From 6e19f4d27b0af25158d09d73fda33918107c5960 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Fri, 21 Oct 2022 16:34:39 +1000 Subject: [PATCH 01/41] Create s47539934-GCN --- recognition/MySolution/s47539934-GCN | 1 + 1 file changed, 1 insertion(+) create mode 100644 recognition/MySolution/s47539934-GCN diff --git a/recognition/MySolution/s47539934-GCN b/recognition/MySolution/s47539934-GCN new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/recognition/MySolution/s47539934-GCN @@ -0,0 +1 @@ + From bc99d1a11ae228277c1f0602e7e3eeb24d891441 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Fri, 21 Oct 2022 16:40:02 +1000 Subject: [PATCH 02/41] Delete s47539934-GCN --- recognition/MySolution/s47539934-GCN | 1 - 1 file changed, 1 deletion(-) delete mode 100644 recognition/MySolution/s47539934-GCN diff --git a/recognition/MySolution/s47539934-GCN b/recognition/MySolution/s47539934-GCN deleted file mode 100644 index 8b13789179..0000000000 --- a/recognition/MySolution/s47539934-GCN +++ /dev/null @@ -1 +0,0 @@ - From 9f93239f2921f560f85e79812bf6bf62ac46da9b Mon Sep 17 00:00:00 2001 From: mr-popo123 Date: Fri, 21 Oct 2022 07:17:21 +0000 Subject: [PATCH 03/41] Added new folder and README --- recognition/MySolution/s47539934-GCN/README.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 recognition/MySolution/s47539934-GCN/README.txt diff --git a/recognition/MySolution/s47539934-GCN/README.txt b/recognition/MySolution/s47539934-GCN/README.txt new file mode 100644 index 0000000000..5ab2f8a432 --- /dev/null +++ b/recognition/MySolution/s47539934-GCN/README.txt @@ -0,0 +1 @@ +Hello \ No newline at end of file From 1d7eb06ba79a1246b02d1a2f6822c29a67bc0add Mon Sep 17 00:00:00 2001 From: mr-popo123 Date: Fri, 21 Oct 2022 18:57:21 +0000 Subject: [PATCH 04/41] Added Preprocessed dataset --- recognition/MySolution/s47539934-GCN/Data.py | 36 ++++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 recognition/MySolution/s47539934-GCN/Data.py diff --git a/recognition/MySolution/s47539934-GCN/Data.py b/recognition/MySolution/s47539934-GCN/Data.py new file mode 100644 index 0000000000..21346134aa --- /dev/null +++ b/recognition/MySolution/s47539934-GCN/Data.py @@ -0,0 +1,36 @@ +import numpy as np +from sklearn import preprocessing +import torch +import scipy.sparse +import torch.nn.functional as F +import matplotlib.pyplot as plt +import scipy.sparse as sp +import torch.optim as optim + + +def load_data(file_path): + data=np.load(file_path) + edges= data['edges'] + features=data['features'] + target=data['target'] + +def normalize_adj(matrix): + matrix += sp.eye(matrix.shape[0]) + degree = np.array(matrix.sum(1)) + d_hat = sp.diags(np.power(degree, -0.5).flatten()) + return d_hat.dot(matrix).dot(d_hat).tocoo() + +file_dir="/content/drive/MyDrive/facebook.npz" +load_data(file_dir) +features=preprocessing.normalize(features) +adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), + shape=(features.shape[0], features.shape[0]), + dtype=np.float32) + +adj=normalize_adj(adj) +num_nodes = features.shape[0] +num_features = features.shape[1] +num_edges = edges.shape[0] +train_set = torch.LongTensor(range(int(num_nodes*0.2))) +val_set = torch.LongTensor(range(int(num_nodes*0.2),int(num_nodes*0.4))) +test_set = torch.LongTensor(range(int(num_nodes*0.4),num_nodes)) \ No newline at end of file From 2205c15f734907e40f55b67c3ce1e465b514b7be Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sat, 22 Oct 2022 18:50:42 +1000 Subject: [PATCH 05/41] Create Model.py having a 2 layer GCN --- recognition/MySolution/s47539934-GCN/Model.py | 61 +++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 recognition/MySolution/s47539934-GCN/Model.py diff --git a/recognition/MySolution/s47539934-GCN/Model.py b/recognition/MySolution/s47539934-GCN/Model.py new file mode 100644 index 0000000000..e9b6e925c0 --- /dev/null +++ b/recognition/MySolution/s47539934-GCN/Model.py @@ -0,0 +1,61 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.module import Module +from torch.nn.parameter import Parameter + + +class GraphConvolution(nn.Module): + + + def __init__(self, input_features, output_features, use_bias=True): + super(GraphConvolution, self).__init__() + self.input_features = input_features + self.output_features = output_features + # weight in the layer + self.use_bias=use_bias + self.weight = nn.Parameter(torch.FloatTensor(input_features, output_features)) + # bias in the layer + if self.use_bias: + self.bias = nn.Parameter(torch.FloatTensor(output_features)) + else: + self.register_parameter('bias', None) + self.reset_parameters() + + # initialize parameters using kaiming-uniform + def reset_parameters(self): + self.weight = nn.init.kaiming_uniform_(self.weight) + if self.use_bias: + init.zeros_(self.bias) + + + def forward(self, adj_matrix, in_feature): + # input * weight + support = torch.mm(in_feature, self.weight) + output = torch.sparse.mm(adj_matrix, support) + if self.bias is not None: + return output + self.bias + else: + return output + + def __repr__(self): + return self.__class__.__name__ + ' (' \ + + str(self.input_features) + ' -> ' \ + + str(self.output_features) + ')' + +# 2 layer GCN +class GCN(nn.Module): + def __init__(self, in_feature=128): + super(GCN, self).__init__() + + # first GraphConvolution layer + self.layer1 = GraphConvolution(in_feature,32) + # second GraphConvolution layer + self.layer2 = GraphConvolution(32, 8) + + + def forward(self, adjacency, feature): + h = F.relu(self.gcn1(adjacency, feature)) + logits=self.gcn2(adjacency, h) + return logits From 9d5c1a20e09df1a697666808c3c5d59a836f89c2 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sat, 22 Oct 2022 19:12:35 +1000 Subject: [PATCH 06/41] added header block and comments in model.py --- recognition/MySolution/s47539934-GCN/Model.py | 31 +++++++++++++------ 1 file changed, 22 insertions(+), 9 deletions(-) diff --git a/recognition/MySolution/s47539934-GCN/Model.py b/recognition/MySolution/s47539934-GCN/Model.py index e9b6e925c0..06862eeb15 100644 --- a/recognition/MySolution/s47539934-GCN/Model.py +++ b/recognition/MySolution/s47539934-GCN/Model.py @@ -1,3 +1,7 @@ +""" +Author: Arsh Upadhyaya, 47539934 +Code for 2 layer GCN model +""" import math import torch import torch.nn as nn @@ -7,16 +11,19 @@ class GraphConvolution(nn.Module): - - + ''' + Starting of graph convolutional layer. + Parameters: + input_features: dimensions of input layer + output_features: dimenstions of output layer + use_bias: optional but good practice + ''' def __init__(self, input_features, output_features, use_bias=True): super(GraphConvolution, self).__init__() self.input_features = input_features self.output_features = output_features - # weight in the layer self.use_bias=use_bias self.weight = nn.Parameter(torch.FloatTensor(input_features, output_features)) - # bias in the layer if self.use_bias: self.bias = nn.Parameter(torch.FloatTensor(output_features)) else: @@ -29,9 +36,13 @@ def reset_parameters(self): if self.use_bias: init.zeros_(self.bias) - +''' +parameters: +in_feature: an n-dimenstional vector +adj_matrix: an adjacency matrix in tensor format +''' def forward(self, adj_matrix, in_feature): - # input * weight + support = torch.mm(in_feature, self.weight) output = torch.sparse.mm(adj_matrix, support) if self.bias is not None: @@ -44,14 +55,16 @@ def __repr__(self): + str(self.input_features) + ' -> ' \ + str(self.output_features) + ')' -# 2 layer GCN class GCN(nn.Module): + ''' + A model that contains 2 layers of GCN , by creating 2 instances from GraphConvolution function + ''' def __init__(self, in_feature=128): super(GCN, self).__init__() - # first GraphConvolution layer + # first GraphConvolution layer taking input of 128 as that is format in facebook.npz self.layer1 = GraphConvolution(in_feature,32) - # second GraphConvolution layer + # second GraphConvolution layer having 32 as input, which is output from previous layer self.layer2 = GraphConvolution(32, 8) From d3fcd493bba0059fcff375cea0e61cbd2487aa6d Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sat, 22 Oct 2022 19:36:17 +1000 Subject: [PATCH 07/41] add header block and comments in data.py --- recognition/MySolution/s47539934-GCN/Data.py | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/recognition/MySolution/s47539934-GCN/Data.py b/recognition/MySolution/s47539934-GCN/Data.py index 21346134aa..068261b7ae 100644 --- a/recognition/MySolution/s47539934-GCN/Data.py +++ b/recognition/MySolution/s47539934-GCN/Data.py @@ -1,3 +1,8 @@ +''' +Author name: Arsh Upadhyaya, s47539934 +To preprocess dataset facebook.npz +''' + import numpy as np from sklearn import preprocessing import torch @@ -15,22 +20,28 @@ def load_data(file_path): target=data['target'] def normalize_adj(matrix): + #add identity matrix matrix += sp.eye(matrix.shape[0]) degree = np.array(matrix.sum(1)) + #calculate L=D^-0.5 * (A+I) * D^-0.5 d_hat = sp.diags(np.power(degree, -0.5).flatten()) return d_hat.dot(matrix).dot(d_hat).tocoo() -file_dir="/content/drive/MyDrive/facebook.npz" +file_dir="/content/drive/MyDrive/facebook.npz"#path in google colab load_data(file_dir) -features=preprocessing.normalize(features) +features=preprocessing.normalize(features)#normalize features +# Adjacency matrix A-- n*n adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), shape=(features.shape[0], features.shape[0]), dtype=np.float32) -adj=normalize_adj(adj) +adj=normalize_adj(adj)#normalize adjacency matrix +features = torch.FloatTensor(features)#transform normalized data to tensor +labels = torch.LongTensor(target) num_nodes = features.shape[0] num_features = features.shape[1] num_edges = edges.shape[0] +#split the data into train, validation and test in 20:20:60 ratio train_set = torch.LongTensor(range(int(num_nodes*0.2))) val_set = torch.LongTensor(range(int(num_nodes*0.2),int(num_nodes*0.4))) -test_set = torch.LongTensor(range(int(num_nodes*0.4),num_nodes)) \ No newline at end of file +test_set = torch.LongTensor(range(int(num_nodes*0.4),num_nodes)) From 52ad3d72dad2208735509ab35415f8efdb96a42f Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 02:18:51 +1000 Subject: [PATCH 08/41] adding bias and dropout, along with minor changes --- recognition/MySolution/s47539934-GCN/Model.py | 67 ++++++++++--------- 1 file changed, 37 insertions(+), 30 deletions(-) diff --git a/recognition/MySolution/s47539934-GCN/Model.py b/recognition/MySolution/s47539934-GCN/Model.py index 06862eeb15..c4827c3f22 100644 --- a/recognition/MySolution/s47539934-GCN/Model.py +++ b/recognition/MySolution/s47539934-GCN/Model.py @@ -3,14 +3,20 @@ Code for 2 layer GCN model """ import math +import torch.nn.init as init +import numpy as np +import scipy.sparse as sp import torch import torch.nn as nn import torch.nn.functional as F -from torch.nn.modules.module import Module from torch.nn.parameter import Parameter +from torch.nn.modules.module import Module +import torch.optim as optim +from random import sample +import matplotlib.pyplot as plt -class GraphConvolution(nn.Module): +class GraphConvolution(Module): ''' Starting of graph convolutional layer. Parameters: @@ -18,19 +24,19 @@ class GraphConvolution(nn.Module): output_features: dimenstions of output layer use_bias: optional but good practice ''' - def __init__(self, input_features, output_features, use_bias=True): + + def __init__(self, in_features, out_features, use_bias=True): super(GraphConvolution, self).__init__() - self.input_features = input_features - self.output_features = output_features + self.in_features = in_features + self.out_features = out_features self.use_bias=use_bias - self.weight = nn.Parameter(torch.FloatTensor(input_features, output_features)) + self.weight = Parameter(torch.FloatTensor(in_features, out_features)) if self.use_bias: - self.bias = nn.Parameter(torch.FloatTensor(output_features)) + self.bias = Parameter(torch.FloatTensor(out_features)) else: self.register_parameter('bias', None) self.reset_parameters() - - # initialize parameters using kaiming-uniform + #initialize parameters def reset_parameters(self): self.weight = nn.init.kaiming_uniform_(self.weight) if self.use_bias: @@ -41,34 +47,35 @@ def reset_parameters(self): in_feature: an n-dimenstional vector adj_matrix: an adjacency matrix in tensor format ''' - def forward(self, adj_matrix, in_feature): + def forward(self, input, adj): - support = torch.mm(in_feature, self.weight) - output = torch.sparse.mm(adj_matrix, support) - if self.bias is not None: - return output + self.bias - else: - return output + support = torch.mm(input, self.weight) + output = torch.sparse.mm(adj, support) + + return output - def __repr__(self): - return self.__class__.__name__ + ' (' \ - + str(self.input_features) + ' -> ' \ - + str(self.output_features) + ')' class GCN(nn.Module): ''' A model that contains 2 layers of GCN , by creating 2 instances from GraphConvolution function + parameters: + in_feature:n dimensional vector, which is input + out_class: n dimensional vector, final output + in this case model goes 128->32->4 + since in_feature=128(known from dataset) + out_class=4(since finally 4 classes) + ''' - def __init__(self, in_feature=128): + def __init__(self, in_feature, out_class, dropout): super(GCN, self).__init__() - # first GraphConvolution layer taking input of 128 as that is format in facebook.npz - self.layer1 = GraphConvolution(in_feature,32) - # second GraphConvolution layer having 32 as input, which is output from previous layer - self.layer2 = GraphConvolution(32, 8) - + self.gcn_conv_1 = GraphConvolution(in_feature, 32)#32 is like the hidden layer for the overall model + self.gcn_conv_2 = GraphConvolution(32, out_class) + self.dropout = dropout + + def forward(self, x, adj): + x = F.relu(self.gcn_conv_1(x, adj)) + x = F.dropout(x, self.dropout, training=self.training) + x = self.gcn_conv_2(x, adj) - def forward(self, adjacency, feature): - h = F.relu(self.gcn1(adjacency, feature)) - logits=self.gcn2(adjacency, h) - return logits + return F.log_softmax(x, dim=1) From c808c20fe3a6058c7889a6242ca86c61215ed34b Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 02:49:40 +1000 Subject: [PATCH 09/41] Change structure of data.py, to better fit test and train set The previous datatypes for adjacency matrix, and especially features were creating problems during testing phase. --- recognition/MySolution/s47539934-GCN/Data.py | 69 +++++++++++--------- 1 file changed, 38 insertions(+), 31 deletions(-) diff --git a/recognition/MySolution/s47539934-GCN/Data.py b/recognition/MySolution/s47539934-GCN/Data.py index 068261b7ae..1fddbfabc7 100644 --- a/recognition/MySolution/s47539934-GCN/Data.py +++ b/recognition/MySolution/s47539934-GCN/Data.py @@ -14,34 +14,41 @@ def load_data(file_path): - data=np.load(file_path) - edges= data['edges'] - features=data['features'] - target=data['target'] - -def normalize_adj(matrix): - #add identity matrix - matrix += sp.eye(matrix.shape[0]) - degree = np.array(matrix.sum(1)) - #calculate L=D^-0.5 * (A+I) * D^-0.5 - d_hat = sp.diags(np.power(degree, -0.5).flatten()) - return d_hat.dot(matrix).dot(d_hat).tocoo() - -file_dir="/content/drive/MyDrive/facebook.npz"#path in google colab -load_data(file_dir) -features=preprocessing.normalize(features)#normalize features -# Adjacency matrix A-- n*n -adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), - shape=(features.shape[0], features.shape[0]), - dtype=np.float32) - -adj=normalize_adj(adj)#normalize adjacency matrix -features = torch.FloatTensor(features)#transform normalized data to tensor -labels = torch.LongTensor(target) -num_nodes = features.shape[0] -num_features = features.shape[1] -num_edges = edges.shape[0] -#split the data into train, validation and test in 20:20:60 ratio -train_set = torch.LongTensor(range(int(num_nodes*0.2))) -val_set = torch.LongTensor(range(int(num_nodes*0.2),int(num_nodes*0.4))) -test_set = torch.LongTensor(range(int(num_nodes*0.4),num_nodes)) + ''' + parameters: + takes file path + returns: + Adjacency matrix: normalized coo matrix + features and labels(targets) as tensors + ''' + + data = np.load("/content/drive/MyDrive/facebook.npz") + edges = data['edges'] + features = data['features'] + labels = data['target'] + + features = sp.csr_matrix(features) + + adj= sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),shape=(labels.shape[0], labels.shape[0])) + + #normalize + colsum = np.array(adj.sum(0)) + D = np.power(colsum, -1)[0] + D[np.isinf(D)] = 0 + D_inv = sp.diags(D) + adj_trans = D_inv.dot(adj) + + #transform data type + indices = torch.LongTensor(np.vstack((adj_trans.tocoo().row, adj_trans.tocoo().col))) + values = torch.FloatTensor(adj_trans.data) + shape = adj_trans.shape + + adj_trans = torch.sparse_coo_tensor(indices, values, shape) + features = torch.FloatTensor(np.array(features.todense())) + labels = torch.LongTensor(labels) + + return adj_trans, features, labels + #split the data into train, validation and test in 20:20:60 ratio + train_set = torch.LongTensor(range(int(num_nodes*0.2))) + val_set = torch.LongTensor(range(int(num_nodes*0.2),int(num_nodes*0.4))) + test_set = torch.LongTensor(range(int(num_nodes*0.4),num_nodes)) From e8891bb1632c8bb08a20e8fd40a7af3bf321f162 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 09:54:49 +1000 Subject: [PATCH 10/41] created training and testing function, and stored accuracy and losses --- recognition/MySolution/s47539934-GCN/train.py | 64 +++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 recognition/MySolution/s47539934-GCN/train.py diff --git a/recognition/MySolution/s47539934-GCN/train.py b/recognition/MySolution/s47539934-GCN/train.py new file mode 100644 index 0000000000..38da07b4b5 --- /dev/null +++ b/recognition/MySolution/s47539934-GCN/train.py @@ -0,0 +1,64 @@ +import numpy as np +import torch +import torch.nn.functional as F +import torch.optim as optim +from sklearn import preprocessing +def accuracy(output, labels): + ''' + calculate accuracy + parameters: + output:result of running an instance of the model + labels: the true value + function compares ratio of two values, giving result<1, + as predicted probability always less than true value + ''' + predict = output.argmax(1) + acc_ = torch.div(predict.eq(labels).sum(), labels.shape[0]) + return acc_ +def loss(output,labels): + + prab = output.gather(1, labels.view(-1,1)) + loss = -torch.mean(prab) + return loss +def train_model(n_epochs): + ''' + parameter: number of epochs + trains model over the range of the epoch and at each train, + calculates accuracy and losses + ''' + train_losses=[] + validation_losses=[] + train_accuracies=[] + validation_accuracies=[] + for epoch in range(n_epochs): + model.train() + optimizer.zero_grad() + output=model(features,adj) + train_loss=loss(output[train_set],labels[train_set]) + train_losses.append(train_loss.item()) + + train_accuracy=accuracy(output[train_set],labels[train_set]) + train_accuracies.append(train_accuracy.item()) + train_loss.backward() + optimizer.step() + output=model(features,adj) + validation_loss=loss(output[val_set],labels[val_set]) + validation_losses.append(validation_loss.item()) + validation_accuracy=accuracy(output[val_set],labels[val_set]) + validation_accuracies.append(validation_accuracy.item()) + print('Epoch: {:04d}'.format(epoch + 1), + 'Train loss: {:.4f}'.format(train_loss.item()), + 'Train accuracy: {:.4f}'.format(train_accuracy.item()), + 'Validation loss: {:.4f}'.format(validation_loss.item()), + 'Validation accuracy: {:.4f}'.format(validation_accuracy.item())) + torch.save(model.state_dict(),'train_model.pth')#this random file just used as buffer + return train_accuracies,validation_accuracies +#test +def test_mode(): + model.load_state_dict(torch.load('train_mode.pth')) + output=model(features,adj) + test_loss=loss(output[test_set],labels[test_set]) + test_accuracy=accuracy(output[test_set],labels[test_set]) + print('Test set results:', + 'Test loss: {:.4f}'.format(test_loss.item()), + 'Test accuracy: {:.4f}'.format(test_accuracy.item())) From c775efe69d2d399cccc8490bf80bd0f773b92439 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 10:25:54 +1000 Subject: [PATCH 11/41] better method to store accuracy and loss Had trouble returning all four(validation_accuracies,validation_losses,train_accuracies,train_losses) together from one function, so decided to save in np arrays and load the arrays during driver code. --- recognition/MySolution/s47539934-GCN/train.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/recognition/MySolution/s47539934-GCN/train.py b/recognition/MySolution/s47539934-GCN/train.py index 38da07b4b5..9047878adb 100644 --- a/recognition/MySolution/s47539934-GCN/train.py +++ b/recognition/MySolution/s47539934-GCN/train.py @@ -52,7 +52,11 @@ def train_model(n_epochs): 'Validation loss: {:.4f}'.format(validation_loss.item()), 'Validation accuracy: {:.4f}'.format(validation_accuracy.item())) torch.save(model.state_dict(),'train_model.pth')#this random file just used as buffer - return train_accuracies,validation_accuracies + np.save('train_losses', train_losses) + np.save('train_accuracies', train_accuracies) + np.save('validation_losses', validation_losses) + np.save('validation_accuracies', validation_accuracies) + #test def test_mode(): model.load_state_dict(torch.load('train_mode.pth')) From 0d47b5d4445c513bf9e48eb38d9adc80bc56d714 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 10:34:33 +1000 Subject: [PATCH 12/41] Added screenshots of accuracies and losses for both training and validation sets --- .../MySolution/s47539934-GCN/Accuracy.png | Bin 0 -> 76097 bytes recognition/MySolution/s47539934-GCN/loss.png | Bin 0 -> 57140 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 recognition/MySolution/s47539934-GCN/Accuracy.png create mode 100644 recognition/MySolution/s47539934-GCN/loss.png diff --git a/recognition/MySolution/s47539934-GCN/Accuracy.png b/recognition/MySolution/s47539934-GCN/Accuracy.png new file mode 100644 index 0000000000000000000000000000000000000000..d2c94b2add425c9a32d7036a0821a7707bc2c743 GIT binary patch literal 76097 zcmZ^~2UHX7*Y=B|5=4QBG($&&0#cP;1OY{wQUiqEdx`WWO#-0^(t8n634w$bdhZ?S zy`zNQTj0y{{>%HFv(8y-W@RSHOfqX`@B7}@_1od^Ro~pd`{*ta5z&3cw{q%4M8uOs zL^o>ycL?vST`N-%UT(nD-^dc7`WgNbc5Ye8sK^izp<~D{O>PtRNuA&7!ib1yp8T`k zP*i`sOGI>?uP7&@>1nu$*ZBz5NnH>3Cu(M2xWD+Pjzc~+K1uyq{&0NP25P%n^7o{j z<(S`Q`I?J%U+qXs?R?D`(s0@FMA>chqmHGvg_c`&T>Q`8_@qB|9z=?s6t`{y0So>O zJ1lR%kjAmddA80s=+Z0Yvja1|KCyD|ie!sk7WvP@ojN?}(c!}IpM@qIVgH|P zh7K=f3Bm0p4UOAf>tx3}e1;!vY{FZYfzQ_ui(JJPbCF0nQ+}sBJ`HVcTZi}oHC=Jh z#bBW&-J~8bP-w#2$Jm(KsAeT}6quvn+xuOiyIfD6m=4s~IIwMVaQO0Ftr@0{DRi&; z+S4jTIJB1X+*@|qwu3A59O^=8F82Tuf^0sfYz4)&cQ>64$)%J3er)#Vdsav#g^lIp zd@v?8Y43lm!J)j{m>1RF0rTc1{3g)-GWhg5gQK#Wo z7QHOxgrb5(mRy@XU*6|sbE`dbXkcO+yz*9c#~5!Tth>}KC9?@pH! zg^lqX3pK&qRT5$oPl$N_&RiPCKRM7IAGj=XKsB@ApA|;ylvZ%V>2Zfk;++Bb^C7RG zhc&&XHPIYWu>an^^p24K=l#QWy8mAvwhAVl+IycHCJCDTy$Ei#R=bMp;!*wnKCahO zAlq5Jmq_ix6C}y>mf?fCx^0)qVevwXJSF0G*Wb5>OAZfJgH}Ffo^tnoNskuVZ?#%% zqso$G^js?4h1En!?CC;C^_K**=f50Au2{u%KLAb!HuhW6zc)Xe59Qz~1A@%ytWrq2 zmqI9<@;a1QP(6pNTBv5OoNL3{{-`i-UAMuj%Q$p!(_n!<<44?MBJ_r+->Xm2-!Z$u~6+^*jH2@Tp5I z{^P=b&-=O`FDJC*ZEikz=VH%Oog~nus1`Hnly%RVtW|PU(uh8zn0X2g$~fK=(qoX3 z=KVt~SQvatTBOgE{qne;?*He!1HU>A*}@0tcGXf!6pVS@vv)-L@uamkvGZ~64Yyez zl^TitE7rKvCi;_17;4P8=jz|eg;lEXXJ_Z!D6neyPd-@J zHCIG_%@|lWxBj6M*H!lIx7P9G?!>pl`&L^CS?3#}6kS;mSky*lVX5hmy3LPw?+n~z zIcd=}`V%tHI0rHNlv!XCv)`e3c*iov_sn&&!l6bk5AAjFA$I9`=@&bC28J)o9Wn1< zu)?zPavNslP!xH+t%Ab2g{|$oUxlDUCdf=}X7N`>f8Pwk6M`c=7DgNwe;dcU$ffI5 z-A}!=$GZ$MY!`atKho`(<4d<*3+#p5p|>uqu@ldT?Eaqc5@RD@UMBR-O4@)({TtkUIB#`TFUTx0DQWry$~BE~ z3JA!W`?flD(tY%ZO+ZY{beuZex%XL(6e`K@2V#G}jPY8xN&a}aS>Bxx42=Fzb7{tZ z1zAfBV)IMsLwO`6D^0^$pOd12K^^Jl?*g@Mv{}c>#%NCk zvur_rzSh+)MFVeCRFCo>gDO{Daz?SIv^?oGPrymf^}$YcN6{lABky!{3NHV=x$cVY zvHAp%3b>jn8ZZjLt5AJuIBio~Z4EcW*!ev*X6U*2 zc>1 zdeR~wr}ygOCpe3D;{J>I_#0hCn90bh8y%*Bo=p`ggH6T`7R{|&5f;=&f9)(ZLXozp zk$?gVujQGY;bHBp#EoujYo3L6MQHO8n_gjIVgApbZ6SiPR5;EhYNf5lR}H?Gdtt|- zOQ5~+83z05u?lF6I$K6Y#(on?17_>YzAZxQ*l#ZsA~zVY+jN1k?9-#J(%-sF2L%qS z+l`|%KGY2Lg_e_>rnICPu%%~XFvOZ!#_Y#*jkz4Yr{t%P`Sa_#I0MojKxGKT7;kOWXn-liGZl6N#eO& z?#%h3DWH}<_nc+T|K#+T&$T9McbG5_VUK+~r~t6f3|2jb%XQl~cTYc^*-dM&YfbLq zc-`EU;c)1Auq_fpa_c!^T0$;Kd*_evhf#}Kri3S47{tw*EmDBJA$&7I1-^Ngda`mL zys`MFr)S`Oc!wsZMbLvAQ&UrxY?U=N({lXVi?3h51}juL&9ly59iQ~7N4%ty3Afwt z>@0y+f0XgZ<4&~V9TPp~&QP0EN9p!cWxDcnRo2$5MuI^&XGb&2OyFPwSjxA8-W4a@iE59;#eRq-|AvL@i<`lZemArGa5o^6bBf%hp#AwdXq zD?km2gqV_$khrBuQO!PHlWoM@=dJJ9zbv8Ax-;3T$UcJBq?I|G#}1jS;!m!x0!jt0 z=dVv1gp=%MTEY!1e{vyvAZV8}OV>{VuUynw;nc-n)-$IIJ9;ON)?B@8oEj{7xhDXg z6-L|un_DlAT0{}=PiT2FlL(3DI`$eLaP6_*fOoo9y;^g+VulfSxG{TpcDwV6Y=2it zJA8yg`lA;#POukdT?3Z0`Cb1WQ8N_?891Ls!6iz{b)^I!MSXm>eljf{^d;FME@n|S zim%O9|6xyWKg%nx4vQ2|b2+ABQ+wDRwLPu9k8mriSx#+QuC*NBlH~i9{nZ}MA2E6< z+DOQA!aXo{>Bbtfrvvlj!!Tw~IQS=K9vfHoXylH6ec|yDMRu~8naLv>aD~*(NB=T2 z$t>=rAX{CBdek^&iF+tSGfCRdn}mW@L4_~YEx^%sO~Ffs?+370!NB*mDC-xdSsKzO z)8qfXi1ghhwu@)Cb=Gj>;=QR6`A0SL_}6F4(pOfThlXf6YdO$fFI)$S!n)9g>ht>u z?*Mc|9_J>~qWgUfebJ1L3|Q2jFwa?=g9uoAy(Uy>-O?wat%;;`q1*3x)24JTZU(Wx z)T|oE-}+>A87m(fy^!g1Y3k*UYN=jDnTC6kZ6dRT=JIz+NAop9j%t+Uv05340 zgu$rFwJi~lCxO)?wy+|79*1D9P;~!nacS7&K6}483lIF09)@v=?&?Yp_lZkJ-zS|B zaRQpo0>?&3FFtVK@%V~|kG3K~68vK$ExzFc6U;Hhmf z<`@nsGU?3I&A*@K%cd2}31tUeRcVyO!yVk?VzZ$7MP#I!@yE2-AJMeCiAS4DtX4n6 zH%%{JuqD~gz{PJp%B}cmlag7eS>Z5klJaXZ(V;(6(k{26rbYwDWrga8n4%fj0h3vf zWstDrggX+g(p=WKpZ=n0c6!=@EyI2p;(SIP+g$~TxGVE+_@$hn-RrL=HE+3T%EcOo z=SD{@6vrvMuy*X6gb zJ=>S3zOu7e?M~1R8==sKwZ0fvPlvxk4xF6*jz(Y!4pPH5zJ}{%b3Wtm(BNREuv%on1AWjxqAEIjf_!dAcrr@y&$1(Z_qzwzja=0EUDib zmJh%lE~gnFxHP4pOY6k?8BKZefGR@bAbr=RCQ1|AA5p^>OjI{1%tPyRPboiQaI`OB zShH|hmhpOqEFP*_Ou@x};eH&Wbg~el8<{lv&AjK=qsg)${(zMDIV)did`OH&WS&?1 zM{eh}jZX}96C7K!X6};WV`E+0_wTz$Mb_D{XSyvfFIuyos|e8l=09teFk5etKc))0 zI>*W%t6Q3zx5oW2dD7kny3t!bLzr`C&Mqwygoq&K4QYARcX(CU_7zH+*WLT zm;arO)lKWb=a-aYY~LA&RZ&qSeV;7ynrT3_YtUNt&YRM>p>^AoOplg*+n?MN$nq1j zUwjT19*2>NH5Eoa13wPQEsmzT%~S3UkM>bxOm#zF;2QE`#dR)Iv<>*B_@8v}-o9Dk z;VCPiCk-h-BTpFDOo}Uio`9(+fA{Fu*D;Q@W_YDis8>y900alGwh}j74TmRW*|on@ z)zvr$1^vm|sTQH3v6-?qm#`Yc;p)s>Rf-GWga%l6L2>!-Dp0=?#y*!BESGX(W(_V$ zoAB>zpRIA_9XKzD`TrSAq6ss6s@!E)Txa4kfHfn|T5OM|rYGYcTdpx`3vqx%+JI8S z(jo}bg10ZW=5%<;9aL50332h*+g;vXQX{$iKI<-jcn9*jmnPViK7xg=4&jy;q`>NJ0d_y6Yve|047o+N3ss{NVTcOCi zLvbxaG)lJf#qT;Cw>n;`{(0%DVnDp?s?xTS4`O%Ez_;j5qXeiuU1^?|rw4n>)c%@# z;jy|Go}Qp+*WB*oF@DD)E0ub`Z69{6@~b}iy-bYtx87M|Ds7sOD~M&cRKt)s4{iBn z0qTC|q}^+9GVls86=FExJLm)klL=%uGX(nnfflFN3t4yM+Cx6ooC^y4XimEA95*#i z&A&z$w)*20gX3$RwFOnOq;9U;Fj0m={C&qUz12Q zYuEF|7=>|16{Bt#%+KcToAHQ?&e^ut5&D=U8mtL>yi4(#Nw~fbzbYd{JPo>jJkQD_I)2 zIlit77d~Exg)Bay_5uuGuXyBp-aY=FOHLL5GpH&XNt#AaHUO4@A0o2vfdLTm%2rgU zVr2C82dXcvV#MPG294PgaJlwaFY472FN>nh*_o#`W24jaupAWl@XV*)?5*n0lKlI? zpHZWVy5rh5+PgXEbiV=fn+{P4*L{)>n0L4`>!|i31{eQbXpC;P4X)y|>jhKjcz!e8 zVy`F`%)K;SruP0jXAzg&c`R9->#1)sBVvGZ(35)5h_YEZp3kMZ^|siZz_}lO2(zLw zj5Bkj`TlU8LwI>=H~{?e4`RegMII_tghU_nSmMu0QtJ!NFm8W=71e&pj5e^?%piCX|zrn;q3Up^w9@7pU@9i zU$5*=e{>oQ#>g|lX+Hhy@SGSJdYk5h zKg#U==Ql9Rq3=9&70PSh`!GsA3VDG~lI@lyEVsrgvZa8hfOk;TGp?KN)2U)!Zf{Ec z`dQT(=+xrUCiz9ee z>JFpyjS9O7G)mQQV#?gDsS0Zd2BA)()81RDoB0YjZO^6YjCKl8cMA$W*+2WsL*INL zQyh5eu;J@$uI_#0yY{GfHn6^>=T>=Q!ykcxs!y0*dNkvrf5B}r|?xm_sJil942qZ+S3>p z*_F=qmCpK>^v$xRY0w;s01D8`b0Kg1v?xh@8dkI@h|S`GMPbWH=Y4Io;}#o1ka54X zq?D3v#@n_C+k~`)GErG>K?wg_ z4V7wbgyw$kv}s!&PAqrUR4OS1B*bhKsS0vv6Siw#Nf`2-wkHYWeQJKZP$LHM_DMZf zz4+@fqseBr#djV`bn%XTtPu5KtCrCR8Gp!$=y$hAX&I43(QV}%hk~YQ`naC044)gg zBXg-g!jMmB*>PKg<9p^v2dSZo9El5I;Z!YgCa!WR8C^AlHg8Gm4r37bjl0v2%5TPH zM!<9alSS6&(^du5$mf;6oaZZl4q@QTQu9`*xdA8pL7hC^$eqtuvKdTugH}fYAwJ zt!yN}D03s_)zEqUydc3S>HJ5;rkU!LKVI(mUtQDibTvoC`we(iAwVG!_o=>u&AlW0 zLh%!k?8N19=Y71(L*3udXFgiBCJC}3{)~M7vMhcdYc+W08TdWMoVIPgzT{M;jFhMJ zfUp6nvDY_=EoU@*Q&-VauIysb&VJ+63|1QHM2VH3@4SLQSKK!Waz% zZ0vtx4934_hz~z7b!oGW=;2@gtkGqgv?TvDL^pih&&%g)#8%^M zGUh;XDfXPj>rw8^lzvQ={B4fy-_N6{g;2P0p?ai4P-9*0QBY^W^$Sgo6~WmCr^j|Q zHr?9Arm^2;Z^4QV!(6Gqhg-Mq>p=uJjt1wkJhj9@r0@r@fSoZIJ}IU+`QpV%-?UGX zew`q_3_WGRs<+W^;jwaU-3KL&X$z_{-GYIN?9dWkL*_UN^7)c*5Lhd@x9NGBU;TCW zqXRl)0edOy6Vlm4MOx_2Pr-AdBRI#6qq2Y=ftsXuWmi9V&+lE_aClp%zJB=poQ2A; zjA}mi2}3d5RJP2N=Tj&*;~Jaz3c=!{A#+$^yVzU)U)_}#n`fFJTMPZ&#J8P*b0dnj zvl^0j%i%kBpBize-y9z$;}obDnSCyNA?R+n6y<>|N^;;d21-Gf zqv~0njWu?DiwJDYryly0Y>Y8ZsHY4x9#ffJhe@uLqkxEVd8mo5`CoQ!*K%r77;$Dy z6`PM{mKVd$IWl4Yw9UWhLXpaFM5i#5iodN&)KFQvp!eh#dvu%J&8?0jcJCDXoLY0+ zt9|8|CrH`d2PcTe+>D4prbCW6%Oi62idTF?^$eicu(xbC=y}?(LhQ|FQr1Jdn=#W_ zi?+(5PsMM>*}Z`PNg~~n1xB>ni+CvueD+-%bR{PfrGm5bcqI#h=nI{P3_v2%U+)-Y z{;j~h9?{S?1UMq+uvzo0vgNLSDAo__MRS{Dh2)+tsn!`9jVQhL)4S^($x8hTt`)NV zlMBD*s8*+b2RiGLW!W6$cNY2ky%k&`x@n5l#>ee0Nv4HbjgZ>}Jgs^Cm z3_j5D5YivcyUTcJ@~j55#0peN^tJJx5&)VFEz_AZS&}j$%i&C|ecIGpcAChUEt)9j zwyER2Jyrb&aL=KQ2$tA~G=fk!;NK|D?>Cb+8wJE@Z&9+{8z0Km!u7wArqSRw3mhFl zy`gAwQGRbzB`Oqj2i=bt2uI^r%nP1mo9f>=1azoy+4dHR{A`Xm9W|r9;GU#DY7%+_NViN%OnN0g&{Tbla(_l565OCTYTs~ zzizX*<1WAz%#wUBJ8F$*QNC`;Vs{9gmdFnhb_bIl+uN6slASl@!P}~w8M)U(LoX}e zIO%1ym23mvH*vn{Ipa8^F%(O@XZ--zvHllY;Q_`!7e2CcQ6 z(Dnqt@t8LvH@CALfA*__ah27D)}@ly$xZ{6AC+%ki zn#ulYcI%Oypnh*rg+^2btA%l#afR)@rE+2ZwjA1kA)Rzm$dh~cbVa)JRo_)_ z2$o!eboWVThogSkMESY88L1oN8aNP!CU-Yvg}c3%2^dSey0yS8aK#}HeJM!9pn zxR8o8{FNdBD)|2xA0)`w(qhSoRhIbAgo$@UTL-1cDIY~Dte5|=F`?wT=THBqz(q6z zzwK`<`nYiKO@~-tZnbRN49I6{CC*<#g46WV#D@@SmrCr60H0-J)F@34716` zGGPfq&cj)d-SJzk2{$gHUTbbo4jOAW=N8(AMTl{uxz}H_73jFXNGp$O^87+dFB4Z9 zC@LFrF#G(ih|pvv@XS7JW>EdM5vTl6yFWydu>ZNy$~F7+qQ{d|=AETAmD;%|PHLG3 z)Vi109Vm$HCr`PZ)({SA_YO~Rt}th1&~e1xW(?If zE!f4{V`ko__>CJuyRMVRJ2%|Ytz|X)xVCQkb_!(6aBAMMW$8fzZN9!UFnh~#!{J* z2a24e!&yS~PhKemsJ&DEV$x=jF;^om+VL97`U+0<&sbxlw84UP|3N2bFIb|0T#R^U$~9w-F2kt2YkNxUg`5M(a(^=y0&gFWdy|j# zuBJcsY@T{2O0W3zRn$z1p0MU`RL9ZP&!#az$|d?|LNhV1hx@U;SY-0dMa zpk+42A;sDF!HrXk`-#-4nEm~`AtDV8Pb`PpRMPSTyu$=61KZm&94KYP$H%%g#5O08Tcwp*aGMl65u(8({f5kbRfIh<50Ypj-p%x!>#Ng_k)va;{0CL$ zyrQn=-W3vACR~wrpzeD3mgDcM&%5=x4s!U)!a=`lvHk%`As8C!vk8G65 zSU`VDND8M0--tMh9l@9ULfI8oz*zaZenf|6RmgZ^dPnQxprVykNuBP^!T(9!F|W;y z2o`C7mNW;!b4W}w^wtt`oZ*&~G;m#Nb=eB@@>v6fB`81u5X*CYr6&!x|Y0^)Z3 zc4Ahegy+)nYfUV<7Z(?r*%V#f30w~MciED7jp7>4(i#%SjRG`|cLHP}R1$ReCF%b) zLIjle;=BAuNJ|S0%-ItuoutopF;(;X5*NWq+NnysUCjzV){dwPo7PG_W( z#ijn7ejR@(m-OUy&&Clu8+wL)W0C?bf0GaT0jK#*;MKQ)l3q&3sx_g)0)<8sttXE3 z`wodP_LIJP~mAYAn z`%-y~z=hX&=2)82yMiw7Kl*`%9tB1fifF$Azr=1=QGwz~0wZwT7b6=&Cm3%wRWy{T zX2L0@;U~#VqD$49USFu!F}AU;eP4Nm|H336%=JPW7kSsv*avy+d6V+D63EzsL#Qgb zs0L8UGv4Q_CX>+%*^~&NEgFGH>gJN7)grljxcPNuB{i5f;(x7Rm8S;#`|3Dgz5Ny;%uwl(x zKC6_@OC%F4H0om=t(B~#sVOlVYwL9U=VX?do$o~wFJG2xCvtSHtb}Ap$3kkUGe}&Z zB*9e-Cq9D(>COb5eiykbu)oPS67#$xf{UM@oY-;LWt;rh@z2ZP+(o z8EJJ4Gsgk)NFQWhx7p_bO8&>Brn*e6+}f{O@6DrCK1kf$PQn7ss^m(Jei60Y)qN-e zXEXKdviGYn>+H8fH$~-GDQ=(T@^d-wLVu*bV37Uq2))Vj`Z?2N^a7ja zGik@Fz(#U8VFJ|^Op9(jWR~K+`)=biUKd82WL5EO47dZ(iGGu{+}5z}v&Xoe%}_>W zk#%9J{>hH5MReUPH;DAXlG9nb-^6Fy=`V{l^?D6zBvb(TrxfeYnDYJ@@afFZEQ(Fi z(0-f=5~UsQdx4C+hU zJfdm%Iqc2mIi>oiPZ791=lkuZ;n*%qjxvAn;i9XLJ0Qsv8t1}Q!K_n+P_~>2F$C6g zF_Ca>F0k-)XsPS!+RT%5wAYEAGMrd;^GitBZx4mbsb6PemSIwcd_tdnn5EZbBp-iO zEZqh_Kq}H*P8{G*Pm`I#R#|TjqxoUKEy<&8YyH>bbps06Rr)Ojwa_I8+ zI`QH{LK#jLnuuLjI%P`r8m`WT%}=)1i|TuNbibP`czb&z>#9_b3skGzk?YE`Ll1rX zkAG@*Z^SI$*~a0$r39uDb?Kjxtgl1RkQu24X2&kV*!kq5@P>{CcVdI6Z2PPWOt&qf zT2)?GL*-qlyyi~Zd`wJ6ObG;o7$`k^b&u*Gyqu~jlupLlxa)OieLzXejNN3kTMV$= zyc3_7q>~0|z6~m5$))jtJ~aK%xvQ$oGtH{q+x&BS>8{`29sW{L@p*)PQkN~YiFp?| zh0dj0`eO4}Ld^2Pc}tRZ7s@S=I^LC0WZDeXqdz{Ps5cD=jH0YKNvSe&gOa89WBpL zX#d4pIpE+>eh3cAXJ}=fFZ9c_RJLC&=yL$s@!h;IK~b@OqGmuyYad5(4(~C+ZS+MJ zgMC^FM#1Mje5v(6fTH-gmj*@$^#_NE*qUzMFAUaQSulbX*1pi<=^C|rjP@><110tO zSMw2&%+h+!f8%)Mt{lpzjNR)ZC6)|6xVw7dr_5JwQ$*P--EULXOjb0L#T{phZ#Bsm=t9_e3AM3RJv!{se#?8 zc29*m#sVx-Y}CNhaJi~>G=($YnsReT#ztahsj@2inN zj%KVmf-vs4(PsT^F;lCsn%cnWdP)J`ET~=OiT~9;Xfn^O> zM?OQY#8%Z`&6tHeYo6eD6Qde#Y8?nw+l>!%_M+7(p~$jT!%LxPsULO zqcUD&{d;R^^8z?eH@74EsD1*-l;5WQV6x5X>?N0g(o)<0)i?dwzho|L#|%_MA#L3s}I_c&){TsGDq3nEj+?q*FY7m>w|KMFua6D2kMT}*A zFnI=d+1Ul6@^=gUf4=4XQId9%BDMNNUF$iJ8WXEawkNyj4fKVlfB#NHWphvSX5o4y zsgS9g^wsI>3x>TYh^-@eg*<#uV}5edhNs(pz=}&7iP=H$-r^3r3Z|G#dTBTIt8>7z~aGd-%p&AGF}H!A_q3(98Z)HfyiaD0{#f-WPb_crh1|Yb4het^`uO-9rRG|G_8Q%^cnNYY#rvO$BOjB3VvW)cd26Dro9npIV0 zFqQaFR~W-okT=%*DB|+BG#$%!H@T37!zo2`a3!QYQH;Gs893*%75Xd*}co7=H|9X!hdmkYFl~@t>u|JbQN|@2B|w;YrR+TNpuDkCI+(_I%O)=Wh!MT z#gCtBc<51Rx&yD0IatvXcyqP%xACU(hqLtqf8EQT%ol6RL{nObf(0~}=b~O=Ry4le zF64lIRDAlm=Vdb)*Ncvpgv--ae|>F$&O#k$HOMxu+Np&6T>PVaMW^)+ zN#X&#O5BVd?3kouXEyafr+fpkr@o2B)c7Eq|&RjSs0^bGBg;6kL2GC{Ovo-V4VHOXQoaI=@VBJbpKb9Tsk{_0Umwb8}4;w91^u6p7yKxz$v+ zAdbYes{IzGEb8l1(GG(vac_&e&q$Hen1gRq=ErB*{nN20s`X@bV~m7Df; zXb=s(^BX)te+<~~dV_JA_dnbAxE@4N=p05zyO*}ZRHumjuWMbzc~R3d9%WQ+ujUK5 z=TF;(kB$;6_TEu1*!0PeOJ+JJ6ed z0MD3r`A7P2H=l35^i$2;Nl@b4ru-FlR?E7>U&2YPQPk1{iHQazx z84YLqNTN`>CW~k^Z4}Hpef9PZeKf4{mZVD;LyV`PfB_Y6)F@-19lBkwfZL!IVQ$QA z(z>4HNS>BlKNJ=!4EanJtERopPV24mDNVf0kO>EmOJ~^hCmITa zM!&4wnNcz)PUEL<_OH!!lVPy5E}g#FPw0^%fPMrW7f3sKm!~#~#~_Xw|L4vAcFj)r zj(^z2rVOY|SV$=K<&I+^Yp~t%#b)Wi%FgxGj$6q!Xqw{0qW-m}WasQhRtaP$&nQMg z3;TEs85mN}|LcSjQ%F+#Qc2}UEx%Iif0RjJ^qp3Jt4YV^y@^Rih;3Ea{H7^Ini>$0(5$pZzoXG{ zm2{2F>=r7Iw`Xwrn903l&4~(!0#e=R(2^Bz;I~bf^bhup2-vWiqKHBUwAQn!zJ)FF zNu~@wMX;a@C0$Y7e(rbphn4EvJ!4=d+!p+j=`kjF#d>@*8OqL3M;&J#3+IeGS~;zI8q+*!MpZA)V(K7rXm%rYut1s@flU z>5N&CKu5@HHAAM7moPM?(T6>&lxU9c%Db%Dz`hd6*Z5eK=*83Rb@B+U&3AH%3!v#Uy-$sSmN z9)aWN5Z`LUd`SFM=+}uNuKRpMz2|;{k4VT;yp2&0aQUuJj;m)NGwBgU^91;(ifxro zSVAk%`|6dD>p=4lfEi2bJb?ZZ%I53Ac$m`@;+D2F5na=MAQG|z#MqnZe(QN>o)=+9 zRbxIp<|^<^HtW^Qay!E9G-|e}Vw<056=MB-GGUC0w1RTFRm#b_u)?g!fpiSizL7q1 zg&Znj!0HcUwtjbe_nv9W6Yqt_ z8`ShV${{;*b^3(HmD|)jTpxG+-`;McFJ%w_1jKzYzh?p}DiTEF%?4CsY6_vh4e49u3QKD?TN^VcKxo zlx})wvUC7hM@Y*r5#rO%k)e%8?AT}GOBJ4e*rDIrZLZ6istouxM7qZym{o*V+p$%^ ztfCPA6uWXM6dZFi?AJctJbw{kRYB#c{q!|Z3yU*JK(7olx0*G#BtSJbXn)a)C^4SiJP zRl@Ejk*!5U{&A)ucbp4sJE|p>fkt1S#$2b=o-T1&dda_7Y>s4+6&Y55lm`mQzS@h1 z$O72MJcr2trwxIa*>dy@0^`0F!p@d;Kxm+lVk{J-fpT}Xt+>SF+oQ<2K#Fs3 z+1f8QM(z5}`|&w#dX#~0qab+dQn%MaN}o?64Vb_DxtHX))bewna-O_jX`M8_v!H! zHK%ftV%m1Fk1&7ln{t$=RoqRTIsC<%7_%({^a{`y#{k| zm&BU$j{IJ9^lV?uc*R&(UpNZBi?u{f`*u*pVja8DFo(M5b)mZj_R$9~g6YyS2dy3# z{`^C?8u+K>-cWmlByz@#LYPDI3l-=5IL9P{_VKj8%J(~Wf-0&h%AWZdzw2oCVx(Hv zDqQucKC0uO+#vf4QcAxaZhvRt-c@)TIMci3^L+xrXs=yTq+dB@%sqmurwjFH` z33@B_rCqOU2SQGrfxUCr*Kv$(o%>n~DR+=99pN5q;3WQo^@-yu`jG@(=4cKbQ?hQQkjVY&%jd|kB`mku^F?*ggC56h( zlKy^NWpwyytM}~iu%#?*rDZpfubT8Xs66~lyDfgZ=GvTp3JYhiRa`2kq>O{BKJbo} zy!fgn^}?-TY7ahH<|bRUWoYK092gDvz1VI#i_RM z_(~-^SNqKk>+fy)x?j}#RWPzd0GGHW-KB8?9|8*jvRu-C_Xq=Gx`W| zw}<`|M*gUf(=(6tr-VBqOW?2NwgZliy}gv=x9!B!R>gU51ze}?*vAK)9p9mKjN6p4;VDbcNA7f)2!)p4Ekls8$kGS(nm9Yr|jb0nu()se(3$tvOE zw0#GB0mzn@!o9|(2D2xFw{v|)^@djSyD`J5T$?-7<=7`DUmSI=l=xtZ_KnCou?_Y! z1n1L;-K<5iWJR+A>i&9Pmq}icQ_C-CT+}x(>~78G<%<=fMolOPO=`oGK*=o4~ zN#8PbkNY7+y{djTXcfB$k09-Ep+@n;fa1KCwYD!&8=k=c&A*K1r29kOXZj-wqcX$0 z)qA-63av}GSM5g`*tO)6;M^HU1Uq{I$O1Ubw8Z@dEi5EwTv$L^CwemUhrz=f|Frp> zjds>=fw0jMSg#FzUK6{rV@1PlL_L&4sR=|uunP6&z|(mHGgm}g^xMR60f=#%H)x)i z-W>?jBBR0`m0C|fT~kR65I^pVR>zYZ{Aak9gwCXQUG9Hwb(5X3l#CdD{dgzlMScYo zKGQ1&^RQ&%9-rG*7_8Q2W}T%<2L&687@CGv5-uq;Rw`DJzEztmaZuv-7=o835LNuX zcvNAc#5coY{4>koZG2IV{~uGoHSZ1$W%0_%Y-9Y&pXO4K-U990HRiFEevs>ULhG5r zq0b1ic{UwneBoCJ-l_q4k{|brsM&HWj~Eyru75v>o@6>2KM>9P(X&SH&i6 zYgirz=~%}=&`*pK*Di7b#W;Pmhot%b(oAS+HQedY52$c{->CS>FPA>-G-IbS_~DPh zx?#b_Wozb7gjOQNE{Qp9v@|tzXndRZS?w2(S=$yCy53xu$6-`rm=Y9YIr%2kJblLF z2{fbCLs1Sn2`M6DLSxNbhkq8gA1~w2A=4o@eS(?d0j3K8E_^0o`VVcSMUsiYa(G*N z(m~P%4iA4qxwma+PNMb?Rn>hEJ`;~^zkMX&pfq|%a^4SdK|k@+K@boi&(4TsTYu`~ zvFPD}RuRCB6c57GQA|mVmL*)~@xY&a-k?Z~R>Z4j8GJ1eba%ClVgtcqBJ`t^R3ksF z5fpqo2xd!(WtWQPA;%n~2L20iK!W>oeX^tcJeY2X2#<$k=vp-y-(J}5+v7wOTJ%E; zsLi*>@p~9-A`LU0m+A;(dejmJ^z{3i>>GNuvn~%}W4UP;h@Dsxdqth&X8V7|Rjb%l znbr?rNDjvQe`I~NTQ3A{b!j*EjmR@(G|4|d1(%k0$+YV9cT$8I3e;|SvHq>f`mEZO z^6(iFd!Z!8;HL+pueS4N(H%!UaoA-xFwcjLA3g=TKv1PdlO3xqe|-0)+gw0CqN&)D zs>ob!W*&vIhpL{tDT##1r84|J^fc=HLi=!{V~QTF6vPqyo*uxM@%pQ5G-VYWjtT&j zhh0iYS-M+B_o+K)*(5yDtDZtCzQXj@vP z#xz4E7i%4=w7cA;cXEcQP}i3Aj%m}hgZ0XtLrF# za_Zv>BYqoIE;!S>8piq6J$);S8W@<^*H!cs$yeZ$8S4nPg?jz_%akfIW_Zp3CX|o= z=-^kek$w$)k7{Xf^pYQVIvNbu)si?g4DPdZfmp}vGmL-+oFYV+o~FC+Fp-{@ZE4iH zuiGaaalL=Kxsi31?bJ1rcr+k6CY(LtkIt~l`{__)GV}xJ<$eDS1zfem3@A(p=b*oN zWDn?ReAmCcif}Mt6D)5ef*p|L$I@ER;2tRxx zw^KXR@Kb1BRFWWdu`sRTRRLt$#n-ZWirba|aO1{eydu$%2yHrlG)V*h)pq-A!SzcRo8&jIU0QD@qn@1rtR*-cP48L__hlnxkU z?(~&z&tqt}^x8#vFn}{K`NKL{KHXE3U*nlDzZUy`piigf>-CD!LPaidNO2mjw<)^2 z=(t3)QrpFz!6cS+M7Wj4w@%%W4~tAr?>;|~fOGTgWl^o|De9DF9I&Xr^P@jD-&+17 z+tQlQu}q&=d6utAKR3TzcX=zX#8wb;UYl=1hR(mg$}hic$!))^y2n&)hEvLZX1Q4< zlvG=>cO6m2DL4K>?D#16SS&n%07Ev#ut&3@4L?`dc(;ukYSH$kwJKU(Rsl!TRFX-H zB9Q6m>qo+wO9diSH--5bnjkxFmZUphgdC&` zl1oeq&X5p;MkxCR<~$FQ_j{>(jCn#P$j~jK7d7?PU55A?7|olHAbv0(CI0uTtVakX zD!NEJ3^STBRCGHVby~A@vrRrv^saYpt$Pkb7HseW7s5pQIIJUiR2`$aSr&c{j1_vAGS5LQ8R*nx7y z(eJ%#9Q=Jnic>C!=aE+9&F}4*QH>4=G&gX&-w;+v>d`(aX)966MvL5E;y8)Zb!T9y zdq}a%>Skree?P|JEGQUT9d!_T%*uB?@{!mtLQJ8)oS(y?eyKxx8j{L^$)Lbgb~N&q zS7+G4hZtz*L6^fr3H|=NS*9l6XI9>%`hP9Dw|PmyegI{mMvmLzuh{gHTTYo>VisyO z5D86`m)hRUO4i-X5Gac$Q%Y z&B-_Y35bk=|M~Q($nk=&G{B~FO1e?1q)=H}%Kk&!ml?#!p-bwY#40$IMKi-MG1GAve`k+axVUz*`k8w~s~c$(M=y%~jWf7pvsrXv;N2M~=u znxLFlqz0A#@bW9yhwbFHdQyRX*HwOexLZ%}u4j#gUh6UaPTLW$`FhRfuU~GYw%-wy z;_Osk^Tad~7M-z@Y*H0=gVlztc0VKH=+RL1;(>uer9qQpjO5GdV>z^t|5kj=Mj3B* z(LoV?{gR5^yq5K z?2lCH`W`l2_!sPc!Rc|Amp5KzVfXvZm;G*$18E`vy;{MiY!M?f*KuP@KMU{uUxXNT ztpDnUwI96kBO%_lrZ*Z{VDg7OQx5OI1Ew0vgAo6x{M^Cd#(X85)k zZ|Y%3-AisMjYgrr9*i0$16B;PBWN|Ko!>^~$!&&<&=ZEO<8vhf!nF8uic;w=ux4c*)W;hD)3~lHIXSaK*16o@l8iQXJvHvwl z*|}UTsj*Hi@Jb`5M6~&k2fOQm0pvQFJjzhFONKL@h}3T8Bs}8j7jm!VVJu zzuJ{R`xj-h_HAwQ4R7r#_p1UwxC~k8zDDIC&OI%8Jvu}!YDr3dt~R0 zrdiK!2o|ZMdi_k(I{9@q-jC|>Z~RJ9yQKFl_k;H~2XL$#rl8+z0xBE=5|Yfw$zat# zJ+z{L^VV%?*+a$cJ+CxR>%#G`XF7u?ltfcS-8yd*n-ypn$w9HhC=0C;Q5H}? zVxE9+|7@)2J?Cv11H-==`SmXb#$vNF)lb0OvfMnWLd9S}c6*5Z=ns61Y3R1XL$`!S zUew?DafTaQ%c^gF@Xv4FpxNS-mGL+#hx4==-V3qP3`(}f*=X{UJGiKG!X$c_7?ENo zrQ6wQo6UXx1ARKFA>NBRPM$c`Qrsn-s&J6gU#7gz4Vb!q z!_M}`v61Lj^h${DT%v03=cTJt{iTM-(>wM;+Kr}Uf){9T*KPK!E3A6HpdbVIo9IuZ zLi-z)nhzVnSZ`8IO|Wn(S=_f5rtgRtKk%SZGi(yIg(^$;MR+156T?e$t;binlc zBZ#G$?mgO?jar&Bnv4*(J>U6Fcw8G^!*SF#ytUzmBin&snj#PC&J6 zw28j&I(J6akCPOyB?a@Oax=fM^55q3;u$YlwH2`HaB4JYzays+rWEv8`aR+Zl$lQlJ_>_gy0)@!{zDP@WH3qb@ydMD1=SMK$Ay2Mj*N!LGuJkrb(|qoO;Gp>*ab?idD*02@of3-<^igG@l0EP|4pX zb5KV)`KCwoc@hDpfbo1`kC?B;&zdCDOH1M!>&nzB>Oe;^<;M2BZT`~pT1kw*y^6%# zU@c?25CC=`_t-^siM^`Fe|;UFLC#~*ye{(@MXa^lhjX``%D_RL0Ln_XM`sU%Ok~DU zMQAVHq~;Kxx^nmZx*GlE%ho0EN%2XlUzn7U_5PpkEFJT?jb`r@4B@aiWk@{}EFq`MaZO`p%xo1D`gieMBy{&%3 ziptiU*VXS;-UxToI5}4nf2!fWZJ*k$%{U-i!$TX1)#W!2?7F?`RF~cz80<|Z%0_4i zF{B#obf+3zfaTd2nBk_S|KaNK{D%@~FxkdCA#;&K{D+21XXr5aLMo2*?(qKletB^y zE`ew-ii#5aGF&9Iwcd+G#^2y>r|ND5_TU<8L(;Det{wZnj!kTKE0McSDbxF!7iTWY zyW>`l+8Ku2-(J+Y34Fi2rmE73Kl>kw;r}wf?Q4Fo;UXwauut4@6P0D4Pep*%vfQi< z-Q)*$Zmg0Z;4Zhd-IBHL%ginM3qsTE}p6dQB+}J{kKK%2M-RFBRxx4l)@~F_>3*=PhDPI zQ6W_!&?hpIPF%J!O_d+d>22zR_;q3G*97h`Wrd;C;q3S&i*Sa_4B)Kin*cFHv1jD% z_~X=Urm0gKdBD|UbQijYw?~S0rh93>%gDC0)e0M_Pgu#K)iPIR(ta;goYlo zG(Ocr9B+0EJ=0nGJ4WR)r%{C?RxGVbzHTWdP3eIk>!2hktIeey(iKaU-Q>PZFo%F3 zo!zs_z-=c-?AaTeuFF&1r|Jwd4_^%?iZyEVlk#hr4?1{NfGKeyy^+3WUFiV2YIDF$ zrLgAoq9Dr=Fyra7x+lMXh7%eDewdXdYpeg*p#9!2vM_&vqy8{(K}r8DTz`3ZJR9)Q zIfhk+UyL~HH^Ekgi=L6b3vo78O5a0{Lg61ef7>Uy1ZfZd$pmmVyXF$l9;XiSc0Bel zzUt8gup+ehh}tReI!(3*2JJMGqCRG|dH3U`#nh(gVF&N3!Vkc=4OaYwD)2SKu=CT*Yx$l*VL=15#`b`i5vY*|ToutNnWZPuy?A-X(q8 zIYq(X!Ee{*10CM&C|C|p23bM7;pg{^tOF(b7@U1I76&lo49O3ak}&%Fx{Dwvg19iF zf$h4`=MXWwZw3vkMoEtEKv+Ewpg~__Xz!q2xNo%K`EStGbVdLM2^pd-4MM&Ya|a+;~50y$vTG zY&ti?fc$4%v!8%|v9}Jb^+s)Wh2p~ch{fE16aAabYLC!Z6kFCuKrmyRsCp)qOrN3^ zD*L`z_L9Mlqa~)XUm=k;;QsCwP<AIPA@(g(%J(9VGhh7WFjodXsw zdeAmzIHYW`*Fx$|vy^KC{zzbRMCkKnV z#S~<|^_;m@=hRY&&{_8Jq(0y=eC@@XQ>)v_mHb3~u!N(=nWHBTDQp8hG&e$Jo=jJ4LKTdFXo#A^|<=oz&7X zX*|#c0w#AIP0PVI!1OQE^tj8>Btv~!P`&!#im6JommJ?yQ?iMG0c~g9RL}B_5&Sz{ z(*(;Hv-I|5wp3%oLi1)IKCP~>AFY{WM^DP9e!YoG=`-V8`gqOB$>sDn#aK+Sm}+)XbX83!#xcNs$z1r2<$})2KIX4N?FQ3)spowhiuo;V>?ag1RKb&v zM!O~dZNUTzWPtBg-k|zD?OU|=mv`-EeuhniEIV#dmRvL>Q{QCa^%bIXcI5jSnXx!@ zPLiolwF+40|8jY>F&k9~?n**OpGOgU>^opXMi~w);EZ6E+iXLViS)yL*du8v&v`kI zd{hAJ&MgQlL*6Hd@{x|HhfF%z32`FrGy+=F7VrvD_0x<&3cL7w7q5}M7iTsjgY9E0 zpk&@}bLib}yOKMXpf53GwCO~9eq8M(UKNUYyJu_b&14m-*V*))s+dus@mJ!sy(4Cs%wf zAw%4p=HHuRL7kz(IM->$mdVCeLDMOcK{v;P_C;H6sQv-kixklZ`5eFgeFl-6aAX&C z8C6-jAcJ}7=;y}4#Cd6aH7sHiHA7qfhGh~0MDGV$VoI_P#mJAPB$Lzc)`-odt~|G^ zO@ck{i(BV1Od1v6xLH^@@C^{D^_}rZWo=$1By72APwefmq=!2M{Apq)6l~K}=A?5u zS8f6)1N22Sc|CGg+e~VyY8y_s(nRR^wL-WWZn1u^cbOPo8Zq`X(7PIojTUV!5BZSE zI8BS=Ug5I*wGnMU>b{>sw0-`8<^>V};%>|5^4WQWYtmLh51@k*CLB*aCW1jH+X_qU z)rj+%zi76BxN12Xdb#VfvK+(Y1gRUbi@Gev+wTKk9|)JUQ&{jdfP~*eoZ+P>SnBp!`j7w?CqUL=$L6 zCeBN7#GFeR`u4H5=W_k#4WVBT+`|6JUhDHmtM&C2#=cWBXlw3|pblVL1i>6u z^Mfl3E@FfAzz07z7qBM7EndCXZ8=g7PN?;H7dpT>|6_L87GcjP2avCm2T9MC|qj0lU8Pv-OWoeWX2$e$890 z`9EvXlEGVF$nFn(w%A?JMbLQVCu@nyHA;sv5#(U+^>x1rN?LuzUV9r&*Tol`3*LCg z=@>jkHT3;i=j;O}8-vp=vqvtDq6n$W-3zTzj;4`c*g+glMr6txMXWa;)3D~nONt}% zsopY@kDV^ggL*{vW>_^8zv(uM;vie=HJDcQ9ihuC3&`8>v2}BOL%&|hgifAd=x26k z0v`x19(VCOj<2r6R0)uV1MH3%Fft^ca=4q0)vuGay^kih<`Ebp=}ujexpEtnPO8IEisq7wFQtqoN?iV+{uAhwri`O{;CB+-ZTuzuInj7F^e>4N z!-XVE$F8)k^AlkI3SvMWs;Y5zt+gs$R$%Vi1jxKmH+Ql_vXJEwAq$au92d$ z+wQO6KePVp=xd=L7o>ADgSt7@=Y7g;*fXtxWkCk{=6*`#qUJXEFPd&?B-4B~Q@>BS zA#9W3!n{xPpk;65D?_;`KRgupBcdp)llphyglzvr=tQhZ@|?Y(>$)->cMTFbTzQkh z!c6*6zh#I*tS&ijZ;(zyM zdst=FhR<0rYcbW^@2V8pU%M+_)xSqpJj5o8Waj<5==o;-yNDF`SM5U;&el?#57N1N zobUZAr`xD1o!SGBo0}US-6MlHrM%TQkyW$D?h;IxRbwm>H?>bFMxd)KZgQZdU$D9< z&27m(+(!u)W<8*M{KCfo| zeBvJgz)|aowVys>0Lt;Mps9w#dV7jKC~o-cPenqAK~nN!b@#e6qT$iR@MV>e! z&{YSs(}r_ED=~QI;lreI&%l0e9#sL~d|~Pzn>CQ)@w|FSY4yM(`q=)IQ`Vdk;}M6R zm<%I;0M&P*GSKKwge?uOOM%9r#G)JV!>_kZQHUlZo3t4r+qGUp|G$kaZ*XdR2*3dY zSpHBida|l)+4r~kf&`xjf=F~mZ8 zNKZX65T!@VfMZ^}_t^T+=BT-G1x*8UVTl5AFSEd1QLo)Y4+XP~Y-!g4`U&K0DDM0U z;eO!Ox7Tw!yA}7Q4E2>78(RMFvmNK)D-&!PWG_LCrdQk14eV#Cuu~1Lk>}}S!}g{FBhOnIJ@ zL&n$>Aelb!5f}8PJw(OM`1yBOQ@j#5dIlR|_N)jnAVAOd>6?wB%S<)hMd|Z6w++9r zbk1^FOZF&=*FW%4%!0dmm2v0-e5Ssv-V|__LrJ6A@?>aFdyhdb_LTToAdFNH z2E-E%KR@%D(CqEJOs@^ETItlS*4z5BiU_HsD`_qHF=CRf3JrlKNt+gWm9L*kBEIr^ZInEo|u=mos7Gg z8BTxdav?78HicM8d+SD`oRFY}NPhA4m`E!hRZ@eD(bZ3CFV$i`u7#>`Qx!wd8%w~Q zp|ghz3{5ZZ2)_1S6qp$(Bv?<)FeCUKEe!wMmr-0jzayv}Q68Pt$S2llx)Z#2DOkJ8 zzKqjoi~4H#v!dY1w|?tq^k5|v8OjNRv#yFyjTL4~VN_cARUoYjBXqMPj!{*pt$_Sz zP`l)lQKxp{p&zd9Rh%kQ>mtw)rjvOz9?GO$XK5MkMdZF%T1qd&M?*~GUomA?AFH6m zArSMio>G^D=3O*pM9BeC2`46@`q|3T-5Ke+iz;PMX#qyR_ET+$4H2p906g80za4zN z>?qbZ0^dwIWkbD?yJaHXYR9lw^P?OqbBC!Pd21(a2vsN;=O-04*>8B3;|#5Dmg>4V zRpti7i3$l8gC}-0B42FxNIlRzS(UqMPva>=k1H`4>4Ga>a?vpTlbOh#W{49*7p1wu zE~a3-qH~^63)SbGJ5f$Wa%)1P?Yiyk(ul5w2LIrBDj#>ivll!x_;pLXXCR{eQhAy2 z*PYV{ZMw+@|6Hr|$zHRMn30{^LFg~?u}!~oOAfA36RG-T7Q!1o0at5I`~eC|eCSXu z{2k~IbD^$#C-39=$B5Fy#skCc|5ViET*o-W-`U!7VEBP-k{O%w?{ja4Wz2mOu|9n< zY&A{NR|-;0m%+p03ziF}-4cFp&t3KCy>*&nd0R?&Rk}f44YSw9XNr?r{qr|~13yKx zdN>G%rG{i6E-n8(gE_m-o97z8D&lSMb@58;r<;(WM1Kkp4SRw}pv7R>qJX|_q(VZu z#6m~N*WL(;b$aCSZmXH|75AeET70fSKnH`<)v;!f{WNp-8^J=kJ0ef;j|&&e^Vi7J zrRqNfM;ycmkToZErT&S-)v_x4VPbREPA4LMQFDmzu@rMSAFXfK>NJppltKC}Gn3K= zpOp7mH7Db`?{k3T&5WD?&(xx?$+)dxm_;sKxyJg-2zJ+)e-;y&tGuy$Yb$pHeT3LR z)sSk)DS*$iuF3HBM{E_Hy30I}7OQPSz1rg)uNP_*Podr;rR#Qo;wJ2F{!-WCWm#92 zy`na5KE%h;p11~8j@CTid@uu0&}veJJ=o2RQ7Y$(*s<8&xCtzD(R(aPsf2V8?BWJ_ zOfz3yD#qbXQ@CwbpS%Dm(YWw9uTszn!=Z_^w8?QV*cvjt*qybzGR@8nST(9$QB!IR zCJ7bhp{=s(gx{E)>@)Le?dALCCkhF!*}UI3zNEF8c5D9*c5ID#K2OPymm>O48=F+n zp#2gpsPt+1Go4%3VG}@q-?)hCx4-^Fg+r9~G>R+NlMVW6`Wf8i^;DFGr|Be#`LYKij#x|MZ+9;6 zk-xoV_yapK*qghetrG)k*h%T)VR1X#!R*fJZN5Xig#F9sRe_!PD%F^e&6v8Gdl+G&>yP&($WCf`WAM`7{NS69Uwb z%JG7OuUR)-$?eYUgPdTg?59vumifStMyFS+vf9st*z<&}$LytUw-4pT>i<~bGF(l6 zqxkoiIdveb(4Vh!nMet6fEiIpPk?8x-UJ*ExKs;ey}s$`C7zlTM5r|Ke`N@^V~>Wldh26 z#h={?9rm^Q57x?{Bo3?1AhFmXekeu{thEdk6!mN( z`NwcH9_7Z~^3MaBRA_1weS68ps4htOn&&BB(5$zB4})onryfGHRejITO;jLwVll-e z&snqT@1D+Y&qYaEzki{*OcR$4eWodbq<`ye{*}gQMh!a$mQHf9Sr|kvWi(6c=16c9 z)oN1F3JcRK=V=!V)jj7B7Al@dZOen5&=3Zd>+B8urg!Ua`ISDs85kBR*}EgRYtuz~ zanSWkqGV5_8$vO%$SM#N({!BjWu@g3SwIcJSCnFep^Ckc!&;F7 z^t_bYqagT&TLl?4?RovmRo-(k=SMItOj)(}6#Zu7=**W!P!uI;Dic#W z)X_$e%YD&3uR(kUe5lp}LS*ml%wOb2g=gpVAGKqZaWd0tbJg#_ickIC9&!b6L+RoI z?I>kEedgNDZ(B5_7dYko>yO^5vsbJR|L9R%x#IgiXkYP8iAVF*8b&)s0664@w0yjt zpSM>BpA|DG>1e7@Hxe_P_cY&M`c8+N0LiwqL{AgHhwIEAyKev}Fdi+!^tM?hgPD>& zE&-4r4Q@{kn7OLz7ttoJVRBTF5O~F4?<`@^Z-|uar^F1ulyK1$Yv7QtyRQ- z2&Q=$hh;s5`vIG=Ii**((RiBuAd3``VaH8bJox357gf3i!l5dSGL(_(a zy5}o!0YZZ#Fz!e{5&6osd@mUh|HV za;0v`MiDQVFqX{9Tiep=z)g7G-ioa-KC{}TNx#goRIQ|W>-&2?&AS1P-3Di0OA`+C zjQx4glk6?@c33P&7hlFBKicjQ#Dpb-wrQ#sEtZ0Qmz~-c4;+IL#UXGvf2KoZP@;on zn%AD@z&@?l0&EI0Ke<2@FA;%7F1CN60>na*{fsjkv1hq_9kl|MzEjW|7J{9~LJ*AD zH<~lX{Rc(ml4a(x87dgFt#FVY*@H)Pfg2RB^|$HwMr7!0u=)078Jngu*inH%?n=q^ zrdQWRui#Z#r?#mBG~`It$Zd;{vNmY;D~l}W%w^sGHQY&1KAN)8Qt=5L)o}TT z$23u0?{kn2UiK58cNpc-Xa?5nmF7Ewll+j_ApY8YnTSHw^V?dfO0;CS?=G zI@8`xwNDSW??iO}o%%xFGuHFzvz|u=sC5~xVE<6z@tBy}v^>zr5LlW?x(y6w5F0Dd z1Rxt(s~K+QKQdpKnwvRnJd0zM9W@LPauUpm7BLTusjV+EG?1p-bp!mk*XiTs;U6sK zV0D!E6oc5J(|i)i%E|ZGSnm;aa!&c0)g8~j`@wwB`Fp%#lao5>1VblCW``N0IZw-0a@62>gSRko9?@+6C@@*@4nPA zF9J5Hu7W0+09^2-CSun4$Ue^H7k&R#8f}7DFnn<;qCOa{4`^s68t#ylnd9W*L+=Jz zu&>N@{(_-;6v(6ffgAe%6e*cD#38fd+p3X2ST+arV#OIT`)*nSHbqL+;J1Gc%Z#Z6 zc1O#uoBTcxiTW7Uw5}J##2ra1FtFKc=I2PVW&Zx5y~>`*@qB;2`X~4qHc?9W$0vNU zFHAygSD1$E`4ezJA5ekeI37Yr?MOF?W(@1Tg+J-35u1bl-l@r0jn%5np>K46sj@o> z&1O{?^Ox995#Btm6Iy(rDh^oI$=@J5 z7sslufPP?+-M?wujkGuM?IJH|25l9>MI9uby9ig#!9HQ@zYO+5x_72)s99vd$CdEg zc;y^S*SbZ#1E1-HTlcZY_VmW7Te#zIPkg$$LH@133ty62MC0z@PUzl~`dH_FU}b;H zcmA=C4w9zEg#IR%5bcbQD^5JN5pr5%9mX+iWIDZ)Cihn2Y6SX3pN>j1H+b7SM%5zyT9Xlr!cVBxG%GXEW^R;u{5~4Gr9(Oq=)d)!r9b06Ui?