code: def test_generation_llava_fused(self):
      from transformers import pipeline
  
      quantization_config = AwqConfig(
          do_fuse=True, fuse_max_seq_len=2048
      )
  
      pipe = pipeline(
          "image-to-text",
          model=self.multi_modal_model_name,
          device=0,
          model_kwargs={
              "quantization_config": quantization_config,
          },
          revision=self.multi_modal_model_code_revision,
      )
      url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/compel-neg.png"
  
      prompt = "USER: <image>\nCan you please describe this image?\nASSISTANT:"
  
      outputs = pipe(
          url, prompt=prompt, generate_kwargs={"max_new_tokens": 100}
      )
      EXPECTED_OUTPUT = "USER:  \nCan you please describe this image?\nASSISTANT: The image features a brown and white cat sitting on a green surface, possibly a carpet or a grassy area. The cat is holding a red ball in its paws, seemingly playing with it. The cat appears to be focused on the ball, possibly preparing to play or just enjoying the toy."
  
      self.assertEqual(outputs[0]["generated_text"], EXPECTED_OUTPUT)
  
imports: import gc
  import tempfile
  import unittest
  from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, AwqConfig, OPTForCausalLM
  from transformers.testing_utils import require_accelerate, require_auto_awq, require_torch_gpu, require_torch_multi_gpu, slow, torch_device
  from transformers.utils import is_accelerate_available, is_torch_available
label_desc: Use image node_id 7 for calling the AwqFusedTest obj's underlying member method code with example usage: obj.test_generation_llava_fused() without return types