code: def test_inference_natural_language_visual_reasoning(self):
      cat_image_path = (
          self.tests_dir
          / "fixtures/tests_samples/COCO/000000039769.png"
      )
      cats_image_obj = Image.open(cat_image_path)  # 2 cats
      dogs_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_nlvr2/raw/main/image1.jpeg"
  
      prompts = [
          [
              "User:",
              dogs_image_url,
              "Describe this image.\nAssistant: An image of two dogs.\n",
              "User:",
              cats_image_obj,
              "Describe this image.\nAssistant:",
          ],
          [
              "User:",
              cats_image_obj,
              "Describe this image.\nAssistant: An image of two kittens.\n",
              "User:",
              dogs_image_url,
              "Describe this image.\nAssistant:",
          ],
      ]
  
      # the CI gpu is small so using quantization to fit
      quantization_config = BitsAndBytesConfig(
          load_in_4bit=True,
          bnb_4bit_compute_dtype="float16",
      )
      model = IdeficsForVisionText2Text.from_pretrained(
          "HuggingFaceM4/idefics-9b",
          quantization_config=quantization_config,
          device_map="auto",
      )
      processor = self.default_processor
      inputs = processor(prompts, return_tensors="pt").to(torch_device)
      generated_ids = model.generate(**inputs, max_length=100)
      generated_text = processor.batch_decode(
          generated_ids, skip_special_tokens=True
      )
  
      # keep for debugging
      for i, t in enumerate(generated_text):
          t = bytes(t, "utf-8").decode("unicode_escape")
          print(f"{i}:\n{t}\n")
  
      self.assertIn("image of two cats", generated_text[0])
      self.assertIn("image of two dogs", generated_text[1])
  
imports: import unittest
  from parameterized import parameterized
  from transformers import BitsAndBytesConfig, IdeficsConfig, is_torch_available, is_vision_available
  from transformers.testing_utils import TestCasePlus, require_bitsandbytes, require_torch, require_torch_sdpa, require_vision, slow, torch_device
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the IdeficsModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_natural_language_visual_reasoning() without return types