def _eager_safe_variable_handle(shape, dtype, shared_name, name, graph_mode):
  """Creates a variable handle with information to do shape inference."""
  container = ops.get_default_graph()._container  # pylint: disable=protected-access
  if container is None:
    container = ""
  handle = gen_resource_variable_ops.var_handle_op(shape=shape, dtype=dtype,
                                                   shared_name=shared_name,
                                                   name=name,
                                                   container=container)
  if graph_mode:
    return handle

  # We do not want two distinct ResourceVariable objects for the same
  # underlying resource in the runtime.
  # When in eager mode, explicitly ensure so here. When in graph mode, it's
  # ensured by always generating different variable names.
  exists = gen_resource_variable_ops.var_is_initialized_op(handle)
  if exists:
    raise ValueError("variable object with name '%s' already created. Use "
                     "get_variable() if reuse is desired." %
                     shared_name)
  with context.graph_mode(), ops.Graph().as_default() as graph:
    h = gen_resource_variable_ops.var_handle_op(shape=shape, dtype=dtype,
                                                shared_name=shared_name,
                                                name=name,
                                                container=container)

    # Tensor._handle_data contains information for the shape-inference code to
    # know the shape and dtype of the variable pointed to by a handle. Since
    # shape inference doesn't run in eager mode we copy this data here for when
    # the handle is captured by an eager mode function.
    # pylint: disable=protected-access
    if ops._USE_C_SHAPES:
      handle._handle_data = get_resource_handle_data(h)
    else:
      if h._handle_data is None:
        ops.set_shape_and_handle_data_for_outputs(h.op)
      handle._handle_data = h._handle_data
    # pylint: enable=protected-access

  # Clean up our reference cycles to avoid making the garbage collector run.
  # pylint: disable=protected-access
  # OrderedDict, constructed on Graph creation, makes a simple reference loop
  # and hides it in an __attribute in some Python versions. We don't need to
  # throw an error if we can't find it, but if we do find it we can break the
  # loop to avoid creating work for the garbage collector.
  problematic_cycle = graph._functions.__dict__.get("_OrderedDict__root", None)
  # pylint: enable=protected-access
  if problematic_cycle:
    try:
      del problematic_cycle[0][:]
    except TypeError:
      # This is probably not one of the problematic Python versions. Continue
      # with the rest of our cleanup.
      pass
  # Now clean up our own reference cycles by clearing all of the attributes for
  # the Graph and op we created.
  h.__dict__ = {}
  graph.__dict__ = {}
  return handle

def _eager_safe_variable_handle(shape, dtype, shared_name, name, graph_mode):
  """Creates a variable handle with information to do shape inference."""
  container = ops.get_default_graph()._container  # pylint: disable=protected-access
  if container is None:
    container = ""
  handle = gen_resource_variable_ops.var_handle_op(shape=shape, dtype=dtype,
                                                   shared_name=shared_name,
                                                   name=name,
                                                   container=container)
  if graph_mode:
    return handle

  # We do not want two distinct ResourceVariable objects for the same
  # underlying resource in the runtime.
  # When in eager mode, explicitly ensure so here. When in graph mode, it's
  # ensured by always generating different variable names.
  exists = gen_resource_variable_ops.var_is_initialized_op(handle)
  if exists:
    raise ValueError("variable object with name '%s' already created. Use "
                     "get_variable() if reuse is desired." %
                     shared_name)
  with context.graph_mode(), ops.Graph().as_default():
    h = gen_resource_variable_ops.var_handle_op(shape=shape, dtype=dtype,
                                                shared_name=shared_name,
                                                name=name,
                                                container=container)

    # Tensor._handle_data contains information for the shape-inference code to
    # know the shape and dtype of the variable pointed to by a handle. Since
    # shape inference doesn't run in eager mode we copy this data here for when
    # the handle is captured by an eager mode function.
    handle._handle_data = h._handle_data  # pylint: disable=protected-access
  return handle

def is_variable_initialized(ref, name=None):
  """Checks whether a tensor has been initialized.

  Outputs boolean scalar indicating whether the tensor has been initialized.

  Args:
    ref: A mutable `Tensor`.
      Should be from a `Variable` node. May be uninitialized.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `bool`.
  """
  if ref.dtype._is_ref_dtype:
    return gen_state_ops.is_variable_initialized(ref=ref, name=name)
  # Handle resource variables.
  if ref.op.type == "VarHandleOp":
    return gen_resource_variable_ops.var_is_initialized_op(ref.handle,
                                                           name=name)

#.........这里部分代码省略.........
            with ops.name_scope("Initializer"):
              initial_value = ops.convert_to_tensor(
                  initial_value, name="initial_value", dtype=dtype)
            self._handle = _eager_safe_variable_handle(
                shape=initial_value.get_shape(),
                dtype=initial_value.dtype.base_dtype,
                shared_name=handle_name,
                name=name,
                graph_mode=False)
            self._handle_device = (
                self._handle.device if self._in_graph_mode else
                context.get_default_context().device_name)
            self._shape = initial_value.get_shape()
        # pylint: enable=protected-access

