code: def _state_value(self, state: State) -> float:
      i = 0
      state_count = (
          self._state_counts[state]
          if state in self._state_counts
          else 0
      )
      while (
          state_count < self._count_threshold
          and i < self._max_iteration
      ):
          i += 1
          mdp = self._log_generator.generate_log(state)
          if self._first_visit:
              state_counts = {}
              for t in mdp:
                  if t.last_state is None:
                      continue
                  if t.last_state in state_counts:
                      state_counts[t.last_state] += 1
                  else:
                      state_counts[t.last_state] = 1
              g = 0
              for t in reversed(mdp):
                  if t.last_state is None:
                      continue
                  g = self._gamma * g + t.reward
                  counts = state_counts[t.last_state]
                  if counts > 1:
                      self._update_state_value(t.last_state, g)
                  counts -= 1
                  if counts == 0:
                      del state_counts[t.last_state]
                  else:
                      state_counts[t.last_state] = counts
          else:
              g = 0
              for t in reversed(mdp):
                  if t.last_state is None:
                      continue
                  g = self._gamma * g + t.reward
                  self._update_state_value(t.last_state, g)
          state_count = (
              self._state_counts[state]
              if state in self._state_counts
              else 0
          )
      return super()._state_value(state)
  
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 2 for calling the MonteCarloValueFunction obj's underlying member method code with example usage: obj._state_value(state) and returns: super