The ks_test function in the hana_ml.algorithms.pal.stats module performs a one-sample or two-sample Kolmogorov-Smirnov test for goodness of fit on a HANA DataFrame, with optional parameters for specifying the distribution name, distribution parameters, and test type.
------
Here is the executable code template based on the provided help doc:

```python
# Import required libraries
from hana_ml import DataFrame
from hana_ml.algorithms.pal.stats import ks_test

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

# Define distribution parameters
distribution_params = {'min':0, 'max':1}

# Perform Kolmogorov-Smirnov test
res = ks_test(data=df,
              distribution_name='uniform',
              distribution_parameter=distribution_params)

# Print the result
print(res.collect())
```

Please replace the `DataFrame(...)` with your actual HANA dataframe. This code performs a Kolmogorov-Smirnov test on the data in the dataframe `df` assuming a uniform distribution with parameters `min=0` and `max=1`. The result is then printed.