code: def skip_if_rows_cols_limit(self, model_type):
      max_rows_limit = AlgorithmsRegistry.get_max_rows_limit(
          self._ml_task, model_type
      )
      max_cols_limit = AlgorithmsRegistry.get_max_cols_limit(
          self._ml_task, model_type
      )
  
      if max_rows_limit is not None:
          if self._data_info["rows"] > max_rows_limit:
              return True
      if max_cols_limit is not None:
          if self._data_info["cols"] > max_cols_limit:
              return True
  
      return False
  
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 16 for calling the MljarTuner obj's underlying member method code with example usage: obj.skip_if_rows_cols_limit(model_type) and returns: False, True, True