class_docstr: Linear layer with quantization aware training capability
class_docstr_tok: ["Linear","layer","with","quantization","aware","training","capability"]
code: def quantized_bias(self):
      try:
          if self.mode == QuantizationMode.EMA:
              bias = self._quantized_bias
          elif self.mode == QuantizationMode.DYNAMIC:
              bias = quantize(
                  self.bias, self.bias_scale, self.accumulation_bits
              )
          else:
              raise RuntimeError(
                  f"Unknown quantization mode: {self.mode}"
              )
      except AttributeError:
          bias = None
      return bias
  
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 6 for calling the QuantizedLinear obj's underlying member method code with example usage: obj.quantized_bias() and returns: bias