code: def roc_auc_compute_fn(
      y_preds: torch.Tensor, y_targets: torch.Tensor
  ) -> float:
      from sklearn.metrics import roc_auc_score
  
      y_true = y_targets.cpu().numpy()
      y_pred = y_preds.cpu().numpy()
      return roc_auc_score(y_true, y_pred)
  
file_path: ignite/ignite/contrib/metrics/roc_auc.py
imports: from typing import Any, Callable, cast, Tuple, Union
  import torch
  from ignite import distributed
  from ignite.exceptions import NotComputableError
  from ignite.metrics import EpochMetric
label_desc: Use image node_id 1 for calling a global function with example usage: roc_auc_compute_fn(y_preds, y_targets) and returns: roc_auc_score