code: def _step(self, batch: Batch, batch_idx, step_type):
      if batch is None:
          return None, None
      waveforms, labels, audio_lengths = batch
      if step_type == "val":
          with torch.no_grad():
              logit_m, logit_u, feature_penalty = self.model(
                  waveforms,
                  labels,
                  audio_lengths,
              )
      else:
          logit_m, logit_u, feature_penalty = self.model(
              waveforms,
              labels,
              audio_lengths,
          )
      loss = self.loss(logit_m, logit_u, feature_penalty)
      if not torch.isinf(loss) and not torch.isnan(loss):
          self.log(
              f"{step_type}_loss",
              loss.item() / logit_m.size(0),
              on_step=True,
              on_epoch=True,
          )
      else:
          self.nan_loss_count += 1
          self.log(
              "nan_loss_count",
              self.nan_loss_count,
              on_step=True,
              on_epoch=True,
          )
  
      # log accuracies of masked and unmasked frames
      correct_m, count_m = _compute_accuracy(logit_m)
      correct_u, count_u = _compute_accuracy(logit_u)
      self.mask_stats[step_type]["correct"] += correct_m
      self.mask_stats[step_type]["count"] += count_m
      self.unmask_stats[step_type]["correct"] += correct_u
      self.unmask_stats[step_type]["count"] += count_u
      self.log(
          f"{step_type}_masked_accuracy",
          self.mask_stats[step_type]["correct"]
          / self.mask_stats[step_type]["count"],
          on_step=True,
          on_epoch=True,
          sync_dist=True,
          prog_bar=step_type == "train",
      )
      self.log(
          f"{step_type}_unmasked_accuracy",
          self.unmask_stats[step_type]["correct"]
          / self.unmask_stats[step_type]["count"],
          on_step=True,
          on_epoch=True,
          sync_dist=True,
          prog_bar=step_type == "train",
      )
      return loss, logit_m.size(0)
  
imports: import math
  from typing import Optional, Tuple
  import torch
  import torch.nn.functional
  import torchaudio
  import torchaudio.models.wav2vec2.components
  from dataset import _get_lengths_librilightlimited, _get_lengths_librispeech, BucketizeBatchSampler, CollateFnHubert, CollateFnLibriLightLimited, DistributedBatchSampler, HuBERTDataSet
  from lightning.pytorch import LightningModule
  from loss import hubert_loss
  from torch import Tensor
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
label_desc: Use image node_id 2 for calling the HuBERTPreTrainModule obj's underlying member method code with example usage: obj._step(batch, batch_idx, step_type) and returns: loss,  logit_m, None,  None