code: def load_hook(
      self,
      state_dict,
      prefix,
      local_metadata,
      strict,
      missing_keys,
      unexpected_keys,
      error_msgs,
  ):
      if prefix + "w1.weight" in state_dict:
          w1 = state_dict.pop(prefix + "w1.weight")
          w3 = state_dict.pop(prefix + "w3.weight")
          state_dict[prefix + "w13.weight"] = torch.cat([w1, w3])
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import mp_utils
  import torch
  from torch import nn
  from torch.nn import functional
  from xformers.ops import RMSNorm, fmha, rope_padded
  from xformers.ops.fmha.attn_bias import BlockDiagonalCausalWithOffsetPaddedKeysMask
label_desc: Use image node_id 2 for calling the FeedForward obj's underlying member method code with example usage: obj.load_hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) without return types