def METHOD_NAME():
    value = torch.rand(N, S, M, D).cuda() * 0.01
    sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
    attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
    attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
    im2col_step = 2
    output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu()
    output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu()
    fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)
    max_abs_err = (output_cuda - output_pytorch).abs().max()
    max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()
    print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')