class_docstr: A PIL image version of Gaussian blur augmentation as
  in SimCLR https://arxiv.org/abs/2002.05709
  
  Args:
      sigma (list[float]): A list of 2 floats with in which
      the blur radius is randomly sampled during each step.
class_docstr_tok: ["A","PIL","image","version","of","Gaussian","blur","augmentation","as","in","SimCLR","https",":","\/\/arxiv.org\/abs\/2002.05709","Args",":","sigma","(","list","[","float","]",")",":","A","list","of","2","floats","with","in","which","the","blur","radius","is","randomly","sampled","during","each","step","."]
code: def __init__(self, sigma: List[float] = (0.1, 2.0)) -> None:
      self.sigma = sigma
  
imports: import random
  from typing import Any, Callable, Dict, Iterable, List, Mapping, Sequence
  import hydra
  import torch
  import torchvision
  from PIL import Image, ImageFilter
  from pytorchvideo.transforms import MixVideo
  from torchvision.transforms import Compose
label_desc: Use image node_id 1 to create a new GaussianBlur object  with example: obj = GaussianBlur(sigma)