code: def __init__(
      self,
      in_channels,
      hidden_channels,
      out_channels,
      num_layers,
      model_backend="dgl",
  ):
      if model_backend == "dgl":
          from dgl.nn import SAGEConv
      else:
          from cugraph_dgl.nn import SAGEConv
  
      super(GNN, self).__init__()
      self.convs = torch.nn.ModuleList()
      for _ in range(num_layers - 1):
          self.convs.append(
              SAGEConv(
                  in_channels, hidden_channels, aggregator_type="mean"
              )
          )
          in_channels = hidden_channels
      self.convs.append(
          SAGEConv(
              hidden_channels, out_channels, aggregator_type="mean"
          )
      )
  
file_path: cugraph/benchmarks/cugraph-dgl/scale-benchmarks/model.py
imports: import torch
  import torch.nn.functional
  import time
label_desc: Use image node_id 1 to create a new GNN object from inherited base classes: torch.nn with example: obj = GNN(in_channels, hidden_channels, out_channels, num_layers, model_backend)