code: def dropout_layers(layers, dropout):
      if dropout == 0:
          return layers
  
      num_layers = len(layers)
      to_drop = torch.zeros(num_layers).uniform_(0.0, 1.0) < dropout
  
      # make sure at least one layer makes it
      if all(to_drop):
          rand_index = randrange(num_layers)
          to_drop[rand_index] = False
  
      layers = [
          layer for (layer, drop) in zip(layers, to_drop) if not drop
      ]
      return layers
  
imports: from random import randrange
  import torch
  from torch import nn, einsum
  import torch.nn.functional
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 2 for calling a global function with example usage: dropout_layers(layers, dropout) and returns: layers, layers