code: def create_and_check_maskformer_model(
      self, config, pixel_values, pixel_mask, output_hidden_states=False
  ):
      with torch.no_grad():
          model = MaskFormerModel(config=config)
          model.to(torch_device)
          model.eval()
  
          output = model(
              pixel_values=pixel_values, pixel_mask=pixel_mask
          )
          output = model(pixel_values, output_hidden_states=True)
      # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
      # encoder and pixel decoder
      self.parent.assertEqual(
          output.transformer_decoder_last_hidden_state.shape,
          (self.batch_size, self.num_queries, self.mask_feature_size),
      )
      # let's ensure the other two hidden state exists
      self.parent.assertTrue(
          output.pixel_decoder_last_hidden_state is not None
      )
      self.parent.assertTrue(
          output.encoder_last_hidden_state is not None
      )
  
      if output_hidden_states:
          self.check_output_hidden_state(output, config)
  
imports: import copy
  import unittest
  import numpy
  from tests.test_modeling_common import floats_tensor
  from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
  from transformers.testing_utils import require_torch, require_torch_accelerator, require_torch_fp16, require_torch_multi_gpu, require_vision, slow, torch_device
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 6 for calling the MaskFormerModelTester obj's underlying member method code with example usage: obj.create_and_check_maskformer_model(config, pixel_values, pixel_mask, output_hidden_states) without return types