def _init_from_proto(self, variable_def, import_scope=None):
    """Initializes from `VariableDef` proto."""
    assert isinstance(variable_def, variable_pb2.VariableDef)
    if not variable_def.is_resource:
      raise ValueError("Trying to restore Variable as ResourceVariable.")

    # Create from variable_def.
    g = ops.get_default_graph()
    self._handle = g.as_graph_element(
        ops.prepend_name_scope(variable_def.variable_name,
                               import_scope=import_scope))
    self._initialize_op = g.as_graph_element(
        ops.prepend_name_scope(variable_def.initializer_name,
                               import_scope=import_scope))
    if variable_def.snapshot_name:
      self._cached_value = g.as_graph_element(
          ops.prepend_name_scope(variable_def.snapshot_name,
                                 import_scope=import_scope))
    else:
      self._cached_value = None
    if variable_def.HasField("save_slice_info_def"):
      self._save_slice_info = variables.Variable.SaveSliceInfo(
          save_slice_info_def=variable_def.save_slice_info_def)
    else:
      self._save_slice_info = None
    self._caching_device = None
    self._dtype = dtypes.as_dtype(self._handle.op.get_attr("dtype"))

  def _init_from_proto(self, variable_def, import_scope=None):
    """Creates a new variable from `VariableDef` protocol buffer.

    Args:
      variable_def: `VariableDef` protocol buffer.
      import_scope: Optional `string`. Name scope to add.
    """
    assert isinstance(variable_def, variable_pb2.VariableDef)
    # Create from variable_def.
    g = ops.get_default_graph()
    self._variable = g.as_graph_element(
        ops.prepend_name_scope(variable_def.variable_name,
                               import_scope=import_scope))
    self._initializer_op = g.as_graph_element(
        ops.prepend_name_scope(variable_def.initializer_name,
                               import_scope=import_scope))
    self._snapshot = g.as_graph_element(
        ops.prepend_name_scope(variable_def.snapshot_name,
                               import_scope=import_scope))
    if variable_def.HasField("save_slice_info_def"):
      self._save_slice_info = Variable.SaveSliceInfo(
          save_slice_info_def=variable_def.save_slice_info_def)
    else:
      self._save_slice_info = None
    self._caching_device = None

  def _init_from_proto(self, queue_runner_def, import_scope=None):
    """Create a QueueRunner from `QueueRunnerDef`.

    Args:
      queue_runner_def: Optional `QueueRunnerDef` protocol buffer.
      import_scope: Optional `string`. Name scope to add.
    """
    assert isinstance(queue_runner_def, queue_runner_pb2.QueueRunnerDef)
    g = ops.get_default_graph()
    self._queue = g.as_graph_element(
        ops.prepend_name_scope(queue_runner_def.queue_name, import_scope))
    self._enqueue_ops = [g.as_graph_element(
        ops.prepend_name_scope(op, import_scope))
                         for op in queue_runner_def.enqueue_op_name]
    self._close_op = g.as_graph_element(ops.prepend_name_scope(
        queue_runner_def.close_op_name, import_scope))
    self._cancel_op = g.as_graph_element(ops.prepend_name_scope(
        queue_runner_def.cancel_op_name, import_scope))
    self._queue_closed_exception_types = tuple(
        errors.exception_type_from_error_code(code)
        for code in queue_runner_def.queue_closed_exception_types)
    # Legacy support for old QueueRunnerDefs created before this field
    # was added.
    if not self._queue_closed_exception_types:
      self._queue_closed_exception_types = (errors.OutOfRangeError,)

  def _init_from_proto(self, variable_def, import_scope=None):
    """Initializes from `VariableDef` proto."""
    # Note that init_from_proto is currently not supported in Eager mode.
    assert not context.executing_eagerly()
    self._in_graph_mode = True
    assert isinstance(variable_def, variable_pb2.VariableDef)
    if not variable_def.is_resource:
      raise ValueError("Trying to restore Variable as ResourceVariable.")

