class_docstr: Quantized Layer interface
class_docstr_tok: ["Quantized","Layer","interface"]
code: def forward(self, input):
      if self.mode == QuantizationMode.NONE:
          return super().forward(input)
      if self.training:
          if self._step >= self.start_step:
              out = self.training_quantized_forward(input)
          else:
              out = super().forward(input)
          self._step += 1
      else:
          out = self.inference_quantized_forward(input)
      return out
  
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 2 for calling the QuantizedLayer obj's underlying member method code with example usage: obj.forward(input) and returns: out, super