code: def test_dead_layer_remove_branch(self):
      convergence_tolerance = 1e-8
  
      input_features = [("input", datatypes.Array(*(2,)))]
      output_features = [("out", None)]
  
      builder = neural_network.NeuralNetworkBuilder(
          input_features,
          output_features,
          disable_rank5_shape_mapping=True,
      )
      # add condition to break from the loop, if convergence criterion is met
      builder.add_less_than(
          "cond", ["input"], "cond", alpha=convergence_tolerance
      )
      branch_layer = builder.add_branch("branch_layer", "cond")
      builder_ifbranch = neural_network.NeuralNetworkBuilder(
          nn_spec=branch_layer.branch.ifBranch
      )
      builder_ifbranch.add_activation(
          "relu1", "RELU", "input", "relu1_out"
      )
      builder_ifbranch.add_activation(
          "relu2_out", "RELU", "relu1_out", "relu2_out"
      )
      builder_elsebranch = neural_network.NeuralNetworkBuilder(
          nn_spec=branch_layer.branch.elseBranch
      )
      builder_elsebranch.add_activation(
          "linear1", "LINEAR", "input", "linear1_out"
      )
      builder_elsebranch.add_activation(
          "linear2", "LINEAR", "linear1_out", "relu2_out"
      )
      builder.add_squeeze("out", "input", "out", squeeze_all=True)
  
      mlmodel = MLModel(builder.spec)
      data = np.random.rand(
          2,
      )
      data_dict = {"input": data}
      if _IS_MACOS:
          before_pass_out = mlmodel.predict(data_dict)["out"]
          if DEBUG:
              print(
                  "\n mlmodel description before remove disconnected layers pass: \n"
              )
              print_network_spec(builder.spec, style="coding")
          remove_disconnected_layers(builder.spec)
          if DEBUG:
              print(
                  "\n mlmodel description after remove disconnected layers pass: \n"
              )
              print_network_spec(builder.spec, style="coding")
          mlmodel = MLModel(builder.spec)
          after_pass_out = mlmodel.predict(data_dict)["out"]
  
          np.testing.assert_almost_equal(
              before_pass_out, after_pass_out, decimal=2
          )
          np.testing.assert_equal(
              len(builder.spec.neuralNetwork.layers), 1
          )
  
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 3 for calling the MLModelPassesTest obj's underlying member method code with example usage: obj.test_dead_layer_remove_branch() without return types