import torch
import torch.nn as nn

class M(torch.jit.ScriptModule):
    def __init__(self):
        super(M, self).__init__()
        self.conv = nn.Conv2d(5, 5, 2)

    @torch.jit.script_method
    def forward(self, x):
        return self.conv(x)

M().save("m.pt")
loaded = torch.jit.load("m.pt")
loaded(torch.ones(20, 20))

Traceback (most recent call last):
  File "demo.py", line 15, in <module>
    loaded(torch.ones(20, 20))
  File "/home/jamesreed/pytorch/torch/nn/modules/module.py", line 531, in __call__
    result = self.forward(*input, **kwargs)
RuntimeError: Expected 4-dimensional input for 4-dimensional weight 5 5, but got 2-dimensional input of size [20, 20] instead
The above operation failed in interpreter, with the following stack trace:
at code/m.py:248:10
            ret10 = x
          ret5 = ret10
        ret0 = ret5
      ret = ret0
    _122 = _0.weight
    _123 = _0.bias
    _124 = [0, 0]
    _1 = torch.conv2d(ret, _122, _123, [1, 1], _124, [1, 1], 1)
  else:
    _1 = torch.conv2d(x, _0.weight, _0.bias, [1, 1], [0, 0], [1, 1], 1)
         ~~~~~~~~~~~~ <--- HERE
  return _1
Compiled from code at /home/jamesreed/pytorch/torch/nn/modules/conv.py:344:16
    @weak_script_method
    def forward(self, input):
        if self.padding_mode == 'circular':
            expanded_padding = ((self.padding[1] + 1) // 2, self.padding[1] // 2,
                                (self.padding[0] + 1) // 2, self.padding[0] // 2)
            return F.conv2d(F.pad(input, expanded_padding, mode='circular'),
                            self.weight, self.bias, self.stride,
                            _pair(0), self.dilation, self.groups)
        return F.conv2d(input, self.weight, self.bias, self.stride,
               ~~~~~~~~ <--- HERE
                        self.padding, self.dilation, self.groups)