 
 
 __global__
-void compute_lb_from_expr_conv_sparse(float_type* __restrict__ lb_array, const float_type* __restrict__ inf_coeffs, const float_type* __restrict__ sup_coeffs, const float_type* __restrict__ inf_csts, const float_type* __restrict__ input_inf, const float_type* __restrict__ input_sup, const int num_chunks, const int chunk_counter, const int output_size_x, const int output_size_y, const int output_size_z, int offset_x, int offset_y, int length_x, int length_y, int shift_x, int shift_y)
+void compute_lb_from_expr_conv_sparse(float_type* __restrict__ lb_array, const float_type* __restrict__ inf_coeffs, const float_type* __restrict__ sup_coeffs, const float_type* __restrict__ inf_csts, const float_type* __restrict__ input_inf, const float_type* __restrict__ input_sup, const int num_chunks, const int chunk_counter, const int output_size_x, const int output_size_y, const int output_size_z, const int offset_x, const int offset_y, const int length_x, const int length_y, const int shift_x, const int shift_y)
 {
     const int last_x = blockIdx.x;
     const int last_y = blockIdx.y;


    while(i < num_out_neurons_current_layer)
    {
        int a = n*num_out_neurons_current_layer + i;
        const float_type aux_cst = aux_csts[i];

        elina_double_interval_mul_cst_coeff_const_expr(&tmp1, &tmp2, expr_linf_coeff[a], expr_lsup_coeff[a], aux_cst);

}


void update_state_using_predecessor_layer(fppoly_internal_t* pr, fppoly_t* fp, float_type** linf_coeff, float_type** lsup_coeff, float_type** linf_cst, float_type** lsup_cst, float_type** uinf_coeff, float_type** usup_coeff, float_type** uinf_cst, float_type** usup_cst, float_type** linf_coeff_tmp, float_type** lsup_coeff_tmp, float_type** linf_cst_tmp, float_type** lsup_cst_tmp, float_type** uinf_coeff_tmp, float_type** usup_coeff_tmp, float_type** uinf_cst_tmp, float_type** usup_cst_tmp, const size_t k, const bool use_area_heuristic, const size_t num_out_neurons_last_layer)
{
    const size_t num_out_neurons_current_layer = fp->layers[k]->num_out_neurons;
    const size_t num_in_neurons_current_layer  = fp->layers[k]->num_in_neurons;
