Hello. I have a deeply nested list of tensors. It’s returned by an external library which was previously numpy based, which I modified to convert numpy to torch:

```
a = list(self.nsgt.forward((x,)))
print(type(a))
print(len(a))
print(type(a[0]))
print(len(a[0]))
print(type(a[0][0]))
print(len(a[0][0]))
print(type(a[0][0][0]))
print(len(a[0][0][0]))
print(a[0][0][0].device)
```

Results in:

```
class 'list'
59
<class 'list'>
2
<class 'list'>
126
<class 'torch.Tensor'>
304
cuda:0
```

I would like to convert this into a tensor with the following shape:

```
(59, 2, 126, 304)
```

Previously, when this was an np ndarray, achieving what I needed was trivial:

```
A = np.asarray(a)
print(A.shape)
print(A.dtype)
```

This would result in the correct ndarray:

```
(59, 2, 126, 304)
float32
```

In torch, I’m having trouble achieving the same with `torch.tensor`

or `torch.stack`

.

torch.tensor issues:

```
A = torch.tensor(a)
ValueError: only one element tensors can be converted to Python scalars
```

torch.stack issue:

```
A = torch.stack((a))
TypeError: expected Tensor as element 0 in argument 0, but got list
```