code: def simple_algorithms_params(self, models_cnt):
      generated_params = []
      for model_type in ["Baseline", "Decision Tree", "Linear"]:
          if model_type not in self._algorithms:
              continue
          models_to_check = 1
          if model_type == "Decision Tree":
              models_to_check = min(3, self._start_random_models)
          for i in range(models_to_check):
              logger.info(
                  f"Generate parameters for {model_type} (#{models_cnt + 1})"
              )
              params = self._get_model_params(model_type, seed=i + 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 15 for calling the MljarTuner obj's underlying member method code with example usage: obj.simple_algorithms_params(models_cnt) and returns: generated_params