text: # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Ops to use variables as resources."""
  
  # pylint: disable=g-bad-name
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import contextlib
  
  from tensorflow.core.framework import attr_value_pb2
  from tensorflow.core.framework import variable_pb2
  from tensorflow.python import pywrap_tensorflow
  from tensorflow.python.eager import context
  from tensorflow.python.eager import tape
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import cpp_shape_inference_pb2
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_array_ops
  from tensorflow.python.ops import gen_resource_variable_ops
  from tensorflow.python.ops import gen_state_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import state_ops
  from tensorflow.python.ops import variables
  # go/tf-wildcard-import
  # pylint: disable=wildcard-import
  from tensorflow.python.ops.gen_resource_variable_ops import *
  # pylint: enable=wildcard-import
  from tensorflow.python.training.checkpointable import base as checkpointable
  from tensorflow.python.util import compat
  from tensorflow.python.util.deprecation import deprecated
  
  
  def get_resource_handle_data(graph_op):
    assert type(graph_op) == ops.Tensor  # pylint: disable=unidiomatic-typecheck
  
    handle_data = pywrap_tensorflow.GetHandleShapeAndType(
        graph_op.graph._c_graph, graph_op._as_tf_output())  # pylint: disable=protected-access
  
    return cpp_shape_inference_pb2.CppShapeInferenceResult.HandleData.FromString(
        compat.as_bytes(handle_data))
  
  
  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:
      handle._handle_data = get_resource_handle_data(handle)  # pylint: disable=protected-access
      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
      handle._handle_data = get_resource_handle_data(h)
      # pylint: enable=protected-access
    # Clean up op->graph->op reference cycles.
    ops.dismantle_graph(graph)
    return handle
  
  
  @contextlib.contextmanager
  def _handle_graph(handle):
    # Note: might have an eager tensor but not be executing eagerly when building
    # functions.
    if (context.executing_eagerly() or isinstance(handle, ops.EagerTensor)
        or ops.has_default_graph()):
      yield
    else:
      with handle.graph.as_default():
        yield
  
  
  class EagerResourceDeleter(object):
    """An object which cleans up a resource handle.
  
    An alternative to defining a __del__ method on an object. The intended use is
    that ResourceVariables or other objects with resource handles will maintain a
    single reference to this object. When the parent object is collected, this
    object will be too. Even if the parent object is part of a reference cycle,
    the cycle will be collectable.
    """
  
    def __init__(self, handle, handle_device):
      if not isinstance(handle, ops.Tensor):
        raise ValueError(
            ("Passed handle=%s to EagerResourceDeleter. Was expecting a handle "
             "Tensor." % (handle,)))
      self._handle = handle
      self._handle_device = handle_device
  
    def __del__(self):
      # Resources follow object-identity when executing eagerly, so it is safe to
      # delete the resource we have a handle to.
      try:
        # This resource was created in eager mode. However, this destructor may be
        # running in graph mode (especially during unit tests). To clean up
        # successfully, we switch back into eager mode temporarily.
        with context.eager_mode():
          with ops.device(self._handle_device):
            gen_resource_variable_ops.destroy_resource_op(
                self._handle, ignore_lookup_error=True)
      except TypeError:
        # Suppress some exceptions, mainly for the case when we're running on
        # module deletion. Things that can go wrong include the context module
        # already being unloaded, self._handle._handle_data no longer being
        # valid, and so on. Printing warnings in these cases is silly
        # (exceptions raised from __del__ are printed as warnings to stderr).
        pass  # 'NoneType' object is not callable when the handle has been
        # partially unloaded.
      except AttributeError:
        pass  # 'NoneType' object has no attribute 'eager_mode' when context has
        # been unloaded. Will catch other module unloads as well.
  
  
  def shape_safe_assign_variable_handle(handle, shape, value, name=None):
    """Helper that checks shape compatibility and assigns variable."""
    with _handle_graph(handle):
      value_tensor = ops.convert_to_tensor(value)
    shape.assert_is_compatible_with(value_tensor.shape)
    return gen_resource_variable_ops.assign_variable_op(handle,
                                                        value_tensor,
                                                        name=name)
  
  
  class ResourceVariable(variables.VariableV1):
    """Variable based on resource handles.
  
    See the [Variables How To](https://tensorflow.org/guide/variables)
    for a high level overview.
  
    A `ResourceVariable` allows you to maintain state across subsequent calls to
    session.run.
  
    The `ResourceVariable` constructor requires an initial value for the variable,
    which can be a `Tensor` of any type and shape. The initial value defines the
    type and shape of the variable. After construction, the type and shape of
    the variable are fixed. The value can be changed using one of the assign
    methods.
  
    Just like any `Tensor`, variables created with
    `tf.Variable(use_resource=True)` can be used as inputs for other Ops in the
    graph. Additionally, all the operators overloaded for the `Tensor` class are
    carried over to variables, so you can also add nodes to the graph by just
    doing arithmetic on variables.
  
    Unlike ref-based variable, a ResourceVariable has well-defined semantics. Each
    usage of a ResourceVariable in a TensorFlow graph adds a read_value operation
    to the graph. The Tensors returned by a read_value operation are guaranteed to
    see all modifications to the value of the variable which happen in any
    operation on which the read_value depends on (either directly, indirectly, or
    via a control dependency) and guaranteed to not see any modification to the
    value of the variable from operations that depend on the read_value operation.
    Updates from operations that have no dependency relationship to the read_value
    operation might or might not be visible to read_value.
  
