class_docstr: CTC-based grapheme-to-phoneme model.
class_docstr_tok: ["CTC-based","grapheme-to-phoneme","model","."]
code: def training_step(self, batch, batch_idx):
      (
          input_ids,
          attention_mask,
          input_len,
          targets,
          target_lengths,
      ) = batch
  
      log_probs, predictions, encoded_len = self.forward(
          input_ids=input_ids,
          attention_mask=attention_mask,
          input_len=input_len,
      )
  
      loss = self.loss(
          log_probs=log_probs,
          targets=targets,
          input_lengths=encoded_len,
          target_lengths=target_lengths,
      )
      self.log("train_loss", loss)
      return loss
  
imports: import os
  import string
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Union
  import torch
  from hydra.utils import instantiate
  from omegaconf import DictConfig, ListConfig, OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from transformers import AutoConfig, AutoModel, AutoTokenizer
  from nemo.collections.tts.g2p.data.ctc import CTCG2PBPEDataset
  from nemo.collections.tts.models.base import G2PModel
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 5 for calling the CTCG2PModel obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: loss