code: def test_large_model_pt(self):
      image_classifier = pipeline(
          task="zero-shot-image-classification",
          model="openai/clip-vit-base-patch32",
      )
      # This is an image of 2 cats with remotes and no planes
      image = Image.open(
          "./tests/fixtures/tests_samples/COCO/000000039769.png"
      )
      output = image_classifier(
          image, candidate_labels=["cat", "plane", "remote"]
      )
  
      self.assertEqual(
          nested_simplify(output),
          [
              {"score": 0.511, "label": "remote"},
              {"score": 0.485, "label": "cat"},
              {"score": 0.004, "label": "plane"},
          ],
      )
  
      output = image_classifier(
          [image] * 5,
          candidate_labels=["cat", "plane", "remote"],
          batch_size=2,
      )
      self.assertEqual(
          nested_simplify(output),
          [
              [
                  {"score": 0.511, "label": "remote"},
                  {"score": 0.485, "label": "cat"},
                  {"score": 0.004, "label": "plane"},
              ],
          ]
          * 5,
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/tests/pipelines/test_pipelines_zero_shot_image_classification.py
imports: import unittest
  from transformers import is_vision_available
  from transformers.pipelines import pipeline
  from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_vision, slow
  from .test_pipelines_common import ANY, PipelineTestCaseMeta
label_desc: Use image node_id 3 for calling the ZeroShotImageClassificationPipelineTests obj's underlying member method code with example usage: obj.test_large_model_pt() without return types