class_docstr: T5-based grapheme-to-phoneme model.
docstr: Lightning calls this inside the test loop with the data from the test dataloader
  passed in as `batch`.
docstr_tok: ["Lightning","calls","this","inside","the","test","loop","with","the","data","from","the","test","dataloader","passed","in","as","`","batch","`","."]
code: def test_step(self, batch, batch_idx, dataloader_idx=0):
      """
      Lightning calls this inside the test loop with the data from the test dataloader
      passed in as `batch`.
      """
      return self.validation_step(
          batch, batch_idx, dataloader_idx, split="test"
      )
  
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 10 for calling the T5G2PModel obj's underlying member method code with example usage: obj.test_step(batch, batch_idx, dataloader_idx) and returns: self