code: def forward(self, x):
      outshape = x.shape[:-1] + (self.outfeatures,)
  
      if HAS_GPTQ_CUDA and self.bits == 4:
          if self.q4 is None:
              self.init_q4()
  
          x = x.view(-1, x.shape[-1])
          output = torch.empty(
              (x.shape[0], self.outfeatures),
              dtype=torch.float16,
              device=x.device,
          )
          gptq_cuda.q4_matmul(x.half(), self.q4, output)
          if self.bias is not None and (
              not self.row_split or self.tp_size == 1
          ):
              output.add_(self.bias)
      else:
          if self.bias is not None and (
              not self.row_split or self.tp_size == 1
          ):
              bias = self.bias
          else:
              bias = None
          output = self.gptq_linear(
              x,
              self.qweight,
              self.scales,
              self.qzeros,
              g_idx=self.g_idx,
              bias=bias,
          )
      return output.view(outshape)
  
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 4 for calling the CaiQuantLinear obj's underlying member method code with example usage: obj.forward(x) and returns: output