code: def create_and_check_model(self, config, pixel_values, labels):
      model = SegformerModel(config=config)
      model.to(torch_device)
      model.eval()
      result = model(pixel_values)
      expected_height = expected_width = self.image_size // (
          self.downsampling_rates[-1] * 2
      )
      self.parent.assertEqual(
          result.last_hidden_state.shape,
          (
              self.batch_size,
              self.hidden_sizes[-1],
              expected_height,
              expected_width,
          ),
      )
  
imports: import inspect
  import unittest
  from transformers import SegformerConfig, is_torch_available, is_vision_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
label_desc: Use image node_id 4 for calling the SegformerModelTester obj's underlying member method code with example usage: obj.create_and_check_model(config, pixel_values, labels) without return types