xgboost for categorical data gives an error when evaluating - xgboost

Question

I'm trying to run xgboost classifier on a categorical dataset.
The only way i succeeded to create a model is with this code:
import xgboost as xgb
df = pd.DataFrame(data={'1':['MM', 'GC', 'AU', 'GU', 'BB'],
'2': ['MM', 'GC', 'BB', 'GU', 'AU'], '3': ['MM', 'GC', 'AU', 'GC', 'GC'],
'4': ['MM', 'MM', 'AU', 'GC', 'AU'],'5': ['MM', 'GC', 'BB', 'GC', 'MM'],
'class': [0,0,1,1,0]})
X2, y2 = df[['1','2','3','4','5']],df['class']
X2 = X2.astype('category')
Xy = xgb.DMatrix(X2, y2, enable_categorical=True)
booster = xgb.train({"tree_method": "hist", "max_cat_to_onehot": 5}, Xy)
booster.save_model("categorical-model.json")
SHAP = booster.predict(Xy, pred_interactions=True)
scores2 = cross_val_score(booster, X2, y2, scoring='roc_auc', cv=cv, n_jobs=-1)
print('Mean ROC AUC: %.5f' % mean(scores2))
The problem that it gives me this error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-200-76bcf397e943> in <module>
----> 1 scores2 = cross_val_score(booster, X2, y2, scoring='roc_auc', cv=cv, n_jobs=-1)
2 # summarize performance
3 print('Mean ROC AUC: %.5f' % mean(scores2))
~\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)
506 """
507 # To ensure multimetric format is not supported
--> 508 scorer = check_scoring(estimator, scoring=scoring)
509
510 cv_results = cross_validate(
~\anaconda3\lib\site-packages\sklearn\metrics\_scorer.py in check_scoring(estimator, scoring, allow_none)
446 """
447 if not hasattr(estimator, "fit"):
--> 448 raise TypeError(
449 "estimator should be an estimator implementing 'fit' method, %r was passed"
450 % estimator
TypeError: estimator should be an estimator implementing 'fit' method, <xgboost.core.Booster object at 0x0000022E071161C0> was passed
And I don't understand why.
Could you also help me with the code to calculate the accuracy of the model?
Thank you!