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
  import contextlib
  import functools
  import weakref
  
  import numpy as np
  
  from tensorflow.core.framework import attr_value_pb2
  from tensorflow.core.framework import variable_pb2
  from tensorflow.python.client import pywrap_tf_session
  from tensorflow.python.compat import compat as forward_compat
  from tensorflow.python.eager import context
  from tensorflow.python.eager import tape
  from tensorflow.python.framework import auto_control_deps_utils as acd
  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 errors
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import meta_graph
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_spec
  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 handle_data_util
  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.tracking import base as trackable
  from tensorflow.python.types import core
  from tensorflow.python.util import _pywrap_utils
  from tensorflow.python.util import compat
  from tensorflow.python.util.deprecation import deprecated
  from tensorflow.python.util.tf_export import tf_export
  
  acd.register_read_only_resource_op("ReadVariableOp")
  acd.register_read_only_resource_op("VariableShape")
  acd.register_read_only_resource_op("ResourceGather")
  acd.register_read_only_resource_op("ResourceGatherNd")
  acd.register_read_only_resource_op("_ReadVariablesOp")
  
  # TODO(allenl): Remove this alias and migrate callers.
  get_resource_handle_data = handle_data_util.get_resource_handle_data
  
  
  def get_eager_safe_handle_data(handle):
    """Get the data handle from the Tensor `handle`."""
    assert isinstance(handle, ops.Tensor)
  
    if isinstance(handle, ops.EagerTensor):
      return handle._handle_data  # pylint: disable=protected-access
    else:
      return get_resource_handle_data(handle)
  
  
  def _set_handle_shapes_and_types(tensor, handle_data, graph_mode):
    """Sets the shape inference result HandleData on tensor.
  
    Args:
      tensor: A `Tensor` or `EagerTensor`.
      handle_data: A `CppShapeInferenceResult.HandleData`.
      graph_mode: A python bool.
    """
    tensor._handle_data = handle_data  # pylint: disable=protected-access
    if not graph_mode:
      return
  
    # Not an EagerTensor, so a graph tensor.
    shapes, types = zip(
        *[(pair.shape, pair.dtype) for pair in handle_data.shape_and_type])
    ranks = [len(s.dim) if not s.unknown_rank else -1 for s in shapes]
    shapes = [
        [d.size for d in s.dim]  # pylint: disable=g-complex-comprehension
        if not s.unknown_rank else None for s in shapes
    ]
    pywrap_tf_session.TF_GraphSetOutputHandleShapesAndTypes_wrapper(
        tensor._op._graph._c_graph,  # pylint: disable=protected-access
        tensor._as_tf_output(),  # pylint: disable=protected-access
        shapes,
        ranks,
        types)
  
  
  def _combine_handle_data(handle, initial_value):
    """Concats HandleData from tensors `handle` and `initial_value`.
  
    Args:
      handle: A `Tensor` of dtype `resource`.
      initial_value: A `Tensor`.
  
    Returns:
      A `CppShapeInferenceResult.HandleData`.  If `initial_value` has dtype
      `variant`, the `HandleData` contains the concatenation of the shape_and_type
      from both `handle` and `initial_value`.
  
    Raises:
      RuntimeError: If handle, which was returned by VarHandleOp, either has
        no handle data, or its len(handle_data.shape_and_type) != 1.
    """
    assert handle.dtype == dtypes.resource
  
    variable_handle_data = get_eager_safe_handle_data(handle)
  
    if initial_value.dtype != dtypes.variant:
      return variable_handle_data
  
    extra_handle_data = get_eager_safe_handle_data(initial_value)
    if extra_handle_data is not None and extra_handle_data.is_set:
      if (variable_handle_data is None or not variable_handle_data.is_set or
          len(variable_handle_data.shape_and_type) != 1):
        raise RuntimeError(
            "Expected VarHandleOp to return a length==1 shape_and_type, "
            f"but saw: '{variable_handle_data}'")
      variable_handle_data.shape_and_type.extend(extra_handle_data.shape_and_type)
    return variable_handle_data
  
  
  def _variable_handle_from_shape_and_dtype(shape,
                                            dtype,
                                            shared_name,
                                            name,
                                            graph_mode,
                                            initial_value=None):
    """Create a variable handle, copying in handle data from `initial_value`."""
    container = ops.get_default_graph()._container  # pylint: disable=protected-access
    if container is None:
      container = ""
    shape = tensor_shape.as_shape(shape)
    dtype = dtypes.as_dtype(dtype)
    if not graph_mode:
      if shared_name is not None:
        raise errors.InternalError(  # pylint: disable=no-value-for-parameter
            "Using an explicit shared_name is not allowed when executing eagerly."
        )
      shared_name = context.anonymous_name()
  
    handle = gen_resource_variable_ops.var_handle_op(
        shape=shape,
        dtype=dtype,
        shared_name=shared_name,
        name=name,
        container=container)
    if initial_value is None:
      initial_value = handle
    if graph_mode:
      full_handle_data = _combine_handle_data(handle, initial_value)
      _set_handle_shapes_and_types(handle, full_handle_data, graph_mode)
      return handle
    else:
      handle_data = cpp_shape_inference_pb2.CppShapeInferenceResult.HandleData()
      handle_data.is_set = True
      handle_data.shape_and_type.append(
          cpp_shape_inference_pb2.CppShapeInferenceResult.HandleShapeAndType(
              shape=shape.as_proto(), dtype=dtype.as_datatype_enum))
  
      if initial_value is not None and initial_value.dtype == dtypes.variant:
        extra_handle_data = get_eager_safe_handle_data(initial_value)
        if extra_handle_data is not None and extra_handle_data.is_set:
          if (not handle_data.is_set or len(handle_data.shape_and_type) != 1):
            raise RuntimeError(
                "Expected VarHandleOp to return a length==1 shape_and_type, "
                f"but saw: '{handle_data}'")
          handle_data.shape_and_type.extend(extra_handle_data.shape_and_type)
  
      _set_handle_shapes_and_types(handle, handle_data, graph_mode)
      return handle
  
  
  def eager_safe_variable_handle(initial_value, shape, shared_name, name,
                                 graph_mode):
    """Creates a variable handle with information to do shape inference.
  
    The dtype is read from `initial_value` and stored in the returned
    resource tensor's handle data.
  
