code: def test_inference_causal_lm_head(self):
      model = ImageGPTForCausalImageModeling.from_pretrained(
          "openai/imagegpt-small"
      ).to(torch_device)
  
      image_processor = self.default_image_processor
      image = prepare_img()
      inputs = image_processor(images=image, return_tensors="pt").to(
          torch_device
      )
  
      # forward pass
      with torch.no_grad():
          outputs = model(**inputs)
  
      # verify the logits
      expected_shape = torch.Size((1, 1024, 512))
      self.assertEqual(outputs.logits.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [
              [2.3445, 2.6889, 2.7313],
              [1.0530, 1.2416, 0.5699],
              [0.2205, 0.7749, 0.3953],
          ]
      ).to(torch_device)
  
      self.assertTrue(
          torch.allclose(
              outputs.logits[0, :3, :3], expected_slice, atol=1e-4
          )
      )
  
imports: import copy
  import inspect
  import os
  import tempfile
  import unittest
  from transformers import ImageGPTConfig
  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 ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the ImageGPTModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_causal_lm_head() without return types