fix ivy.dropout returning native arrays/tensors

[Sam-Armstrong]

Fixes problem where ivy.dropout can return things like tf.EagerTensor instead of ivy.Array.

Min example:

import ivy

if __name__ == '__main__':
    ivy.set_backend("tensorflow")

    x = ivy.random_normal(shape=(5, 5, 5))
    x = ivy.dropout(x, 0.1)
    print(type(x))
    x = x.reshape((25, 5))

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Thank you :hugs:

[AnnaTz]

Why was it returning native arrays? It is a composition of ivy functions so this shouldn't be happening 🤔

[Sam-Armstrong]

@AnnaTz I've honestly got no idea what the problem is here, all the inner ivy functions seem to be working fine and when I create an identical sample dropout function in a test script it also returns ivy arrays rather than native. So I guess there must be some other problem within ivy that's causing this, but I'm no expert on what it could be 😅. What do you think we should do here? This solution does solve the problem ^

[AnnaTz]

@AnnaTz I've honestly got no idea what the problem is here, all the inner ivy functions seem to be working fine and when I create an identical sample dropout function in a test script it also returns ivy arrays rather than native. So I guess there must be some other problem within ivy that's causing this, but I'm no expert on what it could be 😅. What do you think we should do here? This solution does solve the problem ^

I think I figured it out. dropout is a mixed function so it should have amixed_backend_wrappers attribute to define its decorators as in interpolate for example.

[Sam-Armstrong]

@AnnaTz Ah yes, looks like that solved it. Nice spot! Have updated this PR