code: def run_omp(output_path: str):
      X, y = make_classification(
          n_samples=200, n_features=32, n_classes=3, n_informative=8
      )
      Xy = xgb.DMatrix(X, y, nthread=16)
      booster = xgb.train(
          {"num_class": 3, "objective": "multi:softprob", "n_jobs": 16},
          Xy,
          num_boost_round=8,
      )
      score = booster.predict(Xy)
      auc = roc_auc_score(
          y, score, average="weighted", multi_class="ovr"
      )
      with open(output_path, "w") as fd:
          fd.write(str(auc))
  
imports: import sys
  from sklearn.datasets import make_classification
  from sklearn.metrics import roc_auc_score
  import xgboost
label_desc: Use image node_id 1 for calling a global function with example usage: run_omp(output_path) without return types