The accuracy_score function in the hana_ml.algorithms.pal.metrics module computes the mean accuracy score for classification results, which is the proportion of correctly predicted results among the total number of cases examined, using a DataFrame of true and predicted labels.
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Here is a Python code template based on the provided help documentation:

```python
# Import necessary libraries
from hana_ml.algorithms.pal.metrics import accuracy_score
from hana_ml import DataFrame

# Assuming you have a HANA dataframe 'df' with columns 'ACTUAL' and 'PREDICTED'
# df = DataFrame(...)

# Compute accuracy score
accuracy = accuracy_score(data=df, label_true='ACTUAL', label_pred='PREDICTED')

# Print the accuracy score
print("Accuracy Score: ", accuracy)

```

Please replace the DataFrame(...) with your actual DataFrame. The DataFrame should be a HANA DataFrame with columns 'ACTUAL' and 'PREDICTED'.