code: def revert_transformer_layer(
      orig_layer_impl, model, config, preln=False
  ):
      """Revert DeepSpeed's transformer layer back to original bert-style transformer layer
      Arguments:
          orig_layer_impl (torch.nn.Module): the original transformer layer implementation that was replaced,
              e.g., transformers.models.bert.modeling_bert.BertLayer or transformers.BertLayer
          model (torch.nn.Module): user's nn.module representing their model
          config (dict): model config containing hidden size, attention heads, etc.
      Returns:
          Updated nn.module with original bert-style transformer layers
      """
  
      def replace_fn(child, _replace_policy, layer_id):
          # from turing.nvidia_modelingpreln import BertLayer
          orig_module = orig_layer_impl(config)
  
          # copy relevant state from child -> original module
          qkvw = child.attn_qkvw.data
          qkvb = child.attn_qkvb.data
  
          qw, kw, vw = torch.chunk(qkvw, 3, axis=0)
          qb, kb, vb = torch.chunk(qkvb, 3, axis=0)
  
          orig_module.attention.self.query.weight.data = qw
          orig_module.attention.self.query.bias.data = qb
          orig_module.attention.self.key.weight.data = kw
          orig_module.attention.self.key.bias.data = kb
          orig_module.attention.self.value.weight.data = vw
          orig_module.attention.self.value.bias.data = vb
  
          orig_module.attention.output.dense.weight.data = (
              child.attn_ow.data
          )
          orig_module.attention.output.dense.bias.data = (
              child.attn_ob.data
          )
  
          attn_ln_w = child.attn_nw.data
          attn_ln_b = child.attn_nb.data
          if preln:
              orig_module.PostAttentionLayerNorm.weight.data = attn_ln_w
              orig_module.PostAttentionLayerNorm.bias.data = attn_ln_b
          else:
              orig_module.attention.output.LayerNorm.weight.data = (
                  attn_ln_w
              )
              orig_module.attention.output.LayerNorm.bias.data = (
                  attn_ln_b
              )
  
          inter_ff_w = child.inter_w.data
          inter_ff_b = child.inter_b.data
          if preln:
              orig_module.intermediate.dense_act.weight.data = (
                  inter_ff_w
              )
              orig_module.intermediate.dense_act.bias.data = inter_ff_b
          else:
              orig_module.intermediate.dense.weight.data = inter_ff_w
              orig_module.intermediate.dense.bias.data = inter_ff_b
  
          orig_module.output.dense.weight.data = child.output_w.data
          orig_module.output.dense.bias.data = child.output_b.data
  
          transformer_ln_w = child.norm_w.data
          transformer_ln_b = child.norm_b.data
          if preln:
              orig_module.PreAttentionLayerNorm.weight.data = (
                  transformer_ln_w
              )
              orig_module.PreAttentionLayerNorm.bias.data = (
                  transformer_ln_b
              )
          else:
              orig_module.output.LayerNorm.weight.data = (
                  transformer_ln_w
              )
              orig_module.output.LayerNorm.bias.data = transformer_ln_b
          return orig_module
  
      return replace_module(
          model=model,
          orig_class=deepspeed.DeepSpeedTransformerLayer,
          replace_fn=replace_fn,
          _replace_policy=None,
      )
  
imports: import os
  import torch
  import tqdm
  import deepspeed
  import deepspeed.ops.transformer
  from deepspeed.ops.transformer.inference.diffusers_attention import DeepSpeedDiffusersAttention
  from deepspeed.ops.transformer.inference.diffusers_transformer_block import DeepSpeedDiffusersTransformerBlock
  from deepspeed.ops.transformer.inference.diffusers_2d_transformer import Diffusers2DTransformerConfig
  from deepspeed.accelerator import get_accelerator
  from .replace_policy import replace_policies, generic_policies
  from .auto_tp import AutoTP, ReplaceWithTensorSlicing, Loading
  from deepspeed import comm
  from deepspeed.module_inject.tp_shard import set_num_kv_heads
  from .load_checkpoint import load_model_with_checkpoint
  import time
  from .utils import policy_to_ds_container
  import gc
  from ..pipe import PipelineModule
  import re
label_desc: Use image node_id 5 for calling a global function with example usage: revert_transformer_layer(orig_layer_impl, model, config, preln) and returns: replace_module, orig_module