My argument is that `where` makes sense unless `out` is default `None`, in
which case it simply obscures the creation of a partially uninitialised array.
The casual numpy user accessing through the public API likely only wants
garbage values if they have explicitly asked for them using `empty_like`. If
they have decided not to pass `out`, it probably means they expect a partially
transformed copy of the input array and have overlooked the note in the
docstring.
Example:
User creates a function that log transforms values where log is defined .
```
def safe_log(val, out):
return np.log(val, where=np.greater(val, 0), out=out)
```
User suddenly decides that they don't want to specify `out` every time, making
`safe_log` unsafe.
```
def safe_log(val, out=None):
return np.log(val, where=np.greater(val, 0), out=out)
```
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