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
      output, src_lengths, _, _, tcpgen_dist, p_gen = self.model(
          batch.features,
          batch.feature_lengths,
          prepended_targets,
          prepended_target_lengths,
          batch.tries,
          self.current_epoch,
      )
      if (
          self.biasing
          and self.current_epoch >= self.tcpsche
          and p_gen is not None
      ):
          # Assuming blank is the last token
          model_output = torch.softmax(output, dim=-1)
          p_not_null = 1.0 - model_output[:, :, :, -1:]
          # Exclude blank prob
          ptr_dist_fact = (
              torch.cat(
                  [
                      tcpgen_dist[:, :, :, :-2],
                      tcpgen_dist[:, :, :, -1:],
                  ],
                  dim=-1,
              )
              * p_not_null
          )
          ptr_gen_complement = tcpgen_dist[:, :, :, -1:] * p_gen
          # Interpolate between TPGen distribution and model distribution
          p_partial = ptr_dist_fact[
              :, :, :, :-1
          ] * p_gen + model_output[:, :, :, :-1] * (
              1 - p_gen + ptr_gen_complement
          )
          # Add blank back
          p_final = torch.cat(
              [p_partial, model_output[:, :, :, -1:]], dim=-1
          )
          # Numerical stability? Didn't need to do this in Espnet
          logsmax_output = torch.log(p_final + 1e-12)
      else:
          logsmax_output = torch.log_softmax(output, dim=-1)
      loss = self.loss(
          logsmax_output,
          batch.targets,
          src_lengths,
          batch.target_lengths,
      )
      self.log(
          f"Losses/{step_type}_loss",
          loss,
          on_step=True,
          on_epoch=True,
          batch_size=batch.targets.size(0),
      )
  
      return loss
  
imports: import logging
  import math
  from collections import namedtuple
  from typing import List, Tuple
  import sentencepiece
  import torch
  import torchaudio
  from pytorch_lightning import LightningModule
  from torchaudio.prototype.models import conformer_rnnt_biasing_base, Hypothesis, RNNTBeamSearchBiasing
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