<prompt> Theha mofuta oa Keras o nkang lintlha tse 5 mme o na le liphetho tse hlanonan  <response>: Instruction in English: Create a Keras model that takes in 5 inputs and has five outputs . Response in English: To create a Keras model that takes in 5 inputs and has five outputs, you can use the Sequential model API to build a simple feed-forward neural network with the specified input and output shape. Here is an example code to demonstrate this:pythonfrom keras.models import Sequentialfrom keras.layers import Dense define the modelmodel Sequentialmodel.addDense10, input_dim5, activationrelu Input layer with 5 input nodesmodel.addDense5, activationsoftmax Output layer with 5 output nodes compile the modelmodel.compilelosscategorical_crossentropy, optimizeradam, metricsaccuracyIn the above example, we first imported the Sequential model API and the Dense layer from Keras. We then defined our model as a Sequential model and added a Dense hidden layer with 10 nodes, taking in 5 input values with a relu activation function. After that, we added another Dense layer with 5 output nodes and a softmax activation function to represent the 5 outputs. Finally, we compiled the model by defining the loss function as categorical_crossentropy, the optimizer as adam and the metric as accuracy.With this model, you can then train it on your dataset with the desired number of epochs and batch size to fit the model. After training, you can use the predict function of the model to make predictions on new data. . Response in Sesotho: Ho theha mofuta oa Keras o nkang lintlha tse 5 mme o na le liphetho tse hlano, o ka sebelisa Sequential model API ho aha marang-rang a bonolo a phepelo ea neural ka sebopeho se boletsoeng le tlhahiso. Mona ke mohlala oa khoutu ho bontša sena: python ho tsoa ho keras.models import Sequential from keras.layers import Dense define the model Sequential model.addDense10, input_dim5, activationrelu Lera le kenyang le nang le 5 input node model.eketsaDense5, activationsoftmax Lera la sephetho le 5 li-output node bokella mohlala oa mohlala. kopanyatahlehelocategorical_crossentropy, optimizeradam , metricsaccuracy Mohlaleng o ka holimo, re qalile ka ho kenya Sequential model API le Dense layer ho tsoa Keras. Ka mora moo re ile ra hlalosa mohlala oa rona e le mohlala oa Sequential me ra eketsa lesela le patiloeng le Dense le nang le li-node tse 10, re nka litekanyetso tse 5 tsa ho kenya ka tsebetso ea relu . Kamora moo, re kentse lesela le leng le Dense le nang le li-node tse 5 le tsebetso ea softmax ho emela liphetho tse 5. Qetellong, re ile ra bokella mohlala ka ho hlalosa mosebetsi oa tahlehelo e le categorical_crossentropy, optimizer e le adam me metric e le ho nepahala . Ka mohlala ona, o ka o koetlisa ho dataset ea hau ka palo e lakatsehang ea nako le boholo ba batch ho lekana mohlala. Ka mora koetliso, u ka sebelisa tsebetso ea ho bolela esale pele ea mohlala ho etsa likhakanyo ka lintlha tse ncha. 