How to use the molgrid.defaultGninaLigandTyper.num_types function in molgrid

def test_coords2grid():
    gmaker = molgrid.GridMaker(resolution=0.5,
                           dimension=23.5,
                           radius_scale=1,
                           radius_type_indexed=True)
    n_types = molgrid.defaultGninaLigandTyper.num_types()
    radii = np.array(list(molgrid.defaultGninaLigandTyper.get_type_radii()),np.float32)
    dims = gmaker.grid_dimensions(n_types)
    grid_size = dims[0] * dims[1] * dims[2] * dims[3]

    c2grid = molgrid.Coords2Grid(gmaker, center=(0,0,0))
    n_atoms = 2
    batch_size = 1
    coords = nn.Parameter(torch.randn(n_atoms, 3,device='cuda'))
    types = nn.Parameter(torch.randn(n_atoms, n_types+1,device='cuda'))
    
    coords.data[0,:] = torch.tensor([ 1,0,0])
    coords.data[1,:] = torch.tensor([-1,0,0])
    types.data[...] = 0
    types.data[:,10] = 1

    batch_radii = torch.tensor(np.tile(radii, (batch_size, 1)), dtype=torch.float32,  device='cuda')

def test_make_vector_types_ex_provider(capsys):
    fname = datadir+"/ligonly.types"
    e = molgrid.ExampleProvider(molgrid.NullIndexTyper(),molgrid.defaultGninaLigandTyper, data_root=datadir+"/structs",make_vector_types=True)
    e.populate(fname)
    batch_size = 10
    b = e.next_batch(batch_size)

    gmaker = molgrid.GridMaker(dimension=23.5,radius_type_indexed=True)
    shape = gmaker.grid_dimensions(molgrid.defaultGninaLigandTyper.num_types())
    mgrid = molgrid.MGrid5f(batch_size,*shape)

    c = b[0].merge_coordinates()
    tv = c.type_vector.tonumpy()
    assert tv.shape == (10,14) 
    assert tv[0].sum() == 1.0
    assert tv[0][8] == 1.0
    
    
    e2 = molgrid.ExampleProvider(data_root=datadir+"/structs",make_vector_types=True)
    e2.populate(fname)
    b2 = e2.next_batch(batch_size)
    c2 = b2[0].merge_coordinates(unique_index_types=True)
    tv2 = c2.type_vector.tonumpy()
    assert tv2.shape == (10,28)