class_docstr: Performs keypoint detection for humans.
  
  Args:
      model_config (dict): configuration for the model. The dict-keys are
          "backend", "model", and "threshold".
      executor: function that generates predictions.
class_docstr_tok: ["Performs","keypoint","detection","for","humans",".","Args",":","model_config","(","dict",")",":","configuration","for","the","model",".","The","dict-keys","are","``","backend","''",",","``","model","''",",","and","``","threshold","''",".","executor",":","function","that","generates","predictions","."]
code: def _run(self, status: OrderedDict):
      inputs = status["loaded_image"]
      outputs = self.executor(image=inputs, predictor=self.predictor)
  
      if model_config["backend"] == "detectron2":
          # keypoints is a tensor of shape (num_people, num_keypoint, (x, y, score))
          keypoints = outputs["instances"][
              outputs["instances"].pred_classes == 0
          ].pred_keypoints
  
      return {"keypoint_coordinates": keypoints}
  
imports: from collections import OrderedDict
  from typing import Callable
  import cv2
  import torch
  from hook import HookBase
label_desc: Use image node_id 2 for calling the PeopleKeypointDetectionHook obj's underlying member method code with example usage: obj._run(status) and returns: dict