code: def convert_function(
      concrete_function,
      pass_pipeline="tf-standard-pipeline",
      show_debug_info=False,
  ):
      """Import a ConcreteFunction 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.
  
      A [tf.function](https://www.tensorflow.org/api_docs/python/tf/function) can be
      imported and converted from TensorFlow to TensorFlow MLIR with this API by
      extracting its ConcreteFunction (eagerly-executing wrapper around a
      [tf.Graph](https://www.tensorflow.org/api_docs/python/tf/Graph)).
  
      For example:
      >>> @tf.function
      ... def add(a, b):
      ...   return a + b
  
      >>> concrete_function = add.get_concrete_function(
      ...     tf.TensorSpec(None, tf.dtypes.float32),
      ...     tf.TensorSpec(None, tf.dtypes.float32))
      >>> tf.mlir.experimental.convert_function(concrete_function)
      '...module attributes {...} {...}...'
  
      Args:
        concrete_function: An object of type ConcreteFunction.
        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
        ConcreteFunction.
  
      Raises:
        InvalidArgumentError: if concrete_function is invalid or cannot be converted
          to MLIR.
      """
      return pywrap_mlir.import_function(
          concrete_function, pass_pipeline, show_debug_info
      )
  
file_path: tensorflow/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 2 for calling a global function with example usage: convert_function(concrete_function, pass_pipeline, show_debug_info) and returns: pywrap_mlir