class_docstr: A combiner which computes metrics averaging regression errors.
class_docstr_tok: ["A","combiner","which","computes","metrics","averaging","regression","errors","."]
code: def __init__(
      self,
      eval_config: config_pb2.EvalConfig,
      model_name: str,
      output_name: str,
      metric_key: metric_types.MetricKey,
      aggregation_type: Optional[metric_types.AggregationType],
      class_weights: Optional[Dict[int, float]],
      example_weighted: bool,
  ):
      self._eval_config = eval_config
      self._model_name = model_name
      self._output_name = output_name
      self._metric_key = metric_key
      self._example_weighted = example_weighted
      self._aggregation_type = aggregation_type
      self._class_weights = class_weights
  
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 1 to create a new _MeanRegressionErrorCombiner object from inherited base classes: beam with example: obj = _MeanRegressionErrorCombiner(eval_config, model_name, output_name, metric_key, aggregation_type, class_weights, example_weighted)