code: def fit(
      self,
      X: list[Config[ModelConfig]] | list[ModelConfig],
      _y: Any = None,
  ) -> ModelEncoder:
      if self.transform_full_config:
          self.pipeline.fit(
              [
                  x.model.asdict()
                  for x in cast(List[Config[ModelConfig]], X)
              ]
          )
      else:
          self.pipeline.fit(
              [x.asdict() for x in cast(List[ModelConfig], 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 ModelEncoder obj's underlying member method code with example usage: obj.fit(X, _y) and returns: self