feat: calibration with tarp (#1106)

* add: prototype implementation of tarp in sbi

* fix: wrong use of torch.nn loss functions

* fix: wrong use of min values per dim

* introduced overconfident / underdispersed samples

* fix: wrong generation of toy gaussian data

* add simple test to check detection of pathological cases

* added biased case

* separate quite long test file

* prepared test case with trained NPE

- does not work yet

* first draft of TARP implementation for now

* formatting code according to ruff

* refactoring all class based methods into free functions

* removed print statements from tests

* removed TARP class

* removed tests for TARP class

* renamed tests

* added kstest and difference of area under curve

- these checks will help practitioners to identify malformed
posteriors
- added tests for these checks

* refactored metrics into sbi.utils.metrics

* prefer optional over Union[type,None]

* refer to num_ instead of n_

* asserting shapes early on

* remove obsolete assert

* refactored references check into its own function

* ruff reformatting

* check method updated, plot method added

- reformatted code
- added plotting method to make it easier to visualize TARP
- rewrote ATC property of ECP plot to indicate into which direction the posterior is shifted or dispersed
- updated tests accordingly

* removed superflous shape checks as best as possible

* refactored run_tarp into function similar to run_sbc

* refactored check_references

- added docstring
- removed spurious variables

* pyright fixes for tarp code

* pyright fixes to correct parameter name

* Apply suggestions from code review

* Apply suggestions from code review: line length

* Update tests/tarp_test.py

make slow test pass.

* Apply suggestions from code review

* fix last pyright issues

* refactoring

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Co-authored-by: Jan <[email protected]>
Co-authored-by: janfb <[email protected]>