code: def run(self):
          
          dataset = self.source.train_dataset()
          num_samples = len(dataset)
  
          fig, ax = plt.subplots(self.cases, self.samples+1)
  
          selected_sample = np.sort(np.random.choice(num_samples, self.cases, replace=False))
  
          for i in range(self.cases):
              raw_image, _ = dataset.get_raw(selected_sample[i])
  
              ax[i, 0].imshow(raw_image)
              ax[i, 0].set_title("Original image")
  
              for j in range(self.samples):
                  augmented_image, _ = dataset[selected_sample[i]]
                  augmented_image = dataset.denormalize(augmented_image)
                  ax[i, j+1].imshow(augmented_image)
  
          plt.show()
docs: Run the visualization