$ie zo;`*JEVS^`)%6c3cj$4vlnB)BU}l~^4DCqB%S=xy3E|gLn~%k4!HFPn-hk1`}D*1Yv5Jb+1xvKdL3VqaSdcSX#F+M-pF_d3`Y}SjL zwf`vVyyB&~H9usU%)@w3oGvQARa<#v(DhK9B1qS&iCdUh0VqO)-?Yfy;6dGZ`7UU! zAYn5L{;Ru2A9G>ec;XOIWo9Qu`u)YKCua0Xo4*C1(!9Ze7MU*CePXDsB~FU%Tg6st zDtpgay{NqpE@MlCF{*`BDH031!?h)g!o5d!B9fZ1K_X5E8&)Mk6@W`jnPs2bE70MS zpJvZ4*gNJZ*p`2+KYbX01n-c(2{B!T9#-rNid(l7s`BqxJGvHy;|GLnoWx zD_68tSIj91BPPYJgKQov}Vx zV#E>@KW$ELJ?$tf{k7N{lUuw;gQ@-zDE*F_SDzav(PFRGlQPksH+il>q~3o~BSu;$ zG~w@R(K^Xn9MXn^Y8Hj_YLY!{D#MX!Ms+dO#9QOjM4gVrWC;z7Bn*jD1 zXbU}Pr%K1n3Ik7#XiH30c;#S_+R+J9Jr^t5PYs9N{OD_>y>`Eb#c>YF9Fr7h8P2?U z0rfjf4?68ui?y6;ASfHO`)s&wJV%trS6*4Tp4qD7SA>0k2fg!Ae6|?hZghaT`LF$hI!(DJ|g5cmn(TRMHO@A>i|i z#L^Ic1M6@!C$AiFk88XAE+uJQ#hNDLPnNTlhCBY_Gh;rJ>vs;nPIzmfT`^vKD=ija zGNeos&%bt_|IP7*fp?LXV_*-e74A zp@;_|-esB;xx3Sy!VSE14(2Tg5Jw{QQr|19UFM(r0b|qIcDDS^E-A)74MU&O>Qmy= zhQE+_0H@m(O&FMw>V851;@g~z_Yp_kUNW$c&QPz&wwCY8=WZtUyT-=FU>~gqqfs&5 zO9W0*gPrcSpz)phHP3Oou5%IS$>IQk$$}o_cp=FvK1rF<*dE0-5az>Uc8G%qi&3Gi zu@h6NW7DAy0!-F@7ADdM+Zh(Jlem=T8Z_t9yp_E~<|AL=SoAvzmeL~HDLIM{IBQI1 zGH2gK6gJVo*|J4naJ}Z2Mv_cC?Pm8v0S4XBPmLDLjyutpmx+^ zE02}m^g0g>kMXQ&OHo~~G+Lg?M277-8d9KVwWp}!f?@$M(9^A#+~o!b=Rb#45jFfi z5qK!CAXxDlsg=|IL0;LRA{LV%YT{mXDodh8@>9hN{~4r_7m-vg@GO(1K5onX0 zdrce~rC7%YI*B8G3=Y%Z6`R#lo33i-Bd)2Uw>`pcopw*v@M0Y=Z9nd* z#&$_g%P)SeDzaJA@jehuXZ-8_r&?gaX3Rpu{t>r9()1WEvQp7%mvPPY* zfyWWlT_C6g^Kl~`A-rHqzK0;$N+>P$?}z0!Y7$}jW^rbxCrN=A#Hbw?keR`Wp9W_O zoDg@}TuY}pCgDLn5Ek3@+;tI^(hrf&eDyQwb%UMO`&)Aj?ULF*RIb~gq~B6L^Cw22 z6Y9i%%uBt;4OnvksSl(mjTq^YvbV&&lj4K|(Rsp~>@9c)u9vG^-cxp9ox*Ap;nshi zfa*QG0L>uJ{rQEM!n3C5<+fus;B=3rVjXPkwXV-(3lQ9SD*OC2#+j$JfuDT_C5`eQ z$hs};2SVe`&?YY%lDmSU_&4K&rIro z=easV*ua|~B%pR`=eGA;zU{D(j6xUG;0{DNRFP+u`$y*Gd)cx%XzwiT?z;F@JVCAc z4am}QmGw{|5m#jsdXEx@KD<}*CLx_^FN3!WYK0XkM;%ivEA8yZ#OV`x_`dX=80q;##ToG@Cbu98+)A7x@%cE}at zr1@seb4*VYo)p*AfSI|Ar0SPxJK0RWh$vQHT@ z1*o?pD3gYRC0{?oJMQ=mi|Qd}XYy@UcX?v?@9K!-rQt&cV?mRS;P$>UZcdXtp-{8p?bnOH zRc_wc*CjlejUR=b_H90F_6<_IAI~njL0jlt=qezm(%;^Akqb_=u?&D1f`>g%RFRzbW&20<*}25jlO z;|!n174>WQ_#iKbX|@15f@;x`ajy<)qTPVVX#b!S!a<{NpZ;?8dZHQKvLe&TW`aVl zIcVb4D>_@`?r7qI+C_T5=G!u`=y@Q8)S~CJ+(J}8$zk4jVkC|`|FhgZ7^N|;DiM%% zG{XF{>p^)9xIA@YAFQ=A)c2!_>SNSxXk2KBUitM%=6S#~-P)x#%$n1FGv95`$W}wb58;G~oT8aE>S2Bkh8_0K zdXf3&ybHW>7eIJxu0+5*7-gc$;1rBzT&Y%%8RW%SVB6dO|CPVaKJ+09O|8C{*c!Yp z-W+t{L^(*tL&E)dU@03?O7%(cd+OGwCE-5*-y3xN+6vV&jEuZUAAO7Fe(BU3U|MmY)8I4!-1cM%N;HG3eN%K{g2dB{j zzWzVH-UF)1B zk80`j#uakMhbWG(U9hBjUmEYP)q6Ry#1k)*T`6}djc=$rnRAsY!#8CkcKkU4pLmZm zoVH!Nw4hub)j3cpl;bB{EX^wCwpB*HpbYN%u-& zpVxvp%7$QGP=DS^C+$sk@7L-AJ);YdxBcVqDMtM`0;cMx=xI%TB@rb{S6_7dq5aA( z4ShqP&4*4qn{o1}@l~qL$iM7;3{42=y^*twbAQ(nblLikUl4_ zFf{tRPBkKTj=zCd%KE2>x^=UQ2Hj4e_anIz1*;`3joMnR_4{@LdF3TkoI|?%voH5H zaal+?TxQHV-uizx~dZES_mP7yIi;O>urP_lUs#ubMuATUh!y zQ_pgLzjFz>{*4*$G%VO+>n{nk!Fc9-cJ#Rbn4LQ2GU{_Kic^9d+v&nbUe6_vCT*K} zYbD}?NZq1@p$J7lX_X|M=Ta2ZZaOdm&H9bf**ZJfReO*2 z!q_ROMZP3iByhN9H@Q7E8K} z?&&sofyMir<`y!1X;$^s0C!tZfa=Aq>;TgY2v1XCt(1cOf zmWvNp@;mYYyMmiWb`7gRnmlFv*g`7#`V!u_2hS)ptBg#aJppA$cC#oamQCh|G`v%a z?`6{1jepk3Yrlyxa;Q9Su$eP-^xF7G<(a-TJ|ffFMp){}jk^+7i29AQktLfv?3N$A ze%Otp=a(nY;Cd|v=a2Mpp(VdhlC2n38R4z-bS3_PhZRwL=$5ih5#D#II^oPW3xXa; zmrS>1nJcnQPzF=Sr5GZYWNp`m9q4bg`o(Xg2B;ggzoNg#w>fch&A_gLwYKYMEvdO~ zXN~hBFiGwh+97V!5qVDv6&OYuyX6j4sbyw;ho+H_m9h-#yt{oHlKhGMFaI<7(RYc~ zmePNH6{jZ;w9R%SS{YR8`Yt$nH_J$kGP~Onf_l4~o1&74-XaSnPCn~i{=C}G#BH5( zy+6V_q{96JUdgdcUndG1ioP?B_ci9UrFb5;>mE?Ri!R6(=SiKl_wVk`xRZma_EC0h z|8O_hsxM&brk&56Z=mPu3J-sq+l|9@{n$Yv!dD;Vl6FSZc3*p;oiy0a2OBCVk+KSgSgPkP#iK@Fez{%_9DlHg?UM|hMP5ex z54=^PvKJuWpP)Owi`}?Cf+oNh30$~w4%a~X|&*a zzKV8PT{d&rd&;gEqID)>(+Cx7@Npz_Fxxt}aLMf!^~0M#&IXqx?(Gc!cpBIuOvuH1 z>h2hZE`Qk8fs(OqU+rMSy*`ibPCJXIH9;!mk$p4+;DSwUPCKm}REf{Myk5CNW@*PA zf}h2}zs8l?K5e+dm~W+-_P)r)=<;PWTi`uos#re`tY}JS(81%~bgt!o;nA2uaX%mw9NG_)(&V zUh~&gCC_%Z{zU&%=&nx*`Q8E?hT%8dy-F;ev^uAHCurta5FM+{_I++{s@grLs&j@uM;sa_BxLfcUqnt59`X5BvNY>#7q zy8U=4hqE=zvZGI;<<8w5XWNYKq_zIBE7;>g-ND&=HTP8Yb_`^T#liU?=`i3H{bqJK zl!cZexEwm3ZTKD3IWLMqKN`j0{pG3hOSp|B*xl%6>c|;e%zEN%2m@tx&^XO>@j3%0 zskGPopOH@!?iSCluX!lliOo;+E79bh)$}OPoA{)J5}*t$sNyf^TD$PL{p*b$w%umE za+89A?45wfs8r0Sme@9wh4dW%1tr5&2Z>?m{>l^Ku?No9x%7YeNL{3&nzl~_na7$$ z`+xvp0~hiV;Q{}$hSy3%cln4993EV^WisfYWBN9+wf@3m(kg?S{o3Nl2!IfW0RD#T z&HlhecJmMnrU2wEL@XQJ_rE~=(CI2j)=!A2*mJ7(&TcuS(z9B9pW`dK_&_`j;}qZ@ zldHk3Bz^a^qn?lBc6|B+7Cm;N+TVQoX!J#|&oWC!epdt?u09bBUY4NrIrtO zI-683i=Apno*~7nn<`)5O1L zrgI9PJZ=%qVG$nf%i9p5%VyR6+J)HCJG^Ll@^rDoFfEs-o#BM{w_qf^xWo-xa(F1| z@OV?kq${|i?p*jq^73r-TF-BR;VlYK&b)skTK%?>6KTTQ7_^_g>Lw(GZ7!;mW=hAF z%uJWr2ZTJGJ8|RkJHNf};R)!vY>&UCdoy1)6qSoIggbJY!))!U@80{7`;|pkKk{zh z(jG!^=^WY^qlzf$VJ%@>F3gc}E6D-LNoKOtwxm(gXL%ygep8+0~*(QjBG zpDWuc(yNDx4fFSqRD3p9ps67$b?<74N1h&Q=k8kq&*CfLPziOlFLQj2Mp#X$^I4{I zC9ds_9Ft710aBjzPz4p^@6{`x_2F`3~D+d$=4{VKit(z>%V>dzgGs*@4RA+i$wPtQ#-~#c+-V96pdZA@x7#@Z3 z!Rl?i#hJU2zzb5y;}^neXZBj>4iXNyjFl`>OZ}y+Tiqq@-I_>OM#wJWH=U_I)oo+c z1=NYC>oAJPtv%N%0<*S3uaO+P_@P3)PCnDMycW0Si*6htk^NnU-Ioi7OK#B)7kq`< z%-Ex6l=|{6x6f-@Kfpbdq=xUP^m`J_QwE7=l55C0O_y% zQamWx!<3{ND81(9Uh+itZKXR&p`sA5sMr7%-P74R07%j@xGv^$) z2Vl~OuTdgtQ-gaNnzFX&d1*n+U3XkM+=*2>(QZc~ciexXxf$8;Mj3`0_69o#Z3_$O%lqi5*L+7E59wcO10 zC>6GwwZm~PhBdl^K0?2pkR%36KlMAr1`q{*;|oH#S>hPQp^4b3q7kugytTVF+`Vip zwYqd4br6i zLV`b|`iy#e5TCeVlR`;9x1!L z_KeK;ErAz}##5e19;vP-B#1v$yaeJG9H74Flnb%^^o!8=>^@5Wv5Ud|xWMlBOJ==L z?l4TVf_$zXtA7!ZZKT&k-4Lp+oxNs6Q%KH=Va_kZK6MJcu(z0E-VX3wok{csl1+-? zKhpok@dHTC$?n$UITZbDg0%~Afn#y%y_&sCP+uMxONMKmy|@cU#>bER_Z>1HT91+G5!de%2~c zhv`Cphttk7XM|d8siTrv7GncPkUxWk1<#I3-3seB1Z~$B#NGaX*m~6|VEzKbyz_cs z^HT6g_m-V@@orH954ttGK-9ow(NjNZQf+@cHz>=c5Z7)PBflBRbTgteAPPD8B??vP z{+I>t%XamPQxSU;RwK75VzC3IQGlkQY9_#FjYX3L3UsQh((B^XLN)s2{1?SC8vowO zTnWG3D#~DC5k5KKMB|`KsVs+J;Rt){zPBikc>mkp1}Bh2*8iIyJcn8KKsZMG`)+8J z)?BkOXv#vUc8gI>`2<)0bi>0Q{uV%M1Tx&Wg|jRw8V8iE<2L}6(LqA>oO0*WJskzUBf z0%MzQ+7}W0`2j7|oml(i$YJoz-i7-6MJf*`jcmbgSYAEd4Y z+Z_>ZK2%g&WD3ITv*TsOrdI;P_G7#Gko$v`AN8EGGs1g!3x8r8(dCBz*#(czwK)w^ zx@Po4EQD9Ov;Rt1S`vX3)yw8-n$gVE{QP{usyDx3mBGR+axzv3p2>2f#nr%45!08e zwF=#wvcV8nYLxuKwYI|2JvHdBc>*<2${ZpxVj1H~MI6 zL_djGB2wxwzO>;{k!{zIj`vv8c{iHz0hQ2CQYU~ek56n}cUl`wp3-ZmHq^qh+t-c5LVeWO z31T0K z+`^e&rsOSTGPq9B7`0WC zpu|r+Tt0j9=GKZFGoR3NBaC6;qs({ZNKacCQ~^9gZtjc1V^_M@_ZW*^UySFZBSc|m z?5C;@aFgmOxlc{(@5kJ@9%1>U+A@3+o7qn&>fFmHO#_(7OFIqO*-6Z|E-LQpiQq=&oAya;|C3k;`A8W3%&SLRB%iiLVqN;~l^vyhH=}QJN z2fGKQSm^p9A4E%w)b}TKdZQ1br^aDi@VPWXSsB^(I)OM>xRK$X&O2Jr!aayQ(#!m< z!IQN3-Jpfvu%XhvArEnatV)?tvh38K3?W%mt5rbpUZkd0;T zh)z}J$SHpLiL;D+;j0)kc}Lyw?H&g>hg*hTwR+~(LS%QQ8l{a{!KD$+Orx9}lqOad zu3an-YKpZb0~AMLh`+^xC+>lt_U|tPEF0afa^|M1>CC7-QywYdC0O)X>dwxm20-mr zUI*%!Ma5}|O?{heW zlk${PSPS>OzU@ssdV*5xVzh~PlL}nKeK!b|MfJVK!LqPZuiE(8+1WfQAIVDYKEr76 z!OXaML0&NrzKE!5**6hoICy`n@wfH)%P#|50f&x17RSf_{iRqtrSJXED`9q}p6l`J zN#%Ep5{B*FKHr)V;4ck@$$LohP8`(2oz_s6O!rtVWCv4C3?C?9CvZ1ua;J8x^ytK# zWn?AyWY~H2(U?qgj?t<|AgH4tbF^@B@Hh9^B{FzAF5TNYk@UX6zrxYwN>kCBM&rV) zCuxTAv)VZIfjD7&{ce!`N0Wp=jc$3X!dqgPg)V^S(;6?u6CTBfpWL_-GygNRm4Jo1 zFTX@SO$>Dgm{Zw!&Q-NM~O>q^2xq(x)9{BY-} zoSK2OVGCM*+OK$W{O^akjzJ1X&t#oNWR0~AbwKR^B^_PiMi%Uo3WpNg`W3Fqq6Vkz z3 zZXHacx2;tlZ?3=&><^{W*IgVH%q^dB5;IdwCPWdXxW~@c+Pr*H)v|gX29=fstV-w5!W<_<1@BMOguV<8wdD$t`^;iLZp-V#>I~RNl)hq#K1? z`XX=UX^^K{wTyyrJre)kXaD>=?yPf4c3qv@E91(u=h&p~-g9s)5ZooR^el==~kCdW_}`ZBPi6wEf+eR zn~j09^z>}Dn=Udgk^}>3Hc8TKlscvF8tv`J;nhF@vrz;zHB9xO6l79dUP$u}T^Cld z>Lu0`2L8lFZD7f-I5njbYbm*6!gP7;%y{BL>r~ZISsJjE-1eN#-wzF*7y_wv?E>;9 z_6h)$Womm$lB#P^y|0Y9z;2fkM6CIHSYzUEc2)a0Etl#1ydLp(&bJ~z zSF1Pj5^SowB%)ygrBnQnMaT)4cTAo3N?V{!KL7h`;wjt3DDiTBwdG5p%r zT%a9>@zfO7^xxBVvoQu-R_)KpSGvI+c`0P3RcPOQ=6$Ju($aO5Bb3uZD-)@!9a)#0 zijh9E*+AVx1M^zh&G)pgcrGd7{rv5KjojK#C4!X0^WM#bsP)uvRJA=qfd#-|@t%RssAahCsAYJrK;*4PSUQcB5|>+#CDfTQmO(erysCqM(s|du z(fp+;MNsv&nz%?4Q7m>cvdsyD!6;Ie+Tpi0dI) zQlDZ_mR6C*HkA-3n<}%DVwD!fb%O(z_j&cbTaG94`$3whbl^T`dhVsB?)AWVEe$?W zwd(w6g`yUh0tDL66%*cQ{L`g6^6+StylYM5DCAPrQ6jP<5v?i7RYY|PKG?_X7so(p z_2RsYlhK<9c1r9MmtQIYn8fX#4k+b->|~hDGU1zx7FD#jVgtuO$sQ7+rV0;FhC1z* z1nw`MqB|HDh`Hw?Yp4jMdmyaj#$+<;RpocU43@(8z`dBic@?HCzo z2~R)A#KiEsERU_kJ6=YnR65AC>n(&D&v*;MW-< z`!Vbsqc~PM!?$M!CysBqMz_}`P0rF}{QWUucC(^h4=JORTc)e9tA>lQ;`+k3;wTYj z0aK6|y0DBYNni*@%IPMsf!xyW-y1siU6hDbHWHGJb4wPXrh9hD^ zEro52ofum+P2jtHdXbyed*}SgHxwRoa;gz~9`C$k6;aU>6~9aoaK=hdSlDa}MI~ui&}2z(Tp1*-AkJ9W+0^8#`1EO; z_nfc43=8M1qt4(RuG08o6-^Evo&paubMt{jx2M}B!?I`S#qNzIXJ>bVB!%H^*Lwr+ zy~7n7*djl)d9N;)Kn5@Hu!?vff9^ldxW0(ad<$Cq6~Q=@MlBY9unEJmH973OAnG|? zeK-6?gyNPr?;R`gc2&GOLHEXtOk_A z=j%f{QG1yN4|n6VPYRs4rBGL+iFumiV|8z=9?Z^}CM%1an|AWospd|84uR@a>&$5s z$(3ZSLPhH(ULn<(Y?DdTe{K`b5v8yY90ZWJ62x=~KA|elVH&6%yxeSgI_M(yo+A#Zstn|Dm@7f3aeoQ6hJyto@2fQXJZ5wNCH14S10JFCVI z(=}98<UsmF$$ z;(Gt`t+Z@d7zcIlo8Cp6+Y5hzsb08w>P7M_K{??rz^P8F0IbYL3Hrs9|I=Z7+w(4e zhi_!DN{PWxbEWUA#@ml-@CjCvM&);3|C1Wz+Uxh2wbSNq)rz|+V>1e%dn`4Ruy)Wh zN^!#SUS5^)l3oM8!Yr$!`*A+3K`!;}n_F8g9!r(CMQld8F9x;{^Qw7g9koQo#HOA| z?S^G*6)EU>ZKx3{+^~omCTb96PC6XRjwfD^aH&@1PrwI^Jg4k&&rpbvTkFoZEb&%F zy%~n8P=;}FC53P7xir7E%1ZepZY}adBS;%~^#AuK>BfEJW&Bze*xnz5piH?q6#12<$KHS^Oe);lc z91q%hhVa%bzGgj8XW>Rzc+@$hCkvlakvyRSqlH$JO)*K)?&@n-NSP_CBbujf?#vi} z4-iXra>7Pf%<#IM{9`uVoUzNl1rs*B{?~zq&xQubk{9^W#D3Br=$i@a;$|9ypB`#=>yI>X{y6@#e>b_~nQ62E$(mzvPIGoesI0 z=+r9fLGKs-SyH<+B!6ylZA8}KbV#TwXYQ7Yu;5~N`%>+_Xpf9%(Ve4^V$@p-Zq(G& z1uU{W%JAMpCVM83t_!2Lvzbt-BdQ=mn-b&W1-Uk*_G_gyxi^p2r4E#*p?Z^iN2`21 zPxq?#mJY=Lrf(KRkW0D?ySQk*)05|I%umqF-3hESE&0Hj4^V%N4<7mx&PZ^U_|x)1 z0!8Csk6t`)&o3+VQM>AKumg0;^=&0O#t!_Jo*s|I^XI6!h0xqlZ|>4|dP-OwlEB3$ z-;=A0(!$C%8#>P2w)m>oY_(qa!|IxMlL4MQtysm?;2XYW@J_|G!S~neLkF(h^c!Ux zxNvpg>Gt`7&)tHrrIu>s`v=~yo?iOuuuWQ1gIYYBvD6-+qm^gB<7mIbcxQDAJNjXN zL;tAAbNTDJ)i+H0U%0=Qyr%uC6rVk=`K2RC%4Sa`Y+_=fYjBXO++_(JrROdm8w*<2 zN^!P&Uo-WfwUt-C>HC+RE!&Qk0{V@=&cj@%;HD0#a?UrLIHIAzYVRsM<3tYqX!6vFCKkBn%g;WzyJ8Z5#y((XyqP3_?{j{c6_HptOmAohJI!t(R9DrU{ z@f-l-cgb@`4DBC3KYrAAwBaM~XG~S@wX&A`p z<4fG0pFLssrE=20qo!p+c(u;d;LNFuR&`iwJVerHf!N%v@O(2lIe9=O-5L=PAm0g% z39Q$;O?hFj;yexU%{lS`$@9xdXoexX`RlmT<~PeLD|N;H8o-|Onp|x&xNny+y4#-F zU6>uk93$!@3yhVFeyqma;mioZ_#RmnTn0^ur}mY4R}6*iecd_*m&EBq=d*3@af!G$8o#iF_X?+` z@(=Qb1;gO*Z^HVe@Mf0ANTymTlA4#f5*Z)8I8n%->-_SFO#=3TO*@0O%q{ zlUVzXJdWBO+<5F5|F7(wbTpQy1?_t43|KP>gV_d8Gg*yQT8#qZVZ6JJ@dUI{KqNDa;R4;*Jo`iV;X8mNk%G*(7FK=tv-%X`B! z_1p=kol`zRRZs3Y!*l-OyFZrPFI#%kY7+mlrfET#B?Dcoay=$4K3GQJ>GrJba=_WL`o}?He#wDiOF3U7dkz ziU2ZAOlf^Ym*;_x4;%1~iG9Y5HPH#iTV683eQnw@cPkKIAN-Ibs-k;CH!{;AJ+&}2 zsxGq!UgcFD!D}et_=kP(P5=uMX_A(fau>=%McWPoMi+jlg2QY04b&C;M8O)@f_~+I z$==G8VZWW?3EIKtYTxc0D9dx=`J;8l=aK9oR)GY3(_|Yx)Oj+$QdT(;bto`{EIYsm zuIq*IJfcmtGg18Hv;p7Qm(<2(ML#>lp_89#<$h+0%OVPB!4Z+^sce)n5-*S6p&jcF91tI4ViM8P z?5w4W)_C$Oa@j>n={Ay#+a(d)@b!+KbZi<>=EwSwzyR8$OsgC$?*8?xfyi+x=-G= zUgv9kjV~}*0Tgc5MdgKd$|;n|rXfEG@=gi$z>BkGn};O+_&^~*`=0q2&2cT|t}rK6 z6d!?7ub{!0#Csn-Ge}-58jeVi;@q7w0au^n9se^G53uLSAA{JG{jdbIXOJ}vL{&k# zG-Xipmlx{0PcWElc8;Fe=!mla_6BxUL%RJ@n1-DM!CNiO;l97Hpu%a5m7&5*K~ro} zLOeVmn1f(k6F2=ci9DEu%+y`WURReBGnmST4t+5J4J(tKWoTZB zI-mQ)hpB!bxco5RlD3!Vsa!m{x5rjs=qE#egH7Z)CxgTU6q*<%ldY&lUx0jKx@CWC zg&?$mxkyu|C}}h9ZvNugQFPdLvl?YiM&wX;;fHzt7&wKVc1&oqs-j^ShERF@Eju&~ zV7D{;;T@8dB4yUZ9 z#OP4qU;=(c6juPE2K%v7o+IQ*3IH)?G3L!bwt5g{zh8})Zzq=*=1r545!Qu-?>4M< zbxLrCm%i>+txHY461Je*eC*6g4d7y#`P$mrw2o9?m5_kL)!93D&&G6awZ9^%nL-92 zy(R)=vPP6bj@|C@kU+o(uYPdqU8l#Um_k-cB6@mS>be7melkS0AKD?RxOtSEIOOYD z;d5ljW4Vs?1tq@Xp*8)V6)&&H^#`k}Dtyd*n(G(>>wD19+b~jykwy2Uh>yh_uO7w- z&iAR98;oI|HhE30g%O!eiHd6Ym|zOJn+Xw_tI;4hf*Q!&p6@;8fqO8L%kvaR;3dKh z0$hvpdq!a(8ZxyXMDbO?Q_UeYCxjtnci@j-?jG2Wb5J31%a#7HIdi%@GLz<`zL$b# zQmQza>CqbUsGPgO1TM^dQkt1bj|+V>Z!=uRde|UR>puo>VX3-AfvXT zDHOG|pkm-9-w?tSSzz=8GWwmT_Pa4OIyyzXBN$1FC{vE^p)O?;ICLX(Q1YtHZzG+$RO3)`h>ViDKVF(0FC8SGon=h!p(LG@ zy9`DHaMpTY3eGw_vu`ByXUk~lCV;a{%8YUqh0v>a%T=0QJC?PLrMG!8QTOWpHf>KV zxNgb86S=w|mNLHM1%MpFS6(;81T)9*Dz{W4t@lg2i;Fh*Ch|#n zo%SGGky`-C?}*Y=+?B=HSGwqcxJuWv1OSUmov3mwt8dQyRbsC7NfUz&phcS7K4TFo z0)Y+-luo6rg2Ef#PBB4d1_>j|=LSJd(`v$d3CD*nSU@T_7JWzHxf_0KGj`B5rA*ea zB{rq7LMLsb+|pXX{oHM&tsqyM6Gujtq8zstJ7kgLIF1qQpif%p?`z!>O~hn!a>%o# zq+~0mrKPFqC@U;2`yy|XVKOt|=3En1$f)B<+fUktRmyO%OV*0(s;k(wD2c~$HBGfs zz+7oUqVNOi)CB4(%l}48gVCoS{;jP1wAqoJ6%zLT!@R=6g@+o^O-lGwdRAx*Uw>@> zhT8x7iXHe0Gxpv|`j5Jl0Wf4QNO_4kcPgTtda!hrv@5}b}y9E-KOr9S;1L1j8t z(xmtLZ+s#nlL(iy84Jhrfy>~M#Yt`g*Kzg>MHncz|1UUkkNjQ0Jmfq}T*-WwqaOm} z@cg?y)<)-TqXVUXMDwb#S%yNRN%5caV@LA=3~ySO>GgF+fQ(QvUT~}M3<{y~@}d;{ zE-N2WK$!dZZg~f4^WlcS^doq5Q$JJ>=cvKWOI6BYwnR5!VU{f~@5VBRsQ+)Je`&yB z@9AWmd2LR5uqQ==l6DLX3w!_%pdz=gavM!)Nlw5rb+iYDgy!OeC zF8&%_^G{PhIx`DJnA&3<9m78&SzHv1q@)R#P>i+Xs0;5BLWPg{SIDrWVssumJC)`I z{Sm}iU?`Agmk?)xq^82`NM=`BlaErAre`u8~@{gkda26 zq3+*FWTYZcrP|T)C^b9E`$ybnp$T6=|HqH@6D%J7p-excJZ(Z`0Hd&=QDdI|tv_dw z)Op+?@)@QQ4#(OWLXaGSA@h4p^4*%6rWY0$w-f>OJ~{c(*>mTN&CL}|OhT<(YG%B= zyzD0$E)S+}8;-)Fc;#JOiWwZ--QAtZX9{Y%bayHgFJEekz&$Uti$PME(Y4d0WEZM7 zQ>n-kvp}Hx59I>VWc*f^xF$UALZK`gxjJDM95-&_irCAh$j13?I@51t|?wPWpyuazh7Y5jM%8lp*J=1ffvo@bKr@BvAvFucuu_ zbFCtJR!|`Lz?^h>tE;=aP+~298Mt^a3On1@JV78biYPkDR#t2aDGu0U0_8cW;|?ar z-0r}H(K93?Us;EfUc@@WYj0Ej`SWl@hSb6O^Mwi{to8_dm|Q=uL+YraV!aEt37ptb zg*aUEiQb+Jj*Q&g-iy3(!?kYliptY$E^B3qm&xVj1vyu6Q&2j-D3Bad>L0${dgi}_ zJz)R{I#&Sw6;jE+cM@zRoaMOOA%>nSa){V-n*dKu$6^9P(yvr82g(A#6xPD4!aZ<&WHH zbQ1^IV^n+zcx3B_2Z$R8TlU{-Yilz}9Rbe86gY=u7s^_RCP3sZ=&P)(T&-m~ zd=|_g#_qF1>%$D34uS@)i$`Np`{#Ckydj?)JskAm(#BbaWoBj)B$j-wDCVVh@XC`e z)q4r^!{r*AOQU>o!d9`vVrpuYr*lYZ+k$Y}a8$f4MWIUfBC}1hEv5fy zE17IQep}!_d2+r()Fye^zC37^W{K!a)$sM$>-RoqN(6XtH}}snLoBUS1@}=|{(Z@q zTc8X1JwzP|?J?%e?*Hl9^q(hzJz}f|h0R{Ph(4HpYJ2~-^`6vW(x6m;R@|&;zHPwkB7@iHhH&EwYRH2 z{3hlBW%}up4qE7Cm5yWmy>WDkh!9#LT%~Mad21G~ZFj z+9b|nu^>HnJ64IoBR(j>Gq&<>QPRP7?#%owt@8j=l1l`G)3P3Jt9y~}o`}NPPFc+x z1D_9HKOi6Y-0moye9lT$UyXh?1sgmgc;{zyiZeGUfx=;cRI?eLv|q=E-po+;YSyTWGU^n_G0bXYIlO=B8-IVa4O0=- z+!L!5YL}vu;u_DqC|`G%67>{luH6q%ELBmcyGIfrYxa7Nzb-0M_XXJ`dsny zjk7~FS%)Ql8V6>sSp)C2U3+N3FRxs*or8ftR80?(eieB!%$#+BxLKm@I(xA;Q!Q%< z|NOhv%iM+jf?9@XvGTV*9qsMA`8iuJ_zOjnOZ(lIytamCPMRVcnSAyK%lL%7PxaB1 zTEtC8M)A1q7zgy_6u)TGL%$8HoOix!o`o;y*W%5;n*Y~{6DR)O;TA<&oB`0;y@c-W zi0a5Fb*jF*_I@UpPxYBkxGO!}Z~Ix9iIiHYqmtCg8b2VOyg!mC4m;o}M`;uzMrO5$ zFHiC4U>i_^#Tw3z`{#Xo!gk94qp^24XIYnkF%FP8+@7c!sO*s3+pcd%8}SN{$f?ac&Rl6RG3{}s z$KqrsyHjQLl`p0o>h&Xc4+dX*$XI-@6v)(9N-dDBR)SKuv zQPw#BGst4TaAFh7dPU1Br`YbOSf#ecbffWqe`;U(bH za|IIB^=|AkG#2D3THM>N+FK;Paiq?Fyq4dWt4q|^UVItU*NkoLnx$(g*R?1ixBdyR z?a3_n9PKC!D;NlKc=kxk-)n+~);^U}_I zPRwEj>H?0;V#`#!zefdxN_=o6%ZoAd37NO`Y<;(2Ewb@YyS6Tlg6qR6@R%CN|eoiFVsrU&DuK_=gC zEbtRkOUe5Y=s_86<~WmI>yCUdaDzY7&{RO+hKThmGQza5~#A5^?uq{p{Wys#QMA3ZisdZB#E^8{9QqT{};oj%^0I&yy2 zdX9_mk&H00a6Cd z%$YPw^8$F^i5X;v6Zds`hlgUlpY7rji3qfD)$5#{{@A!*lWBVU&Qnah3n>4Gp|CNV zLgb*bAbp0?LJ*X&e&9L(!v5ETIM{MYV_asM6`a4rnJ^xQj9@uUcMN~+VKXx_+yINTtX_^GBWP_~YCz)P!oU80 z9qdpOw$;yqHs3=x)rzdKvF%fnyS#}&LftzepdzmiY1oPU`SXP28ozl<=Z4kXX?+pT zruLftvdV4=Xu#_)@8(}d{HEU9Pzjb*?jpwqKT9=C?YGzd7l=aAaSQC|sZybdZw&*j zc+y+FjFf3&q&72|T`vT5q&Q6zTpnu0i|l}S=Q?L0)G#euq_v$uGcbF|d`2a-Defsa zvtHs+g$xQUIX;QB#H!yMa1~4YcgMhK;OuI}%o;w$oq2U;f~wcb*Sk|dAFtk|{=Zgv zXBw%joyc~0*#EHHcsP5^W;AuswXNfSu8@_5Zw3j(Lz$E_tF@MhkkoDMM|IkEKe7nf zyuVHb*E=v-^L;|kLV4jfB0XyNNjaUCfc;-_AAj@TC%}K7J|jD#8{V8Vn#eV#2eUa0 zcQ@!X1y6i=-%naqK87?VHhkgH=63WRR3Fc1@wn>uQZy01^D1F!Xr(CT@@(W@=&(|A zsM7P_XO-10=(YG(Z9TS>z=A9uT*O0>ThQuE*PGA(C8=x^<6mXu&WY@3mvZl~*nfI3 ztrDY7%;j-N;gJs+NYYatFojmgK8)SnGvcxosrj~g7gwq`eF{e-7Wd-Gp(KIyb}u7= z5c;cB+Bt9LU69D9rqzDGd24eCKg+(Fi5t9(UEQQX`CW2)tY0(=Jcf0d2ZxN*nZ??; zEd@VlyFMQJLPi>(egg=+7n07sJe&meE3|#DO*6;{muWHDZA~nkzvjsw2amX=EU4pY z04a|SPhcxO^RRZHEj08OA9+1kz42E)Xg0xz6sc*lr^Pt!<+8PLFGpJXS-uVUmmtWH z6MpmAdjt0g4)P}Q)6zH#gWu2o#>AFK@obp2{31b1SH}SpF1QtGIb6lX_KOH#K9ERh zUs3`i@FL%NKgH>9bGPW0SZm%q<8A%xO#E$3fqC+BF+4$yENLM&JR!!gD6>D)gsR|| z0x(7TqyBcbHn(TmPXF|49F3#{VL;2c>GydP2j}gMI_**{-09I4{))5@8DCExH3t7{ z@4qpD1OJ0o687Apu7I3?u&zSo@Q>f%ZU6h%b@`JVe7PySdsWZy6*(bn%FWtAm$q!! zBIn*ezZt|!8f=btNr{JGtWtHE>$J3Udfc{xaBV&5&HnKLjP-?_R-=iRi-0hB3>i!% zvLM4Tzy8KV`kMykdmWd*&+ousY0xXJ)_n&36l# zso{fL{5oDBNh5xeulC(_cqmGAK6qvqnH8XbQ>X3l3=>;*z_9*ipa)4;G4Bx5HHBn^ z8@wYgPFJBSZJ**e2};&uXGl-*0z@(N?!;QBU*~011^iRfazIdll+*cDHUAdgZocyd z>gpJ3umEd8?Z-_|;Q^n%jMi{H+vXkpeT9PKY>=ivVz3pG=tVGU^5L^*rNJ!Me(};j zKIm(h?}9}H0>WdlbY>m^>K5KkbgfBrpX2bDiqzoltc)!h=*{a~a9-%q&@VDgbZa=W z-1z3OTUkA8x-z}$3D{T70h41;DY#a=_(7WT7yqt*M@f@x28sRU+RaoxFZ|&c)8W^{ zO`D_M!xg5Z&fLmPEf3&+@X|bVy4LEm`#ovkXjlKJ9F0Wa!Yymx zUak`LYT+sFZ%+_wacO_<=Rxev!MUxOl!M%<=^wfJb1q^}(x8*M&gw1!hHa@laOHb2{s&{+x_B+fqOGl16L%h2s4_89=@|N8D%Cl!{n3+9hSGoCU zCAk*E8S0c^y5XEi+S>YmrK1!Nr=QuKt=87SO5nG^0gY8`wTacL`d}05TyE9K=#d>1 z60$e;<02u}qcZgE>rv-HtEyeExs3Dy$-Q}_9QBqXgg$kC@*NiB(iO1XZb|cVGu^lS;1dEc_Cq*SEi5 z<3-foOjz=4w65Gx_c+{{xzJw@tF0FsHe7#jEe5DSE%smv3Jf8aM5S-amubI^*duJt zn4X#9%B$#Nbep6opG3jt|9NwTQ(vfjf3?NSwd&`mfccV%VC)95FDfru|J6R3{`PC~ zu1s%>5uA6@VfI!=*}=$GRi8$!+VF_3gM-A8W}TGjt-|w!N#vkt!uV3@%`(19WZsDD z8>La7YN7FdVV!-`%}Niw0_`P_k*D|UD-%3h&x)B(#I6pza;aP!sFP_a4jH(xkt`e= zZ%JrX>?<2i@uYF0Y&1{CDJi?WNRIt5(X^GEASTvJ?38utwv=70_&l}S8E=uSU;UZ; zENlid2Q!N;pMa;6zIOVui*+B4t&;(zCpJK{@y~wxvB6nB9DD!_P6*NhVt1Kd3b6iy zi_eyemAQGdMt=2HC@EZfb4k9uL6y?8CaWF~H!z{GcqG;zy0OwWb z2A?Vyp^KuvC?cm+_VcKoEKIwkB}6l=F!>7JO@|98vJskFG7XZYx8U4N#Blfp0(bvF2EqCEeXhETOK)o0mdy^-+F(a` zHP_#FYpmrcS)j1>8cc827}$th5KnSO>*8Se-?0>c#0nU zT)8O!loYXGz#wR98!h{w7!lvEM^hTtQ^)I#PF9ro9xq0Gd6>@x9Du}_n)x+08Aq){{2_b37>i&x{eCAqQt_=8D)7WEKc#kaAt zDr%!=78t^ps(|zyDe3)lKA#{%yxd}&>_*&`lpJ0o;Gw85H1t~gG0~Ytw|b&=&E7@dFqWy3PCqvx|{f5qJtVBhN=qFryA`vLuyIf~ZcYG}l8@4q zKEI&S?-PJJCRdU%un;g#vNEs3NcMCrUZB19s9FEV?NZrYA-;0ihz#ed_4^U^b@g{d zH5q$J)x9~_>Y{RI7>ZpTC7RO?cu^nBLW~N5*7lAv(e#eOykmr5Q~SZqkNs0}v3L!C zY#PP?$xm8d);3RN=vZE{Za1Cm4~Gwx?7+^>C^D><&>=>lcF~XO+pN#nUWmzFyMkqb z1arNu02H26`aTi&3cy#ijf|3L>#)I%#`XG=h6>yE1BqBDbmGJpH#F??Urg1u6?%R> z?m7WD2O0kf)zb4kdpRL;pVKpnSYm$RRDHVWKVy4!d{_r&!P(w?Q%NjfW`$$VjypZl9t9zsp&-nb>=fn%Fc)(B_@DU$baHN>gA=)A7ZuxR zjHpVs`f6O zJ^Wgdm8#kTawIDA)`!((6F94e*$wW!Ex%IcX!8u;)$gMk(-n0PFr0o@WQ?Q`!P8Ub*c{H__L%- zJcPJV6u&+<+B)*rpM=t%Ev-(z47qa0fq;iCe=;KUvTsq7qzGFqgqD~C-`hH2xy5YX zz%Er6PlF_@uiErC7XNZVJ9R+!Sg&^Vb|Bu@_Pg5TCtJmT*KnwUwMW;42|~@=x9^4f z$m^;fUw+&($ISdGh#BbGfsaR>^x^ccee7ii9le%TB}uT^{q4Y5{@Wor(VYvAn!bXe zznbn^y;{Ks07%IdM6V@)65L3%>94r<@aH=aV++<}RBQ29UBsz(6kjlH#5gN=cf=*- zOTJzd_mVpgoqFAr01s;arI zfd-bI2!cj9RQKx9dsW|=Q$2N4ecJ6mW}WjqqZj& zBuk8WOZr0Yh7wskHLMr9wS6f-f0tTXeZnYZSP+6@WU6XB=UbGxwCoW8!2kHkn{gVD zBuV;lfS0Sco1eMOiluIFo*7z@&u0bf8sf>v%N3s&r{-8Yk4~Pl_v1Ws!FK4^$Vk@2 zq;C77uW9E>kXAc)<7)Z~Y>AI~zdTG@dz412ZHP}()NY)$nzoD97R0b+>t0sdfO$eD#;4*Ie{uB#5Qt(n;%?(g-JYOb8p5@+^Al==X7ZJ>NPR6cPDR~q>SG!8%8^bs~EPa z+|6#Euvp;U?=FB6ks{Qu0qzV_B1KqiJjGLdbkEjNyZcqF`aZzE1Q0Qvy~nidKYXl4 z16sE_{5dinwP&jn~dbF3Me>M;CL8{Xl#HJr!6!#G#3Qi|c* zWSx~!2ivBXD`}&f1807oKVi=~NkpIrYW4#gOqOq->so7XfXku|)`CA~l*1uxd#bN0 zwELl>RsNk6U-CUM(lH*wQSLl<4fj?4ZCS=&EU+gS4{<-#k74fWx3xJFX>j+P`w@q? zcYTT+aGP#&dkHBCBD)0s;RX_}fW_Un+;tz`G5n5_2RFC?dQh-}ACvuv%P;v|Y(Kuk zdQ}EB?QqT}N!mfO^`19M?)cX6<@y-h_b`SHepB+)f^?tpZU-V|T3|tguuqUfSX=+) zfQK#qUFkV7G{?L(Iqc4SKk1TJK;4+tMjYvT1x?cHdkn8Q`S|$Ie+?Lu_(E58aFZ{k zDe#RcIAsC8SxbI6lZL(1cEE6;B50=W?c1XqWDFwS23WvcE*<2p3~f@*HLhL5O{cg~ zUPz{&8h>#v0q2q8hm&*0c?juhIQYW=ziIOxmpd`JwVzqvywM&qmzzu(3tXxMnNwpW zxJ6amM7R(I7!t4~Ph9u$&~@(xseKJS6iP84!4l?|7(Amk{!`Nj0_&)%G1t^{URXhR zP+5@V*wyr3GX5mcHBEY>vuYI<3AIQ5#U+V>8nEJ2lqRWSy8dqE8 znQKECYh6y1+3XnXix)4JCA+bnWLHf9PS$M~9m`#2-uXmR>ltGjZ;XwMm_I~ZN`8_Q z&*4{Mbk}`$YY$C#bYT5dTgQFMQ~?(IRnH(AZZcsue$6k2RcSl167Mi>iy>p_A>PCd z_R*!%7CzfMfyR}eGny7(X7vRlY#M?GjS567`@cJMIy%-4QybQHsqeE8Mn(~p)yeOp zT&1GL$72wSl$&S(T};Z45>_8{(y~?XdS13myJ?v#_B07H^{!){sYaw~e?jky7ue2! z3cGt6ysf2o%kO<1XB`5!Ojf=z?pbwblLOn*jM^;A8d>Be;!{2i=L|N*DOjoYKH+Zi znhFt`A|ioc`$bTR%ZY2TsCygV6O1`5h^eV5_EyVoR?{AhugD3&`$&)1voJE_{G|(T z%9TQ`wQNF-)dk$>h53{rwI$3<0|Skvv-1fb-#U>F`INE0M>|n#lJls>p)+ZSRV_F7 z&tW=s>Nt|#tSAuBeam->oN>u!elf^DUSSe!?vZ2Sri2n{>OX5hJ;6gP$jFM?k#TTj ze8`kcyd0V_=~gB>24T=+QX8*%11;?y=86i0bVwwRXzdX(Yj8yX7K{WyrJEqkH%`QV z)EgVZW-jxW&&|0s-b|4q6on)2qsFUzW{ft8h56~O`Dut0^!)|Va-1(MyOxN19(rT^ z^pOhdIF_2@V%h5F50q~)B&^y}MRLgc9TMx6LUHgX0(}# z)|pdtR*YzpgE89i(FV2lt+PcCsbK!lj03f$?K*}%>JJwvov0&wvZVPT15P6qpWN_E zqVWh(l-(~%m>)v20};vLF|Nm__5cHc#sb>e%-~t$+Z=ObXbAOq82d{rFt`URrbOymP%{B4#sj zbG!AB<9^2QsB3qnMVJkv&7x`e^90XiK=Aw9d#|-c%oVMV`Ft?ZY4zjtFgb&^YjHd6 z;8eT$;0SZ5)dw#?wwsow^WcxA7;r zKHnVSzH3I-`XDLHd6!##-ur2u=^>q!+E5x2zGjd5+JLtUTys?!JB2TJ?Ag1Ea`{e= ztpBxPZ!w#lzfYbiEfD5V-P?}T>4s5WPTK(We+~$*W#ysF{7?+Cr1rA~%K9!)>>Kz9 zRpTMJU~t*WJ;xt3VZFu|MbC}id&)UU7@?aCdYF9BhmGFNHBH0Og;L&&Rp`Ai zFwS7+Bp6|I4yb%Y|50+KYAl=olajBkPuV!c+5irlva|MhtsnlV)4N*`-v-aMHFqy6 z^D;`LbnT*slahD{Ao3yKH=lC6#Lo$6=N(Ltk$g{0V6Y(4emseOtqM3*EJA-gRSx`) zqefI|c-mllM#hjNdW7hOQMz@jIwpB1G7>NPKmYjlSO}su!C#|#fS>oi=mcG4!+kQVq$4921nkU#v_iql1WkYAgd&*-_7ePZAEo7m_<0A5);aq9?BY8Vb7#Mx z{5T)fk4MgHBd)DUA*yVixeMkzwXiXK4dP4kC9za)tbYC6GfM5}e>zn~L@`29nR^LZ zvtEbds__*S7q*lgyS)UZL}`cKZXjs`u>;ZhMbB9s$TeLu_j|p0<)NDt2x=o>HL+>_ zMX}^kJwq`yfN`xCHzKpbl|UfF4Lt{UiQ!6?p@49&jC*BdEZJv5ZAC#NPdY|lY^#NyPzyo)$VkMzs6~ppUw>=Ql`obQo6>JByAEY0B_`Qr8gze?P8&<62-DQk{>~} zG;_%IkN-saaf9>iyXIu(YS1r|4g%>{NUPCWt8qBOcZ(EE`}U6ZGjG7Y&JPBlZ`@Q z-V9o;Kt+ncB{_$g8oz)-Q3#HowSD(rT?B`XsslKyWP8CeY+<|@aH3N-HMP7hxjM=F zRxJ-fX;~Hq^I|tiic)IUU%DqW0jgd-EO}HoO6t0nS7d=aQZx zp!0I-jIo|WL(Y{!Z-$>ZK`K5Wf^t4P5r?)nn65;yIDj3?9i-860Ml9|bsKdcnK$ktuvaCO)9 z!T1+?PUOatos$wNishd2D^=7R5^DR%ZVud8BmuhpyWDlal&nrnYPH0lU<|pVq)M(R zuW-fc1~3cb03B`6tNy3kv-i8Gz!h+!vLsOPTT|BaX^6Ucq$*xH^sL!$jLIZE*kuBi zWrz_X1)N#uE6(S<{9J$@tOnW<%~yvuzL`f5Rpv57J_OKDPb)_+Vye#5}Fa99{96WkovPtv`WzK!3U5W_t}!+4zYMx zU>Q+L)vyPwcuhBP%LO+$7Jq~E|3r`9yLs*K$0~p-$2aIzRIiO`FUm{lk{^wHy6ruX z{p;}C)@utV3MC48$yzP{var{^46k^^sMFuD*Jm`V{@P0^$d1M&7^!vu+qyl3n927$ zWwAapJL_zb z%*-BO7UhpM&$Sp@1AGDo3T!og0jjr6gMLD62c zZ6HyJViCf(yB*7e_@UjGn)QBCKsU*%Wx7^Z0=JiNa0`0Z=;mT&I)hr*a+B?V8yriB z(?9OQqE=;8l04Lay#S#1(cS^z<$4sNP-G`<4@yaFXxJ|Spe<*pH--FxjKt~pxdVpX zudF$Oa-yoKGG2-x6U1SbX~Xw!EuKSLOq)d}22>4KY0;iH}WYXSZ{bthLP>eKJK8>}Put3%T zy;)j;E-BbBaDW{8`DEMVvSjJ#|FzgUx{;w=lzoba-4G2l!_O z)tRz?ryX*JiThu@GSD@3fLIVcKdd*8|Fi9VD5V65g8TR2r6wV;s}B4Ni2n(9S+T+W zx`G7nHW8-`{F#gOk3xW(vvN}`AeD>P*D0jc`d=TDxF1$$G1cv>>D?^i)u0{RuF>M(3(AMqRh>pR5%= zQ8yM!#HoFi=f;%%?W`%1KJ^*XMCh6k*4;6$lYcJdi#eRSke9DkVR8+!!1wp0rJWP8 z3j5%uEH3na^1xq(du*Py=)Pkcl?yhH` z>x2Sqhl}20xYfHyoP&XeosC6Ft$^ZO?!dA!{@D=u0(m2NJeWZMJ15-37A;#Riys+z z(@4hWYtT-!GB#I2iJPmUn=P7(#p|yWdVaKg1ReoxwaxVvcJE@L?_=P_e4$jq24_O~ z-wx82AQn1^hED#>4X~pr$z0f{Bl^bp{KRjf7$r@cmNs*{5IbQXb#$}a|zYq!3F>%_vv*4Jh-s&?{S01l=XFMbcC6P!F&rn^H9FwjCf zO1{=3sMebg)K*IMwbO0#s5!CZy#miDMe*cPz+FzUIDt!LWkFc4)W0fu{aXW)qLY8z-SiMnbM3fS)Ru!Da?x-7h419e7!n14d~bEP@E;+oLVT;;}@Sd-0>3qLq`6?bKvtXnXpQ9WQvJYMCTQ3hE93TT*zh02FQZv~ zVeVV<_2IpQr+GFHz<1)SUy(~W{z1d4m+!`~3>ZWwOz>?VjN&C7jt^ zpaL#61zyGruf(2Gz=wG?d1Z-|-RZ?Hp?YVT_L9SZ{8ZTn`1;2M7ds{E+)N2-Xtak~#5IIhPw8 zYJ&EILl%cFbjSyai~d(@{g&39^%e~rmg+kE8D%^0!h@hTI~H|o;zb}Jd(QL7`igBC zqt6KMCF}&G!G?gH+-1O^FpE?O0nT!W!&_N3H~+xwxr~gkfdu@AD8UpVTDQ`HPdxYmi_4eiMIQ)wAE1RPL;2CuSujvgg zShxk$<-dSWnDlc6BV_Wc76J_sTYV-5KFj>u!Cy&*6h;|2HWZ90FSdo*Cc3BWg?cP} z*YBQ>02N}yj}f%MbV2}sOE5SB7Ug!SX@}kj3g86ZhAIL;y(LaM$9~;?$~XWBw!0GD z^9vjVk&*ga;fM~9TQ34DMt4WFczLx8aN4Hab(FI?_w8TJ_7do%U!THyQ-)=cBeig_ zGC3j!{U)z!hw5tA5UH%p(IaR500@|DHK4sBbJKxibtz+FXY~hsqY7GbOJemLs-(M)X zl8DBzWGRAG*$=BlJ5zqOd%xT8dqADmbPt##E~npZc2DE5(8qt+zUje{v*O8nWt_2gmK{B^K+E1Tvgm}x zs_(S|lBTIlK;A>VEYOGNCxOV@3JAxOcOc@W=^pSNJmDPxR0an&Xk!7xP@H`Q%ZuNd zn-jp$^5{aZ4B#8C+43q1j7s8e!ff9paAtf9aAofWzXcF9oOb|FPAg)-L{DwxIG6YR z4h95RP;{{2k&SurFxjBtQpa{KqjN+Vba(rGP217WL#;yH7Jxit1j!o&?;^4DYpVGGe>k zI`SGBnXC!*@3td6;7)z-0BPj^xb~p#MqJWLnUcKP{SRBD=b0dIn^=C{jGEUTATda0 zpG-$rz~biAV&fV2dl{Tf=ZH2K;dVgj-)98(y<(3pD0Ua8I#*X)eb+u$^@B5apcuNDhA7BpyYlF)uhW`#G z3IcEw&n+ZKz$elR!0u>P=7+6Rp}hw&dp_)%y9#y!;8Ow4mz%J6T$l?zK70CxZ+h(+ z>WqWD?P_s#Sn~bK#RSwtLJs%o<(^SAI(-C72cUd_sEbuUiUc$U-chejCGJeKSQY#1~jOmMs1Jzg{n8M#N1lRvUATOzlnrbZ;oP6Y2um$jaUK&U((NI8q66W!RoW-(r&EE3jb;3Nri*Kn;zy#<`jz4t}g*2DbCeu$R+=SG0h-lK|)NQIIshpAE*8Z6NG za;WB~ve%gVIkIm8IcTMOVd%m~uX^v|Ad|^BSDsdX9E!C}nYMdU3A#=+$SNZ~kcA2x;RKAU`e3XHV zjf&9q0XRo|kJBdtq5Tdg+w;NX3xT#WzAhdTny>RcFQ`I~#_ktV9)Zv> zWCPuK;CvJ4MR`(cJA;|IzOIEyN!F52~8oD<@mmNSoDc|wS zvSFEDGlsd6u~{b)F#A5l+VLI@27`9gYt0gQRBH2 zQ{$_j&QXA9j0NV17A%y)TH1h{27t~4Us^K%3;4~kW8TRgH9t?19?pnh>CNM1N;8|$ zfSwIzeS#B3`0AZSAK*g}lW_~wK6d`+j7yD|OlCE3vYRUNz5!F^1&99C$~`kMPOn0a zDti|zyOMtSMkp5*o{tXFB-WJQsXy*vrr+)8@a>x{K4#ihsNTC?Xz*#z)1hQDnYVA} zUiW4hJ;|Ktp%UdfbL1lG{+C)g@W7f=mO>afI!JCHI%%@3;Bh6k2*TZvXnLz4+#Sjt zK*4a>pu6b;SCP{`nzTVvjd!9za7GMUIT2W63=RKf;kD4d#m|@^%chb$J;R#Uv;r6M zNYva^Phh1uN7`s)0Gp4RD2BS|@M{5o;kLiVuP3m3%OB|J*^IKM3>(>~rAp=2XfT4vNd(p8-NFek5a=lTB|U%HJ>EB`A%cU{qxS&uiAD|>oC zwIFDSvfL@pKt{lQ`*k(Wqv4GWL3hVMn!#j53gom8(fw%&4<53tG*+W3a+Uw_W7ZA- zN0GeVw-ZHgjygx^k{2zf6*+9uYv{{#71o$>s0(A`J`(oe^9OoK3!bojL9!E!rQ{w+$sN)51jKZ1y2GvA^nY4N10@1J&;H`rnUiOO>-Ys`w%|3AELm)vTFe*{=MTm)h%71}xmp908T6TzH zSEX~d3b7#XFwdYKsjBzSeXdt@4l4!SXVF!jXhoeCE|8zoeolW_kg=wGgnbOxpJ`rBz|OG2IXVGl>gPnOx0DXTU| zg@jhL)`7wL*LFKb|1e%%E4|iZ1`8LDUJu4p9#xW5>$uPBt2-FBcX2CvZ~j@`#ZKf>WrMgxQLzInR6QUOMqNU`rg?bFQOj8gv) z)E*t&-edb2kcKF+KaZvLz1$JGgKNp!m#rJ&Ze9RLv(U}HI#;rS4gW0I=LYsnanc^D?-t(Mmqbk8MHZn*fi4r}pzS%j?a?n|Q3tp45NeIFdOV7E z%rT5tyqt`xGp=d=%w(Ycp z!6hv-(cLYW+ST&$jcE1IZogsGS^HE~n&N^;z-#ixS6)PF;IH_ujc-dL3zh> zJ*Kt&&%c((L#+!j;1A@#!-P8lSQC^BvOvkn)i%1%9!unME-f!&$>-#*;Vx03ltfVs z3V@%M*4#Pmf5TuwF&zAL8LWCZ$Q^|cpcFx9Ch}hcc{upg7_j&iBw8ZK3_nA5uZ>l2 ztgXgWdP14C9Ihh#5V!(n6k~)3_FttXVg!VWl2MVdum({m_Bi9amkpcukng1)T&PwG~`hK);kwqn-jq&MctwT3Q=~%Pg+i z{OA@ie+rso)I>Vh-%-MMc@O*}h)q!Rm!A@V@+5C_*#RI^WdOVtQvKj|6p+EDl|$vd zC)CbGHaA&s#qjU-+82`nqOPe0(zoOyQb65jjnrIv>+F_4xBxtw1Bk)|F*_kqJojOX zs=+io5_tu&76SDSh|C-qhQJU9ul)KpEKZ`v3SA84-v1l?g3Bj>pb57mnO%|2GLaa?bMY27X{Qo<8R{+2B{h- z=<0$Mm6!*@N-}%72@Getpb?xCuuEGOumfxcZB#sZ2k+BIYn=3kGvC3F<7ceOY99cf zF&Kh_7#(;whxy-kP8-qa0KD5_?#a130R|a21ETHMw_Nk}ws4`2621z|gY?`aNp<*# z;d?)e;!&AUhzjE*Fr0yAPV8Mdd&$MP+Iu3*iOHdNAZ=+&ch}nqeto#K+|GF1N}OlL zYXVp>7yDnmP@>n-4>JpT|U&>nk|K|AH{q!z}D0JZJ?hxth$!-H@i)(4ypU?bc@;_H_Y!C#jW$g+YOyg&xWc*<8P0=L1t zgzhb<{|j{4k6M<)3=sN3fO_m-mqNHG=q06iGL15j?xoD|R_h!Z#sAoNJS*wyWL)^=1K4_0nxCUb}mRgE|& zSnS7=+riloUhURKi2imcVR+R4X_VKf^UwLyou4NrKM!kw*Q;}ef1e8Y#w(GKXVl=u zhuGe=8N$c+5h+uDs z?rLOuF4$AG5iKbPx3&}$t@cF)*?^=IV1!5>Y>R}g#cl?^UOcmdZhNj$v6KCI@(!wf z2iH^d>Nl_rx_l^qYcwfMw6+SW+e!oATxtKIjJJmCqZClD;TbO>YRi2Mjkr^hgK;kn zpw*SKHd0v}H?>|7YE;$Y4GG{&Xb<~0Q=%OJT&pUZu8tny)Vd?P6~tse0T%(FdPW;- zA>j4teVEvmVT5yIQ4FrIGF7+Tv48_&UmbuN>F2%6gDh&pbu4Yme)<~kq${CzS{rdV zsTX1swn1wEcm}G0HQ4;$d2fCBVQ_*?k|30YzNN+6|5Ch9Hgt`fo}Q*aswK$XZUfoE zj#f$F^*o5xi3k#uqzvlw3xuQhN0*nE(}8E02&{8=lh7q`rAmDjsv;3nbA3 z5JvB96j4xsF&c;bLi2SueVA7^r-yI4!*R@8xSu`w)bDrbm4{+%y&^<@&^M_!k9!Lf z|0{9Pd8Q|tBuWGFpk%kB8Ma^8!^{+A(GNwO*7*q#h9Oc%PDnMBwi#q0V<7Iw~wagHo3S z1M9lCs3;}FD-WS;NV4z1wTAob7sf%&8lbcp5m@7AkVMFw4(c~$2hChCuZ2QN+>d5n zs%ZxEbubW{v5l*djio@DA1bMtK86D=lQD>o<0HDHkfMR91XmcQ)pGLF?X6l7&p~0O z;nI8NIT;{F5&m%a%+j4`l}`uMC}-3_ou=T}5P*_{Ml9^@3Wi(>K((HHx(4zaU7>10 zI-N8>7M)7}ER9|rM{P!#Bstbl-%*)BJlNa(+ZKnZ5(x;Euf^NgiRhqKKGp^h;+J-o zhpS5qhyIL&|1zKJ4VqR!0N}w|8*VhnA1K=+8q~L$4w)$}TP;!6wD7f2!}m3z0fUu) zYYZ4re#)v*Q%C@|!be>)&M%!(d(a}4DjeDAZ{L@7$~S|Yv?=VT+9iP!uZ){M?K*a0 z zY(_$NfEq2w4>=f9fkqk%6?(aO>{CbJv$V0=Wkn<7>_z|J^BWW=%?0WH#7;R}S8spA z>SQDpB>q=|DvL?zx*C9PyF44&2-XL_5PLz}dyj)ba{h|Y&CN`5?ylYLZhsIp`z^m!@RwE5!j6DIV0qpcEg4pUyj zUYF~A|92+u9`>NAF(J{?ZZ0AWe!oKk(WkX15uE}HS(`$D++m`wAk3jn#Qz-%0L5^mImDeo))uz<;u+8S_2St$0$BvsutUIzuwZP6siEY#j%+3uj`!Jbuht(3diY#sv28UKJ&VyI~r za#M#{GjPKkGqt&ktUY2T%|B(@dI4+8Lje0I_HL&@YI-_-yN$VL{brJlXBp&L81FIk zDf3?(o%7n0r$S_|srF>s8Cn0KQ}yE?Mz64&epf4p<~?{gq~~;Fs38xr$P6hJO!BBA zK!qzHJ|Os>#zJv<&^EkmL&$PM#>nbwb?B+9q3i{C6|}eW9Sxlb6Lg1UT086$|0+@? zRQ+vaWQ0`Y>UQu%cU6I#MW~+>URizhiS8|XP+hN7NBOGNg2&aow&hf~8FE$TeMph= zJYq;sX1AICibswvAO=*etIwwiq-e37ClpX#7lnpwfJi~f|Lq_DhIBiU-aFDze=|^C zF$E}%b6)-vYF$0T~d|BKL6r;bo>n2hdb>x1SxX?wcy@POoRV{N}6{NCe19yWiCS*qUMDO=+p| z$Ex;zKbd#_=ikJc#`{XZqCFHO1VVJ!++-ba``4;_`gzx>i{Q(grvEhE!2j{ndn+UI zG5{No!}C{TsW%YP2i9flj&0}Wf$0H*C7)`2m!oQHeQU4;E*IdVzoBVp?*IaZ%mG7> zfD$KQDNix|ymho(1E}VDdpa$$Q?ZyoY#5iDmkD>y!~q=mRB@;t=VPPZKRL!l9nAoBWX{vU)U0@{iw+5Gkz<5`eCL#ufbNjEVrMAuxs%vT$;g(Yd55Lse+^Zv?4h4aaOmw-$gzVq`!A;b?dGZ-Y6WEsYJGV<7-PQj8#aBAo zfh#t9eRtDdDb@4rx(4UeTNlN*k0>afKXvOxQA`(!J*#*>e?6q2y9YtecJgD-Tr#mw zNbp$j{d3b_mwm~^8EH`&%lG&GWJ1?IK>Oah>5yY5@9kk0 z^D3u7U0DF94@EqBCmkdWY8Nh=wS~s1)*MJNi1%(PE3OI{fvzagnb}XmZWT$tULObL%$2r+uOZsrNlC_02(m zWf`%uRP+SZ9L;kN=jOm=&9*)L#LacyCUa=(Kb_CG3Q5yHVjTRot@J9lB*6dYU&)D6 zn_}E_Bvu&2kANby?g1blRHhu*v%4J!hKsH4cnjMc37^81#SaP)H_UAcU?=wOh|Fq- zQcpm9yU`125+36XWKA@^XjVNTA;Igx@lH38_0^D85hdv9ksJ{A%>O)y-S$Eygp`Wq!+REPe8b$*$)*wrp{cMre;ZE07T<5Ue9|2bc;`l0UY6J82%$o`_X8+0uXXqg1hK!usdH~2+P&{7VBH_~hx?`Euj zm*n{OmfT!Z=P#M}05)`7&sCo|OC8YQpa=*$WRwc~be*L;h0>iuHgiM%QhmGLDj#x7 zbM*t?y7y%gOfo_BK>Y%2Y5A7+uasyjj<*r;yBoa${4>G23(PvdB@XC!~h6QbY@licGv_Q-k@&0%ahh;&6A9(NDW*ssZ zso@I}xQJk4%*Gg3^nY<4&sDBn=~1HsQ|eC--_NJ>M?^P&in6H1E5D_E-(1>cHwJ4C zoGm2gpMEtr`W0e_XM8~gaP-Kx7|q2PV~AVl+pVfmzZASw?$fJ|uR?_!kIerD9fW1z zMTmn{anTZ@>{1+IW z%(sdd3LHGCntwfT45*hN9%FYc8Pihk4+3%Z)}}eTyrj8T1= za_Oy>lBXw=GO*mBxp{ zUO^fk$ArpzBEYLM^a7ZJ?B69>4Y?+w*M$mEgC`r;r{vi+L@3tXbfRq!i~fzAoV?bM%41i3eI2_^l1HBDav!*jA%98G!dG6^WK92pw(J%de5Oj ziykdPcKD_1p@TXNfzSkbZc$Hub!=9h?FBG)VLSUl+K9k7!&e0))j1iiL# z)VzL}j(=>dW=6efPBRJnDlWSA+{;kp2u(rt@sN>~N8TZE8FbP8?5Fv}OMK=ke(~A7 z;w<3#E=dqbYifr)=i@O&h{3?apXc;D^;svQKAPOU=#jHDbaH+taq0Byx!=s&#MB4D z29xIO3ipScm%%wtR{1S$Cy*1z$Sa`c5ov?rELzx?$<8LmU~N>xWW#QoYx@cJ|I{a0 zNG_|wuV?hFJ(5zHmt>Yl8x~ETy68-bBwm)xhmxK}VVUFE45YDC&>yA#F&YD^1iTY` z`$doJZF{fVn$)_sb{hml+6XN4{8otFr{4g&~(#uoKHL< zme1?gJ}_>wtDV1KBk1qfkhgM!Y#oE*=BwwGVrxAm1?1_{p6gIx~-?g@6y zGrIr0X+Wc%@a&V%OYE_wHIv<+&5m_s#%a0+rg;tWA39+198tmiV^$}ZPs{H{HedRV z$0fgJb@H1~Y1=H45Iw5f`+v_eLKKM<5HIbqU|UJ15)CpNe6z9>jCQ%gT!wpW_FOW5 z?i*{?IKb~oo$VJThPs62y-z9%nSZ3ecR@7mtDC4rx3SG-Nm(M=+*~#7M?sWtbJo%j zf~RjtShX_S=>B1%`Qz(q^Up2|iqq=CKNKz9`sqvQrw@ATd;DT1__M9R{`Q`T$(k`E z_cvTM7b+I6jO*F&8EoSV7klF_vaKKQ?5yB zL9oDpURFb%l}l#e1!kam#vV*X7I?+qrQC}tMPObAJiHgFAO8wH~`a>kM0;xJ|~2Jpi2XMM8V zGaR8Kn&KLfiDsUe`=~3*BSm-~HjT{s~F84=YziX1& z75L9LD)p)FTdzTsRp?`#NN48<$tAAy-%TZR=XLn050GiK2TF)@e_~iVdyFe1vypxoUH~2G= zl9(Iv{*WO}+Wf2UM3MD%L*FNbMb*veJDxBTqm8YzwcVm;M|XA_vvs#?`SxUbpnZ0F zgi^mLe%A5GznAIrTcxRAzg=(vPeecrD5t+)TAx&H{n1D;WGH6vB!4iv55@cOSZ#5n zgsO&1_fBd>|ni} zzY|98%;j9#W8i1B%YDCj|E`0mi)&DUzN*HJg+@B^gj-LvDlWXmwIoqh(IR@f%moKk z3B_(Q;hV^e%{-AJiHL9Uam9~FtOtVplZ^+^l9SIC?6=`8MO|69ckVq zbUMhTB+F>s?|xy;OWqk_*8}wN_3m+wFNg)Dr2lWdU5QsyXBID5ih>Y35~(O4wH+W7 zuvKUY1tLojWJ^L4Sp)^OBs3~RLJKyaSQe}7A_#<#09hdsO`t{z4z0&(Yh=kI`l2{W zCFEhmg@l?In8%sZe_{TCciy?*ckgfc&b?Qg{qAP=Uq96cWKq=B<-r%&;M3Kfb!)I@ zK)>qtW?#YR)}ZTBPH@6qCU$*8);R(npLWz=U0Gx=H$?>NKQijdmUX4+=K@;uT)MH7 zPYi?t=oZ|h66iy9XjE-j^$mNqx>?f#=-QCNR1=Sq|)HB4zb6{>+)$IB?1=DqQ2vUmR<*cSG_E(e7-z2d7AY zB?u9|=fs}+Fsh$1sBgVpKJik}qeyLr-FZ#bw>7ef$kRY3&A6RqFJb|8vXNh3OqwPI z?}gEyBoDzoAW=2+$pI`qq4-KTcey*9yF&seq5RU%y-RcV%$p6~3?*5!HAa!)Wj-Jv z%tvDnVnQ?Fidd=CBw;+yq3&}cL@8ZQ80a0SF+EjQB;y9-`9upv*XJ%lD=KwDmdl1k zkG}P^3`3@vg8^s3W%OF;M1?Ku&`*^^qGJ^A{?cBP`W`v-NTlh`M6hN@fs>V#LHVfF zxeNW$|Jzuc7S`0popN_lCy1eO4X_HGJqvvjWBDSCU&{4$D`A^!vNWHI(6>`P#pOez z)1x=4o^isvO=B-fPHBLNTs@Vy!KskA@(-gJt~&L+B6yR?E+=E4Q`(W%`PT%?9Ea8) z{wZjJcA~!=3noVhJ10Ya1PNBGHO|1hBQT%0tu$YpKHbbQNOGG7ubRk;9b8%#EAT~#!|Ctgnb8aVwQ&Hw<$v`~ zWsxPTU&S-y9*b$f5vmmWVW4);cYZzSt2kn+=O<1V$LyoYp+VR2yLWqj&BVxZv1NIG z)o;oc9sRJY{XxEf1!7o3iX5JEc7>gtt+k_bvg0!_|8QPLzWuf1i&Y*_#Og#w#3Y(C zey35y6Yfv_5ST~%P-{EPEY#!opEvV2mei~rw@EsM@qUXwu2j0m7zDu+L|U=ccGtem zR0K=l^Wz!bb-^QgId?%GD4s<@Owo(EXhPwftSd5 zaIj=ODW5t5{fVP-a+}OV_ z$z?13Mc0ykUoUPgbZlP;m`yn`|3ji~z2m(rz)^Gn(I6Z|TGQTCGQ99NTc8|=3rVhy z;+Q|_N4vBGqHKW^y&z6YW@2<&RLLr^2PskwU5+M)2k0*~C*%nAa^eC}OCc7>r7FAT z0jTdu`0;ZO;JciX2PFHaa1oW^hls{R@12V8`YMwTJkxLE)1<&C@-pC|@a9U6qw0xp z1x?>yJQ>I9)Hv@W-&I#S90RPb4{QP>Sl3~Ps2eaz$}dae=S6~|LGUdsLxjp~-|zGJ z{q6%`+yd@qhHEmNeE?K(%RXJF!ZKhmUmq?E7H2TZCXMIT4J&PU7eV2F#i>bGu zU+u3YM{Pk?BUKUwgc`BRY2qjKin@0}n#`HU{HD!vG7`=wdBqC?${`<&K>a9wL~j%= z!Qx@GdYW8=z40_pOHs;d{lHjCDL2WDv2ci1=0qTdGWC12Ut7Y5vxl~;Z+C+j?{+i} zD2V;#Hsc`9TUwH65ahO-T0n%z69w-W$fwYs<5tGvt|$Emz@oj!3Zk`m-MW%gSE20j z#Gs}kilx(E-yPK$bQpUt|1rOw6O6wx8Febd^|E@ZttEbof6XWvz?S4?^UrPuXzRp= zKsTp)`KzDy|+F#dRfEe4d!lBI}C8=t5rr3(`Qq%t0P0%7h+g3OIILE zGw+8o7QKp+C^SQSJK?@C4nCnPk!;=a)N#Z!|Wzv)|}Hi3vSykO4wQ zO1~snJgj~z7s)#{s!aEEZ(bV#Z}(i`oVfW2xZ5u!zaRug>>Wh*I5C-Zo;vQyqC@wn zYlv8*hnWHC406}ba-^hRNmtDOz% z=tVjjp zi|!`CG2rJL3!YcMm+t~qkUiCt3EaM3-WqU7;7)%ntmlQM&4swPr@l%J_5FH2#IQh* z6y~;KZ&Gab4D)i&E1_Qr%y@%vBMEMvu2CUex{PBL&ek+qPic=xU~VvvTIegOZFRtBqT{_YwJM~H#pvW&+2)^WKLyEp zrrRU&Qr?OA%t+l@pfn*Qo+h?-nR#ZGY9mAW3Vw7Plh&o^a$(Me>`4?OS|x4zC=Q~l z=mzu~+6NLlU)onpb#(sH&b{roaoq1aCC#2C)a;iI%x6nR7Xk zwm`ivPAXE9sC(rN(-}s;FzIPu?~7KBL3)~Jt{5z4{QD9$)~;N$1XCSY7jm9kE>ziG zG^IIKg+p(oRbY%4#=r^hUBQW_ini@Jx9*H&d=3bbNeVpz_i;_1C zuC&V=YJI^W;+qeFz)c>xvb#nPd2<#yW4~`kz2?+vvk~<8~M; zCl^a%q!evF8xGO(XAWgB$~AxqYKb|lL^>S{hac>LVvkK>L5TwMX$gC9xN~wjQFjb! zjFs1^))O)Tuf55B=SJ{ Fe*sJs{y_i$ literal 0 HcmV?d00001 diff --git a/recognition/MySolution/s47539934-GCN/loss.png b/recognition/MySolution/s47539934-GCN/loss.png new file mode 100644 index 0000000000000000000000000000000000000000..96b03c15f4805af4874d114e2a8960a432372831 GIT binary patch literal 57140 zcmb5WeLU0q|3B_15tU9+N^x{?6roO`av7DQj*Aov*(ycMMU<+_k(8^s z7PGBLDq$`c!$LB~=3+KB+rH1)^gieFd4GPl+wYHVF?&5<&)4Jmcs?HY`~C5FzV6!F zTCJAfEH5P`wc7g6qYhG1%UDuU(lv4`z$ZtXS!D3vFV`HbETjsXw~T^cmU|wtIU*%h z9Ivo&K?eN3^6H=ga`#oA%Vq?AH)!*K7{diB!&Z_Ty4=asoc6#lIvuw3| zKyzl7C0@Crqp<5qmsZfFKjV2qwfI+B=?dm!tlR|@srC7$(O(A!gm?1i=4UoW`<(}K zl9Kv%d)+KSTJ+NmRk=tt@egbN|NLNI*p=a5Y1HK%w7@eie4u_zM*HbH`t>$#-rb0d zxaoL<&t^Tlgjcc_vVQ(D-E%y;wiBsMi%B_KD9!IZxBr}r#+Q-3O|k)7x+kyJZ;q>A zYEQ@fmn*?Ajd0c7b(+>bw{BggHl#Q+q>%+(EOSi5Uzi<_;_jc}%NQ9^Xl}8_|M$If zKf|flO9ng?SyhN&{#s1CG`^iMTrT^|A7!Tmt;grBREO1lpP+yK0yCvt;n#RPQ&RlD z%wu2pdwqLUXtIi)&CH=68YyRwB$$>(z#f_X978f-L8U>PmEgccul-P~{_}2f&?l#UyUojl6Am_Up#oQ*x~g$-Qj-!LV280|Il)-V~*L& z<%m5p|Fn?k>FBTHyvs)?H-&e79`=57ruLd%m|UaLM5e9juEOv7>iHpgea8`h2je#N zSI%C2Ug{l0TCw7GI1%q{BEwDLb^iivOF!q-`WOE3riOaeZC{p=v&eE%vwD zGq=(M zR%lr>d%jpKLq6WouZ(v}S)G(j)pe~jUg>LcX}F|oAifTnJ@D)cB577|_(xCS8LjSb z-_Fb3?YwH%G|3xBam)1hR5&F*P_S?an=XtIPWn%r#vp1@Lx!kEb@$NF_qm<9aq%$1 zx^LwzTquutq3*+Ea()cIkzF9%!ot0Kr=hB%GKooP^0?AoRph(>9AtIU5uzanGDsA;(g9Yu5a~cDiUs%&w!A_s!3EPE?Jgl|!Imt+xV| z>MOk3J$St%g!8g%dM{>WWo6(;#-Dql{Orv5XFjTx_PY_!6@E=K37=@SGP@ko7gU*g z=-T1lmi_GMM!i`zvsv?nkK5zBO*p+H6x0(8f4qSWLC=~+m?^b!7=7vwkQZSKgGqcg znwvwF`=w_17yfwneu0Wn@OT*(%{QF*_O5c`lO}r5eQ0)zQf;L_)g#`7?G-*ZV72gU ziy3N<4<~V}3F5t4#H0pOCVhT+8d|U^oHtSFL#dd^=*t1#dh&w5{6IxKWxZWQmw{g| z4I4RZkq!(0zava5f)tCW#eVnZ!y`!3YSaXfh=0x`YhXW|C;HuVJNb?CuS&Ox%7tnJt!Y{Yz*oDtz zOZ)zz0l_R8R_Y%y6hE;aGhc(8fDMg{^DXAa!Qwhpmw#r{VeD1-IYS9+qm)%<>t=% z2xdQ0W6lxZR^W}#9o+0%_$}}GbSGaxYt2$Sq5lDT!ObyLhOW-1$+Cykg>RwAS!LYS zftnadP%3Oq@)uj>@E{zSe~*2nGbHuZ99hVPbrTnogz7wwP(b|($Kri7ZU3I^o^UPj zIkzxNBXoqH=dn{YzC4(_)=fZKF^|??VmK{(+x9C>lm&W8N@w)@&vhsqaUXO{?czK= z(Vf>fMkri4pmbFoHJ8AC-m>sG25NJjGZ@ruh{}boxH4T!;U~e*Jwdt9RuW`rg>hiRvW)XFDH8-nn3TU)vCM?o+ zb{>&S4<@^Xgne@iLB+2+yMKfz;O0S_%)+MMm^E9Sy3uuP z-kBGG|E&+ph8wkpBID?golqXXy9L$n*&rN-3G~&Er%z&DrpB)tuGUMfp;0~A$9NgK zQ|hQ~Qii&VsqKbpa&7+kfyo98~MWnN^d*+pieoE9{mdIvFRKe7&wKSX~v1% z^zWDuo8NK*Cb({B=R=M&`mr^?m$yH=ZgVNe*Yv^7YTLcn5BoAr()aRD9XWSkg_|QH zddfTKt=$3I?_*ln=X8BPwYlG<`NnZ}hU4$&H>JSK7Met`_cwK=^m60gp?I?aW$(Cf z^74S(YWSu%JSO^WQpM@B&50G^J%VXHVK0F{;ol9`#N8-j?qN$P5l+^{GT_#4Fbm&f z2u&OU$2T18--!DH0l)GK+~Lqfyw=!eB9<|OW_t>CddV2K=Xe3)Kr@oVFhO0kSU-bg zhEt~+(A-f?e$)+=V#L}=fjce#Y)(SYXlyC_!=uLFfK0!*4GGp@mQrx*lf_<}4h3aF zIJ1u)or3v89fNH4aN17lgqj5ES-6r__JlIiVn(M|z*ibY`^7ycPE=6v zxrNV%gSLI^_QbF!v2U?-{ULrs!4W-q)>sdAj9|Ns761($2vbL6sa}KCajJ&%2f4dG zS2t)E(W4?0i%(Yy4XF`Lm|Gt@>HG9onQQ7?59dSis}217y>H#J9N2f@@APrb>-B4T zj+>4ndX3LkFx;=#623H-kIU+%m60veg-`96m98NVzTQ64{BZc!#^wJ8r=paV64YIN z_ig~D9Mzau0Ldkf>0+lC@)Ommwbv^?EIgETl{ptgBr_fFGTVn%ty+bsjEHN1kP&>S zd-qP9y3Q?waZWqtBO{Bp{B1|a^*5MmG`7L16B?hP%aW|0G&j%9v~jxk z-9fsxn(q3T_wCh4>y-m_CI$S~d8N#Bl%oB=lP~~BG>_G+vmi5-TP5-0e}yUtw%w`C zv&b=Zndq=I11d@UmG^} zB&l_~EB2OrV*6|w_l-2p*!K88pk4x5Uy=0m;^1g@ch2~DcWHkr|5jNGjN1kokA*Fq zU?!(*R0i{i7Pu?bqM;e_g$1P2+uOk$iqw4jYq}W(9+dZ8Q@z|uo!_B%?m$(4b1Dwu zx=cG(o^MQB%dc;F&=U_kug*sC6%Zdb70LJV)=cg@Z0oJq-2ry*!)$ zu5c;X@Q1bLxgG;kQwAB`yYE$}$HZWLtiho}0~Fn(S*I#TN4?g~e-P6iEF-Wa( za?AMK)DXbhJ-|Xr(cA0yaKN3-O)n{P6QEn6>dM@eSW-*VWo2$))g^%3yaD{E7#R39 zcIZvzuwHU%>a2O1BW_L`T9wrRM2pdlc+(#1-;^^e!q)2^`x~u1>F4-5%L$0ZVr2xpG!Bs;2qN{p|9dWVh;q{6Say*?jv+CXeNw5z;q1{qCe5 z{=C(LEfp z?y+_II2&sHIEv@RohXk(VPkU##{!|dj;e1;JLc9K6y9eiG(C2t28yKs-m*ZrSMMN4 zyGBqyd?bTHE56Q~91rKEnsll9*LXBM*jg^DHWvlFy?(`t6(FsJJyYAVMK1mnD?jrD ze{SxwBD318kKCGNHNG%^&ESteyeXB;BAXNg^!U)&Gx{O3*^UHutwKvrg)gOU#&%(D zbY9bX3;V*`Tl{a|NKZ#ztzVOnz~`l|Y6$*)i|bZdg_cShg%(g2cUzIhK}1YoZH-^8 zE>G>D*IiKujLB@o;dL91t!0wv2h-IQob&UDCefHw&rchv3Lh-}ZRT`lM z`2gjYvOjC#ukPL+Z|uS8y@v7Uoq|S8g@sR)Ll%me)%3I?bR}uYAj#Ht%wHohJ01_wJ)YK&tdYFxb_OazOhO^RyZ3!(O@RX;1>{QiF3#biZ8F- zjDP$JIWAbpSG+wE^49WAH5JVaM=KN3*y|6;_sY*B)GGTCDJBb(+bhsQo&F$UC&X+a zAg}YD`Yk4sX@oKc)MbQ|eO_3O<$E7|pbz568)gHZ)9=seleQT5uHKTzTvi^VBqVfIhX&Z_^~g@d zvb}aIIVmRsh^OA&UFvD^t>078gk>!PL&o;GpBI{qWG!!CDXm#CCPmtBweFa*zYeJd z`d1A*DVF7)(|(eBn26*BhfltXiAa%EkN8^WlkL!?#@c>G)sad?@d_r-<{-$)sb=W@ z{&1#=Vt;*RZbNb0{Z%mygnGLT#$1}aS~EZl^COtm6aob|tmp8MaCP7&(-UvHam@w8 zD4M%zzuQy4pn9PwcToI^b=qUOByG7^l$x4=*3co9BnL-qZ6?T`QOKp%gD+bE*+MMp1p*9_yaZ-}Uq} zA+>rw&pSTeDjlPV88$uFGZ6Q+ZtAFm8ob+rsGnDBse-jGEunWH{=Yb4 zgSH*fRO)awkIya~mT#?wO*C$gkN8|yfZ~Ad6Z4jrChM76^h~jj)2K&OVO)zv{@9Nx zrOw4sEA%1;v{BnE+{PULR*I&@ho75BvAy%k@MZ;{m1E{EqG5^+gOG(wk+x6HozztB zNAYYhFYGlB5a#N!UtvNzx{m1W>V~BP^nI;uNBEe|q&9Jy&UPiv8rH~0=FaBWrcUb4 z%>e~2Y+4>UXW8yo>a!Kt&F|c)*WcZGtt{;OSgOSGmm4Q;g;Xl?%9O?g1;6c*$Aq)T zr>2Hg4gFY!gYwVhAInc~Mi&+dKB!`Of6b4f&@9?1#b?0BK6;lJ#!U%Y<5B0$1XGGN zaBj51h~@8hj8~zzM5E#y$w;kd-k%Pp?y`RiQFe=j63L%r`GV0LOc|QzFGRETgu_Ck z2)@|_dLiDwr@J(#PT;-fhO^nGz3Q7QQGh36?*S)j$HZ3`gZ~==^;UggZEzb|r4C^& zbjT3T=~DVasPv(+**Pu(Y}fgs6~Ke;R-DG(DR8ai?PD_OFKtxuH?g^9rfQL>v`h=9 z42~}6iwHH`Q0=rin*mXFb-dbY{EK{ia*Zt&qBVS#Qyg>J#y;ma{$w47RXjL$qOg#* zc}Bozq8rw1MUa7n6H*V^I0=Yai)BrraMTX{eTY!X;HukXz}$NDgU@Lyf^}Mr<--3k zO+ELu*CguIn2R?Cv!ggCaUBWM?R9XX~GeZ2Bizq}EBsys69sW0}^SO*esrh&2b zqaCC@`xt0ec*QmDIYi@30&90VeX7*n>>HWR;qy8I9KW`(^R8Zob#}tyu?F=G0IZxQ zHsW(<#E@y)GUJpZwDn*>T)WfNGkf#Ns%k%!e^X}Z+X^yb)QVyJPFN+5QQfFrKQ_ou zWkxjOqO|3hRP=Aldo{-uU~?rgfsT3lh0x9j$LAxKeP8t7rPWM-KFR)(f=x+8o*q8O zOMh5!qO9u?CbeKblmipaQdcDrJ>ty-UYIDm{R4VCzIvY{Jh+uhLF5be**l(VIKt6X z2!6yh}mJckKFFc|$F;Du?IOKbIvw zX}^1Szh`sk97GwJ5&eF(szJ|>rWCwYB*W^0OM*=9I!sVV3u51%?*XN`Vf(fuy~J#p z+Y5k(AHSLk(#z0^BqXx47ro4oBgO*``pxiF*ZEfkr0lvLCQR2vTP;(!v@R?MA|HAXHe1RVYBpj{O%z3lz$-nvPnFWn z47PhILhoH4zJ6Un!G~AIxCiAxvYqKodAWu9Bp6~;24-M;VS1{;%pF8zPr1DAV^dX0ih^r^B z3zYw)W$w{9Ycd!gKKuS#^Mie?SHX00dpFqjTnna>T3wiPIAU9Aa(= zjyZ7sNCLkP#dAaPLZ~LRhO^-L3(3s#zHi^s?VztRWcdlI(-+7?b;{L;Hves(r(dU9 zSoN)NXWjt*bOL+OXyf<@7E|8RORPij+j;rt?jcKzzgvkQ-Q~{Baf!sVhyO0MfMSG) z$M5-2Y_wF(PGhFe-}7ZQu}lp%#liU+cA)iK(8*vm?^n#TqUNQIQkogOy!hTo4k z^iezcP$Fa}K~I5t3tHu5?+9ozfm&Y!EAKiQQmx)M65;h>4zM4pp(9<{DF~gZJDPhP zjwy%rm<9Pm^p^4SZ{$&Eo)ZbT6)s(<9B;9+T&7f0PTkDYLu?5uB}06(_+3MzgvQG@ z4|4RUV`ot3g37U-*T0nLvU+Sa?b^zQ;xlqGdE>HIn|@ax3kV<=SGd7uaS#ZvSzM-|MCRCz&OSm z&C8iM&GD&(p(8Mk+IQ`q#4LPUcIMwIe?g8xwevEQu6~#DUn2!t)x9kVH^T?@R>Wm7 zU5rwN$@hUvO8KqHWx#C^HV1NwzpK~K5RUDUo#xS~G+XGiVsbz35;c6_g-Jxi2HtQ3 z`|DTxs*Tf+Hl8{9@A@AgLcc0=hqH_4TTFRRQcc3Ua8J<>+4Co1+~J2~m``N5e}?sI*3(}#BH7yD&%afc`8-S1Iqg_VT%~$7s||h@J1lUBpl@|! zjCftFzQFwZ?=m5Kh%=s;x}6t=3m^()lWBj8Mc~ry41$# zx_6s^uDB-pH!~k1>)RS!(8w#tMr(hC@4u+0|1aY|x*0G1@Fp_3!atqyIU0Jl<5Gv= zXH%?U9`*R)P0Gja%7&$;x82QE+(yFw%HP)nPscomcMr5DurUNq;Y80LmoUilF@43J zUslMms%m9l{kt^jYUleRp6At5>`^)Z!}XPMQX<+f!JnRJ?eU#3vXq&=0&0_e<5{~Z z9A1yZuw+Vik}Rd( zA}j$KcBBpMa-FtR7UxN+^E$uMMs|M>iygX&?mW!$tzMKcq`KCEgc=bRb@kL!=$Q=7 z7Ll$Y)g>Q^`bB9>KOyTKMhuwhVdP5Gs&Q=-~i&9{X*J`wa$4$i2 zqYhvYnVM~jkK&{bta#=*B#F06EA&B&1(Izf8biuyJ@}lPFjz0o8X)FVOq3C#Pv4Gh z2j-~m$+KWantl(}am&&A#}kbO!v2y6#+$@@RC59r=OUH=2Y9kerM(S!c_dkzwOl-m z%Wq(qkSekY_v)#;P-X5K_r;2X6#T}bVfuO1y{9w8^4V%|JrMmMn)O+YVw+BYWg0{_ zc0CX37*3Peei<;!$KUH?YZ{RTx6|bRWBhN`Zj9&RhJhlmSri`H+)HW-#Huh$f8OC2 zaEB}1hvPEhYaz-%D7)PxI^HcX7ydpL@M<@B<;s7U_TdO}6;g=7T%<~DVS<4RuZMosxIdq zo7~6Z>vI0Ow`jt%%VO8Cu3ftpJWwkG3WayjzU3}mZkhuX#OokfirwRKYovgbVae7+ zPiu;*+dOuhe?*-n64%GzvwtKy(w0UyQdq;1pGeY!Rb*Mk_q@9$JHZ-l2YRGelgG8b zGqkLmA4s16+0A=)k+h4*^dM|A2C#H2C=M3|R*h4!8^;3GwMj91sTWp>mumH%gQWQQ zcuELiW~&hBDf3Cb*ROXFVRl?M24%?rk7Q)H>osk^0ztzs+u>92M9*ar&tsBPI8A|e zi+u@F`H>bOYRjnQcRzeIoBs|VORQdxfX|zVlvSJDE0je~$_7)BoNj+1+n)kxdXmy4 zeKryHqxd9z+r4e7o3$wISWL0HF}m0G@04Oa=Y8OQ`E)v?eGb%g}?5`Z3wgY zr)nkXQknP7y1CQ((z0bcV~38lEJIs9gQWW(*c?38v70v)kM;q2Km~EOQjj=7scdOZ zGr{FYLOU4(#`k^g8|AxW+4GgnK%+MHl8uj}=*8?6lFiVr{tEf!DAm!Rqs+SznLoRB zezx#{(EEsevdo{)^mm$(XSKJtL!S!)mAwS2vky!Jb3;G|94zfa^C;ActwzCLbIkZ& z#PL2{NbAs;YrzB9B{f|680+S)r1jxb6W7>TYiZPj5uXUtG$@ab!DUys3j%Iqs#UrKr}DAo-b0zd7L`Y(W7x z^uTDGjG7>Wh7G@&&&cAlEUMlU=yF4gbo4Aqh8G~mYEiE_51V^$%ZqDKn=BtKX3y1O z{*4gC{XwafHm=388gPK$Q_l zB2=Z|H?`smU0H=RXhh1fAX+RuY_W<`BrnH`O?PG>w{j;jB{f(}DsQW3 zc&WTdbD~`I)sBl8B`g_;WCs$(U2vVCKVo@Y1|&TxzY#e`UCdfVE}}tJPJwVQ$IfjI z0f|5o5S2mFJfe(%#h6B5_pw}Jm-42KC@Si5Tw+Y^qX~W1&M{)!JeCeCySWqp9KZn0R&mw^Mt)Qxj|`qGkvEYn69P=-MNb4;XX&<1s*$MT z!q=qA(`}1j>UA>rM!7Ozg!nqQFhAkG4BrdvJ+bq>#gcJXCZ)uJ^RW@3)EOu@j(&o4HfWJW`A~Ro#-BVIek(+j4Gv zmbrSvz@NKGDcKXY6+UirdR1-1X_AE9c`O|xi*3`cv5L;O!0K{xNwEF9({hlDuIRKf z5@l*k$~&PRvlmSH*u1tILw~ngn0RXU03A_2);~+go0?8uu6Jqvd$RSELmZZaS-kjc z7 z+dgqBKf4;-{xitJZVc>JOYC!o^3YQ51_qzky5zqb0;MmfQRJ}18jm7nca;p$3|Ti$IT89_7b zw$Y*c8}WAc$VN9O*8Y${R40W<6d`jic^?bRm*>W?d`#ZMNmf~0sT-Z5#|8V9A%g(u zm+a1SC z8foO1J1sn6&}$X{sWaoWWQRHe+MyRz#k9LOqRcJa834A;!?+BN|6+_j8vsny_9E1J zaA+8Bp{@(@tXkS0W7~jAiB1fMVQnJ`?ZpQpQknuWaT%V8VxHiwL25~mxY$ zK|*+N&#D}WGwOg*$3SFsq#0?vtHd|rWfvylT@t(gE}Cuk;dT{-O$_-|5^pW7bC>|h z02VqU2E}>N=4P-{mmWzY{Z|2G4M`-cATBK37R)N)11UTjDidaNkKEN3e9%AH#bAEW5yDwFM6UR zY2@E=8KSjjp@QZP$rH0APbAvA(+sv6txgedEegZeZIXBgC>ul$dgJNwbPV>DA%NoA z{k#t(?T+xI@FnY8q+@bG4l&rBmOIlIta*<2PToY^?aIN_VWJXXuPH9 z-Oq^69|ErLD_&m@`Wzvl-$gH$;yF181dRdy%q-s-CTyfR zS1cxAQBYNQ>`1HEW^J}qudUHt@{hZK+eJ?j&w`Ehgc+Bi7o`sRBi4(FpD3$JZMMV! z?*lnhF+4g#^3?8a~OEB*XE0+lC)Ixh)O!JN zGp!u~>^KOmt_9GN0W*w>-4Lx*XL0ni`v7eQnfVy@1AG-e6L)W>e^_uL+Vt|p#iS>S zc6w2=AZ_TKt!$qH8T^*%UCU&}GpYV?53$DJqyiXK$vzffo&vCm?rl6a7KjBAF*Lg0 zaB;QI*8`T62@w@e@cUR$(f=TFEpg5~z&XvMFSgc&qqia)Y4}F|(|s(B=A|sN7Z~VO zI1=4mZA+>468SCIFj!1!ka%;Rbp}sof0?jWQ{cGl2hPi?eaLxTOr1*M*mcWvW)xjQ z$&~>vkaMKP2FT3@bQKW#*2orxHyvgTQ0Yt?QE*(_KXmY% z`Z^>UAT?1105~rAvy={G#ElUS(ieiWxbxpG#cF`lOM;J09Dx`BYsUabBwR}UK!Q7kV}=P?#{PFb2#Ph0OOc^g zw7j(oPqmOr^N)izTVn4ai55&Z245oCtcgVJJMc@|ht`l1UCuobYNL(Cf4KjURjU^2=~6fEwcRxfVSL%E=n0*G(rw=TfrnOUs ze@HcG4_UJ84r%_yxso8jWI$?p2j*;b>Rnw;bzjc+u*Ij42YA>5g4}%5RP^`72&clN2U0&tBiBjefdQL9lUtrd!BPTr;F3En0iX?db#KGiZLlXw z`-%;aV!)++$DF95M#FBhR4>%}9pDMP7RwD5FUE3DOVZ8FOiX-p>@XD~>0)ZAflvRG zpy?YC(VN`#qxyNbTi=Au5Urg9@Q53uj=6-@ycFbFdU)1jeyckF5x*VHwVO~E4xxR7 z-!Tj8c_YFF3?+ofa|3-YHRUb^{fUN$9BJ$0GVHK>Tp`sj82+?E$Z?U9L z$-CI6i~2CdR9bWt!izX#M06+4x)LvgF8F7Fn|AMSF%}VU(LkHcV1FvnmO0YeA3Pnr zxf3iXIeF_6$LzWZ3~k|4b*^I9XTO(|Jy#Co_xbT!=*KmT(Q#R1M9`QEl@aCM&J3ht zN$ry87AUXrMDNv;e60l_05KAVyn1V@DUduzqpRO;fc9@#DQNm+SDbs}lsB}|`I}#X zeCkIaZKR+$58WcT&Nx<`de3dmw4x@!r{Oy;^I|pQyX{~mzOaZZl_r54CtMKu3N!Jn z4&Pwxx&z#R+hk}2xCZC|!{u=Yyuy@}^Tm?KyGTJ>_DObuKeDkubZE8WOu0*w zDSyoTmBY2!UYrvTTP~z=Zq`7{N@El$*(0!`8T9N{%G~>*3cew# zu5IigzY?>hf@u~~Ybqq^hA#-V29pW(nBKR8EXtqQ?fCy$UHhp@w4v^T6Bzs^?{|^V zKDwr+W&!9ad;dNP&+dT}BHV!F5ikpmp3r@(uFfJF^%Q!u9kVvR2dAE1#&0QCXd#>* zqRu~9ZL(10yD$tZzH|;0BZNsM;dVNmB*rB-*@o)qhm*J2 z{Wm#==zC^TqjgaQlZpF-H~@4#*vX-h&RKz;U{G(o$&gw`<>9HNu6|YZ4Ly3t3%bgx zsze&l=&^mq=gLRjHOB|9>GcfzqZ?N+anuilX-93p{)W{s(7)^q z4dP0S`)?)3pGI+TG_@Wq@_(9QN^0SCXTSFFW6oux*O)ub0|}1zd{bNw*@wgBHx;gJ zaN4C@r;z=vu`4K?4qB(4GbmdV3l(Z&cc2CU1Bz$U{(y;`K?SF`AG!UPE}Ra8E<}5vo`B+07zmLK`HXA*AQClK$}B!ngnoY zU+Eup&4Bie!NP`>!^d9djDOO^l#O7}{hrlSBASSt&4ZOcdi1Ej%g|7UOFNOyJlo@) zob3h}R^HF8RPsZAcbdT|LmsH05?TJ4`@UDd%0m)ycocMx&5Nts|GH;rc*r-28bN6m z#-RP{eJQ@*+wH!-HR1HMDBASc!HkC7m}b1UYr+Q!Tk)=#G_DW+~DVNk!+d?_GliBgjsBYMZmcc!0jZ=ZV+3aC$?ZHf63MSKbb znim#FG6=LIKpd0dy{YusDv{=$HUnuzKH6ss@@mj3k#d4NDUpwNfmE`alypzv+oQlr z?lVnM(kl;6Ms!NTIl%NgU?t2G(FC*IX&@QAQ~`7^k-EKIEb9Pmzt|!@{y;l2-Fm9J zuLIY+F=70n))&u{WC{Fej5jBqZSBwcCPLo5fmXkLY@t<23U?F~;?Bzvs_xu~^s@!~y3VDj@XLdg#@ zm=M|!Nbq!eYr@E4j@K3qtVTlzB=Yhb@vV}Onkmwpv?2hl(WnUr)bm^U8c+?Jkw@lG z6j*rG#ZimygD*MCy>kQHtDNHJTA9EdXV;HBIc`~K@sF}d zuOy9}(~I$OfNI}xUqA!*~gzOPh_v42^FrN9HSslnJZA?f@UG1 zm_J_zFndSbQ@tG!a)w<>6Eq!YliD9EU6O~>B$6L$Id>zN3uZzWJ+^DWe4sQY#=I{5kFwX~#< zn)M35KqCb`m+4WGN?JDf_|HgFhiD)NgQA^ca20d&^TjKQdckmK?sN{DRykzEBY@=P zK(#$hf1yD-z1Tq`wP(LH-(hDl)(Q=F+B;lt!>LPNax=b7*VVB_-mZJ}r1|Uc=vBaJ_iHP= zpUnK&&|KNGmHxuZAHL{viL#NX*Jm;~`&i`Hq>!fMrBadIrjc;Cr$V6PD{itdd>Mmzb+a`d&AQqlMdG$-|RP}6s35HnJ$aX zBp(S)7zj)EuPk^a76Gx=Q*vT`co;0LR#25*qD6_~T|zt9`<)6b^8Jh)Nw#<{8Xni1 zbWe>2^bLn}Az)XMIg1Rr&TW$=RgpE3xO3nY7tlwJbM#UL6bkIrRuXioSP7^VlM@eu zTyvKNRBn}AX|eJVtGTP?oISB zGm%sm8v*4y8hsQ9lCMdIDIiL_F#uFu632iqXM_E`kyg;o1~LdTQBh&F(zyjUxZytLX8=&y@0b#X~86FLA%!HSfqeW#`ff^fIXZ z!A{fL05O29;Bg{ayw!c=SdAJ{>{sV1EVj4;F(#W{8{%ZUEj9~~s%kBkglzo*LZ7UT zYmPZ)hliIO80**oeBWKX1LpP)>UoJaw+Sp*WJneEQY1mOHoWH*7>&(fXFH5j4!A6l zMbqWn;kB1(g5<@Ij}K z^D=o#;XuEjN#wyqAU|(Xsgc~BK3nh;_0Q_~ck>0A;0r!v4TyfTmufbJlRkf^(R zwoCH^?W0YjfI6FV6&pkYn!GyUY-y^Crp;?{2S%byB3y6b4r*f@YTO*e}4r;31O0k_}uu%K~U=4x@Y!xt!C< zn_0;!*glV3u|5sXbS6EjrdVl^a)2-jV#5IdxGFAY$|DWhk$FquRCJqJ%?|(ofZ~l$ zB99YNr74zJ6Caop?}K8zV}@P0s3!$>A4r`9TZvdIT60iCLkm?9Gy6mZ$nW61#S)cW z0~|{voEBetEsaksz zJxXNbP4kC=h-)PSibTF0CZavGJBsTBUR<$=j~YR@$Ne&&K3SlMq5HL2);>*ZfBf?a zK#Jn2aBBf_^GX(}gy2cs9mEplqHVF3u+g9@f7nDI`m!jOOxQ|N&<23`?gIxM1`s#z zvKRhBA4(JfmwXeHn!w7grJdxf4f-b+$|cRO$8{ixWq2HMhl2oHy!UiQm$pX zKr{SRWyCN?kSoPn*an~^emN5j`f-%j_~I`21ycqs z$S17jxC0@nWpp>j5}1?++f(}zowV`sj;Q$u&Uu%kp--V3Ww?mECW)-M6S%e^mIN^- z!P9FJ3rlSqG~a7)EUg3TsYHK23EHhZy7zEEhm+xK0PA%wYv9kM*nqE@@TSD-UQY?! zQ~`nNWf0ATv=O-)6a}z-3`d%~c9f0MPhh45zOeyWWC2r*BxL{XzY_>+kxLpnnCBnb zQQsNvwAfEPEmk$*yE{!`$x$|ewLSJoSu0ryd!=jjJ8(<^)V=WYTaX^|cKY4fR@kP1W?6p6D@~!8ZbW~%=D)FToJjj8*_n|n0 z{JET~oaxNSa`@Edkq1QRJd$R>I~-_mN8*-35D+n=uO#v;n&jr5Ut6K z7x)6PTB!o=HDx`eL`wn11in1-`$Yk3yC#)yTH~IIZ_!CBncBiGi zF|ynfhqesd1ohR`4Vr7nc>uf9lYIf0lHkVx5ImJTl)r-W_a;1WvSTa>yyU`DFw3I~ z3HXKS#$fiiGdvG|=x@;YF??d~_vy=YC7MECVM`!vRTAKtTP3jW(T_e*doJt?Y}8@T zV$pp90TuLwDGJ`16%lzM!a}g2e8DSER`NvcMxgNsUvTQq`q&f04@Vp`{9Suqh1hB+M>u5%MTo-ZL*%}MT<)LHApszP44e=QoKB3QiEDRAeQ;R`!6AOh~ zl(tTO%~sA{uWD&|8E#;7!OV>N{g`KP*N=W9A5S2k7EL#&8gRQ|CGT*I3Je~6A#DD- z+^h_5e3?