+    "        model.parameters(),\n",
+    "        lr=0.001,\n",
+    "        weight_decay=0.00001,\n",
+    "    )\n",
+    "    scheduler = torch.optim.lr_scheduler.StepLR(\n",
+    "        optimizer, step_size=50, gamma=0.5\n",
+    "    )\n",
+    "    starting_epoch = 0\n",
+    "    model = model.to(device)\n",
+    "\n",
+    "    # train model\n",
+    "    pbar = tqdm(range(starting_epoch, epochs + 1), desc=\"Training\")\n",
+    "    #for epoch in tqdm(range(starting_epoch, epochs + 1), desc=\"Training\"):\n",
+    "    for epoch in pbar:\n",
+    "        train_loss = 0.0\n",
+    "        # timing to exclude potential data loading\n",
+    "        forward_back_time = 0.0\n",
+    "        for _step, batch in enumerate(train_dataloader):\n",
+    "            # NOTE(MS): must remove outer batch dim from dataloader\n",
+    "            # because we pre-batch data due to multi-res training\n",
+    "            input_batch = batch['x'][0].to(device)\n",
+    "            output_batch = batch['y'][0].to(device)\n",
+    "            batch['device'] = device\n",
+    "\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "            output_pred_batch = model(input_batch)\n",
+    "            loss_f = loss(\n",
+    "                output_pred_batch, output_batch\n",
+    "            )\n",
+    "\n",
+    "            loss_f.backward()\n",
+    "            optimizer.step()\n",
+    "            train_loss += loss_f.item()\n",
+    "        train_loss /= len(train_dataloader)\n",
+    "        scheduler.step()\n",
+    "        # test model\n",
+    "        test_dict = test_model(\n",
+    "            model,\n",
+    "            test_dataloaders,\n",
+    "            device,\n",
+    "        )\n",
+    "        pbar_str =f\"{train_loss:.1}\"\n",
+    "        for key in test_dict.keys():\n",
+    "            pbar_str+=f\" {key}={test_dict[key]:.1}\"\n",
+    "        pbar.set_postfix(train_loss=pbar_str)\n",
+    "\n",
+    "    return model.to('cpu')\n",
+    "\n",
+    "def test_model(\n",
+    "    model: Module,\n",
+    "    test_dataloaders: dict[str, torch.utils.data.DataLoader],\n",
+    "    device: torch.device,\n",
+    ") -> dict[str, float]:\n",
+    "    \"\"\"Test model.\"\"\"\n",
+    "    test_dict = {}\n",
+    "    loss = nn.MSELoss()\n",
+    "    with torch.no_grad():\n",
+    "        model.eval()\n",
+    "        for test_label, test_dataloader in test_dataloaders.items():\n",
+    "            test_relative_l2 = 0.0\n",
+    "            for _step, batch in enumerate(test_dataloader):\n",
+    "                # NOTE(MS): must remove outer batch dim from dataloader\n",
+    "                # because we pre-batch data due to multi-res training\n",
+    "                input_batch = batch['x'][0].to(device)\n",
+    "                output_batch = batch['y'][0].to(device)\n",
+    "                batch['device'] = device\n",
+    "\n",
+    "                output_pred_batch = model(input_batch)\n",
+    "                loss_f = loss(\n",
+    "                    output_pred_batch,\n",
+    "                    output_batch,\n",
+    "                )\n",
+    "\n",
+    "                test_relative_l2 += loss_f.item()\n",
+    "            test_relative_l2 /= len(test_dataloader)\n",
+    "            test_dict[test_label] = test_relative_l2\n",
+    "    return test_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3848ab8-76ce-4612-9dce-215edd114128",
+   "metadata": {},
+   "source": [
+    "# Zero-shot super-resolution\n",
+    "\n",
+    "Train a model @ resolution 16, test at resoluitons [16,32,64,128].\n",
+    "Visualize the average spectra across all 4 test sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a94eb5d4-4c0e-465c-a93e-923913f4e545",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training: 100%|██████████| 151/151 [02:12<00:00,  1.14it/s, train_loss=0.0002 test_res_0=0.0004 test_res_1=0.0004 test_res_2=0.0004 test_res_3=0.0004]\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = get_fno(n_modes=8)\n",
+    "train_dataloader, test_dataloaders = get_data(filename=file_name,\n",
+    "        saved_folder=data_folder,\n",
+    "        transform=None, \n",
+    "        resolution_proportions=[0,0,0,1],\n",
+    "        test_res=\"multi\")\n",
+    "\n",
+    "model = train_model(model, train_dataloader, test_dataloaders)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "1135172c-0a7e-449e-a2a8-d54bbc896740",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_887381/3059218876.py:84: DeprecationWarning: `trapz` is deprecated. Use `trapezoid` instead, or one of the numerical integration functions in `scipy.integrate`.\n",
+      "  zero_shot_aucs.append(abs(np.trapz(x, normalized_residuals).item()))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=1000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_887381/3059218876.py:100: UserWarning: The figure layout has changed to tight\n",
+      "  plt.tight_layout()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# fig, axs = plt.subplots(3, 4, figsize=(8, 8))\n",
+    "\n",
+    "fig = plt.figure(layout='constrained', figsize=(8, 8))\n",
+    "subfigs = fig.subfigures(2, 1, wspace=0.07)  # three rows\n",
+    "\n",
+    "img_axs = subfigs[0].subplots(2, 4, sharey=False)\n",
+    "spectrum_axs = subfigs[1].subplots(2, 4, sharey='row', sharex='col', gridspec_kw={'height_ratios': [1, 1]})\n",
+    "model = model.to(device)\n",
+    "model = model.eval()\n",
+    "zero_shot_aucs = []\n",
+    "\n",
+    "for x_idx, res in enumerate([16, 32, 64, 128]):\n",
+    "    resolution_ratio = [0, 0, 0, 0]\n",
+    "    resolution_ratio[-(x_idx + 1)] = 1\n",
+    "    resolutions = [16, 32, 64, 128]  # [128, 64, 32, 16]\n",
+    "    dataset = DarcyPDEBench(filename=file_name,\n",
+    "        saved_folder=data_folder,\n",
+    "        train=False,\n",
+    "        transform=None, \n",
+    "        resolution_proportions=resolution_ratio,\n",
+    "        )\n",
+    "    # sample = dataset[0]\n",
+    "    sample = {\n",
+    "        'x': torch.concat([dataset[i]['x'] for i in range(len(dataset))]),\n",
+    "        'y': torch.concat([dataset[i]['y'] for i in range(len(dataset))]),\n",
+    "    }\n",
+    "    # grab a single input from the last batch\n",
+    "    model_input = sample['x'].to(device) \n",
+    "    with torch.no_grad():\n",
+    "        model_output = model(x=model_input)\n",
+    "    ground_truth = sample['y']\n",
+    "    residuals = model_output - ground_truth.to(\n",
+    "        device\n",
+    "    )  \n",
+    "\n",
+    "    # convert to plotable form\n",
+    "    model_in = model_input[0, 0, :, :].cpu().numpy()\n",
+    "    output = model_output[0, 0, :, :].cpu().numpy()\n",
+    "    label = ground_truth[0, 0, :, :].cpu().numpy()\n",
+    "\n",
+    "    # get spectrums\n",
+    "    label_spectrum = get_energy_curve(ground_truth)\n",
+    "    pred_spectrum = get_energy_curve(model_output)\n",
+    "    residual_spectrum = get_energy_curve(residuals)\n",
+    "\n",
+    "    img = output  # - label\n",
+    "    norm = colors.LogNorm(img.mean() + 0.5 * img.std(), img.max(), clip='True')\n",
+    "    img_axs[1, x_idx].imshow(img, norm=norm, cmap='bwr')\n",
+    "\n",
+    "    img = label\n",
+    "    norm = colors.LogNorm(img.mean() + 0.5 * img.std(), img.max(), clip='True')\n",
+    "    img_axs[0, x_idx].imshow(img, norm=norm, cmap='bwr')\n",
+    "\n",
+    "    img_axs[0, x_idx].set_xticks([])\n",
+    "    img_axs[0, x_idx].set_yticks([])\n",
+    "    img_axs[1, x_idx].set_xticks([])\n",
+    "    img_axs[1, x_idx].set_yticks([])\n",
+    "\n",
+    "    if x_idx == 0:\n",
+    "        img_axs[0, x_idx].set_ylabel('Label')\n",
+    "        img_axs[1, x_idx].set_ylabel('Example Prediction')\n",
+    "\n",
+    "    spectrum_axs[0,x_idx].set_yscale('log')\n",
+    "    spectrum_axs[1,x_idx].set_yscale('log')\n",
+    "    x = np.linspace(1, len(label_spectrum), len(label_spectrum))\n",
+    "    spectrum_axs[0,x_idx].plot(x, label_spectrum, label='Ground Truth', color=\"blue\", ls=\":\")\n",
+    "    spectrum_axs[0,x_idx].plot(x, pred_spectrum, label='Model Prediction', color=\"orange\")\n",
+    "    spectrum_axs[0,x_idx].fill_between(x, label_spectrum, pred_spectrum, color='red', alpha=0.1)\n",
+    "    spectrum_axs[0,x_idx].axvline(x=8, color='gray', linestyle='--', label=\"Train Limit\")\n",
+    "    spectrum_axs[1,x_idx].axvline(x=8, color='gray', linestyle='--',) \n",
+    "    normalized_residuals = residual_spectrum / label_spectrum\n",
+    "    spectrum_axs[1,x_idx].plot(\n",
+    "        x,\n",
+    "        normalized_residuals,\n",
+    "        label='Normalized Residual',\n",
+    "        color='red',\n",
+    "    )\n",
+    "    spectrum_axs[0, 0].set_ylabel('Energy')\n",
+    "    spectrum_axs[1, 0].set_ylabel('Energy')\n",
+    "    spectrum_axs[1,x_idx].xaxis.set_major_locator(\n",
+    "        ticker.MaxNLocator(integer=True, nbins=4)\n",
+    "    )\n",
+    "    spectrum_axs[1,x_idx].set_xlabel('Frequency')\n",
+    "    zero_shot_aucs.append(abs(np.trapz(x, normalized_residuals).item()))\n",
+    "\n",
+    "\n",
+    "handles, labels = spectrum_axs[0,x_idx].get_legend_handles_labels()\n",
+    "_handles, _labels = spectrum_axs[1,x_idx].get_legend_handles_labels()\n",
+    "handles = handles + _handles\n",
+    "labels = labels + _labels\n",
+    "fig.legend(\n",
+    "    handles=handles,\n",
+    "    labels=labels,\n",
+    "    loc='upper center',\n",
+    "    bbox_to_anchor=(0.5, -0.1),\n",
+    "    fancybox=False,\n",
+    "    shadow=False,\n",
+    "    ncol=9,\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ef369b3-7d52-453d-b376-f1a70e8bdce7",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "# Resolution Interpolation\n",
+    "\n",
+    "Filter before downsampling. Assessing if models can accuratly interpolate points in data that has fixed frequency information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5da27c3b-a422-4f8b-84ad-0a1cb1cccd06",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training: 100%|██████████| 151/151 [05:10<00:00,  2.05s/it, train_loss=0.0002 test=0.0003]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training: 100%|██████████| 151/151 [05:04<00:00,  2.02s/it, train_loss=8e-05 test=0.0004] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training: 100%|██████████| 151/151 [07:09<00:00,  2.85s/it, train_loss=7e-05 test=0.0004] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training: 100%|██████████| 151/151 [23:42<00:00,  9.42s/it, train_loss=7e-05 test=0.0004] \n"
+     ]
+    }
+   ],
+   "source": [
+    "fixed_lim = 8 # filter limit\n",
+    "downsample_dims = [16, 32, 64, -1] # data downsampled resoutions: 16, 32, 64, 128\n",
+    "img_size = 128\n",
+    "max_modes = [\n",
+    "    img_size // 16,\n",
+    "    img_size // 8,\n",
+    "    img_size // 4,\n",
+    "    img_size // 2,\n",
+    "]\n",
+    "\n",
+    "trained_models = []\n",
+    "for max_mode, downsample_dim in zip(max_modes, downsample_dims):\n",
+    "    # Handle data transformations\n",
+    "    data_transforms = transforms.Compose(\n",
+    "        [\n",
+    "            LowpassFilter(fixed_lim, 2),\n",
+    "            DownSample(downsample_dim, 2),\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "    train_dataloader, test_dataloaders = get_data(filename=file_name,\n",
+    "        saved_folder=data_folder,\n",
+    "        transform=data_transforms, \n",
+    "        resolution_proportions=[1,0,0,0],\n",
+    "        test_res=\"single\")\n",
+    "\n",
+    "    model = get_fno(n_modes=max_mode)\n",
+    "    model = train_model(model, train_dataloader, test_dataloaders)\n",
+    "    trained_models.append(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "892b0fb4-b15f-4f5e-8c06-2cb21eb2e3c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x250 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "subplot_labels = ['a', 'b', 'c', 'd']\n",
+    "\n",
+    "train_resolutions = [1, 0, 0, 0]\n",
+    "fig, axes = plt.subplots(\n",
+    "    nrows=1, ncols=4, sharey=True, sharex='col', figsize=(10, 2.5)\n",
+    ")\n",
+    "\n",
+    "for x_idx, downsample_dim in enumerate(downsample_dims):\n",
+    "    model = trained_models[x_idx]\n",
+    "\n",
+    "    # get data at various sizes\n",
+    "    for idx, test_down in enumerate(downsample_dims):\n",
+    "        # get filtered and downsampled test data\n",
+    "        data_transforms = transforms.Compose(\n",
+    "            [\n",
+    "                LowpassFilter(fixed_lim, 2),\n",
+    "                DownSample(test_down, 2),\n",
+    "            ]\n",
+    "        )\n",
+    "        train_dataloader, test_dataloaders = get_data(filename=file_name,\n",
+    "            saved_folder=data_folder,\n",
+    "            transform=data_transforms, \n",
+    "            resolution_proportions=[1,0,0,0],\n",
+    "            test_res=\"single\")\n",
+    "        test_loader = test_dataloaders[\"test\"]\n",
+    "\n",
+    "        # get unfiltered and downsampled test data\n",
+    "        data_transforms = transforms.Compose(\n",
+    "            [\n",
+    "                LowpassFilter(-1, 2), # don't filter\n",
+    "                DownSample(test_down, 2),\n",
+    "            ]\n",
+    "        )\n",
+    "        train_dataloader, test_dataloaders = get_data(filename=file_name,\n",
+    "            saved_folder=data_folder,\n",
+    "            transform=data_transforms, \n",
+    "            resolution_proportions=[1,0,0,0],\n",
+    "            test_res=\"single\")\n",
+    "        unfilter_test_loader = test_dataloaders[\"test\"]\n",
+    "        \n",
+    "\n",
+    "        # get model preds\n",
+    "        model_preds_test = get_model_preds(\n",
+    "            test_loader, model, device, initial_steps\n",
+    "        )\n",
+    "        offset = 0\n",
+    "        if initial_steps == 1:\n",
+    "            offset = 1\n",
+    "        labels = torch.concat(\n",
+    "            [\n",
+    "                test_loader.dataset[i]['y'][\n",
+    "                    :, initial_steps - offset :, ...\n",
+    "                ]\n",
+    "                for i in range(len(test_loader))\n",
+    "            ]\n",
+    "        )\n",
+    "        label_spectrum = get_energy_curve(labels)\n",
+    "        unfilter_labels = torch.concat(\n",
+    "            [\n",
+    "                unfilter_test_loader.dataset[i]['y'][\n",
+    "                    :, initial_steps - offset :, ...\n",
+    "                ]\n",
+    "                for i in range(len(unfilter_test_loader))\n",
+    "            ]\n",
+    "        )\n",
+    "        residuals = model_preds_test - labels.to(device)\n",
+    "        unfilter_label_spectrum = get_energy_curve(\n",
+    "            unfilter_labels\n",
+    "        )\n",
+    "        residual_spectrum = get_energy_curve(residuals)\n",
+    "\n",
+    "        x = np.linspace(\n",
+    "            1, len(residual_spectrum), len(residual_spectrum)\n",
+    "        )\n",
+    "        axes[x_idx].set_yscale('log')\n",
+    "        d_dim = test_down\n",
+    "        if test_down == -1:\n",
+    "            d_dim = 128\n",
+    "        axes[x_idx].plot(\n",
+    "            x,\n",
+    "            residual_spectrum / unfilter_label_spectrum,\n",
+    "            # label=f'Downsample ({d_dim})',\n",
+    "            label=f'Resolution ({d_dim})',\n",
+    "        )\n",
+    "        axes[x_idx].annotate(\n",
+    "            f'$({subplot_labels[x_idx]})$',\n",
+    "            xy=(0.8, 0.15),\n",
+    "            xycoords='axes fraction',\n",
+    "            # textcoords='offset points',\n",
+    "            size=14,\n",
+    "            bbox=dict(\n",
+    "                boxstyle='round',\n",
+    "                facecolor='white',\n",
+    "                edgecolor='black',\n",
+    "            ),\n",
+    "        )\n",
+    "        if idx == 3:\n",
+    "            axes[x_idx].axvline(\n",
+    "                x=fixed_lim,\n",
+    "                color='grey',\n",
+    "                linestyle='--',\n",
+    "                label='Train Limit',\n",
+    "            )\n",
+    "        d_dim = downsample_dim\n",
+    "        if downsample_dim == -1:\n",
+    "            d_dim = 128\n",
+    "\n",
+    "        axes[x_idx].set_title(\n",
+    "            f'Train Data \\n Resolution ({d_dim})'\n",
+    "        )\n",
+    "        axes[x_idx].set_xlabel('Frequency')\n",
+    "        if x_idx == 0:\n",
+    "            axes[x_idx].set_ylabel(\n",
+    "                'Normalized Residuals \\n Energy'\n",
+    "            )\n",
+    "        handles, labels = axes[\n",
+    "            x_idx\n",
+    "        ].get_legend_handles_labels()\n",
+    "\n",
+    "fig.legend(\n",
+    "    handles=handles,\n",
+    "    labels=labels,\n",
+    "    loc='upper center',\n",
+    "    bbox_to_anchor=(0.5, -0.0),\n",
+    "    fancybox=False,\n",
+    "    shadow=False,\n",
+    "    ncol=5,\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b072444d-6c1c-454f-970a-97c16a880c06",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "# Information Extrapolation\n",
+    "\n",
+    "Filter before downsampling. Assessing if models can accuratly extrapolate new frequencies in data that has fixed resolution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "fe41a598-f2ba-43bb-badb-0886010a95ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training: 100%|██████████| 151/151 [25:08<00:00,  9.99s/it, train_loss=7e-05 test=0.0003] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training: 100%|██████████| 151/151 [28:56<00:00, 11.50s/it, train_loss=6e-05 test=0.0004]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training: 100%|██████████| 151/151 [24:58<00:00,  9.92s/it, train_loss=7e-05 test=0.0004] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training: 100%|██████████| 151/151 [22:48<00:00,  9.06s/it, train_loss=7e-05 test=0.0004] \n"
+     ]
+    }
+   ],
+   "source": [
+    "img_size = 128\n",
+    "downsample_dim = -1\n",
+    "filter_lims = [8, 16, 32, -1]  # low-pass filter limits: 8, 16, 32, 64\n",
+    "max_mode = img_size // 2\n",
+    "\n",
+    "trained_models = []\n",
+    "for filter_lim in filter_lims:\n",
+    "    # Handle data transformations\n",
+    "    data_transforms = transforms.Compose(\n",
+    "        [\n",
+    "            LowpassFilter(filter_lim, 2),\n",
+    "            DownSample(downsample_dim, 2),\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "    train_dataloader, test_dataloaders = get_data(filename=file_name,\n",
+    "        saved_folder=data_folder,\n",
+    "        transform=data_transforms, \n",
+    "        resolution_proportions=[1,0,0,0],\n",
+    "        test_res=\"single\")\n",
+    "\n",
+    "    model = get_fno(n_modes=max_mode)\n",
+    "    model = train_model(model, train_dataloader, test_dataloaders)\n",
+    "    trained_models.append(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "82318734-efd8-46c7-bb06-2220c6a24a31",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x250 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "subplot_labels = ['a', 'b', 'c', 'd']\n",
+    "\n",
+    "fig, axes = plt.subplots(\n",
+    "    nrows=1, ncols=4, sharey=True, sharex='col', figsize=(10, 2.5)\n",
+    ")\n",
+    "\n",
+    "for x_idx, filter_lim in enumerate(filter_lims):\n",
+    "    if filter_lim == -1 and downsample_dim != -1:\n",
+    "        continue\n",
+    "    max_mode = max_modes[x_idx]\n",
+    "    model = trained_models[x_idx]\n",
+    "\n",
+    "    # get data at various sizes\n",
+    "    for idx, test_filt in enumerate(filter_lims):\n",
+    "        # get filtered and downsampled test data\n",
+    "        data_transforms = transforms.Compose(\n",
+    "            [\n",
+    "                LowpassFilter(test_filt, 2),\n",
+    "                DownSample(-1, 2),\n",
+    "            ]\n",
+    "        )\n",
+    "    \n",
+    "        train_dataloader, test_dataloaders = get_data(filename=file_name,\n",
+    "            saved_folder=data_folder,\n",
+    "            transform=data_transforms, \n",
+    "            resolution_proportions=[1,0,0,0],\n",
+    "            test_res=\"single\")\n",
