For KL divergence, would we consider two distribution has same random variable? from the formula of KL divergence
$$ KL(P||Q) = \int_{-\infty}^{\infty} p(x) \log \left(\frac{p(x)}{q(x)}\right)\operatorname{d}x$$
I feel the $P$ and $Q$ has the same random variable, but, the distribution associated with a random variable should be unique, so how could we have two separate distributions with the same random variable? I can think $x$ is just dummy variable at here but again, how could this be valid from the definition?

When considering
$$KL(P||Q) = \int_{-\infty}^{\infty} p(x) \log \left(\frac{p(x)}{q(x)}\right)~\text{d}x$$
it can be expressed as
$$KL(P||Q) = \mathbb E_p\left[\log \left(\frac{p(X)}{q(X)}\right)\right]$$
meaning it is the expectation of a transform of the random variable $X$ with density $p$ (or distribution $P$). There is therefore no assumption of $X$ being distributed from both $P$ and $Q$. Note however that it can equally be written as
$$KL(P||Q) = \int_{-\infty}^{\infty} q(x)~\frac{p(x)}{q(x)}~\log \left(\frac{p(x)}{q(x)}\right)~\text{d}x=\mathbb E_q\left[\frac{p(X)}{q(X)}~\log \left(\frac{p(X)}{q(X)}\right)\right]$$meaning that it can also be expressed as an integral under $Q$ (or any other distribution following the same reasoning).
