class_docstr: Basic implementation of a general Knowledge Distillation framework
  
  :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 distil_weight (float): Weight paramter for distillation loss
  :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: Any changes to be made after an epoch is completed.
  
  :param epoch (int) : current epoch number
  :return            : nothing (void)
docstr_tok: ["Any","changes","to","be","made","after","an","epoch","is","completed",".",":","param","epoch","(","int",")",":","current","epoch","number",":","return",":","nothing","(","void",")"]
code: def post_epoch_call(self, epoch):
      """
      Any changes to be made after an epoch is completed.
  
      :param epoch (int) : current epoch number
      :return            : nothing (void)
      """
  
      pass
  
file_path: KD_Lib/KD_Lib/KD/common/base_class.py
imports: import os
  from copy import deepcopy
  import matplotlib.pyplot
  import torch
  import torch.nn
  from torch.utils.tensorboard import SummaryWriter
label_desc: Use image node_id 9 for calling the BaseClass obj's underlying member method code with example usage: obj.post_epoch_call(epoch) without return types