code: def _step(self, batch: Batch_FineTune, batch_idx, step_type):
      if batch is None:
          return None
      waveforms, labels, audio_lengths, label_lengths = batch
      if self.global_step <= self.freeze_encoder_updates:
          with torch.no_grad():
              x, out_len = self.model.wav2vec2.feature_extractor(
                  waveforms, audio_lengths
              )
              padding_mask = components._get_padding_mask(x, out_len)
              (
                  x,
                  attention_mask,
              ) = self.model.wav2vec2.encoder._preprocess(x, out_len)
              x, _ = self.model.mask_generator(x, padding_mask)
              x = self.model.wav2vec2.encoder.transformer(
                  x, attention_mask=attention_mask
              )
      else:
          with torch.no_grad():
              x, out_len = self.model.wav2vec2.feature_extractor(
                  waveforms, audio_lengths
              )
              padding_mask = components._get_padding_mask(x, out_len)
          x, attention_mask = self.model.wav2vec2.encoder._preprocess(
              x, out_len
          )
          x, _ = self.model.mask_generator(x, padding_mask)
          x = self.model.wav2vec2.encoder.transformer(
              x, attention_mask=attention_mask
          )
      logits = self.aux(x)
      log_probs = F.log_softmax(logits, dim=-1)
      log_probs = log_probs.transpose(0, 1)
      loss = self.loss_fn(
          log_probs,
          labels,
          out_len,
          label_lengths,
      )
      self.log(
          f"{step_type}_loss",
          loss.item() / waveforms.size(0),
          on_step=True,
          on_epoch=True,
      )
      return loss
  
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 3 for calling the HuBERTFineTuneModule obj's underlying member method code with example usage: obj._step(batch, batch_idx, step_type) and returns: loss, None