code: def get_model(self, name="GraphSAGE"):
      if name != "GraphSAGE":
          raise ValueError("only GraphSAGE is currently supported")
  
      num_input_features = self.__dataset.num_input_features
      num_output_features = self.__dataset.num_labels
      num_layers = len(self.__loader_kwargs["num_neighbors"])
  
      with torch.cuda.device(self.__device):
          model = (
              CuGraphSAGE(
                  in_channels=num_input_features,
                  hidden_channels=64,
                  out_channels=num_output_features,
                  num_layers=num_layers,
              )
              .to(torch.float32)
              .to(self.__device)
          )
  
          model = ddp(model, device_ids=[self.__device])
          print("done creating model")
  
      return model
  
imports: from .trainers_pyg import PyGTrainer
  from models.pyg import CuGraphSAGE
  import torch
  import numpy
  from torch.nn.parallel import DistributedDataParallel
  from cugraph.gnn import FeatureStore
  from cugraph_pyg.data import CuGraphStore
  from cugraph_pyg.loader import BulkSampleLoader
  import os
label_desc: Use image node_id 9 for calling the PyGCuGraphTrainer obj's underlying member method code with example usage: obj.get_model(name) and returns: model