class_docstr: Linear layer with quantization aware training capability
class_docstr_tok: ["Linear","layer","with","quantization","aware","training","capability"]
code: def _eval(self):
      super()._eval()
      if self.mode == QuantizationMode.EMA and self.bias is not None:
          self.bias_scale = self._get_input_scale() * self.weight_scale
          self.quantized_bias = quantize(
              self.bias, self.bias_scale, self.accumulation_bits
          )
  
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 4 for calling the QuantizedLinear obj's underlying member method code with example usage: obj._eval() without return types