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_Functional(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
          x_b1 = Conv2D(kernels_1x1, (1, 1), name=name + '/1x1', activation='relu')(input_layer)
  
          # Branch 2
          x_b2 = Conv2D(kernels_3x3_reduce, (1, 1), name=name + '/3x3_reduce', activation='relu')(input_layer)
          x_b2 = ZeroPadding2D((1, 1), name=name + '/3x3_zeropadding')(x_b2)
          x_b2 = Conv2D(kernels_3x3, (3, 3), name=name + '/3x3', activation='relu')(x_b2)
  
          # Branch 3
          x_b3 = Conv2D(kernels_5x5_reduce, (1, 1), name=name + '/5x5_reduce', activation='relu')(input_layer)
          x_b3 = ZeroPadding2D((2, 2), name=name + '/5x5_zeropadding')(x_b3)
          x_b3 = Conv2D(kernels_5x5, (5, 5), name=name + '/5x5', activation='relu')(x_b3)
  
          # Branch 4
          x_b4 = ZeroPadding2D((1, 1), name=name + '/pool_zeropadding')(input_layer)
          x_b4 = MaxPooling2D((3, 3), strides=(1, 1), name=name + '/pool')(x_b4)
          x_b4 = Conv2D(kernels_pool_projection, (1, 1), name=name + '/pool_proj', activation='relu')(x_b4)
  
          # Concatenate
          out_name = name + '/concat'
          out_node = concatenate([x_b1, x_b2, x_b3, x_b4], axis=1, name=out_name)
  
          return [out_node, out_name]
code_tokens: def __addInception_Functional ( name , input_layer , kernels_1x1 , kernels_3x3_reduce , kernels_3x3 , kernels_5x5_reduce , kernels_5x5 , kernels_pool_projection ) : x_b1 = Conv2D ( kernels_1x1 , ( 1 , 1 ) , name = name + '/1x1' , activation = 'relu' ) ( input_layer ) x_b2 = Conv2D ( kernels_3x3_reduce , ( 1 , 1 ) , name = name + '/3x3_reduce' , activation = 'relu' ) ( input_layer ) x_b2 = ZeroPadding2D ( ( 1 , 1 ) , name = name + '/3x3_zeropadding' ) ( x_b2 ) x_b2 = Conv2D ( kernels_3x3 , ( 3 , 3 ) , name = name + '/3x3' , activation = 'relu' ) ( x_b2 ) x_b3 = Conv2D ( kernels_5x5_reduce , ( 1 , 1 ) , name = name + '/5x5_reduce' , activation = 'relu' ) ( input_layer ) x_b3 = ZeroPadding2D ( ( 2 , 2 ) , name = name + '/5x5_zeropadding' ) ( x_b3 ) x_b3 = Conv2D ( kernels_5x5 , ( 5 , 5 ) , name = name + '/5x5' , activation = 'relu' ) ( x_b3 ) x_b4 = ZeroPadding2D ( ( 1 , 1 ) , name = name + '/pool_zeropadding' ) ( input_layer ) x_b4 = MaxPooling2D ( ( 3 , 3 ) , strides = ( 1 , 1 ) , name = name + '/pool' ) ( x_b4 ) x_b4 = Conv2D ( kernels_pool_projection , ( 1 , 1 ) , name = name + '/pool_proj' , activation = 'relu' ) ( x_b4 ) out_name = name + '/concat' out_node = concatenate ( [ x_b1 , x_b2 , x_b3 , x_b4 ] , axis = 1 , name = out_name ) return [ out_node , 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