class_docstr: Options for generating statistics.
class_docstr_tok: ["Options","for","generating","statistics","."]
code: def experimental_slice_functions(
      self, slice_functions: Optional[List[types.SliceFunction]]
  ) -> None:
      if hasattr(self, "experimental_slice_sqls"):
          _validate_slicing_options(
              slice_functions, self.experimental_slice_sqls
          )
      if slice_functions is not None:
          if not isinstance(slice_functions, list):
              raise TypeError(
                  "experimental_slice_functions is of type %s, should be a list."
                  % type(slice_functions).__name__
              )
          for slice_function in slice_functions:
              if not isinstance(
                  slice_function, python_types.FunctionType
              ):
                  raise TypeError(
                      "experimental_slice_functions must contain functions only."
                  )
      self._slice_functions = slice_functions
  
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 14 for calling the StatsOptions obj's underlying member method code with example usage: obj.experimental_slice_functions(slice_functions) without return types