code: def next_state_reward_dist(
      self, state: State, action: Action
  ) -> StateDistribution:
      sr = self._next_state_reward(state, action)
      assert sr.state is not None
      return {sr.state: RewardProbability(sr.reward, 1.0)}
  
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 10 for calling the GridWorld obj's underlying member method code with example usage: obj.next_state_reward_dist(state, action) and returns: dict