text: Title: extend active learning with multiple annotators to multi-label datasets 
  Body: Requested in the [Slack community](https://cleanlab.ai/slack)
  
  Objectives:
  
  1) Make a multi-label version of the `get_active_learning_scores` function:
  https://docs.cleanlab.ai/master/cleanlab/multiannotator.html#cleanlab.multiannotator.get_active_learning_scores
  
  2) Extend other functions in the `cleanlab.multiannotator` module as necessary for this to work for multi-label datasets.
  All of this new functionality should live in a new module: `cleanlab.multilabel_classification.multiannotator`
  
  3) Make a multi-label version of this tutorial notebook:
  https://github.com/cleanlab/examples/tree/master/active_learning_multiannotator
  
  Simple implementation:
  
  For a multi-label dataset with K nondisjoint classes (aka tags), we compute the multilabel active learning scores like this:
  
  ```
  multi_annotator_active_learning_scores = np.zeros(len(dataset),)
  for class in 1:K:
      y_onevsrest = create a one-vs-rest binary dataset labeling each example in as class 1 if it has class k in its given label, otherwise as class 0. 
      pred_probs_class = pred_probs[:,k]
      class_k_active_learning_scores = cleanlab.multiannotator.get_active_learning_scores(y_onevsrest, pred_probs_class)
      multilabel_active_learning_scores += class_k_active_learning_scores
  
  return (multilabel_active_learning_scores / K)
  ```
  