code: def forward(self, input: torch.Tensor) -> torch.Tensor:
      output = w8_a16_gemm(input, self.weight, self.scale)
      output = output + self.bias if self.bias is not None else output
      return output
  
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 2 for calling the EETQLinear obj's underlying member method code with example usage: obj.forward(input) and returns: output