code: def __getitem__(self, idx):
      vocab_size = self.tokenizer.vocab_size
  
      neighbors = self._cfg.data.neighbors
      input_length = self._cfg.data.seq_length
      chunks = input_length // self._cfg.chunk_size
      chunk_size = self._cfg.chunk_size
      pad_id = self.tokenizer.pad_id
  
      all_tokens = torch.randint(0, vocab_size, (input_length + 1,))
      # make sure the eod happens at the end of each chunk, can add paddings to it
      # e.g. [..., id, id, pad, pad, pad, eod]  each has chunk_size, each sentence
      # has length of multiple of chunk_size
      hidden = all_tokens[:-1]
      labels = all_tokens[1:]
  
      hidden_mask = hidden != pad_id
      # to mask out the token ids [id, id,  eod, id, pad, eod, id, id]
      # so attention is not across eod, mask should be:
      # [false, true,  true, true,  true, true,  true,  true]
      # [false, false, true, true,  true, true,  true,  true]
      # [false, false, false,true,  true, true,  true,  true]
      # [true,  true,  true, false, true, true,  true,  true]
      # [true,  true,  true, true,  true, true,  true,  true]
      # [true,  true,  true, false, true, false, true,  true]
      # [true,  true,  true, true,  true, true,  false, true]
      # [true,  true,  true, true,  true, true,  false, false]
      retrieved = torch.randint(
          0, vocab_size, (chunks, neighbors, 2 * chunk_size)
      )
  
      context_mask = retrieved != pad_id
  
      return {
          "tokens": hidden,
          "labels": labels,
          "tokens_mask": hidden_mask,
          "loss_mask": hidden_mask,
          "retrieved_emb_mask": context_mask,
          "retrieved_ids": retrieved,
      }
  
imports: import os
  from typing import List
  import numpy
  import torch
  from nemo.collections.nlp.data.language_modeling.megatron.base_dataset_utils import get_datasets_weights_and_num_samples, get_train_valid_test_split_
  from nemo.collections.nlp.data.language_modeling.megatron.blendable_dataset import BlendableDataset
  from nemo.collections.nlp.data.language_modeling.megatron.gpt_dataset import _build_index_mappings, get_indexed_dataset_
  from nemo.collections.nlp.data.language_modeling.megatron.indexed_retrieval_dataset import KNNIndex, MMapRetrievalIndexedDataset
  from nemo.core import Dataset
  from nemo.utils import logging
label_desc: Use image node_id 3 for calling the MockRETRODataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: dict