Replace rgb values of image by a new color with the same shades as earlier in python

Given an image, I'd like to change the rgb values of the most dominant colors by the same gradient.

I have read an image and performed k means clustering on it in order to obtain the most color values below -

import scipy.cluster
import sklearn.cluster
import numpy
from PIL import Image

def dominant_colors(image):  # PIL image input

    image = image.resize((150, 150))      # optional, to reduce time
    ar = numpy.asarray(image)
    shape = ar.shape
    ar = ar.reshape(numpy.product(shape[:2]), shape[2]).astype(float)

    kmeans = sklearn.cluster.MiniBatchKMeans(
        n_clusters=10,
        init="k-means++",
        compute_labels=True,
        max_iter=20,
        random_state=1000,
    ).fit(ar)
   # print(kmeans)

    codes = kmeans.cluster_centers_

    vecs, _dist = scipy.cluster.vq.vq(ar, codes)  
    
    counts, _bins = numpy.histogram(vecs, len(codes))  
   # print(counts)
      # count occurrences

    colors = []
    for index in numpy.argsort(counts)[::-1]:
        colors.append(tuple([int(code) for code in codes[index]]))
    return colors       

This gives me the cluster centroids of the top 10 most dominant colors -

[(222, 187, 108),
 (231, 209, 138),
 (203, 158, 68),
 (37, 34, 20),
 (227, 237, 232),
 (184, 195, 187),
 (147, 103, 32),
 (173, 158, 109),
 (92, 93, 71),
 (139, 153, 150)]

I have also obtained the cluster values of each value via kmeans.labels_

Now I'd like to change all values of my first cluster to red. However I dont want all pixels to be a monotonous red, I want them to be spread in the same variation/gradient as they were originally.

For example cluster 1 represent shades of the color beige, I want to now represent shades of red in the same variation.