code: def _coreml_forward_onnx_model(
      model,  # type: ModelProto
      input_dict,  # type: Dict[Text, np._ArrayLike[Any]]
      onnx_coreml_input_shape_map={},  # type: Dict[Text, List[int,...]]
      minimum_ios_deployment_target="12",
  ):
      # type: (...) -> np.ndarray[Any]
      coreml_model = convert(
          model,
          onnx_coreml_input_shape_map=onnx_coreml_input_shape_map,
          minimum_ios_deployment_target=minimum_ios_deployment_target,
      )
      output_names = [o.name for o in model.graph.output]
      return _coreml_forward_model(
          coreml_model,
          input_dict,
          output_names,
          minimum_ios_deployment_target=minimum_ios_deployment_target,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import numpy
  import numpy.testing
  import numpy.random
  from onnx import helper, ModelProto, ValueInfoProto, TensorProto, NodeProto
  from typing import Any, Sequence, Text, Tuple, Optional, Dict, List, TypeVar
  from coremltools.converters.onnx import convert
  from coremltools.converters.onnx._converter import SupportedVersion
  from coremltools._deps import _IS_MACOS
  import sys
label_desc: Use image node_id 6 for calling a global function with example usage: _coreml_forward_onnx_model(model, input_dict, onnx_coreml_input_shape_map, minimum_ios_deployment_target) and returns: _coreml_forward_model