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 time
  import dgl
  import dgl.function
  import dgl.nn
  import numpy
  import ogb
  import torch
  import torch.nn
  import torch.nn.functional
  import tqdm
  from ogb.lsc import MAG240MDataset, MAG240MEvaluator
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)