The SOM class in the hana_ml.algorithms.pal.som module is a Self-Organizing Map (SOM) implementation for cluster analysis, with various parameters for customization such as convergence criterion, normalization type, random seed, map dimensions, kernel function, learning rate, grid shape, scan radius, batch SOM, and maximum iterations.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.som import SOM

# Create a SOM instance
som = SOM(convergence_criterion=1.0e-6, 
          normalization='no',
          random_seed=1, 
          height_of_map=4, 
          width_of_map=4,
          kernel_function='gaussian', 
          alpha=None,
          learning_rate='exponential', 
          shape_of_grid='hexagon',
          radius=None, 
          batch_som='classical', 
          max_iter=4000)

# Assume df is your input DataFrame
# Perform fit
som.fit(data=df, key='TRANS_ID')

# Print the map after training
print(som.map_.collect().head(3))

# Print the label of input data
print(som.labels_.collect().head(3))

# Print the model
print(som.model_.collect())

# Assume df2 is your input DataFrame for prediction
# Perform predict on the given data
label = som.predict(data=df2, key='TRANS_ID')

# Print the prediction result
print(label.collect())
```

Please replace `df` and `df2` with your actual DataFrames.