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Repository: conradry/copy-paste-aug
Branch: main
Commit: dbdabd980f71
Files: 6
Total size: 408.1 KB

Directory structure:
gitextract_7s6sidp0/

├── LICENSE
├── README.md
├── coco.py
├── copy_paste.py
├── example.ipynb
└── visualize.py

================================================
FILE CONTENTS
================================================

================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2020 Ryan Conrad

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: README.md
================================================
# Copy-Paste
Unofficial implementation of the copy-paste augmentation from [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://arxiv.org/abs/2012.07177v1).

The augmentation function is built to integrate easily with albumentations. An example for creating a compatible torchvision dataset is given for COCO. Core functionality for image, masks, and bounding boxes is finished; keypoints are not yet supported. In general, you can use the CopyPaste augmentation just as you would any other albumentations augmentation function. There are a few usage limitations of note.

<figure>
  <img src="./example.png"></img>
</figure>

## Usage Notes

1. BboxParams cannot have label_fields. To attach class labels to a bounding box, directly append it to the bounding box coordinates. (I.e. (x1, y1, x2, y2, class_id)).
2. Bounding boxes passed to the CopyPaste augmentation must also include the index of the corresponding mask in the 'masks' list. (I.e. the bounding box looks like (x1, y1, x2, y2, class_id, mask_index)). An example is given for COCO.
3. The CopyPaste augmentation expects 6 keyword arguments instead of three:

```python
# typical albumentations transform
output = transforms(image=image, masks=masks, bboxes=bboxes)

# for the copy paste transform
output = transforms(
    image=image, masks=masks, bboxes=bboxes,
    paste_image=paste_image, paste_masks=paste_masks, paste_bboxes=paste_bboxes
  )
```

4. After pasting objects, the original bounding boxes may be occluded. To make things easier,
the original bounding boxes are just extracted from the updated masks.

## Integration with Torchvision datasets

The idea is to have a standard torchvision dataset that's decorated for copy-paste functionality.

The dataset class looks like:

```python
from copy_paste import copy_paste_class
from torch.utils.data import Dataset

@copy_paste_class
class SomeVisionDataset(Dataset):
    def __init__(self, *args):
        super(SomeVisionDataset, self).__init__(*args)

    def __len__(self):
        return length

    def load_example(self, idx):
        image_data_dict = load_some_data(idx)
        transformed_data_dict = self.transforms(**image_data_dict)
        return transformed_data_dict

```
The only difference from a regular torchvision dataset is the decorator and the ```load_example``` method
instead of ```__getitem__```.

To compose transforms with copy-paste augmentation:

```python
import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2
from copy_paste import CopyPaste

transform = A.Compose([
      A.RandomScale(scale_limit=(-0.9, 1), p=1), #LargeScaleJitter from scale of 0.1 to 2
      A.PadIfNeeded(256, 256, border_mode=0), #constant 0 border
      A.RandomCrop(256, 256),
      A.HorizontalFlip(p=0.5),
      CopyPaste(blend=True, sigma=1, pct_objects_paste=0.5, p=1)
    ], bbox_params=A.BboxParams(format="coco")
)
```

**Note: bbox params are NOT optional!**


================================================
FILE: coco.py
================================================
import os
import cv2
from torchvision.datasets import CocoDetection
from copy_paste import copy_paste_class

min_keypoints_per_image = 10

def _count_visible_keypoints(anno):
    return sum(sum(1 for v in ann["keypoints"][2::3] if v > 0) for ann in anno)

def _has_only_empty_bbox(anno):
    return all(any(o <= 1 for o in obj["bbox"][2:]) for obj in anno)

def has_valid_annotation(anno):
    # if it's empty, there is no annotation
    if len(anno) == 0:
        return False
    # if all boxes have close to zero area, there is no annotation
    if _has_only_empty_bbox(anno):
        return False
    # keypoints task have a slight different critera for considering
    # if an annotation is valid
    if "keypoints" not in anno[0]:
        return True
    # for keypoint detection tasks, only consider valid images those
    # containing at least min_keypoints_per_image
    if _count_visible_keypoints(anno) >= min_keypoints_per_image:
        return True

    return False

@copy_paste_class
class CocoDetectionCP(CocoDetection):
    def __init__(
        self,
        root,
        annFile,
        transforms
    ):
        super(CocoDetectionCP, self).__init__(
            root, annFile, None, None, transforms
        )

        # filter images without detection annotations
        ids = []
        for img_id in self.ids:
            ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None)
            anno = self.coco.loadAnns(ann_ids)
            if has_valid_annotation(anno):
                ids.append(img_id)
        self.ids = ids

    def load_example(self, index):
        img_id = self.ids[index]
        ann_ids = self.coco.getAnnIds(imgIds=img_id)
        target = self.coco.loadAnns(ann_ids)

        path = self.coco.loadImgs(img_id)[0]['file_name']
        image = cv2.imread(os.path.join(self.root, path))
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        #convert all of the target segmentations to masks
        #bboxes are expected to be (y1, x1, y2, x2, category_id)
        masks = []
        bboxes = []
        for ix, obj in enumerate(target):
            masks.append(self.coco.annToMask(obj))
            bboxes.append(obj['bbox'] + [obj['category_id']] + [ix])

        #pack outputs into a dict
        output = {
            'image': image,
            'masks': masks,
            'bboxes': bboxes
        }
        
        return self.transforms(**output)


================================================
FILE: copy_paste.py
================================================
import os
import cv2
import random
import numpy as np
import albumentations as A
from copy import deepcopy
from skimage.filters import gaussian

def image_copy_paste(img, paste_img, alpha, blend=True, sigma=1):
    if alpha is not None:
        if blend:
            alpha = gaussian(alpha, sigma=sigma, preserve_range=True)

        img_dtype = img.dtype
        alpha = alpha[..., None]
        img = paste_img * alpha + img * (1 - alpha)
        img = img.astype(img_dtype)

    return img

def mask_copy_paste(mask, paste_mask, alpha):
    raise NotImplementedError

def masks_copy_paste(masks, paste_masks, alpha):
    if alpha is not None:
        #eliminate pixels that will be pasted over
        masks = [
            np.logical_and(mask, np.logical_xor(mask, alpha)).astype(np.uint8) for mask in masks
        ]
        masks.extend(paste_masks)

    return masks

def extract_bboxes(masks):
    bboxes = []
    # allow for case of no masks
    if len(masks) == 0:
        return bboxes
    
    h, w = masks[0].shape
    for mask in masks:
        yindices = np.where(np.any(mask, axis=0))[0]
        xindices = np.where(np.any(mask, axis=1))[0]
        if yindices.shape[0]:
            y1, y2 = yindices[[0, -1]]
            x1, x2 = xindices[[0, -1]]
            y2 += 1
            x2 += 1
            y1 /= w
            y2 /= w
            x1 /= h
            x2 /= h
        else:
            y1, x1, y2, x2 = 0, 0, 0, 0

        bboxes.append((y1, x1, y2, x2))

    return bboxes

def bboxes_copy_paste(bboxes, paste_bboxes, masks, paste_masks, alpha, key):
    if key == 'paste_bboxes':
        return bboxes
    elif paste_bboxes is not None:
        masks = masks_copy_paste(masks, paste_masks=[], alpha=alpha)
        adjusted_bboxes = extract_bboxes(masks)

        #only keep the bounding boxes for objects listed in bboxes
        mask_indices = [box[-1] for box in bboxes]
        adjusted_bboxes = [adjusted_bboxes[idx] for idx in mask_indices]
        #append bbox tails (classes, etc.)
        adjusted_bboxes = [bbox + tail[4:] for bbox, tail in zip(adjusted_bboxes, bboxes)]

        #adjust paste_bboxes mask indices to avoid overlap
        if len(masks) > 0:
            max_mask_index = len(masks)
        else:
            max_mask_index = 0

        paste_mask_indices = [max_mask_index + ix for ix in range(len(paste_bboxes))]
        paste_bboxes = [pbox[:-1] + (pmi,) for pbox, pmi in zip(paste_bboxes, paste_mask_indices)]
        adjusted_paste_bboxes = extract_bboxes(paste_masks)
        adjusted_paste_bboxes = [apbox + tail[4:] for apbox, tail in zip(adjusted_paste_bboxes, paste_bboxes)]

        bboxes = adjusted_bboxes + adjusted_paste_bboxes

    return bboxes

def keypoints_copy_paste(keypoints, paste_keypoints, alpha):
    #remove occluded keypoints
    if alpha is not None:
        visible_keypoints = []
        for kp in keypoints:
            x, y = kp[:2]
            tail = kp[2:]
            if alpha[int(y), int(x)] == 0:
                visible_keypoints.append(kp)

        keypoints = visible_keypoints + paste_keypoints

    return keypoints

class CopyPaste(A.DualTransform):
    def __init__(
        self,
        blend=True,
        sigma=3,
        pct_objects_paste=0.1,
        max_paste_objects=None,
        p=0.5,
        always_apply=False
    ):
        super(CopyPaste, self).__init__(always_apply, p)
        self.blend = blend
        self.sigma = sigma
        self.pct_objects_paste = pct_objects_paste
        self.max_paste_objects = max_paste_objects
        self.p = p
        self.always_apply = always_apply

    @staticmethod
    def get_class_fullname():
        return 'copypaste.CopyPaste'

    @property
    def targets_as_params(self):
        return [
            "masks",
            "paste_image",
            #"paste_mask",
            "paste_masks",
            "paste_bboxes",
            #"paste_keypoints"
        ]

    def get_params_dependent_on_targets(self, params):
        image = params["paste_image"]
        masks = None
        if "paste_mask" in params:
            #handle a single segmentation mask with
            #multiple targets
            #nothing for now.
            raise NotImplementedError
        elif "paste_masks" in params:
            masks = params["paste_masks"]

        assert(masks is not None), "Masks cannot be None!"

        bboxes = params.get("paste_bboxes", None)
        keypoints = params.get("paste_keypoints", None)

        #number of objects: n_bboxes <= n_masks because of automatic removal
        n_objects = len(bboxes) if bboxes is not None else len(masks)

        #paste all objects if no restrictions
        n_select = n_objects
        if self.pct_objects_paste:
            n_select = int(n_select * self.pct_objects_paste)
        if self.max_paste_objects:
            n_select = min(n_select, self.max_paste_objects)

        #no objects condition
        if n_select == 0:
            return {
                "param_masks": params["masks"],
                "paste_img": None,
                "alpha": None,
                "paste_mask": None,
                "paste_masks": None,
                "paste_bboxes": None,
                "paste_keypoints": None,
                "objs_to_paste": []
            }

        #select objects
        objs_to_paste = np.random.choice(
            range(0, n_objects), size=n_select, replace=False
        )

        #take the bboxes
        if bboxes:
            bboxes = [bboxes[idx] for idx in objs_to_paste]
            #the last label in bboxes is the index of corresponding mask
            mask_indices = [bbox[-1] for bbox in bboxes]

        #create alpha by combining all the objects into
        #a single binary mask
        masks = [masks[idx] for idx in mask_indices]

        alpha = masks[0] > 0
        for mask in masks[1:]:
            alpha += mask > 0

        return {
            "param_masks": params["masks"],
            "paste_img": image,
            "alpha": alpha,
            "paste_mask": None,
            "paste_masks": masks,
            "paste_bboxes": bboxes,
            "paste_keypoints": keypoints
        }

    @property
    def ignore_kwargs(self):
        return [
            "paste_image",
            "paste_mask",
            "paste_masks"
        ]

    def apply_with_params(self, params, force_apply=False, **kwargs):  # skipcq: PYL-W0613
        if params is None:
            return kwargs
        params = self.update_params(params, **kwargs)
        res = {}
        for key, arg in kwargs.items():
            if arg is not None and key not in self.ignore_kwargs:
                target_function = self._get_target_function(key)
                target_dependencies = {k: kwargs[k] for k in self.target_dependence.get(key, [])}
                target_dependencies['key'] = key
                res[key] = target_function(arg, **dict(params, **target_dependencies))
            else:
                res[key] = None
        return res

    def apply(self, img, paste_img, alpha, **params):
        return image_copy_paste(
            img, paste_img, alpha, blend=self.blend, sigma=self.sigma
        )

    def apply_to_mask(self, mask, paste_mask, alpha, **params):
        return mask_copy_paste(mask, paste_mask, alpha)

    def apply_to_masks(self, masks, paste_masks, alpha, **params):
        return masks_copy_paste(masks, paste_masks, alpha)

    def apply_to_bboxes(self, bboxes, paste_bboxes, param_masks, paste_masks, alpha, key, **params):
        return bboxes_copy_paste(bboxes, paste_bboxes, param_masks, paste_masks, alpha, key)

    def apply_to_keypoints(self, keypoints, paste_keypoints, alpha, **params):
        raise NotImplementedError
        #return keypoints_copy_paste(keypoints, paste_keypoints, alpha)

    def get_transform_init_args_names(self):
        return (
            "blend",
            "sigma",
            "pct_objects_paste",
            "max_paste_objects"
        )

def copy_paste_class(dataset_class):
    def _split_transforms(self):
        split_index = None
        for ix, tf in enumerate(list(self.transforms.transforms)):
            if tf.get_class_fullname() == 'copypaste.CopyPaste':
                split_index = ix

        if split_index is not None:
            tfs = list(self.transforms.transforms)
            pre_copy = tfs[:split_index]
            copy_paste = tfs[split_index]
            post_copy = tfs[split_index+1:]

            #replicate the other augmentation parameters
            bbox_params = None
            keypoint_params = None
            paste_additional_targets = {}
            if 'bboxes' in self.transforms.processors:
                bbox_params = self.transforms.processors['bboxes'].params
                paste_additional_targets['paste_bboxes'] = 'bboxes'
                if self.transforms.processors['bboxes'].params.label_fields:
                    msg = "Copy-paste does not support bbox label_fields! "
                    msg += "Expected bbox format is (a, b, c, d, label_field)"
                    raise Exception(msg)
            if 'keypoints' in self.transforms.processors:
                keypoint_params = self.transforms.processors['keypoints'].params
                paste_additional_targets['paste_keypoints'] = 'keypoints'
                if keypoint_params.label_fields:
                    raise Exception('Copy-paste does not support keypoint label fields!')

            if self.transforms.additional_targets:
                raise Exception('Copy-paste does not support additional_targets!')

            #recreate transforms
            self.transforms = A.Compose(pre_copy, bbox_params, keypoint_params, additional_targets=None)
            self.post_transforms = A.Compose(post_copy, bbox_params, keypoint_params, additional_targets=None)
            self.copy_paste = A.Compose(
                [copy_paste], bbox_params, keypoint_params, additional_targets=paste_additional_targets
            )
        else:
            self.copy_paste = None
            self.post_transforms = None

    def __getitem__(self, idx):
        #split transforms if it hasn't been done already
        if not hasattr(self, 'post_transforms'):
            self._split_transforms()

        img_data = self.load_example(idx)
        if self.copy_paste is not None:
            paste_idx = random.randint(0, self.__len__() - 1)
            paste_img_data = self.load_example(paste_idx)
            for k in list(paste_img_data.keys()):
                paste_img_data['paste_' + k] = paste_img_data[k]
                del paste_img_data[k]

            img_data = self.copy_paste(**img_data, **paste_img_data)
            img_data = self.post_transforms(**img_data)
            img_data['paste_index'] = paste_idx

        return img_data

    setattr(dataset_class, '_split_transforms', _split_transforms)
    setattr(dataset_class, '__getitem__', __getitem__)

