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 __init__(
      self,
      model_config: dict = model_config,
      executor: Callable = people_keypoints_executor,
  ):
      self.executor = executor
      self.model_config = model_config
      self.inputs = ["loaded_image", "bbox_coordinates"]
      self.outputs = ["keypoint_coordinates"]
  
      # generate different predictors for different backends.
      self.predictor = generate_predictor(
          model_config=self.model_config
      )
  
imports: from collections import OrderedDict
  from typing import Callable
  import cv2
  import torch
  from hook import HookBase
label_desc: Use image node_id 1 to create a new PeopleKeypointDetectionHook object from inherited base classes: HookBase with example: obj = PeopleKeypointDetectionHook(model_config, executor)