class_docstr: A basic RL graph manager creates the common scheme of RL where there is a single agent which interacts with a
  single environment.
code: def _create_graph(
      self, task_parameters: TaskParameters
  ) -> Tuple[List[LevelManager], List[Environment]]:
      # environment loading
      self.env_params.seed = task_parameters.seed
      self.env_params.experiment_path = task_parameters.experiment_path
      env = short_dynamic_import(self.env_params.path)(
          **self.env_params.__dict__,
          visualization_parameters=self.visualization_parameters,
      )
  
      # agent loading
      self.agent_params.task_parameters = task_parameters  # TODO: this should probably be passed in a different way
      self.agent_params.name = "agent"
      agent = short_dynamic_import(self.agent_params.path)(
          self.agent_params
      )
  
      # set level manager
      level_manager = LevelManager(
          agents=agent, environment=env, name="main_level"
      )
  
      return [level_manager], [env]
  
file_path: coach/rl_coach/graph_managers/basic_rl_graph_manager.py
imports: from typing import Tuple, List
  from rl_coach.base_parameters import AgentParameters, VisualizationParameters, TaskParameters, PresetValidationParameters
  from rl_coach.environments.environment import EnvironmentParameters, Environment
  from rl_coach.filters.filter import NoInputFilter, NoOutputFilter
  from rl_coach.graph_managers.graph_manager import GraphManager, ScheduleParameters
  from rl_coach.level_manager import LevelManager
  from rl_coach.utils import short_dynamic_import
label_desc: Use image node_id 2 for calling the BasicRLGraphManager obj's underlying member method code with example usage: obj._create_graph(task_parameters) and returns: list,  list