class_docstr: :param v_min: (float)
      The minimal value that will be represented in the network output for predicting the Q value.
      Corresponds to :math:`v_{min}` in the paper.
  
  :param v_max: (float)
      The maximum value that will be represented in the network output for predicting the Q value.
      Corresponds to :math:`v_{max}` in the paper.
  
  :param atoms: (int)
      The number of atoms that will be used to discretize the range between v_min and v_max.
      For the C51 algorithm described in the paper, the number of atoms is 51.
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code: def __init__(self):
      super().__init__()
      self.v_min = -10.0
      self.v_max = 10.0
      self.atoms = 51
  
imports: from typing import Union
  import numpy
  from rl_coach.agents.dqn_agent import DQNNetworkParameters, DQNAlgorithmParameters, DQNAgentParameters
  from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.architectures.head_parameters import CategoricalQHeadParameters
  from rl_coach.core_types import StateType
  from rl_coach.exploration_policies.e_greedy import EGreedyParameters
  from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplay
  from rl_coach.schedules import LinearSchedule
label_desc: Use image node_id 1 to create a new CategoricalDQNAlgorithmParameters object from inherited base classes: DQNAlgorithmParameters with example: obj = CategoricalDQNAlgorithmParameters()