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: Uses the provided configurations to fit the transformer pipeline.
  
  Args:
      X: The input configurations.
code: def fit(self, X: list[Config[ModelConfig]]) -> ConfigTransformer:
      """
      Uses the provided configurations to fit the transformer pipeline.
  
      Args:
          X: The input configurations.
      """
      self.pipeline.fit(X)
      return self
  
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 3 for calling the ConfigTransformer obj's underlying member method code with example usage: obj.fit(X) and returns: self