class_docstr: Apply a list of transformations in a random order.
  
  This transform does not support torchscript.
  
  Args:
      transforms (sequence or torch.nn.Module): list of transformations
class_docstr_tok: ["Apply","a","list","of","transformations","in","a","random","order",".","This","transform","does","not","support","torchscript",".","Args",":","transforms","(","sequence","or","torch.nn.Module",")",":","list","of","transformations"]
code: def forward(self, *inputs: Any) -> Any:
      needs_unpacking = len(inputs) > 1
      for idx in torch.randperm(len(self.transforms)):
          transform = self.transforms[idx]
          outputs = transform(*inputs)
          inputs = outputs if needs_unpacking else (outputs,)
      return outputs
  
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 RandomOrder obj's underlying member method code with example usage: obj.forward() and returns: outputs