code: def __init__(self, transform_full_config: bool = False):
      self.transform_full_config = transform_full_config
      self.model_vectorizer = DictVectorizer(
          dtype=np.float32, sparse=False, sort=True
      )
      self.hp_vectorizer = DictVectorizer(
          dtype=np.float32, sparse=False, sort=True
      )
      self.pipeline = make_pipeline(
          NanImputer(),
          make_union(
              make_pipeline(
                  Selector(use={"model"}),
                  self.model_vectorizer,
              ),
              make_pipeline(
                  Selector(ignore={"model"}),
                  self.hp_vectorizer,
                  StandardScaler(),
                  SimpleImputer(strategy="constant", fill_value=0.0),
              ),
          ),
      )
  
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 1 to create a new ModelEncoder object from inherited base classes: Encoder with example: obj = ModelEncoder(transform_full_config)