code: def _sklearn_recall_at_fixed_precision_multilabel(
      preds, target, min_precision, ignore_index=None
  ):
      recall, thresholds = [], []
      for i in range(NUM_CLASSES):
          res = _sklearn_recall_at_fixed_precision_binary(
              preds[:, i], target[:, i], min_precision, ignore_index
          )
          recall.append(res[0])
          thresholds.append(res[1])
      return 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.recall_fixed_precision import BinaryRecallAtFixedPrecision, MulticlassRecallAtFixedPrecision, MultilabelRecallAtFixedPrecision, RecallAtFixedPrecision
  from torchmetrics.functional.classification.recall_fixed_precision import binary_recall_at_fixed_precision, multiclass_recall_at_fixed_precision, multilabel_recall_at_fixed_precision
  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 4 for calling a global function with example usage: _sklearn_recall_at_fixed_precision_multilabel(preds, target, min_precision, ignore_index) and returns: recall,  thresholds