Represents a probability distribution over linear mappings
whose covariance structure is separable between input and output.
This can be conceptualized as a specialization of Schmear
for a distribution over matrices where the covariance of the (vectorized)
random variable takes the separable form kron(out_covariance, in_covariance)
for out_covariance and in_covariance output coordinate and input coordinate
covariances, respectively, and kron referring to crate::linalg_utils::kron
See also FuncScatterTensor.