original_docstring: 
          Build the sample graph with adj info in self.sample()
          directly feed the sampled support vectors to tf placeholder
          
docstring: Build the sample graph with adj info in self.sample()
  directly feed the sampled support vectors to tf placeholder
docstring_tokens: Build the sample graph with adj info in self . sample () directly feed the sampled support vectors to tf placeholder
code: def build(self):
          """
          Build the sample graph with adj info in self.sample()
          directly feed the sampled support vectors to tf placeholder
          """
          self.aggregators = self.get_aggregators()
          _outputs_l = self.aggregate_subgraph()
          if self.jk == 'concat':
              _dim_input_jk = np.array(self.dims_feat)[np.array(self.idx_conv)+1].sum()
          else:
              _dim_input_jk = self.dims_feat[-1]
          self.layer_jk = layers.JumpingKnowledge(self.arch_gcn,_dim_input_jk,mode=self.jk)
          self.outputs = self.layer_jk([_outputs_l, self.idx_conv])
          # OUPTUT LAYER
          self.outputs = tf.nn.l2_normalize(self.outputs, 1)
          _dim_final = self.arch_gcn['dim'] if self.jk else self.dims_feat[-1]
          self.layer_pred = layers.HighOrderAggregator(_dim_final,self.num_classes,act="I",\
                      order=0,dropout=self.placeholders["dropout"],bias="bias")
          self.node_preds = self.layer_pred((self.outputs,None,None,None,None))
  
          # BACK PROP
          self._loss()
          update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
          with tf.control_dependencies(update_ops):
              grads_and_vars = self.optimizer.compute_gradients(self.loss)
              clipped_grads_and_vars = [(tf.clip_by_value(grad, -5.0, 5.0) if grad is not None else None, var)
                      for grad, var in grads_and_vars]
              self.grad, _ = clipped_grads_and_vars[0]
              self.opt_op = self.optimizer.apply_gradients(clipped_grads_and_vars)
          self.preds = self.predict()
code_tokens: def build ( self ) : self . aggregators = self . get_aggregators ( ) _outputs_l = self . aggregate_subgraph ( ) if self . jk == 'concat' : _dim_input_jk = np . array ( self . dims_feat ) [ np . array ( self . idx_conv ) + 1 ] . sum ( ) else : _dim_input_jk = self . dims_feat [ - 1 ] self . layer_jk = layers . JumpingKnowledge ( self . arch_gcn , _dim_input_jk , mode = self . jk ) self . outputs = self . layer_jk ( [ _outputs_l , self . idx_conv ] ) self . outputs = tf . nn . l2_normalize ( self . outputs , 1 ) _dim_final = self . arch_gcn [ 'dim' ] if self . jk else self . dims_feat [ - 1 ] self . layer_pred = layers . HighOrderAggregator ( _dim_final , self . num_classes , act = "I" , order = 0 , dropout = self . placeholders [ "dropout" ] , bias = "bias" ) self . node_preds = self . layer_pred ( ( self . outputs , None , None , None , None ) ) self . _loss ( ) update_ops = tf . get_collection ( tf . GraphKeys . UPDATE_OPS ) with tf . control_dependencies ( update_ops ) : grads_and_vars = self . optimizer . compute_gradients ( self . loss ) clipped_grads_and_vars = [ ( tf . clip_by_value ( grad , - 5.0 , 5.0 ) if grad is not None else None , var ) for grad , var in grads_and_vars ] self . grad , _ = clipped_grads_and_vars [ 0 ] self . opt_op = self . optimizer . apply_gradients ( clipped_grads_and_vars ) self . preds = self . predict ( )
short_docstring: Build the sample graph with adj info in self.sample()
  directly feed the sampled support vectors to tf placeholder
short_docstring_tokens: Build the sample graph with adj info in self . sample () directly feed the sampled support vectors to tf placeholder
comment: """
          Build the sample graph with adj info in self.sample()
          directly feed the sampled support vectors to tf placeholder
          """$SEP$# OUPTUT LAYER
  $SEP$# BACK PROP
  