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","."]
docstr: Returns the parameters that will be updated via an optimizer.
docstr_tok: ["Returns","the","parameters","that","will","be","updated","via","an","optimizer","."]
code: def trainable_parameters(self):
      r"""Returns the parameters that will be updated via an optimizer."""
      return list(self.online_encoder.parameters()) + list(
          self.predictor.parameters()
      )
  
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 2 for calling the BGRL obj's underlying member method code with example usage: obj.trainable_parameters() and returns: unknown