code: def __getitem__(self, idx):
      start_index, end_index, seq_length = self.samples_mapping[idx]
      sample = []
      for index in range(start_index, end_index):
          sample.append(self.indexed_dataset[index])
      # 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))
      return build_training_sample(
          sample,
          seq_length,
          self.max_seq_length,  # needed for padding
          self.max_seq_length_dec,
          self.vocab_id_list,
          self.vocab_id_to_token_dict,
          self.cls_id,
          self.sep_id,
          self.mask_id,
          self.pad_id,
          self.masked_lm_prob,
          np_rng,
          self.bos_id,
          self.eos_id,
          self.sentinel_tokens,
      )
  
file_path: megatron-lm/megatron/data/t5_dataset.py
imports: import collections
  import numpy
  import torch
  from megatron import get_tokenizer
  from megatron.data.dataset_utils import create_masked_lm_predictions, get_samples_mapping
label_desc: Use image node_id 3 for calling the T5Dataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: build_training_sample