code: def _transit(
      self, from_pos: Tuple[int, int], to_pos: Tuple[int, int]
  ) -> Tuple[Tuple[int, int], float, bool]:
      if not self._validate(to_pos):
          return from_pos, 0.0, False
      elif to_pos == self.goal:
          return to_pos, 1.0, True
      else:
          return (
              to_pos,
              0.0
              if not self.use_taxicab_reward
              else np.exp(
                  -2
                  * self._taxi_distance(to_pos, self.goal)
                  / self.size[0]
              ),
              False,
          )
  
imports: import logging
  import random
  from typing import Iterable, Optional, Sequence, Tuple
  import numpy
  import torch
  from reagent.ope.estimators.sequential_estimators import DMEstimator, DoublyRobustEstimator, EpsilonGreedyRLPolicy, IPSEstimator, MAGICEstimator, NeuralDualDICE, RewardProbability, RLEstimatorInput, State, StateDistribution, StateReward, ValueFunction
  from reagent.ope.estimators.types import Action, ActionSpace
  from reagent.ope.test.envs import Environment, PolicyLogGenerator
  from reagent.ope.trainers.rl_tabular_trainers import DPTrainer, DPValueFunction, EstimatedStateValueFunction, TabularPolicy
label_desc: Use image node_id 7 for calling the GridWorld obj's underlying member method code with example usage: obj._transit(from_pos, to_pos) and returns: from_pos,  int,  False, to_pos,  int,  True, to_pos,  unknown,  False