The function distribution_fit in the hana_ml.algorithms.pal.stats module fits a probability distribution to a variable based on a series of measurements, with options to specify the type of distribution, the estimation method, and whether the data is censored or not.
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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 distribution_fit

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

# Specify the type of distribution to fit
distr_type = 'weibull'

# Specify the estimation method
optimal_method = 'maximum_likelihood'

# Specify if data is censored or not
censored = False

# Perform the function
res, stats = distribution_fit(data=df, distr_type=distr_type, optimal_method=optimal_method, censored=censored)

# Print the fitting results
print(res.collect())

# Print the fitting statistics
print(stats.collect())
```

Please replace the `DataFrame(...)` with your actual dataframe. Also, you can change the `distr_type`, `optimal_method`, and `censored` variables as per your requirements.