    For example, if there is more than one assignment to a ResourceVariable in
    a single session.run call there is a well-defined value for each operation
    which uses the variable's value if the assignments and the read are connected
    by edges in the graph. Consider the following example, in which two writes
    can cause tf.Variable and tf.ResourceVariable to behave differently:
  
    ```python
    a = tf.Variable(1.0, use_resource=True)
    a.initializer.run()
  
    assign = a.assign(2.0)
    with tf.control_dependencies([assign]):
      b = a.read_value()
    with tf.control_dependencies([b]):
      other_assign = a.assign(3.0)
    with tf.control_dependencies([other_assign]):
      # Will print 2.0 because the value was read before other_assign ran. If
      # `a` was a tf.Variable instead, 2.0 or 3.0 could be printed.
      tf.Print(b, [b]).eval()
    ```
    """
  
    def __init__(self,
                 initial_value=None,
                 trainable=True,
                 collections=None,
                 validate_shape=True,
                 caching_device=None,
                 name=None,
                 dtype=None,
                 variable_def=None,
                 import_scope=None,
                 constraint=None,
                 distribute_strategy=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).
        variable_def: `VariableDef` protocol buffer. If not None, recreates the
          `ResourceVariable` object with its contents. `variable_def` and other
          arguments (except for import_scope) are mutually exclusive.
        import_scope: Optional `string`. Name scope to add to the
          ResourceVariable. Only used when `variable_def` is provided.
        constraint: An optional projection function to be applied to the variable
          after being updated by an `Optimizer` (e.g. used to implement norm
          constraints or value constraints for layer weights). The function must
          take as input the unprojected Tensor representing the value of the
          variable and return the Tensor for the projected value
          (which must have the same shape). Constraints are not safe to
          use when doing asynchronous distributed training.
        distribute_strategy: The tf.distribute.Strategy this variable is being
          created inside of.
  
      Raises:
        ValueError: If the initial value is not specified, or does not have a
          shape and `validate_shape` is `True`.
  
      @compatibility(eager)
      When Eager Execution is enabled, the default for the `collections` argument
      is `None`, which signifies that this `Variable` will not be added to any
      collections.
      @end_compatibility
      """
      self._distribute_strategy = distribute_strategy
      if variable_def:
        if initial_value is not None:
          raise ValueError("variable_def and initial_value are mutually "
                           "exclusive.")
        if context.executing_eagerly():
          raise ValueError("Creating ResourceVariable from variable_def is "
                           "not supported when eager execution is enabled.")
        self._init_from_proto(variable_def, import_scope=import_scope)
      else:
        self._init_from_args(
            initial_value=initial_value,
            trainable=trainable,
            collections=collections,
            validate_shape=validate_shape,
            caching_device=caching_device,
            name=name,
            dtype=dtype,
            constraint=constraint)
  
    def __repr__(self):
      if context.executing_eagerly() and not self._in_graph_mode:
        return "<tf.Variable '%s' shape=%s dtype=%s, numpy=%s>" % (
            self.name, self.get_shape(), self.dtype.name,
            ops.numpy_text(self.read_value(), is_repr=True))
      else:
        return "<tf.Variable '%s' shape=%s dtype=%s>" % (
            self.name, self.get_shape(), self.dtype.name)
  
    # pylint: disable=unused-argument
    def _init_from_args(self,
                        initial_value=None,
                        trainable=True,
                        collections=None,
                        validate_shape=True,
                        caching_device=None,
                        name=None,
                        dtype=None,
                        constraint=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).
        constraint: An optional projection function to be applied to the variable
          after being updated by an `Optimizer` (e.g. used to implement norm
          constraints or value constraints for layer weights). The function must
          take as input the unprojected Tensor representing the value of the
          variable and return the Tensor for the projected value
          (which must have the same shape). Constraints are not safe to
          use when doing asynchronous distributed training.
  
      Raises:
        ValueError: If the initial value is not specified, or does not have a
          shape and `validate_shape` is `True`.
  
      @compatibility(eager)
      When Eager Execution is enabled, variables are never added to collections.
      It is not implicitly added to the `GLOBAL_VARIABLES` or
      `TRAINABLE_VARIABLES` collections, and the `collections` argument is
      ignored.
      @end_compatibility
      """
      if initial_value is None:
        raise ValueError("initial_value must be specified.")
      init_from_fn = callable(initial_value)
  
      if isinstance(initial_value, ops.Tensor) and hasattr(
          initial_value, "graph") and initial_value.graph.building_function:
        raise ValueError("Tensor-typed variable initializers must either be "
                         "wrapped in an init_scope or callable "
                         "(e.g., `tf.Variable(lambda : "
                         "tf.truncated_normal([10, 40]))`) when building "
                         "functions. Please file a feature request if this "
                         "restriction inconveniences you.")
  
      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 constraint is not None and not callable(constraint):
        raise ValueError("The `constraint` argument must be a callable.")
  
      if isinstance(initial_value, checkpointable.CheckpointInitialValue):
        self._maybe_initialize_checkpointable()
        self._update_uid = initial_value.checkpoint_position.restore_uid
        initial_value = initial_value.wrapped_value
  
      self._trainable = trainable
      if trainable and ops.GraphKeys.TRAINABLE_VARIABLES not in collections:
        collections = list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES]
      self._save_slice_info = None
      # Store the graph key so optimizers know how to only retrieve variables from
      # this graph.
      self._graph_key = ops.get_default_graph()._graph_key  # pylint: disable=protected-access
      with ops.init_scope():
        self._in_graph_mode = not context.executing_eagerly()
        with ops.name_scope(name, "Variable", []
                            if init_from_fn else [initial_value]) as name:
          # pylint: disable=protected-access
          handle_name = ops._name_from_scope_name(name)
          if self._in_graph_mode:
            shared_name = handle_name
          else:
            # When in eager mode use a uid for the shared_name, to prevent
            # accidental sharing.
            shared_name = "%s_%d" % (handle_name, ops.uid())
          # 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" % handle_name)]))
          with ops.get_default_graph()._attr_scope({"_class": attr}):
            with ops.name_scope("Initializer"), ops.device(None):
              initial_value = ops.convert_to_tensor(
                  initial_value() if init_from_fn else 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=shared_name,
                name=name,
                graph_mode=self._in_graph_mode)
          self._shape = initial_value.shape
          # 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._unique_id = shared_name
          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):
                # pylint: disable=protected-access
                self._initializer_op = (
                    gen_resource_variable_ops.assign_variable_op(
                        self._handle,
                        variables._try_guard_against_uninitialized_dependencies(
                            name,
                            initial_value),
                        name=n))
                # pylint: enable=protected-access
            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 not context.executing_eagerly():
            # Eager variables are only added to collections if they are part of an
            # eager variable store (otherwise in an interactive session they would
            # hog memory and cause OOM). This is done in ops/variable_scope.py.
            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)
      self._cached_shape_as_list = None
  