    If `initial_value.dtype == tf.variant`, we additionally extract the handle
    data (if any) from `initial_value` and append it to the `handle_data`.
    In this case, the returned tensor's handle data is in the form
  
    ```
    is_set: true
    shape_and_type {
      shape {
        // initial_value.shape
      }
      dtype: DT_VARIANT
    }
    shape_and_type {
      // handle_data(initial_value).shape_and_type[0]
    }
    shape_and_type {
      // handle_data(initial_value).shape_and_type[1]
    }
    ...
    ```
  
    Ops that read from this tensor, such as `ReadVariableOp` and
    `AssignVariableOp`, know that `handle_data(handle).shape_and_type[1:]`
    correspond to the handle data of the variant(s) stored in the Variable.
  
    Args:
      initial_value: A `Tensor`.
      shape: The shape of the handle data. Can be `TensorShape(None)` (i.e.
        unknown shape).
      shared_name: A string.
      name: A string.
      graph_mode: A python bool.
  
    Returns:
      The handle, a `Tensor` of type `resource`.
    """
    dtype = initial_value.dtype.base_dtype
    return _variable_handle_from_shape_and_dtype(shape, dtype, shared_name, name,
                                                 graph_mode, initial_value)
  
  
  @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:
    """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.
    """
  
    __slots__ = ["_handle", "_handle_device", "_context"]
  
    def __init__(self, handle, handle_device):
      if not isinstance(handle, ops.Tensor):
        raise ValueError(
            (f"Passed handle={handle} to EagerResourceDeleter. Was expecting "
             f"the handle to be a `tf.Tensor`."))
      self._handle = handle
      self._handle_device = handle_device
      # This is held since the __del__ function runs an op, and if the context()
      # is collected before this object, there will be a segfault when running the
      # op.
      self._context = context.context()
  
    def __del__(self):
      # Resources follow object-identity when executing eagerly, so it is safe to
      # delete the resource we have a handle to.
      try:
        # A packed EagerTensor doesn't own any resource.
        if isinstance(self._handle, ops.EagerTensor) and self._handle.is_packed:
          return
        # 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)
  
  
  def _maybe_set_handle_data(dtype, handle, tensor):
    if dtype == dtypes.variant:
      # For DT_VARIANT types, the handle's shape_and_type[1:] stores the
      # variant's handle data.  Extract it.
      handle_data = get_eager_safe_handle_data(handle)
      if handle_data.is_set and len(handle_data.shape_and_type) > 1:
        tensor._handle_data = (  # pylint: disable=protected-access
            cpp_shape_inference_pb2.CppShapeInferenceResult.HandleData(
                is_set=True, shape_and_type=handle_data.shape_and_type[1:]))
  
  
  def variable_accessed(variable):
    """Records that `variable` was accessed for the tape and FuncGraph."""
    if hasattr(ops.get_default_graph(), "watch_variable"):
      ops.get_default_graph().watch_variable(variable)
    if variable.trainable:
      tape.variable_accessed(variable)
  
  
  class BaseResourceVariable(variables.VariableV1, core.Tensor):
    """A python variable from an existing handle."""
  
    # TODO(wangpeng): Deprecate `constraint` when callers no long pass it in.
    def __init__(  # pylint: disable=super-init-not-called
        self,
        trainable=None,
        shape=None,
        dtype=None,
        handle=None,
        constraint=None,
        synchronization=None,
        aggregation=None,
        distribute_strategy=None,
        name=None,
        unique_id=None,
        handle_name=None,
        graph_element=None,
        initial_value=None,
        initializer_op=None,
        is_initialized_op=None,
        cached_value=None,
        save_slice_info=None,
        caching_device=None,
        in_graph_mode=None,
        validate_shape=True,
        **unused_kwargs):
      """Creates a variable from a handle.
  
      Args:
        trainable: If `True`, GradientTapes automatically watch uses of this
          Variable.
        shape: The variable's shape. This shape can be set to tf.TensorShape(None)
          in order to assign values of different shapes to this variable.
          Otherwise (i.e. if the shape is fully determined), it will trigger run
          time checks to ensure that each assignment is of the same shape.
        dtype: The variable's dtype.
        handle: The variable's handle
        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.
        synchronization: Indicates when a distributed a variable will be
          aggregated. Accepted values are constants defined in the class
          `tf.VariableSynchronization`. By default the synchronization is set to
          `AUTO` and the current `DistributionStrategy` chooses when to
          synchronize.
        aggregation: Indicates how a distributed variable will be aggregated.
          Accepted values are constants defined in the class
          `tf.VariableAggregation`.
        distribute_strategy: The distribution strategy this variable was created
          under.
        name: The name for this variable.
        unique_id: Internal. Unique ID for this variable's handle.
        handle_name: The name for the variable's handle.
        graph_element: Optional, required only in session.run-mode. Pre-created
          tensor which reads this variable's value.
        initial_value: Optional. Variable's initial value.
        initializer_op: Operation which assigns the variable's initial value.
        is_initialized_op: Pre-created operation to check whether this variable is
          initialized.
        cached_value: Pre-created operation to read this variable in a specific
          device.
        save_slice_info: Metadata for variable partitioning.
        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.
        in_graph_mode: whether we are executing in TF1 graph mode. If None, will
          detect within the function. This is to avoid repeated init_scope()
          conetxt entrances which can add up.
        validate_shape: If `False`, allows the variable to be initialized with a
          value of unknown shape. If `True`, the default, the shape of
          `initial_value` must be known.
      """
      if in_graph_mode is None:
        with ops.init_scope():
          self._in_graph_mode = not context.executing_eagerly()
      else:
        self._in_graph_mode = in_graph_mode
      synchronization, aggregation, trainable = (
          variables.validate_synchronization_aggregation_trainable(
              synchronization, aggregation, trainable, name))
      self._trainable = trainable
      self._synchronization = synchronization
      self._aggregation = aggregation
      self._save_slice_info = save_slice_info
      self._initial_value = initial_value
      self._initializer_op = initializer_op
      self._is_initialized_op = is_initialized_op
      self._graph_element = graph_element
      self._caching_device = caching_device
      self._cached_value = cached_value
      self._distribute_strategy = distribute_strategy
      # Store the graph key so optimizers know how to only retrieve variables from
      # this graph. Guaranteed to be the same as the eager graph_key.
      self._graph_key = ops.get_default_graph()._graph_key  # pylint: disable=protected-access
      self._shape = tensor_shape.as_shape(shape)
      self._dtype = dtypes.as_dtype(dtype)
      self._handle = handle
      self._unique_id = unique_id
      if handle_name is None:
        self._handle_name = "Variable:0"
      else:
        self._handle_name = handle_name + ":0"
      self._constraint = constraint
      self._cached_shape_as_list = None
      self._validate_shape = validate_shape
  
