parameters: (self, image: np.ndarray)
return_statement: return results
docstring: Processes an RGB image and returns the pose landmarks on the most prominent person detected.
  
      Args:
        image: An RGB image represented as a numpy ndarray.
  
      Raises:
        RuntimeError: If the underlying graph throws any error.
        ValueError: If the input image is not three channel RGB.
  
      Returns:
        A NamedTuple with fields describing the landmarks on the most prominate
        person detected:
          1) "pose_landmarks" field that contains the pose landmarks.
          2) "pose_world_landmarks" field that contains the pose landmarks in
          real-world 3D coordinates that are in meters with the origin at the
          center between hips.
          3) "segmentation_mask" field that contains the segmentation mask if
             "enable_segmentation" is set to true.
docstring_summary: Processes an RGB image and returns the pose landmarks on the most prominent person detected.
docstring_tokens: Processes an RGB image and returns the pose landmarks on the most prominent person detected .
function: def process(self, image: np.ndarray) -> NamedTuple:
      """Processes an RGB image and returns the pose landmarks on the most prominent person detected.
  
      Args:
        image: An RGB image represented as a numpy ndarray.
  
      Raises:
        RuntimeError: If the underlying graph throws any error.
        ValueError: If the input image is not three channel RGB.
  
      Returns:
        A NamedTuple with fields describing the landmarks on the most prominate
        person detected:
          1) "pose_landmarks" field that contains the pose landmarks.
          2) "pose_world_landmarks" field that contains the pose landmarks in
          real-world 3D coordinates that are in meters with the origin at the
          center between hips.
          3) "segmentation_mask" field that contains the segmentation mask if
             "enable_segmentation" is set to true.
      """
  
      results = super().process(input_data={'image': image})
      if results.pose_landmarks:
        for landmark in results.pose_landmarks.landmark:
          landmark.ClearField('presence')
      if results.pose_world_landmarks:
        for landmark in results.pose_world_landmarks.landmark:
          landmark.ClearField('presence')
      return results
function_tokens: def process ( self , image : np . ndarray ) -> NamedTuple : results = super ( ) . process ( input_data = { 'image' : image } ) if results . pose_landmarks : for landmark in results . pose_landmarks . landmark : landmark . ClearField ( 'presence' ) if results . pose_world_landmarks : for landmark in results . pose_world_landmarks . landmark : landmark . ClearField ( 'presence' ) return results