From d093efe05ea3995391ff835cb473b3b3dfacee34 Mon Sep 17 00:00:00 2001 From: danjust Date: Mon, 3 Dec 2018 12:41:45 +0000 Subject: [PATCH 1/8] improve readme --- README.md | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index f00c1c6..b5698ec 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,13 @@ # mlpredict -A python package to predict the execution time for one forward and backward pass a deep learning model. +A python package to predict the execution time for one forward and backward pass a deep neural network. +To improve the underlying machine learning model see https://github.com/CDECatapult/ml-performance-prediction. -To install mlpredict run + +mlpredict can be installed by executing ``` bash pip install -r requirements.txt -``` -and -``` bash python setup.py install ``` from the root directory. @@ -18,7 +17,7 @@ The mlpredict API can be used to create representations of deep neural networks ### Create a model representations To build the representation of a deep neural network from scratch create an instance of the dnn class ``` python -dnn_repr = mlpredict.api.new_dnn(input_dimension,input_size) +dnn_repr = mlpredict.api.dnn(input_dimension,input_size) ``` and add layers ``` python @@ -33,7 +32,7 @@ and the current network architecture can be displayed dnn_repr.describe() ``` -A completed model can be saved to a .json file using +Finally, a model can be saved to a .json file using ``` python dnn_repr.save(filename) ``` @@ -44,9 +43,9 @@ For a full working example see the jupyter notebook https://github.com/CDECatap ### Import existing model representations Exiting model representations can be imported using ```python -dnn_repr = mlpredict.api.import_default(filename) +dnn_repr = mlpredict.api.import_default(dnn_object) ``` -An imported representation can be modified and saved as described above in **Create a model representations**. +`dnn_object` can be either the path to a previously created .json file (see above) or the name of a default model (at the moment only`'VGG16'`). An imported representation can be modified and saved as described above in the section **Create a model representations**. ### Predict execution time using mlpredict @@ -57,4 +56,4 @@ time_total, layer, time_layer = dnn_repr.predict(gpu, optimizer, batchsize) ``` -returns the total execution time, the layers and the time per layer. For a complete working example see https://github.com/CDECatapult/mlpredict/blob/master/notebooks/Full_model_prediction.ipynb +returns the total execution time, the layers and the time per layer. Here, `gpu` can be a .json file with the keys 'bandwidth', 'cores', and 'clock' or the name of a default GPU ('V100', 'P100', 'M60', 'K80', 'K40', or '1080Ti'). For a complete working example see https://github.com/CDECatapult/mlpredict/blob/master/notebooks/Full_model_prediction.ipynb From c6a188a56f34c86011e891109712d42af419c068 Mon Sep 17 00:00:00 2001 From: danjust Date: Mon, 3 Dec 2018 12:56:54 +0000 Subject: [PATCH 2/8] improve documentation --- src/mlpredict/import_tools.py | 6 ++--- src/mlpredict/prediction.py | 44 ++++++++++++++++++----------------- 2 files changed, 26 insertions(+), 24 deletions(-) diff --git a/src/mlpredict/import_tools.py b/src/mlpredict/import_tools.py index 75e4eb0..1e9d638 100644 --- a/src/mlpredict/import_tools.py +++ b/src/mlpredict/import_tools.py @@ -9,7 +9,7 @@ def import_dnn(dnn_obj): """Import dnn. Tries local definition first Returns: - net: instance of class dnn""" + net: instance of class Dnn""" try: if os.path.isfile(dnn_obj): net = import_dnn_file(dnn_obj) @@ -24,7 +24,7 @@ def import_dnn(dnn_obj): def import_dnn_default(dnn_name): """Import dnn from default path Returns: - net: instance of class dnn""" + net: instance of class Dnn""" dnn_path = pkg_resources.resource_filename( 'mlpredict', 'dnn_architecture/%s.json' % dnn_name) @@ -35,7 +35,7 @@ def import_dnn_default(dnn_name): def import_dnn_file(dnn_path): """Import dnn from local path Returns: - net: instance of class dnn""" + net: instance of class Dnn""" net = mlpredict.api.dnn(0, 0) with open(dnn_path) as json_data: tmpdict = json.load(json_data) diff --git a/src/mlpredict/prediction.py b/src/mlpredict/prediction.py index b890557..bab4e0c 100644 --- a/src/mlpredict/prediction.py +++ b/src/mlpredict/prediction.py @@ -16,7 +16,7 @@ def predict_walltime(model, Args: model: Deep neural network architecture, instance of the model class model_file: tensorflow model - sklearn: skleran scaler + sklearn: sklearn scaler batchsize (int) optimizer (string) bandwidth: GPU memory bandwidth in GB/s (int) @@ -62,6 +62,7 @@ def get_input_features( bandwidth, cores, clock): + """Generates fetaure dictionary to be used with mlpredict""" padding_reduction = ((dictionary['padding'].lower() == 'valid') * (dictionary['kernelsize'] - 1)) @@ -86,26 +87,27 @@ def get_input_features( * elements_output * dictionary['channels_out']) - features = np.array([batchsize, - dictionary['matsize']**2, - dictionary['kernelsize']**2, - dictionary['channels_in'], - dictionary['channels_out'], - (1 if dictionary['padding'].lower() == 'same' else 0), - dictionary['strides'], - dictionary['use_bias'], - (1 if optimizer.lower() == 'sgd' else 0), - (1 if optimizer.lower() == 'adadelta' else 0), - (1 if optimizer.lower() == 'adagrad' else 0), - (1 if optimizer.lower() == 'momentum' else 0), - (1 if optimizer.lower() == 'adam' else 0), - (1 if optimizer.lower() == 'rmsprop' else 0), - (1 if dictionary['activation'].lower() == 'relu' else 0), - (1 if dictionary['activation'].lower() == 'tanh' else 0), - (1 if dictionary['activation'].lower() == 'sigmoid' else 0), - bandwidth, - cores, - clock]) + features = np.array([ + batchsize, + dictionary['matsize']**2, + dictionary['kernelsize']**2, + dictionary['channels_in'], + dictionary['channels_out'], + (1 if dictionary['padding'].lower() == 'same' else 0), + dictionary['strides'], + dictionary['use_bias'], + (1 if optimizer.lower() == 'sgd' else 0), + (1 if optimizer.lower() == 'adadelta' else 0), + (1 if optimizer.lower() == 'adagrad' else 0), + (1 if optimizer.lower() == 'momentum' else 0), + (1 if optimizer.lower() == 'adam' else 0), + (1 if optimizer.lower() == 'rmsprop' else 0), + (1 if dictionary['activation'].lower() == 'relu' else 0), + (1 if dictionary['activation'].lower() == 'tanh' else 0), + (1 if dictionary['activation'].lower() == 'sigmoid' else 0), + bandwidth, + cores, + clock]) features = scaler.transform(features.reshape(1, -1)) return features From c316402b3bbef9598cbaad4b6ebcdb80313125cb Mon Sep 17 00:00:00 2001 From: danjust Date: Mon, 3 Dec 2018 12:57:46 +0000 Subject: [PATCH 3/8] naming conventions --- src/mlpredict/api.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/mlpredict/api.py b/src/mlpredict/api.py index 6528d37..99fc859 100644 --- a/src/mlpredict/api.py +++ b/src/mlpredict/api.py @@ -7,7 +7,7 @@ from mlpredict.import_tools import import_gpu -class dnn(dict): +class Dnn(dict): """Class for deep neural network architecture""" def __init__(self, input_dimension, input_size): @@ -17,14 +17,14 @@ def __init__(self, input_dimension, input_size): self['input']['size'] = input_size def save(self, path): - """Save dnn to path""" + """Save Dnn to path""" if not os.path.isdir(os.path.dirname(path)): os.mkdir(os.path.dirname(path)) with open(path, 'w') as json_file: json.dump(self, json_file, indent=4) def describe(self): - """Prints a description of of the class instance""" + """Prints a description of the class instance""" print('%d layer network\n' % (len(self['layers']))) print('Input size %dx%dx%d\n' % (self['input']['size'], self['input']['size'], From 34622c6c830b33230241db9def10a507f2396cb9 Mon Sep 17 00:00:00 2001 From: danjust Date: Mon, 3 Dec 2018 13:01:08 +0000 Subject: [PATCH 4/8] naming conventions --- notebooks/Full_model_prediction.ipynb | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/notebooks/Full_model_prediction.ipynb b/notebooks/Full_model_prediction.ipynb index 4d68feb..49f2b55 100644 --- a/notebooks/Full_model_prediction.ipynb +++ b/notebooks/Full_model_prediction.ipynb @@ -11,7 +11,9 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "import numpy as np\n", @@ -37,9 +39,9 @@ }, "outputs": [], "source": [ - "gpu = 'V100'\n", - "opt = 'SGD'\n", - "batchsize = 32" + "GPU = 'V100'\n", + "OPT = 'SGD'\n", + "BATCHSIZE = 2**np.arange(0,6,1)" ] }, { @@ -53,7 +55,9 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "VGG16 = mlpredict.import_tools.import_dnn('VGG16') # imports default network definition\n", @@ -141,14 +145,13 @@ } ], "source": [ - "batchsize = 2**np.arange(0,6,1)\n", "time_layer = np.zeros([16,6])\n", "time_total = np.zeros(6)\n", "\n", - "for i in range(len(batchsize)):\n", - " time_total[i], layer, time_layer[:,i] = VGG16.predict(gpu = gpu,\n", - " optimizer = opt,\n", - " batchsize = batchsize[i])" + "for i in range(len(BATCHSIZE)):\n", + " time_total[i], layer, time_layer[:,i] = VGG16.predict(gpu = GPU,\n", + " optimizer = OPT,\n", + " batchsize = BATCHSIZE[i])" ] }, { @@ -193,7 +196,7 @@ "data": { "image/png": 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LvWtra4XmY+r1+hC9Xh9SVVUlmTFjhm+vXr1C5HJ5mJ+fX/DixYu9rVbrjX3379+vFAQh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"text/plain": [ - "" + "" ] }, "metadata": {}, From a2b96c10f2805f0fe8a34122f48c75651a99d3c0 Mon Sep 17 00:00:00 2001 From: danjust Date: Mon, 3 Dec 2018 14:28:50 +0000 Subject: [PATCH 5/8] improve documentation --- src/mlpredict/import_tools.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/src/mlpredict/import_tools.py b/src/mlpredict/import_tools.py index 1e9d638..aab0048 100644 --- a/src/mlpredict/import_tools.py +++ b/src/mlpredict/import_tools.py @@ -7,7 +7,8 @@ def import_dnn(dnn_obj): - """Import dnn. Tries local definition first + """Import dnn definition from local file or mlpredict. + Tries local definition first. Returns: net: instance of class Dnn""" try: @@ -22,7 +23,7 @@ def import_dnn(dnn_obj): def import_dnn_default(dnn_name): - """Import dnn from default path + """Import dnn from default path (mlpredict). Returns: net: instance of class Dnn""" dnn_path = pkg_resources.resource_filename( @@ -33,7 +34,7 @@ def import_dnn_default(dnn_name): def import_dnn_file(dnn_path): - """Import dnn from local path + """Import dnn from local path. Returns: net: instance of class Dnn""" net = mlpredict.api.dnn(0, 0) @@ -45,7 +46,8 @@ def import_dnn_file(dnn_path): def import_gpu(gpu_obj): - """Import gpu definition. Tries local definition first + """Import gpu definition from local file or mlpredict. + Tries local definition first. Returns: gpu_stats""" try: @@ -60,7 +62,7 @@ def import_gpu(gpu_obj): def import_gpu_default(gpu_name): - """Import gpu definition from default path + """Import gpu definition from default path (mlpredict). Returns: gpu_stats""" gpu_file = pkg_resources.resource_filename( @@ -70,7 +72,7 @@ def import_gpu_default(gpu_name): def import_gpu_file(gpu_path): - """Import gpu definition from local path + """Import gpu definition from local path. Returns: gpu_stats""" with open(gpu_path) as json_data: From ca3ec95226715635c8c67d21f86cc163b95de77c Mon Sep 17 00:00:00 2001 From: danjust Date: Mon, 3 Dec 2018 17:10:30 +0000 Subject: [PATCH 6/8] line breaks --- src/mlpredict/api.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/src/mlpredict/api.py b/src/mlpredict/api.py index 99fc859..1cb3bea 100644 --- a/src/mlpredict/api.py +++ b/src/mlpredict/api.py @@ -76,9 +76,8 @@ def add_layer(self, layer_type, layer_name, **kwargs): (kwargs['padding'].lower() == 'valid') * (kwargs['kernelsize'] - 1)) output_size = ( - (input_size - - padding_reduction) / - kwargs['strides']) + (input_size - padding_reduction) + / kwargs['strides']) self['layers'][new_layer]['matsize'] = input_size self['layers'][new_layer]['kernelsize'] = kwargs['kernelsize'] @@ -95,9 +94,8 @@ def add_layer(self, layer_type, layer_name, **kwargs): (kwargs['padding'].lower() == 'valid') * (kwargs['pool_size'] - 1)) output_size = ( - (input_size - - padding_reduction) / - kwargs['strides']) + (input_size - padding_reduction) + / kwargs['strides']) self['layers'][new_layer]['pool_size'] = kwargs['pool_size'] self['layers'][new_layer]['strides'] = kwargs['strides'] From b97b7736b6acac7e56456903cc1aa7539c4f3cd8 Mon Sep 17 00:00:00 2001 From: danjust Date: Mon, 3 Dec 2018 17:50:55 +0000 Subject: [PATCH 7/8] fix merge --- README.md | 19 ++++++------ notebooks/Full_model_prediction.ipynb | 25 ++++++++------- src/mlpredict/api.py | 16 +++++----- src/mlpredict/import_tools.py | 32 +++++++++++-------- src/mlpredict/prediction.py | 44 ++++++++++++++------------- 5 files changed, 72 insertions(+), 64 deletions(-) diff --git a/README.md b/README.md index f00c1c6..b5698ec 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,13 @@ # mlpredict -A python package to predict the execution time for one forward and backward pass a deep learning model. +A python package to predict the execution time for one forward and backward pass a deep neural network. +To improve the underlying machine learning model see https://github.com/CDECatapult/ml-performance-prediction. -To install mlpredict run + +mlpredict can be installed by executing ``` bash pip install -r requirements.txt -``` -and -``` bash python setup.py install ``` from the root directory. @@ -18,7 +17,7 @@ The mlpredict API can be used to create representations of deep neural networks ### Create a model representations To build the representation of a deep neural network from scratch create an instance of the dnn class ``` python -dnn_repr = mlpredict.api.new_dnn(input_dimension,input_size) +dnn_repr = mlpredict.api.dnn(input_dimension,input_size) ``` and add layers ``` python @@ -33,7 +32,7 @@ and the current network architecture can be displayed dnn_repr.describe() ``` -A completed model can be saved to a .json file using +Finally, a model can be saved to a .json file using ``` python dnn_repr.save(filename) ``` @@ -44,9 +43,9 @@ For a full working example see the jupyter notebook