code: def test_for_image_classification(self):
      image_processor = AutoImageProcessor.from_pretrained(
          "microsoft/dit-base-finetuned-rvlcdip"
      )
      model = AutoModelForImageClassification.from_pretrained(
          "microsoft/dit-base-finetuned-rvlcdip"
      )
      model.to(torch_device)
  
      from datasets import load_dataset
  
      dataset = load_dataset("nielsr/rvlcdip-demo")
  
      image = dataset["train"][0]["image"].convert("RGB")
  
      inputs = image_processor(image, return_tensors="pt").to(
          torch_device
      )
  
      # forward pass
      with torch.no_grad():
          outputs = model(**inputs)
          logits = outputs.logits
  
      expected_shape = torch.Size((1, 16))
      self.assertEqual(logits.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [-0.4158, -0.4092, -0.4347],
          device=torch_device,
          dtype=torch.float,
      )
      self.assertTrue(
          torch.allclose(logits[0, :3], expected_slice, atol=1e-4)
      )
  
imports: import unittest
  from transformers import is_torch_available, is_vision_available
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
label_desc: Use image node_id 1 for calling the DiTIntegrationTest obj's underlying member method code with example usage: obj.test_for_image_classification() without return types