Python tensorflow.python.util.compat.as_text() Examples

def _do_call(self, fn, *args):
    try:
      return fn(*args)
    except errors.OpError as e:
      message = compat.as_text(e.message)
      m = BaseSession._NODEDEF_NAME_RE.search(message)
      node_def = None
      op = None
      if m is not None:
        node_name = m.group(1)
        try:
          op = self._graph.get_operation_by_name(node_name)
          node_def = op.node_def
        except KeyError:
          pass
      raise type(e)(node_def, op, message) 

def _write_value_event(self, event):
    value = event.summary.value[0]

    # Obtain the device name from the metadata.
    summary_metadata = event.summary.value[0].metadata
    if not summary_metadata.plugin_data:
      raise ValueError("The value lacks plugin data.")
    try:
      content = json.loads(compat.as_text(summary_metadata.plugin_data.content))
    except ValueError as err:
      raise ValueError("Could not parse content into JSON: %r, %r" % (content,
                                                                      err))
    device_name = content["device"]

    dump_full_path = _get_dump_file_path(
        self._dump_dir, device_name, value.node_name)
    self._try_makedirs(os.path.dirname(dump_full_path))
    with open(dump_full_path, "wb") as f:
      f.write(event.SerializeToString()) 

def testNoOverwrite(self):
    export_dir = os.path.join(tf.test.get_temp_dir(), "test_no_overwrite")
    builder = saved_model_builder.SavedModelBuilder(export_dir)

    # Graph with a single variable. SavedModel invoked to:
    # - add with weights.
    with self.test_session(graph=tf.Graph()) as sess:
      self._init_and_validate_variable(sess, "v", 42)
      builder.add_meta_graph_and_variables(sess, ["foo"])

    # Save the SavedModel to disk in text format.
    builder.save(as_text=True)

    # Restore the graph with tag "foo", whose variables were saved.
    with self.test_session(graph=tf.Graph()) as sess:
      loader.load(sess, ["foo"], export_dir)
      self.assertEqual(
          42, tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)[0].eval())

    # An attempt to create another builder with the same export directory should
    # result in an assertion error.
    self.assertRaises(AssertionError, saved_model_builder.SavedModelBuilder,
                      export_dir) 

def _validate_asset_collection(self, export_dir, graph_collection_def,
                                 expected_asset_file_name,
                                 expected_asset_file_contents,
                                 expected_asset_tensor_name):
    assets_any = graph_collection_def[constants.ASSETS_KEY].any_list.value
    asset = meta_graph_pb2.AssetFileDef()
    assets_any[0].Unpack(asset)
    assets_path = os.path.join(
        compat.as_bytes(export_dir),
        compat.as_bytes(constants.ASSETS_DIRECTORY),
        compat.as_bytes(expected_asset_file_name))
    actual_asset_contents = file_io.read_file_to_string(assets_path)
    self.assertEqual(expected_asset_file_contents,
                     compat.as_text(actual_asset_contents))
    self.assertEqual(expected_asset_file_name, asset.filename)
    self.assertEqual(expected_asset_tensor_name, asset.tensor_info.name) 

def _do_call(self, fn, *args):
    try:
      return fn(*args)
    except errors.OpError as e:
      message = compat.as_text(e.message)
      m = BaseSession._NODEDEF_NAME_RE.search(message)
      node_def = None
      op = None
      if m is not None:
        node_name = m.group(1)
        try:
          op = self._graph.get_operation_by_name(node_name)
          node_def = op.node_def
        except KeyError:
          pass
      raise type(e)(node_def, op, message) 

def _validate_asset_collection(self, export_dir, graph_collection_def,
                                 expected_asset_file_name,
                                 expected_asset_file_contents,
                                 expected_asset_tensor_name):
    assets_any = graph_collection_def[constants.ASSETS_KEY].any_list.value
    asset = meta_graph_pb2.AssetFileDef()
    assets_any[0].Unpack(asset)
    assets_path = os.path.join(
        compat.as_bytes(export_dir),
        compat.as_bytes(constants.ASSETS_DIRECTORY),
        compat.as_bytes(expected_asset_file_name))
    actual_asset_contents = file_io.read_file_to_string(assets_path)
    self.assertEqual(expected_asset_file_contents,
                     compat.as_text(actual_asset_contents))
    self.assertEqual(expected_asset_file_name, asset.filename)
    self.assertEqual(expected_asset_tensor_name, asset.tensor_info.name) 

