src/sagemaker/FD_SL_DGL/code/fd_sl_deployment_entry_point.py [69:96]:
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class HeteroRGCNLayer(nn.Module):
    def __init__(self, in_size, out_size, etypes):
        super(HeteroRGCNLayer, self).__init__()
        # W_r for each relation
        self.weight = nn.ModuleDict({
                name: nn.Linear(in_size, out_size) for name in etypes
            })

    def forward(self, G, feat_dict):
        # The input is a dictionary of node features for each type
        funcs = {}
        for srctype, etype, dsttype in G.canonical_etypes:
            # Compute W_r * h
            if srctype in feat_dict:
                Wh = self.weight[etype](feat_dict[srctype])
                # Save it in graph for message passing
                G.nodes[srctype].data['Wh_%s' % etype] = Wh
                # Specify per-relation message passing functions: (message_func, reduce_func).
                funcs[etype] = (fn.copy_u('Wh_%s' % etype, 'm'), fn.mean('m', 'h'))
        # Trigger message passing of multiple types.
        G.multi_update_all(funcs, 'sum')
        # return the updated node feature dictionary
        return {ntype: G.nodes[ntype].data['h'] for ntype in G.ntypes if 'h' in G.nodes[ntype].data}


class HeteroRGCN(nn.Module):
    def __init__(self, ntype_dict, etypes, in_size, hidden_size, out_size, n_layers, embedding_size):
        super(HeteroRGCN, self).__init__()
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src/sagemaker/FD_SL_DGL/gnn_fraud_detection_dgl/pytorch_model.py [9:36]:
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class HeteroRGCNLayer(nn.Module):
    def __init__(self, in_size, out_size, etypes):
        super(HeteroRGCNLayer, self).__init__()
        # W_r for each relation
        self.weight = nn.ModuleDict({
                name: nn.Linear(in_size, out_size) for name in etypes
            })

    def forward(self, G, feat_dict):
        # The input is a dictionary of node features for each type
        funcs = {}
        for srctype, etype, dsttype in G.canonical_etypes:
            # Compute W_r * h
            if srctype in feat_dict:
                Wh = self.weight[etype](feat_dict[srctype])
                # Save it in graph for message passing
                G.nodes[srctype].data['Wh_%s' % etype] = Wh
                # Specify per-relation message passing functions: (message_func, reduce_func).
                funcs[etype] = (fn.copy_u('Wh_%s' % etype, 'm'), fn.mean('m', 'h'))
        # Trigger message passing of multiple types.
        G.multi_update_all(funcs, 'sum')
        # return the updated node feature dictionary
        return {ntype: G.nodes[ntype].data['h'] for ntype in G.ntypes if 'h' in G.nodes[ntype].data}


class HeteroRGCN(nn.Module):
    def __init__(self, ntype_dict, etypes, in_size, hidden_size, out_size, n_layers, embedding_size):
        super(HeteroRGCN, self).__init__()
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