mnist_ds.map()

`mnist_ds.map()` is a method used in TensorFlow to apply a function to every element of a dataset. It is typically used to perform data preprocessing on a dataset before it is used to train a machine learning model.

For example, if you have a dataset of images and you want to normalize the pixel values to be between 0 and 1, you can use `mnist_ds.map()` to apply a normalization function to each image in the dataset. This function would take an image as input, normalize its pixel values, and return the normalized image.

Here's an example of using `mnist_ds.map()` to normalize the pixel values of images in the MNIST dataset:

def normalize(image, label):
    image = tf.cast(image, tf.float32) / 255.0
    return image, label

mnist_ds = mnist_ds.map(normalize)

In this example, the `normalize` function takes an image and a label as input, converts the image to a `float32` tensor, divides it by 255.0 to normalize the pixel values, and returns the normalized image and the original label. The `mnist_ds.map()` method applies this function to every image-label pair in the MNIST dataset.