code: def forward(self, x):
      if not self.training or self.prob == 0.0:
          return x
  
      b, n, _, device = *x.shape, x.device
  
      batch_indices = torch.arange(b, device=device)
      batch_indices = rearrange(batch_indices, "... -> ... 1")
      num_patches_keep = max(1, int(n * (1 - self.prob)))
      patch_indices_keep = (
          torch.randn(b, n, device=device)
          .topk(num_patches_keep, dim=-1)
          .indices
      )
  
      return x[batch_indices, patch_indices_keep]
  
imports: import torch
  from torch import nn
  from einops import rearrange
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 2 for calling the PatchDropout obj's underlying member method code with example usage: obj.forward(x) and returns: x, x