    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))
        if snapshot.op.type != "ReadVariableOp":
          self._cached_value = snapshot
        else:
          self._cached_value = None
        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
  
    @contextlib.contextmanager
    def _assign_dependencies(self):
      """Makes assignments depend on the cached value, if any.
  
      This prevents undefined behavior with reads not ordered wrt writes.
  
      Yields:
        None.
      """
      if self._cached_value is not None:
        with ops.control_dependencies([self._cached_value]):
          yield
      else:
        yield
  
    def __nonzero__(self):
      return self.__bool__()
  
    def __bool__(self):
      return bool(self.read_value())
  
    def __copy__(self):
      return self
  
    def __deepcopy__(self, memo):
      if not context.executing_eagerly():
        raise NotImplementedError(
            "__deepcopy__() is only available when eager execution is enabled.")
      copied_variable = ResourceVariable(
          initial_value=self.read_value(),
          trainable=self._trainable,
          constraint=self._constraint,
          dtype=self._dtype,
          name=self._shared_name + "_copy")
      memo[self._unique_id] = copied_variable
      return copied_variable
  
    @property
    def dtype(self):
      """The dtype of this variable."""
      return self._dtype
  
    @property
    def device(self):
      """The device this variable is on."""
      return self._handle.device
  
    @property
    def graph(self):
      """The `Graph` of this variable."""
      return self._handle.graph
  
    @property
    def name(self):
      """The name of the handle for this variable."""
      return self._handle_name
  
    @property
    def shape(self):
      """The shape of this variable."""
      return self._shape
  
    @property
    def distribute_strategy(self):
      """The `tf.distribute.Strategy` that this variable was created under."""
      return self._distribute_strategy
  
    def _shape_as_list(self):
      if self._cached_shape_as_list:
        return self._cached_shape_as_list
      if self.shape.ndims is None:
        return None
      self._cached_shape_as_list = [dim.value for dim in self.shape.dims]
      return self._cached_shape_as_list
  
    def _shape_tuple(self):
      shape = self._shape_as_list()
      if shape is None:
        return None
      return tuple(shape)
  
    @property
    def create(self):
      """The op responsible for initializing this variable."""
      if not self._in_graph_mode:
        raise RuntimeError("Calling create is not supported when eager execution"
                           " is enabled.")
      return self._initializer_op
  
    @property
    def handle(self):
      """The handle by which this variable can be accessed."""
      return self._handle
  
    def value(self):
      """A cached operation which reads the value of this variable."""
      if self._cached_value is not None:
        return self._cached_value
      with ops.colocate_with(None, ignore_existing=True):
        with ops.device(self._handle.device):
          return self._read_variable_op()
  
    def _as_graph_element(self):
      """Conversion function for Graph.as_graph_element()."""
      return self._graph_element
  
    @property
    def initializer(self):
      """The op responsible for initializing this variable."""
      return self._initializer_op
  
    @property
    def initial_value(self):
      """Returns the Tensor used as the initial value for the variable."""
      if context.executing_eagerly():
        raise RuntimeError("initial_value not supported in EAGER mode.")
      return self._initial_value
  
    @property
    def constraint(self):
      """Returns the constraint function associated with this variable.
  
      Returns:
        The constraint function that was passed to the variable constructor.
        Can be `None` if no constraint was passed.
      """
      return self._constraint
  
    @property
    def op(self):
      """The op for this variable."""
      return self._handle.op
  
    @property
    def trainable(self):
      return self._trainable
  
    def eval(self, session=None):
      """Evaluates and returns the value of this variable."""
      if context.executing_eagerly():
        raise RuntimeError("Trying to eval in EAGER mode")
      return self._graph_element.eval(session=session)
  
    def numpy(self):
      if context.executing_eagerly():
        return self.read_value().numpy()
      raise NotImplementedError(
          "numpy() is only available when eager execution is enabled.")
  
    @deprecated(None, "Prefer Dataset.range instead.")
    def count_up_to(self, limit):
      """Increments this variable until it reaches `limit`.
  
      When that Op is run it tries to increment the variable by `1`. If
      incrementing the variable would bring it above `limit` then the Op raises
      the exception `OutOfRangeError`.
  
      If no error is raised, the Op outputs the value of the variable before
      the increment.
  
      This is essentially a shortcut for `count_up_to(self, limit)`.
  
      Args:
        limit: value at which incrementing the variable raises an error.
  
      Returns:
        A `Tensor` that will hold the variable value before the increment. If no
        other Op modifies this variable, the values produced will all be
        distinct.
      """
      return gen_state_ops.resource_count_up_to(self.handle, limit=limit,
                                                T=self.dtype)
  
    def _set_save_slice_info(self, save_slice_info):
      """Sets the slice info for this `ResourceVariable`.
  
