code: def _start_trainer_process(
      self,
      create_env,
      memory_queue,
      model_update_queue,
      done_queue,
      n_steps,
      n_episodes,
      eps_start,
      eps_end,
      eps_decay_steps,
      update_target_every,
  ):
      trainer_process = ParallelDQNTrainer(
          self,
          create_env,
          memory_queue,
          model_update_queue,
          done_queue,
          n_steps,
          n_episodes,
          eps_start,
          eps_end,
          eps_decay_steps,
          update_target_every,
      )
  
      trainer_process.start()
  
      return trainer_process
  
imports: from deprecated import deprecated
  import time
  import numpy
  import multiprocessing
  from typing import Callable
  from tensortrade.agents import Agent
  from tensortrade.agents.parallel.parallel_dqn_model import ParallelDQNModel
  from tensortrade.agents.parallel.parallel_dqn_optimizer import ParallelDQNOptimizer
  from tensortrade.agents.parallel.parallel_dqn_trainer import ParallelDQNTrainer
  from tensortrade.agents.parallel.parallel_queue import ParallelQueue
label_desc: Use image node_id 7 for calling the ParallelDQNAgent obj's underlying member method code with example usage: obj._start_trainer_process(create_env, memory_queue, model_update_queue, done_queue, n_steps, n_episodes, eps_start, eps_end, eps_decay_steps, update_target_every) and returns: trainer_process