class_docstr: Megatron T5 pretraining
class_docstr_tok: ["Megatron","T5","pretraining"]
code: def _build_vocab(self):
      self.num_sentinel_tokens = self._cfg.tokenizer.num_sentinel_tokens
      MegatronT5Model.add_special_tokens_to_tokenizer(
          tokenizer=self.tokenizer,
          tokenizer_cfg=self._cfg.tokenizer,
          dataset_type=self._cfg.data.get("dataset_type", "t5"),
          add_sentinel_tokens_in_reverse_order=self._cfg.tokenizer.get(
              "add_sentinel_tokens_in_reverse_order", False
          ),
          add_sentinel_tokens_first=self._cfg.tokenizer.get(
              "add_sentinel_tokens_first", False
          ),
      )
      super()._build_vocab()
  
imports: import enum
  import math
  from omegaconf.dictconfig import DictConfig
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.nlp.data.language_modeling.megatron.dataset_utils import build_train_valid_test_datasets
  from nemo.collections.nlp.models.language_modeling.megatron_lm_encoder_decoder_model import MegatronLMEncoderDecoderModel
  from nemo.utils import logging
label_desc: Use image node_id 5 for calling the MegatronT5Model obj's underlying member method code with example usage: obj._build_vocab() without return types