The GeometryDBSCAN class in the hana_ml.algorithms.pal.clustering module is a geometry version of DBSCAN that works with geospatial data, accepting only 2D points, and allows for customization of parameters such as minimum points to form a cluster, scanning radius, thread ratio, metric, Minkowski power, algorithm, and whether to save the model.
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Here is a Python code template for the GeometryDBSCAN class:

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

# Create a GeometryDBSCAN instance
geo_dbscan = GeometryDBSCAN(minpts=5, eps=0.5, thread_ratio=0.2, metric='manhattan', minkowski_power=3, algorithm='kd-tree', save_model=True)

# Assume that df is your input DataFrame
# Fit the model
geo_dbscan.fit(data=df, key='ID')

# Print the labels
print(geo_dbscan.labels_.collect())

# Print the model
print(geo_dbscan.model_.collect())

# Perform fit_predict
result = geo_dbscan.fit_predict(data=df, key='ID')

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

Please replace `df` with your actual DataFrame. The `key` parameter in the `fit` and `fit_predict` methods should be the name of the ID column in your DataFrame.