code: def test_mogonet_gcn_conv_model_shape():
      in_channels = 2
      out_channels = 8
      conv = MogonetGCNConv(
          in_channels=in_channels, out_channels=out_channels
      )
  
      # Test the shape of the module
      assert conv.in_channels == in_channels
      assert conv.out_channels == out_channels
  
      # Test the shape of the weight tensor
      assert conv.weight.shape == (in_channels, out_channels)
  
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 2 for calling a global function with example usage: test_mogonet_gcn_conv_model_shape() without return types