class_docstr: Implementation of the knowledge adjustment technique from the paper
  "Preparing Lessons: Improve Knowledge Distillation with Better Supervision"
  https://arxiv.org/abs/1911.07471
  
  :param teacher_model (torch.nn.Module): Teacher model
  :param student_model (torch.nn.Module): Student model
  :param train_loader (torch.utils.data.DataLoader): Dataloader for training
  :param val_loader (torch.utils.data.DataLoader): Dataloader for validation/testing
  :param optimizer_teacher (torch.optim.*): Optimizer used for training teacher
  :param optimizer_student (torch.optim.*): Optimizer used for training student
  :param loss_fn (torch.nn.Module): Loss Function used for distillation
  :param temp (float): Temperature parameter for distillation
  :param device (str): Device used for training; 'cpu' for cpu and 'cuda' for gpu
  :param log (bool): True if logging required
  :param logdir (str): Directory for storing logs
code: def __init__(
      self,
      teacher_model,
      student_model,
      train_loader,
      val_loader,
      optimizer_teacher,
      optimizer_student,
      loss_fn=nn.KLDivLoss(reduction="batchmean"),
      temp=20.0,
      ka_weight=0.5,
      device="cpu",
      log=False,
      logdir="./Experiments",
  ):
      super(ProbShift, self).__init__(
          teacher_model,
          student_model,
          train_loader,
          val_loader,
          optimizer_teacher,
          optimizer_student,
          loss_fn=loss_fn,
          temp=temp,
          distil_weight=ka_weight,
          device=device,
          log=log,
          logdir=logdir,
      )
      self.label_loss = nn.CrossEntropyLoss().to(self.device)
  
file_path: KD_Lib/KD_Lib/KD/vision/KA/PS.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from KD_Lib.KD.common import BaseClass
label_desc: Use image node_id 1 to create a new ProbShift object from inherited base classes: BaseClass with example: obj = ProbShift(teacher_model, student_model, train_loader, val_loader, optimizer_teacher, optimizer_student, loss_fn, temp, ka_weight, device, log, logdir)