code: def test_make_list_of_images_torch(self):
      # Test a single image is converted to a list of 1 image
      images = torch.randint(0, 256, (16, 32, 3))
      images_list = make_list_of_images(images)
      self.assertEqual(len(images_list), 1)
      self.assertTrue(np.array_equal(images_list[0], images))
      self.assertIsInstance(images_list, list)
  
      # Test a batch of images is converted to a list of images
      images = torch.randint(0, 256, (4, 16, 32, 3))
      images_list = make_list_of_images(images)
      self.assertEqual(len(images_list), 4)
      self.assertTrue(np.array_equal(images_list[0], images[0]))
      self.assertIsInstance(images_list, list)
  
      # Test a list of images is left unchanged
      images = [torch.randint(0, 256, (16, 32, 3)) for _ in range(4)]
      images_list = make_list_of_images(images)
      self.assertEqual(len(images_list), 4)
      self.assertTrue(np.array_equal(images_list[0], images[0]))
      self.assertIsInstance(images_list, list)
  
imports: import os
  import tempfile
  import unittest
  import datasets
  import numpy
  import pytest
  from huggingface_hub.file_download import http_get
  from requests import ConnectTimeout, ReadTimeout
  from tests.pipelines.test_pipelines_document_question_answering import INVOICE_URL
  from transformers import is_torch_available, is_vision_available
  from transformers.image_utils import ChannelDimension, get_channel_dimension_axis, make_list_of_images
  from transformers.testing_utils import is_flaky, require_torch, require_vision
label_desc: Use image node_id 4 for calling the ImageFeatureExtractionTester obj's underlying member method code with example usage: obj.test_make_list_of_images_torch() without return types