The OnlineBCPD class in the hana_ml.algorithms.pal.tsa.changepoint module is used for online Bayesian Change-point detection, with various parameters for the t-distribution, constant hazard function, threshold for determining a change point, delay, pruning, massive mode, and group parameters.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.tsa.changepoint import OnlineBCPD

# Create an OnlineBCPD instance
obcpd = OnlineBCPD(alpha=0.1,
                   beta=0.01,
                   kappa=1.0,
                   mu=0.0,
                   delay=5,
                   threshold=0.5,
                   prune=True)

# Assuming that `df` is your DataFrame
# Invoke fit_predict()
model, cp = obcpd.fit_predict(data=df, model=None)

# Print the model and change points
print(model.head(5).collect())
print(cp.collect())
```

Please replace `df` with your actual DataFrame. The `fit_predict` method detects change-points of the input data. The `model` DataFrame contains the model for change point detection and `cp` DataFrame contains the detected change points.