class_docstr: CTC-based grapheme-to-phoneme model.
class_docstr_tok: ["CTC-based","grapheme-to-phoneme","model","."]
code: def setup_training_data(self, cfg: DictConfig):
      if not cfg or cfg.manifest_filepath is None:
          logging.info(
              f"Dataloader config or file_path for the train is missing, so no data loader for train is created!"
          )
          self._train_dl = None
          return
      self._train_dl = self._setup_dataloader_from_config(
          cfg, name="train"
      )
  
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 14 for calling the CTCG2PModel obj's underlying member method code with example usage: obj.setup_training_data(cfg) without return types