class_docstr: Implementation of "Deep Mutual Learning" https://arxiv.org/abs/1706.00384
  
  :param student_cohort (list/tuple): Collection of student models
  :param train_loader (torch.utils.data.DataLoader): Dataloader for training
  :param val_loader (torch.utils.data.DataLoader): Dataloader for validation/testing
  :param student_optimizers (list/tuple): Collection of Pytorch optimizers for training students
  :param loss_fn (torch.nn.Module): Loss Function used 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
class_docstr_tok: ["Implementation","of","``","Deep","Mutual","Learning","''","https",":","\/\/arxiv.org\/abs\/1706.00384",":","param","student_cohort","(","list\/tuple",")",":","Collection","of","student","models",":","param","train_loader","(","torch.utils.data.DataLoader",")",":","Dataloader","for","training",":","param","val_loader","(","torch.utils.data.DataLoader",")",":","Dataloader","for","validation\/testing",":","param","student_optimizers","(","list\/tuple",")",":","Collection","of","Pytorch","optimizers","for","training","students",":","param","loss_fn","(","torch.nn.Module",")",":","Loss","Function","used","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: 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
      """
  
      print("-" * 80)
      for i, student in enumerate(self.student_cohort):
          student_params = sum(p.numel() for p in student.parameters())
          print(
              f"Total parameters for the student network {i} are: {student_params}"
          )
  
file_path: KD_Lib/KD_Lib/KD/vision/DML/dml.py
imports: import os
  from copy import deepcopy
  import matplotlib.pyplot
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torch.utils.tensorboard import SummaryWriter
label_desc: Use image node_id 5 for calling the DML obj's underlying member method code with example usage: obj.get_parameters() without return types