diff --git a/README.md b/README.md index c7042c98..9b160b3d 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # TorchCAM: class activation explorer -[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE) [![Codacy Badge](https://app.codacy.com/project/badge/Grade/25324db1064a4d52b3f44d657c430973)](https://www.codacy.com/gh/frgfm/torch-cam/dashboard?utm_source=github.com&utm_medium=referral&utm_content=frgfm/torch-cam&utm_campaign=Badge_Grade) ![Build Status](https://github.com/frgfm/torch-cam/workflows/tests/badge.svg) [![codecov](https://codecov.io/gh/frgfm/torch-cam/branch/master/graph/badge.svg)](https://codecov.io/gh/frgfm/torch-cam) [![Docs](https://img.shields.io/badge/docs-available-blue.svg)](https://frgfm.github.io/torch-cam) [![Pypi](https://img.shields.io/badge/pypi-v0.2.0-blue.svg)](https://pypi.org/project/torchcam/) [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/frgfm/torch-cam) +[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE) [![Codacy Badge](https://app.codacy.com/project/badge/Grade/25324db1064a4d52b3f44d657c430973)](https://www.codacy.com/gh/frgfm/torch-cam/dashboard?utm_source=github.com&utm_medium=referral&utm_content=frgfm/torch-cam&utm_campaign=Badge_Grade) ![Build Status](https://github.com/frgfm/torch-cam/workflows/tests/badge.svg) [![codecov](https://codecov.io/gh/frgfm/torch-cam/branch/master/graph/badge.svg)](https://codecov.io/gh/frgfm/torch-cam) [![Docs](https://img.shields.io/badge/docs-available-blue.svg)](https://frgfm.github.io/torch-cam) [![Pypi](https://img.shields.io/badge/pypi-v0.2.0-blue.svg)](https://pypi.org/project/torchcam/) [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/frgfm/torch-cam) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/frgfm/torch-cam/blob/master/notebooks/quicktour.ipynb) Simple way to leverage the class-specific activation of convolutional layers in PyTorch. @@ -213,6 +213,10 @@ python scripts/eval_latency.py SmoothGradCAMpp *All script arguments can be checked using `python scripts/eval_latency.py --help`* +### Example notebooks + +Looking for more illustrations of TorchCAM features? +You might want to check the [Jupyter notebooks](notebooks) designed to give you a broader overview. diff --git a/docs/requirements.txt b/docs/requirements.txt index a1361b02..7dd534c1 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -3,3 +3,5 @@ sphinx-rtd-theme==0.4.3 sphinxemoji sphinx-copybutton docutils<0.18 +recommonmark +sphinx-markdown-tables diff --git a/docs/source/conf.py b/docs/source/conf.py index 45a5c0b8..e5dd7fae 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -49,6 +49,8 @@ 'sphinx.ext.mathjax', 'sphinxemoji.sphinxemoji', # cf. https://sphinxemojicodes.readthedocs.io/en/stable/ 'sphinx_copybutton', + 'recommonmark', + 'sphinx_markdown_tables', ] napoleon_use_ivar = True diff --git a/docs/source/index.rst b/docs/source/index.rst index 6db8ad6f..faab10fb 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -21,6 +21,7 @@ This project is meant for: :hidden: installing + notebooks diff --git a/docs/source/notebooks.md b/docs/source/notebooks.md new file mode 120000 index 00000000..1ffa21de --- /dev/null +++ b/docs/source/notebooks.md @@ -0,0 +1 @@ +../../notebooks/README.md \ No newline at end of file diff --git a/notebooks/README.md b/notebooks/README.md new file mode 100644 index 00000000..809caf2f --- /dev/null +++ b/notebooks/README.md @@ -0,0 +1,8 @@ +# TorchCAM Notebooks + +Here are some notebooks compiled for users to better leverage the library capabilities: + +| Notebook | Description | | +|:----------|:-------------|------:| +| [Quicktour](https://github.com/frgfm/torchcam/blob/master/notebooks/quicktour.ipynb) | A presentation of the main features of TorchCAM | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/frgfm/torch-cam/blob/master/notebooks/quicktour.ipynb) | +| [Latency benchmark](https://github.com/frgfm/torchcam/blob/master/notebooks/latency_benchmark.ipynb) | How to benchmark the latency of a CAM method | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/frgfm/torch-cam/blob/master/notebooks/latency_benchmark.ipynb) | diff --git a/notebooks/latency_benchmark.ipynb b/notebooks/latency_benchmark.ipynb new file mode 100644 index 00000000..bd8de6a5 --- /dev/null +++ b/notebooks/latency_benchmark.ipynb @@ -0,0 +1,381 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "TorchCAM - Latency benchmark.ipynb", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "GHbltZUohNM8" + }, + "source": [ + "# Installation\n", + "\n", + "In this tutorial, you will need the entire project codebase. So first, we clone the project's GitHub repository and install from source.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 615 + }, + "id": "h10zMBPWhHz0", + "outputId": "438f0bdd-d234-4d54-c7b9-c19ce1cf7eba" + }, + "source": [ + "!git clone https://github.com/frgfm/torch-cam.git\n", + "!pip install -e torch-cam/." + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'torch-cam'...\n", + "remote: Enumerating objects: 3359, done.\u001b[K\n", + "remote: Counting objects: 100% (905/905), done.\u001b[K\n", + "remote: Compressing objects: 100% (434/434), done.\u001b[K\n", + "remote: Total 3359 (delta 621), reused 713 (delta 471), pack-reused 2454\u001b[K\n", + "Receiving objects: 100% (3359/3359), 8.19 MiB | 20.21 MiB/s, done.\n", + "Resolving deltas: 100% (2250/2250), done.\n", + "Obtaining file:///content/torch-cam\n", + "Requirement already satisfied: torch>=1.5.1 in /usr/local/lib/python3.7/dist-packages (from torchcam==0.2.1a0+a40ec75) (1.9.0+cu111)\n", + "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from torchcam==0.2.1a0+a40ec75) (1.19.5)\n", + "Collecting pillow>=8.3.2\n", + " Downloading Pillow-8.4.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n", + "\u001b[K |████████████████████████████████| 3.1 MB 16.3 MB/s \n", + "\u001b[?25hRequirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from torchcam==0.2.1a0+a40ec75) (3.2.2)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->torchcam==0.2.1a0+a40ec75) (2.8.2)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->torchcam==0.2.1a0+a40ec75) (0.10.0)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->torchcam==0.2.1a0+a40ec75) (2.4.7)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->torchcam==0.2.1a0+a40ec75) (1.3.2)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib>=3.0.0->torchcam==0.2.1a0+a40ec75) (1.15.0)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.5.1->torchcam==0.2.1a0+a40ec75) (3.7.4.3)\n", + "Installing collected packages: pillow, torchcam\n", + " Attempting uninstall: pillow\n", + " Found existing installation: Pillow 7.1.2\n", + " Uninstalling Pillow-7.1.2:\n", + " Successfully uninstalled Pillow-7.1.2\n", + " Running setup.py develop for torchcam\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n", + "Successfully installed pillow-8.4.0 torchcam-0.2.1a0+a40ec75\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "PIL" + ] + } + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "03SV_5l0jAtl" + }, + "source": [ + "# Hardware information" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jVjeyZCrjGxK" + }, + "source": [ + "## GPU information\n", + "To be able to run the benchmark on GPU, you need to have the correct driver and CUDA installation. If you get a message starting with:\n", + "> NVIDIA-SMI has failed...\n", + "\n", + "The script will be running on CPU as PyTorch isn't able to access any CUDA-capable device.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aS8tUr6riIH-", + "outputId": "2747049a-af4f-4042-8a3b-7f731a768b48" + }, + "source": [ + "!nvidia-smi" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HmL3QnWyjmcZ" + }, + "source": [ + "## CPU information\n", + "\n", + "Latency will vary greatly depending on the capabilities of your CPU. Some models are optimized for CPU architectures (MobileNet V3 for instance), while others were only designed for GPU and will thus yield poor latency when run on CPU." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "M1AL1-m8iVL8", + "outputId": "2d2708d4-a037-40f7-f3f4-5761c9aa89ac" + }, + "source": [ + "!lscpu" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Architecture: x86_64\n", + "CPU op-mode(s): 32-bit, 64-bit\n", + "Byte Order: Little Endian\n", + "CPU(s): 2\n", + "On-line CPU(s) list: 0,1\n", + "Thread(s) per core: 2\n", + "Core(s) per socket: 1\n", + "Socket(s): 1\n", + "NUMA node(s): 1\n", + "Vendor ID: AuthenticAMD\n", + "CPU family: 23\n", + "Model: 49\n", + "Model name: AMD EPYC 7B12\n", + "Stepping: 0\n", + "CPU MHz: 2249.998\n", + "BogoMIPS: 4499.99\n", + "Hypervisor vendor: KVM\n", + "Virtualization type: full\n", + "L1d cache: 32K\n", + "L1i cache: 32K\n", + "L2 cache: 512K\n", + "L3 cache: 16384K\n", + "NUMA node0 CPU(s): 0,1\n", + "Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext ssbd ibrs ibpb stibp vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr arat npt nrip_save umip rdpid\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZdEfyHTehb2e" + }, + "source": [ + "# Usage\n", + "\n", + "The latency evaluation script provides several options for you to play with: change the input size, the architecture or the CAM method to better reflect your use case." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3hb59oT6ks-Y", + "outputId": "ddf2f31a-d325-4455-d1e8-32db3c66409e" + }, + "source": [ + "!cd torch-cam/ && python scripts/eval_latency.py --help" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "usage: eval_latency.py [-h] [--arch ARCH] [--size SIZE]\n", + " [--class-idx CLASS_IDX] [--device DEVICE] [--it IT]\n", + " method\n", + "\n", + "CAM method latency benchmark\n", + "\n", + "positional arguments:\n", + " method CAM method to use\n", + "\n", + "optional arguments:\n", + " -h, --help show this help message and exit\n", + " --arch ARCH Name of the torchvision architecture (default:\n", + " resnet18)\n", + " --size SIZE The image input size (default: 224)\n", + " --class-idx CLASS_IDX\n", + " Index of the class to inspect (default: 232)\n", + " --device DEVICE Default device to perform computation on (default:\n", + " None)\n", + " --it IT Number of iterations to run (default: 100)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e5DqgXWQkHyq" + }, + "source": [ + "## Architecture designed for GPU\n", + "\n", + "Let's benchmark the latency of CAM methods with the popular ResNet architecture" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1BPl5sMRkcYa", + "outputId": "6a9137e4-4370-4de7-9e81-c4dcc36767e7" + }, + "source": [ + "!cd torch-cam/ && python scripts/eval_latency.py SmoothGradCAMpp --arch resnet18" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n", + "100% 44.7M/44.7M [00:00<00:00, 85.9MB/s]\n", + "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n", + " return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n", + "WARNING:root:no value was provided for `target_layer`, thus set to 'layer4'.\n", + "SmoothGradCAMpp w/ resnet18 (100 runs on (224, 224) inputs)\n", + "mean 1143.17ms, std 36.79ms\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7FPUlYL0kcaq", + "outputId": "c07fd74c-bc7d-490f-ddc9-1fc230cdc2e6" + }, + "source": [ + "!cd torch-cam/ && python scripts/eval_latency.py LayerCAM --arch resnet18" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n", + " return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n", + "WARNING:root:no value was provided for `target_layer`, thus set to 'layer4'.\n", + "LayerCAM w/ resnet18 (100 runs on (224, 224) inputs)\n", + "mean 189.64ms, std 8.82ms\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DCzAbMNQkL-q" + }, + "source": [ + "## Architecture designed for CPU\n", + "\n", + "As mentioned, we'll consider MobileNet V3 here." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "axInvW2thdgO", + "outputId": "f5e3db0e-4c96-47f7-cf53-9646494ab674" + }, + "source": [ + "!cd torch-cam/ && python scripts/eval_latency.py SmoothGradCAMpp --arch mobilenet_v3_large" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading: \"https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth\" to /root/.cache/torch/hub/checkpoints/mobilenet_v3_large-8738ca79.pth\n", + "100% 21.1M/21.1M [00:00<00:00, 71.5MB/s]\n", + "WARNING:root:no value was provided for `target_layer`, thus set to 'features.4.block.1'.\n", + "SmoothGradCAMpp w/ mobilenet_v3_large (100 runs on (224, 224) inputs)\n", + "mean 762.18ms, std 26.95ms\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uY_02W0ch_F_", + "outputId": "f47c70c2-d0fc-4b83-d83a-aa978904c1b6" + }, + "source": [ + "!cd torch-cam/ && python scripts/eval_latency.py LayerCAM --arch mobilenet_v3_large" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "WARNING:root:no value was provided for `target_layer`, thus set to 'features.4.block.1'.\n", + "LayerCAM w/ mobilenet_v3_large (100 runs on (224, 224) inputs)\n", + "mean 148.76ms, std 7.86ms\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git a/notebooks/quicktour.ipynb b/notebooks/quicktour.ipynb new file mode 100644 index 00000000..f6d62f18 --- /dev/null +++ b/notebooks/quicktour.ipynb @@ -0,0 +1,1013 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "TorchCAM - Quickstart.