parameters: (session, export_dir, inputs, outputs, legacy_init_op=None)
docstring: Convenience function to build a SavedModel suitable for serving.
  
    In many common cases, saving models for serving will be as simple as:
  
        simple_save(session,
                    export_dir,
                    inputs={"x": x, "y": y},
                    outputs={"z": z})
  
    Although in many cases it's not necessary to understand all of the many ways
        to configure a SavedModel, this method has a few practical implications:
      - It will be treated as a graph for inference / serving (i.e. uses the tag
        `tag_constants.SERVING`)
      - The saved model will load in TensorFlow Serving and supports the
        [Predict API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto).
        To use the Classify, Regress, or MultiInference APIs, please
        use either
        [tf.Estimator](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator)
        or the lower level
        [SavedModel APIs](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md).
      - Some TensorFlow ops depend on information on disk or other information
        called "assets". These are generally handled automatically by adding the
        assets to the `GraphKeys.ASSET_FILEPATHS` collection. Only assets in that
        collection are exported; if you need more custom behavior, you'll need to
        use the [SavedModelBuilder](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/builder.py).
  
    More information about SavedModel and signatures can be found here:
    https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md.
  
    Args:
      session: The TensorFlow session from which to save the meta graph and
          variables.
      export_dir: The path to which the SavedModel will be stored.
      inputs: dict mapping string input names to tensors. These are added
          to the SignatureDef as the inputs.
      outputs:  dict mapping string output names to tensors. These are added
          to the SignatureDef as the outputs.
      legacy_init_op: Legacy support for op or group of ops to execute after the
          restore op upon a load.
docstring_summary: Convenience function to build a SavedModel suitable for serving.
docstring_tokens: Convenience function to build a SavedModel suitable for serving .
function: def simple_save(session, export_dir, inputs, outputs, legacy_init_op=None):
    """Convenience function to build a SavedModel suitable for serving.
  
    In many common cases, saving models for serving will be as simple as:
  
        simple_save(session,
                    export_dir,
                    inputs={"x": x, "y": y},
                    outputs={"z": z})
  
    Although in many cases it's not necessary to understand all of the many ways
        to configure a SavedModel, this method has a few practical implications:
      - It will be treated as a graph for inference / serving (i.e. uses the tag
        `tag_constants.SERVING`)
      - The saved model will load in TensorFlow Serving and supports the
        [Predict API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto).
        To use the Classify, Regress, or MultiInference APIs, please
        use either
        [tf.Estimator](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator)
        or the lower level
        [SavedModel APIs](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md).
      - Some TensorFlow ops depend on information on disk or other information
        called "assets". These are generally handled automatically by adding the
        assets to the `GraphKeys.ASSET_FILEPATHS` collection. Only assets in that
        collection are exported; if you need more custom behavior, you'll need to
        use the [SavedModelBuilder](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/builder.py).
  
    More information about SavedModel and signatures can be found here:
    https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md.
  
    Args:
      session: The TensorFlow session from which to save the meta graph and
          variables.
      export_dir: The path to which the SavedModel will be stored.
      inputs: dict mapping string input names to tensors. These are added
          to the SignatureDef as the inputs.
      outputs:  dict mapping string output names to tensors. These are added
          to the SignatureDef as the outputs.
      legacy_init_op: Legacy support for op or group of ops to execute after the
          restore op upon a load.
    """
    signature_def_map = {
        signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
            signature_def_utils.predict_signature_def(inputs, outputs)
    }
    b = builder.SavedModelBuilder(export_dir)
    b.add_meta_graph_and_variables(
        session,
        tags=[tag_constants.SERVING],
        signature_def_map=signature_def_map,
        assets_collection=ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS),
        legacy_init_op=legacy_init_op,
        clear_devices=True)
    b.save()
function_tokens: def simple_save ( session , export_dir , inputs , outputs , legacy_init_op = None ) : signature_def_map = { signature_constants . DEFAULT_SERVING_SIGNATURE_DEF_KEY : signature_def_utils . predict_signature_def ( inputs , outputs ) } b = builder . SavedModelBuilder ( export_dir ) b . add_meta_graph_and_variables ( session , tags = [ tag_constants . SERVING ] , signature_def_map = signature_def_map , assets_collection = ops . get_collection ( ops . GraphKeys . ASSET_FILEPATHS ) , legacy_init_op = legacy_init_op , clear_devices = True ) b . save ( )