code: def train(
      self, gamma: float = 0.9, threshold: float = 0.0001
  ) -> DPValueFunction:
      stable = False
      valfunc = DPValueFunction(
          self._policy, self._env, gamma, threshold
      )
      while not stable:
          stable = True
          for state in self._env.states:
              new_actions = []
              max_value = float("-inf")
              for action in self._policy.action_space:
                  s_dist = self._env(state, action)
                  value = 0.0
                  for s, rp in s_dist.items():
                      value += rp.prob * rp.reward
                      if s is not None:
                          value += rp.prob * gamma * valfunc(s)
                  if value > max_value:
                      max_value = value
                      new_actions = [action]
                  elif value == max_value:
                      new_actions.append(action)
              prob = 1.0 / len(new_actions)
              actions = [0.0] * len(self._policy.action_space)
              for a in new_actions:
                  actions[a.value] = prob
              if self._policy.update(state, actions) >= 1.0e-6:
                  stable = False
          valfunc.reset()
      return valfunc
  
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 DPTrainer obj's underlying member method code with example usage: obj.train(gamma, threshold) and returns: valfunc