class_docstr: PyTorch object detection model operator, detect the region of interests in one image and predict the object classes
  in these regions.
class_docstr_tok: ["PyTorch","object","detection","model","operator",",","detect","the","region","of","interests","in","one","image","and","predict","the","object","classes","in","these","regions","."]
code: def __call__(
      self, images: List[torch.Tensor], mask_score=0.5
  ) -> NamedTuple(
      "Outputs", [("boxes", numpy.ndarray), ("breed", str)]
  ):
      Outputs = NamedTuple(
          "Outputs", [("boxes", numpy.ndarray), ("breed", str)]
      )
      outputs = self._model(images)
      pred = outputs[0]
      scores = pred["scores"]
      mask = scores >= mask_score
      boxes = pred["boxes"][mask]
      labels = pred["labels"][mask]
      if labels is not None:
          breeds = (
              [lb_names[i] for i in labels]
              if lb_names is not None
              else None
          )
  
      return Outputs(boxes.detach().numpy(), breeds)
  
file_path: towhee/tests/unittests/mock_operators/pytorch_object_detection_operator/pytorch_object_detection_operator.py
imports: import numpy
  import torch
  import torchvision
  from typing import NamedTuple, List
  from pathlib import Path
  from towhee.operator import Operator
label_desc: Use image node_id 2 for calling the PyTorchObjectDetectionOperator obj's underlying member method code with example usage: obj.__call__(images, mask_score) and returns: Outputs