code: def imagenet50(display=False, resolution=224):
      
  
      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
docs: 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.