https://github.com/CDECatap ### Import existing model representations Exiting model representations can be imported using ```python -dnn_repr = mlpredict.api.import_default(filename) +dnn_repr = mlpredict.api.import_default(dnn_object) ``` -An imported representation can be modified and saved as described above in **Create a model representations**. +`dnn_object` can be either the path to a previously created .json file (see above) or the name of a default model (at the moment only`'VGG16'`). An imported representation can be modified and saved as described above in the section **Create a model representations**. ### Predict execution time using mlpredict @@ -57,4 +56,4 @@ time_total, layer, time_layer = dnn_repr.predict(gpu, optimizer, batchsize) ``` -returns the total execution time, the layers and the time per layer. For a complete working example see https://github.com/CDECatapult/mlpredict/blob/master/notebooks/Full_model_prediction.ipynb +returns the total execution time, the layers and the time per layer. Here, `gpu` can be a .json file with the keys 'bandwidth', 'cores', and 'clock' or the name of a default GPU ('V100', 'P100', 'M60', 'K80', 'K40', or '1080Ti'). For a complete working example see https://github.com/CDECatapult/mlpredict/blob/master/notebooks/Full_model_prediction.ipynb diff --git a/notebooks/Full_model_prediction.ipynb b/notebooks/Full_model_prediction.ipynb index 4d68feb..49f2b55 100644 --- a/notebooks/Full_model_prediction.ipynb +++ b/notebooks/Full_model_prediction.ipynb @@ -11,7 +11,9 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "import numpy as np\n", @@ -37,9 +39,9 @@ }, "outputs": [], "source": [ - "gpu = 'V100'\n", - "opt = 'SGD'\n", - "batchsize = 32" + "GPU = 'V100'\n", + "OPT = 'SGD'\n", + "BATCHSIZE = 2**np.arange(0,6,1)" ] }, { @@ -53,7 +55,9 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "VGG16 = mlpredict.import_tools.import_dnn('VGG16') # imports default network definition\n", @@ -141,14 +145,13 @@ } ], "source": [ - "batchsize = 2**np.arange(0,6,1)\n", "time_layer = np.zeros([16,6])\n", "time_total = np.zeros(6)\n", "\n", - "for i in range(len(batchsize)):\n", - " time_total[i], layer, time_layer[:,i] = VGG16.predict(gpu = gpu,\n", - " optimizer = opt,\n", - " batchsize = batchsize[i])" + "for i in range(len(BATCHSIZE)):\n", + " time_total[i], layer, time_layer[:,i] = VGG16.predict(gpu = GPU,\n", + " optimizer = OPT,\n", + " batchsize = BATCHSIZE[i])" ] }, { @@ -193,7 +196,7 @@ "data": { "image/png": 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LvWtra4XmY+r1+hC9Xh9SVVUlmTFjhm+vXr1C5HJ5mJ+fX/DixYu9rVbrjX3379+vFAQh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"text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/src/mlpredict/api.py b/src/mlpredict/api.py index 6528d37..1cb3bea 100644 --- a/src/mlpredict/api.py +++ b/src/mlpredict/api.py @@ -7,7 +7,7 @@ from mlpredict.import_tools import import_gpu -class dnn(dict): +class Dnn(dict): """Class for deep neural network architecture""" def __init__(self, input_dimension, input_size): @@ -17,14 +17,14 @@ def __init__(self, input_dimension, input_size): self['input']['size'] = input_size def save(self, path): - """Save dnn to path""" + """Save Dnn to path""" if not os.path.isdir(os.path.dirname(path)): os.mkdir(os.path.dirname(path)) with open(path, 'w') as json_file: json.dump(self, json_file, indent=4) def describe(self): - """Prints a description of of the class instance""" + """Prints a description of the class instance""" print('%d layer network\n' % (len(self['layers']))) print('Input size %dx%dx%d\n' % (self['input']['size'], self['input']['size'], @@ -76,9 +76,8 @@ def add_layer(self, layer_type, layer_name, **kwargs): (kwargs['padding'].lower() == 'valid') * (kwargs['kernelsize'] - 1)) output_size = ( - (input_size - - padding_reduction) / - kwargs['strides']) + (input_size - padding_reduction) + / kwargs['strides']) self['layers'][new_layer]['matsize'] = input_size self['layers'][new_layer]['kernelsize'] = kwargs['kernelsize'] @@ -95,9 +94,8 @@ def add_layer(self, layer_type, layer_name, **kwargs): (kwargs['padding'].lower() == 'valid') * (kwargs['pool_size'] - 1)) output_size = ( - (input_size - - padding_reduction) / - kwargs['strides']) + (input_size - padding_reduction) + / kwargs['strides']) self['layers'][new_layer]['pool_size'] = kwargs['pool_size'] self['layers'][new_layer]['strides'] = kwargs['strides'] diff --git a/src/mlpredict/import_tools.py b/src/mlpredict/import_tools.py index ecafda6..71631f3 100644 --- a/src/mlpredict/import_tools.py +++ b/src/mlpredict/import_tools.py @@ -3,7 +3,7 @@ import json import pkg_resources -import mlpredict.api +import mlpredict class DnnImportError(Exception): @@ -25,9 +25,11 @@ def __init__(self, message): def import_dnn(dnn_obj): - """Import dnn. Tries local definition first + """Import dnn definition from local file or mlpredict. + Tries local definition first. Returns: - net: instance of class dnn""" + net: instance of class Dnn + """ if os.path.isfile(dnn_obj): net = import_dnn_file(dnn_obj) else: @@ -36,9 +38,9 @@ def import_dnn(dnn_obj): def import_dnn_default(dnn_name): - """Import dnn from default path + """Import dnn from default path (mlpredict). Returns: - net: instance of class dnn""" + net: instance of class Dnn""" try: dnn_file = pkg_resources.resource_filename( 'mlpredict', 'dnn_architecture/%s.json' @@ -52,10 +54,10 @@ def import_dnn_default(dnn_name): def import_dnn_file(dnn_file): - """Import dnn from local path + """Import dnn from local path. Returns: - net: instance of class dnn""" - + net: instance of class Dnn + """ net = mlpredict.api.dnn(0, 0) with open(dnn_file) as json_data: tmpdict = json.load(json_data) @@ -68,9 +70,11 @@ def import_dnn_file(dnn_file): def import_gpu(gpu_obj): - """Import gpu definition. Tries local definition first + """Import gpu definition from local file or mlpredict. + Tries local definition first. Returns: - gpu_stats""" + gpu_stats + """ if os.path.isfile(gpu_obj): gpu_stats = import_gpu_file(gpu_obj) else: @@ -79,9 +83,10 @@ def import_gpu(gpu_obj): def import_gpu_default(gpu_name): - """Import gpu definition from default path + """Import gpu definition from default path (mlpredict). Returns: - gpu_stats""" + gpu_stats + """ try: gpu_file = pkg_resources.resource_filename( 'mlpredict', 'GPUs/%s.json' % gpu_name) @@ -95,7 +100,8 @@ def import_gpu_default(gpu_name): def import_gpu_file(gpu_file): """Import gpu definition from local path Returns: - gpu_stats""" + gpu_stats + """ with open(gpu_file) as json_data: gpu_stats = json.load(json_data) if not all(key in gpu_stats.keys() for key in ['bandwidth','cores', 'clock']): diff --git a/src/mlpredict/prediction.py b/src/mlpredict/prediction.py index b890557..bab4e0c 100644 --- a/src/mlpredict/prediction.py +++ b/src/mlpredict/prediction.py @@ -16,7 +16,7 @@ def predict_walltime(model, Args: model: Deep neural network architecture, instance of the model class model_file: tensorflow model - sklearn: skleran scaler + sklearn: sklearn scaler batchsize (int) optimizer (string) bandwidth: GPU memory bandwidth in GB/s (int) @@ -62,6 +62,7 @@ def get_input_features( bandwidth, cores, clock): + """Generates fetaure dictionary to be used with mlpredict""" padding_reduction = ((dictionary['padding'].lower() == 'valid') * (dictionary['kernelsize'] - 1)) @@ -86,26 +87,27 @@ def get_input_features( * elements_output * dictionary['channels_out']) - features = np.array([batchsize, - dictionary['matsize']**2, - dictionary['kernelsize']**2, - dictionary['channels_in'], - dictionary['channels_out'], - (1 if dictionary['padding'].lower() == 'same' else 0), - dictionary['strides'], - dictionary['use_bias'], - (1 if optimizer.lower() == 'sgd' else 0), - (1 if optimizer.lower() == 'adadelta' else 0), - (1 if optimizer.lower() == 'adagrad' else 0), - (1 if optimizer.lower() == 'momentum' else 0), - (1 if optimizer.lower() == 'adam' else 0), - (1 if optimizer.lower() == 'rmsprop' else 0), - (1 if dictionary['activation'].lower() == 'relu' else 0), - (1 if dictionary['activation'].lower() == 'tanh' else 0), - (1 if dictionary['activation'].lower() == 'sigmoid' else 0), - bandwidth, - cores, - clock]) + features = np.array([ + batchsize, + dictionary['matsize']**2, + dictionary['kernelsize']**2, + dictionary['channels_in'], + dictionary['channels_out'], + (1 if dictionary['padding'].lower() == 'same' else 0), + dictionary['strides'], + dictionary['use_bias'], + (1 if optimizer.lower() == 'sgd' else 0), + (1 if optimizer.lower() == 'adadelta' else 0), + (1 if optimizer.lower() == 'adagrad' else 0), + (1 if optimizer.lower() == 'momentum' else 0), + (1 if optimizer.lower() == 'adam' else 0), + (1 if optimizer.lower() == 'rmsprop' else 0), + (1 if dictionary['activation'].lower() == 'relu' else 0), + (1 if dictionary['activation'].lower() == 'tanh' else 0), + (1 if dictionary['activation'].lower() == 'sigmoid' else 0), + bandwidth, + cores, + clock]) features = scaler.transform(features.reshape(1, -1)) return features From 9c683ae09c89dc0514e73a7cc9f052a48dffa165 Mon Sep 17 00:00:00 2001 From: danjust Date: Mon, 3 Dec 2018 18:06:42 +0000 Subject: [PATCH 8/8] Fix naming convention --- notebooks/Create_new_dnn.ipynb | 11 +-------- notebooks/Full_model_prediction.ipynb | 33 ++++++++++++--------------- src/mlpredict/import_tools.py | 6 ++--- 3 files changed, 18 insertions(+), 32 deletions(-) diff --git a/notebooks/Create_new_dnn.ipynb b/notebooks/Create_new_dnn.ipynb index 88ddd8c..b7110a2 100644 --- a/notebooks/Create_new_dnn.ipynb +++ b/notebooks/Create_new_dnn.ipynb @@ -62,7 +62,7 @@ } ], "source": [ - "VGG16 = mlpredict.api.dnn(input_dimension=3, input_size=224)\n", + "VGG16 = mlpredict.api.Dnn(input_dimension=3, input_size=224)\n", "\n", "\n", "VGG16.add_layer('Convolution', 'conv1_1', kernelsize=3, channels_out=64, \n", @@ -179,15 +179,6 @@ "source": [ "VGG16.save('models/VGG16.json')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/notebooks/Full_model_prediction.ipynb b/notebooks/Full_model_prediction.ipynb index 49f2b55..7bf8cc9 100644 --- a/notebooks/Full_model_prediction.ipynb +++ b/notebooks/Full_model_prediction.ipynb @@ -11,9 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -55,10 +53,16 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/djustus/Library/Caches/Python-Eggs/mlpredict-0.0.1-py3.6.egg-tmp/mlpredict/dnn_architecture/VGG16.json\n" + ] + } + ], "source": [ "VGG16 = mlpredict.import_tools.import_dnn('VGG16') # imports default network definition\n", "# VGG16 = mlpredict.import_tools.import_dnn('models/VGG16.json') # imports local network definition" @@ -121,7 +125,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Restoring parameters from /Users/djustus/Library/Caches/Python-Eggs/mlpredict-0.0.1-py3.6.egg-tmp/mlpredict/model/model_all/variables/variables\n", "INFO:tensorflow:Restoring parameters from /Users/djustus/Library/Caches/Python-Eggs/mlpredict-0.0.1-py3.6.egg-tmp/mlpredict/model/model_all/variables/variables\n" ] }, @@ -140,6 +143,7 @@ "INFO:tensorflow:Restoring parameters from /Users/djustus/Library/Caches/Python-Eggs/mlpredict-0.0.1-py3.6.egg-tmp/mlpredict/model/model_all/variables/variables\n", "INFO:tensorflow:Restoring parameters from /Users/djustus/Library/Caches/Python-Eggs/mlpredict-0.0.1-py3.6.egg-tmp/mlpredict/model/model_all/variables/variables\n", "INFO:tensorflow:Restoring parameters from /Users/djustus/Library/Caches/Python-Eggs/mlpredict-0.0.1-py3.6.egg-tmp/mlpredict/model/model_all/variables/variables\n", + "INFO:tensorflow:Restoring parameters from /Users/djustus/Library/Caches/Python-Eggs/mlpredict-0.0.1-py3.6.egg-tmp/mlpredict/model/model_all/variables/variables\n", "INFO:tensorflow:Restoring parameters from /Users/djustus/Library/Caches/Python-Eggs/mlpredict-0.0.1-py3.6.egg-tmp/mlpredict/model/model_all/variables/variables\n" ] } @@ -196,7 +200,7 @@ "data": { "image/png": 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rq6WnTp1ybNweHx9vkEgk2Lhx400rtxiNRmHXrl1arVZrnjhxYsWdXo/VasW0\nadMCDAaDw9SpU4vDwsJueU1dDTvuRERE7VV/Hdg0Gbh4CHg2CRg8zd4VUSdqbye7UeRnkSEltSW3\nrEaic9LVbxq/6VxHnKM9srKyVAAwYcKEO3amT58+7QwAY8eOrWr+3KBBg+q8vLzq8/Pz5SUlJVKd\nTndjKotKpbIEBwfXNT/G19e3HgAMBsON3BoYGGgaOnRo5ZEjR9RZWVmK8PBwIwBs3rzZtaKiQhoX\nF1ckk8nu+HreeOMN37S0NLfw8PDq5OTkDvlveL+x405ERNQeddXA3ycCuV8Dz3/E0E5tNjN0Zr5c\nKrc23SaXyq0zQ2fm26umpqqqqqQAEBAQUH8v+/n5+bV4saaHh4cJAEpLS6VNt6vV6hbnozs42PK6\n2XzzJw/Tp083AEBKSop747YNGzboACAuLu6O02RmzJjh+/HHH3tFRERUp6en5zg5OXX5+e0AgzsR\nEVHbGSuB1BeBy0eBF/4KhE6yd0XUjU0KmlQ6/7H5l3ROunoBAnROuvr5j82/1FUuTHVxcbEAQG5u\n7h3XKG/cLy8vr8WWd3FxsQwAtFrtXS8cvZNp06aVqVQqy7Zt29zNZjMKCgocDh48qA4KCqodNmxY\n7e2Oi4uL652cnOz1+OOPV2VkZOS4urpab7dvV8OpMkRERG1RW24L7Vf/Dby0Dhj4nL0rogfApKBJ\npV0lqDcXHh5effbsWeedO3eqBw8efNv54MHBwTXff/+98969e10GDhx409SXM2fOOBYVFcn1en19\n02kybaFSqcSYmJiyLVu26Hbs2KE+e/aswmKxCFOmTClpaX+r1YpXX33VLzU11eOJJ56o3LNnz09N\n59N3B+y4ExERtVZNKfDpc8DVbGDieoZ26hFmzZpVLJVKxcTERJ+srCxF8+cbV5WJj48vAYCVK1f2\nKigouNEkNpvNmD17tq/VasXUqVOLmx/fFq+//noJAKxfv9598+bN7lKpVIyPj7/ljY/VasUrr7zi\nn5qa6vHUU09V7Nu3r9uFdoAddyIiotapKQU2TACKzwGTUoGgsfauiKhThIeHG997773L8+fP9x82\nbNgj0dHR5YGBgXUGg0F66tQppVKptBw7duz8qFGjrs+cObPwww8/9A4JCRk4bty4MqVSac3IyFDn\n5OQ4hYWFVb/zzjtFHVHT6NGjr/v5+dWlpaW5mc1mITIyskKv15ub7/fWW2/12rJli06hUFhDQkJq\n33777V7N9xk8eHDN9OnTy9tSx6JFi7zPnTunAICzZ886A0Bqaqru8OHDKgAYPnx49Zw5c1r8JKA1\nGNyJiIjuVXUxsOFZwPATMHm9gQaDAAAgAElEQVQT0Dfa3hURdaq5c+eWhIaG1q5YscL76NGjLvv2\n7dO4ubmZg4KCahu73wCwdu3a/MGDB9d89NFHntu3b3c3m81C79696+bPn5+/dOnSIoVC0WHd7kmT\nJhlWrFjhAwCxsbEthuPc3FxHADAajZKkpCTvlvZ54YUXDG0N7vv373f97rvvVE23nTx5Unny5Ell\n49cdEdwFUex2nxLcs4iICPH48eP2LoOIiB4EVUW2TnvZJWDKJiAw0t4V9UiCIGSJohjR2efNzs7O\nDQ0NbXfwIroX2dnZutDQ0IDm29lxJyIiupvKq8D6Z4DKfGDqVuChJ+1dERH1QAzuREREd1KRbwvt\n1UXAtG2A/xP2roiIeigGdyIiotspv2wL7TWlwPQvgN5D7F0REd1nu3fvdsnIyHC5234ajca8ZMmS\na51RUyMGdyIiopaU5QJ/ewaoqwCmfwn4htu7IiLqBBkZGS6rV6++ZdWZ5nx8fOoZ3ImIiOzNcMHW\naa+/DsTuBHwetXdFRNRJEhMTCxITEwvsXUdLGNyJiIiaKsmxhXZLPfDabsA7xN4VEREBYHAnIiL6\nj2s/2pZ8FK3Aq7sBr0fsXRER0Q0SexdARETUJRSdBf4WY/v7a18xtBNRl8PgTkREdPUU8LfxgFRm\nC