Given a model (inherited from nn.Module), calculate Jacobian of parameters of this model with respect to output vector.
This looks like a hack to do this,
  
    
    
    Using `autograd.functional.jacobian`/`hessian` with respect to `nn.Module` parameters autograd
  
  
    I’m also working on this issue when calculating the influence function. 
My current solution is to follow this workaround: 

So I write this sample code (which works as a workaround): 
import torch
import torch.nn as nn

_input=torch.randn(32,3)
layer = nn.Linear(3,4)
criterion=nn.CrossEntropyLoss()

weight = layer.weight

def func(weight):
    del layer.weight
    layer.weight=weight
    return criterion(layer(_input), torch.zeros(len(_input),dtype=torch.long))

torch.autograd.functional.hessia…