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: Transforms the given configurations according to the fitted transformer
  pipeline.
  
  Args:
      X: The input configurations.
  
  Returns:
      A NumPy array of shape [N, D]. N is the number of input configurations, D the dimension
          of the vectorized representation.
code: def transform(
      self, X: list[Config[ModelConfig]]
  ) -> npt.NDArray[np.float32]:
      """
      Transforms the given configurations according to the fitted transformer
      pipeline.
  
      Args:
          X: The input configurations.
  
      Returns:
          A NumPy array of shape [N, D]. N is the number of input configurations, D the dimension
              of the vectorized representation.
      """
      return cast(npt.NDArray[np.float32], self.pipeline.transform(X))
  
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 4 for calling the ConfigTransformer obj's underlying member method code with example usage: obj.transform(X) and returns: cast