code: def get_loss(self, mask_logits, mask_label, mask_target, mask_weight):
      mask_label = F.one_hot(mask_label, self.num_classes).unsqueeze(
          [2, 3]
      )
      mask_label = paddle.expand_as(mask_label, mask_logits)
      mask_label.stop_gradient = True
      mask_pred = paddle.gather_nd(
          mask_logits, paddle.nonzero(mask_label)
      )
      shape = mask_logits.shape
      mask_pred = paddle.reshape(
          mask_pred, [shape[0], shape[2], shape[3]]
      )
  
      mask_target = mask_target.cast("float32")
      mask_weight = mask_weight.unsqueeze([1, 2])
      loss_mask = F.binary_cross_entropy_with_logits(
          mask_pred, mask_target, weight=mask_weight, reduction="mean"
      )
      return loss_mask
  
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 3 for calling the MaskHead obj's underlying member method code with example usage: obj.get_loss(mask_logits, mask_label, mask_target, mask_weight) and returns: loss_mask