code: def load_data():
      """Loads the CIFAR10 dataset.
  
      This is a dataset of 50,000 32x32 color training images and 10,000 test
      images, labeled over 10 categories. See more info at the
      [CIFAR homepage](https://www.cs.toronto.edu/~kriz/cifar.html).
  
      The classes are:
  
      | Label | Description |
      |:-----:|-------------|
      |   0   | airplane    |
      |   1   | automobile  |
      |   2   | bird        |
      |   3   | cat         |
      |   4   | deer        |
      |   5   | dog         |
      |   6   | frog        |
      |   7   | horse       |
      |   8   | ship        |
      |   9   | truck       |
  
      Returns:
          Tuple of NumPy arrays: `(x_train, y_train), (x_test, y_test)`.
  
      **`x_train`**: `uint8` NumPy array of grayscale image data with shapes
        `(50000, 32, 32, 3)`, containing the training data. Pixel values range
        from 0 to 255.
  
      **`y_train`**: `uint8` NumPy array of labels (integers in range 0-9)
        with shape `(50000, 1)` for the training data.
  
      **`x_test`**: `uint8` NumPy array of grayscale image data with shapes
        `(10000, 32, 32, 3)`, containing the test data. Pixel values range
        from 0 to 255.
  
      **`y_test`**: `uint8` NumPy array of labels (integers in range 0-9)
        with shape `(10000, 1)` for the test data.
  
      Example:
  
      ```python
      (x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
      assert x_train.shape == (50000, 32, 32, 3)
      assert x_test.shape == (10000, 32, 32, 3)
      assert y_train.shape == (50000, 1)
      assert y_test.shape == (10000, 1)
      ```
      """
      dirname = "cifar-10-batches-py"
      origin = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
      path = get_file(
          fname=dirname,
          origin=origin,
          untar=True,
          file_hash=(  # noqa: E501
              "6d958be074577803d12ecdefd02955f39262c83c16fe9348329d7fe0b5c001ce"
          ),
      )
  
      num_train_samples = 50000
  
      x_train = np.empty((num_train_samples, 3, 32, 32), dtype="uint8")
      y_train = np.empty((num_train_samples,), dtype="uint8")
  
      for i in range(1, 6):
          fpath = os.path.join(path, "data_batch_" + str(i))
          (
              x_train[(i - 1) * 10000 : i * 10000, :, :, :],
              y_train[(i - 1) * 10000 : i * 10000],
          ) = load_batch(fpath)
  
      fpath = os.path.join(path, "test_batch")
      x_test, y_test = load_batch(fpath)
  
      y_train = np.reshape(y_train, (len(y_train), 1))
      y_test = np.reshape(y_test, (len(y_test), 1))
  
      if backend.image_data_format() == "channels_last":
          x_train = x_train.transpose(0, 2, 3, 1)
          x_test = x_test.transpose(0, 2, 3, 1)
  
      x_test = x_test.astype(x_train.dtype)
      y_test = y_test.astype(y_train.dtype)
  
      return (x_train, y_train), (x_test, y_test)
  
imports: import os
  import numpy
  from keras import backend
  from keras.api_export import keras_export
  from keras.datasets.cifar import load_batch
  from keras.utils.file_utils import get_file
label_desc: Use image node_id 1 for calling a global function with example usage: load_data() and returns: x_train,  y_train,  x_test,  y_test