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: Get the number of parameters for the teacher and the student network
docstr_tok: ["Get","the","number","of","parameters","for","the","teacher","and","the","student","network"]
code: def get_parameters(self):
      """
      Get the number of parameters for the teacher and the student network
      """
      teacher_params = sum(
          p.numel() for p in self.teacher_model.parameters()
      )
      student_params = sum(
          p.numel() for p in self.student_model.parameters()
      )
  
      print("-" * 80)
      print(
          "Total parameters for the teacher network are: {}".format(
              teacher_params
          )
      )
      print(
          "Total parameters for the student network are: {}".format(
              student_params
          )
      )
  
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 8 for calling the BaseClass obj's underlying member method code with example usage: obj.get_parameters() without return types