code: def __init__(self, in_dim, out_dim, dropout=0, use_GAT=False, K=1):
      super(GraphConv, self).__init__()
      self.in_dim = in_dim
      self.out_dim = out_dim
  
      if use_GAT:
          self.gcn_layer = GATConv(
              in_dim, out_dim, K, allow_zero_in_degree=True
          )
          self.bias = nn.Parameter(torch.Tensor(K, out_dim))
          init.constant_(self.bias, 0)
      else:
          self.gcn_layer = GraphConvLayer(in_dim, out_dim, bias=True)
  
      self.dropout = dropout
      self.use_GAT = use_GAT
  
file_path: dgl/examples/pytorch/hilander/models/graphconv.py
imports: import dgl.function
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import GATConv
  from torch.nn import init
label_desc: Use image node_id 1 to create a new GraphConv object from inherited base classes: nn with example: obj = GraphConv(in_dim, out_dim, dropout, use_GAT, K)