code: def forward(self, x):
      batch, drop_prob, device, dtype = (
          x.shape[0],
          self.drop_prob,
          x.device,
          x.dtype,
      )
  
      if drop_prob <= 0.0 or not self.training:
          return x
  
      keep_prob = 1 - self.drop_prob
      shape = (batch, *((1,) * (x.ndim - 1)))
  
      keep_mask = (
          torch.zeros(shape, device=device).float().uniform_(0, 1)
          < keep_prob
      )
      output = x.div(keep_prob) * keep_mask.float()
      return output
  
imports: import torch
  from torch import nn, einsum
  import torch.nn.functional
  from einops import rearrange, repeat
label_desc: Use image node_id 2 for calling the DropPath obj's underlying member method code with example usage: obj.forward(x) and returns: output, x