code: def predict(
      self,
      dataset,
      sampler=None,
      batch_size=16,
      n_jobs=1,
      collate_fn=None,
  ):
      if next(self.parameters()).device != self.device:
          self.to(self.device)
  
      if n_jobs == -1:
          n_jobs = psutil.cpu_count()
  
      if batch_size == 1:
          n_jobs = 0
      data_loader = torch.utils.data.DataLoader(
          dataset,
          batch_size=batch_size,
          num_workers=n_jobs,
          sampler=sampler,
          collate_fn=collate_fn,
          pin_memory=True,
      )
  
      if self.training:
          self.eval()
  
      if self.using_tpu:
          tk0 = data_loader
      else:
          tk0 = tqdm(data_loader, total=len(data_loader))
  
      for _, data in enumerate(tk0):
          with torch.no_grad():
              out = self.predict_one_step(data)
              out = self.process_output(out)
              yield out
  
          if not self.using_tpu:
              tk0.set_postfix(stage="test")
  
      if not self.using_tpu:
          tk0.close()
  
imports: import warnings
  import psutil
  import torch
  import torch.nn
  from tqdm import tqdm
  from tez import enums
  from tez.callbacks import CallbackRunner
  from tez.utils import AverageMeter
label_desc: Use image node_id 21 for calling the Model obj's underlying member method code with example usage: obj.predict(dataset, sampler, batch_size, n_jobs, collate_fn) without return types