def METHOD_NAME(iterations, optimizer, seed=0x2019):
    np.random.seed(seed)
    tf.random.set_seed(seed)
    val_0 = np.random.random((2,))
    val_1 = np.random.random((2,))
    var_0 = tf.Variable(val_0, dtype=tf.dtypes.float32)
    var_1 = tf.Variable(val_1, dtype=tf.dtypes.float32)
    grad_0 = tf.constant(np.random.standard_normal((2,)), dtype=tf.dtypes.float32)
    grad_1 = tf.constant(np.random.standard_normal((2,)), dtype=tf.dtypes.float32)
    grads_and_vars = list(zip([grad_0, grad_1], [var_0, var_1]))
    for _ in range(iterations):
        optimizer.apply_gradients(grads_and_vars)
    return [val_0, val_1], [var_0, var_1]