How to use the miscnn.utils.visualizer.visualize_evaluation function in miscnn

# Predict the sample with the provided model
        model.predict([sample_index], return_output=False)
        # Load the sample
        sample = model.preprocessor.data_io.sample_loader(sample_index,
                                                          load_seg=True,
                                                          load_pred=True)
        # Access image, truth and predicted segmentation data
        img, seg, pred = sample.img_data, sample.seg_data, sample.pred_data
        # Calculate classwise dice score
        dice_scores = compute_dice(seg, pred, len(classes))
        # Save detailed validation scores to file
        scores = [sample_index]
        scores.extend(dice_scores)
        backup_evaluation(scores, evaluation_path, start=False)
        # Visualize the truth and prediction segmentation
        visualize_evaluation(sample_index, img, seg, pred, evaluation_path)

score_kidney, score_tumor = kits19_score(truth, pred)
        # Save kits19 score to file
        save_evaluation([id, score_kidney, score_tumor],
                        config["evaluation_path"],
                        "kits19_scoring." + suffix + ".tsv")
        # Calculate class frequency per slice
        if config["class_freq"]:
            class_freq = calc_ClassFrequency(truth, pred)
            for i in range(len(class_freq)):
                print(str(id) + "\t" + str(i) + "\t" + str(class_freq[i]))
        # Visualize the truth and prediction segmentation
        if config["visualize"]:
            # Load the volume
            vol = load_volume_nii(id, config["data_path"]).get_data()
            # Run visualization
            visualize_evaluation(id, vol, truth, pred, config["evaluation_path"])