    return dataset_class


================================================
FILE: example.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example Usage\n",
    "\n",
    "This is a basic example using the torchvision COCO dataset from coco.py, it assumes that you've already downloaded the COCO images and annotations JSON.  You'll notice that the scale augmentations are quite extreme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from copy_paste import CopyPaste\n",
    "from coco import CocoDetectionCP\n",
    "from visualize import display_instances\n",
    "import albumentations as A\n",
    "import random\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=12.05s)\n",
      "creating index...\n",
      "index created!\n"
     ]
    }
   ],
   "source": [
    "transform = A.Compose([\n",
    "        A.RandomScale(scale_limit=(-0.9, 1), p=1), #LargeScaleJitter from scale of 0.1 to 2\n",
    "        A.PadIfNeeded(256, 256, border_mode=0), #pads with image in the center, not the top left like the paper\n",
    "        A.RandomCrop(256, 256),\n",
    "        CopyPaste(blend=True, sigma=1, pct_objects_paste=0.8, p=1.) #pct_objects_paste is a guess\n",
    "    ], bbox_params=A.BboxParams(format=\"coco\", min_visibility=0.05)\n",
    ")\n",
    "\n",
    "data = CocoDetectionCP(\n",
    "    '../../datasets/coco/train2014/', \n",
    "    '../../datasets/coco/annotations/instances_train2014.json', \n",
    "    transform\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TAPChD30I3/jGNwAA7e3tPKlLp9NKfNXChQsBRHEt9L0gCHDOOecAAPbt28djWE9PD7fVhoYGXHzxxdwOly1bht27dwOIYkvoWsZ6NTc380KwWq1y2SxYsIAprHPnzmUjLif9QNQOZ5rceJ7Hvzc3N3P+5OS5VqspbYrqqVgsch+gd2icftC2Wdtmqj9tm19btlmuD09322wgwA1r/gEJu4ax2gIcGF+CMNGBpBUtfqpVCo0AXDe6TiQc+L7PaWlq2oZ7du9ES95DU1MCk1Mhrt40gTAIcOOX43r6bdlm0wjx2feVsXxRHhsW9GLfyTZU3ais0qk0sqksfADHqgvR2JZGI+R4GN1Xq7mwrNjWUlkHgcG/p9MpLhNJNff9gBfZ0fsobrcW04BjWYFXHJqGqqGhoaGhoaGhofEq4YNffbVT8MrAQIA3LP4ubLOKI8ULEMKC63tI//pHT8HQRAJDEwl0hnPw4oujGJsy8abzp3DrXVk8f+y3R09N2CG+dtMILlhTgx+WsPPwYgxNJnlR19DQgBYzEvbJ5bIAXh1KsQEfAP4YwBde6XfPymJxzpw57BkxTZPpTul0Gk8//TSAaOVP3g8A7KWYO3cuqykdOnSIKSy2bbNXp1KpsEduYmIClUqFvyEFKKSQBgD2quRyOUWMQtIKyDtz1113cR4GBwcxZ84cTu/Q0BB718vlMv/ueR57ZI8dO8bvqlQq7PmcnJxUKHGSJiM9HhMTE/yN+fPncz6kuuF9993H75JUnHqFNvLoua57CqWQdlxc11UoNLRrIYPGPc9j71y1Wj2FSkj1TZ6+bDbLnjbXdeG6Lt/n+77iBZSeNEpHtVqdMei+/lqqtUlPucTFF1+Mm2++GQDwhje8gf/e29vL4iw7duxgL9O2bdu4zp588kl+j/QMEqQXlso3k8mw5/3YsWMK1YXSXiqVFK8v/Z5IJBSRGHr/xMSEQjGaSUGvvnwkTvVkzhxoL6lKMr+bNm3Cww8/DCBqo1RnbW1t3DevuuoqLq9t27Yp/U96Wtvbo0D50dFRVCoVxStO5T42NoY3vOENAMD9Cog8ctTW5a5aQ0ODQnOjfjkxMaGkQ7ZR0zTxrne9CwDwhS98gb3rJFxD6aa8btu2jdtIT08Pe/k9z2NK6rp163D06FHOz+HDh9n7eezYMR7f+vr6cMUVVwBQ+2lHRwd7SIkiBqjtolQq8T2pVAq1Wo3zlEgkOF1hGHLZSqpbpVLhek6lUvysZVnK2E1937Ztvp4NUTSN3w60bda2GdC2+bVomw9H2mUz2uauri48+eSTbDd/E9v8nve8B3/913+NSy655BWzzQsWLMAZZ5yBI0eOYE66B5ct+DaSVgVBaOHQ5CaYlgkgCgmRjINEIuoPUVuN6ywS1YnLcN26dQCARx55FI5j41C/haamRtz/hWEMTqSAsB8hBrD3eAseHnjXf8k2dzZX8BcXfB3JRNQuw+BevPcMSskOvH8zLY9CJK0yyrUUdvVvhJNIwckD8xvUXXvP87mcpNCe7/ucp+g+2gmHmMfEdjfqh3H/pWfD0ABgcdlIGiq3u+i5i3G6LBYty2L1P8uysGfPHgAqX3/+/Pksf3306FFukIZh8ADY3d3NCloTExNMZ5HGiQY4SRmTcQfUmZLJJBu9crnMFVMul7nCPM9jw+Z5HneMpqYmHD9+nCkVmUyGG76Mhcjn89wQ29rauIE2Nzczn/zw4cNMMZuYmOBtfakiSTSN8847D0CkSEfb6KOjo/yuo0ePcnorlQqXVf2gKdUQJQVidHSUy1oqgZVKJb6vqalJUW8jap98v+u6igEkgySVxjKZDDo6Ovhd1WpVibGQcVWUJsdxlIFVTvAl1YVoC5IqR3Ew0jBR+RQKBZ44SHltmdcjR47MqHInB3JSkZPGVE7EaAJUKBS43CzLUiTH5btoYpPNZln1srOzk+MB68tKUknp+wSZJllXcsCe6TlJWwLUGKKHH35YWaBQ2e3fv5/fd/vtt/Ozr3vd63jQrFar/B7TNDl/qVQKGzZswOOPPw5ANW7XXnstT07l5NLzPG5XjuNwH6hf6FC+8vk8xxwVCgWWzAZUifUPfOAD+M53vsPpontaWloUJdcFCxZwOmiisXjxYq7XpqYmrvuJiQkcOnQIK1euBBBRgSgWKZvN8hjR0NDAE81EIsHfzufznA8ZwyMn1dTOZcwMjWmWZSl5pTJMJBJ8f328EkEuImVfpEmsxukHbZu1bab3aNt8+thm6cAl/Fdt83e/+1188IMfxNNPP/2K2eYzzjgD3d3d6Kp+Dquad6N3agkKmI/x2hxYdgKWRQrGBgxDHl1iTechyWVeLleQSCSUhTUpnZ533mbs3fssAKB3rBmVAxlkk9GiLG2XcOm6k1g47+eonGGjWEvj6/cvRHNzMxJWDf/y0TS3vebmkygWPSTsAJecOYn9vRkk89HieHxsDLkcaQVYcKcXdY7twDBtjJZbYFqOcN4mQYs607RgWbFtpXqKdBTi43LoXioTmVeKTZTjVtSmTm0XUbuPHSG/Df+tpqFqaGhoaGhoaGhovEq48U3R/3f3b8JnP/tZdoh8/OMfZ2EpwqZNm3DzzTfzAvFv//Zv8ctf/hKLFi3CQw89hB/96EfYtGkTTNPETTfdxLuOAPDpT38aN9xwA2688UbceOONeOihh2BZFu6++260trYinU5j9+7d+NCHPgQAuP766/G2t70NpVIJK1aswMTEBO677z6kUilceOGFSCUd2B23YXzwBZx8/Ju/lZj2QimNQimNdDoN182j7DpYMfcIklYVvRMdABYgmyjj/a+7EymjCM+nnbohdi7s3J/AwYEsVqyIHAcDIx5awsg5nUg4vGBLJpOKE/V3FbNSAmvWrOGg4cnJSVYu7O/vn/Hcm3Q6zZ5JAFi9ejUA4IEHHmC6SEtLi0IjIO/TCy+8AM/zZqRI+b7P3pN0Os33SBEHSgsQrdDJ0yeVFelsNvICFYtFxTtEHXZsbIzT1dHRwVvi7e3teOmllwBEHjLyojmOw2dbycDwMAzheR6ee+45AMCFF16IH//4xwCiHYze3l4AkeeVvKXy4E8ZEC/LRHptiPaiejZOPYT25MmTM3q1pOdNXkvqiWmaipd5cHCQ639kZIQD5OXBuvIb9Wc/zURPCsOQPVMyYJi+T//esWMHXnzxRQCRR5CEAXbv3o0PfOADAKI2RvW/Z88eJei7njojf5ffpHTlcjlW0SKqEaVJUnzovbVajcutqamJxRzkAbv15VufFklLkB7Ll0v7TH+vp8jIoP+lS5eywEomk+H26TgOf1ue6/f888+zwZOH+AZBwP3ddV289NJL/F7f95kW2tLSwv03mUwqFDYpziB3KKSqJ0GOL2NjY9MGJkpLrVbj/jtv3jymvezfv5+9yalUivO3b98+9kqT6isQiQHQWCXpnpZlKRSz8847j789ODjIuy779u3jfKxfv553GW3b5t1VOQaNj49zukkdUJYJjXWJRIK/ncvllB1EeTC59LoTpDe0UqkoQhcapye0bda2mcpT2+bXlm2+fCNgJxvxvotvxZVXXsl98oknnsAb3/hGfldjYyNuvfVWXHXVVRgYGMDcuXOxa9cu3HTTTchms2hpacHBgwfxxS9+EQsWLMC3v/1tdHd3w3EctLW1YefOnXjwwQexatUqfOYzn8G2bdvgui5+//d/H4VCAZlMBl/72tfwjne8Az/60Y+wZcsWbNy4EW95y1uwceNGvPnNb8Yll1yCJ554As8/9TNsXpPAkcdvxrg7D7lcdkZKsmnGNE2p6uo4NpdZpVKB48Q7ZnKMyOXy6OiIdgCHh4fhODHNMwhCHB1uwqGBHJryDs5b8hK+/r7/gGNWMFqdj5fGz2E7a1l2fA7oPAPr2mPRmAULFvC3i8Ui00WHhoY5H3PndiCbjUNEaAc3COK8VqsVTncQhIr6u2wLEb05+l4ikeDrMDR59zEMox3I+rYSlyXUMRbxEZ2vNGZlsZjNZnlr/+TJk2xs2tvb2Ti99NJL3Pnnzp3Lyl/t7e08kDuOw89mMhkeeHzfx4EDBwBEBSX5+oZhKIfI0iDW1tbGhkdy+h3HUegRcoCWEty2HTcyOeCbpsm0rCAI2FicOHGC6WoDAwM8+DY3NzMn/MSJE4oRoDx0dnaipaWFy+FXv/oVPzM+Ps40gfHxcSWWgWIkTpw4oQxO0iBRo6KyidWYAqXcZJzDTLEJcsCT5WxZFi8Q5s2bx/nu7+/H5OSkUocyPowmHn19fcqElcpccrQlnU7GbVB6Ka31oLqpn7SQ4Vi3bh1PKG6//XY+LkHmVS6MyKDLuAg5aZEy8pQuebh8PcWH7j9y5AinMZ1O82KhUCgocUaVSkWpZ4I0oLLc/jO8XByanKRJWlBPTw/32foYC4ozqVar2LRpE4BIqlsuVKRC58TEBA+ul156KdOjEomEQj2T8vjULuRRG5VKRVEgpH4ZGaH4Wdm3K5UK52tiYoIVIeWkp7Ozk6lm+Xye27c8PDuXy/FkS5a3bdtKbItpmjxB6e7uVhRbd+2KtNAfe+wxXHvttfx7rIYWS8a7rqsovxmGodBxpby+rBupoCrLjRbG1WpVkaWXXmI50dQ4PaFts7bNgLbNr1Xb3LLgAixZsgQ/+9nPlGckpfWCC+J7ZHnRQrtWq+FLX/oSwjDEoUOHUC6X0d3dzfnu7e1FNpuF53lYvnw5PM9DGIb44Ac/iCuuuAK2baOpqQmTk5P453/+Z1QqFezatQtLly5FJpPBwMAAuru7cdmiO9DdvhJF750Yd+cCiKiU8eImPtJBOngAg/u153msAhqGAcIQytzAceI43ObmlunyjGNI8/k8MpnIdiWT0XzhhNeFlD+K0HBQDlqRy4HppTI+Morh9RRnCNV/a2srO0sXLICIhTyBNWtWcz7iIzFih5XvB2x/o/qGQseluozEccg2y/jaQCm3mIrvi/YecrlRXihFswW9t6qhoaGhoaGhoaHxqsLA3r17sX37dv4lDEMW5AGiRc3evXtxySWXsOPgzDPPxPz589lxobxR7LDKY2qkeNG73vUubNmyBRdddBE8z8Nf/dVfKaJykmUSBAHSThXzGp/ESPUSOEHMPHgtIEACU36HWKRrQbZXArOyWCQFQCDy1pPXqKmpib0LExMTTId55zvfiZ///OcAIs8gefHb29vZCz8wMKB4aqiTZLNZhX4GxCt0uf17/PhxRdlPij6Q108GfU9MTPB1qVRSaF5yOziZTHLQfhiGuPTSSwFEFJ/4MNPYK+Q4Dnf8vr4+hS5Gnl3btnH06FFFEZG8ta7r8u5HtB0fn6dGwhmDg4MzDg7lcpm9wY7jwDAM9i5KkQu5qyK9YnKX4+WoaNVqld9ZKBT4PLuuri48+uijXNbDw8PKLgfRLH7xi1/wGUrSizp37lz27k1OTioiCNJjKYP569MvvXA0GJ84cQIPPPAAgMgbuWXLFgCRx0oKhhAkfY/OqlIDsqN2NTU1pXisqLy6urqU+APy9HqeN+MO4K5du9hLlc1mkUqluBzqg+NnqhN5j/RW13vqZ3qWjIwsU1lWsr3IXSeiX7W0tODNb34zAODee+9VDk+WZ3Q1NDRg/fr1AIBFixZxmSSTSd4ZyGQyisANeXDl2WalUok94pVKhb2+1WqVxxHP89DX16fQVSnv+XyejS0JXQCRB5loT0SRAtRA9nqRGMorEI1pUoWU0mhZFvdxy7Jw2WWXAYiofmvWrAEQqfXROCnf29jYyNQa3/cVQZ5iscgCHnPnzuX7Ghsbue1IVUXLsricU6mU0hbk4dWybDVOT2jbrG0zoG3za9E2wwhQ6HsU67euwEUXXYQdO3YgDENs3LiRBZwA4NFHH8WKFSuwbds2PPjggwAim3HnnXeiq6sLiUQCb33rW/Gd73wH27dvRyqVwqFDh1i59+jRo2hpacF73vMeOI4Dx3G4PxeLRaTTaVx77bXYtWsX0uk0Fi5ciMbGRrbNHY0VLMgdxIGx1YBjYV5nhkVYgsDlXbRoFy9u6yRW43ke2xLf99ieBEGAyclJ+D4xW6JzGIFITIbYL9ROgYjGGgnNRPRPgmVZ/GwYGryjaBim0tJ9ktkAACAASURBVOcjOnWsQko7maZpcN2apoGlS6N+3dY2h8fA8fExlEpFrj96bzIpz0kMpsc0OlvRRbkcpTOXyzHVNZVKolaLFcnjPhLvwtq2DdmMaFyN1JanyxyAARzFLGDW1FBpUlUoFDjjL7zwgkK1oMHmn/7pn5TBnjrG1NQUqw1alsWHiLa3t3NMRalUguM4CtecBgjZwWu1GjfQMAwVeoQceGaK9TEMA21tbazYJo3TxMSEovj12GOPAQDOPvtsTq+kxkiJ6s7OTrHVvYANTaFQQGNjIw98L7zwAufJNE0cOXIEQNRpaPCfM2cOp10qjdUrM6kqS/F2eT6f53f5vs/flofvnnHGGRzfISWk6+PfJBWEqHmrVq3C3r17eYIRhiE/v3fvXi7D8847jykmUma8o6OD33vy5EmuC9mxZpKuloaJ2khnZycb+L6+PsVYHD9+HEBE06GJlPTMSUpjOp2GbdtK+5HqdmScpKG68MILOd6lXjFNHgchqUYXXnghAODBBx/EyMiIQsGUSnAvZ3xn+q0+nmOmGBfLshT6zqFDh5T80fekkVyyZAlPrA4fPsyLlvb2dsXg0bO5XA7nnnsuzj77bADgdgCocS4DAwOKMiK121Qqxe8ql8s8sFYqFa4XWixR2c6ZM4cXmwCUcUFOZmnyJqXTs9ksl788osI0TaW+pQy2rM8wDDnmpVarKWMVTWybmpq4j8uxSqqk2rbNeaC4Uhk3Rn+TNFR5dIHMq+PEwfwy/ss0TTbQ8uB1Hex/+kLbZm2b6T3aNr/2bLNbGcM111yDv//7v8ctt9yCRCKBw4cP46abbuL7CoUC3vKWt+Dmm2/GLbfcAsdxcOzYMdxxxx3wPA/Dw8Po7u7Gzp07kcvl8O53vxvlchlz586F4zh43eteh8OHD2N8fBxBEKC9vR3f+973cOWVV+Lpp59Gb28vHn74YTQ0NODcc89FV1cXmpubkc/ncdH872N1+xoU3XdgtNoOa+ApzF35Zqy6+EsYH9yLg4//31wG0eKG+pbH8YCyrdNiicozk0nDtuNypwUmMPORLJHCKtHWE0yBjbQCqJ6AeKkTsiorjUeS2kk2X8bkBkEI140WeKlUCmNjhel0xONYMmkp4wDlwfNURdPIsU5/c0FNoVaLj8cJQ6mMa/HiWcZFRpT+xPSz1ZgGHlowDe/DmAVoq6+hoaGhoaGhoaHxKmPXrl24+OKL+d+0QJAU0127duH1r3+94vCTi9BPfvKT+OQnP4lsNsuO3N7eXtxwww18z9jYGD7/+c8DiBb/V199tSIwlcvlsGXLFuzduxd79+7Fso5xrGzajcMvVVCoRjF8vlvCnntuYkEWjf++mJXFYjKZZIpDb2+vcl4QHfybz+fZczY0NKR4LIkCZpom37N48WL22g0ODmLt2rUAItVCGUQPxPSOWq3G3rPh4WFlh4E6maS9SI+HpI7l83nlfCPbtpUDsKkzhmHIgfOu6zJVJpvN8m5CsVhkb/2KFSv4+t///d+Z9vDQQw+hq6tLOWNOemTJo7to0SLeuTn//PNPOfMHiKgU0tMnxUpkMLqksY2OjnJ6zznnHPb6lkol9rzJHYh60KBVLpeZXvjkk0+yJ4vSEgcE+/jlL38JIPIayoOiqQ7279+vqPK9nApc7A0KTqFa0vXU1BSXiTxzqVqtssjI5s2b+cBfmVbp+SYvr9xFkp5FKutMJsOHsg8NDXE+6qkq9HsikWBa4OjoKH74wx9y/uqFDKTy30y7gzPVS/09UihBpokEVWZS75NqcZOTk9yOt2/fzuWTSqWY8tbY2Mje4IaGBr5n0aJFOOOMMxTxHCnioiqHRd/u7Ozk/ue6LnuPC4UC99+WlhZ+Z2trq0LjCsOQqS/1Sq6SVkRlkU6nFSVG8jK7rqu0YUImk+H2tXDhQuXcsvoDsym9cvwKgvhA8GQyqYh50HeGhoY4f7Q7Ir3fUjmWdiyJckT1SG0nlUpxvuWuraTG+b6vUIc1Tk9o26xtM6Bt82vRNh/qPbWe/qu2We6+Aa+cbW7PnMB1y27D4fE1KFTnwPddhdpJu3v5fB50iLzvB9xny+UK75BFu77OdPmnEZ8vGJ0XGFPETf5GdI5hlFfHSXD+HCcWyzOMgBetQeArdUFwHIfHmoaGRvh+vOPp+x7v9EV0+pkFiuIzjG2l7VE+SqUi5y/aoZRnK5oIwygftm2hWqXx1xQL7nj3U4p3RXOueFcy3h0PFDbGbGFWFouFQoE7fEdHB09WgiBgCoykOhw/fpwN2ODgIB+Gu3DhQn52YmKCC4eUnICo8mu1GndgOfmzLEuhIUhVQRlrRI3Btm02CoVCgQdDUiqjg4pHRkYwb948AJFyHD3f2dnJ8VqTk5Mc19DW1sb3tLa2smLas88+y/LcW7du5QOSiUZGRs/3feWQXMpTGIbYunUrACgHf+/bt487RKFQUBTT5ARRTh5PnjzJ1x0dHeju7uY80SBN5UXpoPqQxmnu3Lk8qfQ8Dz/4wQ8ARAa6fmCj70kqULVaZU761NSUIr0tlfFkfAYNhoZhzEino7/R92RchWEYbNRzuRxLeD/zzDOc11WrVjHFR07w6YgC+o5U76zVanwMw5EjRzgGyHEc5cgDWa7UN+TkoLW1levSdV2FmllveOSk5eXiVmRbIMiFX/1z9XLpcuCi/iGVOHt6epi2NDY2hkceeQRANCGUtB6KyTvrrLOUhdnk5KSSD6n4SeXe29t7Cg0NiNqezIc8nkPGGHmex38rl8v8TKVS4Unk6Ogo50MeQi+VXKX6nWEYygKW2iQdRi3bt5zYyjKke+TCuVKp8MJWflveQ2MC5bFWqymqfFL1VE6k5QKRvi2NU7lc5oVCuVxWJhoapye0bda2GdC2+bVom//qNvr9N7fNJ06cQEdHh2KX/v/a5ksX/RBrWvfgwGg3RistADwEQagoftJCcHJyghdNhgG2p7lcTsTbxe0zCHzFBkbqxpF9iSiqMU2b2ki5XEYyGesfyDKW5R/TWW1lAUv0TdetwfcDTpfn+coiPW6HcewkxQZSmiQlOXYCxwrihhH1G8qj78fpko60yAbHzhp1gRjnj2i2nhfPSzxPONUNHwC+B+AKvMLQGugaGhoaGhoaGhoaGoy5uT6sad2DQ2OrMFKe82onR+NVxKzsLNZqNT5Uenx8nL0yS5Ys4dVwsVhUzqiRBz6TN6q/v5/fc/DgQaZfFAoFPvtnzZo1eO655xRlQFqtu66r0LzkbkS9khgQreIpyNv3ffbok3jE4OAgANXLJb391WqV0z44OMhpHBwc5N/7+vrY2zI+Ps7fqNVq7PVZt24dHn30Ufbq+77PXn3DMFixbe3ataxOdsMNN+Dmm2/md0kvo/TU0XuoTigtskwuv/xyFgOwLIvpLevWreP6oLzVl6083DcIAg5WJ0hvm/TIEsbGxrg8Gxsb2esn6XhtbW3sATxx4gSrU42MjLAXLQxDlEolpc4l1Ukqs1E7XL9+Pc4991wAwJe+9CUWNXjXu96FL3/5y/wNuTMkIQ9SXrNmDXvh5blODzzwAHufGxoaMH/+fC5nul96y9avX8+e05MnT54iQCMFWaR38OVQT0Wt/63eGyqpLY7jCBUzn3eYSqUS06/K5TL35YMHD3IZybROTU1xf2hublbOGJNUMOm9npycVKhj1MYaGhoUBTqqf6nQKpVJ6SxGucNBHku5U+h5Hr+3ubmZxxG5a1gv0hAf0BtwHZZKJdRqNX5GUkSlKA4AEfQfU6ba2tpYVCIMQ0UFUlJNK5X4IODx8XHOU6lU4m9XKhXuQ42NjTPuGEg1y3oPJ9W3Pmfx9IW2zdo2U3lq26xtc31apW1eu6iKP1j/HRwY7cZIuRW1motEIt5xdt2ovVarNbY3iUS8OxvtwNc47aSSGlEuiYIclwsxXGgXMFIupR3ZeEcu2jWO6iGdTjFd1Pc9Tp8UiTGMeLcvstnR/RTmQjuNkvJbPw7ZdnyOKFFEo/Nlp/h7MjwlZupYij2vVKqcJ9d1+dtSmCuVSsIw6HcfqVS8SxxTbuNdRin4M5uYlS+cPHmSB4njx49zY5WcfqlUdvLkSUVBiTqv67o4dOgQgFP57wQaLGmiJA8IlpOdarXKnal+cJIDgFTJou+QBLSkYNBAbpom03HGx8eZKiNjrMbHx3lgbWhomJFWNjw8zN/+2c9+pqSpnn5ANKKenh4egJ999lke0KQSl8yT67oKj15K5gdBwOn613/9V/zJn/wJgKgDUMzC/v37Oc6hWCxyGUY0hOh7LS0tTPF48MEHlcGzntMvOyb9nk6nFbobUYEGBwf5/kKhwAY1CAKmF8kgbzraQRoeSbUjeJ7H9drT08OHoedyOc7TgQMHuL5lOuRigf5Nxu3DH/4w3vnOdwKI2iTFy0xNTXE7nJqa4knH4sWLFVoWfe+ll17idFx44YUYHx/niZHjOBwrJFW9ZooxJFD9O46jHIHwn8U40jPpdJr7b7FYVCZv1O6pvOg5KuvLL7+c+0mhUOC4JsdxMDY2xu9KJpP8PdM0eYxobW1lI5TL5fhdY2Nj/A2ZJynXL9tnIpFALpdTjpCQR17I/kiTr3qKGLU9mb96pUfqv9QmJV1JHi1AbT2bzXL+aEELRItkqgdqE5RWEjxob2/HY489xn2F6geIxq79+/fzezdu3MjlTH0/kUhweabTaeWA8zg+w+J7tBrq6Qttm7VtBrRtfi3a5n//bPT/t34q9arb5gVNg/iTTd/GvsFl6BlJApiAZdm8QInorYnpb2fqaOGR7ZV2zDTjOFF5Ckm0qIvpl4lEgvtXtIiNaaGU51KphFwuy89TmkzTRqVSnb42xII0Hi+CIOQYRdO0pmMkiVIeCNqyI+YMzowhG7ValWMcyU5SWmnBnc1mceJED9Lp2HbTQtfzXG5jnuehs5OcE7ZSBhTX6Dg2L1Sj/kOOJpPLfProjFmBdhFraGhoaGhoaGho/I5jQdMg/scFP8aLw90YKrb++gc0fidgzIay3dlnnx0SFWLJkiXsATdNkw89ffHFF3H11VcDAO6++27lUF/yapbLZUVtSKaVvCXpdFo5g62xsZGDon3fV84iI0hPVj3k1rNMR2dnJ3tixsfH+RvZbFZRaaTgYNd18dRTTwGIKDvy/B/ycBWLRaxevRoA8Ktf/Yq9RK7rMrWM0jSTIhblFwCWLl3Kwe6SWieDrgEoOyGLFy9muoJUawPAuxx/+Zd/ye+955572MPS3d2NZ555hu+X4hzkFfE872UVOGVZZ7NZpkcAYPqHbdvspRwbG1M8RS+nuFYfcCzvm8nTJK+TySTe+973AoiC6EnYobOzk9vt/ffffwpFRFJMyOvY0dHBXjypprl48WLuD0NDQ+yJTKVSTDe0LIvbXhiG/CwJUrz97W8HAFx77bV43/veBwA4evSoQqeU3vmZIOtClsFM91G5ZTIZpTylF552K4rFoqJmR/3hyiuv5GcPHz7M7ZbaH5VJNpvl70lxFtu2FeU/KmcSFqDvSbEaSUOVZwTKcwglTatYLLJ3lg77BqJz0mg8a2lpUc5MpLaeSqWUti3VUyUtyLIs3l3xfV8pz/pzE+kb8ow1SRclMY5HHnlEUYg9++yzuRwsy+Jykx7uZDKpeLvlNb3HNE2hZldm73o2m8WNN944Ww5MjVmEts3aNgPaNr8WbfPdn4nS/KaPmq+abQ4nduOTb3oQe/sWYbgUCcfRLpnjqOeCUns1TZOFXyJqJSmBxoq+kYAb+FlKt9xpj97jMF01aidRn6rVXKTTsWAd9bVMJotyOdqBJxXhqNzAdFPbtiGatkIPDsNAObORaLPRjiY9JEVppEBRrL4bBKFCF52cjPpMT89x5PN53s2cN28el4NhxOWmUphthUIb90tJ0TWYfut5MaX//M4HYBrhz7E9fMUFbmaFT2QYBjfK888/nwexZ599VlHNIjUmqXrmuq5Cv5AD8EwH+hK9gTrt0NAQGyF5uDD9m9In+drxtnJ8xoxlWdwQarUali1bxhPGVCrFNLrFixfzYG7bNm//l8tlvpZxQIVCgSddQRBwOWUyGUX6fmJigge6q6++Gk888QSAiDpEym+FQoGvjx49yvmW1IOhoSFlEJEUmlKphFWrVgGIFNvo+YmJCU7j17/+dXz0ox8FEB2M+9WvfhVARLch1bn+/n7+RrFYVAbombj3BBmvISk4VP+VSkUZSOTkRMZSkRGQtIf67zmOU0dvOHWiXC6XmTazdetWNhx33XWXEsNRn6f4YNYkLwSOHTumqHeR4R8fH2dlvD179uCSSy4BACxfvhy3334754NQrcYHrh49ehSGYeDWW28FANx2222n5BFQ42JIppwwk1y5VGiTxsmyLCxfvpypEpOTk1wOyWRS6Te0IOnt7VUoRRTTMT4+ji1btgCI+jhNyhYtWqTQR+WEsLW1VZlIynRR/SeTSeXgeGoL9VLpkkZSrVbZ4FerVX5vU1MT3ycndUEQMOXKNE2uS6Lu0HtiNbSYwpZKpRQamRx7EomEclA0jTfVapXz4fu+EsNJE5h58+Zx+1y4cCGeffZZplPVx87QOJTL5TgdkgYmDbxt23xNaQeiNkmTVDmmapxe0LZZ22aCts2vLdvMS5BXyTYvbh3Fx970C+wfXoFxtxVtbY0KfVQuWjOZNC9WZOoNw2SqZCJhcwyhjGuMQjbi+2V7ieIOqa94oCEmlUqJNhnHMoZhyBRPwzCQSNj8PUKUzlPprNEiMhT588Ui1hDjm8H17PueyEfIMYe1Wo3bVy6XZ5vd0NCIwcEB7vPR4g/8DWpPci5g25I2G9NNo7E4DgkIQ9I4MBRdhtmCDj7R0NDQ0NDQ0NDQ+B3E4tZR/PUbdmDXkbmomJp6eroiDE3A8L8+G++elcViNptVDt+lVfLU1BTvFCxZskShN0iFQHk47EwBwfKwXvKQSwEK2uaXh7JKhSKphlhPe5GeF/LmXXnllRgZGWF6S6VS4bPdnnnmGc7TFVdcgZ/+9KcAgIGBAYVuIA/+prTLXY1yucxehmw2i0qlwt6J4eFhPgxZKmtt2rSJD1KWZ8GFYcgeJ+kZlGXb0NCAwcFB3HvvvXwfpTGbzXKeTpw4gQ984AMAgOuuu45V3W699VZWwJuYmFDyKj1h0nNaTzEhZTbbtjnv9bQLSm9TUxO3F3lgsfQUyW/XCwlID52s8yAIFBoSvWvhwoX43ve+x/VE7TOVSim7RNITLqkZcuempaWFn3Fdl+lJtVqNle327t2Ls846C0BUl3v37gWg0gWpPKUCp1RDk2pzkmoxU3nK63oaIr2TRFdIXZC+A0Te9piCEeD48eOcJsq34zhMW1q9ejX3GVIMpPvl/6WHXQaTZzKZGXdF5SHcchdN3i939mzbhmEY7AWkMqNyl4c1ywOeZXuhtp7Pxx5Ez/P4Xa7rsjeX3iH/JtuL3P2kOshms4oiItWHPN9wcHCQv7Fx40asW7eOdxxkeuVZY77vK0IEVAZynJSCP4ODg0qaKN/1dDuN0wfaNmvbTOnQtvm1ZZsl9fS3aZuv3DoPH7/6fjx3cgms/BJk6+or/j+YTlmrxef/OY6jUDPVvkzlEe+iRYyD+H5JTwYMpoJGgjrguqFykOe1BkHI6Yh2vr3puknwrl90dmPUz3w/gG2rLAj5N1V0icY9Q9RlAtVqnA/asZTCTMVikeml8+d3oqOjXZz/GadXhqEEQajMBagMTNPiXdhIiCrgb9CziYQD1/Wo9AHgAcwCZmWxePbZZ3NjLRQKzJkeHx9X4o6oUdEADKjGwjRNNmw0yQPUiZk8VJj+RpADF6Dy2OUEiiAHpCAIePL10EMPoVKpcJ6WLFnCMQGFQgHbtm3jtFB6wjCMJYjXruW0SynxZcuW8SArJ2YEMjK/+tWvlLzTpPHEiRNcPvWTY1mG9KyME5EHMNN90iDSe9vb29m4/eAHP2Blxc9//vP42Mc+BiCK6SK1NymjXV/OcrAzDAM9PT2cV7lYoHKWhiaRSPAkYHx8nMtGSpdLI0AdUcaByIkO3Tdv3jxccUVE7963b58yOabvL1u2jGkv+/btU4ynPG5BDvyO47DBXblyJRv+1tZWrr+GhgZe3BiGgccff5yfl4aD7qfFBf1NLqbS6TTTEA8ePHiKElx9+upjVGbC+Pg4nnrqKX4X0TaAqG2SYR0fH1faG5Vtc3Mz002XLl3KZei6rhIXZxgGv6tUKik0SDlxoGfy+TwveuTirVQqcbsoFArc/3K5nDJRsCyL+52kZpXLZUVmnPIky0lSYMvlskL9kdQ4KTE/OTkpKCa2Un+Urkqlwvk2jPiIgvrv0/iZSCS4D5MhkgtfWW6U3snJSS7ncrnMY1UymeTydF2Xr03T5HKqVCrc7v93ZOA1XpvQtlnb5pnKWdvm303b3N05hc+89WEcHl+DitmO0A+UuDjA4Fi8SLE3dtIS9TRa3JJyaKxmHlE8XX6W2m2lUuaFXCKREE7gaIFGbQZweaHkuh7HLHqeyyqpcVyhepyE63owTSqb2DkRtT1yXiVRrca2OlrgR++S6uaeJ5VHwWmv/z4tECMF0wo/G/0ml1qSVhx9o1arIpmM80c0Wtu2EATxYnamo3Y8z/uthIZoGqqGhoaGhoaGhobGq4Sv3vXb/V7K8XHLe17AvsGlKIbtv92Pa8wKjGjxegmAH73S756VxeLKlSv5DKZHHnkE11xzDQBVvCLaanX5/meffZbvoVVyvTePns/lclixYgWAiGoyd+5cxUNH5ybJ89Qk9UEegEl/AyKlL6KzTE1N8e8tLS1obW1lBa7+/n4OqF+2bBlv5995550cyHrRRRcpnkk6s623t5cD8K+66ioWErjvvvvYS7Vw4UIcPnxYybs8x4WQSCTYY3X48GElUJiupXdW0vHovDkK4k8kEvyN8fFx3l0oFouK14u8tp/4xCfwD//wDwCis59ImVFurUuqC3m1pHdRnpslA9Kp3KTIiOd57EGU98j2Yts219HU1JSyG2IYBpYuXQogElXZuXMngEjJ7a67olG6UCjgwx/+MADg+eefx/vf/34A0WG/9G1SXgOAJ598EgsXLsSmTZsAgKkxlF7y9O7bt4/rob+/n3fbarUaNmzYACDyZNKu1tDQEFOYpMd5zpw5aGpqYuqJpDfJXS4plEB5p/9LqhG1Nzr3St5L98i2JCkxphl7ALPZLD9XKBRYPKarqwubN28GEPUhuqehoYGvHcdRBA6kF75SqSj0Hdr5KBaLyhmB5ImX6ZMiM7IvJ5NJ5HI5RWiD2mEymeR3ZbNZ3n2QqoWe5ykiHyT6YhjqIb7Ur+T4A0S7pfEBxgl+V72wA713YGBAobRSvzSMWD21WCwik8lw+3Fdl73ilmUpeY29uxUeq+RYWCgUuC24rsvtvlwuKyqwGqcntG3WthnQtvm1aJv/Y3dMzfxt2ObtF66FZT+HSX8h78ilUgnQzpdlRYIqtGso6abRTiotH0I+9L5Wi3cQ5Q4bYCjtja4jwR/6no1EIsG0UBluYlnxGYrRuYekuOoru4FSpIv6ie+rZRefeVybLrt4t5R2TuUYaBgqnTruf1Ocb2IsRffHO4muGwn70K6o7weCqWHwfXJ3VrIz5HcrlTLvqAZBLHznup6YOwQA8Ec4XRaLJ06cwHnnnQcgymx/fz8AYMGCBTzQmqbJEyLf90+hDwDqRC4MY5n6xsZGntSl02kMDw8r6oNUwOVymSuWDu+l93IB2DYrh0mVxrGxMVbyGh0dRS6Xw8UXXwwAeOCBBzgfmzdvxkMPPcRppEres2cPx2YtXboUP/nJTziv8lBvGlg3b96MPXv2AAD6+voUCoU8yBuAYrD7+vr4XTTASMWu5uZmpuzIAX7p0qUol8vcieRBrvRNSi+VievGEr1jY2O4/vrrAQDvfe97cf755wMAfvnLXyoUQUl7kcpTUgnMMAwu62q1qsSs0f3VapVjQ+SCQlJ5LMtiOejW1lace+65+MQnPsH30SRm8eLFXEZ79uzh8gnDEF/5ylf4e5/73Oe4PGkguP7663Hw4EEuj6NHj3L9S149EFMG5YSpt7eXJzOf/vSnmQ6zd+9eVu7bs2cP/z44OKhIsB88eJDLV1KzSqUSp8v3fYXWI+Mqfl2MBMlX07Vt20r8A6G1tZXbsaSYZLNZLFu2DEAUt0NGVtaZpCSl02klVtAwDJ7UyZgqwzC4fcrfx8fHlfxRO6qn+siFjud5itGmekqn08riSLZDSTeKB2mX245cgCYSCb6u1WqKemMymVTKXVK8aFIgVfaam5v5WcdxlCM5qF1QHdMEJZVKcRuRMUdAfOSAjFmUSmxz5sxRYnjk0RmyTWmcntC2WdtmSt+s22bDxD24GW1hIwzPQLaQw/PNJ/CjK57Rthmvrm3evOlc/On530UtSCuLQBlTZ9uOUm6GEYc6RNRlSmscjxjZLWO6nioiH7FTRXU2hrwACkNvmpIcO1FoMeY4ttAHcDkWUo5DUYwrtW+f+0MikeBFmWVZIkbRn14IEp3eQpy0UMw9wGmMKKmkIZAWCs1xzHEQBEzdpXhHSdmXccpy7CAV2Gi+cKo6cCaTFX3UV47O+G04cGdlsbhz507uUPl8Hlu3bgUQDTy33HILAFVO+siRIwqXmgqzv7+fC6etrY0HjqmpKe6kY2NjCIKAO2kqleK/jYyMKAG9hEwmw53JNE2WSL7jjju4gfm+r0xgy+UyvvnNbwJQY3yOHz/OA0wymWR556amJq7AgYEBfq9hxB2rp6cHCxcuBBA1IuL9+76P/v5+ZSCg9EuPRxjG57zUDzxx8GuCDVW5XOb70+k0enp62NiMjIwok0DJ8aa4CHlsAJU7AHz1q1/l35uampQJsOwM1WqVJ7RylysIAp64Sr6+DGSWxnvOnDl8hEC5XOa4G9u28eCDDwIAnpTssgAAIABJREFU1q9fj0QiwZ7lXC7Hzx8+fJjf+4d/+If41re+BSAygFQ+zc3N6O3tBRDFGVD5l0olnsCUSiUsX76c89TX18fv7ezs5D5gWRZ752Ws2KFDh1hquz7egbzKy5Ytw4kTJ7j8rrvuOmX3jPDss89yXEUmk+G6XLx4MXtq5YTAMAylTcq6p2MiHMfByMgITxakt3RgYIB/lzEkjY2NfD5ZNptloyy92r7v86JlampKWSwmk0kuH9OMz/mTnmi50JGe11QqxfVX3/aofVLclYzXkRM/ui8MQ0UERwpzSE+v9ADTe2R+bNtGtVrld1WrVe7ncndPSrsD8aJuYmKC2/fExASPW62trcrRHaZpCk9qXL59fX28AK7Vako8kYwtobYkd1HlPY7jcNnKCa/G6YWdO3fiC/tvAAC8AW9F6/T4D8PA4dohfKdxB/516KdIJpPYXjkTN/RdAi94CwB1Z6FSreLq4CMAItv88dF3YWk4D8awgWZ/+qzScgmVagW/cPbg9sRPkUqlcGZmBT4x9m7UqjWY7qkLxE80/iv2TEW7M38eXoM/CKPzHk8eP6nYt2PFQfzP5P/Ltnnt/xn9bZ15Cf60cgEAIP2zNN45dgZud36KX2aew9GjR3Gpdzb+HG9C4sj0pNsy8fbJaDfRAPBm++MAItv897U/QWelBaZhoORfy9+u1qq4338KX7P/HUEQYKW1CJ+v/XG0u8PbHCFCN7r+G+d2HA+iHdU/86/GZeE5AIDkZBJeMD358wMc8vrwIetrbJvvqEWLqXKpfMpC4lbjbvzcfwLDw8O4PNyIP8c1sKbjqoJKEIdSfQn4Rn43gMg2f6b0Xiz258IAEExPehFGdXBPsBNfNe6CYRhYjvn4YvBnCAEY8TCDcPrFf2PfjpeCKK7x/Xgzrtn3Om4jk5gEQuA8fzUShoOHk89HcdiTFVw5sQHnnVwG8y4L6/F3AADLsGA+YeLf5j2OZ4PINp833o3bO25Cz7TtC3wf5mQ8l7tP2OY/en475k41wvddrE92AwAum5wL23bwWH4/fjRvJ/r6+tDlteML3p8ilUwh6UzHMo4YPI7e5H2Zrzv/LUTrrsiRcqnRgUsQ7T6/r7wZPdUhfDT3TbbN/1L4a+RGc2huakbYRIvsKK07zz6OfyvtwOOPP46Lauvxx7UrYUyHvGXSGd4thwHcuvn/AgD8x24bN1f/CMvQCcDgIzUQAEkksSO1F99p2oGRkRGsTS7FZ8rvVXap0RPb80+3fA9Hcv3c9q7qvQAwAqya+1VUbA8vjp6FEAHiOLp40VKr1ZTFom1bHEsX2dd4J0w6HuRZhSTIIs8zjhZfcXuOzzk0EIaxoyISfSFbFMC244Wn58UiOCTuEi3Y4jMJ40VkiGQyFtOSi2/P8zm9nucrR2/FZzHHizcgEs8BIluezcaOFNr5zGTSytEdcn4VpSV6fnJykhfAvu+LeX18tAigHqNCHTsIQr4nEsGRx5jMDnTMooaGhoaGxu8APv3M24Hkr79PQ+M3RgisCbuQQgL7rRN4zjkK0zRR9IpY7s1Hq5uHVTOxKDEHxxNDr3ZqXzP4s6ujRcx/7J5F5oYRYNGf/j8w85N4cfQsBLAgRVo0NF4OxmxsX37kIx8Jd+3aBSDie9PuQkdHB3vSn3nmGfz85z8HoCoJylgGz4sPwMxms+z1KZfL2LhxI4BIwUxSypqbm/kbvb29/Hw9v58QhqGQ0LWZNmcYBj9LO5rkqTBNk3cvp6am2FMxOjrKz7S2trJ8tWHEByGPjY3xN8bGxpTDdmnHIZ/Po6enhz0K8iBXx3H4Wsp7m6bJsReWZSm7QNLrJGkdhmEoO4sEqXonaRAvR5VwHEeJlyKqw4kTJ2LP2fR7qW7CMOSyGhsbU3a8ZPyK/AZRh7q6uni3c2xsjHeuK5WKouSYzWY5RmZkZIR3+iTXf/78+RwXkclkOH+u63Ld5PN53m0bGRlhykyhUOBDmoFop5DSvm3bNm4v4+PjvBPa3NzM3qRkMsny3LKNDA4Octnk83l+5+rVq5HP57Fjxw4Aqmy44lkEuH0Wi0XF6yfj86ifSQlvKfPd2dmJgwcPKpQk2d7o242NjfzMvHnzmBKWz+cVehm9R6rOUfyhZBDQDrCk0NRqNaVMqKw8z2NvYLFY5Lba1tbGv4dhqOwMyndJZcT6nWx6VzqdVmK65I4ljUm2bfP3pFIZxfrR83JXNJ1O8zOZTIbrZnJykp9PpVJcBo7jcD7q1c9KpdKM8YySyRDFkFT5vVLKnMZoy7KUIzLo26VSia/7+vrwla985dTBVOM1jx+//rbw+PHj+PGi3do2Q9tm+d7f2DbbDv7D/iLmec1wnAScmgkLJv4t8wjmLZ7P98v4uhzS2LRvEUpODZWwBtu20d8wji9eeO/vrG2+6++i+959c8Os2Ob5nXPxw/95EC19rZj6p/dj/wfu5vqmZUAmkxYsE1fZiVOP5PCZChoxUMi+JRX7QWXlujFjJZPJCPsLVkw1TUt5V7TL6fDfaJdSxhA6jq20e8lSoN09Sd81jLg+KNZPhjHRLqNtO/yM7Ne1WlUZk6gMLMvkHc76eO5IRfbUeEbDgOhbJnw/ZiPFx4lYrLJqGCaXVTQ2Bvx+ou5eMP8BmAZ+ju3hFXiFMSs7i9VqFdu3bwcQDdgkw3zs2DF0d0c0gbVr13IAuaRwDQ8P87WcoElKWktLC1eI67qK8IPruryQqNVqM8p+27atDMTyzDYamLPZrFLphmEovGN5RpmUtaZKnpqa4sHXMAymQ9q2zcHcTz/9NC9gJD2RBnF6VyKRUEQCaLDp7u5mAZEzzzyTf0+n04rxlLEIFPtw7Ngx7Nu3jwO9m5ubmUIr4yWAOEZJDvj1cWBU5rZt83vWrFnDQf6O4+AHP/gBB9vL4w/kcQlyYJWTgnw+z+/atm2bYrypLnK5HN8fBAGq1SobymKxyPU0MDDAzxw/fpxpes899xzHd4yMjPCkJ51Oc1D73r17eQHa2tqKgwcP8uLG9+Og44aGBq6bz33uc1wmGzZs4Dp46aWXmOoqz2zK5/M8GK5du5bvHxoawqOPPqosaGTQOP2+atUqrv/9+/fzteM4TGns6upioYve3l6ePDmOw8Y3l8spEzYZi0d9kcqaFj1btmxR4vskJZmokXJxWKvVlLiokZER5UgHKQ9NRqhWq/E3ZGB/KpXi79G4QN+TRwFIKfpEIsH1Z9s2tx/57dHRUW6rjuNwWVUqFU6HjBmSFJZ0Oo3x8XElH/S3hoYG7uuZTIb7kBSokTFAuVyO29fY2Jiy8E+n01w+hmEo45gUrKFnpqamOO35fJ7TV61WuY9L6m8ymeQ6lvRXjdMLD607iNbtrdiO7do249WxzWu/nYYzaSBIAc+9L0rfaWubQ+AX/i1Y6S7E3uZjaG9vRyJpY2KOiwszW1GbnnwnEgmmyYZhCM/3MLjMQmYgA9NPwPMCnPn8Inxu1zsw3DCFYsbFN52HfqdsczIZfWP9+vWvuG02jRD/398cQNL2MfmNGwHXEX3IYGqkXBxGoSNxbGu5XFKOdIjPOgzgeXHoQnxMlXr+atxPAqZNRv2WFsMJJXxExvpHC2A6CzimtJbLZREDaClzj5iG6vNCLNoooDKPRHNkPuhvyWQSlUrsYI5F7XzFyUp5SiQSvLCtVCqKMI/jxFRZw4jKkfJLC+CIeho7oGLbHp977HnxUSKS+mvbFgyDwuwMzNZOsaahamhoaGhoaGjMMtJDBhb93EHghHCKBkZW2Ti5ZfbPSJsVhMDfeu/B8mAe7lu4F1XLhdmeRybzv0c6CJLA1KIQNTfShyg1++jclUTbyTRChLhjgeZLvxIwjRCfvaEXCRt4fngDVruJX/+QhkYdZoWGesUVV4Sve10U8NzY2Mgr/9HRURZoaGpqYkUr13V512F8fPwUGW9A9ZZt3ryZPS8jIyOKEAMAxbsjvX5SYYrub25uVjzl9DupGAKRx6GtrY3fW6lUFMl72r2qVqvs1WloaGAPTTabZa98c3Mzl8GLL77IqnPNzc1MEVm2bBnmzJnD75W7BtKT2draquSVvBGe5ym/y/Ikj6rcBaE8kYftySefZFGUgYEBRQBEQlLhyHPa2tqKs846CwCwYsUKVpFbu3Ytvve97yneOioT2l0CIg8SCcisWbOGlfsymQzfb5qm4lGVMsOSliPzLpXDUqmUsttC18ePH+f07dmzh9vFpk2bWBK7sbGRPbLlchnPPfccjh8/DiDacaF2nE6nuRx6enpYpMZ1XSxZsgRAdEA20WxcN1a0kmqfvu9zO7jmmmvwzW9+k72+lE8Aym/z5s3jfNR74On+VCrFbUDSJAAo7T6RSLDS3eDgID8jg7NzuRyov69evZrvkW0yk8lwG06lUkx/o3zX7zQCkedOivhI2W76PZFIMNUo8u7FanTyeA1qI6RsKmXpZd5lH6Lv5XI55R76XSqNplIpTofjOFw2hUJBEU6S9SHHlKmpKaV9k0c8n8/zNxobG7kPFgoFbttTU1Noa2tTdi+l4BdhfHyc01WpVBSlWdo9SKVSXG7lcpnLI51O87seeeQR3HbbbZqGehriD7e+M9y4cSMKrZUZbXNzczP+4i/+gnfVfhPbXK1Wcc8996Czs/MVs81nnnkmuru7ceedd86ebW5qxueefgcWVttQc12Y0yIlpmXCEaqM0q5ElLj4yBuCaVmxg98Ai5QkJgwMd7sYXeEhNWGi65cpVBsDhIh3a2u5ADu/UEXNeO3Z5p8En8WS2lwkEg4yThqWb2D/pjEY2VikxDBj4TqbjgZALLqHEDBMlTYpd5ZDP0TH8wk09NioZaPfS2YVn73iHlQS3n9b2/zDT0Tt8/qbG14x29yQz+KRr5SRsj28OHYWai7Q/f0rYBgGDr37XgCYProqpkAS7TGiOYbKTiMJyETHasRjAbVweZi9DHuwLEuIpdnK8Rq0m0fKpjRPiPqZ6FOKYnK8oyfvod/rj22J02GyYmp0FEWg0LoJmUyG8yrDVmSISTIZq4gnkymmgpbLsW2t1WrIZDJ1u5cWp4tQrVY4XdHcI84P0XJtOy43141ZW45j87tWZ38Mx/KbsT2M5ZNfIczKzuLU1BS+//3vAwDa29u5cPL5PE/+T548qUxuaPImpeUBKDQoeQ4Z8cwNw1DOVJO0NFWFKFBUkOj3DRs2KGqRtOVfLpd58gdENAOKO8jn8/w3GctTLBYVo0WUAdlwE4kE0ymuueYaPtcpkUhwviuVikKjC4JAkeInDA8P86RbxkLVy0RLA020CZISp+dTqRQfO7Fp0yb8wR/8AQBgx44dzMM/fPiwIvVNZdjQ0IDly5cDiCax69evBxAthkmh89lnn8X69ev5e9VqFYsXLwYQLY4p1qC5uZkVYqlMqQzl5Ham84Uk7VFOPug+uWCQhoqwcuVKrFq1CgBw6aWXsqz8oUOHuM4qlQqn71vf+haOHTumxOJRO168eDF2797N7962bRsAYPfu3Zyn3/u938MTTzwBIFL+pfecffbZfCbV6tWrWf67WCzi7/7u75g6dvToURw7dgzAqeeFUTuRkzUZNyhjbeRiwXVdHqxJYZPOr5Jqo5JXv2HDBp5cFotF5WgJukfSgHO5HPcZooTOVE9ALHGey+WYYiQni3Jik0wmFSNLdVwqlTivdASEVBcmh43nefy8nMAUi0WeaBC9GYgmJzKehCYFcsJK7VOOadKQyBgnasd0FiQQjTVykSypo5JiR4tSgAxo9N6RkRFOuzzfSiopp9Np/vbJkyf5G5JKlclkFFVejdMT73tuG7w9Hm5a/M0ZbXNDQ4PSNn4T2zwyMsL29pWyzRs2bMAb3/hG7Nix4xW1zZ8avB6pMBoLlh+bj4XeHOzvGkAylURjc+RYhWlhWtgUFd9HEAYI/OnYIf4PFMn/KB9xKAhTc20PxUYPqAKVJDB1QRnpKQswDZjT/bztSBIX/A8HhZUW3GyIF/4Ur6pt/qODFyM3mUDrVBZt/WkMbfHhm8Co76LcEiCdyMWanQZ4kQ0jtr8yNgy8Zpwe82HwojIIAoT/i703j7btKusFf3M1e+32tLfLTXLTkA5IAiGA8GgMnSRY4ACKMkMooigkPMVXw8LkwahhvRLkGZsaiEipDJRCURzleJaKGpXuVSAEDAIBEkxiSO693tzmNPec3a521h9rf9/8zX0PoiRHiOxvjDvGvvusvdZcs/2a3/f7AosTl2fYOpBjai9j+ViC//pnr8LJJ2fIl38It72uDhb8ezqbZY5sb28/Zmfzz/7Pe7DQegifP/wkmKBCoxHjwdf9DcqygpmqiFmWe2eH5MI5xlNmGHX6kpS1qOsFO3ZyMd6iKERVxfpZ2FPr+7v8U5eLWkydRVIWzcyUenEs5GLI5Xmm7K01LLdel0nSVIMrDP3gQp4L67jx8jXlO3kP5xRw+blRFGqb6ueSY4Sgo64/YzVK67Y4J9l4PNK2199JQyyKor4+jiN9dr8/oPQyd30cx24/7OweWdEchjqXucxlLnOZy/eYXHbZZXjjG9+oebFf/vKXNb9N5JnPfCbe9a53KVnVO97xDtx22204dOgQPv3pT+NDH/oQnv/856PZbOKnfuqnPGPy53/+53Httdei3W7jxhtvxGc/+1mEYYiPfvSjWF1dRavVwl133YUbb7wRaZrihhtuwI/8yI9ga2sLT3rSk2CtxStf+UrkeY63v/3tWFhYwF/+5V/ijjvuwFve8pZH/f4/tvFi/OD2M3A6nOa1NYDPXfIQsrhEp2MR7audLGFoVI8rihJlVfrRiOnfOKevjkZi+nuX31VWpZdSlLcq5K0KJjBqIPRXC5x7Twd77o2QbAU4/twSp6/4zjBWLoyaeMpd+1DEFcomcPRFBcraN4csnxoVu/FgA4xWXcR5fKCONu75egPN7QAXnrOAB5+y/S1uMpeldorT4y4qG2BeHXcuj0Z2BYZ6wQUX2P/wH+paRydOnFDPNgCNbD3taU/Dxz/+cQC1d4jrBbLnnSNIAqe47LLLNAG/2Wx6cCmGY3C0jjdy9hoY4zbpKIo02X11dVUTzOV6ZjSUSMrRo0dx6aWXAqjZuyTK8aQnPUnZO+M49t5DRBi7gDriwbUNOXRelqV6+zkUzdHDMAw1osMkFQxjmEwmXlHXGn7gmBaZ3EM8Iwxjm0wmGl0YDAZ6zfr6uibHb21tqeflxS9+sZIVDAYDbG9vK2wiSRKvLzhCw21iz6S8x6xHjvuWk/E5ksL9xjU0OXrCh3qj0fDqGXLEST6naYqNjQ0I8+/dd9+N++67T/tNSHgEjgXUc1ciZK9//esVTj0ej9VLyIXRjxw5ogrcqVOncNNNN+nvP/GJT+AHfuAHANTzTcbgj/7oj/Re3AeNRsN7D35vrqXoKT3ErMbwz3a7rWN71VVXqfc6yzKdh+xtG41Gug9MJhONnAlrnEQT+/2+B/mQdnW7XfWqMiwoz3NtX6vV0v2Fk895j+NIvPxe9p48z/XvDLusvZGusDH3AbNDynsPBgN9vzAMMZlMvCLJPB7yPC5SzuNx/PhxhUadOHFCr2f2u0ceecQjFrDWeoW0+Tci7MlmKLZEKaV9HHUVr3kYhnjXu941h6E+DuXj7Xfb5eVl/F8v+zRuueUWvOtd78Lm5iaWl5dx66234m1vext+7dd+Dc95znPQ6/Xw3ve+F694xStw/PhxnH322bj99tvxjGc8A+12Gw888AB+/Md/HH/wB3+Aa6+9Fr/xG7+BG264Ad/4xjfwwAMP4DWveQ3+5E/+BK973etw44034rnPfS6AmgRnMBjAWosPfOADuOOOO/Dbv/3beP3rX49f+ZVfwVOf+lQcPXoUv/Vbv4W1tTX83M/9HH70R38Ur3rVq/CWt7zlX3U2/z/mv+CK/nmeXhHHMaIgRJAbPHxNirKe4lzRDuPJ2O17eYFKiCiquu5ZMSXMsJUlb39QG4Pw95zABIin5BdlUTqYZlnp56IolADGGIMgDFCVFXqnYlz4xS6yHSIGs+ynbMAqgqCqUFKxcPlNkiQOhVNVqAj+y/eNxwYbF+Y49aQcYRDq+4VBqJFBa61GEytb6Wdrrb6fQf1O0mfGGP19ENTvKr/XKLMJ3DUm0DFob0U457/HyNvWo/NIlyr87c+vo4rmZ/PKygquvmiCD731BL564ok4PVlEWZYKdbTW1e+r2WUdBFLOoaIop8i9el6kaaZRP8DpWo1GQ1k6Bboqn92ai4jQKtSoH5sfAn91ep6DiFZVqZFMhl3WMNJq2g7HWlqznjs9WchnmHCrZhctkKaTM9pep9DE035gNlPHrDoYDLC8vDT9PNT2A0bfr98feOQ11oJ01EKfwWTQvH7LslKYexw3lHQnDEMv6rq1VTtOXnTRHQiM/U1cY9+Mx1h2JbL4yCOPKB7+la98JT784Q8D8Jmk/viP/xg33HADAODWW29VRTJJElWsGArGdLvb29teMexWq+VBo+RvaZp6RbYZ6uYYlBxEpCxLXdTWWmVGW11dxfnnn68T/5xzzlFDkBk4GYNcFIVC0XiCMp13v9/3jBMRgQHJ8xgHzpTMs2xMrqin1YXJdNesYMozWJGU9m9vb3tFuhmDLnmVs2xfrLwzLFT6oNVqYXl5Wduyvr6u/calCfhebAwztrzVaulnPvQ4V1P6bCdjkxcaFyHnfJlZg5Q3J2k3UB82r3nNawAAr3jFKxR68qUvfQmvfe1rzxin0WikORZ5nuOpT32q3ksgNH/2Z3+mB8R9992H6667Ttt05513KivgC1/4Qs0V5Pb+9E//tDLEffKTn8QDDzwA4ExHg/TbwYMHdc3xOgnD0CvGPss8Ko6VlZUVj3FX7rV//36PdlvWqDFGjbput6vQb+krnm9iIB49etSjdheZdQKJwcb5WDVMpp6HW1tbaLVanmNFPhtjFM5RlqXHSMrwb9mruOyGMa64M8NZJ5OJV2YgyzIq9ht5c1LelZ067XZb4VNVVXmMsJIvI2tA+p3n9NLSkn4fhqHndJI+4XUzGo08plmmypf7yHPn8viTSTrB9vY2wjDE2Wefjbe+9a0A3Fnb7/eRpile8YpX4OKLL8Z5552Hj370o3qNtRZPeMITcOLECaRpit/7vd/TCGGaplhZWcGxY8fQ7/fxF3/xF2i1Wrjzzjvxi7/4iwDq+XzLLbfgxS9+sTILM1vxHXfcgUceeQRhGOKuu+7SMjxVVWEymeCRRx7RszmyAf7rIz+KhXYXEt9qt1tot+o12T43xup9MY4/u0DVZMOqQlGkSLsVytgirKbKcJbqukmzFLZy+6D+FrVRJoZlZZ1CXJQuvSEMQg25hWGoxpCFRSlwOhj93lBiozwjjmKMz7K4b2mAJJs6wwvnHMP0lwAQcGpGYAArzvBIDa6qsqqdV5WDHlprEUahvi+ncwRJiHTRAuXUiBQIKe3B1lo1IqM4cv0WhZ4OI99rmQ+6lxiVIULvDGbIqhihk6UKD12bIR75sNaVe0Jce/MebJ1fG/c22IcLfuYCIPzePJvf8+bP48v/dD62sh6AOhUiTSe4+n03IIwifPV/+Yj2gUA8AVdqqdFoeMGC2siT9B2jeY7b29uUOsLzogIgeYZWDdVmM4Ex7oyX907TybRkhZQ/K9TIA4yeqVXl9Fsud5VlUKOXy24ABlkmv3Vw1qJIp33NOp9z/Aic1hhXFqNmHXb6t5zH1lq9prZJ5JwNYUzNuip/kz6qy1eJg8B4TieeC2IsMps54KC4dWrMBCTnYxdkDkOdy1zmMpe5zOV7SIwxOHHiBH7wB38QgHPkSj6WXPOVr3wFr3zlKwH4jly+jq/nnGgRjnr/8A//MJ797GfjmmuuQVEUeOtb36pOWcDlQc7+blYiG+CdD70OV43ORzpxDqOoH7u6ZGGAI9fkyJasZ2BVlUU+hVA+HuqR522Lqlu3N8tLBMGZ+c+BCTSfzATQ96ptsqlSWVae8zTPp45VWyGKoMZcnhcaBYwj40Vcv1ukbAFlyxHmAMA/PTvH3rsjLDxc53klQ4OnvSfA3//0+DvX0O+gNOMSp0dtmLmWP5fHQHZlGt18880eVE+iOOPxWA+DLMsUFvD0pz8dT3ziEwHU3ozf//3fB1B7IcXTPxgMlHyEa/9IrTPx3AwGAz2oVlZW9Lrjx4/rNe12W73jBw4cwJVXXgmgrj0ncLrl5WUPlsEF3zl6xtDFLMs8bx0nI8t79/v9M8hXZj8DdTSTvQuudk2pfcvFkEejked12qm+DcPphORF7iXeMhkDJsXh6IK8k9Srm71vmrq6Ncxy1+l0EIahB3GT/uR3Go1G2pbJZKLQY44sMmRvMBh47JRyH4lQcS06mRfMbvXPwU2Z5IHnAsNky7JUKGJZlqpEHTx4UD2Z1lq95tixY/pOf/VXf+V5EIW5bTAY4MSJEwBc/SagZs+84oortF0f+chH8JWvfAVADeuWqG+j0VAIzDOf+Ux89atfBQD86q/+qvZVq9XSOVlVlbap3+9rn8m7yn3vv/9+va7X63kedyaVkGj1YDDQ+cJRtDRNlWBidXXVY0plhbPVauk4Hzx4UL2ck8nE8+jJs9M0pWK/1oNlynpfWVlBmqb6e04ObzQaHsKBmUrle4a9pmmq62/fvn0eSQCTDWxubnr16ZgRWsaAvc+9Xk+fd+TIEX2n/fv36/O63a7HeNjv93VsTp48qX24tbXlFRdmxAEjLViJlOuZdIeZFzmtYC6PL7noCRehrCrs37+OlZUVPO95z8PnPvc5jMdjXHrppdjY2IC1VuvM3XrrrbjlllvwD//wD2g0Grjnnnvw93//92g