class_docstr: Concatenates results of multiple transformer objects.
  Transformers are expected to have pd.DataFrame as input and output
  
  Modified from sklearn.pipeline.FeatureUnion
  
  This estimator applies a list of transformer objects in parallel to the
  input data, then concatenates the results. This is useful to combine
  several feature extraction mechanisms into a single transformer.
  
  Parameters of the transformers may be set using its name and the parameter
  name separated by a '__'. A transformer may be replaced entirely by
  setting the parameter with its name to another transformer,
  or removed by setting to 'drop' or ``None``.
  
  Read more in the :ref:`User Guide <feature_union>`.
  
  Parameters
  ----------
  transformer_list : list of (string, transformer) tuples
      List of transformer objects to be applied to the data. The first
      half of each tuple is the name of the transformer.
      .. versionchanged:: 0.22
         Deprecated `None` as a transformer in favor of 'drop'.
  n_jobs : int, default=None
      Number of jobs to run in parallel.
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
      for more details.
      .. versionchanged:: v0.20
         `n_jobs` default changed from 1 to None
  transformer_weights : dict, default=None
      Multiplicative weights for features per transformer.
      Keys are transformer names, values the weights.
  verbose : bool, default=False
      If True, the time elapsed while fitting each transformer will be
      printed as it is completed.
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code: def __init__(
      self,
      transformer_list,
      n_jobs=None,
      transformer_weights=None,
      verbose=False,
  ):
      super().__init__(
          transformer_list=transformer_list,
          n_jobs=n_jobs,
          transformer_weights=transformer_weights,
          verbose=verbose,
      )
  
file_path: greykite/greykite/sklearn/transform/pandas_feature_union.py
imports: import numpy
  import pandas
  from sklearn.pipeline import FeatureUnion
  from sklearn.pipeline import _fit_transform_one
  from sklearn.pipeline import _transform_one
  from sklearn.utils._joblib import Parallel
  from sklearn.utils._joblib import delayed
label_desc: Use image node_id 1 to create a new PandasFeatureUnion object from inherited base classes: FeatureUnion with example: obj = PandasFeatureUnion(transformer_list, n_jobs, transformer_weights, verbose)