class_docstr: Solarize the image or video with a given probability by inverting all pixel
  values above a threshold.
  
  If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format,
  where ... means it can have an arbitrary number of leading dimensions.
  If img is PIL Image, it is expected to be in mode "L" or "RGB".
  
  Args:
      threshold (float): all pixels equal or above this value are inverted.
      p (float): probability of the image being solarized. Default value is 0.5
class_docstr_tok: ["Solarize","the","image","or","video","with","a","given","probability","by","inverting","all","pixel","values","above","a","threshold",".","If","img","is","a","Tensor",",","it","is","expected","to","be","in","[","...",",","1","or","3",",","H",",","W","]","format",",","where","...","means","it","can","have","an","arbitrary","number","of","leading","dimensions",".","If","img","is","PIL","Image",",","it","is","expected","to","be","in","mode","``","L","''","or","``","RGB","''",".","Args",":","threshold","(","float",")",":","all","pixels","equal","or","above","this","value","are","inverted",".","p","(","float",")",":","probability","of","the","image","being","solarized",".","Default","value","is","0.5"]
code: def __init__(self, threshold: float, p: float = 0.5) -> None:
      super().__init__(p=p)
      self.threshold = threshold
  
imports: import collections.abc
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import torch
  from torchvision import transforms
  from torchvision.transforms.v2 import functional, Transform
  from ._transform import _RandomApplyTransform
  from ._utils import query_chw
label_desc: Use image node_id 2 to create a new RandomSolarize object from inherited base classes: _RandomApplyTransform with example: obj = RandomSolarize(threshold, p)