def __init__(self, data_info: dict, embed_size: int = -1, bias=True, k=2):
        """Simplifying Graph Convolutional Networks

        Edge feature is ignored in this model.

        Parameters
        ----------
        data_info : dict
            The information about the input dataset.
        embed_size : int
            The dimension of created embedding table. -1 means using original node embedding
        bias : bool
            If True, adds a learnable bias to the output. Default: ``True``.
        k : int
            Number of hops :math:`K`. Defaults:``1``.
        """
        super().__init__()
        self.data_info = data_info
        self.out_size = data_info["out_size"]
        self.embed_size = embed_size
        if embed_size > 0:
            self.embed = nn.Embedding(data_info["num_nodes"], embed_size)
            in_size = embed_size
        else:
            in_size = data_info["in_size"]
        self.sgc = SGConv(
            in_size,
            self.out_size,
            k=k,
            cached=True,
            bias=bias,
            norm=self.normalize,
        )
Simplifying Graph Convolutional Networks

        Edge feature is ignored in this model.

        Parameters
        ----------
        data_info : dict
            The information about the input dataset.
        embed_size : int
            The dimension of created embedding table. -1 means using original node embedding
        bias : bool
            If True, adds a learnable bias to the output. Default: ``True``.
        k : int
            Number of hops :math:`K`. Defaults:``1``.
        
dglgo/dglgo/model/node_encoder/sgc.py