In some cases you may find that each layer of your net amplifies the gradient it receives. This causes a problem because the lower layers of the net then get huge gradients and their updates will be far too large to allow the model to learn anything.
This function ‘clips’ the norm of the gradients by scaling the gradients down by the same amount in order to reduce the norm to an acceptable level. In practice this places a limit on the size of the parameter updates.
The hope is that this will ensure that your model gets reasonably sized gradients and that the corresponding updates will allow the model to learn.