class_docstr: The config transformer transforms a set of model configs into a set of
  NumPy arrays.
class_docstr_tok: ["The","config","transformer","transforms","a","set","of","model","configs","into","a","set","of","NumPy","arrays","."]
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"model={m}" for m in self.model_type_map] + list(
          self.attribute_map.keys()
      )
  
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 EnsembleConfigTransformer obj's underlying member method code with example usage: obj.feature_names_() and returns: unknown