class_docstr: KTUP is a knowledge-based recommendation model. It adopts the strategy of multi-task learning to jointly learn
  recommendation and KG-related tasks, with the goal of understanding the reasons that a user interacts with an item.
  This method utilizes an attention mechanism to combine all preferences into a single-vector representation.
code: def _transH_projection(original, norm):
      return (
          original
          - torch.sum(
              original * norm,
              dim=len(original.size()) - 1,
              keepdim=True,
          )
          * norm
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
  from recbole.model.abstract_recommender import KnowledgeRecommender
  from recbole.model.init import xavier_uniform_initialization
  from recbole.model.loss import BPRLoss, EmbMarginLoss
  from recbole.utils import InputType
label_desc: Use image node_id 6 for calling the KTUP obj's underlying member method code with example usage: obj._transH_projection(original, norm) and returns: unknown