There is no convolutional layer in front, I want to know how to merge the bn layer，thanks!
class MobileFaceNet(Module):
def init(self, embedding_size):
super(MobileFaceNet, self).init()
self.conv1 = Conv_Block(3, 64, kernel=3, stride=2, padding=1)
self.conv2 = Conv_Block(64, 64, kernel=3, stride=1, padding=1, groups=64)
self.conv3 = InvertedResidual(64, 64, kernel=3, stride=2, padding=1, groups=128)
self.conv4 = MakeBlocks(64, num_block=4, kernel=3, stride=1, padding=1, groups=128)
self.conv5 = InvertedResidual(64, 128, kernel=3, stride=2, padding=1, groups=256)
self.conv6 = MakeBlocks(128, num_block=6, kernel=3, stride=1, padding=1, groups=256)
self.conv7 = InvertedResidual(128, 128, kernel=3, stride=2, padding=1, groups=512)
self.conv8 = MakeBlocks(128, num_block=2, kernel=3, stride=1, padding=1, groups=256)
self.conv9 = Conv_Block(128, 512, kernel=1, stride=1, padding=0)
self.conv10 = Conv_Block(512, 512, kernel=7, stride=1, padding=0, groups=512, is_linear=True)
self.ft = Flatten()
self.ln = Linear(512, embedding_size, bias=False)
self.bn = BatchNorm1d(embedding_size)
    self.quant = QuantStub()
    self.dequant = DeQuantStub()

    for m in self.modules():
        if isinstance(m, Conv2d):
            n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            m.weight.data.normal_(0, math.sqrt(2. / n))
        elif isinstance(m, BatchNorm2d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()

def forward(self, x):
    x = self.quant(x)
    x = self.conv1(x)
    x = self.conv2(x)
    x = self.conv3(x)
    x = self.conv4(x)
    x = self.conv5(x)
    x = self.conv6(x)
    x = self.conv7(x)
    x = self.conv8(x)
    x = self.conv9(x)
    x = self.conv10(x)
    x = self.ft(x)
    x = self.ln(x)
    x = self.bn(x)
    x = self.dequant(x)
    return x

def fuse_model(self):
    for m in self.modules():
        if type(m) == Conv_Block:
            m.fuse_model()