code: def convert_saved_model_v1(
      saved_model_path,
      exported_names,
      tags,
      lift_variables,
      include_variables_in_initializers,
      upgrade_legacy=True,
      show_debug_info=False,
  ):
      """Converts a v1 SavedModel to MLIR module.
  
      Args:
        saved_model_path: Path to SavedModel.
        exported_names: Names to export.
        tags: MetaGraphDef to be loaded is identified by the supplied tags.
        lift_variables: Whether to promote tf.VarHandleOp to resource arguments.
        include_variables_in_initializers: Keeps the variables in initializers
          before lifting variables.
        upgrade_legacy: Functionalize the input graph before importing.
        show_debug_info: Whether to include locations in the emitted textual form.
  
      Returns:
        A textual representation of the MLIR module corresponding to the
        SavedModule.
      """
      return pywrap_mlir.experimental_convert_saved_model_v1_to_mlir(
          saved_model_path,
          exported_names,
          tags,
          lift_variables,
          include_variables_in_initializers,
          upgrade_legacy,
          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 4 for calling a global function with example usage: convert_saved_model_v1(saved_model_path, exported_names, tags, lift_variables, include_variables_in_initializers, upgrade_legacy, show_debug_info) and returns: pywrap_mlir