class_docstr: Base class for initial design strategies that evaluates multiple configurations.
  
  Parameters
  ----------
  scenario : Scenario
  n_configs : int | None, defaults to None
      Number of initial configurations (disables the arguments ``n_configs_per_hyperparameter``).
  n_configs_per_hyperparameter: int, defaults to 10
      Number of initial configurations per hyperparameter. For example, if my configuration space covers five
      hyperparameters and ``n_configs_per_hyperparameter`` is set to 10, then 50 initial configurations will be
      samples.
  max_ratio: float, defaults to 0.25
      Use at most ``scenario.n_trials`` * ``max_ratio`` number of configurations in the initial design.
      Additional configurations are not affected by this parameter.
  additional_configs: list[Configuration], defaults to []
      Adds additional configurations to the initial design.
  seed : int | None, default to None
docstr: Transforms the continuous designs into a discrete list of configurations.
  
  Parameters
  ----------
  design : np.ndarray
      Array of hyperparameters originating from the initial design strategy.
  origin : str | None, defaults to None
      Label for a configuration where it originated from.
  configspace : ConfigurationSpace
  
  Returns
  -------
  configs : list[Configuration]
      Continuous transformed configs.
docstr_tok: ["Transforms","the","continuous","designs","into","a","discrete","list","of","configurations",".","Parameters","--","--","--","--","--","design",":","np.ndarray","Array","of","hyperparameters","originating","from","the","initial","design","strategy",".","origin",":","str","|","None",",","defaults","to","None","Label","for","a","configuration","where","it","originated","from",".","configspace",":","ConfigurationSpace","Returns","--","--","--","-","configs",":","list","[","Configuration","]","Continuous","transformed","configs","."]
code: def _transform_continuous_designs(
      self,
      design: np.ndarray,
      origin: str,
      configspace: ConfigurationSpace,
  ) -> list[Configuration]:
      """Transforms the continuous designs into a discrete list of configurations.
  
      Parameters
      ----------
      design : np.ndarray
          Array of hyperparameters originating from the initial design strategy.
      origin : str | None, defaults to None
          Label for a configuration where it originated from.
      configspace : ConfigurationSpace
  
      Returns
      -------
      configs : list[Configuration]
          Continuous transformed configs.
      """
      params = configspace.get_hyperparameters()
      for idx, param in enumerate(params):
          if isinstance(param, IntegerHyperparameter):
              design[:, idx] = param._inverse_transform(
                  param._transform(design[:, idx])
              )
          elif isinstance(param, NumericalHyperparameter):
              continue
          elif isinstance(param, Constant):
              design_ = np.zeros(
                  np.array(design.shape) + np.array((0, 1))
              )
              design_[:, :idx] = design[:, :idx]
              design_[:, idx + 1 :] = design[:, idx:]
              design = design_
          elif isinstance(param, CategoricalHyperparameter):
              v_design = design[:, idx]
              v_design[v_design == 1] = 1 - 10**-10
              design[:, idx] = np.array(
                  v_design * len(param.choices), dtype=int
              )
          elif isinstance(param, OrdinalHyperparameter):
              v_design = design[:, idx]
              v_design[v_design == 1] = 1 - 10**-10
              design[:, idx] = np.array(
                  v_design * len(param.sequence), dtype=int
              )
          else:
              raise ValueError(
                  "Hyperparameter not supported when transforming a continuous design."
              )
  
      configs = []
      for vector in design:
          try:
              conf = deactivate_inactive_hyperparameters(
                  configuration=None,
                  configuration_space=configspace,
                  vector=vector,
              )
          except ForbiddenValueError:
              continue
  
          conf.origin = origin
          configs.append(conf)
  
      return configs
  
file_path: SMAC3/smac/initial_design/abstract_initial_design.py
imports: from __future__ import annotations
  from abc import abstractmethod
  from typing import Any
  from collections import OrderedDict
  import numpy
  from ConfigSpace.configuration_space import Configuration, ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, Constant, IntegerHyperparameter, NumericalHyperparameter, OrdinalHyperparameter
  from ConfigSpace.util import ForbiddenValueError, deactivate_inactive_hyperparameters
  from smac.scenario import Scenario
  from smac.utils.logging import get_logger
label_desc: Use image node_id 5 for calling the AbstractInitialDesign obj's underlying member method code with example usage: obj._transform_continuous_designs(design, origin, configspace) and returns: configs