class_docstr: Base class for models that aren't constructed from traditional, sequential layers.
  Subclasses must set self.outputs in _build method
  
  (Removes the layers idiom from build method of the Model class)
class_docstr_tok: ["Base","class","for","models","that","are","n't","constructed","from","traditional",",","sequential","layers",".","Subclasses","must","set","self.outputs","in","_build","method","(","Removes","the","layers","idiom","from","build","method","of","the","Model","class",")"]
code: def __init__(self, **kwargs):
      super(GeneralizedModel, self).__init__(**kwargs)
  
imports: from collections import namedtuple
  import tensorflow
  import math
  import graphsage.layers
  import graphsage.metrics
  from .prediction import BipartiteEdgePredLayer
  from .aggregators import MeanAggregator, MaxPoolingAggregator, MeanPoolingAggregator, SeqAggregator, GCNAggregator
label_desc: Use image node_id 1 to create a new GeneralizedModel object from inherited base classes: Model with example: obj = GeneralizedModel()