Stack tensors keeping their automatic gradient information


I want to stack some tensors keeping their automatic gradient information but screw up.
I have the following script where I want to compute the derivative with respect to x:

_k_plusplus = []
for aplus_idx, aplus_val in enumerate(aplus):
    state_plus = torch.tensor([x[0], aplus_val])[None, :]
    pred_mean = likelihood(gp_kplus(state_plus)).mean
    k_plusplus = torch.stack(_k_plusplus).flatten()

where print(k_plusplus) returns:

tensor([0.3481, 0.3481, 0.3481, 0.3481, 0.3481], dtype=torch.float64,

It seems to be OK for me, but with this syntax, torch.autograd.gradcheck retunrs False.
Here note that pred_mean = likelihood(gp_kplus(state_plus)).mean is coming from GPyTorch.
print(pred_mean) retuns:

tensor([0.2027], dtype=torch.float64, grad_fn=<ViewBackward>)

again it seems to be reasonable for me.

My guess is when I stack the tensors by torch.stack or maybe at some points, I lose the automatic gradient information.
When I simplify the script:

k_plusplus = torch.empty(aplus.shape)
for aplus_idx, aplus_val in enumerate(aplus):
   k_plusplus[aplus_idx] = torch.tensor(0.1)

It passes the gradient check without changing the rest of my code.

Thank you in advance for your kind help.

Sorry this is my bad.
In the above snippet, type(x) is torch.tensor and I overwrite the tensor object by torch.tensor(x[0]...) where I lost the grad_fn information, I guess.
When I modify

state_plus = torch.stack([x[0], aplus_val])[None, :]

everything works well.