class_docstr: MediaPipe Hands.
  
  MediaPipe Hands processes an RGB image and returns the hand landmarks and
  handedness (left v.s. right hand) of each detected hand.
  
  Note that it determines handedness assuming the input image is mirrored,
  i.e., taken with a front-facing/selfie camera (
  https://en.wikipedia.org/wiki/Front-facing_camera) with images flipped
  horizontally. If that is not the case, use, for instance, cv2.flip(image, 1)
  to flip the image first for a correct handedness output.
  
  Please refer to https://solutions.mediapipe.dev/hands#python-solution-api for
  usage examples.
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docstr: Processes an RGB image and returns the hand landmarks and handedness of each detected hand.
  
  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 object with the following fields:
      1) a "multi_hand_landmarks" field that contains the hand landmarks on
         each detected hand.
      2) a "multi_hand_world_landmarks" field that contains the hand landmarks
         on each detected hand in real-world 3D coordinates that are in meters
         with the origin at the hand's approximate geometric center.
      3) a "multi_handedness" field that contains the handedness (left v.s.
         right hand) of the detected hand.
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code: def process(self, image: np.ndarray) -> NamedTuple:
      """Processes an RGB image and returns the hand landmarks and handedness of each detected hand.
  
      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 object with the following fields:
          1) a "multi_hand_landmarks" field that contains the hand landmarks on
             each detected hand.
          2) a "multi_hand_world_landmarks" field that contains the hand landmarks
             on each detected hand in real-world 3D coordinates that are in meters
             with the origin at the hand's approximate geometric center.
          3) a "multi_handedness" field that contains the handedness (left v.s.
             right hand) of the detected hand.
      """
  
      return super().process(input_data={"image": image})
  
imports: import enum
  from typing import NamedTuple
  import numpy
  from mediapipe.calculators.core import constant_side_packet_calculator_pb2
  from mediapipe.calculators.core import gate_calculator_pb2
  from mediapipe.calculators.core import split_vector_calculator_pb2
  from mediapipe.calculators.tensor import image_to_tensor_calculator_pb2
  from mediapipe.calculators.tensor import inference_calculator_pb2
  from mediapipe.calculators.tensor import tensors_to_classification_calculator_pb2
  from mediapipe.calculators.tensor import tensors_to_detections_calculator_pb2
  from mediapipe.calculators.tensor import tensors_to_landmarks_calculator_pb2
  from mediapipe.calculators.tflite import ssd_anchors_calculator_pb2
  from mediapipe.calculators.util import association_calculator_pb2
  from mediapipe.calculators.util import detections_to_rects_calculator_pb2
  from mediapipe.calculators.util import logic_calculator_pb2
  from mediapipe.calculators.util import non_max_suppression_calculator_pb2
  from mediapipe.calculators.util import rect_transformation_calculator_pb2
  from mediapipe.calculators.util import thresholding_calculator_pb2
  from mediapipe.python.solution_base import SolutionBase
  from mediapipe.python.solutions.hands_connections import HAND_CONNECTIONS
label_desc: Use image node_id 2 for calling the Hands obj's underlying member method code with example usage: obj.process(image) and returns: super