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 __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
      requires_backends(self, "vision")
      self.check_model_type(
          TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
          if self.framework == "tf"
          else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
      )
  
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 1 to create a new ImageClassificationPipeline object from inherited base classes: Pipeline with example: obj = ImageClassificationPipeline()