code: def _main():
      args = _parse_args()
      _init_logging(args.debug)
      loader = Loader()
      model = _get_model(args.model_file, args.dict_dir).eval()
      encoder = Encoder(model)
      decoder = _get_decoder()
      _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(decoder).save(
          os.path.join(args.output_path, "decoder.zip")
      )
      scripted = torch.jit.script(encoder)
      if args.optimize_for_mobile:
          scripted = optimize_for_mobile(scripted)
      scripted.save(os.path.join(args.output_path, "encoder.zip"))
  
imports: import argparse
  import logging
  import os
  from typing import Tuple
  import fairseq
  import torch
  import torchaudio
  from greedy_decoder import Decoder
  from torch.utils.mobile_optimizer import optimize_for_mobile
  from torchaudio.models.wav2vec2.utils.import_fairseq import import_fairseq_model
label_desc: Use image node_id 5 for calling a global function with example usage: _main() without return types