code: def test_dead_layer_remove(self):
      input_features = [("data", datatypes.Array(*(3, 4)))]
      output_features = [("out", None)]
      builder = neural_network.NeuralNetworkBuilder(
          input_features,
          output_features,
          disable_rank5_shape_mapping=True,
      )
      builder.add_activation("relu1", "RELU", "data", "relu1")
      builder.add_load_constant_nd(
          "const1", "c1", constant_value=np.ones((5,)), shape=(5,)
      )
      builder.add_load_constant_nd(
          "const2", "c2", constant_value=np.ones((5,)), shape=(5,)
      )
      builder.add_split_nd(
          "splitnd1", "const2", ["s1", "s2", "s3"], axis=0, num_splits=3
      )
      builder.add_squeeze("squeeze", "s1", "squeeze_out")
      builder.add_activation("relu4", "RELU", "s2", "relu4")
      builder.add_activation("relu5", "RELU", "relu4", "relu5")
      builder.add_load_constant_nd(
          "const3", "c3", constant_value=np.ones((5,)), shape=(5,)
      )
      builder.add_activation("relu2", "RELU", "relu1", "out")
      spec = builder.spec
      np.testing.assert_equal(9, len(spec.neuralNetwork.layers))
      remove_disconnected_layers(spec)
      np.testing.assert_equal(2, len(spec.neuralNetwork.layers))
  
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 2 for calling the MLModelPassesTest obj's underlying member method code with example usage: obj.test_dead_layer_remove() without return types