def _do_call(self, fn, *args):
    try:
      return fn(*args)
    except errors.OpError as e:
      message = compat.as_text(e.message)
      m = BaseSession._NODEDEF_NAME_RE.search(message)
      node_def = None
      op = None
      if m is not None:
        node_name = m.group(1)
        try:
          op = self._graph.get_operation_by_name(node_name)
          node_def = op.node_def
        except KeyError:
          pass
      raise type(e)(node_def, op, message) 

def testNoOverwrite(self):
    export_dir = os.path.join(test.get_temp_dir(), "test_no_overwrite")
    builder = saved_model_builder.SavedModelBuilder(export_dir)

    # Graph with a single variable. SavedModel invoked to:
    # - add with weights.
    with self.test_session(graph=ops.Graph()) as sess:
      self._init_and_validate_variable(sess, "v", 42)
      builder.add_meta_graph_and_variables(sess, ["foo"])

    # Save the SavedModel to disk in text format.
    builder.save(as_text=True)

    # Restore the graph with tag "foo", whose variables were saved.
    with self.test_session(graph=ops.Graph()) as sess:
      loader.load(sess, ["foo"], export_dir)
      self.assertEqual(
          42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval())

    # An attempt to create another builder with the same export directory should
    # result in an assertion error.
    self.assertRaises(AssertionError, saved_model_builder.SavedModelBuilder,
                      export_dir) 

def testNoOverwrite(self):
    export_dir = os.path.join(test.get_temp_dir(), "test_no_overwrite")
    builder = saved_model_builder.SavedModelBuilder(export_dir)

    # Graph with a single variable. SavedModel invoked to:
    # - add with weights.
    with self.test_session(graph=ops.Graph()) as sess:
      self._init_and_validate_variable(sess, "v", 42)
      builder.add_meta_graph_and_variables(sess, ["foo"])

    # Save the SavedModel to disk in text format.
    builder.save(as_text=True)

    # Restore the graph with tag "foo", whose variables were saved.
    with self.test_session(graph=ops.Graph()) as sess:
      loader.load(sess, ["foo"], export_dir)
      self.assertEqual(
          42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval())

    # An attempt to create another builder with the same export directory should
    # result in an assertion error.
    self.assertRaises(AssertionError, saved_model_builder.SavedModelBuilder,
                      export_dir) 

def _validate_asset_collection(self, export_dir, graph_collection_def,
                                 expected_asset_file_name,
                                 expected_asset_file_contents,
                                 expected_asset_tensor_name):
    assets_any = graph_collection_def[constants.ASSETS_KEY].any_list.value
    asset = meta_graph_pb2.AssetFileDef()
    assets_any[0].Unpack(asset)
    assets_path = os.path.join(
        compat.as_bytes(export_dir),
        compat.as_bytes(constants.ASSETS_DIRECTORY),
        compat.as_bytes(expected_asset_file_name))
    actual_asset_contents = file_io.read_file_to_string(assets_path)
    self.assertEqual(expected_asset_file_contents,
                     compat.as_text(actual_asset_contents))
    self.assertEqual(expected_asset_file_name, asset.filename)
    self.assertEqual(expected_asset_tensor_name, asset.tensor_info.name) 

def _do_call(self, fn, *args):
    try:
      return fn(*args)
    except errors.OpError as e:
      message = compat.as_text(e.message)
      m = BaseSession._NODEDEF_NAME_RE.search(message)
      node_def = None
      op = None
      if m is not None:
        node_name = m.group(1)
        try:
          op = self._graph.get_operation_by_name(node_name)
          node_def = op.node_def
        except KeyError:
          pass
      raise type(e)(node_def, op, message) 

def testSaveAsText(self):
    export_dir = os.path.join(test.get_temp_dir(), "test_astext")
    builder = saved_model_builder.SavedModelBuilder(export_dir)

    # Graph with a single variable. SavedModel invoked to:
    # - add with weights.
    with self.test_session(graph=ops.Graph()) as sess:
      self._init_and_validate_variable(sess, "v", 42)
      builder.add_meta_graph_and_variables(sess, ["foo"])

    # Graph with the same single variable. SavedModel invoked to:
    # - simply add the model (weights are not updated).
    with self.test_session(graph=ops.Graph()) as sess:
      self._init_and_validate_variable(sess, "v", 43)
      builder.add_meta_graph(["bar"])

    # Save the SavedModel to disk in text format.
    builder.save(as_text=True)

    # Restore the graph with tag "foo", whose variables were saved.
    with self.test_session(graph=ops.Graph()) as sess:
      loader.load(sess, ["foo"], export_dir)
      self.assertEqual(
          42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval())

    # Restore the graph with tag "bar", whose variables were not saved.
    with self.test_session(graph=ops.Graph()) as sess:
      loader.load(sess, ["bar"], export_dir)
      self.assertEqual(
          42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval()) 

def save(self, as_text=False):
    """Writes a `SavedModel` protocol buffer to disk.

