code: def forward(self, x_dict, adj_t_dict):
      x_dict = copy.copy(x_dict)
      for key, emb in self.embs.items():
          x_dict[key] = emb
  
      for conv in self.convs[:-1]:
          x_dict = conv(x_dict, adj_t_dict)
          for key, x in x_dict.items():
              x_dict[key] = F.relu(x)
              x_dict[key] = F.dropout(
                  x, p=self.dropout, training=self.training
              )
      return self.convs[-1](x_dict, adj_t_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 RGCN obj's underlying member method code with example usage: obj.forward(x_dict, adj_t_dict) and returns: self