code: def configure(
      self,
      bits,
      perchannel=False,
      sym=True,
      mse=False,
      norm=2.4,
      grid=100,
      maxshrink=0.8,
      trits=False,
  ):
      self.maxq = torch.tensor(2**bits - 1)
      self.perchannel = perchannel
      self.sym = sym
      self.mse = mse
      self.norm = norm
      self.grid = grid
      self.maxshrink = maxshrink
      if trits:
          self.maxq = torch.tensor(-1)
      self.scale = torch.zeros_like(self.scale)
  
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 2 for calling the Quantizer obj's underlying member method code with example usage: obj.configure(bits, perchannel, sym, mse, norm, grid, maxshrink, trits) without return types