DOC: the recommended ruff rule for migration to numpy2 is missing many detections and fixes

Issue with current documentation:

Hello, Numpy folks!
There is a ruff rule (NPY201) that is recommended for migration from numpy to numpy2. I did a thorough comparison between Python API removals and deprecations with what the NPY201 rule provides. There are many removals and deprecations that are in the documentation but the ruff rule does not consider.
In many of the cases, a fix could be performed automatically.

Here is a summary of what is missing from the rule NPY201:

Changes that are not detected at all

• np.geterrobj, np.seterrobj and the related ufunc keyword argument extobj= have been removed. The preferred replacement for all of these is using the context manager with np.errstate():.
• removed ERR_*, SHIFT_*, np.kernel_version, np.numarray, np.oldnumeric and np.set_numeric_ops.
• removed namespaces: np.FLOATING_POINT_SUPPORT, np.FPE_*, np.CLIP, np.WRAP, np.RAISE, np.BUFSIZE, np.UFUNC_BUFSIZE_DEFAULT, np.UFUNC_PYVALS_NAME, np.ALLOW_THREADS, np.MAXDIMS, np.MAY_SHARE_EXACT, np.MAY_SHARE_BOUNDS.
• np.issctype, np.maximum_sctype, np.obj2sctype, np.sctype2char, np.sctypes were all removed from the main namespace without replacement, as they where niche members.
• np.compare_chararrays has been removed from the main namespace. Use np.char.compare_chararrays instead.
• The charrarray in the main namespace has been deprecated. It can be imported without a deprecation warning from np.char.chararray for now, but we are planning to fully deprecate and remove chararray in the future.
• np.format_parser has been removed from the main namespace. Use np.rec.format_parser instead.
• The experimental numpy.array_api submodule has been removed. Use the main numpy namespace for regular usage instead, or the separate array-api-strict package for the compliance testing use case for which numpy.array_api was mostly used.
• Support for seven data type string aliases has been removed from np.dtype: int0, uint0, void0, object0, str0, bytes0 and bool8.
• np.trapz has been deprecated. Use np.trapezoid or a scipy.integrate function instead.
• np.in1d has been deprecated. Use np.isin instead.
• Arrays of 2-dimensional vectors for np.cross have been deprecated. Use arrays of 3-dimensional vectors instead.
• np.dtype("a") alias for np.dtype(np.bytes_) was deprecated. Use np.dtype("S") alias instead.
• Use of keyword arguments x and y with functions assert_array_equal and assert_array_almost_equal has been deprecated. Pass the first two arguments as positional arguments instead.

Changes that are detected but no automatic fix is provided

• np.cast has been removed. The literal replacement for np.cast[dtype](arg) is np.asarray(arg, dtype=dtype).
• np.set_string_function has been removed. Use np.set_printoptions instead with a formatter for custom printing of NumPy objects.
• np.recfromcsv and recfromtxt are now only available from np.lib.npyio.
• np.asfarray has been removed. Use np.asarray with a proper dtype instead.
• np.find_common_type has been removed. Use numpy.promote_types or numpy.result_type instead. To achieve semantics for the scalar_types argument, use numpy.result_type and pass 0, 0.0, or 0j as a Python scalar instead.
• np.nbytes has been removed. Use np.dtype(<dtype>).itemsize instead.

Idea or request for content:

My suggestion is to add these changes to the ruff rule. Otherwise, it would be really helpful if the provided and not-provided changes were documented (either in the corresponding ruff rule page or the migration guide) so the users would know what they were getting (and not getting) by applying the rule.