The TripleExponentialSmoothing class in the hana_ml.algorithms.pal.tsa.exponential_smoothing module is used to handle time series data containing a seasonal component, with various parameters to adjust the smoothing, trend, and seasonal components, as well as the forecast number, initial method, and accuracy measure.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.tsa.exponential_smoothing import TripleExponentialSmoothing
import hana_ml.dataframe as dataframe

# Assuming that a HANA dataframe df is already created and it has data
# df = dataframe.DataFrame(...)

# Create a TripleExponentialSmoothing instance
tesm = TripleExponentialSmoothing(alpha=0.822,
                                  beta=0.055,
                                  gamma=0.055,
                                  seasonal_period=4,
                                  forecast_num=6,
                                  seasonal=0,
                                  initial_method=0,
                                  phi=None,
                                  damped=None,
                                  accuracy_measure='mse',
                                  ignore_zero=None,
                                  expost_flag=True,
                                  level_start=None,
                                  trend_start=None,
                                  season_start=None,
                                  prediction_confidence_1=0.8,
                                  prediction_confidence_2=0.95)

# Perform fit_predict() on the given data
tesm.fit_predict(data=df)

# Print forecast values
print(tesm.forecast_.collect())

# Print statistics
print(tesm.stats_.collect())
```

Please replace the `df = dataframe.DataFrame(...)` with the actual dataframe creation code. The dataframe should have at least two columns, one is ID column, the other is raw data.