code: def get_default_config(cls) -> AlgorithmConfig:
      return AlgorithmConfig().update_from_dict(
          {
              "reward_amt": 10,
              "dummy_param": 10,
              "dummy_param2": 15,
              "iter_time": 10,
              "iter_timesteps": 1,
          }
      )
  
imports: import os
  import pickle
  import time
  import numpy
  from ray.tune import result
  from ray.rllib.algorithms.algorithm import Algorithm, AlgorithmConfig
  from ray.rllib.utils.annotations import override
label_desc: Use image node_id 1 for calling the _ParameterTuningTrainer obj's underlying member method code with example usage: obj.get_default_config(cls) and returns: AlgorithmConfig