code: def load_data(path="mnist.npz"):
      """Loads the MNIST dataset.
  
      This is a dataset of 60,000 28x28 grayscale images of the 10 digits,
      along with a test set of 10,000 images.
      More info can be found at the
      [MNIST homepage](http://yann.lecun.com/exdb/mnist/).
  
      Args:
          path: path where to cache the dataset locally
              (relative to `~/.keras/datasets`).
  
      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. Pixel values range
        from 0 to 255.
  
      **`y_train`**: `uint8` NumPy array of digit 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. Pixel values range
        from 0 to 255.
  
      **`y_test`**: `uint8` NumPy array of digit labels (integers in range 0-9)
        with shape `(10000,)` for the test data.
  
      Example:
  
      ```python
      (x_train, y_train), (x_test, y_test) = keras.datasets.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:
  
      Yann LeCun and Corinna Cortes hold the copyright of MNIST dataset,
      which is a derivative work from original NIST datasets.
      MNIST dataset is made available under the terms of the
      [Creative Commons Attribution-Share Alike 3.0 license.](
          https://creativecommons.org/licenses/by-sa/3.0/)
      """
      origin_folder = (
          "https://storage.googleapis.com/tensorflow/tf-keras-datasets/"
      )
      path = get_file(
          fname=path,
          origin=origin_folder + "mnist.npz",
          file_hash=(  # noqa: E501
              "731c5ac602752760c8e48fbffcf8c3b850d9dc2a2aedcf2cc48468fc17b673d1"
          ),
      )
      with np.load(path, allow_pickle=True) as f:
          x_train, y_train = f["x_train"], f["y_train"]
          x_test, y_test = f["x_test"], f["y_test"]
  
          return (x_train, y_train), (x_test, y_test)
  
label_desc: Use image node_id 1 for calling a global function with example usage: load_data(path) and returns: x_train,  y_train,  x_test,  y_test