code: def forward(self, h=None, blocks=None):
      if h is None:
          # full graph training
          h = self.embed_layer()
      if blocks is None:
          # full graph training
          for layer in self.layers:
              h = layer(self.g, h)
      else:
          # minibatch training
          for layer, block in zip(self.layers, blocks):
              h = layer(block, h)
      return h
  
imports: from collections import defaultdict
  import dgl
  import dgl.function
  import dgl.nn
  import torch
  import torch.nn
  import torch.nn.functional
  import tqdm
label_desc: Use image node_id 2 for calling the EntityClassify_HeteroAPI obj's underlying member method code with example usage: obj.forward(h, blocks) and returns: h