You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: eval_anfis_seq2seq_model.m
    path of file: ./repos/Emotional-Video-to-Audio-with-ANFIS-DeepRNN/scripts/model
    
    the code of the file until where you have to start completion: function [out_final, final_audio_reshaped, audio_filename, predicted_audio_filename] = eval_anfis_seq2seq_model(Xin_HSL_test, Xout_HSL_test, TLR_database, audio_database, audio_filename)
%Evaluate full model ANFIS-Seq2Seq
%   Given a set of visual features, generate music
%   Look up table: TLR_database and audio_database (also named: output_TLR
%   and output_audio_equivalent)

    global results_path model_anfis_save_path model_seq2seq_save_path model_type emotion_dim nummfs;

    load(strcat([model_anfis_save_path, 'fuzzy_anfis_HSL_', emotion_dim, 'mos_', num2str(nummfs), 'mfs_ep4000']));

    
function [out_final, final_audio_reshaped, audio_filename, predicted_audio_filename] = eval_anfis_seq2seq_model(Xin_HSL_test, Xout_HSL_test, TLR_database, audio_database, audio_filename)
%Evaluate full model ANFIS-Seq2Seq
%   Given a set of visual features, generate music
%   Look up table: TLR_database and audio_database (also named: output_TLR
%   and output_audio_equivalent)

    global results_path model_anfis_save_path model_seq2seq_save_path model_type emotion_dim nummfs;

    load(strcat([model_anfis_save_path, 'fuzzy_anfis_HSL_', emotion_dim, 'mos_', num2str(nummfs), 'mfs_ep4000']));
    out_fis_train_HSL = out_fis_train;
    % load(strcat([model_anfis_save_path, 'fuzzy_anfis_TLR_', emotion_dim, 'mos_', num2str(nummfs), 'mfs_ep4000']));

    if strcmp(emotion_dim, '2d')
        path_to_model_pos_high = strcat([model_seq2seq_save_path, 'net_', model_type, '_pos_high_', emotion_dim, 'mos_12']);
        path_to_model_pos_low = strcat([model_seq2seq_save_path, 'net_', model_type, '_pos_low_', emotion_dim, 'mos_12']);
        path_to_model_neg_high = strcat([model_seq2seq_save_path, 'net_', model_type, '_neg_high_', emotion_dim, 'mos_12']);
        path_to_model_neg_low = strcat([model_seq2seq_save_path, 'net_', model_type, '_neg_low_', emotion_dim, 'mos_12']);
    else
        path_to_model_pos = strcat([model_seq2seq_save_path, 'net_', model_type, '_pos_', emotion_dim, 'mos_12']);
        path_to_model_neg = strcat([model_seq2seq_save_path, 'net_', model_type, '_neg_', emotion_dim, 'mos_12']);
    end