The function condition_index in the hana_ml.algorithms.pal.stats module is used to detect collinearity problems between independent variables that are later used as predictors in a multiple linear regression model, with various parameters to customize the process such as data scaling, inclusion of intercept, and thread ratio.
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Here is the executable code template for the `condition_index` function:

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

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

# Define parameters
key = 'ID'
scaling = True
include_intercept = True
thread_ratio = 0.1

# Apply the condition index function
res, stats = condition_index(data=df, key=key, scaling=scaling, include_intercept=include_intercept, thread_ratio=thread_ratio)

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

Please replace the `DataFrame(...)` with the actual connection details to your HANA instance.