So, I am trying to recreate this TensorFlow function in PyTorch, but it doesn’t seem to be working:

```
d_previous = tf.placeholder("float32")
d_logit = fwd_gradients(T("softmax2_pre_activation"), T(layer_name), d_previous)[0]
def fwd_gradients(ys, xs, d_xs):
"""Forward-mode pushforward analogous to the pullback defined by tf.gradients.
With tf.gradients, grad_ys is the vector being pulled back, and here d_xs is
the vector being pushed forward.
By mattjj@google.com from
https://github.com/renmengye/tensorflow-forward-ad/issues/2
"""
v = tf.zeros_like(ys)
g = tf.gradients(ys, xs, grad_ys=v)
return tf.gradients(g, v, grad_ys=d_xs)
```

And my attempt at recreating the function in PyTorch:

```
def fwd_gradients(ys, xs):
v = torch.zeros_like(ys)
g = torch.autograd.grad(
outputs=[ys],
inputs=xs,
grad_outputs=[v],
retain_graph=True,
)[0]
out = torch.autograd.grad(
outputs=[g],
inputs=v,
grad_outputs=torch.zeros_like(xs),
retain_graph=True,
)[0]
```

Currently the above PyTorch code results in the following error:

```
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
```

How do I get the function working correctly?