eigen: Return type "annotation" does not reflect actual shape for vectors?

[EricCousineau-TRI]

Issue description

Per docs here (as of 2.5.0):
https://pybind11.readthedocs.io/en/stable/advanced/cast/eigen.html#vectors-versus-column-row-matrices

When returning an Eigen vector to numpy, [...] if it is a compile-time vector–that is, the type has either the number of rows or columns set to 1 at compile time–pybind11 converts to a 1D numpy array when returning the value. [...]

Makes total sense. However, looking at the docstrings:

pybind11/tests/test_eigen.py

Lines 609 to 623 in 4d9024e

	def test_dense_signature(doc):
	assert doc(m.double_col) == """
	double_col(arg0: numpy.ndarray[numpy.float32[m, 1]]) -> numpy.ndarray[numpy.float32[m, 1]]
	"""
	assert doc(m.double_row) == """
	double_row(arg0: numpy.ndarray[numpy.float32[1, n]]) -> numpy.ndarray[numpy.float32[1, n]]
	"""
	assert doc(m.double_complex) == ("""
	double_complex(arg0: numpy.ndarray[numpy.complex64[m, 1]])"""
	""" -> numpy.ndarray[numpy.complex64[m, 1]]
	""")
	assert doc(m.double_mat_rm) == ("""
	double_mat_rm(arg0: numpy.ndarray[numpy.float32[m, n]])"""
	""" -> numpy.ndarray[numpy.float32[m, n]]
	""")

For m.double_col, the input type makes sense, because that matters to C++ (as mentioned in the docs).

However, the return type is a bit misleading: it states that the output types is numpy.ndarray[numpy.float32[m, 1]].

This is a bit misleading, because the return type's shape is actually (m,).
Additionally, when the return type has

This bit us in RobotLocomotion/drake#13885.
The docs do have a suggested workaround, which is what we'll do, but it'd be nice if the annotation were a bit more truthful.

Reproducible example code

See aforementioned example.

Possible solutions

1. Somehow distinguish the type string based on return-type? (that could be down by "rerouting" the return type somehow?)
2. Discard shape information (at the cost of sacrificing info in the argument - probably a non-starter)
3. As-is
4. Other?

[EricCousineau-TRI]

FWIW The main "jarring" aspect is that the shape of float[m, n] is truly (m, n), while the shape of float[m, 1] is (m,) (as seen by Python), so it'd be nice to somehow collapse that.