Fix `mean` bug for integral tensors. (#76584)
Fixes #76430
**The Problem:** `opt_dtype` wasn't being taken into consideration when checking whether the input dtype was either floating point or complex dtype.
**The Solution:** run those checks with the dtype returned by `get_dtype_from_self(self, opt_dtype, true)`.
This fix restores the original behavior, before #61643. It also improves the error message so that the user better comprehends what happened. Finally, I also added 2 tests for ensuring the issue was fixed
-----
#### Before
```python
>>> a = torch.randint(0, 5, (5, 5), dtype=torch.int64)
>>> b = torch.tensor([], dtype=torch.float32)
>>> a.mean() # no dtype
RuntimeError: mean(): input dtype should be either floating point or complex dtypes. Got Long instead.
>>> a.mean(dtype=torch.float32) # with dtype
RuntimeError: mean(): input dtype should be either floating point or complex dtypes. Got Long instead.
>>> torch.mean(a, [], dtype=torch.float64, out=b) # with mismatching dtype and out dtype
RuntimeError: mean(): input dtype should be either floating point or complex dtypes. Got Long instead.
```
#### After
```python
>>> a = torch.randint(0, 5, (5, 5), dtype=torch.int64)
>>> b = torch.tensor([], dtype=torch.float32)
>>> a.mean() # no dtype
RuntimeError: mean(): at least one of (i) the input dtype and (ii) the desired output dtype should be either floating point or complex. Got (i) Long and (ii) None instead.
>>> a.mean(dtype=torch.float32) # with dtype
tensor(1.6800)
>>> torch.mean(a, [], dtype=torch.float64, out=b) # with mismatching dtype and out dtype
RuntimeError: Expected out tensor to have dtype double, but got float instead
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76584
Approved by: https://github.com/ngimel
