The LR_seasonal_adjust class in the hana_ml.algorithms.pal.tsa.lr_seasonal_adjust module is a linear regression approach for forecasting time series data with a trend, providing a damped smoothing parameter to avoid over-casting due to indefinitely increasing or decreasing trends, and allowing for adjustment of forecast results based on seasonality.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.tsa.lr_seasonal_adjust import LR_seasonal_adjust
from hana_ml import DataFrame

# Assuming that connection_context is the connection to the HANA system
# df is the DataFrame for the input data

# Create a LR_seasonal_adjust instance
lr = LR_seasonal_adjust(forecast_length=10,
                        trend=0.9, 
                        affect_future_only=True,
                        seasonality=1, 
                        seasonal_period=4,
                        accuracy_measure='mse')

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

# Output
print(lr.forecast_.collect().set_index('TIMESTAMP').head(3))
print(lr.stats_.collect())
```

Please replace the placeholders with your actual variables and data.