import paddle_spline_conv

# Stacked SplineConv layers implemented in SConv
g = paddle_spline_conv.nn.GraphData(x, edge_index, edge_attr)
# Input n_features and output n_features
sconv = paddle_spline_conv.nn.SConv(10, 40)
sconv(g)

# Standalone SplineConv layer
paddle_spline_conv.nn.SplineConv(
    in_channels: int,
    out_channels: int,
    dim: int,
    kernel_size: int,
    is_open_spline: bool = True,
    degree: int = 1,
    aggr: str = 'mean',
    root_weight: bool = True,
    bias: bool = True
)

# Standalone SplineConv functional API
paddle_spline_conv.functional.spline_conv(
    x: paddle.Tensor, edge_index: paddle.Tensor,
    pseudo: paddle.Tensor, weight: paddle.Tensor,
    kernel_size: paddle.Tensor, is_open_spline: paddle.Tensor,
    degree: int = 1, aggr: str = 'mean',
    root_weight: Optional[paddle.Tensor] = None,
    bias: Optional[paddle.Tensor] = None
)

# SplineConv-specific operators
paddle_spline_conv.ops.spline_basis
paddle_spline_conv.ops.spline_weighting
paddle_spline_conv.ops.basis_kernel_1d