The ARIMAExplainer class in the hana_ml.visualizers.time_series_report module is a subclass of TimeSeriesExplainer that provides methods for initializing the explainer, adding lines to comparison items, getting comparison items, decomposition items, force plot items, summary plot items from forecasted results, and setting fitted results, forecasted data, forecasted results, forecasted result explainer, and training data.
------
Here is a Python code template for the ARIMAExplainer class:

```python
from hana_ml.visualizers.time_series_report import ARIMAExplainer

# Initialize ARIMAExplainer
explainer = ARIMAExplainer(key, endog, exog)

# Set training data
explainer.set_training_data(training_data)

# Set fitted result
explainer.set_fitted_result(fitted_result)

# Set forecasted data
explainer.set_forecasted_data(forecasted_data)

# Set forecasted result
explainer.set_forecasted_result(forecasted_result)

# Set forecasted result explainer
explainer.set_forecasted_result_explainer(forecasted_result_explainer, reason_code_name='EXOGENOUS')

# Add line to comparison item
explainer.add_line_to_comparison_item(title, data, x_name, y_name=None, confidence_interval_names=None, color=None)

# Get comparison item
explainer.get_comparison_item(title='Comparison')

# Get decomposition items from forecasted result
explainer.get_decomposition_items_from_forecasted_result()

# Get force plot item from forecasted result
explainer.get_force_plot_item_from_forecasted_result()

# Get summary plot items from forecasted result
explainer.get_summary_plot_items_from_forecasted_result()

# Get items from best pipeline
ARIMAExplainer.get_items_from_best_pipeline(best_pipeline, highlighted_metric_name)
```

Please replace the placeholders with your actual data.