code: def test_concat_dataset():
      token_ids = torch.tensor(
          [[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.long
      )
      label_ids = torch.tensor(
          [[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.long
      )
      labeled_dataset = TensorDataset(token_ids, label_ids)
      unlabeled_dataset = TensorDataset(token_ids)
      concat_dataset = CombinedTensorDataset(
          [labeled_dataset, unlabeled_dataset]
      )
      expected_tokens = torch.tensor(
          [
              [1, 2, 3],
              [4, 5, 6],
              [7, 8, 9],
              [1, 2, 3],
              [4, 5, 6],
              [7, 8, 9],
          ],
          dtype=torch.long,
      )
      expected_labels = torch.tensor(
          [
              [1, 2, 3],
              [4, 5, 6],
              [7, 8, 9],
              [0, 0, 0],
              [0, 0, 0],
              [0, 0, 0],
          ],
          dtype=torch.long,
      )
      assert torch.equal(concat_dataset.tensors[0], expected_tokens)
      assert torch.equal(concat_dataset.tensors[1], expected_labels)
  
file_path: nlp-architect/tests/test_data_utils.py
imports: import math
  import os
  import torch
  from nlp_architect.data.utils import split_column_dataset
  from tests.utils import count_examples
  from nlp_architect.nn.torch.data.dataset import CombinedTensorDataset
  from torch.utils.data import TensorDataset
label_desc: Use image node_id 1 for calling a global function with example usage: test_concat_dataset() without return types