code: def convert_hubert_checkpoint(
      pytorch_dump_folder_path, config_path=None
  ):
      """
      Copy/paste/tweak model's weights to transformers design.
      """
      model = distilhubert().model.model
  
      if config_path is not None:
          config = HubertConfig.from_pretrained(config_path)
      else:
          config = convert_config(model)
      model = model.eval()
  
      feature_extractor = Wav2Vec2FeatureExtractor(
          feature_size=1,
          sampling_rate=16000,
          padding_value=0,
          do_normalize=False,
          return_attention_mask=False,
      )
      hf_model = HubertModel(config)
  
      recursively_load_weights(model, hf_model)
  
      feature_extractor.save_pretrained(pytorch_dump_folder_path)
      hf_model.save_pretrained(pytorch_dump_folder_path)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/hubert/convert_distilhubert_original_s3prl_checkpoint_to_pytorch.py
imports: import argparse
  import torch
  from s3prl.hub import distilhubert
  from transformers import HubertConfig, HubertModel, Wav2Vec2FeatureExtractor, logging
label_desc: Use image node_id 5 for calling a global function with example usage: convert_hubert_checkpoint(pytorch_dump_folder_path, config_path) without return types