code: def create_and_check_model(self, config, pixel_values):
      model = InstructBlipVisionModel(config=config)
      model.to(torch_device)
      model.eval()
      with torch.no_grad():
          result = model(pixel_values)
      # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
      image_size = (self.image_size, self.image_size)
      patch_size = (self.patch_size, self.patch_size)
      num_patches = (image_size[1] // patch_size[1]) * (
          image_size[0] // patch_size[0]
      )
      self.parent.assertEqual(
          result.last_hidden_state.shape,
          (self.batch_size, num_patches + 1, self.hidden_size),
      )
      self.parent.assertEqual(
          result.pooler_output.shape,
          (self.batch_size, self.hidden_size),
      )
  
imports: import inspect
  import tempfile
  import unittest
  import numpy
  import requests
  from transformers import CONFIG_MAPPING, InstructBlipConfig, InstructBlipProcessor, InstructBlipQFormerConfig, InstructBlipVisionConfig
  from transformers.testing_utils import require_accelerate, require_bitsandbytes, require_torch, require_vision, slow, torch_device
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 4 for calling the InstructBlipVisionModelTester obj's underlying member method code with example usage: obj.create_and_check_model(config, pixel_values) without return types