code: def default_params(self, models_cnt):
      generated_params = []
      for model_type in [
          "LightGBM",
          "Xgboost",
          "CatBoost",
          "Neural Network",
          "Random Forest",
          "Extra Trees",
          "Nearest Neighbors",
      ]:
          if model_type not in self._algorithms:
              continue
  
          if self.skip_if_rows_cols_limit(model_type):
              continue
  
          logger.info(
              f"Get default parameters for {model_type} (#{models_cnt + 1})"
          )
          params = self._get_model_params(
              model_type, seed=models_cnt + 1, params_type="default"
          )
          if params is None:
              continue
          special = (
              "Default_"
              if self._optuna_time_budget is None
              else "Optuna_"
          )
          params["name"] = self.get_model_name(
              model_type, models_cnt + 1, special=special
          )
          params["status"] = "initialized"
          params["final_loss"] = None
          params["train_time"] = None
          params["data_type"] = "original"
          if self._optuna_time_budget is not None:
              params["optuna_time_budget"] = self._optuna_time_budget
              params["optuna_init_params"] = self._optuna_init_params
              params["optuna_verbose"] = self._optuna_verbose
  
          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 17 for calling the MljarTuner obj's underlying member method code with example usage: obj.default_params(models_cnt) and returns: generated_params