code: def h2o_shap_results(self, model, h2o_df, X_train, prediction_col):
      pd_shap = model.predict_contributions(h2o_df)
  
      pd_shap = pd_shap.as_data_frame()
      pd_shap.drop(["BiasTerm"], axis=1, inplace=True, errors="ignore")
      shap_columns = [i + "_impact" for i in pd_shap.columns]
      pd_shap.columns = shap_columns
      print(shap_columns)
      y = X_train.copy()
      for c in shap_columns:
          y[c] = list(pd_shap[c])
      return y, pd_shap
  
file_path: explainx/lib/calculate_shap.py
imports: from imports import *
  from rescale_numeric_feature import *
label_desc: Use image node_id 6 for calling the calculate_shap obj's underlying member method code with example usage: obj.h2o_shap_results(model, h2o_df, X_train, prediction_col) and returns: y,  pd_shap