    The function writes the SavedModel protocol buffer to the export directory
    in serialized format.

    Args:
      as_text: Writes the SavedModel protocol buffer in text format to disk.

    Returns:
      The path to which the SavedModel protocol buffer was written.
    """
    if not file_io.file_exists(self._export_dir):
      file_io.recursive_create_dir(self._export_dir)

    if as_text:
      path = os.path.join(
          compat.as_bytes(self._export_dir),
          compat.as_bytes(constants.SAVED_MODEL_FILENAME_PBTXT))
      file_io.write_string_to_file(path, str(self._saved_model))
    else:
      path = os.path.join(
          compat.as_bytes(self._export_dir),
          compat.as_bytes(constants.SAVED_MODEL_FILENAME_PB))
      file_io.write_string_to_file(path, self._saved_model.SerializeToString())
    tf_logging.info("SavedModel written to: %s", path)

    return path 

def save(self, as_text=False):
    """Writes a `SavedModel` protocol buffer to disk.

    The function writes the SavedModel protocol buffer to the export directory
    in serialized format.

    Args:
      as_text: Writes the SavedModel protocol buffer in text format to disk.

    Returns:
      The path to which the SavedModel protocol buffer was written.
    """
    if not file_io.file_exists(self._export_dir):
      file_io.recursive_create_dir(self._export_dir)

    if as_text:
      path = os.path.join(
          compat.as_bytes(self._export_dir),
          compat.as_bytes(constants.SAVED_MODEL_FILENAME_PBTXT))
      file_io.write_string_to_file(path, str(self._saved_model))
    else:
      path = os.path.join(
          compat.as_bytes(self._export_dir),
          compat.as_bytes(constants.SAVED_MODEL_FILENAME_PB))
      file_io.write_string_to_file(path, self._saved_model.SerializeToString())
    tf_logging.info("SavedModel written to: %s", path)

    return path 

def load_file_system_library(library_filename):
  """Loads a TensorFlow plugin, containing file system implementation.

  Pass `library_filename` to a platform-specific mechanism for dynamically
  loading a library. The rules for determining the exact location of the
  library are platform-specific and are not documented here.

  Args:
    library_filename: Path to the plugin.
      Relative or absolute filesystem path to a dynamic library file.

  Returns:
    None.

  Raises:
    RuntimeError: when unable to load the library.
  """
  status = py_tf.TF_NewStatus()
  lib_handle = py_tf.TF_LoadLibrary(library_filename, status)
  try:
    error_code = py_tf.TF_GetCode(status)
    if error_code != 0:
      error_msg = compat.as_text(py_tf.TF_Message(status))
      # pylint: disable=protected-access
      raise errors_impl._make_specific_exception(
          None, None, error_msg, error_code)
      # pylint: enable=protected-access
  finally:
    py_tf.TF_DeleteStatus(status) 

def save(self, as_text=False):
    """Writes a `SavedModel` protocol buffer to disk.

    The function writes the SavedModel protocol buffer to the export directory
    in serialized format.

    Args:
      as_text: Writes the SavedModel protocol buffer in text format to disk.

    Returns:
      The path to which the SavedModel protocol buffer was written.
    """
    if not file_io.file_exists(self._export_dir):
      file_io.recursive_create_dir(self._export_dir)

    if as_text:
      path = os.path.join(
          compat.as_bytes(self._export_dir),
          compat.as_bytes(constants.SAVED_MODEL_FILENAME_PBTXT))
      file_io.write_string_to_file(path, str(self._saved_model))
    else:
      path = os.path.join(
          compat.as_bytes(self._export_dir),
          compat.as_bytes(constants.SAVED_MODEL_FILENAME_PB))
      file_io.write_string_to_file(path, self._saved_model.SerializeToString())
    tf_logging.info("SavedModel written to: %s", path)

    return path 

def Cleanse(obj, encoding='utf-8'):
  """Makes Python object appropriate for JSON serialization.

