class_docstr: Apply Gaussian Blur to the PIL image.
class_docstr_tok: ["Apply","Gaussian","Blur","to","the","PIL","image","."]
code: def __init__(
      self,
      *,
      p: float = 0.5,
      radius_min: float = 0.1,
      radius_max: float = 2.0,
  ):
      # NOTE: torchvision is applying 1 - probability to return the original image
      keep_p = 1 - p
      transform = transforms.GaussianBlur(
          kernel_size=9, sigma=(radius_min, radius_max)
      )
      super().__init__(transforms=[transform], p=keep_p)
  
file_path: dinov2/dinov2/data/transforms.py
imports: from typing import Sequence
  import torch
  from torchvision import transforms
label_desc: Use image node_id 1 to create a new GaussianBlur object from inherited base classes: transforms with example: obj = GaussianBlur()