code: def __getitem__(self, idx):
      sample, seq_length = self._get_sample(idx)
      # Note that this rng state should be numpy and not python since
      # python randint is inclusive whereas the numpy one is exclusive.
      np_rng = np.random.RandomState(seed=(self.seed + idx))
      training_sample = T5Dataset.build_training_sample(
          sample=sample,
          target_seq_length=seq_length,
          np_rng=np_rng,
          max_seq_length=self.max_seq_length,
          max_seq_length_dec=self.max_seq_length_dec,
          masked_lm_prob=self.masked_lm_prob,
          vocab_id_list=self.vocab_id_list,
          vocab_id_to_token_dict=self.vocab_id_to_token_dict,
          cls_id=self.cls_id,
          sep_id=self.sep_id,
          mask_id=self.mask_id,
          max_ngram_size=self.max_ngram_size,
          mean_ngram_size=self.mean_ngram_size,
          whole_word_masking=self.whole_word_masking,
          favor_long_ngrams=self.favor_long_ngrams,
          permutation=self.permutation,
          geometric_dist=self.geometric_dist,
          tokenizer_type=self.tokenizer_type,
          sentinel_tokens=self.sentinel_tokens,
          bos_id=self.bos_id,
          eos_id=self.eos_id,
          pad_id=self.pad_id,
      )
      return training_sample
  
imports: import collections
  import os
  import numpy
  import torch
  from nemo.collections.common.tokenizers import SentencePieceTokenizer, YouTokenToMeTokenizer
  from nemo.collections.nlp.data.language_modeling.megatron.dataset_utils import create_masked_lm_predictions, get_samples_mapping
  from nemo.collections.nlp.data.language_modeling.megatron.gpt_dataset import _build_index_mappings
  from nemo.core import Dataset
label_desc: Use image node_id 5 for calling the T5Dataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: training_sample