class_docstr: The config transformer transforms a configuration (model + dataset) into a
  real-valued vector.
class_docstr_tok: ["The","config","transformer","transforms","a","configuration","(","model","+","dataset",")","into","a","real-valued","vector","."]
docstr: Returns the feature names for the columns of transformed
  configurations.
code: def feature_names_(self) -> list[str]:
      """
      Returns the feature names for the columns of transformed
      configurations.
      """
      return [
          f
          for e in self.encoders
          for f in e.feature_names_
          if isinstance(e, Encoder)
      ]
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Any, cast, Dict, List, Optional, Set, Union
  import numpy
  import numpy.typing
  import pandas
  from sklearn.base import TransformerMixin
  from sklearn.feature_extraction import DictVectorizer
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import make_pipeline, make_union
  from sklearn.preprocessing import MinMaxScaler, PowerTransformer, StandardScaler
  from tsbench.config import Config, EnsembleConfig, MODEL_REGISTRY, ModelConfig, TrainConfig
  from tsbench.config.model.models import SeasonalNaiveModelConfig
  from tsbench.evaluations.tracking import ModelTracker
label_desc: Use image node_id 2 for calling the ConfigTransformer obj's underlying member method code with example usage: obj.feature_names_() and returns: unknown