    def __repr__(self):
      if context.executing_eagerly() and not self._in_graph_mode:
        # If we cannot read the value for any reason (e.g. variable uninitialized
        # during tf.function tracing), still produce a __repr__. Note that for
        # async eager, errors due to uninitialized variables will raise in
        # ops.value_text when the handle is resolved, so we need to keep that
        # under the try...except if we want to suppress them.
        try:
          with ops.device(self.device):
            value_text = ops.value_text(self.read_value(), is_repr=True)
        except:  # pylint: disable=bare-except
          value_text = "numpy=<unavailable>"
  
        return "<tf.Variable '%s' shape=%s dtype=%s, %s>" % (
            self.name, self.get_shape(), self.dtype.name, value_text)
      else:
        return "<tf.Variable '%s' shape=%s dtype=%s>" % (
            self.name, self.get_shape(), self.dtype.name)
  
    def __tf_tracing_type__(self, signature_context):
      return signature_context.make_reference_type(
          VariableSpec(self.shape, self.dtype), self._handle._id)  # pylint:disable=protected-access
  
    @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 __array__(self, dtype=None):
      """Allows direct conversion to a numpy array.
  
      >>> np.array(tf.Variable([1.0]))
      array([1.], dtype=float32)
  
      Returns:
        The variable value as a numpy array.
      """
      # You can't return `self.numpy()` here because for scalars
      # that raises:
      #     ValueError: object __array__ method not producing an array
      # Even `self.read_value().__array__()` and `self.read_value()._numpy()` give
      # the same error. The `EagerTensor` class must be doing something behind the
      # scenes to make `np.array(tf.constant(1))` work.
      return np.asarray(self.numpy(), dtype=dtype)
  
    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,
          distribute_strategy=self._distribute_strategy,
          synchronization=self.synchronization,
          aggregation=self.aggregation)
      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
  
    def set_shape(self, shape):
      self._shape = self._shape.merge_with(shape)
  
    def _shape_as_list(self):
      if self.shape.ndims is None:
        return None
      return [dim.value for dim in self.shape.dims]
  
    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("This operation 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):
        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("This property is not supported "
                           "when eager execution is enabled.")
      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
  
    @property
    def synchronization(self):
      return self._synchronization
  
    @property
    def aggregation(self):
      return self._aggregation
  
    def eval(self, session=None):
      """Evaluates and returns the value of this variable."""
      if context.executing_eagerly():
        raise RuntimeError("This operation is not supported "
                           "when eager execution is enabled.")
      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 _map_resources(self, save_options):
      """For implementing `Trackable`."""
      new_variable = None
      if save_options.experimental_variable_policy._save_variable_devices():  # pylint:disable=protected-access
        with ops.device(self.device):
          new_variable = copy_to_graph_uninitialized(self)
      else:
        new_variable = copy_to_graph_uninitialized(self)
      obj_map = {self: new_variable}
      resource_map = {self.handle: new_variable.handle}
      return obj_map, resource_map
  
    def _read_variable_op(self, no_copy=False):
      """Reads the value of the variable.
  
      If the variable is in copy-on-read mode and `no_copy` is True, the variable
      is converted to copy-on-write mode before it is read.
  
      Args:
        no_copy: Whether to prevent a copy of the variable.
  
      Returns:
        The value of the variable.
      """
      variable_accessed(self)
  
      def read_and_set_handle(no_copy):
        if no_copy:
          gen_resource_variable_ops.disable_copy_on_read(self.handle)
        result = gen_resource_variable_ops.read_variable_op(
            self.handle, self._dtype)
        _maybe_set_handle_data(self._dtype, self.handle, result)
        return result
  
      if getattr(self, "_caching_device", None) is not None:
        with ops.colocate_with(None, ignore_existing=True):
          with ops.device(self._caching_device):
            result = read_and_set_handle(no_copy)
      else:
        result = read_and_set_handle(no_copy)
  
      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],
            backward_function=lambda x: [x],
            forward_function=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 value of the variable.
      """
      with ops.name_scope("Read"):
        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 read_value_no_copy(self):
      """Constructs an op which reads the value of this variable without copy.
  
      The variable is read without making a copy even when it has been sparsely
      accessed. Variables in copy-on-read mode will be converted to copy-on-write
      mode.
  
      Returns:
        The value of the variable.
      """
      with ops.name_scope("Read"):
        value = self._read_variable_op(no_copy=True)
      # 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:
        variable_accessed(self)
        value = gen_resource_variable_ops.resource_gather(
            self.handle, indices, dtype=self._dtype, name=name)
  
        if self._dtype == dtypes.variant:
          # For DT_VARIANT types, the handle's shape_and_type[1:] stores the
          # variant's handle data.  Extract it.
          handle_data = get_eager_safe_handle_data(self.handle)
          if handle_data.is_set and len(handle_data.shape_and_type) > 1:
            value._handle_data = (  # pylint: disable=protected-access
                cpp_shape_inference_pb2.CppShapeInferenceResult.HandleData(
                    is_set=True, shape_and_type=handle_data.shape_and_type[1:]))
  
      return array_ops.identity(value)
  
    def gather_nd(self, indices, name=None):
      """Reads the value of this variable sparsely, using `gather_nd`."""
      with ops.name_scope("GatherNd" if name is None else name) as name:
        if self.trainable:
          variable_accessed(self)
        value = gen_resource_variable_ops.resource_gather_nd(
            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("This operation is not supported "
                           "when eager execution is enabled.")
      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
        var_def.synchronization = self.synchronization.value
        var_def.aggregation = self.aggregation.value
        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("This operation is not supported "
                           "when eager execution is enabled.")
      return ResourceVariable(
          variable_def=variable_def, import_scope=import_scope)
  
    __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):
      variable_accessed(self)
      return _UnreadVariable(
          handle=self.handle,
          dtype=self.dtype,
          shape=self._shape,
          in_graph_mode=self._in_graph_mode,
          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 be used to
      # initialize the variable.
      with _handle_graph(self.handle):
        value_tensor = ops.convert_to_tensor(value, dtype=self.dtype)
        if not self._shape.is_compatible_with(value_tensor.shape):
          if self.name is None:
            tensor_name = ""
          else:
            tensor_name = " " + str(self.name)
          raise ValueError(
              (f"Cannot assign value to variable '{tensor_name}': Shape mismatch."
               f"The variable shape {self._shape}, and the "
               f"assigned value shape {value_tensor.shape} are incompatible."))
        kwargs = {}
        if forward_compat.forward_compatible(2022, 3, 23):
          # If the shape is fully defined, we do a runtime check with the shape of
          # value.
          validate_shape = self._validate_shape and self._shape.is_fully_defined()
          kwargs["validate_shape"] = validate_shape
        assign_op = gen_resource_variable_ops.assign_variable_op(
            self.handle, value_tensor, name=name, **kwargs)
        if read_value:
          return self._lazy_read(assign_op)
      return assign_op
  
    def __reduce__(self):
      # The implementation mirrors that of __deepcopy__.
      return functools.partial(
          ResourceVariable,
          initial_value=self.numpy(),
          trainable=self.trainable,
          name=self._shared_name,
          dtype=self.dtype,
          constraint=self.constraint,
          distribute_strategy=self._distribute_strategy), ()
  
    def scatter_sub(self, sparse_delta, use_locking=False, name=None):
      """Subtracts `tf.IndexedSlices` from this variable.
  
