class_docstr: A combiner which computes metrics averaging squared errors.
class_docstr_tok: ["A","combiner","which","computes","metrics","averaging","squared","errors","."]
code: def _regression_error(
      self, label: np.ndarray, prediction: np.ndarray
  ) -> float:
      # The np.item method makes sure the result is a one element numpy array and
      # returns the single element as a float.
      return (
          metric_util.safe_to_scalar(np.linalg.norm(label - prediction))
          ** 2
      )
  
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 for calling the _MeanSquaredErrorCombiner obj's underlying member method code with example usage: obj._regression_error(label, prediction) and returns: unknown