code: def transform_wavlm_encoder_state(
      state: Dict[str, Any], encoder_num_layers: int
  ):
      """Converts WavLM encoder state from HuggingFace format. In particular, concatenates linear projection weights and
      biases to align with the structure of ``torch.nn.MultiheadAttention``.
      """
      for i in range(encoder_num_layers):
          q_proj_bias = state.pop(f"layers.{i}.attention.q_proj.bias")
          k_proj_bias = state.pop(f"layers.{i}.attention.k_proj.bias")
          v_proj_bias = state.pop(f"layers.{i}.attention.v_proj.bias")
          q_proj_weight = state.pop(
              f"layers.{i}.attention.q_proj.weight"
          )
          k_proj_weight = state.pop(
              f"layers.{i}.attention.k_proj.weight"
          )
          v_proj_weight = state.pop(
              f"layers.{i}.attention.v_proj.weight"
          )
          state[
              f"layers.{i}.attention.attention.in_proj_bias"
          ] = torch.cat((q_proj_bias, k_proj_bias, v_proj_bias))
          state[
              f"layers.{i}.attention.attention.in_proj_weight"
          ] = torch.cat((q_proj_weight, k_proj_weight, v_proj_weight))
  
          state[
              f"layers.{i}.attention.attention.out_proj.weight"
          ] = state.pop(f"layers.{i}.attention.out_proj.weight")
          state[
              f"layers.{i}.attention.attention.out_proj.bias"
          ] = state.pop(f"layers.{i}.attention.out_proj.bias")
  
file_path: audio/src/torchaudio/models/wav2vec2/utils/import_huggingface.py
imports: import logging
  from typing import Any, Dict
  import torch
  from torch.nn import Module
  from ..model import wav2vec2_model, Wav2Vec2Model, wavlm_model
label_desc: Use image node_id 4 for calling a global function with example usage: transform_wavlm_encoder_state(state, encoder_num_layers) without return types