code: def test_summarization_inference(self):
      model = MvpForConditionalGeneration.from_pretrained(
          "RUCAIBox/mvp"
      ).to(torch_device)
      tok = self.default_tokenizer
      PGE_ARTICLE = """ Listen to local radio broadcasts for advertisements that reference casinos in your area.\nIf none are in your area, listen to national radio broadcasts for advertisements of casinos in other areas.\nNote the location that is mentioned in each advertisement that involves a casino.\nIf no locations are mentioned, note any additional contact information, such as a website or phone number. Use that information to find out where the casinos are.;\n,\n\nIf you learn about more than 1 casino on the radio, use the Internet to search the distance between your location and each casino. Sites such as maps.google.com or mapquest.com will help you in this search.'"""  # fmt: skip
      EXPECTED_SUMMARY = "Listen to the radio.\nUse the Internet."
      dct = tok.batch_encode_plus(
          [PGE_ARTICLE],
          return_tensors="pt",
      ).to(torch_device)
  
      hypotheses_batch = model.generate(**dct)
  
      decoded = tok.batch_decode(
          hypotheses_batch, skip_special_tokens=True
      )
      self.assertEqual(EXPECTED_SUMMARY, decoded[0])
  
imports: import copy
  import tempfile
  import unittest
  import timeout_decorator
  from transformers import MvpConfig, is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, require_torch_fp16, slow, torch_device
  from transformers.utils import cached_property
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 3 for calling the MvpModelIntegrationTests obj's underlying member method code with example usage: obj.test_summarization_inference() without return types