code: def __init__(
      self,
      in_size,
      out_size,
      relation_names,
      embed_layer,
  ):
      super(EntityClassify, self).__init__()
      self.in_size = in_size
      self.out_size = out_size
      self.relation_names = relation_names
      self.relation_names.sort()
      self.embed_layer = embed_layer
  
      self.layers = nn.ModuleList()
      # Input to hidden.
      self.layers.append(
          HeteroRelationalGraphConv(
              self.in_size,
              self.in_size,
              self.relation_names,
              activation=F.relu,
          )
      )
      # Hidden to output.
      self.layers.append(
          HeteroRelationalGraphConv(
              self.in_size,
              self.out_size,
              self.relation_names,
          )
      )
  
imports: import argparse
  import time
  import dgl
  import dgl.sparse
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.data.rdf import AIFBDataset, AMDataset, BGSDataset, MUTAGDataset
label_desc: Use image node_id 1 to create a new EntityClassify object from inherited base classes: nn with example: obj = EntityClassify(in_size, out_size, relation_names, embed_layer)