class_docstr: Represents a value that may or may not be present.
  
  A `tf.experimental.Optional` can represent the result of an operation that may
  fail as a value, rather than raising an exception and halting execution. For
  example, `tf.data.Iterator.get_next_as_optional()` returns a
  `tf.experimental.Optional` that either contains the next element of an
  iterator if one exists, or an "empty" value that indicates the end of the
  sequence has been reached.
  
  `tf.experimental.Optional` can only be used with values that are convertible
  to `tf.Tensor` or `tf.CompositeTensor`.
  
  One can create a `tf.experimental.Optional` from a value using the
  `from_value()` method:
  
  >>> optional = tf.experimental.Optional.from_value(42)
  >>> print(optional.has_value())
  tf.Tensor(True, shape=(), dtype=bool)
  >>> print(optional.get_value())
  tf.Tensor(42, shape=(), dtype=int32)
  
  or without a value using the `empty()` method:
  
  >>> optional = tf.experimental.Optional.empty(
  ...   tf.TensorSpec(shape=(), dtype=tf.int32, name=None))
  >>> print(optional.has_value())
  tf.Tensor(False, shape=(), dtype=bool)
docstr: Returns a tensor that evaluates to `True` if this optional has a value.
  
  >>> optional = tf.experimental.Optional.from_value(42)
  >>> print(optional.has_value())
  tf.Tensor(True, shape=(), dtype=bool)
  
  Args:
    name: (Optional.) A name for the created operation.
  
  Returns:
    A scalar `tf.Tensor` of type `tf.bool`.
docstr_tok: ["Returns","a","tensor","that","evaluates","to","`","True","`","if","this","optional","has","a","value",".",">",">",">","optional","=","tf.experimental.Optional.from_value","(","42",")",">",">",">","print","(","optional.has_value","(",")",")","tf.Tensor","(","True",",","shape=","(",")",",","dtype=bool",")","Args",":","name",":","(","Optional",".",")","A","name","for","the","created","operation",".","Returns",":","A","scalar","`","tf.Tensor","`","of","type","`","tf.bool","`","."]
code: def has_value(self, name=None):
      """Returns a tensor that evaluates to `True` if this optional has a value.
  
      >>> optional = tf.experimental.Optional.from_value(42)
      >>> print(optional.has_value())
      tf.Tensor(True, shape=(), dtype=bool)
  
      Args:
        name: (Optional.) A name for the created operation.
  
      Returns:
        A scalar `tf.Tensor` of type `tf.bool`.
      """
      raise NotImplementedError("Optional.has_value()")
  
imports: import abc
  from tensorflow.core.protobuf import struct_pb2
  from tensorflow.python.data.util import structure
  from tensorflow.python.framework import composite_tensor
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.framework import type_spec
  from tensorflow.python.ops import gen_optional_ops
  from tensorflow.python.saved_model import nested_structure_coder
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 for calling the Optional obj's underlying member method code with example usage: obj.has_value(name) without return types