    # Create from variable_def.
    g = ops.get_default_graph()
    self._handle = g.as_graph_element(
        ops.prepend_name_scope(
            variable_def.variable_name, import_scope=import_scope))
    self._shape = tensor_shape.TensorShape(
        self._handle.op.get_attr("shape"))
    self._handle_name = self._handle.name
    self._unique_id = self._handle_name
    self._initializer_op = g.as_graph_element(
        ops.prepend_name_scope(
            variable_def.initializer_name, import_scope=import_scope))
    # Check whether initial_value_name exists for backwards compatibility.
    if (hasattr(variable_def, "initial_value_name") and
        variable_def.initial_value_name):
      self._initial_value = g.as_graph_element(
          ops.prepend_name_scope(variable_def.initial_value_name,
                                 import_scope=import_scope))
    else:
      self._initial_value = None
    self._trainable = getattr(variable_def, "trainable", True)
    if variable_def.snapshot_name:
      snapshot = g.as_graph_element(
          ops.prepend_name_scope(
              variable_def.snapshot_name, import_scope=import_scope))
      self._cached_value = snapshot
      while snapshot.op.type != "ReadVariableOp":
        snapshot = snapshot.op.inputs[0]
      self._graph_element = snapshot
    else:
      self._cached_value = None
      # Legacy case for protos without the snapshot name; assume it's the
      # following.
      self._graph_element = g.get_tensor_by_name(
          self._handle.op.name + "/Read/ReadVariableOp:0")
    if variable_def.HasField("save_slice_info_def"):
      self._save_slice_info = variables.Variable.SaveSliceInfo(
          save_slice_info_def=variable_def.save_slice_info_def,
          import_scope=import_scope)
    else:
      self._save_slice_info = None
    self._caching_device = None
    self._dtype = dtypes.as_dtype(self._handle.op.get_attr("dtype"))
    self._constraint = None
    self._cached_shape_as_list = None

    def __init__(self,
                 full_name=None,
                 full_shape=None,
                 var_offset=None,
                 var_shape=None,
                 save_slice_info_def=None,
                 import_scope=None):
      """Create a `SaveSliceInfo`.

      Args:
        full_name: Name of the full variable of which this `Variable` is a
            slice.
        full_shape: Shape of the full variable, as a list of int.
        var_offset: Offset of this `Variable` into the full variable, as a
            list of int.
        var_shape: Shape of this `Variable`, as a list of int.
        save_slice_info_def: `SaveSliceInfoDef` protocol buffer. If not `None`,
          recreates the SaveSliceInfo object its contents.
          `save_slice_info_def` and other arguments are mutually
          exclusive.
        import_scope: Optional `string`. Name scope to add. Only used
          when initializing from protocol buffer.
      """
      if save_slice_info_def:
        assert isinstance(save_slice_info_def, variable_pb2.SaveSliceInfoDef)
        self.full_name = ops.prepend_name_scope(
            save_slice_info_def.full_name, import_scope=import_scope)
        self.full_shape = [i for i in save_slice_info_def.full_shape]
        self.var_offset = [i for i in save_slice_info_def.var_offset]
        self.var_shape = [i for i in save_slice_info_def.var_shape]
      else:
        self.full_name = full_name
        self.full_shape = full_shape
        self.var_offset = var_offset
        self.var_shape = var_shape

  def _init_from_proto(self, variable_def, import_scope=None):
    """Initializes from `VariableDef` proto."""
    # Note that init_from_proto is currently not supported in Eager mode.
    assert context.in_graph_mode()
    self._in_graph_mode = True
    assert isinstance(variable_def, variable_pb2.VariableDef)
    if not variable_def.is_resource:
      raise ValueError("Trying to restore Variable as ResourceVariable.")

