self.conv2 = torch.jit.trace(nn.Conv2d(20, 20, 5), torch.rand(1, 20, 16, 16))
         def forward(self, input):
             input = F.relu(self.conv1(input))
             input = F.relu(self.conv2(input))
             return input

     scripted_module = torch.jit.script(MyModule())

  To compile a method other than "forward" (and recursively
  compile anything it calls), add the "@torch.jit.export"
  decorator to the method. To opt out of compilation use
  "@torch.jit.ignore" or "@torch.jit.unused".

  Example (an exported and ignored method in a module):

     import torch
     import torch.nn as nn

     class MyModule(nn.Module):
         def __init__(self):
             super(MyModule, self).__init__()

         @torch.jit.export
         def some_entry_point(self, input):
             return input + 10

         @torch.jit.ignore
         def python_only_fn(self, input):
