code: def _sklearn_auroc_binary(
      preds, target, max_fpr=None, ignore_index=None
  ):
      preds = preds.flatten().numpy()
      target = target.flatten().numpy()
      if not ((preds > 0) & (preds < 1)).all():
          preds = sigmoid(preds)
      target, preds = remove_ignore_index(target, preds, ignore_index)
      return sk_roc_auc_score(target, preds, max_fpr=max_fpr)
  
imports: from functools import partial
  import numpy
  import pytest
  import torch
  from scipy.special import expit
  from scipy.special import softmax
  from sklearn.metrics import roc_auc_score
  from torchmetrics.classification.auroc import AUROC, BinaryAUROC, MulticlassAUROC, MultilabelAUROC
  from torchmetrics.functional.classification.auroc import binary_auroc, multiclass_auroc, multilabel_auroc
  from torchmetrics.functional.classification.roc import binary_roc
  from torchmetrics.metric import Metric
  from unittests import NUM_CLASSES
  from unittests.classification.inputs import _binary_cases, _multiclass_cases, _multilabel_cases
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester, inject_ignore_index, remove_ignore_index
label_desc: Use image node_id 1 for calling a global function with example usage: _sklearn_auroc_binary(preds, target, max_fpr, ignore_index) and returns: sk_roc_auc_score