hugegraph-ml/src/hugegraph_ml/models/seal.py [91:122]:
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        self.use_attribute = False if node_attributes is None else True
        self.use_embedding = use_embedding
        self.use_edge_weight = False if edge_weights is None else True

        self.z_embedding = nn.Embedding(max_z, hidden_units)
        if node_attributes is not None:
            self.node_attributes_lookup = nn.Embedding.from_pretrained(node_attributes)
            self.node_attributes_lookup.weight.requires_grad = False
        if edge_weights is not None:
            self.edge_weights_lookup = nn.Embedding.from_pretrained(edge_weights)
            self.edge_weights_lookup.weight.requires_grad = False
        if node_embedding is not None:
            self.node_embedding = nn.Embedding.from_pretrained(node_embedding)
            self.node_embedding.weight.requires_grad = False
        elif use_embedding:
            self.node_embedding = nn.Embedding(num_nodes, hidden_units)

        initial_dim = hidden_units
        if self.use_attribute:
            initial_dim += self.node_attributes_lookup.embedding_dim
        if self.use_embedding:
            initial_dim += self.node_embedding.embedding_dim

        self.layers = nn.ModuleList()
        if gcn_type == "gcn":
            self.layers.append(
                GraphConv(initial_dim, hidden_units, allow_zero_in_degree=True)
            )
            for _ in range(num_layers - 1):
                self.layers.append(
                    GraphConv(hidden_units, hidden_units, allow_zero_in_degree=True)
                )
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hugegraph-ml/src/hugegraph_ml/models/seal.py [224:256]:
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        self.use_attribute = False if node_attributes is None else True
        self.use_embedding = use_embedding
        self.use_edge_weight = False if edge_weights is None else True

        self.z_embedding = nn.Embedding(max_z, hidden_units)

        if node_attributes is not None:
            self.node_attributes_lookup = nn.Embedding.from_pretrained(node_attributes)
            self.node_attributes_lookup.weight.requires_grad = False
        if edge_weights is not None:
            self.edge_weights_lookup = nn.Embedding.from_pretrained(edge_weights)
            self.edge_weights_lookup.weight.requires_grad = False
        if node_embedding is not None:
            self.node_embedding = nn.Embedding.from_pretrained(node_embedding)
            self.node_embedding.weight.requires_grad = False
        elif use_embedding:
            self.node_embedding = nn.Embedding(num_nodes, hidden_units)

        initial_dim = hidden_units
        if self.use_attribute:
            initial_dim += self.node_attributes_lookup.embedding_dim
        if self.use_embedding:
            initial_dim += self.node_embedding.embedding_dim

        self.layers = nn.ModuleList()
        if gcn_type == "gcn":
            self.layers.append(
                GraphConv(initial_dim, hidden_units, allow_zero_in_degree=True)
            )
            for _ in range(num_layers - 1):
                self.layers.append(
                    GraphConv(hidden_units, hidden_units, allow_zero_in_degree=True)
                )
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