Error calling Learner with Custom model

I was trying to recreate a MobileNet architecture, but I bumped into an odd error that I can’t wrap my head around, anyone else seen this before?

class Net(nn.Module):
def __init__(self):
    super(Net, self).__init__()

    def conv_bn(inp, oup, stride):
        return nn.Sequential(
            nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
            nn.BatchNorm2d(oup),
            nn.ReLU(inplace=True)
        )

    def conv_dw(inp, oup, stride):
        return nn.Sequential(
            nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
            nn.BatchNorm2d(inp),
            nn.ReLU(inplace=True),

            nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
            nn.BatchNorm2d(oup),
            nn.ReLU(inplace=True),
        )
    

    self.model = nn.Sequential(
        conv_bn(  3,  32, 2), 
        conv_dw( 32,  64, 1),
        conv_dw( 64, 128, 2),
        conv_dw(128, 128, 1),
        conv_dw(128, 256, 2),
        conv_dw(256, 256, 1),
        conv_dw(256, 512, 2),
        conv_dw(512, 512, 1),
        conv_dw(512, 512, 1),
        conv_dw(512, 512, 1),
        conv_dw(512, 512, 1),
        conv_dw(512, 512, 1),
        conv_dw(512, 1024, 2),
        conv_dw(1024, 1024, 1),
        nn.AdaptiveAvgPool2d(1),
    )
    self.fc = nn.Linear(1024, 340)

def forward(self, x):
    x = self.model(x)
    x = x.view(-1, 1024)
    x = self.fc(x)
    return x

I realised that I should have called Net with parentheses, that seems to solve the issue.