PAC-learning bound with epsilon-cover of hypothesis class

In this video at 43:00, a version of the PAC bound for generalization error $\epsilon$, which I hadn't seen before, is quoted:

$$\epsilon^2 < \frac{\log{|H_\epsilon|} + \log{1/\delta}}{2m}$$

where $m$ is the number of samples, $\delta$ is the confidence parameter, and $H_\epsilon$ is the cardinality of an "$\epsilon$-cover of the hypothesis class", where he defines an $\epsilon$-cover as a set of subsets of the hypothesis class, such that the probability that two hypothesis in the same subset disagree is less than $\epsilon$.

Apart from the fact that this isn't a formal statement, I couldn't prove this myself. Has anyone heard of this version of PAC, and if so, could they point me to resources explaining it, or give some explanation here?

This follows from Massart's finite class lemma. Let $F$ is a binary function class restricted to some set $\{X_1,\ldots,X_n\}$, and let $P_n$ be the empirical (i.e., uniform) measure on this set. Then, for any $\epsilon>0$, the empirical Rademacher complexity of $F$ is bounded by $$R_n(F;X) \le \epsilon + \sqrt{\frac{2\log N_F(\epsilon)}{n}},$$ where $N_F(\epsilon)$ is the $\ell_2$ $\epsilon$-covering number of $F$ w.r.t. $P_n$. This is proved in display (1) here: https://www.cs.bgu.ac.il/~asml162/wiki.files/dudley-pollard.pdf -- check out the course notes: https://www.cs.bgu.ac.il/~asml162/Class_Material