original_docstring: Predict poses from input frame and bboxes
  
          Args:
              frame (np.array): image in numpy array
              bboxes (np.array): detected bboxes in image
  
          Returns:
              keypoints, keypoint_scores, keypoint_conns, keypoint_bboxes
              (Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]): \
              tuple containing list of bboxes and pose related info i.e coordinates,
              scores, connections
          
docstring: Predict poses from input frame and bboxes
docstring_tokens: Predict poses from input frame and bboxes
code: def predict(
          self, frame: np.ndarray, bboxes: np.ndarray
      ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
          """Predict poses from input frame and bboxes
  
          Args:
              frame (np.array): image in numpy array
              bboxes (np.array): detected bboxes in image
  
          Returns:
              keypoints, keypoint_scores, keypoint_conns, keypoint_bboxes
              (Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]): \
              tuple containing list of bboxes and pose related info i.e coordinates,
              scores, connections
          """
          assert isinstance(frame, np.ndarray)
          assert isinstance(bboxes, np.ndarray)
          detected_bboxes = bboxes.copy()
          if bboxes.size != 0:
              return self.detector.predict(frame, detected_bboxes)
          keypoints = np.array([])
          keypoint_scores = np.array([])
          keypoint_conns = np.array([])
  
          return keypoints, keypoint_scores, keypoint_conns
code_tokens: def predict ( self , frame : np . ndarray , bboxes : np . ndarray ) -> Tuple [ np . ndarray , np . ndarray , np . ndarray ] : assert isinstance ( frame , np . ndarray ) assert isinstance ( bboxes , np . ndarray ) detected_bboxes = bboxes . copy ( ) if bboxes . size != 0 : return self . detector . predict ( frame , detected_bboxes ) keypoints = np . array ( [ ] ) keypoint_scores = np . array ( [ ] ) keypoint_conns = np . array ( [ ] ) return keypoints , keypoint_scores , keypoint_conns
short_docstring: Predict poses from input frame and bboxes
short_docstring_tokens: Predict poses from input frame and bboxes
comment: """Predict poses from input frame and bboxes
  
          Args:
              frame (np.array): image in numpy array
              bboxes (np.array): detected bboxes in image
  
          Returns:
              keypoints, keypoint_scores, keypoint_conns, keypoint_bboxes
              (Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]): \
              tuple containing list of bboxes and pose related info i.e coordinates,
              scores, connections
          """