code: def make_union(*transformers):
      """Construct a FeatureUnion from the given transformers.
  
      This is a shorthand for the FeatureUnion constructor; it does not require,
      and does not permit, naming the transformers. Instead, they will be given
      names automatically based on their types. It also does not allow weighting.
  
      Examples
      --------
      >>> from sklearn.decomposition import PCA, TruncatedSVD
      >>> make_union(PCA(), TruncatedSVD())    # doctest: +NORMALIZE_WHITESPACE
      FeatureUnion(n_jobs=1,
             transformer_list=[('pca',
                                PCA(copy=True, iterated_power='auto',
                                    n_components=None, random_state=None,
                                    svd_solver='auto', tol=0.0, whiten=False)),
                               ('truncatedsvd',
                                TruncatedSVD(algorithm='randomized',
                                n_components=2, n_iter=5,
                                random_state=None, tol=0.0))],
             transformer_weights=None)
  
  
      Returns
      -------
      f : FeatureUnion
      """
      return FeatureUnion(_name_estimators(transformers))
  
imports: from collections import defaultdict
  from warnings import warn
  from abc import ABCMeta, abstractmethod
  import numpy
  from scipy import sparse
  from .base import BaseEstimator, TransformerMixin
  from .externals.joblib import Parallel, delayed
  from .externals import six
  from .utils import tosequence
  from .utils.metaestimators import if_delegate_has_method
label_desc: Use image node_id 6 for calling a global function with example usage: make_union() and returns: FeatureUnion