code: def split_row_copy(gptq_linear, cai_linear, tp_rank=0, split_num=1):
      qweights = gptq_linear.qweight.split(
          gptq_linear.in_features // split_num, dim=0
      )
      qzeros = gptq_linear.qzeros.split(
          gptq_linear.in_features // split_num, dim=0
      )
      scales = gptq_linear.scales.split(
          gptq_linear.in_features // split_num, dim=0
      )
      g_idxs = gptq_linear.g_idx.split(
          gptq_linear.in_features // split_num, dim=0
      )
  
      cai_split_in_features = (
          cai_linear.infeatures // (32 // cai_linear.bits) // split_num
      )
      zero_split_block = (
          cai_linear.infeatures // cai_linear.groupsize // split_num
      )
      idx_split_features = cai_linear.infeatures // split_num
  
      for i in range(split_num):
          cai_linear.qweight[
              i
              * cai_split_in_features : (i + 1)
              * cai_split_in_features,
              :,
          ] = qweights[i][
              tp_rank
              * cai_split_in_features : (tp_rank + 1)
              * cai_split_in_features,
              :,
          ]
          cai_linear.qzeros[
              i * zero_split_block : (i + 1) * zero_split_block, :
          ] = qzeros[i][
              tp_rank
              * zero_split_block : (tp_rank + 1)
              * zero_split_block,
              :,
          ]
          cai_linear.scales[
              i * zero_split_block : (i + 1) * zero_split_block, :
          ] = scales[i][
              tp_rank
              * zero_split_block : (tp_rank + 1)
              * zero_split_block,
              :,
          ]
          cai_linear.g_idx[
              i * idx_split_features : (i + 1) * idx_split_features
          ] = g_idxs[i][
              tp_rank
              * idx_split_features : (tp_rank + 1)
              * idx_split_features
          ]
      if cai_linear.bias is not None:
          cai_linear.bias.copy_(gptq_linear.bias)
  
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 2 for calling a global function with example usage: split_row_copy(gptq_linear, cai_linear, tp_rank, split_num) without return types