class_docstr: SOLOv2 mask feature map branch used in `SOLOv2: Dynamic and Fast
  Instance Segmentation. <https://arxiv.org/pdf/2003.10152>`_
  
  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.
      start_level (int): The starting feature map level from RPN that
           will be used to predict the mask feature map.
      end_level (int): The ending feature map level from rpn that
           will be used to predict the mask feature map.
      out_channels (int): Number of output channels 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.
      mask_stride (int): Downsample factor of the mask feature map output.
          Defaults to 4.
      conv_cfg (dict): Config dict for convolution layer. Default: None.
      norm_cfg (dict): Config dict for normalization layer. Default: None.
      init_cfg (dict or list[dict], optional): Initialization config dict.
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code: def __init__(
      self,
      in_channels: int,
      feat_channels: int,
      start_level: int,
      end_level: int,
      out_channels: int,
      mask_stride: int = 4,
      conv_cfg: OptConfigType = None,
      norm_cfg: OptConfigType = None,
      init_cfg: MultiConfig = [
          dict(type="Normal", layer="Conv2d", std=0.01)
      ],
  ) -> None:
      super().__init__(init_cfg=init_cfg)
      self.in_channels = in_channels
      self.feat_channels = feat_channels
      self.start_level = start_level
      self.end_level = end_level
      self.mask_stride = mask_stride
      assert start_level >= 0 and end_level >= start_level
      self.out_channels = out_channels
      self.conv_cfg = conv_cfg
      self.norm_cfg = norm_cfg
      self._init_layers()
      self.fp16_enabled = False
  
imports: import warnings
  from typing import List, Optional, Tuple
  import mmcv
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule
  from mmengine.model import BaseModule
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.models.utils.misc import floordiv
  from mmdet.registry import MODELS
  from mmdet.utils import ConfigType, InstanceList, MultiConfig, OptConfigType
  from ..layers import mask_matrix_nms
  from ..utils import center_of_mass, generate_coordinate, multi_apply
  from .solo_head import SOLOHead
label_desc: Use image node_id 1 to create a new MaskFeatModule object from inherited base classes: BaseModule with example: obj = MaskFeatModule(in_channels, feat_channels, start_level, end_level, out_channels, mask_stride, conv_cfg, norm_cfg, init_cfg)