class_docstr: Inverts the colors of the given image or video with a given probability.
  
  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:
      p (float): probability of the image being color inverted. Default value is 0.5
class_docstr_tok: ["Inverts","the","colors","of","the","given","image","or","video","with","a","given","probability",".","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",":","p","(","float",")",":","probability","of","the","image","being","color","inverted",".","Default","value","is","0.5"]
code: def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
      return self._call_kernel(F.invert, inpt)
  
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 1 for calling the RandomInvert obj's underlying member method code with example usage: obj._transform(inpt, params) and returns: self