+0eQfauiIjoFgzuRETUsxWctM1plznbQruur70rIiJqEee4ExFRz3UlC/j0ecDJFXh1F+AWYO+K\niIhuix13IiLqmS4fAzY8Czi7Aa/9g6GdiLo8BnciIup5Lh0BUl8AVJ620K7pbe+KiIjuisGdiIh6\nlosHgdQXAbWPbU67q97eFRER3RMGdyIi6jkuZAJ/fxnQ+NtCu/qudzUnIuoyGNyJiKhnyNkPbJwE\nuAfa7oiq8rR3RUTUSS5evCh79913PZ966qm+er0+RC6Xh2k0mkefeOKJvuvXr9e0Z+zTp087Ll68\n2Hvo0KH9vL29B8lksjB3d/fQqKiowF27drl01GsAuKoMERH1BOfSgM9iAY/+QOwOwFlr74qIqBOt\nWLHCc+3atd56vb5+2LBhVV5eXqbLly/L9+7d6/baa6+pDx06VJSSknKlLWMvXLhQ/9VXX7kFBgYa\nR44cWeHm5mbOyclRZGRkaDIyMjTLli3Le/vtt691xOsQRFHsiHG6pIiICPH48eP2LoOIiOzph13A\n1l8B3sHA9C8AJzd7V0TtIAhCliiKEZ193uzs7NzQ0NCSzj4vdYz169drdDqdOSYmprrp9hMnTiie\nfvrp/tXV1dKDBw/+8OSTT9a0duwPPvjAPTw8vGb48OG1Tbd/9dVXqueee66fIAjIyck57e/vb7rX\nMbOzs3WhoaEBzbdzqgwRET24zn4JbH0N8HnU1mlnaKcurnTTZm3Ok0+F/DDgkfCcJ58KKd20uct9\nPJSZmekcExPTx9PTc5BcLg/z8PAYNHz48L4pKSk3/YClpKS4RUREBLm4uDyqUCjC+vXr98jChQu9\na2trheZj6vX6EL1eH1JVVSWZMWOGb69evULkcnmYn59f8OLFi72tVuuNfffv368UBCF89OjRgber\nsU+fPgPlcnlYUVGRFABeffXV8uahHQDCwsKM48ePL2sYt03TWmbNmmVoHtoBICYmpnrIkCFVJpNJ\nyMzMVLZl7OY4VYaIiB5Mpz8Htr8B+D4GTN0KKNT2rojojko3bdZeS0jwF+vqJABgLi6WX0tI8AcA\n7ZTJpfatzmbVqlW6BQsW+EskEjEqKqo8MDCwrri42CE7O1uZnJzsGR8fXwYAb775pj4pKclbo9GY\nJ0yYUKpSqawZGRmuCQkJ+vT0dNdDhw6dd3R0vGnah8lkEkaMGNG3qKhIHhkZWSmVSsU9e/Zoli9f\nrjcajcKqVauuAkB0dPT1gIAAY2ZmpmthYaHU29vb0nSczMxM54sXLyrGjBlT5uXlddNzLXFwcBCb\nPnYkmUzWOHaHjMfgTkRED57szcCXvwH8ngBe2QI4quxdET3AChYt7l2Xk+Pc3nGMP/6ohMl0Uzda\nrKuTFC1fHlCxfbtHe8Z27Nu3xmf5u3ntGSMrK0uxYMECP6VSaUlPT/8xIiLC2PT5CxcuyABbRzwp\nKcnb29u7/tixYz/4+fmZAcBkMl0ZM2bMw5mZma5Lly71SkhIKGx6fHFxsWzAgAE1Bw4cOKNSqUQA\nyM/PL+jfv39wcnKy1/Llywsbw/7kyZMNCQkJ+nXr1mkXLVpU3HScdevW6QAgNjbWcLfXVFpaKklL\nS3MTBAExMTGV7fn+NHf+/Hn5N998o1YoFNYxY8ZUdcSYnCpDREQPlhOfAl/MBAJ+AUz9jKGduo9m\nof2u2zvZBx984GGxWIQ5c+YUNA/tABAYGGgCgJSUFB0AzJ0792pjaAcAmUyGNWvW5EkkEqSmprb4\nRiQpKSmvMbQDgF6vN48aNaq8urpaeurUKcfG7fHx8QaJRIKNGzfqmh5vNBqFXbt2abVarXnixIkV\nd3o9VqsV06ZNCzAYDA5Tp04tDgsLu+U1tVVtba0wZcqUh+rr64V58+YVeHh43LXzfy/YcSciogfH\n8U+A3bOBwJHA5I2AzMneFVEP0N5OdqOcJ58KMRcXy5tvd/DwqH9o62fnOuIc7ZGVlaUCgAkTJtyx\nM3369GlnABg7duwtXeZBgwbVeXl51efn58tLSkqkOp3uRqBVqVSW4ODguubH+Pr61gOAwWC4kVsD\nAwNNQ4cOrTxy5Ig6KytLER4ebgSAzZs3u1ZUVEjj4uKKZDLZHV/PG2+84ZuWluYWHh5enZyc3CH/\nDQHAbDbjxRdffOjEiROqmJiYsnfeeaeoo8Zmx52IiB4M3/7VFtr7jgEmb2Jop27H/be/zRccHa1N\ntwmOjlb33/423141NVVVVSUFgICAgPp72c/Pz6/FVVQ8PDxMAFBaWiptul2tVrfYlW6cH242m2/6\n5GH69OkGAEhJSXFv3LZhwwYdAMTFxd1xmsyMGTN8P/74Y6+IiIjq9PT0HCcnpw6Z3242m/H8888/\nlJaW5jZu3LiyL7744meJpOPiNoM7ERF1f9/8f8A/5gFBMcCkTwGZwt4VEbWadsrkUs/f//6Sg4dH\nPQQBDh4e9Z6///2lrnJhqouLiwUAcnNzb/lUoKX98vLyWmx5FxcXywBAq9W2a/rItGnTylQqlWXb\ntm3uZrMZBQUFDgcPHlQHBQXVDhs27JZVXhrFxcX1Tk5O9nr88cerMjIyclxdXa2327c1TCYTJkyY\n0Gf37t3aZ555pnTHjh0/363r31oM7kRE1L0d/jOwZyEw4Blg4t8AB8e7HkLUVWmnTC7te+jg6QE/\nfJ/V99DB010ltANAeHh4NQDs3Lnzjks0BQcH1wDA3r17b1le8cyZM45FRUVyvV5f33SaTFuoVCox\nJiamrLi4WLZjxw51SkqK1mKxCFOmTGlxvX2r1Yrp06f7rVu3zvOJJ56o3L9/f46Li0uHhHaj0Sj8\n8pe/DExLS3N7/vnnDV988cXFjlpJpikGdyIi6r4OrgT2LQEGvgC89AngcMdGIBG1w6xZs4qlUqmY\nmJjok5WVdcvHWo2rysTHx5cAwMqVK3sVFBTcSK9msxmzZ8/2tVqtmDp1anHz49vi9ddfLwGA9evX\nu2/evNldKpWK8fHxt7zZsVqteOWVV/xTU1M9nnrqqYp9+/b91PQi2Paora0Vxo4dG5ienq55+eWX\nS7Zu3ZorlUrvfmAb8OJUIiLqnv71HvCv5UDIy8BzawEp/0kjup/Cw8ON77333uX58+f7Dxs27JHo\n6OjywMDAOoPBID116pRSqVRajh07dn7UqFHXZ86cWfjhhx96h4SEDBw3blyZUqm0ZmRkqHNycpzC\nwsKqO+qCzdGjR1/38/OrS0tLczObzUJkZGSFXq83N9/vrbfe6rVlyxadQqGwhoSE1L799tu9mu8z\nePDgmunTp5e3tobp06f7HzhwwFWj0Zh9fHxMb731lk/zfUaOHFk1fvz4di8Jyd9yRETUvYgikPku\ncHAF8OhUYMJfAMn96W4R0c3mzp1bEhoaWrtixQrvo0ePuuzbt0/j5uZmDgoKqm3sfgPA2rVr8wcP\nHlzz0UcfeW7fvt3dbDYLvXv3rps/f37+0qVLixQKRYfd7GjSpEmGFStW+ABAbGxsi9NkcnNzHQHA\naDRKkpKSvFva54UXXjC0JbhfvnzZEQDKy8sd1qxZc8sbgkYdEdwFUezwm0R1GREREeLx48ftXQYR\nEXUUUQT2L7XNaw+LBcb/GejAFRuo6xMEIUsUxYjOPm92dnZuaGhoi6GQqKNlZ2frQkNDA5pvZ8ed\niIi6B1EE9iwGjiYBEXHAuJUM7UTUozC4ExFR1yeKQNoC4NuPgMdnAmMTAKFL3EySiKjTMLgTEVHX\nZrUC/5gLHF8HDHsTGP1/DO1d1LbCUvzp56vIrzNB7yjDwj698KK31t5lEbXK7t27XTIyMm5ZyrI5\njUZjXrJkybXOqKkRgzsREXVdViuwaxZw8lPgF/8DRC1laO+ithWWYt65PNRabdfOXakzYd45213k\nGd6pO8nIyHBZvXr1bS8ybeTj41PP4E5ERAQAVguw43dA9ibgqflA5CKG9i5IFEX8VFOHRTn5N0J7\no1qriD/9fJXBnbqVxMTEgsTExAJ719ESBnciIup6LGbgy5nA6a1A5GLg6fn2roiaqLVYcaS8GumG\nSqQbKnHJWH/bffPrTJ1YGdGDjcGdiIi6FosJ2P5r4OwXtqkxT86xd0UE4FJtXUNQr8Lh8ioYrSKc\nJAJ+4eaC3/p5YnVuIQrrb7nvDfSOMjtUS/RgalVwFwTBGcCTAJ4GMAyADwAPAAoABgDFAH4AcADA\nAVEUz3VotURE9GAz1wOf/wr4cbftItQn/sveFfVY9VYrvq24jv0NXfWcmjoAQICTHNN83BGlVWOY\nRgWF1LYkp0oquWmOOwA4SQQs7HPXqcJEdI/uKbgLghAGYAaAKQCUjZub7aZv+PMogMkNx50E8BGA\njaIoXu+IgomI6AFlrgM+exU4n2Zb7nHob+xdUY9zta4eGYYqpBsqcaCsCtctVsgFAU9oVIj10SHK\nXY0+zo4tHts4j52ryhDdP3cM7oIgPApgJYBI/CeoGwGcAHASQAmAUgC1ALQNfx4C8DgAPwBhAD4E\n8L4gCMsB/FkUxdtPhCMiop7JZAQ+mw7k7LXdWGnIr+1dUY9gtoo4UXkd6aVV2G+owNlqIwDb9JYX\nvdwQ5a7GLzQqKB2k9zTei95aBnWi++i2wV0QhL8BmAZAAtsUmM8AbATwnSiKt05iu/V4TwDPNozx\nCwAJAH4jCMKroigean/pRET0QKivATa/Avz8L+CZPwPhr9m7ogdaSb0ZmaW26S//Kq1CudkCqQA8\nplbi7T69EOWuRn+lAgJX8CHqcu7UcY8F8D2AZQA+F0XR0pqBRVG8BuCvAP4qCII/gN8D+BVs3XsG\ndyIiAuqvAxsnAblfA88mAYOn2ruiB45VFHGqqtZ2YWlpJU5W1kAE4CF3wBidK6Lc1XjaTQVXGder\nIOrq7vRTOgXAZ6IoinfY556IongJtm77uwD82zseERE9AOqqbKH98jfA8x8BoZPsXdEDo8Jkxr/K\nbHPVMwxVKDGZIQAYrHbGvABvROvUCFE5QcKuOlG3ctvgLorilo4+mSiKVwBc6ehxiYiomzFWAn9/\nCbhyHHjhr0DIS/auqFsTRRE/XjfeWAHmu8rrsIiAxkGKSK0LotzVGKFVQydnV52oO+NPMBERda7a\nciD1ReDqv4GJnwCPPGvvirql62YLDpVVI71hvnpBw42OglVO+C8/L0S5qzHYxRkOEnbViR4UHR7c\nBUFwA2ARRbGyo8cmIqJurqYU+PR5oOgs8PIGoH+MvSvqNkRRxM9NboL0TXk16kURKqkET2tdME+r\nRqS7C3o5yu1dKlGXU1paKpk3b54+OzvbOS8vz7GiosJBqVRa9Hp9/cSJEw2zZ88uUavV1raMXVdX\nJ7z//vse2dnZzmfOnHG+cOGCwmw2C6tWrbo0Z86cko58Ha29AZMPgGgA10RR/Gez5wYCWA9gcMPX\nRwDEiaJ4voNqJSKi7uy6Afj0WaD4HDApFQgaa++KurxaixXflFffuLA0t9a2onJfZ0fE+drWVR/i\nqoRcIrFzpURdW3FxscOmTZt0wcHBNSNHjqzQ6XTmiooK6eHDh12WLl3ae8OGDR7ffvvtD1qtttXh\nvaqqSrJkyZLeAODu7m7W6XSmwsLC+/IOurUd99cBvANgBYAbwV0QBCcA/wDgi/+s9z4cwH5BEILZ\nfSci6uGqi4ENzwKlF4Apm4CHo+1dUZeVZ6xv6KpX4uuyKtRaRThJBAx3c8GM3p4YqXWBv1PLN0Ei\nopYFBgbWl5eX/9vR0fGWRVeeffbZh3bu3KlNTEz0+L//+7+i1o6tUqmsW7ZsyXn88cdr/f39TXPm\nzPFZvXr1fbllcGvfojf+pm1+4eqrAHrDdjOmX8O2dvsV2O6k+rv2FEhERN1cVRGwfjxQ+jPwyhaG\n9mZMVhFfl1XhnZ/y8dSxH/HYN9/j9+ev4Nx1I6b0csffB/XB978IQeqgPviVXsfQ/oA7feCK9pMF\nX4ckzcwI/2TB1yGnD1zpcne0yszMdI6Jienj6ek5SC6Xh3l4eAwaPnx435SUFLem+6WkpLhFREQE\nubi4PKpQKML69ev3yMKFC71ra2tvufBCr9eH6PX6kKqqKsmMGTN8e/XqFSKXy8P8/PyCFy9e7G21\n/qcRvn//fqUgCOGjR48OvF2Nffr0GSiXy8OKioqkAODg4ICWQjsATJw4sQwAfvrpJ0Vbvh8KhUJ8\n+eWXK/39/U1tOb41WttxD2h4/LHZ9hcAiAAWiaL4MQAIgmAAkAZgAoA/taNGIiLqriqvAuufASoL\ngKlbgYeetHdFXUJRnenGRaUHSqtQbbFCJggYplFiqo8PotzVCHRy5E2QepjTB65oD2/9yd9itkoA\noKaiXn5460/+ABDytG+pfauzWbVqlW7BggX+EolEjIqKKg8MDKwrLi52yM7OViYnJ3vGx8eXAcCb\nb76pT0pK8tZoNOYJEyaUqlQqa0ZGhmtCQoI+PT3d9dChQ+ebB2mTySSMGDGib1FRkTwyMrJSKpWK\ne/bs0SxfvlxvNBqFVatWXQWA6Ojo6wEBAcbMzEzXwsJCqbe39033GsrMzHS+ePGiYsyYMWVeXl53\nvQ/Rrl27XAEgJCSktuO+U/dHa4O7DkClKIo3XpggCBIAT8AW3D9vsu8+AFYAQe0tkoiIuqGKK7bQ\nXn0NmLYN8B9m74rsxiKKOFFZc2MKzOlq2z+jvRxleM7TDdHuavzCTQWVg9TOlVJbpG/4oXdpfrVz\ne8cpuVKttFrEm96tWcxWydef5QT8eOSqR3vG1upVNVGxA/LaM0ZWVpZiwYIFfkql0pKenv5jRESE\nsenzFy5ckAG2jnhSUpK3t7d3/bFjx37w8/MzA4DJZLoyZsyYhzMzM12XLl3qlZCQUNj0+OLiYtmA\nAQNqDhw4cEalUokAkJ+fX9C/f//g5ORkr+XLlxc2hv3JkycbEhIS9OvWrdMuWrSouOk469at0wFA\nbGysoflrMJlMWLBggQ8AlJaWSo8ePepy7tw5p8cff7zqf/7nf4qb79/VtHaqjBRA88/oQgA4Azgr\nimJZ40ZRFK0AygAo21UhERF1P+WXgU/GAddLgOlf9MjQbqg3Y1thKX77/SUEf30Gz5zIwV8uF0Ep\nlWBxn17IeCwIJ4Y9gpX9e2OshytDO6F5aL/b9s72wQcfeFgsFmHOnDkFzUM7AAQGBpoAICUlRQcA\nc+fOvdoY2gFAJpNhzZo1eRKJBKmpqS2+EUlKSsprDO0AoNfrzaNGjSqvrq6Wnjp16kYGjY+PN0gk\nEmzcuFHX9Hij0Sjs2rVLq9VqzRMnTqxoPr7JZBJWr17da/Xq1b3Wr1/vee7cOafnnnvOsGfPnp+c\nnZ3bfdPR+621HferAPwFQXhIFMWLDdvGNDweaWF/FWzz3omIqKcovQisnwDUVQCxXwL6cHtX1Cms\noojT1bU3uuonKmsgAnCXOSBap0a0uxpPu7lAI+MtVB407e1kN/pkwdchNRX1t6xG4uwqr5+48LFz\nHXGO9sjKylIBwIQJE+646Mjp06edAWDs2LFVzZ8bNGhQnZeXV31+fr68pKREqtPpbkxlUalUluDg\n4Lrmx/j6+tYDgMFguPHDExgYaBo6dGjlkSNH1FlZWYrw8HAjAGzevNm1oqJCGhcXVySTyW6pzdnZ\nWRRFMctqteLSpUuy3bt3q5ctW6Z/9NFHB/zzn//MCQoKqr/nb4gdtLbj/k3D41JBECSCIHgA+A1s\n02T2NN1REISHYOvOX213lURE1D0YLgB/iwHqq4DYnQ98aK80W7DrWjlm/3AZjx45izHHz2PFxUJY\nRWBugDfSwvvh9PCB+MsAfzzr6cbQTncUMS4gX+oguWk5QqmDxBoxLiDfXjU1VVVVJQWAgICAO4bb\nxv38/PxavFjTw8PDBNimqjTdrlarW5yP7uBg+7kxm803ffIwffp0AwCkpKS4N27bsGGDDgDi4uJu\nmSbTlEQiwUMPPWT6r//6L8OmTZsu5ObmKmbOnOl3p2O6gtb+BvkzgMkApsN2Qaq84c/PAHY323dU\nw+OJ9hRIRETdREkO8LfxgNUEvLoL8A6xd0UdThRF/HjdeGNd9e8qrsMsAq4OUozQuiDKXY1IrQs8\n5Ld2+ojupvEC1OP/yNXXVNTLnV3l9RHjAvK7yoWpLi4uFgDIzc2Vu7m53TJVpvl+eXl5soEDB97S\nQS8uLpYBgFarveuFo3cybdq0srfeestv27Zt7n/5y1/yr1275nDw4EF1UFBQ7bBhw+75QtOoqKjr\nLi4ulmPHjrm0p57O0KrgLorit4IgvA7gAwCNL+5HAJNFUTQ32z224TGzfSUSEVGXd+1H24WoEIFX\ndwNej9i7og5z3WLB4bJq7G+YApNfZ2siDlQp8NvenohyVyNcrYSDpEtMQ6ZuLuRp39KuEtSbCw8P\nrz579qzzzp071YMHD75tcA8ODq75/vvvnffu3evSPLifOXPGsaioSK7X6+ubTpNpC5VKJcbExJRt\n2bJFt2PHDvXZs2cVFotFmDJlSqvuVlpWVia5fv261NnZuV31dIZW32pNFMX1ALwBPA7bijHBoiie\narqPIAhyAMkAfgXgqw6ok4iIuqqis7bpMYIAvPbVAxHaL9bU4a95xZj87wsYcOgMYk9fxOdFZRjk\n4oyVQb1xYtgjSH+sPxYF+uBxjYqhnXqEWbNmFUulUjExMdEnKyvrljXPG1eViY+PLwGAlStX9ioo\nKLjRJDabzZg9e7av1WrF1KlTO2QFl9dff70EANavX+++efNmd6lUKsbHx9/yxufIkSNOJSUlt1wB\nbjQahbi4OD+r1YrIyMhbLmbtato02a5hOcjv7vB8PYANbS2KiIi6iaunbHdEdVDYpsfoHrZ3RW1i\ntFhxtKK64cLSKvxca2sS9nV2xK98dYjWqjFEo4SjpNX9LqIHRnh4uPG99967PH/+fP9hw4Y9Eh0d\nXR4YGFhnMBikp06dUiqVSsuxY8fOjxo16vrMmTMLP/zwQ++QkJCB48aNK1MqldaMjAx1Tk6OU1hY\nWPU777zT6juUtmT06NHX/fz86tLS0tzMZrMQGRlZodfrm88CQUpKim7Tpk26IUOGVPn6+tZrNBrL\n1atXZYcOHVKXlJTIAgICjH/5y1+utLWORYsWeZ87d04BAGfPnnUGgNTUVN3hw4dVADB8+PDqOXPm\ntOqTgJbwKhkiImqbgpPAhucAuQp4bReg7WPvilrlirH+xgowh8qqUWu1QiER8IRGhThfHaLd1bxL\nKVEzc+fOLQkNDa1dsWKF99GjR1327duncXNzMwcFBdU2dr8BYO3atfmDBw+u+eijjzy3b9/ubjab\nhd69e9fNnz8/f+nSpUUKhaLDll6cNGmSYcWKFT4AEBsb22I4njx5cml1dbXkxIkTqpMnT6pqamqk\nSqXS8vDDD9f+5je/KXrrrbeKXVxcrC0dey/279/v+t1336mabjt58qTy5MmTN5ZF74jgLohi279v\ngiA4AdAAuONVOKIoXm7zSdohIiJCPH78uD1OTUT0YLtyHPj0BcDJ1dZpdwuwd0V3ZbKK+K7i+o07\nlv543TZFt7dCjmh3NaLc1XhCo4KzlF31rkwQhCxRFCM6+7zZ2dm5oaGh7Q5eRPciOztbFxoaGtB8\ne6s77oIgqADMh211mcB7OERsy3mIiKiLunwMSH0RULrbLkTV9LZ3Rbd1rc50I6gfKK1ClcUKmSDg\ncVcllgb6INpdjYedHSEInKNORF1fqwK1IAieAA4C6AvgXn/L8bchEdGDIvcwsPFlQOUFvLYbUPvY\nu6KbWEQR/66ssa0AU1qJU1W2FeG85TJM8NQgyl2NJ91c4MK7lBJRN9TaTvi7APoBqAGwCrabLhUB\nuOUiACIiesD8fADYNBlw9bVNj3HxtndFAIBSkxn/Kq1CuqESmaWVKDVZIAEQ4arEwod6IcrdBQNV\nTuyqE9E92b17t0tGRsZd13TXaDTmJUuWXOuMmhq1NriPh23qy2uiKH5+H+q5QRAEKYDjAPJFURzf\ncCfWzQC0sN3UaXrD6jVERHS/XcgANk0B3B4CXt0JqDztVoooijhTXXtjBZisyuuwAtDKpBipVSPa\nXY2ntS5w411KiagNMjIyXFavXt3rbvv5+PjUd/Xg7gqgHsAX96GW5v4bwA8A1A1fvwdgtSiKmwVB\n+BBAHIC1nVAHEVHPlrMP2DwV0PUFYncASl2nl1BltuBAaRXSSyuRYahEUb3tg95QFyfMDvBCtFaN\nULUzpOyqE1E7JSYmFiQmJhbYu46WtDa45wHwEUXxvt5ZShAEXwAxsE3NmSPYPt8cCeCVhl3WA/gD\nGNyJiO6vc2nAZ7GAR6qPlNsAACAASURBVH9baHfWdsppRVHE+Zq6G8s1HquohlkE1A4SjNCqEaVV\nY6S7Czzkd1zUjIjogdLa4P4lgHmCIDwmiuJtb8DUAdbAtnJN4/widwDloig2zqW/AkDf0oGCILwB\n4A0A8PPzu48lEhE94H7YBWx9DfAeBEzfDji53dfT1Vis+LrMNlc9vbQSV4wmAMAApQIze3siyl2N\nCLUSMt6llIh6qNYG9/cBTATwoSAIUaIolnd0QYIgjAdwTRTFLEEQRjRubmHXFhegF0UxGUAyYFvH\nvaPrIyLqEc5+AXweB+jDgGnbAIXrfTlNbm2dbQUYQyWOlFejzirCWSrBU24q/Le/F0Zq1dAr5Pfl\n3ERE3U2rgrsoigZBEKIBbATwvSAIH8F2AWnVXY472IrTDAcwQRCEcQAUsM1xXwNAIwiCQ0PX3RdA\nl5x7RETU7Z3+HNj+BtB7CPDKZ4BCffdj7lGd1Yqj5ddvTIG5UFsHAAh0csSrPjpEuasxVKOEo4Q3\nQSIiaq4tl9ybAeQCGAJgyT3s36obMImiuBDAQgBo6LjPE0VxqiAIW/H/s3fvcVFX+f/AX2eGGYa5\nAcOdQSBRMeWigqZZpiHqqpmF1/VSq2zZ/so1Td1yV3Mr06/Xat3MWFtvpSXeW9cLkJdMUnJR0byL\nyE0YGO4Dczm/PwaIm+DA4AC+n48HD/Qz5/P5vLGy15x5f84BxsG8sswrAPZZVDUhhJCmJe8A9r4B\n+A0EJu8A7OVNn9OEdF0F4is3QTqRX4xSown2AoanneT4g48rIlRKPCG1t0LxhBDSsVm6AZM/gFMA\nqpbIeZhGQ2s1Iy4EsIMx9iGA8wD+ZaXrEkIIAYBftgL73wKeGGQO7WJpsy5jMHGcKyypboG5UqID\nAKjtRRjv4YwIFyUGOsshE9ImSIQQYglLZ9z/DsAbQC7MQfowgOzWWmWGc/4DgB8qf30L5ll+Qggh\n1nZuE3DwbSAgApi0HRA5WHR6ToUe8Rrzco0/5BWi0GCCHQOecpRjcYA3IlyU6Ca1p02QCCGkBSwN\n7hEwt75M5pzHtUI9hBBCHrWfvwT+8w7QdTgwYQsgkjR5iolz/K+otHpWPbmoDADgLrbDKDcnRKiU\nGKRSQGlHs+qEEGItlgZ3JwBlAOJboRZCCCGP2k//BA6/CwSOAsb/G7B78Aou+XoDjucV4ZimEAl5\nRdDoDRAACFPK8JcnPBHhokRPuQMENKtOCCGtwtLgngrAj3NOyywSQkh79+MnwNHFwJNjgHGbAGHt\nzYw457hcosOxXPO66ucKSmACoBIJMUSlRISLEoNVCqhEzVnngBBCiKUs/dv2WwB/Y4w9zzmnWXdC\nCGmvTqwE4j8Eer4MvLyxOrQXG4w4UbUJkqYIWRXmTZBC5A74s58Hhroo0UsphZBm1Qkh7UheXp7g\nnXfeUScnJ0vT0tLsCwoK7GQymVGtVleMHz9eM2fOnFylUmlqzrUvXrxov2PHDue4uDjlnTt3JBqN\nxk6pVBp79epVPGfOnPsvvPBCo8umW4JZMnnOGHMAcAaAHMBQzvltaxXSGsLDw/m5c+dsXQYhhLQd\nnAPHVwA/fAyETAQfsx7Xy43V66onFpRAzzkUQgGeUykQ4aLE8yolPOxFTV+bkEeAMZbEOQ9/1PdN\nTk6+Exoamvuo70us4+rVq+JevXr1DAoKKg0ICNC5uroaCgoKhD/++KPi9u3bkoCAAN3PP/98RaVS\nWRzeR48e3fn77793DggI0PXr16/Y2dnZcP36dUl8fLyT0WjEBx98kPbXv/71viXXTE5Odg0NDfWv\ne9zSGffxMC/D+D6Ai4yxWAA/o+kNmLZYeB9CCCHWxjkQ/yFKf/wMp8P/griuUxB39jru6ioAAIEy\nCV7r5IYIlRJ9HWUQCWhWnRDSMQQEBFRotdr/2dvb15uxfvHFF5/Yv3+/as2aNW4ffvhhtqXXHjZs\nWMG7776bOXDgwLKax7///nv52LFju/3973/3mTZtWr6fn5++JT8DAFi6Nd2/AawF4AhACmAqgE8B\nfNXI16aWFkkIIaRlUkt12BS3Gb/Pd0OPZ/6DqbLfYUeWFt1lEqzo5oOzA3rgeL/u+FuAN552llNo\nJ8RG/nf0P6oNr08LXj1xdNiG16cF/+/of1S2rqmuhIQE6ahRozq7u7uHiMXiPm5ubiEDBw7sGhMT\n41xzXExMjHN4eHigQqHoJZFI+nTr1q3Hu+++61lWVlbvLxi1Wh2sVquDi4qKBK+//rqPl5dXsFgs\n7uPr6xu0aNEiT5Ppt4nwY8eOyRhjYcOGDQt4UI2dO3fuKRaL+2RnZwsBwM7ODg2FdgAYP358PgDc\nuHGj6SW1GjB79mxN3dAOAKNGjSru169fkV6vZwkJCbLmXLsuS2fc78K8HCQhhJA2rMJkQqK2BMfy\nChGvKcT10nJA2AtPOBdimo8nIlyV6O8oh0Ro6fwNIaS1/O/of1Q/bP7Sz6jXCwCgRJsv/mHzl34A\n0CtyZJ5tqzNbvXq168KFC/0EAgGPiIjQBgQElOfk5NglJyfLNm7c6B4dHZ0PAG+++aZ6/fr1nk5O\nToYxY8bkyeVyU3x8vOPy5cvVcXFxjidPnrxWN0jr9Xo2ePDgrtnZ2eIhQ4YUCoVCfvjwYadly5ap\ndTodW716dSYADB06tMTf31+XkJDgmJWVJfT09Ky1n1BCQoL09u3bkuHDh+d7eHg0udfQgQMHHAEg\nODi4XvhuKZFIxAHzGwdrsOgqnHN/q9yVEEKI1WWWVyBeY16u8UR+EUqMJogZw9OGdEy/vQsRnTqj\n8/BFAD1YSohVHf58XafctNTmbTVcw/07t2Umo6HWf6BGvV6Q8NVG/5SEo24tubZrJ7/S4W/MSWvJ\nNZKSkiQLFy70lclkxri4uF/Dw8N1NV+/efOmCDDPiK9fv97T09OzIjEx8Yqvr68BAPR6/b3hw4d3\nSUhIcFyyZInH8uXLs2qen5OTI3ryySdLjx8/fkkul3MASE9Pz+jevXvQxo0bPZYtW5ZVFfYnTZqk\nWb58uXrTpk2q9957L6fmdTZt2uQKANOnT9fU/Rn0ej0WLlzoDQB5eXnCM2fOKK5everw1FNPFb39\n9ts5dce3xLVr18Q//fSTUiKRmIYPH26VB1RpqoUQQtopg4kjUVuMZTczEHH2V/Q+fRnzrqbhQlEp\nojycsaWnP64UbcWOU5PxRz9vCu2EtHF1Q3tTxx+1Tz/91M1oNLK5c+dm1A3tABAQEKAHgJiYGFcA\nmDdvXmZVaAcAkUiEdevWpQkEAmzbtq3BNyLr169PqwrtAKBWqw2RkZHa4uJi4YULF+yrjkdHR2sE\nAgG+/vpr15rn63Q6duDAAZVKpTKMHz++oO719Xo9W7t2rdfatWu9Nm/e7H716lWHsWPHag4fPnxD\nKpVaraukrKyMTZ48+YmKigr2zjvvZLi5uTU58/8waPFdQghpR3Iq9EjIMy/X+ENeEQoMRggZ0M9R\nhr929kKEixLdZRIwbgIOzAbObwOemQtELKbQTkgraelMdpUNr08LLtHm19sFTebkXDFl2dqr1rhH\nSyQlJckBYMyYMYWNjbt48aIUAEaMGFFvljkkJKTcw8OjIj09XZybmyt0dXWtDrRyudwYFBRUXvcc\nHx+fCgDQaDTVuTUgIEDfv3//wtOnTyuTkpIkYWFhOgDYsWOHY0FBgXDmzJnZIlH91bCkUinnnCeZ\nTCakpqaKDh48qPzggw/UvXr1evK///3v9cDAwIqH/gN5AIPBgKioqCd++eUX+ahRo/KXLl1q8QOv\nD0Iz7oQQ0oaZOMf5wlKsup2F3527hpAfUzD7yl2c1hZjhKsjvuzpj8sDg7Cnd1e86eeBJ+UO5tC+\n90/m0P7cQgrthLQT/cdNTheKRLWWIxSKRKb+4yan26qmmoqKioQA4O/v32i4rRrn6+vb4Coqbm5u\nesDcqlLzuFKpbHBWuqo/3GCo/cnDtGnTNAAQExPjUnVsy5YtrgAwc+bMem0yNQkEAjzxxBP6t956\nS/PNN9/cvHPnjmTWrFm+jZ3zMAwGA1566aUnDh065Dxy5Mj8PXv23BIIrBe3H3glxtg/GGNeVruT\n+ZrjGGOTrXlNQgjpaLR6A/bdz8dbV1IR/GMKfpd0DavvZEHAgPlPeOJweDckP90TnzzpixfcneBY\nc+dSowHY/RpwYQcwZBEw5D0K7YS0E70iR+YNfuWPqTIn5wrAPNM++JU/praVB1MVCoURAO7cuVPv\nU4GGxqWlpTW4AUROTo4IAFQqVYvaR6ZOnZovl8uNsbGxLgaDARkZGXYnTpxQBgYGlg0YMOChHzSN\niIgoUSgUxsTEREVL6tHr9RgzZkzngwcPql544YW8ffv23Wpo1r8lGmuV+ROAGYyxLwF8wTm/3Jwb\nVG7aFAVgAYCeAJY25zqEENJRcc5xpURXvQnS2cISGDngZCfEEJUCQ12UGKxSwkXcRHejUQ/ERgOX\n9wIRS4Bn5z6aH4AQYjW9IkfmtZWgXldYWFhxSkqKdP/+/crevXvX63GvEhQUVHr58mXpkSNHFD17\n9qzV+nLp0iX77OxssVqtrqjZJtMccrmcjxo1Kn/nzp2u+/btU6akpEiMRiObPHmyRRtl5efnC0pK\nSoRSqbTZ9eh0OjZ69OjOcXFxTi+99JLmu+++uyMUCps+0UKNzd2/BqAAwFswb7Z0jjE2jzHWjzHW\n6DstxpgvY2w8Y2wrgGwAmwEEAYiFeS14Qgh5rJUYjPhvTgHmX01D2E+X8fzZq/joViZKjCa85euB\nA326IuWZIHze0x9RnqqmQ7uhAvjuVXNoH/YRhXZCiNXNnj07RygU8jVr1ngnJSXVW/O8alWZ6Ojo\nXABYtWqVV0ZGRvVfXgaDAXPmzPExmUyYMmWKVVZwmTFjRi4AbN682WXHjh0uQqGQR0dH13vjc/r0\naYfc3Nx6SVqn07GZM2f6mkwmDBkypN7DrA+jrKyMjRgxIiAuLs5pwoQJua0V2oFGZtw55zGMsa8B\nLATwJoA+AHpXvqxnjF0FkAMgD0A5AGcAKgCdAVQ9KVz1+WwCgEWc8zNW/wkIIaQd4JzjZll59az6\nGW0JKjiHXCjAcyoF3nFR4nmVEp72zfhY1VAOfPsKcO0QMGIF0H+W9X8AQshjLywsTLdixYq7CxYs\n8BswYECPoUOHagMCAso1Go3wwoULMplMZkxMTLwWGRlZMmvWrKwNGzZ4BgcH9xw5cmS+TCYzxcfH\nK69fv+7Qp0+fYms9sDls2LASX1/f8kOHDjkbDAY2ZMiQArVabag7LiYmxvWbb75x7devX5GPj0+F\nk5OTMTMzU3Ty5Ellbm6uyN/fX/fZZ5/da04N06ZN8zt+/Lijk5OTwdvbWz9//nzvumOef/75otGj\nR7d4SchGp3A456UAljDGPgYwCcAfATwFQAwguOZQ/BbSq9wH8A3MbTa/trRQQghpb8qMJvykLcax\nyrCeqjM/z9VNKsFMH1dEuCjRz1EGcUseXNLrgJ1TgRtHgVGrgb7RVqqeEELqmzdvXm5oaGjZypUr\nPc+cOaM4evSok7OzsyEwMLCsavYbAD7//PP03r17l37xxRfuu3fvdjEYDKxTp07lCxYsSF+yZEm2\nRCKx2tKLEydO1KxcudIbAKZPn95gm8ykSZPyiouLBb/88ov8/Pnz8tLSUqFMJjN26dKl7I033sie\nP39+jkKhMDV0blPu3r1rDwBardZu3bp1D3w+1BrBnXFu2Z8bY0wJ4BmYA7w3zLPrEgAamGfgLwM4\n0RbCenh4OD937pytyyCEPEbulpUjrnK5xh/zi1Bm4nAQMAx0NveqP69SwNfBvukLPYyKUmDH74Fb\nPwAvfAKEvWKd6xLShjHGkjjn4Y/6vsnJyXdCQ0Mt6p0mpLmSk5NdQ0ND/eset3gdd855IYD/VH4R\nQshjrcJkws8FJdWz6tdLzc9h+UnE+L2XCyJclBjgJIeD0Mqr71aUAF9PBO6cAsb+E+j1e+tenxBC\nSJtDGzARQoiFssr1iNcUIi6vEMfzilBsNEHEGAY4yTDV2wVDXZTo7GAP1lrLMJYXAdsnAGlngJc3\nAiETWuc+hBBC2hQK7oQQ0gQj5/ilsLR6Vv1SsXl5YG97EV7ycEaESolnneWQ2bXOKgK16AqB7eOA\ne+eAqBggKKr170kIIY+RgwcPKuLj45tc093JycmwePHi+4+ipioU3AkhpAG5FQb8kGcO6j/kFSHf\nYISQAX2VMizq7IWhLkp0l0lab1a9IWVaYNvLQGYyMP4roMeLj+7ehBDymIiPj1esXbu2yU1Ivb29\nKyi4E0KIDZg4x8XiMsRpCnFMU4jzhaXgAFxFdoh0VSLCRYnnnBVwEtnor83SPGDrS0B2CjBhC9B9\nlG3qIISQDm7NmjUZa9asybB1HQ2h4E4IeWwV6A04nl+MOE0h4vMKkVNhAAPQSyHFO/6eiHBRIkTh\nAMGjnFVvSIkG2PoikHMVmLQd6DbctvUQQgixCQruhJDHBuccv5boqmfVzxaWwMgBJzshBqsUiHBR\nYrBKATdxMzZBai3FOcCWF4G8m8Dkb4AuQ21dESGEEBuh4E4I6dBKDEac0hZX71iaXq4HAPSUS/D/\nOrljqIsSfZQy2AlsPKvekKJsYMsYID8V+P1OoPNgW1dECCHEhii4E0I6nFul5dWz6j9pi1HBOWRC\nAZ5zVmCuvxLPuyjgZS+2dZmNK8wENr8AFGYAU3cB/s/YuiJCCCE2RsGdENLu6Ywm/KQtRlzlKjC3\nyyoAAF2l9viDjysiXZTo5yiDWGDlTZBaS8E9c2gvvg9MjQX8Bti6IkIIIW0ABXdCSJsWm5WHj29l\nIr1cD7W9CO929kKUpwppugrzJkiaQpzML0aZyQSJgGGgkwJ/9HFDhIsSfg72ti7fcvmp5tBelg9M\n2wt06mvrigghhLQRzQ7ujLExAIYD8APgwDmPqPGaDEAoAM45/6nFVRJCHkuxWXl452oaykwcAHCv\nXI8//3oXH9zMQFaFAQDQSSLGJC8Vhroo8bSTHA7CdjKr3pC82+bQXl4ITN8LqMNsXREhhJA2xOLg\nzhjrBGA3gD5VhwDwOsPKAXwDwIcx1otzfrFFVRJCHjs6ownv38ioDu1VDBzINxjxfoA3IlyU6CK1\nf7SbILUWzU1zaNeXAq8cALxCbV0RIYSQNsai4M4YkwI4AiAQwD0AewH8AYC05jjOuYExFgNgKYAX\nAVBwJ4Q0KrfCgHMFJfi5oARnC0qQXFSKCl53TsCswsQxy9f9EVfYinKumUO7SW8O7Z7Btq6IEEJI\nG2TpjPv/gzm0/wLgOc55CWNsPOoE90r7YA7uwwB82KIqCSEdCuccN0rLcbZGUL9ZVg4AEDOGEIUD\nZvq44tusPGj0xnrnq+3b0DrrLXX/CrB5DAAOvHIQ8Ohh64oIIYS0UZYG93Ewt8XM5ZyXNDH2EgAD\ngG7NKYwQ0nHojCYkF5VWh/RzhSXIqwzkKpEQ4UoZJnmp0M9RhlCFFJLKPvUguUOtHncAcBAwvNvZ\nyyY/h9VlXTJvriQQmkO7W6CtKyKEkMfG/PnzvVatWuUNAHv27Lk2duzYouZcJzc3V/jJJ5+4Jicn\nS1NSUqSpqakSo9HYoms+iKXBPRCAEcCPTQ3knJsYYwUAnJtTGCGk/Wqs7aWzgz0iXRzxlKMMfR1l\njfaoR3mqAKDBVWXavcwL5tBuJzG3x7h2sXVFhBDy2Dh16pR03bp1XlKp1FRaWtqiVQ2uXbsm/vDD\nD30AwMPDQ+/k5GTQaDStsnKjpRe1B1DGOa//2XXDZDA/qEoI6aAaa3sRMYbQyraXfo4yhDvK4Ca2\nrM0lylPVMYJ6Tem/AFtfAuwVwCv7AVVnW1dECCGPjdLSUvbqq68+ERQUVOrv76/bu3evS0uu17Vr\n14q9e/de69+/f6mHh4cxKirKf/fu3S265oNY+g7jPgA5Y8ypqYGMsVAAEpgfYiWEdBA6owmJ2mJ8\nlpqN6RduoeePl/Dsz79i7tU0HM4tQIDUHos6e2Ff7y64/mwwDoZ1w5IuavzOzcni0N4h3TsHbBkL\nSJTAq99TaCeE1FJ8JkOV8VFi8L2/nAzL+CgxuPhMRpubuUhISJCOGjWqs7u7e4hYLO7j5uYWMnDg\nwK4xMTG1uixiYmKcw8PDAxUKRS+JRNKnW7duPd59913PsrKyeh+zqtXqYLVaHVxUVCR4/fXXfby8\nvILFYnEfX1/foEWLFnmaTKbqsceOHZMxxsKGDRsW8KAaO3fu3FMsFvfJzs4W1n3trbfe8klPTxdv\n3rz5tsAKG/O5ubkZX3zxxSIPD4+HndhuNktn3E8DmFD5tbGJsYtg7oc/3oy6CCFtxMO0vfSr0fYi\n6AhLM7aWu2eAbeMAmau5Pcapk60rIoS0IcVnMlTag7f9YDAJAMBUVCHWHrztBwDy/t55tq3ObPXq\n1a4LFy70EwgEPCIiQhsQEFCek5Njl5ycLNu4caN7dHR0PgC8+eab6vXr13s6OTkZxowZkyeXy03x\n8fGOy5cvV8fFxTmePHnymr29fa2lw/R6PRs8eHDX7Oxs8ZAhQwqFQiE/fPiw07Jly9Q6nY6tXr06\nEwCGDh1a4u/vr0tISHDMysoSenp61grMCQkJ0tu3b0uGDx+eXzdMHzhwQPHVV1+5L126NC0kJKTd\ndYVYGtw3AJgI4H3G2CnO+eW6AyqXjFyJ3x5k3dDiKgkhj0Rrt7081u78CGwfDyi9zKFd6W3riggh\nVpK361onfVZJQyvsWUSfWSKDkdee/TCYBNoDt/xLzmW7teTaIk9ZqWpct7SWXCMpKUmycOFCX5lM\nZoyLi/s1PDxcV/P1mzdvigDzjPj69es9PT09KxITE6/4+voaAECv198bPnx4l4SEBMclS5Z4LF++\nPKvm+Tk5OaInn3yy9Pjx45fkcjkHgPT09Izu3bsHbdy40WPZsmVZVWF/0qRJmuXLl6s3bdqkeu+9\n93JqXmfTpk2uADB9+nRNzeMajUb4+uuv+4eFhRUvWrTofkv+LGzFos8HOOfHAfwLgCeARMbYDpj7\n2MEYm88Y2wIgDcCsylPWcc6TrVgvIcSKdEYTftYW4x/U9tK6bh0Hto8DHH3M7TEU2gkhDakb2ps6\n/oh9+umnbkajkc2dOzejbmgHgICAAD0AxMTEuALAvHnzMqtCOwCIRCKsW7cuTSAQYNu2bQ2+EVm/\nfn1aVWgHALVabYiMjNQWFxcLL1y4YF91PDo6WiMQCPD111+71jxfp9OxAwcOqFQqlWH8+PEFNV+L\njo7upNVq7azVImMLzXnidRaAEgBvwdwyA5hn1pdX/rpqJ9U1AOa3tEBCiPVQ24sN3IgDdvze3Ms+\nfR8g70AbRxFCAAAtncmukvFRYrCpqEJc97hAIa7weLP3VWvcoyWSkpLkADBmzJjCxsZdvHhRCgAj\nRoyotxRiSEhIuYeHR0V6ero4NzdX6OrqWt3KIpfLjUFBQfXaV3x8fCoAoOZKLQEBAfr+/fsXnj59\nWpmUlCQJCwvTAcCOHTscCwoKhDNnzswWiX6bYNq8ebPT3r17XT7++OO7PXr0qLD4h28jLA7ulSvK\nzGGMfQkgGsBAAN4AhACyYF4q8kuaaSfEtqjtpQ24dgTYORVw7WoO7TLXps8hhDy2lBGd0mv2uAMA\n7AQmZUSndBuWVa2oqEgIAP7+/o0G36pxvr6++oZed3Nz02dmZorz8vJqBXelUtngw512dua4ajAY\nas0mTZs2TXP69GllTEyMS1hYWDoAbNmyxRUAZs6cWd0mk52dLZwzZ45f//79ixYsWFCrraa9afYa\nk5zzFABvW7EWQkgL6IwmXKjc5OjnOpscOdsJ0dex4U2OSCu5egj4djrg/iQwbS8gbXMLQxBC2piq\nB1AL49LUpqIKsUAhrlBGdEpvKw+mKhQKIwDcuXNH7OzsXK9Vpu64tLQ0Uc+ePevNoOfk5IgAQKVS\ntWgVlqlTp+bPnz/fNzY21uWzzz5Lv3//vt2JEyeUgYGBZQMGDCirGnfz5k2xVqu1O3PmjEIoFIY1\ndK2XXnqpGwAsXbo0bfHixW22/71VFocnhLQ+antpw64cAL57FfAMAabtBhxoHzpCyMOR9/fOaytB\nva6wsLDilJQU6f79+5W9e/d+YHAPCgoqvXz5svTIkSOKusH90qVL9tnZ2WK1Wl1Rc7a9OeRyOR81\nalT+zp07Xfft26dMSUmRGI1GNnny5Nya49zd3Q0TJkzIbegaiYmJitTUVPtBgwYVeHp66kNCQsoa\nGtdWUHAnpB2gtpd25NJuIDYaUIcBU3cBEkdbV0QIIVYxe/bsnO3bt7utWbPGe/To0YVVfeVVbt68\nKQoICNBHR0fnfvvtt66rVq3ymjhxotbb29sAAAaDAXPmzPExmUyYMmWKVVpWZsyYkbtz507XzZs3\nu9y4cUMiFAp5dHR0rTc+Xbp00e/cuTO1ofOjoqL8U1NT7d9+++3ssWPH1uvJb2uaHdwZY08DCAHg\nDKDRlMA5/3tz70PI46hm28vZQnNQr9n2Ek5tL23The+APa8BnZ4Cpnxn3hmVEEI6iLCwMN2KFSvu\nLliwwG/AgAE9hg4dqg0ICCjXaDTCCxcuyGQymTExMfFaZGRkyaxZs7I2bNjgGRwc3HPkyJH5MpnM\nFB8fr7x+/bpDnz59ipcuXZptjZqGDRtW4uvrW37o0CFng8HAhgwZUqBWqw1Nn2ldr732mk/Vw7Nn\nz56VA8CqVas8t27d6gIAY8eO1U6bNk3b0vtYHNwZY78D8E8AvhacRsGdkEZQ20sH8L9vgH1/AvwG\nApN3APZyW1dECCFWN2/evNzQ0NCylStXep45c0Zx9OhRJ2dnZ0NgYGDZjBkzqttRPv/88/TevXuX\nfvHFF+67d+92MRgMrFOnTuULFixIX7JkSbZEIuGN3ccSEydO1KxcudIbAKZPn95gS0xr+/77750z\nMjJqrQj0448/lNmbaAAAIABJREFUKqt+7efnV2GN4M44f/g/N8bY8wAOw7yCDADcAJANoNF3Npzz\nIc0tsCXCw8P5uXPnbHFrQh7oYdpe+jrKqO2lPfllK7D/LaDzc8CkbwBxi/dhIYQ8AGMsiXMe/qjv\nm5ycfCc0NNQmoZA8fpKTk11DQ0P96x63dMZ9Ccyh/SyAyZzzW1aojZAO7WHbXvpWtr04UNtL+3Ju\nE3DwbaDLUGDiNkDkYOuKCCGEdFCWBvc+MG+u9HsK7YQ0jNpeHiOJG4FD84Guw4EJWwCRxNYVEUII\n6cAsDe56AEWc85utUQwh7Q2t9vIY+2k9cPg9oPtoYNxXgF29zQ4JIYS0Q1u3bnU6f/58kz2P/v7+\n5bNnz9Y0Nc6aLA3uVwCEM8YknPMHrt9JSEdFbS8EAHBqHXBsCdDjRSDqX4CQ3pARQkhHsXfvXqfd\nu3e7NDWub9++xW09uG8AsBnAVAAx1i+HkLaF2l5IPSdWAvEfAkFRwEsbASFth0EIIR1JbGzsHQB3\nbFxGgyz6Pw7nfCtjLALAJ4yxYs75jlaqi5BHjnOOm2Xl+FnbcNtLiMIBM3xc8RS1vTyeOAd+WA4c\nXw6ETARe/CeFdkIIIY+Uxf/X4Zy/yhi7A2A7Y+xjAOcANLbTFOecz2xmfYS0Gmp7IQ+NcyD+A+Dk\naqDXFGDMZ4BA2PR5hBBCiBU1ZwOm1wDMqfytX+VXQzgAVvmdgjtpdbFZefj4VibSy/VQ24vwbmcv\nRHmqql+nthfSLJwDRxcDpz8F+rwCjF4HCOhNHCGEkEfPouDOGHsR5j53ACgB8BMeYgMmQlpbbFYe\n3rmahjKTOYjfK9dj7tU0nNYWwwTgZ23DbS9VQZ3aXkiDODevHHPmn0DfaOB3Kym0E0IIsRlLZ9wX\nVH7/L4CJnPPGWmQIeWQ+upVZHdqrlJs4tmfmUdsLaR6TCTi0ADj7JfDUG8CIjwH6FIYQQogNWRrc\ng1DZ+kKhndhKkcGIi0VlSC4qxf+KSpFcVIqMcn2DYxmAlGeCqO2FWMZkAr5/G0j6N/D0W0DkBxTa\nCSGE2FxzNmAq4JxntkYxhNRVajThUlEpkiuDenJRKW6UlqNqbl1tL0IvpRR5egMKDaZ656vtRRTa\niWVMRuDAbOD8NuCZuUDEYgrthBBC2gRLg3sygEGMMQXNuBNr0xlNuFxSZg7pheaQfrVEh6o47i62\nQy+FFC95OCNUIUWIwqG6N71ujzsAOAgY3u3sZYOfhLRbJiOw90/AhR3Ac38BBv+FQjshhJA2w9Lg\n/gmAIQD+H4Dl1i+HPC4qTCZcLdGZW10KzbPpV0rKYKjM3SqREL0UUoxwdUQvpRShCik87R/8AGnV\n6jGNrSpDSKOMBmDPa8ClWGDIX4Hn5tu6IkIIIaQWSzdg2s8Y+zuAvzPzLNQnnPOyVqmMdBgGE8f1\n0sqQXjmbfrmkDOWVs+OOdkKEKhzwRif36pCutheBWTjTGeWpoqBOmseoB2JnApf3AUPfB55529YV\nEUIIIfVYuhxkfOUvSwB8BOBvjLHLaHoDpohm1kfaGRPnuFlaXt2PnlxUhotFZSgzmRte5EIBQhRS\nzFC7IlQhRS+lFH4SscUhnRCrMVQAu/4A/HoQGPYR8PSbtq6IEELIIzB//nyvVatWeQPAnj17ro0d\nO7ZZbeCnT5922LVrl/MPP/ygTEtLE2u1WjtnZ2fDU089VbRw4cLsZ555ptRaNVvaKjO4zu8dAIQ1\ncQ5v4nXSTnHOkaqrwP8Kf1vd5WJRGYqN5pDuIGAIVkgx1VuFUIV5Jj2ANjYibYmhHPj2FeDaIeB3\n/wc89bqtKyKEEPIInDp1Srpu3TovqVRqKi0tbdEa0W+88YbfhQsXZD179iwdMWKEVi6XGy9evCg9\nePCg6tChQ86bNm26NX36dK016rY0uC+1xk1J+8M5x71yffVDo1Wz6QUGIwBAzBh6yh0wzlOFUIUD\neimk6CqVwE5AIZ20UfoyYOdU4MYxYNQaoC9t8EwIIY+D0tJS9uqrrz4RFBRU6u/vr9u7d69LS643\nYcKEvO3bt98OCgoqr3n8888/V/3pT3964s9//rPfhAkTCiQSSYsnsy3tcafg/pjIKteb10kv/C2k\na/TmDXLtGNBD5oAx7k6VM+kOCJRJIKYdJUl7UVEK7JgM3DoOvPApEPaKrSsihBAAwNmzZ1XHjx9X\nFxcXi+VyecVzzz2X3rdv3zxb11VTQkKCdNWqVZ5nz56Va7VaO0dHR0O3bt3K/vCHP+RGR0fnV42L\niYlx3rBhg/vVq1cd9Hq9wNfXVxcVFZW3ePHibAcHh1ohVq1WBwPAr7/+mvLOO+9479+/31mj0Yg8\nPT0rpk2blvvBBx9kCSpzxrFjx2SRkZHdIyMjtUeOHLnZUI2dO3fuee/ePfu0tLRkDw8PY83X3nrr\nLZ/09HRxYmLi5aVLl7Z4+blFixbdb+j4G2+8kbdixQrv1NRU+7Nnzzo8++yzLW6ZsXTGnbQzsVl5\nTa60klOhx4WishohvRTZFeaQLgAQKJMg0kWJUKU5pPeQOUBCO4+S9qqiBPh6InDnFDD2n0Cv39u6\nIkIIAWAO7YcPH/YzGAwCACguLhYfPnzYDwDaSnhfvXq168KFC/0EAgGPiIjQBgQElOfk5NglJyfL\nNm7c6F4V3N988031+vXrPZ2cnAxjxozJk8vlpvj4eMfly5er4+LiHE+ePHnN3t6+VnjX6/Vs8ODB\nXbOzs8VDhgwpFAqF/PDhw07Lli1T63Q6tnr16kwAGDp0aIm/v78uISHBMSsrS+jp6VkrmCckJEhv\n374tGT58eH7d0H7gwAHFV1995b506dK0kJCQWjPkrcHOzo7X/N7i61njIqRtqru2+b1yPeZdTcOV\nYh2UImH1jHp65a6jDEAXqT2edVZUr+7SU+4AKYV00lGUFwHbJwBpZ4CXvwRCxtu6IkJIB7B3795O\n9+/fl7b0OllZWTKTyVSrx9RgMAgOHTrkf/78ebeWXNvd3b107NixaS25RlJSkmThwoW+MpnMGBcX\n92t4eLiu5us3b94UAeYZ8fXr13t6enpWJCYmXvH19TUAgF6vvzd8+PAuCQkJjkuWLPFYvnx5Vs3z\nc3JyRE8++WTp8ePHL8nlcg4A6enpGd27dw/auHGjx7Jly7Kqwv6kSZM0y5cvV2/atEn13nvv5dS8\nzqZNm1wBYPr06ZqaxzUajfD111/3DwsLK37QLLk1xcfHy27evClxd3fX9+3b1yqrMFIi68A+upVZ\na0MiANCZOP6Rdh/LbmXicnEZ+jrKsCTAG7t7dcG1Z4Nx8qkn8Y8efoj2cUNfRxmFdtJx6AqArS8D\naYlA1L8otBNC2py6ob2p44/ap59+6mY0GtncuXMz6oZ2AAgICNADQExMjCsAzJs3L7MqtAOASCTC\nunXr0gQCAbZt29bgG5H169enVYV2AFCr1YbIyEhtcXGx8MKFC/ZVx6OjozUCgQBff/21a83zdTod\nO3DggEqlUhnGjx9fUPO16OjoTlqt1m7z5s23Ba3c3puTkyOcMWPGEwDw0UcfpdnZWWeu/IFXYYzd\nqvzlDc75sDrHLME55wHNKY48vPvlelwoLsPFypVdLhSXIqNyJr0uBuDKM0FwEtEHLuQxUaYFtr0M\nZCYD478Cerxo64oIIR1IS2eyq6xatSq4uLhYXPe4XC6veO21165a4x4tkZSUJAeAMWPGFDY27uLF\ni1IAGDFiRL3lFUNCQso9PDwq0tPTxbm5uUJXV9fqVha5XG6s+4AnAPj4+FQAgEajqQ4uAQEB+v79\n+xeePn1amZSUJAkLC9MBwI4dOxwLCgqEM2fOzBaJftu4cfPmzU579+51+fjjj+/26NGjwuIf3gKF\nhYWCESNGdElNTbWfNWtWVs2+/5ZqLLn5V37XNXDMErQcpBVVre5SHdCLynCp+LeedADo7GCPMKUM\nRYZCFBhM9a6hthdRaCePj9I8YOtYIPsyMGEr0H2krSsihJAGPffcc+k1e9wBwM7OzvTcc8+l27Ku\nKkVFRUIA8Pf3bzT4Vo3z9fVtcAbRzc1Nn5mZKc7Ly6sV3JVKpbGh8VWz1QaDodYnD9OmTdOcPn1a\nGRMT4xIWFpYOAFu2bHEFgJkzZ1a3yWRnZwvnzJnj179//6IFCxbUaquxtsLCQsHQoUO7/vLLL/Lo\n6Ojszz//3Kr/7BpLb3+o/F7QwDHyCJg4x+2y8loB/WJRGfIrl2AUAOgmk2CQSoEQuRRBCgcEyR2g\nsBMCqN/jDpjXVn+3c4sfoCakfSjRAFteBHKvAZO+BroNs3VFhBDyQFUPoLbVVWUUCoURAO7cuSN2\ndnau1ypTd1xaWpqoZ8+e9WbQc3JyRACgUqkaDOoPa+rUqfnz58/3jY2Ndfnss8/S79+/b3fixAll\nYGBg2YABA6p7ym/evCnWarV2Z86cUQiFwgb3H3rppZe6AcDSpUvTFi9e3Kz+9/z8fEFkZGTXpKQk\n+axZs7KsHdqBRoI753zzwxwjD6ep1V0MJo7rpbpaAf1icRlKKjczEjOG7nIJRrk5IUjhgBC5A7o3\n8eBo1fWbWlWGkA6pOAfYMgbIuwVM/gboQhs4E0Lavr59++a1laBeV1hYWHFKSop0//79yt69ez8w\nuAcFBZVevnxZeuTIEUXd4H7p0iX77OxssVqtrqg5294ccrmcjxo1Kn/nzp2u+/btU6akpEiMRiOb\nPHlybs1x7u7uhgkTJuQ2dI3ExERFamqq/aBBgwo8PT31ISEhzXqIVKPRCCMiIromJyfL3nrrrcxP\nP/00oznXaUqb65dgjEkAnABgD3N9uzjnSxhjTwDYAUAF4BcA0zjnrdqjZC0Nre4y99c0nMovhkjA\ncLGoDFdKyqCrfN1BIECQ3AETPFUIrgzp3Zq5TnqUp4qCOnn8FGUBm8cA2rvA73cCnQfbuiJCCGn3\nZs+enbN9+3a3NWvWeI8ePbqwqq+8ys2bN0UBAQH66Ojo3G+//dZ11apVXhMnTtR6e3sbAMBgMGDO\nnDk+JpMJU6ZMsUrLyowZM3J37tzpunnzZpcbN25IhEIhj46OrvXGp0uXLvqdO3emNnR+VFSUf2pq\nqv3bb7+dPXbs2Ho9+Q8jJydHOGTIkG4pKSnSefPmZaxatSqzOdd5GBYFd8bYJgBazvnchxz/fwBc\nOOeWbElYDuB5znkxY0wE4BRj7BCAuQDWcs53MMY2AJgJ4HNL6reFEqMR79/IqLe6Sznn+CYrD0o7\nAYLlUryidkWI3AHBCikCpPYQsjbxADkh7U9hBrD5BaAwE5i6C/B/xtYVEUJIhxAWFqZbsWLF3QUL\nFvgNGDCgx9ChQ7UBAQHlGo1GeOHCBZlMJjMmJiZei4yMLJk1a1bWhg0bPIODg3uOHDkyXyaTmeLj\n45XXr1936NOnT/HSpUuzrVHTsGHDSnx9fcsPHTrkbDAY2JAhQwrUarWh6TOtZ/To0QEpKSnSTp06\nlZtMJjZ37lzvumPGjRuX//TTT7d4SUhLZ9xfBZAFc4h+GOMB+MIcsh8K55wDKK78rajyiwN4HkDV\nTimbAbwPGwT3xlpeCvQGXCwuq25zuVhUihul5Q98OpcBuPpMMBiFdEKso+Ae8O/RQEkuMG034Nvf\n1hURQkiHMm/evNzQ0NCylStXep45c0Zx9OhRJ2dnZ0NgYGDZjBkzqttRPv/88/TevXuXfvHFF+67\nd+92MRgMrFOnTuULFixIX7JkSbZEIrHa4iUTJ07UrFy50hsApk+f3mBLTGu6d++ePQCkpaXZr127\ntsEHCf39/cutEdyZOSc/5GDGTACyOOf13kk8YPwdAJ0450KLimJMCCAJQBcA6wGsBHCGc96l8vVO\nAA5xzoMaOPc1AK8BgK+vb1hqaoOfjDRLQw972jEgSO6APL0Rd3W/de5424sQrHBAsFyKr9JzoNHX\nb+PysRfh3NM9rVYfIY+1/FTzTHtZPjB1N9Cpr60rIoS0AsZYEuc8/FHfNzk5+U5oaOgjD4Xk8ZSc\nnOwaGhrqX/d4a/e4uwIotfQkzrkRQC/GmBOAPQCebGjYA87dCGAjAISHh1t1KcqPG9jQyMCBi8Vl\nGOnqhGneLghWOCBILoWr+Lc/2iccxLS6CyGtKe+Wuae9vBCYvg9Q97F1RYQQQojVtUpwZ4w5AogG\