The Hierarchical_Forecast class in the hana_ml.algorithms.pal.tsa.hierarchical_forecast module is a forecasting algorithm that ensures forecasts sum appropriately across different levels of a hierarchy, with methods for reconciling forecasts across the hierarchy and weights for the 'optimal_combination' method.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.tsa.hierarchical_forecast import Hierarchical_Forecast

# Create a Hierarchical_Forecast instance
hr = Hierarchical_Forecast(method='optimal_combination', weights='minimum_trace')

# Assuming orig_df, pred_df, and stru_df are your input DataFrames
# Perform fit_predict() on the given DataFrames
stats_tbl, result_tbl = hr.fit_predict(orig_data=orig_df, pred_data=pred_df, stru_data=stru_df)

# Print the result
print(result_tbl.collect().head(5))
```

Please replace `orig_df`, `pred_df`, and `stru_df` with your actual DataFrames.