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: Get a batch of neighborhood embedding tensor according to input id.
  
  Args:
      user (torch.LongTensor): The input tensor that contains user's id, shape: [batch_size, ]
      item (torch.LongTensor): The input tensor that contains item's id, shape: [batch_size, ]
  
  Returns:
      torch.FloatTensor: The neighborhood embedding tensor of a batch of user, shape: [batch_size, neigh_embedding_size]
      torch.FloatTensor: The neighborhood embedding tensor of a batch of item, shape: [batch_size, neigh_embedding_size]
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code: def get_neigh_info(self, user, item):
      r"""Get a batch of neighborhood embedding tensor according to input id.
  
      Args:
          user (torch.LongTensor): The input tensor that contains user's id, shape: [batch_size, ]
          item (torch.LongTensor): The input tensor that contains item's id, shape: [batch_size, ]
  
      Returns:
          torch.FloatTensor: The neighborhood embedding tensor of a batch of user, shape: [batch_size, neigh_embedding_size]
          torch.FloatTensor: The neighborhood embedding tensor of a batch of item, shape: [batch_size, neigh_embedding_size]
  
      """
      batch_u_neigh = self.u_neigh[user]
      batch_i_neigh = self.i_neigh[item]
      return batch_u_neigh, batch_i_neigh
  
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 9 for calling the NNCF obj's underlying member method code with example usage: obj.get_neigh_info(user, item) and returns: batch_u_neigh,  batch_i_neigh