code: def generate_data(batch, use_cuda):
      """Create a formatted and ordered data batch to use in the
      three player model.
  
      :param batch: A pandas dataframe containing the tokens, masks, counts, and
          labels associated with a batch of data
      :type batch: DataFrame
      :param use_cuda: whether to use CUDA
      :type use_cuda: bool
      :return: formatted and ordered tokens (x), masks (m), and
          labels (y) associated with a batch of data
      :rtype: dict
      """
      # sort for rnn happiness
      batch.sort_values("counts", inplace=True, ascending=False)
  
      x_mask = np.stack(batch["mask"], axis=0)
      # drop all zero columns
      zero_col_idxs = np.argwhere(np.all(x_mask[..., :] == 0, axis=0))
      x_mask = np.delete(x_mask, zero_col_idxs, axis=1)
  
      x_mat = np.stack(batch["tokens"], axis=0)
      # drop all zero columns
      x_mat = np.delete(x_mat, zero_col_idxs, axis=1)
  
      y_vec = np.stack(batch["labels"], axis=0)
  
      batch_x_ = Variable(torch.from_numpy(x_mat)).to(torch.int64)
      batch_m_ = Variable(torch.from_numpy(x_mask)).type(
          torch.FloatTensor
      )
      batch_y_ = Variable(torch.from_numpy(y_vec)).to(torch.int64)
  
      if use_cuda:
          batch_x_ = batch_x_.cuda()
          batch_m_ = batch_m_.cuda()
          batch_y_ = batch_y_.cuda()
  
      return {"x": batch_x_, "m": batch_m_, "y": batch_y_}
  
label_desc: Use image node_id 1 for calling a global function with example usage: generate_data(batch, use_cuda) and returns: dict