code: def init_q4(self):
      assert self.qweight.device.type == "cuda"
      self.q4_width = self.qweight.shape[1]
      if self.g_idx is not None:
          if self.row_split and torch.equal(
              self.g_idx,
              torch.tensor(
                  [
                      (i + (self.tp_rank * self.infeatures))
                      // self.groupsize
                      for i in range(self.infeatures)
                  ],
                  dtype=torch.int32,
                  device=self.g_idx.device,
              ),
          ):
              self.g_idx = None
          elif torch.equal(
              self.g_idx,
              torch.tensor(
                  [i // self.groupsize for i in range(self.infeatures)],
                  dtype=torch.int32,
                  device=self.g_idx.device,
              ),
          ):
              self.g_idx = None
  
      if self.g_idx is not None:
          g_idx = self.g_idx.to("cpu")
      else:
          g_idx = self.empty_tensor
  
      self.q4 = gptq_cuda.make_q4(
          self.qweight,
          self.qzeros,
          self.scales,
          g_idx,
          torch.cuda.current_device(),
      )
      torch.cuda.synchronize()
  
imports: import math
  import warnings
  from typing import List, Union
  import numpy
  import torch
  import torch.distributed
  import torch.nn
  from torch.distributed import ProcessGroup
  from colossalai.lazy import LazyInitContext
  from colossalai.shardformer.layer import ParallelModule
  from .gptq_op import CaiGPTQLinearOp
label_desc: Use image node_id 3 for calling the CaiQuantLinear obj's underlying member method code with example usage: obj.init_q4() without return types