from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense # Define the model architecture model = Sequential() model.add(Dense(64, activation='relu', input_shape=(100,))) model.add(Dense(64, activation='relu')) model.add(Dense(10, activation='softmax')) # Compile the model model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) # Train the model model.fit(x_train, y_train, epochs=10, batch_size=32) # Save the model model.save('saved_model.h5')This code defines a simple neural network with three layers and trains it on some input data (`x_train` and `y_train`). Afterward, the `save()` function is called on the `model` object to save the trained model to a file named `saved_model.h5`. The `save()` function is part of the Keras API, which is a high-level neural networks API written in Python that runs on top of TensorFlow. Therefore, the `save()` function is part of the `tensorflow.python.keras.models` module, which is a sub-module of `tensorflow`.