      Args:
        save_slice_info: A `Variable.SaveSliceInfo` object.
      """
      self._save_slice_info = save_slice_info
  
    def _get_save_slice_info(self):
      return self._save_slice_info
  
    def _read_variable_op(self):
      if self.trainable:
        tape.variable_accessed(self)
      result = gen_resource_variable_ops.read_variable_op(self._handle,
                                                          self._dtype)
      if not context.executing_eagerly():
        # Note that if a control flow context is active the input of the read op
        # might not actually be the handle. This line bypasses it.
        tape.record_operation(
            "ReadVariableOp", [result], [self._handle], lambda x: [x])
      return result
  
    def read_value(self):
      """Constructs an op which reads the value of this variable.
  
      Should be used when there are multiple reads, or when it is desirable to
      read the value only after some condition is true.
  
      Returns:
       the read operation.
      """
      with ops.name_scope("Read"):
        # Ensure we read the variable in the same device as the handle.
        with ops.device(self._handle.device):
          value = self._read_variable_op()
      # Return an identity so it can get placed on whatever device the context
      # specifies instead of the device where the variable is.
      return array_ops.identity(value)
  
    def sparse_read(self, indices, name=None):
      """Reads the value of this variable sparsely, using `gather`."""
      with ops.name_scope("Gather" if name is None else name) as name:
        if self.trainable:
          tape.variable_accessed(self)
        value = gen_resource_variable_ops.resource_gather(
            self._handle, indices, dtype=self._dtype, name=name)
      return array_ops.identity(value)
  
    def to_proto(self, export_scope=None):
      """Converts a `ResourceVariable` to a `VariableDef` protocol buffer.
  
      Args:
        export_scope: Optional `string`. Name scope to remove.
  
      Raises:
        RuntimeError: If run in EAGER mode.
  
      Returns:
        A `VariableDef` protocol buffer, or `None` if the `Variable` is not
        in the specified name scope.
      """
      if context.executing_eagerly():
        raise RuntimeError("to_proto not supported in EAGER mode.")
      if export_scope is None or self.handle.name.startswith(export_scope):
        var_def = variable_pb2.VariableDef()
        var_def.variable_name = ops.strip_name_scope(self.handle.name,
                                                     export_scope)
        if self._initial_value is not None:
          # This is inside an if-statement for backwards compatibility, since
          # self._initial_value might be None for variables constructed from old
          # protos.
          var_def.initial_value_name = ops.strip_name_scope(
              self._initial_value.name, export_scope)
        var_def.initializer_name = ops.strip_name_scope(self.initializer.name,
                                                        export_scope)
        if self._cached_value is not None:
          var_def.snapshot_name = ops.strip_name_scope(self._cached_value.name,
                                                       export_scope)
        else:
          # Store the graph_element here
          var_def.snapshot_name = ops.strip_name_scope(self._graph_element.name,
                                                       export_scope)
        var_def.is_resource = True
        var_def.trainable = self.trainable
        if self._save_slice_info:
          var_def.save_slice_info_def.MergeFrom(
              self._save_slice_info.to_proto(export_scope=export_scope))
        return var_def
      else:
        return None
  
    @staticmethod
    def from_proto(variable_def, import_scope=None):
      if context.executing_eagerly():
        raise RuntimeError("from_proto not supported in EAGER mode.")
      return ResourceVariable(
          variable_def=variable_def, import_scope=import_scope)
  
    def set_shape(self, shape):
      """Unsupported."""
      raise NotImplementedError("ResourceVariable does not implement set_shape()")
  
    __array_priority__ = 100
  
    def is_initialized(self, name=None):
      """Checks whether a resource variable has been initialized.
  
      Outputs boolean scalar indicating whether the tensor has been initialized.
  
      Args:
        name: A name for the operation (optional).
  
      Returns:
        A `Tensor` of type `bool`.
      """
      return gen_resource_variable_ops.var_is_initialized_op(self.handle, name)
  
    def assign_sub(self, delta, use_locking=None, name=None, read_value=True):
      """Subtracts a value from this variable.
  
      Args:
        delta: A `Tensor`. The value to subtract from this variable.
        use_locking: If `True`, use locking during the operation.
        name: The name to use for the operation.
        read_value: A `bool`. Whether to read and return the new value of the
            variable or not.
  
      Returns:
        If `read_value` is `True`, this method will return the new value of the
        variable after the assignment has completed. Otherwise, when in graph mode
        it will return the `Operation` that does the assignment, and when in eager
        mode it will return `None`.
      """
      # TODO(apassos): this here and below is not atomic. Consider making it
      # atomic if there's a way to do so without a performance cost for those who
      # don't need it.
      with _handle_graph(self.handle), self._assign_dependencies():
        assign_sub_op = gen_resource_variable_ops.assign_sub_variable_op(
            self.handle, ops.convert_to_tensor(delta, dtype=self.dtype),
            name=name)
      if read_value:
        return self._lazy_read(assign_sub_op)
      return assign_sub_op
  
    def assign_add(self, delta, use_locking=None, name=None, read_value=True):
      """Adds a value to this variable.
  
      Args:
        delta: A `Tensor`. The value to add to this variable.
        use_locking: If `True`, use locking during the operation.
        name: The name to use for the operation.
        read_value: A `bool`. Whether to read and return the new value of the
            variable or not.
  
      Returns:
        If `read_value` is `True`, this method will return the new value of the
        variable after the assignment has completed. Otherwise, when in graph mode
        it will return the `Operation` that does the assignment, and when in eager
        mode it will return `None`.
      """
      with _handle_graph(self.handle), self._assign_dependencies():
        assign_add_op = gen_resource_variable_ops.assign_add_variable_op(
            self.handle, ops.convert_to_tensor(delta, dtype=self.dtype),
            name=name)
      if read_value:
        return self._lazy_read(assign_add_op)
      return assign_add_op
  
    def _lazy_read(self, op):
      if self.trainable:
        tape.variable_accessed(self)
      return _UnreadVariable(
          handle=self._handle, dtype=self.dtype, shape=self._shape,
          in_graph_mode=self._in_graph_mode,
          deleter=self._handle_deleter if not self._in_graph_mode else None,
          parent_op=op, unique_id=self._unique_id)
  
    def assign(self, value, use_locking=None, name=None, read_value=True):
      """Assigns a new value to this variable.
  
