code: def _recall_at_precision_x_multilabel(
      predictions, targets, min_precision
  ):
      precision, recall, thresholds = sk_precision_recall_curve(
          targets, predictions
      )
  
      try:
          tuple_all = [
              (r, p, t)
              for p, r, t in zip(precision, recall, thresholds)
              if p >= min_precision
          ]
          max_recall, _, best_threshold = max(tuple_all)
      except ValueError:
          max_recall, best_threshold = 0, 1e6
  
      return float(max_recall), float(best_threshold)
  
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 1 for calling a global function with example usage: _recall_at_precision_x_multilabel(predictions, targets, min_precision) and returns: float,  float