nIAVwsbnX4ZxrGWM/AOgPwIkxZsc5NwDwAdAqT+s2Jv0BGxqZOPBlkP8Dz6PVXQhpRZqb5vYYQxnw\nygHAK9TWFRFCCCGtotHgzhhbAmBxncMejLGHXb6HA9hlSUGMMTcA+srQ7gBgKIAVABIAjIN5ZZlX\nAOyz5LrWoLYX4V4D4V1tL2pgdG20ugshrSDnmrk9xqQHXjkIeNbrniOEEEIssnXrVqfz589Lmxrn\n7+9fPnv2bE1T46zpYWbcaz45yev8vjEVALYCWG5hTV4ANlf2uQsAfMs5P8gYuwxgB2PsQwDnAfzL\nwuu22LudvajlhZC24v4Vc3sMALz6PeDeUEcdIYQQYpm9e/c67d6926WpcX379i1ua8H93wB+qPw1\nAxAPIA9AVCPnmAAUArjGObf46VnO+QUAvRs4fgtAP0uvZ03U8kJIG5F1yby5kkBkbo9x62briggh\nhHQQsbGxdwDcsXEZDWo0uHPOUwFUL8vCGLsLIJtzfry1C2urqOWFEBvLTAa2vAjYOQCvHgRcAmxd\nESGEEPJIWPRwKufcv5XqIISQhl34Foj7u3mNdrk7oCsEZK7AK/sBVWdbV0cIIYQ8Mq29HCQhhDTf\nhW+BA7MBfWXXXXE2AAb0/xOFdkIIIY8dga0LIISQB4r7+2+hvRoHzvzTJuUQQgghtkTBnRDSdhXc\ns+w4IYQQ0oFRcCeEtF0y14aPO/o82joIIYSQNoCCOyGkbbqZAJRqUW/rCJEDEFF3XzhCCCGk46Pg\nTghpe64fA76eCLgHAqNWA46dADDz9xc+BUIm2LpCQggh5JGjVWUIIW3LtcPAzqmAWyAwfT8gVQF9\nZ9q6KkIIIcTmaMadENJ2/PofYMcUwL3Hb6GdEEIIIQAouBNC2orL+4FvpwFeIcD0fRTaCSGEWBVj\nLOxBX6Ghod2be93y8nL2wQcfuI8bN86/e/fuPUQiUR/GWNiaNWsesMJC8z2wVYYxNshaN+Gcn7DW\ntQghHVDKHmDXTEAdBkzdBUgcbV0RIaQZCg4cwP2162DIzISdlxfc354DxxdesHVZhFTz9vaumDhx\noqbucR8fn4rmXrOoqEiwePHiTgDg4uJicHV11WdlZYlbUueDNNbj/gMAboV78CbuQwh5nF3cBex+\nDejUD5jyHWCvsHVFhJBmKDhwAJl/Wwyu0wEADBkZyPybeQUoCu+krVCr1RVr1qzJsOY15XK5aefO\nndefeuqpMj8/P/3cuXO9165d62XNe1RpqlWGWeGL2nEIIQ1L3gHs/iPgOwCYsotCOyHt2P2166pD\nexWu0+H+2nU2qqh9undvu+rkqQHBcfFdwk6eGhB87972Ntc3mJCQIB01alRnd3f3ELFY3MfNzS1k\n4MCBXWNiYpxrjouJiXEODw8PVCgUvSQSSZ9u3br1ePfddz3LyspY3Wuq1epgtVodXFRUJHj99dd9\nvLy8gsVicR9fX9+gRYsWeZpMpuqxx44dkzHGwoYNGxbwoBo7d+7cUywW98nOzhZa9YdvgEQi4RMm\nTCj08/PTt/a9HjgTzjlvMHAzxl4AsBmABsD/AYgHcA/mmXUfABEA3gHgBmA65/yglWsmhHQE57cB\n+94EnngWmLwDEMtsXREhpBm4yYSSn36CIaPhSUxDZuYjrqj9undvu+r6jY/8TKZyAQBUVNwXX7/x\nkR8A+PhMybNtdWarV692XbhwoZ9AIOARERHagICA8pycHLvk5GTZxo0b3aOjo/MB4M0331SvX7/e\n08nJyTBmzJg8uVxuio+Pd1y+fLk6Li7O8eTJk9fs7e1rdXbo9Xo2ePDgrtnZ2eIhQ4YUCoVCfvjw\nYadly5apdTodW716dSYADB06tMTf31+XkJDgmJWVJfT09DTWvE5CQoL09u3bkuHDh+d7eHjUeq2w\nsFC4bt06l6ysLJGjo6OxX79+pRERESWt/edmLRa1sDDG+gD4FkAigN9xzsvqDLkF4BZjbCuA/wL4\njjE2gHP+P6tUSwjpGJL+DRz4MxDwPDDpa/OmSoSQdkWfkQHtnj0oiN0NfUYGwBjA63fY2nm1SsdA\nm3L5ysJOJcXXpC29TlHxFRnn+lqz0SZTueDa9Q/8MzN3ubXk2jJ5t9IeT65Ia8k1kpKSJAsXLvSV\nyWTGuLi4X8PDw2t9xHLz5k0RYJ4RX79+vaenp2dFYmLiFV9fXwMA6PX6e8OHD++SkJDguGTJEo/l\ny5dn1Tw/JydH9OSTT5YeP378klwu5wCQnp6e0b1796CNGzd6LFu2LKsq7E+aNEmzfPly9aZNm1Tv\nvfdeTs3rbNq0yRUApk+fXq+X/erVqw5vv/22f81jgYGBZVu2bLndr1+/urm2zbG0jeUvAMQAZjUQ\n2qtxznUA3gBgX3kOIYSYnY0xh/YukcCkbyi0E9KOmCoqUPjf/+Ju9B9xI2Iocj/7B8T+fvBevQqe\nH30EJpHUGs8kEri/PcdG1bY/dUN7U8cftU8//dTNaDSyuXPnZtQN7QAQEBCgB4CYmBhXAJg3b15m\nVWgHAJFIhHXr1qUJBAJs27atwTci69evT6sK7QCgVqsNkZGR2uLiYuGFCxfsq45HR0drBAIBvv76\n61ort+h0OnbgwAGVSqUyjB8/vqDma9HR0dlHjhz5NSMjI1mr1Z4/fvz4lREjRuRfvXrVYfjw4d1u\n374tau6fzaNi6UOjzwAo5Jz/2tRAzvkVxlgBAKutTkMIaecSvwAOLQC6jQAmbAHs7Js+hxBic+XX\nr0O7KxYF+/fDmJ8PO09PuL7xBhxffhliH3X1OIHI7rFcVaalM9lVTp4aEFxRcb/eaiRisXtF3757\nrlrjHi2RlJQkB4AxY8YUNjbu4sWLUgAYMWJEUd3XQkJCyj08PCrS09PFubm5QldX1+pWFrlcbgwK\nCiqve07Vii8ajaY6twYEBOj79+9fePr0aWVSUpIkLCxMBwA7duxwLCgoEM6cOTNbJKqdw7/88st7\nNX8/aNCg0kGDBt0aMWJE58OHDzt/+OGHnv/617+s8s+ytVga3J0BgDEm4JybGhvIGBMAkFR+EUIe\ndz+tBw6/B3QfDYz7CrBrlZWyCCFWYiwuQeGh/6BgVyzKkpMBkQiK55+H07goyJ5+GkxY/5k/xxde\neCyCemt5wv/N9Jo97gAgENibnvB/M92WdVUpKioSAoC/v3+jSydWjfP19W3wYU03Nzd9ZmamOC8v\nr1ZwVyqVxobG29mZ46rBYKj1ycO0adM0p0+fVsbExLiEhYWlA8CWLVtcAWDmzJn12mQeZNasWTmH\nDx92PnPmjPxhz7EVS1tl0mFulRn7EGPHwtwq0yb+ZSOE2NCPn5hD+5NjgPH/ptBOSBvFOUfpL78g\n471FuD5oELL+thjGkmK4L1yIrsd/gM8n6yB/9tkGQztpOR+fKXlduyxKFYvdKwAGsdi9omuXRalt\n5cFUhUJhBIA7d+40+pd41bi0tLQGW09ycnJEAKBSqRoM6g9r6tSp+XK53BgbG+tiMBiQkZFhd+LE\nCWVgYGDZgAEDHrpf3cPDwwAApaWlbX4lREtn3PcAmAdgI2Msj3P+Q0ODKjdv2gjzSjN7WlQhIaR9\nO7EKiP8A6Pky8PJGQNjmWwgJeewYNBoU7N0HbWwsKm7dApNKoRz5OziPGwdJaCgYaxMt1o8FH58p\neW0lqNcVFhZWnJKSIt2/f7+yd+/e9XrcqwQFBZVevnxZeuTIEUXPnj1rtb5cunTJPjs7W6xWqytq\nzrY3h1wu56NGjcrfuXOn6759+5QpKSkSo9HIJk+enGvJdU6dOiUDAF9f33ptOm2Npe8sPgJwF4AK\nQBxj7ARj7H3G2B8ZY9GVvz4OIKFyTFrlOYSQx9EPK8yhPXgC8PKXFNoJaUO40Yji48dx763ZuP7c\nYNxfuRJCR0d4ffQhup08Ae8PP4RDr14U2km12bNn5wiFQr5mzRrvpKSkeq3QVavKREdH5wLAqlWr\nvDIyMqoniQ0GA+bMmeNjMpkwZcqUnLrnN8eMGTNyAWDz5s0uO3bscBEKhTw6OrreG59Tp05JCwsL\n6+XexMREh2XLlqkBYPLkyW3yDVNNFs24c861jLHBAL4DEAbzw6oD6wyr+i/8FwDjOefalhZJCGln\nOAcSlgEn/g8I/T3w4j8AAX20TkhbUJGWBm1sLAr27IUhOxtClQqqadPgNC4K9gEP3M+GEISFhelW\nrFhxd8GCBX4DBgzoMXToUG1AQEC5RqMRXrhwQSaTyYyJiYnXIiMjS2bNmpW1YcMGz+Dg4J4jR47M\nl8lkpvj4eOX169cd+vTpU7x06dJsa9Q0bNiwEl9f3/JDhw45GwwGNmTIkAK1Wm2oO27t2rXuhw8f\ndu7fv3+hWq2usLe359evX5ecPHnS0Wg0YtKkSbmvvfZas4P7e++953n16lUJAKSkpEgBYNu2ba4/\n/vijHAAGDhxYPHfuXIs+CWiIpa0y4JzfYYw9BSAKwCQA4QDcK1++D+AcgJ0AYjnnLfoIhBDSDnEO\nxP0dOLUG6D0NeOFTQNDm2wYJ6dBM5eUoOnIU2thYlJ45AwgEkD0zEB6L3oNi8GAwMT13Qh7OvHnz\nckNDQ8tWrlzpeebMGcXRo0ednJ2dDYGBgWVVs98A8Pnnn6f37t279IsvvnDfvXu3i8FgYJ06dSpf\nsGBB+pIlS7IlEkn9Rf+baeLEiZqVK1d6A8D06dMbDMdjx47VFhUVCX/99VeHM2fOKMvLy5mTk5Nh\n0KBBBTNnzsyZMmVKQUPnPaxjx445nj17ttbDrefPn5edP3++endBawR3xhvYLKGjCA8P5+fOnbN1\nGYQ8PjjNCXm1AAAgAElEQVQHji4GTn8KhP0BGLWGQjshNqS7csW8jOOBAzAVFkKkVsNpXBQcX3oJ\nIk9PW5fXLIyxJM55+KO+b3Jy8p3Q0NAWBy9CHkZycrJraGiof93jFs+4E0JIgzg3rxxz5p9A3z8C\nI1ead1IkhDxSxsJCFBw8iIJdsdBdvgwmFkMRGQmncVGQPvUUGL2ZJqTdalFwZ4y5AfADIOWcn7BO\nSYSQdodz88ZKP28EnnoDGPExhXZCHiHOOUp/Pgtt7C4UHT4CXl4O++7d4fHXv8Jx9CgInZxsXSIh\nxAqaFdwZY2MAvA8gtPIQr3ktxpgzgG8qfxvFOS9pQY2EkLbMZAL+Mw84twkY8CYw7EMK7YQ8Ivrs\n+yjYuxfa2Fjo796FQC6H48svwSlqHCQ9e9CKMIQ0w8GDBxXx8fGKpsY5OTkZFi9efP9R1FTF4uDO\nGPsLzEs8PvBvA855PmOsFMCLAEbCvAoNIaSjMZmAg38GftkCPPM2ELGEQjshrYzr9Sg+fhzaXbEo\nPnECMJkg7dsXbv/vT1AMGwaBg4OtSySkXYuPj1esXbvWq6lx3t7eFW06uFeuJvMRAAOABQC2AkjB\nb6vK1LQN5t1Tx4CCOyEdj8kI7H8L+N92YNB8YMgiCu2EtKLy27dREBsL7d59MObmws7NDS4zZ8Ip\n6mWI/f1tXR4hHcaaNWsy1qxZk2HrOhpi6Yz7nyu/f8w5/wRAYx/DHa/83rcZdRFC2jKTEdj7BnBh\nJzD4PWDwQltXREiHZCotReHhI9DG7kLZuSRAKIR88GA4RUVBPuhZMDtaY4KQx4ml/8U/U/n9H00N\n5JxrGGPFANQWV0UIabuMBmDPa8ClWOD5v5pn2wkhVsM5h+7SJWi/24XC77+HqaQEYj8/uM2bC8cX\nX4TIvaEPuQkhjwNLg7s7gCLO+cOuY6oHIG9yFCGkfTDqgdho4PJeYOj75r52QohVGPLzUXjgALS7\nYlF+7RqYRALl8OFwGhcFh/BwetCUEGJxcC8FIGeMCTjnpsYGMsaUAJwA5DS3OEJIG2KoAHb9Afj1\nIDDsI+DpN21dESHtHjeZUPLTTyiIjUXR0WPgej0kQUHwfP99KEeNhFDR5MIWhJDHiKXB/RrMPesh\nAP7XxNgomFeeSW5GXYSQtsRQDnz7CnDtEDBiBdB/lq0rIqRd02dkQLt7Dwp274Y+IwNCR0c4TZoE\np3FRkAQG2ro8QkgbZWlwPwCgH4C/AJj0oEGMsS4AlsO8vvveZldHCLE9vQ74dhrw/9m78/goq7v/\n/69rMjPZV8hGCISAAkGJSAChIJQlUDIuIVhwAa0bFRcUpWql6F3R3q0Vpd/epcXan4q0ognrhC0R\nQVBAQA1o2IclZJmsM1lnP78/BiibwEDgynKej4ePhGuumXknJpPPnOuczzm4Hsb/GQY+pnYiSWqV\nPA4H9Rs2YMnOoeGrr0AIgocMJvr5mYSOHo3G31/tiJIktXC+Fu7/D3gauEdRlCbgj2feqChKMt6C\nfhYQDhwB/tUMOSVJUoOzCT65Hw5/DoZ3Ie1XaieSpFbHfvAgluwcrCtX4q6pQRsXR8cnniB8wgT0\nnWX/BkmSLp9PhbsQolZRlLuAtcDUk/8BcLKDzKldHxSgCpgghLA3U1ZJkq4nRyN8ci+YNsGdf4Vb\np6idSJJaDXd9A7Wrc7Hk5GAr2A06HaEjRxIxMYvgIUNQ/PzUjihJUivkcwNYIcR2RVFuAf4MZAKa\nkzcFnToF7/SY54UQR5olpSRJ15ejAf49CY5ugbsXwC33qp1Iklo8IQRN332HJTuH2rVrEY2N6Ht0\nJ+bFFwm/6060UVFqR5QkqZW7op0bhBDH8E6XiQQGA50AP6AM+FoIITvJSFJrZa+Dxb+Eom0w4T3o\ne4/aiSSpRXNVVWFdvgJLTg4OkwlNUBDhGeOJyMoiIDVVtnGUJKnZXNWWa0KIGmB1M2WRJElttlpY\nPBFO7ISsf8JNWWonkqQWSbhc1G/Z4m3j+MVGcLkI7NeP+DfmEjZuHJrgYLUjStI1oShK/4vdPn/+\n/KPPPPNM1ZnHbDabsmDBgg4rVqyI+PHHH4OsVqtWp9OJxMRE+5AhQ+qmTZtWOWjQoKZzH8tqtWrm\nzp0bu3LlysiioiJ/RVGIj493DBgwoP6DDz447u/vL5r762vpfCrcFUWZA9QLIeZd5vnPABFCiN9f\nSThJkq4jmxUWTYDS7+Ge/w9S7lI7kSS1OI6iIiw5OViXLcdlNuMXFUXUlClETMzCv3t3teNJ0nXz\n3HPPlV7oeFpaWuOZ/969e7d/ZmZmD5PJFBAREeEaOnRobWJiosPhcCj79+8PXLx4cfQHH3wQs2jR\nokP333+/9dT99u/fr09PT7/x+PHj/v3796+fMmVKhRCC48eP61evXh1ps9mKZOF+aa/hnQ5zWYU7\n8BzQBZCFuyS1ZE01sCgTyn6AX34EvTLUTiRJLYbHbqdufR6WnBwat20DjYbgoT8j9pXfEjpiBIpe\nr3ZESbru5s2bV3Kpc4qKirTp6ek9zWaz7uGHHy6fP3/+iZCQkLOK7eLiYu1LL73Uqbq6+nRNarfb\nlbvvvrtHSUmJ/uOPPz6roAdwuVxoNBrao6uaKiNJUhvQWA0f3QUV+2DSx9BznNqJJKlFsO3d623j\nuGoVntpadJ07Ez3jGcIzM9HFxakdr0XKNeUy/9v5lDWUERccx4xbZ5CRLAcCfPFhcWXUvKNlCeUO\nlz5Gr3XMTIorfjChY7Xaua7ErFmzEsxms85gMFS///77RRc6JyEhwbVo0aLjTU1NpxeDLFiwIGrf\nvn2Bjz/+uPncoh1Aq22/5eu1/sqjANs1fg5Jkq5UQyV8dDdUHoDJ/4YbxqidSJJU5a6txWo0Ys3O\nwVZYiKLXEzpmDBETswgaNAilnY7yXY5cUy6vff0aNrf3z35pQymvff0agCzeL9OHxZVRcw4Vd7V7\nhAbA7HDp5xwq7grQ2or3+vp6ZdmyZR0A5s6de8nR+cDAwNMj8Z9++mkHgMcee6xy//79+uXLl4db\nLBa/Ll26ODIzM61xcXHua5e8ZbtmhbuiKPcAocD+a/UckiRdhfoK+OhOqDbBvf+BHqPUTiRJqhBC\n0PjNDiw52dStW4+w2/Hv1YvY2bMJN2TgFxGhdsQWTwjBn3f8+XTRforNbWP+t/PbfOH+7N7jifsa\nbEGXPvPifqxvCnYKcVYbIrtHaGYfLE76T2l19NU8dq/ggMZ3e3e54Kj3lZg5c2anc48lJSXZTy1M\n3bJlS7DD4VBiYmKcqampPu3ps2fPniB/f3+xcuXK8DfffDPB7Xaf/p48//zznjfffPP4s88+W3Wx\nx2irLlq4K4oyA5hxzuFoRVFMF7sbEAGE4e3pnntVCSVJan51Zm/RXnMM7vsUkoernUiSrjunuRzr\n8uVYcnJwHj+OJiSE8AmZRGRNJKBPimzjeBmO1x4n15SL0WSk0lZ5wXPKGsquc6rW69yi/VLH1fTO\nO+/En3tswIAB9acK9xMnTugA4uLiHL48blNTk1JfX+/n5+fH66+/3nnatGnm559/vjwsLMz9ySef\nRPz2t7/tMnPmzKTk5GTHnXfeWdc8X03rcakR9wgg6Zxjfhc49lM+Ry5MlaSWpbYUPrwDakvggWxI\nGqp2Ikm6boTTSf2mTViyc6j/8kvweAgaMIDoJ6cTmp6OJjDw0g/SzlXbqll3dB1Gk5HdFbtRUBgY\nNxCrw4rVft50ZOKC2/56gOYayU796oebzQ7XeaudY/Vax9q0G1vUDAYhxK5L3A7g8xtgl8ulALjd\nbsaOHVvz97///cSp22bMmFFVX1/vN3v27MQ//elPcbJwP99y4OjJzxXgX4AVePYi9/EAtcAPQojD\nVxtQkqRmZC32Fu31ZnggB7oOVjuRJF0XdtMRrEtzsCxfgbuyEm10NB0efZSIrAnou3ZVO16LZ3PZ\n2Fi0EaPJyFfFX+ESLm6MvJGZ/Wfyi26/IC447rw57gABfgHMuPXcC/fST5mZFFd85hx3AH+N4pmZ\nFFesZq4rkZiY6AQoKyvzqe1SaGioR6fTCafTqdx1112Wc2+fPHlyzezZsxN3797dLjdLuGjhLoQo\nAApO/VtRlH8BTUKID691MEmSmpmlCD40eLvITFkGiQPVTiRJ15SnsZHadeuxZGfTtGsX+PkRMmIE\nEVlZhNw+DKUdd6a4HG6Pmx3mHRgPG8k/nk+Ds4GYoBim9JlCRrcMekb1POv8U/PYZVeZK3dqAWpb\n6CozdOjQBr1eL8xms66goMDfl3nuSUlJtoMHDwZGRkaetwg1OjraDWC329vlSnGfXrWEEO3ymyRJ\nrV7NMW/R3mSFKcuh80U3vpOkVksIgW3PHizZOdTm5uJpaEDftSvRz88k/K670MXEqB2xxdtfvR+j\nychq02rKm8oJ0YWQ3jUdQ7KB/rH98dP4/eR9M5IzZKF+lR5M6FjdGgv1c4WEhIjMzMyqJUuWdJwz\nZ06nFStWHLnY+U1NTcqpzjLDhg2rO3jwYOCePXsCJ0+efNb8q507dwYCdOrUyacFr22FHG6QpLau\n2gQf3gn2OnhwBXTqp3YiSWp2rpoaaletwpKdg/3AAZSAAMLGjiViYhaBaWlyoekllDWUsfrIaowm\nIwdrDqJVtAztPJTfJP+G4Z2HE6ANUDui1Aq99dZbxRs3bgxfuXJl1LRp05xvv/128bkbMJWWlmpf\nfvnl+P79+zc+/fTTVQBPPfVUxYcffhj9j3/8I/bhhx+u6t69uxOgsbFRmT17dgJAZmZmzfX/itTn\nU+GuKMptwN+ArUKIJy9x7j+BW4HHhRA7rzyiJElXrOowfGAAlw0eXAnxqWonkqRmIzweGrZuxZqT\nQ11ePsLpJODmm4l77TXCMsbjFxqqdsQWrc5RR/6xfIwmIzvKdiAQ3BJ9C7MHzSY9KZ3IgEi1I0qt\nXGJiomv9+vX7MzMzeyxcuDD2s88+6zB06NDaxMREh8PhUA4cOBDwzTffhDocDk16evqhU/fr16+f\nbfbs2cX/8z//07l///590tPTa4KCgjwbN24MP3bsmH/fvn0bfv/735eq+bWpxdcR9/uAVOBPl3Hu\nNuDhk/eRhbskXW+VB71Fu8cJD66CuJvUTiRJzcJZUoJl6TKsS5fiLCnBLzyciMmTiZiYRUDPnpd+\ngHbM6XaypXgLRpORjUUbcXgcdA3ryvRbppPRLYPEsES1I0ptTN++fe0//vhj4YIFCzosX748YuvW\nraFr1qzR6vV6kZCQYJ80aVLl9OnTKwcOHNh05v1ee+01c69evWzvvvtu7Jo1ayIdDoemc+fO9hde\neKHk1VdfLTt35L69UE6167mskxWlALgJ6CyEuOg7HUVR4oFioEAIocq1+bS0NLFzp3zPILVD5fu8\n3WMQMHUlxKaonUiSrorH4aB+wwYs2Tk0fPUVCEHwkMGEZ2UROno0Gn9/tSO2WEIICioKMJqMrD26\nFqvdSlRAFOOSxnFH9zvo06FPq5pKpCjKLiFE2vV+3oKCgqOpqakXblYvSc2soKCgY2pqatK5x30d\nce8M2C9VtAMIIUoVRbEDCT4+hyRJV8Nc6C3aNX7woBGi5Qik1HrZDx7Ekp2DdcUK3BYL2rg4Oj7x\nBOETJqDvLP+8XMwR6xFyTbnkmnI5UX+CAL8Aft7l5xiSDQzuNBidRqd2REmSfORr4R4I+LIDlh2Q\nkwwl6Xop2+NdiKr1906P6XiD2okkyWfu+gZqV+diycnBVrAbdDpCR44kYmIWwUOGoPj9dFeT9q6q\nqYq1R9diPGzkh6of0CgaBsUN4olbnmBUl1EE69pl62tJajN8LdzLgURFUToJIUoudqKiKAlAGN7p\nMpIkXWsl38Oiu0EX5C3aO3RXO5EkXTYhBE3ffedt47h2LaKxEX2P7sS8+CLhd92JNipK7YgtVqOz\nkS+KvsBoMrK1ZCtu4aZ3VG9eSHuBX3T7BTFBsgWmJLUVvhbu24BE4EnglUuce6rrzHZfQ0mS5KPi\nXbAoE/zDvEV7VDe1E0nSZXFVVWFdvgJLTg4OkwlNUBDhGeOJyMoiIDW1Vc29vp7cHjfby7af3hyp\nydVEfHA8v7rpV2R0y6BHZA+1I0qSdA34Wri/D/wS+I2iKMeEEAsvdJKiKNOA3wDi5H0kSbpWinbA\nxxMgMNJbtEfK7dullk24XNRv2eJt4/jFRnC5COzXj/g35hI2bhyaYDmd40KEEOyr3ofRZGTNkTVU\nNFUQqgtlfLfxGJIN3Bp7KxpF7pMoSW2Zrzun5imKkg1MBBYoivIUsAo4hrdITwLuAPoACpAjhFjT\nrIklSfqv49vh4ywI7ggPGSG8s9qJJOknOYqKsOTkYF22HJfZjF9UFFFTphAxMQv/7nJq108pqS/x\nbo502Mhh62G0Gi23J9yOobuB2zvfjr+f7KgjSe3Fleyc+iDeIv0evK0h+5xz+6nrmp8Aj1x5NEmS\nLurY17D4HgiJ9RbtYZ3UTiRJ5/HYbNTl5WPJzqZx+3bQaAgeNpTYV35L6IgRKHq92hFbJKvdSt6x\nPIwmI7vMuwC4NeZWfnfb7xibNJZw/3CVE0qSpAafC3chRBMwSVGUf+DdYGkIEIe3mC8DvgbeF0Js\nbMackiSd6chm+PcvvSPsD66C0Di1E0nSWWyFhd42jkYjntpadJ07Ez3jGcIzM9HFyZ/XC3G4HWw+\nsRmjycimE5twepwkhSXxdL+nGd9tPJ1D5RU1SWrvrmTEHQAhxAZgQzNmkSTpchz+Av5zr3cu+4Or\nIER2jJBaBndtLVajEWt2DrbCQhS9ntAxY4iYmEXQoEEoGjn/+lwe4eG78u8wmoysO7qOOkcdHQI6\nMKnnJAzdDaREpcgFupIknXbFhbskSSo4lA+f3A9R3eHBld657ZKkIiEEjd/swJKTTd269Qi7Hf9e\nvYidPZtwQwZ+ERFqR2yRTBYTRpOR1UdWU1xfTKA2kFFdRmFINjAofhBajfzzLEnS+eQrgyS1FgfW\nw5L7vTuhTlkBwR3UTiS1Y05zOdZly7AsXYrz+HE0oaGET8gkImsiAX3kKPGFVDZVsubIGowmI4VV\nhWgUDYM7Deapfk8xMnEkQbogtSNKktTC/WThrijK7Sc/bRRC7DznmE+EEF9eyf0kSTpp/xpYMgVi\n+8CUZRAkN6ORrj/hdFK/aROW7Bzqv/wSPB6CBgwg+snphKanowkMVDtii9PobOTz45+Ta8pla+lW\nPMJDnw59eHHAi4zrNo6OgfKqmSRJl+9iI+4b8S443cd/O8ecOuYLcYnnkSTpYvaugs8egvhUeGAp\nBMqpB9L1ZTcdwbo0B8vyFbgrK9FGR9Ph0UeJyJqAvqvcN+BcLo+LbaXbMJqMbDi+gSZXE52CO/HI\nTY9gSDaQHJGsdkRJklqpSxXUCnDuaiJfr3/K66WSdKV+XAbZj0BCf3ggGwJkCzjp+vA0NlK7bj2W\n7Gyadu0CPz9CRowgIiuLkNuHoWjleMyZhBAUVhWenrdebasmTB+GIdmAIdnALTG3yM2RJEm6aj/5\nyiuEOO8V5kLHJEm6RvZkw9LHIXEg3P8Z+IeqnUhq44QQ2PbswZKdQ21uLp6GBvRduxL9/EzC77oL\nXYzsYHSuE3UnyDXlYjQZOVp7FJ1Gx4jEEWQkZzAsYRh6P9mnXmpbFEXpf7Hb58+ff/SZZ56pOvOY\nzWZTFixY0GHFihURP/74Y5DVatXqdDqRmJhoHzJkSN20adMqBw0a1HTq/ISEhJtLSkou+svzwgsv\nlLz11lulV/fVtD5yyESSWqKCJbD819BlMNz3KfiHqJ1IasNcNTXUrlqF5bNs7AcPogQEEDZuHBET\nswjs318uND2HxWZh/bH1GE1Gviv/DoC02DQe6vMQo7uOlpsjSe3Cc889d8GiOS0trfHMf+/evds/\nMzOzh8lkCoiIiHANHTq0NjEx0eFwOJT9+/cHLl68OPqDDz6IWbRo0aH777/fCjBt2jSzxWI5r0YV\nQvDXv/41zuVyKXfccYf12nxlLZss3CWppfluMax4EroNg3s/AX2w2omkNkh4PDR8vRVLTjb1+Z8j\nnE4Cbr6ZuNdeIyxjPH6h8grPmexuO1+e+JJVh1exuXgzLo+L7uHdmXHrDMZ3G0+nELlzsdS+zJs3\nr+RS5xQVFWnT09N7ms1m3cMPP1w+f/78EyEhIWetlSwuLta+9NJLnaqrq0/XpHPmzCm/0OPl5OSE\nvfvuu0rv3r0bb7/99sYLndPWycJdklqSXR/CqhmQPAIm/xv0sj2c1LycJSVYli7DunQpzpIS/MLD\niZg8mYiJWQT07Kl2vBbFIzzsMu8i15TL+qPrqXPWER0Yzf297sfQ3UDPyJ7yaoTU7D7edizqL58f\nTKios+ujQ/0dz4y6ofiB27pWq53rSsyaNSvBbDbrDAZD9fvvv190oXMSEhJcixYtOt7U1HTJX6aF\nCxdGA/zqV7+qaO6srcXF2kFOba4nEUJ81FyPJUlt1o73IXcm9BgNkxaDLkDtRFIb4XE4qN+wAUt2\nDg1ffQVCEDxkMNHPzyR09Gg0/v5qR2xRDtUcwmgyknskl7KGMoK0QYzuOpqM5AwGxQ3CT+OndkSp\njfp427Go142FXe0ujwagvM6uf91Y2BWgtRXv9fX1yrJlyzoAzJ0795Kj84GBgRftWlhUVKTdsGFD\neFBQkOfRRx9tVd+L5nSxEfcP8L3144UI4LILd0VREk+eHwd4gIVCiPmKokQBS4Ak4CjwSyFETTPk\nkyT1bV8Ia2bBDWNh0iLQykJKunq2Awew5uRgXbESt8WCNj6ejk88QfiECeg7J6gdr0Upbyw/vTnS\nvup9+Cl+DOk0hOdufY4RiSPk5kjSRc3KLkg8UFZ31T8khaW1wU63OGvk2e7yaP5n1Y9Jn+0sir6a