code: def __init__(
      self,
      num_features: int = ...,
      num_classes: int = ...,
      device: _typing.Union[str, torch.device] = ...,
      init: bool = False,
      **kwargs,
  ):
      super(GraphSAINTAggregationModel, self).__init__(
          num_features, num_classes, device=device, init=init, **kwargs
      )
  
file_path: AutoGL/autogl/module/model/pyg/graph_saint.py
imports: import typing
  import torch.nn.functional
  from torch_geometric.nn.conv import MessagePassing
  from torch_sparse import SparseTensor, matmul
  from .None import register_model
  from .base import ClassificationModel, ClassificationSupportedSequentialModel
label_desc: Use image node_id 1 to create a new GraphSAINTAggregationModel object from inherited base classes: ClassificationModel with example: obj = GraphSAINTAggregationModel(num_features, num_classes, device, init)