code: def convert_to_coreml_inputs(input_description, inputs):
      """Convenience function to combine a CoreML model's input description and
      set of raw inputs into the format expected by the model's predict function.
      """
      flattened_inputs = _flatten(inputs)
      coreml_inputs = {
          str(x): inp.numpy()
          for x, inp in zip(input_description, flattened_inputs)
      }
      return coreml_inputs
  
imports: import numpy
  import torch
  import torch.nn
  from six import string_types
  from coremltools import TensorType
  from coremltools.converters.mil.testing_reqs import _converter
  from coremltools.models import MLModel
  from coremltools._deps import _IS_MACOS
label_desc: Use image node_id 2 for calling a global function with example usage: convert_to_coreml_inputs(input_description, inputs) and returns: coreml_inputs