Co-authored-by: Jesse Grabowski <48652735+jessegrabowski@users.noreply.github.com>

Co-authored-by: Jesse Grabowski <48652735+jessegrabowski@users.noreply.github.com>

* More precise imports

* Fix gradient of `minimize` and `root` wrt higher-dimensional arguments

Also:
* Allow several inputs to be optimized
* Handle disconnected and undefined gradients

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Co-authored-by: ricardoV94 <ricardo.vieira1994@gmail.com>

Mainly, joining 0 axes is equivalent to inserting a new dimension. This is the mirror of how splitting a single axis into an empty shape is equivalent to squeezing it.

Also:
* Allow default `None` on unpack

Due to https://github.com/scipy/scipy/issues/24345

Remove cases where type-hints are better than bad type-hints

Inline when only used in one place, or remove if altogether unused

* Do not coerce gradients to TensorVariable

This could cause spurious disconnected errors, because the tensorified variable was not in the graph of the cost

* Type-consistent checks

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Co-authored-by: jessegrabowski <jessegrabowski@gmail.com>

This circumvents a bug when DimShuffle of a scalar shows up inside a Blockwise, as the outer indexing yields a float (as opposed to a numpy scalar) which has no `.shape` attribute.

Removes depracated `create_tuple_creator`

* Split blockwise and subtensor tests to deparate CI job

* Skip dimshuffle memory leak test in numba CI

* local_subtensor_of_squeeze bugfix

* Test graph correctness

* Follow template for test

* Remove depreciated AbstractConv Ops and tests

* Remove tensor/conv from test CI

* remove conv.rst