Hi !

I recently looked at Numpy dtypes (https://stackoverflow.com/questions/56022497/numpy-pytorch-dtype-conversion-compatibility/56022918?noredirect=1#comment98695989_56022918), more precisely how they are cast during expression evaluation.

When testing with Pytorch, I realize the same cast was not performed, and that I was getting a runtime error. Here is how I tested it:

```
>>> import torch
>>> import numpy
>>> x = torch.zeros([4], dtype=torch.int32)
>>> y = torch.zeros([4], dtype=torch.float32)
>>> x + y
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
RuntimeError: expected type torch.FloatTensor but got torch.IntTensor
>>> x = numpy.zeros([4], dtype=numpy.int32)
>>> y = numpy.zeros([4], dtype=numpy.float32)
>>> x + y
array([0., 0., 0., 0.])
>>> (x + y).dtype
dtype('float64')
```

- Pytorch is not doing any cast and expect both side of the operation to have the same dtype. Is this statement right ?
- Is there any plan to follow numpy’s design or will it stay like this ?

Best regards,

V.