text: from interpret_community import TabularExplainer
  from interpret_community.widget import ExplanationDashboard
  
  def Global_Model_Explanation(model,x_train,x_test,feature_names = None,classes = None, explantion_data = None):
      #Using SHAP TabularExplainer
      explainer = TabularExplainer(model, 
                                    x_train, 
                                    features=feature_names, 
                                    classes=classes)
      #Generate global explanations
      if explantion_data == 'Training':
          global_explanation = explainer.explain_global(x_train)
      else:
          global_explanation = explainer.explain_global(x_test)
      ##print the global importance rank data
      print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))
      #Return Generated Explanation dashboard
      return global_explanation
  
  def Explanation_Dashboard(global_explanation, model, x_train, x_test, explantion_data = None):
      if explantion_data == 'Training':
          ExplanationDashboard(global_explanation, model, datasetX=x_train)
      else:
          ExplanationDashboard(global_explanation, model, datasetX=x_test)
  
      
  