The question is more what is the (effective) kernel size for PIL’s GaussianBlur.
For any given radius, you can check experimentally:
img = torch.zeros(3, 20, 20)
img[:, 10, 10] = 1

img_pil = torchvision.transforms.functional.to_pil_image(img)
#?? torchvision.transforms.functional.gaussian_blur

img_blur_pil = img_pil.filter(PIL.ImageFilter.GaussianBlur(radius=2))
img_blur_pil_t = torchvision.transforms.functional.to_tensor(img_blur_pil)

print((img_blur_pil_t[0]>0).any(dim=1).sum())

img_blur = torchvision.transforms.functional.gaussian_blur(img, 11, 2)
print((img_blur[0]>0).any(dim=1).sum())

In general, this is a complex topic, the PIL Documentation 1 references a 12-page article (Gwosdek et al: Theoretical Foundations of Gaussian Convolution by Extended Box Filtering) for their implementation accuracy.
Best regards
Thomas