Repository: haesleinhuepf/apoc (language: python)

Issue:
Add info function
This PR adds a `classifier.info()` function that displays all information in the header of the classifier. 

I added this to the `demo_pixel_classifier_multi_channel` along with the error message that comes up if dimensions do not match.

Fixes #23 
Feature request: Print classifier info
It would be nice if there were a function that could show some info about the classifier, e.g. the expexted dimensionality of input data, the used features, etc.


Discussion / hints:
This looks nice @jo-mueller ! It might be worth considering to use the `info()` function to also add a nice `__str__` and/or `__repr__` to the class. That way when people do `print(classifier)` or `>> classifier` they get something that is understandable.

https://medium.com/swlh/understanding-repr-and-str-in-python-65dd41538943
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Hi Robert @haesleinhuepf , 

thanks for the feedback. 

> I also don't see why the notebooks were updated.

I found that the filenames for some notebooks were not very descriptive on their content (e.g., multi/single-channel prediction) so that I changed their names to make it a bit easier to navigate the `demo` folder. I can of course, revert these changes if they're undesirable to you.

> I strongly suggest to send a new clean pull-request with only one commit adding this function and one new notebook showing how to use it.

Can do. That's rebasing/cherry-picking, right?

Also @kevinyamauchi , thanks for the idea, I'll give that a try :)
> I found that the filenames for some notebooks were not very descriptive on their content (e.g., multi/single-channel prediction) so that I changed their names to make it a bit easier to navigate the `demo` folder.

Can you please create an issue for this? This is obviously unrelated to the info() function. Thanks!

> That's rebasing/cherry-picking, right?

I would start from master with a clean branch and copy over the 10 lines of info() code. This appears easier to me than git command wrangling. It's matter of taste obviously.

Thanks again!
Hi Johannes @jo-mueller ,

great idea! I just updated [this notebook](https://github.com/haesleinhuepf/apoc/blob/main/demo/mutlichannel_images.ipynb) demonstrating how this could be approached. Essentially, I suggest to build a `classifer.info()` function that uses this code snippet under the hood:
```
apoc._utils._read_something_from_opencl_file(cl_filename, "num_ground_truth_dimensions = ")
```

Let me know if you want to do this change and send a PR. Otherwise, I'm happy to implement it.

Best,
Robert
Hi @haesleinhuepf ,

I have an implementation [here](https://github.com/jo-mueller/apoc/tree/add-info-function) - but it was much more complicated than I thought Before I start a PR maybe a quick discussion about whether what I did is the way to go or not:

- Printing information about the number of dimensions used for training requires the dimensions to be stored inside the .cl file
- This, in turn, requires a `classifier._dimensions` variable in the central `PixelClassifier` class
- This value can then be printed by a `classifier.info()` function
- I also added a check in `classifier.predict` that check whether the dimensions of the passed data matches the stored dimensions of the classifier and raises an Exception that points this out.
- I added a notebook for multi-channel segmentation that shows the `.info()` and the raised error

Should I start a PR? Tests are passing :)
Hi Johannes @jo-mueller ,

awesome, thanks for working on this! One major hint: There is no need to write anything extra `dimensions` into the header. `num_ground_truth_dimensions` is already stored there, as mentioned above.

> Should I start a PR?

Yes! The code doesn't look very complicated though.

> * I also added a check in `classifier.predict` that check whether the dimensions of the passed data matches the stored dimensions of the classifier and raises an Exception that points this out.

Fantastic! I recently discovered a bug in this context, which you would resolve with that check.

> * This value can then be printed by a `classifier.info()` function

Would you mind also printing the other stuff that's written in the header? What about printing out the whole header? You wouldn't need the `_dimension` property then at all.

Before submitting the PR, please go through all changes in the code and check if they are really necessary, have good variable names and and are well described in comments. For example, I have no idea what `exc = sys.exc_info()` is doing, what the variable `exc` stands for and why it is necessary.

Thanks!

Best,
Robert
Hi @haesleinhuepf ,

the output of the info function now looks like in [this notebook](https://github.com/jo-mueller/apoc/blob/add-info-function/demo/demo_pixel_classifier_multi_channel.ipynb). Is this how you envisioned it?
Hey @jo-mueller ,

the notebook you linked does not contain any `clf.info()` output. Can you please check it again? Maybe it just needs to be executed and uploaded again.

Thanks!
Robert
Hi @haesleinhuepf ,just reran the notebook. 

Edit: I also removed the if statement that checks the passed image data to `.predict()` for the correct dimension. Reason was that I found it a bit messy to check the correct dimensions in several if statements. Also, training on multiple 2D images from a folder is interpreted as a larger number of image channels - handling this nicely would maybe better be done in a separate PR.
Hi @jo-mueller ,

this output is wonderful:

```
Classifier type: PixelClassifier
--- Random forest info ---
Used features for training: original gaussian_blur=2 sobel_of_gaussian_blur=2
Training data dimensions: [2 x X x Y]
Maximum depth: 2
Number of ensembles: 100
Number of classes:  2
```

Do you want to send a PR?

Thanks!

Robert
Here goes! 

#29 

Fix patch:
diff --git a/apoc/_object_classifier.py b/apoc/_object_classifier.py
index da82f11..ab09db2 100644
--- a/apoc/_object_classifier.py
+++ b/apoc/_object_classifier.py
@@ -28,6 +28,19 @@ def __init__(self, opencl_filename="temp_object_classifier.cl", max_depth: int =
             overwrite_classname=self.__class__.__name__
     )
 
+    def info(self) -> str:
+        """Print general information about this classifier."""
+
+        return self.classifier.info()
+
+    def __str__(self) -> str:
+        """Display classifier information upon `print(classifier).`"""
+        return self.info()
+
+    def __repr__(self) -> str:
+        """Display classifier information upon `>> classifier`"""
+        return self.info()
+
     def train(self, features: str, labels, sparse_annotation, image=None, continue_training : bool = False):
         """
         Train a classifier that can differentiate label types according to intensity, size and shape.
diff --git a/apoc/_pixel_classifier.py b/apoc/_pixel_classifier.py
index bae3ab3..114f0de 100644
--- a/apoc/_pixel_classifier.py
+++ b/apoc/_pixel_classifier.py
@@ -43,6 +43,29 @@ def __init__(self, opencl_filename = "temp_pixel_classifier.cl", max_depth: int
         self.feature_specification = _read_something_from_opencl_file(opencl_filename, self.FEATURE_SPECIFICATION_KEY, "Custom/unkown")
         self.num_ground_truth_dimensions = int(_read_something_from_opencl_file(opencl_filename, self.NUM_GROUND_TRUTH_DIMENSIONS_KEY, 0))
 
+    def info(self) -> str:
+        """Print general information about this classifier."""
+
+        num_classes = _read_something_from_opencl_file(self.opencl_file, 'num_classes =', '')
+        classifier_type = _read_something_from_opencl_file(self.opencl_file, 'classifier_class_name =', '')
+        info = '\n'.join([f'Classifier type: {classifier_type}',
+                          '--- Random forest info ---',
+                          f'Used features for training: {self.feature_specification}',
+                          f'Training data dimensions: [{self.num_ground_truth_dimensions} x X x Y]',
+                          f'Maximum depth: {self.max_depth}',
+                          f'Number of ensembles: {self.num_ensembles}',
+                          f'Number of classes: {num_classes}'])
+
+        return info
+
+    def __str__(self) -> str:
+        """Display classifier information upon `print(classifier).`"""
+        return self.info()
+
+    def __repr__(self) -> str:
+        """Display classifier information upon `>> classifier`"""
+        return self.info()
+
     def train(self, features, ground_truth, image=None, continue_training : bool = False):
         """
         Train a scikit-learn RandomForestClassifier and save it as OpenCL file to disk which
diff --git a/apoc/_table_row_classifier.py b/apoc/_table_row_classifier.py
index d038d05..8bc3848 100644
--- a/apoc/_table_row_classifier.py
+++ b/apoc/_table_row_classifier.py
@@ -45,6 +45,19 @@ def __init__(
             self.feature_specification = self.classifier.feature_specification
             self._ordered_feature_names = self.feature_specification.replace(",", " ").split(" ")
 
