code: def __init__(self, input_dim, feature_dim=32, dropout=0.5):
      super(PGNN, self).__init__()
      self.dropout = nn.Dropout(dropout)
  
      self.linear_pre = nn.Linear(input_dim, feature_dim)
      self.conv_first = PGNN_layer(feature_dim, feature_dim)
      self.conv_out = PGNN_layer(feature_dim, feature_dim)
  
file_path: dgl/examples/pytorch/P-GNN/model.py
imports: import dgl.function
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new PGNN object from inherited base classes: nn with example: obj = PGNN(input_dim, feature_dim, dropout)