code: def get(params, ml_task, seed=1):
      np.random.seed(seed)
      keys = list(params.keys())
      for k in [
          "num_class",
          "model_type",
          "seed",
          "ml_task",
          "explain_level",
          "model_architecture_json",
          "n_jobs",
          "metric",
          "eval_metric",
          "custom_eval_metric_name",
          "eval_metric_name",
      ]:
          if k in keys:
              keys.remove(k)
  
      model_type = params["model_type"]
      if model_type == "Baseline":
          return [None, None]
      model_info = AlgorithmsRegistry.registry[ml_task][model_type]
      model_params = model_info["params"]
  
      permuted_keys = np.random.permutation(keys)
      key_to_update = None
      values = None
  
      for key_to_update in permuted_keys:
          if key_to_update not in model_params:
              continue
          values = model_params[key_to_update]
          if len(values) > 1:
              break
      if values is None:
          return [None, None]
  
      left, right = None, None
      for i, v in enumerate(values):
          if v == params[key_to_update]:
              if i + 1 < len(values):
                  right = values[i + 1]
              if i - 1 >= 0:
                  left = values[i - 1]
  
      params_1, params_2 = None, None
      if left is not None:
          params_1 = copy.deepcopy(params)
          params_1[key_to_update] = left
      if right is not None:
          params_2 = copy.deepcopy(params)
          params_2[key_to_update] = right
  
      if params_1 is not None and "model_architecture_json" in params_1:
          del params_1["model_architecture_json"]
      if params_2 is not None and "model_architecture_json" in params_2:
          del params_2["model_architecture_json"]
  
      return [params_1, params_2]
  
imports: import copy
  import numpy
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, AlgorithmsRegistry
label_desc: Use image node_id 1 for calling the HillClimbing obj's underlying member method code with example usage: obj.get(params, ml_task, seed) and returns: list, list, list