\nx74xLrTxrYmpFxz1vhIzZ848b35YUlKS/dTC1C1btgQ7HA4lJibGmZqaar/a5/vb3/7W0eVyKRMn\nTqyKjIz0XO3jtVYXK9yP89OFezRw6gfUBZxaPdzhjMdsACqvIJMLeF4I8a2iKKHALkVR8oCHgM+F\nEP+rKMpLwEvAi1fw+JLUsmz9G6x7GXpmwD0fgFZ2oZCunLu+gdrVuVhycrAV7AadjtCRI4mYmEXw\nkCEofnK0+JQGZwP5x/IxmoxsL92OQHBzx5t5aeBLjEsaR4dAuTuxdH2dW7Rf6ria3nnnnfhzjw0Y\nMKD+VOF+4sQJHUBcXJzjap/L4/Hw8ccfdwR44okn2u00Gbh4O8ikCx1XFOXXwHxgC/A68KUQwn7y\nNj0wHJgNDAL+KIT4uy+BhBClQOnJz+sURdkLJAB3ASNOnvYh3s2gZOEutW5f/QXyfge974SJ/wI/\nndqJpFZICEHTd9952ziuWYNoakLfozsxL75I+F13oo2SO+2e4vQ42VqyFeNhI18UfYHNbaNzSGem\npU4jo1sGSeFJakeUWqHmGske+Eb+zeV19vNGb2JC/R0rnhq6vzmeo7kIIXZd4naAZlkHsmLFirAT\nJ074p6SktNtFqaf4tDhVUZSRwF+B5Xinqpx1qUII4QDyFEXJBz4F/qooyj4hxMYrCacoShLQD9gO\nxJ4s6hFClCqKcsGGwoqiPA48DtClS5creVpJuj42z4PP/wf6ZMKE92TRLvnMVVWFdfkKLDk5OEwm\nNEFBhBsyiMjKIiA1VS6cPEkIwQ+VP2A0GVl7dC3VtmrC/cO5q8ddGJINpEbL75XUMjwz6obiM+e4\nA/hrNZ5nRt1QrGauK5GYmOgEKCsru+rLyAsXLuwI8NBDD7Xr0XbwvavM83h3Qn3u3KL9TEIIoSjK\n80AW8ALe0XGfKIoSAuQAzwohai/3RVUIsRBYCJCWltYcc/Qlqflt+hN88QbcfA/c/Xfwkw2epMsj\nXC7qt2zBmpND3RcbweUisF8/4t+YS9i4cWiCZfvQU4pqizAeMZJryuVY7TH0Gj0jEkdgSDYwNGEo\nOvlmWWphTi1AbQtdZYYOHdqg1+uF2WzWFRQU+F/pPPfi4mJtfn5+RHtflHqKr9VCGmARQlzykpAQ\n4riiKBZggK+hFEXR4S3aFwshlp48bFYUJf7kaHs8cMEen5LUogkBG/8Am/4IqffCXf8HskOFdBkc\nRUVYcnKwLl2Gq7wcv6gooqZOJSJrAv7du6sdr8WosdWw7ug6jCYjBRUFKCgMiBvAIzc9wuiuownV\ny/70Usv2wG1dq1tjoX6ukJAQkZmZWbVkyZKOc+bM6bRixYojFzu/qalJuVBnGbko9Wy+Fu6hgJ+i\nKPqT02J+0sn57sGA25cnULxD6+8De4UQ8864aSXwIPC/Jz+u8OVxJUl1QsCG12Hz29DvAbjjL7Jo\nly7KY7NRl5ePJTubxu3bQaMheNhQYme/QuiIESh6uZAZwOaysfHERnIP57KleAsu4aJHRA+e6/8c\n47uNJy44Tu2IktQuvfXWW8UbN24MX7lyZdS0adOcb7/9dvG5GzCVlpZqX3755fj+/fs3Pv3001Vn\n3nbmotTp06e3+2ky4HvhfgToBUwF/nmJc6cCOuCQj8/xM2AKsEdRlO9PHvst3oL9U0VRHsHb8eYe\nHx9XktQjBOS/Cl/Nh/4PQcY7oNFc8m5S+2QrLMSSnYPVaMRTW4uuc2eiZzxDeGYmujhZhAK4PW52\nmndiNBnJO5ZHg7OBmMAYpqRMISM5g55RcjMpSVJbYmKia/369fszMzN7LFy4MPazzz7rMHTo0NrE\nxESHw+FQDhw4EPDNN9+EOhwOTXp6+nn14qpVq0KPHz/un5KS0jhs2LB2vSj1FF8L9/8Avwf+oiiK\nUwjx4YVOOrl501/wtpP8jy9PIITYgnce/YWM8uWxJKlFEALWvQLb/g8GPAq/eEsW7dJ53LW1WI1G\nrNk52AoLUfR6QseMIWJiFkGDBqHInxkA9lfvJ9eUS+6RXMobywnWBTOm6xgMyQbSYtPk5kiS1ML0\n7ZxygAYAACAASURBVNvX/uOPPxYuWLCgw/LlyyO2bt0aumbNGq1erxcJCQn2SZMmVU6fPr1y4MCB\nTefe99ROqXJR6n8pp9r1XNbJihIAfA3cgrcoL8K78LT45L87420H2QVv8f09MEQIYWvW1JcpLS1N\n7Ny5U42nliQvIWDNi/DNP2DQr2Hc/4LsXiGdJISg8ZsdWLKzqVu/HmG349+rFxETJxJuyMAvIkLt\niC1CWUMZq4+sxmgycrDmIFpFy9CEoWR0z2BE5xEEaOUuw+2Joii7hBBp1/t5CwoKjqampl7J/jSS\n5LOCgoKOqampSece92nEXQhhUxRlFN456HfjLdCnnHPaqapkJfCwWkW7JKnO44HVL8DO92HwU5A+\nVxbtEgBOcznWZcuwLF2K8/hxNKGhhE/IJCJrIgF9UmRrQqDOUXd6c6QdZTsQCFKjU3ll0CuMTRpL\nZECk2hElSZKuO5970AkhaoAJiqIMACbj7TRzqqd6ObATWCKE+KbZUkpSa+PxgPFZ+PZD+NmzMPo1\nWbS3c8LppH7TJizZOdR/+SV4PAQNGED0k9MJTU9HExiodkTVOd1Ovir5CqPJyMaijdjddrqEduGJ\nW54go1sGXcLk3hySJLVvV9w8WgixA9jRjFkkqW3wuGHlM/D9xzDsBRg5Wxbt7ZjddARLTjbWFStx\nV1aijY6mw6OPEpE1AX3XrmrHU50QgoKKAowmI+uOrsNitxDpH8mEGyZgSDZwc8eb5RUISZKkk+Su\nL5LUnDxuWD4ddn8Cw1+CES/Jor0d8jQ2Urt2HZacHJp27QI/P0JGjCAiK4uQ24ehaOVL71HrUXKP\n5GI8bORE/Qn8/fwZmTgSQ3cDgzsNRqeRmyNJkiSd64r/eiiKogH6A12BICHER82WSpJaI7cLlk2D\nH7Lh57Nh+Cy1E0nXkRAC2549WLJzqM3NxdPQgL5rV6Kfn0nE3XejjY5WO6LqqpqqWHt0LbmmXPZU\n7kFBYVD8IH6d+mtGdRlFiD5E7YiSJEkt2hUV7oqiPA3MBjqecfijM26PBDaffPwhQohWvwOYJF2U\n2wk5j0Lhchj1KgybqXYi6Tpx1dRQu2oVls+ysR88iBIQQNi4cURMzCKwf/92P82jydXEF8e/wGgy\n8nXJ17iFm15RvXgh7QXGJY0jNjhW7YhSM1r+XTFvrdtPiaWJThGBzBrbk7v7JagdS5LaDJ8Ld0VR\n/go8gbd7TC0Qwjl914UQNYqi7AIeAAycUdRLUpvjckDOw7B3lbdzzJCn1U4kXWPC46Hh661YcrKp\nz/8c4XQScPPNxL32GmEZ4/ELDVU7oqrcHjfflH2D0WQk/1g+ja5G4oLjeKjPQ2QkZ3BD5A1qR5Su\ngeXfFfPy0j00Ob0bphdbmnh56R4AWbxLUjPxqXBXFGUsMB2oA6YKIVYoilLKf7vKnOnfeFtF3oks\n3KW2ymWHzx6C/au9Pdpve0LtRNI15CwpwbJ0GZalObhKSvELDydi8mQiJmYR0LN979QphGB/zX6M\nh42sPrKaiqYKQnQhjOs2DkOygf6x/dEochOptuytdftPF+2nNDndvLVuvyzcJamZ+Dri/mu8Gy3N\nEUKsuMS5W09+vMXnVJLUGjht8OlUOLgOxv8ZBj6mdiLpGvA4HNRv2IDls2wavv4ahCB4yBBiX3iB\nkFGj0Pj7qx1RVaX1peQeySXXlMshyyG0Gi3DEoZhSDYwPHE4/n7t+/vTHng8goITFoot5218CUDJ\nTxyXJMl3vhbut538+K9LnSiEqFUUpRaI9zmVJLV0ziZY8gAcygfDO5D2sNqJpGZmO3AAa06Ot42j\nxYI2Pp6OTzxB+IQJ6Du379HDWkcteUfzMJqM7DR7d6fuF9OP3932O9K7phMRIHd8betsTjdfH64k\nr9BM/t5yKursP3lupwi5R4EkNRdfC/cowCqEqLvM8z2An4/PIUktm6MRPrkPTBvhzv8Ht05VO5HU\nTNz1DdSuzsWSk4OtYDfodISOHEnExCyChwxB8Wu/L2cOt4PNxZvJNeWysWgjTo+TpLAknrrlKcYn\njycxNFHtiNI1VlVvZ8O+cvIKzWw+WEmT002Iv5bhPaMZ0zuWRoeL1417z5ouE6jzY9bY9j2NTJKa\nk6+Fey0QqSiKTgjhvNiJiqJ0BCKAkisNJ0ktjqMB/j0Jjm6Bu/8Gt9yndiLpKgkhaPruOyyfZVO7\ndi2iqQl9j+7EvPgi4XfdiTYqSu2IqvEID9+Xf396c6RaRy1RAVFM6jkJQ7KBlA4p7b5rTlt3uKKe\n/EIz+XvN7DpWg0dAfHgAE/t3ZkxKLIOSo/DX/vcNbZBeK7vKSNI15Gvh/iMwDBgAfH2Jc6ec/LjL\n11CS1CLZ6+Hfv4TjW2HCQuj7S7UTSVfBVVmJdcUKLNk5OI4cQRMURLghg4isLAJSU9t1QWqymk4v\nMi2uLyZQG8jILiMxJBu4Lf42tBq5gVRb5fYIvjteQ16hmby9ZkwVDQD06RTG0yNvYExKLH06hf3k\n78fd/RJkoS5J15Cvr75LgduB1xRFGSeE8FzoJEVRhgC/x7uQ9dOriyhJLYCtFhbfAyd2QNY/4aYs\ntRNJV0C4XNRv2YI1J4e6LzaCy0Vgv37EvzGXsHHj0AQHqx1RNZVNlaw9spZVplUUVhWiUTQMjh/M\nk7c8yaguowjSBakdUbpGGh0uNh+sJL/QzIZ95VQ1OND5KdyW3IGHhiQxqncsCXKeuiS1CL4W7v8A\nngJGAWsVRZkHaOD01Ji+wGTgQUAHfA/8p9nSSpIabFb4OAtKvoOJ/4I+d6udSPKRo6gIS04O1qXL\ncJWX4xcVRdTUqURkTcC/e3e146mm0dnIhqINGE1GtpVswy3c9I7qzay0WYxPHk/HwI6XfhCpVSqv\ns/H53nLyC81sOVSJ3eUhLEDLz3vFMCYllttvjCYsQKd2TKkFUhSl/8Vunz9//tFnnnmm6sxjNptN\nWbBgQYcVK1ZE/Pjjj0FWq1Wr0+lEYmKifciQIXXTpk2rHDRo0Fnth4qLi7Wvv/563Oeffx5eUlKi\n1+l0IiEhwT5hwoTqmTNnVkRGRl5w8Lit86lwF0LYFUXJANYDo/EW8KeYz/hcAQ4DmT81Ki9JrUJT\nDSyaAGV74J4PobdB7UTSZfLYbNTl5WHJzqFx+3bQaAgeNpTY2a8QOmIEil6vdkRVuDwutpdux2gy\n8vnxz2lyNdEpuBMP3/QwGckZdI9ov29k2jIhBAfL671TYArNfF9kAaBzZCD3DerCmN6xDOgWhc5P\n9tqXLs9zzz1XeqHjaWlpjWf+e/fu3f6ZmZk9TCZTQEREhGvo0KG1iYmJDofDoezfvz9w8eLF0R98\n8EHMokWLDt1///1WgP379+uHDBnSu7q6Wjtw4MC6kSNHWm02m7Jp06bwuXPndv7000877Nq1a29I\nSIi4Hl9rS+LzREUhxEFFUW4BZgO/wttp5ky1eNtFvi6EqLn6iJKkksZqWHQ3lO+FSYug5y/UTiRd\nBlthIZbsHKxGI57aWnSdOxM94xnCMzPRxcWpHU8VQggKqwsxHjay5sgaqmxVhOpDyUjOwJBsoF9M\nP7k5UhvkcnvYcbSG/L3exaXHqrz1VGrncF5Iv5HRKbH0jA1t1+s5pCs3b968SzYfKSoq0qanp/c0\nm826hx9+uHz+/Pknzi22i4uLtS+99FKn6urq0zXp3Llz46qrq7UzZ84sefvtt0+/QXC5XEXDhg27\ncdu2baEffPBB1FNPPXXWyH57cEUrjIQQVmAWMEtRlBSgE962j2XAD0II98XuL0ktXkMVfHQXVB6A\nSYvhxnS1E0kX4a6txWo0YsnOxl64F0WvJzQ9nYiJWQQNHIiiaZ9FaXF9MbmmXIwmI0esR9BpdAzv\nPBxDsoFhnYeh92ufVx3asnq7i037K8jf652vbm1yotdq+Fn3Djx+ezKje8cSGxagdkzpYna8H8Wm\nPyZQX64nJMbB8BeLGfBItdqxrsSsWbMSzGazzmAwVL///vtFFzonISHBtWjRouNNTU2n30EeO3bM\nH2DChAmWM8/VarWMHTvWum3bttCKiop2uUrepy9aUZQuJz8tF0LYAIQQhUBhcweTJNXUV3iL9urD\ncO9/oMeoS99Huu6EEDR+swNLdjZ169cj7Hb8e/UidvZswg0Z+EW0z02ArHYr646uI9eUy7fl3wLQ\nP7Y/U1OmMqbrGML9w1VOKDW3UmsT+Xu9/dW3Ha7C4fYQGaRjdO9YxqTEMOyGaIL922WN0/rseD+K\ndS93xWX3jjbUm/Wse7krQGsr3uvr65Vly5Z1AJg7d+4lR+cDAwNPj8T36tWrafPmzWErVqyI+NnP\nfnZ67rvb7Wb9+vVhGo2G9PT02muTvGXz9Tf5KN5Nlbog+7NLbVGdGT66E2qOwX1LIHmE2onaPeuq\nVZS/8y6u0lK08fFEPfwrRH0DlqVLcR4/jiY0lPAJmURkTSSgT/vsK25329l8YjOrDq/iy+IvcXlc\nJIcnM+PWGYzvNp5OIZ3Ujig1IyEEhaW15BeWk7e3jB+KvfVLUocgHhzSlTEpcdzaJQKtnK9+/Sx/\nMpHywqtvvVS2JxiP8+wXMZddw5oXk/ju4+ireuyYlEbu/r8LjnpfiZkzZ573wpKUlGQ/tTB1y5Yt\nwQ6HQ4mJiXGmpqb+9Na6F/Dqq6+W5eXlhb/11ludNm/eHNq3b99Gh8OhbNq0KayyslI3b968o2cW\n9O2Jr4V7PeAUQsiiXWp7akvhwzugtgTu/wy6DVM7UbtnXbWK0t/NQdhsALhKSiif+wYAQQMGEP3k\ndELT09EEtr9WdR7h4VvztxhNRtYfW0+do46OgR25t9e9GJIN9I7q3S7fxLRVDpeHb45Uk1dYRv7e\ncootTSgK3NolkhfH9WJMSizdo4Pl//PW7tyi/VLHVfTOO+/En3tswIAB9acK9xMnTugA4uLiHL4+\ndkJCgmvHjh377rvvvqS8vLyIbdu2hQIoisLkyZMrMzIy2uVoO1zZiPsNiqL4yXnsUptiLfYW7fVm\neCAbug5RO5EElL/z7umi/UzamBi6LvpIhUTqO2w5jNFkJNeUS2lDKYHaQEZ3GY0h2cDA+IFyc6Q2\nxNrkZON+7xSYTfsrqLO7CNBpGHZDNDNG3cDPe8UQHeqvdkwJaLaR7D/feDP15vMXn4TEOnj8i/3N\n8hzNRAhx0Q02hfDOfLmSN5P79+/X33HHHT3sdrtmyZIlB0ePHl1fX1+vWbJkScSrr76auH79+ogt\nW7bs7dWrl89vClo7X1/hlwOvABnAyuaPI0kqsBTBhwbvgtQHlkKXQWonkvD2XneVXPjinqui4jqn\nUVdFYwWrj6wm15TL3uq9+Cl+DO40mBm3zuDniT+XmyO1IUXVjae7wGw3VePyCDqG6Bl/czxjUmL5\nWY+OBOr91I4pXSvDXyw+a447gNbfw/AXi1VMdUUSExOdAGVlZT6vgp8yZUq3gwcPBm7btq3wVH/3\nqKgoz6xZsyptNptmzpw5ia+88kqnnJyco80cu8XztXD/IzAJWKAoylEhxO5rkEmSrp+aY96ivckK\nU5dD5zS1E7V7nqYmqt57j6p/vg+KAuL8Nr3a+POu0LY5Dc4GPj/+OcbDRraXbccjPNzU4SZeGvgS\nY5PGys2R2giPR/BDifV0f/V9ZXUA3BATwmMnu8D0S4xAo2lxMyWka+HUAtQ20FVm6NChDXq9XpjN\nZl1BQYH/5c5zr6mp0ezYsSMkPDzcfe6mTADp6el1c+bMYc+ePe1yxMLXwj0L7+6prwE7FUVZC3wF\nlAM/OXVGCNE+r2lLLVv1Ee/0GHutt2hPuFXtRO2aEIK6desx/+mPuEpKCTMYCLgllYo/v33WdBkl\nIICY555VMem14/Q42VqyFaPJyBfHv8DmtpEQksBjNz9GRnIG3cK7qR1RagY2p5utpiryC70j6+Za\nOxoF0pKimJ3Rm1G9Y+nWMVjtmJJaBjxS3RoL9XOFhISIzMzMqiVLlnScM2dOpxUrVhy52PlNTU1K\nYGCgsNvtCkB9fb3GZrMpAQEBZ43elJWVaQF0Ol2723wJfC/cPwBOfaMUvFNmMi5xHwHIwl1qWaoO\ne4t2ZyM8uAriU9VO1K7ZDx2i7I03aNy6Df+ePUn4+E8EpXmvfmjDw8/qKhPz3LOE33GHyombjxCC\nHyp/wGgysvboWqpt1YTpw7iz+53c0f0OUqNT5YLDNqCmwcGGfeXk7zXz5YEKGhxugvR+DL8xmtG9\nYxnZK4bIYNlXX2pb3nrrreKNGzeGr1y5MmratGnOt99+u/jcDZhKS0u1L7/8cnz//v0bn3766aq4\nuDh3cnKyzWQyBbz44ovx8+fPPz1nsrGxUXnzzTfjAYYNG1Z3vb+elkARF7gM/ZMnK8pR/lu4XzYh\nhCrDRGlpaWLnzp1qPLXUklUe9BbtbgdMXQFxN6udqN1y19VR+df/o3rxYjRBQUTPeIbISZNQtG1/\ngWVRXdHpRabHao+h1+gZnnhyc6SEYej8dGpHlK7S0coG7xSYvWZ2Hq3GIyA2zJ/RvWMZnRLL4OQO\nBOjkfHVfKYqySwhx3ec1FhQUHE1NTa283s/b0iiK0h8uvTj1lN27d/tnZmb2MJlMAZGRka6hQ4fW\nJiYmOhwOh3LgwIGAb775JtThcGgWLVp06L777rMCLF++PPSXv/zlDU6nU+nbt2/DgAED6puamjQb\nN24MLykp0Xfp0sW+ffv2vXFxcW22UUpBQUHH1NTUpHOP+1S4tzaycJfOU77P26ddeGDqSohNUTtR\nuyQ8HqwrVlL+9tu4q6qImDiR6OeeRRsVpXa0a8pis7Du6DqMJiPfV3wPwIC4ARiSDYzuOpowfZjK\nCaWr4fEIviuykL/XO1/9UHk9AL3iQklP8RbrN3UKl/PVr5Is3NXla+EOYLPZlAULFnRYvnx5RGFh\nYZDFYtHq9XqRkJBgHzJkSN306dMrBw4ceNZ89u3btwf+4Q9/iNu+fXtIZWWlzs/Pj86dO9vHjRtn\nee2118o6duzYZot2kIW7JIG50Fu0Kxrv9JjonmonapeafvgR8+uv01RQQGBqKrGzZxN4801qx7pm\nbC4bm05swmgysuXEFlzCRY+IHhiSDYzvNp74kLa/0LYta3K42XKokvxCM5/vM1NZ70CrURiUHOUd\nWe8dS2JUu1xDd83Iwl1qD36qcG/716MlCaDsB2/R7qf3Fu0db1A7UbvjqqmhYt47WLKz8evQgfg/\n/IHwu+5E0bS93R09wsPOsp0YTUbyjuVR76wnJjCGB1IewJBs4MbIG+W89Vasos7OF/vKWV9oZsuh\nCmxOD6H+Wob3jGZMSiwjbowhPEhOdZIkqfldVeGuKEofIA2IOXmoHNgphPjxaoNJUrMpLYCP7gJd\nkLdo79Bd7UTtinC5qFmyhIr5f8HT0EDU1Kl0fOpJ/EJD1Y7W7A7UHMBoMrLatBpzo5kgbRBjuo7B\n0N3AgNgB+GnkfObWSAjB4Yp68grLySss47siC0JAQkQgk9ISGZMSx8BuUei1be9NqCRJLcsVFe6K\nohiAPwAXnCCsKEoh8IoQQm7SJKmr+FtYdDf4h3mL9ijZTu96aty5k7LX52Lfv5+gwbcR98or+Pfo\noXasZlXWUMaaI2swmowcqDmAVtHys4Sf8ULaCwxPHE6gNlDtiNIVcLk9fHvcQl5hGfl7yzlS2QDA\nzQnhPDvqRsakxNI7PlReOZEk6bryuXBXFGUO8CredpAALqDq5OcdTj5mH2CZoiivCyFea4ackuS7\nEzth0QQIDIcHjRDZVe1E7YbTbKb8T29Rm5uLtlM8Ce++S+jY9DZT5NQ76sk7lkeuKZdvyr5BIOgb\n3ZffDvotY5PGEhXQthfZtlUNdhebD1awvtDMF/vKqWl0ovNTGNy9Iw8P7cbo3jHEh8s3YpIkqcen\nwl1RlHF4N18C+BKYC3wphHCcvF0P3A78FhgB/E5RlK1CiHXNFViSftLuT+Hz34P1BARHg80KYZ28\nI+0RiWqnaxc8DgfVH35I5YK/g8tFx+lP0OGxx9AEtv5ix+lx8nXx197NkYq+wO62kxiayK9Tf01G\ncgZdw+Qbw9bIXGsjf6+Z/EIzXx2uwuHyEB6oY2SvGEb3juX2GzsSGiDnq0uS1DL4OuI+8+THz4DJ\n4pyWNCcL+HxFUT4HPgHuOXkfWbhL19buT2HVM+A82U2qoRxQYNATsmi/Tuo3b8Y89w0cx44RMnIk\nsS+/hD6xdX/vhRDsrtyN8bB3cySL3UKEfwSZPTIxdDfQt2PfNnMVob0QQrDfXEfej95dSwtOWAHo\nEhXElNu6Mrp3LAOSItH6yfnqkiS1PL4W7ml4N2CaeW7RfiYhhFAU5Xm8hfuAq8gnSZfn89//t2g/\nTcDW/we3TVMlUnvhKCrC/If/pX7DBvRdu5L43kJChg1TO9ZVOVZ7jFxTLkaTkaK6Ivz9/Pl54s8x\nJBsYkjAEnUaOwLYmTreHHUeqydvrLdaLqr2vFbckRjBrbE/GpMRyQ0yIfBMmXZIQQv6cSNfcxVq1\n+1q46wGLEKL4Mp70hKIoNSfvI0nXlvWEb8elq+ZpaqJy4UKq3/8XaLVEPz+TqAcfRKNvnb/y1bZq\n1h5ZS64pl92Vu1FQGBg/kMf7Ps7oLqMJ0YeoHVHyQa3Nyab9FeTv9c5Xr7W58NdqGNqjI9NH9GBU\nrxhiwgLUjim1Ioqi1DgcDp2/v79T7SxS2+ZwOHQna+jz+Fq4m4CeiqLoT81r/ymKovgDIcA+H59D\nknwXEgv1ZecfD+98/bO0cUII6tatx/zHP+IqLSXMYCBm1gvoYmPVjuazJlcTG4s2YjQZ+ar4K9zC\nTc/Injzf/3l+0e0XxAa3vq+pPSu2NJFf6B1V32aqwukWRAXrGdsnjtEpsQy7oSNBerl9iXRlPB7P\nGovFMjk2NrZa7SxS22axWEI9Hs8nF7rN11ewfwNvAlOBf17i3CmA7uR9JOnasRSBy3b+cV0gjJpz\n/fO0YfZDhyib+waN27bh37MnCW/9iaC0676B4VVxe9x8U/YNRpOR/GP5NLoaiQmKYWqfqac3R5Ja\nByEEP5bUkldoJq/QTGFpLQDJ0cE8PLQbY3rH0q9LJH4aObVBunput3uh2WweB0RFRETU6fV6p5w2\nIzUXIQQOh0NnsVhCzWazxe12L7zQecrF5tGcd7Ki6IDP8c51f0II8eFPnDcV+DuwAxglhHD5/BU0\ng7S0NLFz5041nlq6Xhoq4V/joL4chs6Anf+fd3pMeGdv0d73l2onbBPcdXVU/vX/qF68GE1QENEz\nniFy0iQUbesYvRRCcKDmAKsOr2L1kdVUNFUQogvxbo6UbCAtLg2NIhcjtgZ2l5ttpurTI+ulVhuK\nAmldIxndO5bRKbF0j5bTmtoyRVF2CSFUGTHYtWtXkp+f3+MajeYXQohINTJIbZeiKDUej2eN2+1e\n2L9//6MXPMfHwn0O3jnrTwJhQBGwESjGu2i1MzAc6AJYgb8BF5xSI4T4/WU/8RWShXsbZ6+DDwxQ\nsQ+mLIOuQ9RO1OYIjwfr8hWUz5uHu6qKiHvuIfq5Z9FGto6/V2UNZacXmR6yHEKraBnaeSiGZAPD\nOw8nQCvnOLcGlkYHG/dXkFdoZtOBCurtLgJ1fgy7oSNjUmIZ2SuGDiH+aseUrhM1C3dJUpuvhbsH\nb4EO/92A6dwH+KnjZxFCXPO9v2Xh3oY5bfDve+DoV3Dvf+DGsWonanOa9vyAee5cmgoKCExNJfZ3\nvyPwpj5qx7qkWkct+cfyMZqM7CzbiUBwS/QtGJINpCelExnQOt50tHfHqxrJ22smr7CMHUdrcHsE\n0aH+jO4dw5iUWIZ070iA7pr/GZFaIFm4S+2Zr9e5v+QSBbkkXXNuF+Q8Ake+hMyFsmhvZq6aGirm\nvYMlOxu/Dh2I/8MfCL/rThRNy51K4nQ72Vy8GaPJyKaiTTg8DpLCkph+y3QyumWQGNa6+8m3Bx6P\nYHexlbzCMvILy9lvrgPgxtgQfj08mdG9Y0ntHIFGzleXJKkd86lwF0KMuEY5JOnyCAHGZ2GfEcb9\nEVInqZ2ozRAuFzVLllAx/y94GhqImjqVjk89iV9oqNrRLkgIwfcV32M8bGTdsXVY7VaiAqK4p+c9\nGJIN9OnQR/ZbbuFsTjdfH64kr7Ccz/eaKa+z46dRGJAUye8MKYzuHUPXDsFqx5QkSWoxWsfKMkk6\nJf9V+G4R3P4buO3XaqdpMxp37KBs7hvY9+8naPBtxL3yCv49eqgdC4BcUy7zv51PWUMZccFx3Nvr\nXuqd9eSacimuLybAL4CRXUZiSDZwW6fb5OZILVxVvZ0N+8rJ32vmywOVNDndBOv9GNEzhtEpMfy8\nZwwRQa1zLwBJkqRrTRbuUuux5V34aj6kPQI//63aadoEp9lM+Z/eojY3F22neBLmzyc0fUyLGanO\nNeXy2tevYXN7232WNpQyb9c8AAbHD2b6LdMZ1WUUwTo5KtuSmSrqyTvZBWbXsRo8AuLDA5jYvzOj\nU2K5LTkKf62cry5JknQpsnCXWodvP/KOtveZAOPfghZSWLZWHoeD6g8/pHLB38HlouP0J+jw2GNo\nAgPVjnaWebvmnS7azxQTFMPC9Au2uJVaALdH8N3xmpOLS82YKhoASIkP46mRN5CeEkufTmEt5g2i\nJElSayELd6nl27sKVs2A7qMg8x+gkSNzV6N+82bMc9/AcewYISNHEvvyS+gTW9bizRN1J/jXD/+i\nvLH8grdXNFZc50TSpTQ6XGw+WEl+oZkN+8qpanCg1SgM7t6BBwcnMTolloSIlvXGUJIkqbWRhbvU\nsh35ErIfhoT+MGkRaOXc1yvlKCrC/If/pX7DBvRJSSS+t5CQYcPUjnUWk8XEP/f8k9VHVqNRNARp\ng2h0NZ53XlxwnArppHOV19n4fG85+YVmthyqxO7yEBqgZWSvGEb3jmV4z2jCAuSaA0mSpOYiYxET\nigAAIABJREFUC3ep5Sr+Fv5zL0R1h/s+Bb2cx3wlPE1NVC5cSPX7/wKtlpgXnidq6lQUfct5E7S3\nai/v7XmP/GP5BGgDuK/3fTyY8iA7zTvPmuMOEOAXwIxbZ6iYtv0SQnCw3DtfPa/QzPdFFgASIgK5\nd2AX0lNiGdAtCp1fy20dKkmS1JrJwl1qmSoOwOKJEBQFU5Z6P0o+EUJQt2495j/+EVdpKWEGAzGz\nXkAXG6t2tNO+K/+OhbsXsqV4CyG6EB69+VGmpEw5vUlSRnIGwFldZWbcOuP0cenac7k97DxWc3px\n6bEq7xWQ1M7hPD/mRkanxNIrLlTOV5ckSboOfNo5tbWRO6e2UtYT8P5YcNvh4XXQobvaiVod+6FD\nlM19g8Zt2/Dv2ZO4380mKK1lbDQohGBb6Tbe2/MeO8p2EOkfyZSUKUzuNZlQfcvsGd/e1NtdfHmg\ngryT89WtTU70fhqG9OjAmJRYRvWKJS48QO2YUjsld06V2jM54i61LA1VsCgT7LXwUK4s2n3krquj\n8q9/pfrjxWhCQoj93WwiJ01C0ar/q+4RHjYVbeK9Pe+xp3IPMYEx/GbAb8i6IYsgXZDa8dq9UmsT\n+XvLySs0s+1wFQ63h4ggHaN6x5CeEsuwG6IJ9lf/50iSJKk9k6/CUsthr/NOj7EchweWQnxftRO