as mentioned in https://github.com/xtensor-stack/xtensor/issues/2629,
the behaviour of the random generator is controlled by the implementation
of the standard library, so it cannot be expected to behave the same
across platforms

This is required for the bindings, because the 2D views on 4D tensors
must be on the correct type (i.e. pytensor/rtensor/xtensor). Although,
casting of the views to become xtensor could have been used instead.

In doing so, there is no source file to be compiled anymore, so this
becomes a header-only library. But to keep the "public" headers separate
from the implementation, a subdirectory "detail/" is added.

The bootstrapping method is based on a non-overlapping block sampling
with replacement, where the blocks are years of data. The number of
samples and the sample length (i.e the number of year blocks) are both
customisable.

The method is accessible both for deterministic and probabilistic
evaluation where a new axis is added. For now, the metrics for all the
samples are returned, but in the future, some summary statistics would
be implemented (e.g. quantiles or mean/standard deviation).

/!\ For determinist evaluation, the n-dimensional functionality became
    untenable such that the number of dimensions was fixed and
    restricted to 2D tensors.

New unit tests are included to test both the bootstrapping generator
and the numerical results obtained with the bootstrapping turned on.