code: def test_large_model_tf(self):
      zero_shot_classifier = pipeline(
          "zero-shot-classification",
          model="roberta-large-mnli",
          framework="tf",
      )
      outputs = zero_shot_classifier(
          "Who are you voting for in 2020?",
          candidate_labels=["politics", "public health", "science"],
      )
  
      self.assertEqual(
          nested_simplify(outputs),
          {
              "sequence": "Who are you voting for in 2020?",
              "labels": ["politics", "public health", "science"],
              "scores": [0.976, 0.015, 0.009],
          },
      )
      outputs = zero_shot_classifier(
          "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
          " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
          " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
          " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
          " machine translation tasks show these models to be superior in quality while being more parallelizable"
          " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
          " English-to-German translation task, improving over the existing best results, including ensembles by"
          " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
          " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
          " fraction of the training costs of the best models from the literature. We show that the Transformer"
          " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
          " large and limited training data.",
          candidate_labels=[
              "machine learning",
              "statistics",
              "translation",
              "vision",
          ],
          multi_label=True,
      )
      self.assertEqual(
          nested_simplify(outputs),
          {
              "sequence": (
                  "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
                  " networks in an encoder-decoder configuration. The best performing models also connect the"
                  " encoder and decoder through an attention mechanism. We propose a new simple network"
                  " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
                  " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
                  " superior in quality while being more parallelizable and requiring significantly less time to"
                  " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
                  " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
                  " English-to-French translation task, our model establishes a new single-model state-of-the-art"
                  " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
                  " costs of the best models from the literature. We show that the Transformer generalizes well to"
                  " other tasks by applying it successfully to English constituency parsing both with large and"
                  " limited training data."
              ),
              "labels": [
                  "translation",
                  "machine learning",
                  "vision",
                  "statistics",
              ],
              "scores": [0.817, 0.713, 0.018, 0.018],
          },
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/tests/pipelines/test_pipelines_zero_shot.py
imports: import unittest
  from transformers import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, Pipeline, ZeroShotClassificationPipeline, pipeline
  from transformers.testing_utils import nested_simplify, require_tf, require_torch, slow
  from .test_pipelines_common import ANY, PipelineTestCaseMeta
label_desc: Use image node_id 8 for calling the ZeroShotClassificationPipelineTests obj's underlying member method code with example usage: obj.test_large_model_tf() without return types