+    def info(self) -> str:
+        """Print general information about this classifier."""
+
+        return self.classifier.info()
+
+    def __str__(self) -> str:
+        """Display classifier information upon `print(classifier).`"""
+        return self.info()
+
+    def __repr__(self) -> str:
+        """Display classifier information upon `>> classifier`"""
+        return self.info()
+
     @property
     def ordered_feature_names(self) -> List[str]:
         """The feature names used in the order they are used by the classifier.
diff --git a/demo/mutlichannel_images.ipynb b/demo/mutlichannel_images.ipynb
index cd15ed0..ca02c64 100644
--- a/demo/mutlichannel_images.ipynb
+++ b/demo/mutlichannel_images.ipynb
@@ -13,7 +13,16 @@
    "execution_count": 1,
    "id": "9143b0ba",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\johamuel\\Anaconda3\\envs\\apoc\\lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated\n",
+      "  \"class\": algorithms.Blowfish,\n"
+     ]
+    }
+   ],
    "source": [
     "from skimage.data import cells3d\n",
     "from skimage.io import imshow, imsave, imread\n",
@@ -41,7 +50,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x2037218afd0>"
+       "<matplotlib.image.AxesImage at 0x203e61e3370>"
       ]
      },
      "execution_count": 3,
@@ -50,7 +59,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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Yq611xpPjhcANI2GdcJHjcrnEBx98gM985jP44he/iPl8jg8//HCFBenCQ7VZnflkfpgXCtmdAxUBQ7fn0Ln5yRdZnei4WdM0rX6ok3VkY4DIPTqQU/eI3kf/XbR74kZeAzF9X8N7Q1DPr3kF7lgT39OZCN1zxOn6iKVpPiLPH4Gvg0EE5PpMAcobLrt+0+m0ZWrajfClBpE+M2eS3avVbwYiXWnUrn1GrGluxkt2dnZa4NCtGepYyIQIOByIXi6XuLi4aBsoG/6rV6+ws7OD5XKJ73znO/jsZz+LH/qhH8JiscDx8XE4Q+esUkGGz5X1aFeRQjbE7pSeCKAbzsn4vL253fhYVNZ+arIRQMRGSpR2RHbQUG/XB4SyeFxc2RkYeWWoF3CWokCgVJaNOBNvWBkIRvnWtD2/kS4ihhHpkV1Ajk2xkXGVNhui6sEHqT3v0ZhOlMfIafB+BEg1u3AP716e91inDMujQMhy1Hkul8t2+Ykui1DdXV1dtVP4HAjmuMxgMMC3vvUtvPXWW/jCF76A7373uzg6Olpx0lo3qiMviy/fiJ6T2Wo5+KeD0JGt1WwxmnDqIxsBRKxM7YNHK02BfHGah81AxcPrAkGtNA2jC/7ceyqrUTZH0YFh7w71AcaIGWSV7Q3M70dA3dVQPY3Ly8uVPVOcMeMAvh6Zkc2UecPPntdYXleZPO8R8GZ6Y/7ZJePM4OXlJfb29tp4OLVN+72+vsbJyQmapmnH+TgVTraoY01XV1ftoDAH/S8vL7G7u4vHjx9jf3+/XSh7cXGxso8tqieOL3r5GY4THTrZwml4xq82zIWbbsNeR5pWl01nslFARKSOKL6G1evIGKMwUcOPlBYBn7/LvrWmrRWRUVUfy/IZPy+Plj9qXJqeg0g0YBwxPL3nYwxRA2+am24ltzTofQJRNEajOuxjqBGwRPXiEpVT9doH/Nl14UwlG+RiscBsNsP+/j7eeecdHB8fYzabtdsZdDOslpP6JIiTdeggL3W/XC5xfn6OUgp2d3dXVmNzLJVhFDS4bYddPa1L/maZeGpE0zQr40IK/LzuM9PZVSd9ZCOACFg1AGUpKt7Nybxc5CWjRqhpu+H7YJt6TF+B6n9RPrwiI2bU1bg8bj6LwnuefdDY484aqbMFekcCjp7Bo2NFUf1lddrlHDJnFLHhPnWf/aaz0LL4DB7HyXZ3d9sNoGQqbPDUE3ff6+JHPZnRu6869gQA8/m8XePDmTmeCMoFiWRiGh+n43VBKoX3uTFX7+uMK/WQ2QrfiXoaryMbBUT6P3oWNSZvZBTe10VhfC9iIf6u5ieateB/nb1ycW/ChhjNKGRAmQFUxJiy9Te1MkfsJwJ3L4PuUKeH1y0i2fgXDTkbO8pASPXAeogYpefb9aB6ct3xXTpDTU83gM5mMwDA4eFhO/bD8THgjlmWUloWBOAe89CGz3y4XrQrRgaqQwV+YqZO9/uRrgQXgphuyWC+GS5j0K7r6HfWJmuyEUCkyooMj1LzjFH3LPOYzqyi/GhDzaYinQVF3TQHiD6sIytXBBZZvJHXch2oRIwySpdxcqm/r0PR/UyZMdZYkTqQ2pIMr1ctQ8aIoucejvXt9zkATHaxWCxwcnLSds0cRBRkfIpd/yuLcval4dRmyUCj+tFV9g7cXGTJUwB0f2fmrCN79PAeLlow2yUbAUTA3SCeHkdZQ1VXVtaX1evIeCmRUtUYstkdZ2kOgJGBRd7c0/DK13sRaHtZvOxdjCt6NwvrAAzcjVEoOHlcETOL6lkbVfbMr6M0IvbD96IGpufw+FousofBYNCeUsmNntFpAqorApGubAdWz8dWVkLgI4hFdqKA4PZAsLm+vm63jjTN3bqj0WjUjiWR1VLXXl+ue7d3La/a97qyEUDEytIzULIGmzEnB6WaRCwjAhDGn3WlPF/umRw4KF4mzZP+jspcM5QsL5H3Z7iMaUTh9T7XzkTdMw3nzKZWZk3fl0R0GbeXw1mdi9aNnsPD93QgmbNRbMDD4bD90owvAHQQLaW0cbCLx3gpes08E4Ac8BXctAxqp6WU9pTEUsq96fnnz5+jaRrs7+9jsVjg7Ozs3rlLXj+q38jJuG4/tYyIFaoDnhkbiLyOins8D9eHGdWe6WB6xA40rHcvVCIQpAFnA/aMJzMCLZ//jsAx01ENpBkXcDdeoVO/HCdyDxuBQwYeWn4dx/JyqZNw/Wazf+5EdF2NbvDkTBMZBned8wgP3c6SsdRS7gb2Nc8EO2eZ0To0da6Ro/B6urq6as/20kFsPUnzxYsX2N7extOnT7FYLPD8+fN7da1d5Fob0vp4HRACNgiIWDHj8XhlOX1EoXU9DqXWiDxMzTs7E2F6vsKVa4g0H0rPa4sVNX5PW8scAUgGQlHD7sMQIsDJ9KnXLJ/XBYGIu7AVqH28KtJHBOgZS+sC9qyeoz/gbt2Mds10huno6GhlM2jNCanNkD1GY098l+t1tCz6PwLpaEyJ5Tg5OQn1zHxfXl7i+fPn7flJ3GzO/LoNuVOhZDNnNUcZycYAEWda2EXzLQNA96yIG6Gjtd6vGbTf965ZrY/uC76YthqQ5icqnzckvV9biVxriFl6GYBFeokASccsqBt2ZbhGJXMAUfk8na5uXeaAND4+0zxy8NmdCM8LItPjTnQOSGenD3pcUf3zWme9+Mydmsep5YlmjyMAyOqTZeChfAcHB+2h+LPZLLW7qFxu09nMbZdsDBBxYE5ZkR6jmTEBPvPrzCA9PKUGTqTobHg+O6J58wHWiDFE5fH0vQF6g+nKc8R+IqCLqHikw0yPfEfHhhS42d3J6q4P28mATNNUdpKxPJZNd52TwZVS2oV+o9FoZb8Xx1g41hM5Aq9fLnrV7hf15VsoKBnAuU5qrFHD+PIVt7emuTkDa7FY4OnTp3j77bfx/vvvtwPzUfyenyjd1wGi9ffrfwKi9I/gw4VcWlk1Wq9xdXmHGivRePiebj3xMQmPVys9a8hRg6t5wi5G4eWiZMsOItoddWW6mAWwegoA64qDsrrRUsupemR8mU4yIGRD56I9htXlAwo6ZNm0K01rMplgZ2cHu7u77Sei2Q3zHenKgl3nXr8KPlp25p9AwY9W8pNUaj9R2WtAyDwyjSiMOh5uyGU37a233lrRp9tnFzvNwnTJxjAiByN+H8lPg8skY0yRN/B3ooauFaGUk9fAqlFGXqMLkKI8Rkyl9p6XpcsbaQNgGbrSqUkp5d5MJwDs7OxgMpm0n53RvGXdVx8L83S0Lmp16gCmA+teJ1xPoyzo7Oxs5SjcTCJ26yDFdDhGxCEHZdn86OX5+fkK6HlaDnBRndWAMbJF2vB8PseLFy/w5MkTPH78GC9evFg5IlbLGdnz64CPysYAEXB/IRR3MbMS+yyQ6+pK6LOsC8J7CjoRyGi8flwC0P0ZZE+7lp8sfJ+wbkgR4DK8D8xrHNEMleuIJ1BubW21Wxy87hTUs/pSUFZxlsaG4gzL01RmwjywK7a9vd3OhhEMmEbmqDTeKAx1yd9ci8TjRHTV89XV1cpJm1r+SMeuKw0TDWB7/vwZcLNI8vT0FADw+PFjlFJaMPLlCd7e+th5l2wMEDHzOmDNPz0nOfOC/jtqfHov6gowH1EDYDh2Kxxw3CtmXY0oL/qOx1FrqBpnlKYzpIwdRl0HT495idZ1zWaztq442MvFgGxgbJi+oTnSibMjrwOKrs+J2ILmV/XJzayckj87O2tnjXwCQvWQORL/X2MgBCPaddM0OD8/D1neOvWt+skAR8E5AlkOYG9t3XwsdLlc4tWrVyvsDrj/wU+vq9dhRxsDRF5ZpZT2jF3dQAisepuI9WSepRaeYVyp2ijUmP2eP6+l51JjW1FedUyn651aWVU/kYd1kIxWfyvAqHfkzBl3efO5DhJHq54jAHYgcQak8USD+XyHU/LsNpKNHB0d3Vv3lDX4dRiSv0c9EIx0GYDu2tf6jPLD3wq0pdwtnHRQZ3h1OFH7oQ6Oj48xGAxwcHCApmna7/K5fWg+u+53ycYAkTcILgLjQB4rURdXUVzxfN41laiAofnok9doNi9a75PR44ixRLrw92vM0OPOntd0kDUwhonGTQhGTdO0R+Cy28PGxkaiegNWv/7qaXmZ1Vk42OvsnDsWjs1wLGgymbRfWGFXLAJiz4f+9jD+nrM74I458zgPfh2VTMzj97rMbNPtL3OE0fsMw/phHvnhz4ODAwDAy5cv07VENZvpKxsDRMB99NZNlDs7OwDQfsQwWteQobNKX+bg195QvQG5N+a9rOI8fgedSCdZOlFcWR6idLQxR2H7gBSNmYOwPEvn0aNHGAwG7eFbmk+KLg7NJFtBzTzpEbvaMNWZ8YsctCF+8DJyYh5/1MBr9ZutGSNoc1c+l6kAuOdkNe4IqKJusufDWbSG8/f5m87i+PgYAHBwcIDlcokXL16E6WSkYB3ZGCBSpVERPHCKnzzRjXxA3siiimT47H7GPjxu4O7gcY2T19oHX4dZ1Bqh5q9rdXLUSKIugjKGaIxLRQewtWyue8ZzcXGBo6Ojln08evQIx8fHbX0qE3Qw1HL4+E+XbjRvupSAoMDZsNls1o7PZDNU0e8+953FMF+qY/0YApcgUCc1JxIBYgRGEThlTFjfUR0yjxzAPjg4aLuwHk8X6+ojGwNEKrqEnTRWv4Cg3/Ou0eKaZJ7Er7XPrYbkZ9ZoA4jiiuhydK2GnHmaGgB1lcnT6hOXjzt4Xr183GJQSsFnPvOZ9nPas9ns3uCxHp2qM0M69qH2oGn5dLzaBt/jHjJ2wbhOzRt9X5vxPPBdd1weRoVlubi4wNbWVvt9O9VtlLcIvDN2m6XrvyPg5PVyuWy3tAB3YOQbbiMdrAtIGwNErnil3E1zMz40nU7b70fp6XLA6sK6GhOJmIr+zhYsemPhQjp+zYKNgKtpM8Nax3i6tjdEZakxjAgYNU9uVBrO9xRFoKzlvLy8xMuXLzGbzfDOO+/g0aNHGA6H+PDDD9ttBDwzmXu8tGvCcR3qE0ALMs52rq6uVhyDbsQlA9LB6Iw16O8u9sxnkePRAXkHKNqoMudHjx5hf39/pZET9KKV0ZH9RDbgdR3ZWdbueJ+A+eLFC/zAD/wAdnd3cXR0tMIqIwf5qQUiYLWBaeF0BehoNMJ0Ol1ZOq8SeRNvJJpe9Nv/e/xOsb2BqheP8thVfpeo++T5y9iXh4sMWt+vgbg3qigdHb/ibOerV69az//o0aP2S6X6PTedbdPV0Q7gql+mQQDit9ubpmkPtmd3kGWMGIeXv6YjBwGfSdRnmei7+okmOlh3mFn9eXwRY+1iyRngevsD0H7+6PDwEABWjg9xZ/upBSIaoytRFcFwnH7loVSuBKBfw/Ln2jWIKl8/06LxM99cyOfjDz6lrNLFWrSROiuJKrvmHaMGVjP6KA/AHTPR56o330V+dnbWAhEBw8949kbO9UfZWBodDM/3UXC6uLho94jpQlP9yxhjzXFkOvL1NF4vytQ1/+pgua6Jx4uo/vx/BDQRyEYAFQ1Qey8gYmKsx+VyiUePHuHw8BClFJyent6bEV3H+VI2BogiBXullVLaSppOpwDujI6GWJtRcsVm3l3/1Mi1W+DgCGClO6ZGqcYRzabw/SjvbhRRuXxdkQOO3uvSkZeP8XLfmOaF4ZWdRHHznGd+epmzaJzpUjAiG9KxI01P09V9Y+zS8HthutQjAuyokWZsI9J7xK5Vvw6YkdBZ8cxr7jdz5xo51YjZ+XXklLxtZbqIwIzdtOPjYxwcHLRT+2dnZ22Zu4ZHMtkYIAJWj8GMaCULya8bTKdTlFJWvtVNiRYcKphovN4VoEcGcG/jJlcPsxuga0TYAHS8I1t3U/O6GVBlhqlhImN1o/OuBMvgn5lhWIbXr1GorrXBex75jGM9+tlqYHXA2R0A2YyPCZVyd2i8A4AyIc+LX9f05PXldeO61vJGcWt4T4uDwpwZJtOPHHK2ijkCqyyvlK4ZWNeBA7uC0enp6YodrisbA0QR1XTA4B8ZEMeLCEaXl5ctCDgyZw2Vf/xUC7tV+hE6D8N3Hz9+3Hoz3Sipe5Wi/5GBaTk9nxmDiTygg1cEVsD9wXfde8VGEAGEUnc+960REVvgVDUBg2noudBR2Qg6TIsnMxCYqJ+mufkqhW+wrXl717P/jpxhVAeqqyhMtPRB4yMr4oQMB9ejdDPAqAFQ9L63r4whRcDHsb/j42Ps7++vMCM9EXId2RggUmFlRd0SKpZdst3dXezs7LRKJUX3mQtVagZGg8EAk8mkHc9gAwDuul16Ps10OsWTJ08wmUzw/e9/Hy9evGi9mUpEdSPQ0PJ3NZyaB3N9aTm9zARbsg12iXSPGEW7gIyL75CBuJfl9dbWFvb399u1YC9evGhBX9ko09HyMj/U/cXFRbvjn46HXb1s9W/U7VJ9ZfrL3s2YawZWEePSOubZ0XQGl5eXvRyQx1/rjnnes2dRGd3uVNdkRltbWzg5OWnrdR3ZKCCKFOKGzW4DgYrdNJ7lop/P9QG3yPvxjBqeScPxCw7CAVjpszPdUgpOTk5wcnKCg4MD7O3trWygZKPpourudWqNKDKcSH8Z6LDharfGx7L0oHX9koTGRQehU+wM5zpW8Nrb28PTp09RSmmnf3Ww1BkV4/KD1XxwVBmF24zrJtJZxGb6MqEItLIwno7WpYLpzs4Ozs7OWmDXuDQNdwyqP3fcWVlqZda8+38OhXDmk6c8DgaDdhnCOvKRgKiU8rsATgAsASyapvnxUsoTAP9vAL8XwO8C+BNN07xcI04AeTfGZ6D46V2eX0SU5uI1N2r15MPhsJ02LaXg/Px8Bcg0fc0fK1vXqehXON999912UR/XyUSbKrWcFAelzFtlXlvvESwGg0H7DXdfY0PPq4ONZEpMJ/veOoGIX7ggGHkZtd4I/npPgcZBRBczErRVdxybU0amenWdZIAUOQh/7nFELNfrJ3ovKh/1MJ/PsbOzg+l02p477e9o/O58agCiZYucn6eR3XOncHFx0TpffjPt5OQEZ2dn997N5ONgRP+npmk+lN8/C+CXmqb586WUn739/Z/0jSwynEihwN3YAMcFCEbD4bD9npMbJhsCd/ZfX1+3H8nj2EUfI2T6/M/1KicnJ9je3sbu7i729/exv7+P8/NznJ2dhV/W9LLVDEjTjxa6+bYTgiO7MQQe7V5m9JzxE0Cd2ZGR8Bk/tUMw9zpdLBZ49eoVDg8PcXBwgN3dXRwfH6/Uj3tybcje4HjfB6YZJpr2jzy+X9e6VyrRu5FEzCkDQ85KLZfLdqxI7SVyPhETyhiap1Vj11H5InJA50P75imX+/v7eP78eaoXl0+ia/ZlAH/k9vrnAPwvWAOIgJiG1rwOvWIppV3xzDUr7k25twdAC0A+uKzx98mfxs9vwXMcYzKZYDqd4u2338Z8PsfZ2Vk7xezUPDPUqEFFYZSpkJ0RqJUhRkzLGwzBPWMHWmaG4w53BXRtLIPBAF/4whfwQz/0QxgMBnj+/Hm7l8l12zR3XUO9zzJyXIvdOwXLrGxehqiR1QDH8xnlWd9xJ5EBnIZbLBaYzWbY2dlpT3PMGIuX00HJ8xCJli9yehkjog0Ad99la5oGJycn7bnz68hHBaIGwP9cSmkA/D+bpvkqgHebpnl2+/x7AN6NXiylfAXAV9KIAyUD99fVKOth476+vm67XRSOYwA3a4/8+AdWRGYwkaF5PjUusiTS7fF43K5K5ZJ5H5SNPF8fuqxhB4NBezAZPwDoM1qe/6gR1jymN2Ayntls1i7M053t1Ov19fXKcRyeZgYa+lxn+ajnaIDauz/ZrJyGz1hUjfFoOE/X03Fw0LExnQGczWbt6QUcD3X79PzWwKaW3+heLc9ZeuoE6ODXkY8KRH+4aZrvlFLeAfCLpZTf0YdN0zS3IHVPbkHrqwAwGAwaXzjoFF2F+5tu41mpFLIbnhjIozkHgwFevXqFo6Ojlc/wekPUCpe83suPdl30fR94LaW04zHs/+/t7WF/f78FCg2bGX7UXdNnHHTnGTdcQJixH157+fhb79eMVuukaZp28oAzP2qQ5+fn+JVf+RV8/etfx7Nnz9pTFDJ2pwPZDkIAVsbe/P2uRqqN2/XurHOdRs7w0cLGKFxUB7QVflNNx9c0X5pGBKSMN2JsmTN1HdXKGDk0tsF1p/A/EhA1TfOd2/8flFL+JoCfAPB+KeW9pmmelVLeA/BBn7iUWt7Gec/gHQR0LUs0+8OuGAC8ePECL168aLscUoZ7efE9TpofxqlbObSbwnf4vg4C81zgi4sLPH36FKPRCMfHx+05Pd4oMralaeg+q93dXVxcXLTrUJi3aGAyA6fot9dT9r4OyF9fX7fdYzqHq6srfPe738W3vvWtlXEqLaMDhM+q6QJL7QK63WSg7vdc35Hn1/Da2F0XUZcskqzMjJ8ztzs7O+2no2tsxeOpMeYov64Lr4fawLbrivrp+tiFy2t/TqiUsltK2ec1gH8LwG8C+AUAP30b7KcB/K2+cUZeKGskOhWt7/M/15u8evUKz58/bw8C5/tO572yMkPk+MtwOGwHgXVbhMYdMS5uxDw6OsJkMsGTJ0/aD/plWwE0P36vlNIyIQchN3LNTxZfV5qZZ9c0uOCN7Ih78Eop7fhY5L35W/VHvfI+gHZRabTpOMpjrZHyvRoI1eyE1zXAi96J0qZwWlzXs0XM0NON8qH56RJ1thqf1q+GjVhR1lXuko/CiN4F8DdvMzIE8Neapvn/llL+AYCfL6X8GQDfBPAn+kTWxYY8rO+Jcg/Ka44HcXzCB9go7gWYDp9R2M1SRhQBqMbrwq4TNxA+fvwYL1++xHw+X/FAmcdSg9ve3sbOzg6urq7aTYlM1xuV58GZXx+W5F4/arjq1QG0SyTIRjnVz7Asd+SBFYQ4zqcD1JH+o4ZfYz1RebP3+j73tKN6jPKsTG+xWLRLI7hOqmZres/1GOUl00ENgKP8e9m6GGEkrw1ETdN8A8AfCO4/B/BHXzdelawhUiJW44rQfTvcUMhFWF3ezxsaAVA/bxQ1oC6DJWtgPh49eoS3334bL168aMevWD4g/lBi0zStxxwOh+3ng6OGqPnjbzfSSBdR/pmvqJFH1xxs5crb8/PzdlnDYDDAfD7H+++/j/Pz85W607SUdfK5Lh2ogYXrvQ+g8j3VYSYZ28nyEsWt9cIy0onu7u62Jzj67GCWnwh0XRdRnmgXtXej8qnzAe63yz6yMV969cqJKKczE18gSOVwHEdnrhaLxco3zPUdV7IavTdYpundn2wcJvIwalTn5+ftweRvv/02dnd327BqFKoL/ufMYLYAU8upY2iRziMvrenVDMvDsHyMj4tER6MRtre3cXBwgB/8wR/EF7/4RTx+/LgFmig+r1OWI/oskf+OHJn/jxqlhvH4ong9fOaMPA3Ng4MvgaiU0n6AMQLbyJloHvvUo8fnf1EY/+/20wXgLhsBRBmVVnEqqEak73PGRtdgsGHoClyNRysuUqB6LADhAGlUOZko49ENhJeXl3j8+DF2d3fDz/56w+RaDS6UVE8ZgaE/U+PJ8h2FzcqUMUEA7ZGj7JY1TdOeuOnvRuyKs4KcfKAX9nR4zfHDLpbqaXqZazaZPcsaYRSf51sbNZ2nd6EzXUV5VFEHq+97Hft1lkcP38dpZbJxe828QOoFaHxKBfUZ+9OllHYRnxuIT69z7EgN25E9WlOTDbZmBu9l0WlXLpNfLpc4ODhou2nc2JuBJ3fK6wFjFJ3ajdbHRNcad804o2daX/qM6XOWbDQa4eXLl+1G4Q8//HBlg6frSusJwL1ZJM+7psmJjFrD9fc9vNZ9TVcZ08mYSSYank5qZ2cH4/G4nRaPuma+3MTzkTG/DDSi/EflzfS5LhhtFBDVBk+dvjoya3fs4uKi9cARk+K0MhfdqdTYQXaP8bIBNM3qNLPHy/C8TzBaLpftNoi3334bR0dHOD09bRuiNhSuk+IAdaSvSK+RML9R+TImpXn36yh+DtDzoPivf/3rANCeSJgxEgUinc3J2J/r2v/rZIY/z5hCxACA1Y8KRIw0ArAsrqjsZEQ8wZF7t6LGn9VbNJ4XieY1cpwRoEZOqTbzW5ON6JpR3NAz6udhALSLv7ifyr2ze5rIi3keInrK/9Fgr//2WQ7Pu3v8prk524fdNB6bEVF6jpf4znhPR9OodRmUddTYgl57/UR60v9kRZzl03N3vNFHeiM7VIbo5daG4CvXa92JqAGpk/P36Gj0kLeonlyfmR1HeaLOeM7WeDxOAU7j0vJkYSL2F4FjBHqZnWVp9pGNAiJKBhJAbHhcYKjreLKZBQ4Q+yFOCgb8zfQ8fT53QNEVsDRWz7cagB7HwfuczuZRCgcHB+3xJAqCXMOU7ZOL9FbTawZUNbB2HUT60nfo4XnmeEb/3bOqLlXPWRl57XWg993z+3PPj5dN6ytqvF7Xrjsg/mik2xLtgQtEOXaoq8wjhhXVp5bD12ZFUrMVZ0o6GbLuqmpgg4AoosJ6P2IkbJDj8RillHYwmgrycQcFG/3TMRZH9Vpl+H9nYDUmwqn33d3dlYWZfIeLMS8vL/HWW2+1myApLHPEIjOpUXlvCNG7NZDJGJHG2TRN29XY29u7t02H4l3aUkq798rrNRJv+BEwONvRaw3D/Diw0dmp7WSslP8d2NSWozxSZ9xIzeNBom/qaVquF30e2UvE2LrsIQK+jyIbM0YUNWiKNmxdUb2zs4PhcNgu5otWNmdevmmae41fleu0XvOnWwwyKu6GRyNmt0JXF0drYditowG+++67OD09xYcfftguYiQTjM4L8jKp/qLfWlbXPcPXBr3dQ/p96vTi4gLvv/8+3n77bTx9+hQffvjhykmNqnvN3+npKfb391u9+9ieT1x43esYUxY+mpRwkPV3PL5I75Euo2dqQ1qHTdO034J76623WnvnkSu+nipKp9YePD9RXfZhXBHI9pWNASKXqMJ5fCn7y+ya+DnFlC7vpJ7MPUsUFxtG3waoz/UexRs2Tx7U+/wCxnA4bBcC6ixTn9Wzmg8HnWj2z8XZluvLdeSi9ziDdnJygsPDw/bTQtpQog2j+rUOp/4RG/F7/ASSskoNRyfhA841thcBegSADgQObL7EwMtCkGHXlpu5eS+zMXeeziQjhqT3M7vN7KUGdl2yUUCkFeYzaGRCTXNzABoX8ukOb/Ug