code: def draw_anytime_result_one_graph(
      y_acc_list_arr,
      x_T_list,
      x_acc_train,
      y_acc_train_l,
      y_acc_train_m,
      y_acc_train_h,
      annotations,
      lv,
      name_img,
      dataset,
      x1_lim=[],
      x2_lim=[],
  ):
      # fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, dpi=100, gridspec_kw={'height_ratios': [5, 1]})
      exp = np.array(y_acc_list_arr) * 100
      sys_acc_h = np.quantile(exp, 0.75, axis=0)
      sys_acc_m = np.quantile(exp, 0.5, axis=0)
      sys_acc_l = np.quantile(exp, 0.25, axis=0)
  
      # exp_time = np.array(real_time_used_arr)
      # time_mean = np.quantile(exp_time, .5, axis=0)
      time_mean = x_T_list
  
      # plot simulate result of system
      plt.fill_between(time_mean, sys_acc_l, sys_acc_h, alpha=0.1)
      plt.plot(time_mean, sys_acc_m, "o-", label="TRAILS")
      # plt.plot(time_mean, sys_acc_m, label="TRAILS")
  
      # plot simulate result of train-based line
      plt.fill_between(
          x_acc_train, y_acc_train_l, y_acc_train_h, alpha=0.3
      )
      plt.plot(
          x_acc_train, y_acc_train_m, "o-", label="Training-based MS"
      )
      # plt.plot(x_acc_train, y_acc_train_m,  label="Training-based MS")
  
      if len(x1_lim) > 0:
          plt.ylim(x1_lim[0], x1_lim[1])  # 子图1设置y轴范围，只显示部分图
  
      d = 0.85  # 设置倾斜度
      # 绘制断裂处的标记
      kwargs = dict(
          marker=[(-1, -d), (1, d)],
          markersize=set_marker_size,
          linestyle="none",
          color="r",
          mec="r",
          mew=1,
          clip_on=False,
      )
      # plt.plot([0, 1], [0, 0], transform=ax1.transAxes, **kwargs)
      # plt.plot([0, 1], [1, 1], transform=ax2.transAxes, **kwargs)
  
      plt.tight_layout()
      # plt.xscale("symlog")
      plt.grid()
      plt.xlabel(
          "Time Budget given by user (min)", fontsize=set_font_size
      )
      plt.ylabel(f"Test accuracy on {dataset}", fontsize=set_font_size)
      plt.legend(ncol=1, fontsize=set_lgend_size)
      plt.show()
  
file_path: singa/examples/model_selection/TRAILS-Database-Native-Model-Selection/internal/ml/model_selection/exps/draw_img_lib.py
imports: import os
  from matplotlib import pyplot
  import seaborn
  import numpy
  import palettable
  from matplotlib.ticker import MaxNLocator
  import numpy
  from src.common.constant import Config
  import matplotlib
label_desc: Use image node_id 3 for calling a global function with example usage: draw_anytime_result_one_graph(y_acc_list_arr, x_T_list, x_acc_train, y_acc_train_l, y_acc_train_m, y_acc_train_h, annotations, lv, name_img, dataset, x1_lim, x2_lim) without return types