mentioned in the Notes section is disabled.

    .. versionadded:: 2.0.0

Raises
------
AssertionError
    If actual and desired are not equal up to specified precision.

See Also
--------
assert_array_almost_equal_nulp, assert_array_max_ulp

Notes
-----
When one of `actual` and `desired` is a scalar and the other is
array_like, the function performs the comparison as if the scalar were
broadcasted to the shape of the array.
This behaviour can be disabled with the `strict` parameter.

Examples
--------
>>> x = [1e-5, 1e-3, 1e-1]
>>> y = np.arccos(np.cos(x))
>>> np.testing.assert_allclose(x, y, rtol=1e-5, atol=0)

As mentioned in the Notes section, `assert_allclose` has special
handling for scalars. Here, the test checks that the value of `numpy.sin`
is nearly zero at integer multiples of π.

>>> x = np.arange(3) * np.pi
>>> np.testing.assert_allclose(np.sin(x), 0, atol=1e-15)

Use `strict` to raise an ``AssertionError`` when comparing an array
with one or more dimensions against a scalar.

>>> np.testing.assert_allclose(np.sin(x), 0, atol=1e-15, strict=True)
Traceback (most recent call last):
    ...
AssertionError:
Not equal to tolerance rtol=1e-07, atol=1e-15
<BLANKLINE>
(shapes (3,), () mismatch)
 ACTUAL: array([ 0.000000e+00,  1.224647e-16, -2.449294e-16])
 DESIRED: array(0)

The `strict` parameter also ensures that the array data types match:

>>> y = np.zeros(3, dtype=np.float32)
>>> np.testing.assert_allclose(np.sin(x), y, atol=1e-15, strict=True)
Traceback (most recent call last):
    ...
AssertionError:
Not equal to tolerance rtol=1e-07, atol=1e-15
<BLANKLINE>
(dtypes float64, float32 mismatch)
 ACTUAL: array([ 0.000000e+00,  1.224647e-16, -2.449294e-16])
 DESIRED: array([0., 0., 0.], dtype=float32)

Tr