original_docstring: 
              Adds an inception module to the model.
  
              :param name: string identifier of the inception layer
              :param input_layer: identifier of the layer that will serve as an input to the built inception module
              :param kernels_1x1: number of kernels of size 1x1                                      (1st branch)
              :param kernels_3x3_reduce: number of kernels of size 1x1 before the 3x3 layer          (2nd branch)
              :param kernels_3x3: number of kernels of size 3x3                                      (2nd branch)
              :param kernels_5x5_reduce: number of kernels of size 1x1 before the 5x5 layer          (3rd branch)
              :param kernels_5x5: number of kernels of size 5x5                                      (3rd branch)
              :param kernels_pool_projection: number of kernels of size 1x1 after the 3x3 pooling    (4th branch)
          
docstring: Adds an inception module to the model.
docstring_tokens: Adds an inception module to the model .
code: def __addInception(self, name, input_layer, kernels_1x1, kernels_3x3_reduce, kernels_3x3, kernels_5x5_reduce,
                         kernels_5x5, kernels_pool_projection):
          """
              Adds an inception module to the model.
  
              :param name: string identifier of the inception layer
              :param input_layer: identifier of the layer that will serve as an input to the built inception module
              :param kernels_1x1: number of kernels of size 1x1                                      (1st branch)
              :param kernels_3x3_reduce: number of kernels of size 1x1 before the 3x3 layer          (2nd branch)
              :param kernels_3x3: number of kernels of size 3x3                                      (2nd branch)
              :param kernels_5x5_reduce: number of kernels of size 1x1 before the 5x5 layer          (3rd branch)
              :param kernels_5x5: number of kernels of size 5x5                                      (3rd branch)
              :param kernels_pool_projection: number of kernels of size 1x1 after the 3x3 pooling    (4th branch)
          """
          # Branch 1
          self.model.add_node(Conv2D(kernels_1x1, (1, 1)), name=name + '/1x1', input=input_layer)
          self.model.add_node(Activation('relu'), name=name + '/relu_1x1', input=name + '/1x1')
  
          # Branch 2
          self.model.add_node(Conv2D(kernels_3x3_reduce, (1, 1)), name=name + '/3x3_reduce', input=input_layer)
          self.model.add_node(Activation('relu'), name=name + '/relu_3x3_reduce', input=name + '/3x3_reduce')
          self.model.add_node(ZeroPadding2D((1, 1)), name=name + '/3x3_zeropadding', input=name + '/relu_3x3_reduce')
          self.model.add_node(Conv2D(kernels_3x3, (3, 3)), name=name + '/3x3', input=name + '/3x3_zeropadding')
          self.model.add_node(Activation('relu'), name=name + '/relu_3x3', input=name + '/3x3')
  
          # Branch 3
          self.model.add_node(Conv2D(kernels_5x5_reduce, (1, 1)), name=name + '/5x5_reduce', input=input_layer)
          self.model.add_node(Activation('relu'), name=name + '/relu_5x5_reduce', input=name + '/5x5_reduce')
          self.model.add_node(ZeroPadding2D((2, 2)), name=name + '/5x5_zeropadding', input=name + '/relu_5x5_reduce')
          self.model.add_node(Conv2D(kernels_5x5, (5, 5)), name=name + '/5x5', input=name + '/5x5_zeropadding')
          self.model.add_node(Activation('relu'), name=name + '/relu_5x5', input=name + '/5x5')
  
          # Branch 4
          self.model.add_node(ZeroPadding2D((1, 1)), name=name + '/pool_zeropadding', input=input_layer)
          self.model.add_node(MaxPooling2D((3, 3), strides=(1, 1)), name=name + '/pool', input=name + '/pool_zeropadding')
          self.model.add_node(Conv2D(kernels_pool_projection, (1, 1)), name=name + '/pool_proj', input=name + '/pool')
          self.model.add_node(Activation('relu'), name=name + '/relu_pool_proj', input=name + '/pool_proj')
  
