code: def __init__(
      self,
      device,
      batch_size,
      layout,
      iterator,
      num_threads=1,
      device_id=0,
      permutation=(1, 0, 2),
      transpose_layout=False,
      out_layout_arg=None,
  ):
      super(TransposePipeline, self).__init__(
          batch_size, num_threads, device_id
      )
      self.device = device
      self.layout = layout
      self.iterator = iterator
      self.inputs = ops.ExternalSource()
      if out_layout_arg:
          self.transpose = ops.Transpose(
              device=self.device,
              perm=permutation,
              transpose_layout=transpose_layout,
              output_layout=out_layout_arg,
          )
      else:
          self.transpose = ops.Transpose(
              device=self.device,
              perm=permutation,
              transpose_layout=transpose_layout,
          )
  
imports: from nvidia.dali.pipeline import Pipeline
  import nvidia.dali.ops
  from nvidia.dali import fn, pipeline_def
  import math
  from test_utils import compare_pipelines, as_array, RandomDataIterator, RandomlyShapedDataIterator
  import itertools
  from nose2.tools import params
  import numpy
label_desc: Use image node_id 1 to create a new TransposePipeline object from inherited base classes: Pipeline with example: obj = TransposePipeline(device, batch_size, layout, iterator, num_threads, device_id, permutation, transpose_layout, out_layout_arg)