def METHOD_NAME(shape, layout, padding, target, dev, dtype):
    batch, in_channels, in_width, filters, kernel_size, stride, dilation, groups = shape
    if layout == "NCW":
        in_shape = [batch, in_channels, in_width]
        kernel_shape = [filters, in_channels // groups, kernel_size]
    else:
        in_shape = [batch, in_width, in_channels]
        kernel_shape = [kernel_size, in_channels // groups, filters]
    # reference data
    a_np = np.random.uniform(size=in_shape).astype(dtype)
    w_np = np.random.uniform(size=kernel_shape).astype(dtype)
    if layout == "NWC":
        np_in = np.transpose(a_np, [0, 2, 1])
        np_w = np.transpose(w_np, [2, 1, 0])
    else:
        np_in = a_np
        np_w = w_np
    b_np = tvm.topi.testing.group_conv1d_ncw_python(np_in, np_w, stride, padding, dilation, groups)
    if layout == "NWC":
        b_np = np.transpose(b_np, [0, 2, 1])
    A = te.placeholder(in_shape, name="A", dtype=dtype)
    W = te.placeholder(kernel_shape, name="W", dtype=dtype)
    fcompute, fschedule = tvm.topi.testing.dispatch(target, _group_conv1d_implementations[layout])
    with tvm.target.Target(target):
        B = fcompute(A, W, stride, padding, dilation, groups, "float32")
        s = fschedule([B])
    a = tvm.nd.array(a_np, dev)
    w = tvm.nd.array(w_np, dev)
    b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), dev)
    print(tvm.lower(s, [A, W, B], target))
    func = tvm.build(s, [A, W, B], target)
    func(a, w, b)
    tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-5)