code: def inference(self, x_dict, edge_index_dict, key2int):
      # We can perform full-batch inference on GPU.
  
      device = list(x_dict.values())[0].device
  
      x_dict = copy(x_dict)
      for key, emb in self.emb_dict.items():
          x_dict[int(key)] = emb
  
      adj_t_dict = {}
      for key, (row, col) in edge_index_dict.items():
          adj_t_dict[key] = SparseTensor(row=col, col=row).to(device)
  
      for i, conv in enumerate(self.convs):
          out_dict = {}
  
          for j, x in x_dict.items():
              out_dict[j] = conv.root_lins[j](x)
  
          for keys, adj_t in adj_t_dict.items():
              src_key, target_key = keys[0], keys[-1]
              out = out_dict[key2int[target_key]]
              tmp = adj_t.matmul(
                  x_dict[key2int[src_key]], reduce="mean"
              )
              out.add_(conv.rel_lins[key2int[keys]](tmp))
  
          if i != self.num_layers - 1:
              for j in range(self.num_node_types):
                  F.relu_(out_dict[j])
  
          x_dict = out_dict
  
      return x_dict
  
imports: from copy import copy
  import argparse
  from tqdm.auto import tqdm
  import torch
  import torch.nn.functional
  from torch.nn import ModuleList, Linear, ParameterDict, Parameter
  from torch_sparse import SparseTensor
  from torch_geometric.utils import to_undirected
  from torch_geometric.data import Data, GraphSAINTRandomWalkSampler
  from torch_geometric.utils.hetero import group_hetero_graph
  from torch_geometric.nn import MessagePassing
  from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 5 for calling the RGCN obj's underlying member method code with example usage: obj.inference(x_dict, edge_index_dict, key2int) and returns: x_dict