code: def convert_wavlm_checkpoint(
      checkpoint_path, pytorch_dump_folder_path, config_path=None
  ):
      # load the pre-trained checkpoints
      checkpoint = torch.load(checkpoint_path)
      cfg = WavLMConfigOrig(checkpoint["cfg"])
      model = WavLMOrig(cfg)
      model.load_state_dict(checkpoint["model"])
      model.eval()
  
      if config_path is not None:
          config = WavLMConfig.from_pretrained(config_path)
      else:
          config = WavLMConfig()
  
      hf_wavlm = WavLMModel(config)
  
      recursively_load_weights(model, hf_wavlm)
  
      hf_wavlm.save_pretrained(pytorch_dump_folder_path)
  
file_path: transformers/src/transformers/models/wavlm/convert_wavlm_original_pytorch_checkpoint_to_pytorch.py
imports: import argparse
  import torch
  from unilm.wavlm.WavLM import WavLM
  from unilm.wavlm.WavLM import WavLMConfig
  from transformers import WavLMConfig, WavLMModel, logging
label_desc: Use image node_id 4 for calling a global function with example usage: convert_wavlm_checkpoint(checkpoint_path, pytorch_dump_folder_path, config_path) without return types