class_docstr: Graph neural networks are well-suited for session-based recommendation,
  because it can automatically extract features of session graphs with considerations of rich node connections.
class_docstr_tok: ["Graph","neural","networks","are","well-suited","for","session-based","recommendation",",","because","it","can","automatically","extract","features","of","session","graphs","with","considerations","of","rich","node","connections","."]
code: def _reset_parameters(self):
      stdv = 1.0 / math.sqrt(self.embedding_size)
      for weight in self.parameters():
          weight.data.uniform_(-stdv, stdv)
  
imports: import math
  import numpy
  import torch
  from torch import nn
  from torch.nn import Parameter
  from torch.nn import functional
  from recbole.model.abstract_recommender import SequentialRecommender
  from recbole.model.layers import TransformerEncoder
  from recbole.model.loss import EmbLoss, BPRLoss
label_desc: Use image node_id 2 for calling the GNN obj's underlying member method code with example usage: obj._reset_parameters() without return types