The SMOTETomek class in the hana_ml.algorithms.pal.preprocessing module is used for combining over-sampling using SMOTE and cleaning (under-sampling) using Tomek links, with various parameters to customize the process.
------
Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.preprocessing import SMOTETomek

# Initialize the SMOTETomek class
smotetomek = SMOTETomek(smote_amount=200,
                        k_nearest_neighbours=2,
                        random_seed=2,
                        search_method='kd-tree',
                        sampling_strategy='all')

# Assume df is your input DataFrame
# Perform both over-sampling using SMOTE and under-sampling by removing Tomek's links on given datasets
res = smotetomek.fit_transform(data=df,
                               label='TYPE',
                               minority_class=2)
```

Please replace `df` with your actual DataFrame. The `label` parameter should be the name of the dependent variable in your DataFrame, and `minority_class` should be the minority class value in the dependent variable column.