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 _forward(self, model_inputs):
      is_last = model_inputs.pop("is_last")
      candidate_label = model_inputs.pop("candidate_label")
      outputs = self.model(**model_inputs)
  
      # Clip does crossproduct scoring by default, so we're only
      # interested in the results where image and text and in the same
      # batch position.
      diag = (
          torch.diagonal
          if self.framework == "pt"
          else tf.linalg.diag_part
      )
      logits_per_image = diag(outputs.logits_per_image)
  
      model_outputs = {
          "is_last": is_last,
          "candidate_label": candidate_label,
          "logits_per_image": logits_per_image,
      }
      return model_outputs
  
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 5 for calling the ZeroShotImageClassificationPipeline obj's underlying member method code with example usage: obj._forward(model_inputs) and returns: model_outputs