message: [tf.data] Remove unused job_token parameter.
  This was originally introduced when we explicitly managed data service job tokens in Python. Job tokens are now managed in c++ instead, so this parameter is no longer unused.
  This CL also calls super(OwnedIterator, self).__init__() to prevent a lint error.
diff: @@ -36,7 +36,6 @@ from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.framework import type_spec
  from tensorflow.python.ops import gen_dataset_ops
  -from tensorflow.python.ops import gen_experimental_dataset_ops
  from tensorflow.python.training.saver import BaseSaverBuilder
  from tensorflow.python.training.tracking import base as trackable
  from tensorflow.python.util import deprecation
  @@ -656,11 +655,7 @@ class OwnedIterator(IteratorBase):
  in eager mode and inside of tf.functions.
  """
  - def __init__(self,
  - dataset=None,
  - components=None,
  - element_spec=None,
  - job_token=None):
  + def __init__(self, dataset=None, components=None, element_spec=None):
  """Creates a new iterator from the given dataset.
  If `dataset` is not specified, the iterator will be created from the given
  @@ -673,20 +668,17 @@ class OwnedIterator(IteratorBase):
  components: Tensor components to construct the iterator from.
  element_spec: A nested structure of `TypeSpec` objects that
  represents the type specification of elements of the iterator.
  - job_token: A token to use for reading from a tf.data service job. Data
  - will be partitioned among all iterators using the same token. If `None`,
  - the iterator will not read from the tf.data service.
  Raises:
  ValueError: If `dataset` is not provided and either `components` or
  `element_spec` is not provided. Or `dataset` is provided and either
  `components` and `element_spec` is provided.
  """
  + super(OwnedIterator, self).__init__()
  error_message = ("Either `dataset` or both `components` and "
  "`element_spec` need to be provided.")
  self._device = context.context().device_name
  - self._job_token = job_token
  if dataset is None:
  if (components is None or element_spec is None):
  @@ -729,11 +721,7 @@ class OwnedIterator(IteratorBase):
  gen_dataset_ops.anonymous_iterator_v2(
  output_types=self._flat_output_types,
  output_shapes=self._flat_output_shapes))
  - if self._job_token is None:
  gen_dataset_ops.make_iterator(ds_variant, self._iterator_resource)
  - else:
  - gen_experimental_dataset_ops.make_data_service_iterator(
  - ds_variant, self._job_token, self._iterator_resource)
  # Delete the resource when this object is deleted
  self._resource_deleter = IteratorResourceDeleter(
  handle=self._iterator_resource,
  