The function plot_seasonal_decompose in the hana_ml.visualizers.eda module is used to plot the seasonal decomposition of a time series data column from a HANA DataFrame, with various optional parameters to control the decomposition type, autocorrelation coefficient criterion, thread usage, endpoint extrapolation, moving average width, and plotting library.
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Here is a Python code template based on the provided documentation:

```python
from hana_ml.visualizers.eda import plot_seasonal_decompose

# Assuming you have a DataFrame 'df' and a time series column 'ts'
# Optional parameters are set to their default values

# Using matplotlib
axes = plot_seasonal_decompose(data=df, 
                               col='ts', 
                               key=None, 
                               alpha=0.2, 
                               thread_ratio=-1, 
                               decompose_type='auto', 
                               extrapolation=False, 
                               smooth_width=0, 
                               axes=None, 
                               enable_plotly=False, 
                               fig=None)

# Using plotly
fig = plot_seasonal_decompose(data=df, 
                              col='ts', 
                              key=None, 
                              alpha=0.2, 
                              thread_ratio=-1, 
                              decompose_type='auto', 
                              extrapolation=False, 
                              smooth_width=0, 
                              axes=None, 
                              enable_plotly=True, 
                              fig=None)
```

Please replace `'ts'` with the actual name of your time series data column. If you have an ID column, replace `None` in `key=None` with the name of your ID column. Adjust the optional parameters as needed.