$2\mathrm{.}6$ Using the Axiom of Completeness ${\displaystyle \underset{i=1}{\overset{n}{}}}{a}_i=F(x)-F(x^{\text{'}}),$ where $a_i$ denotes the attribution of variable $i$ to the output $F(x)$ at input vector $x$ in comparison to output $F(x^{\text{'}})$ at baseline input $x^{\text{'}},$ one can verify the correctness of numerical calculation of attribution $a_1$ for all i$\text{'}$s$.$ But for this one needs to evaluate $a_i$ for AAi, which can be daunting particularly for classification of high$-$resolution images. However, if one is interested in evaluating and validating attributions for only a subset tilde$(x)$ of the set $x$ of all input variables $($i$.$e$.$ specific input vectors $x$ and $x^{\text{'}}$ are different instances of $x),$ then which one of the below expressions should one use in place of the original expression above:

a$)$ and $($:$x-(tilde(x))-=x^{\text{'}}\}$

b$)$ and $($:$x-(tilde(x))-=x^{\text{'}}\}$

c$),{\displaystyle \underset{\text{i}\text{i}\text{n}\stackrel{}{x}}{\overset{?}{}}}{a}_i=F(x)-F(x^{\text{'}})$

d$)$ none of the above.