class_docstr: A minimal child class to test the methods in the DQNTrainerBase class.
class_docstr_tok: ["A","minimal","child","class","to","test","the","methods","in","the","DQNTrainerBase","class","."]
code: def __init__(
      self,
      rl_parameters: RLParameters,
      metrics_to_score=None,
      actions: Optional[List[str]] = None,
      evaluation_parameters: Optional[EvaluationParameters] = None,
      double_q_learning: bool = True,
  ):
      super().__init__(
          rl_parameters,
          metrics_to_score=metrics_to_score,
          actions=actions,
          evaluation_parameters=evaluation_parameters,
      )
      self.double_q_learning = double_q_learning
  
imports: import unittest
  from typing import List, Optional, Tuple
  import torch
  from reagent.core.parameters import EvaluationParameters, RLParameters
  from reagent.core.torch_utils import masked_softmax
  from reagent.core.types import DiscreteDqnInput, ExtraData, FeatureData
  from reagent.evaluation.evaluation_data_page import EvaluationDataPage
  from reagent.evaluation.evaluator import get_metrics_to_score
  from reagent.models.dqn import FullyConnectedDQN
  from reagent.training.dqn_trainer_base import DQNTrainerBaseLightning
  from reagent.training.parameters import DQNTrainerParameters
  from reagent.workflow.types import RewardOptions
label_desc: Use image node_id 1 to create a new MockDQNTrainer object from inherited base classes: DQNTrainerBaseLightning with example: obj = MockDQNTrainer(rl_parameters, metrics_to_score, actions, evaluation_parameters, double_q_learning)