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
docstr: Swaps probabilty of incorrectly predicted class with true class.
  
  :param y_pred_student (Tensor): Predicted outputs from the student network
  :param y_pred_teacher (Tensor): Predicted outputs from the teacher network
  :param y_true (Tensor): True labels
docstr_tok: ["Swaps","probabilty","of","incorrectly","predicted","class","with","true","class",".",":","param","y_pred_student","(","Tensor",")",":","Predicted","outputs","from","the","student","network",":","param","y_pred_teacher","(","Tensor",")",":","Predicted","outputs","from","the","teacher","network",":","param","y_true","(","Tensor",")",":","True","labels"]
code: def calculate_kd_loss(self, y_pred_student, y_pred_teacher, y_true):
      """
      Swaps probabilty of incorrectly predicted class with true class.
  
      :param y_pred_student (Tensor): Predicted outputs from the student network
      :param y_pred_teacher (Tensor): Predicted outputs from the teacher network
      :param y_true (Tensor): True labels
      """
      soft_pred_student = F.softmax(y_pred_student / self.temp, dim=1)
  
      with torch.no_grad():
          soft_pred_teacher = F.softmax(
              y_pred_teacher / self.temp, dim=1
          )
          for i in range(y_pred_teacher.shape[0]):
              t_label = torch.argmax(soft_pred_teacher[i])
              if t_label != y_true[i]:
                  temp_prob = soft_pred_teacher[i][t_label]
                  soft_pred_teacher[i][t_label] = soft_pred_teacher[i][
                      y_true[i]
                  ]
                  soft_pred_teacher[i][y_true[i]] = temp_prob
  
      ka_loss = (
          self.temp
          * self.temp
          * self.loss_fn(soft_pred_teacher, soft_pred_student)
      )
      ce_loss = self.temp * self.label_loss(
          y_pred_student / self.temp, y_true
      )
      return (
          ka_loss * self.distil_weight
          + (1 - self.distil_weight) * ce_loss
      )
  
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 2 for calling the ProbShift obj's underlying member method code with example usage: obj.calculate_kd_loss(y_pred_student, y_pred_teacher, y_true) and returns: unknown