class_docstr: Mask feature head used in RTMDet-Ins.
  
  Args:
      in_channels (int): Number of channels in the input feature map.
      feat_channels (int): Number of hidden channels of the mask feature
           map branch.
      num_levels (int): The starting feature map level from RPN that
           will be used to predict the mask feature map.
      num_prototypes (int): Number of output channel of the mask feature
           map branch. This is the channel count of the mask
           feature map that to be dynamically convolved with the predicted
           kernel.
      stacked_convs (int): Number of convs in mask feature branch.
      act_cfg (:obj:`ConfigDict` or dict): Config dict for activation layer.
          Default: dict(type='ReLU', inplace=True)
      norm_cfg (dict): Config dict for normalization layer. Default: None.
code: def forward(self, features: Tuple[Tensor, ...]) -> Tensor:
      # multi-level feature fusion
      fusion_feats = [features[0]]
      size = features[0].shape[-2:]
      for i in range(1, self.num_levels):
          f = F.interpolate(features[i], size=size, mode="bilinear")
          fusion_feats.append(f)
      fusion_feats = torch.cat(fusion_feats, dim=1)
      fusion_feats = self.fusion_conv(fusion_feats)
      # pred mask feats
      mask_features = self.stacked_convs(fusion_feats)
      mask_features = self.projection(mask_features)
      return mask_features
  
imports: import copy
  import math
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, is_norm
  from mmcv.ops import batched_nms
  from mmengine.model import BaseModule, bias_init_with_prob, constant_init, normal_init
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.models.layers.transformer import inverse_sigmoid
  from mmdet.models.utils import filter_scores_and_topk, multi_apply, select_single_mlvl, sigmoid_geometric_mean
  from mmdet.registry import MODELS
  from mmdet.structures.bbox import cat_boxes, distance2bbox, get_box_tensor, get_box_wh, scale_boxes
  from mmdet.utils import ConfigType, InstanceList, OptInstanceList, reduce_mean
  from .rtmdet_head import RTMDetHead
label_desc: Use image node_id 2 for calling the MaskFeatModule obj's underlying member method code with example usage: obj.forward(features) and returns: mask_features