1: // Initializing an array with some values int[] myArray = {1, 2, 3, 4, 5}; // Adding a new element to the end of the array int newValue = 6; myArray = Arrays.copyOf(myArray, myArray.length + 1); myArray[myArray.length - 1] = newValue; // Print the updated array System.out.println(Arrays.toString(myArray)); ---------- 2: import tensorflow as tf # Load the data mnist = tf.keras.datasets.mnist (train_images, train_labels), (test_images, test_labels) = mnist.load_data() # Normalize the data train_images = train_images / 255.0 test_images = test_images / 255.0 # Define the model model = tf.keras.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10) ]) # Define the loss function loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) # Compile the model model.compile(optimizer='adam', loss=loss_fn, metrics=['accuracy']) # Train the model model.fit(train_images, train_labels, epochs=5) # Evaluate the model model.evaluate(test_images, test_labels, verbose=2)