Blosc2

* Use oldest-supported-numpy for maximum compatibility.

* Updated C-Blosc2 to 2.6.0.

* Support for Python prefilters and postfilters. With this, you can pre-process or post-process data in super-chunks automatically. This machinery is handled internally by C-Blosc2, so it is very efficient (although it cannot work in multi-thread mode due to the GIL). See the examples/ directory for different ways of using this.

* Support for fillers. This is a specialization of a prefilter, and it allows to use Python functions to create new super-chunks from different kind of inputs (NumPy, SChunk instances, scalars), allowing computations among them and getting the result automatically compressed. See a sample script in the examples/ directory.

* Lots of small improvements in the style, consistency and other glitches in the code. Thanks to Dimitri Papadopoulos for hist attention to detail.

* No need to compile C-Blosc2 tests, benchs or fuzzers. Compilation time is much shorter now.

* Added `cratio`, `nbytes` and `cbytes` properties to `SChunk` instances.

* Added setters for `dparams` and `cparams` attributes in `SChunk`.

* Honor nested cparams properties in kwargs. E.g. you can do:


blosc2.save_tensor(a, "test.bl2", mode="w",
filters=[blosc2.Filter.TRUNC_PREC, blosc2.Filter.BITSHUFFLE],
filters_meta=[13, 0],
codec=blosc2.Codec.LZ4,
clevel=9)


without a need to build a proper `cparams` dict first.

* C-Blosc2 upgraded to 2.4.3. It should improve cratio for BloscLZ in combination with bitshuffle.

* Prefer pack_tensor/save_tensor in benchs and examples

* Remove the testing of packing PyTorch or TensorFlow objects during wheels build.