The interval_quality function in the hana_ml.algorithms.pal.stats module evaluates the quality of interval forecasts by comparing the quality of prediction intervals of the same testing dataset using different approaches, under the same significance level.
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Here is the executable code template for the `interval_quality` function:

```python
from hana_ml.algorithms.pal.stats import interval_quality
from hana_ml import DataFrame

# Assuming that a HANA dataframe is already created
# data = DataFrame(...)

# Define parameters
significance_level = 0.1
score_type = 'classical'
percent = False
check_consistency = True

# Call the function
res, stat = interval_quality(data=data,
                             significance_level=significance_level,
                             score_type=score_type,
                             percent=percent,
                             check_consistency=check_consistency)

# Print the results
print(res.collect())
print(stat.collect())
```

Please replace the `data = DataFrame(...)` with the actual dataframe creation code. The dataframe should have the structure as mentioned in the help doc.