class_docstr: A combiner which computes metrics averaging regression errors.
class_docstr_tok: ["A","combiner","which","computes","metrics","averaging","regression","errors","."]
docstr: Returns the regression error between the label and prediction.
  
  Subclasses must override this method. Labels and preditctions could be
  an array, a float, a string and etc. But the output of regression error must
  be a float.
  
  Args:
    label: label.
    prediction: prediction from the model.
docstr_tok: ["Returns","the","regression","error","between","the","label","and","prediction",".","Subclasses","must","override","this","method",".","Labels","and","preditctions","could","be","an","array",",","a","float",",","a","string","and","etc",".","But","the","output","of","regression","error","must","be","a","float",".","Args",":","label",":","label",".","prediction",":","prediction","from","the","model","."]
code: def _regression_error(self, label, prediction) -> float:
      """Returns the regression error between the label and prediction.
  
      Subclasses must override this method. Labels and preditctions could be
      an array, a float, a string and etc. But the output of regression error must
      be a float.
  
      Args:
        label: label.
        prediction: prediction from the model.
      """
      raise NotImplementedError("Must be implemented in subclasses.")
  
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 2 for calling the _MeanRegressionErrorCombiner obj's underlying member method code with example usage: obj._regression_error(label, prediction) without return types