def plot_most_correct(correct, classes, n_images, normalize=True):
    rows = int(np.sqrt(n_images)) ------ np.sqrt는 제곱근을 계산(0.5를 거듭제곱)
    cols = int(np.sqrt(n_images))
    fig = plt.figure(figsize=(25,20))
    for i in range(rows*cols):
        ax = fig.add_subplot(rows, cols, i+1) ------ 출력하려는 그래프 개수만큼 subplot을 만듭니다.
        image, true_label, probs = correct[i]
        image = image.permute(1, 2, 0) ------ ①
        true_prob = probs[true_label]
        correct_prob, correct_label = torch.max(probs, dim=0)
        true_class = classes[true_label]
        correct_class = classes[correct_label]

        if normalize: ------ 본래 이미지대로 출력하기 위해 normalize_image 함수 호출
            image = normalize_image(image)

        ax.imshow(image.cpu().numpy())
        ax.set_title(f'true label: {true_class} ({true_prob:.3f})\n' \
                     f'pred label: {correct_class} ({correct_prob:.3f})')
        ax.axis('off')

    fig.subplots_adjust(hspace=0.4)