original_docstring: 
          Adds a Transition Down Block. Consisting of BN, ReLU, Conv and Dropout, Pooling, Dense Block.
  
          # References
              Jegou S, Drozdzal M, Vazquez D, Romero A, Bengio Y.
              The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation.
              arXiv preprint arXiv:1611.09326. 2016 Nov 28.
  
          # Input layers parameters
          :param input_layer: input layer.
  
          # Convolutional layer parameters
          :param nb_filters_conv: number of convolutional filters to learn.
          :param pool_size: size of the max pooling operation (2 in reference paper)
          :param init_weights: weights initialization function
  
          # Dense Block parameters
          :param nb_layers: number of dense layers included in the dense block (see self.add_dense_layer()
                            for information about the internal layers).
          :param growth: growth rate. Number of additional feature maps learned at each layer.
          :param drop: dropout rate.
  
          :return: [output layer, skip connection name]
          
docstring: Adds a Transition Down Block. Consisting of BN, ReLU, Conv and Dropout, Pooling, Dense Block.
  References
  Jegou S, Drozdzal M, Vazquez D, Romero A, Bengio Y.
  The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation.
  
  Input layers parameters
docstring_tokens: Adds a Transition Down Block . Consisting of BN ReLU Conv and Dropout Pooling Dense Block . References Jegou S Drozdzal M Vazquez D Romero A Bengio Y . The One Hundred Layers Tiramisu : Fully Convolutional DenseNets for Semantic Segmentation . Input layers parameters
code: def add_transitiondown_block(self, input_layer,
                                   nb_filters_conv, pool_size, init_weights,
                                   nb_layers, growth, drop):
          """
          Adds a Transition Down Block. Consisting of BN, ReLU, Conv and Dropout, Pooling, Dense Block.
  
          # References
              Jegou S, Drozdzal M, Vazquez D, Romero A, Bengio Y.
              The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation.
              arXiv preprint arXiv:1611.09326. 2016 Nov 28.
  
          # Input layers parameters
          :param input_layer: input layer.
  
          # Convolutional layer parameters
          :param nb_filters_conv: number of convolutional filters to learn.
          :param pool_size: size of the max pooling operation (2 in reference paper)
          :param init_weights: weights initialization function
  
          # Dense Block parameters
          :param nb_layers: number of dense layers included in the dense block (see self.add_dense_layer()
                            for information about the internal layers).
          :param growth: growth rate. Number of additional feature maps learned at each layer.
          :param drop: dropout rate.
  
          :return: [output layer, skip connection name]
          """
          if K.image_dim_ordering() == 'th':
              axis = 1
          elif K.image_dim_ordering() == 'tf':
              axis = -1
          else:
              raise ValueError('Invalid dim_ordering:', K.image_dim_ordering)
  
          # Dense Block
          x_dense = self.add_dense_block(input_layer, nb_layers, growth, drop,
                                         init_weights)  # (growth*nb_layers) feature maps added
  
          # Concatenate and skip connection recovery for upsampling path
          skip = concatenate([input_layer, x_dense], axis=axis)
  
          # Transition Down
          x_out = BatchNormalization(mode=2, axis=1)(skip)
          x_out = Activation('relu')(x_out)
          x_out = Conv2D(nb_filters_conv, (1, 1), kernel_initializer=init_weights, padding='same')(x_out)
          if drop > 0.0:
              x_out = Dropout(drop)(x_out)
          x_out = MaxPooling2D(pool_size=(pool_size, pool_size))(x_out)
  
          return [x_out, skip]
code_tokens: def add_transitiondown_block ( self , input_layer , nb_filters_conv , pool_size , init_weights , nb_layers , growth , drop ) : if K . image_dim_ordering ( ) == 'th' : axis = 1 elif K . image_dim_ordering ( ) == 'tf' : axis = - 1 else : raise ValueError ( 'Invalid dim_ordering:' , K . image_dim_ordering ) x_dense = self . add_dense_block ( input_layer , nb_layers , growth , drop , init_weights ) skip = concatenate ( [ input_layer , x_dense ] , axis = axis ) x_out = BatchNormalization ( mode = 2 , axis = 1 ) ( skip ) x_out = Activation ( 'relu' ) ( x_out ) x_out = Conv2D ( nb_filters_conv , ( 1 , 1 ) , kernel_initializer = init_weights , padding = 'same' ) ( x_out ) if drop > 0.0 : x_out = Dropout ( drop ) ( x_out ) x_out = MaxPooling2D ( pool_size = ( pool_size , pool_size ) ) ( x_out ) return [ x_out , skip ]
short_docstring: Adds a Transition Down Block.
short_docstring_tokens: Adds a Transition Down Block .
comment: """
          Adds a Transition Down Block. Consisting of BN, ReLU, Conv and Dropout, Pooling, Dense Block.
  
          # References
              Jegou S, Drozdzal M, Vazquez D, Romero A, Bengio Y.
              The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation.
              arXiv preprint arXiv:1611.09326. 2016 Nov 28.
  
          # Input layers parameters
          :param input_layer: input layer.
  
          # Convolutional layer parameters
          :param nb_filters_conv: number of convolutional filters to learn.
          :param pool_size: size of the max pooling operation (2 in reference paper)
          :param init_weights: weights initialization function
  
          # Dense Block parameters
          :param nb_layers: number of dense layers included in the dense block (see self.add_dense_layer()
                            for information about the internal layers).
          :param growth: growth rate. Number of additional feature maps learned at each layer.
          :param drop: dropout rate.
  
          :return: [output layer, skip connection name]
          """$SEP$# Dense Block$SEP$# (growth*nb_layers) feature maps added$SEP$# Concatenate and skip connection recovery for upsampling path$SEP$# Transition Down