code: def FilterOutSlices(  # pylint: disable=invalid-name
      values: beam.pvalue.PCollection,
      slices_count: beam.pvalue.PCollection,
      min_slice_size: int,
      error_metric_key: str = "__ERROR__",
  ) -> beam.pvalue.PCollection:
      """Filter out slices with examples count lower than k_anonymization_count.
  
      Since we might filter out certain slices to preserve privacy in the case of
      small slices, to make end users aware of this, we will append filtered out
      slice keys with empty data, and a debug message explaining the omission.
  
      Args:
        values: PCollection of aggregated data keyed at slice_key
        slices_count: PCollection of slice keys and their example count.
        min_slice_size: If the number of examples in a specific slice is less than
          min_slice_size, then an error will be returned for that slice. This will
          be useful to ensure privacy by not displaying the aggregated data for
          smaller number of examples.
        error_metric_key: The special metric key to indicate errors.
  
      Returns:
        A PCollection keyed at all the possible slice_key and aggregated data for
        slice keys with example count more than min_slice_size and error
        message for filtered out slices.
      """
  
      class FilterOutSmallSlicesDoFn(beam.DoFn):
          """DoFn to filter out small slices."""
  
          def __init__(self, error_metric_key: str):
              self.error_metric_key = error_metric_key
  
          def process(
              self, element: Tuple[SliceKeyType, _MetricsDict]
          ) -> Generator[Tuple[SliceKeyType, _MetricsDict], None, None]:
              """Filter out small slices.
  
              For slices (excluding overall slice) with examples count lower than
              min_slice_size, it adds an error message.
  
              Args:
                element: Tuple containing slice key and a dictionary containing
                  corresponding elements from merged pcollections.
  
              Yields:
                PCollection of (slice_key, aggregated_data or error message)
              """
              (slice_key, value) = element
              if value["values"]:
                  if (
                      not slice_key
                      or value["slices_count"][0] >= min_slice_size
                  ):
                      yield (slice_key, value["values"][0])
                  else:
                      yield (
                          slice_key,
                          {
                              self.error_metric_key: "Example count for this slice key is lower than the "  # LINT.IfChange
                              "minimum required value: %d. No data is aggregated for "
                              "this slice." % min_slice_size
                              # LINT.ThenChange(../addons/fairness/frontend/fairness-metrics-board/fairness-metrics-board.js)
                          },
                      )
  
      return (
          {"values": values, "slices_count": slices_count}
          | "CoGroupingSlicesCountAndAggregatedData"
          >> beam.CoGroupByKey()
          | "FilterOutSmallSlices"
          >> beam.ParDo(FilterOutSmallSlicesDoFn(error_metric_key))
      )
  
imports: import itertools
  from typing import Any, Callable, Dict, Generator, Iterable, List, NamedTuple, Optional, Tuple, Union
  import apache_beam
  import numpy
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.proto import metrics_for_slice_pb2
  from tensorflow_model_analysis.slicer import slice_accessor
label_desc: Use image node_id 16 for calling a global function with example usage: FilterOutSlices(values, slices_count, min_slice_size, error_metric_key) and returns: unknown