class_docstr: Object detection pipeline using any `AutoModelForObjectDetection`. This pipeline predicts bounding boxes of objects
  and their classes.
  
  This object detection pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"object-detection"`.
  
  See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=object-detection).
code: def preprocess(self, image):
      image = load_image(image)
      target_size = torch.IntTensor([[image.height, image.width]])
      inputs = self.feature_extractor(
          images=[image], return_tensors="pt"
      )
      inputs["target_size"] = target_size
      return inputs
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/pipelines/object_detection.py
imports: from typing import Any, Dict, List, Union
  from ..utils import add_end_docstrings, 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 ObjectDetectionPipeline obj's underlying member method code with example usage: obj.preprocess(image) and returns: inputs