class_docstr: Quantized version of `torch.nn.Linear`. Adds quantization-specific arguments on top of `torch.nn.Linear`.
  
  Args:
      weight_bit (`int`, *optional*, defaults to `8`):
          Bitwidth for the quantized weight.
      bias_bit (`int`, *optional*, defaults to `32`):
          Bitwidth for the quantized bias.
      per_channel (`bool`, *optional*, defaults to `False`):
          Whether or not to use channel-wise quantization.
      quant_mode (`bool`, *optional*, defaults to `False`):
          Whether or not the layer is quantized.
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code: def __repr__(self):
      s = super().__repr__()
      s = f"({s} weight_bit={self.weight_bit}, quant_mode={self.quant_mode})"
      return s
  
imports: import decimal
  import numpy
  import torch
  from torch import nn
  from torch.autograd import Function
  from ...utils import logging
label_desc: Use image node_id 2 for calling the QuantLinear obj's underlying member method code with example usage: obj.__repr__() and returns: s