class_docstr: This class represents masks in the form of bitmaps.
  
  Args:
      masks (ndarray): ndarray of masks in shape (N, H, W), where N is
          the number of objects.
      height (int): height of masks
      width (int): width of masks
class_docstr_tok: ["This","class","represents","masks","in","the","form","of","bitmaps",".","Args",":","masks","(","ndarray",")",":","ndarray","of","masks","in","shape","(","N",",","H",",","W",")",",","where","N","is","the","number","of","objects",".","height","(","int",")",":","height","of","masks","width","(","int",")",":","width","of","masks"]
docstr: See :func:`BaseInstanceMasks.expand`.
docstr_tok: ["See",":","func",":","`","BaseInstanceMasks.expand","`","."]
code: def expand(self, expanded_h, expanded_w, top, left):
      """See :func:`BaseInstanceMasks.expand`."""
      if len(self.masks) == 0:
          expanded_mask = np.empty(
              (0, expanded_h, expanded_w), dtype=np.uint8
          )
      else:
          expanded_mask = np.zeros(
              (len(self), expanded_h, expanded_w), dtype=np.uint8
          )
          expanded_mask[
              :, top : top + self.height, left : left + self.width
          ] = self.masks
      return BitmapMasks(expanded_mask, expanded_h, expanded_w)
  
imports: from abc import ABCMeta, abstractmethod
  import cv2
  import mmcv
  import numpy
  import pycocotools.mask
  import torch
  from mmcv.ops.roi_align import roi_align
label_desc: Use image node_id 12 for calling the BitmapMasks obj's underlying member method code with example usage: obj.expand(expanded_h, expanded_w, top, left) and returns: BitmapMasks