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 extract_output(self, heap: _HeapType) -> metric_types.MetricsDict:
      # drop random numbers used for sampling
      sampled_values = np.array(
          [v for _, v in super().extract_output(heap)]
      )
      return {k: sampled_values for k in self._metric_keys}
  
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 4 for calling the _FixedSizeSampleCombineFn obj's underlying member method code with example usage: obj.extract_output(heap) and returns: unknown