      Args:
        sparse_delta: `tf.IndexedSlices` to be subtracted from this variable.
        use_locking: If `True`, use locking during the operation.
        name: the name of the operation.
  
      Returns:
        The updated variable.
  
      Raises:
        TypeError: if `sparse_delta` is not an `IndexedSlices`.
      """
      if not isinstance(sparse_delta, indexed_slices.IndexedSlices):
        raise TypeError(f"Argument `sparse_delta` must be a "
                        f"`tf.IndexedSlices`. Received arg: {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 `tf.IndexedSlices` to this variable.
  
      Args:
        sparse_delta: `tf.IndexedSlices` to be added to this variable.
        use_locking: If `True`, use locking during the operation.
        name: the name of the operation.
  
      Returns:
        The updated variable.
  
      Raises:
        TypeError: if `sparse_delta` is not an `IndexedSlices`.
      """
      if not isinstance(sparse_delta, indexed_slices.IndexedSlices):
        raise TypeError(f"Argument `sparse_delta` must be a "
                        f"`tf.IndexedSlices`. Received arg: {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_max(self, sparse_delta, use_locking=False, name=None):
      """Updates this variable with the max of `tf.IndexedSlices` and itself.
  
      Args:
        sparse_delta: `tf.IndexedSlices` to use as an argument of max with this
          variable.
        use_locking: If `True`, use locking during the operation.
        name: the name of the operation.
  
      Returns:
        The updated variable.
  
      Raises:
        TypeError: if `sparse_delta` is not an `IndexedSlices`.
      """
      if not isinstance(sparse_delta, indexed_slices.IndexedSlices):
        raise TypeError(f"Argument `sparse_delta` must be a "
                        f"`tf.IndexedSlices`. Received arg: {sparse_delta}")
      return self._lazy_read(
          gen_resource_variable_ops.resource_scatter_max(
              self.handle,
              sparse_delta.indices,
              ops.convert_to_tensor(sparse_delta.values, self.dtype),
              name=name))
  
    def scatter_min(self, sparse_delta, use_locking=False, name=None):
      """Updates this variable with the min of `tf.IndexedSlices` and itself.
  
      Args:
        sparse_delta: `tf.IndexedSlices` to use as an argument of min with this
          variable.
        use_locking: If `True`, use locking during the operation.
        name: the name of the operation.
  
      Returns:
        The updated variable.
  
      Raises:
        TypeError: if `sparse_delta` is not an `IndexedSlices`.
      """
      if not isinstance(sparse_delta, indexed_slices.IndexedSlices):
        raise TypeError(f"Argument `sparse_delta` must be a "
                        f"`tf.IndexedSlices`. Received arg: {sparse_delta}")
      return self._lazy_read(
          gen_resource_variable_ops.resource_scatter_min(
              self.handle,
              sparse_delta.indices,
              ops.convert_to_tensor(sparse_delta.values, self.dtype),
              name=name))
  
    def scatter_mul(self, sparse_delta, use_locking=False, name=None):
      """Multiply this variable by `tf.IndexedSlices`.
  
      Args:
        sparse_delta: `tf.IndexedSlices` to multiply this variable by.
        use_locking: If `True`, use locking during the operation.
        name: the name of the operation.
  
      Returns:
        The updated variable.
  
      Raises:
        TypeError: if `sparse_delta` is not an `IndexedSlices`.
      """
      if not isinstance(sparse_delta, indexed_slices.IndexedSlices):
        raise TypeError(f"Argument `sparse_delta` must be a "
                        f"`tf.IndexedSlices`. Received arg: {sparse_delta}")
      return self._lazy_read(
          gen_resource_variable_ops.resource_scatter_mul(
              self.handle,
              sparse_delta.indices,
              ops.convert_to_tensor(sparse_delta.values, self.dtype),
              name=name))
  
    def scatter_div(self, sparse_delta, use_locking=False, name=None):
      """Divide this variable by `tf.IndexedSlices`.
  
      Args:
        sparse_delta: `tf.IndexedSlices` to divide this variable by.
        use_locking: If `True`, use locking during the operation.
        name: the name of the operation.
  
      Returns:
        The updated variable.
  
      Raises:
        TypeError: if `sparse_delta` is not an `IndexedSlices`.
      """
      if not isinstance(sparse_delta, indexed_slices.IndexedSlices):
        raise TypeError(f"Argument `sparse_delta` must be a "
                        f"`tf.IndexedSlices`. Received arg: {sparse_delta}")
      return self._lazy_read(
          gen_resource_variable_ops.resource_scatter_div(
              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 `tf.IndexedSlices` to this variable.
  
      Args:
        sparse_delta: `tf.IndexedSlices` to be assigned to this variable.
        use_locking: If `True`, use locking during the operation.
        name: the name of the operation.
  
      Returns:
        The updated variable.
  
      Raises:
        TypeError: if `sparse_delta` is not an `IndexedSlices`.
      """
      if not isinstance(sparse_delta, indexed_slices.IndexedSlices):
        raise TypeError(f"Argument `sparse_delta` must be a "
                        f"`tf.IndexedSlices`. Received arg: {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 `tf.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: `tf.IndexedSlices` to be assigned to this variable.
        use_locking: If `True`, use locking during the operation.
        name: the name of the operation.
  
      Returns:
        The updated variable.
  
