 These 300 lines of code create the transformer neural network. The transformer has two components, an encoder and a decoder. Let's say that we're performing translation. The encoder will take the input sentence along with some masking attributes in order to understand contextual embeddings. The decoder will take in the input sentence as well as the output sentence during training as well as some decoder masks. The output of this is going to be passed to a linear layer which will then be passed to a softmax function. This will be then used to generate a loss and so the transformer will learn through backpropagation. For more details check out the full video.