code: def _sklearn_precision_recall_curve_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)
      return sk_precision_recall_curve(target, preds)
  
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 precision_recall_curve
  from torchmetrics.classification.precision_recall_curve import BinaryPrecisionRecallCurve, MulticlassPrecisionRecallCurve, MultilabelPrecisionRecallCurve, PrecisionRecallCurve
  from torchmetrics.functional.classification.precision_recall_curve import binary_precision_recall_curve, multiclass_precision_recall_curve, multilabel_precision_recall_curve
  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_precision_recall_curve_binary(preds, target, ignore_index) and returns: sk_precision_recall_curve