class_docstr: Implementation of Dynamic Quantization for PyTorch models.
  
  :param model: Model that needs to be quantized
  :type model: torch.nn.Module
  :param qconfig_spec: Qconfig spec
  :type qconfig_spec: Qconfig_spec
  :param test_loader: DataLoader used for testing
  :type test_loader: torch.utils.data.DataLoader
class_docstr_tok: ["Implementation","of","Dynamic","Quantization","for","PyTorch","models",".",":","param","model",":","Model","that","needs","to","be","quantized",":","type","model",":","torch.nn.Module",":","param","qconfig_spec",":","Qconfig","spec",":","type","qconfig_spec",":","Qconfig_spec",":","param","test_loader",":","DataLoader","used","for","testing",":","type","test_loader",":","torch.utils.data.DataLoader"]
code: def __init__(self, model, test_loader, qconfig_spec=None):
      super(Dynamic_Quantizer, self).__init__(
          model, qconfig_spec, test_loader=test_loader
      )
  
imports: import torch
  from KD_Lib.Quantization.common import Quantizer
label_desc: Use image node_id 1 to create a new Dynamic_Quantizer object from inherited base classes: Quantizer with example: obj = Dynamic_Quantizer(model, test_loader, qconfig_spec)