The step responsible for checking whether a tensor has the right strides
to invoke a C++ routine was incorrect and always accepted C-style NumPy
arrays (because they leave the 'stride' field set to NULL).

The whole conversion logic will be more heavily tested in a subsequent
commit that adds a type caster for Eigen arrays.

- Prefer the buffer protocol over DLPack when converting C++ tensors
  into NumPy arrays. This makes it possible to wrap C++ memory through
  arrays that have their 'write' bit still enabled (NumPy is somewhat
  conservative here and disables the write bit when the conversion is
  done via DLPack).

- Simplified the implementation of the internally used `nb_tensor`
  buffer protocol wrapper. This change requires a nanobind ABI bump.

- Copy the contents when `rv_policy::copy/move` is specified
  to the tensor caster.

This commit adds custom casters for the Eigen linear algebra library. It
uses the existing `nb_tensor` infrastructure to implement a conversion
between Eigen types and NumPy arrays that currently supports:

- Eigen::Matrix/Array<..> and derived types (row/column vectors)
- Eigen::Map<..>
- Eigen::Ref<..>
- Expression templates (CwiseBinaryOp, etc.)

There are a few limitations:

- Eigen tensors are not supported yet.
- Sparse matrix types are not supported yet.