= v2).  The boundaries `v1` and `v2`
can be given in either order.

See Also
--------
masked_where : Mask where a condition is met.

Notes
-----
The array `x` is prefilled with its filling value.

Examples
--------
>>> import numpy as np
>>> import numpy.ma as ma
>>> x = [0.31, 1.2, 0.01, 0.2, -0.4, -1.1]
>>> ma.masked_inside(x, -0.3, 0.3)
masked_array(data=[0.31, 1.2, --, --, -0.4, -1.1],
             mask=[False, False,  True,  True, False, False],
       fill_value=1e+20)

The order of `v1` and `v2` doesn't matter.

>>> ma.masked_inside(x, 0.3, -0.3)
masked_array(data=[0.31, 1.2, --, --, -0.4, -1.1],
             mask=[False, False,  True,  True, False, False],
       fill_value=1e+20)

r