class_docstr: BGRL architecture for Graph representation learning.
  Args:
      encoder (torch.nn.Module): Encoder network to be duplicated and used in both online and target networks.
      predictor (torch.nn.Module): Predictor network used to predict the target projection from the online projection.
  .. note::
      `encoder` must have a `reset_parameters` method, as the weights of the target network will be initialized
      differently from the online network.
class_docstr_tok: ["BGRL","architecture","for","Graph","representation","learning",".","Args",":","encoder","(","torch.nn.Module",")",":","Encoder","network","to","be","duplicated","and","used","in","both","online","and","target","networks",".","predictor","(","torch.nn.Module",")",":","Predictor","network","used","to","predict","the","target","projection","from","the","online","projection",".","..","note",":",":","`","encoder","`","must","have","a","`","reset_parameters","`","method",",","as","the","weights","of","the","target","network","will","be","initialized","differently","from","the","online","network","."]
code: def forward(self, online_x, target_x):
      # forward online network
      online_y = self.online_encoder(online_x)
  
      # prediction
      online_q = self.predictor(online_y)
  
      # forward target network
      with torch.no_grad():
          target_y = self.target_encoder(target_x).detach()
      return online_q, target_y
  
imports: import copy
  import dgl
  import torch
  from dgl.nn.pytorch.conv import GraphConv, SAGEConv
  from torch import nn
  from torch.nn import BatchNorm1d, Parameter
  from torch.nn.init import ones_, zeros_
label_desc: Use image node_id 4 for calling the BGRL obj's underlying member method code with example usage: obj.forward(online_x, target_x) and returns: online_q,  target_y