code: def train(
      self,
      iterations: int,
      gamma: float = 0.9,
      first_visit: bool = True,
      update_interval: int = 20,
  ) -> None:
      i = 0
      value_counts = {}
      while i < iterations:
          i += 1
          for state in self._env.states:
              mdp = self._log_generator.generate_log(state)
              if first_visit:
                  vcounts = {}
                  for t in mdp:
                      if t.last_state is None or t.action is None:
                          continue
                      key = (t.last_state, t.action)
                      if key in vcounts:
                          vcounts[key] += 1
                      else:
                          vcounts[key] = 1
                  g = 0
                  for t in reversed(mdp):
                      if t.last_state is None or t.action is None:
                          continue
                      g = gamma * g + t.reward
                      key = (t.last_state, t.action)
                      vc = vcounts[key]
                      if vc > 1:
                          self._update_state_value(
                              value_counts, t.last_state, t.action, g
                          )
                      vc -= 1
                      if vc == 0:
                          del vcounts[key]
                      else:
                          vcounts[key] = vc
              else:
                  g = 0
                  for t in reversed(mdp):
                      if t.last_state is None or t.action is None:
                          continue
                      g = gamma * g + t.reward
                      self._update_state_value(
                          value_counts, t.last_state, t.action, g
                      )
          if i % update_interval == 0 and self._update_policy(
              value_counts
          ):
              break
  
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 MonteCarloTrainer obj's underlying member method code with example usage: obj.train(iterations, gamma, first_visit, update_interval) without return types