def METHOD_NAME(self, adj, feat):
    r"""
    Description
    -----------
    Compute (Dense) Graph Convolution layer.
    Parameters
    ----------
    adj : mxnet.NDArray
        The adjacency matrix of the graph to apply Graph Convolution on, when
        applied to a unidirectional bipartite graph, ``adj`` should be of shape
        should be of shape :math:`(N_{out}, N_{in})`; when applied to a homo
        graph, ``adj`` should be of shape :math:`(N, N)`. In both cases,
        a row represents a destination node while a column represents a source
        node.
    feat : mxnet.NDArray
        The input feature.
    Returns
    -------
    mxnet.NDArray
        The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
        is size of output feature.
    """
    adj = adj.astype(feat.dtype).as_in_context(feat.context)
    src_degrees = nd.clip(adj.sum(axis=0), a_min=1, a_max=float("inf"))
    dst_degrees = nd.clip(adj.sum(axis=1), a_min=1, a_max=float("inf"))
    feat_src = feat
    if self._norm == "both":
        norm_src = nd.power(src_degrees, -0.5)
        shp_src = norm_src.shape + (1,) * (feat.ndim - 1)
        norm_src = norm_src.reshape(shp_src).as_in_context(feat.context)
        feat_src = feat_src * norm_src
    if self._in_feats > self._out_feats:
        # mult W first to reduce the feature size for aggregation.
        feat_src = nd.dot(feat_src, self.weight.data(feat_src.context))
        rst = nd.dot(adj, feat_src)
    else:
        # aggregate first then mult W
        rst = nd.dot(adj, feat_src)
        rst = nd.dot(rst, self.weight.data(feat_src.context))
    if self._norm != "none":
        if self._norm == "both":
            norm_dst = nd.power(dst_degrees, -0.5)
        else:  # right
            norm_dst = 1.0 / dst_degrees
        shp_dst = norm_dst.shape + (1,) * (feat.ndim - 1)
        norm_dst = norm_dst.reshape(shp_dst).as_in_context(feat.context)
        rst = rst * norm_dst
    if self.bias is not None:
        rst = rst + self.bias.data(feat.context)
    if self._activation is not None:
        rst = self._activation(rst)
    return rst