filtering for isnan can result in NaN gradient

[jonny-so]

This works as expected

function test(x)
    y = [1.0, NaN, 1.0]
    sum(filter(!isnan, x.-y))
end

julia> Tracker.gradient(test, [1, 2, 3])
([1.0, 0.0, 1.0] (tracked),)

This does not

function test2(x)
    y = [1.0, NaN, 2.0]
    sum(filter(!isnan, (x.-y).^2))
end
julia> Tracker.gradient(test2, [1,2,3])
([0.0, NaN, 2.0] (tracked),)

I wouldn't expect the computation (x[2]-y[2])^2 to be in the graph for the result, so I don't understand why this would return NaN. Have I missed something?

[jonny-so]

Tracker version 0.2.2