class_docstr: in_channels: List[int], input dim for P3, P4, P5
  out_channels: int, output dim for P2 - P7
  first_time: int, whether is the first bifpnstage
  conv_bn_act_pattern: bool, whether use conv_bn_act_pattern
  norm_cfg: (:obj:`ConfigDict` or dict, optional): Config dict for
      normalization layer.
  epsilon: float, hyperparameter in fusion features
class_docstr_tok: ["in_channels",":","List","[","int","]",",","input","dim","for","P3",",","P4",",","P5","out_channels",":","int",",","output","dim","for","P2","-","P7","first_time",":","int",",","whether","is","the","first","bifpnstage","conv_bn_act_pattern",":","bool",",","whether","use","conv_bn_act_pattern","norm_cfg",":","(",":","obj",":","`","ConfigDict","`","or","dict",",","optional",")",":","Config","dict","for","normalization","layer",".","epsilon",":","float",",","hyperparameter","in","fusion","features"]
code: def combine(self, x):
      if not self.conv_bn_act_pattern:
          x = self.swish(x)
  
      return x
  
file_path: mmdetection/projects/EfficientDet/efficientdet/bifpn.py
imports: from typing import List
  import torch
  import torch.nn
  from mmcv.cnn.bricks import Swish
  from mmengine.model import BaseModule
  from mmdet.registry import MODELS
  from mmdet.utils import MultiConfig, OptConfigType
  from .utils import DepthWiseConvBlock, DownChannelBlock, MaxPool2dSamePadding
label_desc: Use image node_id 2 for calling the BiFPNStage obj's underlying member method code with example usage: obj.combine(x) and returns: x