code: def _validate_slicing_options(
      slice_fns: Optional[List[types.SliceFunction]] = None,
      slice_sqls: Optional[List[Text]] = None,
  ):
      if slice_fns and slice_sqls:
          raise ValueError(
              "Only one of experimental_slice_functions or "
              "experimental_slice_sqls must be specified."
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import json
  import logging
  import types
  from typing import Dict, List, Optional, Text, Union
  from tensorflow_data_validation import types
  from tensorflow_data_validation.statistics.generators import stats_generator
  from tensorflow_data_validation.utils import example_weight_map
  from tensorflow_data_validation.utils import schema_util
  from tensorflow_data_validation.utils import slicing_util
  from tfx_bsl.arrow import sql_util
  from tfx_bsl.coders import example_coder
  from tfx_bsl.public.proto import slicing_spec_pb2
  from google.protobuf import json_format
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 2 for calling a global function with example usage: _validate_slicing_options(slice_fns, slice_sqls) without return types