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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def imagenet50(display=False, resolution=224):
 """ This is a set of 50 images representative of ImageNet images. This dataset was collected by randomly finding a working ImageNet link and then pasting the original ImageNet image into Google image search restricted to images licensed for reuse. A similar image (now with rights to reuse) was downloaded as a rough replacment for the original ImageNet image. The point is to have a random sample of ImageNet for use as a background distribution for explaining models trained on ImageNet data. Note that because the images are only rough replacements the labels might no longer be correct. """


    prefix = github_data_url + "imagenet50_"
    X = np.load(cache(prefix + "%sx%s.npy" % (resolution, resolution))).astype(np.float32)
    y = np.loadtxt(cache(prefix + "labels.csv"))
    return X, y