Explain the purpose and implementation of gradient clipping in the context of training Transformer models.

Gradient clipping is a technique used during the training of neural networks, including Transformer models, to prevent exploding gradients, which can destabilize training and lead to poor performance. Exploding gradients occur when the gradients become excessively large during backpropagation. This can cause the model's weights to update too drastically, disrupting the learning process and preventing the model from converging. In Transformer models, exploding gradients can be particularly problematic due to the depth of the network and the use of non-linear activation functions. Gradient clipping addresses this issue by limiting the magnitude of t....