class_docstr: The Ensemble class can be used to create ensembles of stellargraph's graph neural network algorithms including
  GCN, GraphSAGE, GAT, and HinSAGE. Ensembles can be used for training classification and regression problems for
  node attribute inference and link prediction.
  
  The Ensemble class can be used to create Naive ensembles.
  
  Naive ensembles add model diversity by random initialisation of the models' weights (before training) to
  different values. Each model in the ensemble is trained on the same training set of examples.
  
  .. seealso::
  
     Example using ensembles: `node classification <https://stellargraph.readthedocs.io/en/stable/demos/ensembles/ensemble-node-classification-example.html>`__.
  
     Related functionality: :class:`.BaggingEnsemble` for bootstrap sampling while training, in addition to random initialisation.
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docstr: Deprecated: use :meth:`predict`.
docstr_tok: ["Deprecated",":","use",":","meth",":","`","predict","`","."]
code: def predict_generator(self, *args, **kwargs):
      """
      Deprecated: use :meth:`predict`.
      """
      warnings.warn(
          "'predict_generator' has been replaced by 'predict', to match tensorflow.keras.Model",
          DeprecationWarning,
          stacklevel=2,
      )
      return self.predict(*args, **kwargs)
  
imports: from stellargraph.layer import *
  import numpy
  from tensorflow import keras
  from tensorflow.keras.callbacks import EarlyStopping
  import stellargraph
label_desc: Use image node_id 10 for calling the Ensemble obj's underlying member method code with example usage: obj.predict_generator() and returns: self