class_docstr: BPR is a basic matrix factorization model that be trained in the pairwise way.
class_docstr_tok: ["BPR","is","a","basic","matrix","factorization","model","that","be","trained","in","the","pairwise","way","."]
docstr: Get a batch of user embedding tensor according to input user's id.
  
  Args:
      user (torch.LongTensor): The input tensor that contains user's id, shape: [batch_size, ]
  
  Returns:
      torch.FloatTensor: The embedding tensor of a batch of user, shape: [batch_size, embedding_size]
docstr_tok: ["Get","a","batch","of","user","embedding","tensor","according","to","input","user","'s","id",".","Args",":","user","(","torch.LongTensor",")",":","The","input","tensor","that","contains","user","'s","id",",","shape",":","[","batch_size",",","]","Returns",":","torch.FloatTensor",":","The","embedding","tensor","of","a","batch","of","user",",","shape",":","[","batch_size",",","embedding_size","]"]
code: def get_user_embedding(self, user):
      r"""Get a batch of user embedding tensor according to input user's id.
  
      Args:
          user (torch.LongTensor): The input tensor that contains user's id, shape: [batch_size, ]
  
      Returns:
          torch.FloatTensor: The embedding tensor of a batch of user, shape: [batch_size, embedding_size]
      """
      return self.user_embedding(user)
  
file_path: RecBole/recbole/model/general_recommender/bpr.py
imports: import torch
  import torch.nn
  from recbole.model.abstract_recommender import GeneralRecommender
  from recbole.model.init import xavier_normal_initialization
  from recbole.model.loss import BPRLoss
  from recbole.utils import InputType
label_desc: Use image node_id 2 for calling the BPR obj's underlying member method code with example usage: obj.get_user_embedding(user) and returns: self