class_docstr: Embed the label space using a scikit-compatible matrix-based embedder
  
  Parameters
  ----------
  embedder : sklearn.base.BaseEstimator
      a clonable instance of a scikit-compatible embedder, will be automatically
      put under :code:`self.embedder`, see .
  pass_input_space : bool (default is False)
      whether to take :code:`X` into consideration upon clustering,
      use only if you know that the embedder can handle two
      parameters for clustering, will be automatically
      put under :code:`self.pass_input_space`.
  
  
  Example code for using this embedder looks like this:
  
  .. code-block:: python
  
      from skmultilearn.embedding import SKLearnEmbedder, EmbeddingClassifier
      from sklearn.manifold import SpectralEmbedding
      from sklearn.ensemble import RandomForestRegressor
      from skmultilearn.adapt import MLkNN
  
      clf = EmbeddingClassifier(
          SKLearnEmbedder(SpectralEmbedding(n_components = 10)),
          RandomForestRegressor(n_estimators=10),
          MLkNN(k=5)
      )
  
      clf.fit(X_train, y_train)
  
      predictions = clf.predict(X_test)
code: def __init__(self, embedder=None, pass_input_space=False):
      super(BaseEstimator, self).__init__()
  
      self.embedder = embedder
      self.pass_input_space = pass_input_space
  
imports: from __future__ import absolute_import
  from sklearn.base import BaseEstimator
label_desc: Use image node_id 1 to create a new SKLearnEmbedder object from inherited base classes: BaseEstimator with example: obj = SKLearnEmbedder(embedder, pass_input_space)