code: def convert_graph_def(
      graph_def,
      pass_pipeline="tf-standard-pipeline",
      show_debug_info=False,
  ):
      """Import a GraphDef and convert it to a textual MLIR module.
  
      This API is only intended for inspecting the internals of TensorFlow and the
      string returned is at the moment intended for debugging purposes.
  
      Args:
        graph_def: An object of type graph_pb2.GraphDef or a textual proto
          representation of a valid GraphDef.
        pass_pipeline: A textual description of an MLIR Pass Pipeline to run on the
          module, see MLIR documentation for the [textual pass pipeline
          syntax](https://mlir.llvm.org/docs/PassManagement/#textual-pass-pipeline-specification).
        show_debug_info: Whether to include locations in the emitted textual form.
  
      Returns:
        A textual representation of the MLIR module corresponding to the graphdef.
  
      Raises:
        InvalidArgumentError: if graph_def is invalid or cannot be converted to
          MLIR.
      """
      return pywrap_mlir.import_graphdef(
          graph_def, pass_pipeline, show_debug_info
      )
  
file_path: tensorflow-upstream/tensorflow/python/compiler/mlir/mlir.py
imports: from tensorflow.python import pywrap_mlir
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 for calling a global function with example usage: convert_graph_def(graph_def, pass_pipeline, show_debug_info) and returns: pywrap_mlir