code: def transform(
      self,
      X: list[list[Config[ModelConfig]] | list[ModelConfig]],
      _y: Any = None,
  ) -> npt.NDArray[np.float32]:
      if self.transform_full_config:
          return cast(
              npt.NDArray[np.float32],
              self.pipeline.transform(
                  [
                      x.model.asdict()
                      for x in cast(List[Config[ModelConfig]], X)
                  ]
              ),
          )
      return cast(
          npt.NDArray[np.float32],
          self.pipeline.transform(
              [x.asdict() for x in cast(List[ModelConfig], 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 ModelEncoder obj's underlying member method code with example usage: obj.transform(X, _y) and returns: cast, cast