code: def test_list_of_tensors(self):
      range20 = np.expand_dims(np.arange(20), 1)
      dataset = TensorDataset((range20, range20 * 2), range20 * 3)
      self.assertEqual(len(dataset), 20)
      self.assertEqual(type(dataset[0]), tuple)
      self.assertEqual(type(dataset[0][0]), tuple)
      self.assertEqual(type(dataset[0][-1]), np.ndarray)
      for i in range(20):
          self.assertEqual(dataset[i][0][0], i)
          self.assertEqual(dataset[i][0][1], i * 2)
          self.assertEqual(dataset[i][1], i * 3)
  
      dataset = TensorDataset(
          (range20, range20 * 2), (range20 * 3, range20 * 4)
      )
      self.assertEqual(len(dataset), 20)
  
      self.assertEqual(type(dataset[0]), tuple)
      self.assertEqual(type(dataset[1]), tuple)
      self.assertEqual(type(dataset[0][0]), tuple)
      self.assertEqual(type(dataset[0][1]), tuple)
      for i in range(20):
          self.assertEqual(type(dataset[i][0][0]), np.ndarray)
          self.assertEqual(type(dataset[i][0][1]), np.ndarray)
          self.assertEqual(dataset[i][0][0], i)
          self.assertEqual(dataset[i][0][1], i * 2)
          self.assertEqual(type(dataset[i][1][0]), np.ndarray)
          self.assertEqual(type(dataset[i][1][1]), np.ndarray)
          self.assertEqual(dataset[i][1][0], i * 3)
          self.assertEqual(dataset[i][1][1], i * 4)
  
imports: import unittest
  import warnings
  from collections import OrderedDict
  from unittest import TestCase, skipIf
  from unittest.mock import MagicMock, call
  import numpy
  import torch
  from torch.nn.utils.rnn import PackedSequence
  from poutyne import TensorDataset, get_batch_size, torch_apply, warning_settings
  from poutyne.utils import _concat
  from tests.utils import populate_packed_sequence
label_desc: Use image node_id 3 for calling the TensorDatasetTest obj's underlying member method code with example usage: obj.test_list_of_tensors() without return types