docstr: Calculate accumulated rewards of solutions.
  
  :param state: the input which contains the starting state
  :param solutions: its shape is (cem_pop_size, plan_horizon_length, action_dim)
  :returns: a vector of size cem_pop_size, which is the reward of each solution
docstr_tok: ["Calculate","accumulated","rewards","of","solutions",".",":","param","state",":","the","input","which","contains","the","starting","state",":","param","solutions",":","its","shape","is","(","cem_pop_size",",","plan_horizon_length",",","action_dim",")",":","returns",":","a","vector","of","size","cem_pop_size",",","which","is","the","reward","of","each","solution"]
code: def acc_rewards_of_all_solutions(
      self, state: rlt.FeatureData, solutions: torch.Tensor
  ) -> float:
      """
      Calculate accumulated rewards of solutions.
  
      :param state: the input which contains the starting state
      :param solutions: its shape is (cem_pop_size, plan_horizon_length, action_dim)
      :returns: a vector of size cem_pop_size, which is the reward of each solution
      """
      acc_reward_vec = np.zeros(self.cem_pop_size)
      init_state = state.float_features
      for i in range(self.cem_pop_size):
          if i % (self.cem_pop_size // 10) == 0:
              logger.debug(f"Simulating the {i}-th solution...")
          acc_reward_vec[i] = self.acc_rewards_of_one_solution(
              init_state, solutions[i], i
          )
      return acc_reward_vec
  
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 4 for calling the CEMPlannerNetwork obj's underlying member method code with example usage: obj.acc_rewards_of_all_solutions(state, solutions) and returns: acc_reward_vec