parameters: (saved_model_path, custom_objects=None)
return_statement: return model
docstring: Loads a keras Model from a SavedModel created by `export_saved_model()`.
  
    This function reinstantiates model state by:
    1) loading model topology from json (this will eventually come
       from metagraph).
    2) loading model weights from checkpoint.
  
    Example:
  
    ```python
    import tensorflow as tf
  
    # Create a tf.keras model.
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Dense(1, input_shape=[10]))
    model.summary()
  
    # Save the tf.keras model in the SavedModel format.
    path = '/tmp/simple_keras_model'
    tf.keras.experimental.export_saved_model(model, path)
  
    # Load the saved keras model back.
    new_model = tf.keras.experimental.load_from_saved_model(path)
    new_model.summary()
    ```
  
    Args:
      saved_model_path: a string specifying the path to an existing SavedModel.
      custom_objects: Optional dictionary mapping names
          (strings) to custom classes or functions to be
          considered during deserialization.
  
    Returns:
      a keras.Model instance.
docstring_summary: Loads a keras Model from a SavedModel created by `export_saved_model()`.
docstring_tokens: Loads a keras Model from a SavedModel created by export_saved_model () .
function: def load_from_saved_model(saved_model_path, custom_objects=None):
    """Loads a keras Model from a SavedModel created by `export_saved_model()`.
  
    This function reinstantiates model state by:
    1) loading model topology from json (this will eventually come
       from metagraph).
    2) loading model weights from checkpoint.
  
    Example:
  
    ```python
    import tensorflow as tf
  
    # Create a tf.keras model.
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Dense(1, input_shape=[10]))
    model.summary()
  
    # Save the tf.keras model in the SavedModel format.
    path = '/tmp/simple_keras_model'
    tf.keras.experimental.export_saved_model(model, path)
  
    # Load the saved keras model back.
    new_model = tf.keras.experimental.load_from_saved_model(path)
    new_model.summary()
    ```
  
    Args:
      saved_model_path: a string specifying the path to an existing SavedModel.
      custom_objects: Optional dictionary mapping names
          (strings) to custom classes or functions to be
          considered during deserialization.
  
    Returns:
      a keras.Model instance.
    """
    # restore model topology from json string
    model_json_filepath = os.path.join(
        compat.as_bytes(saved_model_path),
        compat.as_bytes(constants.ASSETS_DIRECTORY),
        compat.as_bytes(constants.SAVED_MODEL_FILENAME_JSON))
    model_json = file_io.read_file_to_string(model_json_filepath)
    model = model_from_json(model_json, custom_objects=custom_objects)
  
    # restore model weights
    checkpoint_prefix = os.path.join(
        compat.as_text(saved_model_path),
        compat.as_text(constants.VARIABLES_DIRECTORY),
        compat.as_text(constants.VARIABLES_FILENAME))
    model.load_weights(checkpoint_prefix)
    return model
function_tokens: def load_from_saved_model ( saved_model_path , custom_objects = None ) : # restore model topology from json string model_json_filepath = os . path . join ( compat . as_bytes ( saved_model_path ) , compat . as_bytes ( constants . ASSETS_DIRECTORY ) , compat . as_bytes ( constants . SAVED_MODEL_FILENAME_JSON ) ) model_json = file_io . read_file_to_string ( model_json_filepath ) model = model_from_json ( model_json , custom_objects = custom_objects ) # restore model weights checkpoint_prefix = os . path . join ( compat . as_text ( saved_model_path ) , compat . as_text ( constants . VARIABLES_DIRECTORY ) , compat . as_text ( constants . VARIABLES_FILENAME ) ) model . load_weights ( checkpoint_prefix ) return model