code: def load_conv_layer(
      full_name, value, feature_extractor, unused_weights
  ):
      name = full_name.split("conv_layers.")[-1]
      items = name.split(".")
      layer_id = int(items[0])
      type_id = int(items[1])
  
      weight_type = name.split(".")[-1]
      if type_id == 0:
          layer_type = "conv"
      elif type_id == 2:
          layer_type = "layer_norm"
      else:
          unused_weights.append(full_name)
          return
  
      set_weights(
          full_name,
          feature_extractor,
          value,
          f"conv_layers.{layer_id}.{layer_type}.{weight_type}",
      )
  
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 5 for calling a global function with example usage: load_conv_layer(full_name, value, feature_extractor, unused_weights) without return types