class_docstr: SRGNN regards the conversation history as a directed graph.
  In addition to considering the connection between the item and the adjacent item,
  it also considers the connection with other interactive items.
  
  Such as: A example of a session sequence(eg:item1, item2, item3, item2, item4) and the connection matrix A
  
  Outgoing edges:
      === ===== ===== ===== =====
       \    1     2     3     4
      === ===== ===== ===== =====
       1    0     1     0     0
       2    0     0    1/2   1/2
       3    0     1     0     0
       4    0     0     0     0
      === ===== ===== ===== =====
  
  Incoming edges:
      === ===== ===== ===== =====
       \    1     2     3     4
      === ===== ===== ===== =====
       1    0     0     0     0
       2   1/2    0    1/2    0
       3    0     1     0     0
       4    0     1     0     0
      === ===== ===== ===== =====
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.loss import BPRLoss
label_desc: Use image node_id 2 for calling the SRGNN obj's underlying member method code with example usage: obj._reset_parameters() without return types