code: def drop_path(x, drop_prob, dims=(0,)):
      from torch.autograd import Variable
  
      var_size = [1 for _ in range(x.dim())]
      for i in dims:
          var_size[i] = x.size(i)
      if drop_prob > 0.0:
          keep_prob = 1.0 - drop_prob
          mask = Variable(
              torch.cuda.FloatTensor(*var_size).bernoulli_(keep_prob)
          )
          x.div_(keep_prob)
          x.mul_(mask)
      return x
  
imports: import math
  import os
  import random
  import sys
  import time
  import warnings
  import numpy
  import numpy
  import shutil
  import logging
  import torch
  import torchvision.transforms
  import torchvision.datasets
  import torch.nn
label_desc: Use image node_id 17 for calling a global function with example usage: drop_path(x, drop_prob, dims) and returns: x