class_docstr: Common superclass for IMPALA runners, implementing the main training controls.
docstr: Adjusts initial values where necessary.
code: def __post_init__(self):
      """
      Adjusts initial values where necessary.
      """
  
      if self.eval_concurrency <= 0:
          self.eval_concurrency = query_cpu()
  
imports: import copy
  import dataclasses
  from abc import abstractmethod
  from typing import Union, Callable, List
  from maze.core.agent.torch_actor_critic import TorchActorCritic
  from maze.core.agent.torch_policy import TorchPolicy
  from maze.core.annotations import override
  from maze.core.env.maze_env import MazeEnv
  from maze.core.env.structured_env import StructuredEnv
  from maze.core.env.structured_env_spaces_mixin import StructuredEnvSpacesMixin
  from maze.core.log_stats.log_stats_env import LogStatsEnv
  from maze.core.utils.factory import Factory
  from maze.train.parallelization.distributed_actors.distributed_actors import DistributedActors
  from maze.train.parallelization.distributed_actors.sequential_distributed_actors import SequentialDistributedActors
  from maze.train.parallelization.distributed_actors.subproc_distributed_actors import SubprocDistributedActors
  from maze.train.parallelization.vector_env.sequential_vector_env import SequentialVectorEnv
  from maze.train.parallelization.vector_env.subproc_vector_env import SubprocVectorEnv
  from maze.train.parallelization.vector_env.vector_env import VectorEnv
  from maze.train.trainers.common.evaluators.rollout_evaluator import RolloutEvaluator
  from maze.train.trainers.common.model_selection.best_model_selection import BestModelSelection
  from maze.train.trainers.common.training_runner import TrainingRunner
  from maze.train.trainers.impala.impala_trainer import IMPALA
  from maze.utils.bcolors import BColors
  from omegaconf import DictConfig
  from maze.utils.process import query_cpu
label_desc: Use image node_id 1 for calling the ImpalaRunner obj's underlying member method code with example usage: obj.__post_init__() without return types