First internal integration of the new API

Hello again! The GSoC coding period is in full swing, and weeks 3 and 4 have been absolutely packed with progress.

Following up on the foundational work from my first PR, I have just successfully merged two major PRs () into the gsoc-2026 branch for : The DensityEstimatorBuilder

The primary goal of this phase was to replace the old, opaque posterior_nn() and likelihood_nn() factory closures with something typed, inspectable, and much more robust.

To solve this, I introduced the DensityEstimatorBuilder. It inherits from the base contract we established in Week 1 and serves as the unified entry point for creating neural networks in sbi. Using the __post_init__ method in Python dataclasses, it immediately validates the model name against a _VALID_DENSITY_MODELS set, failing early if the user provides an unknown architecture.

Major architectural shift

Initially, our plan dictated that the build() method should take a BuildContext object. The idea was that this context would hold all necessary information, including pre-computed z-scoring stats, and pass it neatly down the chain.

However, as I implemented the body of the build() method, my mentor : Integrating the Builder into NPE Trainers

With the builder merged, the next step was integration. I updated PosteriorEstimatorTrainer, NPE_B, NPE_C, and MNPE to accept the new DensityEstimatorBuilder instead of relying solely on strings or callables.

To maintain backward compatibility while moving the API forward, a graceful deprecation path was implemented. If a user passes a string (e.g., "maf"), the code still works perfectly, but it now emits a FutureWarning.

if density_estimator is None:

    self._build_neural_net = self._wrap_builder(DensityEstimatorBuilder(model="maf"))

elif isinstance(density_estimator, str):

    warnings.warn(

        "Passing a string for `density_estimator` is deprecated. "

        "Use DensityEstimatorBuilder(model=...) instead.",

        FutureWarning,

        stacklevel=3,

    )

    self._build_neural_net = posterior_nn(model=density_estimator)

elif isinstance(density_estimator, _EstimatorBuilderBase):

    self._build_neural_net = self._wrap_builder(density_estimator)

else:

    self._build_neural_net = density_estimator

Overal feedback on my work

The code review for PR #1882 was intense but incredibly rewarding. My mentor provided feedback on how to write tests that are not just concise, but strong and explicit in their intent.

For example, I originally wrote a test that checked if passing a callable avoided triggering the deprecation warning. But I wasn't actually asserting that no warning was thrown, I was just running the code and assuming silence meant success.

My mentor showed me how to use warnings.catch_warnings() with a strict filter to instantly fail the test if a FutureWarning leaked through:

import warnings



with warnings.catch_warnings():

    warnings.simplefilter("error", FutureWarning)

    inference = NPE_C(prior, density_estimator=builder, show_progress_bars=False)

We also did a deep dive into correct type hinting and managing default arguments. I initially left density_estimator="maf" as the default argument in the NPE_C initialization. My mentor pointed out that this would cause the deprecation warning to fire every single time a user initialized the class without arguments! The fix was to change the type hint default to None and handle the "maf" fallback inside the logic block.

What's Next?

Weeks 3 and 4 were a massive leap forward for the API refactor. We now have a working, integrated builder that correctly handles all continuous density estimators.

Next up, I will be tackling the remaining likelihood and classifier builders. Thanks for following along on this journey, and see you in the next update!