code: def load_state_dict(self, state_dict, strict=True):
      # Remapping from our checkpoints that used a different ordering of layers in the block
      # Previous: Attn / MLP -> Dropout -> Add -> LN
      # Current: Dropout -> Add -> LN -> Attn / MLP
      if "transformer.ln_0.weight" in state_dict:
          n_layers = len(self.transformer.layers)
          ln_weight = state_dict.pop(
              f"transformer.layers.{n_layers - 1}.norm2.weight"
          )
          ln_bias = state_dict.pop(
              f"transformer.layers.{n_layers - 1}.norm2.bias"
          )
          state_dict["transformer.ln_f.weight"] = ln_weight
          state_dict["transformer.ln_f.bias"] = ln_bias
          for l in reversed(range(n_layers)):
              ln_weight = state_dict.pop(
                  f"transformer.layers.{l}.norm1.weight"
              )
              ln_bias = state_dict.pop(
                  f"transformer.layers.{l}.norm1.bias"
              )
              state_dict[
                  f"transformer.layers.{l}.norm2.weight"
              ] = ln_weight
              state_dict[f"transformer.layers.{l}.norm2.bias"] = ln_bias
              if l > 0:
                  ln_weight = state_dict.pop(
                      f"transformer.layers.{l - 1}.norm2.weight"
                  )
                  ln_bias = state_dict.pop(
                      f"transformer.layers.{l - 1}.norm2.bias"
                  )
                  state_dict[
                      f"transformer.layers.{l}.norm1.weight"
                  ] = ln_weight
                  state_dict[
                      f"transformer.layers.{l}.norm1.bias"
                  ] = ln_bias
          ln_weight = state_dict.pop("transformer.ln_0.weight")
          ln_bias = state_dict.pop("transformer.ln_0.bias")
          state_dict[f"transformer.layers.0.norm1.weight"] = ln_weight
          state_dict[f"transformer.layers.0.norm1.bias"] = ln_bias
      return super().load_state_dict(state_dict, strict=strict)
  
imports: import logging
  import math
  import re
  from collections import OrderedDict, namedtuple
  from collections.abc import Sequence
  from functools import partial
  from typing import Dict, List
  import torch
  import torch.nn
  import torch.nn.functional
  from einops import rearrange
  from transformers import GPT2Config
  from flash_attn.models.bigcode import remap_state_dict_hf_bigcode
  from flash_attn.models.falcon import remap_state_dict_hf_falcon
  from flash_attn.models.gpt_neox import remap_state_dict_hf_gpt_neox
  from flash_attn.models.gptj import remap_state_dict_hf_gptj
  from flash_attn.models.llama import remap_state_dict_hf_llama
  from flash_attn.models.opt import remap_state_dict_hf_opt
  from flash_attn.modules.block import Block, ParallelBlock
  from flash_attn.modules.embedding import GPT2Embeddings, ParallelGPT2Embeddings
  from flash_attn.modules.mha import MHA, ParallelMHA
  from flash_attn.modules.mlp import FusedMLP, GatedMlp, Mlp, ParallelFusedMLP, ParallelGatedMlp, ParallelMLP
  from flash_attn.ops.activations import sqrelu_fwd
  from flash_attn.utils.distributed import all_gather, all_gather_raw, get_dim_for_local_rank, sync_shared_params
  from flash_attn.utils.generation import GenerationMixin
  from flash_attn.utils.pretrained import state_dict_from_pretrained
label_desc: Use image node_id 5 for calling the GPTLMHeadModel obj's underlying member method code with example usage: obj.load_state_dict(state_dict, strict) and returns: super