class_docstr: GCN Model
  
  Attributes:
      num_layers(int): num of gcn layers
      hidden_units(int): num of hidden units
      gcn_type(str): type of gcn layer, 'gcn' for GraphConv and 'sage' for SAGEConv
      pooling_type(str): type of graph pooling to get subgraph representation
                         'sum' for sum pooling and 'center' for center pooling.
      node_attributes(Tensor, optional): node attribute
      edge_weights(Tensor, optional): edge weight
      node_embedding(Tensor, optional): pre-trained node embedding
      use_embedding(bool, optional): whether to use node embedding. Note that if 'use_embedding' is set True
                           and 'node_embedding' is None, will automatically randomly initialize node embedding.
      num_nodes(int, optional): num of nodes
      dropout(float, optional): dropout rate
      max_z(int, optional): default max vocab size of node labeling, default 1000.
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code: def reset_parameters(self):
      for layer in self.layers:
          layer.reset_parameters()
  
file_path: dgl/examples/pytorch/seal/model.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import GraphConv, SAGEConv, SortPooling, SumPooling
label_desc: Use image node_id 2 for calling the GCN obj's underlying member method code with example usage: obj.reset_parameters() without return types