This is just a matter of translating between expectations and integrals, which are basically the same thing.
In probabilistic terms it's quite simple - just introduce an independent $\mathrm{Uniform}(0,1)$ variable $Y$, and note that $E(e^{itXY})=\int_0^1 E(e^{ityX})dy.$
In measure-theoretic terms, a function $\psi$ is a characteristic function iff it can be expressed as
$$\psi(t)=\int_{\Omega} e^{itZ(\omega)} d\omega\tag{*}$$ for some measure space $\Omega$ of measure $1$ and some measurable function $Z:\Omega\to\mathbb R.$ You are given some measurable $X:\Omega_1\to\mathbb R$ and you need to construct $\Omega$ and $Z$ such that
$$\int_{0}^1 \int_{\Omega_1} e^{ityX(\omega_1)} d\omega dy=\int_{\Omega}e^{it Z(\omega)} d\omega$$
In these terms the answer is to take $\Omega=\Omega_1\times [0,1]$ and $Z(\omega_1,y)=X(\omega_1)y.$