code: def save_imgs(self, epoch):
      r, c = 5, 5
      noise = np.random.normal(0, 1, (r * c, 100))
      gen_imgs = self.generator.predict(noise)
  
      # Rescale images 0 - 1 (from -1 to 1)
      gen_imgs = 0.5 * (gen_imgs + 1)
  
      fig, axs = plt.subplots(r, c)
      plt.suptitle("Deep Convolutional Generative Adversarial Network")
      cnt = 0
      for i in range(r):
          for j in range(c):
              axs[i, j].imshow(gen_imgs[cnt, 0, :, :], cmap="gray")
              axs[i, j].axis("off")
              cnt += 1
      fig.savefig("mnist_%d.png" % epoch)
      plt.close()
  
file_path: ML-From-Scratch/mlfromscratch/unsupervised_learning/dcgan.py
imports: from __future__ import print_function, division
  import matplotlib.pyplot
  import numpy
  import progressbar
  from sklearn.datasets import fetch_mldata
  from mlfromscratch.deep_learning.optimizers import Adam
  from mlfromscratch.deep_learning.loss_functions import CrossEntropy
  from mlfromscratch.deep_learning.layers import Dense, Dropout, Flatten, Activation, Reshape, BatchNormalization, ZeroPadding2D, Conv2D, UpSampling2D
  from mlfromscratch.deep_learning import NeuralNetwork
label_desc: Use image node_id 5 for calling the DCGAN obj's underlying member method code with example usage: obj.save_imgs(epoch) without return types