Quantized feature maps with Kmeans, then visualize them on the original image

I extracted feature maps using ResNet, then quantized (segmented) using Kmeans. Now I want to visualize the quantized feature maps (labels) on the input image using Kmeans. Does anyone have an idea how I can do this?

 model = models.resnet18(weights='ResNet18_Weights.DEFAULT')
    model_children = list(model.children())
    feature_extractor = torch.nn.Sequential(\*list(model.children())\[:-2\])

    feature_extractor.eval()

    image_path = 'image.jpg'
    transform = transforms.Compose(\[
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=0., std=1.)
    \])
    image = transform(Image.open(image_path)).unsqueeze(0)

    with torch.no_grad():
    feature_maps = feature_extractor(image)

    feature = feature_maps.squeeze(0)
    feature = feature.view(512, -1)
    feature = feature.detach().numpy()
    feature= np.transpose(feature)

    \#Kmeans Algorithm
    num_clusters = 10
    kmeans = KMeans(n_clusters=num_clusters,n_init='auto', random_state=0).fit(feature)

    labels = kmeans.labels\_
    labels= labels.reshape(7,7)
    plt.imshow(labels)
    plt.show()

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