3vAytOa1V1Tm+fQdN56MWRC8/KOQ2ug0DjcAbpbd3t7G4eEhXr16tbKym42olg/3jHwWPVd9+HPg/tlJmcFmoNQ0Nydr8nhenhKg8WWNN1pNn/0v5e5w/mhDMoWH8fvzWt36VhrqzB0c609/q766BngZn05m0J6m02m7fcnjzQDSnWhNMmbkz1VeB4SADQMiIJ+d4fgAP9fDPV/+2VuVCHAYnzc8/d13DIL/ucguMk7GGS0TiLyvvqMVvVgscHp6uvJxO6atX0zNPFIEyg6KGrbGlCLm0eUNNW2W++zsDIeHh9jZ2cHJyUmodx0L5Ce+AYRrhJSx8tpnvDSfTE/PumaaGQtgfAR/1xXT5Qry0WiEb37zmytpRrqKbNHZsp7VzTVk/KhAn3pSG4scS+RcXBQ8PR1fYrKObAwQRd6CFa5Gw9/61QaPJ6KgKlmDzLyrxxMxClaOsp6IgXk+ouMS1MMCqyuwX716hffeew9N0+Do6GjFm3dJRsWzsJ4fz2PGOKPfGqcyhYuLi5YVZXsA2eA4Y6TfV+M9jrl52som9B5wt1qbq5f5jHbHuHzxacTW1YYYPxfY6vnfyqbUcej4od6nHrjotZS7zytxjJDfjOsSL4un4XUZ1W80kcBrXyy8jmwMEFEcjUmreRia78B2hWmfPFoaX9sGwTDa7YnoreeX3srva3n8v4bx+DVNbRRcY8RvSalRezlqOqVEYMH7ESOqUfEu0I3qaLlc4uzsrD3GRMFERQ2cbIMNUcd2GGc0aaFl0P8cZ/S6V9vSsvvMmpZH719fX+P58+crS0u0LN7FJLtyHWq+uFG6aW7Wke3t7bUzxXRWyrQddFyXmt/IQWU9Cs2bxtXFpGuyMUCkhVIlqWeiB9RtD1RAVPjMQ2uDzxTsoKH33JijxplVYtT4u4xA0726usL3v//99htY0ayXAq42Lo2rZiyZV+4jzjI9TncynAHa29vD5eUlZrPZSlyDwQCj0aj9Ai2BV79GooP3OqXOdDNw9MPZ2Ig938y7rk/zGVeV6+vrdviANstxIt5juMge+NwH1ll+HRfiTOKrV69WHJM7M9VpBPYOWBnDrTlWjWdd2RggygChae4+CU2qq0egujKirhrv+wCsPndwiboV29vb7U7+GsuolSlqoHwv8ojq2elZuQeNX7nVclEvbAhZWSL2pWARGZyWLzO2Lr34c3Y5+KFM/ZoKgPZzRTzqhHHoRl8FKGVDWlbPo+dJn/kSB8aretZD9viesg5l8gQmZTaRw4hsQxmYshl+g348HuPJkycopbSH0SlLYtyatjJ+L4PmxduLvuf1qu/XwD+TjQEi4P4sAiuBC7jY11Yg0MqPgEkVkhmnG4DOsngDjRA/Yw9RA/DKrjXo6J567aZp2hXWTrO7ZrxqtNzLrM80bxGgZzrN6gRYHYjf29tbAZzxeIzxeNx+H47T/9rQHIQiRlPTcV9nFDFqbZgReOvXgqlPHdNRpqUOQQfFfY0SfxPEuRWIn/E5OTnB+fn5ygJfsitdjR/Vrd5zxhbpy5nU67BoYMOAyEfjgbtC67e5IoOL6Cbfz7w90H9mrWmalfVKfdhO9Nzj1vciz+zvsyESnCeTCQ4ODnBycrKSvygf0f2IMXQZUfSOl8+BrpYfsryrq6t2rOj8/Hzl4wf09Kx/jsvVnEDGhqK6z97tYlTu6HwwV/XhelHGES2w5JKVUu6WfhCA+O58PseLFy8wm82wtbWF6XSKd955B1dXV3j16lX7xeJoMN5ZnOsmuud2kNXrurJRQOSilJIL3zhzQHGWof/5XMEkkohNObD16dLxvSydPl0B5sfFaS896mKxwFtvvYXt7e324wC67y7KQwa+UZrRGFQGopFnrK050nIvl8t29fTOzk6b/8PDw/abbNHRLtGgcsRsIs/uZfWyeFi/jlilp0cQ8Lp1p+h59EFl/a3CMTaehElAPzg4wOc+9zkcHx/jxYsXKyv5OWbl5a85qRqrVJBlGhkxyGSjgEhZAAvGmTJSStJKHSykh3Ra6Yao6QD3BwvVOBzcVDJgizxlVMER+ETxAPe3IqiBn56eYjgcYjwetycdEoxYviyPnreovH28XQT6tfijuKhLrpLnpliOx81ms5UjW1jnHlffGZusu1FjQVrWGmOqOTze57YcOgyynqjegbt1asoEGQ/rn2yYM5F8dnh4iL29Pbx8+bLdSK3MLQIMbx8st04ARHbjzG4d2Sggimg8p2t1yh64v3hKPU6tsVAygMjy5cZbYxW131lDVzqu72g5ffCRMzFcT7Szs9N6xz4ePvOAEeOM8krAj7qQfUXLxhkh3cj86tWrdnxIG07EtqK6j/IU6SAqoy9wjNbQ1LotWoeMR3WWsWw9WbJpmvaLMQDao49LuTlXa29vr13UygF04OZETH6yejgc4unTp9je3sbJyUnLhnTpR7ZcgXnVOtb24EtktLzryEYBkQtBiLNV/jHD2n8gNzKVqKHqb6fI3sCj+KJ8RL89f0zL++8aH9/RVb1cX0LvqIel+fu1PEZhNT/+PNKDOgcNk6WnQMZB24uLi/ZrtTzPW6fOo7QVOLK6UelyRBEgO8N0FubOMGLekZ2yQfOPW5m4dk7D8gu5HD8rpbRfzi3lbnmA2kTT3AyaHxwcYDwe4+joqO3GaV5Up6rLrO7cJiOn1Vc2BoiizBOIAKwcEE/JGndW2ZHH176tM5KIcdFANM0sT56PCGA835GXVe/kbITraE5PT7G7u4vJZNJ+rSRa+r9u10XzkQFqlkbEEjKAcmbE87j9ax0Mn4FhDYTckXQ9j/Su5YvK6IsvPV++votpDIfD9jxvlpusibNf+h09sqbr6+v2e/NkT+rULi8vsbe318b36NGj1lkdHR21kwBqK9oF03K6XpQl+UD9p7prBmClgEplSTuV0vqiRuB+46WoghyIfHUyPQpw//gIj6NrHMaBRvOhlF/f1UqPFr3x3VLKypQ2T3XklomMyfSRKM0IoDI9e734c9eH3tMpem28Uf5q8WSOotZIHEAiB5YxSeD+hmDXnXbL9P3RaIS9vb12Qga4Y70ECZ39ms1mbZd8Op22cSkDapqmXXPG2UcyKdrP6elpuPbI6ylqY6pfX0ISTbjUZKOASCtdB6PZLwawwlzW6U542KhhZXHx89UehvFwYJCL8SLmpfnQtLOujYePGpQ2Fg7mc+/WeDxe6b5FoNFVfg3rXRAPE+U7iitjFhoWQNuAdEywludaI9H4nYVl97J8ZfFk4XmtXWUFIzIcbgVh10vP12IdcmZM4x0MBpjNZnj69Gm7Z44bwjmuNhjcfItvPB6354bTZnWMiTqnzfmiyGxcS3WmCzfXkY0CIlYKBypJP/2LqqwYoL4ORp9FxuMLxSKW442dXi0yiAhgIlaQNSZdqBZtIYjGq/Q3jXU8HmNvbw8AVs7wzgZbMzbh5XOdaJioDiLA8UaqdeXxRedMRdfKimr2oGFqAOp17ukyTNbYnKl5t1/TYJdra2sLFxcX7QwZ41E2FAGifsWFzkhX/+t0Oid7+Mktjr9OJpMWDGezWet43Yl5mX0LSnQiQl/ZKCAqpWBvbw9PnjxpZwGiQeqocfOZXivYMH41Ej3v2Y04MlRlQFo5uhkxygsrVIHOvYq/o+NQmjawuoWAxsGxFXbRxuNxuzk0Ote6ZiwZa/J8ZmxAG5yGzxq/C726A1HG6LL69ee1tKOyOKhl8UT54n+tM88T7TPbNK2LGKMhAHXIFxcX7VEpunLanSz1yk878VQDZWW0GY5ZRbaojtIBKALNLtkoIAJuzmJ+9OgRAOD8/Hxlg19mcCqZgUSD0OoxIsX5fV9X4SxK3+tr8H2NW9PP2AzDzOfz9nvp+mXbrFxZ+T3fUfgs7qhhZuJlItjrrnoNV8uL//b6zvISgWYWPqs/v6eswu1MhxbYkHVsjP9539NyB0Eg4rok9iJcb7ymXXAJiI67aj14eSNWDaye6aTg1Vc2DohOTk7agurxCcB9hURdKJXIKP2ZV6hXWNRINbyznUi0wiKQyn47m4vS0C6h5k1nmzKdRGn6GpeI8Wj4WlzRfaX4zmYo/IgipbY6uwtQo+5sH2CKwC0C8Agc9dn29na7wNQBjDsGOC6kh/3pVqbM/pgnApguViTTYj58GIIApKze8+Y6ZF34JEK00PRTzYiooKOjI4xGI+zv74eDv1EXgffV40QglBmOL9jy+NRj628N19UAa/cjFqGgqCcUqkHpuzQgjhVF06lRPjWerpm2GgBFcWZpdzEJZxEZAEZ17zrJ6sr1rTOkWVw19hg5teirtOz+6GexdcsOAcUbtjs+1YV+/0wHmb0M3m0r5e5QOdW3OqNIl5ENalqfWiByOuhAooPTOuahlR8ZkiuPcWia2gXIDD8DDXobPRFADby2yjRqVO413aNFXlXz68c8UNwjdhlKjeF53j1PfK75z4DanQdFG6MzoD6MJhsbrJUtAhIvl9uFSmSPPk2v9Unb4+SE/o7sVm3Ul5WQFZENKZvyDw8QBNl9Zzhlw24j/B3ZVldd9JGN+a5Zxl5YOD9OIRIFMY/LG7V6I97nYjCNKwOJLG1WaNS4KOrlvKwZe/BvtmnYiOEsFot2NkY9sJa3xsi8bDWA1nQ9b6rDmu60nnUphOtJ44vSztKssUBPw+swy2sUL+9FXWJPm4DDrhiXqXj3p6ZXzSPTVDDifS0bu4QEPgUsnWWLnFZWF9GzdWRjgIiiXoB7YpwKqmK98bIRqifSRsOG2TTNyui+VopKxIiYll/rAKTm17t9HsbLnemFHjZaPuCbgDloqUxoa2sLu7u7rRFq480WbTol78NGnLl4WV23GkZnAB1wPA/6Xk2ifGQsLiq73vP0HLCi+DXvFJ0iB9Ce2a1MUG1CbT8quw4SN83NOU7qvOl8t7e329kyrRMdYPZxnqi82fjs64LRxnTNoo1+ZCg1w9ZCR8aiU+0AVrY+qOdSg3M2wvv6m1LzfJrPqAFrWF9d7V5XByA1XeY3W9ukYS8vL9vP0egaEI8r6sooCER6r0lUXs0rG+RgcHO+kq6ij7bTZCwuA65aXqO8Z0DSR7K69nyzPgG0iw8VSLReNZ81FsiD0ljHbD9cxkGGzI3RwP3Nrj455DrPbCQra1/ZGEakwMJCaqX4GAeQf1wwAiT3LO5JGB/DOmtxg+C7+owNvMaA9H7tuYZjGuzPq5ExDL0f/7T7qXHo1LAzHQcKzU80iKuiBpzVSQTwOvZF3fn6LmeOtbgdKPV/FoffcyYUiTOliFnWGK6O2+h6uSg/QKxfLZM6K7XBpmna+Esp7fnfaheRg9D4o79IT/5sHdkYIALunw+kyoymyL2ytGF6P9k9fORxnHJ6vJqmVopvN2Gj4iZFTdMbfAZOCpRRF4Fl9C4EhUbNVeruyRyw+fVcruzVZxGL8GfZdK+WM8qn5oML63T62j2ul5dp1ZYD8HnU8NaRrO6i+LSOvKzRWVoRSHoZgdWuoOqGdsyexHA4bFdP6zEiADCdTjEajVZ6IYwnyoeXO5tZ9LyuIxvTNaO4d2QB9TB4b7ARqvOZglGE4O7B1Eg8T96YnDVpPmj4utLVvbaX28sW5TkSZwLOZHwq1uPd2trC4eEhDg4OcHR0hJcvX64MjDN/2tWLGKeXpW85mE503Ivr39+L6t/zUMtnpFNtjF5ffZiTgpCO97Au+KdfEdF4okWcmVNQh6VdWmB1zJJAdHV11TJljiPqkbtahkiXbl+Rnl8H7DcOiID7lcpvmXnFE6ic9ZB2siuj8XHMyBkWKa0OFqp0GSKwyl58UFnjcWbm4SLWor/dGDRu7ZJ5uTR+1QPPMeJXQaKvrmYgFpUrylckqgd6cR73ojM+GQhlAOF50rxFjTpyZHpdq/soDgKBg7kClAJuZNtZ/rrC8eRH1R2v+UfAV9CKbMv1EpU3009W55lsDBB5gX0dhDdM4P4Aryoq8toaTsPwvYwi83dGwbNn2q/3JQXKMLwcmp52KbJBZfVojLdpGkwmE5ydnYWA53qYzWYopbRfW/UvTTgrycaMIiOOGpE/Zzl1PVYE5PpuBgxuC/qsBkiRRA0q0ofGkQELu+sc74tmnvoAf6bDCHgZTu2EuqUToqPPwFzLnaWfhekrGwNEaihUFIGB3pxUl5vztra2sL+/j8VigVevXrVG7B/Ny9LT/8vlso3Xv1kF1AGnNuPl6UUNRu8rQAFoPRivmR7D07tqPE7Ro3zq4jV+zocfN/SxGsbL9zJgqYG16kDvsb79MLeIATkr9PEN138GhrXxxgxcsjJRr54+9asOhfVIxhexj4iB1MDQdaULHZkP3qezZZ0DaMeSCI5RPTMNZ0E1QFpXOkeWSil/pZTyQSnlN+Xek1LKL5ZS/tnt/8e390sp5S+WUr5WSvmNUsqP9cmEVwBw16VQNsBwnDX67Gc/i5/4iZ/AH/pDf6j9yBywOtPlBu9MQ9M7ODjA06dPMRqNOhE9qhgtT9+yKitxgNLfNLBoMDAqIwAcHR2t6M3zrvrhzAq31kynU4zH43t64kmC+h16XWqhYTMG5LpgXetGzUz/Efj5swjcKT77pGEj5hTpOQIdf9dtgqxev8kWsUnPU5R+pE9fRsIwbld0LtwScn5+juVy2c6kedo6q6zx1vK3LhsC+s2a/XcA/pjd+1kAv9Q0zZcA/NLtbwD44wC+dPv3FQB/qXdGZAZIp6KdbdDLLJdL7O7u4unTp3jnnXdwcHDQrpfQOIHYICnaiLmOJeoqaVgXH6PydHwqXeOLVjrzfWUsHFhkemq0GfNxr+tp+58vY9DpZM2r5pmeXheKalpaB/pb49Vv1jmz6sq//q6BndZPBNx9WFDWwFgmXZulLIlMm0AUdZEdICNg8RmzDIS928z2onnVdLmQkjNpmf4yhxs51nWls2vWNM3fLaX8Xrv9ZQB/5Pb65wD8LwD+k9v7f7W5yc3fK6UcllLea5rmWS0NelYaCqcbyXz0ywXA3SrQZ8+e4Xd+53fag9a5azvr7zKtiPYvl0scHR3h+Pj43sBu1hCYl8x7OqNxw48WQ/qSgwhMs4bjXbYs71p+n1XUz/bobm591ycBfJuA6kbTivLNOvbV8PqOg4zqwuuTZdeukUoGVq4bBy9N1+8789Z7BFrqTe04AhIHTn8W6cWXTihDi0Bd60EXu45GozavzqQ8v31+ryOvO0b0roDL9wC8e3v9WQDfknDfvr13D4hKKV/BDWtqmYiOC3F1qHtU4M5jPH/+HMfHxzg4OACwygB8/Q69glauz6D5p3hZEdFYhIfj74xBKY33e4Fu7vX1tQug72s8amBRI41+e9k4VkDJGCnzo+co04nQMZydnd07ME7LowxYwc0biwONA7qDgL8XhdHnkYPx31EZPG4FUrdbglBULyqap4jJuS70f60HoHnywXSG1S8I97FTz4emFTG6mnzkweqmaZpSytpQ2DTNVwF8FQC2t7cbenBdeEWgcKagwMEzXKIZKADteS9XV1eYz+crSM/FezxaI6P7KpEBZuATlDk0Dq04NWZ2x6gTgilFQUQNlV1B94DKQjQ+vquD9AyvZ0I5y6OwPvhtNXbTdnZ22q+0OshoXph2Buiqv6h+MkCJwnc9z/IZAVntPu/xC8XKGn0FdQQ60bXn09NyINL3vA6V9TAc979xxrSrPtw5RsDfV14XiN4vt12uUsp7AD64vf8dAJ+XcJ+7vdcvM3KAOAdAeWC4GoavomUD8m9A8R7FKzYak+iL5spaFATcW2v4zLB4X2m7ViQBKTJa6sIXZlJP0bYBBxNvyAQyPtPFpLzn64xYXr7LVdrHx8f3vLnmj3Hr4VoZW/C8q64ckJiXiEVGIEQ9R8Dl4SIA07C6WJBnUUdLITxOfR6lHzkBzwfLrDas9uMgo2kTJHURbA2AsvvrghDw+ls8fgHAT99e/zSAvyX3/3S5kZ8EcNR0jA8Bd2hMBXEA1A8SbzNtXRb++TewmubmSExuBNS4nCn47y5PyHuerz4sSv/YqHVMIWq0nmfqiYZPI9Rzh4HVAVQFE/eGvCaITKdTTKfTcODT9cF7BJSdnR0AwOnpaej99ZrLJRhHNisY1YeDUeaZs4Yb1VPEIPWei95XHQ4Gg/bMZ9dzzb78eU0chHyG1Ae3FUCUKWn5dKkMbSoDTc+3pvOxM6JSyl/HzcD026WUbwP4LwD8eQA/X0r5MwC+CeBP3Ab/2wB+CsDXAMwA/Pt9M6IDeYvFAsPhcOWeInRE472BqCHxPTIEr5Bo0aDGnegl9K5ZJSlg8J5OyUcMiuH0U8JuUN4FozHS80Xl0fEnTZf64VlGwN2aF9WFe0o+Wy6XOD09bc8Z93edNfDsIS6i1MYUeWz9XWM5DkRZo4jYSQYMWVraUJVJjEYjDAYDXFxchOMtXt8RSHuZfYwny7PqLGLtOgzgoMv76iAiQI/E9b6O9Jk1+1PJoz8ahG0A/MxaOcDq2AcbAr/RxD9Heq3MrGL5n39K/7XyfLbGPZxXVlThERipwdaYkv/28TAFKtWJbuPQ6VnVhx7CTomm0LUMi8WiXWntOnOPqsL0dGGcvut6IeP1dTVZ4+jDUr2cXY0nqgPq2Rcl1pyM3icj0u/xRY5S34+AxVlGxkbVbqJ86h/LxZXdUXrsnvniXi9zzRF36d1lo1ZWX19ft18XmEwm7YI67o2JaDAr3vu9bDRZA8pAIar8yOgyo4zu639tZFkjyMY1IvBVI+TANkGXXb6orx95XAArAKjPtEH6ezUA1t+aLsFU6zVbv5R52Mzx+L2MJalteB6jtWGZ41BhHfDExWyhYQbkGm+NIdXuRzrhzgGNO2M52oNQXWictXyuC0LABgGRe0Qd2+CWA3pcFSqZ76qBex9Z36kZeI3B1CpfPYx7DM9Dxqo0HY3L03Fvr316vqdrgjz/kYfm+5GxRVO+KrWyOiiVUla200TOIgIP11XWABiXMsmMmUa6jBpSVh6NV/eSRbvq/Xckvg7IdewD8F6WiHn6X8YUnYHqfddPFyNaVzbmPCKCDccWdPAaWP2UrXbXqFyCjvd7eQ9YneLkb+B+V8BBwMGgxoy6KGofD6veKGNNkYdX/SkbAu6P62h+IgDU5x42ynvX4kHXk7IfDZMBXuYUorT0eVR/WX3qff/vaTN+XTbCI3j1xMUuvTpIR/YRPfe8RPnXNrFcrn6wUm0m0qsDURQm01GNpWWyEUBUSmm7YeodvVLc61PcIL0b50ifGaEeLq8My71MBGa1skUMyRuKliM62N+Nh11WnTmLjtdlvL4wMcu/gk0UPnsnuq/1qPd0nZMPanudaPz63xuyO58of1GZIl1pvJEOvJvKJSdcM+SfEMoadVYud0RRuSMnpXH5bBjHhTh4rvFHTkXHIaO68HLU7KWPbEzXLGIlNWZCI2fYjN1oZWrf1xsHAUx/q7J1ALnGavh+Tfhcuw6a38lk0n7T7ejoCMvlsl3OUEppPz8zmUxW1qgQnLa2tnB+fn6PwTigRnWgeaSO1ft7fFoXUd1F0/EUZQ3A/Y2bGkcGnkzXlxnUylS7l9Vd5LzYmLm96OLiom3o6lA9L6qvKD/esCPmEdmevuvAwPDz+Xzlqy4al9YzHZt3FaPlHJljWkc2AoiapsH5+TmAVWrIgmd932wMgJWv9F8V5mM5wN00OJ8r8KlhqWSAFnkLNaaIEfF6e3sbh4eH+NznPofBYIBvfvOb+PDDD9uvL2h4ghHHzfiBPa5odk/m3k915fnjO1rGbNDS3/f4o0WXGg8bNeu9j3QBhz5zu6mF9zqpORsCPydTOEsWsTXPaxZnll4UJvrvS0GipQPAHesBVm1f80ag9TPEPT98pr+7nLHLRnTNgPuD1Y7uTdO0m/J8BkxH950d6XPgDny0UURdIG0YfZTsz2oU2o1Tw1xf3+yG5rfMOajL8R6ewbSzs4PRaITxeIzd3V3s7Oy061YWiwX29vbufdPM86rdgKwsWTmcEXnDddan5eRRIn5f047GVzw9fR41kgx0oncZvgZeyoK0e3x9fd3qXdN2VuJ6j4DRbcUdhedJn/mso4Oa6paLTx04+afPdUw2y2ctf31kYxgRhY0NuPOoOuAaKYPsQONTwIkASsHLvTDf0/Uwyh6iNS3qjbJZD82bXrvRn5+ft6cAkOUwDLdOXF9f4/z8fOWLDWwUZCG7u7st08zyFA0y670a83CH4TqMJglo1NzXREDiTJN+J969fZYnB0LVpf/O6qVWJ1p21jPzzk/4+OI/De/x629dS+Vp0q77AqzmP5qccF0QZHyzNycQaHtqD16OKH0N11c2Aoi8UdNwSXf1mXcbsgoGVtfEUJkKchGVdc8eUeVokVsEkP6+v0Omo6BIAHz27NnKd6m0Acznc5yfn7dxKdBqH55nUQN32y80jw4WzJMuyFPD1kbpoODibEDTjLrU9NKqn4ypZODn9eD5iZxHxC6iOqcoCHHdW7ZBNAJBT49Ao7OHXnbPW1RWnVTJZusicM7G5bSu9URUX68X6XZdNgRsCBANBgNMp9OVLRBcoau7w3WsQSl9Kas7x1V8oC1bSKcNmum7p40aQA3QKN6Q9H5maAQN/QoIcPeRRI4P+YHzCpK7u7srWz08j0zTmSa9NNlkxKaU5nujdX1putH3vDLvza4PnZGmW/O4WaNX/WtDqzEgtx1+non14ICqaXia3ujZpdN0IrDMyqTXHGDWbT1dTKqLMWmbYvzMd7TOqAvEa7IRY0SDwaA9nlRBAsAKQGTL5XU/mk+3M7xORUbTqTp4x99RA4tE4/RTGPXdzPg1jDMPMiI1BHpRXbmrXbTlctnOmu3u7uLx48ft3ieWLSpDKaV99/z8fGWxYVSeiLlofJoW60dnY9SRaB4cDLK1LlE9a166PHMGQFFd6XIJ74ppXK/DBjRNL0/EkijuBHQVfQ0YXQdRXvRPl1eQGfm+yVp8fWQjGNH19c1XJHSmjIDERq5e2Q2GoszGu2AUNRjdMBrN1rjB1zwtcEdh1z3yVK+94XLshHHwHrtwUZyXl5ftO/yY3v7+Po6OjlqwUfEp2a5V1Kr7qC6YT71WNqTp8D0+89362jV3dqDljlhC1LD9Ha9PHQ9RdslvyTsIRTbojCRrnNHYSy3PUR5r70ZOrQsondlo+xgMBu34pH4KSfPp+e4rGwFENDhtaBQ2SEVnKlcZgipPp/x9sZxWDAGPg9LaaJgv/s8aple854X3ma6vZfJGxcrmtY6TXV/fff5FNwYrw+DCNQLYYrHA7u4uDg8PAQCz2axlUj5u5EsllOm5YdXouDKYiO1oI2bcCsCREUeOyMN3NbQMNPWZNjiWX79qQt1lrKxr8WJ0r/ZMy+VMUcvC8cbIZqMJiSgd1Yu2Q1/rpfXrx+tEZe8jGwFEwCqboTioaCMmPeRArj7nGEd0fIbGDdx5YM7iOMprw4kai4MKZxoymk+D8XwRFPWgdZ4+6Z5QuzVsGDqVzHjZzQKAnZ2d9muu8/m8/XwQ34+8WwY0qsMa/Xede/eX7zHtLI7IS/t/ZS+RRHWv7JJps+u4s7PTgjWZEJ2l68brOWrcNVByh1XLt846+qJI/VM91xxopK+MAfMoWbVZP24mAuc+shFAVMrNkZr0/NrwfEapaRpsb2+3W0KAuxW6bLzqsZqmWfnCBNfmjEYjADeIfnp62ubFDSr6kgXD6UyHglS2jsPvMR1dNa0DxNyzRHDVhuzLGvicp1pyRofd3pOTE0ynUxwcHGB/fx+7u7s4Pj7G8fFxe3CcOwLNc2TYzkb0mdevs9gI9KJ49b7niaJ1kjXkrC40LwRzXTi6tbWFy8vLFcCOytkFQFEDdWZWA/2IdamdEpy64ony0lUW3tOFwXSYkR5fRzYGiCJEV2PQQVkaCmcuyHx8up5xX11dYTKZrJwAybgAtKDEfOhnezl7B6zOBjFfNTqqgKNlAu7v5yH1Z3lYDgVV/5IJgYPxKHixa8b32ZU7OTlpF0ICN0xpOp1iNpu14x/RkSosv0pkrHqt5dc8OiPRenedRXbiNpOJx+ezht5Y9ZQHOoizs7PwcLMsrYx1ZIDooFFjdBFb17LxOlrvpvHUGEvtPv+0p6GsiGnWQDCTjQAiACv0jo1cuyo6CMzFfF2f7dV+LseBCAZscP4d8MHg5osinDa+urpamULXNKKxAjUIDQ+sniCg+SN4uEfza2UlOmCqa5H4wTwFMTXexWKBi4uL9lPU+t378XiM+XzeDna74UflUqOLDF8dCt/h2JSHd3aZbYZ1veq7nrbnW7vf3iVjvLS309PT9sMKWfcmYhaRjjSfbjN9Gy7DKSj6+E/tmecp0r3nl7+V9fA3h0C0XanTXkc2BohoHLoloWnuvkypg6v+HSyK/9b1M7qh9fT0dKVCvUL4NQqNz1mTdgfUqEajEabTabsGKjohkeVS8FFPovlSgyFTowfSLuDl5WVbzohlqNEreBOYzs/PMR6PMR6P224yGVLUjfLyqB4djFmfGTjzXCLVRdQVzH57PXid+jvedVe2wW4yGaKuN3NAjdLSRuvMI2NxWVfKASsqu+oqsjONS8se6a9LXIccV6VNuqNZRzYCiNjt0KMvvIugjUHPVaFEla7/dRzg4uIi9EzaNePKZY1P2Ywqn8C0XN58svng