code: def __init__(
      self,
      in_feat,
      out_feat,
      ntypes,
      rel_names,
      activation=None,
      dropout=0.0,
  ):
      super(RelGraphConvLayer, self).__init__()
      self.in_feat = in_feat
      self.out_feat = out_feat
      self.ntypes = ntypes
      self.rel_names = rel_names
      self.activation = activation
  
      self.conv = dglnn.HeteroGraphConv(
          {
              rel: dglnn.GraphConv(
                  in_feat,
                  out_feat,
                  norm="right",
                  weight=False,
                  bias=False,
              )
              for rel in rel_names
          }
      )
  
      self.weight = nn.ModuleDict(
          {
              rel_name: nn.Linear(in_feat, out_feat, bias=False)
              for rel_name in self.rel_names
          }
      )
  
      # weight for self loop
      self.loop_weights = nn.ModuleDict(
          {
              ntype: nn.Linear(in_feat, out_feat, bias=True)
              for ntype in self.ntypes
          }
      )
  
      self.dropout = nn.Dropout(dropout)
      self.reset_parameters()
  
imports: import argparse
  import itertools
  import sys
  import dgl
  import dgl.nn
  import psutil
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl import AddReverse, Compose, ToSimple
  from dgl.nn import HeteroEmbedding
  from ogb.nodeproppred import DglNodePropPredDataset, Evaluator
  from tqdm import tqdm
label_desc: Use image node_id 1 to create a new RelGraphConvLayer object from inherited base classes: nn with example: obj = RelGraphConvLayer(in_feat, out_feat, ntypes, rel_names, activation, dropout)