code: def __init__(self, in_dim, out_dim, activation=None):
      super(GraphConv, self).__init__()
      self.in_dim = in_dim
      self.out_dim = out_dim
      self.activation = activation
      self.weight = nn.Parameter(torch.Tensor(in_dim, out_dim))
      self.bias = nn.Parameter(torch.Tensor(out_dim))
      nn.init.xavier_normal_(self.weight)
      nn.init.zeros_(self.bias)
  
file_path: dgl/benchmarks/benchmarks/model_acc/bench_gcn_udf.py
imports: import dgl
  import torch
  import torch.nn
  import torch.nn.functional
  from ..None import utils
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, activation)