code: def get_min_max_featurs(self, df_describe, feature_name):
      x_des_var_list = list(df_describe[feature_name])
      q00 = x_des_var_list[3] - 25 / 100 * x_des_var_list[3]
      q0 = x_des_var_list[3]
      q1 = x_des_var_list[4]
      q2 = x_des_var_list[5]
      q3 = x_des_var_list[6]
      q4 = x_des_var_list[7]
      q5 = x_des_var_list[7] + 25 / 100 * x_des_var_list[7]
      ans = [q00, q0, q1, q2, q3, q4, q5]
  
      # round off to 2 dp
      for i in range(len(ans)):
          ans[i] = round(ans[i], 2)
  
      return ans
  
file_path: explainx/lib/modules/local_explanation/apps/what_if.py
imports: from explainx.lib.imports import *
  from explainx.lib.feature_impact import *
  from explainx.lib.feature_importance import *
  import dash_daq
label_desc: Use image node_id 4 for calling the what_if obj's underlying member method code with example usage: obj.get_min_max_featurs(df_describe, feature_name) and returns: ans