add 9.9

cd848d21 · ShusenTang · 2dfc3148 · cd848d21 · cd848d21 · cd848d21
8 changed file
--- a/code/chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.ipynb
+++ b/code/chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.ipynb
--- a/code/d2lzh_pytorch/utils.py
+++ b/code/d2lzh_pytorch/utils.py
@@ -1080,6 +1080,82 @@ def load_data_pikachu(batch_size, edge_size=256, data_dir = '../../data/pikachu'
    return train_iter, val_iter


+# ################################# 9.9 #########################
+def read_voc_images(root="../../data/VOCdevkit/VOC2012", 
+                    is_train=True, max_num=None):
+    txt_fname = '%s/ImageSets/Segmentation/%s' % (
+        root, 'train.txt' if is_train else 'val.txt')
+    with open(txt_fname, 'r') as f:
+        images = f.read().split()
+    if max_num is not None:
+        images = images[:min(max_num, len(images))]
+    features, labels = [None] * len(images), [None] * len(images)
+    for i, fname in tqdm(enumerate(images)):
+        features[i] = Image.open('%s/JPEGImages/%s.jpg' % (root, fname)).convert("RGB")
+        labels[i] = Image.open('%s/SegmentationClass/%s.png' % (root, fname)).convert("RGB")
+    return features, labels # PIL image
+
+# colormap2label = torch.zeros(256 ** 3, dtype=torch.uint8)
+# for i, colormap in enumerate(VOC_COLORMAP):
+#     colormap2label[(colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = i
+def voc_label_indices(colormap, colormap2label):
+    """
+    convert colormap (PIL image) to colormap2label (uint8 tensor).
+    """
+    colormap = np.array(colormap.convert("RGB")).astype('int32')
+    idx = ((colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256
+           + colormap[:, :, 2])
+    return colormap2label[idx]
+
+def voc_rand_crop(feature, label, height, width):
+    """
+    Random crop feature (PIL image) and label (PIL image).
+    """
+    i, j, h, w = torchvision.transforms.RandomCrop.get_params(
+            feature, output_size=(height, width))
+    
+    feature = torchvision.transforms.functional.crop(feature, i, j, h, w)
+    label = torchvision.transforms.functional.crop(label, i, j, h, w)    
+
+    return feature, label
+
+class VOCSegDataset(torch.utils.data.Dataset):
+    def __init__(self, is_train, crop_size, voc_dir, colormap2label, max_num=None):
+        """
+        crop_size: (h, w)
+        """
+        self.rgb_mean = np.array([0.485, 0.456, 0.406])
+        self.rgb_std = np.array([0.229, 0.224, 0.225])
+        self.tsf = torchvision.transforms.Compose([
+            torchvision.transforms.ToTensor(),
+            torchvision.transforms.Normalize(mean=self.rgb_mean, 
+                                             std=self.rgb_std)
+        ])
+        
+        self.crop_size = crop_size # (h, w)
+        features, labels = read_voc_images(root=voc_dir, 
+                                           is_train=is_train, 
+                                           max_num=max_num)
+        self.features = self.filter(features) # PIL image
+        self.labels = self.filter(labels)     # PIL image
+        self.colormap2label = colormap2label
+        print('read ' + str(len(self.features)) + ' valid examples')
+
+    def filter(self, imgs):
+        return [img for img in imgs if (
+            img.size[1] >= self.crop_size[0] and
+            img.size[0] >= self.crop_size[1])]
+
+    def __getitem__(self, idx):
+        feature, label = voc_rand_crop(self.features[idx], self.labels[idx],
+                                       *self.crop_size)
+        
+        return (self.tsf(feature),
+                voc_label_indices(label, self.colormap2label))
+
+    def __len__(self):
+        return len(self.features)
+


 # ############################# 10.7 ##########################

--- a/docs/README.md
+++ b/docs/README.md
@@ -114,7 +114,7 @@ docsify serve docs
   * [9.6 目标检测数据集（皮卡丘）](chapter09_computer-vision/9.6_object-detection-dataset.md)
   - [ ] 9.7 单发多框检测（SSD）
   * [9.8 区域卷积神经网络（R-CNN）系列](chapter09_computer-vision/9.8_rcnn.md)
-   - [ ] 9.9 语义分割和数据集
+   * [9.9 语义分割和数据集](chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.md)
   - [ ] 9.10 全卷积网络（FCN）
   * [9.11 样式迁移](chapter09_computer-vision/9.11_neural-style.md)
   - [ ] 9.12 实战Kaggle比赛：图像分类（CIFAR-10）

--- a/docs/_sidebar.md
+++ b/docs/_sidebar.md
@@ -76,7 +76,7 @@
   * [9.6 目标检测数据集（皮卡丘）](chapter09_computer-vision/9.6_object-detection-dataset.md)
   * 9.7 单发多框检测（SSD）
   * [9.8 区域卷积神经网络（R-CNN）系列](chapter09_computer-vision/9.8_rcnn.md)
-   * 9.9 语义分割和数据集
+   * [9.9 语义分割和数据集](chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.md)
   * 9.10 全卷积网络（FCN）
   * [9.11 样式迁移](chapter09_computer-vision/9.11_neural-style.md)
   * 9.12 实战Kaggle比赛：图像分类（CIFAR-10）

--- a/docs/chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.md
+++ b/docs/chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.md
+# 9.9 语义分割和数据集
+
+在前几节讨论的目标检测问题中，我们一直使用方形边界框来标注和预测图像中的目标。本节将探讨语义分割（semantic segmentation）问题，它关注如何将图像分割成属于不同语义类别的区域。值得一提的是，这些语义区域的标注和预测都是像素级的。图9.10展示了语义分割中图像有关狗、猫和背景的标签。可以看到，与目标检测相比，语义分割标注的像素级的边框显然更加精细。
+
+<div align=center>
+<img width="400" src="../img/chapter09/9.9_segmentation.svg"/>
+</div>
+<div align=center>图9.10 语义分割中图像有关狗、猫和背景的标签</div>
+
+## 9.9.1 图像分割和实例分割
+
+计算机视觉领域还有2个与语义分割相似的重要问题，即图像分割（image segmentation）和实例分割（instance segmentation）。我们在这里将它们与语义分割简单区分一下。
+
+* 图像分割将图像分割成若干组成区域。这类问题的方法通常利用图像中像素之间的相关性。它在训练时不需要有关图像像素的标签信息，在预测时也无法保证分割出的区域具有我们希望得到的语义。以图9.10的图像为输入，图像分割可能将狗分割成两个区域：一个覆盖以黑色为主的嘴巴和眼睛，而另一个覆盖以黄色为主的其余部分身体。
+* 实例分割又叫同时检测并分割（simultaneous detection and segmentation）。它研究如何识别图像中各个目标实例的像素级区域。与语义分割有所不同，实例分割不仅需要区分语义，还要区分不同的目标实例。如果图像中有两只狗，实例分割需要区分像素属于这两只狗中的哪一只。
+
+
+## 9.9.2 Pascal VOC2012语义分割数据集
+
+语义分割的一个重要数据集叫作Pascal VOC2012 [1]。为了更好地了解这个数据集，我们先导入实验所需的包或模块。
+
+``` python
+%matplotlib inline
+import time
+import torch
+import torch.nn.functional as F
+import torchvision
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+
+import sys
+sys.path.append("..") 
+import d2lzh_pytorch as d2l
+```
+
+我们先下载这个数据集的压缩包（[下载地址](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar)）。压缩包大小是2 GB左右，下载需要一定时间。下载后解压得到`VOCdevkit/VOC2012`文件夹，然后将其放置在`data`文件夹下。
+
+``` python
+!ls ../../data/VOCdevkit/VOC2012
+```
+```
+Annotations        JPEGImages         SegmentationObject
+ImageSets          SegmentationClass
+```
+
+进入`../../data/VOCdevkit/VOC2012`路径后，我们可以获取数据集的不同组成部分。其中`ImageSets/Segmentation`路径包含了指定训练和测试样本的文本文件，而`JPEGImages`和`SegmentationClass`路径下分别包含了样本的输入图像和标签。这里的标签也是图像格式，其尺寸和它所标注的输入图像的尺寸相同。标签中颜色相同的像素属于同一个语义类别。下面定义`read_voc_images`函数将输入图像和标签读进内存。
+
+``` python
+# 本函数已保存在d2lzh_pytorch中方便以后使用
+def read_voc_images(root="../../data/VOCdevkit/VOC2012", 
+                    is_train=True, max_num=None):
+    txt_fname = '%s/ImageSets/Segmentation/%s' % (
+        root, 'train.txt' if is_train else 'val.txt')
+    with open(txt_fname, 'r') as f:
+        images = f.read().split()
+    if max_num is not None:
+        images = images[:min(max_num, len(images))]
+    features, labels = [None] * len(images), [None] * len(images)
+    for i, fname in tqdm(enumerate(images)):
+        features[i] = Image.open('%s/JPEGImages/%s.jpg' % (root, fname)).convert("RGB")
+        labels[i] = Image.open('%s/SegmentationClass/%s.png' % (root, fname)).convert("RGB")
+    return features, labels # PIL image
+
+voc_dir = "../../data/VOCdevkit/VOC2012"
+train_features, train_labels = read_voc_images(voc_dir, max_num=100)
+```
+
+我们画出前5张输入图像和它们的标签。在标签图像中，白色和黑色分别代表边框和背景，而其他不同的颜色则对应不同的类别。
+
+``` python
+n = 5
+imgs = train_features[0:n] + train_labels[0:n]
+d2l.show_images(imgs, 2, n);
+```
+<div align=center>
+<img width="500" src="../img/chapter09/9.9_output1.png"/>
+</div>
+
+接下来，我们列出标签中每个RGB颜色的值及其标注的类别。
+
+``` python
+# 本函数已保存在d2lzh_pytorch中方便以后使用
+VOC_COLORMAP = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],
+                [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],
+                [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],
+                [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],
+                [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],
+                [0, 64, 128]]
+# 本函数已保存在d2lzh_pytorch中方便以后使用
+VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat',
+               'bottle', 'bus', 'car', 'cat', 'chair', 'cow',
+               'diningtable', 'dog', 'horse', 'motorbike', 'person',
+               'potted plant', 'sheep', 'sofa', 'train', 'tv/monitor']
+```
+
+有了上面定义的两个常量以后，我们可以很容易地查找标签中每个像素的类别索引。
+
+``` python
+colormap2label = torch.zeros(256 ** 3, dtype=torch.uint8)
+for i, colormap in enumerate(VOC_COLORMAP):
+    colormap2label[(colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = i
+
+# 本函数已保存在d2lzh_pytorch中方便以后使用
+def voc_label_indices(colormap, colormap2label):
+    """
+    convert colormap (PIL image) to colormap2label (uint8 tensor).
