code: def get_uplift_best(cate, conditions):
      """
      Takes the CATE prediction from a learner, adds the control
      outcome array and finds the name of the argmax conditon.
  
      Params
      ------
      cate : array, shape = (num_samples, )
          The conditional average treatment effect prediction.
  
      conditions : list, len = len(set(treatment))
  
      Returns
      -------
      uplift_recomm_name : array, shape = (num_samples, )
          The experimental group recommended by the learner.
      """
      cate_with_control = np.c_[np.zeros(cate.shape[0]), cate]
      uplift_best_idx = np.argmax(cate_with_control, axis=1)
      uplift_best_name = [conditions[idx] for idx in uplift_best_idx]
  
      return uplift_best_name
  
label_desc: Use image node_id 3 for calling a global function with example usage: get_uplift_best(cate, conditions) and returns: uplift_best_name