code: def print_loss(self, name, q_weight, weight_error, timecost):
      table = Texttable()
      length = 28
      name = (
          (name + " " * (length - len(name)))
          if len(name) <= length
          else name[:length]
      )
  
      table.header(
          ["name", "weight_error", "fp_inp_SNR", "q_inp_SNR", "time"]
      )
  
      # assign weight
      self.layer.weight.data = q_weight.reshape(
          self.layer.weight.shape
      ).to(self.layer.weight.data.dtype)
  
      if self.inp1 is not None:
          # quantize input to int8
          quantizer = Quantizer()
          quantizer.configure(8, perchannel=False, sym=True, mse=False)
          quantizer.find_params(self.inp1)
          q_in = quantizer.quantize(self.inp1).type(torch.float16)
          q_out = self.layer(q_in)
  
          # get kinds of SNR
          q_SNR = torch_snr_error(q_out, self.out1).item()
          fp_SNR = torch_snr_error(
              self.layer(self.inp1), self.out1
          ).item()
      else:
          q_SNR = "-"
          fp_SNR = "-"
  
      table.add_row([name, weight_error, fp_SNR, q_SNR, timecost])
      print(table.draw().split("\n")[-2])
  
imports: import time
  import torch.nn
  import math
  import json
  import os
  import torch
  import transformers
  from texttable import Texttable
  from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
  from huggingface_hub import HfApi
  from accelerate import init_empty_weights
  from text_generation_server.utils import initialize_torch_distributed, Weights
  from text_generation_server.utils.hub import weight_files
  from text_generation_server.utils.gptq.quant_linear import QuantLinear
  from loguru import logger
  from typing import Optional
label_desc: Use image node_id 3 for calling the GPTQ obj's underlying member method code with example usage: obj.print_loss(name, q_weight, weight_error, timecost) without return types