      Args:
        value: A `Tensor`. The new value for this variable.
        use_locking: If `True`, use locking during the assignment.
        name: The name to use for the assignment.
        read_value: A `bool`. Whether to read and return the new value of the
            variable or not.
  
      Returns:
        If `read_value` is `True`, this method will return the new value of the
        variable after the assignment has completed. Otherwise, when in graph mode
        it will return the `Operation` that does the assignment, and when in eager
        mode it will return `None`.
      """
      # Note: not depending on the cached value here since this can used to
      # initialize the variable.
      with _handle_graph(self.handle):
        value_tensor = ops.convert_to_tensor(value, dtype=self.dtype)
        self._shape.assert_is_compatible_with(value_tensor.shape)
        assign_op = gen_resource_variable_ops.assign_variable_op(
            self.handle, value_tensor, name=name)
        if read_value:
          return self._lazy_read(assign_op)
      return assign_op
  
    def __reduce__(self):
      return (ResourceVariable, (self.numpy(),))
  
    def scatter_sub(self, sparse_delta, use_locking=False, name=None):
      """Subtracts `IndexedSlices` from this variable.
  
      Args:
        sparse_delta: `IndexedSlices` to be subtracted from this variable.
        use_locking: If `True`, use locking during the operation.
        name: the name of the operation.
  
      Returns:
        A `Tensor` that will hold the new value of this variable after
        the scattered subtraction has completed.
  
      Raises:
        ValueError: if `sparse_delta` is not an `IndexedSlices`.
      """
      if not isinstance(sparse_delta, ops.IndexedSlices):
        raise ValueError("sparse_delta is not IndexedSlices: %s" % sparse_delta)
      return self._lazy_read(gen_resource_variable_ops.resource_scatter_sub(
          self.handle, sparse_delta.indices,
          ops.convert_to_tensor(sparse_delta.values, self.dtype), name=name))
  
    def scatter_add(self, sparse_delta, use_locking=False, name=None):
      """Adds `IndexedSlices` from this variable.
  
      Args:
        sparse_delta: `IndexedSlices` to be added to this variable.
        use_locking: If `True`, use locking during the operation.
        name: the name of the operation.
  
      Returns:
        A `Tensor` that will hold the new value of this variable after
        the scattered subtraction has completed.
  
      Raises:
        ValueError: if `sparse_delta` is not an `IndexedSlices`.
      """
      if not isinstance(sparse_delta, ops.IndexedSlices):
        raise ValueError("sparse_delta is not IndexedSlices: %s" % sparse_delta)
      return self._lazy_read(gen_resource_variable_ops.resource_scatter_add(
          self.handle, sparse_delta.indices,
          ops.convert_to_tensor(sparse_delta.values, self.dtype), name=name))
  
    def scatter_update(self, sparse_delta, use_locking=False, name=None):
      """Assigns `IndexedSlices` to this variable.
  
      Args:
        sparse_delta: `IndexedSlices` to be assigned to this variable.
        use_locking: If `True`, use locking during the operation.
        name: the name of the operation.
  
      Returns:
        A `Tensor` that will hold the new value of this variable after
        the scattered subtraction has completed.
  
      Raises:
        ValueError: if `sparse_delta` is not an `IndexedSlices`.
      """
      if not isinstance(sparse_delta, ops.IndexedSlices):
        raise ValueError("sparse_delta is not IndexedSlices: %s" % sparse_delta)
      return self._lazy_read(gen_resource_variable_ops.resource_scatter_update(
          self.handle, sparse_delta.indices,
          ops.convert_to_tensor(sparse_delta.values, self.dtype), name=name))
  
    def batch_scatter_update(self, sparse_delta, use_locking=False, name=None):
      """Assigns `IndexedSlices` to this variable batch-wise.
  
      Analogous to `batch_gather`. This assumes that this variable and the
      sparse_delta IndexedSlices have a series of leading dimensions that are the
      same for all of them, and the updates are performed on the last dimension of
      indices. In other words, the dimensions should be the following:
  
      `num_prefix_dims = sparse_delta.indices.ndims - 1`
      `batch_dim = num_prefix_dims + 1`
      `sparse_delta.updates.shape = sparse_delta.indices.shape + var.shape[
           batch_dim:]`
  
      where
  
      `sparse_delta.updates.shape[:num_prefix_dims]`
      `== sparse_delta.indices.shape[:num_prefix_dims]`
      `== var.shape[:num_prefix_dims]`
  
      And the operation performed can be expressed as:
  
      `var[i_1, ..., i_n,
           sparse_delta.indices[i_1, ..., i_n, j]] = sparse_delta.updates[
              i_1, ..., i_n, j]`
  
      When sparse_delta.indices is a 1D tensor, this operation is equivalent to
      `scatter_update`.
  
      To avoid this operation one can looping over the first `ndims` of the
      variable and using `scatter_update` on the subtensors that result of slicing
      the first dimension. This is a valid option for `ndims = 1`, but less
      efficient than this implementation.
  
      Args:
        sparse_delta: `IndexedSlices` to be assigned to this variable.
        use_locking: If `True`, use locking during the operation.
        name: the name of the operation.
  
      Returns:
        A `Tensor` that will hold the new value of this variable after
        the scattered subtraction has completed.
  
