code: def test_multiarray_to_image_input_util_HWC_format(self):
      H, W, C = 1, 1, 3
      input_features = [("data", datatypes.Array(H, W, C))]
      output_features = [("out", datatypes.Array(H, W, C))]
      builder = NeuralNetworkBuilder(
          input_features,
          output_features,
          disable_rank5_shape_mapping=True,
      )
      builder.add_activation("linear", "LINEAR", "data", "out")
      spec = builder.spec
      mlmodel = MLModel(spec, compute_units=ComputeUnit.CPU_ONLY)
      mlmodel = make_image_input(
          mlmodel,
          "data",
          red_bias=-5,
          green_bias=-6,
          blue_bias=-2.5,
          scale=10.0,
          image_format="NHWC",
      )
      x = np.array([4, 2, 5], dtype=np.uint8)
      x = np.reshape(x, (H, W, C))
      pil_img = PIL.Image.fromarray(x)
      y = mlmodel.predict({"data": pil_img})["out"]
      self.assertEqual(y.shape, (H, W, C))
      np.testing.assert_almost_equal(y.flatten(), [35.0, 14.0, 47.5])
  
imports: import os
  import tempfile
  import unittest
  import numpy
  import PIL.Image
  import coremltools
  from coremltools import ComputeUnit
  from coremltools._deps import _HAS_TORCH
  from coremltools.converters.mil import Builder
  from coremltools.models import MLModel, datatypes
  from coremltools.models.neural_network import NeuralNetworkBuilder
  from coremltools.models.neural_network.utils import make_image_input, make_nn_classifier
  from coremltools.models.utils import _convert_neural_network_spec_weights_to_fp16, _is_macos, _macos_version, convert_double_to_float_multiarray_type, rename_feature, save_spec
  from coremltools.proto import Model_pb2
label_desc: Use image node_id 17 for calling the MLModelTest obj's underlying member method code with example usage: obj.test_multiarray_to_image_input_util_HWC_format() without return types