src/sagemaker/FD_SL_DGL/code/fd_sl_deployment_entry_point.py [99:113]:
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                      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))

    def forward(self, g, features):
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src/sagemaker/FD_SL_DGL/gnn_fraud_detection_dgl/pytorch_model.py [39:53]:
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                      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))

    def forward(self, g, features):
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