parameters: (self, reduce_func, input_dataset)
docstring: Make wrapping defun for reduce_func.
docstring_summary: Make wrapping defun for reduce_func.
docstring_tokens: Make wrapping defun for reduce_func .
function: def _make_reduce_func(self, reduce_func, input_dataset):
      """Make wrapping defun for reduce_func."""
      nested_dataset = dataset_ops.DatasetSpec(
          input_dataset.element_spec)
      input_structure = (tensor_spec.TensorSpec([], dtypes.int64), nested_dataset)
      self._reduce_func = dataset_ops.StructuredFunctionWrapper(
          reduce_func, self._transformation_name(),
          input_structure=input_structure)
      if not isinstance(
          self._reduce_func.output_structure, dataset_ops.DatasetSpec):
        raise TypeError("`reduce_func` must return a `Dataset` object.")
      # pylint: disable=protected-access
      self._element_spec = (
          self._reduce_func.output_structure._element_spec)
function_tokens: def _make_reduce_func ( self , reduce_func , input_dataset ) : nested_dataset = dataset_ops . DatasetSpec ( input_dataset . element_spec ) input_structure = ( tensor_spec . TensorSpec ( [ ] , dtypes . int64 ) , nested_dataset ) self . _reduce_func = dataset_ops . StructuredFunctionWrapper ( reduce_func , self . _transformation_name ( ) , input_structure = input_structure ) if not isinstance ( self . _reduce_func . output_structure , dataset_ops . DatasetSpec ) : raise TypeError ( "`reduce_func` must return a `Dataset` object." ) # pylint: disable=protected-access self . _element_spec = ( self . _reduce_func . output_structure . _element_spec )