class_docstr: Displays a base image and colored subsections on top of that image. Subsections can take the from of rectangles (e.g. object detection) or masks (e.g. image segmentation).
  Preprocessing: this component does *not* accept input.
  Postprocessing: expects a {Tuple[numpy.ndarray | PIL.Image | str, List[Tuple[numpy.ndarray | Tuple[int, int, int, int], str]]]} consisting of a base image and a list of subsections, that are either (x1, y1, x2, y2) tuples identifying object boundaries, or 0-1 confidence masks of the same shape as the image. A label is provided for each subsection.
  
  Demos: image_segmentation
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code: def example_inputs(self) -> Any:
      return {}
  
file_path: gradio/gradio/components/annotated_image.py
imports: from __future__ import annotations
  from typing import Any, List
  import numpy
  from gradio_client.documentation import document, set_documentation_group
  from PIL import Image
  from gradio import processing_utils, utils
  from gradio.components.base import Component
  from gradio.data_classes import FileData, GradioModel
  from gradio.events import Events
label_desc: Use image node_id 3 for calling the AnnotatedImage obj's underlying member method code with example usage: obj.example_inputs() and returns: dict