def METHOD_NAME(self, out, index):
    x_s = out[0: int(out.shape[0]//2)]
    x_t = out[out.shape[0]//2: out.shape[0]]
    x_all = torch.cat((x_s, x_t), 2)
    x_all = x_all.view(x_all.shape[0], -1)
    weight = torch.sigmoid(self.bn_lst[index](
        self.gate[index](x_all.float())))
    weight = torch.mean(weight, dim=0)
    res = self.softmax(weight).squeeze()
    return res