The TreeModelDebriefing class in the hana_ml.visualizers.model_debriefing module is used to visualize and parse tree models built with the PAL algorithm, supporting classes in the `hana_ml.algorithms.pal.trees` module and the UnifiedClassification class, but it does not support tree models built with the APL algorithm.
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Here is a Python code template based on the provided help documentation:

```python
from hana_ml.algorithms.pal.trees import RDTClassifier
from hana_ml.visualizers.model_debriefing import TreeModelDebriefing

# Create a dataframe for training
df1 = # Your dataframe here

# Create an instance of RDTClassifier
rdtc = RDTClassifier(n_estimators=3,
                     max_features=3,
                     random_state=2,
                     split_threshold=0.00001,
                     calculate_oob=True,
                     min_samples_leaf=1,
                     thread_ratio=1.0)

# Fit the model on the dataframe
rdtc.fit(data=df1, features=['OUTLOOK', 'TEMP', 'HUMIDITY', 'WINDY'], label='CLASS')

# Visualize the tree model in JSON format
TreeModelDebriefing.tree_debrief(rdtc.model_)

# Visualize the tree model in DOT format
TreeModelDebriefing.tree_debrief_with_dot(rdtc.model_, iframe_height=500)

# Visualize the tree model in XML format
rdtc = RDTClassifier(n_estimators=3,
                     max_features=3,
                     random_state=2,
                     split_threshold=0.00001,
                     calculate_oob=True,
                     min_samples_leaf=1,
                     thread_ratio=1.0,
                     model_format='pmml')

rdtc.fit(data=df1, features=['OUTLOOK', 'TEMP', 'HUMIDITY', 'WINDY'], label='CLASS')

TreeModelDebriefing.tree_debrief(rdtc.model_)
```

Please replace `# Your dataframe here` with your actual dataframe.