code: def _update_policy(self, value_counts) -> bool:
      stable = True
      for state in self._env.states:
          probs = []
          for a in self._policy.action_space:
              key = (state, a)
              if key not in value_counts:
                  probs.append(0.0)
              else:
                  v, c = value_counts[key]
                  probs.append(v * c)
          probs = torch.nn.functional.softmax(
              torch.tensor(probs), dim=0
          ).tolist()
          if self._policy.update(state, probs) >= 1.0e-6:
              stable = False
      return stable
  
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 4 for calling the MonteCarloTrainer obj's underlying member method code with example usage: obj._update_policy(value_counts) and returns: stable