The idea behind copula theory is that the cumulative distribution function (CDF) of a random vector can be represented in the form of uniform marginal cumulative distribution functions, and a copula that connects these marginal CDFs, which describes the correlations among the input random variables.
in order to have a valid multivariate distribution function regardless of n-dimensional covariates, not every function can be used as a copula function.