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
code: def __init__(
      self,
      model,
      train_loader,
      test_loader,
      qconfig=torch.quantization.default_qconfig,
      criterion=torch.nn.CrossEntropyLoss(),
      device=torch.device("cpu"),
  ):
      super(Static_Quantizer, self).__init__(
          model,
          qconfig,
          train_loader,
          test_loader,
          None,
          criterion,
          device,
      )
  
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 1 to create a new Static_Quantizer object from inherited base classes: Quantizer with example: obj = Static_Quantizer(model, train_loader, test_loader, qconfig, criterion, device)