code: def test_load_constant_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_activation("relu2", "RELU", "relu1", "out")
      builder.add_load_constant_nd(
          "const2", "c2", constant_value=np.ones((5,)), shape=(5,)
      )
      builder.add_load_constant_nd(
          "const3", "c3", constant_value=np.ones((5,)), shape=(5,)
      )
      spec = builder.spec
      np.testing.assert_equal(5, len(spec.neuralNetwork.layers))
      remove_disconnected_layers(spec)
      np.testing.assert_equal(2, len(spec.neuralNetwork.layers))
  
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 MLModelPassesTest obj's underlying member method code with example usage: obj.test_load_constant_remove() without return types