The ShapleyExplainer class in the hana_ml.visualizers.shap module uses the SHAP (SHapley Additive exPlanations) game theoretic approach to explain the output of a machine learning model, allowing users to plot the Shapley values of every feature for every sample to understand which features are most important for a model.
------
Here is a Python code template based on the provided help doc:

```python
from hana_ml.visualizers.shap import ShapleyExplainer
from hana_ml.algorithms.pal.unified_classification import UnifiedClassification
from hana_ml.model_selection import GridSearchCV

# Create an UnifiedClassification instance
uc_hgbdt = UnifiedClassification('HybridGradientBoostingTree')

# Create a GridSearchCV instance
gscv = GridSearchCV(estimator=uc_hgbdt,
                    param_grid={'learning_rate': [0.1, 0.4, 0.7, 1],
                                'n_estimators': [4, 6, 8, 10],
                                'split_threshold': [0.1, 0.4, 0.7, 1]},
                    train_control=dict(fold_num=5,
                                       resampling_method='cv',
                                       random_state=1,
                                       ref_metric=['auc']),
                    scoring='error_rate')

# Train the model
gscv.fit(data=diabetes_train, key= 'ID',
         label='CLASS',
         partition_method='stratified',
         partition_random_state=1,
         stratified_column='CLASS',
         build_report=True)

# Remove 'CLASS' and 'ID' from features
features = diabetes_train.columns
features.remove('CLASS')
features.remove('ID')

# Use test data for prediction
pred_res = gscv.predict(diabetes_test, key='ID', features=features)

# Create a ShapleyExplainer class and invoke summary_plot()
shapley_explainer = ShapleyExplainer(reason_code_data=pred_res.select('REASON_CODE'), feature_data=diabetes_test.select(features))
shapley_explainer.summary_plot()
```

Please replace `diabetes_train` and `diabetes_test` with your actual DataFrame variables.