code: def compute_ebm_global_importance(input_data, diff, model_task):
      """Compute global importance score for EBM between the features and error.
      :param input_data: The input data to compute the EBM global importance
          score on.
      :type input_data: numpy.ndarray
      :param diff: The difference between the label and prediction
          columns.
      :type diff: numpy.ndarray
      :param model_task: The model task.
      :type model_task: str
      :return: The computed EBM global importance score between the features and
          error.
      :rtype: list[float]
      """
      if not ebm_installed:
          raise ImportError(
              "EBM is not installed. Please install it by running "
              '"pip install interpret"'
          )
      if model_task == ModelTask.CLASSIFICATION:
          model = ExplainableBoostingClassifier(interactions=0)
      else:
          model = ExplainableBoostingRegressor(interactions=0)
      model.fit(input_data, diff)
      ebm_importances = model.term_importances()
      return ebm_importances.tolist()
  
label_desc: Use image node_id 1 for calling a global function with example usage: compute_ebm_global_importance(input_data, diff, model_task) and returns: ebm_importances