The BiVariateGeometricRegression class in the hana_ml.algorithms.pal.regression module is used to model the relationship between a scalar variable y and a variable X using geometric functions, with parameters for matrix factorization type, adjusted R2 value inclusion, PMML model output control, and thread ratio control.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.regression import BiVariateGeometricRegression

# Create a BiVariateGeometricRegression instance
gr = BiVariateGeometricRegression(decomposition='QR', adjusted_r2=False, pmml_export='multi-row', thread_ratio=0.0)

# Assume that df is the training data DataFrame
# Fit the model
gr.fit(data=df, key='ID')

# Assume that df2 is the DataFrame for prediction
# Perform prediction
predicted = gr.predict(data=df2, key='ID')

# Print the predicted values
print(predicted.collect())

# Calculate the coefficient of determination R2 of the prediction
r2 = gr.score(data=df2, key='ID')
print(r2)
```

Please replace `df` and `df2` with your actual DataFrames.