code: def create_and_check_for_pretraining(
      self, config, pixel_values, labels
  ):
      model = TFViTMAEForPreTraining(config)
      result = model(pixel_values, training=False)
      # expected sequence length = num_patches
      num_patches = (self.image_size // self.patch_size) ** 2
      expected_num_channels = self.patch_size**2 * self.num_channels
      self.parent.assertEqual(
          result.logits.shape,
          (self.batch_size, num_patches, expected_num_channels),
      )
  
      # test greyscale images
      config.num_channels = 1
      model = TFViTMAEForPreTraining(config)
  
      pixel_values = floats_tensor(
          [self.batch_size, 1, self.image_size, self.image_size]
      )
      result = model(pixel_values, training=False)
      expected_num_channels = self.patch_size**2
      self.parent.assertEqual(
          result.logits.shape,
          (self.batch_size, num_patches, expected_num_channels),
      )
  
imports: import copy
  import inspect
  import json
  import math
  import os
  import tempfile
  import unittest
  from importlib import import_module
  import numpy
  from transformers import ViTMAEConfig
  from transformers.file_utils import cached_property, is_tf_available, is_vision_available
  from transformers.testing_utils import require_tf, require_vision, slow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
label_desc: Use image node_id 5 for calling the TFViTMAEModelTester obj's underlying member method code with example usage: obj.create_and_check_for_pretraining(config, pixel_values, labels) without return types