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: Transforms the given configurations according to the fitted transformer
  pipeline.
  
  Args:
      X: The input configurations.
  
  Returns:
      A NumPy array of shape [N, D] for every ensemble configuration where N is the number of
          ensemble members and D the dimensionality. N might differ for list members.
code: def transform(
      self, X: list[Config[EnsembleConfig]]
  ) -> list[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] for every ensemble configuration where N is the number of
              ensemble members and D the dimensionality. N might differ for list members.
      """
      configs = [model for ensemble in X for model in ensemble.model]
      out = self._transform(configs)
      lhs = out[:, : len(MODEL_REGISTRY)]
      rhs = out[:, len(MODEL_REGISTRY) :]
      out = np.concatenate([lhs, self.scaler.transform(rhs)], axis=1)
      transformed = np.nan_to_num(out, nan=0)
      chunks = np.cumsum([0] + [len(ensemble.model) for ensemble in X])
      return [
          transformed[lower:upper]
          for lower, upper in zip(chunks, chunks[1:])
      ]
  
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 EnsembleConfigTransformer obj's underlying member method code with example usage: obj.transform(X) and returns: unknown