class_docstr: A dataset that creates window datasets from the input elements.
class_docstr_tok: ["A","dataset","that","creates","window","datasets","from","the","input","elements","."]
docstr: See `window()` for more details.
docstr_tok: ["See","`","window","(",")","`","for","more","details","."]
code: def __init__(
      self,
      input_dataset,
      size,
      shift,
      stride,
      drop_remainder,
      name=None,
  ):
      """See `window()` for more details."""
      self._input_dataset = input_dataset
      self._size = ops.convert_to_tensor(
          size, dtype=dtypes.int64, name="size"
      )
      self._shift = ops.convert_to_tensor(
          shift, dtype=dtypes.int64, name="shift"
      )
      self._stride = ops.convert_to_tensor(
          stride, dtype=dtypes.int64, name="stride"
      )
      self._drop_remainder = ops.convert_to_tensor(
          drop_remainder, dtype=dtypes.bool, name="drop_remainder"
      )
      self._structure = nest.pack_sequence_as(
          dataset_ops.get_legacy_output_classes(input_dataset),
          [
              dataset_ops.DatasetSpec(  # pylint: disable=g-complex-comprehension
                  structure.convert_legacy_structure(
                      output_type, output_shape, output_class
                  )
              )
              for output_class, output_shape, output_type in zip(
                  nest.flatten(
                      dataset_ops.get_legacy_output_classes(
                          input_dataset
                      )
                  ),
                  nest.flatten(
                      dataset_ops.get_legacy_output_shapes(
                          input_dataset
                      )
                  ),
                  nest.flatten(
                      dataset_ops.get_legacy_output_types(input_dataset)
                  ),
              )
          ],
      )
      self._name = name
      variant_tensor = gen_dataset_ops.window_dataset(
          input_dataset._variant_tensor,  # pylint: disable=protected-access
          size=self._size,
          shift=self._shift,
          stride=self._stride,
          drop_remainder=self._drop_remainder,
          **self._common_args,
      )
      super().__init__(input_dataset, variant_tensor)
  
file_path: tensorflow-upstream/tensorflow/python/data/ops/window_op.py
imports: from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.util import nest
  from tensorflow.python.data.util import structure
  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 _WindowDataset object from inherited base classes: dataset_ops with example: obj = _WindowDataset(input_dataset, size, shift, stride, drop_remainder, name)