[GSProcessing] Add saving and re-applying for numerical transforms.

[thvasilo]

Issue #, if available:

Fixes #985

Description of changes:

• We introduce saving and re-applying numerical transformations for all transforms except rank-gauss, which by definition cannot be reapplied.
• For the more complex transformations we re-construct the original PySpark transformer objects, by retaining the values needed for each transformation (e.g. the min and max values), creating tiny DFs that only contain those numbers and re-training the transformer on that tiny dataset. Then we can apply the trained transformer to the desired data and we get the same result.
• To reduce code duplication we pull out the core computations for standard and min-max normalization into their own functions (_apply_standard_transform, _apply_minmax_transform), which we can call from both the original transformation and the re-applied one. The presence or absence of pre-computed statistics in the function call determines which code path we follow.
• We modify the apply_imputation and apply_norm functions to also return the representation along with the transformed DF. We encapsulate the return values in their own dataclass (ImputationResult, NormalizationResult), to make future modifications easier (by not requiring to change the function's return type).
• Introduce new tests to check all re-construction cases.

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[classicsong]

Please add .. versionadded:: 0.4.0 or .. versionchanged:: 0.4.0 for new and updated APIs.

[jalencato]

LGTM

[thvasilo]

Please add .. versionadded:: 0.4.0 or .. versionchanged:: 0.4.0 for new and updated APIs.

None of the APIs changed are exposed to users @classicsong do we want to track all internal changes?

[classicsong]

Please add .. versionadded:: 0.4.0 or .. versionchanged:: 0.4.0 for new and updated APIs.

None of the APIs changed are exposed to users @classicsong do we want to track all internal changes?

No.

[classicsong]

LGTM