    # Create from variable_def.
    g = ops.get_default_graph()
    self._handle = g.as_graph_element(
        ops.prepend_name_scope(
            variable_def.variable_name, import_scope=import_scope))
    self._shape = tensor_shape.TensorShape(
        self._handle.op.get_attr("shape"))
    self._handle_device = self._handle.device
    self._handle_name = self._handle.name
    self._initializer_op = g.as_graph_element(
        ops.prepend_name_scope(
            variable_def.initializer_name, import_scope=import_scope))
    # Check whether initial_value_name exists for backwards compatibility.
    if (hasattr(variable_def, "initial_value_name") and
        variable_def.initial_value_name):
      self._initial_value = g.as_graph_element(
          ops.prepend_name_scope(variable_def.initial_value_name,
                                 import_scope=import_scope))
    else:
      self._initial_value = None
    if variable_def.snapshot_name:
      self._cached_value = g.as_graph_element(
          ops.prepend_name_scope(
              variable_def.snapshot_name, import_scope=import_scope))
    else:
      self._cached_value = None
    if variable_def.HasField("save_slice_info_def"):
      self._save_slice_info = variables.Variable.SaveSliceInfo(
          save_slice_info_def=variable_def.save_slice_info_def,
          import_scope=import_scope)
    else:
      self._save_slice_info = None
    self._caching_device = None
    self._dtype = dtypes.as_dtype(self._handle.op.get_attr("dtype"))
    self._graph_element = self.value()
    self._constraint = None

 def _restore_collections(dest_graph, src_meta_graph_def, collection_keys):
   """Restores collections that we need to keep."""
   scope = ""
   for key in collection_keys:
     collection_def = src_meta_graph_def.collection_def[key]
     kind = collection_def.WhichOneof("kind")
     if kind is None:
       tf_logging.error(
           "Cannot identify data type for collection %s. Skipping.", key)
       continue
     from_proto = ops.get_from_proto_function(key)
     if from_proto and kind == "bytes_list":
       proto_type = ops.get_collection_proto_type(key)
       # It is assumed that there are no Variables Keys in collections
       for value in collection_def.bytes_list.value:
         proto = proto_type()
         proto.ParseFromString(value)
         try:
           new_value = from_proto(proto, import_scope=scope)
         except:
           continue
         dest_graph.add_to_collection(key, new_value)
     else:
       field = getattr(collection_def, kind)
       if kind == "node_list":
         for value in field.value:
           name = ops.prepend_name_scope(value, scope)
           # Since the graph has been optimized, the node may no longer
           # exists
           try:
             col_op = dest_graph.as_graph_element(name)
           except (TypeError, ValueError, KeyError) as e:
             continue
           dest_graph.add_to_collection(key, col_op)
       elif kind == "int64_list":
         # NOTE(opensource): This force conversion is to work around the
         # fact that Python2 distinguishes between int and long, while
         # Python3 has only int.
         for value in field.value:
           dest_graph.add_to_collection(key, int(value))
       else:
         for value in field.value:
           dest_graph.add_to_collection(key,
                                        ops.prepend_name_scope(value, scope))

def get_element_from_tensor_info(tensor_info, graph=None, import_scope=None):
  """Returns the element in the graph described by a TensorInfo proto.

  Args:
    tensor_info: A TensorInfo proto describing an Op or Tensor by name.
    graph: The tf.Graph in which tensors are looked up. If None, the current
      default graph is used.
    import_scope: If not None, names in `tensor_info` are prefixed with this
      string before lookup.

  Returns:
    Op or tensor in `graph` described by `tensor_info`.

