code: def iterate(self, files):
      data = self.load_data(files)
      for samples in tqdm.tqdm(data, total=len(files) // 32):
          for wav, lbl in samples:
              x = wav.unsqueeze(0).float().cuda()
  
              div = 1
              while x.size(-1) // div > self.args.max_size:
                  div += 1
  
              xs = x.chunk(div, dim=-1)
  
              result = []
              for x in xs:
                  torch.cuda.empty_cache()
                  x = self.model.feature_extractor(x)
                  if self.quantize_location == "encoder":
                      with torch.no_grad():
                          (
                              _,
                              idx,
                          ) = self.model.vector_quantizer.forward_idx(x)
                          idx = idx.squeeze(0).cpu()
                  else:
                      with torch.no_grad():
                          z = self.model.feature_aggregator(x)
                          (
                              _,
                              idx,
                          ) = self.model.vector_quantizer.forward_idx(z)
                          idx = idx.squeeze(0).cpu()
                  result.append(idx)
  
              idx = torch.cat(result, dim=0)
              yield " ".join(
                  "-".join(map(str, a.tolist())) for a in idx
              ), lbl
  
imports: import argparse
  import glob
  import os
  import os.path
  import pprint
  import soundfile
  import torch
  import fairseq
  from torch import nn
  from torch.utils.data import DataLoader
label_desc: Use image node_id 5 for calling the DatasetWriter obj's underlying member method code with example usage: obj.iterate(files) without return types