Thanks for trying to replicate the error.
I did this as I realized a dimension was missing:
First I saw that
(n_samples, channels, height, width) 

should be how the input should be given.
So to my input, I added a dimension and then it complained about a float so this is what worked for me:
input = input.unsqueeze(0).float()

The network output the following value:
tensor([[-2.2090]], grad_fn=<AddmmBackward>)

I’m trying to figure out how to get the float out of this Tensor type now…