* actually make sum etc return jnp.ndarray when None is provided

* no cover

* remove the coverage pragmas and just automatically exclude typing.overload

* fix signature for ReductionFunction

* cleanup typing.overloads

* refactor dot to support None

* make precommit stricter, please mypy

* use auto_sharded internally, undeprecate it b/c it has a point

* refactor dot to take a keyword argument for axis instead of it being a leading argument

* fix some deprecation warnings

* move dot to its own file

* add support for out_axes to `dot`

* add docs for the new dot

* move rearrange_to_fit_order to axis

* make "old-style" rearrange accept multiple ellipses

* add support for multiple ellipsis to einops-style rearrange

* try to fix dtypelike for older jax versions

* missed a spot

* remove old scalar thing from docs

* allow AxisSpec for Embed in Embedding

* copy in Equinox compile cache so Patrick isn't disappointed in me

* use named arrays for the where check

* let `named` work with anything asarray likes

* catch some some dumb oversights in calling sharding

* simplify handling of non-named arrays in binary ops

* move losses to their own file, add binary cross entropy loss

* less coverage noise

* autocoerce scalar named arrays to plain jax arrays

* don't reduce losses when reduction axis == ()

* on second thought, less magic in the Haliax API is probably better

* on second thought, less magic in the Haliax API is probably better

* minimize use of jax internals

* make it so we don't scan scalars, which will be nicer

* older jax doesn't expose DTypeLike

* log the name that's being scanned

* parens seem more normal

* change default prevent_cse to False for Stacked

* Adds a new einsum syntax (#63)

Adds a new einsum syntax to Haliax that has three modes: "ordered", "unordered", and "output only".

*    Ordered looks like normal (einops-flavored) einsum
*   unordered uses syntax similar to rearrange: specify the names of axes involved in the operation, others are batched.
*    output only lets you specify only the expected output shape. use judiciously.

* add a method for flattening all "batch" axes into a single batch axis, which is useful in a few situations