4ADHx8d48eLFyqI9hokWnXk+XD/U0eXl5b3FnwyjO/GjrkTUYJgmx4nm83nLjsbjMc7Pz1cYkudL9azMTEUByB0Iv72ueWdZo/f7dCmy7o47EY+bZea2jWy8zOvI0/blDx7G72nXLXoeAVsfkNJwGTh2iaeh972O9MOopZR2sWYf2QggKqW0LEIbrldqzZvUjCa6V/MW7MZ1eT/OUukg3nw+x9nZWWvMaiQR6HicUb6YN+Bm+v3Ro0cYjUbtxwupJ59JcTDSBuj65T2CDj+UyI3GOmCr7DLrRipw65ouTV+dSuRBtWzuOLQMLJvr19ME7h/AB9yNc2xvb7dd0+jg/8gWutga39Wxpy6GVZOoO9fFqKI4NK+vA058z8/+Yn2vEx+wQUBEtuCLxYD408XuJTN2FHmm6H2vHI8z8oRR15BjPhpXJFH+Ms+mxnd5eYmzs7N2sJnGrCCUlT2abdH0lLWxobKrRhbGLpTOVLpe1MMTSKJNyVom99oeNtIf72djIZqHUkr7qSSyYqZJEGJ31J1GVF+ZvUXho+dub9S/5zkDQx/473KamX66gJT3fKBfy652wGNRtL77yEYAEXCfMXhF+n0HgSiMKzoCJX0v837qvaL3VHxwNUrbwazLE2qelsubT/bwmA92abXrlBlT5An9t8fBrtl4PF4ZO5vNZve+5Q7cX8yobCCqY50507xG+amBUqRjxl/Kzbjf7u4uRqNR2+3mH8fEdPKjBhi16z42konbGPUZAb3afwZ02XR6l+Pj+/6OhouAiU64aZp2TLWvbMReM+D+uhK97gsuXmGRMbmnzAypTx6ixuVhNXzEuiIPq3nmtRopTwIEVjemRvFE+ap5ea0HAt9yucRsNmunsoGbaX+egRQNREdA7nnTLTZaVl0vpXVGUIkapobz+uX6oB/4gR/AH/gDfwCf+cxn2vjG4/HKimmNo6vbFOUjuo4k6kb57xr4qj4U7L38zGeU5yicvq/v6nuZ86D9UJ/ryMYwougbUEA+6JqhemT07hnc8P1ZZhT+bg1INO7s3Sw9L2sUJ7tR6oX4PBos1bS0gUbM0nXI2RF2W8iQeB5P1BXVfEZdjCh/ALC3t4fxeIzj4+N7R8C4vlT3zsR0XInvNU2DnZ2dlhkxzGw2uwfCXh7XjcbZxXxqdZHZr5Y7c1Ia1mdaPSwlYlgR4EWg5M+i+3ymWz/6yEYAEY3Az4ym+JoPFtYHMTV85DmztD3NLsCoefmuhqbiIKbvdQEigJVV1J5+9I6XUc//ceP2eHQgn+DHrhqXU+gWCKYXzaJpWX3AeW9vD++++y6+/vWvrxhz1MCigXlfOsAyXV1d4dvf/jaOjo5wdXXVgrDOjtWcSsTw9H6URz7vcjaReLwRS/Fnqocs7sx5RoAVjUFFtuFOU0G9r2wEEAEIuxZRo47ocg04ov99JPLimRfz99z4MnCJDFTDeVl1zchgcHOGkx7W7vF4fKozz3800J2VkcJ9dfyCRSklXEOiTsS7kqWsfiDz+Pi4XUagrC8C68w2OAtGIKTM5/P2c9r8okV00FetzqJwkT49Hn0eMRoPm8UfOSf9cksf8XHJzA6jMnWxRc9zX9kIICIb6moIXqmZ8v3dGv31NPRejdbWKt3f826ClsXjc8DwGSE2LnYtat3OrEzaYD0PEWV30QO4uJJ7MplgMpm0LKN24Drv60I8buPhWFRUlgjY9T71xcY5Go2wu7t775teBCH/WKfnL6vrdRq967bWYGsOLmKt/PPlEX0Ye1bnnhctc5a3KH/rykYMVrPStTAZQ9J7NSUD+QCjSxeCqwFltDNrcH3iV8Py8FpWH5DU1chR+jXv6nnuC0JN06wcj0rgOT8/b1cmTyaTcABbAVDrKdpzVqP2zo6yMDx9gGddDQY3x98SPLt0tg5QZGG80a/bSGvh6cD9kLZsjDDKD9OIbMWBM9KNg19XnjPZCCAC7ldg1NdX5UQSVXpEZWvvZ6jeZfw1o40qKIq/BmT84wzQcDjEfD5fea7dEI3TG73mNwO9mug2EjaGi4uLtsFztbSCkTcM3YXvQOxl8Hxmz3UWiSyIAHl9fd3uHXMm6YzFJWKnWf05y8juZWnodeacNF7g7kwgbx99u0i1dhUxqwh03GGuKxsBRBHSRl2OPp4ou9+HNkYMbJ1rNz6959cu3pD0voJMKXdfxdVd+pquG5aC0HA4bI/lrXUxMiBVr+vPuNiSK9NHo1G7MZJ51FW3kT58StoHu93BRA2PjUG/eMKzl/wYYtdfzWlk9ar5iFh55BAzfXt6kURlZveWWyy6JMqTMtLMkUf5WsepZrIRY0SUUuJFWF2FckOJaKY2xi4wckqqcUTdja68+buRwWpYTVMBikDC8SFvODV2w2cEh2hLheo/0mNNr2RHnMWbTqcrh9ax+8BP2DRN0862qRf1GVLXBa/1SBdnNBxDGwwG7dnb0ULTCDA0vaycNanZwzrglDHJKAwBe3t7u9VNn7xG9duHvWX56LpXk40Comw5u0rNO2UNM2MwHq8DkF/XjCKLgxKBhD+LQKiUuyNRCES18nv8zAuB4uzsrJ09yga6o25FlHfVN8Nx0oGrl/WDBdqd8/QUfPS5jos5O1KWqKc2cDPy2dnZyoxbl370dyR9bTPr5qhu+zgwBeia49Py12xQ44nKFf2O2lbEpKJ768jGAFFm4Pyt6J8pOFJgFEefPGQeib+zvTdZPFllRZ5XPbt+MYFAFK0f6ioPsLq4UNPv4wk1XyoRaHEMaTabtfuOtEFx75luOqVogyKI6LvMvy/MIwviBxtPTk7ufXlVwd0dVgRWEQhnDTbShd/vyxIixlRjKHpcqzodlYiNaVmj9hOVN2PxfdpXTTYGiLyBUKLGlHmlGgpnIODMIgPA6L0o/i5w0TBuoAo+2h3TeJrm7jNC0YFttTw5Y4zKk+nUGV3UmFl/7H5xRoeHr7HroACh5SXolFLaUwB0C4myKr3Pgemzs7P2jG7mIVqsWKvjLmZUY7b+/KNK5CTUVvRalyMoUEfAGYGR34vqXduel/ejgtFGAFGkoGxTH2Ud6lejqQ4GOpUcARP/u0fOjDtb/R0ZFBem+Se31Ti4glnpeFTe2uryjO24zqN3svS83AzPM5oIGKWU9vgUltdBTcd49BRAP+qX65i42pszeAQ2XRBZK3eN1fSxMw2nwNeltxqb4P1oS4a/rxINvGv6NSCNHKiGi3Y3RGlk5a3JRgARJUPwGh1WqQFO5OkyD1FTqAOMe6oobHaP72nXS7sp/iljGgIbt0/FK8WOvB7DROCp+ak1wqwxR+VlWO1Gbm9vA1g9F8jrh6DIZQpkN6oXMgA/Ljjqxnl9u46iMnqZIl1FrMLDZkMJmY1F9hk983rUcmX56VOv0TseRsM5sGX66pKNAKKmuX/+M+/zf58C1gBLDSKjlhpPBiI14PF0M2+ooKNT1Orp9aRFbzRsYGQMZEgRiEZ0O9KV388aXk1H/lyBU7enlFLasRz+LuVuZo1hlfFx64gesaubfRV4vH662KgfZZKBTFT+SKd92ECm38jWo9X1mv8IXKI6Z5xaNw66NVCtlcWd2LqyEUAExN2JdSqWkqF+ptCMKfk7fdiSph9da0PkRwHUCDiTFQ3Sanx8zq5NJH0AsaarzCn0KaumxTpVUOICPN2qwvd147NuASGr4hlIPhibefKoMTK8glYG1n4/Yp9dztHfdRvv4wyjTagUXZvVVW5N3+PxZ33Llzn5dWRjgMj78b4xjxJ5b3/ex3tH8UUGWQOeropWz8xGpV8nYdjr6+t7IKRx+C5z/rF7FhlnlL++AJ/FV5MugFbgINjox/qoc+aNJ/3xwDICWG1rhoK33tPfep3ZkoNaBj7ZeqeIaUTPooYegZEyoyg8QdtnQ6Nydjke14eXxeOMyvs60rkEs5TyV0opH5RSflPu/blSyndKKb92+/dT8uw/LaV8rZTyT0op//a6GYoK6M+Afv1dfa9v41Lj6wK9LH7mzbtfHBfRYzSjcY6maVYGXzVt3uPhU8oc/HjOyBPX9KT3/H/2Xu3a64iMiAPu3IKhJwiUcneaIg+y1+Np2UVTHfVZhZ81Nm9UkY76MCUPm7HlKA5N1+2vq85KWf3uH+9l15kzypxTFytSHWS20Ef6MKL/DsB/A+Cv2v3/umma/1JvlFJ+BMCfBPCjAH4AwN8ppfyrTdN0fluEhXX6yXvZgU5RHNG133PpYgtd7MyNRj2VjwXpwj49+7nLS3L9jTZArjPi88zjReJes+bFI324AUZe2BcnsvzcosJ7/Ggfy7O1tYX5fN4exhYNOvu168DLnbHsGvh6Q410mOlOmUytTjKbi8Jp3MqQMjYTMZYMXLpsPMpLzT7WkU5G1DTN3wXwomd8XwbwN5qmuWia5n8D8DUAP9HnRfegqtyocURePjNO9Z6ZeNzrKFaf6boYXRFNUGJD5Od7fO9T5GEUoN0AunTZJd6AogYceU1vrJnn9ndYBwpQ+oklhue35nV8yKemo9XanmaN7fQVZy3R8yxt1reH+ziYedM0Kx+GzBxzxJCi+zUWo+Xx/LyuXlU+yqbXP1tK+Y1y03V7fHvvswC+JWG+fXvvnpRSvlJK+ZVSyq9kgBNm2MZEoj9JY+U9HT+osRkgNhpnOZomNxzSm/PLEFzE1zRNe+AXZ3syyu9l860Nrg8fF+E7NV06+ETPPH/ORKL3a16WzxSMHIh4n103/2ptFK/GHzWmzGuvI5EuIokYVMaYeO22mzmbzAH7M82zxqf3NO7I+XXp4uMAH5XXBaK/BOBfAfAHATwD8F+tG0HTNF9tmubHm6b58b4g4Peyd/y9jO1kXq4L2BQgdAuGghFXFvOPY0H+GR7Nu4JbRJ/9nWh3OvMRDWxG+qoZcKYbDdMHePw6agQ6JsYxJB3Aj/IfNXC/50w7yrPH6XFpuWqMIrrWuCJwAXCPJfZt5LQ/XcbgEtVt5vxcojijLt7HIa81a9Y0zfu8LqX8ZQD/0+3P7wD4vAT93O29qkQVd5vOPSPy6WBnAJGBZgqrNaIuI1cmBNwZRSmr62GYZ/1dSryKOeom8T+BpwawzAdBsMs4o0FeTc9BJQMmNe5Ih37f64j//Su12lXTdzNmonri2iDPn8fl4zdROWuAru9F5fN8uj6YBy+Lh82cM4HI34uAsXbfJav7GvD0DRfJawFRKeW9pmme3f78dwFwRu0XAPy1UspfwM1g9ZcA/K9d8bERZ40nqjz+JghxMyiNl2srFLBYaTrYy2eRUWqXR2em3MO58ksprZfycQ1lUdFnnBnWu1sKtIyXK449n864NB7GFS0g9d/6XnYvMu7aejB3EA7IHIDX8ED8uSnNi+pfu3GensYbbVmI4ssYYh+2Wcuz6j0aSM9AVJ2S7ymr6ScrZy28liN7/nFIJxCVUv46gD8C4O1SyrcB/BcA/kgp5Q8CaAD8LoD/4DaDv1VK+XkAvw1gAeBnmh4zZoPBzUHwPOEvayS3adxr/PwSKYAVgNH3OHXOM3LoNXU6nA3DwYgVrukyL8oe2JXw6WXPf+ahtXwRQBBwfD+ch8sG5TPm1ZWHLubo+ugDaM6iauzNG1kfYHDdRKwtY1Y1ANDnNQaT1WGWVgZCGQsFsHLoXJZW9m72zNPM8uzyUdgQAJRPAt3Wlclk0rzzzjt49erVvU/+AvngHoFFd2HzL/PWWokcUFaGMpvNVj5eqGMtZFy+fkUBQBtLZPw1ySrTmRfzOxqNWpbHr15oHNG3pfoAoKabNQaXCORq4aKGSn3rAH82Y7YOQ6PQwelykEwPXddepq6G3xcE/XcEYNQVv7rL43A13j5sLXMwEUvU3zVno7JYLH61aZofTwOIbMzK6p2dHQBoj3Lwbosqjt0bbqDUbQPRuIILGQW/5kCwmU6nePToUbvIjkbL+AhQzrh8bIMSedSMaTjYZO/p11H550ao4wZ9HE1kWDXvT3ZWYyB6T+PpYhnRb38nW9Gclc1/+5aEKN2I1WSS1WuNZdXAS+PU9/U32XHWvc/iz4AxGijvus7i8us+shFA1DQ3U7jT6RTj8RgnJycrB1sRKDgmosedcrWtG2xmQGy4qnj+LRYL7OzsYDKZAMC9715Fa1UcBDJW4e9lHjbyfrzvYxr6XNOL8uB6iZ7XDE/TJ0BHefSG7OL583c17my2LIoz0n/GYNSh9W2APhaTAU7EojJgy9hhpBcVZY26XSYaCojykdlbDRxrZV8XdCLZCCACgNlshsHg5sDz6XSKwWDQdjd03IhMiNsCsm9TZRWZKZtGeXl5ie3t7RWA826Bx8P72ogiihw1GAXCyFBUlPEAWNFNBJKqh67yRw3I8xkxpwjEsrzXgNdnjhSMVLc60O6sKctLVyN0vUSgWGMXXb+7mEIfB6rvb21tYTqdrti+bhbWOGuAxudddhfl3fXvcfdh4iobAURs7BcXF5jP5+05NHt7e+2gMrtF9AIcFF43HUpUYWp8/tkZByEfh3IPpBJVMhuVh88Yj8bPw7+8HHxHF0xGhhY1yMwb13So92pA4O9GjsEZl+qI9qHxR6wuq98MCFQvUbl8DDCTCKA93RoQ8HkN7FhmfuZ7f3+//YSTpuHpOGhHdtq3fC4ad1fYLtmYzwnxSAuO3czn87ZQ4/EYBwcHmE6n91iQK6HWUBg+otVkP9GMl8cbVWiWNp9rWlkeMjBYx7sog6s1TpXMcLIGHLE4z8M64vrwBq06iMJE+fJ8+2LRiLF21RPj8bJGDdnz0tU4a2XRTdOcHV4ul9jb21s518nL4fF6uTJn7OJljtJSZpvFU5ONYkTj8XjlNL/FYtEOzp6fn+Py8jJkKsCdErv2kymDcY/AjZcOQH0Yjj8HcM+LZ2xH9aCiDCHyumRFZGfZWpQs/kyy/Hk5ovfcwKPfnhfVkR4OxzBR98jfd/CNdKsNO6tXDx+lp8+zzdhRPXTZjL7j68N0coa2Ctx8Hmo2m7W2oJ9O8jJmgBmBqTNTD6u/s3pdRzYGiDg2w8r1sQ8OTEdjIhoPleOL9vQ5r4H7FN7XMTFMVElZpUbsKwKJrCF4BdfAyAfqa6ulM6NziZhAxiCyhp81YgWErnxo+FoePUyUJ81rH8cSOauusvkzv+eOxcPrmjUuL+GyEgDtsSj8gOXe3l5oM5qvrrpXu9cyZ86jVs8fRTYGiHgIugIJFx9yKr3WiPxZhNxUuA6Mej/XgWSdhuLvZRRZJUqD7/ahxD5LojNBej9iZLU8ZPe1rFH+MqOPyh8BCMvQx9NqeXxKvy8jiRpeFN4bY/YsSr+Uu+NNOLbJNWkMp+vZdBU/QYnrxmazGa6urnB1ddWODykj9DLVmF1UPxGAZc6mJusC1EYAEXC3HsI3cs5mMzRN034Jogvd9dqVG/332ZroHX2mQAbEa1qiKVSKe/GIKUXvej5UB7UuXGZ4nheX7D01zihv+rsGQl5WlkMHp/uwNyCfhu+SqBwaRy2uWp3p0a3ADcjwy7c8VUBP6NS9gVdXVyvDEdTJzs5OeyInD8WbzWYr7JJ/OuOYgaaWt4sdR47SJ1rWBSqXjQGiCNF5TChw9912zgjVDLTLgJz9eKMejUY4OzsLgasGVJEXjwBFPXgELho2M3j1nATp2WwWnues8UV58bSjfNTAJqPrXcxQ60Kvo206Ud75LAPzrC74rh8LU2NfHq4PyNKelNGoA6KNe53xS7X6AUruwdvf38dkMmlZEbcuORPOHFJX2RimFj5azJqtVO8rGwNELLyOA+lM2mKxwGQywd7e3sqnhCNj7WrMGQPQsaksTK1R1BhZjdr7u95Y3aho4JPJBKWU1lPu7u7i5cuX7VKHWhm0zA4WkZFF+YkkA7UMUBVUuUhV0+/6jrvH7XrSdz0cmTeBTAeIo3iBO/Dy41c0TtoPgYGTLK9evUp1rfnmUARF90Oen5/j6dOnKKW0Wzt4miXjdgfnZY902cWeXef+rv+PTgWoyUYAkXoJ/gdWjYUgtb29vTJo5zNGmZJUojEINTAaURetrdHzzLtm7EHjdIDwxky9aHn8OmMTEduJ8qv54O9oA2+Utwg4HRx4rfrX97Kye1q+vy4rjwKHgnkppXV43LvnTFEnR9g1V5vTpR8Mo2vcnKn4+25vGQAsl0ucn5/j/PwcOzs7LSvKbLSmD5caW1aJGKjbvM/49ZGNACIgHnvQSuf/q6srTCaT9qhVvqv/s/iBu0asHksVyM/bRPFFnibKe9SAXLqMrsuz6TT36elpC8o0Al5ro3Y9ZCzI2aWDhYaL8u3v+zgcr5WF8Jky0i6mlrHdGhtQh6PnY3MlPSdMfEW3MzU9sL4GnK7PKL8RkEblaJqbMaSTkxM8efIEo9GoXXKiuqo5UNdlzU7duWT54nXmKPvIxgBR1DCUDdFgecphdGZNpjDg/udyo+fsb2vXJorfASfy1m6Unl5tj1MECh6OXQCCznw+X/HeymA8Hm80WR10gWWWf33X9R4Zr56ekOVFxePxxqH5Zvq0Hy4RoW705AIXzXtt31sN0AeDu48EMJ5sVb3qMIsTuJnAmUwmGI/HKwfzRb2DzL4yMMnATPOi9eYAyLysKxsBRDUE1/670mOusmY/2Qc3XdRovIEy/u3t7XZHvr5X6yJElVXzLl5mb6wetsYsONN4eXnZlkuNsWbMnlYEOBEQu3gaGWVXAyboA3fnRHmDV/ofNdaoTrSx04nx/HDgjsXootgaYPsKftWDsiHPp4O9z7S6DUZl9Lg1/eVyidPT05Xp/ozVRE40yn+Nfao4yFCnGneUjy7ZCCBygydtBrDi9Rnm+vpmc+p0OkUpBfP5vG2UEXB4I3cmxEPuF4vFyqCfvqP/3cPXmFBkpB5v1uD9PwfwCcp62iTj6WIwLHPECr0Bed5qxh4ZYAZKXKwHoNW7phOtmo8avMfLcPoBAzZUfkNN9aXsQcsS6cnLo2WOys970RExfZ1ZZodN02A+n2M4HGI8Hq+k3Wd1vee7C8SiePjcHYwPe/SVjQAiilK+yWSCpmlWzhnSLQ1cS0Ew0i+Cany1rgenVPn9eHrKSKJ4NL6I0uo9N6ya13D2o0AMoO0++skDEfBFjKUGit5IovciPUTX3kD1IwMcIPavt9bym5VB7UaBiPbTNHdfCnGm4mV13XnZXKeqM50xY7gMwLucherXRZ2xds18wDtzjrX613xnzx349GMNfL7uhvSNAiIAK0YLoG1sNJzFYrFyFtHl5SV2dnaws7OD+Xzefv3B41S2xcY9Ho/bRs3uDRD3z2sepOapNZ7MkCna/WR4rilR+s0uGNkiG1/Nw9UYWmS4UT4z8MwaKnVNkOB+qe3t7bZbravpAYTgEKXrXR3tprCRuo4isOujp6gRR/qpdYf72IznI7vHfHAdUSSZk9C8ug6z9zxeFa1jAtCnnhEBd2ivaynYIHVNCcNx1mgymbQDeOyqRWuN9GhZfsZY1ztkXl+vIyOKGk7GoiJQy7yUlkHj1YZLA1CD8Dg8bxp3lK6Xqeu3/ydNZzeb/0ejEQCsMLpsqUQEqNF97epFn2yKGKqWIYvfQStbvhC9n8XnYSN9Rv+jtACs1HuUpjKlSAcZG8x0o2k5CPEd7f72lY0BIq9wXVGtY0bOXPg3n8+xXC4xmUywu7uLpmladsSpbMZH8PGvrGb56qLVESBF7MTBiPciI6Ue3HB8CwTT1FlEXqsO9T/Ti7oSKlG+9X4tPJdCKAjRi/PrrVo2/kVlzhqRAhC76xlwa93UJAONSLrswtPsYlTu8KL8aBhfQ+W2FJW1T5jaKmnaTRSf2+U6shFA5MpRz0OFX15eAlhdsKcFphdkv5kLH7kuiN26aJbNpQY+NSONKH/UiDIvFIGXh+V4kQ9+coqYYy8EXMatmy55rg0PotPub61LkP1mvij+1VvWIxfgReeRs4w6C6NeXX/zHrut2TiTA1IGQhnTq7GxjC3UmECtgUZg4889fyyffnY6S8+Bx/WszzJ7VD0pG1IW5HXQVzYCiKJGGjVqFlSVoO/rdCzXh+j7XQwo81aRd9W8UfHRdgmGcYodgV0EQu59ND9uMCw788HlCJpvDcslEE1ztypY08gaZKQXACvdMOaBXQey0GhWxwc6Cbbu/flMFyHSAQHxmeKRzqL68DJFIKb1kdmos42M2UTsKHNCNZshCDhTjhgT01UQ0rxndavgw9/OYH3pyLqyEUCUUUW9NxwO21kten03BDceV3YXUmcNPWMKel/pbBTeG0AfEMo8bzYo6ve44M33ay0WC5ydna3EqwPlTCcyegce9bDaPXRjJItzvW5vb68sOtS86Ewo88PDwfTDBpGOojxHesocjL7PcL4NReOPADazAf5nd9VnerNyeHn423XQ5dD1vcwOIxDmtZMAB7/XkY0AIgchR2DSe4ZVg40qwhlKDYQyzxmFYTg36Mi7aXiP3+PO2E1XHJ53beiqu+FweO/kPgWnTE+ZUTqz0JMAANxjMwpYml/dkU7GRBAaDofY2dnB8fFxm0fOtnFGzPVZa6yablZHWmbXuT+P0uljN6qz0Wh0b5apy4m56LtdYMpr367i8av9aP51AoLpMm0y1Mxpd8lGABElapRNc9cHjphAVujMS2k6DBc9r+VR06iJA0cEdhFDisLr2pis3B4Xu2Wl3N8+wf9qgH5Urs8SRSDERqVpaDeKCzCdvSiAMV3mgYscp9Npe0g8043WerHhOJBmeo8aul5HdREN4Hq80eyfhnXdjUajdtxM48oALgNUgree5hiF8/g9P1H5eU/H+xhHxIReB4SADQGiiAFExs/CZp8QcuqbNfLa/SxMF1PK8hP9rnnd6FmXp/WNoxoPAcABPupK8r92faPuj6bDlb0+PkAmpt0udhO1bFwPBqCdUKDBz2YzvPvuuzg/P8fx8XHbHdM8a34i9uZ576qzWrx93o+2ixCQ9/b22gkC7qSPFv5FDs4BTsvpa68iUGDYbO9f1i70PR1bYldbde6OZh3ZCCCKDF2VSi+p0821gUm959e13x5PjXZnEnnECNQyqb0b7Skis5hMJvfGaHSg2NPwwWle813t2gHx1gFgdUWvgia3VwB3Zwo5q/OPZFKUyR0fH2MwGKyMFTkrVDbSpV/Xq0tkG1nY6J0o/GQywePHj/HFL34R3/3ud/GNb3xjZcxMnWaUdgSs2cRIH4dZc7Kelt7zPPi9WppdshFAFHl9Njze53e+eU4vwUgbj7/PcJRapdUYQy3f7kmcZWg6alhR2OgdLaPvuaN+tre32yMhdCp+Op22oKPnN7kOPI/A3bfTfADZAUevCS6+980NlWxpPB7j+Pj4ngfnb6569wF311dUhsw59XUGfRtkLQx/7+zstAtto3VwWZ7cVjKQUbuirjIG55MSUZ41LP+TpfqUf9a+PtWMSJXOgnBQbzQaYW9vrx2ozBBd44yMwo0nW9Wr96N4Ikam8Xr+sgbvzzUe/tfGzm4Yn9Gwzs7OVr5OSyakiwp5oBwHGZVeR1019dpe1giQXO8OLJr/8Xi8EiZqaKyfaGwwqrPIU0d5yhyI6jhLy8EuYisOzjzM7Hvf+177uSyPM5LI7jRfPqDch6lks1qRrdOxaLdMmXQEaOsyIcpGABFwHyQig+QWjtPT05VnNZqdeZsI/Dw/+j+Lt3a/r9eN0uG76pX4qW0NT6ZDHfEZAYkLPN0TsuukBqgzOBFbi8qmzClila5LZXccpGW4KF0eg6r3GNbZQiZRXXrDq7EST5vhnLG6fhjGl0pQvHvl9lizac9XzR4zRx+xGdU/65TdaToFX42fpb2ObAwQObV1hXA1LlfTZvSQEjWaqML5O2NY3jBUou6bh8/YWlZuLwMbI7cyMM6IIrt3V+bjwq9FEAR0tXNWJh2jUn0oCHn6CjQ06vF43H7RItKPx+HMTPNXY2yZ9NF51/vKnrQeIoCLnkU26PbId7SMZCd6aD6diOtG44tALxJ3GlrnetROlGcv7zqyMUDkDZfKK+VmDISDmtw0GVFiIB/9jyrI01eJWIDH60DTVQluiLVGyGtdrcxTArIFZMoslAGpIaouMo8bldXf83Cap2hQXXfHl1JWBrN9mQCvuTJcWUek077S5SQituH3IzuL8hTp2N/JWE+UtuZ9d3cXh4eH97Ysadr+ThQnRScnKL6h1bfQdNnturIxQBSxBHpD/YQQjTd6T++pB9GtH/48U2qWv1rYjOlkVLuWjhoCWaBOX1M/7jEVDBSkdNrc3488m+YvMmw1XmUsCjQEKDXopmnald3Mk86Gah6HwyHOz8/TPGWNIpOs7iKnoxIN8Op7EVuLQMrv9WUOmr9SSnvSxPX1daufLK4uBsTyaR75p47CJxQigF23XCobA0SRl+LU/dnZWbvTWgvJHd4A2oVuqhCOq/hRHzUQqhl27T0PE4EDmUk2FR7lg5tHyQz4zCubjZ7X+t8bWeTNa4s/PS5lqw4IPssH3IGqji1wky4HQTkjSHDi7I+PR3heHUgd9JmXaHGm1pnXUcays3rSOPsyh0jPtXpinCcnJy1IXFxcdLLwKI3M8bDeWHfKdl0nwP2tTa8rGwNEXhgaBA1TD7nS7oD3W1Vo2H3Sjgw0a8hZF8EN18N6o/GwFBoCQYgHiDG8Gmh0rQ1P08mYT9TQsvLwWk9Y1