code: def convert_conv1d_to_linear(model, convert_type):
      """
      This is a help function to convert conv1d to linear (e.g., convert GPT2 from HF)
      """
      if hasattr(model, "module"):
          c_model = model.module
      else:
          c_model = model
  
      for name, module in c_model.named_modules():
          if isinstance(module, convert_type):
              old_module = recursive_getattr(c_model, name)
              new_module = torch.nn.Linear(
                  old_module.weight.data.size(0),
                  old_module.weight.data.size(1),
                  bias=True if old_module.bias is not None else False,
              )
              new_module.weight.data = (
                  old_module.weight.data.t().contiguous()
              )
              if new_module.bias is not None:
                  new_module.bias.data = old_module.bias.data.view(-1)
  
              recursive_setattr(c_model, name, new_module)
  
      return model
  
file_path: DeepSpeed/deepspeed/compression/helper.py
imports: import torch
  from .basic_layer import Embedding_Compress, LinearLayer_Compress, Conv2dLayer_Compress, BNLayer_Compress, ColumnParallelLinear_Compress, RowParallelLinear_Compress
  from .constants import *
  from deepspeed.utils import logger
label_desc: Use image node_id 7 for calling a global function with example usage: convert_conv1d_to_linear(model, convert_type) and returns: model