The function regression_score in the hana_ml.algorithms.pal.metrics module computes regression scores with respect to different types of objective functions, using parameters such as data, true labels, predicted labels, score type, tweedie power, and quantile alpha.
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Here is a Python code template for the `regression_score` function from the `hana_ml.algorithms.pal.metrics` module:

```python
# Import required module
from hana_ml.algorithms.pal.metrics import regression_score

# Assume we have a DataFrame 'data' and true labels 'label_true' and predicted labels 'label_pred'
# data = ...
# label_true = ...
# label_pred = ...

# Define parameters
score_type = 'r2'
tweedie_power = 1.5
quantile_alpha = 0.5

# Compute regression scores
scores = regression_score(data, label_true, label_pred, score_type, tweedie_power, quantile_alpha)

# Print the scores
print(scores)
```

Please replace the `data`, `label_true`, and `label_pred` with your actual data. The `score_type` parameter can be set to different types of objective functions. The `tweedie_power` and `quantile_alpha` parameters are optional and have default values of 1.5 and 0.5 respectively.