PD6jTJuyeACvp2dnZblsEz0tv6+H9OieXYwdyDxMkblc73XmG2fBtbFAiJQ6WNT/ows6Pz8vK1rPWOrVv4obrdx1ivj8oWL0fu1vPeV9Y/b/4TEUT+rNJ+d4dqZjCVkYNKVh+ydLvTvSq/LyNUrcY+Y76CP8hE1JJY/W//jYbM4/LnHw2sORPPkS9aNrmEaDoc4ODjAkydPsLu7u2L0BNDorKiIkfUBfn9fw6uXz+o1A26XyE4iJ5QxpSzemr0xHl3n43/6rq++17wRhHTog+3Kl094+TQ9ZVPryMYAUWRIFBql92WBeOBNw9QMIEszMj6XyDvobzfwKGwGZjQKHiCm6Snr4W8tm4+DqQ48nloePF6NW3/zemtrC9PptAWh6N3z83NcXl6ilIK9vT08evTo3uHvDOuAHpXDmVCtjJm9dDmUrH48/syRRmAa6bdvPJ4313MXU6Ho+jEFEF77uCrzpzOgEaBH7KmPbEzXjBJ5OR60pec3Zx4zWpgXxR+JU9sa5fSwnoaziz5UVddtcP2Me6OoTFHZaiAXjQfVGEHGvlgPwOreMv9YoZ4YOZvNVjbDcgyPK8Kzld963cWANIz/j8qW1VXGajy9LpvJ0nDAzMoT2ZyDjy40zBxpBsK6q17BR7vNbjNuF16mPvausjGMKBIW7uDgAD/8wz+M9957L61o4P43l2oNs0aDa2F430EwqgCvqC4QZKPltLWfxBjlL/OUtTBZHj28sxH9o1NgnnXzKz/LdHFxsTKJ0DQ3Cyx5hK+eLQ5gZYrYuxFdIOs6zgDb9RGBUxY+Y11uAxlY+TNPR68zFquglIGZ60EnDTRf1PNwOMRoNLq3Ls4/yZSVMSvDOtIJRKWUz5dSfrmU8tullN8qpfyHt/eflFJ+sZTyz27/P769X0opf7GU8rVSym+UUn6sb2a8Qm/jw+PHj/H7ft/vwxe+8IV286s+1/8al4ZRD6LhPZwadBZn1AfO4vO4o/f4x1nApmnC0wz7MrZaQ9CyZMbiBs4xCAo9Jb0ot5EAaIFId2lzZofGTMakxq9bT1z6MMqsvqNnWv4IqJlXhneQ8/8156hx1zZqO8BE5YqEdqMs0+3cf+uGZv20k+aT4TT+rJyZLteRPoxoAeA/bprmRwD8JICfKaX8CICfBfBLTdN8CcAv3f4GgD8O4Eu3f18B8Jf6ZiZqYE3T4NWrV/jmN7+J58+fdx7qBOSDchpelZzlxZXqRlzz2NG7zjR4ratlAawsWozi7wsqfYxYrzPqzfVczgLIjEajUcuG2CVTMOJnnwhA/M09Z954mub+AGmtLGoDXSwqqgO/zzJHTqnmUDxNt5HsOqu/qEz6Lv9zqYPuM1TwcIbpNqena1K0zn2jtedf8/q6YNQJRE3TPGua5h/eXp8A+McAPgvgywB+7jbYzwH4d26vvwzgrzY38vcAHJZS3uuTmcjwmqbB6ekpXr58if39fRwcHKwcEK/h+ecrcbUi1ANk1FYrLGr0NeOolc2BTBsg86UzRp6/WgVreaJyRfn1uLvYluuYxs5GwK+p+uH4NGoe/Xp+ft5u0tX6yIAn0ndUL15e/XsdoPY6yxhW5rCyNLL6cdal4OAN3W3IB59pU74mKMqL5kf3GypDjRYFR/qqpVWTtQarSym/F8C/BuDvA3i3aZpnt4++B+Dd2+vPAviWvPbt23vPsIa44fGLrDs7O/e2BgBxhSpYsWLc60bpaWPO7muaURw1r+nXXHPDhhotTOwDdFk+/b7nwe/prIkbvMZJT8xxIGUyGfvj9Dw3grLsZFA+WO1MkteRwdecit/nb40n0k8X+Ltuu2yqlj8Nx7giJqfvjsfjlTpSYPKtPhon65nxaZda2agftB/ZYlS2Lnt16Q1EpZQ9AP8DgP+oaZpjU3JTSlkLAkspX8FN183v3yvsYrHAt7/9bZyenuL09HQF9WuNjV8V5RgFPa/OBvCd6P3MkCLxCsiMWgcESY/5ySMduI3ijxqVN1Q+y8DIyxuBkG7diN7Rezq24wCUCd+bzWYYjUbtjKh253xw1PMQOYGozDVQylifs5PITrI81eq/9q6zcC+Hr5vSrpUv8eDSDwI761W/76fthyzcx+kcWGsglDHUvtILiEop27gBof++aZr/8fb2+6WU95qmeVZuul4f3N7/DoDPy+ufu73nGf0qgK/ext9kiEsve3x83HrRyWSysj/pNo6Vd9x4dOZADd3ZR9bYuxDemVjtPv9z+pprbCKGFxl3BEKRZOEisKdoNylif2rI/LKKHxNbM1Ytiy7U1C08tdXwUb5r7DPz1jVmm/32uuzDlrocWJRmxPwouqFYzwOn7nVxr/YIgLvTNrUOlcnqfj9vG1m70Oc1ptclfWbNCoD/FsA/bprmL8ijXwDw07fXPw3gb8n9P11u5CcBHDV3XbjXEhot/wCsjBM5/ec1xyN0BodG7ruLM2PODE7f6QKgSDhNPxgMVtYL+TsOXjUaHL2bMQTNnzZI/Qquhvf3/RhYgnvWwJmOGj7DMg7OovmAqLOTTKd9vbWGdx1GDS4LG4WLJLODPs6NzEfBh7ZPFumH3Smz5kC0DkwrQDFf6gTpIGtgXWOeryN9GNG/AeDfA/CPSim/dnvvPwPw5wH8fCnlzwD4JoA/cfvsbwP4KQBfAzAD8O/3zUwNDHSat2ma9rRBbUysMKX4jEMH3YDVmbUuJfal1VFl+TX/szvCfVgOZhEjqnmgLM8arxsW76kn5MJCZ44KXDqdHx3h6vlXnalOdJr4+vq6BWfNd6TfmoPg8xpzivSn+VXd+HPqS9PI7CN6ljEk6tXfpT70CylkkHqmk5fRWRXju7q6Cr+sovWbHYwf6ZXXGRvuK51A1DTN/x9AFvsfDcI3AH7mtXKDvIvkdJ3fhFL6yONHT05OVgxLDYdnKiuF7YPmDgiZh3PjztY8ceyKR5x4+WtGruH8OgpTA1w1IAdCB0edeQTQnhap73u+s4ateaQnJxAxn8pgM6aiumK4aJuL6yMCowzAMn1mgOhxRM4kS9vj8X2Gy+WydWJaVmD11EoAK0tCtB7UeeiJBzo+FNVZV9c7K0cf2agtHhkIObrTaFVROgCdNTwFBWUBGe3u8jR6P2r0kUcmCM3n83szZBGQ9ekGZEbszxQsojxHeff0lQ05iPbVT8Y4mqZpu2dcge3724D720Aye8nqpqazLqbl4o6jqxH2ScPZiT/jtW6N0fgZN7txANpegh4p4ywvYz+ZbWl6keNZRzYKiIB8FojSNHddLzYKXUDnXlQNxT9b43HXPGSG+n2YC58ThHgURg2Eoji6DCNr9FFZtYzRvajcuidJZ2RcFz7DE4GrGqx2A3SlNr236sl1UNNHBECah8zWugAlczJ99BmBYVS3WWMmOFH/Dii85hoiDldQtH14HfW1lagOMmfeVzZ2r1lUsbzmoDX3ZOkmS18H4eDBv2ya3MNn3pVhIgalYXyBGb/GWQNEzwufRw0kYhW1ODXfNaCKDA1Au5LaZ2f0ryt9xql1xf8c8xgOh9jd3a2uks8av+clY21Z2TPnkqUX1U0XkEX/o7g9z1HZ3EnSGfhOeV0trQsWI4DvkogFdZW7JhsFRF7Rbiy8xzGePuM9kadxbxMZUOaZHJj0vodTo+AnkXTvVS19z3/0bF0vVNOrx+FeluUA7lZK63OGfx3DbpobtsoxM53ZVHaUlbPWkDNAWofRROlEoN3FiqO0azYb5U1tyide9Exwiu/v060gnn7ULmqgWWOc68rGAFFGaTPhIJ6uLCVIZZWYxd2VtoOcU1TfFOhLA8iELi8vV1Yha1oRve7rZboafNYQsw2mHq+XS425D/j3yTcby9XVVasnboxVIOJ7Pv3sZcwaTBf7iBpo9LsL+CPn5fnis4j58hlnuDx806yupHab85XV2pXT8kR7+vro1HUWvbeObAwQdVU87/E/xyem0+m9rkHGePp4PX+edTs0nJ7F49SY/XTfvqFl1HzX8hLprI9Exg+snnbJPz8KQnXWxSTXyY87D4qe5sivvEZ7C50lZPnVe5Go7qP8Zw4jircLuCIbivJDURuKbNvXnrH+9P2mufvogm/ViGwxKpfmu8Yuuxh3TTYGiIB4XCLyGCq6+5vPFQzIRtxAa0bnlDTKk7/H7qHSZG4x8UVmzkQyoOgr61R6ZkjUl5aB4bQB6N6jDLCjMnoeanmmPnW8iAvsIjCq/Y4AIepyZOXwxZW1xpblh+GilfPZuwzj4KHiO+zdqehRw9GCWa9jirMmz1NWVm8n68jGAFGtorOCcR2L01Lec0rPeGvsJvOunjcV7XMr+AF3A+vZZkiNr6uB9pGoS1LLexTeV9nyfaX8DO9srsZCIs+qaWv6egqBrg5WMMqcgufDyx7lIwoX6UvLEqUbPVebzPSTpeOH4/Fd3Vkf5Ts7UiZy6l1Mj2H7MMbXlY2bvqe4YUQofnl5iel0itFo1DZ4BSR/z7seUQPS+/os8sQalx8wRa/u58TwWVbZfe5lErGUKB5nOS4ZGJdS2uM8Ir1FjTFKN8qXgpCys8Vi0f4mK9LGSYYWrYPJypU9V4nsoSa1+LSMNdakaeiKdoZzwNHNrRoH2Ti7t9l0feacvNw1BtXFSvvKxgCRFy4ycP3P2bKrq6uV9RLZ2pwo/shwo3D0PFEFsJGox2ZXTM/lqVVQlIeafmrxRPqLxNOLvLX/55qtzDs6Pe8j2S575o+fxvZzpHyhozPOGpA4EKr++wCQ68vj0nf7NHjPk8aVNXSe5cTJGl3YSDvkolkvT6TzLnFQ0jyuW+eRbFTXTAsUnTfj3kOPLmClKX2PULuLSrohRQalYel92Biurq5wcXFRPdIjMzoF0pp+uqQ2PhPFV4ubeo3OI4riy+pOf2tXQde7RGF1Jk33welYXB8G1EcPHk/E+vydKD19pw8Lixqy6lGdrDphfhFlMpm04blMhO1DeweMV/XWV7QNRG3Edb1O3MAGMSIAK0rrahgUToezUrzRZN4uu47AxtmPNhztqvgxth63iwNBVKHryDqsqebNGU49KI8r0W0dEVDzOtO97vsDsOKxPR/+ZQr90/1WPMFAwTxqLIw/YsDZf82X6iayqS59ZvWhevNZMoofz0L9nJ+fYzQa4fDwsK2npmlwfHy8ssDX89W1qt+BJ2J2Gcj2sUOXjQIiF1cE7/lMhq4W9W5UZFw+nuBxZ56Jv/lOtDaji7lkVD97phI1EH0vYoB94ove4X09w8b3lmUNzfWQpeFrvjSsi9ZnKaXtmgwGN4fGT6fT9iQDPWsqAnfP9+uCTOTMsjqI6i3abe/OzsuhOlV7XiwW7fnfHKDuYmDR7z4OsQZIWRpdsnFApBUWFVCVqwan05O6D80Vq++54ghECkgeRkEvYm5RpWaSeZooTPbcw0SSeTO/9nEiPwrEV+TWylXLSxbOmZbnX+vo+voaFxcXK6uv9cgML4+Dx+s4DM23x9MVn9sgcP/zPgpEylooXi7tXlEnOpHgJytk5ewC5ihsVOaaY+uSjQOiSGnRPS4gdFBQBekMjDc2Vz7ByxkPsDq24ccuRAavUgOPrjD6bJ0KjhpJLUwW1gE5ykvmBTPjzroBXSxP39FxQTI27jQnc/MzqTyOvnqt6a5vmWvxKuPWs9gdfDUt7qKnjV5eXrZr1th9jvKjvYE+ZfNyZAw4eu//EIyodo8KYOXp6XSqbPUw+lupe1YRCm56z/ORMZkamLpxZYC0rmfy39F7WR6ddfk7fgia5yMDmShPUZehlndNI9K5fvKG40Y8l4pT/+5QovJHuqmxnQiEa3Xp7+qqaeDOnnUfnw/6u55Vl/P5/N6kSfRezQHWmF2kjxqTX1c2DoiA+qyEUlIdo1EPExm9VoBT1qxy/LrLe2asiPeUeUVpOCB4mCjdrLFEIBG9W8sDn/MzR9GRE7U0I91E9yKQrYGBPgdwj/mwm+bLDXSjaC3t6L6WuabL7DfFZwjVXv0zUlomd67OEEu5GTfjLgONl8DH97KPItQYksbV5YBq5c9kY6bvVTIQAlZXUQP3d3tT6VGD8XGk6M/ZUDQW5L+zPGu+KTo+FTUs91oaRwaSGs4bksadgUFkRApCwCobysAmymNmoFGDjxiPM7WsLlhvl5eXbcMkk3P9ZuLPsoYWNU7PP4CVpSS6zkxXiiuzd7tQu/a0aa9kfU3TtDsNyIyAu/ai+x5r9ZHpInvH6y/SVx/ZWEYExFSdSgXue0LtoikDUdHKzcCkdj9qKJ5Hz5MPjkdepcamVCeRfroYmr8f5dvZgBqvMgnq1PXgnrrmLT1vUX3UvDOv/T2OnegKe668zmyqr0OJ3s3yr07SAYHPsz1sno8IzFkX+nmgprk7SkV3G6huory+rmR2/7pxbwQjUioZNbgoDBtEZkjRlDufZ5Wu1xmDiPIexeWgmRn/OhUXMalaGTQfUZ4zINM02A3OgDJiax5XJhFL6YpP9RU1ZuaVYMQPcyozYLcts7nI43s4r+fadbQuCEDL4LgCX4cZsokYBxTNS9M07SkPutBW20qNpXo91xxcxhK7WGcmG8mIXByA6KGjWRH3TF5Z0dJ8Ptf3I4CIGokav7KCPg1p3UrLGkJU7r4gyne9O8t7pZTOTbsRA/B8ROXW8bIu9ubPHBg1Hv5xAFsHr/kJp2hNlL7rDC8rs+tR7xPEffuRMsxosWGN6VJvUb7ZVeOZ7uz6Zd+J62KhXQwnIw2vA0YbBUTePdBGAtwt7afSo13gHo8aUm3rgxu2rwDOGpje07Sj5xFArUNjXT8uUZkzBtjFClXXfkB7lq+MoQHxxEGkpwgcIo/t5YgaFBsgp7azdVIan//1aYjOrJQBqZ1699ZtTQFdZ8AikHT9aNzL5bJdzhBtivV3o7LWgCSr58ze+sjGAFFEkbV7pdRaD3nKpoMjY+9iChGYZI0lagAeng06MgBPz/MR5bP2bmZINXodxR+95yDtDaQGzBpvTfcRC6nF2VUmnx3jxwW5xgi4z471HV3iETk6LZfqTGenskWvasdaLo2XrGY6nWI4HGI2m7XdTRcyS9YJ1xXxaGI9SjnSleahxorcnn1leFYXfWRjgCg79kCNM1pJ3dc4I7SOfkfXUfisIWu4Lo/vU/lZvFE6XQ03K2tGxzOa7bp279knjQiEPWxNr/48uo7ipq1wFo0bQoG7KW8XDgB742MaETBFtuuOUp8723Yd6T0FmC4mwnuXl5cYDAaYTCbtoXJNc7cJNgN613ntuo+zXEc2AohKKfemFX2AT89UiWhqFq++44bQBWJRo+3TqDx8ls8ovhp7yBqelzd6pytO9eaU6GiVrvJquJr0DeuNLEqfDd8nKBhWbWdvbw+DwaDdLA3cAYkuduX9viyTLAbAyvEvCkYMx9k9ZRcZo+CUPFlcFlZtG7hZ4AigBSPtRajedLzK44tA3/WRvbuubAwQ+TYMFpoemVRWFeIzOq6krHIjA8gARuPK9u444GQNVPNRo7J9PKVeR3vn9J0ae6OwcWgdcFDXy9kF/mrIft/LnMXlABQxCG8kmm8vN+1oNpu13R2uwWH8esgaf29tba2s8dH0NW61jy6m4TrKwIBsTtPOdOb6uL6+br8WQ1bEweyaTqN8ejpebs/b64DRRgARcOd9tQDKXLzC3Fvre17RNTrqaWYA4nnR55Hia7Nznl6fhh2VS/NQK3NkHF5ONiQ6BV94mTWuCAyi+DMw7DJ211OtgTC+qF4Y/urqqt0ky3vKjKK4t7e324PufMCZcXtjdLYQ5TezNb4fAWCmS4+b7IzH43Bz8GAwWPkYqcepOo7q1p9n4NOVR5eNAaJoM57+1obBvq53JdiAlMa69DH8CByiPLlEhp+lrwDSJTXAy9LKGqzHmb0TNSYPl/3WNGogluU904mDXJQPbbyZ3q6vr9vBax03oWNzAImWBmTpa7isTJ4/1ZWXI5Mu++NvtoXxeIzxeIzRaISLi4v2/CbNd8aQsjJ25Wcd2Qgg8sJnVI+DbsB94OKf0mndEEuJGmdXNyJiIJH0rYwuBlYD5FqeMgP335q2P2cYHTdRyRp4BEaRnqPB2yx+f7fWYBRM3GOrHniY2Hg8XgFbvucf69Rumg8PqM69zF5G14Wenqjhu4C7pq9ML+yS8bhdPbnCw/uwxbrMhvlYVzYCiID7e7oiw1KaqYDknnswGLTKvry8bO9nivUG7QvtosrV9yLJGpRL1uiy3xmIRgwrKysNLgMsbcQ1z5d50D4gE1F7jzMrS980HaB4jw2Te6/4221QdcWBbH4yvE8DzQBX7XWdhp4BTSZaDh8PIzOKVmr3Yd1R3fXNVyQbs8WDu4ZZ+TrTwfs6lazGqgvGos/QRDue+V//ssYQMYqPAkLZu94AvFF0xZWxApcsbwR5/vnXH2rpejyZgapkjbHPausovagc6rA0Pj3gTr+2oulmdV1zFBE77VPuDFz1Xt/wDuAsI7tk7K7pJ6r1L9qS4o6rBkKvIxvBiLwBqfGwLw/g3sKsaDBQt3+QUivF1rCetnYdeK0VkHnfWoPRCquxlqjxet7WAb+MbUS6dyP0fU7KFKN4M28ZLQr0cF268ns1UFT9DofDlTIwHO0jYmcE3y4nUmt8NYcRObsauGi5a7rqkz/as57yqPXss41qd13l7mJTfWQjgOj6+hrz+TwsrE4r60A0RStKGxKfUenRVwsiQ9BGx3v6TEEwo7B9wCcyysizRg1S3/G0PS2Py8tLfbkOFbx9ZrLL2BygssYZAW90z+PN4lHwi2acNB6WSxkzu2vKsDVPr+vto/z3iSsCZC2zXtfKqc84VtQ0zcp4EZ329vY2Li4u2ql/Lbc7H20r1P3rykYAEZAPUrNB8INx6rXU86rXUBBRZhStds2Mo8ZA3ENpA6h5qozx1MJEeXBgqaUX/eb/6GQA/o7OH8pYl7/bt5Gtc7+v8P1aN0vLv1wuMZlM2jEUfifPQcCXMzBOBbUM8D1szQFFToxhs/qu2VZkFzrgTifNb6RNJpOVcTOfrMjyVnOQfWQjxoiAu0OkgPvIW9sgyLCknToop8DjabECPK7M20cMpaZwjytKo+bF/FrDZ/8jvWj6kQHRI/JPz012vfWh3Rkrc4m6GllcfeNw5+V6cRBh+bgTn0eERAP5UZ4ythyVoeuZA33GtrP7XTPD3p64LoosUMdoyZTcnjR+/a1tyXXXVzaGEbEg0YyEehyeGEhkB+JjNH1QW8d7qGzuUo5WiEYex0UrqcsjRhIBTmS47nH8eRRXlOfoN9li9jG+COyiBp6Fj8pQy5OWocugM0D2/PK3Am4EWExXv9gbpVUDnYhFRXn2vNXK1xXW85MNG7ANKGtsmpuTHdm2FNS099GVLvVGW9IFk31kI4CIygBWR/LVSyilnEwmAO4Gr3U2DbjzcF4ZVAx3YbNrooZHUIkYASXz5qwMplUzMAWLLoakaWaA4OG6PKqyRuD+V1E8/36Ehucvy3+UdvZOLX4H+RrYRwBFPY/HY2xtbbXfhNcV/U3T3HOEylayMvKenxYR1Y/+9ueRfrIukEvE4DIg0/us58Vi0TIhAhKAFli8rJ5n7d6Nx2Ocn5+vLJ3pko0AIuAOxTmYBmClu0DmQpAhYPFjcm4wQM4adAyJBqOekkZLw6ptCtR4mWc+95k6Zwz+zA2UcSsg6z0vZ2bQNXbBsisY+yHu2TvZ3rvsXtYw9Z3onoZ39tolXg5lBWxwyoz6MA69F+k+q6sMkDI9rAPyXfmtxeGzz9p7YBvQMbdoNpTPeVSJf8G3SzYGiACsFIoNWVnL9vY2Li8vMZ/PV47C1Pej+Py53ucAOPvKlBqtzrwyWVuUdtYwo+5ZFMa9cgaEKhk70sF9HhpGXXjj4jPdMOnlr3npqBHWGlUEvH1BJ4uPslwucXFx0bJhncpXMGKatMFIIt1HdtFHN1n8EcvpU07/7fr0MjC8jqnq57w9T5Ht830eVbuudA5Wl1I+X0r55VLKb5dSfquU8h/e3v9zpZTvlFJ+7fbvp+Sd/7SU8rVSyj8ppfzbfTOjRq1KWSwW7XGlHCNyJfFe1ECVLWn8TEP7tGqMOq2p4wtMm+m54SlTiFiP5i261r+gPtr4/N3ot+uXz7n6nOc36yAjn43H4/b/7u4uJpNJujg0qgMH6Rori66je7UGnAG51o2eWMilIJmdODDVnEWfsjp415hqrR67QCfKV18WyXL7+Ku2AbWXbGDfj/Xpkj6MaAHgP26a5h+WUvYB/Gop5Rdvn/3XTdP8lxq4lPIjAP4kgB8F8AMA/k4p5V9tmqZz5CpjBzQgnjxHelg7eU4rIGIu/K8GoQCioKP3FGgUzLI1FIwj2oxZy3Ot4WoY/13zpGok+u0vHQfQNVc6TjQcDrGzs4NSCubzeTpLU8tT1hAi9qn5JnOpNdzI+UT50uc+qKsMKSqbly/63YfpePn0vT7A1IdRRr+z9kVRfXAYRB2utgcd3tA2FBGEPtL5RtM0z5qm+Ye31ycA/jGAz1Ze+TKAv9E0zUXTNP8bgK8B+Il1M+ZG1zQ3A2rcb8axjAiVNQ6+W0tHvZ96Q904q5XJMSv9VpXm09N3MIiM18sbGZTGF4G1p8X77hmBu/E3UnAFHN9cTCdwfX3dHqGReeWszH2YWs2zR9s+ovSdAWXp0Yn4ca1R2Oi/pxvlOatXDaP57WrAtfJEtpLZQtQuIialTpf3famHLv9g2p6fPrIWdJVSfi+Afw3A37+99WdLKb9RSvkrpZTHt/c+C+Bb8tq3UQeuUKIBNO0++fSgN1R9T8Ur33/rPZ05c4aglFUBSf8Yj3YBsnxqOXgvYnNRebsq3cMybk7P0sBKuVseAawOUPr2AMbb1Tj65tHrqgZMtfJl15onr2tPtyYR4Ll+s7xHYN1VjxGYROAW5TsDhCiOKAztnwsbvU71g5HaZWN7WEd6A1EpZQ/A/wDgP2qa5hjAXwLwrwD4gwCeAfiv1km4lPKVUsqvlFJ+RQ1ElaMgoJWmB4Kr6HL9qJIjoGBYSuQBCX7cUKsbJpUh8dvrvlDS09Dfzui6vChFPVM2blMDMv+yKN/RM5t1vQ11ro02G8hV6esZmd7rvKthvWul8WtYH8eLWF7UxfG4Ix3qvVoZ+jgQd3x9gb0WJ+OL8pExQCUAPuscsSROcvSVXrNmpZRt3IDQf980zf94m4n35flfBvA/3f78DoDPy+ufu723Ik3TfBXAVwFgMBg0WtHe9fCtCJlB+Ag//9fey8Lqfb3mym1t5L4q2b8yEoFsZOSlrE75R6Jho8YRAZgbL43p8vISo9EITdPcO6sm2punTgC4P7YT5TsC/Oj36zSsCGS1zFH9UTgjq19LdTbl9piVx8vg9lvTB+0nij9qC34/K2vNDqI81UTjoz2r/fHa9y2uI51AVG5i/G8B/OOmaf6C3H+vaZpntz//XQC/eXv9CwD+WinlL+BmsPpLAP7XPpnJ6K0WVL0ZK9/3AUUgpUqrGWiNJXl8yiLUq3qF6AyEL5SssTNNS0UZVNSoIybI9xhWgZN5JyiVUtoZM+0C6xYa16ODYgSSUfhIsrpyWfeZxqeN//p69TD7zCHUGFNXmTS86ohpqRN1MIrSi55H+YwAy22jpmvmkTsRovdoT86U1pE+jOjfAPDvAfhHpZRfu733nwH4U6WUPwigAfC7AP6D20z8Vinl5wH8Nm5m3H6mWWPGjNdqMFphNG6dgvUl+1G8mbKjynGJFO8sh2n7LAOZBnAHRH72TS1PGaPQSs/yzXcU0B0smb6uMibj0Y/8Nc3d6vdSSrtwTVmf59Hr1RuSswEPl7GQGqDVwkZpAPenmqM8Re9lDVrD9mEmNUeU3Yviz96rtQEFwShf+qdxEKiVHHCzrIJSXyk1BP8XJYPBoNHZGxbw9tmKgnTQtA+iA/cNusuwu/aNZcbmxqisQwfwlstlu0LcWZx7w8zAFUSiMEyfcfKZrlrXNVns03PBH8P6plgA7WZJnU1zXfu1Sxd76Aq7zvsa3htnKQXT6bRdDgJgZRwwA/ouFhEBS2artfua9rqg2xWXlsPzD+BevUfDDO7k1IEeHx//atM0P35PQYFsBBCVUr4P4AzAh286Lx9B3sZD/t+kPOT/zUqU/9/TNM3TPi9vBBABQLmZPeuFnpsoD/l/s/KQ/zcrHzX/G3Me0YM8yIP8yysPQPQgD/Igb1w2CYi++qYz8BHlIf9vVh7y/2blI+V/Y8aIHuRBHuRfXtkkRvQgD/Ig/5LKAxA9yIM8yBuXNw5EpZQ/Vm4OUPtaKeVn33R++kgp5XdLKf+o3BwI9yu3956UUn6xlPLPbv8/7ornX5SUm9MRPiil/KbcC/NbbuQv3tbHb5RSfuzN5bzNa5T/P1c+5oP5Pikp+eGCn4o6qOT/46sDX937L/IPwBaArwP4IQAjAL8O4EfeZJ565vt3Abxt9/7vAH729vpnAfzf3nQ+JW//JoAfA/CbXfkF8FMA/j8ACoCfBPD3NzT/fw7A/zUI+yO3djQG8IVb+9p6w/l/D8CP3V7vA/int/n8VNRBJf8fWx28aUb0EwC+1jTNN5qmuQTwN3BzsNqnUb4M4Odur38OwL/z5rKyKk3T/F0AL+x2lt8vA/irzY38PQCHpZT3/oVkNJEk/5l8GR/DwXwfpzT54YKfijqo5D+TtevgTQPRx3KI2huQBsD/XEr51VLKV27vvdvcnUbwPQDvvpms9ZYsv5+mOvnEDub7pKSsHi74qauD8gkdjvimgejTKn+4aZofA/DHAfxMKeXf1IfNDT/91KyL+LTl91Y+0sF8b0LK/cMFW/k01EGQ/4+tDt40EPU6RG3TpGma79z+/wDA38QN7Xyf9Pn2/wdvLoe9JMvvp6JOmqZ5v2maZdM01wD+Mu6o/0bmvwSHC+JTVAdR/j/OOnjTQPQPAHyplPKFUsoIN1//+IU3nKeqlFJ2y83XTFBK2QXwb+HmULhfAPDTt8F+GsDfejM57C1Zfn8BwJ++nbn5SQBH0n3YGLExEz+Y70+WUsallC9gjYP5PikpN2dl3DtcEJ+SOsjy/7HWwZscjZcZgn+Km5H1//xN56dHfn8INzMCvw7gt5hnAG8B+CUA/wzA3wHw5E3nVfL813FDna9w01//M1l+cTNT8/+4rY9/BODHNzT//6/b/P3GreG/J+H/89v8/xMAf3wD8v+HcdPt+g0Av3b791Ofljqo5P9jq4OHLR4P8iAP8sblTXfNHuRBHuRBHoDoQR7kQd68PADRgzzIg7xxeQCiB3mQB3nj8gBED/IgD/LG5QGIHuRBHuSNywMQPciDPMgbl/8dPu/YRGSfcH8AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -75,7 +84,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x2037228afa0>"
