code: def generate_slices(
      record_batch: pa.RecordBatch,
      slice_functions: Iterable[types.SliceFunction],
      **kwargs,
  ) -> Iterable[types.SlicedRecordBatch]:
      """Generates sliced record batches based on provided slice functions.
  
      Args:
        record_batch: Arrow RecordBatch.
        slice_functions: An iterable of functions each of which takes as input an
          example (and zero or more kwargs) and returns a list of slice keys.
        **kwargs: Keyword arguments to pass to each of the slice_functions.
  
      Yields:
        Sliced record batch (slice_key, record batch).
      """
      for slice_fn in slice_functions:
          try:
              for sliced_record_batch in slice_fn(
                  record_batch, **kwargs
              ):
                  yield sliced_record_batch
          except Exception as e:
              raise ValueError(
                  "One of the slice_functions %s raised an exception: %s."
                  % (slice_fn.__name__, repr(e))
              )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from collections import abc
  import functools
  import logging
  from typing import Any, Dict, Iterable, List, Optional, Text, Union, Tuple
  import apache_beam
  import numpy
  import pandas
  import pandas.core.computation.expressions
  import pyarrow
  import six
  from tensorflow_data_validation import constants
  from tensorflow_data_validation import types
  from tensorflow_data_validation.arrow import arrow_util
  from tensorflow_data_validation.utils import stats_util
  from tfx_bsl.arrow import array_util
  from tfx_bsl.arrow import sql_util
  from tfx_bsl.arrow import table_util
  from tfx_bsl.public.proto import slicing_spec_pb2
  from tensorflow_metadata.proto.v0 import statistics_pb2
label_desc: Use image node_id 4 for calling a global function with example usage: generate_slices(record_batch, slice_functions) without return types