  - Replaces instances of Infinity/-Infinity/NaN with strings.
  - Turns byte strings into unicode strings.
  - Turns sets into sorted lists.
  - Turns tuples into lists.

  Args:
    obj: Python data structure.
    encoding: Charset used to decode byte strings.

  Returns:
    Unicode JSON data structure.
  """
  if isinstance(obj, int):
    return obj
  elif isinstance(obj, float):
    if obj == _INFINITY:
      return 'Infinity'
    elif obj == _NEGATIVE_INFINITY:
      return '-Infinity'
    elif math.isnan(obj):
      return 'NaN'
    else:
      return obj
  elif isinstance(obj, bytes):
    return compat.as_text(obj, encoding)
  elif isinstance(obj, list) or isinstance(obj, tuple):
    return [Cleanse(i, encoding) for i in obj]
  elif isinstance(obj, set):
    return [Cleanse(i, encoding) for i in sorted(obj)]
  elif isinstance(obj, dict):
    return {Cleanse(k, encoding): Cleanse(v, encoding) for k, v in obj.items()}
  else:
    return obj 

def device_name_to_device_path(device_name):
  """Convert device name to device path."""
  device_name_items = compat.as_text(device_name).split("/")
  device_name_items = [item.replace(":", "_") for item in device_name_items]
  return METADATA_FILE_PREFIX + DEVICE_TAG + ",".join(device_name_items) 

def Cleanse(obj, encoding='utf-8'):
  """Makes Python object appropriate for JSON serialization.

  - Replaces instances of Infinity/-Infinity/NaN with strings.
  - Turns byte strings into unicode strings.
  - Turns sets into sorted lists.
  - Turns tuples into lists.

  Args:
    obj: Python data structure.
    encoding: Charset used to decode byte strings.

  Returns:
    Unicode JSON data structure.
  """
  if isinstance(obj, int):
    return obj
  elif isinstance(obj, float):
    if obj == _INFINITY:
      return 'Infinity'
    elif obj == _NEGATIVE_INFINITY:
      return '-Infinity'
    elif math.isnan(obj):
      return 'NaN'
    else:
      return obj
  elif isinstance(obj, bytes):
    return compat.as_text(obj, encoding)
  elif isinstance(obj, list) or isinstance(obj, tuple):
    return [Cleanse(i, encoding) for i in obj]
  elif isinstance(obj, set):
    return [Cleanse(i, encoding) for i in sorted(obj)]
  elif isinstance(obj, dict):
    return {Cleanse(k, encoding): Cleanse(v, encoding) for k, v in obj.items()}
  else:
    return obj 

def _get_test_attrs(self):
    x = control_flow_ops.no_op()
    try:
      a = compat.as_text(x.get_attr("_A"))
    except ValueError:
      a = None
    try:
      b = compat.as_text(x.get_attr("_B"))
    except ValueError:
      b = None
    print(a, b)
    return (a, b) 

def load_file_system_library(library_filename):
  """Loads a TensorFlow plugin, containing file system implementation.

  Pass `library_filename` to a platform-specific mechanism for dynamically
  loading a library. The rules for determining the exact location of the
  library are platform-specific and are not documented here.

  Args:
    library_filename: Path to the plugin.
      Relative or absolute filesystem path to a dynamic library file.

  Returns:
    None.

  Raises:
    RuntimeError: when unable to load the library.
  """
  status = py_tf.TF_NewStatus()
  lib_handle = py_tf.TF_LoadLibrary(library_filename, status)
  try:
    error_code = py_tf.TF_GetCode(status)
    if error_code != 0:
      error_msg = compat.as_text(py_tf.TF_Message(status))
      # pylint: disable=protected-access
      raise errors_impl._make_specific_exception(
          None, None, error_msg, error_code)
      # pylint: enable=protected-access
  finally:
    py_tf.TF_DeleteStatus(status) 

def raise_exception_on_not_ok_status():
  try:
    status = pywrap_tensorflow.TF_NewStatus()
    yield status
    if pywrap_tensorflow.TF_GetCode(status) != 0:
      raise _make_specific_exception(
          None, None,
          compat.as_text(pywrap_tensorflow.TF_Message(status)),
          pywrap_tensorflow.TF_GetCode(status))
  finally:
    pywrap_tensorflow.TF_DeleteStatus(status) 

