code: def fit_batch(self, batch):
      amp = AMPConfig.from_name(self.precision)
      should_step = (self.step % self.grad_accumulation_factor) == 0
  
      # Managing automatic mixed precision
      with self.no_sync(not should_step):
          if self.use_amp:
              with torch.autocast(
                  dtype=amp.dtype,
                  device_type=torch.device(self.device).type,
              ):
                  outputs = self.compute_forward(batch, Stage.TRAIN)
                  objectives = self.compute_objectives(
                      outputs, batch, Stage.TRAIN
                  )
          else:
              outputs = self.compute_forward(batch, Stage.TRAIN)
              objectives = self.compute_objectives(
                  outputs, batch, Stage.TRAIN
              )
  
          self.scaler.scale(
              objectives["backprop_loss"]
              / self.grad_accumulation_factor
          ).backward()
  
          objectives["total_loss"] = objectives[
              "backprop_loss"
          ].detach()
  
      if should_step:
          self.optimizers_step()
          self.on_fit_batch_end(objectives)
  
      return objectives["backprop_loss"].detach()
  
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 3 for calling the W2V2Brain obj's underlying member method code with example usage: obj.fit_batch(batch) and returns: objectives