code: def labels_and_scores() -> List[Tuple[np.array, np.array]]:
      label1 = np.array(
          [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0]
      )
      scores1 = np.array(
          [
              0.2,
              0.3,
              0.5,
              0.7,
              4,
              2.5,
              0.3,
              0.2,
              0.7,
              0.3,
              0.2,
              4,
              3,
              8,
              0.2,
              0.1,
          ]
      )
  
      label2 = np.array(
          [0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0]
      )
      scores2 = np.array(
          [
              0.2,
              0.3,
              0.5,
              7,
              4,
              2.5,
              0.3,
              0.2,
              0.7,
              0.3,
              0.2,
              4,
              3,
              8,
              0.2,
              0.1,
          ]
      )
  
      label3 = np.array(
          [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0]
      )
      scores3 = np.array(
          [
              0.2,
              0.3,
              5,
              7,
              4,
              2.5,
              0.3,
              0.2,
              0.7,
              0.3,
              0.2,
              4,
              3,
              8,
              0.2,
              0.1,
          ]
      )
      return [(label1, scores1), (label2, scores2), (label3, scores3)]
  
file_path: gluonts/test/nursery/anomaly_detection/supervised_metrics/test_precision_recall.py
imports: from typing import Dict, List, Tuple
  import numpy
  import pytest
  from sklearn.metrics import auc
  from gluonts.nursery.anomaly_detection.supervised_metrics import aggregate_precision_recall_curve, buffered_precision_recall, segment_precision_recall
  from gluonts.nursery.anomaly_detection.supervised_metrics._buffered_precision_recall import extend_ranges
  from gluonts.nursery.anomaly_detection.supervised_metrics.bounded_pr_auc import bounded_pr_auc
  from gluonts.nursery.anomaly_detection.supervised_metrics.utils import labels_to_ranges, range_overlap
label_desc: Use image node_id 6 for calling a global function with example usage: labels_and_scores() and returns: list