2m0iSBNdffz0+/OEP4/rrr0e73cb999/vpYQI87GgWoSoSuCi119/vRKtAPV5IzDSc845B4cOHcLrX/96vPSlL8WznvUs/LdbPogD9s+iAAAgAElEQVTv/z9XET4CpMvAyZdZ5Nbtz2XlzpzAWFSUhqJM2bDIp7A5ruuWZukZ5CuznwGg1W65SCHB+UIbatQwbsTIpjXp8ip3xlvgImomNAg08lI5KGZVAQbuXnGsmloYugigCYyLBqLQd2rEDS+iJ1G/sig9OK28VyNuIDABRqljkVVUjXXtyvNc94aiKJA0p+QcpdH7FrkbiyzL1NBk6G44jVBpv1HNvrIsFY57Rq1CIjjxEERCqBMAj1wxhTqGAZBZnHtHgute10PZ6OKTPwtsnp9/157N7/6p+jXe/jvtx+hsrvueSd+iKEZlLUxVab2/mjwwmPZ/gVar1r/a7ZbHlMo1/KIo9s4rZhF3hePds8uy0AietSA4q9UoXI0YK/R5QeCzqUrUMAwjjbbVSB3RI12UvywLrQnJdVlZn6318rFGE2t92LGbS+3IGkYq8O2GktJsb2/pO3W7HX0e65R1/2doTtfKcDhU6O1kkhKZT0jnuetnYwC5Fe81ReH2ueFwoO+wm7IrT5CCqUA92Z/+9KcDcAVt5XtR2F70ohfhwQcfBFAraS94wQsA1OyUkptw9913q7H44IMPKrtUp9PB4uKiQrV6vZ5CRDudjkLcrLWKU5eDblYajYanvM2WYWAYHXtQGR4rv+HC4RsbGzqJmbWKMed8/wMHDqDf73vsZJxvx4agPHs8HnuKMm++bBgJJT8zJsp48AYswnmDDINI01SNOs4ZjaJIFfwjR46oUSDQVoHtMexODEm5LyvBAgtmGDIrvczEFwSBx4zJB8F4PPYKpTM7FkMoHV6fqYljzxDnzYbzOxhKwvOtqirN/RqNRgrRvvPOO7W9q6urCrPcv3+/rodGo6GHzlOf+lRsbGwo9fba2prmZKyvr+Ov//qv9XkC39q7d68yB3e7XW/zFdna2lJI6eLioseqK+8C1Aei/H4wGOj8ZGfG2WefrYfpgw8+qMYz5x/ymDUaDTWepF2cx8dwcTHqeL4xTLrZbOrn4XCo7T9w4IA33rx+wzDUws95nmsbGX7TaDQ855TAU+sDrJ5TJ0+eVCNqbW3NyzORsZd7sTLNa1BgoQwvarfb+v3a2prOt8lkojmjYnTKepqFTcvzmem0zjNx0BpZT2ma6t6aJIkyAm9tbWHv3r2Yy+Nbzj29jCA3uPG25yB76F68/z+9G6YXAZHBCWzi//vs7Wi323qG33777Xj1q1+NVquFKIrw8MMP453vfCe+7/u+D9vb27juuuvwMz/zM1hdXcX73/9+dLtdHDp0CFEU4WUvexkWFxcRRXWO0Wte8xosLi7iyU9+Mr72ta9hPB5ja2sLz3jGM/DBD34QF1xwAZ6SXoDPH/wdAEArOAvJ9h6886vXI3w4xuIlTbzkh67F8LINPPKhf0DerQ05Exg1RAycMVZWpUbI0txBTDliOR6P1aCJIlfsOzCBF7WT+3e7XWRphkjKC0wJbwDfAAuCQJ9dUKQOFmpYVUXlGUY78TYA03OXIJwigQl8dhkh4Skd7J1hq0EQIDT187a2t9BuubzmIHB5lYEJUEwV9rgRqzFWVIWnGwz6tS4Rx7G+Xxg6pTeMQs8hpek6YQ2NzfIphLJwbKhxFHulN2Z1MPme4b6c12iNex4aBoefm6IxNGhtBHjJO/bgxBNT2AAIgj0Y7ivxxdf3v2vO5ovOHmofijyas9kYgvwC6PUWMBwOAGunv5fJQ2MT+RwOApMEBCopzKwFwTRd0KGg+cbcIBy0yPNMmYO73a4aqq4khltDjv+gpDY6s6Uur+HOducMDTTXcjgcqkHJgaY8d/mZ7l7S9spbg67Uh0uNY36N+ox2QRmuMlAHU1wQiRn75fnMdBqGgQcxlfVUlgU6na6Ok+jyk0mKTscFe3ZL5gHqucxlLnOZy1z+vYv51OVJI8BoX4X2yQD2ZB+jT9UlBIKRRdIukb5liN/93d/Vn6ytreH9738/gNp5IPlWIn/4h3+Iu+++W8sPAHUU+tWvfrUqeidPnsRrX/ta9Ho9pGmKD3zgA2c6clf3oPvLawj/dozWvqmS9sHjSD94HC0YmLUS41d+ASfOKzE+CJiF7z5o5Fy+CyUAsp5F1iuRxxMsHp0q5DbAvq/GeOBFI6R7vsNtnMtcHgeyK8YiW+TsxeO6Tmxhj8djPOc5z9Hvr776agB11EAOIY5E1UxFLuxdJ+i65FLxHLBVzh58voYJMhgyyxE8qZPGz2AvJUNB2ZPCiclycPIzOQoHQKMU4/EYrVbLg7GyR1GiA9vb2xp16Ha7+v1wONQ2AY48p9ls+vAU6oc4jvX3/X5ffxNFkd6L+5kjkXWirss1kfc4cOCAeu1Onz59RnHynYo1ixda2i2/Z5ISZs9kJsc9e/ZoO7a3t9Fut717cSRMrmNIK9fD5PfjdjK0Va4R7ydDcKuqUs9Pu93We1x44YUKuX7JS16i5E8PPvigtu/w4cPKsHvFFVeoJ/PIkSM4deqU/n+WEVO8nwcOHMDf/d3fAfBhM0tLS9qfg8FAvWL79+/XPuj1eh4kdM+ePZq0L/cH6vkmY9BsNvVeDAPn5HPpE6AeY4lSCeyR14H07+Liogfr5hptvP6Z1U2uYdKdjY0Nj52U34XHU8g2AHikWZ1ORz26HB03xmhfMRENR75brZZHalUzz9X9lqapB291nkXnnZ9MJh6hDKMEHAxl6BX+bTabysrHEfkkSfQ92OM8Ho/1XRneyrUthbyE+3Aujzu5cnigwvHnUJqAwJ2swf4vx7jhP1+MKga+/JNjHHte8U3P5iiK0Gw2ceONN37bZ/Mzfi7Bnq/HLhLYtHj4hSnChSnLn3VRLFvZafQqgKEInpSu0AgU/62yKLP6XfM89yCRqURMLDSiJr+Ra7xi6NMoRZEXiOJII29M1CL9AvhoIA9NkOUKT5X+0t8pQ6RFZV3UMQjdHpVmqYe2mKQOdqkIGxvAhg59xEgduU+32/XqwvJ5V9jCQTtpHGaRUMzeLO9qjNG+qspKf5u0HcO6pAvwveLIoaIU8ZD76R+lJbZXguIa6yJcHLWxlUNCFWWB03ssTu9J9SxZOZrgpTevoorqfa1ono2Pv/PJGO2rviNnc7tdt3VxcfExOpvrc3IawEWWZaq71ugagT27CGWNzupoH3KNQvk7UJPUuCizJf3UjQFHgLnWMNf1Zf1LIoxyPnpQ5dDpGFEUo5xG+uO4gTx3SD0hxzEGGs1jIhqGe0ZR7JHBlWWl0ciqKj14q6t76dZZHUH0CYDqtoaQTSHLaui2RAprYrmhtivLpBJDqO8BxHB1KAt9V4a31nXVR9P2FaiqFnZbdsVYZObDJEk8mCbDP8UIWVhY8PKAOL9np5yesiy9/DOGeQFuQyzLkiaYo+5dWFjYMZRsNTzvG2jb29tnQNFk0BiWwXl4zDTKdPlcDoRhFlxwOwgC9Pt9j7GRab+ZAYtzCDhvUPqAy3bMlqUoy9Izbph6+5uV7eCC5CKnT5/Wd+10OmokMUOXKNAOU17quHHxdl4QnLMwHo+1HUmS6OJnJtWiKHTuyDVs2ImizEyznFvKTLPyPvK9PJvh0wsLC16h3DAMPWZdNpr4N/L93r17cdNNNwEAHn74Ydx2220AgC9+8YtqeBhj9HDq9Xo4dOgQrr32WgB1yRmh4c7zHG9605sAAPfeey8+9rGPAYC+s/TJ+eefr8+Tvp1MJh4MXObBwsIC+v2+tndra8uDaTKT72c/+1kAwPOe97wzGHeBGp4qbdmzZ49es7a2hpWVFb1u1mjkshbymfNz4zhWg2s0GnnGJTt0mK03jmMPju0gNIU3F9jI4rUpc4zhYmzs5XnulfBhNmT+TbPZ1D1waWlJ1zs7VeI49lhZBbYkuWDSt5ubm2rosmNpNBp5DjpWvjj/hftK3nU0GnnOM3aqzOXxKUEQwATT/dWEapCkaYq1p1fYvLxCPDB4ynubOPTRAqbdUSOGWRqttdh4+6dwNeKp8jl1hpLhVNlgCi90hoDAFaM+0Dlm8PD3p0C7hnmWcW1UKJsmlWqwsI45lAw0KRMl75EX7qzldcB5eGVZKoy0Kis1LsvSlbXg/YNh3cYYZGnmlcKQ/mH2dgOjbeJ8xKIkptEwUKNMSmXIZ1tZZ6RZVzYC1uUyzpbtSIt6H2u03B46mUzUEIsbpDuAoPhTBZoZXsMGGXw7nMece5kX5OiKQjWkozjyjAWBmkZh5MFjrXWQVGaa5dzSsiBIY+zU1rLwSwbJ9UmSoEKl/w9MgNL4us6ps0boHyxgpwXQ953s4AduWcHaEzKYYBE/VP1vAICNfX28ufceALt7Nksu3Pr6+qM+m3/m2s8hiUpY08CxY3XJo/POO4R6stZjJ/fq9TpqqCRJW8s4CP/AN3Oau1QnpzvW554YnqG+EztyrSPlhbUVfS4RBD4cG5AgQkVsoy7/MYpCb22KsVcbnsKAWnrwUoHA1v8vyfky7RuIrptpfwqzqh9EcPZJHMeeQ1ngugsLPYzHE1SVy+MUyfNcDWTA0mdDxqn7TVVZJInTMdy57gIv02Iyn8AuyByGOpe5zGUuc5nL94BE28DiPwTYurTa8e9ls47wPfS8CZa/ESEcgowIg0AcQQCqSkJhruwDi60CBByEtgZBOP19YPHwNRmKtkUYAWVx5u/nMpfdljK2qKZR2PWLcuSNCu2NcFpSrb7m4n9awasOfh/+21mf+2fu9N0jV54/xNMvOIlP3Xs2TPNMx94/Puf270Cr5vJvIdYGgKnevxv3NrMsnI+F/Pmf/7nl+n8cbRGPe7PZVI/+yZMncc455wDwozN1+NYlOLOHnb1+XA9GCCwAP0o5mUw86KJY7hKaB/z6JgwlGw6HXiSFo4MMwZHfybtyNJDJJJhERdrERDnye2Yl5GdzPZ2dns2RM+4PjkQKOQvXWWSiGCbqkYjHbMH1nQqbc5IxR1GstRiNRgo/HA6H35SRlKHDPH783uxR5YRshoQuLi56beFossyXzc1NL7Qv48rRYCa44ciiXM/EIhLd4b5mohb+vtlsehEdka9+9av40z/9UwA145rckxlFgdq7KAQkl1xyib7HlVdeqfWi7r//fi9KzO2Q65Mk8VhkeT3M0tfLmJ999tlKJNXr9TRKLJFCGQNhNhyNRjh06BCAOmrLEFaGSXGEjsd5fX3dg7eIF2/v3r0efJuT6+W+zKra7/eVlXH2OvZ+8pgzIRJHtYMg8KDbsgYYmi57mMzDbrfrkd+wV1zaNJlMPOSEtJ0jp0tLS/quwr4qe9fm5qaua15nDPFiMq+iKDyiBJGvfe1rev2FF16o45okCd785jfPk8Yeb2I+9SN5q/pwugwc+/7CixQx62UURUroMhwOHWTbuL02CCk1wsLzsDNElGGpVVWp4VmUhUaaiqLwoIuMPpGImjHGi7zpms3q/UKuK8tSo3UmMBrlkt8BU6ip1EAM3JnBqRFVVWmbmKFT+kr+X5SFRkJnyeckqiXPkWs4UqtlKYy7xpja+JaIIIyLks6iABqxi/xLtNMjgyl94jrpNxMYP4KX5+hM4YdZnn1TRlJOBVBoLu0fURRRVISYXwNqd1EiaTpYamBc5DQM3HwZT8be2S7jWlau/mIQBo7BliKLcn1enIkaMzAe+ZBEk/n7KIr0vtHA4sJPdxFPjBdx/qdgDc+I/yP6ZvSYnM1/9gv1b1/1v0eP6mz+6de0ccOLh7jzgf1T9k53fsi5UBQ+skuQYnEcKbNmFLl2AH6Ejsd5NBp7ep5EKdvtjkb68rygayrS9xzyra6FaLS9fF1JZV/CMNBoWxxHGpGroZwOHu5I8Dg9yGrNxSAIzzgTHfkNk89ZT2f3mHtJX2T9VAmYpnpnOd27JpMxkQRlGiEFXJoNp6HU8F1M+9OR2Jw6dVKvX15e1nF9+r6PIwqKZVxjT+Mxll2JLHIeEJcdaLfb+oLM9nnw4EGFhS0vL3uGjiyUKIr0+mPHjillMC82uU46OknchjQejz3YnMC5wjBUxs6NjQ19xvb2NhUgrcsocH6SXHf69GlvA+VNk0tO7ASnZTbNdrvt5RmyEXLy5El93iz0jaG1rEzz4SKKda/X8/qAoavGGFW6azy0w1VLe1npTtNU2Sk5z5Cha61WS68B6jwyGeelpSUvt1XazlBVhg43m03PmBZlutVqeSVGuK2TycRjWeXcNma0lEU4GAz0es4JE2NN+oMNbGbdnEwm+ptGo+FBoBnGKHNye3tbv+d+vvzyy3HFFVcAAN7znvfg3nvvBVAbRkePHtX+GQwGOp6PPPKIMo/edddduPjiiwEAL3/5y7VEzac+9SnNQeNDj50fAuuSfkqSxINmS9tHo5GOgbVWx2Z5edlzOrASKf24tLSk7ZbvOEdW/nbs2DFVVOtCty29l4wZQ6sZVs2QHYYab29vewXmGcI+y4DLkHnHhhbqOm00GgpJYtgyr+vBYOCV5OBSH0mSaF/z3GFmY2ZcTVPH6Mj5IMKqJnOJ5zrPY3aMcX72ZDLRucMKyNe//nUtRp2mqQeLn8vjVQyMsZrjJopyHMWw0XSeENtnr9dze22z5Rk6AocMgkCv7/f7WF6pc2wZLinXqZIYOkU0L3JnLIShnj0mMMrYOR6P9RlpmqqhI2UUxCiI41iNLF5TRLJYt8M6A1EVM+v6wxhnTMVx7OUZmsDo84bDoT6PDbCyKj09RNdq4fZdA6Pwy6SRaJvisHYwCQTXwBlmYRgiz6Z7URBqe6PAGStl4UpRcZ4hQ9eiOPLWcavV0nFuNptepJj1MYbcutyvyDOm1SnbiB2M2FpnxMd1uRJlWS2dIVFWJSaDifYnO8ADKuQuDgmG+4dB6BnYeZbr84ui8AxMdTyQrsoOVE7lQTfE/deNYCofJht+LMMXxr+JP4o+ifedddujPpv/+u9OalsfzdmcZRmqKSO+tS6XrtlseXqhstaGgRfE4SLw0t/yDMm96/f7ei5ZW2muYBCEdHYFaiQx3DQMI70PYLXUhpRqkvIVnJLC5x3gmE7rv4khaNQQZP05JthyfQ/j9a3kJloLsjdCZXuty1Rg2ieFlrswJlDDuihKhe/WfWz0nuwwK2mu144YTL8vtU+KwtkIRVGQ7uDSANbW1rCwsKjXa65mPckvAOAzkT0GMk8+mctc5jKXucxlLnOZy1x2EgPY0P/3mcWv45Q5jeuLF6BbNb/1Pb6FvO9PDd73p7sP2Nh372XY//Un7vpz5vJvL8ZUAPCu3bj3rkQWsyzzoCciXNheYIlAHRWTSM+pU6c00lfXR3FkN3LPAwcOeJAvay1Z7iWFrt3n0Wjk1T1kIhqJMgixDFB7MtlzxsxMHK0rCld7iL34TErD0MUkSfS9uR4LewDLssT29rZHtiHv2+12d3wet5eZHKMo0uhMVVVeVIyjFhyNZEgcR2s4iZfrE7LHchbKKfeXGn9MEsRsaBzVZEiqXDMLx5DIEkdHGZLBnrbZv3E0kaOVi4uLHtGKREH5/Tg6I6QJch33b1mWXh1KJnDisZR7cRSUo35veMMblJXtrrvu8lhB2du2ubmp43z11VfjM5/5DIC6PinDluT6888/X6PzX/jCFzySJ4YdzcKmuX+5VplE2IIgIHYzBwWJokghjhw5E+QBzxn5m9Rok88cKWRSI3keE1FwNI89tUIqw+uRvcEeBI4YTDmiLnUZOQrO0cQgCPRd19fXUVWVxwIs+9B47GBWQlggIvuhsKnO9v8sZJYh97O1XGW/KctSyxWkaepF+WVtDYdDffaePXt0TjETHzM7z+XxJi7txFoLTI/nggrbCywRqNeHpmOMhhrpy7Nco13GGESBY9lUyJetgIoIkYh1kc8bhp4zZL8sS2X7FGIZYBqJJPSCgWOe5GgdP4OjURwdqqwjQYnCyEOpaMSQmC0rWyFLM4W3loU7H/k8DsPQ1T3MnY4QxZErLh8ais5YLypmAld7UBhfZfh0v7OVi9qGgUYcmTivKl20NAxdJLIqHeFMo9GoiXdCt6dqf1I7ZiGpigAKHWw5pMgSE/swKZZE8Cqc+bfABC5VJnTfN5Omd+7qfkrvx7DlwAQwgVE2XO7fylZeHUqNnJaFP5Ya6XFQVVhou6962lX40t1349mbLfzFw/8FTwnfiG0M9d2/U2ezmdYIra/JIZlKovNd+JnnAQC+eNVDei+nT+UaibS2hDGBRtjK0mpULUmapJu5aKS18PRep9OEM/qeO7MluiakMoVCiSON1tXjLfe1CALHYOqicKVHEgdCHzjSHUN6WU2Ux3qFI6kr9DdJkmjEEnCQe2FTlfcQqdlTHWS2jhQ6PUhJsIzRd60qq3pBWRZelL8ohDwo02e3220ic2zofXbT1bArxmKr5Ye7+YBginZRYrIsw1lnnQUAWtRUrmfDSISp+iXvh40VZgUV4SLUrEDxxsNlGAAoZb3kNTFzqQzaZDLxcgvlM0Nu2ViIokiVNC6SzrC50WiENE31b8wWySxUjJ9uNBo6ebitZ+RhECQ4iiIK1cdeW9jwFAOR+5YZb0ejkTdOjOmWcRNmUtlImLKYDwsu9t7v93XxMqSo2+1ic3NTx0yKiDOFtxghjCPnfNLZUioAvI1jbW3NU1p2yncVmKxsUMw8ybmU3A+SXwb4MFTOIWMlJ4oiZVI7fPgwjh075uWgsmEtENPbbruNitMaD+4thsCRI0c0T6HdbuPCCy8EABw/fhzf+MY3vHFhI0i+YygplyVhtt5ms6n90el0PMcGM2tyzmvNClf/jY33zc1NNcDKsvTmLecPy9piuLC8o7w3l5nhnEl2WgDuAOZ5v7y8rIZxp9PRPD5hTAb8nOGzzjrLyznmvWBlZUXXULPZ1DZNJhOv7WL0spHLxuKJEye8PG7pM8AvccLQ+k6nsyNT8PLyMj7/+c/rfaTPuewKKyZzeXyJETzmFCklBhQqaGkCnu9lWaLXreffdt+lFLDiakKnokRRpNA/2StN6YwVZgUV4X3aGONSIwLjDE8qwwBMIbGY5lrSfsmw0qIgA6gsEFrHCq6GKhkLkm4ifSBtjEKXE5nnOcqi1L8xW6QxRg2MqnLtbYQNl5M545QVaGVlK/0sZSWUyTMM1MAMo9AzPBWSSn0bx7H2eZ7nHvxS20cMisJMqhBaLqlCqSJZlqnBlaWZ57DT6xsG49FYx6zTrc/GwAQ6L6qy8vJfGcZaWb8YeqVGiGOHTUepM3pthTiI9b48lkVRaB5nnufOaLbOcWAr650fDNPfqZSRta40SxAEuPrpVyOzwMZfruPL2fvxlPCN6JvRt3U2r7ZrnebBR3k2t9stwE6mBlyoMMwgCL25KkZgzfQpjpsCzaZAPA2qqkRZipHn9OwkcfeaTMZ6vlaVg5UGQaj5hEHgHKZhGGjeHuAc9tvbg2mZmZb2m9sXZqGnDrYuMNRms0V8Eu78Z/hubZTX9+h2e6jLTohTwO0F9bp2wSK/HBDniQrnSKr5knWfCW/KcLrfQJ/vfttQI7IOLkl/NHym4Ewgwi21jzgYUbO1Ovtot2QOQ53LXOYyl7nMZS5zmctc/rVigE80v4yTOI3frv7Xb/s2v3JThV+5aWeW4rnM5TstuxJZ5HpM7CFnyCB7Qqqq8iBRzP4oEaTl5WUPwsqENkwaMxwO1eswGAzUW8+sgkzgwl5/TkTNskyJKKQWGxM8iFes0+l4DJNMisIwNoaYSjRiNBrpPRmWWxcXjb335Tpt8rytrS39PcP/giDQKEAQBAqz5YiDRFrZI8FQS+4HLhAu95glc2E2VIaOMkSUoy++18jBeplRbGVlRfuEoaecXN1sNj0SFfa4sUdQxk3+xoyWO9XHY+hREAQebJH7mSPIrVbLg+BwFF3mMRdG5zHjPmeoAkfXf+InfgK/9Eu/pH3CUJeqqnTMjTEe8dNFF10EoK6tKB7Lm266CR/5yEcA1An4UhyYE99lbEXY67++vu4VaWcvrMxJHqfZgsnSn71ez2P2jONY111VVfq8brfrRW0Z4svstFxnk2Ev0p/79u3zINRc75NhZAy/ZpZDTjhvNBo4fPgwgHof4GL2MsYCZ50tVC7C92VPO0OYZO5wH2xvb3vEDDyXGo2G9qEQ+khbZJzPPfdcbz3JPm2tVWbq06dP67suLi569UTn8viUKrLIp5xOMXn3PUIV2vtqz7ZDCvD5PRlPycdaDkEA6xPa2MpFz/LMrzfMqSMc3ZFoWZEXXvF1PkNlfwvDEEVZKCtoUbooV9yIXXF5IrhhpuuycvC/yro0gjzP9Z4WVmGnxtSMmUmY6N/0/A+MPm+STvT3chYBQGkcysQYg3TiE/TJOzEKSO4t13E/MNqJEUfSB7z38XkRBS4CHASBF31hVIwQxQBwfQmfWC4IHXQ0SzOFrSJyKIQwcMR8BqZmR1WSExdpNnDkQXxmlFWpkVMYV87FGMd4G0ahp4u1Yodwi6PYseFa66DADYPxpN7XojBSCLM3ZtTnTI7E0fWnXf003H37vbiiuEDP7H/t2SwRq5tvvvlRnc1cvzGgdRMEBmFY60g1S64Ypm69A1YjZEnS8Jg9gyDUttfpMA6JYJR8yCKOHaS1IHbaSEmaIrqnUQhrp9Px9B1rK0/vlQVcVVZ/U3+G9rPAW8MwVL230XBVFWrURKb355SW2m5xa47vK5/ra+u2TyZjjMeOjEcitWlakB7qz6UwdH0ohD7SlsGgHrfFxQUEgaAVM4pkWoUz1yl9ous0tZ93U3bFWJztEM47FOH8nH6/r0oJF+KeZTQUBajX63n5jlyQmiGpDBHlvMEsy3RBWWs9mBc/T4wQYXtkpZThkZw7xDBPh212i2P2M2/kDE/gMHNZll6eE29CAisUA1PaJPft9/v6/d69e/UdJC+RafzlGadPn/bKYsj3999/v+aTct7fcDj0IKUy9nv37vUKrDJ0kQ07ZsydTCaaV7W1teXB5tzCslglS04AACAASURBVJ5izXkizDTFsEQW6V/AN6BZgWEjcDZHy8upoTxchgKORiOd92wcjMdjvR9DM9lQ4RzJPM91fl544YV4wxvegPe9731nvEej0dA1xYbHpZdequNx/vnn6ztdeuml3hrktckHLjtAssxBj4bDod734MGDeoi1221dp2EYqvIl7LRAvX7lPv1+H0VREIyl8tYg5wIwg7F8v7CwoL/lXFt2FDBDcn2wGe8Q4gOY9xE5fDudjpcXyetPoOq8D7DxJYqX9NXS0pL2Axt4UtJD3mOnsjLMmMxsrQInZ/guG6tsVIpsb28rxHRlZcWHHVKup6yPe+65R/tWHA5zefxJugqcuCoHSr98hQc3JObPLM/QSAgyXrr8Pi4UrykMDecgTJIE48nYMzCNMgwWCj201hljZVmqwg4LfXaWZrCB1e8VFgiLMAj9kgl0rgi0lplDk5jKNlSBx/ApRsisUaAlJ2C8nDdbWYTxNGcqd2yhrD+IgSltEiMpzVLtg06748o8TOwZNP6sb2haSemcUBvrG2i1p3sO5f1leeZKSxhnJLU7bZSTUq9vNpte/iPvccpaWhQOKp9O9F0bsTu7FNYMIDQO7slMsWJUZ4WbZyIGzoiUsQN8g41hxHEYe79nWC/rFVzSJc9dvm1g3bmXF85BEDdi7TfOdxUILVCvn6RZ78HLy8s4dOWFuOTvz8Ef2/8Drwn/y7/6bG62Hqi/3//ozuY0TVHZOoev10vOyG2vqqouzTI1QuKYz0eXw5+mdZk2Lr1kFQrudDAA5AB3bUqShH4LcpI4VmRmSK7fzdA7Bt75z/uIGLqNhtO5fRZSi1bLldOTdmdZqhDYuv+gcFNOSWEDL00zYjqF5m1yDm+dfiMOLMeSKnmjcq9mM1FjNc8tja2BqIlpmqpO02q1iO3VqoHIetKpU6dcYGll9/IWd8VYLMvSU5CZREVekPOZZstJiKRpqgrNqVOnvBwbxl6zcl0no7rolxge6+vrXm0+UXxmo21yH6G8F9na2vJqIjJhiiiGXAORlVAmwuC+kXwtuYd4DbIsO0N55NwmjnjKprK2tqbtGQwG+uyFhQWPRIWja+12W6MWtWek7nuuT5hlmb7fxRdf7NV/4RqGTAAiBiU/T6Iz0pZ2u6193e121Xu2Z88er76l9Em/39e+kogWUG8gHMniaOz29rZnYHJOqCxyLvXA+SScGzpbq47nUbvd1nFqNBpqbPR6PX13rqHJUXfOO2Ojl3OGOELZ6XTw3Oc+V2swHj582Muf5MidyKlTpzyDTeoevvvd7/bGj/NoeI2yF3/WQyYbGhsejYar69RqtfT7LMt0/fT7fc8A5/wQzgGaJSXiaLA70FLyIDY8I1AMua2tLf1eiJLYkJR2cT9IbVUZAzkMO52O1x9K808GaBzH6qTodDoIAlcv1Fqrv+G1xfOeCXGMMd78dodO0xtnjvxx1HHPnj0eUZaszVOnTmkfLC4u4uGHHwZQrwfeS44dOwagXq/yW87rnMvjTKxPHCJKs7UWxpJDk3LCtOQBGZRF6Rw8o+HI23vUiDTTOn/G5dtx9EsdoH2Xx8vraDbaJm3icgJAbbiI0Tubjy7R07IsYcupwm9c9ILLQdjKat8Y4+o6xpFzzJRliTIlghy4EiI1QYeLeIoxxKWXWEdIElcuoygKF+2CgY2tRm753ZuJU2jLstRyIiurK95ZxzUMtR5iWahBWRSFRvMqW9XELdYZFTLmjUYDg2G9l7XbbUdqlznyujRLta8YSVZWJZLYXcPRZ0Zn8bM5x7LZanrnktZspNzQPMvdfLZQh4Ix07Ib07FlPoOkkei7l2Xpaita58ivSl8X8CK1VA5E7tOIGzjwhLPxJ/fdgf9x+Fw8qTwPXwsequ/7Lzybm9P+fLRnc43ac8ahrFNxoIuhycEIMW6yLNUyDEBtcDodI0ZVOe4MEWMCr76hdHpRlCgKtx5c+yo0m5JPOCGdIkJRpGoIBoHVaKQYdnUbnfOEc/oajVjJYIyBfm8Ml/9wHCWNRjztNxe5k9/UTlbHP8I5q25fMtpXWZbC1XuMQMPsRf7S1PVdfV/h2CjrXFMAw+GI9ogmtrbqgFCz2fL2EnYcy293k+BmnrM4l7nMZS5zmctc5jKXuTwK2QwHWA/6OICVb33xXObyGEtVk3i9fTfuvSuRRc7LsdYqVLLb7XqRH/busVdMPI4nT55UyNPi4qJ6ByT/Ta6fTCYaRRiNnJez1WrhkUce0et2gi5yUW5jjHp0GDaXZRna7bZ6JOI49jw8nFcllv/m5qYHv2VoLbOWco4cFzxPksQL1e9UeJz7bTwea189+OCDCiULgkA/nz59WiN17XYb4/FY+/rEiRPKSCvRRmmXREK4ALoxRn/LUR+OvJw+fVrfKY7jM5gr5fPGxoa2iwud79mzR/uKobEyJrOfrbUa0ZF3FmF22Th2GPbhcKheKv6ehfM+mNlUxowLB8v7Mh6dc0s4H4S9VExRn6ap9m2n01Fvp7T1da97HQDg1ltv9djb5Lpms+mNgXigjh07hr179+r37OGW9XPixAkv8slRf4a95nmuEb1Op6P9vb6+rnNhcXFR12mz2VQv6mwua6/X07Yzdf729rbHjCvv5BWXjiLtK2utF3GQ9s3mKALwmNJ2inYwnL2qXO5Es9n08g+l3zhXOggCnc/CkMvREhmD9fV1L8oo9wqCwIORivB8YXhqp9PB2tqa9zcZg4WFBQ8Of/z4cR0bjshKf0wmE5x77rna/wxHE080R3zn8viS5kngrE+FeOSaErCOBp5RNbA7l7iwlUNVDIdDNJN6rSTNRCNRnIYgcNHmFA6W545tMI5iDPqD6WWOYTIMXMkJLycL7nyMo9hbz3weh0GoUSdmZmW0xngy9uC3+tso1O+57EMBd84KhJHZ3pXl0zoGVK80SJEjqepzbHNzU6G1xhj9PJlMvLSFIi+0rwfDgTLSSn7dtFNcPiEVmodxazQwjlW1ETc0esmw+bARIggDLX8iZSeAej9m5IXsK+12WyOWDI2VMTnjs3XndNLwkQlF6aKqQejOxzxzXA9hEHpROe0Cyn1khleYKYonPDO/syxKb057zP0Eo5WxDINQ21dWDtYfN2KNMGd5DQO98sorkR8O8aGH34bLqzdgw2z/i8/mra36vl/72vajOpvLMkBV1lG7RqOn0MXRaIyiyHH7De/DwsIiotSdoQJJjaKQInvBNAdQIoU55eU5OGeepwrlrCpDDKgBnRVWf1v/3qFoHLJLIJ2u0H1F7MKM6JJ78ZnPEdyqqhSGmlGutDGuXE3NJWLonLfodCQta6xw0bIs9V7GGI0+OkguAATaVh+e2pgi2Rzi0OWEEhw+CFR3TZImJhMXkZV+LooCi4sL2v8OGuvSVqaz9xvYBdk1ghvOL+S8HKZfl8FfWVlR5YiVtL179ypV8AUXXLAjPE3yHZmYgpVKNupkUm5tbSk8NYoiLx9JDK7t7W0P8pWmqcIfObeRSSA2Nzf1vnHsDjSm5GcIJNP7c86gELtIe0ejkbZxY2ND2zEcDvV5a2treOihh/R6UQrH47Fes7i4qItpMpmg1+tpvzNRDOdlcZkRHkuGUzI5DxvZTEQy+5uNjQ2FKE4mEzVUNzY2PEVF7lWWpY7NToYiAM/AZgIeuU4WFJcp4DIHvPnmee4RyQgUsN1ue+VDkiTx8kB3yn/jHItOp+PlcUp7NzY2PKNVnpFlmRoeknt31VVXAQAuv/xy3HPPPdpe6VvO02QnhLVWDXHpB3k/MRzk+fI9GygK7YFvkHDZFc715HILDMtlp4oQOe1UE3NhYUH3ka2tLY9kSA7QhYUFb52w44DhHjKPpOwNOz2kLexQ4nwLJoJimBtD5hgeYq1Vh0ur1fJ+L5TuQE22c++992p/svOFHQQ8X0S47E4YhlhYWNB3CsPQWxecWyzfb2xsePkZ8q7GGDVma8KARO8xJ7b59yECbU8aiSM1S7Md9/ZWq+UMEso57LQ72Dw9JZ9bWnb17TiNJEtrY0jQhpVVw6qsXE08dt5O0omug6AKXD5SYDU/LE1d6YQ4ilEWpUsFSTO972QyUWNsPHGO0SB0uVAmMGr05HmOwhZ6jUA8OWewFbU8ghx2XI0nY0eQk7lc/9FopA7zPM9d3dQF55xKmi5XrCgKNJKG5m5yzrW11iN3kTXfCJyxb611KSxUM5ONbF7z+pspDHlMpRCKokC3V+/h4/HYjSWddbayaLTOhA57MOIohM2JzIuMMg8KSmUKytKvh6mfS0d8Zq3V8hhxFOv8iuO4rpspxB/Wr4XH5DzSlkbsyn4Zyr3jckdh4GCoXFJLcu8OnHUA6QFge+04vjr8Hfy++VvcXP7Wv+hsdqkn0aM6m2u9IkeeF9NyGc5hL+/E5RbqOn3iVHG5+kEQejqKvGf9G1f3ejJJvdqIYlglSeKtE8dF4CDtrCdL2ZuqEqPSze86R7L+FEWhEtEwEVRVuXnv5z7msNYR+HCdRP59Xe6kvqp2wJ7S/nSGqlEjLU3dO7EhHIauP5PETPVSGafAWxcu0ODuNR6P3f5koO9qDNBuO0JMgc/GcawG+m7KHIY6l7nMZS5zmctc5jKXuTxaMcDnuvfhQXMMr6tegrZt/ot+9tbfDPHW35w75Oby7UtgKgB4427ce1ciiwzV4iK5DHtkSm4AXkSHk3vZW8bXzHr919fXAdRePE3CznMvssEU9ByBYlIaeTZ7RYSgRiAD/X7fIyPhRFMhh6iqSiOOTJDRbDY9UhJmXBQRunvx/Ozdu1cjF0zI0ul0PFZY9ZwGgTJHfvKTn8SznvUsAHXEUtq9tramLIpyL4nirK6uqgcsz3PP48yRWo4AyrPTNCVvkCPDEM8Zk9RI21dXV7V/OFrK8E+OiiZJouPN8OA0TdWzWxdfdd4lZqFl0pjZ0hkc3WOPE3s7mSk2yzIvginPYGhOFEWeR1Hem8mYOLIrz5FrOILHUIvnP//5+MpXvqLP2KlYOreNP4ehK3GxtLSk84XXmTC3MdESw7HE+7y0tOSx9R08eFC/Z8IYGXuOwgVBoPNOninCfbu0tKQRdY54MekLQ6CZBZgJqWSuzkKJAb/UzmzpDIaLyByL41jfhb38/A7iueTSMtLveZ7jsssuA1CvR2ZbZsj8TmU3ZiHZXIomDEO9bm1tzaNtl8/Hjh3DxRdfDMBnHWaPMRMJcZSfo0dzeXyJQc1EKQyWup4LIo2A9bzfWrahKmELx24p0FMLVx6jrFyUSOb+aFyvESb0qkoH7W4kDSVz4bnPzJEmcOs0CiONcBVFAQOjDK5plnoslvJ9EAYegkCgkNZajZzyGSHlBQAoQybg0guU9bzjkCatJpVqaDh0SJIkHiOxnHXfeOgbWqam1WpphGs8qiNZwhAbNxxyo9Vu6V5SlZWnH0nEK4oiLwKo+0dZ6DWAIzeCrcdNoJZcFqPVdpFljpYy/JOjomEUYjxy55VC/MtCyYYkSqh9HTkWWmuprAWXziCilTByhD8G7hqGoVZlhbR0c0GuBaZzJnDjLP2ZIXPpEWHkIdl4T5f3DqNQxygIA2+/PO/88/CxE3+Hm3A2Lg8vwOerezErs2fzPx5zTO6P5mwW9Escx56+KWWfLvmjl9Vln274eP3eWa6RL9bdGg2DNPXPMpGasLD+XJfXcKWXhJSGSV+YfCbLUo2WMVJH5qrTu9wz/TPcoqokIltRFM4ohDUIQnoXo++XZe4d6v5zkb6a+VV03VKRb7VN4VA78t6z5f+YJZX7iVOPathroPd1egg8BObKSk0mV8N1hXgs9CLJDEN1bM0WAF4I4JfxGMuusaGKTCYTVSo5nM61yxoNF/7nz0EQ6ObEBuJk4qAqx44d8wzBIHDQlSRJ1JBj6AjgBpQNUoawcp3DZrOJ0WjkwRL9kL/LG2SIKE90ztVkxVEMSs7VWllZwfb2tqe4isxCUkRp3tra8jY0UTw3Nzdx5513AqjZTEXhE7p8adfW1pYyZXJ+IBtgzDw6m7ckSiiH0MuyVKPOGIPl5WV9d15AZVnq98wox6VFGAfOOX1cJ299fd3rAy4JwOMvea6AD09uNBqeQszwVMa5c94elwDh+cW1J2dZWnmDl7XBxhdvyrNGLh8KV199Nc477zwAwEMPPeRdpzWtjPE2U4Fvcr2n7e1tXHDBBQCAo0ePemyY/E48ZmxkMYNpt9vFvn37APhsuPzb5eVl7bOtrS10u12vvbJJF0Wh349GIw/SzMY7wzGln2dLVLAzZnNz04Mhi/D9GQpalqWup1arpXsKO3usdTWQ2BkhUCFWFnn9Sjv27t2r+yMrMJxTLRBaebbsecK8zBAjcbhwricbwKurq5rPvbq66jm8RE6cOOExWfMeO5fHp3DWFzsfqpSUqIpyCMMQmP4pDENY49a/Km9l5ZWGkO/7/T6SRqKGoDFGlfQwCmvDDrWhog0jtlYTuOttZb28NnbEsRPSWqvlKExglAG1yAsHVc0z7+xRgwtWDcSqqhTCWuTOuGi1Wh57M9cK5DIAsND8x0kx8fZ0gUZOxhMcPXoUALC6survp5VrVzpJdR9gJ6RngJWVwjFn16fsiUXu9ghbWTXqYGpDlxVRGX9bWeTWwZCV/TNoKNzXWHd9Wbi9rqI6eaPxyOuDVqulfT07/vKM2bJYClc0lQdPVUbfwrqyG1Xp1Xb0SnqQEzpLiVnXOoPSwuWBlZVjsLfWooKDXLORy7m+B886iMXlRaT9Aj9f/BiuC26p27vLZ7MxFm96WYo0b0711Ehz6RqNRs0vsFGnGTCTqtyq1XJ1FSeTdFpqSp5iPB1FvmfdXNpSX2MVAmtMAOnm2RIVjQaXFpt4Z7sI3z8MA312VQVqyEVRjDR1+mWlrLV+0ELaZG1tbDoIpxsPa52O0W53NMeyHjNnq4hwUCwIAq2/GARmqrdh2saIAisZ5Xq6siStVhuDQV8/u5IcLkI9GAzVKK/12WkqFHaPEXV+6s9lLnOZy1zmMpe5zGUuj6F8rP0lPBOX4c32h77ltW946QhveOnoW173z8kvvanCYtfiC//Y+9YXz2Uu/wrZlcgie9hWVlbUg8FRH657wyFmjrykaapeCva2R1GkdfmyLMPa2pp6PKSGDFDDyuS+HAHkiOPCwoIHwdopIVfYCcWr3+l0vPbK5yiKFDLALJa9Xs+DITJkU96p1Wqpp2J9fR2dTsd5tspSfwO4CC3XKrLWqmcqiiIcOnQIQF2LT8huHnjgATz5yU/W3zLD4b59+7woHHtJfC+QizjK806dOuWR/8g71SxSro5QGIZe0j/DLl3tGwdDYm+NPFPawUy1HPWR6K8QCTDMliG/Cm8hGATDHjl6nCSJ3rcsS0cqIDAq8dwWhUKM2u22EvIsLS3p+HOErdls6jxkVrBZ6KG0T0iP5Pmrq6t4xzveAQC4+eabdU0wTIq9j3Eca6QXgEaShZFUrpllhN0JZsvJ9bwG9u3b5/WHRAmZKKnZbGrfpmmqJFVAvU5lDfG837Nnj+dt5YL0DFvjyLX0P3twZT7vRMLEz+Z9KAxDL0Iqc1rGWvpcSCwYli3oCN7HJELXbrc9YiiJfjJ8mqF78n+gJqhh1tmiKLwotfTJ2WefrSRhTJy0sLCg+wLXnYzjWN+DIdrMvjqXfwdipuRL6tl2Y8sROa9uIbmti7LwIiTeWTlwTL6j0chFBYyL3HAR8cBQHbvIQUeTINHrg8hdU5WVFmw3xiDLHQt1I2547eV1p6zeDVd/L2kkHrrAg2wKDDF20LjxaIy4EWv0srIVmpE7myfTCERZlC6aZaGMsLwvbW1t6bmwsbGBvfv26rOZ4bDT6TjG1dKH3em4US25sij1eaPhyCP/0T27skrgE0cxTGAQGZeOwecuo3sY3urBEqdRXxin55VlqTDNKHYojKqskIWZB7NV1tq44RAytlLSnbJ00UcLR14WRk6PZAIlRo4B09q1uWOzl/qPTTS9mrYyZlEU6Txk4rNZ6KG0VfZcrTfZbuGFL3ghAOCP//rTeOfwDXgZvg8/iP/8Tc/m5zyxngu//Afjb/ts/h+ebfFXn2+gCgGgjmhJezudjiJmrAV6vQX9HbOTh4oSKJFlqbKpBoGDUFtbUeStrRDTOuru6j1K8ziFqWYkFXZR9w4yn3ciYfKfDY0sGhNQbUVDDK1uTTOSjHUVuZ73MYnQ8VkZx5HCU42BRiKrqvT6XyKO4/FYo531ezpdsv5N3T8LCz1sbtZnbRgGyHOXejQYSPWDgnQXlybHEG1G3e2m7BoM1Q9xu7wjgT7t37/fKyK6E4sh5wQtLi7qfY4cOeIZCMyoxLT8nU7Hgw8qrIOgldZavRe3VXLegFopZMggF/9miBnDFa11ZRxY+a+Zi+r7LiwsaD8x41a32/XyuIIg8AwrhuYyU6k8LwxDNd7i2BUUvuuuu5QWX2AtbLgq5TkJG9llWaoi2W639XnMzMjKNxcjB2oIzU6GGbcHcIrLZDLxDBJmi+OcLnYuMEX1ZDLxjBKZI6wcMMV5FEXan+1222ND+2blI0ajkZdTyHh1zu/jnDnpkyRJPBiyzCl2ctRUzXV7V1dXMR6PdQw2NzfVwPjJn/xJ/MIv/IL21TdT7BnWI2ux3W7jyJEjAICLLrrIg2jOwmZFmJ2WS0Uw/JaL03MuBACvyD3naw4GA31eq+VygBh+0+12iTnOQT65DAbD2eM41jFaXl5Gr9fTMRiPx57BJsIQeGstMcclnpEo82txcVHflSHk4/EY6+vr+nvek9gYY7ZedhzEcazPW1pa0jk8mUy8/uc8bGkDUO8xsraYcdlaq78fDAa6Znnu8Vh2Oh0vJ3Iuj0/JexbrT8hQFDWUT5TjZrOJ/hT61O10XU4Y7XcgFsOQcoKSZqKK/Pb2tsIs4yj2mEejwJWyiBuxwjTLqtScx6Is1IixsHovbiszY+ZZXcSd00LUQLDE2F1ZL0ePnUrMdKlGZJSoAVTkzjBuNBr1fjPVnBhWDjhjsyorNYYaiVtHJjC6z4SBK9Vx7NgxLCy6PZTvxaUlWALjHI+cUxzHsed8LcozlW9jjD4b8CH/bJhxewB3BhRFoTBUdiJUZaW/DYJAvy8Lx2wqqTRa1qKgshbBTKkg65wQTnmPdS4wRNgExpUuKSvkVa7zJwxCnWP8HmfkzE37JAojJL2p7lg43ZGdHAZubbRbbeSF49uYjCcKC770WU/Gh//7x/HjxXU4FOzH0eoU/jkJguDRnc2VRbPtSj1w6RNJ/2K9oqr8uZXnkpYTe2lPNXN8fU0UxWoIMgdB7VR0OpvALGujx+ke1joDSAyrZrOFJGkoy2dR5J7BJsIQeMCSYyDyjETJi2w2nUOIIeRFkWM8Hunv49g928uXLUs1oAXqK/eX53E6U1EUZAwbLwAibQBqXYL5L/idmlPmZ9YLeO4xlDqO3f13C4IKzGGoc5nLXOYyl7l8T0jRshieNY8Qz2Uu/5ayZYYYIcVeLH2nmzKXuXxbsiuRRY4Ospd8aWlJIVgMs2ACkCBw7ESTyUShjocPH/a83AqtyDKv4DdD1BgWGMexQv4A57VjT3pZll59IYlG5HnuFQ5n1tRer+dF2+ReW1tbSvQh0U/ARSmB2gvDcDqOonGkMEkSbRcXyk6SxIN5cgRwdbVmUzr33HOVMfOBBx6gUH4dKZXx6HQ6XrvE2zIajXYkr7HW6r327NmjfctRuMlkopDZxcVFLaAs7yH35TnSaDQ8j46M+XA41Hdlby57WMqy1Eib1O6T/k3TVPuQYQmtlmOX4+gXFyOeJSXiuop79uzxvNTyTpycvbCw4HnnHLTC+WoWFxc9khFXLwgaARKCIWbTlb81Gg286U1vAgD8+q//ugeRlXk0Syolwt62o0ePetdwBJgh3lxv0hijkWy5n7yHvHe329Uaj+PxWPvp1KlT3n5hrdV7DQYDby7JuzKUlqN+DB1m2PYsm2me59qWhYUFj+RJ5mG32/Xem6PMcj1HqIui0GgeR/myLMPq6qqHUlCGQCK4YlZYJo/q9Xq6hvr9vv6t2+16tVuZIEmeI2Mh/cnQYW67MQanTtXe7n379mnbRyMHYet2ux68eC6PX+FoBEPihYjGwpHEpFnqUBXEgFkUhUIdt7a2XEQ/bijpiqBuJMKSpqlG9xgWGAYh0tyhWhh9wpFBjrxL1K6syprZVGBpxJraSBpetE3ulU5SR9QWR/quWZ55ZD4hw+mm721hvUhhFEYeekXaEUahB/NkYpl2q16nC4sLOHmihr1vbGwo66hESmU84kbsMbxKJI0J6mzuxhIWeq92u61EQnmRuwhuVSCdFv5OmokXeR2ljqWR5wiTftnKKlQ5LVJ9V0MkITxfSlsqq6ygleR5ZUEEMnDMjnHk2FSZxTUvco0AzpISybywqPUbhj7qOUHF1Lk2M9d75PO3mTS1Dzn6LH0HTNl9CUIbBAGaiaube/XVV+PUsTFuO3wrntn8jzhuNs88m6f3fDRnszFAHEVU189HzAiLqPSX3Mvpuo6FfzgcAeDahRbNZj1367Vp9DcSSRuPud6wQ6HUTK5Ob3HX+LDqsqwwHvd1bOQ3k0lK7+6imvVgyJ7kIsCsfzH6iKN8dZpUW6OlWZZrxLOqAiSJY0xn5JxIkjSU+CZN3V5V688O0l2WTi+R59TPL2hsrEZeue2AmY6Dr6PXpEIOAeRIugADPIRdkF0xFlnhn1VQeGPl8gCiKDPrVZIkePDBBwHUA8bQLplwq6urZ9C6iwEkihjgK8qsVAVB4DEXMpRT3mFhYQHj8dgrcyAL6MiRIzrgnJ+xtLTk3ZdhhbIwGY+eZS7vQgxpOdDKsvRKejDERKnIq0rzw8ZjV1SXlfjV1VXcddddAICXv/zlCIJA3zfLMg9qIX3NCj8baUVR6PsNBgMq5LpzKQrpZ1F8hfEV8PM7YQCy9gAAIABJREFUsyzTd22329oHDN9kiAAbaKPRyNuIGP/OGHQ2KvLc0YEzrp7zSXmDYaVjNBphMBh4Cr/Su5PxP5k4hi92OnAfFkWxYy4rF7yX65illyG0ko/6rGc9C5/5zGf0GmFMPXLkiHcwMhxiJyjvLJyV10SWZbjwwgsBAOedd54HpxCjjuftYDDQtvIB3ev1vPzlKIo841vmy8LCghqJvDlyWYp2u+2VTpE+39zc1PFeX19Hs9nUvu73+zp/uAwOG7DM8MhjxnD0yWSiDKSz5U7G47F3qHNJHblXs9nUNZtljsJ9Mpl4UDPOBRf4T81Yx0WBwx0ZZbmkETtD+DA8ffq0B2+VfhPWWsA5L+by+JNoYtA7EWF0sGZvFMWZFeWyKGGD6b4SRqooB8ZBSsMoxOZGfc4aOFhYXuS6blrtGkbO54eWyEiISdA66CIrVcYYhV9aOIedCZzTKkkSFDnlF0aRKvPbW9tqxOR5rkZhs9lUaKaFVeijgVHYYhAE2saydDBJMaTF8KmsO2NCorbnPCJOYSlyx0DLSnyr3cKxY8cAAJdceokHEwzKwJUmKUvNLQwDKphOZQaqqtL3y7JMjSkPWlc5+KUxBmVVar6lML4C0xxCOHgqn6fiBGD4JgxgSgdh5nOBFW0+W4IwUGOQ2WzLyu3h1jqmU84n5TzKMAhhGs6QzrJMS6TM5q3LvGdOgEbi3pX7sKpcfl5ZOEbpIAw8eDBzBURRpFDZIAiwd99eDPcBxeY2frb/P+Enza+dcTYDzun97Z/N9VjunQZZFheXKG+zRJI00b/qMGrjRPRyv+C9O6cbMMaxinIgp87vc20UI7F2KIhu5mC6cRyrcWMJHj4eT9QgHI9HXspWmroqAvU5L/q0M+aryt2rTily84LLjMh5H4aRwl4FAivvbgwQxy5AILmQUeQcQjWjuJs76p+xztjj0jN1f+gwwZjAY5QFQbPlPfK8QFVJqodRKG9dBYJ1YEmrm5AjJERgirdhF2RXjMW5zGUuc5nLXOby3SWNbYOl+0KMDs6jw3OZy7+1bMQD7MfKt75wF+XUdV+qI2jZt752LnMR2RVjsWYDcjBBsbK3t7c9WKl4vzkaNB6Pqc7LRD8vLy/vyL4G1Ba6eL03NjY8BiyGujKJCkfCmPhGnsGRxCAI0Gg0PJIS8erv27dPowgMC6jD7q5YOBNZSDuYXIeLkUpdR47K7cTeyd6TTqej7WX2xksuuQSf/vSnAdTeHUmW/vrXv45nPvOZCj+rqkqjeEmSaEQoDEPvvSVSKdFb6Uv24jAUk5kfOQqXpqlXN1O8La1WS/tEng/4UdThcOhFP7gmHRdGZ5ISJqJJkkQJWcbjsVeji8ln+HuGXPI4cVRU2i/P5/nK8AH2ijFpiBZCnoHGMoSSE9bzPPeIj+SdfuzHfkyZLuM4xgMPPACghgsL22eapgpVXl5e1rG/5557Zrxi7t3Yc3rllVfikksu0XbImG9tbXnQNlnv7AFkwqbBYIBWq6XjORwOPTZdJqKRvuV7ZVnmIQU4CstMwzIPtra2PFIkY4wHxRYPHZNE8HrnZ8v/AZ/xlvus2+2eUdRZIKJcy3Mycd5B9q5ubGzoPiltl/6QdxoOhxgMBjoGcRxr1L7VaukYsHefyWt6vZ7HLszQbSbmYTjaXB6fYlHDCoUYRKJOw3SIVrNeX1tbWx5JmUhe5K6w+aTQz81WUz/DwKs9yDDx8Xjs6iZaH+rK0LuyctELub4qKkeWkpdehIMRD7xXdzodjRSewaYpxcJjP+1B4ZewruB6EGg74ij21pGxBpWZRh0rB9kLjCN3iRsOThkYV6twdXUVhw8fBlBHxba2axTF2toazj77bIym8DNrrYNwRg55ZQKjfWit1f1RorfSl9qHeeVBMRXmOt3rNJJalHpfrsUZR7H2ib4j/ChqlmUKv2Q9rdFoOEbXKYsoI5DkvmEUuvOtyB381EJhq2EU6ry1sF50NYCDOjbihgcZlShzVTmWVRh4LLtyvceAagJ9XhQ7xNAsMy2Mq61ZlT6Tu7xT/IyzcPnfnotPJ+/FTxx4r3c2P3jMsVM/mrN5z+oe/X1VuTmdphOKIEZoNh3JozFOv/FTZiKFmOa5Y9OtKqusp0WRKwspk+5w2lF9ndPZXcTRnXWTSarR0botIHZSRhO5CCevd362/B+o9RBXSUH/rAhIn23cMeNKO4qi8Mg4XVTUIfjS1JE31v0R6f04QhoETjePYzcGk4kj1OTUqiRpeOzCAm8tywJlKboxw3J3T3bFWDx9+rRCsoqiUChZp9NRhYqVMVbMWeG/9957cc455wDwi+cOh0OPnWwWGsZGlhgneZ570DAugLuTYRuGoQcTYWr6oih0AbPCxxDFXq+nSiEbkbPlOdggZahbu93Wv43H4x3hmAyPZKZRhs088YlP1LHgfMfbb78dF1xwAfburem619fXveLmjOvm/CZRKjlfghVXNlRXVla83CnOxeONhPMtt7e31ehJksRzCvB8EeGNYDAYeOUuqqpSWKExRjfQLMs0z7QoCu9A4/IOzEgpUlWVB3ucNTx2MgSrig5pYl+dLYkg7zWZTLx8Sb6ejduFhQWPOVbmSKfTwdveViMR3v3ud+szNjc3PceMvCszkLLBLHTgji0s1rF5ylOeouuj3W57uUEivE6OHz+u14vzRdqdpqmyvy0uLupv2LAuCofvHw6HXl4k56IKVLXX62nfNJtNzwEBwHNu7ATNrZnf3LNFGPLJSurGxoa2g5mUJceRx5b3HhnLdrvtzQfpx263q+PEjKRSjFz6s91ue7B5htzLXGcoP+c48v6ytbWlzx6Px7p+mNmYoe1zeXyJndLOi5Ik0MNG7JwHASljYRSiylxukyjNp9ZO6V5gK6tUeXnmDErJTRPjiNeLMcYZJ5UrscDsqXEUawF0NmxNYBAFfroB5zMKPNIY49pCBdqTJNGcvjpXzJXtEIOyrHyDlKFuceyMvzzP1ZArbKHGQhiGjrGTHIeVdfv/3j17vdQDaevhhw9jeWkZ7c50fY7GKAJnIHIBez4fVamsnK5U5A5CHxgHm2y1WtoHRVkzk3Khe4EkR7ErTZSmqWPWjELPKaAwYGJY5VSMLMvcPcP6nlKaAqaG4cp95CypqkrhomEYokwdLFANudCHtjLskdlYDdxc4DPVVu58E0eKXKPvVpX6XkVReKy6MsbigJE5miSJ5nTmhZsjphfh6IsLPO0zl+J3Dv8nPB9vAVCfzbe8X3gGvr2z+UNvq3DWqsVDg7PUeGNdV2CLyfGlOg/30FCfIdezM7woChRFiTyXHMKmlpCooalO1xGIaZblCpsMgpD0AYvRSJz1Tm+tzzEf5eByISsaj0qNzXqM3bNFGPIZEFR9PB7T/uCoeyXHMaSxdedrrPDbMIy9+SCw2UajQWyt7p3qvdXtHaz31uPnUpqcs985T8rSpesVRY5GQwJeE4Sh6EYF0tQZtlE07XNTAsAfArgOj7HM2VDnMpe5zGUuc5nLXOYyl12Womlx/3P6uGxyNm4pr39M7vmSq0u87Bkp/t87msirfz4GdP7vvQiH/u8XPCbPncv3juxKZHFpaUk9I+PxWCNbNYOP85gz+6R8zrJMvfsXXnihRiUZhpokiVd7hJPBe72eF00SOBZD2tiDUFWVFxGQyKfA4+QajrYxgQ8nSHP0k2sB8rO59hxH15gsQwrWi7efvRq9Xs9P5p9Kv9/3op3OM1Fg//79AOroh3iD8zzHJz7xCVx33XX6jsIQ2e12PYIVrnspfcvRUo6QMAyAoYMCk9kJQsuRN47CjEYjD4IpwtBTjvowcYkUe2fmUGl7q9XyoHYyHoAj72DGVPZYLiwseEXcGZYwmUx03nOUUYpTSx+yp5DfT6JfHBkOw9CLxnNkiyPFHKlnqOo111yD++67T8dspzqQHA2U67ivea4L9PTQoUM7wj+iKPJqrEq/LS4uegy7nMAfx85zZ4zRvoqiSMegZi6r51Kz2XQ13pJE1ziTx8zW/pRxPXHihFfongmuOOrLz5N7A3V0j1EMHI3nCDN7gLlPhNFWnsdMrBxRlXnMnnZmh2QSiiyrYS5y3enTp7V/BHkhfcVES8zqy+RPEom88sor8Td/8zf6vUTmzzrrLMzl8SnG1N71PK9r0Cm5V+miSRxBBBwsNChdZGp5eVnZNBmGakPriHLKEjAu6pQ0EiXLgXGQcWbi5DXIjOm2sshsvS9lWeZqMVpbs25KdJDW4f/P3pvGWpZd52Frn/GOb6r3qrqq2SOb3aSapChStAA2JcFypMiMFCS2A8lWBgkxDCeAoACZhAiBIRhxFBgWECQBbFg2YECQBdiBY0MO6Jg2NYRAIE6hm+xWN3uoLlZ3Ta/ee3c+4975ce5a61u3XlMTn6iK7vrTr2/de84+e++z915rfev7UDQdNRe990azMXZreFukZFmYXcO9issCOFOEeoR5lksmDPerCmBotVOiHe89jYbKNM5rRutbevPNN+mZ9z0jz8jvKsI5Y6fPGsWRZOoQcpukqm2JkMymVj1LCl0WV9avyAnEFLNXyBCLayju+bjmY9YHofVN21BVKnIjirTtSZrIPZqyMXsRw1uRMTUmzXDiuTBykdmDm6bR86YiDLvsZa1EOzKPAd7qnBIfBa/ZRxfpuTOOujnCvzfMuJHCCsmts7Ijoi996Bb9zP/75+lPh++iH2z/qz/w3vyDH/P0Kz9X0We/doVKyml3d5dUOzFAti2itm1kv/bSbz3I5tWSwYvjhOI4MgQwiGZpW94zgmQWE2BijWM8C5Cwi3Z7kmbemLRlPl9Q23pzFuDvddng9RMFL5lQvnZ33RTaHsvfXZZRv8vw2apqpU+652glc4rzviwLk1HVsqxAITBElOAslpgsKJJ2FkUB/aMEVUmiqIs8zyDbGoRQB8+kV65coddff10+Hwz0rHJRdiHOYhzH4pQgnHM2m5kaH4aeEZFZyPn7KHfQsRCtBVBBMJ1haOwILJdLs1jxQXLTgRJ8f1EYp5UnArKZMhsSOjrIjIrPgKyn6Ajy57PZTBZ+Zlkl6hYBrNsaDofGKUQqfmRQRTgMMivygTuEQB/84AeJqKtTxFqv119/nX7zN3+TiDq4KkNSX3rpJYGa7e7uGkYqFCTndhRFIQ4NUuwjvLiqKjo8PBTnHxkje70e0DUvTG0h12HN53OzgPLGirBc7IOyLDvWO2Bv4+9hv02nU1P3ydYtuHo/rHfFOjNk9iQiaft4PDbMqOgs4GEGHTyckwhVxE0VnxEdxEceecRAfvjzF154gV566SUiIvr1X/91s/Gjg4/3wHmOzMOXL1+mH/3RH5V+Y+ehrmt5/7A+DwMbVVVJ32Bd4eXLl42EBNbllWUp38UAxmQykQAUOmZt28r8xMAUOoSbcNQQglwL2Yyx7hqdOgwIIBvt/fv35Rlwc2D6dq4VxWABOnJxHMt3kiQx7xMyBeM7x/20u7trYLODwUCe9/bt20Z6BVlnseaR+/Cxxx6TOuaDgwNZf2ezmdQ+WmrvrT1sxpBiFzmRuViUC4qD1mLP5iozhcEtdjCaojGOHIuno2C6i7o9UKQb6lolKFwwQaUsBvmbRPdp59UR4XUJ2UyjKOoO+eDomGBqo8+AYurssCE0tqxKgcPmeW7qHdlRqeqKsjQzTiEGmJBBVWQjfCvtRfh4oCDyWsfHxya4eHJ6Qm+99RYRdXDV4aDbm+7du6dBs7xnZE0qECRnFtCmUYfLsEgnMcVeZQYGg4FAktNIaxORS6GuVOQ+iRMjIYSwUJG1CBAEaBWO3DatOdPgb9q2lb4ty1KcVoYW83OvQreOx1FMcaZOltTUruGiUptIJG3PM92zo0gDIJs1iwJDJqfzHqGKaSL3c86ZZ0QHcTQaGdZh/vzyM1fps/e/Sn/+G3+KMpfSP/7rXft+7K/3f19781/7T1r6zJf7dPj4h2nvET43M6zXC4soQ89D6Gp2A7SpLJnrQdd2LntgJ09rCruaOf5ulmXA/F7KvoKOmfcBJCO8QDk7PgeWktJz+HoUTNkE/6ZptMQHnToMCHR7Yzcnl8sCZDAic67zPgCHQCy1m+jIORcZngF8n3jCdAkhlZZSx6+3ZmbXoD47p/P5XOpB29aLU2gVBSrpw52dXVouu3agHF9VldTvqx9yUbaFoW5ta1vb2ta2trWtbW1rf4S2SiryLtDjdPkP9Pt+HujKXqDpcnuU39rF2oVJZ5zHfLiZ8eII2Y0bN4yYNjN2Nk0jGUpkLSpLFejs9XoGjtc0jdxvOBwaNkyETfLfo9HIRGs4MnF2dibZpMFgYCJ0COfCCATqSKKAdgjBQN04IoeEGqzHxtfHCAYWdGMWB59jb28PdFpq+XyxWND73/9+IiI6OjqSCMl4PKayLCWV/eyzz8p4PP300wJdxCJczOgiO2me5ybjyH0Qx1rgzKQfWASM30dNSrayLA1JCRLbIISV+w0zOtwPPH8Wi4XMPcw09Xo9E7Hm+yNZDmo0ImQvTVNaLpeGQRXhrRpd0iwqZhYNA+CGthLqkfI8RKZKvhZnfoqiMIK0/Pd0OqWf+ImfICKiN954Q7JX2E+b90YILGYKP/GJT0ibcSyzLJOMF74ns9lMMtTIIjoajSQSuVgsCPUGibS4fzAYGOgqQoTPg5HmeS4ZOczILpdL6cOjoyMDoer1eubdRDQBwssxu8eZyNlsJp8jzLYoChm/jjwgMf2GUWOEX3O/LZdLYUDFZ0Xx5CiKJNPHsCJsI0NJ+/2+QXawFUUh19pc2/Ad5Xssl0tp92Z2dmsPj60uB7rzgu/oPoD5cDPjxdm9yWQiWbgkSWg66ZA6m7q3nMFp2kYhkIyoAMifID2y1LBhis5ioplJhNwjwQ0yB3fZAdXHTWJ9byMXUYg168ew0goEtCmQajlClsIHL59HcWQyagi1NJlF3xo4JT836jL7VjUl66qW93wwHAjhTJ7l1LSN6FheunRJMlv7+/tC4hVHut+lPlW9QSiZSeKEatKMDY9l5CJlUvWBQNvcwEpRSzAA5rZptcwmSRJDbCOMt60yyrrIGQRO67U0p65qWtUruXcL+nZY3sC3r6pKnyOOLDNqq9nKuq6VcChRCG3b6HqexIm0sfWtIWdiw7kQJzFFQfVI8SwnxDfrayGSTfrEKxNvWZb0oQ9/iG6NSvr8V/82fZH+Aq1c9Xvem8fDmL70tys6nRP1dp7QLLMjgWzGcUxZpiisDqIcyAeFaGMZSUea2N2jrqt1htWeqYhYN1HPmwwlJSAJQhhpksRUVZzRS4hfDiRKHAyG5H1r5hXCUFtAE2jbvUBM27aV836XTe3+odfLibUfO2ZTRdehGgKRA/iuMygsRPApoZ6eVzvfQfVkOdPHhGLYRj5PJ4lqTzJZDbeRs6xmbUtigATrPeq6lqzkRdqFOItI45vnOR0fHxORZZjEWsHBYCD1cnmey8Hz5s2bcthEZtOiKAx1LkJJ8TC9Wq0MOymKA7MTcXx8LAcirBtAWQKGsPAzzWYzA0vDhQVrtLCmkBeOk5MTOfRif1RVZZzLXq8nDthisRDIH8ICkWUN6xoQkobP/cILL9BnP/tZIlJYJ3/v+vXr9NRTTxFRt2BwnePLL78sdWpnZ2dyj6tXr8qYbdaT8oEdJ3pd13KA5X5AljXsv/MO6eiodi+Hwp64nwaDgcHUo8g61oqdnZ1Jv2MNGpHdKNkQ7okCrSwtgIy0KH/A8wLZuFAKY7FYGEcVN0as28EFGgMSIQRxrFDihMcB+5SI6Gd/9meFJRU3CJzDVVU9UKv7yCOPEBHRM888Y2osz6tNxNoUZA3kNuLY8D24ro+om9PocPM4XblyxdQNoayF1F15L21FZxZrXxm2ynMGJWqQUfT09FTaMR6PZU7v7++bQAPWROPGwe/JJkQUAyir1cr0Cbf95s2bAgXF2kmGi3E/o2yKYe+DtmBtODqRCLPCwA3KkEynU/kOzk98X7b2cBlSrCdxYuqDz6sV3E13Vd4pTgRmOZ1OZV4is2nTNAo1XdeWcc0cOhRNrXsXwgLJ6dq1XC5FzqOplYWy9S25Zu1Erp1eOYBXpcLSfCOfE6mkR97LzZxnx2O1WlG+PvRif+CeRKH7N4Z81lUtTJ5NrfAz773KiQRgKl3XcRJ1IvD83I8/9ji9ef1NIlJYJ//+7OyM9ve6M0qcx1LneO/4npwL2qIVh3Y80nrkXr8n90viRNbNsHYYuD9xLVmtVsoKC7BElAbxQSGbaaqOautb1pbvakDX/dRLe1S1WnKRxqnWWPpWakCLotASoyw196cHt+buHq0yyku9bNM+wEiLay07b3VT62/qVuZqVVdmP+Z7R1Gkdaox1POuGYT5WoFAbitylMBRG51KHzwdP11Sb9qn99ARvR7dMnW377Y352lLv/2/trSqU/rsv+nRhz58ydRYsmPlXCLOn8BYXVe06YHNlp2pjg1Vzxfet2ZOK3Mo7jFD+X0Hp+z6pCyDOdNywBadWVv72tU5snwFStRg+UZRrCiO2bnNQSKrB85pAgkIrbuMIidOXa/HbPtO/o2fva4bqeN0LpG2T6dTgYJirWYIysgcRZFx+HHNDcHL/+d5Jk4eOpFEQcYGz6NYv9qdjdSp1q9dnIbGhWUWt7a1rW1ta1vb2ta2trWtvbtVPU/D0Ps9n/V/8a9WNF8R/auv9Cic50Vv7U+k+RBR5PwvXcS1L8RZ7Bh/NF2OwuoYJcHCVLblcinRsul0alj6MDqHMFCEjGFWEzMFy+XSZDNQdB6zmqhhh9BD/nduO0Ji+R57e3vnaruhll+e5yYTieykaEYgGGCs2C4szo/j2EB8+beoQfmhD32IPve5z8kzIFPqG2+8Qd/xHd9BRERPPfUUPfnkk0TUZVHfeecdIuqyJPx9hOxiBggjeAgL3d/fpyiKDCsoZpA4goLQWhxXnC8MPSLq5ghHXO7evStZW24LFjPj2GLGCzUiVcBWoc7Hx8dm7EVraq0piXBM1LTjsUXSJcxMIpQrBI3CYRE8ZjU5U47ZPY40IrwVs+JIzBPHMf3Ij/wIERH9yq/8CkT6nMzV0Wgk7T44OKBXXnlF5sVwOAQB3UL6DTP4i8XCQHkxko3jbSLtZAmuEMKO4uBMjuS9N5lINoSeYmaeiYG4b5B0C7WSkiSRTCFDwfl+5xHfZFlmMnFIzIPkMWmaSlYbM6eLxULuhwROCDVP01QQFcPh0KwpRjcOsrPY3iRJZE6iFmsURTL+x8fH8kxlWQrb6YsvvigZ1aefftowum3t4bT8hOjS5x3d+WjdMUZGmvHmv9u2lYzcphbsoM8i1KVkVRAqjayjkYs2IGNBxe0hU8DvJH9H1jenDNFxElNb6R6K0ENHTrOOpWY82qYl73Q/NtpuAPMKkeosYiZSSNQQ4uXWDKq8hwOMNc9z6RMXgJjE6foTCKCuXuGNl69cphvfuCH9772n4ahbM05PT4V8bn9vX2DwZVXKOtHLe/J9Fzkl14EMkPde+t9FTsh/+r2+gcfHkUI2m7aRDGeSKhQ0Ih1XnC9xpBDYsiwl07ZYLCRrK/3OSIwoln7Lc2WU5bHiPpTzIkCdl8uljn2reog+6shmYtKziCDFyLJVc19FUSTXQnIeCrA/Ba/fBz1SzpRz251TUhzfemrX6dbWt2b/4Padvreh59wefTL6EFXlv6HR+Jvvzc89cUpfeX1Fh0eX19dSAsamUW3NGjL4TVNRCESv/6X/q0MsQSYLxd69ZyZcy3aLaCQkcHTOUVkqczzumzqOqWS/kiQRspuOlZWZQz1FkROm067Ei89BimSK48S0iWGldd2A35GbTJz2TQtsqB1pDusYYua0qpRIEAmcLGFQLKQ2aZoZ6ChnK5MkXj8Xsgs/WKKH+zmWpyyXSzhbtzRez4s7d+7K2Wp/fx8IfxwR0b+iC7ALcRZRWgLhTlgThOlVXDgWi4VAsFB0HKFVeMArimJNv9uT+yGMESFqfHhEZtMQgqGyZ+vS8erUIcMRMxwS2cM4wiObpjGHaazjQxiX4syVrY2vxVaWCq1BGCo633iAy7LMOCRsURTRCy+8QEREv/Zrv0aXL1+WA3gcx/Tiiy8SUecUMvz3ypUrBrLLmxPCN9GB7RYO/XyTuhqdaTRkVkMHYJMJlMjWlmzWPqKQMjJlLpdL8xusJ0RoNAYR0KFBxwPncLfYdb/Hukh0PNExxWABwimJCLDsuhiuVivze5yHdV0bh4+vhdIby+VSHBrnnEilvPTSS/TKK6/IMyGTJ3//1VdfpSRJ6OMf/7j0Ox640CE57969Xs9I3CB0nJ9vb2+PFouFeU8RsstOHX6naRoZj+FwaGoTsQaL+xnnGtfzYjALnTdkz0UYGtaGcp+fnJzIM/X7fRk/Q5tfVQZu3u/3Daszv0+r1UqeY7FYyBzDNj322GO27gfWmjxXOA7Cd7DWc7MOmtdZrMNYrVbynh0fH8v35/O59CNKimzt4bK4dZQt1jBtCgoZDUEcF5fr3hxCEChfVVe0WEOwsLbVB29qvdi5akJD5BR+Gkjf++AtRI3r9aI4UtkGCsKCiTBrR06cHqK1E9SqE8oHdlMT7pWd1ASb6sbU8QlrKSV6D7dxXgHnsWkbC2mE4G0EdVLS/3GstYL4TM7R4489TkTdujscDkWaxEWO7t7pas17eU/ev9FwRHXe9dtyuRRYJ8I3Xa61nuTIjjHUkrYbtWJs6Nzi2s7jIPcI6rxHAGlki+JI+p8cGQe/rmsd8xhkmGILjZZSgkjv54MnXwH8kmGr63tI6UqlTNAu0rNdACgmBgta3z6w7xKt13auP6tb49giTLf1Gux3pGUaKL2BjN1tHqg4DDQ4yei3+3+Hvr/5L4jcu+/Ny+U75CJHV689uu6frve5jeiQ6Pm7u/fy6P56f9RzMjuGIRBa9JAkAAAgAElEQVTx9pXnPaqq2rynkci+tOLUdYydGqhkRzDL1EHEs99gEBmIJ1vblqYOsHMc+VxYw56tsFKEfzZNI32+Wq3kmbp2ahkKT2FOOum8T+U3XZCeg7eNPEdVqbSI99qm3d0d8z7jmbJzGPk3WlqFtZ6bddAMdW1bfD6tTcQEBJawXaRtKZS2trWtbW1rW9va1ra2tW+T3f/av0Nv3/hheqK4TH+u+eS3uzlbewjNuUBE9Gcu4toX4o4iucR8PteiaIDdTSYTE6HH7BVHT/I8N7p8mIVDbbxNZkYkWOEMRNu2pgAVMwKcDUJSEIwScGYQiSIwgsiRPtTsw+vleW4Klvn7mIbeZMBEBiYk10FyDmSLw/YSaSQMs7OLxYKee+45IupINL70pS/JPXd2doQB9YknnpDxyLJMniNNUxAqLQ0sDZ/vvL+5TZgJRXIXJPrBZ0IyF8zY4Bgrm9bAREHbtjWZPr7WrVu3JHOKUd+iKORzNMxkYjYwiiLDppllmfQbzjeED0ZRJG3fZDfDfsLsIRtnn5EJGMlkMBOGEEV+xtVqJXPnp37qp+jnfu7niMhCbpumkfePSYEYKsmEPnwtbvN8PjeZKX6vkXgK4WhxHBv9JITdYhb34OBA+md/f18yYUjMdHx8bKDHjBI4PT01DK04j5CU6OTkxMwrzPojWoFZZPf396U/QwhyD4zsrVYrMycnk4l8bzqdyu8RLoyZxc0sIffN2dmZYYTlObW3t2feNUROIDHN/fv35ffz+dywCAuJCRAUYUZ2OBzKuGA2fGsPqYX1ODIHC8DuirIw7zmiMwRNECeiy5fEmoXDrF+WZoZlE/WJffCSTfTBC1zROYVQponuuUgKYlg566b7LWQLEP6ZpAoNR4gpXy9OYumDpmmEBdYwxcaJZAM5kxiAgIKvm8RKfBPHMXnSbBsasohj5uvSYVd+88j0Ebp165b0SZ7nwoC6u7dryiDkOSKAETeaAWyaxjwf6hAKAc+6TZgJPY9srW2VLTQQ7MdBs4iOnCk9wNIRZAT1weve4JSYZj6bG4I1NtTGNhaUfdUDuYpzrmPTZN1DQJDgfEudakoiOidOFE4buUjGEPee1mvmkYmW+HtYQhNHSrTjg7Lhxoky6Td1Q8evPk9ERNl7Inr09tEDZS64N4fgKYkz2a/aVs9WTVNL5g4J8ZqmYx69+i+/m4IPdOuHviDPjSQvWg7jKQQvRCohaNYQy4V6vb5kwjrUUPd8HVO87iEM91ytVPewg37ye8lw2LD+3mrj/Kj9rdlOkn2p1+vDnAnCAov7e9PUQo4TRSmVZSHfa5oSMoIKF+7gpQgr1SwhZyI7zW1+1iCQVNX65nvG5t3ix1uttK86dB0j9SL5GxFxeN7IslTGJepg93+ZiP53+hbbhTiLy+XS1JrxQcl7LwfJs7Mz6fSDgwN6++23iaibhHhwRigJQhI266LQWcQ0L/6WO3gymSirFDihp6en5y5U+/v7Jo2OsNDNOkd0PHFSYKqc/0Y5j7ZtTe0UHhIRNkdE5t7oJPJiuFqt5BB6cnJi6vb4MP3Rj36UXn75ZQMT5HF65ZVXpDYRpUVw40DpjLZtpc4UDWs1sc6Qn4FfxtlsZmCz58H/EIcfRZF8n+s3iB6slcX6MBRs7ff7Bo6HEBPelNnp4+/IJgLw0qIoTO0e/oYZbblvN50Vfj62DjahGxKy+yIjGMKxUTYE6xdRJBfbvrnx/+RP/iQREf3SL2k9dFVV8i7u7e3RtWvXxLl1zknwBmspvPfy+eXLl40EDG9m6ITt7OzAJtfSwcGBPON8Ppc2YhDo+PjYzBEUxmUoJ9YMb/YTsoNu1s5iH/I9RqORjBM6ugj31o2gu8d5Mi+r1cqMJ64jWGe4s7MjTjnCSBGe4r03wQ+EpuOmihJFSZKoiHev90Cgg/sKa4D5XR6NRnKd2Wxm3v2tPaQWOjgar83MYorrUlEU4gz1+32azrp5mSapdbhgn+YDcHBB/mboOR+U4zgWZwMP2lgTVpSFOGxxog5QsSrE8cM9r9/rGwhl27TquMYKwcR9OolVTD2KVELCR+pE1HUt7a59rayXUSfVIPDDYNuDjiM6iQzFrRtlw8Y1P4ojYQS9evUq3Tu+J+1q25by9YH2/vF9YUPFsw6ydMY9dXR88FJniua9rb0LIYhzizIXZVUa2Kw4wD5Q2tf1GNcr7kMMNjWN1hlyvaTUF3ov45SkiQbJg8qlOedotdR9RfobAv8uclID2MkP6JnByG6FGByiVp8PILcIT60blXYJIRh2X+l/htnC98QJzZzO6Tg2NZJGgmX94/lhQ//92/8h/fAT30+fev1npB24N3/upT79he8P9MbZkuo2IecyqRskUsbPTi2g22uHwyHVdUP7L76XHBG9/W99ft3/tkxG5Sq84QHo9g7em5Xlszvv6xzhZ+qCQ8ocrud3ZQTtzvLd3zyfNcAcg9PkxKnE8quuFKj7PQb+kySBOj5bPqOMrlySojWTWJuIpUp6XvGw1yqjbAgB2u2EHVYhrDzmiQmgM5tqkiQPBDq6sdG2l2VJ/fW7jKV4newGz1eIzn2L7UKcxbt370pBNh5i8GBNpIOL5CxJksh38G8csOVyKX+PRiM6OzuTgR2NRqaGiQ8+ZVmae6BjxhNvNBoZR44XmNlsZjI6Ozs7cmhEkouzszP5G6PvvHDx/bit6HTytYi6TQQ1IsuyNEQfbJi9qmstyMVMHWc2iLqDOE/I5557jg4ODujmzZtEZB3SOI7p+vXrRET0Pd/zPcYpQEeVsxR37twxzhAT4ly7dk2ueXJyQvv7++agic48ardhO05PT+WZMONxXj0o6x5y+6IoMnWq6DTx/UIIRpqA7zGdTk0tK2ZXUYsP6w5MjUYUmWwN2+bc42thTSbq9KHOEuon8v/jQeW8TOZ4PBYnBDN9IQTR3/zEJz5Bn/nMZ+QZcH7O53MhSMEaO9TfxOzzfD4XgpTbt28bpwkLzpEwCAMHw+HQzDH+vN/vm8Msy91ghhtJcLIsM59j0MA5J3OX5wuPBz87HuRwPJxz4jwjxXlZlkYTltuRpqkh2sK1oK5rmZ+GYALqmvf29oz+Iv/NwRyibozv3LljCLFQx5LHD51QrJ1Exx+z8cPhUNo6nU7p8ccfl2fa2sNp3ZwORvqAyAbjiHQNdpEzhxsM2PHfSawZnLqupW4syzKT8c6yTO6JZDlN25h7iGPmtX4xyzLjyLGjUVZrcrT1MohrBqJJNtdUfE5+VgwKNW1jaicla1M3nUbkutaqiZpz138fvDp7kKXAgF2aplKXWFalOBGXDi91Tvp63UaH1EVO3udH3/OoOCQhaK1gnMTUW2cp5ou59BsF1Vodj8dyzdVqRf2eDdKzHAURCRkQ34eoc4qLldbYY0AZ60H53nGkhHhx0q2bkoWNLIpKngn0PpE4qSxLU8uK2VWeh1mUUetVygKzqBSR3COKoi7AQUSxi4UQKYoiCq0GLTFTy9f0QZ3cOMQPyLSwY51SqpnMWDOZWa6ZwaZpaPT4F4mI6Mx/hOLK0Xe//vi77s2/8A8dHe1n9IMfv0E//398kn7shXui1RfHkXkHlNClotFoLO8rf14UtZDadGPIQYous6XJhkycoDhO4HM9Q3foMT5PNcbZZ6e1I64BIqCE6yC7DGeeK4pLhgzezbpuxMFE3USCrHaXWWUCJk0aIBlPFMWGaAvXAu9bIOEhqRWMIpUl6fV6RhqO/x4OR8SvUp5nNJ8v4N0KhDqWfIZCJzRNE2oalRNhx997dUizLKXZTJNXiK66KNvWLG5ta1vb2ta2trWtbW1r3yY7+s5P09F3fpqIiCaXK0rqb3Y8d/Q//eN9+sqNK/TX/r3/m2LXfpPvbm1rf3i7kMwi1holSWKi7ChWjdAuzJghIyVCn/hvzLqFEOjo6MhAHDm6gKLZWAOI353NZqaGAFkvEdY3m81MnRVDNjGzgRk9rDtCVk5LJ5yYTBvWdIWgchkhBJOpxAgpMiJxJgz7E6l3Dw4O5Pm+/vWv0/ve9z66ceOGXBPhBpzJPD09NexdnD1xzhl4G1KfY93e4eGh9AdnaIksvBWzihh57SJhI+k3zF4i+yqKQyMsZz6fyz2m06nJ+vL3dnd3z436YqQIszBt2yp1+VryA3+DUGf+Htf+8b2V1ro2EVmNGmVmrmKtBtYzdrCEvozzedIgOBcODw8FZluWSrv+Az/wA/S1r31Nnu+9730vERF9+ctfpslkInNkMBhIRg/hjTy+fL87d+5I33KW6vLly9Ify+VSxpWfm687nU4N6+l59X1t25rIKfY/yqDwO1OWpfw2TdMH3lNkdUXILRteC9mTMRs/GAzkXUZICc4tHk9ek5AdmttP9CB8i8cJaxYWi4WsQXfu3CHvvak15fa/88470oaDgwNZi6tKo5qIqEBIWdM0BrbM99vaw2tNP9D0ciM14JzZimLN6PRyFXLH+jX+f6I1lJChZLHC77I0I58oRHMwHBj0A8LjhVUa2C3xu2Wl9VpJnEiNI8oSxHFMVVnJ/7deobVYu4drewx1R8jKaeCkwBOw/qJ57sY38oyYqRRRdh/kO23bSiYM+5OZmYm6PYaf7+T+CV06uKRM5RHAdwFNVKwKrYVrPeWDXJ4pAXibZEidyhf5oOtFXHV9iBlgZDHHPuFx3pT2QjF6ZF9NokTaJH3oHFWN3q8sS5v1XX+vl+s6HUex3LsJjWRhkzRRltvgFVrftJS6VNsL4vJlodJGrW8F/tu2+p0WWCsZeky0zorBXOXMJ5GtZ2xarX+t69rAofn3OBcGg4Gp++S58c335in9jV99in71v7xBKU2opANpbwLtUthlQ4vFvMuaQ6YOz9N1XZvyi7b11O8rwg33MS35UFk773VPjCInGcC29YBKcHCeamAvjzbYSWOK4560Rc/A+J4C02zQukhkVUWujSiKBDqKiAIeTzxzcwaR29+1yaIvGGbrXA/e60rqIOfzheHe6CCv3ffQ9+jY1BVBhvIqCvFtTBaU+yEELzWgF2kX4iyGoIvS8fGxTMThcCiHVXTGsIg+TVND1YsLkhQWQxE7Q13kgQC62jSNTLDBYGDo/bGW6rwXBWsO0XkisvVCCBPDtuNBCwun8SCJh8K2bc3hFguI+/2+ITxBIgusk8KXgyfbaDQytXN8GB6Px/TBD37Q6C6iFhySbXD/jsdjc/jH2jS2EII5tOJYYLv4Ofk3eEDFgzy3A/sAD7QdDl/hCvzb2WxmIIMIY4zj2BAfoWwHEidh/6MDy+PEBDVIeIIOG84fdIz5fv1+/1zNTHwfyrKU+ZJlmUCU+TdIDMRORZ7npi4Sa23xfWKnvigK+vEf/3EiIvrlX/5leuONN4iom1N1XZsaRh7PwWAgv0ftSaz7HY1G5zp4zjmZRyxvwwcj7GvWKCSywYxNODvbJnEOBnG4nxeLxQNrD4/naDTSA1QcG8eKLcsyEwTC+gx+pl6vJ0RJb731FoUQRMsVncPpdCpOMq4R/X5fPl8ul3T58mW5BxPtGNr1dQADa7p5Hbp06ZK8p/P53OiI8ucodYS1yLju9ft9gb/xgWVrD5+VI09nz7YUUzdfRYYpzWi56uYvOmNN3QjkM450jUJoJgVSGQaQ0YiSiEJjHTA8bGI9O0oLMJSwbbQswLdaAhEn5ztPRGuylLVzlPb0ulEcmf1DzFmtV3FCUD7CK6y7bVpKk1Tuj+tS65UAhusAiaiDrHI9YaRrdpapY437f5ZndPmy1V1MQQuO+zpOFL6f5VrDNBwOxfHnseuGSQ+trW/NWGC7iEjGAMlrsPYbiYGS1Ore8edZmhmZEdlDy4ocOSrXB+000cO8i5w4zcFb2Q4Z10ydwDRJxWHG4DkT1MgzBTIOmxD9tA3VrrtfUytJTJqkCod2AE8GqZW2UemMOI6pbdTxTCNLOMeyJnGiZ0eU0cAzVxzHFPUdxd7Rp/7ZM3Tvm+7N79Bi1dBytSKfqOwT78ddaQ3DTVeUpn2Bs+q4IieGws79Wt6GnZg0TcT5q+tKnJg4jkSOomnOl3FwDt99G8RhR6yq6jUsFaUi9OyjcO9IoKQYqMGEEJEj1YlUoiwMck8mZxQCUduydrFeqzuD9+RaUcRrVSIObJccGEqb9EzpwcHjAAbvzeqc9vsDqcnEs2fnQOu5cDOJxM+tQe7U+EAXZVsY6ta2trWtbW1rW9va1rb2x8B8Guj6Rxb03Bf26Nqdnd/1+3F0MaQmW9sa24VkFqfTqcmemEje2obDoUTJe72egZ5ipJCj31jc672X7ApLcPBvFgstJo3jWLIfnGnia3KUYzBQmEye5wZ6iEyHCJNFNkckfQkhSHYHiW+IrIC2wA0Anmpom9ewN46AIGRnkyEU+xajVJhl4KjKcrk0xENvvvkmfeADHyAiouvXrxuWRimuJzIZHcwmchYHM5zD4VDaenh4+AC8mJ9xOByazAiPZ5qmcm+EtxZFYcaJYYzIBIkELgwJQZgvZxl7vZ7JoiJDJGYfMUusNMqNaTcysDrnhFAJ2VdRmoDvy2MjkdoN6QzOUuH4MUkMFpPzGCBUbLFYyP8jGRNKtSDhTFEU9MwzzxAR0SOPPEIvv/yy6QOOmJ2cnEhWyXtviF6Y1AgJlTCLhuxdm8ydCD1BMqe6rg0p1XnMugjxeLd5iBkNXl+QJQ8zmfjOczs23038nO83Go3o+PiYiLoMPD/r7u6ugcGGEOSZcB3ZXN943ZpMJvLb2Wwma8LOzo6snzs7OwZxcHJyIvMMia8mkwk98sgjMuY8d85bT7kPed4eHR2Zdm/tIbXSUzojqkYdhA6hl2xplso7nyQJRc2aJCRSpksiJQlB0foQgmRXWIKDMzFVXUl2KHIRJXn3DlalSnigsH2aatYoTmIjqWGYDp0+Q57nkhULIQgzKgXS7M7GGUME3r1XBlMHWbkEso+uY7DkzGsURXo/H+RzzMJ1twfoG8N3vUI267qmwXC9rpCj07NTOjrs9uqzszNlN20tdB2lMDCbKJA/yHBmaSbkQwh75N/L+pqlKhXiG8lqxpESbyG8FZlO40TZwpvaynbws3L20RDqAPIKs6htpSRcLnUyBkIyY0hJvIUBwzmIHAmhkvfeZA253/i+Ml7A3sosqz5WZmsKJP3JZD4oD8JjgOQ6dVWTyxS1JWibVFk9QwgURzHNHm3pejujn/5Hn6RXPz6hzzzymXP35l/91zH9lR+9T18/OaSySU3ZUhQ5ms2U+bsoSppfOqbIKWNql5FTQhU2JS7ic2ULZx+QPgFoZxTFcPYkIZ+JYR6maSYQT9z3nIsoBgRAHGsms6rqc/f8JFF0j3MRpamS3WAJG+9dWRYL+2me9wwMlkjPWsgK6zbWNy250RK0qiolI4rlbJ1P4aVfu3MXy4a0co+iKGXfRbKxDl2Zynf43UrTTMZvOBzouTfEFLnmL9IF2IU4i6PRSDTRLl26JBAsIpU6QIcS6+rqujaLCB9osiyTARiNRuYgjgv/pmYbsh2iHt4mcyWRhVMul0s5FG6yluI1q6qS51gsFoa1FJk58WDGhtDaTe1GxGgjmyLqPSKE0nvF66NjhRBBhO+uViva29sTZ/G1116Ta6FW5XK5NM4GyhHgGPB1Z7OZgY4g3BBr3FBOAmFwKHeBh/GyLOXzEIK8pMiAuumQbNZVoFOJciL8TMiyugk3ROgv1q+hI4LskaiPhFqcuCmjlWVprsv1YQh75TZiv/PY7OzsGHpo/nu5XBqYLS9IKNWCTKw/9EM/RC+++KLpQ36+xWJhdEGxbpedZNy867o2mqI4D7H+FIMIzjmBqiOcpSgKhcxB/TJCM/DdQqg5vutpmprf82f8TDx3mfaf7bxaDYRP13Utz/fOO++YuYp1qrPZzNSTcv/0+31Z37z39Du/8zvyOTtyOK5f/OIX6dq1a0TUOaRVVZnf8/eOjo7k8/F4fG6Qi9vC/cnfqetaWFezLJN24Dq5tYfLhrOULn/J0asfO6P+oK/14oXutbjfYV2db71hfMQADDNbZplCDyOnshREto7PB0+k1QOmBhkP18jwyYYQ7KZuTJuwJKEOepaoK8tajcyceDDTxpI4Oka7MY4M/DxNU3LeybXYCUEIJcLCm6YxsEl+riSonEfddOUbh0ddvf/JyQnFQQPgqMGK0P4gUDeVd3CRk+tWZWWcGT1genFyuV2i8RhrKYj3Xtrr4DDeNvocnTSL1hniXoW1jKZ/KBinUhhXY5WTSJPUzDdZm5X0kqJYpSja0BpHJM00wNC0VsbDOPggmcFm9uxAImOCZ02itVwDO12R6nTmeU4JaXBBJFngrNvWLTmUyFqP39nVkm60M3ryKyP6ob96/t78v/2znI4uZfTjf/o6/cKn/2364Q/fII6RN00rcgtdcLqm1/9SR6KTRPo+KCNpEAcvBPvsznV6gPwc2D94duXO7ZhTdU5ijR06YmxRFJvfd9djTodYmFg7FngPv9OaQy/jF2DeKhR3NpvJ3yHYOtWq0ut2SQGu+02prvVsxkHhJEnFkUOn+tatd+Rs1Ovl67OW/p6/NxgMqaq60o48Vwcaz6rclu4eTvrN+5ZGIy1HYgf0Im0LQ93a1ra2ta1tbWtb29rW/pjZfLempI7IPQjQE/s7n75En3vtvfSzP/wvKNoyo27tAuxCMouDwUCi2QipY9IG/pwjFqenpwbahcyhGJ3jyDZmu/I8lywmkdWi2yReQVinaqIM5VrL5dKwEG0SnCCJB+rKMeMnCrxj9guhhFioi8Kh3nsT3UdGy9VqZaL93I/ee6M9x/A/jPqsViv5f8wgDYdDmkwm8vtnn31W4J9PPfWUfA+hb4vFQjJT+PlyuaQnnnjigXagJmGe5zSZTEyGDiO6SBojLHBA4IJ9hWyW0+nUwAjZmIWX9WdQy4/HgchGxVAvarFYSPuKojAkCPwM3Cb+HhJGoA5hFKnuEbah3++bbCIbkhUh8Q0X7GOxNsIHkRTHsn9pET5GO7mfO8Hb7vtPPvmkQE2vX79uIm/37983cFpmPX3iiSckE42Q6RCCEL30+32ZL3Vdy/fzPKc8z40mJepe8jhNJhNDfPRumXPMRPN1iqIwDKar1crAd/n30+nUwFP5uQ8ODuj27dtE9CC8jMcPEQZHR0dy/evXr0sfENm1jvuRyGo2rVYrev755+U73J/37t2jN998k4iIPvWpT9Fv/MZvEFE3V5988km5f6cl1f3m5OTEQNi5nxENMBwOBT6PxEdN08j7jhDW8wgMtvZwGGeZGLXBa0m/p6gPzLAUK9UnJEeGORR1CBl6itmuOIlpsdQ1OXiFhSZxIhmhQKFjrqT1erWO7mepaqXWdW3WMcw4BQoCGV0VK/kbCZpwHQwU7N/AdMkZNUTR4F4euQ4hwb9p6kZgqL711FuTYrRBzyFN01CxJuQwGYPalqRwNilLMyoLXcMvXbqkuqv7e3JvZIGtKyXnK6vSMHHu7e490I4QKZFQEifd50ycA0RGyPYaJzE1xbofEu0f7KvNkgIeJ8488/eLshAtSBc5gwrh50Z2UaOzVylUuWmVGdU5zaK62D2A/JDsV6T7oAvOnMHY0iQ12US2JEnkmVzkJKscKJiMbhql8k6URSnvTdsoUY93nqKgEO+3P/3z6372VCzW84UcVcNAxbCl/+Yf/gD906f/KXkXzt2b/9Fvfyd97/tepSwuabFgtvc9U2ajJC54Hk4FQtnpCzJcO6EkiYVspSOpYUSQns2LoqQQmOjNSYe1bS1MrFhy0zSlZO26DB5nxQI1jTLKO9eJ2BN1c0nPaTF5r8SHfJbArF13f0UYKnHhQK5/dnZm0G4dAZCiFLld3ZxmtFRDR0eX4R7dPFoslnR21mmCv+9976Pr198iIqKquk97e3tK4OQIsoMr+b33gULQDCLn8bIsFf1MoiAalPbsrxDWqIso/I9E9J/Rt9gubNfnRTrLMjlkzOdzQ9GOhzQ8oLDTglTx6ICi40jUHXbYIcXDo3PO1DLy38gKOZ/PZSDxkN7RBneDyk4AsgfyPaIoEpbAXq8nixAedNE5RQgk3iNNUyOqHkWRmbgo+I0Hdn6x0VG6dOmSqd3bhOfxdXZ2duS6u7u75pDO17179y4999xzRNQxOzLcEA+k/X6fTk9P5W+21Wolk7iqKhqPx3Ionc/nAklGCZEQgpFFwRoyTu13tMbanwh75fufnp7SaDSSfyvL0jgxyL6LEFE2hkfyvdEQvpemKWDunWkLvwPImIqQ0qIozpVO2TwYYdABGT/btjVSD0plrfBfhBXi5sn9zt/He3/v934vEXWODjLHhhDolVdeIaLuHXjyySelr9FhZgc9hCA1srPZzDDQsgPFC7lCQSrT3vPqe1H6BPsgjrVeBh0jrDlFtlT+Nx6nw8ND+RwDPPP53Mxrfg58PyeTiXw/TVN5vr29PZpMJqYGDJ0tlNbguTMejwWKG8cK5To9PaXHHnuMiIjefPNNcSjv3r1Ls9lM3qfZbGbWX5xLKMmCbM24JnEQYXd310B0EQa2tYfXoshJ7TOuz1LvFVToHOH/KPXyADSfnYFga7+yVEsDmrah2K3njiNTyyg1cq0yUlaVwiaTRBlFnXPiILAT0ED9Fbc9d8rkbQKzcNBFBtSmaQTu6Z1XpssoFgxWmnXspwivxfOGCK6nKn+EjtKgP1CoKhygiTrHha/DQTQioryXi1OIzKyLxYIuHXb78eRsQv3BWkKKnDiCaZLSqljJ32x1U5u1Ms9yYSetqkrWSZQQCaRwWuNwhyByJYGC6U9Tw7d+hmLV7Xv8b23TGvZVZN9FiCjJV2wgHk1qVNe/F2cTWHJ9sDJcuG+ydWLvOsbCvutbPSM6G4CIkPEzaL0lOW0nwn9d66hds2wGstBDlPCgmOi1j0/omc/v0L9c/QJ96Ykb9ATNEV4AACAASURBVB/Qf33u3vz2PaL7U0cH+xwoXRmHudfL6Tt+8ceIiOjtn/8XRNTV2zFM0nt1Ir0PFEXO7LVNg/IVPF/VqevOEiS/b1utndTEjweheU9xbGUpuH4S5dIGg4HUGoZA4lh1JR4wr2u+X0w8YYqilN9GkZYEdVwpJfwmMucj3BMZApvnmUBxnYvkukWxop2d7txzenpGly93557FYkFVVUKJmJZfYQlNJwHC7xP3LxHWXjrnhJk2z3vCuJoken5y3e+epAuwLQx1a1vb2ta2trWtbW1rW/tjaD4mevWjZ5Q3CX3o7Wvv+r3/9h88QS+89026Nr71R9i6rf1JsAvTWTyPHASFq+u6lsj/e9/7XiM8jSxGyPDJkQfMLHDUn7NOh4eHEhkfjUbye4QSYjQKGSUxysRwNb4fFiMjKQpmMIg0GoFtxwwJZhaKopDM3mq1kmwAk4/gtTDLidkRvu54PDbC6Px5r9c7F3rGUE7ut9FoJNmo3d1d0fPZ1JdEUeXzSGIwGrRYLGS8T09P6fDwUO7nnBaA43VxviDEE2HLi8XCsGTyXMMMYBeFKd41M8URZ8xQ49wry9JAYBDKi7BHzGzi/F6tVnKt+Xxuskmo5cnPgUyeTdOYOYUQSuc00oTXREbhwWBgslyYZVZh3Mj0Ld8bGXOZHRizfsx6+vTTT0tUc2dnR57p/v37Mk47Ozv0jW98Q/qfM18oDp+mqck6IjEUEhRghgP1UBHqjFlYAzuDLDH3HbLp8veKojARa4nuthpNxqzh8fGxKeznd3k8Hku2k98NfqamaeR7mGFldALfm/tjNptJ1h4JmPb39+nWrVsyrkh8gZAtzGpiBhf14ebzuVlz+Tr7+/syD5Ho6DwGza09PMbEDp1OumYHBRXhW8mQ7R/sC4uoc87oyrEhW3jbqtZgVXUwSc46DQYDgUFmWSbB86hR6ClC/rz3lCWQsWKCDArSvrZtO8F1ZlmNVTjeB091o4Q3CO2X+wCRDa7zSLRWN0qOE3zoROGRYIuznK1C6Fqv4vJ5lku2s2ka6Z8kSeQ5kIiGoZxMpILEbb28J+sBIhtcpNlWF+kegf3BLJtE62wuwwhXBQ0GA7mfI0AaxDo2rpsw687V7BfCluu2NsRpbf3gmYuc7QdknkU9XsxQI4FP22jf4nnBOSfX8cGbzKZhEa+VZAiJBYl0Xidxos8RbxD7wZxiqGmg0D0Pz+koIk+aMUNCPs4sx1FsssyPft/fIyKi25/7K9q3kSJASl/T9afO6Lk3jt51b37nHaJ/8sX30r/7sbepri8Z0rblckYheHlfp9OJDAjvK/1+XzQMoyimqqqpqpQ0UAkEFQnVNC0lCb+PoIcaK+w1AqhxN2bKmIpspERYItRSCA+eUSx5jUJPMWu4XC7h/OYoz7tzbJ5nsE92ZDp4PlIURaDhUM/TbJ1vw9qIJa1Wilzgd7/f79FsptDYLmuI59vuWmVZyLUwg9u1S9dQfr44VoRiv9+D7KxFjF2UXYiziIeuTQgmwxixTomdMaLugIkOCk+cKIoMbTNf/+DggJbLpXEK2SaTidwbD9O9Xs/AynCCYe0cH9JZcgDpnfmFQAkCIou35wE8PT0VJwmhjmmayqES2UEZTonMbgiRQOcP65TYsM87pisdZnxuFCcfj8eGmRXlGlBmgl/Y1WplngUlR3gzw82XnQIUWUVYGy7GKH2CThM66Aj94WtiLRs7kQj/QwF1NgxCYI3rJkMrbkg8j9gB5XmBcEdk7MP7YV3kfD6Xe65WK0PbjHBodFoRPljXKlqLDKObdOL8fFjXiKLYGHRAiHee5zSfz82hgCHi8/mc3n77bSKycFqkA0e492g0MoLwfE12ZlAiBzcVfqadnR1pI8KLiTSggfMcneTz5FvYWUXHGmu46loPPYPBwIwTP99wODRziv++ffu2cXjxv+Px2ASFGG6K7LkIZ75586bUhj777LMy11566SVxePM8pzRN5b27cuWKfA/Hr65rE6zhPt/f3z83SFKWSg2OgRhcl7b2cFkgDYpGPqI2qKQTSwskcfKAM0ak84xN63CcSAiESAOum3sY7hdlUYoTgvt5kiSGmVNq8mCtS2KVV3B1F3QUKYQoIu+0nhDhogKJhODtqliJk9T4Rk5EUaxs5G3bynV88EawnT+TZwTnT9Yow/iosFdHzjoqKbTVKUtnnqmIPI5H3dTirJh9oanN+yr9nCYCSUUnK4ojKqtS2oVBJRc5gcdinSrFZB1xlgYJljHVBBfAkWrbVuC5pk8ARopnKAN9jwmcBa91iqQBaK6vRdi97ImtSmzg/XCvrapKHfxa51cSQ82iU+c5os6hlIBGG+TZg1cnHaHHGJAsi5LSnS74V9e19IdvvantTdKkO3dk7743F8UhVXVN0+mMBgOF03a1fl7awM+aZRmUzzSw/nfOTCPj7IzTzzDIPE/EAcoyhWwSaUAD53kUqXzFefItDLXsAujqTCs8tZW/O+4LDsTovE/TTOoMnYvk7/l8DmuSM//NshzOUIGWSw6UNsSP3UFuu+9Pp1Oaz7vz5aVLl2Su3bt3DyTYEorjSJzK0UjPDLacxlOeZ3IP7vNer09aA9pIW5tG+Rn+qIK3Wxjq1ra2ta1tbWt/Amy129KND8y/3c3Y2ta29gewQERJE1Ea3j3PE4KjXtqQSdNvbWt/SLswghvOWFVVZVhFOeKO2oFZlsn3EVaGWTBkVUXylzRNTToef4fitSjeHkWRYW9kCCxq/CCDonPOaDsicynq1TnnzL0RpomsoJiZYnPOGSHP5XIJEaHEZIGQ0RLhcRht46go9w9fB6FkTdMY4XIUNP/IRz5CRERf/epX6aMf/SgRdYLtCF3kzARmYfb29iSq0uv1DHQXCW4Wi4XA8TDqhzqNqIdZ17XJHGEGj58PM4B8T4R2cl+tViu5N2ZxqqqSOdbr9TaYsVK5Dvdtr9cz83uTCAczgJhxRsNMFs/h2Wwmf+MYc9YVIS34b5h55fvNZjMhnOF+J+oyXFjYj7BxjFQh3LiqKrn3K6+8Qh/+8IeJiAxRRtu29J73vIeIuveQ743kGPh3WZaGnAkJfxBCg8+E8zbPc8Nsex7LLUIokyQxuqfT6dS8dzjH+G/UmkNobEcY8CAkHOEsd+/eNYRKRGQy1rimoU4iM52enZ0J+/FkMpHn5vlL1K2lZ2dn0te4HvZ6PfOOY6Sdr5EkiaxJZVnKuofkOr1eb8uC+v8D80mgds9RQus9Zf2qp4lm4nEvieNYiFHiKJYMImb/MCPvnFNG0TgiX3kltSElWWnahiK/fg9AvN05Z+DUDIH1XsXsm0aF4smtyzQgAyHZ8OCNtl8a670FRhrFhhVUWDIxHeiUpIbhiZKRishkgYRMKoFMWhzJ9ZCcDSGehlwjrJEfjWZfZJ/PUiEMvHv3Ll29epWIOrSFZJkaLxlO36oAPe7HSZKohmUUU5Zn0tdVrSSBmAlDnUbUw2x9K2MpY8X9ERThgn0aRxv6uOu+qivVq01I24jnrCRJZI5xP/J18KyD8/uB7AtDlVtvMs5oPHdarwRwZaXIiyRW0iVP3hDeRHGkTKmQcaSg2qFlVQojLBFRQOLDdcbfB5gH5Gg+rGg5rOk3T/4Wfbz3n1MVMSRS9+Z/8H9O6N//UxG5UFJROMoyhVbu7Ox0c805ynMtv2JWzqYJAh1t20bIX4hY8/BB3cMQvPzdneVa+Q5nHxExhiy3CKHsyJT0jNppT9dwLcxO8v1aaholg7HairxGecl8diSBqhtNFCQzSYRncrumoU4iM512OsmsTVtIH+Jez6z4vZ6OAZakNfCOe2H11fMCMum3rTLHdrBenesPLQw1z3OBTmE94Wq1EmdqMpnIAfPVV1+Vv8tSaWBDCHLQwYPV0dGR/F3XNQ2HQ/keUtjv7OwYllQeJBT4XiwWAgtDR47lMoi6zQjlCJBSHuscq6oSBwrr3zblGfhQuLu7a+ooMT2NLIYoxB5CkHsPh0OBHqRpKnVPo9HIODp4AOeJt1wuqSxL4zRzG1erlRw867qWzfvg4MBAJfk5EDqIB2N0bHd3d00dHzqxuBGgLAYe8tERrOtanE50phBaUxTFA5AphLfimPH4I2Rqk4kT5yHOA3RIEV7ZNI307WAwMAcgnmOb84L7CgMpuFgwLBDHk79XFIVhZUUHmvtqb29P5gvKdiRJIn1w9+5dI/OBsFsMWty8eVPe6+/7vu+TeX/p0iVxbpqmkTlZVZXULGI97v3792l/f99I5OA7gfXO6AxzH1RVde64Jkkiv8X1gdn9+P+Hw6G5Lr6zGMDiPiiKQqRyTk5OpD9Ho5H0Ia5bXJN4nrzKW2+9JX118+ZNkQD60pe+JN/p9XrSb+gEInvx7du36dKlS2b8ee5j0Inbxs+NwS/ucwyKOefkmZbLpVyT59PWHj5zkROnIEkTW5e3duSKsjA1yBKAaWHtCSSHWHQWB0OAdbe+k79oFdKKslb8vba0LJQSjKkAyp9AkCxJ5ZAex7GVIwhKKR+qYN5tgYg6rS3blGeo14fTvJeLExiCMnzyM4RGyx3YqQgU5N5ZmumaE+uan2UZhVidks3aT6L1vtk2Zs+Q+rC6oZwPnq2+t/1+XyVAkpgIkHbiBAHTKDq2eS83dXxxpHWY6HCjLEaAQ773CpX0rZf5hQ4zQjabxsKDIxeRi9d7LdRbYmkFwn6lPpCIggtmHrLj5703tYZt0xIG884LpiOPwua8wMA/ngvkrNK0VHsNClOLNX0QxHYA2fVeGGh7vZ58B2U7MDGyWCzIk6ebVyf07PEh7bkR3VjdkrHh33ztlVv0H/+t99Ev/fRb9NbsA7QqWyJyNBj0qd/vSdkPJjw46OmB7XW5XFG/3wNY6aboPa3vnQpMs5PUUMg0/zbCOtooIp4MeZ6QitHH6/o+lq/T63b3xdIahrfGcF6pZb9CuTmUq+vqtbvv8/55nrzK2dlEHMEuoNz5Ebdu3TJnNu03PavkeU+kQebzOQ0GfZgzCt/t7sdzsutTog5Cq8yotUBoO+4SL9fhZ+qSEet3kRzFFP41XYBtYahb29rWtra1rf0JsGwW0ZW3Br/7F7e2ta39sbO8jOk7fucy/cXh/0D33fRdv3e2SOgv/y9P0PNXb9CTl+6bf7v9g1+k2z/4xYtu6ta+DRY63c6/exHXvpDMIkbLNjXpOAMxGAwM+yZCwRBKgFqFqAm1yfyJ2RCE/7GhniIyfxZFYeA3qodiM1Oo85hlKhbc7/dNpAHhdahvxn+fnJxIRqYoCpPxQIIbzE4g46aB2YAwJ/YJwjqRGGczw9Xrnc+sFkKQ/3//+98vzKjvec975PcIEcVC9MViIe175JFH5P7z+Zx2dnbOZclFptLFYmEggzh/+LeY5UWmyTzPzTWn06lkZcqyNFFYNsz0cpaa+wcJUhACg/2EUUPMDmL2i0gZTRHyibqeyMqGWcIsywxjKs4LzAYjXAijokR0LrMqwkKQaKnX6wnpCmeGkfwG+5eF6l977TW6cuUKEXVkKZj1Z9skDOK2HhwcUK/XE8IZhH9vkq1g2yUqDlDVuq4NWRV/HwltWJuU50UHJVHIPOqectYQGXdHo5FkS5G5dTabCXkXQqabpqHFYmEyxZjNZ0bT27dv0+c//3npW4xk8zx65plnDFkRr4ePP/64YXWu61ravlqtjD4sZ0WRfRkhzJPJRD4fj8fybvT7fbk+9+XWHj5LC0d79zO688RSyUqoyyYxYQmSQ+W9XLNtSW6ISFCrEDX7OHvVNB28FMm2EP7HhnqKyPyJxDVxFJt3CmGIqPOIe22apCY7xBkohKFiJmu1ArKbpjEwULmfJ6JIs6pN00g/bmoPGgh9cg7BidcM42aGK0mUjAazcIGULOXw8FD2752dHUO2hbBXIW2pK2nfaDSS+/PZoV2pLqDAgoOTttdVbdYx0cAkzWq1TasQ3aB7QJxomxx1SB1eT5q2oVCvM7UAb46iSMbVt15JjZyTeYF/IxSPx4LvidlB1NkkUkZTHyyMlecq6hDjfogw/TiKiRwZoifZNzET6oOB43K2m4jIwV6OEGFEAEWLmlZZRZ8JX6Sm/uZ7820i+udfOKAf+NApee+p1+sIp46ff7XLUK1vjSz53V7Qfd7v99dnWkVCMQwSyVaiqKUsU91KZQpOJHPWtl6ybR35n64XTGiTZSnVtepsd/dTllzOaiZJfG5pRZZlpsSrXjPxlmWX3eN2YEa8rivTb3g+ZkbTjszvnfV3kFVZNUgPDg6UrKgsKMtUvxxJBr1vpe113QijaRzHgHBrjA4oj0dRlPJ5luUCjU2S9I+kROTC7oB1dQhxY3jjbDaTgb1y5QrdvHlTvs8Pvllvh3h7dAiLopAD0Xg8lgFHyFgIQQ5pmJbGFPymDAbCrkajkXE8EO7Ig5+mqRxQse3ee8OOyPfI8/xcxs2OvtgyEWI/4GGcnaxer2dq2VBOgK97//59I53QNI1IIbBcAvch//7xxx+nr3zlK0TUsTGenJwQUQdp5H4eDodygEah2LIspa17e3t0enoKqfr83JpOdATKsjR9hSKpvEkeHBzI5x02XCmgl8ul/B4P/9gnRMqmicEMhEN67+VgjnIcvFDhxoMLER+gvPfnjnm3AGpN5XlO8nK5NPWcOPcRTo0ONJFl4cR5gYabKwcwbt26JX3L9ZjnwW6cc/K9N954Q/pnNpsZaREeD5R/uXz5sryL9+7dM0zDfF/+L76n/DkGTNDBJyLDfovQbe6b6XRq5GS89/Ic2EdVVdGjjz5KRBZuulqt6PLly9L/2H+45jFUc7Va0Xg8NrIm/G8YAAshyDs4nU7l+88//7yhR+f1E523EDr6b34HB4PBA8yw/Dk63CqCrIENrNXFOipcYzfn0dYeLgvrmrg4iQ3EjeGNVan1T6PhiKbtOmgSa3AqipXBEh2HKEQKs2s7+QJ2QvNMA8FN0yhsm1RqK45jwzwqNfY+WMmCOJO2ZlmmezjIKqDDF2exOBtRGxmIaVl3e48jJ/dI4sQcoPmaaZJS0zbGKQxO+wEhuOxkYd031khGLpLrLldLAyP33ss6MRzo+pZnufx+d3eXbt/pAnaXLl2S97PX6+kBOtUDdOQieb62saUfxaqQtiSx7jFRrE42OgJN24ijEydaf+ico7JQzgmE9cu5bl0fy22Jo5hW9ZqlFSREKtL1P4ojuV+gYMYP69TFIXUdEysGRIxjFevf54158Db4ik4yXxNr2ctqXYICdbVytmsb0w5VDLESJfMb3/VAW4lIAhiz+YxC2bEI/1735pMTdYqqqpR6vQDvVgdLVb4KfhcXi6WRLCPS5ENXEhHLtbR0hKAdXpxIIgKZCBWzD0HZRcuyXAfcdd9mFtGunXxuCrSzo6VtWiJUG+Z+6b9cS7o6R7OS72dZDvt2Kv+GAbAQiIbDgbSRv3909CiwmbZSl5imCey/HXSX38E0TS0DcqxqAexAI0tuNy66jvD3MdnSNOp0rifgU0T0ZfoW25axYGtb29rWtra1rW1ta1v7Ntn9F3+EiIhAzeMPbXVLNOrVhMyoBy8+QyF4mn7X9W/djbb2x8KiTjbobxDRn/1WX/tCnMXlcinQLiy8HQwG5zI5Ygo2yzKJ4g+HQ8MsdB68jbXqkIwC2TTZsiyTKD6RRh7w89VqJb/t9XqSpRgOh4YwA7M1Bk7RNKZtGMni9iGJBkbqhsOh3HuxWFC/3z+38BoJPWazmfQJZhow0rrJQoVFv9PpVCCHjz32mCFx4UwTwmNRvBtFwKfTqUQ1T09PZczu3Lkjz82ZZI78YFQuilQgPssyA619N/H0TX0ebtPx8bFcp21bA6NDch+8LkIPmCEyjjXqjmQ+CEmp65r29vbOJdhBmC73/abheGBWE+czihQT2agTFuqjVqXRpCKFzm5CLhEmzfNoPB4LvFTIHM4hasJ+ePPNN4UBNUkSeX+RlGZ3d1c+v3fvntyvbVs6Ojo6VzPo7OzMQNiQeOU8IhsiMvMedSdRMw3fQcz6D4dD6euiKIyYPc+LEIJAR5fLpSHv4nufnZ2Z6yyXS1nfFouFaQs/0wc+8AFp740bNwysntkPB4OB3CNNU2n3ZDIx62ySJBJZxsj77u6uXBfbizBbzMJiZreDMSlEf2sPpwXqsjdJ2mUJma0SibqiOKJ4Hc12kZIlITFUmqUCNyUik1FjY606Js6J4siwabKhXjARmXVJska1CqwnSSLQ2DTrIvUo8I6kIwr/8gb2Kdp1IUj7fFCYbCBlsEyzVDJDVV1RmqRGqB5LPriNZVVSVK/LUyDTEMcKp0W4YQhK1ML9vFrrvO3u7Eo20XtPmeixOcnCVlWlZDIgAl6WpWTOVsVKxmy+mMtzl1VpGGZ965XsJigLJZLEIRqhbmqzRyDMUrJMkZP1Ko47Ah2GmKaJzr26rs2Y4xlIGCIdzKMklrngg8JOW79GtShRpvxbGqdWYu8chYkQgvQ5QrSTOKE408y32dcDCQtIaIH11mm23WQYyTJj83i0pPuTi5xCwrOcTlen1K6JjX4ve/Mv/soJ/blPjimPZhRFR1SWJV39zMfIuYgmH7lORES9Xi7v9WKxBESOp8FgCHuydlRRFOYci8Qr5xHZdKyjMO9Bd1IRXF0WTslygrQlyyyzPWcpnYvARwgCHe1Qd+v5WWtWu64Lcx3Uw66qGjKkyup6dHQoGb3JZGLOJHyO7PQeGbWn8OSyLNaZVO0T1m8kCuBv5PAONNTv89qaEFEr33fADsx9GMfBIIkuyi6MDZUPWrPZTOCbeMAhsixEKJeBjiPitXkS9vt9gUOyAypwgHUqm40H4ODgQBwjhDru7OwIJA7FqZ1zps4sTVMjxs0QkYODA6nhwcMVwuAQbohMl0RkDm/427JUfDLC5tAhRYeNaxD5O9z/6JBirSY7X9x2FCs1dRUAdUCmRKxrjKJIWB2x5gTrPrmmCiG/uNByn6DTtOkQYm0aLxBvv/22vLB37941lN9EZBYG7uuzszNTZ8iH39FodG6tp/defotsr7u7u0ZMHWtscY5hbQMK1S8WC3MQY8eDSOmX8d3Y2dkxCyhKoaDTY5gGwdlEOBQRGcgt9/9yuRRnERl9eTzOc3qjKBI482w2M2yZ3I/T6VSeo9frCZSTAxA4X6XGBmoIcWPEdqHzhQEePHQgqycy7PF1sc6U5xjK7iAUxzknbb99+7Zh7uP+PDo6kjrfvb09Ojk5MbB5rm187bXX5B7D4ZA+9rGPERHR1atXZU7iHMF2Y91tmqZ0//59897ge8ZzBGU/NgNYzF6NgRtkih6Px/IM/NnWHj7DpEVVVjJPojg6N9DpW6/SEiEx6xUyNvI5MkkTA1f2Xh2PtrESV7ifs2OEe0+e5/Ju93t9FUMnJ45N8KGToFivS1maCcNkv9+nolTGR3asoihSqQ2AGyLTJRHsHbXWSEZRRE2rbJ5Jkogj4b0yh0YuMmsGrtPs3AQK0m+4b7LzxW2vqkqd8IiMQ8sDmmWZfieonIRzjvq9vo6ZVxiwwH0rb/aS2tfGgZKyi1hhrJsOoUhceK3PnE1nZq/jzzflLHCvLIrC1hk2ygR5Xq1nCFAnCofvvJdT27TGSUOeA9lLYJzaRtf/qq6kf1zkBApKRPIcrW/Fwc/znCgoa6uPvFlrsXaTxwmdzSiKKN3p9tBqctVAbiU43da0Wq7I+/b3vDcvipj+o795SH/vZ96hJV2iWRmtYbUkNW8dtFIDjVjrTxRANoKEARVrCDumU20HD3BdtyaAyddpW01gIKtnx6SKsGGoM/VB4Krc1u5zLxBaIj2zd+cKkusytHUwGEoglc/VyrjaSm0j+xdEHTvp1avXiIhoNNJyk7qupL2xqaFW/yOOO+eQ7+9cH9aCAOuhg7UnCNtrCJ4Wi7n0rfpM6mt00ii8Nl6cbdlQt7a1rW1ta1vb2ta2trVvkz36/X+fHv3+v/8tv+6iiOk//Z+v0WN7d+mJg/u/+w+2trVz7MLYUDmSMh6PDZkEZ7Kcc0I+0zEiKeOSRh2UcS2OY4mCrlYrIwLe7/flHgg3RBFahFM2TUPXrnWRguVyKZlPZJdEIXXO8mG2jbNAyEIax7E8BzIMIssqEuhgFBOhYESWFXa5XBqCnPOyqGVZakE8QFoRWnt8fKyR5CgycEeEGCEZyf379+n5558noi6Tgn3C/TybzeRZxuOxyZAigQJmgJEACLPN/F2iLnPH88V7LwQe/X5f+nlvb0/+Rs28PM8pTVOJEE2nU0NKxNmRpmnO1cZEaOZwODQQZP4tM5jibzBSiAQJ57Gp4RwhIpO5wawkMnw65yRai6QjCNNBGEocxyZSiFBOzJZicTzDLJkECSHCOFYIHWX47/Xr1yXLjJDpXq9n2Gz53p0O0UDGFvu6KBQyMhgMZC4dHR0ZghuMXvL7u7+/L/20CSO+f/++/P7atWvyfJcuXZK5dPPmTXmfkKAIYe5RFMkaNplMDBMyrynMyspjiM80Ho+lPxF+/eijj0r0E+F6SZLI+3B6eirfHwwGdPnyZfrGN74hfcptYX1TbjtqSyHMFtlluT/zXIv/kRwHCYG29nCZT4iKXkO+9ZRnuWSKQghCTEJOs8c+KKoCM0iOnEABXeQoceusVKMZ/eADJWkiWZIkVWRDEif6e2Ab9d7L3lrXtdmvhEzCtxSt49y1rzuynXVkPVCQLBAys0YuMuudZJxizaQjMyWG6JFxkdvOMNS6rjUjC5DGprUEJ65cQyAB0lo3tfyN+qvOOUriRGCJLnKS3c3jXO69Wq7o8pEiNPhaSMBTVqVk6rI8M9lV7vO6qk0GOI5i83xoqEPJ8yWEQMtqzdidpOb8JWVHGWhjJh2JEa9LZVkaUiJBLHnIZDpnsoTCYJ+lBoLsUtT400GMnO7TWC4SgmZnfatERjhHzkSdfgAAIABJREFUiEghyXAOjFwk7K11XZMjPa82XgmAyKm+ZfB6v4gigRRvlkZgtlTaTR2Ut207Dcnfz958TET/z6tH9J1PTojWmVUs5VDiGp07jJRTcpZMMot4louiVDJrg8EQsoGYfdT3t9dDsspYIKVJEmi5XJn9kd+hwUDP9VU1papSMhhlFI0k+4jntLIsJBvZtpY1PMtSyeLhM3Vso4rg4/bu7IyFdCeC4tJOP7t7H1YrVS9g0svpVNGH3JZeLzdt54xkx+TKqgF4vkwkixrHupYachy6uOzihTiLVVUJsx/XFBJ1nY61Ygz/RJjX2dmZYTTiWiFkfkQIJDMj8gRAkXaEdpycnMhBCx2MTcgqMk0i/BKhC8hQGUWRHMAmk4mhEEd2M3RIcNM6jxmTKXwRxsKGrJC4qCBL6ia8EyGJ6JxEUSSQunv37sl1kZH2rbfekrHs9/syZmVZysuIh3Rkhx2NRtL2t99+m65du2agkvw9dICR8XG1Wknf4oH9+PhYxn80Gpl6ToS9oNOLtVgHBwfiCKCY+aa8BtYmIqQRpVY2JUAQZsU2n88N7JkN5xj+dtN5xoUf35WiKORvhCqj49E0jYF/cr+hw12WpZlTCCMdDoeGsfM8aDW2ebFY0LPPPktEnVPHTLr8fvK9ud1VVdFwODTOO7//u7u75wrdo8OFziLWwWJ9NAZ09vb26PDwUK6FrMy3b9+Wez/66KPSh5uizOxM4zuAQZmTkxP5/OrVq1RVlWEF5nE6PDw0MPB79+4RUccWi+PEfTefz+W9RFZVnts8TvP53ATGeF6tViv5e29vz/QbSoawYT02s05vjuXWHi5b7rV077tbGlAno4TOFNag4XrAsL+iKFRmwHt5p3COIQSSYXbC8hwn1AStL2fI3mq1EscDHYxNyCof2LEuLssyA+fENcA5J4f5slC2aB+8OJuOnBzqEcqHcHgT1HPrtQ9qFqWNPpj9BmHzWL7B5pwjz0LjwRvnBMtgloulBsTihKJmLRw+OaPBmqUxTVKthW4Uao+H9KZpqCWVGeC2T2dTGo/H2sZY6/UcqZPWtq0w5jZ1I32LjKLL5VLGf5Bp0NO1jqIEnNPYSx9S0DNOv9+nas1I2Qcx86ZttL42SY2DJ8kFcgoDZlgg1CbiOYqNWUWJyPBjGGZVgAhuOs8ynoGoDTovmqaR9wahyuh4eA8Qb6i9z3KVZuMae+nnWkXaf7978xdeG9Knvusm3bs8p73JJanvwzNw5zgp42mWpaZundlNO+F53iuCOEDocKGz6H0rcE+sjy7LlpKEE0I9GgwGa1kOohJYmbs9rVt7xuMdk1DS9yyWZ2pbDTrUdSOOHZZ7jccjwxvQrT0MVx2IZEi3fyt/CTvGKFmB0mcduz8nfro6Q+Th4FpDTCLUIIPS6/Wk30LQPkSoa9tqoKBtNaC3haFubWtb29rWtra1rW1ta1v7fduXrx/QP/nqn6HHfvpvfrubsrWH0C6M4AazYhzxGgwGBgaF0Cf29r/yla/QE088QURdVJ7haZgZsh55bWCes9nMMEwifISzOnVdS1T+8PDQZOc4OoAkIQwFZNjWZjYLMwUcxUERccwsIDEARvoxAoiF0URdpOE8XUe+HpHVlSEiyQAi8U2WZfIMnNHhZzw7OxPWxXv37kmU7eDgQKCcTz75pGFpRM1M7gOEs969e1fG8pFHHjHZF8z8IIwVSQ2KotBod13L/XZ2dugLX/iCtJUF4ZF9886dO3TlyhUhE7lx44YhLOLsEBGZomjMZHMfsDgtUZc5w8wiZll6vZ6BBTOhEhKCdAXVDxKFoFZlVVXynmxGMpE4ifuC+53HnCG/3EZ8b5Alj+Gi0+nUQKy5z27evEkHBwfyjO+8846BKOK1VJtpIeN07949ufdkMpH7TSYTM29v3rwpcM4rV66YOcbwVoT/YAYYCYcwy4vENQgp34SDj0Yjgb1574XoJY5jA5Pnsdnf35d77+7uSjaQqFuviGzmkyOXHAHe2dkx7wr3SZqmdOPGDSLqMpzchwgvmk6nggTACC7DfVGPkZ8JIaao94gQJsyQcj8SdfOT14ter0d3796VPtvaw2nOOQMlxYxyAjAoIe1KE4Eu3r5zm/Z2O3j1aDySuRTIsnpyeLv1HamMzLOqFEIQ1Gkk0qxO61shqMH1HPfHJE5UlD1ylCUZzSvVUMZ3j7OGg8FA1s2mbSRDipkFFzmFkUKk3znV/pPsFEMq01jICpumkUwREVESKamNWKM6skh8E8fK6tm2LaVZKs9YFIW8c4vlQrLBWH6zt7dnWBpRM5OzWpGLpH2LxYJ297o1ZjQaGbbZNE3J115+j/qLqJMpzKi+pV6uyC4mO1suljQcqe40ZyjnizmNhiMa9Ls1ZzKZGMKi2VyRLfw5okOaplENyxRKh6pa+gznHZE92zVtQ6uFQvMlY103Bm6KbeC53ratspZ6PVsSrc+0wAYs+1KkZ+C6qsknqrlp3hsggur1u/4sy9IQGuW9HkWrDp32B9mb3yk/QfH409S2tWTbiqKkvtyvEMZN7z1Np1PZG4bDAcyxAs60kWQNOxirsqeyrmP32vD51sF1WvJeyQaJSKCZWZYJo2lHgsdELxEgjjwgXvqSdc3znmQDiTpiGqLNzGfH4orlKvM5a0E66gkxVGQQQPl6rm8SF6KGMUE23jlFKXTZWj6vROQcj5lmk1HXs2ksIR/3YZqmAN9NtMzmIKLEtf8dXYBdiLOYpum5Qtf40MfHx7IAOufk0LS/v08HBwdE1C2SSNWMkE2UvtiEkuKgIRMo/41OGt+fyKbyvfdSd8QQLD6Yb9YjsiHVd5bp4KMQ+3K5NKylbE3TPCCXwC9BHMfi3CKbI9Z64iaJB0E8BOZ5br6/Wq2kr46Pjw1cGKUN2Anp9/v09a9//YHrosj9eDyW73vvxdGs65oee+wxuS7WjaFTOJvNAKKgNZneezmsDodDevrpp4moWwDZoVitVgKZffrppztGufXzOufo5ZdfJqLuwM6/R9ar8XhsmEelbme9aBJ1c4TnQZ7n5JxSy89myv4WQpC+3awPROeGF/Llcimf41zF4ADXYfD8wcAD1jKmaWqCAnwPhIuNx2NxSNq2NQ4TOz3MxMp9hG3/5Cc/Sb/1W78l7eD2fuMb35DauZOTE7kWvnPozBwcHKxx/F3/XL9+3QR1eI1AFuDxeGxgofz3ZDKRe9y7d8/A2Xge9Pt9Go1GMrY49waDgQRMyrKUMV+tVkrbHimr3sHBgYGeIqwapTKw3vrevXsyp+/fv28OX/z+j0YjGb/BYCAbVZZl8nx37941LNP4Pp6d/X/svVmMpdd1Lrb++T/zqVNzVXdXN3sQB4uDaLVlG7LDCLY8KAaSF+fGiAIDDnBj4CaPgR/ykIc8XCAPRgDf5OE6ug9BrmzAMAIbhiFb8iSZMmVTokiKZJPsZg/VrK6qU3XGfx7ysM/69trFdiRZatvtnPXC08Vz/n//e+9/773W+tb3jQQEJ/9IzSv3A7c3jmMjUMWfz65tPBb/GDTdS3s01jq26dxf+fT+dbWfgOVTQJyiLNJSFhbh4NoIG5hLRaGdC6oEe2ptskCfhZLCKRCwUt/3NYy0yA24IGCetQnHh0TOAoIlWbBlPSKuY5v8B2ijq/euPM+N/YZNQmn5+QD7si3AJqu60gyjotZTro/yIGi8X65jfL/ItUMbRZGGCwcC0lrVWNs916OT6OQj15Ui937gUzbVEjngkigr6va6uG4s6sakU5iWmvlb1mRKNmvP93CWy7Mc+32RF4DMrqysENXi/GMRHR+pPTwIAvxe8jv4gY9AgxtoLgRZnkC1dtAc1yGLdC1slur6+Zr0PLTIIgIattZyMJZm8s7zHH+Xc7VIdHCAaxEhn2BpWRq71nuGYzuQDCktDTGtqgpOUhRFcPbrqkZdY13X1Gw2yM4c2lzd/AftzbduH9BblzZp+Ov/lsJqk4gsAwKpnBkdjJCQ1tFoJHgRtJOlzsMMbw5