class_docstr: A `Dataset` that asynchronously prefetches its input.
docstr: See `Dataset.prefetch()` for details.
code: def __init__(
      self, input_dataset, buffer_size, slack_period=None, name=None
  ):
      """See `Dataset.prefetch()` for details."""
      self._input_dataset = input_dataset
      if buffer_size is None:
          buffer_size = dataset_ops.AUTOTUNE
      self._buffer_size = ops.convert_to_tensor(
          buffer_size, dtype=dtypes.int64, name="buffer_size"
      )
      self._name = name
      # pylint: disable=protected-access
      # We colocate the prefetch dataset with its input as this collocation only
      # happens automatically in graph mode.
      with ops.colocate_with(input_dataset._variant_tensor):
          variant_tensor = gen_dataset_ops.prefetch_dataset(
              input_dataset._variant_tensor,
              buffer_size=self._buffer_size,
              slack_period=slack_period,
              **self._common_args,
          )
      super().__init__(input_dataset, variant_tensor)
  
file_path: tensorflow-upstream/tensorflow/python/data/ops/prefetch_op.py
imports: from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import debug_mode
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import gen_dataset_ops
label_desc: Use image node_id 1 to create a new _PrefetchDataset object from inherited base classes: dataset_ops with example: obj = _PrefetchDataset(input_dataset, buffer_size, slack_period, name)