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.cpu().numpy().astype(np.float32)
          for x, inp in zip(input_description, flattened_inputs)
      }
  
      for k, v in coreml_inputs.items():
          if isinstance(v, np.ndarray) and v.ndim == 0:
              coreml_inputs[k] = np.expand_dims(v, axis=-1)
  
      return coreml_inputs
  
imports: import numpy
  import pytest
  import torch
  import torch.nn
  import coremltools
  import coremltools.models.utils
  from coremltools import RangeDim, TensorType
  from coremltools._deps import _HAS_EXECUTORCH, _HAS_TORCH_EXPORT_API, _IS_MACOS
  from coremltools.converters.mil.mil.types.type_mapping import nptype_from_builtin
  from coremltools.converters.mil.testing_utils import ct_convert, validate_minimum_deployment_target
  from ..ops import TorchFrontend
  from ..torchscript_utils import torch_to_mil_types
label_desc: Use image node_id 4 for calling a global function with example usage: convert_to_coreml_inputs(input_description, inputs) and returns: coreml_inputs