EgEffWzmh6h7zuS7jkmcj1/XI932jhELW/umzQClk3nLhkFpwdBuNhgE9ZWirZRG5LicgMrIsyf4diXN6DMdTvf/MPOrDSVNwU31G1883F7XWjvE+Kp+G/Rh9XfXusdPsob0KFiuCVlzvajtEpOVDGg2WHrKIiG7RI7BHsuuPRiN0lsy8SMIa/jeRKWuQJIlZL7GYbK1Wy4jIycyEbdtYWJrNpkHuwgd2KcnQaDSMmkN5qJSfpYahrOuRGkN1rXXMhsMhDnBVVeEwPp/PceBrtVq4jqw7siwLB1o+pMsNUNagSVIT2Q75wsvaOymdwNdkyQcenzt37uCgfP78eUOegSOFUicxSRIc+KWsiNTvm81mRr1Uv983yEHYtra2cI84jo26T1lXx7+9ffs2+rPb7SIT1mw2MUem0yn1ej2096mnnsLh/5VXXoHj6XkenT9/Hn0g9QIlcYqMwEudPO5j7hNJdiPrCWVGnRePJEmM7JdcbHjzLYoCmVM+aPDiNhqNjMMb99uDBw8wNr7vG5ThshZOR/JyI5vL3z89PaWrV6/CGbcsiz75yU8SkRl4kFl/uaHs7e2hz8uyxHX4vSBSc0Q6izwHiVS0XAZUZEZXzmN2lCRaodvtGk6NlLc5OjpC27vdrjEGcr7y3LFt23gO3qhGoxGu+8QTT+CzpODnoBFnsmW/d7tdvP+yFvXw8BBzOs9zjKUMAnW7XTjirEfJ4y/X3G63axx6+LmDIMDm+84779DFixfxd0lExCYd7qV0xuNrda0zWobTb5kacOxgyLp4OUcNZ8/TteEW6cwlI4Cgp+d5cDCLXOv6SR07z9UIAsuydG2gqCc06sZdlYHj79WVXouo1sibONK6onVd4zCeZzkkKnzPN3Qn2Qm0SB9oWS5BykCgfqqyDGkJ2Q70p3CmJeGPXdpwSIpSZbh4fMbjMd7VXreHjJZt28b6wf1ZFAUcDMd2qHJ0plWiEeAkF4pISBIOsbXbbdwjLzRCo65qjQApdeBgPBqjP4MgQPbQ62qUV5qlFAYh2ru2vkbpllpr9vf3sffZjk3dXhd9wEFE29ZZaeOzo4mIMI61IC8RBDn8/13XRQZYEi0VhSZ6MYhv8lrrOlYVMqd8/pLcDdDvrHRd5Xw212PjiMwi1eQtxrvVbmkt5bIyAoqWZZNFP8jeHNL/+crn6N/81P9Do/kh3T1dpbqWslt672DeDO4HiShRdXUyoMJz0kENosyKSbSCTJ4QERyusiwpijQqKgi09mBVlcJfyFG/aFkW9qm6ruBkqSC7uu7KygrIZxzHhsPFQSOuc/Q8lyzLwb2zBcJBynNMpzHkNapKZwllEEjtrR20Q46BXHMVvwq/m1rew3V10un4WPsUjmMSEbFJh/tR2jJEvLSlLW1pS1va0pa2tKX9E9m9v/jVf5T75KVHd0dbtBIcffcvL+2xMtuqiIj+WyL6oRemPjLpDJl9kTVTkg2MowaNRgPp9MFggPqgTqdj1NXwdWazGSLvzPbIGRbOABCpzARHI6IoMjKOfO9WS0dxZD0aX5t/e1aGQ9bSPex+MoMohU65T4jMjKGsw2Jxd772dDo1GKkkjFHCWzjCImtDoyhCJkdGE2U2l/8toX3c3jAMDSipHD/uawmPOwtp4wgXkYoacpZESj2cFafnaJmERErx9XPnzuHvs9nMqBPlOrP19XXyPE8LHYu2P/nkk3T79m0iMuvOHMeBIP358+fx/U6nY2R2pcyDhETLWlqFtw8wHhKqKmURZN0um4RW2rbGyzM8VWbeJeSXMz6yHlRSiEvIThiGBmxV1yW0EMFtNpsGnLPZbNJXvvIVIiK6du2ahtwIankp4bK3t4dnPTo6ouvXr2MseE42m006Pj7Wkdf5HP2+tbVlZJm57VIORFKtS6kOWQ9UliW+zzBinjOz2QzPJ5mY5TNVVQVo1MHBgQFVk1I73G8yIz6bzajf7yMqe//+fYOpUCIweF7JTD0RGXOPn29tbQ0IA14zH7amRVFkMArzvSXjsed5BnMzz5fZbGa0D4LXYq4u7XG0WmXj8sqomTKkAkTNokTkoIwh0Gt+URa4TpZl1ODIe15RWZSakVRkqsMwRLYtz3NkpliQnkhl+nguZnlm1IOBDdlR0FFkRS2d6ZPfD8MQ2SHP84zMFMM9ibTguxR4L8rC+L4TaGH1NEsBPa1rE8bI3VlRBQil53maFTTPUetXlqVm+BTZXP43oH2OjYyc6+qxKauSxPAZayX2/LzAPc5mIybTiRaeF1IPWZ4h2+bbun66rmvqLLIneaZRV91ul9q1WuukVFNRFnp/ajXJsTUbblVWaPva2hqNR2q/C8KAorm6n23Z2Ot63Z7Oage+cb6EzIOvxlhCl0EV6ejn8zwP45dmKSULWQTLFnW7jj4m244pwcEsvnKf4vvxPi0RPXJvlzIaVBMN91V/uu7MgK1CfN3zyfc8smz7B9qb/9XffJKubL5DxxcPqapqms8j2t3dXfRhieyi53mK3ZbfzTxDbWK73RaIpYrSlBF5FrJ+VSUhl6WQtZC8HBW+T1QbXCESpTafJ2BiVedePa5cbymRbCbSrhIoPQ0Pz7LMOAdNp1Mwlcrzg+u6oiTMNt5NOff4+ZrNDrKEvGZqhQaNOlJ8K5pRmPvKcXR9rcqE6n7jrGia5kb70LfqvfhP6XFxFvM8N2q02MIwxGIjdde4rodIHXz29/eJSDlfknhDyl3wIVRpjHg4UMtDUJIkOCjNZjMDY812cHBgyBmwyboj1r2TRDgMlZDQA+m8SaeJSKfwpcafdHSk8zQej41U/dnDo3TEJb5b1rkZdQuC9EMePFnjjkgtJLduKajzpUuXjAWRbTqdPvRQ7ziOIXHAzoLv683+5OSEBoOBsdnwhiYd3brWGpbHx8cGSQy3/caNG4BmDgYD3EMGAWazmVGDeraO88d+7MeIiOiP//iP6VOf+hQRKWeB4anHx8eof5xMJoa0C4+TJDIhUk4lzzceNx4v6WBKR1xCGuVY8BjLPmQIrAxQyOtKiBGbnJO+7xuEEXy/4XCIvzcaDbSbJRj4fpPJxKgJZEciSRKjnpgdblUwrjb4c+fO4R2V85Z/z/0WxxoulqapQQcuN2K+VpqmxhyV5FGydoptPB4bRD/NZtN4N7m9coOXsj2DwQDBBeksniWA4WDWYDCg4+NjQ6pF6hvKmlNZi8pz7PDwEPfu9/v4LINijUaDgiDA37IsM3Qe5fsvSYL4fTo5OQERlYSqdjqdh85VOb+W9rhZTTXX/9UEKQVJnCVh91mewRFrNBo0mao5FoahQbyRFWpO2LaNA7ft2OTYDg7UjqtrGItCSy/III88L0jZLcMskwfhIzIVmdY9xEGLTPIayTEv90S5Zid5gt+yg5cmqVHn6NiOsQdIGKQjKPKl7IespYTzJmoqq6qCxh2RIjwZnS4Itvor4LCXsh1ZmmmHO86JFsurbdnkBYt/1ATyIMdxcO8kTlBqo75WU2nrtRakPTUBQhlFEUVxhDHgtg+HQ0AzG42GDgiIIECWZWruVToYJ886u+eU4/Lee++hLCRJEqybURSh/jFNU0PaxQEEcnEeWnDQBH4AWCiRrmc1eB8833DE2cGX56my0PuQ7EOGwMoAheG4CsITmAgoOI6Dz5IHIY5iTbrjqqCeRT/Y3tw79smbExULGbZutwvIZlXVcJi4Hz1oSUq5hoLKkh22Skg9aFipktPQZxX+Tp4X4nyp34UkUaVR/D3P8/BuNhqFcL5JtFHXzioSymgxftpZlOd13/epsYBGNxoNiqIIMheNRvOMviGPpXYcVY2jPgPL/V9KsLG5bk2uq0kfy7I05Kgk6U9ds3Org8JxHFNvQSomJTKCwIeTrdbrJQx1aUtb2tKWtrSlLW1pS/sXazuf/BMiIjp67Rf+iVuytKV91B6JsyizHJJafjgcIuIuCVJktkSyOp2enoIZ9SxkUkaipHD1aDQyaOOlUL0s+uUoRbfbNaCKEprF3j1LC0jRTH4+CfM6S23Pkf5ms2lQZHO2bDAY4N5SoL3X6xnRUlkUK8lAJOGA7/sGvbMUOZeMm5JFrN/vI+Kxu7sLFksp3RBFEbIOw+HQYKfie8gI13Q6RZv6/T6yTJzJ5IzHvXv30JatrS2MuYwmbW1tIWp7cHCA7Mz169dxj9lsBnKc1dVVtIOzPJyBdl0XmcJ2u41nffHFF0E+sre3Z0CBud/29/fxrDKCxKQyEuLI873dbhsZQB5bmTUcj8fo/zzP0QeS+EQSK/F1+HuTyeShzJSu60ICQpIKra6uAuIr4bTdrha5vXfvnoBAOAYTqCRIsG3bIDuScB9mzL148SLgLScnJ3gv4zg2CJ86nY4B4WSIqm3bBvpAktfI90ZG8iQTsiQlktk1vg+Rmh+SIZg/93o9zQ4Yx0Y0maHO8/ncyM7JDMV7771HRCpLv7m5CdjzycnJQyHpN2/exFons5dSLDsMQ2Q+V1ZWsLbdu3ePNjY2MBfOohDkeigJvCQcWjIe8xj3ej1cczabGYRIS3s8zSKFhAmDkOaCWj6aRiAjKYoCkMQyLalcMB1Ktsg4ibFH1KVZWiKzbq7nGkifoKnlBOSeDyQSaWH7IAgMqCLWJdtBFi1NU6qpRgbLdV2dxcsKAwIr14Ys0aQ7QAbZFrJljUYD95YC7UEYENWkyVIEUYchhl5pkhXHcaguFuUidUWerUtKJPkUZyKpVu86Z7Y63Q6NJxqV4Xr6PWQpkyiOkAG2HU2iZju2AZnlLJxcSziTyWeoyWSCtrTbbc3E6mhWT8nYPZvNkLHc3d3FPbIswz7WaDbQDs7yxEmMPmw11dj4vk+TsTo37Wzv0GxBPtLr9wwoMPfbdKJLdKSwPZPKMMS4phrz3fd9Q/aCx7Yo9XkqTVJkVKuyQsYyCAI8a1VWGGO+Dn8vnafG3GCzbZtmCwmIMAx11r7ZoMGVN4mI6ME3f84gIuO5PplMgEX8QfZmyCEFAXW7ncV1GEZbIDPo+x4FgS6nqaqawlCXQOm5mxnkNZV4b2TGyxdMyJw9dF3J8MnlMqq9cawRinLvCoIQ98hzLdVh2xoNmOeZkZ2TWXsuq1pZ6VOr1abxeITvcRtVX6jrnp6eYK1T2UieR/pZ5XkxDBtGxleWhZxFITBBjpQTUWSXTGKpz0Ce5wIGHAT6HJplmYbfkgGa+KHaI3EW5/O5TsELjPb6+joOLqenpwb0hKFgdV3D4XrrrbfwfQkvlRuSZVl/r3ZdFEVYEB3HMe4n4WoS3ob0/xl5DUkpL2t/iPThSVL6y83p7IGfD4VlWRoyDFx/wE4IXyvLMk1lTprt0HVdOKSSLY4XEr63PPzzAZP18HgiXr16lW7evElECub5/PPPE5HaFPhF2d/fN7QK2Unr9/sYpw8//BBjyYyrRBpC+bDaKMkWNZvNDOpsfr5GQ9O2v/HGGwY87o033iAikwW01+vR9evXcb+1tTU4FZPJBDBWZvzitrOjcv/+fSyqGxsbRk0mzyl2dORizHNUziVZN8j9T6TmKjvPcRwbQQDpBEpJBtmHRs1KWcIBk7BMGVA4ODjAXJUOy2w2gyO9tbUlFtzcYMeU15ZQFxm84Wfh9vEcUTTTWqqB34HJZEJhGGptrigynB2eP9Pp1DhccjtGoxH6sCgKHE5cV9Orn2VSPsvyyXNP3jvL9AbY7XYNiDjPb8lyJ+ew1C8dj8eUZRnWtCzLjLFlh21vb89gLebn293dheOv2NP0pszzk2uf+Vq9Xg//r9/vY/zn87lRy8JBkna7jXUoDEOs3UmSGMy10olc2uNpSaukD8/PKY6VcwK28WZLU/THNQ6oNem6eqqJ3JaaP0fHR3AoHdeBA1TX2nEj6+/XrpMMzLZlaybO2qKiWqyDeaEP47WGDn5EXsOyNFtlXWuny9N1i4Gg9C+LEnDDswd+OMCVDlq7jguWU2ZbxbUEJwMRGe8X1m1XlwJYtmZMrUp9jrEtLYmR5RmFQYj3dnWwind7OByCMdr3fTiOk+lEO0aVLgUJwxDjOpvqUhxmXCVSzrfcoywS/Um1cShtLLTnirzA87mei3YcHh6iHa7rImg7m88wX4IwoN3dXdyv2WzCMUvTFDDWstQst7PpDAy20+kUDLutVsuoySyyhb6w7xljTrVw7GsyJE5k7Syb1FDOi1ywclbGXsd6lKxZKvkaoG9Z2YBiy5O8hCfPZjOqhbPAlmUZILftdpsmrkoq5Nk/fG9W9bEWahht21lARhXTKL8DLF0mA9eGZihxSUMGp0mxLav/mySJlripKrCL2rYNGKnvSy10C+3g52GYqry3+r7eBxkWmiS6tKKua+F8akfU83y0NUlSg89AyvOpNqt9sN/vGRwSPIjdbodmMy4pcYmXN9d1AGelRe1zknAALIT0hko68RlfntMtMLRK7gvXdY0zIjvuCpe+WF/o0dlHQx9LW9rSlra0pS3tX5zlYU2T9WVmeGlLezytJre0ya6XR/el/ePaI8ksqsiBZhiVWocc8SbS5BwyWmHbNiIbjuPQq6++SkREn/70pxFFcV3XIFohIiNjIrN7bI7jIJshyR2qqtJp+TA0CDU4S8VZIqlf9rAMhCTk4OJZ/j1na3zfR+ZFZhPb7bbBrCh1FmW7zpLdSPZG7h9JOCN1Kzc3N/Gs7XaboihC2zc3N5ExeeWVV+iZZ55BX3PkR8JYJXNoo9FAZkIKFidJgozR5uamUUx+7tw5RB2Z0ZafQ8LjGLY8Go3QBxKeur+/Tzdu3MBYcNbv9PSUPvzww4cyxz711FMg8/nxH/9xECo99dRT6NcnnngCz3d6eopxarVaH2H3lVl0HsPpdGrAitmCIADEUEafZT+XZQn4JreBSEUFp9PpR+Y1m4TjcLat2+2i7SrClaA/JLRSZuSkVqhRkE9vCDmBAAAgAElEQVSCJdF18T6sr69jjsnM68HBAfpgMBjg78PhEH3CGUfOIBdFAajs2e9JuDfPb6nFqNjFdB/I94fXmul0ShsbG2j7ysoK2t7r9bAuuK6L9zeKIqwFEi4siXpkP00mEwNSPBqNME5ST7MoNENglmV4NyXZhGTxPZt9PsuYKzWtuK9830d/ynWTn4u/w1YUBdbJ+XyO57Bt24CtLe3xNIt0BiLLM2SBbEcQwxAZJRv4rWUhg2RbOjN9Ye8C5r9t6ywhX48hf77nG9k9XNfW87UsSiNbzxkk13WRJazqClmqmlQmUWbrjQwEk9qUBdrhubpsoqba2NOKhTC64+rPkmTsrM6i67q4rlM7lFciOyXYG9E/loaIFpZGfbTaLfQXM6Fz1qnVbuEd3d/fp/WNdfR1DdIQDWO1bAvvquu5FJDWa5aambzmt1ttqgRLZLfbxVpUVZrJ1bIsDR21bLDeJkmCsZHw1OlkipKbckGmQqTgp7PpDH1okYVnXV9bp9OR2v/PnztP04lq49r6Gvp1ZWUFz5fECTJ4vufrjPiC3Rewe8EqmWYpUjCh0BV0yAH7qmVZyHbKfq6rmrodddbhNhCp7GOapR+Z12ycFZcM2EEQYI5ZtgUyW4u0vqht2dD7tG2bkn5N6VFJf5v9H3S9+HWS9r3uzQy3ralesIk2RKlSDDZUx7GpKDQLeVVVYk+MkdlSwvMa7s3zW2oxlmUFtk+lFa3fH62lnVGr1cK73Gjo7HoQhOQ4OX4v98cgYKRPievWtYvsZS2QA0pVwUX/q/IvJgDShDMSPajmLpN/echMKoIfJq4ys88GY26aCJZVjT6QSD/lM+n5ws8n9+uqqrBOKpgts6FamvxHvU0f0COwRyadwcb1d0Sq4+RBmx2MPM8NJ4YPdjs7O/T7v//7RER05coVHNhkOpZrw2Tqnjva932D0lg6eLKtfCCVgvASytlut40XxfM8LLTz+RxOlqThlY5VURRokxQU9n3fOCzyRLAsy6ifkoezMAyNgyn/RkJV5WFVpePVy3BwcGAc/hqNBpwx27bp2rVrRKScRSktwQuPFBSXGw+3mceMN4jJZEJXr14lIiV4L9lUFQOXWoQkXFWKRcdxbEAwuZ+73S5w50EQ0EsvvURERK+++irmUZZlxu9brRb+X7PZpI997GNEpBxBnldSokSyTXIbuQ/4+2maUhzHmE+TyQRwTv4OkcmaJfHrch7Lw85sNoMDS0QYI8dxaGVlBb8fj8dw7Hq9HpzsszWrDIGcTCZ05coV9A/PQwkb930fzgLDXKQcBffb1tYWXb58Ge3iPvjCF76APszzHO2I4xjvu+9rCvYgCOj09BS/abfbdP78eXyWsGDuq5WVFfRXFEWY9+Px2KCT5vlSlqUhqTIYDPC8h4eHhmPF5jiawUzSea+srBhwce6re/fuGQEaHrPpdErdbhf3azQaOGTkeY57+r5vzAHpnPJ4N5tNjLFkgWUmS8nwCoifgNnKNYaIEDB57rnnDKg62/b2tnG4lLDxpT2e5qU2dQ89mmwo2YYa8CULOKO60BDMqqwMJ0Yy5b719ltEpAJBDHW0bU0tj9owEW+CYyXKQqRQvVGfbVs4zEtBeNfWsFBmeOb303b04TrL9Z7oOZ4RsOHDe1VVcHQssoz2ycMiO8lUmPVTEsLquq7+t0Ng35RQ1TRN4STVpOH0s9kMtYHslDepiX/zerK/vw9piTAINceCqwOV7ATojiSMGddkpmlKqwN1zfF4TGmWon4u6ARwlMqiNOoz+Tt5keM7FlnoZxkAd1yHLl66SESqxAPBg7JUkNHF733PR1DA8zxaW1VrdZzEmFdZmsEZnqc64cBtJCKq3RrBj7JQ95CyEQznhEQIqTkCuSsh1SLnsW3bOClnWQZGYCJCna9t2dQIG/h9kmoIZhiEcLLrSgcnqqpCsD9NUwQXgjDAnMxyzVHhOA6FrZDe+PghffyvV+nHyifpZfc7agy+j735z/1v07lwQvaCyb8odADUcRxR7uFQHGv5C9/3qddTe/j2tg+ncjqdChk8XUuX5/qdTRJdwyml5+paSn61qNFo4HmVpJp2rNgsywZcVQnVq3urWkEtYyaDy/Is3mzymVklZPh+nqfrDpVz6+DeEp7KdYZVpWHHnudRHEsWWD2PyrISDquG3UqYbVmW5Aq23tEiYLK5uSXmpFRX6OAdV2V56u812URU/QY9Alvmspe2tKUtbWlL+/+B+TObNm43vvsXl7a0pf2ztMqpaWbF5JHz3b/8EPvtxh/TB73jH3KrlvYv3R5JZlEKzAdBgAi9JPeQqW8ZxUuSxIgufv7znycilZli0hXJOsYEN5J0giM68/kcny3LQrZIsnJKPUNJEiN/ezayINsXhiG+l2UZnlWyKUrik7IsjayDFH7nbCXRQrBX3J+vJaNRkhUsCAIjM6mZmTTMQmrosZYPR3hmsxl98pOfRF/zWMVxjGjy7u4uvf3220RE9MILL9C9e/fQDqn3x5GayWRiZC7DMAQkjkls+B58v5WVFTzH6ekpMnXz+RwMpvfv30em6Pz58+jbzc1NjPH9+/cpDEP01WAwQF/fvXsXkbSNjQ1kHFutFqKiDM8gMolhJPnM8fGxoYe1tbWFaJTMojabTcyfk5MTfF8SqsRxbBTYa5FcHXVnuKjMNHJ/TiYT9GGj0UA/yDlS17WRseQ+WFlZAZSXNfuIVCRRQpobjQY0Kf/sz/4MY2hZFsbjypUryFhJrcpms4nrjkYjzPULFy7QU089ZegHcp+cnJzge1KH8OTkBO+7hJ1LeJKEw2ZZhnngui6NRiPM+7W1NfxeZplt20YmtK5r9CeTGp3t806ng+eTWVuGX0mEA/fV4eHhR8h2iLTOKn9fas1yH9R1bcD7R6MRspy9Xg/tGg6HxhooURWyDICjl41GA22V6yERGdHnpT2upuB5rANYFWp8WVOQSGWpJGySs3BFURiZtOeee46I1Hxl0pW60lktiywq8sIgnQCjbqaZxx1LZ/ElK6fraKInsnRGW/5WkuMQfTTTJ/ddzmSFQajZMAXxSVVXyBSlWQoIqx/4yFYSqWyrvH9Z6fWVM1uWpaGEjusgM8lEKNw+2efczxZZBvNolmW0u7OLvgYEr9BskZ1uB3vB9ta2QXwndQRliQ4/KwuQQwO7oc8MeZHr0pqwgWxpEifI1OVZDrbW6XSK/aLX7aFv2602pW6K77iuSw1fM0xyX0/GE2RYd1u7Oqvt+cgmU62ztrPZ7KF6jVEWKcKSRZ+2221kRWUW1Wtopuw4jjWJkiBUKfLCYE9lGKpkrLdsi9IkNTKN3J+yPMlzPQMtBXgr1TQ7VPt6NI+QfW6EDSC1XM9F3xApVvR+p48+/H725iT2qSwrynOlLapJYhIQ0fR6PVpfXzP0A7lPFGqLSePIQIMhmy9g542G1lUvilxk8EowkNq2rbQyxblJl2AkYGyVjPBEWq/b9/V5Vva572sSHKXXzHDR2sj6yyznfD43mFw1m30C+GdV1WAnrWv5PtfimRxDRzoIdElZHEfiN5bhV8gyAF4CXVejIyxL61YSEdr0KCluHomzqJh61IKj0quaxVQeRPgwt7q6is6RDkaSJLS9vU1E6sD+7W9/m4iIrl27hmuGYWjU4sjaryAIMMjz+dyAksn6MNk+WRvIhzHf96ksS4Ph8GFSDxKuFscxnIJms2lQ9/OLkmUZrlMUBSB3fEhjyJmERxaFFuyUNWW2bRvQPg0L0LA3CU9rt9s0nU4xTtLhvnLlCv4un3VjY4Nef/11IlLsqSyYe3JyAqhjEAS4zr1794wNcWdnB3WKt27dwjP1ej3U8a2traFO79KlS3BI67rGAjgcDuGEsANJRPT000/Ta6+9RkSKXfL9999HW2Qt3ubmJpzeO3fuYJz39vYMiIikouZ5OB6P6YMPPkD7hsOhAbXkcZawV+kszGYzfGc8HuPeTz75JBa9+XyOe8/nc/THxsaG4QhKBkwpHD+ZTHC/0WiE60oB+slkAmdByivcv38ffctC7wxv2d/fp9/93d8lIrUYy77/+Mc/TkSKSVfWbfJG1el0MFelJMPq6ipNJhM4hQcHBzh4yiDN7u6u8Z5KB8+sLVDze2trCwv04eEhmGInkwk5joPnle/52toaNmYpj9Pv9+E4SirytbU1rFvz+dwYe35HR6MRua4L9t27d+8a7MmyHlg6ghBFzzLcT9Zk8n35741GA39bWVkx6jU5AMKHQr4HBw5kjZKUzpCM0FKYWrJaLu0xs5rIXsyDutIlCk6tYaFk6XrDZqMJhyRNU9SpFUVBnbZ6h2azGRh7V1dXDZi9rGG0Sdd+Oa4WNM+zHLC7RtiAk1VXNdnuAlpZ1/iO7diA7DmOQ3VV4zCfJhrm6VriXODo2sK80PV9nudpYXXLNs8Fjl5vmInTIlX7zrDCNEs1s2ZVmTVzlS6/YUfHcTWTqud6ghZfO+he4FGWatZk6XAPBgP8XT5rq9Wiwwdqb10drGK9iuMY65vjOkapigyAdzodrAGj0xGcpjAIUcfXbDZxluiv9HWpRk1gSY3iiJqW2g/ZgSRS8P+DB2of6/V7dHpySm1f7UWyjq/VbsHpHY/HGOdev4d+5npE7lueh0maYE9b6a9QFItSoLzAOEvYq3QWsizDd9JEw0LX1tYAdc3yDPfO8sxgRpeOoOM4mCOSkVTVzGnHjAMYnufRnT/7b9T3y9SQQWMo7nQ6hcQI18H+Q/bmy/UOBUlFCh9eLyQgWFJFJ08ajSalaQqncDab4fxg25rNvtPpagi0cKCTJDbqBnl+t9ttQC7n8zmkOlRpRQqn0LYdOF3NZpPiBYTadbWERBiGxhlaqiLosrMcDpfn+YBzzmYJ2bZN7QX77ng8gSPp+wGgo1JSj0jLfshAblWVgOtKTpGyrMjzdLlRGFpGvSbDZi1LyvDUNFnU6qpSIF0vyVGVPNdlIconWEB8qSAi+o9E9PP0Q7YlDHVpS1va0pa2tKUtbWlL+xdu/2v1r+kTs8v/1M1Y2mNmjySzKPVmfN8HGQmzfPHnhxFT9Ho9RIc2Njbw2xdeeIF+8zd/E9e/ePEiEWnBewNKsPDQPc8z2IY4myDbV1UVGDulVpqEDjJBDUfSJDtps9nE3yUbpowUSoZQ/jcRGWQTs9lMpKoDiqIIGavRaGRAETmaIbOMkpmx2WwiU1NVlRBV1Wl2Jh/h68rIony+lZUVg0yGsz7vv/8+XbhwgYiI3nnnHWROWCOSSEWZZXau3+8jwxMEAaKf7777LrJ+fC8ilZnkzBRnaIgU/Imfb3t7G8/07rvvYow3Njbo4sWLyCBWVYWM4I/8yI/QZz/7WSIi+uCDD9B2qY/X7XbxHHt7e8iIzudzPENd19Tv95GNarVaBvMpz2NJwJDnOb6ztraGMZ9Op5gjEp4qYa+sTSbnAlu32zWyqFrA1tRi4jnS6XSM7D+/D+fPn8ezDgYDOj4+ppdffhnPIaGIMvv85ptKUPisppOE3HL7VlZWMPadTsfQ8lxd1VHxLMvQh/P53CBt4fki330Jc5tOp3j3HcfBPAqCgBzHwTsria+YQIjvB1hWqIv2W60W5qiEikuEgYSztlotStPUyFjy/SQxgMwAS+1Ox3G09ltd4zqdTseILMp5cfv2bfQPazASqXVBZjI5eu26LrIPlmWJgn+d+ZAEPGfJn5b2eFlda41QyZRMzM0iyGeSNMHcD8IAc0ZC9re3tunrX/86ESkoJ8893g+hYyf0ER3bobzUpG/QeKy0TmNda71AqjW7pOd5xv5dlZXJKs2sp0Lb1bEd496cvbRqE2ots35sWZbh347rfCTDjz3OsgGnNLKMVW1kMlnUva5rTXYjdHmrUjEx8nX9wEemVz5f2NCoAdfRhF4npyfYY4bHQ2q3PqoL22w0jexcGIbQAnRcB+v88GRooI/YJCN1kug5stXdwvN12h080/BkCGKZVqtF/X4fGcS6ro0z35XLioRtNBpBc9H3fDw3r+FERP1eXyN48gzPUJOC7MeRarPnewbzKZAZtSZdqkqdQW42m4AOZ6mJ7niY5mJVVpSWqTkXFhYEAdZgSZzjuI4BPeQ5EvhaU1y+D72u1s917DbleUEvv/x36MPvZ29mciXbdgyitTDUZShB4C/W/WTRJw2jPIL7MM+1VrUsF1Ha5LToKxLZVV2mYdsW5pHjuGTbWge5qjSJomXZFIaaCZR/L/d/z9NKA6WAijsC0i3hrErjsaAo0rrQWts8FRnSBIQ6QeCLM60FAp661u+Huj6vYSXluc62jscjgSDSZzj1vus5BhZf20Z2VUFPmXRJzx2JwKrp0QFRH5l0xsMY+GR9jqT0lweR2WyGCZmmKZyToijo137t14iI6PXXXxdUvxF1u11MHo1lNmsC5ERotVqAPcqaw+PjY/GiBDg013VNcRwbBzC2KIoMpjNeHCXElGUqiEzHUb6kkgWKhTh5o5TQMFm/KF8Ufi4itUBISJust5Ntl7Dgfl8vujs7OxC6lzWIaZrSL/7iLxIR0R/8wR+A6fTo6AgHaK6FJDIP3PxM/O+dnR1M8O3tbRyuT09P6f333yciJa/B193d3cV8OTo6gkNx7949tGNtbQ11W0dHR7S3tweGV4bq8XPws0rW2vl8juvKg3IURejDnZ0dfJ5MJtTpdLDYSaiybdvYsPf39zGPL168aDC0Sbgp942kNXccDR3yPI+CIPh756g8zPOclOy5aZoaNZIPgxOmaQo2UmbclLBnNrlpSgdD1gxXVYXx29jYQB3mt7/9bUPoudfrYWyiKDKcvJ2dHSJSTiT3SRRFuLfrungOObeLojBw//L68jcyKCQdrmazaUCSpTQPj+t4PAYMScpdKIa5As89HA4NmnWeL4PBAPID8t2UtYWy/+VaKgNbHJCT84LvJ/tEtosPZ9yHfC21wev6Rf4sGeXkoWNpj59ZloZcshNUiPocSekvDyJlpqGZZVEaPAGf+MQniIjoweEDrDcsM8OHcV1vtaDPJy1gDzZs3wXsUdYcRlGE37uOqyW4auWEYr8jPd/zPMehvSgLHMbLUktfyAC2ZPguKw1P9FxPH/Brh1zHhUPkuZ5mCxX1i7Zt61ObTahlTFJdLkA1AapaVZVxypPvdxiGcBY7nQ7WfFmDWJQFXb2m9sEb79wA0+k8mmsnz9IOSSYO3I6jnknC4wExbHfgyCdxQienaj2X57dupwuY7TzScjuTyYQGq2o/bjabeg2eR9Tr91BuVJUVHLOyKAERlXIgWa7PU444KOd5jj7vdDqAKqdpSoEf6L1BQJWtUrPsTqYTzON+v48gQllo2RXZV1VVoX7VsvXabDs2uY6rIdCOS/Zcs9NKGCrPSYaCY/z+i39LRESv/8f/QUNuxXwuyoK6CzZS5ezRP2hvthMV1MjzHDDlbledvx48eKBlYsqSgiCEQ5Tnun7VsnTypdFoos5RSj0pOQgOhpjJkqLg9taApOZ5bPwmCByjLQjkeJ64bg0pC9/3IOeRJKmQ2tNyF7IOv9VqURTFDw2mNxoNmk6lVJQWNtGOMQk208xwHB2Hy86UA8vQU8fRjKsGYbFlEQ+hLDFR+7HmVOFFQt2L6yhTwFAfXcXiEoa6tKUtbWlLW9rSlra0pS1taUt7iD2yzKLUJOSIQLfbRZTj9PTUYBF9mAC2FPWcTCaITF2/fp2+/OUvExHRiy++aEA+JURhOp0iK9NqteCxS8Y1melTERYtLs+ZuiRJPkKKI+F1kt1K3o+vOxwO8XyDwcDIUHLmpSgKPB+L1PN1JQmPzCBJiCJnP4lMQfI0TZHJkBk1hs1xu2Tqe29vj/7u7xS8IY5jQ6+Gs0OWZSHT0+l08BydTgcEKVEUaeHfdpsODw/Rv6PRyCAJklkuLtqeTqfo8/l8/lBmxo9//OP0t3/7t0SkiGs4o3LlyhV688030fZWqwX43Lvvvov7DQYDZIpkNlAy98p5FMcxnu/mzZv0xBNPIDL54YcfCvavBvp9b28P15JwyjAMjf7n68znc7wDEmrKREs8zhICVRQFiHBarZZmDhTF2VIDy/d9g/iELYoiQDE7nQ5Idfh7UuuUyYe63S7elbfffluwk2ndwjAMAQmWYu+c1ZI6lrLfZUZdF4mHRtaQ7yMjqlLzVJIKcZaex1/CVYMgMN4t7oe6rg3WWTnGgAUtmGqJzCz9dDol27YNAhlee6Q+bBiGIFE6OjoCNFpBYHSWR2aiJdRIwr+VPpXJeszPxP1zeHiIZ5XZSylALuGmEs68ZEN9fG26mtN7z46JcpUN4cyFJEVLYp39kmiJPM+RZVIkE+q3aZoiM7S7u0u3birGxe2dbXUPQWojGSll5o7n+Nm9DpqNnm9oB3OmriiKj5LiCEbLrFhkDUQWRmbkolijRuS7WZPeT6uqQga2qiolar6ApVV1JSBgOoNUVRU5ltaR5MybbdvIkBZlocm5REbNsR0DQVRWmv293+vTh/cVGiEvcrQrTVOwdJJFyHwGvljzA5/Cpl57OEPm+76xJidJggyZ7djIchERNBDTLMW4ZnlmIBj4+TY2N1Di0261sX4MBgM6PDpE2z3fo3Sm2js8GeJ+jUZDEwtZQnNTsIbLeVTkBRhaT09PaWVlBWvidDbFXK9cLbje7/VxrUzAKVm7lkhlPvk6WZ7pNgkWXiZa4ox8URT6flWFc4nv+frvpZ47gavfP8dxcB2ZKsrzHERCtmXTSn+Fdvv6PPa97s0qi60v7Lou9rcsy5EFY6F4hmC6BjQ7Jc62WZaG1soSGAmzrKpS7OWugZaRbJ9ENbJkEq7quo7xbjEklag2WGd5GirNRA11zTJ9dsgX71maZgvdc52B1CR6pWiXS+0FiVIUzUGII/dTIhv3LoqMXJfP3MmCLJE1XjNkRRWpjc4My71dKzRofUXVZzqbyKa0JoWY7SOyR+IsSrp2x3EMp0s6ZlKonh9+bW3NmEgQnRUORZqm9HM/93NEpBie4jgGG6c5gBqKKuv+XNc1HDxeJCVDq+/7xuSUzqOkxudn4TaCUtu2jcMj10xNJhOwEPb7fTgUWabFV2ezGaVpCqdSOkoSNiMP0LJ+kYjAkul5ngFbZYfkLC1+s9k0pBf4/ynqXr2w8IReXV2l73xHCcJevXoVn3d2drApPPPMM3Tnzh0iUlDTsiwNwVYpPM7jMRgM0Mbj42PD6eF2NJtNOAv379+nH/3RH8X333vvPSJSi8Lzzz+P8RyPx3i+J5980uhb3gg6nY7Rn+y4hGFoCDrzPLx69SrdunULh/y9vT3cv65r/P3w8BB9Ipk8kyRBOySDpm3bRu0dz0/uA66ZlMx4s9nMcEJ5Tk6nU3yW9Xme5xntYJhsFEU4XHzuc5+jV155xZBfkc74Sy+9RETKuXnnnXfwHb7ffD5HH0gn+emnn8bnVqtFrutivkr4lZQlqaoK71MYhnC+ZI2ErMmRMFSuz+XvyzFotVpGvbMMNElHjpl/R6MRnKh2u425Kg9LQRAYMh+u68LxnEwmoiZDHxYGgwECLr1ez4ARMd05w615zKTD7DiOqLfIDEFh/l4YhujPBw8eGPXV/KzSCcjzXEMQRYBMwnaW9niZITtlW4bTxX/3fA9wPrI0RLXZbOKAXJYl2fWCSdV24KCVRUlXrqias+FwSEVeYD2oqopqy4RFE52p+7NtQ+wdsFcqjWCFrFOWzmNZlobMAVhTbUccwCw4U2EYGsFUFlyX74rssyzLqCg123ue5UZAC4yt4gAt6xeJCCyZju0YsFUwxS8YV/EMcg/39PtJNRnYMHZCmo0mymwGqwN8luzLG+sb2EfanbaqjWPIX1WS76i2VGWF8ZCyOk7kGE4POzWepx3/6XSKMoIoirC+ua5LW1tbGM800TIHa2trRt/yOhb4gdGfHJxwXRcBiKiMMA9XB6t0OjoFU2q/19d8CIF6ZiKWSFjAGGtdj5YUCdrhN32Mn7UQsidSjioYc0n1AddMNhoNo7xBOqG+q/owSiPMT+V4a8kZBIHzAntMnucIOmRbHv27w/+efv2T/4FiT7OVEn33vdmJHKoXXBadTtuoOVxfX9esvJ46A+eQ1akNEXkJBWUYqutqJ01KaqhzpD5Pa2hsSbYta/L0Xur7Hhw2xf6p+5MnnOu6BlO5DFRryR48PrmuA0eO+U3Y8VSSf+zo6veh0WiIesRQvJsWnZ6qco7VBdyaiKgoyHCYbVuPp+KH0OsQf08Fze3FOGpmddXH/Fk7pJV4X+u6wlg8SlvCUJe2tKUtbWlLW9rSlra0x8BuXjilU39O/92bn/mnbsrS/hlZpVy6f/8orv1IMovtdhtRjna7jSyAZIJqNpvIJkiiFwlDKYrCgIjJwk/2qi9evEhJkgCCl+c5WDplRO4sKyQK6kUkXUK2JOSLIwCSMIejL/1+34DzyeilzLBoZibNzHhwcIBsycWLF/FMq6urNJvN8P9kxlKS0kynUzyT7J8gCHAPSZwiM1ZZlhn/Pjk5QZbr/v37iLYeHh7S008/TURktOnFF1+k3/qt3yIipTfI/ZymKdp3/vx5EOVsb28bGkNRFKEPm80mCDd6vR6gK9vb24iKVVUFBtwkSYRIampkc5nl9Atf+AL90i/9EvpkOBxibCaTCaKGaZoC8ieLqPM8N7IzbNvb2wZpz9WrV+n27dtEpObU9evXiYjotddeg9ZRs9k0yQQEuym/D+PxGP2WJAmgrjKTFQSBIYwrWYQ5Q8fPxNHInZ0dIwsnM+0c9eW5QKQyXPwObGxs0GuvvYYI9MWLF+mnf/qniUjpU37jG98gIqI333zT0Dri30vilHPnzqHfpCbh8fExeZ6HeSWF5yUETrJ3yqyhzLTGcWzoLEnCBjYmuJHZcratrS38Xq4VWZZhDBzHMTK9bFJLVTIy87/5fbJtG+wF+jgAACAASURBVGujvFYURVgPpR7m7du3NRGEQAKsrKzguTsdxTrIv2+1WobosXw/uF0ffvih1jZrtQwSJTYJ95UkWw/ru6U9HtaZBnThO226eW1Evq/15ixLR789z6OkUONelTpbX9UVSGnKSr+bjqsRLupi6j/9fp+KosB8L6sSMH/H1lk49yyMahE9txxLE3xYpEXrbfPvMkPP7SRaMA8LOB/mraUJZ/JMZ89ty8Z7OpvNANPs9/tU2wvoebNBWZbh/9mOfqckKiLNUp1NcjQ0z3VczUie5Qa6Smbx5b/jOMa7KjWX5/M59olJNkGbtne26RuvqLV5pb+Cfi5KjT7q9rogyml32lSUhSYDygtATD3P0+eKUJPutTttGh4P8dx8diiKgkprUa5TlMZ56PIVJdfwrW9+iz72sY9p9u84euh+XpQFtbwWxtRe5DbKqiSrtDBmtNie2522sc+uDlZpNF4wklONEpqDBwc0GWsGe7DyBhbuQSLDlaSJgVhhqGuSJJQWmkG2LErjN/zsvudTYRfoE1pUZ3Q6Hbx/teCxTJLkocz9jUYDEF237dI3s3do9X6HvnT4pe9rb/6N4Lfpf2pM6KqlyFMkQaSCZKr7KfJGGzqLioxGC9IzaYwsYTKZ/y1oKCo9QT2nWM+QmUn5OspHoI+Y2g91do6tLEuBErQBoa0kusDz8ExSn1D1iUaKmcRyNoTu8zynZIEGkKzn4/GImk1NOMfWaISULDLMQeCTbTvi9z7ar7KO+v3gds1mU/Sz73sCcabPoRLu63lad7KuLSKLvvzRHvzB7ZE4i2trazjg7O/vG5S+3DlcZ0Nkyi1I7C6RPrxIx8/3NVygKAra3NzEwffll1/GIWpzcxMDMJ1ODZkJ/vt8PjcYBmVdlEwRS8dVUs0TkQFxkzBbnsRFURj1hHzoW1lZwWIvGUwZ8sXQNdd1jYMym+M4BqyUnYKqqgynUEL55MIhD/O7u7vAra+urmJTv337NhbZ1dVVOHVSsuLo6MigTuaD9fb2tsEoOh6PwbT57rvvou2SfU0y1dq2jXGdzWbY3OTzERG9+uqrRER07do1wGE/+9nP0he/+EW6fFltUFKeodls4nvPPvus4QhI5lAtQKuZv2azmeHAuK6LmsTT01PMvX5fixZ3u11D5kDWjXH7pGSMlGeQ9WcsXsz3D8MQEIzJZIK50ev1MPfG47HBEMzjsbq6+lAHVspK7O7uGtIgn/vc5xAg6PV6BixSUvDzMw2HQ8zv09NTozZIOkaSbfTGjRvohyiKMA/lQWxzcxNzPU1TvDdFUaA/2u021gjJhtput40xkGuSZMaV80VKmdi2bQQ5uJ8li7Nkz22329RqtfCbXq9n1Pc+jCU1z3P0uZw7Ej4bBAHa5ziOAbOXNOpxHBusldwnx8fH6CtZG0akN2MJPZXBE7k+L+3xMreyqVOE1Ov3aDqZ6to0S7PgpklqQP7kuEt2RnbeJJzScRxDPqLVbmHtvHvvLmB6rXYLkgBpphkiq1pDGvMsB9yQah0skXsoB6AlEzE7gkQi+CFo533P1zWHVQWH0rZtqsuFUxg2IIAuGUztStXwOQ1dMiAPyugbyybHX9QmkaWdgro2nELuw7quNaSRnd9FP3Q7XZw9mo0mauZG4xF1FiyWzYYuzWh32tqhjOZaJoR0uU/bbxuMommSgmlzeDLEecwPfEPMnplqZY1clmVYj+XzERHYnldXV7GvX75ymd544w1aGajzjWTDD70Q39vc3ER7bUvXvpZFqSG7lgVJjizL4Biz48E1iXESU5JqOD5fNwgCPN/ZurHBygDf4b71fA97XZZncN44EAvm3zO8EVW+CGAEIeZeWqR4/7jviFRAAsyttg4aeLaWgul2lLRXnSrn7Pvdm8d0n3x/jVqtJsVxAufKdT3DMSoK7RQOh0OjtCOAFImGmLbbLcBQy7IwkhzcH+r8jqdGLaTv+8YYWGJNUvuPje9JhlZZslFV0iFV72WW1XAi1fdLXMf3PfwmDCWng2sEjPUZuqQ41gksLZGh5TEcx0X7LMs2YPbqebktBfm+K36vvhFFkajPNvdbltdQ9aC8jlSoa3yU9kicxaUtbWlLW9rSlra0pS1tad/d7n9jCSld2g9mlqoJ/wwR/d4P+9qPxFl0HMcgS2HogutqCIb0nsuyNIS4JSkJE9fI6LckjyFaaO0somS7u7t08+ZNIjLZSTkrwybZUzmC0O12H8psSqQiC5w1ajQaiDT4vm+Qs3DkRUYcLcvC3x3HMch4OCLHGRb+fHh4iDZWVYUo3nQ6RdaWSIvYt9ttaAzevn37I9ko7n8IEy8IMSThDP9+Pp8DmvnVr34VBDKDwcDQvXnqqaeIiOiNN95AluPGjRuAQ1RVRU8++SQREd29e9cgDZJEMVILsK5rwF4lkUG320UG8ZlnngHEMwgCQGBu374N8qCjoyP6+Z//eWSmbty4AdbUT3ziE/TMM88Qkcp4cR/u7OwgC9Ptdo3ibAlPlfPi+PgY2dIgCDD3zp07ZzBgcibt5OQEmdqLFy/iujKCb9s2xmI8HhuZ77quMRck7JKIDCIpvpZkAS6KwiCV4Pkmtbu63a6RgQ+CAPc4ODjAnD45OcH4nTt3DuPRbreNrLQUbgajXJYZzG3vvfceYORXrlzB+Pd6vYeiEiTE1LZtg+iF144oikA2dHh4iLbu7u7SYDAwsoYSVioRC1IwV5IVSLIjyWAM8oiNDWTpiUxSqrMkOmDDE9nfOI4B3/6pn/opvL/37t0zGE/5WQeDgTEvzpLayAwyQ6YnkwnmrURKSCZrmZGV5Ei8Vi/t8bS6rhU82XM1nNK2zSwyw7mKipqB1pGVpCQ8Z2qqRTbAMiLyValJbbqdLtZByU5qWRZZriBRE+ypcp/mjIxFmr2XSGU2OWvkuZ7OeDqOQc4i92NkAS2N1rFsyyDjYW1DyejueZ4inKtFFp8zW3lGjaZmc5bM0/wejUdjIxvFZCcyk2hbtkGwUVYlfp/lGbVbao+6c+cOkDeNRgO/r6ua1tZVGcrhoWYdHQ6HtHdxD+3mUpXxZGzstVVZoQ+lFmBNNZAilm0hMxwEATKI6xvrgHg6rhCHH41BHhTNI7py9QrYTYfDoVF6srGuzkdJnKAPO50O1r4gCAwGWs52S21MooUG9yJb6rgO5l6328XYup5LlOjxYkZZr6/1M+u6NghuoNecpEbmuyY9F/IiJ5GEN4ik+Fqe7xlZ6dObzxIRkW1rzd3AD3COCILAyMA7tkMeqTKq72dv/s/3r9O1JCLHKUFI5Di6DMxfPINt23RycmLsMzz+QRAiQyYzgIo4T6PMJNELrwt5nhvIKc6QdTrdxdlaZw1NWKk+K5UMQ7ZNLUbOsFVViYylylZKbUXNdK/mywKmXeSCRCc1SN/Y8rzAWWlvb++h2tZ5nuFZ1VypRZ+apDYMx7Vti8YLyHSaptTtaoZ+39cw2TxneK3OyNZ1RQw4tNWz/Bo9Ls4idyaRmiB8uJDOWl3XgFpJqn/JbgjhXVpgxRcHz/l8brCnyYPv2tqaAX3jQ+jOzg4OUDLVLVnP5HUk+6Ya1NKAMcr6IP67ZKSUAqa2baMPJLNaVVX4u3pp1GQ7ODigLNMitIPBAL9pt9s4iNq2bcga8He2t7eNF0gKykpYr+M4WIzrujbqk55//nkiInrllVfwd3ngz/Mc8MvXX38d0EUpJTKfzwGlZRkE7p+rV6/C4bcsC2M7Ho/hKN27dw+/j6KILl26hHHlTXI6nRqO1bVr14iI6G/+5m/oK1/5CsbpM5/5DP3Kr/wKERH96Z/+qQFp5k1zfX0d/TMcDuGUHx0dGcytEm64traGxaeqKrCKyvly//59sILOZjPc486dOwbDLo/FuXPn8Hl1dRU02Nvb2xQEum5EMZVpRwB08FFkzF1+B6SzLp2ee/fuGfBbXgCbzSZtb29jzG/fvm1g7Nkxl++T7/uA+Hqeh77Jsgxj0e/38Z3NzU166qmnMN8k5KeqKswlz9Pwn+PjY6wRSZLAwXccx3hHecyks8+sfNyulZUVPJPcFIpCb9hy3tu2bdSyAiYlhMXv3r1rOPTsPBOp+SPHXNZzczAjDEP6iZ/4CbRJrm3ch7dv3wZD63A4NOo42u02vsf1jHxdyYwn1yQ2KSviOI4BQZRszUt7PE0FXWswNEoJG3yHaoqjBRTN14dmEuyGua3rdeW+kmc5vs+HRVlHjHqvOME8kizUdV3jkO1aLhwreR1FPKkZAqtaS1t4nge2Ud/z8XfJSFmVlYb5CTb0qtbrmIRyZ3mGQ/psNqOyLOFMy1peKUVlWZZ2JCwNJWt32mh7Vet21LWW/LAdm2zLBvMl1ZqR1nM97DH7+/sPZUOvygrwy8MHhzRLVT/neQ6HO89yChuLuv0sJar1OjBYHWjh+Y7mcUiT1JDRYhmHPM+pv9LHuPLal2ap4Vjxfnpv/x7dunWLklit85eeuEQff/bjRKTkqCSkmc8FzVYT/RNHMZzyaB7BQavKilxfOxoyqFjXtWaurmqMzXQ6BWNqlmW4x3g8Nhh2Garc7XbxudFs4NzTaXfIcR3db5almW4tvTfkea6DAmfq1w2nx9L8ChyYkDWgnudR0i/pP5ldo79q/jv6H2//X9/z3vwb4/+MzlcbFJf3kGiJYx1cZBhwu92i9fU1zDdX1Car/YDHqV7UJOrSHCKzvMy2LTiBnuehVlE1TfdNHMc0mXC5SkiWpR1XNvVMtOir3Ag6aVkKGzBUVY9Ni/4cw3HL84KCICSOL1SVdsxUYEzXE3Mww3VdBFnlnq8Y+tW6MxqNsbdGUQxHlceA+5rrGdV1fTpe1ACr/8cwVFnAqWVFLMsWjrF15nuPxh6Js1iWJQ60HPUmUocuPjidnp6iQ3q9nnH440Ph+vq6QXDDByuZzWOMvDzU8EvX6XTwd0kUcufOHfz+05/+NBa3oijgzMjJ6XmeURuVZRmcGCIyDu+y/knWHMp6QFl3+DApA6lxxdeXEX5Z2yT1VuShj78vMxCyaJ4XUj64Sp2tk5MTg3iHnZVWq4UDapIkyM698soriBTJCIuU6vB9n5JEF30///zz9Id/+IdEpBxHzrbduXPHcNj5Wr1eDw7i/v4+DsNbW1tYGNvtNhyNj33sYxSGIf3VX/0VERH93u/9Huikr127Rn/0R39ERES/+qu/Sn/+53+Oa/HGGASBUX8oD9D8d8/zjA1pNpthjsjaNjknq6rCPJQSILKO4v79+3huqVvJv+X5IQmfskxHs7h9fG+eCxsbG0amjsf+9PQU/TkajfCeua5Lzz77LN7H3/md38E4HR4eYgG9fv06IstXr16ld999l4jUmPPGEwQB+mkymUBLs9/vU1mWuGcYhoZEBvendLLlOPu+bxyY5PsryX+kVuH29jbm2FtvvYVnP3/+PNrIwRRuu8w+yvdTar/xNbe2toyD8GQyMWpsJA2/JMX59Kc/jXE+W9PJzyoz15KUhgMCfC2pIyqJhVjrkvuX2y4PAXIt5j5oNBrGwWZpj69VVUlJkqh1AdpgBWrhkljr7IVBaBz++KDcbOl1z3VcZGocx0HdH9WLrOHiUFNTDUfJD3zq2GrflZT34/EYDs2FvQvaEagqXRfvaYIb27KpzEvjnWyEOrvHEhmSQCLPtaMkM+nNhn7X+J5EKnMpg9NEOvuZpRkcj6rWTrOUnJDfJyJ8PxMZCIt0+zzPo4oqOCuOq+8fx7FBvMN7ie/5OKAWVkHrG2rP2N/fx/qdpinGQkp1sAQX339ra4tu3LhBRMpx5LrI8XiskTCllgMJwgBnqMl0Aiew3W7TdKLWcrlOr62ukeu6dPuOQjm89Z236OKli0Skzke8f7zw/Av0wQcf4FocOHBcB7WvYRgaxEecYXZsR9WtLRw2KcNmWRYcMMErQ3VdG8FJg7Oi1HIgeG4/QF0r/5bnhzzjlWWpg7GJPq9RTQhmtFotWrnybSIimt1+EZIaSZwg+JEkiYEE6O0M6G+79+gn37pKxZuT73lvtkgjAObziFzXQTYwTVNaW1tF3yoiG9YhbILsptVyDV4MWU8o91qdGNHnU3kOVTWDXHNoU7vdEXX1R3Cser0uiG9s2xE1ei6yj6XQrazrCgQ1RDXVtbp3u631PoPAN0gXic5KXKi/5nlBFy7ojLys6ZSyOTJzrQPKthFczfMCfdVoNAxiIYlG0qg2vQ7VNRlrsXY0vX8UOatlzeLSlra0pS1taUtb2tKW9k9k68/9MREpZ/F7tTjIKfFzatUhEU2/6/eXtrR/qD0SZzGOY0D7sixDBiNNU0BPJVSuqip432maIlovGUylrIXv6yiaZVkGnbTJJKTreNbW1pAhe+aZZ5DV+uCDDwCnrKoKmU8pIJ+mqQGVldHI+XxuUGfLmjc2SZF/enqKzKCMLEjIXlkq7D1nlPI8N1hIOVuTpqkBQ+VrDYdDIyPLzzSbzdAuhvVxRCKOY0NmgjM9586dQ+bt6tWrBmumFEZ+2HV2dnaMetXpdApZE35GIqJbt26BvUtmmYJAs1PN53NkFgeDAfp5OBzSJz7xCSJSUVSOom1vb9O9e/fohRdewD2+9KUvEZGKnDLM7/T0FBng9957j5577jmMH8OpO50Ooj6u6yIi22g0jCxgv9/H2HS7XaPWUGZoeJw2NjZQQyazvGVZGhBKtizLaDqdYmzDMETbuW6Tx4nfs6qqdG1RXaNvZbZLsgm2Wi0Dnrizs0Nf/OIXiciUaimKAnP661//OqJfV65cQR/IOt+LFy9iDq+urhpQ8W63i8x0kmjmQZlt5/YQqbku1wXJZstzcjQaGUgA7h8FgdHzand3F5FQhsEQqUy2pLWXtaWSepvvvbq6ivbFcaxrlBY1p9wnch3haxOZkJbj42Nk8NM0RcTyt3/7t/HcFy5cQB9evnzZqGWeTqcGlb1kUOW5I6WBzkoKyciw7AM5Fkt7PC31S5ptKXRKWZa6lqosAD2VUDm5thdlgYxjlmZaaF7IWsi9jiz1Pf5/eZ6D8ZNqQtaw2WziPdpY30BWazQaAU5Z1zUyn4aAfFGqLAwjZcU+mOU6c081GTVvbLZlo01xojPynquzUo6t34OqVhm1ZkPt4VVZGSykzBZalAWyRmQtRNtJyURIyRGu25NlJ7ZjU5HrGui80NIEcRSDWbXb7SLztjpY1TBEUU9YU/3Q63Q6HSNLlaWZZp6uasAgR6cjGiwEx+uq1hlk10GWOcszsjs6W8L9HEcxyi8m0wnNpjP002Qyoe2tbdzj/fffV/+v3QbML05iZIBPTk5oc2sT48f7WD/oG9JgqDN3PSMLGIYhxiYIAj02NeFZXc8Fm2qr1aLxSO1dZaXfk6quKPRD9C1bWZaUZqnOtrsuMtyy/MobeBTFuiacryvrfiU6q65rzEPf8/XZtlJn2zfeeIOy/DzNs4iGldrzv9ve7M49cGqkaUKNRh9zuNlsUJNZgJPY2Csl54Etsu1EBEZR1yVk/eqaDFkLfhWTJDOQANw/nueQ6zqAtHY6XbS9LCsqCpbBC1HrZ1kMZWUEF3/W9242G2hfnheo9UvThFzXoyiaL+4vWVqJeBFUa4guY9HnowKZz1df/Saeu9froQ8HgxVK04wai/UiyzRCUUKEHccVe62N+xmSQiR5Wyz0gcyIPkp7JM6ihJxJGJUkgXBdFwdi6XAVRYFFSx5opUajdDQZKiWdLnkIlrp+TCzTbDbpU5/6FBEpCYfXXnuNiIiefvppXFdhi2M8jxxYSbAja4pk/YIkAxiPx8ZhVdY5sclnYKgpqJQbDdxjf3/fgCjyJitrjdbW1tD2qqoMXUAp4SHJS7rdrkHCIw++/PcPPvgAC7kkHPF936gVlHVqvFns7+/T9vY2rhVFET37rCrovnHjBjQUG40GPXjwgIjUQZ77SrZ9NpvBAWq32/i+rD/Msoyef/55jO3nP/95aA+Nx2P62te+RkQKksrP+gu/8AuAtO7u7uLwLcc4TVMc5G/evElbW1u4fxzrzfHBgwd4pqOjIzhK7XbbgKFKYicJ/+N+KssSvy2Kgs6dO2cEQ3iuD4dDzDcptyGJSSSZU7vdxvhVVYX5GUUR/j6dTkEURKS0I6W2oJSGYfM8D471YDAwNkqGyayvrxuLWxiGeDclfFtCRCaTieH08vMVRYE+lzIx7bamhk+SBH3O8GBJJiQDRDIIxL+XJDGNRgOOeKfTwfilaWq8S9KBzfPc0EmTGqjsVEpnz7IsBLY6nQ7m16VLlwD9fvvttxF0unHjBu3u7uIA5TgO5kUURfgsa6rrusbvGYZGZEL85cHfsiwEaJb2+FraKungfERUmjCqqqoAzbNtW0kmkOlwVVUF5yZNUzhZMpgiHc2iUNp9fAB3bLMGlg/yMlDmeZ6uxT0Z0sEDBa9eX1/XdYqOrudzHdeoc7QsC9BTGYDxXF17WdUV1bn6nCb68BY4gkRHBIQsW9ef1VVNXqCdU9dzKagXwe2JCVFEqQzVgFA2m004WXVdGzXn/EyBHSinfHGPRqDJeWzLpka9INXKC/x9NBqBzEXKoDiOY9QKyjNGu6Pe58l0orQWRdCMCe6GwyH2BtdzaTZXDl+30wU003GE45hlgFD6vo/vy/rDsizVvnmg1rXnnnuO9u/vYzzu3lGELKurq5iTV69eBaS10+3AWZdjXBaaKPH09FTpfS/uX+QFnnc+m+OZ5tHcKGkADLX0DAJGSEAUWkewqiujvr7b7eKdqEkHiOMo1g67nWu5lEof/j1Xv4u+7xuB+NANMS6Qt8lSiuYRXb1ylcJJQJTQ97w3V96Y1p+/QfuF1u/uLIh9lJMjINOC/EwRurG8WwEyGKWtzWQrHuCRVVVRu631IiVUlZ035bBqqHhdV7iu4i9hGLp+V/I8Q42e4hPJFs/nUhTxmV071qq2ckEYFASGA6uIcBhOm4oSrww1lmmaURBwUkYHiIPAp/lifq2s9Gl9QczE2tFE6v3pdLoL/UrWb9QEefxZ1lQT6fONbesgrTqXF7iOJhjSgbBHafZ3/8rSlra0pS1taUtb2tKWtrR/btapG9/9S6SkFX70f/n3lLxzme6e9L77D5a2tIU9kswihHDJhL6FYYjI9vnz51GE2+/3DTIJydDJkfckSYzotyTxkOLYMgsXxzqiI9kpXddFBOqll15ClnA0GgHWsb6+btDJm56/hv2VZYm2ZFlmsCPJ9oKpTGQoJSHHfD5H5J4zGVL8XULDuH8nk4mRFWEoryThkNnVMAxxnSzLDAILST4jI1iO4wCa+dd//df4LEkuxuOxkUlh2Fyea9hLu92mZrNJb775JhGpqCH/ZnNz04B8/smf/AkREf3yL/8yxqDb7YIC+vLly3Tnzh0iWrCCJbqgnvvj6OjIkLUYjUZG9pMJQcqypBdffBG/eRhkU0rBtNttQF37/T7dv38fMOabN2/iedfX1w1Zk1u3bhGRSfs9n88xTlIm4r333sN10jQFC2yaKjpnbpd8JyzLwpyU0Mxut4t5K8csyzL8lklmiEzWYcdxaGVlBf07GAyQNZSRdynEfHx8bEjJ8HiMRiOMxe3btwE7XllZoeFwaMhJyKyYZGbl9+b09NRge+X3r9Pp4J2RjMmSoKgoChqNRgaplSQiYuY33/cxjx88eIBsYFVVaJ9kIJTMqFEUYa3hOfAwCNzGxgbeWbmm9Xo9PN+3vvUtZGRffPFF/P2ll16ir371q7hOlmWAur744ot4plarZWRY5ToiGTDl2ibXASkf8jDyr6U9XuaUFgWRQ2mrJKoJkEZm/CYi6va6BskUw/Qk9FCSc0ios+M4BomH7dkGkycykEVOTr3IMlY1rmXbNt6RSxcvIUuYJAkQFs1WE/A9y7KoLEuDQAbETVUNwpOyLEG8wwQy3F4Q8NRahkES6uSZlktyXddgUzVEwW0LGcQ0TQ3YLDL6VW2UrViOXsc05K40JBYk+UxZlciwWrYFaObdu3fxmceLSGXqUksL2zP5TFVWBnGW53l0dKjWj2ZDn6da7ZYmFgx8uvm+Ith65kee0WMQWJDLWBmsYB1zbBOxwP0RzSND1iJJEmQ/9+/tY02s6op2tnfwG85Yynlr2Rp27Ps+TWd63k6nU+r1NaEipMlaTUPWZHSq5pXj6jKGLM+0rFEYIGN4cnKikXJlQSv9FXy2yEK75DthkWb4J0tn/YIgwLyVYyYZ88MwRHZVsg7blk1hQ53n7u9M6ffe/Z/pR/N/TbesD/8/92arTql9eUJv/Yf/koL/+svof5ZqGI9HNBgwwU2DoigGvDJNTdkoyczKQCFF/qJLK9KUs7YBSHQUy6hWI5CZSMU8rkmtKrzXtcF6ziQzs9kc5xVZ7qPOvQyZtSjPNWkiw1DjWJ9Z1X/1fthqtfDOqvvxmIWAuh4cHCAju729g79funQR51Pefxnqur29g6yogr1q1KUk+jHZXxmqqs9dVVVSVWn5kKpS71lZO+Raxb+iR2CPxFmsqgoDyC8lkXrRJByL4WaSvY9IH0bYSeNrykOM1IhT2jCa5pgX+bW1NQN2wTadTuEsqgmprnX+/HlsVC+//DKcCHbQeKNLU41Nt20bk1iyYY1GIxwYHccxDtks9eC6Lr4TBAEOllxnJiEOPEnkobnZbBq/4XZIras0TfF5MBgYsBd5kBwOh4aTIPWtmEX06OgIdX8/+7M/izZdvXoVC7zUzJP1h6PRyNCCtG0b458kCZzFzc1NeuKJJ4hIORX8eWtrCw6XnFNBEGBDms/ngCTy+PK/ZW3qX/7lXxr1bHy9r33ta/QzP/MzuDcf0i3LwsHa932jdk6y/a6srBi1peywybl7cnJiOFBXrlwhIiVfwf3f6/UwrhcuXAAkkdlXuS3tdhufoygyHFpsesIplPWARVHAYfJ935Cr4XeJx0dCfnk8oygymMC4t8FF/AAAIABJREFU7RsbG/j95cuX0VeNRgP9vLe3hzl59+5dWl1dNSDfEqbNf5drwebmJt4nz/PwrFI/yXU1W1uaprhmv983oKSyxknK9mRZhmeybdu4h9Tf5DZ5nod+cl3XgB3Le9R1bTyT7EMpSyNZednJPjg4QP9/85vfNJz/t99+m37yJ38S95D11vxMZ7Vb5dojNyd5mJVsyw+TGFna42WtiUcX3+nS+9en2vEjtaYCjpVmWhahyOEAEenDCjtpRCbbc1mVGk4X+FRX4gBXV4CkNptNfK4E7XuWZgbMnq/V6/ZwYL977y6cCHbQoLVXlIYsBs4FRWmUrrBzatmWlrsgraFn2za+Uzu1EcCW+nh1rRlePU/DU6XUjywXsEj3W1EWVGZ6feT+sGxLSZYsDolRHBlOAveX67h06aIKJEbzCHV/ly9fRpsGqwOsUVIzr6q1jiRLA0n+A64VlFrQrXYLe8J4NMbndqNNo2pRKiHmlONqNswsz6gTqP2Gx5dhobI29XZ526hnY2junbt36PITl9X3xyPoIVqkmTUdx9FBA4sMtt9G2DBqSyWbvmSkZph1kiQ4q0wmE70eh4FR147yBsc29