The QuantileTransform class in the hana_ml.algorithms.pal.preprocessing module is a Python wrapper for the PAL Quantile Transformer, which applies a quantile transformation to numerical features in a dataset, with options to specify the number of quantiles to be computed and the marginal distribution of the transformed data.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.preprocessing import QuantileTransform
from hana_ml import DataFrame

# Assuming that a connection context to HANA has already been established
# and 'data' is a DataFrame object containing the input data

# Create a quantile transformer
qt = QuantileTransform(num_quantiles=200, output_distribution='uniform')

# Fit the training data
qt.fit(data=data, key='ID', features=['X2', 'X6'], categorical_variable='X5')

# See the quantile-transformed training data w.r.t selected features
result = qt.result_.collect()
print(result)

# Transform the test data using a fitted QuantileTransformer
# Assuming 'test_data' is a DataFrame object containing the test data
transformed_data = qt.transform(data=test_data, key='ID')

# Print the transformed data
print(transformed_data.collect())
```

Please replace `'ID'`, `'X2'`, `'X6'`, and `'X5'` with your actual column names. Also, replace `200` and `'uniform'` with your desired number of quantiles and output distribution. The `data` and `test_data` should be DataFrame objects containing your training and test data respectively.