class_docstr: Generic feature encoder with flexible data type input,
  optional bottom up encoder type inference, and
  top down `FeatureUnion` configuration acceptance. Also
  includes feature origin method to track down the origin
  transformer of a given feature. Can optionally distribute
  feature union fitting using spark.
  
  Includes `extract` method for slicing off peices
  of the fitted `FeatureUnion` pipeline as a copy of
  itself with only the requested steps.
  
  Args:
      transformer_list (iterable or array-like):
          List of transformers, similar to that of FeatureUnion
      transformer_wieghts (dict):
          Multiplicative weights for features per transformer.
          Keys are transformer names, values the weights.
      n_jobs (int):
          Number of jobs for joblib parallelization
      size (string, 'small', 'medium', or 'large')
          Size of default encoder if using encoder inference
      col_names (iterable or array-like):
          List of column names if using numpy input
      config (dict):
          Configuration of column data types. Keys are column names
          and values are names of default encoder types.
      sc (sparkContext): Spark context for spark broadcasting and rdd operations.
      partitions (int or 'auto'): default 'auto'
          Number of partitions to use for parallelization of parameter
          search space. Integer values or None will be used directly for `numSlices`,
          while 'auto' will set `numSlices` to the number required fits.
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docstr: Extract copy of fitted self with slice of transformer list
  
  Args:
      step_names (list): White list of transformer
      names to include in extraction slice
  
  Returns:
      fitted Encoderizer object
docstr_tok: ["Extract","copy","of","fitted","self","with","slice","of","transformer","list","Args",":","step_names","(","list",")",":","White","list","of","transformer","names","to","include","in","extraction","slice","Returns",":","fitted","Encoderizer","object"]
code: def extract(self, step_names):
      """
      Extract copy of fitted self with slice of transformer list
  
      Args:
          step_names (list): White list of transformer
          names to include in extraction slice
  
      Returns:
          fitted Encoderizer object
      """
      encoderizer = copy(self)
      encoderizer.transformer_lengths = [
          encoderizer.transformer_lengths[x]
          for x in range(len(encoderizer.step_names))
          if encoderizer.step_names[x] in step_names
      ]
      encoderizer.transformer_list = [
          encoderizer.transformer_list[x]
          for x in range(len(encoderizer.step_names))
          if encoderizer.step_names[x] in step_names
      ]
      return encoderizer
  
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 2 for calling the Encoderizer obj's underlying member method code with example usage: obj.extract(step_names) and returns: encoderizer