  Raises:
    KeyError: If `tensor_info` does not correspond to an op or tensor in `graph`
  """
  graph = graph or ops.get_default_graph()
  return graph.as_graph_element(
      ops.prepend_name_scope(tensor_info.name, import_scope=import_scope))

#.........这里部分代码省略.........
        kind = col_def.WhichOneof("kind")
        field = getattr(col_def, kind)
        if field.value and (
            not input_map or
            sorted([compat.as_str(v) for v in field.value]) !=
            sorted(input_map)):
          raise ValueError("Graph contains unbound inputs: %s. Must "
                           "provide these inputs through input_map." %
                           ",".join([compat.as_str(v) for v in field.value
                                     if not input_map or v not in input_map]))
        break

  # Sets graph to default graph if it's not passed in.
  graph = graph or ops.get_default_graph()

  # Gathers the list of nodes we are interested in.
  with graph.as_default():
    producer_op_list = None
    if meta_graph_def.meta_info_def.HasField("stripped_op_list"):
      producer_op_list = meta_graph_def.meta_info_def.stripped_op_list
    input_graph_def = meta_graph_def.graph_def
    # Remove all the explicit device specifications for this node. This helps to
    # make the graph more portable.
    if clear_devices:
      for node in input_graph_def.node:
        node.device = ""

    scope_to_prepend_to_names = graph.unique_name(
        import_scope or "", mark_as_used=False)

    importer.import_graph_def(
        input_graph_def,
        name=(import_scope or scope_to_prepend_to_names),
        input_map=input_map,
        producer_op_list=producer_op_list)

    # Restores all the other collections.
    variable_objects = {}
    for key, col_def in sorted(meta_graph_def.collection_def.items()):
      # Don't add unbound_inputs to the new graph.
      if key == unbound_inputs_col_name:
        continue
      if not restore_collections_predicate(key):
        continue

      kind = col_def.WhichOneof("kind")
      if kind is None:
        logging.error("Cannot identify data type for collection %s. Skipping.",
                      key)
        continue
      from_proto = ops.get_from_proto_function(key)
      if from_proto and kind == "bytes_list":
        proto_type = ops.get_collection_proto_type(key)
        if key in ops.GraphKeys._VARIABLE_COLLECTIONS:  # pylint: disable=protected-access
          for value in col_def.bytes_list.value:
            variable = variable_objects.get(value, None)
            if variable is None:
              proto = proto_type()
              proto.ParseFromString(value)
              variable = from_proto(
                  proto, import_scope=scope_to_prepend_to_names)
              variable_objects[value] = variable
            graph.add_to_collection(key, variable)
        else:
          for value in col_def.bytes_list.value:
            proto = proto_type()
            proto.ParseFromString(value)
            graph.add_to_collection(
                key, from_proto(
                    proto, import_scope=scope_to_prepend_to_names))
      else:
        field = getattr(col_def, kind)
        if key in _COMPAT_COLLECTION_LIST:
          logging.warning(
              "The saved meta_graph is possibly from an older release:\n"
              "'%s' collection should be of type 'byte_list', but instead "
              "is of type '%s'.", key, kind)
        if kind == "node_list":
          for value in field.value:
            col_op = graph.as_graph_element(
                ops.prepend_name_scope(value, scope_to_prepend_to_names))
            graph.add_to_collection(key, col_op)
        elif kind == "int64_list":
          # NOTE(opensource): This force conversion is to work around the fact
          # that Python2 distinguishes between int and long, while Python3 has
          # only int.
          for value in field.value:
            graph.add_to_collection(key, int(value))
        else:
          for value in field.value:
            graph.add_to_collection(
                key, ops.prepend_name_scope(value, scope_to_prepend_to_names))

    var_list = {}
    variables = graph.get_collection(ops.GraphKeys.GLOBAL_VARIABLES,
                                     scope=scope_to_prepend_to_names)
    for v in variables:
      var_list[ops.strip_name_scope(v.name, scope_to_prepend_to_names)] = v

  return var_list

#.........这里部分代码省略.........
    meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including
      the path) containing a `MetaGraphDef`.
    clear_devices: Boolean which controls whether to clear device information
      from graph_def. Default false.
    graph: The `Graph` to import into. If `None`, use the default graph.
    import_scope: Optional `string`. Name scope into which to import the
      subgraph. If `None`, the graph is imported to the root name scope.
    input_map: A dictionary mapping input names (as strings) in `graph_def` to
      `Tensor` objects. The values of the named input tensors in the imported
      graph will be re-mapped to the respective `Tensor` values.
    unbound_inputs_col_name: Collection name for looking up unbound inputs.

