code: def forward(self, x):
      if random.random() > self.p:
          return x
      return self.fn(x)
  
imports: import copy
  import random
  from functools import wraps, partial
  import torch
  from torch import nn, einsum
  import torch.nn.functional
  from torchvision import transforms
  from einops import rearrange, reduce, repeat
label_desc: Use image node_id 2 for calling the RandomApply obj's underlying member method code with example usage: obj.forward(x) and returns: self, x