A few years back, it was still common for machine learning toolkits to support only particular classes of models of a rather fixed and rigid structure, with only a few degrees of freedom (like the kernel of a support vector machine or the number of hidden layers of a neural network). Inspired by foundational work in Theano, toolkits like Microsoft's CNTK or Google's TensorFlow were among the first to popularize a more flexible view on models, as computation graphs with associated parameters that can be estimated from data. This view blurred the boundaries between popular types of models (such as DNNs or SVMs), as it became easy to blend the characteristics of each into your type of graph. Still, such a graph had to be defined upfront before estimating its parameters, and it was pretty static. This made it easy to save models to a self-contained bundle, like a TensorFlow SavedModel (such a bundle simply contains the structure of the graph, as well as the concrete values of the estimated parameters). However, debugging such models can be difficult because the statements in the Python code that build the graph are logically separate from the lines that execute it. Researchers also long for easier ways of expressing dynamic behavior, such as the computation steps of the forward pass of a model being conditionally dependent on its input data (or its previous output).