      Raises:
        TypeError: if `sparse_delta` is not an `IndexedSlices`.
      """
      if not isinstance(sparse_delta, indexed_slices.IndexedSlices):
        raise TypeError(f"Argument `sparse_delta` must be a "
                        f"`tf.IndexedSlices`. Received arg: {sparse_delta}")
      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.compat.v1.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:
        The updated variable.
      """
      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.compat.v1.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:
        The updated variable.
      """
      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.compat.v1.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:
        The updated variable.
      """
      return self._lazy_read(
          gen_state_ops.resource_scatter_nd_update(
              self.handle,
              indices,
              ops.convert_to_tensor(updates, self.dtype),
              name=name))
  
    def scatter_nd_max(self, indices, updates, name=None):
      """Updates this variable with the max of `tf.IndexedSlices` and itself.
  
      `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]].
      ```
  
      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:
        The updated variable.
      """
      return self._lazy_read(
          gen_state_ops.resource_scatter_nd_max(
              self.handle,
              indices,
              ops.convert_to_tensor(updates, self.dtype),
              name=name))
  
    def scatter_nd_min(self, indices, updates, name=None):
      """Updates this variable with the min of `tf.IndexedSlices` and itself.
  
      `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]].
      ```
  
      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:
        The updated variable.
      """
      return self._lazy_read(
          gen_state_ops.resource_scatter_nd_min(
              self.handle,
              indices,
              ops.convert_to_tensor(updates, self.dtype),
              name=name))
  
    def _write_object_proto(self, proto, options):
      """Writes additional information of the variable into the SavedObject proto.
  
      Subclasses of ResourceVariables could choose to override this method to
      customize extra information to provide when saving a SavedModel.
  
      Ideally, this should contain the logic in
      write_object_proto_for_resource_variable but `DistributedValue` is an
      outlier at the momemnt. Once `DistributedValue` becomes a proper
      ResourceVariable, we should remove the helper method below.
  
      Args:
        proto: `SavedObject` proto to update.
        options: A `SaveOption` instance that configures save behavior.
      """
      write_object_proto_for_resource_variable(self, proto, options)
  
    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 __complex__(self):
      return complex(self.value().numpy())
  
    def __int__(self):
      return int(self.value().numpy())
  
    def __long__(self):
      return long(self.value().numpy())
  
    def __float__(self):
      return float(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(
            f"Incompatible type conversion requested to type {dtype.name} for "
            f"`tf.Variable of type {self.dtype.name}. (Variable: {self})")
      if as_ref:
        return self.read_value().op.inputs[0]
      else:
        return self.value()
  
    def __iadd__(self, unused_other):
      raise RuntimeError("`variable += value` with `tf.Variable`s is not "
                         "supported. Use `variable.assign_add(value)` to modify "
                         "the variable, or `out = variable + value` if you "
                         "need to get a new output Tensor.")
  
    def __isub__(self, unused_other):
      raise RuntimeError("`variable -= value` with `tf.Variable`s is not "
                         "supported. Use `variable.assign_sub(value)` to modify "
                         "the variable, or `out = variable * value` if you "
                         "need to get a new output Tensor.")
  
    def __imul__(self, unused_other):
      raise RuntimeError("`var *= value` with `tf.Variable`s is not "
                         "supported. Use `var.assign(var * value)` to modify "
                         "the variable, or `out = var * value` if you "
                         "need to get a new output Tensor.")
  
    def __idiv__(self, unused_other):
      raise RuntimeError("`var /= value` with `tf.Variable`s is not "
                         "supported. Use `var.assign(var / value)` to modify "
                         "the variable, or `out = var / value` if you "
                         "need to get a new output Tensor.")
  
    def __itruediv__(self, unused_other):
      raise RuntimeError("`var /= value` with `tf.Variable`s is not "
                         "supported. Use `var.assign(var / value)` to modify "
                         "the variable, or `out = var / value` if you "
                         "need to get a new output Tensor.")
  
    def __irealdiv__(self, unused_other):
      raise RuntimeError("`var /= value` with `tf.Variable`s is not "
                         "supported. Use `var.assign(var / value)` to modify "
                         "the variable, or `out = var / value` if you "
                         "need to get a new output Tensor.")
  
    def __ipow__(self, unused_other):
      raise RuntimeError("`var **= value` with `tf.Variable`s is not "
                         "supported. Use `var.assign(var ** value)` to modify "
                         "the variable, or `out = var ** value` if you "
                         "need to get a new output Tensor.")
  
  
  class ResourceVariable(BaseResourceVariable):
    """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.compat.v1.Print(b, [b]).eval()
    ```
    """
  
    def __init__(
        self,  # pylint: disable=super-init-not-called
        initial_value=None,
        trainable=None,
        collections=None,
        validate_shape=True,  # pylint: disable=unused-argument
        caching_device=None,
        name=None,
        dtype=None,
        variable_def=None,
        import_scope=None,
        constraint=None,
        distribute_strategy=None,
        synchronization=None,
        aggregation=None,
        shape=None):
      """Creates a variable.
  
      Args:
        initial_value: A `Tensor`, or Python object convertible to a `Tensor`,
          which is the initial value for the Variable. 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.
          Defaults to `True`, unless `synchronization` is set to `ON_READ`, in
          which case it defaults to `False`.
        collections: List of graph collections keys. The new variable is added to
          these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
        validate_shape: If `False`, allows the variable to be initialized with a
          value of unknown shape. If `True`, the default, the shape of
          `initial_value` must be known.
        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.
        synchronization: Indicates when a distributed a variable will be
          aggregated. Accepted values are constants defined in the class
          `tf.VariableSynchronization`. By default the synchronization is set to
          `AUTO` and the current `DistributionStrategy` chooses when to
          synchronize.
        aggregation: Indicates how a distributed variable will be aggregated.
          Accepted values are constants defined in the class
          `tf.VariableAggregation`.
        shape: (optional) The shape of this variable. If None, the shape of
          `initial_value` will be used. When setting this argument to
          `tf.TensorShape(None)` (representing an unspecified shape), the variable
          can be assigned with values of different shapes.
  
