code: def setUp(self):
      torch.manual_seed(1234)
      np.random.seed(1234)
      self.multi_dataset = MultiDatasetLoader()
      self.multi_dataset._num_datasets = 3
      self.multi_dataset.current_index = 0
      numbers_dataset_a = NumbersDataset(4, "a")
      numbers_dataset_b = NumbersDataset(40, "b")
      numbers_dataset_c = NumbersDataset(4000, "c")
      self.multi_dataset.dataset_list = ["a", "b", "c"]
      self.multi_dataset._loaders = {
          "a": self._get_dataloader(numbers_dataset_a),
          "b": self._get_dataloader(numbers_dataset_b),
          "c": self._get_dataloader(numbers_dataset_c),
      }
      self.original_config = registry.get("config")
      registry.register(
          "config",
          OmegaConf.create(
              {
                  "training": {
                      "dataset_size_proportional_sampling": True,
                      "max_epochs": None,
                  },
                  "multitasking": {"enabled": False},
              }
          ),
      )
      self.multi_dataset._per_dataset_lengths = [4, 40, 4000]
      self.multi_dataset._total_length = 4044
      self.multi_dataset._total_length = sum(
          self.multi_dataset._per_dataset_lengths
      )
  
file_path: mmf/tests/datasets/test_multi_dataset_loader.py
imports: import unittest
  from collections import Counter
  import numpy
  import torch
  from mmf.common.registry import registry
  from mmf.datasets.multi_dataset_loader import MultiDatasetLoader
  from omegaconf import OmegaConf
  from torch.utils.data import DataLoader
  from ..test_utils import NumbersDataset
label_desc: Use image node_id 1 for calling the TestMultiDatasetLoader obj's underlying member method code with example usage: obj.setUp() without return types