parameters: (node_features,
                   source_indices,
                   target_indices,
                   num_nodes,
                   num_edges,
                   input_channels,
                   output_channels)
return_statement: return  gcn_2(X,source_indices, target_indices)
docstring: An example 2-layer GCN kernel.
      Args:
          node_feature (Layer): Node feature matrix with the shape of (num_nodes,input_channels)
          source_indices (Layer): Source node indices of the edges with shape (num_nodes)
          target_indices (Layer): Target node indices of the edges with shape (num_nodes)
          num_nodes (int): Number of vertices in the graph
          input_channels (int): The size of the input node features
          output_channels (int): The number of output channels of the node features
      Returns:
          (Layer) : The resultant node features after message passing kernel ops
docstring_summary: An example 2-layer GCN kernel.
      Args:
          node_feature (Layer): Node feature matrix with the shape of (num_nodes,input_channels)
          source_indices (Layer): Source node indices of the edges with shape (num_nodes)
          target_indices (Layer): Target node indices of the edges with shape (num_nodes)
          num_nodes (int): Number of vertices in the graph
          input_channels (int): The size of the input node features
          output_channels (int): The number of output channels of the node features
      Returns:
          (Layer) : The resultant node features after message passing kernel ops
docstring_tokens: An example 2 - layer GCN kernel . Args : node_feature ( Layer ) : Node feature matrix with the shape of ( num_nodes input_channels ) source_indices ( Layer ) : Source node indices of the edges with shape ( num_nodes ) target_indices ( Layer ) : Target node indices of the edges with shape ( num_nodes ) num_nodes ( int ) : Number of vertices in the graph input_channels ( int ) : The size of the input node features output_channels ( int ) : The number of output channels of the node features Returns : ( Layer ) : The resultant node features after message passing kernel ops
function: def GCNConvLayer(node_features,
                   source_indices,
                   target_indices,
                   num_nodes,
                   num_edges,
                   input_channels,
                   output_channels):
      """An example 2-layer GCN kernel.
      Args:
          node_feature (Layer): Node feature matrix with the shape of (num_nodes,input_channels)
          source_indices (Layer): Source node indices of the edges with shape (num_nodes)
          target_indices (Layer): Target node indices of the edges with shape (num_nodes)
          num_nodes (int): Number of vertices in the graph
          input_channels (int): The size of the input node features
          output_channels (int): The number of output channels of the node features
      Returns:
          (Layer) : The resultant node features after message passing kernel ops
      """
      input_channels_1 = input_channels
      out_channels_1 = 8
      input_channels_2 = out_channels_1
      out_channels_2 = output_channels
  
      gcn_1 = GCNConv(input_channels_1,out_channels_1,
                      num_nodes,
                      bias = True,
                      activation = lbann.Relu,
                      name = 'GCN_1')
      gcn_2 = GCNConv(input_channels_2,out_channels_2,
                      num_nodes,
                      bias = True,
                      activation = lbann.Relu,
                      name = 'GCN_2')
      X = gcn_1(node_features,source_indices, target_indices)
      return  gcn_2(X,source_indices, target_indices)
function_tokens: def GCNConvLayer ( node_features , source_indices , target_indices , num_nodes , num_edges , input_channels , output_channels ) : input_channels_1 = input_channels out_channels_1 = 8 input_channels_2 = out_channels_1 out_channels_2 = output_channels gcn_1 = GCNConv ( input_channels_1 , out_channels_1 , num_nodes , bias = True , activation = lbann . Relu , name = 'GCN_1' ) gcn_2 = GCNConv ( input_channels_2 , out_channels_2 , num_nodes , bias = True , activation = lbann . Relu , name = 'GCN_2' ) X = gcn_1 ( node_features , source_indices , target_indices ) return gcn_2 ( X , source_indices , target_indices )