- Always enable __array_function__ overrides.
- Remove special cases for Python 2 compatibility.
- Document these changes in 1.17.0-notes.rst.

It will be good to see ASV numbers to understand the performance implications
of these changes. If need be, we can speed up NumPy functions internally by
using non-dispatched functions (with ``.__wrapped__``).

Example behavior:

>>> x = np.array([1, 2, 3])
>>> y = np.array([1, 2, 3.0001])
>>> np.testing.assert_allclose(x, y)
AssertionError:
Not equal to tolerance rtol=1e-07, atol=0

Mismatch: 33.3%
Max absolute difference: 0.0001
Max relative difference: 3.33322223e-05
 x: array([1, 2, 3])
 y: array([1.    , 2.    , 3.0001])

Motivation: when writing numerical algorithms, I frequently find myself
experimenting to pick the right value of `atol` and `rtol` for
`np.testing.assert_allclose()`. If I make the tolerance too generous, I risk
missing regressions in accuracy, so I usually try to pick the smallest values
for which tests pass. This change immediately reveals appropriate values to
use for these parameters, so I don't need to guess and check.

* Review F401,F841,F842 flake8 errors (unused variables, imports)

* Review comments

* More tests in test_installed_npymath_ini

* Review comments

BUG: Fix misleading assert message in assert_almost_equal #12200

Fixes #12200 by making a copy of the matrix before NaN's are excluded.
Add a test for it.

* ENH: __array_function__ support for np.lib, part 2

xref GH12028

np.lib.npyio through np.lib.ufunclike

* Fix failures in numpy/core/tests/test_overrides.py

* CLN: handle depreaction in dispatchers for np.lib.ufunclike

* CLN: fewer dispatchers in lib.twodim_base

* CLN: fewer dispatchers in lib.shape_base

* CLN: more dispatcher consolidation

* BUG: fix test failure

* Use all method instead of function in assert_equal

* DOC: indicate n is array_like in scimath.logn

* MAINT: updates per review

* MAINT: more conservative changes in assert_array_equal

* MAINT: add back in comment

* MAINT: casting tweaks in assert_array_equal

* MAINT: fixes and tests for assert_array_equal on subclasses

* previously, stochastic failures were possible
when running the NumPy test suite in parallel
because of an assumption made by assert_warn_len_equal()

* we now guard against this failure by gracefully
handling the testing contexts of both serial and
parallel execution of the module

After the pytest migration, test classes no longer inherit
from unittest.TestCase and and the fail method does not
exist anymore.

In all these cases, we can use assert_raises and assert_raises_regex instead