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They are not supposed to be used since they can be deactivated by
`python3 -O ...`

This resolves deprecation warnings in numpy>=1.2 .

This was achieved by exploiting the diagonality of the Fisher metric.
In cases this is not true one needs more samples to increase sensitivity.
Tested that a factor of 2 only in the inverse covariance can now be detected.

The zero-mode is kind of accounted for as it is discarded in the
calculations and thus the FIXME is kind of fixed.

Adapt the density_estimator accordingly.

Re-introduce the FIXME on how the zero-mode is treated in the
`_Normalization` operator.

If no amplitude total offset (i.e. zero-mode of the amplitude operator)
is specified, the correlated field is still able to produce a useful
tensor-product of amplitudes. However, their overall zero-mode is of
course not determined. As convention, set the zero-mode of the final
amplitude operator to zero.

Adapt the statistics summary to not require the zero-mode.

Convert the return list of operators to a tuple as to disallow the
caller to amend or change it in other ways.

Since `noramlized_ampliudes` is not a property anymore, name it more
actively as to highlight the need for it to be called as a function.

The zero-mode must not be known for constructing amplitudes. Only when
finalizing the model, the zero-mode needs to be specified. Honor this by
pulling all parameters relating to the zero-mode to the `finalize`
method.

Move `moment_slice_to_average` outside of `CorrelatedFieldMaker` as it
does not depend on nor require the inherent structure of the class.

Handle the intricacies of factorizing power spectra in `finalize`
instead of relying on normalized amplitude models.

The information theoretic interpretation of drawing samples from the
metric of the KL samples is not clear. This may be the reason why it was
not documented before.