code: def forward(
      self, residual: torch.Tensor, x: torch.Tensor
  ) -> torch.Tensor:
      if not self.training:
          return torch.add(residual, other=x)
      else:
          with torch.no_grad():
              mask = F.dropout(
                  self.mask,
                  p=1 - self.survival_prob,
                  training=self.training,
                  inplace=False,
              )
          return torch.addcmul(residual, mask, x)
  
imports: import copy
  from collections import OrderedDict
  from dataclasses import dataclass
  from typing import Optional
  import torch
  import warnings
  from torch import nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the StochasticDepthResidual obj's underlying member method code with example usage: obj.forward(residual, x) and returns: torch, torch