def METHOD_NAME(input_shape):
    input_shape = [1, 8, 8, 1]
    inputs = tf.keras.Input(shape=input_shape[1:], name="input")
    first_conv = layers.Conv2D(32, 1, name="first_conv", kernel_initializer="Ones", bias_initializer="Ones")
    linear1 = layers.Dense(16, name="linear1", kernel_initializer="Ones", bias_initializer="Ones")
    last_linear = layers.Dense(1, name="last_linear", kernel_initializer="Ones", bias_initializer="Ones")
    x = first_conv(inputs)
    x = linear1(x)
    out = last_linear(tf.reshape(x, (-1, tf.reduce_prod(x.shape[1:]))))
    return tf.keras.Model(inputs=inputs, outputs=[out])