code: def __init__(
      self,
      in_channels,
      hidden_channels,
      out_channels,
      num_layers,
      dropout,
      num_nodes_dict,
      x_types,
      edge_types,
  ):
      super(RGCN, self).__init__()
  
      node_types = list(num_nodes_dict.keys())
  
      self.embs = ParameterDict(
          {
              key: Parameter(
                  torch.Tensor(num_nodes_dict[key], in_channels)
              )
              for key in set(node_types).difference(set(x_types))
          }
      )
  
      self.convs = ModuleList()
      self.convs.append(
          RGCNConv(in_channels, hidden_channels, node_types, edge_types)
      )
      for _ in range(num_layers - 2):
          self.convs.append(
              RGCNConv(
                  hidden_channels,
                  hidden_channels,
                  node_types,
                  edge_types,
              )
          )
      self.convs.append(
          RGCNConv(
              hidden_channels, out_channels, node_types, edge_types
          )
      )
  
      self.dropout = dropout
  
      self.reset_parameters()
  
imports: import copy
  import argparse
  import torch
  import torch.nn.functional
  from torch.nn import Parameter, ModuleDict, ModuleList, Linear, ParameterDict
  from torch_sparse import SparseTensor
  from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 1 to create a new RGCN object from inherited base classes: torch.nn with example: obj = RGCN(in_channels, hidden_channels, out_channels, num_layers, dropout, num_nodes_dict, x_types, edge_types)