code: def _main():
      args = _parse_args()
      _init_logging(args.debug)
      _LG.info("Loading model: %s", args.model)
      model, labels = _get_model(args.model)
      _LG.info("Labels: %s", labels)
      _LG.info("Building pipeline")
      loader = Loader()
      encoder = Encoder(model)
      decoder = _get_decoder(labels)
      _LG.info(encoder)
  
      if args.quantize:
          _LG.info("Quantizing the model")
          model.encoder.transformer.pos_conv_embed.__prepare_scriptable__()
          encoder = tq.quantize_dynamic(
              encoder, qconfig_spec={torch.nn.Linear}, dtype=torch.qint8
          )
          _LG.info(encoder)
  
      # test
      if args.test_file:
          _LG.info("Testing with %s", args.test_file)
          waveform = loader(args.test_file)
          emission = encoder(waveform)
          transcript = decoder(emission)
          _LG.info(transcript)
  
      torch.jit.script(loader).save(
          os.path.join(args.output_path, "loader.zip")
      )
      torch.jit.script(encoder).save(
          os.path.join(args.output_path, "encoder.zip")
      )
      torch.jit.script(decoder).save(
          os.path.join(args.output_path, "decoder.zip")
      )
  
imports: import argparse
  import logging
  import os
  from typing import Tuple
  import torch
  import torchaudio
  from greedy_decoder import Decoder
  from torchaudio.models.wav2vec2.utils.import_huggingface import import_huggingface_model
label_desc: Use image node_id 4 for calling a global function with example usage: _main() without return types