code: def _test_builder(
      self, builder, input_shape, expected_layer_num=None
  ):
      data = np.random.rand(*input_shape)
  
      # Mlmodel before
      mlmodel = MLModel(
          builder.spec, compute_units=ComputeUnit.CPU_ONLY
      )
      output_before = mlmodel.predict({"data": data})["out"]
      num_layers_before = len(builder.spec.neuralNetwork.layers)
  
      remove_redundant_transposes(builder.spec)
  
      layers = builder.spec.neuralNetwork.layers
      if expected_layer_num is None:
          self.assertTrue(len(layers) < num_layers_before)
      else:
          self.assertEqual(len(layers), expected_layer_num)
  
      # Mlmodel after
      mlmodel = MLModel(
          builder.spec, compute_units=ComputeUnit.CPU_ONLY
      )
      output_after = mlmodel.predict({"data": data})["out"]
  
      np.testing.assert_almost_equal(
          output_before, output_after, decimal=3
      )
  
imports: import copy
  import unittest
  from sys import platform
  import numpy
  import coremltools.models.datatypes
  from coremltools import ComputeUnit
  from coremltools._deps import _IS_MACOS
  from coremltools.converters.mil.backend.nn.passes.mlmodel_passes import remove_disconnected_layers, remove_redundant_transposes, transform_conv_crop
  from coremltools.models import MLModel
  from coremltools.models import neural_network
  from coremltools.models.neural_network.printer import print_network_spec
  from coremltools.models.utils import _macos_version
label_desc: Use image node_id 1 for calling the Redundant_Transposees_Test obj's underlying member method code with example usage: obj._test_builder(builder, input_shape, expected_layer_num) without return types