code: def action_dist(self, state: State) -> ActionDistribution:
      if state in self._state_space:
          actions = self._state_space[state]
          probs = list(
              map(
                  lambda p: p * self._exploitation_prob
                  + self._exploration_prob,
                  actions,
              )
          )
      else:
          probs = self._uniform_probs
      return self._action_space.distribution(probs)
  
imports: import pickle
  from functools import reduce
  from typing import List, Mapping, Sequence
  import torch
  from reagent.ope.estimators.sequential_estimators import Model, RLPolicy, State, ValueFunction
  from reagent.ope.estimators.types import Action, ActionDistribution, ActionSpace
  from reagent.ope.test.envs import Environment, PolicyLogGenerator
  from reagent.ope.utils import RunningAverage
label_desc: Use image node_id 3 for calling the TabularPolicy obj's underlying member method code with example usage: obj.action_dist(state) and returns: self