code: def discrete_planning(
      self, state: rlt.FeatureData
  ) -> Tuple[int, np.ndarray]:
      # For discrete actions, we use random shoots to get the best next action
      random_action_seqs = list(
          itertools.product(
              range(self.action_dim), repeat=self.plan_horizon_length
          )
      )
      random_action_seqs = random.choices(
          random_action_seqs, k=self.cem_pop_size
      )
      action_solutions = torch.zeros(
          self.cem_pop_size, self.plan_horizon_length, self.action_dim
      )
      for i, action_seq in enumerate(random_action_seqs):
          for j, act_idx in enumerate(action_seq):
              action_solutions[i, j, act_idx] = 1
      acc_rewards = self.acc_rewards_of_all_solutions(
          state, action_solutions
      )
  
      first_action_tally = np.zeros(self.action_dim)
      reward_tally = np.zeros(self.action_dim)
      # pyre-fixme[6]: Expected `Iterable[Variable[_T2]]` for 2nd param but got
      #  `float`.
      for action_seq, acc_reward in zip(
          random_action_seqs, acc_rewards
      ):
          first_action = action_seq[0]
          first_action_tally[first_action] += 1
          reward_tally[first_action] += acc_reward
  
      best_next_action_idx = np.nanargmax(
          reward_tally / first_action_tally
      )
      best_next_action_one_hot = torch.zeros(self.action_dim).float()
      best_next_action_one_hot[best_next_action_idx] = 1
  
      logger.debug(
          f"Choose action {best_next_action_idx}."
          f"Stats: {reward_tally} / {first_action_tally}"
          f" = {reward_tally/first_action_tally} "
      )
      # pyre-fixme[7]: Expected `Tuple[int, ndarray]` but got `Tuple[typing.Any,
      #  Tensor]`.
      return best_next_action_idx, best_next_action_one_hot
  
imports: import itertools
  import logging
  import random
  from typing import List, Optional, Tuple
  import numpy
  import scipy.stats
  import torch
  import torch.nn
  from reagent.core import types
  from reagent.core.parameters import CONTINUOUS_TRAINING_ACTION_RANGE
  from reagent.models.base import ModelBase
  from reagent.models.world_model import MemoryNetwork
  from reagent.training.utils import rescale_actions
  from torch.distributions.bernoulli import Bernoulli
  from torch.distributions.categorical import Categorical
  from torch.distributions.normal import Normal
label_desc: Use image node_id 8 for calling the CEMPlannerNetwork obj's underlying member method code with example usage: obj.discrete_planning(state) and returns: best_next_action_idx,  best_next_action_one_hot