code: def __init__(self):
      super().__init__()
      self.num_steps_between_copying_online_weights_to_target = (
          EnvironmentSteps(10000)
      )
      self.num_consecutive_playing_steps = EnvironmentSteps(4)
      self.discount = 0.99
      self.supports_parameter_noise = True
  
imports: from typing import Union
  import numpy
  from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import QHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, MiddlewareScheme
  from rl_coach.core_types import EnvironmentSteps
  from rl_coach.exploration_policies.e_greedy import EGreedyParameters
  from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
  from rl_coach.schedules import LinearSchedule
label_desc: Use image node_id 1 to create a new DQNAlgorithmParameters object from inherited base classes: AlgorithmParameters with example: obj = DQNAlgorithmParameters()