ut3_constructors

Constructors and file IO for uniform Tucker tensor trains (UT3), on the raw .data tuple.

ut3_zeros / ut3_ones / ut3_corewise_randn build the padded supercores + masks directly (the uniform feature ragged round-tripping cannot express: ranks may vary per stack element – the determinantal variety, docs/uniform_ranks_and_varieties.md). ut3_save / ut3_load share common.save_core_families (2 supercores + 2 rank masks + the shape ints).

There are deliberately no ut3_from_canonical / ut3_from_tensor_train / ut3_to_tensor_train round-trips: they would take ragged CP/TT data and round-trip through TuckerTensorTrain, which is ambiguous (ragged vs uniform input) and trivially composable from the existing ragged ops + UniformTuckerTensorTrain.from_t3 / .to_t3. Be explicit at the boundary instead.

Following the layer-wide rule (docs/contributor/uniform_pytree_composition.md): supercores (data) -> ``xnp``/``use_jax``; masks (structure) -> ``np`` (host). The pure constructors keep a use_jax flag for the supercores (there is no array input to infer from). ut3_load keeps use_jax for the supercores but always returns numpy (host) bool masks.

Functions

ut3_zeros(shape[, tucker_ranks, tt_ranks, ...])	Uniform Tucker tensor train of zeros. tucker_ranks/tt_ranks may vary per stack element.
ut3_ones(shape[, stack_shape, use_jax])	Rank-1 uniform Tucker tensor train representing a tensor full of ones (every real entry == 1).
ut3_corewise_randn(shape, tucker_ranks, tt_ranks[, ...])	Uniform Tucker tensor train with random N(0,1) supercores (padded regions masked to zero).
ut3_save(file, data)	Save a uniform Tucker tensor train (2 supercores + 2 rank masks + the shape ints) to a .npz.
ut3_load(file[, use_jax])	Load a uniform Tucker tensor train from a .npz file written by ut3_save().