Phiml

**Highlights**

* NumPy 2 compatibility
* `Tensor.numpy()` and `.native()` now support dim packing
* `wrap()` and `tensor()` now support shape spec strings, e.g. `'example:b,(x,y,z)'`
* Compact sparse tensors can now be created using `sparse_tensor` (experimental)
* Support for SVD and eigenvalues
* Shorthand notation `dim in Tensor`
* Various improvements for sparse tensors
* Support save/load on Stax nets
* Added `tensor.T` to transpose a tensor. This switches primal/dual dims.
* Added functions `ravel_index`, `d2i` and aliases `length`, `rand`, `randn`.
* Shapes can now be stacked using `stack`
* `unpack_dim` can now be used with non-uniform targets

Fixes NumPy compatibility and other bug fixes, adds convenience features.

Bug fixes, improved CompactSparseTensor support.

**New features**

* Sparse SciPy and ML tensors can now be wrapped like regular tensors.
* Shorthand `shape & dual` to add corresponding dual dims
* Added experimental compact sparse tensor
* Removing dims from a `Shape` can now be done using the `-` operator.
* Generic type conversion via `Shape.as_type()`.

**Improvements**

* `scatter()` is now more flexible with `treat_as_batch` argument.
* Improvements to linear tracing. Improved rank deficiency detection. Linear solves will only use matrix_offset if confirmed by user.
* `minimum` and `maximum` can now be used with `None` values.
* Stacked trees may now include `None` values.
* `reshaped_native()` and `reshaped_numpy()` now support `()` / `None` for singleton dims

Numerous fixes and various new features, see 6

**Highlights**

* JAX buffer management for variable-sized tensors
* JIT improvements and fixes
* Periodic pairwise_differences() and fixes
* Extrapolation by side with '+' and '-'
* Added `substeps` to `iterate()`
* Regularized linear solves for singular matrices
* Add `safe_mul()`