code: def _test(self, loader):
      self.model.eval()
  
      total_si_snri = torch.zeros(
          1, dtype=torch.float32, device=self.device
      )
      total_sdri = torch.zeros(
          1, dtype=torch.float32, device=self.device
      )
  
      for batch in loader:
          mix = batch.mix.to(self.device)
          src = batch.src.to(self.device)
          mask = batch.mask.to(self.device)
  
          estimate = self.model(mix)
  
          si_snri, sdri = si_sdr_improvement(estimate, src, mix, mask)
  
          total_si_snri += si_snri.sum()
          total_sdri += sdri.sum()
  
          if self.debug:
              break
  
      dist.all_reduce(total_si_snri, dist.ReduceOp.SUM)
      dist.all_reduce(total_sdri, dist.ReduceOp.SUM)
  
      num_samples = len(loader.dataset)
      metric = Metric(
          total_si_snri.item() / num_samples,
          total_sdri.item() / num_samples,
      )
      return metric
  
file_path: audio/examples/source_separation/conv_tasnet/trainer.py
imports: import time
  from collections import namedtuple
  from typing import Tuple
  import torch
  import torch.distributed
  from utils import dist_utils, metrics
label_desc: Use image node_id 5 for calling the Trainer obj's underlying member method code with example usage: obj._test(loader) and returns: metric