parameters: (
        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)
docstring: 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: Ignored. Provided for compatibility with tf.Variable.
        caching_device: Optional device string or function describing where the
          Variable should be cached for reading.  Defaults to the Variable's
          device.  If not `None`, caches on another device.  Typical use is to
          cache on the device where the Ops using the Variable reside, to
          deduplicate copying through `Switch` and other conditional statements.
        name: Optional name for the variable. Defaults to `'Variable'` and gets
          uniquified automatically.
        dtype: If set, initial_value will be converted to the given type. If None,
          either the datatype will be kept (if initial_value is a Tensor) or
          float32 will be used (if it is a Python object convertible to a Tensor).
        variable_def: `VariableDef` protocol buffer. If not None, recreates the
          `ResourceVariable` object with its contents. `variable_def` and other
          arguments (except for import_scope) are mutually exclusive.
        import_scope: Optional `string`. Name scope to add to the
          ResourceVariable. Only used when `variable_def` is provided.
        constraint: An optional projection function to be applied to the variable
          after being updated by an `Optimizer` (e.g. used to implement norm
          constraints or value constraints for layer weights). The function must
          take as input the unprojected Tensor representing the value of the
          variable and return the Tensor for the projected value (which must have
          the same shape). Constraints are not safe to use when doing asynchronous
          distributed training.
        distribute_strategy: The tf.distribute.Strategy this variable is being
          created inside of.
        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
docstring_summary: Creates a variable.
docstring_tokens: Creates a variable .
function: 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: Ignored. Provided for compatibility with tf.Variable.
        caching_device: Optional device string or function describing where the
          Variable should be cached for reading.  Defaults to the Variable's
          device.  If not `None`, caches on another device.  Typical use is to
          cache on the device where the Ops using the Variable reside, to
          deduplicate copying through `Switch` and other conditional statements.
        name: Optional name for the variable. Defaults to `'Variable'` and gets
          uniquified automatically.
        dtype: If set, initial_value will be converted to the given type. If None,
          either the datatype will be kept (if initial_value is a Tensor) or
          float32 will be used (if it is a Python object convertible to a Tensor).
        variable_def: `VariableDef` protocol buffer. If not None, recreates the
          `ResourceVariable` object with its contents. `variable_def` and other
          arguments (except for import_scope) are mutually exclusive.
        import_scope: Optional `string`. Name scope to add to the
          ResourceVariable. Only used when `variable_def` is provided.
        constraint: An optional projection function to be applied to the variable
          after being updated by an `Optimizer` (e.g. used to implement norm
          constraints or value constraints for layer weights). The function must
          take as input the unprojected Tensor representing the value of the
          variable and return the Tensor for the projected value (which must have
          the same shape). Constraints are not safe to use when doing asynchronous
          distributed training.
        distribute_strategy: The tf.distribute.Strategy this variable is being
          created inside of.
        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)
      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)
function_tokens: 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 ) : 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 ) 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 )