code: def __init__(self, cf):
      self.file_name = cf.plot_dir + "/monitor_{}".format(cf.fold)
      self.exp_name = cf.fold_dir
      self.do_validation = cf.do_validation
      self.separate_values_dict = cf.assign_values_to_extra_figure
      self.figure_list = []
      for n in range(cf.n_monitoring_figures):
          self.figure_list.append(plt.figure(figsize=(10, 6)))
          self.figure_list[-1].ax1 = plt.subplot(111)
          self.figure_list[-1].ax1.set_xlabel("epochs")
          self.figure_list[-1].ax1.set_ylabel("loss / metrics")
          self.figure_list[-1].ax1.set_xlim(0, cf.num_epochs)
          self.figure_list[-1].ax1.grid()
  
      self.figure_list[0].ax1.set_ylim(0, 1.5)
      self.color_palette = [
          "b",
          "c",
          "r",
          "purple",
          "m",
          "y",
          "k",
          "tab:gray",
      ]
  
file_path: medicaldetectiontoolkit/plotting.py
imports: import matplotlib
  import matplotlib.pyplot
  import matplotlib.gridspec
  import numpy
  import os
  from copy import deepcopy
label_desc: Use image node_id 1 to create a new TrainingPlot_2Panel object  with example: obj = TrainingPlot_2Panel(cf)