code: def test_transpose(self):
      def _build_and_test_network(
          input_size, transpose_layers, expected_layers
      ):
          """
          Helper function for testing transpose removal.
  
          Args:
              input_size: Size of the input network tensor.
              transpose_layers: Array of transpose axes definitions.
              expected_layers: Array of indices into transpose_layers indicating
                  which of the transpose layers should be present after the
                  graph pass.
          """
          input_features = [("data", datatypes.Array(*input_size))]
          output_features = [("out", None)]
          builder = neural_network.NeuralNetworkBuilder(
              input_features, output_features
          )
          spec = builder.spec.neuralNetwork.layers
  
          last_layer = "data"
          for idx, axes in enumerate(transpose_layers):
              name = "t{}".format(idx)
              if idx == len(transpose_layers) - 1:
                  output_name = "out"
              else:
                  output_name = name + "_out"
              builder.add_transpose(
                  name=name,
                  axes=axes,
                  input_name=last_layer,
                  output_name=output_name,
              )
              last_layer = output_name
  
          spec = builder.spec.neuralNetwork
          # Check the network before the graph pass.
          for idx in range(len(transpose_layers)):
              np.testing.assert_equal(
                  "transpose", spec.layers[idx].WhichOneof("layer")
              )
          # Run the removal pass.
          remove_redundant_transposes(builder.spec)
          # Verify only the expected layers remain.
          np.testing.assert_equal(
              len(spec.layers), len(expected_layers)
          )
          for output_layer_idx, input_layer_idx in enumerate(
              expected_layers
          ):
              np.testing.assert_equal(
                  "transpose",
                  spec.layers[output_layer_idx].WhichOneof("layer"),
              )
              np.testing.assert_array_equal(
                  transpose_layers[input_layer_idx],
                  spec.layers[output_layer_idx].transpose.axes,
              )
  
      _build_and_test_network(
          input_size=[1, 10, 10],
          # These transposes are not inverses.
          transpose_layers=[[2, 0, 1], [2, 0, 1]],
          expected_layers=[0, 1],
      )
  
      _build_and_test_network(
          input_size=[1, 1, 10, 10, 3],
          # First two are the identity, then an extra.
          transpose_layers=[
              [2, 4, 1, 0, 3],
              [3, 2, 0, 4, 1],
              [1, 0, 2, 3, 4],
          ],
          expected_layers=[2],
      )
  
      # A slightly more complicated test case where there are two transposes
      # in topological order, but are actually in parallel in the graph.
      builder = neural_network.NeuralNetworkBuilder(
          [("data", datatypes.Array(2, 4, 8))], [("out", None)]
      )
      last_layer = "data"
      builder.add_transpose(
          name="t1", axes=[0, 2, 1], input_name="data", output_name="t1"
      )
      builder.add_transpose(
          name="t2", axes=[0, 2, 1], input_name="data", output_name="t2"
      )
      builder.add_stack(
          name="stack", input_names=["t1", "t2"], output_name="out"
      )
      spec = builder.spec.neuralNetwork
      # Run the removal pass.
      remove_redundant_transposes(builder.spec)
      # Verify nothing was removed.
      np.testing.assert_equal(len(spec.layers), 3)
  
imports: import copy
  import pytest
  import numpy
  import unittest
  from sys import platform
  from coremltools._deps import _IS_MACOS
  import coremltools.models.datatypes
  from coremltools.models.utils import _macos_version
  from coremltools.models import neural_network
  from coremltools.models import MLModel
  from coremltools.models.neural_network.printer import print_network_spec
  from coremltools.converters.mil.backend.nn.passes.mlmodel_passes import remove_disconnected_layers, transform_conv_crop, remove_redundant_transposes
label_desc: Use image node_id 15 for calling the Redundant_Transposees_Test obj's underlying member method code with example usage: obj.test_transpose() without return types