class_docstr: Quantized Layer interface
class_docstr_tok: ["Quantized","Layer","interface"]
docstr: check if model is loaded from quantized checkpoint or regular checkpoint
code: def _load_from_state_dict(
      self,
      state_dict,
      prefix,
      local_metadata,
      strict,
      missing_keys,
      unexpected_keys,
      error_msgs,
  ):
      """check if model is loaded from quantized checkpoint or regular checkpoint"""
      super()._load_from_state_dict(
          state_dict,
          prefix,
          local_metadata,
          strict,
          missing_keys,
          unexpected_keys,
          error_msgs,
      )
      if state_dict.get(prefix + "quantized_weight", None) is not None:
          if self.training:
              raise RuntimeError(
                  "Can't load quantized model in training mode, first change model's \
                           to evaluation and then load the saved model"
              )
          self._imported_from_quantized = True
  
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 11 for calling the QuantizedLayer obj's underlying member method code with example usage: obj._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) without return types