code: def action_dist(self, state: State) -> ActionDistribution:
      self._model.eval()
      dist = self._model(
          torch.tensor(state.value, dtype=torch.float).reshape(1, -1)
      )[0]
      return self._action_space.distribution(self._softmax(dist))
  
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 PyTorchPolicy obj's underlying member method code with example usage: obj.action_dist(state) and returns: self