code: def test_wrap_xgboost_metric():
      eval_metric = wrap_xgboost_metric(roc_auc_score, "roc_auc")
      d_matrix = DMatrix(
          np.array([[7], [14], [21], [28]]), label=[1, 0, 1, 0]
      )
      assert eval_metric([0.9, 0.3, 0.7, 0.8], d_matrix) == (
          "roc_auc",
          0.75,
      )
  
imports: from hyperparameter_hunter.metrics import ScoringMixIn, Metric, format_metrics, wrap_xgboost_metric
  from hyperparameter_hunter.metrics import get_formatted_target_metric, get_clean_prediction
  from collections import OrderedDict
  import numpy
  import pandas
  import pytest
  import sys
  from sklearn.metrics import roc_auc_score, hamming_loss, r2_score, f1_score
label_desc: Use image node_id 27 for calling a global function with example usage: test_wrap_xgboost_metric() without return types