convolutional neural networks nn batchnorm ( )nn relu()nn maxpool (kernel_size= stride= )#fully connected layers self fc nn linear( * * self fc nn linear( #connect the units def forward(selfx)out self conv_unit_ (xout self conv_unit_ (outout out view(out size( )- out self fc (outout self fc (outout log_softmax(out,dim= return out #define functions for model evaluation and generating predictions def make_predictions(data_loader)#explcitly set the model to eval mode model eval(test_preds torch longtensor(actual torch longtensor(for datatarget in data_loaderif torch cuda is_available()data data cuda(output model(data#predict output/take the index of the output with max value preds output cpu(data max( keepdim=true)[