code: def remove_unseen_classes_from_training(
      train_mask, labels, removed_class
  ):
      """Remove the unseen classes (the first three classes by default) to get the zero-shot (i.e., completely imbalanced) label setting
      Input: train_mask, labels, removed_class
      Output: train_mask_zs: the bool list only containing seen classes
      """
      train_mask_zs = train_mask.clone()
      for i in range(train_mask_zs.numel()):
          if train_mask_zs[i] == 1 and (
              labels[i].item() in removed_class
          ):
              train_mask_zs[i] = 0
      return train_mask_zs
  
imports: from collections import defaultdict
  import numpy
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: remove_unseen_classes_from_training(train_mask, labels, removed_class) and returns: train_mask_zs