Pytorch's autograd issue with joblib

There seems to be a problem mixing pytorch’s autograd with joblib. I need to get gradient in parallel for a lot of samples. Joblib works fine with other aspects of pytorch, however, when mixing with autograd it gives errors. I made a very small example which shows serial version works fine but the parallel version crashes.

from joblib import Parallel, delayed
import numpy as np
tt = lambda x, grad=True: torch.tensor(x, requires_grad=grad)

def Grad(X, Out):
    return autograd.grad(Out, X, create_graph=True, allow_unused=True)[0]

xs, ys = [], []
for i in range(10):
    xi = tt(np.random.rand()).float()    
    yi = xi * xi
    xs += [xi]
    ys += [yi]

Grads_serial = [Grad(x, y) for x, y in zip(xs, ys)]
print("Grads_serial", Grads_serial)
Grads_parallel = Parallel(n_jobs=2)([delayed(Grad)(x, y) for x, y in zip(xs, ys)])
print("Grads_parallel", Grads_parallel)

The error message is not very helpful as well:

RuntimeError: One of the differentiated Tensors appears to not have been used in the graph. Set allow_unused=True if this is the desired behavior.