class_docstr: T5-based grapheme-to-phoneme model.
code: def _setup_dataloader_from_config(self, cfg, name):
      if "dataloader_params" not in cfg or not isinstance(
          cfg.dataloader_params, DictConfig
      ):
          raise ValueError(f"No dataloader_params for {name}")
  
      dataset = instantiate(
          cfg.dataset,
          manifest_filepath=cfg.manifest_filepath,
          tokenizer=self._tokenizer,
          max_source_len=self.max_source_len,
          max_target_len=self.max_target_len,
          do_lower=self.do_lower,
          grapheme_field=cfg.get("grapheme_field", "text_graphemes"),
          phoneme_field=cfg.get("phoneme_field", "text"),
          with_labels=True,
      )
  
      return torch.utils.data.DataLoader(
          dataset,
          collate_fn=dataset.collate_fn,
          **cfg.dataloader_params,
      )
  
imports: from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Union
  import torch
  from hydra.utils import instantiate
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from transformers import AutoTokenizer, T5ForConditionalGeneration
  from nemo.collections.asr.metrics.wer import word_error_rate
  from nemo.collections.tts.g2p.data.t5 import T5G2PDataset
  from nemo.collections.tts.models.base import G2PModel
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.neural_types import LabelsType, LossType, MaskType, NeuralType, TokenIndex
  from nemo.utils import logging
label_desc: Use image node_id 14 for calling the T5G2PModel obj's underlying member method code with example usage: obj._setup_dataloader_from_config(cfg, name) and returns: torch