HjPakpLh5bHRjqFRaT7raoqwMBlUC/PdPCOx8f3fBpfruj+3ffpG/H/Tv+392X6N+X/9vfvzZ5PGUV045f/iLxaPZPreqgH7PV04HgyGVOj0cT/U9IZ+t3i2kLft0Rwsi3q4m1ix1FCZKVqgLqeLveQUFLLIjiYRJZgPS0xHorlmHAPvXeRCJLb5Hm6nE3CjokscQ8tASLlNmzbIsvSeo/s3DqOS71FwEOVeKhn/PDDDwRbq3LSL1w4j3toiZQCY6uC28x6asGhdRytjc7t4ufjsXBduS7TI7NliHhpS1va0pa2tKUtbWlLewzt9fAD+rrzFv1X+WcWzKhLW9oP1x5JiFhqz53Vm+PoVa/XA9SFmdn4+zL9K8kWOKLjeZ4BKY3jGNFz3/cRCZvNZogi9Pt9RB2krpPM1JRlCYbBvb09+ou/+AsiInryySdpc3PTINHhNp7NNMkibo4aHR4eIko1m82M7B5HSOI4RmaQ4Qay+FVCUqVmG/+9KApk06T+4mw2QzRxNBqhHa1WC4KsRCZ8V2pBRv8ve28abNd1nYl9Zz53vveNeJgeAAIEQBAkxUk0RZEURbUoKrZlS7Yjqbptp9yptl2uDK7YcbpTqVQlduzEVXGnU+1U223HlmTJtCzKaimyTJGSLFEUxUGcMBEE8DC8hzff+Zx7xvw4d629NoSOuyqCFcZ3V6l0+XCGPZ2991rrW983HPI4fehDH2JimDfffBNHjx4FUBBv0N/L5TK/TzJHua4L13W5ry9fvsx9TaybQOHFO3LkCICCuIOiKo7jMBRQJuCfPn2ar1lbW+M50mq10O/3Ocp59OhRJrh56KGH8OSTT3Jfv/zyywAKshsSLfd9X4tUkRfVcRxun+M4WFhYwMmTJwEUHl2KLklIqyRt6Xa7PHemp6e1CJesu4QOUkSUYIGSpETqEJLHdjRSidqu62rETNLbSe2T89n3fc3zJvWp9u7dy9HdLMs4aiuJWqheQEFoQ+8olUqaB5f+XqlUNHZhGSne2NjgqOrMzIymdUiRV5nA32q1+BuSnn3JFNvr9VCpVPgbkrqO3W6X52Ge51oEkYqMyAVBoGlxKT0kdW8URZidnWVo1mAw4HZ0Oh0eP9u2taR4Go99+/bxenb69Gme9y+88AIeeughAMCTTz6Jj3/849o4y0go1XdlZUUjGJGRQslyLHU2FVOdcw0z26S8LYsBIM8LnTzDUFwWAlbq+R4zZTqOinDJaKJhGgwvlQLylmmxjqBpmojiSJtbBNOTiAff93m+JkmiYKsiUpPlGcqVYk1rNpq4sHQBQLEuVCtVjblUwu5lpIk1EHOVCiLXriiKVFTSNJiIJImVuHie5zAtk2GDaZpqkFSO9htQkdMsg+GN4WNCf1Fq47LuJYpoUQZFOCThu3KfjuOYYbpHjhzBxUsF9G1rcwszs8Ve2Wq2+O9ynY/jWNPclYR13W6X+5pYNwHS4Cueu7m5qQh1LFOde7IU1UqxBm9sbqAxhjcOBgOeI36pSEeifXR2ZpYJbvYt7mMt2DzPcXWl2L+np6exsakYyWWkiqJslmkhiMYIFctDtVbFxnpxT2uqxTDW4WDIEFqpuTkajdR5qFxi1lIY4LqX/BLPqSRJGOZKupO8RgpNvCiKmEU4SRNNs5ki4oZh4K3P/0ZRd0+xk8v5bNu2lkYgGfsbjQa+0z6Be3EEc3kT1QXV79fbmwvSFTrP2qAIYJLE4u+udsa0LJs1F4fDoUbURxDONM00ckUqpZIvyCotjlZKArwoGsFxVMqWbduIxvN7NBrxPARyLYIo22YzND5m+GZxbf5996ZpikqlzFDZKIo5ehmGIx6/4tw0JisKhjwezWaTo4kbG5uoVovz3vLyMvbtK85sp06dwvHjt3E9i3WOIosKEtvv95jYR0Yf5ZnNMEwmypE60pZlcluNAl7/WwB+ET/gckOMxW63q7GKSlgoTYTt7W2ePDLXzzAM7YBJH1av19Ny/aQURalU4v8Ow5CvGw6Hig46z3ly27atCdtT/STj5gMPPMDGkOM4+NznPqdBHDm/o17nd/u+z/fLw79pKmFOz/O4TXTgBYqDJ30MBBGVIthSsJc+sEqlwgbJ6uqqJr5L10hIYrfb5Weapsn3UjskIy0ZDFEUce6W67psmEVRxLCXvXv38vvK5TLnTgVBoNH2Z1nGfXXmzBneeJaXlxnqeurUKYZmvvTSS1yPra0tNjyXlpZY1L3RaGgwW5mDmSQJj/nCwgKOHz/O7/joRwtY92c/+1mG7125coXfd+XKFc5x3N7e1uYhzc8dO3bg7Nmz3KbLly9rbKhkIBITLNWL2f42N7WcReq3drvNz5RSG6PRCGEYavkM1O/1ep3HybZtAVdQUEkJBZXwFnou3SshOgVzWfG+d7/73SyjQtTbAPDwww+zUS7fLfMrq9WqNt/pmq2tLayvr7PY/JEjR9iJcOTIEWbMHY1GGjSTjKYkSTRIGBn7/X6fDT8512mTlW2neV+v1zX5Grqm1VJ5OPKgQhB4QDGk0TVUSDaD+kEaxpKRVjqRgiDQWKDp74cOHeLx+9jHPsZj8aM/+qPfB7tVVOjKEXfx4kXt4EF1juOY67dz504tP4vqJ+v090HTPSk3phQizznn29LBt1wuc45cGKhc9jRN1TnLUHAyx3bYmIpGEV+TZRksWPwc27HVQTlJ+Lo4jhnyhxwagykzOWcqF1Eybu7du5eNIcu0cPLUSdSqCuJIRpbnqVwz27b5wG/AQJIpuBk7p22LIbcy1SGJE4YFmoZZQPkilTNnjSFqlqnWVMd12CDpD/pc90RAzyQkUaaLGIbB91I7JCMtrXdpmnIOomVZbJilaYrtrWIvaDQamhTRYJw7lcQJf8fEKEvjJFNSer0e9u3fB6Bw3pGzeWVlRcl5BAHvV+1OGzPTxdj4nq85o8nApvwsZjqvupifm+d33Hr8VgDAyRMnsWcM3+v2uvy+XlcxR4dBqPJEhXQF7TfUpm63y3Nk0B+wgdjtib8nAz5jBMOA1z7f87nfwjDkZ0qpjSRNkIxEn9o27Nzm8aNxMk0TuVlcI2VNHNvhOWKYClqbpGp/M02V/0tsq/S+vYt7x3wbhexJkhR9e7292csdvON//8f41s//ftH/4twL+CBjpnDqDvhcMTMzw5IQMzMzYg9WTKeWZbPRJFO88hzYHBv7URTxWbeQxKO9rmBDVcacytWVCguZyPstlXzOJywUE+gs7zGs83qpFACxpw7Y4JOGsWSkjaIUaUpMrIqnoJCTKt43PT3FENbjx48z98nNNx/+PtgtjWeR2lbUsdPp8O8i13M0bmvKBmKtVkOWKRZ3qp9Wp2KB3YcbUCYw1EmZlEmZlEmZlEmZlEmZlLdxyZFjB6Z+2NWYlP8flhsSWfR9XyODIQtYQtx6vR57F+r1OlvSMnIi9Zska2G9XlfCr2NhebonyzL20BHUEoAWiq9UKmzdJ0mi4B+uqxLARQg9CAK85z3v4bq8/PLLHDUiCAegazxKGCN57YHCQyIJYGT7qJRKpULkVUBJJexW6jeSB1BCGrMsY8bOWq3G70/TlD1IURRpupBRJASMoaIjvu9zpEeKstdqNX5fFEUa/I/+vry8zCQx5XIZp0+fZsKaI0eOcLRGRl7r9TqTg8zNzTEkpV6vszdRakfu2rWLhe137drFHp1Wq4XhcMj9s7m5ydHIOI41nUx632233YYXXniB3y3hptRuCfckuC/BRNfX1zk6OD8/jzfffBNAwYxKc3f37t3aOBFRS6vV4vZJKG8QBFoUvFqtcjRREhbYts1eX8kEGoYhwzB7vR6P0/b2tsakSu8YDof8zLm5OQRBwN/Q3NwcR33PnTvHHsfRaMQsot/61rf4WRcvXtQi0bJO1Ofdbhf79u3Do48+CkAnegmCgJEBURRx++he6isJK7n77rv5eoKwXrhwgetUKpUwOzursQVLjSKGqmUZR0I9z+N6yCicjObJKFyWZRpkXn4ftm1zPwRBwGuHXCfPnz/PUXD5PUmtw0984hMcjd+xY4e2bkZRxN+H7/t49dVXeTwkDFVGKCQLsNTFlYgGid6YlLdnMVDMI8uyiijFeMmXELdRNII3ZiT0PI+jGUmiIidQOtWaRmPJK/EcI2F5uifPVSTFdVyGb7JAOoqInNzLCdJ6LfyeSpzE2L9vP8P5rq5c5aiRaZgcLTUtkyOeEh1AsFigiLBJAhjZPr7GseHkjgYllUyunD7QC3ktKZfKmhA7idO7nsvvz/KMI0tpmsKpK13INE15nOg91A6Ci0Yjxdbuei6CMUmIJP+zTIvr0ev1UKsX5y/HcbCxucFnmpmZGWaRlZFXz/P4269UKrx3eZ7HuodSO7JWr7Gwfa1e43W75Jc0Eq9hMORoZJqlDIGWZCTz8/PMni+ZKk1L9XkURRxJztIMaZIyTHQ4GHJ0sFKtYGuzWNvLlTLP3Xq9zv2W57laQ0s+t0+eBZJYQUqzNNPSbOhcSu1gqHMc8bgmSaII1UYRDvyjTwIA3vzyf6zOwKnB74jjWDvbJnHC31ClUsHU1BReGL2FLw5+C/dv/QpOGRe/b28ulUsABojjmKOERf+pOlGfj0YjNJtNPrNJjcAkieF5Pt8vVQuoSPK5LMsZ6ZOmKaNa2u02t8+2HVQqaj+X7KZFP6q9S+mFW6zFmKaZBtOURJkSaUURQHMM8ZUoHtUPMcNCC21DY1zfbcyNo+CWZWpkPPScV199Ffv37wNQRLjlupmmKX8ftm2zhnKn0+H3FegPWgNzjrzmuVr7in8nxQKluXgjyw0xFpMk0Q5dFK4GoMESaOLbts2TLAxDjdWTJstwOGQoX5ZlfNgPgkCDU37/BFM4fCqSXTQMQ+3wLaEVdCis1WoIw1BbTCnnbWpqig+6g8GA6zIajbgdEloj4Sa1Wu260C7P88ZhaZWnQB9jv9/nekm42dTUlMbwSu+TuVuNRkMzIgvB0BrfQ3WRkD95kIyiiA0uCQttt9ssQP/0009rUGPJFrmxscHPWlhYwGuvvQagEOWlXLibbroJr7zyCoDCePvUpz4FALjzzjt57jQaDd54pqamuG/a7TYbpxcuXMDx48dx9uxZbhN9pHv37uW+3dzc5NzU9773vXz99va2JlFB9ZbQ6M3NTSwsLHCfzs3NsQElYZqAypsbDAZc9507d7LB3O/32bBaXl7W8lJl7uO1+bnkUJDsndIp0Gq1uB1ra2vMOhsEARshALTcR5m3J+eY4zj4iZ/4CQDA7/3e7/E3tLGxwd/Z2bNn+X4pB0H5jzQW1Kb9+/ePqalVniQd2Ki/qL7UbzJXU8JI8zxng8Y0TZ6Tpmny9adOncIrr7zCRm+1qpjxpNGcZRkb72ma8sFodnZWc2bRGO/YsYON01arpbGfep6n5RNKyL0SDk5w5swZbqtkaJVrxNNPPw0AuPfee3muU+4j3XP27Fn+TjudDp555hl+loSz0VyQUFeZpxhFkVY/ukbmgE7K268YhnLEUn4ZAC1HTnIIYLyMJYnK3TNgsCEWxzEzW+Z5zpDSOIk1OCVyXSKABcYFTEyyiyZJoh2+OVcviZnJ0/XcwuE7Zm8tl8vsYCyVSnzQje2Yc9aSVLXDMzyG06ZJqhlcfI4QqGvLHkuDEOup7bBBG0URG3+pgJvJNJk8z2GVx1Ayy2QD0fd8ln3wLA/DYMgGuzzTpEmqibpT/6ZpiumZae4r6dih7/X8+fPc/0GoZMVMq4DG07OqtSrWVsfs5DvmWVqiNdXC6tXicDs/P8/798LCAs8d31MBgVKpxH0ThiEzmLbbbczNz7EzTjLYNhoN7tthMMTShcIRvP/Afr4+DEIe/yRJuN4SGj0MhqjWqmyYVyoVBKFKEZIOAFrv4ijmutdqNTaYoyhiiG+v19PyUmlOxXGMJFbz1bIspImSbaMzQ57lWv4jtWMwGMCsXOJ3kNEK6LmPdF7LkWsQVcu0cORowfXw0rNv4j/Dh/Er9v/2fXvzgX07uA8oV0+y3ydJCnfcplarCcNQ5xjpsCnqRfmvysDMRa6mhJEWf1fwTSnzRddvbGzg6tVVdgq7rivYV5UjN89zlMvF955lOUajwqgvlyvIc3I8qTGuVqt8/vf9EtKUxj4f51qq/VgVxb6aZTafleI4Yehpmko5spzPsLt27WJJjdEoHPMZFPdsbW1heuwYGY1CnD9/gd9Hr5eybRLqKvMUizQZJROi2nTjyoQDfVImZVImZVIm5R9A6ddjXD46+LsvnJRJmZS3ZelggMV8HkZ+A3UUJuUfXLkhxmIcKw9No9HQdNOkODZFhDY3NzXvPkUjpNYgsSbSNSyY6nlaoq98h4RsSp3Ea5lHpQeQvBGDwUCLzkmvw/Hjx/n+l156ibUH77rrLvaKNBoNfq7sA+mdTdOUI6SA8nANh0PEccyRjXa7rSXVkteQoiD0XLpGMsJKEpTCqzHNz5EMaPK3HD/XdTW4CbVDesviOOYoY5FkXPThuXPnmDymWq1yhJb6jSIj7Xabo3O33HKLBlcgON7Gxga/4+abb2YGUqCIRgLA888/z9E5impRn0i4ommaDGk5duwYj9Nrr73GnujNzU1ux/b2NpPxtFot9nBWKhXNMzYcDnleLC8v87OWl5f5fiIvouul5iaN2dTUFI/xysqKJiAs61WpVPi6IAg0Vk+a93Iez8zMcF0lSZP8Nvr9PkfwNjY2cPHiRQ32RPPnpptu4igsjS9QzBHq2ziOue5TU1PcbwcPHuQ+oIg2vWNqSumC+b7PbZUakUEQaOQsdI3jOPzunTt3at58uv7AgQPYs2cPRwElNNdxHPbcpmmqwd4kUZaEm9JzZRRuMBhw3xK8VJLe0G/P8xSkSUBVDh06xN92GIZ8zblz5/je2267TVvbbNvWPJsUnZVRG8lommUZj3Mcxxp64HpMz5Zlcftk5HdS3l4ltTJ0nQCIiigQwSkNQwnKS/jnMBhyhMSAUUTVAE1r0HHV/mhARa9tyy6ij4IJ0LTUGiwjW3TNtcyj/G0LRtA4ivk5lmlxJBIA5uaVzuvKygqTsC3sXODzhu+pdcX3fG5fIeQ9hrplOaJERlHGWndBjDRLGZYYhiFHWA0YDIOUutFZlnFEz7IsxKmK4ss1jZ4JYwyhJUZE8TvNUi3Sw5p2topk5VmuoqVZyt+tTOvZ3t7G3j17ARTfuecqFuqVlRWOAoZhyBHP2dlZhUTKM8zNzwEo9jF6x/T0NNY31rntU61if7ty5QoaY4ZOyyqis9QnpqPaZxgG72Nzs3OKUX51TelyDwN1lgtVmoZfUgSDruNqGoxxHPO8kBrfvV6P7yd9S7peam5SxLhUKjHBTb+niIssq4D4Ur0c14HtjedMolhrJeOqnMeSlX/H/A6O1EoN0SiK+Pw0HA7H0MXxHGmaPH/OT3Xxgc078bfR/4p/lv8bbW+mc1YB5VQRYLlPK9hjDssyWPyd0qMAPZ0iSRLRz4lGzqLO5RaPa61W46hYnqs9qtVqoV5viD11pDGoui7B2TOhsACGw0oWUakRnKYps7XGccREMkmSwjB00ht11rG0vZIWqOnpKc2GoWu2t7f53vn5eYHMKM7V2+PofK1W5/01TYUmqWA0zfNcaKynPB5SQ96yLMWAaryNYagy/0aGuNfX13niSuio7/sajTZNqiiK+Dp5oPQ8j6+ROHGg6FBa0DqdDn/wW1tbvCjIiR7HMS8cEnKXJAm/m5hYafJ1Oh3O43vssccYzvUXf/EXOHbsGIDiQEyw2X6/rx3aqE7Ly8tsLCwuLjJ+mWRCJOadDGg6rFO9qB+kPEOlUuE+kayczWaTDRKS46BFQrJVZlnG90jxdonjr1QqSszYULj6Wk3lJmRZxm0i8VuCaR46dIjb8frrr/Pf19bW2Fi8cOECw+lOnDjBsMIgCLg/pTj83XffzWyY+/fvR6fTYQj0cDjksTUMgw1P27Z5nLe2tng8yuUyG9mSAXV2dpYXsHq9jkuXLjGkwnEcDX5I7x4MBnx/t9vl52ZZxvmOFy5c4LGo1+s8TnEc8+G82Wwy+y/dT21aWVnh39LYuPaQT5vvcDjUFkmaR6VSiedYkiRYXFzkf3vllVewf38hAv3hD38Yv/3bv833UluzLOP2ra+vazm8xC7caDQ0+Rh52Oh0OtrmQX0SBIEG5ZbfMrXJNE0ei3a7zfVotVpcj5mZGayvr3MeR5qmuHjxIl9H38DU1JRmlFORAtumafI8koZ/r9fTIDMSYi6dOtc6XOibq9VqWrvJsM3zHI899hg/h77xZrOJs2fP8kGg1WrxPY8++ig+85nP8D0SAkvrlsypNAyD+03CgyV0S+Z4Tsrbq+R5rqQwTEMTfqdxpQMzoH9ftm1zPlmapgz/s3LF1m3bNh+MpQMSKIwYglaGo5Bz/eS3bZomH46zNGN4qYTcZVnG7x5FI4bkAcAoHHEe38GDB3kdPXHiBGbnij17amqKGTCjKOJ7AZU/2ev1lMxXs4FBf8B9liXqsGoYyoAulRUvQjSKFBw3jrkdjqvkQCQrp+/7CsKapsjSjOGKkq0yz3N+lhRvRy6Yal0HGCtLGFBOAM/1BFwwR38whmiiAhhgmOb0lDpjrK2t8d8HgwGfBdptxXq6vr7O626cxGy0xnHMY7lz506Wvmg1WwhHISpj1sw4jlX+PAwtlYDGOQgCHg9yjlE/U7vLlTKvrZ7nodNV0kSmZTJzKXLwu6NY3T8aKbmEPM/RbKi9hIxCz/MYOppmqebYNEKDnSx5rpwN/V6f2zcljA3TMNnwlPDOOI614AR9c7Zjs0GYZRmajSbPsdWrq2i2ivoeOnYYn/rmV/Gx7L34t2/8Mn7jkSeQG8U95ZIPIB7nzqk0sJmZ2XE7PIY0mqYxloMhKPGIDRzfL7H8RRwnnE+YCmmQa1MuaCyk47dU8jWH/mCgHO55nvG51/eVQVtwbRCjqNqL8hzXDapIwz+KRlxXINekN7Is438rGFeJeTSDP/7mPM/lnMckiUV6W86pLcVzxky6vj/mlqDzla85rpPkDb5H1cXQeEZouzUM5bSS8GDDUGlsBYg/fxo3oEzYUCdlUiZlUiZlUv4BlNLQxsLl6t994aRMyqS8bUtkJPiU+TRm0jp+9MKd2r8lRorT994Qe2JSfsglL0i8/s2NePYNiSy6rkoOl1CtUqnEnqkwVFpO3W6XLWOCKwKFVU4QSPI2AIUXX0a4AGiwS/JGkLYfoIh1AJ3R0PM87d+oTlLbsFar4erVq/wsmbC+tbXFunm/9mu/xlG1ra0tjqrNzs6yt0WKEe/atYv/3m63+e9xHGvsdI7jaNdR3aenpzUPJ9U9CAJNZ00yMdE1vu+j3+9z1EgydklCjkajwe+Tot7b29sc3ZGRoj179mgQOLrXcRzMz88zdLHZbHKC/Hve8x58/vOfBwA899xz+LEf+zEAhReF6nTlyhWeC5L0pd1uC09Uzr/Pnj2LarXK9584cYIjVuVymT2hvu8reIsgFhkOh9z/hw8f5giS1OKhOUglCAL2fpXLZY70yEhMu91m2OxwOOT5IhPn19fXGbYqhbCHwyFqtRpHvzqdDrdjdnaW25GmqSbcLD3yNB4y4i8h051OR2urbOPtt9+u6QWSKHwcxzxfjh8/zpqJEtbVaDQ0IhpqN0E2pFCxJJChb7BSqShSCs/ToqWSzIHeV6lUeB2Q2qtRFGnzyjRN3HLLLQDA7LXX1p2eT22VorxSK5bWOdM0NeiXnFe1Wk2LqkoxchmFofF44YUXuD8/9KEPadBTih5evnwZly5dwt69Bazs61//OmuEvvzyyxrEX8JYFNRFRYOvJRWRRGX0HKlBNylvr+JGFqa2SljfF2poGduxOUqVJAlDBEexInMxXZOvsW2b1wa5vsWxEvVm4fNIwS6JNIS0/QBFrAOMozVDpXso/41hi5bJ7KCe66Hf7yt9O8dmKGgQBLyWvetd7+L5HgSBiqqVKxx5k+tmrV7jv0um4TRLtVQL0zIZShiGinilVFbi7ZIMKIkTbV9nxtVcQVUN28AwGiKN1N5OEU/Ltvgez/dgRsU9vX6P7w8DxcTql30mj6k3BAQuSbmuVs1CtaKQRb7vY3WtOLvs278Pp08VaJ3Lly8z0sc0TY6idXtdhTKCgnKGYYi6W/R/DoXI2Nraguu63Kb19XWORkodadu2OfIaJzG3Q6YeTM9Mq7UWBkcPaQ5SSeKEI7WO4zDxjSRDDMOQYbMyFWcUjdT4DRMFW4XB8zOOY3iup7SuQ6V1Xa6UVTQ4zxR8M02EDmGu/Z3mjuerM+woHKm9yNDbOL9jnsczt3LsW9xJYbfGAAAgAElEQVRXjNn2FmaDOvI8x/Hjx7F8+Swy5Lhy8DU4BqF+PAHfVIym1WoFWZbDdZUovIoUjisBwHUd7kfbtjgSWey1SmPVNAlt6LA2YvEtqPNpkUZG0TODEXybm0qjuUgFoWep9SHLUoZm+r6vIXjkHqggmwaSJOZIque5WlSVgruSEJP+GyiQgYSIO3LkiNhbTT4LdLtddDpdTmO5cGGJkVsrK1cFFNUQOouKOC/Pc425Xek1xho7LD0nz3ONOfkHWW6IsUiHWqA4fNIiUa1WBdY458OcFKeW4fhOp6PlKUrmRyqEw6fBvJZ1kTH9QuIiDEMNy84bxLjOVD8acKL8lgawhOao0LDKCTh48CD+9m//FkBhZJGBUKlUtHwGKTFCv0kGQ2G/M970kiRRwrFBoOUgkgG1sbHBi4qE+A2HQ343XS/hedRXpVKJF4xrocDU1q2tLY3Gl8Z1fn4eb7xRhNbb7TaPMUFVaDF9/fXX2agcDAbcjtdff50N2IWFBTYQDx8+zId513V5wd7e3r4uMy71J43z0aNH8clPfpL7/OGHHwYA3HPPPfwhS0ZKmfdZr9d5fvV6PWbG8n1fyw+L45gXN2lUDAYDLVeQ+lYa3FNTU3xNv9/nfrIsi9/XbDaRZRlvEvv37xfCsUo+RrKwynksmVwtSwlIS1iIpLcnY42e2+/3eZyuXLnCBu3Vq1c1iLgUlz5y5AjXhQw0y7IYskk5xnR/p9PRoJkSBkmbbxAoweTRaMT1k8K9pmkyDFwaazQuJHEiRauPHj3Kc0k6BUqlEv+WfStzCGR+lWSNK5fLcF2X75cGmGR+dl2X++fkyZM8D9966y088sgj/E567qlTp9jZsry8jKNHj+K5557jZ1Gu78mTJ5URKnInZX1lHo1lWQrWJRxNBMWnaybl7Vkk7GoUjrQ8dzqgIgfCSOWsc76ccKqOQpVqYsDQmB+5kLzG+PCisS6mil1XSlxIuZYsVQdrz/Uwwojrx2kd+ThHj2jnDXXolvT5MMCQyKmpKSxdLFg2+70+WlOFk811XDb8ikePD7FJyr9JBiMRhzPaX7MsU+zBccKwVGlADYdDhh7GcQzXdvk3vduAUUiLWEVflctlBTG1Va5omqb8XAn5DYKA65vnORs01UoV62vFuheGIRvug+GgYFMeG1Nra2u8zsRRzO1YW1tj2GO1VuXzwsz0DEtRWJal1rcgvC4zLvUnjfPszCxefe1VHk9KN9m5ayd8T6Vs0D5oWRZDdj3P4/k1ikYsDWLbNuoyPyxLUSGpiCzluR5HMUxf7V3Mhh+qdbpUKmnnU4KkGqahpDb8Yt6RsdpsNTmXVsrHSBZWOY9tx0bn/G1F+0wLufn9+YA51FgaMAADTGITRRGPU6+r8jCxbTDHB0Fo8/FHSfs3ADbQTNPgfagIQKh80tEoFHDTTINBck5vnIj1IkGeU/BFMYIahoHamD1ZZxfNNHZT3/e47bOzMxiNIn4fGXWOY/NvmVuY5xLWKdcEtQY5jjNek8jgT2BZKveSJWcsi/tnfX2D5+H29han5RT1L567sbGBtbGzpdfrYWZmlh27lmVhfpzru76+IYxQQziLheMgUQawaRqi3Q63L4pG/Nso2r8fwMv4AZcJG+qkTMqkTMqkTMqkTMqkTMoPqVx96f0AAPMHGBlKjQyLvWmYY5KaY4sJssjGzjdvRb6w+XfcPSlvt2IaGQD8JoAP/KCffUOMRQlxWlhY0Lz6MpJGER0pVCk1v65lEZRwSrqX3icTSum6brfLUbjBYMBeVNM02WsgYVcSethsNjVh616vpwnaU0RAelJ7vZ4Wsbz33nsBFFGYEydOAChYJCVLIxGDeJ7HUbTRaKQxLVWrVY54yKhmu93WWEgpilculzUyEGp3q9Xi54RhqLG8SjYngsEChceIIlPVapX7dn5+XiPIoDHbvXu3puUmdS5N0+RxK5fLHMJ/5plnGHp68uRJ1pL70Ic+xNc3Gg187nOfA1BE1ORzKLK3a9cufrfneahWq+wFKpVKrBEYRRH+/M//HEChj0dkO+12m6GcKysrGmslzZf5+XmeI+SJpHkiWbCk7qEk9pmdneV+k4Lru3fv5r7a3t5mmKaMohJckOooo19pmnI/SGY1yfZL407PlfpN1IZGo6E9JwxDnq8SGbBz506Ozr3xxhs8j1dXV/kde/fu5eeSdihQRM6pTsRYTP8tv0cp8JvnuUbCQ8/Nsoyvl15YGRGV85xIGqRXlby7/X6fI55ra2sa0QuNx44dO7geksjL932u07XIBxndH41GGsxKQlcJBvalL32J4cX33Xcf9/nly5cZknru3DktgvLGG29we9/5zndyP5w4cYLfJ9fGgthDwZvZ22rbsKg/41hjS5ZR6Ul5exYDxbeUJAmqtSpHQtIsVYykaaIiOnnG1xiGwVBAQ/OEqyhclmd8L1BEXyT5DV03GilIXRzFCoIp0ink/iERQFJTlAh1KComRbYlhDUaRRqjKDFx97o9XsemWlMc9ZMkKLalILcEHaTIlOu6TEAioYRy3TQMg6N4juMo2GuScrtLfomjcEmSaFDbMAi1uktWb1q7XNfl8atUKxpBBo1fvV7nZ0o01mg0QmREPG6O4zCpzfkL5xl6urG+wYzLR44c4et9z8fJUwU7ebPV1J5Da2G9pt5txRZc1+UooGM7OHrkKNeXkEnVahVT08V6HF4JGcrZ66tzlm3ZPF+qlapCd9iWhsKpVCs8H2Tag2u4CMesoOVKWaVpmBZHDSWyKExD3pfkmkhwQYLNGlDfSpZnWvqATMeQkUbJckvXyLOx7/nac5IkYci1RAbUajUMhkU7XrXP4ccH9+FfPftz+OcP/nf4/V9to/Ov/wmOn70XL/7I/1n0oW0xLLRcVooAWWZq0GwdBinRS+Con+PYGsmfZA6XUTSFUjEZ7hlFMTzP1Vj+CV4ZRRFHPCVSyzBMxDHBZqtcD2I6Ldpna+ckVXQCnzRNYNtjQiTxbVEqCVCkqtB+unv3bo76dbtdPuNtb2+rSJ9hYH19jdu7e7c6o66vr2vnbLonTVMN3pxzNNmGaSnUhWIQNmHbN55+5oYYi4ZhMASs2+1qwu802SW0K89zjVWKPv7/J8gXwQ1t28b29rYGwZTvkAyf8sBGE7Lf72ti5vTOzc1NRUtsmtoHLOmdSSgdUB8wvY/ubzabeOCBBwAATz31FBt4R48eZWMhTVMN6mjbNrd3a2uLjdM4jrV8TYI9SrbX4XCoLSqSiZPymiqVCjY3N7VNReYnSUgLFTlmgILtygOw7/vaZkaH28ceewxPPfUU7rvvPgDAF77wBTbePv3pT/M43XHHHQzfPXbsGLPLBkHALKnD4ZD7v9FoYGlpiftZ1mNlZYXnheM4GvTxV37lVwAUED6mu3Yc7sMkSdh4l2yYNAZAsThJEXlpyFcqFZ4LpmlyXqt0OjSbTV4AS6USrl69yteTYSVzQCuVCqanpzkX1jAMjcWS3hdFEY9rqVTivqrX67w4XctYTPDSKIq0PsyyjN/v+76WY0dtOnbsGBtf3W6XIZSSrdX3fe7/zc1NjZZaOoUkPDbLMu4raWBKdrPRaHTdfOBr85VpzBqNhvbN1mo1jX2P7qexBQrDkX5Lp5NkAU4SxQJH7aA2eJ7MCck1o1JCyui7qdVqDD19/vnneX5LiJ7neRpDYp7n/H1IaZBLly6yQPDMzIxgcgVSmi9hwN7sNMtgUt62WINM4ZiQzM6T8vYrtm2hWqsWBhsJv6c5kmycS2c7fNBFrg5Ytm2zcZNlGTA+52ZmpkFYCRppmibCIGT4oXRK2I6tzV8y0tIk1b5nKWZO33MwDNjwMAxDk5YwYLDxFscxG5GmZSKLVX4YQQR93+c98dy5c7wWzM7MsrGQ5RlfH42iguUxUzJcbJymGYJ4fBZwHWZlldDzOI4RpmNjw7S0b4ocoJmTYRgMVQ6aqaQwZE4Ste3aMXPg8LgmaaIdSKVEBR1uDx48iHPnzvF6fub0GRZ4f/3113mcduzYwfDdudk5ZpdNYsWYHsdKJsLzPXTaxGbpa/Xo9/pqnlimBn28952Fk73X66GRN/gaeT7hfFnHBcg3VwEbxm7F1dIBpCHvOq6WF0/nsVE0Yoip7/uck2k7Nu+B0vBv1BvqnOU6KJVLzJoLQ+XyGkIgPk1ThtA6tqP6yvNQni6cyMONORi2kk0hBtRCiF0aF+rcbNu2ylXLwG2K5iJ8z1/BXWd241/uPILX3jqN6e8dRbWaaw7bcplk2wI2TvLcg4RHGoapGbrEhloqlUX7MoZsJknKsE5pdFoyXznLGVrp+94455n2cJcNuWvZuGl/lftxFMWcy5hlEMymiuUUAF9TSIPYbFTmglFYN5Jzse+6DD29cuUKOh3F/E8DYNsWt4EYV+n7kNIg3W4Hruth+p4YwzsSOH/egIHCeOb+TGI1rHkOa/xfhmkqI1LAcgXq/wdeJjDUSZmUSZmUSZmUSZmUSZmUH1LZ/2jBqfDGp/+LH+hzMzPHizdfxgOZhZmVmykAPSn/AeW1R14reBzGjpjw3SEulIvoev5VAM8X1+W35sAj4zxDI+fkcO7rf6ueufzwMqJGhOyRDJE5wtVjGVILaCyuwznronnaQ4YccS3D2TvfxGXn8vhOhdKAARx79RiqgyJYcOGmC9jYsY433Ryhjbv+kxvQFzfEWNy7dy9Hikaj0XV14a6FbEimLIpSSEFqwzA0LxFZ54U2y0DzsJCHTkYKkiTha6QoaxAEmsabZCqTWowy4mGaypNJ0RIq5IWThDOO43Ck6IMf/CCefPJJAAVEjAhAJCyExN0pylUul7VoIkWWJDlHtVrVBFepDyR5Sa/XY5HiqakpDS4stRkljG56epq9Levr61wnGSEplUrcVsuy2KvV6XRw5coVAIWnZvfu3RzxCMOQvcyPPvoonnrqKQDA448/jr/5m78BALz00kuc7F4qlXDrrbcCKOCiFC0rl8scXZUsdY7joFQqKW+tiPrJiPWdd97JxDkS6rRv3z7uj3JZ6Tetrq7y+y5fvoxWq6URwFB/zs7Osud29+7d/DtNU4YYhmHIvzc3N/k59Xqd+0ZqeiZJoukQep7HdZT6i+VymcXp0zTlue44jgZDpb9vbKhEaxmBJ9Y6gpi2220NuiS1NSkaeeDAATz77LMAgDNnzvB8OX78OMNvy+UytyHPc2xtbfGYbGxsaCRK1G+lUon/7rou94kkj5FarNd+r3JuO47Dc1QyAo9GSgRYfr8AeL0hkiF6lkRB0HdTqVQ0ljsZOZXstGmqBLNd1+U+ecc73oFPfOIT/HfWUBLkQ3mea9GKhYUFPPjgg1zHF154YTyeufCWZmiN51vNjGD1i28xXj0P37yjeK4gISGIMKB7Kycw1LdvyV0DQz8uSI2SFJmt5imvi7mClUpd0CAINOQFzRPXcBW8S5B2OI6DKI6EQLUJz1fQTAlvo2tG0YihfEmsYHZhohhJLVMxgqZZCsMU0Q/H4CggrYdUOBJnKKZW0zJZJ/HmQzfj1KlTAHRGaumuJ3F3ijpJbV3HdRD1xwgpTyFsXNdl9s0cOcM0JWx+FI2wtb3F9TaFjprUZpQwunKpzIfY4UCR18FUERLbsbmthqm050bhCL1use7lWY56vc5rXJIoNs4DBw4wOujQoUN461xxflhZWeG9y3ZszM0VpB1hGHJk13FUdFWS/1mmBduxuR8kCY+EZS4sLDBxjiQ7ajab3B+O4zA5Un/Q5/d1u12U/JIOux/3Z6VcYZhtvV5nGGqW66zQTKg2DBTRkudxXcNRyO/LsgyjcMTER7Zlcx2HwZDni+u4CHsh9ztBayWxkmVZ/A0Mh0NYhmK9lN+iZVkMlQ7DkOd9kiYMdXY9lW4S+imc1EMMA3EUC0byOY21Xh2PcgRBcF1t5oJxv+hD23a0fbdSURrPRF4jmb8NI4NE6cm5bZoWqlWPxyxiaHYqED1KX9G21Xoj0ywKpJ5CQShSG30/NQyIumRaNPH1974+ZjJW2q+e56FLCgIDEwZZg7kwDCUjaV5AoklP2/d9jEYRR2Vz5HCvAv400GsBpeMRcDgG8hypBQwrgLfuwMxswDD4HQaKb0KH1d7YckOMxdXVVTaaZmdn2VCSQpmSetv3fT7EtlotLc9JHsZk/iIthhsbG8iyjDeGMAy159ICIQ+bkrFxfn6eJ0+9XueDo+M4GotkFEV8wNza2lKsZ3LTdF1esKemprQcR9p4oijC448/DqAQYqd8CWlklctFWJ/q0mq1eOGK45gFxaUUQhzHPCFXV1e5TqVSiTf4Xbt2aXTCWZYxxb5kEpX5YRsbG1z36elpLU+KoMaS8VbKEhRipMUmeeLECdx///144oknAAA/93M/hz/5kz8BAHzgAx/gvn7xxRdZyuD06dPM8Hjfffdp8GKq69TUFBuknudx/y8vL7M8CFAYLu94xzt4nCT+ndoRxzEb3DJ3RkI2y+Uy/240Gpifn+f5trKyouXx0Aa6ubmpQaCpSAMjiiIl1iygFc1mk+fFpUuXMDs7q0F2rnewj+NYkxOhv0uRXNu2NbZXMsrkM7Msg+M4PBcAaHAcaSxSH7RaLf67zF8tlUoMuU6SBBcvXuS/12o1vkca6YuLixpsiq6RsFIJue73+xpkl/ptZmZGg5RLmYtSqcT9uba2pgkK05qUpimP5dLSEj+3Wq1qjhW6NwgCTepHfqdS2LpWq2nMzWSUnzhxQltfJG23ZDOmvx84cAD3338/f+ff+MY3OEdaP5TnqNcK58l8JYdvj6Vosj46lwrocHl+EaZf1F3mefV7PfjjfpLOlkl5e5VedYQzBzaAUXFoJkidAYMNM8kbICWc/JLKFbRsiw/pMm0BUFDAYTwscrkYlpYwFNS2beXYE4dN21LQ50q1oh3SyAgxLZVPZGe25nQJgoDbkWUZH+wty+IDdKlUYrhhmqZaGorMX6e8L9NQUjjkRCb4YMlX54o0S7V1V4q3N5rFdzfoD7hOtmPz91yv1bk/86wwnig1QDKJ5si5H4bDIde9VC7xadUwDFTHbJOS8dY0TZarCPKA15719XXs2bMHb5wocgXvuOMOvPLKKwCAg4cOcl+vLK+wk25jc4Mdv7t37xY5zw7DS0ulEhuktmWzQURpNvT+zc1N7FjYweNEdYyjmNuRZil6Y2FzYgIFCsPISA0eG4YXez4q1QrP136vr62jdH4IhgFDoJNIrWvSwJApQlEcMRTb932eF91OF+VKGU46NtgNsMFtmqZ6VqZyanPozONUTNPkueN5HhuzMAAzUzBQy7R4LgDCyW+A57rn6gzhMIq+sx0b5TF7qm07CALFAUDnddt2xlDQ4p3SSG82G6BBKAwuMmgzwS6qGOELw4+cCC73W3Ge4pZrMhe27cC2Kc1jwPWQKTd5nvFYdjptzfkqDSmqX5LEmtRPkuSIIuJUieCO4duep665/ZnbGA773HPPwR4oeGtOE/F1A9YJlZNJ41mv17Fnzx44XtGOpaUlBC+MA0qZCdu2kALY9V+m6MZlvPLaLlTdHHZQ7M0HPrCNg6d9lM/ugVNpwrCVMztjR8gIi2cXsXh2EWcf+RJuVJnAUCdlUiZlUiZlUiZlUiZlUiZlUv6eS5YDRvb9f7dGJjb3bsPITJhdC3Y8/fdfuXG5YcYiRaOyLGPv12g0Yu+CJLvp9XocLZNaaRLuJElppJeCmD8pYlIqKTYnScjSaDQ07TrJFkrRCEmKIYV447iA7Vy6dAkANC+qjKpIkWMpFn9tRJTa4bouJ5W/8MILTGRx0003fR+jqYQ6knfP931uE8FjgMLrQL/TNOUIyfz8PEeJTNNEu91m7wtFS6iQt0Yyx/b7fS1SRH0r+82yLA3+Q20/efIk7r77boaunD9/nmGlzz77LN73vvcBAL7yla/g/vvvBwCcPXuW4XQHDhzgerz44ot49NFH+fnUvqWlJdazbDabWF5e5jm0vb2Nb3/72wCAW2+9VYvC3XHHHXz/hQsXABT6iwTlvHjxIte7XC4zI9z09DTOnDnDcE6ag9TvNC+mp6eZvCZJEp4XMzMzDNeWkd1Op8PR1Xa7zfDNOI5x8eJFTReS3n0tjIgi0dvb29xvw+FQY/iU3+L1WFxJH0p6puU9MlJP/Vav1xlaLVlIu90u1w8Ae/BnZ2fR7XY1fUrJekjvjuNYY+Okb9ZxHI0ch0oYhgzX3tra4vldKpU0ZEG/32dv5IEDBzjSLwlr5Hctx7JWq2kJ8TJ6Sd7ZPM+1upumyfNC3pMkStepXq/z3JNzSkJ5PM9jVMBDDz2E5eVlXj9uv/12/t3r9TToKr2jXLYQrY6h9FUHo9UCXubbgD1VrElGqQ6D9TNtZGPWOfdGZdBPyt9LkakEHK1PdAI5CQvlfcyyOWpoGiYw3gbTJGXPeZqmSHOhY2ZaHB10bLVPO7bDz/J8T9Ouk2yhnF4iSDGSJFEEGWmGNEnR7RRri4zQeK7HkUzkYIigTHsxU7WXp1mqrTG0ZiwvL6M9JrKYak1pjKZJmmhQR9Lys22bYa9ZmrFmIgy1TuWZgv5WqhWOEhm2UZw/cqUfS0VqNkr0SxRFHG1zHZf71rItJaRuGhzlBdQ5Zn1jHTt37lR7RnubkRSXLl3CTQeKPfWtt97Cnj17ABRrKq1RrVaL67GyvIIDBw7w86l97U6bBe+JXZ4idEEY4PKlIko5NzeH1Em53+hc2Gl3GDa5a+cuPsd0Oh2ut+M42G4XCJlyqYzNzU2GeeZQpEZZniEdjZFX5ZJGUKfWx7I6W4nIbhiGvFeFYagIArO0qAuxCKcZLN/i8ZOa2bRfBWGg7W+MojEN7VtkFldB/pRnedEmEqfPVfoAMrBOo2UrJm+g+N4N14UBFW0bjUYa4mlqqjBIKpXy+NxdGdc9FcgkR8A8U36Waap0McsyBcRTfWdJkmAwKPpcpoE5jg3LUrqJURTBthWT/3Ac6ZeENXleQFSBgmiHxrLZ9JCmRKCpyGcMw2DmV6Ag15ERS0W6mTHks7hGIRxo7uU5YBhKA1ES3BAqYHFxH3q9HhPh7Ngxz79Ho4jZVAHAtMk2MZD2x4yp352B+c5NbN++gmHpCu5/+i44vRYM24NJLNOWiTxLYIwZxzYqOIkbUG6IsSiZNfv9vkZtLw8/tJDs3LlTO7zTYdE0TS1nScJTmcFqDBGUkEEJ56OJuL29rUlIXO+wKPOcJONqo9HA9va2lucoYbbXSmxQO6hIXLbMTarX63zvu9/9bnzhC18AAHzmM5/BRz7yEQ16Sr/n5+fZwCiVSmwozc3NaRBDalMURfwBtVotrrfM5aMxk5BdWjQrlYomDyDHjw4dGxsb3DemafLhQsIvtra2sLq6yobZH//xHzMs9JZbbuHxOHz4MBseu3bt4kPv888/z0bkK6+8wqLjAJid6rnnnmNozKFDh7Bz507u0/vvvx9/+Id/CKA45JOhurKywof/vXv3smEmISbD4ZA3wH6/r4m1StYs27Z5nKJIQPj6fWaqJCMCKGCM1NY9e/ZcV5pAGugkXULvkDmS18o1UKlUKtq3KB0QZEzJ3BnbtjlHZTgcYm1tTYOMSEcMvXPHjh1Md16r1Xhzmp2d1Qyo22+/netH896yLPi+z3O6Wq1qObnkdNrc3NRkVKjdEu55LVyYxkw6hM6dO4d9+/bxdVKaZH19XWP1o3ukQ6hWq3E9KJcZ0I06ei4Vy7K0PFM6YAD690djU61WtZwOmYdBc/3BBx/kb4vyaGmtPHbsGD71qU993zu63TbW1wuYdTUpozHecKvlEhw6+A86sCvFt1yqVDEyxgy06RBWMqa6/0EKgU3K32upb7u4+QUbb93b16DvBaRKzWGaM7VaTTmS0kgZHobBuX6OrfL2bEvtPWmSFtC58UExTXQ4H8H/wiDkQ2/JL2mOK7rGlEyMyPnw7vkegjDQ8hzJ8KyUKxqc1ogU7TwVQ0DGLMsq6gzdab13cS/OnC5g2q+/8TpuueUWlQ+dqXzoaqXK+6vtKPhupVJhiKhpmgyTTaGM4ZJfYuNS5vIBOhOs7SipCNdx2UCUsiSAgqsPh0M2mKQTuYCxjdllgwD9QZ8Ns+9973tY2FGkZszOzvJ4TM9M89per9XZgL5y5QobkVdXr2JuLDpeRRXNVuFkvXz5MjsLp6emUavVuE/37NmDl15+qWiT57Kh2u+ptIJGo8Hrpm3bPC/iOOazh8whN0wDeSzF2NU4pWnK/RZFkWJ1D5XB5Lout7VRbzCE1hBrn2agj6VLZG68zE2nQgYrUIwfPVfu31maoT9QBqzcQ2mNj+MY1sDifpABE8d2NNm19bXCAWoaBafDF3/if8LU1DQM8l8YBubnd4x/q7NrIcmgWIsL5vdiDHzfE+z7AXyf4Jsq9UvugYUEhoIL05glwiG0vb09dsqr71HN4wFDRLNM3eMKmK3nuVyPwoAlmLy+X8nzlWFkLM9hWTbimKQ3gHz8zT30s0/j2U//R8iSwsmkUmOYxwZZlqPVKs4Fi4uLbAQOBn1UKmXO75ydncNrr73G72cH2HcsdD/Wx0OvruDbwxZ8lsqp4Op3C1tn982r+Po/ehHl0fVlMsw8hxPZCOzk5ete8P+y3BBj0XVdLfePSqVS4UPe5uYmf6TSw+44Dh+sut2uZiDS9Xmea3T+cRyzYZemKb/bdV0+/O/YsUPLa/j36dtIWQNanCSxDr2TrpMH1F6vp+iZhVGY57m20NHfy+UyRzLCMMRP/uRPAiiMus997nNMnz8/P69JYdA7hsMh96fUsPQ8jxdm2f9ra2t8vW3bmJqaum59e72epu1HC4+MIErKcLqO+l/mW1GJogjf+MY38JGPfARAQXhCUdW33nqLcwUffvhh/MEf/AGPGT335Zdf5mjUPffcwwaN67oc9XvggQfwlYyvaOEAACAASURBVK98BUAxdxzH4Q3wxRdfZIPtmWee0aQiqJ5TU1P8rOXlZX739PS05n2kZy4vL+P48eM8hpKGO4oibR7KnDUyNqThKOuRZRk/Uxrccn4COhX9tVE1SSpFxnAcx1wn27Z5g5eETfKQlCSJdg9JyND7aL5sb2/z2Dz//PPc1qWlJdx22218PY1xtVrlTXV9fV0zbmVEfzQaCS+eImoZjUZMLDUcDnkDlVFGGXXNsox/N5tNrK2taf1F9S28jooASEYWpYHIB94o0saQ1ieJfKDvQR4Yrmfsk+OB6k5zQfbN4uIiG4unTp3iOh0+fBhf+MIXmODmr//6r7U1jX6PRiO0x3mjnWSIfXPFXK+0ZmDXC6PbKTVgkwZt0EG/XxyYtrdW0RrncSg32KS8/YqBNMswikYcHQMKEhWaJ8NgqCQ1MhWRMS2To3Oj0UgzEHkvyNV+adtFJJIiYbmpcv0sy+LvrlqtsgEVJ7HmeGGJA9PgPC7f91U+oCDWoXeS0ZWkCR/mR9GIr5FGYY6cpQxMU7XPcRzOWXQSB0ePFjqAfsnHqZOn+DusVCtqDRZRVElklaXKELcttf9LUpPBYKCkj8xCU47ukfWNRpG2LkldQT54xom2vnEUNs+0fE4qaZpiaWmJ0Szzc/OKn2F7C/282Pv27duHl156iceMnrtydYX1EHft3MXrMTkCgcIRS+R6o9EIpqWcyssry7x2Xjh/QZOKkM5JepY8Z5VLZe18Rs/s9XqYn5vnMTSg+DLSNOUormVZPJeiWGlNSsNR1iPPc/VMYXCbhok4jYHxkSeOY+3fqCRJwtFdeY7NUqWHHAQBGvUGv4MdApattBuzQv+UvieSkAHGkcnxfAmDkMdmFEXIswxRFKPTaXOOvGka6PfVWYWJeYYDSGozGexJkpQ1DSlCV/RVwmePOI4RBLS/mZpjqlQihFPOv31/BwaDARt5ADifEDC0PU1JYeRsIBZ1MHjMQjGGlIMoJfiyLIdtW2zYmWamGfszr44JrvYCYTBCOibZUrIr/Hg0mw00m4ow0BvPo5mZaZw+fQaL+/YDeYa33jor7leEjKvftrHY9HH1R7cx+oyHZmUcLffLMMd131ibwdQwgGXGgKEIu+IohGOaSJ0Uy7dfBgz8c9yAMslZnJRJmZRJmZRJmZRJmZRJ+SGVM1/6mR92FSZFlOalJv/ec+tHMLfvQbzTKuPLn/pVnHzxLwEAN936fjzy4d+E47iIRx089+/+BbbW3sT84r24633/Ar3113H7Y/tx4ZU/gV+ZxYE7Popg2IdhmPjs738U68snMb3jZnzg47+H+vQs8rKFLx/6I5xb/ux16zQcFYa/YRoIo8IQD4M+fHLiZ5fxS9/FL+E+/M8/6P64IcaizAlyXZe95zJq2Gg02AMRBAF7oxqNhpabQN5zCXvL85y9FElSeDLI29br9bScx2ulLah+5NWU8ATbtrUcJCm7IaOf0qMXRRG3D1C5fqPRSMtVJE+fhHVKSGGB4y7q9CM/8iPYt28ffvd3fxcAcOTIETz22GNcd8kcS5408lBRm8hrdPXqVY4SytyptbU1VKtVxt+naarJncg8MKqvxJdXq1WO2pqm8hpRP9L/S+z9+fPnmZr8wQcfxMmTBbT6gQcewJ/+6Z8CKHL0fuZnikVzdXWVoSCbm5v44he/CKBgUv3GN74BoIhQ0jVxHHMu45e//GUcPnyY58+LL77IuXQXLlzge8Iw5Cje9PQ0du3aBaCIGlIEEVBRo4sXL3J/+r6PSqWiRfsIQul5HrOsbm1t8dzdvXs3R30vX76sRcQl5TzNPQm5pDlJfW2aJkdLt7e3mW20Wq1y+4Ig4AhupVLhugdBoPKSRiOOtA6HQy1iHMcxf0OO4/C8P3fuHH+n1WqV+2pzc5O97u12W8trpeje+vq6BnPetWuXFnmlOTo9Pa1FMqkfer0e96dkOk2ShMdCMs1KaLrjOFhYWMDZs2cBFDArGS2nyLKMtEsxa+m9Xl9f5z4sl8si30HJyiRJokUWDcPgtkrmuTzP2Zu/srKihL4FDGl+fp6htZJB+s/+7M9gWRbDt4gdGCjWJ1pnbcuEN/42K5UK/FYhRm7N7INVHo9lrQokY9jq5gbC4ZiRuWIj6hftC0dDTMrbsxj8P0OLOpumydEI31OC5HESMxuj53scncuyjKMwtmNrET4pw+PYKr8vSiMt5/FaaQugiJ5QpE+mb5imyfdKWGaWZQXrpKCql7BtiW6hCGeSJnBNtcZRRMZ1XI6i5sh5H8uyjOu0Z/ceNJtNfPvZIv99ZmYGBw8eLK6zVHSo5KtUjmtTBEjuQKYFpFnK/TQYDOC6Luc/5lmu1rVUrf+GqWRC4iTmqKFEskjWZAN6fxJbJ3Kgvd3GxkbBwLi4uIj1jWJP3Lt3L1595VUAxV557NZjRR37A15/hsEQb54p5KfuuOMOTh2Zn5vna7I047Xr7NmzODRziGVUlleWOd2g3W7zPUmScBSvVC6hXivWx16vp6UfUBS80+lwf9q2Dcd14CZqnAdjCKVlW8yyGgQBorQYn3q9rtBV3R5DnbM8UzmgpsVzz/M9FT3OM22tNgyDzwxhEGI4ZhuV5+E4iXnNdx0Xjlms32495rFJ0oQjrTLtgfqUviHLVJHl7e1tjkq7rqulBdmOjfc994tIkwRvfvTLxb2WzdG9wWAIzyNoq4t6vaaxhVJ+X7lc4jxE0zQEeiXS9sRSSa0FJBcBZAJFo87VlmWiVqvyObRcrghbINb4Fgg6Wsxn9fErfoYBi9Y7jqNE63MFsy3yR0t8XZ6b3NY8z7XIZBR08a0nfhbdUQOP/9wf48z3Po9SdQYf+qd/gj/9nUdg523c/Z7/FA//1L/Ec5/7BVQqVbTmbsb/9YlfxtWl7+K2227H3T/7ZfzVv34Mb7zyLCzbBWDCcV18+J99Cv/uj34ebu0iOr+U4Gdu/x7+MjqHzf4FmOUmTGc8lp5bMOEAGA0DJPFYMsQxkUbjdRU53BSPAG8TY1ESW2RZpsEHJLyKPhrP8/hQLxd4qd8mDz1pmn6fMSMPiVJ3jxbNubk57QAtMd3SuJQLO71bSn/Q/0v4Hx3A+/3+damQZWhfwj9c1+UFhTYIoFjoDh06hN/8zd8EUJDB/OVfFp6MUqmEd73rXQB0PT7qRyq08EvYa5IkWi5KGIZsZJZKJe53CZWUpBgyV3MwGPAYSFIhSZd8Le2/aZp4/vnn+X5KkN+xYwdvFt/85jfx4z/+4wCAp556CnfddReAAjpKkMQnnniCx/jJJ5/E+9//fq4f1enOO+/EE088oRmur7/+OoBifn71q18FUBitVI/9+/fzwnrmzBm1EFtqIZaG4fb2NuJY6RVJEqVSqcRQWWl0SqfDgQMH2LiU/ST1SGu1mmbMSkkHz/MYYuq6Lm8k6+vrbETu3r2b+63f72t6ndfLhXBdV5vv11KIU/t27dqljTnV6c4778TXvvY1bh8ZMJZlMQypXq/zNzc1NYWlpSWNDIoM6DAMtYMffStSAoT0pqg/6FvsdDpslMtcZILPHjtWHHqWlpb42yTNVqA4tDB1fRxrWlA03kQARPWgbwZQBF9EjkUlTVOu16lTp1jiolarsfTNxsaGZgxTru7i4iL34ZkzZ3hcNzc3cf/99zMBFxEwUXtp/JqNFuZmi7m4sHMOi0f3Fe8LujDCon+GuYF+Mj4wxX3uWyerIEkK5xAnu0zK268YY2KUMWyRvotRpEjUYKgUAtuy+VAvjTSp35amKcsdZHkGMxf5XQIulWWZprtH8D+ZnhIn6qDsui7D7kzD1PY6rvf4QMe5YFKywDDZeIiiiP8u88akM/RaMikyQqJY5cKZponpqWm8973vBVCQwZDT03Zs7N1TOGAyLwPEZ0KGHABegyXsNcuUjqDnepqzy3aUzEgcxcWzASAHTHvspE1UP0exykW1HUUMlGappmco+8EwDHYyxZFKPZB6zxcvXsThI4cBFM7ChZ2FM/TC+QsMh3zjxBts5J46dQo3HSzIceIkhhkVdVpYWMCJN05ohis5ni3Twrnzxb65uLio5MOaLT6HbG5usjPDMA1UUeV3UAmDEFmq9lHf8/nfbcfm/U4anRJy3Wq1OG9Q9pPUI3U9F8PBWGu6XNIkHSxbkbBZlsUOieFgyPtYvV7nfouiSNPr5OlsGKwJaVmWJnWT57kyKrOE21er1zQCKJlDGMQJ/NUmHMcRJIYGtraK/igcmwr6224rWTTTNFnfsICkUp9knE9YfEvKwCOD0rYlqWTIe6Jh6CQ4aZpgdrY4M3Q6StfZcRw25Io0G4IqZ7BtsjVyxGMStizLuX5xnDBUtehHdS8ZnXQP1WtjYwNXZ6/yv7363J9hNNzGlSvfQ721CzAs7D/8LvQ230SzEqPR2IUT3/kEjj/061i6tIJW1MLhtbfw1omvYc+ePeh2O1hb+g7u+cD/iPLMZ/Hmq19Ee/08Zncew8yOI/jQP/0EjKkMcHNYmY/FXceRbrUxjEdAUvRPDANRNjbKM/WNW7mNLCvOLYkFfPo47vwF/ODLBIY6KZMyKZMyKZMyKZMyKZPyQyo77ir4FjZf++APuSaTAgDJQ8r5kCbEPzKO4Fm2znBznRJHfe2/n/ur/xyJtQMzu+/GP/mvnsaX/vQX0d26jGF/A3/92l1YfzDG1c/vhG1MY2Gu+e956g+v3BBj8dqoE3ni+/2+xsIpCWCoXEvUQtEymVgqCSBarRb6/b5GKEEWt+/7/I7BYKCR0lCRouVF4q0iBpHvtm2bPWyO43A0Skazpqam2PspyUek5ICEawZBoBLJt7b43ZRITO8/dOgQQwm3t7fxR3/0RwCAxx9/nKEdEhoro0ylUokjU5VKhevabre1SGoYhlo0WLLTSpgf1Un2uYx2VatVTbhdwoUNw+BI2ubmJjOafu1rX+N7er0eRx+73S5Onz4NQI/6rK2t8W8pyfDTP/3T7KFcWFjAxz/+cfb6njhxQotSEdnK1taWFi0lyKXv+wzrvPXWW7l9BFMECu/25cuX+Z44jvmeHTt28Fx3HIehFWEYctQpDEP2bEaRgm/IeSHZbOfn5+E4jhZFJy+1JH05ePAgj5Nt24q5z1Ri1lmWabBJOdflHM3znL27vu9zmyTbq5QJqVQqHJGfnp7mdvu+YklrNpvcnxTtpLpfunRJE9al62REV7IfW5aCBY1GI763UqnwNd1ul3+vra2hXC7znNm1axdHUuX3IOHeaZoqivQ01QiAaCwlWY2EmpbLZVQqFf4GV1dXmQ3tzTff5Gd1Oh2ek0mSMPnQxz72MYZ1ffnLX+a58Mgjj+Cpp54CUESPZV9R/9P4kWfY9Vws7Crg6Tcd3ovVftGnm50+Du0qvLmODzj5GKrqVNDZHDO3mjkWdhdRkzBW6+ekvM1KXpxv8iyH5ShodxRFGgsnRbySXHne0yxVRDKWzdGyPFesk3KfLjklbV3LcgXTs22bYa9xFDODKf2tqKqS8MjzXCMGYRbI3BlHPMbSNJapGMkThQgolUochZHkIwYUSshwBJlHrPa6IAiQ2+P2GQVZDb1/emoa9TuLtToMQrz8vYKI8NChQwy7l9BYy7KYUdOxFYmO6yj0URiGmnxCkiiiHilrkmYpRy+zTJHXyD6X0S7XdXl9kyywWZYBhoJpLg2XmNH0woULfE80ijj6OBqNsLlR7HWO4yCPif1xwBHj0WiE0RvFWnHs2DFFaFSr4vhtx7GxXqCf1tfXGTaZ5Rn6PSWrQPMiigWc3rYRDIu1a25ujtsn1z3TNNHtdtV5Ls34nmq1ynPdMi0EcfH3btLlM56UMkrTVM2dJNaJcsZ9U6lWYJmWFkXnlKYk5XpMTU0phl/TRMlUTLWtm4p9YePVxxXkGrn2nUkEUp4rwijbtrlNMgKf5aoeGN9P+ycz99oqci33aYp2Ut273Q6L0xeIFYKnqjUCMJigxjAUAihJEr7XcVy+pjiPFL8pBYXOKLVaHSOKpBom8pwQgIppOM8zZjDNshyZIACSUnsKjm7w9Y7jwHFcluTo9wdYW6Pz6ZbGmnzlyhXEowH3U73WwHQ1RGv+KKbmb8bp15/FoXf8FFYvvYK5mSaTwNXr9XFf5Sg3dmL1rVdw+tWvojV7E+b3vANLp7+GJA6w7/hHMTz/JBJjGof3HEd7eBVROkQQRpiqFxFgywajNhzTwSgYv8XIURsTIm3jxpUbYiyWSiVNb5AOkq1WS2MUpcUxjmMNLipziCTLpmRllLlXhmFw3sDS0pKmuyOlG+jAODU1xYuepPGXkFlpkK6srGD37t0MXajVavx+SXMv6yjzKkslpeUjD29ysWk2m5rxNRqNuL5SN2/v3r349V//dQDA7/zO7/CG9PDDD2sH1+tpUiZJwtCDLMvgOA63fWpqitvrOI4mD0H1CIKAxy+OYy3fkd7Rbrc1qK1iftKhPkmS4MyZM1xHmVf53e9+l/uBoHV79+5lp0O5XNby+8gg+au/+iv81E/9FIDi8N3v99kY++AHP8hQXrkRnD17lnML19bWuE2bm5v8OwgCHr89e/bwoX5xcRGnT59mmKDcyDudDufura6uKlpr0bdUb/o7FQn3dV2XNydppFDfUh2vzQ+la9vtNs8dKcOg6ToZxnXz88jYJ9gM1Q0oxpzaIWVthsMhMwd+/etfZyfH+9//ft50pKE5HA5Rr9e1/qF+aTQamtODDDzJfizp/6XBJKHiUkqGIOtkQPd6Pa7XhQsX+LpGo6EOVsL4k9Iw/X5fy7Wkd5fLZa5Tt9tFv99nJ8OBAwdw9913Ayjg1N/5znd4LKWTjCCwTzzxBLf1yJEjPE+++c1v4p577gEAfPe739XWVnLIUNvp75VyCXMzxVg6boS1q8Vhb+f8TtD06/Q7GI7nTsn00CoXB+HMSpCM4adGonK0J+XtVcJKitW9w3FOTsjrRMkvaYyd7AjKUg0uyuuHYC3N8kzLM6Q5GicFax85gtqdtoKDm5Ym3cDSGaUSur2xHqug8ZeQWWmQ9vo91Ot1hup5rsfvlzT3eZYzIYTMq3RsR6We5IpbIMsV/N73fc34SpOUYYUJEq5Xo9HAA+96AADwrW99i+U89u3bp+3HnKeoMTNmnCOX53lheNDYlEqwMov7jSGxhqnqESdsyKdZynDfPFN7koT103uon4vmK4OfnJ5S0ifNUixfWeZ+6Iw63G7JS0GOBtMyeb84ffo0bjlWsK2OwkJvm/aPQzcfYqdulmXcvq2tLdSqKk2H2jQMhgx1TWIF16036mxoNpoNbGxusNMNOXi+jcIRS3oM+gONJZfPOpFyTlqmxSflOFGODctS0hVkWEjWVKqjaZr87hw5XxuGIc8dA4Zi0o1iBUOFgQQqP0/CrQ3DYKg01Q0YO4IofUDI2pCuquF6CIKADaObbjoIx1GQcrJH45gMY2ILVQam73sMMXUcleqUphlc1xHPUjIYKgUNgoHUZCPSdQ0kSQzTpFzfEewxvL3dbrPmoudZ4ntUjKlSGqY4945h2VEM01RnW6oT6b7T+ajVanH6zokTb+ASLnHf0ppDdS2Vfbzy0rfQG/0y3vnY/4B7P+hhFGzjxb/+b3Hx4kUcv+c2OI6LJEnh+yWYloV3Pv5beKfpI80SdLcu4enP/jfIswR//q9+Aj/2X/8vuGX615AslhCm2/j0d38VozhArVIDkSaHUYh4PHccw4LvjNMDjAyZoc7cN6pMYKiTMimTMimTMin/AErsZ+jMRn/3hZMyKZMyKZOC//7nTS2i+3/8xgL/vnTmGVw68wyazSaT/wDA2sXn8ZU//jD/d5bGeObP/jGWlpbY4DYME5ZlYnvtLJ65/OOI8houXLwJrZqDIP7/HoncDTEWZVSuVCoxCcf09LSmNyg1F8milwQz5XL5urpn9Xqd4Yazs7MMRQUKD4CMfsgIlGT1lHps5A2Snp6rV68yM2aj0dCId6SO3fb2NkcjTNNkQg+qH/UBPTcIAm5HGIbKK1avM7TV8zzNQzsYDJTHM8s4gvgLv/ALHIX75Cc/ySL39913nwZJYxFfwXJJJCFUd8tSCdkyAtnpdPgaCc0zDIOjfrVaTUuCllGYaz2ZLDQsNIbku+n99A5699LSEv