human
Can you provide a the Python code you requested given the following:
2.3.1 Uncertainty with forward dynamics
assistant
#Here is the Python code you requested.
class ForwardDynamics(BaseIntrinsicRewardModule):
    def __init__(self, states_size, actions_size, hidden_size, alpha=.1):
        super().__init__()
        self.module = MLP(
            actions_size + states_size,
            hidden_size,
            states_size
        )
        self.alpha = alpha
        self.mean_reward = 0
    
    def forward(self, s, a):
        sa = torch.cat([s, a], dim=-1)
        return s + self.module(sa)

    def get_intrinsic_reward(self, state, action, next_state):
        with torch.no_grad():
            r = # <YOUR CODE>
            r_centered = r - self.mean_reward
            self.mean_reward = self.alpha * (r) + (1 - self.alpha) * self.mean_reward
            return r_centered

    def get_loss(self, state_batch, action_batch, next_state_batch):
        # <YOUR CODE>

agent = QLearningAgent(
    epsilon=.1, 
    alpha=0.5, 
    discount=.9, 
    get_legal_actions=lambda s: range(env.action_space.n)
)

forward_dynamics = ForwardDynamics(
    np.prod(env.observation_space.shape), 
    1, 
    16
)

train_with_reward(env, agent, forward_dynamics, n_episodes=2000, update_reward_period=100, batch_size=100, n_iter=25)

test_agent(agent)