I am having a look at neural networks in Arraymancer. After training the model,
how do I test it at a particular point. See ??? in the last line of code below:

> let (N, D_in, H, D_out) = (10, 2, 3, 1)
>
> # Create the autograd context that will hold the computational graph
> let ctx = newContext Tensor[float32]
>
> # Create random Tensors to hold inputs and outputs, and wrap them in
Variables.
> let
> #   x = ctx.variable(randomTensor[float32](N, D_in, 1'f32))
>   xx = [0.000783'f32, 0.153779, 0.560532, 0.865013, 0.276724, 0.895919,
0.704462, 0.886472, 0.929641, 0.469290, 0.350208, 0.941637, 0.096535, 0.457211,
0.346164, 0.970019, 0.114938, 0.769819, 0.341565, 0.684224
].toTensor.reshape(10,2)
>   x = ctx.variable( xx )
>   y = [3.000602'f32, 5.738535, 4.316186, 6.618693, 6.045585, 4.771489,
3.230002, 4.798756, 3.455619, 4.285201].toTensor.reshape(10,1)
>
> # ##################################################################
> # Define the model
>
> network TwoLayersNet:
>   layers:
>     fc1: Linear(D_in, H)
>     fc2: Linear(H, D_out)
>   forward x:
>     # x.fc1.relu.fc2
>     x.fc1.sigmoid.fc2
>
> let
>   model = ctx.init(TwoLayersNet)
>   optim = model.optimizer(SGD, learning_rate = 1e-4'f32)
>
> # ##################################################################
> # Training
>
> for t in 0 ..< 500:
>   let
>     y_pred = model.forward(x)
>     loss = y_pred.mse_loss(y)
>
>   # echo &"Epoch {t}: loss {loss.value[0]}"
>
>   loss.backprop()
>   optim.update()
>
> # ##################################################################
> # Test at (0.25, 0.5)
> let res = [0.2'f32, 0.5 ].toTensor.reshape(2,1)
> ???