def METHOD_NAME() -> None:
    from torch_cluster import radius
    from e3nn.math import soft_one_hot_linspace
    conv = Convolution(
        irreps_node_input="0e + 1e",
        irreps_node_output="0e + 1e",
        irreps_node_attr_input="2x0e",
        irreps_node_attr_output="3x0e",
        irreps_edge_attr="0e + 1e",
        num_edge_scalar_attr=4,
        radial_layers=1,
        radial_neurons=50,
        num_neighbors=3.0,
    )
    pos_in = torch.randn(5, 3)
    pos_out = torch.randn(2, 3)
    node_input = torch.randn(5, 4)
    node_attr_input = torch.randn(5, 2)
    node_attr_output = torch.randn(2, 3)
    edge_src, edge_dst = radius(pos_out, pos_in, r=2.0)
    edge_vec = pos_in[edge_src] - pos_out[edge_dst]
    edge_attr = o3.spherical_harmonics([0, 1], edge_vec, True)
    edge_scalar_attr = soft_one_hot_linspace(
        x=edge_vec.norm(dim=1), start=0.0, end=2.0, number=4, basis="smooth_finite", cutoff=True
    )
    conv(node_input, node_attr_input, node_attr_output, edge_src, edge_dst, edge_attr, edge_scalar_attr)