code: def __getitem__(self, idx):
      # Generate data of the expected size and datatype (based on GPTDataset).
      np_gen = np.random.default_rng(seed=(self.seed + idx))
      tokens = torch.from_numpy(
          np_gen.integers(
              self.vocab_size, size=[self.seq_length], dtype=np.int64
          )
      )
      labels = torch.from_numpy(
          np_gen.integers(
              self.vocab_size, size=[self.seq_length], dtype=np.int64
          )
      )
  
      if self.get_attention_mask_from_fusion:
          return {
              "tokens": tokens,
              "labels": labels,
              "loss_mask": self.loss_mask,
              "position_ids": self.position_ids,
          }
      else:
          return {
              "tokens": tokens,
              "labels": labels,
              "attention_mask": self.attention_mask,
              "loss_mask": self.loss_mask,
              "position_ids": self.position_ids,
          }
  
imports: import os
  import time
  import numpy
  import torch
  from omegaconf.dictconfig import DictConfig
  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.indexed_dataset import deallocate_indexed_dataset_memory
  from nemo.collections.nlp.data.language_modeling.megatron.indexed_dataset import make_dataset
  from nemo.core import Dataset
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the MockGPTDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: dict, dict