class_docstr: Converts various DensePose predictor outputs to masks
  in bit mask format (see `BitMasks`). Each DensePose predictor output type
  has to register its convertion strategy.
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docstr: Convert DensePose predictor outputs to BitMasks using some registered
  converter. Does recursive lookup for base classes, so there's no need
  for explicit registration for derived classes.
  
  Args:
      densepose_predictor_outputs: DensePose predictor output to be
          converted to BitMasks
      boxes (Boxes): bounding boxes that correspond to the DensePose
          predictor outputs
      image_size_hw (tuple [int, int]): image height and width
  Return:
      An instance of `BitMasks`. If no suitable converter was found, raises KeyError
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code: def convert(
      cls,
      densepose_predictor_outputs: Any,
      boxes: Boxes,
      image_size_hw: ImageSizeType,
      *args,
      **kwargs,
  ) -> BitMasks:
      """
      Convert DensePose predictor outputs to BitMasks using some registered
      converter. Does recursive lookup for base classes, so there's no need
      for explicit registration for derived classes.
  
      Args:
          densepose_predictor_outputs: DensePose predictor output to be
              converted to BitMasks
          boxes (Boxes): bounding boxes that correspond to the DensePose
              predictor outputs
          image_size_hw (tuple [int, int]): image height and width
      Return:
          An instance of `BitMasks`. If no suitable converter was found, raises KeyError
      """
      return super(ToMaskConverter, cls).convert(
          densepose_predictor_outputs,
          boxes,
          image_size_hw,
          *args,
          **kwargs,
      )
  
imports: from typing import Any, Tuple
  from detectron2.structures import BitMasks, Boxes
  from .base import BaseConverter
label_desc: Use image node_id 1 for calling the ToMaskConverter obj's underlying member method code with example usage: obj.convert(cls, densepose_predictor_outputs, boxes, image_size_hw) and returns: super