  Returns:
    A dictionary of all the `Variables` imported into the name scope.

  Raises:
    ValueError: If the graph_def contains unbound inputs.
  """
  if isinstance(meta_graph_or_file, meta_graph_pb2.MetaGraphDef):
    meta_graph_def = meta_graph_or_file
  else:
    meta_graph_def = read_meta_graph_file(meta_graph_or_file)

  if unbound_inputs_col_name:
    for key, col_def in meta_graph_def.collection_def.items():
      if key == unbound_inputs_col_name:
        kind = col_def.WhichOneof("kind")
        field = getattr(col_def, kind)
        if field.value and (
            not input_map or
            sorted([compat.as_str(v) for v in field.value]) !=
            sorted(input_map)):
          raise ValueError("Graph contains unbound inputs: %s. Must "
                           "provide these inputs through input_map." %
                           ",".join([compat.as_str(v) for v in field.value]))
        break

  # Sets graph to default graph if it's not passed in.
  graph = graph or ops.get_default_graph()

  # Gathers the list of nodes we are interested in.
  with graph.as_default():
    producer_op_list = None
    if meta_graph_def.meta_info_def.HasField("stripped_op_list"):
      producer_op_list = meta_graph_def.meta_info_def.stripped_op_list
    input_graph_def = meta_graph_def.graph_def
    # Remove all the explicit device specifications for this node. This helps to
    # make the graph more portable.
    if clear_devices:
      for node in input_graph_def.node:
        node.device = ""
    importer.import_graph_def(
        input_graph_def, name=(import_scope or ""), input_map=input_map,
        producer_op_list=producer_op_list)

    # Restores all the other collections.
    for key, col_def in meta_graph_def.collection_def.items():
      # Don't add unbound_inputs to the new graph.
      if key == unbound_inputs_col_name:
        continue

      kind = col_def.WhichOneof("kind")
      if kind is None:
        logging.error("Cannot identify data type for collection %s. Skipping.",
                      key)
        continue
      from_proto = ops.get_from_proto_function(key)
      if from_proto:
        assert kind == "bytes_list"
        proto_type = ops.get_collection_proto_type(key)
        for value in col_def.bytes_list.value:
          proto = proto_type()
          proto.ParseFromString(value)
          graph.add_to_collection(
              key, from_proto(proto, import_scope=import_scope))
      else:
        field = getattr(col_def, kind)
        if kind == "node_list":
          for value in field.value:
            col_op = graph.as_graph_element(
                ops.prepend_name_scope(value, import_scope))
            graph.add_to_collection(key, col_op)
        elif kind == "int64_list":
          # NOTE(opensource): This force conversion is to work around the fact
          # that Python2 distinguishes between int and long, while Python3 has
          # only int.
          for value in field.value:
            graph.add_to_collection(key, int(value))
        else:
          for value in field.value:
            graph.add_to_collection(
                key, ops.prepend_name_scope(value, import_scope))

    var_list = {}
    variables = graph.get_collection(ops.GraphKeys.VARIABLES,
                                     scope=import_scope)
    for v in variables:
      var_list[ops.strip_name_scope(v.name, import_scope)] = v

  return var_list

 def _get_tensor(name):
   return graph.get_tensor_by_name(
       ops.prepend_name_scope(name, import_scope=import_scope))

