class_docstr: MediaPipe Pose.
  
  MediaPipe Pose processes an RGB image and returns pose landmarks on the most
  prominent person detected.
  
  Please refer to https://solutions.mediapipe.dev/pose#python-solution-api for
  usage examples.
class_docstr_tok: ["MediaPipe","Pose",".","MediaPipe","Pose","processes","an","RGB","image","and","returns","pose","landmarks","on","the","most","prominent","person","detected",".","Please","refer","to","https",":","\/\/solutions.mediapipe.dev\/pose","#","python-solution-api","for","usage","examples","."]
docstr: 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.
docstr_tok: ["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","."]
code: 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:  # pytype: disable=attribute-error
          for (
              landmark
          ) in (
              results.pose_landmarks.landmark
          ):  # pytype: disable=attribute-error
              landmark.ClearField("presence")
      if (
          results.pose_world_landmarks
      ):  # pytype: disable=attribute-error
          for (
              landmark
          ) in (
              results.pose_world_landmarks.landmark
          ):  # pytype: disable=attribute-error
              landmark.ClearField("presence")
      return results
  
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.image import warp_affine_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.tensor import tensors_to_segmentation_calculator_pb2
  from mediapipe.calculators.tflite import ssd_anchors_calculator_pb2
  from mediapipe.calculators.util import detections_to_rects_calculator_pb2
  from mediapipe.calculators.util import landmarks_smoothing_calculator_pb2
  from mediapipe.calculators.util import local_file_contents_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.calculators.util import visibility_smoothing_calculator_pb2
  from mediapipe.framework.tool import switch_container_pb2
  from mediapipe.python.solution_base import SolutionBase
  from mediapipe.python.solutions import download_utils
  from mediapipe.python.solutions.pose_connections import POSE_CONNECTIONS
label_desc: Use image node_id 2 for calling the Pose obj's underlying member method code with example usage: obj.process(image) and returns: results