I am training a network with siamese structure, meaning that it has two inputs propagated through the same base network.

When I visualize the graph using tensorboardX, it looks like one input is used and the other is not:

I do not know, if this is an issue of `tensorboardX`

storing maybe just the most recent graph or if this an error in my network.

I would like to obtain the graph during the forward pass of the first input, before the second input is forwarded.

I know that one can obtain the the predecessor in the graph using:

```
loss.grad_fn.next_functions[0][0].next_function[0][0]....next_function[0][0]
```

Is there a way to obtain the `grad_fn`

attribute directly from one instance if `nn.Module`

, like e.g. the `Conv2d[0]`

in the picture? The weights are stored in `Conv2d.weight`

but is there a `Variable`

where the itermediate value of the forward propagation is stored?