      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
      """
      if variable_def:
        if initial_value is not None:
          raise ValueError(f"The variable_def and initial_value args to "
                           f"`tf.Variable` are mutually exclusive, but got both: "
                           f"variable_def={variable_def},\n"
                           f"initial_value={initial_value}")
        if context.executing_eagerly():
          raise ValueError(f"Creating a `tf.Variable` with a `variable_def` arg "
                           f"is not supported when eager execution is enabled. "
                           f"Got: variable_def={variable_def}")
        self._init_from_proto(variable_def, import_scope=import_scope,
                              validate_shape=validate_shape)
      else:
        self._init_from_args(
            initial_value=initial_value,
            trainable=trainable,
            collections=collections,
            caching_device=caching_device,
            name=name,
            dtype=dtype,
            constraint=constraint,
            synchronization=synchronization,
            aggregation=aggregation,
            shape=shape,
            distribute_strategy=distribute_strategy,
            validate_shape=validate_shape,
        )
  
    def _init_from_args(self,
                        initial_value=None,
                        trainable=None,
                        collections=None,
                        caching_device=None,
                        name=None,
                        dtype=None,
                        constraint=None,
                        synchronization=None,
                        aggregation=None,
                        distribute_strategy=None,
                        shape=None,
                        validate_shape=True,
                        ):
      """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.
          Defaults to `True`, unless `synchronization` is set to `ON_READ`, in
          which case it defaults to `False`.
        collections: List of graph collections keys. The new variable is added to
          these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
        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.
        synchronization: Indicates when a distributed a variable will be
          aggregated. Accepted values are constants defined in the class
          `tf.VariableSynchronization`. By default the synchronization is set to
          `AUTO` and the current `DistributionStrategy` chooses when to
          synchronize.
        aggregation: Indicates how a distributed variable will be aggregated.
          Accepted values are constants defined in the class
          `tf.VariableAggregation`.
        distribute_strategy: DistributionStrategy under which this variable was
          created.
        shape: (optional) The shape of this variable. If None, the shape of
          `initial_value` will be used. When setting this argument to
          `tf.TensorShape(None)` (representing an unspecified shape), the variable
          can be assigned with values of different shapes.
        validate_shape: If `False`, allows the variable to be initialized with a
          value of unknown shape. If `True`, the default, the shape of
          `initial_value` must be known.
  
      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
      """
      synchronization, aggregation, trainable = (
          variables.validate_synchronization_aggregation_trainable(
              synchronization, aggregation, trainable, name))
      if initial_value is None:
        raise ValueError("The `initial_value` arg to `tf.Variable` must "
                         "be specified except when you are not providing a "
                         "`variable_def`. You provided neither.")
      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(f"Argument `initial_value` ({initial_value}) could not "
                         "be lifted out of a `tf.function`. "
                         "(Tried to create variable with name='{name}'). "
                         "To avoid this error, when constructing `tf.Variable`s "
                         "inside of `tf.function` you can create the "
                         "`initial_value` tensor in a "
                         "`tf.init_scope` or pass a callable `initial_value` "
                         "(e.g., `tf.Variable(lambda : "
                         "tf.truncated_normal([10, 40]))`). "
                         "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(
            f"collections argument to Variable constructor must be a list, "
            f"tuple, or set. Got {collections} of type {type(collections)}")
      if constraint is not None and not callable(constraint):
        raise ValueError(f"Argument `constraint` must be None or a callable. "
                         f"a callable. Got a {type(constraint)}:  {constraint}")
  
      if trainable and ops.GraphKeys.TRAINABLE_VARIABLES not in collections:
        collections = list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES]
      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],
            skip_on_eager=False) as name:
          # pylint: disable=protected-access
          handle_name = ops.name_from_scope_name(name)
          if self._in_graph_mode:
            shared_name = handle_name
            unique_id = shared_name
          else:
            # When in eager mode use a uid for the shared_name, to prevent
            # accidental sharing.
            unique_id = "%s_%d" % (handle_name, ops.uid())
            shared_name = None  # Never shared
          # 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.
          device_context_manager = (
              ops.device if self._in_graph_mode else ops.NullContextmanager)
          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"), device_context_manager(None):
              if init_from_fn:
                initial_value = initial_value()
              if isinstance(initial_value, trackable.CheckpointInitialValue):
                self._maybe_initialize_trackable()
                self._update_uid = initial_value.checkpoint_position.restore_uid
                initial_value = initial_value.wrapped_value
              initial_value = ops.convert_to_tensor(
                  initial_value, name="initial_value", dtype=dtype)
            if shape is not None:
              if not initial_value.shape.is_compatible_with(shape):
                raise ValueError(
                    f"In this `tf.Variable` creation, the initial value's shape "
                    f"({initial_value.shape}) is not compatible with "
                    f"the explicitly supplied `shape` argument ({shape}).")
            else:
              shape = initial_value.shape
            handle = eager_safe_variable_handle(
                initial_value=initial_value,
                shape=shape,
                shared_name=shared_name,
                name=name,
                graph_mode=self._in_graph_mode)
            handle._parent_trackable = weakref.ref(self)
          # 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(
                f"The `initial_value` passed to `tf.Variable` {name} is from "
                f"inside a control-flow  construct, such as a loop or "
                f"conditional. When creating a "
                f"`tf.Variable` inside a loop or conditional, use a lambda as "
                f"the `initial_value`. Got: initial_value=({initial_value})")
          # pylint: enable=protected-access
          dtype = initial_value.dtype.base_dtype
  
          if self._in_graph_mode:
            with ops.name_scope("IsInitialized"):
              is_initialized_op = (
                  gen_resource_variable_ops.var_is_initialized_op(handle))
            if initial_value is not None:
              # pylint: disable=g-backslash-continuation
              with ops.name_scope("Assign") as n, \
                   ops.colocate_with(None, ignore_existing=True), \
                   ops.device(handle.device):
                # pylint: disable=protected-access
                initializer_op = (
                    gen_resource_variable_ops.assign_variable_op(
                        handle,
                        variables._try_guard_against_uninitialized_dependencies(
                            name, initial_value),
                        name=n))
                # pylint: enable=protected-access
              # pylint: enable=g-backslash-continuation
            with ops.name_scope("Read"):
              # Manually assign reads to the handle's device to avoid log
              # messages.
              with ops.device(handle.device):
                value = gen_resource_variable_ops.read_variable_op(handle, dtype)
                _maybe_set_handle_data(dtype, handle, value)
              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):
                    cached_value = array_ops.identity(value)
              else:
                cached_value = None
          else:
            gen_resource_variable_ops.assign_variable_op(handle, initial_value)
            is_initialized_op = None
            initializer_op = None
            graph_element = None
            if caching_device:
              with ops.device(caching_device):
                cached_value = gen_resource_variable_ops.read_variable_op(
                    handle, dtype)
                _maybe_set_handle_data(dtype, handle, cached_value)
            else:
              cached_value = None
  
          if cached_value is not None:
            # Store the variable object so that the original variable can be
            # accessed to generate functions that are compatible with SavedModel.
            cached_value._cached_variable = weakref.ref(self)  # pylint: disable=protected-access
  
