code: def forward(self, *inputs: Any) -> Any:
      # We need to almost duplicate `Transform.forward()` here since we always want to check the inputs, but return
      # early afterwards in case the random check triggers. The same result could be achieved by calling
      # `super().forward()` after the random check, but that would call `self._check_inputs` twice.
  
      inputs = inputs if len(inputs) > 1 else inputs[0]
      flat_inputs, spec = tree_flatten(inputs)
  
      self._check_inputs(flat_inputs)
  
      if torch.rand(1) >= self.p:
          return inputs
  
      needs_transform_list = self._needs_transform_list(flat_inputs)
      params = self._get_params(
          [
              inpt
              for (inpt, needs_transform) in zip(
                  flat_inputs, needs_transform_list
              )
              if needs_transform
          ]
      )
  
      flat_outputs = [
          self._transform(inpt, params) if needs_transform else inpt
          for (inpt, needs_transform) in zip(
              flat_inputs, needs_transform_list
          )
      ]
  
      return tree_unflatten(flat_outputs, spec)
  
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 2 for calling the _RandomApplyTransform obj's underlying member method code with example usage: obj.forward() and returns: tree_unflatten, inputs