The get_relevant_doc function in the hana_ml.text.tm module returns the top-ranked documents relevant to a term or multiple terms based on Term Frequency - Inverse Document Frequency (TF-IDF) result or reference data, with various parameters to customize the input data, reference data, language, index name, thread ratio, and created index.
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Here is a Python code template based on the provided help documentation:

```python
# Import required libraries
from hana_ml import dataframe
from hana_ml.text import tm

# Create a connection to HANA
connection_context = dataframe.ConnectionContext(address='<address>',
                                                 port='<port>',
                                                 user='<user>',
                                                 password='<password>')

# Create DataFrame for pred_data
pred_data = connection_context.table('<table_name>')

# Create DataFrame for ref_data
ref_data = connection_context.table('<table_name>')

# Call the function
result = tm.get_relevant_doc(pred_data=pred_data, 
                             ref_data=ref_data, 
                             top=5, 
                             threshold=0.5, 
                             lang='EN', 
                             index_name=None, 
                             thread_ratio=0.5, 
                             created_index=None, 
                             key=None)

# Collect the result
result_df = result.collect()

# Print the result
print(result_df)
```

Please replace `<address>`, `<port>`, `<user>`, `<password>`, and `<table_name>` with your actual values.