               weights, pair, indices_kernel_num, arch, out_features_real.dim(0),
               inverse, is_subm,
               static_cast<int>(tv::gemm::SparseConvAlgo::kNative),
-              reinterpret_cast<std::uintptr_t>(stream), bias, 1.0,
-              /*bias alpha, only used for leaky relu*/, 0.0,
-              tv::gemm::Activation::kReLU);
+              reinterpret_cast<std::uintptr_t>(stream), bias,
+              1.0
+              /*bias alpha, only used for leaky relu*/,
+              0.0, tv::gemm::Activation::kReLU);
         } else {
           // regular conv use numbers in indices_kernel_num to perform gemm
           // so we don't need to slice.

        int num_act_out_real = std::get<1>(pair_res);

        tv::Tensor out_features =
            tv::empty({is_subm ? real_num_act_in : out_inds_num_limit, 64},
                      tv::float16, 0);
        auto out_features_real =
            out_features.slice_first_axis(0, num_act_out_real);

        // real_pair_size) this valid for pair_fwd, pair_bwd, pair_mask_fwd,
        // pair_mask_bwd, mask_argsort_fwd, mask_argsort_bwd.
        int pair_fwd_size_padded =
            is_subm ? static_num_act_in : out_inds_num_limit;
        tv::Tensor pair_fwd_padded =
            tv::empty({KV, pair_fwd_size_padded}, tv::int32, 0);
        // you can find equivalent python code of following code in python
