code: def action_dist(self, state: State) -> ActionDistribution:
      weighted_policies = [
          w * p(state).values
          for w, p in zip(self._weights, self._policies)
      ]
      weighted = torch.stack(weighted_policies).sum(0)
      return self._action_space.distribution(weighted)
  
imports: import logging
  import gym
  import torch
  from reagent.ope.estimators.sequential_estimators import Action, ActionDistribution, ActionSpace, IPSEstimator, Model, NeuralDualDICE, RandomRLPolicy, RewardProbability, RLEstimatorInput, RLPolicy, State, StateDistribution, Transition
  from reagent.ope.utils import RunningAverage
label_desc: Use image node_id 2 for calling the ComboPolicy obj's underlying member method code with example usage: obj.action_dist(state) and returns: self