code: def forward(self, x):
      x = self.main_conv(x)
      x = torch.stack(torch.chunk(x, 2, dim=1), dim=0)  # 2, B, C, H, W
      g = x.sum(dim=0).mean(dim=[2, 3], keepdim=True)
      m = self.mixing_conv(g)
      m = torch.stack(torch.chunk(m, 2, dim=1), dim=0)  # 2, B, C, 1, 1
      return (x * F.softmax(m, dim=0)).sum(dim=0)
  
imports: import logging
  from enum import Enum
  import torch
  import torch.nn
  import torch.nn.functional
  from vissl.config import AttrDict
  from vissl.models.trunks import register_model_trunk
label_desc: Use image node_id 2 for calling the SelectiveKernel obj's underlying member method code with example usage: obj.forward(x) and returns: unknown