code: def from_native_module(
      module: nn.Module,
      process_group: Union[ProcessGroup, List[ProcessGroup]],
      *args,
      **kwargs,
  ) -> ParallelModule:
      LazyInitContext.materialize(module)
      # get the attributes
      out_features = module.out_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 out_features < tp_size:
          return module
  
      if out_features % tp_size != 0:
          raise ValueError(
              f"The size of out_features:{out_features} is not integer multiples of tensor parallel size: {tp_size}!"
          )
      linear_1d = RowW8A8BFP32O32LinearSiLU(
          module.in_features, module.out_features // tp_size
      )
      linear_1d.tp_size = tp_size
      linear_1d.tp_rank = tp_rank
      linear_1d.process_group = process_group
      linear_1d.a = module.a.clone().detach()
  
      split_row_copy(module, linear_1d, tp_rank=tp_rank, **kwargs)
      return linear_1d
  
imports: from typing import List, Union
  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 .linear import W8A8B8O8Linear, W8A8BFP32O32LinearSiLU, W8A8BFP32OFP32Linear
label_desc: Use image node_id 2 for calling the RowW8A8BFP32O32LinearSiLU obj's underlying member method code with example usage: obj.from_native_module(module, process_group) and returns: linear_1d, module