parameters: (self,
                 input_dataset,
                 size,
                 shift,
                 stride,
                 drop_remainder,
                 name=None)
docstring: See `window()` for more details.
docstring_summary: See `window()` for more details.
docstring_tokens: See window () for more details .
function: 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(
          get_legacy_output_classes(input_dataset), [
              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(get_legacy_output_classes(input_dataset)),
                  nest.flatten(get_legacy_output_shapes(input_dataset)),
                  nest.flatten(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(WindowDataset, self).__init__(input_dataset, variant_tensor)
function_tokens: def __init__ ( self , input_dataset , size , shift , stride , drop_remainder , name = None ) : 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 ( get_legacy_output_classes ( input_dataset ) , [ 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 ( get_legacy_output_classes ( input_dataset ) ) , nest . flatten ( get_legacy_output_shapes ( input_dataset ) ) , nest . flatten ( 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 ( WindowDataset , self ) . __init__ ( input_dataset , variant_tensor )