def testWriteEvents(self):
    file_prefix = os.path.join(self.get_temp_dir(), "events")
    writer = pywrap_tensorflow.EventsWriter(compat.as_bytes(file_prefix))
    filename = compat.as_text(writer.FileName())
    event_written = event_pb2.Event(
        wall_time=123.45, step=67,
        summary=summary_pb2.Summary(
            value=[summary_pb2.Summary.Value(tag="foo", simple_value=89.0)]))
    writer.WriteEvent(event_written)
    writer.Flush()
    writer.Close()

    with self.assertRaises(errors.NotFoundError):
      for r in tf_record.tf_record_iterator(filename + "DOES_NOT_EXIST"):
        self.assertTrue(False)

    reader = tf_record.tf_record_iterator(filename)
    event_read = event_pb2.Event()

    event_read.ParseFromString(next(reader))
    self.assertTrue(event_read.HasField("file_version"))

    event_read.ParseFromString(next(reader))
    # Second event
    self.assertProtoEquals("""
    wall_time: 123.45 step: 67
    summary { value { tag: 'foo' simple_value: 89.0 } }
    """, event_read)

    with self.assertRaises(StopIteration):
      next(reader) 

def testSaveAsText(self):
    export_dir = os.path.join(tf.test.get_temp_dir(), "test_astext")
    builder = saved_model_builder.SavedModelBuilder(export_dir)

    # Graph with a single variable. SavedModel invoked to:
    # - add with weights.
    with self.test_session(graph=tf.Graph()) as sess:
      self._init_and_validate_variable(sess, "v", 42)
      builder.add_meta_graph_and_variables(sess, ["foo"])

    # Graph with the same single variable. SavedModel invoked to:
    # - simply add the model (weights are not updated).
    with self.test_session(graph=tf.Graph()) as sess:
      self._init_and_validate_variable(sess, "v", 43)
      builder.add_meta_graph(["bar"])

    # Save the SavedModel to disk in text format.
    builder.save(as_text=True)

    # Restore the graph with tag "foo", whose variables were saved.
    with self.test_session(graph=tf.Graph()) as sess:
      loader.load(sess, ["foo"], export_dir)
      self.assertEqual(
          42, tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)[0].eval())

    # Restore the graph with tag "bar", whose variables were not saved.
    with self.test_session(graph=tf.Graph()) as sess:
      loader.load(sess, ["bar"], export_dir)
      self.assertEqual(
          42, tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)[0].eval()) 

def save(self, as_text=False):
    """Writes a `SavedModel` protocol buffer to disk.

    The function writes the SavedModel protocol buffer to the export directory
    in serialized format.

    Args:
      as_text: Writes the SavedModel protocol buffer in text format to disk.

    Returns:
      The path to which the SavedModel protocol buffer was written.
    """
    if not file_io.file_exists(self._export_dir):
      file_io.recursive_create_dir(self._export_dir)

    if as_text:
      path = os.path.join(
          compat.as_bytes(self._export_dir),
          compat.as_bytes(constants.SAVED_MODEL_FILENAME_PBTXT))
      file_io.write_string_to_file(path, str(self._saved_model))
    else:
      path = os.path.join(
          compat.as_bytes(self._export_dir),
          compat.as_bytes(constants.SAVED_MODEL_FILENAME_PB))
      file_io.write_string_to_file(path, self._saved_model.SerializeToString())
    tf_logging.info("SavedModel written to: %s", path)

    return path 

def Cleanse(obj, encoding='utf-8'):
  """Makes Python object appropriate for JSON serialization.

  - Replaces instances of Infinity/-Infinity/NaN with strings.
  - Turns byte strings into unicode strings.
  - Turns sets into sorted lists.
  - Turns tuples into lists.

  Args:
    obj: Python data structure.
    encoding: Charset used to decode byte strings.

  Returns:
    Unicode JSON data structure.
  """
  if isinstance(obj, int):
    return obj
  elif isinstance(obj, float):
    if obj == _INFINITY:
      return 'Infinity'
    elif obj == _NEGATIVE_INFINITY:
      return '-Infinity'
    elif math.isnan(obj):
      return 'NaN'
    else:
      return obj
  elif isinstance(obj, bytes):
    return compat.as_text(obj, encoding)
  elif isinstance(obj, list) or isinstance(obj, tuple):
    return [Cleanse(i, encoding) for i in obj]
  elif isinstance(obj, set):
    return [Cleanse(i, encoding) for i in sorted(obj)]
  elif isinstance(obj, dict):
    return {Cleanse(k, encoding): Cleanse(v, encoding) for k, v in obj.items()}
  else:
    return obj 

def load_file_system_library(library_filename):
  """Loads a TensorFlow plugin, containing file system implementation.

