code: def __init__(self, g, 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']
      self.relation_names = list(set(g.etypes))
      self.relation_names.sort()
      self.dropout = 0.5
  
      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,
              g.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,
              g.ntypes,
              self.relation_names,
              activation=None,
          )
      )
  
imports: import argparse
  import itertools
  import sys
  import time
  import dgl
  import dgl.nn
  import numpy
  import psutil
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl import AddReverse, Compose, ToSimple
  from dgl.nn import HeteroEmbedding
  from ogb.lsc import MAG240MDataset, MAG240MEvaluator
  from ogb.nodeproppred import DglNodePropPredDataset, 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(g, in_size, out_size)