class_docstr: Interface for scalar regression metrics 
class_docstr_tok: ["Interface","for","scalar","regression","metrics"]
code: def layer_op(self, subject_id, data):
      metric_name = self.__class__.__name__
      metric_value = self.metric(data["inferred"], data["output"])
      pdf = pd.DataFrame.from_records(
          [{"subject_id": subject_id, metric_name: metric_value}],
          ("subject_id",),
      )
      return [pdf]
  
file_path: NiftyNet/niftynet/evaluation/regression_evaluations.py
imports: from __future__ import absolute_import, division, print_function
  import numpy
  import pandas
  from niftynet.evaluation.base_evaluations import BaseEvaluation
label_desc: Use image node_id 1 for calling the BaseRegressionEvaluation obj's underlying member method code with example usage: obj.layer_op(subject_id, data) and returns: list