code: def test_img_pil_to_rgb_mode(self) -> None:
      one_channel_input = (torch.ones((28, 28)) * 255).to(
          dtype=torch.uint8
      )
      one_channel_input = Image.fromarray(
          one_channel_input.numpy(), mode="L"
      )
  
      # Test without modifying the image size
      transform = Compose([MNISTImgPil2RGB.from_config({}), ToTensor()])
      output = transform(one_channel_input)
      assert output.shape == torch.Size([3, 28, 28])
      assert (
          output.sum().item() == 28 * 28 * 3
      ), "Background should be black, center is gray scale"
  
      # Test with modifying the image size (try the two valid formats)
      for size in [32, [32, 32]]:
          transform = Compose(
              [
                  MNISTImgPil2RGB.from_config(
                      {"size": size, "box": [2, 2]}
                  ),
                  ToTensor(),
              ]
          )
          output = transform(one_channel_input)
          assert (
              output.sum().item() == 28 * 28 * 3
          ), "Background should be black, center is gray scale"
          assert (
              output[:, 2:-2, 2:-2].sum().item() == 28 * 28 * 3
          ), "Paste should be in the middle"
  
file_path: vissl/tests/test_transforms.py
imports: import unittest
  import numpy
  import torch
  from PIL import Image
  from torchvision.transforms import Compose, ToTensor
  from vissl.data.ssl_transforms.img_pil_to_multicrop import ImgPilToMultiCrop
  from vissl.data.ssl_transforms.img_pil_to_tensor import ImgToTensor
  from vissl.data.ssl_transforms.mnist_img_pil_to_rgb_mode import MNISTImgPil2RGB
  from vissl.utils.hydra_config import compose_hydra_configuration, convert_to_attrdict
  from vissl.utils.test_utils import augly_test, gpu_test, in_temporary_directory, run_integration_test
label_desc: Use image node_id 2 for calling the TestTransform obj's underlying member method code with example usage: obj.test_img_pil_to_rgb_mode() without return types