code: def on_stage_end(self, stage, stage_loss, epoch=None):
      stage_stats = {"loss": stage_loss}
      if stage == sb.Stage.TRAIN:
          self.train_stats = stage_stats
  
      if stage == sb.Stage.VALID:
          print(self.acc_metric)
          stage_stats["accuracy"] = sum(self.acc_metric) / len(
              self.acc_metric
          )
  
          self.hparams.train_stage_logger.log_stats(
              stats_meta={
                  "epoch": epoch,
                  "steps": self.optimizer_step,
                  "lr": self.optimizer.param_groups[0]["lr"],
              },
              train_stats=self.train_stats,
              valid_stats=stage_stats,
          )
  
          self.checkpointer.save_and_keep_only(
              end_of_epoch=True,
              num_to_keep=5,
              meta={"valid_loss": stage_loss},
          )
  
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 7 for calling the W2V2Brain obj's underlying member method code with example usage: obj.on_stage_end(stage, stage_loss, epoch) without return types