code: def main(args, logger):
      # Acoustic Model
      logger.info(
          f"Loading acoustic model from {args.tts_model_path}..."
      )
      feature_reader_cls = get_feature_reader(args.feature_type)
      reader = feature_reader_cls(
          checkpoint_path=args.acoustic_model_path, layer=args.layer
      )
  
      # K-means Model
      logger.info(
          f"Loading K-means model from {args.kmeans_model_path} ..."
      )
      kmeans_model = joblib.load(open(args.kmeans_model_path, "rb"))
      kmeans_model.verbose = False
  
      # TTS Model
      logger.info(f"Loading TTS model from {args.tts_model_path}...")
      tacotron_model, sample_rate, hparams = load_tacotron(
          tacotron_model_path=args.tts_model_path,
          max_decoder_steps=args.max_decoder_steps,
      )
  
      # Waveglow Model
      logger.info(
          f"Loading Waveglow model from {args.waveglow_path}..."
      )
      waveglow, denoiser = load_waveglow(
          waveglow_path=args.waveglow_path
      )
  
      # Dataset
      if not os.path.exists(hparams.code_dict):
          hparams.code_dict = args.code_dict_path
      tts_dataset = TacotronInputDataset(hparams)
  
      iters = 0
      while True:
          in_file_path = input(
              "Input: Enter the full file path of audio file...\n"
          )
          out_file_path = input(
              "Output: Enter the full file path of audio file...\n"
          )
          feats = reader.get_feats(in_file_path).cpu().numpy()
          iters += 1
          if iters == 1000:
              gc.collect()
              torch.cuda.empty_cache()
  
          quantized_units = kmeans_model.predict(feats)
          quantized_units_str = " ".join(map(str, quantized_units))
  
          tts_input = tts_dataset.get_tensor(quantized_units_str)
          mel, aud, aud_dn, has_eos = synthesize_audio(
              tacotron_model,
              waveglow,
              denoiser,
              tts_input.unsqueeze(0),
              strength=args.denoiser_strength,
          )
          sf.write(
              f"{out_file_path}",
              aud_dn[0].cpu().float().numpy(),
              sample_rate,
          )
          logger.info("Resynthesis done!\n")
  
file_path: fairseq/examples/textless_nlp/gslm/tools/resynthesize_speech.py
imports: import argparse
  import gc
  import logging
  import os
  import joblib
  import soundfile
  import torch
  from examples.textless_nlp.gslm.speech2unit.pretrained.utils import get_feature_reader
  from examples.textless_nlp.gslm.unit2speech.tts_data import TacotronInputDataset
  from examples.textless_nlp.gslm.unit2speech.utils import load_tacotron, load_waveglow, synthesize_audio
label_desc: Use image node_id 3 for calling a global function with example usage: main(args, logger) without return types