      Raises:
        ValueError: if `sparse_delta` is not an `IndexedSlices`.
      """
      return self._lazy_read(state_ops.batch_scatter_update(
          self, sparse_delta.indices, sparse_delta.values,
          use_locking=use_locking, name=name))
  
    def scatter_nd_sub(self, indices, updates, name=None):
      """Applies sparse subtraction to individual values or slices in a Variable.
  
      `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
  
      `indices` must be integer tensor, containing indices into `ref`.
      It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
  
      The innermost dimension of `indices` (with length `K`) corresponds to
      indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
      dimension of `ref`.
  
      `updates` is `Tensor` of rank `Q-1+P-K` with shape:
  
      ```
      [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
      ```
  
      For example, say we want to add 4 scattered elements to a rank-1 tensor to
      8 elements. In Python, that update would look like this:
  
      ```python
          ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
          indices = tf.constant([[4], [3], [1] ,[7]])
          updates = tf.constant([9, 10, 11, 12])
          op = ref.scatter_nd_sub(indices, updates)
          with tf.Session() as sess:
            print sess.run(op)
      ```
  
      The resulting update to ref would look like this:
  
          [1, -9, 3, -6, -6, 6, 7, -4]
  
      See `tf.scatter_nd` for more details about how to make updates to
      slices.
  
      Args:
        indices: The indices to be used in the operation.
        updates: The values to be used in the operation.
        name: the name of the operation.
  
      Returns:
        A `Tensor` that will hold the new value of this variable after
        the scattered subtraction has completed.
  
      Raises:
        ValueError: if `sparse_delta` is not an `IndexedSlices`.
      """
      return self._lazy_read(gen_state_ops.resource_scatter_nd_sub(
          self.handle, indices, ops.convert_to_tensor(updates, self.dtype),
          name=name))
  
    def scatter_nd_add(self, indices, updates, name=None):
      """Applies sparse addition to individual values or slices in a Variable.
  
      `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
  
      `indices` must be integer tensor, containing indices into `ref`.
      It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
  
      The innermost dimension of `indices` (with length `K`) corresponds to
      indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
      dimension of `ref`.
  
      `updates` is `Tensor` of rank `Q-1+P-K` with shape:
  
      ```
      [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
      ```
  
      For example, say we want to add 4 scattered elements to a rank-1 tensor to
      8 elements. In Python, that update would look like this:
  
      ```python
          ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
          indices = tf.constant([[4], [3], [1] ,[7]])
          updates = tf.constant([9, 10, 11, 12])
          add = ref.scatter_nd_add(indices, updates)
          with tf.Session() as sess:
            print sess.run(add)
      ```
  
      The resulting update to ref would look like this:
  
          [1, 13, 3, 14, 14, 6, 7, 20]
  
      See `tf.scatter_nd` for more details about how to make updates to
      slices.
  
      Args:
        indices: The indices to be used in the operation.
        updates: The values to be used in the operation.
        name: the name of the operation.
  
      Returns:
        A `Tensor` that will hold the new value of this variable after
        the scattered subtraction has completed.
  
      Raises:
        ValueError: if `sparse_delta` is not an `IndexedSlices`.
      """
      return self._lazy_read(gen_state_ops.resource_scatter_nd_add(
          self.handle, indices, ops.convert_to_tensor(updates, self.dtype),
          name=name))
  
    def scatter_nd_update(self, indices, updates, name=None):
      """Applies sparse assignment to individual values or slices in a Variable.
  
      `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
  
      `indices` must be integer tensor, containing indices into `ref`.
      It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
  
      The innermost dimension of `indices` (with length `K`) corresponds to
      indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
      dimension of `ref`.
  
      `updates` is `Tensor` of rank `Q-1+P-K` with shape:
  
      ```
      [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
      ```
  
      For example, say we want to add 4 scattered elements to a rank-1 tensor to
      8 elements. In Python, that update would look like this:
  
      ```python
          ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
          indices = tf.constant([[4], [3], [1] ,[7]])
          updates = tf.constant([9, 10, 11, 12])
          op = ref.scatter_nd_update(indices, updates)
          with tf.Session() as sess:
            print sess.run(op)
      ```
  
      The resulting update to ref would look like this:
  
          [1, 11, 3, 10, 9, 6, 7, 12]
  
      See `tf.scatter_nd` for more details about how to make updates to
      slices.
  
      Args:
        indices: The indices to be used in the operation.
        updates: The values to be used in the operation.
        name: the name of the operation.
  
      Returns:
        A `Tensor` that will hold the new value of this variable after
        the scattered subtraction has completed.
  
      Raises:
        ValueError: if `sparse_delta` is not an `IndexedSlices`.
      """
      return self._lazy_read(gen_state_ops.resource_scatter_nd_update(
          self.handle, indices, ops.convert_to_tensor(updates, self.dtype),
          name=name))
  
    def _strided_slice_assign(self, begin, end, strides, value, name, begin_mask,
                              end_mask, ellipsis_mask, new_axis_mask,
                              shrink_axis_mask):
      with _handle_graph(self.handle), self._assign_dependencies():
        return self._lazy_read(
            gen_array_ops.resource_strided_slice_assign(
                ref=self.handle,
                begin=begin,
                end=end,
                strides=strides,
                value=ops.convert_to_tensor(value, dtype=self.dtype),
                name=name,
                begin_mask=begin_mask,
                end_mask=end_mask,
                ellipsis_mask=ellipsis_mask,
                new_axis_mask=new_axis_mask,
                shrink_axis_mask=shrink_axis_mask))
  
    def __int__(self):
      if self.dtype != dtypes.int32 and self.dtype != dtypes.int64:
        raise TypeError("Non-integer variable can't be converted to integer.")
      return int(self.value().numpy())
  
    def _dense_var_to_tensor(self, dtype=None, name=None, as_ref=False):
      del name
      if dtype is not None and not dtype.is_compatible_with(self.dtype):
        raise ValueError(
            "Incompatible type conversion requested to type {!r} for variable "
            "of type {!r}".format(dtype.name, self.dtype.name))
      if as_ref:
        return self.read_value().op.inputs[0]
      else:
        return self.value()
  
    def __iadd__(self, unused_other):
      raise RuntimeError("Variable += value not supported. Use "
                         "variable.assign_add(value) to modify the variable "
                         "value and variable = variable + value to get a new "
                         "Tensor object.")
  
