code: def __init__(self, num_nodes, h_dim, out_dim, num_rels):
      super().__init__()
      self.emb = nn.Embedding(num_nodes, h_dim)
      # two-layer RGCN
      self.conv1 = RelGraphConv(
          h_dim,
          h_dim,
          num_rels,
          regularizer="basis",
          num_bases=num_rels,
          self_loop=False,
      )
      self.conv2 = RelGraphConv(
          h_dim,
          out_dim,
          num_rels,
          regularizer="basis",
          num_bases=num_rels,
          self_loop=False,
      )
  
file_path: dgl/examples/pytorch/rgcn/entity_sample_multi_gpu.py
imports: import argparse
  import os
  import dgl
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  from dgl.data.rdf import AIFBDataset, AMDataset, BGSDataset, MUTAGDataset
  from dgl.dataloading import DataLoader, MultiLayerNeighborSampler
  from dgl.nn.pytorch import RelGraphConv
  from torch.nn.parallel import DistributedDataParallel
  from torchmetrics.functional import accuracy
label_desc: Use image node_id 1 to create a new RGCN object from inherited base classes: nn with example: obj = RGCN(num_nodes, h_dim, out_dim, num_rels)