Hello, I need to learn network with two different transformations on input data and than weighted sum of outputs, I want to learn weights w of the summing as well:

```
w = torch.tensor([.5, .5], requires_grad=True)
optimizer.add_param_group({'params': w})
learning loop of one input data:
output[0] = transform_1(data)
output[1] = transform_2(data)
output = torch.matmul(w, input)
loss = criterion(output, label)
loss.backward()
optimizer.step()
```

I am working with images and transforms include resizing, so `transform_1(data).shape != transform_2(data).shape `

therefore I cannot stack them together along new dimension.

Using this loop I am getting

```
Variable._execution_engine.run_backward(
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [5, 2]], which is output 0 of PermuteBackward, is at version 10; expected version 8 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).
```

If I put whole training loop into `with torch.autograd.set_detect_anomaly(True):`

I got error

```
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [5, 2]], which is output 0 of PermuteBackward, is at version 10; expected version 8 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!
```

Do you have any idea how to train network with learning weighted sum of outputs? Thank you very much