code: def generate_cifar100_batch(fname, num_examples):
      images = np.random.randint(
          256, size=(num_examples, HEIGHT * WIDTH * 3), dtype=np.uint8
      )
      fine_labels = np.random.randint(
          NUMBER_FINE_LABELS, size=(num_examples), dtype=np.uint8
      )
      coarse_labels = np.random.randint(
          NUMBER_COARSE_LABELS, size=(num_examples), dtype=np.uint8
      )
      labels = np.vstack((coarse_labels, fine_labels)).T
      dump(cifar100_output_dir(), fname, images, labels)
  
file_path: datasets/tensorflow_datasets/testing/fake_data_generation/cifar.py
imports: import os
  from absl import app
  from absl import flags
  import numpy
  import tensorflow_datasets
  from tensorflow_datasets.core import utils
  from tensorflow_datasets.testing import test_utils
label_desc: Use image node_id 4 for calling a global function with example usage: generate_cifar100_batch(fname, num_examples) without return types