code: def set_weights(full_name, module, fsq_value, hf_weight_path):
      hf_weight = access_by_string(module, hf_weight_path)
      hf_value = hf_weight.data
  
      if fsq_value.shape != hf_value.shape:
          raise ValueError(
              f"{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."
          )
      hf_weight.data = fsq_value
      logger.info(
          f"{full_name} was correctly initialized from {hf_weight_path}."
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/data2vec/convert_data2vec_audio_original_pytorch_checkpoint_to_pytorch.py
imports: import argparse
  import os
  from functools import reduce
  import fairseq
  import torch
  from datasets import load_dataset
  from transformers import Wav2Vec2Processor, logging
  from transformers.models.data2vec.configuration_data2vec_audio import Data2VecAudioConfig
  from transformers.models.data2vec.data2vec_audio import Data2VecAudioModel
  from transformers.models.data2vec.modeling_data2vec_audio import Data2VecAudioForCTC, Data2VecAudioModel
label_desc: Use image node_id 4 for calling a global function with example usage: set_weights(full_name, module, fsq_value, hf_weight_path) without return types