class_docstr: NNCF is an neural network enhanced matrix factorization model which also captures neighborhood information.
  We implement the NNCF model with three ways to process neighborhood information.
docstr: Find the community neighbors of each node, i.e. I(u) and U(i).
  Then reset the id of nodes.
  
  Args:
      partition (dict): The input dict that contains the community each node belongs.
      relation (list): The input list that contains the relationships of users and items.
      community_dict (dict): The input dict that shows the nodes each community contains.
  
  Returns:
      list: The list of nodes' community neighbors.
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code: def prepare_vector_element(self, partition, relation, community_dict):
      r"""Find the community neighbors of each node, i.e. I(u) and U(i).
      Then reset the id of nodes.
  
      Args:
          partition (dict): The input dict that contains the community each node belongs.
          relation (list): The input list that contains the relationships of users and items.
          community_dict (dict): The input dict that shows the nodes each community contains.
  
      Returns:
          list: The list of nodes' community neighbors.
  
      """
      item2user_neighbor_lst = [[] for _ in range(self.n_items)]
      user2item_neighbor_lst = [[] for _ in range(self.n_users)]
  
      for r in range(len(relation)):
          user, item = relation[r][0], relation[r][1]
          item2user_neighbor = self.get_community_member(
              partition, community_dict, user, "u"
          )
          np.random.shuffle(item2user_neighbor)
          user2item_neighbor = self.get_community_member(
              partition, community_dict, item, "i"
          )
          np.random.shuffle(user2item_neighbor)
          _, user = user.split("_", 1)
          user = int(user)
          _, item = item.split("_", 1)
          item = int(item)
          for i in range(len(item2user_neighbor)):
              name, index = item2user_neighbor[i].split("_", 1)
              item2user_neighbor[i] = int(index)
          for i in range(len(user2item_neighbor)):
              name, index = user2item_neighbor[i].split("_", 1)
              user2item_neighbor[i] = int(index)
  
          item2user_neighbor_lst[item] = item2user_neighbor
          user2item_neighbor_lst[user] = user2item_neighbor
  
      return user2item_neighbor_lst, item2user_neighbor_lst
  
imports: import torch
  import torch.nn
  from torch.nn.init import normal_
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.model.layers import MLPLayers
  from recbole.utils import InputType
  from recbole.model.general_recommender.itemknn import ComputeSimilarity
  import numpy
label_desc: Use image node_id 5 for calling the NNCF obj's underlying member method code with example usage: obj.prepare_vector_element(partition, relation, community_dict) and returns: user2item_neighbor_lst,  item2user_neighbor_lst