class_docstr: Quantized Layer interface
class_docstr_tok: ["Quantized","Layer","interface"]
docstr: handle transition between quantized model and simulated quantization
code: def train(self, mode=True):
      """handle transition between quantized model and simulated quantization"""
      if self.training != mode:
          if mode:
              if self._imported_from_quantized:
                  raise RuntimeError(
                      "Model imported from quantized checkpoint cannot be moved to \
                              training mode"
                  )
              self._train()
          else:
              self._eval()
      super().train(mode)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from enum import Enum, auto
  import logging
  from abc import ABC, abstractmethod
  import torch
  from torch import nn
  from torch.nn import functional
  from nlp_architect.common import Config
label_desc: Use image node_id 8 for calling the QuantizedLayer obj's underlying member method code with example usage: obj.train(mode) without return types