code: def _step(self, batch, _, step_type):
      if batch is None:
          return None
  
      prepended_targets = batch.targets.new_empty(
          [batch.targets.size(0), batch.targets.size(1) + 1]
      )
      prepended_targets[:, 1:] = batch.targets
      prepended_targets[:, 0] = self.blank_idx
      prepended_target_lengths = batch.target_lengths + 1
      features = self.frontend(batch.inputs)
      output, src_lengths, _, _ = self.model(
          features,
          batch.input_lengths,
          prepended_targets,
          prepended_target_lengths,
      )
      loss = self.loss(
          output, batch.targets, src_lengths, batch.target_lengths
      )
      self.log(
          f"Losses/{step_type}_loss", loss, on_step=True, on_epoch=True
      )
  
      return loss
  
imports: import itertools
  import math
  from collections import namedtuple
  from typing import List, Tuple
  import sentencepiece
  import torch
  import torchaudio
  from models.conformer_rnnt import conformer_rnnt
  from models.emformer_rnnt import emformer_rnnt
  from models.resnet import video_resnet
  from models.resnet1d import audio_resnet
  from pytorch_lightning import LightningModule
  from schedulers import WarmupCosineScheduler
  from torchaudio.models import Hypothesis, RNNTBeamSearch
label_desc: Use image node_id 2 for calling the ConformerRNNTModule obj's underlying member method code with example usage: obj._step(batch, _, step_type) and returns: loss, None