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "09c415f1f091438e8de16ba3bf8e3a7a": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HBoxView", + "_dom_classes": [], + "_model_name": "HBoxModel", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.5.0", + "box_style": "", + 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+ "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6-isjCf8eouw", + "outputId": "81cd4abd-aee4-4103-97e7-9c93ebea445b" + }, + "source": [ + "!pip install torchvision matplotlib" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (0.10.0+cu111)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.2.2)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision) (1.19.5)\n", + "Requirement already satisfied: torch==1.9.0 in /usr/local/lib/python3.7/dist-packages (from torchvision) (1.9.0+cu111)\n", + "Requirement already satisfied: pillow>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision) (7.1.2)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.9.0->torchvision) (3.7.4.3)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.3.2)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.4.7)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.10.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib) (1.15.0)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CkH4BlzmdJ4c" + }, + "source": [ + "## Latest stable release" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "i_H5vMoMc6Rw" + }, + "source": [ + "!pip install torch-cam" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VVEOrDRpdNd0" + }, + "source": [ + "## From source" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 510 + }, + "id": "IC2H2zSwdRz1", + "outputId": "0dfc4920-c33d-4c1d-c8e8-855322a7762f" + }, + "source": [ + "# Install the most up-to-date version from GitHub\n", + "!pip install -e git+https://github.com/frgfm/torch-cam.git#egg=torchcam" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Obtaining torchcam from git+https://github.com/frgfm/torch-cam.git#egg=torchcam\n", + " Cloning https://github.com/frgfm/torch-cam.git to ./src/torchcam\n", + " Running command git clone -q https://github.com/frgfm/torch-cam.git /content/src/torchcam\n", + "Requirement already satisfied: torch>=1.5.1 in /usr/local/lib/python3.7/dist-packages (from torchcam) (1.9.0+cu111)\n", + "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from torchcam) (1.19.5)\n", + "Collecting pillow>=8.3.2\n", + " Downloading Pillow-8.4.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n", + "\u001b[K |████████████████████████████████| 3.1 MB 14.1 MB/s \n", + "\u001b[?25hRequirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from torchcam) (3.2.2)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->torchcam) (0.10.0)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->torchcam) (2.4.7)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->torchcam) (1.3.2)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.0->torchcam) (2.8.2)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib>=3.0.0->torchcam) (1.15.0)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.5.1->torchcam) (3.7.4.3)\n", + "Installing collected packages: pillow, torchcam\n", + " Attempting uninstall: pillow\n", + " Found existing installation: Pillow 7.1.2\n", + " Uninstalling Pillow-7.1.2:\n", + " Successfully uninstalled Pillow-7.1.2\n", + " Running setup.py develop for torchcam\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n", + "Successfully installed pillow-8.4.0 torchcam-0.2.1a0+a40ec75\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "PIL" + ] + } + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QnqnIbRegkeE" + }, + "source": [ + "Now go to `Runtime/Restart runtime` for your changes to take effect!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SZp62o6qdlTc" + }, + "source": [ + "# Basic usage" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RJ5L0BrUfgLm", + "outputId": "88b67588-0577-4362-e595-c690ccac12e4" + }, + "source": [ + "# Download an image\n", + "!wget https://www.woopets.fr/assets/races/000/066/big-portrait/border-collie.jpg\n", + "# Set this to your image path if you wish to run it on your own data\n", + "img_path = \"border-collie.jpg\"" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2021-10-30 22:57:35-- https://www.woopets.fr/assets/races/000/066/big-portrait/border-collie.jpg\n", + "Resolving www.woopets.fr (www.woopets.fr)... 104.26.12.50, 172.67.73.8, 104.26.13.50, ...\n", + "Connecting to www.woopets.fr (www.woopets.fr)|104.26.12.50|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 312888 (306K) [image/jpeg]\n", + "Saving to: ‘border-collie.jpg’\n", + "\n", + "\rborder-collie.jpg 0%[ ] 0 --.-KB/s \rborder-collie.jpg 100%[===================>] 305.55K --.-KB/s in 0.02s \n", + "\n", + "2021-10-30 22:57:35 (13.6 MB/s) - ‘border-collie.jpg’ saved [312888/312888]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 66, + "referenced_widgets": [ + "09c415f1f091438e8de16ba3bf8e3a7a", + "19ed4f0e37bf4af8ab85aa46e0d31ecd", + "911e3f4554bc4874b7ca892b98f54cd1", + "7b542b84ecff48beb755259c6cc3dd36", + "37bcbf69f39442109227e5306cf25632", + "3aa6f45a02db49e4afa3a9514b640662", + "c41137d7842e4915b9129e672c732587", + "34bd9d08937a48f3b4255c10634c8f67", + "5fdf3871054d40febe8f5a6e6190722f", + "e5ec6d4f061f472899975a299a28d913", + "9a3b90814c7d4b5ca68545c56301baec" + ] + }, + "id": "Zymmsgcbf_6m", + "outputId": "8b80e383-0451-4ae0-97d5-c13d02b942c5" + }, + "source": [ + "# Instantiate your model here\n", + "from torchvision.models import resnet18\n", + "model = resnet18(pretrained=True).eval()" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "09c415f1f091438e8de16ba3bf8e3a7a", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + " 0%| | 0.00/44.7M [00:00" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 264 + }, + "id": "_FIofDSdhC6K", + "outputId": "ac3d6aa4-fa2b-4595-9aff-36f067abd05b" + }, + "source": [ + "# Overlayed on the image\n", + "for name, cam in zip(cam_extractor.target_names, cams):\n", + " result = overlay_mask(to_pil_image(img), to_pil_image(cam, mode='F'), alpha=0.5)\n", + " plt.imshow(result); plt.axis('off'); plt.title(name); plt.show()" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VsKWEYueiZqJ" + }, + "source": [ + "# Once you're finished, clear the hooks on your model\n", + "cam_extractor.clear_hooks()" + ], + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8wvuEmcGd287" + }, + "source": [ + "# Advanced tricks" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MUr7Em4rd5aK" + }, + "source": [ + "## Extract localization cues" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nPtcif9daxLg" + }, + "source": [ + "import torch\n", + "from torch.nn.functional import softmax, interpolate" + ], + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "BKIiNXKWd8f7", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0c8d33e7-6159-4d8a-8d48-5f7da2be9fc7" + }, + "source": [ + "# Retrieve the CAM from several layers at the same time\n", + "cam_extractor = LayerCAM(model)\n", + "\n", + "# Preprocess your data and feed it to the model\n", + "out = model(input_tensor.unsqueeze(0))\n", + "print(softmax(out, dim=1).max())" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:root:no value was provided for `target_layer`, thus set to 'layer4'.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor(0.9115, grad_fn=)\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wjcWMbbtbVxv" + }, + "source": [ + "cams = cam_extractor(out.squeeze(0).argmax().item(), out)" + ], + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 252 + }, + "id": "0Rc2TtqNb1VX", + "outputId": "82ea719b-1707-4b18-8534-c0e6e8ad02b2" + }, + "source": [ + "# Resize it\n", + "resized_cams = [resize(to_pil_image(cam), img.shape[-2:]) for cam in cams]\n", + "segmaps = [to_pil_image((resize(cam.unsqueeze(0), img.shape[-2:]).squeeze(0) >= 0.5).to(dtype=torch.float32)) for cam in cams]\n", + "# Plot it\n", + "for name, cam, seg in zip(cam_extractor.target_names, resized_cams, segmaps):\n", + " _, axes = plt.subplots(1, 2)\n", + " axes[0].imshow(cam); axes[0].axis('off'); axes[0].set_title(name)\n", + " axes[1].imshow(seg); axes[1].axis('off'); axes[1].set_title(name)\n", + " plt.show()" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DNdwZO7Fd8mC" + }, + "source": [ + "## Fuse CAMs from multiple layers" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "G6TDPWKJeCIq" + }, + "source": [ + "# Retrieve the CAM from several layers at the same time\n", + "cam_extractor = LayerCAM(model, [\"layer2\", \"layer3\", \"layer4\"])\n", + "\n", + "# Preprocess your data and feed it to the model\n", + "out = model(input_tensor.unsqueeze(0))\n", + "# Retrieve the CAM by passing the class index and the model output\n", + "cams = cam_extractor(out.squeeze(0).argmax().item(), out)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4vdTWfmjjE4w", + "outputId": "71ab5bf2-0f85-4fef-c414-e0e7cafa2cc8" + }, + "source": [ + "# This time, there are several CAMs\n", + "for cam in cams:\n", + " print(cam.shape)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "torch.Size([14, 14])\n", + "torch.Size([7, 7])\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 199 + }, + "id": "6q4O5zrvjKXv", + "outputId": "d5006663-da42-4b74-e204-2b79b590b3db" + }, + "source": [ + "# The raw CAM\n", + "_, axes = plt.subplots(1, len(cam_extractor.target_names))\n", + "for idx, name, cam in zip(range(len(cam_extractor.target_names)), cam_extractor.target_names, cams):\n", + " axes[idx].imshow(cam.numpy()); axes[idx].axis('off'); axes[idx].set_title(name);\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 511 + }, + "id": "E1WTyofWjNw9", + "outputId": "cece40d9-4e33-496c-f399-c3ccd575cd38" + }, + "source": [ + "# Let's fuse them\n", + "fused_cam = cam_extractor.fuse_cams(cams)\n", + "# Plot the raw version\n", + "plt.imshow(fused_cam.numpy()); plt.axis('off'); plt.title(\" + \".join(cam_extractor.target_names)); plt.show()\n", + "# Plot the overlayed version\n", + "result = overlay_mask(to_pil_image(img), to_pil_image(fused_cam, mode='F'), alpha=0.5)\n", + "plt.imshow(result); plt.axis('off'); plt.title(\" + \".join(cam_extractor.target_names)); plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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Z5sLptIZJe8p4m4FCTgmVqFssOVHxB0NaSiKpR/a0hJmhKN0FN4nXWQVvUaxrQrXjZlFUqUex2p2ukEQQlcC5D0kgFgk6foexs3nsxu4b5kIpUZyVVOiqtCSYrWh5zeAVA9FgDKwNnNThRAN64GGPLOUOkqJyL1OKClYLIoXe7ukN+thOk0JSBTy2OBJuiknGzMkK4oqIorqnm9E7pC5omlnma1QKjQtWu8ToOM7p8NP0rQc1VxRNguSZabkk62OkVdCF3TRTtNDN8Lby9P4ZdwdDRSlFeOPigjf3O+Z0ZlbOkCIefnfD1ejNKflJVMg+0eoLnr1/w27eM+VfzuKFKcVOgsPj5ZJJE2ob77z3l3B7l5w+ExRWUnIKJqJbZKCL6YLn95+ntw33ikiNij0JeAI6bhnJO5JXil3R24rbLZoXxBOQ6A5mjW5RpOXJcOukPGO9403oFtcZjszRYdMBMxQnqZI1YIlpJ2cjZw+4KLysGTxhbsGUMAI3vSzeerBlgYFdMYcuUdVlUTQlUspstbOtQirt9YLXrCAYWTrixqDx4kbNsT6WliRsYLOchamvlInIgOSRBTsjkWESW6h3sK64R/ChDSGKl7pF5nR1XBKSbGy9R9AnSCok9uRkmN8Hv+oNyYpIxijMcyZNb3NsjZI6uVyxK/tBwUDKOTLE7k1uT/e8/+LLceP+aeZUSEshy3nrhzkrSSJ31CZc7i5x+zRz3iGqVPsUZpXaKk/vX7Are0qCKU3MOXORhWW6ZD99D/N0Se0ba9vIUxoJIJ5wbLsW7EStuM+4bVi9g/kS0QtwZ6v3WD8hPmFb7E7YHrN7ct4QS3Tv2GnF5YDohijBDLhjAi6F1ju9NfA9Ij4gHYCBbJQCu52SM/SumAlunZwbqoITtQdSsC5YB6uG40gGFx9QYyRkj4g2GzuvN5psbKK0blg/cTyMis+n1wveuq2BQbQwFSEnC/iwBuUF4N1BemwD7mzm3IugWrAOdTtwPPaBayIKHKc1x7vQW30oCCRDUsNsT22DqvAOYkyzM0/g9pzWLiJripDzgrlxOnye9f4ZZZrZ7ReWZceyu6JMTzBfMCbmvGcpCwKDuoMpZ+ZS2JXCth24Ox5id6kbV3MJzK6gqogHxlTNqCg5K3NaEI+iVtMl7nDcVu4O97zXvkDvT3nz+g0+88Z3o5IRhLlkrndvcrseuTncs7YDzR8xaWzz7s6pOffrHcfDU6zXCMTaqYf3selE743T4YbWKnl+g+QZNwOxAc+OiB7BO906kjzw/NgpEaH1GoHjjsuMog8LqPeN3u5JxcgZcmmkwb92M9wNn0PIcIv7UnHqoC/72XYwdn1Rx8+ilivKBGN3BaGt8Z6aHetOtwBbvb4mVWbuqMUq1KJIcloHXIj1xlBQ4kJDuxB6c25uNkQ2+qh6JVgW0GAlRIismiI+GZkwl0SehJydVHxgKCPlxlSU4C7vIru2lbo6Vt/Ht+dBbEsG2bO7/CwXu0+hOoMKF/OOq2UJpoQQFcygFB0qobBbrrg5fhHnEcrFwzw8qG7ykg5UhERiP+fArTkUs60bJSWExN2LZ9zdHKinI5fzE968eCOKI48Aa7ay9c7xHu5OP8t+viRp4rjecHP3RV48+xz3NzdIF5IuKOCWaOtKqx1rirvg7X1Md5g3vB8oqSFn/lQGAxAVFyoEPDGjmbE1o3dHaIhuKMsQF1acgGvdoHdDs0Wm7Y50yDlUVNKCSgckMOzUMItMG0HiY/78AxKmkh5o1e7xN4Kt0IGLFfx1g9fA1XAMMw3s5Glow1vMh2iE8Rn7qI7XGyaDbXiIAIn7sPgQPEQMHQVfyZSSyQnK1EhpoEx3et2wKQUvXBs5Oa3PrDUyeNKMJyPlC6blLabyNqo7ejPmuXA571hKZgoJjWqOYXR3tmZs1emsJKtMaWOegstsdl5sFtSlQzdhbc7WOlPJlByStQ52wh2mKfHm1afZTdckFV7cdWp/n8vlMubGDWuddX1BN+fu9B7PeQfRzHp6n7qutFbAdpidUAFNCynHNupecYHkCe+G24ne7lBpoIKmHMV2VhJpiAVD3HDH2UiSUO+B5b0Bp5EJV1T7CHKoazywbhGIvQlKRiiITyAZC+4N1ULOYCUyqWCoAyZjR0iozIgUkgrTJIHF3RAPydoNxBTriSof3tDx0X7egVNqr7EIPFNbC9zjOTKwaCyuNCih4BIwr1jvEagiYAlxwQXwwFyi4Yso6SwjdsyE3pV1G4E9RH134f6+sk1w2r6MpBXVzw5q6YIyG9Qjmh8HJdRXsl2guXC57FjyTNIzgxDUDMBhq9wej1TbmHTiM9/6fVwsC0Vj9a/dOdYGYmQpdIO7estaldad2e9YprdCvNUoeqasXMvMnDJX855T21i3zmntlEEfiieudk84tU7bnuHmiHTcZ+DivDWh0mhWMVE0XaBSQJQ8daiGWcOlY9YQMUw6pIzmiZSDokuDFcBzsAl1jeckCayP4jjFA8cQlJyucG+hilrldDROJ0aCcnJSEnMUaAatKaQtKDnrOME+uL8UHOJZZpBMykrOgqRO0rgGWqKZgCluGoJL/3Dv2EcHr0TWWytUa2A+5EePVSUFI8fqUnCrAfZR3KKadDxoF0m4CIINLtBe0igitO5Ya6EC6URvhSoa1bUMk4drcLUquD/n4hpyekxOl3S7xo/vhzRZLjltR3a7jblcAvIgQPTBkDRzDm3jvbt3uD04c1Y+dfWYrEZRpVmle2etyvvHA8d6YJ8vcIynL95FkrJMF+CZvu9RKCKoQFEhaXqYRHfnJJ3NNuY+U8a0b61yOr7DVu9QvcIxWl/Z6kpdjbateA9pXlJG0mXwrrkgtmE9RAwUfHhL0MG9VgcKqkbHMIu5FwfvilkU2WY56iIhnoHUwVI4Qg9O3jKtW1BkDyHuJEZBgNG9BTZXH9t9zDmeEMsIOQo9B1IPSOIBECQJYgGB8PEaYgHwut4GZGjTBp2gP/r5AswRHTSGBNAXkSFee1A5rsBZH2yDduoPWQ8GNGHgIwPEkKTAjJFxE1xaqGnaEUvxIMVp9RlptzCViVZntnqk1qd4v0XlMZfTFfuyAyJ7nhqsrbP1W26Pz7k5HLm7fZ/d8iZZ9jSvFN3RMU7bkeeHe05147CemPObnHqQ+3O55PHFJfspsvmcMubCmXxxAic2c5o3Du3E3ek9bm6/zPHyLR7vP4W5cHN4h5tnX6K3F8y7PWl6i3q6Zz3csh2e0dtKioIcI9HagZQeY55o7Ujvx5hf74HB0wjIrtQ1ao9Rm0Wx1Azzjd43sDD+aEovJWUcl47QEWXI2x2XGs/wvIsOMap6JbaRjssWXC4hZsRCckopiI/d04TeDdQHmdHxFsWwtxQJTxqihhOx5Q8g+W80eMfwUHTxIOUeONiiGU0K9FiZMLCsBvXzCj+aUnwg4TYyBzfBWnxPGdBDlaQdZMVFcAqOYBbvr56jcFHY1ue0VtlyQcsbdDvgXjH7Bda6cmqPudh9B2KJu/XIs/vPcTo9Y10v6W2ldqH359h8zXo68ZT32O0uOJ1+gdPxGaafBr/iYvcWV7srxKNIEdlzuSwkCbPLWet3QNw5VuNYK8+Pz3j6/OfZ2hJWwk15+s7P8dT/b0QSdWuB5fsRTSGsKIWc9mzpBeJOKkLKSioJ456tnmBrWN2CCSijOhcFSfReUC2IZNw8smkfBVQzrNeo6nUUoL0PK2MU2+YtuPhRVZ8pUtehjqognvBB3narQEO0kbOMTG0je0oUgdIwCyHEtdHpVIcsoRuoC3jg4Kns6H0LtVIirl4zeAUdVIYZkW09vp7SRB7mEzTT/YBbDwnTwWn4SP2qQp6iWHA2ejO8RRCHlBxZO2lBkyIU3Az3G0xSaOG+Yd2RlMYWqLTtBHJkY0b0Bii4r6zHZ/R6wztpx/3JmaeJ0/aMp+/+NG7K5fUj