code: def adjust_validation_params(self, models_cnt):
      generated_params = []
      for model_type in ["Decision Tree"]:
          models_to_check = 1
  
          logger.info(
              f"Generate parameters for {model_type} (#{models_cnt + 1})"
          )
          params = self._get_model_params(model_type, seed=1)
          if params is None:
              continue
  
          params["name"] = self.get_model_name(
              model_type, models_cnt + 1
          )
          params["status"] = "initialized"
          params["final_loss"] = None
          params["train_time"] = None
          params["data_type"] = "original"
  
          unique_params_key = MljarTuner.get_params_key(params)
          if unique_params_key not in self._unique_params_keys:
              generated_params += [params]
              models_cnt += 1
      return generated_params
  
imports: import copy
  import json
  import logging
  import os
  import numpy
  import pandas
  from sklearn.preprocessing import OneHotEncoder
  from supervised.algorithms.catboost import catboost_eval_metric
  from supervised.algorithms.lightgbm import lightgbm_eval_metric
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION, AlgorithmsRegistry
  from supervised.algorithms.xgboost import xgboost_eval_metric
  from supervised.preprocessing.label_encoder import LabelEncoder
  from supervised.preprocessing.preprocessing_categorical import PreprocessingCategorical
  from supervised.tuner.hill_climbing import HillClimbing
  from supervised.tuner.preprocessing_tuner import PreprocessingTuner
  from supervised.tuner.random_parameters import RandomParameters
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.utils import dump_data, load_data
label_desc: Use image node_id 14 for calling the MljarTuner obj's underlying member method code with example usage: obj.adjust_validation_params(models_cnt) and returns: generated_params