class_docstr: Stochastic depth module.
  
  See :func:`stochastic_depth`.
code: def forward(self, input):
      return stochastic_depth(input, self.p, self.mode, self.training)
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from einops import rearrange
  from opt_einsum import contract
label_desc: Use image node_id 2 for calling the StochasticDepth obj's underlying member method code with example usage: obj.forward(input) and returns: stochastic_depth