class_docstr: Abstract class for preparing data in order to train a surrogate model.
  
  Parameters
  ----------
  scenario : Scenario object.
  considered_states : list[StatusType], defaults to [StatusType.SUCCESS, StatusType.CRASHED, StatusType.MEMORYOUT]  # noqa: E501
      Trials with the passed states are considered.
  lower_budget_states : list[StatusType], defaults to []
      Additionally consider all trials with these states for budget < current budget.
  scale_percentage : int, defaults to 5
      Scaled y-transformation use a percentile to estimate distance to optimum. Only used in some sub-classes.
  seed : int | None, defaults to none
  
  Raises
  ------
  TypeError
      If no success states are given.
docstr: Returns vector representation of the configurations.
  
  Warning
  -------
  Instance features are not
  appended and cost values are not taken into account.
  
  Parameters
  ----------
  budget_subset : list[int|float] | None, defaults to none
      List of budgets to consider.
  
  Returns
  -------
  configs_array : np.ndarray
docstr_tok: ["Returns","vector","representation","of","the","configurations",".","Warning","--","--","--","-","Instance","features","are","not","appended","and","cost","values","are","not","taken","into","account",".","Parameters","--","--","--","--","--","budget_subset",":","list","[","int|float","]","|","None",",","defaults","to","none","List","of","budgets","to","consider",".","Returns","--","--","--","-","configs_array",":","np.ndarray"]
code: def get_configurations(
      self,
      budget_subset: list | None = None,
  ) -> np.ndarray:
      """Returns vector representation of the configurations.
  
      Warning
      -------
      Instance features are not
      appended and cost values are not taken into account.
  
      Parameters
      ----------
      budget_subset : list[int|float] | None, defaults to none
          List of budgets to consider.
  
      Returns
      -------
      configs_array : np.ndarray
      """
      s_trials = self._get_considered_trials(budget_subset)
      s_config_ids = set(s_trial.config_id for s_trial in s_trials)
      t_trials = self._get_timeout_trials(budget_subset)
      t_config_ids = set(t_trial.config_id for t_trial in t_trials)
      config_ids = s_config_ids | t_config_ids
      configurations = [
          self.runhistory._ids_config[config_id]
          for config_id in config_ids
      ]
      configs_array = convert_configurations_to_array(configurations)
  
      return configs_array
  
imports: from __future__ import annotations
  from abc import abstractmethod
  from typing import Any, Mapping
  import numpy
  from smac.multi_objective import AbstractMultiObjectiveAlgorithm
  from smac.runhistory.runhistory import RunHistory, TrialKey, TrialValue
  from smac.runner.abstract_runner import StatusType
  from smac.scenario import Scenario
  from smac.utils.configspace import convert_configurations_to_array
  from smac.utils.logging import get_logger
label_desc: Use image node_id 10 for calling the AbstractRunHistoryEncoder obj's underlying member method code with example usage: obj.get_configurations(budget_subset) and returns: configs_array