When I try to construct a tensor (A 3x3 matrix in my case) from multiple other tensors that require grad, the resulting tensor does not have require_grad = True by default, and even if I set it manually, when backproping the tensors that were used to construct the matrix do not get gradients. Why is this so?

example code:

```
test0 = torch.ones(1, dtype=torch.float32, requires_grad=True)
test1 = torch.ones(1, dtype=torch.float32, requires_grad=True)
test2 = torch.ones(1, dtype=torch.float32, requires_grad=True)
test_mat = torch.tensor([[test0, test1, 0], [1, 0 ,0], [test2, test1, 1]])
print(test_mat.requires_grad)
sum = test_mat.sum()
sum.backward()
print(test0.grad is None)
```

False

True

How can I construct a tensor out of other tensors such that gradients flow properly? I want to assemble the individual entries into a matrix and multiply another tensor by it (a 3xN tensor), instead of optimizing a full 3x3 matrix (since some of its entries are constants, and I require it to have some properties such as symmetry, which can’t be enforced directly if I optimize all the entries).

edit: For now I am creating an empty 3x3 matrix and replacing elements directly. Is this the intended way?

Thank you.