original_docstring: 
          Adds a Transition Up Block. Consisting of Deconv, Skip Connection, 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 name:
          :param input_layer: input layer.
          :param skip_conn: list of layers to be used as skip connections.
  
          # Deconvolutional layer parameters
          :param nb_filters_deconv: number of deconvolutional filters to learn.
          :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
          
docstring: Adds a Transition Up Block. Consisting of Deconv, Skip Connection, 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 Up Block . Consisting of Deconv Skip Connection 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_transitionup_block(self, input_layer, skip_conn,
                                 nb_filters_deconv, init_weights,
                                 nb_layers, growth, drop, name=None):
          """
          Adds a Transition Up Block. Consisting of Deconv, Skip Connection, 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 name:
          :param input_layer: input layer.
          :param skip_conn: list of layers to be used as skip connections.
  
          # Deconvolutional layer parameters
          :param nb_filters_deconv: number of deconvolutional filters to learn.
          :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
          """
  
          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)
  
          input_layer = Conv2DTranspose(nb_filters_deconv, (3, 3),
                                        strides=(2, 2),
                                        kernel_initializer=init_weights, padding='valid')(input_layer)
  
          # Skip connection concatenation
          input_layer = Concatenate(axis=axis, cropping=[None, None, 'center', 'center'])([skip_conn, input_layer])
  
          # Dense Block
          input_layer = self.add_dense_block(input_layer, nb_layers, growth, drop, init_weights,
                                             name=name)  # (growth*nb_layers) feature maps added
          return input_layer
code_tokens: def add_transitionup_block ( self , input_layer , skip_conn , nb_filters_deconv , init_weights , nb_layers , growth , drop , 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 ) input_layer = Conv2DTranspose ( nb_filters_deconv , ( 3 , 3 ) , strides = ( 2 , 2 ) , kernel_initializer = init_weights , padding = 'valid' ) ( input_layer ) input_layer = Concatenate ( axis = axis , cropping = [ None , None , 'center' , 'center' ] ) ( [ skip_conn , input_layer ] ) input_layer = self . add_dense_block ( input_layer , nb_layers , growth , drop , init_weights , name = name ) return input_layer
short_docstring: Adds a Transition Up Block.
short_docstring_tokens: Adds a Transition Up Block .
comment: """
          Adds a Transition Up Block. Consisting of Deconv, Skip Connection, 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 name:
          :param input_layer: input layer.
          :param skip_conn: list of layers to be used as skip connections.
  
          # Deconvolutional layer parameters
          :param nb_filters_deconv: number of deconvolutional filters to learn.
          :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
          """$SEP$# Skip connection concatenation$SEP$# Dense Block$SEP$# (growth*nb_layers) feature maps added