code: def test_mogonet_gcn_model_shape():
      model = MogonetGCN(
          in_channels=2, hidden_channels=[4, 5, 6], dropout=0.5
      )
      assert isinstance(model.conv1, MogonetGCNConv)
      assert isinstance(model.conv2, MogonetGCNConv)
      assert isinstance(model.conv3, MogonetGCNConv)
  
      assert model.conv1.weight.shape == (2, 4)
      assert model.conv2.weight.shape == (4, 5)
      assert model.conv3.weight.shape == (5, 6)
  
file_path: pykale/tests/embed/test_mogonet.py
imports: import pytest
  import torch
  from torch_geometric.data import Data
  from torch_sparse import SparseTensor
  from kale.embed.mogonet import MogonetGCN, MogonetGCNConv
label_desc: Use image node_id 6 for calling a global function with example usage: test_mogonet_gcn_model_shape() without return types