code: def call(self, inputs):
      self_embedding, neigh_embedding, adj = inputs
      adj = _sparse_ones_like(adj)
  
      degree = tf.reshape(tf.sparse_reduce_sum(adj, 1), [-1, 1])
      agg_embedding = tf.sparse_tensor_dense_matmul(
          adj, neigh_embedding
      )
      if self.renorm:
          agg_embedding = (self_embedding + agg_embedding) / (
              1.0 + degree
          )
      else:
          agg_embedding = self_embedding + agg_embedding / tf.maximum(
              degree, 1e-7
          )
      return self.dense(agg_embedding)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_euler.python.utils import layers
label_desc: Use image node_id 2 for calling the GCNAggregator obj's underlying member method code with example usage: obj.call(inputs) and returns: self