# Import the necessary packages import keras from keras.models import Sequential from keras.layers import Dense # Create a Sequential model model = Sequential() model.add(Dense(units=64, activation='relu', input_dim=100)) model.add(Dense(units=10, activation='softmax')) # Compile the model (with any optimizer and loss function of your choice) model.compile(loss='categorical_crossentropy', optimizer='sgd') # Train the model (with any training data of your choice) model.fit(x_train, y_train, epochs=5) # Create a predict function for the trained model predict_fn = model._make_predict_function() # Use the predict function to make predictions on new data predictions = predict_fn(x_test)In this example, we first create a Sequential model with two layers (one with 64 units and ReLU activation, and the other with 10 units and softmax activation). We then compile the model with a categorical cross-entropy loss function and stochastic gradient descent optimizer, and train the model on some training data. Once the model is trained, we use the `_make_predict_function` method to create a predict function for the trained model. This predict function takes in new data (here, `x_test`) and returns the model's predictions (here, `predictions`). The `keras` package is the library this example is using.