Correct transformation of second-order tensors in adapters

[han-ol]

Currently, only first-order tensors, a.k.a. vectors can be transformed in both forward and inverse direction through an adapter object.

Can we transform also different orders, particularly second-order?

The question arises because a PointInferenceNetwork can estimate the covariance matrix of the inference_variables.
Some adapters represent a change of basis and origin, for example, standardize is a linear coordinate transformation from "original coordinates" to "standardized coordinates".
Thus, the inference_variables live in the standardized coordinates, and the covariance matrix needs to be adapted in inverse direction to relate to unstandardized coordinates.

When a covariance matrix is estimated in the standardized coordinates, the inverse of the coordinates transformation is different from naively transforming matrix columns as if they were vectors in standardized coordinates.
Rather, a covariance matrix $\Sigma^{-1}$ transforms as an order 2 tensor: the basis change matrix $A$ is multiplied from both sides $A^T \Sigma^{-1} A$.
For standardize, $A=diag(\sigma_i)$ with $\sigma_i$ the standard deviations for each dimension, so $A^T \Sigma^{-1} A = \Sigma^{-1} \cdot diag(\sigma_i^2)$.

Solutions to this issue are likely related to a separate issue on keeping track of jacobians of adapter transforms, as mentioned in #245.