def __init__(self, input_dataset):
"""See `unique()` for details."""
self._input_dataset = input_dataset
if dataset_ops.get_legacy_output_types(input_dataset) not in (
dtypes.int32, dtypes.int64, dtypes.string):
raise TypeError(
"`tf.data.experimental.unique()` only supports inputs with a single "
"`tf.int32`, `tf.int64`, or `tf.string` component.")
variant_tensor = gen_experimental_dataset_ops.experimental_unique_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
**dataset_ops.flat_structure(self))
super(_UniqueDataset, self).__init__(input_dataset, variant_tensor)
def __init__(self, input_dataset):
"""See `unique()` for details."""
self._input_dataset = input_dataset
if input_dataset.output_types not in (dtypes.int32, dtypes.int64,
dtypes.string):
raise TypeError(
"`tf.data.experimental.unique()` only supports inputs with a single "
"`tf.int32`, `tf.int64`, or `tf.string` component.")
variant_tensor = gen_experimental_dataset_ops.experimental_unique_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
**dataset_ops.flat_structure(self))
super(_UniqueDataset, self).__init__(input_dataset, variant_tensor)
def _as_variant_tensor(self):
return gen_experimental_dataset_ops.experimental_unique_dataset(
self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access
**dataset_ops.flat_structure(self))
def _as_variant_tensor(self):
return gen_experimental_dataset_ops.experimental_unique_dataset(
self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access
**dataset_ops.flat_structure(self))