docstr:        INPUT
           |
           v
          [t_0]
           |
           v
          [t_1]
           |
           v
           .
           .
           .
           |
           v
         [t_999]
           |
           v
          RELU
  tk are randomly generated,
  under this certain seed, the result should be
          INPUT
           |
           v
          [t_0]
           |
           v
          [t_1]
           |
           v
          RELU
docstr_tok: ["INPUT","|","v","[","t_0","]","|","v","[","t_1","]","|","v",".",".",".","|","v","[","t_999","]","|","v","RELU","tk","are","randomly","generated",",","under","this","certain","seed",",","the","result","should","be","INPUT","|","v","[","t_0","]","|","v","[","t_1","]","|","v","RELU"]
code: def test_remove_thousands_random_transpose_layers(self):
      """
             INPUT
               |
               v
              [t_0]
               |
               v
              [t_1]
               |
               v
               .
               .
               .
               |
               v
             [t_999]
               |
               v
              RELU
      tk are randomly generated,
      under this certain seed, the result should be
              INPUT
               |
               v
              [t_0]
               |
               v
              [t_1]
               |
               v
              RELU
      """
  
      import random
      from itertools import permutations
  
      random.seed(1000)
      input_shape = (3, 10, 5)
      input_features = [("data", datatypes.Array(*input_shape))]
      output_features = [("out", None)]
      builder = neural_network.NeuralNetworkBuilder(
          input_features,
          output_features,
          disable_rank5_shape_mapping=True,
      )
  
      num_layers = 1000
      dim = 3
      input_name = "data"
      debug = []
      for i in range(num_layers):
          axes = list(permutations(range(dim)))
          random.shuffle(axes)
          output_name = "layer_" + str(i) + "_output"
          name = "layer_" + str(i)
          debug.append(axes[0])
          builder.add_transpose(
              name=name,
              axes=axes[0],
              input_name=input_name,
              output_name=output_name,
          )
          input_name = output_name
      builder.add_activation(
          name="relu",
          non_linearity="RELU",
          input_name=input_name,
          output_name="out",
      )
      self._test_builder(builder, input_shape, None)
  
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 8 for calling the Redundant_Transposees_Test obj's underlying member method code with example usage: obj.test_remove_thousands_random_transpose_layers() without return types