code: def load_data(label_mode="fine"):
      """Loads the CIFAR100 dataset.
  
      This is a dataset of 50,000 32x32 color training images and
      10,000 test images, labeled over 100 fine-grained classes that are
      grouped into 20 coarse-grained classes. See more info at the
      [CIFAR homepage](https://www.cs.toronto.edu/~kriz/cifar.html).
  
      Args:
          label_mode: one of `"fine"`, `"coarse"`.
              If it is `"fine"`, the category labels
              are the fine-grained labels, and if it is `"coarse"`,
              the output labels are the coarse-grained superclasses.
  
      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 0-99)
        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 0-99)
        with shape `(10000, 1)` for the test data.
  
      Example:
  
      ```python
      (x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.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)
      ```
      """
      if label_mode not in ["fine", "coarse"]:
          raise ValueError(
              '`label_mode` must be one of `"fine"`, `"coarse"`. '
              f"Received: label_mode={label_mode}."
          )
  
      dirname = "cifar-100-python"
      origin = (
          "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
      )
      path = get_file(
          fname=dirname,
          origin=origin,
          untar=True,
          file_hash=(  # noqa: E501
              "85cd44d02ba6437773c5bbd22e183051d648de2e7d6b014e1ef29b855ba677a7"
          ),
      )
  
      fpath = os.path.join(path, "train")
      x_train, y_train = load_batch(
          fpath, label_key=label_mode + "_labels"
      )
  
      fpath = os.path.join(path, "test")
      x_test, y_test = load_batch(
          fpath, label_key=label_mode + "_labels"
      )
  
      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)
  
      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(label_mode) and returns: x_train,  y_train,  x_test,  y_test