class_docstr: Population class
      
class_docstr_tok: ["Population","class"]
code: def generate(self):
      self.collect = []
      while len(self.collect) < self.n_states:
          config = self.config
          for i in range(self.n_steps):
              value, policy_dist = self.actor_critic(config.to_torch())
              dist = policy_dist.detach().numpy()
  
              if random.uniform(0, 1) < 0.1:
                  action = random.choice(range(self.dim_actions))
              else:
                  action = np.random.choice(
                      self.dim_actions, p=np.squeeze(dist)
                  )
  
              log_prob = torch.log(policy_dist.squeeze(0)[action])
              # entropy = -np.sum(np.mean(dist) * np.log(dist))
              flag, new_config = config.step(self.action_space[action])
  
              if (
                  flag
                  and new_config not in self.population
                  and new_config not in self.collect
              ):
                  self.collect.append(new_config)
                  self.log_probs.append(log_prob)
                  self.values.append(value)
  
              config = new_config
      # print([math.exp(float(i)) for i in self.log_probs])
  
      self.waiting_rewards = [0.0] * len(self.collect)
      return copy.deepcopy(self.collect)
  
imports: import math
  import random
  import logging
  import copy
  import torch
  from torch import optim
  from torch import nn
  import torch.nn.functional
  import numpy
  import nni
  from nni.tuner import Tuner
label_desc: Use image node_id 3 for calling the Population obj's underlying member method code with example usage: obj.generate() and returns: copy