class_docstr: A dataset containing the unique elements of an input dataset.
docstr: See `tf.data.Dataset.unique` for details.
docstr_tok: ["See","`","tf.data.Dataset.unique","`","for","details","."]
code: def __init__(self, input_dataset, name=None):
      """See `tf.data.Dataset.unique` for details."""
      self._input_dataset = input_dataset
      for ty in nest.flatten(
          dataset_ops.get_legacy_output_types(input_dataset)
      ):
          if ty not in (dtypes.int32, dtypes.int64, dtypes.string):
              raise TypeError(
                  f"`tf.data.Dataset.unique` does not support type {ty} -- only "
                  f"`tf.int32`, `tf.int64`, and `tf.string` are supported."
              )
      self._name = name
      variant_tensor = gen_experimental_dataset_ops.unique_dataset(
          self._input_dataset._variant_tensor,  # pylint: disable=protected-access
          **self._common_args,
      )
      super().__init__(input_dataset, variant_tensor)
  
file_path: tensorflow-upstream/tensorflow/python/data/ops/unique_op.py
imports: from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.util import nest
  from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import gen_experimental_dataset_ops
label_desc: Use image node_id 1 to create a new _UniqueDataset object from inherited base classes: dataset_ops with example: obj = _UniqueDataset(input_dataset, name)