code: def _sklearn_roc_binary(preds, target, ignore_index=None):
      preds = preds.flatten().numpy()
      target = target.flatten().numpy()
      if (
          np.issubdtype(preds.dtype, np.floating)
          and not ((preds > 0) & (preds < 1)).all()
      ):
          preds = sigmoid(preds)
      target, preds = remove_ignore_index(target, preds, ignore_index)
      fpr, tpr, thresholds = sk_roc_curve(
          target, preds, drop_intermediate=False
      )
      thresholds[0] = 1.0
      return [np.nan_to_num(x, nan=0.0) for x in [fpr, tpr, thresholds]]
  
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_curve
  from torchmetrics.classification.roc import ROC, BinaryROC, MulticlassROC, MultilabelROC
  from torchmetrics.functional.classification.roc import binary_roc, multiclass_roc, multilabel_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_roc_binary(preds, target, ignore_index) and returns: unknown