code: def __getitem__(self, idx):
      # Generate output values randomly with the expected size and datatype
      np_gen = np.random.default_rng(seed=(self.seed + idx))
      tokens_enc = np_gen.integers(
          self.vocab_size, size=[self.max_seq_length], dtype=np.int64
      )
      tokens_dec_in = np_gen.integers(
          self.vocab_size,
          size=[self.max_seq_length_dec],
          dtype=np.int64,
      )
      labels = np_gen.integers(
          self.vocab_size,
          size=[self.max_seq_length_dec],
          dtype=np.int64,
      )
      enc_mask = np.ones(shape=[self.max_seq_length], dtype=np.int64)
      dec_mask = np.ones(
          shape=[self.max_seq_length_dec], dtype=np.int64
      )
      loss_mask = np.ones(
          shape=[self.max_seq_length_dec], dtype=np.int64
      )
  
      training_sample = {
          "text_enc": tokens_enc,
          "text_dec": tokens_dec_in,
          "labels": labels,
          "loss_mask": loss_mask,
          "enc_mask": enc_mask,
          "dec_mask": dec_mask,
      }
      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 4 for calling the MockT5Dataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: training_sample