class_docstr: Zero shot object detection pipeline using `OwlViTForObjectDetection`. This pipeline predicts bounding boxes of
  objects when you provide an image and a set of `candidate_labels`.
  
  This object detection pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"zero-shot-object-detection"`.
  
  See the list of available models on
  [huggingface.co/models](https://huggingface.co/models?filter=zero-shot-object-detection).
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docstr: Turns list [xmin, xmax, ymin, ymax] into dict { "xmin": xmin, ... }
  
  Args:
      box (`torch.Tensor`): Tensor containing the coordinates in corners format.
  
  Returns:
      bbox (`Dict[str, int]`): Dict containing the coordinates in corners format.
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code: def _get_bounding_box(self, box: "torch.Tensor") -> Dict[str, int]:
      """
      Turns list [xmin, xmax, ymin, ymax] into dict { "xmin": xmin, ... }
  
      Args:
          box (`torch.Tensor`): Tensor containing the coordinates in corners format.
  
      Returns:
          bbox (`Dict[str, int]`): Dict containing the coordinates in corners format.
      """
      if self.framework != "pt":
          raise ValueError(
              "The ZeroShotObjectDetectionPipeline is only available in PyTorch."
          )
      xmin, ymin, xmax, ymax = box.int().tolist()
      bbox = {
          "xmin": xmin,
          "ymin": ymin,
          "xmax": xmax,
          "ymax": ymax,
      }
      return bbox
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/pipelines/zero_shot_object_detection.py
imports: from typing import Dict, List, Union
  import numpy
  from ..tokenization_utils_base import BatchEncoding
  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 7 for calling the ZeroShotObjectDetectionPipeline obj's underlying member method code with example usage: obj._get_bounding_box(box) and returns: bbox