Most people struggling nans at the output  know that when you feed nan or zero input to the model which has layers such as batchnorm, then the model may become broken. During the training, if you do not do backward pass, the model does not break down. However, since you already did a forward pass with these undesired inputs once, the eval mode outputs nan while the train mode does not. Any explanation on this?