<*<*5b*;uQ_wsPg~$f*#Qs<^S||!3-8WDrn}|6!3154}v~m57S`510zNe z7B!m(4v2o}-=L86vQPVhY5a47{YPk7kJSX-%35`G-qu4tF+!p+=4pjrX%mK*A7`@g z&}6>(1^-+0dV)`1l6oH;wtC|pOmx|=T`T^(^jHSVMAFgdQkU!LW(d|RyW2URrj&k| zajIInL7Q8F!)sQ>**8vdRn;H2g}{%E%AC^G>;k-D%_AYv>dZ84=Me2cq(Kh!W>=dz)(aW6%hdHT*Cz~MAa54P9qZHYATn-@0{+ z4Yd3=6ek2tMva7^8bI3{s>zhs-O}S}Il*`nOG^IDVyGz+)d@eGmFoKySkpKrxw%t>aQ(K=Z=R=nl1~+D>=e z=@mU)zLh#Fk3bU!kCD&NoU6sva{J}nry%0&0y3ao4Gvk^TJ(yt64XnnU`Yo+2~3=q z_u7G!^deyT5$Lk{ov;F*SQrZm7A7QE>d#i8XwZfKV%Ku+iF$eKR&9W)^#Hl@IW^~| z>6pa3Y9i?hG6%Oib0-31ERAjlq%Op^51Ax#sj-=L%tP${FP zDA_3^beut_P_0Bj16dwouL0=v_edzH0N*3~Rz`d}+;m+}f>mMxR9*G-Wnxt#)#6l6f&JX==Q31B2(l%LihE7mNDhI_$V%q9U00j0%L zb1(ZwZ=dwu`dLh=Wqu=JZ^Fh!{VByefn-z(ZwmGXFHaM>dyy2lE*(HSX=IPL%NT$o z`&a?_=9nNHMAAYv50*?CboGFfiK%fJiz?|paEx?Gv;c0Fmu}yo!Yv6h0)=!JC)!KU zpqQ+PZM8-2S%jfdGoqOTL8@vCVRvXW)O|G8`rSHapq+#YUj;KDDYBw1zDM?3r0an$ zdB7GBr8d`D^qKn@vDoY#WX8jb~GKta6@S*7H<#R65(UxLVMr)*x_`QS{k=xc;@ zQ41SwPZx*U!r0@&|@DTi@q5GOv+po{=q&kkBb*`uNk=CU?i#|^I~gt-0c|dgo?MhSLGH9PdlM)E-nH2q3yrYleVhI5uc&MKR9z}a#D zEKxaJY4@6&)3=sRR*(Chf4Uq-z;b>Rym3&UODzq((g|?f$c0Od>@L+dw8d|X+V77k zeGTrpBe%dV$fdk}D+toH*b-ixFvwNT;;Md8W#{jMF9Mlv_K{ue?`avOr3v5qWnb_@ zhTv6Bh26#0dFt3-2tfJOc5etFEQaVQ7e0z&O0rUWU0krI- zUz30VO!K{y)b6#2)1iJ0S-GN&eq9 z3d*_lJ>w-!jp};0!IkVQP*Jc-@Th5Lt6nXxyvhBK#gYIxz?Z&$sC1YP?20Y$KZ?C^ zWCvhlxTGWdXAr;l*$;3z>}3w*>)i9p^rd(dTvQmZ2h=lkj@oYv;0J0DM$eyU+`PFC zrvW+%vhuG1)r_ev8mZ~!P~`TjL4hsj&nDsIP4rJ2*!T=qQ8Dpxt(R)Q z_Rnl5JOpsXIoPVspfyCVw2WBv%n@>67f!9VxvTSA`PttY*oq(!>JbbEjEP(EFWi+7 z=$^zL48Onuu+@0mas*iUeZRrb2w+)*)Q%b|UziCp9h)RQjFarTjIb=S1Vnh(=orlo z$nRbk0bl|-!cGIwOgE3%<1V@7H6#iT7948u|3C!DwYgoyuf42*f|s2F{WH>_Tv;xa zb=Dq||3ItZB*vq{DHm+aVDw)&PYjQ)_A|IZ3<9Sr^(92^G)1Pm2bgmxi2 z0Krrh*1KHD{B>{yz~7s6_pJRPuw)bqz57t~@M)v{w;%*t_=kSUU&g2qdpQEPKBf^CEd1lM zRCC<7pVwFFVj@=S?{-^LWPf91TZ_@md&jYDAUj|*_3xsxUd-JynDkuf<$wFcrACk< z-!GohAY{0@>cv!nV^PpOvvO$7L#o^f`6*~l{09H{& z()Lc%_Qh`R5}^Arc=trzJsHKxnXe-U_qG4>#-mrNN#jSWRC97}nCd?q@`@L#C@#sq zmj2wPkPy(V*FTy6x9xP*`wTm*{yzZ|*vrY#Q%w&EtStp0$u6n;^)20ezfl8;>uH@l zTmD_!p_KffG&lot;nkkCX<(*vw(V(JtSU-q+czb-S0{YyFf=c;KHmTNy;^Ng>>h6$jn@F*Ye>b42`?DFLaB*7f)oO~;P|BNzzEth?8W?Vt3Q>_Xa)UDQPX zx)s3Nzk7by3UE5>Ye|kqp7vjEN*C)-si$Hi--h>R|H6X-((Zl#v_ZN-nSs#Qp6R}i zVP~B=#y`KQ0NoV7j@|)1%e+o%IPh8UH?Y7vC~y^&l=}%cfWhh_{@Q`((!8967y(k zj-AFJzi95k0^=72#JdRNf2x^*^cYo?IymP7{BRrV<|=xXaNNMnh`Y5=_Gh=o!QXv} z+e`Ke(Er0DWK&WwZiUu&Y%}=Ptu|9UD}Pg;^MPpF(HC5tXT`-A+^3YvlWy4ATd1D| z{T?8oEls)b>%`={f=@jNgB}*+9~I6QRYRIPH8*qampS>mLqQQrLPkk%N5c6I;9~%f z63dRXgg7^-WGK={8NHeawmd zfc_QFJ~jb6tpjTJUX}o`{>*_Sa3CEy;svLaOhi#m1I8)8TnXiH5H#Y^+gNGgC@!u7 zz&234YCb;TnD)-#e59j&rd{c;@Y#R>&K3Z@qwd}SvP9I?Z$Cda;mhmoyE$cUh@$KU z+zO`q*XFlNz@HSsEzG-uv2^@ehX7j+49M%C_lQGs#MQ{FO1_oW4_!5X6?X&O!G(Vq zcB0s_*Lrg>^$-@{#D}RIDpz8>G~9o~V{!c@&iiLchr+ba_PwP15d_TwA>&;o3|#q= z<@DElg&6Tte!Gr2;Mdy?n&dPUX5-aGsEdz=ji_2Qy{#h?NDtoni3db!E~oOv!r}Dz zKa-JEVGV6m8Nd2T)@N!m;*y_=w>f@9<*J>Clqe3q?SSxn;NIG0`hv2{NGU-YJM@Ga z$>WfE#c3hK)q};@Nh)=3<=sHknyKoLl*|Oo8+uRg^^%=nY#7s-~5z?T0UY zwWA!pYDwbylT?Rn#k|y|8gMV&>eM2BzSpaG@71n)}362Agn`Gf3|_|CJ?w@=;Bz3;L|C zt~XTHlt-VQed*pJPYkag;6Q>vUL(#O%Xj7T?Oqdo4&t}rTOlC{kR#$#QTXz7aG=$*B|s#TskQA2 z1L=X_wYqFX0o`bEe&-G`EZvt-Dl?~qIr`!x` zg`o2Sef^$mysA<%K=0yh*KrdJ7?F!iIAI zTbxwseS0;)r0t<7dF}s2LjSE5_7S(_=@-0`S*JGW)(Ls4=(j{xbzDpODsPKVx4i_x z=iq7%VYEm?Tg7UuQ`rGtx4cJmeFjZ@NHZ?U8ogozZVP*KP@bciT$7$$ z^TvWzBhRbd}}vz_TKTP}6V zajr|rW1$Y&c$eg1Fdi36oc#u$ZK2&8>~%&l7nb9naML7l4#73m3`9|c^XQXm>^|>| z4eR(UNCW*p4I%)vMum4`X6xe6z`>Y`ebVYKE*&tjM zEs~>BUagRz2^sxb)E}0^`%p=XU)aVU5Lc-?wr#rspd#^0-+htr@}+_I=1!DL8sm!w zge|f;?Bq>5%t}_WXJ7;Dve;C!%EpZ>`7I_c$(C+S8c~Ee^_C~NKS(IsHmIT|>#@Nj zOo?2*JfaM`<_QpX$yWg#@Eh#e+5BkGS`YNTCff%PBuoL8{=+Vu!4;7MIo>66e7*Alx+Vb#G)}FoGPb%8TX*hJ>Nd0 z|2@Cn3!G9_<dG#&sZCylUoeXu2&SR=g*gq!zCKplY?VGVA%M?3daSQ3j3gLng6=8~=8(e&)M|dT|3(zVYjZ2>GGaCCN%o&RoQ#~_Q`*}?{p3=Q z*(Xv!)S`Cf@F>Bcw|+qw74;pU=I2q3*y@DOafT?^nc%J9HY#q1MMX=y1y3a26*_b7 z%t(W~k{F0H6Y!$Ic+}4T8H43s7#76uP3eeS0hql!rg^q9Y7a05j7l!gK@ssmt2=&6 zvSXiqp80Q(_NgXT;E6kqjp6(lAgR1XNZ&F${@qL#g9=YL7uzKBx%dW9nw)H}_8@&9 z_9V3mp~@F)*$H@iycb)TFYU` z9G=4#7G~=u@k|=l%ctiHt>#Jfo2q`B$=MEzF^I0ig|T1uI}P}$Ji(c<`fI=$+o=l# z^n8J147`1YoX{yH@ZN@ftW}>#*P(93c>|Q4YPz@q>AK z8W_*DkBDRy^kHr)ohdh1T-uAbwaZam8`(OepQ3@&k6S9Ai3rd4VTnQ9HbV~fJ7BQx z-E*;0vkjFzTOv9@#%SRrb^9kS@7o2~**f!v^BN>9>C><${Hot@R{@E!U;*iSTk0y3 zNsafEAN+Z*cMvqAZ~e>AY3^mp^$UvD<49J%Pm37D;Ev<$;vG`*S3{)zpsCU`qL#_K zVxrIQjQto5eEfF)!mo9u)eywO^`p060kRVTeIo2qp(lMj{-w25a#rMPEEL zi`yt?$`Or2inrtJSDw4q+)s-iW;ijM3gEJKUx}ZH79Q=*?{9<^ie0^EHQC~zXA!(j zN@-O9&wC(VCc$yYb{xr7mYZ_JMtgvI-1>MVF$@I09!`VJHb;J!)V%>XkoDfaiI5@h zG~(Dclus>0VGHywYDO@t5{j$OSq#g?jcq@vDR|NxwPMSR6s3X+M0L~nd!jEFVzP}e zTf5+y?>8%!eATU04(IuGLJNh#hw6{=$loa*(;10~iRjDu3HBT{e=7%gB|Q++`H-j( zMn9T3C662lcdG8VnR`R2>xwfxrq((v%ir?h=xTz=MtfaR`{r0Ogx=R#;Jx;Uwhbo+ zmV-B){E;q+?hzRAtczCOm=>Ic*^IZO7P%_XN(fO(vken$Jqe<%U7nRI5Ugu%6H0)H zTE9eZhq(#>h?wl7L&cAlEvDii%b!9Z>u);^7ZPh7Mdnt^aK+S}N>9I=Cs$MbW#$IV zJ4$U-y09%Rhqbp828WlbvCK0GPe|piAqWz#={>*bY=678O}MAg^axjhPNBhA-BUw*Lo+3&m0fi|xARh;`lJTz5UpGZD_QS$Lvro?(t<^jHh>Sq(tmf3cH3*&pv3 ztK$3;i6H5eD=}ZLcG66#PR zZh{os{$h0BNJU@qQU<@?P}T4e$*-MaP{)p?$$XMM-D#V_Zy>wd$1nTY8O)Yj-w-v#hD1{BU0a)S>g6;n=|aWTh2Mxs(*oHd(0%^!~5tBppLPdO6D&wqUfHzzh2C zP;IO2BczpNhpT#xM81)JYWq8uUpB+y$2EQ@NqQVX;u3e#{o8Gu<_1*kS(sRZ;r&Wi z;AJ@+Oe0^ClZL2!viJ0jWbcJNc{qAzYyrxjaX6X#TELGiSn*qZQ%rMP96qN5AWYN4 zGF$KaI0rn30q}FwA!SRZ#DFbF{QH!@rw4O1xg4Jn#PCOl9ZPS;5|`WMiOa5?gMAmZ zN#iJXpWOOenOiXilp#Y}D*6t+%5JcFdnvcrIOpX#;y;U5AmX-k zyk0a7m|nT$Bwv1$+P4vrUx|!Wkjg5#%ch`7<8hO->z0jj+Fu7hE71l%3)I?(Y%EET znqZp`lYM6HIHW=m(?iCz)a`I0>W|G2X-syKL%AZ}$uplsHFGNqxSO zl0ygIWPv+5xLn?>q)e|g`w>R%je%s0FSF(nr83&-z<+lByxk{f;RhJ93&|ol{`W03?d~VihG_^=N&AJ?tqqa-)u*If`9*Mg2cTTWY)beMZrh zzXHTpq{X*WaVJX18mWKM)*_N?>VR<`+$-+4}t;51y<6n;6zz|QUSkJl~> z^Qi_)LzRx4>w4`!$gf>q@qw~Wfj8r2fB83dx@W$}JR;ebqG`4?qP_dxOW*RXGNY0L z1Noo07F!tx(D|Wk6*tUwY-HD?imNjH=Ps?;@c(vgXm z6y>=V;Ntb^g($?R%skGJn+{C{BZVBoqIMyT&~Vld?>yX6Ob1RVxTe8xrS|FUa|NxV z0%oym_f{2m>SX`bn5fG~GQfo#~dtN4NN<`9_ik&0U2 zq2E+b8@-@Mms>6^DC}?(5*@D{3 zNWFa`(dD#zS_bTSauNH!hJ&kVPAn(q*}Dy*5WHw2A7SLy1mkk)AiqcA*S{HaGmq>$r)--te*}m? zMKb^QY&4LBfttb}IBpQdak&Q&b{|OM53t8rwS3cL(Wx%8rpjL3Bgzn`md?%rx9Z0h zBnYVx?rFDD6Yn`!5(KDF_1emoTXA*L96r1-<7e|^=t!29 z(bDo6%GYme=XRtVn+he|hv(E1ge6w%S^uB0a^Q@E;cy^Qcy79T_gZq)$i3nD(e#IL z9up5bv_b`8S4m}{vQQuNKBZ3Cra!0zL98S~hI~G8I$wR9flxYoSbX8_ELx=bgA4Kk zraqU|h#`4`{K{5LccwEHv*sI-@A30Fu#XDm86f%`5gzs2UHR$%=OHwXj@-^oGE2>6 z?e(DF6D2usy=6ksI4$*=42fqr1hc}|z{ znva7Vy&OrpOcG)uun|t9Pd=y<7d?z-<`#T0K&$en@Vdw-?z_q2q`DNcr;H-P*5}?7 zzKp<-xg<(dHnDYfO8zXY<4SjnA8C-}Oj<7OT*+UpRDo~vj`*&J`R}} zd4Q!acFQCSokaJ?bYmjlT65F+0TxDdD;OV}6;*6DS281+W)Xet%~?x{4ve1kD36up zkDB)`J9-t_Ha+NmW8cJiIp(cyF8MT#H=GXg?E~FuFBN;8tz1y$%pvC%6S$xCYsb~s$vnL z1w?Cl%K&33Pj7!-=w(FZgR)CJiO>281%x*qNjzp6!%dBi(2XC?10V z7+g5NYKIdq zUpQDG)6;VfC)^m&8-+0$-o8-dB*`Iyx>9x$I%>{_l^G;B>x=j+Tx6xS$lYGS!K~uD z2FdPE6G+a6i_p*pKgyz2viF=}@_M&G{?i{`6@?ZH-(TI3aE1F_`oFCf8g}Ps zjKi7Y^t(TVCbz=B0#Q!CVCCpo2>_svPltoi7Rz7DOy1?fuZC`NIg=bojUH9(%7rQ} zKg|ZLcVn79U3Zw^xI&ht3Nt4_oQyF7v=H8=&%95)Ldv1g$>y+qy$>r5&V z8#w<2-YhM?3?SB6G|bfw--t`7t)BeE$@iZ6Mn-I6Jh_^0QcNl0u$@_Ok{gfgvyP2Cnl1huHNE65wNjQ z*Wr>;E!yC!k0FGVqmU$)%pbJzdDV^>Pg-r7|!mi|b!En2+FQcC_? z=qs2bmIj2uJ)+mzg6^JKES@jFJX-Wt1ce<`;!=5jD?YcW@i>UO``?hwHx1)iYuz8n z&#nx))`vqEUh%6e>#RUZninkel)So*w8X4n;yJJbQzEstTP{XA5m~pe@L!JYp0#ac z>uikkTzbi)DUra^(7Y_Oa>7UNkoeD>>RyB@I_dJh!I%yX=!EGQ)%|{$f7$~8A875q z7X~{e^pGF*nAY>iL}eD?QsmfFzf)kYshA&xMBcY)fPC~erI<0^j#mu$mbbKKYB3uj zz8=aCaxq=T92V4QoIeQdxSw;>>7ql2>!e?aGT=QCQ)_+3B_#ff<1+#jI3VCTf6EGS z`h-J+l6$#+Hi1c~l@*@3#d){3kZrp%$b*(}2SdO7o3)Jp<_W5GIIb=%I&#IDL&?*~ zYIEmSN`HrYD!0}5ZQU?vL*kv{rS_AYO1nG#@L#H#Ui4vgR##Cx#2Et$L!*V>6J-io zse7^7NM$5ec<#6{+G%Y=*JMSB?Eb!x9TdS9;M^#39z0dhVc%7^xlu(LzW!ep8u?dd zJNt;vWx!FfD89s#TJ=-p%|J{_Y6)SVNl~JJ$#O2Pc`$(5jl)><2F9x^dB6+Kx?me9 zn%5LV=YpGiBPe`#yv8B|3+-!b1I#M)b_kFzb*7Y8dX5&ip*BwJUNOSdBDvSsax)g+ z2VFzYNP%$dX(K0$Ck+L=5}7)er*hT296Pcs9biY8D`Gd#K6QRj6Z`LC#lM47s8jE_ zKmSxdAd#F+z-pf<*0j(??J=vzJ$mVwIf4OUpYnWSgD8A>Nxq|OcE0JVFKg(XSTZld zP^_%GK(yeJ;*+-4=km^;!);K~Rr^hau%n<@X z_~CQ*M*hipHPPwo#^nb{6R&eS6i6NuPdcRfDX|?eKfoi0`L&sP)861fog1z1xp6n+ zXC2dtCUH%8*~_KtE*xw4{wZD3PwMywf0IXNy$$rwIcuLDKr!c5sqKk1PbBp7`~i`z ztfOYFL=(n(S$>JNkr8FO3TnY&x2BBJjWLBj`=XhR{3Rd(+xg5`GLql2Q;xQoDyAida(%EXEjd`daYg>*@FDrWdCi z*gny9ga@7#dG9pTs2Z`{mFjj+`{o}7P2Bh!dF60Jhz%`qdTJ@XDT$OsSS*= zA^MX;!g>$j!!PD&!|50$$BFnvM@GMXzV}8AKXfGxb=}sa)-hVBMP**(GJ^4TS%zz$ zfj7ZPf-|ja^vcr)>;li~-5W9pl6eM|RFECVXJJ7cWL@1y|8GZ#7o8}_1QI^qWAAR5 zprh@hF)4M%rIRC2XAq~7W{pXZSY0(2i6U~)dy{whAWkz9rwL)57f&ugqV*g>INM8x z_UQ&VR?LzpYpXZMowCH#kd2CQ4{INpQVTBUgYj9ySOha;ZsyiL7KC@M5ul~-pEczz z(LdY*#ln}d1@fOGPk#77Twy&qll(g1NkcW=vpPrk{Q!sGrAt3mb{D=RjaGF3DCVzK zMs2)z@11>_GpCK?E>wE=IR5|kQsNp5=0G3TeReh@Nep_?Dlv;9KIn-!yi8o^jP4}% zI=?Fw8Lj+o!Vs7}RBV#WMI9PXlK0KZ-C$^jcra`EJz7%lix%d>{hc9e-Verc{wpfU zE8PNq%E-&RkvKZ%TooxCx|%H?o0T? zGc0k@lxvO8;PgSX@t>MqS;gz^q5t_{{*!1Jyby3Wj{6FyUJ706lbJIbtv!GNd}zHa zhn7xF66Ka$#Nz<`#1Vu{_^A8Cr8{&tRPKng*p^J7b?j($2Ppjc1>;s9=+M62BMtdR zIIaD3E9S=xq_AA2bGI+ugJXdl&Svf~z;w_5T`0#LdPu_DVxP6utxRYY=@ zHhj?0OSi7VWO${ta6L&%l^jqhz4o7v>>pbbn{TM5wLCbBiM|xGF*}}wTb#R*{0^}u zv9rJdX3&-p3xL$->Iq7Djx>8tRvm*F3ykKEcg!{_yE`qC__h~Qpk)t=HE#s*8uz_S zI=>mPr_2SWaz7?A=So2@3J&fU*nq62n$W=DB-0!ERw_I}L)tsCNqz&T5DF@v9Fo7S zSUH4gz(q6*MVx5br+7)GhY2t$wjVk5zIY{tXt;^v3K(Y;)fFd#vTuuA;eE9(N_ZN1?~S+EBs~F|K$ZUq94`k+oCTIjx_pdIImXUtk{^J zzT-Qc5(TYHm0pKmeuSXmB}tW5U6_}8D(iWCwPp>-X+%aO^AQ!r%esH231gn^nB0hI zQu9JOtow2ona0Z)^n_Q3a#Xz!YmPZDn@)S!~eKtfkD zgEnO1lKodqE?s%0`y=Y4S(MquB?mPa7}2;hT!Qr%KWuS8L27@)hMm~v%?h_cGV}%X z8Y-aQb?y20QRfe9v+p#`-p#Xh2pe8rKWA1ppecP_*SbSP$o?>|nxYFU@Wg-D^Zg$H zAvVL{uIO}1T2X4a^MPBec0$-~yJ+i{Z?T=k*B2i$7Sp51EF*4;7%d51R0ybK^OS`T zF3EaX7OwOd?N$O0dNSzllWO&q&B5co2vT#dvoEeIucb~|7yD4e2Dw-Z1Z!*?IWk^Z zi3rXJ2P?2@bC-@>epO(TXd=;oZKgTgtCLHz)Q+3Z!3M@+2@*9fa9t{;CB}&Q9q6yl zr|C~4^TA{=FZrFiYoaaMD-hnFAS*8@^Toz{&X(P7Uo;W@0!tgcc~MGhuu0z(n}dQK zZdfnkAM)1sdU?z5@TZO9Zr85Q!A7~-%)C#r@hziO|2`}LWa`r&e){k%=d0K4$ParP zpka3$x1h_f|B4_A%d4QQsmWJJL1i|kbclJ2Ned$~Q|9oL(4K%4ErNPwZ8I67F4-Ep zFTUnMfOpAU>_Z;xVLoO$3x{{aS=q?-&#q!SN$^`O)a49AGpM%IUe3Ix5xu1}ww?l9 zWyhoQOf>Pv*jV{`if5MoD`0$OmYO<%DoR=*MR;~B-8@RoD+W2izC+7Iqr1Pq@|;A0 zzP4oK_{ls8=&ZwJ-uqErGi1%?bZ>3G=~#taOHG!lQ8K-J4GC)yRnF}k=*vSBKVR%z zsR-zpLnAKn)iqNb;2oe{YEEh6N;!6Pu|=n%^VAZnX@uVZ`X4UHYp69k(H&4N`94X+ zufy9Q54s3m-W`E;&a5KpFvKzWrnLBg0(>K!T;ho%Br!zQ2Sx!G$BB6L2^S{T=X>3` z^OL&%d)(Y733qm}i|*lCV*{UV#t^taJZ&yljgl(DEMSN7K*h zcDS+H>iX@E;}CuErei>92`SNFK(oplJ@axpHbQ!1E#%n`K(%`MvqWWS;wtINgJLe{ zVM|n%O)~134uO3!(V7|-GD$5jiVm)>ni3gnmAm&1$W$RpI|H6%p_IGs1onSC%+yUm z`AnQi_Mfmr-LE6@)>jx~f=Eu-EgBRk-Qf3KgU7E2m2TNB?^j&DJS zlWd7KmHK9jZ7kjpbqHe|?M*k+^$PO>gx!|r_3NtG;RT_cO$buH3e<_JAtJjEn=|5_ z=(&j=@_|DheOJ5`8_2jJbx4t%_lQ=}t9|Tqf9~cwG=2C|fU574A#E+UQ&O@=#7lZG zROrXYCw+IXeBmSy%v^uYATnJo`z8;roUZ-(xMGE82X*uU^>_f~-^1}Ox%1A^m+594 zK7R;K6(8_Me3&kh&#l^=E$^+J!|mD(5^ID`r)u%u`*w-i)AtT_v|=+s)0C>;wP`ot zZ?Uow1iwtO*kW6Z{PfL})+}9EcE(Mo!{eXI+UeuQK&f(M?3zixW0DsSd1lxqc)PJ@|XysB2C zP>p7Y0c&wQe_mJ#zc}+? zb88F|bxa7lntD3-%tz@TgThv7HA+p7+yjDLoSzDZ5yxiFT9?6bmV~}Hkuv@b7 ziS%7v6`QSZDn>;gTQ$^nzLIEIo28(A&gr^5I<`^4TasOk3!VXmB(G!IpvU3T1xIz1 z&k&~sa)_qAvrMcIg&eoa!>xu#EZTFIg)m2K09Ly+>Zulp2rGgu{osRmPMJ8E^;r7B z9p~$}rMi4RIla4z0DaP@i2>mTHt)L)xCS zqS+vWfb~WyU0&_dxhe7>%9EQRKGVp~Rh*MLqLy74F`1cLd-^fusqi{|y$q#jswnwg zies5&ssl#iQE33>F)?{iWK>MP1+zL%J~RzE5-tc~u;wDXb(gYG{$H$3z1#Gq=Evxt zI1ICy{kxz=dmmrs-{g~jw?WZjrA4JWymDV9=!8@WD?xPF z77B$)J1T+A8o6Qgj57)yFzK#~>XH`lc}*}z_=nT`DvE<+x!4%#X3 zX2W;SH@$Y>9en~gi&8MC!j)DdwhKUt?F7_?J@&da3Ja)QM!brz`i{{*FlSiwi=8Ty zI~5V(EQzCwsk@Jq7N+l9Xw8mSo7ba-{9p(UMntebUSorCh3iJm^0T)GQ1e3yo2p*M zVtjjiQcl0Jbo;b_&{_4ho0744^6LORwF36XvLCnH?uQ;`--M^R%{~7u6k3tX+8! zehq-lpin|*-xr5Ph8{a@v?3}tT<(z1SOl?hU;77^^Zx2e@)1Kcn|Nm3Wg~8G74N7>-BuK zZmDWDRnru)_oVm+iCp`W`|6>^pjjcD^93|=Anz5wsCLQ7hwV3Z!J1$uTH17)DcdJwT~c>n`b{dX^Im(P^GY2jb%)%8`ZYAE#KK; zaqLK;pn&N`tm+kOC^|?^u{KEC=TNH6g$oCrbmrssca%>0O@*GCl-Uzkh4gN zl%x-Wwi$pU)GM~s{cEx7e+n4@hOl-#t~+U_;?`X~?O?(>`~CEi?FFb+rsEUneJ{d_ zuVxw+5W_@oeHlRp6eWc|7FR>cAM$ov{Q77D=S~XR}fVN)E4yh?FGcML$9~rK&iT%GXU=O{rPRlV39nrc^A%?{!p`%TO=_@qz! zGB+$*8aDJwomOL!UwlPv)#`}^v(m~CXVx7qfhMGVZiB+b$%xS$&tS%=uTl2+!<5I; zD{Ya_2C~vqNclpheDs{@qRm8PMTpbI^&D-|cNJ#%7^Yocwd>Bl0RPEvt4QxoUR`!0 z(??EGvIj6(9~9`EJ2w?~nh~+bD_bBoPl$66LwbOYpDiZ_q~~e6nT4WPySJ$uiv|hs?-Rq`H9=L>_z_Oce%{?F$kt%fism}+%R9f z3=7I*&Yf+fpsKs_1%rT;n4hj;G*Fy;zE#s5UT%KXkYDj<4RLyojH?`O}agT=JBZKPVj9C1lc2k-y+cq4SLnmR{~$n^oj#M|BxJ~b)D5+S}pTGv6cw^h4T zj2A7+GXJu1eT3}GH7KQW#xlf5$`-o2+@{2Pt86?R8|@?cmFb>)Y_49fMH}&vd1t_r zUSu0<#t;%pO1{QYa2ZAF5vx0Wt(WauTZhO@zuTcP5vdkWDHs{t;e_6j+SrC#``{ZX z2lh*tK5Z``w!OGf)AE{pQT?}!=T_{q2^vg)gQ!g%LpNtQ(Xn{8%X_(1@flIWB2)nu zR{|B5>n)W{-j{AlaKQK>CTYtK&}2#hnoiSoCmizAtjNk(Ii>bo|8IRO(|+Wz<4szJ zbxXMP4WRS2@Pf^V)+H%I9w*=YqQ^^O zRRGG}tnpC8)BYemVOVlE3~sFxR9AK~v{Nuiqp=Zkp~v9^(ItS)9O3vLO+fxo+oQ6_ zYR~8+EI-U)W2Z`DS$EmoyIoJc)&*yUt@}e%ndry)(4xc#D5F^K8N9aL3DPa=y{ZK|@e zcFZ-NKIMM2#3G&b;1D5FDCJsvjNLGn9(OJGNd)_0EKhkouob{K44dMltWEv9BW$KZ zRqa?>v^JfQ%H9Zf+W=OlW@nT3=`f4Q`by6WQoj4h4g;MVG}vuwUL+q^`|I+vB{z>? zs1jHg(nciraALyWv#?2lMN=t}Z@wf57$-kb>FSU3Nw$r=V{++wLms`yAwq__WL_p< zzFD>yGLhcJ!;x~Y=coWwzFBo8mq3k}C3FOL?)@jR0|*NH;Z}#}uSBHm^@ZC9`*e%- z>82J8DVt=|a_7*;H1n(b>Sby=9|hH2F=UQXeB4I~V3to^9qrT2YKPJAc~=6VJAl^r zQNEHO(T?U>cN%`H%h0XYPu#dNM2mM~ll>c7d|(l2Ed1XA>}&lV93dx@`KcEVGw!qc z@ID*7T4K8CrIkC~E~L+lZ931v8kW*sGIc(VaFcJO(yy$L1>TUki9Q8uX7$PBp?}>$3p(XT^@74nQVYi^CKF<`6b<) zmtN)bR(q=R$R7iM)V}r4jn}zHjzo||zeAJN-Ia@Yksj>2YUaX-4^*1+K6LxU2 z({LP5Lad}mAP-+JP&~&P%!B^C#xy&fxPBtAlaj`B1b~KWUXFF@Z25iEY<6MjC#s5+ z+5qA1ZuVqGoFsYS&1v5C$C;+F|; zZki2BNUi-;_!w=cQA_!7iHB|0M5It{L+~l{KI#24*T}JA7!AlvVrv!@aZak$;fF2==}{J%Hr4(iZGg;ancui7GO;fSBD_8PV6A zpG;jDv!)I**bIxl>P`o3|EY>)GiEJL%XIp^(ABE@o6K6xmE#{fg?zpZIu9o@^jQTS zU34~!J2tPwz2P2$96KIe=Ow|U-DmhUp64$?^0D$E1~2B{4ee(lGp615Z^-+0tolAd zFg$Vo*7IYdzICgEMC_UoDZs+dZ{)&<=bI&^Vp;8yy0dp3)L3d&u zsYW9d4x5#~(>*{UFSu>49nZC|oAY$cK7f%mk{O!sTRp7gbQ~eb9!Q%FqiKcurqGV9z-LpQL&1dc1m{LZ?P2T!wQwSR32D zx4zUP<9(qE`dq%y&fI3o$DmjWc=TgAPCsWh3I+4oOYwUfHfB5u7BmvVb@Igb_`EwE zYU++6Aaj)J1e&_I0tHW%I&DfGh={`It`H*m-RrUQ*9?GWfATF=sdJojIKo{ePu~hA za(;D>TGw*ddkva~{MXaZ%_>c4x$`g+vq2mwLU-@TW?EB{SvqlE=ua?)nGZ4V!plKg}uO*BWsryQk1xe315b62))6VdC7Pb_K!#JZa;B zzUCyCr*kQuC2_K3gY4YGPawj(Pffd9Ef6%27Fb&!8!O=6E%>6y1Wv2IJ2ny;Z_mXapd%0x z9)88P{6&}Ph?Y8eKV}+b+~wNipv}x;Eo2<)ryNsG^zc$)eN0R;xHyz$ddsNoT4#I2 z+}ZkDY~PQ}Qj`}j+5&fZT_JPqP|PZ8goE!VEpEPsy}OlnBievh#gn$s!%fpj$>(h$ zYM12)(bwKeCg&^E1T;`gr3RKgmC-6gE5?ai)Hd8lo_{chV2IHbFllZ^7%#jo{Eg`( zRsxzFZANLmv@&Xw;LlEz)TLrZqEA0kmm%DGl6B989s;-pD-!)KX5#?i;bpl3nnd?E zpS__1W#sq97bz+{4?yQM=&xz2?{voDuM)e`1|^#&xO z7YH>wlhV!1qMBMG&P>(96@vsB*QAcP?fUb9YVAZVp;`bb5f)PTxbKJr^C`QnSAeeIv~`grx1xFy8(e5Cdq_#)irw*UKG?1mukvQKZ%VnLFI{cFMCqkP9!xf+~z1zBv_ttlEga_*m$g99;!hh7xY`CkbrF(+Ziq33BVQ^#>kx>BsIsD>HXh>sKqF)V!e3&cI#H1DDYT;rz<;p_6&v z4T;!M8oYmE?KM+vN{o39R^%R44o_FHF6|e3muj|Y5RT{>G{&nRn~gi=^62?;q_BVl zL-T+Wj;nEl13DSV$nyVD(Jz2=Fe8A3eeQWy6ADV9y~Ys*6$B{ilNjU5i`N zuU$_fl}ybPOus7M;BYJG&mr}LmRz|+8pYlDp~#$(l;8T_p)5TCj`Cuynw3b=@{n+& zmECc1{=Ntb>yMbXruvFOX_8|8FNL4E|2RIRhbH>pc#wWminD}$K4351zN;j5Ls6%b zho&GnC{q@#b!cjLsMdy`%b%L`{qr8&)Q{AD89T3!PdYFOXZf)LEqBpk{OVor1Qc_| zmu`kqO9+2!y?Df4VE=qzYc@)7@pE_dgEhL+Ej|)5Og+gzTI7y8r?k%8v%3*FyIb#q z;ty&btG9MWaX68S?GHCqm(q2+hx}Wc<~8MEbpE*bURs#B0NQ?Ih?DN0I@ z{VH7%zcI*1{YXd}sZ)IWfl@>D4Uq5ay(Lw#Jh)5>ZRwIe?c>yRclyg{6*1@R&$R`9 zR@&qbDN!Gq6zrS-%gpr!x)LmtOixg;#%OO8EBYZ(^R(A7a1XOOuUVw83XM|M{@xDu zGl|vbx?~Ok4r_ELPF&%29~k<>jsF= z(ej{8v3)Il4WnwX5fG0stF#4knML8WDd-wY$SKQWhGwR6vtq;yrC8{bV&|VUpQ`bK z*D^iY-aT2obK+umq@tKMzf}*EwNMx14_{&Fqa#K#0&0-EbW|o|mFCP@8F)>J33Y0R%R2PD;}t5T7_H@%?yC8vwN$jt~;Gy z=YQTf`jV&RkDvk=fN^P-MAt!E&0X?0s~_BaF8wh=AeXnh zO6!_Qyg$2X!6Ww|&c4oKEYNd7e}KS9%$(WkbY>pX>EFbdd|Uj;q$2kQ`u_+S0Vtrk>K35P3p7)uZrhd#L(N)N%bNzrhxhVt9~w^H6Ed`p3Ydy zBiClGH$J5&kk+q9Xk9q{d^vBkUumut`rNFvlh?QuT{|l!;I?=zzHjzWli_Lp*sOE8 zE@MG$jgAawtPkAwWXOyOa|M~_iwiK|mzblkB<;ShejrIoo9=`b_<|FX;^kD_nI@2= zcwSHq6?TC-^<4nJtUg_K=R3g`i~S8FNTg0y3OZ zGRGvZO6#0{9D!!H;Mv)i?|RexVQnBOUJogN?x6e(5{HwlWB(AYf*ji5EU$j8ph$k& zP)ng_>7Jd~S3BNZ+8W#HCIMgumbcB}ajQe$ve|&sY0{ z*6QaDssPYhzopNNrFfl4A&@TA#P}uuKi$1$SliLEH;SYXiUx|iLyHs(!QF}%C~l=# zffC$3gyfR_q5tsQ?FhabjXTDxd!+LE3(RU$#odr=J`!x zQ6E0x&&ayd`2K#;7ZU9)SUTSY_D6euH*ag)v~*jRAidsld)FPgP;uiE_q%Hs4D&iP z&nH4Rx4s=S{oK$koJM@`0*mxXbz>=chPcZ@itBli~_fxri<^}zBqL5s$H&Pa#7JZe7@AJ*mt9B@xOa#I&H#=cV7^L zaC`)N+lwWeI5zot8lP=eZaYp%TzeEc$lTr@1N*cp409ZrL_-l_YO(JU4tJ_LdmcYn zR$xD7QPMa!_iLO?WFy^L4awliQUEF ztmxA2-Y!!RjZBbnuFd{;P^T zDL0c?7$caU&hLKh2UuLqOdaFjHf7IgA2Ryc{0?LOxN$_UySOV8jk&gfne~FUl9XYt z;(4Bl1Fn=@l7>$Ec}Ml*^33+}d9EsLeEFRVW$TXN{ajn?qezPly5IDKpWTz7G*Z)- zoL(ij)V><1cU+WnZHw}PG2WCTYEyUbf+qL7+;8$;=2r@wBCo>wthzmZ+N6!`3cH9y z?w}+=X=%vBN=#4h>c)hvZX|auDKAvp@M*2?5x2CdU6|sJfnVC6iAU|oG;eiP1t)bP zasa^F)iKR69a`Zo2T>?2wy^!vOA4JX%<|>9#{i$rYszh%*5#U>i>AZ!mKLUr1xjk^ zNPu+SD_^My0zzNO73|zsfwBICrdRk^0BLDB3lm{5p`v0wizFL8y+gu&i|77%)BXql zF}EBc6KnlHtJ5|9taG}&BCBcNGF`GK4>l78BN+j&VnvdQnJLC_GGi?;q=w-%=JEY= zm73dXo4jbGQ&hikP|8RAer+R2ZJoiwL#~7Bv?ApVAkv~^xE>J!6)CukvOZyw?~YsAv-`L0Pj^|KBuWitEXiTA$}1cN~3 zEes6ygd1_+fZrw<{Wl#abfp2g3I_5wW`YyR4b@RWzWx1tZ?S_$zn@ru+?<-JQ0WiI zL498Bn|v~?ryFq5Dh7ZdNz&I>%JMt>=*-=IeQs05jQE8U_W|&( zVkPiYX_RMz1qQBxLf4L5apXrp>D@v6KldUbTw@=|->Fl@?X76K!XSWIXNrLl_qS&fFAhF`;ctzK0TM z!inmb*3Z@l=HinMAL~xLg-TFaPKUSX_p7H9KhxOgy*Ql0s2-jW76Hc_Ks{xf;x`Hs zl4Io3Qws6Z^@+}@`1BjNy&|0R*5#dOF5mxY*0~&XGnRcZq06U>N7H})b~=t%eNIVY zROghPdTNdJ7c}b0 z{fdQ@1SSW`=NJcV)e_4SML+(N<3?_Pm}xemZ7j?pEuwAr$H3X%=qlm4_k%dG%UAm6 zleeI4QPP}o5BaO>VV84q2Wv1&{`ua;*qQ%<`oj;NW3|m7+e*f$-wK~r#zdc+iiFzn z>d*#p$1T!i?OHlLb1HRv3za`|4O|%gJKS;&%BAxrl4L*Y^N1j-5O45_{HuHxJ6F$cq1d#nhm*J1I97g^P!1|-zYQ#irw z6Os%qVBTjx%;k(q_#o7pk`Udj4*+dAwfq57YjH9uB3w=AI5yS%y^U+IxxV1!YJR>^ z28Am9Z}@~B5UZ2$a;iiRhRKs>+iAo)YW+-Q$_$pIsb|Fo@Ve-b6z=+Y0HFb~p^jXG z)UCMaZUii;%t0!>T9-q?@rPt_eQBN;%OuMTty-WbRh)1ZDq z+~jrzIFCZy{8_r@45CE(0T&>h#h~W;zn=bEXw~SMq6vIqVILoD!-! z>hPzUIi~%i7y+U{$dJ-g6YqO#A^=K^te_P{yN$XL=v?9(rFpP2v$(-LE zC*lFv7k}AvAB#Lm0wFp+)E$~US*Q2kG}>PUT?e0}or?46NOzwiEC|25aPE=zCD1(Ao14w}9N_tjge5^`fuLq>H5Pv{Wnc zo8hVU`D>FT^)>tc8ahQgtC-5W;I?ZT!(H#V$3{}~we*A`!~U?j5YppbRLXsDE-OVU z{YOqAnm*L+m{{f*Ge-O5)%4ZR0a!0s6G-1zCkC^H7EWOfhmX}etfDlg_=;zOkN$E@LZ_HNV;8bY{NO)S29*wPXY(zs!& z@e47(N06De)+uN{Yk%lmtTTWgVJ(l6afypl^=zrufA_uhYU|vm z8to;GyU||XzA$+Kx14BH(4PHsI8osEb$J8V_X8Kx_r;Vhn&jSt0AeJF z-Pgc

ZutDxN7r5yz>l6RO@LHY&tTU=NFQWocO_fcq@L;$P-7hu^_s86DU8+p#nZ zJ*d6ti*;+c<4BeGs61dqPQz@Fi@wGV4s1>#_#FUpt7p>1D0uohvOyl`l<9Au7(OD& z=fogNxH}g@E2PEKxsCQD7y+3a@85`9+x zMY`Zy_ql?OF8#%N0Tz!R7_d80Q(T7;FcYep{!7}6(Ihbr1g44R4WCad;_iEc>?WtV z&QtDlppy|yW6t8BlkB0OztCp7olmY5w@^6RlMIQ~i)a^TT6>Y{Z=rcnmxG?a1Cp2* zJP`&n~5zjA#!!PhGGT$0k_}URiwNX zne1&<6YHYgM6W@yvS1Juz+X=2KwWv15dPx$k4r7S_bHeG1OebdGDCk$*8k=Hr92 zInw1yMQtg(etaEl#w{NpKry6f4@8kq(^nW0*>uWi$dq4q6=_`^Vuw4n1K}s6b8^VH6091_qSAb@koQ3s*0iy_B6^xqFOYB^ZOp$g3+^Bf_WF z;A}aoEd`h>a82)IT@FfWHRY)<%0q%lX;==z;xU8@qMam0)@5{S9`j+f^v2gXz7qph1R|s8r_ZETCiw-x&xJC79(FP{x|t;alZ|aCvf8Q38V0a9zd-Oca_Y@ z3&J;3zxSj>84l($Zg;ZBA3pjqyV@Z@vivpDsSF6Mph~(Mr|dON>k~B0_?#1g#2CBf zH?oSiCoAH0-k~l@8*6onF7jVN4+Xha?_up6*U`Hhb?ZVguZ$E7mgTLvBf_t2IU>5= z&4lm155+))Dq<$dO%U~AUZ=bytY_y=(h(=hZP3jo=X)Mhw!IU#0r}Cuf=37dPtL6? zGR&_Ub@tvTs_W;;hzqyptH-b*_C^A)thZq_GlPUMc-T%fZ((aPI3jE!;$=eiT3Ca^ znL?sE!y(VE;^oXYU4*Rsn$-3;e;IcQSVQkZ6N^*IhzqrJL#h)`)2Stv=LxnAd)EeM z>8*ofN=sJn06 zwhTB#%rH=(0rD5&_yW&|FjF5sIK z2%~YtJY)~@VUzXjo3TrhoKWO`E&K>$nA}^{1CVAE(+@)d!^||9Ln`6CJ3|}*)qVM4 z?q}l!8@AV7PbK(N;eYIh4G=3$asq6*yVbh?wGlnE zE9f3IbyStDAr9JpCfwk>je>Fttd60ZTSk=;TB^y@Q*trx?IJR5S@>^1gf`fo1sf!x+9s)|R?<2GqFYp$}>ft$hMPedrI?eN|_32SK%)N6`H^ zAFpfFOD1~*D+1bA#A7(9?kA?-%ZyiYEHU9wu@s4gp;nY`TU(=i?v)?3eHGyrQ@z%i zn46K_r2bpJ8^ z{Iw3H{ts@#e=F9hcP%LYr)YA_{1vD*s;Gu%QM*qt=xDdf$LRyfgc2In z`0$yh{3QAE;Zx8U-_;n_nl4i(K8;9{dl-Dd&=b7IN zoRSWAsfpogD*r5wG@k$U;AQ`7?dK8|Z0`#-M|MA4DZI@I^5aJzG&%F7__a~U3}94qB1+H1?RK6BhTR7r8z z`vsI3dk9!b3$Y!GGyfR0NWPx3y0ka*lPHf$K{l;?%*Tn~UDZO_5zz-L!OCv4w<}*{ zeIExeYCq_>)2u<(n6Y|zKH-Aw0{R{kwtFaZ&UHDloD!k5R!Ws^sA{+r?wQzHtEyB= z0kgCw#a?n$`UaZmO2#7PLw-*DnLh8O+-sLo%?AK|E*x#3H>3JCNGbm3itkuZ>g1Ho z!t1Ak{Xuj@dOtF*%_xxoX0!b1rl{#>;{BCI(J*>&HPGa}7eZ>9A$^8J9hds;cNPe9 z#Ob4cWqn=2y@Fa%$|a6s?JAC$Eh-;AK_}HH--BRSf7```&UX6O&rEChG?e4dd}VJ+ z2`+OTnIAy?@EvDA*~(7ZMI!NTakQ&78+R~WHE3i{pgjr`CeB9!1#F3#3_mj~$T@|o z{V*e9O{RUxhYD5P6MmU!vLI1X3|P8b@=bnODT-2zI>odHVM<+Zj}Ki9u}Dy6R1{o^ zv_Msm?28}~qJlz^zDwe2aIJL_KwYlgNfL5J9R|oDrAW#D`|N+AdRqQhaTz#ozW$Rb zpdUbTW+jgNqz`OhR1F`+rS80u{&D2y_EoV@Evf)LEbMGr93Z^==*2^Xf;u#NGbDIk z@B)1PijJ>(%yAe)@>vZ|AW5i&mn_}eH1>;YH-kKCDMb5kMc=uU(%VeKY%UG?=G>^* zRh%XAyJ2ZMi6qV2J+=}d-j$wMkM}7AGbg$+yYC)1eA&W`Hj2E2P3^s-Po*EIV1UAXYU$Y#!>uEN$=H68(2-p zJEN)p&KUjL|H+s#anfo~FtO(F={Y`?2(#1h!Hp%%8*F1saL$1xdB!*kN`YVNj8E5x zN?KQo_Pu=Wf7K0#arN1b1wo4UL;@9meStNwu<(+J*YQ^T5wUdM5+?7oM7I)k<7F-Xn2&2|5spl$bFSoJl-E#Hz#Kc&LPEwOV`ToEYQGb`ZUs`Ed{iUgQh ze582@{I_{PIw~&ol;f{81YLBdErL%R!H8g{bs^ zh|h*a&X8&4&1*OO8@$zZ9Ay8POSJ)Gu{gFyd@KKH$I9#(4LIamPgy+Z=VVY`ed)!o zr6zo+8{>Bbb}o#*!FmIG7vv%@Hh&T5+|6&d z3(AF{kVRXib;c2KXpyO6#^elAKK6L2Oj_TF>f{jgjoGVo|nO4pmK_$n_2`} z`wyZ1c-;BQhG$+p{5gqxnQoMWynfmrlcjT z2yuSn@y+&BI`bLXM{>#n^wH$Kqk--2^J^1zUb3tom)*Ew9Z4CwMK8Xc6{+4E8xTTm z%w*Lamq0NEtaun!0WY=khFN|L2Pzgf)Q&FCb|Xtg!8)WQGsDr%doegrJ4%BmL;Iu% zH_98pPHQIU$A4mPXxPtP*A~5aMDie?;mYpdwLbOtr8Pl*#0UW!k$o9VejF7m*TBdHI-YlmNjjdmxhH6bpb!)e ztC=xT6$ALFYcqp1ud1puQCXGTK_yzxvT_N<5n_HfQNoHG=5#6$c_4cJsq2gC@)50# zbv+xyA_a<-W(dutr8{~Id$YG4{AQ%Z0HbQPJr>Nh_RtN|Y$NQGg1b@yEm=AulGmTo zUdlw_cZ^4t$X%u#~%+!h6H32uqRDbXOL=EjCj_yv-c%itqy)*PNqhRGUoK-w0Ew@x;Zb-;F5VC%(Qqu|;FaU?{H8$JY@2D@0H|FLz)zKm+C z;<-#c&_;-CY7hA+Cz5XeP;3wcGtM$+SG-CPrt=pQ+u!lQlW*+?H}y`~#|Rk>@&msq z{KJ&p8T|K%=)YF$(?>Z^x>3+1Q1|yp4YmYB2K!BvEMJRX*;1Zu5qsv%Xx&p+wI=Cy z1Pn+k4POgcNpYDxl}S)cRUy`c$qkVeMGT&dwyb2u*-!yjiM(pWU{LaNtKTS$3nDVU zhr*CVL}&>!R}>Na1svW-hABNMo+JktON~v%}CqL*0-{D zc4_<3*h1nxVJor)w3nyX{XIZZJdy?0NhZP| zSIHHU?+Fh*Hzj#sWJB?E zd4R=Z7{ewfb5=cbWofn%I9@HD)4zLLjcsrw}* z!-hVJkH|P-R!S@!VTa%8m0caw6}|Ye&w~` zH%SK)TEWh$uI}|<>c_fYKm%T90@^80d zw>S?d34!%sPw6<{pahk&C)8h~zL8Z;*kmrn&LS*$FyLL&a^;K7gbiQap|=r^x|s@B z*3OD|70VYTH`>4C6D9t)7x35rw)zG##=ih9m15Z^_Gx=wfF#*+BqE}Yda&9ld^xe< zV}V-}4?y~0`4tiQQ`0#gX{{BIwMtOid*$qjn0{#{p0XhvEPsW-4_kIqS|kk^biANi z7Mg)kt%zoTG)6OP#jrM&-kxtyp ztGD6e6Eoz9U6CV$`X-V((cx}=bLN&mcS1S4 ztbLK?q#X;E^zTZOd06SON+`r01+|z*5;}svFwJAB51=<89>%5^52nMej#v2lmOhQQ z*x@}n$ZdhFcw`#*Dl~hsW;d%_?&QGKz|k&^7?(>%dmqJ!d#~=wTvnJaggQ{Wz9df+ zbA!3A(|t&h=vAf9<`vgqSv*<&bDU;fbi$>+iEDBF*Rm-PF5#qk14;o>UUuTi2JzFW zHIJ$fJrbAA(wP4ruHj#ym|sHJQvlv z!e+*lXh&~PfFU;r6~C03KFSeZHA1g?R&q)+#XiMGCk}zxCF6-gIRW#_1ZkEIVl`M- zc;=mMpsp->ZSt>HV3eV!o(kHG_E3$Nt3j%$GxjmNF7~EyvIS-qEUmdnC_SJNe7V7A z&Jqm|k|jKjI&?N7VDzHJxKJR=f@BHGMNR$am*u3c<|McnwwbQaH4ILztSJa)CWHt= zlI`L{sm6f5BvlYOXJ7sa>O8~zdl0J?7qmRtY)f1yntX)igGvl{J)`)ZkL0Wrs5-NY z=X$O=H}Ap%xIDZi!N<&`$LK(?yQkTKLttfCS&Yu6%Ba>XJe$(@WB~hyy-uz{L>l7$ zYo%)e_#F>tK|6|E|OH7vv1gJg_` z?y(C%YD54rH3E~hq=ixNh?zNwg2`0QDZ_PC8k4Bl+rv0>A<~9TOG=-K_2bU8U^sn+ zS!5SPGxT&Lwx`PF!vMxi(FM)p(Wqd-x8wjAb=tl3ER^Qfz}|T3>j+&V{_$qwTgDTf z(Of?ec;cho#by1_KyPDrr0Z*cT&Q;(Sp5)Ev<@jCj=Aa1bv|c)p|w~x%1=|@qG7DS zyKxssl=xx;vSv4iS*~=4uW)l*fH#QiMcg2~t@yx7%~tU$?0T_&yW~9*DE~)3?cbT4 zoxpa>ciHSqfAfco`j6hmonhWsGA%OG#;Y2Mjh0{1Lu>!F*Hbie=3*_ z4lGIj6s7AV{v$GfO4}8XrPrkC9C#FgUVWv&KLs|3LLLJ_gk9kP_ZXVU$eHKh@*@#u zHL~H;9A8k)1sk;kuVk~koiwO0I5^E&WN8LyboC-@(84e_#|Mfi4OV)9;LAzpkSi5W zkWvAbS|Y;+s6{lCFn2_eXZd;l59c?sX+K^E0tN3q0rj#@R52k>6epRLQ;N7UN#m@1 zBaPvFE0=}$$P7wu!c&R-2g`TDf8y*cMHqG}8S@|EZOs5wg(0CZjk#CHOf&`LHH^Tp zOwBtQ%tX;Ztzd4Y7bmFZDHdh!qTEr@8mwP{UhCf)FzV$wYV1Ffa|cH2%S%ryPR1wy zq_-T99}sz$J-hy7nME|SPb~0gg>J}w_?8(eHp=L@^p1hX8SwEXHy;gj;pJ+ug1!5f z{u(_+k3iacY&N3CDs1JnEIZ*(ZHt13vEaCHg{jD7Z=T_fnS zEhr>)!nf}~Uc*Yht=tQ_a;h+*aY>`&K<)z8TysszDe33u|GWx$*r@t8z z?Rk0EM*?0Im!b?4;S+2uG+>q6(H48S13-vzmzsWq>t_dx3Y>}x1^7cN&K-^g00sl9 zLf8~ihcVD%3d!Ja4qnjv(oD|4p^UsaivpkGZeryKKSaq_?5tFUI(T~nq@z6hONkgx zx55bu=~ZST15ZIla3Sxy0#3%py_)tCt|=MVF=cR+J_U!wveHwIbu<1<$Ps15gdmsL zyS%PzprsYb5}Ty2vpRqxFaET8dMsvxi4&5OEn)jr`9vXlKz&_iF2ptAlx1bm4;pXN zhAQi7l%hQn+zQrW*~9dmqUogm?u6U<5w2~t0_o%&Cj(0+u0ZHip={xXxZ`t7Z2M?_@zt;uY!RYWqD)> zDke@1Z@-~NGRHFoJ(FIdYjMKN(*~C7sOnB+c@}Ud>C_9a7SYb6wyGQ|+~ZEAZVq5) z{!2Mw3lS>SVW9anX3h*I;8xQg%0ht9ZF%or@g9jOdjCg6@Xy2-xxH)vwmDi9%vAAg z6qCyshCi=*5T|fkl#@Q}L|A3*JPlFH<5(c>748~n`PH-DY>UVlmCp=SoUo51FbVYJ z%1iYq>Q|4(G#^o>8Xft>#tomelZMBQQN{4CC_x0e=th5WL^QCzO$Hs%gF~ZQc^Hx{ zn1oh6yD6oiG67+-WOF}%m(TcH5;<6zo{8LNe|%?G^moH&;Bgk(?0a?M%Gm=PN%|_Wb_=gGtr72yIOAD~#JW>5`Wjpi^r0?yU^yL4!!uuU z_&})846m8Xl@?aw5&^kunB~!vXyyn$^f(oY#IvQ}Pjxly+n-!zH^f<=8Qw{nu=mxN zKAGAS$wXoHgT7nib8aHYejQrrGmrrbIqY|77jQFbbZl0%bKs@e4*dFV@z?&VZ)LyT z3SyqzGf1*jeiA(G0GIu@1oq!;FCEHp0R?+{X;5@C-$*8CYQjOmmHsKY+iy0{yJ(_d zO*?r<&=_1umW^?jq6gn;^x9bz*1y)X&LUTN@O5Yx(oOXiq&aF8V-#};LJUhT_0*o} z^24*_n51J_#cX!Gfakm~0*{ot!#>GFczuH7tTLYh=c+vd<#W+tg32`0DR0?ix%;xC zk506slRZJ!2O*N9(jqY=DZn*P%Lt+n+Z~lG>)!x8Tv-uY#k&TRR*VzDzK}&INu_Sc zg)iI!g852#XMlY88QE~nlfi22?52)Ya3c*~yYI71 z@G*Se(Tq6YswD1r=MpJaN4|VO%Ey&4i7ppf+JCK*e*S?wUbo#Kx~iwHBA`G zU%@=D+8~ zufwupLq5hk)98Rc9~K})ea-30PRd4sNo z-N-e$l?Aw5X|-cIpKT`D9x)>_1GQH#zx+BK#wrg#;^xD!3O+6B&G1;s>XMy16>!Z& z45*v;Uq-F2_PoZHzW`l|js!2(f7wlz2@>Nh(?h_2KSq2C@ar2Tvxws9C7Vp76RkUJ zKWPRTf~qUzhS68D>=KbVbAvisQk3#OS8o`X)?ONF`%k|Ep?SZnCgkbUkO6^1M#6JdW0QC0U0+Ull8b_ z+wtdOr7!%s?H)!}-oE%J2^LXl$t#}ije#}NL(g_j*;6qrq#Xa!_Lo3@dXlq$z9X|x zA$t4&#{n0@v;6#g+p%o1?bh1^Wr>^dmoh1UD|;`0B%N4VGD}ZQEy=R{*P^A$SFI&|y6`ek%Mt%N zeo1?`gt>j(hirgy^ZOgqMhb^ML)GK_p}(NmQ9JtfXVd%lO&7CvTGrOq`@@Yq!os<_ z)KUh|lDUl2o0?`PuCCI6#VMrPpnvWX{g?jy$H~?*rsPx_O5s+erv7R>Q$2Mle&mLA zQ&q(iJ-q05IlrA|ovpNZ)hjr`oMQC)RzPX1Ciwx$S=XlJP|DY)rl$KTjZPPPFB0Ci zMx^@>#An)KF`5hf>iXZ<{*MISKk^4KY@p%wop<2}sW!W*a@WI_%c9Mk0CB&4J5y!D zZy&zir`P@w3w}t*xhnwN&(_)5wf#%Zd5Ss7e_ z!}8=NfEYtdaj`SqLhjywWSt0Dn(U$XBDr*LiJqb#?B^|Cu8 zTA!+J7OA2ir9;&~1^Ci{Zv~uR^s$q`@`O?2k z5{_Q2jQjJY_hm-(jzdP}6L(Q$zb!<@d+b?ed)db7=nXZo^^)v*ED>{wz3%hSurStr zrRxs)MAiqeivU5Cz5wgi)6W%kQ*9bNj*au^B5HzfmXekUuySAi=)T|I`U_-_yH6Dr?HaQJ6$QTpY-90x3R4`2I&Bwtwm! zc2v@OWeTM&j&2tpO)exW6Z}d=-&B}nEj(O&EB@B9?@-nqaFUQykaNFhZ99@KaMPmw z?C#;fqsw+Zd+yEd5E(bbv+bg>3TCq{U3njdsT@%AMz(- z;d<0#M-KCIrZov3g%-t57>zfZ*u}I(oukWj-tk7=hxsPgDPxbqw)=g@n%TG$tBt+{ zb@9@R^~TL?E&Wfb*TdH03FR#p25wP-h#v@Qk%^Yeg^Y{C#ftjy+JHWhg}8_}2E-8SYnl>v?{DUWo6vCG%HVQft4L=6DZkia8Lu z=h4i4bIjShzFLj>yilOpv=*;Cf$R;rTx&bY60X4~XjKjBEMKU%J>3|wJzQwn7FoPT zWLiweldb~|+cw1xm5`(x&sLht>_0CudA0Dk&K;2EelW-0p(d1@&G;CwgifpBgLa;b zY*vYAXqz%izBkplwdX=Hk>=ga7V}Busc71HZYj-sfV2rZ$KN{}AR48sxCJ$Xw}mMt zWu$poqg+V@?j3(_izV)rh;fK}1g4DgpIQwibMCO2SFGTIql0P{a3(&P{6+YaA#U9p zQ}-FmR{<0U0r$J6+c5!m`GW*)yz)TI`(E@c1PqCs6bFKB z*CX2!kJn?h?gdhPh!3z$d^ZXQTnFs^r&&3I&C9sXqHh^JH`1&ph}1=Md8KEyE;YmG zr%NASZ4NlRu}B?|FK}sbAabLio~3=f>a+b-rsLfIL)>A$NoP}|eh`?NMtPo?pEg_c zx>&Qk1p6;xyA+w~i>M;2@ZN0S-fXK6!&dp;e^?e8N$}LDtOXZ5^X+BzS3X)`SW~$3 zx`Z@6#bmz7MdaDX7Qv5r->WyhR@-4`oIXSTTiC4+Zf~$ZMu~RnXqO^4IZ@0awZ~j8 zf`}Z%&6q?OT;(~fYGE!+k|aosMxFcT!*^9``sXyFA6*lV&cyG}CT%Yd=C^Bd?!U;d z9k*&Nr>Ir*_*z_VN<6Yl9aqKpscfsGtK3+;C=!@36Fuo?BxLjSVK)?Rws#+7t)L;;Ory?=< z?q{($^B>JeW54rX6vnlw0sAV}`3zgu?=xOy$=||8Qn$l?nAL(Z7#PyZ9}fc_4cFn` z6T?~C_Ogqg7V5El-bNx_4DI!ueA;^(?_I;ivTE7;H$}%8jWWgk*gGvVgFrmK8#g4D(0ooOr>j|xW|z!E9Sjdkcf%rPy-7q0eK_ zcc(rd7p{a&pqNooz+aQ6y_dEQXLnXveQx3@x;=gI+t5+R`eMd9Ckcgz6jO9-8eM^G zJ*t<)Cp8lNItLVs?Ivw&NXD7_HFqR;pS>o^Cc!2nHF(T%`o}+o>qACahYC0ydpZIf z;T>l;E^n@EAL%I@NGCfom-ab&4s7h(zYBM=!ZEKvTh#0u_m6$tWoJA%G#2I9vJS-Q z{rPD^Z&Iq$XQ-AyJi!ZePqjKa-h*lwd%zFyE#ftQkDr7|jA^pK+a#5L_h<3pO}@@w zU05GjhuHUc6;){j@#S4bn%+r+J&!stCnLFA*_=9o#94--)!HYP_msHk7pq1Txc2WU z@Gsi6)b!t;74VO~%%U#6HA0YJoQS(oc;$FIJpZX%%TwTC>*HC>x#F@t_m)*&e$GlD zG|e91Vw>@F_{%4Uw~4z*H*&I8d(Y-iGuWy|)4X0d8XWO}KJ}auX^aP~yMgYUlM%+4 zqtV_D+Nq@u&tEEspU4Qkofl;6P#O1lT&|Nc$$8I8mt%pQ*_VjwUr`*@(B^Sm`6&nW zxreM6dD7DY74@|&HA3@biy4ie3&Tk+5T&-$i#!8@jh7N9#T}Gf9$0U4vk3n2dO^UK-fjd#4 zRWLd?TNsx|S}3nsGx_OsbQ`-m6|#4$^G`kG_fdR~sNFv~DqQDMRn=}GK{0-b|1`{> z?B1$qR_nvy1K>On1QQOXo?EozAUP{4HHV=e3KL@|;p~!Z*H(iMG8XTwZU%K*<6DnD zau=xkt`P0?SNkIdTeV*n@l=A(PIDf8Z%$I$I4CRC5~Cfar+1NX7)?1FA;mZXB+i(v z)K~3r=ijK~G5!PiB6L5uDW1~$a9&Z&?YFJ`+~N!UIy(j=n?16Kr$Nn*>o2{UUYl7S zMcn?!k=4xLb)_fM1ct9MM(*gvXg!xuabuTpENDrSRa4)aOG%(x-cT(VG%En5m#AY1 zOjxg&7{DTNM3+oq^~i4UZ@-*21XNRJ3Hx>w(Q~X?h@g98Z3& zw14}{Tkhu=&k6UWEjz5A)9>}TI_}v}pc4yit6$UKdmT55ng>?Pq;+tqfO@mFcbcaw zBQbKn;JQCq)O(eFnrD(PBkL7Bv$y5!>O?os&i&K4aDO?gc9YM6s6KD_rS}Th?l|?U z1DgGlM0(+7Uv(jiTI5w$CtQ1Ll5Fo?eyi7XdIXnnf_kQ?3oeDz5a;%64h~B*eS&}*d$@ws}6#nKdKeiL)2_M(}ctPY%;w}IHCm@^?}&WeGuzp%}1WW zwxUOXj9w|BYh-j>frmUnbLg{pTbui?#r<7No`~wH_2y{SD{FAz+ZgeRD|E7mLM-^!3vVjB) zcFE6fLeyrLJ3KeT9K;ziyi$<}Ze956{7pG=UVJqZ`j|#}mNKJ#grqqe0Kr=qY}rXdUB_9+1W_~nRZ`wF7LIm?p-1g%;5&1; zF}N}9!mB)-o~GWE7MIIRt4@K5q1{W*%yfMQjjC8j`3I3H$!Q7O9ibxe{!I?*6)er4 zhP5KKtiEBuO}>jyqQA7{x9qff|5^%~+?*%zVK}NEyCM->X*HhtTV##PEs$r1bUXUH z@^U0TEZXHQT~l;}&icgV`V$_g!m?}{w(r!$id@UC zM)~n3sH-#wG0w@i#YshG4y%|k*DUwN@p7r$0- z8ZppAh8J2vP9;A!RHndAD&*!I=QQrmp?LM?y~jQ{7l$Rt?F4lBp8>ZG30@+(d!FIES;@cCP8LpqJtaMTO$e*Pj$)yO(~8 z(wIu}9z!B9@_7BfGClnC?#AT*3ei>JPyQX&Pr~$mQ2u{C;979@n14aG{^_Ec;%^62 NB{?R{|9qwzXkvR literal 0 HcmV?d00001 From 94585caba49d9f8bdb1434f0ccff3903b5db3d0b Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 10:49:44 +1000 Subject: [PATCH 13/41] accuracy and loss for test set --- test.png | Bin 0 -> 26154 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 test.png diff --git a/test.png b/test.png new file mode 100644 index 0000000000000000000000000000000000000000..b677fded98d54b328278768f61fa765106153c60 GIT binary patch literal 26154 zcmeF3dtB0Y|MzWetFBh9c~!GgH?7>Va%t*R&~16DELP?@wUR>%C(AP+(AKq;|qUj_ySEB1K09x^a^=Pd(+MT?id3#>%6pyPl~i=qyBd}Tms-#7|9cpLHM z{x1y-%2StXj=cjsUiO`5K$L;O+RgfZi}oDaG-+S}WA1VN@^E~}ME|1nK|9L!uhmx9 z6wK>_^Mrw#)r$+4y>Ia4`IdT%uO4q*_O{ihpB7!ezD1*OVQS=?@tUVoggK2;CYsY4 zo}P^Ql9*rzUHKDYO#^$o)ZC!2P(?~DC0T%U#hfcucoSdgx?9PU$}@^}j|{KjSAVYh z=u*Vo#dy`??kfu0bgSB|4sdJA=tJtklr#%Ta@*`ZO<5$hp(91)-7-rZe7v(`oUbw} z;6Gqgt+wtpl--Rhu4f)BRD^SxxfU`(G&M(!#+lig6y&&KV6!l&ZB^DpRqouU-D>%_ zGtM!RgK`t7wcAM#hntx(QFET{W1vD?R}AY^W{qDIl8-M{E{c61g^6rRuo(e!;Y2Uo zX2{$4)lWY|K|CD%OxLd+-A59W+fFc#HD2|!(bi+KA*oD~$;(Sp1%+8xxbir(i<$CB z0@dT{mJ&M8OL4@4zIgl|XS#Vz7Cc&DlrbUg&d8K!;a6AJ-iFj{XJ%SN;w#rS9DweJ z_T3=*hi!#BG#Jl{AY5X!WQ{x>S8Z=3=XAB^Pb2qL;(wFq464eB%9Ob8vG4N{+&oI^ zSfc$;_hYDptA3gGRu{TlV`g(liDKw%P{Keiq6|FRV>xCswlgo=Rm6=tjJv`3q z2~_s-(vE|hj5+p-k7%j9y7aS@WL3sNQd_-I5MlKv;-R+^dAoV({QNYXF&gf0rJl&O zb>s-6k~5p?oHvxh#O7w;m=y$0eZ?N`#+*PinJcaAVifx@?>0QDtSfHf0xzm5;R8kL zY~|P}h;(75jY~Z@{IR3qhU9MBd%5P``=CV6tn=Ypve<^j+2YJABAY1BLdWf$b(myV znrl4RX}FXU1NDXQl!2o9fXo_Y{l8i30$NX^*g@Ie7m4DK$U|1bi!Buk?3OqZcsJbZY6!*eJdxA(aq`$rFX@== z2dobs%f~NU)p6TFf zzzx^tagneh83g3QM++|rTWxA*f719ec;?=OJEMw@-VM{UoV)L#tcdYjXtIV+nc0YF zvN-f(XTy|KB+6I|ZtZ(UL3 zI=ge9LM$O18YHrEy}sqPJdCiu&=%H@EES}aU0_Y>w@#%YzT!ep(tR5-P@&h{_~me% z+f`;+PWRr-oq;h&vW-a=wV?=tB(vW>#KN>FDRNq7mb#;x7}*zIMyVL{;0-yTiD(`u zGJm7EUwlyO4PBel)Pm{`qRETKF{U+E3L-g(E4yqAB^tWRCPSJF_f-^awBJyQB&#}N z&g>LfCyalD+B$mKWA>3QGUXE!I&xxl2g4v8lS-D z{1RruKcnVw9qxohZxL(SKH0sQw-nK#Ez=pR`>xSQD3(hr`mUqzG_qeFT>$JdF>TDZ zUDYYuQz-MDSjv-x++E5kmIW$F%_oY-dxNGqk&-}uC$OJw`cA!&HN16`@ym>QFE`F7=vbl4Wm&iE<15^2#w0M^>o@9Sz zGHE5CjtHP)$VHi;|9SOyd}qbS&u<+VopxeENfW zw8lf0G|gc3$_Q^^D1&quSg|dL@I(8}Jp1g1wd~)p@sY;nAZ9~R;Sq>X;e+BZd#ffm ziSj_HB0~yhGmv;mL5{h1>r(-&DXrzh70jje_uJPwPTwMfE#9oN}*EgL$& z$4qr>j_j;6P4dK3(9^$+ccD2Ey8=lutLWH+{Z8wfJhC$tn={TLuW$&Jx?VTQZMHS% z;$U(scV9GfDj_yUZ1AL~90r5RF*{^AE_;+xTAl4x4xcG7>dIy}k86io7AhdBgu2CKWlY4sohr~^co zf8fwi7lLTzQXkcrsSR`N>+G-Ax^&2lGBx)$R0X=U-lVN6;|2 zkB20m^%iE1XJyDUHErwFU6?|V4LqRv2Gc&G0-yB_%=7>`n{|qwkkgaY&nN4+m%~k{ zL|_4f+HRAoar@{UlOHSB9`DW=SLXSPdoufTrH1}uRhRq&6uS^vCyr|!{*TCCF}Ph( zlI!BMBgz>=G0RK!X)32^*6dQ8&;{p|DG!tn32*n*1tVvyCvxfDc5@O{ zKh2>ID(Wu~EwiUo3=mzXrGbUn-|+I+T3xVs!_gj&g2J;b&E)I8572CA>))^lU_t3= z(hA^{f#4k%CT^{!^()}(N9UU7HUmg$U?BOPBy?T{{DcBuR~l(2&ykkTFaHQEZ+Nzx za(h*peoguVu-3|DE`3}7NdIx#>5GG#KDlFMaV~@}0>GU%Ys5+Uj)Z+w-T> zuOuwe%%5)le;+M>wC?*C+Zlu!yms`bZxQBXty1cuh6kypE_i~R7wSULX|!t$K#KtE zj7Fl>x~@4B@bSpz@j`2Zb+~G@K_OtY<*_v6m)D#T>yv zFz4P4si#8V-W(2TTP55q)4_cN!sq;`NNx)*pymdrl(sQ)gIZ^U*mIZ0v?@Cc$x-nM zW|ezPZl+}(p$St{#Irm!U^H>?YW7CI9PBf6H9cyf4T*IwthJv~d5Aq?a&0*&@+a%Q zUnQH}HEn=K227C25nRC!(TuhiF#Yw+B_D5W+aZglx}u}Y*tg`k)aDA>4bbsEn!P* znZ41FutM;_>McL}r=M00RZY9LbGUbSiDgrb9kyan`Ebd@4q3)gf(|;dM-+0Zl{w=F0X`hZf4~2*#)UI4Parh zLv_{&rW9hwS%^!kcwy1WoU{(5*IK04Y(gV9^z9amE*A}s-k!rY8WpA?vdmk=-vk%R zG+>@Tx?W5#53F=Hd*IERRI~-)4=B^KNEYh!;$Utrq376x?OoM4GV`=H2S&G@*j~~TTT?1vbdzl zdrtTFNR0_7vSkqAE!h2tS zA{HQ14#|SIH$qj`EqW#L01{MA36NLuwdgB~@0oO%IrJnXaXc(bvTl^)P(7Q4JKtv$kWtJIH0s?C zPeYesznv$@X2eWvr1W!pgz3aLmAYm59j)d_>xux&Ffa{uoe=9)*xdLt`5C-P z9D8>?&VQ4*C-nc@s+0FBzKH#as=swEZI*nWrMt0@u;Jz)BT?Y2dOh z1R#s?FJ!In)eTQIJ2-H{LrY@Is%9>H(NL^94Z`hqQk?3td_antyd4XExW;kAK(^}w zv>N7XMQvS=45iB<4~&dp-A#G*VCImw)GzA@slvjiFeFEp&)(q8;>6VI60!TE#s>Ba zh;!_x{A3BPQulVwm(0}QYN|v^CQ)J2u8sNrsCS1t^aq)XAl15X>tw~^ie`BXf%qM& zpV!mb(boZr+`*y9O_UeDn2Zs8os6Znm0Jeci{JTiu7k5^BBu()R~bfMH;zy4K*NDI zOQbOdLYLcPcDr+WWrzZQY65YyShH-JG1kbSMKM|enk3$vd?tVg5g3(>d7| zCmR;7()A?Bexf+jjdF0FV@dAPxpCuWE@>uutuovLDy}FHS72J(RPkr3M8b*QSy|DSvz^K2}oVj#nt#e>kcwzlc;~VQtgU#$2s-ylTN+%w!Cy(xt#BCDI z$TIpbj0rkEcdjj~x5@z>F~lioa^a51RS{X}pEjY6REC`A`nUmi2Nv|WvjFi9b%_%H zT`bBCXvowA#u__;3*6cvp$J*uCDK&Qh-~tzmu@@+AvQ`LC~R#}*X0&P8TTP8iJ5X@ zSB?FeNW981Pjp?A#9<8SFvt&CuUI%Xv>3QVUuQ_{pivEsvHBTF6v3Bwei_R%L?v~* zV2CV-vfT4tu}``Uk->q`0&^#>M4r_3Ye4J={ig744-Udd=6`@^;}8)8*<_V!581^D zXSk+F%S?w#C{;g*&W}T&l5xRB{)eT`80W%tQv86g7?I;U`?w2}v(dgayO{m3pnnXN zJT?89gIykhD-7Dl4Ns*ZH}f@4VoPO??QF5!or5T7K?RTzk$q!C&kz$7@dU92gZ6?J z&~>h}#gwY!l^*=v*NfQ6>efwH}iu2a!pzK>k^8F)XrgTj+JWu(;W9 zLaDR02Q;)qoE#7QPWg1^lj@2OONmg9@LnK~GR{q~={{u!Cl&|N&N5PY_ zUc_zYHa%sgOzNyw>Q}!r)o5nXlk=$fD|_UuhvvxqvK6q*f42PHd-!wued5;vYXOxy zw~zE&%zyMPBK+S>rdPz-VSbA6OJReF*v^{vHI|C8yb;`(pfG%HFL7*RFtS?~RI9N2 z3XVOFf4U=6VSjRl=q8L=%Q7_fW*OebF+$STLu~6_5$9`S>5NIz*v*m8t>!3*AJp!{ zly*P%jwYYs5H=a6CJ!%pxQCftz_}sWU$5&>9V~#e>0ykgZjbKPgOfpD2m)!0Yi*#_ zA!|pkV`ZBJR^v?sGN{vrFUZ`+y?g@4lRbiE*A6ePJ~(8Y(lSedmN!DxfXo?*dL252 zPd4y2-d0=FYW+e`W@^sK#P*C)2u*I(*DGHAN>(*+nxcbfsst<}Q9@!_i}$-LNFE$*{`pGvn;8?A{gt2a|hG39`cY?;y^!QnrCVX+8;s9 zk<=W3FM0SICdG+}qBf1JCz%(@a)75lu1q2`mtK-yQ5>n4(G>9-Kwe!{}yOy}Ifi7PF%KpR#HB7#+9G zLe<9o$Su;yY(Ib8QXZ^+BJbNtx~DZ~kOQe6*IiiLpshJi0g-hzYaysL ztL1*S;Y~_yE5$YlRen+lVja2QIBm5&?aQ2)h-hNGh#Zm?u04>m!H>Z*f=rw_Ywae< z$?RVrx`q|`L)8w;!WjIfAi|x*xT8x1^j?AD{7BR>um$&N37`z`#RD(E?_g4Sos*Gf zAY$_sl2(1^qoppgeBLS5=?`68CVp1zlhYFS)kajWPl2h z#!ALEJ~CLoro*Akfjo7u7Tzs{D?~+tB|2R(X+s=4NagmC3rID2|E;G-xgQ2>D7N5! zxbOPxrDkXNC_aSH5Zg)gOv7$@hMje+ZPuwVR5E5?rS9K>Bn3!#K8k7UasgyW+v1&9 zVa+vH!&QhTsMyj>3uBl0mLzqQ4AhRto5nDWrB0&ZFn1oMkDk=A5mW`LJ&bKRMK7=A zmY4=-iw4od!LsI4k!6bTq%o2iy&2?-pBHJ}8Zrv1trRJgATE;Dbc2aF?6RTr*sdj< z?Qf7_TNPO7Vd0mv>={l9i|gmAYRwHGA2urVX^KfBeOfx`49KVxWN!|MWjJcZ@+wWy zxcwaR@Wd~IE&dz4jds@~%@1>$Rsw>aUWz0*oNeUAl25rsOCd1kvDA8XCYu)wX!8%h zbZM;{{+O>g?rRlA@2JUhlTNg&%_ed!dU)4vn>;juS5@%F|o7HrF?i~BG z682LH8oEm25+quhT5g?;fs2aaou3yi{WX02t!H!J@DwfprNU zvFy7u2@rA1UG;Tr03%Ks^)8|firJ?NXb%=Q&$21UU~D4*IIWxzb}poWQ(gorz$o)) z3zo$X=I0|St$dug`d37T)`-b0j7erq2<33t@9g61c2w#`$J+GFc8aWZsQo7ut`8^eG z{0=ULfYK(gf!mfVxMe&<)5I6gFqTjS={MC%euV^6-6))%jI9!~IuPra>y+!!KE$sO zm;-;&8rnCMoRT(goVhr9W>kub#x`si5gm;Mc-dma%V~0bq0Ch*m-ma~A9Qk9B~>Dh zV|j=eO_&D%TocFU0EC*a0Tm~ee`lrLmOOk*w7BewXs>fnG^Ab)X#Za#ddL8v+VJ;L z%;`l1$f{ve_S6h6RIV3%dv*E#R5>NK;cmeHIYbrQ>DgD~j;p^72)pYUbKIyVK!OE` z(76|dn7DSN&{^ipqaOBZ&Su@_-(ZGmFky-+^ofMe2L$f?bAL^pK$yZti(sa#o1!N=*N8TB|u znx=65pE-5&G(O0-8r4i`@NCs&g1K}rAtwLJjj2PK%v;EJE1Jme5-@aExZLsZ{M?Y7Pq=lnpci~+dPy27ZA9;7Aj z3^Cc#1ebACSR^ZV;$SR!Qyu~FiaN`@mD@|@UMWHShOE{BwjtX)z$!Jd%qEYSUUah3 zgTBo*wRKqdn(i8w^<$C>y8sh|dxa&bZL9g&V;FP&t=7N5|BBe&SYpAK*_U%GEbDBA z7Qb@k#p2C^Tn}C>PnyL>Z?F^vO(W`YK2Y8d3I_?Op#}-i( zuIrcA!J=YE1F7BSy>*rlcfZ*Kg*9{sa{wSZT5lo`T*&?{!#LZ)6zrGAZYmk@_tX6= z7rTq#x1|(vlbK0v8RPBr7_)kdZq1wk0P{5G>;i)7Qt8~2dDc|i=AcMRp7CaH5D5)cl)t?DogvcTDl*^Y@hVZYptnmI*XA$GzCX`m3V+N+CoV=r%W;XIxip7b;#p6V2eziS zl4;Te_e#aytPzpx%^eri7_FJ@!2$-nR^*tYT`Q~}UMk2=>t;-2G7b`uUe~jrVpa>L zriy2N$grD_ypE-0@E65CL7<^>C1s{Pr9)0{tJD8gc{KK}%TT4pm)NZ22R?O7vlUba za1L0AFy`YN65z)7S&nU1e}}RjBauzZr(0#d`?LGCiy{os(vs>WX*f7zRCNO|RF(!S z0D*X`nbuJ>-Y!KSr#iFkDd@(Y3<~3lbD;?x%?ghw<+c*p8LLM-cCHdX4?UX$Di%i3?H!YsweEtSRMXh5r zaltqLD4gz>-XbHDo!4=rjwe&kLyK<=R#9TQ{ai)A4&Q7v6zBR7@_-*O=&Z7`0!}LK ze__;5tWc6M09ot)(?_j3r8NW6nv#k7|ZGuJw4QJT_GT0fj%Iqz8KbYZ>i`S+YnP7-m;AJDS1lZT^rfl)GwMVWh_*zL9R)qy z6qEQ$BZa=Ek!JmmHB!B)pYi#HkfI7n9?fr7wq1#EK>0WA#gpgd*Tv-E2J^b%V;JXe zqVdrO;DdNyPEKa|2Z#rZGC`ff;fxL3nz4Cqn+S-jsNuX{X~XZmMBb^-uQPckHUbi> z#(MYsb0Cp3CYsGBMnVA!-4Am72qL`Sj@u}oR>?HeN3BIGqMaM95q9#jfPnas zjX&p$bwo=?kL%98>R2x0=a_l@Q#f6$u-vCd#*%|nFGBnECV!Fe9BC)I9x%+AMlN$5 z432*9uN#b|yA<78vfKkX6l3RD#M04piRv#G#!ui_0docDW{Uo$ct63(mnFe6zUE?jk;>GGp&tj0qeZx&iq zC7OF%#mEIK=GE2y`kI-FfF14a9&RyxOBD|k(Yu;D6*%Bn(%Em-QO6nFx*p|9M92K` zavZEH);OsK)_uRONBLEhv|!bk;b85|?<{=aH4+OwpG%+r(88w;VoxnbTEPy?_;dS#sd9Yu7#m1!`2<#i_E!ZhB zV*dol2VK2i_=tPF43iEf0P1fYEl7sm8b&+{OV~ZT^`D#f4QUmtAV8Yn4c2|~aJj1e za1;60VW)!K>)8ctBNh{x9CAH4DzJVValE=sIZ|^z+}YQ-sdn1$eqW&y&_;*D>SW{N zB!I8B=~lpb?f?nis(XxB{E`>nnWO2w`p=Kg5)$>8c)IS9sETuD-Zww}t-A~8iAe9X zpT<50mVbJ-95%Uo6R>t`ep8zKHFSKm^6CG+P5(PEeX1sf;t4AzGxe!abtzM)0&=5L zs==dW@urJ@ZUy|qN%Ydmkf)kOr}J>#p>ID}lB@Z8arH76(ll_1Ph%lk(EzK)Ik6t{ zaCdxN5mA&8wdj^&kJjzc_}!7&xr);=VV1WPxhXVydm%P!l;itc7&WJZce<1cN>QEK z@RNL9CJ3}8mw{#1lCEJ<=Fm!u;j_{Q+K4^+9H~og6ZR~_A+{_V>po0ww+}?UZpXhS zjt-k7o#b!xj$TnmTqY6A&v(U_@Q12f`bujm!kOI~1E*01O=i^1V3iRzLuLy$6-YO~ zNRq}ds7|4_C%*)~89s+2v8>2V)=u`_lTm2l;|J zG3$?h{O_dED{4QQf0(=6f;%Qpe3|HsJzelLz$5_IV9B6|MdNlw#pGYpi=ul-kwJJ% z$e<>CdqP3;dQ`qG{A_>@bylm)sIVx&x-p=plKt*l8@Y9nrpG$3&U@3UTNd*f(%YCg z#5{184c`Q0Lw7T<H;FT&xfi0Jg-l~l2bYPe z2b9AmuCr4ZgsfQ>5=wS#rvs^!!dq?PUyv*y=`xoBqHjDH0uiq-5J_tqTLyxwC~QpZ z`L=7wr`Tcg{uj18?8kiM_1cwod12QYkfHTNr{Roh!p$llh)8+lDk4^}CKIFq|5wS; zdnKHiDeho!>*O?`hr+l6-IRZ(hmz5SQSdQF&v<=S%f^I|z~EIu6J)eF{Yp%(2`@+3 z>dKqB+zeVBjJ%wj*wrfE+ab$wxO%W4Yc!bQZWdrxPc)Xnc!8|lfclGa2EHLtgQ089 z?0*Olxr-9x1YX% zR0M8Rf4+`&=pYLf{0B}%=|IKR-!S-ylT|Nx61y8v3dL!~E{|u_4=a2nvSAJcX*HrT z_Zij@0 z+vm6qmrDwKPynPv_R4COQ)QKMcjB^V?!7n-XQQQu(d28=bTqiHrBCCD?o+|B^`W)G z6aeF7t24==wzbUZoX2mwJ*oK3+qlDVUjAgcPjI-UwF^t8ihY+K>y+J%73mxJ*ua#W(XH8oa7)sUoHQ z7o=HO#MG!M#&{;`3+(Vj5O943-z*6UizbHSE=j7e{RKq)0bd4~zy)%k9BBr}_E`?J zpiqVWpqK+^J!;T<^k?Tide|$EJ}U?5TT1m98|p~LjsfzkcvRg0L{#ThNI*<2=%3o* z@rp!^q6zZbGl~GfA^XucV&pxAVCrY*J^zhO?+Anlw(xGur$!-#3c{=x5WcPbv^8QH z+;h8Up92uf{euWvkTdA)(O>P+qc`a7qHcaeXDb?`f1XchdiGnDXOlz^Z2y)>5B5P2 zDP@o0!i$8%1Ah*cx(fVCujg7E;@Ea`43Q~-|9+{zGvby1-v1o|VXdTN3&;o{8ok3( zI&hNM_IYU*eN=4B08$29{)q~j$bnGtn>p_#>%o_!n&$|xZSUuLF_hJ}k&;YAM6N8o zZkACHFTWRol<5GyO9b?!YX3w4g zu7vI~_)qM5JpgaXYbv!!V>~fQ3*-QjzUMCZx%QsP;N ztQzj9m{&3z3Mwe}WePTbk1Zkg$Z*2U5jQ*2(v+}ZrR51pho;UtKo&DS{3~Umeb*CM z!d^Ja_eFBFfToPo>#6+B^bo2YHbjPzR_5Z)zOI)R{nq+~mC~A@N-S+24(mQuW=VL5 z4bBC`P^5FLBc!M+^lf;Iw(N_sluv zkb-9rI@*+K%4Dj8!c)S#O0r*-=X_cLpi?diNp z*{7?3rv;=(3C_fPaYnQHKj8mVVaL$U4(kjONWizF-8m^@E_C_lbKe58rTerS&$<>- zxRZ|E=R3Ro0SR;prH4;M2Ub1-w3DOK#j%_ z2ZXvR3N3E2V;e$W`Sb2!SLA|vLB);Nopk;DIP;v~y2WST{nQ6s~pqxlBaO`HMP}CG4b)8SWp|nM}H|;CjrJ zrg&aSeS#OSc_Va}FYkS1ir4luv{}GbT8_UR+{v%iUJ!kiP;t%Aa+wmQ4x-JNLX>{E5c#Ac}mA4)qW+snh9GXdhul(k45e(x;P4moJN<}m!@GIcrM0@vvz~l0d(?ZNW zbg+-DKcTlLw;M|9yIa%A!cU_HO6-*rR}>Fx&(7QMhV?N4Mr0WPIfe}r#?_}79ils! z17%Y_faaU2;Z{&o=SMbj^SSZIgXv=^*|#JMRl}qLGJZy$bYE#fBrO1^So@&ThIV1x zVEqK0+q2x!rMJlVg=V^7xoKSI)uFy6bdd*CBK0xlXN-F5&y3nSBkBp7{p1gfS~1V4 zUx`~m9Q*rd8zm|THJEeJHm9d7v)qJl-|6DCB`9?Ds1*`kaR`>=Ys>@sP}kI8xvoWh zM?AQ#Xco6?l_>p95l|vy!T4xlOlwr33i4Ut5^9i1a0z&*n(q%Jcv2~?QPUt91wHt= zLl&th?I3Q+LRa91hm+IQw{SI8RR_R?O{wFi#RxuzqleWE_C1~<_eXLorWTM#>-R)U z39gG`tlmY~bhkLaZkRPYgx$5? z{`bI|kd^o<-f1jrHt8c&wy!oZWM3jn??sKp1d8!yh1Y1PH+G+5rX>RrprVkx3a*89 z;8yj2mm}RqKUEvogxSYjI+$0os=3qtW|XN1t&fpXc^n~UY+Ixz=xaDmFQ!Uoi`9j~ zgR}D~(g5tC-xx8WNQg%i7F|sRUAAp{JxD6eA6Uz6T|8GvTHIrU5(38dONZXU$#iv2 zPaa5{Uk1cK%M8U8!~qMEv_QH}(ca#nRoc|)}UuLN;GmEXBQr3@;^M?qg$t3Nv|7_s(oPwDBGi#nRkX%lIi9Q6>!J}*E-ldy=qNhpIg|M`K?t)%J zHRxLcw4h9Y?iTQMxQ*)ZYL|8M%gcdfyJyQA&zPF&OHK2IsVwqq(NTlATd$e*|GdZa zW_-ORoYPFJk*BFM15K&IP_3ND0l1X#jsfJBdZ|IMK!^T;v-`B-j%bxZ-*~Cc?4tAi z!L#dc?SHDgv;+`z0T6XDg|y4}4C3yX-020&L8ScU1`QA(t$;9fubQL2lRsCPj|nwb zUO^i5+n~HEolkbf0Ws2fwY0ZF0VKh_6(%q>V$4zz--PjSaDNpfbrM*`PUAR#@5=MB zU)Laj;7mhLOKlAS`fWFQD|Ywb0y=3T&&AuN1eUYYSp1vSTc%mK52FE@v|G^A(!j>h z^Cawyc;>IBbD6m7HjcvVaBxQ$F|hQe~w^)jYbRns)wpsEpku;RnE$S8TkDd$9>X#bQe7? z5ZX+HKt2|`a-pJ4^qQ@)Gn!2gNiIIb{%tQ@`C?v0Qd8M%=wq0{Q&+ByaB7FYbhOMq z*tFXa_1-Vykhv4VZdXG^`Ta^TWW2jb*=Wo0$3Cd=>q2yebqVvLV!!P=n>6N8uRHj9 zv~=aK7vvk`K*EsJ4qImrcTpj3Tc+-L#r~^vZ)VckEOO|Y(PF($3d<}53^3Q>L}s!hi%k3zWt5L!wDAu^ZOX%mo{S^Ze}hRmyVrMr zlOyOxZc5*LN6g=tcL9)ofHGE5cgH-C|Fi)d|9!~JRe+HvR9sn>( z=n)-zS+;NbGm7R-I6U5X1lwwI%H1lpjMW7vCrxYt(wuZGS-hRE@*cENZOPB03oEGy z|B+2kP%5Y1EzH-{zqvRsxS{)0-|8Q#8q=TQ1g?OzDoGtF?Wi59IjCJ(r$!c{s0YpT1W6g;s=iilb3fZyU>XE=2Kl}>65d#RK51t}_K zqjyzhpH;Vds=SQ zud|0<^_!UTzH(qUdsyN{=~u3m_#Z2>t6RkP$9`&VkHU=jqKMkpMEI6xL8*i3P!B!Jynvr-~&hi+1~J|Jv%$?5mgM_nykTne{pZNeO7b1xeBW%l?51Eg-;;uuQa zn_Z?F z$y|;#dHim%+}a@`%gQ^i+^McxxPD;hSO;T$H(SNQAlhbc(755cf>*VA`hlUUGVW_# zDu_szG@JvNnHxhSE9u}sR>bR_{lCnPRt-PwcNx%|_>T%Fxj(#a;_npqP(THOd`8R5 zev|M?v9^yD$O+;Gyb(-=%{^x=6o~(0SejwvJ&Jk(rIy4ngf5!-uFwsYPk3UM#c zkm@TO2RC=6o(zDeq{t&V&Gp?%YpnS~qo53_1fZK(kFeD0Z;OF?lkrfOToCAs-B*eo zmS;uO+C!db@8C5&Z_WvxI^AfXJ3Uk0j-hvh->D9mZ#M}7OwmH1cCUSN?Oq}baB0CG zCx?*U0H&GSFyl9z^DGJ%sx>k8T*Y@&8c1)cWkq~?Z@FLBzS+kDXqSsOYG`9Y)yc#- z@_geSCTQ9w`4UPBK-D^zSU5$dr#PpX1f%!7M$4}&w#rX>jzM4{5^YLB|;% zZky;Q8d?ATO>6kJX+#KDml z%^b{f{zqczW<4QS06KP08X;imlFYKGLfJhhl$-_?$F=m;XKv8KJFwyXyO9Y#n_8C% zYRi(i$~1rkP=qBkr94EYfJk=F)O^_ezJ7y!Zkw4ge;C}9ASPTFe5Cf8$a>LKLN%iR zh1ptek+0L_kbOc$QkSWy2mb|{#hLBPDxezPHu$k}Ga>_YN*A)7JfhNZcPHJwF^1>39}0d zr5UWlIYo;RU9uo_Y(M9Gi!)Q75M3emXHlQU2Y#+-jf3()UL4-`&OD?Ow+%c4B***J z`3L!D{|iK|$2~?G2Oy%{F5y=<+xwVav_KtI00W}XTA)sUe^L?>+$VgChtjZ$B8 z^Pf|yVkCb&=r;Q4QA>rYIrQxxfaL3e>F*QU(#T9!%Z179Gq`JD+3fcD8BdF;I~^g{ zozn?jkQA)S#I^q8LecY)Q4)mo?tz!x`?+)BID2BK%f7-RPZLi2%p74ME=UsNLg%H| zy5!*};EOycWyHjdw=yFpk_`+n4?m4TjsZ;OchP1plU$~#! zg_$FZQeg8&|H|opJ{u5Ezg$o}t>MqR{D^2n#P6lka9z9HdB~9a$Y@?Y6?JKgUe`|D z#D3F+^k#5%z_3wz{V6`?(?Gv9qs(+t8xgBV$V^4~b9euT@aw&xg3wnje0z;(->b;! zP$I{aTeI?8ZXQ*r+9v7CTl$J+BdSGj$eSvaMlgBN5iXF8Ag08;niF+Kx{!W)v)S=l zyR8keRcu^*?4J~!#xTz*wOz6zC%GIT+P|~*EtTk`?7F-2ZG8Gpi@$2)>m#R~0i1d{ z6rkF4*niQ)r^6*_qa175i3LYRani|z^h_X9Zdq@eN|~Stx97?1_!^)Dg^v0Q4YlAv zTgUB|r_kBgP5W0RCsunND@@+7zbO!Si?w^%q{pzdXWjdPq?XwRvhx`%78slQ#yV5mwhJcNqx)+YPM0zO zVU+_fhW>sWRJ_AzE{IdHsi#P+Id2ShSq3!jEr^hL+4Ko}1N+xHLiYl*N%stEPR^C_ z>o>isP3hwW#PZpzNa_vFji&IB^qipCJQy#hBRbUrvBJVVbJvKpK!O zr4gC%kX6C^;8E_XxWSyEbJIY1I757#uPZ>Cx#!phn@3&j@DJYoSBj}iZZ;>tc6FWF z!&N2zLo0uuo1J0n@SBpIK0HV35@A2{N_rw7mu^gJ`-gHVQZSysE2Zf$=CG){-kEFr zKW;IxxxiSH#)^AmyUiHx3k-phH@d;p(su9F3pJU(>y)8;>#%!gYJMdezKKZtR4KSK z<2DsRFG8cQ7wnsQH>9&=g97sO?32WpzZw0cES?|zBx($n0V8jlSEV|GZW=gWwsq&a zk0shRrWb=Z(^4&I+BgyGk7J(@^JAa-W)tvv%{CwZ#jK~+6pmrP^job{vV@udfBFV- zQ$XKYLO9=MCi4d>S@WHJeGC=QM)|ccaH>(7R&exEhT^L?81-Ci)*GbJY`)0nr@kdX zFqIKArZh)G=RCo!T&%uIFR1QtpZ>v}SC&jo`dCo0x&-L3<96G~;<2}Az<5PqY?6m{ zev5Jj|Gu;V@jO!cxiSHx?mGsEUsn{fUAeLBjEOv)1zqm5aH;)Og}x&vRzZDa;&?W* zG^~9`CNBO>YA9rVfufxZpBUR+eYV}H1rjTba)+7f=RrmB9BDW@(mx>T-35oB9^-*b z94tgR6Bl512NV*26rg@5c*_@|RX@RJ)0!}0`e&67g%xU4rwLPdH^mmcn0XYuI5?gX z95pT>q{mo2tx40%n134Xw4Po!w@j17dhW>AJRj`z{q#iqx*DnkMJD*RhPH~=1mk?a ze)ly~9`bjlyidf91WyD_Yl)6H$^cEes00s)Xm!@Sc=Oq z&e?t~y_$s2a+lR>we!*^3HvHDI?TRCMOW0vO&1U75PkF-TlU<6PE+k~d0==DgBa`RZk>mKh3$KrX2ASDr z0apgN@<#JBpnm7d8xPje^WH3i?rXx#_n92@eWuPAeWs)FYMTwDiBNV_T{||mutQab z*!0SnceX)S!+G?Ey15B6#UpqQ!gIP2Ccbte*yrlpUc5DSoqkNz0UR(M14AiJa0XuL zuVNu}Eaj*;oR~Ll;~XajXTO#jO~W4RE*l1BJ=M(EmU100>xieA9Tgi6D{fxMCpEMW z*L>C;FFC5g-&=`YkE1uY2j8=EFo4A`Ybt$ z0!s09A(oIWMc4j%+*54a+!{K#1e)`AlSm=a{e5AFU0Qcvk+uDod@0B+EOnXUuATyJ znGTH{{0_O(7tA6u6Duf`aXxStpBIl*9e!D%U&bkyu!1B$Z;p)e`3DiUTpZD&ubP{J{orp}Oh5n?cSTE1FhM27Y*-X)+c^#`#pVrJThv+%<@qBIR(TAG%mjR2< z*fd=Gs=Z`Tz4+O@QvEIle>qq5&3^n}j4*xCq~GcSG@G>Da~fbEzQS)ZwwgJALIVTD zj|5=Yz14&{M6(XvKFbX?118Pw=nnli8Xz?V z_@+{HS9?m4_|p(O45UPw510R{!phf{^3L&Gdgh}JIv*YLUD>J-drkJ!JlbL4VwUBA zRsYdyDizD6xAeX9`6=)nwpP3hFJA{pZ2OI1oMlAIThe1-dt*+<^C_EX>Z(NOHPs3I zNL7Q$Z|B^8)*MTPHO0GV(6#u_k_L};$cfFIsP9P4j#re|JVUR@J`>mPDm5D7UI}At zbmekRmYQL|au>I!7d7PjtKqu3={8(orV*wF^6K? zZs5E8#Qss6T@uT_Y$*!}y|Cac_HbZM1~gi(e@{K#5;RA>ty!EqTmY0O$AIa(ETYTi z+LUnJ31JZKE8YK^;QK$fufJaFyZO~#xtYI&{r*qCy>4sK{G5O4-=j8fs(9{xeO^?s znfgZbE!=nK^H<6L_#ItW`F+}N(0F~pY~Z30i&Yctcf8yf=5IBB?s8|d^DE|^7u3H0 z?oZAX?W(>PHDP&H>(2|WpVxD2g9mT-wJP8WP!AP<*A(8xQu?#6PCl3Z{H55;!>7+b zFiEG|Cs%Gk=kLS|#uCb!5ZHaVsHGW@f9H-9ei zJ8uZQxd6CYgrUJc_jk9$&*pg{KNr5;CjZ5)((3x{UZXQbHJ|>Tn)MWT1iH=B+wZ1t zjhl4#KKKZq#%t$ZtAjQ;gLjymwccKx8yGgvYv1x~Q|4aZ&5nAA5Au%Y!|N(-iu}$Q z-xfDo)!8$7Q875r|Ihiq9he|9KW+HHKY8LUVXexP+>$fG8M%x9ADVEo{`TKfRn>J$ zo6leS!1?;@iFMzP{hcVOA)o%{2=I`cy>+qAuX=5+`WF?qpEdPzd+TTSEdPm-c_*(p z+WWr=zMeht_2zF&x~gCAy9PWtFYQH5d8I{r@$TN6)*x zr?!6I+kZ)W|5fGRk)NBn{Y=c}sM@=$pYNRpJZ1cT*ERL)KT_VAzX@LsT8tO}e&;=3 z`(oJ&#QHhgU24FS=X=13uIzTb;0b$yKED2S)xY*%aru103OGHfVJC2vt=9K@-0Opf(e-&4MJtMV!@&3@wUC%%K zs{ZNz*y@`0+I6zIR-6cl}kiZhAy<~qC-E0W5 N!qe5yWt~$(69D_#Dn$SQ literal 0 HcmV?d00001 From 2327c1e9a3da96ecbbc4d3e0ff7da189fe1fb8d1 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 10:57:54 +1000 Subject: [PATCH 14/41] mistakenly added test result in wrong directory, so deleted --- test.png | Bin 26154 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 test.png diff --git a/test.png b/test.png deleted file mode 100644 index b677fded98d54b328278768f61fa765106153c60..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 26154 zcmeF3dtB0Y|MzWetFBh9c~!GgH?7>Va%t*R&~16DELP?@wUR>%C(AP+(AKq;|qUj_ySEB1K09x^a^=Pd(+MT?id3#>%6pyPl~i=qyBd}Tms-#7|9cpLHM z{x1y-%2StXj=cjsUiO`5K$L;O+RgfZi}oDaG-+S}WA1VN@^E~}ME|1nK|9L!uhmx9 z6wK>_^Mrw#)r$+4y>Ia4`IdT%uO4q*_O{ihpB7!ezD1*OVQS=?@tUVoggK2;CYsY4 zo}P^Ql9*rzUHKDYO#^$o)ZC!2P(?~DC0T%U#hfcucoSdgx?9PU$}@^}j|{KjSAVYh z=u*Vo#dy`??kfu0bgSB|4sdJA=tJtklr#%Ta@*`ZO<5$hp(91)-7-rZe7v(`oUbw} z;6Gqgt+wtpl--Rhu4f)BRD^SxxfU`(G&M(!#+lig6y&&KV6!l&ZB^DpRqouU-D>%_ zGtM!RgK`t7wcAM#hntx(QFET{W1vD?R}AY^W{qDIl8-M{E{c61g^6rRuo(e!;Y2Uo zX2{$4)lWY|K|CD%OxLd+-A59W+fFc#HD2|!(bi+KA*oD~$;(Sp1%+8xxbir(i<$CB z0@dT{mJ&M8OL4@4zIgl|XS#Vz7Cc&DlrbUg&d8K!;a6AJ-iFj{XJ%SN;w#rS9DweJ z_T3=*hi!#BG#Jl{AY5X!WQ{x>S8Z=3=XAB^Pb2qL;(wFq464eB%9Ob8vG4N{+&oI^ zSfc$;_hYDptA3gGRu{TlV`g(liDKw%P{Keiq6|FRV>xCswlgo=Rm6=tjJv`3q z2~_s-(vE|hj5+p-k7%j9y7aS@WL3sNQd_-I5MlKv;-R+^dAoV({QNYXF&gf0rJl&O zb>s-6k~5p?oHvxh#O7w;m=y$0eZ?N`#+*PinJcaAVifx@?