        # Or get the initial value from a Tensor or Python object.
        else:
          with ops.name_scope("Initializer"):
            initial_value = ops.convert_to_tensor(
                initial_value, name="initial_value", dtype=dtype)
          # pylint: disable=protected-access
          if (self._in_graph_mode and initial_value is not None and
              initial_value.op._get_control_flow_context() is not None):
            raise ValueError(
                "Initializer for variable %s is from inside a control-flow "
                "construct, such as a loop or conditional. When creating a "
                "variable inside a loop or conditional, use a lambda as the "
                "initializer." % name)
          # pylint: enable=protected-access
          self._handle = _eager_safe_variable_handle(
              shape=initial_value.get_shape(),
              dtype=initial_value.dtype.base_dtype,
              shared_name=handle_name,
              name=name,
              graph_mode=self._in_graph_mode)
          self._handle_device = (self._handle.device if self._in_graph_mode else
                                 context.get_default_context().device_name)
          self._shape = initial_value.get_shape()

        self._initial_value = initial_value if self._in_graph_mode else None
        self._handle_name = handle_name + ":0"
        self._dtype = initial_value.dtype.base_dtype
        self._constraint = constraint

        if self._in_graph_mode:
          with ops.name_scope("IsInitialized"):
            self._is_initialized_op = (
                gen_resource_variable_ops.var_is_initialized_op(self._handle))
          if initial_value is not None:
            with ops.name_scope("Assign") as n, ops.colocate_with(self._handle):
              self._initializer_op = (
                  gen_resource_variable_ops.assign_variable_op(
                      self._handle,
                      self._try_guard_against_uninitialized_dependencies(
                          initial_value),
                      name=n))
          with ops.name_scope("Read"), ops.colocate_with(self._handle):
            # Manually assign reads to the handle's device to avoid log
            # messages.
            with ops.device(self._handle_device):
              value = self._read_variable_op()
            self._graph_element = value
            if caching_device is not None:
              # Variables may be created in a tf.device() or ops.colocate_with()
              # context. At the same time, users would expect caching device to
              # be independent of this context, and/or would not expect the
              # current device context to be merged with the caching device
              # spec.  Therefore we reset the colocation stack before creating
              # the cached value. Note that resetting the colocation stack will
              # also reset the device stack.
              with ops.colocate_with(None, ignore_existing=True):
                with ops.device(caching_device):
                  self._cached_value = array_ops.identity(value)
            else:
              self._cached_value = None
        else:
          gen_resource_variable_ops.assign_variable_op(self._handle,
                                                       initial_value)
          self._is_initialized_op = None
          self._initializer_op = None
          self._graph_element = None
          if caching_device:
            with ops.device(caching_device):
              self._cached_value = self._read_variable_op()
          else:
            self._cached_value = None
        if context.in_graph_mode():
          ops.add_to_collections(collections, self)
        elif ops.GraphKeys.GLOBAL_STEP in collections:
          ops.add_to_collections(ops.GraphKeys.GLOBAL_STEP, self)

    if not self._in_graph_mode:
      # After the handle has been created, set up a way to clean it up when
      # executing eagerly. We'll hold the only reference to the deleter, so that
      # when this object is garbage collected the deleter will be too. This
      # means ResourceVariables can be part of reference cycles without those
      # cycles being uncollectable, and means that no __del__ will be defined at
      # all in graph mode.
      self._handle_deleter = EagerResourceDeleter(
          handle=self._handle, handle_device=self._handle_device)

  def __init__(self,
               initial_value=None,
               name=None,
               trainable=True,
               collections=None,
               dtype=None,
               shape=None):
    """Creates a variable.

    Args:
      initial_value: A `Tensor` or Python object convertible to a `Tensor`
        representing the initial value of this variable.
      name: The name of this variable. Automatically uniquified.
      trainable: Whether the global read of this variable will be used for
        training.
      collections: Additional collections to which the `read` operation for
        this variable is to be added. Defaults to [].
      dtype: The type of this variable. Can be omitted if it can be deduced
        from the initial_value. If different from the type of the initial
        value it will be cast to this type.
      shape: The shape of this variable. Only specify if there is no initial
        value but shape inference is desired.
    """
    if initial_value is not None:
      initial_value = ops.convert_to_tensor(initial_value)
    if dtype is None:
      assert initial_value is not None, ("Trying to create a resource variable "
                                         "with no dtype or initial value. At"
                                         " least one of these must be set.")
      dtype = initial_value.dtype
    elif initial_value is not None:
      initial_value = math_ops.cast(initial_value, dtype)
    if shape is None:
      if initial_value is not None:
        shape = initial_value.get_shape().as_proto()
      else:
        shape = tensor_shape.unknown_shape()
    else:
      shape = tensor_shape.as_shape(shape)

    self._dtype = dtype
    with ops.name_scope(name, "Variable", [initial_value]) as name:
      self._handle = gen_resource_variable_ops.var_handle_op(shared_name=name,
                                                             name=name,
                                                             dtype=dtype,
                                                             shape=shape)

