class_docstr: Equalize the histogram of the given image or video with a given probability.
  
  If the input is a :class:`torch.Tensor`, it is expected
  to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
  If img is PIL Image, it is expected to be in mode "P", "L" or "RGB".
  
  Args:
      p (float): probability of the image being equalized. Default value is 0.5
class_docstr_tok: ["Equalize","the","histogram","of","the","given","image","or","video","with","a","given","probability",".","If","the","input","is","a",":","class",":","`","torch.Tensor","`",",","it","is","expected","to","have","[","...",",","1","or","3",",","H",",","W","]","shape",",","where","...","means","an","arbitrary","number","of","leading","dimensions",".","If","img","is","PIL","Image",",","it","is","expected","to","be","in","mode","``","P","''",",","``","L","''","or","``","RGB","''",".","Args",":","p","(","float",")",":","probability","of","the","image","being","equalized",".","Default","value","is","0.5"]
code: def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
      return self._call_kernel(F.equalize, 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 RandomEqualize obj's underlying member method code with example usage: obj._transform(inpt, params) and returns: self