code: def fasterquant(
      self,
      blocksize=128,
      percdamp=0.01,
      groupsize=-1,
      act_order=False,
      name="",
  ):
      self.layer.to(self.dev)
  
      W = self.layer.weight.data.clone()
      if isinstance(self.layer, nn.Conv2d):
          W = W.flatten(1)
      if isinstance(self.layer, transformers.Conv1D):
          W = W.t()
      W = W.float()
  
      tick = time.time()
  
      if not self.quantizer.ready():
          self.quantizer.find_params(W, weight=True)
  
      H = self.H
      if not self.observe:
          del self.H
      dead = torch.diag(H) == 0
      H[dead, dead] = 1
      W[:, dead] = 0
  
      if act_order:
          perm = torch.argsort(torch.diag(H), descending=True)
          W = W[:, perm]
          H = H[perm][:, perm]
  
      Losses = torch.zeros_like(W)
      Q = torch.zeros_like(W)
  
      damp = percdamp * torch.mean(torch.diag(H))
      diag = torch.arange(self.columns, device=self.dev)
      H[diag, diag] += damp
      H = torch.linalg.cholesky(H)
      H = torch.cholesky_inverse(H)
      try:
          H = torch.linalg.cholesky(H, upper=True)
      except Exception:
          # Addition because Falcon fails on h_to_4h
          H = torch.linalg.cholesky(
              H + 1e-5 * torch.eye(H.shape[0]).to(H.device), upper=True
          )
      Hinv = H
  
      g_idx = []
      scale = []
      zero = []
      now_idx = 1
  
      for i1 in range(0, self.columns, blocksize):
          i2 = min(i1 + blocksize, self.columns)
          count = i2 - i1
  
          W1 = W[:, i1:i2].clone()
          Q1 = torch.zeros_like(W1)
          Err1 = torch.zeros_like(W1)
          Losses1 = torch.zeros_like(W1)
          Hinv1 = Hinv[i1:i2, i1:i2]
  
          for i in range(count):
              w = W1[:, i]
              d = Hinv1[i, i]
  
              if groupsize != -1:
                  if (i1 + i) % groupsize == 0:
                      self.quantizer.find_params(
                          W[:, (i1 + i) : (i1 + i + groupsize)],
                          weight=True,
                      )
  
                  if ((i1 + i) // groupsize) - now_idx == -1:
                      scale.append(self.quantizer.scale)
                      zero.append(self.quantizer.zero)
                      now_idx += 1
  
              q = self.quantizer.quantize(w.unsqueeze(1)).flatten()
              Q1[:, i] = q
              Losses1[:, i] = (w - q) ** 2 / d**2
  
              err1 = (w - q) / d
              W1[:, i:] -= err1.unsqueeze(1).matmul(
                  Hinv1[i, i:].unsqueeze(0)
              )
              Err1[:, i] = err1
  
          Q[:, i1:i2] = Q1
          Losses[:, i1:i2] = Losses1 / 2
  
          W[:, i2:] -= Err1.matmul(Hinv[i1:i2, i2:])
  
      torch.cuda.synchronize()
      error = torch.sum(Losses).item()
  
      groupsize = groupsize if groupsize != -1 else self.columns
      g_idx = [i // groupsize for i in range(self.columns)]
      g_idx = torch.tensor(g_idx, dtype=torch.int32, device=Q.device)
      if act_order:
          invperm = torch.argsort(perm)
          Q = Q[:, invperm]
          g_idx = g_idx[invperm]
  
      if isinstance(self.layer, transformers.Conv1D):
          Q = Q.t()
  
      self.print_loss(
          name=name,
          q_weight=Q,
          weight_error=error,
          timecost=(time.time() - tick),
      )
  
      if scale == []:
          scale.append(self.quantizer.scale)
          zero.append(self.quantizer.zero)
      scale = torch.cat(scale, dim=1)
      zero = torch.cat(zero, dim=1)
      return scale, zero, g_idx, error
  
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 4 for calling the GPTQ obj's underlying member method code with example usage: obj.fasterquant(blocksize, percdamp, groupsize, act_order, name) and returns: scale,  zero,  g_idx,  error