class_docstr: Options for the hand landmarker task.
  
  Attributes:
    base_options: Base options for the hand landmarker task.
    running_mode: The running mode of the task. Default to the image mode.
      HandLandmarker has three running modes: 1) The image mode for detecting
      hand landmarks on single image inputs. 2) The video mode for detecting
      hand landmarks on the decoded frames of a video. 3) The live stream mode
      for detecting hand landmarks on the live stream of input data, such as
      from camera. In this mode, the "result_callback" below must be specified
      to receive the detection results asynchronously.
    num_hands: The maximum number of hands can be detected by the hand
      landmarker.
    min_hand_detection_confidence: The minimum confidence score for the hand
      detection to be considered successful.
    min_hand_presence_confidence: The minimum confidence score of hand presence
      score in the hand landmark detection.
    min_tracking_confidence: The minimum confidence score for the hand tracking
      to be considered successful.
    result_callback: The user-defined result callback for processing live stream
      data. The result callback should only be specified when the running mode
      is set to the live stream mode.
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docstr: Generates an HandLandmarkerGraphOptions protobuf object.
docstr_tok: ["Generates","an","HandLandmarkerGraphOptions","protobuf","object","."]
code: def to_pb2(self) -> _HandLandmarkerGraphOptionsProto:
      """Generates an HandLandmarkerGraphOptions protobuf object."""
      base_options_proto = self.base_options.to_pb2()
      base_options_proto.use_stream_mode = (
          False if self.running_mode == _RunningMode.IMAGE else True
      )
  
      # Initialize the hand landmarker options from base options.
      hand_landmarker_options_proto = _HandLandmarkerGraphOptionsProto(
          base_options=base_options_proto
      )
      hand_landmarker_options_proto.min_tracking_confidence = (
          self.min_tracking_confidence
      )
      hand_landmarker_options_proto.hand_detector_graph_options.num_hands = (
          self.num_hands
      )
      hand_landmarker_options_proto.hand_detector_graph_options.min_detection_confidence = (
          self.min_hand_detection_confidence
      )
      hand_landmarker_options_proto.hand_landmarks_detector_graph_options.min_detection_confidence = (
          self.min_hand_presence_confidence
      )
      return hand_landmarker_options_proto
  
imports: import dataclasses
  import enum
  from typing import Callable, Mapping, Optional, List
  from mediapipe.framework.formats import classification_pb2
  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.hand_landmarker.proto import hand_landmarker_graph_options_pb2
  from mediapipe.tasks.python.components.containers import category
  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 1 for calling the HandLandmarkerOptions obj's underlying member method code with example usage: obj.to_pb2() and returns: hand_landmarker_options_proto