          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)
        initial_value = initial_value if self._in_graph_mode else None
        super(ResourceVariable, self).__init__(
            trainable=trainable,
            shape=shape,
            dtype=dtype,
            handle=handle,
            synchronization=synchronization,
            constraint=constraint,
            aggregation=aggregation,
            distribute_strategy=distribute_strategy,
            name=name,
            unique_id=unique_id,
            handle_name=handle_name,
            graph_element=graph_element,
            initial_value=initial_value,
            initializer_op=initializer_op,
            is_initialized_op=is_initialized_op,
            cached_value=cached_value,
            caching_device=caching_device,
            validate_shape=validate_shape,
        )
  
    def _init_from_proto(self, variable_def, import_scope=None,
                         validate_shape=True):
      """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(f"The `variable_def` you passed to `tf.Variable` is "
                         f"Trying to restore a TF 1.x Reference Variable "
                         f"as a TF 2.x ResourceVariable. This is unsupported. "
                         f"Got variable_def={variable_def}")
  
      # 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
      synchronization, aggregation, trainable = (
          variables.validate_synchronization_aggregation_trainable(
              variable_def.synchronization, variable_def.aggregation,
              variable_def.trainable, variable_def.variable_name))
      self._synchronization = synchronization
      self._aggregation = aggregation
      self._trainable = trainable
      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._validate_shape = validate_shape
  
  
  class UninitializedVariable(BaseResourceVariable):
    """A variable with no initializer."""
  
    def __init__(  # pylint: disable=super-init-not-called
        self,
        trainable=None,
        caching_device=None,
        name=None,
        shape=None,
        dtype=None,
        constraint=None,
        synchronization=None,
        aggregation=None,
        extra_handle_data=None,
        distribute_strategy=None,
        **unused_kwargs):
      """Creates the variable handle.
  
      Args:
        trainable: If `True`, GradientTapes automatically watch uses of this
          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.
        shape: The variable's shape.
        dtype: The variable's dtype.
        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.
        synchronization: Indicates when a distributed a variable will be
          aggregated. Accepted values are constants defined in the class
          `tf.VariableSynchronization`. By default the synchronization is set to
          `AUTO` and the current `DistributionStrategy` chooses when to
          synchronize.
        aggregation: Indicates how a distributed variable will be aggregated.
          Accepted values are constants defined in the class
          `tf.VariableAggregation`.
        extra_handle_data: Optional, another resource handle or Tensor with handle
          data to merge with `shape` and `dtype`.
        distribute_strategy: The tf.distribute.Strategy this variable is being
          created inside of.
      """
      with ops.init_scope():
        # Here we are detecting eagerness within an init_scope, so this will only
        # be true when we are running in TF1 graph mode.
        self._in_graph_mode = not context.executing_eagerly()
        with ops.name_scope(name, "Variable", skip_on_eager=False) as name:
          handle_name = ops.name_from_scope_name(name)
          if self._in_graph_mode:
            shared_name = handle_name
            unique_id = shared_name
          else:
            unique_id = "%s_%d" % (handle_name, ops.uid())
            shared_name = None  # Never shared
          handle = _variable_handle_from_shape_and_dtype(
              shape=shape,
              dtype=dtype,
              shared_name=shared_name,
              name=name,
              graph_mode=self._in_graph_mode,
              initial_value=extra_handle_data)
          handle._parent_trackable = weakref.ref(self)
  
          if self._in_graph_mode:
            # We only need to add the read_variable_op in TF1.
            with ops.name_scope("Read"):
              # Manually assign reads to the handle's device to avoid log
              # messages.
              with ops.device(handle.device):
                value = gen_resource_variable_ops.read_variable_op(handle, dtype)
                _maybe_set_handle_data(dtype, handle, value)
              graph_element = value
            ops.add_to_collection(ops.GraphKeys.GLOBAL_VARIABLES, self)
            # Do *not* add to TRAINABLE_VARIABLES here, even if self._trainable,
            # because retraining or frozen use of imported SavedModels is
            # controlled at higher levels of model building.
          else:
            graph_element = None
      super(UninitializedVariable, self).__init__(
          distribute_strategy=distribute_strategy,
          shape=shape,
          dtype=dtype,
          unique_id=unique_id,
          handle_name=handle_name,
          constraint=constraint,
          handle=handle,
          graph_element=graph_element,
          trainable=trainable,
          synchronization=synchronization,
          aggregation=aggregation,
          in_graph_mode=self._in_graph_mode)
  
  
  _pywrap_utils.RegisterType("ResourceVariable", 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(BaseResourceVariable,
                                          _dense_var_to_tensor)
  
  
  class _UnreadVariable(BaseResourceVariable):
    """Represents a future for a read of a variable.
  
    Pretends to be the tensor if anyone looks.
    """
  
    def __init__(self, handle, dtype, shape, in_graph_mode, parent_op, unique_id):
      if isinstance(handle, ops.EagerTensor):
        handle_name = ""
      else:
        handle_name = handle.name
      # Only create a graph_element if we're in session.run-land as only
      # session.run requires a preexisting tensor to evaluate. Otherwise we can
      # avoid accidentally reading the variable.
      if context.executing_eagerly() or ops.inside_function():
        graph_element = None
      else:
        with ops.control_dependencies([parent_op]):
          graph_element = gen_resource_variable_ops.read_variable_op(
              handle, dtype)
          _maybe_set_handle_data(dtype, handle, graph_element)
      super(_UnreadVariable, self).__init__(
          handle=handle,
          shape=shape,
          handle_name=handle_name,
          unique_id=unique_id,
          dtype=dtype,
          graph_element=graph_element)
      self._parent_op = parent_op
  
    @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]):
        result = gen_resource_variable_ops.read_variable_op(
            self._handle, self._dtype)
        _maybe_set_handle_data(self._dtype, self._handle, result)
        return result
  
    def assign_sub(self, delta, use_locking=None, name=None, read_value=True):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable, self).assign_sub(delta, use_locking, name,
                                                       read_value)
  
    def assign_add(self, delta, use_locking=None, name=None, read_value=True):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable, self).assign_add(delta, use_locking, name,
                                                       read_value)
  
    def assign(self, value, use_locking=None, name=None, read_value=True):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable, self).assign(value, use_locking, name,
                                                   read_value)
  
    def scatter_sub(self, sparse_delta, use_locking=False, name=None):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable, self).scatter_sub(sparse_delta, use_locking,
                                                        name)
  
    def scatter_add(self, sparse_delta, use_locking=False, name=None):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable, self).scatter_add(sparse_delta, use_locking,
                                                        name)
  
    def scatter_max(self, sparse_delta, use_locking=False, name=None):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable, self).scatter_max(sparse_delta, use_locking,
                                                        name)
  
