class_docstr: The AUC Metric measures the area under the receiver-operating characteristic
  (ROC) curve for binary classification problems.
class_docstr_tok: ["The","AUC","Metric","measures","the","area","under","the","receiver-operating","characteristic","(","ROC",")","curve","for","binary","classification","problems","."]
code: def get_metric(self, reset: bool = False):
      if self._all_gold_labels.shape[0] == 0:
          return 0.5
      false_positive_rates, true_positive_rates, _ = metrics.roc_curve(
          self._all_gold_labels.cpu().numpy(),
          self._all_predictions.cpu().numpy(),
          pos_label=self._positive_label,
      )
      auc = metrics.auc(false_positive_rates, true_positive_rates)
  
      if reset:
          self.reset()
      return auc
  
file_path: allennlp/allennlp/training/metrics/auc.py
imports: from typing import Optional
  import torch
  import torch.distributed
  from sklearn import metrics
  from allennlp.common.util import is_distributed
  from allennlp.common.checks import ConfigurationError
  from allennlp.training.metrics.metric import Metric
label_desc: Use image node_id 3 for calling the Auc obj's underlying member method code with example usage: obj.get_metric(reset) and returns: auc, int