          # Concatenate
          inputs_list = [name + '/relu_1x1', name + '/relu_3x3', name + '/relu_5x5', name + '/relu_pool_proj']
          out_name = name + '/concat'
          self.model.add_node(Activation('linear'), name=out_name, inputs=inputs_list, concat_axis=1)
  
          return out_name
code_tokens: def __addInception ( self , name , input_layer , kernels_1x1 , kernels_3x3_reduce , kernels_3x3 , kernels_5x5_reduce , kernels_5x5 , kernels_pool_projection ) : self . model . add_node ( Conv2D ( kernels_1x1 , ( 1 , 1 ) ) , name = name + '/1x1' , input = input_layer ) self . model . add_node ( Activation ( 'relu' ) , name = name + '/relu_1x1' , input = name + '/1x1' ) self . model . add_node ( Conv2D ( kernels_3x3_reduce , ( 1 , 1 ) ) , name = name + '/3x3_reduce' , input = input_layer ) self . model . add_node ( Activation ( 'relu' ) , name = name + '/relu_3x3_reduce' , input = name + '/3x3_reduce' ) self . model . add_node ( ZeroPadding2D ( ( 1 , 1 ) ) , name = name + '/3x3_zeropadding' , input = name + '/relu_3x3_reduce' ) self . model . add_node ( Conv2D ( kernels_3x3 , ( 3 , 3 ) ) , name = name + '/3x3' , input = name + '/3x3_zeropadding' ) self . model . add_node ( Activation ( 'relu' ) , name = name + '/relu_3x3' , input = name + '/3x3' ) self . model . add_node ( Conv2D ( kernels_5x5_reduce , ( 1 , 1 ) ) , name = name + '/5x5_reduce' , input = input_layer ) self . model . add_node ( Activation ( 'relu' ) , name = name + '/relu_5x5_reduce' , input = name + '/5x5_reduce' ) self . model . add_node ( ZeroPadding2D ( ( 2 , 2 ) ) , name = name + '/5x5_zeropadding' , input = name + '/relu_5x5_reduce' ) self . model . add_node ( Conv2D ( kernels_5x5 , ( 5 , 5 ) ) , name = name + '/5x5' , input = name + '/5x5_zeropadding' ) self . model . add_node ( Activation ( 'relu' ) , name = name + '/relu_5x5' , input = name + '/5x5' ) self . model . add_node ( ZeroPadding2D ( ( 1 , 1 ) ) , name = name + '/pool_zeropadding' , input = input_layer ) self . model . add_node ( MaxPooling2D ( ( 3 , 3 ) , strides = ( 1 , 1 ) ) , name = name + '/pool' , input = name + '/pool_zeropadding' ) self . model . add_node ( Conv2D ( kernels_pool_projection , ( 1 , 1 ) ) , name = name + '/pool_proj' , input = name + '/pool' ) self . model . add_node ( Activation ( 'relu' ) , name = name + '/relu_pool_proj' , input = name + '/pool_proj' ) inputs_list = [ name + '/relu_1x1' , name + '/relu_3x3' , name + '/relu_5x5' , name + '/relu_pool_proj' ] out_name = name + '/concat' self . model . add_node ( Activation ( 'linear' ) , name = out_name , inputs = inputs_list , concat_axis = 1 ) return out_name
short_docstring: Adds an inception module to the model.
short_docstring_tokens: Adds an inception module to the model .
comment: """
              Adds an inception module to the model.
  
              :param name: string identifier of the inception layer
              :param input_layer: identifier of the layer that will serve as an input to the built inception module
              :param kernels_1x1: number of kernels of size 1x1                                      (1st branch)
              :param kernels_3x3_reduce: number of kernels of size 1x1 before the 3x3 layer          (2nd branch)
              :param kernels_3x3: number of kernels of size 3x3                                      (2nd branch)
              :param kernels_5x5_reduce: number of kernels of size 1x1 before the 5x5 layer          (3rd branch)
              :param kernels_5x5: number of kernels of size 5x5                                      (3rd branch)
              :param kernels_pool_projection: number of kernels of size 1x1 after the 3x3 pooling    (4th branch)
          """$SEP$# Branch 1$SEP$# Branch 2$SEP$# Branch 3$SEP$# Branch 4$SEP$# Concatenate