8ejUb/N3tvHizZdZcJfne/eTPzZb6X+bbaN1WpSipJSLJWCy3e5N1hGugBE0DjCNOM3cS0Pd0dQwQTtmmajqAxmIgxY7DBhGnwRngsGy9YNrKwbMlySSq5rCqp9u2tmflyv/udP+49v/M7VWLEgApTkOcfZT1l3nvuuWf5Ld/v+ygaxJkuz507hz/90z8FkGsxzs3N0fd27dqFO+64A0Ce8RKt0+lQ5qzT6Sh6emKcl5aWKNPjOA5dc2pqCu12m4hstm3bhn379tFvRMZS0zRaAwcOHCBI4uzsLH2emZlRhN9FFm1hYYGeb2lpCc1mk+b05s2blTkp3lmr1aL5xrOCvu/LiCODe/CsZhiGCsGMruuy8N6yFKImHvkW7991Xcqofve736UsoxgHQIWU6rqOdrutkOiI/qyvr1P/eebU8zxlnxDXa7fb1KfZ2VnKAI5GI4KamqaJRqNB4ysy7EAO8xXf6/f79P77/b4CF+e/Fc8tGJPFGIq/dzodvPWtb6X94gc/+AE90/z8vEIyJD7feOONlI256aab6F1+5zvfoed785vfTH3a2NjAxsYG9u7dCyDPeHKI8Gwxdxeb05hy8ve3sb4K2y6QHeEQG0KXKwwRF9HL2LFRqRQoCKRwjPyavWEPk3aVtkxmPyzTon3XK3mq3iDTXOR6yKJxxmbDMEgzkDONl70yHFcyo2dZRlmZzMgIcqobMmPCoaM8285ZoAeDATFjuo6rEO9wHTt/7FPmR9M02tdE/4Cc7IQQTrFkm9RiiUxwHIcQQ4ZpABokw2sUUnYvyzLKIN588824cDFHLz17+FksLObEUlu2bJGwSdOl36pC4ZoCddV0DXEo0SSC1dUPfIIoGibL3EBDr5uvUduRJSmcVEjcAwC0TMuTR0SOKktzdE1X9lpOgiTu3d3oUl/jRGZaE8Z02e118cwzzwDItRh5Scz09DQhUM6cPkP38sc+Zc78sU9ES2kiIYb9QZ/YQk1Dllw4jnMZy36z0aTfVOIKPYdYA7Ozs0RY45U9+myXbMnqbTt0blYqFamxPci1qcWcrk5VlTkpssaj8YjmG88KxrHMDOuGZCbWDOmkJElC1xFlPYIAytAZUZOW0d8t01Lev2lZ0C0Tvg80m7OQrVh/DFIqUDiSLTQlhyhnTRd2r0mZQsvSlH1CXC8nlcyvUy57dD7mBHCyXKtU8pTyNF0XjOtlYicNw4DefxCEClyc/5bbzMIMTZKEIeV8XHvtPsperq6ukh5juSyJj3Lobv55YWGeznJum507d54gsPv27aU++b4P3/cVXW8x1lmWwnVKxbMH0DPAMTX4oyEMQ5zfIXxixk2QCjSHYcC2i+dDBoM5s5EGaWC9jO2KOIvtdluBjAioZKfToQl9+PBhMtJ27NhBg9vr9cjI43VqURTRBPF9n5yySqWiYPRHo5FiQO/YsQNA7vRwR4czh4r7CZgjkB+G4n4XL17E9PS0whYqrrW4uKg4f8KRcF1XgX8KI5TTH6dpKusr2PP1ej0FQuN5HjkStm3TJjQ3N4f77rsPQF5XJ9hTDx06RIypd911F03oS+u+OCulrusEaVxZWVEOSg7HE33nEge8ViAIAnKAeOOwQOByx1F85vfg9aRctgGQQYVL2WDFxv+Vr3wFBw4cICZSTdNIOP7xxx9XoAiCPXLfvn3KvDp79iz9XXyf1936vo8bbriBAgMcCthoNGi+VCoVuvexY8cIGrO2tkbvfH19ncbRNE1is52bm1PkNUzTlHTbto3z588DyJ1N8ez1ep02Y9+XzGP5ZizlasQauFQug8OhwzBUoODie7wWJggCpb6Gs+du27aN+iHm8IULF2g8d+7ciXq9Tn3xPI/mFa8bdl2XnpXDgjncu9ls0vMNBgNy6hzHUWqGOp0OzbEwlIcNh8eurq7SBm8YhuLIcwkPsbc5jsOgODJIdfDgQRiGQXW41157Le0zIhglxlMEM3bv3k39ffrpp0lu5rbbbqOAEJDXPIo+dbtdCoJwiLFj25ht5o7n7m3zMJOi3jUIUS5EhNdaa6gWkCnHtIldDlmGQVgIkxsZ3HIO3erwEpVJu6qaPs5QXTXRaeSQxJJb7Af+mBy2lfYKOUP1ep2cgiAIaG1blkWORwoZcOGBRtu2c0HzTLImcwNaQLMDP5A1RZrKHMr3MdEMXTIr9vt9uCUXZlrU60WyPqlaqZITG8fSieGBpzRNiWEyTVLF8RP1YEkqA1iiPESMlWVJmJ+hSdbscrlMtsfc3BweffRRAHnATzCmbt26ldYpZ/IUAvICrqhpGkEwh4Mh1c+ZBpMs0UBQ3iiM6Lm5fFecSCkKpTEGWaCAowqJlCyVTiU0ugdSefZy2QZA/j3NUujQqa/iXDl+/LgSzIOW134CwIXzFwiCGScxOhu5s9doNpR5JeyphtWg74dhSA5lHMeYn58n+5HDrL2Sp/A7CNH69dY6akXt12g4onc+Go2Uun9eny8c9yzNzyEefBfnD5d948H7OI6RRBLeTAG+OCHn+1K5DAUOzdi44zSWTiE0uc6SmN5thgyaBrQPnsDZs2fJjonjmOZwv9+DGNDp6Tpc1yX4Zx4slraZ+LtpmqxEJGbBISg2rHDKwjCk8zGHdQrbT4fvjwnemts7Bn0W/tBwOCQ2VF3XmDyXrdgkQg7ENA3EsSjRSenz/PwcNE2nkrRms0n7zNSUDLJrGiiYMT09Q/1dXl4mO23z5s1YWJAZx1YrL6WxbQdB4OPMmdPFc6T0HIZuwvNKKG0JsLQtQu2ICz2NkCYJ7GJfHo5GcIp5aOgG6JaF7gAAIABJREFULLH+kJHki64DplnUmwNYreI5XIGmv/RXJm3SJm3SJm3SJu1qb97IwuL5ykt/cdImbdL+RbbzDzyOQ9c/9KPuxqQB0IwU6+/sYeqFOtba8//4C6YaAPzRP/5Cl7crkllsNpsU+RmPxxTt39jYIM//mmuuocjS+vo6RQdqtRpFE7vdrkJeIb7DC+03NjZQLpcJblAulylbt3fvXsnkxXQaORHJeDymSP/s7CzdOwgCuo7IWIlIU7VaVYg3eFEzZ1AkqASDsfV6PSKg4KLEnL1JEGeIaFa5XKaoGs+2lctlIlupVqv44Ac/CAD48Ic/jC984QsAciIMAY288847KdMjIKI8iyPGpFQqKQQkPPMiI0IymjgajejdnD59mqJ5PNrIGTYBVVeQ/1to+136Hc6syYlaHMdRYMQ8CnfkyBGC9rZaLcrc7NixgwrqOcxydXWVtO7279+Pb33rWwDy7DEXRhefW60Wtm7dSvMnTVOCDx4/fpw+j8djyhratk16j3v37qXxFyRKYmxFRrTf79O8MAzjMo1B0top/h+QZ/AFhDIXw83X3MzMDL2bPPImmdhEpq5SqdB4GkZOsiD6zgmn+BrixAmcBGlubo7my+rqKmW2Z2dnJfEA8oysWL+cmc1xHIrihmGoaDZy7VAR1ez3+wq7r+h3FEW01wRBgOnpaerLpRFxkU3kDL+8cT0sz/Po3ZumqcCnBRT3wQcfxDe/+U1aQ+vr6zRHFxYWaL698pWvpOd79tln6bpbt27FvffeS/fjmXQRGXZdV0EciP4DeYR065b8uo3pEkyjYHObqWNc6DeNRwnmZvL9yXNtpAUDZhD6GPTyfsxMT0F3innoli8bl0m7Opqm5TkxDRqiOCJ9RN/3af40ZhpM3HpEkDbXkVmRwA8U8gqRBbNsi7JSvu/Dtmz446KUxLIpW9doNBTouYCOciKSOIppDZY9yW4cJzFdxzBzBmORCXVsh7KDXGMQmSxvyNIMllNkORiMLQgC0tYT4wKoTKwI88xdoif0vERMw9hbLdtCf9CnPj1w/wMAclSLYPg+e/YsGs18v9m6ZSutZ03LYaEiO8j19EzLpExaFEWUhU2ShIhlNF2jjF4URfRuNjY2KBsssmx0P9a4rqDyb03ul/w7Ja8k67CQ9/2V6fVYwAy+oT2Njpbra1NWEsDq2irZV6PxiM6+er2OtfU16pfo+3A4RFgrtGdnm8QQXalWqC+VcoXGZjwaY6o2RfMnyzI67y5lnucavAIl1Gg0aPwH/QHZb5quybMkDOj81vQiMyiyopHUmNY1HZmRj5s/9uk85wz2pVIJo1ZT9knoSEKjM822bTn+eg5BpXKqKJZILTD9xTimd51lGbEhl8tl0hgcDoeIY3GmlVGrTdF7ct0S/II8itsepikJmJIkIftUaCUCquJBEIQK+kgiDxNWypGTwcSUZZe2XJpmlE3kqCbecsKoYh+yLHr3uq5D14XdoqNez9fZnj17cOrUacpejkYjYmmtVCqY/tPcftt2cBs93+rqCss+1rB9+w66n4Dr5uMm2ei5rSz6D+SZ0PpsCW0LuLi0A64hdFZdRGmhQBClKJfya1mmgQwC5RUjLBARpZIDzSj2q0wHkDx82eC8DO2KOIudTkcx1IQx1+l0SC6BQ9e4KC+XmQjDkBZpkiSKbAN3pMbjMf1+ZmaGjPRut0sbUr1eV5wYXu8jHCgA9P1arUb9rtVqcBznMmkL0cSzcsM+TVMydC91gObn8wiCZcmakTAMqX8LCws4ffo0fY/LH5w5c4bgoo7jELzx0KFDBHt53/vehw984AMA8gUgGBe/973v4ed//ucB5DV9g8GAFiqHq/CaFS6jwZuARIrvC/a0Rx55RHH8+Djxxmmg+SbE/87Zrfr9Pm1OmzZtIueC1yzatq30W9M0Clo8+eST9BvHcRQoCBezFxDaRqNBz9dut+n9hWFIcMhdu3ZdxtInYAlbt27F8ePH6VriULn55pvJUV1bW2OCtKochJhT3W5XmVOO4ygwX/G8nIW2Xq8rsF4OMRVjy6ExfGPltUH9fl9huuNswbzm0TAMcm5y2IqUzRHjXyqVaO1zFlgx/znTrbjH8vKyhJRVq0owQ6zTarVKNVWNRoN+OxgM6JpBECiQokqlogSnOAuweKZSqUTz5VIItAj2VKtVus7CwoLyWzHO586dU9Z/t9ulvpTLZaprfeSRR2j979u3jwIbSZLQfvbUU08R6/OFCxfIuZybm8Pjjz+Op57Kafsdx5HOYrWMzfO5QTo7M42aU9CwJxmWz+XzKI4zzBT1PIaWwA/yfmi6gzTNn2/bpm1kuJU9OZ8m7epq+b6Q7zVJKuHSY3+M+bn8vImTmBw2y7aUIAh38EhuJ0sJNqewE1t2zh7J+AvEGRr4ATl8rusqTgyv9xEOFJA7dgDguBLy7TouDNMg5y5NU8UREntRGEgWUs4WrUFDFkgHqFLUKumGDiOUEDjRv0qlkgeoK5LpUjxvd6NLcFFLtwhaubS0RJDbu+66S4GLXzifQ+AuXriIG2+6EUBe0xeGoWQ9ZYFWvpfwgB1vAhIpvi8kI86cPqOwbOLFj+Ycrgh2hstiRvo9NMDU5diKvaFaraLvD7CYNvAn2lfwK8lb8Tr7P+Nn8Wql39BA8N/hxSEFFAxTCtinaUoO5mg4IgitV5Js0zw4mSQJ2WzT09O5w61Lh1vYY1O1KTqDSl4JrpGfGYuLiyTnMRwNUdEkTFb0Q9d0mlN+4CtzyjRMDAMJ8+VBbK4CwGG9PLh96q9/DgBgWhJuqhs6CjJUsmkAIPIjuI6UueB2iFLzqGtK+ZWm65jqzGKm36caQMsyMV9IpfBSJR54zb9n0T0GgwGD8jqsZtFEINapY1NdY6nkybUYhszeSKAXgZcoigrouggWuyxwLZ1Q07SYA6s6YsMi2JP3Kb9upVIh6KllmQQD7XZ70FgAJAh8gtlalo1qNX//Z86cJmbVZrNBgY3cgc33s+XlJTqne70+OZflsoXz5y9gaWm56LusLbUdE4O3r6He1zEsleCYxTpPMwx6QqEBKBX2n44McSJKwkxyTmvVGgUHLC3Bf/g2/hx34/V4mduEDXXSJm3SJm3SJm3SJm3S/kHNyHR4cHB7sA+NqIo3lP4PnItW0NWH+Gr43/E140lsZEOMtInszo+67f6fr8Vc/04cvu5//qi78q+6Nd40QqceY+nR3ajXXvr7f5+WacBzTTRf8fJcTmlXxFms1+sU8eh0OpS237lzJ3nxg8FA0T4TGYHRaKRkAznURUSJdF2nyEuv1yOyFvE98XshiA2ocDPOgFoul+kenHCm2WxSRsd1XYxGsuBZXAPIIzEie8Jhs0EQ0L1nZmYo4sWF6ldWVhStSRFF7ff72Lx5M0EiTNNUyE9EP1qtFsENOTR2bm4OP/uzPwsA+NKXvkRQzCzL8IlPfAIA8K1vfQtvectbsGvXLnp2Ee0RER5AhdmmaUrPFMcxZdFc18WRI0cA5FkjHun7u+CmPJvIszY8G3n77bcrou4ioxOGIR54IIf1vP71r8fnP/95AHmmTgiei3EW88S2bRoHzkjLI7U8a7t371767YULFxSiBa6ZdeHCBRoTTgZkWRYx/PIoXKfToQzwysoKMcpyogROtCLuCeRzcn19ncan1WrRvQ3DUBh+RcRL13UFViquG8cxPQdn2zVNkyKwcRwrREZxLIvXecaZw5mDIMDTTz9Nzyq+Mx6P6Vmnp6eJHdZxHMRxrMBKxbjzTL24huiveL5+vy+jrVFEa7FcLlNmnpPrCF1VcS1d1xVmNnEtrk/KM9FZlimMuRxBIfodRRH+9m//FkA+jzZv3ozXvva1APL9SpDdZFlGWdFbb72V9jCu6drv92lO79mzh+ZIvV6nMX/88cexd+9eylivr6+hUsyLxnQN01N5ViMOgJVekZEtebCKqO/crItypcgSxWMUxIswoaNWzvukJxrGQT5mliUJrSbt6mqCSEPXdfhjn+b+dH2aMh5hGEqSGU1m56IoUrKBIsukQzL5appGUe4gCAAN8pzXNURBwazsSfgmz3BxBlTbsinDxQlnOAmWaZo5CYhuKNcAcmheUGRP/MCXe18i2Sm5JrTQrgNyIhmuNSkEz4MwQHWqqjB+c4SN6Md4NKa9y3ZsykyVy2UcvCFHWLzw/AtSlxkZnnk6Zws9c+YM9u3bR+RZWZaRvZGkks3RMi0FkSOeiaOaTNPE6lpuR4zHY4W4hqCkYvzZ+1Sgi2D/v/jnli1baA+d0sq4u70PXubiXLqG//aaL+N692b878XZ/LnzT+LfPn8XvMTC72l/SeMs5gm32aKYvUuW9dnoblDWttFo0G/7vb6CEuIalr1+j8bEMKU2n6EbtO+GYUj38Mc+ZYAHwwG6vS59X2ROEyQyuwppBxIJYTE+o/GI7q3p0uZL0xSmVkBMma2j6RLCyhm6ozCSWU1WMpOmubapGCteRsQzzlmS0XeyLEMYBIgLyLmEhUbodgUZj8tY6s2ijEWSQaW0tsNL7OG8X1EUEpFNEISUxUvTBEEgMpQ2KhVpR1KWX8v3GHEtjrbixDBcn9QwTIKOZlmejRT3I1beJKUsXJKkRFzYaDRQrU5h9+7Zor8Bkd1kGdB9Yz4mmx/ZRHvY1NQUxkUWPAwD0qqcmZmhjGhOYpS/7/PnL6DRaGC9gFaPRiPYYtxmEnirFWjGFNIEGBb7kG1Z0IvnzpljCzRQGqG4BXRdg1PoLGqphigpIMEa8Mwitl01zmIURQTVMwyDjKssy8jBmJ6elsK6/T4t0iRJyBAsl8svWo9WKpUIYhaGIXbs2EHGVRRFlA7mkLo4jhWmKmHkcUF43/dpg77UmQmCgA4IDsfr9XoEJbNtm4z3jY0NOmzW19dpY+XjMT09rdRO8r56nkdOxdramiJ/wfslfjMzM0PwxIWFBdxyyy0AgFtuuQXvf//7AeTOrDgYT506hd/5nd8haODdd99N6XUxFuKdiXdQqVRoU+fiuUeOHCG2N0B1CLnTw6/F2b8AKNBFwfjY6/Xo3dx5550kDfDxj3+c5sv58+epbnP//v208fR6Pei6Tk4Jr/XkQuVcLNb3fWKevOaaa5RaTdHvVqulMGCmaUp957WCvC6C19TOzMzQGM7Pz9O919bW6N14nmRrEw6E6EcUReTQcggtb8Ph8LJxF8/HIZeSRcxR5qpovV4P1WpVgSjzwApnzJVivRI6Mj8/T/vA6173Opw+fZqej9ft8r7y+lzXlTAbzjbK2dcE6514PtF4DRantHddF/1+X4Gti8+NRkMJSIh+dTodqkHkjM6NRoPqjzZt2kTz4utf/zrtT51OB7fccgv1ZcuWLXQgLSws0MHFofTf/va3aX6vra1Rfed4PKbgSbVapb7ecsstqNdr5CCOhwNsWcjnYb1so2zlc2RqagpplM/JJIxJ8FozMgQFfMq2DTr0/FGAxQLqGvgDaJqAJk5gqFdzi+MI3V4Xmq4ReyQykINRcksSmhVKmaIszQjuaVvyHOL1aJYpIe1JkqBer2NcQMbSJKU9kUPq0lTCWLVUY0ZeogSqBHOrqOkDCkcqiaFF0qkUvw+CAJUCSmYYhmSu9DMyzEejETGR6ppO4+GWXHJIojhS+mpZFq3P0XCkyF9wJ4tD/oUMQ6VSwabFPIi4aXET/uaRv8n76gdUc7jR2cB3vvMdggVv3bZVOQeoXiuTe7tty3rQLMvofayureLsmbP0Ww5D5XWHGbLLHcOiCRvDtEwsFoyPAtrvJQ5eG9yMF94wwKHXnMfHP/5x3LBwA+qo09n8x8YSvvv2s1h+5hz+6NS78UX3caVOntd66rp0VHVNBjp5cLox06D5whlBR+MRzRHDNBSmbF4rOB6PqSbQsi2CwJZKJRrDSlnW7o+GI3o3eW2a5EsQLUmSXNKjLEtGxLzgLYzCy8ZdPN/+n/wfAICjn3kfOaeGacAszHSxPsX4246tckEUiGQjMwhGbhomzW9+znK21j17dtN5lZdGCXk7C3wiWJYN25ZBGjE+Odto/h1dB53TaZoqTqFoahlXpgR+gkCyGZdKJeaMl6hf+VzJ++X7Y9QLVu+cSRXFbz20Wvl8qVarcIt5cfLkSdqfxmMfmzYtUl+mpqbQK4KplUoFxkI+trxU5ezZcyyxNcTUlAgcx2QD2bZDcNhNmxZzToEicBCZIaYqgsm1D00DLCNnP85EOVOSSlkVLUMch8U700k6I45iVClxE0LT/g5M+cvYJjDUSZu0SZu0SZu0SZu0Sft7tWpcQimzcf/GQTxy20mcfc1L/6Zt9fHOXb+Pjx7/X9HRB3g6y6V/IkRoo3+Fezxpk/bPo5lTMexGglEzgd6/egQproizyAkzPM+jaPjs7CxlM4bDoZIhE1GjqakpJpo5JrgA11/0fZ+u02w2sbGxQd4+Z0o0DIMigP1+n6Kap06dUiJyIrJRr9cpS9XtdimSI0h3RIQ/T8fLjCfPpHGhedEnDj3N0+wFpITBNDmboud56Ha79PtarUbZqNnZWSU7xEmCRJaCQ+WazSbe8pa3AAA+9rGPKZBSwzBw+PBhAMAzzzyjwClFlqRWqylCt+LdbNq0iSJ1Dz/8sBIZ5MX1HGJ6uVCqjMIK9s/77ruPIjSWZeHaa6+l5ztx4gSAXJBcjMGhQ4coI1oqlWhe3HTTTbjxxhvxta99DQBwww03EARX0zSaI/zdZFlGRD1PPvkkPTcX9700Y+c4jqL5JbLPXDh+fn5eEkmMxwqhjiBXKpVKNN+63S69Pz5HRqMRpqamaB2cPn2axrDZbNKzDwYDRcxe3I/DYZeWlhTIpciIcs3DxcVFBZYqnhHIo4ccGs11K8U8OnnyJN72trcByPUlecZB9G92dlZBE3CSItM0aTxHo5EC/xTzM01ThdmUM8eJz1EU0T4A5NlF0RfObsozw2fPnqX53e12KfIqhIrFdcS8HwwGtPYPHz5M8L7vf//72LZtG71nzuq6vr5O7/K3f/u3aQzuvPNOgqDX63W6XxRF9PcDBw7gwIED9NztdgdbthS6jUmAG/buAABULQ2eWRS/mzFQFNEHqY5KqWB3rlUQhfk7zlITpiHElhMSRY6CAdLiHVu2ZHGetKur9WcifG/rWSDO91cxt8pembIZYRQqmTux/h3HkVp+cUTR7yAIYBtSLFywp3qeB9/3JUxT0ylDo2s67R9BGFDGsrPRoXsbhkGMlK7hEruoFmgMupaT7gh9PK7ZG8exQtTCheZFnzQwfbs0kWiEVGYfOZTfsiwEfiDPRNch4h2vLBEhuqFTdse2pC5gNsgI0up5HjGVP3XoKWkPZXlWSiAHlleWFTilyJ45riSyMg2T3k21WqUz6dTJU9QPjubRcAn5XAZFY1Jy2mS0R+3YsQNRHGFPex57urMYVmN8487jeOLuJfgn8v30pc7mFXTwXx/4En79+X+D1yzne/JcWsdj4bN4Qj8KaCBylkyX2dkMGRH1XLx4UaKwopgygwqLa5aPiZg/o2CUz2uodlOlXMEgzvftKI5oDIajIZ03lilROEESUPaZz5EoiuA4Du37GxsbNIae59GaCMOQ1o2u63Q/Doftdrt0Xc6q77ouZVQr1YoCSwUkAVQYhwo0mkO5NS2/Jz9D19dbjEguY8L0HtI0g2VJtlia37qOKIrp2QX7Z5KkpG+YE0mJjGLGoKOA4GVK00TR6eZ2MGc3jWNfOY9l2UvCEEUa3S/PfEpYvVj7KysrZA8tLV1ErVZTbC1hu+TMqPm7eeKJ79EYbNmylWySHPkkGGFT+vvsrEvoryzLbT6h21jePsb6u3pwIqA0MNA6vw2WjpytVbAFZxJ+azs20gJimq9RAWHOJMQ3CZGp1V5XpF0RZ7Hb7Sqit8II4kYhxx2bpknGY5IkilMoDDDHcdhkMwj6efHiRZTLZTLyzpw5Q59LJQmnSZKE4F98AVqWRYdhpVKhe/B6R8FIyNmhxCbm+z79fWpqipwKwzDohfPvA1CcIQ6NFN+pVCo4f/48bTxTU1O0SNfX16lf/PdxHNPBc/bsWUXu4PrrrwcAvPrVr8bDD0tWXQ4N4MLjg8GAnoNDXXn73d/9XTKgOZSPO9KXNk4hLASbgfwQe+Mb3wggFx7/1Kc+BSB3VPlmKPrUbrfJqTt//jze8Y53AAA+8YlP0Ji5roswDPFLv/RLAIAnnniC/l+lUqGD+NK+imcVgudA/m7E8wk2TTF+s7Oz9P+4I59DKnIHMYoikmRotVqK3IUIkiRJQvOCM3xGUUR/X1hYQBzHBK11XVcZHy45IfrU7XYla2GaKnNKbJLcUeUQXd/3Fec/SRKCQ01PT5MBxOsXPc+j/nqeR87lzp07lfpFMTYbGxvK+HIG1VqtpgQ3eJ2hmDthGCpj9WLsgHze+b6vMKu2220aE8/zaDz52HLjyzCkIPjq6ioFkJaWlvDMM89c9i5PnTqFVqtFkHLTNMlxDcOQjKlyuUww6zNnztD+VK/XFaZaMW+vv/56GhvP86BpGqzCQNi2aREHduew2WS0AaeAj6bhiA6VTNMJMlX2SuhHBXQoTqFpRU2UbSFJC7Y9JIhTAd2awFCv1pakiRTpziSsNM1SguZlaUYOhq7rZNymWcpo7mX9smFK2JumawRv7vf7sGwLnp3Ps43uhjx3TUtCNtMMvX6PPov1pRvSGLZtWzL+WbLeMc1y+4LT8pN8Aat/dhyH4LCarpGRzr8P5M6HuHcayzEQ37FtG71ej852x3YUCSly6nRDsYGaBdtwt9uVNaBxRIGjXbt24eSpk9QPRQZHk+MQhiE5RxzqytuDDz6o7KecMfPvYifXdE1x0un9Q8c1e6+BFRnY7W1F9/lVLI5q+E+v+gywPZ8XddT/f53NQT3FH/3097B7924AwPFvfAfv+ovb8U3zGTStOsmXXNbXwgxptVvyT4y5nXMtpGkOCaUAPHfkdckWmqQJSl5uk45HY6SanMfiTEyzlKCcju3I9ZDIvwvJKWGvmqZJwRf+LKYhnSE/8Kk2mD+r4zjkkHJHVdd1ekdxEivOf5ZKFmG35FKtLq9fzAMEKQUjwgJyW69Ps/UTUY2iOP+lTSOTQI7jKsF+4aTlCQJpcwvHM0lkvSNvmqZDPHpeKmZTfSEPrOeM5NJfEJ9t26H6QE3T6fNwOCQJkH5/gJUVUY6UIo4Ff8QGxuMRnce6LgNYnO3fti2y37rdDVarKctk0jQl6Onc3ByNjWVZ0DRpV7p3JtBWXZx/7jpsRD5KjgUTGbJE2nGZplFdo22ZCMQZnMr1brBgFJAiza58hnICQ520SZu0SZu0SZu0SZu0y5oVGbjz+Z04f3eAh7YdwtHaRcRlHU04L/3jv0drV0Z4g/dr+KvRf8WFrIVj2VmsYgORdrmW3qRN2lXbtBTDxQh64L30d/+BLQOw4eDsS37xH9CuiLOo6zpFzKIoomiEiOoDeQRCRBl5dIxruXDNpiiKSMA6CAKKXk1NTcF1XYV8RmQvz507p2R0RDZyNBopGQtBDKNpGmVbOHHJcDhEuVzGqVOnAOSwQtEvTsLDIXitVoueq1qtKjBGkXUolUp0Ha65OB6PYds2/dvzPIoa8awFh1OapknfuTTDIqIf999/P2U/wjBEu91WskacTODFMoA8SvXRj36UGB55dosTxlwKNeXF4VEU0b0PHDiAr3/96wCAbdu24dZbbwUA/PCHPyRNqsXFRTz22GPUPzHmubBq/l56vR6NwfPPP499+/YRDHX37t24/fbbAeSi55ywgGsScoFerv0pooxra2uUJRJMnCIyZRgGvZtut0uR91KppAgui2vVajVFM1GsDc/zCA7Nhdh1XUen06F1w/UGR6OREskWWfRarUaQCMuyFAgFJ4IQbTQaKXqBc3NzNA9t26bf84ycZVm05nzfJ6gkAMq8xXFMGdkoiihiqGkarS/xDsX/4yRRnDFvZmZGYcDj70lAa8bjsRKN52zLo5GMJvIIPmdmu/baa/GDH/yAvsMJqgSr6/PPP0/7y+HDh2nc+Jo5e/ZsEV2Ue5x4jkajQQy9pVKJdBK3b99O/Th+/DjtWyJbDuQQ5B3Ffnjs2DHMzc1iPMrf076tmzDfzMdhuBGg4hWwpzhFIFjTdAMoBH6jKIJlSui/uLdh6ojF2o8zREUE2Pw7shOT9s+/Vfo2blzaiuN72kjShPbqOI5hF1kATdNgCZFnlrgydEM5L2gPTVLUCu73JE4wGkvYqmmaBP/jmcxurwuvENkejUeUjYyiSDkzBDGMBknGwwlcoiCCZVs5fBU5rFDA7hQSHsaAOhqP5HccW4ExikyRa7ly79ENieCJclIxznpN2aE0k2Qi0IhwRNd1SciSJjJrl0p4646dO7BcZD/EGU/jm6UK66XIyPIMIDLQ5+9///uUtRPwRyDPlvGsMoeaGpok50lS+W4XGwvY/lwVf1b+Bjbun0Y8qmMP6i/72bzlzt34Nf+zeOcT9+Am7IEDC1/OnsAQYxpPvUgt6prMdmmahCQPR0Pa14MwAAZSA5tDoANfanybkUmwYGjSlnFch+YeAFoblmURHNqyLBpPTdPgj6XuommZGAX576MooneTZTKL7jouoTv42uI6uaLP4jpEtGNaKJfLElpt6LTOeEZO13SJGMgyJGmCx171p+j3B9gylZ9dHHGUpgmxiwIawjCCVaybIAigFRn5HC1XfItlKXObVrCTp8p7EvDUKIopM5gkCdJUsi1zhnGebc0J9fIxbDabZGPwrKaua8Tq2mq14BUZ45WVFcqW5igjqXms6wZ4cj4kxmVJvGeaFukk1us16ke73SYtRtM0aU5tbGygXuyH6+stlMse4iiE8XPLCDwgPLMfZc9G5MewLUHsldFazTQdoLWYwmA+kLi3prMSr5S2GkDTMHQyyVL4MrZnRaNzAAAgAElEQVQr4ix2Oh2CytXrdQViymElnOlQTHruDFmWRUZkp9PBmTNnAOQbgDC+gyCA7/vEiDgej2kicSN0YWGB7rG2tkYGmKZpZMTyOsPBYECG52g0guu6Cv0xF2PldM0cxsalBUQ/+G+DIFBYzsQ4ZVmG+fl5gpy1Wi3aBOfm5mgcuFFqmiY5pNu3b6fx37ZtGxn7HK577NgxBbsPSBFWDjvlrJC8/q1cLpNxvHnzZuordyj5dTjeXXzvxhtzEeJbbrkF3/3udwEAH/nIR/C6170OQF67JTaMQ4cOkWHOaxZ0XcerX/1qAMCf//mf4w1veAOA/HD69Kc/jVe8IicR9jyP+v6ud70Lv/Ebv0F9uVT0WIytmGOtVove6/LyMo3n+vo6kiShcZ+dnSUIZaVSocOx0+nQXOf4/CRJ6JlOnDih9INDZsTcPnXqlCIzITZXIN/4qPYmSbB582YaZwEXNk1TocHnUg/CATUMg/ptWRa63a4Cj+XSGxyKLX7TbrfJGRqPx0o9oRi38+fP09937NiBwWCgBGk4rJTDR0XjYt2czTjLMgWyKfonjFZAMJ71iAmuWq1SXzjD74ULF+hdHT9+nGRQTNOk5zh58iTV0XJnkPf7zJkz6Pf7Sh2GOJh5zer8/DzVdq+vr9O9r7vuOlr7+/bto/m1c+dOnGVSJL3uBqan8nGYrnnQtCKIZGeoFn83MhNxoRkdI8NQQKY0AwU7NwJ/AF+IKpc8RIWHGA58ZIXhpuPFYeaT9s+/6ZEGs59Rnb8wgrM0kwyMbK/mkgMcjmfokl3UH/vobuTz0jSl8R0nMeI4JgH7OIop2BSFEWIrXxO8/GM4GkqWXmi0VjRdg63nn8MwhEuGZ6SUcGRZRnVqGdjnNEOcyhIBOvcySJZU9tskThS2SzFOyIo6twJyNhqPaK2Wy2VsdDdoDIUjAR3kkNZrddo/arWarLG2bNrHWuutXK6BOYK8ZEO0JJEyDrquw4CEjwunYqo6hWHBIpllmeLcZJpkHb20flFwCOyt74DZM/AHW76Kzh90rujZ3C4P8ch/6eA3Pvhe/J/Zz+MX8SBOYgl/rH0ZO7FIz2oYBgUOxqMxGdmDwYDGczQaIUszGnev7BE007Ztct79sWTN5qzfWZoRPLXT7ijjzoPLYm5vdDZymYli3O3Upn75gU/BiTRLMVWdKoY5UwKdYM66ePdpIu1FTdeIGVU3dPiBT7Bp25EyM2kqZVHiOKZnTbMcCrpePo9B1sdCXPQjk7Zfr9ejOrx6vZ7XWDrSeROw0jRNGHxUBptzKKmEcko7GUoSQbzMXPpCOslBEJB94zg2OYI5s23+m36/R+8qd9iqxRia9BydTgedTrvolXx3vN/d7gbCMGCJI8mpMRyOsGOQl3KcrpwiG2E0GtG9Z2fnaO03mw34xfyanq4rUiRB4MN1LJzZESP5m2vglQxoSKAbgO0UkHvo8EPRS8m9oGs6ii0JSRxS8NY0LaSF456EMQV6tERHZib/C65Am8BQJ23SJm3SJm3SJm3SJk1pzV4FbbOvZJiveNOA9+MTeCh7DPPaND6S/W94Ci/gME79E3bin75dfPLvQSk7af9k7brl6wAAp1+GeWdf00OqAVFkAVeQH05Hhl96Eq/Crfjcy33tK+IsGoZBmaZarUaRAh5Jq9frUpA2yyiCaJom/b1WqxFkz7Isgp42m00l0hZFEUXr4jim3+/du1eBi4gojmmaBJszDIMyi1zgu9/vUwQhyzLU63XqC9fvSRIJ5eHkF6ZpEhSwXC4rjJsiy9hut2lsdF1nxcOOAsfjsL2lpSXql2VZNG6OIwvtbdumMej1eoqW4z333AMAOHr0qAI3vVRXkjOacsigyGStrq4S+yofA3GtS68j/i76wufCX/zFXyjj+bGPfQxAzmD6pje9CUAOu+PjKSI9mzdvpvF88MEHiT3193//9zE7O0vZyyeeeIKE0s+cOYO7774bQB4JFVCZcrmMn/qpnwKQC53fdNNNAHK4oIDZ/OAHP6BM3fT0NA4fPkyw2W9/+9uU+Wk0GpQRqlarNN8qlQo9a71ep/EQUTwgnzucBEdAVW3bRqPRoKyx53k0d1dWVugz10CcmppSYMKiXUoEw/8t5nC5XFYIcqIoovk6NzdHmTBOLDA1NYW77roLAPDQQw+RtiLPSt9yyy0Kk6phGDRu/NnDULK6KQLGDMrd6XRor2k0GpSFHQ6H9Bxcr1HTNIW8ptVqEWSeMxB3Oh0in/nhD3+owFBFVvLSaLN4Pv55OBzi+PHjpNPYbDYpElqr1RStRAERr9VqFNm/7bbbFA1SgQw4dOgQoSPiOMa1e3fBKzIkXsmBVZAwxCMTfkHs4bgOrFIxtsMIKKLghhtQ1B1ZhjgSDKhjGAWEFUmMQZiP2Ti4XNtz0q6SpgFxnGAwHMBxHSL6cFxJIOe6rtTsi+VcNmHS392yXEO6oRNTped5kuwGGuJUEuGkaUq/bzQaiki6WJ+6LmFzuqZLUpQwIIclCAN5niJT1nMGxqCZSl3fPEJv0D0EFNCyGUQ8A0FVx/GYskSapkmSDyMnLhMEOWki9fT6g77ct3UDRaIvh6ky8hgxBkEQ0FhFcYTt23JY+fr6et7v4jeX7jP0bw0Ex9WgUSZrOBxK8rFMJZzjGVh+XU3TqC9iLuwbb0Z5oOPfVn8b586u/UjO5kOnnsCr8T48jP+BbZu24Wyjg/MXztP+6JU8KgtYXV0lG8otuVhZWSFEyLmz5+AXovBeyaN93nZsWOOCTMS2aUxcV+psctZrPZba0bqmE1TVMAyUvBLZodw2Gw6GSvaaxtlxFBKc7sl8PLI0IjIf8Q5FE5lv27LhB77MGiYyq8nfQZpItIzhxnAdB/cc/Wmsrq5iefP382vqOsFLN21aVJlUNR1B4LN/S/SStO000GSFRplJ3x9jbS0fj1LJI83FKAqhF2uR6zVqmkpeMxqNaf4EgWRVHo99QuStra3R/XSds9wDGVGEZvR8gMxQhmGEdrtNOo2el8Euzk3HcYmwZnFxE5aX83PadR2yszZv3szKyExCBiwtLaFSoCPSNEXzQIbn3tRC6fFphIYDXUCEI04eZMCwirENU0DsoUlMENr8feb/DeMYWgFhRZYiFPMzS1EN8E7gKnEWPc9TRLOFoTwYDOglx7EUsZyZmSFHsFwuY2ZmBkBuvHHhcIHDH4/H9GIEQ6CYJDMzM7RIR6MRGdY7duxQjHQBHQQk/NOyLHIEKpUKTU7B8CiMylarpTARig2qWq0q8gVig1ldXVWcJmF8m6ZJ9+NwXWEkC2O31+uR8weAIB9cMoTLA3S7Xezfvx9AvuGKsRoOh7RhX3fddThy5IjiSHAJkBdjTdu1axcxq/7lX/4lMXnmePQXZ7nl9SfciL777rtJsL3RaJBDFMcxPfeJEyfw5JNPAgB+67d+C88++ywA4P3vfz+NIWeK3b9/P37zN3+TrnnzzTdT3dloNKLnu+6667Bnzx4AwJe//GUcPHgQgMrS+eCDD5Ihz+v2tmzZQoyyN9xwA+r1Or7/fbnpChZVzup78OBB7Nq1i8ZKOJ5PP/00yR/kArYVeq9iruWyCLlTJuBaYgx7vR6tm3379smD0nWloPBoROM5HA4lY5rr0joZDAaS3a+AggC5McPnQRRFtH7DMKTfXwo9/vznP0/9FXNk//79dG/DMAiOJqDf4rrr6+tK7SyHynI5EDHfdu/erQSBxHpSDnh2yPX7fYXpuN1uK9IAXIpG7B2GYdA78H0fx44dw6WNMw1yGF+apjh58iTdg9eH1mo12usOHjxIa+tNb3oT9W84HNJzD4dDGs+9e/eSkXTzj92EJPRRKeXjW7ItgqtYlkMHc4YMmhBr1gHHzd/fxbV1TFUETB5Ii4M/1RIEcWEoxBHOX8yDQ1P12cuef9KujqYhh2IFQQ6/0kwpn8NZkwW8rVQqkSNo2aw2OfAV4fDpmdzgiiO5f6daisFwQA5pqVRSAqBiT63X64qRLqCDgIR/GrqsIbdtmxlpFqI4kpT345HCREilLrYj5Qv8kByB4XCoOE2i/pAHb13XJQZMYSQLoz0IAnL+AFAdJpcMSZg8gB/4mG3m66ff79NYRWFEgaC52Tmsrq0qjgTIP9QIcib+DeSBS3FmPPfcc7TfaNBenOVW0+V1sgKyWxjU27ZuQ6/Xw/ZwFh9qfh6nxktA+qM9m/9d98P489X/jJleGbdaOxCfy9/rcHOKM8FpxFrO1C0YZefn5+G6Li4u5TXhmqah3cr38GxaJifm5+ap3AgaaE9dXl5m8gfy+77v01zLkDEh9pytV4xhEAS0bhrNhsKSznkbRAAjjEKaezywGoYhXVM3ZN1lnMRg0wBJKmHT3EYQtawze55C9Z6v45k/ei2mntmMndpWnBw9CgBoNmdhmiIhoCMMpfPFuTByOQlZOyvYTdM0ISdL13WYRV3y9PSMEgTi57SoG8wZXVGMWVg42fnvlfp5Q0ph2Lbk99B1GTiO4xit1uXlelkGEq3XFMh1ik6nQ/dIWH2o6zoIavm6np+fo7W1d+9e6l8YRgTLDcOI1lyj0SAY6uLiAsItL6CxYuGHy3OYqRlIRYLGMJg0TNFR5Aoa4n30RyM4ZGdBMkhrGRIBKU8T9Pp5cMgxgC9dg/0/fdko/OPbFXEWB4MBTp7MF+0dd9yhRLNE40YQN6x53SDXVuSkNjMzM2R0jUYjlEolItKIoojIQaampig7wElteIF6uVymCW3bNjlcXJPO8zysr6+TYbexsUE1U67rklPBteds26bNHwBlAXzfV4gwxEY1HA4Vx288HtOBODc3R+M5NzdH41mv18nh4uQsWSY3sXa7TYt9ZmaGnE5O8SzeAdE7s2gPzwYOh0OqB73vvvvw+OOPA8gX9Ys5l9w55H8DcmkKkQVqt9tKVlO08XhMz/2e97wH7373uwEAH/rQh/CVr3yFxkA4JwDI+dI0TXGCDh8+TNd+6qmnyOi+//77aTxfeOEF+s7KygqefvppALnkiOjf5s2bybH94Q9/CEAWxQ+HQ8ogvec97yHH3HEc+rtpmnQArq6u4qtf/SqA3HCgCD7TGhJzS4yd7/ssEqfRvBJOEFDUaxT9DYJAkXYRc4zX0QVBoAQquEEXBAEZiNzAq1QqSr0FX08cDSAO3G3btkldtSCgOSk0kER/Z2Zm6Pd87vi+r9Dg80NIPLfjOPQZkHO81+vRvUXNp7i267r0Pufm5ui6g8FA6aMwEHj0Wtd1JZtIpARxrCAJDh06hPvvvx+AKsMRxzGtvxtuuIGkb44dO0ZrgxvSvV6PstjPPPMMuqIW1TCwvrqObc2CAjxLMS6ioo6RIc0E8RGA4rORJfALuQwYOgZkXGbQxPezBHFWZEi7G9jo5c8dZD1M2tXZsixfx5Q5l8F3amEUkpPG5TI0XZ4xQRBIGY0kJsO4VCqRQZsTJ1mYKijs0yQlchDHcShAxGt8dU1HlBW8BpatnNliDfP9QkhfcYItUTNpmibVFiaupMI3DEPZJypVKYWUMCIMqosMI8XxiyNZD10ulynwXC6XaTxd16WAORh1PjJQ3ed4PKbMUKlUIqfzUu6ANJNZRh7I5dnAMAopALdjxw6cv3Beji1e/GzWdE39W/G9VruFb+w+irdu3I4l/PM5m//LrZ9Dc1iBrhs4duwoAOC3pt+Nm0/swl9MP4parYbjZ87iq9aT+I9r/ybvM9MOnS4ySLfdfhvZZqZhoj4tpRPm5yQy5fiJ4wDyTLYYK665KWpBxdhxpJYGjeZVWDhB4n1w+Qt+pjX3HgEArBy9lu4RJ5J4KkkSJaMdJzFcy6V/c41hjqRJkgT73vYHGH3yF9E/P0YlTaHpgOflZxrX0k6SmBwVEfgXph3XsRZOIyAIEVP6u5gmmqYxpJ1E3QFyjgeB1CzNEVExZf5yneX8XOJEknkyRfZREPAMh0PlucV1UiY5kaYp80fyLOCOHTuLd2ArzvC3tn8LAHDv2r1oNgVR5jrK5fy95vuRRs8hstgrK8sIhI2gaUiSGJqmo1xyoWUZYiE5pGfS+ctSiImhI0UsUoiahlBoyiKDJgK5WYq0SD+Hvg8/KHwYAH33ygBdJzWLkzZpkzZpkzZpkzZpkwYzM/B7z/4i/p/F7+E7yXPA6KV/80/RRnaIs3YbhmHg+VKeMXzs37XR++HzeFX/RriRi9cHB/GO8FU4bJ5Col1dRFzzN+fssCtHr33Zr61bIZKlzQCOv+zXnrT/75baV9c8/LvaFXEWOaRO13WKeG3evJk8f16PNB5LbHKSJJT9WlxcJIjfpk2b6LfLy8sUddu8eTNarRZlEdI0JQ+fZwDPnTtHfRoOhxSJ5HIArusqqXIRMRTZPBGNyrKMoqLVavVFo61hKKNJtm0r0RMRneFRjjRNCfam6zrq9TrVHayurlKm4ezZs8pziGznYDCgrF+tVqMo7KZNm4ix8cYbb6TaAiHoypnAOGT0UrF6IM9yiawtlxa5lMWTP5O4joDpib63223q+80330zP95nPfIbeJc+QWpaF9773vQDyd/7KV74SgCoCD4DqDEVtmqiFEPASIIcSiKzh7OwsZXc5nPKhhx6i9/ShD30I73vf+wAAH/jAB2hsa7Uann/+ebpupVJRmEtFXUWj0aBxbzablG2r1Wo0D9/1rnfhgQceAJBHx8UYcMZckeES81KMH5BnDblEDc/mi3nLs+v8XXCZDy4lkkNKZA2xbdsKWzCP1Iso8Wg0ondz5MgRYmX1fZ8ixrVaja45Ozur1BcOh0MlmyCyjJyprt/vK3OVZxx4vaN4Pl7POxgMlExGmqaUZfB9n8ZhOBzSvPJ9n/p78eJFJbLPoa68zlfMnWq1inq9TrIY27ZtU9iTxXPx9ddsNqmeBwDOns3Zj9vtNu1VzWaThIZba8vY1KhDK1jptMwiKJ9ZspCkBYwpzZAkQk5Eii9ruoUNUQZQcoACshPHIRI9z65eWFmFHxQQq1Sut0m7ulpgxziXrVN9VlRAzqpTVVp3vB4pjmJZb5VmCAum3GqlShC/apX9diBlF6pTVYxHY4UVXCAYfN8nRstetyfPzSgkeQ3DMEgOgKMtdF3HuKjDLZfL0CBluJDJMg/bkXDVOIlpj0qShGCkhmEoKACir88yyhJyJmdxroj6t+FwiEpZ7qPiOaIwor09DGXWz3WkZFG1WkW7YGxcmF9Av1eUmmh5ZiqFRPdwmYsXQ/EMB0MM+vLc5HuRgKoqz8SvU8DbRL2mMcpgZjo+sfBN3Dz/z/xs/r0P4d3//d1YQn42p80E/9fGu3GrcS3+dvg0kmIMbdtWmEvF2eCVPBp3z/NIysJxHVxr5U7bFx/6IiGD4kzW4MZJrEA/bcemeVmyZHKHI2HiOKb3gQw0bxVegUzCr/1AImp4qUOeOdNpPRqGAduUticx0mo6ZUWzLMttjkEJYRhiaqpKfRLnrOs6CAKBEvIQhhFl28KQIQB0jWUZpf2X/1bMz0xBrvF6R8lRYlDmMgxDha2UKw2oJSkh4liyvYr+5nYBaHxSxvwrS6w0qom0bQeu62J5Obdpa7WaLNnIUoKq59n8/Lqe52FhYZ6eV9joOWQ2pO+Q/bb1FE4cHKL85CZoaQINrD7b1JHRu9GQsf1X9l2HX7wb2zTpbE7TBKmWj21/OKTxuJLtijiLvu8rBqrYNNvtNn2enZ2lOiDLshSCE/F5MBgQtXyWZQocThjQy8vLcByH9Mr27Nmj1H6JvwtnFFCNvCCQmju+7ytQOVEbWKlUkKYpGbtBEBC5S7VapYNgNBrR5uE4Di2mlZUVxUHkh544wLjmonC4hFPRarWoX3v37iXoqetKLShe1Mw3i1KpRNDa9fV1XHPNNTSGf/zHf0z95bVU/LABpFPyEz/xEzh06BCAfEF8+9vfpu9cKosh/nspDJUOctsm53YwGFC91gMPPIAvfOEL1HexMXOD/9y5c/Quut0u6R4+//zzuO+++wDkRfNbt26lzeoXfuEX6NlrtRr144UXXiCHZnp6mjanqakp2kCfe+45fPrTnwYA/Oqv/iq++MUvAsjlFe655x6CR3Mn7bHHHqMaW4F3F43XL4hD6M1vfjM59aVSSSGcEWMroJ9i7ov1A6jQLPFdMW5iDDkxDK/v4xCPOI7JoBMOIddv5BIWHBYixnMwGBD0W8hUAHkNibgOPyzE/xfzj0vnlMtlGk++Zrm8hq7rytzl81D0aTwe099LpRK63S6923q9rsBH+ViJubOyskL7yHg8VmRz+H35oS6uOTU1hZ07d1It64ULFyQVOquV4mRQrVYLv/7rvw4gr18U47Fr1y4plbKxgesLWFdzuop41EGtWkhkaBoSqouJYQrDETrSoo9xFiONROAggD/IDz3XqMIQXN2BD78Yt/WNPlK9VoyBrJ+etKurDd0Ax0ur8OARTBQo5nXxuezJs0Q3ZL0vJ48Jw5ACsVx+gtdODQYDmIap6KPy2i/xd/FboHCSMgnBEwYbh2zHSUy1gbadaymK9RwnMfxuUdvo2OQMR1FENWGGaRA8cTge0mceJE21lIxs01KlOaDlYwTkNZKiX41Gg/Yz0zTJKbBsC1okDWWSorJMRf91pjFDY/j0U09Tf7nWHjJ13xA20YH9ByiQa1kWzp6TutyXymLQdQQMtXBOxf16po/z0TpWV1evyrP5w+nX8d7vvx6vmrkdz+gnkWqp4qSdO3eOamy5XQioklUCtrp331502h16ZySJwsZWQD/5mSNakiQKFFisAS5Lo0FjUE5Z38e1QtM0pdpZYeNx3UxOMkh1kYmc92mawfFcmKalnJvlcoXuzeUj5Jkc0/8TZDI59FOczSDnhstr5MRQ0o7h81CYhVEkobuWZcL3A0SRTN7wxIO0iTTmIwxpH4njiM4uZcpnGfUPbA9zHAf1+jRaRS1rv9+j32uazSCi8jej0Rjf/OY3AeS2uJD2mJ6eJifU933MFbb76fuOovp0E61eE5ZZUAFlQiIlhZ6ItSydxZQ5qhmAuICnm5ok50EcIy6uM/JDZJqU+blSTX/pr0zapE3apE3apE3apE3av+R2Y7wLbx3fgffMfeRH3ZV/cEv0FO9d/CMEeoTX926Gnk3M3En7ETQzRmBl6HbLL/3dq6BdkcxiFEWUnuVMUu12m7J14/FYIdIQUcpGo0GZs6mpKcpqra2tURRI0zTKSqVpiq1btyoRCBHxmp6epoj+4uIiRSY8z6NoRJIkBAVwXZeiKYZhKJlBLmFQq9Uoglir1SiDtLS0pDCB8ewcv4d47sFgQJHBbrdL3+/1eqpwMGQ0UWRUgDxTKKIUcRwrMgoiCmcYBmW+6vU6/X0wGFBUll//0mcXvwNyWmsBlTx27JgC3xGNE+VwmKuMzEjyEp7lFPIVFy9epGjkNddcQ5nlQ4cOEaMoZ7+97bbbKNtpWRZBWDqdDprNJn7yJ38SQD4vDh8+DAD47Gc/S+LCt912G2WJP/WpT+Hee+8FANx777340pe+RJ8FnfdTTz1FYsKtVgu33347PvrRjwIAXvva11JEsVKpEFvcnj17FKkIzvAp5ss73vEOvP/97wcgi78Blc47TVMsLS0RNKdWqymyLWKsm80mZbuTJKHPpmkqcE4x9wDQeiiVSvRZZNHEnOFkORxixKGguq7jzJkcNvnCCy/gwQcfpGtRVozN9VKpRKQzwOXrhqLwjCHOtm2CeUxNTSlQTvH98XhMmVcOrXVdF4uLizRuvV5PEZEW35uenibo8MrKCs1bDmHl8BbLshS43Rvf+EYa/6997WskWXPkyBHKvJZKJfq953l0j+3bt+Ptb3/7Ze/pySefpEz0zl070VnPYaiukaE36iERGRrHg2EWiIEoQJYUIseWAc0sIqemCa1QAY7HY1iGKK5PZAQxStDuFTCbCDCt/P31BxuYtKuzGbEOd2iga4yATJJIjcdjeNMFJC6OKOuUJilMJ19fdsmmc89xHJqXo+GIsoM2ZFlHlmWoTdUUOKfY+0puCa12fi5VK1XKJlqWJWU70owRZJiUYdM1XcpEDIbQdI0kDBxXsjk7rkMZpEF/QGQyjuMo5x2/h0a0+hIO6wcSMRIEuWxHprPURXEpkfUBCsh/AYE0UkMyTw8HxICp6RoRA7kzrmTGLEjGSGibiRxqupTIAED77kZ3g/aG9da68nyicaIcaPLcFtePoghzSR3/d+VLmN4/j2nMX7Vn81daX8FX3nwWd3x4Azs3mhjsAYJUEsAIpMjMzIxSTiMyOmmW0ny54YYb8MjfPAJAlZgyDEOB+PYHfZTcfE04riNtH12jcfc8j7LdWZrR5/wM0uj7AhIMgKCmpiWla+Iohm5IORhOlmNZFrEZZwwKqmlAEIzRmboAfxzALfoaxxGSIrseRQE708wCCSdJeFS5FVDfRcZL0wwl48zPVvH9KIoV20EMjmmaqFYrNG5BIPuS/zf/XqnkoldAh4fDAUPtSQirpvH+GQz1A+zde03x/QwnTpzA9u35PF5dXSPUlmlaEhlgWXSPer1GSgPie0C+NqYLoqTp6Wn44QY2fuYUZtoWnu9qKLsF4snQoOmSTEhoYRipJjP9ug4UY57FMQzB5IpM7gRphnGxzqIE0I0iKwngwhQO4Qq0K+Is9no9grhxFtP5+Xk6bCzLok1l+/btOHo0Z7eanZ0lp5BD7kzTJGfNMAyqSwzDEBsbGwosVThN4p7iN8TeFks2s3q9TvCNPJVcwK7W1wk+OBqNUKvVyKHVdZ36yNmxHMdRcPziHnEck3HKmUP7/T49k67r5DgI6RHxvVqtRoYrh83Nzs4qeori4G80GvRMtVqN+tRqtQjeUKlUsHPnToI+cpiPGG/RrzvuuAMAcP3115MW3MGDB+m6Dz/88ItqOYnfA7J2Tvx7YWGB+ut5HskRbN26la57000H2LAAACAASURBVE03kXO8Y8cOusfa2hrVgJ0+fVrRtBO/veuuu/DlL3+Z6iRWV1eJWfRd73oXzc8vfvGLZPTccMMN5ISUSiWCwBw5coQMoN27d+NP/uRPAICYKX/lV34FAPC5z32Ofv/2t7+dDtmnnnqKatAsyyLn2/M8mhfNZhO33347jad4Jj5mo9EImzZtos2Yy7MsLCzQM/m+T5sbd0gsy1IcQdHCMFRq78S9BYxUzAXf9xVYsTCyBCOh+I74fNNNN9E743qIw+FQYU+tVqs0DqPRiPrmeZ4C2RT3np+fp7+PRiN6Vtd1yXhyXZfeK5e6GY/HiixNo9GgwJOmabRHbGxs0HNzljUOMU1TqWHFIdfT09PkEO7atQv33HMPPvKRj1B/hQ7lwsKCpGHPMqKM/7M/+zNcd10uCNxsNula119/PVaKuuZjR49iy2JeEztoXYQeRYgCUbPqwqsWhgBi+H4BxQsTWG5hOJiAURjftp7BNgUkTVJyD8cBzl/M9yc/1uDqBXT/RXQ7J+3qaFNDB3dm1+LzpccQJzFG43ytVsoVqflr6MQKWqvXCFZY9srkFHLIna5LvTlN1xSWc9/3FViqWF/inuI3llbIbaQpGWmu6xJ7askt0Xk4Go1o3YyjMTzXIwZUTZOGNtdmNUyDDGhDN8jB43X1USyZKoMwkM+kaeQ4CLtFOHKu41LNW+RGMvhT9sjID4IAkZavGa/k0TO5jiuZUUdjlZV9uk7QxxfTKxb9EnvD3NwcVgotuPm5eXrWk6dOqjBU5kNyfUnP8/BZ72/xC63XYTCX/Is4m1Mjw5P/cYSf/sJu9M628U3jBXzbOYL37PoZbHTyeywvLWO+qEEzdMm4a1mWwoa/eUt+v1MnT5FR73kejaGQdhLnaBhKeRbOHB7HMTn7psAlIl9zxLI6lCyrSZLQdTRNOhRC4kph4NbkHBFOJAX+MxGsSfHd13wSnldGOSvW0NgnTcEwjOgZbNuCbUunN4oiBkO1qL4vd8z04lnL9Pcokk4oD1Tn52/+eTSS9nAUxYX9IQLJHo2FpsnAlu/7jJsgYjwMck7n57TG/q4X70zKdE1PT2P79m343veepP4KVYVKRbIFW5ZNSatnnz2M2dk5ev/iWnNzcxgW83N9fR2lW3poWRlOP7IDWhohFZInsQmrCL6l0MkRTxLAKAK50HNGcwAwtAyGLs/mNBPzLUavX0DvU8AsyJyyFNB0fANXoE3y85M2aZM2aZM2aZM2af9Km5Hp+Ez71/D7lS/giakXftTdedlaamT41FuexWPec5hLa/hk+z/B862X/uGk/atv9128D/ddvO8f9NuoGqA8NJH+E0OgC9WrP7wS175imUUR7b9w4QJBT5vNJmUWPc+jzN3y8jJFkPr9PpHj2LaN5557jn4rIpTVapWyF57n4dy5c0o2UkS8ONNpr9cjdkrP8yibUKlU6FqLi4vKdUT0QugOcv1HXsgsImHid4DKemoYBmX6KpWKkn3k0RYZ0bGxuLio6MSJiCwnABqPxwpMV4xbHMcEVVxeXqaMxcMPP4wf//EfBwD82I/9GI4ePUoZr4ceekiB2pGwMissLpfLlP36whe+QEygvACYQ0/VgmYDW7dupbHasmULjU+73Sa4MIcRHz16FHv37gWQZ3rEHHniiScoosOL4KMoImjM2bNncfDgQTz22GN0f5HpK5fLdD/BQAvkWdi3ve1tAHKYrdC9u3DhAkEPzp8/T+/lr//6rwGAoDlvfOMbab7ato0PfvCDAIC/+qu/IkhkvV6nOcIhnlEU4a1vfSsA4Otf/zrNzx07dihQTF3XiQ11enqast1CEwnII9kiSs3JXDiBi2VZNIY8687nYZIkaDQaFNHTNE1hCxVra2Njg9ZDqVTCLbfcAiDPqIo5cObMGRrPAwcO0D0ajVywWNzDMAx6z71ej75nGFKnqdvtUoaUM6bxz5xsqlwu05oZDocKVDpNU5pjJ06coCyKWOuASlzEI/zi92LcBNrhne98J7E4nzhxAq95zWuIIfDRRx/F8eM5ffmuXbsUGLqYu9u3b6d5yddJr9fD9u3b8z6NBrDCYgyiUQ4xLaKX42EPOtF1p0izIqoZZUiLCKSd6cgKZtQsDqALtmbDRauTX/f5c8tY7+XvWHerGHcKDv1Ujs2kXV1NMComaYJ+r4+Sl8+/1Esps2hZFp2Dg8GA4JhBGFA5RWqkWFvPs86e50mIpy2J3SzLQrfXpUxfHMcE1eRMp8EoIDSQZVm0r9i2LVmFK1Ulq8nXY5Zl0E0phi76EkexQsjF0QEiu6NrOmX6bNumjJxu6AqETmR3DMNApVpRdOLE3pVmKQxN3lus7eFoKMctTQmqOBgM6Pw+dfIUre3FhUWsr69jYX9urxx7/phCkGfoEs0g+mVbNmW/jh07RmguZKDsVU6ikl3+d13DTHUaZd/F5+a+g/1b9v+LO5tHP+fgG889i1d87wDKkYv7H7if+ijsMdd1Jfkgg3imSYpr9+XMqCdPnqSMc71el5lBPSeCEizUbslVtEoFzDqJEwmz9APlvX7/E/8+Hw/doLNL0yWi5lKYrFfyKPulQRLAhVFI8Fbf92k9aJqGaqWKxcVNBQNoVry/LukIzs7OEhmM55UKskWxDjSCXQZBQKQ4miazgb4fIBQw8gz09zjO6HMURTAK0h3blgi8nG01RZJIYhnhO3Q67RdlRs/3AtDz8caZWyuV3Ce4+eabsVSwOLfbHezevYvNyzNkp01PT6OZ5iQ1g3RAdlatVldKYASpTRAEqNXz7/g7T+AH13XhPLkDehrB1DUI8ck4CqAZkmwnK6DqWgpkxXMbYDZ0mkAT6DzNxLgoHWn1BhgFhf6iaRN6yNaA//AY/hvuwr/Hy9yuiLM4Go3IKVhaWiJI1WAwIAiepmm0GYzHY8K8+75P9Y6lUokcoFqtRofZ+vo6Dabv+wqD1szMDL3wwUC+5MFgoAiVi8PQMAy6R7fbVTDpYsOdmZlRoH1RFCkSEMIx47DCUqlEB4SmaXQA1ut1GhvXlTTa/JAUTFXimdbX1xVHUBjEa2trCn2xGM+dO3cqtVeiH/1+n5yChx9+GFNTUwo1sWie5ym1aZydViyUBx54gER2//AP/1ARQ32xmsVSqYSNjQ3JsqfrZECfO3eO3q2oUQDyAIGAJ7/iFa+gzeKOO+6gfrRaLRIHvvXWW+l+jz/+OBqNBvVxdXWV4LSdTocc6KNHj1J/H3jgAZo7b3rTm/Doo48CAO6++26qqeDfNwwDDz/8MH75l38ZQM4WK773yCOP4LOf/SwA4JOf/CQ5UD/zMz9D9zaM/5e9Nw2z66rOhN995jvfW6OqSiqpSrMsW5YtI4xtwMjGYAYzGMhgAqRDJyTpJtB8STOFphPClwSSkA6dpPkgEGxoIBAbAo6xjWywZcuSJduarZKqSqoq1XjvrTud+Zzvxz57nXMMT3iSWAFBbf+5Lt17hj2tvdZ61/vKKZi1mEdvfvOb8cUvfhEAXz9iDquqCsMwaDyTsjRJJ2Z5eZnGtb+/n5yYpPMlSRKNRXLO53K5FASZQ0/42Nq2Tf2bPFD29PTQd86cOUOO1sTEBH1+xSteQZ8vXLiQEuROwrSTbKjJOkxFUeh+hmHQGCiKQvOi0WhQvXM2m031jVivlUoFy8vLtG5s2ybq9nK5TGuo2WzSdZPBkGR/ybJMf5dlmeZXT08Pdu7cCQC4++674fs+QZYff/xxPPDAAwCAW2+9NcUU/fWvfx0AdyKFs3no0CEKsG3ZsgWNZR4o0FiAdRX+24zsQ/JddCLHzmYKHEQHbkOFElFsB55HMhoydPg27+fAdyFpvH8W6i2MX+BOwELbBNP4mDUbLbBomoTSCiDlUm0heG2PZVlotpro7eMHIsdxaH0yMNpfPTcOPHqeR5IRqqLGdfi6QfWEnU4nVUdv6EZKeD7J2C2kMxzHIVin58dSHUyKJTEs26IDNwCCz4qaZ8Fo6gcx07JpmjAyPwwrVJU4SAcGOlg/VzqLnEVZou8wxuB7PjFMdvxOyhEUkhyddoccUiFCDwCVciWGsGaMuB7ciZ3Os+Nneb2XHMPdRVNVNVWblmSnFTZ7ZGSEmNQPPXko/n1c9pWqWVSVuCTINM2fadssW0XoxyycOnISJzbOw3pyBoMD3D5efvnl5LwziUHS42CBmEeXXXYZnnma36/VbNEcliU5Vd7AGKNgSNJ2WLZFTmi+mEdB5k6Mbds0vxljFLQIvHjsNTWWgmGM166yICp18mNWUVmSuTo7uB0MggBgkSRDGGLnp9+KdruNfb/6twA4p4Kw+a1WMyEHEkTOooChhsT+KUks4YwJdlQhAQXqt6SdTZagOYn1JNZrJmPAsmxywDzPRyOqmTcMIyEn5iQ4NRJRDyDlwMZOpERwbS59MQCAz5cgCOl8NDU1RfN148aNBJU1TRMnThwHwM8Pwtm8cOECrY2enh7YkT1deMMMik/3YbpagMpaYKEPN3LsfCbBRyR3osiQInBnGASQIoipBJkkrsIgAJOj/cxyUGtyu952PDBZSPY5EJKijAGLWWwr4vlvF8VZXGkrbaWttJW20lbaSltpP/2tGGTgwf/xX7zE22NvnIPWWMRaNoS3fm0n7mMu4EvwpJ8N4fSV9vy1gyVeyzg4M/Sv+l3IAMu6+FIWP6r
Download .txt
gitextract_7s6sidp0/

