class_docstr: MediaPipe Selfie Segmentation.
  
  MediaPipe Selfie Segmentation processes an RGB image and returns a
  segmentation mask.
  
  Please refer to
  https://solutions.mediapipe.dev/selfie_segmentation#python-solution-api for
  usage examples.
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docstr: Processes an RGB image and returns a segmentation mask.
  
  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 a "segmentation_mask" field that contains a float
    type 2d np array representing the mask.
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code: def process(self, image: np.ndarray) -> NamedTuple:
      """Processes an RGB image and returns a segmentation mask.
  
      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 a "segmentation_mask" field that contains a float
        type 2d np array representing the mask.
      """
  
      return super().process(input_data={"image": image})
  
file_path: mediapipe/mediapipe/python/solutions/selfie_segmentation.py
imports: from typing import NamedTuple
  import numpy
  from mediapipe.calculators.core import constant_side_packet_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_segmentation_calculator_pb2
  from mediapipe.calculators.util import local_file_contents_calculator_pb2
  from mediapipe.framework.tool import switch_container_pb2
  from mediapipe.python.solution_base import SolutionBase
label_desc: Use image node_id 2 for calling the SelfieSegmentation obj's underlying member method code with example usage: obj.process(image) and returns: super