code: def forward(self, features):
      # mask features (x4 convs)
      features = self.mask_convs(features)
      return self.projection(features)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from mmengine.model.weight_init import caffe2_xavier_init, kaiming_init
  from torch.nn import init
  from mmdet.registry import MODELS
label_desc: Use image node_id 3 for calling the MaskBranch obj's underlying member method code with example usage: obj.forward(features) and returns: self