class_docstr: The Adversarially Regularized Graph Auto-Encoder model from the
  `"Adversarially Regularized Graph Autoencoder for Graph Embedding"
  <https://arxiv.org/abs/1802.04407>`_ paper.
  
  Args:
      encoder (torch.nn.Module): The encoder module.
      discriminator (torch.nn.Module): The discriminator module.
      decoder (torch.nn.Module, optional): The decoder module. If set to
          :obj:`None`, will default to the
          :class:`torch_geometric.nn.models.InnerProductDecoder`.
          (default: :obj:`None`)
class_docstr_tok: ["The","Adversarially","Regularized","Graph","Auto-Encoder","model","from","the","`","``","Adversarially","Regularized","Graph","Autoencoder","for","Graph","Embedding","''","<","https",":","\/\/arxiv.org\/abs\/1802.04407",">","`","_","paper",".","Args",":","encoder","(","torch.nn.Module",")",":","The","encoder","module",".","discriminator","(","torch.nn.Module",")",":","The","discriminator","module",".","decoder","(","torch.nn.Module",",","optional",")",":","The","decoder","module",".","If","set","to",":","obj",":","`","None","`",",","will","default","to","the",":","class",":","`","torch_geometric.nn.models.InnerProductDecoder","`",".","(","default",":",":","obj",":","`","None","`",")"]
code: def reset_parameters(self):
      super().reset_parameters()
      reset(self.discriminator)
  
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from torch.nn import Module
  from torch_geometric.nn.inits import reset
  from torch_geometric.utils import negative_sampling
label_desc: Use image node_id 2 for calling the ARGA obj's underlying member method code with example usage: obj.reset_parameters() without return types