code: def __init__(
      self,
      policy: RLPolicy,
      env: Environment,
      gamma: float = 0.99,
      first_visit: bool = True,
      count_threshold: int = 100,
      max_iteration: int = 200,
  ) -> None:
      super().__init__(policy, env, gamma)
      self._env = env
      self._first_visit = first_visit
      self._count_threshold = count_threshold
      self._max_iteration = max_iteration
      self._log_generator = PolicyLogGenerator(env, policy)
      self._state_counts = {}
  
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 1 to create a new MonteCarloValueFunction object from inherited base classes: TabularValueFunction with example: obj = MonteCarloValueFunction(policy, env, gamma, first_visit, count_threshold, max_iteration)