If I am not mistaken, they use an average pooling layer after convolutions. So the image data can go through the model without popping up errors. That is not to say that the comprehension of the model will be the same for all image sizes. It depends on the structure of the model.
For instance, a Conv2d layer with a given kernel may be able to capture a particular feature of the image important to classification at one size, but not when that same feature is much larger. (Read up on kernels to understand why.)
Due to the way neural networks learn, it’s not like there is a one size fits all answer to this. It may also be dataset dependent. But generally speaking, if you have enough compute resources, you should trying resizing beforehand and see what size works best for your application. Or if you don’t have those resources, just preprocess your images to the smallest size allowed, as that will also equate to less calculations/compute time during training or inference.