def _create_none_optionals(func_graph, template_tensors): """Creates none optionals in func_graph to represent template_tensors. Args: func_graph: FuncGraph. template_tensors: a list of tensors in func_graph. Returns: A list of tensors in func_graph. """ with func_graph.as_default(): return [gen_dataset_ops.optional_none() for _ in template_tensors]
def _create_none_optionals(func_graph, n): """Creates `n` `None` optionals in func_graph. Args: func_graph: FuncGraph. n: `int` the number of `None` optionals to make. Returns: A list of tensors in func_graph. """ with func_graph.as_default(): return [gen_dataset_ops.optional_none() for _ in range(n)]
def _create_none_optionals(func_graph, template_tensors): """Creates none optionals in func_graph to represent template_tensors. Args: func_graph: FuncGraph. template_tensors: a list of tensors in func_graph. Returns: A list of tensors in func_graph. """ with func_graph.as_default(): return [gen_dataset_ops.optional_none() for _ in template_tensors]
def none_from_structure(value_structure): """Returns an `Optional` that has no value. NOTE: This method takes an argument that defines the structure of the value that would be contained in the returned `Optional` if it had a value. Args: value_structure: A `Structure` object representing the structure of the components of this optional. Returns: An `Optional` that has no value. """ return _OptionalImpl(gen_dataset_ops.optional_none(), value_structure)
def none_from_structure(value_structure): """Returns an `Optional` that has no value. NOTE: This method takes an argument that defines the structure of the value that would be contained in the returned `Optional` if it had a value. Args: value_structure: A `Structure` object representing the structure of the components of this optional. Returns: An `Optional` that has no value. """ return _OptionalImpl(gen_dataset_ops.optional_none(), value_structure)
def none_from_structure(output_shapes, output_types, output_classes): """Returns an `Optional` that has no value. NOTE: This method takes arguments that define the structure of the value that would be contained in the returned `Optional` if it had a value. Args: output_shapes: A nested structure of `tf.TensorShape` objects corresponding to each component of this optional. output_types: A nested structure of `tf.DType` objects corresponding to each component of this optional. output_classes: A nested structure of Python `type` objects corresponding to each component of this optional. Returns: An `Optional` that has no value. """ return _OptionalImpl(gen_dataset_ops.optional_none(), output_shapes, output_types, output_classes)
def empty(element_spec): """Returns an `Optional` that has no value. NOTE: This method takes an argument that defines the structure of the value that would be contained in the returned `Optional` if it had a value. >>> optional = tf.experimental.Optional.empty( ... tf.TensorSpec(shape=(), dtype=tf.int32, name=None)) >>> print(optional.has_value()) tf.Tensor(False, shape=(), dtype=bool) Args: element_spec: A (nested) structure of `tf.TypeSpec` objects matching the structure of an element of this optional. Returns: A `tf.experimental.Optional` with no value. """ return _OptionalImpl(gen_dataset_ops.optional_none(), element_spec)
def none_from_structure(output_shapes, output_types, output_classes): """Returns an `Optional` that has no value. NOTE: This method takes arguments that define the structure of the value that would be contained in the returned `Optional` if it had a value. Args: output_shapes: A nested structure of `tf.TensorShape` objects corresponding to each component of this optional. output_types: A nested structure of `tf.DType` objects corresponding to each component of this optional. output_classes: A nested structure of Python `type` objects corresponding to each component of this optional. Returns: An `Optional` that has no value. """ return _OptionalImpl(gen_dataset_ops.optional_none(), output_shapes, output_types, output_classes)