>0QDtSfHf0xzm5;R8kL zY~|P}h;(75jY~Z@{IR3qhU9MBd%5P``=CV6tn=Ypve<^j+2YJABAY1BLdWf$b(myV znrl4RX}FXU1NDXQl!2o9fXo_Y{l8i30$NX^*g@Ie7m4DK$U|1bi!Buk?3OqZcsJbZY6!*eJdxA(aq`$rFX@== z2dobs%f~NU)p6TFf zzzx^tagneh83g3QM++|rTWxA*f719ec;?=OJEMw@-VM{UoV)L#tcdYjXtIV+nc0YF zvN-f(XTy|KB+6I|ZtZ(UL3 zI=ge9LM$O18YHrEy}sqPJdCiu&=%H@EES}aU0_Y>w@#%YzT!ep(tR5-P@&h{_~me% z+f`;+PWRr-oq;h&vW-a=wV?=tB(vW>#KN>FDRNq7mb#;x7}*zIMyVL{;0-yTiD(`u zGJm7EUwlyO4PBel)Pm{`qRETKF{U+E3L-g(E4yqAB^tWRCPSJF_f-^awBJyQB&#}N z&g>LfCyalD+B$mKWA>3QGUXE!I&xxl2g4v8lS-D z{1RruKcnVw9qxohZxL(SKH0sQw-nK#Ez=pR`>xSQD3(hr`mUqzG_qeFT>$JdF>TDZ zUDYYuQz-MDSjv-x++E5kmIW$F%_oY-dxNGqk&-}uC$OJw`cA!&HN16`@ym>QFE`F7=vbl4Wm&iE<15^2#w0M^>o@9Sz zGHE5CjtHP)$VHi;|9SOyd}qbS&u<+VopxeENfW zw8lf0G|gc3$_Q^^D1&quSg|dL@I(8}Jp1g1wd~)p@sY;nAZ9~R;Sq>X;e+BZd#ffm ziSj_HB0~yhGmv;mL5{h1>r(-&DXrzh70jje_uJPwPTwMfE#9oN}*EgL$& z$4qr>j_j;6P4dK3(9^$+ccD2Ey8=lutLWH+{Z8wfJhC$tn={TLuW$&Jx?VTQZMHS% z;$U(scV9GfDj_yUZ1AL~90r5RF*{^AE_;+xTAl4x4xcG7>dIy}k86io7AhdBgu2CKWlY4sohr~^co zf8fwi7lLTzQXkcrsSR`N>+G-Ax^&2lGBx)$R0X=U-lVN6;|2 zkB20m^%iE1XJyDUHErwFU6?|V4LqRv2Gc&G0-yB_%=7>`n{|qwkkgaY&nN4+m%~k{ zL|_4f+HRAoar@{UlOHSB9`DW=SLXSPdoufTrH1}uRhRq&6uS^vCyr|!{*TCCF}Ph( zlI!BMBgz>=G0RK!X)32^*6dQ8&;{p|DG!tn32*n*1tVvyCvxfDc5@O{ zKh2>ID(Wu~EwiUo3=mzXrGbUn-|+I+T3xVs!_gj&g2J;b&E)I8572CA>))^lU_t3= z(hA^{f#4k%CT^{!^()}(N9UU7HUmg$U?BOPBy?T{{DcBuR~l(2&ykkTFaHQEZ+Nzx za(h*peoguVu-3|DE`3}7NdIx#>5GG#KDlFMaV~@}0>GU%Ys5+Uj)Z+w-T> zuOuwe%%5)le;+M>wC?*C+Zlu!yms`bZxQBXty1cuh6kypE_i~R7wSULX|!t$K#KtE zj7Fl>x~@4B@bSpz@j`2Zb+~G@K_OtY<*_v6m)D#T>yv zFz4P4si#8V-W(2TTP55q)4_cN!sq;`NNx)*pymdrl(sQ)gIZ^U*mIZ0v?@Cc$x-nM zW|ezPZl+}(p$St{#Irm!U^H>?YW7CI9PBf6H9cyf4T*IwthJv~d5Aq?a&0*&@+a%Q zUnQH}HEn=K227C25nRC!(TuhiF#Yw+B_D5W+aZglx}u}Y*tg`k)aDA>4bbsEn!P* znZ41FutM;_>McL}r=M00RZY9LbGUbSiDgrb9kyan`Ebd@4q3)gf(|;dM-+0Zl{w=F0X`hZf4~2*#)UI4Parh zLv_{&rW9hwS%^!kcwy1WoU{(5*IK04Y(gV9^z9amE*A}s-k!rY8WpA?vdmk=-vk%R zG+>@Tx?W5#53F=Hd*IERRI~-)4=B^KNEYh!;$Utrq376x?OoM4GV`=H2S&G@*j~~TTT?1vbdzl zdrtTFNR0_7vSkqAE!h2tS zA{HQ14#|SIH$qj`EqW#L01{MA36NLuwdgB~@0oO%IrJnXaXc(bvTl^)P(7Q4JKtv$kWtJIH0s?C zPeYesznv$@X2eWvr1W!pgz3aLmAYm59j)d_>xux&Ffa{uoe=9)*xdLt`5C-P z9D8>?&VQ4*C-nc@s+0FBzKH#as=swEZI*nWrMt0@u;Jz)BT?Y2dOh z1R#s?FJ!In)eTQIJ2-H{LrY@Is%9>H(NL^94Z`hqQk?3td_antyd4XExW;kAK(^}w zv>N7XMQvS=45iB<4~&dp-A#G*VCImw)GzA@slvjiFeFEp&)(q8;>6VI60!TE#s>Ba zh;!_x{A3BPQulVwm(0}QYN|v^CQ)J2u8sNrsCS1t^aq)XAl15X>tw~^ie`BXf%qM& zpV!mb(boZr+`*y9O_UeDn2Zs8os6Znm0Jeci{JTiu7k5^BBu()R~bfMH;zy4K*NDI zOQbOdLYLcPcDr+WWrzZQY65YyShH-JG1kbSMKM|enk3$vd?tVg5g3(>d7| zCmR;7()A?Bexf+jjdF0FV@dAPxpCuWE@>uutuovLDy}FHS72J(RPkr3M8b*QSy|DSvz^K2}oVj#nt#e>kcwzlc;~VQtgU#$2s-ylTN+%w!Cy(xt#BCDI z$TIpbj0rkEcdjj~x5@z>F~lioa^a51RS{X}pEjY6REC`A`nUmi2Nv|WvjFi9b%_%H zT`bBCXvowA#u__;3*6cvp$J*uCDK&Qh-~tzmu@@+AvQ`LC~R#}*X0&P8TTP8iJ5X@ zSB?FeNW981Pjp?A#9<8SFvt&CuUI%Xv>3QVUuQ_{pivEsvHBTF6v3Bwei_R%L?v~* zV2CV-vfT4tu}``Uk->q`0&^#>M4r_3Ye4J={ig744-Udd=6`@^;}8)8*<_V!581^D zXSk+F%S?w#C{;g*&W}T&l5xRB{)eT`80W%tQv86g7?I;U`?w2}v(dgayO{m3pnnXN zJT?89gIykhD-7Dl4Ns*ZH}f@4VoPO??QF5!or5T7K?RTzk$q!C&kz$7@dU92gZ6?J z&~>h}#gwY!l^*=v*NfQ6>efwH}iu2a!pzK>k^8F)XrgTj+JWu(;W9 zLaDR02Q;)qoE#7QPWg1^lj@2OONmg9@LnK~GR{q~={{u!Cl&|N&N5PY_ zUc_zYHa%sgOzNyw>Q}!r)o5nXlk=$fD|_UuhvvxqvK6q*f42PHd-!wued5;vYXOxy zw~zE&%zyMPBK+S>rdPz-VSbA6OJReF*v^{vHI|C8yb;`(pfG%HFL7*RFtS?~RI9N2 z3XVOFf4U=6VSjRl=q8L=%Q7_fW*OebF+$STLu~6_5$9`S>5NIz*v*m8t>!3*AJp!{ zly*P%jwYYs5H=a6CJ!%pxQCftz_}sWU$5&>9V~#e>0ykgZjbKPgOfpD2m)!0Yi*#_ zA!|pkV`ZBJR^v?sGN{vrFUZ`+y?g@4lRbiE*A6ePJ~(8Y(lSedmN!DxfXo?*dL252 zPd4y2-d0=FYW+e`W@^sK#P*C)2u*I(*DGHAN>(*+nxcbfsst<}Q9@!_i}$-LNFE$*{`pGvn;8?A{gt2a|hG39`cY?;y^!QnrCVX+8;s9 zk<=W3FM0SICdG+}qBf1JCz%(@a)75lu1q2`mtK-yQ5>n4(G>9-Kwe!{}yOy}Ifi7PF%KpR#HB7#+9G zLe<9o$Su;yY(Ib8QXZ^+BJbNtx~DZ~kOQe6*IiiLpshJi0g-hzYaysL ztL1*S;Y~_yE5$YlRen+lVja2QIBm5&?aQ2)h-hNGh#Zm?u04>m!H>Z*f=rw_Ywae< z$?RVrx`q|`L)8w;!WjIfAi|x*xT8x1^j?AD{7BR>um$&N37`z`#RD(E?_g4Sos*Gf zAY$_sl2(1^qoppgeBLS5=?`68CVp1zlhYFS)kajWPl2h z#!ALEJ~CLoro*Akfjo7u7Tzs{D?~+tB|2R(X+s=4NagmC3rID2|E;G-xgQ2>D7N5! zxbOPxrDkXNC_aSH5Zg)gOv7$@hMje+ZPuwVR5E5?rS9K>Bn3!#K8k7UasgyW+v1&9 zVa+vH!&QhTsMyj>3uBl0mLzqQ4AhRto5nDWrB0&ZFn1oMkDk=A5mW`LJ&bKRMK7=A zmY4=-iw4od!LsI4k!6bTq%o2iy&2?-pBHJ}8Zrv1trRJgATE;Dbc2aF?6RTr*sdj< z?Qf7_TNPO7Vd0mv>={l9i|gmAYRwHGA2urVX^KfBeOfx`49KVxWN!|MWjJcZ@+wWy zxcwaR@Wd~IE&dz4jds@~%@1>$Rsw>aUWz0*oNeUAl25rsOCd1kvDA8XCYu)wX!8%h zbZM;{{+O>g?rRlA@2JUhlTNg&%_ed!dU)4vn>;juS5@%F|o7HrF?i~BG z682LH8oEm25+quhT5g?;fs2aaou3yi{WX02t!H!J@DwfprNU zvFy7u2@rA1UG;Tr03%Ks^)8|firJ?NXb%=Q&$21UU~D4*IIWxzb}poWQ(gorz$o)) z3zo$X=I0|St$dug`d37T)`-b0j7erq2<33t@9g61c2w#`$J+GFc8aWZsQo7ut`8^eG z{0=ULfYK(gf!mfVxMe&<)5I6gFqTjS={MC%euV^6-6))%jI9!~IuPra>y+!!KE$sO zm;-;&8rnCMoRT(goVhr9W>kub#x`si5gm;Mc-dma%V~0bq0Ch*m-ma~A9Qk9B~>Dh zV|j=eO_&D%TocFU0EC*a0Tm~ee`lrLmOOk*w7BewXs>fnG^Ab)X#Za#ddL8v+VJ;L z%;`l1$f{ve_S6h6RIV3%dv*E#R5>NK;cmeHIYbrQ>DgD~j;p^72)pYUbKIyVK!OE` z(76|dn7DSN&{^ipqaOBZ&Su@_-(ZGmFky-+^ofMe2L$f?bAL^pK$yZti(sa#o1!N=*N8TB|u znx=65pE-5&G(O0-8r4i`@NCs&g1K}rAtwLJjj2PK%v;EJE1Jme5-@aExZLsZ{M?Y7Pq=lnpci~+dPy27ZA9;7Aj z3^Cc#1ebACSR^ZV;$SR!Qyu~FiaN`@mD@|@UMWHShOE{BwjtX)z$!Jd%qEYSUUah3 zgTBo*wRKqdn(i8w^<$C>y8sh|dxa&bZL9g&V;FP&t=7N5|BBe&SYpAK*_U%GEbDBA z7Qb@k#p2C^Tn}C>PnyL>Z?F^vO(W`YK2Y8d3I_?Op#}-i( zuIrcA!J=YE1F7BSy>*rlcfZ*Kg*9{sa{wSZT5lo`T*&?{!#LZ)6zrGAZYmk@_tX6= z7rTq#x1|(vlbK0v8RPBr7_)kdZq1wk0P{5G>;i)7Qt8~2dDc|i=AcMRp7CaH5D5)cl)t?DogvcTDl*^Y@hVZYptnmI*XA$GzCX`m3V+N+CoV=r%W;XIxip7b;#p6V2eziS zl4;Te_e#aytPzpx%^eri7_FJ@!2$-nR^*tYT`Q~}UMk2=>t;-2G7b`uUe~jrVpa>L zriy2N$grD_ypE-0@E65CL7<^>C1s{Pr9)0{tJD8gc{KK}%TT4pm)NZ22R?O7vlUba za1L0AFy`YN65z)7S&nU1e}}RjBauzZr(0#d`?LGCiy{os(vs>WX*f7zRCNO|RF(!S z0D*X`nbuJ>-Y!KSr#iFkDd@(Y3<~3lbD;?x%?ghw<+c*p8LLM-cCHdX4?UX$Di%i3?H!YsweEtSRMXh5r zaltqLD4gz>-XbHDo!4=rjwe&kLyK<=R#9TQ{ai)A4&Q7v6zBR7@_-*O=&Z7`0!}LK ze__;5tWc6M09ot)(?_j3r8NW6nv#k7|ZGuJw4QJT_GT0fj%Iqz8KbYZ>i`S+YnP7-m;AJDS1lZT^rfl)GwMVWh_*zL9R)qy z6qEQ$BZa=Ek!JmmHB!B)pYi#HkfI7n9?fr7wq1#EK>0WA#gpgd*Tv-E2J^b%V;JXe zqVdrO;DdNyPEKa|2Z#rZGC`ff;fxL3nz4Cqn+S-jsNuX{X~XZmMBb^-uQPckHUbi> z#(MYsb0Cp3CYsGBMnVA!-4Am72qL`Sj@u}oR>?HeN3BIGqMaM95q9#jfPnas zjX&p$bwo=?kL%98>R2x0=a_l@Q#f6$u-vCd#*%|nFGBnECV!Fe9BC)I9x%+AMlN$5 z432*9uN#b|yA<78vfKkX6l3RD#M04piRv#G#!ui_0docDW{Uo$ct63(mnFe6zUE?jk;>GGp&tj0qeZx&iq zC7OF%#mEIK=GE2y`kI-FfF14a9&RyxOBD|k(Yu;D6*%Bn(%Em-QO6nFx*p|9M92K` zavZEH);OsK)_uRONBLEhv|!bk;b85|?<{=aH4+OwpG%+r(88w;VoxnbTEPy?_;dS#sd9Yu7#m1!`2<#i_E!ZhB zV*dol2VK2i_=tPF43iEf0P1fYEl7sm8b&+{OV~ZT^`D#f4QUmtAV8Yn4c2|~aJj1e za1;60VW)!K>)8ctBNh{x9CAH4DzJVValE=sIZ|^z+}YQ-sdn1$eqW&y&_;*D>SW{N zB!I8B=~lpb?f?nis(XxB{E`>nnWO2w`p=Kg5)$>8c)IS9sETuD-Zww}t-A~8iAe9X zpT<50mVbJ-95%Uo6R>t`ep8zKHFSKm^6CG+P5(PEeX1sf;t4AzGxe!abtzM)0&=5L zs==dW@urJ@ZUy|qN%Ydmkf)kOr}J>#p>ID}lB@Z8arH76(ll_1Ph%lk(EzK)Ik6t{ zaCdxN5mA&8wdj^&kJjzc_}!7&xr);=VV1WPxhXVydm%P!l;itc7&WJZce<1cN>QEK z@RNL9CJ3}8mw{#1lCEJ<=Fm!u;j_{Q+K4^+9H~og6ZR~_A+{_V>po0ww+}?UZpXhS zjt-k7o#b!xj$TnmTqY6A&v(U_@Q12f`bujm!kOI~1E*01O=i^1V3iRzLuLy$6-YO~ zNRq}ds7|4_C%*)~89s+2v8>2V)=u`_lTm2l;|J zG3$?h{O_dED{4QQf0(=6f;%Qpe3|HsJzelLz$5_IV9B6|MdNlw#pGYpi=ul-kwJJ% z$e<>CdqP3;dQ`qG{A_>@bylm)sIVx&x-p=plKt*l8@Y9nrpG$3&U@3UTNd*f(%YCg z#5{184c`Q0Lw7T<H;FT&xfi0Jg-l~l2bYPe z2b9AmuCr4ZgsfQ>5=wS#rvs^!!dq?PUyv*y=`xoBqHjDH0uiq-5J_tqTLyxwC~QpZ z`L=7wr`Tcg{uj18?8kiM_1cwod12QYkfHTNr{Roh!p$llh)8+lDk4^}CKIFq|5wS; zdnKHiDeho!>*O?`hr+l6-IRZ(hmz5SQSdQF&v<=S%f^I|z~EIu6J)eF{Yp%(2`@+3 z>dKqB+zeVBjJ%wj*wrfE+ab$wxO%W4Yc!bQZWdrxPc)Xnc!8|lfclGa2EHLtgQ089 z?0*Olxr-9x1YX% zR0M8Rf4+`&=pYLf{0B}%=|IKR-!S-ylT|Nx61y8v3dL!~E{|u_4=a2nvSAJcX*HrT z_Zij@0 z+vm6qmrDwKPynPv_R4COQ)QKMcjB^V?!7n-XQQQu(d28=bTqiHrBCCD?o+|B^`W)G z6aeF7t24==wzbUZoX2mwJ*oK3+qlDVUjAgcPjI-UwF^t8ihY+K>y+J%73mxJ*ua#W(XH8oa7)sUoHQ z7o=HO#MG!M#&{;`3+(Vj5O943-z*6UizbHSE=j7e{RKq)0bd4~zy)%k9BBr}_E`?J zpiqVWpqK+^J!;T<^k?Tide|$EJ}U?5TT1m98|p~LjsfzkcvRg0L{#ThNI*<2=%3o* z@rp!^q6zZbGl~GfA^XucV&pxAVCrY*J^zhO?+Anlw(xGur$!-#3c{=x5WcPbv^8QH z+;h8Up92uf{euWvkTdA)(O>P+qc`a7qHcaeXDb?`f1XchdiGnDXOlz^Z2y)>5B5P2 zDP@o0!i$8%1Ah*cx(fVCujg7E;@Ea`43Q~-|9+{zGvby1-v1o|VXdTN3&;o{8ok3( zI&hNM_IYU*eN=4B08$29{)q~j$bnGtn>p_#>%o_!n&$|xZSUuLF_hJ}k&;YAM6N8o zZkACHFTWRol<5GyO9b?!YX3w4g zu7vI~_)qM5JpgaXYbv!!V>~fQ3*-QjzUMCZx%QsP;N ztQzj9m{&3z3Mwe}WePTbk1Zkg$Z*2U5jQ*2(v+}ZrR51pho;UtKo&DS{3~Umeb*CM z!d^Ja_eFBFfToPo>#6+B^bo2YHbjPzR_5Z)zOI)R{nq+~mC~A@N-S+24(mQuW=VL5 z4bBC`P^5FLBc!M+^lf;Iw(N_sluv zkb-9rI@*+K%4Dj8!c)S#O0r*-=X_cLpi?diNp z*{7?3rv;=(3C_fPaYnQHKj8mVVaL$U4(kjONWizF-8m^@E_C_lbKe58rTerS&$<>- zxRZ|E=R3Ro0SR;prH4;M2Ub1-w3DOK#j%_ z2ZXvR3N3E2V;e$W`Sb2!SLA|vLB);Nopk;DIP;v~y2WST{nQ6s~pqxlBaO`HMP}CG4b)8SWp|nM}H|;CjrJ zrg&aSeS#OSc_Va}FYkS1ir4luv{}GbT8_UR+{v%iUJ!kiP;t%Aa+wmQ4x-JNLX>{E5c#Ac}mA4)qW+snh9GXdhul(k45e(x;P4moJN<}m!@GIcrM0@vvz~l0d(?ZNW zbg+-DKcTlLw;M|9yIa%A!cU_HO6-*rR}>Fx&(7QMhV?N4Mr0WPIfe}r#?_}79ils! z17%Y_faaU2;Z{&o=SMbj^SSZIgXv=^*|#JMRl}qLGJZy$bYE#fBrO1^So@&ThIV1x zVEqK0+q2x!rMJlVg=V^7xoKSI)uFy6bdd*CBK0xlXN-F5&y3nSBkBp7{p1gfS~1V4 zUx`~m9Q*rd8zm|THJEeJHm9d7v)qJl-|6DCB`9?Ds1*`kaR`>=Ys>@sP}kI8xvoWh zM?AQ#Xco6?l_>p95l|vy!T4xlOlwr33i4Ut5^9i1a0z&*n(q%Jcv2~?QPUt91wHt= zLl&th?I3Q+LRa91hm+IQw{SI8RR_R?O{wFi#RxuzqleWE_C1~<_eXLorWTM#>-R)U z39gG`tlmY~bhkLaZkRPYgx$5? z{`bI|kd^o<-f1jrHt8c&wy!oZWM3jn??sKp1d8!yh1Y1PH+G+5rX>RrprVkx3a*89 z;8yj2mm}RqKUEvogxSYjI+$0os=3qtW|XN1t&fpXc^n~UY+Ixz=xaDmFQ!Uoi`9j~ zgR}D~(g5tC-xx8WNQg%i7F|sRUAAp{JxD6eA6Uz6T|8GvTHIrU5(38dONZXU$#iv2 zPaa5{Uk1cK%M8U8!~qMEv_QH}(ca#nRoc|)}UuLN;GmEXBQr3@;^M?qg$t3Nv|7_s(oPwDBGi#nRkX%lIi9Q6>!J}*E-ldy=qNhpIg|M`K?t)%J zHRxLcw4h9Y?iTQMxQ*)ZYL|8M%gcdfyJyQA&zPF&OHK2IsVwqq(NTlATd$e*|GdZa zW_-ORoYPFJk*BFM15K&IP_3ND0l1X#jsfJBdZ|IMK!^T;v-`B-j%bxZ-*~Cc?4tAi z!L#dc?SHDgv;+`z0T6XDg|y4}4C3yX-020&L8ScU1`QA(t$;9fubQL2lRsCPj|nwb zUO^i5+n~HEolkbf0Ws2fwY0ZF0VKh_6(%q>V$4zz--PjSaDNpfbrM*`PUAR#@5=MB zU)Laj;7mhLOKlAS`fWFQD|Ywb0y=3T&&AuN1eUYYSp1vSTc%mK52FE@v|G^A(!j>h z^Cawyc;>IBbD6m7HjcvVaBxQ$F|hQe~w^)jYbRns)wpsEpku;RnE$S8TkDd$9>X#bQe7? z5ZX+HKt2|`a-pJ4^qQ@)Gn!2gNiIIb{%tQ@`C?v0Qd8M%=wq0{Q&+ByaB7FYbhOMq z*tFXa_1-Vykhv4VZdXG^`Ta^TWW2jb*=Wo0$3Cd=>q2yebqVvLV!!P=n>6N8uRHj9 zv~=aK7vvk`K*EsJ4qImrcTpj3Tc+-L#r~^vZ)VckEOO|Y(PF($3d<}53^3Q>L}s!hi%k3zWt5L!wDAu^ZOX%mo{S^Ze}hRmyVrMr zlOyOxZc5*LN6g=tcL9)ofHGE5cgH-C|Fi)d|9!~JRe+HvR9sn>( z=n)-zS+;NbGm7R-I6U5X1lwwI%H1lpjMW7vCrxYt(wuZGS-hRE@*cENZOPB03oEGy z|B+2kP%5Y1EzH-{zqvRsxS{)0-|8Q#8q=TQ1g?OzDoGtF?Wi59IjCJ(r$!c{s0YpT1W6g;s=iilb3fZyU>XE=2Kl}>65d#RK51t}_K zqjyzhpH;Vds=SQ zud|0<^_!UTzH(qUdsyN{=~u3m_#Z2>t6RkP$9`&VkHU=jqKMkpMEI6xL8*i3P!B!Jynvr-~&hi+1~J|Jv%$?5mgM_nykTne{pZNeO7b1xeBW%l?51Eg-;;uuQa zn_Z?F z$y|;#dHim%+}a@`%gQ^i+^McxxPD;hSO;T$H(SNQAlhbc(755cf>*VA`hlUUGVW_# zDu_szG@JvNnHxhSE9u}sR>bR_{lCnPRt-PwcNx%|_>T%Fxj(#a;_npqP(THOd`8R5 zev|M?v9^yD$O+;Gyb(-=%{^x=6o~(0SejwvJ&Jk(rIy4ngf5!-uFwsYPk3UM#c zkm@TO2RC=6o(zDeq{t&V&Gp?%YpnS~qo53_1fZK(kFeD0Z;OF?lkrfOToCAs-B*eo zmS;uO+C!db@8C5&Z_WvxI^AfXJ3Uk0j-hvh->D9mZ#M}7OwmH1cCUSN?Oq}baB0CG zCx?*U0H&GSFyl9z^DGJ%sx>k8T*Y@&8c1)cWkq~?Z@FLBzS+kDXqSsOYG`9Y)yc#- z@_geSCTQ9w`4UPBK-D^zSU5$dr#PpX1f%!7M$4}&w#rX>jzM4{5^YLB|;% zZky;Q8d?ATO>6kJX+#KDml z%^b{f{zqczW<4QS06KP08X;imlFYKGLfJhhl$-_?$F=m;XKv8KJFwyXyO9Y#n_8C% zYRi(i$~1rkP=qBkr94EYfJk=F)O^_ezJ7y!Zkw4ge;C}9ASPTFe5Cf8$a>LKLN%iR zh1ptek+0L_kbOc$QkSWy2mb|{#hLBPDxezPHu$k}Ga>_YN*A)7JfhNZcPHJwF^1>39}0d zr5UWlIYo;RU9uo_Y(M9Gi!)Q75M3emXHlQU2Y#+-jf3()UL4-`&OD?Ow+%c4B***J z`3L!D{|iK|$2~?G2Oy%{F5y=<+xwVav_KtI00W}XTA)sUe^L?>+$VgChtjZ$B8 z^Pf|yVkCb&=r;Q4QA>rYIrQxxfaL3e>F*QU(#T9!%Z179Gq`JD+3fcD8BdF;I~^g{ zozn?jkQA)S#I^q8LecY)Q4)mo?tz!x`?+)BID2BK%f7-RPZLi2%p74ME=UsNLg%H| zy5!*};EOycWyHjdw=yFpk_`+n4?m4TjsZ;OchP1plU$~#! zg_$FZQeg8&|H|opJ{u5Ezg$o}t>MqR{D^2n#P6lka9z9HdB~9a$Y@?Y6?JKgUe`|D z#D3F+^k#5%z_3wz{V6`?(?Gv9qs(+t8xgBV$V^4~b9euT@aw&xg3wnje0z;(->b;! zP$I{aTeI?8ZXQ*r+9v7CTl$J+BdSGj$eSvaMlgBN5iXF8Ag08;niF+Kx{!W)v)S=l zyR8keRcu^*?4J~!#xTz*wOz6zC%GIT+P|~*EtTk`?7F-2ZG8Gpi@$2)>m#R~0i1d{ z6rkF4*niQ)r^6*_qa175i3LYRani|z^h_X9Zdq@eN|~Stx97?1_!^)Dg^v0Q4YlAv zTgUB|r_kBgP5W0RCsunND@@+7zbO!Si?w^%q{pzdXWjdPq?XwRvhx`%78slQ#yV5mwhJcNqx)+YPM0zO zVU+_fhW>sWRJ_AzE{IdHsi#P+Id2ShSq3!jEr^hL+4Ko}1N+xHLiYl*N%stEPR^C_ z>o>isP3hwW#PZpzNa_vFji&IB^qipCJQy#hBRbUrvBJVVbJvKpK!O zr4gC%kX6C^;8E_XxWSyEbJIY1I757#uPZ>Cx#!phn@3&j@DJYoSBj}iZZ;>tc6FWF z!&N2zLo0uuo1J0n@SBpIK0HV35@A2{N_rw7mu^gJ`-gHVQZSysE2Zf$=CG){-kEFr zKW;IxxxiSH#)^AmyUiHx3k-phH@d;p(su9F3pJU(>y)8;>#%!gYJMdezKKZtR4KSK z<2DsRFG8cQ7wnsQH>9&=g97sO?32WpzZw0cES?|zBx($n0V8jlSEV|GZW=gWwsq&a zk0shRrWb=Z(^4&I+BgyGk7J(@^JAa-W)tvv%{CwZ#jK~+6pmrP^job{vV@udfBFV- zQ$XKYLO9=MCi4d>S@WHJeGC=QM)|ccaH>(7R&exEhT^L?81-Ci)*GbJY`)0nr@kdX zFqIKArZh)G=RCo!T&%uIFR1QtpZ>v}SC&jo`dCo0x&-L3<96G~;<2}Az<5PqY?6m{ zev5Jj|Gu;V@jO!cxiSHx?mGsEUsn{fUAeLBjEOv)1zqm5aH;)Og}x&vRzZDa;&?W* zG^~9`CNBO>YA9rVfufxZpBUR+eYV}H1rjTba)+7f=RrmB9BDW@(mx>T-35oB9^-*b z94tgR6Bl512NV*26rg@5c*_@|RX@RJ)0!}0`e&67g%xU4rwLPdH^mmcn0XYuI5?gX z95pT>q{mo2tx40%n134Xw4Po!w@j17dhW>AJRj`z{q#iqx*DnkMJD*RhPH~=1mk?a ze)ly~9`bjlyidf91WyD_Yl)6H$^cEes00s)Xm!@Sc=Oq z&e?t~y_$s2a+lR>we!*^3HvHDI?TRCMOW0vO&1U75PkF-TlU<6PE+k~d0==DgBa`RZk>mKh3$KrX2ASDr z0apgN@<#JBpnm7d8xPje^WH3i?rXx#_n92@eWuPAeWs)FYMTwDiBNV_T{||mutQab z*!0SnceX)S!+G?Ey15B6#UpqQ!gIP2Ccbte*yrlpUc5DSoqkNz0UR(M14AiJa0XuL zuVNu}Eaj*;oR~Ll;~XajXTO#jO~W4RE*l1BJ=M(EmU100>xieA9Tgi6D{fxMCpEMW z*L>C;FFC5g-&=`YkE1uY2j8=EFo4A`Ybt$ z0!s09A(oIWMc4j%+*54a+!{K#1e)`AlSm=a{e5AFU0Qcvk+uDod@0B+EOnXUuATyJ znGTH{{0_O(7tA6u6Duf`aXxStpBIl*9e!D%U&bkyu!1B$Z;p)e`3DiUTpZD&ubP{J{orp}Oh5n?cSTE1FhM27Y*-X)+c^#`#pVrJThv+%<@qBIR(TAG%mjR2< z*fd=Gs=Z`Tz4+O@QvEIle>qq5&3^n}j4*xCq~GcSG@G>Da~fbEzQS)ZwwgJALIVTD zj|5=Yz14&{M6(XvKFbX?118Pw=nnli8Xz?V z_@+{HS9?m4_|p(O45UPw510R{!phf{^3L&Gdgh}JIv*YLUD>J-drkJ!JlbL4VwUBA zRsYdyDizD6xAeX9`6=)nwpP3hFJA{pZ2OI1oMlAIThe1-dt*+<^C_EX>Z(NOHPs3I zNL7Q$Z|B^8)*MTPHO0GV(6#u_k_L};$cfFIsP9P4j#re|JVUR@J`>mPDm5D7UI}At zbmekRmYQL|au>I!7d7PjtKqu3={8(orV*wF^6K? zZs5E8#Qss6T@uT_Y$*!}y|Cac_HbZM1~gi(e@{K#5;RA>ty!EqTmY0O$AIa(ETYTi z+LUnJ31JZKE8YK^;QK$fufJaFyZO~#xtYI&{r*qCy>4sK{G5O4-=j8fs(9{xeO^?s znfgZbE!=nK^H<6L_#ItW`F+}N(0F~pY~Z30i&Yctcf8yf=5IBB?s8|d^DE|^7u3H0 z?oZAX?W(>PHDP&H>(2|WpVxD2g9mT-wJP8WP!AP<*A(8xQu?#6PCl3Z{H55;!>7+b zFiEG|Cs%Gk=kLS|#uCb!5ZHaVsHGW@f9H-9ei zJ8uZQxd6CYgrUJc_jk9$&*pg{KNr5;CjZ5)((3x{UZXQbHJ|>Tn)MWT1iH=B+wZ1t zjhl4#KKKZq#%t$ZtAjQ;gLjymwccKx8yGgvYv1x~Q|4aZ&5nAA5Au%Y!|N(-iu}$Q z-xfDo)!8$7Q875r|Ihiq9he|9KW+HHKY8LUVXexP+>$fG8M%x9ADVEo{`TKfRn>J$ zo6leS!1?;@iFMzP{hcVOA)o%{2=I`cy>+qAuX=5+`WF?qpEdPzd+TTSEdPm-c_*(p z+WWr=zMeht_2zF&x~gCAy9PWtFYQH5d8I{r@$TN6)*x zr?!6I+kZ)W|5fGRk)NBn{Y=c}sM@=$pYNRpJZ1cT*ERL)KT_VAzX@LsT8tO}e&;=3 z`(oJ&#QHhgU24FS=X=13uIzTb;0b$yKED2S)xY*%aru103OGHfVJC2vt=9K@-0Opf(e-&4MJtMV!@&3@wUC%%K zs{ZNz*y@`0+I6zIR-6cl}kiZhAy<~qC-E0W5 N!qe5yWt~$(69D_#Dn$SQ From 562404978cdc7990dc87fb07fb6619357bad9249 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 10:58:54 +1000 Subject: [PATCH 15/41] accuracy and loss values after 200 epochs on test set --- recognition/MySolution/s47539934-GCN/test.png | Bin 0 -> 26154 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 recognition/MySolution/s47539934-GCN/test.png diff --git a/recognition/MySolution/s47539934-GCN/test.png b/recognition/MySolution/s47539934-GCN/test.png new file mode 100644 index 0000000000000000000000000000000000000000..b677fded98d54b328278768f61fa765106153c60 GIT binary patch literal 26154 zcmeF3dtB0Y|MzWetFBh9c~!GgH?7>Va%t*R&~16DELP?@wUR>%C(AP+(AKq;|qUj_ySEB1K09x^a^=Pd(+MT?id3#>%6pyPl~i=qyBd}Tms-#7|9cpLHM z{x1y-%2StXj=cjsUiO`5K$L;O+RgfZi}oDaG-+S}WA1VN@^E~}ME|1nK|9L!uhmx9 z6wK>_^Mrw#)r$+4y>Ia4`IdT%uO4q*_O{ihpB7!ezD1*OVQS=?@tUVoggK2;CYsY4 zo}P^Ql9*rzUHKDYO#^$o)ZC!2P(?~DC0T%U#hfcucoSdgx?9PU$}@^}j|{KjSAVYh z=u*Vo#dy`??kfu0bgSB|4sdJA=tJtklr#%Ta@*`ZO<5$hp(91)-7-rZe7v(`oUbw} z;6Gqgt+wtpl--Rhu4f)BRD^SxxfU`(G&M(!#+lig6y&&KV6!l&ZB^DpRqouU-D>%_ zGtM!RgK`t7wcAM#hntx(QFET{W1vD?R}AY^W{qDIl8-M{E{c61g^6rRuo(e!;Y2Uo zX2{$4)lWY|K|CD%OxLd+-A59W+fFc#HD2|!(bi+KA*oD~$;(Sp1%+8xxbir(i<$CB z0@dT{mJ&M8OL4@4zIgl|XS#Vz7Cc&DlrbUg&d8K!;a6AJ-iFj{XJ%SN;w#rS9DweJ z_T3=*hi!#BG#Jl{AY5X!WQ{x>S8Z=3=XAB^Pb2qL;(wFq464eB%9Ob8vG4N{+&oI^ zSfc$;_hYDptA3gGRu{TlV`g(liDKw%P{Keiq6|FRV>xCswlgo=Rm6=tjJv`3q z2~_s-(vE|hj5+p-k7%j9y7aS@WL3sNQd_-I5MlKv;-R+^dAoV({QNYXF&gf0rJl&O zb>s-6k~5p?oHvxh#O7w;m=y$0eZ?N`#+*PinJcaAVifx@?>0QDtSfHf0xzm5;R8kL zY~|P}h;(75jY~Z@{IR3qhU9MBd%5P``=CV6tn=Ypve<^j+2YJABAY1BLdWf$b(myV znrl4RX}FXU1NDXQl!2o9fXo_Y{l8i30$NX^*g@Ie7m4DK$U|1bi!Buk?3OqZcsJbZY6!*eJdxA(aq`$rFX@== z2dobs%f~NU)p6TFf zzzx^tagneh83g3QM++|rTWxA*f719ec;?=OJEMw@-VM{UoV)L#tcdYjXtIV+nc0YF zvN-f(XTy|KB+6I|ZtZ(UL3 zI=ge9LM$O18YHrEy}sqPJdCiu&=%H@EES}aU0_Y>w@#%YzT!ep(tR5-P@&h{_~me% z+f`;+PWRr-oq;h&vW-a=wV?=tB(vW>#KN>FDRNq7mb#;x7}*zIMyVL{;0-yTiD(`u zGJm7EUwlyO4PBel)Pm{`qRETKF{U+E3L-g(E4yqAB^tWRCPSJF_f-^awBJyQB&#}N z&g>LfCyalD+B$mKWA>3QGUXE!I&xxl2g4v8lS-D z{1RruKcnVw9qxohZxL(SKH0sQw-nK#Ez=pR`>xSQD3(hr`mUqzG_qeFT>$JdF>TDZ zUDYYuQz-MDSjv-x++E5kmIW$F%_oY-dxNGqk&-}uC$OJw`cA!&HN16`@ym>QFE`F7=vbl4Wm&iE<15^2#w0M^>o@9Sz zGHE5CjtHP)$VHi;|9SOyd}qbS&u<+VopxeENfW zw8lf0G|gc3$_Q^^D1&quSg|dL@I(8}Jp1g1wd~)p@sY;nAZ9~R;Sq>X;e+BZd#ffm ziSj_HB0~yhGmv;mL5{h1>r(-&DXrzh70jje_uJPwPTwMfE#9oN}*EgL$& z$4qr>j_j;6P4dK3(9^$+ccD2Ey8=lutLWH+{Z8wfJhC$tn={TLuW$&Jx?VTQZMHS% z;$U(scV9GfDj_yUZ1AL~90r5RF*{^AE_;+xTAl4x4xcG7>dIy}k86io7AhdBgu2CKWlY4sohr~^co zf8fwi7lLTzQXkcrsSR`N>+G-Ax^&2lGBx)$R0X=U-lVN6;|2 zkB20m^%iE1XJyDUHErwFU6?|V4LqRv2Gc&G0-yB_%=7>`n{|qwkkgaY&nN4+m%~k{ zL|_4f+HRAoar@{UlOHSB9`DW=SLXSPdoufTrH1}uRhRq&6uS^vCyr|!{*TCCF}Ph( zlI!BMBgz>=G0RK!X)32^*6dQ8&;{p|DG!tn32*n*1tVvyCvxfDc5@O{ zKh2>ID(Wu~EwiUo3=mzXrGbUn-|+I+T3xVs!_gj&g2J;b&E)I8572CA>))^lU_t3= z(hA^{f#4k%CT^{!^()}(N9UU7HUmg$U?BOPBy?T{{DcBuR~l(2&ykkTFaHQEZ+Nzx za(h*peoguVu-3|DE`3}7NdIx#>5GG#KDlFMaV~@}0>GU%Ys5+Uj)Z+w-T> zuOuwe%%5)le;+M>wC?*C+Zlu!yms`bZxQBXty1cuh6kypE_i~R7wSULX|!t$K#KtE zj7Fl>x~@4B@bSpz@j`2Zb+~G@K_OtY<*_v6m)D#T>yv zFz4P4si#8V-W(2TTP55q)4_cN!sq;`NNx)*pymdrl(sQ)gIZ^U*mIZ0v?@Cc$x-nM zW|ezPZl+}(p$St{#Irm!U^H>?YW7CI9PBf6H9cyf4T*IwthJv~d5Aq?a&0*&@+a%Q zUnQH}HEn=K227C25nRC!(TuhiF#Yw+B_D5W+aZglx}u}Y*tg`k)aDA>4bbsEn!P* znZ41FutM;_>McL}r=M00RZY9LbGUbSiDgrb9kyan`Ebd@4q3)gf(|;dM-+0Zl{w=F0X`hZf4~2*#)UI4Parh zLv_{&rW9hwS%^!kcwy1WoU{(5*IK04Y(gV9^z9amE*A}s-k!rY8WpA?vdmk=-vk%R zG+>@Tx?W5#53F=Hd*IERRI~-)4=B^KNEYh!;$Utrq376x?OoM4GV`=H2S&G@*j~~TTT?1vbdzl zdrtTFNR0_7vSkqAE!h2tS zA{HQ14#|SIH$qj`EqW#L01{MA36NLuwdgB~@0oO%IrJnXaXc(bvTl^)P(7Q4JKtv$kWtJIH0s?C zPeYesznv$@X2eWvr1W!pgz3aLmAYm59j)d_>xux&Ffa{uoe=9)*xdLt`5C-P z9D8>?&VQ4*C-nc@s+0FBzKH#as=swEZI*nWrMt0@u;Jz)BT?Y2dOh z1R#s?FJ!In)eTQIJ2-H{LrY@Is%9>H(NL^94Z`hqQk?3td_antyd4XExW;kAK(^}w zv>N7XMQvS=45iB<4~&dp-A#G*VCImw)GzA@slvjiFeFEp&)(q8;>6VI60!TE#s>Ba zh;!_x{A3BPQulVwm(0}QYN|v^CQ)J2u8sNrsCS1t^aq)XAl15X>tw~^ie`BXf%qM& zpV!mb(boZr+`*y9O_UeDn2Zs8os6Znm0Jeci{JTiu7k5^BBu()R~bfMH;zy4K*NDI zOQbOdLYLcPcDr+WWrzZQY65YyShH-JG1kbSMKM|enk3$vd?tVg5g3(>d7| zCmR;7()A?Bexf+jjdF0FV@dAPxpCuWE@>uutuovLDy}FHS72J(RPkr3M8b*QSy|DSvz^K2}oVj#nt#e>kcwzlc;~VQtgU#$2s-ylTN+%w!Cy(xt#BCDI z$TIpbj0rkEcdjj~x5@z>F~lioa^a51RS{X}pEjY6REC`A`nUmi2Nv|WvjFi9b%_%H zT`bBCXvowA#u__;3*6cvp$J*uCDK&Qh-~tzmu@@+AvQ`LC~R#}*X0&P8TTP8iJ5X@ zSB?FeNW981Pjp?A#9<8SFvt&CuUI%Xv>3QVUuQ_{pivEsvHBTF6v3Bwei_R%L?v~* zV2CV-vfT4tu}``Uk->q`0&^#>M4r_3Ye4J={ig744-Udd=6`@^;}8)8*<_V!581^D zXSk+F%S?w#C{;g*&W}T&l5xRB{)eT`80W%tQv86g7?I;U`?w2}v(dgayO{m3pnnXN zJT?89gIykhD-7Dl4Ns*ZH}f@4VoPO??QF5!or5T7K?RTzk$q!C&kz$7@dU92gZ6?J z&~>h}#gwY!l^*=v*NfQ6>efwH}iu2a!pzK>k^8F)XrgTj+JWu(;W9 zLaDR02Q;)qoE#7QPWg1^lj@2OONmg9@LnK~GR{q~={{u!Cl&|N&N5PY_ zUc_zYHa%sgOzNyw>Q}!r)o5nXlk=$fD|_UuhvvxqvK6q*f42PHd-!wued5;vYXOxy zw~zE&%zyMPBK+S>rdPz-VSbA6OJReF*v^{vHI|C8yb;`(pfG%HFL7*RFtS?~RI9N2 z3XVOFf4U=6VSjRl=q8L=%Q7_fW*OebF+$STLu~6_5$9`S>5NIz*v*m8t>!3*AJp!{ zly*P%jwYYs5H=a6CJ!%pxQCftz_}sWU$5&>9V~#e>0ykgZjbKPgOfpD2m)!0Yi*#_ zA!|pkV`ZBJR^v?sGN{vrFUZ`+y?g@4lRbiE*A6ePJ~(8Y(lSedmN!DxfXo?*dL252 zPd4y2-d0=FYW+e`W@^sK#P*C)2u*I(*DGHAN>(*+nxcbfsst<}Q9@!_i}$-LNFE$*{`pGvn;8?A{gt2a|hG39`cY?;y^!QnrCVX+8;s9 zk<=W3FM0SICdG+}qBf1JCz%(@a)75lu1q2`mtK-yQ5>n4(G>9-Kwe!{}yOy}Ifi7PF%KpR#HB7#+9G zLe<9o$Su;yY(Ib8QXZ^+BJbNtx~DZ~kOQe6*IiiLpshJi0g-hzYaysL ztL1*S;Y~_yE5$YlRen+lVja2QIBm5&?aQ2)h-hNGh#Zm?u04>m!H>Z*f=rw_Ywae< z$?RVrx`q|`L)8w;!WjIfAi|x*xT8x1^j?AD{7BR>um$&N37`z`#RD(E?_g4Sos*Gf zAY$_sl2(1^qoppgeBLS5=?`68CVp1zlhYFS)kajWPl2h z#!ALEJ~CLoro*Akfjo7u7Tzs{D?~+tB|2R(X+s=4NagmC3rID2|E;G-xgQ2>D7N5! zxbOPxrDkXNC_aSH5Zg)gOv7$@hMje+ZPuwVR5E5?rS9K>Bn3!#K8k7UasgyW+v1&9 zVa+vH!&QhTsMyj>3uBl0mLzqQ4AhRto5nDWrB0&ZFn1oMkDk=A5mW`LJ&bKRMK7=A zmY4=-iw4od!LsI4k!6bTq%o2iy&2?-pBHJ}8Zrv1trRJgATE;Dbc2aF?6RTr*sdj< z?Qf7_TNPO7Vd0mv>={l9i|gmAYRwHGA2urVX^KfBeOfx`49KVxWN!|MWjJcZ@+wWy zxcwaR@Wd~IE&dz4jds@~%@1>$Rsw>aUWz0*oNeUAl25rsOCd1kvDA8XCYu)wX!8%h zbZM;{{+O>g?rRlA@2JUhlTNg&%_ed!dU)4vn>;juS5@%F|o7HrF?i~BG z682LH8oEm25+quhT5g?;fs2aaou3yi{WX02t!H!J@DwfprNU zvFy7u2@rA1UG;Tr03%Ks^)8|firJ?NXb%=Q&$21UU~D4*IIWxzb}poWQ(gorz$o)) z3zo$X=I0|St$dug`d37T)`-b0j7erq2<33t@9g61c2w#`$J+GFc8aWZsQo7ut`8^eG z{0=ULfYK(gf!mfVxMe&<)5I6gFqTjS={MC%euV^6-6))%jI9!~IuPra>y+!!KE$sO zm;-;&8rnCMoRT(goVhr9W>kub#x`si5gm;Mc-dma%V~0bq0Ch*m-ma~A9Qk9B~>Dh zV|j=eO_&D%TocFU0EC*a0Tm~ee`lrLmOOk*w7BewXs>fnG^Ab)X#Za#ddL8v+VJ;L z%;`l1$f{ve_S6h6RIV3%dv*E#R5>NK;cmeHIYbrQ>DgD~j;p^72)pYUbKIyVK!OE` z(76|dn7DSN&{^ipqaOBZ&Su@_-(ZGmFky-+^ofMe2L$f?bAL^pK$yZti(sa#o1!N=*N8TB|u znx=65pE-5&G(O0-8r4i`@NCs&g1K}rAtwLJjj2PK%v;EJE1Jme5-@aExZLsZ{M?Y7Pq=lnpci~+dPy27ZA9;7Aj z3^Cc#1ebACSR^ZV;$SR!Qyu~FiaN`@mD@|@UMWHShOE{BwjtX)z$!Jd%qEYSUUah3 zgTBo*wRKqdn(i8w^<$C>y8sh|dxa&bZL9g&V;FP&t=7N5|BBe&SYpAK*_U%GEbDBA z7Qb@k#p2C^Tn}C>PnyL>Z?F^vO(W`YK2Y8d3I_?Op#}-i( zuIrcA!J=YE1F7BSy>*rlcfZ*Kg*9{sa{wSZT5lo`T*&?{!#LZ)6zrGAZYmk@_tX6= z7rTq#x1|(vlbK0v8RPBr7_)kdZq1wk0P{5G>;i)7Qt8~2dDc|i=AcMRp7CaH5D5)cl)t?DogvcTDl*^Y@hVZYptnmI*XA$GzCX`m3V+N+CoV=r%W;XIxip7b;#p6V2eziS zl4;Te_e#aytPzpx%^eri7_FJ@!2$-nR^*tYT`Q~}UMk2=>t;-2G7b`uUe~jrVpa>L zriy2N$grD_ypE-0@E65CL7<^>C1s{Pr9)0{tJD8gc{KK}%TT4pm)NZ22R?O7vlUba za1L0AFy`YN65z)7S&nU1e}}RjBauzZr(0#d`?LGCiy{os(vs>WX*f7zRCNO|RF(!S z0D*X`nbuJ>-Y!KSr#iFkDd@(Y3<~3lbD;?x%?ghw<+c*p8LLM-cCHdX4?UX$Di%i3?H!YsweEtSRMXh5r zaltqLD4gz>-XbHDo!4=rjwe&kLyK<=R#9TQ{ai)A4&Q7v6zBR7@_-*O=&Z7`0!}LK ze__;5tWc6M09ot)(?_j3r8NW6nv#k7|ZGuJw4QJT_GT0fj%Iqz8KbYZ>i`S+YnP7-m;AJDS1lZT^rfl)GwMVWh_*zL9R)qy z6qEQ$BZa=Ek!JmmHB!B)pYi#HkfI7n9?fr7wq1#EK>0WA#gpgd*Tv-E2J^b%V;JXe zqVdrO;DdNyPEKa|2Z#rZGC`ff;fxL3nz4Cqn+S-jsNuX{X~XZmMBb^-uQPckHUbi> z#(MYsb0Cp3CYsGBMnVA!-4Am72qL`Sj@u}oR>?HeN3BIGqMaM95q9#jfPnas zjX&p$bwo=?kL%98>R2x0=a_l@Q#f6$u-vCd#*%|nFGBnECV!Fe9BC)I9x%+AMlN$5 z432*9uN#b|yA<78vfKkX6l3RD#M04piRv#G#!ui_0docDW{Uo$ct63(mnFe6zUE?jk;>GGp&tj0qeZx&iq zC7OF%#mEIK=GE2y`kI-FfF14a9&RyxOBD|k(Yu;D6*%Bn(%Em-QO6nFx*p|9M92K` zavZEH);OsK)_uRONBLEhv|!bk;b85|?<{=aH4+OwpG%+r(88w;VoxnbTEPy?_;dS#sd9Yu7#m1!`2<#i_E!ZhB zV*dol2VK2i_=tPF43iEf0P1fYEl7sm8b&+{OV~ZT^`D#f4QUmtAV8Yn4c2|~aJj1e za1;60VW)!K>)8ctBNh{x9CAH4DzJVValE=sIZ|^z+}YQ-sdn1$eqW&y&_;*D>SW{N zB!I8B=~lpb?f?nis(XxB{E`>nnWO2w`p=Kg5)$>8c)IS9sETuD-Zww}t-A~8iAe9X zpT<50mVbJ-95%Uo6R>t`ep8zKHFSKm^6CG+P5(PEeX1sf;t4AzGxe!abtzM)0&=5L zs==dW@urJ@ZUy|qN%Ydmkf)kOr}J>#p>ID}lB@Z8arH76(ll_1Ph%lk(EzK)Ik6t{ zaCdxN5mA&8wdj^&kJjzc_}!7&xr);=VV1WPxhXVydm%P!l;itc7&WJZce<1cN>QEK z@RNL9CJ3}8mw{#1lCEJ<=Fm!u;j_{Q+K4^+9H~og6ZR~_A+{_V>po0ww+}?UZpXhS zjt-k7o#b!xj$TnmTqY6A&v(U_@Q12f`bujm!kOI~1E*01O=i^1V3iRzLuLy$6-YO~ zNRq}ds7|4_C%*)~89s+2v8>2V)=u`_lTm2l;|J zG3$?h{O_dED{4QQf0(=6f;%Qpe3|HsJzelLz$5_IV9B6|MdNlw#pGYpi=ul-kwJJ% z$e<>CdqP3;dQ`qG{A_>@bylm)sIVx&x-p=plKt*l8@Y9nrpG$3&U@3UTNd*f(%YCg z#5{184c`Q0Lw7T<H;FT&xfi0Jg-l~l2bYPe z2b9AmuCr4ZgsfQ>5=wS#rvs^!!dq?PUyv*y=`xoBqHjDH0uiq-5J_tqTLyxwC~QpZ z`L=7wr`Tcg{uj18?8kiM_1cwod12QYkfHTNr{Roh!p$llh)8+lDk4^}CKIFq|5wS; zdnKHiDeho!>*O?`hr+l6-IRZ(hmz5SQSdQF&v<=S%f^I|z~EIu6J)eF{Yp%(2`@+3 z>dKqB+zeVBjJ%wj*wrfE+ab$wxO%W4Yc!bQZWdrxPc)Xnc!8|lfclGa2EHLtgQ089 z?0*Olxr-9x1YX% zR0M8Rf4+`&=pYLf{0B}%=|IKR-!S-ylT|Nx61y8v3dL!~E{|u_4=a2nvSAJcX*HrT z_Zij@0 z+vm6qmrDwKPynPv_R4COQ)QKMcjB^V?!7n-XQQQu(d28=bTqiHrBCCD?o+|B^`W)G z6aeF7t24==wzbUZoX2mwJ*oK3+qlDVUjAgcPjI-UwF^t8ihY+K>y+J%73mxJ*ua#W(XH8oa7)sUoHQ z7o=HO#MG!M#&{;`3+(Vj5O943-z*6UizbHSE=j7e{RKq)0bd4~zy)%k9BBr}_E`?J zpiqVWpqK+^J!;T<^k?Tide|$EJ}U?5TT1m98|p~LjsfzkcvRg0L{#ThNI*<2=%3o* z@rp!^q6zZbGl~GfA^XucV&pxAVCrY*J^zhO?+Anlw(xGur$!-#3c{=x5WcPbv^8QH z+;h8Up92uf{euWvkTdA)(O>P+qc`a7qHcaeXDb?`f1XchdiGnDXOlz^Z2y)>5B5P2 zDP@o0!i$8%1Ah*cx(fVCujg7E;@Ea`43Q~-|9+{zGvby1-v1o|VXdTN3&;o{8ok3( zI&hNM_IYU*eN=4B08$29{)q~j$bnGtn>p_#>%o_!n&$|xZSUuLF_hJ}k&;YAM6N8o zZkACHFTWRol<5GyO9b?!YX3w4g zu7vI~_)qM5JpgaXYbv!!V>~fQ3*-QjzUMCZx%QsP;N ztQzj9m{&3z3Mwe}WePTbk1Zkg$Z*2U5jQ*2(v+}ZrR51pho;UtKo&DS{3~Umeb*CM z!d^Ja_eFBFfToPo>#6+B^bo2YHbjPzR_5Z)zOI)R{nq+~mC~A@N-S+24(mQuW=VL5 z4bBC`P^5FLBc!M+^lf;Iw(N_sluv zkb-9rI@*+K%4Dj8!c)S#O0r*-=X_cLpi?diNp z*{7?3rv;=(3C_fPaYnQHKj8mVVaL$U4(kjONWizF-8m^@E_C_lbKe58rTerS&$<>- zxRZ|E=R3Ro0SR;prH4;M2Ub1-w3DOK#j%_ z2ZXvR3N3E2V;e$W`Sb2!SLA|vLB);Nopk;DIP;v~y2WST{nQ6s~pqxlBaO`HMP}CG4b)8SWp|nM}H|;CjrJ zrg&aSeS#OSc_Va}FYkS1ir4luv{}GbT8_UR+{v%iUJ!kiP;t%Aa+wmQ4x-JNLX>{E5c#Ac}mA4)qW+snh9GXdhul(k45e(x;P4moJN<}m!@GIcrM0@vvz~l0d(?ZNW zbg+-DKcTlLw;M|9yIa%A!cU_HO6-*rR}>Fx&(7QMhV?N4Mr0WPIfe}r#?_}79ils! z17%Y_faaU2;Z{&o=SMbj^SSZIgXv=^*|#JMRl}qLGJZy$bYE#fBrO1^So@&ThIV1x zVEqK0+q2x!rMJlVg=V^7xoKSI)uFy6bdd*CBK0xlXN-F5&y3nSBkBp7{p1gfS~1V4 zUx`~m9Q*rd8zm|THJEeJHm9d7v)qJl-|6DCB`9?Ds1*`kaR`>=Ys>@sP}kI8xvoWh zM?AQ#Xco6?l_>p95l|vy!T4xlOlwr33i4Ut5^9i1a0z&*n(q%Jcv2~?QPUt91wHt= zLl&th?I3Q+LRa91hm+IQw{SI8RR_R?O{wFi#RxuzqleWE_C1~<_eXLorWTM#>-R)U z39gG`tlmY~bhkLaZkRPYgx$5? z{`bI|kd^o<-f1jrHt8c&wy!oZWM3jn??sKp1d8!yh1Y1PH+G+5rX>RrprVkx3a*89 z;8yj2mm}RqKUEvogxSYjI+$0os=3qtW|XN1t&fpXc^n~UY+Ixz=xaDmFQ!Uoi`9j~ zgR}D~(g5tC-xx8WNQg%i7F|sRUAAp{JxD6eA6Uz6T|8GvTHIrU5(38dONZXU$#iv2 zPaa5{Uk1cK%M8U8!~qMEv_QH}(ca#nRoc|)}UuLN;GmEXBQr3@;^M?qg$t3Nv|7_s(oPwDBGi#nRkX%lIi9Q6>!J}*E-ldy=qNhpIg|M`K?t)%J zHRxLcw4h9Y?iTQMxQ*)ZYL|8M%gcdfyJyQA&zPF&OHK2IsVwqq(NTlATd$e*|GdZa zW_-ORoYPFJk*BFM15K&IP_3ND0l1X#jsfJBdZ|IMK!^T;v-`B-j%bxZ-*~Cc?4tAi z!L#dc?SHDgv;+`z0T6XDg|y4}4C3yX-020&L8ScU1`QA(t$;9fubQL2lRsCPj|nwb zUO^i5+n~HEolkbf0Ws2fwY0ZF0VKh_6(%q>V$4zz--PjSaDNpfbrM*`PUAR#@5=MB zU)Laj;7mhLOKlAS`fWFQD|Ywb0y=3T&&AuN1eUYYSp1vSTc%mK52FE@v|G^A(!j>h z^Cawyc;>IBbD6m7HjcvVaBxQ$F|hQe~w^)jYbRns)wpsEpku;RnE$S8TkDd$9>X#bQe7? z5ZX+HKt2|`a-pJ4^qQ@)Gn!2gNiIIb{%tQ@`C?v0Qd8M%=wq0{Q&+ByaB7FYbhOMq z*tFXa_1-Vykhv4VZdXG^`Ta^TWW2jb*=Wo0$3Cd=>q2yebqVvLV!!P=n>6N8uRHj9 zv~=aK7vvk`K*EsJ4qImrcTpj3Tc+-L#r~^vZ)VckEOO|Y(PF($3d<}53^3Q>L}s!hi%k3zWt5L!wDAu^ZOX%mo{S^Ze}hRmyVrMr zlOyOxZc5*LN6g=tcL9)ofHGE5cgH-C|Fi)d|9!~JRe+HvR9sn>( z=n)-zS+;NbGm7R-I6U5X1lwwI%H1lpjMW7vCrxYt(wuZGS-hRE@*cENZOPB03oEGy z|B+2kP%5Y1EzH-{zqvRsxS{)0-|8Q#8q=TQ1g?OzDoGtF?Wi59IjCJ(r$!c{s0YpT1W6g;s=iilb3fZyU>XE=2Kl}>65d#RK51t}_K zqjyzhpH;Vds=SQ zud|0<^_!UTzH(qUdsyN{=~u3m_#Z2>t6RkP$9`&VkHU=jqKMkpMEI6xL8*i3P!B!Jynvr-~&hi+1~J|Jv%$?5mgM_nykTne{pZNeO7b1xeBW%l?51Eg-;;uuQa zn_Z?F z$y|;#dHim%+}a@`%gQ^i+^McxxPD;hSO;T$H(SNQAlhbc(755cf>*VA`hlUUGVW_# zDu_szG@JvNnHxhSE9u}sR>bR_{lCnPRt-PwcNx%|_>T%Fxj(#a;_npqP(THOd`8R5 zev|M?v9^yD$O+;Gyb(-=%{^x=6o~(0SejwvJ&Jk(rIy4ngf5!-uFwsYPk3UM#c zkm@TO2RC=6o(zDeq{t&V&Gp?%YpnS~qo53_1fZK(kFeD0Z;OF?lkrfOToCAs-B*eo zmS;uO+C!db@8C5&Z_WvxI^AfXJ3Uk0j-hvh->D9mZ#M}7OwmH1cCUSN?Oq}baB0CG zCx?*U0H&GSFyl9z^DGJ%sx>k8T*Y@&8c1)cWkq~?Z@FLBzS+kDXqSsOYG`9Y)yc#- z@_geSCTQ9w`4UPBK-D^zSU5$dr#PpX1f%!7M$4}&w#rX>jzM4{5^YLB|;% zZky;Q8d?ATO>6kJX+#KDml z%^b{f{zqczW<4QS06KP08X;imlFYKGLfJhhl$-_?$F=m;XKv8KJFwyXyO9Y#n_8C% zYRi(i$~1rkP=qBkr94EYfJk=F)O^_ezJ7y!Zkw4ge;C}9ASPTFe5Cf8$a>LKLN%iR zh1ptek+0L_kbOc$QkSWy2mb|{#hLBPDxezPHu$k}Ga>_YN*A)7JfhNZcPHJwF^1>39}0d zr5UWlIYo;RU9uo_Y(M9Gi!)Q75M3emXHlQU2Y#+-jf3()UL4-`&OD?Ow+%c4B***J z`3L!D{|iK|$2~?G2Oy%{F5y=<+xwVav_KtI00W}XTA)sUe^L?>+$VgChtjZ$B8 z^Pf|yVkCb&=r;Q4QA>rYIrQxxfaL3e>F*QU(#T9!%Z179Gq`JD+3fcD8BdF;I~^g{ zozn?jkQA)S#I^q8LecY)Q4)mo?tz!x`?+)BID2BK%f7-RPZLi2%p74ME=UsNLg%H| zy5!*};EOycWyHjdw=yFpk_`+n4?m4TjsZ;OchP1plU$~#! zg_$FZQeg8&|H|opJ{u5Ezg$o}t>MqR{D^2n#P6lka9z9HdB~9a$Y@?Y6?JKgUe`|D z#D3F+^k#5%z_3wz{V6`?(?Gv9qs(+t8xgBV$V^4~b9euT@aw&xg3wnje0z;(->b;! zP$I{aTeI?8ZXQ*r+9v7CTl$J+BdSGj$eSvaMlgBN5iXF8Ag08;niF+Kx{!W)v)S=l zyR8keRcu^*?4J~!#xTz*wOz6zC%GIT+P|~*EtTk`?7F-2ZG8Gpi@$2)>m#R~0i1d{ z6rkF4*niQ)r^6*_qa175i3LYRani|z^h_X9Zdq@eN|~Stx97?1_!^)Dg^v0Q4YlAv zTgUB|r_kBgP5W0RCsunND@@+7zbO!Si?w^%q{pzdXWjdPq?XwRvhx`%78slQ#yV5mwhJcNqx)+YPM0zO zVU+_fhW>sWRJ_AzE{IdHsi#P+Id2ShSq3!jEr^hL+4Ko}1N+xHLiYl*N%stEPR^C_ z>o>isP3hwW#PZpzNa_vFji&IB^qipCJQy#hBRbUrvBJVVbJvKpK!O zr4gC%kX6C^;8E_XxWSyEbJIY1I757#uPZ>Cx#!phn@3&j@DJYoSBj}iZZ;>tc6FWF z!&N2zLo0uuo1J0n@SBpIK0HV35@A2{N_rw7mu^gJ`-gHVQZSysE2Zf$=CG){-kEFr zKW;IxxxiSH#)^AmyUiHx3k-phH@d;p(su9F3pJU(>y)8;>#%!gYJMdezKKZtR4KSK z<2DsRFG8cQ7wnsQH>9&=g97sO?32WpzZw0cES?|zBx($n0V8jlSEV|GZW=gWwsq&a zk0shRrWb=Z(^4&I+BgyGk7J(@^JAa-W)tvv%{CwZ#jK~+6pmrP^job{vV@udfBFV- zQ$XKYLO9=MCi4d>S@WHJeGC=QM)|ccaH>(7R&exEhT^L?81-Ci)*GbJY`)0nr@kdX zFqIKArZh)G=RCo!T&%uIFR1QtpZ>v}SC&jo`dCo0x&-L3<96G~;<2}Az<5PqY?6m{ zev5Jj|Gu;V@jO!cxiSHx?mGsEUsn{fUAeLBjEOv)1zqm5aH;)Og}x&vRzZDa;&?W* zG^~9`CNBO>YA9rVfufxZpBUR+eYV}H1rjTba)+7f=RrmB9BDW@(mx>T-35oB9^-*b z94tgR6Bl512NV*26rg@5c*_@|RX@RJ)0!}0`e&67g%xU4rwLPdH^mmcn0XYuI5?gX z95pT>q{mo2tx40%n134Xw4Po!w@j17dhW>AJRj`z{q#iqx*DnkMJD*RhPH~=1mk?a ze)ly~9`bjlyidf91WyD_Yl)6H$^cEes00s)Xm!@Sc=Oq z&e?t~y_$s2a+lR>we!*^3HvHDI?TRCMOW0vO&1U75PkF-TlU<6PE+k~d0==DgBa`RZk>mKh3$KrX2ASDr z0apgN@<#JBpnm7d8xPje^WH3i?rXx#_n92@eWuPAeWs)FYMTwDiBNV_T{||mutQab z*!0SnceX)S!+G?Ey15B6#UpqQ!gIP2Ccbte*yrlpUc5DSoqkNz0UR(M14AiJa0XuL zuVNu}Eaj*;oR~Ll;~XajXTO#jO~W4RE*l1BJ=M(EmU100>xieA9Tgi6D{fxMCpEMW z*L>C;FFC5g-&=`YkE1uY2j8=EFo4A`Ybt$ z0!s09A(oIWMc4j%+*54a+!{K#1e)`AlSm=a{e5AFU0Qcvk+uDod@0B+EOnXUuATyJ znGTH{{0_O(7tA6u6Duf`aXxStpBIl*9e!D%U&bkyu!1B$Z;p)e`3DiUTpZD&ubP{J{orp}Oh5n?cSTE1FhM27Y*-X)+c^#`#pVrJThv+%<@qBIR(TAG%mjR2< z*fd=Gs=Z`Tz4+O@QvEIle>qq5&3^n}j4*xCq~GcSG@G>Da~fbEzQS)ZwwgJALIVTD zj|5=Yz14&{M6(XvKFbX?118Pw=nnli8Xz?V z_@+{HS9?m4_|p(O45UPw510R{!phf{^3L&Gdgh}JIv*YLUD>J-drkJ!JlbL4VwUBA zRsYdyDizD6xAeX9`6=)nwpP3hFJA{pZ2OI1oMlAIThe1-dt*+<^C_EX>Z(NOHPs3I zNL7Q$Z|B^8)*MTPHO0GV(6#u_k_L};$cfFIsP9P4j#re|JVUR@J`>mPDm5D7UI}At zbmekRmYQL|au>I!7d7PjtKqu3={8(orV*wF^6K? zZs5E8#Qss6T@uT_Y$*!}y|Cac_HbZM1~gi(e@{K#5;RA>ty!EqTmY0O$AIa(ETYTi z+LUnJ31JZKE8YK^;QK$fufJaFyZO~#xtYI&{r*qCy>4sK{G5O4-=j8fs(9{xeO^?s znfgZbE!=nK^H<6L_#ItW`F+}N(0F~pY~Z30i&Yctcf8yf=5IBB?s8|d^DE|^7u3H0 z?oZAX?W(>PHDP&H>(2|WpVxD2g9mT-wJP8WP!AP<*A(8xQu?#6PCl3Z{H55;!>7+b zFiEG|Cs%Gk=kLS|#uCb!5ZHaVsHGW@f9H-9ei zJ8uZQxd6CYgrUJc_jk9$&*pg{KNr5;CjZ5)((3x{UZXQbHJ|>Tn)MWT1iH=B+wZ1t zjhl4#KKKZq#%t$ZtAjQ;gLjymwccKx8yGgvYv1x~Q|4aZ&5nAA5Au%Y!|N(-iu}$Q z-xfDo)!8$7Q875r|Ihiq9he|9KW+HHKY8LUVXexP+>$fG8M%x9ADVEo{`TKfRn>J$ zo6leS!1?;@iFMzP{hcVOA)o%{2=I`cy>+qAuX=5+`WF?qpEdPzd+TTSEdPm-c_*(p z+WWr=zMeht_2zF&x~gCAy9PWtFYQH5d8I{r@$TN6)*x zr?!6I+kZ)W|5fGRk)NBn{Y=c}sM@=$pYNRpJZ1cT*ERL)KT_VAzX@LsT8tO}e&;=3 z`(oJ&#QHhgU24FS=X=13uIzTb;0b$yKED2S)xY*%aru103OGHfVJC2vt=9K@-0Opf(e-&4MJtMV!@&3@wUC%%K zs{ZNz*y@`0+I6zIR-6cl}kiZhAy<~qC-E0W5 N!qe5yWt~$(69D_#Dn$SQ literal 0 HcmV?d00001 From ba4ceb12836de989480231234703b254db7b0eed Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 13:12:04 +1000 Subject: [PATCH 16/41] creating a predictive model to train model in real time and plot relevant graphs --- .../MySolution/s47539934-GCN/predict.py | 26 +++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 recognition/MySolution/s47539934-GCN/predict.py diff --git a/recognition/MySolution/s47539934-GCN/predict.py b/recognition/MySolution/s47539934-GCN/predict.py new file mode 100644 index 0000000000..51193901c8 --- /dev/null +++ b/recognition/MySolution/s47539934-GCN/predict.py @@ -0,0 +1,26 @@ + adj, features, labels = load_data('facebook.npz')#returns normalized adjacency matrix, tensor features and labels + features.shape[0] + num_nodes=features.shape[0] + #split data in semi supervised quatity, i.e train:set:test=20:20:60(since n_train Date: Sun, 23 Oct 2022 13:13:48 +1000 Subject: [PATCH 17/41] removed data split and doing it in predict.py instead --- recognition/MySolution/s47539934-GCN/Data.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/recognition/MySolution/s47539934-GCN/Data.py b/recognition/MySolution/s47539934-GCN/Data.py index 1fddbfabc7..302d946232 100644 --- a/recognition/MySolution/s47539934-GCN/Data.py +++ b/recognition/MySolution/s47539934-GCN/Data.py @@ -48,7 +48,4 @@ def load_data(file_path): labels = torch.LongTensor(labels) return adj_trans, features, labels - #split the data into train, validation and test in 20:20:60 ratio - train_set = torch.LongTensor(range(int(num_nodes*0.2))) - val_set = torch.LongTensor(range(int(num_nodes*0.2),int(num_nodes*0.4))) - test_set = torch.LongTensor(range(int(num_nodes*0.4),num_nodes)) + From 61c3787a9c522ab060e5d669bc08035b2e3da4ec Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 13:19:05 +1000 Subject: [PATCH 18/41] Added header block to train.py --- recognition/MySolution/s47539934-GCN/train.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/recognition/MySolution/s47539934-GCN/train.py b/recognition/MySolution/s47539934-GCN/train.py index 9047878adb..8676761dab 100644 --- a/recognition/MySolution/s47539934-GCN/train.py +++ b/recognition/MySolution/s47539934-GCN/train.py @@ -1,3 +1,8 @@ +''' +Author_name: Arsh Upadhyaya +roll_no.4753993 +assigning accuracy and losses to all 3 datasets, by training over n_epoch +''' import numpy as np import torch import torch.nn.functional as F From da34c7a6f4df05239636407b2a9f8f892891a19d Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 15:03:00 +1000 Subject: [PATCH 19/41] tsne map for GCN with 200 epochs --- .../MySolution/s47539934-GCN/GCN_tsne_200.png | Bin 0 -> 64342 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 recognition/MySolution/s47539934-GCN/GCN_tsne_200.png diff --git a/recognition/MySolution/s47539934-GCN/GCN_tsne_200.png b/recognition/MySolution/s47539934-GCN/GCN_tsne_200.png new file mode 100644 index 0000000000000000000000000000000000000000..c793327c14a13ace5ddbcf618862143a4a9b2cd7 GIT binary patch literal 64342 zcmcG!V~}QDur*k=jV`;oY}?hvF55xbelzzxgp4vE$)6XCrgv zN`%SFio?U;zyJXO!AnYrC;|Zi*8&D16a?U#B}#I0zzdjzkfah6Ks=$0LIIzl|4FDj z00F_G{5OE-x4b_AUvfB#syY6(HF0#&w>Ji|)_448W$S2VZb0a4Z0}%hYr{;(M8`x! zXy)knkBgrE|NKs8Yi~-QOb{6c1VjiVDI%!knt7S!;`(c8DP)J6S#zmF%V?r&-#-d| zwU1>%6KXhE;1r=y5Cw`fFkav^3oNt@fh;h_gh8*@*Mq%PvpU{*EWT2w`DXI#TTOD= zU3DtH&S>U?$K6V%&A~*)!^h#8_e6`RC^7Ya#F06X#M(FPKN-u@gGv4GjbzIp8Urdo z5^Tb#^vM5{EtJ2#fButpvSQz~|E_eD{sYX+2#~YnR1gybfXve}f=>)xg1w4_{ESQwj{ItopZDIh5c zjf4~3agyzk%3_XUXJ?n`w5*<(l*HQHVlV_F%lBr4f{KdG>&^m;L1Xs3zP7f;^L|fYFBA%;u-WeP zn|;TVc_M|Lf{iU1uyeAm{^aD^VCDw)aPuv;x^GV_y6oTI^V0NVuym~g?OJr$a1cl7 ztd>SjPECo5<5Yi(N58*5CagD_C0jOVOa7!0-*i66QWYhFgM%}1a;kejubC>YIK3^Y z>W&>A8nyZSe4Es8yFZ$x<>b(B*|f zl#}cZhLH&fc3OYhFRBf1cl$VgA<*@F+AmjWLiTFfb`80mWQ_nuNIvDCEUu)xgOP`~ zlN@%Bs-3TE7b9ciy?-1Uyt)c#Y}V_6BL{inG5C>KjQh8IPyl@l{7)YPgVAUBKCDb8 zQw3+TdEXXtzC{R;%rcG$U3%`#08JYXzX&>PEa*>jZ z5dki0{&iuuq^x>(R+4jnl%kt|!KG7^0uGBIFE2k!&aJ`KQ&2SPj-&VV3H8?!X zw$5TQiG@L{A>!@Lx3sjx^LaZ!yKN~b2<+X{Bg2-K>WB%j1Wxk*j&m{qkx#NXvQ6cyMHd*=m)c_)l6tU`<;V-0JG;lWEK$A^7g0jg2hz_4TfwcO#5&1v1W! zj@|lLJWq>%A1~Ju0Q!VMr!^Wu-woBfSoSxK&x@--EXrWB)lTbQANZ8c!Q+CQ>0l_b zk%>usdir`Zq>;fqJd&c6TVWK#f4ISDPG1c9{{=X%Byj(=O328NadUtCXa4}isT!S_ zfc>qqmBeWQP6NOJR?FpI=FI@Yyh$z?M~d;LHGOKDeMnEIcqETPr(^@(P9Uw&CGSLy?7yqj)Wz4!fbbCRrS!`Yvl{;Q($_sr2sg)W&usa_3y(ucV2G&XNEcYktP>Mu=4 zD&UKy3e1)rZ*H&m$G*LP|CZHF8ErN>05CMy7+com@lsq{dkdv$VPBai9T4-NdgnRK zv&rNMwGlF%v+|R||E8_A{4z*llovKuC!T^*J3cOTJeekJV?zhP`#%|MQEm^%O>dv- z4JM41%QQez($a`2Y2+h>E;S$YJIPkj?Q%H2fg$`q@f78l_BJ<+aQJbfmL5HMVou04 zTx~Y9Xw|3uV_I7CgN3MPiXd!0-<|;26W7qd0ZP$vr;<*gD=IG!*JB7RHb+B4o2^i- zDLt9VlQ z=EHT}PcUYInoy8Khdwx0B1fQ7sphwFB_=fnfYE{5y#dav#s$}(uNUA{UJo`*UC%q@ z=EXy`{U~kNb?00d17BR@R9GUR{Uyhp5b)on(5?s}VdMZ?*Ou!2@92$Fw1@9T2xja? z@Iv&lCIRykC-6!v$#xI-1BFxsz#BaK=D$K2R?c9vu@8%IB*OhClOE+@IyVXzXg=QA zuw}qmb^L1j%@jkxzfD7CJWur}^96JLo-7Up%DE8Cu>_%#Prvn8q z3`9Zzl^XMf&yPQ?Z4CtqE^Uz?3kA7p=|#bwwTIm`dY8Rjlu?A;mb);8MXvgsdm0AIBa2*;^5wCf~~LeIMuDZ29(~ZQfw;o+DIK2!I3V7%y-r`ZpH_hONHT@ojUC zk%4`rJ^-#1VkT}5K4)@zEuRG77A0J(|ItlExY*B>Vcs@~$|1*c0cDiG+>6d}O^;%Y zv6E$JSa-2pV$#oa?9SUGMBZ`=cw+==I~ zs_CXo=8zp&74up7e>YNU=q8Stu~6~DFeaB4$S%1c@BH~{orr0K&YEsc_qw!8+J}Bk z>$ve~R>A}l@d^W)oqHRaH+B1=aLFw{k1;QoWbXw~gaVix{M`osrbJiE*J3l!w`~}C z#7X5oKYe~GUrJz1ZNIXh5{Lhp((Yf_;EUNL3gv=aLJxu!YW{XUZ4+OQl>?m{8Yk0lojs=DfQ=CA zofY)gFfsk}#9+hoK{^b7y;ZhiL6{54`H%s5hn3ahy|Ypjw7F$omFDR3pC_PpHJcC@ zUk{WO7OZWw!o_^#>efENp4T%}=IgkSN=s#m&7LMrzS<$K$r$D2vMXf{l4;ROHTSM{6E^D<~(q#y+nKo0J z9hi6X({Ha)dKuBS-ql3~EPv{qI_*Hm#_RE}P--*Jg-oi`gIa=^lnrVy(~K?S8zJBp z)ah}lz)yC*4nI@0i?3lX-#J+(XQ2CPL6Vk}LWJ?3o@q&LX7BIuN1=LQRT zI~%`#XK(P;Ozne*$aPkpItIH6mS!py^VUs`csv2IS6Qid0vX9!9)co)xWm>$2&&FL zSbrK0QG^&cgN1Ya+KM17ao*84TPfrQ&{xS8Bkwr~osF(tiHx~W(M5WgWoWbTelm56 z#+RGbh9#311~?Wi*46xF@S;Y=kwfo*n%$5zsQp7`!;6bx6CP z+EG)kt5E1Hyj&#-Q}b=`#-;9TH0dL1*-)S8hl`#%eAdUjze;;Su}w~5DjRw1{nO5( z;}d(4c(@kAYU4JSBY^> zo_|d=eaTrrwN@#6wAs(`ep>Bi4OZwNAYdVfj8UP3d`kJkcDp6Qqq|-4`FI^=BWUY_ z>tz1>8?w&8>3(Jp;(SrnVn5A!5}S}P0PvXDY*tt?JoiwhnRXD|&X>E9%+tGZ;`p5N zCh4D0pAH8pG+S=7pJB0~a0J8)AqshTclPq$2(+%kKrc_PoNu~kS`VWgoh^EJZ%rR* z-LK}dnJ(-J-JK!gY+i7^msvaZQ{kAaX9$Woj*0~v`8p5(_#(diENrw`BcLC?`@`q2 ztod#-vkNx7suB_)7>T>tTj0{iKbdbxj*#M$oy=>yEC`0O%bjy9=e>{=o&ClF-ODQ~ zu$fKZ08|!16Vz-pmWW2J))$}GU0z9Pu&g9|yxDp~!>WRtHo_WZ>~96%NmWaWukN0J zDQ%xk?w>LNGv9Tm3+(3Lc-#P=fQMd)DFNOB5UV{uw6$rL`>l_RG}DObJ?Uk>UR#Kq zDf)Lg^MyKo?N^SV?B=NS)u7@0hM#TZh17%>VC?S_Y)n-t0cx7k>ygq*%rm@=7U3)W z-~Z!G-Spbp+6s$`?6#a%7@aTGY&!46wCFI&$jBZ~=RkcYC;v!ENk!Kdk%vUs>(ctD zy|UR({Q!f=3`NZTfPy@4AZfgy(?{(1`xv?s`j0;Qii&Oi&F(-e$h!K$nmG!((pA8{ zZ9H3jq4@C&8w17lX`#aL!S`moOXt08#abFpG5u~5cCMn*o}gOqij3HZp(b?}J!uTw zO2nMn-D#OI(KwFM+I}F{`ML0!dpeGQ&2U6)S3(kJz&iZ6={W%eC#L&@VVmw3RmcND zLZK$AW?8J37!4*KQT%}8f%F0J?&@19rKjt}MXzkQ$nspfm z2_JLZrW?gro^CY(Z$B?o^H5i=A90B4uiy4MJMWck`wr~I($H!* z+g)c{9qgzSvS7RQ6Lbdv%_Sls3CPdSA0`@ne0=;roR5foweW+m=#c(m8{Sy1jL-nS zFf?AeNGkNg+jcr#K5hGj)H|6q?#J^T^v78HpnKI7QNur^T{u&YTai>Pvx;C5rpqAy z41M9b@M?K1{mt^rME^JV}dn^%c+b9<~+ zrKXhJyoFc@viUDPW&swbK2l1QBA1tkZm@rXHFicEC~rfHp!BNdR86ZhGy_$5JX-5b zVak_qVHe2FQzekAYH4yhTi+(r=RDp<28K7XnAOx-@S0Ms_j4_3X$dZ? zSL|oM0LlW{+wOGH3dlO#eSH0wcv#W(!5d&`{k)$To0%!G-RbUp+4dl%X!%@T&LSt4 z(K9^4w^6&hspZph2On@!cX(F=-`6~O25UezLtIoTm++#v(B3Ec>9fmh}sMP zC4161x%U_`(OUs|d+x1hWrv4fuc_FY#b4<|a?cnF0V*tQy%9Lw14&CL`CxbT%v=hs zG0SF zSH50pK!02xjS25j3s*@!mK7ruK0n%#D3dlMEmQ;V!h$`VC^RRrud8HoH!u-6#K@lr zp4`iupnbhLCDpsx0)ngjt+?Dpq>Y|rCzz&|M4C3RBJWW5MYmTHI{?h-H!*PA2DTI~6!oYO?t4F}5usL8`gTsDFN9E84!aVfu291x zjK4H$T*g5x8QbV>USx%}a%baE)1Y=%#8nQN89mxTn*py2&nu0GmT`#DNOL){W6>49 zYc+lLo3grsg|h$Xecqo^F>ELKCM4zi5+_@NcrS8(I&JFEfy33~$jP|L413P!^55#p zDZ?d4Ygka@pU%y08w_*weUx+Z^ZpWKi*+u;v+1IxU`897r;5!5f9y}or_S1t0^NIQ z=npsY#zD&cMxXZ+fXOF6V1ZQf?qBaz9TeB=&f=Vm{~%6+`S!?pWd45tjIY_zK&q32 zU?bVOA@Y)rtM5{mydsgT7%pDpB+CmO`Jg`;`<{4Jo|#$|-B>P00Y>-Iu`s)?)G%*O zu&qd8;&h^nDBg})52pJoczaZqCp%*cW(PhHAmbfhOaG@62t(;bNX0Tr*q3-JhA9sx zE;odS`huwSFYT5C_h9GT25N4$MwGohx*0@%52SHvg>ubmeIwoZ6Pj>*`>x^m%MXSy zy-kN;Z*5@qZp39X*P&B6q5G>qTxd35;$C&_mUo%M#X5WGtxIQhf#it+%qrt_$c)#p zk}*yMznZAJAb8BzzV098Vqn4|f`WZ_qc`-?bRWVB?*JNZ+%ke;N@i;;q4xncnMnG* z`$oLds=`N@Ah8n201up-WpvMQZ5J4r8j^rRgGUiR~EyRa{;gjvj>S%y>E3f<%R zAUTG&3J-N13?2J2E;ABWdGQZzWm}O+zYmlF?L2A{yu36IR1tEimeoHA_X$G4aVP@V z$`Pk$!YXR9-6}Y+68YXn!G+TWbU_wOj!2_#z?nI zKi;9jqf*J3PnGtI2N&s2@;BNqJSiKq~KP*-&1eeAkMnAOAq+pc0qq@lPYwy|iTF%#dm5b6Dn60IxAy|tE zd5$)HZ+4L1s|El+5^H^b0~OSHcjQ!3pY4$vUd4)tLk;Mr zLH?RBicEqYwMH;167zt72?W}Uh0>frBwjJb5I?m@bF@7dG#Fq4!HPPCQO=7{T^2+`U`ttbMPOtuQ0pzY(_(?)!oWRv5rE zw(Hbl^2Zr)-pvgN9w-Dp1u;}zK#>6fEG54MG44o|6kH$<35Z5-g$5A+fDhn49z4IR z*Xm=^wG3a3d?kCQ<-N5MP}{$?>cRJscNsr8*!X94W2tmRo7!)R1t#!6K9|75y$f*SM!#pK;Bb~CE|dpI6X>b$>97OT0cshK(GyO`d~L=6VH9jNxo z>tP%ch^!V3Tj(<+8A)Q~X>SD$<_thzNPfepiruDn!a8!K1jmJ)KA)5n-(XhMR6 zTF{KD!NJGnJ*GW3U$qm_cF)EsJ%JiqxE386Lu#mq+X3VzMrIre7;1C=N2h(Hh04ON zpm5iy0|`D|(L$==D^;G1<+QwA*qKhMr95diSiZaa3F7}w9v3Jb#oaY%r`pa~RjxJX zQ=mbu{}8ysjFd#?Hk)zhyRwGs=SEeDmG8%b$|bsnNpKMAz~_^~g;63BFVMzOEXmoA zF~JTGtCcD%$kq6^nrLCAgiFBsl4~MD#O-o-v-b_MSaC~B;;V_C6{0=rN0Wy@g@4Hw zs_trH8z&%X>ax!j7yPLd82p8WfIcQT7`8-&SUCyPgfnczeNJfm z8=^U;C#tRU?*{W3N3k#7z~|YMW~=k9HSz{xXJo{%j~6~!xht?*CB#^A#n=v8;x_2E zz?=bbbXvJ=M5!f5i4B{q>BUhf9gZiD=`%s!=BCpk1drCLE!qb(m#NE!`XpqKJ>hoV5|0DIU)Xk&(|xorn1HoQ~{7H$N#YGD?`dQ<qe)_fV^U~HDuXDq6xX4QSx zqQ6cs@!(ttKeW%!tWU*Vj0gy}lw!#rm}w`vZfI0$D9I>CBVvAcHB{%XONsYNI@*n*ZsE81^1Q&0!u3b}(8loV}qZK@3fuuc|1u z`vvr||Lm_q7?dDixHXJ&Y2u5fzkTU8yAuftqL-I=|yriz3;b&!DD++$Z;Sw)82Hxb=U1z}tYi}Iub!bi+L_8x^&n{Q|E zt~ZUxn%q;RDUrqQDbcJd#ZwZ)TWd6t__+5;;iAxSzZMAP*83hVGAl6lNQ@d#ie1$a zN08+%zCmwvUtkEx+Cd__ZsFjn4A$uz)BL5c*?pA${rd6rb=!LHXHJ`)m6*1=|D8X- zWwqgqIQA^Wgu*&sk1t*qWxbXk;I2djh6+jAU?Au8%3vUWk_qhv4Y7%K*&ZLcYE3C2 zbT z?$t`likXpru$1vr-cs_JC;^urZK*3{EB$gLZaVi}RY|)u)EV;A4{GZ*l`qp)8ot|! z8f<4^j@R+Oy#ODLe+Qy(3?&iab(guXtPxxl1bDk(3^PDUC?MumAhDewal#BLWf(g~ zs-x8>Unon6sNO~j&T{@f@J68R;>AakEl(fFZT7C;xG?=xi9#vw_2bSQp0H<*rA+V} z3v3EMN|cffV+J_!NQO zjU96zPFI-toI1ZmNS^rchrv->j7Q|qo;b~%NQf`&O*HHNm5O~+5vh+hi?7bLvNL?F z7wY#jbsX?rcst5v-f+X*%DG;2Nw2{YhvxU}@av%W1nXh%(KMeYQG2ZFSN!kd#xxIInZ)oC%GQ_N`+-__M!oNB#rANKk9CzMFM3#iiILQ` zLbuNi3gmfFdqvdh_vQykTJ-H+L-0AOVn67Y$^&LzePyxuV4H$%1-#Jgzeth$9bRdL zAb+|jw&y&E&GY;`y^BmaX}Lr7GtUcu$QP&H4tV?R?wCClZi@ z&Z|-91eV(_{D2n?FABzFQ1&6oj|74|duKzvABW71J5&KI>RXJ3wLL`nobxOGs_5E+ z?(LTIlTqhaG*Ag?c5jV`P5vzAzy;X=7F0o?^NBaAxv~H^%E(} zrmtc@M1`9!{f@wxm+IpZUh-S-x$uA(;&wHb7G5q0fB0&p?`;=!&CM{LRQ~M!-n`&| z78W(sda9}YJPxF6F(L*I#^%5y)-qa^Q$m|>$Fz93O*R`>~Z%=C}dob@|3zwAP*tuJH4E#j}zT)0*wz=lZDyOD9 z(FVoaucXE{^cHCq3xgGBtXxI3TfOx?2vzf-D)48u`@Gc@f?YxUa>!|Ni)b|?Bk$-N zrPbk?HJmpE6n7NvNC;sgv#BkPSeT!tTrF1INlQ-HXy68F@>>xXEjiOBs>BG&C;f*! z+1EXRR+>%|FY|@odYVwJpIS+*MQzLLZM-NwO0T(<{5_j))IDpgMezG%B;V+?oz;Pc z47}EmY{(*urlgiw9Ozln3(iDN$Qn$=pqVT@8FODAyR~eUdt|T97Xlb3k4JNt#&C8@ z`SLx!TFm|x`V5l_eG;aeJ=b+{5j+EJPutjL;bQ(v=HaUB`O@xs?bXcQpRVpt=R=!q z4r5bO#>0`=i76?}fJ*VU=Yve;@@QziVcdSbMIimE)k^%W4&_S4URT$Ifkfk%lSBm$ zbp)EJ;qyG>ru#5cvNy`i=SxDZ8mXzSLSJix*5HP0DL?I^0=3Mzke1tc&RUPBs+TgP zBq2$(c}n&q{Wo8hsxxiypx(h}*q!h%`yBMA$?;m^b`b5R~0(G%UTh?xtXxO*!W6{#$Ql#k*ylB@SkI6P=w-lK7~ zS5w-S-pUv=h-!mB;}D9w5r%;cndau)Zk@ZY?|;#a^3dJiNJPFK|DyFd^n6q4 zR<8}8R5TkbDbaCyb0>UU>f5*>HhZANK@C%p9fpHZJ9?x}!EgoqTI%FG)C98sZIJ4G z@>HeM!t2@Sj3;NC9-;b#auIeQcR;_NSvSD-ghNi$Ug*pFaoGTe%NCiQE-yRszkL9J zW|Y1u_MJ&Ud(k*;%cgx`)g{j7$CYgcJV^xG>!L9u>GY9XRF9Ohym`)Ojt)(pUIuy& z#bTI^26}*rA13T?H=dKf$w}72gsF*afgQoRlQ|{)GvCG?NW*?7`c&0N18X~VuLVi1 zIoL4H0`Ro+lrL)nNqSKjxkRg%Qowo&%81u%%ymNop~a-;I@wn}soTw5a9|kvx8|a3 za%hp@LOChTtEgJjN^VGbx&QR;ZtFaG+}FE z@0S{+u={;H|1s*INR*(JrWy6*&R-8=8pVULRNYk#nBGqZE@>W?B2?KmM(X;VmqP9FMyH{Ls=}0gdDDtK@n#N5sR^ zaUktwTe>MMD?^mwf_WF@0$&I!Bu`P%+Oo^P2u^=ekE#)!&1%lCE!ztN1M>=Kuxb6c z?r=LVD~VOSziQdJpXR;+`ZvY8@()n<&j3-8vrB^YQ2|-zRp$2>5Pjf5pgpfVQ=e-$ z&XZ>AYz3)Za6A*q5g)qn!>-{A^r=NJ@Gdnw`?l87eL!*2wH0FRAJPH}pO7<`r|25> z{eI%svM6n}l^1C(DKH%8;5J9RvjC#n8|$yL#-R80c(2UQ>4J!`&)tJfc%XdPa5KD- z&-9@an7Gp?4L%1s=l)e1z^hM8PA0)m*&L9AwFe1)A&j$>%pW2g!D2N5y*G0CO#bgC z8+D*M0(kfs>vk#SejPg?`-0RYJfbJZo~pz+(yT@wvbuUuU_4}3_s>u26i@0LtT}fC z6)R$FD>b&R4RPX1d{ReJ&ZdHxer8r@Cn=vNVH)@Mn?Og+3H*CnmnHItmof+X?;q9L z`tO_A&=8s;fn-hB%3G3b+8-zaK|oz^2c9!V`r{ABp$8lE+!5Gi^M=hq-3Wd`b2EnT z{k{GFv&lpveYJ?=X)v!crvc|8ySb`l(XO9cF>kiI=r@jrnbZU`?_9m5;EqxVA4i#8 zLbRYvZxn`EUVI0Uq*qIPR!bRDL5MdTGWBc62!dA^6ukGAA!tg`b$@Z-{@G@*n0jsz zXUnL5cZWTNowq%H&rK741&X@>+mN0~3@yz@lGq&^L22&Dit6gjTyBiO`hde!AaShV z+C7ebITWc_Sm^gp66Z#5kwdea}zBg^)^`jXwGhL<*u? zqV~AHcJz_GGFXP=#<;zUMCIZc`qPOAThd&#S5B{8 zGyJbpjd}MHmgc>sFP^3+=I-BXk>pBrQ|Y}?pmK;4zdda(Fgu$}?WBWQzE!1SonXYW z{IPZiG=Da?1o>mE-cn(7;n~yUqVe*jr&Ivlaw(6Gj>A#7E)pj*>8ufeR=EEFTuM47 zs>!1`a&ow>tW|hhb>n#WQ1B;(vV6|w&R-S^s17Rfa1?(F@6nJ{V&>y0ikBWy& zA417eFK%`MHr%g{6GL6EBt~VRu`!E739pC`YoPd4O~jHNC;R6(g*r#|@hGlqM11wG zBhd*8S_o2F0X~@;(ZFa|ASou;5S);7zNqn&h$>F*VFayILTDI<)EU`mxl~D~)ePAb zVMEZ_f-@b5^m|d!%1b!u!4d>ts+>Q+-Uwp;2`BtSKbpXcEP-4*V2U?7KFXBnYwp6C zO+0f5qi*Wddg!CVu@lkQ?#9Ch4wb$KfA{kS|LDMfNeOZ@@x#*1ZH4{QV2vO67VOJY z;Z=BI-w8>`HnVZ?S^RRQFD%*}Y zR8It*7br$_(Nv4P25moQL zRxUWb-@{xX$ZE?9DHA&i{pyI46Xu)6SF51M$f!DAb3Fn7y4w`TyfYdNw03U*{X3&pwdGET7od?P>qY%aW@2lmDc8n;Ei67prpXZtvWLdT zj%gYrE-@)dAxZ0}jEKn2csOab}U^@+4?XGGL)l}^ozFvnp9BoJUVm^{*ZnXG*N>}~G&XCTo1Py&W|K)hKzU5d^KM9db-sF_ zm~128JIi7kTa1CEy3R4)R)Wi~QUqm3GbEJQRmMD2jc1m&i8)gf*;VYhx7*q$1FV5I z&}#{P*83JE^Uz8wLF(LBZYU=?JiDHtX%mleqi#AsJ21yb1QH z5k{mknxAae)zhtRIDQ7@?gJb%s}5ARe%KU!&8)vy;5Op%*D$BF+LhGF!??G?)pDSq z+ibKl*5WA!{w6nq%dCI8dZ@-PC^E;DqylSuJvEj7_+@}^&Gy@_Byd>Mp{}!&wo>FPezi>$m4O`_Ro%_jguJG zL~=ZD#DiKMJi)iWhBc)U`tu@ivu@j$2S8&v@~j@{bawNIU^~ul1&P9QW*7?7I3jnxqYI>vU?9PG30ph8 zbM^#Pth2T3+7}GzzlIMpG@Q%JmT6>vuLX+n6}%h>k^t_GK)KzHjovgJSVecg& zTV+Ex+-XHbo{hsmuVU*otAWBf>}-o{wUmWP>jZ@Dp70-^jH_q?w)}q%&-hLJpxn}b z+;|c;@Mn(LQ>+SU^e6_-hpVw9pbHLukCjQ}OK`#ns02P;P(K2* z@wgcI(z1w=k#WodVoUuF`mg}z9}Vexn~xJzn7SS!2a1(QSA;~hl<$gpp z^scqk_+VvNAe2Udj2db%wL8BT?ZOvr`Fg@5+CZExDXDPU$GHE^U|L5;70GE*NW76ClB!+ zAg)-EhW<%U8rmq36<9|AQ(6^4(uVO@*xtN~LD$!o$Hafn6S@t=+I)EitMPNB!V-jU zA4Sj~e^3n6x(~@psApP$KSBD=YXAe|j9SAPi5Q`%Kg*0$~9t$b@ zdY;dEpSIcaOY6Nd{fv6rv9|d9YWz)};I)VK&1p)t%6dMH%K<~w8_7yHVqAXW-^ors)uNWI z?ks6vzq89n!YgWMn)DO*bnffZawO%^o}8J}%Zx(z;T&gM7E(V%Wi!lS6)EBDK$FwV&%I zPy^g;lGtPpUI+s|UNv_^XckCJxfHOjK=v@+a0`LZs>)1mwV%Ol@-X?7lb`uFm}@x5 z>?wo34yuo2*cAnz_Hg;|HxdcQ{z z=v!r{oTXS}I34gnlqw>F<%wsvB~uDI{J=lqp+~Vh80Zuj+>eCg}tT>lY1)~RC zB#*d6Y5s9Dv{gOYQ6P>4obM7LT|soi%#MKH(wL1451mixL)Altr`meVMue=8`H0d) zKmhr}WTZ=4yw+$Vb432Ks};4PjkaNG0}AM=zJvI$g~yi#6j(q+=9;lWWEq{SH2M;B zKiKK+`*Ud{3U>Z2>J?S@uaI<1C%T7koMi)VgCO1L?Q-*dWdJ zmQZUCoEtR=TwZ;x5hZL3?jLL%m<8L?U1gO*hLfg$z_<+a0VYYUo;aF(7l_wbAf>zh zdSWokaYY;}7=$2#Xgz+7(1kL)ZB*!=5aabg-YyWy{xFYd6v4Z_u+jn}cfv#(HmvVg z2*1+sv$1PaiK3;Hh@hpwQ>yR(+)=t8950+(!91ptOU)PR)kNk?Jz-t!BcE=*uH>r( z@CEux-VH@Y(2Al%B&*m{zOSCU`n+~Ft0q~IzpYa{#D0WI0(28vI&l6)L&Nh zw^0)eU5Rtiw2qkW-6Wvdc{&gwTf--rRV6^n?O7X!koNKuqC0|CHAb%nbs$X4nm9B| ziwai!D`@>xcwR1mUYrYCq`Z6mAkQlq{5Rs^g&nWbKSe=jswahAB#QCP*kF4Qn`y!- z&wBgK7BzMe14_kE58ell1C}T+S0s?@L#@0`R%46(RjWyhK4>l@=W) z(&01b0*B#23%pdwY5&{Q5t|m&gx$hwE*=Jw=MFrza7$bg_#g^_$@)%tqjf=%W2YdL zEclGzeFPSp7>%>qJq}xjN&y>@4~s7Dc@+vde&#C8pstnA=UX|tNBTTu9~TBaJiW zR`9$VpecAs{7%|sn_4#dteq||U=sJF`+Fw`UwOCcKNYyjlpIRG*_%}6w%za+c@YCC zXmLwB>pV!M)ag7T_SY1$xAL?=sRWPL8J&j(yjUtKm65~dz`tz^isuI623y=TU3pE3 zY_?