class_docstr: A combiner which computes metrics averaging regression errors.
class_docstr_tok: ["A","combiner","which","computes","metrics","averaging","regression","errors","."]
code: def extract_output(
      self, accumulator: _MeanRegressionErrorAccumulator
  ) -> metric_types.MetricsDict:
      if accumulator.total_example_weights != 0.0:
          result = (
              accumulator.total_regression_error
              / accumulator.total_example_weights
          )
      else:
          result = float("nan")
      return {self._metric_key: result}
  
imports: import abc
  import dataclasses
  from typing import Iterable, Optional, Dict
  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 6 for calling the _MeanRegressionErrorCombiner obj's underlying member method code with example usage: obj.extract_output(accumulator) and returns: dict