code: def __init__(
      self,
      key,
      typename,
      *module_args,
      forward_fn=None,
      tied_weight_attr=["weight"],
      **module_kwargs,
  ):
      super().__init__(typename, *module_args, **module_kwargs)
      self.key = key
      self.forward_fn = forward_fn
      self.tied_weight_attr = (
          [tied_weight_attr]
          if type(tied_weight_attr) == str
          else tied_weight_attr
      )
  
imports: import os
  import glob
  import re
  from functools import partial
  import torch
  import torch.nn
  from deepspeed import comm
  from deepspeed.utils import logger
  from ..None import utils
  from ..activation_checkpointing import checkpointing
  from .topology import PipeDataParallelTopology, PipelineParallelGrid
  from deepspeed.runtime.state_dict_factory import SDLoaderFactory
  from deepspeed.accelerator import get_accelerator
  from deepspeed.checkpoint.utils import clone_tensors_for_torch_save
label_desc: Use image node_id 1 to create a new TiedLayerSpec object from inherited base classes: LayerSpec with example: obj = TiedLayerSpec(key, typename)