code: def create_and_check_for_image_classification(
      self, config, pixel_values, labels
  ):
      config.num_labels = self.type_sequence_label_size
      model = TFViTForImageClassification(config)
      result = model(pixel_values, labels=labels, training=False)
      self.parent.assertEqual(
          result.logits.shape,
          (self.batch_size, self.type_sequence_label_size),
      )
  
      # Test with an image with different size than the one specified in config.
      image_size = self.image_size // 2
      pixel_values = pixel_values[:, :, :image_size, :image_size]
      result = model(
          pixel_values, interpolate_pos_encoding=True, training=False
      )
      self.parent.assertEqual(
          result.logits.shape,
          (self.batch_size, self.type_sequence_label_size),
      )
  
      # test greyscale images
      config.num_channels = 1
      model = TFViTForImageClassification(config)
      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, self.type_sequence_label_size),
      )
  
imports: import inspect
  import unittest
  from transformers import ViTConfig
  from transformers.testing_utils import require_tf, require_vision, slow
  from transformers.utils import cached_property, is_tf_available, is_vision_available
  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 TFViTModelTester obj's underlying member method code with example usage: obj.create_and_check_for_image_classification(config, pixel_values, labels) without return types