class_docstr: Zero shot image classification pipeline using `CLIPModel`. This pipeline predicts the class of an image when you
  provide an image and a set of `candidate_labels`.
  
  This image classification pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"zero-shot-image-classification"`.
  
  See the list of available models on
  [huggingface.co/models](https://huggingface.co/models?filter=zero-shot-image-classification).
class_docstr_tok: ["Zero","shot","image","classification","pipeline","using","`","CLIPModel","`",".","This","pipeline","predicts","the","class","of","an","image","when","you","provide","an","image","and","a","set","of","`","candidate_labels","`",".","This","image","classification","pipeline","can","currently","be","loaded","from","[","`","pipeline","`","]","using","the","following","task","identifier",":","`","``","zero-shot-image-classification","''","`",".","See","the","list","of","available","models","on","[","huggingface.co\/models","]","(","https",":","\/\/huggingface.co\/models","?","filter=zero-shot-image-classification",")","."]
code: def preprocess(
      self,
      image,
      candidate_labels=None,
      hypothesis_template="This is a photo of {}.",
  ):
      n = len(candidate_labels)
      for i, candidate_label in enumerate(candidate_labels):
          image = load_image(image)
          images = self.feature_extractor(
              images=[image], return_tensors=self.framework
          )
          sequence = hypothesis_template.format(candidate_label)
          inputs = self.tokenizer(
              sequence, return_tensors=self.framework
          )
          inputs["pixel_values"] = images.pixel_values
          yield {
              "is_last": i == n - 1,
              "candidate_label": candidate_label,
              **inputs,
          }
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/pipelines/zero_shot_image_classification.py
imports: from typing import List, Union
  from ..utils import add_end_docstrings, is_tf_available, is_torch_available, is_vision_available, logging, requires_backends
  from .base import PIPELINE_INIT_ARGS, ChunkPipeline
label_desc: Use image node_id 4 for calling the ZeroShotImageClassificationPipeline obj's underlying member method code with example usage: obj.preprocess(image, candidate_labels, hypothesis_template) without return types