Python ops._name_from_scope_name函数代码示例

 def _init_from_args(self, name):
   """Initialize the CriticalSection from constructor arguments."""
   with ops.name_scope(name, "CriticalSection", []) as name:
     with ops.control_dependencies(None):
       # pylint: disable=protected-access
       handle_name = ops._name_from_scope_name(name)
       container = ops.get_default_graph()._container
       # pylint: enable=protected-access
       if container is None:
         container = ""
       self._handle = gen_resource_variable_ops.critical_section_op(
           shared_name=handle_name, name=name)
   if context.in_graph_mode():
     ops.add_to_collections(CRITICAL_SECTIONS, self)

  def __init__(self,  # pylint: disable=super-init-not-called
               initial_value=None,
               trainable=True,
               name=None,
               dtype=None,
               constraint=None,
               initialize=True,
               **unused_kwargs):
    """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`, automatically watches this variable on GradientTape
        whenever it's used.
      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.
      initialize: if True, runs initialization in eager execution; leaves the
        variable uninitialized otherwise.

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

    if isinstance(initial_value, ops.Tensor) and hasattr(
        initial_value, "graph") and initial_value.graph.building_function:
      raise ValueError("Tensor-typed variable initializers must either be "
                       "wrapped in an init_scope or callable "
                       "(e.g., `tf.Variable(lambda : "
                       "tf.truncated_normal([10, 40]))`) when building "
                       "functions. Please file a feature request if this "
                       "restriction inconveniences you.")

    if constraint is not None and not callable(constraint):
      raise ValueError("The `constraint` argument must be a callable.")

    if isinstance(initial_value, checkpointable.CheckpointInitialValue):
      self._maybe_initialize_checkpointable()
      self._update_uid = initial_value.checkpoint_position.restore_uid
      initial_value = initial_value.wrapped_value

    self._trainable = trainable
    self._save_slice_info = None
    # Store the graph key so optimizers know how to only retrieve variables from
    # this graph.
    self._graph_key = ops.get_default_graph()._graph_key  # pylint: disable=protected-access
    with ops.init_scope():
      self._in_graph_mode = not context.executing_eagerly()
      with ops.name_scope(name, "Variable", []
                          if init_from_fn else [initial_value]) as name:
        # pylint: disable=protected-access
        handle_name = ops._name_from_scope_name(name)
        shared_name = handle_name
        if init_from_fn:
          # Use attr_scope and device(None) to simulate the behavior of
          # colocate_with when the variable we want to colocate with doesn't
          # yet exist.
          if self._in_graph_mode:
            with ops.name_scope("Initializer"), ops.device(None):
              initial_value = ops.convert_to_tensor(
                  initial_value(), name="initial_value", dtype=dtype)
            self._handle = _eager_safe_variable_handle(
                shape=initial_value.get_shape(),
                dtype=initial_value.dtype.base_dtype,
                shared_name=shared_name,
                name=name,
                graph_mode=self._in_graph_mode)
            self._shape = initial_value.get_shape()
          else:
            initial_value = initial_value()
            with ops.name_scope("Initializer"):
              initial_value = ops.convert_to_tensor(
                  initial_value, name="initial_value", dtype=dtype)
            self._handle = _eager_safe_variable_handle(
                shape=initial_value.get_shape(),
                dtype=initial_value.dtype.base_dtype,
                shared_name=shared_name,
                name=name,
                graph_mode=False)
            self._shape = initial_value.get_shape()
        # pylint: enable=protected-access
#.........这里部分代码省略.........

  def _init_from_args(self,
                      initial_value=None,
                      trainable=True,
                      collections=None,
                      validate_shape=True,
                      caching_device=None,
                      name=None,
                      dtype=None,
                      constraint=None):
    """Creates a variable.

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

    Raises:
      ValueError: If the initial value is not specified, or does not have a
        shape and `validate_shape` is `True`.

    @compatibility(eager)
    When Eager Execution is enabled, variables are never added to collections.
    It is not implicitly added to the `GLOBAL_VARIABLES` or
    `TRAINABLE_VARIABLES` collections, and the `collections` argument is
    ignored.
    @end_compatibility
    """
    if initial_value is None:
      raise ValueError("initial_value must be specified.")
    init_from_fn = callable(initial_value)

    if collections is None:
      collections = [ops.GraphKeys.GLOBAL_VARIABLES]
    if not isinstance(collections, (list, tuple, set)):
      raise ValueError(
          "collections argument to Variable constructor must be a list, tuple, "
          "or set. Got %s of type %s" % (collections, type(collections)))
    if constraint is not None and not callable(constraint):
      raise ValueError("The `constraint` argument must be a callable.")

