code: def run_pr_auc_ltr(self, tree_method):
      from sklearn.datasets import make_classification
  
      X, y = make_classification(128, 4, n_classes=2, random_state=1994)
      ltr = xgb.XGBRanker(
          tree_method=tree_method,
          n_estimators=16,
          objective="rank:pairwise",
          eval_metric="aucpr",
      )
      groups = np.array([32, 32, 64])
      ltr.fit(
          X,
          y,
          group=groups,
          eval_set=[(X, y)],
          eval_group=[groups],
      )
      results = ltr.evals_result()["validation_0"]["aucpr"]
      assert results[-1] >= 0.99
  
imports: import numpy
  import pytest
  import xgboost
  from xgboost import testing
  from xgboost.testing.metrics import check_precision_score, check_quantile_error
label_desc: Use image node_id 16 for calling the TestEvalMetrics obj's underlying member method code with example usage: obj.run_pr_auc_ltr(tree_method) without return types