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How does the feature contribution being calculated in the Explainer Dashboard in Python?

I built a soft voting model and wanted to calculate the feature contribution for an individual prediction. However, I found discrepancies in the result between the feature contributions calculated by myself and the table in the Explainer Dashboard. I fit the same model and processed data but still the result is different. Is there anyway to solve it ?