recurrent neural networks output [torch size([ ]nextwe define the train and evaluate functionssimilar to our previous exercise the only difference is that we need to handle text_lengths as an additional parameter in the model we will also define the accuracy function required to compute the binary accuracy and define the model' loss functionoptimizer and load the model and the loss function on the gpuif available these steps are identical to our previous exercise in listing - we train our improved model definition listing - training the improved model #define train step def train(modeliteratoroptimizercriterion)epoch_loss,epoch_acc,epoch_denom , , model train(for batch in iteratoroptimizer zero_grad(texttext_lengths batch text predictions model(texttext_lengthssqueeze( loss criterion(predictions reshape(- , )batch label float(reshape(- , )acc accuracy(predictionsbatch labelloss backward(optimizer step(epoch_loss +loss item(epoch_acc +acc item(epoch_denom +len(batchreturn epoch_loss/epoch_denomepoch_accepoch_denom