class_docstr: AutoSAGE. The model used in this automodel is GraphSAGE, i.e., the GraphSAGE from the `"Inductive Representation Learning on
  Large Graphs" <https://arxiv.org/abs/1706.02216>`_ paper. The layer is
  
  .. math::
  
      \mathbf{x}^{\prime}_i = \mathbf{W}_1 \mathbf{x}_i + \mathbf{W_2} \cdot
      \mathrm{mean}_{j \in \mathcal{N(i)}} \mathbf{x}_j
  
  Parameters
  ----------
  num_features: `int`.
      The dimension of features.
  
  num_classes: `int`.
      The number of classes.
  
  device: `torch.device` or `str`
      The device where model will be running on.
  
  init: `bool`.
      If True(False), the model will (not) be initialized.
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code: def _initialize(self):
      self._model = GraphSAGE(
          {
              "features_num": self.input_dimension,
              "num_class": self.output_dimension,
              **self.hyper_parameters,
          }
      ).to(self.device)
  
file_path: AutoGL/autogl/module/model/dgl/graphsage.py
imports: import torch
  import typing
  import torch.nn.functional
  from dgl.nn.pytorch.conv import SAGEConv
  import torch.nn.functional
  from .None import register_model
  from .base import BaseAutoModel, activate_func
  from ....utils import get_logger
label_desc: Use image node_id 2 for calling the AutoSAGE obj's underlying member method code with example usage: obj._initialize() without return types