Custom function passed to Learner() requires to have learner functions. Why?

I have a task, in which I have to take in 3 input images. For it I wrote a custom u-net model
class ThreeInputs(Module):

    def __init__(self, n_channels, n_classes, bilinear=True):
        super(ThreeInputs, self).__init__()
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.bilinear = bilinear = DoubleConv(n_channels, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        factor = 2 if bilinear else 1
        self.down4 = Down(512, 1024 // factor)
        self.up1 = Up(1024, 512 // factor, bilinear)
        self.up2 = Up(512, 256 // factor, bilinear)
        self.up3 = Up(256, 128 // factor, bilinear)
        self.up4 = Up(128, 64, bilinear)
        self.outc = OutConv(64, n_classes)
    def forward(self, x1, x2, x3):
        x =[x1, x2, x3])
        x1 =
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        logits = self.outc(x)
        return logits

I passed it to a learner:

learn = Learner(dls, ThreeInputs(3,2), opt_func=ranger, metrics=acc_camvid)

where dls is:

field = DataBlock(blocks=(ImageSequenceBlock, MaskBlock(codes)),
                get_items=SequenceGetItems([0,1,2], -1),
dls = field.dataloaders(fns_train, bs=2, create_batch=create_batch, verbose=True)

I am pretty sure that when I used to pass custom models to Learner before it would deal with summary and lr_find on it own. Wouldn’t require the custom model to have it. However, now it does.

My question is how to I overcome it?