class_docstr: Basic Implementation of Quantization for PyTorch models.
  
  :param model: Model that needs to be pruned
  :type model: torch.nn.Module
  :param qconfig: Configuration used for quantization
  :type qconfig: Qconfig
  :param train_loader: DataLoader used for training
  :type train_loader: torch.utils.data.DataLoader
  :param test_loader: DataLoader used for testing
  :type test_loader: torch.utils.data.DataLoader
  :param optimizer: Optimizer for training
  :type optimizer: torch.optim.*
  :param criterion: Loss function used for calibration
  :type criterion: Loss_fn
  :param device: Device used for training ("cpu" or "cuda")
  :type device: torch.device
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docstr: Function for printing sizes of the original and quantized model
docstr_tok: ["Function","for","printing","sizes","of","the","original","and","quantized","model"]
code: def get_model_sizes(self):
      """
      Function for printing sizes of the original and quantized model
      """
  
      original_size = self._get_size_of_model(self.model)
      quantized_size = self._get_size_of_model(self.quantized_model)
  
      print("-" * 80)
      print(f"Size of original model (MB): {original_size}")
      print(f"Size of quantized_model (MB): {quantized_size}")
  
file_path: KD_Lib/KD_Lib/Quantization/common/base_class.py
imports: import os
  import time
  import torch
label_desc: Use image node_id 3 for calling the Quantizer obj's underlying member method code with example usage: obj.get_model_sizes() without return types