The AdditiveModelForecastExplainer class in the hana_ml.visualizers.time_series_report module is a tool for explaining the results of additive model forecasts, with methods for initializing the explainer, getting summary plot items from forecasted results, and setting seasonality mode.
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Here is a Python code template for the `AdditiveModelForecastExplainer` class:

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

# Initialize the AdditiveModelForecastExplainer
explainer = AdditiveModelForecastExplainer(key, endog, exog)

# Set seasonality mode
explainer.set_seasonality_mode(exogenous_names_with_additive_mode, exogenous_names_with_multiplicative_mode)

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

# 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')

# Set training data
explainer.set_training_data(training_data)

# 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
comparison_item = explainer.get_comparison_item(title='Comparison')

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

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

# Get items from best pipeline
items_from_best_pipeline = AdditiveModelForecastExplainer.get_items_from_best_pipeline(best_pipeline, highlighted_metric_name)
```

Please replace `key`, `endog`, `exog`, `exogenous_names_with_additive_mode`, `exogenous_names_with_multiplicative_mode`, `fitted_result`, `forecasted_data`, `forecasted_result`, `forecasted_result_explainer`, `training_data`, `title`, `data`, `x_name`, `y_name`, `confidence_interval_names`, `color`, `best_pipeline`, and `highlighted_metric_name` with your actual data.