old_contents: # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # =============================================================================
  """mlir is an experimental library that provides support APIs for MLIR."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python import pywrap_mlir
  from tensorflow.python.util.tf_export import tf_export
  
  
  @tf_export('mlir.experimental.convert_graph_def')
  def convert_graph_def(graph_def, pass_pipeline='tf-standard-pipeline'):
    """Import a GraphDef and convert it to a textual MLIR module.
  
    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://github.com/tensorflow/mlir/blob/master/g3doc/WritingAPass.md#textual-pass-pipeline-specification).
  
    Returns:
      A textual representation of the MLIR module corresponding to the graphdef.
      Raises a RuntimeError on error.
  
    """
    return pywrap_mlir.import_graphdef(graph_def, pass_pipeline)
  
new_contents: # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # =============================================================================
  """mlir is an experimental library that provides support APIs for MLIR."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python import pywrap_mlir
  from tensorflow.python.util.tf_export import tf_export
  
  
  @tf_export('mlir.experimental.convert_graph_def')
  def convert_graph_def(graph_def, pass_pipeline='tf-standard-pipeline'):
    """Import a GraphDef and convert it to a textual MLIR module.
  
    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).
  
    Returns:
      A textual representation of the MLIR module corresponding to the graphdef.
      Raises a RuntimeError on error.
  
    """
    return pywrap_mlir.import_graphdef(graph_def, pass_pipeline)
  
subject: Update link in the mlir.experimental.convert_graph_def API documentation