HestonModel.sample_paths returns variance converging to zero about tf-quant-finance HOT 3 CLOSED

tf.random.set_seed(42)
dtype = tf.float64
kappa = 0.5
theta = 0.04
epsilon = 1.1  # make it a constant so that Atari approximation can be used
rho = 0.1

initial_log_spot = tf.constant(1.0, dtype=dtype)
initial_vol = tf.constant(0.04, dtype=dtype)
initial_state = np.array([initial_log_spot, initial_vol])

# Set up Heston model
heston = tff.models.HestonModel(
    kappa=kappa, theta=theta, epsilon=epsilon, rho=rho, dtype=np.float64
)

# ATM European call option price seems to be valued correctly
maturity_time = 1.0
strike = tf.math.exp(initial_log_spot)

# Using Atari approximation
approximate_price = tff.models.heston.approximations.european_option_price(
      variances=initial_vol,
      strikes=strike,
      expiries=maturity_time,
      forwards=strike,
      is_call_options=True,
      kappas=kappa,
      thetas=theta,
      sigmas=epsilon,
      rhos=rho,
      dtype=tf.float64)
print("Atari approximation: ", approximate_price.numpy())
# Expected 0.1445071937036353 

# Using Monte Carlo sampling
samples = heston.sample_paths(
    times=[maturity_time / 2, maturity_time],
    initial_state=initial_state,
    time_step=0.01,
    num_samples=1000000,
    random_type=tff.math.random.RandomType.SOBOL,
    seed=42)
log_spots = samples[:, -1, 0]
monte_carlo_price = (
        tf.math.reduce_mean(tf.nn.relu(tf.math.exp(log_spots) - strike)))
print("Monte Carlo price (Andersen method): ", monte_carlo_price.numpy())
# Expected 0.1449164116664759