tensorflow_model_analysis/metrics/aggregation.py [116:139]:
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    total_value = 0.0
    total_weight = 0.0
    for sub_key in sub_keys:
      child_key = metric_types.MetricKey(
          name=metric_name,
          model_name=model_name,
          output_name=output_name,
          sub_key=sub_key,
          example_weighted=example_weighted)
      if child_key not in metrics:
        # Use private name if not found under metric name
        child_key = metric_types.MetricKey(
            name='_' + metric_name,
            model_name=model_name,
            output_name=output_name,
            sub_key=sub_key,
            example_weighted=example_weighted)
      weight = 1.0 if not class_weights else 0.0
      offset = None
      if (child_key.sub_key is not None and
          child_key.sub_key.class_id is not None):
        offset = child_key.sub_key.class_id
      elif child_key.sub_key is not None and child_key.sub_key.k is not None:
        offset = child_key.sub_key.k
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tensorflow_model_analysis/metrics/aggregation.py [207:230]:
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    total_value = 0.0
    total_weight = 0.0
    for sub_key in sub_keys:
      child_key = metric_types.MetricKey(
          name=metric_name,
          model_name=model_name,
          output_name=output_name,
          sub_key=sub_key,
          example_weighted=example_weighted)
      if child_key not in metrics:
        # Use private name if not found under metric name
        child_key = metric_types.MetricKey(
            name='_' + metric_name,
            model_name=model_name,
            output_name=output_name,
            sub_key=sub_key,
            example_weighted=example_weighted)
      weight = 1.0 if not class_weights else 0.0
      offset = None
      if (child_key.sub_key is not None and
          child_key.sub_key.class_id is not None):
        offset = child_key.sub_key.class_id
      elif child_key.sub_key is not None and child_key.sub_key.k is not None:
        offset = child_key.sub_key.k
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