code: def __init__(
      self,
      in_channels,
      out_channels,
      hidden_channels,
      num_etypes,
      num_layers,
      num_heads,
      dropout,
      pred_ntype,
  ):
      super().__init__()
      self.convs = nn.ModuleList()
      self.norms = nn.ModuleList()
      self.skips = nn.ModuleList()
  
      self.convs.append(
          nn.ModuleList(
              [
                  dglnn.GATConv(
                      in_channels,
                      hidden_channels // num_heads,
                      num_heads,
                      allow_zero_in_degree=True,
                  )
                  for _ in range(num_etypes)
              ]
          )
      )
      self.norms.append(nn.BatchNorm1d(hidden_channels))
      self.skips.append(nn.Linear(in_channels, hidden_channels))
      for _ in range(num_layers - 1):
          self.convs.append(
              nn.ModuleList(
                  [
                      dglnn.GATConv(
                          hidden_channels,
                          hidden_channels // num_heads,
                          num_heads,
                          allow_zero_in_degree=True,
                      )
                      for _ in range(num_etypes)
                  ]
              )
          )
          self.norms.append(nn.BatchNorm1d(hidden_channels))
          self.skips.append(nn.Linear(hidden_channels, hidden_channels))
  
      self.mlp = nn.Sequential(
          nn.Linear(hidden_channels, hidden_channels),
          nn.BatchNorm1d(hidden_channels),
          nn.ReLU(),
          nn.Dropout(dropout),
          nn.Linear(hidden_channels, out_channels),
      )
      self.dropout = nn.Dropout(dropout)
  
      self.hidden_channels = hidden_channels
      self.pred_ntype = pred_ntype
      self.num_etypes = num_etypes
  
imports: import argparse
  import math
  import sys
  from collections import OrderedDict
  import dgl
  import dgl.nn
  import numpy
  import torch
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import tqdm
  from ogb.lsc import MAG240MDataset, MAG240MEvaluator
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 1 to create a new RGAT object from inherited base classes: nn with example: obj = RGAT(in_channels, out_channels, hidden_channels, num_etypes, num_layers, num_heads, dropout, pred_ntype)