code: def __init__(
      self,
      head,
      roi_extractor=_get_class_default_kwargs(RoIAlign),
      mask_assigner="MaskAssigner",
      num_classes=80,
      share_bbox_feat=False,
      export_onnx=False,
  ):
      super(MaskHead, self).__init__()
      self.num_classes = num_classes
      self.export_onnx = export_onnx
  
      self.roi_extractor = roi_extractor
      if isinstance(roi_extractor, dict):
          self.roi_extractor = RoIAlign(**roi_extractor)
      self.head = head
      self.in_channels = head.out_channels()
      self.mask_assigner = mask_assigner
      self.share_bbox_feat = share_bbox_feat
      self.bbox_head = None
  
      self.mask_fcn_logits = nn.Conv2D(
          in_channels=self.in_channels,
          out_channels=self.num_classes,
          kernel_size=1,
          weight_attr=paddle.ParamAttr(
              initializer=KaimingNormal(fan_in=self.num_classes)
          ),
      )
      self.mask_fcn_logits.skip_quant = True
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import KaimingNormal
  from ppdet.core.workspace import register, create
  from ppdet.modeling.layers import ConvNormLayer
  from .roi_extractor import RoIAlign
  from ..cls_utils import _get_class_default_kwargs
label_desc: Use image node_id 1 to create a new MaskHead object from inherited base classes: nn with example: obj = MaskHead(head, roi_extractor, mask_assigner, num_classes, share_bbox_feat, export_onnx)