class_docstr: Mask RCNN target encoder to generate mask targets.
  
  Parameters
  ----------
  num_images : int
      Number of input images.
  num_rois : int
      Number of sampled rois.
  num_classes : int
      Number of classes for class-specific targets.
  mask_size : tuple of int
      Size of generated masks, for example (14, 14).
class_docstr_tok: ["Mask","RCNN","target","encoder","to","generate","mask","targets",".","Parameters","--","--","--","--","--","num_images",":","int","Number","of","input","images",".","num_rois",":","int","Number","of","sampled","rois",".","num_classes",":","int","Number","of","classes","for","class-specific","targets",".","mask_size",":","tuple","of","int","Size","of","generated","masks",",","for","example","(","14",",","14",")","."]
code: def __init__(
      self, num_images, num_rois, num_classes, mask_size, **kwargs
  ):
      super(MaskTargetGenerator, self).__init__(**kwargs)
      self._num_images = num_images
      self._num_rois = num_rois
      self._num_classes = num_classes
      self._mask_size = mask_size
  
imports: from __future__ import absolute_import
  from mxnet import gluon, autograd
label_desc: Use image node_id 1 to create a new MaskTargetGenerator object from inherited base classes: gluon with example: obj = MaskTargetGenerator(num_images, num_rois, num_classes, mask_size)