code: def __init__(
      self,
      name,
      indexed_dataset,
      data_prefix,
      num_epochs,
      max_num_samples,
      masked_lm_prob,
      max_seq_length,
      max_seq_length_dec,
      short_seq_prob,
      seed,
  ):
      # Params to store.
      self.name = name
      self.desc = name
      self.seed = seed
      self.masked_lm_prob = masked_lm_prob
      self.max_seq_length = max_seq_length
      self.max_seq_length_dec = max_seq_length_dec
  
      # Dataset.
      self.indexed_dataset = indexed_dataset
  
      # Build the samples mapping.
      self.samples_mapping = get_samples_mapping(
          self.indexed_dataset,
          data_prefix,
          num_epochs,
          max_num_samples,
          self.max_seq_length - 2,  # account for added tokens
          short_seq_prob,
          self.seed,
          self.name,
          False,
      )
  
      # Vocab stuff.
      tokenizer = get_tokenizer()
      self.vocab_id_list = list(tokenizer.inv_vocab.keys())
      self.vocab_id_to_token_dict = tokenizer.inv_vocab
      self.cls_id = tokenizer.cls
      self.sep_id = tokenizer.sep
      self.mask_id = tokenizer.mask
      self.pad_id = tokenizer.pad
      self.bos_id = tokenizer.bos_token_id
      self.eos_id = tokenizer.eos_token_id
      self.sentinel_tokens = tokenizer.additional_special_tokens_ids
      assert (
          len(self.sentinel_tokens) > 0
      ), "Provide the argument --vocab-extra-ids 100 to the script"
  
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 1 to create a new T5Dataset object from inherited base classes: torch.utils.data with example: obj = T5Dataset(name, indexed_dataset, data_prefix, num_epochs, max_num_samples, masked_lm_prob, max_seq_length, max_seq_length_dec, short_seq_prob, seed)