+    "        test_loader = test_dataloaders['test']\n",
+    "\n",
+    "        # get unfiltered and downsampled test data\n",
+    "        data_transforms = transforms.Compose(\n",
+    "            [\n",
+    "                LowpassFilter(-1, 2), \n",
+    "                DownSample(-1, 2), # don't downsampled\n",
+    "            ]\n",
+    "        )\n",
+    "    \n",
+    "        train_dataloader, test_dataloaders = get_data(filename=file_name,\n",
+    "            saved_folder=data_folder,\n",
+    "            transform=data_transforms, \n",
+    "            resolution_proportions=[1,0,0,0],\n",
+    "            test_res=\"single\")\n",
+    "        unfilter_test_loader = test_dataloaders['test']\n",
+    "        \n",
+    "        # get model preds\n",
+    "        model_preds_test = get_model_preds(\n",
+    "            test_loader, model, device, initial_steps\n",
+    "        )\n",
+    "        offset = 0\n",
+    "        if initial_steps == 1:\n",
+    "            offset = 1\n",
+    "        labels = torch.concat(\n",
+    "            [\n",
+    "                test_loader.dataset[i]['y'][\n",
+    "                    :, initial_steps - offset :, ...\n",
+    "                ]\n",
+    "                for i in range(len(test_loader))\n",
+    "            ]\n",
+    "        )\n",
+    "        unfilter_labels = torch.concat(\n",
+    "            [\n",
+    "                unfilter_test_loader.dataset[i]['y'][\n",
+    "                    :, initial_steps - offset :, ...\n",
+    "                ]\n",
+    "                for i in range(len(unfilter_test_loader))\n",
+    "            ]\n",
+    "        )\n",
+    "        residuals = model_preds_test - labels.to(device)\n",
+    "        unfilter_label_spectrum = get_energy_curve(\n",
+    "            unfilter_labels\n",
+    "        )\n",
+    "        residual_spectrum = get_energy_curve(residuals)\n",
+    "\n",
+    "        x = np.linspace(\n",
+    "            1, len(residual_spectrum), len(residual_spectrum)\n",
+    "        )\n",
+    "        axes[x_idx].set_yscale('log')\n",
+    "        f_lim = test_filt\n",
+    "        if test_filt == -1:\n",
+    "            f_lim = max_mode\n",
+    "        axes[x_idx].plot(\n",
+    "            x,\n",
+    "            residual_spectrum / unfilter_label_spectrum,\n",
+    "            label=f'Filter Limit ({f_lim})',\n",
+    "        )\n",
+    "\n",
+    "        axes[x_idx].annotate(\n",
+    "            f'$({subplot_labels[x_idx]})$',\n",
+    "            xy=(0.8, 0.15),\n",
+    "            xycoords='axes fraction',\n",
+    "            # textcoords='offset points',\n",
+    "            size=14,\n",
+    "            bbox=dict(\n",
+    "                boxstyle='round',\n",
+    "                facecolor='white',\n",
+    "                edgecolor='black',\n",
+    "            ),\n",
+    "        )\n",
+    "        f_lim = filter_lim\n",
+    "        if filter_lim == -1:\n",
+    "            f_lim = max_mode\n",
+    "        if idx == 3:\n",
+    "            axes[x_idx].axvline(\n",
+    "                x=f_lim,\n",
+    "                color='grey',\n",
+    "                linestyle='--',\n",
+    "                label='Train Limit',\n",
+    "            )\n",
+    "        axes[x_idx].set_title(f'Train Data Filter ({f_lim})')\n",
+    "        axes[x_idx].set_xlabel('Frequency')\n",
+    "        if x_idx == 0:\n",
+    "            axes[x_idx].set_ylabel(\n",
+    "                'Normalized Residuals \\n Energy'\n",
+    "            )\n",
+    "        handles, labels = axes[x_idx].get_legend_handles_labels()\n",
+    "\n",
+    "fig.legend(\n",
+    "    handles=handles,\n",
+    "    labels=labels,\n",
+    "    loc='upper center',\n",
+    "    bbox_to_anchor=(0.5, -0.0),\n",
+    "    fancybox=False,\n",
+    "    shadow=False,\n",
+    "    ncol=5,\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4daef795-7cf9-493c-bbc8-c77c128872da",
+   "metadata": {},
+   "source": [
+    "# Multi-Resolution Training\n",
+    "\n",
+    "Train a model @ resolutions [16,32,64,128] (each resolution comprises 25% of dataset), test at resoluitons [16,32,64,128].\n",
+    "Visualize the average spectra across all 4 test sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bfc8ada1-af9f-4948-bd1f-64f95924569b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=9000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training: 100%|██████████| 151/151 [09:24<00:00,  3.74s/it, train_loss=8e-05 test_res_0=0.0004 test_res_1=0.0004 test_res_2=0.0004 test_res_3=0.0004] \n"
+     ]
+    }
+   ],
+   "source": [
+    "model = get_fno(n_modes=64)\n",
+    "train_dataloader, test_dataloaders = get_data(filename=file_name,\n",
+    "        saved_folder=data_folder,\n",
+    "        transform=None, \n",
+    "        resolution_proportions=[0.25,0.25,0.25,0.25],\n",
+    "        test_res=\"multi\")\n",
+    "\n",
+    "model = train_model(model, train_dataloader, test_dataloaders)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "69bdc5b3-0e7d-4724-a3f1-af2e74efb381",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_887381/3059218876.py:84: DeprecationWarning: `trapz` is deprecated. Use `trapezoid` instead, or one of the numerical integration functions in `scipy.integrate`.\n",
+      "  zero_shot_aucs.append(abs(np.trapz(x, normalized_residuals).item()))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.num_samples=1000\n",
+      "self.num_samples=1000\n",
+      "self.num_samples=1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_887381/3059218876.py:100: UserWarning: The figure layout has changed to tight\n",
+      "  plt.tight_layout()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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/6G9u+q9vY8P/ydrc9Lc33OCFwA03eO/gqc0am+s1coxRXNmHdROYnSteAHB1rVgIMCJAQ0r274wNqcMiAqklWg9LDSqKZp61jAjIJY4ZEeh2Q2ujaXQAgwFQFLBlF3m3D4sMW50uJmWGIrPolBaDoT+dwSDYaqwt4ArFdR0KiGlYKY+MovDzdns7eP/jTT631q3QsWPkB/tenbE1TiwEZBSAt7JQmCt7pZYCjle6k8ybu3FEQAqBVEQgXtWM94fDIALKsrmowHAI0+0im4xhUWO97CDPchSFnf1keMo8HbbZZTDkelpNW4WAoiiKosyBTvIsCwJgoRDoAhv9GhvdCXAwAB664K2By5e9IZUSArJGgE37aUlIISANqMOEQLzU8LUQAjT8YyHA+1MhYPp95GtDoChQbgGu7KDTydHtWwyFEKA91+/7j8oC46oKdqcKgWtDv+/n6/a2T9Ha3g6Gvxw7dQrYWKvQcUNkw2kjfa6oFbfV4XyO04JkjcA8IXBY69t5czcVEbia1KA4PUgKAbnq2HQzkwnKjQ1kZRd5ViDLLKwNH4GnyGCePAxX3151VAgoiqIoyhysDanEzfaJYev1pt1Uihpl7pBXQ2AwghlMDabhMHhRR6OwCpHMsWZqEA0p3mdqkFw+dV5qUCoSkEoNSgmBuva3VRWMKLo869obUDLvAfBjNOTYurGqwvtVlb94dY2sLlFawGUGvW4GY8zs+tW1v3UuXFOuXqwrEF8btraaKUDcpCA4fdphewsoxgewuzthBS0pBJgOxPksU4O4RG+cFjSv5e2iL/WwiIBM/5G3VyME5G2nE+pyer3Q3mr6WbK1NfT6fZTbvVlEADCNnw/1ED/ycOgXH1t1VAgoiqIoyhzy3Gd4ra+HAsuNDV8gzMjAjTcCvbLGqbUROlkFu3MJ2L3ijaWHHjo8IiBrBKQlEXtSpRCIt5TxDwSjXS4etkyNgIwSSCOq0wnqiPnX3a4/zv6+35ftlIrCf75OB53+OvK1DfS6GbKzPYyqbHbI3d1wOcZj/3F6PX/JWEvASIJydRjjIwFcLExuXDtglh60DZTjXd/rlUvqyk2mBrG/ZiwE4ohAav2A1NoXFJ3yxOOFxKRwXVQjkEoN4mSLhYDcRiM/rykCer3wGaafLVsb4/SpzdYpUa/Ln+xo5C/bQw+ttpBVIaAoiqIoc6DNG/cT95sLXUNyh9JWKMwEGA9Db0waSvSi8v5kEp4fj8OSvXFqkFwzIM6tBtoRgTglSK4dsEgIUEzQSOMYb/n+8vUyP4LH4msZQShLGOdgigLWdX0P+7KGdXZma1WVQa/nX8rFmwYDb6vRSas8PKwNqT/cZCoQbzfXa3TgF3wwjADs7ISIABd7oAhgREAWCMcucUYF5qUEyTkMtOdtXBwsFxNLpQfFRcKy6D0WBaNRUwww/CQXQ4va9prp53IAtjY3G/qbNQIyNWgw8HUuly8HgbuKqBBQFOVRY329Pfa//+/Nxy/84nF7p19LFK3/4R+2x+65pz12773tsStX2mMp6yLL2mNF0R5LFQavrbXHtrfbY+fai4fh44ni+/vua7/0Oc3P8ZKXPHupQ2mx8NVDW7bb9cYS+6yzxzpbLJ7aBrY2HYpqjGzvMjAZ+XyAS5e8JXDpUogIMAleRgRoOaSEAK0HKQQWdQuaVxsQCwF5S0OKHYK4GJOMEshWKZNJ2J91BOwkVNf+NfQIF4U/x2l6EJyDzUt0+0CRldjs53AuR1l6m7LT8ZdkNPJve+ZMyNg4OHiMJ8AxwFo/f2UbUK4NwPUvNjaAtb5Dt5jA7u+FdKB429tr1wgwVCMjATK1TaYHyXS2RWlBnK9AUwTEWywGpp2sGvcnk9Dpqtm6qn1+cSEzH0v4e5uuWLzW66Kus5lm39vz15NaiY/X11d75WwVAoqiKIoSIZvksNsKPap8vLHhRcDmuoM5mAAPTb2mly/7bTDwt3FqEHNh5DoCg0FwK8ZCQBop8SJMklgIpLz/TP+ZN0aj3tpg9FdVEAJVFZ6jt5UGmBQCNLiMCW1QAZhOB2W3BPIaVbcLk+ez9KCi8BppMPC7nzrl3/bSpcfymz8+cLmH9XXvp6AAiO93ywpFNQzG/t5e8z5rAxYJAVloGy+Al4oIzHOP07gH0oXBqTUEpAFPz7+ck1zti/M31cFI/q5iIcDz4Rye/nHorGeougaTiS+CX19vZk9RbK2vh7KgVUSFgKIoiqJEyDUD5m4dIMcEZjj2q63KFVblFndVobHPMdkqVPYfpCeVEQGZWy1rA0hKCNDQ52tk9xXCakdjmv3dZXoQoTiQ6URMjKbBZW1Ia6IIKEtgMIBxDhgMvF1mc5RFjU5p0O0a1LVppV9NJiEgoUXDVwdbhnIRPG5SBKytOZT1MKQBUQRIMUBBIAUAawRkNGCeEFhWBADzOwRRgHI+y/kZp7PR6JeRs7puFsHH0Qm5yVocoJkGx/ctCtg8R6cE1tc7GI9No5aaEYHNTb9durS6US0VAoqiKIoS0emEhZXiTiuziMA60K0OgIs7wMF++NeeEYGDAz/GAmEaTkwNYkSAooBCgIaITA2SBo0kVQ8Qd1eJUyxo9M+rEaA3VXYNYloQDTJGCeQYxQBTg2i9l2X4TKL9aHcTKNdLFJnFwakMvZ6ZeU6t9UWWZelXG754MZRVrGqu9aqR59Oo1TSSJdODuHUwhD3YCyJA1gbs7TVTg+JoAGte4rz6eUXCwPxoVhwFmLdwGOciXyMNf0a05qXOyTGeD8+vKMJYLAS4n6yJmdYj5JsG/a5FvVXOhAC10f5+szaDP/lVQ4WAoiiKokQw/T21FYVDWfjbbFIB41HaIJIeUhpI8f24xaJcPIxGTZy6IEl1CJK3NPrp1QSa3lM+zzHuIwuH4/1kmoVMJ2Lug7w/mYSoAesQRiMYa2HqCaypUFigLDNUU83Q6TQ31nNKraIshrUB6+u++JqRALkS9vo6kO0NYGQ6EIuA50UE6O5mWpAsFJaLiKUiAkB7DqfahMr5mUJ2E4rnJkWC9PbLeS0ZjUIUS3r/F/3euM9UCBhjkBmD9fUSm5thLQEZEaAI46VcNVQIrDBf+qWH7/MlvcRqqym+718tt98f/dFy+w0Gy+136tRy+91663L7/fVfL7ffRz966C5f/3//30sd6mc+80mH7wTgve9darcTxed8Tnvssz4rGvjTP23v9J73tMdSqwh/8IPtsfvvb48tO187nfZYquI5hUyfIKlC41RB8jhRMJ0ai97jhkQx8hd90RNaY29/e/tQOl8XU5bNIktpRK2vAb1OhV5eI6sGoaPKzo43kOhRpTVA92BqHQGmAjEiQGOKouBq1gyQhZZAs62ofC4+1iIPKhAMLWl0Sc8r0BQFHGc0gr8DFh6Xpf/cUws/Q4H13gbKjp1dwqryURdr/T8jV66EbquaHnQ4ZRk6MM3WuphuvluTQ+4mwajnLe/Tmh0kUt5S7ULjTjs0wmMP/TwRIOetTOsB2q+RY3FxfOrYqQ5bUiAwHY6iVR5bRtJ4TLbNFV2Hsm4Xa2sd9PsGa2v+px6nY3W78zXJUaJCQFEURVEi8jx4UlvGVNehW9Yo8wqoRs12inEetWwfKu+L3uStGgHpTT2sVWjqPlnkXU15Oue9l3xeel+JNJhkBIIpRnERJ1dR7naBTgdZ0UV/fQ2FCfnr47G/resgwPhRlMMpitnlbdS18HGnA5jJqFnbssjoT0W5UmsGpDrwzBOaMiVtnoEv76dS31L348XHZNpRLJTlnKaY5WsYtZO1Ceymxf62RQFTlnCDAXrdzrSlsGktOMjbaanMSqFCQFEURVEE0unHFuON1KDcwUzGMGayOP1nURpQvPJqvGjYvHSKealBsZE0rwf7vJ7sqZaiMjc7fg9JyqCSqUFAKzVo1k1oOARgkbkJnM1Q5hbdrpluflcarmX5qH3lxw5GBLhWA+/7zaHbAXCQEAA0/GVheyoCsGjxsHlzWCLT0eJx3saRrrhdaKqFaDzObldxkXz8vjKViIJAnkfcsYgW/fQPhOl00NnYQL9nZxEBCgEZGeCixauECgFFURRFmUL7oSybHVbk1s1qZKOD0Dx8XmtFuYgYx7hCFtcMYASAEQF24IkXXZpHykua6rcuVwyWt3IF1lgYlGXb4yoLNeNUo7jAkqkWTBFirQALiqfnZPsTlBvryDNgrVtga6tAXfvcamPColejkUYEloEdr3q9YHzKyECnA3Q7DrhwMD8aIAUBn4sFLwVfXCAsu1wtmr/S2F7UJjS1avC8VYRjcUD1uChiQKQQAMK5x0KYvYXlasXTRTC63fVZepBMC5KPL1689t/5I0GFgKIoiqJMkf/Wy8VJ5b/5mXUwdQXUiZSI1JZqUThvi2sClj3heZsUBdJAiiMC87ZUJCCVhiTPVXY3ondVFkDLaMh4DDOZ+OvpJshtPisOjjdqFWUxLLKm0U9RwOhAt+PSAoBpa1IQMOd/XqF7qj3oYXDexEb/IgOfC93JMWn4c5/YyE9FFWS6UAyjWkCzFW78O4j/OOztAf0++mf6GK5ZHBy027YyzXDVUCGgKMqjxpMSddZPiGtZ3/HR9k6f+ER7LLHSLi5caI8t26y5222PbW62x86cWW4sVVScJ/7EpgqIU6RWUfrwh5uPz59v7fKsRLHwc57TPpQWC7exNnSpkTm+Mte32wVK52D2R8AkyqdOeVXjXOq4s0pKCKRIpSqkvP6x978s24ZSvB+VT+wxvdqNSIOQxaI8JhcG4C1/D7u7MJMKHWuxsV5iMvF1Ac6FGoHdXf8SWY+qtIlFADsx8XLnmVvc4SoWt/KCz1sb4LBFHlL5/rEIlca1NPapAqVQkPN5kXBNpbItEgdxlICtcZnWxv2Gw3BeTBMaDGBGA3SyLnq9vFEfIP+GdDrphe2PChUCiqIoijKFqwmzHiA2pjodIJ842GoyP3c6VRcwL1IgDalFIiA2pOIUiNjApweVQkC61KUgSNUByPdZNLaMEGC6EN/DubDIGAVTlk0Tpw2KXh+9LjAYeqNpPA457hRpXJ5ASSPrW1jj0oisZACGiSJfGa2J1wBICQAZvQLS3X1IbKTL+Zry9EtRwBXl5ByWq2LLY/K94zl5mChIjRsTPiOvC3978ncvxL8ZDJCXBbrdvBGJiTcVAoqiKIqyYjDiH6ejtGwTN00NmtczfZHXdF4HlThfWj5PA0caU9Igig18Wn+MCMT50/HtMkbTPMN/0ZiENQT0rgJNITAcAtYi601Q5DXKwqDTCUXDMr9dLlyrtOG8lYXu0o7OMze/gH2eYI1766fS12g4p4zrVNH6ojQg+YObhTJEBEum9iyaj/PEwTxREM95vl4WEdP4l38oREQwL/volG42d+PVsrtd3w53VdbDUCGgKIqiKPD/xrPtYmx8yihB5nzXoEZXldTKqiyaTKX/zGsFKkUAC3OlUUMvqPTq8z6NflqArBqVRj/fg8dNvf9hYw8nXUj2lGfRNI0753yOdV2j3NhE0akx6hqsr2eN1CBuzoXgi9JGRq/atRaumRo0r9uPbF87z+gHwnxdpjA41c0njgLEKpxzmAJAHi8WGvOei+ejFL+pmoI4PSiODvB3L9OURLvgfG2ETl6i389nhr9MLez1rv47fTRRIaAoiqIoU+bV1lrr/GMDGDjAzUmRmLeliI2NRc/RkJLe/DhXmulA0oiK87Dl8eadT+q8Fu0fn+e854F2m0YhmkxVAXUF4ypkxiLPLPLcNOxD6RRW0sjrJDPEigLIbQ1M5qQFHSZcJTJylVrojvdlBCDl/ZcpQKk8ptj7H59D6nFqns8z8ucR7xsfi0KAF5ldwfb3gV4P2VqBfj+fdQ2K6wSoK1YBFQIrzPOet8ROy1b8ffKTy+23bKPmxz9+uf1uv325/Zb9q762ttx+u7uH7/O2ty11qP/r//rupfb7f/6fpXY7UTzuce2x9Swq5k0VxabWYU/lAaTma2K13WRh8Nmz7bFbbmmPLTs3UysGL1ssvOxYnFj6gQ+0dtl6TrvQ+mlPu7l9LKUFM25a6wbQwW5qoKqBasE6APM8//FqpTJvXhIbVBQAPLm4CHgWpsjC0qUyf3pe6o+8jcdigyr2ls4biw2/eRECRgeYFgTMIgLY2wP29pCNCqz11gDTbr84mQCXLz/87/m4E3e3pBjI86mQlQt9zetqdZh3f96cSEW5OGdlQbBMAZIqr9drz51FXG2KkHxuUbGw/A3Mm8vjsY8C8I+GiArYTg/d3trcGgFrV6fORYWAoiiKoqDpdE+2JzcOpq6nnutDWoACi+sBpPGx6IQoAOalATEPhEKA4nMZI2qZCMA8Qyp+j3mRg5QIkR2FRiN/ztMaAdYN2AooC4eJC2kuckvpbqXpfI8b7Vg7jWbNa1cLpAVAyvDnm/BNY/GwqJ5FFjBI7z+fX2buynNLRSF4DhyTz6UEsrxNCZGUoHXTWovBILQCmtYK2LURCluhLC3K0jTmbrfrP3q87MZRoUJAURRFUaakMhhmmTUpIyrun09ioyPONaKBxByB2D0oPZGyGJgGU7fbTg2SjfaXTX+YZ7gvMpLisdT+87oQyfv0TDPhXwoBY1EWDpVpd22iBlLayPmbrAuHa87deB4vk+ITz2N5LJ6EFLqpBTnKMgjXR5LvFc/hh1vjMu/5Ra8DwiKAbB08vTWjETI3QVmWrQ5knMNcV/CoUSGgKIqiKAgRgVROep5Pjahq0u4KlBIGNIKyzD9H6wwI+9MYTuVY04Ayptn5h+7wfr+dQ3217sVlhYBM/UgVHMfbonQKubHw0hifElRVs9QgWwLdzRqmbOZXr62FRYmVNnENrmwMxagWqiX7/sdGP+eynKspQRwLhvgHxR69Mg2IXG2+zDIRgVTq2rw5m/r8h4ndum6kBc224QDdbtloOCAbEHDKHzX6U1IURVGUKfPsBAPAGAdIW3vZImDpHZWLa6WiCTRGpFtXFgHHBcHy/ZZdjVie2zJCQL5Hyuia95pl96E3WRYO5zUy65DDCaPWzIzbh+M8PglIAZBy5BtzyAJgsTdfpvRIj/9k0qwJkG1DZYtPmQYk04Li1jk8tgz1HCYKUml18wQqx+IC4EV1AvE+8cWU7wN4418IAjM4QG9zs7UgIUXBoqzAxxIVAoqiPGqkFtv1CwcJUi6RVKFsqgi402mPpQqDEyvwJtd6n0zaY6nzWzYvIeW2TBU4LzsWf97UqjSJAuInPUmLhZdlXmqFzbwYWNhOMeVB5Rzg98le5M75eR4fgxacXB+AaUD0pMYGDc8rFgGLFnqaZ0kvSq04rJgyZUzN877GXtjBwF+b/X1fLJwV6Hdq5LmPAkhj6uBAU4PmEU/BZUpRAMw3eDkPmdAun0vVBaRy62RuDKNYhPOWx4s/DDBfsMy7APw88fyNfzfz5m08Z1PiQL4Hnx+NmmJgNEK3A3Q6bromhgqBx4y3v/3tS+/7kpe85FE8E0VRFOV64XAb1mEWEkgVAi8ypnjL19Z1OxXBmPRCYd1uiABQDMZ53fOEgPTUxu817yLEhs7VbLFASXlhU8YVBfe0v72txrC5A8TquLK2dFWMqFVjng2bJPU9pwx5zkXOpSxLO01SrW1ZDyDXAjBm/noawPzF9pYZiz8TL4ocSwmDeQJgkYCNhQNThKZCwAyHMNUYnbJoaCHeX5Wo1rEUAi996UuX2s8Yg2oVErQURVGUlSA2nub+Y52ytmgI0QByLhg/jHLRSErVE1AI0Ihii1CmA0kxQS+pPE7Kc5oaW2RYpQyplFWZihLMM6rkfvPSLogsvBwOYVyG3OQoy6zR0lUjAm1S9ivH5+48bx5LY34yCZEtpgPFXwAFQrw+gFzWmFBQpLoVpQrwl7GYY3GbugCHRQTiObxI6KY2IHTC4kKDoxGKIkdZmpmI5RxeFTF7LIVAvSrNWRVFUZTrinm2QHLH2DsYL/IF+H/x6T1lsWUqpSIuDGZXFXkCcWeW2KPKMUlqbN5nmTcWG/2pscO8q4sMLmkRjce+irLT8YLAWRS2h14vQ68X8qxVCKS5qrnL+/L7iudwVYW5zPQdWewuj5daH0CmM8atSikGJPLx1dS8xFb1I4kIzBubFxGIBQfXyJiKgbLot9YkUSFwRAwGA3RT+cOKoiiKgkO8qJjjNZ+XNiC9+DSAUrnQsn86hQCNKSkCUi0e49ajcv+r+dDzhEAqMjBvn6vZ4uPxNl5xOK9gUSfbufLjKwFp5/JxawdpIM9b5Zd1Acxh4byVxryc95yzcoVgznugmQ4Xv15GtGS0bJlIQIrY8F80tqzxHwv/edEFwAvY3d1ZilCx5hqtQ2V62yrM4WMvBKqqwmte8xr823/7b3H//ffjAx/4AO688068+tWvxh133IGv//qvP+pTnEuqNrLFsivtPvOZy+33ohctt98yK/cC7cLQeSwrjZdd+Xhj45q95/Ofv9xbKm2SLf7iwdR3dcMN7bGUCzD1HZ450x4bj9tjo9Fy+y2bPpg6l9Q/ZKlC6NS8Tjkt4v3izhsA8OEPt4Zu/6y/vdThl/25Hmfm2aytHeLFkeo6fD+TSegvztVbufpw7MHnseI0IL4XkF6lWG6xF1XeLhshX+RRXWTML2tMzdvifaoqtF88OICBRWk7s2iANKbYyEYzfNvEggCYTpdaPBn3GpUdfua1wZXzTgreuBuQnJvS8AeaHYuksOAJx5GAazGHryYqMG8+p56bN++d87//4RDlKdeYt3GKW6rc4rFkRQITjx4//MM/jDe96U34sR/7MZTiH9GnP/3peP3rX3+EZ6YoiqKsIvNFAJr/+MdbqsAyNrDijc3FmfPCjVbCIiM69XzsMj/MEJ+3b+pzxkJgmbHUPqkLzMdRG1FMJrCoZ4a/bI/Jj6oEpH2bqidvCNl43sZpPXIVLDk3ez3vhGQ7J95fXw/5L/JLiouI49DOorHU6+ZtqdfGkbnU7/UwEXDY7zD1G6EjYDxGnjkUhWt1UZXr/x0lxz4i8OY3vxk/93M/h+c///n4J//kn8zGn/GMZ+Cee+45wjNTFEVRVpWk8x1zjBSmUbAdKHOrmeYQ922nARwfIzYk6OqOV4KVY9KgThVcptYqiEmpnkURASBYMMumT8S9LOcJKo7To5qNkJmqUWjJ+wyuTSZHn16xSsh0k7iMxAHzxasUrXENi4xmSQM5jo5xjqYEonw957g8SeDwGoFFX7Scr3IsnruHidRFkYDDNhkRGA59B6y6QmYz5LlprK0mg39HybEXAp/4xCfwxCc+sTVe1zXGqTQARVEURUFbCMyMnpTxVNfBMqXRJNcMiI2o2CtLw1rmYUvjSQoK+R6plIpFrURTHCYE4rQKORYbSfFY7HFdZGjFqRWjEYzNkXcqlHMCKkB6OY2TTsq2rmvAGQvYBZ52dvoBwnfEVDdijFhuW8xjWUTMx/wN8KTmCdhUC9GrmcNA2r2+aO7Gc5HPLRKwy0YEhBDAaITM5Mhz2woUakTgMeBpT3safv/3fx+33357Y/ytb30rnrls3ryiKIpyokilVTgYwCzwZjOPWhpJsXGeSsWJDfGUOzf27MciAQhG2MMxohaJgUXRgavd5n321HtNDUkDt5TdpXjk9JG6sqqm9nyeiASwuxXDK9b6cda5SCFgbQjNSJHK1YYpCPglSRHL53nLE41Vy8OtEwDac3mRqF0UxUr9VmNhMC8Vj+c8Gvn0IFs3IlrMnFIh8Bjwfd/3ffi6r/s6fOITn0Bd13jb296G97///Xjzm9+M//Jf/stRn56iHGuSRVDxir6p4t477miP3XJLeyxVjbyz0x5LuQxTxcKpsWV7s6dI/ZVPxYJTBcSpat742qVWVk4UHt94Y3u3s2fbYx//eHvspCGNp1a9ozFAngGViO8zj9paP38oAGgA0XPPg9Pw4uP4TWmtyQ4q0pqLO7ekxMKiyMCyLDL+U2OHGVAylUR6kuflXwP+ehqLvF+1IgG8/EddaLlqyDkrbeswh227z/947C9k3Op2MvFzO44GWBsK5OWcZfSARn5KAMgxaazHaXOxOHg4OTTLRLWuRhDI52Qq3yKVyohA5loiQCMCjxF/9+/+XbzjHe/AD/7gD2JtbQ3f933fh2c961l4xzvegS/5ki856tNTFEVRVpiGbQ1vLLjpP/5GplcwEuBcSBWi4R/nW1P4ScOInth5OdGpXP9HUwgAywmAeCw2ruY9jl368X3AG5hVDWPc3PrPOIhx0omnSRwN4Dw2skWtVFWdjr+oMjoANL9r6eVn6k98P5UmxNCNPIaMlM1Lc3uk1vIy81gK0HieLlOALyekfP3eHjAeI8vrVhBmVebvsRcCAPC85z0P/+N//I+jPg1FURRlxYmjALRvpKPewgZDilEBY7wRRYOAXv/Y0ymRB5VGUuz1lx7XwyIC80TBog8skYYSb69WBKS8qinPf8qokvkS4zFgLKyr5jaIWRVjapWIo1nNOW1QO4OMF1KKAba4BcJznJuxoGXdgDT8jfGvl+lBUiDIOR7PVVnIID+EHJv3YSWpybBo3s6LChwW4VoU+ZLHnAqqOL1NHv6oORFCAAD+9E//FH/1V38FwNcNPPvZzz7iM1IURVFWjVRKEIXA1EENOAOb5TApIcCUizhnhYYB1xRwLhhTsjA4JQRSY/FJxie/jBBIPRd7X6+VEJiXPhELANnScTyGgUHmqsZaV7EgUJqkbGw5l32mTdQylCJAGvfSIx9HAYB0NICP40iAFLvyWHFEgPNKzk0+N++DSuZNiMNSg/h40bxNRQjkc/MUal3PrXNRIfAY8PGPfxwve9nL8K53vQvb0xW6Ll26hM/93M/FW97yFtySyjtWFEVRTjRtB7uZ2S7GGVhpwMrUoNijLg0VOca8Z+kpZSqRNMSAYDGkvKXx61MnH59H/CEl0jBKjcUGVUoIyPSeealA88aS7z9fi6yCIbVKtCMACUFbGxRxREvmDtH7L+efPAixthmtYjRA1gnwO07Vt6RE7NUUC8+LFBw2h3nuKREg9z0sHSj1fKxQp8ef91NZBY69EPiGb/gGjMdj/NVf/RWe8pSnAADe//734xWveAW+4Ru+Af/tv/23Iz7D+eztLbHT533mcgc7fXq5/e69d7n9Ll1abr/9/eX2W3ZpyGULhtbXD99nyWuSqltVliNVe7uz3/wON26+ub1TasXc1Fy6fLk9liqyTb122WLhVDVi6h+g1BxOzdeUxypVLJwqBI4/W2pl8bigGOnLmbpMSttOofE0mYSaSlcbZFkBlM5/T+NxEAHcWVpkQLOlKA2jRr6RbaYBcT9pmEljal7+x9WIgWWFQPzcPANK3i5Kr0gVC8s2o1FOtjFmbhaH0iY1NZpRLRNWtmI0gH/TpHEPhPQWOXdp7MdzWYqBOC2IY3IeS8GRGgOa5xXP1XhfMi8qkBK0VxMViA3++P6iFKHEqayKGDj2QuCd73wn/vAP/3AmAgDgKU95Cn76p38az3ve847wzBRFUZRVZVGetXWYGibCAwgEA5eCIOXVZBoE34TQ0JFji4z4RTnVy6YIXa0Q4HnK/Q7b5kUEUsZSysqPzkU+VDHQJv660xEBNIuEZWcrGY2ahcBMcHTE32kcEeC+MiIwLxIgx+T8lyIg/q0sE9maNzEWRbY4Nk8YpIz9lBhIpRCt+EQ99kLg1ltvTS4cVlUVbrrppiM4I0VRFGUVOSy3mo1UjDFweQ5k8NGATqe5CjAtLunhl95SGlvSc8r0HjkmowTzIgKpaMC8CIG8je9LroUQkHnTsRBI1QPE+822aZcm17b3lDapqSGDT7PCd2dg8hyGgkBGAOQL/YQPm0wPkqI3Fnhyrsrag3lzPLVxHi4Ss/J5yWFCQN7KcwcWC9aU4S/H5qQROaR/jqvAsRcC/+pf/Svcdddd+Nmf/Vk85znPAeALh7/1W78VP/7jP37EZ6coiqKsEgs9qVMxYDMDVxb+H3nmC8WFkzR+mHZBg0p6OKVHlUaSNMSY+iBTjaRxJT2pqZOXY6kPebXM89jPSxdKRQTmGVHzjCljAZiVMpxWnUViwG8Gde2QsQJbpgLJVB9p4ANhbqa+e/mDiR9zXxkdkMeVJwq0I2cyFfORqMHYM5+as3L8sHk8TyQkolysMYq/p1XgWAqBU6dOwYgve29vD5/92Z+NfNrObTKZIM9z/KN/9I/w0pe+9IjOUlEURVlFUtk5DRvb8h991/RqS0MIaBr30sspjR7uJ9+M+3OMx089F0cCeDweM65nSaUfLcu1EAKHjSXGpTf1sGwnxROLADl/qwqYVNPOQVwdGAiG+njc/h5kKhAfS3FLYcox+WaL0oNSRn/8e2AxvmReofAirrUQOCxdaE5EIBWwO0qOpRD41//6Xx/1KSiKAmAwaI/F9b0bt93W3ilR8JosUE9VvO7utsdSlfertNpwaoXkxArBrc+buk6JsUR25MP6d/QkMM+bSqe+b4gyTQ3Ks2YKEA0W7ijH2KpRFgHLyAGPw/3lWCqnOjUmBUE8Fn/A+P4yxIbTvLE5XtGWsZTKsZa3eQ6XZXCwqER6Sxx0UTzxtIjHOS1Z+N5ZK5se+iwLaT9xKhsfN0JjtvmFyLFFa18sms9xATE/gPxA3C/13CJiITBvDsd5/bEgkKuHL5rD4r5zpqF9Ut/TUXEshcDXfd3XHfUpKIqiKNcx86ICM/saFo4RAWthuKM0bIBmqkSc+sDH0nsvW4rKE+Ex5o3x9TGLIgKPRAgsig4s612NPa3xGNMqou9glbypq0qcJZYStVUNZFkO00H4ftglbTLxc5HGPW/lJtce4Jg06GMhG4sDIC0I+AHi34zkapwxKeeLHIuN/sOiW8Y0lwY+TNxOIwJS28cf7yg5lkJgHoPBAKPIu7e5uXlEZ6MoiqKsGqlUoDjHWkYGrMlhu91mCoTMm+YYDSdZD0AvK59nakXsHZUGlVzMKfacxjkg8gPxfupDXg3LCAEaR0A6DUgWCMfFwnFL0SxDDdta3XnVvKqrRCpjrL0ZP4fzDDCAiQ9gTLpGQH6PHJNRBfkmccRApgzJlKBYtch5Om8ec37JLz9l8PMcYx6JEIjHUtGsSAzUzrQiWqsyd4+9ENjb28N3f/d341d+5Vfw0EMPtZ6vlu1fryiKohx7Ul5nadvMaoArwFZAbjPYDCFVgYaLNKRiccA34n5SHPAYUoXIdCEaTvH+QFMcLDL6H6lbfdnIwDzjaUF3lVZHIZv5tIpIBGhUYD5xNEvejx30VWaRGQA5YOS8ir9ToDlX+Vga+3yTWLFJkRAb/PNUdzy3583ZhytmF9UKXI0QWFT4Lra61tSgI+O7vuu78Du/8zt43eteh3/4D/8hfvZnfxaf+MQn8O/+3b/Dv/yX//KoT09RFEW5zpjl+1YONQzqzHrbIctgYg9pbLRL7740kPyB/S2f474yR5v78ZjSqyrHFuXSzDOoroZUfnVKFMRGP1+bEgJxGpG1gPWpQbVrG1GrYkitMqnrI4NLkwmA3CKzAAoAxnhBMO97oRiV3zO/jDgKJje5X0oMAIvHeOLyA11NepBkUTQgNXZYRGCZ1KDKNKKKqzR/j70QeMc73oE3v/nN+KIv+iK84hWvwPOe9zw88YlPxO23345f/MVfxNd+7dce9SnOZZlFecdPf+ZSxyq2t5d70zNnltvv4sXl9ksVbqZIVZU+EpZZNnXJa7LsR1XapP4mxzW64xvac65IFc+mvtNUsWxqzqV+TAcHh58ckC4WTkUSH0l0MRXSThULx2Opa5JYVTt1SZb9aZ5UUjm80nHJ5iiAAWwGC4ussDCogxEgjSLmTMsC4jxvWmTSOpBrESwaS3lUF40B7R/mtfaqXm1EgDnX81KDnG0Uucqi12XsPsUTz2mWAzgHuNyiyM0sn92kPNucf/I7leEFrkAsQ2dxbl1KDMQnlxoD5guCaxHZko9TEYJFIkDOXxnN4pbnqEchNUj2ElAh8Bhw4cIF3HnnnQB8PcCFCxcAAJ//+Z+Pb/zGbzzKU1MURVGuM5o2tkHN+wbIjIEzFrAZYGufd01DITbE47zlVPoQ31DexmPzjKJFEQAZoYhftyyxEcXPKp+bZ0Ats832bbcPTTmOlUCcwk9kJov0b0h7O88sbFECWdQhSBq5FAM0/Clu8zyoM5nOxo3C17mmilv0xc6LCMwTBfPGUhdJXpj4QsmLuExUgGIgjgrk+VTMmpnxz208fmT+o2vFsRcCd955Jz7ykY/gtttuw1Of+lT8yq/8Cj7rsz4L73jHO7C1tXXUp6coiqKsEC07VNgH0rE5GjVtHGu9V9Vah8zmsB2RRsFbaURJD6mMEkiPv/SwyjePPf1xbvVhUYJYXDySFKFUFCC+kMByudY0poQn1dl0RGA8DvdVDLRJObdps4/HIZMMkE53v+hVlvlUIZsSZ/EqwpzbMo0tlcMVRwL42tQcTgkE+ZgnLW/J1YaI4h/5w00PiqMBUd1LNS0WpvHP21WIaB17IfCKV7wCd999N77wC78Q3/M934Mv//Ivx8/8zM9gPB7jJ37iJ4769BRFUZQVIrZlJdJOiQ1Q/tvvYGDyDDazTWMlzpNnHjaFAfP8ZRIxPffSiGInodiw4mvjKIAsIJbjqWiCfP5qL9iiWoFYXcnUID4Xdw0SxlTtDKpJ00GtqUFpFgVZgGCEAs2p2JwiBsgsnDWwuX+x4cFT6UK8H9cCyC0WsKn5ey0iAg9HCMQXL3Uh59WwpIRAVCPgxaxpzF+ZDXjUHHsh8G3f9m2z+y94wQtwzz334N3vfjfOnj2L//gf/+MRnpmiKIqyaszLaKHRxFA+7XHu75yZ7kejxMA4Awt/IJP5gxseSIoDafSnxlKGUtyuFAhiQnpRU2PXUgjwPXjx5EWUz12NV1UaUmYqBGq0UitWKc96FUjZq7H+lPUtnF5yC2MGWWaQ5wY2M3DTAxmm/ch1BWQNjIwOxCGzw0RAKoKVimjxgwDt+fpI8sVS6UJy/i4jBOL5m2VwxncN4nyVdS6aGnQE3H777bj99ttx99134w1veAN+7ud+7qhPSVGOLUXRHpuW6cxI1QWfOdNO21u/KVEYnCiMXbpYOFWgnhpLFQuzyu6wsZS7J/WXn60nJfIfJbJMsXCigPrBB9u7PfBAe0xp/ntO516e+7nsnK8n59eV6upJO6iqAGss8tzCWIcsz2DgmvnTqcbiciyVGiSFgDS0gObtMmPLCoFFxtW8EMqyEYHYmJLRgDwHTI7JyGI09j/F4bC5qQhokrJHrQ3zN/4zJacV/xYzld9vBlmWIc8zmKyGqyuYOL1NFsHLBvnxHI5T3BZFAOS8l+M86VgQkNTf3EVu9zg1KB6LU4XiORvfjyMCeY6xSGUbjfztcBjSC4+aEycEFEVRFCXFopQKINgmzH4wJjhBAWq80CbQWcA6wAKojUVmHJxBMB6mb2pkNIAnAjTDDs3wQ/Nx6vXLjC3jTV3GwzrPkIoNKulVPWybubFNQ/fIzCkVAW1STmljvNEppw4Qrmeet/VnnodyDQqDLDPIbA5DIcuoQCxs5RckO2PFgvZqUoMOqw9YVDOQcrTMu3hknhhYJpoVdQ2i4S831mmsAioEFEVRFGWKdOrxdjJpep9pn2eZ97TKWl86smkHUCjk2TR1yGTIMgNjHExWA4wSyA4s0uJdFBGYl2axbIpFXDcQ3yeHRQRSY6lcaxpUy0QEphfXIcd4L8PBAWbbYLAaKRWrSMom7XT89JL2sBRS86aOzPjxc9ogyxwya2CzDLawQFb5dCG5pdqI8gdytXUC0sBPCQR+gHkRgnljKWIhnhK0h4kAzmERihmNTUsIDIerEQ0AVAgoiqIoyoxUBkDK+clxoBkpYIqQtGesNXDOTQ0pwGQZrAFgnU8XcnVw18YFmPMMppRhJQuDH44QiMfI1eRdH2ZExalB8ZhUYXkOuAyT2jTSKsbj1TGiVo3YDpUZPPRCO+cFLL8WOb2A5pSJbfKq8nM4n85rawBjffqbcSJPro7ux5GAZQTAonkbi4BrNX95EeNwoJyrfLwoRYjb2hqG+0EIjEZhHmtE4FHmK7/yKxc+f+nSpcfmRBRFUZTrBv5bTjuUHtHxuJ1usUgISBvGj5nGmDEO1gDW+KJigwwwDiYzMJbhhcoLhZT3P2X0x8KBJzdv7DAxQB6O1T1PEMjIQOxF5ViWwWUZ6qKDCjnGdY7BAA1jSiMCaeLsFGv93OXllV8vtWeep+3sVFq/LAPwa2jZ6fs4GGQwJtQPzAqLUwdaJAZ4oovEwKJoQDyHH65qnBfZkhd6QbqQyzK4/joGF01r/mpE4DHgsDUCtra28PKXv/wxOpuHx403Hr7Pn/7pcse6887HL7XfDc+9bbkDxhWf81hWcC2zjDKQLtxMkSq0jOl0ljrUH//xcm+ptEktVH3vvc3HqRrbVM3u5ma78nhjo/0G66dPt1+cml/LFhCn5ty1LiBelriyOrX6cKKA+INL/p046UgRUBT+TwTzp0ejZroEnZ1V5fel45OpQVxbiJ5XOTZrmT9NFzI2Q55lU3vYeVvD1cHalUKA92NxEO93LYTAInFw2IWU9+dFBhYYU67ooOptYIIcB5XF7q7vA7C/77fUT0tpFwgXhb9WnL/x11sUYVpJQSDrtUNqULMxkAxc+XQ3IM9z2AwwroZzNfyKe37uGakglo0MAIvH+EEeLTGwqG5gUc2AtUDZwajOcHBgsL/fTG0bDFQIPOq88Y1vPOpTUBRFUa4z5L/nRHpDY5tbZvDINZVkpk9c0wsAxnjDycEXE1fcxxrA+FFjnD+ehTemgKmBMz1G/GZ8XhosPOHU2NUKgofDPK/qvCjB9LEzBhUsJnWGSaLl4qqkVawaYi02AP668T7naNxwika+TPHncVg4LMdk7YyPCjRFgxe0BtZmfpFt/pbq6nABkBrnyS8aAw6vGVg0j7nfPCdi3D2IY/NEQJbB5QX29poC9uAA2Nvz91clqnVshYCiKIqiXA303Jelvy8XXuLiPzLtOfaMzgqDhfefKUUc43vIuthGlCCnTWGQmQwwgIX19kfukBUOgDCApDGUigjE+8VjVysEYkMqZVzF+dVyLGVIxfUC1mLiMuztAYMJsLPjjamdnXCf340S4GUsCqDX8/OYhe7S6I/taEa5ZBdQuXFMRrQ4p+VyAhQdQQhIG9khsxlslsFkfg6bRdGAw9KCUhEv+Xje/J3nhpeCWV5QeX+RqOUH5XNFAbe5if37gwiQYmB/XyMCiqIoirJy0NABgneTTVBim5XGO1/HW5lCxKgAx3ifNQZSPHDMWl+Q6abpFs4CBkBmAZdhVmDsYiGQKlCoKh85kEbRYasNHzYmmedlXUYMSKOKF3O61bXBcGgwSLReXKX86lVDRrOcay4exjFpE8u5Gi9aHUcNOH8pBFgXI4uTuQ8jCEEMGFSyjtYYGK9yfaExnJ+nh9UNpNKHlhW188RsfGHIsoI2ERWoswKjSdZICYq7Xq1KVEuFgKIoiqJE1HXwOssuNVIISAc8bXAaQ7I3O8cOM6DiMdlIR9YvGAOY2f8caKoYa/x9N21N6gDkCYOJOQmxlzUeO0wUkHgsNp44tsirKlMrsgz1qMBgaLA/aHtTBwOtETgMfs3zhIDc4nSi2N6WHUBlnYCMiskC+uirjCIDMlpgxFfvvJg2tRcEWXNucl4fKhCAZrrQorm7jKhNpQrNSxPiB8xzVDb38zchBA4OVkcEACoEFEV5FEmtXhvnRabqc69caY9tbrbHNjbaY2trbSOk319baqy73T5eYROJnMsWEC9bVPxw3ZupZZkTqy3/2Z89vMOfZFggzK8mXkVYplPQQJKGEVMmZDrQ1aUGNeyK1lhGIyozyKxt2dIwQSAArulFnZdu8XCiBLO3mBMZIIvSKoBgXBUFkOeYVBl29wx29vzfA7lpatDhMNdfXnLnQkGwtJllNIACgmOcf1LIyrFYvMpIgKwdmFdbG/alOMga+2EqdBuFxxQHi9KDDqsdkOPxhUsRRwcW1Ay4vEDlikZNgNxUCCiKoijKiiL/XZceUaBdGBwji4WB8I+9TNeQKUTcR9oU8fukx8zsOa4RZYwPAPDWWgPAzaIHficAcJg9NMKop6U4PWkTG/2PVAwcJgSMLxB2xsIhQ1VbjMah77pcQ0DXETic0cg7WaTznIvfTSa+fkAWCsvFgjnGOcfXxGOjUXNMFg7L8fG4LRjmCYP5Y9bft9M1OBDNx8PqBlLCQO4777FkSTEwtiX2h8WspoUFw7u7/v6VKyoEFEVRFGXlmNb4odPx9weDptefXWvmFQbLKACNGNk+VHpYH2lEQBpUqRSiRsYNAGMs7NRYMVMxYI0XA/55ti2F/8/VRgkk84ypWAjIcR/GwLAqMJrkuLJvcPmywZUd4PLlsF265A2qZTtJnzTyHOj3/X1eI5nrL41+pq9xzvJ5rjsQdw6S8437xGNNL39bFKSMfjmH24KgWXQMTNOIjIHh82YqEGYWbVRvwIsANOfm1UQKYmTKkPyxZRlGwwx7e2ZW4C5FAB+vEioEFEVRFAXBmGbGFYuE6YknIde5mWctvfbScJFRBOnpn18s3DSIuH/KaypXOmZxc9jPNAys2pjgiAcAKRYs4ES9geMqsTPDH5gJhAVCwCTGkhdaCgFgWqFtUFUWo4nFaGIwGIae60Nxn55upQ2vCxfB4xg3Gvy0k+UiYXK9gLhVqFwzgIZ7LGLleDr9pzlvU4JAjsXjIYgUdyRqb170miAe7DTShSWEQTyeIo5wAYC1qG2O0dhifz+kAlEE7O76tKBVaRtKVAgoiqIoCoKxTs//eBz+0abBLVN5pCfV2nArUytkHrXsvy49/XIsXVyZHpPG19WkWcRGVhxF8JEBA+P8BzVwPo3IXyW/j53en461LqS8jYlFwPRiOhgMxxa7e812odwODjQl6DBo3EshQGj8y4XDUh2CYhEgo2IpISDnpDT656UCzRMHywiBRXM8jiJwrY6wycJ6evSn0QODkBbHi3WY2oyjWsZgNLY4GJiZEJCCgAXvq4YKgRVmmQK/VFFlig99aLn9brghO3wnAGfPnltqv9Onl9tv8+xSuyULRlNk9bWrJPs3d12zQ504/vqv22PxH8K9vfY+yxcGt8cStbLo9ZYbY0hd0u22fxPdbvvFqbEi8R6pBa1TNb+mTriNYitIhqenvOfP2+erq2MvD4XAaNT0+lsbVmGVBv681CBpRMXpETI1KB6Ljap5xo/cPxYH8XFiY2m5MQMzNXSyacTApxAB4PsawMkIQywIgOUtd2uBGhiMTCMV6MoVnw506ZL/W7FKbRdXEc5fKQKk0S+L3mOjn8/FRr5cQ0AKzzhlLX7NIo//1Rj4Vyt0U4LX3xrhvA8igfMZ8KI3TC/nC5T9E5DDjccAlTF295rpQIwGyIXFVg0VAoqiKIqCZroEWyYC7VWEKQKkwSHTd3gs3mckgYYWEF7DY3KMx5Q1vCljR+7PMXle8jjLj5nG+UidaVwzGwIQIsC7UH1RspE2UspimnPdJ0BdG4ynhcFyzQA+Ho9VBCzCuWZRNSNcseHP+R0XBssFwqQ4iKNX84TAvGLhlKGeej6139XsK39/h4mD1L5+Hhsx7/3ctRZtjZt4PB57ESA7XFHMctvZeTS++UeGCgFFURRFQTCkBoNmGhA7pozHPiowz/sv24PSCGGxsDSq5O0jiQjM85zGRtTVeFHnjaWMqOQYANYeAD6q0HKeTgdYdjCpfNrPZAJcuAA89FC4vXIFePBBvzEioMxnPA5F7tNOrA1xy/lKEcC5nWV+7scdgxgNkMXB0vA/zLhfZMTL18dRA7nvomL5Zcek0R/vG6IE7f04HiMFP7cLF3zkKo5oXbzot93d1Zy/KgQURVEUZQpbKNJQkYW9QDoSIAuKU55+PkePvbzlfrH3n0bbPEMn5dVvFwvPTyHiecVjUkykUojmCwGT3E9GEHj9OMRrx1aUoxFwMGgWBQ8GYRGm4VAjAougkB2NvAiQNQAUAsz1j0WATA2KPfEySrBIjMp9YiNcjkuRkLqfErgpkRAb8/OExLz9mpGA5jiweB5LceDmRAPkuhc7O+k1c1YBFQKKoiiKgmBISYOTXn1rgyEVRwQWLRQ2rwg4NqxS3v9FQiBl4MeGU2zMHzYGtI0zOcbxeCwlDlKe1djA4jUeDr3BNBx6j+rFi6EugF7VnR1dP2AZKATK0j+WBcIUBGwbuqg70Lx8f7nxNxDPo5RBPk8gpObkvEhYalu039WIA6A9lhICqShBVfncf9YExEXuXEtgVVveqhBQFOVR42Mfa4/FOZIXLrT3SRX8psZSxb3pgt+H/9pUcW+qSHnZ9112rCzbRb/xmPzHiPy//2977Hd/tz2mtKFnL04Nki0+47UCll0xODZ0UgbWtRQCi4ypqxEC8tznRQ5iIcDjAvONKelhHQy8sb+/70XAhQvhlqkVly9rNGAZWB8wGrULhDkvZFQgLgyO24TKuR7P39RcOsxgT83fRdGDRaJinnGfev2890oJgZQYANICt6799b58OUQEpABgncD+/uquhq1CQFEURVEQ0lRkISVTcDKhwWgMSCEmjQVZ6Bu3FuXz8rlYKPBWth6NjZrUegPyubigWe4bH5eviY+bOk/ZGfQwIbDIq2pMKGSV6T+pjes5KIdTVT6yQq9/UTSNf7mNx8HIX9Tl6mpy/xfl8z/cAuJ5x5w3ftj7LBsp4C2QFg2Av9Z7e00BG2+XL6+uCABUCCiKoigKAG9sDofee1eWwQBmapD0ni6zYvA8L730qh/mIV3k/U8ZWod5/6/lWOz9j/fnGJAWBDT09/aA++7z1/2++4D77/dG1X33rW7LxVVmOAzFwkwFkqsDc07luY8cxEXr8vtexohPpQ5Jcbiscb6o5uAwQz8eS0XbDovAzTv3eSKgqvzcvXw5FLentkuXVrNImKgQUBRFUZQpslg4TjGQhcFAGKM3XXrQY4+8HGNRr0zDSHnpeX+R9/8wwbDMmHz/1Jg08OmZT0UpFrUqjTfWY0jP//5+uOV9RgSU5RmPvRjgXE6tFsyoV2zAx3NjURrOPON+3hxdlLKzjIE/T5zICMGi4y0SB8tGDjgGhHqAy5dDTculS14UyG3V61pUCKwwy+T1fuADyx3r9Onl9jt1arn9zpxZbr+zSy4Udv78cvudW259Mpw7Vxy6zz33LHcsza9++Hz4w+2xeLGwa537v+xYakGxZXP/lz3n1H6p93i4NQepRQff+tb2mLIcde0NKBrpsn0oDQsaDnFf9ZTREhvRcv+UUTXPwF+24Ddl4M8rUo73S702FaWQUY9lzim+BcK1ZYvFvb3QHvRTn/Ibi4b391e3yHJVGY2CaKPRH9cDyHksvzMpDubN33lG9DJRq9S2SCwsSlGSEQSmOl2NeFi036LPZIx/L7lWADcWuFMU7O+vflqbCgFFURRFQfBSA81i4Tg1SBrdqbHDPIvLFOumxg7zbi7y/i9KV5LnlFo4KmVIxQZV6pxS517X/hpXVfCe7u4GIfDgg14I0Jha1ZaLq8xw2BQC7BiUMqpjw39Rb//DjOV5UQH5+LBowiLhGj83TyTMux/P6WWiH6nPCvg5Gy8cJqMCFATXQzRLhYCiKIqiTKkq7+1LeTyZ3kMjK075SaXGpG4P85zHY1IczDNwGMWIx2R6TywEWFAqjyfTkFJRgriAmK/h54/POb4mVeWN+/HYG/7xxhaMBwern1KxqsRCYFFL0GULa1PRIWB50bBons8z8OV7x7+f+Jzisaute0jdT4kBwBv3LAKmAJCL4D34oL+9fPnIpsBVoUJAURRFUeANz4MD/w9+txuMWNlZZV4Kz2FGldwWpQEtO3a19QAp7/9hEQG5KnIsDg4zKud9xtHIG0yDQTCYdnd9q+G9PX97773e2FrlTiurzGAQhMBhRu+itJzUHAaWM+6XGUsZ3PPSf1LiYJk5mHp9PLbo9bFYqSofAWAR8OXLXhQ89BDwyU/6AvcHHmi3yV5lVAgoiqIoyhR6oSeTkDYBBMNqUUvPRREBuaVWAL5ao2pecW+8r2xfKtdD4DFSYzR6gPZYavVkubIyxwivE89jPPZiazDw+dOMBPA+i4SZmqU8PBh1YXeg2KC9GmNeevxT+wKPnRDg/cMM/9R+saA97LXx+08mfn6yS5Bc+O7BB70AeOABP49XuUtQjAoBRVEeNVLFwnERLFfAlKQX2Hr4Y49kQbFUIe/GRnssLoKet9/W1sN/7cWLzcepwuD/+T/bY8rVM5l4gzQ2mqRBtSjlJzaeFhlUD0cIxHnTqYhA7OmMjadU+oR8Lo4I0FiKIwfxmIyc8JYLXQ0G3lja3w+Fwbu7wCc+4Q2oK1dUBFwLeA2l2IsNbmD+fON9YH50Kx7j8ebN23i+z6tziX9Tcn7K+ZUSB/E8Txn8Ml0qHosjZdyPrYV3dpqdgVjUztqWvb3roy5AokJAURRFUSK4sBhw9Tn9sac0HpO388bk8WSNAA24OL0n5e2V4uCwFZCl0R+PSQM/ZfSnRIR8DyCsF3Bw4IUAb5lGwXUExmOtDbhWcIE8Ro/mGfDx/ZRxL/cDHp4QSN2/mnqA1BybJwTkltovFq0pISB/T6ORn59c6Zo1AVw07MEH/XPXYzqbCgFFURRFWQC90/SyMjUGaBtR8VhsTM2LDswzyg4TAvM8/IcJgXh/WVDKcbnuAZ+LVyDmWGy08TqNRv4+e64PBj61Yn+/2WKRnYQ0EnDt4foXMfF8AxY/Ts3peYI3Fg5yXsbbonks55QUC4sM/HlCQB5r0X7x8YBmd6CLF733XwqBhx66PkUAoEJAURRFURYiPdQ0bJZ9zDFpQC0rCJaJPsiIwKKWptLAj72hsfef46kxvnbRWJ7719a1N/xlq9D9fV8MvL/vVxC+/36/33g832BVHh0YMViGWACkxuT41UQFDhMBcXrbPMN9nnEfiwE5ltpfCoXRyM9dKQQeeigIAba/vZ5RIaAoiqIoSxIbqssarrEIkPelpz8lDg4TAvO8+vTcx0KA3vp4jLdsiZoak59l3hivCVuFTiahNShThOTqwcrqI0XaYalbsRg+TNTKOZxq9Rkvhia9+rH3Phaoi7z/i17D3yNrAuRaARcvegG7s3P9RgEkKgRWmA9+8Nrsc5Qsu7LwnXcut98TnrDcftvbh+/zutctdyzl2rK/v/jxUZIqNE4V8qbmV2ostaJ3amzZFb3f8Y7m449/fLnXKUePFAy8LwUB85NJSjDI+/HjVP50nBo0z/vPsdjoSo2VpX+tfC7ej0bjeOwNp+Ew9Fdn29DhcLV++8q1IxbHrFMAmvOWt1IMx5GqeP4tk94TG/SLbuft55yfq1euhKJg1gcwsjWZHI8IlgoBRVEURXmMSAmCa4E0pKSBH4/FqTy8L8UB95VGP187mbTHyrL5HuwQNBr5FIr9/ZBOwVSL4+BJVZaHc33RnI+jBbLOZZ7Bn0rrmXc/9Zycx/J3MxqFaADXDLhyJSx4d5wK2lUIKIqiKMp1Dr3wbBcpVwCWBb+y8FIaRCkDn6+pqjAm1yXgGN+LkQGm/wyH3vu/vx8KhbUrkDIPzou4bkFGAhaJgbKc7/GPjX8pWuM6Aq4XQO8/IwIHB14gHDdUCCiKoijKMYAGlDSkFqViSIOIXv2HOybTkC5cCC1C77/fG1VSoKgQUK4G2cqXGBMMf5nKxlS1eWPyflE05zHFBjsDXbjgheylS8cjBWgeKgQURVEU5RhxWPpRXGcg+8zzMYsl2QmmrpstHHls+VoupMRVgg8OwirCinIt4QJfRApb3nKjwS+FQCwCGA148MHQFvTy5ZPRyUqFgKIoypRU2PfBB5cbS7G+3h47f365sT/+4+XeQ1GuFlnMyyLl0ajdzpHe/7IMRhXHOp2mYTWZeONpMPDGP4spj2MqhbJ6cD5PJl4gyNQgafTzcVk2x4zxnv/77/fz9ySlsKkQUBRFUZQTiGz/GSMLKaXRNB6H+gHpTR2NgE9+0hdULjquojxaxC1OqyoUrVPMFkUQDJzTznnj/9IlX8uy7NoKxwUVAoqiKIqiNOBiU3JNAFlAPJlgtuqqMcHgkusIKMpRwflLMcD5KyNYTAcajXw066RGr1QIKIqiKIrSgF59mTYkW5TKCAEFgBQOirJKsKUt0KwjYCerk4wKAUVRFEVRksj+73FxMfOyT6onVbk+mUxCYbuiQkB5lFm2qHLZ/f7n/3z456IojzW7u8uNffjDj/65KMojRbb+lOsUKIpy/aJCQFEURVGUpZA1A4qiXP/Yoz4BRVEURVEURVEee1QIKIqiKIqiKMoJRIWAoiiKoiiKopxAVAgoiqIoiqIoyglEhYCiKIqiKIqinEBUCCiKoiiKoijKCUSFgKIoiqIoiqKcQFQIKIqiKIqiKMoJRIWAoiiKoiiKopxAVAgoiqIoiqIoyglEhYCiKIqiKIqinEBUCCiKoiiKoijKCUSFgKIoiqIoiqKcQFQIKIqiKIqiKMoJRIWAoiiKoiiKopxAVAgoiqIoiqIoyglEhYCiKIqiKIqinEDyoz6BVcc5N7135UjPQzlJ+LkW5t4jQ+ew8thz7eawzl/laNA5rFzPLD9/VQgcws7OzvTerUd6HsrJY2dnB1tbW9fkOB6dw8pjy7WYwzp/laNE57ByPbPM/DXuWrkdjyl1XePee+/FxsYGjDFHfTrKCcA5h52dHdx0002w9pFn7+kcVh5rruUc1vmrHAU6h5XrmauZvyoEFEVRFEVRFOUEosXCiqIoiqIoinICUSGgKIqiKIqiKCcQFQKKoiiKoiiKcgJRIaAoiqIoiqIoJxAVAoqiKIqiKIpyAlEhoCiKoiiKoignEBUCiqIoiqIoinICUSGgKIqiKIqiKCcQFQKKoiiKoiiKcgJRIaAoiqIoiqIoJxAVAoqiKIqiKIpyAlEhoCiKoiiKoignEBUCiqIoiqIoinICUSGgKIqiKIqiKCcQFQKKoiiKoiiKcgJRIaAoiqIoiqIoJxAVAoqiKIqiKIpyAlEhoCiKoiiKoignEBUCiqIoiqIoinICUSGgKIqiKIqiKCcQFQKKoiiKoiiKcgJRIaAoiqIoiqIoJxAVAoqiKIqiKIpyAlEhoCiKoiiKoignEBUCiqIoiqIoinICUSGgKIqiKIqiKCcQFQKKoiiKoiiKcgJRIaAoiqIoiqIoJxAVAoqiKIqiKIpyAlEhoCiKoiiKoignEBUCiqIoiqIoinICUSGgKIqiKIqiKCcQFQKKoiiKoiiKcgJRIaAoiqIoiqIoJxAVAoqiKIqiKIpyAlEhoCiKoiiKoignEBUCiqIoiqIoinICUSGgKIqiKIqiKCcQFQKKoiiKoiiKcgJRIaAoiqIoiqIoJxAVAoqiKIqiKIpyAlEhoCiKoiiKoignEBUCiqIoiqIoinICUSGgKIqiKIqiKCcQFQKKoiiKoiiKcgJRIaAoiqIoiqIoJxAVAoqiKIqiKIpyAlEhoCiKoiiKoignEBUCiqIoiqIoinICyY/6BFaduq5x7733YmNjA8aYoz4d5QTgnMPOzg5uuukmWPvItbrOYeWx5lrOYZ2/ylGgc1i5nrma+atC4BDuvfde3HrrrUd9GsoJ5GMf+xhuueWWR3wcncPKUXEt5rDOX+Uo0TmsXM8sM39VCBzCxsbG9N7HAGwe5akoJ4YrAG4Vc++RoXNYeey5dnNY569yNOgcVq5nlp+/KgQOIYTxNqE/YOWx5FqFkHUOK0fFtZjDOn+Vo0TnsHI9s8z81WJhRVEURVEURTmBqBBQFEVRFEVRlBOICgFFURRFURRFOYGoEFAURVEURVGUE4gKAUVRFEVRFEU5gagQUBRFURRFUZQTiAoBRVEURVEURTmBqBBQFEVRFEVRlBOICgFFURRFURRFOYGoEFAURVEURVGUE4gKAUVRFEVRFEU5gagQUBRFURRFUZQTSH7UJ/BoUlUV3vSmN+G3fuu38MADD6Cu68bzv/3bv31EZ6YoiqIoiqIoR8uxFgLf+q3fije96U34O3/n7+DpT386jDFHfUqKoiiKoiiKshIcayHwlre8Bb/yK7+CL/uyLzvqU1EURVEURVGUleJY1wiUZYknPvGJR30aiqIoiqIoirJyHGsh8B3f8R147WtfC+fcUZ+KoiiKoiiKoqwUxzo16A/+4A/wO7/zO/iN3/gNfNqnfRqKomg8/7a3ve2IzkxRFEVRFEVRjpZjLQS2t7fxFV/xFUd9GoqiKIqiKIqychxrIfDGN77xqE9BURRFURRFUVaSYy0EyKc+9Sm8//3vBwA85SlPwblz5474jBRFURRFURRiLZBlQJ77W2OA8RgYjYBoGSjlGnKshcDe3h7uuusuvPnNb54tJpZlGV7+8pfjp3/6p9Hv94/4DBVFUZSTijHe+JGPi8IbQvI5GkXcAED2wOC+ZelvuZ9z7U0edzTyhlZdB2OLx60qYDLxj9UIU5Ylns/WhjnJMTkHOd+s9fO+LIFez/8OjAGGQ2B3189Hzk/OY8CPVVXYdK5ePcdaCHz7t3873vnOd+Id73gHPu/zPg+ALyD+lm/5FnzHd3wHXve61x3xGSqK8lhw003tsfPn22Nnz7bHqqo99qEPtccefLA9tr9/+LkpJ5cs8xuxFtjcBLrd4Bm11htFWRY8pkAwimhkFYV/rRQDzvn5y9uqCscDgCtXgL09b2xdueINfx53NPLPSeNLUWKkMDXGz13OP4rTXi+I2Szz86/T8XOrrv14nvuxTqcpBMZjPw+lsT+Z+OPUNTAYAAcH/m/tzk4Qtpyv8dzVedzmWAuBX/3VX8Vb3/pWfNEXfdFs7Mu+7MvQ6/Xw9//+31choCiKojzqxJ5RabzTQKKRv7UVhEBRBGOKokAKAaCZTrG5GQwoIHjzpRDg+fAYPD7Q9KqORn4/OUbvK+HxU2JZOf5sbPj5xjmyvg7cfLM35oEwx3s9P8aUH85tOSfz3M97zn2gbcBzoxjY3/dCYDTyjxnhqip/fzj0ImE49ON8fHDw2F6nVedYC4H9/X3ccMMNrfHz589jX111iqIoyqOIFADr60C/743ujQ1v7NDA73SAtTVvHG1sNCMCWeZfRwNfigl69ykoOh0/Nhz6jR5YadCPRsClS94wOnfOP3YupAGNx/6+TMlgpODgwL+WosA5v8/Fi5qScRLgXCsKP3fX1oLRnmXA9jZw661h/maZv7+21kx3o9FPo14KhrL0c4m1AVUVfidZFqJVly41IwDG+NvJJMzzgwMvFCgOBgMfXbh82YsIigMKiJPKsRYCz33uc/H93//9ePOb34xutwsAODg4wA/8wA/guc997hGfnaIoinLcoceeBlGvB5w6FYwpGkDb2/7++npbCKyv+31lnYCMFlAA0Ojf3/ebMSFViEJgfz8YU9Ija20Yp4G0s9OMCOzsNKMLFBCMMCjHC5mW1uv5eUjvPuezFLPb2z69kt7/ovD7rK/748k0oG43zBuZLpRlwbtPr7614Zh17dPYmObG86TYpUgdj73hPxwGgbu/74Xr1laIJHBsZyfM75PGsRYCr33ta/HCF74Qt9xyC57xjGcAAO6++250u1389//+34/47BRFUZTjAo19GvA0hHo9f//UKe/t7/WA06eD4VMU/nWbm36/9fVg9NDA5zEYBaBXlUKAIoFCgEYU0EwDcs57STsdbyABwfChR58RAZmbTa/q3p7/DLJw8+JFf8403uo65GtrfcH1CeeUnFtbW2EO9/t+znI+l6V/vL4e6lRY+Lu+7sWALDrnb4XzmL8VvhejUPTq13UQCeOx338w8OdBsQz41wBBoDIy5lyIBlAIMGqws+OFxYUL/lic/9xOwtw91kLg6U9/Ov7X//pf+MVf/EXcc889AICXvexl+Nqv/Vr0er0jPrtrw5OfvNx+P/RDy+33f/y9JWf99HoeSqoiM8FHrpxZar+//Mvl3vY3fuPwfbREZHW55Zb22POe1x572tPaY6lgX+p4N97YHpOFm2Rvrz124UJ77N5722MPPNAeSxUa/9mftcdSvgrNaFxdsswbRp2ON1D6/eAlLcumEDhzJggBGlvr694QYfoQiyaZYiGNJhkRYKqGjBbIfGoa53xuf98ba8NhOC5TKmRxMT2qk4k3lliUefly2IdC4Nw5v//Ojn+/Bx7wx2eaBg3Ak2BUXc8wdYdGf78fokmnTnlv//Z2EAPb26GwN89D1ICPiyL8FoBmWhsFc6d009+A86k/NWCMg6uBSQUcHBiMJ2HeD4cGWWZmhfJlGQqHKQSAZnrQeOxvd3f9xvqX0ciLgMuX/WfZ3vZ/77kx9UgWHh9HjrUQAIB+v49XvvKVR30aiqIoyjFCtkG01ns4mTaxvh5SfCgEtrdDR6CU0e/zrR36nRplXqMogV4JGAtk1sEah8wAmQGsATLnYGuHfALkcP58AP8fBwAOzhmgMqgdYIyBsQYVgH7mGp2IamcxKTI4AHXtjaxJBXQ6BlVlGpEHGoasG2DEYDRq1ifs7DSjBBQFFBtaU7BaUMhubHihur3drAG44QYv+DY3/TxfW/PiQBb2djp+zEew3KxuRaYK0aB3NVCUQGErZNU0F208nRScKAA2u0Voc2UtKmQ4dbrEuXNApwTy6VNMdSPjscFw6A36g4Ng3O/vN+cqU4IoCHZ2QqrQpUt+4/xm0fFx49gJgbe//e148YtfjKIo8Pa3v33hvi95yUseo7NSFEVRjgvSE9nve6Oo2/Ue027XB0LPnAne/07HpwNtbYUUHhZXski43wcy49CZ7CGvhqHLEBzMtNrRADAj58cmY8DVMK6GdczBqWciwPdztLBZ7tVEngN5hqICusahLrxmMAZwnRJuYxPIcjh4ReFMhsoWqGrv9ZfFlVXlDScWFJ89G9IshkNvUN1yix+7fNnf0riiF5ZFnioIjhYWslMErK8H7//p0yG1hyIgz/33n+d+/jIdjsc5f97fZtbBuBqoHcwktPMxEz+GugYuDtsLWFBdsh8pDz5V3jbPsd7vo79WwuztAgMHlNPimtLnDzmbYSIiAmwvur/v71M0MDrGubm52ZznV64A998fWuvu7/vfwqc+daRf2TXn2AmBl770pbjvvvtw/vx5vPSlL527nzEGlfY8UxRFUR4GTG2gh1/ebm8HIcDiyTNnvBBgrrS1DltbwMZ6qCXIUKM8GCMfD8Mb1TXghkAt8nHY35OudRpOMil/mjth6cZnDpFzKOGm4QN+mB7Q63uLYGpwuQxAmaOaGuplAVR1yJ3OMmB/38xqFcZjf/jhMLwV7w8GIZLgX9dw+ipHBGtSNjf93Nzc9GLg1KkgZjc3/fw9d25q4E/z9I0J6TTdrk/t6ZU1yv1LyB44gKkrGFm5y5CQLAAYDkMPW5mnFvfG5bgxMFPVnOW5t9Dr2p/AeDQrGnBZhgIGOSw6HYuNfonhZob9AzMTAhSu3W6IWhRFqK1hqltZeuO/qryI4PVg3QwLkq9njp0QqIV7oVZXg6IoinKNoPEvF+86ezYY/efPe8PihhtCJGBjw++3teWNplmhr3FYswfoVCNk4zGKnQFMXcEO9oDxsLm0L/NwWAFJ40om38fLDjOJmsUE3JivIT2ttOzZsmUymaqTPqyx6A8tcmfhihLVWg+1M+h1DYbCmJpM/Gcdj30UgAXJZemNJRqcw6Hfjx5XdiaiY1h5bOBCX6wHoFHPx+fPhzoBFuuykJ3T7exZYLMzRCcbI8cExXAMXHrIKz2ZO8ZG/5zT43HoDwo0FxeIhQCVpuwzOhj4Ezk4CAKYJ2btNNLlfw/ZtGq5yDooewX63RyVy2aH4NoFLHCWNTLdbuhaxBS4aQPKWXTh8uXwc7xeOXZCQPLmN78ZX/3VX40OV7eYMhqN8Ja3vAUvf/nLj+jMFEUhL35xe+xlL2uPff7nt8cen3+sPfjhD7fH/vij7bHUUsAJS2R9e7s1dkOi0vh/S+yHpyfGXtSuXP7rK6daY7/7u+2XxmO//uvtfVIFysq1gZ18ut2QC33jjX5bW/MCgOlAzJ9mfvSpU6FtaL8PZHCwl/Zg93a9VXHpEjAZhyrb0chbHrLtzmDg8xikEJBeUxpNsRBgBTEQChaYsJ1lYTEBVhPTStrYgMkyrBUF+lkOFGvAqRIuy3CqMqhqf9qdTrDvqgp46CF/6NHIX5fh0B+WqxifOuXf4r77/CnxI6sQePShLb2x4YUZ5+WZM2Hesg0oF6jb2wutbTc3HIrc16yc2qrRObgCu7vnv9DBIOSHydY7TK6XEQEpdFlsw1oARrZ4suwbCgS3/eZmKAqglS5Xz5O9SssSeb+PvN9Hr9eDK0oMOgad0qDfN9jbM7MaH54aBcDubtDKg0FYfI/FxwxWMDhxPXKshcArXvEKvOhFL8L5qHPNzs4OXvGKV6gQUBRFUZaGfdHlmgDMr+73QyHlxsZ0EaXModtxyHOHbl6jcDWKSY1sfwLrJrB7vjLRDAbAwX6wNmhVS+MGCKsfMTpAy4PRAFZiMjJgrd+PDdeB0EtxMglCgK1RfFsWYDyGcS54assSZlpjgNEAzmbInIFxBh1n0M8tJtZgZDNMaou1teD9Zz45T4ktGvM8XENrQwGnXNtAufZQxJ4+HdKAzpzxEYAbb/QpQP2+jwywfuXgwIuDm2922FqboKimUanLl33ezN5eyJEZDPwXSI8/K23puZdLUdP1zi+dUSvuy4Uw6IanMKChz/krWwbJ41Nc0LV/cADT7cJ0Ouh3Ouhv9jDayDEYWWxtATs7IXVofz+kvPV6YVViRg9Y6yI7du3thSDH9cSxFgLOOR8eivj4xz+Ora2tIzgjRVEU5Xpla8uvnNrreYOp1wMe9zi/ra/7Atl+PxRYlrnDZn+M3NTIR3vIBwOYwQHslUu+AHjnSuhvSAOK1YoUAzRsKAjiW1Y2SthnlFEC6VnN8+DuFEWYs0UI2E4oz713V67mtL7u3y/LYB1gYbBpcvT6JSbIsWs2MDIdrK15w3E49MWWLNTc3fUv39jwBhNPkQYWC413d4/k6z32rK15o/+GG0Kh+unTQQTcfHNIaVtbC4uBMRUuv3IRuH86Z1kxvrsbPP5ctUvOX6YFca7JBQXqOvSlZcobJ4WMEozHzdXD9vf9ezOfh255FqGwkp/RsvX1ZjTi4MDvs7uLIstRFDnWN/vY3OzhwgWD0Sj8hovCR72ocRgJGA595Euear/v57tsY3o9cCyFwDOf+UzfKs0YPP/5z0eeh49ZVRU+8pGP4EUvetERnqGiKIpyPcFOQVxdlWsCyG4rm5tAv+/Q7wO9LtApHDb6NXJTAZMRMBkAw33gimilIyMArEhkITA99XwuVQdAV6VMIeKSxakiTNmRhcYVj2lMqJ5kxIFueh6v14PJspmTzRYFiq7DxNaY2BrGupmuGA4N9vaCscSMJqaN09ikGGBmkvLowN7/p055Ubux4QUt61xYMMzFwjbWHdb7FcxggOzCPnD5Egz7cbIYhI35OYfZn5M5NnJ1O4pNuVw1owYUAhSi3DqdppudkS/WFMhwE3uYxkJA/o64atm0H66hIC5L9M6cw+bGOsYTi6oKdUD9fhAB+/v+8d5eOH1+3E7Hj1265Pe9XjiWQoDdgt773vfihS98Ida5vjWAsixxxx134Ku+6quO6OyWh7loi3j3u5c71vpP/ovldvzi315uv/vvX26/229farfHP+c5y+33xCcutd+Xv/IzDt3ncz/3mUsd6x/+w6V2U5bkK76i+fgbv7G9z5d8bmIVr9/7vfbYn/5pe+yDH2yPpfq9pf5SDwbtsbNnr+3YHXe0hm6/7bbW2Nf9/c9sjT3hCc2FEFPr9f3UT7XHUh9LWY489wZSp+M9qefPewHAegCO0cjqdoF+NkLXjpFPxjAX94B6EoymwcB7M5lbwMJKGkI0pGQFLXNsZISbBj0NMKCZfyMjAnI1MhpHchEEGlW0yLkvDSquGsXUJC6CwPZAkwlsVqC3VqLIa1TdHON+yeDB7CNfuuRfTu8/DaiDg7AfPxKzpLSz0LWh1/OGLTcWr585E1rb0gu+vg6s92v0ywmKwX5owL+zEwo9GL6JawIoZKUQ4PLAFJ5yzjKth8a6LG6XRTZAqHuRNTG8z3H+lqg+8zyI5IODcPx+31+U6e/A5DmyskR/q0BVlHDGwNVAnluUZViLYGcn/HSoYfhR1tbCR+LP9nrgWAqB7//+7wcA3HHHHfiar/maVrGwoiiKoiwDC303NsKiSuwQ1O/7W3YL2t72ixx1qxG61b63BC5d9JbCpUvBe7q35w2WS5fCfaYGSUOK4kBaG4SFljKfmt5R2SWIScz0sMoliGlEsfhBigRWRmeZf67TCfk9ee4tyLW1WRtIU5To9jqAdXCdHupeiar2u0wmfjXuuJOQDIJkWcgQYd1pvEiU8vCgmGVXIAqB06f9JiMEGxvAxlqNXjFBMTkIxv/+fnNFLi4iwWWn5ZfJ1CAWvXMMaBr+NNY5DjTrVbLM/7CmbW8BhLkZd8LiUt3cJ66bqWt//kx7o7Uu5/uVKyh7PaBbATaDyyyyzQJFYXBwYLCz43dj6g8/JhBqKWSUgKURq86xFALkaU97Gt773vfisz/7sxvjf/Inf4Isy/CcJb3QiqIoysmCtkWvFzZmzbDDSK8XFhTrlDUKUyGva2STETAewoyGIU+axhPdiDT+aUTFRr8co9Ev1wmQrUNlzjXQ9JrSi0rDRxr73KSYkClI3F+mFfF5nt+0WNNMz90ZA+MAYy0sLHLn04jK3KDbtTDGzOw11lMw7ZuZSWXZDoAoDw9rvZFPwXr2bOgUxI3pQqdPO5zaBjp2DLu/F4o7GA3Y3w/1AFRs7KPJqBXnr6wRoKLjRmNfilMgWNWy69Vw6CcKEESunBzSsJcds/hcrxcia5zXXDhAttetKv8ZGYUoS5iyRGdtDZ1TXQz7zNIws8J2BiVYU8GIFy/Z9VI8fKyFwDd90zfhu77ru1pC4BOf+AR+9Ed/FH/yJ39yRGemKIqirDIspGRh8Pp6SKNYW/MG1dpaKLLMXIXOaBd2PIbZvQLs7YY6AOZTczlepgZdvNhuB0qPKwuIAW+BsAPQcNgs7qURz56H0tinR1WmBnFMGmA09oHmgmRMeo6NN+f8Z2AaEXO1q8q/pt+HXV+HzXLYbh8uy1H1ShjbwXBqGDFtgoGGPA8Bh91d/zaXLl1/hZerRLfr5+u5c36T7UHldvasFwmnTwPZ3pVg3MuWoJcvB4Ofc5PigMY/vf/M+acgkJ5/qe5oTXP+c/7SaKeBztqBOBUIaLrcKaDp4Wd6G38/sjZG1stwo+CmUOj1YKyFq2t0igIb/Rx1nc26XkmnQFmGn69sonQ9tHM+1kLgfe97H571rGe1xp/5zGfife973xGckaIoinI9wFWD+Q+9XIGUHQ27Xeef7wJ2UiMbjmGqkV/ldCiiAXFUIF6WVHrbuR9fI41+dj2JDXiKAbnsr0x9yLLmh5Pe0biTi2zzKKMPcgPaUYppRMBQiEwFQlYWcBYobI6ydHDw188537u93/cv73bDulBsBqMRgUcGHeLr635jWjwjWbJ96NZ6hWx/D9jbg9nxbW1nRj6LPK5c8Vbu3p5/noJWrmzNjj4HB2GO0IDnPGQ0jHObhbzMreG+zMPhuhY06uUck6k/rF/h+8lJJIvjeZ4UtvztsBsSf/DT35KZFst3yi62t3uznyXfxpig+Tc2fASGNQUXL67+YmPHWgh0Oh3cf//9uPPOOxvjn/zkJxudhBRFeXSIfnoAgL/7d5uPv/iLEy98W2KlrFRh8P/3/7XH9hKFxly5SHLlSnssVVV7S3sBMNx0U3vs0qX2WCounHqPCxfaYx/6UGvo85///MbjjX/wuNY+H/hA+1C/9mvtMWUxMk3+zJngOd3aCmsGrK0BPTuCPRjBjkfA7o7/zh96yFsAcl2Ahx4KuQOMEjD/mgZ5VfnHNIiYAkHDnR5XoOlljT2aLJCkkc6WR2zFSC8tx2SLR+ndZ+oPvamy3zsXCGBXFhpUXCiAXty6BsoSZVFjvWPRsRbVZo7h2MxO0VpvQFkb8teBULbA01OuDjras8zPWW4ypW1tDeiWFQoz9i1tOWd3dkL+/2QS0oO4URDIFa5lBS2FAMUqDXHnQv4M0BQHjHbRmJcCN5UW1+02fx9ASPnhfszjkVa7XPWY78PPDTS7GjENrtuFcQ5512JjozMz7vlSXls6D+SKxfxZrirH2hr+0i/9Unzv934v/vN//s+zdQMuXbqEf/pP/ym+5Eu+5IjPTlEURVlVZNOSU6d8asXp02wRGhYR62RjZIN9YDQMHVUuXfKdqmS/zAsXgnuQRtLurr9PA5zFlhSzFAi0OmhgSatCthSVHn4+ZgRBplywqwrDHBQZ1nrXMbuvjEZtTy6LPGWHF4oFGY3gmgQAUJYoNi2KvMQ4yzBBhs6k2TyGaeBsw1pVIVog14tSlod2c1mGbkGy3S2jBGVWw07GIWLF/BZ2ueL6FgcHIT2I6ozRKwpQuXYAF62TEQMa3BSZdMrK+pPUxtdSMEtRICNeTP+RdQeTSTO1iMcAmu8PhPal8gJOhYMxBtZm6JcZRuu+9oVaQwoBRg6ZOrTqLXGPtRD48R//cXzBF3wBbr/9djzzmb5V5Hvf+17ccMMN+IVf+IUjPjtFURRlVaHDXG6ySLjX8ylBBSpgPIKhAcXWijRs2FqR43LNAJnTzHaLcs0A6dkEQmoQEDqnyOdlZIGvl/nR0usPBKHClIksay7tS88p0zdYQQ00C4z5mJs8z+HQr1I8HALjESxylCaDyRy6pUWvZ1DXBr1eSA0qinDNZamEcnWwHOTUKV8DcO5c6BzETkHr6w6FnRrne3s+Usq2oJzP7HQl29iyNoTtnRh6oAjgnCT8LVBQyIW/ZEQBaKajyS5BTCGSz8v5CzRrWihU2VFI/pgpfhkx4y3T81JRMGthrUVpgO3Njak+MbOf9N6e9wFQDDDycunSane/OtZC4Oabb8af//mf4xd/8Rdx9913o9fr4RWveAVe9rKXoeAfM0VRFEWJ6HSaPddpPJ096/9xP3sW6PccsotD4NIusL/nKwMHg9AmlGsFyGJhaezTw8oxNh+nwS498XyOnlT29pceWRrlFBdlGfajRU1DiCk9PB5bhcrFmGgA0djqdpsRA8LiTQoBLizFKAHTL7IMWZ5jvedQFzmwUSIrSvR6/jS6XR9IWVsLKxBzfYFV96quGvy6Tp3ybW9PnQqpQJubIcq13q2Q7R/4uXnhgl8j6KGHmp1+DqZtRBkhiOtdWCBMi1iuayGb6svuQmUZ2n3KNDYgpK9x/gHNsBDT4uRvhRvTi5j2k+fNCIWsC6ChD4Q1MdiBSIp2IIjkwQBmdxflqTG217eQ58VMp8g1AnmJdna8ENjfX10xcKyFAACsra3hH//jf3zUp6EoiqJcB8gW+3R6ytpFb0c7bzdkDgYVMBmHlAgaENIzSWOdxj6NHHnLFocc48kAzdaLcQQgLvSVHyS1n/TUSkNI3soIBO/LaIE08KTnVqZr0ECTq7qOxzAAMjeBhUGZ1+iUwGTiposZm1nZgowIyAZHq5xrvUoURchTl+t1MVBEj7UdHsDs7nqheuGC37hsLj3uLAC+eNFbtnKtC9nxhx5/RpGYdsN0OGl0A81uQ3E9lVwgjNXj8kcYryMQ1wEQWdULhNQknjd/5PK8pDCXi5mNRjMBY4xB4YDN/jo657ozIUCDnylDFy964UXtv4ocOyHw9re/HS9+8YtRFAXe/va3L9z3JS95yWN0Vg+PH/qhw/dZ/8V/t9zB/v2/X26/j350uf2WLbbe2Fhuv3vuWW6/hx5abr9UIWjE//n3EwWfCX72c25Yar8//uOldjtRPOMZ7bHP+Zzm4+wP3tne6c/+rD3227/dHkv1ZkuNpZZ4XNaiSOUl3HtveyxVGJxqe5IqDE6NnTvXHvvDP2w8fMaLXtTa5Uu/dK01lrp0S/xEThwsWi1LHwFYW/PGEm0IrivQLWrkoyFMPYHZnbZWZPNw6SE9OPDPsW7g8uVmm0+ZIkQPLN2JNHicC95WWnHGNBd3kt2FZDcVIHhh+TzQTH3g72AwCKk/fB+5yitzvVnTQAtdrkDM1YYZfWDUITbiphez0zGwWx0UpZm1i7982b+EtdXMPpE2aZx1orRhRyZ+nUDIkPFFrA55Bhimrcn6E36/TBdiByG5qJiMCMl1JmQxOrv+8PhSYTvXbJUrvf10sUsBK+te+J5EpvfwNyBT6+Sq2vT2y/nLyFr8WabdghpRCb7XaAQzOECWZyhKi36/xNqa/7shGy1tbHghMBz6l67iasPHTgi89KUvxX333Yfz58/jpS996dz9jDGoVlWeKYqiKI851oZuKuvr3uhnExJArDWU17CTIcxkFIwjWUTJjYYUb9llZTBoFjXKugF6W7maqnNhZVam+nBMtgxlKhDDFzxpGmDMkZYFxLKmgJEJmRpEo4cpHBQqPAYNKRr7bNcoF4SiIcZUEBpgZYmy10O5BmS5v3Sdjk+54ltsbvqXb2wEEcCmScp8mNZGMcuvXhawdruAdRVMFUWnZP9+prmxPoBRAGk7ySgWjWga9TwuowWxuGSUTKYBUYwyTSfl+Zf1LlIwcM7Luc36Fnm+MsolFz7jOcctc1m4Ihcvm342M5kgyycoyxLdbki/YvEwi7Jll+BVK3w/dkKgFiqxVreBoiiKsiSyBpKpE3JVYToyXQ1vBGDS9FbGxbJxVxVZdEurQBpS0hADmgaXNHiAprHP56WxwhQJGks0rqSRRONKrlUghYA0vnjufH/57yvPix1m5Lnx+vBaTNOEDIs1x2PYyqLIM1SlmX0ERl+mnRunqUO6tsAybG35lrdse8tWt7KbTb8HYDgIAlR+r5zDzOtnlEqm9lAsULhSJMRrS0iRwPeh1x9ozne5eIfs3sP35Jgs6I1/KzKiwOPLDkUyHU626I07axE5LtuKijFTVyhKh14PWFszMwcCozKbmyGLajJZvWjssRMCiqIoivJwkPW1Gxu+XejGRliBtdud2jdVDYyHgBuEYkIaPjRSmGrBzitACC/IjkKsKZAdTLhoEr2U0ivKSIL07tM7yhWDpTeVt3QLS0ONCfk0wKSYkKlGcm0BmQInPwvbnLJVzdo0RY3XZDj03mXme08mM6s0MwXWej0UpZ2t0trr+YJWpgbJzBFlMZy/LHLf3g4pb9STnQ6AT14MawJwHjFdR0aNmOPPQl+Zv892t5zjcX0L1TUQxKNMywFCyg6/eLnyNQ1z7s8JwDUEZEcha6cLI3TDGCMRQCiIlwssyLQhilmZDkdhzDoXwE/K4XD2WlsU6K7V2N62GA4d+v0gBjY2vCCjVmb/gFXi2AmBn/qpn1p632/5lm95FM9EURRFud6QjnDaCHSwz4zQWeqA8KRK6HWUiyABTW984zh1M7JAo0QiXycfx177eekUDHVIzyzHeMv9ZTRB3pdFwSmkNzn+TDIvQhYSj8cwGZAVXeSZQzHLIjKzTCLqlbgOVEnD7jVXrni7mM581skeHAD7B8D6pAqrBbMWikpB1nTEufr8TmURvIxeycgT54EsVgCadSj08sve/3zMTlZyEnDjQgiyX6e1Xszs7npj/Y47wspefP1k4gUQo3OysJ6droDwHKMKjCzw3NkcYFpz0N8+he2tsrEMAzUSdXC8yPcqcOyEwE/+5E82Hn/qU5/C/v4+tre3AfgFxfr9Ps6fP69CQFEeZVLFwqdPRwMfTyRMplbpTbX8TblWUgmYzHE+bL/19fZYahXh1JLJm5vtsRSp9eZTBcl//dftsbjSLPGeX/7l7cUSUysL/+Zvzjm/E47M2KHNE2fTWOeAauI3aQjReJKeTBbHsrOKrAegoSMjAnFqDtA8ESJThKhU6EmVG3OauKSszOHe2PCWIlN3eDzpEZV53fxscj+54Jg818HAv44rhdF7zFakrIEYj2G6PRSnM2R5BzeczZEXGR580P+8O53Q1n5vr50hpbSRqfepWtu69l/P+tqan28s7O71QhoQ5ykjAzTI6RnnnI0X92KnnVTaDk+Ac0h+mbH45FYUwcsvxWq36wtKtrdD9On3f9+vBM8Pm+ehla9Mk2ONDaNoDJUA4WLxQsm1C3iefC2LjusaJs+BXg/9Xo6trWxWz8KlGeRPZ9U4dkLgIx/5yOz+L/3SL+Hf/Jt/gze84Q14ylOeAgB4//vfj1e+8pV41atedVSnqCiKoqwocl2suBvnbHNo5gzL3GcaNNIY4X16w+O2nHyOQoBGCY2WuO0nT5TGDYUAvftyvNPxtz6BuZniwATy8dgbTGz7KIs2mX4hU4X4uWQUQn4GmdbE85aRDwqCqbFlJhPkW5tAYbG9aVH2M1jrT6+uMUsXip3KShoufsfCd9rQHOdjjKOLSeN3NGquIkwDWHrPZdGwrHmJo18xnEeyqF3ux6pmGami5591AmXp88b+6q/8JDl9OghooBlZi415mU4kRS8/V7yYH1cB7/ebIp+iHgidssZjFJ0aa32L7W2DgwPv02J9RlxPvyocOyEgefWrX423vvWtMxEAAE95ylPwkz/5k/h7f+/v4Wu/9muP8OwURVGUVcLakF/Ndbhod8iFSfMKMAPxIrbJjFIFZr3I4/SfONQgva2yM4rshiINf2Oa3Vekl1QuesBIAA2quC0ooxHOBQ+9rA+I04yAdmQCaEcqYs8rFxWjKJLG2WDgvamTCVxdwZp6lpEijdm1NR8V4OWRXm4lYIy3m2kfnzrltR5XxV5bAzY2HPrdGtgbNsNfcvVfurS5vkWczkYRIIt+pSCWNQIyAhCnHEkxYExQKpwvjCqtrYW53e/7D/eRj/gPe+6c/9HecovfT64+x+PJQhP+kIGmuOW1kAb/zk74vbFWgIXu/D0xh20yQeYm6HUsNjczDAahUJtNBygIUoHho+JYC4FPfvKTmCTC/1VV4f777z+CM1IURVFWFXqhz53zt6xbpCHKf9DzMWD3xItoODAXgIZU3FYxFgPSMGdb0LgbEN9DGucy5QcIxheFwEyxTCMBs3ZHLiySkGWh2wsVkHxfmeaU8vLKMfm62BvLND/2UqzrUMzJNBQaVmWJoqiBqX7Z2vIvPX06XFJZ16lCoE1Z+rm7teVtYylqKa7Weg7l5KC5yBfTw1jcTuOfF1umtPHi0zhm8S3gv0eZLsb9ZEcruZ6EFLeMYHFuyzShovDrrVy86B9/6lPAl395+N1wHlG1M6Vpc9M/lqKGvxOZ8iNFt6xvYMrfpz4VjP4nPjG07wX8uVHw1jXyqXbZ2AjBjK2tsMowAwirwrEWAs9//vPxqle9Cq9//evxrGc9CwDw7ne/G9/4jd+IF7zgBUd8doqiKMoqkXJihpQg5+0SC9gMMNJTHieup7zm8k1iQ1+2NpTed+4/L29fGvgyNYhCgGP0ZNLbyY3RAJ6DfN84zSlVoyBzw+d9VpnaJL3JMu1JtE81hZt+VDc9TdOoadaC4cORa3hx8V652G+Ww99ZWwtddVi8LTs+AaFFKFuJSpEAhLkvi8nj2pF4iW75BUpDnwKXyoXz8fJl/9z2NnDjjd7CPnXKz/HhMIjvuKiYP16KF/k729kJC+3x81M8yGhbHBlzzi+8euONIfpXVaHN1fT3JDPqOh1GYrwwSJW7HSXHWgj8+3//7/F1X/d1eM5znoNieuUnkwle+MIX4vWvf/0Rn93h3HLLEjt9PFEsmWLZNRXOnl1uv7/1t5bb76lPXW6/VPFlitRqqymWkdt/8AdLHepLv/SrltpPVxZuk+qQMK3bD6RWCNrdbY+lCmpbB0N6Dne77bHYGAOA225rj6V+iKmVtVNjqWUkUysQp4qj5T/GJF41+b3vbe1yy2d9Vmvs2c/eao1psXCbqvJT7+JF78UbDBpRfxhMO4BmFrbfBXITWoXS3VrX/sXSGgOCEQ+E4nV6WLk4F5BeAyDOf5Z5zjR2isL/Hjqd5muZEiS7B3Ef2R6UXlLp0Y8rHKXxT4NwMPBGGC2euFCUn5WpR1UVqn5puNGYA5DlJUy/j04J9Lr+8jIzhPXO7GaZ+imddEYjb+OyYw072NJG945ugxFKFBsbMKzXkKtZS+HI+RYXsLOdrMy6kOllXIyDkalYKANhzsh2t52Od58bEwxzuQAC92UKE41xnn8ceZMiR4pZGY2SqUyMVlEAFEUQrnzsnL+4jBpwBfFp9yWbFciyrKElZOkDf36rEtE61kLg3Llz+PVf/3V84AMfwD333AMAeOpTn4onP/nJR3xmiqIoyqpB+3Z/P6QUy7W+MPPyGaBTemUQpzzQWCjLZktEaZzLgl6mHvAEaDjJYkamSsjc7LiVEZuWU/RKTz+FglxbAGhGNGTkgMfnecpUCbnoEr35ctVYWdErBYuMAEiBTE/scAiT5zCugs0csszMLqPc+BYpja2ELBnOWy5kJW15B4PaIXxPcWtXGtY0nuW8lmlBouC7IRho7dLy5Xcfw/khxW+/H+Y0fwu0pPk7oXChsASarXBlpIvnJM+RKUT87IRiXqbgSYHOegOK//X1ZvHwzg5w+TKsschKIM87szmcKrdZFY61ECB33HEHnHN4whOegDzltVMURVFOPLSXpf0ivXrOAaMxkFUO2aSCoeFEw0T2bWRtgOzgw5wAGlPSE8l9mRYhUy/Y5kV2NZEefpl3LYt6aUDJ9CXpiqRxI9M2Urn/7TypZichaVxJEROfpyxYjgswGRmcpp+YSYYsy5FlZmZrAensEqVNrBObNbnOX7+RmK8UALJIWM612JUtxaxcPZoFv/wRpYSwFJWAnzdra2GROZl7T+OekSX+3rgAGZ/nYy7aRzEg552c95zDspVvqj6B4rXTCaEpRgCkqJ5MfBjm8mWYLEO2kaHT6TQiAbLUZ5U41lbx/v4+7rrrLvyH//AfAAAf+MAHcOedd+Kuu+7CzTffjO/5nu854jNUFEVRVgXaAmxXKTMEmFo8HABZDfRGk9AliMnYsuUijRXp3QRCZxbm60vvo7WhxYtcsXdtremN5YlmWThhekdlDkiee68l0ziYK81bekBlCpEskJh3kRiBALwBxNfS4pGFxjLiwIsq87/Jzo6/dv0+0O/Djgrk2drs0FIIrHJP9lWAekzW3kqnvLVAp3TAQ3vB4GctAPvuA8HAlgW/QJiDLDzgWFU1O1UxPx8IEQL+kAaDEHnY2ACe8ASfIsm0UKnC+fsAmkb75mZIIZoWm89ECMWNFLn8bVA0y2WrZWqb/D3yolEI8Y8Dw4Znzvjf2OXL/vNcuQIUBWyni872Jrpd09DanP6rxIoFKK4t3/u934u7774bv/u7v4uuyBF+wQtegF/+5V8+wjNTFEVRVpF4QVwyMzwNYCBy5eMNaLpgZUhBjsle6nFqkUxzkD1MaeHJnqY0vqWlERcx8+TlOaaKOjkuXxPvF6sjvrdMPYo3eVwZxZCufV74mWCpkU0LhuVlkm+jtKGRKZeqAJoL5VnU3uAeDMIc4YJvgDfM2XaIAlYWqBsTIglAiDyx0Jd9Mjm2tQWcP++N5n4/FOu+4AXA3/7bwO23BwHA/r2bm35j2yMa6WwnurXli3OZDiejWbI+Ib4YUkx3u+G9+n1/DM4/mUrEizoahaLkm28OqUGsSRRhF2OAsgB6PTfTHN2uf8nGxmpFBY51RODXfu3X8Mu//Mv4nM/5HJ/TOeXTPu3T8KEPfegIz0xRTgapBX1b2Xk7O+2dUsW9N9643Bs87nHtsRtuWOJE5oylSLkjU+fCf4gkqSLg1IrGKUsnvi6pwuj3va819JSnPLe9n9KintpHWebtDJkSX1W+Y1C3rJFNnDemYgEgDRWu2FpVwcNKo0IaV/SssiCRHk2S574IuNsNqxNbG9qP0LhmKpBUMDTwqioYbrLzEC0UpnXwnHg8Hourr/I4QLOjUNyMQooL2W6Sx6OgYZqHjIpMW1jmVY213hpM7u20rS1vg5096095by/dU+CkM12aAfv74Rqxa+ZM4Do017igR52tNTlvGOmSOfVsu8kcF5lzVNfh75YUi4xoHRx4j/nOjm/9ubvrt8nEH7/XC955KRT5oWSHKabj8Ty4LgFT9IBmnYz8W8y0PJ4fPy+PLc8dCJ+PkQx2COr1wm/7hhtCETzg0wYHA6ytdRtCYHPT66G1Nf/R5aLLR8WxFgKf+tSncP78+db43t5eQxgoiqIoCmv+mCItm5x456BDkQPGOV8onIoESM8pc5RpaNMSo4ERF+HKol7pPZ+myzQ6EDFFQbbn5MkSmQZEQ4dudenZl8Y6z4uvl6kU0mqJ3fJ8DxpZcecZ6Ynl+9JwlNGC6cq2xuQoC4eJSN3mglhcn0xJw6+ZmSwy5b3RJVYKOZk2Iyc+5yC/R3lgft8yqsPnZUTLWq9MHv94/wU++KDfXxrdjHgxZ59eeSkCZPtZma7E3w1TkWSkgrn9FKYUE0wFkl2SKIbkxZIRMnlOLGwGQlogV+medjIyB3vo97uz4F2v5/X7+rqfz/v7KgQedZ7znOfgv/7X/4q77roLAGbG/+tf/3o897nqIVMURVECdMhz4TDaMKyjHA4NBkMgqywKk/kVcWlV0WCiwVzX7bacsnMOXyOdUjJPWaYMra/7k6JrkYaP9L5KaHRXVegdKb2fQLNTjHQX01Msc/mlwS+LmOP0H0YyZKFxfF4yfUh+Bl6zwQDIc+SdDOvbNXIXNI/cVim1YtXglGF2T5yT7riTLKKVAk622uS8GY/baW0Ul3GnKZmmRgP69Gkf1d3a8s9zxV65CB4Ne3r1h8MQNeLxOf8YFeB78tzi/fkbpMik5S27XMkImewsJNPr+J78vVRViJQxZWhry0c8pscyZQe9M6HZUJw1tSrpbcdaCLzmNa/Bi1/8Yrzvfe/DZDLBa1/7Wrzvfe/DH/7hH+Kd73znUZ+eoiiKskJY6+3tra2Q3ZNl4d/5/QNgf2BQOIvMZLBSCNCIl+k4zvkD0YiitzUWArKmIBYBXCp2fT20LZQJ37IugOdDw55dYLjgETuysFiZnlMpBJjqIN3JtLqlISYjCjTo4ygBjassC4XUca0BEFa0ZbEpgCLLsLXh0Mv8R2c5RLfrT1+FQBpjwnpbp0+HrEPWtY9GwKQycB1vmRquAREb/TTOKSi5+Bbgn2OqGr8Iet+lSuMcOzgAPvuz/dz9xCd8YS3QVNs8dpzuJtN16KFnxEC+T1yLAoTfD393UvDIDleyoJ+iXab78TfF1DqKFIoXLm5x5YoveKZHod9Hrwv0+w7r6wY7O0Ej8U/FKnCshcDnf/7n4+6778aP/MiP4NM//dPxm7/5m3jWs56FP/qjP8Knf/qnH/XpKYqiKCsEbR46FafO6akT0IQsFxi4LAOQKPYFmik/7FbCfWTvc+lhpcEhu5zwmDSK+Lzs4x8X90pPrjSgpOHNc0oVFNMoir26MlVJ5odLj7I04ORYXHAcR0FShcnWwhqHzNTIp2sKFIVp1RgrAWO8ZnQOeOih0Or/7Flvt9Iu7nQssrxEf33DC7BeLyyCJ+cYOwlxnrAjFoUb35TtibjiGxfhKku/SON47Ffpkyk5FMc0vLkEMtOKmAe2teXfU7YslTUvMqLFNDrWUvEDA82CYVmHIwuBOLnk/KU4oGiWqVDyd8LuS5/+6f7ajEYwdQ0Hh17XtDqRsZnXKrAip3HtGY/HeNWrXoVXv/rV+Pmf//mjPp1Hj2VX7n3lK5fbb2Njuf3uuGO5/f7iL5bbb9lEuVShZYrUirMx99+/1KG+7MuWe8sf/MHl9jtJfOQj7bHpAqIz1lMr96bmdaqgNrVidqqANvUeqUrD1Aq/Fy60x1KL4zBfVJJaNTn1G0sVGqcKpmPilYbn8PTPaKdCpsLSyy5AflypKj8FZMr89rYv7uv1RDOR3E6/79LPI6btMDVhNAre+9Go2TdQFtzSiJApRHIdAWncHxz4cf4NlB586eF3Lnj/mccsc7Bp8DDfW6bp0NPb6bQLg2nB0LDib4Cfl8eQ7RylZ9YYf178XFIo8FyyzH++U6eAbg+oK5h6hH4nx/Z2hv39UCqhEYE2zvn5e3Dg7e4HH/TO9yc/2dvThPZ9sdlF0V+D6e/5Aebd09iX3yGL3Nn2k8KU3zu94xSOm5t+Hj34oD8O5ziXiL5ypVkXQmHLY7PnJv82smhHikWguYIwOw7Jupv4HGUaEj37spCeF5I/9qoKaxPEolyKA3oMuFLz9DdjnJu1EJWNlDY3/emye+lRcmyFQFEU+NVf/VW8+tWvPupTURRFUa4DnAtpJ9O1gWYp042aV7A4UeQZAyG/X0YEWGgpO5DItAMa6VzIiDlJ8qS4VKwsJOYxpsW1jXQhGibS6JdpENKjSY8u35+VjKORV+3SmKLnl8a+fE+eK2/Z710uSpVaAEAWWgPhWhQFUDsYVMjzrLFGlbYPnQ+ni7TTqS9ZauK1pmmKKdl9R7bO5HfJOcY5QEOaKTVc10Km41BAUlgyLa4sfd6S9K7TKpYpSDyP4TB4kGRdCs+FQoJzh/OTgobnEveilQZ/XDMjx7mgmBTTg4H/IxFHzvb3w2e2FqgqdDp2VicQd/ldhcjWsRUCAPDSl74Uv/Zrv4Zv+7ZvO+pTURRFUa4jbGTjs452MABcDax1DJALAxcILUBpUMgUHx7UWh9mkCk9MiJAg5yRAqDp9Yw7mci2i/v74X3jtAnej48hPxwNHCB4SSkiZOU0iQ18WufSM8zn5MWMFz6T1tB4DOzuwnX7mPQ2MbEFHCyssA1T6zwoAc5d2utyCYr4q28IMfbRl8YyEDznLIBnFyu2smXxxmQS5rHMu6eRzwhpUXhVwjkJ+P0HA6+++TvivNzbC0Ig5DeF35XsmiWjC7Ijkix8l787fnaKGs5pHkNeI/4m69qfjyxWLooQRZFzvq5RiNWFqT9kGdFRc6yFwJOe9CT84A/+IN71rnfh2c9+NtaitJJv+ZZvOaIzUxRFUVYZmUFAx+h47G1tVxvUGwhGg1z+NiUCaMSzT/upU8HoYv2AXHWXj/v9YFAZE3qnA800BZ7cpUveOJM51NKzyddJi4Qb00DoxZdtEqWnmOlCcfcfadQzsgEEg4zGG9M/gKZ44O206LKugOGmxTgvUAOwUZ3oKhhQq4osxZA6jU7yg4OQqWVli1Cmt8iWt7INLIXn2ppPwaHBzjDNYBDGAT++seGF78GBT7McDv1vgGOykJ7tQ+XCB4wGsGBHpqnJVB7Av5YF6XEnJFkrw8/GCAJTnoCgmORkA5pRBTkBGZWgSJavmUYn5GnHwYpVSMc81kLgDW94A7a3t/Hud78b7373uxvPGWNUCCiKoigzaB+w7i8O3ctMAUAYvjJdgfnu9BzSw08jgd5T5hzRA0shIL2WMj2DJyjvxx5LWn4ck8WYqeclJvo88lb2eJcLj8k0JR47le8gU0x4XvKi03Ms3zPLfVG2M3BoahaNCCxmOPTlQ855m52RrMuX/bU7OPAO+V4PyGBgOF8pYtm9qdcLF5rCrtPxCe7b28EAvnDBb497XLB0KQg53+R3LNPLKEBlihvQNMTZhYc1Bt1uEKXynClcmUcma3coXNlSd2cn1Cvws7Ml6NpaEOqDQfO3BITIHWtwWPsCzM7JlaUX/MaiOmimFrIZwaoI2mMtBD6SqlRUFOVIietn11Or76YKg5/2tPZYqih8c7M9lioCTiUZ0yt0GKlC3pRr5/GPb49xMR3J6dPtMXblkJw61Xx8003tfRLF0ol1FbVYOEFde2Npd9d/JdKbSgEwHk8X4zUWsGh2C2L/dRbrTiahypgdV/p9/x1lWfB0Mn2CxhE9ivT+yzQHGQEgPEEZUZAeXooN2QZRpkJIQ4l9OlmYLEMje3shXUiuNkxxwPQLOUZxQRHB3onWhqLqPPdzm5EFAK7owdmsJQB4mJM+Vw+DdjdrApifzikATO3yyai9EJzsq9/v+7/Hu7tB2G5t+e/rL/8yiN4bbgjiV+bfcyVhuRIw593Fi6EmgBEnigjp9ef+MpI1XbSrde6cr+yEJJs4yLDeYBAuSrfrn+OaGzxPptcR1ulwDvM+Lfpez//7U1UYdTZw+SE7qzeaNhJqlNTw4x31omLHVgj88R//Md7xjndgNBrh+c9/Pl70ohcd9SkpiqIoKwztBNrOtEGIzE93MiIg23FKjzc9ovJfeno2ZUSAidw8CVkITBdibPlKLz+FQOzRly0X5blQsMj2njLlQqYmySJL2UVGpkQRmVstrR2eL8+R7yFXpmWaBrFT73EUBJGXR0ljrdeaN9/snQB0jMut03HInIjQ0Kgl/P44l5kytL7u033+5m/8weM5J+tkZPEtC96tDbUFQJgLshgn/oI5LzinpfBkJyBGAOiplwY60CyoLwrvRNrcDBeHaUhxbQ3QrPFhwTTX2OBnc85fl60tXxcwGGHzvg/gge0nYzw2jVNh/QZPXYXAo8Bb3/pWfPVXfzV6vR6KosBP/MRP4Ed/9Efxnd/5nUd9aoqiKMqKYq23c8oyLCrGLAaZFlRXgKsqoJoaBWtrwcihASQNIWuDa5bGg3NNr6Jcg4D7pDryyEJHGkYyR1rmKdPwpkChxRG3uu10vBEj6wekYcjCSHr0ZW4/EM4x1TI0NviYViGPQ6tINFZ3JkNVGUyEnqDzltkbrDdlx0nFU5bBXqezm/atTN83kzEMU3WYHgOEF8gi2Y0NP2/q2kc189yP8UfB7z1uhyPnIY/NMUaaOP8Z5Wr82OrQeYqv5wdg2g9F5PZ26GYkC+MZSpL1LWtrYb7J7kW8BrJAP+64xRoYRrV4fuwLWlWw9WUUly+gmjQDdGzQxeUO8jwE/o6KYykEfuRHfgSvfOUr8bM/+7PIsgw/8iM/gte85jUqBBRFUZS50N5ZXw9CgA5GaZNUlQMmld9iIcC8ZxrO/FdeLrTEMabXyLQf2VaERbXSm07jmQWaFBs0sHh8mYZBtyNzv+np54fq9/1iCdLDL72qu7uhVSKNeBry8vxkYTDzwbmPdIGy1STPjfvTKwzA1TkmEzOzsWiPdbthramNDZ9xRYes4uHl5FQaDHxqCg1P6tHZGhcUbTJSNBIpQ0xv6/V87tz99wN33hmMbiKjQzyONLY5T6QQkNGreUXuTGOTXX04D/lb4BoCeR48+xQJnCCyiBcIv095XjznuLaB58oLzOYzskaGq3EzFdA5wMSRGP9zW1sLP4Oj5lgKgfe///345V/+ZWTTK/wd3/Ed+L7v+z488MADOJ9KllUURVFOPLRVaFvTnqYzkXZ6ZgFjppYnLVSg1c7QwcCtrQPOBftaFhWLfVurCslUoDhHSRpV0rCSgkF65HnLtAbp4Uy1E43vs6OLTDCXhr+MOsTw3GWUgceilS+3qWAxMMiMQyXqp+XGU1AB0Ib6bTj0AmB/3wd1qMWcmwaFWJ9CASaNWrmdP+/rAR580F/8G27w+7OlaDwniPw90OLlXOWKxnzv0ciLDC7kQeL8f85FdtSS0QYa/9yPUQN66/n+VEf05vd6zdqAuF0YhQfHe73QslcW5Metfeuq0WxILpgsazaOmmMpBPb397EpCgbLskS328Xu7u51JQTuuWeJnb79C5Y72Gd+5nL7Xb683H4f+9hy+/V6y+135sxy+/EP0GGkCkYf5nt2jzhsdz0jV7MkdHLOSBX8PutZ7bHUSsBXrrTH7ruvPbaz0x5LuWJSf5VTqxI/9FB77MlPbo+lVv5NFRqnYsOp/eILmqqYTBzr1lva1tLTnmZaY3/+5+3DnSRoHzjn/xReuOAvJy/7qVPTJiHd6VShkcwe4kAjjcEVJar1bThjkV38FOzO5ZDWAwTjY7ZAgfBGSqNKFiUyf1l2TOF9WXMQtx6lSKAHlUY9Pfg8H5nWQaNwc9N7W/f3w78RfD2vAQ02GkTchyk/zOmREQkgrErMzz21Wm3ZQ2dzDdY0s0dkIISLwqoYaMK2/9x4jVjne/r0dIHzugwF33LuUA1nmf83/NQp4NnP9kLgypXQnUeKQVkHQLEZi0kazVTWsusUFxPLMm8p0xCnF1/WmnCOSfHJAmK2BWW6E6GQoLd+MgmF8czPoUjlaynQZXobvf68bpy7jMTt7YXORMPh7DLJUgzW8K9K0fuxFAIA8PrXvx7rovPIZDLBm970JpwVHTW0faiiKIpCZIZAnOETtxMHDJxzgPXGtqGhAYgCyBKuvwZnLbA7NaCZ2iA9+jQ4aC3QOCLSo5864UYVs7A4pBCQ1kgcAaBBFVvUfEwjjmkUMpWJr5fiQQpqWRsgE6Vlq1FpME5FjslKWOuQiSiAzIyKT0EJ8LrIlva9nhcAZ89O17MDgI0Nr/UGB8EylUW7nQ7cjTcC3Z7/TvhDYIrYYOA3mY9P8SjnE4WiTL+hl16KaEKxLNNx+BuRP0bOIZ6vFCFM02NelMwh6/X8hWDEgu/PVbXlwncUQzT++dtJpTExnW8qCkxdz3SVFLF8S20f+ihy22234ed//ucbYzfeeCN+4Rd+YfZY1xFQFEVRJLTRuQiurEE8fdrbCUUB2MxgWGWoRxaFrVDYUdODP21baKxFVo3gJg7GVcEg7nT8fkxPiI0ooGk00XUoPassdtzdDVEg5n3QcJHWsjTg+WGlKJAGGg1yihP5WoZHaMzL7kM03FICQYoTGbXq9YKXmNGNSJTIzJB4O+qOK6uIzMSig5u16jRMDwYGWZajsBMYOS9o4LJqnrnubB0rc+bH4zB/pMiUcyaePzKfnkXr0rBnlEDWwsiieaA5p2SrUlnoy/m0u+t/Z2xLWpahEIgtTfkcC1AoumWXK6lG5W8q7prE381UHPEnyb8pMhAdZ/wdFcdSCHz0ox896lNQFEVRrjNiIcA2f5ubsRAAhpMM4xro2wkKO24a2TTExxPYffber0PqwcZG0+Dn6qQ0+uXyudLoAMIJstpQLsLElB8aNEzXkBEDIBhMsliTxpa0sJkDznNin3Tm98uCirj7DMcIjTymcPC9KFjY6aUhPprfC7e4xbzShGn0vZ6fs3R0A2HxXaYK5Xnkaa9rTEwO21+HzQzMxYvhRezwxHUg+J3K7lTSuJY1LLILERB+XJwnnD8ysga0i4hlF61YZHAO8/0YDQBCCg/PH/DnybnI52Q6kRQE8r1kFyMel6KAYrauYfKsIQRkfT3ntPyJHBXHUggoiqIoysNB1hjK1Pxm0bCZPudgp0aIA1CbHM4YWBiYqoKBKCCkoS9znenhBxppMY32o3GKEL2v0tCmtcF9aKzI1CEpJuLuLnIsLhSVaR68ELH1LdN8YqTgiRcZk0InTjWydpoaVMOy5sKZVm71KqRWrBoUAZubvhSO6xDu7QVbdVY/y+90MICra6Ds4GD7JnQm+yj3LgfD1hgvYPk9Hxw0o1syd4tCgOKOPx4pHmT6Gz31stURfw9c3XcwaObnUXRwnrN7UZzfx9+eTEGjQBgMmmlI8VoGsgiZ1wAIwoG/A/l7YJ1CXQNFPluiQGYwxT/Ho0aFgKIojxqpQqjWH79UUWzc5xxIr/r7N3+z3JumXptyJaYKklNsbLTHUqsXx7mvQLowOLWScmosvlYXLrT3SRVQJ65Jt7sCfetWDGv91GPmAD2pw6G/rLL/98bGtM5wWAPDMWqbY1T0UNscZT1BPhz5+gH2Hy3LYFAcHASPOJ+nl50GSspwlgY0rTrnQj932cWFzfXpiaXHV7oi42JI2Z4xFi2MELBAUq43ID3E8fkSrujK1BLmhserJ0+ft2WGMq9R2RqZtTDGoK79pdvfb9qASpPtbT93pYP7ypXguGfWj7WAcQgedwC7Z25HvxzDHjgAUzUhDX+Z5F7XoQCchvlgEBQz95EGPEXAcOjnMMcYwaIBLyNUrH6OC3elcQ6ED0zrW/6u9vaCRd7thrkuV/cCmuJE/i7ENQLQLLCnOpWpQtPfRpaFp9mFbDDwl5MZS0eNCgFFURRFQfi3P65lpW0i7RNjvJ0PuOn/DWqbwdkMDhXgajjnD2Kk8QKIHuOm3RMTaKfypAxrGlfSAJdFvylPe5zPHB9T7hOPAc3jxbnhhx2Hr40FTVzxK87R33Wt3WU5hgqBgDGhnqXb9ddob8+LAApcLsZGm9mMARQF3NYW6tPnsGZGMMOBLybmzkDotEPx2ul4RUZPvuwcxUIEdt5hbYhcr2A4bKa1SaEqo1D8nXC+yeLduB6F1bhS0PLcWfgrPf/yt8PWobKYne8v1y2Q4peCZ3c3dEHiWgZTwSTX9pM/a9ns66hRIaAoiqIoCIYmm38wQCSdi7QxZnYDOqg7FsgsisIA1sFVGYZ1B9YB+aSCMVWz4pWGkbQEeOBOx7tsqT4k0rsqi3zjfu7xa2iM0YtPI4ofOi4ijg1zGnrMowZCD3ZeLBpR/By85RirVmWB8mTijUm+B1VYnqPOOxiOc1SwGE9Mo24zbjykBNgsZzgMGTWjUTOStb4usnOKYhZRMpmBGY9hpmk8LssAY4G68h2DpBqbTLyBzS+F+fT0uLOlpkzR4XyVaTfch959uaidLC5m/QuXTaZXH2gqxDiKxokil6VmREK+Vi6eJlufMgIm1xbg+zKtij1a2Vp0Gu1ytZtpEP5spRaX2UVHiQoBRVEURZlCe2I08jYqW/rLf8CBYNu4ooArCmQWKIsa1jgMncW4zpC5Gvlk6PMvaITL9Iq4Qw4NYqBZYMlNek2pSmR6TYw0kJh+URTekJJrFVBMSANNnhM9qOz0wtcw/QMI3ln5GWPxE+dq05iULR+nYqNGgeEkw6S2mFQAvI+61TUo1W31pMIpsrMTlm7g1yMd6UXhBcKVK8DpbYui0/GF2ZMgWF2Wo7a5r3GXcw5orl8xGrW7QbE3PxUJIwUyjMP5K1OBOAdknj8Qfhc0tJn3JFuLDoch5Y6ChPOJc4+tk+S8l7UIsSAm/A3I9T/42fkbYASE7UdHI2A4mmmlOI2NwYl8BazwFTiFR5cPfehDeOMb34gPfehDeO1rX4vz58/jN37jN3Dbbbfh0z7t04769BRFUZQVgYYUMxZobMapKXQE1rWZtVXPrANgYI3DeGwwngDOAFWRwcHBOMDUtW/VKNMZZL5+7NGP03ykoc7nmRIxD6Zs0JCJvfY8TrwomIQWN40qmfYh0zZkypE8xzifR+ZHyM/F964qOJMBxsE501jvKpXppAT29/26X6wFoFNc1uKORn7dxYsXgc/43yqsbxo4m6NyBkWRw9oMFfxWjxwyUwerlcY7v3sg1ALs7/vb1CrUvOXrpNdddsRidb6cM3I14bU10b7LhkLhVGE9n6eBPm3rO5tQFOI8J/l74+vjVj9SBMi1CWSrsenvxRk7K62QaYX8qFys+Kg51kLgne98J1784hfj8z7v8/B7v/d7+OEf/mGcP38ed999N97whjfgrW9961Gf4kI+/OHD9/nQg1tLHesJtyWKL1N8/OPL7Zcq8EzxnOcst988j1ZMqhAyxTLnlyruTJCdWu4tlTaphXXjhX/PPv321j49Gy8/jHRh8LJ/RVPx19QcSa0ifHDQHkutcN1aMnkOd9zRHkvN61SBc7xf6nfz+Me3hnYP2oXBq1CktmrQlnHOrzckjSfaCWyEQmeiXCOr3zfIMoPxCBhPgKKwyHIfLcjrCTIXFRrQcGDOdJz8TkNZCoE4pUem6xBZvEiDq98PPfvlqqyyRmFezj7TImTvd54/78e5UzxHuRYCLx6LjtkJJl4YYDIBMgdXrsMh2FwsrpTZVKuQWrFKsBYdaKbD8/7BAfDAAw7vehfw9V9vcHBQYPdTfp+qMuh0gFOnytlcdzXQ7eVweQ7DuWmtn0sbG+EHIotopJddtsMF/HNMHaLnniKUx+J+fA0LgKVoBoJRTiHAv+lSRMi/kZzXTI1jnQLnJdAskAfCuXEf5/xFlAXMsnsSu2pNIwLjDJjYUCjMw8ig2VFzrIXA93zP9+CHfuiH8O3f/u3YEF0+vviLvxg/8zM/c4RnpiiKoqwiMktARgT4nHTaN73RZmZ7V8IOqWFg4aMFcM63xjcGRhbNxhWD0gOZKqSVxN16SNyfUBrp8vh8jq/hB5VWtkzxYRElowMylWKeZS7PJU77iBdrmu033VXsnmq7qClBbbpdv+7b9rZ3oK+t+RaiWeYbje3vA//kVcDffMwhz83Muc6mPLK0YzwGJsiRW+srj50LgpArEcuI1JUrIV2GPwi5NoCsOaH4BUIuExcqkx57IMwbFi3H0SUZ7mD9iqwxkB5/a31UodcLOf58DdAu3GcEgJEObrJgpd9vraDs6hoTAFZk9UktH6+TdlQcayHwF3/xF/ilX/ql1vj58+fx4IMPHsEZKYqiKKsM/22nrUMnOuBtgStXQro96xBpY9ATK53ugE+/dtYCdroaGY1pGioy5YJub2kYy1Qieu7F6qUzI5wFlLLZPvOceHwyTwQQGnlxVyAaVr1eWNBMuqBlu0caSzKtiN5Sa71HWbYhFWlJ1aTA/sBgVIWPIAMY9HwzM0MJcO6yQJhzeH/fF8Dv7QH33++vIee4tG/lAr11bTAcAVm3B0MPuGz3CTQX3KBRDTQNbykWZCoZEOZHWXrVwtw7rkfAyBIt6fi4UpSmWpGyoEd21eJjnpcs3o9/ExQzfC8g/JGgauJF5G/QWmBUY7wXLo3sIMQUxFWIzB5rIbC9vY1PfvKTeHwUKn/Pe96Dm2+++YjOSlEURVlFpFNbOihpF7AVPp2gHJepw86FzIdGF0Rj4TLA0KCAMG5iF+E8lze9j86FtAT5HNVJbABxP5kLDTQNfHkBWBzJ95KpHTSiZOEvDT8KgdgbK72nfB+mKzFaH7n6a+QY7VoMhC0oX0pbMV6wVgl2seysRI3p01IMPnlfyKSRDvPx2AuFODXedQq4bg+oK6AsUecd2G4Xht+97Awlc+Yo+jiXJLLjULxUNEUoNxa38zl+uLh9VL8f9uW50aMf18bENQs8J+5HDwBFCguUpVCP1tKoO91pxM8CowrYC28lf5ZcT0AuTXBUHGsh8DVf8zX47u/+bvyn//Sf4BcjqfGud70L3/md34mXv/zlR316iqIoyoohu3uyIYqsXaRtTdtYLgokjVUgOCjr2oi25C6kIzvAGOPXGZB90uNCStnRRFq+MqWGBpE0mOI+hXwfqWL4Hnw/GnAy11sWHLP9Ii8KvaB8LQ1CPseUCXZTmX5GF6eMsJB6eh4mt8gLoHDN6y2NKZkhogSkXSyL23nJ19d9idz2dlhc1xgvANjoh2K305kuim0sXK8HYwxclmMydijzornIHNN+ONllHYhUxXE0SkaL5CJhMpUtnsOy05QsGGaUiak+ZennHot/WPjDqFq8WrHcKIjZcomrg/P8eJH4HsbAZSVg4MOAZdlYrFjOYRUCjxGvec1r8E3f9E249dZbUVUVnva0p6GqKvyDf/AP8M/+2T876tNTlGPP+fPtsWXq0c+f77TGbk8V8qZ6r6VWJd7ebo+lisVTBcSp1YZTedC33toeSxX8nj693LmkCqFpNJFUt5jNzdbQzk57t49+tD120rE2dCgEQot7FvjJbp80rPgPe5xXPRz6r1CO01nu//E36GZAbtE0nqXSoFEvVxqWnXpkpTL7EbJbC8UDPas0gGSXHxpP0vXb6fjfgWwpyv1pGda1n1SjkX9OevWBkDMthQDPUVazFgVQTo2rqrkyawaDXs8gy/3PQ3arTKWQKwF+5fKasTYWCPNxfx946KEwLbhiM9OK+v1gBwNu9vfWwKEsAIyESKRQlaKWj5kuRMXMecoTYy4TI13yB8O/8XIFY5k2xx/bwUHI/WfV/86Of7y11TTe2TlIpuHR2z8YBLHMC2kM3NrabP4aRtmmvynX7fp5DIe6AqwBrHEAHLa3Q2mF1Oey1vioOdZCoCxL/PzP/zxe/epX4y//8i+xu7uLZz7zmXjSk5501KemKIqirCCxHSM32ufMDuDjOKU/tZJo2Iw3Xp1Dbaf1A5gWD09FwWwlYhrGwms+M2hkSoVsCRoXA9NjKg2ouGiYH1xuFABxZ6G4VSjHgGY1JMfjhv/cpo+dMX6FAGN8ITXMbJMpK/JayvtKG2ovajk21BkMQgCHgpQakwKWHW34dc1qMwxghoPwA+h0gKJszysWAPMxjX45F6VwLQqvvPv9oFQ4/2WEK66XAYIwplde/m46nWDQxyK43w8ueUYier0QDuF7M9+/rr2hn2VAlgPdgygKl6N2gIVDjgqYTP8IZBa93PuSeKo8LJsmrUJE61gLAXLbbbfhtttuO+rTUBRFUVYYZjYwC4A2wsFBcKp3OiEthXYOgzfSTpE2bywQ/A7AuM5Q1dbbUhlgUCPLCphiepBu179wba15YHkw5i/LFCKgaYzz5MbjsD8NHBpSa2tN68+Y8P4s3JQpG91uM0IlC5jjtA2ZRsQLl2U+3cQrIRgYwACVs6hrOysSHo6adaOr0mllVeF6WzIlpSyBe+7xwcL1dT8uFxqTXyNtcymGuWhuBp/GVucFBgOLoiiQTedM7SzynglflowOxR2maAXzzWhQd7vNAndGoRi5YpRM1qjwQ3BOs5UnRSzVjRQnFDO9XoiIMQdQilcgRMXKEshEXQDT96Zitq4BW41hpguj1UUHY1diuNtseys7qq6KkD12QuDbv/3bl973J37iJx7FM1EURVGuJ2gPxMaQzONleg/rENfX24XB8nhxqj/HnTOoaqCCgQVQZL5ewMrCSZ5A7ElnuhBXY6WRRYtDFkTyBKQVHRdnMpUny0K+AjuqUAhIC5zPyTQkuf4BL5R05wNNF+gshOKjIsYAcMZfl+m1GU9Moz193DRGacL6azqyAf81nToFfOAD4SuhCOC0YbdP2uacq+NxcPJnmUFpc1jrUNcZxhODLDcwNkS5XO5TxwznBb8oYTTP5l6clkZByvkiW3nKCFecFsfIVVnCTX8LlS1gMgtrHAxzcPhhYmM/jqIxiiDT8gCYbhcuFPr4fZ2DMwYOftG7fDqRx5XFpLZwxuDgwDsSeApcsZyLIK8Cx04IvOc971lqPyNDooqiKIqCZtZLM6WnuZ+0c2nTAM3MGmmn076RNkdVmVm6gE+/cDAmh8n9iRg7TZtJpfJk2bQtaYYaGYyxsGUn5IRUlX9tVfmUG7qIsxyu7ADGAjD+cV4AzsLVBrXL4WoDa4C8djBwzdaJ0iiLW8uwziFavZjGkoOBQQ0ztUBrZNPrZmCdBeBQO388uaIwU8C5qBjTXZQ2a2uNmuyZKJDlGVwNuyybDZ749cpF84Bwv9fLG63ynQMmlQ2NfXLAxtGmWWV81hS4LPIAgsrj3JWtZOMUI9nBB2imz5Ul6qKDamJgqhoWYgKxHoHefflaOYf5g407D00mft7KC2L9Z6+dxWTi0CktnLMYDCzGlV+fYX/fb8Oh3wYDX7qwt7c6c/jYCYHf+Z3fOepTuGZ88IOH7/Pbv73csS585oIl6AV/61nPWu6