class_docstr: Apply randomly a list of transformations with a given probability.
  
  .. note::
      In order to script the transformation, please use ``torch.nn.ModuleList`` as input instead of list/tuple of
      transforms as shown below:
  
      >>> transforms = transforms.RandomApply(torch.nn.ModuleList([
      >>>     transforms.ColorJitter(),
      >>> ]), p=0.3)
      >>> scripted_transforms = torch.jit.script(transforms)
  
      Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require
      `lambda` functions or ``PIL.Image``.
  
  Args:
      transforms (sequence or torch.nn.Module): list of transformations
      p (float): probability of applying the list of transforms
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code: def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
      return {"transforms": self.transforms, "p": self.p}
  
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 RandomApply obj's underlying member method code with example usage: obj._extract_params_for_v1_transform() and returns: dict