The KNN class in the hana_ml.algorithms.pal.neighbors module is a K-Nearest Neighbor model for classification problems, with parameters for the number of nearest neighbors, thread ratio, voting type, statistic information, metric, Minkowski power, and algorithm, and methods for fitting the model, predicting class labels, and returning a scalar accuracy value.
------
Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.neighbors import KNN

# Define the parameters for the KNN model
n_neighbors = 3
voting_type = 'majority'
thread_ratio = 0.1
stat_info = False

# Create a KNN instance
knn = KNN(n_neighbors=n_neighbors, voting_type=voting_type,
          thread_ratio=thread_ratio, stat_info=stat_info)

# Define the training data and features
data = df
key = 'ID'
features = ['X1', 'X2']
label = 'TYPE'

# Fit the model
knn.fit(data=data, key=key, features=features, label=label)

# Define the prediction data
pred_data = pred_df
pred_key = "ID"

# Predict the class labels
res, stat = knn.predict(data=pred_data, key=pred_key)

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

# Score the model
accuracy = knn.score(data=data, key=key, features=features, label=label)

# Print the accuracy
print(accuracy)
```

Please replace `df` and `pred_df` with your actual DataFrame objects.