code: def __init__(
      self,
      in_channels,
      hidden_channels,
      out_channels,
      num_layers,
      dropout,
      num_nodes_dict,
      x_types,
      num_edge_types,
  ):
      super(RGCN, self).__init__()
  
      self.in_channels = in_channels
      self.hidden_channels = hidden_channels
      self.out_channels = out_channels
      self.num_layers = num_layers
      self.dropout = dropout
  
      node_types = list(num_nodes_dict.keys())
      num_node_types = len(node_types)
  
      self.num_node_types = num_node_types
      self.num_edge_types = num_edge_types
  
      # Create embeddings for all node types that do not come with features.
      self.emb_dict = ParameterDict(
          {
              f"{key}": Parameter(
                  torch.Tensor(num_nodes_dict[key], in_channels)
              )
              for key in set(node_types).difference(set(x_types))
          }
      )
  
      I, H, O = in_channels, hidden_channels, out_channels  # noqa
  
      # Create `num_layers` many message passing layers.
      self.convs = ModuleList()
      self.convs.append(RGCNConv(I, H, num_node_types, num_edge_types))
      for _ in range(num_layers - 2):
          self.convs.append(
              RGCNConv(H, H, num_node_types, num_edge_types)
          )
      self.convs.append(
          RGCNConv(H, O, self.num_node_types, num_edge_types)
      )
  
      self.reset_parameters()
  
imports: from copy import copy
  import argparse
  from tqdm.auto import tqdm
  import torch
  import torch.nn.functional
  from torch.nn import ModuleList, Linear, ParameterDict, Parameter
  from torch_sparse import SparseTensor
  from torch_geometric.utils import to_undirected
  from torch_geometric.data import Data, GraphSAINTRandomWalkSampler
  from torch_geometric.utils.hetero import group_hetero_graph
  from torch_geometric.nn import MessagePassing
  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, num_edge_types)