code: def forward(self, x_dict, adj_t_dict):
      out_dict = {}
      for key, x in x_dict.items():
          out_dict[key] = self.root_lins[key](x)
  
      for key, adj_t in adj_t_dict.items():
          key_str = f"{key[0]}_{key[1]}_{key[2]}"
          x = x_dict[key[0]]
          out = self.rel_lins[key_str](adj_t.matmul(x, reduce="mean"))
          out_dict[key[2]].add_(out)
  
      return out_dict
  
imports: import copy
  import argparse
  import torch
  import torch.nn.functional
  from torch.nn import Parameter, ModuleDict, ModuleList, Linear, ParameterDict
  from torch_sparse import SparseTensor
  from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 3 for calling the RGCNConv obj's underlying member method code with example usage: obj.forward(x_dict, adj_t_dict) and returns: out_dict