code: def download_dataset():
      print(
          "Downloading dataset of %d samples to:\n\t%s"
          % (DATASET_SIZE, DATASET_DIR)
      )
      print(
          "and corrupting the data (%d%% duplicates)"
          % (100 * CORRUPTION_RATE)
      )
  
      # if not empty, delete current contents
      etau.ensure_empty_dir(DATASET_DIR, cleanup=True)
  
      (_, _), (x_test, y_test) = cifar100.load_data(label_mode="fine")
  
      dataset_size = min(DATASET_SIZE, 10000)
  
      x = x_test[:dataset_size, :]
      y = y_test[:dataset_size, :]
  
      for i in range(x.shape[0]):
          if random.random() > 0.95:
              # pick a random sample 5% of the time
              idx = random.randint(0, x.shape[0])
          else:
              idx = i
  
          # get label
          fine_label = FINE_CLASSES[y[idx, 0]]
  
          # read image
          img = x[idx, :]
  
          rel_img_path = os.path.join(fine_label, "%d.jpg" % i)
          abs_img_path = os.path.join(DATASET_DIR, rel_img_path)
  
          foui.write(img, abs_img_path)
  
      print("Download successful")
  
imports: import os
  import random
  import eta.core.utils
  import fiftyone.core.utils
  import fiftyone.utils.image
  from tensorflow.keras.datasets import cifar100
label_desc: Use image node_id 1 for calling a global function with example usage: download_dataset() without return types