|By2Ko|>=NoqP1v`79sg2e-yMs}I|r5daFZ5v5)rDOg>STHpVH@`l>k=!J9}VN zdJWoU7#EZ;#NP)7xs(z~I?D`P7Jhuv;)qQILQ4cBO7W*NeVG22Y#|Wuc62sQfT@*j zp)zbnOgx6)OE4P92}ORSV_sxgKq1g72xT%?B**V=#;(_2yBaSNXso=9xj-YacLb{tj+DRUZLRlXzg?f^womag5#pJc zQVY)b%+%=vee1j;(pb#sHJ^*(R54&w>@TPR4fJ#DRX3n8B7>dOTn>;BH390DDB^Mx7}?4%d|Cc#4;zX&(K!put&Zp&19(Z!T|@9Y-Mh z9#$*swqgMUxuUn^FyBUXdcG#0D z_RHEDh#aQF`Pa>?r%n{1tq^(w7r)Sv67j0#=q@6yL%tWPd14M7Y~kvCP{?2=!u8aM zv|!>T!Cx!+D;BA=-0PePTyHTW`-14g5hTHv>Y@~N)j)vMz};H)BvoE{Ak0kRL?iP) z=UG=d^@mhY|NB0*R#OVtKUeDv?&tG?+fjKbp4ATH#!4%|gcp?-mHdJtbI?4cxN^vR z_M6PU$A!>gK_h7hkQ~AXH`G4t&#NB{r&$SoE_YrSPf|@}stdr>|tRBxehu z(%bU&n&TxtGXhb1Zg(xja8~uZpcl{XLzjfhw zi>r3BqUTc+ABfnTs}gu3|K^Xet;rkj(mi7$Drm9Fr(TfnljOgN1*?oB5f+Cpp0n-r zxhp+_-l!Mo1urag+{4tw=Tp>F$(FXi?H8^-Zk8EOrm!MED;h-A(A&Iqa60gR9l zH0Hw*Ll)zMfy^+MIKcWz4s>yC&tvFeNacjrnykowLk5qg>m{K1TTIJC6`8l;OU*hD zG^-9mrK67^dwoKC0rFQf=|zC3Is|;?gu`4yX6aF$(eW_m4p+b`KSu6Lok5{xX@@6e z($f;QBk0IF$aygWv}KPc=A<_t@}t2oRExV$B2aVHIn^QI+%R7#pt0WsY3uz(|d(?oyvz zk(VndjII?l}Q;rn?*j)52zek4FM13w75k~dl~ z8*HXEI!T!4q_oXGEC&i&!9vIg^YvG!nk(Qgs_k=$1Z0E3q(QJGl#v=W<$3*vePaT8 z?n`vEmMC@L&2WCX7~rXN_|1GAz`;NX4S8A8b0PS15&o+lGYw@=`6l5>L@%?_zz#_# z!0#j^H7j!3ivoAN%m_!`p7l~sZbw4@__wtI$)ljcf&pR>5W2lxds2Y88hc?Nj0;!V z>CRL4(9C4#4*r&nwpl?A%PLj?MZsaH8|qm7(IiE*1=*^#Q%`o+lJ>#h*-JvwE&JGx60gDHNW!&?9( z|MIr#^#*=yu;KgxBS>{^-avy6HfR%};IS7J$BSpM7-KriCh;5&g0 zRE`~5KN*IOD4g|O;fDgcX!E3%Qut}|>!!97IS3Q`e-21v&l#6vG0owpq&J^VWn~H4 zap5Gblg6DW5J`4pex1)9SnVPPQ-9&20L;j01F@2y0%$^ozOH9o`pK9o4)jS#NM5fp zMi_fvtPnhw4!n(yuJSnV)8~1bTN7ng_kVi(b06$(laB?1YC zKguhK^s0)~`Gy-KPrV(8JIw!p9XZ-iWZH!)0$)98*eE(9t z(Yil-5i;CB1v!iv?9CsFBU))iX`rLbt>4l|d*$Ft$qRlyk3Ey93ZTTFR^KI*jvvg! zwVFFQ;~m%YZ&zLX$AKr_# zZ7p3)DlcOCgv`8&VVivR)thOG^^kly03B{Xfv%CP4wI}7BO*z(wkXD=B1AHce{*}z zxcyim$34X$0c1}xnzBC%7BaQ;5`QS=cj6HVAG47Rg6Qpt)g7vE87gc!(ox@Utiz5#pB z9iW9Dv-CDzZ`{Q7FKs7gCQ&tstc8F8PAyIG)!CoJ>zOw6x!rLFSR3BM0kh!?cqs~o zD0Zbdw4j}UHp~QE5_XE^zgqncoBdB>#qZ|;@rR82Hi+%X@IEnK0H(5dvh^KKs-C}3 z#`{meegpO|hLJY;=IGZG{3#!hUZ5<~h`qIjieuTi4N^>?hTv4H&NxZ~wqQ zRmh7pOp5M#YiLtet=Fi3|EmyPT8IDDov0i}=#SS_anNB=$|j`o`fQR3Ng^is)#VeN zoKM;!Ra1bcp=Y0;mjciwN($nKOd?%dJhW|cX%BvPCSCa}S2ywGUrMsrcS3=uv4=ow z7iEiGMB*mV-t1RN0Kt`8iJUykIk?PGZD$G8x&TxeITQaVxVL>8zuPc%_yfd{LGXr6 zE}O8HQ;Qo|v2H5gf9H6pN+Eo9wt~GU`zZ)%1~YuUbfvwtr+vH`E#r_-rcv@qYv6jE z@An#U*7Y_L7UwcYt;{e^N;&Ok21}QcaY5r&50w)Q!bN*lXJM-}=f83neU^?{Z4Kw> zMmIT+zn#989s(tP;)m2?R)*;6kMoJE?&OJ| zUc_B1-sIu6Zxivjpxs4Yzexy^2UZbyeOJcEw$Ft$Os6hufSHmA6ye7=<~iTJN4hdZZ0S_&La)Qg z9)xA{>dkP**XJ^AUV_j6pqZKT6KFb2o1b9wo0*|0DHZ2l*2)FnaF`WJ!5hy9`SC?{ z7$b6cLs>up-glZw&Z*4SuLMX-WQl>j%aalEb0FxdkjOk0;z_0RmyVg7QQE*q=WU}s z65!V>4|8%`qYR?zbU{sOXoA0}Vm+U(sK-ze9YsSbM8tIQN`D1+w$G#^?c?A;2d_oS za0!b81D*VS>+}3+#|yZmhOHFEu1|79-6K?c;wS|_xp)$bj*fEZ3H>Bvl1Khn%EPyo z;`Z9yw|;$A{&O@G8f}Kotur3y+FkQ`JywZ8VJL}5Sj6mMZ&nkEW5T;>?hR5Nit?T# zYl*wCgO?(;;+&h$oOlSYcWvi^&dr=rvSUbPWU!vUOFNSq0QV7)`bYM)C|DaS&H8+A za6;vQ`*geyJNEYjdyk03lPCq@fOq_zN8rgnKF1B0{()3{m=vINN&hKxFe^hjzA^Z- zj4_qlijv>IGW-nz>FNT~lZx=K-kDc9V|e^rJjVBDIuw8q^qju{<@bPT5m-@=cV};Y zIj4cY|9PQ8g{CVkOMnt6Dd;_S0k+RIY`|=Vj3g<%=dGcfp2uhNtB1BSZC-+xUoPZF z=hZ@LctLQ<&vtXld0mVYNKgu1{(G1of39x$GhNbm`dqBC040BUot~5CU>9W?Lja^g ze6$nwdKkK014c|aEV$Yv`rNTwpA={*o}Bk60Liq*(w}{P*m(I&#Do-R3Kh}N7Dhoh zV!Ho5X-$UoQ`#1Er9FId(}_d1Yyu}rQ0bLd_Cng!ybuJV3-28u7*RI0|n2IbWP^E=7UL%sHt-a3k}C>_x`>U@1`Ud zf47N6$M#XArwD51d#^Y1o{lX1pD}l7jkU3U_eCrUY{w@ZFTF!7rMmJKgwRn+q1qRd zp>Pi(6-T6v2RyRxlN&_D?V{aJEvUG z%~wvEPEUu6f(PFrdc+jclbu3k_l_7WWf0O0l4*V5&N{zdvk}a?zW_Fh)?_~o>O4w9A)?h^LhDLo?I#2jmYNxl zdvkA)Z`^${eMt}B`(%U-Y-UZgf*N;(j0`uuUe)L!)^XEoz3u{oz z`n^MCpX+E;1}dnW`51=TN;0*Tpl1dX^kI!sAq1-Q8R-2oq~E|fkxH8^|KaVKpIM4?etH*={^)!rRE%xJ zqP;Pq{h1O^3g|y=4pu>K_4sk26#my6u?HqYVJobl+vyEfs5^zv>G8dr#M~<4hfD%Z zfRq@u#kgAf3H@VZUL&TYlV}iDW>`yFHm=5QJR4gb(uXEcwi=A0W#N6#IRoDf7o`zW z;qfWD+g+HW7=xxQsW)oa@#-vUK5x_cn-p4qlJL{@V~?){>sALj=h35SuPufd5WmW4 zML)sUJtTnJm3Cv;8kKG*_hO*Yf8_-1LGNd(g=X|{R%9~UHdN8@Vm&J0cE}^zK(fXH z8oeNFTpkzcIs@iKP%jlhN}`kimm6!7A1%^_SUjBR)UokOlP^^d_lmo@c{ciKox>)5 z6ctn#7N-{Npis}PT45?lSWD9}PH(i4(1avvrkEP4Wa5|S(sbGDh!Hs(r6gABx&cKnq0!>SJPghJy<@E2 z+PaY6um0|c@iiVg@^Z@j*?slgdL?c>yO1A1Y6Vd8=d8~kJo1~Iyz&~Xu|z@@SWO=2 z(NS$4NNEHLHU56l7YKUaTcqdz8a_}4OxJa;z4lrbELcD!5@F%Og&cqU@%-_Rf8^L> zkL8LhuHc$$uHl+%uHolD|M|b{y#D&!n|ZgL_M^Lay0_ zlN0%KVDZE1NGzO)j2T!S9ei$ZNw5`(!|U*^+?q3tIA{QDuTFf)@6OwPvMNCJpso zqJ3l$EtDpRET6**!bj>|7rL}CKL(*^HsjtoK0zcA(@@{)CRoysu{4BnZV^a9o8hA) z?V&zi%#v^$O0$SsF51&xno>a&nPDI0wt29$fx=g|A^`u}&G^=}Ur?3mW$XRhL%{e!OVo|6K!`5l;3Sf<*9vVs# zH(e|~sR#9*o+1~J8L`-sn#=EZX0}&JD~nTJ_yucDy!t(i4G*mPBksh5_*^M=#ujtV zN4`VA8>Gk^s%a&1JKlUnmU&e8d z|JKcPMU#2APbv-Z&`&-$_85nM3-Lw+YTzA8&MYT(L>)tYA@V^fd_s_T&s=2itZgE= zdS~uo0b@!D(W9ph)Inv!y`_WTvs*F(nSEy;gjM7lHo8h742#0QzJ*MtQMz=DOJvV9 z=`zkoI@uOE4pB`3y~}4)^za6RaPkBILOPz_AYsisxPs{7TC7pLagj19`qOLUE*8+(A5K=1u4+Ou$(ih*w%;On-cFHoo zIOBvd@8uxweBz6oHTMPFx``$sZs@FUu3*kVj}Y|bUJQJY@nOn=ePAo%8X6jS>#es~ zvSbNuZEaLlRRK_0SxH;lo;5ApS1_!xXVu2e2pzq-)d2=ENOElN zYe2r+_tG}ZP1!9{_5`W9RoJ7K*aI-j0t3g$vC*~>hMBRxH7i0`;ZYu=5GYNMJfIBS zZ6h=rk+Xa7o*{bNOlp?_qLYFl%;HS9qu1g`Bp zxEuSBgPN4KLRdDz=eFR#r5)L25eQnG^{I9|E}L?9k~?QU&1ogu5kp2s0FtEAd9t^b zXZmVrF@n4vt>E(QNAOg44Ymz^aSySS&aUn-k)#V^Hz73_vhZifir0`8FChcxmbnMmhWJv$^iN>j;O#8QX*qdBb(L+;R)I+;R&59UUDx_jy0K{!7dG?62+{ zbD!1qjhwnoKcQ{T}$3e61I)rk)M?}|HyuhI<=2Yj~mdJYXFhehGtJ7 zfUa#iEj?bQ`*Pp+N>~0?Hr7cYxVaO6qG=X4K3Y$}XW@0*EViSZrCRuNY$CsH$}@mc zkhY*kX8rC-rSrGW@pSSOZejDWl5NZnc4cfamE@b-m(pu_AZ6qEO)IG{lsV6+1;VS) znhbOlKe>H7vlqtk_-#j}x>r z>ns2&Dk^AcX&DM3mX~LDDF(Rs;)}WX;)?;t4c=FnKfPw$eS+SxAI#dyVt)VikMZr_ zKZf#+!F(DuIf)`Si#{m{c((ESjtTT7ot3xN7Qy${ZsJEyb@C~>pp>HoK{f+Wm^S9* z9Q7|s;d`wSy)#C9el>{&6Nkf`1LK7scn4uAgi@r-gBTNwu>5YcxPevZ!_^kaC}LKc zL}4Q=P?DUA69RBI_JTHSb#fazkcs@op;l~XqBSM)5F}kZ8UB3 z(6;-%wT7`ub^fQ}U=FW(GJBhX_3_;GL;#kq5&J;}`kEM*Ub~gTP5D zYy?+tTR?9_N0=7AU$mmHjpKUFp!d85&hzLuNqi*5%rny5Rym(-I|}e=78g});FRJ< z#7N~12|j7^ol{=myx)Cd_!2ak@q3!Hy~M5@P}3KHF4+1|2QGJJPD<<{9k$lf&jsc6 z%=Gn8m+aw=wgYGoVWM*le(=g6tlj(r%n?SW*cIf$x@FwDep? zKp8Nl6c=4|5wm8^;v3)i#?a@dpME-b+;In2TyX_=+;IoXmoNX9pWB-^f1L$i`rn+} zd~Th?XHOn?j0MwcSn-Q5Vp=v!FS`L_98X|YRXaDF{}}qf1n1IhZdkE|zpgn5t2Bsm z>x3U!NAI}{vb`~-(3|_vM(7HToC@4qJ4nv2$s}-CHhN1x{&!li-I~Le8(DZO1j;2z z%_zsWu7&8bX&7}SVA-VRRymaP=eLoVUya@#Mem4W1wEK$K|Gtzd;D~e=9WL#GGA)1EMN;9prw2K}{`1`5i~eSU*tInvV6wZ9T5klU zAZ}~)m>#53JoD!g*8CX)RukH`)KF7Wk+jfv^kALrK~71aq?ff~(m*3)frUZB)=AhJ zel0s}Jforux1Po@ohc?YyA02+E=VbqE{Of88UmU3sqgeM{_^Z3&ON4a$O}6#&^4|R zo9*xB#;MQa5;nS2)Oq_^R?x&(wjIT~cnNQ6#n_`fr0#T-2X?RFsIpnS(6KRN+igp} zm=Wc%l+C8kN^tpz&+s}VnS>(ngC<^oMaLTD9X{V%%};kNBx1R-eQCr=9at|ELcfc9 zUR=wHp11k@Nr!UQ#6!^KzLA;wzlsl(0b8|d6?fiwC-dgbWBzU6XwGQ^)e7 zKRumyA5B`^aQcXwB^zPZ9-1KP^@GdkBL1R^;sjidx54y-0MrxNAT zaP8{FwX=)#+P)w!_?b(AH!|NcdE9vR}IMQbVdBzU*4l$)Mf z!p=283Lbn1F*q3^-%XGBu zDZGE|M5|9>O?Sh2CbGmHaY*MMy0H1f^k=DZ$8hIqe_$%X_2)m%4_BRv?bqo&$s*+{ z#QSIu*6AV8b6kg8aQph{l=npV=qJ0868cRy2W;-*^~n{(r3a(%jR|WrYT7gjJ}kaj z^A?}kd>ocPldtFz7Pa1fy3;X!bl~L#U0!xaI|;dc6u2|iVtYEo4;l`^7rC&a-BSk**%rpPuv&oHLUVJ){M2a^z@5bxaSumpp*Ql!2v}w`N7o#}j z$D4!U2XOjfb6K--7f-ylDd+aZbxlK_*GRZ^i{%S8vVM0NBAG^-cJ5WVQW;FU=rPkB zgEJ6PGp3dzQYPUC-@z{MqD4|@ow4Df<3u`deoV#;QnM?w8I&%K)a*(EYZ{SB6C$x8 zC5PnP;c#B;@H&DoY3BeCbp?JGwIrbUtX26k4Q%)0a2l^(o?p&J9l=#uM+% zAZ<&^&gsCquL&(WcCo2Q*nonx1h37DzA|2{KMtWeg{RB_yAwoO;eM(gyV8Ym<^bbD zDX?Mr2sxtBPzp9Bis-exydWw#Yx!H;+c|yw8)7Luw{+lsG&|uZ(>9;)Tgzu=xA11P zly7c70wX0bXGmJT0~F4LR)*^&Zxq0{tKbAdz8~#rrU)pHIL(EMoyj$QqX_$Y{HMP z$9C)3ZXLZlHfAq0Z@@+=xa7{$sBbC7u$)J~#a)3qv&1O{7=uA6do0ImaM{QM;^0z4 z1SR3;d!VRkP@_P&*SmQMM!sSr;$Hi6agd+}Tp{nvpvrD0ZuVT1uwVnAs_*Kjp-qw0#W!(QhYQs94W zCt6n=y*+|i8D=!}_Q=q`1wh0`+6FS3a>f~eIiY~m^m6=p>eR9!z*Yp;?Luzp0o%q7 zc+h&{Xeu>y?)lOs8(s^bCIs2qRx)I~z-c3bOCvR-4Bz_vR9T3Ecl+w%Lu z2euCE!-z-@ZNfo>6;@IJ03ZNKL_t(sd;iLzf1i8Ronsm=fWCy6-~OhKWI|#MymN`T zWYarA)_%7OS4)3JNDvsH*eg66S_r(lbBJiH^(XU&0|$kP2LB9F?_-D6l3Y-OT^w*c zH_f4MCl*d3v1lUhZJl6^2wz$@q%8ugc4Qnv0N5J-RXZFS_~8Dw!qwFK-V`|$xLRU>46F^)VR_XY)VV;{h@ZQ*#C80I) zVnxh$76C3XWV|NNOz^ws>Y2SDj?5!}8Mic^?4FEAn7D-PcwnjVYU&2OfD)bl>n35< zc|ZrHgFVqj|L12o`AIo=R{*7OKh{g|Gut8QT!(B*Bl-u9m8WiSz$7A2ywF!QIMfhK zThM2@dA4T)rxkB!dm_XQ&5m+<)N&EllbCIeB$mC)<22q>3fD9JxSs06|Faf+SGKV2 zT_2a9HI43e$2dpPDg1teRrmOL`SCEy zQka!_yedtSo>)YxEsEA1NAFEI;lS>=Gf9sM2l`&$m1#sQg%&lOkYkRRhw|to=a0%o z*b04D4}sN<=)IXG0!kAk51N2?T??TnHjrE}(IIzq#`4y4jU6-t%c~=-{0%R~;5l{0 zunK%=y$RkvUq$d_^*P@=8%Ss{ze?)Hh4wG1$_A@ zP51)#kmohhVOBU4__v~EycQ`Vtt2}WLCSaZ5dU+G=w(&l7sy>{@CfWG_lUD$Xh3_i zhvfXpLL!}gU*o#%Z4zEM`-Jle*KS}WUZRnlZMkKF?kq=T}R)rCUl@-QQQj(fgO7hTB3V-)Dp(i$s zX`IG3UISZni7>_|a0pwWMN-5LsRf`r?nm~-p+L)Mry?vH&*t$v3lE-`_J)Z%!VA-r zU^^Eq8!X&+L`co5!1z)C-)$|3x19Xm+$FKRy{gv5ig=)FD*x!2j7nTQV(!{MRqnfKXe`5 zI~)UHoLYcgt|Ob$c<$=LTJ4%r%+0O1owtE^h6q4mqd@7NqW*CBMv9$v3e(ZXLPtb-@qnwLIJ5MC5U9& zA!O_c#n-mMtQc%cvy(m9HBev4@FT#D{~ zbBM7icclqj(M04=b@{`6X@&cNZj4WtIcoPIlfX?5NhpKZZ-ejlF1&YkQ98pU9f!0b zaDT0V_z_{!hX*kiJE7GyT2Ap_mryjM3WomglLmF3e(DoBin=urg=tj44K5qI-|HkU zM+A0jrY8B?&v!HL(D(MP>@DIcojYFGz`R-8IclDx!g1`Jqu7{uiaCLvj0H&A7Chfq z{XUPRDA(gGD{SKYIk!^i9zS%wANjH0fb9eP=D`=a?WuhremNqkP|}y;ilhIVC8sPT z5+6|uHb}e}wf|`JpFS6vHqpA`q^k>t4Uqt(M)acxfMw%q?!))SZuIu3Q0`KH#fJ;8~*Nqw2VDU-YOS#Y;K?VEIX1RN2l)i%3@&;CZ{rF<#@TH=Ji-MCu%@odSRn^e;Ah|mPlkJgf&R1CU;c=j3FNbY}a4vDl2N{dLG zQN#@mbyTfP9CeyQVK>@0b3^m|p?$U{ z3%P9jk$io^TBiGYiCAv#>6peHZL>yyQZ=s zUc{%%>$#w!98_ce!Oc+Pq zebA7l4z9^R-WcM+%AFa?^u4*;DKfUyKpTx=Bo@>R8OX@Vv(efkxZ0ywULDJ;Q}|>( zjOrscG_>XLBt6@^F(wz`ZtQWIW{y0U!AlzPw@aAZ_QP_Xf25F9LS}4NrtQdIt=z!vw7o8(M^7a^sSr&ADQ#>eP-)F+Eu}fLpEmfp61eOnDUsQvG!+hU`K#wJX-xxD zX138#T+Hr@Q6aXFnR|g^m_z;61|a~?<9)bZicoTlpk;aq=>z?UUW@P>y`=AokUXal z{2JCG9}n{`ED4cXoW%2bB*XI+Qt`PTG-q{~KDTTu4|PppS1L4gN8XN=@|n%YF*XF2 z(kRuU+%oM|<^_6&9>2p9vwrNNaQ@{0#p3 zE?wj8?adr}@N}e#a9+vtT+&j^T^+MBzFVfpxo*l!gmmXQyr^mu5!=mQJ7(lLn&P~Q zEf`AUpwg56sn_*?1^W%yUZSl#GVVTuFK{mOS$3xJN?W*hbfNVovE35ACrwzLlK+9tH#WQM`YI?beH#r?(DmcaJvBxjam zl?2c`qWISCMvobp6FP&2P|<(fbnL1yNQtGuvPOl7?18-ph$|kKN!;+|ZnUTg8mCpz z*jWP&I)qHLSusS(02w7@GStj$1Wi#UOTBac=^BR>i)B_-+=8Un!9uL?8wBt z+6vy;k*|l@@S0!tCMbCLU4&^nl3JQT7y}`=v^7SJPHxDkffh9gKemD1^A@58y|KO2 zUentmgdX1j49ZmrQZvek9XVypeqefM46~{r@Ah)Y{d$bHX*tw_zR zz^E#q=T-jhE*E zSQ^gX5@#$3oRY#3JyZAgaUBLSf?MzjFN6hAru`y;MRD0{nRWqbpRn0 zEBkV550ViY&h=XgT+)7Dk6YS?=H3u#Q<5-rZ1vA~$1DU-bk_nPbg*iBFuTqHYxU4R zMJ|H}X8)Z-Lapf76luBTyDHoXuBv?%jREb-yCu&2Nq5Kr(e_m73}xG z_7Wbq&Y4Hd<-t|!_HLlGMgK=;z+l3D@a+$INQXR`6H>lqsjZxRm6XU>3gyBY2> zgZZMp^}gC87?X-f&a1}uxo|c25_q`*6i#EyngS=}W-HQ@itw-6j(1BZR-un{u|IDv zE!VjjGt21@dI&$hVK}rG)Tt@&qyNb!?51Q+e?V$Bi;jtK$yH5keA7?e>;$*|x_Ed` z(;RkgTY5pYSHnZ90%y$Q-fF*+hacJ~zV=-L^a zwQy6$y5g7H4sx^^b6noh;U?aWmEo5L?dbsKsGTG>I}#y^^{$*hb<&o2M+j9b1?jY- z=Hc@wwzQRS<_+fqaN9+HW6p&33=eT%d1XpD!o%NdIFz>ZaDG|CM4D@}u05=$xqj*@ zAfx&u3x|Z-uo1YixMae+oL$_R`$qo<`Q5p%)2s?(OfEuii!k$`7+2r+F6ffi zp9yjAZB^`eJ-gu5<%Z=KcH;3V4mv#MTrM`&fO4(xt=;JDkv*dEBOEmm=aOuc5_t49 zNz(<=Y3ixehu}i8vgfe*m_MDhLW6Ew39PR zwxbKh#(3elLDI4EbdAX%3V|*aXO`?h7cS6qhZV*R{eVqC0#gw+U&>QiPFcm%qg@_gY zkd2iVeCfwcLt?N(KsqJxCSt@qA&>%3eS3!S8a#v1ModXaPcFpow-BV6-M^XG>OR^! z{mALX{PGuX^23Ktq`Sw1(ltn1$V8g(qwA3=lgJ4Nke*lomIS{EDV%=~9aIez^5(qL z;IR=ClKJnAOJE)v!2Mh_^K%Q_*Y-2vCz9^4A5ADuD&EOOl^aQ0lGWR5h^0JuD&quZ z_3>w|mc~{$A1~cD#HgF=n-Pb-(Y|Q7O2mV;Wvz^403M zaZ3$6#~}3D_dZtaR*2mKj8L6P{1ugW3>CZ#((6IBgX>U4;FAcMKfnB+iv0#`FYtPO zBfoxl6|Ftd@psOJL3-kNOUFH3TJE8@rbg58-wQ^J=W6aBVhhS-nw<0vDZsFBHT9C7 zR<=jymjO)>KWs9%bX*PH=qonUD;#6vvlYstAyO8arnut9oy=VlBM{1Dh6xF-`|8n7 zngwM( z;IkWv+&u*)H0&-N`gGJ*KZBt~F)88b6lSHO6%-rDLn4`ai#hIH>Z~GME29}g#^{p7 z+=*;n5#W1={hgDJY9wt4NGh5-{H)wq%eIpNWr zT>-AFe1$`UeGt415xDR@jbmGV5WU`!wj!5gS`?yJ4gc3zg^FE=DtQX&n)v=s{zHln z4T<9)|M&+pXU=5Wv}s&(%{Bk-)8XP@-N(h({f%`y^0(mFBaG*LjZkmT`2XHuZ+OZh zj&FPQA(Dmnoo0uiF%p)I))hyK8Yo|$*!_6dBPF4iv@LjhRRArKrr?2hnEdKm4m>uAre_{kfukQeVipvpjj(KF zPaN;2wu}axBCU{}7Q3F+h}2Y&nq7h3lNd5yN*BbhtOi{m+zMHlMmr#Zc2XDGvE67V zbs^`*5#>2_{4|9;tRLwa|F^cMcra&p9IAj@U>@uz@~ay7R1zj8x$2dZIpa6y@#9C9 zfd+MxV_bgfDz=w;Nux11y&FoS+w^j6(|m4kn?;}Uk#Fi(2wKy*7eQaBUhmwWQC}Tg ztIaw*PEm!47-7F0qbqoF@oWG(OyAg6M=5qD!$Z5gx^1E(5S+am&o@ut-j1m}*;7L* zPY8E!gC8SR@S8VD8c&UI?U56pbTu;gd6e0U-F_l=>p|EZr(vh_GnehYE1_dCB<{i~ zHr!KK&~+?!>kRDnW3Y|&dv#487JMi?Fw3&IDbP`svJ`J^SBs zKCf)t$?IEokH6#IVz4jMH+GR& zG|?$KmIAFeLHManAjsJ}3;+eL#%}bkI97qzX%RDMU`Kj;+q-gWA6hm>T`{p^rt`lm z1PY1YZ7`vzpBvA87&j(2|IlI62_RXY;NqWeNW-Gw!FNbaE<#C>9d-yeV}=XUmgpm7u51NYHZ113z8ZX*$tOy;(Bca)Sw{CcEq~;Ab+b%Ls|26D=1cg3 zKJ3{25c%%#4pu#MEyfQ24(UH8V;tMq2#NQZj>s6O3A)cll|GHo|Fb=^4}}3+vt|v` zrcGnY6h~z9lb`$~4?q0yzs-Q%@Zd}Tngb1Wd=(fIi}0;$AyK{u`e~Lu{M|iaFTPJG zk52!|vk5&_?{sS3(1vG6H?cz}<9lm2TCbD8iJUYmXMRkiOu`Spi;Sk6+BaR|e?_N% z`CJO`T}$kUI#LHz;M&lR=k51;lZ~&5rgW{@;yD>HTXfy`3Y79s&X( zn_Hf(=f%eh*z~5K%E@VNYCRE41wbkKPoGQp;dLlo5yyF4I4R2umkPv*kl?_8YyDhnO_^LqJZ`DzZCGTgMZ zK&CKEZ7BXytS7mo>Rpa3Xy$tjhw@sqobdy1*}=IYJ(atNdjvw7$$^jUWMj9R)W8V{ z1AI4kkeaGt9|S+EeT7T59mQ;aF9!!Z=rKJ!(>npF6u+ObGQ)TQ96fUG^;g}yhd?*Z9Q&STYCl0S9YHDg&y?XT+-}vXkw8vL2-(&3Q z+Z1p+6ZLp7Oajku0|~Jsra0b=OLM67u~fDk)n3A^3e$7JBD78?4``JJ9p;{{Ax#To zVqsp7(Z6~pT3>P~>?YDS$}|YPupMFA1Xk_9s0j0IMC=K3=Gb1F3$*J*od_VBn8OBQ~sIX&*-3C;9@wzQ;pYaSOdJ_v`w(V_BGeniR*wfHe4Jr27~1Qp9#h!n;a~HKqQeR6jxfs)vh*& z$sOwZqi4hH&TLRZ*m|Fb2klH(=$@H+>zq30J!M#4$w~QJsWc)PVf%YEfKNcUU=>Qm zQPyA^5rtAVs^wy^YxmAvtPgOa4?9CitZ&BC*o6}`(3>L2_Bb7v95Qqt zBZU+Lq^hhj>4>O7?#tNN+(Ta75DGkXZp}0$1bSnHw#yf|Z#u3^)wgm7f#q9?9a=@= zpwYS4TPa-EUwRBF72o{r){H$vJf(BaQdb` z_!Ox{UP$Djg1d3Nya!E$QAP~YcB5W2saW$)fa^a$4zFF0^`p_8Jg<(hmO_{AGnyV~ zWk)K2Gvx93!1?8CN&eY9o^8o6(*o@rU$7mo%p|nNIs@IKNcH1up+IahSrI9r!}O5v z={^7H)=j*A{Bl0E`V=ZWQ9fC`73dC}(5TbBmCsIk7C(bBuCymik4h=UF(DvYl za|k3I$$x#QnCQEbFODgqWRQ)Fir%~T$g3M&H=+XPBlfkt|31RUg1{;(E7`VfThHIy zw{NGia-d4U)mLB5)mL8)!2J31_sG~k`RlX%^YuRE!GtpG(_|G?UKWJwPyT~^KrsHI?kW#D%WppA#v~+oV;|>x$U^3zO&mg zH< zs>z+xpTm>GiVH}S?^V6cQTeV+RX5N3HJgJ&Ev$(*@m{o?F5BQZe?9ABh3rfPNIG%= zky^^l2j5+E&%sfyrTBR3cviRj zwGDG(aSsutCKlse-;`m6DZm_+hn?>yF{>i`V%|+&S)ZHgQSi|>x6*#W+@6|cGG_92 zG{qZBLg-5sRYmR8HfJfkm<l)FTBKT^WNKGszIlc(JDT>k1Lj2$|to%{{ zy+A_0K%lk+v9|;e*9_{FL86-96pE^55p4cg@YiG4y}(n9?X>FwjHzi-!bp;M9Vlvd1D-% zU(acuZsCc0ORy4sB0dfH-gjyS5KUkBq|H(J^%&`E`rf`@v$?cvJ@+pHjIdMl@#atgL>bLEv+GIQq4y*}=; z4>t{ck#Z~aCPzYGX}Kq0?~zwGq#!x9jKti^UW;KQ=xhB*LG+}lZY)JERs3|kv|SWH zivBSpUsqaL@QV6qm;ffwP5#NXPneMs%V+@k4BSKxMMlaTc zl8o)IlP7WVTv;y5f!gLGG?6ZaGT1_`6c`%jq!P^PVuXUwi!~UvEvOk5sc#ly7LG+U zm`E*(L-%tQsLcWF%>nRPh;kEAmPC}B?gEuAzMON+p4CgCqtfEBiaagFSZ^n9cU9mZ z24Ca^2wUNMupaMY?Q|EmR08k_T2v4r+A^9<^mXv9vF~#6+LH(AxECHQEVKb`SPPAp z+AH}&#VYRHIfFty!R$aYU-5^GJ#&zxZTD7s$SWHxB^l@K zVokh|Ie{k5F5bkl)(YAzFBLMLp*EtFpf;XAVgggj4Yio)>tJ21kQ=uhOqm`-3V170 zo^fr=@vt&l!lJ@);4IJCUw@~BQcx2QGs&M_zrExOblu5x z+6E~lDd?`9_4Rtr`|l<~nvJO>9_b*55T+c;8u18+M$?Y~Ci>lQ`J;*&T=}VfZN1Xr zsDg&R1;vL2A>2h%*Bs`CT9_MhgM=IfN9FI}>zj_I%XZz?erZzW>jG!wIa%9NAs%d+ z%y+Ba-90PJd-`rP-v9BjA+V1E3#L~MeNo>m#^EAYmgm2NzM0=fJGu>Yh0wvO_S1Q4 zHNk7Q7#xLF8tPTu&-P5xC3J&J*3osrL1}lPpvSaGNYwKDVMQd1 zxQXw%jrbk63#CbfQdq@7x=x;k3L3r9;^}$F&II1IJCX4O>db!DbNX}C=S8rd)S+93 zsnF~pF93a6C!SX#SVx4gj?Hp@subFiPOKC2Dd>nX(`dtED5eK8Ev3?60tOAaSA4nb zeGd6{J)76~c=(P|wr|X|!mUnt{a;~@IJvj3UEI;=w6c6wS^7UZuZ~InE*6A$vb?Jt zyPqYSR65D@5!*yxXU4}_;Bi$Z^lqKM-Pp$^9LXD<6|`7BJi@_7pr>^hMUH-T29`q^ z3HsiPCSX>uoo8n~#?toD+_K{!l1k@wt@gzVN9vp-s2y- z^XQ{4;@R3p!GrJP|6m8cwJrE=X(ITwZII~0mO_Ap{FheZ3vGslZOHPZd-fH#37@-x z;Eg*mmPHBtd?(>^HX!O#1LkI*oJXFXgj-t3m9?=hmXGM>nSQIkK=H+jm0VO_%Zzz3 zPQIX(+n!y|j6=@xcv%G{OAvCHXS^R% z9I(I!!KZJ!mw119Bn6@?f!3KoZ;6r|UyKUn{vmjE$&@i=tlYetl2acOi1O3`x0sMX ziN)}g=@@b)i=8)kTJZ&XCmf9mzP$060d2bRxr$YMwtRh$g`s;u_b9${({{di{M6oNz}qX! z`QuAPd~(iu^8884R2*}NXXz0R)xHjpii9I5@g#U)`ZK)IS;6*Hh<4M%zuK#4HvLTS zb#l$|DNf-qD=&tv~GD$?i{Zgj+hKKZdh=9F7~c z?-^Q?fwMY@oDqdo?(?#&Zb%?Cr4(M?m~$wZNMWpR!Wxz5CQoN60wq!k&(f|;0y7ob z#w0l4d#jn~qCD6bK--oYK;Hsf8K_okpqfVd;>AB6jnD6J+&S%7ra)?n-`+o&=pnh! zBQGtj$?6`fnw-G!IiwQ3=XqvjK6k%9mOsCI2>Bmw#rJLTk5#pz6G2wZ0q$mYtitlyajsb8J;rtsnd8;&j(J zSh9cH?yxUSVCQ*ra;seuB!2if!jG?lsENq6UH0~geNH(9e!0`FkgPyNQ|bE>gOa%H z%Cevefi*vXX-j_njVdmhJCm<`ax$NqJBw$wjb$)vHtJ;mURtKWN|U{k0w>^M_E|Rn z{O^0Y?S%VT;8};20RK5HjC_{66lJh>o+OkA`C(vXbpFd}|wt z9X5_!DzN&-W~}N`APdO};pSKMk2vgk+sQ0AnfWf!!zYmU?Am^18zEI4#+xUJn?53^ z&&0QKM-Tt>9m()I9Cdyx&%K(Ds<-ey(}}jj{Z1%dVvg;#%(`_F2h3ChXKW_ns5TvY zO_0t*i_pf!F=`s=EXiYgNdPSp$2m+P24TbpnkI2Y#Txv5&R`O@xaH_wCE5Emc2Rf(lQ1c{AC3(Gb6mxb>826v!I-JN8Gq$C zpk>Llacq!%bOe+Fp-(`R{GI=#VwVR9ER9z_+{FLf_RkTwlR~(aQ?tA`uA-RN)@OJu5`8J9QWp3Rqv|lr-K+3pbU-pl|@))K9=c{iV7IS4;xQ%N*Vc& zeF#Y_9rUQMEWxIGCC83m$MWa%QA*$x>m<+0$C~cPG6Jy4hPX!L#L478@?j5IcHd3> zs?v-K*{W5?eyt!~gjZk}g@_*(>`7jB^8Ex>q&Ri_2k0T2H@eEHOLa?mxmn!zG)`fx zH^Rw^PQy+KjH9xLpc+2K z;iq)&akAN4e343m5CW~AOI`?<*cSh!inBi7Ny(^ujKT{M!OtMXUd}7s&v1ai0LO9o z+U<|-Qrq$6g?#DEBlz~8p2Y4Jr4?||F$eLt7vI~H)7(4AW>)O>mQp~Q2i4~3Lpr@g zsW(Q5&l`h`CNZ{kAb{X&o3ZnJSXF(`l59fHttE2$5J~G9hxn0Y}mHJ$My|wc)C5-AoA-m;1OU*gq=j2 zkiuS;k1fNrUo?l%vKsXI7{V)v96kZ-6g|V5vR4OTKSI=)QehP%b=drMq z0ouzczKDH!6Tu`==Lqg5?G#h*J7CwE%2LlQgbU!lP z1EG9}(=Tt~i7f~9kQcbcC1bYEtDO~`Re+)I!GYzNtq=uO~t(uCmA=qZhxCJC*+ z6T2_~fgA-Yv!P$YOl;8v5CUU;BPzWv3n2fQwb+J6YJ8EaZZCvOUVSAL`onRMEis&u z{z@o=uS~|Kf#p#EPzujW3G`JloPt)ov-~8j-k*tYO#}U<`udI(|6(clWlwgjyCGJ{ zIT2JuqMAHJFCOETOtBO{@_xY0H3!pPMenb%BlpkJquKoGqNfRZdo3SLIGk6qkvF=^ zc{@_h2hkGBJTbmM?p-GP-9RK;xgOdcVU=N?e(N5^xkVefp)NBV`-+M+gXTExD|GK$ z@=I(a4wrqWo^!5h#x@0~U*5vj8ZS@XUBr>6b~5M4%oGA4z;py2xsMO;a=^Ag)4@!F z?FfGPKVw<;Q~_^1o5xS@-Ne{Qea_MY|2csggW&*y0j*u}-D-d5v&;GBr;g>=xsw1m z^vgHziD~USC`I7)&B&CE*4Txx6;?%J~r*WCNmH8{0+ zE3Zc?c(KFP(j4pU;^NZ5bqo8#QDn9B_5`~7oPT5&55BXKU|u@p+~+={@l%tW|J_y! z{Hd(tKh!jl3(Gd{RzmZhLPwkA)h7#RZ}#x72MgJ`&5P6=G+i;Kx^Ey@0W$CNAK5(j zCme`E1WrF}_X=aScExz%y&8`H=IzY;(rHOpj+CF==& zlui!L-%R@*TL~@Ogr_-*lkdYElb@Zu@-4(+?NB^0F*gJ0mBbpAhYA=Bs0J?u$?0W- zYy1e2E~43hx2D-W1M3f^29g(mqnJE%U_!3~lIIs;f7?sfJrha(ybw{Hq%YH@#uW~p z5s=8fs0#s@4x-T_aAUn&N&599R@^<1Khvn?sp*gL^sL7@VpJX3*%kC0q#;G7PT*O^ zn}$soNxZ>vG-kdQbH(yV7`

qiYI?ICp}yQ%NxXj;{t)Q)Snp2MW#2x!zq&=G4r8iQJbhmg?aewK!(r;dac+9B zw)eh#-$GVo*H!#a!~qKoD9Q_P))6y#^tF*|mH|wtDB_!opV-5*+J{Jolmo8Pds=1w z#Wh`&uic31vu0YQp&VU*n;j!D0+q=?n4~pcO1tc#m0>ix@fkIA; z;#dN;#f@r<9a@FArU_+dCM0)1662{3QfC!_Z{Xfge>H;dZ!KU!@T=R9%@#j?VI1q< z50Xj=d;y2cu5IAwmp;Skw_ikidmjBw)OWXxM$6*zirE^^we?D$WrUUw1J`Ukn$3wY zRlY99dAkN!Y#wYbEUw`vTMi{4&8&qh_t!HeA)vxYWS!dt@5p7Ec18J3DTt-qh9nJ-ohFipx_#3FQ9-=|5q_bNzS60Rl_o zho3o_DPv0b#RE%6)UGh(=jr8ZhrV!MVs}@PblaVsVb(;7(_77S*n1?Ied{R{?DS%9 zOCd^3L~+Uu;uvbGL4&R{X1S70DH{Twj5!Q8N4q3Q=jk&DKfMO6Erwkh8g^Txr2Ukc z$c{Kht*gjje#VRFceE#TkW*vWX#!ILgZ2yNlK0OKk=-d2Qec$?QI5iUZwt<757uEp z*YwZ;Tj6=C1OMXYp7S$(qidzUzbs7mvyG%AfBNZYp6wco_3TCrUpc8cqv%Uwv6R8} zj~&Yo&Uz6k6uRb+Fm;y4%2?Vl8c_Izol^=j+gD>nw3xb75DSeGJ%KgABzDit#?-cO z=&h@`>dv)UGag1_@$E{x-9fG~t{wY65nID_TsLb*So#nSfRP}Y^ze(P7XgB0HC1%P zd&B(8@**Tm4L$#Q44+Fz(zi+S0(? zOWqx_P0F&VYwp+wv->C@cp){9LVX1)?G^OYbs!TKjwUlB0i*(3qTUS;OLSuZJevF8 zNXR!`RwZ)sRMa5FH8Y@8ujZlqt>p%lMugGcD3_P7WqKgbPJv!6WTU3A!6MIjJshzY1`&SH%53z5qG%_!L5T@cQ5OmEr&kZ*H|yu|PGLUZsC=IXr$5Ur)ytR{ zY94S-(=9Z6cIjsR)!JLww8``ViiG3dV~6eKzQ!rU>|Taa3X}s+{J&z7amgJ&sKo4J zi0{f*YfqBbP~#70enRYhOYr)0U!XJ`C19j5Gz-5c#k5h4*rJp#p8c70AF#S>UV;g6&{zXd?8|MwFq^_UZWp=c!!5UoDzKNr4^m({a{8=yjds zKeHyQmg1=KDe5=7=Y95Q5Akcuv8#P<#pINeL2trP=tp*-ViJ-P!WKez3n}_!reT2%1^G*s13IMqA#vA$bpZ|=e zY20$lEu3=7Df?tj5B=aWP8mcK(P!vf`H2ObwqOQL?J;h9^i?d!b-z@PD&o)IypS*4 z^6);HD*%pz-qc0jb8A7lBCP;gV;7O*r|eeOc5#q+anQB4w%gF9fh`%DJ+fhtHoH<& zW2h!d5qxDM-kK(&M^11{hh$kqRfB=EJRdPViU``Mh=y7dz-W{Fzc1d;#EK49y%pq# zHy=vdz~j+n+Z{#wCtV$IkcTH{)D(?PebSLmBJW z28sS^3@WVkL|-az_U9`C4g~tB;^Sm}1i2n%p(n|Ct52oVHgXiL6uj6mhA)g-)k8*J zuR;igQi3tn2}X@e@yvb2yw_38=N7k6<=cX#B&HI0P`ezK?n+}y41Mqr{gHT?cNdNW zu`Y?35)_sW42~K07)}F5fqs5R-g|zV{}izatJ@W)(zJNj4f>_33i?z zt8zCEg8ObUZ3*prz~U-QWlBu#D!NUL!M4FdU{&PdTi-RoORkk_8UenVg zubO?5ZG`3E+18Gywgq!sk!$Tp0dW)jo~+MF0Id=CoKs3Tta=-ZmmZ4M-;A>yjIC|R z?izAZx=Q;V$qR3fWn;L4_>_D=qL5e$UfWoO-!NgE)1yiPZUM+6@)`(eCYxhnRF}pY zyZ|7GNUnQ)Wv|2ur&fptBQ%Q`kmSdIaKW8Bz=pR0L?Mp#k%MyeOY##nYDKi&DIpmng$Oj2t zvFj5&fUMYW2&^yu@|VAG;e{6hP*+#S!i5Wa-WxM!40UxMy@X9){N|yh8RXR$Z>`_H zPv_zk2IxF{Hdv0k8t$$II21YbgO;`iY$sP$WCezU)8OfSTLM7~y7U}vhB?n%uod3* zO}&3RitzI_Smhy{fET?f0s`7EIyh@TrY)I0ws+S(b8HKTjoreVb>p$pL?9doeOnuP zL$9SHJEO=$qNpwpFK(NfcDZIEX?aLm9*Flw1Pck09_X*u)envXmme{Wt~Q-yLgMu~6qH!p{P+g) zi><6l#v?&rl5rfcOg9=b7eNjN+i}06-EPsf^(BOTNhXwLk0>?_#fFuB5;4h>e=TO- z(UFnO-#7~C|K!LgdH{oBpAy)KC!R<{!@w;5pZ)A-oORY&J^$8qol7shbj1BHUc8va zix&gX)YLS@)q4i7uG+-i&kr4L-xsc9XfLDZq+GcLCNlypMu`3j@mYVlilAX~!px0) z>DV`UdgT;uS#m`09D43rxK`JSK1sR`5YxbKL3}V0%OX`Z7#s&2@V{4wrzvyCEKr8q5zh zGTGO`x_AM}9+6(RJd1|n$h>;KU-kCzo^d}^&xoUW;KMhAv^RT5B?Y%#H=6I>u@&Wj zkh^C&nJJgBK72FC-+x!e_y4pRO&|HMNdSL(c^;3yKb@4N(GmACXKVw1zS6Bo;W)h> z=6F={;O|R`bm(Zh;_S~i4|Dz!j3XiRUr6JS5zp}ehQ>Z6uq8_dS5Lm{uDf{Ri6?me z`R99l#FdqmY}?kWc)fl5b}B0?v$ntb>Z`f>>Z<{mKY#unn)Tqg{qfgFbiJPbRdfe? z>|LY@!`_3G+2@xl)7F9INIaU2ra4R;)67QlC?DOPeJ*Y%1N*fl#D_z&-R}<_JmQJO z2t2+PJu)zo^o%Q-_}q03_yg{9nK3`c?`OQhr?2s`dF+(5wIpK_mKA`QE%ljZ*~iE_Q20d2$#gJ zJp5g5dg>S~(?h*C4=Sar;`Qh%9`6eB#Ls8qF%&6FpbCLMIXzXCC5nixQQ(2!O?sL8 z8YlB)>v$A`PZVz9xPtAJc@pIHE0Ho>!VvJD8O1He9i-0^5OP zOz`@%c}$v>prcjikw2F5diowddu=_l77Uaj%MJmagAS0_Uc`RmLVV_#XZZQgf6mgS zO9=!5J+Gg6=9ye_$t8UK>tAQbjvdt0)UaT|f{$>fnP_SsnJ;|b5f@}GntT4x*rVAv zj!QbGtzl1!bE}$$uXYj^61k`i=goXDhim=i3c^rOkwkbM)HZ(~YqFnZ2Y^Bf2Sc;) zJ+ThikGvF+npnUU-&)y2UfnQ!itk?j7T>y}i&Rx1D8&O#CHdh_&Y4?F&@j={wV7;3 zAjz)1Cp6Fs9sHdZ5zin|k^`7QV5C4gYM?Auy6=V6X_vGvUCUJnJX=~3@l;QV7c(W? zA)^lS-rvH77uMsI7Bd2^gwvLtS2|o?zK$y@*5#Pot|1ui{eEBk-#c0Jc90L3yT50{ ztB7_=Y}?H%(}xyn$$*e>@KKQ-@&b7GJ>=Bg0=G(cBJTc-U_U?3!z? zA(>2a(n%))ZXm}Ucih3unKQZIf(w{2V+OjebK7mV?UM+^;i7s}F-;xA-I47hNHvJd zYFEPEvmSaJ;VzOG!j| zDm}ga&pmtNw>MX2+E6%vQnNWT~&wKgBHd2C;nZcvObt&2d>ZE}$pg8F8SeB~?hetZJMEsmEeIpkw zT*2u4%wm_9eyxf5$8~Z2=O@tI;NgmI*Aonn)RSRC^e-s?xd?syi060!gJZuTunikF z#pOv>#o~}vl%|Vdfri7^s{>hy~d+UlmaW8V*1{|VCOA+=>Sev$1@UuAsuK? zmU0)|0pWAQAX4EV4vHWvlQ`SGsFmRyePuKPz!4%Ns;@6>Tc8X{y5w;Nc5KcR5jLlX zm|wLcgS>`=DsP0t!VSF9SwZT^cI-Fu!Aj3Yfk;MCT4+GKXwu8CA78+?&v9MTeHYYt zO5?Au&vgS{B7F)>76nkc98l`T7qpo?JwBixa04$BjzClNBr; z&Y8CczhPy)ZtC0wKe}@h-#BLy2k%Pcp*tERm%;%8+vC_zbdEm0m@=k}(t<$F_a|4D z@U`=fVRB`5^@qKKL9DO6m18ql;;G?~mVED>2Y_zl`oXfC`IMy4KGBM-N`eM*R0;|# z_pGdkz})}K<|w{*x8ZwhE5^D;ux#w&P}T@crAZQp=ks9WMB?d1&`d$i3Ll%+c)9zR z6`AwUB~4w~!|xnNkQij+|AWf6nBi@MJR9xgRCX zb{5gNhNB?m2okpBPa9_QN=X5fn)LmQ%`pWyK`*LL_OR3x;R2f@Ps^>-sg&fl9kcmW z-ND>kHLarJ;@mrL{6AO_}R56rLaclV-*DuwnO5q5Vt(Gh`%KdBTq|m z_93e|citLSFAq>x+iQVonxde-le=C$m>bSrlCjVwO^r8}6|wC-$+=fG;SZ(r%=#?G z0o`U^Q#*|^L}SQ66{eWv>)=ZjEBUECme|^znA6HLpO+9=V+(nKqj=#LM^Q1Mm9p6_ z)XEU?xCgb}2b=tWgA}g26e;LDb0)!8H{spb0w`w8kMi9+He+~mueL(K8(kG_PUgAK zQrHC2XJksbVfYS$#kY-N$uIIS{9(pFTEm3t1FfANT|~!Ca8ASot&JWwuQNFOIc0xn}yd`ZN9Q_c%ks!?!zQHrm`@0W!tY$-GxR6Fcc0D*ls{6 z#Rs)JSW#QgjPaGcxxTKay0a88ZEP7n!+pf(E}F&fAAgPZ$nI5t9zL?2x9s^qj&3+> zgJ=~gl&%n(yO804^Pa1}C`wbv$+3)bB}ze^6U6G5k0i!quib=Dh|x*Z*0gxCWn&ix zaPqt$WzStEXH?+X)`mH;gtm(g23?{w&8>MRfpp+#6F!;<^V=mya@&iCQxJ%A_;*Ol?eJ2U7?c$q$`kxRevK4XjA`d0c7`;@q?@nT0Mo0EBH zIFuVPVz$PK1#ZNz0xTto?tmq~%p(=_(>bG@C955-oU(d=8hy7*u(@8RsovnpyNg-+ zXaRGN>e{0OHXL+|4>vdHM2rwmt(bykYn(V^Bf92rC`nl;!q3iMg3puNE0IhH zq8*YmE^nb>hmI`R6(77r82eA)*}oHnQu{*ga^GYA{Q127_O9i!3 zLR7l0!2WBrz0rrU)=$#v-Al=-5#-eP0KQcYIBSBa4X(>pYl>hO_}wxr{h-%(VvWk{ zZpm=TTbDM5nZ$S33cR|-?f+UwL50PCJ$@Qn8j2VlYa{YOgyd^&2wP$J975F|Y8D;l zcAz&6i@Ob#Ab-xCoDu0?3*_K+5ZQefJGw^}OrUc&;;F0SSC zqt+l%7}6e4=j-j|g^ZsNrOgg<&uep7wqYDke(fH-ea2GCttu_k)o>CqK`J3w|9+6o zHD1oVycu8af(?5GmI;@A?Kn1%s>ZTiSFEAi*pBRD?JFL+<~}M5yRsGu?TcThk{*{`#mOVYS z{SQ(hCdQCcV_-^%NeJ5rL!m6N*94K9yl7Hkrt@+1(l{)L4Db&q3+!bD(BVcu%Jw); zz~fpk{fBLf;pF*tsh+)#h41yvoLIG8spAi+PbV=mmF*p}{A!d_z*=C438O~i5l62lR9^scr6z3If^4^UO0I6+CuVG3S4= z8LdxQH`}5o?0j#?|K$n zuOr0%6FEH;K28L7Ac$jxxa_9;hD}}ojXCnf8rp{MR_XcO>`nouJ%aw@sw}VdgCkJ2 ze(Yz9vHw+s{YF0av&C2|gD6YDRv(ViddLgFYVzSU8K^#I>&gPv;O!wV04Lu^R@rka z?GoBW87&K)@+19x=l8vd%7)*)&*E~6Yk#|q|696)&F3#-{iNBw%6flOJze%1skqV_vw43(9M9&Tly7NQMKi<_xg7()ru`^WA9( zp)y>Be#7FdgV$u_VfFOoSNQyjO{;x9{eY{Qu`g+2IMiuj;*@I&ZsC_sxOumi@B z*X}*!S`3CkrjAV4!V*I5h!iPYds+ z`UvMr0j7oRpa!n4 zUd8$ISMt!?vwNR|&qB7F+57BDLZ^;wwIDI@nWd7Fww*dB<=1m`zWcq%wrF;rH94w3 zcWd;G=(HU-z{8^rw3IHKGm{0!b#mR}ty#xNSsF7(H)btqV%ga>E^P}sT6K!b2ao96 zT@1H!a(*C@qom8g&)#+fl|^0IE*>#5EVa^C5!w&}k_WI`IADS8WqkMI6SKblor_Nx zQ9riNGxE6OJqej>(a-YQa1`uq>5@=R2-J2T&TIKNAB1pL2eIGDM|Ek4`R(Ab0R;gA zG>J%>h`0$#p?qoPiRx(q-9ALCIpB4`XM^k?-tWTjIGlU%DgaCizW>-s-2B?{?A%^J zY)gpMk5A%%mmJ5q`8%m9awW4CR&T{ffD`s$hrPt+RuY{*7BgL~*$H{UXJb$Efp6e5 zGrSJd4~e1;<0BU7{I=dBR1LJzpdZ!eZwLptavR*JhFF}lr7EqyjfexKi-|jzgA7i>5Q0E-r6&N zfnr>78>0){02kRO|Ie$N%pbplNoB1go=e|~*C|Jka0ES7m%=w750KYh#{mm$FXInS zyv_}uJ1yh26}3CK{1<;8Sx36|5xNd>H}}I0h8Q-%=DtphN+PF5A>g1oG@R8T=+seN zL8uM5@>JctBm~Oe*E-EMPvJ%}PKzQZB;2iAL_;M7d>Fv#=xtw8x|2)aJC;9KnIOnsQg&12?bx4T7GFFW#mR{s4Hbe%B~+h5SbLWzS$V{B+b6(~f=!I~SuuJ)qU zBoQh1^Yr>0rp$>m=ZKLhx|o*0>rsFN|7RicO8}a>{5-W{GN!Hb$lJ46IB6@fq`}K; zs<0!i6!~izRh7NcxKeQ2^9xvAU(PX8w`8UcSdtI6jp8pa&*Rq@JWaswzSr$`y#BA7 zdGD1VqbHBLI_1H zdb?jS%BX z!fa_CzPsU>x7YEdGml_wX+h6xBPOqIP`GI=4JQ?X&vi*E zKut^_{R-#Zyo~PDRv(0H=+vn!=)iHn4i}N}oTNr-Y8i527m^f6LG)*p7>~76@O&$! zMx0Mv)WTU;wv1@e7lJf-;f@#kBjDy|y|kt`8M&@KkB8r$)$_Mj9>z=xOjBf))3P1G zBk#=wCAjY46Z!7xO9}Z>NTGPGW*k3#>}WzY^=zDCFl(XJ6ZoL(pobjhAJ^p)nghus zczq*x7z}p3->|sviw~j+rzZ-tuO-yC?Fln#IevO=#%n&0&4TJ3J%7)r9C_6WrJysW zlNab;BE|vpS%l|?5%+ljyM_Y9lNqYroh|~00f?mO(d%|@W_kr9d@$U{un528JYcei1F_7jWye=NM%~@r*#KN_9&+$?j=r z2&Y&5f9j|fOgqc{+YWkb495{Xb6+u^xTFQ2zjxe}Bl$zaY(#Y&RpaI5^%HpI*W)S6 zixNqCh^KV&j0Ew2H8JD%Sk|aL!6bTCG1lB;o^P+7nYT5VEY+Sa zc&QY`Q!)#Ab)yT%N+5OLo*DQ62E~ChuwB5rw||Z5ih_*S^#0*oeGu^Wp7p%9aYxSU zK6VIMXh*jrMkf&g2U%hv7jz(xX+t}?1?{9(v=ds9r?epEbTO=e4_&$cWr4`I5o42x z(p2wppzi*sE$DRnZ#JMXM<9U!XOj=rVBoarZa&$Id0@JWeeku-*pq$W$?{p+!nOGN zVByql1FT9sqxcK7) zIV}pB%8-u|I*^}Ea!p6E2|$fW;_UFDnuds^v_x*6i%UdRVt{q#P6jBjA)-NtPOn=v z-I+&0z=W8N^FbJr5<}Mr7&`4{5)n%w<0jgsB=%Gv=>1kiO`#&a39w%}{!NaYyom`X z+=gE_c=(kS-1cY;L}nFZa4m+5=6%Rrsma*=_KY32mmhCkzjMdIF?YLS6}atsbeuTLCW6M zE$6;C?;0LNu{mrJRWuOAsi6rBfwM7ytd3<6m9kv9Dlu^wqNphi_c=L`Z@CeN3bgsX z$zVUO6 zF6iPXr@e?B9?hzbuM)Ig;I8@-I&FjGu@Stg1KoDGb^0sJ2()tc>{GEVKWJ{vFejwg zGDTu?S+&=do6(#Ed$&d zQdcZ`AAn7XFq6Fb{Pz=25Ps+bq7m@YhUeaQNKPmQO>)h(A2N4#jL$6o6iC6iGqsfzkjEOVjS)l%r?XA(Auj+c zC26kr5H3*6Uf8wYSX;T~;gt??+W@lbmI?ud!6ZA|{gfBPbM%u01*-jjpzL*o_$YHY z|9x?Q!1gevRh9F-OHUf~8eP|U{HLGK`kqpX+aG(CKRxqSj@J9ZuRje{6F|-D9AGJt zE!R~E)OIh!f&#TRh`lc0o`;zVxu_E{Gln#JBhP@Il3w=mJU5p&U_qzuCP`^-)kHbB zcOjB*rqEtaqVI^HrfS?CdTNACsO*gOH;4_dGl#x196d zGn4b)^Paba809! zWDe4*Ak1avvx*PVgrXn^@G+ao)i*g4vSKN6F_)8 z1`XA!AWM+>dekFkPhWu!$TmWe9rLa-R;lW_J?1Gdht|TrEJ>tswLCP=^AO zD7X-{zW~WK%Ij)A=+r=?!+`kwXb=t#<|n#6xa(dyuh3}bI8ohBrGfp zDJA#bdoQb2t-@ll@XkB$(63*=mKL+o+_~seHXb}#<=8z}U(8Ko239%t$`?O1r&#BR zM5b79A2Wl+?QtnDH66LX4)MfU8Qds6iXhjE3B~F5)Uf(36I6*i*h2Az9dX45RZBG? zkQ;+g!yFLh0H+|lAe`JmCy>ETl)erqkm$d(;YB#|J5brZniS(wiFD;nv-Ff5>X|5*ci^4001BWNklu*sC>`RiFv&BOcV~AKvgTMCI^MN7&xMF zCiC;X5)-2ID?!zMY^s7*X1!2r#mb>@#`*3rmkEPk=z7QYgDFH zDX^0#PqKFHS~_&7u;YLA)mI!oe3-+B53_mmW^TXzb~bO`+=5azp4kgOX4|2(8pmJ$ z{3nvzM=&_KB9Z9vjK-(5sY-J?d6f1eg(q!O^>c-KM6wML=$T;U8Do_oWB;`WFr*hC zN*!ACIaUHzA0%&TA5SLvYVFA^6J&YJkOgil9alKX*>N1yisZPe0`}f90QmlR6w|>o zRnB{qqQw8(!{ppB5POek0zchF!R5&We6=50q5=Y;suU0I$igRYVECNHwT{3~`;PP0 z(#M%Q>zfDnQqLxzx-mq&Vf!;;{(7Q891GM;#UO_krT8$F=W?e|M) z*XVni1u4;!k5E7vP=JUl0i8#IDDFZG85PQ_996|h2ID$;HmN5>ay{UD}rro*Y3Z z3{MJ7d^>)Pu%Be<-`DS#(5oP~X4~|*DT8t@pktk0-njjZ7 z`b!G%TSUKKK;oZ?2(z;7D!p7a6(2^QZf~!A5*~~fdOzPD6XtjvVUwV2Vw}TfGO-~> zOAvdq_<{Rdb_YoXRWJSZG8eTqCM%{2Gmh^Ksb`Pp8#3XiJ5XOzT-y5x znBeTovUn{~`7MA!r#U(;4cqJKQt!Y+TO0RcO%prb*w|QFR+fy1r@c*_kI7_TW740C zxtw1>f}c~ef2(X5@q8?HYb=mTCn7N{iMkY?g<1pu1}a!T@9bOj`HrXldx}v}Dg#b_ zAmgx(g3xrb^^@z)J}6ODI+e^Fn}&Gmkz>Qt3kAWT`m4TtF=hRu{gpJEX@LrgT-_?c zgvVjOw*4}l-7Xm)4zrn{D+kwRcN!9=c!ELMQHeL3R_%~e@1^v}G5tN7Y{Li^?Z`v^ zfi1d$atd4Rt(1q_PHt+M&&QLlzCnGe`|r)@d5rC^rK%)z0j$hK#jzlNor=Wp8;m_U z&^B0jTl1cOQm+TSop!g(ZRQEFavhLfTDL^8kd!}cme~@sh!OTw z-BD`@`p(x<;w2Op(rEgK2!(GdD9U1bSATD8310F~lqj^+faJ7Vpk`%dwZaFY#7lv& z26L?rw~Mb$-6o@`DfHjKgv`wLoU@uBNIp1Z7e5-W70jkSy6p=cJKQAvbck`^Gm3!q1VPuZ0stkVnr0ri} zL^k=xMkgLGVJTLeIFA>IY$pCZLi5I)&N(N4y zjChPnf<)*>Q<-V1*N+@b)C=_TdNnDWn1L()p6%7s{UU|-Va|ar-t^jpAfhULr_f9l zY^7K1W}YQ2ceq z=1Aak%)!{%(~w~|0^$m3Ys0uH1H;%=f|fmOVx3si0je&1*Ny%KTfnc-E ziin&X0+pf$w_s682`bv2Z(k0-H)n(SG!&n@_!^kKfP;LDab&^uEZH(RVawkScY0cW zRT@vanJ!=MD}Hvg7Frg(ha`5R#U)*IL}`gCRTR-|rPCY+w&>Ql=e_XpK@WPY9Y%gc zsjk3kN&ZS}(AZ<4q(XIYqhkGD*Y8yK0#N6U`8j`TteUA|^U%;^rD++&XoPD?d31h; zRit*u6DFE_5)QLcR5{%Z0_(a71cV3sZ=j2MKX$p`-^?6excp0%s`*2uU>(_ZfGkSh z1lgR8h|{LdUjxhghU4}goK_Wk8hnrfWM|Hs)Z$b^RCOC@5#Q&P5d<;5cW|QP7?+u! zDBoof@W z&Cx#gcs-tvrn8xIyPabLp)_QioGApnZpauIgTb(99#>sY@hZP8=PAL!!Pl~092Qku zJoxm>pq4$adU&yv)g9G~B)vX&&X7g&mTCCh`mygf>7JYLMTkXxm9sxumqR=% zSp6|V+Zm8`;l%sVXim_AtT>4u;HD6T_YF4SPjn9JwZ0v#m& z8p}rx6wBE78Y}t6jQnZ^;!qj_3i5CM89Pmzyi(sFcr~isGMr-LZy!Rik&#l2Y^ECS zD8c=uqLm6GOYYA43_h}`QgKHdBuP1hi!B_TH43CucduR4}?NGoxu|sTmN{eS}Ehp{94%^ zNSB0Qp*Z?uHPED@hMP{6j~TH%kar}S|LYiA&~Y-{uoMxvr|PRAj|Y^fw!jqQ_txEh zz8eKQMJHZg8P1O_BH<(rS2ySvC3bdDcQueQFwYrpVUY+CH{~V-y208!8D5#5toGqT z^su$wfva4}=6T1(ye^;mVyfiiKWUfUEKWU!Hh-mD^i$dLl7@WcEtYdlOL4UhgrEyr z7u+gAh8lcNQ1;v8swOU_{JYuy6vg_K{`C7;`J+Z)N5id)I#M_ss#JCQQw6!InK*q%mvi)x z3Ynw^U4n;>ck#|{SXst%%;8FR#R{i|LkE_R9j+0&nsl{hTN0d&3+6Tr__eq}wM;Nv zt`x8AK%EEFyfYWHw<*jd6*l9QlebE*-B|jyu*wsB`#Iipej7Y4{YLmwT51P8SdOF= z8Q7P!?_0j1^3~1x8WDzFkOn~!3kj_DEps2vuJ_3+^n@R_Cl%w-3n2}>XX=+;zF#?; z78c6j#A-!c2yBCK-(Rl#ys=;Pz^#eb7RW~y1M4z{*ztBy-$Tz_wEEX zc0?Y@lfKz!-P^kzyPMAH|I8qw6sSL!dA>0!K>6FL_?b24*2fCeK zn_<+BXWVvAM@%$W*H?^ZH_424{+EJ{To2ny(*|7%LjsWj9EF6YGpAv61PmKG@ER-P z$o=jHup)-3UOX@8$kslXN2V9dCa26Y7^1BNB$H{XMl@qCj-U0P!sI#f#TiH^mIle; za#RBL=DX+Cy#o9Y?uyP|aICtv;LQfbiMHf8F*Sl6J(4t69u}eXknQT08mZnCl2R%H z)r2*9DpQ7YAiGxzoZfS0(Y4TSw{!K$E?&X+*I8S4Yk%E$zZv0uYPbDF4NF|B?W~jj zix-c-On%YGNNd)>2I-eYbM0|Lz(X)}b1sD2E;B<1Rwx`-yCP+l!`0%0G^gZ>rv(zZ z`0b?Smaca>W{rgU7tZtRQU9ole9^rc^O>$sE>^$RCmO`J^S3u0%)@&ruo`O0wUg0z zE|wC(xce-GA8UH45t@em+pvhkd&TYxm_L++B--o2-7E(l5{cT{+~NcIOB;GogYCrh zDNMJO2_H9s- zEM|q#Lh{(ZdM#4Wse_nj{}hD?JdvsMb-?7}LZsO#flo;D+`uxt81E*&<{M;?y6`D?F>OLIg0o4;i%_~*Kq<@nUJ`0Y)HX<=G*wa z1lD`2@GNT{C$Xp02ZzoKUnrDN$YsCTeJ9X*7V-`TtIz@Ql=HrZ zaOCG%SPPZdv&7I3@5ZxXtXoKM?7FX=IdMzWQEN%nHqFWM`l=bDAaM-BH%P`i1obmn zb>8>yA(o)&PU%)+vcZHXo5bRZUC?3h7^vL##$v@c%Rh2U{EP{fqOZ*4ZDg=4O%U-i z5z{&B!CkBR$@<$_7eNUY;t{TSEHsiCU`%oCJDcu!)37tfs|HA?$dO>IuqT> z(oQ&4HshD64=I0a^eBG@(^hY99>$$@5U+Rm{#=@CWmOHqDu5Q2&gg@Gg!Nf?RS1mv zZNp?Y{8_Bqmo(FVe>XW092Kcck~94)Op#JzXNX)EI|RPafsN)K26J$-;>|-Wd7Tsl zwZY-WsV@%VlmLe2#*+!J`^hQ_w$jV~r_R=PcJr8`=0RoY8K@tU$aXi$zB7zhV#T8> zXuDYO=LQJ1z&na?l+Xgb?Sfk8qXUBJmLiRK`R(oX<5o&ei{Iz51zjEGzyis@ohU(V zw2DZ$fB#2(q%ES~ALk@Ol=}6*1LBSF4<-?1*8Tuc@;+ZZb&2epSdX+Hlb`==jF9(_tYYDqxLfsOiHAhi_-oet=q&2 ziRKD~;MfSCSPoi=k)nNk^BIEB$Q~Xzf^yXav2uak5+9z`%N@q(LaMX#H=6I6w6Vao8CE zm3w}o|8*X64->s@x53Dn`w`KEBl#zdPcSdPJPmBsn0^^_S5F*wMD1SH6A2n~D(XB8uE2B<2)bOA$gSbOcmcFeFr&tp_ zSUjDAzvUNq3f0oID|ONC@gqs<#~xUXcz^BtZ%+xOh5oo96)U!?oNFGs<8;Uw;kGQ| zbgmqf{RI|NBCKjCKf=#6y_ug*$@t3Tcy3@QV z_{$vz)`E9!|AB9u>1AX71omd!iK72ZnYy8l(Z!?a9|8qVBED}qvE#2)B+5b2`-&Ga z3FeaA8?9tMGchBer9JP)$XXbR$PEgCh)M8w$o!GYPtPQZNeVIwy{27$#u~d!F82&Q zmkZ8Yrg&-ZQ(|^FR~tJf4?0^3=@}scWXO{IOpqeb5M}6w)cy&H3W9IwSWvp$;~IpF zTmrAOzt(NEnqwaIjN4p4Cg#Xy5iXfA*V<)QoKh3nyU67;96FrRc|Xw&$n_Pjv3O6c zd8B_%4tSx~$Ib{VODYOxy3N_5E91nEq(SRA55>CcqYy3*JxN^k8Nik~tzdxD zI3-=*udeDT<#yJ8B(ZXaMZFprC`fbMEqNQ*?*;CkW?p|eA|mpiM(T)Tbv7{|^nO~O zb89mDy_KnQ;0}8%vfk?Kav+%`d;7CoVtRAw8g)iitX=Y^5}HA&s&XXj3UfQ9tmADK zX;Sn=Jvh3FY{JeYHsIls+K&gf*O;K0m6ZF7db`Kt-^DjBSL_Bf>kKEZ>OZj?r$XIr z*u$m)YBw~s$&uFNaERsc?9|x?oNgd^*s(`Oo5~zrOLM6yHxJKpW)ukKaxV5xNYi^n z?&8~Z>Kk95mqpDex9(V$-x&z9r{?EHFTv$>SQ1owg*nK7qK<9$Lg{Odq%?tM30R8I zXxwx>k^f!soA7kN62xdU_7=tli_k6`eiw~5??f?N{Q2mMQ0fI;SK*5fCjD*I6f$|3 z$FAI$dO@P3&dEGr4_at9ArKEK#>lfpJ;tg~fO0)+Qjre)PglMZE_?6(Yb`NzQO$w{ z?onV#Pe$5kB4cq++4b01g(DG|$4gCRcobZDOUz_9;nS&=y?eXXZ$~0g@1eW*%|dgE zt-~Oyl@FbZl^g%sR_FU*F`UuSI`1U)`0>OZt{J@#${H712Jl>~3YEvfpdUW`b2LIs z%6+m1j6N4-BE5_lIecH^;lWs$&(_6sBLAe^7HUFbq7pnMp|=*!xkcjE>FR8Lz29V75e|M>8>x=Fk-IyCuoty_e-UBoYD`Sb)Kwy9miwQB8DdfUrf|=aaYDR4pi`}ph%5T zMXMgb^vUQ8$TrjHB8to;w|f~@4PI`BuAC*efIH`N0KkH+(VsQ| zG+RIuKG^ z4OH>{j%#1;FBBUQlN7Uy84`6yf)Y~FoAzy0pVfy}aC}7Bm0l_6dASCZg_FbI)aN38 zspNvSl6l#C-s)u&%3&toESuhKX_B?s=#vl~Qtrj`;LkP4HI(z1NaD4Z^}!iLIZ(C7 z^R457_m(6bjY7ng7KolQdq3c^6Te+nm?zN+X5yWn(X7$mE9+D??{Mgj~z24Y8Wj!GQp87=!%y5y- zzaXzQ4KL?4EjkMf?A>Q%LQpYt;S215V>`dM{}RPaOkONVOIf z_l?!ai4po2*dH7FFnJAwdK#1V33YtP5Rh{4Vrv#`+N5xygVKoX0d-FumXw9p)|Iog`e}}_M z7BCSt_ef#YwBIZI#_YT8Qv ze3IQ-A%_N5s`hN4aYVc9Cg+68az!e$;1&5p-A?)kj2p?=L-u|^TDC5hXX}{e6vIx@ z1{gN-K1ZvsB9Rv}V!llUovOY{3MQEaX%U1NJ49Cy>hp4mwN{}5uw(}J$Lm@QAgnlb zaXnOfd!7xYfPos*yUj!(ad3m{zp8vsYbo7rV8b}C!BjW$%HEEmvfHivM-2t7@WU2%zWcpJmz4O(Cc*MIyjh0!97|bY3(lEs4GDGp!YbjiQMRuS=7$h~4X0eGNKTH1 zR}o>E#qL|?`WCAi6Iyr?&;-VKg0q!(7WjUO6z+3+c*Ezpr{(uGC1AyZn1vI+apGPSC2TDtE;aUk|Fvea^%Su!Y5mQT_}=?q=YR96nH;2-LxZ=?&N9zDr-) z51KrF6p#d)=bOy7r4zM8oIp$5DVvd|LS>VVX2mf-(sXmOw-VVSM(#EQDb@{J0sK#S zN*vd!!nVewZKTiCac=9dJ#%a~2=FpgeM%w$Ya_w%(Bf#GxG(1D}tmR1yIeKsH_*L)g$8SzTz;0Y#INURPmy_88YMJJXP*>AHL zQwpJ#U)r%N3vv`|LlX`6mO0YCAl16U$yE*Y`ygR$f5))R_dUTZge&*JHh9InW!VpY zj>H6LV_?++eP>0{Me5B!;b_!7_LqN0I@8vdQtoa!!ia*z;*+Qffz(;a+tOjh9S2&U zMVbg-k{twAAcVL;cSzxZ+pY}Nh60YK3(uEdM+++dR%4gv^4XRfvAmR6Q_;|5@$~59 z_eE>{RyQ?uv*QDuEcI~~?C;ClClC^owzPh*d5@z0sax+_SV5WCOWenD%^8+P_~pSR zz3Zl4>ii#|WgGo1(LBjnjIbyUL?j|415|aECiC3Z>}W=G`R+&9o1TVN)ko$r1OwLs zu<~=uwN$L9-N*Q|u)?2=^~c&}GA>W8QipPQ8V7&s!tJop&T}SklL-znFjiNP&8{kI zWx*9$SkAEvVomD4-izF1ZxXMpCTI1WTt`PSY=|5BAtLelqY^cU*@J8-(EUa#DcI7E zmU1uH(u&>!r&4}g!(p$8&-`!-je9!Z>vQxx{zCiC$go}WVmaVW*L%DNMeY?8IV*Ya z8%u$8C)B27Vr+jj$_=%w;YN3ka>5K_3uIdGQwXC_+VsLd=&b1x4m7|EtG+~p=jV9l zU7|Aj)r%mwkMHySln#zrjOaTf%+qwh2m&~&6zkEAN7y9as-I4>S|((L;P>zT`R)sb zffMpYogm+Hgcu&o0MFi?B4{oRv5d365tJSnHk9yoRCwKix{8`*BYm|M)Y-4w>m>mkh`Plqe-qUvC{dQ+i@o671Iav28*bBnQ+tL-B+n;M&gdTG}KJA z)QUMO5-4W^7LynX&hs~0i=d-vzty@;T|p}l)sGKG4V{ZYf=t{72T=_6@}=3 zxk$xcK9RI_V|;oB(_-2a@O<69nxAJXST z@Lid4;x`5^&AQUW9zk>76Xq-V_Omb>-ZFRF_OAjjdE1THQnn{W*x$7&g9!taCh7ra;bUd!a10O-TtjIw zVYLF{XAYCg4I$edN-&haG<`GR7D53^F_@)`)O7$WM}y4@tJ~FP%&5sOkh%(TzSW&u zESvhveoH_hn`f|0xscoCSP5K4rBSay6g=F{im6v782SYT4O2*4L%!wOj-Y?Vzmufg zt8A2n&yKgB?=pM+=+xe$h%>yibPmcwUJSxh2?D<%(|l^}>Y@*OHcjxq3>l7<=w*TH z)QcRX{^iY8*xOcsrx=9etV9HYMiRdrT$)#wMBZvNNy5OuslvilmKPy_K|yETv}c;L z%Sad`ik6xv+GyJ5`>XV&&+`am-~^8v?N9kDR6TjRM&@XS@eN9b85paFnm_n^s-e%L z5%o)Md6Y&sh^H|Ij8u=zi~i`#z=H7As_0~2g{zhbU;tqJFTMS;R%7y-uLdkt zqA|HA%kR+Ykv7z?%8Nl^vNURX*WmMH3T* zPbmXf1zwwq9K)^{tX+Sz%h{+g$=-_72zHnc*Bg;w=A|XQNps}DZN^-Ozp2*YXBpxV z>Pl&){lf(X)W|4%`xjrJpC22DW6#;pwyJ|s5^w9CvvrnWNUYq&XHQ7-B|~m$U%P*y z((!&=Hl`I&Pw1v3A z9&Gt=uFwzp9yfCON4wpkMF0;&0&CSlsQLg!RPr$nCvK|5s zYw%gkUF*qI21k_R-VlrFSQ5_~9Hb`$D!X5@Rr&#D^7S?z z-5)n<4A?XT9!42NLz}rz?Del02C6KEpuXN>BXc|(nceHx@Nr$AP0P-+Bp=**w24e7 zqq##!63XZ{h*+tcpV~1=nqJv`NKBg%akF=u$v-ov3+=0gOI1DomN*7Oa(vk8%Zz^P zmC`tJpAcdVUM8s#uDroQ=6k|G1}=hNe!+?Ce)!(!a`Hzlr1oyL;a-mQ6Ze+;dx|sL zFs2D1@%xCfzW=r!p|3BK1d*a!IIHQBids#mZaV3*4V-KZ1&;vg&L&tJmqy1t#15Xa zi{B(=rNsPP%HfomUO)^kM`Uz#1|o!_;5$2GYE)JhKBw*Kz*4nNI;SnJWE>~s_lpDy zS*>3tupsH_G2>eAlEr!!#1|sgUaF9<=`YK^9N98O-t=>SY06Gu_8_;}N;VgVld&ER zuj&y5-W~0e_pT;8LJ702+BILXAKtF&@!DP&r@uN5C3@!jdLT1xd8)VFf`!8&?r$N< zS2v&bJL zAEn}eL;g7ZqgX9$d1^mNl{G|^q0s>Cwy3UI@AvK%ibOP(FB;~+jUOBm!t%Wj zqNeLn`?CAf?;rN=^oQZFTPVp!y>|3mXn0;_+S;u8XDQ6)ue0osg3-%gfkH7GU7j5X z%Jx{fJAIxR+-@3_%#$&;3)|vzJ7%~q*@-46q?5hQ7OJApYyR$=P4Y2mhn=8JfxsHC zX^&HKUgP|ZA0Y5TDU}_v~%0^}AlXZx2yJERs<9SmMid z`LzR-LMc^!77)~r91;eDD)O@j}w>466fy#YGobkucfoAma@*~mCAJl%mJ+PuVsBYsC{ za{`f=Um3nDf9#L|<`@BFq1hCKMN@@14L z`XK%A;*^oMgLv1G)RjFy?yk~Y9BX;lo^zS--Kdaad?VlSkanEfXHGDDzK+JsX3_Hl$d}SbVlF zY7@MwKfw?zJ-N^A+;YAyQKx4{B7J4P&Y>(Z=228=Cph8ey2~&+ z4MM#|%G;?o-=uEnDzhSkhdQP;xG=T38k;h|n!W$a zR5c`+YR`={r*`m#spjuuo1IBFZ8seSq6xzyjH`GK@xGEfA$48p@pBuzC#KJB+yPAf z?dob!=gU0FrQabsd@9k-;^M2L$<#p3>uQ_(?&tQmEm{inm3yD&E;>B_N?&oUZ}pyi zw-68l?o01h7VH!mV6Wf~lr3e#bhW1@ho0j|e;g?F|5Q)iE$gmE#VV6LG|zL9q{w=y zbv|@}`ylJ5AgKIcTirr~v2im#HtOH{!&#=mk%VjpR!pN>8#RyqGF1dg))2`1Bt1p%UQKj`c28UjIC||B^VaSHhmm2b7AmiV z5y1>$aRQ_~T)Yb_LfTEe5qfN`1@p%4UOXQu&KT#tuD3>lPsi)ce4`6O(-r6n$ z2|$XDUTo zm~k6!*0Ufoz-cHD&one@Ji@a0_)GR3$tm4S3@rF0iaoccvnryV-0imIPEnzTvJ5Tc zB$9RCRYdY9rSb+F6jRSwogO~uo{x9i2DC&f8(rD?@jB+kmmg8SFsj$Cx@}LT$L>*e%{ft6%_o2tUuY7n!9Wovo4fc}Rlys^q> zQ>P<_=&5+yrA=uAHFZti#|4`!4sW2Ce@{nyfKyh&Pe4=P2GdH7rTtPv0xD}eVoGP0 zPW`hp(gup~uM2yeUQ-UzEt*!wN(jkEIvmfyCVzt5kIAvKr;Farirv>|z&s~tZP#==beIM!LJ!K-aoWt?3+`f3r5DfSdf-~?8 zeYf1Iuv#Iz=N?hg!>;|M*6)Um$fqIZ*Yo~*0V<)dIfp_^W&~EV{_;gufzdP))S~LQ zZ!${nAPo(JLc`(9&8zhC_6CP9b49OB`h8;U($t>-k`5xVv-1p9>s@M1fQz=?r zb`QVDs)?ys9UdgfurVF*-$Tn*bWeJpgSaZywNQpC$~>!D?|lq5uScIZ9|koPw0}OU zOy_5w`3q@)UJ00;VYY5=$Pqekr_picU`Q1sXX9Lu`LQ{QN6cbiB~8IokISe>KDnw4Wxb%?|5m`=@`l z;X!8RvB^|vzDz?9MUwsSM@$dq$W~eYET$y~oXuvZC*R{A>nKb%*j!3xo^uzVAsau5&wSKxHFN?6Mdfr z=7Rtwf~!>D#$to>ieT{3p4v_H2?Kt8_qC>z)>(Qtm43sY2$K%4OC>3)ihKc+qs=hTm zA7Z`%t&Q*{W{aS@BumE7kl54H6NV&S1r-}xN>~`Q zWy1}xKlF?7P&5t*IR(YdhWplH2bi@i1t>dEwBZ3Pw&75~zgTVBd%fcShWY#bQ3n+b z4K$7UI~Wj;N~j_O%>Tp;4fD(d7G{8A!S8Ko-+2S|FDr&I6`qJF*GYBz`DX81lNAOm zEUXqi=BD541t5c>Kq=&yv62f1M%CI-))p2PjhL%*>np$&-2Y0u|NZUrhmol@LM-3W$Z0ag;19KW7Ui6Eic{NN?LV?EXGpjALl( zf(psVzy(1gQ36VCUDE~?)<1jBdT?+6_$$!c-7aLDoLKR=>})XhngFo@Ua1gA$p4Qy zjx)1bJzBGYgkubc->Xr#$tpQXWh|Laa}1a)rhM|bookg>RIIl;7UBaF#D=klioNw)yVsL0SfU93@gKb%t7a%S>*-Iqnj#icT)R(><(b=(ccXETdFpUDrJ zn3zaXX@2vl{?;JDldV8goOE|MnfUMccePEI5r>F~DGvWD>4jUz`C?`2dW*e5!-K(4 zxoYV?piad}Dwyc#BLf3sZGgYH&z2#_{iKyl9Fa%3Kp3Q6`KGndl4QK)I zv#~gVzfv+Z2evI6l%D`uA!mmi*SbTH&t42yQIg8)g>+} zLnlD{>@fBgb%Bs58TRHfGv|7b8zVxWYhgJ`G!PUNlmjRETmgc_QL$2mY$+)zY6A{V ztfvikB6v)Cq43CxAqIYZK#zoLpNd}aSxsPo?ZywBn)W?xSAK;A2Se=c?Xlp-$mI%T z13<;0^Jyw3DLD@W%!9nWze_79Py*QT55a&cf;j-E+d(V?_6{51B~`j@2|D&&7}r&Y z-GKI$$feI2IUO7w-Ss00GFvavgG5V^2@4BX8+Gb3Vo^{~wCXZeY0%Bi&N2w-PXc2L z+Iovw(!;52+1JMlAhsM4=r(98riv9^e1K|*$p7tUZxA#a;C^kk+gPRD*KW4KVPRpp zzuf2;yGco@WVP>lz}y>(vBq#}a{1i+7n#oMera;O-J2!g+uk`Rs-}iss+eo%=J|2; z`SC9UD33a)oQ9V-GcPai7AqkIyCEqFy|2H&?d34@WUYni-#(jzgM&cjfX@Kn8HPx} zgG@jm4?xrV>y!P>jg^A7Jm=rTT_3DPJ|ZVTJe-`I{_Uu$(_&}3+GvRn$m-yM*Z;}R zux%Jo&Tik&;C3{6nfB_+0oupe;$a(DP(b<*9}C2zU|?X%oD^V`P1QH>tF-C^!^2_z zK3z=%+p;cK`7Vcy9~c3F_MNw5bO?leY2H^|EkhrsveBA$c8udG3}h4(A$fVkB?{SN zfO{}gJv8MIskG-*MGBB6(U;K2g>u#LNo=4UfX@DUYhPGUGcq#Dkgox#W!Ws}$b*p) zaM@wRl}E{C28V`F@$kmCdxJpJSWV%k1zv5U;+DC#d_)299lIk`0N9*! z{Xg0UhbzAVz?N)LEzVKfeoDJkYk=f25rG&MR9DALDVNp*5PjCKT@)1+IRG{taCj}y zvp$@!B(j-LIh_At`G>tWHuSu1=ft$M6@&lb8NF8B4}d7zxL#{4u4Uf?6ak+*9!S$m zOh^dq-!VLyE0#-R&R_Y>albh4^ zwub1KnB*?+r<8vOU{rNp+ENH>|A8rhTmSGa*Xy+0?8-o9`UyXt-}{lCsPNy-sw(=g zfPX7JKAu=I7N4e}p+QGXfTmct+t0TRYZn09{hk1D)1#^EgCrW&u`b8V#B5&ov5)uH zaUiE=ECP*cEGsKZgGJx}b#2Q;MJ3^1g*z|X&x$6^tp9_ubnbt4oPCu%16KJzlPh09 x!~6v(F`WOV9shd|Z2mvnooE3k_y65PZ-PaMemo*py;>kZkrr1Fs}V5_{2!mrEY$!2 literal 0 HcmV?d00001 From 315fc58f17614f0a0ada58ba103e1be4c31ca16a Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 15:11:07 +1000 Subject: [PATCH 20/41] Added tsne map with 50 epochs --- .../MySolution/s47539934-GCN/GCN_tsne_50.png | Bin 0 -> 63201 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 recognition/MySolution/s47539934-GCN/GCN_tsne_50.png diff --git a/recognition/MySolution/s47539934-GCN/GCN_tsne_50.png b/recognition/MySolution/s47539934-GCN/GCN_tsne_50.png new file mode 100644 index 0000000000000000000000000000000000000000..491855f8401b511635e69b90b48c752141be603b GIT binary patch literal 63201 zcmcG$V~`|G*fu)0ZSL4Mc5K_;v2EL)9eZ}H9b?C~ZL`Oo)6e_HiSzG7d_TTBI-|R? zqBAnHuDWhkMJXvrBEsRpfq;M@N=u2UfPjG30~-kp6tHKBn$imR1>qthtp)=WUl_AU z;4!SDl(q{92t3+<4Rn6X{{z^`?JBP2s_J0j>S63`4q|8Q>S*iWYHMXe;%@HjV&!1Z z#>mRZN>5_x>gveL#Pt82XLNA>#gtAQ8wCPF0wOK;Q_VByGS?#$Lwz}7M|a7|*$8Jo zkqzxfMI=l`BpH;3rd+1`P;+hV8_jjJm3~uIZ44~9_%d=+ZJWCNH;p^xBO-_S=1)F2{-!1jp-* z2O{I*#21>O67q!t1iO8nDFp@d47$Amz}bO2?)W`Bt(j-2-?Vz%7=)sb7PXmAVAXbB z^ZLJ?QSI;VOUcMshtXM@%u6IvQc98vct0e@U@=tsfA+&Ojan^~NoPz@aLA`rY1XBe zmX@l>&``-`lK4DdGql=mP;S|`<}e!vcim6(m8Pm#n46EKreeGfVH-;6uN-mh_>!xt zsF)cT7!2Mc5ew>BSXjukZfxxWFflP%1qBW4w>s2iXw2Q+TY&2=PE{HAx#`w4@IEVD zy1HI#k_2N#fl=Gwci85qDNU{Ge)>(oWv@qP_!B*4Ab|&rjRmw?gDxyA>~gamU0ofQ z!x{q?7Ix{z&}~Wm;bw=pd|@mM+hBD4uMMc@MNMlauN$puxq`Sd2Bl)&8VBzt2rFDzALJJ zhwENE-!%zva=kl$RLjqIOifHGDk?yXc6$7LU-lw?P*A9U)9bdxhCm}r@M}s)NIc#k z5)$L%PrTps&cHKu37<^o@?2Cmt#-IwkiI+s5sVH*aJ)CL&T2nEvhv|r77HgPz43^XF?oTKrf>DkY zJ#b-LTYCG}&W_xioSX`ws;IG~VBno1NpW^|cDdc%`S$p{6EG8wh(GI=8yX4$+{Ebl zm#hCU0$BT%4|rTLEWY41sLP;`O;1cq!%QPYB+Spwo(P77cWl>Ny>bU)rqT|N#RNj= z;}#qU>D2V}yW0U|t{3g5#>U(CPvtc;lX(MX$jQTsLm-MfoIGTNIDlY)xu~(Y@BndOH~;&C z3b^Z}6ck~lvKep%BPu8~qr}DaB|5 zO(maGQ(0nGW;0v-DzzATij>rj3Uw}`CmmN?!M=G30?EFEKy}Ms4p*!C zA|vRH38eXU}+22GG=2 z(4~KO?Aa>dt+%RAJ5hUCjgUQQ9HJ-kg4k~*Xd>VK3s3Oed$B=ka-*a?CdeF{i!23> z(+?$c5lhec>)82h;3hN@`(J+kdo0z?cZ>S83-#;yZU=CR{lbksX2kYW9+}mwEPwj` zn*$PR0RC=8>k_OJ7rEF4H^sI8;{?=BfX^6|yLqd92a#r21W!QT$l`TV=JGo)QSXgD zyS&54KNxnk^X2Mbn|ZAN!Jp`Aym(0$*z#T=`r>t>qmazy5raKnc||A-m*CX)=Dx)9 zr_oslR(>5^fUxQQVdrbVro{AOkeTB3NT*BhVIv^G#ywlL17U!6k#_?km^2D^Q0J0? zV{NcxDAVP?TdJQED(TI*s+I52p-#NpLF1=YRqk`BkP!SpCzK%!>c{s7s_jnhOtQlY z3!r&V-u`~QC*`%>5t}CuZL@>8+T@5Vb>Pe4q*R-3cU5aW zp(~v@eWB@n*nClioD}h7=tCbvJu`n5oJT_|S)EQ7$Va$DSydn=Bp)}6op4qD!r^GY z?;s10b&`tn%|{*2N#4p!QMxB6hNkE1La&fjGBw38Z}B!4&2ryrpMYo!G<&Wrv3Ck= zfLHqaj{a!v<_FXU4ZTZ}>ZO%q{vm4Ni50vp<5YhYv`9;*n^3lF6HndO)UnKtd%aq&?t^5bN*tmB%ajjpK*>x5>np z?|3&T)<=EX&dxI>`uCdyZP(tw^7A1E>P3B6d`~4xFq!56m`B^`r5s%L(!ihf(?fP_ znQZp>|Jap*$-kj!&X4nIinP3fzw)W0gu-bqbhf<=wY&3nio7Y)WzCG}^+4PAbCe#+ zO|hc0ZI3<{_bL@kOD?5)VZXI+WXp1#AFaImIxmm)gWh z@&_nkK*6icc>kV~-6Y>0FVqj$VOq6nk|;WqDskn=_B=EVtV@!Otrr!jlqdOY zxc$`@JA5{a$qIj<0b)<&xeF>IBa_MHKxAdrM>Q5fb4H(o**@@2e3j3X?O&z6jinSn+@LJ^S{Uuz_v~ zgG3iG8*4dSXR^RXFnDI${2rEt*92p>IR|Lb$L)NbOnZfWLYlj^AYN}LwO)5L&zTk% zO4>qia|0AH-swQ3T820}8K!329X=!I$2MZ+l>zv?RIaBZLg9vHlq7;8s4ASz+h4bZ z7rgqN?&e41XHVrz((mIx!=c;zk+gZXq}yd$pe{L$cuS``CF1G$41YSV z<0=FDYtSV>%tP7#{q(PImo2!9&^ycd6G}42%+`2wZ?}lMa$Lb=`=wf00zMRkWGblq zh!yfxwS)IlFa1te-E@nKBEe)gTe%qjQD=@F4hpFJ=BEK2^D0L)c|cTLLS+i&J3e1HFe2&a)DzZ%$MgEP8kADC~6_HKOjaOBhAOr3w-nSE+e+% z>ksdZ(9<3=>WuqBub}8hpeZ9jL@4y4>31lRKMo; zIH{~JHQ;AHUwvH%{t?fvWvC9S5`X{uO$d!5GD|Ly<-?dQcanOF4UV1Kp<|Y^U}5~q z#$6sFe^^ZV_&O{zzglf}A!f_} zU;^Q+>W2>K%(*`6^~3qNJtV4TTqkVwGZN8MW2+iLmCnYf2f851N#v5rc|-^twQt0` zzin!b*PE@36S6sMu$nBUz=HktQbZNQj*nUlw~b`aK|lILPOybAOLh8r$Kd2k9?*~JdXgi(E1lHjrHmoFYsuyAIwd{1Wxl}~Op-%)fT zV~Ka)&>7ZLetqtF^d4j{ZDe{}gz0(Cv-sBAny#8MW^)tJP@p?x%eI#f+npfecL4qFck$}{yGXu1#PrR$fcI02)> zimO_ULRwsv&R@pT*OYJ9(kFM@=tvRmHNjifefD>M!t_W1zY(p>k~+ebj< zR1_fci}B10P2aRAzn9(0u(_RDEMr3a`Fd-u+lLG2&Vu}B)Bm+uAqy?|HIvu%vLCY) zj1~2SA-$mCWF>g5^NwT_{iGH_VYq2Z!1W+RNDAREYC2!^E*eb)=IZ+6?sbq4`Hvry z?-Thw_O~2JT-y(L#l`%EO;UsBKfTt%pt5erAAN6Rujn4>vS zwPiRM)6X}i{<&c*i0?A;5wxezrn8ktYDl2I%eaepG0bJ2vVb9*lp6P$Ni@X%$9VvpLCx(hI!pqF@(Jt<<7 z02~)_OnoO#70CQE_;QAv@q1Hk+IW5Gp*v0TKb27SsPoAucIG;$c_7gW6^Ukue;(cu z$tm^UTOiN>kD;Zpi^lfKMegrFQ8-Yr4Npby}4NwFzBgq|NiI?El3IJNjC0 zB`L`!sX%{Qw_w~H{8nr|5L%AYp>wUL#VnAU&+G;eiUk`Gn$q3ddPdXh<=*3sAYS*g zQRzp?M|~_IpYn#27CVzFd|t=`dHc4%KTQs2E&_9A8|{f(J<8u-sqK?{g;4mEW9GLA zo03fTs>ksGekRc|38(`8R}W#sVLIR`Ted*;BGn-(-D(6G%*b`ep27NLK4RNKySpHL z*`&8Bk)l2O+-z{ABtWDJBa25J9tJk#Tf>#7IwJ>OT? z8wXf;I3=R3$dFY7pd9p9^KS;8BmB1al@qNS1u<{TQA-k!8c3-M41L0fBWb1DU`FAv zv~1Zdja!Ik#J0#4>`%F@i4m*fHx|==7zEL~1P|={&-GlKa5JUn_{nx#*3Ztc8H&@p zmrQQ)0@5KjmU%smou8aQ^8W_J?#j}Nr0Bicv5t5MMjP2976Sb^Z+~#1XHqj zfm^hsCs?1dHrsg)N5uA-G&k)@uf&5*R$%)lUWYNe^h2?^%^E1ZNTL5|+zQOC&76Vi zzjbb4s3(+SibyeMDQZU3V=6h=NUmmX2}s^u0@sw=8_(oEbay!Eiy`nG`Lovd&^B<- zj$1P~Aku*#J_mmF6*;VPt;51ukg?<754(zeinOitnF(%wEY!i+%QpvC4*rFv*8D*J zRX>s1Y-Y+3;(rD8(Bn=r^dKsbEH`7!V@AuCr|2yS{{C?A3vqwp0Pj66T|7F33Qc6B9m55WI;5mb}(1K*~~q$=k1+t@#4y# z;h6a-U$gsavtl~X{o}7=H%};StQn~tD$}~Us3DXuePIH(sx0$w_J#cdyJzwX6G+=# z_NJMeUY2f{dg9cZ9EbeZ2Jb|IqM~FCFqq}m2kZXjOIB67ap8;KA`qFDtsibbG>+Rt)%&1_e`)p+OkZ&T@RCNri~Z>UHdwJr{#Fud|3SLrYb*8 zgjt;}LEz;}Sv{exV9T&k#+aVrCep|2YVe`CxqeTI4vYwb)o-_(We@-H_e-x*dVwW` zJ6^=q%JHC5GZ+$*j;c1dGEJ2a@hGnldvVPJsD-yT@4?@$PcU9lZS*O9w^!GqO>yL12OslcNFMfajZ4+Lpf7n}aC9%e;scK>WPnwUUry_zUv4c{9es+rPmSh=jL`71K-Z5C3h44( z_ob}lp{qBV1d15)JlB3F8pb@4gpy1V%?OAx?XIR8@b|o`3QoM0z6pXlR%je2bO&ua zq3X3>Lr+M#4zRyBg z`|c1i$Me1k^;z0+o#vk1?x*`>0}E$5zZW@3{#RSWoU1;_%(f@dLHrr!BhNS(k*{|k z(vxQ>N-8A3+~?OGFDMa;YjOUz7Y;H)K5AjKGRel370(~J^s0s2!JeQHYLQX5ODh;!#5?KiN#^z#u|Fw=en`N zA&mOsz#(X^+smiA(I<=)>x5rYB3#wcGXE;@m&)M6e(1W+IUP8>Vlx_U9WecNMDWmK zZx?TwHh3V}v0^+%{z%%6XRVg0UmzyZ{nzEc)p_ec=$JNL2~YSWYO<2s{kRK$xzi!!GhicW^;O_?3)Uot#M z#{(WJ3H|ntcHifV>58GMX#F+FhqL)Y?URdrIoNhdk~X;#2mD)=%;r7Y2g;fOWG~$| zhXF}5pJ3b%c)z1+`qiJ@BMr0|!yW{Smf0s;_cD>5q!c%PP3eH*@BUi(tf|JwCi#mO z1s5kdThy(qqt(>k(r)5`>T^7N=89J-;dYXPXQZtb}-gLq~ zya^d6X}errf_opQ{$tIA8yKItl+nj-)NgP!&ySk2UnzNHhP;INb=^`YYV$)HG24W+ z@BD|v-vc!}*Ji5QO{aiCRPm$r$&bd_IFUg4FlVL}qW1v$&jQ#XJUTCjlas)LPJxe< zE>V`#1A6k+SG++up(S%kYqwD{Y4}=5^uK&oxWmQNQCh_%kleOdwb`6lq!43H3Iv?+ z3fXZ$?Ofe3S4Bz&!BR;vb>m8&Odu@K4dbRKefvv>^oQiM8x8|!)i|yxRIOY7v-@j_9;TeZnzV7fT`HqVP5nWQsBcHPYB8X6>-A7cd5LZojA3{08V0*!NpRI zK2uzE`S61|$He={>$S*~H)+%Fd{reDv0)S(i8=5GB7GxkJ@_IS7r@Y=Z-)c+c{LC5 z7$X)-9d(fJN-tnKD#y+DRO%^+Zstd^DVzhnPTWYM6r@;11f0MaZbt)!7Q$jfDU1z6 zLyHXX$7zfT$?Qk(dt0ACmfswWp5wRgZ$_qwbZK%O1D5roicMxCI4*?DVA42^c1bxM zWYvH1jvd1#^_^r~K;R8YR}b~E)O6*E)4(e%${HOs*2MlM@U#1)992B}^fk(<$g0af zh7d*5Z*V5DNMWhcr&7>pW$du*_E_xdDE>eSQzb^I9mL;4W*N&EZ2Tw^Y>{}oCK0MR zpq(BAbLu(liX6T`PC6coyfu&39&mg{uu+HKVZ8eU8TogkvdK=3wDd4{$mV5*Y6A9@ zx51rGQ7?e=*?~7wD@}VOj#?e^IrCcCu*nid-|tbPSlvNN44c^5@E+1->)O^CZemn$ z@ZNCBc;I>&v6*?O>dN>_Jl@g%Qix(O;zp@jZ^MDLC@V&HNh(6xu^JwVmRiKfW%c*s zHG`=lRr@^MONKrbU?+Gx)k>qQBbv&HR08&PN1Xf?s`?Zwh!MZVaQTiNk4Ho77jaQO zzR$crbL@FTw&^d>v71`bqclwJfEq%}xq2VK0(9;1>NvY}Wf96vGdYAF;FK@NedOMD z6W#WruN4b$7~oH$lm$0_1n+nelkaj6l2H(M6}8t)2FkD|{hA6|L;TN}t$*i?*2rP4 z?N7z#zzvaSx40bvE!_AfPQ*WP+j)0pNh3+_13sj~zCAWi_YF<6c6e!*AIw!Ze{!~J zULl1vKi(~dH(%s##nss(El|E6HPiy?Q``qvaeYm`VHiTGrj1XEX`TU#2{#S>e}lN5 z5R3uC+if0$tR=Ff+=w#WeV4neGf?1UC#2P^6I#Tsl#>(kymDa-#u-mqsOS&4#eOY5qbVZ>iv;EG+Xk&2r3BQ1L*4OLH&Y+|bn zgWR_KFR%Jjl?NCn^=v1pbd`n<+Zhu+Uxs6}e+q155W+X{e2XW8`HdoKdyK$#>ZMLm z0{s-s%Fx?qg8W>NSSA|#Wm1gQVO(V^SHwJ(M;WUxSnoak+y*bWN-6cFKe>XxF|`w4 zIi>D34e9=27nt&yR2^HYzs!3YVE&fU|G<|kL+ z`LA-Bp?8`Xx;+@HuTZH&GH`@9xKnH{BkvaqYod>U|Er+m21z#3ih-SK90M zLUGE1zap7C_RtMPoD$I&(XbJYe#VbqtWGB;40>;74kZ|hI_qudNp1gbF7H{$0cW$q?08T(nH0(yf7u}n7#)N+dNN1v zGY+&4qz$PZgjo$kT2rv@1oIQ+Q*}MU8CSfDNyiFNg9sPz6X;?yUkF!A7qb zw(5~vhXy)b%{IKll=tPN0sH$Oz06!b%tsw@vhyu$v~wX+`hi8m;Jek;NFNV~Cxupc zOaZTKB2r(D!