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 __init__(self, transforms: Sequence[Callable]) -> None:
      if not isinstance(transforms, Sequence):
          raise TypeError(
              "Argument transforms should be a sequence of callables"
          )
      super().__init__()
      self.transforms = transforms
  
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 1 to create a new RandomOrder object from inherited base classes: Transform with example: obj = RandomOrder(transforms)