Shape-typing support for many functions and methods

Many functions and methods now have shape-aware return type annotations.

Type-checkers can now infer the number of dimensions of the returned array

through common operations. For example, ``np.linspace(0, 1)`` is now typed

as a 1-d ``float64`` array, and ``np.sum(x, keepdims=True)`` has the same

number of dimensions as ``x``.

This covers ``numpy.linalg`` functions, array creation functions (like

``asarray``, ``from{buffer,string,file,iter,regex}``), range functions (``linspace``,

``logspace``, ``geomspace``), aggregation functions and methods (``sum``,

``mean``, ``std``, ``var``, ``min``, ``max``, ``all``, ``any``, etc.), sorting

(``sort``, ``argsort``, ``argpartition``), cumulative operations (``cumsum``,

``cumprod``, etc.), set operations (``unique_values``, ``intersect1d``,

``union1d``, etc.), and various other functions including ``nonzero``,

``transpose``, ``diagonal``, ``atleast_{1,2,3}d``, ``clip``, ``round``,

``inner``, ``bincount``, and ``fft.fftfreq``. Several of these also gained

more precise return dtype annotations as part of this work.

Shape-typing is still a work-in-progress, so coverage is not yet complete.

Because of limitations in Python's type system and current type-checkers,

shape-typing is often only implemented for the most common lower-rank cases.