code: def __init__(
      self,
      model: "ParallelDQNModel",
      n_envs: int,
      memory_queue: Queue,
      model_update_queue: Queue,
      done_queue: Queue,
      discount_factor: float = 0.9999,
      batch_size: int = 128,
      # learning_rate: float = 0.0001,
      learning_rate: float = 0.001,
      memory_capacity: int = 10000,
  ):
      super().__init__()
  
      self.model = model
      self.n_envs = n_envs
      self.memory_queue = memory_queue
      self.model_update_queue = model_update_queue
      self.done_queue = done_queue
      self.discount_factor = discount_factor
      self.batch_size = batch_size
      self.learning_rate = learning_rate
      self.memory_capacity = memory_capacity
  
imports: from deprecated import deprecated
  import tensorflow
  from multiprocessing import Process, Queue
  from tensortrade.agents import ReplayMemory, DQNTransition
label_desc: Use image node_id 1 to create a new ParallelDQNOptimizer object from inherited base classes: Process with example: obj = ParallelDQNOptimizer(model, n_envs, memory_queue, model_update_queue, done_queue, discount_factor, batch_size, learning_rate, memory_capacity)