  In this article, we consider the random sampling in a reproducing kernel
space $V$, defined as the range of an idempotent integral operator of mixed
Lebesgue spaces. At first, we approximate $V$ with an element of
finite-dimensional subspace of $V.$ Similar to classical case, we estimate that
if the sample size of both variables are large enough then the sampling
inequality holds with high probability.
