1. 1. 1. 🐛 Describe the bug

`input` argument of [nan_to_num()](https://pytorch.org/docs/stable/generated/torch.nan_to_num.html) works with `complex` type but `nan`, `posinf` and `neginf` argument don't work with `complex` type as shown below:

```python
import torch

my_tensor = torch.tensor([complex('nan'), complex('inf-infj'), complex('-inf+infj'), 4.+0.j])

1. ↓ # ↓ # ↓
   torch.nan_to_num(input=my_tensor, nan=1, posinf=2, neginf=3)
2. tensor([1.+0.j, 2.+3.j, 3.+2.j, 4.+0.j])

1. ↓↓ # ↓↓ # ↓↓
   torch.nan_to_num(input=my_tensor, nan=1., posinf=2., neginf=3.)
2. tensor([1.+0.j, 2.+3.j, 3.+2.j, 4.+0.j])

1. ↓↓↓↓ # ↓↓↓↓↓ # ↓↓↓↓
   torch.nan_to_num(input=my_tensor, nan=True, posinf=False, neginf=True)
2. tensor([1.+0.j, 0.+1.j, 1.+0.j, 4.+0.j])

1. ↓↓↓↓↓↓ # ↓↓↓↓↓↓ # ↓↓↓↓↓↓
   torch.nan_to_num(input=my_tensor, nan=1.+0.j, posinf=2.+0.j, neginf=3.+0.j) # Error
   ```

> TypeError: nan_to_num(): argument 'nan' must be float, not complex

> TypeError: nan_to_num(): argument 'posinf' must be float, not complex

> TypeError: nan_to_num(): argument 'neginf' must be float, not complex

In addition, `input` argument of `nan_to_num()` works with `int`, `float` and `bool` type as shown below:

```python
import torch

my_tensor = torch.tensor([1, 2, 3, 4])

torch.nan_to_num(input=my_tensor)

1. tensor([1, 2, 3, 4])

my_tensor = torch.tensor([1., 2., 3., 4.])

torch.nan_to_num(input=my_tensor)

1. tensor([1., 2., 3., 4.])

my_tensor = torch.tensor([True, False, True, False])

torch.nan_to_num(input=my_tensor)

1. tensor([True, False, True, False])
   ```

1. 1. 1. Versions

```python
import torch

torch._version_ # 2.3.1+cu121
```

cc @ezyang @anjali411 @dylanbespalko @mruberry @Lezcano @nikitaved @amjames