Add Split bijector#82
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Split partitions a PyTree input into several sections along a given axis, analogous to TensorFlow Probability's Split. Also widens AbstractBijector.inverse_and_log_det's type hint from Array to PyTree, matching forward_and_log_det, since bijectors can now operate over arbitrary pytree structures.
lockwo
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Jul 7, 2026
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| def forward_and_log_det(self, x: Array) -> tuple[tuple[Array, ...], Array]: | ||
| """Computes y = tuple(split(x)) and log|det J(f)(x)| = 0.""" | ||
| y = tuple(jnp.split(x, self.indices_or_sections, axis=self.axis)) |
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I think transformed has to be updated first since it does not support Split outputs. Split.forward() returns a tuple of arrays, but Transformed._infer_shapes_and_dtype() assumes jax.eval_shape(...) returns one array-like object with .shape and .dtype
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Yes, is this not updated in my latest PR?
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Adds the Split bijector similar to tpb.Split.
Together with a "Restructure" bijector, this can be used to model the probability distribution of arbitrarily complex PyTree's using simple flat based distributions.
Also updates the type hint of AbstractBijector's "inverse_and_log_det typehint" to accept a
PyTreeand not anArray.