#.........这里部分代码省略.........
  graph = graph or ops.get_default_graph()

  exclude_nodes = None
  unbound_inputs = []
  if export_scope or clear_extraneous_savers or clear_devices:
    if graph_def:
      new_graph_def = graph_pb2.GraphDef()
      new_graph_def.versions.CopyFrom(graph_def.versions)
      new_graph_def.library.CopyFrom(graph_def.library)

      if clear_extraneous_savers:
        exclude_nodes = _find_extraneous_saver_nodes(graph_def, saver_def)

      for node_def in graph_def.node:
        if _should_include_node(node_def.name, export_scope, exclude_nodes):
          new_node_def = _node_def(node_def, export_scope, unbound_inputs,
                                   clear_devices=clear_devices)
          new_graph_def.node.extend([new_node_def])
      graph_def = new_graph_def
    else:
      # Only do this complicated work if we want to remove a name scope.
      graph_def = graph_pb2.GraphDef()
      # pylint: disable=protected-access
      graph_def.versions.CopyFrom(graph.graph_def_versions)
      bytesize = 0

      if clear_extraneous_savers:
        exclude_nodes = _find_extraneous_saver_nodes(graph.as_graph_def(),
                                                     saver_def)

      for key in sorted(graph._nodes_by_id):
        if _should_include_node(graph._nodes_by_id[key].name,
                                export_scope,
                                exclude_nodes):
          value = graph._nodes_by_id[key]
          # pylint: enable=protected-access
          node_def = _node_def(value.node_def, export_scope, unbound_inputs,
                               clear_devices=clear_devices)
          graph_def.node.extend([node_def])
          if value.outputs:
            assert "_output_shapes" not in graph_def.node[-1].attr
            graph_def.node[-1].attr["_output_shapes"].list.shape.extend([
                output.get_shape().as_proto() for output in value.outputs])
          bytesize += value.node_def.ByteSize()
          if bytesize >= (1 << 31) or bytesize < 0:
            raise ValueError("GraphDef cannot be larger than 2GB.")

      graph._copy_functions_to_graph_def(graph_def, bytesize)  # pylint: disable=protected-access

    # It's possible that not all the inputs are in the export_scope.
    # If we would like such information included in the exported meta_graph,
    # add them to a special unbound_inputs collection.
    if unbound_inputs_col_name:
      # Clears the unbound_inputs collections.
      graph.clear_collection(unbound_inputs_col_name)
      for k in unbound_inputs:
        graph.add_to_collection(unbound_inputs_col_name, k)

  var_list = {}
  variables = graph.get_collection(ops.GraphKeys.GLOBAL_VARIABLES,
                                   scope=export_scope)
  for v in variables:
    if _should_include_node(v, export_scope, exclude_nodes):
      var_list[ops.strip_name_scope(v.name, export_scope)] = v

  scoped_meta_graph_def = create_meta_graph_def(
      graph_def=graph_def,
      graph=graph,
      export_scope=export_scope,
      exclude_nodes=exclude_nodes,
      clear_extraneous_savers=clear_extraneous_savers,
      saver_def=saver_def,
      strip_default_attrs=strip_default_attrs,
      **kwargs)

  if filename:
    graph_io.write_graph(
        scoped_meta_graph_def,
        os.path.dirname(filename),
        os.path.basename(filename),
        as_text=as_text)
    if save_debug_info:
      name, _ = os.path.splitext(filename)
      debug_filename = "{name}{ext}".format(name=name, ext=".debug")

      # Gets the operation from the graph by the name.
      ops_to_export = {}
      for node in scoped_meta_graph_def.graph_def.node:
        scoped_op_name = ops.prepend_name_scope(node.name, export_scope)
        ops_to_export.add(graph.get_operation_by_name(scoped_op_name))

      graph_debug_info = create_graph_debug_info_def(ops_to_export)

      graph_io.write_graph(
          graph_debug_info,
          os.path.dirname(debug_filename),
          os.path.basename(debug_filename),
          as_text=as_text)

  return scoped_meta_graph_def, var_list