class_docstr: Classifies nodes with a simple Graph Convolutional Network.
class_docstr_tok: ["Classifies","nodes","with","a","simple","Graph","Convolutional","Network","."]
code: def graph_call(self, graph, **kwargs):
      graph = graph_nets.modules.GraphIndependent(
          node_model_fn=lambda: self._dense_features
      )(graph)
      graph = graph_nets.modules.CommNet(
          edge_model_fn=lambda: self._edge_model1,
          node_encoder_model_fn=lambda: self._node_encoder_model1,
          node_model_fn=lambda: self._node_model1,
      )(graph)
      graph = graph_nets.modules.CommNet(
          edge_model_fn=lambda: self._edge_model2,
          node_encoder_model_fn=lambda: self._node_encoder_model2,
          node_model_fn=lambda: self._node_model2,
      )(graph)
      return graph
  
imports: import functools
  from typing import Dict, Optional, Text
  import graph_nets
  import neural_structured_learning.configs
  import neural_structured_learning.keras
  import neural_structured_learning.lib
  import sonnet.v2
  import tensorflow
label_desc: Use image node_id 2 for calling the GraphConvolutionalNodeClassifier obj's underlying member method code with example usage: obj.graph_call(graph) and returns: graph