code: def __init__(self, graph, in_size, out_size):
      super(EntityClassify, self).__init__()
      self.in_size = in_size
      self.hidden_size = 64
      self.out_size = out_size
  
      # Generate and sort a list of unique edge types from the input graph.
      # eg. ['writes', 'cites']
      etypes = list(graph.edge_type_to_id.keys())
      etypes = [gb.etype_str_to_tuple(etype)[1] for etype in etypes]
      self.relation_names = etypes
      self.relation_names.sort()
      self.dropout = 0.5
      ntypes = list(graph.node_type_to_id.keys())
      self.layers = nn.ModuleList()
  
      # First layer: transform input features to hidden features. Use ReLU
      # as the activation function and apply dropout for regularization.
      self.layers.append(
          RelGraphConvLayer(
              self.in_size,
              self.hidden_size,
              ntypes,
              self.relation_names,
              activation=F.relu,
              dropout=self.dropout,
          )
      )
  
      # Second layer: transform hidden features to output features. No
      # activation function is applied at this stage.
      self.layers.append(
          RelGraphConvLayer(
              self.hidden_size,
              self.out_size,
              ntypes,
              self.relation_names,
              activation=None,
          )
      )
  
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 EntityClassify object from inherited base classes: nn with example: obj = EntityClassify(graph, in_size, out_size)