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def objective(trial):
     eta = trial.suggest_float("eta",0,0.5)
     max_depth = trial.suggest_int("max_depth",2,32)
     min_child_weight = trial.suggest_float("min_child_weight",0,3)
     reg_alpha = trial.suggest_float("reg_alpha",0,3)
     colsample_bytree = trial.suggest_float("colsample_bytree",0.5,1)
     n_estimators = trial.suggest_int("n_estimators",10, 6000)

     model = xgb.XGBClassifier(eta=eta,n_estimators =n_estimators, max_depth = max_depth,
                               reg_alpha = reg_alpha,min_child_weight=min_child_weight,
                               colsample_bytree=colsample_bytree, use_label_encoder=False,
                               val_metric='mlogloss',seed = 0)
     model.fit(X_train,y_train)
     predictions = model.predict(X_val)
     return accuracy_score(y_true=y_val, y_pred=predictions)
    
study = optuna.create_study(direction = "maximize", pruner=optuna.pruners.HyperbandPruner())
study.optimize(objective, n_trials=100)