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 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 ``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.
  
  n_jobs: int, optional
      Number of jobs to run in parallel (default 1).
  
  transformer_weights: dict, optional
      Multiplicative weights for features per transformer.
      Keys are transformer names, values the weights.
docstr: Generate (name, est, weight) tuples excluding None transformers
          
docstr_tok: ["Generate","(","name",",","est",",","weight",")","tuples","excluding","None","transformers"]
code: def _iter(self):
      """Generate (name, est, weight) tuples excluding None transformers"""
      get_weight = (self.transformer_weights or {}).get
      return (
          (name, trans, get_weight(name))
          for name, trans in self.transformer_list
          if trans is not None
      )
  
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 5 for calling the FeatureUnion obj's underlying member method code with example usage: obj._iter() and returns: unknown