code: def monte_carlo(gamma, margin):
      """Calculate Monte Carlo advantage.
  
      We assume the values are a tensor of shape [batch_size, length] and this
      is the same shape as rewards and returns.
  
      Args:
        gamma: float, gamma parameter for TD from the underlying task
        margin: number of extra steps in the sequence
  
      Returns:
        Function (rewards, returns, values, dones) -> advantages, where advantages
        advantages is an array of shape [batch_size, length - margin].
      """
      del gamma
  
      def estimator(rewards, returns, values, dones, discount_mask):
          del discount_mask
          (_, length) = returns.shape
          # Make sure that the future returns and values at "done" states are zero.
          returns[dones] = rewards[dones]
          values[dones] = 0
          return (returns - values)[:, : (length - margin)]
  
      return estimator
  
file_path: trax/trax/rl/advantages.py
imports: import gin
  import numpy
  from trax import fastmath
label_desc: Use image node_id 3 for calling a global function with example usage: monte_carlo(gamma, margin) and returns: estimator, unknown