+       "<matplotlib.image.AxesImage at 0x203e62ea4c0>"
       ]
      },
      "execution_count": 4,
@@ -84,7 +93,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -127,14 +136,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\rober\\Anaconda3\\envs\\bio_39\\lib\\site-packages\\skimage\\io\\_plugins\\matplotlib_plugin.py:150: UserWarning: Low image data range; displaying image with stretched contrast.\n",
+      "C:\\Users\\johamuel\\Anaconda3\\envs\\apoc\\lib\\site-packages\\skimage\\io\\_plugins\\matplotlib_plugin.py:150: UserWarning: Low image data range; displaying image with stretched contrast.\n",
       "  lo, hi, cmap = _get_display_range(image)\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x20372306d90>"
+       "<matplotlib.image.AxesImage at 0x203e63692e0>"
       ]
      },
      "execution_count": 6,
@@ -143,7 +152,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -214,15 +223,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\rober\\Anaconda3\\envs\\bio_39\\lib\\site-packages\\skimage\\io\\_plugins\\matplotlib_plugin.py:150: UserWarning: Low image data range; displaying image with stretched contrast.\n",
+      "C:\\Users\\johamuel\\Anaconda3\\envs\\apoc\\lib\\site-packages\\skimage\\io\\_plugins\\matplotlib_plugin.py:150: UserWarning: Low image data range; displaying image with stretched contrast.\n",
       "  lo, hi, cmap = _get_display_range(image)\n",
-      "C:\\Users\\rober\\AppData\\Local\\Temp\\ipykernel_16816\\2170673470.py:4: UserWarning: cells_result.tif is a low contrast image\n",
+      "C:\\Users\\johamuel\\AppData\\Local\\Temp\\ipykernel_24476\\2170673470.py:4: UserWarning: cells_result.tif is a low contrast image\n",
       "  imsave('cells_result.tif', result)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n"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+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -242,17 +251,97 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7cde71a-101d-41cb-ab26-a0f388cb3006",
+   "id": "e3c9cdde-0dd2-494e-b1b9-e44be93bb1ba",
    "metadata": {},
    "source": [
-    "## Reading the classifier file\n",
-    "You can also read the classifier OpenCL file as it is just a text file."
+    "## Inspecting a classifier\n",
+    "\n",
+    "In some cases, it may be necessary or informative to inspect the classifer you are using. For instance, using multi-channel data for a classifier that was trained on a single channel or vice versa will lead to errors such as this:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "9ae5dbfc-14c7-46e4-94d7-71827ea967ba",
+   "id": "f8e0ae92-f3c1-42e6-a870-e82438040f8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "clBuildProgram failed: BUILD_PROGRAM_FAILURE - clBuildProgram failed: BUILD_PROGRAM_FAILURE - clBuildProgram failed: BUILD_PROGRAM_FAILURE\n\nBuild on <pyopencl.Device 'Intel(R) UHD Graphics' on 'Intel(R) OpenCL HD Graphics' at 0x203e11592b0>:\n\n1:47:26: warning: unknown OpenCL extension 'cl_amd_printf' - ignoring\n#pragma OPENCL EXTENSION cl_amd_printf : enable\n                         ^\n1:615:16: warning: implicit conversion from 'long long' to 'uint' (aka 'unsigned int') changes value from 18446744073709551615 to 4294967295\n        return 18446744073709551615;\n        ~~~~~~ ^~~~~~~~~~~~~~~~~~~~\n1:625:16: warning: implicit conversion from 'long' to 'int' changes value from 9223372036854775807 to -1\n        return 9223372036854775807;\n        ~~~~~~ ^~~~~~~~~~~~~~~~~~~\n1:628:16: warning: implicit conversion from 'long long' to 'int' changes value from -9223372036854775808 to 0\n        return -9223372036854775808 ;\n        ~~~~~~ ^~~~~~~~~~~~~~~~~~~~\n1:652:84: error: unknown type name 'IMAGE_in3_TYPE'\n__kernel void predict (IMAGE_in0_TYPE in0, IMAGE_in1_TYPE in1, IMAGE_in2_TYPE in2, IMAGE_in3_TYPE in3, IMAGE_in4_TYPE in4, IMAGE_in5_TYPE in5, IMAGE_out_TYPE out) {\n                                                                                   ^\n1:652:104: error: unknown type name 'IMAGE_in4_TYPE'\n__kernel void predict (IMAGE_in0_TYPE in0, IMAGE_in1_TYPE in1, IMAGE_in2_TYPE in2, IMAGE_in3_TYPE in3, IMAGE_in4_TYPE in4, IMAGE_in5_TYPE in5, IMAGE_out_TYPE out) {\n                                                                                                       ^\n1:652:124: error: unknown type name 'IMAGE_in5_TYPE'\n__kernel void predict (IMAGE_in0_TYPE in0, IMAGE_in1_TYPE in1, IMAGE_in2_TYPE in2, IMAGE_in3_TYPE in3, IMAGE_in4_TYPE in4, IMAGE_in5_TYPE in5, IMAGE_out_TYPE out) {\n                                                                                                                           ^\n1:660:13: error: implicit declaration of function 'READ_in3_IMAGE' is invalid in OpenCL\n float i3 = READ_IMAGE(in3, sampler, POS_in3_INSTANCE(x,y,z,0)).x;\n            ^\n1:637:27: note: expanded from macro 'READ_IMAGE'\n#define READ_IMAGE(a,b,c) READ_ ## a ## _IMAGE(a,b,c)\n                          ^\n<scratch space>:57:1: note: expanded from here\nREAD_in3_IMAGE\n^\n1:660:38: error: implicit declaration of function 'POS_in3_INSTANCE' is invalid in OpenCL\n float i3 = READ_IMAGE(in3, sampler, POS_in3_INSTANCE(x,y,z,0)).x;\n                                     ^\n1:661:13: error: implicit declaration of function 'READ_in4_IMAGE' is invalid in OpenCL\n float i4 = READ_IMAGE(in4, sampler, POS_in4_INSTANCE(x,y,z,0)).x;\n            ^\n1:637:27: note: expanded from macro 'READ_IMAGE'\n#define READ_IMAGE(a,b,c) READ_ ## a ## _IMAGE(a,b,c)\n                          ^\n<scratch space>:59:1: note: expanded from here\nREAD_in4_IMAGE\n^\n1:661:13: note: did you mean 'READ_in3_IMAGE'?\n1:637:27: note: expanded from macro 'READ_IMAGE'\n#define READ_IMAGE(a,b,c) READ_ ## a ## _IMAGE(a,b,c)\n                          ^\n<scratch space>:59:1: note: expanded from here\nREAD_in4_IMAGE\n^\n1:660:13: note: 'READ_in3_IMAGE' declared here\n float i3 = READ_IMAGE(in3, sampler, POS_in3_INSTANCE(x,y,z,0)).x;\n            ^\n1:637:27: note: expanded from macro 'READ_IMAGE'\n#define READ_IMAGE(a,b,c) READ_ ## a ## _IMAGE(a,b,c)\n                          ^\n<scratch space>:57:1: note: expanded from here\nREAD_in3_IMAGE\n^\n1:661:38: error: implicit declaration of function 'POS_in4_INSTANCE' is invalid in OpenCL\n float i4 = READ_IMAGE(in4, sampler, POS_in4_INSTANCE(x,y,z,0)).x;\n                                     ^\n1:661:38: note: did you mean 'POS_in3_INSTANCE'?\n1:660:38: note: 'POS_in3_INSTANCE' declared here\n float i3 = READ_IMAGE(in3, sampler, POS_in3_INSTANCE(x,y,z,0)).x;\n                                     ^\n1:637:52: note: expanded from macro 'READ_IMAGE'\n#define READ_IMAGE(a,b,c) READ_ ## a ## _IMAGE(a,b,c)\n                                                   ^\n1:662:13: error: implicit declaration of function 'READ_in5_IMAGE' is invalid in OpenCL\n float i5 = READ_IMAGE(in5, sampler, POS_in5_INSTANCE(x,y,z,0)).x;\n            ^\n1:637:27: note: expanded from macro 'READ_IMAGE'\n#define READ_IMAGE(a,b,c) READ_ ## a ## _IMAGE(a,b,c)\n                          ^\n<scratch space>:61:1: note: expanded from here\nREAD_in5_IMAGE\n^\n1:662:38: error: implicit declaration of function 'POS_in5_INSTANCE' is invalid in OpenCL\n float i5 = READ_IMAGE(in5, sampler, POS_in5_INSTANCE(x,y,z,0)).x;\n                                     ^\n\n(options: -I C:\\Users\\johamuel\\Anaconda3\\envs\\apoc\\lib\\site-packages\\pyopencl\\cl)\n(source saved as C:\\Users\\johamuel\\AppData\\Local\\Temp\\tmpoccl_qz_.cl)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [10]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mclassifier\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimage_ch1\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\users\\johamuel\\documents\\biapol\\projects\\apoc\\apoc\\_pixel_classifier.py:145\u001b[0m, in \u001b[0;36mPixelClassifier.predict\u001b[1;34m(self, image, features)\u001b[0m\n\u001b[0;32m    141\u001b[0m     parameters[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124min\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(i)] \u001b[38;5;241m=\u001b[39m f\n\u001b[0;32m    143\u001b[0m parameters[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mout\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m output\n\u001b[1;32m--> 145\u001b[0m \u001b[43mcle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopencl_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpredict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    147\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output\n",
+      "File \u001b[1;32m~\\Anaconda3\\envs\\apoc\\lib\\site-packages\\pyclesperanto_prototype\\_tier0\\_execute.py:3\u001b[0m, in \u001b[0;36mexecute\u001b[1;34m(anchor, opencl_kernel_filename, kernel_name, global_size, parameters, prog, constants, image_size_independent_kernel_compilation, device)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mexecute\u001b[39m(anchor, opencl_kernel_filename, kernel_name, global_size, parameters, prog \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, constants \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, image_size_independent_kernel_compilation : \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, device \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m----> 3\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mBackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_instance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43manchor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopencl_kernel_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkernel_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglobal_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconstants\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimage_size_independent_kernel_compilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\Anaconda3\\envs\\apoc\\lib\\site-packages\\pyclesperanto_prototype\\_tier0\\_opencl_backend.py:41\u001b[0m, in \u001b[0;36mOpenCLBackend.execute\u001b[1;34m(self, anchor, opencl_kernel_filename, kernel_name, global_size, parameters, prog, constants, image_size_independent_kernel_compilation, device)\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mexecute\u001b[39m(\u001b[38;5;28mself\u001b[39m, anchor, opencl_kernel_filename, kernel_name, global_size, parameters, prog \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, constants \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, image_size_independent_kernel_compilation : \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, device \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m---> 41\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43manchor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopencl_kernel_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkernel_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglobal_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconstants\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimage_size_independent_kernel_compilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\Anaconda3\\envs\\apoc\\lib\\site-packages\\pyclesperanto_prototype\\_tier0\\_opencl_execute.py:311\u001b[0m, in \u001b[0;36mexecute\u001b[1;34m(anchor, opencl_kernel_filename, kernel_name, global_size, parameters, prog, constants, image_size_independent_kernel_compilation, device)\u001b[0m\n\u001b[0;32m    305\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    306\u001b[0m         warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    307\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe `device` parameter of pyclesperanto_prototype.execute is deprecated since 0.11.0. It will be removed in 0.12.0.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    308\u001b[0m             \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m\n\u001b[0;32m    309\u001b[0m         )\n\u001b[1;32m--> 311\u001b[0m     prog \u001b[38;5;241m=\u001b[39m \u001b[43mdevice\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprogram_from_source\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdefines\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    312\u001b[0m     \u001b[38;5;66;03m#prog = OCLProgram.from_source(\"\\n\".join(defines))\u001b[39;00m\n\u001b[0;32m    313\u001b[0m \n\u001b[0;32m    314\u001b[0m     \u001b[38;5;66;03m# Todo: the order of the arguments matters; fix that\u001b[39;00m\n\u001b[0;32m    315\u001b[0m     \u001b[38;5;66;03m# print(\"Compilation \" + opencl_kernel_filename + \" took \" + str((time.time() - time_stamp) * 1000) + \" ms\")\u001b[39;00m\n\u001b[0;32m    316\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    317\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    318\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe `prog` parameter of pyclesperanto_prototype.execute is deprecated since 0.11.0. It will be removed in 0.12.0.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    319\u001b[0m         \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m\n\u001b[0;32m    320\u001b[0m     )\n",
