code: def create_and_check_for_masked_image_modeling(
      self, config, pixel_values, labels
  ):
      model = DeiTForMaskedImageModeling(config=config)
      model.to(torch_device)
      model.eval()
      result = model(pixel_values)
      self.parent.assertEqual(
          result.logits.shape,
          (
              self.batch_size,
              self.num_channels,
              self.image_size,
              self.image_size,
          ),
      )
  
      # test greyscale images
      config.num_channels = 1
      model = DeiTForMaskedImageModeling(config)
      model.to(torch_device)
      model.eval()
  
      pixel_values = floats_tensor(
          [self.batch_size, 1, self.image_size, self.image_size]
      )
      result = model(pixel_values)
      self.parent.assertEqual(
          result.logits.shape,
          (self.batch_size, 1, self.image_size, self.image_size),
      )
  
imports: import inspect
  import unittest
  import warnings
  from transformers import DeiTConfig
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
label_desc: Use image node_id 5 for calling the DeiTModelTester obj's underlying member method code with example usage: obj.create_and_check_for_masked_image_modeling(config, pixel_values, labels) without return types