Interpretable xgboost - Calculate cover feature importance
A more detailed explanation of cover can be found in the code


  cover: the sum of second order gradient of training data classified to the leaf, if it is square loss, this simply corresponds to the number of instances in that branch. Deeper in the tree a node is, lower this metric will be


You can find this here: cover definition in the code

This basically mean that for each split the second order gradient of the specified loss is computed (per sample). Then this number is scaled by the number of samples.