A TensorFlow demo that guides users through building an image classification application using the ResNet32 network and the CIFAR10 dataset. The demo consists of three main components: Inputter, Modeler, and Runner. The Inputter encapsulates the data pipeline into a function `input_fn`, which reads data from disk, shuffles, preprocesses, creates batches, and prefetches. The Modeler encapsulates the forward pass and computation of loss, gradient, and accuracy in the `model_fn` method. The Runner orchestrates the execution of the Inputter and Modeler, distributes the workload across multiple hardware devices, and uses callbacks to perform auxiliary tasks such as logging statistics and saving trained models. The demo includes an example code snippet that demonstrates how to create these components and run the application.