Hello,
i have some questions for the NeuralNetClassifier.

is it possible to set a weight decay (L2 penalty) in the built in NN classifier?

how can I define the hidden layer sizes? I know there is a parameter called “n_hidden_neurons_multiplier” which is dependent on the input layer. So if i understand it right: input size = 20, module__n_layers = 2, n_hidden_neurons_multiplier = 0.5 the hidden layer sizes are (10,10). Or is the neurons multiplier dependent on the layer in front, hence the second hidden layer is half of the first hidden layer (10,5)?

is there a way to generate loss and accuracy curves for train and validation data? the metrics are already printed from skorch and there is a history_ attribute which is empty though

how to get rid of the following warning?
[W shape_type_inference.cpp:1974] Warning: The shape inference of onnx.brevitas::Quant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (function UpdateReliable)
Thanks in advance