class_docstr: Class that performs pose landmarks detection on images.
class_docstr_tok: ["Class","that","performs","pose","landmarks","detection","on","images","."]
docstr: Performs pose landmarks detection on the given image.
  
  Only use this method when the PoseLandmarker is created with the image
  running mode.
  
  Args:
    image: MediaPipe Image.
    image_processing_options: Options for image processing.
  
  Returns:
    The pose landmarker detection results.
  
  Raises:
    ValueError: If any of the input arguments is invalid.
    RuntimeError: If pose landmarker detection failed to run.
docstr_tok: ["Performs","pose","landmarks","detection","on","the","given","image",".","Only","use","this","method","when","the","PoseLandmarker","is","created","with","the","image","running","mode",".","Args",":","image",":","MediaPipe","Image",".","image_processing_options",":","Options","for","image","processing",".","Returns",":","The","pose","landmarker","detection","results",".","Raises",":","ValueError",":","If","any","of","the","input","arguments","is","invalid",".","RuntimeError",":","If","pose","landmarker","detection","failed","to","run","."]
code: def detect(
      self,
      image: image_module.Image,
      image_processing_options: Optional[
          _ImageProcessingOptions
      ] = None,
  ) -> PoseLandmarkerResult:
      """Performs pose landmarks detection on the given image.
  
      Only use this method when the PoseLandmarker is created with the image
      running mode.
  
      Args:
        image: MediaPipe Image.
        image_processing_options: Options for image processing.
  
      Returns:
        The pose landmarker detection results.
  
      Raises:
        ValueError: If any of the input arguments is invalid.
        RuntimeError: If pose landmarker detection failed to run.
      """
      normalized_rect = self.convert_to_normalized_rect(
          image_processing_options, image, roi_allowed=False
      )
      output_packets = self._process_image_data(
          {
              _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
              _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
                  normalized_rect.to_pb2()
              ),
          }
      )
  
      if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
          return PoseLandmarkerResult([], [])
  
      return _build_landmarker_result(output_packets)
  
imports: import dataclasses
  from typing import Callable, Mapping, Optional, List
  from mediapipe.framework.formats import landmark_pb2
  from mediapipe.python import packet_creator
  from mediapipe.python import packet_getter
  from mediapipe.python._framework_bindings import image
  from mediapipe.python._framework_bindings import packet
  from mediapipe.tasks.cc.vision.pose_landmarker.proto import pose_landmarker_graph_options_pb2
  from mediapipe.tasks.python.components.containers import landmark
  from mediapipe.tasks.python.core import base_options
  from mediapipe.tasks.python.core import task_info
  from mediapipe.tasks.python.core.optional_dependencies import doc_controls
  from mediapipe.tasks.python.vision.core import base_vision_task_api
  from mediapipe.tasks.python.vision.core import image_processing_options
  from mediapipe.tasks.python.vision.core import vision_task_running_mode
label_desc: Use image node_id 3 for calling the PoseLandmarker obj's underlying member method code with example usage: obj.detect(image, image_processing_options) and returns: _build_landmarker_result, PoseLandmarkerResult