code: def layer_op(self, subject_id, data):
      if (
          not self.app_param.output_prob
          or self.app_param.num_classes > 2
      ):
          return []
      pdf = pd.DataFrame.from_records(
          [
              {
                  "subject_id": subject_id,
                  "roc_auc_i": data["inferred"][0, 0, 0, 0, 1],
                  "roc_auc_l": data["label"][0, 0, 0, 0, 0],
              }
          ],
          ("subject_id",),
      )
      return [pdf]
  
file_path: NiftyNet/niftynet/contrib/evaluation/classification_evaluations.py
imports: from __future__ import absolute_import, division, print_function
  import numpy
  import pandas
  from niftynet.evaluation.base_evaluations import BaseEvaluation
  from niftynet.evaluation.base_evaluator import ScalarAggregator, DataFrameAggregator
label_desc: Use image node_id 1 for calling the roc_auc obj's underlying member method code with example usage: obj.layer_op(subject_id, data) and returns: list, list