This commit improves handling of evaluated loops with grad-enabled state
variables. Previously, the AD variable ID of each differentiable state
variable changed in every iteration, even if the loop did not touch that
variable. This is an implementation detail of the loop evaluation code,
that should, however, not leak into user code. This commit fixes this
behavior.

This commit fixes bugs in the compilation of reverse-mode derivatives of
simple loops (i.e, loops with max_iterations==-1) and updates the test
suite to cover problematic cases.

Cooperative vectors enable efficient compilation and evaluation of
expressions involving matrix multiplication. They cater to a specific
use case, where each execution thread performs a sequence of independent
multiplications by reasonably small matrices (e.g., 64x64). This enables
the fully fused evaluation of small multilayer perceptrons within a
larger program. That said, the feature isn't specific to MLPs and could
also be used in other ways.

On NVIDIA GPUs (Turing or newer), cooperative vectors map to the OptiX
cooperative vector API leveraging the builtin tensor core for
acceleration. On the CPU (LLVM) backend, Dr.Jit compiles cooperative
vector operations using available instruction set extensions (AVX512,
NEON, etc.).

For further details on this new API and now to use it, refer to the
documentation in ``docs/coop_vec.rst``.

Fix derivative of ``nn.matmul()`` in simple symbolic loops

This commit fixes bugs and adds tests to ensure that matrix
multiplication can be correctly differentiated in reverse-mode when it
occurs inside a "simple" loop (i.e., a loop with max_iterations==-1).