keras/src/datasets/mnist.py/load_data
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)