code: def __init__(self):
      self.vectorizer = DictVectorizer(
          dtype=np.float32, sparse=False, sort=True
      )
      self.pipeline = make_pipeline(
          self.vectorizer,
          PowerTransformer(),
      )
  
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 DatasetCatch22Encoder object from inherited base classes: Encoder with example: obj = DatasetCatch22Encoder()