docstr: Given the network predictions and targets computed the binary CE
docstr_tok: ["Given","the","network","predictions","and","targets","computed","the","binary","CE"]
code: def compute_objectives(self, predictions, batch, stage):
      "Given the network predictions and targets computed the binary CE"
      predictions, lens = predictions
      targets = self.targets
  
      predictions = predictions[:, : targets.shape[-1], 0]
  
      loss = self.hparams.compute_BCE_cost(predictions, targets, lens)
  
      self.train_metrics.append(
          batch.id, torch.sigmoid(predictions), targets
      )
      if stage != sb.Stage.TRAIN:
          self.valid_metrics.append(
              batch.id, torch.sigmoid(predictions), targets
          )
      return loss
  
file_path: speechbrain/recipes/LibriParty/VAD/train.py
imports: import sys
  import torch
  import logging
  import numpy
  import speechbrain
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.distributed import run_on_main
  from data_augment import augment_data
label_desc: Use image node_id 2 for calling the VADBrain obj's underlying member method code with example usage: obj.compute_objectives(predictions, batch, stage) and returns: loss