1\nGsLjwbp8BeVvv427upqIe+4h+rln0UZGqh0Nt8fN+mPreW/PexysOUhCSAJzBs/hru53ofdrOfPs\n2xshBHtL605PgdlTbAUgqUMQDw7pyujesfTvGolWzleXJElqMWThLrUMLjt8cj+UFsDkxZD0M7UT\ntRpNe37APHcuTQUFBKamEvuPfxB4Ux+1Y+F0OzGajLz/w/scqz1Gcngybw59k190+wVajXzpUYPD\n5eGbI9Xkn+yvXmxpQlGgX2IEL47rxZiUGLpHh8j56pIkSS2U/Ospqc/jhpxH4Mim/5+99w6PszzT\n9s/pvRc1yyq23MAY08E2EGxDaKGEtgQMBMPuwm5CdsMvyUdI8mVJSEiWhCzfkSymGxJMHMdgCKEY\nCAYCCQSwwcaWrWJbdYo0mt7e9/fHOxppJBlc1Gw953HMMdLMO/M8o3rN/V73dcPFv4HZ5070jg4L\ncj09BO79Bb1r16LxeKi4+24cF30JlXpiK6SpXIp1jet45JNH6Ix3Mtc9l3vPvJel05eiVonq7XgT\nSWZ5fXs3r2zr5vXt3URTOYw6NYtn+vja0pmcNacMn80w0dsUCAQCwX4ghLtgYpFleO42ZcjSOXfD\nsf800Tua9Mi5HD1PrSHwq18hxeO4V6zA+2+3orFNbGNnPBtnzfY1PP7J44RSIRb6F/K9U77H4qrF\nooI7zuwJJ3hlm2KBebcpTE6S8Vr1nHd0BcvmlbF4pheTXgwyEwgEgsMNIdwFE8vG/wv/eByWfBNO\nvWWidzPpSfz973Te9SPS27djPvUUyu+4A8PMmRO6p0g6wm+3/ZYntj1BX6aPUytO5aZjbuKEshOE\nYB8nZFlmS1uEV7Z28dLWLj7tjAIw029l5ZJ6ls8r49hqJxq1+H4IBALB4YwQ7oKJ461fwZu/gBO+\nCmd9d6J3M6nJdnXRfc/P6Hv+ebSVFVTddx+2s5dPqDAOJoM8vvVx1ny6hkQuwReqv8BN829ivm/+\nhO1pKpHO5Xl7V4hXCs2lXX1p1Co4odbNHefNZdm8Muq8YmiZQCAQHEkI4S6YGD54Al6+E466BM77\nOYjK7IhImQzhxx4j+OvfQC6H95Z/xXPTTahNpgnbU0esg0c+eYR1jevISlnOqT2HlfNXMss1a8L2\nNFXoiWd4bbsS2fjGjgDxTB6zXsMZs3wsm1vGF+b4cVtEUo9AIBAcqQjhLhh/tj0Hz/47zDgLLnkA\n1MJrOxKxN96g60c/JtPaivWssyj7zrfRV1dP2H5a+1p5aMtDbNi1AYALZ1zIjfNvpMZeM2F7mgq0\nBOO8sk2xwLzXEkaSwW8zcNHCKpbPK+PUeg9GnfgdEggEgqmAEO6C8aX5DVj7Vag8Dq5YDVpRHRxK\nZvduuu7+CbHXXkNfW0v1qgewLlkyYfvZ0bODBzc/yIutL6JT67h89uXccNQNVFgrJmxPRzKSJPPh\n3l4lX31rF43dMQDmlNu49QszWTa3jPlVDtTCry4QCARTDiHcBeNH+wfwu6vBXQdf+T0YrBO9o0mF\nlEwSfOABwg89DFot/m/+J+4VK1DpJ+bNzZbAFh7Y8gCv73kds9bMdUddx4p5K/CavBOynyOZZCbP\nWzuDvLy1i42fdhOMpdGoVZxc5+bqk6ezbG4Z1W4xXVYgEAimOkK4C8aHYCM88WUwueDaP4LZPdE7\nmjTIskz0xZfo+ulPyXV0YL/gAvy3fxNdWdmE7OW9rvdYtXkVf+34K3a9nVsW3MLVc6/GYXCM+36O\nZIKxNK9u6+blbV1sagyQykrYDFrOmO1j+bwyzpzlx2HWTfQ2BQKBQDCJEMJdMPZE2mD1JYAKVqwH\ne+VE72jSkG5spPNHPybxzjsYZs+m6mf3YD7hhHHfhyzLvNn2Jqu2rOKD7g/wGD38x/H/wRWzr8Ci\nE8kko4Esy+wKxBULzLYu/rG7B1mGSoeRK0+oZtm8Mk6u86DXiiFVAoFAIBgZIdwFY0s8pIj2VASu\nfw48MyZ6R5OCfDRK8P77CT/xJGqrlbI7v4vryitRacf3V1KSJTbu3siqzavYFt5GuaWc75z0HS5t\nuBSj1jiuezkSyUsy77f28Mq2Ll7e2kVzMA7A0VV2bls6i2Xz/MyrsIu8e4FAIBDsF0K4C8aOdBSe\nvAx6WuDadVCxYKJ3NOHIkkRk/TN0//d/kw+HcV5+Ob5v3IbW5RrXfeSkHC80v8CDWx6kKdJEjb2G\nH572Qy6ovwCdRtgzDoV4OsemxgAvb+3m1U+76Elk0WlUnDrDy1cX1bJ0bhmVzomL8xQIBALB4YsQ\n7oKxIZeGNddAx0dw5RNQu3iidzThJLd8TNddd5H86CNMCxZQ9r//i+noo8Z1D5l8hvU71/Pwxw/T\nFmujwdXAPaffw9k1Z6MRsZwHTVdfile2KSkwb+0KkclJ2I1azprjZ/m8ck6f5cVmFG+IBEc+2za9\nxqanHicaCmLzeFly1QrmLvnCRG9LIDhiEMJdMPpIeVh3EzS9Dhf/GuacN9E7mlBy4TCBX/yC3rV/\nQOPxUHH33Tgu+hIq9fh5mRPZBGt3rOWxTx6jO9nNfO98vnXitzij+gzUKuGpPlBkWWZ7V5RXtioW\nmI/2RgCodpu45uQals8r44RaFzqN+NoKpg7bNr3GSw/cTy6TBiAaDPDSA/cDCPEuEIwSQrgLRhdZ\nhuf/A7Y+A+f8GI69eqJ3NGHIuRw9T60h8KtfISUSuFeswPtvt6Kx2cZtD9FMlKc+fYrVW1fTk+7h\nxPITuWvxXZxScYrwVR8g2bzE35vDvLxNaS7dE04CcGy1k9vPmc3yeWU0+K3i6yqYUqTiMQKtzQRa\nmnjzqdVF0d5PLpNm01OPC+EuEIwSQrgLRpdX/wvefxSW/CeceutE72bCSPz973Te9SPS27djPvUU\nyu+4A8PMmeO2fk+qh9VbV/PUp08RzUZZUrWEm4+5mWP9x47bHo4Eoqksf9kR4OWtXbz2aTd9qRx6\nrZrFM73ccuZMls7x47eLJl7BkY8sy0RDAbpbmulu3kWgtYnulmb6Al2f+9hoKDgOOxQIpgZCuAtG\nj7fvh03/DcdfD2fdOdG7mRCyXV103/Mz+p5/Hm1lBVX33Yft7OXjVoXtTnTz6CePsnbHWlK5FMtq\nlnHT/JuY65k7LusfCbT1JtlYSIF5pylENi/jtug5+6hyls0t4/RZXsx68adTcOSSz+UIt+2hu6Wp\nKNADLU2k4soUX1QqXBVVVDTMZsHyc/HX1OGrrefJO/6DaDAw7PlsHjG0TSAYLSbdfx+VSvUwcAHQ\nLcvy0YXb3MAaoBZoAa6QZblnovYoGIEPnoSX7oB5F8H598IUswtImQzhRx8j+JvfQC6H95Z/xXPT\nTahN45Mesje6l4c/fpj1O9cjyRLn1Z3HyvkrqXfWj8v6hzOyLPNJe18xX/2T9j4A6r0WvrqojmXz\nyjhuuguNemr9TAumBulEYkCctzbR3dJEaE8r+VwOAK3egG96LbNOXYy/th5fTT2+6bXojMPPNC25\nakWJx73/8UuuWjFur0cgONJRybI80XsoQaVSnQ7EgMcHCfd7gLAsyz9RqVTfBlyyLH/r857rhBNO\nkN97772x3bAAPn0e1lwLdafD1WtAa5joHY0rsTfeoOtHPybT2op16VLKvv0t9NXV47J2U28TD255\nkD81/wm1Ss3FMy/mhqNvoNo2PusfrmRyEu80hYpivSOSQqWC46e7WD6vjGXzypjhs070NgWCUUOW\nZWLhkFJFb2miu7WJQEszvV0dxWNMdgf+2npFoNfW46+px1VZifoAEqfGI1VGpVK9L8vy+E+qEwgm\nAZNOuAOoVKpa4LlBwn07cKYsyx0qlaoCeF2W5dmf9zxCuI8DLW/C6kuh/GhY8SwYpo7YyezeTdfd\nPyH22mvoa2spu+P/YF2yZFzW3hbaxqotq3il9RWMWiOXzbqM6+ZdR5mlbFzWPxyJJLK8tr2bl7d1\n8ZftAWLpHCadhiUNXpbNK+OsOX681qn1plNwZCLl84Tb9xYEuuJJ725tJhXtKx7jqqjEV1NfItQt\nTtdh0VwthLtgKjPprDL7oEyW5Q6Agnj37+tAlUp1M3AzwPTp08dpe1OUjo/gt1eBqxa+snbKiHYp\nmST4wAOEH3oYtFr83/xP3CtWoNLrx3ztD7s/5IHND7CpbRNWnZWV81dyzbxrcBvdY7724ciecIKX\ntir56n9rCZOXZLxWAxcuqGDZ3DIWzfRi1In8esHhSyaVJNDaooj0ll10tzQT3NNCPpsFQKPT4a2u\npeHEU/DXzsBXW49veg16k3mCdy4QCA6Gw0W47zeyLD8APABKxX2Ct3PkEtypVNpNTrj2j2A+8oWj\nLMtEX3yRrp/eQ66jA/sFF+C//Zvoysa2yi3LMu90vMOqLav4e+ffcRlcfG3h17hqzlXY9OMXLXk4\nIEkym9sixXz17V1RAGaVWfnn0+tZPq+MBdOcqIVfXXCYIcsy8d4eult2EWhpLjaO9nR2KDG8gNFm\nx19Tx8IvXlhsGHVXTkOtEW9OBYIjhcNFuHepVKqKQVaZ7one0JQm0garL1Y+vnY9OKomdj/jQLqx\nkc4f/ZjEO+9gmDOHqp/dg/mEsT1TK8syr+95nVVbVrEluAW/yc/tJ9zOZbMuw6wT1bJ+Utk8b+8K\n8vLWbjZu66I7mkajVnFirYvvnj+X5fPKqPFYJnqbAsF+I0l5etrbCz70poJIbyYR6S0e4ygrx19b\nz7wlZyl+9Np6rG7PYWF1EQgEB8/hItyfBa4DflK4fmZitzOFSYThiUsh2QvXbwDv+GWTTwT5aJTg\n/fcTfuJJ1FYrZd+7E9cVV6DSjt2vTl7K81LrS6zasorGnkaqrFXcecqdXDzzYvSasbfjTDbWf9DG\nz17cTntvkkqnidvPmc3ps3xsLAxCemNHkGQ2j0Wv4YzZPpbPK+PMWX5clqn3tRIcfmRTKQK7W4qJ\nLoGWZgK7W4rJLBqtFk91DfXHnVjwpNfhq6nDYBZvRgWCqcika05VqVS/A84EvEAX8H1gPfA0MB3Y\nDVwuy3L4855LNKeOMukYPH4RdG6Ba/4AdePTiDkRyJJE5I/r6b73XvLhMM7LL8f3jdvQulxjtmY2\nn+W5pud46OOHaO1rpd5Rz8r5Kzm37ly06sPlPfbosv6DNr6zbgvJbL54m1oFUuHPVrndyLJ5fpbP\nK+eUejcGrbAECCYv8d6egYbRQiW9p6NtwOpisRaq53XFxlF31TQ0Wt0E73xyIZpTBVOZSacGZFn+\np33ctXRcNyIoJZeGNddA+z/gyieOaNGe3PIxnXf9F6mPNmNasICy//1fTEcfNWbrpXIp1jWu49FP\nHqUj3sFc91zuPfNelk5filqlHrN1Jzt9qSw/fG5riWgHRbTbjFp+u/IUjq6yC2uAYNIhSxI9nR1K\nFb2Q6BJoaSLeOzB+xO4rw19bx5zTTleSXerqsXl8h/3P8453O/nrM7uIhdNY3QZOvWgGs04un+ht\nCQRHDJNOuAsmIVIe1t0MTa/BRf8P5pw/0TsaE3LhMIFf/ILetX9A4/FQcffdOC76Eir12IjneDbO\n09uf5rFPHiOUCrHQv5A7T7mTxVWLD/t/3gdDLi/x4Z5eNjUGeXNnkA/39JKXRj4jGEvlmD/NMc47\nFAiGk82kCe1uLVbQu1ubCLa2kE2nAFBrNHimTad2wXHF2EXf9DqM1iMvhWvHu5289uSn5DISALFw\nmtee/BRAiHeBYJQQwl3w2cgyPP+fsHU9nH0XLLxmonc06si5HD1PrSHwq18hJRK4r7sO7623oLGN\nTWJLJB3ht9t+yxPbnqAv08epFady0zE3cULZCVNKsMuyTEsowZuNAd5oDPLOrhDRdA61CuZPc/Kv\nZ8zgqb/vJhjLDHtspXN8JtIKBINJ9EUGBhgVGkbDbXuRZUWo6k1m/LX1zD/r7GLDqLuqGq3uyLK6\nSHmJWG+aaDBFXyhFXyhJNJSi8b0upFzpm+1cRuKvz+wSwl0gGCWEcBd8Nq/eBe8/Aou/Aaf9+0Tv\nZtRJ/P3vdN71I9Lbt2M+9RTK77gDw8yxabgNJoM8vvVx1ny6hkQuwZnVZ3Lz/JuZ75s/JutNRnoT\nGd7aGeLNnQHe2BGkrTcJwDSXiQsWVLKkwctpMzw4zUpj6Uy/dZjH3aTTcPs5nzt/TSA4aGRJore7\nsyDQm4uNo7FwqHiMzevDX1tPw8mL8NfW4a+tx+4rOyLefMuSTDySKQryvmDhuvB5LJxGGnw2TAVW\np2GYaO8nFk6P084FgiMfIdwF++av/w82/RyOuw6Wfn+idzOqZLu66L7nZ/Q9/zzaygqq7rsP29nL\nx+Sfbkesg0c+eYR1jevISlnOqTmHlcesZJZr1qivNdnI5CT+sbuHTY0B3mwMsrktgiyDzaDl1Bke\n/uXMGSyZ6aXGYx7xa3/xQiVqdGiqTP/tAsGhkstkCO3dXbS6BFqVSnomqbypVKnVeKZNZ/pRxxSr\n6L6aOkw2+wTv/OCRZZlkNFsiyPtCKaLBwnU4NUyEmx167B4jZXUOGk4wYveasHmM2L1GrC4jGq2a\nx/7PWyOKdKtbTCQWCEaLSZcqM5qIVJlD4MPfwfp/gblfgssfBfWRkdYhZTKEH32M4G9+A7kcnpU3\n4rnpJtSm0bdetPa18tCWh9jQtAFkuHDGhdw4/0Zq7DWjvtZkQZZldnbH2NQYZFNjgHebwyQyeTRq\nFQurnSxu8LKkwcuCaU60mqnbeCuYGJLRPgKFRJf+dJdw2x6kvHJGR28y4asZSHTx19bjmTYd7ThM\nRR5NZFkmHc8pgjyYKqmW94v1XFYqeYzJpsPmHizIC9ceIza3Ea3+8/8HDPW4A2j1ar7wlTmjapUR\nqTKCqYyouAuGs/0FeOZWqDsDvvzgESPaY2+8QdePfkymtRXr0qWUfftb6KurR32dxp5GVm1ZxYst\nL6JT67h81uXccNQNVFgrRn2tyUAwluatnUGlqbQxSGef0pRX57Vw2fHTWDzTyykzPNiNR5bPVzB5\nkWWZvkBXoYreXJw2Gg0FisdY3R78tfXMOP7kgtVlBg5/2Zg1o4826WSO6GBh3l8tL1TPs6nSNCaD\nWYvNY8RVbmH60R7sHiN2jwmbVxHmeuOhy4F+cS5SZQSCsUMId0EpLW/B76+HigVw1ZOgPfxPcWZ2\n76br7p8Qe+019LW1VK96AOuS0Y+z/Dj4MQ9sfoDX9ryGWWvmuqOuY8W8FXhN3lFfayJJZfO816LY\nXzY1Btna0QeA06xj0Qwvixu8LJ7ppdotprsKxp58Lkto755C7GJhgFFrM+lEHACVSo27ahpVc+YV\nqugz8NXWYbZP7lSibDo/qEo+vGKeTuRKjtcZNNi9RmweE1WzXMOq5gbz+LxxnnVyuRDqAsEYIoS7\nYICOj+B3V4FzOnxlLRjGJlVlrIhs2ED3L35JrqMDbUUF3ltvIbt3L+GHHgatFv83/xP3ihWoRvG0\ntyzLvNf1Hqs2r+KvHX/Frrdzy4JbuHru1TgMk1sY7C+yLLOtI8qbOxWh/rfmMOmchE6j4rjpLm4/\nZzaLZ3o5usqBRn34N+YJJi+peGxYw2ho7x6kvCJitQYDvpo65iw+U6mi19TjmV6DTj/5ChC5bJ5o\nqN/GkipWz/s/TkazJcdrdWpsHkWYl9c7CoLchN2rXBss2iOiMVYgEHw2wuMuUAjtgofPAY0BbnwR\nHNMmekcHRGTDBjru/B5yKjXsPvsFF+C//ZvoyspGbT1Zlnmz7U1WbVnFB90f4DF6WHHUCq6cfSUW\n3eE/iryrL1WwvgR4c2eIYExpOGvwW1nc4OX0Bh8n1bmxGMR7f8HoI8sy0WCgpGG0u6WZvkBX8RiL\n01XMRVcaRutxlpejniTWvnxeIhYuCPFCxXyw3zwRKY05VWtUBY+5Is6V64KdxWPEbNcLYV5AeNwF\nUxnxX1cAfe3w+MUgS7Bi/WEn2gG6f/HLEUW7xuOh6uc/G7V1JFli4+6NrNq8im3hbZRbyvnOSd/h\n0oZLMWqNo7bOeJPI5Hi3OcymHUHe3BlgR1cMAK9Vz6KZivVlSYOPcsfh+xoFk5N8Lke4bU+JQA+0\nNJGKKz+DqFS4K6qoaJjNguXn4q+pw1dbj8XpmtB9S5JMvDc9kMxSTGhRPo73phlcF1OpVVhdBuxe\nI9OP6veYG7F5Tdg9RiwOAypxxkogEHwOQrhPdRJhWH0pJMNw/XPgbZjoHR0wsiyTa28f8b58ODwq\na+SkHC80v8CDWx6kKdJEjb2GH572Qy6ovwCd5vBrupQkmY/bI8WG0vdbe8jkJfRaNSfVuvnycdNY\n3OBlbrkdtRATglEinYgXUl0KVpfmJkJ7W8nnClYXvQHf9Fpmnbq4WEX3Ta9FZxz/N4yyJJPoywxr\n+lSq5sl9ZpnbPEbFY+41DjSAeoxYXQbUIklJIBAcIkK4T2UycfjtFRDeBdf8ASoXTvSODgg5nye6\ncSOhhx7a5zHaikNLcsnkMzyz6xke2vIQbbE2GlwN3HP6PZxdczaaSXJKfn9p600Wp5S+vTNIT0Lx\n0M6tsHP9olqWNHg5sdaNUXd4vS7B5EOWZWLhUMHqoiS6dLc2EenqLB5jsjvw19Zz3HkXFe0urorK\ncbO6FLPMhw0ZShW95/lcaWSi2a7HVsgyn3mCsTSZxWVEoxPCfPPmzWzcuJFIJILD4WDp0qUcc8wx\nE70tgeCIQQj3qUouA2uugbb34YrHoe70id7RfiOl00SeeYbww4+QaWlBV12N/dJLiP7phRK7jMpo\nxP+N2w5qjUQ2wR8a/8CjnzxKd6Kb+d75fOvEb3FG9RmoVYfHP+doKss7TWHeLKS/NAWVlA2/zcBZ\nc8pY0uBl0UwvPtvka9wTHD5I+Tzh9r0DfvRCPnoq2lc8xlVRSVndTOZ/4eyiL93idI2pZ1uWZdKJ\nXGlUYjBJX3igaj44bxzAaNVh9xjxVFmpO8Zb6jffzyzzqczmzZvZsGED2axSFIhEImzYsAFAiHeB\nYJQQwn0qIuXhj/8Mu16FL90Pcy+c6B3tF/m+PnqeWkN49ePkA0GM8+ZR9Yt7sS1fjkqrJXLqqSWp\nMv5v3IbjwgN7bdFMlKc+fYrVW1fTk+7hxPITuWvRXZxSccqkbwzL5SU2t0WKPvUPdveSk2RMOg0n\n17v5yik1LGnw0uC3TvrXIpicZJIJAq0thdhFxY8e3NNCviDUNDod3upaGk46FX+NItB902vQm8Ym\nGjSTzBU95aVDhhRhnhmSZa43abF7jTj9JqbPcxcjE+0epRF0NLLMpxqyLJNIJAiFQrzwwgtF0d5P\nNptl48aNQrgLBKOE+Cs11ZBl+NM34ZN1sPyHcNy1E72jzyXb1UX4scfpXbMGKR7HsmgRnnvuwXxK\nqZh2XHjhAQv1fnpSPazeupqnPn2KaDbK4qrF3HzMzSz0T277UGsoXpxS+vauENFUDpUK5lc5uPn0\nehY3eDm+xoVBKyqFgv1HlmXiPeFiLnp/42hPZwf9HZdGmx1/bT0Lv3hhsWHUXTkNtWb0ftaymXwx\nkaVoYylUz/tCSdLx0ixzrUFTbPqsnOUs8ZjbveOXZX6kIcsysViMcDg84iWdTn/m4yORyDjtVCA4\n8hHCfarx2o/hvYdh0W2w6OsTvZvPJL1zJ6GHHiby3HOQz2M/91w8N34V47x5o7ZGd6KbRz95lLU7\n1pLKpVhWs4yV81cyzzN6a4wmkUSWt3cF2bRTEet7wkkAqpwmzp9fweIGL4tmeHFZDq8R7YKJQ5Ly\n9LS3D6qiK5dk34DYcpZV4KutY96Ss4p+dKvbc8hnbvJZiWh4eFRiv71laJa5RqcuVsfLau2lQ4a8\nRowWnTibdJDIskw0Gt2nOM9kBuIrVSoVLpcLt9tNdXU1brcbt9vNhg0biEajw57b4TgyZloIBJMB\nIdyPdDY/DRt/CJG9YHRAqhcWXgvLfjDRO9sniX/8g9CqB4m99hoqoxHXFVfgvuF69NNGL6Zyb3Qv\nD3/8MOt3rkeSJc6rO4+V81dS76wftTVGg2xe4oPdvcUppZv39iLJYDVoOaXew01L6lk800ud1yIE\ni+BzyaZSBHa3FBNdulubCO5uJZdRKqYarRZPdQ0zjj8JX009/to6fDV1GMwHN5tAyTJPFxNZSppA\ng0niI2SZW91KxbzuGG8xKrFfnJttehGZeAhIkvSZ4nywzUWtVhfFeU1NTVGcu91unE4nmhHOrCxf\nvrzE4w6g0+lYunTpuLw+gWAqIIT7kczmp2HD1yCrVGVJ9YJKAzWLYZKJPFmSiL3+OqEZ6AS+AAAg\nAElEQVRVD5L84AM0TifeW2/Fdc1X0LpGL6+5KdLEQ1se4vmm51Gr1Fw882JuOPoGqm3Vo7bGoSDL\nMrsC8WJD6TtNIeKZPGoVHFvt5N/OamBJg5djq53oRLSc4DOI9/YQaGmia1DDaE9H24DVxWLFV1vP\nguVfVER63QzcldPQaPf/30J/lvnQqMT+6nmsJ1WaZa4Cq0upjlfPcw9Uywt2FovTIOJHDxFJkujr\n6ysR5KFQiHA4TE9PD7ncgL1Io9EUxXldXV1RmHs8Hux2+4ji/LPo97GLVBmBYOwQk1OPZH5xNET2\nDL/dUQ3f+Hj89zMCUiZD34YNhB56mExTE7rKStw33IDzy5eiNo9eQ9u20DZWbVnFK62vYNAYuHz2\n5Vw37zrKLKM3TfVgCcczvLmzMKW0MUh7REnGqfGYWdLgZfFMH6fO8OAwCX+uYDiyJNHT2VGIXVQE\neqCliXhvT/EYu6+sUD1XBLq/tg6bx/e5Z2lkSSYRzQwR5P0e8xSxcAopX5plbnEYCoOFBvvLC0OG\nXAY04g3nISNJEpFIZJgw7xfn+fxAU65GoymplvcLc7fbjd1uR60+/L4fYnKqYCojKu5HMpG9B3b7\nOJKPxehds4bwY4+T6+7GMGcOlT//OfYvnoPqACp+g3m+6Xnu+8d9dMY7KbeU8/Xjvk6VtYoHNj/A\nprZNWHVWVs5fyTXzrsFtdI/yK9p/0rk877f08Eajkv7ySXsfsgx2o5ZFM73cepaXJTN9TPeMTRKH\n4PAlm0kT3N1SbBjtbm0i2NpCNq282VNrNHimTad2wfGKUK+tx1dTh9FiHfH5ZFkmFcsOSWYZaACN\nhlPks6WRiSa7HrvHSFmNjZnH+QuRiQWR7hZZ5qNFPp8vivPBwrxfnEvSwPdFq9Xidrvxer3MmjWr\nKMzdbjc2m+2wFOcCgWBkRMX9SCWwHX69CKTs8PsmsOKe7e6mZ/Vqen73FFIshvmUU/CsXIll0WmH\n5NF+vul5fvD2D0jlB3Lc1aiRkHAanFw771qumnMVdr19NF7GASHLMtu7orzZGGRTY5B3m0OkshJa\ntYrjpruUqnqDl2OmOdEIm4CgQKIvMpCL3tJEoLWZcNteZFkRbAazBV9tXTF20V9bj7uqGq2u9MxM\nKp4dMSqxv2qeS5dGJhotumKzp80zEJXYb2vRiSzzUSOfz9Pb2ztMmIfDYXp7e0vEuU6nG1YxHyzO\nJ0uPS0fnMzTt+jmpdAdGQwX1M75JRflFo7qGqLgLpjKi4n6kIctKasyLd4BGr5hK84MawHQmWPq9\ncd9WuqmZ8CMPE1n/DHI+j+2cs/F89UZM848elee/7x/3lYh2AAkJu97Oi19+EbNufKvX3dEUb+0M\nFjLVg3RHlea/GT4LV504nSUNXk6u92A1iF/BqY4sSfR2dxZz0ZXG0V3EesLFY2xeH/7aehpOXoS/\ntg5/bT12XxkqlYpMamDIUEdTZzE+sb8ZNJMsjUzUGzXYvCYcPhPVc9xFkW73KhVzvUn8TI4muVyO\nnp6eEZtBe3t7GVw80+v1uN1uKioqOOqoo0rEudU6+ecvdHQ+w6ef3oEkKX1VqXQ7n356B8Coi3eB\nYKoi/kIfScQC8Oy/w44XYMZSuPjX0PyXgVQZxzRFtB9zxbhtKfnhh4QeeojoKxtR6fU4Lvsynuuv\nR19TM2prdCe66Yh3jHhfNBMdF9GezOT5W8vAlNJPO5VINJdZx+IGH0tmKlX1SqdpzPcimLzkMhlC\ne3cXIxf7K+nZlCJ0VGo1nmnTmX70goEqemUNmbSuKM4De1Ls+jBAX3AP0VCKVLz0rJpWry5Wxytn\nOgeEecFvbjBrJ70APNzIZrP7FOeRSKREnBsMBjweD1VVVcyfP79EnFssh086lCznSWcCpFPtpFId\npNLtNDf/T1G09yNJSZp2/VwId4FglBBWmSOFxldg/b9CKqIMVjrpZpggX6Msy8T+8hfCDz5E4r33\nUDscuK7+J9zXXIPW4xm1Nd7tfJentz/Nq7tfJS/nRzyuwlLBS5e9NCprDkaSZLZ29LGp4FP/e0sP\nmZyEXqPmhFoXSxp8LGnwMq/CLlIypijJaB+B1ma6m3cVG0ZDbXuQC/YHvcmEr6YOb3UdNm81Jlsl\nqN3EI/lBQ4ZSJPtKIxM1WnXBU24sxiX2e8ztXiNGq8gyHwsymcxnivPBmEymYQ2h/Rez2XxYfH9y\nuSipVLtySXeQSrUXRLryeTrdiSznPv+JAFCx9Kydo7Y3YZURTGVExf1wJ5uCV74P7/4G/PNgxXoo\nO2pCtiJnMkT+9CfCDz1MurERbUUFZd/5Ns7LLkNtObgc6KFE0hGe3fUsT29/mpa+FhwGB9fOuxav\nycv9H9xfYpcxaox8/bjRGzLVEUkWppQGeWtnkHBcEVRzym2sOKWGJbN8nFTrxiQ8wFMKWZbpC3QV\nc9EVX3oz0VCgeIzF6cbun0798UehM5Yjq7ykEyaioQzb30+DDNAL9KJWq7C6Ddi9Jmrne4Yls5jt\nIst8rMhkMsNEeb//fOhgIbPZPGLGeb84n8xIUoZ0uqsozNMFYZ5KF4R5qoN8PlbyGJVKi8FQjtFQ\ngdNxPAZjJUZjJUZDhXJtrOTdd88jlW4ftp7RUDFeL00gOOIRwv1wpusT+MNK6N4KJ/+rMlRJZxz3\nbeRjcXrX/p7wo4+R6+zE0NBA5U9/gv2881DpRifC8JPgJ6zZvoYXml8glU9xjO8Yfrz4x5xdezYG\njQEAr8k7LFXm/PrzD3rNeDrHO02hglgPsCsQB8BnM3DmLB+LG7wsnunFbx//r7lgYshls4T27i7E\nLjYV010yyYRygEqF2V6GwTINz/SF5HIe0kkneUz0BKAnoLSdWFwa7B4N1XNcg4YMKc2gIst8bEmn\n0yMK83A4TCxWKlYtFgtut5v6+vqSxlCXy4XJNDltb7Isk82Gi0I8neooqZqnUu1kMgEK7xaL6HRu\njIYKTKYaXK5TC6K8EqOxAoOxEoPeh0r12UWJ+hnfLPG4A6jVJupnfHMsXqpAMCURVpnDEUmCv/0v\nvPx9ZRrqxb+GhmXjvo1cMEj4iSfo+e3vkPr6MJ94Ip6VN2I5/fRRORWczCX5c/OfWbN9DZ+EPsGk\nNXF+/flcOftK5rjnjMIrKCUvyWxpi7BpR4BNO4N8sLuHbF7GqFNzUp2H0wvpL7PLJk+Cg2DsSMVi\nSqNoSxPtjTsJtDTR29WGLCm2LJVaj1bvR8YDah9qjR+VxoNKpcPi0CuJLF5jSbXc5jFhdYss87Em\nlUqNKMzD4TDxeLzkWKvVOmLGucvlwmicfG/K8/lk0VM+2F8+uHouSemSx6jVBozGSgz91XGDUiE3\nGCuK4lyjGZ03Io++/iz3/yVGMGnHa+rj386wcv2ZXxqV5+5HWGUEUxlRcT/ciHbC+ltg10aYdS