The KMeansOutlier class in the hana_ml.algorithms.pal.clustering module is used for outlier detection based on k-means clustering, identifying the farthest point from the centroid as an outlier, with various parameters to customize the clustering and outlier detection process.
------
Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.clustering import KMeansOutlier

# Initialize a KMeansOutlier instance
kmsodt = KMeansOutlier(n_clusters=None, 
                       distance_level='euclidean', 
                       contamination=0.15, 
                       sum_distance=True, 
                       init=None, 
                       max_iter=None, 
                       normalization=None, 
                       tol=None, 
                       distance_threshold=3)

# Assuming that `df` is your DataFrame
outliers, stats, centers = kmsodt.fit_predict(data=df, key='ID')

# Print the outliers
print(outliers.collect())

# Print the stats
print(stats.collect())
```

Please replace `df` with your actual DataFrame. If you want to use different parameters for the `KMeansOutlier` instance, you can replace the values in the `KMeansOutlier` initialization.