├── LICENSE
├── README.md
├── coco.py
├── copy_paste.py
├── example.ipynb
└── visualize.py
Download .txt
SYMBOL INDEX (29 symbols across 3 files)

FILE: coco.py
  function _count_visible_keypoints (line 8) | def _count_visible_keypoints(anno):
  function _has_only_empty_bbox (line 11) | def _has_only_empty_bbox(anno):
  function has_valid_annotation (line 14) | def has_valid_annotation(anno):
  class CocoDetectionCP (line 33) | class CocoDetectionCP(CocoDetection):
    method __init__ (line 34) | def __init__(
    method load_example (line 53) | def load_example(self, index):

FILE: copy_paste.py
  function image_copy_paste (line 9) | def image_copy_paste(img, paste_img, alpha, blend=True, sigma=1):
  function mask_copy_paste (line 21) | def mask_copy_paste(mask, paste_mask, alpha):
  function masks_copy_paste (line 24) | def masks_copy_paste(masks, paste_masks, alpha):
  function extract_bboxes (line 34) | def extract_bboxes(masks):
  function bboxes_copy_paste (line 60) | def bboxes_copy_paste(bboxes, paste_bboxes, masks, paste_masks, alpha, k...
  function keypoints_copy_paste (line 88) | def keypoints_copy_paste(keypoints, paste_keypoints, alpha):
  class CopyPaste (line 102) | class CopyPaste(A.DualTransform):
    method __init__ (line 103) | def __init__(
    method get_class_fullname (line 121) | def get_class_fullname():
    method targets_as_params (line 125) | def targets_as_params(self):
    method get_params_dependent_on_targets (line 135) | def get_params_dependent_on_targets(self, params):
    method ignore_kwargs (line 204) | def ignore_kwargs(self):
    method apply_with_params (line 211) | def apply_with_params(self, params, force_apply=False, **kwargs):  # s...
    method apply (line 226) | def apply(self, img, paste_img, alpha, **params):
    method apply_to_mask (line 231) | def apply_to_mask(self, mask, paste_mask, alpha, **params):
    method apply_to_masks (line 234) | def apply_to_masks(self, masks, paste_masks, alpha, **params):
    method apply_to_bboxes (line 237) | def apply_to_bboxes(self, bboxes, paste_bboxes, param_masks, paste_mas...
    method apply_to_keypoints (line 240) | def apply_to_keypoints(self, keypoints, paste_keypoints, alpha, **para...
    method get_transform_init_args_names (line 244) | def get_transform_init_args_names(self):
  function copy_paste_class (line 252) | def copy_paste_class(dataset_class):

FILE: visualize.py
  function random_colors (line 18) | def random_colors(N, bright=True):
  function apply_mask (line 31) | def apply_mask(image, mask, color, alpha=0.5):
  function display_instances (line 42) | def display_instances(image, boxes, masks, class_ids, class_names,
Condensed preview — 6 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (419K chars).
[
  {
    "path": "LICENSE",
    "chars": 1068,
    "preview": "MIT License\n\nCopyright (c) 2020 Ryan Conrad\n\nPermission is hereby granted, free of charge, to any person obtaining a cop"
  },
  {
    "path": "README.md",
    "chars": 2971,
    "preview": "# Copy-Paste\nUnofficial implementation of the copy-paste augmentation from [Simple Copy-Paste is a Strong Data Augmentat"
  },
  {
    "path": "coco.py",
    "chars": 2419,
    "preview": "import os\nimport cv2\nfrom torchvision.datasets import CocoDetection\nfrom copy_paste import copy_paste_class\n\nmin_keypoin"
  },
  {
    "path": "copy_paste.py",
    "chars": 10956,
    "preview": "import os\nimport cv2\nimport random\nimport numpy as np\nimport albumentations as A\nfrom copy import deepcopy\nfrom skimage."
  },
  {
    "path": "example.ipynb",
    "chars": 396311,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Example Usage\\n\",\n    \"\\n\",\n    \""
  },
  {
    "path": "visualize.py",
    "chars": 4147,
    "preview": "\"\"\"\nCopied and lightly modified from:\nhttps://github.com/matterport/Mask_RCNN/blob/master/mrcnn/visualize.py\n\"\"\"\n\nimport"
  }
]

About this extraction

This page contains the full source code of the conradry/copy-paste-aug GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 6 files (408.1 KB), approximately 271.5k tokens, and a symbol index with 29 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.

Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.

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