code: def get_core_ml_prediction(
      build,
      input_placeholders,
      input_values,
      use_cpu_only=False,
      backend="nn_proto",
  ):
      """
      Return predictions of the given model.
      """
      program = Program()
      with Function(input_placeholders) as ssa_func:
          output_vars = build(**ssa_func.inputs)
          if isinstance(output_vars, tuple):
              output_vars = list(output_vars)
          elif not isinstance(output_vars, list):
              output_vars = [output_vars]
          ssa_func.set_outputs(output_vars)
          program.add_function("main", ssa_func)
  
      proto = _converter._convert(
          program, convert_from="mil", convert_to=backend
      )
      model = coremltools.models.MLModel(proto, use_cpu_only)
      return model.predict(input_values, useCPUOnly=use_cpu_only)
  
imports: import logging
  import numpy
  import copy
  import coremltools
  from coremltools import converters
  from coremltools.converters.mil import converter
  from coremltools.converters.mil.mil import Program, Function
  from coremltools.converters.mil.mil.passes.pass_registry import PASS_REGISTRY
  from coremltools._deps import _IS_MACOS
label_desc: Use image node_id 13 for calling a global function with example usage: get_core_ml_prediction(build, input_placeholders, input_values, use_cpu_only, backend) and returns: model