code: def test_DML():
      student_1 = deepcopy(mock_vision_model)
      student_2 = deepcopy(mock_vision_model)
  
      student_cohort = (student_1, student_2)
  
      s_optimizer_1 = optim.SGD(student_1.parameters(), 0.01)
      s_optimizer_2 = optim.SGD(student_2.parameters(), 0.01)
  
      student_optimizers = (s_optimizer_1, s_optimizer_2)
  
      distiller = DML(
          student_cohort,
          train_loader,
          test_loader,
          student_optimizers,
          log=True,
          logdir=".",
      )
  
      distiller.train_students(
          epochs=1,
          plot_losses=True,
          save_model=True,
          save_model_path="./student.pt",
      )
      distiller.evaluate()
      distiller.get_parameters()
  
imports: from copy import deepcopy
  import pandas
  import torch.optim
  from torch.utils.data import DataLoader
  from KD_Lib.KD import BANN, DML, RCO, TAKD, Attention, LabelSmoothReg, MeanTeacher, MessyCollab, NoisyTeacher, ProbShift, SelfTraining, SoftRandom, VanillaKD, VirtualTeacher
  from KD_Lib.KD.text.BERT2LSTM import BERT2LSTM
  from KD_Lib.KD.text.BERT2LSTM.utils import get_essentials
  from .utils import MockImageClassifier, MockVisionDataset
label_desc: Use image node_id 13 for calling a global function with example usage: test_DML() without return types