class_docstr: Concatenates results of multiple transformer objects.
  
  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 transformations 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``.
  
  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.
  n_jobs : int or None, optional (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.
  transformer_weights : dict, optional
      Multiplicative weights for features per transformer.
      Keys are transformer names, values the weights.
  flatten_transform_index : bool, optional (default=True)
      if True, columns of return DataFrame are flat, by "transformer__variablename"
      if False, columns are MultiIndex (transformer, variablename)
      has no effect if return mtype is one without column names
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code: def _transformer_list(self):
      return self._get_estimator_tuples(
          self.transformer_list, clone_ests=False
      )
  
file_path: sktime/sktime/transformations/compose/_featureunion.py
imports: import pandas
  from sktime.base._meta import _HeterogenousMetaEstimator
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.multiindex import flatten_multiindex
label_desc: Use image node_id 2 for calling the FeatureUnion obj's underlying member method code with example usage: obj._transformer_list() and returns: self