+      "File \u001b[1;32m~\\Anaconda3\\envs\\apoc\\lib\\site-packages\\pyclesperanto_prototype\\_tier0\\_device.py:26\u001b[0m, in \u001b[0;36mDevice.program_from_source\u001b[1;34m(self, source)\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[38;5;129m@lru_cache\u001b[39m(maxsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m128\u001b[39m)\n\u001b[0;32m     24\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprogram_from_source\u001b[39m(\u001b[38;5;28mself\u001b[39m, source):\n\u001b[0;32m     25\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_program\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OCLProgram\n\u001b[1;32m---> 26\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mOCLProgram\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrc_str\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdev\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\Anaconda3\\envs\\apoc\\lib\\site-packages\\pyclesperanto_prototype\\_tier0\\_program.py:28\u001b[0m, in \u001b[0;36mOCLProgram.__init__\u001b[1;34m(self, file_name, src_str, build_options, dev)\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_kernel_dict \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m     27\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dev\u001b[38;5;241m.\u001b[39mcontext, src_str)\n\u001b[1;32m---> 28\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbuild_options\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\Anaconda3\\envs\\apoc\\lib\\site-packages\\pyopencl\\__init__.py:532\u001b[0m, in \u001b[0;36mProgram.build\u001b[1;34m(self, options, devices, cache_dir)\u001b[0m\n\u001b[0;32m    528\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    529\u001b[0m     \u001b[38;5;66;03m# cached\u001b[39;00m\n\u001b[0;32m    531\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyopencl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcache\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m create_built_program_from_source_cached\n\u001b[1;32m--> 532\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prg, was_cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_build_and_catch_errors\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    533\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_built_program_from_source_cached\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    534\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_context\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_source\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions_bytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    535\u001b[0m \u001b[43m                \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude_path\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    536\u001b[0m \u001b[43m            \u001b[49m\u001b[43moptions_bytes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions_bytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_source\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    538\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m was_cached:\n\u001b[0;32m    539\u001b[0m         build_descr \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcache retrieval\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "File \u001b[1;32m~\\Anaconda3\\envs\\apoc\\lib\\site-packages\\pyopencl\\__init__.py:580\u001b[0m, in \u001b[0;36mProgram._build_and_catch_errors\u001b[1;34m(self, build_func, options_bytes, source)\u001b[0m\n\u001b[0;32m    572\u001b[0m     err \u001b[38;5;241m=\u001b[39m _cl\u001b[38;5;241m.\u001b[39mRuntimeError(\n\u001b[0;32m    573\u001b[0m             _cl\u001b[38;5;241m.\u001b[39m_ErrorRecord(\n\u001b[0;32m    574\u001b[0m                 msg\u001b[38;5;241m=\u001b[39mmsg,\n\u001b[0;32m    575\u001b[0m                 code\u001b[38;5;241m=\u001b[39mcode,\n\u001b[0;32m    576\u001b[0m                 routine\u001b[38;5;241m=\u001b[39mroutine))\n\u001b[0;32m    578\u001b[0m \u001b[38;5;66;03m# Python 3.2 outputs the whole list of currently active exceptions\u001b[39;00m\n\u001b[0;32m    579\u001b[0m \u001b[38;5;66;03m# This serves to remove one (redundant) level from that nesting.\u001b[39;00m\n\u001b[1;32m--> 580\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m err\n",
+      "\u001b[1;31mRuntimeError\u001b[0m: clBuildProgram failed: BUILD_PROGRAM_FAILURE - clBuildProgram failed: BUILD_PROGRAM_FAILURE - clBuildProgram failed: BUILD_PROGRAM_FAILURE\n\nBuild on <pyopencl.Device 'Intel(R) UHD Graphics' on 'Intel(R) OpenCL HD Graphics' at 0x203e11592b0>:\n\n1:47:26: warning: unknown OpenCL extension 'cl_amd_printf' - ignoring\n#pragma OPENCL EXTENSION cl_amd_printf : enable\n                         ^\n1:615:16: warning: implicit conversion from 'long long' to 'uint' (aka 'unsigned int') changes value from 18446744073709551615 to 4294967295\n        return 18446744073709551615;\n        ~~~~~~ ^~~~~~~~~~~~~~~~~~~~\n1:625:16: warning: implicit conversion from 'long' to 'int' changes value from 9223372036854775807 to -1\n        return 9223372036854775807;\n        ~~~~~~ ^~~~~~~~~~~~~~~~~~~\n1:628:16: warning: implicit conversion from 'long long' to 'int' changes value from -9223372036854775808 to 0\n        return -9223372036854775808 ;\n        ~~~~~~ ^~~~~~~~~~~~~~~~~~~~\n1:652:84: error: unknown type name 'IMAGE_in3_TYPE'\n__kernel void predict (IMAGE_in0_TYPE in0, IMAGE_in1_TYPE in1, IMAGE_in2_TYPE in2, IMAGE_in3_TYPE in3, IMAGE_in4_TYPE in4, IMAGE_in5_TYPE in5, IMAGE_out_TYPE out) {\n                                                                                   ^\n1:652:104: error: unknown type name 'IMAGE_in4_TYPE'\n__kernel void predict (IMAGE_in0_TYPE in0, IMAGE_in1_TYPE in1, IMAGE_in2_TYPE in2, IMAGE_in3_TYPE in3, IMAGE_in4_TYPE in4, IMAGE_in5_TYPE in5, IMAGE_out_TYPE out) {\n                                                                                                       ^\n1:652:124: error: unknown type name 'IMAGE_in5_TYPE'\n__kernel void predict (IMAGE_in0_TYPE in0, IMAGE_in1_TYPE in1, IMAGE_in2_TYPE in2, IMAGE_in3_TYPE in3, IMAGE_in4_TYPE in4, IMAGE_in5_TYPE in5, IMAGE_out_TYPE out) {\n                                                                                                                           ^\n1:660:13: error: implicit declaration of function 'READ_in3_IMAGE' is invalid in OpenCL\n float i3 = READ_IMAGE(in3, sampler, POS_in3_INSTANCE(x,y,z,0)).x;\n            ^\n1:637:27: note: expanded from macro 'READ_IMAGE'\n#define READ_IMAGE(a,b,c) READ_ ## a ## _IMAGE(a,b,c)\n                          ^\n<scratch space>:57:1: note: expanded from here\nREAD_in3_IMAGE\n^\n1:660:38: error: implicit declaration of function 'POS_in3_INSTANCE' is invalid in OpenCL\n float i3 = READ_IMAGE(in3, sampler, POS_in3_INSTANCE(x,y,z,0)).x;\n                                     ^\n1:661:13: error: implicit declaration of function 'READ_in4_IMAGE' is invalid in OpenCL\n float i4 = READ_IMAGE(in4, sampler, POS_in4_INSTANCE(x,y,z,0)).x;\n            ^\n1:637:27: note: expanded from macro 'READ_IMAGE'\n#define READ_IMAGE(a,b,c) READ_ ## a ## _IMAGE(a,b,c)\n                          ^\n<scratch space>:59:1: note: expanded from here\nREAD_in4_IMAGE\n^\n1:661:13: note: did you mean 'READ_in3_IMAGE'?\n1:637:27: note: expanded from macro 'READ_IMAGE'\n#define READ_IMAGE(a,b,c) READ_ ## a ## _IMAGE(a,b,c)\n                          ^\n<scratch space>:59:1: note: expanded from here\nREAD_in4_IMAGE\n^\n1:660:13: note: 'READ_in3_IMAGE' declared here\n float i3 = READ_IMAGE(in3, sampler, POS_in3_INSTANCE(x,y,z,0)).x;\n            ^\n1:637:27: note: expanded from macro 'READ_IMAGE'\n#define READ_IMAGE(a,b,c) READ_ ## a ## _IMAGE(a,b,c)\n                          ^\n<scratch space>:57:1: note: expanded from here\nREAD_in3_IMAGE\n^\n1:661:38: error: implicit declaration of function 'POS_in4_INSTANCE' is invalid in OpenCL\n float i4 = READ_IMAGE(in4, sampler, POS_in4_INSTANCE(x,y,z,0)).x;\n                                     ^\n1:661:38: note: did you mean 'POS_in3_INSTANCE'?\n1:660:38: note: 'POS_in3_INSTANCE' declared here\n float i3 = READ_IMAGE(in3, sampler, POS_in3_INSTANCE(x,y,z,0)).x;\n                                     ^\n1:637:52: note: expanded from macro 'READ_IMAGE'\n#define READ_IMAGE(a,b,c) READ_ ## a ## _IMAGE(a,b,c)\n                                                   ^\n1:662:13: error: implicit declaration of function 'READ_in5_IMAGE' is invalid in OpenCL\n float i5 = READ_IMAGE(in5, sampler, POS_in5_INSTANCE(x,y,z,0)).x;\n            ^\n1:637:27: note: expanded from macro 'READ_IMAGE'\n#define READ_IMAGE(a,b,c) READ_ ## a ## _IMAGE(a,b,c)\n                          ^\n<scratch space>:61:1: note: expanded from here\nREAD_in5_IMAGE\n^\n1:662:38: error: implicit declaration of function 'POS_in5_INSTANCE' is invalid in OpenCL\n float i5 = READ_IMAGE(in5, sampler, POS_in5_INSTANCE(x,y,z,0)).x;\n                                     ^\n\n(options: -I C:\\Users\\johamuel\\Anaconda3\\envs\\apoc\\lib\\site-packages\\pyopencl\\cl)\n(source saved as C:\\Users\\johamuel\\AppData\\Local\\Temp\\tmpoccl_qz_.cl)"
+     ]
+    }
+   ],
+   "source": [
+    "classifier.predict(image=image_ch1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4050343e-6614-4587-8a75-04770d4c0f47",
+   "metadata": {},
+   "source": [
+    "To avoid this issue, we can print the classifier info to see which dimensions the classifer expects for the prediction.\n",
+    "\n",
+    "Note: For the classifier to work on new data, only the first dimension needs to match. In other words:\n",
+    "\n",
+    "- A classifer trained on a `[2, 256, 256]` image will work for new data with dimensions `[2, 512, 512]`\n",
+    "- A classifer trained on a `[2, 256, 256]` image will **not** work for new data with dimensions `[1, 256, 256]`\n",
+    "\n",
+    "To obtain all information about the used classifier, you can simply print it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "58b7dc53-3e91-471d-8847-a8aeaa1a6ce6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classifier type: PixelClassifier\n",
+      "--- Random forest info ---\n",
+      "Used features for training: original gaussian_blur=2 sobel_of_gaussian_blur=2\n",
+      "Training data dimensions: [2 x X x Y]\n",
+      "Maximum depth: 2\n",
+      "Number of ensembles: 100\n",
+      "Number of classes:  3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classifier)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1744168e-a370-4985-b17a-d56650a277fb",
+   "metadata": {},
+   "source": [
+    "# Loading a classifier from file\n",
+    "\n",
+    "If you want to inspect a classifier from a saved classifier file (`my_classifier.clf)`, you can do this as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e64cc093-36f9-42c2-beab-fe384b874a80",
    "metadata": {},
    "outputs": [
     {
@@ -275,9 +364,9 @@
       "\n",
       "max_depth = 2\n",
       "\n",
-      "num_trees = 10\n",
+      "num_trees = 100\n",
       "\n",
-      "apoc_version = 0.6.8\n",
+      "apoc_version = 0.8.0\n",
       "\n",
       "\n"
      ]
@@ -301,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "id": "8c9555f1-3db3-4049-9b1e-13e4a6ccd6f9",
    "metadata": {},
    "outputs": [
@@ -311,7 +400,7 @@
        "'2'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -345,7 +434,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.11"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,


