code: def get_metalearning_suggestions(self):
      # == METALEARNING suggestions
      # we start by evaluating the defaults on the full dataset again
      # and add the suggestions from metalearning behind it
      if self.num_metalearning_cfgs > 0:
          # If metadata directory is None, use default
          if self.metadata_directory is None:
              metalearning_directory = os.path.dirname(
                  autosklearn.metalearning.__file__
              )
              # There is no multilabel data in OpenML
              if self.task == MULTILABEL_CLASSIFICATION:
                  meta_task = BINARY_CLASSIFICATION
              else:
                  meta_task = self.task
              metadata_directory = os.path.join(
                  metalearning_directory,
                  "files",
                  "%s_%s_%s"
                  % (
                      self.metrics[0],
                      TASK_TYPES_TO_STRING[meta_task],
                      "sparse"
                      if self.datamanager.info["is_sparse"]
                      else "dense",
                  ),
              )
              self.metadata_directory = metadata_directory
  
          # If metadata directory is specified by user,
          # then verify that it exists.
          else:
              if not os.path.exists(self.metadata_directory):
                  raise ValueError(
                      "The specified metadata directory '%s' "
                      "does not exist!" % self.metadata_directory
                  )
  
              else:
                  # There is no multilabel data in OpenML
                  if self.task == MULTILABEL_CLASSIFICATION:
                      meta_task = BINARY_CLASSIFICATION
                  else:
                      meta_task = self.task
  
                  metadata_directory = os.path.join(
                      self.metadata_directory,
                      "%s_%s_%s"
                      % (
                          self.metrics[0],
                          TASK_TYPES_TO_STRING[meta_task],
                          "sparse"
                          if self.datamanager.info["is_sparse"]
                          else "dense",
                      ),
                  )
                  # Check that the metadata directory has the correct
                  # subdirectory needed for this dataset.
                  if os.path.basename(
                      metadata_directory
                  ) not in os.listdir(self.metadata_directory):
                      raise ValueError(
                          "The specified metadata directory "
                          "'%s' does not have the correct "
                          "subdirectory '%s'"
                          % (
                              self.metadata_directory,
                              os.path.basename(metadata_directory),
                          )
                      )
              self.metadata_directory = metadata_directory
  
          if os.path.exists(self.metadata_directory):
              self.logger.info(
                  "Metadata directory: %s", self.metadata_directory
              )
              meta_base = MetaBase(
                  self.config_space,
                  self.metadata_directory,
                  self.logger,
              )
  
              metafeature_calculation_time_limit = int(
                  self.total_walltime_limit / 4
              )
              metafeature_calculation_start_time = time.time()
              meta_features = self._calculate_metafeatures_with_limits(
                  metafeature_calculation_time_limit
              )
              metafeature_calculation_end_time = time.time()
              metafeature_calculation_time_limit = (
                  metafeature_calculation_time_limit
                  - (
                      metafeature_calculation_end_time
                      - metafeature_calculation_start_time
                  )
              )
  
              if metafeature_calculation_time_limit < 1:
                  self.logger.warning(
                      "Time limit for metafeature calculation less "
                      "than 1 seconds (%f). Skipping calculation "
                      "of metafeatures for encoded dataset.",
                      metafeature_calculation_time_limit,
                  )
                  meta_features_encoded = None
              else:
                  with warnings.catch_warnings():
                      warnings.showwarning = (
                          get_send_warnings_to_logger(self.logger)
                      )
                  meta_features_encoded = (
                      self._calculate_metafeatures_encoded_with_limits(
                          metafeature_calculation_time_limit
                      )
                  )
  
              # In case there is a problem calculating the encoded meta-features
              if meta_features is None:
                  if meta_features_encoded is not None:
                      meta_features = meta_features_encoded
              else:
                  if meta_features_encoded is not None:
                      meta_features.metafeature_values.update(
                          meta_features_encoded.metafeature_values
                      )
  
              if meta_features is not None:
                  meta_base.add_dataset(
                      self.dataset_name, meta_features
                  )
                  # Do mean imputation of the meta-features - should be done specific
                  # for each prediction model!
                  all_metafeatures = meta_base.get_metafeatures(
                      features=list(meta_features.keys())
                  )
                  all_metafeatures.fillna(
                      all_metafeatures.mean(), inplace=True
                  )
  
                  with warnings.catch_warnings():
                      warnings.showwarning = (
                          get_send_warnings_to_logger(self.logger)
                      )
                      metalearning_configurations = (
                          self.collect_metalearning_suggestions(
                              meta_base
                          )
                      )
                  if metalearning_configurations is None:
                      metalearning_configurations = []
                  self.reset_data_manager()
  
                  self.logger.info("%s", meta_features)
  
                  # Convert meta-features into a dictionary because the scenario
                  # expects a dictionary
                  meta_features_dict = {}
                  for dataset, series in all_metafeatures.iterrows():
                      meta_features_dict[dataset] = series.values
                  meta_features_list = []
                  for meta_feature_name in all_metafeatures.columns:
                      meta_features_list.append(
                          meta_features[meta_feature_name].value
                      )
                  self.logger.info(list(meta_features_dict.keys()))
  
          else:
              meta_features = None
              self.logger.warning(
                  "Could not find meta-data directory %s"
                  % metadata_directory
              )
  
      else:
          meta_features = None
      if meta_features is None:
          metalearning_configurations = []
      return metalearning_configurations
  
imports: import typing
  from typing import Dict, List, Optional, Sequence
  import copy
  import json
  import logging
  import multiprocessing
  import os
  import time
  import traceback
  import warnings
  import dask.distributed
  import pynisher
  from smac.callbacks import IncorporateRunResultCallback
  from smac.facade.smac_ac_facade import SMAC4AC
  from smac.intensification.intensification import Intensifier
  from smac.intensification.simple_intensifier import SimpleIntensifier
  from smac.optimizer.multi_objective.parego import ParEGO
  from smac.runhistory.runhistory2epm import RunHistory2EPM4LogCost
  from smac.scenario.scenario import Scenario
  from smac.tae.dask_runner import DaskParallelRunner
  from smac.tae.serial_runner import SerialRunner
  import autosklearn.metalearning
  from autosklearn.constants import BINARY_CLASSIFICATION, CLASSIFICATION_TASKS, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, MULTIOUTPUT_REGRESSION, REGRESSION, TASK_TYPES_TO_STRING
  from autosklearn.data.abstract_data_manager import AbstractDataManager
  from autosklearn.ensemble_building import EnsembleBuilderManager
  from autosklearn.evaluation import ExecuteTaFuncWithQueue, get_cost_of_crash
  from autosklearn.metalearning.metafeatures.metafeatures import calculate_all_metafeatures_encoded_labels, calculate_all_metafeatures_with_labels
  from autosklearn.metalearning.metalearning.meta_base import MetaBase
  from autosklearn.metalearning.mismbo import suggest_via_metalearning
  from autosklearn.metrics import Scorer
  from autosklearn.util.logging_ import get_named_client_logger
  from autosklearn.util.parallel import preload_modules
  from autosklearn.util.stopwatch import StopWatch
label_desc: Use image node_id 7 for calling the AutoMLSMBO obj's underlying member method code with example usage: obj.get_metalearning_suggestions() and returns: metalearning_configurations