The auc function in the hana_ml.algorithms.pal.metrics module computes the area under the curve (AUC) to evaluate the performance of binary-class classification algorithms, taking in a DataFrame of data, an optional positive label, and an optional output threshold, and returns a float representing the AUC and a DataFrame of the false positive rate and true positive rate.
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Here is a Python code template based on the provided help documentation:

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

# Define your DataFrame
# This is just a placeholder. Replace it with your actual DataFrame
df = ...

# Define positive_label and output_threshold if needed
positive_label = ...
output_threshold = ...

# Compute Area Under Curve
auc_value, roc = auc(data=df, positive_label=positive_label, output_threshold=output_threshold)

# Print the AUC value
print(auc_value)

# Print the ROC DataFrame
print(roc.collect())
```

Please replace the `df`, `positive_label`, and `output_threshold` with your actual data and parameters.