5c\ndD9YvOO6hUxrK6FHHiGy7o/I2Sy2ZcvwrLwR04IFo/L8zZFmnt7+NM/seoZoJsoMxwy+c9J3uHDG\nhdj0tlFZo5894UTRp/7WzhCRpNLod3SVnRsX13N6g5fjalwYdcL+cqQiSRLB3e3s2bqDjp27CO1p\npi+wh0xyYIARKgtqjQ+1/njUGh8meyWOsgocPnNRkNsLAt3qNqAVPy9jTjKZHFGYh8NhEolEybE2\nmw232z0s49zlcmEwGCboFQyntOFzqL9cEejZbM+QR6kw6P0YjJXYbPPweZcWrSv9Ql2nc495sUGW\nZda+v5efbNSRyjoACCYd/HSjBqejjYsXVo3p+gLBVEEI98OJT/8Ez/4bZBJw/r1wwleV8+7jRHLL\nx4QefJDoSy+h0mpxXHwx7q/egKGu7pCfOytleX3P66zZvoZ3O95Fq9aybPoyrpx9JceXHT9q/3Qi\nySx/3RXizZ3KlNKWkPIPvsJh5Jyjyljc4GPRDA8e6+T5Zy44dNIJZchQpCtGx65mAq3N9HbuJt6z\nl0yyE+SBCqVK7UZrKMNRcRxO/3Q80+vwTvMPDBnyGNEZhDAfa2RZJplMjijMw+EwyWRpeondbsft\ndjN37tySCrrL5UKv10/QqyilpOFzmJWlnXS6a1jDp0ZjLQpxu/2YQqV8kI3FUIZafWCvT5Zl0jmJ\neDpHIpMnnskRT+dJFK6V23PEM3kS6cJ1Jkcs3f954XHpget4Jk9eGn4GP5nN87MXtwvhLhCMEkK4\nHw5kEvDSHUo+e/kx8OUHwTd7XJaWZZn4m28RevBBEu++i9pmw3PTTbivvQatz3fIz98V7+IPjX9g\n7Y61BJIBKiwVfG3h17ik4RK8pgM7k7D+gzZ+9uJ22nuTVDpN3H7ObM4/poKP9vQqU0obA3y0N0Je\nkrHoNZxS7+G602pZ0uBjhu/wiWGbaux4t5O/PrOLWDiN1W3g1ItmMOvk8pJjilnmocJwoWCK3q5e\nwm0tREN7ySY7kfIB5HwIKFhdVFoM1gp8tcfhqaqlrH4GVXNm4K50YRBZ5uOCLMvE4/ERhXk4HCaV\nKp3N4HA48Hg8wzLOXS4XulHqpzlYhjZ8FkX5/jR8GitxOk4YseFTo7GWiOxYOkconSPe1y+0uwdE\n9WABXhDdsUHiPJHOF0X3SCJ7JFQqsOi1mPUarAYtZoMGs16Lx6Kn2m3Golc+txq03P/ayMkx7b3J\nEW8XCAQHjvC4T3baP1QaUEM7YdHX4AvfBe3YV4/kXI6+F14g9OBDpLdvR1tWhvu663BecTka68jj\n0/cXSZZ4t+Nd1mxfw+t7XkeSJRZVLeLK2VeypGoJGvWBVzPXf9DGd9ZtIZkdiIVUq0CnVpHOy6hV\ncMw0pzKldKaXhdNd6LXCZzzZ2fFuJy89uI50bBNIUVDb0JmXUL/wNLR6LdFQkkgwSSrag5TrRs4H\nkPLKtSwNRPTpjFZc5TX4auupmt1A5ayZuCqrUB/Ez5rgwJBlmVgstk9xnk4POtuhUuF0OkeMUXS5\nXGi1E/OGSmn4DJX4yRVRXtrwKcsyGUlHOmcgnTeQU/mR1BVIqnJyai95PGRlF1nZRkaykMoZSGSk\nfVSwB4T2fmps1P0i26ApubYYFOE99D6rQYtZr1XEt0FbIsL7jzPq1Ptd1Fj0k1dpG0GkVzlNvPXt\nsw7kS/6ZCI+7YCojykqTFUmCv/4PbPwvsPhgxTNQf8bYL5tI0Lv2D4QffZRsezv6GTOo+PGPcVxw\nPqpDPN0cSUdYv3M9v9/xe1r7WnEZXKw4agWXz7qcalv1QT9vLJ3jh89tLRHtAJIMGo2aX1+1gNNm\neHGYJ7YiJ/h8ctk8vV0Jwu1xwh1x3tvwIunoS0DBPiBFycZepPGdvRitJmQpQCbZST4zkOri8JVT\nVj8ff2GAka+2HovTJc6ojCGyLBONRvcpzjOZgSx6lUqFy+XC7XZTXV1dIs6dTue4iXNZlklm88TT\neaLJGD3RTnrjASLxEJF4D32JCNFUnFgyQSydJpXTks4bSOUMpPIGMnkrGWkhaek00jkDyZyOZFaF\nzP78nEVRq6JYDNpSMa3X4LMZqPVaiiLaYii9tg75fLAIN2j3X2SPBbefM3tYAcWk03D7OeNzhlgg\nmAoI4T4ZibTB+n+B5jdg7pfgwvvA7B7TJXPhMD1PPEnPk0+Sj0QwHX88Zd/9LtYzz0B1CIOcZFnm\n4+DHrNm+hj+3/Jl0Ps2xvmP558X/XBLleKDPua0jyl92BPjLjm7ea+kht4+SVDKT59z5IkN4spHN\n5OntTBDuiBdFek9HnL5gEkmSQY4jy31kYq9SFO1F8kjZzWRierzTa/DVLsFfowh03/Qa9KbJnaF9\nuCJJ0jBx3u8/7+npIZsdmOCqVquL4nxozrnT6USjObAzHYNFdnxfHutBXuyhdpFoMkksnSKezpLI\nSCSzkMyq9yGyjUBF4VJ4PSoZs07GrFcrYttgwGU0FESzUqm2GIZXri0GTaHaXfi4IM4tk0BkjwX9\nPvahlkXhbxcIRg8h3Ccbn6yHDV+HfBa+dD8svGZMG1Aze/cSfvgRetetQ06lsC5diufGGzEft/CQ\nnjeZS/JC8ws89elTbAtvw6Q1cdGMi7hi9hXMdh949aUnnuHNncGCWA8QiCqn1+dV2Fm5pJ617+8h\nGMsMe1ylc3JmLU8VMqkcPZ0JejoUcd4v0CPBKHK+DzkfASLoDXHU6iiqfA+5eJh8bvj3cij//tjv\nUR+gABR8NpIk0dfXN2KUYk9PD7ncwJsojUZTFOeDc85dbjc6o4VUXi5aPeLpPHszOXa0ZYg3txcb\nHgcL76E+7KIQT+dIZPPsr6tTrZIx6XIYNRkMmhR6dRyjJoVBm8ZhTGOwpDHpZCwGPTaTCZvJit1k\nw2Z24jR7cFh9OK1ebEZT0WJyJIrsseLihVVCqAsEY4gQ7pOFdBRe+DZ8+ARUHQ+XrgLPjDFbLrV1\nK6EHH6Lvz38GjQbHly7E89WvYphxaGs2RZp4evvTPLvzWaLZKDOdM7nj5Du4oP4CrPr998bnJZnN\ne3uLQv2jPb1IMjjNOpY0+Dhjlo/TGwaGH80pt4lTtBNIJpkrEeah9jjhvd1Ew93I+V5kKYIsR9Bo\nokj5CLl0X+kT5Iw4y8pxlNXi8J+Cs6wCR1k5z//qXtLxyLD1TDa3EO0HiSRJRCIRwuEwgWCQzkCY\nzlAvgXCEYCRGRoKsrCGLGkmtQ2eyojWWo3bXodIZkTR68iotWVmliO1AnkRbjli6j0QmTCKT//xN\nFNCqVSNWqiscRkU061QYNGn06jg6dRSdKoKOEBo5iEbuRpXvQKfqxahNF8R5Bp0ajMbyYQOEBjd8\narWjGysrEAgE44UQ7pOBve8pDai9rXD67XDGt0AzOn7syIYNdP/il+Q6OtBWlGM//3zSn2wl/vbb\nqC0W3Ddcj3vFdejK/Ae9RlbK8uruV3l6+9P8rfNvaNValtcs58rZV3Kc/7j9rlR196V4o1Gpqm9q\nDNCbyKJSwbHVTr62tIEzZvk4ZpoTzQhTJcUp2vEhFc/S05kg3B4jtDdCd+teejo6SESCijiXIshS\nL0h9yPKAdQKVCqvLg7O8HId/Nk5/OY6ychz+cpzlFZhs9hF/TpbesJIXf/M/JRV4jVbPF667YTxe\n7qRCkmQS2QFbyEiV6lghxi+WytITS9ITTRCJp4gmM8qx2TypnExW1pBDTY7+Nz/2wmUECr2jWjVY\nDDmsBjDr80WhXenU79t/bRjU7KjXlDRJmvRq1HJvMXElld5dEo2YSnXse8JnYXCQwXhS8eN+Ua7X\nez93wqdAIBAcrohUmYlEysOme+H1u8FeCZc+ADWnjdrTRzZsoOPO7yEPiVRT2Wz4/vlmnFdeicZ2\n8JWnzngna3esZV3jOgLJAJWWSi6ffTmXzLwEj8nzuY/P5CT+sbtHqapvD7C1Q6nC+mwGzpilVNUX\nz/TiskyODOapRCqWJdQeo7Opk66mPYTb2ukLdpJOhBVxno+AXBptp9EZsPvKcFdWFKrnFUWBbvf6\n0R5kc/O2Ta+x6anHiYaC2Dxelly1grlLvjAaL3PMyEsyiSE+7H5RHR8S2/d5MX799w1tvv4s1Ejo\nkNCSR6eS0KskTAXhbDPpcZiNOG0m3DYLbrt5oElykLhWkkUGquAHmsKUzycUQV6Y5jk4JjGV6iCd\nbkeSSi1RarVxkCgfHo1oMJSP2oRPweGLSJURTGVExX282Pw0bPwhRPaCYxqceitsfQZ2/xWOvgzO\n/28wOUd1ye7/vneYaAfQWK14Vq48qOeUZIl32t9Rohz3vo4syyyuWswP5vyARZWLPjfKcU84wRuN\nilB/e1eIWDqHVq3ihFoX3/riHM6Y5WNuhU34SceJvlCMtm2tdOzaTWhvO5HuThKRILl0uBCnWNoY\najA7sXnLcFc24K2uKgj0cpxlFZjsjjH5vm23zuKx6mtptylnUvzWWcwdxefPS3JpxvUIHuvhg2hK\nM7GHxvilstJ+r6/XqkdMEHFbzJh0arTkUeUzyNk0UiZJPhUnm4ySSUTRynm0Kgkdecx6DX6XnTKv\nG7+3NErRarWO6vdGlvOk091DohHbS4T6iBM+DWUYDBXKhE/fsiFWlkp0OpH+IxAIBJ+FEO7jwean\nYcPXIFvIt43sgT9/GzRGxct+zBWjulx61y7Cq1eT6+wc8f593f5Z9KZ6i1GOu6O7cRvd3HDUDVw2\n6zKm2abt83GpbJ53mkJFr3pTIA4oub4XHVvJGbN8nDrDg80oohrHAlmWSUR66WreS3tjK8HdbfR2\ndhLv7Vaq5/loyfEqtQ6DxYPDX4Wz/Hj8tdX4a6cpAt1XdtBV84NlaD5/W2+Sb6/bTG8yw+KZvmGV\n6hIbyT7E+ND7DkZkD43x81j0hbSR/uSQ4XYRSzFTeyDGz6TXoJLz9PT0jBijGIlEGHxW1GAw4PF4\ncFe5cbvrSsS5xTI6Q8RkWVYmfKbbSRer5AMV83SqnXSmC1kuPQOg1dowFKrjDsexRftK/23KhE/x\ney4QCASHghDu48HGHw6I9sGYXaMm2mVJIvbGG/Q8vpr422+j0utRmUzIyeHraiv2Lx5RlmU2Bzfz\n9Pan+XPzn8lIGY7zH8ctx97C8prl6DXDRZwsy+wKxItC/d2mEOmchEGr5pR6D9ecXMMZs33Ue8Wk\n0tEil83SF+iit7OD7tY2Ai176ensIBrqJh0PIQ+xI6jUVvRmD66K2TjLyvFNr6J85nTKZ1SPat65\nLMsl49SHVqUVkV1qFxlcyY6nc2zeGxkW9ZnKSvzg2a2fu75Bqy7xVPdH8/lshpGH0wwZUmM1lApw\ns16DTnNw0aiZTKYgzgN07S5NbOnrK23UNZlMxYzzBQsWlIhzs9l8yN8fZcJn54B1pSDM04MEej4f\nL3mMMuGzAqOxAqfzJNHwKRAIBBOEEO7jQWTvyLdHD7zyPZR8LEZk3R8JP/kE2dbdaMvK8N12G84r\nLif+1lvDPO4qoxH/N277zOdMZBP8qflPPL39abaFt2HWmrmk4RKumH0Fs1yzhr+MVJa3d4WKXvX+\nyXkzfBa+UhDqJ9e5MepEw9jBIMsyyWgfka5Oers76e3sILinjXB7B9FgF+l4L6UNfFpUagcavQub\nrwaHvxzPtErKZ1QzbU4Ndu9wK5Isy2TyEj2J7CBh3e+vLgjvEl92oRkykxsW7afcd2ARfnqNurSR\nsSCq95XPD/Crf1o4osVESSPRoD1IkX2wpNPpksr54CjFaLT0zIbZbMbtdlNbWztsQqjZfPA59AMT\nPtuLUz5LGz7byWSCjNzwWYnZVIvLdVpRjPcLc9HwKRAIBJMDIdzHA8c0xR4z0u0HSaalhfCTvyWy\nbh1SPI5p4UL8X/86tuXLUemU09GOCy8EGJQqU4H/G7cVbx/Krt5dSpTjrmeJZWM0uBq485Q7Ob/+\nfCw6S/E4WZbZ2tFXFOrvtyoDkKwGLafN8HDLF2ZweoOParcYhLO/KFXzbiLdnUWBHunqJNzeTl+g\ni1ymtFdBUlnJaT1kNTXgWojO5cXo8WEq86L32FFbdGRVEM/kCGTyfJjOKZF9TTtKbCWDmyA/SyQP\npn+susUwuGqtodxuLLWLDB5MM6S63d/4aC3YRfbV+PhZI9S/tKDywL/Qh0gqlSqK88HCPBwOE4uV\nNutaLJZhGecejweXy4XJdHANloMbPof7yxVv+cgNn4qH3OM5syQmsd/KotEYD/prIhAIBILxQ6TK\njAcvfx/e+mXpbToTXPirA7LKyLJM/K236Vm9mtgbb4BWi/3cL+K+9lpM8+cf1Nay+Swb92xkzadr\neK/rPXRqHWfXns2Vs6/kWN+xxcpsTzzDpp1B/rI9wBuNpQOQzpitJMAcN911wMkTUwHFMpIjFOql\ns6OT7q4AgUCIcKiXnt4IPZE40USKjEpHVq0jq9KR1RjIqk1k1CayagNZlY6cRkdWoyGDiuwB/N6a\ndJpBFel9i+nS+wYJ7CFVcKNu/IbRDPW497+euy+dP2ZRn6lUakRhHg6HicdLLSRWq7WkWu7xeJQh\nRC4XRuOBieHhDZ/91pUBf3ku1zvkUUrDp9FQgaEgxAdHIxoMFaLhU3DEIVJlBFMZUXEfa8LN8P6j\nYKsCFdDXrlTal35vv0W7FI8TefZZwk88SWbXLjReL95bbsF11ZVofb6D2lZHrIPf7/g96xrXEUqF\nqLJWcdtxt3FJwyW4jW7ykswHe3r5y/bCAKS9vciFAUinN/g4fcgApMnA+g/aRiXHPZ3Ll0x8HOrH\njqcHN0IOWEfi6SyRWJJYIl2sYifzkJHUyMOEk7lwqQQTygVQyzIGVBi1Gsw6DVajFr9Zh9NmwGbW\nlYjpgXHqIwvsfrE+Uu794cJY5fMnk8kRhXk4HCaRSJQca7PZcLvdzJo1qyjM+8W5wWDYr/WUhs++\nAS/5kKbPz2r47I9GLGn4LNhYRMOnQCAQTC2EcB9LMnF46ivKxzc8B+76A3v43r30PPlbeteuRYpG\nMR51FJU//Qm2c89FvZ/pHs83Pc99/7iPzngn5ZZyvlj7RZr7mnlj7xvIsszp007nytlXsqhqEcFo\nho0fB/jLjhY2NQaJJLOoCwOQbls6izNm+5hf5Zg0QjCXl0jlJJKZPM9+1MY9f95OOqckhLT1Jrl9\n7Ue8vSvIrDJbiQAfnKkdSxdGqg+6LZvfv2q2ChmDSkZHDl0+jSabQi9l0MlZLFIWF3lMWi0GlRad\npEWT06KTDehVJgyYcdoteH1mysrMlFdaqZpmwz/NhsEkfi0HU68JcZnhIyLGCA6Dg3qNF/hs4S7L\nMolEYkRhHg6HSQ5p2rbb7bjdbubOnVtSQXe5XOj343dNktKkUp3D8soVG8vnNXxW4nSdVKiaV5ZY\nWUTDp+BwI/5BN30vtpDvTaNxGrCfU4tl4cEP+BMIBKUIq8xYIcuw9gYlq/0rv4eZy/bzYTKJd/9G\n+InVxF59DVQq7OecjevaazEde+wBnfJ+vul5/r/n1pLoXoqcc6LS9qL3v4jL07MBIA0AACAASURB\nVMjVc6/movpLaQ+Zigkw2woDkPz9A5BmKwOQnOYDiwDM5SWS2TyprEQqqwyOSWYK19k86eJt/ccN\n3N9/fKrkMdKgxwzcv78Cux+DVj2kIj20Wq3BpFOjyaVQp+KQ7EOK9ZKPhMlGAmRCXZCMopey6OQs\nWjmHyebAZPOhMziRcZDNWEjGTIADVEpyjt1rxF1hwVVhKV67ys3ojUKgfx6bN29mw4YNZLMDU1h1\nOh0XXngh8+fPJx6P71Ocp4bMMHA4HCUV88HiXKfbd9ValmUy2VChUj7gJx88WEiZ8FlKf8Pn4EjE\nwaJcNHwKjjTiH3TTu64ReVDEqkqnxnlpw6iKd2GVEUxlhHA/QPbbjvHmL+GV78OyH8Dib3zu46RU\nisiGDfSsfoL0jh1onE6cV16J65+uQldeflB7Pfb+f6G37RyQBwlvVQZL2esstF7N+y1hkjkJjRoa\n/DbmVNho8NtwmnUDovtzBHUqkyeVOzRBDUrDo0mnwaTXYNAq1yadcjHqFUFt7P9cN/z+O9d/POLz\nqoCPfnB2MWVElmVS8ZjSANrVQaSrU2kI7e6kt6uLaDCALA/809HodNh9ZZgdPvRGF6gc5LJWkjET\nsYgRZG1xIYfXVBTn7krl2llmRmcQ4uxgyOfz/PKXvxyWyAKgVqvRarVkMgONmCqVCqfTOUyY94tz\nrXbkN0pKw2epKB8cjfh5DZ+KdaVCNHwKphSyJCNnJeRsvnAtEVi1GSmaHXasxmmg4tsnjdraQrgL\npjJCuB8A+90ot3MjPHkZ8tyLyF7yEOs+aOP76zeTlgaq5XqVzK3LZnGiQ0X45Y2EN71FOpWGqmp0\ni5agOepoMioNmZykXPJ50lmJTF4q3pbOS4Nuyyu35STimTShRIRoQgccmmgcLKiN/aJ5iKDuF9Mj\nCWrlPvWw20yDnsuoV6PXHFrD40n/9090J4f/LLs1WX45r6cg0ruIdHeSTpRaFswOJw5/GXZfOQaz\nB5XGQT5nJRU30xfWEA2mirGGKrUKh89UqJybiyLd6Tej1QuB/lnk83kSiQTxeLx4PfTjwZ/3V8x9\nviZq6z7EYIiTTltoaT6WQKCek046qUScO53OYeJcknJkMt2lWeVDpnwOb/hUYzD4h1hXBlXMjZVo\ntU7R8CmYdMh5GTlXENIZCTknIWfyynXxtvyg+0qFd78QlwZ9XHrfwG0cYIFm2k+WjNrrFMJdMJUR\nwv0A2Fc0nVatotxhJJOTyOZyZJJxsmjJjGILgVatQq9VKxeNcm3QqtFrlSg9g0ZNVk7SmdxLINmB\nWp0nEzkGpeY8FJmfX7YAUyEhZKigNuk1GLWjI6jHgnwuSzQYpC/YTSTQRV8gwOqNH/KKYxG5QY16\nWinLWcHXmZduwe4vx+kvw1FWjtVdhlbvJC/ZyCTMRIJ5wh1x+oLJYry1Wq3CUWbGXWEeqKJXKAJd\noxPJOaAI8WQyOaLoHkmQD7Wu9KNSqTCbzZjNZiwWCxaLpfhxY+MT1NS+gUaTH7Suhra9Z3H99b9W\nGj4H+8mHxCR+XsPnYFFuKFTQDQa/aPgUjBqfKaYz+xLIByKmBz4/UDFdRKNCpVOj0mkK14Mvym3q\nEe/XoNKrUWnVqPRqep/dhRTPDX96UXEXCEYNYbI9ANpHEO0AOUnmpDo3epWEfvsGdIb/v707D5Os\nru89/v6eU0tX7z3d0zPDsAzbsCkwigRxQASuCIlLEI0EIxqvmlWNSW5MojH6kGuWJ3GJPvdeNASC\nRjS4gddLJAgoLrigDssMgiwCM0zP1j291nLO9/5xTnVXd1f3dPf0TFPdn9fz1HPO+dU5p35VUzSf\n/vVvGSB35uvJtq4inwn4h9u2Qb3w684nOn7JqosvomXdGvLZiVCehPFwfH+2AaFbdm3hU/d/irue\nuotCezPnFF7Oww9vYpeVJ3eTSQVBmSvOOmrBn8OhVi4VGdy9i/19O9m/e1cazvuSx+4+hvbtZdLK\nPmac6E5cLvO9rnMYzLTSVhnixfu+z0nDj/Lyd/4f9j07yr4dwzzzi2EGf1ANkIME4RCda5rpPbqN\nk89ZS9faJKB39BYIV9jUlrVBfKZW8Nr9qQM8a9WG8DVr1kwL5LX7hUKBIEg+a3cnjscol/spVwaI\n/b5pwTsMI44+5pvc/a0z6gz4zJLPr60Z8HnElGkSNeBTasL0PILz1JbneFJZnfPTkH5wYbp+kA6a\ns2mQDrBcOB6ck+2UazJpuJ4peGcDbJEmHPCYun3c2y/ZsCj3FxEF93k5orMw42Iw//S6M5LBqNFX\n4bduhhPOGX/+hi9/n77mrmnX9Y7282t/93sLqou7c++z9/LpLZ/m3mfvpTXbzkn5K3hg6/O4p9jE\n+RtX8/x18M1tO4HaABrzm2efsKDXXCyl0ZE0hE8P5ft39TEyMLnrQhCGtHX30N7TyzHPP5P21b20\n9/TS3NVDJtsJtHLz37yLk4Yf5aThRye/WNDGnTc+TJgJ6FzbzNrjOjj1JetYta6VrnXNdKwuEBzm\nFTYPlziOGRkZmbEVfGo4P1AQrwbt3t7eSQF8aiAvFAqYGVE0QqUykITw8j7KlQHK5e1UygOUK/3s\n3jPAs8/2p+X9lMv9VCr90/qT1xdxxLrXTelfXl3hc3n+ey53HvmUADy3MB2XYyjHxLWt2FNbtSuH\nIEzXBuVMQNiSnSE41ymbFKAPbZg+nKoDUDWrjMiho+A+D396yUl1+7j/6SUnJQssPfhluPiDcMJF\nAIxt28bef7uRqx98nI9veh3FzETrd75S4re3fxd447zqEHvM3U/dzafv/zRbdm+hI9vNkfHr2Xr/\naewJC1y+aT2/vflYNq5JWhXf95X7+dy9TxG5E5px5a9s4JrXLGyxprlwd4rDw+PdWAZ39TEwJZiP\nDU0ebBhmMrSv7qWtp5fjX3g2bT29tHR0k8l3EoQdRFEzI/vLDO0rMrSvyC+3jTHUX6Q4vA/Yl9wj\n/xIqI7cDtX+mzZBp2sxVHzqH9p4CQQP+j7BWHMeTuqYcKJBPnY+8VqFQGA/aq1evZsOGDdMCeDWE\n5/MxUbR/vBW8Uu6nXB6gXHkyCeHlfvYP9rNnb1JeqSRb9+mD1KqCIE8220U200Em20lz8waymU4y\n2Q6ymU6y2aT84Yc/QLm8Z9r1Tfkj2Ljx/YvyucrMPKrfv7nucRqU49rgXO/cqeG62s1jjiv3TpOx\nSa3KQTaAbEiQDQhasljHgYLzPI4b/GfI4dCyqVdBXeQQUnCfhxkXg2nbCl/9IJx2Of7iP2Tom3ey\n94YbGLn3XqxQ4NfOPBN74Ctcv/FidhW6WD26jzc/8l9c+XtXzPm1K3GFbzzxDT51/6d4tP9ROrNr\n6Rx+A089dRo9LS380UUbuOqco+lpnbwgzDWvef6iBnV3Z3Rwf9qNJQniAzWhfP+uPkqjkwNjJp+n\nvaeXjtW9rD1+I83t3WQLXQRhO9BOuZRjeCAJ5ru2F3n8oSKVYgQMpo9EoT1Ha2eetu4C607opLUr\nT2tnnpbOPLf/a479fVAZuwfiQQjayDRtpnPdmXT2Ni/a+19M1SA+l4Ga1RbxmcakFAqF8dDd09PD\nMcccM6UVvIlCwcnlKoThGHE8mAbxfsrlviSMp4F7bKyfwaGBtAV8YFpXlVph2Ewm00E220k200FL\nywnJfnqczdaG8Yn9uc64Escltm37S+J44q8BQVDguOP/ZH4f9jIyY5iu23+6tm/0/LuEHFSYrgm/\n08J05xxbneuVTWnpVpgWkZVEg1MP1t7H4NoLiAtH0t/539n775+n/OQvyaxdy6o3XkXn615H2NHB\nwK230veRj1LZsYPMunX0/tG76XjlKw94+1JU4pZf3MJ1D1zHU4NP0Zk5kqGd57Ov7zROXtvJWzcf\ny6vOPIJ8pv6MJlu/fSffvunfGNyzm7buHs57w5s45byXzfh6HscMD/SzP+3CMrCrj8Hdk1vNK8Xi\npGtyhebx7iuF9m7yzV2E2U4saCeKWhgbyTAyUGJoX5Hh/iLxlD9TB4HR3JlLQ3hTEsi7kkDe2pmn\npStPS0d+1j7nP7/3We787DYqpYm+lZlcwMuuOpmNv7Kw6TTnK45jxsbG5tQtpdqFZab//pqammbo\nhtJEc7PT1BSRy5XJZIoE4RhxtD9tBe8f74pS7X6StIIPMD7yto4wbE0Dd8dEy3cavjPZzvHgPTmI\ntxMEc1s59GA88f0beXLfJ6jkdpMp9XBM1x+w4ZzfOuSvOx+TwvQcBh7OPUxP71u9GGE6mFNgPkCY\nru1bXfu8wvTKtuULcMeHYODpea8SPlcanCormYL7PE0K4GvXsOqEfVT2DtH/ZCfx0DCFM85g1Zuv\npu3ii7FZFnU5kJHyCDf//GZuePAG+kb76AiOY/fTmxkbOJkLT17LWzcfy7nHd88648vWb9/JN679\nBJXSRNDO5PKcd+Wb6D32+PEW8okW82Qe86gyeVaApta28VDe1LKKMN9JELbjcRvlUjNjwwFD/UVG\n9pemZcNMNkgCeFcSwFs7m8aPq+G80JZblG4s//mlb/ODn32HiDFCmjj7jJdwyeULn4KsGsTnMlCz\nun+gID4RwpMAXigkITybLZHNlgjDMcxGqURpl5S0/3c1jFcq+2etcybTPjl4p6G7NohPDt8dZDId\nz9lZVIZ/0kfxy9fSzvWEtpvIe9jPm8n/+ttn/XO8u0Pks055N/NAw/mG6QjiGasyu0xwgCA9S5jO\n1Qw8HA/QM5yrMC2Hw5YvwK3vhHLNeJlsAV758UUN7wruspIpuM/DwK23suP9f4VPmtbOAaP9sstY\n9aZkddODeo3iADdtu4nPbP0M/cV+WuOT6Ht6M9nSRl77gqN4y0uO5YTe1lnvEUcRA7t28rn3/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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -205,7 +209,7 @@ ], "source": [ "fig,ax = plt.subplots(1,1,figsize=[8,8])\n", - "plt.plot(batchsize,t_unique.transpose(),'o-')\n", + "plt.plot(BATCHSIZE,t_unique.transpose(),'o-')\n", "\n", "ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n", "ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n", @@ -224,15 +228,6 @@ "\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/src/mlpredict/import_tools.py b/src/mlpredict/import_tools.py index b9d3967..f80016c 100644 --- a/src/mlpredict/import_tools.py +++ b/src/mlpredict/import_tools.py @@ -40,7 +40,8 @@ def import_dnn(dnn_obj): def import_dnn_default(dnn_name): """Import dnn from default path (mlpredict). Returns: - net: instance of class Dnn""" + net: instance of class Dnn + """ try: dnn_file = pkg_resources.resource_filename( 'mlpredict', 'dnn_architecture/%s.json' @@ -58,7 +59,7 @@ def import_dnn_file(dnn_file): Returns: net: instance of class Dnn """ - net = mlpredict.api.dnn(0, 0) + net = mlpredict.api.Dnn(0, 0) with open(dnn_file) as json_data: tmpdict = json.load(json_data) try: @@ -97,7 +98,6 @@ def import_gpu_default(gpu_name): return gpu_stats -<<<<<<< HEAD def import_gpu_file(gpu_file): """Import gpu definition from local path Returns: