code: def make_union(*transformers, **kwargs):
      
      n_jobs = kwargs.pop('n_jobs', 1)
      concatenate = kwargs.pop('concatenate', True)
      if kwargs:
         # We do not currently support `transformer_weights` as we may want to
         # change its type spec in make_union
         raise TypeError('Unknown keyword arguments: "{}"'
                         .format(list(kwargs.keys())[0]))
      return FeatureUnion(_name_estimators(transformers), n_jobs= n_jobs, concatenate= concatenate)
docs: 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.
  
      Parameters
      ----------
      *transformers : list of estimators
  
      n_jobs : int, optional
          Number of jobs to run in parallel (default 1).
  
      Returns
      -------
      f : FeatureUnion
  
      Examples
      --------
      >>> from sklearn.decomposition import PCA, TruncatedSVD
      >>> from sklearn.pipeline import make_union
      >>> 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)