docstr:         INPUT
           |
           v
          [t_0]
           |
           v
          [t_1]
           |
           v
          [t_3] --.
           |      |
           v      v
          [t_4]  RELU_1
           |
           v
          [t_5]
           |
           v
          RELU_2
  t_0, t_1, t_3 can be merged.
  t_4, t_5 can be merged.
  The output should be
          INPUT
           |
           .------.
           |      |
           v      v
         RELU_2  RELU_1
docstr_tok: ["INPUT","|","v","[","t_0","]","|","v","[","t_1","]","|","v","[","t_3","]","--",".","|","|","v","v","[","t_4","]","RELU_1","|","v","[","t_5","]","|","v","RELU_2","t_0",",","t_1",",","t_3","can","be","merged",".","t_4",",","t_5","can","be","merged",".","The","output","should","be","INPUT","|",".","--","--","--",".","|","|","v","v","RELU_2","RELU_1"]
code: def test_branch_structure(self):
      """
              INPUT
               |
               v
              [t_0]
               |
               v
              [t_1]
               |
               v
              [t_3] --.
               |      |
               v      v
              [t_4]  RELU_1
               |
               v
              [t_5]
               |
               v
              RELU_2
      t_0, t_1, t_3 can be merged.
      t_4, t_5 can be merged.
      The output should be
              INPUT
               |
               .------.
               |      |
               v      v
             RELU_2  RELU_1
  
      """
      input_shape = (1, 10, 5)
      input_features = [("data", datatypes.Array(1, 10, 5))]
      output_features = [("out", None)]
      builder = neural_network.NeuralNetworkBuilder(
          input_features,
          output_features,
          disable_rank5_shape_mapping=True,
      )
      transpose = [
          [2, 1, 0],
          [2, 1, 0],
          [0, 1, 2],
          [2, 0, 1],
          [1, 2, 0],
      ]
      input_name = "data"
      for i, axes in enumerate(transpose):
          name = "transpose_" + str(i)
          output_name = name + "_out"
          builder.add_transpose(
              name=name,
              axes=axes,
              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",
      )
      builder.add_activation(
          name="dumpy",
          non_linearity="RELU",
          input_name="transpose_2_out",
          output_name="dumpy",
      )
      self._test_builder(builder, input_shape, 2)
  
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 10 for calling the Redundant_Transposees_Test obj's underlying member method code with example usage: obj.test_branch_structure() without return types