class_docstr: Computes weighted labels, predictions, and examples.
class_docstr_tok: ["Computes","weighted","labels",",","predictions",",","and","examples","."]
code: def merge_accumulators(
      self, accumulators: Iterable[_WeightedLabelsPredictionsExamples]
  ) -> _WeightedLabelsPredictionsExamples:
      accumulators = iter(accumulators)
      result = next(accumulators)
      for accumulator in accumulators:
          result.total_weighted_labels += (
              accumulator.total_weighted_labels
          )
          result.total_weighted_predictions += (
              accumulator.total_weighted_predictions
          )
          result.total_weighted_examples += (
              accumulator.total_weighted_examples
          )
      return result
  
imports: from typing import Any, Dict, Iterable, Optional
  import apache_beam
  import numpy
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.proto import config_pb2
label_desc: Use image node_id 4 for calling the _WeightedLabelsPredictionsExamplesCombiner obj's underlying member method code with example usage: obj.merge_accumulators(accumulators) and returns: result