code: def make_image_input(
      model,
      input_name,
      is_bgr=False,
      red_bias=0.0,
      blue_bias=0.0,
      green_bias=0.0,
      gray_bias=0.0,
      scale=1.0,
      image_format="NHWC",
  ):
      """
      Convert input of type multiarray to type image
  
      Parameters
      ----------
      TODO
  
      Returns
      -------
      model: MLModel
      A coreML MLModel object
  
      Examples
      --------
      TODO
      """
  
      spec = model.get_spec()
  
      if spec.WhichOneof("Type") not in [
          "neuralNetwork",
          "neuralNetworkClassifier",
          "neuralNetworkRegressor",
      ]:
          raise ValueError(
              "Provided model must be of type neuralNetwork, neuralNetworkClassifier or neuralNetworkRegressor"
          )
  
      if not isinstance(input_name, list):
          input_name = [input_name]
  
      spec_inputs = [i.name for i in spec.description.input]
      for name in input_name:
          if name not in spec_inputs:
              msg = "Provided input_name: {}, is not an existing input to the model"
              raise ValueError(msg.format(name))
  
      builder = NeuralNetworkBuilder(spec=spec)
      builder.set_pre_processing_parameters(
          image_input_names=input_name,
          is_bgr=is_bgr,
          red_bias=red_bias,
          green_bias=green_bias,
          blue_bias=blue_bias,
          gray_bias=gray_bias,
          image_scale=scale,
          image_format=image_format,
      )
      return _get_model(spec)
  
imports: from .builder import NeuralNetworkBuilder
  from coremltools.models.utils import _get_model
  import copy
label_desc: Use image node_id 1 for calling a global function with example usage: make_image_input(model, input_name, is_bgr, red_bias, blue_bias, green_bias, gray_bias, scale, image_format) and returns: _get_model