class_docstr: Base implementation of unsupervised GraphSAGE
class_docstr_tok: ["Base","implementation","of","unsupervised","GraphSAGE"]
code: def _loss(self):
      for aggregator in self.aggregators:
          for var in aggregator.vars.values():
              self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var)
  
      self.loss += self.link_pred_layer.loss(
          self.outputs1, self.outputs2, self.neg_outputs
      )
      tf.summary.scalar("loss", self.loss)
  
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 6 for calling the SampleAndAggregate obj's underlying member method code with example usage: obj._loss() without return types