class_docstr: CTC-based grapheme-to-phoneme model.
class_docstr_tok: ["CTC-based","grapheme-to-phoneme","model","."]
code: def _setup_dataloader_from_config(self, cfg: DictConfig, name: str):
      if "dataloader_params" not in cfg or not isinstance(
          cfg.dataloader_params, DictConfig
      ):
          raise ValueError(f"No dataloader_params for {name}")
  
      if not os.path.exists(cfg.manifest_filepath):
          raise ValueError(f"{cfg.dataset.manifest_filepath} not found")
  
      dataset = instantiate(
          cfg.dataset,
          manifest_filepath=cfg.manifest_filepath,
          phoneme_field=cfg.dataset.phoneme_field,
          grapheme_field=cfg.dataset.grapheme_field,
          tokenizer_graphemes=self.tokenizer_grapheme,
          do_lower=self._cfg.tokenizer_grapheme.do_lower,
          tokenizer_phonemes=self.tokenizer,
          labels=self.vocabulary,
          max_source_len=self.max_source_len,
          with_labels=True,
      )
  
      return torch.utils.data.DataLoader(
          dataset,
          collate_fn=dataset.collate_fn,
          **cfg.dataloader_params,
      )
  
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 13 for calling the CTCG2PModel obj's underlying member method code with example usage: obj._setup_dataloader_from_config(cfg, name) and returns: torch