Suppose $Y$ is a random variable in $\mathbb{R}^d$, and we want to find the covering number \begin{equation*} \mathcal{F} = \big\{ F_{Y|W} (y | W) : y \in \mathbb{R}^d \big\} \end{equation*} where $W$ is another random variable in $\mathbb{R}^k$ and $F_{Y|W} (y | W)$ is the conditional distribution function. Denote $P_{Y, W}$ is the law of $(Y, W)$, what we want to find is the covering number $N(\epsilon, \mathcal{F}, L_2(P_{Y, W}))$.
At the first glimpse, this question is quite simple, since if we treat $W$ as a determined number, then \begin{equation*} \mathcal{F} = \overline{\operatorname{conv}}(\mathcal{G}), \end{equation*} where $\mathcal{G} := \big\{ 1(Y \leq y) : y \in \mathbb{R}^d \big\}$ with $N(\epsilon, \mathcal{G}, L_2(P_{Y})) \lesssim \epsilon^{- 2d}$ (This is because $F_{Y|W} (y | W) : \mathbb{R}^d \rightarrow [0, 1]$ is a distribution function). Then by the result for convex hulls, we have \begin{equation*} \log N \big( \epsilon, \mathcal{F}, L_2 (P_Y)\big) \lesssim \epsilon^{-2 d/ (d + 1)}. \end{equation*}
However, I think the randomness of $W$ complex this problem: what we want is $N(\epsilon, \mathcal{F}, L_2(P_{Y, W}))$ rather than $N ( \epsilon, \mathcal{F}, L_2 (P_Y))$. And I try to find some articles about this, but it is proved to be futile.
So can anyone help me with this question. Thanks in advance!