code: def __init__(
      self,
      in_size,
      out_size,
      ntypes,
      relation_names,
      activation=None,
      dropout=0.0,
  ):
      super(RelGraphConvLayer, self).__init__()
      self.in_size = in_size
      self.out_size = out_size
      self.ntypes = ntypes
      self.relation_names = relation_names
      self.activation = activation
  
      ########################################################################
      # (HIGHLIGHT) HeteroGraphConv is a graph convolution operator over
      # heterogeneous graphs. A dictionary is passed where the key is the
      # relation name and the value is the instance of GraphConv. norm="right"
      # is to divide the aggregated messages by each node’s in-degrees, which
      # is equivalent to averaging the received messages. weight=False and
      # bias=False as we will use our own weight matrices defined later.
      ########################################################################
      self.conv = dglnn.HeteroGraphConv(
          {
              rel: dglnn.GraphConv(
                  in_size,
                  out_size,
                  norm="right",
                  weight=False,
                  bias=False,
              )
              for rel in relation_names
          }
      )
  
      # Create a separate Linear layer for each relationship. Each
      # relationship has its own weights which will be applied to the node
      # features before performing convolution.
      self.weight = nn.ModuleDict(
          {
              rel_name: nn.Linear(in_size, out_size, bias=False)
              for rel_name in self.relation_names
          }
      )
  
      # Create a separate Linear layer for each node type.
      # loop_weights are used to update the output embedding of each target node
      # based on its own features, thereby allowing the model to refine the node
      # representations. Note that this does not imply the existence of self-loop
      # edges in the graph. It is similar to residual connection.
      self.loop_weights = nn.ModuleDict(
          {
              ntype: nn.Linear(in_size, out_size, bias=True)
              for ntype in self.ntypes
          }
      )
  
      self.loop_weights = nn.ModuleDict(
          {
              ntype: nn.Linear(in_size, out_size, bias=True)
              for ntype in self.ntypes
          }
      )
  
      self.dropout = nn.Dropout(dropout)
      # Initialize parameters of the model.
      self.reset_parameters()
  
imports: import argparse
  import itertools
  import sys
  import time
  import dgl
  import dgl.graphbolt
  import dgl.nn
  import psutil
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn import HeteroEmbedding
  from ogb.lsc import MAG240MEvaluator
  from ogb.nodeproppred import 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_size, out_size, ntypes, relation_names, activation, dropout)