  Pass `library_filename` to a platform-specific mechanism for dynamically
  loading a library. The rules for determining the exact location of the
  library are platform-specific and are not documented here.

  Args:
    library_filename: Path to the plugin.
      Relative or absolute filesystem path to a dynamic library file.

  Returns:
    None.

  Raises:
    RuntimeError: when unable to load the library.
  """
  status = py_tf.TF_NewStatus()
  lib_handle = py_tf.TF_LoadLibrary(library_filename, status)
  try:
    error_code = py_tf.TF_GetCode(status)
    if error_code != 0:
      error_msg = compat.as_text(py_tf.TF_Message(status))
      # pylint: disable=protected-access
      raise errors_impl._make_specific_exception(
          None, None, error_msg, error_code)
      # pylint: enable=protected-access
  finally:
    py_tf.TF_DeleteStatus(status) 

def testSaveAsText(self):
    export_dir = os.path.join(test.get_temp_dir(), "test_astext")
    builder = saved_model_builder.SavedModelBuilder(export_dir)

    # Graph with a single variable. SavedModel invoked to:
    # - add with weights.
    with self.test_session(graph=ops.Graph()) as sess:
      self._init_and_validate_variable(sess, "v", 42)
      builder.add_meta_graph_and_variables(sess, ["foo"])

    # Graph with the same single variable. SavedModel invoked to:
    # - simply add the model (weights are not updated).
    with self.test_session(graph=ops.Graph()) as sess:
      self._init_and_validate_variable(sess, "v", 43)
      builder.add_meta_graph(["bar"])

    # Save the SavedModel to disk in text format.
    builder.save(as_text=True)

    # Restore the graph with tag "foo", whose variables were saved.
    with self.test_session(graph=ops.Graph()) as sess:
      loader.load(sess, ["foo"], export_dir)
      self.assertEqual(
          42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval())

    # Restore the graph with tag "bar", whose variables were not saved.
    with self.test_session(graph=ops.Graph()) as sess:
      loader.load(sess, ["bar"], export_dir)
      self.assertEqual(
          42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval()) 

def save(self, as_text=False):
    """Writes a `SavedModel` protocol buffer to disk.

    The function writes the SavedModel protocol buffer to the export directory
    in serialized format.

    Args:
      as_text: Writes the SavedModel protocol buffer in text format to disk.

    Returns:
      The path to which the SavedModel protocol buffer was written.
    """
    if not file_io.file_exists(self._export_dir):
      file_io.recursive_create_dir(self._export_dir)

    if as_text:
      path = os.path.join(
          compat.as_bytes(self._export_dir),
          compat.as_bytes(constants.SAVED_MODEL_FILENAME_PBTXT))
      file_io.write_string_to_file(path, str(self._saved_model))
    else:
      path = os.path.join(
          compat.as_bytes(self._export_dir),
          compat.as_bytes(constants.SAVED_MODEL_FILENAME_PB))
      file_io.write_string_to_file(path, self._saved_model.SerializeToString())
    tf_logging.info("SavedModel written to: %s", path)

    return path 

def load_file_system_library(library_filename):
  """Loads a TensorFlow plugin, containing file system implementation.

  Pass `library_filename` to a platform-specific mechanism for dynamically
  loading a library. The rules for determining the exact location of the
  library are platform-specific and are not documented here.

  Args:
    library_filename: Path to the plugin.
      Relative or absolute filesystem path to a dynamic library file.

  Returns:
    None.

  Raises:
    RuntimeError: when unable to load the library.
  """
  status = py_tf.TF_NewStatus()
  lib_handle = py_tf.TF_LoadLibrary(library_filename, status)
  try:
    error_code = py_tf.TF_GetCode(status)
    if error_code != 0:
      error_msg = compat.as_text(py_tf.TF_Message(status))
      # pylint: disable=protected-access
      raise errors_impl._make_specific_exception(
          None, None, error_msg, error_code)
      # pylint: enable=protected-access
  finally:
    py_tf.TF_DeleteStatus(status)