code: def _load_data(path, labels_number=1):
      """Yields (labels, np_image) tuples."""
      with tf.io.gfile.GFile(path, "rb") as f:
          data = f.read()
      offset = 0
      max_offset = len(data) - 1
      while offset < max_offset:
          labels = np.frombuffer(
              data, dtype=np.uint8, count=labels_number, offset=offset
          ).reshape((labels_number,))
          # 1 byte per label, 1024 * 3 = 3072 bytes for the image.
          offset += labels_number
          img = (
              np.frombuffer(
                  data, dtype=np.uint8, count=3072, offset=offset
              )
              .reshape((3, _CIFAR_IMAGE_SIZE, _CIFAR_IMAGE_SIZE))
              .transpose((1, 2, 0))
          )
          offset += 3072
          yield labels, img
  
file_path: datasets/tensorflow_datasets/image_classification/cifar.py
imports: import collections
  import os
  from etils import epath
  import numpy
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 for calling a global function with example usage: _load_data(path, labels_number) without return types