code: def forward(
      self, x_dict, edge_index, edge_type, node_type, local_node_idx
  ):
      x = self.group_input(x_dict, node_type, local_node_idx)
  
      for i, conv in enumerate(self.convs):
          x = conv(x, edge_index, edge_type, node_type)
          if i != self.num_layers - 1:
              x = F.relu(x)
              x = F.dropout(x, p=0.5, training=self.training)
  
      return x.log_softmax(dim=-1)
  
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, ClusterData, ClusterLoader
  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 4 for calling the RGCN obj's underlying member method code with example usage: obj.forward(x_dict, edge_index, edge_type, node_type, local_node_idx) and returns: x