The multiclass_auc function in the hana_ml.algorithms.pal.metrics module computes the area under curve (AUC) to evaluate the performance of multi-class classification algorithms, taking in two dataframes representing true class data and predicted class data, and returning a float representing the AUC and a dataframe representing the false positive rate and true positive rate.
------
Here is the executable code template based on the help doc:

```python
from hana_ml.algorithms.pal.metrics import multiclass_auc
from hana_ml import DataFrame

# Assuming that connection_context is the connection to the HANA database

# Create the data_original DataFrame
data_original = [('1', 1), ('2', 1), ('3', 1), ('4', 2), ('5', 2), ('6', 2), ('7', 3), ('8', 3), ('9', 3), ('10', 3)]
df_original = DataFrame(connection_context, data_original, ('ID', 'ORIGINAL'))

# Create the data_predict DataFrame
data_predict = [('1', 1, 0.90), ('1', 2, 0.05), ('1', 3, 0.05), ('2', 1, 0.80), ('2', 2, 0.05), ('2', 3, 0.15), 
                ('3', 1, 0.80), ('3', 2, 0.10), ('3', 3, 0.10), ('4', 1, 0.10), ('4', 2, 0.80), ('4', 3, 0.10), 
                ('5', 1, 0.20), ('5', 2, 0.70), ('5', 3, 0.10), ('6', 1, 0.05), ('6', 2, 0.90), ('6', 3, 0.05), 
                ('7', 1, 0.10), ('7', 2, 0.10), ('7', 3, 0.80), ('8', 1, 0.00), ('8', 2, 0.00), ('8', 3, 1.00), 
                ('9', 1, 0.20), ('9', 2, 0.10), ('9', 3, 0.70), ('10', 1, 0.20), ('10', 2, 0.20), ('10', 3, 0.60)]
df_predict = DataFrame(connection_context, data_predict, ('ID', 'PREDICT', 'PROB'))

# Compute Area Under Curve
auc, roc = multiclass_auc(data_original=df_original, data_predict=df_predict)

# Print the results
print(auc)
print(roc.collect())
```

Please replace `'1'`, `'2'`, etc. with actual IDs if they are not strings. Also, replace `connection_context` with the actual connection to your HANA database.