original_docstring: 
          Adds a Dense Block for the transition down path.
  
          # 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.
  
          :param name:
          :param input_layer: input layer to the dense block.
          :param nb_layers: number of dense layers included in the dense block (see self.add_dense_layer()
                            for information about the internal layers).
          :param k: growth rate. Number of additional feature maps learned at each layer.
          :param drop: dropout rate.
          :param init_weights: weights initialization function
          :return: output layer of the dense block
          
docstring: Adds a Dense Block for the transition down path.
  References
  Jegou S, Drozdzal M, Vazquez D, Romero A, Bengio Y.
  The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation.
docstring_tokens: Adds a Dense Block for the transition down path . References Jegou S Drozdzal M Vazquez D Romero A Bengio Y . The One Hundred Layers Tiramisu : Fully Convolutional DenseNets for Semantic Segmentation .
code: def add_dense_block(self, input_layer, nb_layers, k, drop, init_weights, name=None):
          """
          Adds a Dense Block for the transition down path.
  
          # 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.
  
          :param name:
          :param input_layer: input layer to the dense block.
          :param nb_layers: number of dense layers included in the dense block (see self.add_dense_layer()
                            for information about the internal layers).
          :param k: growth rate. Number of additional feature maps learned at each layer.
          :param drop: dropout rate.
          :param init_weights: weights initialization function
          :return: output layer of the dense block
          """
          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)
  
          list_outputs = []
          prev_layer = input_layer
          for n in range(nb_layers):
              if name is not None:
                  name_dense = name + '_' + str(n)
                  name_merge = 'merge' + name + '_' + str(n)
              else:
                  name_dense = None
                  name_merge = None
  
              # Insert dense layer
              new_layer = self.add_dense_layer(prev_layer, k, drop, init_weights, name=name_dense)
              list_outputs.append(new_layer)
              # Merge with previous layer
              prev_layer = concatenate([new_layer, prev_layer], axis=axis, name=name_merge)
  
          return concatenate(list_outputs, axis=axis, name=name_merge)
code_tokens: def add_dense_block ( self , input_layer , nb_layers , k , drop , init_weights , name = None ) : 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 ) list_outputs = [ ] prev_layer = input_layer for n in range ( nb_layers ) : if name is not None : name_dense = name + '_' + str ( n ) name_merge = 'merge' + name + '_' + str ( n ) else : name_dense = None name_merge = None new_layer = self . add_dense_layer ( prev_layer , k , drop , init_weights , name = name_dense ) list_outputs . append ( new_layer ) prev_layer = concatenate ( [ new_layer , prev_layer ] , axis = axis , name = name_merge ) return concatenate ( list_outputs , axis = axis , name = name_merge )
short_docstring: Adds a Dense Block for the transition down path.
short_docstring_tokens: Adds a Dense Block for the transition down path .
comment: """
          Adds a Dense Block for the transition down path.
  
          # 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.
  
          :param name:
          :param input_layer: input layer to the dense block.
          :param nb_layers: number of dense layers included in the dense block (see self.add_dense_layer()
                            for information about the internal layers).
          :param k: growth rate. Number of additional feature maps learned at each layer.
          :param drop: dropout rate.
          :param init_weights: weights initialization function
          :return: output layer of the dense block
          """$SEP$# Insert dense layer$SEP$# Merge with previous layer