class_docstr: T5-based grapheme-to-phoneme model.
code: def __init__(self, cfg: DictConfig, trainer: Trainer = None):
      self.world_size = 1
      if trainer is not None:
          self.world_size = trainer.num_nodes * trainer.num_devices
  
      # Load appropriate tokenizer from HuggingFace
      self.model_name = cfg.model_name
      self._tokenizer = AutoTokenizer.from_pretrained(self.model_name)
  
      self.max_source_len = cfg.get(
          "max_source_len", self._tokenizer.model_max_length
      )
      self.max_target_len = cfg.get(
          "max_target_len", self._tokenizer.model_max_length
      )
      self.do_lower = cfg.get("do_lower", False)
  
      # Ensure passed cfg is compliant with schema
      schema = OmegaConf.structured(T5G2PConfig)
      # ModelPT ensures that cfg is a DictConfig, but do this second check in case ModelPT changes
      if isinstance(cfg, dict):
          cfg = OmegaConf.create(cfg)
      elif not isinstance(cfg, DictConfig):
          raise ValueError(
              f"cfg was type: {type(cfg)}. Expected either a dict or a DictConfig"
          )
      OmegaConf.merge(cfg, schema)
  
      super().__init__(cfg, trainer)
  
      # Load pretrained T5 model from HuggingFace
      self.model = T5ForConditionalGeneration.from_pretrained(
          self.model_name
      )
  
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 3 to create a new T5G2PModel object from inherited base classes: G2PModel with example: obj = T5G2PModel(cfg, trainer)