class_docstr: Transformer pass through used for hyperparameter optimization
  in a pipeline
  
  Args:
      encoderizer (fitted Encoderizer instance):
          Encoder from which to slice
      step_names (array-like):
          List of step_names to extract from encoderizer
class_docstr_tok: ["Transformer","pass","through","used","for","hyperparameter","optimization","in","a","pipeline","Args",":","encoderizer","(","fitted","Encoderizer","instance",")",":","Encoder","from","which","to","slice","step_names","(","array-like",")",":","List","of","step_names","to","extract","from","encoderizer"]
docstr: Extract appropriate transformation steps from Encoderizer 
docstr_tok: ["Extract","appropriate","transformation","steps","from","Encoderizer"]
code: def transform(self, X):
      """Extract appropriate transformation steps from Encoderizer"""
      return self.encoderizer.transform(X)
  
imports: import ast
  import numpy
  from pandas import DataFrame
  from scipy import sparse
  from copy import copy
  from joblib import Parallel, delayed
  from sklearn.pipeline import FeatureUnion
  from sklearn.base import TransformerMixin, BaseEstimator
  from .base import _parse_partitions
label_desc: Use image node_id 3 for calling the EncoderizerExtractor obj's underlying member method code with example usage: obj.transform(X) and returns: self