recurrent neural networks model eval(#explcitly set model to eval mode for ibatch in enumerate(data_iterator)with torch no_grad()predictions model(batch textloss loss_function(predictions reshape(- , )batch label float(reshape(- , )acc accuracy(predictions reshape(- , )batch label reshape(- , )epoch_loss +loss item(epoch_acc +acc item(epoch_denom +len(batchreturn epoch_loss/epoch_denomepoch_accepoch_denom herethe contents are similar to the previous experiments we create the necessary boilerplate code for our training loop note that we need helper function within the evaluate function that would compute accuracy (binary outcomesin our casethis part is not mandatebut it helps to view intermediate results in accuracy after each epoch listing - defines the function and the necessary bits for our network listing - defining the accuracy functionloss functionand optimizerand instantiating the model #compute binary accuracy def accuracy(predsy)rounded_preds torch round(torch sigmoid(preds)#count the number of correctly predicted outcomes correct (rounded_preds =yfloat(acc correct sum(return acc