+    """
+    colormap = np.array(colormap.convert("RGB")).astype('int32')
+    idx = ((colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256
+           + colormap[:, :, 2])
+    return colormap2label[idx]
+```
+
+例如，第一张样本图像中飞机头部区域的类别索引为1，而背景全是0。
+
+``` python
+y = voc_label_indices(train_labels[0], colormap2label)
+y[105:115, 130:140], VOC_CLASSES[1]
+```
+输出:
+```
+(tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
+         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
+         [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
+         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
+         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
+         [0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
+         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
+         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
+         [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
+         [0, 0, 0, 0, 0, 0, 0, 0, 1, 1]], dtype=torch.uint8), 'aeroplane')
+```
+
+### 9.9.2.1 预处理数据
+
+在之前的章节中，我们通过缩放图像使其符合模型的输入形状。然而在语义分割里，这样做需要将预测的像素类别重新映射回原始尺寸的输入图像。这样的映射难以做到精确，尤其在不同语义的分割区域。为了避免这个问题，我们将图像裁剪成固定尺寸而不是缩放。具体来说，我们使用图像增广里的随机裁剪，并对输入图像和标签裁剪相同区域。
+
+``` python
+# 本函数已保存在d2lzh_pytorch中方便以后使用
+def voc_rand_crop(feature, label, height, width):
+    """
+    Random crop feature (PIL image) and label (PIL image).
+    """
+    i, j, h, w = torchvision.transforms.RandomCrop.get_params(
+            feature, output_size=(height, width))
+    
+    feature = torchvision.transforms.functional.crop(feature, i, j, h, w)
+    label = torchvision.transforms.functional.crop(label, i, j, h, w)    
+
+    return feature, label
+
+imgs = []
+for _ in range(n):
+    imgs += voc_rand_crop(train_features[0], train_labels[0], 200, 300)
+d2l.show_images(imgs[::2] + imgs[1::2], 2, n);
+```
+<div align=center>
+<img width="500" src="../img/chapter09/9.9_output2.png"/>
+</div>
+
+### 9.9.2.2 自定义语义分割数据集类
+
+我们通过继承PyTorch提供的`Dataset`类自定义了一个语义分割数据集类`VOCSegDataset`。通过实现`__getitem__`函数，我们可以任意访问数据集中索引为`idx`的输入图像及其每个像素的类别索引。由于数据集中有些图像的尺寸可能小于随机裁剪所指定的输出尺寸，这些样本需要通过自定义的`filter`函数所移除。此外，我们还对输入图像的RGB三个通道的值分别做标准化。
+
+``` python
+# 本函数已保存在d2lzh_pytorch中方便以后使用
+class VOCSegDataset(torch.utils.data.Dataset):
+    def __init__(self, is_train, crop_size, voc_dir, colormap2label, max_num=None):
+        """
+        crop_size: (h, w)
+        """
+        self.rgb_mean = np.array([0.485, 0.456, 0.406])
+        self.rgb_std = np.array([0.229, 0.224, 0.225])
+        self.tsf = torchvision.transforms.Compose([
+            torchvision.transforms.ToTensor(),
+            torchvision.transforms.Normalize(mean=self.rgb_mean, 
+                                             std=self.rgb_std)
+        ])
+        
+        self.crop_size = crop_size # (h, w)
+        features, labels = read_voc_images(root=voc_dir, 
+                                           is_train=is_train, 
+                                           max_num=max_num)
+        self.features = self.filter(features) # PIL image
+        self.labels = self.filter(labels)     # PIL image
+        self.colormap2label = colormap2label
+        print('read ' + str(len(self.features)) + ' valid examples')
+
+    def filter(self, imgs):
+        return [img for img in imgs if (
+            img.size[1] >= self.crop_size[0] and
+            img.size[0] >= self.crop_size[1])]
+
+    def __getitem__(self, idx):
+        feature, label = voc_rand_crop(self.features[idx], self.labels[idx],
+                                       *self.crop_size)
+        
+        return (self.tsf(feature), # float32 tensor
+                voc_label_indices(label, self.colormap2label)) # uint8 tensor
+
+    def __len__(self):
+        return len(self.features)
+```
+
+### 9.9.2.3 读取数据集
+
+我们通过自定义的`VOCSegDataset`类来分别创建训练集和测试集的实例。假设我们指定随机裁剪的输出图像的形状为$320\times 480$。下面我们可以查看训练集和测试集所保留的样本个数。
+
+``` python
+crop_size = (320, 480)
+max_num = 100
+voc_train = VOCSegDataset(True, crop_size, voc_dir, colormap2label, max_num)
+voc_test = VOCSegDataset(False, crop_size, voc_dir, colormap2label, max_num)
+```
+输出:
+```
+read 75 valid examples
+read 77 valid examples
+```
+
+设批量大小为64，分别定义训练集和测试集的迭代器。
+
+``` python
+batch_size = 64
+num_workers = 0 if sys.platform.startswith('win32') else 4
+train_iter = torch.utils.data.DataLoader(voc_train, batch_size, shuffle=True,
