code: def roc_auc__roc_auc__ner_f1(
      true_onehot1,
      pred_probas1,
      true_onehot2,
      pred_probas2,
      ner_true3,
      ner_pred3,
  ):
      from .roc_auc_score import roc_auc_score
  
      roc_auc1 = roc_auc_score(true_onehot1, pred_probas1)
      roc_auc2 = roc_auc_score(true_onehot2, pred_probas2)
      ner_f1_3 = ner_f1(ner_true3, ner_pred3) / 100
      return (roc_auc1 + roc_auc2 + ner_f1_3) / 3
  
file_path: DeepPavlov/deeppavlov/metrics/fmeasure.py
imports: import itertools
  from collections import OrderedDict
  from itertools import chain
  from logging import getLogger
  import numpy
  from sklearn.metrics import f1_score
  from deeppavlov.core.common.metrics_registry import register_metric
label_desc: Use image node_id 11 for calling a global function with example usage: roc_auc__roc_auc__ner_f1(true_onehot1, pred_probas1, true_onehot2, pred_probas2, ner_true3, ner_pred3) and returns: unknown