    def scatter_min(self, sparse_delta, use_locking=False, name=None):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable, self).scatter_min(sparse_delta, use_locking,
                                                        name)
  
    def scatter_mul(self, sparse_delta, use_locking=False, name=None):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable, self).scatter_mul(sparse_delta, use_locking,
                                                        name)
  
    def scatter_div(self, sparse_delta, use_locking=False, name=None):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable, self).scatter_div(sparse_delta, use_locking,
                                                        name)
  
    def scatter_update(self, sparse_delta, use_locking=False, name=None):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable,
                     self).scatter_update(sparse_delta, use_locking, name)
  
    def batch_scatter_update(self, sparse_delta, use_locking=False, name=None):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable,
                     self).batch_scatter_update(sparse_delta, use_locking, name)
  
    def scatter_nd_sub(self, indices, updates, name=None):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable, self).scatter_nd_sub(indices, updates, name)
  
    def scatter_nd_add(self, indices, updates, name=None):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable, self).scatter_nd_add(indices, updates, name)
  
    def scatter_nd_update(self, indices, updates, name=None):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable,
                     self).scatter_nd_update(indices, updates, name)
  
    def scatter_nd_max(self, indices, updates, name=None):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable, self).scatter_nd_max(indices, updates, name)
  
    def scatter_nd_min(self, indices, updates, name=None):
      with ops.control_dependencies([self._parent_op]):
        return super(_UnreadVariable, self).scatter_nd_min(indices, updates, name)
  
    @property
    def op(self):
      """The op for this variable."""
      return self._parent_op
  
  
  @ops.RegisterGradient("ReadVariableOp")
  def _ReadGrad(_, grad):
    """Gradient for read op."""
    return grad
  
  
  def variable_shape(handle, out_type=dtypes.int32):
    handle_data = get_eager_safe_handle_data(handle)
    if handle_data is None or not handle_data.is_set:
      return gen_resource_variable_ops.variable_shape(handle, out_type=out_type)
    shape_proto = 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 (indexed_slices.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)
  ops.register_proto_function(
      ops.GraphKeys.METRIC_VARIABLES,
      proto_type=variable_pb2.VariableDef,
      to_proto=_to_proto_fn,
      from_proto=_from_proto_fn)
  
  
  @tf_export("__internal__.ops.is_resource_variable", v1=[])
  def is_resource_variable(var):
    """"Returns True if `var` is to be considered a ResourceVariable."""
    return isinstance(var, BaseResourceVariable) 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 = UninitializedVariable(
        trainable=var.trainable,
        constraint=var._constraint,
        shape=var.shape,
        dtype=var.dtype,
        name=var._shared_name,
        synchronization=var.synchronization,
        aggregation=var.aggregation,
        extra_handle_data=var.handle)
    new_variable._maybe_initialize_trackable()
    # pylint: enable=protected-access
    return new_variable
  
  
  ops.NotDifferentiable("Assert")
  ops.NotDifferentiable("VarIsInitializedOp")
  ops.NotDifferentiable("VariableShape")
  
  
  class VariableSpec(tensor_spec.DenseSpec):
    """Describes a tf.Variable."""
  
    __slots__ = ["trainable"]
  
    value_type = property(lambda self: BaseResourceVariable)
  
    def __init__(self,
                 shape,
                 dtype=dtypes.float32,
                 trainable=True):
      super(VariableSpec, self).__init__(shape, dtype=dtype)
      self.trainable = trainable
  
    def is_compatible_with(self, spec_or_value):
      return (isinstance(spec_or_value, (type(self), self.value_type)) and
              self.shape.is_compatible_with(spec_or_value.shape) and
              self.dtype == spec_or_value.dtype and
              self.trainable == spec_or_value.trainable)
  
    @classmethod
    def from_value(cls, value):
      return cls(
          value.shape,
          dtype=value.dtype,
          trainable=value.trainable)
  
    def _to_components(self, value):
      return value.handle
  
    def _from_components(self, components):
      return BaseResourceVariable(
          trainable=self.trainable,
          shape=self.shape,
          dtype=self.dtype,
          handle=components)
  
    @property
    def _component_specs(self):
      return tensor_spec.TensorSpec(self.shape, dtypes.resource)
  
    def _from_compatible_tensor_list(self, tensor_list):
      assert len(tensor_list) == 1
      return tensor_list[0]
  
    def _serialize(self):
      return self.shape, self.dtype, self.trainable
  
    def __tf_tracing_type__(self, signature_context):
      return signature_context.make_reference_type(self, id(self))
  
    def __repr__(self):
      return (f"{type(self).__name__}(shape={self.shape}, dtype={self.dtype}, "
              f"trainable={self.trainable})")
  
    def __hash__(self):
      return hash((self.shape, self.dtype, self.trainable))
  
    def __eq__(self, other):
      return (type(self) is type(other) and
              self.shape == other.shape and
              self.dtype == other.dtype and
              self.trainable == other.trainable)
  
  _pywrap_utils.RegisterType("VariableSpec", VariableSpec)
  
  
  def write_object_proto_for_resource_variable(resource_variable,
                                               proto,
                                               options,
                                               enforce_naming=True):
    """Writes additional information of the variable into the SavedObject proto.
  
    This allows users to define a `hook` to provide extra information of the
    variable to the SavedObject.
  
    For example, DistributedVariable class would fill in components in the
    distributed context.
  
    Args:
      resource_variable: A `ResourceVariable` or `DistributedValue` that has the
        information to be saved into the proto.
      proto: `SavedObject` proto to update.
      options: A `SaveOption` instance that configures save behavior.
      enforce_naming: A bool determining whether to check that names end in the
        expected string ':0'
    """
    proto.variable.SetInParent()
    if enforce_naming and not resource_variable.name.endswith(":0"):
      raise ValueError(f"Cowardly refusing to save variable "
                       f"{resource_variable.name} because of "
                       f"unexpected suffix in the name (expected ':0')"
                       f"which won't be restored.")
    proto.variable.name = meta_graph._op_name(resource_variable.name)  # pylint: disable=protected-access
    proto.variable.trainable = resource_variable.trainable
    proto.variable.dtype = resource_variable.dtype.as_datatype_enum
    proto.variable.synchronization = resource_variable.synchronization.value
    proto.variable.aggregation = resource_variable.aggregation.value
    proto.variable.shape.CopyFrom(resource_variable.shape.as_proto())
    if options.experimental_variable_policy._save_variable_devices(  # pylint: disable=protected-access
    ):
      if hasattr(resource_variable, "device"):
        proto.variable.device = resource_variable.device
  