def _read_variable_op(self):
    with ops.control_dependencies([self._parent_op]):
      return gen_resource_variable_ops.read_variable_op(self._handle,
                                                        self._dtype)

  def set_shape(self, shape):
    self._shape = shape

  @property
  def op(self):
    """The op for this variable."""
    return self._parent_op

ops.register_tensor_conversion_function(_UnreadVariable, _dense_var_to_tensor)
ops.register_dense_tensor_like_type(_UnreadVariable)

# Register a conversion function which reads the value of the variable,
# allowing instances of the class to be used as tensors.

# Note: registering for Variable after ResourceVariable because inheritance will
# otherwise lead to the wrong behavior.
ops.register_tensor_conversion_function(ResourceVariable, _dense_var_to_tensor)
ops.register_tensor_conversion_function(
    variables.Variable, variables.Variable._TensorConversionFunction)  # pylint: disable=protected-access

# pylint: disable=protected-access
ResourceVariable._OverloadAllOperators()
ops.register_dense_tensor_like_type(ResourceVariable)

                array_ops.pack([state_ops.is_variable_initialized(v) for v in var_list])
            )
            # Get a 1-D string tensor containing all the variable names.
            variable_names_tensor = array_ops.constant([s.op.name for s in var_list])
            # Return a 1-D tensor containing all the names of uninitialized variables.
            return array_ops.boolean_mask(variable_names_tensor, variables_mask)


# pylint: disable=protected-access
ops.register_tensor_conversion_function(Variable, Variable._TensorConversionFunction)
Variable._OverloadAllOperators()

ops.register_tensor_conversion_function(PartitionedVariable, PartitionedVariable._TensorConversionFunction)
# pylint: enable=protected-access

ops.register_dense_tensor_like_type(Variable)
ops.register_proto_function(
    ops.GraphKeys.GLOBAL_VARIABLES,
    proto_type=variable_pb2.VariableDef,
    to_proto=Variable.to_proto,
    from_proto=Variable.from_proto,
)
ops.register_proto_function(
    ops.GraphKeys.TRAINABLE_VARIABLES,
    proto_type=variable_pb2.VariableDef,
    to_proto=Variable.to_proto,
    from_proto=Variable.from_proto,
)
ops.register_proto_function(
    ops.GraphKeys.MOVING_AVERAGE_VARIABLES,
    proto_type=variable_pb2.VariableDef,

    """Pass resource_variable_ops.is_resource_variable check."""
    pass


# Register a conversion function which reads the value of the variable,
# allowing instances of the class to be used as tensors.
def _tensor_conversion(var, dtype=None, name=None, as_ref=False):
  # Try to avoid assignments to and other mutations of MirroredVariable
  # state except through a DistributionStrategy.update() call.
  assert not as_ref
  return ops.internal_convert_to_tensor(
      var.get(), dtype=dtype, name=name, as_ref=as_ref)


ops.register_tensor_conversion_function(DistributedVariable, _tensor_conversion)
ops.register_dense_tensor_like_type(DistributedVariable)


class _MirroredSaveable(saver.BaseSaverBuilder.ResourceVariableSaveable):
  """Class for defining how to restore a MirroredVariable."""

  def __init__(self, mirrored_variable, primary_variable, name):
    self._mirrored_variable = mirrored_variable
    super(_MirroredSaveable, self).__init__(primary_variable, "", name)

  def restore(self, restored_tensors, restored_shapes):
    """Restore the same value into all variables."""
    tensor, = restored_tensors
    return control_flow_ops.group([
        _assign_on_device(d, v, tensor)
        for d, v in six.iteritems(self._mirrored_variable._index)])  # pylint: disable=protected-access

                                         self._variable.dtype, name,
                                         as_ref=False)
    with ops.colocate_with(None, ignore_existing=True):
      with ops.device(val.device):
        return math_ops.cast(val, self.dtype)

  def _should_act_as_resource_variable(self):
    """Pass resource_variable_ops.is_resource_variable check."""
    pass

  # TODO(reedwm): Define operator overloads.


ops.register_tensor_conversion_function(
    AutoCastVariable, AutoCastVariable._dense_var_to_tensor)  # pylint:disable=protected-access
ops.register_dense_tensor_like_type(AutoCastVariable)


# We have DistributedVariable subclass to pass
# isinstance(..., DistributedVariable) checks when wrapping a
# DistributedVariable.
# TODO(reedwm): We should not wrap DistributedVariable, but instead have
# DistributedVariable wrap AutoCastVariable. Subclassing DistributedVariable is
# messy, because we do not fully implement the interface of DistributedVariable.
class AutoCastDistributedVariable(AutoCastVariable,
                                  distribute_values.DistributedVariable):
  """Version of AutoCastVariable that subclasses DistributedVariable."""

  def __init__(self, variable):
    if not isinstance(variable, distribute_values.DistributedValues):
      raise ValueError('variable must be of type DistributedValues, '

# Register a conversion function which reads the value of the variable,
# allowing instances of the class to be used as tensors.
def _tensor_conversion(var, dtype=None, name=None, as_ref=False):
  return var._dense_var_to_tensor(dtype=dtype, name=name, as_ref=as_ref)  # pylint: disable=protected-access


def replicated_fetch_function(var):
  # pylint: disable=protected-access
  return ([var._dense_var_to_tensor()], lambda v: v[0])
  # pylint: enable=protected-access


ops.register_tensor_conversion_function(ReplicatedVariable, _tensor_conversion)
ops.register_dense_tensor_like_type(ReplicatedVariable)
session_lib.register_session_run_conversion_functions(
    ReplicatedVariable, replicated_fetch_function)


def replicated_scope(num_replicas):
  """Variable scope for constructing replicated variables."""

  def _replicated_variable_getter(getter, name, *args, **kwargs):
    """Getter that constructs replicated variables."""
    collections = kwargs.pop("collections", None)
    if collections is None:
      collections = [ops.GraphKeys.GLOBAL_VARIABLES]
    kwargs["collections"] = []

    variables = []