%n2;To~fBEbd$mz5om+Cc-d;H%w6Gl7h*rgCQmjF#`podDi}RNL{5F zIb^m|oi=M|?boBOrvPly&;lPFl~V;uyF5;+|FV8R1Oe_ z%yb9)mCIAsXIeciUE=W-_>OJ}ZpXCBC^k4LR(1c67b;(eTHx%D>*76;Ga`jr^I zQ3IWsUEzv7)#fb^Z!*+j-fUeD!vt8v01Rhy5b`dXCH5S|^GQid8$VsH@!yS-V_;+7 zpB5(oA>b2T^2z`V)S3Hp$({%fRe97=32GngXS?t?$>0jAAE~c6KH*Y1>+dY!<}OF5 z({a{6yyr`k=h3h!l*#66=rpOpPt9s4cV^LR3f>5{ChN7Q*1*713zMU*zqknN@-ji)?E}s@k?!LNmA;7cebs*4`=Rpem+ll130iUm;hsukc zj&@@aM0|>wj_*l+V&fpF!TiQSoB6? z$rK=ZPD7Y-Iou7(pf-x0>_S-LeU;5$`Q$s@AsH=xQ^A{f#J$u=C?1Ab>3xKryG!}6 zyt5&#F_I})34KiJMnYV*UVrcQ^!rg@(Y7=12|aS2e5SkJ7B!k~wz?<0tZf|qMPzo_ zmGt4>&OND{fmg1sCOv*|E4hwJEnU^$LLBp%MjTNCspfBuQ- zZXfD;-@=2N5YTgaqozc>*)RdD>-`>D-|t@9heS!_1^U}qC$?tRE2g@OUvpB~_(8yI zb===7qZY4X9VjjdFb2Z zFynPvKIPk!I_zv!O@5Z#o;e#Wr}(68g>n$4>3bo~+d*6%PuNPPnY)58`>|3u7*D(h zStmR6S{S2f=$|==6o;K&J=1X+psMnP3Z5gb6=v4?t!2MGm-d!xyTqkph!3B|0)t?m z2LQ)@*a@e^G{Ha|fO*ROn_-*gQ+-M6o2_grA!a@^dXW#;DZnv3#8A%gYnm!W)HcTc zaDH%Z$`}>Q1%fh=IIkmVRkZE0nDcu~Z)`yqJ6tUSBy-yi!Gn+Ll5U)QP=;`2=TbKIJLan19qCrtcNwUo-GDdcx#mRrzogf-C zjb|ndZT)0~58UjB0hF!nVjgC;ySQ&%Jz02ENaI;YNh~-8EW6(|wYPD-{e<`<#4Vzf zqz(rCE2brl0{3FBwY=WA@`SF3KAJ{Xi^cKXnPT=?BF+Rwd8}>oylbsew;~qKVV@AuRFrcb`~(j04xHs z00O9_wDi-0lPe;DRWn@82T>|F_lU=qX~JH4qcghqhq9B?`?gb(hqFxjX)c|9I3g;7 zwmUATtGYOnM*;L*mf-hUq>c5t>OP-Tybr!~Wt~FOxS_t47`+S)ZGf$#RqGWugZJqB zUc7YNbJO)j75-nV>tIa8E;>i|ugqU{o8w`ooG3T9CaN5lSYcH&IHDPgFA^IGP_ha< zfQcuRPS%uEE`Q3$rL-TuJyD@yy1J#Gql-29?#(P^hQIv3o~IDtQt-|zM|KX9BX;(> zDQsS!gO6f6kF?XPx7KJ+1bRe;(5k0VP@AY}7dql?(_9GbnrT2?+VxU1GBm%xrnq(- zCs{@ur+H3p-!A%MSL%Adcu(?tuJ_oR)K~SBWQD`ALR7*;()pY@11)bM8iBD*;~1(i zsbWyl$}cM&Ns9{FAet!86@tU%6~R8@Sv&y?zc1w2sEL6;%f8#Th6s?L&@vb&ib|hx z*Qz5DD|M5noH15_3@7p4Y^`t6Q|}tO8GcV!%_s@YKH$vd!#^&gN2^c#+cV}O?+f|Do$HfS6%+10K7VMoD+!*v}zs}cm{9;(BeCB&x4f>a%?6-;58zY+`6r)U| zrP55IB`TH5Qd$uBR)f#LfHc#fKbMlDbvGUbEV;RP} z+x=-TpLjP6+A)d?%9wq`GXw*~(>O6h^S3Z7de2L|z(m>LauI{Xd{>tXi^Js?XnrAS z5;q2&bc>eEr@aY1XPJjke3g%)b=uHib_`p)^U?ik*mV7>$a1F&U~JjbAc@+?7e`y8 zdEeFK%H5#b59+gnlfI)Cd;mr&SI9PBj4C^#yT>yc3`s>D*((UA1SPUEDGme1*a$WG zjE}4F6&G@e4A-T1`T`&dyXbYDtd-csVF&Xl~~F0uOS!%vW} zF@}8I_aruo|1tj?l8+Pe^f-)O@(8A^ov>3ZOE1z<=D{JkPxr>bL`tk3JIm7DQ5n4< zN6t*J?Va__y@pTmYelnXw~9LDn^WSu_D`4N#0|c0y-ii~P!AiLoi&8}kCZD$i?8d_ z7n^p3Zi&F;z?guz_Gr{G0o_MmKhwR~62HwP03B4YHXmif#s*xPKZT_Mm31_BwD1CA zhnr=aKFue$I@2kf1L6l(Z{ekr4F7FFSdKdB+Rz0u54>|k9klPiPBfE2d1v8JX@ zfb%7cNSJ5=Xuu4?6kB&jp)Pq$ufVXuXQw?5Vl?)@;v1$vg3wTjXBA#s7%E`YqO>Sc zc%GQCB6O~|AxyC~(UuGQOH2a!pW@P<;Oa5<$UnJzQY#=axmym6R~cTQTnDABcYmve z)o}K0dF_QbG#tP7F0OID@2hHe_#+bqSZ4aDuK5=vj(PNq{%7o%*iK{&Wrm)dW{(NT zeW=wFq8>{;pMj)L*3{byAvvT1WA1NVRM*r**AE=b77IBtQ;e6y11`l&y8c%@kV0IZXV_b8A zn)pSyAS^_)dZ}PMKHNkj!~knm)mkeTIAul7*f7kn(xq}gby|@ER$(fuq{1EwN46|{ zb}5Oqw!Xz$P4s7|%lnaP)9*+Yuh z?5x*jpT_A3(X9Il)kfDaRYsQQ!No=Iq6&p5#2@^0;Cc})cGvAMr{fB-O`q_b}cOdHZpYL8w&eh`Qc<XEZlZ%bM6v_NUYJDs2XV2wibjDdU67s&n`XE zt;n%xm-nVCFGUW)HcWA%CDhB&@3bE=#I5#ybPTIu;qojvxY;A+Lxn}?sQEuHzpo@y z@zq9xL8h_cXYCaRiD&DzFZ{P=@P&epve~q|IGr`(m5Q!DAje3Q!FI^YpEQ{JamC# zB0>5!+{QsHgk>wq=BjWyn_nWDs=qaQwzHelBB}Yp+{<)QenqLV1cL+Cs}`v)y2Z|Q zO)yi3Y2_v%O$&8^e(z~}S!KMDF`qEWhh%mD?<~;KU$k#u?NL6rp*)@?on`A-&iZS| z>BYnq`Ij4IL2Gb~veaNbYbt^enVgGC@MNQD^&h-2aXP5ETG5n>a;%@RKd%mvRlq+x zF>cu(*<>W#XDkkaToGV@7jpI;&$gT%i(j4m#L{JM84lKj?SNG;t<{2bz0`FEAf7F< zqE=m=ixtMaQ1CTiE&FmQetp7DdVRMfif#%D{o3Y8a8|t6>&eJr4c-V4`s4LPM7eIg zgNp==fD0qsuu@xb=O$MZ7%wS=(@~tl&=(!k?X5tjaLP#{v;A&_%l*!M(!}8K#VAsC zhV~S)TK;v5d;321E4h4V)`&{`_2(rqrfEQNq9Y3=8uKM>JP-Pr;hyP1lMqyl*BMC! z)$NSOjj?4$jnW&{rXW1BKW7rfhIiown0|Q2rrKPyj**EGq)dR~Y6R>-Afl-oKiO8o z=xq>4`%cj09}W=}vsnmHwJ*him6B*Rp>u$Fq<69KpODR?!BsTu&xi^h-VPVZg?L(J zD(oPzVSm2HQ7~wNy$vq2Ehf6aGU!%Z^$4|g#Qq=S5Q%6f=fIi=v#?JHrka3cgOSh< z%+KG$G5K}7orWICnkw;BlfUcVwA8{T6bQ%i%M%=2cUmO=H#3MD{!nM5)>T=-WFQhK zeioK0E~Sm6)j(^2K|0{RDnp)|<(Lz08y;xqOyRN>9|ch<&bI0G18gj`jXwSQEOdDy ze2elQzI6o%M!o{z{L%KK5oE9G?@R})eU=k(jNm)2xNxlgK_PsPxuG#x)wApI(svwL zYc=J)ze#Ke^0D4{mSRt!vKeg364`_IXfDg_2)c8tG~yvB^mrtkm?m-2o!{DA0>74x znMK;J=09WM9H>`Lu}b2(g%H=O(VB)nrHQw6?*5q_7)&!;7jQD233F;;Xg_tZHu?hC zS$ruw4a2UIYShZgx+sglMP->!oTzvu)CGj<_9IlGzL6RF!)33PFxysb`c2nu>HnJT zM*heUayMb4;>sdKUe`6jo7>gwej^>_oGIKYYG|sz830rH3ZIb-1;i9tCc*(V9iilb z#6^;sh9%}krei4zqPf7udj2QI-=Id?mJ}HOA-n(+#C772cH@&!89=3-nf!=__(_t1 z5DTRsF+9{v?^;-b&<%wDT0zOMuexFv2x?*lJ}vb)+o=*=t4+e9ig!smtKXpp*01id za6cl6$BW_6y>6bfY#$UVyoyq+SaA0eP_n6n4;~iZ-<0xwTKEahwqEM?hr6dtJK?VI zsXF_RebnN-?<8xmL+QUCO^7i0;fm_;%>(h(67fyQpI#dW@d&z?c4~tr9pH!1Au+UV z{Oj%mOe4bA6j4d&air7imY)itUFp3rwUAOJZ0FA{KYk;Oo{mnm8vNV3@1<<5AP`sP zHWwrQa#~uHXmZGG7Ay-SlzvGMvE@EzqJ5C7HzNr}KkJI!=;)pS=9^wYseY@~!-Obs za%j|HTni1191HH7h z+`_dSTo5vADIH6;bw0B6i@L@-$R#Wx^KosJ>!gT^r?030s~z?4GFW=T<+x3dib0h+ zd96qJ55bP-%EX1(%Z$GMfR6?@TTUy~|8`?EdidE212o;AqYmwwQd#;SH)y7@l0VFE z+tZuZj$q|Noj!e7^f%_n*l2uJQl6sl24aholDDZ_sF|G%+)QCnHh4eAM-{7`wYA0Q zaox6TR4qKaa3#sP*9?Z$ZKltU^uLZy3How!mY48qoRP|VBG1xp!hE4XeR5B;6vT+RWWtgCH02JJSJSl~;4uFt zKm%*g>_%2mRnKZ|;G2sG_wr&xL9!uBHzm&?2Uzwm(=KpHuipt*pDjoHRZ*?wyE#Z9$i8?}UoLP~aPcabu}}*K z$xVJ~^m3+p6uzjK)i|>P|F9MBm)gYX+aG{b4Yv?&R=~#om@-14)yn_-uUg-O=%eJmuH@L+{ARyKvmvZe-)A)4H`T$YW4;^ zG7b=;(s=>wTv<7y`hmn-WTHFaHViTK3j*Qm=pY1Q6H7>EKfO2s;DbarVmpRB`}2sl zKGF3A^aE1$2Nyf2XVqNe3y1_Z$VN2Isw{=b%_=`fL}k>Yt{qny)NLl7~DXMYA&apJ2iandCS3CPWJP7UFqF>wi~RyW)I=1zdY%73~CY1 z(9x-$*6}M@d~Hp1cEOqq%{1e3u%{I!pl&hT&cUt{tO%Uf`X{8;qLIdzW8r-`!dC@Z zbDU$anL3NfMU9`$kWoxvZIkT)k-G(UJy&_8J~SM4rQ<@Y4fJ(3Pgwkz#agOp5RH*t zW)Umb&PQXMN=62YpRri0JPZXA!7q~3lKK&9rxjzZk-!Tcm`ex>&^&Q+S>5eO-X7DY^S2PySZUlbA;b!w_$yzo#GU-X zfoh4=lb0NM3vc;R#+JLhQRkFiRn_5@pzxn~XaWj6!{6 zaOnew+A)}{yFEU3jMs*w5^P_P`pTS4aGHyTawM2)f-Rwfu&DT18mSYar%Wz^_~S9n z=_%2!=BK|jq$d9p<7+AA>0@oc&o%$)mx~F6v!v(Q|MAYXcRLuVp{5*#ec1|Cs%1yfKvjiB1nHuP+%n`1?Rn&iMWlBGy- z%M8ZMpS#b&a;{O>6&C6WXS-4)ElIq;r>b6IR|Hcg40-5kB=T=DmQP}3@T}V+E7Mah z%4y>M_ntv0;pQ&QRl*f)8o-6-nRk1sLxVR})lc%0^QI*64OzoG`Bi!$A0f0W!{GwM52?e6@kIs|iCQV6XwV^6#zVs3# zymoD@T7F}caMD1NR0lhy0B>>3z^ODzq5_T6A!W)!pkYurwb$a(F1{EIr*lliSzCk}P~(gQJE)}Du3H}ar1 z=96*+b`S&ik-PvTTI*nRfdhhKA~Cq?ZB`CRs2nk*fPi#`cRz zcEu$3q6{W*3G-u$S$%uX@hqK)jT4aF;eP*u#CG=yy)%K|TTYA2h0lnbf+Zz&k_YL?=A?RsmblfZ@q<{M5Ck%^cYkYj&aIvu+IyO z^Os#$a!XIUzIj&+50E5FQA}R{VtZk6>)03(4_hUKM;Rx1I1-8U!V0oBl(uT?pKb#I z2qWv@gVoy!6-Ot@V9m|k<7F$Z)qs45e((C~X-jz>3>kkRF!DW= z#mg5{Z@^^SiF$7yU^n}cTWPlB_+o{`Cg#6F0X;7doWveX9LQg$hX{kCX9-Dc1v*^? z!=;Ar<(!2#xJsP(1MhB&oG_#){kVTUrHgR}zUexj6n|Mx_C>CFpPQ=x@XIy-sCmcR z^j3E@h?5;-Qn%j`h=1kzY`3|II<#WxYowkRu_u8d4C{$UlNdTukI`J>0HJ2;2o)at z#0iJi=B0}Dgq$IVq}Sh$>V7}CeaCQSy&~8&rLr)`UFiKz6TG=Be*f@8nC6LOt?puF z@3F=pMb!vNk+}Xbt1n5QiHDJ@g*Z15!}|v(C#}B{8XFGZ1`Wo?XNlNOTP>6;kP-9E zgRw-{`A9^U<|m#W&zEKnr_gCytrShcV($|p@Qnoj`(6MzLCnt)G3o}j=?spqkz1rI zxdK_R`63c^$bm|e!PN@xzxGpAcQoLD$E!fKkQI5^xI)|YL?r<04{Lj#mfY94JU&{a zp<-9IyczO#@b6Y9WYBI(XU@)g`b+;LsYt6=0==l6si($}wq^~j8!w32GQG(6=bMwe z2_`BoE^JZ=;Il3J$Jq|YJ@411+g)BIrH~UI{13CDa=E;Z%(@%w2wO9G<|&bi)J4oQ z<4=DT6!MLYJcO&Z+9|m9qVgkZpU9`@O4Wa-s)(k45Wn<*6nJDJ2*i;>&{LoOEEDR= zRu#P!nO5o#4+IY}i5@T@B_$a<9y#$w?mQuE`922`xo3UzF5So5!M9QtM%6-2MQios z^CwO{($(f)k&dV}@P2+ucB{+H{0Wz51^Tc#J{QL&{Bg43mF!NsD)L zu;l;QJMZ{7s;lpR@62r9Rj-zt+$%OVrZ>}UdVm0-hftD`gybRQC5<$aCkam=C{yRlnJr1v605cM&`|% zM|XEO^XJd!>8GFOvBw_6@Aq@_%{LFbtpiC7wxV0H+RtX7vSXQ`j^Gwz;FIidex0Ji z8~3TL=FSj*|M&Y;`tRY~L)YNa_C2<2OC*=CY(J5hwQuQ*JVK|~5#>;SD_50nVU#O9 z!@ZQy(V}t5-0Ae!eDq>J!Lw(kk`PP_t*M*b=Qq+rddR7C(^V4#RS=(0XfNJE*o(Vv zf&|CTc8oE8WS#>tF^V!VW}EITkUJCN4Sp>RN%5X;NUnK#caKOYDLm_B_v z4Gj&S@Tm3=R%{1mQg{tC{f(sXf^?H(e&UZrqI>ruTJdd!>Q7K!h^fep0nClI z#vvSFb!vkrqXpFFZd-`;hi>BeU>A|Y$040cpSF{<(VBh_&#sz`5R$HlhhWrAXLJxv zM@p2oFy7a;;@sYX5=r#+xwj87+xh)oG@0wM$DB=g?QNfm7@p>o4Z{##BAOuQxs8}* zIaq~0TX-4Pu!tx3zq-n1^#X@fmh-zC&L!99!J!&BRRf9SFh%XBaUF{(xv?IkALL8`|jhg z!w%z)JMJJqKY!>(K0#Q)-$Ue2fNc@^xV8KqONiO=4e%@*)-_hk0Q$`e>Uq>2n#G0YM_t}S~=Oe#jYHI%Lx-uHLnY6}vo zn3maerU>p|ItK$MJuJ?ic}qi*;h6jEMxwLJNlYspRIrGU?MIOTclyGgP4HwgxeayB zQ0S@cBj|f^_vHsOOkt@thX|cK8QDL>h^F*ue}la6mL&yF ziT9DQx7fBj!r$H~r#q3u`Nz!W)cI5P%gmRs40M^<0@9`L+otf-f&)^3g+d`NxZnbQ z_q*Sblas?&zxq{fz4cav5Zrp}t$h32-{#(X(+eryefQnmefQk}w6u(nV|gE7#X5V1 z7kqNWDj~p0=b-0U;eSEsFCg1fuIqu^2jMla4FFBR(K$^i$I2XGx=a@dsp1wUF*$UB zM2W&X!BH%J=5mOtSWEFKxF`R`{N|bQIHah{hTEOM7p$;ew?-KxVG#83GpcA6QK5upT9RdB7`g?DQ+NQlfVC4Te-yw3Dc z0wDyrjzqYQWLhrVF@1Q$9S%IIlTvpsyQAr;)Mn+48M%%QLL6`cOxJZTxZnaVyX-Q~ zKi{T>mzMU`xw!7S>p1JIvoc->W^68%M3@&yBNK*14Q-;B1#-kDn7D6!e2GB3V8pDhdH!yTAYMu9Ua=nN~>cVt^SP~_Y zAosbASbi6=X~njgaBqZ{VUe?^1>a0Bd%6Qy17qO^!jec;L2C}7Hh1IQ(1b875@Yj; z&MvnFsActOsM*FMgne$(f2edu?aDR%f}`@4`{nfp2SbZ}n2& z`X>DAo8S-M;N$eZ2qE}?GtS|b)ejLLkm%Z{x`>-PF7-etkMr^4fbxM!DY@pFYnVQL zIyc>PQ}6M{#=bO2OO`BQ)~s2d#PIe4gKIq>Cp@AZEA&#@aVx(3V-;SyC3v%Q6b@nH z5(a1H*Rn8oH-1&;7vtY&s<)HhPkfixXD^{pi!mrOQ-bX7TND$9$~WISm9-s(_;0F1 zTN6Q8AS_Tq2^_oHv$q8}x3}V6+nj2`GfaCoubYrC5D^{Uo7;&THU__6r#KLTZVg%I zg(^R?8IE7pfYmq{P1803HY+aFWC|#cU-FZG)KE~f3ukp37#8a8t`y+~pcod;x(++N z(*S631Dq-ym(3@9OeMyo;sM=e8BhOoO#O~oE)Ao^CcnrW_uxLMp43gr7&oDt99~w# zzyIW+8R!eNH_YK=`0IvSBI?~(ii+8fiu0A@%hqg zxMIQ$ta^PN0MGvUCC)Ftnnliwxn%5DSo+KdgO77Badh!izCHaclmQQHS?e}#TmR&U z_H{7ffYQL0E?vs~_utR#*|V8DcP?O4<{y6eVb-i!gAju8Aqc#=9pl4YeJsR*Ff1H(ofu`QMMR@>MiHA(VEc4>w5j2w zSQ^xgCR3+NDIR>%{wBHTuLNq^9zrcd>9x3ANdfIL`SH1L(i{x1uD&eo zdeIrhvwk;`qbHz*;=RGs;UmXoG=Yjx9JM8cQJH0~b(5UuHlQT>gpq0_$a!W1U7wpp zVoW|hY6-lyjjnTNAsuScbO>sD7!(EHdo^gyA*53!IkK)ubHk0Rx>cnbv7ceAI?iJ{wqLhDZ0 ze1=SqM-@o-z@Mz7AU8_8X4C3Ldjy&98GOev)Q&_|&JR6$O z%lxPfS!&clity@zk?-LrcRoeyC_fjTFgNS9dxQFLTgKxKq0$oT{!D;k2qc~x^z=-zogW7oMM}^U@aB>Np8la9PrM!X!|lYbEF^JZ0r~C- z-KrbI5?GeN zcZ1PiYq1%2`q+G;5gp&s8bVuO-4@%|f*HshzAZE4*R!w_>kdGBI2jYx6-9~bnCzv` zY9NMropd{pLLidf&7v|LBOy`**!J}vc2&6$g)z`ge9LQbZELapUP%qh%(I1+bZI2U z72w?2ZWCn!>E2rr)lm!!--mSsD{|BBQ;Fy)sj_;nOaAMsZtpb(3d<(S09Xzc*Ut7d z%~zNfj>dGeZ8lg6bX_o~H>mrUkG#RJ9(jW&etadBWkYG`gJX97Sn48e8MUyOnXTOT zryHLMu#X6e`vfT4hLllc=_fiK;hYVCWpzULk3P|{WP>H)#rq2Q>rX1s``5x! z1;!D6+`{1h9s2==hZsDuaweK;F}1XXKfZP(F~bRl#kYR(4&H#(Yxz==R;auplYG&| zcTRnVw{Jd-__R_ihrrp@0r?VRk`rx50^*Wy{7(Zp`DWTWo=FpRsFHCN?I?*D(g3DK zFyGFHNRRnT*q&xZB{9AL-+MLC6RT_PooX8&pPycQvZoJ#95-f8KObCH@Kk;&!cIi}IGv}jJrw0V(?ZkwL* zd%O}goWT2Go!xK0bByUO^Tp%VaL-Rm*}BG? zvJnv}Jk-86w5}LN&$t~bp+(aWQN53}(es@U7-RD2K4Bv69c?)3I*A{W8q{r+2GGj_ zICcj!mW}~1y$*zFA>t-xz|~6@aWn=|J0j^qOb#u)Bp{tC`q;dTFN$GFIzl1x{rhg_ z8{;eF-lM+CpSHft`>jR+?r|0 zv_1W~VLfdKtbujBGN5=~-ww8aE86BbS5DoA$DMiiZkspz+Se^X$a3&Z`-I+OLII^% zM^n=SPlYwm_|p%H?aTA{O+0?9S9471-!+7A$+pYLvXG~plS%UTlz$lvsvvw!C5dqb z+`IHJZhLMauRKwZszsAo2nb7}wntJKhEm#^rc=wPJ?fK+2_HU|`0P<=JKOCVZ9UhT z7U5$iWSwV;ZDv&BCi9gA|_$u#X9fRtDaihCXq>U_t(~h|;Z~RAxNG zlqOq3IU_>M7%A9!3H!I~4BzSvk9QdT0)Md9JmRtG1p@x0%|Wg3KsL6Jzr2 zVCjL-6y{z6U;J(pH~+4V-#$}?=14!!(bPRWjY>+i`cAy>RO4A*n@SJQ1j|Be3E^E< zV{2hO^zvRN4}(+Kg}^NGC(V6=*EUTc9vj}$VyOydfw#Avlz+4JrwFVfKWGY$Xg7fu zH{;mZfx4#~&#HQIm#jm{Aar|=XbN5D%%XeoboBCE;xo#yd`^7t?4bL!sTc)bTcZ*T zDZGtBAKj--wKMhi5QH*~+c23jrevt;7&c;NW>SeMt(;~eC>L`cxG2Rd{omh4-%<59tnudd|TdyDzfqC+_2e=g@& zOOK{Cp87nc31brxZdmw!T1V37Bz*Wdq^clA((I%u#3vRq=Y$9sezk>T&*-9ZdYl`6 zUXRmd;qjSh4tqiJF0TdM>LVIh8H9?I_+P6c=jAPEO+hTbJA1)QDSKnw{I6Bv*|dk8 zXEyXL+5-hj*7=kmB=g4Y=8>;F%JN(9V$RW_w7rHLFe4H`Q;1J1-8&m~@?7WkRy-@3 zaqjFQ=jAQrJ-r_9>V^~wy(GQg@R8#%iv7JgE^(-sg??f)%Fyb&=sIT>`q(^#9H`@` zBRV%juFd{J%(QIs%Cep8+0{Zcv~MQ-fjGQq5+cLMrsY=PRQ~UHhW_u|=>Y(tO;(8uoub4yA>Es|@#awIo@ei`Rn-jTAFEZaVs7uQck zn;3$ISjHw?qXn03Jp(=0!Dv^AV{)2U?62XLW5?0iYHM0*FovV96J5WZ(zwG9cGyBdv{n8GOJtN49 z*8|-3<56_ADQN8x@}68nbZ!Mki66Biin^zp=rMgJOh*ufp$ggXU(?QHO-C&;Qzu~= zC&8fAL_!X9DbFhS0_%orgkntW47z)f`aXnwxod>%OVLvFsW znFp55WzEh~qJ|qqSXk2qW!sjK=$D$YXQv&i5pczB% zq84~{8P>8x%gI<3?eUPHu!DQ#h7 zj+@Y-qkGwYW}YYe4n>e$CVOTqsoMy_H4E3^)cUfeS4>Fok9W85{QX7zbICNKc{#+V zmm=L79pek_%ZI4@<=EbWbZU6lG}xImgg`I$qqcTqmgS;^_l-K9`{Y`}Cr(NW2uD@y zuz-Y#);T;)No(%L%=e^gVG?qmwF9aLl5`@$2w$9XBma&)yiel?sPGo>_;LTm!!=8J zu=ZUHY4=kXY2)@y&#*n%$hW4SIb=HrJ3bR&p9EsBW&ci;fE-?ty#6LRDIwUQ@ZIxw z^U&(~Xu}XnLfDz&hYA^z65_hbzuuk47mr(&_Wkze0Ee&y8^tT-C!`23djBu^l%n0KRHz!(Uh#3mZq3of8qx;bp=>iQ$c-u4i>iGXF^GP+8Ul)XmajX z+Ii7a%+{vj-Z41pI_wfMM~uf9+pnsGVIoYKq{+t!yuQ6JW-EEEvnM4~gRLnJm)@Ro zA6+qWUf4wEg>(C=Oj{CdcNd9iB}kWs29(|XE7w>zfj(n#dhpHlkNO@#2Q|F1g_qlSy2^6?K-NDVD`M?H2L!_MtYTn^r z)o)P~8h+)~A))3B^4)02e;kzY4EA_T1G+mELR|{sZiQDL&*SpLX7J@7Hv#q!$B<%3 zpVe^8fotnZxZqEh@ci1Ts2Kuf{`Tf!#EjHT@-f}PKi{24Oz+d4gn+N!wwsnHKe%HDKmXw>UcTi4W-JJ& z2(SMAc%26Kz4S$nJico{+esJ_^^GW2vjZ7XKodkRFGo5B(yJgH0)2s(*bfR2dV+7v zd4q*hci?iE1l$QbzSluT;p{COKWabh)&NTkn~rKv;?AB$83waRE7 z?K+-+FDa$H-7$2nZkfzlk$lh@&13zbw1x33+llwx9jLK>gpHKx$^23%3Y;{(mhYeS z4w@>HNqX&bw>B1IX2|a1P%Y})Q{Q)eTmixJ=h)`Teu2ZD4|d^uy^5TdwvhMKx|Eo$ zv?2B!7fG^362o@US{Cm0yORSl5xR+MYctU~6@4toVL`k^LIHvM{!PqlK%i;b={VvzNaoU>z3tk+jV0J2UE>! z$DG-PmVBxXMWR~*N5<$(8^cN1Frl~;muAu$@^aUkhjIOhD{-i1Z|%WNyNi)BebE#` z($p1Td{JL=V^xEN(`&LY3MB>pFq0>T5kTeiD02_(-U-NLzotIqkR0~PtSAX*D_bv7!E4D1+4AZnUnimJ1i`n#iT&lAXg6!#Wf$xb5VLiUGJA{;nn}#+*`2%KLFVQ zA{QmeFsk15EaBjNX>AYup=d((`oD(~RHl)PV^VC^ot_Z*ax8K@2_C)SQC?YF$*Ofl zys~l}ix$+NYW9z2OhFfoUFlk-VJQ^(Q)6F*fSVUD<(2i5dNU!|om(K_nqxoY^^N1P zG=$uj}qgKZ;C%H+SlZPzS(2w=u_GKm$_*DBH zN=TI_|5Ac@$Voif)2D@ZRXs{Lj@D++lkbCFL=PQ}<#%C@%BAD;bMbw!3+K*uo4u$A z)b4oib5>%8EsXlr-ZF~=sX@R=?MwtiIHz=?&jG8=f!yY2_y#yZjypzOdw|zBPsE{` z==}nhmxAT+Fy@5kD(NxLtQ);>7h;>}| zk8?@%ixrbDjn2=_#!=mdXH7j?_s~AB#Eepm^1g*S_p#Mz9mDfqsa;W|+kxrpD|)K# z=_cpptr@mfdvB`DcD+;;;**MyxgMT)f75;muuH}t!>f(!iS;Y9q6w8rfif!nBS+gm zQ1O`n%Ye6H&=0MIRiR~y&uw*UiBa(|BHzXd{nwxx#eYba`JR)OwloM+y8p87p2 zF)8J=BhP2SRNMJqJf<*d`|kT`Xbyu|RN!>W6bAHQKfInYOUqu);raUuIrH*1@`?K{kQ^#=g@9J=SCD$DPA#3Z(h#URL2A09% zS=;ct(-$EbGALWJg0@)`(8m{mY2mJFW@5CK|8bo_!cZ`;gzo4H+}_&s>m?jsS;vYW z{gu@#a;V?rK+W(bJ2ZP97EZ6hsp>d1iD4=%E?mQVcY&#=YabR6pIVYmJ+}nZcyKN0 zL|CA$j^bL3CYISHRQi7B||peR;Sc%?4un;MF_=HByq$DuU_$yvF9>c>0y!Gq(t>Xs(v z9395%GMG@@!MTTSX33HT8F-BzpzSpX7Wznd5>G7(PVn&D@T}j}j zEnsHI`Y`}%%pmva^+Xno#V8G+gcAf_*_ts{Z(oI1TG#+!23&-XwV9*w{dD3?4wUnY zxtH+Y8=fL;#4)irtY88^JLKa1Yxp3+X96q(1U-Ub;BiKjnyRP)$i1cavy>K6--=M2 zgkq)q=k2Sw;E+uurl~kolg+!A@c5fQWTH{ZZ@%>BOmac8G6jW>V2+41->iW)GhKS0OTJ5O{NY%JFuO#4#&G)SnLyn0ZK)TP~WwwzXdJ z3Jos)dNYeJZAXYc`7-bDAn!b0K|HG98K4FA1OscT!_Kxm%5uXzbbAq|o|(cd zAgd2aI@I1$Edr2+#Q*DR@F@IY!Wiy&bUO~Wg{Ip6$AqPDXM>%IT(e9FWj5ueP+269 zFz~FdCw$z*)Du;uH%V;A<%ikhR28J6WK|K=J>8TY(@0BR0O{%XATk6>+c!CwO<7<{ zf!WKh?8hMLQSp<%8%KR7ftR)*Op72I8{7Tddc_2Ocy}#_pWKbdX>i)C9lUyd2!x>P z!a24tFR2O1WJ)RZ@il1Oee6#uAbfPC9XqB9lu$fnyn1;KX26A-fkl{UinR6!IWKQX zIcFpx1w@Xi#J7BBZ*2&B12r4L>rWMM!)?29y7%s9CWz+jmy8?N2n13uKa&Z zRBv=MVP*`E&%(0OP(C4^ZLNc}=ew8miHM-5`v7L!<%0|Hie zTU>Ta9@ieVoyy~Nws?wouz4!=2|pSpKF!48veMEkWgLTKXkU)2Pm?oC16h~?Ac{>i zzr-4y8jmIl^{m%qK0cJQ)B@DrbG*xH)3UId8_@oQAYqqkjr&o>j7L@e z^H@O|vogQfWX6IBaYGP`h#`}9jGi=jkgETkYCEM;&jM`N*%X|$?Szh-KxFO4fUek+84ym^by&d32pK z3!EB>0cCL1&KS8*t>;^_Z)WT2{ZI1!$89gum52~e`oeT+5;YV2e8W@w-S&Zu&oreJzbnTLhRFV^X3Nge3vm$e}VY=K;E$;<^|#!ZQ_bDJ9@p-@z&V_7x>QBmNm zE8AG|w_=QhKqfiRPM1Y!;W*;`*@B7!3V0TkvWQxQ0-B>Qwx+&4hhM&M93Rw-MpG~v|LaX5-lNI9tUc7y6_yR^hix1I(KxEh(Y#M z)#~TqR7AR#Bm_w4ZqLJ6+kiFBX$#yQwRenM-!VCIwh$tA^&3DM=Ud zicIc#dkc5mG?q2*`f<4}7G2WL-()(I@$5O9x#i4v>>Pm5%Y5vPIl1bd^U3!``0|M# z;BzH-r)nHuz5gukzwW85b9(20j>EK6l#2?;dtxopOb@9B(7NOJ-m78M%Xw_sHVU)N zYga1-@X>2A8wji1fmP|G`;IY~FBYS=M+m&M8AUgdR_3}={XaWg78Mr-_|Tiz_hm-& zodDUB7g5*(tcQ{=k|gx;QPc@B&?Hvag9s(;m?>S_f-EFZ#>BCzed*>qDe*3=MS2~? zrj(?UMEVPoq*Kz$^$c7B|skFhT1fL180jTg4urrdLPq5sbH)6sIKnDM3zpfVoO2_E@ z{QheG_j{wc^jmv4^`edu8eJ+B1%7yUEf4;-l=aK~6j$hc;Tz4Aj>){+RaHsdcH1{m zIy#XtV^Dsv$gmJf_{QoN_cbgcut$Koo_B$hN@Vo zisYO_x3IKoEW0~=R1}0$+LjWQ{U--#;dx%#uQ?LurY8LFrjDaOsuOt_js1id z*i6DqE0HNtx5tQnr34%bSQa_YZ|u#;&_}lck!3m49FmKU*oZqCVCnYpS;u8JD9C&G zLlP5*59c2{-s&|W0yu=qaV67nsUN|v_5i_W z0<0fawb#%XOMlT^LgU7%r;luB#-{ylLI{|DVu*h(%N|5IQYas%bMx=&hdeJH6Kq-S zrK8!w(WiHE(GA1TU~=(-4LtPzA;feC$4#l_N9Vj@3$F|~Zb~huA5w+ZFaFExG&p9` zF5cTdno;@Tlst@8>pcAGhH*HeUF1K!s<(DiVn!*vSDUeHhAdJ^sE%|`u_ds!`cS5Z zdj$lR1XE&GIFaLQzm2oGEvw(CryyrqypaUUfUlnP0ZX@!>pLS&3zMKH=sJH6(xD{% zLP?XNkdUYMjSbu9C8dCfKsGv&-45h#7Z@3K7nX%|s-%^*HZ1~At;NwEB|fbr&94K1 zrVyJmiujB&q$=>Nti!w3&Um4h2nd$l0Rn>yKswLxm^&s%_RwDB&NK_VL z`CK>}gZP%#pvKZkSNjVAC{Z2Pj=>XT_aIS`Yt!TXZY+-z=cXnCFK$K|!V(NH?TE?y zHP$ODPhe}Xk)$3Y{&UV7;zzWL2>Vwxt`UVAOy{qA>1 zw0%oj{BZ3fX=zyma7y`XPA(rg_Mwy(uy2V-KWPj`Ml(%V{&IlUJq{jRwFx2QUYRt% zyl@3^L!r90fM0(8Ke!#)bIIj;GtZybWsvBQeD{%4IIG|Tx|&p8ex!hHYkfd+5s4W% zwzr~BE(Re8FBpsO{hb3A7>WF%ZN^i^N3q&9+fUTA1k-1LeaDUs83f|j$72{&M1=21>~MD}I#U_i_l{e+kTXoA?)h5R5` z#4p?{`ffIx4=n3E2rvY)#ewKg>M0drbqe zIiqYAWO7ahj?=ZhCA+X22umXNvPj*&kSzB4XO~Fd&oYcp%|yC z7gBl1IPPBko4p-f3dZ`180*WR8GlM~PzbOUD^@UR(j+EKu%p+%@P#k%%rno7r~uQ1 zij`%z>lmQ|aZ;Md$O91iQ_AuC-~=E8tRTXB4W?6 znP&^5AD#0Ci)U^_k1HhNf^*+l#7T!VakeMMEsr0Ma##$nUng7^M>aVirUDWs#URI| zcWKHXRjqL@^b-R(-tI=kWadT8B`{`tFvmL4PtL)rbg?t8^Xm_e-1kXH91=?a+N2 z>;Ewv6auWFp@A`D`hxLBj~>nY@4r8yeU!O#aVc7g6BNL$I5>aIg8gb+nytvtpCI}K zkgnyOI%5YrTl1J)+CJi1SdUEJhk~~N=%(Q9t>gLY8;5h%v8y?4)^?m~pP;?{#<4`Y zll~VV7FKDl7uR(rfWGBYK(!Z33e;KY zWRd>!7yV`l`g||$f3>0P(Gde>x&_g1l_CrJeu^Bf4eUxHrK!co_Wh#MHCek_**|64oKG)OfR zC;#y^NC6#JF0fNOCE=*+=+)SUgMHGDKH|0{wrLYH%LXI~M`Mt9k*|06rF6&0U9#Se zC)`VXfAN3sH?98v3JwYZHeB3&_ubrm_uT-rv<&VH9*Cc=e}b5q?pK*#IDxZAAGXh9 zl2#|OWigPMo)0jtsH?ZC#c;C2_qHt|60+-C`2v|ESsQ8tRE*Ewccwn=+G2uhj-7$W zZ?SXSBqB5O`R&U`Gkf$Niu@7W4xO$xg|8ZMr@HtqVD)KG;)?r|o=1?&NY_GIuGSpr$>K~4*& zkR>t=$~Z1SXug-g4ZEPjKqU7_(k+Oek((mC04zg5OaVnAM#m9TqxM`F-B>Gfk-as- z`X)%kbkxo$X26YfsW_`z2;8%i$f4y#=M^PM9uiTcr)vs|K*=|-gaj#2+rs2LztPqp zESrHKhtNWV1n*nh37-V&@ecWNP|jB(q=)+Lj`aLLe18BStoQe18Ebl7Go8 z=K{Dl?;$>;te5cPdviM?rVm^D{0#esgCY}0MMVX5b$w3T`uch*D$*U4*I$1$ z<;s;57Z1N&_nv4cZ#8cwAyaots!$l?E!;2qyzKZ6g9)!5^kl$*biaKj%chi2wa&%Y z7fpe9z*E{iju#5TImvi>qt^D-q6WP_4 ziy|Pu$l{STo4EVUEzCST)z6`2%e6NQ^q4H08`L4|ne~TmfNEL%=Be%cd3`m?3@2!* zHPRoJBJ7MK!!9|SqB>Hf3D0fbiFE}Ka3^*xqQsg4veWTA~%*2`%+Qr zJxU1*h|&b=N$sd}L%r{YaDZ||7mSTTY220?(!#s6n*0Y=68QIK@*n;X|Et?@ZEZ%X zf<$F;FXdd@9fVkBjYwM9OiC#Uys!}wGf+&4kbT6)p&o_1wu8KfR^g~^!@I0DE&E3{ zNWjc-4SC+)!16fKlvOG0M7mN+gl-LbL;Z!9F#B#k16LKWi)7|Ly8*4C3*tJC#$Z_`Q&U4Ck>HU>9^ssG&Ka@xYr`!#Gx)ho zX|cJhVV~Qzj6I;gwO7|=jmMtW#AraHg>V` z122ZL@7l~EM@9J2-*;dopy6GUOJ=R)?_YX`Y2~fG3zpyGiiyQT+=CS0=_$6E&1fZi|HvGb_|-*(?ixqvH)GK!7n0v)B1F$Q_RR}b zqKr-;GL&c$8fb@xP><+FJ-mzJs#@Gznh~Z%N!S&Qo%Nkq1wJ}2KGe=$k%Zb6#raYf z#8caqCdfKB2nB6VcQ278rQgA4qOXsrPR=Xaa5i)e>l&GCT#fWG{q`3EP~x_x**E+4 zUustj#ge`0sfK%BspltK6q3B6eq~Hr!{k1{k-~p|FsSDBjXzyG;zF!G(#DnV{gKbT z{TnWP=T0to=T0_t4ITD!u;ZXGA!?e&AOHAA7A;zYX_|cLOJ8E<%$XzB{#aj8#;T^8 zP?(ZazV8i3{<+VWW-{yDdDCd#elCw#WI#y{0rfjvOqv~~p~lIrSKLlp(?k>n@=JC8 z@>&%x=iU=Ugn*+LhbSyh5DlrsLL#dmbT}lR|4K7=|8x}7=SR5khP^Z4X6t`%&vzuV zepI?MYrPI(a!l@SUVk!|Yk$~8VVREFZ#q>0Cl&AJ__G@^OqG?jqxjCFi!rQzC*|rW z6>(54+^SBTbTeuX6eOqS?c%l0(HN){X)&gFI=H56GqEWe->yDpfM08POSljqi*)3N zt!cUcgeh@tYbJccSdexgvM?+h^_@7DbzqHgV$Ab`6i7`VI~-UWl9j8C?3&3)6DE2^ zzGSa_Wy66TKyBa1$P+yTn$zOkFShXH-FsJaE2l8P;;T<$-pRB1@}b`$)DccQXTqW5 z_o_|9GP(Z!yJ?GudhfYD(#8!d{?3vUzD>R}Q&sX$2M!7i4B(72&S2ZNZS2^wgCG3h z2P4{cxhJ2ail(3qFcnJRf-#5hd&B1Ns>5c&K(n6&#!QMMGn8$&EEqp6hLmv2g%jCb z<02MOiG);I8l1y6zubbU3ku7!GpaNP9C1pJUq8NsUp!om)3yJ5>7kPHWM$6=r$kR+tS&7;>a)7QbHt&@n!fddd&T`J~I7gnny&4j9A zu`sueGA)c~aUi4F7JiBAlvdowwxPBL$$54I&f0bqL*l!w5&sRlkZ(CL-^#~&FAtI_ z!i+*+_Q@SVd|U>ukBX(Js*dMKlD2;#cqj@%Z?FW#|M+rDQS+2|u;W`3|f}Qy_I|f_4bt!nWvz$c*4dfJ<{O^lZ{PoA9 zSoL-Qr^{l|7uvb(<|dwN8wZ`yw00)Vj&RhXuKf}Y@4T4HcYZ&-%m?APgPR^cgMd3u zMShTuh?_rO@jNAgFpgxLTc-Q{sDij>``Y>Cg!i-dI~+JQ+q^8w_91(Uln_nk5_BnG z!CPGwEPm)>+H3N9vwO%E2l!3sR_!eFJwf4!-U zf}d# zKbN!syg)<0i?$PKvj@DY;9l;ODzY_87g@IE0B zanclYVM>8=fQP8^`ZTV1Vi6HT!?bLY$-grE=iqq0 ziP)*B7_&N5McOQk$!^TiE<~#Z>^)&e0FGTP7+D@bpoC*6?a{-=t2tPBc=_E2Wxv0e zdJ)f7e8}H-t{^5&PR^gi*N4tMU@Ag~D~@9hsR0yq-wlQkcBSSU?%&ClRUWpk-Lv4R5b%>T|BcTT!=>BC+U9op4O&lhPv2dE-pZ+HfjmVSk_7#*t)X_n#@@-%SNPQZ<3E zZa$gjaVFYn4QMAcbrP+T#`gzYqw za7^Q^^}|6zbdI0YkDKvpQM4Whbqa&o^P0Klu1b3QI3gG$b7rF*c1(gn&UTRCSkek1 zI}ITEr8FYvXJC$TgAgbc@yar}#SxC0-^z>y&4@kRZvp7_LG=0n>VL8E+LPz#^g5V0 zcPwvN&ogTJ@PpC^O;NdU$ZVdUa|5r;y^*U&o`hFBu(@@IBfi+?c`s1x&E(I=Uds)i zJw{_GN=qTaM$`*xOd70`+XcC1nA=s`Z%n%yTPi7`@QF&+9{YIE!b7#VS7^% z@3a*0$gpMHTR9ao$^1hCQ)aet{`~I2Z+{6A894?SIR=m4UciH|ZQo=2VL45_`t85) zuQems*_g_>!aC-U*|y)I!`|mn+2p6HIi<_f>x5xw7-e2+N}}9RF`avce8}JGhqEq_ z$t{CEBhL{;?HF=`rl2n7VRzI+o2l_}Yd(vc2NO+T2lhfwSGbQ8Q`#)__uGigONsZw zViH;F1O!@R5G4tbL73owVhVn^SA^1#`m`U5bbY^+$ zP=#bgTP`le;GC@OoSVHJ?GnjrPi3)VbN5=VH3k}TmADB|*KZQGhEfp$3sG;Pz7fJW z!`r1gqP^3K_DKM3W2j>biOXZr+NiPiV~uby`LhWJHj36)_T8aE=Q+uarS9H+@uhot zx#7t}+o}H{95G-$P~^#^A(H568PekY+Km{NiFP2Xo2ESsjbEYjHAtpgXs<&#n0-_| zmz3<{XnzfwkR0vb%|~td^nPW$s=-Ki{LFoS4ToKsUUJ424PAZO(?0#KC|@~x{T}zd zH^#LywZOt&|2IsvBMCSWZeem$@u!@Wz7s<#T%5a^M#IU~+vXr7nAq}ClbrRu-&}y9 z4=flVgNn@(R3sulis*;U7`h@0tHf!?IXZ7{L?T*D&>f&z7+G#CT}db*(wQIS{h$oX zuM-=Z(N&+y@%~Q!@rM}T$1}W)=rM)p;cVIqblz-g<(ma-xViW<9KAdsZ;DCJAwRY# z4MS0QvSu`w~}f_B502(cHE(NO^`DHGPm_7{p{ z5@f#%&V`_sAe{4g|IKNHLxNZoj2LWN>t@M6F6W{dbsU?r8?9r&{zb`$c$D7jX_B80 z{xCu6Zhs&ply7#j=>O{3ada*#+OiMmRoIivRkHS-&7`^$#gUy_jIU2y#e%d- zoQj24HSnqi`FfDMN|sY=cxf;^G#M`5Z7<-|+;aXj@-3bp{V%S{TZ>Qa!8YvCIdh8} z^X&|*wO*_>KFl{Wv1%NNowOua5^IzTT#D^Viw**aDUrIp2Bs`%!1#&|-u_nxeM`21 z*F@?jzLz)P*jR@!Ow4I6#PBe#HQQ;6dC+4jnQK&Dt{;ZkD=rwb1hX%0>pD*1&s~T36>}lZM>ljij^5CZtTJ)6geA4PO_BeP~Va{E=ASP^LA zmWoV5md2}%LwU7vXu>rP{h$ElnsU?~Mt6Ub^>F~O1YO#Sy>a>_4Ybs2yz}?;u4MhT z<454!T#FJhyN+`(!jQ1{p~&GA0(es-r(K%K+>3sRP;5gr>Zsw2I_lu8a~(k{kBtF0|5!benid~ZORAVNsvJeg zq@HNVRJmyLiBv{?6gk>?dh}wPN__Y(XsSH8Ycj7k4(U3GxyFkubCWR4sTSFTt7#h7 z5lie8e$|s0uwF3UNrP5TT2x&-c!q(^%3}Ohl@vSQvDd#_4&H zmC7Kg*H&Of)x>Mi)zf6X@8PFu!ZuD;XYQCi^c;6;Gq?U>2W`zN-#L9amD^ny9tVn) zh)^u)Ug@J3`-+}};6V0!(hp8zuv4)( zIpg37gmxOFy(OJwctc!tY+lR}VZtpT z45{Gkm1yo!O{V*6Sk;z6n{?6=b71zNx}aE+2ColA=?FXSMf_HASY1ap3(}Cf0K;VGDvKBB_Zr^qRV6A_L?-+^(g&fNCXn0 z+UXf?uf=)S)N;~!wpxTg&Eyv^Z{w>I$D_ndM9fS$n(5MMT{Ipm)rE){xK{1Ly}GJT zqoX-A@`q%zb8~ecExLNgqqMz8|#0e zJGz%8+4mL))AJGnwuk)ut!_BqDO{5f0!#+W>aO4{C771LuoP~4Zx$bCrXzu$iQyKB5rRS2b{c(N%r+9bo2ZQ~BX@H_%km%w?m$jnS(RWm--O$A9%W zzWLx~{qE;L!L&^NQvM!q>{?B_)5{OXokgKH^N<|-zX?YSSPy(yx0$j)ZJcKlwqmSo zDq~&CP9~%cI^f5YW;L?mT(H`JP>)|M-hKXK9=^GfXhdKd5hl+r<=O{#u~E(8H`U|W z8A;4>D)M9;7U8{pfg$0x@}oIDtDLZ<;!sSYmVzQI97!T_$(ZaLsZi5;aS)hb?QkL66@(_CF$PT<=qhcq#-cVgBlCJ0 z6BDLr^_+k7T7s6wj4qa{9mcb-kLZ&KAtkH}q+#^3=IX{U+Q&h{kv%g61<=^2)7a=B zuO!wlvE$L(3OQ)#HAj*nqvP=IAXwhV0BXkp(ItAP0M0! z^NxdJz|5u}^Wp1JA|VHJ&IqG-3}l-=_i+F9gNTH>2db6t`MLia#a#WTT7Fo(f>y)H zGj$_a705vslADTF@}-Q@1K!6&g@~#0+Y!rfDF(VCv82s7{8Q~{>Y{Eg$=gh(7A9<| zn1&$TW#6mLu_|gP{B6Yq%8V2=HNFynQl7|Glu~fhnZtPM<9HEg(*i7k5fkVg$|FL+ zZHt!jrAeE){(DD(VUgQdP1BbO2uMeA*R)HZFfk^ob#Pkckf^Pz*Yjr;l%ZiT1?CDr z%8X{Hbz*()$4Yf$ZPHLPTR=#J2D&uK(L>xn`P~kdrX=4HOwtih`>2ry@XZbLpcko9 z{X9UVCJA zmlyZh_k#f$d7o#tJjU; zU2Y6Z!R?Q5*O^P0J;V+lIW&u#Pg}-mEgQL|dIq5$l1kF6bjTjXqpev>2+27dFkxnZ zvJFl~PVOG8g@DOL)p$QHM@m5?L-uXF00hX0KsKr$w-XA?u4H9tLfBV=HP->U3Z_MPd_MlSx8SH~>e{=R?gm9cX$}$_ngy!D=b_1|?&Re8pFdlQ(;Z*I+Lvl^_g_o-)@dV=ia?k$js{|Rbb`kXNqVW3pgozn{O1=Y z{k=D`3Ny(nv_o?BE$x)oG?AI&Vd?5pUi)+%x}x&sV<&OetO;o9!8H_RHOshg`9Fy2 z#lW7|fomcyJhpWSSC2gDp!Vy(0Y?m251f=giQjBl9QU07bVXxM&Y1n)e>7(B;8U;i z{5zYGSsISvnWSw9vi=hvQ)ah=51Q&#{`u)J>MCViSrkpTIOS^%oN|5>shPHcRfIk0 zpPRiNhca+g@VzBTNC^Q`BWx;UIrl-4eSi=o=Rl-rQF8SF%^znIsdr&?2-}8QUEJ{W zLSDG)Sq5j>I+0$d!O>aO`0ApBV)o;$z2AliSP48i=@iUAuc@nsSWg&Hf!h~v@M2N; zcyVgieFf~<6cQz9+1$H_?^sh+Us>mh(N1K~0d z3MkXtARGYQLTJz|G#(!HDS3`Sm+=al3Tp$IYz$?9B^jo-u)|6r)}bu&UTY!8r&gl( zNOCqUSkayf#rC>S=ksBeyO4?#I+MIr1ypE1egx@%T7jA3qV3c%SZ*DpL|78(v@d2u z(948NGYS5xDPEOB*v5I-_;AvY@W_cFA|tZVcQg_jorPz8w^EJe(UH9$keG>k)h>i3 zNzYAX$)A@|G%}x*jJ_qrI`P5!GH(9;KWPtlJkx0r)Z|#2} z+6Q_ayt1K*uYC9>xYW4o(eGiTdW1){zxhL#sg3B`Q~~oMJ3bQ&TVJB!;ImT_Q0(`EGCFag@@OT#rJ9h z|9mo&SX4m>$=%PEGGgJ-m7FuK0=-x0EE?$!A4&_tA%(^ojT_D!PD?{a$Vrt*T|tB; z(j`C_T=;Y|U!7WkCM+x|Sh8afw>2EqT|=x#4?0d@kLTW7nWgKql=#CDE*Z3u({p#A zAo+diIG*1<0z2AHexC1S{R2S37INEOXKpb^Qn%; z7qc{8&+JmnkH0tu{(o(t{iHESmloea!^HdVtwbi~W8|fh>1tza%#n*ym^yL}GrR`(5%5=20v-&l_xh@mxxx~TWDf)oPBlwf9f5TO|EmAi1Q z-yKgC5%AMjf56Psrp0yp(}wN*Y|V>QN1CAuOv3?f8gjb_kf_HugU9T;S5ituSTf}= z<;**wict%qENL6Zf|8q=nt5Pyy}tbW$EYZ**sJ%*DGVY~!>_t|aY}z8AB`h^#%V-s5q?a5*?EZ#%)LB8@%&E zI#w%8JSM=XDZ!-1>(kb3zEwI0fML#N{xIqtoc*kG7$#_YX}ka&ie#v(1+O>4H5=#i zs|iaF8CgUKXle1Ft7f7BlY)x+bS~X8pX&-%b56FsNI%-s%xmNR!2?eXW0~qs2$~55 z%H}YCm7_V$s6zYSRyjSl;)W @$>kx#}ODgiq0yiB4XhG zw1(VVgTiewjFl-kb0Zu*t^!Ni#$a2UpJy9KvZSS$u$5#eNH?H9QE9~Gk%&SsQca%g zW~I7G`TKf8QwnLATEvIOILvveaczQ89ZNB}Wb1;2>mdZ-6T}|LChLki0tCC(#q_FH z(q3GH!|foiBpWFedRMBG5`KE_{rofd3;-b`%Foxm%)fT8>SzGu3luAgwL>7LwLr5D zE;DYTAv$7to2p%uUYkRDj=_U(Eoa5aP3~bM*@pkDOIcqW>X)K zyt{iX3ks&}S3mxnam0Wnfe?b?158<6TUtuYlzivJ54qr&)g8x4-Zo1Q^CY8qwnfr;y|ILa=@oGl^v;1W7}fANj6RQRNqDh-C`bEt zGuWM2hD9PMat2uM%p`2=`?^4BD21|!pFz%cMz~x0ZQJQ_49jUGY9?XADH1YbM5M;w z>PK*PR$15iZ4RaLc0)0$Y)Hz#Q+FE#FRSNwSu~LY<8onN{m?iHIPzJw%$d1i-IrJzF(@4oRzhmN2(_!Julg*k<`O3+G3A% z9X}~_1uIWStBxT}NvL2*_Xes!dK@$zKbFnan<)0TLW@qM!O2S-j^ZVbvfHLdQD^t< zMJq!>SWQ|lFH90O6cmS^Jl?CRSYErTgL~yJ!lSY=GrS4Se*Dr2s7H6+`@T9LnD*)^ z4EoH^`VTfSc5p6>zIk#SBk&YQ@@na^aEvXVZpS+|opYDn$8_IrRB~+r?9Rg}pp--~ zKTJhrMw5?*EnS-rG#>dUz}MZ+(g%Px)T*eh&cEp(g(C<3~|~_S@Uv zghn87Dh4r2!67Uz&D+H5jbWY*45zK}`#jk&m`f)7ZlC+|-;5&$><}T>U(M_hJNWWZ z>-IR~62Qy#!#QhIDc9YztIzh8Q6Kd&SDYIAeU(#K-j>US*_)Z@YXt11-Ve78roPS3 z+0)h~-Ipoh(W>z*NZY|6S1{>1nTo~sa0*|~-NcNvx<1#^`uaX(3cg=<9IFFas6t{% z!5B{?za08LZZ*+B$1Dh08mf?_`9fSadj%z#Elez~p|;J-6Yovs-O>`9u?N&e-9)8= zQ&?CMepo((;4~e-*^t=Bj(?R}gB^Fm&G#1b+qXAUJS>{D?>XKWJI)6XH4$HC3*CZXs%M2)rh=3h0@T-L7;P_T&$k$d5u%7%DnheKsM(LM1q+AbF>v zCXOCuU{YUPL-S=b~sM#+x{{(KZOrNV_DzWfLHEWsJcJgvF;`2 z_*U`FudU&fb!QNhYQl5pRicb^H?tsZC&#Dl!l76MO^pmaj9W256U<7j#-r@y$@MpL z$+&}4YwU2wVZ+0`@x~h&J$f_~Cr;$7v(BQq*?tMi%gb@Q-AtZ5naPtUbJbP-CnO$@ zkY*Kgi|^sexhoP12lj?x2{r`NX*QiaxNCC1Z6lU~lAy*eSk_X=y_HkDzOkgv=etg4 z?C^#CMy~!Ze*>eutqGw`uwV2x)4^0<9hVhs#Hm<)8m~YyPYHOWY67bQ*+is9(9#J@ zjrGAyepNLoX?qjFvX*>YiisvH{%`gwP8eH8PD+sRg>~HfmBqaFtv|7-U>jhI>}?39 zBPEoEQwUlbGD9PDMHbQ_kSbl(grldW(Dt_>#3tHht9){yqXYW#0( z!fG`8RdN=9>&+VU&9!KoYEfQU!}VXjkJdmpGj!Z>qmpjx&X@eM{XPiRurusSFkamq z1e_+a+6ks&pK9k%7V$5FD!`?1{qp%Z+P&;p^EbjRYx?YeUx=77%33#YW6?@{n#mYX zQ_{)eb7L0s$na&HlU+uJ9>u2`w0#C41eEF9Hr519sv$ z_L~&|03ZNKL_t)EC$etcI@YXN!^n{%x%=+!*~GA6!&tRy6{}XQ;?YMR{SWqX-ymCm zhC^#(cy3dlZ4Aq1`qjid{J3HUW84j#o4FOUABxRTcPlO>(I7g7!HMbJD+5l=$tCkn zrFQu`>Z>%&K5VE<>3i)=2yiO~m-Z^Vkqk?ki-{&tZTM%?kc2v0QK|B;#=%L)F^7Ny*{#oi7pJGYaHHb}N_=Pk$`(!$jv7E_S@BWV8$+twaUEY`nN zRc~CHyP@mlZfg5THK0}FfWl3Dk6Ti22#0wTsP4o#}VRx5r<6(@uZWwa~fvO zoXHC>yzn3F%>hQ_U7&wztRmp?&7*m~eFVq(s`&Yc<=s^{I-c#`$`h)9pA7zlo63&G zl!BO4aVs(WN|Z&J-3iStjqP|nr*ZTwP!xfpKr{@hP8V^ZpGZy@+^Rw1YDPSSfLuL9 zUCc$g7Qv(1Y8s{#M6BeVSyZa=%Rn9HC+Pp0b@$8S%KUXK%G^$hW~UgGI9u_?qJ$L{ zgup6t5V;`9x~2+pniKIxjFs>DxcI2iTzT6rPWnnSwGkIrf3N^$Rtsn{E;NWsA@D#k z>go{c_6SyvM)(*vi=8pfG|F%(CLvR$s=~>Qe?NsM{_jObWah}wq^1DYb%I-a|9b&cJcI%YMNds!D@(7Qrd{#8e+@Q1sqpc z$2IwDId9X+{XRPYcr9pAAfkXFy4NK85Fe?IdbxeaQCwHBhD<%e;H+jgZatfcV?WyS zu>hy4Qz&$v_<0@`n_XlqXn^q@boh>Ar)!Cc_h7#dW#JUQS+tJnsdnLAOTwmLI>!v? z=wB=xHU_LG{`ki~a^898b$u=?D`U!(DWs&NaK{~YFnjjw|6uR-1$!Qa6#>_eS;qy@ zt)y!adge~{C7}dxw7-_;M!(6c4JA}ZJa}tDytrX7*J$(j%c!>iNQo2o?O@F*f|nl4 z$j~E9_t)}CTTWa!MsKjCcL|RsjfZb8 z#-C=gdSnq+iiJ>n8Lu84H79#8CwscS`fc?@ep@}U^Qr)XKV0+=#%4Fz*QXIK&8aE8Z(T7JXL1}hieVheCf+itm^YO0#c{QhwC`VJeue+v7(!F7xy8K19wWRS* zuz=T_N=VgWv_^Cs!idXb3Ya=_m)j1VRe0=&a7;V0Nk{H>fF@CjBZ%xMvOz}_g%Jf& zFa)yPNoe{YJ0>dx?fI!FjRD*%ccM49(0*GU4D(=?xsdC8)EE&cPA8U^LS$SXsV}cZ zm=^BmTNt*+L&>wVXbd!C)WsO#%;mK!UPEn2!j_fLK0LzwrGFyQ<;@Go)YzVx-SzqE z{Ix7=DeNFf^bH|EJ+>KZwI7ikMdU`4q&&pKK;g}%K`d!1=656h%~)?U!_%5ryz>oC zDn6zEje#wnm-3StE-cg5xRgr;JZ*CJpfWz(SxmLl#mcrUv?Ofeps6y^Q%`}*_P8u* z7ay(BF5lyEzlb=b25iBC1?=9vJK_5~@4S;mixzeLU(+-$xZnZ+^7HfAv113BnVEd{ z*=L-2=9#Qtzn+woL}iS}AAg+3AAcNxy1Kf9cqV%b9T=G4YmOuN%&R}-Izn8LzmaG} zaL%dYvHsvf7m0E8o)AJ33G38UO{AcB&0ZxpCu`gL^Jq^CqdoBq!|xTYBAWB#_@J5YFWg#E05L@ zo8u#VX(m>Ui5%f1Hs24Lz<9r%XCBPup?_@T)#jlzk|<3yFbE3uwr=Cq18#@GS3TQ! z<=-QCV|ocEj3~$Bh##<3MY6oKsN<4=uY%L23k#(jLv9cNLmZ$=WWFT zzO{WWb+P#K5JI2~31M!r{c4FsuJl8#6HGvWx!r@9(1tpx4RqP9(vcm-+U7=X?oJP| zGQ0#%9EG}|5#hFwF@ap~vkQTBP5^WjGsQ)CR1WS9yHOn~J0?Wg{@G_ZZrh1cZ?F}K z8tG&1B0vP&iO6Iu;ZCwW6gmUcMBNNzl|<;EPH)@G1b=}ET(xx`m*#E4r5L=?^v?kq zu$xc01Ix50H&TJ@pZmCb;`>bX)w86vou9lum)-N>mHMHao>_*cBV->kRdxjY{O*Hk zTzc$E^4$S81v6-JUe1|AFB)jazfd@&25iZa{udgbeDXC>ktY=7mIS90Z*R{|vc{Xd6;r(V$$iXs{OeFs`%M^cF+0qfh-5JFJsY^O5fBWS8j_t)?7#AHR=Dg+PnouMcH&_3gWpdp`p}T9!dhPL#U$+R@)@Pt<{3+K#?F zK=_O_qUU9F)bN7n{1nzN_W|2DA#*2xs~b7sK6sQElf3nWEsZsSOoq5y`)unMK~jFR ziOmj$cOJ-O{;731opu7WVJY1H+B|~M#5}AoP1=N~FR!f!oQj1*MA;GX(q=lak`3lP zs~}f7?GRe0;(mu0EZ6#xc@6Z;mlYZ)BSM(vZs;lWD{Tc2B{zy?2-|b)dF2&}C^U#J zNXJ*xh=E1$Oh3NsDiBR337n)}5E7g(H4!;4y;~t9z6Bsz9mw44%H(^W*C>dh^1Iz* z(S=1ss@%2i6>b@SW}jOGT7$IKcSj1;A7t?Bb3Pzlv)QjFjNQ(fow>a5*+_mVCb6@Zi@!N8QsX0K}q#=9ZZqk~@!J zg_c0e4QvUyc%g0>b5kpsm0FYVQd<&kDL;l9qkA>%yM>=K&0jyDXrC(Jg!C#-NUvhS zn$yt~$#;wF1XBfjspXHm$58Q-B(=7UwaYz>pAk&TjNB810?`gJoO-0lW^`K-nVy*H)OQ*oAq(XXmzHS)jWNqK$AT0!V zRiitQChY`lB&e%`=~^wtW1rCXVj6Vb*FA90&Y0@L*|s1bJrWQS(jCWQlha{cT=`xFa+#1 z$Es$yYT@b8z489MzG zqTwhGmjlJU089BJMdsIetGSqWTMK!{vzQD$+(q!PB)n8VG|qSd1T1|ID-Fq!p+|5i z21}a@nCh#?shE7&n$O0zbnd+TW3-rss!3uIsIAgT&o=P-J2mP0-?tI775!Tp`+vlZ z>E5I)a;Xd6Z7JZI{I%rj0d{vOY4pxI1`rH!wQy7M%6L|&3g0bS!xdYOr`dE7ur!9b zTDUG}H6zU?{``x)xMObq)Xkbdq!YO)tLq#^nu(a*f^5*SmZzc|)6DN9qfmBPq^c1X zq*w0Ii0N3wI$5DYbiMWFBsap6r2b+JZ4VE^*p&`DT_iR4eZ}+QlDRvsO~-noZ78Mv zr-M4P6`{-c?G=HUsnLE%A!>OP=POO9JNFQ77a&vnzpv%(el#i3h1I14d8qJA#ru4F z^unaS`Of304NW<; z=XAK@urXlUwrxxL{W<5H!#U@ibBKF$fFTZ?_a&vB>-1dhFc5-;89SKaw*^mo59)>~ z_|pfId2Yo7kOEyZdGP#y@cP{@-g!A4_$|M_`gh#@=8u>(cO1eo4WH{MemZzLw^q#H z@~!hYKYJU;rtHS4800#_JeVYB>+PmOF3#D!*XQ!gsKs5zv9s?B(spuE`VRYhS9jau zVKM5eG#-*WEnQOqq=9&Pl*^3LKztZSKm_{Imm$&0&9em z*ztb6w^gC74bdr_-}8z{ryz26`W{9uK@&c1nszJqth$?(Z4i}qx?^DG>okSY9=jP9xCp; zUhl%)JEwB(_PH!>DPeK*U~VoyhL`GxB;B8;N=_WNolsOK5OvTTbn}fT&tSHK`iM?5m(Jsuyqlz)wTm6`tC9mdN>sv1G-*iWmWQxW!27j?3vc~HXIlZisp zNlEHjslryYPX+~>5!hq=ib<9GE2pw4m=@=iO9?Sc5HL0F*g2g))sDff_A!ZyNo1~0 zY^E2HVIYPF?2w^qfo6efq0DF@sa~uQh%xP`^O{k{wWCaILp`|xGHee@3=g7=3m`hm zru6AGchM*na4CeY%fWI8tRW79H|G&PJ(bo!45sDv;j}(8l=+^)eVFR2OIoIWf6&*yvhxgn`_>ljxUz&5|Mv6H zO@o+udVtK-SlqS_S20q$;|s(E17WR|uRp$ZEr`$!NL0%NKN)hRJ7(X}QO zJI0HZnf#R$3gAfG*{sxRhIS-x_q+y{ge$ksV|zH2OfAeYDZ6>CVTi2-*b|B%ba5uu zXq%y^jt(IQMUZt4P$lF>P?8k?6#}F#QHF$>Q11nc2d|4*3 zlTyK@V9f4e)ZL<|>@P@(vN?n`+(of3lk*23$0r=y%jse4E^e!L~FH zm4=t0u2#;^-op3FX6=0k`>7;wZt+a=2Ot2Rk(bUD58RKeOMd*G`JdjjqJ8M&{|1hD zV21*}@35yZg%FTkXfb_$D_0&rlF|+K3mB7hkpezy&Fct-#A9nXjk>TaVX%yb1&`le z$mf4|lJU}N^zue9LERoB^0f?%3HG2Rr6e5atIBQ%G@CecOiB&+4*if(?k3zyjMFp9 zy24in8$z*_(7*TK4iv>!+PHerxFijOT>s~n7-(on|1?O-1*Nzjs>St4Eg4srbI0r) zM$GsPS4@7E38{9W!OEzQ&7pM8$}HvI<6mY@YE{=z_FhXqw^qzxX-g4NsS@gt%8K>% zig^?Q3vCZlST?&(|2Tm6dzJQ0PXen&MOg8_&G7H;qFKkal?vlMjW{v6Z^(ySn7zr) zE9&`>L@o5!ny^N==yD$c;jvH#hf#`>8?UZ*3Q5_SLXn8vsO`xm!JY~@NktGnJsn&F z7@mZ{8tFp$Jjl*aBfsDN?=Iuj3B@rA!}m)*{(Lkgr7`|I^lk1b`H1QMI?m17inE6o zHqhcyQ__a;#__lClL_ZCEqw^HvPbd6%&WO^+!+Jg0ib9(h<+@*5Fq?F4S0Y5i#Xze z9Ret78dk;&(D;8qbD!;mfI%ao3>zEbufNLShmY;Rp}=RYnM_Nm$DnYaK2khcuo$H(^72)n1E>~PNZL?{1ob5 z2VjQ3mKmeo-p8x4|8RIDg|keyY3WqwY0O)720k@Lx)!0;a-srLvYzAO-(1L3-~7vf z_9;RV$1*!SI{D;@DCs{&O3C~sKP6zs=jK&J{5)MZihnhg&}k%16uT&7g3(OA?jjmY zoUgYcdhiuQM50zk(cfz$Hb0)lW>q_|R{3$xZb0+L2uU56V$fkd@1pZ_C!`6o+5sxI znD78$O|~avjj;gh8h0{2qd4yOg)_#InQ}4DF8LSX*lK)EyL$Phxtkd3YUR255!6QA zG@DNLJn<3ydg>)O91cz`oWiMv`%%s$b1^iWKw^;F38@dExR2lWeg6;Ohygo9P#hDG z+5bY;^)L7$1e|?U9nbvIW~c>CwM*x0LHbU9Q$0R$-$F7ky<3az=JSWL`U4*cFQRGT z`LGO?C@2EyQ;U<>R8*|C?kW(y|g5kN=lGz3f8I=#K-{T53nOb z*g1S1xMSUmJUiz*3C(7V8O)3^S7C)mK=U<#g-efg>K+A%qYrA!g!~ z%lafL2mv0S#nE4CMH3ckv*5L@dHl^YmI{&5iw-O)bhI-oHC~?Uj7uBXzTQo^hqAg< zz#8Nra=agPp% zQiFkNvnG8quKj@Ek|@(!_j_tR_pRHxXUYZ4%^jQgqnZyw1KC@)%>`FGLvbw2)S=pmGd(e#i>(~(-P#iH}hZwu+cQJGi#h!%#k81JYcZ*oOCXbdo2H9#< zj*jVU*I*#n5lvx5TMkG0Yr4vzOgpWOk!A4NKV7J05rhOQMI~@+0amV#*q|cwLCG+1 zDi#+HJE7mj1((eFoUhGXlfZx+C^`uUzpI%5NPfS2EXSr)Q{V`& zEtJag)*J%TfrUUtVv^ju>l8wMeex!tpIO<0tL zQyAiEV^_q>trbVJC1jH=oT7s+cp&nIR6*dbB0RV5LTNR-WJp5a%E2nJ!?_M&upq6H z#m$4T#s;uHO6^V(1{%E@N(Vh&O3|m0*gK8CzR~}F;(zGPirgEB+`g-Z4-^)X8(02? zABrdj>Ec_|mq=4@fuIA$0P5iKYCToM;Ia#Ju802gtW(ls`I*&%f z*`=NzOTqx%A6SDNv_DaTvMquLTcnHyp*iu2C^4xrE2Wxi3)WB*^YEF>ftf8>>%Gvf zfy?a5x3YIe(()ftf^1h1{(*@i_u3j2NwKq)vT!QNo?0p8UflP1<3A8l=>5fg8(!tC z;-lkK_=V6R;~8Z0^&3MQ%fcGmKe+`gXW$GIt`N>GPiMB(QQ=ia+0dWGum`TlH&;T;IQ{Od&8-;m`)tR zWQ?bg;qF%c-CRh>(t1_$?IcEYhs+ctRw5Oo|IWpop0H``NUkdWjQOd%*ctVas|U%{ zBea{{uc(q8Lz&kHi!}-3PfCz23L<4+jF%n0ynpC>Jg{p@!u9T|*(SPk-S%vJ61}to z+iV;~aAk79KhEVhR-pLUhwm4$I4k5ns_eU_(9o)WS z20tFOf>|lm7*cS}i>FdPSSPHhc7*8o0PY9Oob7-a#kTfaSBI^&b=KNeTdj)LRV&mDRRqDsaDWH`0U?kC5=cVGo}Y2= z`Tg+;8RYZHB9O%2c|IP|8A6EOP&N}w*Vy_vO!AU=xbL^6j7FXY79$5eW#8+&CEK` z0#rHCF}XT!4+|1@QLyb|c7&ZlqHQHMKXFMGVRA|Ie%w+g$yP;(?!jY1p*zTLRq5vI zkHMOU#r?MPOyL*|1YW5Tq1RLF4eH>1`0UY;Tot>AX(0uK=pIG}HC67;sS9IEb)fah zCLxv8ns1%V*8yOFEVCej%r0f~7nocXo5c`kC5PR?t?sA0MCW9R*D;QItGRL0GX!Zi zKAiJgY|YY&2)&*8d_}KD3D_yb2=f)ot1~V9CU+WPx{EN~O@I;VEHWu$;evKtj@f<^=zjoTHl<>MU+$i(NBWl&}kr)UazNu7)+H z;m%U5n#3D$@FH-T8qqcvtH%6~L#|-LEpBYW;GW#6d{NcD54euxcLTqo&>hTc#Y0(I zJd9Xt9lso~g>k_plz8mivhPg%iQ##Xz5w!-rGxOIBO(lBsRa>YfJ*IPT0-Vnr;?-@hmS*zqQ4bqN4Aj+~`jw z$Bh6g4LkYnP+yItwUF6{_aMudTcv&VH?4DIWC5@GD%9PGGh^r`4I6O zIIg9{W9O(hKo<1lunL2b&Ju#&EyD3e389N}vHiD%=xg^AcF{hP@7&A(m2PU06~4N7 zAH%|j`fX1(1OeMM6Dh&2Nvs--3n}5Tlnq4bf!(>iXynv~VG)u+j%p$-Zh~}!vm+0- zdmrEne!lPEG@dC;r_yj>pi$@v=FaRH{QYnSSM8bCI`xy!YEm4P+}Lk-i`s1>%8Rf| zM6iiAq85ElnV)mpL!#m9i&bTVc&{|Mp|)k({p3JAGeU4YU5x#WGDNLOhYCP-5?u&p#13z5L&=T6(Z;_W zR)e&-68?I{2OuPIp*1X?xrxUvU&VDZwh(IbkYDQJ%?&%y!tck~?|q7#t-WPXBv0@* zxUjgpF0i<}ySx2xcbCE4-QAtVVR3hN*Tvo47aic8eedG_aS<1B_u(d@=Sz2WXJ=Jq zW@kP1TIKEDEBh zI;QlPQnLP1P~a%Rbm5Zb`pX?D^=L0dvLj%*oiMx1&^mhbVYKe@bnd`5X-LP$)8>$d zPQXCOfUr`x zo)!Wd>bB8V5_LG(UDFkOnX;>`{%f~w{@L-)e~$k)K;tq{rSR#z4hYf8_3<*r#*g!n zOrg(sl}49FinJ*kR*|*$af1>8nZo1X901?#>z4yOvU&mY&z_7Q?kc;#)YA{H+vLUuDq58e*eMh-|1U+&r>mU1!!pytDjinH5x!>rPgu!GtWaUSEJ(cDL!4 zga#2G+dMD4w~!{$ivadX+v$9;oVB{=r$PGAs|RY!tah4b5AxYETmHw@;1WW8K)IfuC5!^7-NalUi^Y#%y(Z|^dm zsCb!c`$FwxAy8$UZq~wH4a_wRU(ZrAWTx8$i-ofwSGTwbny0y6bBeJNHmB$C+3?-&vX%s5_~OcdY|Ax+KX1|b?q0I)=SbPk3T zYh?MsVYHXW43C4iykp;4!-M^t^Tjj_&8l?|t4a51;+msg^v6y9d$zf;O@?(+145MI z!SwHo*Y`j#2yoFXK3Lrpwn$zH-M)hFZUaT#Q&Dt%3BTac2_k!G}Oi_J_V}VWHrGp0;T9 zqR)q{MU9^q-5OoRl7>E*P)F)MO7Gyzb%gZY9a}n6ziAE^2jpgCa2DxX4poWz4GcWx zg4rQ?%kP(!b`p*!5-7shJPl7aatJxW!?)Fs6+#7<@HCN0LUuKZ7)1+>u=NQQw}e85 zG)I%D!mVUORyu0aDS~ zM#4vk!_e7v@QlP*_R-pwu^DJ4nkuk}uykDy1En9t^yn!5YPgEs2H3A@ZE~^vuBf(* zF(-e%khoU7Su6L(7hAS)nsCsiF?U748{LpBdN0yM3pqxI~^5Q&R{*S8pjYim; zoe7dGJ^;fBf(FWc>Fzopj1)Y9<@&ld0WYH7(?uZ+? z&S;y+)8aT+E{brol;4wgdoSOHWuxYotZaCA3krB)m0cK@c8Bu{uD<$V&j@Z~t%b8W z#5>DX?A4awDQzJgLl9eht#!?@nVbXc_!EtwhGm6)(X-C5!{SSPJyjqtjt~Nf$Qe>~!|= zt{tfxy?Uk1tuY@3RG~Q3`#ZA2H)2hmebJi8jo@nW-!#I3m0hA}ETmNd*kOJX=ghPo zeq)qV(J;46DQM?YVaFR@tY@1|rV)N5DkYR*k3n<|{dYB=VYjJfVC$Xn+JnRClDz8d z-TZIdq&T>FAxCZ0(&6T2h6!ID0@NjVzOIS-q=wI1?nIH1k*21m35kjQ6REWL&-}pK z!QzqS+D(XWpwT6-Dx4ISq8s{Mf-<+frdove48$}02A5SD zitx82WN$uC7}TM&WNT^sBWqj6e)Y+VL3KxvTL)HsxuqfLObbHQo(@gZs`(Aw;&DAbi1Q+Lev5Z(-;G8cbY$sqZ#oO1lJ#I7s{ESfVsoAd{RdmTb77p>>M1bcc{zlD%O%))aN-zLDu@_==$0$wwuJoRzmb@@4adoZ zc1@t(jcUv;P)$7I_SY=6c>cm0Boj(LmYG|&v_GCZrd0uKwbyM^TY+CF&6u%;YTNIQ zO-!#pc&m(lcktI;4>MRBZddEW4Q7*LQ&WexdxKM%tl{0>&xIGhq}RnoTtzow>qM=q z91Mq4v&gR*y~h@HB|J!4ro)?<`?S?YoO2^;q<9UCt0Dbrk$amgZ+Q$GKP&S`tZlqK ziyJqxZyI5w9MvECelflt1WK=ljNj*^w;Rs&&P~(~pZM-OYwzS@&P<_(>ooE5S0xA{ zy(HZkNzNx)iZnDh{G9#d>&Og{2^u69rO5xY042-9ie@iHxCsSwuUoL$@`a!+XsXjY zPAY`2HhA|M7mBoDW0HxbJ+^$VsjEC16iGog-XinVx$agLUk8b5tqAdsZp&a0uj4m& zd>1Iyv~p`^#@9QM83v)N{QMW0;JkoRp5H3CQ7x>dl@FuO4L#_-iCkvWvX}pED zAr^_oNMhAUp9W9bmy)HP%{aZME1?Nv5Iarih&IRyt4G+CFBBp#E53SR0B3cFpbe*22(>faLbO8oIJFtiH!rW zKGbAIz*sSl4TG33u4RQ_gtUWwwu+(G&>iJ3=ZR6t4@XU|1Lw4s^d<1cz#n06d#ocE z-Qv~mG$s!)DCY0-_T@HG-1qWT@TF=s&5hKtUdqN=R?7lXyF=Q{ZpJLWbKtz5#yw;* zol6KZJ3C02PA)KG4;>rpsg67QU&faP{y{?iyutP}a2Ob1w2+Wv$B(-%Iuky7@742& zY+OpXfdyn7+pH2@|NX%GF?J?;E3ZbUG6OFG$j9I0z%@skrH2PwA0TI$J~N%Gs=(9LRv^3EA6-3jTJ=t!TEF4 zH*(-1j*E`xJA2QZ<@U6o{8D5beIdh>Ly2V2UM@Ss>8wO@f6EcFrR5dM?CkhcnTVbI zGdi|ap*Ocywh^`wikY1P(FWRF665nsjsN*DS@HfKk-6i(*ynwEFA6U1c!6km29K-t z0kgEU^ba1Fa_DM4Pq9*;nfb0x*nM9%Bjr5>Qkpx=DmMeUM3kV&50#A7ud*qct+9*t z8zohP*``ym<1$^zh%1PP4lT+5^zs$k*d3Vr(2usitCX|Z#v-$W*e=Hlrx|b8V>a;O zSsj0hy{ibR)8_cgQ_mPCCpe4`O)eWXLrNvV0L=yvb%AU7)aCXvQ5Q>mG04=%^(nKc z;Gb$-IVW(EU;?8!01@&f8~~^?5c(Q*`|>p2=}i2RrG?x!1dd_E`Vf7^3;> zZYX1K0K%sP1A|q6fhQA?E7K*f&+bt;3tPT#XwH}-42#o_Q~t&MTI!yz9}LR3Cl)kk zUR#OlNZix5~hA|qT{@u=NHTHp-b($F+8Ki@#%2ISwPAzo50K}Vlf zy_+u!g^GY_khK^X7*O9J^0^hpV$`3y@q6YCK>=R7_a4OYUhjrt>z}H=%cjv8jVF@gSHeD{|JIW<#soc6dJO@6fIZ5Ef+E6bic^ zgF9>;(;IBLnvystlyMg=7gSz}*v7)b!Us1uT@Oe_lJQW6pTJi-ofd@jNpS@QBrPp1 z(6*-bvMJzp(HbjG#tT&Zu^9vv<$|jVSo+AdJwlg2m>SNIjCQZjk+UUz8A&g((N|cWWLX8itXhs}0{{Lltz2OX1VXW=iROaun~q5ba-f*=ezvOgvV};k*?>4ODY*RWL$r{ zq@w71!-wLH|6K&F&3o0RRDRo!MW7$G!XBd+5&m8%*n z6IaI{{N|LT#T6i7>lzQoW!^k|8H^nQxX@6v&xQ$!11DFF4cWVaLhU)z=)sMhm ze|e%(Zin_<(lFl{kbByDH(*3ADI`KESZe*D%7zh53>~JVyVVVf8Tom= zr(xvgJ5fY}$1D=XlLMpd02>wfIoPf~EfFn}f3#opG5~O^L>qj9wTGU{xg}gprKlzH z$sy-<4M;mP<-(Ke^&OEo-c-zBzct{AJ?!pD?B1+l^UUF2Psn3R+Io@bbOPY24|SPOqiPx0}30q?Xv!H5$jv!YudvWw}O zFm%_dk$U+!qHAdmUVrlt`n!{ZiNYYQ2l;v{I!z2iFEBpqcnZ&Rp0V2)Ms0_KzQ5K9 z|GMC}oDpzS?Gje+U;;o}gQ71lqXAutRRNe@a3(JLXtsAfl-vqRyKAwnQaBiYl4(8Ez{LwQI4ufG;HGay3(WQ!Re3wH!S^( zS+ouC;`!(-@vW!z4H>>g<*4GIY1Jqu5@)r<({DO0@T?8veCPC9$5H|&L~yx>{=>}} zOJS%rW6{TZLnT}}i%c8N(Yjia!A@lmV4abQEId|isV!DUCn%xh3pwjwpXY8PVBhT4 z1iZz9xS~|&*|+XsM7^Ulz2i$rMSR}n{hHoaMQQ^dO?OR|NW6y;E61L+k}tZ(GV>(q zKD9^}N{w402UVjGKb?vrimihr6(<`jm6wP^&RfUnyj9x&I=!F*yC?!$ zU)b2vtc6z^RUaJ`%Jncz?@hDXon$B3`n7(z%E@(=_Ah+L&&1_S5-c`YFG*q79tzIb z#xFQF=t1m{7FrH=7hEkP6=O1l3t^|}SE%|h*|1QZB1Gf2OIB22L9vK9 z&wQ&79zjmV%6y8Mq?6}BD@=-y>Z3NAuUMrgqUB4SsVZttG}Hw*J}r|C<@>|!KS1wA z>@iElzRbBeIyT;>QUMfgj3M91s#ngXo-j6~TO}hjn_j#y_TeE?#j1#sl$S7PM56GK z9;>1_^#f*bm}ktudMhl#skVmt(wrDiBAFU+R_t%JO-hHbWz3oN5PSnC;~r78O`9EO zo*a1(eHGp1`^AM2Rme#yzQv&)U>Q>92%PpmUV_q{1qT0AvDkI*8EqwQ*-~u};yUV0 zzWmZsCIRyI`6ows3=5I&M zZl6tVP#aH_#9;Cn2vIbL^(91itBMSEtsk6Dy?wMBXpFxc!I^J`-6?&e^&Wy?Yt~bJ zJUlpW)TT)3=`x8*@`QU&Ii)V2!84jwSD**2Dc6hP#ENfzKg{hFq> zl7|PUhY9#3PTbt5+I6l>1+dqj5)2h6 zL&sm2Xg`U3dwD3nMcB8NV?XKS`LL_OOzvrj%1nUIF!KSN(evrp6+}zX@El15|)hx3~L$!^Q$!> z>AZ~jDbM8mLla56-D68_3cnlKUWUN)FR@3~^bPL9;mSr@!&BAH{fRiX!;13_JG*ij zIuY84l0|zytqXzGz*vNs9U)J8cKFP23I+K5(X8xONV_WLY}s~qkW6iCo^oCBFLXJN z>Qgn4`3Z(0eimFt4ooddKXB9AzVvb3I=DIOWQrJN)Za@Xkq zlCR}KMn===S5i8BG83Kz$K*q=`Ch>ICiA`s-TL@!al_{Zh@lU@%;cQ}-bN31z}CyK z*3QESjPO8-u;;J+YE^YFLxDP_cx!O` zY~&`sSa(5Ue6YX209Fq>lZE;l9J~4jis{cEN!qp1Ip@YRc!D9*irXWTHa!%*Z9%ja z@su1S8J-u{H%W-oi&E@fP%@EK^VIho(bDD7o(Q5CA=V^8v{I) z9JiH-`pwgQc6!fn32aVyxbAGlWP+FbMnxsJpvm-yGI< z*PKPIyCM(2uS5l8s$vMwIWP$oB2NVPLJ3hnTqtr5gPc@qln*_onvVLc+M0_^hnbX- zhtH<-!5KZEht9q2n8SDaE+aNZh^4b7EcY)eY0brUmlr9029FRYcR)I{@d0V_>rr{M zG_2Lcn;-%gy&KLq{n~W#?DPb!z398>Ca6Z!36eMN4lIg-(_GMKImiIR>S8N!)V^eD zNj-Xt6@;2p=r18z% zDGAvuMC)DANv`he1gK1ia(Sd<1Qw2^uIc?t6js zWRT*H>b=Zd^gVsoe04su8*jI(4DOz{XgyhP zDzWnSKI34n@tvs!H|~w$88=KcLuX>G8rzL-t2kNPzawP(4Is1*o6BZKSYadtQK zBfn{)8`}u}xPPj3{5PE?y}qT`x=6;4MOSyX!Q1ZY2U`^CZ{IL4q{W0)ZL&sYd;L(? zz=-T3f6fvtdu*ncXidGJf-O(I>ZM;LEX30F!s`+R)&;vH zw&OWR-s%I`SC$w2_h6HV`Y4Ds$W9O6d z*OWgRjorX+XQ;suE-(Az+uR2V>WBXF>T1L$Bv33Ye33kZ(_^<}0{~&lX$J8f5m-&4 zli!DnFHKa*!R(S1?3=6Xv>hnA0+u|oPu0U$qZihi9{!(v*Lc0xPgnINWZC)lo~W&B znD#Zx;2L|?Ci4Am#}vix1^fxA@c|bmeTWMuQP?NC!`<-Ex4$zdt{oFSuqF3=KB=%4 zhi>6YXR3E+Mjz^}k`hr%Vgy;k0e^amYXlqU^&$qs?KBk_ zS@WdRF{_#XeH zK$N>SHPItoKpmL!E7G=&b_{V8UB6Uu@)h3^2dQYZV8g@c-1r|x#6}D0E)HhqD!;#y z`=WwX`(6FQa_WjROjA;`tXA!+#^mn!q;v2Ri?7jLz6^4zN()LY9=y8p7S4)^mOh=; zltC>lw9bWbAE}&Xv0js$cyAhem`wV~x~JL-g^A29{cwv>0^QWv*)1&8;N!Lxu+Gxu z&-az(L;LLe#PIrtdRdwp8maOcG5sIqeAo)f_byr(L>LYodFCi7Mq>%?5lW^1DwEMv zB!U{#{0Rw%tLpgjbGJ!)Xfy{?mfCgwUu^ZOG`CooN{1tu0vfEqb9fpQM6p)6oOh*a z+HF}5&^lMzB@4K4sleU|4d-1z7TMI?M`gOoLz2sFdYyz6IzX=##pI^T0}N<%qC;tF z@$()@-QmFCbF#KJLtfk1wsSNa6Io=N8rnmi1J%HKV(0}{QXfaFHO^vt~04%3gQ{g>%C*VH&rAR$`FHY6VXHOs! z5ID0KYQi*!D#g-TyVsJnMVIG@&*`ObklU@pbx3dYcOH=9`J3C1hZQAj@$e>He>g=` z?OuQ0{Xg(0elz_6&Cm8{QQhel*uLLd+Aw++V>Q?7JhD1cunR^F?2Pe~gg&Vjemx1} zgW$a%*`;fx9t4WsD%KoE_XmERV^y&v6Uwt%i6s!jA0>lV(EEu~n3S7gC_?4^g)pf%SSAYOa&nXT%D2nnui95DBG8L70uw-1M9$W{%CEPD(SlHMC zyR^TyJ@mw3VEZZF9^qmXN!m_Aku@L6h@b`f-}N{Rh(^K*&3sPl(I}rd;%^)}0hh@P zdzBuWiR4nmV=j`n9-la2P-=A*G$5>v6@2{jLo3BO3gyivu+f-N+fl#Afc%m&(tZHyEEF=R+>8yM=FYe^ zB6oU24yK;?{70Cn>Af*y{2jiJ=m5#=Z4xf8H&}z_nK{pH52lAA=ZGebd~-yJi`Ks( z8?)6k`wg**P3sKZKV|77H0>#WFpz;qiv1$hVmF5@@RF@nSqzyPn2#aY@q42UEu?>2 zXjC~80p^j07z#N&!bW2YMNE%Jt6-a;Z{Y{z82SKxG8Pne4uz`QNBz(w3#m?M4OtXn}VWp#Ha@OY34Wol9DCK-EkVqy?j zSXiVK6evI&eFHW(b#oLf4tjL#+DpRe)qWhzC_SvX%!pEMD#RKE;^o4S#`JzY7P`f+ zRE=AHxQVWk8g>Vmkg`x`JH2OuQH+Y#((nhb=}?c2SYakHd^wSWF3_g#ZDQ1TMxVrmdkSA+|TFo=)iYC z1Tow-lOf*vgrBWTn7umDvA}mWmkx8$mkFedBF7(PpQlyS&`CvjAQ=fBge++D*Slga zP%Ap+xsIcUvk1Y>M9+xJ1a1J#{Qn@zN!@h3xP{I}Ki^hX*Y2f4>034QyzuHz>&?Q0 z`h2CjE4ww46Uk3Hch5+DQ>q4^FB&FQTy#G65f6+vEF}q4b}%cdfvr#_y&XCze(mzV$$OjGOaBtr`=}-~os>OzpjYUNDhmxh+JJW7xkWm51 z#N=FY?=8K;zKvXiJcII}sWOS=GZUC)EI>VFyZJTJ&-i*MMIo#B?imd?12-HwcL8Dh z@%DFUbd=R}EMWw|&CMNy&ly)&$3*C{3#K6WG1NVWu{C`;i^%M*5d0mvdB6Tqi5xb=n1D!^nNEk~7Jq zGr-C25bPSa4N@;4BHvBfrk!9msj9gvsSzo=^dObO@rkK-G1P8xazb?HKTU&q62US_ zyp(pEm)%=+Oy|;5j{j>3Wf{}<-x3N#$5`uAy~~+$O;>{z4H+?UU@$Z?8Uev{8of^2 z%YO9u)D#(Al`BUixoldU={VZAsOV_L1wE=b$|=IBQfI<_#!OmGt-4q-3$|JLF4)ek znZyn_EWan58mP6ofZn*Td1>t0V;CdI(KXZJoav5Y?d=)QEa19~Ipvh0uFo@h)yLl^ z`1Yqt%EUsgnDkz@M?F)=0R=WNO^JNvySFAwQ0tmefET_vb#DvXZibFb9Za=a;;Gl* zASh0j>BHEPsEzvm;MGk=K>=D*#!5;8dKcw_h&A}0R!!t`xay}{-jwNr_umIKpc`I4 zj$5gl(_cwi>9$A>d7$uFwuKEv#d>4wkglL?be!8-V#wc@>)Oe(rF202cih3PNPgoj z_us2Q(74jk0-bgmPpC;JPGxUh28HOkcA8f2R$Lnz8^__VTx5u z+*OfSB6Q!>>Ee=A^Y1>VDn)wp_FANpmbJqVL4xNw>3j|!b_)X6uuAd3aj(JPQMS5Y zdnIO8HhPv_yblU3=>)|~dea#Zfw5o@ii$mvJ3gH$dYG@4mSU?xZT3?okI2gP)=}3* z8uG+$l)hFtRWG0WPadAW4_Mn{`teI$rU)_=i(F#uoncCQ-`f~mDQeAYElkGquofK^ zwnm1=RQ&{ix=>+F`31eaZPtT3YZ_Z#u{=_(2XpjG9(rwZjNpNKjSjP?vRs{Cs~j72 zj%rHw9;Y`kd?godQ8kO{dAZ%z5T*aQq=NFi9O;*&OXr7=ZF^jg$v5iTsJ80xD{}b* z?2F$Ob7SM}ve2G6FyNXkDaTXm5z&GRR}J)5t@S2p5%cnzGF&Cy9){<;hqwpYNj!N- z2RCN3&;ZE%+#@s?RA4HPj>Rd!tIlUnN_%whKngZCF#mjy??t3gE^V=jQ7oW@M*0%2 z-SMNW#a`byKS>ZjvF_-L(FCrKCtIaBB6s#&CQ7P|jO7ndxiMYdm!X3rjHv6vmEt62 zs*V9r{+Dj{3orewWc%?yo3`?&j<$nr4W{BXzwdoJJ>L26Ze9H=C2OYYhYxbgcy@31 zYgmqQpe+rGaPAG~w;VEh)s;g2L{jlT#%@M|0WdZwNYhbji`?{fT*ziQ>j*?};*v_F zOyc?Go}cFDF$bh^eP$kO`fdS^O4NWn15J&Y4bN=th}6l)1Pz^m^&og@T9Yl8l0ttpU9xdivnw2=yc6=c8wW^hJxf0RGYY$}W6UVM z)Kg1Px@YTD76$_j1lieJ!5ymD$ROBhzh{ICE%a6bFFk#*6fyC-WGWp+x>$G(7BTjG zc-$DTVJMurWul`4t2}Z9M31eb6m*SZU`q1jYF6>c6&RN$01tq zADbw*BNYZn4#h$Uxq|i?F$*zxcI{wUYF*v zuhcp6@!krj%h1?E%R$eI68I-K=;n{okMGwYLXsBzTGH6qxX2e& zm)Lgm`F0RFIW2AEA2(pPmn*XNT2kkANA%uccyeCeOb{eI6(b|Gm%l-+c5{@`U|1sP zQqp+7g~j6+az@5@twwWZr$Z@;SiE6$MLw{m5AHL1&|_K6r*J^=T?UsU$+wb{lBrzY zbYjskCiiRIiHQlX)d`BH%eBSTe2|PGp`mv#2l3C5J=e^|`^C=hSFMwRz&&JT8ER)I zr^9lkBCplLKH%qbZ@T~IL%t4Rc4{i+?Ch+er$^u1!eVl1N%icnmv`U5a+Q9!wih2E zbxDf*j&ExhNP8tI-JP}F`n7WM@~Q97H|Y%NvZZBZlT%Yuc^s9P;mAa3IxV(|$}*sJ zXXd*SQXYK)AIPHt{) z3e2zYhvEh1nii*YeZF1jdLE_n114bWeO{ev+VzKp>E?rQ8>U9}?5n#;d5PSheN&*9e3#+FmqoCIN*KcL$^2QSk64B2Y=W zTu%Q(*7@W54}a9Wq7UB(`uoS0voVPEdQucGV0^pVkATNkUM7F`hYE~${2wpY%Zo=C zg4OTs?BwLZHphbuZfA;%mD(HO3*Z9?V3P#uTSU&yIj&)+(>Y=&1e`e!Ib~(#TAzgO z+X46-?`sf5<$1a1n)YKY2Na#R*lmHh+*H1O9vkz|$uxSKyM>L7_l~mvz}>+ALDc4pBwOFCJ}J6g&K9tUhC)IhkO=w4 zmaBDMFATmW)HL+O&CJYd>%H&xhc9<}^FdK|VnRY6f%~ST3;}93~ zXx*5O!lZ3`T#yBO6Ek?m_)YQvTL(gLa6Ef=>x1~D04=s_3vJVOttPD-UrO%<2B-h3b!WScf9|BX^*>PcY&A8t zgBv2h7h({8!yE+P=mOqPe*5>Q>oPCSZ&hj#m*0GL<-1k%*n~a)oM}_rtuFBK2*N~n zzaD2h9gZbBU9Q$UZMHc&4M$<}o96o0v>)Sv%%deY$Yyx%gN>wQ>eF z_V}`G5Ik=H)RgE-oH{I8pK6pHJtDoIs|Cg`QqM&vjX= z0i@q>dVsX6YjaFgRMOGWQS647x4|T2_0?6rtoW(pP$cg>m?K#M` h{(tl;|3BQ~3;A|fIxq=vMEni(lNMJHs}V5_`d?M{OO*fs literal 0 HcmV?d00001 From 789ebab62e8eb388899ddbd6523d8d72fc495de2 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 15:13:41 +1000 Subject: [PATCH 21/41] updated predict model to include plotting tsne --- recognition/MySolution/s47539934-GCN/predict.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/recognition/MySolution/s47539934-GCN/predict.py b/recognition/MySolution/s47539934-GCN/predict.py index 51193901c8..a9e5fbc493 100644 --- a/recognition/MySolution/s47539934-GCN/predict.py +++ b/recognition/MySolution/s47539934-GCN/predict.py @@ -24,3 +24,6 @@ plt.plot(range(200),validation_losses,'r') plt.legend(['train_loss', 'validation_loss']) plt.show() + #for plotting tsne map + outputs = model.gcn_conv_1(features, adj).detach().numpy() + plot_tsne(labels, outputs ) From 4072e1ab7ff0b577abd74cee86dc0ebdb2e94b7b Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 15:17:12 +1000 Subject: [PATCH 22/41] add tsne map with 100 epochs --- .../MySolution/s47539934-GCN/GCN_tsne_100.png | Bin 0 -> 65250 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 recognition/MySolution/s47539934-GCN/GCN_tsne_100.png diff --git a/recognition/MySolution/s47539934-GCN/GCN_tsne_100.png b/recognition/MySolution/s47539934-GCN/GCN_tsne_100.png new file mode 100644 index 0000000000000000000000000000000000000000..c2903796eb4bb524dc67da801f61ddf8b43da3f1 GIT binary patch literal 65250 zcmcG#V~}Lc7e3gWwr#toZFAbTZB+NPZF}0bZQJ&=&1u`%dVl}e*w~NzVNb-Z8+EH9 zZ)V=-JelX5=SC>XOCZ4F!h(Q+AV^7yDT9E3)&UP<=pVo<%hZ%sz!QX%h?FWc(7d5d z!-3yn93-`zKtSM7|2sezwtPQ;7rC6pHJw!e=FVB2>zn!cA z_N)vn3@r4-md?%&JdBM0&+iO?-xiE1M9~o-AjBY2V#2EKSr^%EY3OQy!*(jp*tn@e zNi4|83`xuKNY5&$DgOq0Of!TkXt=qkO&Q5yGhJnvQ?moo&%g{_z%%@x&Edj>vN8ef^ba&-745`~FW;vf$*bA(YsFZ%lW`eEU(VE&9N4Da4I(bKGip&lKT&)Rzt;qy=e6YcI@Q2eYbK3Zlm{R2-}C!Xhs(LC zmR5R_@`O|(Q*IoDWG{s3!NQ6nZx3_muj6LxkHIucyU)m$1 zqmzMwf$^WQ*vukVJG}-x*)zXtYpbe~l$4Zyzdv0nOVc?0{(X2nomrfuyzTe>dAA?M zSYzb|8~|#(KyK~Gv4(+;@sZ1N$c>BXjVus9*sc|E0q-5Y1>nG=IfYM=Dx?&%t3u&yBtEx}-)B*r}J}Ou*RmwAoyqu&&Fp8i<&f z*se2=&HDJ{q|H+|F7}5$(|;(Gl9q<`db%ioeSg31wOA~XJMEBnc4jkg#8I<*J8>MR zNWFPI$-Q6JFvvgWv2V=;;>_`8x8Hnie)aM7^_534Sn1NlBTSn%=w|#E}JCrVzyFF$V9Jm*y1c zwAv4ij@}O<3EnKoFoH6$m$x$#f*Lwis z1B9`J5|k%mIR^eCl)k)TY{LI9j3P^5C*+lomL^%N-_5fQ|33y2S65f>HeJq6->LS0 zJX?-(Isfa@g!4a6imW#ob!~!~9!V2n`#+TC%meE6_OBF&$C+A!zy%#k-<yZtM(JkR(pa>PQb4f7obDryP<4KdJFhr(qy!zdK^Zv<|Ex)j^@B*ChG;SwKkW_t7np~eJEhrtU z8!u7m2z2=(TYW@PAc@A7sz5pxm6ke<61dG3N+1C}C5^)l*JLD`go6V!k;c&a`D$x? zX6DLJ2Emt6k|yC5E#PC_#8hbEy-Ztq6bH!CbL80*;B-HKn(YmQTx@lwW@X{yBSOif z&|5t{-k;1`T&&hd6Y!=0TXt<)%Bv&`hBNYmNEeLn*vHrfK_`que~M+A20o8;XIjwr zrwzcrtxithgHM|_pW!~iSFh2H*s>qJeg<~{0%~D#F-YI%6LwXH_|;?5~tg%w0nEl2>kA{KPI?