    if isinstance(initial_value, checkpointable.CheckpointInitialValue):
      self._maybe_initialize_checkpointable()
      self._update_uid = initial_value.checkpoint_position.restore_uid
      initial_value = initial_value.wrapped_value

    self._trainable = trainable
    if trainable and ops.GraphKeys.TRAINABLE_VARIABLES not in collections:
      collections = list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES]
    self._save_slice_info = None
    # Store the graph key so optimizers know how to only retrieve variables from
    # this graph.
    self._graph_key = ops.get_default_graph()._graph_key  # pylint: disable=protected-access
    with ops.init_scope():
      self._in_graph_mode = context.in_graph_mode()
      with ops.name_scope(name, "Variable", []
                          if init_from_fn else [initial_value]) as name:
        # pylint: disable=protected-access
        handle_name = ops._name_from_scope_name(name)
        if init_from_fn:
          # Use attr_scope and device(None) to simulate the behavior of
          # colocate_with when the variable we want to colocate with doesn't
          # yet exist.
          if self._in_graph_mode:
            attr = attr_value_pb2.AttrValue(
                list=attr_value_pb2.AttrValue.ListValue(
                    s=[compat.as_bytes("loc:@%s" % handle_name)]))
            with ops.get_default_graph()._attr_scope({"_class": attr}):
              with ops.name_scope("Initializer"), ops.device(None):
                initial_value = ops.convert_to_tensor(
                    initial_value(), name="initial_value", dtype=dtype)
              self._handle = _eager_safe_variable_handle(
                  shape=initial_value.get_shape(),
                  dtype=initial_value.dtype.base_dtype,
#.........这里部分代码省略.........

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

    """Creates a variable.

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

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

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

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

        self._dtype = self._initial_value.dtype.base_dtype

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

  def _init_from_args(self,
                      initial_value=None,
                      trainable=True,
                      collections=None,
                      validate_shape=True,
                      caching_device=None,
                      name=None,
                      dtype=None,
                      expected_shape=None):
    """Creates a new variable from arguments.

    Args:
      initial_value: A `Tensor`, or Python object convertible to a `Tensor`,
        which is the initial value for the Variable. The initial value must have
        a shape specified unless `validate_shape` is set to False. Can also be a
        callable with no argument that returns the initial value when called.
        (Note that initializer functions  from init_ops.py must first be bound
         to a shape before being used here.)
      trainable: If `True`, the default, also adds the variable to the graph
        collection `GraphKeys.TRAINABLE_VARIABLES`. This collection is used as
        the default list of variables to use by the `Optimizer` classes.
      collections: List of graph collections keys. The new variable is added to
        these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
      validate_shape: 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).
      expected_shape: Deprecated. Ignored.

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

    if collections is None:
      collections = [ops.GraphKeys.GLOBAL_VARIABLES]
    if not isinstance(collections, (list, tuple, set)):
      raise ValueError(
          "collections argument to Variable constructor must be a list, tuple, "
          "or set. Got %s of type %s" % (collections, type(collections)))
    if trainable and ops.GraphKeys.TRAINABLE_VARIABLES not in collections:
      collections = list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES]
    with ops.control_dependencies(None):
      with ops.name_scope(name, "Variable", [] if init_from_fn else
                          [initial_value]) as name:

        if init_from_fn:
          # Use attr_scope and device(None) to simulate the behavior of
          # colocate_with when the variable we want to colocate with doesn't
          # yet exist.
          true_name = ops._name_from_scope_name(name)
          attr = attr_value_pb2.AttrValue(
              list=attr_value_pb2.AttrValue.ListValue(
                  s=[compat.as_bytes("loc:@%s" % true_name)]))
          # pylint: disable=protected-access
          with ops.get_default_graph()._attr_scope({"_class": attr}):
            with ops.name_scope("Initializer"),  ops.device(None):
              self._initial_value = ops.convert_to_tensor(
                  initial_value(), name="initial_value", dtype=dtype)
              shape = (self._initial_value.get_shape()
                       if validate_shape else tensor_shape.unknown_shape())
            self._variable = state_ops.variable_op_v2(
                shape,
                self._initial_value.dtype.base_dtype,
                name=name)

        # Or get the initial value from a Tensor or Python object.
        else:
          self._initial_value = ops.convert_to_tensor(
              initial_value, name="initial_value", dtype=dtype)
          shape = (self._initial_value.get_shape()
                   if validate_shape else tensor_shape.unknown_shape())
          # In this case, the variable op can't be created until after the
          # initial_value has been converted to a Tensor with a known type.
          self._variable = state_ops.variable_op_v2(
              shape,
              self._initial_value.dtype.base_dtype,
              name=name)

        # Manually overrides the variable's shape with the initial value's.
        if validate_shape:
          initial_value_shape = self._initial_value.get_shape()
          if not initial_value_shape.is_fully_defined():
            raise ValueError("initial_value must have a shape specified: %s" %
                             self._initial_value)

#.........这里部分代码省略.........

  def __init__(self,  # pylint: disable=super-init-not-called
               initial_value=None,
               trainable=None,
               caching_device=None,
               name=None,
               dtype=None,
               constraint=None,
               add_initializers_to=None,
               lifted_initializer_graph=None,
               **unused_kwargs):
    """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`, 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.
      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.
      add_initializers_to: if not None and not in legacy graph mode, the
        initializer tensor will be added to this map in addition to adding the
        assignment to the function.
      lifted_initializer_graph: FuncGraph to try to lift initializers to.