AqSVjHwmp4ssUy0roZVYqfupTlzrUh359ubdU2sSrCKfGUovqpmp2L272WmOnlk35i1deBYCbbmqPpYp7U2OpQt5U1VeqMDh1vMc9rj2WKiDe2mo+Ts1zxv4F99zT3u3ChfbYSUc6LKUnPxYD0iHuXKhTpDN+PA6GFg0semppxwAh3YWZDZk1ML0OTFHCooZxlU+XpwEVL8SVZaiRY4IcJgPysoS1zq/C5eqQJF7X/jdQduBgULNOoeNmHvnaWbgJMK5KTGqHEhUyO4Yxrpm+IT2nsSHFPGyZxjQ9d1cbTGoDY2rkJWBcjQoZxqNpRAD+3wAjspO41tPeHnD5sv9bsbMTcq6VJsyYYZkGU+//6I/8ounSiGctLFNVnAt9FgaD4GjnXKYt3emEiBnr1+V30et24fIJUNUw1SRUdMv6Fqmys8x78lkrIg12zndZvCsKht1UbBoxJ/1v0CGrxjAD4XqnKBVpPYZCRR5f5v4zegbAZflszhsWB5UlamdC86E8x6Qyfv2Lka+hHwz8Kezv+3m8s+Pn8s7O6szhYycE5vGxj30MAHBrqrOHoiiKokTQVmFKsWynL9vqSztHpiBPhB3EFHtmQ0h7GeDrDWoHVA7wWfPCcELwlHum3kgYn17kAGem+04fOAdffAATPLJTjeCzGKh2wDedvl1Il3AA3MyAszyjhjJyUyMejGCYIDAAAziDehoRAKYGnA9nNCMtJvh6ZMF13DJU24bOhw5wY7wQOHXKG6F0tgvbtpHVxa+YNbRAs8usLNTmmmKc69SDIQqWo8hq5HDNxcXkmgPArC2ny3JUzqLIXHgz/pj4hVNZM2xhpxEtf/BZ+9mycNPx6ZxfWwudh4oCLi8xrixKjEJEgsqeqxpTSRUFcO4ckOc4GOeoBwa5rdGlY6nfR23KWUrVVA3M1glgh6vJxF9TCtq9vdXqeHWshcBkMsEP/MAP4Kd+6qewO20BuL6+jrvuugvf//3fjyLVek9RFEU5kcjcaBqb47H3QBvTzDCgkSQzHqThRG/q3p4f4zHK0nsK89wHb9iqlEbvYDBdvyC3KKfHs7WFsUBdW1S1N/AnrIGEFw9ZBuTWAcahqg3q2gLOwBYGgIPNsll7cxp1WebrH1HXcG4C5xzyzKC2gM0MkBdwmF6PGsitQWGM7/c/vUgTFBjBRwJm9cHT94EDqrHP+6eWMTCoTOEjDbDI/bCPZDjgyo7B7l6IBrCzjfSq7u+vjjd1lXDOzzMa8pcuAX/2Z8CNN4a8dGu9578sQ+mHnLvMY2exPCNcTMmSgaHt7aadDZjQ1rVrYfMcVrZ4lmlC0xz/yllMKjNLEcuzzAtRmQbE1kbTE3ZF6QVuXTULSKrKp+9MP4zr9QBjMZ7+luvK+LQhA5RmOoHyHHXZxaT251mUPlLgYOBshtEkRz3xKXxeHFu4fh/o9TEc+98jjEOndMDBECgynD1VYThwuHClmM3TqvLXn2lBq8SxFgJ33XUX3va2t+HHfuzH8NznPhcA8Ed/9Ef45//8n+Ohhx7C6173uiM+Q0VRFGWViFuGAu2Vba0NgoBpErJLJ1vtA8EJSs8rm52UZcjnbr+nmeZcZ97bCninpzOopl7ZUbTYL5yDc1NPvAOqGgAskFkY4+Cs8J9O92EAAcbBoPJvYulNtUCe+dSHMTCu/dO+fCG0P6mRYWx8qgaFxvR04QBMhMd4VupgQ5saA18bkWf+3CcTYG+vWSTMyIpM916V/OpVgk53WdzOum96qTlnKbJY0sFuWPK7ou3N9QVkNy1GADodoFM6H1SqfHGsf0+DzFgUZTek2dV+Mb2qNqgmFqa2U2FqphlAzR+ZyzIvIssSprGEdAjDGfnDYW7erBA+80KDUaZZNtI07FaWGKHEcFxgPDDTzCM7FanwaXSMxMHP1doBE5dNxZH/nRb59Nk8n7ZotZjUDjs7ofaekRYKWWbsrQLHWgj80i/9Et7ylrfgxS9+8WzsMz7jM3DrrbfiZS97mQoBRVEUZYZMS6bRKTsT0uMNhA6J7DRU1yElg+JgZnAbXx/Q6wURUVXeOyizIIBmOox0jNJbSwNCNuoxBqgyIB8YTKaFxrT6aRQWOYA8OFFnjU8AoDaAmyaQT/v3u9qinqYvyDxwM/XqW1fCGIthleNgbGCjRi4yrUp2feTx5PXOLFAW/n2r2sz2Y4Hl/r73dO/uBs+2CoE0slurTNmJC95Ho7DQFWtZGAnrdn3UgFGxy5dDrQvX3ZLzQq5kzN+On7sW6Fi/BoEBavjI0mgq7GRn21l6DVPSrIWDReWALCvgYGFcHdSrc3DO+EJ569PoDGsORGpbnWjTaQzgrEWdFRiOcuwPs9lCauFiwafYwTS6jwJmlurDJQcAg/HEwZoCV3YN8tzMri87BsmC9/39ZofVo+ZYC4FOp4M77rijNf74xz8eZarYT1GUa0qqEDguXE0t+ru/3x578pPbY7fdlrXGtrcf3xo7nagpTp3bqdH97cFlC4NTYymXT+rDpaqj41WEgXYh8JIrEr/3ve3dlDQ0svkPebyuQJ77r5rGKAuBmdZDg4mt0ikOul2fEjQe+ylA42d31x97bc3vxwJiRhVk2sZgEF4rC5dn6f/OzIQLjbtGNMMGQ03WNvhcfW/J2amGqGugGgevMI1Eb+QZ5FkHxpY4GBns7fmOMVxMmO/H1KrxuFlHLMUMr2Gv5zsVUQBVVTD6L1/2U50Fw3t7y/WDOGnQGc7rzgWch8MwT9gQh6KS4zIVjnOP7fwHAz8/u11/2+v513Jx6W7XoNv1x2ZXWyBEFYDQdleKWf5GKFg6HaDTK+HM1Bs/jRRUyGBtNq0R9pUmVe098zX8vC5KB4s6RLSMaUTxpNE9HhvYTt//jkfhfPxvwsFNP4Bf2C9rpFAx02n22wFrAAyqKsPly03HwMFBU8RS1K5KNAA45kLgm7/5m/Ev/sW/wBvf+EZ0psUdw+EQP/zDP4xv/uZvPuKzUxRFUVYNGRWQCwDR65la0Cr2tjJLQXr2aQDJhYTkisQ0lPk8xYhMOZrlX7umgeXf3zQWAuZ95l7LFBGem2wDKdv7y6Yt0sMcIhe+z7+1hkGExrWLr5/otpj0WEOkX8jX8vPG30UjS0RpQP8BF8GrKuDBB0M0is8zRUh2hqUxz6gOU/MpVKsqtL10zoszitbNTS90ZfSH9QasR5HzmreNepXMYDSyjbXwZCtfzkHfqcs0xKB/jW0sgcEUJjnfpNDn75Zr43U60wMZbxo7YzEaBUHFawOEc2EZA0XQHXd4gfTQQ34/XrPdXX/L1LZVmr/HWgi85z3vwW/91m/hlltuwTOe8QwAwN13343RaITnP//5+Mqv/MrZvm9729uO6jQVRVGUFYAedJnPS2OlKLzHu98PnlcaBewIxP4T47H3XMu1iu6/33dd7veBM2f8a1kQWxT+/qyNaBbeHwipSTw3aUx1u8GQYSpTp9NcCJjnSCNufz90TZS54BQRNMJYz0AjCmh+RooHXhNGSqTo4XmyyBQIhmSnE/rc87ijkb92+/s+AsC1A+itZuGwFgunkZEXijIasjTQpdHPOdft+oAjI0+yhWin441+RhDW1nwzHbYSlYv4cr5QsLKDLd9LpnwBTS+7T1UyM+OaTYKaXnjT+B2E7rVm1qdfNgFityS+lxQYQEi7YzOi4dAALpvVPDCfn1E3znkKgaIAel2HIquxn1mMx2Zm7FO0UhBR1K6SCACOuRDY3t7GV33VVzXGtH2ooiiKMg9Zdyg7CdLTSIO20wmGley8QkNIphIYEwouq8oLAb5OGgdywVPmIQMhIkGPODuQ0DPO53iuQDCQaHzLz0XDmt5WGmt8PSMUQHOJF5mtJttI8jPyPSikZGSEwgNoFl7TEKORyJQKufFz0sDUiMBidnb8MimdDnDxov8u5NoVg0GYGzJthnODtSusCWCa0cGBHzt9Gjh71gsCFh2zIxE7a3KustiYhjlrZJhaRxHSEWvhyToSHodzifNfpsYBYd7ws8k2qJx7FBgUQbK+uJkiZWa/4UY9jYhmUYRkmU+LM8bMWobKVCBenzhCtkocayHwxje+8ahPQVEU5f9n777DpCrPPo5/Z2Z7X1hYWLqAKIKAIIiKgqKIXaOSvBoRLNGIMcFoNBqNsUYRW4iYKIIxGo0tVlBRxIJSFFGRpvSybO995v3j8OychQW2zO6Z8vtc11w7c2Z25t4998Jzn6dJiDBXNc0VwMpK/0aj9mVBzX3TCI+Pb3il3T5HwN6wMQ2jkhLrPU2jNj7ef2XcPlnZzAew9yzsPUzCNHDM1U5zZX7vJQr9Y6AbXtU0X+29B9Bw2VTwN9pNT4N5L7PEu/1Kq33lGXM113/ltuHnmp6F4mL/BGp7r4y5mUaVfZiH7Kuuzrp6b3KjtNQaspOU1HB4jn0omL0AM8tbmiLB5KNp0JpjYH0158UUj2ZDM9OINmPkTVFhhh2Z3Lf/XZll/O0Fr8kZU5ib2O3DhqBh8WB+D/ZhcOZv0l6gm+fthYD9/fZe7cocN4WtfYO2Oq/1ArNpmNn4zuSvmTRs70kLFmFdCIS6xnYD3VtTRzRlNzIHsjG7djWyA2sjEhKa1rPS2IaujTFraR9MYxNGG5McV3PQ11R5m7aPxP/+17TPlH2tW7fvsT57zeW1X3E0GpsDu2nTvsea+r2rVu17rLFNicviM/c5ltlh39c1dbPhxiYkZzSyobGruGjfg439cI3sGry3zZX7/gzLlh302wTrP3UzLt80AMwVRPuwCnshYIYMVVT4xwAnJlrHTOPE3givrLSGvJiGiGkUpKT4G1nmymxpacOriKahZW+cmDHe9pWHzJVJ+2Rhc/N4rJ9n754L+9Vh01Czr4xkfnbT6LcPqTDvYXpJTGFhvsf+vuBvfJrfd1WVtdO1afyb+M3v1IyvNr0gWjGocSa/Skr8OVNYaP2zkZHhP1/2vQDsDW1zLkzvln3vruRk6/9pM6yors7qbcjJse4nJFgbn9tXhjK9AaWl/vwwk+4bm/Budjq2D6OBfXvOoOGSveY97Ksa7V282K/K239mU4zYe/kam2Rs3y/EXozW1VkThaurrZjKyvw7YJvcNZPdzZC3YMvfsC4E8vLyuP322/noo4/YvXs33r3KsPz8fIciExGRYGMaMPZGCPivLkLDK6X2Bo+5wmkaCfZJs+CfvGgvAOxXVs2Vxf0Ng7BPdLRfpTex7d2os8dhv3puGi+m98HeQ7B3r4B9Iqf5XvsVUTOUyD6J0x6nYW9Q2Sch2xtX9p/fFARmlRXToKqoCL6rqcHGTBI2jdqSEv9wmdhYq8FuhuLYi8faWn9PWEqKf7iZKWw7d/bnW3W1Nd+loMB6nJRkvcZslNfYBnD2otTkg31omlmO01w5txeTJjf2Ht9vb7Cb58zwI/P3aCby2iegm7k/5n1NYWSfj2CKWPO8vTAyQ9bMMCcznyU62vpqioCyMqvA3bHDuuXk+OdaBJOwLgR++ctfsmHDBi6//HIyMzNx7f2vnIiIyB5maEpZmX8stMvlb5ykpVmNKJ/Pek1trfUfu7lybxow9tV+TKO7Wzdrh9eSEqtRYJZpjIryD0eyFwhmF1Lwv84M37Bfkbfvsmsa4vUbd9nGgdvH4ZuGyN4NLfBPsNx7ZSL71WFouOqKfVM08A9rgn17UEwvgM9n/X7NBNWoKCuusjJrxZWCAqsXsLgYNmyA7dv9V3hl/0zjOD7ev1KQueptH09vH4NvH8YD/jX+zXk0RUV8fMNerepqq/FvCgtTxO29fr596JkpCOwrUZn3tK+UZeYUmJyz9wDYr+Lbe9AqKvwFt/k7NKsY2XvF7CtTgf93ZHpSTD6aSfzmNSZ3c3Ks+6ZgAqs3JDfXX7SWlFj/NuTmwq5d1qiM3NzGe4mdFtaFwCeffMKnn35av2KQiIjIgZiGpn1pT3PF3efzD4UxjQnTOLePUbbPDTBX8OPirELC9DjsvRKJfS7B3sMazLAz+9V4E8/eewKYGPeeN2AaVPahRvbPNz+3fYWWva+47j1Ouq6u4TAPE5e58mrYR7nZV5QxRYQpMszv3wwJKi72D6soKGhYbEjjTI+TGSZjhoGZHiaTy9BwIq7JOdMQj4/3F5KmB8A0+usnydpWnTK9NXsXtaYwNfljGuhmNS7T+LcPyTF/YyZPTBFg7y0yV//tq0mZVbRMb8fe8wzsw9xMo37vuSv25U5Nz4LJaVOwFBdbDX/zOeb3aJ8PYC8ISkqs7zG9NcEmrAuBww47jApT4oqIiByEaQDYxx/br0aa/1JMY8g+VtgMfUhPt652V1RYVwErK2HrVv+V0rIy6/vMPnLmaqRZ7cQ+WRKsq7J7N2ZMnObKqYkJ/DGZBo99KJN9DXWzpKO9EKir81/dtf/3ufdQI/sQJmh41daMCzcFkOnNMIWCGYpRXg47d1qfU1jon1hqlgy1Tw7VBOHmsffwmDHyNTX+AtHMK9mbKTwTEvyTd0tK/CsGmZ6BmBh/vptGsPkM+9wS+9wTM1HWXsCayebmvU2D314w2osYM2TM42m4bK0pxu1zH8zfkH0SsH0Og8lvr9f/80LDv2XTO2JfEcvt9vcMmmNmKJQZ4mQKFpPLpjgKRmFdCPz973/n5ptv5vbbb2fQoEFEm4WK90hJSXEoMpHI0NiGuXtPlPrgg31fM3bsvscamwS8e/e+x3Jz9z3W2Ga+jR3r0mXfY1u37nssqpF/ORv7Rz4jY99jjXUN9+6duu/BpH2Pedx7tYQaCeS95/d9q3ff3feY7Mt+pdNcoXS5Gm6yVFHRcCKsvbFqhhHExVnnvrDQGgZkll7ctq3hEqSmYWwKAXPF0b5UKTTc+dTEaW72q772ZTztBYB5D/M+poFlxk/bYzdXYu3Ll5rj5oqsfQ6EvRAwn29WTjGTTM0YbXOlOjXV+lpaag0Dqqqyfldm6UXToLLPk1BPQNOYf18rKvxr/IP/92gfkmNfwcfkVkyMdX7S0vxLh/p8DScg24tg08Nl/g7MfBnTE7B3b5W5mZ6J6Gj/fgT2pUft32f+Hs2VfzN0z+xPYa7umx4vU3CYr/YeLdM7ZV/ly0x2ti//a/YBMIWEvYfL7K5sih9T+JpC3z5nyPR+2BcGCDZhXQikpaVRXFzMSSed1OC4z+fD5XJRF2xTt0VExFGmAWIaFPYrieAfamB/nf0G/quBNTX+hra5Emu/Cru/K9z297Zffbe/3j7cx14A2GMzPQxmgqNpGJnGu31+gGno2MeH25eFNJNH9y5I7GO27cMuDPtOyGbCp71BaB9mZN+AyXy+WTJUmsacx9xc/5Vpw35+ysr8V/XNMJbycqs3y76Zl89nzeOwX2U3vTbFxf4GdGKify6JaQSbfDT5ZS/oTM6YpXftOWKP1X7MvrJRbKx/tUEzrAn8uWdea4pPU8zb5x7YN9EzvW4m70zPnHk/83cWF2fNDXC7rd4s8zPZJ7SbQsD0jphhS8EqrAuBiy++mOjoaJ5//nlNFhYRkQOyj9E3K4HExVmNHfvVUzMm2mz0ZTYiMuOkd+/2N3Q6dGjYC2AmWtob++YKK+w7AdK8xj60BxquuGIaK3tP7jXvY2+Y24sEM5fAPjHYXF01kz7N+9pXJrL3PoB/VRpz9db0Nni9VuPI6/VP4rRPXjU/B1iNL7NKUHm5dQU6N9c/LEWaJy/PXwSayeamh8lMRDfL41ZVWY36wkLr3CQnW3MyzITc9HSrh6C8vOF6+GbJTFPA2Tso7UN6TC+S/TnTcN87r+1DhcwV/spK/87T5m/BPGdfncgUJCYX7ROhzbK5iYn+JX/N34j5GzarFpm/SbNvRlmZv2iJi2s4J8I+l8b8nZhcts9xCeZJ7mFdCHz33Xd8/fXXDBgwwOlQREQkBNiHLpgx1dCwR8AMgTCNFvuVSzN5uKzMajQkJTVs2DS21GZjPQMmBnvPgL1Bbp/IbK7wm9f7d0j1N8Lsq8aYn2fvIRvmiqi5Wmp6BEyDb+/lG+09AvZixlxlNfHah4HYewDMz21vNO59JTXYr6YGq6oqq+FqlvU0xYB9uU4z3j8hwX9u4+L8v3fT+O3UySoYzLKbZqK7mSsA/nHw9vX67efU3htmX47XNJztw4zs+xyYwjs5ueGGe6bIMct0VlZaV+pNHtv3xrBv4mV6D9xuf6PfzJUxvzfTa2BfGMD0QMTH+3tF9l4NyQwTMisG5edbKwztXQgFm7AuBEaMGMHWrVtDthBobAzz3hrbsKkxjY19bsx//9u013Xr1rTXHXZY0143eHDTXtfUDcoGNeGUL1rUtPfSBtUt19jGW+vXN3zcr9++r2lsc67GNDa/oEOHfY819o/wXlOGAOs/vL01Nh8gIeHgsUHj+381NjWpsXkDjX2v19uwV7O8PHGf17zxxr7f19j8CjkwMwzArEJihhOYHgMz/MFc6Y6O9g9ViIvzD68wjQz7ZkJmSEJBgX9zMftERfsYafuQCfswCfv6+6axYhrN9qu/0HBysb3BvndvgX1Sp7mSC/4VT+wbipkGkH1nYzNO274akX3seVKS9djeiDKNuaIi63h2trXcYkGBlbem8SnNY86tyUV78WmYTbzMCjxJSda/bWalIDOh3DSmzVwXs/StvWgrK2vYwwUNh87Z89VevJpza8b/V1U1LARMTtmXv7XPRzAb0NkLBGhYDNjnR5hCoKjI//9TbKx/2VD734d97oyZw+B2+/8ewF+clJX5G/95eVZv1u7d1uNgL2TDuhC47rrruP7667nxxhsZPHjwPpOFjzzySIciExGRYGZ2GjVFgPnvw36V01zpNlcP4+P9jQjwf5/LZV0l3L3b3+gC65i5Wmpet3cDG/wNavvKO/YVVsx45tJS/7wE02A3GyzZlxQFfyPL9ADYx2THxDQsWMzV1+johrsSm9ea9erNZ8bF+dect/8ukpKs9yooaHjl1RQC5ne0bZt137xOms9+ZdyM0TeNatNbZN9jIirKOj+pqf5J6+bqvBkKU1Ji5ZhRWuofEmTyzLyvacxDw6LPPLYPlTHFg5lsa+9Bs+8ZYAoB+8Rze3FsrsjbJwib94CGPWmmmDe9ID6f9X4JCf7cNz+36eEyfzNmqI/5HdfWWg1+s19ATo6Vx7t2+YfXBbOwLgQmTZoEwNSpU+uPuVwuTRYWEZEDMo0Vs9GVvYFvb5BXV1sNJFMw2F9nxjDDvsMmwN8QN40Ve+PFXFGHhpOFzeeaK7mmoWSGVuzdc2De1zxvigN774a9kWZfdWjvnjRzzH7ldO99CuwNT/skUTO51BQuppAyDUxzKy3VLsKBYBriZkUq8A+JMUNboqL8S3GaQsAso2nf78LskJuX13CjOLPkq2lMm1XiTLFr/ibMMDp7npjGuf1vxhQCpgA3xbdZwtS+d8HeOVtX55+waxruZh6B+Rx7gQxWvOY9KysbLudreihKS63vNfN1zETr8nJ/bldWWo3+TZusrzk5/lsoCOtCYOPGjU6HICIiIcg08M1QBfMfvxkvn5pqfS0osBovKSn+hoX9PQoKrMZCUZF/TLJ9V15o2MA3vQtm2IaZWGmGephGTnx8wzH39kaRvZG096RM09NhX2/dfJ/PZ/2c5uqoudJvH/tvGufmPew9DaZRV1NjNfrtBUFFRcOGkVmS0tw2b/Z/3b694Thyab7sbOt33KOH9XutrPSv7GMmw5uGssmroiIrX9LT/ZOAzfkxQ19ML4AZs29/P5PP9onvbre/mLBPjDe9XWYir2nM21f2MSsQ2ZcHNQ13wz53Bvx5bAoA83723gnz92OG+phbbKy/oW+KIPt8FfN7MoWDmRCcn+/vCTBDgkJpOGZYFwK9evVyOgQREQlB9gmJphFiJko2NhzBNJzs4+LNuGrTmLBvimQfLmHGN9vHVtsn8TZWCJhhEvbeCfuEZPvkSPsEXftqLI01quw9FvaJzfbJxfYru40txmcvAPbeHMq+4pBpSNl7A8zKNNI6ZjUdMybeFJlJSdY5LyqyXmcmBpvJw6YYNCvmFBT4h5uZ4swMl4mO9g9LA//qUOb82ifdm4a1GTpkrsybcfomN2Jj/fNd7Ov52zf6Mz0Oe899sK9KZRrsZo6EGQpkH5Jm7w0z8yLMqkj2ni97EQDWe9bVWTlrhgHl5/tXXiovD60iNqwLAWP16tVs2bKF6r1mHJ199tkORSQSGdas2ffY3ht+NTZZuLGJ8suX73ussQ3LGtvEq7HXNTaRubHJ7faVVozOnfc91tik4sYmBje2jFxj/2k09n57x/x8I5uHvfXWvsek+Wpq/FcGzXh301AxjQbTuADrsVnu0jSYTZFgH4Vqlg+1N0JMA8NMyjRXMPeeHGlf3ccM8zAFgxmbb3oT7IWCucJvjpnX25cPtV/NNQ05e2Hjdvt/Hvuma6Zx5fNZjSDT+LMvXWkKDzOp2QxJys62bqWl1rwAUxhI6/l8VqM2L8//+05Otob+mDkc9n0y7I12s4xmaamV07W11vKhey83as8bk0/2OSn2oTj2PSHsQ4PsRat9IropSk2zbe9CwP63sHevln2JXfMZphixF/D2yfb2n9kUK6aHz8Rgn7xviqT8fOtm7hcWht68lrAuBH766SfOO+88vv322/q5AUD9fgKaIyAiIo0xV+/NFVCXq+EKLKZxYBo0NTVWo8ssOWiunpohE2YN8spK/1h5+zAec6ur83+GfRdUew/B3sOKzNVPs1SpaTDZCwH7GuimCDGbndlXezETnk0hYJ+8bMZNR0VZjUrTmDQ9Fmb4lGncmV2E7WPOzfj06mprQ6atW62Ca8eOxotzabmqKuuKdXS0lcOmIEtJ8ffQ2FfiM8WAuSpeVOTfj8C81uR1cXHDpTdNj5kpAO3DxswQH/OZZv6LeS/7cB37xnfm78i+K7DpTTB/K3v/7ey9P4XJOXsRYF8NyLyH+ZnMZGX7fIG9d0g2xXxOjvX7KSjwFwL2IVKhIqwLgeuvv54+ffqwcOFC+vTpw9KlS8nLy+OGG25gxowZTocnIiJBzuwLAP4GBTRcE90MuTENINNwt69iYoYdmCUSTYPfNIpMA8q+14BpgJgegcYa+PbXmV1N7UN27I0fc+3LNJzMMA77Mfv3mONmvXj7cInqav8QCPvyjaahZwoS+54L5rVm7kVxsVUUaK+AtmGuWickWOcqL88qFpOSrHOTmmr1oNqHcpn5BGZyd16ef63+hAT/UJmiooYTeu2NbXtvk30tf9MzZYb/mLwGfwFdVtaw6AV/fpv3NQUtNOxNs3+m6ZHYe+ic6bEwPXom/6ur/b1RZr+A6Gjr95ac7P/bMkuFZmdbc1lML4ApAkJRWBcCS5Ys4cMPPyQjIwO3243b7eb444/nvvvu4ze/+Q1ff/210yGKiEgQq621GlMHGhMPVgMjJaVhj0BNzb5r5oO/sZSebn2fWTnHLNtoigozf8C+drsZpmQaXWaFFfCvmGJn3seMqbYftzecTFFiJmmaXgK322osmsmhJjYztt/eYDM/X2ys1dj0ev1LmppCobzcGgZUWmqtsLJrV8PJnhJYpkA0jWlzlR+sycNm/oDZI2DbNn/RZwrZ0lKrxwb8OWI2KzONfPP94O85Mw1608NgJqqbSej2YT9mDoKZT2OKBvuyuuazTV6ahrn9OTMcae+9NMzGaTEx/pig4ZwZE7vZYK262sr97dv9xXt1tdUT8OOP1nGzclIoF7JhXQjU1dWRnJwMQEZGBjt27GDAgAH06tWLtWvXOhydiIiEgoM1Us3YZNOItw8NMhMP7a/de5yzfVKtabCZ15oeCTOe2zRuTOPaXP209xzYP8s+D8G+uhD4r5iaoR32ycGm4W56OMz7HmgzMtNjYR6b15mrr2apxfJy/1KRoTaeOhR5vVbRmZTkH5plemXy863nzSTtxET/fAD7xHf7e5lcMJOMzfh6s5uxySszMb6oyL9mv2ncm78p+5wW+x4H9qLTfjXfLOUJDRv/9uFG9onK5jPsG+SZScr2VYrs75GXZxUDaWnWBpi5udbPVllp/d5yc63CyD5sKpSFdSEwaNAgvvnmG/r06cOoUaN44IEHiImJ4R//+AeHHHKI0+EdVFN2U/zpp6a9V1Nf11SrVjXtde++27TXnXZa0163enXTXte5cyPbxu7lppua9l7Sco2tA75794Ef709Td+RtTGMTfhvbubexv5PGdjlubPfixnbv7t9/32ONdR9v2rTvscZ2Pt571+DPP9/3NdJ27JNw7Q1h87i0dN/xxPbhDfbn7GOS7eOnzRAIaDj50TT0zQZF9rH9ZrJuYw038z723ZHNVdCEBKvhZ34G8/dqGmH2CZr2FWPsvSOmqLEfKy21Gn+1tdZXM4zIDAPKzvYXBNI+ysr85zU21j+8y/RYmaFq5t87+2o/JlfsE4tNAWCG3pjhcOYz7MvGmj0hTC6ZAhcaLu1pn7BuegjA32C3L61r5p+YHjEzmd3+N7n33hamIDW9GGY1IvucmfJy62etrraWst21y18AFBX5VwcyCwmEehEAYV4I3HbbbZTt+VfzL3/5C2eeeSZjxoyhY8eOvPjiiw5HJyIiocTsnmsfD2/Yr5rv73vNFUdTRDS26+iBNtEy4+ztGluFqjH2K/xmHL/HY13BtRcT4C8W4uL23STMXKAywy9MI85ebFdU+HdfLSnx78aan+8fi93UuCUwqqutfDNzN6KirAauyUUzvKa21j9O3xQC0HBpWjOEp7HC037V3d74hoY9Uub77L1g9rH89vH79iFDpuiMi2t4MccM2zF5aIYU2QsPe4+ZKXTMZ5uCwPSQ+HxWwVpQYN03qwOZHDaFTzgI60JgwoQJ9ff79evHmjVryM/PJz09vX7lIBERkaawX/ls7pVA+5r/TlxFtA8/Mg0fc5XYPtzD9DKYVYLsm5eZxhQ0HKpkGpemt6C0tGEhYOZKmIZnuDSgQo0ZymMmgO896sA+dr4xpmfoQEWcfTftxuyd+40V0Kb3yRSa9knu5j3M6j0VFQ335zAb4Zn5M6bwtRcFpkfD7Bxu8rGqyhr2Y/Yw2LXL+htxuazjhYWNF++hLqwLgZycHDp16tTgWIc9ffrffvstgwcPdiIsEREJQfargM1tzB+sx6CtmaEQ9jXXq6oaToQ2jX6zsooZGpSaah037BOYY2L8Sy2aCZVmaJC9ELAXQSoEnOXzNT4s60D52ZShyoG092pce1+Bj4ryr9lvH7oWF2cdi4uzbqZIte+mXVXl318hIcHK7dJSaynbvn39qyWZfUHMZOhQnhB8IGFdCAwePJinn36aM844o8HxGTNm8Kc//YkKbV8oIiLNEMpjgu0r/EDDpVDt8wHMPAVzzDS2zGvNkA+zaot5b3PF30w8NZOlNQxIWmp/E/XN8CWTlz6f1ehPTLQa72YeQUKCfyM1Mxm5qMhq5MfEWDlcVGR9T6dO8O23/uVS7UuZhrOwLgSmT5/Oz372M6ZMmcLMmTPJz8/n0ksv5dtvv+X5xrbkFJGg1ZodRxu7AtnY+zX1M5o6+b57932PpaXte6yxq3OBnuAvsjczTMe+upBp9EdH+5d5NMM07MNCzHKjZWX+gsCMnd57bwGRtrD3ilNm9SLT6xUVZa2WZPY+MFf2zaZfHo9/p/ukJFi3zr83RiTlblgXAjfddBOnnHIKv/zlLznyyCPJz89n1KhRrFq1ii6NLfEhIiISIcxwncbYhweZYSFmUzS32ypo4+KshpPZZVbj/6U9NZZr9sZ7TY1VzJphaqYXy+ezeglKSqzXuVzWMKPCwshp/NuFdSEA1iThQYMG8corrwAwadIkFQEiIiIHYN/t2Kit9a+WZMZMm3kBkTCEQkJPdbWVo2aYj5ngnphozY9JTLR6v5q6jHU4CutC4LPPPuOSSy6hQ4cOrFq1is8++4zrrruOd955h9mzZ5Oenu50iCIiIkHH7ItgLwTscwzMxGNN/pVg5fXuO9zT7Mexa5f1uKio/eMKNo1ssxM+TjrpJCZNmsQXX3zB4YcfzhVXXMHXX3/Nli1btGKQiIjIAdiXPN37ir99KVURCV1h3SPw3nvvceKJJzY41rdvXz777DPuueceh6KSxsyfH9jXiQSDbduadkxERMQJYd0jsHcRYLjdbv70pz+1czQiIiIiIsEjLAuB008/nSLbwK/777+fwsLC+sd5eXkMHDjQgchERERERIJDWBYCCxYsoMq2wOy9995Lfn5+/ePa2lrWrl3rRGgiIiIiIkEhLAsB315rmO39WEREREQk0oVlISAiIiIiIgcWloWAy+XCZV/8eM8xERERERGxhOXyoT6fj8suu4zY2FgAKisrufrqq0lMTARoMH9ARERERCQShWUhMHny5AaPL7nkkn1ec+mll7ZXOCIiIiIiQScsC4FnnnnG6RBERERERIJaWM4REBERERGRA1MhICIiIiISgVQIiIiIiIhEIBUCIiIiIiIRSIWAiIiIiEgEUiEgIiIiIhKBVAiIiIiIiEQgFQIiIiIiIhFIhYCIiIiISAQKy52FA8nn8+25V+xoHBJJrFzz517rKIel/QUuh5W/4gzlsISypuevCoGDKCkp2XOvh6NxSOQpKSkhNTU1IO9jUQ5L+wpEDit/xUnKYQllTclfly9Qlx3DlNfrZceOHSQnJ+NyuZwORyKAz+ejpKSErKws3O7Wj95TDkt7C2QOK3/FCcphCWXNyV8VAiIiIiIiEUiThUVEREREIpAKARERERGRCKRCQEREREQkAqkQEBERERGJQCoEREREREQikAoBEREREZEIpEJARERERCQCqRAQEREREYlAKgRERERERCKQCgERERERkQikQkBEREREJAKpEBARERERiUAqBEREREREIpAKARERERGRCKRCQEREREQkAqkQEBERERGJQCoEREREREQikAoBEREREZEIFPaFQGFhISNGjGDo0KEMGjSIf/7zn06HJCIiIiLiOJfP5/M5HURbqquro6qqioSEBMrKyhg0aBDLly+nY8eOTfp+r9fLjh07SE5OxuVytXG0IuDz+SgpKSErKwu3u/W1unJY2lsgc1j5K05QDksoa07+RrVTTI7xeDwkJCQAUFVVhc/nozm1z44dO+jRo0dbhSeyX1u3bqV79+6tfh/lsDglEDms/BUnKYcllDUlf4O+EFi8eDEPPvggK1asYOfOnbz22muce+65DV4za9YsHnzwQXbt2sWQIUN4/PHHGTlyZP3zhYWFnHjiiaxfv54HH3yQjIyMJn9+cnIyYP0yU1JSAvIzSWTzer2sXbsWgAEDBuxTrRcXF9OjR4/63Gst5fCBHex8SPMFMoeDOX+VO+ErFHJY+Sf705z8DfpCoKysjCFDhjB16lTOP//8fZ5/8cUXmT59OrNnz2bUqFE88sgjTJgwgbVr19K5c2cA0tLS+Oabb8jOzub888/nggsuIDMzs0mfb7rxUlJSgu4/IQlN1dXVzJ8/H4Bhw4YRExPT6OsC1YWsHD6wpp4Pab5A5HAw569yJ/wFcw4r/+RgmpK/QV8ITJw4kYkTJ+73+ZkzZ3LllVcyZcoUAGbPns3bb7/NnDlzuPnmmxu8NjMzkyFDhvDJJ59wwQUXNPp+VVVVVFVV1T8uLi4OwE8h0n6UwxLKlL8S6pTDEkpCuh+purqaFStWMH78+Ppjbreb8ePHs2TJEgCys7MpKSkBoKioiMWLFzNgwID9vud9991Hampq/U3j+iTUKIcllCl/JdQphyWUhHQhkJubS11d3T7DfDIzM9m1axcAmzdvZsyYMQwZMoQxY8Zw3XXXMXjw4P2+5y233EJRUVH9bevWrW36M4gEmnJYQpnyV0KdclhCSdAPDWqtkSNHsnLlyia/PjY2ltjY2LYLSKSNKYcllCl/JdQphyWUhHSPQEZGBh6Ph+zs7AbHs7Oz6dKli0NRiYiIiDTBpv/Ap5Ng0/NORyIRKqQLgZiYGIYPH87ChQvrj3m9XhYuXMjo0aMdjExERETkwLasWgVbXuK7hYudDkUiVNAPDSotLWXDhg31jzdu3MjKlSvp0KEDPXv2ZPr06UyePJkRI0YwcuRIHnnkEcrKyupXERIJNh6Ph3POOaf+vjhL50NaSrkjrbUxpzc946E8p/nzCJR/EghBXwgsX76ccePG1T+ePn06AJMnT2bu3LlMmjSJnJwcbr/9dnbt2sXQoUOZP39+k/cJEGlvHo+HoUOHOh2G7KHzIS2l3JHW6nFYL9gMA7pvbPb3Kv8kEIK+EBg7diw+n++Ar5k2bRrTpk1rp4hEREREWu+Qwb1hM6RGb4W6OtCVfWlnQV8IiIQbr9dbP9ytX79+2hbeYTof0lLKHWm1hJ7W17pSKMuGlKwmf6vyTwJBWSPSzmpra3nhhRd44YUXqK2tdTqciKfzIS2l3JFWi4rHF2sNZS7dvuEgL25I+SeBoEJARERExCHfb+5lfV22xeFIJBKpEBARERFxSEmdVQi4KlQISPtTISAiIiLikKPH9gZg5GE/ORuIRCQVAiIiIiIOiUq1egSo2u5sIBKRVAiIiIiIOCWxt/W1svmbiom0lgoBEREREYcU1lg9AqV5O+Ag+yaJBJr2ERBpZx6Ph4kTJ9bfF2fpfISw3C9h66uQMRp6nNvuH6/ckUDw7BkalBRbQNHOXFKzOjXt+5R/EgAqBETamcfjYeTIkU6HIXvofISu0g3zSfrpATZUXUS/Kee2++crdyQQktOTKa/rQIInn+jSDUDTCwHln7SWhgaJiEhIWpM/FoCUqsX4vBpSIaErIaO39bVWKwdJ+1IhINLOvF4vmzZtYtOmTXi9XqfDiXg6H6Fr4JhRVNXG0jllFzW7vm33z1fuSMAk7lk5qHRTk79F+SeBoEJApJ3V1tYyb9485s2bp23hg4DOR+hKSI4jNms0ADHZ77X75yt3JFBq43oDUJC9renfo/yTAFAhICIioStzrPU171NHwxBpjc9WWj0Ca77PdjgSiTQqBEREJHR1HgtATc7nmicgISt+zxyBzkmbnQ1EIo4KARERCVm1aaOorIkl2pfD1pXtP09AJBCGn9gHgL6dN2ovAWlXKgRERCRkRcXGsXq3NU+gZO0HDkcj0jKeFKsQoLYAynKcDUYiigoBEREJaf2PGwfAERkfOxyJSAtFJ0NsZ+t+wVpnY5GIokJARERCWnK/sdadwiUaViEha0fJIQCs/Ex7CUj70c7CIu3M4/Ewfvz4+vviLJ2PMNBxJLjjoCYHcr6BzkPb5WOVOxJIG3P6ktXtCwp2NG3CsPJPAkGFgEg783g8HHfccU6HIXvofIQBTxw7a0fT1f0RX/zvI465cmj7fKxyRwKoa/9DoByGHrKhSa9X/kkgaGiQSKTy+WD7O1Cs8agS+n7IGwuAL/9zZwMRaaFDjuwLQHrsJmcDkYiiQkCknXm9XrZv38727dud3RZ+1W3w8Rmsf/lW52IIAkFzPqRVeo0aC8DRvT5ut3kCyh0JqCRrjgAVTRsapPyTQFAhINLOamtreeqpp3jqqacc3RY+N+kXeL0u+se9wg+fLHUsDqcFy/mQ1ul7tDVPIMq7Z55AO1DuSCD5Eq0eAW/ldmrLKw76euWfBIIKAZEIldF3EF/s/DkAPXZHdq+AhAFPHGRY+wmw7X1nYxFpAV9cFyqq43BTx/bVPzodjkQIFQIiEezoK/4CLg9JVR/ADq3BLqGtMm0sAJu+WeJsICIt4Pa42VFsDQ+qyW3ahGGR1lIhIBLBotP7QZ/LrAcr/+hoLCKttTrP2lgsoepzfF7tJyChp++RViHQL0OLOEj7UCEgEukG347PFQOFn7PyrflORyPSYocfN5LKmjg6p2RTte1rp8MRab5ka54AFZscDUMihwoBkUiX2JMlOVcB4P7hz7qSKiErPimWuO7Wuupxu991OBqRFqhfOWiLs3FIxFAhICL0P/dWKmriObLbl/g2vup0OCIt13WC9TX3I2fjEGmBzflWj8DGdVsdjkQiRUQUAueddx7p6elccMEFTocigsfj4cQTT+TEE08Mmm3hO/XsQtzgaQC4v7sdfJGzJnUwng9pha6nAuAt+Jzayso2/SjljgRabZzVI9AxfhO+2roDvlb5J4EQEYXA9ddfz7PPPut0GCKA9Y/32LFjGTt2bFD94+064g8QlQxlq2HD806H026C9XxIC6UNJq8sE7evgh8WLmrTj1LuSKB1O7QPPp+LlPgSfMXbDvha5Z8EQkQUAmPHjiU5OdnpMESCW2xHfIdNB2DbB3dTW33gq1EiQcnlZnX+KQDUbXvP4WBEmicuMQ7XngnD7jxNeJe2F/SFwOLFiznrrLPIysrC5XLx+uuv7/OaWbNm0bt3b+Li4hg1ahRLl0buLqkS/Hw+H7t372b37t34fME1Mbe853TyyzrQPXUtn817xulw2kUwnw9pmYEnW8ODhnZd2Kafo9yRNpE22Pqaf+AdspV/EghBXwiUlZUxZMgQZs2a1ejzL774ItOnT+eOO+7gq6++YsiQIUyYMIHdu3e3fXDVhW3/GRJ2ampqeOKJJ3jiiSeoqalxOpwGEtNSWOP6AwDHp90FddUOR9T2gvl8SMt0PGK8daf0Wyjd2Wafo9yRtlAZfyQA2zasO+DrlH8SCEFfCEycOJG7776b8847r9HnZ86cyZVXXsmUKVMYOHAgs2fPJiEhgTlz5rTo86qqqiguLm5wa1TpRnxvHc7iv99NdZUqcQkeTc7h/Tj2smkQ0xlPzRZY3XgBLtJWWpu/AMR3hdTBgA82vx3wGEUOpLU5vHKTVQgU71rTFuGJNBD0hcCBVFdXs2LFCsaPH19/zO12M378eJYsadkW8/fddx+pqan1tx49ejT+wm3/w1W5ixPS/sRHf/0VeGtb9HkigdbkHN6fqAQYfJt1f+0DUFsR+CBF9qPV+bvHNu9pACxd8HEgwxM5qNbmcNfDrUKgX+fVUBv+vbLirJAuBHJzc6mrqyMzM7PB8czMTHbt2lX/ePz48Vx44YW88847dO/e/YBFwi233EJRUVH9bevW/azle9hv+T7uMbxeFxP6/RM+PAtqywLyc4m0RpNz+ED6XUVtdA+o3sWiJx4LfJAi+xGQ/AW2ea15Aj3jP9QmedKuWpvDvY44BDwJxHgqIfe7NopSxBLldADt4YMPPmjya2NjY4mNjW3Sa484/zrKfsgi8ZuLYfd8mH8CvpPn44rv1NJQRVqtOTm8X55YllbewbGeKxgUM4Py4mtJSEkKTIAiBxCQ/AUGjzuemjfi6JK6A1/O15B5VACiEzm4Vuewy21NGM77EnK+gi7KXWk7Id0jkJGRgcfjITs7u8Hx7OxsunTp0i4xJB7+MzhpIUSlQfFXbJ0zmi8X/tguny3Slkb9/FKyy/qRkZxLwtr7nA5HpFkSU+KIzjoBANeWtxyORqSZ0qzhQb6ibx0ORMJdSBcCMTExDB8+nIUL/UvEeb1eFi5cyOjRo9svkM7HwWlfkFvRi57pP9Lvx9HUZn/Zfp8v0gY80dFknnKn9eCnv0FlnrMBiTRX1wnW17xFjoYh0lyrd1qFwLLPNjgciYS7oB8aVFpayoYN/j+EjRs3snLlSjp06EDPnj2ZPn06kydPZsSIEYwcOZJHHnmEsrIypkyZ0r6Bpgwg8fwv2PyfifRKXQkfjYPjXoAe57RvHBL0PB5PfaEa9LtB9vo5fHcvFH9P7Ze3EnXibKcjCriQOh/SLN7MU3EDNblfQEUF0fHxAX1/5Y60lZrEI6EcshK/3+9rlH8SCC5fkO9CsWjRIsaNG7fP8cmTJzN37lwA/va3v/Hggw+ya9cuhg4dymOPPcaoUaMC8vnFxcWkpqZSVFRESkrKwb+hpgQWnQc5CwE3u7rNotPxV6O/UWmqZudcG7+fb8d7uBZNoKYuik2HraL/0Ye3OiYJb4HM4da8l7fOx+5/dKNL6k6+TXmLwWee0apYJHI4ncOl+QUkze9gPThnNyRq7qE0XXNyLuiHBo0dOxafz7fPzRQBANOmTWPz5s1UVVXx5ZdfBqwIaJHoZDj5Xeh5CeCly/ZreOX2P1FRHtT1lsh+ubJOZfmO04n21FLx2e+dDkekydweF5sqrdWDOlXPdzgakaZL6pAO8d2tB7tXOBuMhLWgLwRCkjsajnuWNe4/AnDREXcTvfRS8GrnP7G2hS8sLKSwsDBktoXvNOEh6nxRHNn5Hdj6rtPhBFQong9pumPOs+YJdHG/H/D3Vu5Im0ofan3NaXzOofJPAkGFQFtxuTjs5/ewIe3v+HATteM5+GAi1JQ6HZk4rKamhkcffZRHH300ZLaF7zX4MDwDrrEefHUDeOucDSiAQvF8SDNknQauGChfC7uXB/StlTvSlkrijwVg/cqvGn1e+SeBoEKgjfU7/RpcY14DdxzkLiTvheP55stdB/9GkWAz+M/WMrllP5Dzyd+djkakaWLSoftZAOxcPMfhYESablPp8QCk1C3VpnjSZlQItIceZ8P4RVTTkY5R35D65Wh+WrnG6ahEmie2A0V99iwnuuYuCnYXORuPSBN9V34pAJ7c1/DW1jocjUjTHDr6aKrrYshM3UX1jtVOhyNhSoVAe8kYRdW4L9hedAi9MzbRZ91o2P2p01GJNEvikGv4KXcAnZJzKPzwDqfDEWmSvsefRm5JBp1TdlG4SpuLSWiITYgjJnOEdT/vQ4ejkXClQqAdJXftR6eLl+BNHoarthA+HI9306tojo+EiqiYaHxDHwKgj+8JKNZmNxL84hNjSDh8EgAd8p91OBqRZuhkDQ8i/wtn45CwpUKgncWkdMY98RPIPA28VfDZhfz79r+heT4SKvqOOR06nwy+avjieqfDEWmShCMmW3dy34VqDWuTENHpOAAqdy9zOBAJVyoEnBCVCOPeJC/lctwuL5ccfh3b37gRfF6nIxM5OJcLRjwCuCH3HdYtWuh0RCIH12EEJB0K3kqKlz/ndDQiTVKRZK0cFFe3nuwfdzgcjYQjFQJOcUfR8Yx/sibqzwD0rpoBi38BddXOxiVtzu12M2LECEaMGIHbHaJ/gmmD+Ml9JQB1X/2euprQXU40LM6HHJzLxdpaq1dg4+f/DchbKnekrcWnZfBjrrWbe953ixs8p/yTQHD5tAvFAQVym/H9Wv80LPsVUEdNygmsy3qNI47q0DafJUEv0DnXVjmctyOH6Hf7kRJfTGn/J0g6+uqAvbeEtkDmXCDf68dvt9D3214AVI7/ibjOfVr1fhK+gimHyxddRcKOf0KPq2HME62KRSJDc3JOJWQw6H85jH0LnzuB6OLFJHwygg9fWel0VCIH1DGrE76BtwGQtPkObZYnQa/v4J4URJ8IQNympx2ORqRpEnqPte7kL3IyDAlTLSoEysrKAh2HZJ1GxfGfsbO4N306bWRs1bHw47+cjkragM/no6ysjLKyspDfFj515G8goQ9U74av/ux0OC0STudDDi79qD2Thne8SGuXbFPuSLvoOgFwQ9kafPnr6w8r/yQQWlQIZGZmMnXqVD79VOvgB1JC96F0vnQFRbEn46YCvrwUvpyGr05LCoWTmpoaZsyYwYwZM0J/W3hPLAy3lhOtWT+LbWs2OhxQ84XV+ZCD6/kza6f38g14d37eqrdS7ki7iO1Iju8YAD7819v1h5V/EggtKgSee+458vPzOemkkzj00EO5//772bFDs9kDwZPQgdTzFsCAP1gHfpzFVzPG893ybGcDE9mf7ueyOvdEot2V7Pjfda2+yirSpqJT2Bl1HgDLn/+bw8GINM26sjMASCh73+FIJNy0qBA499xzef3119m+fTtXX301zz//PL169eLMM8/k1VdfpVZbuLeO2wPD74fjXqa8JpnhPRbT+avh+HK+dDoykX25XEQf8xhVtTGM7PE2fPuA0xGJHND2lBsAOCrzv1Rka1M8CX6HHG8VAqP6fAjVGp4tgdOqycKdOnVi+vTprFq1ipkzZ/LBBx9wwQUXkJWVxe233055eXmg4oxMvX5GzUlL2VFyKJ2TtuP64ARY96TTUYnso//II3EfZQ0R4vvbIPsTZwMSOYDhpwxnc9WpRHnqiF9zl9PhiBxU18OPhPhuuH2VsHm+0+FIGGlVIZCdnc0DDzzAwIEDufnmm7ngggtYuHAhDz30EK+++irnnntugMKMXKk9DiNr6jLIPMPayXX51Wx96QpysqucDk2kgegjroXuF4KvFu/iSeTtyHE6JJFGuVzQ6wxrxSt2/AfKtjsbkMjBuFzQzeoVYMfbB36tSDO0qBB49dVXOeuss+jRowfPP/88v/71r9m+fTvPPfcc48aN45e//CX/+9//WLRoUYDDjVDRKXDSG3DEn/Hhokft02ydcyI7ftR/XhJEXC4Y/TSVUf1x1+xk47/+L6Q3GpMw13kMdDwWfNXULLvH6WhEDqqui1UI5K5fSE215mJJYLSoEJgyZQpZWVl89tlnrFy5kmnTppGWltbgNVlZWdx6662BiFEAXG4Ycgdber9JUUUaR/X6kq5fDYPsxQf/XpH2Ep3MtkNepbwqnhE9PqD00z87HZHIfpX3tXoFqjc+S0lursPRiByYr/N4SiuTyEjYwrI3PnY6HAkTLSoEdu7cyZNPPsnRRx+939fEx8dzxx13tDgwaVyvY8/Addpy6uIPx1WTAx+ejPf7h6mt0dWBUOF2uxkyZAhDhgwJy23h+40YxKbO1u6XqTvvge0LHI7owML9fMj+xfY+je93DiMxtoxNb85o9vcrd6Q9RcUl8FP1uQAcHvOc8k8CwuVrwS4UxcXFjb+Zy0VsbCwxMTGtDixYBHKb8YCqLYNPL4MdLwPw3rpLGHb1k3TqkuBsXNJqgc45x3J4yeWwcQ5Ed4QzvoGEbu332eKoQOZcW+fvt++8zODCCyEqFc7bYg3FlIgXtDm8/R34+AyIzoDzd4AnunXvJ2GpOTnXohIyLS2N9PT0fW5paWnEx8fTq1cv7rjjDrxeb4t+AGmCqEQ48SVK+z5AbZ2HUw99jqj3R0PpJqcjE7Ec/TdIGQw1eXz/5EXUVGnDGwk+gyeeD0mHQm0RrHrY6XBEDqzrKRDTAWpyYZMmDUvrtagQmDt3LllZWfzxj3/k9ddf5/XXX+ePf/wj3bp144knnuCqq67iscce4/777w90vGLncpE06ka29V9AWW0G6Z5V8M4w2PGe05HJAfh8Pqqrq6murg7vbeGj4ik56lWKK1I4IvNzVs75g9MRNSpizoc0zuWGQX8EwPvj36gsKWnytyp3pN25o6HnRQCsX/Rf5Z+0WosKgXnz5vHQQw9x1113cdZZZ3HWWWdx1113MWPGDF588UVuvfVWHnvsMZ599tlAxyuN6H3MyST+7CtIGQq1hfgWTWTxE/dpVYEgVVNTw3333cd9990X9tvCJ2f1Y03qHACOTn0YNr/qcET7iqTzIfvR+/8o9R2CuzaXFXP+3ORvU+6IE3bG/gKAtNoFyj9ptRYVAp9//jnDhg3b5/iwYcNYsmQJAMcffzxbtmxpXXTSdIk9YOIS6PFLXHg5IfWPLH/4Qqhp+tUtkbYw8oKfQf/fWA++nAolPzkbkMje3NF85XkUgJEdHqcub7XDAYnsX9cjj2dXSQ9SExufrynSHC0qBHr06MHTTz+9z/Gnn36aHj16AJCXl0d6enrropPm8cTB8fNY6f4b1bXRjO7xCrxzNBSsdDoyiXRHPQjpR0NtEfmvn09poXYdl+AyZtKZbPedTrSnBs/Sa0BDLSRYudxkHHOV01FImGhRITBjxgwefvhhhgwZwhVXXMEVV1zB0KFDeeSRR3jooYcAWLZsGZMmTQposNIELhdDf34tNWM+gphMKFsL7x7F9leu4KfV2U5HJ5HKEwMnvEJZTQc6eL5h7ZNnUltZ5nRUIvVcLuh29qPgioGCxbDxv06HJLJfUYdeAa4op8OQMNCiQuDss89m7dq1nH766eTn55Ofn8/EiRNZs2YNZ555JgDXXHMNM2fODGiw0nSJfY6DM1ZCl3MBH92qniZjSX9+fON+qKt0ODqJSIk92NLnTYorkhne4yOiFp1mLYMrEiyS+8HhNwJQ9vnvqSxVz5UEqfgu0P1sp6OQMNDsQqCmpoaTTz65fpLUq6++yquvvsp9991H79692yBEabH4LnDSa+QeuYh1OUeREl9C39Jb4I3DYMsr6vqWdnf4CcdSO2Y+eJIg/1N4/xSoKXU6LBG/QX8kt7wHie6tfPnUPU5HI7JfpZn+4UGb1xc6F4iEtGYXAtHR0axataotYpE2kjHoRA79zTJKBjxlDReq2AyfXsD6J07ki3e+djo8iTAdDj0WTnofPMlQsIRNc06lKE+T2iVIRCWwNsnaT+C4jIegeIPDAYk0LqnPcf4Ha/7hXCAS0lo0NOiSSy5pdLJwsDrvvPNIT0/nggsucDoU57jcJA+/HM7ZAP1upM4XR/+0TxiZP5zyhVOgYpfTEUYMt9vNwIEDGThwYORuC9/pGDj5A8qq0+idvISt807FV+3MChg6H7K3Yy86n6rUk4hyV8GnU8DX+OaYyh1xktvjoX/XOAYmfU+fuiegVsN+pflcvhbsQnHdddfx7LPP0r9/f4YPH05iYmKD54NtbsCiRYsoKSlh3rx5vPzyy8363rbe3t4phds3s+GlmxiR+ZJ1wJMIR/yRmn7TiY6Lcza4CBfonAvmHF77+XIyV59KWkIBpI6AUz6AmFSnw5JWCmTOOZa/xevgnSHgrYRB98GRN7ffZ4vjQiaH66rgf4dA5Q44YiYM+V1g319CUnNyrkWXML777juOOuookpOTWbduHV9//XX9beXKlS15yzY1duxYkpOTnQ4jqKR168WI370IJy2G5GFQVwarbmXnU4ez4J//xVun+QPS9gYcO4L4Mz6AqDQoWg7vnwTVhU6HJQIph8LwRwCo/uYOfvj0K2fjEWmMJxYG7png/t1jlBbXOhyQhJoWFQIfffTRfm8ffvhhs95r8eLFnHXWWWRlZeFyuXj99df3ec2sWbPo3bs3cXFxjBo1iqVLl7YkbGlMlzFw5nIYMYfCqix6dtjEhMSLcL1/POSvcDo6iQCxXY+CUz6CqHQo+oqf/nkyBdkFToclAv2uYsWus4nxVBP71cX4arSKkAShfldSVJlBomsTi+c863Q0EmJaNahxw4YNLFiwgIqKCgBaMMqIsrIyhgwZwqxZsxp9/sUXX2T69OnccccdfPXVVwwZMoQJEyawe/fu+tcMHTqUQYMG7XPbsWNHs+OpqqqiuLi4wS3sudxw6BSSfrGOpSW34iUeV/7nMP9o6j6ZzI/fNf/3KPtXXV3NnXfeyZ133kl1dXXA3z8kczh9KJyyiMKKDA5J/4q8F0+Gqvx2+ei2Ph/SPEGVvy4XfX7xNAUVXTgkYw2uL3/T4GnljjSmvXK4Pv/umcEPrt8CMDrtXvDWtMnnSXhqUSGQl5fHySefzKGHHsrpp5/Ozp07Abj88su54YYbmvVeEydO5O677+a8885r9PmZM2dy5ZVXMmXKFAYOHMjs2bNJSEhgzpw59a9ZuXIl33333T63rKysZv9s9913H6mpqfU3s1NyJIiKS2Tkr+7Gfc5a6HIB4MOz9Vkylx7KR4/fDbUVTocoTRCyOZx+JHlDPyK/rBP9Mr6Gd0dB0Q9ORyXtLNjyt0PXDNJPn2c92PI0bH7d0Xgk+DmRw4PPuwZvVAbpMT/C939v88+T8NGiQuB3v/sd0dHRbNmyhYSEhPrjkyZNYv78+QELrrq6mhUrVjB+/Pj6Y263m/Hjx7NkyZKAfY7dLbfcQlFRUf1t69atbfI5QS2xB5z0Xxj/GRvyRpIUV8a4jn+CNw6FzS9q/4EgF8o53Hf4INIv/AhiukD5Bph/NHlfveR0WNKOgjJ/u54Kh14PQO2SK9iyZrvDAUkwcyKHoxOScA/5k/Vg7b1Qo80apWlaVAi89957/PWvf6V79+4Njvfv35/NmzcHJDCA3Nxc6urqyMzMbHA8MzOTXbuavtzl+PHjufDCC3nnnXfo3r37AYuI2NhYUlJSGtwiVudj6TftC9akPIsvNgsqt8FnP6foP8N55/GnyN6mjaCCUajnsCvtCDjzG0g/DurK6LhmEh/OmE5NlSbBRYKgzd9h91PoG0yUN4/it86mKF/zBaRxjuVwv19BQi+o3s2KeTPa5zMl5LWoECgrK2vQE2Dk5+cTGxvb6qAC7YMPPiAnJ4fy8nK2bdvG6NGjnQ4pdLhcHHbmL3Gdsx4G3AbueFJ9X3N6xytJ+ygLll4DBSudjlLCTVxnmLCI5SXWUMOTsh4m6qNxUJHtcGASsTxxVI76H3mlGQzK+oqEZT/f7/4CIo7wxLKz810A9PU9zPpV+vdSDq5FhcCYMWN49ln/zHSXy4XX6+WBBx5g3LhxAQsuIyMDj8dDdnbDZM7OzqZLly4B+xxpgqgEGH4XvrM3sbz8LrYW9CXWUwIbZsO7wyh7ZSTv/G0O2TvUHSkB4o5ixK9msDzmRbzuRFz5n8I7QyGnbYYFihxMl359qBn9Gj5XDNF5b8LXtzsdkkgDXY/5P37MH0ZaYhFdttzodDgSAlpUCDzwwAP84x//YOLEiVRXV3PTTTcxaNAgFi9ezF//+teABRcTE8Pw4cNZuHBh/TGv18vChQt1Vd8hroTOjLjiNnr8eh0c/w50OhNcUSRWLeP0DpeT/H4WLLsWCr91OlQJEyMuuAj3xGWQ0B+qdlH33ol89OQsfF7NVZH212Xw8bhGPmk92PCIo7GI7MPtofs5jwKQXPwcZGu5dTmwFhUCgwYNYt26dRx//PGcc845lJWVcf755/P111/Tt2/fZr1XaWkpK1eurN+IbOPGjaxcuZItW7YAMH36dP75z38yb948fvjhB6655hrKysqYMmVKS0KXQHG5oedEOOVNOGcLK6vvZFthHxKii2H93+GdI6l58xjefnwe2Ts0ltbO7XbTv39/+vfvj9vdqhV8I0fq4XDGCgoTzsXjqmFc8jR2vvJLqG19bul8SLP1vQzfgBtx46Nv/AY6pXRU7ki729+/XbE9xkDPSYAPll2nBT7kgFy+liz+H0CLFi1qdPGh3J8AAI+WSURBVDjR5MmTmTt3LgB/+9vfePDBB9m1axdDhw7lscceY9SoUe0Sn2Pb24cinxe2vAtr/w5574HPmtxZXJlGyuBLoP/VkHaEw0EGv0DnXDjlsM/r49N/PMixSX/E466D5CNgzAuQNtjp0MQmkDkXrPnr83pZ8cjPGNHldcpr00k462NIVx6Gi5DP4bKt+N4cgMtbwX83/oMLb72yfT5XgkJzcq7FhUBhYSFLly5l9+7deL0NJ0xdeumlLXnLoBSs/wkFvbIdrH7tSdJK5pGV6l9JytdhNAs2XcNR51xA567xDgYYvFQINMHOhfDpz6EmF58rik/zbmb45NtISA6+xQoiUcg3opqoqqyMklfHkeFZBtEZMOFTSBngdFgSAOGQwxv+dz/9ym4hvzSdvFHf039I13b7bHFWmxcCb775JhdffDGlpaWkpKTgcrn8b+hykZ/fPjuCtodg/k8oJPi8+Da/jWvdE5D7HlAHQGF5OqlDL8XV/1fWsA+pp0KgiSp2wqdXQM47AGzKP5zek56CTsc6HJiEQyOqyaoLYMGJUPItvpgu7BryGV37H+J0VNJKYZHD3lq2PjWSHklf4+t0Jq5T3my/zxZHNSfnWjSo8YYbbmDq1KmUlpZSWFhIQUFB/S2cigAJAJcbV++z4NR34JxNrPfcxs6inqQlFOBa9yi8PRDfguNY8PADfPLmN1RXhf9Yxurqau69917uvfdeqqurnQ4ndMV3hVPeZkXMf9hdnEnvDj/A+8fD0mlQ0/Q9LnQ+pKWqq6u5d8YT3PvtJKriBuKq3kXN/JPZtDoINkGTsHfQf7vcUfS4cA64PLhy3oL1z7d/kBL0WlQIbN++nd/85jeN7iUgsl+J3ek/6S66/uon6ka+Ch1PBdy48j5nQuYfGFMylKg3u8Lnk2HT8xRl54TtHKeamhpqamqcDiMsDL9gEin/txq6/R/ggw2zqHh5IB//Z0GT80fnQ1rKyp1aike8y6a8/vTsuImMr8ZB+Q6nQ5MIcNB/u9KHwuE3AeD76lrWfbWlfQKTkNGiQmDChAksX7480LFIpHB78PQ7DyYsgLM3ktPpflZln0ZVbQLu6mzY9Cx8fjHJH2Sy6oERbHjlVti9GLxqqEnj4lI6wIn/hhPexRvTg3jfVk70nsb6uZdAVZ7T4UkESO3ShYSzFlJBD5LcP8L80VDyo9NhicDgP1MVPwxXXSE5b04mL7fO6YgkiES15JvOOOMMbrzxRlavXs3gwYOJjo5u8PzZZ58dkOAkAiT1pNMpf6DTKX+A2krYthC2vYMvbxHustUM6bECqlbAB/dCVDIFMeNYvOE0eh0zgaHHaxyu7KX7adRMXM3XT9/CyA6zODT23/DGfBg+E/pcYi17K9JGOvfuARkfw/snQcUmvO+O5odu73PEcUOcDk0imSeGumP/Q9k7R3Fc/0Vs/+IuOPPPTkclQaJFhcCVV1rLUP3lL3/Z5zmXy0VdnapNaYGoOOh9BvQ+AxdQlb+NHxe9w+HJ83EVfAy1+aTXvsE5WW/AFuB/fSFrAnQ9je21Y+nWO9npn0CCQGxiEsf85nFqtv8c97KpUL4OvpjMtg9msCbur4z7v9PwRLkO/kYiLZHUB05bQt2C8XjKv6fbDyeyougNhp9+gtORSQRLyDyUnf0fI3HL5XQrvgu2HQfdT3E6LAkCLbo85vV693tTESCBEtuhOwPPvwrXKa/ChTkw7jPW+G7jh93H4/VFQdmP1uZli8+m0+KOfHnPWKpX3gf5X4FXeRjporsdB2etgsP/TC0pdE/6lvFRp1P19omQ+6XT4Uk4i+9C5djPWLVzDGkJRQwrnABb/+d0VBLhuh43BXr9EvDCkv/DV7rN6ZAkCDSrEDj99NMpKiqqf3z//fdTWFhY/zgvL4+BAwcGLDiRei43dD2Wwy6+i8N/+wnuC3Lg6Bcg61LKfb2IiaphVJ+PiVn9R5g/HF5KZuMTI3n3z1fw47uPQfZHGiseiTyxMOwOak7/iaUF11PjjSWh7BN47xj4+DyyN6x1OkIJU4lpqRz26wWUJE7ETSV8cj58P0O7vIpzXC4YNRtSBkFNLitnTWLLpiqnoxKHNWto0IIFC6iq8ifNvffey0UXXURaWhoAtbW1rF2r/1ilHcSmQf+fQ/+fkwCUbF1D6fdv0dX1PhR+BnVl9EldRp/UZVAALLS+rcrVlS/XD6Em8UhOPv9ISD8SkgeAJ6bdQne5XPTq1av+vrS9+LSOjLz2ESj9HSy7GXa+CNtfp+OWN3nv31Pp3nUEnpgYnQ9ploP9LcfExxNz1huw5ErYPBe+uZFP3/mO4dc8SXySNr+T1mnR/yVRCXjHvEr5qyMY1uNzFr9wFT1vnmsVCRKRmrWhmNvtZteuXXTu3BmA5ORkvvnmGw45xJq0mZ2dTVZWVlgNDwr6zWxkX3W1ULCG9V98Tcn27xjcfRXRVauhcj/LprmiWb/7cH7KH8yQsUfSZcCRkHYkvriuuNzt/4+jNhRrB/mr2P7mH+gWPR8AnzsO14Dr4fDpENfZ4eBCX1hsxhRIPh91qx6Cb/+Ax+3lp6JjOOSy1yE+0+nIZD/CPYd3fzOfjO/OwO3ywsC7YOhtTockAdScnGvRZGGRoOaJgoxB9D9zUMPjlYUUrl/B9u9W0cHzHV0TvoOyNVBXTP9Oq+jfaRXs/Dfs3PNyb0e++ulI3B2OZPSEARDXBeIy+WR5JgkdMhk8LImY9utIkEDqcCTdJr/Lli8X0WHzTSTVLoMf/opvzSN8vOVSEkfcwIiTBugimQSGy4VnyO/5ZvtADtn1fxyS+gW8MxzGvQEdjnI6OolAnYecBrEPw1fXw+rbIbE79L/M6bDEAc0qBFwu1z7dT+pKl5ARl0ba4JNJG3yy/5jXi7fwJ7at/Iqizd8yqPsqXKWroeIn4t15HNfvI+AjWOb/ljEABeDbmGBdPY7PZFN2F75e04WsPpmMOjET4qzbe59kEp+eydGjk4mL199KsOk5aiyM/BI2vQLf3Y2r5BvG9vgn3p1P4f3wTDyDb4JOx6nbXAJiyOmn4yv8Aj46Ayp+ggWj2Zg+k96n/tqR3keJcAOug+K1sOHveJdeznsfxHLaNb9wOippZ80qBHw+H5dddhmxsdbYxsrKSq6++moSExMBGswfEAkJbjfuDv3oeVI/4CL/8eoySjZ/w67vVpLh+Yb02E1QnUNdZS415bnERVfg8pZD+SYo30TvKOhtOiBse+2dCrALfK/HQXxniMtk3Y4sXtl2JFExMVw//SZi1K3gqOqaGh59eSNwMRcc9wDFXz7KkMx3IPtN65Z+NJ8X38SRp59HUrLH6XAliFRXV/Poo48CcP311zfpb9mVdhicsRw+/gXkLKBP/jQ+fWAxo37zFNEJWgJZmq4l+deAywVHP072jgoyy59hfNIv+XFBOX0nXN4G0UqwalYhMHny5AaPL7nkkn1ec+mll7YuIpFgEJNIcv9jSe5/bIPDHsBTVwfVxVC2o/6WvWU3+dm5dErOJiNxl1U0VOVSVZpHQkwZLl8llG+B8i30jomm2jeM6qrwmUsT6srLywHoNmIsfY49FQq+h2/uh53/hYJlHMuFbHrmEGJO+h0xAy6D6CRnA5agYXKnWWLSYfy7fDnnAY6Kvo3je74E762AE1+BdG0+Jk3Xovyzc7npfPZTfPFILcd0+Rd9866AL76Aox8HT1xggpSg1qxC4JlnnmmrOERCh8cD8enWLeMIADIHwt7T/jxAgtcL1SX+oqF8F3mbd8CPrfzHW9pW+hEw9l9Q/iA/vfkw6SVP0TvjJ1h1HXx/M/T6Od9VXs6ho48hJlZDOqQFXC5GXf4H1n56PIfuughX+Y8wfxTVA+/BM/C3eKLV+yTtw+V2M/L6ufi+6Y1rzd3w01OQtwyO+zekHeF0eNLGWrShmIg0kdsNcanQ8XDoeTIcdjEdx13vdFTSVAldOGTSX0n55VbK+86E+D5QVwY/Pc2gHceycdYRlCybARXZTkcqIWrA8cfhOmsVZJwCvipivv893z16Ipu//9Hp0CSCuD1uXEf9BY79H76oDlD0Dd53hsOaR7X3RZhTISAichCe2AQSRv0Ozv0RTnif7KhJlFclMKDLDySvvxFe7w6LzmbNwjcpyKt1OlwJNbEd4ZQFFPSZRUllEkOyPqP7N0fCulng8zodnUSS3mfx1Lavefeb03BTBV/9Fj4+WxtyhjEVAiIiTeVyQffxZF70H2Im7SC/x2OQfBT4amHHmxyWfTaVL/Zgx9s3Qt5yXUmTpnO5SB/9a4pGr2JH1fF4KIfl0+D9k6jOW+d0dBJBfnFVT/665E2+KLgXXNGw4y14ZwhkL3I6NGkDKgRERFogKiGVDmOug7NWwIRvKO7wa/LLOtE1bRdZRTNgwdHwei9++u9vWfDcpxQWaHK4HFz3w/uQddnHMHgGuOMg92N8bw/m0yfupFYr80k7SEqCDxdFcczU38IRL0NMT6jYDgtPghU3QF2l0yFKAKkQEGlnLpeLrKwssrKytA9HEAjI+eh4JCmnzSJ96nby+78Anc4EdwJUbOWQmkeZ4B5D9FvdYOnVsPN98NYE9ocQR7TZ37LLDYNvgDO+46eSk4mNqub41D/jmT8Ysj8O3OdISGvL/0vcbiA+Hg49lfIBb/PO95cCPlg7E94+EnZ9GNDPE+e4fD71XR9IMG4NLuEt0DmnHHZITTm+TW+wav7/6JP4LilxRf6nSOeDtWcT3+9Mxl40zhojHkYCmXORnr8+r4+P5z7PsfG/I8aXYx3reTHF/WeQmtnF4ejCl3LY74F7a/nDrVH83/Gv8a9pV+Guy7We6HERDL0Xkvs6G6Dsozk5p0LgIEL9D1hCjwqBMFRXDVvnw8aXIHcB1OTannRB2hDoOp6PfhhPv9HH06NPomOhBoIaUW2guhC++B1smwf4KK5IYU30nxl5yXXgbtZK4NIEymG/ujr4/e/qOO+EPE7o/yPk/B12/xvwgSsK+l0FR/wREro5Hars0Zyc09AgEZG25omB3mfDuOfgZ7vYdej7fF14DeWuAYAPClfCDzMYx2lkfpJO3fwT4Nu/QM5neGs1jEiAmDQ44Rk4+TPWZI8gJb6YkVHT4W1N4pS25fHAw495OOHcDtC5N3T5A1s7vUNt4hhroYT1f4c3+sKyaVC+zelwpZnUI3AQoV7JS/Cpqalh1qxZAFx77bVER0c3eF49Au3rYOejzZVuh83vkPPDIupyP6FLytYGT1fUJPL9zqPoNGAEvYaNgA4jILmfNY48SEXK1VSncqem2ssX82ZzfOptuGoLAChLOomCHnfRfdixB/luaYpQyGFH8s/rZdfqfI6ZkEKHdB8LZr1Bp8L7oewr63l3DBxyGQy8BZJ6t3080qjm5Jz6E0Xamc/no6ioqP6+OMvx85HUDY64kk5HXAk+H3W5P+DZ/i7kLMJX8BnxFDCi5ydQ8Ql8bn1LLSl8s204NSkjOGbiCOg4AhL7WMubSrtxKneiY9yMufLXUHkRLL0J3/Z/kVj6IYk/fMiO708ha8Kd0Gl0u8UjznAk/9xutld2pKzKR0xpLdGJw6DfG5D3MWx6GEqXw4Z/wI9zoM+lcMQt1oULCVoqBEREgoXLhafTQOg0ELgBl89L7ppv2Pr1lwzusoyo8m+g9DuivMUM7/4R8BF8tud7PYlsyDmU3MpDOWL0oSRnDYCUQyG5vzWsRMJPXAacMIeKXbfy8d/vYXy/f5EV9T68/z50Oh76XQ09fwaeOKcjlTAyfISL5StcVOZVk5YCFBdDxxMhYyzkfw4bZ0LJEvhpDvz0DGSdDodOgy7jNZ8lCOmMiIgEK5ebjMOHkXH4MOBq61hdDbtWfcXGFcvomfQV3RK/gtIfoK6Mfh2+ph9fw2as2x65pZ2pjDmU7of1h4TuEJ/Fxl1ZpHbNIr1rFq74zvoPOoQldOnLxL/MYdM3t9A77w7Y9RLkfAo5n1Lx2fVED7iMqAFXWYWhSAD06gX0SoLK7pCdzZuv1fLyoo488dfjSBh+LBQshY0PQ/Fi2PG2dYvtDL0mwSGTIf2opvdgZn8M29+E8q1WUTv4z5DUpy1/vIiif/lFREKJJ5ouw0bRZdgo/7GaSiq3r+arj9dTXbCesUPWQPmPULERqrPJSNoN7IafPq3/lj4A9dMR3BCXSUFVFjsKsujQpQNdeyRCVCJeTyIbtyQSHZ9I916JuGOs40QlWf8Za6WQoNF7SH/geSi8m+Ivn6Bo3Yv06LgV1j9k3TqdAP2ugB4/g6gEp8OVcBAXR0lKNy673U1+gZuBfav4w/VVkD4S0l+A0g2w5WnIewOqdsO6x61b6iDo80voeeGBG/Xb34LF54DP6z+2YwGMexc6DGv7ny8CqBAQEQl10XHE9T6KY3sf1fC4z8fODblsWrmGXimryUr+Eap2UV60m7U/5NE5ZSdZ6btwUQeVO0lnJ+lpK6ASWG+9hRvoC1AMZO/1uUf8CYb8pa1/OmmutEOIG/cgL6y/i4KP3+YPZ8/CVfQx5CyGnMX4lk3D1edi6HMZdDxac0ukVZLTo3j5ZR+PPVTN7y7OgRIXJCdbTyb1g4H3gfcvkPMh7HwZij6Aou9g5R+sW/pw6HUh9LyoYVGQ/xV8OskqApLHQPIxkP8GVK6FD06Eo2dD718of1tJhYCISLhyuejavxNd+3cCxtQfTgCGnQu1VXW4vJVQsgPKtvLF4t1s3bibIf1zObRnMdSVUV5WwQeLfcR6yphwfD7UVUBdGT5vBS5XmkM/mBxMTAz8aloc8DOoPAN2fk/dT/9i++r/0bPjJlj/hHVL6gu9fm7d0gY5HbaEqHEnuRh3UgyUWkOFfHn5PPFGNy69qJKkRB+4oyFzgnWrLoAdr0Hu21C6FApWWLeVN0PncdDrIiheC5ueg7pySD4Wjnga4hKhdip8cxmUfAlLLrZ6F4Y/DBnHOP0rCFlhXwgUFhYyfvx4amtrqa2t5frrr+fKK690OiyJYC6Xi06dOtXfF2dF8vmIivUAiRDfH+jPMX1g7/9OE4CzzwB8Pv/N6wWvD6LD/r+QAwqZ3ImLgz7DWV88iPH3PcgxvT/iP394mKjyj6H0R/j+HuuWOgi6nQVZEyFjtOaNBLmgzL+kJIiJ4Z8Pl3Htben8fV41X7+fS4OVTWPSofdU61a5G3a9taco+BJ2f2TdjLgBcOgsqwgAiEqBYS/AT4/B9ich7wt4bzRknQFD7oH0Ie3644aDsN9HoK6ujqqqKhISEigrK2PQoEEsX76cjh07Nun7g3kNawlP2kdAQl0orMEeqSorYc2qaob2KYK8HbB7Pl98spjhPT8g2lPtf2F0KnQ5xSoKup4GCVnOBe0A5XDrLF7k5eKLfVx/cR6/n5IHKSkHH8JTvhW2/RuKPoPYvpByLHQ+FRLSGn995U7YcD/kvgp4ARf0vtgqCBJ7BvgnCi3aR8DG4/GQkGBNiqqqqsLn82ntdhERiUhxcTB0ZAzQCVJT+aGgF6Nv+wMdkgtY+9w/yIhaDCWfQ00hbH3ZugGkD7OWgcw6HTqOVG+BHNAJY918txqSPUmQVwEFBWwuTKW8LpbD+9c2/k0JPeDQm5v+IXFdYdCjUDoNfvwrFLxrDSfa8l849FprU7O4jMD8QGHM8a0pFy9ezFlnnUVWVhYul4vXX399n9fMmjWL3r17ExcXx6hRo1i6dGmzPqOwsJAhQ4bQvXt3brzxRjIylBgiIhLhYmI47Jg0Fn9Uy61/iCPjyEnQ+0EYtJinvlvAZ7t+T23MkYALCr62hg+9fxy83BEWnQU/zISClQ1XdBHZIzUV3EkJ0L073k6ZXHZDB4admsF/34wN7Acl9YchT8GwNyHpaPBWwZqZ8MYh8O1dUFMa2M8LM46X9GVlZQwZMoSpU6dy/vnn7/P8iy++yPTp05k9ezajRo3ikUceYcKECaxdu5bOnTsDMHToUGpr960w33vvPbKyskhLS+Obb74hOzub888/nwsuuIDMzMxG46mqqqKqqqr+cXFxcYB+UhFLTU0N//znPwG48sorA74tvHK4edr6fEjzhFL+hkPuuFwwZmwUY8ZGgbcnlJdTuLWE6x85gvKKU/n4+d9ywqjdkLMQ8j+Eks+gthh2vGXdAKLTIWOk1VNgbnGdnf3BHNReORwy+efxUBrfibjUWtxuOKpnHlTHWDPaAyn1KBj+GuR8ABvvh4o18O3tsG4WHHEzHHKZNldshOOFwMSJE5k4ceJ+n585cyZXXnklU6ZMAWD27Nm8/fbbzJkzh5tvtrqQVq5c2aTPyszMZMiQIXzyySdccMEFjb7mvvvu484772zeDyHSDD6fj5ycnPr7gaYcbp62Ph/SPKGUv2GXO243JCUR1yeJxx+u4YP3qhlzdCVUxkDamcz7fApFpS4mn7KE1JpPoOhzKFsBNQWwc4F1M+KzIHUgpBzu/5p0iHXc7XHuZ2wH7ZXDoZR/KSnwzoIofvi2lr5d0iAvDyor+XBVR44ZXktCfIDid7mg8ynQ6WTY+TpsegCqtsJXv4NvbrX20Oh0nFWsph0Z9rnYFI4XAgdSXV3NihUruOWWW+qPud1uxo8fz5IlS5r0HtnZ2SQkJJCcnExRURGLFy/mmmuu2e/rb7nlFqZPn17/uLi4mB49erT8hxBpZ8phCWXKX+fFxcHUX0Uz9VfRUNsLysvxFhRx16PJ/LglhrSEEVz6i6Hg+g3UVUPRKij6GkpWQtm3UPUTVOywbrs+aPjmrmhI7GWtF5/Y09ptNq4zxGVCXCeISgZPLLjjrF1k7Td3bPPWjPf5wFsNdZXWsrflW6Fkg7VSUskGa7OrI/4EWRMC+etTDu+HywUDj4wCOkNCAuuXFXL6LzuSlVnH52/k0aVzAIeYudyQdT50OdOagLxjHlSuh03/sm4AMR2gy3jofi50PydiN9kL6kIgNzeXurq6fYbxZGZmsmbNmia9x+bNm7nqqqvqJwlfd911DB48eL+vj42NJTY2wOPXRNqRclhCmfI3yERFQUoKdXHJ3DC9hudfqOH8U8ugoBw8Ht76vAMffnkSUyYdw+Aj9wzRrS6C4h+gdB2Ur4eKDVC1Eap3gq/GaoCXbmhZPO7YPROVXdbNtecre5a2Zc/NV2cVAAeT+1XACwHlcBMkJbHbE0+nDB/9elaTGZ0PdcngCfAVencM9JwCPS6D/CWQ8z6UroLyb6E6H7a8ZN2ikqDrROg8xlo6N2UARCcHNpYgFdSFQCCMHDmyyUOHREREZF/RMS6uuT6Ga64H6qxeAkpK+PuzSby7OJHkmCoG96+0CoeYVGuDp703eaqrgYrtULYRKrZC1Q6oyYPafP/NW2FdxfdV+b9iGzbirbJuLfohOkFMT4jpAbE9ILoHdApsESBNd9wJHlavgZLdblxxqVBQQLUrlnlvZ3DZpAoCOuXB5YKOx1o3AG8N5C+H3A8g/02o3g5b/2vdDDO8LfUIa4fkxJ5Wr0F1AVTlQcUuqMqFqHhrud3EXtZeHKmHWz1YISKoC4GMjAw8Hg/Z2Q33tc/OzqZLly4ORSUiIhLBPB5ITobkZKZNryGlYzW/OK/SKg5qa1m+LoVf3dGFSy+s4PorymzfFw1Jva3bgdg3r/P5rEZbXSV49wzx8dVhXfX37ukF2KO+dwBwRVm9B/ZhRW53wyvOBQWQ0D0wvxNpESuN4sGXBUlJPHxPLTc/mM5Lb8Tx/ksFbffB7mjryn/GaPDdBvlfQt4nULIcKlZbRen+hrcdjMsDyQMgbTAk94XEPtbSqAlZ1lC42A7W5weJoC4EYmJiGD58OAsXLuTcc88FwOv1snDhQqZNm+ZscCIiIhHu9HOiOf0crIZcVRVUVPD8I26++i6GQ3tUcP0vyqxJBx4P63/y0K9P3cGH+btce80FiALi2+6HEOe5XJCaStbhtXTs4OWXZxZAYaFVKQR6uFBjn93xGOtmVOVDyXooXQNla60eg+odVi+VJwWiUiGqI0R1sHqt6kqgaos1D6GuCIpXW7f9iU6D+C4Q1wViO1rzFaKS9hSusXsK2Zg982VircLBHW0Vv95qqC2D6kJrv49DprRqR2XHC4HS0lI2bPCPFdy4cSMrV66kQ4cO9OzZk+nTpzN58mRGjBjByJEjeeSRRygrK6tfRUgk1LhcLlJTU+vvi7N0PqSllDs2LpfV4I+L45Z7vAw8qorDemENFSotJSfXxYATB9CtSx2rF+WQnBTcq9yEgnDMv19OieKs8yAlKg0KvVBUxMdfp/DukjRuua6U1JR2ypvYDhA7CjJGNe/7vF6rF6H4WyhdC5VbrCKiZjfU5EBdIeCzGvA1hVDctPmuB5Q6PLQLgeXLlzNu3Lj6x2am/eTJk5k7dy6TJk0iJyeH22+/nV27djF06FDmz5+/330ARIJddHQ0v/3tb50OQ/bQ+ZCWUu40rlOmmyt+HQvEgi8FKiv5dm0NcbE+OqbWklydB8VREBPDjGc6Uud18YtzK+jZrc7p0ENKuOZfWhpAAiTE4U1K4bcXxbJydSx11XU8eGe5w9EdhNsNid2tG3stje/zWfNkqgugKgeqdkN1DtTkW0WBt8Ia/uarsYa/+WrAV71nrkytdXO5rZW3XLEQlQLuZIjr06qQHS8Exo4de9D1b6dNm6ahQCIiIqHG5YL4eE46N568PB/bNrqhew8oLcVXXsHMJxPYmRPNiEOL6dm5DqKj2bHLze5cN4MPr23zUSESxNxuXKkp3HVvHXffVcNNV+RDfjkkJlJWF0d8nA+32+kgm8HlgqgYiMqEhABdzC4ogNSerXqLUPoVioiISIiKT3DR/4gYSE2Fbt2o7d6bW27yct4ZVRx3TB1UVkJBAc8/D8MmdOb/rkltMBk4J09NlkjjcsGZ53j4Ynk0nYZ1h86dobqa6/8Qx/AJGSxZHjyTbkOV4z0CIpGmpqaGuXPnAnDZZZcF77bwEULnQ1pKudM60fFRXPf7KK77PeDrCTU1UFVFmcdNcpKXo48otyaM+nyUVsfQ5ej+ZGbU8cPHOfVjxXfnukmI95GUGHlzDiIu/2JioHNninwpvLIwhsIiN66yUqj1WHNRpEX0mxNpZz6fjx07dtTfF2fpfEhLKXcCyOWyGnoxMdzxV7j1bh/VZanWGu01Naz9osZ6GT5Sa/OgwAVuN7fclcWcl1OZeXsBv/tVBQBl5S6efy2erp3rOPOUFu45EAIiNf9SM+NYvwHe+G8Vx5wQA0VF4HLx2qed6NQJjh9Z7XSIIUX9bCIiIhJUoqJdJKTFQFISpKczfGJnigt9fPCeD3r3hqwsSE9nZ651FbxLSrk1XrqggHUry7nqpjSuujHV2tugogKqq7noqlSSD+3C3P/E+fcokJCUkQFTr4m18qBXL8o8KVx9cxpjzsvgrQVh3jMSYOoREBERkaCXmOzm8CExQEz9sXc+gvzdtcRGZUBsGtTV4cr1ceYplaQm1VlDRurqoKaG4iIoLXPjrq60riJ7PBx8UwMJai4XJCZS1TGBc8+p5ePFNUw4KgeK3ZCYSGFpFKkpPp3mA1AhICIiIiGrQ+corOZMLABDT4Q3TzTPJlpru3u9PPeil8LCKjp17AAp6VaPQGysQ1FLIHXo6OLJOdFUVfqIru0JBQX4ioo57eJDcEe5eeqhIgYeWut0mEFJhYCIiIiEL7cb3G4ysiAjy+lgpC3FxrmAJEhMZENeOSvXxuHCR0d3AdTEQbhPqG4BFQIiIiIiEj5cLvoPTWTTRh9ffFxNZr9kKC6G0lJufrwbnTq7uPLiclKSNU9EhYCIAxISEpwOQWx0PqSllDviJOXfgXXp6uLcn8eBryukp7NtTSkPPZ1Kba2LE4aVcPQorZmjQkCkncXExHDjjTc6HYbsofMhLaXcEScp/5phzw7XGYfH88RjNSz5tI6jB1dCfiXExfH6x+lkdfEycliN05G2O5VCIiIiIhL24uLgimuiefrfcdCrF3TrRlW1i2tuTmHUmZ1494PIm0OgQkBEREREIkt0NKSnU9KxN6ee4qNPj1rGD8mxdrOuqmLld1Fs3R7+zeTw/wlFgozZFn7u3LnU1EReN2Sw0fmQllLuiJOUf4GRkelh3vMxrFnvIbp/b+jQAWpq+PVNSfQ+JpMXXw/vJWY1R0Cknfl8PjZv3lx/X5yl8yEtpdwRJyn/Aism1gUkQEICpTEdiE124fHAiUfkQpEX4uP5aWc8bjf07lHndLgBox4BEREREZE9kjrE8NEn0Wze6KPLUVmQkgJVVdx5fwyHjO7MzNnhs1qTegRERERERPbStZsbSIbkZHyVVRTWuPD5XBx7eAEUVkBcHNsLElizIYqxx1bj8TgdcfOpR0BERERE5ABccbH8750YNv3kZdSZnaBjR6ir45m5Lsb/PIOLf53qdIgtokJARERERKQJevVx40pOgsxM6N0bV1oqHdK8nHZcCeTnQ0kJ5cW1/OmBZFatjiLYp2+oEBARERERaa7oaG69K46d2W5+8et06N4d4uN5610Pdz+azDmXpUNtrdNRHpDmCIg4IDo68jYtCWY6H9JSyh1xkvIvOMTEADGxQCykppJ1ZDXnnlHNkH7luCrKoaQWX3QMYy7pyeGH1nH3TSVkdvI6HTYALp/WnDqg4uJiUlNTKSoqIiUlxelwJAIEOueUw9LeAplzyl9xgnJYAsLng6oqqKjgh6/KGTi+G7ExXvKXrCMhNRpiYnhnUQK7ctycfHw1vbo3c1nSggLo2ROSkxscbk7OaWiQiIiIiEiguVwQFwfp6fQ5Nov5b1Yz894qErrumVhcWsrfn4rm8hvSeeMtN1RXg89Hbr6bv85K4s332n4zMw0NEhERERFpQ3HxLiacGQNnAsSD1wtVVRw31kVJdTWjjqq2CoGyMr5eksjN93bh8L5VnDWmENxu8Hi47PcdWfdTNDPvKOKY4YHZTVqFgEg7q62t5aWXXgLgoosuIipKf4ZO0vmQllLuiJOUfyHO7Yb4eG65E265E/BlWROLa2pI3e3l/35WSVZGjdWrUFsLVVWsXNWZb9bGULC9HA4ptZ5rJWWNSDvzer2sX7++/r44S+dDWkq5I05S/oUZlwuioyE6mpHj4N/jAOLAlwR1dVBXx+zZPnbsqGTIiI7QtYM1ByE+vlUfq0JARERERCQYuVwQFQVRURwzNvBvr8nCIiIiIiIRSIWAiIiIiEgEUiEgIiIiIhKBVAiIiIiIiEQgTRY+CLPxcnFxscORSLiorq6msrISsPIqJiamwfMm1wK16bdy+MAOdj6k+QKZw8Gcv8qd8BUKOaz8k/1pTv66fIFqbYSpbdu20aNHD6fDkAi0detWunfv3ur3UQ6LUwKRw8pfcZJyWEJZU/JXhcBBeL1eduzYQXJyMq4AbNxwIMXFxfTo0YOtW7eSkpLSpp/VUooxMA4Uo8/no6SkhKysLNzu1o/ea88cDpRQOIf7E8qxQ2DiD2QO2/O3pKQkJH+3oZoTkRx3W+Ww+Tc4kn+37S0SY25O/mpo0EG43e6AXJVtjpSUlKBPVsUYGPuLMTU1NWCf4UQOB0oonMP9CeXYofXxByqH7flrGlGh+rtV3O0rGHN4b5H6u3VCpMXc1PzVZGERERERkQikQkBEREREJAKpEAgisbGx3HHHHcTGxjodyn4pxsAIhRidFMq/n1COHYI7/mCO7UAUd/sKhbhDIcbGhGLcivnANFlYRERERCQCqUdARERERCQCqRAQEREREYlAKgRERERERCKQCgERERERkQikQkBEREREJAKpEBARERERiUAqBEREREREIpAKARERERGRCKRCQEREREQkAqkQEBERERGJQCoEREREREQikAoBEREREZEIpEJARERERCQCqRAQEREREYlAKgRERERERCKQCgERERERkQikQkBEREREJAKpEBARERERiUAqBEREREREIpAKARERERGRCKRCQEREREQkAqkQEBERERGJQCoEREREREQikAoBEREREZEIpEJARERERCQCqRAQEREREYlAKgRERERERCKQCgERERERkQgU5XQAwc7r9bJjxw6Sk5NxuVxOhyMRwOfzUVJSQlZWFm5362t15bC0t0DmsPJXnKAcllDWrPz1SaP+9re/+Q4//HBf3759fYBuurX7bevWrcph3UL61pocVv7qFgw35bBuoXxrSv66fD6fD9mvoqIi0tLS2Lp1KykpKU6HI2Ggurqahx56CIAbbriBmJiYBs8XFxfTo0cPCgsLSU1NbfXnKYcP7GDnQ5ovkDkczPmr3AlfoZDDyj/Zn+bkr4YGHYTpxktJSQm6/4QkNFVXVxMXFwdYebW/f7wD1YWsHD6wpp4Pab5A5HAw569yJ/wFcw4r/+RgmpK/miwsIiIiIhKBVAiIiIiIiEQgFQIiIiIiIhFIcwREREREREJBZSWsXg3ffQfffgtXXw19+7b47VQIiIiIiIgEG58PfvoJvvgCliyBL7+Eb76Bmhr/a4YNUyEgEko8Hg/nnHNO/X1xls6HtJRyR5yk/AtTW7fCggWwcCEsXgw7duz7mg4dYPBg69aKIgBUCIi0O4/Hw9ChQ50OQ/bQ+ZCWUu6Ik5R/YaKyEj79FN5917r98EPD52Ni4Kij4JhjrNuoUdCrFwRoifGIKATOO+88Fi1axMknn8zLL7/sdDgiIiIiEqny8+GVV+DVV+Hjj6Giwv+c22019k89FcaOte7Hx7dZKBFRCFx//fVMnTqVefPmOR2KCF6vlw0bNgDQr18/3G4t3uUknQ9pKeWOOEn5F4LWrIG//AVefrnhOP+uXWHCBJg4EU45BdLT2y2kiMiasWPHkpyc7HQYIgDU1tbywgsv8MILL1BbW+t0OBFP50NaSrkjTlL+hZCvvoJLLoEjjoAXXrCKgCOPhHvvhVWrYPt2eOYZuOiidi0CIAQKgcWLF3PWWWeRlZWFy+Xi9ddf3+c1s2bNonfv3sTFxTFq1CiWLl3a/oGKiIiIiABkZ8Ps2TBmDAwfDv/+N3i9cM45sGKFtfrPLbdYE34DNN6/JYJ+aFBZWRlDhgxh6tSpnH/++fs8/+KLLzJ9+nRmz57NqFGjeOSRR5gwYQJr166lc+fOzf68qqoqqqqq6h8XFxe3Kn6R9qYcllCm/JVQpxyOUOvXw/z51jKfK1fC2rVWwx8gKsq62j99ulUUBJGg7xGYOHEid999N+edd16jz8+cOZMrr7ySKVOmMHDgQGbPnk1CQgJz5sxp0efdd999pKam1t969OjRmvBF2p1yWEKZ8ldCnXI4wnz6qXVV/9BD4Te/sYb+/PCDVQSMGAF//Sts3mz1CARZEQAhUAgcSHV1NStWrGD8+PH1x9xuN+PHj2fJkiUtes9bbrmFoqKi+tvWrVsDFa5Iu1AOSyhT/kqoUw5HiKoquOEGOOEEa5ff6Gg46SS4+254+21r/f9ly+CmmyAry+lo9yvohwYdSG5uLnV1dWRmZjY4npmZyZo1a+ofjx8/nm+++YaysjK6d+/Of//7X0aPHt3oe8bGxhIbG8usWbOYNWsWdXV1bfoziASaclhCmfJXQp1yOALU1cEvfgGvvWY9njIFHnqo3Sf6BkJI9wg01QcffEBOTg7l5eVs27Ztv0WA3bXXXsvq1atZtmxZO0QoEnjKYQllyl8JdcrhMOXzwa9/bRUBMTHW1zlzQrIIgBDvEcjIyMDj8ZCdnd3geHZ2Nl26dHEoKpED83g8TJw4sf6+OEvnQ1pKuSNOUv455M474R//sFb6+fe/4dxznY6oVUK6EIiJiWH48OEsXLiQc/ecCK/Xy8KFC5k2bVqr3ltdetJWPB4PI0eObPPPUQ43TXudD2meUMhf5Y4cSFvnsPLPAU88YRUCAH/7G1xwgbPxBIDL5/P5nA7iQEpLS+t3zhs2bBgzZ85k3LhxdOjQgZ49e/Liiy8yefJknnzySUaOHMkjjzzCSy+9xJo1a/aZO9ASxcXFpKamUlRUREpKSqvfT+RgAp1zymFpb4HMOeWvOEE5LPt48UVrXoDPB3fcAX/+s9MR7Vdzci7oewSWL1/OuHHj6h9Pnz4dgMmTJzN37lwmTZpETk4Ot99+O7t27WLo0KHMnz8/IEWASFvwer1s2bIFgJ49e2pbeIfpfEhLKXfEScq/dpKba63888wz1uOrr7YKgTAR9FkzduxYfD7fPre5c+fWv2batGls3ryZqqoqvvzyS0aNGtXqz501axYDBw7k6KOPbvV7idjV1tYyb9485s2b16bbwiuHm6a9zoc0Tyjkr3JHDqStc1j518a2bYM//tHaH8AUAdOmWUOCHNwJONCCvhBwimb7S6hTDksoU/5KqFMOh6gNG6zlQPv0gfvug4ICa8Owzz+Hxx+HMJuYHfRDg0RERERE2tTGjfCXv8C//mXtEwBw4olw/fVw9tlhVwAY6hHYj1DolhY5EOWwhDLlr4Q65XCI2LYNrrnGGgI0d65VBJx+OnzxBSxaBOedF7ZFAKgQ2C916UmoUw5LKFP+SqhTDge5nBz43e+gXz+YPRtqa+HUU60C4O23IQDzTUOBCgERERERCW75+fDuu1aDvTVKS60hQH37wiOPQFUVjBkDH38MCxZETAFgqBAQERERkeBVUwMnnWQN2Rk3DvYsm9osdXXw9NPQv7+1/GdJCRx1lNX4//hjOOGEwMcdAjRZeD9CYVdLCU0ej4fx48fX328ryuGmaa/zIc0TCvmr3JEDaY+dhSMm/x59FL75xrr/6acwZIg1dn/AAOjZEzp1gvR0SEyE+Hjweq3iIScHtm+Hjz6CN9+07gMccoi1ItAFF0CE778Q9DsLO007Akp7087CEuq0K6uEOuVwENm8GQYOhPJya0jPW2/B0qUte6/0dLjtNrj2WoiNDWycQSSsdhYWERERkQj1m99YRcAJJ1iN+Jtvtq7uf/cdrF0LO3daV/4LCqCsDCorrav8Hg9kZEDXrnDkkdYSoOPGQVyc0z9RUFEhINLOvF4vO3fuBKBr167aFt5hOh/SUsodcVJE5N/u3fDGG9b9J56wdvSNjobzz7du0mphmDUiwa22tpannnqKp556StvCBwGdD2kp5Y44KSLy77PPrK9HHGEND5KAUyGwH9oIREKdclhCmfJXQp1yOAA++cT6OmaMs3GEMRUC+6GNQCTUKYcllCl/JdQphwNAhUCbUyEgIiIiIsGltBS+/tq6r0KgzagQEBEREZHg8sUX1iZgPXtCjx5ORxO2VAiIiIiISHDRsKB2oUJARERERIKLCoF2oX0E9iMUtreX0OTxeDjxxBPr77cV5XDTtNf5kOYJhfxV7oS4tWvh0EOttenbQFvncFjnX02NNTQI4PjjnY0lzLl8Pp/P6SCCmbYGl/YW6JxTDkt7C2TOKX8loHw++PBDeOghePddWLAATj11n5cphx326adWT0CHDtauweG4WVobak7O6TcrIiIi4a26Gv71LzjqKBg/3ioCXC5YutTpyKQxTz9tfT3jDBUBbUxDg0Tamc/nIycnB4BOnTrhaqNuaWkanQ9pKeVOkNu9Gz74AN5/32r4Z2dbxxMSYMoU+O1voV8/R0NsjbDNv/x8+M9/rPvXXONsLBFAhYBIO6upqeGJJ54A4JZbbiEmJsbhiCKbzoe0lHInCK1eDS++CG++6V+D3ujSBa67Dq6+2hpyEuLCNv/mzoXKShgyBI45xulowp4KAREREQldP/1kNf7/8x9Ytarhc0OHWnMATjkFTjgBwqWxHK68Xpg927r/61+32URu8VMhICIiIqHnyy/hnnusq/9GdDRMmAAXXmh9zcx0Lj5pvrffhvXrITkZ/u//nI4mIqgQEBERkdDg88HixXD33db4f7CuGp98Mvz853DeeWEx7Cci/etfcOWV1v0pUyApydl4IoSmYu/HrFmzGDhwIEcffbTToYi0iHJYQpnyV/bx8cdw4okwdqxVBERFwWWXwQ8/WBOCL788qIoA5XAT1dXB738Pl14KVVVw1llWoSftQoXAflx77bWsXr2aZcuWOR2KSIsohyWUKX+l3rJl1jj/sWOt3WZjYqzVZNavh2eegQEDnI6wUcrhJiguhnPOsfZ1ALjtNnj9dWtokLQLDQ0SERGR4LN2LfzhD/C//1mPo6KsoSO33grdujkbm7RedrY1ifvbbyEuzlotaNIkp6OKOCoERNqZx+Nh9OjR9ffFWTof0lLKnTZSW2tdIb7jDmuoiNsNv/yl9bhPH6ejCxohnX/bt1vzOtautZZ1feMN0BAqR6gQEGlnHo+HUxvZ0l6cofMhLaXcaQPffmtNFF2xwno8YQI8/DAcfrizcQWhkM2/7dutuR4//gg9esCHH4b0xm6hTnMERERExFlVVfCXv8Dw4VYRkJZmDRV5910VAeEkP98q7n780erdWbxYRYDD1CMg0s58Ph9FRUUApKamhs+28CFK50NaSrkTIB9/bO32u2aN9fjss+GJJyAry9m4glzI5V9ZGZx5Jnz/vXVuP/wQevd2OqqIpx4BkXZWU1PDo48+yqOPPkpNTY3T4UQ8nQ9pKeVOK+XmWsOAxo61ioDOneH5561VY1QEHFRI5Z/PB5Mnw5IlkJ4O772nIiBIqBAQERGR9uP1wtNPw2GHWcN/AK66yioGfvELa4MwCS8PPQSvvGLt/Pzmm3DEEU5HJHtoaJCIiIi0j8WL4be/ha+/th4PGgRPPgnHHutoWNKGFi2Cm2+27j/6KBx3nKPhSEMR0SPw1ltvMWDAAPr3789TTz3ldDgiIiKRZeNGuPBCa7WYr7+GlBSYMQO++kpFQDhbtw4uuMDaPfiXv7TmgkhQCfsegdraWqZPn85HH31Eamoqw4cP57zzzqNjx45OhyYiIhLe6urgwQfhz3/27wlw1VVw553WnAAJX9nZcNppkJcHI0bA7Nka9hWEwr5HYOnSpRxxxBF069aNpKQkJk6cyHvvved0WCIiIuFt0yYYNw5uucUqAk4+GVautFYEUhEQ3oqK4PTTrZ6gvn3h7bchIcHpqKQRQV8ILF68mLPOOousrCxcLhevv/76Pq+ZNWsWvXv3Ji4ujlGjRrF06dL653bs2EE321bk3bp1Y/v27e0RuoiISOTx+eBf/4Ijj4RPPoGkJJgzB95/HwYPdjo6aWulpVYR8NVXkJEB8+er8AtiQT80qKysjCFDhjB16lTOP//8fZ5/8cUXmT59OrNnz2bUqFE88sgjTJgwgbVr19K5BYlXVVVFVVVV/ePi4uJWxS+yN7fbzYgRI+rvB5pyuHna+nxI84RS/ip3GlFQYI0Df+kl6/Fxx8Gzz8IhhzgbVztqrxwOyvwrL4ezzoLPP7c2hXvvPW0YFux8IQTwvfbaaw2OjRw50nfttdfWP66rq/NlZWX57rvvPp/P5/N99tlnvnPPPbf++euvv97373//e7+fcccdd/iAfW5FRUWB/WFE9qOoqKhVOaccFqe1JoeVvyFs8WKfr0cPnw98vqgon++ee3y+2lqno2oR5XALeL0+30UXWec/Odnn+/JLpyOKWM3J3yApIVumurqaFStWMH78+Ppjbreb8ePHs2TJEgBGjhzJd999x/bt2yktLeXdd99lwoQJ+33PW265haKiovrb1q1b2/znEAkk5bCEMuVvCKqthTvusDYG27rVugK8ZAn88Y/g8TgdXbuL2By++26rJ8jsFTBypNMRSRO0eGhQWVkZiYmJgYyl2XJzc6mrqyMzM7PB8czMTNbs2ao8KiqKhx56iHHjxuH1ernpppsOuGJQbGwssbGxzJo1i1mzZlFXV9emP4NEHp/PR3l5OQAJCQkB3xZeOdw8bX0+pHlCKX+VO1gTgi++2BoKAtbusY8/DsnJjoblpPbK4aDKv9deg9tvt+4/8YS1TKyEhBb3CGRmZjJ16lQ+/fTTQMbTJs4++2zWrVvHhg0buOqqq5r0Pddeey2rV69m2bJlbRydRJqamhpmzJjBjBkz2nRbeOVw07TX+ZDmCYX8jejc8fngmWesCcGff27tC/D889ZOwRFcBNi1dQ4HTf5t2gRTplj3r78eLr/cuVik2VpcCDz33HPk5+dz0kknceihh3L//fezY8eOQMZ2UBkZGXg8HrKzsxscz87OpkuXLu0ai4iISETYvRvOOw+mToWSEmtDsJUr4Re/cDoyaW81NdZ5LyqC0aOtPSMkpLS4EDj33HN5/fXX2b59O1dffTXPP/88vXr14swzz+TVV1+ltrY2kHE2KiYmhuHDh7Nw4cL6Y16vl4ULFzJ69OhWvfesWbMYOHAgRx99dGvDFHGEclhCmfI3SL3xhrUE6P/+Z40Fv+8+WLwY+vRxOrKgExE5fOed8MUX/h6h6GinI5JmavVk4U6dOjF9+nRWrVrFzJkz+eCDD7jgggvIysri9ttvrx+/1lKlpaWsXLmSlStXArBx40ZWrlzJli1bAJg+fTr//Oc/mTdvHj/88APXXHMNZWVlTDHdVC0UCt3SIgeiHJZQpvwNMrW1cMMNcM45Vo/AoEGwdCncfHNETghuirDP4Y8/hnvvte7/4x/Qu7ej4UjLtHofgezsbObNm8fcuXPZvHkzF1xwAZdffjnbtm3jr3/9K1988UWrdvJdvnw548aNq388ffp0ACZPnszcuXOZNGkSOTk53H777ezatYuhQ4cyf/78fSYQi4iISAvk5MCkSfDRR9bj6dPhnnsgLs7ZuMQ5+flwySXWXJEpU6z8kJDU4kLg1Vdf5ZlnnmHBggUMHDiQX//611xyySWkpaXVv+bYY4/l8MMPb1WAY8eOxefzHfA106ZNY9q0aa36nL2FwooVIgeiHJZQpvwNEsuXw/nnW8uCJibCvHnws585HVVICNsc9vngV7+Cbdugf3947DGnI5JWaPHQoClTppCVlcVnn33GypUrmTZtWoMiACArK4tbb721tTE6Iuy79CTsKYcllCl/g8DcuXD88VYR0L+/NRRIRUCThW0OP/88vPwyREVZ95OSnI5IWqHFPQI7d+4kISHhgK+Jj4/njjvuaOlHiIQlt9vNkCFD6u+Ls3Q+pKXCNndqa+Gmm+Dhh63HZ50F//oXpKY6G5c04Ej+ZWfDb35j3b/9dhgxon0+V9pMiwuB2tpaiouL9znucrmIjY0lJiamVYE5LWy79MRxUVFRnHvuuW3+Ocrhpmmv8yHNEwr5G5a5U1AAP/85mLl9t99u7RocToVOO2nrHHYk/6ZNs+YHDB1qTRSXkOfyHWwA/n643e4D7mLXvXt3LrvsMu64446QvlJSXFxMamoqRUVFpKSkOB2ORIBA55xyWNpbIHNO+duO1q6Fs8+GdesgPt6aD3DhhU5H5QjlcCNeftnKh6goWLbMKgYkKDUn51rcIzB37lxuvfVWLrvsMkaOHAnA0qVLmTdvHrfddhs5OTnMmDGD2NhY/vjHP7b0Y0TCjs/nq98FMjo62tlt4UXnQ1osrHLn3Xf9G0P16GHtEzBsmNNRyQG0a/5lZ8M111j3b75ZRUAYaXEhMG/ePB566CEuuuii+mNnnXUWgwcP5sknn2ThwoX07NmTe+65R4WAiE1NTQ333XcfALfcckvID6MLdTof0lJhkTter7Up2J/+ZK0Gc+yx8OqroCW4g1675Z/PB1dfDbm5cOSRVq5I2GjxmJ3PP/+cYY1cLRg2bBhLliwB4Pjjj6/f+CvURMSOgBLWlMMSypS/7aCkBC64AG67zb8k5IcfqggIkLDJ4X//G15/3do1+NlnIRQLXtmvFhcCPXr04Omnn97n+NNPP02PHj0AyMvLIz09veXROShsl/2SiKEcllCm/G1ja9fCqFHw2mtWw+6f/4TZsyE21unIwkZY5PC2bdYEYbAmju9ZpUjCR4uHBs2YMYMLL7yQd999t77aXb58OWvWrOHll18GYNmyZUzSbnMiIiLB4/vv4bjjrPkA3brBK69YRYGInc8Hl19u5cnIkVolKEy1uBA4++yzWbt2LU8++SRr164FYOLEibz++uv07t0bgGvMxBIRERFxXk6OtS9AUZHV+H/9dejSxemoJBg9+aS1jGxcnLWCVFSLm4wSxFp0VmtqajjttNOYPXt2/UQVERERCWJVVXD++bBxIxxyCLz1FmRkOB2VBKPCQmtTObAmkx92mKPhSNtp0RyB6OhoVq1aFehYgkrYTPKRiKUcllCm/A0wn89a/vHTTyElBd58U0VAGwvpHJ4zx5pMfsQR/p2EJSy1eLLwJZdc0uhk4XARFpN8JCi53W4GDhzIwIED23SzPeVw07TX+ZDmCYX8DanceegheOYZa4fgF1+EgQOdjijstXUOt1n+1dXB449b96+/XrtKh7kWD/iqra1lzpw5fPDBBwwfPpzExMQGz8+cObPVwYmEo6ioKC6M0N06g5HOh7RUyOTOW2/5h3k8/DCcdpqz8UhAtFn+vfkmbNoEHTvCJZcE/v0lqLS4EPjuu+846qijAFi3bl2D50J6d0UREZFwsXGj1Zgz+wRcd53TEUmwe/RR6+tVV0F8vLOxSJtrcSHw0UcfBTIOERERCaTqapg0yVoh6JhjrOEeulAnB/Ltt7BoEXg88OtfOx2NtINWrwW1YcMGfvzxR0444QTi4+Px+XzqERA5gOrq6vbZFl6aROdDWiroc+emm2DZMkhPt+YFREc7HZEEUJvkn5n7ed550L17699Pgl6LZ4Dk5eVx8sknc+ihh3L66aezc+dOAC6//HJuuOGGgAXolJCe7S+CclhCm/K3lV5/3T/EY9486NnT0XAiUcjlcFUVPPecdf/yy52NRdpNiwuB3/3ud0RHR7NlyxYSEhLqj0+aNIn58+cHJDgnhcKKFSIHohyWUKb8bYVNm2DKFOv+DTdYG4hJuwu5HH7zTcjLs3abPuUUp6ORdtLioUHvvfceCxYsoPteXUf9+/dn8+bNrQ5MREREmsnMCygstHYO1qaf0lRz5lhfJ0+25ghIRGhxj0BZWVmDngAjPz+f2NjYVgUlIiIizVRVBRdeCEuXQlqa5gVI023bBgsWWPdNb5JEhBYXAmPGjOHZZ5+tf+xyufB6vTzwwAOMGzcuIMGJiIhIE1RWwvnnwxtvQGwsvPQS9OrldFQSKv75T/B64YQToF8/p6ORdtTioUEPPPAAJ598MsuXL6e6upqbbrqJ77//nvz8fD777LNAxigiIiL7U1EB554L771nrfv+xhswfrzTUUmo2L0bzCaw117rbCzS7lpcCAwaNIh169bxt7/9jeTkZEpLSzn//PO59tpr6dq1ayBjFAkrbreb/v37198XZ+l8SEsFRe6Ul8PZZ8PChZCYaO0iPHasM7FIuwpY/v3lL1BaCsOHwwUXBCg6CRUun8/nczqIYFZcXExqaipFRUWkpKQ4HY5EgEDnnHJY2lsgc075ewC7dllzAj79FJKS4N134fjjnY4qLERMDm/YAIcfDrW18OGHoKHdYaE5OdeqDcUKCwtZunQpu3fvxuv1Nnju0ksvbc1bO27WrFnMmjWLuro6p0MRaRHlsASczwdbtsDXX8NXX8EZZ1gr07QB5e9BfPopXHQR7NwJKSkwfz6MHu10VGIT9Dns88GNN1pFwMSJKgIiVIt7BN58800uvvhiSktLSUlJabCbsMvlIj8/P2BBOimoK3kJS+oRkKDg9cKPP1oNfvvN/m/7nXfC7bfv860RczXVCT4fPPKI1YCrq4MjjoBXXoEBA5yOLKxERA4/9xz88pcQFWX9bQ8e7HREEiDt0iNwww03MHXqVO69995GlxEVkcZVV1czY8YMAH7/+98HZlt4aTGdjz1yc62rzJ98AsuXW1f9S0r2fV1UFAwaBEcdBSNGtH+cQaTdc6ekBKZOhZdfth7/3//BP/5hzQ2QiNOq/NuyBaZNs+7fcYeKgAjW4kJg+/bt/OY3v1ERINICNTU1TocgNhF5PrZssRr9n3wCixfDDz/s+5q4OBgyxGr0m9sRR1jLUwrQjrmzeTOceSZ89521N8Ajj8A114CtN14iT4vyz+ez9gooKoJjjoGbbw58YBIyWlwITJgwgeXLl3PIIYcEMh4REWkL27ZZk0kXL7Ya/43tAD9wIIwZY401Hz4cDjvM6gEQZ335pbUy0O7d0KULvPaa1YATaYnnn7cmBickwL/+pb/xCNfis3/GGWdw4403snr1agYPHkz0XrsXnn322a0OTkREWuGnn6zx46+8YjUm7Twe6wr/mDHWJkLHHQcZGc7EKfv30kswebK1YdiQIfDmm9Cjh9NRSagqKbHmlwDcdps2D5OWFwJXXnklAH/5y1/2ec7lcgXvLHkRkXD2ww/+xv/Klf7jLpd1pf/kk/1X/ZOSHAtTDsLng3vvtRprYA0LeuEFnTNpnXvusVaa6tsXfvc7p6ORINDiQmDv5UJFRMQh335rXTl+5ZWGY/09HmtzqZ/9zNp5Vps9hgafD377W3jsMevx734HDz5onU+Rltqwwb+D8MMPW3OAJOI1eyu6008/naKiovrH999/P4WFhfWP8/LyGDhwYECCExGR/SgqgtmzrZV7jjwS7r7bKgKio601wZ96ytpw6oMPrEmlKgJCQ10dXHWVvwh4/HGr8aYiQFrrllugpgYmTLB6mERoQY/AggULqKqqqn987733ctFFF5GWlgZAbW0ta9euDViAIuHG5XLRq1ev+vvirJA6Hz6fNdH3qaesJSQrKqzj0dFw+ulwwQXWf/B7/j2WthXw3KmpseYDvPACuN0wZ471WKQRzcq/JUusfzPcbpgxQ6tNSb1mFwJ77z/Wwv3I2tV5553HokWLOPnkk3nZrL8s4pDo6Gguu+wyp8OQPULifOzcCfPmWQ3D9ev9xwcOhCuugEsugU6dnIsvQgU0d6qqYNIk+N//rFVcnn8eLrwwMO8tYanJ+Wd2EAZr2dBBg9o0LgktEbFm1PXXX8/UqVOZN2+e06GIiDRNdTW88w7MnQtvvWUNGQFrsujPfw6XXw6jRunKXjgoLLTmcXz4obVHw8sva+iGBM7rr8Nnn0F8vLUbuIhNswsBl8u1TxdUsHenjx07lkWLFjkdhojIgfl8sGwZPPss/Oc/kJfnf+7YY63G/0UXaeWYcLJxI5xxhjW/IzHR6hE4+WSno5JwUVEBN9xg3Z8+Hbp1czYeCTotGhp02WWXEbtnZ8nKykquvvpqEvdscW6fP9AUixcv5sEHH2TFihXs3LmT1157jXPPPbfBa2bNmsWDDz7Irl27GDJkCI8//jgjR45sbugiQaG6uppHH30UsHqrmrUtvARcUJyPzZvhueesAmDdOv/xrl3h4oth6lQ4/PD2j0sOqNW5s2QJnHMO5ORYDbS33oKhQwMfqISlJuXfX/9qFZvdu2sHYWlUswuByXtNXLrkkkv2ec2ll17a5PcrKytjyJAhTJ06lfPPP3+f51988UWmT5/O7NmzGTVqFI888ggTJkxg7dq1dO7cGYChQ4dSW1u7z/e+9957ZGVlNTkWsAoZezFTXFzcrO8XaYry8vI2e2/lcPO15fnYr+Jia7nPZ58Fe49lfDycfz5ceql1ZTjCVosJtfxtce689JJ1jquqYNgwa6MwXa0NC+2ZwwfMv59+gvvvt+7PnKmeRGlUswuBZ555JqABTJw4kYkTJ+73+ZkzZ3LllVcyZcoUAGbPns3bb7/NnDlzuHlPdbvSvmlOK913333cqTF0EsKUw0FuxQpr2c/nnwfzn7jLZa33f+ml1ljx5GRHQ3RSROTvQw/B739v3T/rLCsX1EgLG0GTw7/7nVVonnyytaKYSCOavY9Ae6qurmbFihWMHz++/pjb7Wb8+PEsWbKkTT7zlltuoaioqP62devWNvkckbaiHA5CZWXw9NNw9NHWuv9PPWUVAQMGWLvHbtpkTRS97LKILgIgzPPX67UKAFME/OY38NprKgLCTFDk8PvvwxtvWCtQPf64FhWQ/QrqVYNyc3Opq6sjMzOzwfHMzEzWrFnT5PcZP34833zzDWVlZXTv3p3//ve/jB49utHXxsbGEhsby6xZs5g1axZ1ZqUOkRChHA4i338PTz5pDf8xGzHGxFhX566+Go4/Xv9B7yVs87emxprr8dxz1uMHH/QXBBJWHM/h2lprYjDAtddqfpEcUFAXAoHywQcfNPt7rr32Wq699lqKi4tJTU1tg6hE2pZy2CFVVfDqq/DEE9bmX0bfvvCrX1lX/bXm/0GFVf6WllrF34IF1pyPOXOsYWAS1hzL4aeegu++gw4d4Pbb2+9zJSQFdSGQkZGBx+MhOzu7wfHs7Gy6dOniUFQiIo3Iz7eu/j/2GOzaZR3zeODss62r/+PHW7t6SmQpKoIJE+DLLyEhwdoj4ADz4kRapbgY/vQn6/6dd1rFgMgBBHUhEBMTw/Dhw1m4cGH9kqJer5eFCxcybdq0Nv3ssOuWlqDhcrnqV7Nqyz04lMNN0+rz8dNP8Mgj1hwAM/k3K8u6+n/55VoJpoVCIX8PmjtlZdYeAV9+aTXI3nnH2gROIkJb53Cj+TdzJuTmWvOPfvWrNvlcCS8un8/nczKA0tJSNmzYAMCwYcOYOXMm48aNo0OHDvTs2ZMXX3yRyZMn8+STTzJy5EgeeeQRXnrpJdasWbPP3IG2YLr0ioqKSElJafPPEwl0zimH28gXX8CMGdZkT6/XOjZkiLV5z6RJ1lyACBXInAvZ/K2stHYHXrgQUlPho4+sZUIlJIRkDuflQZ8+UFJiLU974YVt91kS1JqTc473CCxfvpxx48bVP56+Z4LL5MmTmTt3LpMmTeL/27vzuKiq/3/gr5FNNkUFWRIQN0TLBUxFM00tTH+GmkrpV3FJLTElc61c0lI0LXPXSjA/7rlkllvmihuouCRuBIYJYu6g7Of3x4mRYR8cuDPM6/l43Aczdy533jPznjvnfe+55965cwdTp05FYmIimjVrht27d5dLEUBEpCErS47EMW8ecOzYs/ldusgTPzt25Mm/BKSny3MC9u+XIwLt3s0igMrenDmyCGjWTA5DTFQCihcCHTp0QHEHJUaNGlXmXYHyMoTD0kRFYQ7rkBCyb/ennwLXrsl55ubyqr9jxwIvvqhsfBWQweZvZqbMi19/BSpXllcLbt1a6ahIAeWawwkJwOLF8vYXX/B8JCoxxbsG6TuDPSxN+kMI4MED4O+/gb//RlZcHM5s345/6tZFtyVLYGZmprE4uwaVr4yMDCxZsgSAHOUj7+eB8HC5t//ECXm/WjVg5Ehg1CiAgxYUyCC7VZRCvtwxNQWGDZPni5iby6NHfn4KR0mlYQg5nDv/Prx5Eybz5wO+vnKbxSOTRs2gugYRGbzUVODmTdnQj49XN/g1bqekqBc3AfAygKyUlGKPhlHZE0Lg4X9j/Gt8HlevApMmyXMAADniy/jxsijgBaAIBeTOrFmyCKhUCdi4kUUAlamc/DNLS0OlH36QMydNYhFAWmEhUAiDPSxNuvfwIXD5cuGN/KSkkq3HwQFwdUV2rVqIuH0bcR4e8C7DsJnDpXTnDjBjBrB8uezmUamSHP3n888BZ2elozMahpa/ldavBz77TN5ZtAj4b6Q7Ml7llcPNzp2D6sEDoF49eYI6kRZYCBSiQl3MhrQTHy8PrR49Kqfz52X3nqJYWQGuroCbm5zy3nZ1BSwtAQCZ6enYPXt2mb8M5rB2TDMyUGnuXHnF10eP5Mxu3eQJeI0bKxucETKk/HWPi4PJrFnyzrhxsusYGb1yyeHsbLTK6bYYHMxzA0hrLATIuGVnA3/++azRf/So3Mufl4sLULt2wY18Nzc5RjgPxxosrz//hN+ePTDNKQC8veXIQLlGNCMqiP2dOwjYsAGqnJGC5sxROiQyIg2uXkWNe/cg7OygCgxUOhwyQCwEyLg8fQpERDxr9B87Jrv+5Fapkhzq75VX5NS2LbuEVFT378Pkgw/Qd/NmAIBwc4Nq1izg3Xe5Z42Kd/cu+q1dC8vUVGS3bo1KP/7IvKFy9XJEBAAge+hQmPDcJSoFFgKFMLT+qVSIf/+Vjf2chn9kJJCRobmMtbUcaSGn0d+qFWBrq0y8OsQcLsa+fcDgwTD55x9kq1Q42q4dWv/yC8z1bGQaY6X3+SsETIcNQ7UHD3CvWjXY/PQTzP/r/kcElEMOJySgzl9/AQCyhgyBSdk8C1VwHD60GPo8dB0V4P59OX73oUOyn390dP5lnJye7e1/5RV5NVjT8quJMzIy8N133wEAhg0bxuFDy9uTJ8CECcB/w+6J+vWxxd8fSR4eBX4epD1DGHrxuS1aBIwejSxTU/w0bhx6zZjB3KlADCGHs776CiYTJiDRwwM1rlxh/pEahw8l4/LwoRyve+NGYO/e/Hv8vbw0G/4eHor25zczM8NInkyojJMngYED5dCgADBqFFRz5qC3lZWycZFhiYqSJwUDMJk/HwGjRysbDxklk3XrAABO48cDLAKolFgIkGF6/FhesXPjRmD3biAt7dljL74IdOkCtGsHtGkD2NsrFyfph4wMYOZMOc57VhbwwgvAqlXAG28oHRkZmpQU4J13gPR0oHt34MMPlY6IjNGFC7IgNTMD+vZVOhoyYCwEyHCkpMhuP5s2yb+pqc8ea9gQCAiQG8RGjZSLkfTPpUvAgAHAmTPyfr9+wOLF8grBRNr68EPgyhVZTIaGcrQwUsaaNfJvt25AjRrKxkIGjYVAIfT+RDVj8fQpsGuX3PO/c6fs352jXj3Z+A8IkEcBDOQHubhzBHTF6HNYCOC774AxY2TRWK0asGyZzJdcyuvzIO3oZf6uXy8b/5UqAWvXIqNKFXy3dCkA5g7lV2Y5LITMRQC7HRzw19KlzD8qNRYChTCki9lUOGlpwJ49svG/YweQnPzsMQ8Pudc/IABo1sxgGv+5CSFw584d9e2yYtQ5nJwMjBgB/NeHFn5+siuQi0u+Rcvr8yDt6F3+JiY+u1DYlClA+/YQ6enMHSpUmeVwVBRw8yaElRUia9ZE1p07zD8qNRYCpB/S04Hff5eN/+3bn13ZFZAX7OrbV04tWhhk45/K0YULQJ8+svuGiQnw5ZfA+PEc352ez5gxwIMHgI8P8NlnSkdDxuy33wAAomNHZPEoAD0nFgKkHCHkGP+rVgHbtsmhP3O88IJszPXtK8f1ZyOOSiI0FAgKkl3KXFxkYfnKK0pHRYZu5055bpKJiexuVo7DDRPl8+uvAIDsN98E/jsiRVRa3JpR+UtNlQ20hQufncAJyPH9e/eW3X7atGHjn0ouJUUWAKtXy/t+fvJkOgcHZeMiw/f4MfDBB/L22LHyquNESvn3X+DECQBAtp8f8L//KRwQGToWAlR+bt0Cli+XU85ejMqV5SguAwbI4T5NeG1E0tKlS/Lo0aVLsnicMQOYPJmFJOnGp58CN28CdeoA06crHQ0Zu9275dH0Jk0AV1elo6EKgIVAIfRyxApDdfIk8O23wObNQGamnOfqKvfgvvcehz4rI0aRw2vWAO+/L0eTcnKSI2l06KB0VKQDepG/J07IoWYBuQODF54jLZRJDv/XLQjduulunWTUuMusEEFBQbh06RIiIiKUDsUwpacDa9fK/v2tW8sGWmam3Ou/eTPw11/AxIlGWQSoVCpUrVoVVatWhaoMT3yu0Dmcni4LgIEDZRHQqZMcSaMURUB5fR6kHcXzNz0dGDZM7n0dOBB4/fV8izB3qCg6z+HMTHlEAAC6dWP+kU7wiADp1u3bwIoVcrz2xEQ5z9xcdv/58EPA21vZ+PSAmZkZgoODlQ7DcN25A7z9NnDkiBxBato0OYpLKbuV8fOgAn37LXDxorwy+fz5BS7C3KFydfKkHLmqWjWgdWuYmZgw/+i5sRAg3Th9Wp78u2GD3JMGAM7Octzt4cOBmjWVjY8qhvPngbfeAm7cAKpUkUeaunZVOiqqaJKSgJkz5e2vvpLFAJHS9u6Vf19/nefTkc6wEKDSy8iQw35++60cBjRH69ZyzO1eveTRACJd2LZNnlSekiKvKr1jB+DlpXRUVBFNnSpHC/Lxkd2CiPRBTiHwxhvKxkEVCgsB0t6//wIrVwJLlwL//CPnmZnJYT8//BBo2VLZ+PRcRkYGwsLCAACDBg3iZeGLIwTwxReycQYAnTvL4WerV9fJ6vl5kIYLF+S1AgDgm2+KHH2KuUPl5v594NQpefu/81WYf6QLLASo5C5fBubNk+MWp6XJeTVryjG2R4yQXYGoWEII3Lp1S32bivDkCTBokDzBHABGj5b9tXV4QSd+HqQmBPDRR0B2thyStl27YhZn7lA5+eMPmZcNGwJubgCYf6QbLASoeCdOAHPmAD//LH8oAXnIfMwYeeVfCwtl46OKKT4e8PcHzp6VR5yWLpXDzRKVlZ07gf37ZZfGOXOUjobomX375F92CyId4/ChhViyZAkaNWqEl19+WelQlCEEsGuXHI7R1xfYvl3O8/cHjh4FIiJkf20WAXrLoHP42DGgRQtZBDg4yMYZiwCjUu75m54OjBsnb48dC3h4lM/zUoWlsxwWAtizR95mIUA6xkKgEIqPYa2UzExg3TqgeXM5GsuhQ3Jv7ODB8sqt27cDbdvKYRtJrxlsDm/aBLz2mhy5pWlTWXQW00WDKp5yz9+lS4GrV2V3x8mTy+c5qULTWQ7HxABxcfK3uH17ncRGlINdg0h68gRYtUr2v46Lk/OsrWXf/48+AmrVUjQ8MhJLlsgTzoUAevYEfvwRsLFROiqq6B48AGbMkLe//FIOTUukL3JGC2rblttD0jkWAvrixg05Dn9cnOwS4esLvPyybIyXpXv3ZONr4UI5GhAgu2KMHi2vAaCjkVmIiiSEvDBYztjtQUFyWFqOlU3l4Ztv5KgsjRvLo59E+uT33+Xfzp2VjYMqJBYCSvvzT3lS2rp1QFaWnLd1q/xrYiK7Rvj6Pps8PHTTLSc+Xv74rVwpx2UHgNq1ZR/ZwYMBK6vnfw4qlBXf32eysmTRuXKlvP/558CUKeXa/YyfhxG7d09uCwGZe1oWn8wdKlNZWcCBA/L2f8OG5sb8o+elEhxzqkiPHj1C1apV8fDhQ1TR5eHi48eBkBB5UaQcnTrJL3pkpHw8Z4z+3BwdnxUFbdrI0XssLUv+vNHRwNy5cgjQzEw5r0kTYNIkOVyeDodlpNLRdc6VWQ7rQmoq0L+/LH5VKtlP+/33lY6KnpMuc67M8/eTT4DZs+VOlzNnirxuABkPvcnhiAh5bZ6qVYG7d3mUlEpEm5xjq688CQHs3i0LgMOH5TyVSl6Bd+JE2RUot/h4WRAcOyb/nj0L3L4tT9jdvl0uY2oqT+zNKQx8fQFX1/x7UwsqPDp0kM/r58eTf6n8PXwI9OgBHDwoh2tctw54+22loyJjcueO7BYJyKMBLAJI3+R0C3rtNRYBVCZYCJSHzEx5QaSQEOD8eTnPzExeun78eMDTs+D/c3WVU9++8v7Tp3KPVe7iIDFR7jGIiHj2g+bi8uyogYsLsGwZcOSIfEylko2viROBVq3K9GUTFSoxEXjzTSAqCrC1ldeoeO01paMiY/PVV7JrpI8P8NZbSkdDlB/PD6AyxkKgLD19CoSFyR+b2Fg5z9padn346CPghRe0W5+lpRw1oG1beV8IeZLx8ePPioOoKODWLWDLFjnlMDOT4/6PHy+vTEiKycjIwNq1awEA/fv31//Lwt+/L/ecNmigm/XFxMixsP/6S3Z127VLHtVSiMF9HqQbt28DixfL2zNmlOqoKHOHytTTp0B4uLzdqVO+h5l/pAssBMrCgwdyL/yCBXIsdACoUUNeiTcoSHcj8ahU8gTf2rWBd9+V8548eXaOwfHjwLVrcs9raQoPKhNCCNy4cUN9W68dOyaPIN25I0f0+fTT5+tGduaMzMekJKBOHTksXt26Ogu3NAzq8yDdCQmRDa1WrWROlgJzh8pUeDiQliZ/uwvoOcD8I12o8IVAfHw8BgwYgKSkJJiammLKlCno06dP2TxZQoIc8nDZMuDRIznPzU2OxDNkSNkPBQrI0X5efVVORM9j7VqZt+np8v6UKcDly8D33wOVK2u3LiGAH36Q1whITQWaNZNHApycdB42Gbnff5cF59ixhQ9+cOuW3E4DpT4aQKRTd+/Ki3j+v/8nt7WAZrcg5iiVkQpfCJiammLBggVo1qwZEhMT4ePjg65du8Jal43y69eBefNkN6C0NDmvcWPZD/+dd2S3HCJDkZ0tx/T/4gt5v0cPoGNH2bBau1Z26dm2TXbrKYnHj2V3uHXr5P0uXYANG+QoGES6dOuWzNeUFMDCQh6FLcjnn8ttddu2BQ7JSFTuNm0CTp0CLl6UOw8tLeXOEqDAbkFEulLhh0hwdnZGs2bNAABOTk6wt7fHvXv3dLPys2dlQ9/TE1ixQv6w+PrKkXnOn5d98lkEkCF58gQICHhWBEycKM81+fBDYM8ewM5Odjlr2RK4cKH49Z0/Ly+Qt26dHPEiJAT49VcWAVQ2Pv302XVRpk2TXdryOnUK+O47eXvWLO5pJf2wb5/8++SJvG5AbKzcflaqJI8UEJURxQuBw4cPo3v37nBxcYFKpcL2nGExc1myZAlq166NypUro1WrVjh16lSpnuv06dPIysqCq6vrc0YN2QXI2xvYuFHuQe3aVQ4JGh4OdO/OYejI8Ny6BbRvD/z0kyxgQ0Nlwz0nlzt2BE6eBOrXB/7+Ww5X++uvBa9LCNnYatUKuHpV9nE9eFAWFvxuUFk4fVoelQUAd3c5PO3UqZrLZGbKo1NCyB017EJJ+iAzE/jjj2f3d+6UI6kBMkdr1FAmLjIKiv8ip6SkoGnTpliyZEmBj2/cuBFjx47FtGnTcObMGTRt2hR+fn5IyjkJF0CzZs3w4osv5ptu3bqlXubevXsYOHAgVuZcvbQQaWlpePTokcZUoG7d5Njn/frJkXp+/RVo1457l0hxJc7h3M6elXv5IyPlj87vvwODBuVfrkED4MQJOdRncrIccvGbb2TDKsfjx8D//R8wfLg8HyBnmNBXXtHVS6QKrFT5KwQQHCxv9+8P/PijvL1yJXDu3LPlli6VuW5nJ7tzEpUBrXM4MlIWrjl27pTdLwHA37/sAiWCHhQCb775Jr744gv07NmzwMe//vprDBs2DIMHD0ajRo2wfPlyWFlZYdWqVeploqKicPHixXyTi4sLAPml7NGjByZNmoQ2bdoUGc/s2bNRtWpV9VTo0YN69YCbN2Wf6aZNS/fiyWiZmZmV2VBvJc7hHNu3y0b6P/8AXl5yr39Re0qrV5fdhIYNk0fDxo4FRowAMjIK7gq0cydgb6/T16hrZfl5kHa0zl9AHsU6elT2qw4Jkfnbt6/MzzFjZKFw6xbw2Wc5TwLUrKmTeJk7lJfWOZzTLahbN5nD8fHPLjpaTCHA/KPnpRJ6NOaUSqXCtm3b0KNHDwBAeno6rKys8NNPP6nnAUBgYCAePHiAn3MOnRVBCIF+/frB09MT06dPL3b5tLQ0pOWc8At5mWZXV9eyu7w9UR7Pe2n7EuewEMDcucDkyfL2G2/IE9ZK2n9fCNlF7uOPZYPL2xu4dEkeBahVS54QnHPNCzIqz5PDWm+Dnz6VBeyNG8D06fLcAEDeb9hQ5uPmzbJY2LhRHvk6fpxd1KhI5ZrDr74qL/q5fDnw22/yPENA7mSMinqOV0HGSpv81est4b///ousrCw45hmdxNHREYmJiSVaR3h4ODZu3Ijt27ejWbNmaNasGS4UcZKjhYUFqlSpgjVr1qB169boxLP1ycCUOIenTAEmTZIN+qAg7U/iValkd4wdOwAbGzlkY2qqPF/m7FkWAVQqWm+Dv/lGNvpr1ZIXTMzh7g5MmCBvDx8ui4BKlWRji0UAlSGtcvjxY1mYAnIEq+7dnz2WawcoUVnR6yMCt27dwgsvvIBjx47B19dXvdyECRNw6NAhnDx5ssxjet69s0Ta0nXOFbq+6Gh5Xsvnn8tC4HlcuCCPLHTqJLtisKFl1HSZw0WuKyFBnryekiK7afbrp/l4Soo8KnDzprw/erQ8ikVUjHLL4Z07ZeO/Th151fWEBOC/bs04c0bRq66T4dImf/X6OgL29vYwMTHB7du3Nebfvn0bTrwQERmozMxMbNq0CQDQt29fmBZ20aOy5uUlf3h0MZTnSy/JHzQDpDefB2lv2jTZ2G/d+tnV1XOztpbd3/r1A5yd5dWxdYi5Q88t5/yAnOtZODvLo1YPH8oLLxaB+Ue6oNdZY25uDh8fH+zfv199lCA7Oxv79+/HqFGjyvS5lyxZgiVLliArK6tMn4eMT3Z2Nq5du6a+