code: def load_data():
      """Loads the Fashion-MNIST dataset.
  
      This is a dataset of 60,000 28x28 grayscale images of 10 fashion categories,
      along with a test set of 10,000 images. This dataset can be used as
      a drop-in replacement for MNIST.
  
      The classes are:
  
      | Label | Description |
      |:-----:|-------------|
      |   0   | T-shirt/top |
      |   1   | Trouser     |
      |   2   | Pullover    |
      |   3   | Dress       |
      |   4   | Coat        |
      |   5   | Sandal      |
      |   6   | Shirt       |
      |   7   | Sneaker     |
      |   8   | Bag         |
      |   9   | Ankle boot  |
  
      Returns:
  
      Tuple of NumPy arrays: `(x_train, y_train), (x_test, y_test)`.
  
      **`x_train`**: `uint8` NumPy array of grayscale image data with shapes
        `(60000, 28, 28)`, containing the training data.
  
      **`y_train`**: `uint8` NumPy array of labels (integers in range 0-9)
        with shape `(60000,)` for the training data.
  
      **`x_test`**: `uint8` NumPy array of grayscale image data with shapes
        (10000, 28, 28), containing the test data.
  
      **`y_test`**: `uint8` NumPy array of labels (integers in range 0-9)
        with shape `(10000,)` for the test data.
  
      Example:
  
      ```python
      (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
      assert x_train.shape == (60000, 28, 28)
      assert x_test.shape == (10000, 28, 28)
      assert y_train.shape == (60000,)
      assert y_test.shape == (10000,)
      ```
  
      License:
  
      The copyright for Fashion-MNIST is held by Zalando SE.
      Fashion-MNIST is licensed under the [MIT license](
          https://github.com/zalandoresearch/fashion-mnist/blob/master/LICENSE).
      """
      dirname = os.path.join("datasets", "fashion-mnist")
      base = (
          "https://storage.googleapis.com/tensorflow/tf-keras-datasets/"
      )
      files = [
          "train-labels-idx1-ubyte.gz",
          "train-images-idx3-ubyte.gz",
          "t10k-labels-idx1-ubyte.gz",
          "t10k-images-idx3-ubyte.gz",
      ]
  
      paths = []
      for fname in files:
          paths.append(
              get_file(fname, origin=base + fname, cache_subdir=dirname)
          )
  
      with gzip.open(paths[0], "rb") as lbpath:
          y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)
  
      with gzip.open(paths[1], "rb") as imgpath:
          x_train = np.frombuffer(
              imgpath.read(), np.uint8, offset=16
          ).reshape(len(y_train), 28, 28)
  
      with gzip.open(paths[2], "rb") as lbpath:
          y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)
  
      with gzip.open(paths[3], "rb") as imgpath:
          x_test = np.frombuffer(
              imgpath.read(), np.uint8, offset=16
          ).reshape(len(y_test), 28, 28)
  
      return (x_train, y_train), (x_test, y_test)
  
imports: import gzip
  import os
  import numpy
  from keras.api_export import keras_export
  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