code: def _quantize(self, x, scale, zero, maxq):
      if maxq < 0:
          return (x > scale / 2).float() * scale + (
              x < zero / 2
          ).float() * zero
      q = torch.clamp(torch.round(x / scale) + zero, 0, maxq)
      return scale * (q - zero)
  
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 Quantizer obj's underlying member method code with example usage: obj._quantize(x, scale, zero, maxq) and returns: unknown, unknown