def __init__()

in src/sagemaker/FD_SL_DGL/gnn_fraud_detection_dgl/pytorch_model.py [0:0]

• 12 lines of code
• 5 McCabe index (conditional complexity)

    def __init__(self, ntype_dict, etypes, in_size, hidden_size, out_size, n_layers, embedding_size):
        super(HeteroRGCN, self).__init__()
        # Use trainable node embeddings as featureless inputs.
        embed_dict = {ntype: nn.Parameter(torch.Tensor(num_nodes, in_size))
                      for ntype, num_nodes in ntype_dict.items() if ntype != 'target'}
        for key, embed in embed_dict.items():
            nn.init.xavier_uniform_(embed)
        self.embed = nn.ParameterDict(embed_dict)
        # create layers
        self.layers = nn.ModuleList()
        self.layers.append(HeteroRGCNLayer(embedding_size, hidden_size, etypes))
        # hidden layers
        for i in range(n_layers - 1):
            self.layers.append(HeteroRGCNLayer(hidden_size, hidden_size, etypes))

        # output layer
        self.layers.append(nn.Linear(hidden_size, out_size))