Test patch:
diff --git a/tests/test_multichannel_training_and_prediction.py b/tests/test_multichannel_training_and_prediction.py
index fd63430..5952ea2 100644
--- a/tests/test_multichannel_training_and_prediction.py
+++ b/tests/test_multichannel_training_and_prediction.py
@@ -38,3 +38,6 @@ def test_multichannel_training_and_prediction():
 
     assert np.allclose(result, ref_image)
 
+    info = clf.info()
+    assert 'PixelClassifier' in info
+    print(clf)
diff --git a/tests/test_object_classification.py b/tests/test_object_classification.py
index 5d4246a..60ff67b 100644
--- a/tests/test_object_classification.py
+++ b/tests/test_object_classification.py
@@ -15,7 +15,7 @@ def test_object_classification():
             mean_max_distance_to_centroid_ratio mean_max_distance_to_mass_center_ratio
             touching_neighbor_count average_distance_of_touching_neighbors average_distance_of_n_nearest_neighbors
             """.replace("\n", " ")
-    
+
     import apoc
     oc = apoc.ObjectClassifier(num_ensembles=10)
     oc.train(feature_definition, labels, annotation, image)
@@ -27,6 +27,10 @@ def test_object_classification():
 
     assert np.allclose(reference, result)
 
+    info = oc.info()
+    assert 'ObjectClassifier' in info
+    print(oc)
+
 
 def test_object_classification_statistics():
     image = np.asarray([[0, 0, 1, 1, 2, 2, 3, 3, 3, 3, 3]])
diff --git a/tests/test_object_segmentation.py b/tests/test_object_segmentation.py
index 025d6d0..30207b7 100644
--- a/tests/test_object_segmentation.py
+++ b/tests/test_object_segmentation.py
@@ -24,3 +24,6 @@ def test_object_segmentation():
     assert result.dtype == np.uint32
 
     assert np.allclose(result, ref_image)
+
+    classifier.info()
+    print(classifier)
diff --git a/tests/test_table_row_classification.py b/tests/test_table_row_classification.py
index 3ebac9b..507ab72 100644
--- a/tests/test_table_row_classification.py
+++ b/tests/test_table_row_classification.py
@@ -39,6 +39,10 @@ def test_table_row_classification(tmpdir, feature_table):
     assert result.dtype == np.uint32
     assert np.allclose(ground_truth, result)
 
+    info = oc.info()
+    assert 'TableRowClassifier' in info
+    print(oc)
+
 @pytest.mark.parametrize("feature_table", [feature_table_dict, feature_table_data_frame])
 def test_with_nans(tmpdir, feature_table):
 
diff --git a/tests/test_training_on_folders.py b/tests/test_training_on_folders.py
index 673e338..985c114 100644
--- a/tests/test_training_on_folders.py
+++ b/tests/test_training_on_folders.py
@@ -34,4 +34,8 @@ def test_training_on_folders():
         labels = segmenter.predict(image)
 
         # There are at least 4 objects in each image
-        assert(labels.max() > 3)
\ No newline at end of file
+        assert(labels.max() > 3)
+
+    info = segmenter.info()
+    assert 'ObjectSegmenter' in info
+    print(segmenter)