code: def __init__(
      self,
      agent_parameters,
      parent: Union["LevelManager", "CompositeAgent"] = None,
  ):
      super().__init__(agent_parameters, parent)
      self.quantile_probabilities = np.ones(
          self.ap.algorithm.atoms
      ) / float(self.ap.algorithm.atoms)
  
imports: from copy import copy
  from typing import Union
  import numpy
  from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNNetworkParameters, DQNAlgorithmParameters
  from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.architectures.head_parameters import QuantileRegressionQHeadParameters
  from rl_coach.core_types import StateType
  from rl_coach.schedules import LinearSchedule
label_desc: Use image node_id 1 to create a new QuantileRegressionDQNAgent object from inherited base classes: ValueOptimizationAgent with example: obj = QuantileRegressionDQNAgent(agent_parameters, parent)