8vImbX3KdndPzu+RLz/L2mcON+9y/+IZkhYeXT+hpIlH+4X9NLH1TtGAPyqhjJmHSteGqL+1xlobN6c7bu6fc7y/o8xhYPHcsX4BsqeUS0re6P2eba3kPLPsHmN9j6Q7rL+gUslFKFMh5Zne7wAlp5myFJalIRi9S0C77Qm9jUJand5X3GvQkYN/PauEDxTgqAEettgh6T5IvVGk4BpskkpUNe7BUkALjt5jEWdVTFp4lYliF7WwjvZtMA+CtdiZYnvX0BPUEA0PiXkiJ+ejOvC/SsFW0FGpysC5Z/yYi5CSgVpslylkRh3NGY5GwTZk1d4aysTIsWHW0T48EErWQkoLLgyez4ePtYVSdpYbtSIkBMObhY8AD5KYE6rhEvPeOd2/g8hC3a5o1ViW7wqt3Dpm93g9Iiwcb5/S2z37q+8gywW73XciWZjnhTd2V+znEixIC/jSuodPwULXv183VmtYM9w7SWf2ZWH/+NPw+FOc6sq6rZzWW+ZdouRrMFimAurcHX4ZKs687Gl1xeyWabnAfENSB804KzkXdvtvZVm+hbmA8Iy2fo4mYeRRnfF+iVMxr7jUMwUPktGsiIQ6yrA7xsMa6pgZpChmHxTe4dRPZ5bCDHQU0hb1Cx6CVW9OTiXMQXR66yMBOtY7dobIA1maCdYG1MToWtE5XGuihJvuwxPvVwleCxynGjgmvgIwmndECyaOWR8YRR5MGU4UZL0Hv9es47UFRypxg7lElpjKFMDeE6pK75EtBjLCLaifNMC7WA/1zRxMMY+qNqWEah5LR/FuZFGmvGdODadgKNswgLs9DQakG741lrxnP78dt6jC5TJxfbGQNe4rTyEptxR0VTcha2E/J0pvyHRmM8B9eeB0a28c68qL+0K1TtKFjHO57GnWEJytVer2HqU8ZpoLdYNSJ1q/w3pBZcf+8g0eP/7eMYcVfKb3p7jfYs0xu0PzLliHuoH3B0nZiCgOf0LgQOu8dIFZJBl9CKyBNv1lEIclk9gVh8wbvpOAw7UZmgbF5oGpeyfKnmH8eehcMYnOmyFyqUaXyGnr5KyjeP3oc0++Cs/bEdkwD9NEekWjbt7pNSRfG0KGuIPaIOHt4eZ7GwqLVKY8gWqsrrFCU5qAjMoU7+c+WkNGS4kYcaBhcKkyrC4dwX10cHgEODYhYiAFa8J6esqyfAolqnnrR3p7n9PhjroeyeURu+tv5/LqMZf7x+zmhTlnpjwFc5ASU9IRz0L3wJFhRBrcZYeiJSDS8Hl0D2gRwVGofTAJmzEtM/M0g2gIQSzAC9bTO6Q8cbF/A5WC2+fhZKTymKnM7C++i6VcA5XWE9Uqp7pyOt7Te8FoJGujaOwgLYw9RWnN6L3ijKAjVMYuIUQ4L5kfHwWen9mJ4QxkwIMQIEa3Q2gfgXW70/oasi+Oj+cXNRIPseL+sh3IhmNtQGTMO2sdQXsmDF4reOlRZKV4IxUdjMOAERbmmrgq4mK1oCqYhxvpDCIY3QY9hbE5KtmG9UZtmXnaUcqMZiNPIGmjtT7gx3hvT7in4GtVyCnk5NUa5pneFJ0U1VgEbsbh9gW9/iWm5VNYCwxX5j0XF4/gEpZlz5QfMRfl0W7Pfp5jN8jR4pNkFCmj6lUJPlfH7EbAahhO4hGE6d7i4aoGl9q8U3sGVdb1hNsRL0/o7lQ/cX/4eTQ5c3lEIpH1CcrCs/d/EkkXzLtHlPRo7HCJxombu/+Hm2d39BauMbRiKXY6kZmkO1QCQ4o0zI5Y3zBxUtZXukYedCPcooAyY2DRQVUODt9xUhI0y4NI9WCXHBDDWqJXQbwPcW0cxzrmUUTwMzPx0noX9dOog9z95fdeJ3gZq+4s+0KJm7HAmiFGOHFbSpKMetBYZzotJwtfgGisOOmk1Icl0gePu4GfWJaJeQ4hYN4trOuJ3oS6JU4np1tk7JImpqIYKyD0pKGYWcNMhg8BejWsO6f7G1I6Mk0L+6u32e3eHixIZ9ldkSgsk7KbE8uUSUN4ieuO7rGHDCCMLDT4zvFwRIQyyvZuoeNXC+/r2hr3pxOlTMN2OGM07usLajtwPL5HPdxwdb2npN2Df3ZeHqGitPaCZteITrSWaHbPzd1f4+bZF4fPeQeE59ktMn7SGdVCkoyl4NqTNqw1NBmiQs4CKFRj6+M5w8CvgTjA+30AACAASURBVGtTGt7s7JQ8hXFdx46LozIFbBR7iBcTC7ZjJczsY1Grn6V5fZDaH3rcZBi1jAdHmqRzkniN4FVJWAv1Q3MOCsYJoUFje+oeVE2STMkTc8lhrKZxqgfMa3T85oyoIOmIpsBhwSJoqFdppUx3qCYcYZcLWKWJ4JZRDZyl2XA5gZTgE7f7WDwaxSXeoj1oNEUiHu4xorWltqe4fpYlh5lG5H2WecfF9DZzzmQ52zUlAnewLQ/kvEOzVypvedm4mfSciWPupCtbM9yVknYs+ZLWG90rtecwst9+kVZXSmYUgkdyekRvna3eBRW4HqnbT1HrlylF2NZnHG9vcIOkPUQL0fFs5AGzqkSQJlGMqCc0DVUzG8suBKVcgBO0s/vwTIkNEUkzzHOm5CVk46IhRgi4J7btSGsjc7uDtiFCWCS386KwcAYWLaQ8GJEtPNxmGhTb2RPBMKu/fvCWs28ZXMiaEDfy7Mz78JXWOnF/16lrbC05CSUnEg4yYdLIKfg7xGNbz3eh3mTFemRM0YZ5Y2t7zBNqhoiOIiwytKuTlw7SWf0Or0JtDRcl5fHwRHHfQoe3xFQmpmWKjpCcqdsNL178ZaY3PsPj3Q71jZJumZJQ9DNhkB88dbTznPc0YJzGnUe3q4z/PCQPiW3WfPTyqZC0M5cZ1RyeBpuovVH7u7T1QF9DDpWcwK85HU/0ojRrHI8vhtAj1PWO7b0XIdl6xz0KI5EFtfAXiNcBK1psvdrCOCMdSY1StoAxYsy7FEHrFRFhh3A4RJGdUwLLnLsiVBopTaOtPpNyohSANOqNgXfrRm9Or862dXrU8A9FGhKQY5mhLOEJrhsc7oW2RdFt473chbOh57WCV9RRA5FOUiWps+wal1fO9XVUtVubeLGH9945oG6IjJYcr4EB1dAslBIVpHvGbQI9BuayCbTi6twfgwPuLdq95938gDWnxYecWZEUHHHdgq4SDb4x6ZmtMFQrmox5n1iWxjwm+ySd0+3nOJV3yVe/mv38FjlfhsysBdXE+d8ZiYJkBO8I1nOxcx4vCwoP+ZOw/a1mNHMcJQmUqWBmHLeNU+9s20pKb7G7uqDXG6Z5oZRr4JJt3dhaxWwiTVdMaUGHotbrFsKt32MmYCnmWSLb+blHTKO9yqXGDpI3kgz5FqJrWcJklUXobhHMQEoZtzzYgRA/nCgu3QTpwdvGzuWjqyIOX7AGtcqoUyI7p4eaKRZ+mRqXl7ED1Azuxr1J4F+GiNXOVNprysNhAA9O0DxokIurztVV4mK34GT60ZG0IjlhbaMRHjoTp9qJ3gKsT1koeUdnYz3dU5uw3xX28wWSnbWeON4dWLfo48pZcauk5EyzsZsBOq0Jx0MKO2CPdnrBmHbGspwQNswyqfiwUd6y373BVASlMU8Tl/tL9otEh0Dak9OOJI8QyaMA0Q8E7ZiNhwA+fx71SeD/rXdOrUW1bsLaGnMulJSYS/hduxgtO3sEu/gUKlfcn97FegZP1HbA0w1uO1praFrYX303ZpV1fZ+2vU9PJ1oDrw3vYWTKKTq2UxacJdgaHyxR6GOIFrAVSU6cvaAcj5VcBrtjxJy7AX1USnreXugtzOuathFUBSsrCENli4ZZzZC6B39LBGwZKqEQ9Ka5PXikAz4CEgvj7Plt3aGB6avP4G8geHMZq6Aneoe11qiitdHcWKtzc2w8e36kbpVEprUwPXeGZS9fsuzeYtm/RUkJ843WVk61otMFWRYwCzO1N/qQMsP448w75/IiIzjrpngKLNctJjVpYtk7F5dOKUIuSimdnAgIUx4hXAVs0Euu9o/ofUN8RXWJTOkTSfIHgvfVgP1gEMfXL/9hD2XrztPDkeNW2U0L3aKlKHVhSmlg6PCsLnlhNwmX857rXeWdG7g5TPR6z+l04u7mZzDf0HTBsnwbJSdK2pF0o5ZMXe/R7RbrO3oPz4HmMOpP0x73RN2M2ksEsEVgixop6/CQJNyMtUHrQpkSOSumnVZj57J+Alc8FVTOlkoghYunW8e3YFiCPk3jHIihmKqjeWBmzWSdgwqjUbeNuxunTEGX9SrYdlZx4VzInZXC1wreyyXRmnI8gYngljidFp4/b2iunLaVm9vO8RBZ4EH6qxuiiVwKLpmUElOZKXmm9wum5Vs43N9jTfAeokL4OENtiR7EwFtzyezm0P2RTj82pgLLAjmv7BaYJqFMCyVd4JqZy0pOPg5IuRyFVXCPKm/QaqO1I2ub0bQiuqfIhEgarzsH7C8WvOfPxwQ7zNl5a/+EOjsiIUyYhyldx1FQeHDGZ7OZaSangui3o2nh5u4dtrbip422QpoTrUPuhk4zc36LpPd4X2mbk9McWFmDvdEikVU96gQj0WsF7aSkkXmlAhGwZgI+R2E5+skCXW9Bp1kU5m4VT1HYMVgC0dE/hw0DeXs45eZh2s78rwoqE0oJqyNhhD/cDMHAA+8G/Wq05iMGYn4/IvF+dPC++egxp20j54b1gqiwNcNOYcXbWsY9Uco91VrgQSkomaSZ3hvNTtR5pftGHhl5G8cEHWQj1wtEMsd1pbbwQaiMC9dQltwSmlNUn37P7nLj8bWzTMJ+zojs6LbgsqeZkNPMVAyVE6ovyHoFZHp/SvdMl8/SJLOuKym/yV4fD25Y+cDsf0TwnuFDSkGDlzSsoq8E97mDJESLl50nwVxEgbsvyhv7DJ5ZTyeW/TX4UzTNOIOw13fRcoGqMJUr2AnW4+yxbsZWjTQ72EZvjd4KvcfP8DocupHWRAuqUzAjHh4Fs7PZxsErSc4JRBApiOrwGHicQyFxbJePxtfz4Sq9jVOWhpEwDQoVh1494qH38BYTsM+6jXPLRquYevze2fT0ujyvqHGxu+B6l+mWOG0bx21la+ebSUFtZUc7TJrZzxNQxhbQAguennN7WDhtM7Xdcn/3HluvnGrn1JyUF2rbYrLNx2Enhd7Cv9v6FAe6NUVSYbd0LnfGMsEy7YEr1lZolhF3XCaQOCwjMfq6+h3NKnfHz3NYDU2PUd2DPqHogj7AhQ+DDB8WzDwEcxpZNbZR+8DL9UzWM9Qk97HlOlNamPM1Kb2JeWWaNjRdgW5Ap20nlAOlXDLNO9L0JshEWu9Zj3ds9Qa/t1BA7WUTgKQwvvdWx5POI9sWSHGWhjUf1KdhPTy2osP7qzrasFIUg9axmvDUR/COW3ShN8b7Bc5OOVqhVBuaagRoG2osZyYhqDyjh9MtMdrIYq7FP5pw+Mjgbd0pyUjJmNJEyTuqreHsV8IY0464HZkm5fHFFVNqQWVtE9t9AzO24w0v+pGcJ5o1tvU0iHzjeDqS0zqcR+H/zLqACK12bm4suiXO5wlI45RDfhWdcXaozuQkNDuLJdMoDBLuCwUDcphVeAORb8F9R8mXTHlP/Cutivuwcg6t8tzW/zIjP4TqKx/+yv/P4yX5fmYj4mt78Ah0c9be2LqyWuOu3rHWG+rWSfqEaXdNkg3rhnFEZEPlipw6kiam6ZLD7RdIeklte+r6bsAuBEhoBok9AbMKbRjJPehJLChB3Gh1BSrR7Ho+BjTUQfewUQrnszB6sEpjh3RnHIEQxh5NMM1T9D1mJU9KXRu19lg7o11MkIc+t34G1DJagMZ79lfFob/R4N22xtFO7KcLdsvEWo/cH59zWs8udwkXVXZyNvYXG1f7Pd0ax+2eu9Mx+DqU3jpup6BmVJhTidMNe2OejDQRxYle0rYg3XtvrJtwkkwqidpXTqfM8ahkNfTNBZYdJe+AxJSDWMsprIxIw32jtYbLt4LuWOZrdsslQmDOpDuaBdYNJe1lhn35uXxFVj4H7C+2p41CbgTuS67YR1A7mA9I0KgeJ+xc5CfU/Yn3t89F169kpukxJRdav6O255BmUp7ChccdtruOnrfN8P7+yGoxd5rOYkv4Qqw7MtqvuvVRtMV2H1cX3HF090LKJTy4BI7HgsdOGgpmLiuiPZiPB3gU7EGZEuIBJ/IcbEd3R91JHg41JVE15spaZGI/z08S1HQkoNcM3vdf3NK7cT81pvKc09pYa3+wsyEO6qQCkqDrHZsfYkVZJ+Xo6RJPo4fJmUvh0cUbTHnidNo41hvy7siyh6mEsWbbCiqZdT3Rbht3hyPdQ9NXie6F+zvnRU4ImZZrFIjpmpyvSDoFga8e5pmS6f4t1JbDeqmXqEQ17gjNQw2L1p0IVpVXs6vyiwfr/zd4fWSRl8LFBws8CJPTROd6KcAFtXXWqXE5PyZP1xxP9+yWhTeu3ozz1/obHLZLbg6fp/vFyIITJe8x7zQLTjvOxphCIEkZ0Yy1YcbpPjy4G63XYSeb4gCYHGcxdNOo/k1IqXDusg7QYCBpCEc+BI/wl1T1h+NtWxO6V6ayG4zBiVTi3+aK42ujOVXUKZtyEuBkARXEH+yvboa14UF+neA9rh1cqW2Nnv1XvJgDsSEu1C3I6MPJSNnGBETxJYShpjcliXI177laZgSYLmdSzVCEi52Q0uitkhSmmyJUM9a60U6dlAwtwWKoKmtV7o6wlAyayHlF0zVzuWKSRlYhyRLav+yZ8wW1g5NoZrReOazPYVZIcxSGw67J4EfdzxlXXwnEryzoPhC+L8UMjdfGrz2gXeLUoM4yoMSUnJLDYnp98RZLuWI/zVwvFwjOqZ5Q+RStN46nF/R8QiVjfod7PfthOJ+8mHNGpbzsUu6CUwfONvA+doXGNDnz5LgvnNYe+FdkiFI6DoiOLhihDMXTyCmRdcW1kSQWQxKhNqP3RpW7OPZL4oyGJDAVxzJMswDRrOmjXawiuBjzEspkdH70AW9eI3gFcLFhYxMmLaOqbKBnRiB43e0Ez1ZBekiH28lZT+NNhoWuYUMNimLAcVpd2Y7CvASOrS1kxSk5Zc60i0Zd0zBBdzSdQJ2tz0gV7NDZpjlOZu9GKgcuL55QSqKk2M9kOKtC1y9s7Yib0bohJtR0FycFaLTGRzX8Moh9yM4fLOa+EkLwEJju5+/DBwPcX/m+jdcagpM0TjITmXlyecWuFKYcfHKcRXFimd4a8vIttT+j144zx8lGcoHKRs5KyYmzi1tUsbbFyToC4j6wLKTUWBalpHF8gSgn2tj+4+grzUO0GJeeh6NvSol5UsxPeF/Y5UzrneN6R+01jvrqE2m0iYkF/RWqY5wrIQnyJFiLXTklZd7FoXxKBHaRCz5sfGTwTrNTN4EklKxMeRoSITiN87+8LQ5FglZpxwu2w0btld6GJU4c1071zs2hcXkdjnvz+/CknoQvfxlyMsp0YFoS5hrnjjWllIndLpFLp9MR3eg0thpdxtvWxjmxj1lyYq1/nee3K34Bu+ktpvSr4sgoERLGnE6Ir9T2JZpd0O2zIVtLeAqMRBbIWgAdXGN0t34w+34lIHuFJiPa9aNI+iB88HOH9fi8mXO3HXl6d4f6TJ5nShoOvhTO6NSNKc/4fI22wnFd6Xak1ue0rYHtAi5lxnkXG9470gbzQAkcKtG6LiUU01QiUN0jGHIvIQEzrIn95X2lHMeSTkXZzYZq9PPl6ZLeBLPGVAq1v8B9F64+wlWIp8Cw1uPEfRjmnYompeBoCnrs8mLharkODri+Zut7yWPigZwAWaMQIrFt4OIxwUo0Y44epuNJOG2xnaHRnyQWsOO4Nb707BnXj8IbKkkoM6xHYVvb/8vZm/ZIkiTpmY+IHmbuEZGZ1TUzBAkSywX4kf//nywWWGB4LJbTM91dlVeEu5keIvtB1KOqh+QsNxvoLnRVZqWHm5iqyCvvQWmTfmbsskcb4RIy8z2vgrgvPZUgMkEK5vuSiL+tFuXgNk/GvLPVX/h0Ea7bfySnLZxdJBYGl5J5O79i8x845caYGyofUL1Avrw/xKwaQL787px92P78d/9Zd/gahsYaiDKLzeP+zkwbBsONt/M7f/72J+6n8Hcff0I1RKflkaPAgLrcafzGmB3xbXEJFJErKSVSavHzqQMJG2HLJVrwoZgvo76cyCU4EC4eRnhzwVWa3kUAD49f91hABZ6bw4M5pQfiTRINSqQIW7lE/43TVWjNmHMylvJaHijVg6NN2J46BhISr5ovZL1gvVHSD7YNqolLjdWpKpyt08cIDikFXNbpMRcsM2jzOy4Vs7Sm/Nhhp6TkbEgKucg5jJKFWqFuYSs/Gowu3O8WKMR20keQR0rJ8SabBNFZFc+rN9ZJ2SpJP+Ioos+YfeZoztEyo/+Jv/nwD1y3f0dJTyAXhFdyeqYkMGvMcQctTDlgHqheQ4UgwnwsTVIMMA9V9Hp2/6z1jYZXLOpYLbYSMR+sX7Jk8a+tcWuNX7/9N+73TqmFPr8x7WdE1hRM2I6WlMKBnE7SWNyIFHK+gt+CL+C2+AwOVhbcNdbnTyE7X44LscYN6GvMeKbTCUycMPE2ZIkO4mO32WA6W97xLQf10YMNaDPk/yZjrc0nYUiSuB93zGJdP2deEncBzywiJZoCZps+Oc472S9gsdj4oeJNfqWmGK5QZySYLTYiwaqPzYi6MJe+bTbD5kBkQ1PFGaQyKcUXcL0UxVIXx7eRUsAzs0qkDvUg0UwzctUQZtqI62fG9khMKfkncnoJFpVcmd4Qu9JvsvDED+T0xFY+cTsMsz/ytP8dSR9mKc/L4fEaxSpP74ws0YbnhsraySeB5MundlXs7723/tnwJqtl0LUF5bGkiBrDEXQqu1b+Lv97xBPTOq3feTs+M2znw+UTRYPcjytzJsZUjv6N1r/ivgcF1BrTZnCicyXl4OYGSyvI5FMnmipJfRFijFxC8dK6BRY8B+aGvMc9yXLoeXAbNFQSo9LPDfRENNPsdbl9Km2+riEvhKpjnotbvXzn5sOYZu3fdFvMNCHlWHbc7g3mHfXKv5QT+C+fvGTKgo+aDXoftN556IKmrbfOhTHies1aQApSNOQjWqJwS+CDZo05hXYU8JNUhK0KaVvwG8JxF/Yt2FKlVlJO2HhBt2dsGG3eGGbU+ndrzamIX5h2p42TPipzfKSkQq0VfYo3vY87b8f/w1b+Pcgnsu7UFaKiWZfRhqwCDvtTVYmzQR2S8m7V9let2GNjtC5S/+tiXvazcRWvaIMkwpYy0ifV0pLeFM4zcTs+8+X1P/N27ny8/m9kLZyzc2s37v0bb2+fgSu5XrB5pzVfmRUHyFPo+STjNnELK6eSE66Qi5I0vBmQUF/MpfxlrXpTMlwHKokkQY0UV/CdOSZv9zuiM3gReWBmCwdOYDuS4rNMm8y5+N0plMeqYT5jltZ3bYsRV2LVnoTejF9ff8G64PMH24a6gdEY1jlG55yNbo7bAJf3nC0bjqbMzy8/c9kvDHdu88ZczHldeQjmBdwQn4x+vuuiMCerMAiYpBblcinkfKHkn2LQKj+xlw+IKM1eOfoXNG0omWEpoDl38IrPhMszcwwsXbjdjK18CfRhfsLYAwradkQV0omUsL5HDFKPI1OcqTOITqJIXjEESycWRfv7Ov1r5OEhF3qculiQZ5ZjADIVzRL9sTlzGJmM9c7t+Mzx5Y1f6i9kfQaBpJ+YtnF9+g/gnfvxjyDh6dDPGyn9AeGK2QBuqMzQtlkgDFpqQF6Lgxsey3F9i0QRRQSBhZWBOyl1lC2+04UFH+fJ2Q5yDj9m0cJeM3vZIj2KyKto7Rvmib1s5BTrZbAwW5kxMCoZyZNUUjD/JAa5+93oU5ZvxI8U72Xy9fsr9zP4lXNdQ++bosfzU7huleueKQWSxGTZLf4Il0gA8vnQWZ2kNILwY85xQi1CG0JrgsiO6E6pH9jqE0mUrDtbvoZGzSaaT9zb8gIwbAxkbqR0xbfKtK/Y7Ix257tnxvzO9QK5FPa9U8qVhFJzZsidyffQ0i0FLZpwSbhMUtpXUTuaMynLb3Sn37YRvwMffkMW3ovYJWCqB/JkYRWa0pL/TKPZpOnBff4CpZK9089vNPvGtv8rctrZSywVcOjtL7TxRtLG9emJlF+WamFBi/SQAiVDk5BkC5Uvd9wNnx23ESgClaThbG/eeLfg94nrxCUtJCaUIuYj/IJHwHyKoDJJy2AkvpKHOckZBjVJ0FnWZm+R5wnescsdzeHknotS9zgk849SIrdLJR3QX8PSH49eFzRMKGQxjYBag1A95sQSTGuM+ZvbzoMTHM35RHQiKZjyr6/x6+YU8MS+bVzqz1S9cCkvqH7D7Fe6TfoQhh3UYqSknG2891bhB2CYzzXtJ5w7oydeZ+V2V/KWef7wCy8vFWanz5DIzHEw2o1cNlL5KX7KHKR4yWvzo4o+5qj/L8rvo3Yfp+4jgMwEffS9E0aH++y8njd664z2Da0bL+XCON94e/tGO26UfWOvG2kdGOf8ugYsKPUk6UeS/oE5T85+Mu0gvCxi8E7lA/IQx46IIMDH0rU1kuzkXAFlWAnh5Ao6iYEsxUs2ejxjG6QE05WBwYory5eCMzGfiD+jD11ajy9o9jjcTA9EAoGwucxMCD5wrY4/z6C1/g+/2P+F4q1bel9GYI/dZnp/WDkVWKYjr8eNWirZM61/4dv9LaZaHhLpyDrbdiOVECVKyognRrewcTflsie2UtgLlDzI6Q7acPkLo3+mzSCElHwlJaVNYTbosy2G1ACCWJ5z2LAGaWRjiqMMpFSG3DC9cB8HNr5hCrfRYRau+sSWK1tWag1eRV4hK/J+6voqYPkfFO4q1sc68vFXe/S/0SrJAMMYfTDVaXRyLTxd/gbmYGwbWjbebr9w3Z95qmGkch532vkL5/FH3O/s25Vt+4mUPtLnoM1/XMlAI0j2yanlb8DDSUesAOGaLi4okSMikoMNNzJmNQrqQZLpy83eVwyD5UW6jwy1jnEct9XLToSCdYUpgdnjqzUKVUlJEqdqgknD5gCJ1kEw2DJKx/r5Y8Ub5hpwvYYYsTdW7wqth5ohFJ5O98mfvvzCu2mIs/ooQYtSq1EqPD3F75kmdHPavDKHk9IIz9ZSY62shLrUbrj/SkrGvnVyytR8oeQnjnFn2uA4d+5vwf99uoR9f7psYIXjbNyb4ilRdnj5dOXDx0quQipg8sx5wuRGlzeMk5wm+7ZRLhdSLeSspNXvxiTOX5+8/7PD4X1vy2+nsMu7SYJLKK/LltmYaKmoOVUqapnRE6RE3jY2/UgWjRd0OkjkUZgZXpfWzwuzf8NGx2zDZw9Zjpzk3BG50ttAta0PpUBCltkLwtLhBTfXzcLetM8wLFl0RhUlp4oQqE/K4duWhPh1Y4l1Kfic4dNmC9HwRBKh5C1MuasHx9uPZdeaEVUSA0psHX+oeF2+BwmjRsCyuwQ9cV170w1WYo54cD3CKX2BnOshP7Y6KcO+xVU2huPDSFnYLx+A0Hy5H0zu9Bma6zkbc8FkH/bKpfxMLU+IbnT7gsqjTbhSSub5snOtF2ragczbCXXAaYnrB+HjJ+XpwwXNmeYT4yvoHro3z2gCy9/w8oJUCaOeEuADy7VV1P+65/198cr6n99vgh9F+/h7QTGOK1IzlsC0xIOasXJPFDx58AbyCwzDOGNYzG+Qj3h6Y2HqMyNzRYGtnvr9FFRhzj+T0jOoMMavOHd0bRDnEA7vQbtMGts5C71gOwc2J6EyySSNGyilTpKQBNV8CQ5DCmNrZwXwVALis4jYwie1buzpmVoyT08VtDH85OjKt/tnmrNIQQEu5/qDGzbzg+0S4LU2edfqx+ORlcAePZVqCjI5cfUM74jM+KGUlVIpkRqfw1JoTkV0Y9sTexEu2wXVC8JJ0gvmTmsnw2LJMcrGXl6ADZsJt59WgIdwvWQ+PCc+Pj2x5SdK2sMhfIOnVGg+KNfJ5Zoo+3c0/yucQe8NlztZla38K1J9QvMTeVN0E1IRco08DjRaBdfffRfyuyL+qzf/nxWxs07ix8kbyENOQhah4SRPtLNx+J0shS43DvuMyk9I+cMyMfnOMf8Lbf5K3gxLgovSx47NG+fxndEP5rTYNs4HJbKD/8IYB300wPFH9NRIMA3RjtodkR63zLDQo6GxCnaJ1jALOS+Ia3FbUpK1qOiUkpBltGjFGa3TxySJs9cPFCnsm/Ph+Q+ITM7xPZY49y/cXyPRPmdHs4QM/0eKN2TlAXI7Egz9NYAEHhghdLpMRXSOFXP18IoMb7EisfUZU7jdhPsRU6VZotYbn553nlcKZMkXVH6mjxt9JFIq/PLtF8yV73dD5KCmgpE4e6H1C7U4l014vm7UulOzkopCEmo1yEbVj0hJpF2Z+iVOO9kDG9XMfv038Xu35yDEl0TJYT9PJrjLy/YzSGYPUxP+WfH+Di57tA3/vIAfF9MIuDGFewujN97sj4zjV7IkZhuMdqL+Z7bytxESfv8T9/sXUCiXwuyd0V6ZdtLvIY2f8/YOa/Wu5NywPFCftO74LGvVbjzc3HNSSs2Uoqhs9DLpxegttlwPAvlW9Dfv3hRGfoGHVsRz8CwE6rbFjsDDME/mIJgOnbrD9am+R8pmTVzKlZoLByHCjbyN8GX+seKdypw7cwpznkuusR7PQt5dolA+Pj+TNPN2v/H6dos2g1gJ5hXhiQndwTtsm/F8FX56Ljxdayhk0yCpIlxjkyXBbVASx6Hc785xHly2ElsjM6Y5OTu1BONMxMPeKN0hTUxfmXqQtoLnjww5Vy7Yd4a/MVXJ2x8o+xOlFra9UmqhlDjteSALvy/cx98T/rpteEfIfgfvvDvFy8NS5jdjsPADIYmQp1Jsklrh7ErvsXqVDFjn7fx7Rjtik5U1rvc5cBuYNo63QT/Be5DO8aAp9jUkRchfCYiD8Y5Tp+zUGqSobXkow6SkSRMn6X0lYq7wR7Elp89MIidZ/JWpGz4KvU+OY7KP+Pe1Phg9eN0+jbfjVy7XCelnjAacsaCYg6rPXPfC2d6Y3lc+8v+8Pv/F4j2OyZwntzenn5NEQlMQPCBsS00mH7eNy7ahkpEU7oX9NtazDOcbkcB5+/pAI6JfqwAAIABJREFUz0/Czx8+8bQ/xeSZFy7IVxxI8rckOUl0smbGEEZPnOfk2L+T0hk8g7X3lxUZMNMXSB9xTtwnU53DBox/AD+DE0BC5Y4xIO/U7SP5Utn2K9tWKFXf7fTRAFrsYU+lv4PK3vveRzv1z5rf37cKj7feHm2Dw3R8BNiYNthM+MDfIMm5vX6OIVMEtcE4T1wr4z6x5RZu1ul+MByGzfhup5MZZJ3Roy+PBWYhZQeZKBVhoDqXTYBQSmTtBcMsOA+5LK8GP4gY3/jsNkfMIoylQobkxhyTOYTWndvtTq0dJYejplbMMtNOXu9/ppYDlZOcAsJzc/ayU+vO0S7czzf6OOIZ/Ujxfv5LfNDR44tXMcQq6v5g5iJq1FLYy45jdAsvhZzTMo+Y5BlvXujWlLqHyVtOe0BZMhmzIXILfqjHMqM1Y4yE6obqWCB6ZtNnkhZcI2Aw5UA3TCSINbLHOjd3PBlume6V6S18b7VT8x1SCb5FfUJqFFDZwwZJHq3C6m3NQR94b/pdy/De9/6O5/CO7T7+f2zXongdn6E8eHyHDqu92iGB6SdSLngfMA58nCAjCvV+Z/gKM5HwVZi03zzBxKj75FLizx8jMXtdHOWIlgrFcGy7zNbQnTOphA0BFoNf62fwEDSBpoUC+ZIOKd4KbgUwhim4IgnSMjFpZ493PHtAjlrwrowm3DuUcpAnZH0i5Z3na2XMhs+v1P0npsO9ffux4n37Gt9+WHWuJdI6SXKusd4T4+j3OJkozHlnjBmAOA5u3A97hzpFnFydszttvKH8TJvG2b9xu9/IWalpwnSmJYYL98Nxz+zbBz5cfqaWnZIrJpM230j5Tt2dUq+kClIcyYrnzBDnkq74CLlPrp1UIW8VLcLQjtSMpYQXR6ojRSIwQaOde8jVNfOb5f3DUvG9bVj8hvdB7VG5q/If/IYJoztTAuO1JZH1paB1m1Qu5LwzW2e2k35+5hz3GEBrQi2HJ8MSj2mu1IsAnbI3Xp4KBQJjnZneA40IX7gTs7beMWc+7ExJ9JkwG8wxOI7BecQaudTg/HaJzDbzEetyMnicvnNOUh5oFvYaCFU7jX4fzK6YsojpheOmETXQjee9ULKhcielsE142j5Q9Blwpv3tjxXvg8v7YAnF+TBIC78lhTfZ7d7545//K1krra0QD3tghktxmglyswvtgHsR/kneqLnjlugTjq6Mrvi48bRncsmco3F0R6VQtitbuZI1B69YJ5oaZbuwXydahVQvTD2RAp6CdGN5YxtB1dv2K7meaK1ILtyt0/y/8rT/jNQnvLKiwjxUtOuEVQ3m/3u0/YOcI1EGjzr97ct7DG38Vb/rI2A2WfZJ3SfTGiYHloyZJ9NnrNjJARcyoP0T3u+k6tjohL4sUy8/oU8vjHPS3r4i8ytbhSJLmmuK3BVp4FlwthWA3jAPFEBaQpMspp3TZ7SEgeluccp6rPLnMGYHSSNW3CQQGDPSTrd9ICi1QNbEfSbaGVRXm4vLIol2dlp7oz07T5eP5BJLC5VMzTt7fVkyqh9cD6vGwJVynD5bVra8o5IYEvZGcTJFnzO8M2eEH0+LKyjCkWO7ossCtHfl65dAHlRnGOtZeAPNESvi19cT8zO0aSXzdNmB2MDkfAl6ZJokFcquaClo7mhxPCkzdbRkJHU0Dy4X45KMy3VH8jOef8LTvyY53OY3bvbfKOkFFWdbKEPAY+ApSPP8rg+Ot9rfT15flfqbGSrvp+3vC1gSpAmSha5Os8awg0EDMTwZfbyRJJPqC5NG44blhtZBnr48EIQqlZqe0fnEzAdb+cRsmSJ3EhNmox8Dk0GphVp33JTjhNYbrU1sdlpP63AJI5Ex5N25xkwZy3dsmqzijRs1YiwidNseaZd9opLJuZByxbcEMzgQo8146db3Oqbz5fUz53zjw9MHLvUleMlzcNl+Ipdn0o96lZUi4f+VlJwTHy8flpQ70U3odoIuOp0En2jOGRZAXhcqMf8KG1YJ75bZhd5AJGOegLwccpSSKo94z+EDn5lExb1z75/Xejne/KkgvCLyDOKodIzvTAg1cG5oPqm7kupB3Ta0/mvI/xZLFxJOsk+cfKfrSSmFngxL0d9qfhStx2o4P4rWf0MfHtgXBIS0flr/q+WEBLZrEj52w5k+OM83Tu/Bh/ZBt2/08Qs6C2qfGW3Sxl+YdosbcNNwHmq6PJEV9UxKG10GTmXOFlTHMemj4XJQaqKmUHXvUkGvMYP04BUct8TJADnj5ZgJ8UnO4CtpU6SjEgOeWaWPzpwTEQuhAf5uraW6B4m+CL6Dn4PuzmiON0NytB+5Ckc7SOUN0y9U/YnJldf7P5LLv0bl448Wb2KrW0i1c+aybWQBlxwYpeR11owlG7GgQZJwP9eKeMaVuxSpjsDU9UA1Cjfu+JhacyanvPy1EpUgf9Sa2aoi2hnzFpFXOdan385Gls88QunSNtFaKa4UcSQfaC2kmiP0ZBMoGyk/oaJsOTP1D0x1PI+wpi0aLKfV55IdVsSBpGXrKQ+ywm/F+7BF/c24RBZhSMAjllSmQIqh7ZDGQYsESTeO9srr11+wdmBHi9N6dLJMihSSVMhK1o2aKuIn+HfcKu1oTO643SKhPiXKtiElUVJwBrIFXKIzUuuFLWQ6Yy56ZLR5EMbakak2MFv+vo/FhD7EoULeYKshWxIK7mn9PgmBJaDDkSUPsvC/QmY40Jcy0eIMuzPmJMlO68+c/Rs/f/y3P1a8NX8gaWJao8+w79S0x4zsJ1l3TCaplPf20M3iWjo6ZjGouhNJPxLMeEN/o1S68TBpjr5ykqrxdKlc9g0TYUwl58H18pHLvkX6e3Es3enaOPrgbTh9GGVPAbYvzZgmJZcEZUPqBiVhOaH5RGo4V6ZaYspPYWA9deLJ8WJ4Esi2Tl6C85rW5kk89rweD0VW8T6K2N+Ld/3X4hq2qbjAKSc3/8bdDlwvmH3n7f5HjtEYx8k8BoyJ90nCuFRhy3ssTzRI4nM4o91o/Y17+8IYX8h0tryTZUeykKySxEmu+FCmn6geoUNzW5KdSNKMFJ8e9v2iMcDN2HppMkqNzdr0ifhALdw5n57hUgtzJHoTxghKZi6K5MawkABpirZKkr5zi0P+PnE1psUGUN0528bt+L9+rHhxx2cHh6M1fuUbLxciJ2HqElwKH5+feN6f2OuVdja+3U/011/48nZ7VxHMCZ6UorFguM8eH5p4ux/ZX25OzsLzS6ZmAa6YdJIK22bBGb7skJ2uCfe/sJXOHNANvDp5V+plp+6Vbd+o+xUpiuewAXA9SekNLRMtGa0xjD1aBHC6NE49SYtNlnP0/jk5qhYPF1vmHQ8JO8vzLE5dkNUtxLDrSSO7TQI2fPM/8fnt/+TtyzfENubdmeeS5CukTZk+mEs6U7XiObLppoBYJAB1N47WeDte8XFyqROKormQraBTkdnwGb212UnJO+IahiF+Ar7yhVcodjJSWr2sLFKSKCGsmLhFLnHS4GKXlKllx9SXrcAAucH6rvYr9PZEO0PFnJLH0IcxunJ/0zALBNrpjOM7W72T8w+uh0O8GplaR5+430hSKKXiONfLlZfLzqfrC5d9J6Wdvt1x+U4bzyuTItLTS078/PKBkp4424C3V77fZ/xzIfxc3YFMyoXLFpBJSpVhryDO9VIp+4VUBKqADgxh80CjxmAV7oXtemHbn6j7hbztpC2hW8H1juWTmQ5Ev6DlZ6Tk6GdTDGdOB7kx/BvoJGnFU0U0he8uAco/hIaBncZSJq32RwjOgXuoaycL655wG5Ov7TvfXv8PbHxDizHbEXBdFtLulF1JCPMUxmnMPtA0ML3jmnGc1u84gwFMmeRLCCTrdmXbPpARdA5kOnYaoxP8hfz4nBPzioSsg3BQj43b05Ozb8IwpzXhOMLPYr5b0ep6PZdnmYXGLmxOO2ETsJwzJ8wZ4Sw5JaI7juWDMSINcwpy5vhOLbIs+lv77zkj/6vFe9oN8ULrLSwzk9EWeVs1+uG9FvZtZ6tXRCtIWP+X9oX9KTNnrBb/8PyJj5cPCMpWL5grR7szVg5BOLQ8VlrhC+ybonohc4ZAryhaJlKeIRuaD5IkyoKVsm7odmXbXqj7Rt1fqJcreb8iVZAauKokwXPH0n+i61c8/YGcP0IqWBqI3lG+U/wN0ROhIl7Bwl4q5P6rH/Iwuw6Hdkddl8DwwXHIDLeIdTXn1r/z/Tj4dvsL4q88vwDaGUkZEvivVGdLwpYEH4l5lpjUZwsLAQSZE0wo10rNlZJO/Gkjkdjyf6D6TnJjnP+JdvsH+ozeO61EUcZ9LQBT9Lum1JrYNnh+Nj69XFA1xnjjfp6IeDgjPbaFeNh4kZjDOc++hjoPuLSFTbrlFrPNTOCFnCP1M9qI2NzZkoPVUig1eMMMweUR2fAjxduWB+uaOnNS9prCWMKcMTtjbrTZSTbBvnHOxunfudsX0gaMiVK47Pv7lqVborZ7mEKrMt24XJRc48vIxRhmvN4nT97RdCXJoPsAFg0ScE5ML4gmrvXvoHzEVKiXZ8qeybVQthfSfonFQ10+D/IlHAuz4OkXhvzKQFF9Cl2cBpFbvSH+RuEjMm5oClGjcWPKhsolvIhXgNi7Iw3GsBs2v2NsmL8g5lj7ynn7L9hwdoe0CYfGQx9ZmZvRz4qKUjEumlFL2Mj007ETxglqE+khEiiayXXnsm0wlSSfoF9JVGSA+7/HW6P7Z8a8k3G2RCyYbIuhyxNb3SPOazeen4S9TlS/40UXYzBO5XYE/ptS3ECa4nY5jkQ7Y4sXMp+O9S34Fr7g0JHC99dCm+Yj1DUG5Apl75QslFwjumysofhHitemLS5uJMR8ev7AXrdwxk6d19tX+vzO66FcLzuSKt1P3to3jtFA1qACjDGZVVEtmJ8YxuXpGZODlJT9ksk5hiubwjBDbPB6b5HXWxLmgQf7iCk/1Q0tTsqVsr1ArmH7XytSFPndtk1KwrNC2jFXGl8RDaedkCUN4I8IH/C1jFHA5q/gN7J+IpmSpYPfcE5EGkmvqOaAEP3R/574fMXG54iQTZ/IegH9ihXlUhL3duPeG66DSzFmzohegp4okEwpfiUZ2Bjh25a+xb9/LNdKGZh1UlFS3ZBFVHcVdGamHbFIyj8h+YZJoEFtDopEjzuHU9IiJJVELRErpuphfMgB8pU+Y0jdi1Ny4PZjRH6IU2Bo0AiInt8mSy0eUQJjKLl0aomWYxowlrWWOnV3rk8RQTDapJ2JborP8mPF+0Df3UCShliyHQu2dPo4eDtGFN/tzn7ZcG0cvcWNOkOXNobzS//K2Rs5ZyaKpAupjJD+XCr7VgJTrmG2FxFMB+ZGm5+wqWTvTM+RFZwgWyPLEXKf0pD6jCew3BnJ0bzjBawIkh1PxkxGn0rziJlCDVSWW6KQ0yvqn3AruL0y2xc83ZA8VkCiEnKaO+hAXEiEM6P7wp/9DZlfSXaQ5YL4d4QRVkxcOeZ3fJ6xItVB04Grsl0uBGazjLXHHqdnO0h2iQm+dOYUuh2R1KPGkDdSInIfhjF4Y7LhyRh00Bi0JNfw3ZglQiAtU7dIXaq5kFOKJVS6o9IZ82DagZlQcyKJU2sY6pkJYz1bCLx+dOFsZxj8TY2WwMNFffTYhmLG7IVcWgzMOVbJpYIQggdJ4bjezxp18GPFyxLhwdkHX96+B30vZ0qWtVVhcTkK52lIir7QlmHeaDCH0fygz8G2bZSyIUlxz6iEigHZQ+emnVrCF2JMaA6yFboU+kzkCa6JMTrab5TyyvVS8HQn6WemZu7zDZnGExeMv6Xov0PSR6YYzW8c/p3ByS4lFK0pkfMgMSmcqPyCS2PaK2KxRmX5IEx87fMNSc9BQPIeEJNP3E7c/oL4pKQYblULLrFwETtw64xkWA5DOUFw3SKHIQ/QC30UWlNmU2wWZhqYwlTFVDEJVpiorriqBhZG0sYXpr9iXPF0x+RPa5UuWC7MM1qbUiZ7nlRRMpU97yTZYj8+T6afnOPEydgcJIWSw+IrKSGUBFQuiBSEwbTC7XZynA+H8w1Vw6ozvTOH0fuO4tRtsu9hgCIaedO9O8cBx1047rY8h3+keIVFowuXx/sZ5OW9OpDQlNBSFlkjFgRiYW4xOowma88dm7pIpRkMU3xGmIcKtDOYaHNstF64XOMN16xs9W/wdCXrBU/CsBtH+8ypr9TaERVGCgL0wZ9oBqfMpfe/MfiV1P+Eyr9hqNB8MOQeVqwS4XtKJwPJJ2qOcoA1zA6Kn4iNGD4ApeN2MOeBZphWQGrgutZIvJK5UdIV1UKWgWr4rA3CzKPzSvbGrk4uiZoLniquhSmJqY7lSTKnDeHuB23ew0opO1oJUz1zNoVwj5t4v0cmhUz6vNOHYeM1xI+pomUtSYqhtNiGpYnIG8kzSV+Cq+vEgcIGfKSPz5xnzDopBYSpcif6fCNnFvF8BC1gn7jf6ZKwmcm1RfpQS1iaIJ05l7QKUFmaOYE+hbfv8P1r9Ndu/3O44V8s3ssloHe3IKq0I/oYlYxIjQGGwZjnUk5k1CQKd8SRnJKzLVecgIqUYU7vYRWfU/Sd5mEJhWbO0fn0Sbi8FHR7YWoO+p86UKn6gXMaooYWxfSkMSNQRWFISKhn2hlqdE/YeI1/hlCqk3IUvgJiHaajOvHZFmWwwRwkGm6N8/hvFC2B6/obwrZ6u6/kvJGkYnZjy86WMjknUgpTExELco2dVIzTGrs4JQdrrq9T2fRgcuWwFgMNoYo+Z+i8UlaKZFwnOo2KUElUL0H0sQNLgSc36XS/YzZwEqIbkpXkgkonSw1aJYpzo9sd5mc2/ZkkOwicJxw9c7tHcE4s3ipWZBW+LanXwDUTmX2NlE9yHvQhTMv44qv0bpyncNyXmfVU3AbbHsy22yt8f4XXb9Dab+v2HyrebYd2gi2BZS1KSZmkQi4JST34qVpofWIcy+v1kVkQ2qZtS+xbYppynso5on++1sJWnjBP9KGMPkEDFz6mUnyw51fq5ROuyjHv9PPGVCgV8vaEqXAfAb+4JroLVsDEOXwgWtGaIYcnVtFBznNBMB2bnTkPztGYnCiGuCF+kmioD7BG79EaqBlJYc/CJneqPodlFA10sqXClmXFVslaGhvDg0c73dg0oSKY7DQTkjvdje4n08aC5XKoCCTSmLw21HThzhrDoycKBTGNtmRCF6f5pNlkuOOyETnNQippYbMrCZ4RXJQB9A79O5U71+1T4LbOIlkJSUvYrtqgnR23Hc2GpG/0cbDXBcFxp4+D+ym83aGdwbNQdfpZaKdw3oXwdjB6F95u0V7eb6FQnz0OFdfFIf2h4q0SZiMWnlE5pWjc1YDBWMQMJ07OiA+d71bxjpBLEJ1L3dmpbDljs5O5cN2eqPmC2Y4Mp42YVFVgKx+o9aTUN6QcWFbUchiVaCfvkPaEpUwzC8qdZbyGIbKKcZqhDpec0drIyUA7cMI02tHwZJieiN84rVFFKFooYiEc9RP8pBCR5FvK1JTYE2y8sudKloiKSirUNChKZGYQuci21LTIZHqjmJHSB1w+PcTnZL8h8zsmM26y2VFLVHV6CfaX5g2ZirWJoeGH8GBepsQUZeqg+aAD01OQ6GW5W1oPXkMWpq9AwdmXd4OgYnTvvB5/BpQxBn0ExXEvF2rOoV/DOOeN+72BbojC6/e1rBGWfakwLdbmx7G4xIP3W3lavGxhAyJhmWu+VvqPEMEQJfxQ8bYjyBdzEajDWTzsjoyT3sODd847LvLwpos/OK2tVw9DCiuV5/ozsl24/g0cbeCWybli87rI3Y4kpxRHZENywvPAtdPllZErg52+cnJ7emVS6cAwweSODSFVZc+JVCtlU/brxr5XNIdD5Wwnw0F9oBaCRbEbWSYiQlFn0wTWUOLkrZIoRbjUyqV8oOgTWXJwm+VEvJNxdilU3eLHWcHV0yOL2ewg+2DTguszBizAAJEL078ulURs8HQayZzsI9hzAk7GVehTMOtMmVSCc2vqmCRMNqacDDJmk+QRczUNnCP8HEZCpuE97LOqQC6TtOyoZlf6jIEp552tPFPSyl/zuCGHwRyKymUNqy02atqQFEZ87iW2tMMYPeA1W5Ko0RSzvFw3HySniWpEOogT1gM/UryzXbARzPq01L5Hn+TlOQthlGzu77iep2DnqweBO6RagRluJR6qsZOS0S2jcmWOuI4KERZYNxieuZ+TuU9SdboIJ4OZ4nqSqnSddD+YUnFVLAcnwZns1wuffrpwfbqyXxK1aqwmZ8dIuEZk6BxxneNG9kmSgVoE6WEhcExmFE1ckrGr8lReqGkLKyKbmL8tpMLY5EKRWCMHYUfjBPZG9kalgThGYxL0UJccLYk7yWJQSyvY2sTYVNB6oU0YfuI5WHhTCOed1aYNUSxlpEykOPSOzPAKFu5RYEvn5mqhhJgbKjtwI5VJRZCpgXJ7YXiLuSQbOQVTf86OcJDUV0yvk6hMz5g1endQRcm4BW4tSeKzrNJxwjIhfoQlaXLDl/2/KiF//9ENW07pneI3DSLDi1hhii9hpb17zo6+mFUanlS5xjHcu3GcJ592iWRHr7jdOcZrECr7wHwPil4RBsYcjX4omgpqxig7XgXZhbwlKCVyvlRJqTI107QjTLbnzsvHzNNz5Xrd2bZC1oH4iWiHrGE2J0Zncj8a4p2Eg801eBCGgD7JKLtO9mRsOqiqVEJHFoTsgfhBRciuJEuIj5W7IbhNxO7IPME6PmO54Poz4hviA+yGenuPe8p4oAtakJxIvjRvaqSUmYnIchgpMFQOPBU8BeNOc0JK8AfUDbE4fU2MPgP6o3dScTKFPSeuZbLrjvfMSZjLlGW1bzaYosuV03AkQsfFmLxivAXGPjolVZJujDFpw98plrJisIKsFat1Ung2I2OdvhIxXAnqHuPkjxVvIQKtGViP62KaklxBlg5q7blDsg42omBFoR/xxoHwy9c3VP7C8/UnnBuv50kfO3M0Hk7daMIRxjSsGaSBZidpj4CPlMPQLzudgeTK88sehJuSaSSaT14+Oi8fEpeLsm+JnEG9R1g2B/F6nLh0NIUCd45Bt8GmnbmCubcsFMkUMbZk1JSoqZP8lcRl9bqyWAyDjJL8DDM812UeRwwd4471N8ZsjCm4HEy5MWVnePzMvRlzZkrNkCwE5qmQpmJjkiSWKlMXd0J2YK68MkFMoQg6gqFVHEyNZIKMwFMTCR+DZLBfM3RIU9hTON9k3wPXrWcEtvgGHLgPjhnuOTaJRYT5+vk3xhz0fienxFP9iaIZM+P1eOX1fl9evA8Sv8bNJLa2nBZ8mUVkikKP1vBdTvX/t3inrwR2iVTLh73lmErRFG+khyFFSLviTvDHgLBgtlAOwx9/+Uz6covrSjdKyqjuIXS0g84ILDILVoxUjXyF/OTUp418LZRLQDWTSb0qLx+eqdcXdBOGHsyUqZtzvTqX/Ym9vJBo2PiF1j6jfqdKIvkd6Cg3KiGVmd6wGSSUmjeecgxuNRmFM2CmqcCfkfQ3JN1DbuP3xSjr6GwoMRhNewTzNeY4meOOz04ixxe2ctHevjr/9I+NP//aSNeN//0//h0pC0UKA6FmBR0MnzSLtbvkBMzVT9fwBpNwtUnuoUJJMHKY3UlXfMlw6rVRPLFJRodi7UCZoCVc7ZOSSkXzYLaw+HcG0watx9UuKxRGtJCRd1XJnisv20eyCn12+uzczjf6cCRG0+BAJxAeB9QgF6E+2qEZvBkbjuYfRBvu521piJa3LMTCwiIgJeVIXI+1Z2B55hYhIo+T+J32usjYM9oNF2VIJ+c4VUOlGmvcei1cP1x4+liQXZjbnfzieIF8cWapuAiXlwv1kri+7KRtZ2jCtVPqE7VM9vKRqnuQW+QT9/FfONorZKMwY/kwJ5cUeLTlBjbZ0uSpZC7Z40TSSsZIAlmeSAhV7pFg44GuiCvQUHsjLn0W2B8xBz4byaGKYBJTkbjz53+883///Xe+fG/cOuh+Yd6f2T5UTJXOHbHMnqDlO9kSUy4ML3RVTgI5EFHEg9zk4pQkzJQZ2RgH9OlYAmVyqRvFCzon0hXYmCPMvbNmxEtI3ZMzdSDzypyTo33l3m7InGy5UvMlVDOcIB6pTVXYthfEw+u4zkTOSg/gn5KDhTZ94ky0DHJ19k0DdzZBl1DQCSO/Hyremrdg5GtCL8bZJ20GdyHncNGuJU7cJBUkBRHD4/S1OTj7Wn8OQVwRwhc/Alg25siYCKlG/1PKxvP1hY8frjx93JHdGfXAr41RHPaEpGB7Sb2iW0KKo5uxZVlbQaipUikkOxDroUrvd/q9c1djcFDVuOQXyl5IfsaGaJxkGVQ9yIRVVRalilP1icwFdSG5BpyGkyViS906eMPnGT+zC8lDUWFEan2RQkSWwLdf7/zx7z9zfG8wgmM7TmhvN54+KHFc+BralGuGZoVOxAAgzrA1hyw7giQ5mFSp4RJ/vvkI6C1Nrmnjokr1C95gnuG52w5d3NyCWmLOgYkiemV26G0yesV7DjUIO906pTg5XxeECkkN8xjmzA+0DMp+YCSsQV9E+yCLDUo1ti02sGKRLy0S81ZOFZEfJKM/bx8oWXjaLqSUVzI7vLXPeDpw7ZE7LHE1zenMsGlhGqRUSbNhKEkLeA07TV2DIAlfUaSxqYsWJ+VEromyC3JRdMu0fGJ6Bo6Y3hje6PKdkV6YOYD8lBuaW/jtzobkDyR5RnCmfUPmZFdBp6AUMoNNXrjIv6Wqk+Wgyz9g/GeqdhIn6pDdSCjZnSwniRLDlRlZnOSGeEP8xP0IMgoZJWOiKBFXOnxEKCPO6+3gn/7TdzgPshvXUhlHDwea3vF5BEGc8pDOsWkOt0YPn15jsuVC8hSGg3L/LYtCWzDqppOXALTKxkU2qgh5Zlw1hpR8xzOMmegev95XVKugjDmYM2HC5PSOAAAgAElEQVRzw2xDvKOSKGv9nYTlMafAwXSjjRspDa5bZS//ht4m5+nc3xrf3xrH4bhmtpLZa6hTXMKF1HxSy04i8y8IKf7l4v3p44V9F2quzG6kFhldpV7CyjJ/Z/pczDNnnJEwO0YPSiOFcBjcCHJveGZpCiDYLU7f/5e0N+uRLEmy9D4RXe69Zu4eS0atPd097OEMCPD//w0+kBxySBCzdFd1ZeUSi7ub3UVVRfggN4JP04MJJhBAAplAOMzVVGU55ztGghQ1bl0WtCqenS4p4o3U6bqyszIsx3guCc/HR6Q1JrmcELeO9wgIHN3Y/f9A8wfEO7fX/8BodyoVaCQivTyZsN+e+fCbd0z5ge3+e/70z7/w9t0nygXEejReSTC/4TKfLoDzqdbTTMqG2a/42FCpqEwhCURxcbLAboPund47P/3TjfvnlWTOkjOHd5KHT9Bax0cLnkO64qfdSL2QgCyCpwk0xO/HOR89fKUZSKr4ENARWoKz8c6eSZpIUqPW9Q2tRl0icE8OIsBEKmqKnU3sMTw2oD1SRktJUd5NS2iXx/M5fdiBwGh1/8JDubCU9yQKMsG4GOu0camv/PzxE1tzHqYLP7x5pGTltn3hl/YzLoN5nim5kPU7b95pOZiny4ngj9oo6WlVL40mia1/Yd9HcMiO2CjllDnaHvFK8hC5bGScHIj8s+Qwz+Ah5pa58uHDW+rDwu43vH9kO/5EmWJrtHPQUmZ8VRnlRCqJfTT28ROFg2QOfgc/ZYW+svWfwRqtfaamwpJgkor6HnWsHbx/+8BcBR0H3u48/3jj5S87/8P/mJnfho9vjABdD1GqZtByOqLPZ9CP2KKd2ofIgEgR6vdV4yqR9/bTPz7z6c87HOHYTVrxFuZkN6KBGhN+8tfwgNslHwz5qkRzis4o7RxZCtA5JOSE3nMA8qTElKEoYtO3kkVTQtIcPLJpRzxhOpAWW0CXQnc4zmyRtR8cxxlTlRQj0exOUmjmwIr30DY4O87GdXpHkkwWzmbSyMljQ1lmbDhP81veXn5LKZWn5Q9cpic+3f+JlJyanKz/9SP63xiVGSY3xrjT2wxjQXzCFcbhrKPzvDrrKkFo8X6KdULSqGTEQ4kvSaNRSWeWroaD2K2jKVEWeHh8QifBpLGOzv3o5D3SeI6WYFLylMgltmfUyHhb905Jr0jewzA6EkudMR0xaRmdopWpJN7UK0u6on5H7IXnXz5xW688lHfc71+4ff6F5AfPvz7zj0fiH/6nhTdPoGeC0cAYyckndFhSaAuSDQSNmA4g+zg7eAkTIiGc+fjxMz/+x185tgBIX/KEZWHbzy+fCrLfyf41mf50Jks0MInMcCP5dqryMi4Zz8rwQRsDk6/yyWicA8UvDISxR0q8i1NlIuXMUidMDEuDvo4YVaowxLAEXQfxWxBSmr6xig3i1VWjjRVkBwat30E6W7tFpkhaYjFxprrnGr9D2SL6LOsSJ0Y7b66/YZ4W2tg5xmfQ9fsOr0vD5PTaD0FHonUi2mp0nrfB62b0LjzMiWVe0JRZ9xt9BO4rpZBOumQGgyESda9MBOY7PPuSO41nrssfyDJw7mxjQU60UL5mymUiL0KaFZkGaYJcnaQT9zWxuZ3SRsePDc9BzxEbzDnxVB95rG8pMlAH685lGtx+/sS2CK+ff6Xdn5nUqDp4/fTCX/7vgf5d5u07ZxKPurmkaOQs5qWJTHJFXc+pQ6K4kIV4BQx87By3G3/9L59pr7fIMEvC2zfveVk7VTqHEFrh7QjdcjbkHLshFiZOc4Ybw2toXSU2c3IyMkpJdEJLKzlQ7k5w38z1nD9Hu5w1oVpIVcmiDGuQO5YPeg4Ku6cB+evNfYlQaw0ROcLJLQskQhuNYWvc/CZ8fv1M6zeWGlFWSiHpFbLhqTPceL194v2735KI2FeoFN8pOXHRJ4bfv+/w7nfBZA/cT/NodlTo1rnvz7yuN47uTKUw1ZmsJRoMbeR0kDPnNCKC80ZPqBdEZrCMaiLXQX4QZKm8to+MYzAmo+saa5YsaFVyzeQqlEXQGdJMWIAYFNljyTEiOTPGKyluLbevbSFTUqo6RSLt3XQw1xssb/C2cty/cLw+Q18pOlAZ3D5/5q+9wB+VDx8qctIVpymThqImJJmRkeAM1hZ2xCTQ9lkZ1vn5L7/w48+fef7LZ6QdSC7kUikyKHaQ/EDODJCx7RTpoJ1zt8qwhHhBXVEPvUNnPRcFFU5xTUpho0o18pKjeRTEU4wsRULAQ2VIEO1lRNnhyuksCVPkSIYXQ6tRLNzQYn4yKRRzo2ZImnEq27GwtY4NJTGDD173jdfjhZoTNRWKrrQjZAOSlJ8+vaL1f+V3H/5AKZfQtOAkvYQ6MX9n2fA4/R3NXvmy/UprFpGm1tnacQpDQlF/mQqPyxVV5bBOlcCYzrMwpcKxlcgAHuAeg2iRgkpmqh4XS1akdNbxEUjoeauOLHgO10GaM3l20gXy1HAO/DgwX8m6h7bUoSgU6RTpiIU6DFNG+0SXV5LOqK/09ifGUD7+8s+8uiJ94/7lI8f2Cu1Ax0ESwQ/j139syL3w9l3i5dZ588cnEpGlFgGGA0VxSbHTlxFZDh0+fn7ln/6fT3z5/EzbVvQMJn54Sujxiu8ryZ05ZxrOnJ2iRxCH/Otn5uQQbHJGiIeg56uD2TOJKSY7OeDSXQRNBY4YqzkJscw4YBs9BDhA9kQ6rVcm8Wech1myU87QckSgB1HHfUdSZ54rNWfcd3IGu210T2HVMgk7lbRYSgQcI6K1UnAf1Cvbofz48Z9JafB0fct1+iPqC2af8e8NVJlSJaf32OJ8OZ6pJX7QuVd0V7b2imHUqtGBqoIPkjvLPLOUmaQXxlR4eS6MNjhGIqWKphnNhbka9ZpJDxWugy1v7HlHS8ay4zqCSVDOeKNJSKUheo9JgEtstEaMqtQbNWWWXCgSN7Ow0ptws8/QlY2Gt51kBRlX+nbj9fMLc7IgMVoowIJ0aLT1jqrx6c+Ovl6YVHnJB2/eXPAh3yKf9KuWIcSBkIzO4M//6RfWTzvH685oHZIzpcQixmh3vO+oKYWA0L15yhGkcrpsxXMIhRi4xGbOnNPJrIiEbpnToiSST0acMMjhC/OwXXEScoYbr21H24Z2o/ZCHgnrTrNTxVagUjDx0GK4hr5iROzqVIVlCjjhGI2Sw20xevAk3EcwldMSsQUImJ0eRVB3lulCdov+SAf3bWWMPzHnQCnkf0FW9t8W5rhT8sI037guF2peaGMw0uBlDw7VNClabt8EHJdcuc7vKXoFKo3OXDN7NaxHuncuTsnGdblwfarI1Rizk1ICHezaaefCA4ysTtNBSY2UDuCAMZAxSGJkPWtPEhmhilGTovYViLGzHY2+3clyULxyTY/YcaGvPzP2OzKBWosPf79DUZbrROud4/WVJJnXzeBy5XM7eJgnhEIhk2VGLWjl5i2sUa3TWuPzT3e25wgJdDPEoA5hNmNtG2Pf4jVg8DBnnh4LMmLXnz2BNrIWhkis4S0wU6opiOQS0xs8NMcRARfhNkKjO7iHPNLHwGuM1vzMi3B39jFg99A6uFKSBCkTRW0wioSbfARXI+uVOYdKMGlHuNDHHk3rScJP4qQU0yfEvi2tsEHbiTKLsIJljfJDxLGuHBwkGexfHRX/vYc3lOxGzoPLUhA1zLYYESW4PiQkDS7Xg+tyjQ9Wj3CiakF9xu2C+KDlwfVqSB/nNiixTFemWsgpROaSjVoru8xsehb+SUGFfZwpCDriwxoNuqOevmX5Zk0kPLgG3knmiI8TCUoIWiX+hJK/024bVYJaSG8sOQAa7gfWjLY2JnfGPjj6CIdxq9Qx0e7BD0uykyRGY2PssZd3476uPD/fWb+s7K87vXc8gbowkan7YOsdsU6iIcDDUphLJnnDLTPngpSZLvF/ZBeyCtkTlYnDErsNhD2YFCTczg79RFe5Q3c9z3Yc8jEOyGFR55RJkjQENB6g8ISCe/AtcoJ02uq1UPQkd7YNiuAe+c+jR4YIAqkkgsZ+hmxrCNRtyDfpwBgt6KCaUGlxo+cFZcbM6Md3hgje9p8oOWEelnXxAbHRB+3U4tQpcV0q1/lKzQXRazwBXMAnzAqGUCaoyfGRT85ZoWikYkpKSA5SOfmGW8XlCMV/pHgwcI4WGb3ZB0vOJHLoZW1FvVNwsgTfdbRBTwO1I7LNRkwD0iliybIh/hl1p2+3+GKMA1GoOqLWGo3ZE8WVw5S+d/ZtR1YhdyW1KyV7bOe8x2axx+2+t4P/8p8/8ctfX2jNafeGYWgRas4UV6Q5tg0mnGPsTCVRdDBXyNmYqyM5Y5owrexmHKTY3XmKzDIFMcfPwJtMJrwPBSPcG64BEPR8Nm22n7nDhArRIi+E7sE51oQOOQVgwhA/k+5P64MQfQsKXOh95eiv7FtHpZA1nMLIONWGX/fBobLzr3hYh/t2hHSzQNadcr68ETI6TsD0dxzebj3Igi50O2KTZMbwg+PEjNayMJf3lDRT8hLkRI445JbADyRBmQRPieyO+RXsijBhZJqNcOuOTpcMydG0QD6QYnh1dBKmSbjUwUUrD+lCTQ7pYOyFrAfXkoOssx/c1y8cPqIDd6WKnpqmFLmAUsmszJfMeoXt84EdK92c4p0pCdaN9nIn6US2FOhVU5Jnxmocr43aN64Pb0iEiqzfV/7y52c+/nrw8txpW4kgaB20scOASTLFM/saB0jNSTLIWVmKQg87eikxThvitK8gQm9hM7fwgQ3v4ejAotSwQrJC6Mc8blEZaA4u2VeLftTSpyzchRSpMSgaK2gUTM8JRMEYAQxUA+mIVWz8fyKjMSKoJauHW9mDDSyEf81OOaVKQrNCdnoS9q3x+nowxLmIY+yI/Mqkpxbme9fDdo5aHI1kljGI7IIdU4tlhQluobrHF0RzPLk+MPvM8HMblYz4SGZUHhF/CMBxGaxtZaxh5CQrWpw6TfS8IctAFkEXmKpzyXAtlYtCoQMBxrtOhSVlEo7XC7/2n9m3BiMC8GqeKVrjAItTNW6wJK+8e1/4dDttKvtKSc4iwQX27rgYb65vWeqVvnU4nLYN/sP/8syydN48KYnM509fOFonpcq2Qd8Gj8uFaU68SmI/VkjE7dKFPkKX68OZJg0Xwnaj316RN5XkIBQgXj9lgHVELiQJFpm6gx34GGAzGl8j1ATxEWUVEuQcOrhG+g5w1hXhLigglhHPp6orgIHWBh1niJ7p74A4zRo6AsIYjpoUF9ZJnFQNxOzejhDaaEQzfAUVak2M1mhHpBv1Br3pqVpcsfIrWZ74F7To/40oq7VhrYfm1h7prZFz0M33vnHsB8ch5LO4xhuq0amHWyx/W12mlE4b+BwfvL7D04VRN3RKrLpBdWQqTNfMsXRMjbFAulZ06kwloZZINJROOYf3KXceamVRI3nHVRhL4eN2IBJsgyQHRaLLr+etqmSSF8QGbVuZNDIiaEFWTD2ojjaMNjr95RU/jL4N6IpYIjWHMvHb3/6O269QE7x9/8QvH39lSVNEPh3GUhZKLkhyVDptW/Hs7L2Ta6ZoYu2dsd1Zv7wyP10oUzy7UaMnercQsqtE1IBHTcrw83XhRK+GCEhdwx0iBUGwFJ1R/2o/GoonQ7JCFzRnxFJ8oSxcxUNC/O5JzqWFxeeLBXU9a/QPFvT0YRaLGReSlFiuWMf8azxEAzQg1flEFHSNTI0shLh7ZtvuLLWz1B++7/CKVNoReHw/Qz4C4dPo/cB6Yj0aH8dfcdu4Lu8DDuLhFqxlCsR7fgAJD1lK71H9AZX3UIQxK1KFVI30lNCnGXkUxqVQ+MgtDayupGJk7RxH5+Y7Oc/B1eKO+iuZwqRvUK84d+a8MaeBEQmLGSN5OxsNp+ZYXsgh6IjG66FW0pzYP3+h3zfa/cB2Q63QbJA90+8NP4QsMzWFIP8ild8/veftv3sCd0yc/WXl1y93zEMroAW0hAVGk9IOZ992uhofPvzAbWwcY2Opypu3jet8R22O+AM2NisMT6crF2x4HMIhCBNFczz5HJgaC9GjNAodR3F6QCpAiSWFOT4U6QTm1TSc4mY06ScrQ7A0qNMgpYHLiC90s5g6ecc46GPD/Tjd0GEXqhICqi77GZodhCURhfNLKZGiHhR1SSxpRhKsR2Lbx8mW+I7D62wMS+z7EfjJEiLhrAtzzgxVRA8kvzK80cY9zIxkVKLm1RzxRKoXJKXIFpYUIvesSJnQOVGXij5OyEOGy2BcnvByR/QvrL6RkpO94cM53Ln3Z4o5uXSKhFYXDbr3aF/wMciS6L4HPCRPMY7xRtYRIpzDsQPoDds3Xm+vFHP0MJY8Y0Skqo8QovctwhGz5fgl5PCEvf56Y//NztPDI+7GdjSe6gN7vXPfHGtnGuWx0cWYr5V+SkhzTWyvG1RlTpV2v0EXEo8Ig942Di8REmgZkxLethRxCFEWhKdQJQTf/jWdNOXIM3YYRNBJHGBCAeSKWnB6Fc4ZMFEGjwD8kYLamdNAJXx7LjsHRrsX9g7uPXJIrCN+UBXmnE77ekclMTQA4jFVcFwa0xIkpq9bwLlMPCwLtUy8ubzlef1Mt+8MEbzvKzHunHm8XKm1Ygy6JQaKilHqW+b5wlLfIaoMP+hjjQ/Pg3CDlNhyooH4l4GzI+mClopOFV8qMk3IJDAJ1Ead/sikK+b/RJJGtTs57UzETUrgI0jFkAH9eEVYacdO2/3spKPLHd7OuiygIoqRT/KLT8otCZNnMGc/Ot3hzfUtq23cv9zpe8c2Q1qshG0Mmjf0vrL6yvPPNx7zQ+BJR+KiC8ULk0yYN8Y4EFF63/F5Qr2Erd6dl5eVpw9PSDbWbaPowjyH/6wPOblvwvAzv01ju2kS6aIqYSeXFMuMLMRESJTQ0BgmSpfB4YmUnEM6zUNeGpFlUVy69TjXyU9hk5PSQNKdJAdZOzYglTnc2t1DuukngMaVTrx4SUGRuMF72JbkZO4KFnqQHFkVAfFT5mmhpBO4srznZfvyfYd3WEO0sEyVqSwkUbpnsihFQ6GftfAwv2XKD7GDd6PnG8MNk5W9DZL1AENLCakgGXRDZUa0xnq4clq2H/AS/+5poHkij07mxrUcXNSo1lGzkO/1FgJv7cEJFuXYBuOIKCexM/XRwotlHivPnB0dYH1jrDtjXbndO0uamXNFunB/udPXgQ6lbQ3fiR1/B2nB904Fjs93ts8b+j6cHOrK/rKzfX5lPe6US6YfFgeiGcd9D8s4cQtqSXh35gfhw28e2L7s+NGxUnFzvHcwjxfsdGbEPymMsAqiBTupk0gQeYo0khpVZ0ixaxwShMw1w12MAw8A+Ghg+Zz5GuSIQfDUGdYiCkEbJTmjn1SiFK4H/NROkL+JddZ2MBc/xTYRJSvnhhlLoZTzyLfICbKWM9+4klNlcPAVQvhdh9cYYT1PErecg5Bo44YhoBn3TmIm6XIq7xPH8Tm2UjIw+xGo5Hal5Dcn0/bGNL9nuhSSFCSFdcV1w+UXTAqdmGiktDJrY/LBNRsX7VR3pB8cx/1MV1QiDDpWn9aNdBJqjKCJZwltgBox08Sx0fC2sVTh7VPm1093bmPwOD1E/eeJsUfd6w04wLpBE9QS8zzRbwfUTH/Z8dXRpAwzXn554cf//M8cvvH+9z8wPSy8rM+oK+vrnTRlug2SKHWZyJLZXxu//VcTNQvZBlkDETrrOdKyQO+Lg1vcuI6FXLMTigcLC5bqRspKqplU9EyPjcO7u6EarN7Rc1Dou53o2KhxR5JTURYEn+bj9N8JkgqlzDGDH3EY7Ss/goAxDjfW0ck2SDng5HJC8yKQhwCTqFNKosjM6LDtX8j5A0YArf9/TBsMsUHxg0ttkGe6dV5ud27bHUlwWRbePZxuCgTXFk4DGZgLx3D2fT9RUQuikTnsaaBjw/01bDIcIGs4eK0wJDP0E2Y/U7VzScqShEWUbM7XyNP96DSEqYYOwsaK2krNjugN143eSijLNFFTDslkN9Q7WQamQBuoB761HZ3qYRSaHt5CMn55/hh5ZIejLVAirW2szys9Nb5cPrP9bmO5Loyjw2ZIg5JylBsTPF3fcDtuPK+fz0ioRBLlUmeO7aA8KO1uXN5nFo0YLZdQcRWc6tAQNgvonbljRNRtiJ2+dutBFM8iJBmo38kKaJQe7jFBODJsCkbQKaPcHfGSSRAttRgZY9HHsA6tjYi7mhnjVM+7EEk1GcmxvXQ/OQzKGWV2XiZDzyy+IJ93CxdyyYWpJvpQnm9/xezEp/XvbNiyP8YhPgafby/ktHG0g23vuEeox+iw9ztlRExmtxuSlZwq1u/0wxgW3yLR7WwuNvb2jG+VsjbS0pFjQ/pAeMTF6Xxh2CtlmqhjovpMdajpleQ3xrgxYZH91TuHNwaN4gdLcZYMk17ipWiN3gZiI9bHDIRO9lgQrJ8HNQnvPzxCS+wvzlgHYxuk7iGo2TqpJXQkMjX+dCW1xP668eWnz9y/rCx5QU15mh/JFnPr1Jz7xzs9H1zeX3lYHnhZn7m+eyDPEUW7bhvX7NBjRZyGgYXIiDNsJlHYPYEl+ldn8umyyJyHSDgZwRVshzFCTSYZ1fCmhaG0BOJJYmJyML7lzLmebpdZKJp5evgNtRdka3gf9NQx+VYlBz+EBHqcvrtgMyQVchKEgUggoIxB74b1RB9xgKNcEEqdyMnjgtxeaMfXV/I7Dm/ViaSFww4+v9wREjkpU66Ueirjs9B9o3uof47xEqJkwv3Q20PEGnmil5jrdd8Z42e21VB9RFCmBNMlncinC6k4acrM+Ur1QbWDosHVygyylCCrLHEoMUNsZS7G0yzMqtSk5ORYPziaBPFyhJZA20HqsH/JLHMmDeFSEvTET582+t7YbzvscLw0vIFaik2bKbJ3VN4wifBy/8j2fLA/H/iDBzvBErZDEedJlfL0FtWP/On5jtfIILZmjGPw8vmFIR3vylSUyzzFGtyITDgBtAI5biQ3igrdwwAK/RxPxTM+TDiOjvlGkUrywhgvSMtIfghv4VDSCJDfpWo0tinHresWi51qvHv798z2t8jWGfJX9v6Rg87eOs0F1x4JQyGBB77qfeP2T1pQ7eTcSDJoXxOCFCBu9jYKzRpjVDQFuqqUTOs9NBffc3inWqhlJg+NkcZQLnVimQpTDSuP6U7RKWoTAeUC7Ixm2CgcR+V+G6FJ1djaRWQSeG9IO2BXxjqT/YGqj6Q8wZTJ88pEo1piSYklRRBlEUh5Qb1RfCfRz21TY0rOQ70wJaVEgUv3cA4kc/bRUYs/Y83RVLhTLJGXiS9/PSia2fuOnOvSvq1kL8iAsQ2WVPjd48J9feDzMcIYKRN44QxU593DWz68+x37/pG310eO9pk1J9p+Y1s3hnSOdedSL9xvN8olYS3yzLJkFEctnBuSlCTRbOrJexDPsTmTSFIySoB5GFFbOhz9oPUJTRWjnBachqvhUsgizAmYjJIm/HgKW1M54NipXbnq35OOBJaR9lvufGTrd9YRul80xPlfY31BzxySQbeDCtRi1CkzRtCEuhDB3GeCqHnitm6UnM+M53HqKBou38ltKDUxT4XJKkkLLy83asrMpVKSxq67RHizuIAL7o3eV4ZXjqNwbM44Ei6F/Z7BZ0RzrI1R0tQoWtECuZYgtZRCKkopE6UPSjoo0skiTB6uiJyFhAWQDgnhuTtVJRi8TJHS4yHcdkKonkd8AcTjWUsa7ILLtbA+H4zWsB5zT+nOcduIoLPI6aXB3uOD1fUXrHVKubJc35HzHBI+gcfpyr/7N/+Wf/9//W/8xz9/pj5meuoc6xFdvELbGlsJ2ubD20rNQVM8xXyRH5FGDAF0DfrjiO2gj0hOShLRCIMYkSUfJAm+WyKdUbnhI0zqsRCwhOQREBU1ColcF1zC6a35ipSV3BPZZtQdbyEoHzzTpTOkMqyfVBn5RrlHwpIvco4hfZCSkGQBdYY84+qxGvfwq5oPjmPnvmbq9EDO4dlL+YTwfc/hTTkMcmjiQRfaHkyCCM+LOKPYnBVyWjCU1u48f/nI2jbcOjYSdVpOq/vEcX8DUvCLI1N07yoH5Am0IyWmD5pbTAg0gk/EnhE5ayMPuorKRpKAiqgEF7cmQdnotgX3yj8xTpWZ7RNpeJQmojRixZrTEdGveaKtDfWE9zhcxxaOg+CohtXoth78p2fhkh+4N8jTxPz4yOHC1sKcWbLy4e07Pvzwe/7xn18RV/b1CCIMGttKI3BK3bCj8+H9I8vJYRi+I8NQD2NrF2PzEfBsa4hlklaMev6CV4SJJLE+Dw6FnjLGr7dcD+K7RKrmcA9OhM+IXtBypl26o0wxquobpDeQKsgzcAHKGeYSirWkmZIB1tNkaRgLZkG9d89nZNYEXGntTh8HInZuYIFDGMMYRzR4Oc+odMy+04CpmhE1xOWkbMN9WxE9eKrxvKsGETCnGXOhJFimC8cIfWmeKnpa34cVRguqDn4GKWdDioa2VIzhL4gswAtqN9SDBTbsC5DIaaEQt2zmArbifWdIo2QjbNcZvLL1M+ugZ8aekOYUH6iUc0s6cBesK+M4SB630+g9pJItnmCJ6iO6ehOsw7odjLzTPHF9eOLy7i27OzoGi2RsCKUWfvP+Az9//CvtuJ/kd6BHXWjNIIf+q20DtcSbhzk0E6LsvYWU0QxPDRjYCFkocjoorBPBhvn89xL9gKb4u1SCpStC94H7cT7re8y8UYYdYZhNFeXCECGLIao0+4SOHeudo61oestlOZh6TDYGg7F2kkhoV1IQJId3UGKTOso51x60XulH1LIRCRsXiaZQs/mQUMzZGVD4vYEqL/dnpqffxmbID277yuiQvDJxhBRPCykt3yAbtWYelge6WRAlk5z1cKKNHM9YErzEL05LaE2lxB69+wGmFDuwHoEaYrGNKVypTLp1y/0AACAASURBVBSNp0gchIownYk2DexGt1fEa8wwD8cPOJn3wVKjhQaixxcpUPKOeyMnZ5mEtMCXAXjw1eLyUugRR2rNeNnuTI9vKdcrZVlO/1e4S4ZBduXd0xt+ePcDTS/c/nrHu0ft34yROtKFXArWhGMtZH0KBKo73S3CYpKHFy/F/r+cXNvhMZFwIt9XCec0X5FPemYfK2czRTz1oXME11P956z9TvN/pPCZPD6Q20TpwmQVtTXed4U6TaRLOmmRZwNpLRAGmpE048TrpVxPL5vTRmVvjfsuMUXw+OxDrxtf5tY6fTK0gVvjaONbVNZ/9+H99PopQqM1cVtbkM6x8Dj5QGSQynxmVFyAjMvBXBam2qLpYYA6SDACrYAkx2dHZyXPwWLQHLI8Tw66kTCyKZNPTPJHlvTAxBZuYZQk+WQEwzRNVM2cLjBsrBz7ShqGtTgofjRGg1iEtkA09RKOY1OwcmoYhOs1w3bqXCW4XT4k6jv7enMmNE9oKQw/Z/Uq53/22CwmZZkqf/z9H/jrlx9ZpoW2hhqva0dqbOPMjKyV9aVi7UJbf+TurzRxeulIUTQbWTXUXdg36eJX7a27k9wpYQON0o6zITo3TIaTiVeUYTE+c0VMIurVnW00/HhFto6swtw3HvQ9VS+QnTQRarsjY7swMnjOYELOMWPutoNVis700TmOlW47t32l9eOELoZ4y082mYiwHh17fgl5qDQO29mO//r5/BcPb+uD5/udpMqwhJ8bndaFYzQajeKd4TvJWgjRfaB6Ab7E73qEEiSVgWalakJnIb1xylvIbxf8QcJFkTqShVSEmpRZLkxmzO7MBNsg+4ZyWsVp5+huYlJD7Iay0EbHxs7ojnaLxqcJ1iITTs0pMsiUwJZScQ3gnDC4PCj3n5WaItlxH/uZqRBSSBWlLA90L2xt8IfHB4YNWmvo6AxgM0g6cS2JH96+4+cvP+MjSgTrsSoWC8vMtsY27OPPH/nxT8bltxu7BubVPcTcKcXmCiFEORaYgdhqhg0Hz7EocmNYENi/bldDlO+nID9/m1wkcbJWigWayhAOC4GTu3IcO7uvPDAzpxwwvbKQaomARt1BjSz1xNlCTtf4+yyhcsTqvu/0HudHVFHAbAS85ZTNDh8831d0A2TQfXz/kqLTGdKASrOdox+YZfY2+PJyR9MenqPxQMnRIpsN2nhlazeOfmIsxU5rtgfO8lGob4T8ZiE/vcGfCrZ0yBvkE6isUEgUzRSDdJIZ1YFxw+0L6nYe5BBcJwEsqOy+K9IdjoE0sJjP4Ec0m4HwFIokck64SIA5phFhekQzub1u3xwsow3SgJIql4cre4u+4PHNEyLC0Rov64r0QUGwfsVG5fHDhbks0GHsDTOLmj+HNcmasW8H+fXOT3/O/OH6gC+dbi8x4C+KetSCw4wkBdccW033U5cMofoN+73hZyRXi92FhEogoRQNuWPwdSObzUkR+qgWkQsyzjm+Mg7nIDI3RBNmt5gqSTpraQdLnGEAiJcI1ZaEpkq2GsQfV8wjJZ5zczjGKbEdetbKxtECSOjuoQ/+nsPr4tz2G8oeYowmIJ1uPfbnryGouJaDkp4RXeg0dvsV9E4uweVNKVGzUsvEMs3UpUSw9fwWna/4LIyl0+tHTJ9BJ066EOIR2mf2Sh+/IuML+Osp4xsMdrofsRSwHZrR7hu2CX03+pFiMTHsvGVjGZ0cZPQT/BGWb/GJyyT0NcKhj63FGpn0DUXvRGDiVDJ/+6//ltWg5My27vz66yd+/fEnHupMb41JDv7+737L//zm3zJPEzmX2KaOqHtrrhTN+IgvRlsPPv9y54e/eURSw/IgF0ctGAj0Eyebo3SIw1riZyIUZOYxqkpyMtt9hNpAGgki/wINvprE1MNRmhhZLKY4OWxD1LghxYKbG2hTA+2YQLPB5p1uA/FE1YApukdz5oRUEyGc0Klgrcd0xE9v2+liFgFNFjNoDPOOu3//qEwEejfEO6NHCaBayKmGhtMd65nRnSOt4AeNBhLRnCF/lFOrWZlKpdYHan1AC6TyFFOGDJozpEBCtfGR5spOJ/kNGz/T2k8YDWEnjRHwO38lsTFkZfdXaI5tjm2Ct8jIiMCRc277de/vFnxbC0KNSGyYZCg1ZdyNYxvBWROPeeW5Bk0p8bBcmeZCUuV6uWCj8+Nff+JP//gntO/YtHAphcN2Pv5sbPd/xePDhYfLE8fY0e0L97Ey5cqxtphiDYmwkza4fVp5vEIpGSRi0b0H11glRciIjlgcYFEduCNoZGG4oykDwVeIb6qcel3D6WeYSyCgTkBVTF/Ywp1SYn5cUmaZF9Ke4HbQbENc43cvQS6fZtAjANNjrNgJ4CNFcIxrJDrlpPSRz/Isn7IIR6XiErkUoobbSfwM+sX3Hd6HpXDfGr0Z4YxOTGkm5Rr2c2+4x3NZcth8+njFPGoZ0QjkyOlCTTMlzeQcPjdRO1eLNbpi7XGL2s59/wW3ja1t3OzORe486MaUGsZKG3e039iPz0hfSXaQ+oE0QZrA4Kwnw1uQEyh2plvK6aAFIdPpkQV2anSzpyC9NKGkQtdz2C6GizNNlQ/vn3j79glyoo1BOw5sv7O/fGFfb7RaWIkB/WX6HbeXZx5+85bf/PYDa3ul2YEPmJcr9+dP31y8NWXePr0hy0I60bHxljuuwRxDQjhD6qgY5idU5LT6CCdX1+Mm1ZSjJBLDziAUJ/BbASax8L87cPYQWgJq6Lsy15na3uJ9jVhZOq29xkgLp+ZMnh6ipOnxKo/R6HbQ3ahlIhWYPZPSQtJMb43eG1uP8xNIBT+pm+lbJgjfftrvObzzO9w+8fmIHISc8il+huBjFVrP3LaG2RdSKTQ/cHVyypTySMlvKGkmqaLSI2nnJAz66DBaCLvHPTIMutENjuPGMg5c7mh6oejKUTeKbgg7zgvu9xiPjQ5nrhjD8R4eqvhZBbXIF3Z3kHRGb8UIZ2uNzh0l4c3pfePYlGWeyWn9VntBcMhKTjw8FEpy7FxzJh9MWVlKYezKaB2pmd/88IHf/vb3XB8faUfj3dMTP/9yYWsbZQrtbHjp5NyUJt69ecfbH94hZePQF4wN56tzO/xqoo5KNH0qofPAcwjA5Sz8iRLDLSHa8NOaGbyKk1zjMR0xEj70RE49hNU9WyALeiW5xiRFFbTQmiMtB0MiXSmndUcsM2Rj+BZQPzpNV5JEYlNOBbGNTqJrZRyvdAshvepZzg05E5EsiJn5O3FPmcxSFu6p0zxsHFkh55jnukZhGsntMY/tA0iZlCsqD9HbpuXce2+4vGIcmGfMD8YQeh/01mnDGf1A3fAjMoqHhpBjaKfJwSF3hJVsz2TZSDrIMkhezyQZ4ITKyZnWg4NIiUyMPrCmjO7QB80CApIJ7YIOYzSlpik4w7Z/O7w5J969vbAsORo6j3y6NgbLVHnzcCF5p6hSk3C5LPz+9x9iu4Tjo5M0SpNyvfLL51+jpk4ar1qZ+M0PH3h8+x6fnbt/ZuUXmr1gfYsb9zy8Kcl5C592colphHqiucZt7iEEd88hIdXQ+546s3Aeu4ElkheqzgwNpKlJCyxtD5e468zwjb6vzPKBrBNJjST52+H13hjtRjtKrM+TgjZMWhQpqbJMlZEyx9ZoU+G+9yBNKmfsQ2jkElCrMl2+E/c0lwUQ3j0qr/fnuNpzWMEHDfFQ7Kc0KFN47HMKkrZZZXTQk2egWoIsrhvdFtyukZyoGTSU81v/iKcbk/S4jfhq7Y4tU7fOSIbJBt6DiuODAojEUiQaogEjRZ2XgpboQ7AutB2shW+LAfQY2Zhl1Agugxbm5QGVT1EXi5JL5vEy8e7dlemyxJdlH6wvG9O88HrfebxMvH/8HU8PDxz7xtv3T6DQj53DG58+f8R7Y72tpClHVJQ5OSuX5cLT9YmnxzfM08IhBwynpJm9f2J4iwGBWLhu0XBJlIQnD5qOKZ0UUxbC0a7nkiJYDRaaDhJZAx79VQORJfTB8cLMdMkc3jj2G+vtjr8m9ped0gqTXFCcohNTfQg8ACFmP47GcQSXWJJHuZmcmmMWnXUh15i25MPJJozRz+iuzBkkS5bCZSrM83cKc7ZtZ9hBorBMC+tudAu6d7LEPF1BBNEWT5lWkkSizt4E94pZovuG6isqH3BbGCd9R6zjY2CeMIlyIqdEBkoeVBtkaRScZAotYSNhEhGmKhKY0ZHiNxVJq9hxNj942LU1QNfjSIwjcmXFJeJLB1gPKIacNPEqZ7BhH9RamefK07TwZl64Xh4iCLE7WTpzdbCdSY2hStsP/vn5LxEg7oO5JoSNT6+vPLx74seffqamwvpyP7llUYNfpoUf3r9nma8oCUW4lkde7Y7g5y0Ztam4hAQyQGGc835yFg7T87OMeFWH0MsSixURAvvqQaUJpqXSR3yeWEW0MkakObVhtB3GejCOESKqnCNIxeZQEYqdzXQj2A1fE0CFbe2oDiwGEYhu3xrnqU4MBmLhETbJNBmn5emU335vfOv9pZ8SNYk6iRFPFZ0+lPWIYLvr8gHRgqYeT347eN0Gbo2SNq7TEw/zH3HecLTOSIrvK9pjTWsMyI1cB3WCaQyqdao3Co1JjDqcIiEeF3dkxK5MDsEPPQ+tY11OcuPXEjEUStGlBe2mt35eR4E58nbae3pHPOHWub/cefv0RH4QrtNMMSGNRCrTSWjs9H4wlULOhak88XKb6CNuoFoTP/zwjrfvnkgFfvjDHynLDKnwv/+f/5777cuZkZy5Pl35h7//O373N3+kaAEX5nSJyYA5WQqd7ZwPD/DYjGUi7R2BxAGnuy1LCm63+EnOIepzPCLIvs7FPQB3NqBIBgrDMvsRIOt2Jr7H9nCEjNUdckfKJS6M0wkBStJKzTOwIRLsiLDEO+0YFA0ns9iOSCalTMlCzVfMnN4SzI3jvmEjaJQlf6cY/fXloM4Zk8ZmjWkZzEt8RPcVjqOz+sG631jmB8R3+ghWgWjhaMpcEpP+HulvEXlE08reG203bH1mfrqe875GOTdrBSefxIElC7MIiym1Q+qOnrRCsUQgxeLW/TpD9THOsJbQQJQkMXHyGHdhAnY6VEesfL/ewGqO6sybNxfeP17wbcAx8G2QczgY8HDG5qLIGPjYSanw/t2CneveWhKXS6WUzHy9UOYJycof//gH/vSXH/ny509MWvnwm/f8zb/5W/7mH/6W+rBErsXpjpjywkU+0Psrg9dzDXySHkcJGpD7t0ZteMMtDrd4QZiIQMMcAnAfoSITibQkEoUc5CM6ZuGC6ES+XBAmGx2llkqaMjaUra20vpNb5nrCtV0G7hHdCmcOhrWYGrgyxmDddqaSI0NQwypf8mBaZoZt9DwQn9hX5/ZysK2d1r6zbPjpp2dyzWhW5keJLludlAsqnc8tUMdJHhltAR5BfkHklSnDnAsP+T1FMvEjCOhEtzsva8MmZ3u9US+FxeZA8qNkKkkM4SCpUbQzp0hiT+Lx3LmgRwq3a4/DG5Of8zB6rCFzEkouoSX9fzl71yZJklw97wHgHpFZ1T07ey4iZUZSEs1E6f//KcrIs7uz092VGeHugD4AkTWUmQ7t9K6t7UxfqrIi/AK8eC/h+Kh0s6C0V5Cjqpp60ZnrzPCV/QaqLB7ZVAx4/jh471uB6nl1R9nrr3nQe+f29sa+3dhu71jfsMJcAbbe+Xf/yz/xL3/9r3z95cb//r//b/y7//gfebu/p24sZp2VaRO62xu2WjEWLBlmobgL6yR1aZun23nMhMpqLB/MlETGygbZSRadBCYbu7Q0LvE0Q02PsYnJhlRyp4sS1rHe0SH0pjCUI07OeWCubGS+8pgHItkriQrmk2MmN0SwDBKMmcMUTcORbdsxNVQ6JsIcwbYJ59Y4j8WPHz8JlTnC8bz8dIUYDd0SEul6o9sD0454EjOS4P0nNj1B31D9krankdH0aw1+e/53/iofrK+K3ARm1p57TbCS+J2CPi/rUUPpptzU6eL03lN6MoU4JB3EJel46oK3xGytVLfXghChhJskgjG8KI+1IZL9DCtplaJO399SGs7gnAfPx8FNU/FgPTm5SXLJsXZrnfv9nfv9K9bvRWAqAXdyEHm7b/z7f/4n/vN/+U+8//kXbts9bx8fyWEiXcqF4GZfuLV/Yvh3PH5AJE0zFqyVwTYuk6mTWWOGFYkqJBUikvNQRtziimhq9