Function final_layer_norm_modulation

src/models/heartmula/codec.cpp:876–896

    auto timestep_mod = modules::LinearModule({estimator_dim, 6 * estimator_dim, true, GGML_PREC_F32})
                            .build(ctx, modules::SiluModule{}.build(ctx, embedded_timestep), weights.adaln_single.linear);
    for (size_t index = 0; index < weights.transformer_blocks.size(); ++index) {
        const auto & block = weights.transformer_blocks[index];
        s = flow_transformer_block(ctx, s, timestep_mod, positions, block, config.num_attention_heads, config.attention_head_dim);
    }
    s = final_layer_norm_modulation(ctx, s, embedded_timestep, weights.scale_shift_table, estimator_dim);
    ggml_tensor * first_stage_state = s.tensor;

    auto x = modules::ConcatModule({2}).build(ctx, hidden_states, s);
    x = project_layer(ctx, x, weights.connection_proj, config.in_channels + estimator_dim, estimator_dim_2);
    auto embedded_timestep_2 = adaln_embedding(ctx, timesteps, freqs, weights.adaln_single_2, estimator_dim_2);
    auto timestep_mod_2 = modules::LinearModule({estimator_dim_2, 6 * estimator_dim_2, true, GGML_PREC_F32})
                              .build(ctx, modules::SiluModule{}.build(ctx, embedded_timestep_2), weights.adaln_single_2.linear);
    for (size_t index = 0; index < weights.transformer_blocks_2.size(); ++index) {
        const auto & block = weights.transformer_blocks_2[index];
        x = flow_transformer_block(ctx, x, timestep_mod_2, positions, block, config.num_attention_heads, config.attention_head_dim * 2);
    }
    x = final_layer_norm_modulation(ctx, x, embedded_timestep_2, weights.scale_shift_table_2, estimator_dim_2);
    return {project_layer(ctx, x, weights.proj_out, estimator_dim_2, config.out_channels), first_stage_state};
}

}  // namespace

class HeartCodecFlowEstimatorGraph {
public: