The cdf function in the hana_ml.algorithms.pal.stats module evaluates the probability of a variable x from the cumulative distribution function (CDF) or complementary cumulative distribution function (CCDF) for a given probability distribution, using data from a DataFrame and distribution information provided in a dictionary.
------
Here is a Python code template based on the provided documentation:

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

# Assuming that connection_context is already defined
# Create DataFrame from existing HANA table
data = DataFrame(connection_context, 'TABLE_NAME') # replace 'TABLE_NAME' with your table name

# Define distribution information
distr_info = {'name':'normal', 'mean':0, 'variance':1.0} # replace with your distribution info

# Define column name if necessary
col = 'COLUMN_NAME' # replace 'COLUMN_NAME' with your column name

# Define whether to use complementary cumulative distribution function
complementary = False

# Apply the cdf function
res = cdf(data=data, distr_info=distr_info, col=col, complementary=complementary)

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

Please replace `'TABLE_NAME'` and `'COLUMN_NAME'` with your actual table and column names. Also, adjust the `distr_info` dictionary according to your distribution information.