{pOid}+>A6Pqn!o|jBi`%gio&@3rH=XONPx6{^Y$CNU z7lstea3pzn7kWN=6 zxt;uACzc|J)bp4(BKnZHnk;bGerXy^g=~gs2XdpV zrELDF8t3$!V zuR{V!s)JAYO!D1sS8KCA^rcl44?6AUGH~AsMB!3civJc&F{w{mFRfs4OLcrWfp^u; zFC*?roV9XgXs~6lFR#&ruLQ?gt0)OL-lQuo@VyJeTQDXht7S9gS>aWH_+i{HbbP2` z3l)VfTw$fYBB=p--#>-;r}i;=nLBpL1a>hFohZpf#G9SG>*zr{G@(%Da})^+aZTTl zy$2G;#@+;Wh#(ay3;Q-jb!1U9pjJ>2V-xDn0A?6@?7CbFeXbwoO&#eR!iNY@o6UaU zV%;z;IeB9F74vHUIwyo+MAfPZOv{BeMv$3;Y9ee%78+p_%bmiphDeI>#Os?+Y5#pd z7bRAZeAUgP3oCg*Fj!Ew)Co|N{2d6sg4n4w#0|HWwoUA@S)a+3)VmF=u!A{D74}%} zRl}I`?>oV2d)EyAqO*SSu(OWqzsCA+GRYSnQ6@-&Oh=U?n|1mg(M@04af}_JlmRm#-!NY%H0~vX44}UX#u-D$3z{`kxks)`g zo^_l^IQ%P#}bQM>SP2RkgAs zbnKzQsSXlvwfWbxz4%J)AnbQ{u8q~pr=5KQP{QP5f{-mH(JrjuWU%vj2QT}HY9GoH zU4bOdq=(W2(lNoEG#BjRm+U063i zQ&3Q+6e=#glqg^pSDzO_CcV+4zYMr{%)*X{VdSwti>$qE#wvRzP_zb7gYWw<;4-%A zIdn%on2{ti-F$;JM4wsww&EoyAQD!~^f~#VhYUn(ESJGoR}6(si_wP4(i9zf;0cQ= zH6wfd69(NU01i4WzHWmcD72`x8sA?J&(74oVb9Tq{*wzduJUUrXg@{Fwk$8Txl8LW z1VIu8n%L$-*GBy(4BrmN^`?3Oh?~X7z-k#-Qwnr=yUTVW^(UVDHo#KCD?)?)3nL7_ zOq%5Q3!aZIVSJ~6`j=<*4jm9c_>KZ^zTkVg_CuIpZxbeMCo?}}UQGzZv-&`f^oQ0h}7ze`5Fqk1|j-kv#_k~Cb9B(3_nHc|Yz z46grS?l4pjSlO*2PJ{PDxw_ z>`La~b|!kX7$uZCDq!nfQUe%>2iTY9?)oKm}xb^Bsu~6Ju8myTqF3l+S zCIP>ma-U1;v&6KHui&{-`$R`#IGSeP@HoDgCH!{hyauce#MjH4B3n>sIz@t!#kn|K zf6#M{gqpabDp&jM>*0L`89awI@yFK-yeKegYWg`ju6WRnZ2aQIjnc{;DS11c|-G{T^!A2f~Yz9VRTi z6|p?4xj$!Q88AT1NG)6%Ny^;7U2osyj^M#`mO#34#wOJ{x^QVI;HZ*mGJaO$0R;X; z7K&KqhbNO{6SbE4u|F~X^B9x9MbLFd-vr ztA~Zy$0#Va_d6raO>{PI+pJJ^GinUae+)0$0bu>+4RRjx^WqSC|Ff>}CzB-=PqzP- z_x*I1=UKV!PER%h9tV^AjUl1WgOWVYKXTX0^Wk|l*-&$suN#x0_W0?vG3C3+E9Pr{y|UAuewEh~GAYxi-dg2}vWb?y}X%{nyU zHWFGQ6gwBCbM_~c1=CVXfNB(6BQ2i0#Pn?nVNVTI?Pl)CHzxVbmRdb16{+pNnOpBb=_YE3dxvAM4IoR&#t+fc!E5uq^XN$m@i zSZF%~w996yiFh~UF2NJ=zeAmc%kkGjM5)zzIbo*^PPZL;BJX`+Bybi?u;Tes^WDcM zis0?6vv(gCfS=RtGlzXhV`rxO)dDxw8=Y>9zzwx7FdB}=Wsl@`I^K2Q8k?P^`uS7w z9|t>o^2f&qV#`BZhyy^lBZ!|3y3PP3WR&C{iuB?>UYYn4Dxsig?a~5X*5VFF@NUT> zz3A}<6*J$Y1-4?L?Qa%Uz#Yve2_1gAo%oqtGrf}ax}pOtYa9{&^p;ngur${{U0#4- z4$v8JQps*qZagx3&Dm%xg5Nv7Bb6=0XDiZPy!SWrlmLh)xK-8;h0mR2N~(B~9Bt#i z3K?2bzukyR^;Bh5t9s1^aUuTR6$R5>jhpRKQ_V;xQ+!8^p4e^khYSh8IV^uw10)WY zDL02*VZ(l~!+{PbbBJxIM#4lU1qe;rr~KO29t7A5g2VHmi?eN`dbl} zEYVdACY)j^MQCiySARb?Wq`25(2wS=%0rx^qfI7WLBW82P&Z+UDoT zboTPOI$*uhFVcM?A13XX6A|5mkX&vr`B@WVba=t)v`KShdF(nCjq(X${gN)J6iF|D z^g!Adk+l10Q?-`e-`1lQoNmKN=`uFs zW6S9IR>Bw<;8p3Ye0gDP1Oj7F4-XI7G^T*6DtZv#k6VfVNCFtGYPHznSXo&e9v`!K z+?l#wtj=5N==~Etr7lXMXIve@d{kS>=9C_gpte>G(g@LypvNX?;*ZlYdYeacAV>~%GyHuC zZHsDIiouD(PmP18)z>j0N=G5XAidrnv&B(JT?Z&lZk8#m4}p{C>W*F=nj^Vk|0$^` z`>BK~Af!Zw$FFh9gN}&Up&=uQOl_$asPp{z*ZjzGB z4Mu=J5n2a;{@L;BaRAH1#?hP!2D+4mqGj_EhNr?%+MWM);w0jqxBpY9=clG;Eu|tl#;`Xj=1!|@ID?^xb3ciz z3qZN6C<)z*_NAOFQCWdvhsZlj){WlY$XF;Xn-vw78*s||i(yz^wiK1g|0LHh2bfF5 zu3eGI74XrlGXxcZo)P@^*7y5#@qD@%47d=6b2(#-qJ1eE z(RkAL=6azUVCz1!q;u>+5{8gnW|Nk-8yadqN9eGx1+FI+#29lEx~CB+?A>(ro{tOa zh3=+Y$Dbg57nLe8I+NEAA)D;S=$UkQ*?0 z)P@B{!NyKyh_KwogqjbPAY*rafzV%p_3EdLkEp^lb1Q0Jq-R z@_oNz@o|=X^g5iapfvB_MWFj@-x5H0+j$%>K0nK|7l zTE4Nkp5=xy85}&P)7bhm_;EgLp4mPutTow)3RxRo%?vn$Rr~3|_blXONq%&eLWNF~ z|D;FspSNJ#`=r_#viWB8>-a4=?-&5uw)dH?Xlx+b9MfOKCRn8aOT`tW!EJx$1OKRP zQ)}ZTtLP0Mcx-k{wXbeW8dd(@KJ|VFfvNjSV?G_XyqWfrerq%=W+vBc)#x1T8?F?j z@O@cHf7r0h_?MTG0{v|em+deG7w&g~me#F;J%J|yjL|f8vWbJ(KWAXf>RZKk!{s#@ zx%LlNQs$$~q1si>Qq6<5kPNP#MlKZOhr}$n%CDfo3htmW-;MGyuOPRS+PzLatBaqJ z2wf%61J%BS+}AcN_h{~~?}e|`Y&5`1NX!bU5KR628lCm^D5m|<{(B8*jc`XQ>18WO z4v&S6VY)vmr(oF)M`)6OY5%RzjZoC!ZBlGmDh8?`8;w3}@D2ouMxnL&vA*g^WN&vs zm2ytE_^s~4goiHY5tTe%4$h$`tMr|9ksD{pf)>JWl~8Y@!s8#!$2|x{Y)y*g4Xojp zS~=Jae97LQg9lj&e};St?QQmb5n{=+X`{FEK3{hFT{e5BXL^5!-8Xkzz^QAHTMS== z8rJegdOgV-N=YH{6?|!wa;qiyh>$GKyYsoJo?+T+5h z9SZfr-rx;esnHzsUwno{+C?V*`-}8WL;}+OColGgJe@&ak2~n;u3p3x5$@`Dk|5z8 zz`Z*ORq2bNz!Gr)rHqAOtRKt!O;HI9hh4yj9W=M})vLGy>O2;!_So>pxmDRbXP!0a zt%5@2-s8qB6%9w7t0ntu`dn@N_+SCan`2#${BFB8=puGfQLWOFCH|2DP)j>_rz0V7Up)4^8L=qYsdxl*7m^p{{rH7@c2vmz5MCixsGMJ5Sscm zNf;uGr$H*UoJkz{-a3}I)rpE35XcBM40dqaty1IGIVL)uC>gK2hm=+ilYz&F7#=p3 zI?TorW{WrXV_703{~H4T{lv3u*4B4V`%Qk7T<9gC1hs_s{Z?qn$Nb_j%!$R05bAa6 zj09GN6p{Jycx~Qzm{D5OS2IZ%Ue4fz-fwBo2e*W|bMk^gQ8u!@?$X3pQ(u;Jh+hQwec61P?T3Pv##=_&Hwp->eWOiBxFIoX2Tmh&JPd^`0sj_P{@k{)se7X!L15Jd-Z3%w9(@Sy29e_Iyc0iv|KlaC3KPU` zWrP)*5mzz+rx`_utH6v)Vz{Y+qJS@d6uSL{*X;MimnO4`N^p!LhU>L-(3PhCGbqXG z0;MuI+dQT>DiEyzqegZs zw(KGZ)lqBN{8AEKWk)C|#iW06%8+ChH#h?tBcRk#Ehu4B<8Fwmv;DT|3xCt@VC883 z^3q1A8M93JT}rZ-ghG z(}L=4#t_)v`m=>}CWxwR6cPV#jhywiNTf#CcuY`4jA2sZu04IqfTE@I-KJus`_r;0 zZVszHBdmi-=s2p{hQQC9zaB344WoCuSCZxwSESna%4Zrk8sdtp=z4b<0hd@lH7dZ6}F zRAcesIQiH65SGE{I6F{SHflp5_{>DygR_90 zvGEUUI$D5@}uDYf&8~PMGsIr=^a>y2Sytl9qmoeEsaiEeyt5;O{SYF9u`+eY_UxFhQDo)u^)Tc!y%%rRo7N!s*Kh?K!Ul1_GmCG}4(-g>+ zXo&B3J-U(w{bk+Y!EIf0E}i_TkYY(E(d3#avNoWyXI6uI9@m?LAxVG4W!vm0?WMEp zhqhZ^Ykfc9PdEt@^Vq3NZUpPYha_6*Hm!%3rRJL=BJ%~<8o3(AXZlK^tO#OzzPU~n zNq^mM{KcK;ajuGK@Myn1%$1BOKWqoYQFO9pfR+#2IYRv`=q4jX!eAB;V3K3{c?IIy z8=~X(Y?#B9m=xTpM5e9oMn8_Z7@9(>dPYd=V^2D5$Jz~WaK}dLxa@1V82)g3Z}!&T zOw675Lw%oy@@n41Y(`B(m{d2?UE2{ZIeyYkA-U6{zNV~yWvEbskgNEjVvHFT_m=ex z#!1uqhw%&6g@7)&$HZr$nu-zj#$z#e@xqX?;xLwx&x~hGF&Mg>7gE4%BdOVDYfV zY;_h0HH}ZYSY>)pJX4zv?<5pdiC>mBmHR`ERayEF@CCIDBiC^pPs^pU>bEDe?z{c5 z{q6AFa}dt-{yG{Vcymyu&|duxNXu-8(Nq$um*gwPEb@}7yztnak9)B_Qhcb0TQ8DS z{>eZp%adAkvELnFfEIvN9DyzrW-MYMCk|_GL^^&!9!bFG;XrP0C8GXFq38&QreG0A zJmKLs=2Dg3I&FBEl%KYQ%pr-}MOiGAv>h^r7dnOqpHr)2>UR#q@JBgt&gjxjDeY&V z4dECGeSzJ;mYN!q$)RhPZ_Lj%smBUtsLbt3fu0SV`Ql*&u=19XUl$6C`!9Kg+KPLy%sF7fJF^WUpOG!<7i9DxF`gGtXY0F_(1k)ksZNWcX)Fx2H>x$# z*~bY=YbxBP_LS-^QJvx=7f>BwlLI0{(aKV7Cr|qNC@M!uSf@|U=)O387}zLu)uiY)ZEcn z39^0L!FlcDM3_c6rToKe6BAu$kh=3Y9vS0TKEsRkC2~sb^{8Zh$m5yPiwg$7?wv_3 zbx*_>5vE$ak<*R(mk_LvQBhj9?eC$3#FD(AJR$^0O@}EL_*i-2;~k0Mvr!UUhTjs_ z0(rNdT9q2Mdwb%8H#)T3K11?T8L2s4pj<;;l6 z1q7ElXEX!a7@>qkM3CW%;+;3s zpj9gGcs0=Ynp0c`-q7K;-Ve{BV4}tOaz}8(%@|+iJP0kQ!xSnzySE)$l01OJ4MAf_JPGg?HZh6y$O>cpivBa2ruiD&1gH1bro5 z@wOELy<`j#LlsCji>lEASU!=XhNR2QjuQwDE|H>f6n}%_&6;>MMcLZJ(B|q44MbA-^n%#gp%U!GU;h9BedpkrDFcwYUySWMgaF(l6cQo6a6?= zB|P}G2ESnnJ&%o;U7RP1!u@&Cr2BbMmV*FZ&@1`uBZx8B53n&SccBei7^8*V>WhPz z9p2GNd6RwX>P$M|R%4{p527`k(b$axdf-2dV?7C%RUZkGVj$0h^$j@6anqjyD>hYP z*ieGmGV8CFahQSblD=w(3&%k?8Qs=QtxSp$Vn7gqemTU1&tmQGMFn*%;kM*4k#RLJC^8$;a%-_!p6ngo~~1 zU?SoirqMZ2Rcc;Ai6Y2F=PP$vT(rx2JwP1dhBf`yIca9de$n0hZc?^sUGd4w(G2EJCP=WKCeqYUlP1xmA$`S$qO};XJMtjs z&E^jCjot7=hLh78z}af&8D-|7qiv?3r38umQdfux5OsX*JD0F;ArAUW4jN4EdCNf$ zHQ8n;?uUu*#1CH3$=*3j)F^n6kjr?`ZZLCHyz?b%K0g|M7nT5-n7>bg>?O3`-}*y( z=J2w!hf4Q8|ID}L=m+C3cQMCq(K>An0s=1OEWrNoyK>MEL)TzCfqcVf9pElyb|Pb94SuLD%mBlAimPaAfqEtRHnad^P6-uwam$!BG|RY~oOKnxqmw zEAUPal)RO6@iF;tdYoi$dY)J7I`l*8dR;bmeZ8IQy6K(duD|vk*q^9g4@$!1w!Qx@ z>?zkbGtm=?r3!@d`owp>K|wWULmFm*kGr{_OHtqw@Ia<)HbZ+6jm1&C2dI=swYjbA z$;64T%Tl`vQlpysfTP1`dl2{TJvH~kB7~aH=V1Mt1hc=uEX$!Q60sjveZFIA5FcSNF7Ue@jTyT9m}pXUbaJfO31Vo8;k?ed(ay= zqE5Pd9b1Xoe4J>$de<|B1Ve7i(G0EN}T9H?kz~la?Aggt{7y_Id{cxm7VKC(Hq&VO7xMNQU0?6^~CIsSR?i zm*6nx-?!;?iO959LF57mA=)zj6L1y)HZRr<6pw!3-P*47BDjH9qo6Z@YR33tP+koNBc zCeJfV8e2&d3?>q}T^8YKU1Y{C2O_~F#8d7xbCYG`CPlF92bTVFPsjPX7zCMMW*Pzu zdEeSyQE-5XQB!;D$I>!b3yoq=9`po1Qg=+H&OF1?HU~r%;|7~RHS0HJNHWsQ+)>7< z4Ds*6-w~G_IBEzzSeJ@L!C(OFwVb*98DQn}76w5sLCo??sLYq_(&f#}vsdzIgbKO1HJ>;20L|Cg50Ug+({!F@ z>`znzqzUp-f0VBD|HM51%x6b@bOlanIezAdYL5`|F%*Akm*j-d^OZ*6ahA!HW{BuE zS)z=Ok4NLMh607Q%`b1S_b04=U)~_FaBv%|9fN}v>D(qm4%kXMBM0>_1RriIWO6AP zP33_)3!L%)B1<|uP`NsXKW#F8sd|%RP%JA%{i#o!Y`-E}gU)9%q)qFI8)%(ZvWWr% zTzbMseLITn@i09Gmh~27ETHu86XP9&M>BgUiNaHA+pt);_rkm?KdNFX_tRn2tT88K zZ$oUK_9i*o42pF|f)y4@=UbgT6uwkqDj#4t_Am7^#$cmEUIjF>I0d)a}R z>fuMyaj*U<0)fBmiknq_K!bR6dUUFUzV z4p%WK?2QK%rYGVK=b}bcXvgn*qZ#2wEbldiTN*oW~xGd@F$^wc3 zGHZL@B7uTsSxU4@joRY?D8k*!+8*nsYM^Yh!G5cQ%+%w&ys1|#;uB+`dWLeM(lY6F zs%U8DSB#Y&hksQyRNGnEhV@s?8WIO_f}FOy_> zoqTWD)E|X5Vw!lvw)LwsPzxC?{GbBqa|GKC2t8U&kzfr@`N6sq|DSbrWmJ`P3orRy zc*|(88b!883rUykIv5#23}D?5XrOunhcOoSinHvp(c_nM|2m&@74`?Q{vS04DACH7X* zb~B#YX1UAhzV!$xTd~W_jl1%bCRi{d$V{9PQL;!&86+a4#5Ko#^~2uFA#fvoe|-k$mM&WSMMVk~|?HtA*aHARD~461)G z&q_ZFb+!zQTv6QQy}~cD+tcWAPd0sjQ0cnR(=aD%Hmf$cX~ZYx2m!T>1vj!qeqJ$h z9l=O&?{~s`AQv4WIF4FVQX9Xj%3Y<+0gmvkjNIuOvj$>mXH>e2j%T=p<&G-HFkRdZ zG}=*+eLvcQdiK#k9dBi-M`k3aCpW-*D7G&~N??N_YL^_~k~I?<%QW^ERUA?zi z4}(FqK6Y>fk@2pK&O02w-lb99_?+^lQPEprv=x?$w2}1MRswwxcYjG!)3-Vf<5{Te z!AMA^v2-Ct^d{Y1ZV2u={`Ivemdn3;B@B0lV?MDEF&iXNVucY{?+OXGR6+w$J#74A zT8@B!dj^|9t8@EV6PLvF)o+8WOu-|2b9vBeY>mXp3880p5iHLUf*t8`f9%)~iRA!% zlZ3^3iMpoi4k)EBpH@VNv)+6k`{RU5fQCX}co9)WR~W^-{HD_|gJc`<2yzmy!0` zN2g3#TjZvx39(5l@!j3Qe<-GRgBPiI-0qa#8pw6anc{}&sk9@Xu?bJI6kuTriuY6eeRA8*CrEg^RGOhvSLqd>$+yK5q|3 zm+Q?0Hj2Cj+HoSSS;4dQ=!xxn4>7ZFDK`o6b05-8VUs-ts8a%P<&=WT1HNwLGM8JG z3&xN)g-U0Qb!|mdCzO|;thpG^{6%Wx92)pk!)C3vc9g;Hph>6ds-|j= zGupH+Z~!d$M2q?b^zu&MNj*p17+g_-hca!lhJ!tiN3<9r{y%7zN ze6>1Pp^2Q?%fzqFZ|x2a0kNZMyn@yZ&?VUh(KM3G$7k0L&Ed@$hzdfA6tI{oytB)$ zMm*L2pYwC5`o_ZxwKo{sqs*o2^&z5HzG!fS|AyP$1g7Inv{R>An>U-!1jVqj`ksDn zl{x4{2$Vo4Y~Em<^-p<9D9890kESLZg$JbVZcoJD2!)0yN?Gbm5bMxrCsQqBs}IlS z)hX*w;|=)1zOUeHqOZ0__P0P_!rvwS*`xA`gX}dRp%~Pm(6@^!5UO7pxx0h+bt-a@ zx^p$Byb`G5R~2AP`&sRU6r!lv+zADPBA?TKGwEs7RRJu)V9#ISJn4kot8t0-A!4qd zM_~2IG-9Ee-T!U$6Hz7HNiHtTbMTl@XOP;CunV3ExUc4Ol=%VWx-s`T2S|$o<_PWX zzBcx|6Hpsu$SN;Vhs0K~=ep5sFRqJ#>F|g&=P#O%J2~2C-8Yi<&ihl|2F}#a1F#>2 zTi4<1duYC{_N`>k&tQ6xYWtF46qZ#*LzQ$~&S?7O%JUm>n}A;tFap@4)`WzDOsK7M zTr?b+CI$D_WDS;lxM&q!SSVcFiuEg_W;7e_orcI)$p1Qo_EUlCAp~%8+=2;{%1gUU z-hj^M=)o%3uWbvxPNb!;ikO3=ju0%Tuzb>Xax;y}ji_+zZm2w~z*8p9#jtBJZt{tS zny`7A1Oy_clIV-cE+m`tKrJ@GPi5R1z3sG~1OwW{b`Co<3rf>vS%fF--Mw`2bX#2v zVcP=vTH*c`gN-Oix?dj3dw^CJ(bn-*k9ThOTJfm z;5nhYC0l6oO7Ugh8Y%t3Wx9k{k7$voFxot)ozO+FabUQ|gDU8A73QiHx#6{vTk?WB zI8KeRa8^eMTO2pKGm=HBH-&*ZqUx5^dtemG64D{pldK>iUq)uSe{|_6FPVKWTqIUr zWsH<%<>&6T_aE8ZYB%#w(pAToNIIorD~8PN&!@-i6mmG>!|W-i_lY+({O6(+o7b~4 z#~qbq`6@UkIj^gL@0;_W=S5=Q?6993?nATlr#qv21{mO;hEX7WaP4GiJ+IDx)tVUI{Zw9p2PDKl z&+!w;(}M)bn^gHe!SFjU7x3Vgw>=~Plw|o;N{lrH39fMv@_Gjc1KUFxT}7H%)It$% zL=C&{!DZiTf=+|EZF^HD5%7)s588FSL5#@1GI4jNe2dDr2bhRGSj~&kYIt2);5`5s z2%O_TxEfRt?D{3n0Lbd4p}{30vY(sb4Z3i9Fp>kRf(%9@1wYRGLMUG#@`a3)9Em6? zX7czbUOA$_jxQyJ4GdfNh&S z>gO7>8k>-xm95{f0;4=h{QVD0O*bv9*OXB)dj)p*k6# zC|T4UPt1sLh9^S`B`dS7b^Fm=T(R6nYz+|fl6scycVupJ%c%>Tup=rdLnAE|lsg5|D+ZMW_vmK%c=$Dy@z!aHMl z5X1lAP0-$jvQc;bC64}E?M(eg$OiW*!b$$8MC0&U+9>KrYK`Qn{iDNV(lbz}1hzew zdQBdmbbRoE2MbQ9iPoZUfuO#Ii2eR&FF* z>-#Ta)A>s`_}iQMo9e|aFI=REk5AbCs<|4IhoS%j>$9Gz1bHLl&nJPMaO1ozZY1l= zX4ZNom{}e;J=h2A+Z%O!Kgw>-dzl&e&Dj7u zgy+zIzR8gH9r-fU<#rR!nxXx;zuW1!UD)Yhen0J}{{+}}--Fmz%t!>K#v3C}A-fk( zgh7Vcn?c8xNN?(+>Guv)dWeqwmN-B)cUG+d5}<<4u#rM@b4V*{6+*4zi^i2ArQbUe z;mal21M>ZJMLCxBM`hXgBnf4Fry_m;&3s1$i1w0!=_&2;4^SX=n=CDn;W4kG>$j$U zcee#ACCxhp7|KgdpnaQGOuaO`Pf3PC(eH5qlF4o0sq9ZN(-2Yn((Aetc z)#~Mj+W!*PGg~Op+Q_=a-_5G0r?ykUlU1EBTNKP`qwa9CvEn#sbFIAcr^^}1e?KEg zvNzzUXFmzApmuTxivy#_a&`}i*IW_H@qkkmEHscLUtnK0$9vraGqR%=*4b~>LgYg* zdWFzE?47}b?Tb|4W@D85n$d~-dA8If6jStuTfgV;IL))`9LqAC5)4N`3-gfO$wy$h z8JXf4)xin{=WOQifCEWL*pIr1?Jr!lzJ44vRnC&f12`G6!!r09Z%392JCa{bZk>?D zxUhf0y#8IgIz4rX)-&pkMnJ9X|JI4Buo5D#$}9V03rMo*HgIqmPYssCh|7=^dR1(smp#sk2RkEqiw%4(V&DL-?mQH%VaFx`6s!X~b9C3~%J8fEFrS{fi4jD&?;q zvjmON{{_@QE5AWQ88_{oLb%D~@|{aK>Ac2-3?R`6e7+!^a6m&%QYFCcHIn8c6auoe z2p1HtU(fHr`wv+S14 zH);y%qHcb+`EY!OonnWdo60}s*@hA94toI+?@Pzv#r98l@!ECV@rx5VY<%)frII$6 zX~TX&>g0Dj4IWI!%rp|9T;fMoiy*m@OA$`W`^`QEI(A8Y-K( z_u@x;?INGN?pazkod~Tzf$)4B#;d%2(E^?-bMk}DL%1&OcozTuP7WS24yS5I%m0WB zM;^cb!6J&947M#TLN}5Ns5A4op^2W0Z6AxD5J->4Hh+MbKbw{GlpmbiwTsTNg?akq zMO2Q><;j|n)HFKqwJU5`?q>Ts7svjnC4R15G4X1N!YgDdtdiNNN3=`3%{<=TqrgftZq zl{|505gS*y3Hk*=zaZdKsNCk{?%$VMGidZ)Nh4vAa)A{B&duM<%j4#!7)?w#BsuO@ z4JLXLpRvVlh3I>!jRXvpu&LlTG#Vmq8lvt2nId~Ww>CwRZwqiy@mdTND#9LqwRINn zx0Yh`xDz2j`NHr()|nDN0*<@-=Q%7nQT^m zvV@{BCo`#ZSVG{WqUJ#Uhh*iNy!e+OcYO2_Tid>5WL6W-9`QAKwh;FWTg3IdrqdX8 zVj>vsY-K~RZ#uf|-SvF3vb8HWh5|BXJ9_d~Cm*~pia$TPld=(E(nW;JOI9abKXzIm zUYN4yqTZPyKVYbsN--7UcvRPsQaJxLNx9hVN`P`mc)rrHsm{U^bl%(JxrG(8CQE3?>}x%pPGaoHmDsqN+$K znKWx07v1?|p1A%wK74jb$~JF$W*bvxS#gcE3Ysu^u3-cZ)QpeclL!c8w_0B6iU4VUtp-EVn+Id1K37$VX-KZmyWjJ1yn$c8Y6oIa7{zID+o5N1c;PrwK}r)DdL7!X$29#Z zOUiT}i$KI8~3U1mxg|}J;1CY_sL7O*?Qm>Dv#=MU+Md*Z-uyR)+i?$3U zVn3U!=FaY8M!tUhFRh*XG_da=Ow;7TNtbidx!cIj^YX|I#e{?1w|dqa6AkNF)o9_1 z`7612@zn&{`wo88>|HPYGj|oxP-E*B^JV=X@`rihj@Oz0?}a?N@;oxNUjtpfosq!E zZ19IN3CRwewnRFz5Ks{Fz0^0_Q9ch~?B@oT(shEAsH+3GZ>~+EVDu1YQV6zn3ks*)6vkkt6|u9;+OA@XcND#M;$31jWL zBy1253o!O~)A2w#_BY$Gz2igmb=9lV>=O5(cIx-fVouve(j7XpPHtuB*snL{P{#aI z*Y2JX7hWLARKo-fjXFJzA(~~*4VfyB)sE(^*yiA<%nB|nT8F2bU|QqP=Ck%fY{EcA za$4?ICVLuCdzqLAPBS-5_@H%AwhZG+k)wQ(AGI# z$=IEB7;7U;Iy;QoPjcw}%4^xLyq0kEbIj;`6kB=!6ZAdAJ`Lg#m?KrgqQ7F>ZPXx4*m{MS;`CjKkN~_a5f> z^IG}!ExY2M4Ff)YEtB_N%t8oBZgGTDuWY3>&tf1Z?7{G z8_{Nvc6~J>&@<&~a!5rGnp{kHzhVrvyD*Tve98Z#EGKn*k}jbpoW_|OkHJ7;sJ)%z zt1CI{$n|Vk=H}1m4#nSLNs>ti`ndk_9c=v4&HWFRv-Yx4y?kKTTq zV9+EY*1Xh#O*XuL-hh6P2N9KMi+wn4CU?HGm6e%=y@GfXdX!x~_h+I@CNP?!4o+Bi zd^d)rL?9XJ?BIm#9b~FuhPm4r;cVmQTMlP)Ff-0nvRDNtQI5r*OFzt_Gm6o-C<&1)PYQq z7C03;jvhsHP)1i0#2B*D0u*E|VeyjC?H3c?77ge)Bq z6_mAxh}PFp`{J;!ZcG^a`8I}+2r^8j7G9lL)}F&8Zwq$Je$VY(s7(pFRCum-I3glZ zH65ua7zj?t-pLgutI&jjCM;iGyJ2HT$eTd3k4lyLsEd!=ia8>)E1pjgBosR}3lCnYi%k?8lT=hJH;=e4&JZa8Tj#Tk|>nR}JJA>KuPg3lURr*|tO29LnH_x!d`9 z(Ru)!ipe7*-$x*@3DeqG*t_m;AP@qi*@l!RqUR0n!#)<`H;tF?c{4$Hb;8%;=aH+Q zPB`Xo52O%615m9748cne<@4KX`Ao_;4-;EhZ`h>* zNB3|870a}huA@xuKrN4;6h@IIJUQ-Nt~qQmwTI_htA3{qOBF0Va2PdVSHA*D2$-JH zghNR#yri`9B!*0tTPr8Aq`d$mMsTq!CO<3Kz%SL!ObfK(h)6tYlq*VB;#4f*1>jN* z+=|XY={3FD512MK1hSHjt+RrFp~IFnZnmy*(FZXwem|)ed6!}^vS(Hhd#aO7aCUVj zfz{0`y1w_{-|LcxKblHdPrkP|O?c&zJkFUqmgn!Y%ADAQWW1-DcEiceu(zu(2@WA~ zDCSoV0^hPTwO{-DjPH~HtEi~p@WT&h?AWo48#j)-@4g#==H_N*&z{YQ5hIvAdp0dC zE&a6g-NA#GKaujg=l?!0{&&@uYJODq%YIt>`h?hy4QUKSqd4WKUAgq$dnuD>B-tY) z2>KMByuAqf_9m1TKUq&K#kH{>GYHY3LTkO9rSGRhO6@Dfy(wT%5(fpKDh4%;Zj3(M z32Qz}{BqM)Tm* zH#k5D0Y1~tSsQ2bYSRdAteDD5UsncqhuvgiUL&6_9D%YZol|W)F_SR-Y>G_KvO3`s zI)fa^v$qRjnGEd?m@w1N*r@@c;l#EL2~X6G`xpv0}|cLAb3xi)WThBx>~TTQ>wB&dUS@(iA*-M6QJCO;Nz+knG-Ur+9D_%`TAw{=9Pr3)@QKX2E5?0`9EZkKYVhg{w!QPoH6D zYcM0>SSNuafjMYGmNURz!#+U)ZrnYY75)MaEIy^{B->bsu8e>Da#t~iRJi8Fqq*bE zw^4+|VKeZDZB#X+@#qaj@pGdQ!8^}pGkm`)7Pb~6ki6A0nC1R_ew4SF(e4&>8CSmf zu&tQq8%EG!*f_vj!&&)T$hIXu*h0W-O{LscF%6IyC_L9Vf?4U6%-sAFjvM`CzjOS3 z!gor56%`dxRAfCMUayxiW5!TbRmHq{^Z4+?4*@v;{PUSLYu5ja0Lv@Op<#DZ%I|+X z@={zLS3kn*+e3G+Tkxkjyz&?f9UDTk$(Y%vZz1$m#H>(EWVcIghwKK#EwQNXQBte(UKhrXSnJKlS|q_^hLd z!&aZlM0X>0#b8@7otMVWM-wtmMo+_nlOn<6%+XVj+wpYTOgV4sby6lkBC&svju60I{43+1#x!dHD$}Wx$10=U3fdH z5FB4TnM;TM5z%rMoowDLcMw&PEZtGUDfj+}<0fn-#}i=L_F`5(E1C1dwamG&k+gJ! zW$&l6ZJm=FHcg=|>_DinDVW8kU?!ip7jUSzl21AY@w<{0Yz=1ca`Uiw(bb({FAG}B zcxLpw36;3pbsM)=PPJ;*`3zkDs>k+G2TLS;d7vQwzSo&L=ERi0&-V@ADFN0UJ9qA6 z#flZom@$L8y1KYvD=aLeu5REj^tX+d?s}aEFCX|W$uuPXwmr-t_$|RcuZ8G=9v=Af zAdWt>89n-SYk{4RWEVuq9~5QVS{I56=~+4>CIxuo@jN_^_4p1OMzqZGv5fLoljHRF zVsY8+AWel+Moi?0Q(O4;t(6>pax2RhdI<#*eQ3Ham~@~IA>ipd3V7tN#Z0(7gl^A( z4jZ@(R*ZI_6h%-lXpTB5cJ%(8=H%|6DeB~@hA|1p$ETOj8g8U29O#uhah$uE({s0x zt%dlcy^s~YJcc^jXz6gqDED34hyq)HLo#~%oO;cmv@(H3%&z-1bolE9gITf2%gH}& zVD=eJ?C-QlFUf>gPtrp7wnL_hA|xwYa#<`(XjvJ?dt)_12jPpQ^g6~w*TEJeiV8YO zA_BXT*tXki39q1`azn-Rr2U#G{9)$-TvM_PziDSnFq1_!1?akfwC?JbWQ4PoJBKfh zr^fOd8kcQ7n4Fk~BPINC=S;rn=s=IfFFdnrF^d{*VPCaQpm zX-(8cU99ZLB4B8I?kmKmNE9JCCUZBJ4O*Qv7hy7%o?XBoM}V+YaA5M<izR{VMVC(<9R;C)%DSa#9D1VVR9`aR+p9ZamLu2^~pMv5*j0Rvh9(CWW2974&lL? zae(C2ar1fW>1@J5mBUVM#Web6vKl7nhKi~fy{173D0le?8<1lQ^8SQZ;c-bQpz!G5 zig@?gocMW3#|y0R9L4$MO;O#@p2^Ijo>7OUKM%Ls@v+ z#cZu^BB~qYs491UekBn@#ZIEB-=>+2&TPY6=0w)pzyvWoh%zh)j{Y~S8-iI}x_%ZT zGn+XzcROXy4jhU})n+?p60d>?N!F}R!_+HKw9 z9zTB4Nhk5d6Be8Ai6@@m`leGxWlcQw-B3nKTVbZ-oXA-LnWrDWtp`# zgkXT8Qb-g7oYLrW}uSGWgfaM+8 z2@CTn+1t2u(3+GLk?jiHGUPLchMU>a-g+vBZ&Uk9Hjdt~QtMc;jok1ck_vfCH(S2Y%h{91B28#-NtM8~GowS-SUUd>`nndxH=j(d zr6N>*@CdJT`%jw2+k*SXBmfhHugPHExA`}6sHK|0FcQ~S4sfT1HoTrpx@!mRW( zQ6_`aE&0vuyBR+tn?Xau=uvu^Dxa(y#p%PG5N%C3o~cI2)bZIF60X{DFeZYK)L7-uO-Z!**|5Wt-&{9r4W-9? zSk^p#+*ZVG6_ew0E(D+sJS|pth|)04hLfLfJ%TgyHgi$&I;PC(AS+)dQXw#6W<;rg zbSj*DLMw_|)K9D559~`N^)=#_^X}&Tr$0$JCIQpqp!G+SyPoW*fopjM?p4)$*h61N zxc>%{jYlFB*^NyoW7fN_kmJ7Zz{KS-W5Q_f@6{SvfAiJ-(HFhGi44xhe{m`5xYQ(@ z&IL;#QH6MYN(hjn)LF3f>~>J+xYR!u_)K&;G32~G*Zm;UDpfEr-X;irhB%6AsC+*nrW9VD+bIeEeD_7yYfu(xhB6K9=TsjhsQ_ zK{kU?jg-BUGxvw?iEeu?;C@5pZ@Z>&^YBVAdq3}=wHI*J_5+B-$>vrHD+LnDq&V-W z#r}RruU~WI4??WHY;>$bwgnA?!Ojkf?0y!-%)2Tke<@$apLWfNpC^DnjX#YOOQ!UD zkB7W-EzgafN1-E#A>sT@N3k`O+3$6G3a8MyclgIlNNc9GQRD9458|_TGcctfI?0WC zdI9fVekQ7;XE~>@5BpL{eNWI_+rl$9y+WWpz)2S$%dhVINiPAW=cVC0dUWDjZBq&D zHwgEt>OPjxBoMKLaC@<^4@C5(7BG^LlY*2E4^gwz&idt@LC`5;$r1uMyMnX^GHRg; z#!d}THX=mD7AIeR?2Quz_C$P(0rZ%ViYUqTwwgASv3-|~@b+dnfQVW4G>t%(k+001BWNklqW_QgJAL?TSf zB`fUU`$YlK9s$ETDpip*4oI(MQ7qI=6DAE|8>bbGW@%dk1&$1UH||7+cnbQxzom^^ zQH0>!&9fQjYGQLRlNMvoV)F)yuvD1eT*id7W-@YguKU+63 z>zsF*%V^e}JwgqVOu9p%{h@N4Pc@-!3}KYoiOkKW-fg1my?to{s0v%ANhu*DHSVt6 zAJ_m0DgsTM|LOhwbK1|CkXaVLt})ck-J9NKQT+xqMP*F-AXc{QB5XzhJD>RqyY=w( zLn1JxNO(3B1;!Ly%;mQnl#+q{eZ@Wjwijq^Y)=qgoiL5wFSgnez{u%o;|HZRxCgI* zBqJwEThrbbw(GRs4~*X4_1CX&so;hC3uvmfSqv~NEn)Qje%3APn{2SB_|;#lC>|Wf z7F)RO4wGU>m|y+1irX$Nw+@;h?J7i!_&p6oC@TRSC7@%vG*A>&ezn(}ohM=kO`Pwq{9UjzKt-RSXn5Y!|vS=Oo_8_$pHw{rIqg-u7rQn_BGUi{C zPD8bcqD4vIocGa_Kr+nP%7XXD5N*w1%wg4pO%*Lk%`GY6;o31)ZbG4AO8Bt3ods13 zI5Io+EH3ZJ<rC_x`yNA}-L#s2Q+_d%;{@=lu0T9$9od43f{m|(!? zO9d__H*605+_6e}Pz{~%5W5DO8E!jc)v17p2 z?In#Gqa(o-NUD|D&x`~nDL~9G}p-B*ke}H-Yh#zpywKFfKly&b{pmzH_C(n@3xl53q1BJ0&0MAfFS4njj%g{ zV|Ng9q?2g3hE*h*FcKDP3B1=@LTGy)gfupPP>MQqIXw|E1)sDP@pS#@UVXo`^IeWq z-%2`fQo_|c4vaG@KWrPso6Y5%oU?O4+G%CjgTs9n9nH6L(d^M2cWwh0-&9Ra)XghR zL;1Kpd0C->!bCxrB&4=-2%X8EdOqza?stsaeRYJ42u{_`J2flu=^;ya^@%sG!B*W)`kU(sOfAL8cB8fg zaWAjHR%03G2j#bs=yZuy@W$a5}SUFSvlgS)D#=>p2vkL5z()Z*wiqQ zP7M*Vw3MBjTr}6&&}^2Ev1~+$r3*6HxWbJ)To)f?`1B0Ir{yBO3W%8ht4)EZfGi`< zY?eC6HVxTo9gsWHU~Is8+ag%OD?OBHp@Lc_n0s+Ot3OSnbkiDYrVZkg_F^PZWDD?z z(&bb|(s-(V6cu4_+-DS(8p`kh<~lFihuB#g$Yz44DQ@aBrB(9ep6ZDlno-T%ysf;_ zIFtpgrAP#m+?7D`RAzodqoYSb&d2J;a%}c)9DS5+F=AOkZmO8Vpp32995(c*!7Ek6 z`1s*NupidUjz|U`C4%3yC1D5-+~aA){DTT6s*tf{xC7f@sVHc%2W&%I6+t7?&lUZk z!yxUF-2|^H#2hz38*~hIq4M5Qe~x{+_N$HKdxCueY%g%qCCBpe-N|>X$zZGNAmh#T z@y`;hcra<@lIss~-iF+J%ln@Cj06@%S&wu-^39;LHmJQF-v>5!JapJ=MI zVygGvj3WTq&D>kF(~*!-V)JS@0xRCE>!`%}VjGSZ+lbEa68>p6W~m(_3L?ws;;Kk6 zcRP{mJRl^Vx7K5?Z6{onLHL4f(3GUnm(qlMGt9YHc2AR5Nyt&0%D^iaEqiebmj14Z}Dqe@idL)07Z2tPB&+G>l|d zIGq}OJE#5jcwW5Y4O)*ZW_NWa_8pSnOk2*J+#NjEFpT?aCX)D!?dkI-FtNfp1d?5m zjQ%eHjw+sj7Gru2@#N!FY=q3cjJK2$*ZmEs9VXgmej?-Ddv^Uk|JEL3p8(qn3>sF1 zX4m#Q0j~g}S#COx9);(_t=OvD`}gDYv0nE_1`)AcY5%Oe-q^f4)lT;7f7$N4ri77` zHski1xIHETpF$v@a^Igz5>HeFBB<}RSd{`4VUH+W*VUp1B1z_= zi>hH;+JSAAkB+-b5hXp=9RW&d7-NGQA_7}o2O?_Xc(VnyB1GWklH{^iz*)oBC-`i1 z2@YZ4)Qp~|(yn*eTBQVn3Zg88&@BJk%@tF*rD94fMNyZ;j7o*tu#3SiAG5P6c&E9P z>4g`gH@<6Wpgr)`BiUr!yd9|tFu}~T;lH7noU#zfV)8?#%0H_2MI(_a!Cjf;P1EAwoaSA#&25QS1eR={dvq=$d`_j%DKbUxJ}0 zH(?D_FR(TG$#``wrb9z&eV1h%2uY#4536TesgxC|2z&<)q4o4h1V`t`mxJ#8S;Z^6 z=^+9lckonSLPYJs%Q46`XswA5r3_b?*LzCKjQWW)YfLOxxW3(6jp9ISUCgERmJA zc<^)2o>f;nj#bqt5hH$$wk8Oz$>Xeqgq?PG0xPjH?CBOTZNZSR&Y#8FfK}Q?Mg_)d z59X&Cm`gG-zVISLom^6Zft6W9wra>W6)9nb-zvl?rQr4bw#d z%G^!I@LtOxhPm34YagA(u~K618LtQBjb`3qzr(FL;>Wx;7yp{^OD1NPp&-zN!Xf!% zxN+=Rc-zx4{Q_yG7?KbeM`zOU?k~%1G{4B4-Q2m-R9zoF((XY z?EBD#eHz$a!r^uh^7S3p(;w781a(LA*EAvv$vILfUTOiT0wX7lz=17 z2$yBxpEbfN9dB1jTd*1Xj^@;wCr~^*Ox9?Pjf?G=L6d?>3UvpJ=i&7>B9ntiP3;PE z&=jN;_zxaJ#_Mb8YcbRZWngBxQPg0PekG1=H8__hW}XnHL|qj`9OlNR$i&=yiln4l zK}bbFiNnu7Dkkykk~Ksl3W|UQZ6(}NJvp9z#P}owI$~v5Opw(Mj5<4L5)1(uI<%|S z-vD;W=d%YfCwB+Z6bK|QKNw~ExQw1VN)=Rt)Wa#%HGbiwIe|XP1?Es?bq2NDv3BXgs{wg4N*6L+dYk&hkVLt zR}1R{*+e=8wjsf9fJ*^e-%pOZCWzztW|X=p`ZyQiGqaHe$+bC;>g4Ss{(wVG{(ZS8 zk4I+yf{3Z3Ao#3$E0=x#B)Cl!%`){83&-p!qsrVl><%PnS}6$ZipJk3pTmsY5u8*u zjaMp`vZbSzvFRn8Tsnu}q&?td>;3|v!Njyijy7tzCv-5u?)A>3u=p6AF?x;N%d-GM zSTuh$zOkPX)m3&kWz$e)nIqROP3QRXi5&Z*W)3;Nm2EX1l08i(3jSpt=7i^R!`bg~ zKzc0^sq$8HIS9y#Gq{gGQW4UkG$J}2%p4XcP z6N+l6U;1(1-+)nWCvbgnN{z<0upQSu^$@lUhe}Np`ywB{drL8kdIi$qGeRkaS9f%3 zM^}IJJY<@2B9Vwqs|*VcFB!v}@)^v?86J<%`mAv?x37PLx=Vbo|MpP!3Q|)DjmpQ&aCIFt zP4s+ET!8fy2H_ETy-jzDf|2jRu&bz%M6;?^-G*~@HQ^C?DAAs|6$Xy@brO&g$Id2% zZsI$3H0>vhBkhyzsEz)(`Lp}DgMO9g|Cz_>7d22HOsBfl+Z8k`Q)~;2uF6eP0aOq@ z{Q62zM>g7^N`Ye2(i8xKCbo||P24#9J?ac*isL}ZWaC3>D0y%w%|=m?Z!618u;FgQ4)hP!q+ z2#4%oiaulTE5jZdq7FW49~2J}Fs7u@@u1r(a+V5)#Q9JIA}o=1L3E<^EURmSIG$=E zc-2?W7Z`Sile1%0$ryV%Gjm5I9G;mwk{1uXJjn(7@J(Q!0Q(BK;$IiB;=^^+?x^o` zagOGD2u>+Q&vxV3(S&=+&b{DReO-{~nQj8p%ZLun?(v;Ki3RvdMIfadF!M1P$wc)> zaIdJuv8BP%bo$8Fzb6oe#9rBg@JG-KytK`kK=!|vCTL>a@#I~FRPS;y?MDU)-bR5{ zN2GfO72UwGsU9;|BXDH_#u#f+RCb1O-%*FU+ah@Axz>WOugSsn$u7(Z3PP7?3p(&F zZ{vc$*YNCyp;YE&a?Y5oTyVs86t%Y^p$W-7!xzUt$EKX-sFPbNrXa%tvq`mtSGMIp z`)T0@?218Yb{m>%64}Fuf>2cBuBFpa4{2j-MJD-kTd*&$C(_MaXmgmjk5uS*Peo}o z5YZT|K~wP09Bi484HMh44)p0RM3xRnY{KMsCCkau!suvSrpt%|rhp_xGqbfIXKXx{ zxCgd#RZ;~wN$Cd@jXEMMiB5GBxVot8JRxx1Uq_-b-5N)_=IYf>pDQ>w z__nc+#PJnTdeqoHzcSI zYF4I>Zi`poz@fb0J(fSbG=pHcoB1eeqSgCAHz2HoZeXi!$GvnHzS~PM#yKtji%nn- zbKv`X38q^CMG%-?W(hwD!OS7(Uu44@Du$8C&);j|u5FX3TAf94h2;EMJ5jadRGgsT-*z|3J4VO82=R*d{>~MJ)Qg z9cuG(7BS7y{Jh0BpDP zUZ>D`am;*9%-Ib{$}$s8o8|^LG6`{aM>rE}DM}?Ql6!WgSHbis1gt7w(f08&Kq**faa$YWwyLtW0of1Q#oqzxTM$Y!?%Zh0_-b5O3C6hE6-l4Wy5#q zP;0juTYdBbFXF#a0HEt-Ry)F^zq1kH4@1-d-9&5fk^cHR>kJ86eFx4hjTmlQd0b#Sb4ekTy$*NfnG7H)2c#&Q@WlKhAFRF5E);8|%n?aTVT$Tde=z-hf%AAw3G| zcAY~rs+j7jLscZ$1mR;cX+3QcM!pve6Z^I%l-3|hFp6HBPH<)sx7{=re_JVmiRB!0 zOh@;eyeYADt&{T(8qebSS%m5Y%EkzuKUU)Sq85=KNhtrcJ(z(hK~*G={`6H29=Quu zF;Rqx(pS7i=WM!kl;IZnLDae6STu7%p8?%^ZW@8pIVTLMGJo|*4jR6jS)+II!x`&&rDzJ~ z?f|ZIgJ44=a*P)t1Y)y_w1J2i*eY5rW?)+gwXMsKBRWjZDBI4(#cPPhvcE@6mCLpt z!luJZFxSP{Vm1}e2U|c@&~wvhKY1LuL&!E4W|6~UHTL)W#Nxhg|4kWYn7D~k7!oO< zV8eFCjPUSbxHulq^;q2)rl;585(e36L2jS>4!(%S8()s%_IFZg7!uqDVn`5WSg2P9 zdFhrsv3?A%J){th6^;}Vyf1H~-8K?(BG6**7FBOG85x?3IZonM4CdxpBoxakYw+W# zf8o_7XJd9|&{rg~#tDN%2zU1%ynl+4CL{-@SMp(7Vin@(;ohXlvU>-qS)V6o59xhw zAFydQmYjVpZ*5z}OPiN5EHj_0rX7c-?cLJZ6zbrnb+7Pw!xnT5GPE>)GJF2{6wAMOuQqO^r@f3^eru2yT&4@HsgL^hOB;Gy-5{mJ~#7bu}F zX5Kd?$ogc{wWrP}*nM2xW5nzgtrq*Q+j;)!r8qY?ptgn*t_=_C+9B(O>G+O~?ck#Z zNN}#N#qc`N+QUhgv1q=B_7lcg(z&pmgVxV^N!$}S8P9sD+aBkz!qS+FP|lig}m}^s=8JZio&SVI{HlZ@bH!iqz5;G zT@XFcLu8s8V?Q^Vw-qTB%O_gx#9W&OG4hDMzl$*$)m&Y&oB~^rh%S&)peiOs89tu= z`Sbkf;WJTH9Wg9~EDt9LFB^J7zQiaZp})TH|=0ldNJd(${6Y?;6FP)=Ao_cC!E`&`}xPl zH@SD?+q`_}l@z$Y{u*Qdm+?O%z~;}N&t;cghG7_7aKQy!ef8Dt3Y^cA?`0LyAH?x|2h*l_0>+NVPzowNmYQ6 zJja0moa^goJ9{d&&Gn>xQDKomOlkR}l9ZW25pfMlYftSX3v?0&dS06ZQBz^ztC`d_ zIA}e2BBsk87bHg11F_Tpy?84xXrOQ~kCCv(Ej%lbi zM}UKmSj*IHTX^K7kpvDH5#NkdwPbMbf|>k!_GjJZOY+#gCA|2rVj`t>KH2|Z?8dsJ z_hhq6KI!XCY=ciTz~4RiTtYzvZF)yPW;=}X@o8- zv@Umu_;V{_`7LB=2*qY%evtvDB<(-j5bt(y^W@326cljw0joK4hIKDOz=*;Y=Ip9W>FdS-(1gPE6Hf2vn56SUAA;1j7;NpufX2F65l$4Y(ZQ3+WI_V_Fj_sR3@onIqOXhIajZahG z)NT2MxjV@%EJ8 zt1%N~bXYE}BlAgrX%$+dAA4mR!Tn7{*ub^C68o+eq+KO6wg6j2O9Jh^yQ^McQT;8p zU;l8~q8vYQGhf{ArFC!-rqh;4`M0SM>=r001pZmW@xHUsqRmT7NK2cFyVRCc=8BLc z&9288mz{HG|EPh@D|A)|3PJbb$fzSYwKOhxpIbJOk2aKZ#OQ6Pb$*sUENR@Wp%TQ| z8iX}9NwsgyE;;tBCO+sGAJ4R&WK>;$x=qx>TQENJLYp0wnDE-)*Llk+A%wt~;l?;5 zE#(FQuqfq45ikr~pYOt6>!-R_fi+D`{9z+!{-(DezUIWml;w7C_kx2MI;s}$=vJZ{ z+%WiaaV6e23oUKI8tlyVc%4bdqt&XE{##?)0XT+m@+1?)p&M!BU8mJbRtYa zWO!EUlu=TGX>Ax>Hl0bOHB2h4vMDVRymU#Mq=V<7+)w1Ya5e%~*gN9kRCXN|702y2j^m2!s0=7BGa{p+h{C9#h-|XR4niP=gpfU*q?2^I z)7|O4_PWdY{c)?SySl2nI}ld$eV#m>s(b6)b5Gqm?|I+zeZMbF$9LdA%3)T1zHS5O zm$mWSa19>CWKW`qyZf4RFRTK^3UrSfhiw5zt8vE2-k!|#Q=+h{dI9Iv77DLU4IQz5 z9_!k~{@n+7;MvurmV2{raP0j`Fir_zF7QDQ*n@7Axrr%#7q{*o!madU?f4ZRs@lp& ztG77;RPVo_gueEg?%|z?w}k=g=;)xnesuG;uC9(3UU=b6?9nKO`@`IG!$W8|lQn=6 zP7*n-1tpdua_UUy7oBG|@owtMX-@#^A=J4#T##-J3^PNf7=L~LD+jU?e8(fYtYI|V zWMmuOqG#JcWao;F7%u?DupC3?SBUyc!3^+8TG3-AT+$?Uq7OrBcJh>fM>aR}z9pNPUDJ!tWsc72 zLNaSnjF+Ao?>Uc~#B7NtxgYyviHvKAQUiQ8qT9l3^`LKwP4H{RH-f4JS7_h10rkif!rkid8;NZc7Z^$(tx%H`>_XOZuy@$aomN>?1BqWob z2}LZ0?}fb?mfvf|l=iU3^etz_kK;i_PM+h?>ywVsdzRy9PP|br{bSV8J=u@dm_>2! z`(yu~0_~{;=6Q~nBQ#K!hOwW}Ag+y$pJlRO>l*I6t{BZV$Xcs}c^|G|BIBh9o4U{- z(ckSsb|o_X46mGpA4g zxbeUOEL$OEtBzo=S7xM*rV43;^;`(i97m}#4k<=0Ef6;Ne%4+1oC(?+nrEUm_F-=b z(7wHtu2a1W7xRd>}T!XV>1szF2SW!3n~vSr7#l2opl`;mR#sR@I6pUCYfFQ|5=rPvD3F%fj0t z53IJfmiG4c%(3LIm0916ns;y#Bz4IrF;94driqdgGE+Uo`>5&b5LX`?K2LSonweM1LF|Kn+% zy)T53Qiw-Xl5vHn+L|X!CiCi&{d+tNEo&y#Tn>PeFbO`q4Yl8ZK^XL+t4?%*RTxpD2Tfn(>&Yl2iJkwf{Y8ye-sm_)G$u)W1LWg^o~AHcA3g6iL#tFr36a~cA3Qpk3?BA z3?9i>+Ey@ZxrkaCDO(|KsjQ1saBtsqzCZn0TD%UK#w9Eo-C=&-@)Xy6?pfTuQ4j{A zC5&?70LthTwUxHEHfn2Yx&8Lr`NJRn@Md=lfYYxyi64FJ7uZ(z zL}Ufr`Pp~6iuAh*8U(2+7*zqMj?@HuU=ol%-BHWXRTA^69fCxf98!GOD;Wsf8o`67Bdwx=*P=rX;onr~=RRT`27k z`Eo5^-ciq8iyk1T8tGvW#4VLCtUeh=>XIs;OiQshS`uSCn(5-Z`% zl6H;{c5_0gn_k0%Ed`b71Zhjcw0{k-&+K&^ZUtm1sC5zSEnY;_B-CBb-t88fd|fP< zQG=8TkTDP?lae8B+w%-5dprYmD!+om!yQgYjmHP4@!RgX>`a94DFzpp@8rtrE$HeI zwfgaRTNp4+)A-rXe#Tj6orPstTzTb{%$xVtka5v8jc@$v^L*$1KXUS9(g=inDOfiqqkQBz3Hsmv&s zm^DF$FFcyS{hJ7MY$tr~ToQ|F@$JbiS>bK#BPK`O#_`OCanMy#&1IMyI?x85nnhh zH+eRtfIE9KbpN>@eN7Z$OXAUV8i8hGmuZ;gI_5DxQs)&T1CEl2TbPvUNq&081AO() zRXn<-1rWG317$c$;s?EiIvwIr;_M2{D_oO?6-6lBr(qrI@1=-i2T>|>jbC1phzl&C zI2wh3j4MP=oC{G6wn>Jz2+nN3h^Cevyt+loQrMj=Miz~#`wIo6&*U3Ny@vdFb8`p4r@IdCe<$AW)F=V>gm*@r8Xx~QuDo4e!9w=kLGFU@9Yqs6aM^B6J z$&>F$pHKq9|3r9B&lwXbB|-=`4YubTn;IrIUWXD&{Lg*MNu~EYqqd7%dXD0*-s#+R z+_3~E`RqR%-WCQ7;M{Z1<=k`6eY3lf7bjkLEcb?Qe^U^3UFo zbFMlRM{m!~TM;-CPo9kjFw2XG9otBJaV>i15Z)K}p@wr4eeHlddoLiT~|za>C^Avw0&PVff7&pc(xL=c9JhhKh?g_xxAL)*U&1?v znRw2^9hkPl|E?~_D)Td5Gs@oSMOhHZl7VvFKa`{HZkh^Oze)I}Mkj@0OhUo88eYuY z%C|0flJ$FQ&{c`94iLS$0}|;nB1sefpWBHIS7Cgj$QeGhz&Iq+{Zu7lb`+(;BD3i^ zW&x0Q2u1c2V7(A@>XD^4zmx?Ls86xSC`OD7l^EFvl}^nvMWAXnwLM?r`srP`6$?Wu ze7t&#lRK&Ak37UJmEU#G&N5!veFH)U^p6&)y!i<~A6#xzs-Zlp8 ztqzaR!xdl8PLzD$nvZemHMcUH7M=BbT+Hr>vZsKPBLJ;GM)6-ZIJ9)gl6V>qy>p0U zOGPFjcr4VeG?f|<1xlG%K{rDeE<~zIMqNBPzlPx58_@;}6HKHIXaZ@1u+r=>;TT}6 z3b1WN+(LSEtYR;Ul>$rR-r0lERE*Zvk9$uam`=ZpGCz@XjzW6#{4j+;20ci(z_+^< zC6-4G7Kz(y<62P(2btQ0AtfSVIQoW{l!6qvf+_y(q?fp0=@y2TyCEew&{xDmcNB5u z2iIpAF95I0;`~M1`0c^@7+ob;CEl#CvE1ju_E{)1<4$-|k%)MKFW-h+njho0gGbSt zC`QBPOZDqn5*Xc!@#rRfQO7t1HnyQBvlk`8wDCRM%|J~ZbLaKYRjDvotYr;{w83um z0MhACV{(kgHbOQ^KR1)D0m3RoMva%KNg)~%`OA-xZm`clZ%cC8S^dnc?#8P+-r(4M zpPcJRFeUuq;4JRwojGB@)D^?n+kMELqlW07^>evm#VhGoNo4ew2-p9zGu*}J*8MK) z^8-e19XB$b{j0zsu{gxIzuCtuBxgPTUp#)sH&G6iz4H#m5d-!*qPjG|lRx@&)_ay^ z@&1Y{i44XHuIVHo;>M(V$F8=1)T0wvp)ss1!$RLT;E3+#X?rZ9$4_^ZGDbWHRY59( z$SKWv3sg$LJjP4x>(kJljUr$4LdvG-`Mpk!sG6V?A~Yp|$9A9}7|wjsZt^j-YN1m< ztRhJ2s46@gI`FUAhdvNT^v9941y+`}M3@qNeWbws2vXHSp6s2+R5#p(1jQXAdR-lM zgB#Vh`O2~p=Qc@g=p;F-!kMs? z(TPZIE5h38gCY|WDyf=2TzmR+%Fi^+N-(E%nCnVkD7a`h$1u{g)(Pig4_j=C^eA-7V45=L_W%U+GCp3c`yOpa>9u*6)`=Yo=Y6_=j8Wo6_|iu}G$D#9Ww| zC=(=~Pk6BjkH+j2-+cKFzIXIxucGaD9*!8WHyEm_^4I>~v31Q3Zu`$WdGW#ZdCxOL z9<;%USx3T_gznx*eBm^bE#(Ny#=WH*_tx$yF$ayh;+(J-LSR(|u*s&VO2Dqrh<>vU zY(YY;g=UrFzr5lU$2_VEJK)KjEZrJjzs0E+4AL+s!P??QJib+qPQn5bi)#@n3-8Mv zcy{y-^?@l76XbO#hb4grw_ud{Ni3-YRn2N+RI2A|VOX|@(haKz;#OHMpRn zEkmUqxrM(!-dz!?GbIr~<1-!&D2oX}M64JlNkj`H3Sv)IiLPBD>rO;DV#Kj&=bR;W`?Cz0pHR zwVCk8L$q_G%BL?hE!%@p;@tu3V#5u0qm6QOq z!Ko!P3)9B8Za?02`yCR7oqw+@FUA*h96_Eo1fUF9SpNvXu*+HM3!CJOQp~C#Mq|n7 z$*Mx5r-%P}ej6^2jn^+ZEBQ2^Jncn3eDnJlil+^o5X6_%;eEMdw8V@MS#cz#KZgIw z)(MT51oXi;rmB!!I4#?Fjc?PGs73;rNGpa&N!|7^o1SgJ{AhrW-gkU@pGR?8_6V>b zh4U|#f~)^>2Dg3uPHOx^{QMiWjLdG@V4UDX+mNcD|6?m4Wf8h(BT6`dV%SbO8AT92 zYc{Su1I|Ljv@s3Aw?8r+_cQD_D(KrIs*6hgCvit%t;D$wI0lWT^mG? z>g4_iKv9V=Z*(NDMrONot*x`ZkRniQp`@GkyA52b#ivhyf&cp3DWh$w3btD%)m)CQ zTAVR=CqZw5w%!tU4;fgu^^iwbHwUrhjU8vWkdAu zDa713<|zUGt9>aqb}pbsjne*5i0BqKntv#B9dlYSkyDx-Nv@RRi6v)M5M9y4+(pj- zu%xJmHIb3Rj$<%lxDJV2PAXJcMdTmqaR7GIT^6<=14Sj}(Y3|&;RznINAFQRv z@DWgx{Jd*nR$nw>vo{gIkSfOqyHJokHm=Ny5TG14%&qP@95`TQ`u^l_Ees#vZ(ZL< zSElX-7KY@!k~Th2xg86^%aICHCyM^M;+pO|cy`@JW)xN7QZ?RFx0F)%KOaZ&hyi;O zQB+(s={aMKSmrmO3a4IF`ekfsJ!V5O>Wh1)?0reVw`MO^sSmTVC^M;PeNl>5x8?tg zt~!!aV`16Z)OaC4SGjThDV%n~0jfizD_mW(SvhxCwgIzjQuQU!RK%?xzmwXMVSH)o z`k!vB;I?FS=G0VuF|lJCu|gh{a1!5(`*5}OW7LL7EU1}yLeAlXr!gY)mG2`-6Yopy zxOVq}B1p`sB(bO#OI7I_D&o^0?C0tGN@(Bd&UhYmGm?Dy+P!?>m&Lf+!Z~?&0*8r; zB67Xlq{MaaAjT=qL^Ry3Vty-(+R;xyDhSI)Ntnb=ZUzCVmI_kM<%qc9>}6^ym==Fp z-^78+09qlpHSah%iXg99Gd86-?91jT_zGMqVS`U&b-0H2=(}@*04_yxdT|G)RQTNP z6CFvYOgjQYDhyhV7ys1IGyv2E3gYel$VO5TLYl)*B^1*n+ z5not~d&faYj+Xq$7yOZ$LVR&8iAA-JqI;UiAp>5*D`ydSY)4ioQVNKi+)Qd_9(gW7 zUISoQDm}y5IW@_o!h^*CS$cf60jUa7OC0Y@*_%MwpUU0)vm~BZ zIuX)IqZkQGdNy_8YVRZR?%K)6VUoSC?8B0Osoc&9!45vYbLE813IP-wh`J;)tRnk0 zL{(~xgcZnP%?Y2nEOsVBT(fTpU#VY*M=^7YDC6ueHM+y>O**04fSSUVf~Y;JNRpHq z2aP-`6*)0@$pxitTvoY*Z|zym_C(3}6Als1_Zqcy0@^6sQ=MqT_6@=v`=95I{m=8a zQ@%`v_l-B~ZxoKuSPnB{i4?28_=_y#1+aR{K9+pyzcEaUKRmvkXMXxHdQTW(*%{)6 z)D+5&o53~rUk!k}t^_rl!uIQVu7r~WJSO}l{Vs;yzlh|VDy)DTv($?eI7!W_KMF72*$gVFDOkbWiorm^iBOm(Gs_P0?2dZY?yP1wtkSmI&&|JS zhO}7l*m`Id8beE_6Ae4shPyX=`P!%EP<>SusiqPqRUq$$z|}eln@q}rVZuW9pmzY96=Vn1lM8HN}5=J|95OqN;L#vm= z&f-IqSSY2IW6W&9BZKvvv*}!h%={@7LVzI^8r{SEX69o&Fz-Gdntw09Y=@t+}60OZ${>wq^=jqyYRZ=hzE8UVObXEfBFWbUU(P#${*grzRo^edk080mLlC6dgl=8Py)rYIrz$` zl+9@;&g1t#co_Y_FsT`3ImSjBp4<$EMbTqBQ2XP==TwncSc4KX98YDu zCo>KrnoY45ww#z8F=9wZ&arHWn_U0MDX6NAl2%+dO#w}#V`J84uMDNv<&Ziwg`qQM z(=#my?ZBC$umLhGT>A!zotUS8QaWDF*uG71Z@>zA99HMJJuGR%v%mN8?CpLKg5Q7W zEP5{VV2mXMxFG(OYV`XDNxr89<<~k|cMQA8O{%HHxyO))p$k2DHg%zdlNj~IB<55h zY>T3AbfTp4ui~XDumWzB;arxgod9i99Ao9k!a(xxwJ-CYvNjBw+wRDT9Wzxeo8}0w#G{6H624EG*E2GH|p1LQgJ6< zVdXf6wuA#I9|NX`zs~&|0oBNOY4d%3{A%VCT)5>Ngp>^0?(rASpvfI3V(DC1z8kk< z6E$=WfvY?a*iN&xmgKmc3H4l7xVdBLY zxVQJ9#troS0~n2^qvuKqNt5E+)}ja)xO@rHTfmde-0A2w6ys-@9ykh8eVNMHSR+r=)w@UraVq@(6Z0ac}xGaTJ>@+#E0=;u6=eXv!F>6K* z>A59)IlpudZpFr(_Ch>LoS*!9CoBJB0p_I&!Z*s$%eqeihHx<@E^Nfcea9fisUc+5 z=onY%aemtTG*x;G8>iOQUA;5-pUy>qz*Yo5?p)02B^~_!;4GTlVU`Db5J)WPkX*jn zwt~-1TTe(evK|3d0k1SUv$&lH2O0|Z(UA0ibr7w`;77;T@uAOmQ9C_>Uoi+zVDzRA z$)M%pj)N_{5-sDYp;}D&)(ULxi}t=_4cJ?2q~FH4|B-bhpWls~fYp|Pu7X>0_9}FT zJ(iqZNvf#?v)s=m-@T2;*S8YzdARC-u0$P55cGiB zLU(VZ^f%8_a?1+@9&61QF96rhJavuoK}!73wPj79Bh!NSg?$A-L)>tTgOc{H6r~_B z!nb}u{-<^kx^FX~zr5neIf1j>))j^?T$pRT#$QiLNSSE+`XOOr`E-oBl5A?Y5TGiU z&E?pBx5Lt#$oncWAgx9BkrQ9yk#GJV*Z*ZF^(_fhbz~DKPj@^bm)HW}65v)GH3%C= zwy7b3vUCV#UIdCpu9poFP31LU5=hmB7<%6#jOk^#{EmwKSm>JM-IpCiOD`ENuGpE8 zYRa@Pz^z%Fc5)xq6D7!~D0pw_PfT#kf%lg^C<%%8#vVx7ndkr7rq$HD!}wH-BGtsL z*j!Muhn2erF`o`$b+{;2l6-&v(cIKEk1y>xflsua$_*V0`D)t=-nZp!>f8~|E!~&* zt8)p9Vl6qL;)S|12^&(F2P9AbwU|$zHH-G0ZvLb0g8WpH0_T%@u`Rs)!a?<=66H-&} zx$`-v9+zcLJW)daZZ%R9)zZ1QU@pH^B4Uk%`}0nIO420wz!t)1%tmUCSgf#|#M-gpM3FQRi9G5KFq&wb=Ux=k#`a9) z-HbxIG!(;`RMmkPMNjS|eAb+_5fBqEsidU%w`-jKq%2ScQc+0GD0RY?7#YkON z=9If1H!grvUrd##Cl7+pLTFAHQfZ81KUj)(Ac{H=$13(>RtGR^0?18DZYdp2A+e+( zlg6N3I-QmmH?V7?7a^dcHpQ22*hk}xBt5VAc+A~Mky=114{a8(F@Wc|J`!?* zMZWom< zuPNgQ)+L-;U4AP=Cl?B^2ZDdugf|xZZf2UNw*Fj=ti}?#Ani zBL)nRQf3*ieAwGPz+KP0!o{a9&U^ez|8XIg{?Ef`2O}6h*VIdok9H!l%AE zw`0`j?w^dTP|cQd)G;AK+pUqDRe>=rh*1>)w~lx1KD-+`5w=9@b(mXHRZzl7EZ-RR zm#UDMQ-x=1cXk~wO(E4#fQoC`xOVoEoL4=u_heYOclDz6=d$F4X=fSIys#x|=MeoL zS)NwTmv}ZE!1uyl^xb`MVsn0a_;{l~{e+=L1y$qi>kCNZ5 zK^V?=V3zv`ubeeXyB7lAb8YF;+Z{^=Ow-Bm?k&+!oZUTq#PP!N+kzv`OH2P$`P zP5Uy8oN^<1kd(Nd&!a34^ap&b-1r_W1m>nPh^r{moqLrr#VHRn-Z)wccAFRFZDPPa zJpB|tHT$eLyBn`Nju)h;%hGVaJy>AYCfnmpc(j6D#B)cKmd# zs$A)r*l~>r+s3`UCv(l%l6s7pmHGQF1j(7@qai#gohAM_Pu0Dx8{g_Zx&04%CLvd( zVOSWmD@Vsc6C@T*!wh*a8%v!^%C9DLYgj1Z1p1C%TnkIsa-fo>O&vMwkG_bHp5y1D z&WfUU3?V%_$%Yd214GUNqhCe#kBAvdWS1NF-hKEU>?HbwI_$aG1R8BmELVxcmbiEI z55lobCuEcdR4TR0;_6J^iS$6cWKhe<rXA&~IWo*KUX*E6te6_0M9s}|uz`WZ^7&Od;iewZE4viT!XWc!H{seUThg-2pxKys)w}i)w>X6q6F4*3{)%9yRqof^8IQ6^|b^+rf1Ud1>(S!W4 zXBKz%Hj|p_9+OYOIH?HzA7Mnwi4vF;O! zqK1bM0SiJV4CvUce$?6&(r-Dl;&^co#YRkf>v>-ag1@i)Izc^89^5|x-ZBR4j5E&Q z+H0>x(=@*Pm0kRg-6%5=09QV4^%j$1#@QhM)Z46oX>sV{&QwB@;$hP*aZt1zZ_P2 zBN)x)7!tJ3L9Al$#9oLH7}Y^U%s`gJ2gCn;D+u>3AkwIS4j7*bL2t=*Ry2JIga zTys67&JBSHQdh7;D$0y2UsJx;wf)5iDQ zZpxaXbbh+l5zv*u-@G6T+`A?5ynFyPoW$#MVFlczK2XHIr&0v}*qKquNUaJGyQ~_9 z-N=3&{9r#)4DmeAfrKhEkuhF5#~sI=cisuWJ@?$hrI%ic+wErh^y##;w6JE)8dj`WaoDaM@Vcof z_H$q`f6N>p%%f|!Pf(L;492}ygy17*FXz+$@Asqcjg}=R6_|e=$U+?FJug+Vk8RWZ%MaCVsY)npB+7$ zQ8vM03f)*XzUSI-@9sl_qqH$XG?S9}vIdOmAYFI8!Zm-b8zoT)lcI+0C~?C{ARcE= zlTD>WR?c)@sE!fR`bM860eElf#r@Yo%()&SD`pcpbq>LMHWNLz5i2yNlCw)eYSLjw zRuo~CJO47!RbnT%5Yr^0EsD~daBjdVFh)`mx_6T!T?MGI6iPgWX?QqhYda_XBFrvV z8Ah+oj$bC&bC*KmV-<*|gfr}3tAGI$bmL`T!cV$b->$ui)4p&cL($x- ze6wpS_`}z(n6kq2(pmL*U)oRnnEI@Jt;v3rA-{(kKl@&4N`q82mJ{xGsKfE4^|_xP z7baBuqXh5U?CeEJhxVSBQ$_5A>DfkPD#mM~zNU*X9x_QK(}Af@PE$B1r}*V0A5#_L zOX@IYlwy|oaSisM?6>f~a)9KlN|Mdx_}1)0nAugdge0R4Ilg(RDMU`6nO%}bSFyc1 zY9#wvQVyDE&&80lU6|Ew1}Jp| z(kqZs;PTjXt!(7fo*nf4dllE~3!HJQk*Hs@X}EhoZB{vG!r`jSjAO6WvkjzTBWhA4 zb(OE~IgVe=e3E)s7&DIt_^NEt6bP*oetcYz0}YhLVXTK;qh6{6nM#jsk%_u&IL(Y4 zU7e_Na|uUpHC)_y_!jbSN4%j1Y}Kk&bai#*ynoF#*KolF7i9jgX&P5tap?5Vn{K*^ zn{K)ZfP)7Qq1tL1PyF!HJi2ZN*Z=8B1|o4ZRpI?7E#&`Qc+w%4Cur(V()EXzFlvH` zlu7)kD$MC+6aJc?_~9or|Nqw?eV8wt{%v4{ral3rB=FESM8eGa-Fvq8U``7;GE{JQ zJQ=|#QRCILH_-|^VL16|9^IL^4`ud~a`H*fw$ZvH2-8B=Xe4WEaS!((g&?`026Ppz zC!FJ9LHf4yG-=cX^M*L%i|g<|zb7l7%T8CWCS`%D;OZD8IjaCU2Ap(;oK?R>Sr|q% zIUd!>@TdzTSkH&jdpBt!f+D&6ybXNz3!B-Q&KLVTM|11F4NNTQG~VCv`WDZ8tK$v*iXMIR z(Fu?L_P4*~{`>FevBw_E7>wH5TH4!3^#S+q-%o9A{xH;4S6#(bS6v0b(xp=i_l?J@ zV`j1P=$S;~Njz>H{Z;O@{pyt;abf94P|^eer7O(P^s<7#>X6?FZ^YvXPWb%Koj0#a zudW6sUJ0swF+|+RWwn_$?k!!T2JCQP#3Pu>zEmLy;SeaINwoeL%21qCb2;Ai9ggb! z$TbRuFvqqtzTJH13W2(BfY8I+MswVr+(m3zJ;~Ws7#9Yyi=6SdN_=QNk*PZXBa_`Y zBOA%SR2=H7a_ zfqK#a_6{F1tWj1L=3^(k#6>O6UTo966x+MXx$E}j_+7)?Vb1|~uIFc#>!_^>tohE= zAQVuiCy>DLiCuw^E?$5|N=5V8+(Nu5}lL1tTXo{ow zZ0Gz615K#76&;}nLb{Kxo?d5sTq2vG^cKow{^JS%hIUwra=v}>mRVyw^w2~6;0Hh8 zsi&U8=Nm1JaQ^w{bNS_$^ZCzzp3crrT3cILzI^%X+7}@N0pAqJ1rxv*@bR#52e*9v z4?Oj!)kKSQB8Y-p71w?AT>zuT%jt;ox42Mc!1QSosDI=rIvg&%+WoPS^K2XGcVl>T zM0JqB16znMuA7`qSg8GiOMM_|5PEQ{bK00gzI7d>rk7!t`Z9vV@kP^c@94?zZfOcK z%^Vfd2_I^m!v*`3SlU2haV<(Th3#`WB#pR%`~J;@&zucHU0z}Lnz4S;Z zP63JxoYds;9T1swqyU*v5UK?48&_6DrimycTAVq@3;`}1iYzDRt2!Ni6+p4c4^Md? zKA+Blk_MjW+RQb^T_h}H)XJt6Zlp3A-+VJ|*}t`%L!JLHE==F1c*_{D&wS=HB$G+b zIO7b!A#(iU7r$WMym?%F@x{!YI~PsUxbemt4=V%l^~OiOcR3&V-sMOsdHm&_-2TMN zY;5Z!l{PHpK_B1!$T=K4r-3j0;-10{O<)|%a(~|NnN$^$EfYxX98Mf+xNl_7NKuhe z5;=V)YUl9bUF8aaBb40jyc~brw()H1CbnXFCTwLFdx@@S!vEZ!>>6r-)K!MwJr~c` z9!EjGE<|!h*=YXT$Ti2@gRrC{|1@HleJ+MBT$nj718!u(K<^j?OJddpk-jlGgz1EJ zJQf%#=##rGLP$#I4f5o}Gk9!kZkmE!ZK4z!XB*5$S6&0|5f(x~+|FYqGS>!54P=`K znkFVb9FDJ9S~#`G`gD}W{MQo#q+6n$I+%M9$MBw1KgyCkvt}Wk@d19_`WS{t^XUay zRkU9Z?{PBdsMHsg^MyI*v9!E-@(=tIKuGy!d=;-hmM&e&+O=!n>~2hoSRzHzG$|?a zvE-9KDV!%W$;7Vj=_l~m4#zvNB~nu`OP$2#2}_~8RbbnA*6by5Y|}Vm#%nidzGrsf z+0vbN%1zEFBXY(Vino-wwsfNn#_(+IPVYYoPzA&1&2z{VV^N@gBATM;@g3-0&R&sK z>?LwqOE#l0FA6ajB*lOHDc?DmGs$UB@?XIXkC4F(-mLE0#Cps%(v8Ru-X@ z=Xy^HHY);M3|lTX#zt2Ed59ZHf%QZQ>PZ9Oldr3%B2!>*3^-baRfV-5CujkI;8(|f zf`uj7$-fCJ#mYy%gF${z^7W1=3P2Ycr&Z14OY_br@CMQ4-!5|}yoEfle>Uvt?&qo> z-%0PVL$~(0G+e4evQV=0B)=4+sg%Ks7USO5jS@{^G?X~X4HG3Hzm<>&hf~ zW7#Mv3uP#QRqSey?8$Q?f9EGJ<)i=1SGtHg!B(jaVSYXLfk6*kApxPSir zoU`#f!glU=AtlIR)ybJs3-|GGP5#0K%F$tmRrspfbR@w(yY?1tKk?tF@s2(Z9@Rxl zu!gO}`B~4ecL?$BlfOWXuk`i3$lDx84A`3h+qSv%d;dqu98LF6q;gf@54qi-hxoW^ z%9B9}CvffRLl_pR#uBW`TzRR(4qN6L10kHS;#dMl%An}!UAWo@k*b2QY?968j(W!- zFxAZJ5Q$k8#7>;yq%|lCW@QnHc{SMv-mvg)I)Jbw(PQhdLmpCjh!oiv<1y(rqCSDv zHb`P_6^Z%N(o}fiBuziE9ryMgCl_u+u|ydq#fV4}QL1AbOCz1EKA!KH_C3T({-8Ehk6NCthRD-96 zrjfGMyypl3qQZoV!q4;Jj`>VP7MUsx+v3iB&-2;Y=VZM9fW4m3ghHkFpF78E zDtMbL&)-?N^x1Z2`oAE!r>-D|vh?g-zd6p6Q zUfPeVeGp+uM}^*!xOes8UAO;`FB?0x#o31%F&LWa$Wti-k|vlo?p?ij*S0&nkA)S8 z#sp$|9DEaeSC&LcrJ&eAJ)m=#4tPi;XRijUr>1f@5MdN7Rd zncdW_c?o^Rz{o(2{!@TC5$wlHowp1Hxh;V8XgSv7CHV;_IKeS%%uiehpvZ>#VFH0T z!5&bgMLcVk=)dq4@^G^ zj>Mn`NQMud3xh+%0qrWb^Zy|m512gK7|~$ za02}9>_q6p2$3`?dTMue{V>zU-FlE@a~WpsVP5@7>4XPe+9Y93$)`?xfmL&N@yXvd zVUDA13d_c``M_v$_f(N@#uNff3w{3}{?&U>(@E^%D;iMG9kby-5A5weWb3&7FT=vW zu>;@c4hC+S3A#iY0z)l@@$}|&NfeRH_DtM+`tZH9AKh)U;_4U=mnAXI?L}_)Ls*4! z1JN8ugjFolF?w0c&V+^>)S%eR@4tukB1xCoSb-Jpb48L zMcr(SmZ4&28OIU4#hB((hFj5DRMM35eyy*Rn~(cfzVOm*^cfN4_^@jhMt(~fsz-X9 zR05h81sF1&kuTBx{AkGsnP1$%aTPPUZu8yjj`UFO3G%tw=W;^j8!Nr_cEu3`c9>yW zHi{4=4U==e_G^ZtiOjKwUfj;tE?>#=IrZGTdh^5$1-*z!(%JLLm*4anX9>N15UGF| z?_8KR?w!4d-++PRjZ7#NBsqWaHr}^%Ggtq46-Kfn@7hA$Fn?5vO4=a%M(V-kHg3NdL_J{$JQhBefhiTHxgxYD19^>1)@R3zaIx;8 zJC#pP%?XoA1gQgX?9M&>Lsno_=gCt$%*N}DFo+7^*WA3Pep%k*^NQ#RvJ| zb2l(#jMGF+M-z(G1gat#G~M_WgA2>|a{kQgs4HrjPzYgWu!fsge6ry2BPfg`2JA3l z>)vjzxb6>=p7&pWe4H0<_$=SQ;|apCiPf#8FOTtp!?gPiB2VGU!`6PjHYZ3l2B-=~ z_}rP#a>HXQSiip(Z`lA^Uv?6=1dQs#v`;rOJF0@n%31iIZzcNqY55J13QAoPyTf+0 z1tZCfVuv&YR+lSnye3F`)pAShc-#B1EkVjs;UMUL9VB%@F?iBy7B*lC>{cf?QW;KQ zk9g)IZ6#E!$4Y3L*~5ocZD4+&4^s-RY+Xs70^NC`|Gb+6_YI-`cLUaRSC&EtfH#?9 z+O_R`dBb#cm&BiHM?cz!{gn!=JqC$N4}|hpOn5aI8NS!YcsYY2ka$%Or&Z14le5m8 zR1~+`Tg+|8f0keGe4H10cVHpuO@=cIi{nGxTvpjir3>8NRd_=`N2n7htsKE?#t{Q{ z7|=TunUe7WkW%u*hF#qE-7ERb4R^75Pq!mQR~qC;pE!^IzUvv*Y)>nXyhcf#iIA!< zuy>* z3cMf>y0EwU9d!%K#qF6Jdn3asl8!P-Q6z+-0PxnS(ND6CGZjdl&sBU z@=`*ijkX2WZZAqpx)`XGnF`FW-KYtCii-zNzZ$Q0Xsp(1Z!uqA@ZQYp-ej1YS|4F` zZ!7!k26~IW#q5eB;lDQuM-13uzzugldr0Ri0)jpdw|@EZoMY>DzCny9mr8imOahPW zfDvAWDu~aocBGBo2#lMFEL-7k8)jjc&i;{E6=3jvi|}sfK<$rVRuqv~G;L}nSPlU} zPl6x(%Y!Va>fxZ_;iApwWR-icw~x zpz(_Q|Mt#1JgO>f;J+B% zi$ET;Aj7>-WI~phAVfoE*$~}zGrXYX?1>tZtwsA872D&a`iZrM^I-p}jW;wnB^Yil zEx`BNGn?F25rT8l8!F=1dU56MZm|?zI}FEwcr-=ejG53VEfW^p3A_ za?zqfC?A_f$))L3oR!+*$_rrf;Gc0$r#CbpDxMA-b1iggJzT=iE!)wQCVnRo(6qUCVRQgsVdofBEW6T$;dL>0fa;FN$ok z@@nWSpzR7p+Y*kpE)vnnh0?DKrDp{q)uCV4!uCXm=H{#1EiKlcLEB)gz?#YCJ=uJ` z>PL{TNJw&C9RwV)~*t{~sVv^wi~BEJg-Eia9jK@`3Z9XnEn@1H#b!XD^eY9s)*`W8jiCl! zG8?oLj8sz1V8-(wur0HGIhdv(fOqb{n!YJ4p? z0F$32%;KWVW#N=5um}(8*+rm8JqfJ_LW2Q^%9%g;3jlUjL~`Adi;>&IPD~CVL9H@1 znzT9vW&i*n07*naR3ysq666sJvbI7Db3?Jd52NOn;q73r8<{I4SiP7s%Q5AZqlH;; zb&c^oXegx)BDU|K{7(sJHnl2eA85aXBKK4$#g>FwoxFAPr=-Ld0U?O!R}t2;DJ(2x zT8CRWcGD=oDaQnoDbqKxY-I}O$M+z-nx3jm&+1FXL-BY<)mLK8^&a$Y2}2$;p^h!8 z=KN)Z<5h5K z3g2cYH?F*do+ZOCQh-MwlANe#7a=0Nhz@SBYx?TTE1=8_QF{EDH#W?Vr>1~%h0y=BFkzs2CHdz~IDcB}s+!Cnpf`u$jT<{^+svx(9R`shh;Ywwk`nwiqS7oLc5*DM4} zRAhk}Y#PM6C~=yQn#wz$jI4KEJp_nzj-j|!yqm+Js5;M~{-m$;DMzGM)~qTzxlub5 z;r%8W%2i#7RhR7*z(;u$II33P?BOP=FMI!uqhf|ENIS&D8;>H zP#sOwHHv$1*8ssCf;$0%OK^90cemid-QC@t;O_43?)puh_ty90zW4u~s&k5B&P-45 z>E6A2ZCMNQpf8N3NZ4@Yq0PMGlS;+Z{tnRhR~j0Y?-Ss3#L8uPn99i4SwvKssZp3_ zW#Z#|7TI!k#_Z|q2BGRtXfx*|p2LV?m3RxH9u}Y%$I5?7gBzLf-Iml@HcMhTMM%|} z$Py%6&@{_g|BP~doun4xt;4>&F|}1LDx0~Z#cfc=5b{<0CJ}94Pp1@xYn`Klkf?-K z#Uo=ZlD!ZXg63P24JLmqwDN{09|$%>OFZqzRb3u6|F8|7haK-qPrQ%Fr``~h5fa6G z(*EJO@G6Aq)s*4nQM*K6C-;j6#W^zUL@2>mKY`NVj+P7#>AWx8hOq%?_#N>??NDjK*wAx=U=sDXxFANuOp9`m9`^F1Jt= z=}{cj%d21OX_)Mkb@sNC3|}-4wB+!;G8LwMPKmvJRivuf#pe&~UggABu}4n~5ZPM5 z-ALp>N!9R)IC(u?G<_{cCUM#?m``12ecEaBzE2PnfH2Ljh!P#<2tHB|T~kW1r=BZ& ztXOEsfQ9^Bc`tx|0LhMg?ALyxuBr6+=nDJ1{Wa{h`HWQQQiP`Q=@HCPT$lo@&{f6D z&5y!|Yv}QEQpEfPy3TW#Q0N?!&ub5Aqmdrbbhu1=RTYCJ!zyM>iaYuzpC|n5ZV2*4 zsVl-4>Wj0$Lm%0z_s>~Q^-0`qXAjQ9%$9R?H&BjQeX3@=Zz3v)Ld`rdAr#ppR0OL8 zvV)Agu<~}Z?cYzcO@DhA{LvKTRk=BsaG!s*Q5pg#9(r%Y-B+5uf@oj3x2SK38T!$f z`l?9%GIGL^522=9K%y8bMzdJOsSjE4LP6DY(G!~@JJh(=x(L2ui^n`C^gMO3w7~#U zbWnyH@ncrn{>vFf#rT+zyM^qr{|y}0W5@j_)TY^GHOEU38A~kR+JYXO77Z7wyDXbT zXmD}-Z@5rfv9n2{9>;2Pq{@@L#|uQkS9-XX&ET<`94oj*mF}XoNU7?aa+cjDY2yb! zVnc2VfP9^1qq`47c6Uam6E{%s6wMLttRG897)H>$^Fo5YV6So2nv{+%^6t)&&B{E@ zE01s3E|R@e#L*RI^7DaY^E71RBNv0TU54PIl1ZsXmU?4iTdZyQ^P(Nu{eClrN+lWJw_ zPJ3dNLOBkg;FTINJ`M8R1YQdk4&^Nnn$E^57CW8m0@8Ray(W9YWF1;|=S9}O zpL2e~ZNex1jM`SiFN^L{5d1(@XFX85oGm-jPAQPi3YKXX3&_^2t1iyQTf7|8l`ZJx z(o{+p$SF%dY+{%Dvi{W+mKtrRCjeTl)saf8)$y9(6{usj)ZxM2=5lGg-s&`#${Ox; zy3o*SNggM0xhL~xe`OkQ{^D{zb_e?xo8>D;c#E;?;)Q9JhxUCj!QX`?YB3nbGS}GL z)GqyB0_O@jI>unX@T+O&ur~8C<4>1gI#Mqjrzn+-#61s$a+1WU9(5K^`ieKB8#%#@ z1gHut6JQ{@QmI?momfbN@dd?(2Wn-?HC9-v5ik>WMxpeVb$pM-s5WeA7C>QLE!+?a zo$33|zw|5be9O@wEEHo0?}fw0cMnW^Fq}&mgtxciz-OEPyie=MX5~o~;rH-WhvcMr zQxU}UwQaq}W-AjuP<4zR-`y-y5xi*lyT$CYK6m-}jh{L+`xnc#?E=il%DYcF%Z z<<8OE^|Qw!h4avW?W~lFI|oKA^t3xNF4*DYY6G-RCL~Cevf?hQot>U5f^70_;qj#x z`Y=vosh-Gj%2rc`@B@7XFPwmV8 zC}FKiqB-@dBFd1pbSpxBJ2mpv8dKqJLAP2AHUQTa&x=gU8i1m zvWl(RbjBfZCuxDMqM#>1f6Z7PF)=xyEp8skXuTa;!CX`ejq2cG^%77mR1Qn1mXp{=X!7g=Xn z{`c>bJhupvN%XuWABW>T(Dab9q3I;O6#hb7728FrgXd0lX2g+}0khZ1 zdXEVqwowo!2QK>QA`xj(qlGL4L>hs5OXoOf)*3@u;bCDtS5 zP|cMbJx+Cmo+P~2tNnqE;;>*91v9F}F$?3a$>7C{?u|C`5=YC5TO$ma_qt(f$g1%o zQ;&AO{g+Ga;gSt6G#NtMpwk36LyzQ4T1CK&!;2?1PrRuO=s zj(Efxt1=!@F+FC7g?;o4IiB&0lOoebgA>tL;Vx!j!|{>nN>z11xB33uWLF+9^|FoG zY$H=8$VSU$=B~jet5s2B<3gMJgp7>wKg2&&yS#aUL4?oaIsE7dz}{trQS!4Lcu*V_SQV?+%6Bc-TmgPI}kP(`I1*Eqv>OOg*1rIE56 zw(-As#g!Tu+s-s(`-qmr>ID1yabj`BA_=Bg?gHI>qk@h5dyHGnn`-8s&{}@Wn#H@0 zHtO=MP?a&xeZ?>numpWG(e_{SQ42h9#QcusD1F+Fpk~_o>rC&E+UwnM_US45T6;9( z8c0Vh4x|q>>NJjs`T}a79dAVFEO|~=t$oQe2 z+QYPtusdQ9^@oyx(Wkq;g#QTu$mh+)~hOFRSJtL7+Ip(t9K34v+ZlDp8mlIk^k$U^7 z0f9eFeyOYOx>2ErvWon&*P=T_5UDkKMWOe;twR4sAOcn@cjee55=Wln_iqyCW0)Uvx}B|Qa-Fq`D@1HWzj;TrBh2a)VCWCM@LJBncg(4 zO*3RohypNft>s$wkbCYrJj-2MH^wqJb{X7>)GX#@k0WeCeh>V4sIu(*5mqfnyRUUZ zxVC6j?$G40nYv87bA)Q3rNTIx{=XfL(VG64atp_bgxm1Phpr%jb{LJ~mb0bqUJ??j8R99G?|!n70q z&AsFaFPzzys>^%!kVcl*Ls=#&a^c2=AN(!yvFT!E!D0_YnLC#~VuDj--xE*UGRFJG zdzxger+6aVbt{Jcs5zvrJ-kHZthp^G8*8CyiitUYJ;P<5RW9q+XSHew%FA^P`C(17(WUL!kDpHIS8Gek)x)s6&r%(B%U+TCiqH}j* z6~0MZeBILhou#A??ja@Zw8&3o?)D|v9DpZIXhhN67kC4gRJ6LRS;%2x>__pPN7 zqNwxTg)g61UeR!xMq{e4T$2dxsSGCT7ZxtX<-RF+2M&EDqlDJAdZ^Sv)?Om!N@F}R zeB*N3ML5A=7GylyHoKAL)BI$ar`k_P=;E`9-vv1{Osnl#_T_@(kF3|`!@=r=gWcra zm9njnz{3$ti!d4+sf2LNS#GyKwJtsA3ieWv5?`%oa{W5H2+QD0pkfl~c>Jp*W)d-8KK)il}Lyw6V|#4v=R zKQ}*>e)?Eny?<;jKzj50Q~12F<6MJ#R-yIkKV3Gz;+>6d-TjpV-jJ) zU0AcH6jv7xf5h)87iN+mBpo#=WEvxg)zml(?rfJFa8y?CR6F@x?_T zwx-s?&`2k4LB*@o)5Qt$U2I`<;oizXex|4^m`Z+SZXvS3`+QSSZ7JNu2md3O$~yvK zjW%=_{JNB$F_%nJ3b@fB%eQwFsY3EO8y1cVn8b`*9Y_~xf?3nFz+~Hvj!$!PK1L*2 z(zZCO%Jyt8>Z$=b0UV`3cW1i}H%*Cdnki>C;^x*MTaWf{eWD8r+8}He;9-hjWQFPs z7>YSHU#p-w_=Ijx$WQoC+`DVdEFd3}@SbE~&CiV_;>Vx-!uwsX+Ql=H7|Tn-ALIAg*7Q{raq(Yo zrG5)Tk97Y6%--J<^j`ecIFkf$sPKdWGyHqlUB~s{ni^n@mw56nlP9PSnA>a=h3c21 z#@916&UlOMuHZ;EnUXPOZ%@_;G711lNyCyWtHL(kfi^|J4FQ)x&n}uJYVAJq1`M7#Pz5+B9029r=~#W5 z+jUD?)qEdmX!u0kk%-5RbqNBB$dxLygR!uUkh zmtW-`RIpNZB?V(>8KlS;%}h*0&^Hsvo-EnE@T)xWo1thM57?Lp zNwKAk8kDW1^sw>i$|BKyMBLb%`R=KjxLQBj(jRq(+~(FXEtWc0sBQ^lsrH#e4-R0} za$RCg9M6T1kRIwMgm^JqOnm0-nPm5SN9s!;+NuOR_%k^`5sTNL7#=p6V7(aI=F*g7 z+7nJc1m==+#uy)K*oh;Vrm)3dL~%LP-1(2y9W8T!=`N>TE`FngRAmPChR2$Zfglap z{zL1}=pPRVcl$Eyj|Ds1%afA>H1`M(G~O2zG^5&E4Xf697M=(!kzjutyL%gJZ&%l@Q+39{4$!slSG4c0*oEEk|t_eeq!xHJ< zTY3l4Y&Dn>k8BdKHyhKY4_zK?yg__pyTFp0MV@9L4fKI73NU&u*G>T^_j7kf2{bKd zchc36fvDOmbkOLHzC)tR&TH|fm4#THI05?$Qnkgf`Z+3>`Aw0qlC+UTK;n!1-PgWO zhu+JAFBEhR>Xo`{XiM*e7`fE?G^5ljmP;^F#awFOka1-frHuOwTS+&IZ&<>T$3 z5+3#v27q#Cx_-UFL!KM1jyY_!duPCjca>ceS*PX!+=6MU;b?-`ft#VI?p}F|%T_PE zBKq&T_FmEyS0Du|*D(iR(TdM4B&o7Dvm9Fp(AjMj$rQg9;w-81rt)f^r=s^bC9w%t zJG6!L0v`Nwy8Yva7`ZP$H=RHyyBvHXSFfto9AxyK@4pt1p{vB1YKwZx7BQxq)u=x1 zEmAVY2^oF5){A`>*51@9P!TO-=&_Ju2nH!Iki}*k_LM16b;vPDEK0y!lSHTVloR_P zES{!X3`5_rad0E3)T2}d%C%pY2PE1hnzlR&$~ zKmXfO;!~ibIQy7UDNWRWvow#-m7@XQNh{Suhhdm-#!Zu#-13UgclJ(pNp#-R<_3RP z$ej~F77;zs8fPmxyUY4zh43$?6K?hPJ@<-wU?Z{W&VV^7#J)kR0&@-f#6|`%yQPMM zyh`wwF}hOaU>Hkuo4zKrUKX{=6&&pe^A>?jcc79D2mN-KpHsV~eBlrAZ{+hAcEAZE zk0D$?D>-lc>y78q)fi7)cVK%zdl{=DCn|dQ;oaXw?1WG5z@8rn)|8MnVqjX*o*JPT{1U_keo09YdDcV%-z&3;UaM zKu)&|cp$Q6ss{z^^meDgyoZrqW3V*8ydH6~5+oURf}n>843D^ceDt}@bFw0j-FCXv zIf#*1389VBy+y+7yU`PtSWz$Kja%Cwb1*)Y2yM9VhyGyR{M_eCidVcOkx#gXhvvwg zwE?5j@#KxFX%q6aZ(mu<+x%{)n5Zu?z)y8q4O ztw^{;W~x|eyNzn@Q%1#y<PxtRHK&(3ZSI2a%bsd9Zj4~zrCZbA)D_t2N$-45 zxSE__?OEpgXj^zg=Z_Gaul2%W<_-)=_G)Oi|4$bcVa<}jfFR`VR)iKM3Bpx*^ zyj_h)X?+<&4152T$?-iys41ba+%+(ZSq~1#cS;^Ay^fv*s4%AaK)g9`WXpTQ)#p>h z#Wp{8a}lpqN1&`FmsavS!NgYbm9y|IIgm;OZIKmOhG?qVg)TQbju)$l9@ky6E}zbp z>%=7`^Hi#|J@4mLtSI$f^Oy<@Sr_lOP6#)@vrG;n>oZ-y4x2Ey+Kfp3uT(q5$1R6B}V%2D}?l*6+zdMo# zaXwoDcuC-wI=#3<5D7>~NdvUnoO9UX!6&+Ze#Qq&7W%v_eR0y>WI4w-?s&_>O!{lg zX16+5m%>xZ;G7n;=8vkaai^8HNY)qVP+vS|qjZwpeKMaxsR(N)6DTRjX@1M8`#msK zCD>_JFsu?g&}Nu;T0v6H!aH@tDW*6t{ExBpA{lkDLD-*VHp5EDa$J)B9FxVW0*gSf zK392BERtx|yOSkCuadi>5}e49#c9l*8Hdm4(JrqTeH){^^Qt3vC=(c{5`3XOd(J*ny)csSl<#innC-(iI3A@eb z!y%kc@>L?W0AZ?VIbkT5C0MVw#v~>VyxgC1?nbbz{91DkK=%LS;R+~;?$Wwk>z;$y z(eFLQKgC9I7J3Z*g49ErQ#|%5jHS=|8lN;+R^w!29rxnY+OfuH#&Mfe&QIJTLj@LJ z6GTbOIwIk&o1Mp!miVgkJU7tFnpPWL)(%?68Z9sxuIdq8A=Llsgg~+oDP~I&i$A2# z!n~QkczJSGTGW0rgncR~+M1(5wqcVe4;3G~&=Qw5a35N~J??Ejn>CyF_jV*L z!q4aUH4Io&v7Q7Yxq>DM1g1X)L~lo8zd=O8+AUUUm1}pT#*qGGc0N;Ovz(Ho-P_yy zldg%z9UC7nEF&YM5xPvT_Ic}FiOu4>nZ=J#Cr(Vq7fhtx72vbs1xe)7mQb{zF|f#g zbu!zaVA$<}uEY@vXEmfrRkKR_c1#qGa~+c8b`?n`9n0IIfWDZ)UN3H? zk%8?(0HAuvSCiYX*bAgJ)1&KJT#8!g+39xc;6tA9%!^@-vr&y~zjXWvYICf@1T=`Y zUErmu?vcI6`ACY+h;MGRgv-~$PUsB&2B(PG%o38s{^gn*h0{s1&LMbyOl|{TxC47y zZ}nMlqoYC3y6HU;!BGvCyN4KYYLhRGPr7=KgW+LQnv5b5#7qSsC*pOmW0aR`PnHdv z+?XgdogA|7(-t$?=ce-*s-mJ$AxMi=?00vwvb)!=l$4aZ0JVYTQnjDiY+;SfW~cEz zzs&~E!FU?9fVQ@F_|RIW+kM^V^?-_#1)Wm_!hYUQ1I`fZmTa zhkn&Y2qS0jP_8wfPKl;F=F3VmkTw3#E&vgZ-gMrO0o}<3qXH|#+QA<>-+t-4JQe$T zJ2u3_k{j1{&`b{7!2n%QO#XZ1E-s+D4e6#T$6C~jnLs~@t=P5H?Q~4z`Sqge`RggS zW@<=gPDV?^B7`UeBi4ek%`r;*V9?4aN!*2239N8>8H!}~XI!tMQUm!Q9JHk?(#qZ0 zD?Ma7Truv=Z;a{r>Eeiw1s+`X35?`R+xu^na7o>#GH7KHDD3`{c+ z#t+z&V}yPzgV4P?Ui2s@6?WF;BiWlg->`%)Oswqie-bDe+mx6{%eSidD*Yg2{VTp# z{7mA)vY4Og)Vqk6?(RPj=n7{@Hi9F2DPQ7G>nUtD+@~CD4=x9}XEbLIPE>gh2J+7?S@){8Yd=z zG`w?n*rm((=EtYuP&p^U>$_xfJlf_chmz*vF73b|l7M0f1Y}Qw);pqeX1-`n_uQIfkg^JS)O0Cf@zqr^lXNKu%<7-s}`&InBG)Y(4*5F@BiM$&Gtm7 z0j8wX!dCDhAO?BEL=B#d3KTY)4lg{n8yu@#sHRmOYJOZb_tQ%gxf#$RP;p<- z^ruMkd3YUCknq?dBJq4W$k42P-W8N>M&VpHjM=trJ=A5hp^`c3p_8Hs`iOMi(UVqp zt8m2y^htzV?FXLUJ`%bsKN&upNn9q8(D|{O-EF@rid>zIW7%(P5q267j-Qr+;Qy|8 zMXcdHg&}n(i%C??<1p^LgT-?u43t9}Nfz3hklXW4BkaT96_sX~RiT|nJxsf~5IPzq> zA1^nn;)BVb1zki&P8IP3uksM-^JJOFI6rWBimeICRz@uPE_F)Ln%Ewr6|k4t6Z}eG z5u_??5YMsI_Q&L{g)R_9j#Uuf?=k9ip!-$XPx1|hJnJgk_1E1i%H6*}?#q%R^PPg^ z)^|dkvbVaFQ8D;B>_XXEulaHfN@!vjmb;$xaUp5C6kK=e8j)riid#uX zLzny@kTHIR{;aeAnbqwEpGTXcDkpd%B18vk{X`WpWtGvqoe#7F`}@C6?5_lSzL+s#MObav*ttXc{jJ=fd0%GA6adqv&{ErTkcCRISJZ3$U7VY5Jn zG_1j~l19L6UvoK!A&=rY4BY2VK!2yb!VJudqP@33yD4&z64Kj9KK$J{uZqlv>wt9{ zb#bt5MYJ==qZz{!#($ToSVr{2EyMa>hbPL1O>WMk$KkP|U3Xq!H64w++y{M@GR8G@ zth&$VliA2K9Ln$sjHR%9B4A0E5;A_O6~x`m1#;o=Q>RNixOiX6E-tuLu!A^U`#)zC~nuJ!}!yG(rs-z8G5f1NdTtvnGvIU1pX>2!z1t4e`uaL%oH>B>O` z>Tz`#(dE0fTArNJcOE11YL$Hn$AHIIN=jyfI?6jKiM=(wFKKeCvHpMQDI2%n*Wd`& zUN#hC!j6uBSXV!oM|~7y&6!BGV$DB{$*-@{g7>@MS!xxKHp2k7$)KEWm%TeV+hk-b zaoo4=DME3Ca}@0n!>lTDh!kAbZ+&|Ph|-zL58DmEWX9wbX{ z-FrG_IZr!PY?bGnOq-on#AuB_`Gc;IX`VlXP``iKzF~BdgEXY;l3_rTM+&YB%>gSM=z9^1MIwH|we}k0gTW=>V8nxuD)J1Qa(8Nnj(~Ey>^8 z*|8$e0yuyFkmYiL*%z>*`nshWIj(q6s>zW<5gplFejdmIV~zl6Ea0dL69gK2wNU5+ zvOkO3Zhu-J^`xL{5+M+FJ7V|4)oI!#p8Lc&T@IrC?N#ywUqLD1Ql~vYB+YwobMswH zDlh|@0SFwtYK4ttmA<+C!2hT-h8E8t8RF5D>+&s7(dSj{oo8P(EX3xnh7Rkt!4|N3 zZo1&^J0W_UCZ$RY{p$|xISH^5Z2wq1ZMt>8HjVY6RU4!z^XdCRM!