def METHOD_NAME(test_datagen, model):
    gen = test_datagen.flow_from_directory(
        config.test_path,
        target_size=(config.img_size, config.img_size),
        batch_size=config.batch_size,
        shuffle=False,
    )
    class_cols = []
    class_names = []
    for class_col, _ in sorted(gen.class_indices.items(), key=lambda c_i: c_i[1]):
        class_cols.append(class_col)
        class_names.append(class_col.replace("_", " "))
    cards = []
    true_class_is = []
    true_classes = []
    true_probs = []
    pred_classes = []
    pred_probs = []
    class_probs = [[] for c in class_names]
    num_batches = int(math.ceil(len(gen.filenames) / float(gen.batch_size)))
    # num_batches = 1
    for batch_i in range(num_batches):
        examples, truth = next(gen)
        preds = model.predict(np.stack(examples))
        this_true_class_is = [np.argmax(probs) for probs in truth]
        true_class_is.extend(this_true_class_is)
        true_classes.extend(class_names[i] for i in this_true_class_is)
        true_probs.extend(ps[i] for ps, i in zip(preds, true_class_is))
        pred_classes.extend(class_names[np.argmax(probs)] for probs in preds)
        pred_probs.extend(np.max(probs) for probs in preds)
        for cp, p in zip(class_probs, preds.T):
            cp.extend(p)
        base_i = batch_i * gen.batch_size
        for i in range(base_i, base_i + len(examples)):
            cards.append(
                f"""```Predicted: