code: def __init__(
      self,
      weight,
      bias,
  ) -> None:
      super().__init__()
      device = weight.device
      weight = torch.t(weight).contiguous().cpu()
      weight, scale = quant_weights(weight, torch.int8, False)
  
      self.weight = weight.cuda(device)
      self.scale = scale.cuda(device)
      self.bias = bias.cuda(device) if bias is not None else None
  
imports: import os
  import torch
  import torch.distributed
  from torch import nn
  from torch.nn import functional
  from typing import List
  from loguru import logger
  from functools import lru_cache
  from accelerate import init_empty_weights
  from text_generation_server.utils.gptq.quant_linear import QuantLinear
  from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM
  from text_generation_server.utils.log import log_once
label_desc: Use image node_id 1 to create a new EETQLinear object from inherited base classes: nn with example: obj = EETQLinear(weight, bias)