Yeah, I felt that it should work, but I haven't had luck. I put up an example on github pages here https://scottyhq.github.io/codeautolink/intro.html .

The build log includes

WARNING: Could not resolve mean on line 3 using path numpy.array().mean.
Unable to follow return annotation of numpy.array.`

But it returns an numpy.ndarray type that does have the mean method, so I'm guessing there is some issue with following the source code structure

In [1]: import numpy as np

In [2]: test = np.array([[1, 2], [3, 4]])

In [3]:  type(test)
Out[3]: numpy.ndarray

In [4]: test.mean?
Docstring:
a.mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)

Returns the average of the array elements along given axis.

Refer to `numpy.mean` for full documentation.

See Also
--------
numpy.mean : equivalent function
Type:      builtin_function_or_method

I had a quick look, and np.array seems to be missing type annotations. I couldn't find it in the source with a quick search but

import numpy as np
assert "__annotations__" not in dir(np.array)

reveals that. Now, it may be that I've missed the memo on some part of the type hint machinery but at least this is what we currently support. We don't run code to resolve types 👍

Thanks for checking @felix-hilden ! Taking a step back I think I have a much simpler example to work with, which will avoid some of the potential complexities with larger libraries, but still illustrates the functionality I'm trying to achieve.

I've slightly modified the docs example to capture chaining methods on a class that returns a new or modified instance:

    def heal(self) -> Self:
        """creates a fresh Knight :)"""
        return Knight()

# how to link taunt() here since heal() returns a Knight?
print(knight.heal().taunt())

master...scottyhq:sphinx-codeautolink:chaining

Oh that'd be a good feature to have, I've added #147, contributions very welcome if you're interested!