inputs = tf.keras.layers.Input(shape=(128, 128, 1)) x = tf.keras.layers.UpSampling2D(size=(8, 8))(inputs)

The code above defines an input layer with a shape of (128, 128, 1) which means it expects inputs with a height and width of 128 and a single channel. Then, the layer applies an upsampling operation with a size of (8, 8) which means it will increase the height and width of the input by a factor of 8 in both dimensions. This will result in an output tensor with a shape of (1024, 1024, 1) which is 8 times larger than the input tensor.