    def __isub__(self, unused_other):
      raise RuntimeError("Variable -= value not supported. Use "
                         "variable.assign_sub(value) to modify the variable "
                         "value and variable = variable - value to get a new "
                         "Tensor object.")
  
    def __imul__(self, unused_other):
      raise RuntimeError("Variable *= value not supported. Use "
                         "`var.assign(var * value)` to modify the variable or "
                         "`var = var * value` to get a new Tensor object.")
  
    def __idiv__(self, unused_other):
      raise RuntimeError("Variable /= value not supported. Use "
                         "`var.assign(var / value)` to modify the variable or "
                         "`var = var / value` to get a new Tensor object.")
  
    def __itruediv__(self, unused_other):
      raise RuntimeError("Variable /= value not supported. Use "
                         "`var.assign(var / value)` to modify the variable or "
                         "`var = var / value` to get a new Tensor object.")
  
    def __irealdiv__(self, unused_other):
      raise RuntimeError("Variable /= value not supported. Use "
                         "`var.assign(var / value)` to modify the variable or "
                         "`var = var / value` to get a new Tensor object.")
  
    def __ipow__(self, unused_other):
      raise RuntimeError("Variable **= value not supported. Use "
                         "`var.assign(var ** value)` to modify the variable or "
                         "`var = var ** value` to get a new Tensor object.")
  
  
  pywrap_tensorflow.TFE_Py_RegisterResourceVariableType(ResourceVariable)
  math_ops._resource_variable_type = ResourceVariable  # pylint: disable=protected-access
  
  
  def _dense_var_to_tensor(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
  
  
  # Register a conversion function which reads the value of the variable,
  # allowing instances of the class to be used as tensors.
  ops.register_tensor_conversion_function(ResourceVariable, _dense_var_to_tensor)
  ops.register_dense_tensor_like_type(ResourceVariable)
  
  
  class _UnreadVariable(ResourceVariable):
    """Represents a future for a read of a variable.
  
    Pretends to be the tensor if anyone looks.
    """
  
    def __init__(self, handle, dtype,  # pylint: disable=super-init-not-called
                 shape, in_graph_mode, deleter, parent_op, unique_id):
      # We do not call super init on purpose.
      self._trainable = False
      self._save_slice_info = None
      self._graph_key = ops.get_default_graph()._graph_key  # pylint: disable=protected-access
      self._in_graph_mode = in_graph_mode
      self._handle = handle
      self._shape = shape
      self._initial_value = None
      if isinstance(self._handle, ops.EagerTensor):
        self._handle_name = ""
      else:
        self._handle_name = self._handle.name
      self._unique_id = unique_id
      self._dtype = dtype
      self._constraint = None
      self._cached_value = None
      self._is_initialized_op = None
      self._initializer_op = None
      self._parent_op = parent_op
      if context.executing_eagerly():
        self._graph_element = None
      else:
        self._graph_element = self.read_value()
      self._handle_deleter = deleter
  
    @property
    def name(self):
      if self._in_graph_mode:
        return self._parent_op.name
      else:
        return "UnreadVariable"
  
    def value(self):
      return self._read_variable_op()
  
    def read_value(self):
      return self._read_variable_op()
  
    def _read_variable_op(self):
      with ops.control_dependencies([self._parent_op]):
        return gen_resource_variable_ops.read_variable_op(self._handle,
                                                          self._dtype)
  
    @property
    def op(self):
      """The op for this variable."""
      return self._parent_op
  
  
  ops.register_dense_tensor_like_type(_UnreadVariable)
  
  
  class _MixedPrecisionVariable(ResourceVariable):
    """Represents a variable that can return in desired dtype when read.
  
    In mixed precision training, it is usually desirable to use different dtypes
    for variables and computation. This class will be used to wrap created
    ResourceVariable when mixed precision training is enabled. It allows layers to
    perform computation in a different dtype than their variable dtypes, in order
    to achieve higher performance without causing quality loss.
    """
  
    def __init__(self, var, read_dtype):
      """Creates a MixedPrecisionVariable.
  
      Args:
        var: A ResourceVariable instance.
        read_dtype: A tf.DType, the returned dtype when read, default to None.
          Casting is performed if read_dtype is not None and differs from
          var.dtype.
      Returns:
        An MixedPrecisionVariable instance.
      Raises:
        ValueError: if var is not a ResourceVariable instance, or read_dtype is
          not a tf.DType instance.
      """
      # pylint: disable=super-init-not-called
      # We do not call super init on purpose.
      if not isinstance(var, ResourceVariable):
        raise ValueError("InvalidArgument: var must be a ResourceVariable type.")
      if not isinstance(read_dtype, dtypes.DType):
        raise ValueError("InvalidArgument: read_dtype must be a tf.DType type.")
  