+                              drop_last=True, num_workers=num_workers)
+test_iter = torch.utils.data.DataLoader(voc_test, batch_size, drop_last=True,
+                             num_workers=num_workers)
+```
+
+打印第一个小批量的类型和形状。不同于图像分类和目标识别，这里的标签是一个三维数组。
+
+``` python
+for X, Y in train_iter:
+    print(X.dtype, X.shape)
+    print(y.dtype, Y.shape)
+    break
+```
+输出:
+```
+torch.float32 torch.Size([64, 3, 320, 480])
+torch.uint8 torch.Size([64, 320, 480])
+```
+
+## 小结
+
+* 语义分割关注如何将图像分割成属于不同语义类别的区域。
+* 语义分割的一个重要数据集叫作Pascal VOC2012。
+* 由于语义分割的输入图像和标签在像素上一一对应，所以将图像随机裁剪成固定尺寸而不是缩放。
+
+## 练习
+
+* 回忆9.1节（图像增广）中的内容。哪些在图像分类中使用的图像增广方法难以用于语义分割？
+
+##  参考文献
+
+[1] Pascal VOC2012数据集。http://host.robots.ox.ac.uk/pascal/VOC/voc2012/
+
+
+-----------
+> 注：除代码外本节与原书基本相同，[原书传送门](http://zh.d2l.ai/chapter_computer-vision/semantic-segmentation-and-dataset.html)
+
--- a/docs/img/chapter09/9.9_output1.png
+++ b/docs/img/chapter09/9.9_output1.png
--- a/docs/img/chapter09/9.9_output2.png
+++ b/docs/img/chapter09/9.9_output2.png
--- a/docs/img/chapter09/9.9_segmentation.svg
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Z5W5b8qt2123AK7q1dGy1IVZNnZW092JFaMgxn3qfWnjlsMKw35ywrDt72VIv7o7A1HLM7hzvP09qmnF3HOxDuYMX+7juBWhps/k3KMcg56g5BrOVyo+XkCrduEBDYI55x2rWexEdz1W1fzLdHznIzUtZnh+XzdMj+bOBitOtou6TOaSs2goooqiQooooAKKKKACiiigAooooAKKKKACimlgOpqpc38cQIByQDSuOxNO4CnnoM1hX/7xHHuKle/8yTHQDqfpWTPekg9cDPPrUN3NIqxCLHdgFVHcewp4tVRdzNkqD+dRtO7Z/hH8qaJSyZBLe5pFal6OCN0HTGaf5UXBAGO5NUY5HVcg8Yxn3NLHcsxxtx2yaZOpb2QrGdseB296bJbRSxngbjzz3NM85CdoYEjoKUTYPTO1sZ96AIprCSLlfug9qSzWaK4EjZXPY81orcqB8x9qlLQsPnAJosFzQt7jegwc1djcdyKxVIVfkbAqXzdijJ6d81SZDRseYpOAaytS1A48uE8nqRUVzfeXDhTjI496zlYySEOFyepzzWc6nRFwh1Y5Vycgjd3aho8Z5J5/KnyzLCq5I9s9KrPeMeV+QDsKg0LltcPAwyfrg8VuWV2lzGCp5HWuU+1bvusAavaJc41ARls7x0qoT1sTOGlzp6KKK3MAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAphwfmfgDsafTdoLZbnHT2oAbgudz8KOgP8AM0x2LoWx8nZf7x/wp/Mpx/AP/Hv/AK1JI3z8DOwZx6k8AVLKQ0DaNuc7ep9W/wA/0pWQsuCSqegPJp6JtVQeSOT9fWnEgDJ4Ap2Fc8g8QxNFqc6kbQWJHGKycADmvSvE2hR6qHuIJFWUcKv9415hfCWCdrZwNynBxSRRMtyi5wPpSm5PG1Dj34osYVCgPjJ61cmtdhGBkkcUwM77TIsu/aBxS/a853KOfarEluSxyB+FQGFckdPrQFgNxFuJ29R6UzzITnJ/OlaABckcetMeJQOeM9OaAAojcjHHpUXlYJOM+1MaNg3y0C4dPlYUCFkXDAgU1UB9jTxOp7UZHYimIlhAz8wFXklURFGxtx/EM1nCRV5JFRyXLNlcGkO5eDbWBVwauWp+cSNuPPX1rEiLbuD1rpdHiMiHIJ4wQO4qZaIqOpu2UIWJWiOdw646e1aiRlQBgK3bHQiqtnA0Clcll7ep/wDr1bWaPy8M+T1HrXJM6EVpmO4gg5FCPuHv9aRpBKSGHzD0700Aqxz+Fc7NSbcSDginowZMNwaj7jgCnLw2D+dNCYYJBUjP9KqXAwpP51eHQn0qndZweeaoSOZu4jJKTg/jWTfQEg46Cumlg+YkjrWfeqqJjA3dvatoTsxSjdHNxRENgjnpWpawCAB5PvdQKj2LADI33ieKYspZwc9TW/xGKtE1UZmJOOKcylcsp5psMq5H+0MY96eGXqenQigogYEE7cjuDV2JiVyR0qrIOcjt+tPHyAHJKnpn+VTLVDjueheEZvMsWTOcGugrlfBUimKVAOeCa6qtafwmFX4mFFFFWZhRRRQAUUUUAFFFFABRRSUAHaq11dLAMk9KfPOsUbEkZHP1rn7m7NzOx52rUydioxuT3eosTkHAJwPesiW9EpDjhRnr3qO8YuyfNyp3Ae9Oe3CxuATiQ5H51k2apJDYpJSZmbjChQPc/wD1gaidMR/OeSTnPap5sQxKoIJZyeemBwP5ms+7ucYbOVJyM0DJs7SQTtBPX1pY5YVG1jx/SsW5umcbUBJJ4HrT1MpGGOQRjA6mnYDa+0QFVXPA55pWmSRWCkkL6dKyrWB5ZMN0C5Yk9P8AOK0Y4kZEC8xuRnnrRsIhaVYG+bBLDORSNe7SzH7y8EemanlggTIA5U9RWJc7kPlx8qzZJprUDaiucxhi2OMipRe84B/CsO0kkbzAFZm78dq1bfTLyW4KeUdgYAn0oAv29yWfJPH9KurOZslxthHUnv7CqhsI7RfNvJlCrn5V71BLqKylY449qH7nBH6etS5DUbliafzHZ2BVRyMdqiikRz8znHXIHP5VUlkVSVRzzz0IOfpUCGVmKb1U/wB0jj8/8ahIs2WdHTa5Gw/dJwNw/wAay7wsjEDOBznH9amjgkYhW2jd/DkHn8+KZOPIYLkFh/BngVbRKdilJKxVe2f1rb0Lc2pRbfugZNY0TSzXIV8AHjjFdV4ct90kk7ADacLxUxXvDm7ROj7UUUV0nKFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFADXbaOmT2A70xYzuXd2+Yn1NSYGc96WlYYUyQEpxj8aVnC8cluwHWmGMyD970/ujp+PrQwINhkEaJzx8zYGP8A6/NeeeNtBjsrgXUZ4bsQPm/z9K9NJCkfTgCs3XdPTUdPMTqN2QF9eTilaw73PIoZo0BIHzehrTgl3hlf5W4zx0H+e1R6joc2mXzJKpyp4x3+lQR2skp4kwO/19KBplmRPOZUCYAGTjsKS4ijhh27SXHGfSlFrLHludowdp/rUy/N5bOcnHOT36mkWZUu5TsI6joarMwGUblfetG8hfcAQA2QAB37f4fnVF8JkkAZP5GmhMqswYgDjHemyIoA5zQ8m0EGq5JJpkBJjtikGR3pQpzz0NSBeMUxEJU56nmpAWx0yRUgjPFT29qXYZ6UmwSI7S3aWQY4ya7vRrJre3UkAN/SsvSNPTd844HXiuo4VQF6e1c1Wp0R0QhYSRwikHuOg7GqG/zmIcbXXnI/Q1aYgjDdKrS5V1cDBHy5x1B/+viufmbNkkhEcsxVhtcfl9alVt64JwemKYwEgGRtYfdYUmzP1HUd6llllQSvXINSAMSMHOelQqOnNBdgcHqKQib7ow+apzjcxJIwO5q0WaRPmHHeq7hd2FBzTEiu0QPc4qndwRqpJwDWoUwtVLx0SBmIHAoT1KON1F8zYBOM0yDqp/OutttOtdVsyksYD9dw6isO/wBEutNk3FS8ZGQwH8664VU1Y55U2ncLfjjqSeKlctuwQAVJGaghBHJ5qwWHQnJ7H+VPmDlHLhsKOO3NLGcccZA5BqEk46E9KkEm5wGGSeQaTKR2/gdTtnfPHHFddWF4StfI0hXI5kOfwrdramrROao7yYUUUVZAUUUUAFFFFABRRRQAVUurny1Kr94DNSXMyRod52+/SsSe4ZyzZyF6+9TJ2Kiri3dwZRycqeuKwbm5xK6I2CB8xAp9xelpXWE5I4x9aYY0hYuDl5MjBrK9zZKw5I9hJZuASRn/AD9KivNRiSRPmwAoIP6f5+lGxmDCXOQmAO3Nc7eqbm4SOInavy/+PE/yoSuDZo6hqRknjgTkJEOfqM/1qpvkkypJzgnFNELmeWeRSmWztI5C54FSDIbyz17npkf/AF+KegIWCH9/8+TsBO4GrUUaBxuPHXn34x+WaqvMELhQcl+fp1qSSUzTAbhySzAd+nFK4WNBXWM7AfvZ+b6jj+VTaeQCgc4UNwO5xWcLuMlBuHJzx61ctSnmwpHxuBLN3A9KQy9sh3vnp0+n+c1Yh0y1kxJKoHORzVe6aKOAeUAWD7mz/wDXqob65ClduWHIyOCKewrXNVpdO050WJFLMeDUFxq0rShIj5YA5GOuelc/cXO4qwCFlboDnmm/aFMjRyNgfx+gx0x70ndlJJF2a4eW6iUEyoQT8zcE+/0qSSeaaQxgrGgGB8gGfx//AFcVFaTWscQiO2QtjO3qa1I7q2SMRrEyjsuzcB9ccfrTSJbG2enq+C0Tjb3YD/69X0tY1GGUKx/izk1XaS4eMCObC9giDj8sU+KB9uWjlz2YZJP4dRVJEtsbdKscfzXDlBkbUXIPsTzWHJLL5jfPvt26bR932ycEVuXCXULEeYmG7zldv5dSaoNHGWZZE2Z53R8Kfw70PYcdyvbRbz8gyTgAg13OkwC3s0U/eIya5fTLURBHbBDEYHTNdRE+FBLYFKmtbhVfQv0VFE24Z5/Gpa2MAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKadx4Hyj1706igBFULnA69feloooAQKASQOT3pGXI59RTqKAMzWtLi1CAkgCUDCsa87ube4sbkxmMqegJHBOeTXqkiB1Oaw7+2jdisgVwfWs5y5dTSCvocnbRmdAXcMJCMgdueBVk6eo3MY/4f6GpZtKWN82ztFznio45dSg+RhHInPPSoVSLNOR9DH1SJplwoKsrdf8/SsKU4LblzuPP1rr7hZ5w2IACTnr0/zxWNd6VKoJK53D6nNUpxJcGc8w3nkEc80nkkGtT7BPKxyh3Dg8UjWE4wNh64HFXzIjlZQWLnipREo5rTXRL9huEOABnrT/7FvQcCI7fSlzofIzNjQAjvWnY2u9xgcCp7bR51JMi4I/WtywsQiqzAj2NZTmuhpGNtx9tGY1Cj8DmpyCR2/OrJjVT93OP7tNZVGe/seK5mjVMqvnPaoZlHlMTkFfm/LmrbAqCCCR7CoiFZSpOAePpU21LTGqox1NLjdgrzj/OKkiBKKwxkjnAp+Ax6AMO/SiwrkSYIHPHbPWlMZPJ59O1KVZGOduD156UjZ246/hQ9AGMdvBbH1qNBz1z9DSkBjhRTS20hRnNZuRaQ9jngdKytWbAEYPXk81qKOM5rM1CPc4z1FOO4yTRnEbBSOtdIirKNrKCD2NctZYVx1BrprUbox6j3q09SZop3vhu2ny8K+U5HbpXO3nh+9tmyELr6rXdROehzU/DDBrRMyv3PMEt5ghVo3Dg8ZFbmg+H7m9vI5Jo2SFTksR+ldlHbwGQM0anHTIrUiZNoCgD6VtBKW5E52WiFijWKNY0ACqMAU+iiuk5gooooAKKKKACiiigAooooAr3jwRwE3GCp4we9cnf3IgPlRDrnA9KdruribWBaRHKxttb0B71QZQ8wlkOTnAH41lN3djaEbK7HWtv5IVyAWY8/5/KrUVsv+sf5hnPP41V88bsk5C9x71ZDtJAV42nv0NQUzI1O7kmuxBb/AHmwOK1bTT7SxjMjKPNY7jnnB/yap2ltHZ3LzPtL5JUn0ptzqFu053XCkdCCegNO/YGguLqCZyqrhum7tk8VXnhR7mLAyoJY/hirUEdvIIjGOEzgEZyp5z+opqxeX5pXnKcgZznvRsMzJYiu4IPmU4J9AMVGvyxqAuXPzFh+Vac0ZaLcBywJ9x61WaHEDSDABwB+BpXHYzkOFZlwMc89vT+dXrS4RTt5VVHAHXOO9RS27xjp95ssPfstQFSpOGA4JPPvTEdBpdwyoybgwBJLdwvXA/T86tXcKTxl4lLHAwmMlvzPtXO2dx5biNDycZz3P/6qu2+qNtjwwCpglT3HIA/SmL0KN3A8LbppJIi5+WNhzmoXk/dIHiyM7mwOvH/1q29SK3p3NIE2ZAyuQT6+vUis+1s2XH2ghn5Q7OmASc/y/M0WC5DZwCUMkxCE52t2zxWrawOuPPVoyD6ZVv8APrTLq0eIL5e5T6hcgd/y6irrzwQQgGVYpCg++Pl6UxXLKeaANsceD6TEfoanlu4oov34P/ff9TXK3sltuLXECPxx5U7qQOxAxjH0p0PkXMH+jmdQccNMW7809hWudCdZtFbYjIQwzgtuP+FMubi3Nq8zRExqcYTjJ+hrDtdPlZo/MyCMtj0z/n9K0NRAg00Qx5wTk+hOen86hyKUSzpTm9uzcEMsMQwinoM9K6OP7wAGW7Viacn2aKCAdcZf6mtnLbPl69OaqJM9TRgcbdxOT2qwOlVrZdsYz1qzWpiFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFACMMjFY95GY3yec1sHpxWTfqQ3LE1lW+E0p7mdJg5zUJQEdKmcA9arEqp56VyHSOEQxUZi+YcdB+VWIyGx+gp4XqeuelWoktkEcEQJAUcf4US2sZjU7R98H/x7NSiPFRys3lt6Bc0xE4iUDtTWTI/CnK3HPWkLDI56UxFVlO5iKkt2DoSRhgaV+ASPWoslG4FJoaLRxn2pCM8Nn2pVOV46U4jvj8qLBcjZTjBIOfUc1G8QY8Bc/TFTZGMH8KZJx95c/UVLQ0yug2blYdG4/nSljngnFOwMsQBj/61NLY6j8xUNFJjN27gbmHptqPzEyQCCfY9afNtlG3acdv8mmpDgcnA9cj+lQy0HuePYVWdyzALwO5qxM6ohx6VXi4POcnmsnuaLa5KcheuKzp03yFueK0JCwXOKYsSscnr+tUtBJlONSG6da2LGUrgHkVRVAGwD+dXITjrVIUtTai2uMjrUuKqWzjvzVsYI4NbrVHOxw+tSRvtqPijNGwblxJTnrxVgHIzWcpIqxHLjvW8J9zKUexaopqPuFOrdO5kFJS0UAJmlpCKAfWgBar3d0lvEzE5IGQKlkkWOMu5wBXPzXEs0zyhMIePm70m7FJXOfS2/wBMurubqZG2/rUU8nyFExx3H+frVu9aeR32ptTORVSFNzZJwOprDqdHQksoPN2sc8VellW3T5sYxk/WnRvHa2hkd1x1zXHa7q8k7lUO2M9B0zVWuRcg1nVpbi5aG2c7ScZB5NUms7mCFZDGW3jPWobd8JLP3HA+tW7DUNls8csnzFgEZhu25/ya1jFbGcpMt6TqckLiKVevygseFH+Fb8bRo5kjkDfKCSpyT3wf89q5fZuvMJcechi3E9MHOMY/Cp7N5opWWL/ln85PpyKmUdbIuEtLs6ASmRec8inqBuXP3DxtPrxWHFekSFJXIGc4rQgvIcn95znoDWLTNbokuEcHKR5LNkfyz+tUZbR4SQT7DnIrYWVGJYMNuKqXO1hnaDj7vtQmKxiA7ZTz1Y8mpBIxChDkBun1NLPCSTIuMjgVXTcpOVwpGOe1aEFkXUhYBnzk56+9akOqLCT5S8DqxP8AKseCPAA2dOcnvT5A7qIyeTSYzpItXDsN+CPl69uBmsLVp5pj5n3gzHbn0/yKhEErYVDkdOKkljdkxIMrjAAoA56WRxNnccg9c9663QRjTlkIAYniufnt0MqjGM5P0rp9Pj32cUYXGDzg9KcnoKO47UL+eFhDaAFwAWJ5zn0otI55ZFu9SJIT7kfbP0p13aEXxlAxnFWI4RIQASTUaFFqyaSWUyNzk55regzgE1QsrcIBnNXJ22JwSKqJEi+k2ZMDnHQGrcfPJOayrIl+eta0eAOtaoyY+iiimIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKzdRUdutaVZeovyRWVX4S6fxGQ/wB488VWkbnGD+dWH7k1Wc57n6gVw3OwfHIEXLYXPbPWpBO24dADVIrHnJ3sfrzUiDBBIAPbmtFIlxL+7nHemPgqQRxjpUYYZ4oZske/FapmdhkcreWCTzjmjzuGB69KYv3Djrk5/OmhDknoT/jSY0WN+5gPWonfGfpQMjLeg4pjqScdgOakaJI5ijhM9Rx9auI4K1l8Zz+dOSYo55O3OaEwaNFhj/aU0zg/dJFIsoYbk5B5xQxB5HOfzpsREPNUsV2sM+tMdifvxnP5