code: def _sklearn_precision_recall_curve_multilabel(
      preds, target, ignore_index=None
  ):
      precision, recall, thresholds = [], [], []
      for i in range(NUM_CLASSES):
          res = _sklearn_precision_recall_curve_binary(
              preds[:, i], target[:, i], ignore_index
          )
          precision.append(res[0])
          recall.append(res[1])
          thresholds.append(res[2])
      return precision, recall, 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 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 3 for calling a global function with example usage: _sklearn_precision_recall_curve_multilabel(preds, target, ignore_index) and returns: precision,  recall,  thresholds