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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def gae_advantages(td_deltas, mask, lambda_=0.95, gamma=0.99):
 r"""Computes the GAE advantages given the one step TD-residuals. The formula for a GAE advantage estimator is as follows: A_{bt} = \sum_{l=0}^{\infty}(\gamma * \lambda)^{l}(\delta_{b,t+l}). Internally we just call rewards_to_go, since it is the same computation. Args: td_deltas: np.ndarray of shape (B, T) of one step TD-residuals. mask: np.ndarray of shape (B, T) of mask for the residuals. It maybe the case that the `td_deltas` are already masked correctly since they are lambda_: float, lambda parameter for GAE estimators. gamma: float, lambda parameter for GAE estimators. Returns: GAE advantage estimates. """


  return rewards_to_go(td_deltas, mask, lambda_ * gamma)