      with ops.name_scope("IsInitialized"):
        self._is_initialized_op = (
            gen_resource_variable_ops.var_is_initialized_op(self._handle))
      if initial_value is not None:
        with ops.name_scope("Create"):
          self._initialize_op = gen_resource_variable_ops.create_variable_op(
              self._handle, initial_value)
        resources.register_resource(self._handle,
                                    self._initialize_op,
                                    self._is_initialized_op)

      with ops.name_scope("Read"):
        self._value = gen_resource_variable_ops.read_variable_op(
            self._handle, dtype=self._dtype)
      _register_variable_read(
          self._value, trainable=trainable, collections=collections)

  def _init_from_args(self,
                      initial_value=None,
                      trainable=True,
                      collections=None,
                      validate_shape=True,
                      caching_device=None,
                      name=None,
                      dtype=None):

    """Creates a variable.

    Args:
      initial_value: A `Tensor`, or Python object convertible to a `Tensor`,
        which is the initial value for the Variable. The initial value must have
        a shape specified unless `validate_shape` is set to False. Can also be a
        callable with no argument that returns the initial value when called.
        (Note that initializer functions from init_ops.py must first be bound
         to a shape before being used here.)
      trainable: If `True`, the default, also adds the variable to the graph
        collection `GraphKeys.TRAINABLE_VARIABLES`. This collection is used as
        the default list of variables to use by the `Optimizer` classes.
      collections: List of graph collections keys. The new variable is added to
        these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
      validate_shape: Ignored. Provided for compatibility with tf.Variable.
      caching_device: Optional device string or function describing where the
        Variable should be cached for reading.  Defaults to the Variable's
        device.  If not `None`, caches on another device.  Typical use is to
        cache on the device where the Ops using the Variable reside, to
        deduplicate copying through `Switch` and other conditional statements.
      name: Optional name for the variable. Defaults to `'Variable'` and gets
        uniquified automatically.
      dtype: If set, initial_value will be converted to the given type.
        If None, either the datatype will be kept (if initial_value is
       a Tensor) or float32 will be used (if it is a Python object convertible
       to a Tensor).

    Raises:
      ValueError: If the initial value is not specified, or does not have a
        shape and `validate_shape` is `True`.
    """
    if initial_value is None:
      raise ValueError("initial_value must be specified.")
    init_from_fn = callable(initial_value)

    if collections is None:
      collections = [ops.GraphKeys.GLOBAL_VARIABLES]
    if not isinstance(collections, (list, tuple, set)):
      raise ValueError(
          "collections argument to Variable constructor must be a list, tuple, "
          "or set. Got %s of type %s" % (collections, type(collections)))
    if trainable and ops.GraphKeys.TRAINABLE_VARIABLES not in collections:
      collections = list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES]
    self._save_slice_info = None
    with ops.control_dependencies(None):
      with ops.name_scope(name, "Variable", [] if init_from_fn else
                          [initial_value]) as name:
        # pylint: disable=protected-access
        true_name = ops._name_from_scope_name(name)
        if init_from_fn:
          # Use attr_scope and device(None) to simulate the behavior of
          # colocate_with when the variable we want to colocate with doesn't
          # yet exist.
          attr = attr_value_pb2.AttrValue(
              list=attr_value_pb2.AttrValue.ListValue(
                  s=[compat.as_bytes("loc:@%s" % true_name)]))
          with ops.get_default_graph()._attr_scope({"_class": attr}):
            with ops.name_scope("Initializer"), ops.device(None):
              self._initial_value = ops.convert_to_tensor(
                  initial_value(), name="initial_value", dtype=dtype)
            self._handle = gen_resource_variable_ops.var_handle_op(
                shape=self._initial_value.get_shape(),
                dtype=self._initial_value.dtype.base_dtype,
                shared_name=true_name, name=name)
        # pylint: enable=protected-access

        # Or get the initial value from a Tensor or Python object.
        else:
          self._initial_value = ops.convert_to_tensor(
              initial_value, name="initial_value", dtype=dtype)
          self._handle = gen_resource_variable_ops.var_handle_op(
              shape=self._initial_value.get_shape(),
              dtype=self._initial_value.dtype.base_dtype,
              shared_name=true_name, name=name)

        self._dtype = self._initial_value.dtype.base_dtype

        with ops.name_scope("IsInitialized"):
          self._is_initialized_op = (
              gen_resource_variable_ops.var_is_initialized_op(self._handle))
        if initial_value is not None:
          with ops.name_scope("Assign") as n, ops.colocate_with(self._handle):
            self._initialize_op = gen_resource_variable_ops.assign_variable_op(
                self._handle, self._initial_value, name=n)
        with ops.name_scope("Read"), ops.colocate_with(self._handle):
          # Manually assign reads to the handle's device to avoid log messages.
          with ops.device(self._handle.device):
            value = gen_resource_variable_ops.read_variable_op(
                self._handle, dtype=self._dtype)
          self._graph_element = value
          if caching_device is not None:
#.........这里部分代码省略.........