class_docstr: A fixed size sample combiner which samples values of a specified key.
  
  This CombineFn is similar to beam.combiners.SampleCombineFn except it makes
  use of the numpy random generator which means that it accepts a seed for use
  with deterministic testing.
code: def add_input(
      self, heap: _HeapType, element: metric_types.StandardMetricInputs
  ) -> _HeapType:
      # TODO(b/206546545): add support for sampling derived features
      sampled_value = util.get_by_keys(
          element.features, [self._sampled_key]
      )
      random_tag = self._random_generator.random()
      if self._example_weighted:
          # For details, see Weighted Random Sampling over Data Streams:
          # https://arxiv.org/abs/1012.0256
          weight = element.example_weight
          random_tag = random_tag ** (1 / weight)
      return super().add_input(heap, (random_tag, sampled_value))
  
file_path: model-analysis/tensorflow_model_analysis/metrics/sample_metrics.py
imports: from typing import Any, List, Optional, Text, Tuple
  import apache_beam
  import numpy
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.utils import beam_util
  from tensorflow_model_analysis.utils import util
label_desc: Use image node_id 3 for calling the _FixedSizeSampleCombineFn obj's underlying member method code with example usage: obj.add_input(heap, element) and returns: super