code: def DecoderTransposeX2Block(filters, stage, use_batchnorm):
      conv_block1_name = "decoder_stage{}a".format(stage)
      transpose_name = "decoder_stage{}b_transpose".format(stage)
      bn_name = "decoder_stage{}b_bn".format(stage)
      relu_name = "decoder_stage{}b_relu".format(stage)
      conv_block3_name = "decoder_stage{}c".format(stage)
      add_name = "decoder_stage{}_add".format(stage)
  
      channels_axis = bn_axis = (
          3 if backend.image_data_format() == "channels_last" else 1
      )
  
      def wrapper(input_tensor, skip=None):
          input_filters = backend.int_shape(input_tensor)[channels_axis]
          output_filters = (
              backend.int_shape(skip)[channels_axis]
              if skip is not None
              else filters
          )
  
          x = Conv1x1BnReLU(
              input_filters // 4, use_batchnorm, name=conv_block1_name
          )(input_tensor)
          x = layers.Conv2DTranspose(
              filters=input_filters // 4,
              kernel_size=(4, 4),
              strides=(2, 2),
              padding="same",
              name=transpose_name,
              use_bias=not use_batchnorm,
          )(x)
  
          if use_batchnorm:
              x = layers.BatchNormalization(axis=bn_axis, name=bn_name)(
                  x
              )
  
          x = layers.Activation("relu", name=relu_name)(x)
          x = Conv1x1BnReLU(
              output_filters, use_batchnorm, name=conv_block3_name
          )(x)
  
          if skip is not None:
              x = layers.Add(name=add_name)([x, skip])
  
          return x
  
      return wrapper
  
file_path: segmentation_models/segmentation_models/models/linknet.py
imports: from keras_applications import get_submodules_from_kwargs
  from ._common_blocks import Conv2dBn
  from ._utils import freeze_model, filter_keras_submodules
  from ..backbones.backbones_factory import Backbones
label_desc: Use image node_id 5 for calling a global function with example usage: DecoderTransposeX2Block(filters, stage, use_batchnorm) and returns: wrapper, x