class_docstr: Image classification pipeline using any `AutoModelForImageClassification`. This pipeline predicts the class of an
  image.
  
  This image classification pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"image-classification"`.
  
  See the list of available models on
  [huggingface.co/models](https://huggingface.co/models?filter=image-classification).
class_docstr_tok: ["Image","classification","pipeline","using","any","`","AutoModelForImageClassification","`",".","This","pipeline","predicts","the","class","of","an","image",".","This","image","classification","pipeline","can","currently","be","loaded","from","[","`","pipeline","`","]","using","the","following","task","identifier",":","`","``","image-classification","''","`",".","See","the","list","of","available","models","on","[","huggingface.co\/models","]","(","https",":","\/\/huggingface.co\/models","?","filter=image-classification",")","."]
code: def preprocess(self, image):
      image = load_image(image)
      model_inputs = self.feature_extractor(
          images=image, return_tensors=self.framework
      )
      return model_inputs
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/pipelines/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, Pipeline
label_desc: Use image node_id 4 for calling the ImageClassificationPipeline obj's underlying member method code with example usage: obj.preprocess(image) and returns: model_inputs