Joël Larose added the comment:
The same problem occurs if the argument is a `list`. The same inconsistency
happens depending on the position in the list that `nan` happens to be.
>>> max([5, nan, 3, 0, 8, -10])
8
>>> min([5, nan, 3, 0, 8, -10])
-10
>>> min(
Joël Larose added the comment:
Forgot to mention the environment:
Python version: 3.9.0
OS: Windows 10
I have not tested this on any other version of python.
--
___
Python tracker
<https://bugs.python.org/issue44
New submission from Joël Larose :
If `math.nan` is the first argument for either max() or min(), the result is
always `nan`, regardless of the other values in the results. However, if `nan`
is in any other position in the arguments list, the result is always what you
would expect if `nan
Joël Larose added the comment:
An easy workaround would be to alias your import or to import your class
directly.
```
from ... import losses as l
class A:
losses: l.Losses = l.Losses()
```
or
```
from ...losses import Losses
class A:
losses: Losses = Losses()
```
--
nosy
Joël Larose added the comment:
OMG! Ok, thanks guys! Switching to super().__new__ made all the difference!
I can't believe I didn't think to try to change this line.
Regarding the call to cast, I know it's only for type checking. Trying to
write code that works checks with mypy.
I
New submission from Joël Larose :
Hi,
I'm trying to implement a metaclass for the singleton pattern, with the intent
of creating type-appropriate sentinels. After trying several approaches, I've
come up with what I thought would be an elegant solution.
However, I've run into a bit
