This code uses the MNIST dataset, which contains images of handwritten digits and their corresponding labels.
The neural network is trained on this dataset to classify the images into their corresponding digits (0-9).

The code first loads the dataset and normalizes the pixel values. 
It then defines the neural network architecture, which consists of a flatten layer to convert the input images into a 1D array, 
a dense layer with 128 neurons and ReLU activation function, and a dense output layer with 10 neurons and softmax activation function. 

The model is compiled with the Adam optimizer and sparse categorical cross-entropy loss function.

The model is then trained on the training data for 5 epochs. Finally, the model is evaluated on the test data, and the accuracy is printed out.