code: def __init__(self, cfg, trainer, tokenizer, name, size):
      super().__init__()
      self.name = name
      self.tokenizer = tokenizer
      self._cfg = cfg
      self.size = size
      seed_val = parallel_state.get_data_parallel_rank() * 131 + 97
      torch.manual_seed(seed_val)
  
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 1 to create a new MockRETRODataset object from inherited base classes: torch.utils.data with example: obj = MockRETRODataset(cfg, trainer, tokenizer, name, size)