VIcqMnkdz3FITuXAwazZSIA45GVU/nUgZyMAgj15pjbwfv7fYmnKwBz1NZM0ILjKjBXr71X3ZfIBwPap7rqHIJI6DpUCOSxGMVmtzRbFlDuTnnNKqBl44qCGR92C/PbHarAkXGW5559qoloQQjPUqakVSp5/MUoGevIPNPVcjrVEliAkdDmryPkcVmKSrYP6Vbjc4rSLM2i55hA5/lTg2RnioQ4I5zTlYY471oSPJP8A9epFfpyKgOcetNRwpxmpuO1zQjf3q0jbhWYrEd6sxPjvXRCZjOJcopFYMMilrcyCkIzS01jhSfSgDK1W8RUMJycHLY9Kw5b4krEQATwAvOBTr69txLK0sg3E1gXOoRrOWt5ATn5sjOaxbuzeKsi9qV1Gi8Pjb0IPesCfUnYAw7lz2J5FMvHe8kJXHPT60wWmIjubkDkdzQkluO7ew6e+nuIhG8hAXnFJPp1xfWSyIoZug+XBwKbFDCy58wAjgDPWpHubmxkAjZgo4+Wqv2Jt3MaO2lijmt5kZJAM7SOtRiNJFCh8Y9q62ZxqUS+aMyDGGxyM1mXGlrFNviMZ/wBpm24PuKanYThcjs7P7Las7sN7+vpVjSo3kM/lj5pMKW7Ko/xq3aabbSASajdiQL0jRuPxNXzc2/liO1jVI1x8vTNTzWuyuW9kZEulq78AtIRkjsKrTadPbsXBZsdT0rqIiuwySLtJ6YqlPK8lyFxgN2xU8zK5bmNFcSxn5s8dBT3vtwCZ7ZOa1bjTklXhMAe+apTaenlHaCPqQM0XQWZWjkEjEkc+tEykYD4we2cVEAYMcYCnHB61JlJmznNOwhE2jpgc+tTRu2wAjAPt1qowMbdBV61XzFUMQPbvQwRLDIQQuAcntViQoGUHGeuAKcLVsg7CPTipUth3wPrzSuOxi6hp7C5EqN8vWtjSi0ZAwOeSakaMMNrc46Yq1a2y7FIBFDdxFi7iEiKVzS2kQVsnFTOvCpyQKnhjAApNBfQsQ4AGKbclXHBHFNmkEcRxw3vVA3DHcTyw7Zq0QasNwsaZxg+npVq2uGeQbjxWJFKz4LYH17VbSTbjBJzVXJsdCsinoafWRazsOOpJ61qI4I61SZLQ+iiimIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigBsjbUJrGum3EmtK8fCbe9Y0784zXNXl0N6S6lObvVKQNu+VzVyXBPFVZR7gVxN6nUhBnHOSfrSLIQcFRUJx2LH8KXygWBZT9M8mrTBotowZv72Ow6U9iQdoOWNQmZUUKuM+g5xSqCB1+Zjkk9q2TMWiVRy+OnSlAAbn1pMhQdo4x/Km7wmAepBzVEjgNxPp1pi9yfXNBfpjvTSWBCn05oaGhGQKN3TPSmMuDtxyeTUrcso7Dmk7MxPJqCiCCUxbVPRhmrayZPt/MVSKbnUkfLnbURm8mXax+Qjr6c0noO1zTZyhxu4PQ+lV5GcHIIBH605XBQA8qentSZwMHkDg5qGxpEaszZ3HHuDTkjUNnd+ZApBGpYMMqRSOig7uWPtispGiIJpd0uB0zimO4jBP580Ssq8gcjoBzUKbWGXYfN2qUaE8f97A9jmrG4tt55PBqkrKITwCOmPWkgYl9ylvoaqxLRoxyskY3DJFTBwTvTv1FUkYsTkcn8KmhJIB/P3ouS0W1cNg4wRVlMN9fbvVNDwQR0qWI471cWZtGghIHPI7U4kY9KrJKQCDmpVYkYrW9yLC7sDGeKYzkdAKPmzSkEdPzpbjJ4XGMHGfSp0Y5qhG21uAP61ZD5Gc5qosmSNOB88VPWdA/I5rQU5ANdcHdHPJWFpkiho2U9xT6Ksk8l1aR7KeSOUZZGxz/ADrHuLsScRLmQ8ALXReNYWt9XZyp2uOtVtH02KPE9y6RNKuUDDk+4H9elZJcqOlycitpumXO9J7rjJAVOw+v4VY1AOZzDBE7mIDJ/pXS7V8rzLdQ7r93PAP4D/Gsprq489y9nEvzkFs9alvW4JdEYp0i6cuRBnYM8NjJp6QXN3DIlxA0Uka5+7jA+tbuby5jEaKkLlxnYxyR9Oar6uoKCCO52OuGk2jO4jt9BmjmDlMuzLohjbAYevGae8YlJJIIPTI4q5ZxrfwmRVKSjgjHX3pJrKWEsQp57gZzS5h2McwlclVJz2qxEr7lLKR2AFWykrYJg59RxmrEUEjEZATHPvQ5Aok9qh8sFiwXoSeaWUbiPL596mjgO0bgzADjjg1WvvORcoNg+lZlly2gwAGYtn1pLq2jVQT261jQ6rNCCGJP1q3Hq8cq4cFQe5qrMkgu7RJFPlcn6VlvYzwA5P5V0ts6TY2gfTHWp57cleANvoaFJoTSOOQnfgj67q07KPDAjGParWo6f8uVjy3piqdsk8LYMZ2irbuhJWN1D8g3dakCK3UYJ9ax/tPTdlfwq7bzOQCDuU1IzQW27jt7VYjQqAoGRTYJdy81ZUjp0pkMjwQ3INWU4XNRuQpBOMUSuypuXBGKaEypfXKH7oyapoy7jlQzH+92pLnc7goTg+naoofMjY7VHP8AeNUBoLGUjGWHrUySFQAe/vVdSx+8QSP4QM/rShtrZ6mmI0LeQr1JGDWxbSptzmueWTIAGQTWlYudwyaaZLRtKcjvS0yM5XNPqyAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKDwKKjmJCECk3ZDRRunyTzWTcZ3cYrQnbGazpiDmuCq7nVTRWZgMgc1CzZyWGakkPIB49qrzHI9BWBuhGK9c8+i1GGz3wO/akCnqenuKUBWOWbgU0xtCpgMMDNWOSck+5NRIQSAo2gfnUinnscflWqMmSEk8nhR096jnwEdyMkjAFPQFsMx4HQ+tJIAxJA4WtUZiRcIGJzgf5/lTVyZCT1AHNKBtiC+uP8abkKCT1Y4AptgkS5GcE8mkPK44GaRRyTnk9/ag54x6/magYhUlfl9SagkgDLj2xVyMfLj2pjKC59uaY7lO1dioVh0FWeTjA5qsgKyHHr+dWFDZ46+lZyiUmIGKtwMfyp7Dchz16cUuM9eD6GjI5DDDfl+tZNFplJoiu4/LntiomQENkjGOvcVdcgZUoy+9VWiVHzuyCOWqTRMiMJ2ptxtPQk96kYKkyjGAwPPpUTEuhVSDj9KUMXGWznHH+fwpgSh92HJwCAc+9XI/9TuzzVe2j2xFGOVIyKkiASMJn86QmPjkJfPr1qVHbqvPeolwoyozjn/P602N+hUjb79qtENGgr7u+CfWrEMm4YNVYTnG8de9Wki7jrWkbmbHvtXkGkRgw5zTW+Xgn9KRfc4p3EOZSB8pqSJ+KjYkjH9aRCRS6j6F2J8HFads25MViBuh71pWMgY4zXRSl0MakS/RRRXSYGdqmlW2ormVP3ij5XAHB9eeK871KwvNP1Bhch3ycibqHHrntXqTkY+8RWVdRBp9275SO4qZK5cJWOMtZHZY0RpAFO7INW3sZ03s04VHbeFI+bHpWpJDaW0hMESh+5A4H0FNFtEI/tk2WCksA3c+prLlNue5mQ28kNs+6UrLNzGc5wvcn3PSkuvs8DRxMu6VV2+Ywzz1Of5VbWGK7uEkkO5i2889h/8AWFJKioFJaMM7M7Z75P8A9akO9zMsp7q1uSrLG0YbDxg4I56it8GF4g4IKH17H0NY109ilwJW27pVB3Dvjg/qKc80VkV3SZSQfczkEf0oDlbNZYYSDjv6GpBDEOQADWEdQt4XG6QlCMo3qB1H1FW4tThkX93MrfLu2miwmmapQY4wKzbxkTiVM+lXEuUcDBBz6HmlmRJB84zSaEnbcwpLKC4TegGPpWDe2jRSkDOB7V2yWyxklRgH0qpcWvmt8qf0pJtF6M5O2mnj+65G09K6Sz1KOSMeewDVah0yNl/eRqG9cVXutDRuYsL3PWhu4vIvrNDIFUupB6AdRUF1bhc9SPUGs2PT5oH+8CoPetR2zAu7kj5SetK47WMG6hBfqSPY9Kls1Ke61aeEyvkL+IGKkgtihJJ5+nWncCzaEc/Nnn8q0RjaM4BrJTfG33SCatPPlcYwfemiWh9y4b5QTxTVlJj2hs4qq0rbsAgHtSIZVYktgj9aqxIkvmZPyggnqtQIxibDb2B67gQKWaQuQX2qf7uev0oRwwA+8c/lTAspMWXaq7VqZT0K4z+VVT2C5X2FTo7gABPqTTQmTqQWznp6Vct5PLbnqe1UEJzjP1NWYmIKgtkmmI6S1bdGCcZParFVLEfu81bq0ZsKKKKYgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAqC5YBcd6mPSqVwxySaio7IqKuylPzmqL8fWrUzVTlP61wT1OuJA4+YmoJFOMnk9an4B5NQyktwvSsjRFVicFjjrUZkLsAAcDvU7pkc1WX5ZB09AO1CLLMZJyCQoPU96k3AfKBgdh3NRA4B2/e/vGpIsISwBZsdTWsTNk2eg6UqjPHQZ5pqcgsTlm7jtRKwCEA4yMfQVqjJgDuf86hILTGQnAHAFSqDnanUjGaHTaUPbrTAYH5wflGOg9KcuSpc9TwPam7e7dWP6U8ZKKR1J4pASAYGD1PJ+lMGSxz3peQCffH1phJ34z7tQBWk4cgA5H6ipYyWGD+dNmUlS3OdwpyAg8gf40MY/I/vYPuKdkFcEcfpQcYzjNMwRnB/Csmi0SjAHQMv6iq88fB2KBmpY85+9SShsbcDaemKzZS3M5VxLgEjcP1/zilXaCysOMj6jJqTH8X93nNJIh8xXH4/TNI0JyApYdscGlfBBz97OAKYIy8bgH7pqTBaJT3z1osSPTlAO/wDPihIx5mOx7GnfwsygZHOKWHbKvTp2PUVaRDZOgx8uehq0jkcZwfQ96qxjBAB5HY1MCT/hVLQhkjyA8Hj8ablXBAIppAPtSAEc7s/hQA07kGGP0p8cu44zilkBK81XXh+Dz2oeg0XQQOtXLBwHHNZqsWXtU8EgRgM1cJWkRJXR0lFNjOY1PtTq7zkGuARzWZe7WUquOK03GRjGao3SrsIVcH2pMaOeZN8h3k4B6dNx9KrXV5JMrW+AVZhgg9FHXin384G/jAAwBWU7IkZfaQT8oP6n/PvWZqjRSVYree4YOpC7UB9+n6DFYGpzv52zJ+SNB+OBn9Sa0pTLJY20Lsf3z7jk54JwP5H86wb25WW5lcfxuT+ZqXq7HdQilDmZICZbaBm52sy/yI/VjRO2/DHPSnWQzYy8/wDLRT+h/wARTLyQJCw70+pSS5blVJxJm1kYAMcox/gbsfoeh/PtVVJ5ILjbyrZKsp7Gqc8o38VdnhMtpDe+YoY/LJ65GcH8QPzBrS2hxTlroTx31xBOArkFM5we+K7HT7uSW2QsdzY5FcRYwyXd2JG+7n0613umQ7IVG3HHNRIXmX48sAcdakK4HApqgrjNDyYGevtUNCIkmbfg/nTZbkBuB1ppk3kr/SkECScuCGHXFRcu3cj8p5jk9KnSzXyiOeoNWIo1UAAcVKWAGKpIlyKxiQYBGTUbqB2b8qmfHrUMsqqvJIx7VVhDHHynnH1qruCsdw+Unrmobi7UsRlSPTuagEolG4HYc8g9CKLDHTODKdi/MOvFPS4ypSRMH1PNVJWMbnj3JqJ7lTzgA+gI5piNDy0K5yGPon+FRA7SeSMetVYpxuysqDJ6N0q35zMoJwT2wSaBkkRMoBwzGpY42BJZl9TiqyMzMAX4+oq0gO3ghh6UCZLE+G6kn3q0gywYcCqqlRjIGfSrMTYHPINMk2NNuH+6xyK1lORWHYHcPlXGDW1H90ZBrREMfRRRTJCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAGv92qFx1Oa0D0rPuR8xJrGrsaU9zPn9e9UpM5z1Jq/LjHNUplOOciuKZ1RK5OTTWKqKHIUZA4FQZJbk5zWaNAckjNQJGTJkde1TOcDJPApI8nJ6YOM+tNILht2+5HFOQlhk9Kl2qMDoP50fL6bQOgFaIlsdyAM/gKawGSW9Of8Keoy2W4pB88mFXgdK0RmxYlwATwepFOk5ZvRSB+mTTkAJJPKg4+ppyqSNx75NaJaEETDKjPU8f40xznOP92nTHDcden+fzpv8b+gOKhlIco49s/0qMfebHrj8KepBO3tSIATk9TnFADcZyh9M0qqCu09Ka2RIG9sYqQHj0NIYhUrx+RqNhjBHT+RqdTuUoexphGAe47/AONQykyIdiD9R6U/ep3IxOOx9KaQMZH4fWq8jMHyO/8AnFZstajpVwTuHfnFL90liPlHymnLJvUE9R696dnbuUjj/OKSQxit5UpXtj/69S8LwDyelJsJC9scHnpRJkTj0piZJHtIJA4PTP8AKmQqY246ehp0SgqwHPHT1pQ2BgjPvVEkzFiAQ2D9KBM/oc/XimxuOgxn0Ip2cn5SM+hpiHqWcfPmgqQMr+VCF8/eP0qaNiTjp+FCEyqLllk2OpHvimTcNuWr8gQj58ZqndRjblM8U2gTJIm3JnHUUm7HLnjoBVe3k42g4NR3chUZJxikh2OthnGyNAeuKvVx+nahvlhUt/GAAPcV14ORXoo4mhkhYdD+lZl/P5cTHJzWjOwUdMmuZ1W5cBlI/pikxpGPf3CMCTwc5qpJPukCbCxXgKR19cfjUNw7CQu5BC84I6+n60zRoZLrUQQ+HjBkCk/ex2qTQu3hnnvYltyAsAYtjPRRgH8wfzrlJ1dXIPautbzbeGVGQ+cU6ZyTkgdfxrJuNOxF5s0ihjztzzSuddOFokelP/oNznqoz+q//XqhdyC4d137MDI4zUyzKiTxwfdMR3H3rPUHbvbOCetNLqTKqkuVEKxhCTw+RjkdK0dNheVzDJxDIu1sdvQ/gcU6wsWuTuxlf510+m6cigArzmhysYqPViaVpYhJVlwVOOneugiULHzQkYCAdx/kUjMc4GMVAm7j2bHcVUuJCeBUkjfLjODVPeWfG3Oe4qJMcUADs3ynitC3jxy3UimwQjAarijC04xFKQ0fSo2I5wOae4PrVdiR/EaokJCR0P51l3249Cfc1dkc4IXn2rHvmfkgN9QelA0UrhljHzSKfxzj8KrxzksCZCyg8DFQ3EivJuYNu7sKIkG4YbI9uKqwGk8hZOGP0HUVRmL4+VdxHt/9ap33KnykA+/FVy8mAZFDKe+OQaEDFgcFvnVBWhGwMeG6ey1TjUYBRuO+OtWUJzgdOhFJgieIKPuqTn1PSr0RYEDaAe45qrGjDGcHB9RVuKYsPunjoM80ATH5sFV+bp0q3AFGNo5FV0kJQqVPPap7VfnUE4JNNEs07NWLg7iD6Vsp90Vn2oCYxWgp461ojNjqKKKYgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAqndxjljVyq92R5dRNXRUdzIk68mq0pHpk1YkPJqu49OtefI60Z1ySMDqfSoS21STVidck4quyZyCc+1QaoiO6Vwvbqas8ZGPwqIFULHr6mn24LAs3fk+3tVITJDhBubljwBT41wRnlqjBLSBiOBzU44BY9T+g9KpEMQnoFHNSEBR5cY+Y9WPakiPy7j09aC3Xt61rEhgvIwvAHT3qRmCsqD0NEC5Te3GRx7CowP9ZIw+8PyFaEEfGQx5wcmkjAK5PPPWkALADHJ5NOYfIu3kc/4VDLGJ8xz05zSg4JYfw8fyoUBYGJ6nP60iDh8/wB7/CkAPjeMHrQRkZ6d/pTEJYJ6kZ/KnjrikxjgeAw4P9aU+uOe4qMEqPX+tDsMA5x7+lSxiMdmG6oetVrlRyRz61YDfeDY9xVaT5Tt7dj/AEqWi0APOT+NTsflz1IGPwqqDjIfoP5VLESvynPsfWpsUWY/m4PUjj3oZd4LZ+ZOCPUUzeBtP8PqO3+f61Ip+fHRv5iixI0Hy3AycNyDUxUEZ7HnioJflxkZQ+33TUqEhMqeDTExjqQdyruHp/8AWpQ6twRz+dODbRkLx3A6U7dG4znB9CaAFVecgt+eKnAJ6n9ahVF9c/jmplA6sePSmiWPXP8AFtPpzUpjDJgHFRKyg/IACamUgck4q0SzHmj8mfDZHPBFR6gM2zEnjHX0rangjuo+vI6Gsq8gc2kkXRgKm1mWndGbosmLmM+Yv3gxr0eBtwP5ivIbCfyr4KTtCcN6Aetem6Nei5iQgj7u0jNd8TkkXLuVYkORXI6tcl2LKm1ffrXW3kJlHOQPaua1dOGJU4HSiQROVu5I9hXqWOST0FMsH+zxyzq2HOEVl577j/IfnUV8sgm+5hQO9aNncx2n2eNYFZsb25/ve30xUliG6jntXuDdLuLKPmIBHt/Kn31rZ/Y2mW53bl4BPIOKrX2hxzQrJYP+7Ls209unH6Gsv+xrwkgP060tDVTmlZFaFfLkdSc5jc/oTT7ZHu2WGNDtJ54rQ07QpTKWkbPyMM/UEVvafpsdvH8owT69aHIhILDTPLiVeBxzWvBCsQyByetNiXYOao6lqqWqMFwWA6ZqUDZptMqA5OOKotfISQhBrlX1maa5UO/yE8+gH/6qSC8YNsY4I4ptMSsdV5u8Y65qWAHd0rJs7xWABNa1tMjNtByaixVy8p4qQNxiq4kAOQaQzAYwasgmZ/eqN2pbnkfSlkmBXcD06iqc0zAEqd69x6UmNIryyrE2Wb9ajmmWZeHQnuCaqTr5xYxMQR1U8/lVYRA/fAyPagZK0CO5IIx6UG28oZGOR1NJ5iRDlvxBqvJdSkkxEMvp0pq4nYc7FAQYyyeo6UqxZjJTJBI43YI/GqsZMkn7zdGTV6OEjAySD0I4/wDrUwRLCsi8bn+jGrSKgHzcH0JxUMcDR8q4IPYmrUcqoeVyD29KkYRRbZflDAd8Gr0TADufeo4fLdsIFx/vVaEW3BGATTEyN5TuwVAB75AqWGVjIvBA+oqFpAr4dSfckCrFtIMjEfP50xG3afPg5rUQYArPsQrD7oBrSHStEZMKKKKYgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAqtdrlM1ZqOcAxHJxUyV0OLszDlqvIcDA6nvVmYcnJ4qjKx7DNedI7YkUhAHAqlLkf8A1qsyOBnkVUmlVTyRn0qS0MKnHOMnoKsIcIFUE9h71V+78zEnPSrKPsUAjDH9KY2SAnn0PFPZv9HAXgtUTMNwC84HGPX/AD/OnOcnYD93H51aIZOoG1VzxSSYZivYnJPtTW4dVz0GP6UZ3Tbey8k+v+c1aIZOW+VUHrTJiAhGeMU3P7zqcAGklweB34q7k2HD/VA45I4/GhhhSO6jAp7feXsABimA7mcf7QGaQDFXkJ/COtRA/usnqxzUpPyFgP4qiBx1PekMQcBMepFKOYwR2NCjCkdl5FMj+6V6ZpDH5zznHemOwPPTdx+NRs+OfQ/5/Q0xH3wvGTypzn29aQxTJtIJzg8VG3zNgjjofan53LkjBDc1GwIfryen9KVihyjdhD1Pr3qRWKquew/l/n9Ki3c9wMhh7etTsN8WR15qRi7gvupqRRxgdunNQD5o8DuKepZcGgCUyfKRnp+lJGcH5CAO49KiduQynnuKVQGXKY91/wAKQFlZCpB4H8jTiAxyUAqukwUYbp3Bp3mp0VvzoFYsJGoOQc+3SnMzDPB9qrqyk5wB9KVwhHT/AMeqhD1kf+Lp7VICTwWxntiqhyDgOq+3enoSDhec9+9AWL0SEH79SyQ+anUZqqsyp8o25PXJqxHOQB05/CnoTqcHr1pLYXxBQiNju3Y+8a1/D+rvZeTKfmjztce4710tzBDdxGOeNWU+vauP1LSrjTC7wDfbN79K6YVFszKUep6hHPFc24kibcjDIIrC1oJ5Zz8ijpxkk1y/h7xFJpziNxvgbtnpXVzy2+oWvmwSqc/mPwrXcytY4e7bzZtue+B3zVS4nBuZB8yKGIA7Y7Vr6rbJET5ZHQ8474rn2UAgOM/jUlm5p11jyhvJwnzccDJP9MVpCSNmIZOvcdKyYVHmbEIUKQp2nBOOP6VfYhQAAR7ZqZIpMtrNFEGOegqCXVIY/u5OayrssIX3sVywxk/Ws442nDZHVyO3tSsO5sS6zJKCEO3HesS/mMsmSee9V3nYOSBwRjA+lM3E7nIJ9c1SQmxuwtyOnap5SEnZ+Tu+fPbnmo1bgs3LnoPQU9wWjGScAcCmIks7ts7c43Hj8BW1aXbB1VTzgVztujI5fHI6D61etDIjlueopNDR0b6iAQd3B4NK92UG5jgVzkkjkDPGWzV2/mP2FQvLE/oKkZcmuSMlG4IpqXHmjaWw3Y+tZMUrOjq2eQcfzFS20wfAPBxn/GhoEW2bHJHzDrjvUcs6qMjHPf1qKdJSMx5JXt/Kkgi+0KVcHrnHpQMqTuksgByhzwR0p0Ebo2cqy9+KtpYSRyHjcvcEdRVqOzUH5Tj0z2p8yFyjEtgwDDFTRqVO3pnt61KkTIxByD2IpCAGCydD+VTcdh6Boue3XpUpaGVSHAGeNw6GoxKIhje+PTrSrIrgmNlVvXHH40AOhiSPo7KfbOK0IhJjg5HoDVFZJFB3IpYeg4NWIpmOD8ox2xincViyqsVLFQcetWLLYWzhQw7DiollEq/KRuH1qxbQoZPMXIJGGGaaJZtWgAxzV6qlpENoNW61RkwooopiCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACqt5JhNoNWHYIuTWVcy5JNZ1JWVi4RuypO3WqEr4z2qxM/pVCV8ng81wyOuIxnwajzz9wE+tDt/eApilQflK/wAqhGguAX3MOnc9qAQSXcjaOgprbifvLtHYCkILsFUc9/amA6Jz5bO3XrT7dgpLPzj5j9e1RO3AjTovUjuafFj/AFfbgn+lNCZaX75Zuv8Ak/4UJ90E/wAWSarxymVz24JH4/8A6qnBI3nHCjAFaRM2h54z7AH86AMmPj+HJ/KmyEGSXaemF/KpBxt56LzVkis3K/QZpqDk89/161G2d49v/rVIPvD3Of0oAbH94r/tfpUe3IJPqP0//VSjiRW7fNmklO2Hd+dSMdHj9cVGuVU47EgVIGAPseRTVG5GHfrU3GV5B1PYjH+fzqMApOHHRuD9D/kVOcEFvb+VNjQkkNz0/DFFxjEX92698Ar9R/8AWFJcKSqkDJ3DH5VYAHmZ7IpJppU7FXuSTSuMrFT5rKOw61LGzKnsMGnAZDHp2B/GmucHAwMf40DHgBXCjrk4pgfKsfQ805zuPHBAzUCOSWYDkHJA7+tADix3b8bhnBFOXH3kY4HpzSE7PmGCp/L/APVSgLuyMo1IZOrKwG5fxFSqiZycj6cVAMjnOB7YpWPfKfU80CLBlVeNx/Oo3mJPCg+5NRebtH3kP0FRyOjZL4GaYrCvNj5dwBPYcmkEu0HHGe5zUTPEBiIHPeoTIUPLZHoBSKsXUm5GCPcmrMb7sESbj7dKzFZWYA5JPbNXbcop5Vm9iakGjThbPRqlkVZI2jYdRUcBOOFUfSrI+cYK8/StEZM8+1yxksLolQPLbkEDpVS3v7iF1McjAjsO9d3q9iLm0ePCk9RmvOJ0e2maNx0PBFdNOV1ZmU1bU3l1FbsNHPnLDGSeRVVIAk6ncHwwOM9RWZFcEMDu6eorQtplZiBkEBsgfQ1pYi46AgSbpTsI7YrZiuYnj2xk5/3ayklBxyuf93NadrKHQqrqGHU7QBUspFLURiJd/wAx3Hj8BWW00oTAjUZJ5HFbGpeYEXlTknkfh6VjyFUXLK7D3NCGyBpdoJbnsPSgMXwBx07U4XaOdpjVSOjYqSHZJIHaQgr0DYpkj9nzhUGSxycmpY7fzJdoOQp+Y01SsZLEF2PAPYfpUfmzKm1AI1zyFIpFGgYYzIFUDjk1Ygt8A8cKM1TtnZxvZMDpnvVtLslmyuFOAAKhlIb5W+6CY4GDVyW3R40UDkdf51JEIz+8P3jzmpgqtMozwFxUtlGX9i2EMBwKdDpxWXcOgJx7VsMEIAOMt/jTowoIA+oouwKcUGxulSi0j3eZHwT1Aqy4Gdw4INGAGyvfqKQEcRVgRj5h1pJbYEZHHepCoLblGGqGe6Ma4PPtQIaBtHzcjNNHltkZDA9/Sqv2objk/KeCrVBPOIm7H0P+etUkDJpZI1BWRSQD1AzVffH1imXJ/Aj9KjmlW6iLKSkvfB61BEQYyZAd4Hfv+NUI0rd/mCTtkgZB68f1qaad41LRshVcYyh4/KqCyRvtUuVb+E56exqWOR0cAp8mCDj/AOvRYRqWV2zRqzbATxweCfxrXtpxIuUIEn865oRfZ32MOGHU9Ca0bGN43+VyBngZyKa0E9TrtPmLgZ4z1Hoa0KxrInZvHU9frWpBKJV9COorRMyaJaKKKokKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooqG4k2IQOppN2VxpXK93N1APArHuJeTVi5kPNZkzHmuKpO7OqnGwySUDvVdpFPP8AKo5i3XAI9qpSysvQ4rC9zdRLMjDtn8Ki3k9WB+vWqL3zA4b86elymOe9VYZcWZFGGHPuppxk3qcYVO/vVEzgN1I/WpUkSRdu8fhTsIswlWztwOwx+tPJxlunHb1qKLyowAD1p+Y2Oc5A6YosIfAMOpPGSOPapllHmc9Dz/X+lVlcyMx+6Bx9Pak80G6J6ryP8/nVIlosxtiRsngu2fxxVgHEW49zz+v+FViwWIHuwLfkM/0p6TeZEqZ/iJ/CrTIaJQM7ifX9KNy8AnBycfTNNSQCYhumP0pJQUxntkUCEXIRlbrkf4UvUFT0P/66R8Nhj0x81N+ZTtJ6cg0higFPl7AkfQdqUErj8vyzSZ/eKWGA2P8AD/CmxHczKe5yP8/561DKQhG5FAwASfy45pSdgHHXk/5/GhTwH7LxinH942wHjPJoAR+fkHRjk0SEbueMY/8A1ULtJZ2bCr3qu06u4ByADk07APRhtGerEkVGzZV2x1A/LFHLnceFXAFLIAwcjPQcfSiwwJwQezDmoo+Mk9e49RU8YMgxgcjv3pREynodv8qAGo+R/Cw7570rBSOGwB0IoZFUHOMH1ph2LyAMGpGBIPPmA4/Co22seQT75zSF+cBeRSFz04P40xigDoIwajk56heKC+FqKSYDOFH55oAGYAcnP0qrJd7TheMfjUNxcbiQCRVaEPdTiKI/UkdKpRC5p27SSn5ep79TXQWVqY4wXOCfzqDTrdLdFxlmxySK0F55J5/lWMmrjbZOh2irCtkc8VUHPGT9alQkD/61VFmbRNKhC5zkVxXi3TFMRvIE5H3wB+tdoGIB+WszUVEsbxnOGGDzWqlyu5Frqx5YHxxjNWbabaxLdlOKjvoGtrx4mP3TxmiIxlDufB9hXbujm2Zqw3O84XAPoOc1s2SqkOfKJ3f561g2iRIwOGcHowbH9K6iCZFiAigKn3ORUMuJRuXVl8thtbOQKzWIVircA9auXyyTS8OFZT2FVHhI++yZ7jGKkso3EWxjtAPpjvUgjDxxtjJJ+X29RVtLdT8pTbnkc5FTW6hS0Mq4yAQ1O4rFdbdxEgDEqOSD/n61BDM4fbKgK8jNdAkStFvHZcEVUk09sI23KFDkj1FK4WKNvGSxaGRgDwVb1q9bRybmEnHocUlvBt3bflGCMj9atQXC7isgz7kVMikWPLzEu3qeppvksuWVjuAqdSCuR3qPJEn1qShV8wqpZssB+vrTrcyAsG55GDQzGPBx8tSAgqHTrSAmLg7g3Ge9N3EjBOMcg1H5qt8si80zOz7pOPegB7OyscGoZG8wg5/OnSAMMg/nSJCZGFUkIqvau4wqkjtVXUIXgWMHk47101vAsSHcKx/EjoQArDK8YqrE31OdDuXI3cdqtW8rsSrEg9Mis8syvkdR0q5DOGjwFUP/AH8d6dguTAsJMBAcVdjVgoJbIU5qojecQsrE8ZVuhHtVnDglUO7IyTtzQBqwNvVVlwxBO1gOlX7VAWBxz0PofwrJtJZCBuJOO1bliATzn8aBM1LfMYwTxVu1c/aeOhFUGdVxk4NaFioxvPU1aM2aFFFFWQFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR2oARmCqSay7u43HGamu5yeFPFZz5Y1zVZ9Eb04dWQytkVTkwc81bdR/EarOFP3VHFcsjoRnznA5rOnwx5Jx71tuDg5xj3FZ9xGp6KDUGiZlzQnblefrVOdZ4Vzj5a18FGwBgHtzSbVI5IIFUp2G1cxUvHXHz/AIZ4qYSrjcMjPPymrVzpsVwGKcMe4rJuoLi1Jz0B6gVtFxlsZu6Ne2vYwfn+Y+pOaurdI4zwF/lXMrOCAXA9M4pyyneOT/hT5CeY6XzWcgQqduevanJEybOc4GTWDHfyIcbsj6VfTUF2ZOSenWlyhc1UYEKOcBD/ACqGNijDB9CPrimQXUW1VDAkqc1Ogjf5UIJJyv5CnYVx6nBH6fSrLt5sXI5FQqvII6A/pUirxgEn+tOxNxit+64+8BzTQzK67sex/pUjIQcjrUbDgqRx2/wqWikyWT5lDJ6dKjUD5mz9Px//AFU9G/d+vNNBSNC7nHAFZspDgNgx6MW+npUTkqGVfvN1PpSfaYgoJYkE5pDKmBjI/rTTCxKFG1YwvAPNQvHvdm6gccd/WpDOixlVJDHv3phuY40C4AwOmeM0xDiNo2D72KYP3dvlzx0OTVYXpXd0Dtxz2qnJcNJgFgR6GnYDVW6gjY4YkY6HvUE2p5jLLxg45HNZT5JPGc846Ukr4w2ScDjinygXDeSP94/iaYbhm/8ArCqbOGwCdx9+1IWcPwQV7U+ULlr7QccDk+9BuD0DZ+tZjSFcgsR71GJJE6HOe1VyC5jRkmGcs3PpVeW7WNevPoKrNKx4OKYItzc80cqQcwhkluCCeFJ7V0ekWsQQHaCQc5xisqziVpAoXJ7V0EciW0IiQjjkn1rOrKysVCNy+rc8AYHepIyuc8n3zVBZSzYB4IyOKsxZxjJ69PSuY1aNGPacAHFTbDjhiaqRlyAAw464HNWVJ25wc1tExYh3KCMZqjeY256fWr7S4OGB+uKhkVJBg8U2JHnfihB9rWULjcOT61ix4BzyPcV1/iewlaMMEB2nI+lcukYDEEFeewrspO8TnqL3jQ0mVlbaqRurcHcTiuuhVY7IttCkjouTXPaTAGAZGJP+0h/WuglkCxoh5wOlOQ4mTLk7mAYfhVcQXEjAgKVPUkird/NMuAq8dcDio4HlaPc6IPwqSgWNYeqkAckBs08ywGP5uGHIDd/oarzz453AdsbetVsGUYVlH4jNFguacN2FJycdmJ/rWhAVYBc5X0IrFih2MCxyMcknPFX7djvADAqeOD0qWUjQ+xK6YQ7eoJrOuI1WXYV3KvLdsVu22QuGPJ6mo7+3MsJCqfoOpNJMTMi3mGNrZx0qVsZPcg5qo0b25/eDB64qVJBjdntTsFyz5oK7SPekSQDIH41TMoVwPSnc5pWHctuwcc8Ed6SMNuw3NIikrg+lWIk6Z7UWBscseSBV23gCYJFMiAXk04zbs4OO1WkZtkc9wC5x90Z/lXJarcmaZu+K3tUkEFnJjO41yrYaYJu+Y80wRDGSZgO56VZgSbG5V3A1bt4xCu4gM3o3b6UeeRcAbcZOR15NK/Yq3cVI3OEdcrjjkflVqOCZAuHbb6Hkiqyah5c43YOMAc1fOoxLKdsZBJyeetF2FiS3kkWXaeQexFb9owXCtlaxo76NRkR4Ixz1NS299HLLgO5xySwpXBxOqiRHHIB+taEChQADxXMwTyEja2VHRTW1p90JMLjB+lXGSZnKLRrClpqmnVoZhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVHM+1DUlVrs4Spk7IcdzOmYs/X8qgY4FPc4zVdmribOtIY5JBwagKEDHP1qcnI4A9qik5O0A1ky0V5Dk/MTiq82CDk1YcDdgHmmOvGCKgtGYyZJKbgO45NRgAsMrj6datzLxhQAO571XMecfwr3J70XLRGZCpwv40MySIQyh8cU8hS27G4ZwCKcCFTaeDnIzRcZnzaXbyjKbkz69qh/sZlz5cgOOOeK2cgKSygj6YFNLrtYgkeuRVqpJEOEWc89lcxElomxnqBUJ3K2TxjtXQmUnAPPPeoZoYpgPNQdOo6itFW7ol0uxjrKwGQSM9avW00sbK6nkciq91bPb8g7kz19PrSROAMH9K2TTV0ZNNOzN6K6OwBjwc9+1TxXe5DkAsvX3rHifOCOcelWh2wM/SmSakd4jgbhg+/epDNG2f5VlpnqrfgakDOAMGhhYv4wMg5BpjgSAr6iqyOV6SD3BpGnUHOSMVnKKZabHPbx7DvGeOwqirqrMoOQDgEHrT5bobSFO6qkbZZsnjPJNRYssNKxGB1PvTDu7qM0m5M53OR9KUt6EDHoapCGnKrgbQPrUbEMRgH3OKcxX1/Gmbwp43f400IRhwSePTNRNuLZfNK55yeMetV3m655JqkJjy4zgnikJTOenuartLj0zTDNkVSRLZNKVbgDP1pFQBeev8AKo1kBNP8wEe5PAp2FcAoGSaVD8p9uaZK3G0fSljOVP0pWHc09O2qxc9VHpVmN90vzNn9ap2YIgc4PbnOKVHZc4BI5HPXPrXPNXZvB2RrQsBwz/4mtC3bIGcgdqybQNw3G49/QVpRyA/cy1YNals0osnA5+tXIjWVHId2AM4qykxJ4/SrjKxjKJfZcjkA1Vmhycg4pPtJUEk4xTPtTP0P4VbmmSotFae2M4MZXIPBqlF4TtTKXd257LWzG/PzdanWYA7R/KrhNomUblCHQLeAALIT9RUU+ky796srD0A5rZEm4c81Gz+XyRgeprTnZKjY5K+glFyE8t1HqRUrQqIuSMCukkNrKNsu01n3tghUmE59QeuKpSuBx9yBNL5aAgZ7Z5p0NqiKcoC3bv8ArWjLGkZZFTn17moGGwjeQoI4GOn0qrhYZHHsI+bOOqnvViOUmQ7ABgdKY0eRuBB+pFOCyK+5VGe+KTGjSgmZQoJzzWmjLOhAbDdqwUuCrkse3GO9acEgyp6MOx+lRsNq5k6tG0cxKhmI6s3GKoRyZjb69K6LX7U3NmJlBJQZYA44rlEba+Oce9aIzLX3mbJ7Zq7aruXBPQfpWbEx2gHvxVzz/LQBOccGmO5eV1GMVMjk9OlZfmEHCnirUMuFAzzSA0ck4HOKeq8AYqrHPU6zU7isJfWq3MDI3AxXFzQbLgkAkA4zXcGTchGetcvf2rxTMDyM5BpoRAjbh1Ix2qMFxcDBywIxilcqgBA7cCoXa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