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 __init__(
      self,
      metric_keys: List[metric_types.MetricKey],
      sampled_key: Text,
      size: int,
      example_weighted: bool,
      random_seed: Optional[int],
  ):
      self._metric_keys = metric_keys
      self._sampled_key = sampled_key
      self._example_weighted = example_weighted
      self._random_seed = random_seed
      # We delegate to the TopCombineFn rather than subclass because the use of a
      # TopCombineFn is an implementation detail.
      super().__init__(combine_fn=beam.combiners.TopCombineFn(n=size))
  
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 1 to create a new _FixedSizeSampleCombineFn object from inherited base classes: beam_util with example: obj = _FixedSizeSampleCombineFn(metric_keys, sampled_key, size, example_weighted, random_seed)