code: def save_images(self, images, base_filename, flattened=True):
      self.ensure_dir(self.dirname)
      for i in self.saved_image_indices:
          label = self.labels[i].lower().replace(" ", "_")
          image = images[i, :].copy()
          if flattened:
              image = image.reshape(self.image_shape)
          image[np.isnan(image)] = 0
          figure = pylab.gcf()
          axes = pylab.gca()
          extra_kwargs = {}
          if self.color:
              extra_kwargs["cmap"] = "gray"
          assert (
              image.min() >= 0
          ), "Image can't contain negative numbers"
          if image.max() <= 1:
              image *= 256
          image[image > 255] = 255
          axes.imshow(image.astype("uint8"), **extra_kwargs)
          axes.get_xaxis().set_visible(False)
          axes.get_yaxis().set_visible(False)
          filename = base_filename + ".png"
          subdir = join(self.dirname, label)
          self.ensure_dir(subdir)
          path = join(subdir, filename)
          figure.savefig(path, bbox_inches="tight")
          self.saved_images[i][base_filename] = path
  
imports: from os import mkdir
  from os.path import exists, join
  from collections import defaultdict
  import pylab
  from sklearn.datasets import fetch_lfw_people
  from sklearn.impute import IterativeImputer
  import numpy
  from fancyimpute import SimpleFill, IterativeSVD, SoftImpute, BiScaler, KNN
  from fancyimpute.common import masked_mae, masked_mse
label_desc: Use image node_id 3 for calling the ResultsTable obj's underlying member method code with example usage: obj.save_images(images, base_filename, flattened) without return types