code: def _perform_torch_convert(
      converter: TorchConverter, debug: bool
  ) -> Program:
      try:
          prog = converter.convert()
      except RuntimeError as e:
          if debug and "convert function" in str(e):
              implemented, missing = converter.check_ops()
              print("the following model ops are IMPLEMENTED:")
              print(
                  "\n".join(
                      ["  " + str(x) for x in sorted(implemented)]
                  )
              )
              print("the following model ops are MISSING:")
              print("\n".join(["  " + str(x) for x in sorted(missing)]))
          raise e
  
      return prog
  
file_path: coremltools/coremltools/converters/mil/frontend/torch/load.py
imports: import os.path
  from typing import List, Optional, Union
  import torch
  from torch.jit._script import RecursiveScriptModule
  from coremltools._deps import _HAS_TORCH_EXPORT_API
  from coremltools.converters.mil.frontend.torch.converter import TorchConverter
  from coremltools.converters.mil.input_types import TensorType
  from coremltools.converters.mil.mil.program import Program
  from .converter import TorchConverter
label_desc: Use image node_id 3 for calling a global function with example usage: _perform_torch_convert(converter, debug) and returns: prog