The get_suggested_term function in the hana_ml.text.tm module returns the top-ranked terms that match an initial substring or multiple substrings based on Term Frequency - Inverse Document Frequency (TF-IDF) result or reference data, with various parameters to customize the output such as the number of results, score threshold, language type, and thread ratio.
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Here is the executable code template based on the provided help doc:

```python
# Import required module
from hana_ml.text import tm

# Define your dataframes
# pred_data and ref_data should be defined according to the structure mentioned in the help doc

# Single-row mode
pred_data_single = # DataFrame with 1 column, Document content

# Multiple-row mode
pred_data_multi = # DataFrame with 1st column as ID and 2nd column as Document content

ref_data = # DataFrame or a tuple of DataFrame as per the structure mentioned in the help doc

# Call the function
# For single-row mode
result_single = tm.get_suggested_term(pred_data=pred_data_single, ref_data=ref_data)

# For multiple-row mode
result_multi = tm.get_suggested_term(pred_data=pred_data_multi, ref_data=ref_data, key='ID')

# Collect the results
print(result_single.collect())
print(result_multi.collect())
```

Please replace the `# DataFrame with ...` comments with actual DataFrame definitions. The structure of these DataFrames should be as per the help doc.