How to understand the task about bootstrap?

Question

I have the following task. Let $X_1,\ldots,X_n$ be a sample (i.i.d.), $T_n = \overline{X}_n^2=\left(\sum\limits_{i=1}^nX_i\right)^2,$ and $\hat\alpha_k =\frac1n\sum\limits_{i=1}^n|X_i-\overline{X}_n|^k.$ I should prove that bootstrap variance estimation of $T$ is $$v_{boot}=\dfrac{4\overline{X}_n^2\hat\alpha_2}{n}+\dfrac{4\overline{X}_n\hat\alpha_3}{n^2}+\dfrac{\hat\alpha_4}{n^3}.$$ My question is how to define $v_{boot}$ in these terms. I know that when we use a bootstrap we generate subsamples with replacement, find $T$ for each subsample (and have $T_1^*,\ldots,T_m^*$) and find variance $v_{boot}$ of $T_1^*,\ldots,T_m^*.$ But answer for $v_{boot}$ in the formula depends on $\overline{X}_n$.