code: def test_TAKD():
      assistants = [deepcopy(mock_vision_model) for _ in range(2)]
  
      teacher_optimizer = optim.Adam(teacher.parameters())
      assistant_optimizers = []
      assistant_optimizers.append(
          optim.Adam(assistants[0].parameters())
      )
      assistant_optimizers.append(
          optim.Adam(assistants[1].parameters())
      )
      student_optimizer = optim.Adam(student.parameters())
  
      assistant_train_order = [[-1], [-1, 0]]
  
      distiller = TAKD(
          teacher,
          assistants,
          student,
          assistant_train_order,
          train_loader,
          test_loader,
          teacher_optimizer,
          assistant_optimizers,
          student_optimizer,
      )
  
      distiller.train_teacher(
          epochs=1, plot_losses=False, save_model=False
      )
      distiller.train_assistants(
          epochs=1, plot_losses=False, save_model=False
      )
      distiller.train_student(
          epochs=1, plot_losses=False, save_model=False
      )
      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 2 for calling a global function with example usage: test_TAKD() without return types