code: def _pre_hook(
      state_dict,
      prefix,
      local_metadata,
      strict,
      missing_keys,
      unexpected_keys,
      error_msgs,
  ):
      # https://github.com/espnet/espnet/commit/3d422f6de8d4f03673b89e1caef698745ec749ea#diff-bffb1396f038b317b2b64dd96e6d3563
      rename_state_dict(
          prefix + "output_norm.", prefix + "after_norm.", state_dict
      )
  
imports: import logging
  from typing import Any, List, Tuple
  import torch
  from espnet.nets.pytorch_backend.nets_utils import rename_state_dict
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.decoder_layer import DecoderLayer
  from espnet.nets.pytorch_backend.transformer.dynamic_conv import DynamicConvolution
  from espnet.nets.pytorch_backend.transformer.dynamic_conv2d import DynamicConvolution2D
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
  from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
  from espnet.nets.pytorch_backend.transformer.lightconv import LightweightConvolution
  from espnet.nets.pytorch_backend.transformer.lightconv2d import LightweightConvolution2D
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask
  from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import PositionwiseFeedForward
  from espnet.nets.pytorch_backend.transformer.repeat import repeat
  from espnet.nets.scorer_interface import BatchScorerInterface
label_desc: Use image node_id 1 for calling a global function with example usage: _pre_hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) without return types