text: Title: Setting a list of one or two `float` values to `kernel_size` argument of `GaussianBlur()` gets an indirect error message
  Body: ### 🐛 Describe the bug
  
  Setting a list of one or two `float` values to `kernel_size` argument of `GaussianBlur()` gets the indirect error message as shown below:
  
  ```python
  from torchvision.datasets import OxfordIIITPet
  from torchvision.transforms.v2 import GaussianBlur
  
  my_data1 = OxfordIIITPet(
      root="data",                     # ↓↓↓↓↓
      transform=GaussianBlur(kernel_size=[3.4])
  )
  
  my_data2 = OxfordIIITPet(
      root="data",                     # ↓↓↓↓↓↓↓↓↓↓
      transform=GaussianBlur(kernel_size=[3.4, 3.4])
  )
  
  my_data1[0] # Error
  my_data2[0] # Error
  ```
  
  ```
  TypeError: linspace() received an invalid combination of arguments - got (float, float, steps=float, device=torch.device, dtype=torch.dtype), but expected one of:
   * (Tensor start, Tensor end, int steps, *, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
   * (Number start, Tensor end, int steps, *, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
   * (Tensor start, Number end, int steps, *, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
   * (Number start, Number end, int steps, *, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
  ```
  
  So the error message should be something direct like below:
  
  > TypeError: `kernel_size` argument must be `int`
  
  In addition, setting a `float` value to `kernel_size` argument of `GaussianBlur()` works as shown below:
  
  ```python
  from torchvision.datasets import OxfordIIITPet
  from torchvision.transforms.v2 import GaussianBlur
  
  my_data = OxfordIIITPet(
      root="data",                     # ↓↓↓
      transform=GaussianBlur(kernel_size=3.4)
  )
  
  my_data[0]
  # (<PIL.Image.Image image mode=RGB size=394x500>, 0)
  ```
  
  ### Versions
  
  ```python
  import torchvision
  
  torchvision.__version__ # '0.20.1'
  ```