The UnifiedReport class in the hana_ml.visualizers.unified_report module is a report generator for PAL/APL models, currently supporting only UnifiedClassification and UnifiedRegression, and provides methods to build, display, and customize the report.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.model_selection import GridSearchCV
from hana_ml.algorithms.pal.unified_classification import UnifiedClassification
from hana_ml.visualizers.unified_report import UnifiedReport

# Initialize the UnifiedClassification model
hgc = UnifiedClassification('HybridGradientBoostingTree')

# Define the parameter grid for GridSearchCV
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]}

# Initialize GridSearchCV
gscv = GridSearchCV(estimator=hgc,
                    param_grid=param_grid,
                    train_control=dict(fold_num=5,
                                       resampling_method='cv',
                                       random_state=1,
                                       ref_metric=['auc']),
                    scoring='error_rate')

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

# Build and display the report
report = UnifiedReport(diabetes_train)
report.build()
report.display()

# Display the model report
model_report = UnifiedReport(gscv.estimator)
model_report.display()
```

Please replace `diabetes_train` with your actual DataFrame.