      self._var = var
      self._trainable = var.trainable
      self._save_slice_info = None
      self._graph_key = ops.get_default_graph()._graph_key  # pylint: disable=protected-access
      self._in_graph_mode = var._in_graph_mode  # pylint: disable=protected-access
      self._handle = var.handle
      self._shape = var.shape
      self._initial_value = None
      if isinstance(self.handle, ops.EagerTensor):
        self._handle_name = ""
      else:
        self._handle_name = self.handle.name
      self._unique_id = var._unique_id  # pylint: disable=protected-access
      self._dtype = var.dtype
      self._constraint = None
      self._cached_value = None
      self._is_initialized_op = var._is_initialized_op  # pylint: disable=protected-access
      self._initializer_op = var._initializer_op  # pylint: disable=protected-access
      # This needs to be set before read_value() is called.
      self._read_dtype = read_dtype
      if context.executing_eagerly():
        self._graph_element = None
      else:
        self._graph_element = self.read_value()
      self._handle_deleter = (
          var._handle_deleter if not self._in_graph_mode  # pylint: disable=protected-access
          else None)
      # pylint: enable=super-init-not-called
  
    @property
    def name(self):
      return self._var.name
  
    def value(self):
      return self._read_variable_op()
  
    def read_value(self):
      return self._read_variable_op()
  
    def _read_variable_op(self):
      with ops.colocate_with(self._handle):
        res = gen_resource_variable_ops.read_variable_op(self._handle,
                                                         self._dtype)
        if self._read_dtype != self._dtype:
          return math_ops.cast(res, self._read_dtype)
        else:
          return res
  
    @property
    def op(self):
      """The op for this variable."""
      return self._var.op
  
    @property
    def read_dtype(self):
      """The dtype of the returned tensor when reading the var."""
      return self._read_dtype
  
    def _dense_var_to_tensor(self, dtype=None, name=None, as_ref=False):
      del name
      if (dtype is not None and
          not dtype.is_compatible_with(self.read_dtype) or as_ref):
        return NotImplemented
      return self.value()
  
    def _should_act_as_resource_variable(self):
      """To pass resource_variable_ops.is_resource_variable check."""
      pass
  
  
  @ops.RegisterGradient("ReadVariableOp")
  def _ReadGrad(_, grad):
    """Gradient for read op."""
    return grad
  
  
  def variable_shape(handle, out_type=dtypes.int32):
    if getattr(
        handle, "_handle_data", None) is None or not handle._handle_data.is_set:
      return gen_resource_variable_ops.variable_shape(handle, out_type=out_type)
    shape_proto = handle._handle_data.shape_and_type[0].shape
    if shape_proto.unknown_rank or any(x.size == -1 for x in shape_proto.dim):
      return gen_resource_variable_ops.variable_shape(handle, out_type=out_type)
    return constant_op.constant([x.size for x in shape_proto.dim], dtype=out_type)
  
  
  @ops.RegisterGradient("ResourceGather")
  def _GatherGrad(op, grad):
    """Gradient for gather op."""
    # Build appropriately shaped IndexedSlices
    handle = op.inputs[0]
    indices = op.inputs[1]
    params_shape = variable_shape(handle)
    size = array_ops.expand_dims(array_ops.size(indices), 0)
    values_shape = array_ops.concat([size, params_shape[1:]], 0)
    values = array_ops.reshape(grad, values_shape)
    indices = array_ops.reshape(indices, size)
    return (ops.IndexedSlices(values, indices, params_shape), None)
  
  
  def _to_proto_fn(v, export_scope=None):
    """Converts Variable and ResourceVariable to VariableDef for collections."""
    return v.to_proto(export_scope=export_scope)
  
  
  def _from_proto_fn(v, import_scope=None):
    """Creates Variable or ResourceVariable from VariableDef as needed."""
    if v.is_resource:
      return ResourceVariable.from_proto(v, import_scope=import_scope)
    return variables.Variable.from_proto(v, import_scope=import_scope)
  
  
  ops.register_proto_function(
      ops.GraphKeys.GLOBAL_VARIABLES,
      proto_type=variable_pb2.VariableDef,
      to_proto=_to_proto_fn,
      from_proto=_from_proto_fn)
  ops.register_proto_function(
      ops.GraphKeys.TRAINABLE_VARIABLES,
      proto_type=variable_pb2.VariableDef,
      to_proto=_to_proto_fn,
      from_proto=_from_proto_fn)
  ops.register_proto_function(
      ops.GraphKeys.MOVING_AVERAGE_VARIABLES,
      proto_type=variable_pb2.VariableDef,
      to_proto=_to_proto_fn,
      from_proto=_from_proto_fn)
  ops.register_proto_function(
      ops.GraphKeys.LOCAL_VARIABLES,
      proto_type=variable_pb2.VariableDef,
      to_proto=_to_proto_fn,
      from_proto=_from_proto_fn)
  ops.register_proto_function(
      ops.GraphKeys.MODEL_VARIABLES,
      proto_type=variable_pb2.VariableDef,
      to_proto=_to_proto_fn,
      from_proto=_from_proto_fn)
  ops.register_proto_function(
      ops.GraphKeys.GLOBAL_STEP,
      proto_type=variable_pb2.VariableDef,
      to_proto=_to_proto_fn,
      from_proto=_from_proto_fn)
  
  
  def is_resource_variable(var):
    """"Returns True if `var` is to be considered a ResourceVariable."""
    return isinstance(var, ResourceVariable) or hasattr(
        var, "_should_act_as_resource_variable")
  
  
  def copy_to_graph_uninitialized(var):
    """Copies an existing variable to a new graph, with no initializer."""
    # Like ResourceVariable.__deepcopy__, but does not set an initializer on the
    # new variable.
    # pylint: disable=protected-access
    new_variable = ResourceVariable(
        initial_value=array_ops.placeholder(
            shape=var.shape, dtype=var.dtype,
            name="unused_initial_variable_value"),
        trainable=var.trainable,
        constraint=var._constraint,
        dtype=var.dtype,
        name=var._shared_name)
    new_variable._maybe_initialize_checkpointable()
    # pylint: enable=protected-access
    return new_variable
  
  ops.NotDifferentiable("VarIsInitializedOp")
  ops.NotDifferentiable("VariableShape")
  