def METHOD_NAME():
    op = ProxylessMixedLayer(
        {name: value(3, 3, 1) for name, value in OPS_WITH_STRIDE.items()},
        nn.Parameter(torch.randn(len(OPS_WITH_STRIDE))),
        nn.Softmax(-1), 'proxyless'
    )
    optimizer = torch.optim.SGD(op.arch_parameters(), 0.1)
    for _ in range(10):
        x = torch.randn(1, 3, 9, 9).requires_grad_()
        assert op.resample({})['proxyless'] in OPS_WITH_STRIDE
        y = op(x).sum()
        optimizer.zero_grad()
        y.backward()
        assert op._arch_alpha.grad.abs().sum().item() != 0
    assert op.export({})['proxyless'] in OPS_WITH_STRIDE