class_docstr: The FPMC model is mainly used in the recommendation system to predict the possibility of
  unknown items arousing user interest, and to discharge the item recommendation list.
  
  Note:
  
      In order that the generation method we used is common to other sequential models,
      We set the size of the basket mentioned in the paper equal to 1.
      For comparison with other models, the loss function used is BPR.
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code: def _init_weights(self, module):
      if isinstance(module, nn.Embedding):
          xavier_normal_(module.weight.data)
  
file_path: RecBole/recbole/model/sequential_recommender/fpmc.py
imports: import torch
  from torch import nn
  from torch.nn.init import xavier_normal_
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.loss import BPRLoss
  from recbole.utils import InputType
label_desc: Use image node_id 2 for calling the FPMC obj's underlying member method code with example usage: obj._init_weights(module) without return types