The K-Optimal Rule Discovery (KORD) class in the hana_ml.algorithms.pal.association module is a machine learning tool that generates top-K association rules based on a user-defined measure, useful for tasks like market basket analysis and recommendation systems.
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Here is a Python code template based on the provided documentation:

```python
from hana_ml.algorithms.pal.association import KORD

# Initialize a KORD object and set its parameters
krd = KORD(k=5,
           measure='lift',
           min_support=0.1,
           min_confidence=0.2,
           epsilon=0.1,
           use_epsilon=False)

# Assuming that `df` is your DataFrame
# Perform the fit() and obtain the result
krd.fit(data=df, transaction='CUSTOMER', item='ITEM')

# Print the antecedent items for the mined association rules
print(krd.antec_.collect())

# Print the consequent items for the mined association rules
print(krd.conseq_.collect())

# Print some basic statistics for the mined association rules
print(krd.stats_.collect())
```

Please replace `'CUSTOMER'` and `'ITEM'` with your actual column names for transaction ID and item ID respectively. Also, replace `df` with your actual DataFrame.