code: def from_native_module(
      module: nn.Module,
      process_group: Union[ProcessGroup, List[ProcessGroup]],
      *args,
      **kwargs,
  ) -> ParallelModule:
      LazyInitContext.materialize(module)
      # get the attributes
      in_features = module.in_features
  
      # ensure only one process group is passed
      if isinstance(process_group, (list, tuple)):
          assert (
              len(process_group) == 1
          ), f"Expected only one process group, got {len(process_group)}."
          process_group = process_group[0]
  
      tp_size = dist.get_world_size(process_group)
      tp_rank = dist.get_rank(process_group)
  
      if in_features < tp_size:
          return module
  
      if in_features % tp_size != 0:
          raise ValueError(
              f"The size of in_features:{in_features} is not integer multiples of tensor parallel size: {tp_size}!"
          )
      linear_1d = ColCaiQuantLinear(
          module.bits,
          module.group_size,
          module.in_features,
          module.out_features // tp_size,
          module.bias is not None,
          tp_size=tp_size,
          tp_rank=tp_rank,
      )
      linear_1d.process_group = process_group
  
      split_column_copy(module, linear_1d, tp_rank=tp_rank, **kwargs)
      return linear_1d
  
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 the ColCaiQuantLinear obj's underlying member method code with example usage: obj.from_native_module(module, process_group) and returns: linear_1d, module