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)
docstr: Fits the embedder to data
  
  Parameters
  ----------
  X : `array_like`, :class:`numpy.matrix` or :mod:`scipy.sparse` matrix, shape=(n_samples, n_features)
      input feature matrix
  y : `array_like`, :class:`numpy.matrix` or :mod:`scipy.sparse` matrix of `{0, 1}`, shape=(n_samples, n_labels)
      binary indicator matrix with label assignments
  
  Returns
  -------
  self
      fitted instance of self
docstr_tok: ["Fits","the","embedder","to","data","Parameters","--","--","--","--","--","X",":","`","array_like","`",",",":","class",":","`","numpy.matrix","`","or",":","mod",":","`","scipy.sparse","`","matrix",",","shape=","(","n_samples",",","n_features",")","input","feature","matrix","y",":","`","array_like","`",",",":","class",":","`","numpy.matrix","`","or",":","mod",":","`","scipy.sparse","`","matrix","of","`","{","0",",","1","}","`",",","shape=","(","n_samples",",","n_labels",")","binary","indicator","matrix","with","label","assignments","Returns","--","--","--","-","self","fitted","instance","of","self"]
code: def fit(self, X, y):
      """Fits the embedder to data
  
      Parameters
      ----------
      X : `array_like`, :class:`numpy.matrix` or :mod:`scipy.sparse` matrix, shape=(n_samples, n_features)
          input feature matrix
      y : `array_like`, :class:`numpy.matrix` or :mod:`scipy.sparse` matrix of `{0, 1}`, shape=(n_samples, n_labels)
          binary indicator matrix with label assignments
  
      Returns
      -------
      self
          fitted instance of self
      """
  
      self.embedder.fit(X, y)
  
imports: from __future__ import absolute_import
  from sklearn.base import BaseEstimator
label_desc: Use image node_id 2 for calling the SKLearnEmbedder obj's underlying member method code with example usage: obj.fit(X, y) without return types