code: 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
          `saved_model.SERVING`)
        - The SavedModel 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
          ),
          main_op=legacy_init_op,
          clear_devices=True,
      )
      b.save()
  
imports: from tensorflow.python.framework import ops
  from tensorflow.python.saved_model import builder
  from tensorflow.python.saved_model import signature_constants
  from tensorflow.python.saved_model import signature_def_utils
  from tensorflow.python.saved_model import tag_constants
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 for calling a global function with example usage: simple_save(session, export_dir, inputs, outputs, legacy_init_op) without return types