code: def __init__(self, p: float = 0.5) -> None:
      if not (0.0 <= p <= 1.0):
          raise ValueError(
              "`p` should be a floating point value in the interval [0.0, 1.0]."
          )
  
      super().__init__()
      self.p = p
  
imports: from __future__ import annotations
  import enum
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
  import PIL.Image
  import torch
  from torch import nn
  from torch.utils._pytree import tree_flatten, tree_unflatten
  from torchvision import tv_tensors
  from torchvision.transforms.v2._utils import check_type, has_any, is_pure_tensor
  from torchvision.utils import _log_api_usage_once
  from .functional._utils import _get_kernel
label_desc: Use image node_id 1 to create a new _RandomApplyTransform object from inherited base classes: Transform with example: obj = _RandomApplyTransform(p)