class_docstr: Implementation of Static 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 (calibration)
  :type train_loader: torch.utils.data.DataLoader
  :param test_loader: DataLoader used for testing
  :type test_loader: torch.utils.data.DataLoader
  :param criterion: Loss function used for calibration
  :type criterion: Loss_fn
  :param device: Device used for training ("cpu" or "cuda")
  :type device: torch.device
docstr: Function used for quantization
  
  :param num_calibration_batches: Number of batches used for calibration
  :type num_calibration_batches: int
docstr_tok: ["Function","used","for","quantization",":","param","num_calibration_batches",":","Number","of","batches","used","for","calibration",":","type","num_calibration_batches",":","int"]
code: def quantize(self, num_calibration_batches=10):
      """
      Function used for quantization
  
      :param num_calibration_batches: Number of batches used for calibration
      :type num_calibration_batches: int
      """
  
      self.quantized_model = deepcopy(self.model)
      self.quantized_model.eval()
      self.quantized_model.fuse_model()
      self.quantized_model.qconfig = self.qconfig
  
      torch.quantization.prepare(self.quantized_model, inplace=True)
  
      print("Calibrating model...")
      self._calibrate_model(num_calibration_batches)
  
      print("Converting to quantized model...")
      torch.quantization.convert(self.quantized_model, inplace=True)
  
      return self.quantized_model
  
file_path: KD_Lib/KD_Lib/Quantization/static/static_quantization.py
imports: from copy import deepcopy
  import torch
  from KD_Lib.Quantization.common import Quantizer
label_desc: Use image node_id 2 for calling the Static_Quantizer obj's underlying member method code with example usage: obj.quantize(num_calibration_batches) and returns: self