code: def visualize_reconstructions(autoencoder, x_test):
      n_samples = 4
      image_vectors = x_test[:n_samples, :]
      images = image_vectors * 255.0
      predicted_images = autoencoder.predict(image_vectors)
      predicted_images = predicted_images * 255.0
  
      # Compare real and reconstructed images
      fig, axes = plt.subplots(4, 2, figsize=(12, 8))
      iterator = zip(axes, images, predicted_images)
  
      for (left_ax, right_ax), real_image, predicted_image in iterator:
          real_image = real_image.reshape((28, 28))
          predicted_image = predicted_image.reshape((28, 28))
  
          left_ax.imshow(real_image, cmap=plt.cm.binary)
          right_ax.imshow(predicted_image, cmap=plt.cm.binary)
  
      plt.show()
  
imports: import numpy
  import matplotlib.pyplot
  from sklearn import datasets
  from neupy import algorithms, layers, utils
label_desc: Use image node_id 2 for calling a global function with example usage: visualize_reconstructions(autoencoder, x_test) without return types