XVZKlMMcz7/cPg/STony97PP5OhVo0cXPmLbu+/KPG/QANDxUV3mDhWlRDmcM1BK7i5BI0aUaP3MP9IFxQuB5ORkXL9+XX0/NjYWUVFRqF69Otzc3DB27FgEBgaiRYsWaNmyJRYsWICUlBQMHjy4TOMKCgpCUFCQ+vAKkaFhDpMhK1H+urk9u2J1UXhBJlJAiXK4Qwc5ESlE8UIgMjISr732mvr+2LFjAciRgcLCwhAQEIA7d+5g6tSpSExMRLNmzbB79+58JxATEREREVHJKV4IdOjQAcWdrzxq1Kgy7wqUF7sGkaFjDpMhY/6SoWMOkyHg0B6FCAoKwqVLlxAREaF0KESlwhwmQ8b8JUPHHCZDwEKAiIiIiMgIKd41SF/lHNLLzMwEIMdkJdKF9PR0pKamApB5ZW5urvF4Tq497yU+mMMlU9znQdrTRQ4bQv4ydyouQ8hh5h8VRpv81asLiumjmzdvwtXVVekwyAjFx8ejVq1az70e5jApRRc5zPwlJTGHyZCVJH9ZCBQjOzsbt27dgq2tLVSFjVOtI48ePYKrqyvi4+P19irGjFE3iopRCIHHjx/DxcUFlXRwhd7yzGFdMYTPsDCGHDugm/h1mcO58/fx48cG+d4aak4Yc9xllcM522Bjfm/LmzHGrE3+smtQMSpVqqSTvbLaqFKlit4nK2PUjcJi1OW4/0rksK4YwmdYGEOOHXj++HWVw7nzN6cRZajvLeMuX/qYw3kZ63urBGOLuaT5y5OFiYiIiIiMEAsBIiIiIiIjxEJAj1hYWGDatGmwsLBQOpRCMUbdMIQYlWTI748hxw7od/z6HFtRGHf5MoS4DSHGghhi3Iy5aDxZmIiIiIjICPGIABERERGREWIhQERERERkhFgIEBEREREZIRYCRERERERGiIUAEREREZERYiGgB2bPno2XX34Ztra2qFmzJnr06IErV64oHVahQkJCoFKpEBwcrHQoGv755x/83//9H2rUqAFLS0u89NJLiIyMVDostaysLEyZMgUeHh6wtLRE3bp1MXPmTHDgLmn69OlQqVQaU8OGDZUOq1CHDx9G9+7d4eLiApVKhe3bt2s8LoTA1KlT4ezsDEtLS3Tu3BnXrl1TJtgCFBf/oEGD8n0eXbp0USZYAEuWLEHt2rVRuXJltGrVCqdOnVIslsIYYk6U5PcnNTUVQUFBqFGjBmxsbPD222/j9u3bCkUsLVu2DE2aNFFfedXX1xe7du1SP66PMeemz/lsqDmRW0HtFH2Mubh2S3lsM1gI6IFDhw4hKCgIJ06cwL59+5CRkYE33ngDKSkpSoeWT0REBFasWIEmTZooHYqG+/fvo23btjAzM8OuXbtw6dIlzJ8/H9WqVVM6NLU5c+Zg2bJlWLx4MaKjozFnzhzMnTsXixYtUjo0vdG4cWMkJCSop6NHjyodUqFSUlLQtGlTLFmypMDH586di4ULF2L58uU4efIkrK2t4efnh9TU1HKOtGDFxQ8AXbp00fg81q9fX44RPrNx40aMHTsW06ZNw5kzZ9C0aVP4+fkhKSlJkXgKY4g5UZLfn48++gi//PILNm/ejEOHDuHWrVvo1auXYjEDQK1atRASEoLTp08jMjISHTt2hL+/P/7880+9jTmHvuezoeZEjsLaKfoWc0naLeWyzRCkd5KSkgQAcejQIaVD0fD48WNRv359sW/fPtG+fXsxZswYpUNSmzhxonjllVeUDqNI3bp1E0OGDNGY16tXL9G/f3+FItIv06ZNE02bNlU6jFIBILZt26a+n52dLZycnMRXX32lnvfgwQNhYWEh1q9fr0CERcsbvxBCBAYGCn9/f0Xiyatly5YiKChIfT8rK0u4uLiI2bNnKxhV0Qw1J/L+/jx48ECYmZmJzZs3q5eJjo4WAMTx48eVCrNA1apVE99//73ex2xo+WxIOVFYO0UfYy6u3VJe2wweEdBDDx8+BABUr15d4Ug0BQUFoVu3bujcubPSoeSzY8cOtGjRAn369EHNmjXRvHlzfPfdd0qHpaFNmzbYv38/rl69CgA4d+4cjh49ijfffFPhyPTHtWvX4OLigjp16qB///74+++/lQ6pVGJjY5GYmKjxXalatSpatWqF48ePKxiZdg4ePIiaNWvC09MTH3zwAe7evVvuMaSnp+P06dMa72WlSpXQuXNng3ovDSUn8v7+nD59GhkZGRpxN2zYEG5ubnoTd1ZWFjZs2ICUlBT4+vrqdcyGmM+GlBOFtVP0Mebi2i3ltc0w1dmaSCeys7MRHByMtm3b4sUXX1Q6HLUNGzbgzJkziIiIUDqUAv31119YtmwZxo4di08++QQREREYPXo0zM3NERgYqHR4AIBJkybh0aNHaNiwIUxMTJCVlYUvv/wS/fv3Vzo0vdCqVSuEhYXB09MTCQkJ+Pzzz9GuXTtcvHgRtra2SoenlcTERACAo6OjxnxHR0f1Y/quS5cu6NWrFzw8PBATE4NPPvkEb775Jo4fPw4TE5Nyi+Pff/9FVlZWge/l5cuXyy2O52UIOVHQ709iYiLMzc1hZ2ensaw+xH3hwgX4+voiNTUVNjY22LZtGxo1aoSoqCi9jdnQ8tmQcqKodoo+xlxcu6W8thksBPRMUFAQLl68qFd9o+Pj4zFmzBjs27cPlStXVjqcAmVnZ6NFixaYNWsWAKB58+a4ePEili9frjeFwKZNm7B27VqsW7cOjRs3RlRUFIKDg+Hi4qI3MSop95GRJk2aoFWrVnB3d8emTZswdOhQBSMzTu+884769ksvvYQmTZqgbt26OHjwIDp16qRgZFRW9PH3pyienp6IiorCw4cP8dNPPyEwMBCHDh1SOqwKxVBywhDaKXnpS7uFXYP0yKhRo7Bz504cOHAAtWrVUjoctdOnTyMpKQne3t4wNTWFqakpDh06hIULF8LU1BRZWVlKhwhnZ2c0atRIY56Xl5dedS0ZP348Jk2ahHfeeQcvvfQSBgwYgI8++gizZ89WOjS9ZGdnhwYNGuD69etKh6I1JycnAMg3IsXt27fVjxmaOnXqwN7evtw/D3t7e5iYmBj8e6nvOVHY74+TkxPS09Px4MEDjeX1IW5zc3PUq1cPPj4+mD17Npo2bYpvv/1Wr2M2pHw2pJworp3i6OiodzEX124pr20GCwE9IITAqFGjsG3bNvzxxx/w8PBQOiQNnTp1woULFxAVFaWeWrRogf79+yMqKqpcuwkUpm3btvmGN7t69Src3d0Viii/J0+eoFIlza+ciYkJsrOzFYpIvyUnJyMmJgbOzs5Kh6I1Dw8PODk5Yf/+/ep5jx49wsmTJ+Hr66tgZKV38+ZN3L17t9w/D3Nzc/j4+Gi8l9nZ2di/f79BvZf6mhPF/f74+PjAzMxMI+4rV67g77//1rv3Pzs7G2lpaXodsyHksyHmRHHtlBYtWuhdzMW1W8ptm6Gz046p1D744ANRtWpVcfDgQZGQkKCenjx5onRohdK3UYNOnTolTE1NxZdffimuXbsm1q5dK6ysrMT//vc/pUNTCwwMFC+88ILYuXOniI2NFVu3bhX29vZiwoQJSoemFz7++GNx8OBBERsbK8LDw0Xnzp2Fvb29SEpKUjq0Aj1+/FicPXtWnD17VgAQX3/9tTh79qy4ceOGEEKIkJAQYWdnJ37++Wdx/vx54e/vLzw8PMTTp08VjlwqKv7Hjx+LcePGiePHj4vY2Fjx+++/C29vb1G/fn2Rmppa7rFu2LBBWFhYiLCwMHHp0iUxfPhwYWdnJxITE8s9lqIYYk6U5Pfn/fffF25ubuKPP/4QkZGRwtfXV/j6+ioWsxBCTJo0SRw6dEjExsaK8+fPi0mTJgmVSiX27t2rtzHn0Pd8NtScyCtvO0XfYi5Ju6U8thksBPQAgAKn0NBQpUMrlL4VAkII8csvv4gXX3xRWFhYiIYNG4qVK1cqHZKGR48eiTFjxgg3NzdRuXJlUadOHfHpp5+KtLQ0pUPTCwEBAcLZ2VmYm5uLF154QQQEBIjr168rHVahDhw4UOD3NjAwUAghh36bMmWKcHR0FBYWFqJTp07iypUrygadS1HxP3nyRLzxxhvCwcFBmJmZCXd3dzFs2DBFGyqLFi0Sbm5uwtzcXLRs2VKcOHFCsVgKY4g5UZLfn6dPn4qRI0eKatWqCSsrK9GzZ0+RkJCgXNBCiCFDhgh3d3dhbm4uHBwcRKdOndRFgBD6GXNu+pzPhpoTeeVtp+hjzMW1W8pjm6ESgpc1JSIiIiIyNjxHgIiIiIjICLEQICIiIiIyQiwEiIiIiIiMEAsBIiIiIiIjxEKAiIiIiMgIsRAgIiIiIjJCLASIiIiIiIwQCwEiIiIiIiPEQqACGjRoEFQqVb7p+vXrSodGVCLMYTJkzF8ydMxh42GqdABUNrp06YLQ0FCNeQ4ODhr309PTYW5uXp5hEZUYc5gMGfOXDB1z2DjwiEAFZWFhAScnJ42pU6dOGDVqFIKDg2Fvbw8/Pz8AwMWLF/Hmm2/CxsYGjo6OGDBgAP7991/1ulJSUjBw4EDY2NjA2dkZ8+fPR4cOHRAcHKxeRqVSYfv27Rox2NnZISwsTH0/Pj4effv2hZ2dHapXrw5/f3/ExcWpHx80aBB69OiBefPmwdnZGTVq1EBQUBAyMjLUy6SlpWHixIlwdXWFhYUF6tWrhx9++AFCCNSrVw/z5s3TiCEqKop7MQwUc1hiDhsm5q/E/DVczGGpoucwCwEjs3r1apibmyM8PBzLly/HgwcP0LFjRzRv3hyRkZHYvXs3bt++jb59+6r/Z/z48Th06BB+/vln7N27FwcPHsSZM2e0et6MjAz4+fnB1tYWR44cQXh4OGxsbNClSxekp6erlztw4ABiYmJw4MABrF69GmFhYRobgYEDB2L9+vVYuHAhoqOjsWLFCtjY2EClUmHIkCH59l6Ehobi1VdfRb169Ur3hpHeYQ6TIWP+kqFjDlcwgiqcwMBAYWJiIqytrdVT7969Rfv27UXz5s01lp05c6Z44403NObFx8cLAOLKlSvi8ePHwtzcXGzatEn9+N27d4WlpaUYM2aMeh4AsW3bNo31VK1aVYSGhgohhFizZo3w9PQU2dnZ6sfT0tKEpaWl2LNnjzpud3d3kZmZqV6mT58+IiAgQAghxJUrVwQAsW/fvgJf9z///CNMTEzEyZMnhRBCpKenC3t7exEWFlaCd430CXOYOWzImL/MX0PHHDaeHOY5AhXUa6+9hmXLlqnvW1tb491334WPj4/GcufOncOBAwdgY2OTbx0xMTF4+vQp0tPT0apVK/X86tWrw9PTU6t4zp07h+vXr8PW1lZjfmpqKmJiYtT3GzduDBMTE/V9Z2dnXLhwAYA8PGdiYoL27dsX+BwuLi7o1q0bVq1ahZYtW+KXX35BWloa+vTpo1WspB+Yw8xhQ8b8Zf4aOuawceQwC4EKytrausDDWNbW1hr3k5OT0b17d8yZMyffss7OziXuE6dSqSCE0JiXu09ecnIyfHx8sHbt2nz/m/vkIzMzs3zrzc7OBgBYWloWG8d7772HAQMG4JtvvkFoaCgCAgJgZWVVotdA+oU5zBw2ZMxf5q+hYw4bRw6zEDBy3t7e2LJlC2rXrg1T0/zpULduXZiZmeHkyZNwc3MDANy/fx9Xr17VqKgdHByQkJCgvn/t2jU8efJE43k2btyImjVrokqVKqWK9aWXXkJ2djYOHTqEzp07F7hM165dYW1tjWXLlmH37t04fPhwqZ6LDAdzmAwZ85cMHXPYsPFkYSMXFBSEe/fu4d1330VERARiYmKwZ88eDB48GFlZWbCxscHQoUMxfvx4/PHHH7h48SIGDRqESpU0U6djx45YvHgxzp49i8jISLz//vsaVXn//v1hb28Pf39/HDlyBLGxsTh48CBGjx6NmzdvlijW2rVrIzAwEEOGDMH27dvV69i0aZN6GRMTEwwaNAiTJ09G/fr14evrq5s3ivQWc5gMGfOXDB1z2LCxEDByLi4uCA8PR1ZWFt544w289NJLCA4Ohp2dnfpL+tVXX6Fdu3bo3r07OnfujFdeeSVfH8H58+fD1dUV7dq1Q79+/TBu3DiNQ2lWVlY4fPgw3Nzc0KtXL3h5eWHo0KFITU3VqrJftmwZevfujZEjR6Jhw4YYNmwYUlJSNJYZOnQo0tPTMXjw4Od4Z8hQMIfJkDF/ydAxhw2bSuTtkEVUAh06dECzZs2wYMECpUPJ58iRI+jUqRPi4+Ph6OiodDikp5jDZMiYv2TomMP6gecIUIWRlpaGO3fuYPr06ejTp0+F//JSxcMcJkPG/CVDZ4w5zK5BVGGsX78e7u7uePDgAebOnat0OERaYw6TIWP+kqEzxhxm1yAiIiIiIiPEIwJEREREREaIhQARERERkRFiIUBEREREZIRYCBARERERGSEWAkRERERERoiFABERERGREWIhQERERERkhFgIEBEREREZIRYCRERERERGiIUAEREREZERYiFARERERGSEWAgQERERERkhFgJEREREREaIhQARERERkRFiIUBEREREZIRYCBARERERGSEWAkRERERERoiFABERERGREWIhQERERERkhFgIEBEREREZIRYCRERERERGiIUAEREREZERYiFARERERGSEWAgQERERERkhFgJEREREREaIhQARERERkRFiIUBEREREZIRYCBARERERGSEWAkRERERERoiFABERERGREWIhQERERERkhFgIEBEREREZIRYCRERERERGiIUAEREREZERYiFARERERGSEWAgQERERERkhFgJEREREREaIhQARERERkRFiIUBEREREZIRYCBARERERGSEWAkRERERERoiFABERERGREWIhQERERERkhFgIEBEREREZIRYCRERERERGiIUAEREREZERYiFARERERGSEWAgQERERERkhFgJEREREREbIVOkAipORkYGsrCylwyAiIiIiI2BiYgIzMzOlwygXel0IZGRkICYmBkIIpUMhIiIiIiOgUqlQt25doygG9LoQyMrKghACLi4usLCwUDocIiIiIqrA0tLScOvWLWRlZbEQ0BcWFhaoXLmy0mEQEREREVUYPFmYiIiIiMgIsRAgIiIiIjJCLASIiIiIiIwQCwHKZ/r06WjWrJnSYQAAOnTogODgYKXDoP8cPHgQKpUKDx48KPH/1K5dGwsWLCizmLSRN7cHDRqEHj16PNc6dbEOKl9lnZNxcXFQqVSIiop6rvVw+2cc8m5Xw8LCYGdnV6bPqQ/brZLkd1l8V/XhtesTFgJlKDExEWPGjEG9evVQuXJlODo6om3btli2bBmePHmidHilMn36dKhUqiKn0ihNA5M0DRo0CCqVCu+//36+x4KCgqBSqTBo0KDyD6wYuXPK1NQUtWvXxkcffYTk5OQyf+5vv/0WYWFhJVq2sMadNusg7RS3rZk+fXqp1hsREYHhw4c/V2xFNWJcXV2RkJCAF1988bmeY+vWrZg5c6b6vj4V1fooZxsYEhKiMX/79u2l/m1SQkBAAK5evapoDDm/yTmTg4MDunbtigsXLujsOfLmNymDhUAZ+euvv9C8eXPs3bsXs2bNwtmzZ3H8+HFMmDABO3fuxO+//17o/2ZkZJRjpNoZN24cEhIS1FOtWrUwY8YMjXm5paenKxSpcXJ1dcWGDRvw9OlT9bzU1FSsW7cObm5uCkZWtMaNGyMhIQFxcXGYM2cOVq5ciY8//rjAZXWZU1WrVn3uPW+6WAcVLPd2ZcGCBahSpYrGvHHjxqmXFUIgMzOzROt1cHCAlZVVWYUNExMTODk5wdT0+Qbmq169OmxtbXUUlXGoXLky5syZg/v37+t0veX5W2ZpaYmaNWuW2/MV5cqVK0hISMCePXuQlpaGbt266ey9YH7rBxYCZWTkyJEwNTVFZGQk+vbtCy8vL9SpUwf+/v749ddf0b17d/WyKpUKy5Ytw1tvvQVra2t8+eWXAIBly5ahbt26MDc3h6enJ9asWaP+n4L2Tj548AAqlQoHDx4E8Kyi379/P1q0aAErKyu0adMGV65c0Yg1JCQEjo6OsLW1xdChQ5Gamlro67KxsYGTk5N6MjExga2trfr+O++8g1GjRiE4OBj29vbw8/MrNta4uDi89tprAIBq1arl23OdnZ2NCRMmoHr16nBycir1XkBj4O3tDVdXV2zdulU9b+vWrXBzc0Pz5s01lk1LS8Po0aNRs2ZNVK5cGa+88goiIiI0lvntt9/QoEEDWFpa4rXXXkNcXFy+5zx69CjatWsHS0tLuLq6YvTo0UhJSdEqblNTUzg5OaFWrVoICAhA//79sWPHDgDPuvN8//338PDwUA8l/ODBA7z33ntwcHBAlSpV0LFjR5w7d05jvcXldt5DxNnZ2Zg7dy7q1asHCwsLuLm5qb+PHh4eAIDmzZtDpVKhQ4cOBa6juPe1pN9Lgsa2pmrVqlCpVOr7ly9fhq2tLXbt2gUfHx9YWFjg6NGjiImJgb+/PxwdHWFjY4OXX345346XvHvWVSoVvv/+e/Ts2RNWVlaoX7++Ov9KI+82L+cz37NnD5o3bw5LS0t07NgRSUlJ2LVrF7y8vFClShX069dP42hx7qMOHTp0wI0bN/DRRx8919HXiq5z585wcnLC7Nmzi1xuy5YtaNy4MSwsLFC7dm3Mnz9f4/HatWtj5syZGDhwIKpUqYLhw4eru+zs3LkTnp6esLKyQu/evfHkyROsXr0atWvXRrVq1TB69GhkZWWp17VmzRq0aNFC/VvZr18/JCUlFRpb3q5BtWvXLvLoe3x8PPr27Qs7OztUr14d/v7+GtvqrKwsjB07FnZ2dqhRowYmTJhQ4gu11qxZE05OTvD29kZwcDDi4+Nx+fJl9ePFbf+XLl2K+vXrq3tF9O7dW/1Y3qNqSUlJ6N69OywtLeHh4YG1a9dqxFKSdk9WVhaGDh0KDw8PWFpawtPTE99++22JXquxMthCICVFTrlzOT1dzktLK3jZ7Oxn8zIy5Ly8bd7CltXG3bt3sXfvXgQFBcHa2rrAZfJuxKdPn46ePXviwoULGDJkCLZt24YxY8bg448/xsWLFzFixAgMHjwYBw4c0C4YAJ9++inmz5+PyMhImJqaYsiQIerHNm3ahOnTp2PWrFmIjIyEs7Mzli5dqvVz5LZ69WqYm5sjPDwcy5cvL3Z5V1dXbNmyBcCzvQ+5v7irV6+GtbU1Tp48iblz52LGjBnYt2/fc8WoFSGAzBRlplJcVXvIkCEIDQ1V31+1ahUGDx6cb7kJEyZgy5YtWL16Nc6cOYN69erBz88P9+7dAyB/XHr16oXu3bsjKioK7733HiZNmqSxjpiYGHTp0gVvv/02zp8/j40bN+Lo0aMYNWqU1nHnZmlpqbHX6fr169iyZQu2bt2q/hHo06ePuiF1+vRpeHt7o1OnTur4S5PbkydPRkhICKZMmYJLly5h3bp1cHR0BACcOnUKAPD7778jISFBo9jKrbj3NUdR38vylJ6eXuiUdw97UcvmPZJZ2HK6NmnSJISEhCA6OhpNmjRBcnIyunbtiv379+Ps2bPo0qULunfvjr///rvI9Xz++efo27cvzp8/j65du6J///75PrPnNX36dCxevBjHjh1TN94WLFiAdevW4ddff8XevXuxaNGiAv9369at+Y7Alhshnv04lvek5TbQxMQEs2bNwqJFi3Dz5s0Clzl9+jT69u2Ld955BxcuXMD06dMxZcqUfF385s2bh6ZNm+Ls2bOYMmUKAODJkydYuHAhNmzYgN27d+PgwYPo2bMnfvvtN/z2229Ys2YNVqxYgZ9++km9noyMDMycORPnzp3D9u3bERcXp1U3zYiICPVnfvPmTbRu3Rrt2rVTr9vPzw+2trY4cuQIwsPDYWNjgy5duqi/b/Pnz0dYWBhWrVqFo0eP4t69e9i2bZsW7yrw8OFDbNiwAQBgbm4OoPjtf2RkJEaPHo0ZM2bgypUr2L17N1599dVCn2PQoEGIj4/HgQMH8NNPP2Hp0qVFFkwFyc7ORq1atbB582ZcunQJU6dOxSeffIJNmzZptR6jIvTY06dPxaVLl8TTp0/zPSa3DkIkJT2b98UXct5772kua2Ul58fGPpv3zTdyXr9+msva28v5Fy8+m7dypXZxnzhxQgAQW7du1Zhfo0YNYW1tLaytrcWECRNyvRaI4OBgjWXbtGkjhg0bpjGvT58+omvXrkIIIWJjYwUAcfbsWfXj9+/fFwDEgQMHhBBCHDhwQAAQv//+u3qZX3/9VQBQv6e+vr5i5MiRGs/TqlUr0bRp0xK9Vnd3d/HNN9+o77dv3140b95cYxltYr1//77G/7Zv31688sorGvNefvllMXHixBLFpxMZyUKshTJTRnKJwwwMDBT+/v4iKSlJWFhYiLi4OBEXFycqV64s7ty5I/z9/UVgYKAQQojk5GRhZmYm1q5dq/7/9PR04eLiIubOnSuEEGLy5MmiUaNGGs8xceJEjc9p6NChYvjw4RrLHDlyRFSqVEmdY3lzJK9p06Zp5FtkZKSwt7cXvXv3Vj9uZmYmknJ92Y8cOSKqVKkiUlNTNdZVt25dsWLFCiFEyXI75z0TQohHjx4JCwsL8d133xUYZ0F5nHcdJXlfS/K9LE/Tp08vdMr9OoQQ4ssvvyx02dDQUI1l586dW+BypRUaGiqqVq2qvp/zPm7fvr3Y/23cuLFYtGiR+n7enAQgPvvsM/X95ORkAUDs2rWr0HW2b99ejBkzpsDH8uZKQZ/57NmzBQARExOjnjdixAjh5+dX6HMU910qM8nJz350y3tK1n4bKIQQrVu3FkOGDBFCCLFt2zaRu7nTr18/8frrr2v87/jx4zW2d+7u7qJHjx4ay4SGhgoA4vr16+p5I0aMEFZWVuLx48fqeX5+fmLEiBGFxhkRESEAqP8n7+9f3lzPbfTo0cLd3V29PVyzZo3w9PQU2dnZ6mXS0tKEpaWl2LNnjxBCCGdnZ/X2RwghMjIyRK1atdTvVUFyYsppswAQAMRbb72lXqa47f+WLVtElSpVxKNHjwp8jtz5feXKFQFAnDp1Sv14dHS0AKDO+ZK0JQoSFBQk3n77bfX93HlSkKLanhWRwR4RMESnTp1CVFQUGjdujLQ8hy1atGihcT86Ohpt27bVmNe2bVtER0dr/bxNmjRR33Z2dgYAdZUdHR2NVq1aaSzv6+ur9XPk5uPj81z/n1fu+AH5GrTdS2BMHBwc0K1bN4SFhSE0NBTdunWDvb29xjIxMTHIyMjQyDEzMzO0bNlSnWMlyY1z584hLCwMNjY26snPzw/Z2dmIjY0tccwXLlyAjY0NLC0t0bJlS/j6+mLx4sXqx93d3eHg4KDxvMnJyahRo4bGc8fGxiImJqbE8ecWHR2NtLQ0dOrUqcRx51WS9zVHUd9LKrm8287k5GSMGzcOXl5esLOzg42NDaKjo4s9IpD787C2tkaVKlV0/nnkfg5HR0dYWVmhTp06GvOYA7oxZ84crF69usDfzMJ+X69du6bRpSdvbgGAlZUV6tatq77v6OiI2rVrw8bGRmNe7s/x9OnT6N69O9zc3GBra4v27dsDQLE5mdfKlSvxww8/YMeOHert4blz53D9+nXY2tqqt4PVq1dHamoqYmJi8PDhQyQkJGhsC01NTQt8bQU5cuQITp8+jbCwMDRo0EDjKH9x2//XX38d7u7uqFOnDgYMGIC1a9cWOlBKdHQ0TE1NNdoPDRs2LNX5V0uWLIGPjw8cHBxgY2ODlStXav1eG5PnO5NJQTkDiuQ+32v8eCA4GMh7flbO99HS8tm8oCBg2DDAxERz2ZxudbmX1XaglXr16kGlUuXr85uzwbfMvfL/FNaFqDCVKskaTuQ6bFrYScZmZmbq2zldkrJz933SsbyvRZtYC5I7fkC+hrKMPx8TK6Bv2Y9gU+hzl8KQIUPUh2eXLFmiy4g0JCcnY8SIERg9enS+x7Q5OdnT0xM7duyAqakpXFxc1Ieec+TNqeTkZDg7O6v7heZW2hN3C/pelqXy/l4WZvLkyYU+lvPdzZH75Ny88nZ3HDNmzPMFVkJ5c2PcuHHYt28f5s2bh3r16sHS0hK9e/cutltSeWxn8n7mim/bSsrK6tmPrhLPXQqvvvoq/Pz8MHny5FKPllbQ73JBn1lRn2NKSgr8/Pzg5+eHtWvXwsHBAX///Tf8/Py06ip34MABfPjhh1i/fr1GQZmcnAwfH598/ekBaOw8KS0PDw/Y2dnB09MTSUlJCAgIwOHDh9XPXdT239zcHGfOnMHBgwexd+9eTJ06FdOnT0dERESpttMlaUts2LAB48aNw/z58+Hr6wtbW1t89dVXOHnypNbPZywMthAoqN1sbi6nkixrZianki6rjRo1auD111/H4sWL8eGHH2rdyAcALy8vhIeHIzAwUD0vPDwcjRo1AvDsC56QkKA+CbQ0Y1Z7eXnh5MmTGDhwoHreiRMntF5PUUoSa07DL/feGL2hUgGm2n+GSsrpH6pSqeDn55fv8ZyT0MPDw+Hu7g5AblAjIiLUJ295eXnlO2Eyb254e3vj0qVLqFev3nPFa25urtU6vL29kZiYqB5utCDa5nb9+vVhaWmJ/fv347333iswRqDoHC3J+6pv8hZdSiyrS+Hh4Rg0aBB69uwJQDZWCjrJ3RCZm5srs41UqQr+cdRzISEhaNasGTw9PTXm5/y+5hYeHo4GDRrAJO/ewed0+fJl3L17FyEhIXB1dQUg+85r4/r16+jduzc++eQT9OrVS+Mxb29vbNy4ETVr1kSVKlUK/H9nZ2ecPHlS3T8/MzNTfV6VNoKCgjB79mxs27YNPXv2LNH239TUFJ07d0bnzp0xbdo02NnZ4Y8//sj3Oho2bKiO6+WXXwYgzxnMPaR4SdoS4eHhaNOmDUaOHKmel3OUmArGrkFlZOnSpcjMzESLFi2wceNGREdH48qVK/jf//6Hy5cvF7uxGT9+PMLCwrBs2TJcu3YNX3/9NbZu3areI2dpaYnWrVurT5I7dOgQPvvsM63jHDNmDFatWoXQ0FBcvXoV06ZNw59//lmq11yYksTq7u4OlUqFnTt34s6dO+UyhnxFZmJigujoaFy6dKnAXLO2tsYHH3yA8ePHY/fu3bh06RKGDRuGJ0+eYOjQoQCA999/H9euXcP48eNx5coVrFu3Lt/JdBMnTsSxY8cwatQoREVF4dq1a/j555+f+2Th4nTu3Bm+vr7o0aMH9u7di7i4OBw7dgyffvqp+kdW29yuXLkyJk6ciAkTJuDHH39ETEwMTpw4gR9++AGAHD3D0tISu3fvxu3bt/Hw4cN86yjJ+0plq379+uqTys+dO4d+/fqV2V72O3fuICoqSmO6fft2mTwXIEePOXz4MP755x/8+++/ZfY8FcVLL72E/v37Y+HChRrzP/74Y+zfvx8zZ87E1atXsXr1aixevLjII16llbNnfNGiRfjrr7+wY8cOrcbOf/r0Kbp3747mzZtj+PDhSExMVE8A0L9/f9jb28Pf3x9HjhxBbGwsDh48iNGjR6tPlh4zZgxCQkKwfft2XL58GSNHjizVNXusrKwwbNgwTJs2DUKIYrf/O3fuxMKFCxEVFYUbN27gxx9/RHZ2dr7CDJBHhbt06YIRI0bg5MmTOH36NN577z2NI7UlaUvUr18fkZGR2LNnD65evYopU6bkGw2PNLEQKCN169bF2bNn0blzZ0yePBlNmzZFixYtsGjRIowbN67YDUGPHj3w7bffYt68eWjcuDFWrFiB0NBQ9ZCFgBwNJjMzEz4+PggODsYXX3yhdZwBAQGYMmUKJkyYAB8fH9y4cQMffPCB1uspTnGxvvDCC/j8888xadIkODo6lnlD0hhUqVKl0D1EgNxb9vbbb2PAgAHw9vbG9evXsWfPHlSrVg2A/AHbsmULtm/fjqZNm2L58uWYNWuWxjqaNGmCQ4cO4erVq2jXrh2aN2+OqVOnwsXFpUxfm0qlwm+//YZXX30VgwcPRoMGDfDOO+/gxo0b6lF+SpPbU6ZMwccff4ypU6fCy8sLAQEB6r6+pqamWLhwIVasWAEXFxf4+/sXuI7i3lcqW19//TWqVauGNm3aoHv37vDz89N6z2dJrVu3Ds2bN9eYvvvuuzJ5LgCYMWMG4uLiULduXZ10+zAGM2bMyFcIent7Y9OmTdiwYQNefPFFTJ06FTNmzCiTCy46ODggLCwMmzdvRqNGjRASEoJ58+aV+P9v376Ny5cvY//+/XBxcYGzs7N6AmTj/PDhw3Bzc0OvXr3g5eWlHio5Z/v/8ccfY8CAAQgMDFR3l8k5YqatUaNGITo6Gps3by52+29nZ4etW7eiY8eO8PLywvLly7F+/Xo0bty4wHWHhobCxcUF7du3R69evTB8+PB811Mori0xYsQI9OrVCwEBAWjVqhXu3r2rcXSA8lOJ3J2t9ExqaipiY2M1xg4nIiIiIioLxtb25BEBIiIiIiIjxEKAiIiIiMgIsRAgIiIiIjJCLASIiIiIiIwQCwEiIiIiIiPEQoCIiIiIyAgZxJWF09LSlA6BiIiIiCo4Y2tz6nUhYGJiApVKhVu3bikdChEREREZAZVKBRMTE6XDKBd6fUExAMjIyEBWVpbSYRARERGRETAxMYGZmZnSYZQLvS8EiIiIiIhI93iyMBERERGREWIhQERERERkhFgIEBEREREZIRYCRERERERGiIUAEREREZERYiFARERERGSEWAgQERERERmh/w9s2cnbflMHoAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x800 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# fig, axs = plt.subplots(3, 4, figsize=(8, 8))\n",
+    "\n",
+    "fig = plt.figure(layout='constrained', figsize=(8, 8))\n",
+    "subfigs = fig.subfigures(2, 1, wspace=0.07)  # three rows\n",
+    "\n",
+    "img_axs = subfigs[0].subplots(2, 4, sharey=False)\n",
+    "spectrum_axs = subfigs[1].subplots(2, 4, sharey='row', sharex='col', gridspec_kw={'height_ratios': [1, 1]})\n",
+    "model = model.to(device)\n",
+    "model = model.eval()\n",
+    "zero_shot_aucs = []\n",
+    "\n",
+    "for x_idx, res in enumerate([16, 32, 64, 128]):\n",
+    "    resolution_ratio = [0, 0, 0, 0]\n",
+    "    resolution_ratio[-(x_idx + 1)] = 1\n",
+    "    resolutions = [16, 32, 64, 128]  # [128, 64, 32, 16]\n",
+    "    dataset = DarcyPDEBench(filename=file_name,\n",
+    "        saved_folder=data_folder,\n",
+    "        train=False,\n",
+    "        transform=None, \n",
+    "        resolution_proportions=resolution_ratio,\n",
+    "        )\n",
+    "    # sample = dataset[0]\n",
+    "    sample = {\n",
+    "        'x': torch.concat([dataset[i]['x'] for i in range(len(dataset))]),\n",
+    "        'y': torch.concat([dataset[i]['y'] for i in range(len(dataset))]),\n",
+    "    }\n",
+    "    # grab a single input from the last batch\n",
+    "    model_input = sample['x'].to(device) \n",
+    "    with torch.no_grad():\n",
+    "        model_output = model(x=model_input)\n",
+    "    ground_truth = sample['y']\n",
+    "    residuals = model_output - ground_truth.to(\n",
+    "        device\n",
+    "    )  \n",
+    "\n",
+    "    # convert to plotable form\n",
+    "    model_in = model_input[0, 0, :, :].cpu().numpy()\n",
+    "    output = model_output[0, 0, :, :].cpu().numpy()\n",
+    "    label = ground_truth[0, 0, :, :].cpu().numpy()\n",
+    "\n",
+    "    # get spectrums\n",
+    "    label_spectrum = get_energy_curve(ground_truth)\n",
+    "    pred_spectrum = get_energy_curve(model_output)\n",
+    "    residual_spectrum = get_energy_curve(residuals)\n",
+    "\n",
+    "    img = output  # - label\n",
+    "    norm = colors.LogNorm(img.mean() + 0.5 * img.std(), img.max(), clip='True')\n",
+    "    img_axs[1, x_idx].imshow(img, norm=norm, cmap='bwr')\n",
+    "\n",
+    "    img = label\n",
+    "    norm = colors.LogNorm(img.mean() + 0.5 * img.std(), img.max(), clip='True')\n",
+    "    img_axs[0, x_idx].imshow(img, norm=norm, cmap='bwr')\n",
+    "\n",
+    "    img_axs[0, x_idx].set_xticks([])\n",
+    "    img_axs[0, x_idx].set_yticks([])\n",
+    "    img_axs[1, x_idx].set_xticks([])\n",
+    "    img_axs[1, x_idx].set_yticks([])\n",
+    "\n",
+    "    if x_idx == 0:\n",
+    "        img_axs[0, x_idx].set_ylabel('Label')\n",
+    "        img_axs[1, x_idx].set_ylabel('Example Prediction')\n",
+    "\n",
+    "    spectrum_axs[0,x_idx].set_yscale('log')\n",
+    "    spectrum_axs[1,x_idx].set_yscale('log')\n",
+    "    x = np.linspace(1, len(label_spectrum), len(label_spectrum))\n",
+    "    spectrum_axs[0,x_idx].plot(x, label_spectrum, label='Ground Truth', color=\"blue\", ls=\":\")\n",
+    "    spectrum_axs[0,x_idx].plot(x, pred_spectrum, label='Model Prediction', color=\"orange\")\n",
+    "    spectrum_axs[0,x_idx].fill_between(x, label_spectrum, pred_spectrum, color='red', alpha=0.1)\n",
+    "    spectrum_axs[0,x_idx].axvline(x=8, color='gray', linestyle='--', label=\"Train Limit\")\n",
+    "    spectrum_axs[1,x_idx].axvline(x=8, color='gray', linestyle='--',) \n",
+    "    normalized_residuals = residual_spectrum / label_spectrum\n",
+    "    spectrum_axs[1,x_idx].plot(\n",
+    "        x,\n",
+    "        normalized_residuals,\n",
+    "        label='Normalized Residual',\n",
+    "        color='red',\n",
+    "    )\n",
+    "    spectrum_axs[0, 0].set_ylabel('Energy')\n",
+    "    spectrum_axs[1, 0].set_ylabel('Energy')\n",
+    "    spectrum_axs[1,x_idx].xaxis.set_major_locator(\n",
+    "        ticker.MaxNLocator(integer=True, nbins=4)\n",
+    "    )\n",
+    "    spectrum_axs[1,x_idx].set_xlabel('Frequency')\n",
+    "    zero_shot_aucs.append(abs(np.trapz(x, normalized_residuals).item()))\n",
+    "\n",
+    "\n",
+    "handles, labels = spectrum_axs[0,x_idx].get_legend_handles_labels()\n",
+    "_handles, _labels = spectrum_axs[1,x_idx].get_legend_handles_labels()\n",
+    "handles = handles + _handles\n",
+    "labels = labels + _labels\n",
+    "fig.legend(\n",
+    "    handles=handles,\n",
+    "    labels=labels,\n",
+    "    loc='upper center',\n",
+    "    bbox_to_anchor=(0.5, -0.1),\n",
+    "    fancybox=False,\n",
+    "    shadow=False,\n",
+    "    ncol=9,\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfacbcd8-8c15-4ea9-82c5-756950836a26",
+   "metadata": {},
+   "source": [
+    "# Multi-Res Training vs. Zero-shot Super Resolution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "630bccc3-6b60-46db-87e8-2d53186766b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "res = ['16', '32', '64', '128']\n",
+    "w = 0.4\n",
+    "x = np.array([1,2,3,4])\n",
+    "\n",
+    "plt.bar(x + w/2, zero_shot_aucs, w, label='Zero-shot')\n",
+    "plt.bar(x - w/2, multi_res_aucs, w, label='Multi-Res')\n",
+    "\n",
+    "plt.yscale('log')\n",
+    "\n",
+    "plt.xticks(x, res)\n",
+    "plt.ylabel('AUC of Normlaized Residual Spectra (Log Scale)')\n",
+    "plt.title(f'Device: {device}')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "updated_operator_env",
+   "language": "python",
+   "name": "env_updated_neural_operator"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}