The ModelStorage class in the hana_ml module allows users to save, list, load, and delete models in SAP HANA tables, with models identified by a name and version, and can be saved in three ways: for the first time, as a replacement, or with a higher version.
------
Here is a Python code template based on the provided help doc:

```python
from hana_ml.model_storage import ModelStorage
from hana_ml.dataframe import ConnectionContext
from hana_ml.algorithms.pal.neural_network import MLPClassifier
from hana_ml.algorithms.apl.classification import AutoClassifier

# Create a connection to SAP HANA
conn = ConnectionContext(address='<address>', port='<port>', user='<user>', password='<password>')

# Create and train a model with functions MLPClassifier and AutoClassifier
model_pal_name = 'MLPClassifier 1'
model_pal = MLPClassifier(conn, hidden_layer_size=[10, ], activation='TANH', output_activation='TANH', learning_rate=0.01, momentum=0.001)
model_pal.fit(data, label='IS_SETOSA', key='ID')

model_apl_name = 'AutoClassifier 1'
model_apl = AutoClassifier(conn_context=conn)
model_apl.fit(data, label='IS_SETOSA', key='ID')

# Create an instance of ModelStorage
MODEL_SCHEMA = 'MODEL_STORAGE' # HANA schema in which models are to be saved
model_storage = ModelStorage(connection_context=conn, schema=MODEL_SCHEMA)

# Save these two trained models for the first time
model_pal.name = model_pal_name
model_storage.save_model(model=model_pal)
model_apl.name = model_apl_name
model_storage.save_model(model=model_apl)

# List saved models
print(model_storage.list_models())

# Reload saved models
model1 = model_storage.load_model(name=model_pal_name, version=1)
model2 = model_storage.load_model(name=model_apl_name, version=1)

# Use loaded model model2 for new prediction
out = model2.predict(data=data_test)
print(out.head(3).collect())

# Save a model by overwriting the original model
model_storage.save_model(model=model_apl, if_exists='replace')
print(list_models = model_storage.list_models(name=model.name))

# Save a model by upgrading the version
model_storage.save_model(model=model_apl, if_exists='upgrade')
print(list_models = model_storage.list_models(name=model.name))

# Delete a model with specified version
model_storage.delete_model(name=model.name, version=model.version)

# Delete models with same model name and different versions
model_storage.delete_models(name=model.name)

# Clean up all models and meta data at once
model_storage.clean_up()
```

Please replace `<address>`, `<port>`, `<user>`, and `<password>` with your actual SAP HANA connection details. Also, replace `data` and `data_test` with your actual training and testing datasets.