code: def forward(self, x):
      if self.training and self.drop > 0:
          return (
              x
              + self.m(x)
              * torch.rand(x.size(0), 1, 1, 1, device=x.device)
              .ge_(self.drop)
              .div(1 - self.drop)
              .detach()
          )
      else:
          return x + self.m(x)
  
imports: import itertools
  from collections import OrderedDict
  from typing import Dict
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import SqueezeExcite, SelectAdaptivePool2d, trunc_normal_, _assert
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling the ResidualDrop obj's underlying member method code with example usage: obj.forward(x) and returns: unknown, unknown