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 __init__(self, **kwargs):
      super().__init__(**kwargs)
  
      requires_backends(self, "vision")
  
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 1 to create a new ZeroShotImageClassificationPipeline object from inherited base classes: ChunkPipeline with example: obj = ZeroShotImageClassificationPipeline()