    Raises:
      ValueError: If the initial value is not specified, or does not have a
        shape and `validate_shape` is `True`.
      RuntimeError: If called outside of a function definition.
    """
    if not ops.inside_function():
      # If we've been init_scope()d out of the function definition nothing to do
      # here; we can't really do the capturing or conditional logic.
      resource_variable_ops.ResourceVariable.__init__(
          self, initial_value=initial_value, trainable=trainable,
          caching_device=caching_device, name=name, dtype=dtype,
          constraint=constraint)
      return
    with ops.init_scope():
      self._in_graph_mode = not context.executing_eagerly()
    if initial_value is None:
      raise ValueError("initial_value must be specified.")
    init_from_fn = callable(initial_value)

    if constraint is not None and not callable(constraint):
      raise ValueError("The `constraint` argument must be a callable.")

    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

    if trainable is None:
      trainable = True
    self._trainable = trainable
    self._save_slice_info = None
    self._initial_value = None
    self._initializer_op = None
    self._is_initialized_op = None
    self._graph_element = None
    self._cached_value = None
    # 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
    with ops.name_scope(name, "Variable", []
                        if init_from_fn else [initial_value]) as name:
      # pylint: disable=protected-access
      with ops.init_scope():
        handle_name = ops._name_from_scope_name(name)
        unique_id = "%s_%d" % (handle_name, ops.uid())
        shared_name = context.shared_name(unique_id)
      with ops.name_scope("Initializer"), ops.device(None):
        initial_value = ops.convert_to_tensor(
            initial_value() if init_from_fn else initial_value,
            name="initial_value", dtype=dtype)
      with ops.init_scope():
        self._handle = resource_variable_ops.eager_safe_variable_handle(
            initial_value=initial_value,
            shared_name=shared_name,
            name=name,
            graph_mode=self._in_graph_mode)
#.........这里部分代码省略.........

  def __init__(self,  # pylint: disable=super-init-not-called
               initial_value=None,
               trainable=True,
               caching_device=None,
               name=None,
               dtype=None,
               constraint=None,
               **unused_kwargs):
    """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`, 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.
      dtype: If set, initial_value will be converted to the given type.
        If None, either the datatype will be kept (if initial_value is
       a Tensor) or float32 will be used (if it is a Python object convertible
       to a Tensor).
      constraint: An optional projection function to be applied to the variable
        after being updated by an `Optimizer` (e.g. used to implement norm
        constraints or value constraints for layer weights). The function must
        take as input the unprojected Tensor representing the value of the
        variable and return the Tensor for the projected value
        (which must have the same shape). Constraints are not safe to
        use when doing asynchronous distributed training.

    Raises:
      ValueError: If the initial value is not specified, or does not have a
        shape and `validate_shape` is `True`.
      RuntimeError: If called outside of a function definition.
    """
    if context.executing_eagerly():
      raise RuntimeError(
          "UnliftedInitializerVariable should not be created "
          "outside of functions.")
    with ops.init_scope():
      if not context.executing_eagerly():
        raise RuntimeError(
            "UnliftedInitializerVariable does not support legacy graph mode.")
    self._in_graph_mode = False
    if initial_value is None:
      raise ValueError("initial_value must be specified.")
    init_from_fn = callable(initial_value)

    if constraint is not None and not callable(constraint):
      raise ValueError("The `constraint` argument must be a callable.")

    if isinstance(initial_value, checkpointable.CheckpointInitialValue):
      self._maybe_initialize_checkpointable()
      self._update_uid = initial_value.checkpoint_position.restore_uid
      initial_value = initial_value.wrapped_value

    self._trainable = trainable
    self._save_slice_info = None
    self._initial_value = None
    self._initializer_op = None
    self._is_initialized_op = None
    self._graph_element = None
    self._cached_value = None
    # 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
    with ops.name_scope(name, "Variable", []
                        if init_from_fn else [initial_value]) as name:
      # pylint: disable=protected-access
      with ops.init_scope():
        assert context.executing_eagerly()
        shared_name = ops._name_from_scope_name(name)
        shared_name = "%s_%d" % (shared_name, ops.uid())
      # Use attr_scope and device(None) to simulate the behavior of
      # colocate_with when the variable we want to colocate with doesn't
      # yet exist.
      with ops.name_scope("Initializer"), ops.device(None):
        initial_value = ops.convert_to_tensor(
            initial_value() if init_from_fn else initial_value,
            name="initial_value", dtype=dtype)
      with ops.init_scope():
        self._handle = resource_variable_ops.eager_safe_variable_handle(
            shape=initial_value.get_shape(),
            dtype=initial_value.dtype.base_dtype,
            shared_name=shared_name,
            name=name,
            graph_mode=False)
      self._shape = initial_value.shape
      self._unique_id = shared_name
      self._handle_name = shared_name + ":0"
      self._dtype = initial_value.dtype.base_dtype
      self._constraint = constraint
#.........这里部分代码省略.........