The CrostonTSB class in the hana_ml.algorithms.pal.tsa.exponential_smoothing module is a forecast strategy for products with intermittent demand, which modifies Croston's method by replacing the demand interval with a demand probability that is updated every period, providing a non-biased forecast and a probability forecast for estimating the risk of obsolescence.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.tsa.exponential_smoothing import CrostonTSB

# Create an instance of CrostonTSB
cr = CrostonTSB(alpha=0.3,
                beta=0.1,
                forecast_num=10,
                method='constant',
                accuracy_measure=['mape'],
                expost_flag=True,
                ignore_zero=False,
                remove_leading_zeros=False)

# Perform fit and predict
forecast = cr.fit_predict(data=data, key='ID', endog='Y')

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

# Print the stats
print(cr.stats_.collect())

# Print the metrics
print(cr.metrics_.collect())
```

Please replace the DataFrame creation part with your actual data. Also, you might need to adjust the parameters of the `CrostonTSB` instance and the `fit_predict` method according to your needs.