code: def create_and_check_lm_head_model(
      self,
      config,
      pixel_values,
      input_mask,
      head_mask,
      token_type_ids,
      *args,
  ):
      model = ImageGPTForCausalImageModeling(config)
      model.to(torch_device)
      model.eval()
  
      labels = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size - 1
      )
      result = model(
          pixel_values, token_type_ids=token_type_ids, labels=labels
      )
      self.parent.assertEqual(result.loss.shape, ())
      # ImageGPTForCausalImageModeling doens't have tied input- and output embeddings
      self.parent.assertEqual(
          result.logits.shape,
          (self.batch_size, self.seq_length, self.vocab_size - 1),
      )
  
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 8 for calling the ImageGPTModelTester obj's underlying member method code with example usage: obj.create_and_check_lm_head_model(config, pixel_values, input_mask, head_mask, token_type_ids) without return types