docstr: The aim of this test is to verify that the mixed int8 is compatible with `pipeline` from transformers. Since
  we used pipline for inference speed benchmarking we want to make sure that this feature does not break anything
  on pipline.
docstr_tok: ["The","aim","of","this","test","is","to","verify","that","the","mixed","int8","is","compatible","with","`","pipeline","`","from","transformers",".","Since","we","used","pipline","for","inference","speed","benchmarking","we","want","to","make","sure","that","this","feature","does","not","break","anything","on","pipline","."]
code: def test_pipeline(self):
      r"""
      The aim of this test is to verify that the mixed int8 is compatible with `pipeline` from transformers. Since
      we used pipline for inference speed benchmarking we want to make sure that this feature does not break anything
      on pipline.
      """
      # self._clear_cuda_cache()
      self.pipe = pipeline(
          "text-generation",
          model=self.model_name,
          model_kwargs={"device_map": "auto", "load_in_8bit": True},
          max_new_tokens=self.MAX_NEW_TOKENS,
      )
  
      # Real second forward pass
      pipeline_output = self.pipe(self.input_text)
      self.assertEqual(
          pipeline_output[0]["generated_text"], self.EXPECTED_OUTPUT
      )
  
imports: import gc
  import importlib.metadata
  import tempfile
  import unittest
  from packaging import version
  from transformers import AutoConfig, AutoModel, AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoTokenizer, BitsAndBytesConfig, pipeline
  from transformers.testing_utils import is_accelerate_available, is_torch_available, require_accelerate, require_bitsandbytes, require_torch, require_torch_gpu, require_torch_multi_gpu, slow
label_desc: Use image node_id 3 for calling the MixedInt8TestPipeline obj's underlying member method code with example usage: obj.test_pipeline() without return types