lYNLhpBF2FKMsRWZIkSlyYOcqH1jdYNnQZtMuzkcX5nHJM4YR6D2+2OcSPhnqRnOmm418QQhLEO5nJu1ki7hCeqjsiGtqB3pXVnnMHjx0+evPveksg9F4/vyuPN2bcthYAnrHFHPfBF0tzIlJ57+4+o3jMUbhqDScTB37/9ld/GwL+0YmmRM3FVQtJZ3FTRlQ2VekfcaeZ0fbLL4t6Fd1H6rSFt58niYyaJmmUv4peTUFmOP3McuUI450pJ04y03XeS2mmWkJPX5wgBOROb7EAo8zFTpt3rc6JJlVRBpdHaznb7ytev/0jfbjm2PT7J1O7O8sHb3vm//6//wpc/f0X2VJ/FSk9h6YavS2IgNFPe+6/8eDRUkkoopCzfr1um+L3UOFjIyWD6gkmRcNIsJWgl2Unt2wpDCYg0E4mwgjEnEeNTHRGL1jpta9hU9vc7a6XP73pUYpGmokLKTMW0cWu3zNlYB+fIZFDTTvjImNhFUg7WIwN82JFWVM4gIc2fWby3W7LJ1lqMw/nbf5+ch7DfhLGEwGh341wnb/Ylea/zhmhHVHG/4XEy/TsejuxpYHI0WB3aDfrNaFsyyVrPZPf02Q2+2I3dntVkON3gZo23ZmxD0K3TbgfxOBmPJJmskn3nNR25jL2xXBhjMUeOH/NBl/DuNdCAZoU1BkCU4lZBjd6EP/3pvRwmBSOVt0qqpffbL2zbV1rfi4hSSlp3whWPyZwHX778QrtvrPXIptEXMSbSIUITOppR3rgr0yW5Qfz2uXCX548oK425jZy4FciYKm1L3ELSUkmDxIoha2MsUZ/IzyhVSuX4nCRSmTDLmNCZaSzdd+TcMkbXBOsT74vYpMbPOb1UudPbgbpAv9H0W9q8kqhHkw2m4AumGo/zd4LxWriBcJw/afe0vwFqzNGwNhnP4Pe/LaxPrDX2m7C9vXE8gg876JZpNV4DA9eNFY+azhm6N6TMg+MuyB70zem70G/Kts3yuWrcY+MtlD2gxwL/AF9ZwkjQK1q0i9JV8YpoSnFiDkrXWpxzZtc9YByLcK0rPE9rkXQMjwocSbeZnrN+6cmMKny196QgWijmeQ1qCN1u7PtXWttRTW5EBEAOIKKGAeErLT6tEWPgx4/0rrXKLRbQWXV0wFyDGSuDxd2IsJz4zQuTzYFFzIXLYmlLpTGeWRCSt0lEbupFWta6p/lf3lDKnE/cb7lwCRTPZyrGEsM3Q1ZyJ1DQ3sGScBXiWM+Ey1QsrXSAr6D1xp7TQF1UpChjHFXCUTeIcY5MlBeBIJErEVg/K33fbw3rHT+d04ynBvFjMs6Br0oIz7Q61inolmk2oi0DmfUbk5N929HIIEFrxtYV31LN3dpMy58mWR6YcxPljY3dBy1d4hhrcPjJkJly8IvptAIfGTqScJemEqGyFhJdmKlmnqm+0DLVULGsgX2xytlSMVQ2mv3Kbn9GV0bIhi60JYylIXS1LDeks21faLZn6RLZRYdLkoAuauLMq1dM8eODWA/88Q2xVI/ImyLN8PFA+waFv4YmU0y8g284H3idbgjIdNBLSp6/6JDcktqkWi1g+KR0j7i0tCdwZ66M0I04yuHTMEvXod6E2Q0fjblSWb04Mb0n1dJSKKvbjqxgkZkez+PEdNF7R2Ox/MfL1BoSwvSY4DDnyp/Je0rvPUk52gT/2cUb6ogs7vcbe1dMBmscSbyYg+dT+Hic9PaV43Qe5weLQOy3bBxo3N87X99vvG2aJ8e1+/GsG1sDJuHJ4srrOEknykRk4bF4PiaCY8fAtpy4yWPy8W2xzsYakaqJlZ66a62i3kUt3k6sQDQNqZsmVyE8LUhipRO86M7W3+n2K11uBUuQo9fWkRBa6ItolHKXXCi1RIg1KR/zUjIIc57pCL4OWA84M74qnk+kbVhsWXvK9ZWEEKGr8vQDk71epBVNML9+aBTikiaGi8z8AM+vL1U6RSIZeJkyibAim93lRlpIJxqQ055seJsm1jqL1zsYsH6wRRRHZHCzndbvyAhkdcy2HO17qqzNcrEu90R8SMfJvKHSrO+YJy6RVNBwWmvMVjDozyxeIZ2yb71h247RWUOYbsTzZI7JX//6jXMs+rbhaAb/dehbw5rx+FB8wLN/pGPKvSO/JFdoehBD0OH0lUSdVGssRvygxQM5H5zfv9FPp6E8j+C7nqyx4GOxniBn4KNeoksGccy8uv0aAa9V8E/SBVVbksNnpgUl7TSY68HThV2+sLd7TuNKBNjbRrOdTfZ0WZfELH3OWuQX99Srpkx0Ier3xEfGbZGNk3x9g1tD7huydaQJ0rJ5DSQpkCrIkrLN78z1qDHx9bXJn1tzoVZeKXMORFoNmPKzmBas7eSwKSRrU4J0rKx0FZ94nMj2nt/fcpO01pCZGSKLwVgDXxPsT1jfM9lyZkMGaaIXOMd84HImFChen9tZrPw5bBEzWD4xjRQHdGOkNcjPLd5unVtPz4bdGvaeu/84n/iE4xycY/L92xPrYH3LlErL0V5vDdWdtYzHlFRP33LXVbIfYwR2CPtQzodz9sHWTnz8nXk+6I/BPiZWvly5IIX5UDiEGE7MtLFPfH/haySgTzGr9OJIAJGnrEviw75ysb2UBXFd90J4QmxrTiyM3m9YkUekam6huLNl0c/F/I8kWaP5Z0wcmcWQag379WsmEPWr8RJEk86YU7Pr60iKJnGCgVpxnytYMQcMaZmUJPcSWi4InsgylggeqcbNz5JNaCOjwhrOiIWsdL3RSpH3eELsmKUzUts63Tf67NjWYXvw5GTGQUim/mSU12K/dfbtHbXJDOHj+UxERfJ8cw+CmaJSYBdNlTaSqZwizAnj+ZNQ2bY1unXebju3fq/8WuH374txLuZcGV+0UnnrS4hQ+p61o0nPxs3L2TDy4QhBFEldpQGJBsy5eM4sVfpSbqvRxirnmmLjq9F8JhDvqSuTGYkseTBXwkNpvF0W+EERm+uEjayl0qVc6tfz3/M6vqJYLW+DNVhr8XRBV6AruO9f2Pst5Tl5CCY26ovwScXZZCcvUaCsg5yI7Pl9SpYuqUDkSg9JTVo2euFe+WR5ClvfkJ6WriqSJoHhrFi5YZCiPdZijl6NqLzqdSRZZSFS3KSMyJqaKIVL1swuM6N4JRiazWHvb/Se5Z7eNvw209fNH/lc/WDF4Hb7msiNHqh8ZcUPYDLWRKPXtHMWFBc0yannjBM1Qa2xz2CeP7l4m+0Ewf32xn17Y8xgTGHrPzDrmC3mSuM0Mydk0cLxpazZMjncheMAOGkCLcqz1TIeSVqDbWepMPzJ6QcWE5kGYSXdSQFhQllWjjKe3axt+Ex5TnDUIZaq1BCtg7AozbmKi/y96qsWjngtoAq/lgLUIWrIsBjPbzAW/hysfbD2N5o2trZXbR0QmWR/5RC7Z9cMUfX9H2A6UUKNjIeq71dDgShzl7Wy6Xnb/swc38AehJyZCyHZnSflsGrzKMKjJx8Xn3htXUcoPC4hNsmsNaNM8UJKhJkRuKHGGas2w+CcT76YpF9Ha9hm3G6D9Xyy/EDCcFI317Ybphd64FV+eIo/PTkYIV63YVq3Kg2VpI0GQmvQ9592zEk20n67s/V3VOEYH/lBIK/gIsDkdaZZtyGsYRxrY47OHJox97dUNIhMRGbK3JviNhgELpknvPyAglpEklFVbgisOIjlNUC1XGjViGQ49Cogv0asACTF0kVq4SakFtcUqYrjtKxKbkKedmmkoXnPERKsmERMnj/+xsff/js+lX/8x39m7x3VTuYuZNrjGs+0mNo3bL9n/oJsiKakB1VEq7DLQjY3Ycl1iKwNVRrv+585+Y2DR3KjNfmOFYqat5jPLDciknhUT82DhMnI+Fe/lCEvI+oaXoe/nlrT/HOHO5MHK4zzFL60v7Pbl3wmomzbO7MHMa7PmzdXrIG096yt15FpUuVSf90MK2YqXqTTdMtba5WqgpVG5P/KCv3Xa9775G5v9JbmItEE0Y9yXLmwRuolJ5bbLLtNYcdHww9jDUuPAocVsxZw1XU6cRks0tm8zQfmB2rZKFUiUkFfqU6ea2HTYSndU8q9wplTWaOu3WtGQS5I4nJiWXnoVhJkKoM7EprNxxJcZk6kRNL4upG48GYlchTiGHyMJ7//9oHOB2/3d96//gPWDB9PxuN7eoHd3mjbWy3YQNqemrJSJYgaLw49+fKzCYocvbdOaMKSTd85oxGMhJMkCjbLgUYec7kJtQzqIuRF0o/IQYOrpKwoVmrFpKGypXNNBN1SFWwSaDhznIx5Ml35OL7xdYtUHRcNdtt/gdMIyfjWrX1hzr/jLfHcOZ58+/GNOQ+2rSfS4MKaBuFsuqOeoV2oMtb3NJXTIq39zOINedD7lyygNePq5xw1Rkwrn/t9Y9u2hMjU0mYIS1MJr+tXNLkE4Zi36vAFjRyXSiZ9sXyw5vVAHY+zMNPFRZzJazmT1pnOFi2bFV/4HMyTzDwQLXgoFy4AXnTJVd1/fS18EstgWU7z2i0HDqK0Vs6HDs0aiyQXuZ48fzyrAZr4+M74gNje8HHQb+/03ouUE2AdrgRz7VXfClF1brqFJy6t1jJeSjO/gasklpZw2MoaN8iaWctYxEg6JGoEqRLGtaqZz6ljeOYDIZaqDEmtb/J74dL6hzu+wFdkIKDeCO+seGB6Ry1YBcOJ7dmA2kn4DtwThZGR75rGOX4w1krPXk+ftK43mqaIVXBMbjxXQH7JLAAAIABJREFUinlX+fj+3OKNF8wJYjiD8zxTSy/B2/uNt9uNL1/SEPr7j5MfT+eMrIENqmGzzOI6nHiCH0o7GjYEHw0dqWOLAvdFkljjZD5wlPZN2wX4z6IWC2NOZHo2kCO9yRCypgxAy36TxEgT45h1zJWL5NWpL6HLDbNbMZ2Ublt2+eT0sEkjZILd+Nge7P2D2/3O3nfa9obajt6+oi3ZZKpKRHJt0QThRY2oAyGfbf52UASjuscTyko0QhFMN+YZeFv5Z+qvShGFEEnrLDVm4m2Y1kU9V73TxJKDhMUkHPHLST3htpCcskWkQFNVUYNtb2zrzhhPWhyI3EBO1jozz040D5tYtHZDSjdostHtDdHvnOOJz3wP3dKI27RjrTHWM9N/vLDn5YWX/8Tixd8I31hrgf/gOC7vqFxg9/vGv/vHX9n3O+eawMb0B99+DNROmm5IBB7Cipay9EcQH0I8YT0FfTbaAJ8b2FFXvVE2YaSGfyCtOAuxcngS2aKY5gllJmgNB6JUslmbR4VB87LIF9FqqlZtUIFopJvNibccb4qmNSgWNG0wHXrWc9qU+9sX7B8Wb1//RG83rGdATD2hHIULOVbNVZZliGSNG1xXe27KHL9evwd5HmcuGi0TJPOzXtpBBV2oGaEpRfdkFqWl0ouIKgnrRerc5IICo9AQsoFTSWdJbYbYhkfjOZMTYV1gnPS2wxDG/B2fZ34mNSKKNywL9yfWd64IWJFWP2cNUuoS0KaJLq4nqjfWCMac+EpzklW4/U8t3nX2jDzaFs7gOE7GjJcDS0Sw3Tbe39/p8yRkcqzBj8dgjIPFhkpDdcvJ2jLGA+IhrA/QD8eeAaOCm8W40FNcmZEvI5GEBHVCVjYX1Zi4eELGmrdBxiTVFVzElbKgq98XRMkU+Pgk5WAlK1JFNJsJyJ9TrSX5XQVtQvSE1t6//srb/pbhiNIR27JMytWbdT3x2ZxZLl6XP3TQF9hRGO+VfiMiL57C9YdMG2nxlBZQmCOa2KiXb5uSnATV6+VHlhj5MF7lSdrvN9K+ZIIbMNCW3y/njsmxNYKmweGTKd9p9g/JuxgnPTI4cfrAYiu4vGxYOQlGpglxDQ81J28Iaok1uy/WevA8HxzzZEa+bfcyBPypxTuDRwy6fqfbnY/n4hyLVCWnNOj3H9+5v73RtsYuzj7hdgTyFPyMupZTU2XlXOjTGaegRzAfTjyC+/D0KVMrROAP0ynJB+pXzVu1oWnSGt09gzkk6Yzld1wMpwTPTfMEjOs3JZsF5KqRV+KooUnN8xxdpj190g3F0m8nTGDBdnuHdiNm5qwnV6JOWfI6j/B6a5HfU7Kfj4Lt0hNCqoEtMRr1c0edkPVrZobJRoilqYcGrrAqNPD6xssHw2GFMKu2DDEuyPm6CIJMBMr+osa5stJqlkQ8NDT9yQzEgt8ef+HPLmhssBaydjQ0lRe6pyeH2CubzdcPzvlI+ZfnZhQbOTL3J1t/R6Ixz4Oxagjl5IeMz2f5b168Jh1U+H48EB8ch/B4JpVOy/nxnM734wf3rx3r8MV2pgo//nbjEGOeypwDRLJ5sYTTECGWMg8nHovj4RyyOLuzK7lYKDywUhKjZbO1ZqBVNlRpV/hoNTc1bLhcbvKWKrit/p7ohf2WSlkyyE7d2frtD518LiTVWrhOdtrLyzhZWStNkkVfaGstxs+S4fp1XlhuOvEgF5pMNpBxXfNSp1Xi2yHJlmtqrFrjOXWOXMQrg/umevL5kSQbkUOQl6NOjZbTFLEmaWsxGUxphDwRfSvbKK8SA0Qc1WDG4rfnv3Cbb+gyetxp7Z2235DVkTrRPRZjnBzjgzEP5sq6vG8b7rB8MeNkzE6TrfjXiWApCtWc2s+Oh8/nNYvOdMIxC/esqZDWiRKW38VUud+N6Y04N1g7rJ5mwpY4aeuN7X2HuzG3NHObx+D4yMU7vbihdmWKAa61IxNlsCKyz+WwBuKSV4/PzDWINKTL3INMvUz+gn2+OOWlps1/zQ4YBfeTiJX+t+QDNcmNk7VzXb+1+KK2msh1TNSCEerW0M9Ndi3j689ecvP6tdeQr/5+1umZNhrumIGWi/yM83XeCrzw2oxyTQ8yCSv1hKZCJa5NHrWp66T3KKstz2DF1limuG6kcO+KXwVK8xYBKx6o/enzVpFG+Ek4nOfJOQfugYnR+i05EZEbVyh/YBmvg2atuAon7DLI/pnF+/yRdLvtTcoeSMs1Z/7haoTUrlTAHZmptW03/HbHB8yRZtLWg7YL+93Qt8a4GWzwsT44PganTYYultamIXKgICW2FMuTs/iy7sE5Z+rX3HnbdvZ2Rwua+zgenGsyX7owqkygTqP655KfXy1/lIuhWjK48iHn75lkiDiqxCjKJfLCWbMhuhq1uv5VayHXqLo6fnmdxHIVhFXP1wu4jtf6xfBULlwydpMNcFzARHBxTBamQUiqAaERNMaqXmBFBqFcw4l4PRZM9DXuz7q60aTTlEqvjExg0paL78pTCyfWA/VOZrSt7HlW+fSYYJyENZSN6d9etNK73TFVhiwej+vv1I2jxmWK/W9evN+/n6DBuxkSLWXh9Y7zedYIc2bdiZbt0HDEN5rmB28NZIN2D/pbY7t17N7RezBaMrLmMTnbYJrnMMSsJmhWhneGiaGeO/aqGQEiFpsKv7x94Wb3NDjxZDd9HE+e85msMmoiJZ8PRKsGzUVrlVb02TStyuIVb+lhFqs4v4JXXbjcab1GvPJ5pwc1jKghzhVonXCYVumgn83ZdQ28kAD+cLJm2eUubKr1vbP5TLVhNWySi5ALb8fwMBarRuxBUHajxcCTgL11NrvT4s7UG97uTL1jbWOY8DEX58pPopV0GiQTLPXGWWtHnAw/cE+IzaSTyaRbau3Y6PrOism937n3XzBRug2ex8GPx8mcXgoWeSk//s2L98fHgzEXK3bk145pMMcgPMopB0IWz+cjwe4uzOGMZ8IklwohO3Zom9G3RuuW3rTqtXiA8ASlBzALUiJjczIZJnMaVDLCyaO8x6q5a81427+w64ZqyxfmJ+dwujrDz2yARLliXq9lUXriz+s00gQZqOFMLlIPMuY0onI1vNx2apxclfXrxBVqoeaLkOuou8aGRQ/k4hbX1fkHcJ3rNhBg6zf2/hX3H7gWX1k/DerC0t1cCte+avpwhwXqGTs7C6e/Jm8Z4HdjhOEofbsh2xee0UEbd93Yjg/OlSSepJ1mGXWMk75+sPGOr8E4fmRYi19TyobZROSepoTjyTyUtZS239ms08zo1nm/H/z925MxDmblgmA/uXiXHMxwfvwOSqf3YjuterFUXRapMo7HKI+s7XWd/o8Y81WUvZrqXDqSeG0SWVKyM8nwHVdBWzGqVGiSqYquLTHNRLvyZNIsWVQ7K/LfE/+9TresvUCKtCKftwg1Rg1hrZGy+ZaZaE0VdSXGYs2ZFMURSURxZ7+/I5Y5DXIhJPBZDlz1a9XJPkfVK1EqiYS4rv9EbYELoL8azKaNr+//K38//spaR56mLohp1eFVf/tkFV83YuKu2fRWnt4fuloQMIGuiT+vELCkt85I820rw0Ih+x33ReZZRJ6S5zfOxxM5MttDPRvsplSIYP7dpjvGDuuZLj6i9O1LDYLgqwdv++8ZVhM5zX1RTP+ti/ftT0o7YD6dj48zeZ1qaTdkiq6yW/LsRK+JnGe4VmVs8erY47rayqpJ6gX3TXm7d+59w/TkPCfHnOy60treom7TXJBqjjYlpqRMBinwe4De6+SpJkVywVjrZTEFzqqvp6/9lE6FQhl7sfV7mmJbuiRSI226lJLkRElkYttuL9nRi4f7WrfXFC0XzDifrBipvhCF5ehSYhmaeVB/aPyuJs5zsCJw277y+/HPjPkN14IPkxOTKoVYTJwZE/fJWjmqX66sKr/EdpBk7K0oSqgumjValRgeJyp3ipKR5ordOKwlPFf/JYQxz4y1LcpqMFHRvL0EpBTLaGNrN3r7KGPFAzPF2h1fJ73debu/8dfff7x+9tfz+7cu3q9fWzpEmuEnxFo8j7RK6q1n4W+OmdI1xYpeR6tf5PC4Olx/wT4qhlrCbWo5qXv7snHXk76EmItjnhwyOHxyyOSUySYpVsx+Q9IMUkDVqvZc+dII1goWM5sJol5CLY6St3929vkPuUCU3r6mj3DBXcmd+EQJEqFyWttT41flQ0TkrET/P6eopPRprcHj8QPrSddMQpOBWTr9OOWU+NlcvnR4UGUPuO+MtYONmn8k5XE5lUaUN9j0xTmcWILqBgi2acGA0EQR6QxNiwNqIPQcT3p7EO2ez06ht51mnWktrQCaE01xTYTovRn7/o4sxf1knM+CGlPRYaYYi+UnvStnOIc/mHGg60Z4uv201sl7NEsO/f+nNvxPFu/7r5h9sLae07YT/MfBY5ycc9FX0vu2JrjlxCSoRiHSwDjqigqtK7CclIVPx5m2N273nZse7GtDH0Kc6Qp5nCcPBlsM2jrRPmhu9BcUJcXihxnOOR+owlzKYjJl5Mldl7CoIJadLCtSwAn1++n8fr//Ga1Te3n5GSzHxyLG59AFgTlPlucCV3IKd0FX1Nh3LS/PholVfftCJ15wW461r1LrKmX4w9eJq4ODQl2KS+A5ao4ipuem0Vr0VwDATMqqZZPXQhMZwDAVHOMYJ0c4kx31PH3T35dKX0qDcTEFPdNqwEDVadpp3tFuyH5DRZizYK/ykhDJBr5vhqyT5wy+P/4bv7x1Yq0U2ZbDuqrlBO5f0QH960qKvrO1Hd+Nj+85+54LjjOVFO4DM6O3SbPFdnO220J0cjyDccprKKBCEpXxGhdURy25DtqmdOtsvqGx0WND1skczsfHwMZAtwHb4kb60GpkLebVfBzzpHVFHTyUc5310rkQfQJJpn5BbrLqs0mOWptuWCmBRcCLO5wKCX99ZjPL07ZqIw0Ba0VFvPwL8sTOdPZVcp486j9B+QvKzI2S6ZNlyf+Hd+HuYKlle/z4jvdnSn5CahpYIbXXIr8Wv0j5OmgKACzxQblQGy//Y5mE9KJTWvGZt4S66DmwiiTwN+vJLykeBbLyuWnSHaVt7PKFdgUKjt/za2mj942dnXYKc5387cdfcHeavPHxPPj7j29/sFqtm+lnFu9tz2AQYkPeU0PWT6epMSW77HPMsrE8+PKr8P5LFt99z1Pt+YzXVRtCUvj0IqSQpxteD90Rk/wBfaePE6ZxnouP88TOyXYLRg82Mj/txQsWeI6T1nJxLodjDlY4awFRoR2SwSCfCopkUUnFPwhJfVTNKRBOvmghF4DHa3Dj7i/lsMdKV3Zrr+lcejV4jV1rKaqWc0xKxiFwTzJ9GSlARVxR/UNoTmsk4Hkc/P23J/G2uP1atM4VqVrBixWW5VoJOxKNsI62jADwlUpelwWRiNACehPMdrresb6x+hsrNqbcaUtpMnEa4YZGY8WozxiZGnU1p6o02YpC6YC9oEFCUN0x60xPDP4v3//Gmr9xHsFz+uu25mpyf2bxmkXieU0Z60pmSVjm8gEjnLkmYp3WHZWVEJoHPeMmsm6rKzo8Tx290AhyADJXsMwJzdjTTW50TjjyNAtPQvo4nUWOKXFoYZgm79YkGL4KzA+mZyOWlqSr9FItHWFy5Rb5peAolQyitlZ17SJWeqUpysWrcM+Rappe54LOMmRyybmjhvTXM7tOxos8btZqFFpjRM8XexW4wQXySsFnDi4cZ8JIf/uvi3//DrZnueF1OLh4IQG5gb1QFxEjnTM1w0DHTNpmT0xYe0duN2T7EyNurLYj7R2bOaQxu6FxIr6BG7HOtBnwZOA9jt9pvqNLaL5qkOF1oq/Sp86cqDkIG+7JIlwLxnDGTEtVj6hDKS5Z37998SJgrXPOxffjyfNMOCkbr2zWLgVL5nNdNVpeemsWAuHBmp55XsekH4t2//zWEXndjVgpk1TDLP1bwwxtuTibdoQM6zuK62kqfL3f+HJ7R4Bznfw4vn9SDF9ydK3QjmzNR83QPZKxppoNQtN7chbw15XLytjXKEJ8voCZsUxjJvqinW3ba7HVbaNCKnarPKjfg3QDT1PswLaWTZ5VgyhJ+bwayatxW8v5+HjweDz48W3w21+CP/+7ZN4JFze5psAXA4dsygqWSdKSS5UbJfkxw/Y79B3dviLRObXjIoi0lxeaYKi0NAxZOZ6ffrJ88HEYOv9OO41tGbftDdWVC5ZJ+CMXfU3M3JXwjq9Zqu8/TB8LLHSCkJ+csI1KCP/2fPA4jIj0IKvnlBRBy38+z+DHD6O1jjX49nvw7fdgjAzuCFfmCfp0zsdk3cC3YPZiXRXGqhduW82YmWK9sWlnU8E8GMcTmwsTo3f49csv3FqaMW++p9QlRpYDs76W5gryOrGjnBYT569JleRo1yNPTC24TSIXcSypr52uLnMerOPEp9P7hiDst6ysTTVDWxJCqAXoL25xCjFbTabSG0wiGXHqiV5o0Tiv4tfDOY6DH99/sObkX/5rcPuq6aKkkUYfErg5EYlnUwXSWrV4PZEJccWaZnffN7T1EoPqC5VxH8xVN2NM5jpZnlO+tE2dyXFQZ8bJ43zQzzSq7dHr8Mhx8WN8JFbO4lwfSTSPqPImuPggScGQ4lGk0uOnFu/jHJxz8PgIIqxGkflAxC5QvBGiHMfib38bfPvdiut5lE6p1UuEOUGO4HFM7BmsNll9ZJatQ0SeEiopUw+y0eja2O3GLidtLuI8OdeJSYa5dNsylzey3jK9sIXIZiUShkITE70Ywjlizu/jTsqy25PhJ112NCJTx0P/RxZbrBc0NlZ5jXrQ2o05J71vvM4PvU7b69FJciYSr0sR4qpMNY/yesuhxTWsIIq3W7TFtRZzTfwRfPut8Q9vhSzUYWLNcAwt18w5FsJkLSlz6azrzawGDy3VF9IJPwjZWD4Z/sG5Gud0jniwXJnL8/v7KIOYLMtWCDMcC4iwGoY8WeuRCvO1mOOR2LMfVTrktFNUXsPGi/FRGoPaAD+xeP/lL78zF0hsNEkozOVKK0xYyLphW87avz+fyfKOOxfjLtaeNUzkCTensz4eyKZEd+K56AfMM1jqsF2CwfpRNM1LtgZdoJ0TzgbHeF2xy08ok7vlJ3N5nZ5SvIgsbQgpCdAVVJc1Zvg1m4JzDGY7iwWXeDRkvb3WwmeevFHoyRgH8xhptLLdGfPAitmWJnslNq3RdLBSAi+eNEZdsIR5HpgafdszGuHiLmB18kqVcVpKiQAVPr7Drw7aUk1CVzBleRGPLMe453lmEKQkBKUXLVUyY1rmZMnkHN8Y4hx+43A4540f5+LbGvw4d8Y5iFFGKNUYUvJCIa391TPTgnDmWOXnkTfMWonD/3FDK4mExMqh06rPpyYv/fe/efH+7fcfiBi3rdH7VetKQUWKbkbfG7plZ8u8OufPpJ25Zi5ej0qdaSDU1ZuZCzGc83CGBWMEyz77F7O82vqmdAnET2QzZBPU0/v7+/mBacNsy2mPWEZMedo7mRpbawgw5snzOFORoVkjRmRzZzjIyZhPYovX58yjJbkEK9KnLWbyVStRjrUm43yw5smKzJ27eAopZVrFLkv8WFTLht+Zc2HNOM+J3Rt6eSsQxXCDbCoTprvdN348LgVwNnbWhGjZXCeFUlOlHYnFqZE3ZSQ641p0mjVZOlnnkwjh1A1vNzKcMCU80yfnDM6pzKUZIO6JruRgKkMmV2SSfPY5sFBmCHMFz2MxRqpTEo25vDaKjiq8uB85Tr7O4Z8sG9wvHdhllylFAilGlGZNaJYG0dZJAvTakNVZR2NNTV1YFNm7K21v+BYsE6YEc0yex+Boi6MHY/PKCiPZZdZQE0Qc6wZbw26NttK68/vzI+tfzryOYkeYnMeD5cLblxv71pEgJ4MeaVoXCXutqJO1pru7PZhrpIV9nXyXKFK0YS0/s6dXVLrurKiNui4MMEt5y5NJuJqxXIiJPqx0Tk9oGGspVLVoXFmVUs2f11W25mLfOn1rnDGzsW2Wn8+kuNaaHF6BtG8SpNXvhbJEyWz6bGbn8nTeDJASS86VRKP04zVkkSqOEo6ugHme6fe2gpiOnCdvupgr1dzIyTmePM8HK4JzjSqBLtQhDzYVUtl9UQmo/mClFOynFq9qp2nPaYde6rKVokfk4rXUCDipfyo5pTK7MWKnj/RBoINuivQU82kjTwlNSGrFYqzJYyzu4myyMA/cBEzRlrCdaIMmtJvRlhEzOH2kD68W42kF41jMM0AyHqs8OggavXWeMmtPywuXXcOxMI7zwVgnu25lhSrMNfPEXWnpNOeJr0nbOiFpPIempy6Xh4JEUh2yzkolNDlpC3GkZc13jgOzxu3+Rp7iA1ma/mfVyF4c4DXTYrb1xpqOtZyu+frs2C9MOWvKlPCEFXZc8pqFcMyBSCdkq/OvvIntK7u9MaYi8zrJM38kVg42IrI0GcdkPR15LnwIT/1A/I6zQJI8dPUXHs6ceVtdBuBBcm+iYhbcL2izZEj+k0OKZhsvESG5UVPAeJFqcjozZ4L81Aw/1Q/ZpKmBYcgu9F1pm6XvahPEyvqoZZe7PHLowWSXQRdnKUA1S0wWBdN1wVpO2OZz8TE+YLtj2hlzcM5UOot4Geb1vD7rBPR8uy/iOSU98lg8Ht857g/u7Y6FskYu1nDPgEIPxIzt7Q0fNe6uRixqPFs7pRCMIqmQ30OEgtF4sdDWHPlSUZanHN1H5M9pmnlyRCmStZ5JjarrdkjnxbT7j4CoPgXN6/kyuLJIw2mPTBklghYNoyP0mrC90UmnIGe+aJemxoqTuVZGJMzkVGgWtXw/M8agm2P2KWKNENYSzpWOQjldr8W7csG6XxPJnAks4udlQF5g/kW41gLgLz5BOtiA+0KmVOdpWHnqEquanmrwLgEmXjVPwkhXAyGWaZnnGDyZbDI5Y/HUxU0cYdJXsJVNUla2iSd/nIkSbO2NuXKZe6wcXMzBtjUIOOfJY5y1ySLfu1e32wQ/8lR8Ph/MPZXKPifzPJCZ16NJNlYhC2nC89s3rLXyabDCmLNmuw7e10KmeBVy4bNZHuQA5pmTsGh1y+Sz98jhzorFj48f+T7mrPFpbohACokQXDLNPedAn4qHqDIpX245YlYTLW1VDkcSY4JsWK/b6TKPwZP0NFceWmtEKkpmAszPY9Lkie5FvKpbYK0SgnoiVX5FHtQk8LXhLlybqBP+J3HeqMGBro7JREPqYa56GGQzsnIXeay8tiVjVSUypDkoEWRNndaKtBn1GgSULy0t6+y5FmcMngy6T3YWe9VoEUEzLWVeqn2lWWrWoNxgsv7r3WiqHM9F+ANtmsqKY7LW+qwTr0LLQSOv68fzg+N+YtstSTVr4NOZz5MuG23/Qt835vPIpqNZ0Q2riZLXfcVFwFEMJ0/5VCzn92smHGvy+++/8aZfuG13mm2V2F6qXhd+fPzgPA6ez2c6F2kKP305MYNBxXXpliSdUjO/yiLkhfPGNQhI9JDnGHg7uO2S0vNYTBdCGlgSQC618VqTc0zmLB/kBTpX3kIjOKDU2ilsTQfQ4ByL6TlV1At2jSjlcgoEpJ5V26wcc36Sz+vFB4DsGKXGorl0eUmyo06OiOo8ok7t6o6vP5MM/EgOQNVxck3o6mRWaYg0fJ2M5TzX4Ps6sbkIGyCL1p1dM4lcVGibYtXlqjbWhO5SbjBZTx3nxE/hec6EhigzDZNcTCZIq1PlODlj8P3Hd7o2em9gxnwcyQkYD77cf6nv9a1KqTIoiT/o717LN2oBg1SUgBqvEWjHOY8TiXQEvyZqrXfcJpPMefjLX/7CXDmONbPsBTTAEgmZK5hx8WjttYESJUnHdI+EOy93dFqN6IHv55Oz/YWtvTOjcfqeKmS8/m6NoD1hvjWTMRczEVIbIDM4qwqbc9Bs4u48joMx/bVxCvuoWx2mJ3zZmrHv2SzOsTji/LnFiwvW00VcrofgnsD9tXBroeZrsvxQkhCLXIMGcoGqXVepv+bXOVyTOoGNxoauhTKJ9WQ4PNek+Yk2R2yhMdk0mD0HDlurOKwAoWEa7NYZKlmvjoSjzjNJRASfmya0nGek3GYc2xrPvz/4y/Mv3Pc3bm+/sN9uyMxJ0vJZbKqskZPaqXXqLi7ieDr3FOBd9Dm5MGcrayZLgng0ZddF328pkyoEB22oBOPIsfB5nGzbxjhzI68pzAWu1RSReKqUTZOT7+u6mp2E2LKDtSwNytDaVflYkzm+sUw5YnEEzJBPmM1zwZmmns8LJ08mXypLxpwIwVgnpumqeZaXc1ybuU7fq5F7yalCMrgmsnn7V2De/wkxp/IWLG1ZXt+ggKBCGj6bA7ncxakOMQ8WRGHrLcNENDtV3F+f68WArVpWpWeAiBULyimo5aTFifqis7ircatUGnUvCZLSDDSMbsLj40jexHCe5yjPh/y+uhnm5Q22qplT45gn50j5zJw5Zm7bhveBH5N2v2G94WdahooZ0lrdJJ+DjetFXYR00awdQy/eRI6+3TqCcfiTT1vVrFORrJF731GzdKXXnAqoCWP4q1ZcVLOjF5NPakHnJBSVNMTTjdBOSENKYYwYocJEOMcHwcaTYISxStZ11aC5MVJYGyYs+XyHHjn08HBUT1Qzg3rMWRkVn3yXFwbu+T9C6jRfr4WdNmI/sXhzu0qZ4FGOgU7es1dRlw8orsX6qu9yoKGm6LbRumaNppqwUr3WhC8vZ/Oca0fOOMslro5IVVyyHhvuPIdzLGXlKD1HrpUjnMnxDbfsyM+YxDMbw9YFVi5gJXFPKzGhT2BWE6V59X///o1f3n/hbltuPpyt7wX/5EwzJ2bZD1jv2eFfKEZtcKl/lnJDv4YPovqa/olZXaifGrYgBz33t3f+w3/6DzzWwX/7y/+DkwOYy+XgutWuUzRfh5Xwk5fsyHWH1nGt2nA+AAAgAElEQVS5XCQLCZAaKBTDb8rkGZOTPDG9UAAi1cmuTreG23ohIXiUfWzRScVRzVp2LhI6rK2Up+olbK2mNLKp+3gemErGRsyfXLzaJ9ayLnU3Zs3GL0GdmdQHqVT4alS4JN4a9E3YdsN2ZfbBUV1vduBV/ax0d5wOk5XkEoFQRXqeDqoLkx1J2QJuwRJYGkgTmnRozjgLfqs6tEvDphWLraJdR0JRdg1eCg+NmFgIuzUOC9aYPM9nYpaaJ2XbOuEkMiBaqoyW5Ydq2u7XpCgkXrfLH8lGIuTQIc8d3JMrYJum6kOkyE+54FvvaFO+/vKV//x//h+4Tr59/M7H+R1rXqemvzZK5MmQhBfRzMwoObxIw7WBNAhjSZYFp8MJTBMWylgwIt3GFsqchSVXAy61YUzkc9wRhTUX+R5x1P1zG0Y27Y4nElVTxCvQPM2ng+fhKXyoEuWnFq/1HKGKOkgjUDxSgElpsDSy5p8F+ly8gdCUj1tTbr0RFtkgVGSVEEXPoxq5dA+cAiMWPTIf12yj6T09IOKgtUzDTOAdbO/YvtGjl7LiyRwzR58iTCa0oN3SLT2a4pbIiVFBKImKJ5zn6X+rGnyMDx6PG+cYvO2C9Y71xnqWwNAdsVbizjQkyZDET0edF7WyKqPQC1PmhdOOOBhxsustseimVToIwyfff//OzXf6+8beO+9fbvx4/MZtN6RLcl6blK1u3lKhl4NNLthPB/Z8j8uD0xenK4c7z7k4cbzn3wkzkE54Y4VmuMlYmSdXfm8Zf3AZHabpyefQl6sYLJw3J4rAiyh1pSRdpZXKhShRJc2rtfqZxauFtTshA7QlnlhxOhfcI6Sjtl/1iywUr+lKYr2xPIHncGTTAu7rhwyQaESB51HdlGhHZcM0uQCJECyaeFYUNnHTJFKvdN8VeyPkI4MOA6ZMoqWnRE1zayPYa/HGDHykBCciaCs3h8fiHM9XCSBmWSL0YJ4DdU0ijbaC26o8kCTtpDdCNk6pIqn1VP3BYrDWzPw6ObF7o2nDWqP1jKHqKunldnyw/3LDuvKnP//C4Mnf/v5XpA20g1vWnXIZihQpieuwIXPwMjgwON05HY4lnG6cy3nWFS7V0WaSfKZQRqRrUXjgU1gz80aS301Jgj7LIsJLiJCn7Gu7VhPmUcjT1bzhbOVQeXtviL5znpM5fxLnnTMX6MtVRkiFaWS+gXO5p9Qkp1zI9XVlOuOcPARkgzMWq+diFwe7xp4YRAey+02YJ6+2kJ7GE+qEN4JO2ITWERUeyzkj2LeEynwulgWnTOZaVZNmwzmqflKTDBFxzSFFZZ+JRfYuIuhxIOqITaTnhm3dsK2jAcf5SAWwfZpExxj1tXKE/DISUUhzEM+/cyEVvjjn4G+//Q3pjtyUL2+/fH7NOoX/4Z//Md+UZZxUDnwE2wy9F6asacwdGMslZVUXHbiYX67CrMU7Ak6H6VKXTzrreBEtfAUnKwWZnp4PUXwc93iJSv11glaj2Xg55+fZlGgDcv0in3+n6psg9XCmeSi93d6zwQzF2vFzi/cY5RBYO1pI39f6rBVcR86+a2Zu0tIg7dV9rkyI9zSoQBWr20Ii6928LZSIhjNwURwtGMey5nLBQhgB5sKG4SY8/eTv54N9/wIiDEbiwxXY0rVhNdLXlbVmRmMlR9dIFxkMYiTQbq70Xbm9KffbG9YbM0a6BHVL2VPLCVVIpIfEWjlosYsrkabP1CRP6vSVq3Sofvf5ePD792/QFvomvP36lh359WxfGLTmjdaEr3/+lUMO1nbg+wf05D874KGsMJbnsETsOnjSDWeGMDyyOYt8zh7J//VIqVCE5p+RZNsdYxZSQw4OVsJxL+HnCyoVMhQ7SPVyNmJEpl7Kxa6LbEgvd3dYKTowrZsrD8E8qX+y5h0jd49pGiKpKHLtZi020EtjVdlrxX7PAZgCxbyfIxdOaj3qesvFGyuhKl+Kt5y5JziuTIThlWIegqI0rAqSpPYdOB8x8Z4kmVFTHV3FiPPMNrby5ooJi7SxVyl97GYwF8cj5/b73fj65y98+frPhDrfv33w1t+51dSLJsQE7fpSHqv1PHk10jlT63R58WGyw853nKfb8zz4OA/G80l/F/6RfyrDvE86pFgl+Wgy+by8jN9+vTO3wWgj41FDWK64W03TpODl/AC5SGtzoTidYCOkE5GISW6AvMhXpJ3AMRdjrdKsjVy4ldpzqTyjkIasgxPjXmuVkXQuXP5Ak9FaHxpaxC9huuaw5fsHve08nzlQ+qnFi18SEnnVa3pps4SXSiBDj6WA65ycpGK5/AT8gtI+a9w08sg6OGfjgU5hdmFJQTcOS/PfJ5XYiCXMowItuO+dP73vWaOK4w2GD2az5DOQi3INZ2ku9tCyt+/l/B3pZiimtHC65zTKbl/Z+8a5Bh/PH6xz0L/+kp29BmiJ+cXL+aVx+d8SfHJxLyGhfJYQQebtPsfJMQ4OP/g4W7rVsBJ1aJKEJy1hpabebWenzYZ6w26d0z2zlyPtmtxz43uVKFJlWGjaniYgbkhsSGyobWx0QjqnWKE9OUdd8MoR9gVrrJyqebyMUvi0RSorAIjiUrsXh+GFvCQS1Hs195GNmha3ei3h+f1EdTJXQWo/tXglWU5aLyDnN4kytCI9X0OEy7FGtGzb58B0gpRvWcG1RMrcJYpNVI2cTPCV48sZeeJOlEldgxgzjKWGi9WUKLjfNvb9nS5ZV3kMlhxov6FqKRakDEx0EC3otFQ/q4Mm3COrOuEOthu2GsSeE8V5cv/TG1sYMzwb1Zbh3MMPtn3LW6Hr62flqnUjUiVQpQKa8vQVzukn33585/fvf8fNOdfOZGaUgaToMaS6d60SwKDfd/Z453l+MGRnyWCFFmSVizbECG3QagiSxr75/gIUo8kObIjuZPRCy/ExxqiBQ17faaqdA4TAR3IpoiLBMgvvEwpLvLaIQNWUXSrgHG6U31zk7T5X0CzhslWDsDn9k+L5M4s3O+PsoC92kGhgFvRu7D1l1B+RsFeaNuc1korRAYyCf7PMcCVji6a8fvhrFyfbUOpFKFM0F6zk4r3+N2nMol663pI8ooLIlgph3RFLQnv4mVJ5gdlSnGh1zXtMXBWalfp8sWThGrBtqQ6eZ0qgehpwfP/7D27WEFm4BP3tlv/fUznsK8fFHnkaJ0ln1Wbjhf+OlTq8Yx08x/P/Je3dmuvIjny/X65bVW0AvDS7WzrSTHhOhP3q8Pf/Ig6HT3jkE6MZSX0hCWDvqlqXTD/k2mj5wZo49AMj2GwQBIlVqzL/V/roDHuij+oLX/CcjFoP4hIY2ijLCiiNThdlhEBVx2OHxNnl4CiBhQQ5Q8rusAj5DS0yE1JcicF/dBxKU4t+adzjbIegrh93s+qYt+38uc8YMpnNyVBOVNdRKB8PHVab2LsMnE9R7o/bcQ7PnxNzsuYNHvsNJ/+mw+sU+LRlyJx1k7AsULIQenAzHjJV/zZ/E/MV0pCQSClg2ejB6DOD4D6UozoL/gJdAz0wZ11oCC34yODLRqDdoZ0Be4dTPVQDUZoKSkGZlVuW6DboCBozlOZagDiF6yiV012qEVrsVFE6hoVIWqO7BUTRGDn6wcP2hKRCzomhnct2IYZIO3diSNNR63OvW9Y7KcS5PDl9+3rcOOrB3k6qNrp1zuF4b9WGNmFdF3c2J7dDqXiEaZVBwxEDRw1mU6VFOv5m0hiR5IdXgxMUOpdfCRHCAuISziDTcMAsU9TA6E48jSEue+z2Vh8WdBpmh3nYYbe376dMblAmS+iBe45UhRm7paae7jvbRM28iixnz+CI4p9j3FnMbzm8YeZqMUUKErylO0xhcgjmLe/mLlUdd5yPKc7xn6cohGT0CN7QOKuhYvBDItMAOTHFHh1VSIJ/k5iWIMc7aEM4u3Ctxtej8bSubkw0o5mPGyZupXHBTp9zciEsCe1GDx7fqSbs9UTMvW49KiMqmnyG1ea+vJwzROHxwyNl3dCqtFvl8fEjMWd6q45IpIhpn6OCz/ZjBrF4CI4nmXerVD0ZdJp1NDiPP6xTx8mXzy98v37H44cnSEazypfbF1oC2wqnDg49ObVNBZhyDEcRLCZIBUJBZ8EJcl+CIyJlog9Tg33HXSXSNVKHUYdQu1CrMerU63bz7Fl15EvnDTyZ4Nl7M8cEewtc/bsT5bOv2jSfqtuB5u8gx8jvPn1iWy68Xp/56ctXavvGmdeYKqLpNLgbgQxhrxVm42UIkZh8CTLXtpCSK+ZtqGdWzTmQaSMqWfx0Rsfd1PzJ1u6v9yaQEL9VzRdFNxUKyQJRjdiUvRuv1VtsYnQBStUTwW9iRdFYPDgjOT5JUqrn+RPdXsD52mi3SjBhNEFrZM2Gtcrr1yu///53SILLuw1RYdTG+nShrAs2jHaeftNGr3XKyfO8hsxvUnC/2bx7kOzsX+0Hkv2e2bvfvBaUl9szl6PwEB+Q5Mq9W9uJy8VhLiJqK9VuHKPRNNIMP6wxQywubJI7HAYu53caPyBTzRfezmRVqF1oPXL2wFGFc4dRwRpO5nSFrrN0cepBVLDhl4cfWneX3M2pcCdvHCM1Gx7CqMwoLiFK4LsPH/j9d//CmjbebVda/7/42+dfv+3ware35cM7Cdy3JijnMJ/DSpqAsk2IzMhF8ULG6YlWczvW0NmkKKxrwAoOGQlvqSk6hK5++zoWCXWOLw77CKcFZHgHxmszbs3LPpbiIR63rlMYpFMA7oZDmxiriN9+zUCzz7+HwH/85YXb50YKiXePH7lcdupLRcZAQ6dqJQRP0ykPG1t5JFig9hONvpB5KuXAYp66WTc3Opzm/56qSkfd2xfND68Kx+h0OqEID+83LAFJ5gIXePfjD1SJ7OfJ0SvNAkMLdVSq+s0psUBavC42Jict3mAvm5Aa7iyOru7zYH4f0+qQeXADx01oZ0BPg8PQ6osaDbR5kLSpw5xy/z6rgnbUPFTQsL9DYKZ+G08gUu67kuuq17Ky5Y0kgR4iS76AfP7Wm5dpgfAfJjD+7jXQegdruIl64r7BVfAx+pwmJFqLtNPeWKe1JJYS0Wz0VAlvVhfzuE5LqDgx8PfIQyOSTImmrk1Q4fVUvuydYYFFG3XstN4pKZFj9K42GzSmxjjaW0QV5nilobAtxHcXQt29FtY6ezvY91d+/P57P7w0juvOp3cfWdYLgUSv3ZVVwd+bw2YuQ+LNdWJB0dlUSfSUcovGaYMelFAiMg90Hc3bLtc0D2iFGGkChA0soM1oKhy9cg5o6hBYzAXJxcu2Y3ZUZh5ct6EH2puh07dnp9CFauI7RDNuJ+ynce7QD0PPgVXgBPMNEW1z3p3NTIlEjMIIlT4NgjZhMd+65I0+l+CXlJcmTmF+MI7zhWE3sI39vHGcO3zrzOvyp+l6mNilA9DzAIvHnHpJtTG/Z871B5tZtH6jtimhzBHWnCkxTBo3kOLg3rTU9TeyYkyorJn4YRX/PJEZcTr8FaTPN57PQEkGVsmhsy6BbEJ2fTwdF6qUoEhOb6KYMSr9hOcX4zoi8emRLRVCN+pRWR4XlofsENY4EabIJ4sD82F42mKeC4waMQka/WG9m2403LHbuRcUmx64+w3so9UxGkRYlsKvz5/p0RfF69EJy+JvPAtouND0oGkDIjknJBXCPLxdMnW6H95Y0hDBEiouiRRJDBLniOwaeG3CtcKtwnkG6imMw7DDsAPsNOw0pBrS/QBaN6IJSZJbD1J6g30FnY2iM+LKJvU0SRPXLPkOBEbtB//+858YHa5H4/PLszNy33R4baYHTD54zNHBa61khnb0N9pYwoyV7w2RgoXufiURQlBCD+QQSAHWvNGisibHVluGHg2kewS9BjQGFGFYpFun8luurCvdZpftMI6mlKwkgWXOk0WZxInf1B5uEaEHEp4LcN4SX/4m9FrID48eqjw6McPDZWWRSEzC85dXfvzh9556mIAiaHPRj0ZXz/XaZwChW3HGHFUQfB6+d8SoH+JyiZQ9kXtyiG9x5o8EHz59YPl0QbbVF6mg9LNRTbieg6MKt1vkJJDWTFmyH9yYsJQxks/zNi+E+wNvEZXMIDHUYce9CdduvHbjVuE44TyEfoCeAz3BTvz2rYZUIXSZ9h8jDJuOlJmrEQK9C3GIe9lCIJhrLuReJsNdH+wH19TYz0pJJ798+crZXNBu/wDo/cdjgzJdvnOm8T8Tm/rXOw09NWzOjCWfhWBgY5DScDhEHZjOyZm4yHQUlwfKsrCHG4eMN9uKqWA5OnCuHbPMQGkaJ4sfZ6RtJ0rwCHsTcgicHY5urMXfBsFgyg5AIOXgREMMjD1zauT6ciCjEkRZc+DjuwfElNHcDGrJGbpGJ22ZED3FsLdO3KLvBDbt7xPiab165FMEywoZn3EtICVQHgrplig9+YHeAiMNNCqyJh4enmgEmgrElWHqO8BoPF8bv371t8iHh8CyZiQtWChoWEgUiJ4fxnByIUhGhhMRTQPV4FC4VePWhVuHownHMRe1Q+eo4DMuzRe30AW8dxztRqtKl5NIIhUjkugt0Vql5Dix3Zmsr3USEdOAcD+/wH4Mev/q5dlTpnpXHv4PH16XvNnM+pqJJsab/wxnSN8O+l1kLibE7jeJiL+aoVCyl3KUmMjBl4sQPTHGUqbboOog458/Tk1pMCPY4l1ow0Uj84nCxLUOKjIPdyDQSQPOYZToX3+ORg5uJ5LYoUA0b8l8fjl4/nojB+OyFh7fPbGPQR2Ngve3DRr5EtmWR+L6m5wzrbMeoLtOmdmWM7rSQyPn4lm+ObzdvABxDeSRSJfESnHn8hrROBhxUKOL5Z1ZFMe7UZoZXRLdMns1Rhs8WWZZNySuDEk0ViKZe1hJpxNF6ZbdVKCBcziSs3fjVgOHzkWtBc4T6gGcBtVg3rLeCyt/R1D8huf2fpJCZwnRdSQziiplT5GPIWNqHKcvc31MmnnGft2p47PqZOcG9xKebzq8bzCd3AkFfP613/6/TjblvtzZELTDskAKbhkX8TDqPDsGxEC7IH0QipGAcwywRkouopHoRso4bypseBaaDXR4AAjiHRhD5rw8t2gv+TOqOu2YREjByMFYOrSZeqPV+PVn+PpqnB2sZC7rI69HZy2BNRYsK/uxs2yRtEXeXT5wz2xzoREwPAMtFv/n9OinhhSB7A++/9yJEATi6imOoQRKyESJaDSaKK1EbgNiVaSsdBFONfY+6CYczfjycjKI5MvC9lgYsv7mTaOglqfpEoZEKp1jCKcGjiHcGhwdjh64tXl41aNqewtT4zyQ5kTUJE65d28kwZVu0b10njmjEyadD2j0UTLMMcEQxnCD5hiDMfrMc+hTmThDa7A3xRrf2gZ0H7v9CZia0Hly74bY+0dhzoiMqTYS/AuKSYhBXa/KjFoewWG45mElQypDxnTNejJ53iKWOxYObJy+/CTDLCIUzBzaURMyDgFNfoSAdz92E6K6Ks5D/oyzK2c36hnIQ/j8rDzfbgRLRIsc1bMq1m2FLHTpEDqPTyu2Bj+McSriTBET2uGaCeJslEyCjZnEnsylkhmkeOCGCdAhbZm0RWKLlDXxch7cUA6Fujfq6wtPHxckbzTtNAvs3XjZOy+3xvVQHi4R8orGxRcxMp1CIzp2a8quxrUl9gZ1wD5wRKEJVYWqiaqR1iM6PAzRrUp+UYniIdiAMUghkBLEEZDFl/VwzoqBaYS12QUd1JGOMQ6Os1JbdSp83rxjjClQ7xOGndya6ZQV/H8TxP/JzeuvZ9dWuLvzN+LZ5n/fP3TezuarSmvObMXkr3+zSCpKOwWOQJ8Rlho8sC4WXwgjiRQiOayMYPQQGSjnbI5f10IJkTAq4xDQRjchWEOsz8XMA/jilFK6B1pdnzrNgM2E4wu8/Hql6yzKI9LISEpUij9oBilvhHXjtTfWbVDi4LydZAnEEulnmwfT/5lEovcZAxZxli/Z28dgEBbXFFsylpLRIuztpDfjHBA08pefnpHtI2tOaMhoEM7RaCROjXx5qXw9hcfvL3z/oxCWTLdEwziH0Yaxd+X1VK7VCYiqcHZh74FDI51Mt8IYkdEDooki/rD1GKcbBsS805goJAZRE/carRDFdczdLVSNQeudWitYRMU4W2c/To9IxcVYd1G6+q3oqMjbRft37O43Hd63Q8zEe++idLiv0SIy3XeTARO3cduAOpRQjRB1uo5BpRExGM0PlwkzyWEyT2BNsDZt4jFjZPJa2HLgcVlYQod20paAteoplD0iY3CPprpPSz6aCfFOS1pAxRgt8Xrr7E1c+SZKjn5jocre8KyGmJD5yrW9EctJbY328soff/gd2gbH2Nm2x8miO7ohw6E0i+pJd/PmJfjtdHc3yBqREjh6Z1ehK1SLbOXCS/3KL193fnj4hKULte7srXJrhqYFTRvPzwf/x39r1GS8+6iM6Lft0ZVbE27VeD2hdndNNA10ot/MkrCwzKJB1zWk4JG2QqQV5Yzz75DEI2aLuaDEBHrGuuuS6UarndYrIsroB/VUf8PiAdetzpAS7M18CXAvSnwbS95w+H98LP+xh23aqE3D7FGbth/zw2sSpvjYG8bjnUfHs7LGiNyTnbUn6u5zmDTDzk7ogdSd5g0Phi6DYEYyYViFLdHzgBxZ3114eBQeMmQa2iKlZMZZsV7RfrhMb4bhaRtvI4T3HxvhLorG5+FjDKolj+NM0EgcGkjbisbICAVNGejc2sFZB4QDnhbeffeELYGhJ/GS0ehpL6PN0JEZZxWz3zyaFCtO82p09LcPJT4tkCLXX565VqFsC8eIPK2P/PCHP/Ll+Ua5NUiFYwi/vhx8vTbOIXQyzZS//WToovwXAuVROA2u1d4Ih7O7HkSJrqCLi+djqMOQyRI2MqELa4ks6t6+lAKhJHo7PbyaTMpGP5SzDvresEOwvRN6plWduH9Dh6MK4354e58L2v0+9Mvs3pTpsWD3hKV5sIVvJynCFHQwnbLG3S7+WxqiR5/eX85xanydmEgEYsqkvPicWotn4JpDScOEMYyohnRDFyFUI7UIFfTaYRHKw4qtby9/BtXLPkKdi90CcSUU0N6xrnTxilZf1oQoHhQo1hm9c5w7ez2nHNA5+RwX0mWbirAAaYUSOY4XtBrRjLwK//ThBy5b4mgVSsAuiaNW8lKmGMi1rzFEd1yYYQVkFerpjpIRxFVgZeEccO2BSkYoHCPSZeG7Hz+Q3zWO6qhKo3BrwvNtUEfi1ES1QW+Jv/0csLXw7oeCJmHvytmFs09nd3AkJK0LMUcsJpJmRsuMULBQXCtsgdBtCv4GrUO0QDAlWYQuaFfGCXU3xm2g+3CLlfZZd3Di3rXBmBKBeygjTJnt1HjAJFHeDu3br0wg4BsPr4ivWO7BipMGjF4+HV0o7QbMab7kbjfBZz6JpBgpOYMmRo/IiJ5OM8TFHqeiLx0ZhrXE6FPscQY0GyyKtcRVEgmhba5ZsL6QLJCsEKzDaO60xTNwRe7kiMxkpkEQnabHg+vZ2DUCy5QIGpofaJIROo/LhuZCx9hPw6o/jP/lw/eMtHEoXF9f/FUaYARlyZMMQGnZIE9mUgTJQsvwepxIyqhkbreA5gf2dtIsIWlFysppiSaZEDfS5YGzXvny+coIgdfT+Pw62M/BrQmnZafQT2H8FQ5dePy4EHMiiVGS17SqCORAXCIhL6hF0siM4ZJJE3GmsAOtM06j752xd9ptOGRmyrhBfemcV6XtxjgG1JmKjmP6vg/Z26E19I2dfVvy7X5Ip/7hftPOGTfM9fDuvvkfPrwx+G1qU7YWQiIRSCG5zkHGZCp803TBsccGpRSw4WohGYZpmw5oz4eN5sC+dsG6IA3fzLswTodJpBtM3v6wyDghb5Gci+s+LRLwoJAoiRSG14xO9FvEYbgIJFxDCkoX4dpvnFZAAlkyEowmC4cGymUlXB6gJI5649bd9h3NOK2wj8htP/jl51c+Pq1c1kxYMzcCzIzeXiJWMkECquphfSFwVTeCxrLQo3LoFQ2RU69oECyuVCmcVoiWaRYIyxOxR16uN355aVQp7Ap7HxzqMsegCe0RfQ5oSjy+3xyBFfHMBjPA4wX6mVANaM9Ii0gNWB2Mw9DD0Bqor539CxxfhXoV9ICjdzgD4xYYN4GmzrS9tR7pWxSCg5Zeog3mCZry96jt3y/++Ah6HxdsSnDfMNhvOLxhvnLBVU/0u9hYEBKe/DSxOIFwTz9036W3pY847T6uPQxiiBr07pH30Qj39scOtOmPw1GKKMkfAA3IKZ4mWCKpJDQ2mDWuEaVMVZsHH3ZiAMthLkljiuCVY3T2njhZ3t4silFlwfrgw/YOlgcqg9YPqnlqT0I4R+TLrvz6l1/IInzIG7JkWm2OWY5568iChBXMvOlRE6ML+/AGnhQiPW5UKtde0bQx+umjAAu3HlzsLt7lu8YHbl//O/nywP78lVMLIwYajW7RCRyNjArhEFgilpIjMbF4jFONcA0YGa0BaQmaonWgFcaZ6efADmPshfoSGK8BOTKyK/1QpAnSArE7c2fTHX1/490peCP6eOhApp8ZwWWq6vEJ/6+Fze5dFOFNkfYb4vANh9flnvY2075JF8356hgKMeYZB+XuiBySLykGQROBzCCCBVLIxLTMuXgw+vD8suYH7K28RI0SE1tYZ5Wq/wX0zupYImj3W17cqeFhzeZJOgzElJAiKUVy8rASG53RZtNP2qioz/W4IPv1hI/vH0mX92he2fcX1BKaNhfhWOXWhdSMuD3yux8+8O7pwmg7+/4FHeNtafXAjYT2gVomDGeuDnUJaWhCtcw+Es/7jsUVjYERVk7N/O3LwYNdKJfVXbWpMOKFk2fSwzvO84qzty6FjGGjWuaoRnt1639Z7ylH3sPWuivBZARoAq372FYV5q/pKViL2KlwZlL1Odemd43uI4OR29EAACAASURBVKExGHiRnYpxn2KDRISEcjJkOKlFxQeqCSPKPT1Tfrv4zPxik4lEmKvPvhnnvUNLcfqQnI69z3WTrFADC1i7e/GNmD1f1dQtxzF4fJACOqaKSHAoQI0wJnHR3HiYcqKwkMgkDa5zaB4jxRhujbE0JY6FoSd1NPKaIGUXuWv3mowUkOxJl6PeaONGj0qVhS59BmAbQT0g5WN5hOUdFWGvz0QWhlTUGtHg1gMPUvj+D//M5eHiLJMKh75wHI11KaSZXHnWWeMkrrrSAXt36aKNSNVApdAss/dGk5URVqw88qd//4Uf5cKPl0/+tZxGI2Npc3lkEepxo8lgkFjzRpVC68a5G1YCEjOPU7q5j+GmxjMgXYiasGr0s2Gn+IjWwY5BP308oAvSIqF7SIxxl63OKdXw6gBxAZe/L8PUngAzJd7MF+0gv5kZAvJWFogE7gJ2ppBLZJY38o0Mm3AfrOeHpYHM1EYPEO/00QiWEY8KpOrARoPJggXzDIJ7jGjXjqjTq5b8KQ7q0UT3uKdkxQuzby5ECRqhRHqYjtzWCDWQe4IUOLpTpkuFJwpjiQhe7neqsEomZ7fLV5TPx86umSYLqo0wG3OWGOnxQk8P9HqjWfaveWRMC0UilTwP/kqlEEKmJ+XWf+EcER2BJSSCRc6jTn1GoGliDON6QgkRIXM05dDEYYVrH2hIbNt78uMnnqTx62vlA4UqgT/9+c883w5G3CALIxw0BtU6Ia/sI9K7QcgsLLSaaSGR8sYWV89kt0YdfntGjQ4pDgEVZJot3akasGqE7rexdUOGZyx47oUiPficbS5dE7nrhDtKnbOuco8B8DfSb9b4O9vqBJjvTvdbVu7LnMk/Qsr+M5zXu82iZLqdMNX3QZwVCeZVR0kWgiT3RAWjmSI2QGdH16SKCWl+gRMftuA0YtNZVRUJGghWXOw8k819dhYo2amMYS6NjIUgmdGAFmgGV4xY3AYv4hUA59nJxXNlW4ucVjg0c442X/VCNKWsC+X995yWsRE4e0Rbh+GdxKdVWli5joWtRxYWT/oJG+nhI7cOpwIU+l45T2VZCoiQJFK78lphTQ5Dvh6DfUSuLfBSBWJkK+/Ye+Tdp09sFjg0US0g6yNtP6BsaI6MCE0GljJSLiiJ8xg+UlEooTCksGNYHPQGekZ0d0dEHx1rg3ZU6EIY/m9IC4TmgS1MyaPN6H7rijZFqyENpIfZYzEPrxhNj3mg713NQpA0e5fbjMudCJXMzg+8aejuNzRrgMyClW8cG3JYCXJPHZ/lGgKqDdQXgUQm3Tk9sxnd6UJjewtt84XJNzIfJwYQ2px5IjAclQhEoiYfcM1dB2MfjiAMQUogR39Y1vjg+tWojjOOQb8pegqEQEiREZRj78RohOzC5msVjpGo6rahaIOIkh4/EtZ3VDNaj7weRpFECitDHeftsvH1Nnj/YaXFC6rNK7DWJ45fX3xus8Lr7cZ5dD4smd6VHKLLECn00ygxcFrmZfeFsLNwnKCfb+jlK3+8fCJtT5yWOIewvfvAOpRfPr9AWgmLYFlnCvqKdsfBz2OiBreOPHR0aWSZqE2Dfg707D7HtoHOKH4bPk6gkdBB1C+I+6zbWyO0eYkM87SgPrFYuUfddkR01sIKNsudZebWeZ6wC62cZXMncxBvBA0zZFG5a8j1H7HD/8nYYPgXqkYMaf7hdxFmJIrXHr1F+5i6OihkP+hjoOIBTR4Ham/0qarL7fwJNE85DEAzLPnsc6ei3wb4N75X0D44xpW0ZcyGh+bpXB7ilHBGsOApOyOClELrB/s1sB+RPjKCZ3aVKKzvv+daBdkyRw9UKyCDNhTRTAmBFgofvntHXJ8YcfVsr+TNOKdlAoYeytdrQySxj4Sqzrw2oUvhPAdrglszV3dpoorRgnBa5PNL5UcysNAtU804uvB6wi/PjU6CfCEtRn+9MUbCRmJMJEDwNMdbP8mPyctedF4QPUIbUDthBOjOpsWeZveboEORAcEcYvMDPJAR574x5uv8brY0L9XuJ5KEOCl1T1KaeO8cC5a0QRxeezuURH4LJ3QJgkNsah2lfTtJQZ+4W/AgDTQRomfQtqZYM0j+pLnQV2ZYWkJkmTHz/mIYo1PihftoH2P2DrGuTiFH8wUuibsO1LxgJCT//dr8QSwRbcoIDW5KWjOh8Bt9nbyAWvHIIgvCsJlYczq/f3sN7LcAdg8rCZRtQdOFRmbvQrVCCwvX6wuXUvz2skq1gpQnetxoZEouSCj0quwtsCyZ2/XG3oWlFI4RHEKb8r694vbyW+doxus52Kv6LI1wWR9598OPNCmuzyVTe6fHhefd+NuXg/0w9n1wnkKtHmEaLTLOgVZX73URyhbo+yDERhSQET13YXiDkEx8fbQOw3Fz8eJKwvCxLgwjkxmS8CR5c5f3lJvFOFMvEcALuGVGt/ZegU4MaZJFQooZHU7ZBxW8z8YDU5gqvRQyw4Sq/z+cFL0PlJMSM0u5YMmhi9YHx3Hjsl5Y80JZCl0HfSi9w3mcBFEIGSNSQiCUgFlFh7As74l5cyU96k9Yd+2oC1mmqL0pkiYujNuug4Hd4z1DgOozsGeA+fIXkjs9hrapfPLNuHclEGhXQ08fGSz0mef1QNNEtYhVxeLG8+0zaKBIJgZo3ThGoMtCw2WEMrMtTk3sXdAgHFXpI3jmcAVV745IS2RvnT4zDl5uJ1+undrVs9jWjbBcWJ8+0sn++0NhBJ/nB4mmkT6gnsJ5G+jwUJSUA9SB1FmognC0Sn4I5C1gVKwaWvEylmGkUVxc3u1NXDWOztgbORQfB6dLJqiCBurpoYBieEVC8Ns3JQ/M81wKF5PLlInJ1PyKyWz/BEIgljSTmBxmtQmnRVk8XFC9KeibDu9lXXh6/5GU3KOWcsIkUZvy8OERTBhVaUPnx2S38HAQc+by+NGF37Wy5gf6CPTmJkzVTooFE48YIg5Sml1uw3l0U2W05jGoCdbikUrNzHsloofGaZ+YYIrQIKcCYpyjYwYprSiDc39lb143Gru7kQ23GwkbvWdqjWgKtP3GrQk5FCyt1O4Uc5eFHhY6hS4OIgYJHCNydnFCovvtKha4vZyeF9aVEt2237o7L/ZqHM14OfxgH1V4YkHDyggLasn3A5kHWBOPD9/x/PmLz6iWkDazw/AWy2Hii9UwRjeO0SgULpdHuh1AI5hCd/JH8Qc6EkjmUbK9DVS6p0d2puXd295fXj/z8vLKmgvvn55ICLU3j/YS81ZTmx3P95ua5uwbyW/ilAlxnUJhY2ilsDG64/8hRJJkBNd7fNPhVRK1QutKa40QXOkeQiblRKudeg76OFmXjW0J/PD9D+zHye16Qj9IEmgyqPUkhGV+jgoSKOvFy/CGklJ2giGppzyeg1ACMTs0p6bQg2eBpVmZlSIkY4mB0Tv11gkx0EYnZHesSjQygSEQY0JvjWRxhgF2v3CGILrQduGmxrYFfv1y4ziV948u+jlrpcSFLgWLqwvAg/diwEDKA0cXtJ/uCNAAPbLvJzE7wnJcO4REU/WykCF0Eg3hqObWqHLB4gL5gkqhz9w2s8i+Z15fhLoLMrxstdlw4sV8wYoafUPX6ehQqK/GqWPS/Z13xVN+tA/Oo2JijiwpxLwiRZwubhU9vcgvSiBJYM0LenEKeDr5nbAwnd5AJ0RiWBjjQCaSYGqEmFB1jUmM2Rd1hD6EYMkb5a1NM+c0zNo3og3X/eD1emDiswmAqRKTF5GownJ5T8reWnk7jXi9crbOWTttfpFjCLUNolRyLIQcibmQ8sJ5HIxWISaqdtKWMBt0MRhCid5iyXDLERHSupCyuDdO40Q7jLq/eIJ5KixrZntwIqB1JWZjiYWE17DqMeV54qxQsMz+0olx5cuvO89fKzFlyuWJJuLNQ3hOWrfor3BzQB0JrA8fyJf3fP35bwjmc1tzV7PXBXg5+LKtWMwcvXqWsAUncCxRh7A+vPMF02YCJwklM0ajHoJpZiuP7C+HM2PNSZ7euzsghnh4tslMMjeOdhKqsa6BogkNxpKCj1E9oh3OW0fUuGyfSEEY7C6sJ7xpR0SEx+2Jh+U9IYDqbZoNLsTQKWvmrFfq6WHRgcPT6c11MqouU7XWiSWCijvN1ej9xNQZz8iMxVXmbf0Nh/feLN5bQ3IiL2XaQmzOVVDPG1Urpsa2XhiWXU3fBikuHGcjpZUYjdG9DbGrEJpR6/xiQ6GfHkZHd8YqlYiFKXGsDqH00SkPC0tYaXWHENwysmZSSDw9fmL0g1IKFO8t2xL0USkpUA+lXx1KshmcjcC6LYy9IyboRfjpr1+QGIkpMTRz9M45AnEolgoDP3QRz1SziV3/8Id/5qe//USvnXUpCMbZnNjpLqyiyyAuhXO4DTwvG61WhgV+97vf8/77/zKTggSzyBh+6zJZxXYdJDJLNCqN0U9/sNug4+EfMpMbIxG6UbVjVZG2sj6uYIFxgh6GNCFZpraGjc7RnxmnkVR8WU4T12+4Xre7tl5wm5P25gF6wYBKTIFFNnIUDzCcfr+lRFQ7tcMYjdba35Fg8ncqNPGzFUDIpPCNC1uImdF2yrISUySWjevzV0QdM2i1UbYHYli43p5BE7bsxFRYSsbVRDOqHRc3j+Gi432/EqORoh8yEyUOoQSjHietg7ZA2hLtbHSB9LTBCa19YTCQLJ6lm70RsqsymtKPndOuAGwPK/my0TRzXq/U23DVWncOXaLToG2vLKXw8uuV/eVke8hee6WJ3jtHhzVkJK/YLBpps6dYMdp+ogT6DAGU5KmZR4d2+i1uCKc2Qg/s3WPtQyqUNfL1NkjLhddr5fLuiUECTaAFHQm6IZppR6deT6LNhHhJYC729oZLJc2imD66dzObUM/B63kSxpXHNdNr8iVZ3YHykAsSy9z0FT2726KGEQZYU/r15nrdJKxPnhHRe0dQAm6BNzybzeaO5Afd6K0RA6w50jFu+w0zoyyLJ2u2js49ZqJwHkKd4rcd3tGqdynMT3beXkE7MWdSKn5zJB8fLpcHkDiNdQfr9vAm3tTRZ3NmRyTTm9Bq9/aZcWPbNh4/PPFwCSwrfP46qGpECpYjobhVaOwVbYJEpVon5oDtjdvrSUjR+fM0ZnJLpg6l6Q1eruQSeb3ttHPQq9uSZCrmzuvpDNCD8fnnzw7PLDIJmIIl2HkllsyyPSF5o5s5cTK1sK9H4+dfv3AOP7D11ggYZxNqU6R3QnC7jyT3l3m7lLBsF5J2Xm4nX19fePjuB7xGyg2RjEQwYwmFS86EMuj7wLqyxszNHLbszXFRg/nqhXt8rAxoDA45KcxF1AZIIKaCoNTa0NHJEh0rHkoyGLVT90opkaUsDOssS6aP0wVQEVSN2+szKRtlW6dswKhnY4zB68szpUTK4kxajIlUvBRHQiJpfOMjQooTdYAYvlHboFqxYYQQqcPhp5imbUaVpWRq665HyGlSeW6sO4+DmBePFDLj3G+kklizUB4WyuUjubzDzp3fff+ey8PqhSE58Mc/Fv7tL59pdXCcXyEHQoloMMqWPCo02MwBuKE2ePjwDpVBVPWUEXEcsR+VvGyM07Dq4unRvPE9IjQdb51sr5+vPF0WLk/v+PTDO2o/eFxW6jAuywOXp4XH9x+RtDCoHlanXjCYLk/EZSFtF87rST0aSwocTalNYUDOkVsdWGsg0bl9PApv3Vb21nj88ImYH8A2bGSsR/8eDOEPP/7Iqo0smRIzx7Xx7//2M/9x/AwZ9vNAh9G0oUSi+QgWZkuTmlJvjTMmynZQ0oaNSJw3NwyyZIJ5XJenA0XCkqlVIURvY69ursxrZowT08YYJ22cNB3c9hfyQ2YrT4Toyr6u68wz9pj/ZV0pizeJ1lpJKSICrTViTC5sut04z9u3Hd6ny4bWikSXNRrmCSgItSq9GyVlRAJpKbPBMDJ6Z78dhFC81VGE9SnS6sHlYePDhwumC1ECjz98z7ptjrnGiC2ZD2Hweqv89PNX3zgn8xYjjGvl3Y8fiWuiaZ/l0IPQPSf2uDk2HVKZCrjB9cvhN2SAdjbEzItQ7vLLtdBulY/fP/LH339gebqgenIe3Zm7Do/ryj//0z+RZv8vwQsW70mHFlY+v3iSTdPA0d2Ob8zo0QGneidd624V8oeycKsQER4eHrg8fUJkpfeANdDakSEwEksqxGE8bultI/9f/qd/xk7403/797cI0iyJVhtt4BqH4XBYjtGd3UejpYyEjp7nZNJcc9Bqp90ObtdnLpcnEo+EoR5wLW4nkpRo/WAcfvN2GjFEPn3yMpjr6wsRz+HQ0WhjUEqhLIVaT1Kcru/eCDHS60FM0RvvJzUsNliWSNjefdvhFRFyTuQUZ9OM8bQWroeD/5fHhRJd7R7WC4gLz00bH54ePMSY6K3rAiM8oMP4+rkiAteXL2TJPL17z149mC89FMqW6Pvg6bLxfFaOcyfdg0vKwsP2hAZD+45U73c4zBMsgyhlydOCExl9kGalquJgvG++vEXvizS0G5+eHnn+5can5cLz52dCEdqpCIU//Mu/8HiJHK+vXB4LtVWUQUrJi+4soiHRNLAPn4fPY1By4hzD1VNBiCV4n4U5ySKSHe4asErmdlPacaXugxI2Xj7feFw2lvJAJLGFRBxGzCut7UTp/Nd/+ie+/HTl+vnGOAelZH/r9EHT5gouiUg30pKQYRQCUYx0yfSXRj8rjORBKgmWsrDEC7029utBjJmQA2N44o3JoFuFPoh50Gon6EIIg7UkRu8MO4lLIGoGgdaqd7fN79PobcosPbvBxL0UJSdEXVpAKt92eB+2lbMOzrqTcyHnzPXwOfjxYUNCYtSD3pVeXz13IXjX1cOWHeMTIW8F2iAvfpgNYT8OtocLvYuH5K0bOgZ17/TmwXWhZH54fKD1jbwt5MviWbS3gy7KfrwgEdb1gtSD2puL1yW5JlWUdhgxKXlxc2gOhbNVJAe0DfJTRGzw/vGJcQ5uVVk+3/jzn3/lux/fQ658/09/4Lv3v2d/+TP5w4bEBQuB/eUXHh6TH8A5CuRl5QFjvx0cNI4+kOj1rohQVjeh7rs6zjqqJ1iaIXHh3/7tr3x695H3Tz9gR+dSMiKJXgfaAyFsvH7+wvvv34MsPP/yFSmP/PDdR3758y/Uhj/o60KXRj86AWFNgaCBqJElbWRZSRI5qyNJ9ty5lJVY9G3xNXnxfWa7zKXrinXX5Zo01suC9opYnyRER0ann1diiZzSQIrf2FTPUA6zWTUk9lYpURlr4qgHT5ePjvubob0Tc6H2b0Qbhg5SgjGiv+a0E/BURg2d5+crx9FmJRXU2tm2hacP74glc948XSXguFRZE+30QpNtzRDcnCkakNFJU4DcuyvRzped9WFBTXltr+jLqyfXBKcjny4PlEUYJuR3F87mBYS9Xp2QEI9TdTTGuJ0na87A4OOHB6Rc+HrsWAxYG3z9/Mq1Vl5fr1yPTv/rV36Mif/53Xe8/vQLL9dn1ne/5ziV2/Mz6Xgm50zKGxYy+9GQlPn4tLG83rhdbzT18r2YfY6THIiLEL5WjmqoRHJISFj4/fc/ohXqCfKYQQcyIJeN+nrw8vkrt9fG+fUg5Z2f//qV51+fibxSls7/+r99T7spx7NRj0i0TFFjmyiPdOP6vNOOwU1PtuIQZT0GuTtVbpaQHrg8PjBOY7QrcfEI1W0rjHHQ6kGIG9SOiLCPStUb2TrZOhKV05jz9wtdvV734WFD25hNpIOShRihnTsprNTaPehwyWiKmA7aWb/t8Abt2FAelsh+PQkxEVLiqIN9f3XYRI33jwutGYfAh3cXlhTIrXv8UB+83E7evXtynLfDelnpQ2itM2xgmjmG39IpuBKshIRko9dK2RKXZfXUcIGlFM8mG66MUgxrg2gGo/GwFBSIJVH6oGwJlcBQj8wfDBpQ1sjHT99z6o3byw1kkGMgr5neA+fZ6E356T9+5uX1Cx++i1xvjZ+/vPLn//tPfNo6zxWWstJaJaTE5d0jJQVKyfzw/ROnVSKF2gblspFi4Rwn2+Wkt8Cp8HxtqK1oV37561c+fFgYT0bQgNWOFeHzLy/cfv4b5fKOr6+Vv/z5f6edjXZWUsj87scnHh8XRlbePwbCCMQmvP7cOK6d1hW9DUpyUU5Qt0feD0ssgSUUejVGH7TWkT549/RAPSvnsROj15Xt1xsxC4+PBQNCT9ACp3Ximlx/3iupBEwj15cXvvvuA1Hg7F6gEpPQRmXvO4FIzpnezTuTdTh1HQJb+UaorB07x6E8fXjP4yWx18HrS2Mtiactcq3wsEQetkx5786JsiycZ+fXLzfi+kgsG2EYela35YyBnLDljQBUzbRhLCmSykJKQm3dAYOUKClxVOfO0xpJubgJ0zopuh/LI18DS0mOoNM5zxNRfCY9vYX+8lhcsRaU6+3k/eWRKk6zJossSyStBUrE4g9UqzRRvvz8V9ZLJCH86//5J0Y6OGvndVXay2dCNy5rpGRHXVofBMmk4sydp2L5DaspEsKFDx8fiGHjy9dX0qXx018Gf/7zL6gW5GXH/vW/8+PTO9p1x7jxr//6Hxwvz/zuU2C5bI7jtk5OifeP79hYOH7qxJT45a9fub6cPK6Fx1KwAE/vHggPiVaN7eLi8+vzM40bUTcsJHZt9Jl2bpoIvdOmMuZShP3sfP3yBfTgEheIGzkmzuszj2ml9ohYIkXPbEY7DeXyuNG1eraZuCE1rxd6uxJ6ImTjsSxcb87mDsn0MXi4bLw+P3/jzZsyeVVer1eieAbselHq2Tnr4Ho2UoyoGi9/e2UphXVtnE0d8H9+JmXl6f0T5+sruQhlXWkWvcQjBI/VN/j6euX9k4AFpPvSdQyjo7Q+sN64hI0uTiHHsmDJMxmSDVLOEON06nopYW+V9f0jGr12APWUQguDXIQ+dvpQ8lZYPlxYlwu1HV49lQJLWeH/ae/MduTIkSx6uJPuHpGLluoqzPz/Xw3moRtTi6RcIsKdO+eB0T1P3cDoqQuQfYASCTGdRrN7z1WDQiN4w2gNLwXCaM4fVpwfaNfQQ1PrQR2dsFj2y8H1ukN3LMHQxiTy7HX2+XYIbukdJxfMonlkxf6iSRHSIXn98srX+JXb+cxiA7e3g80NwvCQD2RXnB8+4MyJfr3ONHYcflmosbOtYFVhcyutCIKomL/7yUpDSksteT4Yk4JRkUYRy47tG0qDEeMfAdnLaSHFg5Qy28NCjp1YCi4mWt35n7/9gZAN6ySf/vLMQPL0sNJqZc+aXCO5H1ivqClhrUPKguqS0guabW7cxIQl1lSQ2qCMZ9m+k4yubMB6wRELQ0rqGHPjMQS5ShZjeVgXYi6EOzE9lTwDmEfHGIkxHc0ky6ScaV0htablhDbhvh9stC54f985PyxoNR9xYXV0Nbdk3CnsbnXkWmhpIJRFCMi1z697r7SaZ2i1FLhlnYuIflcrdTBKIrWb/14dWBqUgfVyYqPatHC3Omit4VaDd3bKPXPBLQLrT3P3LyJHjZzdI1Y6WtxR2uNcp4+MtIIqINZjmj5rhWQocaZZZnWgjKQXzaIdYfM0NfjgN473RE2Dep+2GKHmzVI7ey6Ukli1ZjjPH7+9E0Xm04cnlF0mEZ4DyVzp95RJxw1l1AwiLIMhNCGsfDw/Icrg9csFKTeCC4gKq/N0J3h7fZ+r/+BQRsxYL2dwTpDijUvKuFPgfA4ICs8fH6fQShTej4gQUyFGU4hqMUwc1ml54mX/HwQaJdTU0OTEwzlwiREVPM48IPt3qsqu1wNjNdZopLonr6uJITVj9ifOO6QyLKuk1cLL243eB8Z5fJCkWPn88YHrNVP7DJRrFbx09D6dxRqNdRofAkoZcsycHldiikhtGHX2qhXQpaOtoYlGoxIbqCARVmORxNbYThvpuJGONHtfI7FWUlvHezdF8GM+GmuvZDrx2jk/WtxqeHmPaGNYF0kZBYEiBM/e5vIjpT77uaYwytHz9Mppu7Afgy9/XDhtCh8eEErwdD5ThyKVOuO/vKDWKU6vVXKNIHpmWzb21Dhy5bwEhG18/e0dZzxaauKloIQkrGcMhi9//Y39fcdpwfm0ooTg+u0rkpl7p4agpIjsA+fsnUppSTGSY+G0rBPRpQTbaaM3iRCWUiKxz+zh4O3EU3H3DGhJrYNWKsEa/NOJ/hhotXDkzn6r7Ncr24NF+IATg7wPlIHRMsF7SkmU28FpOXOkPLUPztAUhM1hg6M0wf72gjHf2fMKKdm2BWsUNRcUfSa+oNFGsscpZxQCjlvEaMXJe/y6zTFabVgL12ukizl7FcASVkaTKO0pqTOEoaQpzAjB0Vvj8rKzp53UdlywPH16RsnBGFBy4ygRYwVHrSzrgnaBWgvaOFruCKlxWoEAbTWtFaTU03sl5rartsblFrmVhF8tvS+k42ANgqqmAcVaO3GhZSBy5/Vy4fzzxlBMDXUWEBpNVpT0uKD45T/+Qu+RWiFYx9fLzuvLCwiBMYafP58RWhPzDOtrY+C0JZVOa3B932las/kVH1ZGmUBnvzikHIg25qxXCp4eF1a3MKog3vLM/DB2SgvrwEgxLVx54L1DoLEo9FAoNeMMtFQYpXHG0nJDO81+jXQhOZ0CpRRqjQil/gHKk0qiDEjh6T0jgiZ0DaOwLIocI0JKvJa0u9FChzBh3+bvyZaTRN8HeO9ppdD64BYzdQi0GMTynW3Dtnq0ltTauFwPgtMsfiGV8Y8fPPrAGU1vnZTyJHHXirEKwcBYRypzxOaWhZQLyE4rgpwT1lj6mGHKjInaP2+B98uNIAxBn9F2GtysUaA69MaqF5QG6xrGLwS/UsqFVitDCIxy1FbuIcxgnaf0hlKKPga1zvw3pQyfzwtlDEqaq9z1SZGPg31vrGuYj4jRSKXcfy9FqpHSKkZptu2MVBUvLUZbao7U0tlvYyvWgAAACm5JREFUN8QOp4dHlJTcjsL5+YkExFLozDxgHxR0RbpVjLJ8/ukDVIVXnqDmXLtcMykWWCVeO0Sr/Py0wZDTuV3GZOqmNlfBaqCRs2Uqje104jjKzNBoghLzzMUwGrpk0YoUy0RRGUvWlettJ6kZ6bZuHiEGOe6M2lkXh1JyRtYqTe1lrssRaCkn+rRWhvP44BFyZkvLlunDYOxs+Xqd7yalpla898q2ruTaaengX4S+/+vD64zASLilxvXIXK87tQ384lFCYowhpkbt4KzGOUspffrc6Dir6UhSmhgoWsZpjRwdo0GP6WXSAqqYxsGY8+yTpJqrQ6Z+mD4ouaCsxBszDXpCsu8J0Xey1WAk9LkIGEJSc0NbhwtT+KxGw5jZ79VeMEbht0AblVEzxg2E1wit2LwELVFqvnxrb2zPTwgLapmT60V4Rs3sxxsheI7jIMuEMZLepstZWzvRR9bysKzUWoitIrWd8QaxA5aYQEmLtQv56PR8cNkveCkRTWCcmZDpv3vRpOa6X1FDs1pBjTO0pnWJt56UEjFlzHA0NCNWhNBo2Rk5UUvG3HEErTb2Osiv72hrqWiCVSgxdR0TvD2VY3I0Hh5OlHSjtEaNN9xqMHrmFEvAeMOoBRs8dlnpJcEoDGsoVWF7nbfrkfB6THmlM/j534xWc9ml/XafCX/H4RVC0vr8an3+bGnlwAgQuSDF5IKNNlVMIbh7anvDWEevZfbHA4KXKCkx1jGEYX89UEohjafVehdvTFtKKWMKr3OdkB4FvQisnnv3LvQMdxQNhWBzmtoqJSaCP9OUpTGvTlkK8XahxfmKffj8cRIRFZQ09cWCezplH1Akys5Ey2XxXOOBDhmMQ2k1p8NDkFOm9CuKxuV458EKTBusbsFJgbYS41cgUHun9ggiTdQRA69n5vHona4t+1simIXFnci3ytffXzifNtTqkENRYp0yzD4Iysw/gOuO1YrLt3eOIcl74dOnj4Q1EKPAq5Uh0kysR0BrGCmIx47WmvO2ooVECotwirdfv3G8vrE6R23TIVyQhIcVKwybmVyOphXH5UoZc7pwejrRe56LCyHQ1oAYhNOGkJCPRGsJbQ3ee7yAVgs5JayG47bz9r7zeA4sq6NIhRYKc3eAm+9VldVaoTaMtTirqVhur++MMcUhMoMVitf3G7U0nHe01kmXHWPUFIoPeRfyTCuzlCCduTPCpnH6dt35/PnMsqx8+3phNHCbp+WIDpLSp6Jp2RRDQTMd4910wuYpdJYSKBXjHEfcyelKbXOxIoxgWw09Hwg9iTBBDbqW8xFoLeWojFahV3orXI+DIQVVDlAZweTxjt4JwSB1w0mHWR44rRZjBK1UmjKzt5WCLhVGWFq8ggbv7sHdfRDzoHWLqBIfIB8doyqlJoIZ7K9f8ToQj8qybSzhkVgi+2XHhxPr80J6e+fhg8IoS7wkRm20kaAqFGC9h67Zb4njkjgtC84atFToAOZO9tFC83vtXMpEWArg2BPLtiD74Pryztvvie2ksUFRyo7Sii2sUyWmFA+PJ6yauNf9tnO7Xam13kVJjU0MwtAEZ+nOcVGCY694b/GLRcnpSg9akW5XpNEo49Dqe8XocobwKTmIx07Ole3phJR6Hroyr/TtwU8vvxgYI6caSGmUNvTW6WXgV3dnV00/Wcpt6h+kIuXO68sbzm2czye+fXmD2uaV26el5Mu3F7qopDbD+JZHx6efTig/EKVhlEK2CLedoEBaTRvzmh0SchkIPd3FKKi5zYhXPV1WYV1pJEaDk/Nop8iyIIyktEQDvGgo6VnlQlSD4A1WbdSeUFpinSeEhZTeeK+vOLFSi+PWI1Zbyt4wVuLDgrKa67USQsAquJWIEYKwLnjpiLdMLZk+KhoFrVLHQdOZ456U08POULDIM8ackLUihkYdmRozShhKm2gC7y1OS1rplJTxPmCFIadGzoWfPj9zPm04Jk2x5oI0hsfnB/brhRSv7LcXLq9XrIWXtwtCNE7nBak9jc7R2iSVaYv0K6ZmnOw0IKXCr3/9lQ9PYY4410es3xADcmlcLxecNyznM3g3PY3m/yDm/+/Dy/2qqrmjrZ7ZWbkyrOHIjXgktBQ45/GLIafOsgbUgFLbvGYqaG0YUpL2hFASt54xrt0DNWAJjpQGL1+/4vxCGx0FdFGJ14wJjsV7vl0vvO8XyijE7nh7e+HzxweE1KyrRaSK0p10NLb1HpwqwK0eY6YVp46OHoLT8zPvt3ckHb9YwnmlUyktYZ1GrxtFDJrsZG6YIOmq0dWEDjhr6CKilaBkeHuJLF5Dna5hJzzWGIYRtAhITRWSWiqp7IwhQXi60Fz3nVIqvWZkkyx+waZOL/ODsKcL55BQauY9FCo1ThaGUQZjF7QJtP1GTwWnJNJoGB1rzfSkiUyrA23MxBM0CNvGSBdynprsZV1xDJSEdkSOGDluO27d8Kvj/PyAoLC/f6W1znHsSNF4+vRx8utqI+87X377g3VVhG0hpYSxErN4vu0Xvl0P0vHGh18MQypKuy+QxGRuANTW0EYTj0jO3zltaEIzaoFR6X2ifWqtGD01D9ZIRuvUlqllZtvm0jFK3qGS07u/7w2YW5MQ5ljrj7/9MRkjSuPX7W7oK+TjhtSaoQygkXoijTqdjw8nnp83LmkH3ThuFSMcn57P3PbIkTLaGbQ15D6xq00AuVFLIaaE3Sax5pYTWEFNlVtKLDmBVhivOPKOyB3pJH0cd4i2oarO0FBTQg+JUoJdVkoXhHVDymlpr6VBN/QqMSHwsFr220HPFR0cR270VPBGko83eqoT2F0LA0O6xenJezij9YJImZ4bt7eEtZrNnaZlXQlEFZRbIuV9CvHbYL9Nfa2RE02b77j9fNkRseKdo+rK26sgKId/Cnz7/QutRmqBIAW5TD3D5f2Fpw9PDKat6MOHFXl6QFo7/YJmThdqyYjR58q5F9odbRtjRCtLrn3y15ykpsyyOmLMaOswznF6ONFTBATOOURvWDFmTO/3HN7jdpBTnQJ0CUYJhlIcqaC0xVp7D0lu1PuVIZUjPD7SciJeLtz2OV9FSpRx7McNbvvUokpBH45+WJSeTIZcZ5D0cY0IxHw1l4RbDHa1uDDnn9uDoT4L3t4P/vu/fkcYgTQarybNvNSG9Rq7uqk2641vX1+R30Aayfa44O1KjHUG3RloRSALSNWRvUGV4BTCTu5CbY1WZ8h1ixLU/Ir2XljC4z09r6KNIqcxN4ltbgJdUGQO4pG4XBLpKDiVSFUxyqTVOGnxZtBqZlRFvN14v0RqjqzW8/BwJsad47jhcQQd6PePx2iNVCpCONCa43ajSomUglYaqg9+/eMLomTObuPDLz8hqZQ22PeINdCHQWlNrpX9cpAvN+S4cft9R4XAXipCN0LwWCkod9pQr2kyyqzj5//8C88pT27dqJx+/gS9U3vmKAV6Z1sDRopJrlOSvB9zXS0axgd6bRgzVYp2Of3T8yn+FU7nR/2of+f653OIH/Wj/s3rx+H9UX/a+nF4f9Sftn4c3h/1p60fh/dH/Wnrx+H9UX/a+l88yWOyWAjXhQAAAABJRU5ErkJggg==\n", 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