class_docstr: Apply single transformation randomly picked from a list.
  
  This transform does not support torchscript.
  
  Args:
      transforms (sequence or torch.nn.Module): list of transformations
      p (list of floats or None, optional): probability of each transform being picked.
          If ``p`` doesn't sum to 1, it is automatically normalized. If ``None``
          (default), all transforms have the same probability.
class_docstr_tok: ["Apply","single","transformation","randomly","picked","from","a","list",".","This","transform","does","not","support","torchscript",".","Args",":","transforms","(","sequence","or","torch.nn.Module",")",":","list","of","transformations","p","(","list","of","floats","or","None",",","optional",")",":","probability","of","each","transform","being","picked",".","If","``","p","``","does","n't","sum","to","1",",","it","is","automatically","normalized",".","If","``","None","``","(","default",")",",","all","transforms","have","the","same","probability","."]
code: def forward(self, *inputs: Any) -> Any:
      idx = int(torch.multinomial(torch.tensor(self.p), 1))
      transform = self.transforms[idx]
      return transform(*inputs)
  
imports: from typing import Any, Callable, Dict, List, Optional, Sequence, Union
  import torch
  from torch import nn
  from torchvision import transforms
  from torchvision.transforms.v2 import Transform
label_desc: Use image node_id 2 for calling the RandomChoice obj's underlying member method code with example usage: obj.forward() and returns: transform