docstr: Returns accuracy on contrastive objective. 
docstr_tok: ["Returns","accuracy","on","contrastive","objective","."]
code: def evaluate_batch(self, batch, stage):
      """Returns accuracy on contrastive objective."""
      out = self.compute_forward(batch, stage=stage)
      objectives = self.compute_objectives(out, batch, stage=stage)
      return objectives["backprop_loss"].detach().cpu()
  
imports: import logging
  import sys
  import time
  from functools import partial
  import speechbrain
  import torch
  import torch.nn.functional
  from torch.nn.parallel import DistributedDataParallel
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain import Stage
  from speechbrain.utils.distributed import run_on_main
  from speechbrain.dataio.dataloader import SaveableDataLoader
  from speechbrain.dataio.sampler import DynamicBatchSampler
  from speechbrain.lobes.models.wav2vec import w2v_mask_collate_fn
  from speechbrain.lobes.models.wav2vec import sample_negatives
  from speechbrain.core import AMPConfig
label_desc: Use image node_id 5 for calling the W2V2Brain obj's underlying member method code with example usage: obj.evaluate_batch(batch, stage) and returns: objectives