code: def forward(self, x):
      shortcut = x
      x = self.conv1(x)
      x = self.conv2(x)
      x = self.conv3(x)
      if self.se is not None:
          x = self.se(x)
      if self.drop_path is not None:
          x = self.drop_path(x)
      if self.downsample is not None:
          shortcut = self.downsample(shortcut)
      x += shortcut
      x = self.act(x)
      return x
  
imports: import math
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import SelectiveKernel, ConvNormAct, create_attn
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
  from .resnet import ResNet
label_desc: Use image node_id 3 for calling the SelectiveKernelBottleneck obj's underlying member method code with example usage: obj.forward(x) and returns: x