*$fKby>g!%h>4 z@v{j<4^dC5sI&E_A@_*W`&0p1%YK3E36vHMZXpK)5>nf@Ehi`_QT$|3VCQ6T;e*Fu zb+c^1K2=cnS&mBABkl9?d)#|Qv?7%yoH4DU$0F;~7V=ZO1-Z0};lY}Ir~9LKZtjVP9sP$l9@3|zY}x`#4LCgE%;OLbVdQA0 zYg^P{!MNql-;gSbmfaG(#fR?RFRQNZJT(}11Vl@sMa>FkaOq{2Yc5~Z8~vY^~t$mtvJ11Yt7-G@A3V$SjM&bLlqQW zRR>%1-#4D@f8?=x6VM}VcDxOubxxLAePkHHjZ$;|Qk6J7vh6zbp@Sj*9a_!4G1Z+7 z_rnk837fy3VmD+Ve(-3$z#;se_t?)3-|E{&`PjDpN37GE_stR1YQuM<1bWR(g)HUK zGDw-HB!}`Rm8xz8gw*hu(7#*px366vZSwm{<4qcmnfhiYXqfsv#%Ahfaapx8JRTlD zUNzafo|s(PA8-c26k!<|8H=NHx)EeJH$OeIhpqDdYHSAPQ^T<^;Lqm^BhO}ObZ@3^ zUF}4h*tBN%c$B|T#le4G5*f0SRwVlMRN?#$G_{pjj+<>4*yK;of3D77>lrNb-s_$g zY6Vy&bLG9#>Z|MlU(QkmX6CysC%wzx2HpcYwZTH(<@T45642ejx9K)X6Di*|KQ{X4 z0l+(d5TrqGZQn)L|C@oR{6!KReL>c}aNdZK(G(FFDI5hb({~~tU(iCBzOr_z0n~EZ zAGbZ!wax_kf*;fFho7CUbXA*d34ZH%^kGziTd~vcEryOh_V5zTH^VNJ4iFQFwRS3q zw~6JuND8Tywn$j~ZE4Xx&PE)iM!-nRaWQ7I$6CSD?P;`kM>q)M)d}n8i*@s$;Z ztYb}z(v)X^{RJrktLg&`?cCvg^~MWsVY`;R>tNp^!08LBPx>RRe$siqxo7D!+I zho|;>6W_Y|h8Y9||2P270WdwHXucOpjYiAuP>Rg0&esd@t#F1;-N%ddvyF~4x7!0I zi-q$4nvb~HjNE4!+lys+oo#SHPgJL2PV4Xcv1oXe7Wy}B{xIjvK=ESqhuBtZFokCo z`RO-A>)!_bex)RtJjW&1ef{6l)ppi;)>HU8F(j9{RT?%r5D?UpvzNiqgyeqx0u!*a zV}gZ!$?We9f=}V|;u3-pjl>m^mq%%7Y3T#9_pAD{twIs&Zinu7@obiasj2a$D~S@8 zi2VZ*W2DVj(|MbdtN)NULB4Z;4DilA?UrTL9!}M89eT5P!vH%5DWMQ$nT-5ECbBmQ z%*4W1&gFWEc_=6=5nzLrr0pIWibi2S$@`dXhP&xi)JA>y52#5$vq<_6sENwy_TJAg z?9UTkju{+5$+8lZAwaD@PdDBZIYY-7NCtLX4Rm$WjnR|IOMBjB$M^&jKape)Z3U?? zWUzn$^gL=SQWbDT(7=59E@!P9GvFPp8%Pz!BVvz0&-UNY~ zLo0&ep*av<^JJh5rxfFCJjh=Fm{0rw4+lpa5WNsge!Q@#C=fW;JC?%2d6@3d{BlwT zdNf~#wdr{)okvss5dUXpt8e-KR#4mL1r70%d(1D>j>BB-K9TOm1%Ykyd#8q zoXjRC9^+k6#}8o3|F_>U5RwuT+gi2h+D~_w(CZJwxI4=E{7o*KIaXHHIr6U#O{>%C z?}&)+VwPsTsLXqICl@}jtM}BKo!c_lH$Cej$yfok#|Q%jAO%Y^^+iV2etv#})l-=V6WO8QEdS2^kFxpi zk0flkv2NhV*0Nu@@arEKh>eY%(M@Y_=ivg7VlFV&INiiA{u+Eg8qp|{_u-9~zc%k% zL|Lk-sqGyeX6c8j2hRe8%|W^wCIb)eC-~aWNR0bG7kelCZxy10-foprw5NV?yjhyc=^bbqClt1yZ!BE6a*a|ozeTv)&1_M=jnPE$QiR_!RgRu08prd+;DoZuPiKa04f>_ zfW-+a+M%QS<}AxV1I@st$LnqEUN3jsua{j&sWoXVCW!AZ_jx{_ADRFGJO_SMHf|fH z$Ojw_zjwQ4tzl^VuaW+6tkD{S!IzKB?`E-c-=)1j-yiM!exp>-uZCgJ?71CgP!J%7 z06!%C86`$lu-56-CM7GIgc$PUVzo)Ou&_`fdT{pShJeFXKqi?{(D`^qu#jO&StZ`(BUh6qjw6d{joZ5gCKKF(oA>_JC((=GburkWmZJedUB4Ho$`r z%l~#20JIkyi@Kcm6A3uu>~`7lII>)4B1u({&g;==gRtt4rQ<2D4fN@z7oH*hmxNnC zhRP5U-;)5)5-`>FPpi5%jBBN{__QXn_;nUw?NC4X+Sc#oNk7l z=ap3S8q|*$D*i1uoe2gO_J-LvD(7=P!Z#Zh$Pg<@q3uih%(stVU8wY7cn093V=)Z*UL|2WNOH=f{6N$S#3im@zaow0*kT zb|1|0elbgA(3t?Ve)C!F=+jYNbn|gxnrfq^n%vOA{}&jZ{pTF0IjDC$Q&2%3&W{^w zGZIhL*(FVmwy5o4Wf8u3K}kciTRTd>3zQ)wAtA|~;3O8#NN~2eem=~aEm5EdMkE*k zG~?{aqYXmz;NL>&G}rwE6>LuXZ%v!t9v!^njks;Sy+Z$FWo%3ibX3!>AHx$UNmW7r z)530rx;hwr2C$O6(iVV9Zb6e$G0Dk8fEw^RudS;Smy)ssoW8&+Hdms6BE+cQ2ZJs` z%+KF7ZnWBHg_n|&(mAiz67c^}CJzSmVv;8K-OzI6J3AP~z zVbkikQIgg`!=m=Tn}Hs!wXpnW4GhMmrJb_qHIW0hiQaUs2(4NzM9ANNqaS#FZV?d? zR~|wsa)q=gonOCD#l^)V@HtU^fiad2j6XKY0qf;jBgfN)@uWxFlev;|wK{Usfy#ns zrBq}zRMe-3RU1&%8le27`yGhx&ED|asm`a6#4u3RC^RDCE(Jn8T#~*eFo8w|td(pynE+;&p|SCfeTyn^yEqD2Yb6|W(t zgPy%TL+6V%*zTMC(c{%78@ULrBzi5?HfK5v3=Ak0jfQ5G>PFc z!{el+$k5!p6z~%lO@`N29IZ5zO~Nx_4@gT;0Xb9jaj~&`-G1Qv|2}}C^ZK`_U@~aa zdfXimD<~-JE6DQBlQS#Ux!;?^4gmsu8nC$D>1XEW*8!$d3o{*^bf(LO+s#sqffe;3 z;LP>X{_XL2kpKuUX}!%QDM`n>Wm2JuZmHd^4xh`h7@twsH=WrijFyrTCbh<6FXpFV zm3D_=m-qV~AWUFo_$e2uC8=+?^FLNV(i^q(|Fb$1{omCYzzKALV~U;${eKqUwmv!C i|MwF9|ND>jK0((NwouX^nL0s$LtI2gxKc>h|Nj8vv#W^! literal 0 HcmV?d00001 From 1ab79b1e2a58059dead80b66f6312500b911280c Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 15:22:53 +1000 Subject: [PATCH 23/41] create the tsne mapping function along with header block --- recognition/MySolution/s47539934-GCN/tsne.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 recognition/MySolution/s47539934-GCN/tsne.py diff --git a/recognition/MySolution/s47539934-GCN/tsne.py b/recognition/MySolution/s47539934-GCN/tsne.py new file mode 100644 index 0000000000..2e18831296 --- /dev/null +++ b/recognition/MySolution/s47539934-GCN/tsne.py @@ -0,0 +1,12 @@ +''' +Author_name: Arsh Upadhyaya +roll no. s4753993 +to plot classes of dataset facebook.npz and see how same classes come together in a cluster +''' +import matplotlib.pyplot as plt +from sklearn.manifold import TSNE +def plot_tsne(labels,output): + tsne=TSNE().fit_transform(outputs) + plt.title('tsne result') + plt.scatter(tsne[:,0],tsne[:,1],marker='o',c=labels) + plt.savefig("GCS_tsne.png") From 47ea171eb22613e664e0d39188c326928d4bf33e Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 15:29:16 +1000 Subject: [PATCH 24/41] adding final working jupyter file with all functions and results included --- GCN_final.ipynb | 648 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 648 insertions(+) create mode 100644 GCN_final.ipynb diff --git a/GCN_final.ipynb b/GCN_final.ipynb new file mode 100644 index 0000000000..53e71b8e31 --- /dev/null +++ b/GCN_final.ipynb @@ -0,0 +1,648 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyOdKktAKTsIX4iuqCL4y12Z", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "4Tpyn_NtoRSE", + "outputId": "927e952a-8a55-4448-b8f2-fbd1a0fb6b4d" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'\\nAuthor name: Arsh Upadhyaya\\nRoll no. s4753993\\nCode for 2 layer GCN\\n'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 1 + } + ], + "source": [ + "'''\n", + "Author name: Arsh Upadhyaya\n", + "Roll no. s4753993\n", + "Code for 2 layer GCN\n", + "'''" + ] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive" + ], + "metadata": { + "id": "jsqqEDjjqjZi" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "p_dNPCQKrVqa", + "outputId": "b49f52ea-d538-45b0-9ebf-de7ab4d024fe" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import math\n", + "import torch.nn.init as init\n", + "import numpy as np\n", + "import scipy.sparse as sp\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from torch.nn.parameter import Parameter\n", + "from torch.nn.modules.module import Module\n", + "import torch.optim as optim\n", + "from random import sample\n", + "import matplotlib.pyplot as plt" + ], + "metadata": { + "id": "V2CFruE7r4yV" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def load_data(file_path):\n", + " \n", + " data = np.load(\"/content/drive/MyDrive/facebook.npz\")#path of file through google drive\n", + " edges = data['edges']\n", + " features = data['features']\n", + " labels = data['target']\n", + "\n", + " features = sp.csr_matrix(features)\n", + "\n", + " adj= sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),shape=(labels.shape[0], labels.shape[0]))\n", + "\n", + " #normalize\n", + " colsum = np.array(adj.sum(0))\n", + " D = np.power(colsum, -1)[0]\n", + " D[np.isinf(D)] = 0\n", + " D_inv = sp.diags(D)\n", + " adj_trans = D_inv.dot(adj)\n", + "\n", + " #transform data type\n", + " indices = torch.LongTensor(np.vstack((adj_trans.tocoo().row, adj_trans.tocoo().col)))\n", + " values = torch.FloatTensor(adj_trans.data)\n", + " shape = adj_trans.shape\n", + "\n", + " adj_trans = torch.sparse_coo_tensor(indices, values, shape)\n", + " features = torch.FloatTensor(np.array(features.todense()))\n", + " labels = torch.LongTensor(labels)\n", + "\n", + " return adj_trans, features, labels" + ], + "metadata": { + "id": "KDdp_bTnru0h" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "\n", + "\n", + "class GraphConvolution(Module):\n", + " '''\n", + " Starting of graph convolutional layer.\n", + " Parameters:\n", + " input_features: dimensions of input layer\n", + " output_features: dimenstions of output layer\n", + " use_bias: optional but good practice\n", + " '''\n", + "\n", + " def __init__(self, in_features, out_features, use_bias=True):\n", + " super(GraphConvolution, self).__init__()\n", + " self.in_features = in_features\n", + " self.out_features = out_features\n", + " self.use_bias=use_bias\n", + " self.weight = Parameter(torch.FloatTensor(in_features, out_features))\n", + " if self.use_bias:\n", + " self.bias = Parameter(torch.FloatTensor(out_features))\n", + " else:\n", + " self.register_parameter('bias', None)\n", + " self.reset_parameters()\n", + " #initialize parameters \n", + " def reset_parameters(self):\n", + " self.weight = nn.init.kaiming_uniform_(self.weight)\n", + " if self.use_bias:\n", + " init.zeros_(self.bias)\n", + "\n", + "# parameters:\n", + "# in_feature: an n-dimenstional vector\n", + "# adj_matrix: an adjacency matrix in tensor format\n", + "\n", + " def forward(self, input, adj):\n", + "\n", + " support = torch.mm(input, self.weight) \n", + " output = torch.sparse.mm(adj, support)\n", + "\n", + " return output\n", + "\n", + "\n", + "class GCN(nn.Module):\n", + "\n", + "# A model that contains 2 layers of GCN , by creating 2 instances from GraphConvolution function\n", + "# parameters:\n", + "# in_feature:n dimensional vector, which is input\n", + "# out_class: n dimensional vector, final output\n", + "# in this case model goes 128->32->4\n", + "# since in_feature=128(known from dataset)\n", + "# out_class=4(since finally 4 classes)\n", + "\n", + " def __init__(self, in_feature, out_class, dropout):\n", + " super(GCN, self).__init__()\n", + "\n", + " self.gcn_conv_1 = GraphConvolution(in_feature, 32)#32 is like the hidden layer for the overall model\n", + " self.gcn_conv_2 = GraphConvolution(32, out_class)\n", + " self.dropout = dropout\n", + "\n", + " def forward(self, x, adj):\n", + " x = F.relu(self.gcn_conv_1(x, adj))\n", + " x = F.dropout(x, self.dropout, training=self.training)\n", + " x = self.gcn_conv_2(x, adj)\n", + "\n", + " return F.log_softmax(x, dim=1)" + ], + "metadata": { + "id": "dZwEJTIVt9-C" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def accuracy(output, labels):\n", + " '''\n", + " calculate accuracy\n", + " parameters: \n", + " output:result of running an instance of the model\n", + " labels: the true value\n", + " function compares ratio of two values, giving result<1, \n", + " as predicted probability always less than true value\n", + " '''\n", + " predict = output.argmax(1)\n", + " acc_ = torch.div(predict.eq(labels).sum(), labels.shape[0])\n", + " return acc_" + ], + "metadata": { + "id": "qsMqOqxTwjd9" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def loss(output,labels):\n", + "\n", + " prab = output.gather(1, labels.view(-1,1))\n", + " loss = -torch.mean(prab)\n", + " return loss" + ], + "metadata": { + "id": "WUessmBVwpQb" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def train_model(n_epochs):\n", + " '''\n", + " parameter: number of epochs\n", + " trains model over the range of the epoch and at each train, \n", + " calculates accuracy and losses\n", + " '''\n", + " train_losses=[]\n", + " validation_losses=[]\n", + " train_accuracies=[]\n", + " validation_accuracies=[]\n", + " for epoch in range(n_epochs):\n", + " model.train()\n", + " optimizer.zero_grad()\n", + " output=model(features,adj)\n", + " train_loss=loss(output[train_set],labels[train_set])\n", + " train_losses.append(train_loss.item())\n", + "\n", + " train_accuracy=accuracy(output[train_set],labels[train_set])\n", + " train_accuracies.append(train_accuracy.item())\n", + " train_loss.backward()\n", + " optimizer.step()\n", + " output=model(features,adj)\n", + " validation_loss=loss(output[val_set],labels[val_set])\n", + " validation_losses.append(validation_loss.item())\n", + " validation_accuracy=accuracy(output[val_set],labels[val_set])\n", + " validation_accuracies.append(validation_accuracy.item())\n", + " print('Epoch: {:04d}'.format(epoch + 1),\n", + " 'Train loss: {:.4f}'.format(train_loss.item()),\n", + " 'Train accuracy: {:.4f}'.format(train_accuracy.item()),\n", + " 'Validation loss: {:.4f}'.format(validation_loss.item()),\n", + " 'Validation accuracy: {:.4f}'.format(validation_accuracy.item()))\n", + " \n", + " np.save('train_losses', train_losses)\n", + " np.save('train_accuracies', train_accuracies)\n", + " np.save('validation_losses', validation_losses)\n", + " np.save('validation_accuracies', validation_accuracies)\n" + ], + "metadata": { + "id": "1tJrfwlHw2bC" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def test_model():\n", + " output=model(features,adj)\n", + " test_loss=loss(output[test_set],labels[test_set])\n", + " test_accuracy=accuracy(output[test_set],labels[test_set])\n", + " print('Test set results:',\n", + " 'Test loss: {:.4f}'.format(test_loss.item()),\n", + " 'Test accuracy: {:.4f}'.format(test_accuracy.item()))" + ], + "metadata": { + "id": "dnk6qMppxA1h" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + " adj, features, labels = load_data('facebook.npz')#returns normalized adjacency matrix, tensor features and labels\n", + " features.shape[0]\n", + " num_nodes=features.shape[0]\n", + " #split data in semi supervised quatity, i.e train:set:test=20:20:60(since n_train" + ], + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "

" + ], + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n", + " FutureWarning,\n", + "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n", + " FutureWarning,\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "from sklearn.manifold import TSNE\n", + "def plot_tsne(labels,output):\n", + " tsne=TSNE().fit_transform(outputs)\n", + " plt.title('tsne result')\n", + " plt.scatter(tsne[:,0],tsne[:,1],marker='o',c=labels)\n", + " plt.savefig(\"GCS_tsne.png\")" + ], + "metadata": { + "id": "GCA-iK6PIxtg" + }, + "execution_count": 28, + "outputs": [] + } + ] +} \ No newline at end of file From f74b4cfa43add60b13ad63f954516369e2530976 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 15:32:44 +1000 Subject: [PATCH 25/41] mistakenly put in wrong directory so removed --- GCN_final.ipynb | 648 ------------------------------------------------ 1 file changed, 648 deletions(-) delete mode 100644 GCN_final.ipynb diff --git a/GCN_final.ipynb b/GCN_final.ipynb deleted file mode 100644 index 53e71b8e31..0000000000 --- a/GCN_final.ipynb +++ /dev/null @@ -1,648 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "authorship_tag": "ABX9TyOdKktAKTsIX4iuqCL4y12Z", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "id": "4Tpyn_NtoRSE", - "outputId": "927e952a-8a55-4448-b8f2-fbd1a0fb6b4d" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'\\nAuthor name: Arsh Upadhyaya\\nRoll no. s4753993\\nCode for 2 layer GCN\\n'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 1 - } - ], - "source": [ - "'''\n", - "Author name: Arsh Upadhyaya\n", - "Roll no. s4753993\n", - "Code for 2 layer GCN\n", - "'''" - ] - }, - { - "cell_type": "code", - "source": [ - "from google.colab import drive" - ], - "metadata": { - "id": "jsqqEDjjqjZi" - }, - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "drive.mount('/content/drive')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "p_dNPCQKrVqa", - "outputId": "b49f52ea-d538-45b0-9ebf-de7ab4d024fe" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import math\n", - "import torch.nn.init as init\n", - "import numpy as np\n", - "import scipy.sparse as sp\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.nn.functional as F\n", - "from torch.nn.parameter import Parameter\n", - "from torch.nn.modules.module import Module\n", - "import torch.optim as optim\n", - "from random import sample\n", - "import matplotlib.pyplot as plt" - ], - "metadata": { - "id": "V2CFruE7r4yV" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def load_data(file_path):\n", - " \n", - " data = np.load(\"/content/drive/MyDrive/facebook.npz\")#path of file through google drive\n", - " edges = data['edges']\n", - " features = data['features']\n", - " labels = data['target']\n", - "\n", - " features = sp.csr_matrix(features)\n", - "\n", - " adj= sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),shape=(labels.shape[0], labels.shape[0]))\n", - "\n", - " #normalize\n", - " colsum = np.array(adj.sum(0))\n", - " D = np.power(colsum, -1)[0]\n", - " D[np.isinf(D)] = 0\n", - " D_inv = sp.diags(D)\n", - " adj_trans = D_inv.dot(adj)\n", - "\n", - " #transform data type\n", - " indices = torch.LongTensor(np.vstack((adj_trans.tocoo().row, adj_trans.tocoo().col)))\n", - " values = torch.FloatTensor(adj_trans.data)\n", - " shape = adj_trans.shape\n", - "\n", - " adj_trans = torch.sparse_coo_tensor(indices, values, shape)\n", - " features = torch.FloatTensor(np.array(features.todense()))\n", - " labels = torch.LongTensor(labels)\n", - "\n", - " return adj_trans, features, labels" - ], - "metadata": { - "id": "KDdp_bTnru0h" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "\n", - "\n", - "\n", - "class GraphConvolution(Module):\n", - " '''\n", - " Starting of graph convolutional layer.\n", - " Parameters:\n", - " input_features: dimensions of input layer\n", - " output_features: dimenstions of output layer\n", - " use_bias: optional but good practice\n", - " '''\n", - "\n", - " def __init__(self, in_features, out_features, use_bias=True):\n", - " super(GraphConvolution, self).__init__()\n", - " self.in_features = in_features\n", - " self.out_features = out_features\n", - " self.use_bias=use_bias\n", - " self.weight = Parameter(torch.FloatTensor(in_features, out_features))\n", - " if self.use_bias:\n", - " self.bias = Parameter(torch.FloatTensor(out_features))\n", - " else:\n", - " self.register_parameter('bias', None)\n", - " self.reset_parameters()\n", - " #initialize parameters \n", - " def reset_parameters(self):\n", - " self.weight = nn.init.kaiming_uniform_(self.weight)\n", - " if self.use_bias:\n", - " init.zeros_(self.bias)\n", - "\n", - "# parameters:\n", - "# in_feature: an n-dimenstional vector\n", - "# adj_matrix: an adjacency matrix in tensor format\n", - "\n", - " def forward(self, input, adj):\n", - "\n", - " support = torch.mm(input, self.weight) \n", - " output = torch.sparse.mm(adj, support)\n", - "\n", - " return output\n", - "\n", - "\n", - "class GCN(nn.Module):\n", - "\n", - "# A model that contains 2 layers of GCN , by creating 2 instances from GraphConvolution function\n", - "# parameters:\n", - "# in_feature:n dimensional vector, which is input\n", - "# out_class: n dimensional vector, final output\n", - "# in this case model goes 128->32->4\n", - "# since in_feature=128(known from dataset)\n", - "# out_class=4(since finally 4 classes)\n", - "\n", - " def __init__(self, in_feature, out_class, dropout):\n", - " super(GCN, self).__init__()\n", - "\n", - " self.gcn_conv_1 = GraphConvolution(in_feature, 32)#32 is like the hidden layer for the overall model\n", - " self.gcn_conv_2 = GraphConvolution(32, out_class)\n", - " self.dropout = dropout\n", - "\n", - " def forward(self, x, adj):\n", - " x = F.relu(self.gcn_conv_1(x, adj))\n", - " x = F.dropout(x, self.dropout, training=self.training)\n", - " x = self.gcn_conv_2(x, adj)\n", - "\n", - " return F.log_softmax(x, dim=1)" - ], - "metadata": { - "id": "dZwEJTIVt9-C" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def accuracy(output, labels):\n", - " '''\n", - " calculate accuracy\n", - " parameters: \n", - " output:result of running an instance of the model\n", - " labels: the true value\n", - " function compares ratio of two values, giving result<1, \n", - " as predicted probability always less than true value\n", - " '''\n", - " predict = output.argmax(1)\n", - " acc_ = torch.div(predict.eq(labels).sum(), labels.shape[0])\n", - " return acc_" - ], - "metadata": { - "id": "qsMqOqxTwjd9" - }, - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def loss(output,labels):\n", - "\n", - " prab = output.gather(1, labels.view(-1,1))\n", - " loss = -torch.mean(prab)\n", - " return loss" - ], - "metadata": { - "id": "WUessmBVwpQb" - }, - "execution_count": 8, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def train_model(n_epochs):\n", - " '''\n", - " parameter: number of epochs\n", - " trains model over the range of the epoch and at each train, \n", - " calculates accuracy and losses\n", - " '''\n", - " train_losses=[]\n", - " validation_losses=[]\n", - " train_accuracies=[]\n", - " validation_accuracies=[]\n", - " for epoch in range(n_epochs):\n", - " model.train()\n", - " optimizer.zero_grad()\n", - " output=model(features,adj)\n", - " train_loss=loss(output[train_set],labels[train_set])\n", - " train_losses.append(train_loss.item())\n", - "\n", - " train_accuracy=accuracy(output[train_set],labels[train_set])\n", - " train_accuracies.append(train_accuracy.item())\n", - " train_loss.backward()\n", - " optimizer.step()\n", - " output=model(features,adj)\n", - " validation_loss=loss(output[val_set],labels[val_set])\n", - " validation_losses.append(validation_loss.item())\n", - " validation_accuracy=accuracy(output[val_set],labels[val_set])\n", - " validation_accuracies.append(validation_accuracy.item())\n", - " print('Epoch: {:04d}'.format(epoch + 1),\n", - " 'Train loss: {:.4f}'.format(train_loss.item()),\n", - " 'Train accuracy: {:.4f}'.format(train_accuracy.item()),\n", - " 'Validation loss: {:.4f}'.format(validation_loss.item()),\n", - " 'Validation accuracy: {:.4f}'.format(validation_accuracy.item()))\n", - " \n", - " np.save('train_losses', train_losses)\n", - " np.save('train_accuracies', train_accuracies)\n", - " np.save('validation_losses', validation_losses)\n", - " np.save('validation_accuracies', validation_accuracies)\n" - ], - "metadata": { - "id": "1tJrfwlHw2bC" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def test_model():\n", - " output=model(features,adj)\n", - " test_loss=loss(output[test_set],labels[test_set])\n", - " test_accuracy=accuracy(output[test_set],labels[test_set])\n", - " print('Test set results:',\n", - " 'Test loss: {:.4f}'.format(test_loss.item()),\n", - " 'Test accuracy: {:.4f}'.format(test_accuracy.item()))" - ], - "metadata": { - "id": "dnk6qMppxA1h" - }, - "execution_count": 10, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - " adj, features, labels = load_data('facebook.npz')#returns normalized adjacency matrix, tensor features and labels\n", - " features.shape[0]\n", - " num_nodes=features.shape[0]\n", - " #split data in semi supervised quatity, i.e train:set:test=20:20:60(since n_train" - ], - "image/png": "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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n", - " FutureWarning,\n", - "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n", - " FutureWarning,\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "from sklearn.manifold import TSNE\n", - "def plot_tsne(labels,output):\n", - " tsne=TSNE().fit_transform(outputs)\n", - " plt.title('tsne result')\n", - " plt.scatter(tsne[:,0],tsne[:,1],marker='o',c=labels)\n", - " plt.savefig(\"GCS_tsne.png\")" - ], - "metadata": { - "id": "GCA-iK6PIxtg" - }, - "execution_count": 28, - "outputs": [] - } - ] -} \ No newline at end of file From 7f783210fc84bf6bf71b22de0b5411add9e5b69d Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 15:34:47 +1000 Subject: [PATCH 26/41] final jupyter file containing working version of GCN model --- GCN_final.ipynb | 648 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 648 insertions(+) create mode 100644 GCN_final.ipynb diff --git a/GCN_final.ipynb b/GCN_final.ipynb new file mode 100644 index 0000000000..53e71b8e31 --- /dev/null +++ b/GCN_final.ipynb @@ -0,0 +1,648 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyOdKktAKTsIX4iuqCL4y12Z", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "4Tpyn_NtoRSE", + "outputId": "927e952a-8a55-4448-b8f2-fbd1a0fb6b4d" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'\\nAuthor name: Arsh Upadhyaya\\nRoll no. s4753993\\nCode for 2 layer GCN\\n'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 1 + } + ], + "source": [ + "'''\n", + "Author name: Arsh Upadhyaya\n", + "Roll no. s4753993\n", + "Code for 2 layer GCN\n", + "'''" + ] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive" + ], + "metadata": { + "id": "jsqqEDjjqjZi" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "p_dNPCQKrVqa", + "outputId": "b49f52ea-d538-45b0-9ebf-de7ab4d024fe" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import math\n", + "import torch.nn.init as init\n", + "import numpy as np\n", + "import scipy.sparse as sp\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from torch.nn.parameter import Parameter\n", + "from torch.nn.modules.module import Module\n", + "import torch.optim as optim\n", + "from random import sample\n", + "import matplotlib.pyplot as plt" + ], + "metadata": { + "id": "V2CFruE7r4yV" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def load_data(file_path):\n", + " \n", + " data = np.load(\"/content/drive/MyDrive/facebook.npz\")#path of file through google drive\n", + " edges = data['edges']\n", + " features = data['features']\n", + " labels = data['target']\n", + "\n", + " features = sp.csr_matrix(features)\n", + "\n", + " adj= sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),shape=(labels.shape[0], labels.shape[0]))\n", + "\n", + " #normalize\n", + " colsum = np.array(adj.sum(0))\n", + " D = np.power(colsum, -1)[0]\n", + " D[np.isinf(D)] = 0\n", + " D_inv = sp.diags(D)\n", + " adj_trans = D_inv.dot(adj)\n", + "\n", + " #transform data type\n", + " indices = torch.LongTensor(np.vstack((adj_trans.tocoo().row, adj_trans.tocoo().col)))\n", + " values = torch.FloatTensor(adj_trans.data)\n", + " shape = adj_trans.shape\n", + "\n", + " adj_trans = torch.sparse_coo_tensor(indices, values, shape)\n", + " features = torch.FloatTensor(np.array(features.todense()))\n", + " labels = torch.LongTensor(labels)\n", + "\n", + " return adj_trans, features, labels" + ], + "metadata": { + "id": "KDdp_bTnru0h" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "\n", + "\n", + "class GraphConvolution(Module):\n", + " '''\n", + " Starting of graph convolutional layer.\n", + " Parameters:\n", + " input_features: dimensions of input layer\n", + " output_features: dimenstions of output layer\n", + " use_bias: optional but good practice\n", + " '''\n", + "\n", + " def __init__(self, in_features, out_features, use_bias=True):\n", + " super(GraphConvolution, self).__init__()\n", + " self.in_features = in_features\n", + " self.out_features = out_features\n", + " self.use_bias=use_bias\n", + " self.weight = Parameter(torch.FloatTensor(in_features, out_features))\n", + " if self.use_bias:\n", + " self.bias = Parameter(torch.FloatTensor(out_features))\n", + " else:\n", + " self.register_parameter('bias', None)\n", + " self.reset_parameters()\n", + " #initialize parameters \n", + " def reset_parameters(self):\n", + " self.weight = nn.init.kaiming_uniform_(self.weight)\n", + " if self.use_bias:\n", + " init.zeros_(self.bias)\n", + "\n", + "# parameters:\n", + "# in_feature: an n-dimenstional vector\n", + "# adj_matrix: an adjacency matrix in tensor format\n", + "\n", + " def forward(self, input, adj):\n", + "\n", + " support = torch.mm(input, self.weight) \n", + " output = torch.sparse.mm(adj, support)\n", + "\n", + " return output\n", + "\n", + "\n", + "class GCN(nn.Module):\n", + "\n", + "# A model that contains 2 layers of GCN , by creating 2 instances from GraphConvolution function\n", + "# parameters:\n", + "# in_feature:n dimensional vector, which is input\n", + "# out_class: n dimensional vector, final output\n", + "# in this case model goes 128->32->4\n", + "# since in_feature=128(known from dataset)\n", + "# out_class=4(since finally 4 classes)\n", + "\n", + " def __init__(self, in_feature, out_class, dropout):\n", + " super(GCN, self).__init__()\n", + "\n", + " self.gcn_conv_1 = GraphConvolution(in_feature, 32)#32 is like the hidden layer for the overall model\n", + " self.gcn_conv_2 = GraphConvolution(32, out_class)\n", + " self.dropout = dropout\n", + "\n", + " def forward(self, x, adj):\n", + " x = F.relu(self.gcn_conv_1(x, adj))\n", + " x = F.dropout(x, self.dropout, training=self.training)\n", + " x = self.gcn_conv_2(x, adj)\n", + "\n", + " return F.log_softmax(x, dim=1)" + ], + "metadata": { + "id": "dZwEJTIVt9-C" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def accuracy(output, labels):\n", + " '''\n", + " calculate accuracy\n", + " parameters: \n", + " output:result of running an instance of the model\n", + " labels: the true value\n", + " function compares ratio of two values, giving result<1, \n", + " as predicted probability always less than true value\n", + " '''\n", + " predict = output.argmax(1)\n", + " acc_ = torch.div(predict.eq(labels).sum(), labels.shape[0])\n", + " return acc_" + ], + "metadata": { + "id": "qsMqOqxTwjd9" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def loss(output,labels):\n", + "\n", + " prab = output.gather(1, labels.view(-1,1))\n", + " loss = -torch.mean(prab)\n", + " return loss" + ], + "metadata": { + "id": "WUessmBVwpQb" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def train_model(n_epochs):\n", + " '''\n", + " parameter: number of epochs\n", + " trains model over the range of the epoch and at each train, \n", + " calculates accuracy and losses\n", + " '''\n", + " train_losses=[]\n", + " validation_losses=[]\n", + " train_accuracies=[]\n", + " validation_accuracies=[]\n", + " for epoch in range(n_epochs):\n", + " model.train()\n", + " optimizer.zero_grad()\n", + " output=model(features,adj)\n", + " train_loss=loss(output[train_set],labels[train_set])\n", + " train_losses.append(train_loss.item())\n", + "\n", + " train_accuracy=accuracy(output[train_set],labels[train_set])\n", + " train_accuracies.append(train_accuracy.item())\n", + " train_loss.backward()\n", + " optimizer.step()\n", + " output=model(features,adj)\n", + " validation_loss=loss(output[val_set],labels[val_set])\n", + " validation_losses.append(validation_loss.item())\n", + " validation_accuracy=accuracy(output[val_set],labels[val_set])\n", + " validation_accuracies.append(validation_accuracy.item())\n", + " print('Epoch: {:04d}'.format(epoch + 1),\n", + " 'Train loss: {:.4f}'.format(train_loss.item()),\n", + " 'Train accuracy: {:.4f}'.format(train_accuracy.item()),\n", + " 'Validation loss: {:.4f}'.format(validation_loss.item()),\n", + " 'Validation accuracy: {:.4f}'.format(validation_accuracy.item()))\n", + " \n", + " np.save('train_losses', train_losses)\n", + " np.save('train_accuracies', train_accuracies)\n", + " np.save('validation_losses', validation_losses)\n", + " np.save('validation_accuracies', validation_accuracies)\n" + ], + "metadata": { + "id": "1tJrfwlHw2bC" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def test_model():\n", + " output=model(features,adj)\n", + " test_loss=loss(output[test_set],labels[test_set])\n", + " test_accuracy=accuracy(output[test_set],labels[test_set])\n", + " print('Test set results:',\n", + " 'Test loss: {:.4f}'.format(test_loss.item()),\n", + " 'Test accuracy: {:.4f}'.format(test_accuracy.item()))" + ], + "metadata": { + "id": "dnk6qMppxA1h" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + " adj, features, labels = load_data('facebook.npz')#returns normalized adjacency matrix, tensor features and labels\n", + " features.shape[0]\n", + " num_nodes=features.shape[0]\n", + " #split data in semi supervised quatity, i.e train:set:test=20:20:60(since n_train" + ], + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n", + " FutureWarning,\n", + "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n", + " FutureWarning,\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "from sklearn.manifold import TSNE\n", + "def plot_tsne(labels,output):\n", + " tsne=TSNE().fit_transform(outputs)\n", + " plt.title('tsne result')\n", + " plt.scatter(tsne[:,0],tsne[:,1],marker='o',c=labels)\n", + " plt.savefig(\"GCS_tsne.png\")" + ], + "metadata": { + "id": "GCA-iK6PIxtg" + }, + "execution_count": 28, + "outputs": [] + } + ] +} \ No newline at end of file From 26964300e9bd03e893e59e2ad2f92960e835127e Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 15:37:22 +1000 Subject: [PATCH 27/41] fixing directory --- .../recognition/MySolution/s47539934-GCN/GCN_final.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename GCN_final.ipynb => PatternFlow/recognition/MySolution/s47539934-GCN/GCN_final.ipynb (99%) diff --git a/GCN_final.ipynb b/PatternFlow/recognition/MySolution/s47539934-GCN/GCN_final.ipynb similarity index 99% rename from GCN_final.ipynb rename to PatternFlow/recognition/MySolution/s47539934-GCN/GCN_final.ipynb index 53e71b8e31..1bfab0bd5e 100644 --- a/GCN_final.ipynb +++ b/PatternFlow/recognition/MySolution/s47539934-GCN/GCN_final.ipynb @@ -645,4 +645,4 @@ "outputs": [] } ] -} \ No newline at end of file +} From 81dc37e52a1fea5b77364a6c61a4760732e11449 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 15:42:01 +1000 Subject: [PATCH 28/41] again changing directory --- .../MySolution/s47539934-GCN/GCN_final.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {PatternFlow/recognition => recognition}/MySolution/s47539934-GCN/GCN_final.ipynb (100%) diff --git a/PatternFlow/recognition/MySolution/s47539934-GCN/GCN_final.ipynb b/recognition/MySolution/s47539934-GCN/GCN_final.ipynb similarity index 100% rename from PatternFlow/recognition/MySolution/s47539934-GCN/GCN_final.ipynb rename to recognition/MySolution/s47539934-GCN/GCN_final.ipynb From 80210308fd1820673b96adfdb31039c4012fad96 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 15:46:12 +1000 Subject: [PATCH 29/41] deleted an irrelevant file --- recognition/MySolution/s47539934-GCN/README.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 recognition/MySolution/s47539934-GCN/README.txt diff --git a/recognition/MySolution/s47539934-GCN/README.txt b/recognition/MySolution/s47539934-GCN/README.txt deleted file mode 100644 index 5ab2f8a432..0000000000 --- a/recognition/MySolution/s47539934-GCN/README.txt +++ /dev/null @@ -1 +0,0 @@ -Hello \ No newline at end of file From e6bf0ec1f10f3c8fb1621424f6da2ebb7c630eb6 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 16:18:39 +1000 Subject: [PATCH 30/41] Added initial readme file --- recognition/MySolution/s47539934-GCN/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 recognition/MySolution/s47539934-GCN/README.md diff --git a/recognition/MySolution/s47539934-GCN/README.md b/recognition/MySolution/s47539934-GCN/README.md new file mode 100644 index 0000000000..0cf3a2f03e --- /dev/null +++ b/recognition/MySolution/s47539934-GCN/README.md @@ -0,0 +1 @@ +nfj fjj \ No newline at end of file From af33631aaffd0b046c646edf01f7a2e0a5f770bc Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 16:20:39 +1000 Subject: [PATCH 31/41] Update README.md --- recognition/MySolution/s47539934-GCN/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recognition/MySolution/s47539934-GCN/README.md b/recognition/MySolution/s47539934-GCN/README.md index 0cf3a2f03e..ce02cc116d 100644 --- a/recognition/MySolution/s47539934-GCN/README.md +++ b/recognition/MySolution/s47539934-GCN/README.md @@ -1 +1 @@ -nfj fjj \ No newline at end of file +#Graph Convolutional Network for facebook.npz[didfjid]"www.google.com" From dc5cc686107101bb97afe0419343ff0f4f8e63d9 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 16:43:21 +1000 Subject: [PATCH 32/41] Delete README.md --- recognition/MySolution/s47539934-GCN/README.md | 1 - 1 file changed, 1 deletion(-) delete mode 100644 recognition/MySolution/s47539934-GCN/README.md diff --git a/recognition/MySolution/s47539934-GCN/README.md b/recognition/MySolution/s47539934-GCN/README.md deleted file mode 100644 index ce02cc116d..0000000000 --- a/recognition/MySolution/s47539934-GCN/README.md +++ /dev/null @@ -1 +0,0 @@ -#Graph Convolutional Network for facebook.npz[didfjid]"www.google.com" From b08e1ba309d11167212bb35ecba5421e9fc67c21 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 16:46:34 +1000 Subject: [PATCH 33/41] Add files via upload --- recognition/MySolution/s47539934-GCN/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 recognition/MySolution/s47539934-GCN/README.md diff --git a/recognition/MySolution/s47539934-GCN/README.md b/recognition/MySolution/s47539934-GCN/README.md new file mode 100644 index 0000000000..f7cddf3510 --- /dev/null +++ b/recognition/MySolution/s47539934-GCN/README.md @@ -0,0 +1 @@ +#Graph Convolutional Layer on Facebook.npz dataset \ No newline at end of file From 95d86892c96b80e26ed4a7c197f06483c1fc99d0 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 16:54:06 +1000 Subject: [PATCH 34/41] Delete README.md --- recognition/MySolution/s47539934-GCN/README.md | 1 - 1 file changed, 1 deletion(-) delete mode 100644 recognition/MySolution/s47539934-GCN/README.md diff --git a/recognition/MySolution/s47539934-GCN/README.md b/recognition/MySolution/s47539934-GCN/README.md deleted file mode 100644 index f7cddf3510..0000000000 --- a/recognition/MySolution/s47539934-GCN/README.md +++ /dev/null @@ -1 +0,0 @@ -#Graph Convolutional Layer on Facebook.npz dataset \ No newline at end of file From ae2050e88fd030e655e82cf43f4ab18fea4cbdd6 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 16:57:41 +1000 Subject: [PATCH 35/41] Add files via upload --- recognition/MySolution/s47539934-GCN/README (1).md | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 recognition/MySolution/s47539934-GCN/README (1).md diff --git a/recognition/MySolution/s47539934-GCN/README (1).md b/recognition/MySolution/s47539934-GCN/README (1).md new file mode 100644 index 0000000000..e0fba3443f --- /dev/null +++ b/recognition/MySolution/s47539934-GCN/README (1).md @@ -0,0 +1,5 @@ + +## Graph convolutional network with 2 layers on facebook.npz + +A brief description of what this project does and who it's for + From a3b8d0dfedc4b50b6922d67af636db3c54591e18 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 16:58:39 +1000 Subject: [PATCH 36/41] Rename README (1).md to README.md --- recognition/MySolution/s47539934-GCN/{README (1).md => README.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename recognition/MySolution/s47539934-GCN/{README (1).md => README.md} (100%) diff --git a/recognition/MySolution/s47539934-GCN/README (1).md b/recognition/MySolution/s47539934-GCN/README.md similarity index 100% rename from recognition/MySolution/s47539934-GCN/README (1).md rename to recognition/MySolution/s47539934-GCN/README.md From e71cb3a51e7750f23eedd4aa85587c949da3c949 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 17:02:02 +1000 Subject: [PATCH 37/41] Update README.md --- recognition/MySolution/s47539934-GCN/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recognition/MySolution/s47539934-GCN/README.md b/recognition/MySolution/s47539934-GCN/README.md index e0fba3443f..183dd0a25b 100644 --- a/recognition/MySolution/s47539934-GCN/README.md +++ b/recognition/MySolution/s47539934-GCN/README.md @@ -1,5 +1,5 @@ -## Graph convolutional network with 2 layers on facebook.npz +# Graph convolutional network with 2 layers on facebook.npz A brief description of what this project does and who it's for From c661e4827962201b08096efc6fc1d29f643d8905 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 18:36:28 +1000 Subject: [PATCH 38/41] Creating basic structure of README file --- .../MySolution/s47539934-GCN/README.md | 27 +++++++++++++++++-- 1 file changed, 25 insertions(+), 2 deletions(-) diff --git a/recognition/MySolution/s47539934-GCN/README.md b/recognition/MySolution/s47539934-GCN/README.md index 183dd0a25b..e06ff20c2e 100644 --- a/recognition/MySolution/s47539934-GCN/README.md +++ b/recognition/MySolution/s47539934-GCN/README.md @@ -1,5 +1,28 @@ -# Graph convolutional network with 2 layers on facebook.npz +# GCN on Facebook.npz Dataset +### By: Arsh Upadhyaya +### roll_no: s4753993 +## Goal +node classification +## Why GCN +## Working of GCN +## Process of solving problem +## results +### accuracy plot -A brief description of what this project does and who it's for +### loss plot +### test_set +### tsne +## Dependancies +1.numpy +2.matplotlib +3.pytorch +4.python +5.sklearn +6.scipy +## reference +[1] https://arxiv.org/abs/1609.02907 + +[2] https://github.com/tkipf/pygcn/tree/1600b5b748b3976413d1e307540ccc62605b4d6d/pygcn +especially helpful in training and testing functions From 7f007268fadb7544ede897d50834ad801c332baf Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 20:47:42 +1000 Subject: [PATCH 39/41] adding all results in README.md --- recognition/MySolution/s47539934-GCN/README.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/recognition/MySolution/s47539934-GCN/README.md b/recognition/MySolution/s47539934-GCN/README.md index e06ff20c2e..51d7d3febe 100644 --- a/recognition/MySolution/s47539934-GCN/README.md +++ b/recognition/MySolution/s47539934-GCN/README.md @@ -9,16 +9,34 @@ node classification ## Process of solving problem ## results ### accuracy plot +Accuracy + +we can see that training_accuracy>validation_accuracy, but only by a little bit which is a good sign ### loss plot +loss + ### test_set +test + +The test_accuracy and test_loss is pretty similar to training and validation accuracy and losses as expected + ### tsne +![GCN_tsne_200](https://user-images.githubusercontent.com/116279628/197387517-f2536959-50be-4cbb-b4a1-f1d5fb506dc5.png) + +After 200 epochs there is a significant accuracy, and the 4 different classification categories are very rarely overlapping + ## Dependancies 1.numpy + 2.matplotlib + 3.pytorch + 4.python + 5.sklearn + 6.scipy ## reference From 904ea47da540cb70c1db2cf1298580f8a6ea09d3 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 20:55:04 +1000 Subject: [PATCH 40/41] Updating ReadMe --- recognition/MySolution/s47539934-GCN/README.md | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/recognition/MySolution/s47539934-GCN/README.md b/recognition/MySolution/s47539934-GCN/README.md index 51d7d3febe..586b9ce387 100644 --- a/recognition/MySolution/s47539934-GCN/README.md +++ b/recognition/MySolution/s47539934-GCN/README.md @@ -3,8 +3,18 @@ ### By: Arsh Upadhyaya ### roll_no: s4753993 ## Goal -node classification + +The objective is to accurately classify nodes into there respective categories(total 4) +as per the facebook.npz dataset ## Why GCN + +GCN and a conventional CNN are similar in there objectives, they both take some features +from existing/given data and perform some operation on it, be it classification, recognition or even +creation(like in GANs). However the difference lies in there flexibility. A CNN requires data in some +clear format(like in images). However in a lot of real world applications, this is not the case. In fact, +the internet itself is a graph with the websites themselves being the nodes and the edges being hyperlinks. +Such is the case with the facebook dataset, and we have to classify the websites to certain classes. + ## Working of GCN ## Process of solving problem ## results From 868d3f6ecff540e3e818492b11ed742a6c0a3907 Mon Sep 17 00:00:00 2001 From: mr-popo123 <116279628+mr-popo123@users.noreply.github.com> Date: Sun, 23 Oct 2022 21:31:46 +1000 Subject: [PATCH 41/41] Finished Readme.md --- .../MySolution/s47539934-GCN/README.md | 23 +++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/recognition/MySolution/s47539934-GCN/README.md b/recognition/MySolution/s47539934-GCN/README.md index 586b9ce387..dd92300640 100644 --- a/recognition/MySolution/s47539934-GCN/README.md +++ b/recognition/MySolution/s47539934-GCN/README.md @@ -16,12 +16,31 @@ the internet itself is a graph with the websites themselves being the nodes and Such is the case with the facebook dataset, and we have to classify the websites to certain classes. ## Working of GCN -## Process of solving problem + +GCN use graphs as objects, and to create these, they multiply features by features by weights. They use adjacency matrix to store data. This matrix is used in forward pass(function used in model) and thus forms a graph, in which adjacent nodes have information about each other. For a simple 2 layer convolutional network, like the one used in the model, each node has information of nodes within 2 edges. Hence one can Imagine just how strong a deep GCN can be, as unlike the neurons in CNN, the nodes in GCN are not bound by euclidean geometry. Especially on a bigger dataset(internet), it could be assumed there would be a lot of hyperbolic and elliptical non-euclidean connections over space, thus increasing the interconnectivity of the model. + +## Program Flow + +#load_data() +this function is responsible for first extracting features,edges and targets, then create an adjacency matrix, and normalize that matrix. +Further it returns features as a tensor. + +#GCN_model() +Since the given data has input of first layer as 128 dim vectors, a hidden layer of 32 was used and then directly finished with out_class=4, which is the number of required categories. Alternatives, a third layer could have also been added, however the accuracy was already pretty good. +Maybe a dropout was not necessary in hindsight as the dataset seems to small to need it. + +train_model() +considered 200 epochs, and at each epoch, found difference between ouput of model and the target itself. +Similarly for losses as well. +Done for both training and validation set. + + ## results ### accuracy plot Accuracy -we can see that training_accuracy>validation_accuracy, but only by a little bit which is a good sign +we can see that training_accuracy>validation_accuracy, but only by a little bit which is a good sign. +Reached accuracy of 0.923 on training set after 200 epochs, went as high as 0.9446 after 400 epochs. ### loss plot loss